diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb
index dfebaef2..09156b64 100644
--- a/Ch02-statlearn-lab.ipynb
+++ b/Ch02-statlearn-lab.ipynb
@@ -146,7 +146,27 @@
      "shell.execute_reply": "2024-06-05T01:13:21.524324Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mSignature:\u001b[39m print(*args, sep=\u001b[33m' '\u001b[39m, end=\u001b[33m'\\n'\u001b[39m, file=\u001b[38;5;28;01mNone\u001b[39;00m, flush=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[31mDocstring:\u001b[39m\n",
+      "Prints the values to a stream, or to sys.stdout by default.\n",
+      "\n",
+      "sep\n",
+      "  string inserted between values, default a space.\n",
+      "end\n",
+      "  string appended after the last value, default a newline.\n",
+      "file\n",
+      "  a file-like object (stream); defaults to the current sys.stdout.\n",
+      "flush\n",
+      "  whether to forcibly flush the stream.\n",
+      "\u001b[31mType:\u001b[39m      builtin_function_or_method"
+     ]
+    }
+   ],
    "source": [
     "print?\n"
    ]
@@ -291,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "id": "8bcb5c5b",
    "metadata": {
     "execution": {
@@ -308,7 +328,7 @@
        "[3, 4, 5, 4, 9, 7]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "ac55fedf",
    "metadata": {
     "execution": {
@@ -401,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "c4d9f140",
    "metadata": {
     "execution": {
@@ -440,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "106ef1ea",
    "metadata": {
     "execution": {
@@ -458,7 +478,7 @@
        "array([ 7, 13, 12])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -487,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "d111ed03",
    "metadata": {
     "execution": {
@@ -506,7 +526,7 @@
        "       [3, 4]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -538,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "d9562687",
    "metadata": {
     "execution": {
@@ -555,7 +575,7 @@
        "2"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -576,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "4fe5b7cf",
    "metadata": {
     "execution": {
@@ -594,7 +614,7 @@
        "dtype('int64')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -616,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "c4c43c7d",
    "metadata": {
     "execution": {
@@ -634,7 +654,7 @@
        "dtype('float64')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -655,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "62851cb3",
    "metadata": {
     "execution": {
@@ -666,7 +686,135 @@
     },
     "lines_to_next_cell": 0
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mDocstring:\u001b[39m\n",
+      "array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0,\n",
+      "      like=None)\n",
+      "\n",
+      "Create an array.\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "object : array_like\n",
+      "    An array, any object exposing the array interface, an object whose\n",
+      "    ``__array__`` method returns an array, or any (nested) sequence.\n",
+      "    If object is a scalar, a 0-dimensional array containing object is\n",
+      "    returned.\n",
+      "dtype : data-type, optional\n",
+      "    The desired data-type for the array. If not given, NumPy will try to use\n",
+      "    a default ``dtype`` that can represent the values (by applying promotion\n",
+      "    rules when necessary.)\n",
+      "copy : bool, optional\n",
+      "    If true (default), then the object is copied.  Otherwise, a copy will\n",
+      "    only be made if ``__array__`` returns a copy, if obj is a nested\n",
+      "    sequence, or if a copy is needed to satisfy any of the other\n",
+      "    requirements (``dtype``, ``order``, etc.).\n",
+      "order : {'K', 'A', 'C', 'F'}, optional\n",
+      "    Specify the memory layout of the array. If object is not an array, the\n",
+      "    newly created array will be in C order (row major) unless 'F' is\n",
+      "    specified, in which case it will be in Fortran order (column major).\n",
+      "    If object is an array the following holds.\n",
+      "\n",
+      "    ===== ========= ===================================================\n",
+      "    order  no copy                     copy=True\n",
+      "    ===== ========= ===================================================\n",
+      "    'K'   unchanged F & C order preserved, otherwise most similar order\n",
+      "    'A'   unchanged F order if input is F and not C, otherwise C order\n",
+      "    'C'   C order   C order\n",
+      "    'F'   F order   F order\n",
+      "    ===== ========= ===================================================\n",
+      "\n",
+      "    When ``copy=False`` and a copy is made for other reasons, the result is\n",
+      "    the same as if ``copy=True``, with some exceptions for 'A', see the\n",
+      "    Notes section. The default order is 'K'.\n",
+      "subok : bool, optional\n",
+      "    If True, then sub-classes will be passed-through, otherwise\n",
+      "    the returned array will be forced to be a base-class array (default).\n",
+      "ndmin : int, optional\n",
+      "    Specifies the minimum number of dimensions that the resulting\n",
+      "    array should have.  Ones will be prepended to the shape as\n",
+      "    needed to meet this requirement.\n",
+      "like : array_like, optional\n",
+      "    Reference object to allow the creation of arrays which are not\n",
+      "    NumPy arrays. If an array-like passed in as ``like`` supports\n",
+      "    the ``__array_function__`` protocol, the result will be defined\n",
+      "    by it. In this case, it ensures the creation of an array object\n",
+      "    compatible with that passed in via this argument.\n",
+      "\n",
+      "    .. versionadded:: 1.20.0\n",
+      "\n",
+      "Returns\n",
+      "-------\n",
+      "out : ndarray\n",
+      "    An array object satisfying the specified requirements.\n",
+      "\n",
+      "See Also\n",
+      "--------\n",
+      "empty_like : Return an empty array with shape and type of input.\n",
+      "ones_like : Return an array of ones with shape and type of input.\n",
+      "zeros_like : Return an array of zeros with shape and type of input.\n",
+      "full_like : Return a new array with shape of input filled with value.\n",
+      "empty : Return a new uninitialized array.\n",
+      "ones : Return a new array setting values to one.\n",
+      "zeros : Return a new array setting values to zero.\n",
+      "full : Return a new array of given shape filled with value.\n",
+      "\n",
+      "\n",
+      "Notes\n",
+      "-----\n",
+      "When order is 'A' and ``object`` is an array in neither 'C' nor 'F' order,\n",
+      "and a copy is forced by a change in dtype, then the order of the result is\n",
+      "not necessarily 'C' as expected. This is likely a bug.\n",
+      "\n",
+      "Examples\n",
+      "--------\n",
+      ">>> np.array([1, 2, 3])\n",
+      "array([1, 2, 3])\n",
+      "\n",
+      "Upcasting:\n",
+      "\n",
+      ">>> np.array([1, 2, 3.0])\n",
+      "array([ 1.,  2.,  3.])\n",
+      "\n",
+      "More than one dimension:\n",
+      "\n",
+      ">>> np.array([[1, 2], [3, 4]])\n",
+      "array([[1, 2],\n",
+      "       [3, 4]])\n",
+      "\n",
+      "Minimum dimensions 2:\n",
+      "\n",
+      ">>> np.array([1, 2, 3], ndmin=2)\n",
+      "array([[1, 2, 3]])\n",
+      "\n",
+      "Type provided:\n",
+      "\n",
+      ">>> np.array([1, 2, 3], dtype=complex)\n",
+      "array([ 1.+0.j,  2.+0.j,  3.+0.j])\n",
+      "\n",
+      "Data-type consisting of more than one element:\n",
+      "\n",
+      ">>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])\n",
+      ">>> x['a']\n",
+      "array([1, 3])\n",
+      "\n",
+      "Creating an array from sub-classes:\n",
+      "\n",
+      ">>> np.array(np.mat('1 2; 3 4'))\n",
+      "array([[1, 2],\n",
+      "       [3, 4]])\n",
+      "\n",
+      ">>> np.array(np.mat('1 2; 3 4'), subok=True)\n",
+      "matrix([[1, 2],\n",
+      "        [3, 4]])\n",
+      "\u001b[31mType:\u001b[39m      builtin_function_or_method"
+     ]
+    }
+   ],
    "source": [
     "np.array?\n"
    ]
@@ -681,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "816dac08",
    "metadata": {
     "execution": {
@@ -699,7 +847,7 @@
        "dtype('float64')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "9a81115b",
    "metadata": {
     "execution": {
@@ -737,7 +885,7 @@
        "(2, 2)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -762,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "8f051245",
    "metadata": {
     "execution": {
@@ -780,7 +928,7 @@
        "10"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -800,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "906c3a0e",
    "metadata": {
     "execution": {
@@ -818,7 +966,7 @@
        "10"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -846,7 +994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "e8b8d6db",
    "metadata": {
     "execution": {
@@ -897,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "47c39f33",
    "metadata": {
     "execution": {
@@ -915,7 +1063,7 @@
        "1"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -935,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "d74a5638",
    "metadata": {
     "execution": {
@@ -953,7 +1101,7 @@
        "6"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "5a19217f",
    "metadata": {
     "execution": {
@@ -1034,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "116ec7b8",
    "metadata": {
     "execution": {
@@ -1051,10 +1199,10 @@
      "evalue": "'tuple' object does not support item assignment",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[23], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m my_tuple \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmy_tuple\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m my_tuple = (\u001b[32m3\u001b[39m, \u001b[32m4\u001b[39m, \u001b[32m5\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mmy_tuple\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m = \u001b[32m2\u001b[39m\n",
+      "\u001b[31mTypeError\u001b[39m: 'tuple' object does not support item assignment"
      ]
     }
    ],
@@ -1074,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "ec5dc91e",
    "metadata": {
     "execution": {
@@ -1095,7 +1243,7 @@
        "        [3, 6]]))"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1118,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "31fbaf86",
    "metadata": {
     "execution": {
@@ -1136,7 +1284,7 @@
        "       2.44948974])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1155,7 +1303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "c0e63f89",
    "metadata": {
     "execution": {
@@ -1172,7 +1320,7 @@
        "array([25,  4,  9, 16, 25, 36])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1191,7 +1339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "365d1248",
    "metadata": {
     "execution": {
@@ -1210,7 +1358,7 @@
        "       2.44948974])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1238,7 +1386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "e2756bb9",
    "metadata": {
     "execution": {
@@ -1252,24 +1400,25 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.63214394,  0.34288365,  0.85005043, -0.47385094,  0.32864279,\n",
-       "       -0.78796854,  0.51125391, -1.12947776, -0.71391649, -1.92367418,\n",
-       "       -0.42609273, -0.7309404 ,  0.83090301, -0.47570241, -0.5852834 ,\n",
-       "        0.89788488, -1.27638353,  2.59421427,  1.25973331, -0.61486026,\n",
-       "        1.3979914 ,  0.79368659, -1.14506316, -2.00869422,  0.19502313,\n",
-       "       -0.45956661,  0.65944298,  0.69723473, -1.09211872,  0.15972909,\n",
-       "       -0.22530418,  0.32276968,  0.50374192,  0.80506415, -0.48224507,\n",
-       "       -0.53214642, -1.09662644, -1.52722433, -2.10241781, -0.42403843,\n",
-       "        1.32310673,  2.00932483, -0.16726754,  2.30113584, -0.68976675,\n",
-       "       -0.67621251, -0.78371139, -0.62709974, -1.39453964,  0.25195549])"
+       "array([-0.4508872 ,  1.17883376, -0.32853253,  0.20756942, -0.32215492,\n",
+       "       -0.05519633, -0.8680871 , -1.11742288, -1.37391049, -1.46642031,\n",
+       "        0.42209949, -1.700909  ,  0.04053901,  1.24846134, -1.17289235,\n",
+       "       -1.67739525,  0.05835306, -0.39049108, -1.26939779, -0.48574264,\n",
+       "       -0.1772349 ,  2.06224408, -0.07338148,  1.43027811,  1.28497703,\n",
+       "        0.89882284, -0.49436552,  0.52077866, -0.23063094, -0.42880002,\n",
+       "       -0.40939347,  0.24736457, -1.13203337, -1.22495674,  0.33685895,\n",
+       "       -0.64899066, -0.10449238, -0.40618371,  0.43670778, -0.45498746,\n",
+       "        0.48986445,  0.08621589,  2.63460155,  0.33753548,  0.30457715,\n",
+       "       -0.78430298,  0.66188239, -0.40779866, -0.70139484, -0.90822623])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "import numpy as np\n",
     "x = np.random.normal(size=50)\n",
     "x\n"
    ]
@@ -1284,7 +1433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "d336dde1",
    "metadata": {
     "execution": {
@@ -1311,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "cad26f2b",
    "metadata": {
     "execution": {
@@ -1325,11 +1474,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[1.        , 0.71781515],\n",
-       "       [0.71781515, 1.        ]])"
+       "array([[1.        , 0.70871734],\n",
+       "       [0.70871734, 1.        ]])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1351,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "a621aa6c",
    "metadata": {
     "execution": {
@@ -1367,8 +1516,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.97181857  3.2315884 ]\n",
-      "[ 0.73365791 -7.23683363]\n"
+      "[-6.01168657  9.41451861]\n",
+      "[-0.69335752 -8.33379176]\n"
      ]
     }
    ],
@@ -1418,7 +1567,7 @@
      "output_type": "stream",
      "text": [
       "[ 4.09482632 -1.07485605]\n",
-      "[ 4.09482632 -1.07485605]\n"
+      "[  4.09482632  -1.07485605 -10.15364596   1.13406146  -4.14030566]\n"
      ]
     }
    ],
@@ -1426,7 +1575,7 @@
     "rng = np.random.default_rng(1303)\n",
     "print(rng.normal(scale=5, size=2))\n",
     "rng2 = np.random.default_rng(1303)\n",
-    "print(rng2.normal(scale=5, size=2)) "
+    "print(rng2.normal(scale=5, size=5)) "
    ]
   },
   {
@@ -1502,7 +1651,7 @@
     {
      "data": {
       "text/plain": [
-       "(2.7243406406465125, 2.7243406406465125, 2.7243406406465125)"
+       "(3.027045156273903, 2.7243406406465125, 2.7243406406465125)"
       ]
      },
      "execution_count": 34,
@@ -1511,7 +1660,8 @@
     }
    ],
    "source": [
-    "np.var(y), y.var(), np.mean((y - y.mean())**2)"
+    "np.var(y,ddof=1), y.var(), np.mean((y - y.mean())**2)\n",
+    "#np.var?"
    ]
   },
   {
@@ -1525,7 +1675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "id": "2499a37e",
    "metadata": {
     "execution": {
@@ -1539,16 +1689,16 @@
     {
      "data": {
       "text/plain": [
-       "(1.6505576756498128, 1.6505576756498128)"
+       "(1.6505576756498128, 1.7398405548422828)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.sqrt(np.var(y)), np.std(y)"
+    "np.sqrt(np.var(y)), np.std(y,ddof=1)"
    ]
   },
   {
@@ -1562,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "id": "3c308346",
    "metadata": {
     "execution": {
@@ -1588,7 +1738,7 @@
        "       [ 1.02130682, -0.95964476, -1.66861984]])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1608,7 +1758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "id": "7b5e8fcd",
    "metadata": {
     "execution": {
@@ -1625,7 +1775,7 @@
        "array([ 0.15030588,  0.14030961, -0.34238602])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1639,12 +1789,12 @@
    "id": "d2617ab3",
    "metadata": {},
    "source": [
-    "The following yields the same result."
+    "The following yields the a different result."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "id": "157d4a21",
    "metadata": {
     "execution": {
@@ -1659,24 +1809,17 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.15030588,  0.14030961, -0.34238602])"
+       "array([-0.13604387, -0.70466596,  0.40038349,  0.45675943, -0.04765406,\n",
+       "        0.73533095, -0.04256834,  0.39038958, -0.68884708, -0.53565259])"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "X.mean(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cdaef318",
-   "metadata": {},
-   "source": [
-    "    "
+    "X.mean(1)"
    ]
   },
   {
@@ -1714,7 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 44,
    "id": "8b11e94e",
    "metadata": {
     "execution": {
@@ -1727,7 +1870,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1739,7 +1882,10 @@
    "source": [
     "from matplotlib.pyplot import subplots\n",
     "fig, ax = subplots(figsize=(8, 8))\n",
+    "rng = np.random.default_rng(1303)\n",
     "x = rng.standard_normal(100)\n",
+    "# testing with reseeding\n",
+    "rng = np.random.default_rng(1303)\n",
     "y = rng.standard_normal(100)\n",
     "ax.plot(x, y);\n"
    ]
@@ -1756,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 45,
    "id": "04eb327c",
    "metadata": {
     "execution": {
@@ -1769,7 +1915,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1794,7 +1940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 46,
    "id": "e678d266",
    "metadata": {
     "execution": {
@@ -1808,7 +1954,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1842,7 +1988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 48,
    "id": "b4cff8b7",
    "metadata": {
     "execution": {
@@ -1855,7 +2001,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1866,7 +2012,8 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.scatter(x, y, marker='o');"
+    "ax.scatter(x, y, marker='o');\n",
+    "ax.grid()"
    ]
   },
   {
@@ -1882,7 +2029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 49,
    "id": "9e59d427",
    "metadata": {
     "execution": {
@@ -1897,16 +2044,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x122230260>"
+       "<matplotlib.collections.PathCollection at 0x7f2143e6c8d0>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1945,7 +2092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 50,
    "id": "c7408ab0",
    "metadata": {
     "execution": {
@@ -1958,7 +2105,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1969,7 +2116,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.scatter(x, y, marker='o')\n",
+    "ax.scatter(x, (y + y[::-1])/2, marker='o')\n",
     "ax.set_xlabel(\"this is the x-axis\")\n",
     "ax.set_ylabel(\"this is the y-axis\")\n",
     "ax.set_title(\"Plot of X vs Y\");"
@@ -1986,7 +2133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 51,
    "id": "52bac447",
    "metadata": {
     "execution": {
@@ -2000,19 +2147,20 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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eParMzEyXPqe0tFTR0dEqKSlRVFRUQ8MGAAAmZrXZadEzCHdqthuFGdfJVzhXEexczUMN1eXeXVlZWRo6dGiVacOHD9ekSZNqfc+JEyd04sQJ59+lpaW+Cg8AAJgIyZSxmLFLtRnXyRc4V2Emhupy767CwkK1b9++yrT27durtLRUv/zyS43vmTNnjqKjo50/8fHx/ggVAAAYWObuAk1Yub1a6bDCknJNWLldmbsLAhQZ6mLGLtVmXCdv4lyF2Zg6offEjBkzVFJS4vzZv39/oEMCAABBzGqzK2NtTo2DkTmmZazNkdVmyKccAdPgXIUZmTqhj42N1aFDh6pMO3TokKKiotS0adMa39OkSRNFRUVV+QEAAKjN1rziaq19p7JLKigp19a8Yv8FBaAazlWYkakT+gEDBmjDhg1Vpq1fv14DBgwIUEQAAMBsio7VniB4Mh8A3+BchRkZKqE/fvy4srOzlZ2dLamyLF12drby8/MlVXaXHz16tHP+O++8U99++60efPBB7d27V88995xef/11TZ48ORDhAwAAE4ppEenV+QD4BucqzMhQCf3nn3+uXr16qVevXpKkKVOmqFevXpo1a5YkqaCgwJncS1JCQoLeeecdrV+/Xj179tSTTz6pF154QcOHDw9I/AAAwHz6JbRWXHRktfrfDhZVjqDdL6G1P8MCcBrOVZiRYevQ+wt16AEAQH0cI2dLqjLgliNxWDiqN+Ww/MDd2uLUIg89nKswClfzUBL6epDQAwAAV1DbOrDc3f7sr9DFvocRkNB7CQk9AABwFS2+geFodT39S21tra7uzg/z4VxFsHM1D23kx5gAAABMLTzMogFd2wQ6jJBSX21xiypriw9LjFV4mMXt+WFOnKswC0MNigcAAACcyt3a4tQiB2AmJPQAAAAwLHdri1OLHICZkNADAADAsNytLU4tcgBmQkIPAAAAw3K3tji1yAGYCQk9AAAADCs8zKL01ERJqpakO/5OT010DnDn7vwAEMxI6AEAAGBoKUlxWjiqt2Kjq3aTj42OrLEEnbvzA0Cwog59PahDDwAAYAzu1hanFjmAYEUdegAAAIQUd2uLU4scgNGR0AMAAEMyY+uqGdcJdTPbPjfb+gDBjoQeAAAYTubuAmWszVFByW+1wuOiI5WemmjY55/NuE6om9n2udnWBzACnqGvB8/QAwAQXDJ3F2jCyu06/QuMow3QiIOamXGdUDez7XOzrQ8QaK7moYxyDwAADMNqsytjbU61pEGSc1rG2hxZbcZprzDjOqFuZtvnZlsfwEhI6AEAgGFszSuu0p33dHZJBSXl2ppX7L+gGsiM64S6mW2fm219ACPhGXoAAGAYRcdqTxo8mc8T3h70KxjWCf7l6r4sLPnFx5F4B8cwEDgk9AAAwDBiWkR6dT53+WLQr0CvE/zP1X35yDt71DQiPOifPecYBgKHLvcAAMAw+iW0Vlx0pGprD7eoMsHul9Da65/tGPTr9K7FhSXlmrByuzJ3F3i03ECuEwKjvn3u8FNZRYOOLX/hGAYCh4QeAAAYRniYRempiZJULXlw/J2emuj1ute+HPQrUOuEwDl1n9fFKAPKcQwDgUNCDwAADCUlKU4LR/VWbHTV7rux0ZE+K43l60G/ArFOCCzHPm99RuM65zPKgHIcw0BgNPgZeqvVql27dqlTp05q1aqVN2ICAACoU0pSnIYlxnp1cLq6+GPQL3+vEwIvJSlOv5y0afJr2fXOa4QB5TiGAf9zO6GfNGmSevToodtuu01Wq1UXX3yxPvvsMzVr1kzr1q3T4MGDfRAmAABAVeFhFg3o2sYvn+WvQb/8uU4IDrFR5hpQjmMY8C+3u9y/8cYb6tmzpyRp7dq1ysvL0969ezV58mT99a9/9XqAAAAAp7Pa7Mrad0Rrsg8oa98Rnz9fzKBf8BVXBsiLjWrCsQWgRm4n9D/++KNiY2MlSe+++66uv/56devWTbfeeqt27drl9QABAABOlbm7QIPmfqiRSzbrvlezNXLJZg2a+6FPRwJn0C/4Sl3HlkP5rzatzyn0X1AADMPthL59+/bKycmR1WpVZmamhg0bJkn6+eefFR4e7vUAAQAAHHxVOs4VtQ361fqMCC34M4N+GZG/e3rUxnFsRTereYC8kp9PGqJ8HQD/czuhHzt2rG644QYlJSXJYrFo6NChkqQtW7aoe/fuXg/wdAsWLFDnzp0VGRmp/v37a+vWrbXOu2zZMlkslio/kZHGeP4IAIBQVFeC5cvSca5KSYrTzCvPqTIy+ZGyCj3yTg7JlsEEoqdHXYYlxiqyUc2NY0YpXwfA/9weFO/hhx9WUlKS9u/fr+uvv15NmjSRJIWHh2v69OleD/BUr732mqZMmaJFixapf//+evrppzV8+HDl5uYqJiamxvdERUUpNzfX+bfFQlc4AACCUebuAmWszanS+h4XHan01ESlJMW5VTrOV4NyZe4u0F2rdlT7p4KjhwDluYzB0dMjmPbj1rxiFZYG9vgGYDwela277rrrqk0bM2ZMg4Opzz/+8Q+NGzdOY8eOlSQtWrRI77zzjpYuXVrrPxMsFovzmX8AgH9ZbXbKF8ElriRYJ361ubQsX5X3qq+HgEWVLajDEmM5zoNYsO5Hf5RGhPFwH0V9XEron3nmGY0fP16RkZF65pln6pz33nvv9Upgp6uoqNC2bds0Y8YM57SwsDANHTpUWVlZtb7v+PHj6tSpk2w2m3r37q2//e1vOvfcc2ud/8SJEzpx4oTz79LSUu+sAACEmPpaWxFcAvml0dUE64nrerq0PF+V9wqGHgKu8Ma+NHMSEaz7sW3zJl6d71Rm3p9mxn0UrnApoX/qqad08803KzIyUk899VSt81ksFp8l9D/++KOsVqvat29fZXr79u21d+/eGt/z+9//XkuXLtV5552nkpISPfHEExo4cKC+/PJLdezYscb3zJkzRxkZGV6PHwBCSTB2Z0XtAv2l0dUES5bKuApLymtM/i2SYn1YOs4ILaje2JeBPh58LWj3o6uPxrv5CL3Z96dZcR+Fq1waFC8vL09t2rRx/l7bz7fffuvTYN01YMAAjR49WsnJybr44ov15ptvql27dnr++edrfc+MGTNUUlLi/Nm/f78fIwYA4wuGgcvgukCOGu/gauL04/ETAS0d52rLv696CNTHG/syGI4HXwvW/fhj2Yn6Z3JjPik09qcZcR+FO9we5b68vPabbkGB7y4Kbdu2VXh4uA4dOlRl+qFDh1x+Rr5x48bq1auXvvnmm1rnadKkiaKioqr8AABc5053VgRWsHxpdCfBqq10XGx0pM9brPoltFZcdGSttcItqmz59FUPgbp4Y18Gy/Hga8G6H739j4ZQ2Z9mxH0U7nA7oe/du7eys7OrTf/3v/+t8847zxsx1SgiIkJ9+vTRhg0bnNNsNps2bNigAQMGuLQMq9WqXbt2KS6O7ikA4CtB250V1QTLl0Z3E6yUpDh9Om2IXhl3gebdlKxXxl2gT6cN8Xn30/AwS0B7CNTFG/syWI4HXwvW/ejtfzSEyv40I+6jcIfbCf3gwYN1wQUXaO7cuZKksrIypaWl6ZZbbtFf/vIXrwd4qilTpmjJkiVavny59uzZowkTJqisrMw56v3o0aOrDJo3e/Zsvf/++/r222+1fft2jRo1St9//71uv/12n8YJAKEsWLuzorpg+dLoSYIVHmbRgK5tdHXymRrQtY3fkq9A9hCoizf2ZbAcD/4QjPvR2/9oCKX9aTbcR+EOt8vWPffcc7ryyit1++23a926dSooKFDz5s21detWJSUl+SJGpxtvvFGHDx/WrFmzVFhYqOTkZGVmZjoHysvPz1dY2G//o/jpp580btw4FRYWqlWrVurTp48+++wzJSYm+jROAAhljlamQA1cBtcF05dGR4J1+uBdsUE4eFdKUpyGJcYG1ajh3tiXwXQ8+EMw7kdvngehtj/NhPso3GGx2+1uPzhjs9l0zz33aOHChWrUqJHWrl2r4cOH+yK+gCstLVV0dLRKSkp4nh4AXOQYiEmqOiCz42syo/MGB6vNrkFzP6z3S+On04b4tYRdMCVYgeDJNnB1X370wCXa9v1PNS47GI+HUOWt0oPsT+PiPgpX81C3E/p9+/bpz3/+swoLC/XCCy/oo48+0uOPP6777rtPjz32mBo3btzg4IMJCT0AeIZSScbAl8bg0pDzpr59Of4PCXp7Z0Gdy+Z4MBf2p7FxHw1tPkvoW7RooSuvvFKLFi1Sy5YtJUmfffaZRo8erRYtWmjHjh0NCjzYkNADgOdobTUGvjQGh9rqTruTfNW2L6/qGafFH+e5tGyOB3Nhfxob99HQ5bOE/qWXXtItt9xSbfqxY8c0adIk/fOf/3Q/2iBGQg8ACAV8aQwsR/fo2kYld6d79On7sk+nVrr48Y1uLZvjwVzYn4Dx+CyhDzUk9AAAwNey9h3RyCWb653vlXEXaEDXNkGzbACAb7iah7o9yr1DTk6O8vPzVVFR4ZxmsViUmprq6SIBAABCki9LjFG+DADMy+2E/ttvv9U111yjXbt2yWKxyNHAb7H8/y5aVqt3IwQAADA5X5YYo3wZAJhXWP2zVHXfffcpISFBRUVFatasmb788kt9/PHHOv/887Vp0yYfhAgAAGBujrrTtT3VbFHlQGae1J325bIBAIHldkKflZWl2bNnq23btgoLC1NYWJgGDRqkOXPm6N577/VFjAAAAKYWHmZRemqiJFVLvB1/p6cmejSQmS+XDQAILLcTeqvVqhYtWkiS2rZtq4MHD0qSOnXqpNzcXO9GBwDAKaw2u7L2HdGa7APK2ndEVhvjusI8UpLitHBUb8VGV+36Hhsd2eB64b5cNiBxfQYCxe1n6JOSkrRz504lJCSof//++vvf/66IiAgtXrxYXbp08UWMAABQSxkhISUpTsMSY31SYsyXy0Zo4/oMBI7bZev+85//qKysTNdee62++eYb/fGPf9RXX32lNm3a6LXXXtOQIUN8FWtAULYOAAIvc3eBJqzcrtNvWI40hBZGAAgMrs+Ab/i1Dn1xcbFatWrlHOneTEjoASCwrDa7Bs39sErLz6ksquw2/Om0IbQ0AoAfcX0GfMfVPNTtZ+hP9corr6isrEytW7c2ZTIPAAi8rXnFtX5ZlCS7pIKScm3NK/ZfUAAArs9AEGhQQn/HHXfo0KFD3ooFAIBqio7V/mXRk/l8gcGgANTGzNcHI1yfAbNze1C8U3mhtz4AAHWKaRFZ/0xuzOdtDAYFoDZmvz4E+/UZCAUNaqEHAMDX+iW0Vlx0ZLX62Q4WVX5B7pfQ2i/xnNraNu+Dr3Tnyu3VupwWlpRrwsrtytxd4JeYAASfd78oMP31Idiuz0AoalBC/9577+nMM8/0ViwAAFQTHmZRemqiJFX70uj4Oz010S8DLmXuLtCguR9q5JLNuu/VbD31wdc1zufov5axNsdU3WsBuObdLw7q7le21/iama4PwXR9BkKV2wn9xRdfrBUrVuiXX37RoEGD1KRJE1/EBQCAU0pSnBaO6q3Y6KrdNmOjI/1WEslRmqmuAaBOxWBQQGjK3F2giat2qK5c3UzXh2C4PgOhzO1n6Hv16qWpU6fqnnvu0Q033KDbbrtNF1xwgS9iAwDAKSUpTsMSY7U1r1hFx8oV06KyG6c/Wn6sNrsy1uZUq7PsilAZDMpqswdk38B72IcN57hWuMos14dAXp+BUOd2Qv/000/riSee0Ntvv63ly5frD3/4g84++2zdeuutuuWWW9S+fXtfxAkAMBhfJAfhYRYN6NrG73HUV5qpLqEwGJTZB/4KBexD73D3WhGI64Ov/nHjyfUZQMN5NMp9o0aNdO211+raa69VUVGRFi9erJkzZ+ovf/mLrrjiCt17770aMmSIt2MFABhEsCQH3orDk1Y0iyq7nPbp1EpZ+44ErNXK162ujkcRTu+94Bj4iy63wc+X+zBQrf6B+lx3rhVhFumnsgofRlNdsFybAXiPxd6A2nNbt27Viy++qFdffVVRUVFKS0vTgQMHtGrVKk2cOFFPPPGEN2MNiNLSUkVHR6ukpERRUVGBDgcAgl5tyYHjq7S/n3n3RhxZ+45o5JLNLn+24zPG/yFBb+8sCNiXZ19/ebfa7Bo098NaWyQd/9T4dNoQut4GKV/uw0Alj4FMWj25VhjxmgjA91zNQ90eFK+oqEhPPvmkkpKSdNFFF+nw4cN65ZVX9N133ykjI0MvvPCC3n//fS1atKhBKwAAMJ66njX358jO3o6jvtJMp4uNjtT4PyRo8cd5AStZVdsgft78/Pq6F5tp4C+z8tU+9MfxF0yf6+DutUIy5jURQPBwO6Hv2LGjXnjhBY0ZM0Y//PCD3njjDaWkpMhi+e3Sdd5556lv375eDRQAEPyCJcHzdhz1lWaySJo8tJvm3ZSsV8ZdoI8euERv7ywI2Jdnf315d7V7caAG/rLa7Mrad0Rrsg8oa98Rr25vXy7bn3yxDwOVPAZD0lrXtaImRr0mAggebj9Dv2HDBl100UV1zhMVFaWNGzd6HBQAwJiCJcHzRRyO0kynd+WNraErb9a+Iy5/efbFIFLufHlvyOe7OqBXIAb+8mW3azM9h+yLfeiv4y9YPvd0tV0r6mLEayKA4OB2C319ybyvLViwQJ07d1ZkZKT69++vrVu31jn/v/71L3Xv3l2RkZHq0aOH3n33XT9FCgChJ1gSPF/FkZIUp0+nDdEr4y5wtsZ/Om1ItSTO1S/F7+0u8Enrrr++vNfXvdiiykS3X0Jr5zRvtWzXtRxfdrv29rID3dLvyT6sj6vHVWFpuVfXPZiSVse1YuaV57g0v1GviQACz6NR7gPltdde05QpU7Ro0SL1799fTz/9tIYPH67c3FzFxMRUm/+zzz7TyJEjNWfOHP3xj3/UqlWrNGLECG3fvl1JSUkBWAMAMDdHclBYUl5jt1fHAFvuJAfBFocrpZlc/VK8Iut7rcj63uutu/768u7oXjxh5XZZpCrb2pEgpqcmOgdT81bLdl3LGZYYW2e3a4squ10PS4x1e5A3q82u6W/u8tqyg6Gl39196ApXj6tH1n2p4rKTzr8buu7BlrSGh1mUdmGCXvg0z9TXRACB5XYLfSD94x//0Lhx4zR27FglJiZq0aJFatasmZYuXVrj/PPmzVNKSooeeOABnXPOOXrkkUfUu3dvzZ8/38+RA0BoqO9Zc8n95MCIcbg7MJa3B+xqSKuruy3Gju7FsdFVk6TY6Mgqo2Z7q2W7vuXM//Abnz0rPP/Dr3X055O1vu7Osr21PbzRwu/qPnSVq8f/qcm81PDzwBe9DRoq0NeiYIsDgPc1qGydP1VUVKhZs2Z64403NGLECOf0MWPG6OjRo1qzZk2195x11lmaMmWKJk2a5JyWnp6ut956Szt37qzxc06cOKETJ044/y4tLVV8fDxl6wDADcHQ8hjoOBwJm6QaW8RO5+0Sb7V9fl0lqhqyveqq++2t0miuLCe6WeM6k26HeTcl6+rkM+ud79TP7vPIeh39peHL9tb28Pbx7c3a7XUdf3WdDw09Dzw57v2BayIAd7latq5BXe7Ly8sVGemfbks//vijrFar2rdvX2V6+/bttXfv3hrfU1hYWOP8hYWFtX7OnDlzlJGR0fCAASCEpSTFaVhirNeSAyPG4e7AWN4esMudQfyk2mtUO1pN60uE6noUwVuDlbmyHFeSecn9btdb84pdSuZdWbY3tkdD91dNXHmcxFW1HX+tz4jQkbKKWt/X0PPA3ePeX7gmAvAVtxN6m82mxx57TIsWLdKhQ4f01VdfqUuXLpo5c6Y6d+6s2267zRdx+s2MGTM0ZcoU59+OFnoAgHu8mRwYNY5Tvzy/t7tAK7K+r/c93hywy9Uv7/WV+2rIc+eS9wYrc3U5LZs2VskvJ736rLDLn92scb3Lbuj28PX+8paajr/Ckl80+fWae0meqiHnQbAmrVwTAfiC28/QP/roo1q2bJn+/ve/KyIiwjk9KSlJL7zwgleDO1Xbtm0VHh6uQ4cOVZl+6NAhxcbG1vie2NhYt+aXpCZNmigqKqrKDwAAnnJ8eb7cxZZBbw/Y5fj8q5PP1ICubWpManxdo9pbg5W5upyxF3aW5N1nhV3+7IEJ9S67odvDSDXFTz/+YqObuvQ+bwzYWN9xDwBm4HZCv2LFCi1evFg333yzwsPDndN79uxZa9d3b4iIiFCfPn20YcMG5zSbzaYNGzZowIABNb5nwIABVeaXpPXr19c6PwAAvhKMA3Y5+Lrcl7fW3dXl3D3kd14d5M2Vz5YqW+fvHnJ2g5dV3/YIpvJs7grm8wAAjMjthP7AgQM6++zqNyubzaaTJ117tsxTU6ZM0ZIlS7R8+XLt2bNHEyZMUFlZmcaOHStJGj16tGbMmOGc/7777lNmZqaefPJJ7d27Vw8//LA+//xz3X333T6NEwCA0wXzKNO+LvflrXV3ZzmOOuCvjLtA825K1ivjLtCn04Z4/Ax1XZ/t8H/X9nBp/zV0ewRbeTZ3BPN5AABG5HZCn5iYqE8++aTa9DfeeEO9evXySlC1ufHGG/XEE09o1qxZSk5OVnZ2tjIzM50D3+Xn56ug4LdSJwMHDtSqVau0ePFi9ezZU2+88YbeeustatADAALC2+XBvMUfrabeWnd3luPtbte1fXZcdKQWubn/GrI9jN7KHaznAQAYkdtl69asWaMxY8ZoxowZmj17tjIyMpSbm6sVK1Zo3bp1GjZsmK9iDQhXywUAAOAqb5YH8xZ/lfvy1roHcht687M9XVawlmdzRzCeBwAQLFzNQz2qQ//JJ59o9uzZ2rlzp44fP67evXtr1qxZuuyyyxoUdDAioQcAhIrM3QV6+O0cFZZSo7omwZaAUlMcAMzLp3XoL7roIq1fv97j4AAAQGDVlJxWqvp/fg/+729KNSXPrc9orGuSz9TQxFhqigMAAsKjFnpJqqioUFFRkWw2W5XpZ511llcCCxa00AMAzKam5LRls8Y6+nP1wW2N1IXbVxzd2+v6wkTLOADAm1zNQ90eFO/rr7/WRRddpKZNm6pTp05KSEhQQkKCOnfurISEhAYFDQAAfMuRnJ5ex7ymZF76rb0+Y22OrLbQa6232uzKWJtTZzIvSYUl5ZqwcrsydxfUMycAAN7jdpf7tLQ0NWrUSOvWrVNcXJwsFrp1AQBgBK4mp6ezSyooKdfWvGIN6NrGF6EFra15xdX++VETuyp7M2SszdGwxFi6vQMA/MLthD47O1vbtm1T9+7dfREPAADwEVeT09oUHfP8vUblzjqH8j8+AACB4XZCn5iYqB9//NEXsQAAAijYRvCG9zU0IY9pEVn/TCbjyTqH4j8+ggXXMQChxqWEvrS01Pn73Llz9eCDD+pvf/ubevToocaNG1eZl4HjAMB4KH8VGjxNyC2SYqNPHQk/dPRLaK246EgVlpS7/KhCKP7jIxhwHQMQilwa5T4sLKzKs/J2u73as/OOaVar1ftRBhCj3AMwu9pG8GZ0c/Ox2uwaNPdDt5JTjgPXRrmXfvvHx6fThtAq7GdcxwCYjVfr0G/cuNFrgQEAgkddg6QxyJf5hIdZlJ6aqAkrt8uiqhXnHX+fXr4ulhZOpSTFaeGo3tVaf0/lODvSUxM5V/yM6xiAUOZSQn/xxRc7f8/Pz1d8fHyNLfT79+/3bnQAAJ+qb5A0Bvkyn9qSU0fiPiwxlmeQa5CSFOfcNutzCvVW9kEVl1U4X+cfH4HDdQxAKHN7ULyEhAQVFBQoJiamyvTi4mIlJCSYrss9AJiZq4N3MciXuZyanNaUuJP01Cw8zKIBXdtoQNc2+uuVifzjI0hwHQMQytxO6Gt6fl6Sjh8/rshIBoEBACNxdfAuBvkKfu6O7u1ITs0gECObm2n7GR3XMQChzOWEfsqUKZIki8WimTNnqlmzZs7XrFartmzZouTkZK8HCADwnfpG8A7l0c2NJJRH9w7ldUclrmMAQlmYqzPu2LFDO3bskN1u165du5x/79ixQ3v37lXPnj21bNkyH4YKAPA2xyBp0m+DejkwyJcxOEb3Pv0Z4sKSck1YuV2ZuwsCFJnvhfK64zdcxwCEMpfK1p1q7NixmjdvXsiUcKNsHYBQQCunMTnK0NU18rpZy6iF8rqjZlzHAJiJV8vWnerFF19sUGAAgOBT3yBpCE6hPLp3KK87asZ1DEAocjuhBwCYE4N8GU8oj+4dyuuO2nEdAxBqXH6GHgAABJdQHt07lNcdAAAHEnoAAAzKMbp3bR2KLap8htiMo3uH8roDAOBAQg8AQANZbXZl7TuiNdkHlLXviKw2t8ab9Vgoj+4dHmbRzCsTay1TJpl33QEAcHA7oV++fLneeecd598PPvigWrZsqYEDB+r777/3anAAAAS7zN0FGjT3Q41csln3vZqtkUs2a9DcD/1WMi0lKU4LR/VWbHTVruWx0ZFaOKq3aUf3ztxdoEfeyanxNbOvOwAADm6Xrfv973+vhQsXasiQIcrKytLQoUP11FNPad26dWrUqJHefPNNX8UaEJStAwDUxlEH/fQbqaNN2J9JpdVmD5nRvWvb7g7P/bmXrjivg19jAgDAm3xWtm7//v06++yzJUlvvfWW/vSnP2n8+PG68MILNXjwYI8DBgDASKw2uzLW5tSYVNpVmdRnrM3RsMRYvyTWoTK6d13bXarc7o+8s0fDk+JM+w8NAAAc3O5y37x5cx05ckSS9P7772vYsGGSpMjISP3yyy/ejQ4AgCDlTh10eA/bHQCA37id0A8bNky33367br/9dn311Ve64oorJElffvmlOnfu7O34nIqLi3XzzTcrKipKLVu21G233abjx4/X+Z7BgwfLYrFU+bnzzjt9FiMAIHRQBz0w2O4AAPzG7YR+wYIFGjBggA4fPqx///vfatOmsnvftm3bNHLkSK8H6HDzzTfryy+/1Pr167Vu3Tp9/PHHGj9+fL3vGzdunAoKCpw/f//7330WIwAgdFAHPTDY7gAA/MbtZ+hbtmyp+fPnV5uekZHhlYBqsmfPHmVmZup///ufzj//fEnSs88+qyuuuEJPPPGEOnSofeCbZs2aKTY21mexAQBCk6MOemFJea2l02Kpg+51bHcAAH7jUgv9F198IZvN5vy9rh9fyMrKUsuWLZ3JvCQNHTpUYWFh2rJlS53vffnll9W2bVslJSVpxowZ+vnnn+uc/8SJEyotLa3yAwDA6UK5Bnwgsd0BAPiNSy30ycnJKiwsVExMjJKTk2WxWHRqtTvH3xaLRVar1etBOj77VI0aNVLr1q1VWFhY6/v+/Oc/q1OnTurQoYO++OILTZs2Tbm5uXWW1pszZ45PexsAAMzDUQM+Y21OlYHaYqMjlZ6aSB10H2G7AwBQyaWEPi8vT+3atXP+7i3Tp0/X3Llz65xnz549Hi//1Gfse/Toobi4OF166aXat2+funbtWuN7ZsyYoSlTpjj/Li0tVXx8vMcxAADMLSUpTsMSY0OmBnywYLsDAOBiQt+pU6caf2+o+++/X2lpaXXO06VLF8XGxqqoqKjK9F9//VXFxcVuPR/fv39/SdI333xTa0LfpEkTNWnSxOVlAgDcZ7XZTZWIeVoD3tfbwWzb+XTubnezbw8AQOhxe1A8b2rXrp2z5b8uAwYM0NGjR7Vt2zb16dNHkvThhx/KZrM5k3RXZGdnS5Li4uiKBwCBkrm7oFpX6bgQ7Crt6+3Adq6K7QEAMCOL/dSH4YPY5ZdfrkOHDmnRokU6efKkxo4dq/PPP1+rVq2SJB04cECXXnqpVqxYoX79+mnfvn1atWqVrrjiCrVp00ZffPGFJk+erI4dO+qjjz5y+XNLS0sVHR2tkpISRUVF+Wr1ACAkZO4u0ISV26uNTu5oI104qndIJFe+3g5s56rYHgAAo3E1D3W7Dn2gvPzyy+revbsuvfRSXXHFFRo0aJAWL17sfP3kyZPKzc11jmIfERGhDz74QJdddpm6d++u+++/X3/605+0du3aQK0CAIQ0q82ujLU5NZYac0zLWJsjq80Q/2f2mK+3A9u5KrYHAMDMAtrl3h2tW7d2tsbXpHPnzlVG3o+Pj3erJR6A//E8a2jZmldcpbvz6eySCkrKtTWv2KPn0Y3C19uB7VwV28P4uFcAQO3cTuj3798vi8Wijh07SpK2bt2qVatWKTExscqo8gBQF55nDT1Fx2pPqjyZz6h8vR3YzlWxPYyNewUA1M3tLvd//vOftXHjRkmV9eGHDRumrVu36q9//atmz57t9QABmI/jedbTW80KS8o1YeV2Ze4uCFBk8KWYFpFenc+ofL0d2M5VsT2Mi3sFANTP7YR+9+7d6tevnyTp9ddfV1JSkj777DO9/PLLWrZsmbfjA2AyPM8auvoltFZcdKRq6yhrUWXLW7+E1v4My+98vR3YzlWxPYyJewUAuMbthP7kyZPOOu0ffPCBrrrqKklS9+7dVVDAf0oB1M2d51lhLuFhFqWnJkpSteTK8Xd6amKdz8ZabXZl7TuiNdkHlLXviCG/zHtjOwRy+ZKx9oM/tge8j3sFALjG7Wfozz33XC1atEhXXnml1q9fr0ceeUSSdPDgQbVpw2AyAOoWys+zMrCTlJIUp4Wjeld7JjbWhWdizfQsbUO2Q6CXb8T94Ovt7QrOf/eE8r0CANzhdh36TZs26ZprrlFpaanGjBmjpUuXSpL+8pe/aO/evXrzzTd9EmigUIce8K6sfUc0csnmeud7ZdwFphpx2ohJkC+5m9y8+0WBJq7aXm26r+qI+yv5cudzPInJG+tx6jK++7FMT33wdbV5jFLPPVBJNee/+0L1XgEADq7moW4n9JJktVpVWlqqVq1aOad99913atasmWJiYjyLOEiR0APeZbXZNWjuhyosKa/x2UiLKlvNPp02xDStV46BnU5fX6MkQYH27hcHdfcrO1Rbr25vHzPBmHwFKqaaPrc2Zjx3vYHz3zOheK8AgFO5moe6/Qy9JIWHh1dJ5qXKOvBmS+YBeF+oPc/KwE4Nk7m7QBNX1Z7MS9WfpW3I893BOKq2JzF54xn32j63NjzTXB3nv+dC7V4BAJ5y6Rn63r17a8OGDWrVqpV69eoli6X2i+f27dW7RALAqYLheVZ/cWdgJ7qNVuVIhlxVdKy8QS3Z9SVfFlUmX8MSY/2WRHgSkzda8+v63PrwTPNvOP8bJpTuFQDgKZcS+quvvto5sv2IESN8GQ+AEJGSFKdhibGmHySKgZ08V18ydLrvfizT0x98XS0JdbRk19e1ORiTL3djqq17t6vbwNXPrQv13H/D+d9woXKvAABPuZTQp6en1/g7ADREeJjF9K1SriY3JEHVuZPkxEVH6pWt+Q1qXQ/G5MudmLzZw8CTdXQ800w9999w/ntHKNwrAMBTHj1DL0kVFRX64YcflJ+fX+UHAPCbfgmtFRcdWe0ZUAeLKpNRkqDq3Elybup7lgpLT9T6uivPdwdj8uVOTN6s2+3uOvJMc804/wEAvuZ2Qv/VV1/poosuUtOmTdWpUyclJCQoISFBnTt3VkJCgi9iBADDYmAnz9WXDElSmEV67s+91bltM5eWWVfLczAmX+7E5M0eBq5s+1PFRkcyWnsNOP8BAL7mdkI/duxYhYWFad26ddq2bZu2b9+u7du3a8eOHQyIBwA1cAzsFBtdtdWTJKhudSVDDvNH9tIV58V5pXW9vs+zq7InwLovDno8cry73EkIvdnDoL7PtUiaPLSb5t2UrFfGXaBPpw3hOK4F5z8AwJfcrkN/xhlnaNu2berevbuvYgoq1KEH4C1Wm52BnTzgyqjt3qxZnbm7QNPf3KWjP5+sugyLdOod05+16f29Ddz5XLiG8x8A4A5X81C3E/q+ffvqqaee0qBBgxocpBGQ0ANA4LmSDDlGeJdUJaF1zOVqa2htI8Wfzt3lNpQ/t4G7nwsAALzLqwl9aWmp8/fPP/9cDz30kP72t7+pR48eaty4cZV5zZb0ktADgHE0tEXZ0crtask2T1q9fY1WdQAAjM+rCX1YWJgslt++qNjt9ip/nzrNarU2IOzgQ0IPAMbSkBblrH1HNHLJZrc/85VxFwRVWS1a1QEAMDZX81CX6tBv3LjRa4EBAOBLDalZ7WmNeX/WpncFdbsBAAgNLiX0F198sfP3/Px8xcfH19hCv3//fu9GBwCAH3laY96ftekBAAAc3C5bl5CQoMOHD1ebXlxcTB16AIChuVt/PRC16QEAABzcTuhren5eko4fP67ISFooAADGVV8t+lOdXgceAADA31zqci9JU6ZMkSRZLBbNnDlTzZo1c75mtVq1ZcsWJScnez1AAAg2DDhmbilJcVo4qne1keLDLJLtlGFkYxk53ms4pwAA8IzLCf2OHTskVbbQ79q1SxEREc7XIiIi1LNnT02dOtX7EQJAEKEkWGhISYrTsMTYKklmn06ttO37n0g6vYxzCgAAz7lUtu5UY8eO1bx580KmhBtl6wA4ZO4u0ISV23X6RdOR0i0c1ZsEBHAD5xQAADVzNQ91+xn6F198kcQWQMix2uzKWJtTLfGQ5JyWsTZH1v/fJ9tqsytr3xGtyT6grH1HnNMBo/L2Me3uOQUAAKpzuct9oD322GN65513lJ2drYiICB09erTe99jtdqWnp2vJkiU6evSoLrzwQi1cuFC/+93vfB8wAFPZmldcpUvw6eySCkrKtTWvWCW/VNCFGKbii27x7pxTA7q28egzAAAwO7db6AOloqJC119/vSZMmODye/7+97/rmWee0aJFi7RlyxadccYZGj58uMrLa/8CAQA1KTrm2nXjg5xCTVi5vVqiUlhSrgkrtytzd4EvwgN8xtEt3tvHtKvnlKvzAQAQigyT0GdkZGjy5Mnq0aOHS/Pb7XY9/fTTeuihh3T11VfrvPPO04oVK3Tw4EG99dZbvg0WgOnEtHCtLOfq7AN0IYZp+LJbvKvnlKvzAQAQigyT0LsrLy9PhYWFGjp0qHNadHS0+vfvr6ysrFrfd+LECZWWllb5AYB+Ca0VFx1Za21yi6Q2Z0SouOxkrcs4tQsxYATudIt3lyvnVFx0ZTUBAABQM9Mm9IWFhZKk9u3bV5nevn1752s1mTNnjqKjo50/8fHxPo0TgDGEh1mUnpooSdUSEMffVyd3cGlZdCGGUfiyW7wr51R6aiKlAQEAqENAE/rp06fLYrHU+bN3716/xjRjxgyVlJQ4f/bv3+/XzwcQvFKS4rRwVG/FRlftAhwbHamFo3prWGKsS8uhCzGMwtfd4us7pxhEEgCAugV0lPv7779faWlpdc7TpUsXj5YdG1v5xfrQoUOKi/vtC8GhQ4eUnJxc6/uaNGmiJk2aePSZAMwvJSlOwxJjtTWvWEXHyhXTorJLcHiYRVabXXHRkSosKa/xmWOLKhMVuhDDKBzd4n15TNd1TgEAgLoFNKFv166d2rVr55NlJyQkKDY2Vhs2bHAm8KWlpdqyZYtbI+UDwOnCwyw1ltFydCGesHK7LFKVBIguxDAifx3TtZ1TAACgboZ5hj4/P1/Z2dnKz8+X1WpVdna2srOzdfz4cec83bt31+rVqyVJFotFkyZN0qOPPqq3335bu3bt0ujRo9WhQweNGDEiQGsBwOzoQgyz4ZgGACB4BbSF3h2zZs3S8uXLnX/36tVLkrRx40YNHjxYkpSbm6uSkhLnPA8++KDKyso0fvx4HT16VIMGDVJmZqYiI3l+FYDv0IUYZsMxDQBAcLLY7XYKItehtLRU0dHRKikpUVRUVKDDAQAAAACYnKt5qGG63AMAAAAAgN+Q0AMAAAAAYEAk9AAAAAAAGBAJPQAAAAAABkRCDwAAAACAAZHQAwAAAABgQCT0AAAAAAAYEAk9AAAAAAAGREIPAAAAAIABNQp0AAAA77La7NqaV6yiY+WKaRGpfgmtFR5mCXRYQRsXAACAUZHQA4CJZO4uUMbaHBWUlDunxUVHKj01USlJccQFAABgInS5BwzMarMra98Rrck+oKx9R2S12QMdEgIoc3eBJqzcXiVplqTCknJNWLldmbsLiAt+xTUKAADfooUeMChaPHEqq82ujLU5qildskuySMpYm6NhibF+7eYerHHB97hGAQDge7TQAwZEiydOtzWvuNrxcCq7pIKScm3NK/ZfUAreuOBbXKMAAPAPEnrAYOpr8ZQqWzzp2hpaio7VnjR7Mp+3BGtc8B2uUQAA+A8JPWAwtHiiJjEtIr06n7cEa1zwHa5RAAD4Dwk9YDC0eKIm/RJaKy46UrU9hW5R5fPL/RJa+zOsoI0LvsM1CgAA/yGhBwzGSC2ejHDtP+FhFqWnJkpSteTZ8Xd6aqLfB54L1rj8LZTOBSNdowAAMDpGuQcMxtHiWVhSXuMzqhZJsUHQ4skI1/6XkhSnhaN6V9vusQHe7sEal7+E2rlglGsUAABmYLHb7eZtJvCC0tJSRUdHq6SkRFFRUYEOB5D02wjSkqp8YXa0cS4c1TugiYIjvtMvLsESn9lZbXZtzStW0bFyxbSoTJyCoQU8WOPypVA9F4L9GgUAQLBzNQ8loa8HCT2CVbC2+lltdg2a+2Gtg2I5Wuc+nTbE9MkcQluonwvBeo0CAMAIXM1D6XIPGFRKUpyGJcYGXYunOyNcD+jaxn+BAX4W6udCsF6jAAAwExJ6EwjFbqyoFB5mCbpEgBGugUqcC8F5jTIy7vcAgNOR0BscXRoRbBjhGqjEuQBv4n4PAKgJZesMzDHo0OldOgtLyjVh5XZl7i4IUGQIZdQdBypxLsBbuN8DAGpjmIT+scce08CBA9WsWTO1bNnSpfekpaXJYrFU+UlJSfFtoH5itdmVsTanxpJAjmkZa3NMXesYwYm640AlzgV4A/d7AEBdDJPQV1RU6Prrr9eECRPcel9KSooKCgqcP6+88oqPIvQvdwZbAvzNUXc8NrpqV+LY6EjKVSGkcC6gobjfAwDqYphn6DMyMiRJy5Ytc+t9TZo0UWxsrA8iCiwGW0KwY4RroBLnAhqC+z0AoC6GSeg9tWnTJsXExKhVq1YaMmSIHn30UbVpU/uIuydOnNCJEyecf5eWlvojTLcx2BKMgBGugUqcC/AU93sAQF0M0+XeEykpKVqxYoU2bNiguXPn6qOPPtLll18uq9Va63vmzJmj6Oho5098fLwfI3Ydgy0BAGB+3O8BAHUJaEI/ffr0aoPWnf6zd+9ej5d/00036aqrrlKPHj00YsQIrVu3Tv/73/+0adOmWt8zY8YMlZSUOH/279/v8ef7EoMtIZhYbXZl7TuiNdkHlLXvCIMzwWc41hBquN8DAOoS0C73999/v9LS0uqcp0uXLl77vC5duqht27b65ptvdOmll9Y4T5MmTdSkSROvfaYvOQZbOr0ubSx1aeFH1EaGv3CsIVRxvwcA1CagCX27du3Url07v33eDz/8oCNHjiguzjw3PgZbQiA5aiOf3kbqqI3MKN7wFo41hDru9wCAmhjmGfr8/HxlZ2crPz9fVqtV2dnZys7O1vHjx53zdO/eXatXr5YkHT9+XA888IA2b96s7777Ths2bNDVV1+ts88+W8OHDw/UaviEY7Clq5PP1ICubbi5wy+ojQx/4VgDKnG/BwCczjAJ/axZs9SrVy+lp6fr+PHj6tWrl3r16qXPP//cOU9ubq5KSkokSeHh4friiy901VVXqVu3brrtttvUp08fffLJJ4bpUg8EM2ojw1841gAAAGpmmLJ1y5Ytq7cGvd3+W+tM06ZN9Z///MfHUQGhi9rI8BeONQAAgJoZpoUeQHChNjL8hWMNAACgZiT0ADxCbWT4C8caAABAzUjoAXiE2sjwF441AACAmpHQA/CYozZybHTVrs6x0ZGUEYNXcawBAABUZ7GfOpIcqiktLVV0dLRKSkoUFRUV6HCAoGS12amNDL/gWAMAAKHA1TzUMKPcAwhejtrIgK9xrAEAAPyGLvcAAAAAABgQCT0AAAAAAAZEQg8AAAAAgAHxDH09HGMGlpaWBjgSAAAAAEAocOSf9Y1hT0Jfj2PHjkmS4uPjAxwJAAAAACCUHDt2TNHR0bW+Ttm6ethsNh08eFAtWrSQxUJpJHeUlpYqPj5e+/fvp+Qf3MKxA09w3MBTHDvwBMcNPMWxA1fY7XYdO3ZMHTp0UFhY7U/K00Jfj7CwMHXs2DHQYRhaVFQUFyt4hGMHnuC4gac4duAJjht4imMH9amrZd6BQfEAAAAAADAgEnoAAAAAAAyIhB4+06RJE6Wnp6tJkyaBDgUGw7EDT3DcwFMcO/AExw08xbEDb2JQPAAAAAAADIgWegAAAAAADIiEHgAAAAAAAyKhBwAAAADAgEjoAQAAAAAwIBJ6+MVVV12ls846S5GRkYqLi9Mtt9yigwcPBjosBLnvvvtOt912mxISEtS0aVN17dpV6enpqqioCHRoCHKPPfaYBg4cqGbNmqlly5aBDgdBbMGCBercubMiIyPVv39/bd26NdAhIch9/PHHSk1NVYcOHWSxWPTWW28FOiQYwJw5c9S3b1+1aNFCMTExGjFihHJzcwMdFkyAhB5+cckll+j1119Xbm6u/v3vf2vfvn267rrrAh0WgtzevXtls9n0/PPP68svv9RTTz2lRYsW6S9/+UugQ0OQq6io0PXXX68JEyYEOhQEsddee01TpkxRenq6tm/frp49e2r48OEqKioKdGgIYmVlZerZs6cWLFgQ6FBgIB999JHuuusubd68WevXr9fJkyd12WWXqaysLNChweAoW4eAePvttzVixAidOHFCjRs3DnQ4MJDHH39cCxcu1LfffhvoUGAAy5Yt06RJk3T06NFAh4Ig1L9/f/Xt21fz58+XJNlsNsXHx+uee+7R9OnTAxwdjMBisWj16tUaMWJEoEOBwRw+fFgxMTH66KOP9Ic//CHQ4cDAaKGH3xUXF+vll1/WwIEDSebhtpKSErVu3TrQYQAwuIqKCm3btk1Dhw51TgsLC9PQoUOVlZUVwMgAhIKSkhJJ4jsNGoyEHn4zbdo0nXHGGWrTpo3y8/O1Zs2aQIcEg/nmm2/07LPP6o477gh0KAAM7scff5TValX79u2rTG/fvr0KCwsDFBWAUGCz2TRp0iRdeOGFSkpKCnQ4MDgSenhs+vTpslgsdf7s3bvXOf8DDzygHTt26P3331d4eLhGjx4tnvgITe4eO5J04MABpaSk6Prrr9e4ceMCFDkCyZPjBgCAYHPXXXdp9+7devXVVwMdCkygUaADgHHdf//9SktLq3OeLl26OH9v27at2rZtq27duumcc85RfHy8Nm/erAEDBvg4UgQbd4+dgwcP6pJLLtHAgQO1ePFiH0eHYOXucQPUpW3btgoPD9ehQ4eqTD906JBiY2MDFBUAs7v77ru1bt06ffzxx+rYsWOgw4EJkNDDY+3atVO7du08eq/NZpMknThxwpshwSDcOXYOHDigSy65RH369NGLL76osDA6FoWqhlxzgNNFRESoT58+2rBhg3NAM5vNpg0bNujuu+8ObHAATMdut+uee+7R6tWrtWnTJiUkJAQ6JJgECT18bsuWLfrf//6nQYMGqVWrVtq3b59mzpyprl270jqPOh04cECDBw9Wp06d9MQTT+jw4cPO12hBQ13y8/NVXFys/Px8Wa1WZWdnS5LOPvtsNW/ePLDBIWhMmTJFY8aM0fnnn69+/frp6aefVllZmcaOHRvo0BDEjh8/rm+++cb5d15enrKzs9W6dWudddZZAYwMweyuu+7SqlWrtGbNGrVo0cI5Vkd0dLSaNm0a4OhgZJStg8/t2rVL9913n3bu3KmysjLFxcUpJSVFDz30kM4888xAh4cgtmzZslq/WHPpQl3S0tK0fPnyatM3btyowYMH+z8gBK358+fr8ccfV2FhoZKTk/XMM8+of//+gQ4LQWzTpk265JJLqk0fM2aMli1b5v+AYAgWi6XG6S+++GK9j5MBdSGhBwAAAADAgHgYFQAAAAAAAyKhBwAAAADAgEjoAQAAAAAwIBJ6AAAAAAAMiIQeAAAAAAADIqEHAAAAAMCASOgBAAAAADAgEnoAAAAAAAyIhB4AgCCzadMmWSwWHT16tNZ5Hn74YSUnJ3u0/O+++04Wi0XZ2dkevb8mrsRsJGlpaRoxYkSgwwAAoE4k9AAABNDgwYM1adIkt983depUbdiwwaPPjI+PV0FBgZKSkjx6v6cxG8m8efO0bNmyQIcBAECdGgU6AAAA4L7mzZurefPmHr03PDxcsbGxXo7IXKKjowMdAgAA9aKFHgCAAElLS9NHH32kefPmyWKxyGKx6LvvvnO+vm3bNp1//vlq1qyZBg4cqNzcXOdrp3e537Rpk/r166czzjhDLVu21IUXXqjvv/++xs89vcv9Tz/9pJtvvlnt2rVT06ZN9bvf/U4vvvii12OWpDVr1qh3796KjIxUly5dlJGRoV9//bXGzyovL9e5556r8ePHO6ft27dPLVq00NKlS2t8j2Oeq6++Wu3bt1fz5s3Vt29fffDBB87X9+7dq2bNmmnVqlXOaa+//rqaNm2qnJwc53qe2uX+jTfeUI8ePdS0aVO1adNGQ4cOVVlZWa0xAADgDyT0AAAEyLx58zRgwACNGzdOBQUFKigoUHx8vPP1v/71r3ryySf1+eefq1GjRrr11ltrXM6vv/6qESNG6OKLL9YXX3yhrKwsjR8/XhaLxaU4Zs6cqZycHL333nvas2ePFi5cqLZt23o95k8++USjR4/Wfffdp5ycHD3//PNatmyZHnvssRo/KzIyUi+//LKWL1+uNWvWyGq1atSoURo2bFit20KSjh8/riuuuEIbNmzQjh07lJKSotTUVOXn50uSunfvrieeeEITJ05Ufn6+fvjhB915552aO3euEhMTqy2voKBAI0eO1K233qo9e/Zo06ZNuvbaa2W3213avgAA+Apd7gEACJDo6GhFRESoWbNmNXaBf+yxx3TxxRdLkqZPn64rr7xS5eXlioyMrDJfaWmpSkpK9Mc//lFdu3aVJJ1zzjkux5Gfn69evXrp/PPPlyR17tzZJzFnZGRo+vTpGjNmjCSpS5cueuSRR/Tggw8qPT29xs9LTk7Wo48+qttvv1033XSTvv/+e61bt67O9enZs6d69uzp/PuRRx7R6tWr9fbbb+vuu++WJE2cOFHvvvuuRo0apYiICPXt21f33HNPjcsrKCjQr7/+qmuvvVadOnWSJPXo0aPOGAAA8Ada6AEACFLnnXee8/e4uDhJUlFRUbX5WrdurbS0NA0fPlypqamaN2+eCgoKXP6cCRMm6NVXX1VycrIefPBBffbZZz6JeefOnZo9e7bz+f/mzZs7W/p//vnnWpd5//33q1u3bpo/f76WLl2qNm3aOF87dVl33nmnpMoW+qlTp+qcc85Ry5Yt1bx5c+3Zs8fZQu+wdOlSffHFF9q+fbuWLVtWa4+Gnj176tJLL1WPHj10/fXXa8mSJfrpp58820AAAHgRCT0AAEGqcePGzt8dyabNZqtx3hdffFFZWVkaOHCgXnvtNXXr1k2bN2926XMuv/xyff/995o8ebIOHjyoSy+9VFOnTvV6zMePH1dGRoays7OdP7t27dLXX39drdfBqYqKivTVV18pPDxcX3/9dZXXTl3W7NmzJVVWAFi9erX+9re/6ZNPPlF2drZ69OihioqKKu/duXOnysrKVFZWVuc/QMLDw7V+/Xq99957SkxM1LPPPqvf//73ysvLc2/jAADgZST0AAAEUEREhKxWq1eW1atXL82YMUOfffaZkpKSqgz6Vp927dppzJgxWrlypZ5++mktXry41nk9jbl3797Kzc3V2WefXe0nLKz2ryS33nqrevTooeXLl2vatGnas2eP87VTlxETEyNJ+u9//6u0tDRdc8016tGjh2JjY6sM3CdJxcXFSktL01//+lelpaXp5ptv1i+//FJrDBaLRRdeeKEyMjK0Y8cORUREaPXq1W5vAwAAvIln6AEACKDOnTtry5Yt+u6779S8eXO1bt3a7WXk5eVp8eLFuuqqq9ShQwfl5ubq66+/1ujRo116/6xZs9SnTx+de+65OnHihNatW1fnM/iexjxr1iz98Y9/1FlnnaXrrrtOYWFh2rlzp3bv3q1HH320xvcsWLBAWVlZ+uKLLxQfH6933nlHN998szZv3qyIiIga3/O73/1Ob775plJTU2WxWDRz5sxqPRvuvPNOxcfH66GHHtKJEyfUq1cvTZ06VQsWLKi2vC1btmjDhg267LLLFBMToy1btujw4cNujVMAAIAv0EIPAEAATZ06VeHh4UpMTFS7du2qPeftimbNmmnv3r3605/+pG7dumn8+PG66667dMcdd7j0/oiICM2YMUPnnXee/vCHPyg8PFyvvvqq12MePny41q1bp/fff199+/bVBRdcoKeeeso50Nzp9u7dqwceeEDPPfeccyT95557Tj/++KNmzpxZ6+f84x//UKtWrTRw4EClpqZq+PDh6t27t/P1FStW6N1339VLL72kRo0a6YwzztDKlSu1ZMkSvffee9WWFxUVpY8//lhXXHGFunXrpoceekhPPvmkLr/8cpfWGwAAX7HYqbkCAAAAAIDh0EIPAAAAAIABkdADAAAAAGBAJPQAAAAAABgQCT0AAAAAAAZEQg8AAAAAgAGR0AMAAAAAYEAk9AAAAAAAGBAJPQAAAAAABkRCDwAAAACAAZHQAwAAAABgQCT0AAAAAAAY0P8DJW830NPiC0wAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1200x300 with 1 Axes>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "fig.set_size_inches(12,3)\n",
-    "fig"
+    "fig\n",
+    "#np.corrcoef(x,0.5*(y+y[::-1]))"
    ]
   },
   {
@@ -2040,7 +2188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 52,
    "id": "0e4d4d4e",
    "metadata": {
     "execution": {
@@ -2054,9 +2202,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1500x500 with 6 Axes>"
+       "<Figure size 1200x400 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -2066,7 +2214,8 @@
    "source": [
     "fig, axes = subplots(nrows=2,\n",
     "                     ncols=3,\n",
-    "                     figsize=(15, 5))"
+    "                     figsize=(15, 5))\n",
+    "fig.set_size_inches(12,4)"
    ]
   },
   {
@@ -2080,7 +2229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 53,
    "id": "12d9932f",
    "metadata": {
     "execution": {
@@ -2094,19 +2243,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1500x500 with 6 Axes>"
+       "<Figure size 1200x400 with 6 Axes>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "axes[0,1].plot(x, y, 'o')\n",
-    "axes[1,2].scatter(x, y, marker='+')\n",
+    "axes[1,2].scatter(x, y[::-1], marker='+')\n",
     "fig"
    ]
   },
@@ -2133,7 +2282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 54,
    "id": "17d46f32",
    "metadata": {
     "execution": {
@@ -2160,7 +2309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 55,
    "id": "24a361a3",
    "metadata": {
     "execution": {
@@ -2173,19 +2322,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1500x500 with 6 Axes>"
+       "<Figure size 1200x400 with 6 Axes>"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "axes[0,1].set_xlim([-1,1])\n",
-    "fig.savefig(\"Figure_updated.jpg\")\n",
+    "#fig.savefig(\"Figure_updated.jpg\")\n",
     "fig"
    ]
   },
@@ -2210,7 +2359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 56,
    "id": "facce2ec",
    "metadata": {
     "execution": {
@@ -2224,7 +2373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -2251,7 +2400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 57,
    "id": "1683862d",
    "metadata": {
     "execution": {
@@ -2265,7 +2414,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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aY6Qyt1D7i2MMGpB05iKJHE8wHmo3N1dgo1VdkyTyPB1yy/HUkqXM9GzSDcxPZ6qJM0XxrPDqO9t1aqdnWTcUueQg6j6znaMNFlbSnXp0arFhxVO89rrv4p8wFmoXBMFoqF13zygU8pzqCHpjclEqddstLBRYWhi+Hrp7PDfF828iaZryl6rNV7TPQxmitMxsVFqFP791PHVGyMIiii8r1J7jaC8kwlxYSEh/NedVlHgtcjx79uxJt27dqFSpEpUrV2b27NnY2dlx8eLFojwtAB5aqTgiSdpk8DpC92b2KpVUcnmJc9I9IBMKQV2VyWQ5C0BCHkLl/zcfnTM+j/vrFixTTEk6FSZRwkGud1ydOmpAiYS/zRjJRo5nZ2d8jM4UYsiZnlPLM9FALU9dEXkJcvl3Afl0yXJI1raWWNmIxD02IlHy+J5aVfDZ40jJ7S9Cp3iG3X+ep3qsLxOh958DpuV3PtfW+/TwNdxeVUdOnQ0Uj0+MMx5qT9R+b7rv8UVkZihRZovfm51EDqfunrGzt5Y0Nmk0wgs5ntLETldNwS4XoqhLgclNmUxKMW2cDn8XkLcwaTz8/YywUJhhZYKBSfIYunJKheRqTyjk0L0hxGrXBudXSFnMUqpI0r5AuNi8OvMqSmSrVMRqXwJeG1e7Wq1m48aNpKWl0bSpfm9fgKysLJKTk//vL7+o4SUqFWeDQ/N9jH8bKriLi8nNZ7kvqC8L5dydMZPLiElJIyKxaF8Cyrg6AfAgPKZwjudesOP5eoj7P3wem6f9SrmJSlNoZEKuY73dHZHLZCSnZhKbYFzdL60NMT8JMzwf71LimNAwwy7unDGh0mNK+4j3YbCB8LS3lvQEP5Le7lvOHYBH98L1trl42GPnaI1GreHRXf3tAOWrlwbg1oVHkturN64IwN1Lj4k0ci0A/BuUx8LKnOePorh3Wfp4xY2j684CULNllVzHnth6CYAaTStLbldmq7hz9QkA5auVlhwTdOspAL4SDvrsbBWhj0Ryq/seX8STh+I2ZxdbbGz1iVmwtlh/aR9nyXM/ex6PWq3BwkKBi0TXJIAQ7b3rbaRAvkqtIfSZOK60gbJSOXN+Kt4jPp6Gj6eDIAiERoi/W29348d9EbpnRBkPJ5NaDkqd98Fz8Tmle24VBGqNhkeR2jkVsOd7brj5NAKASl7G00ReJm4+F9dQNzsbnF5COalXAedDnqIRBDzsbIu8hFSRE8/AwEDs7OywtLTkvffeY8eOHVSrVk1y7HfffYejo2POn6+vb77P29FfXFwuhTwlsZBqO77qaFq+DACXQ56hUhfcYFMYsLOyxN9LXICuhzwv0nPVLOsFwO2wwjGC1CpXCoBbwREF2v9OSGSevg//MqJyFRSae3jX2socPx/xgX3vifEXjioVxetz34jS519RJBNPQmPINlC7snJl8TgPHkofx7+qt3iMx9FkSrTerFrDR5xv4DPJ/avXFe/jOzf0Xxrlcjk1G5YHIPDSY8n9G7WrDsCV49IpPaX83KnbuiqCIHDwr7OSY3RwcLGj9YAmAOxZdszo2OLAwxvB3L/yGIW5GV1GtTY6Nj4qiQsHAgDoNqql5Jjblx+TkZqFs7s9lWrpP38z07N5oCWetZtU0Nv+6F442VkqHJ1t8PFz09t+/7b4nVep6SNJsO5pXyaqGCC9D7X3XMUKHpgZaEcZ9EgcU7mi4dJSYc/jyMxSYW1lThlvw+ovwF3t76VqeS+j4wCi4lNITsvEzExO+dKmE6nAJ+Izpmb5Uibv8yJCoxNITs/C0twM/9L6hD+veBIVT3qWEmsLcyoWISEUBIHrIeJ33sBP+jsvDlx8EgZAk3Jl8vUi8G/E4fvii3UH/4rIi/gzFznx9Pf3JyAggEuXLvH+++8zevRo7t69Kzl28uTJJCUl5fw9ffo03+ct6+KEv4cbakHg+EPpBep1Q7VSHthbWZKSmcW9yFcnJ61uWZGI3AiVVqgKCzXLaIliWP6I4j9R269gxNPPywU7Kwsys1U8Djdd9aysJZ4Pw0z7DqtqO+zczSV0XFVHPB8ZHufl6YiDvRUqlYYnIdJKbyXtcZ48iUYlEe5297DHxdUOjVrgUZD+uSpX80YulxEbnUxMlH5Uo3rdsoBIPKXC2zUbicTz1qUnkvNr1F58sb157iFZGdJGs26jWwFw6K+zuRaH7/lOewDO7LhMnIHwfXFh59LDALTo0zCnhqkhHF5/DrVKQ7VGFfCrKr3IXzkmPpsbtKkq2e7y3o1QVEo1Ht5OeEqokneuiy8L1euWlSaWWuKpe/n4J+7fFV9Oq1Tzltz+QKuY6u5BKQRpFVf/SobH3NP+BvwreBoksACJyemEx4ipB7rfmTE8CBN/M+W8XUyq+alDYLA4n5rl8kc8b2qfUdV8PTFXFLyvemCYOJ/qvp5F2qf8aXwScanpKMzkVPfJndi/LFwMFrlHk3L5F7/+TVBpNBzT8qROWtGuKFHkxNPCwoKKFStSv359vvvuO2rXrs2iRYskx1paWuY44HV/BYFO9TwS9N8gnmZyOY38xAf6Be0b26uAen4vi3iKC8PjqDjSCsHZrlsEHkXk73hyuYwa5cSHqU7RMAWVy4iKxYOwGJPyCqtVEOd5L5eSP1W0odHgZ3FkGPg8MpmMytpF/cEj6eN5eztha2uJUqkmNFSfUMtkMqpqicN9iXC5lbUF5bRq1P07+qpn5Ro+KBRmxEWnEPVcP92gVmNRabt9NVgyz9OvijdupZzIylRy88JDyc/QtGttnD0cSIhO5uKhW5JjdPCvX56qjSqgzFYx/63fcm21+bJwfs81jq4/B0Dv9zsaHavRaDiw9gzwN+mWwuXjIvFs2L665PabWpW5ZuMKksRSp1LrXh7+ifuBfyue/0R2loon2vQLQ8RTp3hWrixNAtVqDY+0eaz+RsipjnhWqWCc7NwL1qYNeDmblOP5QPuyqHt5NAVqjYa7oeJ5dM+LvCIwRKuY5pO46h1PSzxr+uZOtgsC3ZpQ3dsz37mthY20rGxuadPVmpT/bxDP60/DSUjPwMnaioZlil55ful1PDUaDVlZphfULgh0zP3skxDSsgtORP4N0L2hXXySf7W4sFG3rHgjP4iMJS2r6L4Hdwc7vJzsEQS486zg4XYPRztKOdujEQTuhOYvb7ZqWS3Bemp6nmi50q4ozOSkpGcREZt7nrO/n1YhzSU07+5qj6uzLRqNwEMDaib8HW4PMhBKl8lkVKokjjEYbtcRz7vS6RVVaor3xH2JcLullTmVa4jbb1/XD7eXq+KFnaM1GWlZknmeMpksR/XUKXj/hMJcQadhzQHY/+dpyTEv4tNfx2Jla0nAqbv8OXNbruOLGs8eRrDgnd8B6DehM9UaVzI6/vqJu0SFxWHnZEPLXvUlx4QHx/D8STRmCjn1WvpLjtGlN9TSqs4vQqPRcDdAfOGtUc9Pb7tO4ZbLZVSuqk8sHz2MRK3W4ORsi6dE3qVGI/BQq3hWNqBmPn2eQEamEitLc8r4GA6hB2nJaVUj5BT+/k3pfmO5QZce458H4hkWnUhGlhIrCwXlShkP+xuCLiqji9IUFDnEs2zhHM8QdMSzrp/0i0Zx4Groc1QaDb7Ojvg4m56n+2/G4SDxBb1dpfKYmxVcMc8NRUo8J0+ezOnTpwkJCSEwMJDJkydz8uRJhg8fXpSnzYG/hxu+To5kqdSceRzyUs5Z3NDleV4Le07WK9Jf2tPRDm8nBzSCwK2nhRMGN4SaZcSF5FZo4ZxHl6d5MyR/x/P3FdXLoKempz6YK8xy8sN0oTtjqODrhlwmIz4pnbhE49UDTAm360KUhhRP+Hvhf/BAeowuR+++AQNQTp7nbWli+mK4/Z8wJc+zoTbP8/LxuwZV464jWyKTybh+4i4RRog4QJkqpfn017EAbPphL+f3XDM6viiRmZbFt8N+IT05g+rNKvPWrMG57qMj1x0GN8XSgNv6sjYntkajCthKlDrKTM/OMRbpVOcX8SwkluTEdCytzKlQRZ+w3Nd+134VPLC20Z+D7l6pUrWUpJoaHp5AWnoW5uZmlC2rnz8K8EB7X1cykgOqVKp5pP2+c1M8dS9olfxMy5vU/V51UQtTcF+rklYq7Z6vsHZaZjaPIkSjVK1CUDzTs5Q8jBAjGbrnaVEhQEc8y746xPNCTn7nf0PtFAQhJ7+z40sIs0MRE8/o6GhGjRqFv78/7du358qVKxw6dIiOHY2HhQoLMpmMTlXEC3k46NV0pBY2Kri74G5nQ5ZKTcCzoiV5eUFOnmdI0Ybba2kflLo39oKiplZBCMwn8ayiDf8/fBaLUm16iFYXqntgQp6nlaU5vqXEfLuHocYJlG6hvWeEeOpC7cYMRrocO0OKZ+UqpZDJICoyiQSJLkP+WuL58F64ZJ6oMeIJued51mleCXNLBdHP4gl7ID1Hr7Ju1GsrKqO6MLQxtO7fmL7jOwOw4J3feW7kGhYVBEFg4YSVhNx9hounI1PXTMi9dmdEYk46gdEwu1YdbthO2vx593oIapUm1/zOKjV9UJjrqyYvGoukcD83Y5H2Rah8eQ8UBvIY/zYWGSZMj0NjUKrUONhZ4Z1LXuxDLZGsVDZ3BTMlLTMnQlEpH8SzSh5U0hdxJzQSjSBQysUBd8eC11+8+yxKdDc72OLlJF1SqzCQnJHJo2iRMNcpU7TKal5wSZffqRVxXncERkQRmZKKjbk5zctJp8gUNoqUeK5YsYKQkBCysrKIjo7m6NGjL4106qALt598FEz2f6Bvu0wmo3E58QfzaoXbX1KeZ46zvXBIQe1yOuIZma86jj5ujthaWZCtUhNiQnkkHV7M8zQFuoUut3C7Kc52Lw+HXA1GOmf74yfRqCUc+7a2lpTRqlJSqqdPGVfs7K3IzlJJllWqVke8h8OexEjW88wtz9PKxpLazcTwsy5vUQo6InZ4/TmUJnSoGjt7MNWbVSY9OYOZQ38mM5dWpYWN3cuOcmLzBeRmcqasnYCrtrSVMRxcdxaNWkONJpUoU1l6gc9IyyLwkvhy3riDdH7nrcsiyc81v7OegfxOreKZq7GouiFjkZZUGjEN6YxFxhztunu/SgUvo47ljEwlYRHx4vFMIJ6632opV4c81dLURUOq5IGsvghdNKaWX+Goky8rzH4zLAJBEMvgub0i/dAT0jK4Fyl+j43LSd+nrxuOaEW51hX9Xlqe7WvZq/1F1C5dCg87W1KzsrkQ8uoQsaJEU21C9IXgV8dgpMvhufk0slB6qRtCNR9PzOQyopPTiCyEuqH+pd0xV5iRkJpBWExinveXy2VU9tGG2010qcPfC50piif8HQrMlXhqDUZPwxMMdjsyxWBU2tsZGxsLsrNVhEgYjOBvg4gU8ZTLZfhXN5zn6eBkQ5kK4jXQ5Q2+iP/L87wjHa7XlVW6fMxwp7TGnWri7OFAYkxKTqkhY1CYK5i6ZgIuno6E3H3Gog9WvbTC8ncvPeT3L9cD8PbsIdRsLp2H+SLUag0HtWpuVwMllABunAlCla2mVFk3Shvo924svxOMG4vUKg0PdMRSQvFMTEgjIjwRAP8qBojnA+PEM8/GolzyO588i0UQwMXRBlcDfef/b366MHtZ0wmkIAg5iqe/b/4UT11+Z2GE2QECtVVBijrMfv0VDLNf0nKESh6uRV7L8lXA/4fZjeeJFyZee+Ipl8no4P/fCrfrQgSBzyNJLUIzT15QydMVO0sL0rKyeRCZt4LqeYG1hTmVvESlrTDC7RbmCqpqF4T8h9vFheh+HvI8K2tzQyPjUnI6rBiDLhT4MBeF1MnBhlLatpI6g4UUdHmehgxGcrmMSlpV6WFuBiMJZztA1Zq6PM+C1fPMrazS3avBpBjokqQwV9B5RAvANJMRgGspJ6asnYDcTM7xTefZ83vR1/dMjE5m9ojFqJRqWvVvRN8JnU3a7+qx28Q8T8De2dagqQj+JueN2leTVAFzy++Mi04m4lkCcrmMKhL1P4MfR5GVpcLO3gqfMvp1IYPui78tX60S/k8IgsBDXRjdgKPdVGPR/RxHu3HHti5txdSweX4c7RHxKSSnZ6GQy6nonfd6mYIg5DyXahWSQeeW1khZq4iJ541XkHjqooRN/yNh9sex8QTHJ2BuZkabin4v7byvPfEE6OgvPiiPPXhcpGrbq4LSTg74Ojui1ghcDZVe1F82zORyamnzeF5WuD2wkAxGOqfozXzW89TlbgXlwdluZ2OZ0/nkoQn76RTSsPAEMrP0i7b/33wqmF5IPshIHmMlncHIEPHUKlcP70dIqoJVtCHX+waJp4l5npelDUaevq6UqeyFRq3h2qn7hj5Gjsko4PR9wg10W9I7d3N/xmpNPcsmrePuJemyTYUBtUrNnNFLiA1PwNe/FJ8secvkotYvmooM9WYXBIErujJK7aTD7Heu5ZLfqVWly1X2wlai97muWYB/9dLI5fpzf6AlnpWrSqt2EZFJpKaKxiI/A4pikAnGoswsJcHajkWmOtormehoz4+xSBdmL+/tmqe6nzqExSSSmJaJhcKMKj4FLxwfnZRKVFIqcpmM6kVYSkmpVhP4VPy+XiXiqYsS/lfKKOnEuGZ+ZbCzNN5itjDxnyCejcr44GhlSXx6BtefFS3peVWg++Fc+C/mefpqne2FZTDSdTDKt7NdSzzDotFoTA/L+msXsKBcDEMArk62ODvYoBEEHj81riib4mzXhdqDQ2MNdzDKIZ7Symm5Ch6Ym5uRkpJJuEQ9Tl2o/XlYvGTf9hpa4vnwTjhZEh2QdMrbHQN5ngCNtEYZQ12MQCSoDbR1K/evMU31BOj3QRda9muESqlm9ojFROdy3fMDjUbD8ikbuXn6HtZ2Vkxf/yE2Ej3OpRDzPJ4rRwIB6DbacJj98e1nxEcnY2VjQc0m0q7WwMu51O+8blp+Z5Ua0sahB1pVPLcwezk/d8wljEvwt6PdmLHoQbD4G3R1tsXd1bhx5oGOeJoQOs9WqngSLhJafxPyQXX4O8yeP9KoC7NX9fUo1MLx5T1dsLE0vdd8XnE/PIZMpQoHa0vKu+evhFRhIyIphdC4ROQyGQ3L/rfyO3Um7JeF/wTxNDczo31lcZFadel6Mc/m5aCZNlRw+O7DPLmpixL1tS3Rzj8MLdJSTzqFMjAskuRCaJdau5xWuXseS1Si8X7oUihXygUrcwWpmdk8fG666umvrQF6I8g01dq/nLjg3Qwy3pq0aiXx+gTcNdxa1cvDASdHG1QqDbcN1OL09xeP8/BhJKkS+aLm5mZU1JqQrl8N1tvu4GiNr7at4lWJvuqepZ1x83RApVJz5ewDve3lqnjh4GxDRlo2189JK46NtITy/MFbJMYZ/u50JqM9K07w9KFpLxgymYxPl76Fr38pYsMTGN98OlcO3TRpX1OQHJ/KjEEL2bHkEACf/TaWMlVMK+4sCAK/Td2ERiNQq3llfCsZzv87sP4CAHVb+mNhqa+6aTQazh2+DUBtiTC7Wq3hklZRlqrfqVZpuK7ND61aU19Jys5SEagN4/tL1PcECLgpElv/yoZJ5Y1bolpVxYj56HqgOCY3tTM9M5sHIbqczdyJ5O0nYg1SRzsrPF1Md4Jff6g1XJXNn7p47l4IALXLF45qeD5IvM6FVQ/UEE7eF9Nj6pb1llTAiwP7A4MAqFnaC3url6f+FReuhD3jTmQ0ZjIZ7StJ520XFf4TxBNgbJMGmMlkHH3wmMuvSPi5KNHOvwJudjZEJKVw4Lb+ol0cqO9XGi9HOxLTMzlyp+jybct5uFDRy5VslZqDN4IKfDxPJzvqlvdGIwjsu2zYIW0I5mZmNKvhB8Dhq6Z/Fy3qiA+Di4EhpBlo/fgimtcVx5+8YjzsW9PfGycHaxKTM7h2SzqMLZPJaKFVv46euic5xqe0M2XLuKJUqjkrQQwBWrQWDTAnjkgrjm06icTw0J4AyTm071EHgH2bL+ttl8vltOtdD4Dda89JHr9G4wpUrOlDZno2W381nIvZuHMt6rWpSlaGknnvrjDJ4Q5gY2/N7J2fU6muH8lxqXzV7wdWTt9c4O5G9y4/YnyzaVw6EIC5pTkfLX6Tln0bmbz/kQ3nObf3BmYKOW/PHGhwXNTTOA5vughA37FtJMfcOPeIZ09isLa1pHnnGnrbr51/SOTzBOwcrGnUsrLe9qsXHxEfm4qjkw11GpbT237p4iPSUrNw97CX7FikUqk5dVokti2a6x8fRGX+cXAMCoWcpg31ybEOR8+K93KrRsaNFBcCgslWqijt6UTZXHq5Axy7LN7/LWqXNzkNIiYplesPxbWoVa28L/zJ6ZmcuCU+RzvXk74ueUGmUsUB7fOyS+3cjWv5hVKtZtsV8UWmV92qRXaevCBbpeLPi6IoNbhBzWKeTdFDIwh8d+QUAIPq1sTF1ualnv8/QzwrursyqK54Q3139BSal+RELS5YmisY0bguAH+cu/rSnLfGoDCT07+h+B1svmS8TWFBIJPJ6NNIJDQ7r+SdKEqhdxPxeLsuGS5Ibgwd64sLw5FrD0zev5KvG76eTmSr1Jy9KW2geRGtG4qLaeCDcKLjDTv6FQoz2jQR53PyomGS2qGNGKY+dTZIMtwuk8lopw1lHzNQsqhth+rIZBB48ylRkUl62zv2qINMBgFXgonUuppfRNcBDZHJZNy4+Jinwfpqcc8RzZDJZFw9FUTwfX2lUiaTMfKzbgDs/fMM8dHSnaDkcjmf/fIm9s62PLoVxl/z90iOk4JnGTd+PDaNnu92AMQC8190m0tseLzJx9BBEAS2/3KQzzrOJvppHN4VPFl4fBrd3mxr8jHCg6P5dfJGAEZN7k2l2oZr861fdAiVUk3dlpUNhtm3/nESgI79GmAjkb+5d5P4UtCxd12sJIrTH9odAED7brUkw+THtWpq2w41JNWva9dDSErKwMnJhnoSiirAMe3LUeP65bGXMCcBhDyLI+RZPAqFnJaNjYcWT2iJZNtGlXIlkmqNhhPXxN9R+4amE8Bj1x8hCGIqj7dr3ttDH7oeRJZSTSVvN6oVQj7m8cBHpGRmUcrZnkYViy7H8dT9YGJS0nC1s6FdtZcb4jWEPYFBRKek4WFvS4+aVYp7OkWO3bfvcTsyGlsLCz5s1fSln/8/QzwBPmzVFFsLC+5ERrP7trSK8zphaMNa2FiY8yAqlrOPpZWtl43+DWpgJpdxLeQ5j6KKzt3eo15VFHI5gWGRPI6MK/DxOtatjJWFgtDohHyZjFrWLIeVuYJnMUkmu9tlMhkdGonKw7EruSul7s521KosKkanclE9WzcRSeqZyw8l63AC1Krug7urHalpWVyWCJUDtNOS0xsBocRL1Nt093CgtjZX8/iR23rbPUs5UbuBqIId2Rugt92rtHOOirZ/yxW97d5l3WjZVXyZ2bBUWtFs2K4a/nXLkpWpZPOSI5JjQHSrf/TTSAA2LzpI4HnT1WkLS3Mm/DiKKWvGY2Nvxe1zQYxrOo0NC3azZeF+k/9mDFrIsi/Xo1apadm3IYvPfEPFOn4mz0OtUrNg3Eoy0rKo2bQSA4y438ODYzi6VbymIyd2lxxz93oIAecfYaaQ0+8t/eLzkc/iuXJGvE7dB+orsokJaVw8LaponXrW0duekpzBpfOiate+k7Sx6fgJ8aWmdasqkqYhQRA4elIc06GNYQXt5EVxng1qljXadz0zW8n5G+L93rZR7kTy5sNwYhPTsLO2pHEN0wtwH9FGPzo2yJ9aueui+Jl7Na5usspq9Hjal/ReDaoVafh7k1Z06Fu/OhaFkJdaUGg0AqvOXQVgdJN6r8ScihIZSiU/nhAjRO81b4jrS1Y74T9GPF1tbXiveUMAfjxxjgylcffvvx2O1lYMrCeGxlacvVrMsxHh6WhHmypiWGnL5cAiO4+rvQ0tqvoBsOuKYWOJqbC1sqBjHXGB2HUx78ezsbKgZS2RYB02gUTqoFNQzt8MNinc3ka7UJ64bJx41q3ui72dFYnJGdy8J516YmYmp10rcSE/ekpa0Sxd2pkqVUqh0QicNBCSb99JvAePHrotqfZ27iUq84f3BEiar3oMbixu33WdzHT9azBkXHsAzh4MJFTC6CSTyRj9uUis9v11jpiIRMl5ArToUY9Ow5ohCAILxq0kLVm6DJMhtO7fmMVnZ1K+ZhmSYlNYPWMrf0zdaPLfxf03UJibMe6HkUxdOwFbx7wtCht/OsC9K0+wsbdi4tIxBt3dAOsWHkSj1tCwXTWqGlASNywRyXyHvvXxLK3vZt+/9QqCIFCvaQV8/PTbWB4/cAu1WkPlqt6UkyjqfubUfZRKNeUqeFBeYntmppKz2vzd9oY6KgVFEBGVhLWVOc0aGVbQTmnVfZ3abwiXboaQkaXEy82equVzVxJ1YfbW9SqYbPCJTkgl4LGY39mhXt7rJz6OiON2aCQKuZzuDQuu0EUmpHDhoShO9G4ofZ0LA2FxiZx/GIpMBgMa6adtFAdOPQzmUUw8dpYWDPoPhNlXX7pOZEoq3g72vNGoXrHM4T9FPAHeaFSP0o4ORKak/ieMRqOb1sNMLuNi8FNuhxuu2/gyMahxLQB2Xb9HRnbRkf8+DUUFZc+1ewZNNHlBrybiA/lowEOU+cjhexnh9rba3LWA+8+ITzLct12hMKOlNhfutJFSQDoF6dylx6SnS3fpaadtO3n8hDTxbNmmCuYWZoSFxPJYghg2b1MFWztLoiOTCLiir6zWb14Rr9LOpKVkcvKgfopGOf9SNOtYA0EQ2Gggj7NOi8rUaFwBVbaajT8flv6wWrw3ewil/NyIfhbPkkkbjI6VQumKXiw8MZ3R0/vTfmjzPP11fbMNC09Mp/d7HfOsYt2/9oR13+8FYML84Xj6Gq4LGfYwkhM7xH7zulSEfyLoZhhXTwchN5Mz6N12etuzs5Qc0h5D93LwIgRByAmzd+pVR/Icxw6JKni7jtJq58VLj8nIyMbT04HqBlpp6tTOFk0rYWWgZNTzyEQeBkcjl8to0chwDij8/dLWpmHloguz33iIIECdCt545cGMpMOBa9qc1+p+uNgXXLHafe0uggANKvjg6+pU4OMZgk5saF7JD1+XojtPXrBCq3YOblDztTcVxaSmsey8GOWY2K4FloqX06non/jPEU9LhYLP2jYH4PfzV4hJNbw4vw7wdnKgWw0xXLvy3KuhejarWBYfZwdSMrM4eKvojE+tqpXDxc6a2JR0zgWFFPh49SqUxt3BlpSMLC7cz3vqQvOa5bC2NCc8Lpm7oaa9BOQ13O7l5kC1Cl4IApy8YtzA1aqxuFCeuvjQYJmnShU8KePjQna2ijMXpY/XtnVVZDK4e/c5ERJqoq2dFU207SuPHdYPt1tamdO2s6g0HJYwGcnlcroPEsO4ezZekiTtQ8eLqufpfTd5JpELKpPJGDVRJFiHN10k6qnh9Asbeys+//Ut5HIZx7dc4uR2fWNTbrC0tmDYpN588ce7efr7ePEYKtXVN+DkhozUTOa/txKNWkObfo1oN1CfCL6IdT8eRBAEmnauSSWJgu/wd+pC21518S6rT2LPHLlDUkI6bp4ONG6lb0Z5eC+CkMfRWFgqcr7fFxEdmcQtbf3Pdh2l1a9jJ8ToQru20oXtVWoNJ86IJEyXkyyFU9qXqzrVfHByMEzUspUqzl4XHfhtG+euROY3zH74qph+kJ8wuyAIHLouPgu61C+42ikIQk6YvU8Rqp3ZKhU7r4nf56BGr4ayGPA0gquhzzE3kzOqSfGofy8Ti06dJ12ppJa3F92rFZ2BLDf854gnQPdq/tTy9iJdqWTRqfPFPZ0ix1vNGwBw8M5DnsYnFu9kELveDNSqnpuK0GRkbmZG93qiYrfzcsHD7WZyOR3qiovRwWt5d8tbW5jnuFfz4m7XKSkXbgWTnpl7uF2nep68bPwcDWuXxdrKnJj41Jw2gv+ETCajfWttuP2kdLjd1dWOunXERffESePh9uNH7kjmlOrC7WdP3CUlWb9TU6c+9TC3UPD4fgRBEi02K1YvTeN2VdFoBDb9elxyDjWbVKRuy8qolGo25KJ6VmtYgaGfiuH5XyauI/pZwfOEixLLvtpMeHA07qWdGT9/qNGxwfeec3rvDQBGftZVcsyjO8+5dPwecrmMIe9JG5v2broEQLeBDTGTCDEf2i2eo3mbqpLdiI4fFX+TteqUwcNT31yTmprJZW1/eENh9hs3Q0lITMfRwZoGdQwTP52q3zqXMPuV22GkZWTj5mxHjYq5lyjKT5g9Mj6Fm48jkMmgfd28h9nvPY3maUwiVuYKWtXI+0vKP3EjOJyw2ESsLczpWKvo2iYevfOY+LQMPBxsaV3l5ZbvMQSd2tmzVlU8HeyKeTZFi6DoWLbeFH9zkzu0KpS84PziP0k8ZTIZkzuIifJbb94hKLroTC6vAqp4udOiYlk0gsDqCzeKezqAmFiuMJMT+CySe+Gmt5LMK3T5SifvPiHBhNaTuaFLffEt8WTgYzJNLLnzInLC7VfzHm7PUqo5G5B7uF2X53n97lMSjeQoWlooaFZfDDueumiYpHbQEs9rN0JISJSOEOjC7Ybc7Q2bVMDe3or4uFRuSnQiqlS1FOUqeqDMVnNSQhV1dLaltbaUj47w/BNDx4uu8uO7bxARJk0UdSaaI1suEy6hjP7f8T7rhn89P9KSM/h+/CqDJqzixrl9Nzj411lkMhkTl4zBPpe+4mt/OABAqx51KVdVOny9Uat2tupWGx+J3u2P7oVz7+ZTzBRyuvRroLc9K1PJiUNiWLWzoTC79ntuL1GiCeDM2SCUSjV+fm6ULyddS/Oo9kWnbcsqKAwQv5i4FG4HiQXqW+eiYp64JP4O2jSsmKvBRqMR8hVmP3pdq75WKI2Hc97JzqHr4ktvyxrlCqXI+05tDnzn2pWLtGi8rpLJgIY1URjJPX5ZCIlL4Ki2T/mYZobbyb4umHfsNBpBoEuVStT3Na0ecFGh+L/9YkJ939J0qVIJjSAw75jp3Ur+rdCpnttu3CYhreAErKBwtbOhY3XRCFCUpZX8vd2p6uOBSq1h3/WCVzKo5VeKUs72pGcpOXdX2ultDM2q+2FjaU5kQgqBwaZ1VpLJZDkL21ETwu0+nk5UKuuOWiNw+pp0O0kddO72kxcNE2Gf0i5UqeyFWiNw8qy00tuyhT/m5mYEh8TwJFj/RcLCQkGrtiKBlQq3y2QyOvXUmoy0eYH/hC6P8NSh25Kdjvxr+dKglT8atYbNy05IHqNqPT8atquGRq1h+bc7jZJ/hbmCL359CytbS26de8D2pYYd8cWFuMhEFn2yBoABEzpRu4Xx8FnAuQdcOBSIXC5j+KddJMcEB0Vw7vBtZDIZQ8bp53bC33VVW3Sojoubfo7iuZP3SUvNwrOUY07Vghfx5FEUIU9iMDc3o5UBJ7ouZ7h9W2m1MytLyWlt5QFjbnad2lnD3xs3F8NET6VS5/xeTHOzPy+Qm71TPsPsh7Vh9s71Ch4qTc9ScuimeLw+jYouzP44Oo4rwc+Qy2T0b/hqmIpWnb+GIECbyuWo6GE4H/p1wOnHIZx9Eoq5XM7Edi2Kezr/XeIJYnKtuVzO2SehnH4cUtzTKVI0KedLtVIeZCpVrLscUNzTAf42Ge0NuE9aVu4h5PxCZzLaVQg1PWUyGR21xZoPXs97uN3KQkHr2qLKqMvzMgW6PE9Tw+3ttPmbJ3NxtzepWw4LCwXhUUk8MtKas0NrrbHKQCjd3t6KRg3F8Nnx4wbC7Vpl6+zJ+2RJ9JNv17UmCoWcB/fCeSJhQvKv6UPFqt4os1Uc3iltDNTleh7dcY0oiTadAG9+2ROFuRkXj9zmyGZp9VSH0hU8eXfWIAD+nLOT41uMj3+ZiAlPYOaopSTHp1Ghpi8jv+xldHxacgY/froOgC7DmlHGQIefjdpUheada1BWYkxaSibH94kdmnoMki5qf2iXGFnp1LOOpHKoe/lo3KyiZBg+Pj6VGwGiMt7WAPG8cOUJ6RnZeHk4UN1IR6dTOWF242rntbtPSUnLxNnBmtomdIg6mo8we3hcMoHB2jB7Ptzst0IiiEhIwcbSnBbVCh5mPxr4kPQsJb6ujtQrV3Qq2OZLovrduko5vBzzbqYqbMSmprEjQFwPdKLM6wqVRpMjro1oWIcyzk7FOyH+48SzjLMTIxuKKsv3x8++1kXlZTIZY1uIP7B1l28WqZvcVDQs50M5d2fSs5XsC7hfZOfpXq8K5mZm3A+P4f7zgof1u2iVhjO3g0nPB2HWKR1Hrxk29fwTeQ63a4vJX7kdSnKq4bahNtYWNK7tB8CpC4bV1HatqiCTwe17z4mM1i8ED3/n4R0/KV1kv3pNXzw8HUhPz+biWX1C7ORsSxOtSeXAzmt622UyGT0Gi0Rn3+ZLaDT6oe9q9fyo06wiKqWaLb+flJxnuarejNQajX6bsd2o0Qigy4gWtOrdAJVSzfz3V/DTR2vINODwf1m4cjSQ8W1mEnQ9BBt7K7747S0sLKUd3Tr8+vU2YsITKVXWjbFf9ZYcE/YoijP7xQjEUG2Zqn/i6J4bZGUqKVvBgxr1/fS2P38aT8DVYGQy6NC9jt52jUbguLaTVbtO0urXyVP30WgEqlX1xruUk+SY46fFF5x2raoaDIsnJKUTcFfMCc41zK59SWvVoBJmcuNLY77D7NfE31j9Sj64ORpPiZCCzlTUpmYFrCwK7kjW5b73blg4tUClkKlUsfu6SPIGa8WG4sa6SzfJVqmp7eNFg7LFG3YuauwOvMfDmDicrK0Y19y46fBl4T9NPAHebdYQO0sL7kfHsONW4XS5eVXRqWolSjs5kJCewcarRRfeNhUymYxBjcQH0foLAagliERhwNHGirY1RDVu68WC1w6t6uuBr5sjmUoVJ24ZD2VLoWm1sthZWRCd+HfLvNzwYrj98KXclVK/0q6U93FFpdZw6qppxeSPnzccbnd1saNWdR9x3Gnpl4QmjStibW1BZGQSgbf1P5dcLsshGocOSN9/3fqKuVbH9t8iNUWfMLfpWgtbeysiniVw6ZT0ddARpkNbLvM8RFrF7f9uO6o1LEdGahbfjfuTNIlz6SCTyZj0+1iGf94DmUzGoXVnGdf6W05uvyxJfosS4cHRzHtvBdOG/JKjdC4+9hVl/Y0bYQ6sP8+xrVeQy2VMXDgCa1v9sjGCILDmp0Oi271DdcpL9E1XKdXs3iCqvj0GN5YkK3u1RenrN6mIl7eT3vbrV4OJjUnBzs6KxhLdkgRB4MhRbZklA2pnaloWF6+KL2BtWxoOOZ+6JL7cVS7ngben/lx0yFaqOK39nbQzwc1+PehpnsPsgiBw8HL+3exKlZojOWH2grfIDIlJ4MrjZ8hk0LtB0YXZ99y4R3JmFqWdHWhWyfSUhKJCQnoG668EADCmeYNiNdkUNVIys/j59AUA3mnaEEdrw40TXib+88TT2caad5uJReVnHjrOw5jX12ikMJPzbktRMfr5+HlC4xKLd0JA7/rVsLey5GFUXJEWlB/crDYA2y7dJjRGOgRrKmQyGT0biw/qFYcv55kwW5gr6NRQXCxXHDC9VE/XZmIe2+kbj3n01LgxBqBzC3H8ur1Xjc6xRcOKWFuZExYez7mrhol053Yiady2+xpZEsYqKytz2rYWy7us/Uu6d3rnrrWQy2VcufiY27ee6m2v26g8Zcq5kZaaxV/LT+qfw9qC7gPF3+vvC/aTlamv3NdqXIG6zSuhUqqZ+/F6srP052pmJuezH0dg52hNUEAoX434lTQJN/2L40dO6sWcbR/j7OFAeHA0c9/5gwntZnHp8K0ib0kbF5HILxPX8XbT6ZzYKhK/nm+14acDX+ItYf55EfvXnefnSZsAGPxBJ6pJ5FwCHN56lXOHbyM3kzP8g46SY3atv8Dz0FgcnGxo16O23vZnoXHs1uZ/9hmsH4YXBIHV2u+1fecaWFjqq3aXrzwh6EEkFhYK2hjI3dyx5zpZWSr8yrhSqYJ0kXe1WsPG3aJruWMr4z3B9566Q0JyBm7OdtSrmnu7yNV7tW1CG1c2Ocx+7nYI959GY2WuyFfR+F0X7xCTnIarvQ1NqxScwC3cdxaAVlXL4eVcNOHvpIxMfjkiVo4Z1rROrkryy8C3+06QlJFFJQ9XOlQxXtP13wxBEPhy72HCk1Mo7ejA8Ab6v9fiQvHfBa8AxjZpQPNyZchQqpiwbS+pRZhvWNwYUK8Gjcv5kqFUMXnnoSJTGU2Fo7UVH3RsBsCiQ+eIS81bpxhT0aiiLy2q+KFSa/hx75kCH29Iqzo42FjyJDI+X6WVxnRthMJMzqV7YVwN0idgUijn7UoHrenhj10Xcx3fv0Md7G2tCA2P59hFw3O0s7Wkf1cx5WT1lgsGSVTHdtVwd7MnNi6VA0ekXxKGD2uGQiHn2vUQbt4M09vuU8aVLt3FB+Dypcf0ziWXy3hPa3rZtfkyIY/0UyOGvN0GNw8HIp4lsHmFtDHwk+8G4uBsw6M7z1n1/X7JMd5+bsxZPx47RxvuXw9h6vBfSU0yfv/VbVWVlZdnMXpKb2zsrXhy+xlfD1vMZ93nc+tc3u+D3JAcn8ofM7byZsOp7Ft9CrVKQ4P21fnl2FTGzxuGhYGC6TrsXXOWX74USWfvt1obLJ8U+jCKX2fuBGD0J52pUE1f7YyJTGTtUjH/861POmP7j77tgiDw248HUak0NGxWkQbN9NXM08fvEXQvAitrc4aPbq63XRAEVq4Wv9Peverh4qwfjk5Pz2LzTlFVHTG4qUHF6tj5IJ5FJOBob03vjoYX3WylijW7RDI/qlcjg+54HW4EPePynTDMzOSM7i6d4yr1uZbuEgnYoLa1cc5j0fcspYrlh8Q5ju3cCAvzgoXZrzx+xrHAR5jJZXzSvWWBjmUMPx8+T1xqOuU9XBjWtE6RncdUHLzzgP23gzCTy5jTp9MrQYSLCisvXedI0CPMzcxY1K871ubGnxUvE6/vVc8DzORyfujdFU97O4LjEpi670iRKxjFBblcxuzeHbGxMOd6WDh/vgLllYY0qUVVbw+SM7P44UDBSaEhTOzZCjO5jOO3H3P5kWlkzxAcbKwY1U7Mmf3twMU8d0bydnWgbwuxiPLS3edNvt/G9m6KTAbHrz7kQajxfFVbG0uGdhND1yu3XzT6kjG4ZwOsLBXcfxzFxRvSbn0LcwXDBog5Quu2XESp1O/eVKqUE926iov8ilWnJT/XyLdaYWVlzt3bzzl7Wp+s1W9SgWZtqqBRCyz94YDeMWxsLXn3CzFHc/PK0zwP1Y9SuJdy4tO5oilo5+qzXDwmnUZTqZYv320cj72TDUEBoUwZtpQUCcf8i7C2s2Lop9358/p3DPqwC5bW5ty9/Jgvev/A1EGLeBiQ9+YC/0R6Sibrvt/LG/WnsHXxYbIzlVRrXIEFuycya9NHVKqdu+K1Z/UZlkzdAkC/t9vw7td9JUlaVqaSuR+vIytTSd1mlRjwdmvJ4y2bf4DMjGyq1S1Dx9519bZfOvOAK+cfoVDIee/TLnrnUirVrNTm3Q4a2gRnCYf5mbNBPHwYhbW1BcOGNJGcx879ASSnZOLj7Uy7ltIF1NVqDWu2iiHGwT3rY2NtuEzQ3lN3iIpLwc3Zjl5tcy9s/vtO8bi9WtbA290x1/EAx2884v7TaGwszRnduaFJ+7yIbecCiUpMxdPJjn7NClZ8XaMRWLD7FAADmtSiglfROLrvPI9i0yXRhPZVr3bF3gM9NjWNGXvFMmHvtGhEzdLS5rrXAVefPuf74+JaOrVja2p5v1qftYR4auFia8PP/bqjkMs5cO8Ba68GFPeUigw+zo582VlcXBYeP8fjmOItjm0mlzOtt1i2Zdf1u1wLeV4k56ng5cqAJmJO6YJdpwqs9g5rUwdnO2uexiSy93Le84PHdmuEpbkZAY/CuXDXNLJSvrQrHRuLYXrdAmgMAzvXNUn1dHa0oW/nOoBx1bN751q4utgSHZPCoeP6ZZEARgxrhoWFgtt3nnHlqj6JdXOzp/8QkcCu+PUEKon2o+9+3AkLSwU3r4ZwRoI0tuhYnfrNKqFUqlkyZ4/kfBu3q0afN8TSIT9N3mywR3vFGj7M3TQBB2dbHt56ypRhS0hJyL2jmb2zLWOm92Pl5dn0eLM1Zgo5147f4YMOs5n15m+EPYjI9Rj/RHamkh2/HeXNhlNZO3c36SmZlK/hw8wNH/DD3i+o2cy03L5dK0+xdNpWAAa8356x0/oYVAZ/n7OHkAeROLnaMfH7IcglVKArZx5w9ugd5GZyPpjaS29MdpaK3348BEC/YU3xkeh0tG/XdcKfJ+DsYssACVKpVmtY9acY/h3QrwGOEn3qMzOVbNouqp0jBzc12Iv+5MUHhDyL/z81Xwr/r3Y2xDIXw87Ve2Fcu/cUhZmcN3uapnaqNRp+2yP+Vod3qIeznbVJ++mQka1kxWExtP9258ZYFlDt3H31LveeRWNvZcm4ztLkvqDQaAS+3XkcQYButf1pXCH39IWihCAIzNhzjMT0TKp4ufN+61fDZFMUiE1N4+Pt+1ALAj2rV2FovVfD0PUiSojnC6jr482k9mLYYd7R0wQ8z/vC8W/BwPo1aFnRj2yVmik7Dxd7yL12mVI59d1m7TpeKL3VpTCucxPsrSy5Hx7D7qsFq+tpY2nBmx1E9WLZwUt57t/u7mTHwNaiOrh0Vx5Uz15NkctknL7xmHshxltv2r2geq7aYVz1HNKrIRYWCu48iODqLf0wOYhF54f0ExfcvzZdlCSNbm729O4ltp9baUD1HDSsCU5ONjx/Fs8BiTaZXqWdGThSTMH4feFhMjP+P/1FJpMxfkoPzC0UXL/wmNOHpEnwmxO7UbF6aZIT0pn/2QaDReDLVyvN3E0TcHS141HgM74cuoRkE8gngGspJyYsGM4fF7+l3UDRcHN2z3XeazGD8W2/ZUK7WSb/ja43mWVfbSYpNoXS5T2YvPxtFh//ikYda5psgtix/AS/fb0dgMHjOzBmck+D+545cIv9G8S0jYkLhuDirp/rl5WpZOl3ewDoO7wp5Srrqydb150n4nkCru72DHurld72tNRM/lotkspRY1phbaOvQB4/cZfQ0Fjs7a0YOECa1O0+EEBiUjqlPB0N1u7UaAT+3Cp+pkE96mNrY7j/9r7TWrXTyZZebY0v0IIg8PsOkUD2aV0TL1f9bktSOHz1AY/D47C3sWREh7y3Zdx85iZxKemUdnWgdxPpnvamIj1Lyc8HxPzrdzo0wsWu4H3epbD1aiCBzyKxtbTgi27698PLxr7bQRy9/xhzMzlz+3YudvW1qKDSaPh05wGiU9Oo6ObCzG7tX0nzVAnx/AdGNaxL16qVUGo0fLhtL/HpxV9svSggk8n4tlcHbC0tuPksko1Xit/l/mnnFjjZWPEgMpZ1FwKK5Bwudja800FrsNp/Nl/lkF7EwJa1cHOwISI+mZ0XpcmPMbzRuSHWlubcDY3i1M3cyyQB+Hm70LmpGGJcvsN01TPkeTzHjXQocnW2pXdHcfFdtcUwEe7VtQ7OTjZERCUZrOs5dHATrK0tePAwkjNn9c9pY2PJyDHiS96alWdIlyhPNGh0CzxLORITlcwmLWl5Ed5lXBmsJTm/zd9HkgRRtLBUMHnRcKxtLbh9JTinI48UylX1Zt6mCTi52fHkznO+HLyYxLhUg+P/iVJ+7nzx61v8eno6zbrVQaMReBz4lEe3wkz+S4hOxs3bmY9+GsmyczNo3behpAJpCFt/O87v2lzNoR92YvSkHgYXnqhn8SyaKqqig95tS/2W0mrqX78eJ+JZAm6eDoyQKCgfHZnExpViWG/shx0lSeWmdRdISkzHt4wrXXvU0duuUqn5c634HQ8e2Bg7O333bVaWkg3bROVvxOAmBnMxz1x5xJOwWGxtLBjQzTDRU6rU/LlTq3b2bpSr2nnl3lMCHjzHQmHG6B6mqZ0q9d9q56iODbC3yZurOC0zm1VHRIX3nS5NTDYyGcKqE1eISU7Dx9WRYS3rFOhYhpCQlsHCgyK5ndChKe7F3IoyMT2T7w6IqQXvtWpMFS/3Yp1PUeLnUxe4GPoUG3Nzfu7fA1uLoutEVRCUEM9/QCaTMbt7R8q5OBOZksrEXQeKXQ0sKng52vNpezHB/8dj54hKNn2RLQo42VrzSRcxNLr4yHmikopmPsNa1sHH1ZHYlHRWnrhaoGNZW5gztpMYtll+6DJZyry10XRxsGFoOzEUuHT3eZPrer7Vqwlmchlnbz7hzhPjyrydjSVDumoVyB0XjN7Pw/s0wsLcjFv3nnPjjnQerJWVOYP6ikrv2k0XJFVEJycb+mtbKa7684zkmG696lLax4XEhDS2bNA3S1lZmfPOx50B2LL2POHP4vXGDBrTkjLl3UmITeWnr3dIkmXvsm58MLM/AOsXH+XWJcPO/bL+pZi3+QOcPRwIvhfO5MGLSYxNMTheCn5VSzN9zTh+OzuDbzd+kKe/77Z/wsrLs+g6siWKPIZUNy85worZuwAY/kkXRk7sZpB0qpRq5n6ynrSUTKrUKcPIjzpJjrtx8TFbtaR//JSeWEuoh78vOkxWlooadcvQVqL9ZUx0Mts2iYRx7PttMVPoLzsHDwUSHp6Is5MNfftIty/cdziQ+IQ0PN0dcios/BOCILB6i0j0BnSrh4MEgdVh76nbeVM7t4vmoD5tauLpYpoLfO/FuzyNTsTJzpqh7eqYtM+LWH/qBolpmZT1cKZ7Q+PO/NwQmZjC6pNifdxPe7TEQlHwOqBS+OngWZIyMqns5fZKGIq+P3KGuLR0Kri78HaLvOfX/ltw4uETfjsv/s5mde9IRbdXtxtTCfGUgJ2lJT/374GVQsHZJ6EsPfvqdCspbAxpWIvaPqVIy8pm1n7pNoMvE/3q16C2bynSs5V8f6BoWplaKBR82kNU21afuEp0AQluv2Y18HK2Jzoxla3n8q4cj+pYHztrSx49j+Xo9dxbYgKU8XKmazOxpNPvJqieg7rUw97GMlfV083Fju7tRfOCLlwphT7d6uBgb8Wz8ASOn5Gu6zloQCPs7CwJDY3l+An9PE2Fwoy33m8LwNYNl4iTIHjN21ahbqPyKJVqlmlzCF+EhaU5k+YOwtzcjIsn7+e0cvwn2vaqS8f+DdBoBOZ/toGkeMNh9DKVvJi/eQIuHg6EBEUwafBiEmKSDY43BL8q3jTsUDNPf3VbVc3VqS6FDT8fZtXcvQCM+KwrIz7tajTEtmbhIe4HhGFrb8WXPw1DYa6vpCUlpPH9V1sRBIFuAxvStK0+8Qm4EsyZo3eRy2WMmyh9zjUrTpOdraJGLV+attBXVbOzVaxdJypkw4Y2xVrCCJStVLFeW0Zq6IDGmEvMF+Dc1Sc8DI7G2sqcQT0M999WqtQ5uZ0je+Wudl68HcqtR+FYmpuZ7GRXqtQs3yeeY0yXhthY5U19Sk7PZM0xkSi+17VJgfubLz54nkylinrlS9Ohpn7FgcLArbAItl0VIz/Tercr9p7sV0KesfW6OJ+ZPTu8tiH2p4lJfL77IAAjGtShR/WCt1MtSpQQTwPw93BjZjexEPXiMxc585q21DSTy5nZqwMKuZwj9x5x/H7eC6IXJuRyGV/1botMBvtvBnErrGjybDvUrEgdv1JkqdT8ftT0WppSsDBX8HZnUfVcefgKGRLtII3BwdaKkR1FRfK33RdMzm99q1djzOQyLgSGcOtRuNGxdjaWDDEx13N4n0ZiSaTAMG7eky5wb2NjyaA+WtVz4wVJpdbOzoohg0Xzwuo1ZyXzQVu08qdajdJkZipZu1K/ooFMJmPcxC6Ymcm5eOYBV87pF8OvUKUUY7TK6O/fHyDkkXTe67jpffAp705cVDI/TtpkNKfWp4In87Z8gKunI2EPIpk0eDHx0Xknny8D6xYeZM2CfQCM+rw7wz+W7sGuw7UzD3K6On00ewCePi56YwRBYOGMncRFp+Bbzp13JuqXYVKp1Cz9/gAA3fs3oIJE7mfwk2gOa5sFvD1OOt9s994bxMSk4OHuQM8e0kagg0dvExObgpurHd06Sbu6RbVTVCX7damLo71hE8++U3eIjNWqne2Mu8QFQWD5Du1x29XG3dm00PHOc7eJiEvGzdGWAa3zXkPxrxPXScnIokIp1wL3ZX8SFc8ebU77xJ6tiiTvTxAE5u0TQ9q96lalnl/xdgTKVqmYvucoAIPq16T+a9qhKEul4qNte0nOzKK2txdfdij+nNrcUEI8jaBPzWoMrlsTAfhs1wEikvMWcvu3wN/TjTebiaRk5r7jxV7HtFppT3rXE9W8eftPFUlpK5lMxoddxTSDbZcCeR4v3QbSVPRqUg0fN0fiUtLZeCYgz/sPbVcXJ1srQqISOHjZtPahpT2c6N5CNBuYkuupUz2Dn8dx4pJh1dPL3YFubcVQ5p9bDKuefXvWw87WktCncZwyUMOyb+/6ODvbEhGRyIGD+mqwTCbjbW2noQN7AwgN0S+NVKacO320LvhffzxItkTx+t7Dm9CgeSWys1TM+3IL2RLk38rGgskLh2NuoeDyyftsWGI43xPAp7wH87d8gFspJ54+jOKLgb8QcM40RfplIDEulSVTt/DXDyL5e/PLHgz9UDpkrkN4aCzff74RgG5Dm9Cyq3SIef+WK1w4cQ9zczMmzR2IlYQKuWfLFUKfxODgaM2od9tKHmfFbyfQaARatqlCtRr6C39GRjbrN4j37sgRzbGQUB5VKjXrtmjVzv6G1clLASHcfxyFlaWCIb2Mq51/auvgjuzVCCsL4wrz+VvB3H4SiaWFglHdTAvVZmar+GO/tu5mt8Z5bm+ZkJrBupNiqbv3uzU12BLUVPx6+AIaQaBt9QrULFM0pXUO335IQFgE1uYKPu7cokjOkRf8fuYKwbEJuNnZ8FnH4p9PUWHOkVPcjozGydqKRf26Y2H26qu6JcQzF3zVqQ3VvTxIzMjkw217yVbnzbn8b8H4Nk3wdXYkMjmVn4+fL+7p8GHH5libKwgIjeDwbeMtH/OLhhV9aVzJF5Vaw7IjBUunMDcz490uorr359GrpGXmjbzbWVvm1PdbtvcCShPvszE9G2NmJufSnVBuBBlvv2lnY8lgE+t6jujbCDO5jMs3Q7hjoCyQna0lA3qLx1u7SVr1tLa2YPjQpuKYdefJkiCENWr50ryVPxqNwIrfjkuea/jY1ri42vE8LJ4dEvmgcrmcz2b1x9HZluAHkaxceFjyOOWrevPu1J7ifBYdZuOv0ufTwbucO/M2f4C7txPPn0QzecgSJg36hduXiy8ykJKQxuq5e3iz2TfsXSPmX741tReDxkt3G9LheUgMXwxfRmJcKuWqlOKdKT0lx4U+jmbZArHo/psfd6KiROvMhLhU1iw7KY4Z1x4HR311MeB6CJfOP8LMTM4YA8R02/arJCamU9rbmc4G+rYfPnGXyKgkXJxs6dFZWjkUBIFVm8XnVu9OtXE20gddp3a6mqh2LtO+1A1sVxtXE/urbz19i5jENEq5OtCned6d6H8eE58hVXw8aFerYGHxoPAYDgaIL0zjuzQt0LEMIVul4qeD4r34Rsv6eDoWr6HoSUw8y86IpqwpXdu8Mq0iCxu7Au+x4fotZMD3vbvi7WhapYXiRgnxzAWWCgU/9++Bg5UlN8MjmXu0aPIOixtW5gpm9BSVp78uBRD4PLJY5+PpaMcbLUVS89PBs2Sr8mbaMRUfaFXP3VfvElLAVprdGlbBz8OZxLRM1p/Me2H+QW1r4+pgw/PYZHafu2PSPt7ujvRqKS7YK3bn3s1oUOe6Jqme3p5OdG4jLph/bjWspg7o1QAbawseB8dw7tIjyTE9utfBw92B2NgUdu+Rvi5vvdcWuZmMC2cfcitAv5STrZ0lYz7oAMD6FaeJidIPezu72vHZrH4A7Fx3gctnpFXY7sOaMlrbHenPHw/y18+Hjarq3n5uLNr7GT3faInCwoxbFx7xef+fmTJsKQHnHry0fu1xkUms+X4/o5t9w6YlR8lMz6ZiTR9mrnmXAe+1N7pv2KMovhj+G3FRSZSt5MnsVWOxlMglzc5SMnfSZrKzVNRrVpE+w6WJysolx0hPy6JilVJ0ligmr9EILNd2OerRpx4+vvrh/JSUTDZplcw3RreQdKmr1Br+2qQtBN+vIVYG8l+v3grjzoMILCwUDO1tWJX8P7WzZ+5q57mbwdwPicLa0pyRJqqd6ZnZrDqorbvZrXGeuwzFJaex8XQAAOO6F1ztXHJQvH5d6lTG37toHN3rL9zkaXwSbvY2vNmqQZGcw1QIgsDXe4+hVKtpVcmPrtUL3tf+VcSD6FimHxBTCca1aEyrCn7FO6E8oIR4mgBfJ0fm9xRzyP66GsDCk6bXXPw3oXmFsvSsVQWNIPDx5n3EpppWx7Co8GarBrjZ2/A0PokfD+qX0ykM1C5bitbVyqHWCHy79ViBKhiYyeW8101cqFceuUJodN6IrLWFOWO6isaFpbvPk5BqWimvN3o0wsxMzuU7YZy/Jd11SAd7W6sc1XPJhjOkZRhWZkf1a4xcLuP8tSdcuC5d6sne3op+PcX81F9XniBDQum1sFAwaqRI8NesPUeERCF33zKudO8pEpiFC/aTka5/nPZda1G1pg+ZGUrmT98umTPaqKU/vYeJ38HcLzZz76a0M3/I++14U5u3uO6Xo3w7fo3RjkXO7g6M+3YAK05/RbcRzTBTyLlxJojJQ5YwttVsNi0+QnxUwdI1pKBWqbl45DbfjFnOqCYz2LDoEBmpWZSr6s30P8by876JNGxbzegxju+6zkf9fyE+OgU/fy/mrn0XZzd9V3Z2topZn20k+EEkjs62TJzVX7KU0+mjdzisrb06/vOukkXcd2y5zIP7EdjYWDD8Dekw59Jfj5KWlkX5cu60bSP9GbbuvMrziEQcHazp1a2O5JhspYrFq0VjZK8OtXAzkoO5bu/VHLWzd3vjaqdSpWbpVvG5079tLZwdTKt5uWzvRRJSMvD1cKJ707w50QVBYMG2U2Rmq6jp50XL6uXytP8/cfz2Y07ceYxcJuP9TkWjdj6KimPxUZHcftixObaWxVvCZ+Gx81wJeYa1uYLp3du9knUsC4qHMbG8uX4bGUoVzcuVYULLomkEUFQoIZ4mol3lCjnF5Zeeu8TcY9KFsf/tmNK1DWVdnHiemMz763eRnp03o0xhwtbSgq/7iCrX2nM3OHCr8HthA0zs1RprCwWXHz1lVQHLK3WqW5kGlXzIyFYydc1Bk0PmOvRvWZOK3q4kpGTw/aaTJu1Tys2BQR3qADD3z6NGySTAkK718XJzIDI2md82GW5R6lPKOacO4rxfD5Ocmil9vP6NcHez53l4IktXSM+5c6eaVK9WmrT0LL6ds0uSNI4e2xpXNzuehsbxw9y9kn3cP53WCxtbC25dD+XX7w9KnuutTzpRu2E50tOymPr+aoJuS6cgDHq3LeNn9EVhbsaFI3eY0Hshd6+HGLgaIjxKu/DBd4P549RXdB/ZHGs7SyJCY1k9by8jG89g5lt/cPnYHdR5bCbwT0SGxfHn/H2MbvIN34xZzsUjt9GoNVRvWJ4pv77B4oOf07Sz8aLyGWlZ/PDFJhZM3Ehmeja1Gpfnuz/fwclVn5gplSq++3wTl08HYWGpYMr3g3GRIKePgiL4fsZOAPoPb0q1WvodaW4FhOWonWPfb4ezRL/1w0duc+jIbeRyGR9+0ElS1XscHM0fa8T78903Whtse7l6y0Ueh8Xi5GDN6AGGO9IEP4tj5XaRII0f2ipXtXPN/is8ehaLo52VyWpnYHAE645eB2DioNaY5zHfbt+Vexy8LvYS/6J/mwKRpsjEFKZvElNORraqR3lPfdW5oEjNzOLjdXvIyFbSuIIvfeobfwkqamy+GsiyM6LaPK17O3ycTWtp+m/C3chohq/dQkxaOv4ebvzQp9u/ruf8v2u2xYy3mjRgWicxV2nVpevMPnLytSOfzjbW/D6iL042VgQ+j2Li1v3FWse0XbUKjG0tPvSnbTvCo6jCb+/p5+7M5D7i97r44HluhebfSS+Xy5g1sgv21pbcDo1k+cG85Y5amCuYPqoTcpmMA5fvcybQtKLy7/VrTik3ByLjUvh1m3F12NbagslviyaUbUcCuBlkuEXpO8Na4FPKmdj4VBatlM6HtLezYvInYu/03fsDuHhVf85mZnKmTumFnZ0l9+9HsHKVfsqKo5MNX83sh0Ih59Txe2xapx/iL1POnUkz+yGTwd5tV9m7Tf9FwcLSnG9+GUnN+n6kp2Yx9d3VPLwr7frvMbwpP20Zj3dZV6LDE/l82G9s+u14ruFzrzKuTJgziPXXvuXTH4ZRrWE5NGoNFw4H8vUbvzO66Tes+X4/107e49qp+yb/Hd9+hSnDlvJm85ls/OUwcVFJOLjY0u+dtiw7Ppnvt39Eyx51cy0qH3w/gg/7/czRHdeQy2WM+LAjcwyQTpVSzdwvNotmIgsFM34eQe2G5fXGJcanMeOzjWRlqajfpAJvTeigNyY6Kplvv9qGWq2hbYfq9OijX8D96bN4Fv4slsYaPbIFtWrqk9dspYrZP+xDqVLTvHFFg072e48iWLdD/I199k4Hg7mdao2GOcsPoVSpaVa3PF1aGFcig8PjWLlbe9zhbU1SO5UqNTPXHEEjCHRrXIWWNfWvoTE8j03iuy2icvte16bU9CuVp/1fhFqjYcr6gySlZ1LVx4OPujXP97EMQRAEpm49THBMAl6Odnw/pHgJ0JmHIXyzTzQMjmvdmH51C9bl6VXE7YgoRq3bSmJGJjVKebJ2xEBcbPLWgvVVgEx4hZlTcnIyjo6OJCUl4eDw6iTNbroRyLT9Ym7FyAZ1+KpTwd5MX0VcDwvnjT+3kq1SM7xRHb7qVnyfUaXW8O6q7Vx8/JRy7s5sHDcUOyvDbfDyA0EQ+OKv/RwMeEBpFwe2fjaiQOc4dD2ISav2I5fJWPHRQOpWyFspj5+2nmbtkWt4Otux5etR2FnnPpfLd0KZsGAbMhksnzqEWhX1TSEvYtayg+w7dYey3i78OWekQbfw7aBwxn21AY1G4LtJfWjZSNrs8Mvvx9i66xouzrasWvImThK9tk+fCWLGzB0AzJsziIYSBGfPzmv8/P1BZDKYvWAIDZtU0BuzcdUZVi09jpmZnLlLRlKrvp/emIz0LKa+/yd3b4Rh52DNvD/GUKGK9GKelpLJ4q+3c1IbQq7bvBKffz9EMiRtCKEPIjm04QLHtl0xud2mMdRt6U+XoU1p0qkmFpam5QkKgsD+DRdZNnsPymwVrp4OfPHDUGo11r+GIIby503ewulDtzE3N+PrRcNpIFFrU6lUM2ncGu4EhOFTxpVFq8diZ///ho2sLCWfjFvDw6BIKlTyZOGvo/VyMrOzVUz4aC2PHkVRp3YZFswbIhmq/23lSTZsu4yTow2rl76Js5M+oczKVvHW52sIeRZP++ZV+ObTHgavy5ZD1/nxzxPYWFuwfv5oPI20u9RoBN6Zs4lbj8JpXqscP35iuM/9i1i25wLL9l7E2d6arTNG56knu0qt4a1Fm7kZHEGd8t6s+GhggUjc8qOX+fnAOawtzNn86XD83J3zfSxDWHX6Kt8fOIPCTM7adwZRq0z+iXJBcS8imuErN5OeraR37arM7dv5tVuTA8MjeXPDdpIzs6hbuhR/DOmLfSGvgwVBXvhaieKZDwyuW5PZ3TsiA9ZeDWDmoROvnfJZr4w38/t1QSaDdZcDWH3herHNRWEmZ8GQbng52hEck8DUrcbNIPmBTCZj2oD2eDs78Dw+mVnbjLudc0Pnev70aFQVjSAwdc1BUjL0W0Iaw3u9muLj5khUQiq/7DAtv7VR9bL0aFEdQYDZKw+TnUsXpQ+Ht8HF0YbQ8HhW7zSszNbw92ZIL9EwMP+3wySlSOeevjO6FWV9XYlPSOPHJdLfUauW/vTS5nLOnb+X+Hj94v09eteja886CALMmbGT5xIdiwa/0YI2nWqgVmuY9eUWIsMT9cZY21gya+loqtb2JTU5g8nvrCL4gbRpztbeii9+GMrHcwZiaWXOjXMPGd/zJ25I1A01hLKVvXjn676svTKTL5eMpkHbqpSvVjpPf5Vq+TL0w06sOjedOevH0apnXZNJZ2pyBnM+/IvFX+9Ama2iYesqLN71sWHSqdawYOo2Th+6jUJhxlc/DZMknYIgsGT+fu4EhGFja8mMH4bokU5BEFi04AAPgyJxcLRmxpwBkkag3/84yaNHUTg4WDP5y56SpPPm7ads3C6GSz//oLMk6QRYsfEcIc/icXWy5dO3DZurImKS+XWjtvvSkJZGSSfA1uMB3HoUjo2VOZNGm9br+tHzWFYcEOf8xeC2eSKdACuPXOZmcAR2VhbMHtWlQKTzZmgESw6JDv8pfdsWCem8/ORpTt795B5tipV0RiSl8O66naRnK2lSzpdve3V87UjnrRdIZz2fUqwY+mqRzryiRPEsALYG3GbqviMIwLD6tfm6c9vX7oZfee4a8w+fRiaDhYN60LlapWKby62wCEb+vhmVWsPEri2LxD0ZEBzOG0s3o9YIzBnamZ4N8p+zlJqRxeB5f/E8Lpm+TWvw9TDj5W7+iStBT3n3R7GX9vLPBlK/sk+u+ySlZjB4yp/EJ6fzVq/GvNvPeIjtxOUHTFm4BzMzOatnj6BiGWnXq6gurSXkWRwdW1bl64+7S4578CiS9z79C7Vaw5TPutO5nX64KytLyfgP1vAkOIb69fyY991gvRy/7GwVEz/4i3t3nuNXzp2fl72h1wM8M1PJZ2+v4tH9CMqWd+f7ZW/g4KSvsqalZDLlXTHX09HZlnkrxuBX0dPgNQl9GMXcj9cR8iASmUzG4PfaMuLDjpi9wl1P7geEMfeTdUQ9S8BMIWfM593o80YLgyF5tVrDj9O2c2xvAGYKOV/9MFSyMxHAlrXn+OPno8hk8O1Pw2jYXP8ZsGPLFZYuOozcTMZ3PwylXgN9U8z5Cw/5avo2AObMGkCTxvrKeVp6FmMmrCYyKoluHWsy6WP9wvUAtx+EM26qqMLP/bIPLRpKq/CCIPDJvO1cuhVCnSqlWfKV/r32IiLjkhky9U/SM5V8PqIdA7W508ag1mh4c94mbodE0rp2eX58v1ee1oH7T6MZ8f0GVBoNs0d1KVBrzJSMLAb++BfP45PpWsefeSOMd7DKD6KSUhm4eB1xqen0qluVOQOLT11Mycxi+MrNPIiKpZKHK+vGDMLhNSuddPN5JGM2bCclK4t6Pt78MaQvdsVs4JJCieL5kjCgTg3m9OiEDFh/7SbfHDyO5tXl8fnCm83qMaxRbQQBvth2gBtPjXfIKUrUKlOKyT3aAPDjwbNceiztWC4I6pTz5r1OokNw1vbjhMUm5vtYdtaWfDuiMzIZ7Lhwm/P3QvO0f0N/X/q1FHPbZq49QqZE4fR/wtHOmokj2gGwet8VHj2NMTq+baPKtGlYCbVaw5zfDxnsmmRpoWDqB12Qy2UcOXOP05eklcDKFb14c5hIdhf9eoQoiW4/lpbmTJvaGysrc65dD2HjZv0yUBYWCqbP7o+Lqy0hwTEsmLNHT0G1sjLn6wWDcXG1I/RJDJMnrCUlWV+NtbW3YvZvo6lUzZukhDS+HLuSsCfRBq9J2Uqe/LR1Al0HN0YQBDb+epwvRiwjRsKNX9zQaDRsXX6SiUOXEvUsAS8fF37YOI5+Y1oZJJ0ajYaFM3ZwbG8AcjM5k+cPNkg6d2y4yB8/i2lFYz/sKEk6b14P5bfFRwB4Z1x7SdIZE5PM/O/F2qAD+jeUJJ0Av/x+nMioJLw8HZnwTjvJMVlZSub8chCNRqBz62oGSSfAgTN3uXQrBAtzMya/LW1i0kEQBOb+eZT0TCW1K3nTv51p3YY2HLvB7ZBI7KwtmTLMNIVUB6VKzfR1h1FpNHSoU4luDaqYvO8/IQgC3247xvP4ZEq7ODBtQN7mYgqyVWo+Xb+XuNR0Knu5Mb1P4Z/DVCjVaj7avJcHUbG429nw2/A+ryXpfHPDNlKysqjv++qSzryihHgWEP1rV+e7nlryef3Wa0c+ZTIZU7u2oa1/ebJUat5fv4uQuILVuywIBjeuRa+6Ygj78437iSpgn3UpvN2+EfXLlyY9S8kXf+1HWQCHcr2KPgxpVQeAmRuOkJrHkPtH/Vvi7mTL0+hElu3JvTsRQPuGlWhVtwJqtYbZq47kag777I122NtYcu9JFJsOXDM4rmrFUgzT1kj8/vcjBkPuQwc2pnoVb9LSs5nz4z7JwvJly7rxwXjRnLJy1Wnu3NU3OLm52TN91gAUCjlnTt5nw1r9xgYeXo7MXToKJxdbHgVFMmXCX6Sm6Lvv7RysmbPsDSpUKUVifBqTxq4k9LFh8mllbcGHs/rz5cJhWNtacvdaCO91+5Gl3+zkQeDTYk+tSUlMZ++683zU7xdWzN+PWqWhZddaLN79Ef61yxjcT6VUs+ibXRzZdQO5mZwv5w2iRQdpE8buzZf57UfRBDTsrVYMGNFMb0xUZBLfTt+ORi3QvnMN+g3S72OuVmuY/d0ekpMzqFzJi7FjWkue78yFhxw4EohMBlM+7YatjXQoccWm84SFiyH2j96ULkwPEJ+UxqK/TgLwVv+mlCll3NV96MJ9zt8KwVxhxtQ3jZNUHZ7GJLJ0l3hffjKgJe5OeSucvuLwZR48j8HJ1oopgwpW+mf31bscuCE64ucO74q9CXnhecWC/acJCIvA3sqSRSN6Yp1LZYCigiAIfL3nGOcfh2FjYc5vw/tQ2unVi4oWBDefR/Dmhm2kZmXTwLf0a0M6oYR4Fgr61arO3J6dkQEbrt/i6wPHXivyaSaX88OAbtTw9iQxPZN3/9pJQpppNSYLGzKZjOl92lPZy4241HQ+Xb+X7AKWrvknzORy5g7rioO1JXeeRvHLwYJ1cvqwZwt83ByJTEjhp12GyxdJwV6rogCsPXKNuyG5F/aXyWR8MaodttYW3HkSyaYjxovZuznb8cEIkQws33qeZ1GJBseOGdwMPx9X4hPTWbhCOg9WYSZnymfdsbYyJyDwKVt2XpEc16VzLdq1rYZGIzBrzi5SJAhj9Zo+TPhErKG7evlJLl3QL1Jftrw785aMwtHJhgf3wpn64V+kpeoTfHtHG777/U3KVfYiITaVD4f+ysHtV42SyNbd67B418dUqulDemome/4Syd773X9k6/KTL7V/u1ql5vKJe8z58C+GNfuWJTN28ujOcywsFXwwsx+TFw3H1kh/8shn8Ux88w8OaZ3un8/uTysD3YL2b7/GkgViK87Bo5sz6t02emOyspR8M2UrSYnpVKrsxSdfdJMkTn+tP8+twKdYW1swbWpvybaYcfGpLPhZLI81pF8jatfQd7qDGGLfuEesZPD5e51wMPJ5f/zzOMmpmVQq686wbsbTchKS0/lhvegof6tXE/y8cy89JAgC3649SqZSRQN/X/o0l76WhhD0LIY/Dol5oZMHtsPF3rQ6oVIIjUlg9nZx/uM6N6WOn3FjYX6w98Y91l8IAGDuoC6UcXUq9HOYil9PXWL7jTvIZTJ+Gtid6t6GU2f+jQh4HsEb67eTmpVNwzKlWT6kD7YWrwfphBLiWWjoW6sa83qJ5HPTjUCm7z/6WpFPGwtzfh3WG28nB0LjE/lg055CJ3ymwtrCnEUjemJvZUlAWAQL9hd+NykvZ3tmDhZLDq06cZULD/IWJn8R1pbmzBimLV90LpCL9/N2rNa1K9C5oT8aQeCbtUdMqg3q4WzPR4NbAfDbtnM8jzFe3LxH6xo0qF6GrGwVc5cbNm9ZmCuYMiH3kLuPtzPjxopq1PI/z/DwcZTeGJlMxicfdaZUKSeiopL54acDkuft3rse3XvXRRDgu2928uypvtnIr6IH3y0Zib2jNfdvP2fax+ski9A7OInks07j8mRlKlk4YydzPt9EqkSIXgfvsq78tGUCs1aOpU2POlhYKgh9GMWK+fsZ2XI208eu5MyBW5L94QsDIQ8i+WPuXka2msPX76zizIFbqJRqbdvLHvx5agrdhjYxqpad2H+TcYOWcP/WU2ztrZjy/RDadpMOJR/afYNF3+0FxFqdb47XD6cKgsBP8/fz8EEkjk42zJgzAEtLffXr5s0w1v51DoBPP+5M6dL6RhdBEJi78ABJyRlUKOfOmJHSBeezslV8t1gbYm9VjRYNpU1TAKevPuLYxQeYyWVMfaezZFekF/HD+hMkpWZS0deNUbmQVB12nrvN1aCnWJkrmDayQ95C7Go1X2tD7O1rV6RTvfx311Gq1Hzx134yspU0rODDW+1MqzmaFzyIjOXrHWLKxbttG9Omat5KRRUmDtwO4ucTYvRneve2tK5csCL7rxpuPAvnzfXbScvOplEZH5YP7vtakU4oIZ6Fij41qzG/VxfkMhmbA24z7TUjn+72tvw+vA+2lhZcDX3Ot/uPF1vIsYyrE3MHiW0P118IYO+Ne4V+jvY1KzKwqZhjOWX9QeJTDXe2yQ0NKvkwuKW40H+z/kiee7l/PqgNTrZWPHwWy58HTSty37t1TepX8SEzW8V3q48Y/a5kMhlfju2IpYWCa3efsudkoMGx1SqZFnLv2aU2zRtXRKlS8+2CvWRm6hMzW1tLpk/tjUIh5/SZIPbsDZA81viPO1Otpg9pqVnMmLyF9HR9RbNCZS++WzwSO3sr7tx8yrSP15MpUUzfycWWOcveYMzHnTBTyDlz+DbjBi7mtpHi8WZmcuq3rMykn4ax/sI0PpzVn2r1yqLRCFw5dZ85H/7F8OazWPz1DoJuhhX4d5GckMbutef4sN/PvN/9R7atOE1CTAoOzrb0Ht2Cxbs+ZumeT+j7ZivJ2pw6pKdl8f1X25j35RbSU7OoVqcMSzaPNxheP7LvJj/N2g1AnyGNefsjaYfwji1XOHboNnIzGdNm9sPDS79Qd1JyBrPn7kGjEejSuSbtJYxmANv3XOfytWAsLBRM+7ynwRaTKzadI/S5NsQ+xnCIPTU9iwWrxHqOw7o3wL+ccTXsbMATDl8MQi6T8dWYTrmSVIDohFR+2iK+8I7r3Qxfd6dc93kRq49c5f6zaBxtCh5i/+XAOe5qjzVnWMEc8VJIyczio7/2kKlU0axSWcZ3KL4uObfDo5i8UyyK/0bTegxpaFoe7r8F15+FM2bDDtKys2lc1offB/fBppjSGYoSJa72IsDu2/f4YvchNILAgNo1mNW9A/LXyO1+6kEw763fiSDA1K5tGNlEv1fzy8LPh8+z7MQlrMwVbBg3lMpeboV6/IxsJUMXrudxVDytqpZj8Vu9871IpGdlM2DOWsLjkxnYohZTBxvvr/1P7L90j69WHsRcYcaGqcMp7+2a6z5PoxIY9tUaspRqpr/VmR4tjRdV3rDvKj+vO4WdjSXrF7yBu4H2gy+63Du1qsr0j6Rd7olJ6bw5fhXxCWn07VGXj9+XdvZv2XqZX5cdx8JCwdLFoyhfzkNvTFxsCuPHriQuNpXmrfyZPqu/ZB5e0J3nfDl+LelpWdRpUI5vfhpqsMd3UOAz5n65mYin8cjlMoa925ahb7c22cH+LDiGozuucWzHNWIj/1aVfSt40KFvfarUKZOn+yU5IY0TewK4dPwuKqWobJsp5DRqW5WO/RrQoJU/5gbqrf4TD+48Z+6kzYSHxSGXyxj6dhuGvdvG4Gc7fjCQBV/vQKMR6DmgAeMNhM5vXAvhy0/Xo1ELjPuoE30H6itsgiDw1fRtXLj4CF9fF35b8gbWEp2HnoTE8O7Ha8hWqvnovQ457Vf/CVNd7ABz/zjCruO38PVyZs3ckUY7FKVmZDFk6p9Ex6cyokt9PhwinX/6z8/2ydLdnL71hBp+XqyaNDhPZO9heCxD569DpdYwZ3TXAhmKzgeF8u7v2wFY+EZP2tc0fF3yA41G4KN1ezh+9zGlnOzZMmE4zrbFU7A8OiWVgb9vICo5lVaV/Ph1WO9/XcceY7j29DljN+4gLVtJk7K+/Dao97+KdOaFr5UQzyLCntv3+Xz3QTSCQI/q/szu3hFr83/PTZQbVpy7yoLDZzCTy1g0qAcdqhbuA89UqDUa3lu9k/MPQ/F1ceSv9wbjZi9d9y+/CAqPYdiiDWSr1HzSvQVjChDKuhwUxjuLxZIyv7zXJ0+9mAVB4KMluzgbGEwNPy+WTxyIpQF16EWs2X+FxZvPYG9jyV8zR1LKzfBvSa3R8M7XG7j7OJJmdcuz4LM+Bk0Wdx9G8N6U9Wg0At982oP2zaUX0CvXg5k4bQsA30zuTZsW/npjNBqBqdO2cunyY8r4urJo4QgcHfQXuLu3nzPxg7UolWqGjGjGmHelGxvcC3zGlA/Wkp6WTe0Gfnz13UDJUksgqoJLZu/hmFZtrVLLl/e/7I5/jdzLV+mgVmu4dfExR7Zf5fzh22RJqLt5RYVq3nTs14DWPeoYVTX/iYz0LDatOM3WVWdRqdS4eTow6buB1JRwm+twYOd1fp67F41aoFvfenzwZQ/J7z3kSQyffbCW5KQMOnapyedTe0pe//UbL/DHilOYm5ux9JdRVKigrzqmpmUx4fN1BIfG0qRBeebO6C95rNS0LN75ch1h4fF0blWNaR91M/g5Lt4M4ZN54u9rybRB1KsqnSsK4u9p1srD7DlzBx8PR9Z/OworiXSBf2LvhbtMX30IhZmc9VOHU7G06S+7GdlKxizczL2n0bSpWYGf3pa+fqYgIiGZYYs2EJuSzqCmtZg2IG8vsqZg8ZHz/Hr8EuZmZqx9dxA1fb0K/RymICkjk7FrtxP4PIoK7i5sHDvkX13H8p848fAJn+7cL5JOP1+WDer9r+MLJcTzFcHeO0F8vusAakHA38ONn/v1oJxr4RfzLQ4IgsCUnYfZEXAXM7mMb3t1LLYWZQlpGQxavJ7wxGQqebqy6u2Bhf5WvuFsAHN2iMn7Mwd1pG/jvBkJXsSczcfZfOYm1hbmrPhoINXKmJ4YHxmfwqCZa0nNyKJzQ39mj+maq/tWpdbw1qwN3AuOonxpV5ZPGYy9reGyI4/CYhjz1TqUKjVDu9XnwxFtDI5dtu4Ma7dfwsLcjJ+mD6R2NWmytuSP42zecRVzhRnffd2PhvX0SVBiYjpvv7eSuLhU/PzcWDB3CK4ShOvA3gB+nLsPgH6DGvHeB9L5dXduhjH1QzHX07OUI9PmD6aSgc5FAMf3BbB41h7S08QwfodedXnzw464euTt2ZOWksnZg7c4sfsG8dEpedrXTCGnXotKdOjbgHJG5ioFjUbDsT0BrPr5CPEx4nmbt6/GxzP6YC/RRQrEeqlLFxzgwE6xQUTnXnX4eGovyXvqwf0IJn+2geSkDCpXKcWPS0bq5XUKgsBf68+zarVoovvog0707qWvYmZkZvP5tC0E3n2Oi5MtKxa/gYtET/dspYqJs7Zx/fZTXJ1tWfvTGwYNRY/CYnjvm42kZWTTr0NtPh+j39LzRaw7cJVFm8QaxYs/H0DDaoarAehw7cEzxi3ajlKl5r2eTXmnh+lhZ5Vaw8QVezgZ+ARHGyu2ThmJu2PeXPA6xKWk88aSzYTEJFDRy5X1Hw0tdIf5H6eu8JO2SPyMvh0Y2Ei6bWlRIzollbFrd/AgKhZHays2vz2UssVobCpMqDUaFp46z7LzogGzqZ+odP7bSCeUEM9XCpdDn/Hxjn3EpqVjZ2nB/J5d6OBvOCn+3wSVWsO03UfYEXAXgC86tWJM8/rFMpewuERG/76Z6OQ0qpRyZ8XYATjZFF5NN0EQ+GHPGf48dQ25TMbCN3rStkb+vkelSs2EX3dw6cFTXOxtWPPpYHzcnEze/9K9MD74eQcqjYY3Ojfgw34tc90nKi6FMd+uJyYxjQZVfVn0WT/MjYSTj5y/z/TFIrn7dHRbBnaWDoGq1Rq++n43Zy4/ws7WkqWzhlK+jL4CpFZrmDl/DyfPBmFpqWDBzIGSzuXQ0FgmTtpIXFwq3t5OfD9vCF5eTnrjdm67wpKfxFyvrj3r8NHErpJdcIIfRTHzi82EP40X3d+TutOpZx2Dnzs2KplViw7nqJ9W1hYMfqsV/UY1x9JAuP5VwJ0bofw2fz8P74hlqUr5ujD20y40a1fVoKIWHZnEt5M28+BuODIZjH6/HYNHt5AknYE3w/jqi82kp2XhX7UUc34YisM/FGlBEFi2/ASbt4hO7dGjWjBqRHO982dlq5j8zTauBYRiZ2vJorlDqVheP7VCoxH4ZuFejp0LwsbagsUzB1O5vPRLWnR8Cm9PX090fCp1qviwaHJ/g7miAMeuPGDyEtFA9dGQ1gzvkvtz60lEHG/O30RKehbt61Vk3tvSqrAUBEFg1qZjbDsXiKW5Gb9N6E/d8nlro6tDWmY2Y37dwt1n0Xg7O/Dn+EF4OZve2tUU/Hn2OvP3nQLg487NebuNfpmsl4Gw+ETGrNnGs4Rk3O1t+WNkP/w9CzedqrgQn57BJzv2cSFErEc9skEdJnVohYXZq9uowhhKiOcrhuiUVD7esZ+rT8VF4f3mjfiwVdPXIj9FEAQWHD7DyvNi/ce3WzTk0w76i83LQHBMPKN/30JcajrVS3uyYmz/Qg3HCILA9E1H2HnlDpYKM5a/15+65fK3eKRmZDFm0RYePI+hjLsTqz8ZnKdyKrpwH8DkYe0Y2Dr3JPsHodG8890m0jOVdG9ejeljjXccWbP7Er9uPItMBnM/6U2rBtLpFFlZSj6euZXA+8/xcLXn1zlD8ZQI5yuVaqbN3sGFK0+wsbbgp+8GU6WSvqoXEZHIxC82EBGZhJubPd/PG0KZMvr5rIf23+THuWKd0JZtqvD5lJ563Y0AUlMymT99B5fOPgCgR/8GvPdZF8zNDT/ggwKf8dv8fdy7KS4KHt5OvPlBR5q1r/bKEFBBEAh9HM2G309y6qBoBrOxtWToO23oPbypZNkiHQKuBjNnylaSEtKxd7Tmy2/70aCp9Pd77nQQ332zk6wsFbXqlOHb+YOw+UeNTbVaw8+/HGbPvgAA3n+vHQP765MVlUrN9Dm7OHfpEdZW5vwwezDVq+iX/hEEgV9Wn2Tz3msoFHIWTOlPw9plJeeXlpHN+zM38jA0hjKlnPn9m6E4GmlZeetROOPnbSFLqWZg+9pMHJG7uSc2KY3R8zYSEZdMrfKl+O2TAViZmGsL8PvBiyzddwGZDH54qyftaucvNUmpUjPuj51cfBiGi501ayYMpmwht8TccOEms3aLpdLGt2/CuA5NC/X4puJ+ZAxj124nNjWdMi6OrBzVHx9nfRPbvxGB4ZF8sG0v4ckpWJsrmN29Ez2q66cg/ZtQQjxfQSjVauYfO8OfV8Saii3Ll+X73l1xtimeRO3Cxh9nr/D9ETEsM7BeDWb0bF8sxPphZCxvLN9CYnomdcqU4vcx/bAtxKK7KrWGj1bt5vS9YBysLflzwiAq5tPQFJOUyqgfNxERn0yNsl4s/2AA1ibkmOmwfN9Fft19AblMxo/jetGqVu4lTs7fCuazhTtRawTe6duUsb0NLyqCIDB/5VF2HruFpYWCJV8NonpF6fBvckoG477aQMizePx8XFk6eygOdvqKc1aWkkkztnHjVhgO9lYsmjuU8n76bTpjYlP4YtJGQsPicHKyYd53g6hUUT+/7PSJe3z3zU5UKg1l/dyYNqs/Zf30vw+NRmD9itP8tfwkggBVa/rw5az+eHk7Gf38Jw/cYsVPh4iNEut1WlgqqNO4Ao1aVqZRK388ShnevyiQnaXk1pVgLp95wKVT94nS9qiXyWR06VefURM64GwkH1St0rBt/QVWLTmGRiNQ0d+LafMG4SVR5kit0rBi2Qm2bBA7SzVqWoHps/rrhddVKjXzFuzj2PG7yGTw6cdd6S5Rqkmt1jD7h30cO3UPCwsF82b0p54BMrl+1xWWrhEVt+kfd6dTS+nOSiqVmonf7+TSrRCcHWz4Y+YwvD0Mk5Nn0YmM+XYDiSkZtKxTnvkf9sr1OZWemc3YH7ZwPywaXw8nVk8akqde7Dsv3GbGerGz0+SB7RjcKn9ObI1G4Mv1BzhwIwhrC3NWjRtA9ULOudx6JZCvt2s7VbVuyMedi0dEuBr6jPfX7yYlM4sqXu78MbIvbnaFm7tfXNgacJsZB4+TrVbj5+LE4v49qezx71dxS4jnK4w9t+8zdd8RMlUqfBwd+KV/D6qXej2K3265FsjXe8Ti+R2rVuT7/l1NMr8UNu6FRzPmj60kZ2TRoFxpfnujb6HmP2VkKxn72zZuhUbg6WjHXx8MyXeoKzgynjd+2kRSeiatapTjx7G9UEiEjKUgCAIz1x5h17k7WFkoWP7ZQKr75b4Q7Th5i+9Wi4vLjLe70K254X70KrWGST/s5HxAMM4O1iz/ZhilPZ0kx0bGJPP+lPXExKdSu6oPP04fgKWEKpSensVnX23mblAELk62/DJ/KD6l9Qt2JyWlM2nyZh48jMTW1pLvZg+kRnX9HNLbN5/y7fTtxMelYmVtzmdf9qBNe+nPdOXcQ+ZO205qSibm5mb0HtyIIW+2xF7CyKRDZkY229ac49D2q0RH/H89VL9KnjRq5U/jVv5UqeUrGe4vKGKjkrlyJohLp4O4cfHx/xmXzC0U1G9WkZHj2lPBSE6oIAhcOvOAFYuPEhYcC0DH7rX54MvukgpuXGwKs7/eQaBW8e0/uBFj32+nV2ooO1vFzFk7OX/hEWZmcqZ82ZO2bfRJoiAILPjlEPsO3UKhkDP7q740MVCH8/Dpu8xcJLbXHD+6NUN7SZv5BEFg7h9H2H0iECtLBUu+Gky1Cobv/6TUDN6atZGwyASq+HmybPKgXF/0VGoNn/66m7OBwTjZWbN60hDKeDgZ3edFnLkTzMe/70KtEXirUyM+6Nnc5H1fhCAIzN91ir/O3EAhl7NkbB+a+UuT9vxi9/W7TNl6CEGAUc3r8UX3VsVCOk8EPeHjzXvJUqlpULY0vw7r/VoYibJVKr49fJJNN8ToRPtK5Znfq8tr8dmghHi+8rgfFcOEbXsIS0jCUmHGN13b069W8RhzChuH7z7ks60HUKrVNCnny+KhvYqlzdftZ5G89YfYbqxpxTIsGdW7UElwYloGo5ds5klUPOU9XVgzYTCO+cwpDXgSzruLt5KlVNO3aQ2mDzW9GLVSreajxbu4eDcUVwcb1kweSimX3H8rizefYc3+KyjM5Cz+YgD1/A07uNMzs3l/5iYehESLocwZQ3E0YPB4HBrD+K82kpqeRevGlZj5WU9JMpaSksnHkzfyKDgaD3d7fpk/DC8JpSo1LZOpX20l8PYzrKzMmTmjHw3q6xuTEuJTmTNjJwHXxeL8fQY04J3xHSTD6RHPElg4ew8BV4MBsHOwYvhbregxoKHR8LQgCIQ+iubS6ftcPv2AezfD/q8dqL2jNQ1aVKZRy8o0aF7JoKEnN6jVGh7cfs7lM0FcPh3E4/sR/7fd1cNeS3arUKdReawk0gtexIO74SxfdJhb2mvj4GjNmAkd6NK7ruR9dvN6KLNn7CAhPg0bGws+m9yDVhK93DMysvlq+jZuBIRiYaFgxvQ+kj3YBUFgyfLjbNkldkz6elIvycoGAFduhjBx9nbUag2De9TnAyMtMVfvvMSyzWeRy2TM/bQXLesbDl8rVWo+WLCN60HP8HK1Z+W0obiZ0N5y7objbD55E0tzM5Z9OpBa5U03fAWGRPL2L1vIzFbRs1E1Zo7olG8it+L4FRbuEyNK3w3rQo/60gpwfnHgVhBfbDyARhAY0qQ2X/VqWyykc2fAXabuOoxaI9DWvzw/DeyOVTGIF4WNiOQUPti2l1vhkciAj1s3493mjV6rMoslxPNfgKSMTCbuOsipx+LiN6x+baZ0bP2vTSx+ERefhDFuw27Ss5XU8Pbk9xF9cLHNfzu4/CIgNJyxK7eTka2kZWU/fh7ZEwtF4T3EIhNSGP7LRqKTUqldthTL3+ufb2X1xK3HfPbHHjSCwHtdm/BeN9PzqlIzshizYDOPnsdSsbQbKz8fhF0ufZo1GoEpS/dy/OpDHGytWDltKGW8DOeKxSak8vbX64mMTaFmZW9+njLAYH3E67fD+OzbbShVavp2qcOnY/U73wAkJKbx4aQNhD2Lp7S3E7/MG4ariz4ZyMxU8vU327lyNRhzczOmTe1Ni+b6nV7UKg2rV5xio7ane5Vq3kz7tj8envrPDkEQuHL+EX/8fITQJzEAeHk78eb49rTuWN2kRTc5MZ1r5x5y6XQQV889/L/uR3IzOdVq++Jb3j1PC3h6ahY3Lj4mKSEt538ymQz/mj40alWZxq2qUN7fy6RjRoYnsmrpMU4eug2IaQJ9hzRm8BstsJVIg9BoBDavv8Cq30+i0Qj4lXfn61n98ZHIr01JyWTy1M3cvReOtbUFs7/tTx0DYfMVa8+wZqPYaWbyJ93o0kG6IkTQkygmTNtIRqaS9s2r8PXH3Q2ad/afvsO3v4ktNj8d3Y6BnQ3XEhYEgW/+OMT+c3extbLgj6+GUMEn99DmxuM3mL/pJAAL3u1B+3qVct1Hh7CYREb/uJGE1AyaVS3Lond7Y57PZ/vOy3eYtkk00n3eqzWjWksb/fKLo3ce8en6vag1Av0b1mBGnw4mm6YKE39euM53B8X0ij51qjGrV0eToz+vMi6GPOXjHfuIT8/A0cqSH/p0o1UFv+KeVqGjhHj+S6ARBJacucjiMxcRgLqlS7GoXw+8HPJXYuNVQuDzSN75aycJ6RmUc3Nmxch+eDu9/O/wavAz3l21g0ylinbVKvDjsO75XgCk8CgyllGLN5OSkUWrquVY+GbPfB9/69lbzNokdlyZNqQD/ZubXr4kIj6ZUd9tIC45neY1/PhpXO9cH9qZ2UrGzd3C7SeR+Ho6sWLaUJyM5K4FP4vj3RkbSEnPonXDisz+qKfB/Ljj54P4+sc9CAKMHdKcNwZKE+no2BQ++GI9kVFJlCvrxqK5QyXrd2Znq5gzdw+nzwQhl8uY9Hl3OhogMBfPPWTet7tJTc3EwdGayV/3oUEj6fxXtUrD4b0BrFl2gvjYVAD8q5fm7Y86UrOu6aFMtUrNvZtPuXwmiEunggh9HG3yvlKwsbOkfrNKNG7lT4MWlXFyMT2/LSU5gw0rz7B782WUSjUyGbTvVpvR77WV7DCk22fBnD1cOCu2QO3QuSYfTuwiWfQ9ISGNL77cxOMn0djbWzF3ziCqShiEANZvvcSyVVp39Psd6NtDmjQ9i0hg3FcbiE9Mp14NX77/yrAr/XJgCJ/O34FarWF4jwZMGGa86PuKXRdZtuM8ZnIZP37Sl6Y1/YyOBzgT+IRPluxGIwh82K8Fb3Q2vXZvXHIao3/axLPYJKr5evDHRwOxyWfU5/TdJ3y4ajdqjcCbberzac9W+TqOIZy894SP1u1BpdbQq25VZg3o9NJz8wVBYNHx8/x2WqyG8EbTenzRqVWxkN/ChCAIrLx0je+Pn0UtCFT1dGfxgJ74Or0eBql/ooR4/stw4uETPt99kOTMLNxsbVjUrzsNy5hevPpVxZPYeN5as52IpBS8HOxYObo/5d30c/mKGhcehTLuz11kq9R0qlGJBUO6Feqb9I3g57z92zayVGr6NKzOzMHSbQZNwZK951l+6BJmchk/v9uH5tX8TN73Tkgkb3+/hUyliiFt6/DFEMNhSh3iktIY8+0GImKTqV25NEs+N16G5sa9Z3z03VaUKjUDO9flk1GGQ3Jb919n4QrRHfvRmHYM7C5NOsIjEvlg0npi41Lxr+jJj3OGYGerr9iq1Rq+/2E/h47cRiaDjz7oTK+e0kpXRHgi3361jYcPIpHJYOSbLRn+RkuDi1lmRjbb1l1g85pzZGaIOZRNW/vz1oQO+EqYlXJD5PMErp17SGJ8ap72MzOTU7V2GarXLYvCiOteCtnZKvZsucL6ladJTc4EoG6j8oz9sAMV/Q2HiB89iOSbqduIjEjE3NyM8R93olsv6TB8dHQyEydt5NmzeJydbVkwb7BklymAHXtvsPBX0VTz7hutGTawseS4qNhkxn+1kciYZCr6ubN4pvT3D/AgJJr3v91EekY2HZtVYca4bkYJyqGL95n2m5gv+uXoDvRrW8vg2JxzPI1hzIJNpGcp6dO8Rp76sGdkK3n7563cDo3Ex82RPz8ZjKtD/kwxASHhvP3bNjKVKno1qMq3gzsXKhk79yCU8Wt2oVSr6VKzMvMGd33pCqNGIzBz33E2Xr0FwKcdmvN2i4bFEuYvTKRlZzNl72EO3BNf5PrUrMo3Xdv/K+tzmooS4vkvRFhCIuO37iEoOhYzmYxJHVoxuqH0w//fhMikFN5au53HMfG42tqwclQ//L30XcxFjTNBwUxYuxuVWkP32lX4blDnQn2zP3H7MR+vFkPlY9s35KNuLfJ1HEEQ+HrdYXZfEsOCqz4eROXSpl+vY9cf8vkysT7hF4PbMKRd7u1MnzyP461ZG0jLyKZr06rMeKeL0fvu6MUgpv0snuOD4a0Z1r2BwbErN59n5SYx9D1lfBe6tZNWKUPC4vhw0nqSkjOoWd2H+d8MwEZCbdNoBBYvPcLOXWLB83fGtmHIYOki3tlZKpYuOsy+3WIliQaNyvPl9N44GuhgBBAfm8ra5Sc5uOs6GrWA3ExGtz71GfF2a6Nu8eKEIAicOnKHVUuOEal1uftV8GDshx1p0LSCwe9SEAQO7A1g8U+HUGar8SrlyLRv+1PZgEnp+fMEPvtiA9HRyXh4OPD9vCH4+Ei/SB48epvvfhIJ38jBTRk7SrrWbEJSGuO/2kRYeDw+pZxZOmsILk7SRC0yNpm3p68nNjGNetV8+WlSP6MvSQEPnjN+vviSNLxLfT4yoR1mTFIqo77bQFRCKg39fVn8YV+j9W5fhEYj8PnKvRy7+QgnWyv+/HQIZT3yV+roSVQcoxZvJik9kxZV/Ph5TK9CjdRcfvKU91btIEulpkP1inw/tFuhHt8UqNQapu46zK6b95DJYEaP9gxukPuLwauO4LgExm/dzaPYeBRyOVM7tmFY/Vr/+rU8N5QQz38p0rOVTNt/lD137gPQo7o/s7p1/Ff1a5VCfFo6Y9fu4G5ENI7Wliwf0Y9aPi+/9drxu4/5ZN1eVBoNfetXZ2a/joWqIGy/dJuvN4sKz6TerRnRKn+5WEqVmnFLd3Dl4VM8nexYO3EoHnnocLL64BV+3iGaLhaO702Lmrm35bx8J5SPfhTDl2/3acrbfYznmOp6ugPMnNCdjs2k22UKgsDi1SfZtFc0lnzzaQ/aNpU2ljx4HMUnkzeSmpZF9SrezJsxAHt7/VxEQRBYufoM69aLhHb4sGaMeaOlwQf7kQO3WPT9AbKyVLh7ODDt235UrW68/mpYcAwrfjnKxTNi7U9rGwv6DWtC6441KFPO7ZVYRFJTMrlx+Qmb15zjwd1wAFzc7Bj9Xls69qhj1GGfmanklx8Pcni/qDQ1blaRL77qpVcUXofg4Bg+/3Ij8fFp+Pi48P28IXgY6Oi0a/8Nflp6BEGA/r3q88E70nUyU9Iy+fDrzTwMjsbDzZ6ls4bi5S59zOTUTN77ZiPBz+Mo7+PKb18PMdp962lUAmO+3UBSaiZt6ldk7vieuf7WM7KUjP1+M/fCovHzdGb1pCE4GDnHP/HjztOsOXYNc4UZv0/oT90K+avxG5mYwshfNhGZmEKtMl4sf28ANnkos5Ybroc8551VO8jIVtLavxwLR/TEwkRyXVjIVqn4bOsBjtx7hEIuZ16/LnSv+e+uYwlwNOgxX+w5SGpWNh52tvzcvwf1fKTTUF43lBDPfzEEQWDt1QDmHj2NSqOhioc7Swb++/NCkjMyeXfdTm48jcDGwpxfh/WmcTnDfZSLCocCHzBxw340gsCgRjWZ1rt9oZLP5Ucv8/OBcwDMG96VbvWkCVluSE7PZPSPmwiOiqeKjwcrPzY9T+zFMks2luas/GIwlX1yV013ngpkziqROH/zThe6NjNcZkkQBBauPcnmg9cxV5ix8Mv+1Ksm/X0KgsD83w6z52ggCoWcuV/2pUldaTJ8LyiCz6dvISU1k4rlPFjw7UDJVooAGzdd5Pc/TgLQq2ddxr8v7WIHCH4czTdTt/H8WTwKhZx3J3Sgd/8GuRLIW9dCWL7oCA/uhef8z93TgQZNK1K/aQVq1imbp/zLgkClUvPofgQ3rgRz5fwj7gU+RaMWH9/WNhYMGtWcfsOaYCWhFL+IZ2FxfDttO08eRyOXy3jj7dYMHt7M4O/gVuBTpn+9jeSUTCqU92De3MGS34kgCGzYepllq8UXkr496vLhu9JGlYzMbD6duZXAoHBcnGxY8u1QfL2l1cGsbBUfz91GwP1nuDnb8cfMoXi6Gl4PXiybVLWcJ8u+HJRrD3aNRuDzZXs5EaBVKycPxdfdyeg+L2LL2Zv8j72zDm/rPtvwLZmZmRkTJ3HsxGGGpkmbMsOg28rdN+jWdVu7dh13hZXbbV2ZAg0zgymJmZkZZLF0vj+OLNuJQXYccKr7unQdkWVZPjrn+T0vvfi5mFby0v1rWTt7Yt/7brmSB/71BWVN7YR6ufHho7ePq2foWOTUNPKDD76hT6VmXmQwr09ytw9TUKg1PP75No6WVWFlYcErt61jWezUnuan0+t549hpXjsq9rydHRTAKzetw+sa6T1qCmbheQ2QUVPH499sp71PjoO1FT9bupA7k6dP6fYLfSo1j3y6lVOVtVhZSPn5yoXcO/fypxNsO1vE01/sRBBgZWIkL96yetKazAuCwEubDvHp8bNYSCU8c9Mybk2bWPiorq2Le/8uVsZOD/Pj1R/dgKuJM+g1Wh2PvLqJzOJa3JzseO2xjcSHjN0vtr/NkoWFlGe/t2rUHp86vZ5nX93GwfRS7GyseOHx65k3c4QiHp2e51/Zzv7jxVhZWvDzH60cMexeXtnCz37zJR1dfXh6OPLbX6wfdrwmwJZvs3n1tT0IAoQEe/Dk46tJShp+5nZfn4q/v7SNo4fEiEJouBc33z6HZSsTRm2jpNcLHNmXz95t58jJrkKt0g55PDDEg8QZwSTODGHajGB8/F0nZZ9WKtQU5taRd7aGvDM1FObVDenhCRAU6snchdHcfHfamKkA5aXNfP35aQ7uy0er1ePq5sCvf38jM5NDh32+TKbkvQ8O8+22MwgCxMf589KLtw3rQsvlKv7+rz3sO1QIwD23zeUH9w3vQre09/KrP2+muLwZJ0dbXnvudiKHGSIAoiv6q5e3klVQi72dNW//7g4ig0deRLV1yXji75sorW01tE26C88RQvf9aLQ6fvef3ezKEPfNt566mZmRpruVnx85x5++OoAgwCPr5vHDNcPnso5FQ0cPj7y/mbKmdrydHfjfY3fgb0JrNFPZm1fKr77cjUKtISU8kDfvv3HS57uPRUVrB09+uZ2S5jbsrCz5150bmBcxuf1ILzdlbe08s20vZ+rFtmf3pczgl8sXXfbUhSuNWXheIzT29PLUph1k14luS3KQPy+uW0m4x+Uv0JksVBotv9y0i135hurZ2AhevHEVLnaTN1fdFL49U8hvvt6DVqcnxs+L1+/dgL/b5Oxjer3Ab7/Yw5YMcYb9A0uSeWrdyIUto5Fb1cTDb3xDr0JFmI87/3p4o8kno54+JT9++WuKaluwt7Hibz9ez9z40Q/yer3A8++LrWcAHr1tIfeuHdkZVKo1/PLvW0jPrcZCKuFnD67gxuXDC22tVsdz/9zOwZNi+PqODbP5yT2Lhg0J1zV08qvnvqamrgOpVMKDd8/n7lvnDvvc4ydK+MfLu+jskgOwemUiP3poGa7D5HIKgsDmrzL54J2DxiIidw8HNtw0m/U3zsJ5jN6bKqWGnOxqMk+WcTajkqphqtc9vZ1InBFCYr8IHfUVh6JUaoxis6yoEZ1OP+RxJxc7EmcEMzstktlpkaNOXur/ezNOlfP15+lkG/qWAsxKCePnz6zH0/PCwQeCIHDgYAFvvHWATkNbp9WrpvH4oyuHrXIvLW/m93/aSl1DJxZSCT/+3hJu2zh8FXh+SSO//vNm2rv6cHW24y+/von4YcamgpjT+X9/+YaKunbsba34y//dSHLC8IsKgOqmTh7/29c0tvXg7mzPv35xy5htk/qUan7+1recKqzBUirlhe+vYdVs00K+giDwr+0neG+3WI1928IkfnXrxPpf5tU08egHW2jvlePl7MDbD91ElN/kTLMRBIG3Dpzm9X1iS6u0yGBeuWf9pE50M4Vvcwr53bf7kas1eDra8+rt1zMreGLpCFcDGp2Od09m8q9jp9HodDhYW/Hs6qXXTE/u8WIWntcQekHgk6xz/P3gMfrUGqwtLHhs0Vy+Nyd5yq6oBEHgk/Rz/Gn3ETQ6HQGuzrx867rLnvd5prqBJz76lnaZHA9He/559/XMCp2cA6EgCLy97zT/2iUe7FdOj+SPd62dUDPkssY2HnljE81dMrxcHPjXTzaaXHAkU6j42Vvfkl5Ui6WFlD88uIbVKaOfWPV6gde/OMJHu7IAuGPVLJ68Y/GIwlmr1fGn9/ay/Ug+APffMIcf3Tb8qD29XuDfX57g31+In8vcmWH8/qnrh61ilivU/PPNvezeL75uclIIz/xs3bC9Pnt7lbz3/iHjrHBnJ1se+uFS1qyePuz7lvUq2b71DJu/yqCttRcAGxtLVq2dzk23zyEwyLTFXU+3goJzNaIrebaGkoKGC8TixeDl4yyK2JnBJM4IJjjMy6QFjFqlZf/ePL7+7DTVVeKkIqlUwsIlsdxyxxxi44ffz+vqOnjltT1kZVcBEBTkzpOPr2bmjAsXLIIgsHn7Wd547wBqjQ4vTyd+98v1TIsfviPH7sMF/PnN3ag1OiKCPfnTrzbiN8J4y5KqFv7vL9/Q1tWHp6sDf//FTUSHDl89D5Bf0chTL2+mq1dBkI8rr/zfTQSOMWGoo0fOY69torCmBTsbK/76o+uZlxA66s/0o9HpePGz/Ww+Je6bD69L44er50xIdO7PLePpj3ei1GiJ9vPkX9+/ccKT0M5Hodbwm6/2sCtXXOzdM28mP79u0WWtXldqtLy44yBfZov9ZOeEBfG3m9fi5TR1w9C5DU38evteilvE79biiDCev245fs6T83+bipiF5zVIfXcPv92xn6MVVQDE+Xjx0vWriPcd+WB8tZPX0MxTX2yntrMbKwspP1u5kPsuc+i9oauHxz7cSlFjK5YWUn6/cQUbkydvxbo9u4hnP9uDRqdjerAvr37vBjycxt9Mv7mzl0fe3ERZYzuOtta8/NAGUqJMy5FVa7Q8++/d7M0STz4/u20xdy0fu/Dp411ZvPKZmK+3ak4Mv/3B6hGriAVB4P2vT/L+N6KgXDUvlmd+NPLzD5wo5sXXdqJSawkJcOdPT28cMcdv1748Xn5jL0qVBjdXe37zs+uZPTN02OfmF9Tz8iu7qDA0hU9MCOSpJ1YTFja8UNdodBw5WMhXn52irKQZAIkE5s6P4pY75jItKWhc+6NSoaYor568s9Xkn6uld1BTeVOQSqWER/mQODOYaTND8BnnLPiuzj6+3ZzN1m+y6DK4lfb21qxdP4Mbb0nBd4TXU6u1fPr5KT759CQajQ5ra0vuviuN22+dM2waQq9MyV9f3cXh4+I+NS81gqefum7YHqw6nZ53PjnGx5tFZ3BhSiTPPnHdsF0LAE6dq+KZV7YiV2oID/Tg77+4CV/PkY//J3Iqefr1b1GqtcSG+vDPn27E3Xn071hdaxePvLqJ2pYuXB3teO2xG00aNwtiEdLPP9jOsYJKpBIJv7ljOTfNM73nbj+CIPDR0TP8dethBAHmx4by93vX4WA7OU5kU3cvj324lYKGFiwtpPz2huXcnDJ8esulYnBoXSKBhxfP5eHFcy57r9DJQqHR8NqRk3xwOhu9IOBqZ8uzq5ZyfULMVVFweCUxC89rFEEQ2JJXyB/3HqZLocRCIuH7abN5dMHcKTtWrFep4jdb9rK74MqF3uVqDb/+chd788oAeGBhMj9ds2DSDo6Z5XU8+Z9v6ZYrCXB35o0fbCTcZ/zpEj1yJU++s5Xs8nqsLC144d7VrJ5lWlhQrxf42xeH+OzgWQAeXJPCozcO70oOZtfJQp5/bzdanZ6U+GD+/Nj6UacibTuUx5/e34tOp2dWfBB/emrDiNXHxRXN/OpPm2lp78XJ0ZY//N96Zk8fPhWgqqad5/68lYqqViQSuOe2NB64e/6wzo1Op+ebTZn8+79HUSo1WFhIufXmFO69Z/6woWIQv1vnzlTz9WenOXWizHh/dKwfN98+h0VLYy+YU341UVPdxjefp7N3Vy5qtZiD6uXtzMZbU7hu/YxhJxX1k5VdxSuv7qauvhOA2clhPPH4KgJGWAgUFjfy+z9vpam5G0tLKT9+cAm33JA87L7UJ1fx3D+3cyKrAoD7bp7LD+6YP6Jru/VgLn95fy86vUByfBAvjbL/AGw7ls+LH4gjFuckhvDnR9djP4ZwK65t4dFXN9HeI8ffw5l/PXETIT6mtT3q6JXz+NtbyKtuwtbKkj8/eB2Lp42/MEan1/PnzYf59PhZAG5Lm86vNi6dNCcyp6aRxz7aSluvHDcHO165+3qSwy5vb+jzQ+t/vXktaeEjp0pc7ZyuruU32/dR3dkFiF1nfrNyyRWZync1Yhae1zhtsj7+sOcQOwtFtyHM3Y0X1q2Ysk3nzw+9+7s688/LHHrX6wXe2H+SNw+cBmBhdCh/vfM6nGxHHz1pKlWtnTz87iZq27txsrPhlQc3kBIx/v+XSqPlmQ93su9sGRIJ/PymJdy1ZOxenSB+zv/elcHrm8Wq+/Vp8fzm3hVjpmyczqvml6+LDlR0sBf/eOpGvEcJBZ7OqeLXr3yLXKEmLEB0rPxGaJPT3tnHr/+ymfySRiykEh7/3jJuWjNjWBGjUml4/d0DbN15DoDpCYE8+4v1eA+Tpwhis/PX39jHMYMr5+PjzOOPriJt7sgzvcEg4r5IZ+/OoSJuw8ZkZiSHEBHlO2L1/OWkpbmHwrw69u3OvUAs33LHHBYuGV0sd3T28eZb+9l/QMzn9XB35JGHl7N4Ueywn78gCHyxOZO3/30YnU6Pn48Lv3t6A3HRI/T9bOri6T9torK2HWtrS55+eDWrFg4/Y1wQBN796gT/3iRWBa9ZEMevH1o9Yg9NQRD4z7Z03vxa3JfXpsXxm++vGrPnZkZxLT99Yyt9SjVRgZ68/vhGvExsVVbf1s1P3viGmtYuXOxtefXHN5AUNv5WOXKVml98tIPDBWLO7U+vX8gDS4YX7hPh2zOF/Pabvai1OqJ9PXn9vg0EuF2+rijnh9bnhgXx1ykcWu9VqvjrgaN8diYXAB8nR55bs4xl0VO7En+yMQvP7wh7i8t4btcBWmRiSO2u5CR+tnQ+jjaTI5YuN/kNzTx5hUPvu3NL+PWXu1FqtIR7ufP6fRsI8ZxYE+jz6ZDJefyDrZyrbsTSQsrzt69iffLwJ+LR0On1/OWrQ3x+VBRgD6yYzePrF5hcvLT5eB4vfrQPnV5gfmIof/7hujFdoqKqZp78xyY6euR4uznyj6c2Ej1KdXFpdQs//csm2jpluDnb8cIT65kVN3xqgEqt5S9v7WH3YVEA3bAqiSe+t3TEMP3+w4X87bXdyBVqXJzt+NVT15GWOvJJ4MTJUl59fS8tLT0ALJgfzaMPrxixD2U/w4WtAaytLYmO9SM+MYC4hADiEwNxv8TN5TUaHWUlTRTk1VGQV09hfh2tLb3GxyUSSFsQzc23zxkzPUCvF9i2/Szvvn+Ivj4VEgnceEMyDz6wEMcR3MXuHgUvvbyDk+nlACyeH8Mvnlgz4oSh7Nwanv37t3T3KvBwc+BPT99IXOTwAlWuVPPCW7s4mC5GPR64cS4P3TpvxL9Bo9Xxp//u49ujYn7lPWuSefS2sUcs7s4o5rf/2Y1GqyM5OpB/PLwBp1Hc+8EU1bbwyJubaO+V4+fuzBs/2UiY7/ijFq09Mh55fwuFdS3YWFrwx7vWsCopetyvMxw6vZ5X9hzn/cOZACyLj+BPt625rEVE54fWH1k8l59M4dD6gZJyfrfrAM294gSyO2ZO4+fLFk6aIXEtYRae3yF6lEr+sv8oX5wVV5d+zk78fs0ylkYN39bmaqdXqeLZrXuvaNV7QX0zj364leYeGc52Nrx81/XMjZycEJFSo+WZT3ex55z49z28ai4/XjV33OJaEATe35PB69tEx+f6lDh+d/dKkwvOjuRU8PQ721FqtMQFe/PqYzeOOdqvobWbp17eRGVDB/a2Vrz4k3XMTxp5P2tu7+EXf99CSVULFhZSnrhnCbesGt7NFASBT7dk8OZHRxAEiArz5vdPriMk0GPY165r6OS5P22lpFzMy7zlhmS+d88CHOyHPyEoFGr+99Fxvvw6A51Oj62tFTdumMX662fiN0YeZX+hzvEjxRTk1Q+bt+nr5yoK0cQA4hMCCY/0vqjQfHtbLwX59RTk1lGQX09pcSMatW7Ic6QWEiIifJg+M5jrb5hFYPDwn5Xx71BrOXqshC+/SqektAmAqCgffvrEGmJGGal5KrOCv722m9a2XqytLHjkh8u44brh/49arY73Pz/BR5tOIwgQF+nLS7+8Ec9hCsIA6pq7+OU/tlBR24alhZSff28FG5aOnC/Z26fkl69/S2ZhLVKJhJ/ds5Rbls8Y9e8WBIGP9mXz8ldHAFg+K5IXvrfW5P6Vp4qq+el73yJXaYj29+T1hzeOa6BDPyWNbTzy3maaunpxd7Tj1e/dQFLIyJ/7eOhTqfnF5zs5VCimNDy0JJXHVo7cm/VS8G1OEb/7dt81EVpv75Pzwp5DbC8oBiDEzZUX1q1gTsjl7z09VTALz+8gJ6tq+M32fdR2dQOwLj6G36xagscUzD8Zrur9n7etY1rA5Qu9t/bIePyjb8mpbcJCKuFX1y/lzrSkSXltvV7glR3H+OCg6ExsmB3H729dafJovsFsOZXP85+KOXFpsSH87fvXm1yckFvZyBOvb6FLpiDA05nXHx871+2CE/+9y7hl2cifi1Kl4Y/v7mHvCcM0riWJ/OyB5diM0DfzZFYFL7y2k+5eBTbWljz24FJuWDn8uDm1RstbHxzm661i9b27qwMPPbCI1csTRzzhVlS28M9X9pCXXweITuGc1Ag2rJ9FyuywUaf9gLhv1td2kJ9XR2FePQX59VRVtHD+UdTW1oroWD+8fJyRjKOhkkqloaSokeam7gsec3G1NzisoriNjvMbMWd1ME3N3WzffpYdO88ZW07Z21vzvQcXccP6WSP+zXX1Hbz+7gFOZohiJijAjd/9cgNREcP3g61v6uL3/9xGoUHUXrcskf/7wXJsRmjefjqnimdf205vnxIPVwf++OR6pkeP3FWivrWbn45j4QPid+3vXx7m0wPi2NQ7l83gp7cuNtmB255RyO8+FtuupUQF8Y8frjfZJR3MieJq/u/DbciUakK93HjjhzcS5OE67tcZjtqOLh79cCtlze1YW1rwh5tXcf2MiTWwnwhKjZYXdx7ky6ypX7UuCAKbcgv4074jdCmUSCUSvjdnFo8tSrum56xPBmbh+R1luIq7p1csYuO0+ClZcZfX0MyTX2yjrrNHbDi/ahH3zhneabkUqDRafr9pH1vPiE2xb58znV+tXzJpbay+PJnDi98cQKcXSI0M4h/3X4+L/fid3aP5lfz8g20o1Vrigrx57cc34jmGe9lPTXMnj766ibq2blwdbPnnozcyPXx0F+b8UOddq5N57PaFI57MBUHg0x1Z/OuTI+gFgYRIP156agNebsO7Rm2dMl58bScZ56oBsQr6lw+vwnWESuXTmRW8+vZ+6hrE4pi4aD+e+PEK4kZw8fR6gZOnStmy9QyZWQO9Lf18XVh//UzWrpmOyxj9PAfTJ1NSVNgghsDzRIeyT6Yy+eeHQyqVEBrmRfy0QOITAohLDCQg0M3kfV+vF8jKqmTLt9mcOl2OXi8e5j08HFm/bgbXXz9zxIlQcrmK/31+ii82Z6DV6rGwkHLLhmQeuGse9sM4yoIgsOtQPv94bz8KpQZHBxt+8eNVLJs3fOGbIAh8sj2TNz49atL+AJBX3sjPXtkykOrx5I1Eh4ze0UOl0fLsB7vYly1GF566ZRH3rJhl0mcoCAL/3Z/FP7ccBWDVzGheuHfkLg2j8c3pPP7w1X60ej3J4QG88uCGCX3PhyOjoo4nP/6WLrkSLycHXr13A9ODLt8CvbKtkye/2EaxIbT+k0VzeGTJ3CkZWq/p7OK3O/dzorIGEDvHvLhuJYl+Yw/eMGMWnt958hqbeWb7XgqbxZYy88KCeX7tcoLdXK/sG5sAPQolv9m6lz0FYvHEyrhIXrxhJc6XKfQuCAIfHMnk5d3HEARICQ/kn3ddb/IEobE4VlTF/324DblKQ5i3O2/84EYCPcZfCJBb1cTjb2+mU6YgwMOZNx6+iRBv03JT23v6eOL1LRRUN2NrZclLP7yOxUmjJ86fX9yxeFYEz//oOuxGGU14vsP10pMbmBY9fHGGXi/wxbYs3vr4CFqtHg83B559/LoRq941Gh1fbc3kv5+eQGFoDL9mRSIP3b9o2L6f/dTVdfDttjPs3J2DzCAWrawsWLI4lhvWzyIuzn/cCx29XqC2uo3C/Hp6e5Tj+lmphYSwCG9i4/2HFXlj0d2jYPfuHLZuO0NDQ5fx/lkzQ9iwfhbz0iJHTAHQ6wX2Hirg7X8for1DzGlNTQ7j0R8uIyRo+DB+b5+Sv729l/3HxZDkjPhAfvP4dSPOXFeqNLz07h72DHLAf/7g8lEF3YGMEn73zk5UGh3RwV78/ckb8XEfvV9id5+Sn76xlTNl9WI+9QOrWZNqmguo1wv8bdNhPjkkuqR3L5nJ/20cuY/taK/z+q4TvLtfbCO1blYsz9++EmvLyelA8kV6Di9uOYhWrychwIfX7t2AzwRSACbK9txint26F7lag4eDPX+9ec2UnEKk1ev5b/oZXjl8AqVWi42lBY8tTOPBObOmbK/sK4FZeJpBo9Pxn/RsXj1yEpVWh62lJU8sTuP+1FlYTrHVqCAIfHT6LH/ZcwSNTk+Qmwsv37aORP/LtxI9WFjOLz7biVytIcjdhTfuv5Fw78mZIFXc0MrD722mpVuGu6M9r3//BqYFj9+1qGnt4uE3vqGurRs3Rzte/dENTAs1LYdMrlTz9Ls7OJYn9iZ8+s6l3LJ47NSCvaeLee7dXai1OuLCfPj7EzeOOqLwwpy+5WxYOvJI0ZKKZp7753aq6zuQSODODSn88M4FI1aVt3fIeOc/R9i1Xwz72dtZc/+d87h5Q/KolehKpYaDhwrZsjXbmP8IEBnpww3rZ7FsaZxJYe0rRVFxI1u2ZnPwUKGxEt/BwYbVq6ax4fqZBI+R/1lU2sirb+0nv0ickhbg58qjDy0jLSViROF9rqCO51/ZTnNbLxZSCd+/Yz5335g6Yui+sbWHp18eyPl98t4l3Lxy5AiGIAh8tDOT1788Kva5TArjhR+vw2GM/0NjRw+PvrqJysYOHG2t+fvDG0iJMS03T6XR8pv/7WLvGdEl/emNi7hvebJJP3v+6zz72R52nhUF+Y9WzuGR1WmTEq3R6vT8dccRPjohCuO102N44ZZVl62lnkqj5aVdh/ksMweA1NBA/nbLWrydLp/onSwKmlr4zfa95DWJE8jmhATyh+tWEOo+OQWl3yXMwtOMkaqOTp7dsY/T1WJOW4KvN79cvoi5oVMvSTq3voknv9hOfVcPVhYWPLEsjfvTLt+qtLSpjUc/3EJdZw9Otja8cMsqViSM3prHVJq7ZTz63maKGlqxtbLkt7eu4PpZw7e1GY32nj4ee3sLBTWie/ncPatM7vWp1en548f72XxcFG33rkzm0Rvnj5l7eq60np+9soVumRJfDyf++PD1JEaMLHjlSjV/eHMXhzLEk/uGpdN4/O7FIxYGKVUaXvvPIbbsEav4o8O8+dmPVo44ahGgoKiBV97eR1GJKCKDAtx48O4FLFkQM2Ye50gibvGiWGJj/YgI9yYs1Atb2yuT8yUIAk1N3ZRXtFBW3sypU+VDxXKENzdsSDZJLNfVd/C/z0+x+0AeggB2tlbce3sat26cPaILKVeo+c+XJ/ns20z0eoEAX1d+9+S6Uf8fx7LLefHt3XT1KnB1suPFJ0fucgBik/a/fXTAmM5x6/Iknrpr7D6X58ob+Pnb22jr7sPb1ZHXHt9IVIBpoydbu2U8/Z8dZBlc0j/cs5q1s8efK9nU2csvPtrBmaoGLKVSfnfrCm5MnZyhFK29fTzz5W6Ol4ppKI+tnMePlqZetvSj/IZmntmyl6ImsZ/ujxeKofXLOQlpMuiQK3j7RDofpp9BJwg42djw9IpF3JKUMCXT0q4GzMLTzBAEQeCrc/n8ef8RepRiOHF+WDA/XTKfaf6Xd0zlxdKtUPLM5j3sKxLbukR5e/C765cxO+Ty9DDtkMl54uNvya4SnaHrZ8Tyq/VLcZ2EnK0+pZqff7SDo4Vi3uHqpGievWX5uPPB5Co1P39/O8cLqwBYnxrP07cuNanoSBAE3tl2ire3if0U44K9eeH7a8dsHVPb3MlTL2+mpqkTqUTC/den8oMb5o7ah/G/W07zzpfHEQTwdnfkl99fybyZIxeLHDldyp/f3EN3r1hVft3SBH509yI8RshV1OsFdu3P453/HDYW1QT6u3H3bXNZtTR+zKrzkcLWIOZgBgW6ExHhQ0S4N5ER3kRE+oyYNzlR1GotVdVtlJe3UF7eTFl5C+UVLfT1Dc0hHW96QEVVKx99cYqDR4uM+Z8rl8bz4wcX4+kxfBhbrxfYfaSAtz46QruhvdR1SxN48vvLR5xC1Nkj5+UPDxqLy6JDvfnzT28YdRJRTmkDv393J3Ut3Ugk8NSdS7h95eht1TQ6He9tP837O9LRCwIR/h689thGfMcIyfdz4FwZz3+6l64+JQ621rz8g/Wkxoy/Int7dhEvfn2AXqUKJ1sb/nH/9cyNvvjKbkEQ2HGumBe3HqRboRRTYm5bw6rEqIt+bVPoUSh55cBJPs04h14QcLO34683r2VB5NQKrctUav6Tns37p7LoU6sBWBMbxW9WLZmSju3VhFl4mhmWVlkf/zp2mi/O5KLVi/OkV8dG8uTi+UR4Tk7Y+HIgCALfnMnnr3uP0iUXc+hunBHPz1cuxMPx0lfxq7Va3tx/mvcOZ6AXBDyd7Pn9xhUsjbv4hsJanZ73DqTz9p7TaPV6vF0ceeGOVaRFj+8Ar9HpeHvnKd7fk44gQKCnCy/et8bkhtf7s0t54aN9dPeJJ7mf3rqImxcNX13eT0+fkr99dIBdJw0iI9iL5x5aS0TgyI5TVn4NL727h/oWsYp79fw4nrx3yYiFRG2dMt766Ai7Dok9P+3trHnglrncum7kULqsT8U332bx5eZMenrF/cXX25l1q6ezbGEsgQGj7/t6vUD2mSoysypFAVjRQpdByJ6Pu7sDEeHeRET4EBnhjbv7+ISoWq2jqqqV8ooWystbqK5pH3b2u6WllNAQTyIifIiN8WPxolhcXUff95VKDSczytlzIJ8Thn6cAGmpEdx/x7wRi7EACkob+ef7BygobQQgwNeVxx5YyoKU4fd5QRDYc7yIlz88QLdMrA6+c10yP7hlHrbWwzvFao2Wdzef5H87MtELAt7ujvz2+6tJTRh9369p7uQ3H+wir0p0fdfNjeMXdyw1qfpcodLw128O8c0J0eWPDfTmj/evIdx39NSE8+mWK3nx6wPG0Pr0YF/+eNcaQrwuPmTbLpPz/Ob97MsX89zj/b35462rifI1zcm9GARBYFtuEX/efYQ2mbjPr5sWwy9XL5pSQk2l1fJpdg5vHk+nUy4uXGO9vfi/pfNZHBl2hd/dtYFZeJoZlZrOLl47eoqtuYUIgFQiYeO0eB5bNBd/l6nzOXfKFfxj3zFjGw9nWxueWj6f22ZPuyxVlTm1TTzz1W4qWjoA2DAzjqfXL5mUnqN5NU386pNdVLWKldr3LJzJE+sWjDuPK7usjmf+t5vGjh4spBJ+uHoOP1g9x6TQWEunjN/9dzenC8Uqz4XTwvjdfavGnIO9L72EP3+4j26ZEitLC35ww1zuWTt7RPdTqdLwzlfH+XyHoRuDkx0/vHU+G5ZOG/F95pU08MoHB4yte/y8nblj/WzWLZ+G7QgFTnKFmq07zvL5Nxl0dA00hI+O8GHZ4liWLozF13vswi5BEOjo6KOsvJnycjHcXV7RQl1dxwVtlSYDJydbIiK8iTSI2YgIH4KDPEyanqTWaEnPrOTA0SJOnC5DoRQLryQSWDQvhntun0v0CO2RQPycP96UztF0UfTY2Vpx/y1zue365BFD8eW1bbz20SFO54rh4MhgL3790CriwkeOruRXNPLiv/dSVtsGwNp5cfzs7qWjjssUBIHNx/P42xeHUag0ONnb8Ou7lrM6xbTUkvyaJn79311Ut3QikcD9y2fzyLp5425rdrq0hmc+3U1ztwwLqYQfrZjDD1eY9h0bi925JfxhywE6+xRYSqX8eNkcfrAk5bKkF5W1tPP89gOkV4lpWmGebvx23bIp1ZtTq9ezOaeA146eorFHHLgQ6u7KE4vnsTYuGqk5rD5pmIWnGZMoaWnj5cMn2F8iuh9WFhbcnTydH89LnVLzZ8/WNvLctv0UNolV/In+Pvzu+mWXpe+nSqPl9X0n+c/RLNGlcXbguY0rWRR78atouUrDP7Yd4fMTYhJ/pK8Hf7p7LTH+I08MGo5ehYqXvjjAjkzRiZwe5scf71tDoKfrmD+r1wt8euAMr206hlqrw9XRjl/esZRVs6NHdT/bumS8+O+9HD8npg2E+XvwqwdWMGOUPo0F5U388Z3dlBvER1iAB4/etYi0GWHD/i69XmD34Xze+ugo7QYh6epsxy3rZnHT6hk4Ow3feUCl0nDgSBEHjhSRdbYKnX7gEJgQ68+yRbEsWRAzYsh5JBQKNZWVrYbcSzE0fn5IfCykUilBQe4G11QUm15eTuPKO9NqdWSdrebAkSKOnSpFNug9+Pq4sHxRLKuXJ45YqS4IAqfPVvHRptOcNfQ7BVizJJ4f371oxGbw7V19vPvlcb49lIdeELCytOB7N83lnutTRkxrkMlVvPHVMb4+eA5BAFcnO351/wqWzh49hNzU0csfP97PsTxx/5odE8TzD6w2KbSu0+v5z75M3tx+Eq1ej4+rIy/cu4aU6PHlvas0Wl7ZcZz/HckGINjTlZfuWsP0SWgK3yGT88LWA+zOFfOgY/y8ePGWVcT5j95CajLoU6l58/Bp/nMyG61ej62VJT9ZNIcH582atIr8S40gCOwpLuPlQyeoaBeNAR8nRx5dOJebpsebq9UvAWbhaWZcnK1v5O8HjxkLkBysrXggdRbfn5s8ZcZvanV6Ps04xysHTiBTqZFI4Pbk6Ty5fP6k5F+OxdnqBp75ag9VbaJDedPsBH6xbvGkjFY7UljJbz/fQ3uvHEsLKY+tmcf9S5LH7eruyCzij5/vR6ZU42BrzS9vWcL6VNN6vJbWt/GbD3ZSWieKwsVJ4fz6ruV4uY4cbhMEgV0nC/nnp4fpNORlbliUyKO3LcTVcXhRqNXq2LQ/h/e/PkG3TAyLpyQG8/g9S4gcYUSnSqVh+8E8Pt2SSaMhZG9na8WGFdO5fcNsvEcRkF3dco6cKGH/4ULO5dUaHUuJBJISg1i2KJbF82NwHUdfzyuBTqfnXF4tB44UceRECd2Dpit5eTiyZGEsyxfFERvtO+L/W6vTc/BEMR9vTqesSlzEWVhIWb0ojrtuTCV0hClSSpWGz3Zm8b+t6cgNjurS1CgevnMRgT6uw/6MIAjsSy/hH58cor1bXDSsnRfHE3csHtVR1+sFvjmWyytfH6VPqcbK0oJHbpjHPSuSTWp31NDRw7P/20VWWT0AK2dG8ewdK3Ae5zGiuKGVpz/eSVlTOwC3pk3jZ+sXYz9KOzFT2ZtXyvOb99PRp8BCKuGhJak8tHQO1hcxCcsUBEFgT2EZL+08RFOPOCJyWUw4v167hMDLOOv9YhAEgROVNfz90HHyGsWpZq52tvxoXip3Jyddtsr/7yJm4Wlm3AiCwPHKGv5x8JixtYSrnS0/ni9+YW2myEq3tbePv+45wtYc0d1zs7fjZ6sWsjEp/pKPj1NqtLy65zgfHs9GEMDXxZHnb1rF/HHmZw5Hh0zO77/Yx8F80Z1ODg/gj3euwd99fN+Lho4efvPhLrLLxRPv0ukRPHP7cpMazmu0Oj7Ymc77O9PR6vQ42tnw01sWccP80StBu2UKXv/iKFuOiCkRrk52PHHHYq6bFzfiz/X2KfnP5tN8ufsMGq0OiQSuX5zIQ7fOx3OERuPDCSdLSymrFo4unPppa+/l0PESDhwuNLYVArCQSpg1I4RF86KJCvchJNhjxGKay4VeL9DU3E1ldSuZZ6s5dKyYjkHz5N1c7Vk8P4Zli2KZFh846r6vVGnYfiCPz7ZeKNxvW5+MzwiFQHq9wO7jhbz1+VFaOkShEh/hy+N3LyYpduRiv/qWLv7yvwOczK0CINjXjV/et5yU+NFDuLWtXfzhw71klogL5Onhfvz2vpWE+42djykIAptO5vH3TUfoU6qxt7Hi6VuXmrzw6kevF/jwcBav7jyBRqfD3dGe525bwZKEi8/v7upT8OK3B9lxTswTjfLx4MVbV5MQcOnbxlW3d/HCjoMcLasCIMDVmd9ct5SlMVNn9PK5+kb+fvA4p6prAbC3suLBOVPLQJnKmIWnmQkjCAK7i0p5+dAJKjtE9863P0SRlDBleoCmV9Xx/Lb9lLWKYZZZwf78bt0yYnzHF6aeCFlV9Tzz5W5qO8ST+C0pifz8ukU4XqT7KQgCm9Lz+dPmQyjUGhxtrfn1xqVcnzyygBsOnV7Pv/dm8tbOk2h1epztbfjlLUu5brZp7ZvK6tt47sM95FeJjsKcuGB+c88KAjxHd0XOltTzp//uo6JedImS44J4+r7lhPiNXNxT39zFm58fZf+pEgDsbKy4e30Kd103G7sR2hn1h4o/3pTOmfxa4/0LUyK5e2MqiTFjF1g1tXRz8GgRB48UUVzWfMHjfj4uhIV4EhbqRViwB2EhXgQHuU9oss1oCIJAW7uMyuo2Kqtbqahuo7K6jeqadpQqzZDnOjnasmh+NMsWxjJjevCYOYY9MiXf7DrDV9uz6TI4pKakKgBkF9by2keHKaoUPxtfTyd+csdCVsyNHVHkarQ6Pt6VxftbTqLS6LCytOCB61O577qUEUeogri/frr/DG9sOYFSo8XW2pJHb5zP7UtnmOT6N3b08Pyn+zhZJOacJoX58cK9awjych3zZ4e8TmcPz3y6m4xyUfguiQ/n97etxMPp4t3wAwXl/H7TPtplcqQSCT9YnMJPls+55KFtpUbLO0fTee94JmqtDisLC364YDYPLUydMu5gaWsbLx86wb5BKWN3zZrOj+enTsmR0VMVs/A0c9Fo9Xo25RTw2pGTNPWKbkaYhxtPLp7H6tioKZGUrdHp+PDUGf516BRytQYLqYR7Umfw2NK0ixaBYyFXa3hl9zE+OnEWEN3P3964nMWxF+8g1LZ18atPdnGuWqwwXpUUxW9vWTHutksl9a387uM9FNaKDveSaRE8c/syvEyYfqLV6flkfzZvbj2BSqPDzsaKx26cz21LZozqrmm0Oj7ZlcV7W0+hUmuxsrTg/nUp3L8udVTxkVNSz2sfHSavTPybPd0c+fHtC1i7YHQnO7+kkY83p3M0vdQYRk+KC+SejanMmRlmkgte19DJwSNFnMmpobKmbYizOBgLqYQAfzfCQrwICxHFqNsYlebno9boqK3roLK61SA224bkaA7G2sqC4CAPoiJ8WDI/muQZoSYVHLW09/L5t5ls3ZtjLDby83bmjg0prFuWOGJxFkBNYwf/+vQoRzLFYiN7O2vuvyGV29bMGrFaHSa26AAob2jjuQ/3klcpFpGlxATx7L0rCDRBNAqCwDcn8vjHZtHltLGy4NHr53PXkpnjSlMRBIHt2UX88ZuD9CpV2Flb8YsbFnPznMSL7vnYLpPz0rcH2ZkjLqzCvd158ZbVl2Xs5aGSCl7YcZC6zh4A5keE8Oy6pYR6TI3m6XVd3bx65CRbziuSfXTRXAKmUJHstYJZeJqZNFRaLR9nneOt4+l0KcScu0Rfb55aMp8F4SFTotluU3cvL+06zO4CMVHfy8mBX65exLrEmEv+/tMravnt13uN7ue6pFievn4x7hfZ9kmr0/P+gQze2nNKbLvk7MAf7ljNvJjxt136z75M3t55akLuZ01zJ899uJczhpy5GRH+/Pa+lYSO0fezvrWbv/5vPydyqgAI8nHl6ftXjBpuFQSB/aeKeeOzozS2iifL6FBvHr97MckJo4dpq+va+WRLBruPFKDViu2JPNwcWJASyYKUCJKnBZvsVnZ1y6mqEUVhRXUbVdVtVFS1jigQLxYLqYTAAHfRYQ3xJDzEi9AQD/z93EyunK5p6OBoehnH0svIK2kwivCIEC/u3pjKsnkxo75Wd6+CD745ydf7zqHT6bGQSrhh2XS+f/M83EfJfx0uzeLJOxazdpQ0CzDsl7syeHf7aTGtw9aap25dxI3zTRN7jR09PPfpXk4ViR0ZksL8eP6e1SaPke2nq0/Bi98cYNdZURhOD/blpbvXEmxCYd5oCILAtrNFvPTtIboVYrupBxYm8+iKNGwusdNY39XDH3ceYr+hF7KPsyO/WrOY1fFRU+J43irr463j6XyWnYPG0BZwVUwkTy6eR6TX+NpgmZk82jo78XJ3v3aE54Z33uPO1BTWxcbgZM7VuCLIVCo+OJ3Nv09n0acWXZLZQQE8tWQeKcGXp3n7xXKsrJo/7DhAdXsXAHPDgnh23VIiLvHBSqHW8Pq+k3x4TGwX5OZgx6/XL2Xt9NErw00hv7aJpz8eaLt089xEfrZ+/GH90oY2fvvR7kHuZzjP3L7cJPdTrxf46kgOr35zFLlKg7WlBT9aP5d7V84eVcwIgsD+jFL+8clB2gxV6WvT4njiztELTFRqLV/uPsN/Np+iTyE2gZ6bFMqd180mJTF41M+0tb2Xz7dl8e2+HPrkauP9drZWzJ0ZxsI5UaTNChu1jc9If0t7h8zoUvZf+uTjE6MWFlL8fV2NIjMs1IugALdxh/D1eoHCsiaOppdyLKOMqrqOIY/PSAjk7htTmTtz+I4B/bR1yvh67zm+3nOGXsPfMm9GGI/etZiwUXJmBUFg54lCXvlsoLDsBkNhmcsIhWX9FFQ389yHe4yFbAunhfHru5fj4zZ2xbogCHx9IpeXNx8d5HIu4K4lpoXlB3Mgr5znv9pHe69cbJO0ci4/XJ560W2SGrt6eX7zfo4UixX50b6e/OHmlSQGXlqXU63V8e8TWbx55DRKjRZLqZT702by8OK5ONhcveNg++lSKHnvVCb/yziDQiNOFEsLDeKnSxaQdBk6mJi5EEEQONPQyJe5eWzPySH3lz+/doRn2PMvIrW1xdbSkrUx0dwyLYHUwMApsTq71mjvk/P2iQw+yTqHWqcDYGF4KE8tmUei3+WbnT5R1Fot7x/P4q0jp1FpdVhZSHlwXjI/WTQHu1FChZNBbm0Tv/1mLyVN4gl1SVw4z96wDF+X8bXtOR+FWsM/tx/jk2NnAfBzc+K521ZOqOn8xbifDe09vPjRPk4WiLl0scHe/P6+VUQHjZ5XK5OrePPr43x14CyCAE72Njy4fg63Lp8xavi9s0fO+1+fZPP+c8aWSOGBHty+dhar5seNGvpVa7ScyavlaEYZxzLKaTMUx4Ao/mYmBLEwVXRDRyquudpQa7Rk59WKzmZGmXG6EIh/06zE/r8pctRKf4DiymY+25nNvpNFaA0N7CODvXjs7sWkTht9v8ota+D1L49yplh0wcMDPHj6/tFbaQEo1Vre2XaS/+3NQqcXcHWw5ed3LGVNimmRiYaOHp77ZC+ni0WXc0a4P8/dvWrcLme3XMmfNh9kW5ZYoBjm7c6Ld65mWvDFiRu9XuCL9Bz+vvMocrUGKwsLfrJ8Dt9bNPuSt/c5VVHD89sPUtEmLkBSQgP57bqlRHlf+ib0F4tMpebDjDO8dyoTmUpcLCb5+/LUkvnMC5s6PUWvJVplfWzKL+CrvDwqDLUgeqWSyt8+c+0Iz1f3H2RbZRXlHQOr9mBXF26ZlsjGhHj8nC7uxG1m/DT19PLGsdN8dS5/yBSkJxZNjXBHXWc3L+w4yKES0XXwd3XmmbVLWBYTfkkXNGqtjvcOZ/D2QUMI0caa/7tuIbfMnnbRVfcZZbU8+/ke6jvEMPStadP4v+sXmTQqczBlBvezwOB+Lk4M55k7luNtgvspCALbThXwty8O0ytXYSmVcu+qZH5w3Rzsxmg1U1DRxEv/3Udxtfh7fdydeGhjGmvnxY/qMtU2dfLl7jNsO5SHwlBw4+pkx43Lk7hpZRJeI1TB96PXCxRXNHHkdBlH08uoqmsf8nhMhA8LUyKZOyuM0ECPUXMgLyd6vUBLey+5RfUcTS/j1JlK5IoBF9fezpq0WWEsMLz3sVxcrU7PsaxyPt+VxdmieuP906P9uX1tMotTIkd1DSsb2nnz6+McyhLzP22sLPj+DWncvSZ5zKbs6UU1vPTJAaqbxZPYqtnR/OL2pWMOK4ALXU5bK0seXT+fOxeP3+U8lF/Oc1/uo61XLPK5f0kyj6y++PB3VWsnv/1mL1lVhpSUYD+ev3klEd6X9ljZ0ivjL7uPsi1XFNGejvb8cvUirp9m2mLySqLUiNOG3joxMG0oxtuTpxbPY2nUpT1Om7kQtU7H4YpKvsrL51B5BTqDdLSzsmRtdDRrQkNYnhB/7QjP7u5unJycONPQyFd5eWwvLKZPM1DROcvfnzUxUayJjsLfnAt6Wanp7OLVIyf5Nq+I/h1pcUQYD6TOZF7Y6GHPq4EDReW8sOMgDd3iVIvkYH8eXZrG3LCgS/rey5rbePbrveTUikUT8f7ePLF6PvOjLi5vVq5S8/L2Y3x2/BwAnk72/GR1GjelJo4rRKjV6fnv/kze2nkKjVaHvY0V9y+fzb3LZmFvQliurbuPP316gANnRBHi4WzP/atmc/Pi6aM6yzq9np3HC3lr03Fjix4/T2fuXDWL9QsTcRiljVFvn5JvD+Xx5e4zNLWJ4lsqkTBneijXLU5g4ayIUR3UfmobOjmaIYrQvOL6C6YRebk7EujnRqCfq2HrRpCfKwE+rthMsijV6wXaOmXUNXRS29hJXWMndU1d1DV2Ut/cjVqtHfJ8DzcHFqZEsiA1klmJQSaF6MtqWtlxNJ89x4uMjfgtLKSsmBvDbWtmER8xstMnCAJnS+r5eFcWR8+WIwjiZ75uQTwP3TgPnzGc1bNl9byz7RSnDNOxPF0c+NVdy1g6I3LM9w2QUVrLPzcfJb9GrK6fGe7P7yfgcpY2tvHy9mMcLRQXoqFebrxw52qSLrIZfFtvH+8cTOfz9By0Oj121lY8tXo+d8xNuqTT1dplcj44kckn6edQaLTiyNKUJJ5YlobzJExWu5Q098r4NCuHT8/kGAVnqLsrjy+ax3Xx5mlDlxOVVsvx6hp2FZewr6ycHtVA6tBMfz9umZbIdTHRONnYXPvFRXK1hl0lJXyZm0dGXf2Qn0ny82VNdBRroqMJcp0aTW+vBUpb23jl8En2FpcZBWikpzv3pczkhmlx2FldHS7RcMjVGt48fJr/nspGrRXTB2aHBPDokrnMuYQCVKfX89GJs7y+9wRyQ95sSnggT61eQFLwxZ3w0stq+f0Xe6ltF4uawrzdeeK6+SxLjBjX31PW0MbvP9lLXrUokN2d7Pn+qhRumT/dJBfo0Nly/vHlYerauo0/f9+qZG5dnDSqA6pUa/hq/zn+uz3d2Eje0c6GG5dM4/YVM0cVNFqdniOZZXyxK5tzxQPHByd7G5anxbBuUQIJkX4mfQ4dXX2cyKzgSHopecUN9Bjey0h4ezgNEaTOjuM7yWu1Ohpauqlr7KLeIDJV54nLwVhYSAkNcGfe7AgWpkYSG+FrknPe1SNnz4kidhzJp7iqxXi/q5MdNyybzk0rk/AeZQqQVqfnQEYJH+/OorByoN3UopkR/OSW+UQEjB7CPWMQnP3jWC2lUm5eNJ2Hb0jDyYTuDHnVTbyx/QQnCsW0DnsbKx5eN4+7Fs8cV+SgqauXf+06ydbMAvSCgKVUyj2LZvLImnkX1U6oV6ni30ey+PB4NgrDd3tBdCi/vXEZAZewGXtHn5z3jw8IToCkQF+eXbeMRP+rOxUqp6GJDzPOsKOgxBhFC3Bx5uEFc9g4PX7KtPKb6qi0Wo5UVrGrpJT9ZeXI1ANRFE97e25MiOPmxESiPIe69de88BxMU28ve0rL2FlcQmZdPYP/mGm+PqIFHBNFsKvrZXnP33WqO7r4X+YZvj6XbyxCcrWz5dYZidwzewZ+zldvWkRzj4x3j2XwRVbuUAG6NI05oZcup7hDJufdQxl8euocGkPe7LL4CB5bOY9o34nnYGm0Or44mcNbe07RJRcF08xQf366fiEzQsfuZdmPXi+w90wJr287Tq1BQPq4OvLQmrlsmDv2+DmNTsf2U4V8sCPdKEDdnOy4b+VsblsyhgBVadhxopBPd2dR3SSGYS0spKxIiebuNcnEho5+Mq1u6GDn0QJ2HSugub3XeH+wnxsLZkUwOyGYpNgA7E1MR+jpVVDbKDqOg93H2sbOS1fVbiHFz9tFdFV9RXe1X9j6eDmb5GQLgkBlfTuZeTWk51ZzOqfKmLtpYSFl/sxw1i1KIG1G2KhhcZlcxZbDuXy+7wxNhs/TxsqC6+bHc8eqWYT5jx46zi6t451tp0gvEvurWkqlrJ8Xz/fXpuI/Rh9YEIvg3th+goM55cafv3n+NB5aMwcPE4Yg9NOrUPH+gQw+OpKNyvBdXzk9ksevW0Co18TbCSnUGj49dY73DmXQ3d8FJNCHp1YvYG7kpctH7OiT88HxLD5OP2sUnIn+Pjy2NI1FUaFXbeRJo9Oxt7iMDzPOkF3XaLx/dlAA96fOZHl0hFlwXgaUGg1HKqvYWVLCgbKKIRFlH0cHVhvMvOQA/xGd+u+U8BxMq6yP3aWl7CopJb22Dv2gPy3Bx5u1MdGsiY4i1G1q9CmbyvQqVXydk8+HGWeo6xLDnhYSCatiI7k3ZSbJgf5X7cFwJAH62NI05oSNb57zeGjo6uGNfafYki26LxIJrJ0ewyPL0y7qZNirUPHvg5n870g2SsNJacW0SJ5YN76TrEanY8upfN7ddZrmLjEMHuTpwo+vS2NNcsyYoUONTseOU0W8v+P0EAF678pkblucNKr40+sFjudU8snuLLIKB5rCz4oJ5K41ySxICh/V6dLrBbIKathxpIBDGSUoVQMuooWFlIQIX5ITgpmdEExilN+4q8gFQaC7VyEK0YZOgxjtQqFUj/3Dg5BKJfh6ORPo50aArytBfm74ejmPOOt8tPfT0NJNZn4NWfk1ZObX0tkjH/KcmFBv1i1OYGVaLK5j5FI2tvXw2d5sth7Oo8/wN7k52XHrihncvDQJtzF+PqtEFJwZxQOCc8P8BL63JsUkwVnd0smbO06yO7t4IJyfEseP1s4hcBytjdRaLV+cyOHtvaeNi7FZYf48df34FmPDve6X6Xm8c+g0bb3i5xzu5c7jq+axIiHykh3rOvsUfHAik4/TzxmjJon+Pjy6dC6Lo0bvWHAl6ZAr+PxMLp9knaPZ0CfaSiplXUIM96fMJGEKFKpOdRQaDYcrKtlZUsrB8grkg8Smr5Mja6KjWBsdzcwAf5PSG76zwnMwbX197CktY1dxKadqa4eI0DhvL9bGRLM2Opowd7MIvZTo9HoOlFbwv4yzxlFmAAm+3tybMoN18TFX7TjO5h4Z7xwVBWi/E5kSGsgji+dc0hB8eUs7/9p3kt25Yt9RC6mEG2bF85Nlc/F3m3gOc3O3jDd2nWRzRj56QcBCKmF9cjwPrUwlyMPV5NdRabR8dTyH9/dk0NF/kvV15+F181g2PXLMUKdGp2Pn6SLe23GaulZRgLo62nHfymRuWzK6AAUoqmrm093Z7EkvRmdw7fw8nVk1J4aVc2KJCvIc9X/Tp1BzLLvcIMhqjD1B+7G2siQpxt8oRGPCfC66hc7loLVTRnZ+LRn51WTl1xrzXPuxsbYkKSaA2QnBzJsZTkTQ6G56b5+Sw9nl7DldREZBjbFzQJi/B3etnsWatLgxc2azSup4+9uTxjGXlhZSbpifwINrUvH3GHtfrm/v5t1dp/k2vcD4+1fNjObH180l3Nf0whyNTsfWjALe2ZdOg6Fhepi3O0+tW8CShIkXqmh1erZkF/DmgVM0dokOcICbMz9ZNpf1M+Mu2X7TLpPzn5NZU05wFja38r+MM2zNKzJ2RfFwsOeOmdO4KzkJL0fTXWsz40eu1nCospKdxSUcqqgwuuMA/s5OrIkWzbkZ/n7jzqU1C8/zaJfL2WsIx5+qqTVWYwHEenmyNiaaVVFRRHq4X7Vf2GuB4pY2Psw4w9a8QmN4y93ejjtnTeeOWdPxcRq7avpK0NTdyzvHMvgyK88oQKcH+PLQwhSWxURcshnwhQ0tvLb3BIeLxIIHSwspt6ZM46GlqXg7T/yzKm1s45/bj3HEUEhhKZWyYbYoQAPcTc8/U6g0fHr4DP/Zn0mPocdjbKA3D69LY2HC2Cc/rU7PzvRC3tuRTm1LFwCuDrbcvSKZmxZNw22Mfo/NHb18ue8smw7lGHtMAoT6ubNqrihCQ3zHXlgOdgazCmqNBTb9ONhZMzMukKSYQAJ9XfH3csHf2wVH+yvTU1ir09PS3ktDazcNLd0UVzWTlV9LdcPQXp2WFlISo/xIjg8mOSGYhEjfMZ1chUrDsbMV7D5VxMncKjSG7ylAakIwd69OZu600UO3er3A6aIa/r0zfYjgvHF+Ig+uTcHPfexjeUu3jPd2p/PNiVxjSsDixHB+si6N2EDvMX++H41Ox7eZhbyz77Sx24Onkz0Pr05j4zgL7gaj1wvsyCnmX/tOUmPoC+zt7MCPls7hptmJWI/ToTaV+q4ePjiexVfZucZjaIK/N48uSWNJ9NUpOHV6PftLKvgw4wzpNXXG+xN9vbkvdSbXxUVf8tGg32V6VSqjs3m4ohKldkBsBjg7szZGDKMn+fle1P5jFp6j0CFXsLdMFKEnq2uGiNAwNzdWRkWyKiqS6X6+5uq5S0SnXMGXZ/P4OOscjT2iS2AplbI2Lpr7UmaQFHBxhTWXiqbuXt49ljnkoB/p5c5DC1O5LnH0yS8Xw9nqBl7be4JT5aJjbGNpwV1pM/j+4hTcHEYXZ6O+blUDb+4+xYkSsUDDUirlhtR4Hlo+B38TxEE/PXIlHx3M5qOD2cgN7Yymh/nx6Lp5pMaMndem1enZlS46oDUGAWplacHymZHcvGg6s6ICRj0gKlUajudUsudUEcfPVRrTIwBiQrxZOSeGlakx+JnQj1MQBKrqO0QhWlBDdn7tEFE7GGdHW/y9XPDzcsHf21m87u1CgLcLvp7OE57bLggCHd1yGlq6aWjtprG1m/qWbhoNt1vae43u32AkEogN8yE5IZjk+GCSYgJGnGc/GLVGy8ncKvaeLubImXKUg4qZwvw9WDU3hlVzYgjyGV3Et/f0sfVEPpuO5hlTKSwtpGxckMgDa0wTnJ0yBf/em8HnR8+i0oj/xzkxwTyybh7Tw0w/Lmh1er7NEgVnnaHAzsPJnu8tTeHWtGkT7tkrCAL7C8p5fe8JSpvFtltuDnb8YHEKd8xNumTzzctb23n3WCbbcoqMhTdJgb78aFEqS6OvztZC3QolX53L46PMc9R3D6RbrY6L4v6UmcwIMK3Az8z4ae+Ts7esjL2lZZyoqTUaJgBBLi5GsTnN12fS/gdm4WkinQoF+8rK2VVccsE/x8fRgRWRoghNDQq85A1+v4to9Xr2FZfxYcZZMmsHqo+T/H25L2Umq+OisL4KP/d2mZz/nsrmk/RzxobGAa7OfH/+bG6amXDJTj6ny2t5dc9xztaISfj21lbct2AW9y+YdVEtUs5WNvDGnpOcLDFUGFtI2ZiawA+Xp+I3jtB+p0zBf/Zl8PmRc8Zc0tToIH64eg6zo8YuztLq9OzKKOLT/WcorBmotA71defmhdO4Pi0elzF6UcoUKg5nl7P3dBGn82uMoXiA6ZH+rJwTw/KUaDxdTQvp6fR6SqtaycyvpqiymYaWHhpbu+kyTOMZCYlEnCfvNE5XVKvT09zeO2olO4jC3M/LGT8vF0L83ZkVF8jMuCCTq+i1Oj2ZhTXsPV3MwcwyZIoBcR3g5cKqubGsmhNDRODo4Xi9XiCjuJZvjuZw8Gy50Z10tLNhfVoc966cje8o1fH9tPf08emRs3xy6Ixx8ZIU5sej6+eTEmV6XrVWp2d7diFv7z1t7Ojg7mjP95bO5rZ5o7fyGg1BEDhWUs1re0+QXy9W8TvZ2vDgomTumTfzkk3+ya1v4p2jGewrKjO29ZoXHsxDi1IvacHjxVDW2s7/Ms+yObfAGMp1tbPljpnTuSt5Or5XcYHpVKa2q5s9paLYzKofWmgd6ubKqqgorouJJsHH+5LsN1eN8HzppZf45ptvKCoqws7Ojnnz5vHnP/+ZmJgYk37+cs5q77ej95SWcbiickhVl4utDUvDw1kVFcnCsNCrujXQVCW/sZn/ZZ7l2/xi4wLA08GeW5ISuW1mIoFXYWusXqWKT9LP8d9T2XT0iULE09Ge++fO4s6U6eMeW2kKgiBwtLiKV/eeoLBBFGfOdjZ8b9Fs7kqbcVEnwOyKet7Yc5LTpYYCEAspN6Um8sPlqfiaMK6wn9ZuGe/vyeCr4zlGIRLt78ldS2aydnasSW2YCqqa+PpoLrsyio1N4a0tLVg5O5qbF04jKWLs4rSuXgUHMkvZe7qI7OI644lbKpGQHBfEnMQQkqL8iQ3xMam/52D6FGoah3EjG1p7aGzpNr7niSKRgLe7E35eooPq5+0ihvi9nPHzdsHT1XFcKR6CINDQ1kNOaT1nius5nF1mHGUJ4OXqwIo5MayeE0tc2NguSGevnK0nCvjmWK4xTQIgMcyXmxdOZ1VKtEkir6i2hY8PnWFXdrExrB8X5M0j6+YxP970amytTs+OM0W8vfc0NW3i+3F3tOPBJbO5bV4S9hfRXzWzso5X9hwnu6oBADtrK+6dP5MHFibjcgl6YgqCwOmqOt45ks6Jihrj/SvjInloYQrTrsLxkGpDdfpn2Tmcrh4Ip8d6e3F/6kzWxcdcsgX5dxVBEChubTOKzcLW1iGPJ/r4sDIqgpVRkUR5eFzyRcpVIzzXrFnDHXfcQUpKClqtll//+tfk5eVRUFCAg8PYjsPlFJ6DUWm1nKiuYW9ZGfvKyumQDxygbS0tWRgawqqoKJZEhOFmN/FQp5kLae+TG6sdW2Rirp0EWBwZxp2zprMoIvSSNl6eCEqNlq+z83j/eKaxEb2TrQ13pyZx39yZuDuMPX1lvAiCwN78Ml7fe4LyFjG3z9XelvsWzOLOuUkX5YBmltfx5p5TpJeJAtTKwoKb5iTyg+Up+LqaLkAbOnr4z75Mtp7ON4Zv3RztuHXBdG5dMN2kOfAyhYqd6UV8fSSXkrqBA2uEvwc3L5zGurlxJvV9bO2UsS+9hL2ni8iraBrymJWlBbGh3iRF+jM9KoCkKP8xq7RHQxAEunoVNLR0oxynAJVIJXi5OeLr6TzmtJ/R0Gp1FNe0cK60gZzSBnLKGmg7L3fVxdGW5SnRrJoby4yogDGFrCAIZJXU8fXRXA6cKTMKRQdba66bE8vNC6ePOR4VRBf5cG4FHx86Q1bZgEiZHurH/cuTWZZkehW4Tq9nx5li3tl7mqpWsd2Wm4MdDyxJ5o75SSYNOxgOQRA4WVbD2wdPk1kpRmOsLS24c24SP1icgrvj5H+n9XqBQyUVvH00nXN14j5qIZVw/bRYfrgghchLPOVoItR2dfPFmVy+OpdPe59YaCiVSFgRHcH9qTOZHTR6moyZ8aHT6znb0MjesjL2lJZR09VtfEwqkZASGMCqqEhWRkVe9mE6V43wPJ/W1la8vb05fPgwixYtGvP5V0p4Dkan15Pd0MCekjL2lpVR1z1QKWohkZAcGMCKyAhWREaYe4VOIhqdjgOlFXyWncPxyoFVv7+zE7fPnMYtMxKvugpIjU7H9txi3j2WQXmrKAZtrSy5dVYiD85Lxt918vdhnV7PjnPFvLH/lLHIwdHGmrvmzeC++bMuKgc0o7yON3afJLNcFAf9AvSBJckEepjuQPfIlXxzIo/Pj5ylsdOQ02shZc2sGO5aMpP44LFbpwiCQF5VE18fyWVPRrExlG9rZcnK2dFsmJfAjMiRe8wNpr6li0PZ5ZwrrSentIGO89oMAQT5uJIUFcD0KH+SovwJ9bu6Cw97+pTkljUYhWZ+ZdMF4XoLCymxId5Mj/JnbkIIKfHBJrVpau2SsTujmG+O5lJlGGkJEBfszc2LprMmJcakPqi9ChWbT+bz2ZEz1LeLx1FLqZQVM6O4e8lMpoWansOp0erYcaaI9/ZnGAWnq70t9y9J5q4FMy5KcB4qquDtA+nkGsSflYUFN81O4EdL5+BjwmJpvGh1enbmF/PO0QxKW8S8URtLC26elcj35iUTeAkbzk8EnV7PobJKPs3O4Wh5lTGk6+3owG0zp3HbjERzOH0SUWo0HKuuYV9ZGQfKK4YYYdYWFiwIDWFVVCTLIiJwt79yRthVKzzLysqIiooiNzeXxMTECx5XqVSoBo1k6unpISgo6IoKz8EIgkBhSyt7SsvYV1ZGUWvbkMejPT1YERnJisiISU3a/a5T1dHJ52dy+fpcPl2GpsyWUikroiO4M3k6c0Mu7XjL8aLXC+wvLuedo+nkGvLBLKVSrp8eyw/mz74kzoVWp2dXbgnvHkqnzFD0YGdlye1zk3hgwSy8LqIKPr2sln/tPkl2hej8WEglrE6K4XvLZhPjP7bDNfg9Hswp4+NDZzhb0WC8f2a4P3cvncWSaREmFWj1ypVsP13EN0dyKGsYmKvu7mTP4qRwls2MJCXGtHGRgiBQ19JlFGznShuoHPSa/Tg72BIf5oOfpwu+Hk74ejjh4+GMj7sT3m6OF+VOmoIgCHTLlDR39NLU3kNTey/NHb00t/dSXt9GRf1w79mGaZH+RgEdH+aDrYk5jjXNnRw4W8aBM2XkVQ44xHY2VqxJieHmRdOJDzGt12JNaxefHj7DllP5xvxNF3tbbp4/jdsXJuEzjjQOuUrN16fz+O+hLJq7ZcbXemBJMnfOn4GDiYMAzken17Mvv4y3D6ZT3Cg667ZWltySMo0HFyXj6zL5Qkqh1vDNmXw+OJFFvaHXsaONNXcZIiWeV9nCurlXxldn8/jibJ6xKBRgQXgId8yczrLocHOz90miXS7nYHkF+8rKOVZVPaQS3cnGhqXhYayMimRRWCgO1pcmv3i8XJXCU6/Xs2HDBrq6ujh27Niwz/n973/Pc889d8H9V4vwPJ/arm72l5ezr6ycjNq6IRXyPo4OLIuIYEFoCGnBQTjbXt3zcacCKq2WXYWlfJp9bsiUizB3N25OSmBdQgwBLlfPfiIIAicranj3WAYnKwZ6mC6PjeCu1CTSwoInvRWTXi9woLCctw+cpsCQA2ptacHG5ATunJtE1AQnIQmCQEZ5He/vzzBWwQPMjw3lrvlJzIsJHVdVf151E58cOsOe7IHxeH7uztw6fxrrUuJMEiOCIJBT0cg3R3M5dK58SPW5g601C6aFMT8hlDnxwSaF9fvplinILWskp2xk93AwEok4Y9zHXRSj3u6OeLs64uXmaPJEpH50ej3t3XJaO3tp6ZQZxWVTx9gFR4Nd2umRoktr6v6l0erIqWjkRH4VR3IqKD9PfE8L82N9WhxrUmNxtBs7d1mj03G8oIpNJ/I4kl9hzK8N93Xn7iWzuC4ldlyFPnXt3XxzOo/PT5yjx1AI5elkzz2LZnH7vInnU/ep1Ow4V8yHx7KpMEQp7K2tuDMtifvmz8LTafLFX2N3L19n5/FJxjljbriHgz33zp3J3alJOF2C3PCJotZqOVJezZa8AvYVlxvPca52ttySlMDtM6cT4u56Zd/kNYBeEChobuFYdTWHKyrJqm8Y0nvc39nJEFmNJCUw4Kosdr4qhedPfvITdu7cybFjxwgMDBz2OVe74zkaXQoFBysq2VdWztHKqiFTAKQSCdN8fVgYGsL80BBm+PldlTvOVKKouZXPzuSyJbeQvkGzZGcF+rMhMZY1cdFXNOxwPsNVp/q7OnPLzARumpkw6Y6KWIVbxVsHT3O2ekCkzwjx47bU6ayeFj3hZP+Cumb+fTCTPedKjQdHb2cHbkhNYGNqwria0bd0y/jyaA5fHc+hUyaehCUSMd9vxYxols+INKm1k0anI6u4joNnyzh4tpy27qH5jFGBnsyNCyEtPoSZUQEmFTgZX1uro6SmhdKaVlEIdshoau8xisLB7ZsuNe7O9gbH1Rlvd9F5DfR2JTHCD/dx5KUKgkBNSxcnC6o5VVBNZnGt0Y0E0aGfHRPI0pmRLEmKwMt1bOGu1mg5VVzD3jOlHM4rN/Z2BViYEMZdS2YyNybY5OiESqPlQF45X5/ONRa8AQR7uvLg0tmsT44b1/9xMIUNLXxxOodtZ4uMDdidbW24d/5M7po3E1cT8obHg0an41BJJV9l5XG0rMr4vQl0c+b782az8RJ2wxgvOr2ejJp6tuYVsbuolN5B5+TkIH/unDWd1bFRV+3gj6lCQ08Px6qqOVZVzcmaGjoN0bx+4ry9WBEZwcrISOK8va6qqN5wXHXC89FHH2XLli0cOXKEsLAwk3/uasjxnAgqrZaTNTUcrqjieHU1FR2dQx53sLZmTlAgC0JDWBAaQpib21W/U12tyFRqthcUsz2/mNPVtcZ8I0uplAXhIaxPiGV5dAT2E2yjMtlUtHbwcfo5vs0ppEcpHtClEgkLI0O5JTmRJdFhk7ooEQSBjMo6Pj5xloOF5cb+j862NmyYFcdNsxOJ8TM9XD6Y2rYuPj1+jm8zC4zjBwHmRAVxU2oiy6dFmiwMlGotu7KL2Xoqn+zy+iGPJQT7sGJGFMtnRBHs5Trma+n1Yj7okZwKThVUU1jTzOCjnI2VBTOjApkW5ktssDcxQd74uTtN6DsoCAKdvQqjEG1q6xXdyi4ZrZ2yMV3K85FKJLg52+PlJjqmPgZx6evhjLeb44R7g8qVakrqWimubaWotoWMoloa2odONnJ3smdOXDDzEkJZOC0M5zFaV4EYLj5RUMW+c2UcyaswjtMEsW/mdbNjuWXBdEK8TZ8QV9LYxqbTeXybVUi3Yb+SSGBuVDC3pk1nWWLEhAoMZUoVu3JL+DI9l7y6ZuP9oZ5u3Jo6jVtSEie9E0VlWydfZ+ex6WyBsfgGIDU0kNtmT2NNfPRVMRVLEAQKmlrYml/E9vxiY2EngI+TI9fFR3PT9ARivCcWMTEjds45XVvHcYPYrOwcWRcsj4i4qqJ3pnDVCE9BEHjsscfYtGkThw4dIioqalw/P1WF5/k09PRwvLqG41XVHK+uoVMxtAegv7MT80NEETovJNhcKT9BmntlbC8oZlteEXlNA30g7awsWREdyfUJMSwID7kq3GalRsvewlK+zMojvWqgstfDwZ6NM+K5aVYC4Z7uk/o7W3tkfJOVz1fpeTR0DYiOeH9vNs5O4Lqk2Ak5PWqtloP5FXxzKo+TpdVGkedsZ8P62XGsT44jPtD0nOfmzl4O5JSx72wZ2eV1Q0RjdIAXK2ZEsmJGlMkjEztlCtILa4zuXothzvxgnO1tiA7yJjbIi+ggL2KDvAn1db8qRMF46eiRU1TbQnFtC0U1rZTUtVLT0sn5R3orSwtmRPiTFh9CWkIIUQFeJoXm+5RqjuZXsu9sKccKKoc0nPd2dWR5kvj/mRFuWrEXQFefgj05pWxOzye3ZiCn1MfFkY2pCdyYmjCuqVr96PUCmVV1bMrMZ09eqbEozdJCysqESG5NnU5q+OT2w1SoNewxfLczqwcWUZ6O9myckcDNsxII9bg6RjVXd3SxLb+IrflFVLYPCCFnWxvWxEaxITGO2cEB5mEqE0Cr15Pb1MTxqhqOVVVztrHRmFYEYnHydD9f5oeEsDA0hOl+vlfFuWmiXDXC8+GHH+aTTz5hy5YtQ3p3uri4YGeCuLpWhOdg9IJAQUuLcdWTWd8wpHG9BEj09WF+SAjzQ4OZ5e9vDmlMgPK2DrblF/FtfhE1nQMtJ1ztbLkuLprrE2OZFeh/VRxQq9oHXJE22YArkhToyw1J8axNjMZtEtMGdHo9J0pr+Dozl4OFFcZem1YWFiyPj2Dj7ATSIoMn5Co1dPSwOSOfTen5NHUNFCAEeriwZkY0a2bEEO03+iz1wbT39HEwp5x950rJKKkdMrEn3NdddEKToogOMO01BUGgorGdjKJaCmtaKKlrpbyh3fgZDMbGyoLIAE+iA72IDfYmwt8DL1dHPF0cJtyIfLLQ6fV09ipo7+mjtqWL4toBN/P8NIN+vFwdiAnyJibQi6QIf5KjA7Ezsb9lj1zJ4dwK9p8r5URh9ZD0An93Z1bMiGLFjCgSQ3xNzivtUSg5kFvOrnMlnC6pMZ6ULaVSliaGc9OcaaRFT3A/7Oxhc3YBW7LyqescWGSFe7mzcXYCN8yKx2MSWyLp9QLp1XVsPVfIrvwSY/heKpGwMCqUW2clsniSoxkTpVXWx87CErbmFZHTMCDybSwtWBYVwYbEWBaGh5jHWI4TQRCo7e42CM0qTtbU0qMaOvUs2NWFBaGhLAgJZu41Vvtx1QjPkU4E//73v3nggQfG/PlrUXiej0KjIb2ujmOV1RyvrqakbWhSv42lBbMDApgbHExacBCJvj7mysFxIAgCOQ3NbMsvYntBMW2Dwl0BLs5cFx/NmtgoEv2ufBcCjU7H4ZJKvszO41hZlVFkWVlIWRIdzoakOBZHhU3qHOjOPgXbzhbyTWY+JU0DXRp8XRzZMCueG2clEOLpOu7X1en1nCypYXN6PkcKK1AMcsVCvdxYPSOaNTOiiRxHsVNXn4JDuRXsO1PKqeLqIWLR182J5MgAkiMDSY4MJNjL1eT/p1qjpaKxwyDeWiiqbaGktnVIzuP5ONha4+nigIezPR7ODni6OODpMui6swMeLvbjbuujHSQo27r7aO/uo7W7j/Ye+ZD7OnoVQ4oPBiORQIi3m9G5jQkWxeZ4ckBlChXnKhvJKqsjs6yOgurmIW5NsJerUWzGBZk+CUWmVHEwv4LdZ4s5ft7/MNbfi3WzYlk/Ox4Pp/GLQqVGy/78MjZl5XOqvMbo8DrYWLN2ejQ3zU5ketDFzaM+n4rWDracK+TbnEJjD18QczdvnpnIxhnxl6Qifrx0yBXsLylnZ2EJJyprjPuOVCJhflgw1yfEsjImEsdLNIHpWqVZJuNkdQ2namo5WVNLfc/Q9BVnGxvSQoJZYDCSruWWi1eN8LxYvgvC83yaZTJOVNVwtKqKE9U1tMmH9hh0tLYmNSiQNIMQjfbyvCpcu6mAVq/ndFUtW/OL2FNUNqQoKcDFmVWxkayOjWJGgN8V/0xbe/vYnlvM1pxCChoH0gZc7GxZlxjDDTPimB4weSdRQRAobGhhU1YB284WGiuHAZJDA9g4O4FViVETmowkV2k4UlDB7nMlHCkcOks90tfDIEJjCPUyPfzYq1BxJK+CfWdLOVFYZZzr3Y+XswOzIgOYFRFIclQA4T4e4+ogoNcL1LV1G8LVojNa1dRBe7fcGK690kgkYl6mj5sTMUFeopsZ5EVUgOe4q+l75Eqyy+vJKq0jq7yeotqWC4RthJ+HUWxG+pk+CaX//7/rXAlHJ+n/348gCOTVNbMpK58d54rpVQ7st6nhQdw0O4EVCZGT6lB39inYkVfMlnOF5NQPOIZOtjasSYjixqR4ZgWPPVnrUtMq62NfcRm7ikpJrx7adSXJ35f1ibFcFxd91bVtuprpVCg4bRCZp2pqKe/oGPK4pVTKDD8/Y/3Gd8koMgvPawRBEChr7+BkjbiiOjWMde9mZ8fc4CDSgoNICw4m1M10p+e7jFKj5WBZBbsKSzhUVmmcKQxiI+RVsVGsjo1kdlDAFZ+UVNzcxtZzBWzNKaK1dyCMGurhxo1JcaxPiiNgEpvTqzRaDhaWsymrgBOl1UYBYmdtxcqESFZPi2ZeVPCEQnF9SjUH88vZfbaEY8VVFzheq2dEszopmqBxuKwKlYZzlQ1kldWTXV5HblXTBZXmbo52zAwPIDkqgOSIQKICPCf0fxUEgT6lmvYeOW3dBgey34nsv6+nj/ZuOZ0y+QV5laZga21pcFANzqnBRfVwscfLRbzt4WKPm6P9hHNQO3rlZJXVGcVmaWPbBe810NOF5MhAZkUEMDsykABP03MsFWoNx4uq2HW2ZNIc78FUtHSwN7+UHeeKjX1rAfxcndiYLIbSAyeQEzoSaq2WQyWVbD1XyOHSSjSG/dZCKhYG3pAUx9KYiCtemd7U08ue4jJ2FZaSVTt0Xne8jzerYyNZlxBDsJvrlXqLUwqZWk1mXR0nqms5WVNDUUvrkM+0PzUuLTiIucHBJAf4XzV9NS83ZuF5jaLT6ylsbeVkdQ0na2rJrKsf0rYJwMfRkdSgAFIDg0gNCiTc3VwxPxYKjYaj5dXsKS5lf0nFECfUw8GeFdERrImNIjUk8IrmaOn0ek5W1LL1XAF7C8uGiOWU0EDWJcawIi5iUh2Mpu5etmYXsikr3zgZCcTw5ZLYcFYmRrIgOnRCjtJIOX4AIZ6uzI0OYV5MCKmRgeOqNlZptORVN5FVVkdWaR3nKhsvcCkd7WyI9vck3NedMF93wn09CPf1wNvFYdK+L1qdfkj+tilIkGA7zrnxo6FQaahs7qCyuYPyxnYqmjoob2qntrXrgueG+bgzqz9dISJgXI3d9XqBksZWThRXc7KkhuzK+iHif6I5vv0IgkBpczt7ckvYm182RGzaWFqwMjGKjckJpIYHTVpvXK1OT2Z1HbvyS9mZX0z3oChAvJ83NybFsW5a7KTmik6E2q5u9hSVsruojLP1jUMem+7vy5rYKFbFRprFpgn0KJVk1TeQXltHel0deU3NQ5xigChPD6PRkxoUiMs1lKd5MZiF53cEtU5HbmMTJ2vE1Vh2Q+MFJzoPe3tSAgNIDQokNTDQHJofA7VWy/HKGnYXlbK/pJzuQaE7VztblkdFsCImgnlhwdhZXbkCE5lKzd7CUracLeR0Va3RrZJKJCSHBLA6PoqVcZH4XMTEosEIgkB2dYN44s8ro7lnoDLczsqShTFhrEyMYnFs2ITC8V19CvbnlrHrbAkZ5UOLiCykEqYH+5EWE0JadDCJQb7jcvo0Wh0Ftc1kl9WTWVbH2YqGIW1/BuNoaz1IiBq2Pu74uTtPerP/yaRXoaKyqYOKpnYqmjuoaBSvN3T0jPgzUf6ehpzYAGZFBODhPL4FS3O3jJMGoXmqtJoO2XndOtycWZUUxZoZ0ePqatCPIAgUNLSwN6+UPbmlVA9a+FhaSEmLCGZlYhQrEyNxtpuck79aq+N0ZS17CkrZV1RO56DxhD7OjqyfFsuGpDiifa5cWyFBEChtbWd/aQV7ikrJH9TBQwLMCvJndWwUq2Ii8Z9iLXkuNx1yBZl1oshMr62nsKWF8wVRsKuLMbVtTlDQVTeq+WrBLDy/oyg1Gs40Noqrtdo6zjY2ojov5Ohia8PsQFGEpgYFEOft/Z3JQRkvGp2O09V17C4qZW9x2ZAZuTaWFswLDWZpVDhLo8LxcZr8Gc6m0tjdy7acIvYUlhpHdPYzM8iPVfFRrIqPmrRwvF4vkFPbyN78Mvbklg5pzWRtacH8qBBWJUaxJC58QoJAplSRXlbHyZJqThZXU93WNeRxJ1sbUqOCmBcdTFpMyLga1oPoZJU1tlHRL9QMIq22rWuI4B2MrbUlwZ6uuDnZ4+Zod95l4D5XBztcHGwnJT1DodLQIZPTKVMYLuL1LuNt8dLY2UPrCJXsIKYZDAhpUUzHBHrh6jC+TglylYasijpOlFRzqqSGsqahhZB21lakRgaSFh1CWnQIYd7jj7bo9QK5dU3syStlb14p9Z2Tu28Nh0qj5XhFNXsKyjhQVG7srwvi/PcVsZGsTYxmbljQFUu7Uet0ZNbUc6C0nAOlFdQN+s5JJRJSgwNZHRvFypgIvK/gsehqp0UmI6Ou3uholrZdOGo21M2VlMBAo1kz1fppXinMwtMMIDayz21qNn7JsusbLgjNO1hbkxzgz+zAAFIDA5nm62Nu3zQMOr2ezNp6dheVcbC0gvruoU5Sop8Pyw0iNM7nyk2ZqOvsZm9hGXsKSjlTOzTslujvw2qDCA0Zp1gbiX5Xak9uKXvySoeE4/tdqaXx4SyMCcN/gsK3vqObkyU1nCiu5nRpzZDCJxDDuCkRgSQG+zItyJdIP48JpURotDqqWztFQWoIS1c2d1DV0olmHNOJpBIJLg62uDnajTvnTycIdPcp6ZIpxl3E5OXiYBSYEb4ehPm6E+bjjvsEKsRB7PuaW9NMXm0TZysbOFs1NKIikUBikC9zo4OZFx1CUojfhGbWqzRaMirrOFJUyb78yXfTh0Oh1nC0rIrdBaUcKqmkTzXggHs62rMyLpJV8VGkhAResV6uXQolR8or2V9SwdGKKmSD3qO1hQVpocGsiIlgZXQE7g5XNtx/NSIIAnXdPWTU1ZFRV09GXR1VnV0XPC/Sw4PUoEDmBAUyOzAAH0ezcJ8IZuFpZlg0Oh0FLS1GRzSzvmHIODQQD2hJfr6kBAaSEhjAzAB/HL+jydIjIQgCxS1tHCytYH9pxZBeeAB+zk4sjQpnWVQ4c0MCr1g/vOYeGXsLy9hdUEpWdf2QKuVYXy9WxUWyMj6SSC/TK5RHQxAESpraxNBoXinlLUMrPqN8PFgYE8aimDBmhExsbKxOr6egrsWQS1jNuaqhTZlBdKPjAr1JDPYlMciXacG+BHm4TPhv1Or01Ld3U9fWbXQdh14G7us9TxRfLNaWFqKTOoy76uZoh7ujPZ7ODoT6uOF8EWMeZUoV+bXN5NY0GbfN3Rc22vd3cyYtJpi06BDmRgXjMsHf2dDZw5HiSo4UV5JeXjskX9ne2oolceGsSoyacP7wcMiUKg6XVrGnoJQjpUMLCn2cHVkVF8nqhChmBpne+H6yqeroZH9JBQdLK8iqrR+SX+jhYM/SyDCWRoUzPyzkqpnGdrWgFwTK2tvJqK0ns14MnTfLhu7DEiDW28sQ8ROFpoe9WbRPBmbhacYkdHo9Ra1thhyXejLr6mk/r32TVCIh0sOd6X6+JPn6Mc3PhxhPz6uiEfLVQqusj0NllRwoKed4ZQ1K7cAJzdbSktnBAaSFBpEWGkycj9cVOam1y+TsKxJF6OnKoTmU/q7OzI8IZn5ECHPDgidtTnVFSwf78ss4UlzJuZrGIcLX3tqKmSH+pIQHkhoeRHyA94T2qT6lmsyKOs5VNRpF0+CWOv042doQ5e9JrL8Xsf5eRPt7EeLlOukjEjU63ZBQ+HjnuEvA6Ja6Odpjb2M1qe65VqenqauXsqY2ihpaKa5vpbihldr27gueK5FAhI8H04J9SQz2ZU5kEMGeE+ua0djVS2ZlHRkVdWRU1g1xxgG8nR1YGBPG0rhw5kWGTHgG+2B0ej35DS0cL6/meHk1Z2uHLlICXJ2NEYDpAaY3vp9M2mR9nKqu5URlLaeqa4aE0AGivTxYFhXBsuhwpvv7mvPzB9Esk5HT2EROU5Nh23yBkWIplZLo40NqUACzAwNJDvA3FwNdIszC08yEEASBqs4u0uvqyDSEJuq6LyxOsLG0IM7LmyQ/X6b5+pDk50eIm6v5oIjYpulkVQ0HSkXXYvDMYxBzbFNDgoxCNNzj8ncd6JQrOFBcwZ6CUk6U11wYPvX3YV54CPMigpkZ5D8pDeu75EqOl1RxtLiSY6XVdPYNLUSxMwjR1PBAkkMDiA/wmVBrGr1eoLqtk7yaJnJrmsmvbaKwvnXE6nJXe1sCPFwIdHchwN2ZQA8XAtxdCPJwwdfNacotsARBoKtPSV1Ht+jSdhgu7d3Ud/TQ2NkzYh6rv5szicE+TAvyJSHYl/gAbxzG2Q+0/z3UdXZzpqqBDIPYrO0YKmylEglJwX4sigljUWwYMb7jr3QfjrrObo6VVXOioprTlbVDKtFBbEG2Kj6S1fFRxPuZ3vh+spCpVKRX13GyqpaTVTWUtA7NMbSUSkkNCWSZIW0nyHXyWkJNZXqUSnKbmjnX1ESuQWw2yy7Ma7azsmSmn5g6lhIUwAw/vytaBPpdwiw8zUwaLTIZOU3N5DQ2ca6xidympgt6iQI42dgw3deHab6+JPn5Mt3P9zufK9NffXqyqoaTVbWk19QNydMC8HFyZG6oKETnhQbj63x5p5z0qdRkVtdzoryaExU1lLacVzBiZUlqaBDzIoKZFxE8KWF5vV6gpLmNjIpaMirqyKysp1uhHPIcS6mUKF9PEgN9mBboS2KgDxHeHhPKt9NodVS0dFDcILp7xfWtlDa1XVCFfT5SiQRfVyejIPVzdcLZ3hZnOxvj1snOBmc7W5ztbbGxtLgkQkan19OrUNGrUNGjUNItV9GrVNEjV9KjUNHeKzcIS1FkjjZxCcTRqKHebsT6exHj70W0vycx/l64T7AtUFtvH3l1zeTVNZFr2HbJh/4/pRIJcf7epIYHMjsskFmh/pNSHNSjUHK6qo7jBrFZc57AdbK1YW5YEPMjgpkXEUKwu+tF/87xoNJqya5r4GRlLaeqa8ltaLqgPU+cjxdpocHMDQ1idlDAd356kEqrpaClxehi5jQ2UdnZecHzpBIJ0Z4eA+ccX1+iPCeW323m4jELTzOXjH5XNKdJFKHnGpsoaGm5oHoewMfRgem+vkwzHBQSfLxxs5u8meNTDa1eT15jMycrRSGaXdeA+jwnLszdjbTQIOaEBpEc6H/ZK1Sbe2ScqKjmRHkNJ8praO8bmnrh5WjPrOAAkoP9mRUSQKyP10UXX+j1AqXNbWRW1pFeUceZ6gbaZfILnmdnZUlcgLdBiPoyLciHQLeJ5272KdVD3MDB4q2+vXvYfXo0rCwscLYXhagoSG3GHTLW6fX0KdX0GERmr0KFbITWT6Ph7exgdG/F7YCb6+3sOOGwcp9KTX59M7m1TeTVNZNb10RjV+8Fz7O0kBLn501KeCApBqE5GWkNLb0ysmsayKquJ6umgaKm1iEpHJZSKUmBvsyPCGFeRAiJ/j6XtThIrdWS19RicDVryK5ruGA/CnFzFSMeYcHMCQnC3f67e0zU6HSUtXeQ19xsDJsXt7ZdkLcNEOTiwnTDuWS6nw8J3j7mPNerCLPwNHNZ0eh0lLS1G4VoTmMTpe3tw86SDnB2JsHH23hJ9PHB0+G72RdNqTG4IQZHNK+x+YLPLNjNheSgAJID/ZkdHEDYZRwIoNcLlLS0cbxcFKKZ1XUXnETtra2YEeRHskGMTg/0u+iTgSAINHXLyK1rMgqc/PrmIZXH/bja25IY6Eusnxfh3u6EebkT5uWG00WKHEEQaOvto769h9oOUZA2d8mMYrBHMeA49ipUI85Nn0zsrK0GXNZ+59XOFjcHuyHCMsDd+aJzJLU6PfWdPVS2dlDR2kFZczt5dU1UtHZcMOFIIoEwL3ejMz0t0JcYP8+LLqoTBIHK9k6yqxvIrKknu6b+AkcTIMzTjXnhIcyPDCY1NOiyOoY9SiVn6hrJqm0gs7aenIamCxaTXg72pIUFkxYaTFpo0He2t6ZKq6WkrY385hbDpZmi1rYLPi8Q+09P9/UxCs1pvr7faYE+FTALTzNXHLlaQ35LMzmNzeQ0NZHX1Ex1V9ewz/VxdCDex4cEb28SfX1I8PHG19HxOzdxqUepJL26npNVNWTU1FN83ng2ADd7O6MITQ4MIN7X67KFllQaLXkNzWRW15Nd00B2bcMFhTyWUinxft7MCvYnOSSAWUH+kzLZRa8XqGzrGOK0FTe2jZi76e3sYBCh7oR7uYlbb3d8nCd/vxIEgT6Vmh656gJhOt7JRUgkONpYDwnpO9mK7ulEWhWNRZ9KTVVbJ5UtHVS0dlLR2k5layfVbV0jvnc/V6dBKRC+JAR4T4qbqdHpKGxsJaum3uhoDm7gDqLIjfHxEhc6If7MCvLH1+Xypac09fSSWdtAVm09WbUNw35H3e3tSA4KYE5IIPNCg4nwdP/OHcsUGg1Fra3kNbWQ39JMfnMLpW3twzqZjtbWJPh4G5xMUWj6Ozt95z6zqY5ZeJq5KulVqYasdvNbWqho77jgwA3iwTvOy5s4b09ivbyI8/Yi3N39O5W/06tUkV3XQFZtA1l19Zyrv9BNsbW0ZEaAH8lB/iQF+DHdz+ey9fTT68UcVlEkiGKhqefCFjwBrs7E+3mT4OdNgr8P8X7ekyJG1VotxY1t5NU1U9rcRmVrJ5WtHUPm2Z+PvbUVYQYh6ufqaDIXMgABAABJREFUhLezo/Hi4+yIx0XMP7/aEAQBmUpNc7eMlh4ZzT0yWnv6aO6RUdPWSUVrJ03dF4bJ+7GxtCDUINzDvdyJC/AmMdAXL6eLj1CotVpKW9opaGwhv6GFgsYWiptbL3DUbSwtmB7oR3KwP8nBAcwI8rtoN9tUNDodJS1t5DQ0kVUnLrSGK7YMcXMlOchfjEwE+V/WqMSVRhAEWvv6KGxtpbiljaLWVgpbWylv7xg2CuBqa0uCj8+QqFewq7kw9VrALDzNTBn61GqKWlrJbxkQpKVt7Rck4ANYSaVEengQ6+1lFKOxXl7fmRBMf/5Yv9uSVVs/ZKRnP4GuzmJerb8v0/19iPf1xuEy9WJt6Oohy5iDV39BsVI/Ps6OxPt5i4LU35sEPx+8nSZnTnqPQklVaycVrR1GMVrR2kFN+8iTifqRSiR4ONqLQtSlX5Q64O3siKejA4621jjYWONoY429jTUONlaXbTEkCAJytQa5So1MpaZPpaZPpaFLrrhAWLZ0y2jp7UOhHr3QCMDD0d4oxkWH2J1wbzf8XCZnTKhSo6W4uZWChhbyG0WRWdrShkZ3ofvlYmdrFJmzQvxJ8PO+LH1w9YJAdUcXOQ2GqumGZgqbL8xdl0okxPl4MdsgMmddgTzsK4Vap6O8vYOi1laKWlqNIrNDPnyRnqe9vTGCleDtTYKPj9nJvIYxC08zUxqlRkNxWxuFhoNbkWElLVMPX2Th4+hAjJcoQqM9PYj29CTCw/2an8CkFwQq2jrIrK0ns7ae3MZmKtuHr/6M9HQ3CFFfpvn5EO19eXqx9iiUFDa1Gl2tgsYWKtsvzBMEsUF2vJ83cX5eRHi5E+7pTpiH26T12lRrddR1dFPZ2kFlW6fRCey/tPb2jSlMh8PG0gIHG2vjxdHWGgdraxxsrcf9GfeLyz6VGplSTZ9aTZ9SFJlytWZCuaTOtjZ4nyeig9xdjEJzsvq26vR66rt6qGgTw/bFza0UNLZQ0dYx7OfqYmdDgp/ogMf7i4uQYDfXy9JPs7lXRk5Dk0FoNpPbcGEPSBA/u0Q/H2YG+DE7KICkAL9rvupcEAQae2WUtrVR2tYuHoMNLqZmmFC5VCIhzM2NGC9PYr29iPPyIt7H+zvf1eS7hll4mrnmEASB+p6eQWJUXG3XdF1YbADiwTDE1ZUoTw+iPT2I8vQkytODUDc3rK/hcH2PUkleY8uQk2pz74XhbxtLC/Ek4eNFrOES7eV5WU6qMpWa4qZWo/uV39BMeevwoTkQ3dFwTzfCPEUxGu7pRoSXx6Q5pP3o9Ho6ZHJaevuGDU+3y+TIjU6jetxV75OFVCIxiFwrHGyscbKzwcfZES+nAZfWx3lAaE7W5J9+5GoNlW0dVLR1GrYdlLd2UN3RNWLDfHcHOxL8fEgwCMx4P28CXJ0vufvV72QWtbRS3NxGUUsreY3NF/TXBXFqW4KvN9P8fQwLNF9C3K/dMLAgCLT1ySlpa6O0vV3ctrVT2tY+4iLfycaGWC8x/alfZEZ5eph7ZZoxC08z3x361GqKW9sMq/I24yq9S6kc9vmWUilh7m5EeYjOaKSHOxEeHoS4uV6zgrS5V0ZuQ7MxjJjb2EzPMCF6gCBXF1GIensaBWmgy6UXCAq1hpKWNgoaWihqbjWKmrZh2ir1Y29tZRSiwe6u+Lk64+/ihL+LM77OjpMy/WY0NDodfSoNfSqVYSu6lHK1wa1UqYctphgNCWBvY4WDtRjK7w/t9zuoDjbW2FlZXtL/hyAItPfJaezupaG7l8auXuq6uqk0/E8aRskLtba0IMzDTfy/eLlPehrFaMhUKjHP0CAwi5pbKWltGzIasx+pREKUl4chJUUUmlFe12YPyH6BWdbRTnl7B6VtAyJztONkqJubMYLULzLNoXIzI2EWnma+0wxeyZe0tVPa3kZJazul7e30jbCSt5BICHZ1JdzDnQh3NyI8PAh3dyPC3R3na2zEml4QqOnsIq+xWUxjaBZP0sO5QACONtbEeHsS4y0K0kgvD6I8PXCZhAbgY9GtUFLZ1klFW4fRZato66CmY+x8TU9He/xcnAwXUZT6uTjh7+qMn7MTbvZ2V2RM4pVGrtbQ3COjsbvHKC4busTrjd29NPb0jjnq093BTkyF8DSITMMCwN/V+ZKPhNXodFR3dlHW2k5Ja7u4/7a0XjBush8bSwuiDGHgWB9P4ny8SPC99npAavV6aru6Ke/ooLy9nYqOTsoM2+HSCEBc6IS4uRJtiAhFe3oS5eFBqPu1HRkyM/mYhacZM8Mg5i71Glf7JW3tlLeLLkCfZuQiDC8HB1GEGsRoiKsrIW6uBLq4XFMH5w65wngSF8VoG+Vt7cPmdQF4OzoQ6eVBpKc7ER7uhLi7Eeruiq+z0yUPT6q1Omo7u0Qh2tpBXVe3UTg1dPUM63Kdj1QiwcXOFncHO9zsxcvg6672trjaDWyd7Wyws7K6ZBOKxotGp0Ol0SJTqelSKOmSKwa2ciWdcgUdcoW47VPQZbitNOGzkUjA28nRINZF0R5mSHcI83TD7TIU9MlUKqo6ugyXTkpb2ylra6eqvXPEfdLHyZFYby9ifAaEZqi7G5aXWAxfLgRBoF0up7qzi+quLio7O6lo76C8o5PqzpE/F6lEQqCLMxHu7sa0o2hPTyLc3bA1h8nNTAJm4WnGzDgQBIFmmWyQQ9BBeXsHFR0dw84D7kcqkeDv7CQKUVdXgt1cjaI02MXlmjiga3Q6Kto7jYK0pEU8+Tf2jN6GJ9hN/BxC3V0JcnUh0HDxd3G65EVfgiDQpVAOEaLG6909NHT3jtpyaSwkErGNla2VFXZWlthYWWJnZYWtlSW2g69bWo67NZNeEFBrdSg0GpQaLQqNFpVGa7yt1GhQaLQoNdpxh/EHY29tZRSV/S6wv9EddsLH2fGSh537IxN1Xd3Ud/dQ29VDdUcnVR1dVHd2XTA1a8j7t7IiwtOdKC8P0Yn3ER35a6HDhV4QaJHJqO7qMgrM/m1NZ9eoi2RbS0vC3d2J8HAzbD2IcHcj1M3tmi+2NHNlMQtPM2YmiV6VioqODiraOynraKeqs4saE04AAL5OjkPc0SAXFwJdnAl0dsHTwf6qcM0mikyloqytg7JWsRihsr2T6o4uaru6xxRE3o4OBiHqTJCrCwEuzsbbvs5Ol8Wd0uh0dMoVdMqVdPbJja5gh1xBZ5+CLoXoGg52EuUmtCa6ElhIJYOcWVtc7e1wtbMVnVuHASfX3d4eN3vxfgcb60u+/wmCQLdSRX1XN3XdPdR1dVPb1WMUmvVdPSi1o7uvHg72xgVMpKeH0WH3d3Ge0kU/Co1G/Ey6u6ntFj+Pmi6DuOzqHvVzkQABLs4Eu4jHFlFguhPh7o7fZYg2mDEzHGbhacbMJWZwyKvqPGeiqrNzxKrQfmwtLQlwcR4iRgMNAizIxQWXKZpXqtXraejuodoQIq3u7KTOIDbqunqQjyHWLSQSfJ2d8HFywNvRER8nR7wN170H3XclWtr0h7aVWq3BjRxwJZVGR3LQ/WrNsP1oR0MC2BjcUltr0VEd7KKKoX5Lw/3i49ZXIPSv0elo75PT3NtHi0xGc6/YN7RFJqNF1kdLr4zGHtmIuYX9SCUSfJ0cCXAV9/1gVxdjykaouyuONpenWfxko9bpaOzpNQrLfpHZv22Tj+zmgvg9CHBxNi5cQ1zdCDVEEQKcnc3upZmrDrPwNGPmCiIIAp0KJdVdnUZBOvjE09TbO+y0psE4Wlvj5+yEv5MTfk5O+Ds74+fkhJ+zeNvXyXHKnXwEQaBTrjC6X0ZB2t3vgvWaPGLSwdpqiBj1dLTH1c4ON4PT597v+tnZ4WJne01WK08mgiDQp1YbckKVdCkUdCoMjrBcQYdcTqusjxZZH829Mtr75GPuw/14OtgbHe3B7naQqwu+zk5TLk9aEAQ65Aoae3tp6OkxbHuHbFv7+sbsuepobT2w8HRxIcjVxSAyRXFp3mfNTCXMwtOMmasYtU5HQ0+PmNc2yAXpD7mN5Yb042lvj79BiPo5O+Pr6Ii3oyjEfJ0c8XZwnFKVu3pBoKW3j4aeHlp6ZUaR0++g9btrMtXobvJwONnYiGFmO0PhkJ0tjjY2OFiLrYscrA19Ma2tcbQWJxOJ91njYG2FvbX1VVugIggCCo1WbDavNvQZVWuMt2Xn3e5TqelWimkEnYbioy6FcsTClJGwlErxcnQQ9zkncd/rd6h9DM50gKvzlOrxqNPraZPLRRdXZnBzZTIaBwnLJlmvST1cx4pqONvYTOl0GzNmBmMWnmbMTGEUGs0gB0V0VBrPc1TGyo3rx9HaGh+DIBW3jvgYxKmPoyMe9vZ4OthftpGak0GfWi26b4PCvO198gEhpVDQabjerVCa7MyNhbWFBdaWFlhbWGBlYWG8bSU1bC2kFzxmIRmfWBUQ0Oh0qLU6NHq9uNXpUBsuxseMt/UotdoJTTQaDjsrS9zs7ET32JAPKt62HSouHR1wd7CfMvmEWr2eDrmctj45rfK+QYsaGc19/SkCMlr75CZ9lhLEbhf9EYj+iIRxIejkNOXzuM2YGQ9m4WnGzDVMfyj/fGHaLJPR0ieeUJtlsjHzKQdjZ2WJp70D7vZ2eDo44GkQpKIwHXrbycZmyggOnV5Pt1Ilho7PCyOLTqA479zoBqoN7qBKjczgDo7XCbxSSAB7a9Gh7W8673je7X6H18XOdkBc2vWnJthhe4mb7k8mSo2GDoWSdnkfbX1y2gzCsl0uv+C+LoXC5AWIhUSCp4ODcYHWH0HwN6S6+Ds54+PkOOVSBMyYuZSYhacZM2aQqdVGR7C5v+ijTwwhNvfKaO3ro03eZ1LPy8FYGPpfutnZ4W434Ii5Ga4PXMT7nG1scba1uWpD1WOh1mqRqTUoNJphHUe1Todaqx3WoZyIE3m+g2ptYSletxxwUwe2UmwsLXG0scbOymrKLAgGIwgCco3GuEDoUirpVIgdBrqU/Q62gg65csjt8e63UokEdzs7PB3sh7j/Po5OgyICDnjY21/yJvhmzFxrjEevTZ3lrRkzZsaFo7U1ju7uhLu7j/q8PrXa6BS19fUZXaI2uZz28273qdXoDMUVHXIF5eN4Pw5WVjjb2uJsY4Ozrc3A9f7bBoHqbGNjdOccrYfmYV4JYWVtaYm7pSUw9XtETjaCIKDW6ZD1O8RqNX2agdzSbqWKHpWKXpXSeL1HqaJHpRyyHW/1fz+WUike9nZ42Dvg6WA/yJkfetvT3gFXO1uzoDRj5irALDzNmPmO0y/qQtxcx3yuSqsVHan+XMpB17uUhj6Yg1yrTrnC2O+0T6OhT6OhsXfk5vNjYW9lNSRk7Gh47/b97YYsDS2IDFux9ZAVdv0N3y0tjc3frQe5hzYWQ3M0p7pAEQRBdGB1gxxYrQ6NXtyqdIY2UNqBhvRKrRaF1tAKynC/Siu2h1JoNMj6C5Q0Q9MSLqaR/WAspVJcbQfySt1sBxWD2RrcdXtbXG3tjOkBjtaXvh+pGTNmJhez8DRjxozJ2FhaihXLjo4m/4xWrze6W70q1SDnS3S7ulVKeg33dSuV9KhUQ6uwDS4rgFyjQa7R0DrxwUMmYSGRDBQOGcSolYUUC4kUC6kUC4kEqVSCpVSKVCIx3C8xPta/Ha8oEgC9Xo9OENAZtnq9gFavRy8Mf79O0A8Rl2q93uS2VJOJnZUlDlaG3FJDTqmLjQ0utrY4GbbOtjYD141Ot3jb1tLSLCLNmPkOYBaeZsyYuaRYSqW4G3prTgRBEFBptfSpNcZwrmyQKJWp1Qb3brCDp0FhcOuUg7b9bp5Sox1SKX5+AZHO0KJIwfjyCK9WpBLJEAFtaym6w/0usbgdOu6z3zW2sxSb1g9pMzUo/cHR4DhPdZfYjBkzlwez8DRjxsxVjUQiwdbKClsrKzwc7C/J79ALwnkFQ1pj4ZBmkEDVCQI6QS+6jYat6D7q0Rq2RjdS0DOR2k0Lg4s64KYaHNR+p9V4e8BpNRYg9bdyshhaiDRVC7vMmDFz7WEWnmbMmPnOI5VIsLG0nHLToMyYMWNmqjEljrJyjQbLcfQkNGPGjBkzZsyYMXN5GE/f6CkhPOe+/xZSW9sr/TbMmDFjxowZM2bMnIdeqTT5uebEHzNmzJgxY8aMGTOXhSkxuaixrc08uciMGTNmzJgxY+YqpKenBz9Pz2tncpG9lRX2VlZX+m2YMWPGjBkzZsyYOQ/tODSaOdRuxowZM2bMmDFj5rIwJRxPM2bMmDEVvSCIjeS1g8ZADtmK140N5PUDfTpVg28btmqdDrVe7O+pE/ToB00PMk4REs6bLmR4bCJIDb06h0xFGjQNSWrYWkqk4vQkycCoT2vpoBGgxqlLUqyllkN6fNpYWIiN4w2N5O36G8YP2lpZWEzyf8aMGTNmzMLTjBkzVxhBEJBrNXSrxHGZvWoVMrUamWEmuEwzMB9cptEMuj5wv3yQqFTpro1pQ1caS6kUWwtLozh1sDJMK7KyxtEwGtPByhonw9bBSpxi1P+4o7W1OA7TxhYnaxtzE3szZswAZuFpxoyZSUKl09KlVNKhVNA56NKtEue096hU9KjFbbdKSY9aZXxMd4lqHK0Nzp5d/4hIS8M4SAuxWby1wRkc6hAanEHpoClAUnH6j+V5s9ilkkEThCQSLKTiff1u5XgnjwuIQnywm6ozTkjSo+vfDnJZdYJ+wJk9z7FVDZq8NPgxlU6HQqsxXMQRo0qdKNz1hv+FVq9HphcF/mTgYGWFi40tztY2ONsYZrUbrrvY2OBsbYurrS3utna42trhbmuHm60dTtbW5hnuZsxcQ5iFpxkzZi5AEARkGjVtcjntCjltij5a5XI6lHI6lcohwrJDqaBLqaDvIoc8WEqluFjb4GRjM6yD5mBtNchNszE8Lt5nb2V1QejYxsLSPD98nAiCgFqvG5hvbxCj8hGc5v7rMrWaPsNzxOtqetWie92/X4iPa2igd1zvyVIqxdXGFjeDEHUbJErd7ezwsLPHw84eTzt7PO0dcLe1M7urZsxcxZiFpxkz3yGUWg0t8j6aZDKa5TLa5KKoNG4VcvEil08oZG0hkeBma2dwruxxtbXFZZCj1R96FZ0u0fFyMbhfdpZWZmfrCiORSLCxEEW7i83kDO3Q6HT0qlUGh7vf+R503eCEd6tUdCkVhoWNuMCRazVo9XrjfmnS3wC429rhae8wRJCKW3u87R3xcXDEx94BN1s78z5nxsxlxiw8zZi5BtAZTs7NfTKa+mQ091/kg673yehSmT5dAsDe0gpPe3s87RwMzpId7rb2uNme50DZ9YdFbZBOoRO5WqcTXTqDc9erUYlunmZojqlMI+aRagyFRmqdFo1ej0o3cH3gPq2hGEmHnvGnEFhJhob4rc8L/Rvvk1pibSHmYRodYivrIa5w/+3+++yvgLi3srDA3c4edzv7cf+sUqsZ4rD3u+sdBoHaoeh35OW0yfvoUCoQgHalgnalYszXt5Za4O3ggK+DEz4ODvg4OOFjL94efL+9lfUE/nIzZswMh1l4mjFzlSMIAj1qFfW9PTTKemmQ9VAv66Vh0O2mPpnJeZI2Fpb4Ooiuj9cgUdkvMD2N1+2nzAlXLwj0qlV0qvpFiZwOpYIOVb9gkdOlEvNPO5RyOlUKZGo1ar3uSr/1y4oEcLSyxsUQuna3sRcXDzYDIWx3w21xgSFet75CFe62llb4OVrh5+hk0vO1ej2dSoVRiA7ZGq43y/to6ZPRoVSg1uuo6+2hrrdn1Nd1sbHFz9GJAEcn/B2d8Xdywt/BsHV0xsfB0RzeN2PGRKbE5CJTOuGbMTNVEQSBVoWc2p5u6nq6qe3tpr63h3rZgLA0JX/SQiLBy94BHwdHvO0djeKy/9J/29naZsqEFwVBoEulpEneS7Pc4ObKZbTIZUPu61DKL6pAycbCcqhbaDW0OtvB4BZaW1gOLTwa5ELaDHEoLbGSSrGQjE+MCIgFQ2qdDpV+UGGQXotGp0dl2Kr1WmOxkEKrHci7NLizMrXqgvsu5kDvZGWNt71hv7J3xMfeUXQFB932tne8YgJ1Iqh0Wlr6+sR9qW9oZKD/0tQnQ64d+7snlUjwsXc0ClF/RycCnVwIcnYh2NmFACdnbCzMPo+Za5fx6DWz8DRj5jLQo1JR29stisvebmp6uqg1iMy63h6U2rHzKT1s7fBzHHBZAhyd8HMcONF52TtMuWIapVZLnUz8HGp7u6mVdVEv6xFTBAwCU6Uz3ZV0sLLCzcZ+oPjE4OSJDp4dbrbiY642tjhZ2eBkbYO9lRVW0qkjmCZCf8uqflHarVaJ4WuVwQHuD1/3O8SGbadKYaxyNwUPW3t8DELUz8EgvhxdCHISLx629lNm0dNPr1plWACKi8CGXsPWcLtR1otmjJ6tEsDHwVH8HJxdjKI0yFn8XHwdHKfcd9eMmcGYhacZM5cZQRDoUCqo6u6iuruTqu5O8XpPF1XdXXSPkVsplUjwc3Ak0HAiCnByJsDJ2Sgq/R2dsLWcemNjtXo9jX291PZ2UTtYYBpEZou8z6TXcbe1w9dedNj6c/B8DG6bt70jXnZioYjZVZpc9IJAj1pJu0JOi6KPpr5e46LgfAfalLQFe0srowgNcnQduG4QqI7WNpfhr5pc9IJAm7xvQJjKesXwvWFhWdvTPaZraiWVEujkQoiLK6EuroS6uBHiLG4DnZzNzfzNXPWYhacZM5cAQRBoV8ip6u4yCsuqnk6qu0Vx2atWjfrzHrZ2orA0iMv+bbCzK36OTlMqTHk+3Sol5d0dlHe1U9HdQXl3B2Vd7dT0do3pBjlYWQ0RIYGOogPkY+9kCOE6XFZBKQgCvRqVmBOqkhtdwe7/Z++8w9uqzy/+kWzJ8pZlW957xnFsZ9nZiwxWmAUClFEKtKVAKaN0/gp0l1VaaAuFQtk7QBIgk+w9bCd2vPeQbVmSZWtL9/7+kKPYiXeckIDP8/ixrOUr6ere8z3vec9rt7ozL10nYobcOZh9L1tcTvfUJJejNxNz9NOLfLx6I6G8enNHvXpzSL36REb1Xvb19sbPW96r7Pq5f/v4ofRRnFMVVxRF9DaLh4i2mXtoMRn7LDQMtJl7hi33h/v6kxwcQkpwKClKFcnBKlKDQ4kJCLpgFcETi9LGPkS0sbu/rWao74iXRHIaKU0MVpIQFEJ8UPAEKZ3AeYEJ4jmBCZwBHC4X9UYD1QYdNQYd1Xod1b2/jcOQy+iAQLdaEawkMUhJotKtXMQFBeN/gTTqDAaXINDcY6Sqq9P93nTpqO69rLUOHnUjl3oRExA0qNIV4nP2I21EUURrNdNiMtJqNtJiMvY2H5kx2HpLyzb333qrBecYCOP5hkCZT2+j0AlC6m4YUsp93X5Ev0Ci/YOJ9A9EcQ6Ivc3lpLnH6CGibtW7i4bey0MlLvh4eZEYdCohdf++EFXSvnAJAhpTDw291ZG6rhOLWT11RsOQNhxvqZT4ICXJyhBSlCpSQlTu30oVSoXvOXwVE/i2Y4J4TmACI0CXzUqVvtNDKqsNOmr0Ohq6u3AOokBI6EsuQ0gKVpLQq0DEBwVfkOXwgWCwWSjTdXBc18FxXTtlug7K9VqsQ2R7RvoFkKIMJTlYRUqwitTey1H+gWc9YsnksNNqNtJscpPKVtPJyy1mI62m7lF3sPt6yTwd3iE+vgTJFfh5y/qpkScu+3qUyRO3uycjySSjU6NERPdUIVevmtqrog6lrnY73N38J34MNsuoG4nCFH7uLm3/IKL9gk5e7v07zNf/rH+G3XYbtb1quVs9dy9s6oz6IX2+sQHBTFKFM0kVTqZKzSRVOAmBygtWIe0LURRpM/VQZzRQ32Wgtkvfa+VxE1PLEKQ0VOFLSkjoKaQ0lNjAC1c9nsD5iwniOYEJ9MEJglmh66RCp6VS7/49lL/Qz1tGSoiK5F714MTlpGDlN4ZcgtuDWdOlo0zXQZm+g9LOdsr0HbSaBp4u4+PlRVKQyqM6nSCYSUEhZ115cgoCTT1dVBs7qe7qpMao81zW2YbPbJQAat8AD6EKVfi5VUAfv5PxQT4niabvBfo5uwSBLrsVXS8JPaHiulVdM51WM+2WHppNbkJucQ3ftS2XepEQGEJKsIqUoFCSg1SkBLt/B8nHJ2h+qNdzQmmv6UNIa7r0dFgG/g77esvICAkjMyScSaFqJoWEk6kKH7dQ/PMBoiiiMfX0Lpw7qTHoPVWalp7Bp0P5eHmTGqIiXRVGWkgo6aow0lWhxAYGX1AZvBM4vzBBPCfwrUS33UaFrpNKnZYKvft3pb4Tjaln0MdE+QeSeoJg9papkpUqIv0DLrju2+HgEFyU67QUa1sp6tBwrLONSoN2UDXp61KSjHarm1R2dVJt1FFj7KS6S0ddt25IL1ygzOc0pS7aP9Dzd4Tvhe2jPRsQRRGD3UqzqatXKe6mpVctPmFJaLP0DNnZrvYN8BDRlCAVyUGhpASriPE/+0TGYLNwXNfRq863c7xXmR9s6laMfxCTQsPJCYsiNzySnLBIVIrRB9uf7zA57NT2ElFPNcego8agH/S98fX2JjUklLSQUNJUoaSHuAlpzAQhncAIMEE8J/CNhksQqOsyuE822g7KOt0l4eYhQqCj/APdB9PeA2pa72o/QH5h+y4HgyCKVHfpKO5opViroahDQ6mufcCTjp+3jIyQcLJCw8kMcRPMDFU4QWdZwRRFkVZzNyW6Ns9Pqb6NZtPgn6OPlzfJQao+RMdNduIDlWdFeXMJAp02E22WHjptpt5sTBsmpx2T88RUoxN/23ozM2309N7uGqax6lRIJBIUXt4EyHzw95Z7fnsuy+QEnPJ3sMwXtW8AEb6BZ0WldQguNKbuk4sAz6Kgc1DFEdxWhUkqNZNDIpisiiBbFUGaMuysN4q5BIE6o77XJtJBmb6d450dg+5XsQHBHhKaGx7FlNCIC943OhhcgkBjd1e/BXqFTku1QYd9kAWov0xGhiqczNBwJoWGkRnqvhz4DX2PJjA2TBDPCXxjYLBaON7ZQVmnluOd7ZR1dlCu6xx01a728z+lhOS+HOTzzT1IiqJIU4/Ro2QWaVs5pm2jx2E/7b5Bch+mhEWSGxbJlLBIJoeqiQtUnnVFQxBFao06N8HUt1HS6SaZg5XIT1XRTpR1x0tFO9G5rrF0027p7v3dQ5ulm7Y+lzusI58IdT4gUOZDpG8gat9AInwDieglpGrfAM/14Yrxm7LTV50+aX0YXJ32lkhJU4YxWRXhIaRZKjUBsrP//eyyWSnTdXCss41irYZibSs1XfrT7icBUpSh5IZFkhMeRU5YJFkqNQrvb25Ul1MQaDAaqNBp3ZYkvZZKXSc1hsGrDLGBQb1kNNzzOyHom+GtncDoMUE8J3DB4YSJ/mhHG0c72jimbaNU2z5omVzh7U2GKsxz0MsMDSdTFfat6OR0CC6OadvYp2lkv6aJwo5WOgfoKld4eZMdFtHvBJoYFHJOymY6q5kD7U0c7GjiSEcLpfq2AbMMvSQS0oLDyFK5SchkVQSTQtQEj5N6aXM5qTF2UtWtpdqodRMjYycNPfoReRvBnbEa5uNPmMKfwF6V0d+7V4nsVSA9yqRHqXSrkqONNBJEEYvL4VZOexVUt7Jq8yisJ9TVE+qrwW6lzTIyrya4iVWkXxBJgSpSAsNIDQolJSiM1KAwwhT+42IxcQoCdd26fmr2MV0bXfaBO9cTA0PICY1ihjqGmeo4MpTh52Q/7bJZOdrZRnFHK0VaDcUdrbQM4G/2lkiZFBrODHUs+ZGxFETGETqG2fMXGhwuF3Vdeo73WfiXdWoH9YCfOC5PDotgSrj7J10VNmFx+RZggnhO4LzGCVP80Y42jnVoPGRTaxk4kicuMLhfmWdSqJr4oOBvzcra6nRS1NHqIZqH2ptPI3EyqZRMVTi5YVFuRTM8kjRl2DmbH91s6uJAWxP72xvZ395IVVfnafdReHmTGaImuw/JTFeGj0uUT7fDRrVRS1WX1kMyq4ydNPboh+zwDpYrUCsC3eH0ioB+ymBkr1IYNo4K4dmCKIr0OGy0WXtoM3d7lNu2XuX2hKo7nIIbJFOQGnSSjKYEhZEWHEa035krzaIo0mwyelTv0l5C2mo+ncQEyxXMCI9hZkQcM9VxTFFFnjPy0mExUdyh8VQQirWaARd2qcpQCnpJaEFkHJH+I5sn/02AwWqhTKf1WJ3KdB2U67QDRj/JpV5khIaRHR5BTnjkBBn9hmKCeE7gvEKbqYeids2wJFMqkZAWEkp270o5OyyCjNCwb52XyOywc6i9pZdoNlLY0XpaA5DSR0F+RCz5kXHMiIhh0jksBYqiSLWxk/1tTRzoJZoD+efSgsOYqY5lengMOaFRJAepxmWxYHbaOabTUKRroaizmSJdCy3mwX2hQTIFKUGhpPUSqZSgUBICVET5BV2wnetjxQnPaqOpi9o+SnBVl5ZGk2FQku7rJWNySCS5qmhyQ90/MX7B46KOdlrNlOjaOKJt5mB7E4c6Tl9YKby8mRoWzcyIOPLVcUwLi8bvHOXiiqJIc4+RIx0t7Nc0sU/TSLlee9r94gOV/YhoXOD4vD8XClyCQL3RQKm2vVdUcB/rB8o+7ktGp/QS0gxV2EQY/gWMCeI5ga8NPXY7xR0aitpbKWxz/x6oXO51KskMjyQrNBxf2beLCIC7LFms1bCjuY5dLXUcbm85zVcV7utPQaSbaM6KjCMtJOycdZqKokiNUcf2llr2tjVwsL2JTlv/hYOXREK2KpKZ6lhmquOYqY4dl25hlyBQaeygqLOFQl0LxZ0tVBg7BuyyVisCekvGJ0vH41k+trucdDmsGO0WjA4rXQ4LRrsVo8NCl93Se5v7b6PDitFhxTHK7FAJECDzIUjmS5BMQZBcQbDnsi/Bvb+DZL3XyxUEePuMy+uzOh3Uduuo6nYT0ROqcV1P54A+v1Aff3JDo8hVxZAbGk2OKopg+ZlbXRyCi1Jdu2dRc6C9Cf0pXmBviZTJqggKIuKYG5VIgTrunMac6a0WDrQ1sbe1kf1tjZR0tp+2T8YGBDMvOoF5MQnMjU74RnbPDwdRFGns7jppoer9PdAIYR8vbyaHqcmLiCJXHcnUiKhvHXm/kDFBPCdwTuAUBMp1WgrbWnuJZiuV+s7TVBOpREJ6SChT1JEeJTMrLPwblYc5Goi9Hec7W+rZ1VzHntYGuk9pBIrxD6IgKs7jJ0sKCjmnB+Buu41dmjq2t9SyraXmNEXTp1eByu8lmtPCY8ZlMpPW2sOBjsZeNbOFY/rWAb2hkb6B5KiiyQuNJkcVTVZIxBmTHpcooLEYaTLpaTTp3b/NBs/fevvg05m+TsikXsT6KYn1DyHOP4Q4vxDP5Vj/EPy9z+xzcQoCtd2dFOtaPQpzmaF9wOlOSYGqXiIaxdTQWLKUEWescrsTGjo9JPTAAAq7j5c3BRFxLIxOZmF0EilBoef8+3KovZl9rY3s0zRR1NF62vszOVTNvOhE5kUnMDMy9luntp+AKIo0dXdR3IeMFrdrBlRGVQpfctWR5KqjPIQ05Fvg478QMUE8J3BW0Gbq4ZCmmcNtLRS1aTiqbRvQ0xMTENh7kIgiTx1Fdrj6nJXFzle0m3vY1VLPrpZ6drbUn2bOD5YrmNurjsyLTiA+UHlOT5yCKFKia2N7Sw3bWmo43NHS78Qpl3oxUx3rUZemhEaNi0fL5LBzQNvALk0tu9rqKO9qP+0+/t5ypqiiTpZ5VdFE+o3teCCKIs1mA8e7NDT2JZgmPS1mA45hRmVKwKM+BskUBMl8CZYrPJc9CqXc/bd8tM1FuL2axhMKaq+q2ldpdSus7t8jmcakkvsR6x9CvL+KWH8lcf4hJAeGkxkcgcJrbOTH6nRQamjzLBCKdC009JzeIR4sVzBbncjciETmRiYT7z8++3VTTxcH2hvZ29bA9pba03yiMf5BLIhKYmFMMnMiE856yP2pMDns7Nc0sbOljp3N9ZTpO/rdLpd6MT0ihvkxbiKaHXrmBP1ChiiK1HUZKGx3ixhH2lo5ru0YcP9OCFKSFxHF1IgopkdGk6kKnyjRnweYIJ4TOGM4BYHjne0c1rRwSNPCobaWAXMyA+VyD8HMVUeSGxGF2s//a9ji8wsOwcXBtma2NFazran2NE+Yj5cXMyJimRedwPyYRLJU6nN+4tFaTOxorWVbSy07WmpPK58nB6k8J+8Cddy4LB6cgkCxroVdbbXsbqvjSGfTaWXczGA1U8NiyFPFkBMaTUpg6JjeG1EUaTIbKDG0UKJvpcTQSqmhlS7H4FOOZBIpMSfUQj+lRzWM8w8hyjeYQJnivArTtrocaK09NJkN/Uh0k9n922Af/LV6SSSkBIYzWRnNZGUUWcooMoMjx6zE6WxminuJaGFnM4c7m+lx9FexYv2DmRORxNyIJGarEwhVnPmxQhRFKru0HnV+X1tjP8LiJZEwLTzGsy9nqyLP+WfYbu5hd2sDO5vr2dVSd1rnfJDch7nRCSyOS2ZxbDJqv4Bzun3nI2wuJ8e1He5qWruGoraB468U3t7kqiOZHhHD9MhopkVGT6iiXwMmiOcERg291cKRtl6SqWmhqL31tDnAUomEzNBwpkW4iWZeRBTJStV5dSL+OtFhMbG1sYYtjTXsaK7tVz6XANlhEZ5S24yImK/FalBr1LG+sYIvGyoo1Lb0u83fW86cyAQWxiSzMCqJuEDlGf8/URSp7u5kl6aW3W217O1oOI2MxPgFMzcyiXkRScwaIxkRRZFGk54SQ6ubaBpaOW5opctxupdMJpGSFhxBckCYRwGM7S1Rq30D8ZJ8c5SnboeVJpOhHxltMOko72qj03Z6+LuXREJyYDjZ40BGnYLAUV0ru9pq2dVWO+AiI0sZ0UtEE5kZHj8u5WeL0+FRQre11FBj1PW7PUzhx7LYNFbEZzAnMuGcd1eLokitUc/O5np2tritNqeWmaeERbAkLoUlcSnkhJ17ony+wmC1UNzRRmFbK4fbWjisaRmwRJ+sVLlJaEQ00yOjSQ0JnXgPzzImiOcEhoQoijQYu9jf2sSB1iYOaVqoNuhOu1+Q3Ke3nOFeSeaqo76xk37GAkEUOabVsKWxhi2N1RRpNf1uD1X4sSg2icVxycyLTvxaVuGiKHJc386XjRWsb6ig3NC/5DdZFcHC6GQWRCcxLSxmXE7CDsHF/o4GNjSVs6m5Ao2lv7qjlPsyW53gJhyRSWMqvwqiSFmXhj3tNeztqKVY34xxIJIp9SI9SM1kZZRb2QuJJi1IPeoS+KkQRRGry4HObsJgN2Owm9HbTeh7f7uvM42oFN4XEgkEyXxRyv0IkfujlPuh6v2tlPkR4uNPkMz3jMmxKIq0Wbsp0bf0kvVWSg0taAcgo1IkpAaFMzMsgdnhyeSHJxIoG33p+oStYremjl1ttZSdYquQS70oUCewPCaDZTHphPuOj+rX2GNge0st21tq2aWp6zdYIVDmw5KYFC6OT2dhdPLXYglyCgJHtRq2NdUOeBwJU/ixKC6ZJXHJzI9JOusTxS4kCKJIjUHXK5g0D3sumxkVy8yoGHLVUd/ogQBfByaI5wT6QRBFynVaDrQ2sb+1if0tTbSbTz/BnFglTu9dJaZMrBJPQ7fdxvbmOr5qrGZrU+1pIwPPB6VCEEWOdDTzZWMFXzaU09jT5bnNWyJldmQ8K+IzWB6bNm4lPavTwc62WtY3lbO5paJfULhc6sXM8HjmRiQyJyJpzA0nzSYDu9qr2dNRy96OmtPKyDKpFxlBER6l7kxIpiiKaKxd1PZ0UNPTQW1PB81mfT+SaRMGnp51tiFFQrDcF6XMD6XcnzBFAEn+4SQFuH8SA8LG5N0URZF2a/dJ1bjXnqC19U+l8JJIyFbGMFudxBx1Crmq2DG9x1prD7vb6tndVsvOtlpa+0RiSYBpYbGsiM1keUwGcQHKUT//QHAILva1NfJlQznrGyv6fX99vLxZEJXExfHpXBSbitLn6ynXDlU58ZZImREZw+LYZJbEpZCmPLdNVBcCTlTvDmtaOaRppnCA6p1c6kWuOtJDRKdHxnyjp9udC0wQz2857C4XxzraPIrmQU3LafEVMqmUHHUk+VGxzIiMmfDFDIGWHiMbG6rYWF/FXk1Dv3Khv0zG/JgklsQlsyg2mYivyZvlEFzs1TTwZWMFGwY4oS6MTuLi+Awuikkl2Gd8Gi26HTa+aqliQ1MZ2zTV/brPVT5+LI1JZ3lMBrPVCWOyFehtZvZr69jTXsOejhoaTP39XX7ecvLDEpkdnsT0sIQxkUyH4KLR1NmPYNaZtNT2aLG4Th85eirkUm9C5H79FMoQuX/vdf74SEenqgiiiNFhweAwo7eZMDjM6Hp/620mup0DT/7pCwkSon2VJAaEkRxwkpAmB4QTLB99pE+7pZtCXRN7O2rY01FLXU//4QC+XjJmhCUwJzyZ2epk0oLUo15wiaJIbbeOjc0VbGgqo1DX3waSpYxgeWwGK2IzSQsKGxeyJYgiR7QtrG8o58uGChp6DJ7bvCVSZkXEsyI+nRVx6V+b59LucnGwrclTVanu6q/mxQcqWRafyoqENKZHxJz3gw6+DjgFgbLODg5qmjnQ2sz+1iY6ThFepBIJk0LDmRkVS0FULDOiYgif6FUYFSaI57cMZoedI22tHjXzSHvrad3mft4ypkdGkx8dy8yoWPLUkd/aOKPhIIoix3UdbGyoZEN9Fcc62/rdnhwcwuLYFJbEp5AfEfu1TeCwu1zs0tSxrr6MjY2V/VTGQJkPF8WmcnF8OguiksathKi1mtjcXMH65nL2tNX1KyVH+QWxIsZNDqaHxY5a1XQKAod1DezQVLGno4ZSQ2u/aC4viYTckFhmq5OZo05mSkjMqEZS2lwOSrtaKNQ3cMzQRE13O41m3YCxQOAmH/H+oR7iFuenQuUTQEgfcunrJT+nipNDcNFlN7uJaK/y2mbp8pDm2p6OIZunVHJ/kgLCyQiKIk8VT15IPGrF6I6tLeYu9nbUsLu9hj3tNehOiZkK9fFnVngSc9TJLIxMJ9Rn9CfwVrORjc3lrG8qZ39HQ7+MzKRAFctjMlgRm0GOKnpc3v8TlpT1vZaUsj6WFAkwPTyWSxMyuDQhk0i/r29CUYPRwObGar5qrGGvpqHfYIkQH18uik9hWXwqC2ISv/VJIoNBFEXqjQbP+fJAazP1RsNp90sODmFmVCz50bHkR8USGxg0oS4PgQni+Q2H0WbzqJn7Wpo4pm3DeYppX6XwZUZUDPlR7i9NVph6YjU8BFyCwD5NIxsbqthQX0VTn/K0BJgeEcOy+FSWJ6SRHKz62rbTIbjY2eommxsaK/oZ68MUfiyLS+fi+HRmR4xf04TW2sOXjWV83nicA9rGfiQgOTCUFbEZLI/NYEpI1KgPzGannZ1tVWxuLWebpvI00pQaGM5sdTKzw5OZGZZAgGzk5TC93cQRXT2F+gYKdQ0cN7YMGOju5yUnMSCc5IAwD8lMCggn1k816lnrXzdEUURvN1HT00Fdj7YfIdVYuwZ8TJSvkryQeHJD4pmqiictMALpCD2kgihSaWxnT3sNuztqOKit7zczXgLkqeJYEpXBRVGZJAWGjvo16WxmtjRXsr65jJ2a2n6LnUjfQC6OzeTS+CymhcaMGzGoM+p7m/DKOXJKE9708BguS8j82kmoyWFne3MdG+sr2dxYjaFPVcvHy5t50QksS0hlWXwqYb4T6t1QaDP1nLSitTZT3tlxWh51lH8gM6NiyI92q6KpIRM2h76YIJ7fMLgEgaJ2DTua6tjeWEdhW+tp85ajAwLJj3KrmQXRsaQoVRNfihGgtLOdj6qO8Wn18X7laYWXN/NjElmWkMpFcSlf+4G7vlvPu5VFfFhztN92hvv6c0m8W4mZGT56lXEwCKLITk0N79UUsqm5op8qmB0S6Sl7pgaFjfq5XaLA3vZaPm0sZnPLccx9iEqwzJcFkWnMU6cwS52EWjHyE7vZaeOQrp792mr2ddZQYdScdh+V3J+8kHhyQuJJD4okOSCcCMWZKxl2wUmnrZtOWzdam9F92d6NfZQ+UAkSgmV+hPkEEeoTSJhPIKE+gQR6+57xNpqdNmp7tNT0tHPM0Eyhvp5KYxvCKadYpcyPGaFJzApLIT8smTi/kR9L7IKLIl0Tu9ur2d5WRamhtd/t2cpoVsZN4bK4KWNSQnscNra2VrOhqZytrVWYnCftEEmBKlYlT+XqxCnjEtN0AhpzN1/Ul7OuvoyDHU2e66USCYujk1mVlsfimJSvdWHvFAQOtjWxob6KjQ1VNHQbPLd5SSQsiEnimtTJLE9Inah0jQBdNiuHNM3s61VEizs0p4k7Uf6BzI9LYH5cIvNiE771VrUJ4vkNQGtPN9sb69jeWMvOpobTPJpJwSEU9JYA8qNjiQ0M/pq29MKDxtTNJ9WlrK4q7RfsrPRRsCw+jeUJqcyPSfzaJ4vYXE42NlbyTmUhuzT1nuvDFH5c2qu4jCfZBGizdPNhbRHv1xTSZDqpkOWoorgsLouL4zKJ9VeO+nlFUeR4l4Y1jcWsbTzWr2Elzj+EpVGZLI7KYKoqblQn8NqeDja2HmN3RxXHDE2nlc1TAtRMVSWQGxJHXkgCsX6jnwDlFFzUmtqoN3WgPZVc2rrR2rsxOs7uVCO51JtQeWAfMhrkIaWRihDSA6MIlo+FyFk51tVMoa6eIn0jRfoGzKd4W6N8g8kPTWFxZCazw1LxGUXjUqu5i680FWxpLWdfR63n8/GSSJijTuHKuByWRGWO6btmcznZoanhi8bjbGyu8JBQmVTK8phMVqVMZZY6YVwb/AYjoRG+AVyfmsP1qTnj1gg1VoiiSIVey4aGKjbUV1Lcp0s+QCbnksR0rk6dzOyo+Inm0RHC7LBT2KZhX2sj+1uaONzWis11clEpAXLUkSyIS2RBXCJTI6K/dRXGCeJ5AcLqdLK/tYntjbVsa6ijUt/fwB8o92FebDwL4pKYH5cwQTRHiR67jS/rK/m4qoTdLfUejUcu9WJpfCrXpGaxMDb5a/Nr9kWNUcc7lYV8VH0UXe+MagkwPzqJm9LyuCg2dVxLwC5BYEdbDe9WH2FLS6VHTQ+SKbgqMZtVyVPJUKrH9Nyt5i7WNh3ls4Ziqrr7kHy5L5fETOaK+FxyQ0ZXIq3r6WBD6zE2tpZQ2d3ffxvtq6QgLIWCsGTyQ5NR+YyuKcTmclDdo6Hc2EJ5dzMVxhaqezQjUi69JV4eMhjqE0iYPAiF1+h8di7RhcFh8hDcTptxRA1FAJEKJelB0WQExpARFENGUDRhPqM7bjoEF8cMTezvrGG/toYifSNO8WRpO8Dbh4URmSyPymZ2WCpyr5E3T+lsJr5oKuGzxmKK9c2e6/285SyPnsTKuBwKwhPHFBXV47CxtqGU92qOUKw7qbImBIRwQ/JUrk3KIWwcVVCA6q5O3qsq4sNTvqfzopK4KS2XpXFp54VVo6ZLx8dVJayuKu1nIYryD+SqlCyuSZ1MesjoKxffZlidDva3NrO9sZYdjfWU6/oPCAmU+zA3Jp6F8UksiEskJvCbzV9ggnheEBBFkWqDjm0NblVzb0tTvxWUVCIh17OCSiJXHfmtW0GdKZyCwI7mOj6uKmFDfSXWPu9vfkQsV6dmcVlS5rh1eZ8JnILA5qYq3ig/zE5Nnef6CN8AbkjN4bqzoKS0mo0edbOlT5TNjLA4VqVM5ZLYzDGV5QRRZFd7NW9V72d7W2U/kr8kKoOVcTnMi0gdVQd6fY+2l2weo6IP2fSWSCkIS2Fx5CRmhaUQ6zdy/63ZaaOyu5WK7mY30TQ2U2tqxzVAs5G/tw8pAZGE+wT3K4GH+QQRKnf/HSTzOyv2FqvLga5XWe20GU9TXRvNnTRbOgd8bKg8kIygaNKDYsgIjCYjKIZIxchzUy1OO0f09exsr2STpoR268n9JMDbh0URmSyPmsKssJRRkdDa7k7WNBazprGYJrPBc320XzCrEmdwbeJUVGMoxQOU6DW8V32ET+uP0dNHBV0anc6qlKnMiUgaV6XvRGXi3cqi0767N6blcVNa3nkxiUgQRQ62NbG6qpS1tWX9/OFZKjXXpE7mypRJ58W2XmjQ9HSzvdcKt6upAb21v1c9RanykNBZ0bHfSLvDBPE8T2G02djdXO8hm809/YO1I/0DWBCXyML4JObGxKP8lntGxgJRFDnW2cbqqhI+qynr54dMDg7h6pTJXJWaRfw4TOUZD3RazbxbWcTblUdoNrlP6hJgcUwKN6XlsWicvWOiKHJI28R/K/axsbnC0yiklPtydeIUbkjOIy04fEzP3WW3sLq+kHdqD9JgOhn7MiM0gSvic1gRnTWqmdkesqkp6efXPEE2l0Vlszgic8QRQXbBSaG+ll0dZezvrKTe1IF4WgsBKGX+p5G1aN+QETfcfB3ocVip6D6p0pYbm6k3dZzm3wQIkvmRq0xgdlgmc8MzUStGVj0RRIEifSMbWo+xqbWEDtvJ41eAt6KXhGYzOzwF2QhjpERR5Iiukc8ai/myqcQzaUou9eLimMncnJxPjipmRM91KsxOO+saSnm3+ki/eKb4gBBuT5vJd5Jy8R/nzu/6bj3vVRXzflURWqvbfuEtkbIiPp1bM6aRr447L7z3VqeTLY3VrK4q4aumGk9EnFQiYX50IlenZrEiIW2iM34McAkCRzva2N5Yx7bGWo60tfZryJR7eVEQHcfCuEQWxiV+Y5qUJojneQJRFCnTadlSX8PW+hoOt7X0awqSS73Ij45lYa8vJF01Pvl030a0mrr5uKqEj6tKqDKcVH9UCl+uSJ7E1amTyQ2LPC/eX7E3P/CN8sOsqy/zdOmG+PhyQ2ouN6fnjbu66RQEvmwq47/l+yjqcxIuCI9nVcpUVsRm4jMKxaovSg2tvF1zgHVNRz2qcqDMh6vj81iVNHNUncytFgPrmovY2HqM8gHJ5mQWR0waMdlst3axR1vO7o4yDuiqTsvmDPcJIiMohvTAaDKC3CRT7RN8xvuJU3ChsxuxjiALtC+kEgkh8kD8vc980Wlx2qnqaXUT0V5Vt6anrV/pHCA1IJI54ZnMCcskWxk/olL3UCQ00FvBoshJXBw1hdnhKSMm7FaXgy+aSni75gDHDCf30WxlNDcmz+DS2OwxBeMDlBnaeLe6kE/qj9LdO7I1SKZgVcpUbk2bQZTf+J5f7C4XXzSU80b54X5e0ExlOLdkTOOqpMnjTnrHCr3VwtraMj6uKuFw+8n33c9bxsWJ6VybOpk50ePrlf02octmZVdTfW/PRh0tpwhO0QGBLIxLYnFCMvNi4y9Ysj9BPL9GWBwOdjU3uMlmQ81pO1myUuVWNeMSmRUdh6/smye5nys4BBdbGmp4t6KIrU21nlWlj5c3y+JTuCZ1Mgtik84LnxW4fUGf1ZXyevlhjulOlotzQ6O4NWMalyVOQjFG8jcYuu1W3q8t4rWK/Z5yulzqxVWJU/heej7pY1Q37S4n61tKebvmAIW6PifW4AhuTJrJ5XFT8PMe2QFUEAX2aWt4r34f29vKPSqdl0RKQVgyyyKzWRw5CeUIyKZLFCjtamR3Rxm7tGVUdvfvqg7zCWR2WAZzwjLJUSag8hl9HI5LcNFpN9JhM7h/rAbPZa3NQLvNgN7ePaCaOlL4efkQ5qMkvM9PmI+ScIWScJ9gwn1C8PcevUXELjip6m5lf2cluzrKKOlq7LedQTI/ZoWmMyc8k1lh6QTLhn/PBVGgUN/AhpZjbNKU9Gsci/UL4br4fK6Kmzaq8PpiXTNv1xzg8+ZjngisYJkv1ybksSp5JnH+IaN41SdhcTpYXVfMfyv2U9vtVuW9JVIui8/ijvR8slVRY3reoVCia+PN8sOsri3ptzC7NiWbW9KnkRI8+oips4U6o57VvX7Q+j6d8bEBwdyQPoXr0qcQ5f/1RUhd6BBFkSp9J9sa69jWWMe+lkbsfbJY5VIvCmLiWBKfzJKEZBKClV/fxo4SE8TzHKPRaGBLfQ1b6mvYc8qO5OPlzdzYeBbHJ7MoPpG4IOXXt6HfENR26Xiv4igfVh7rV0rPj4jlO+nZXJKYcV7NM67v1vNmxRHeryr2hLzLpV6sTJzErRnTyQ0b/5Nds6mL1yr2835NocfnpvLx45bU6dyUOn3MjRYt5i7erz3IB3WHPcHhMomU5TFZ3JQ8k6mqkZcSjQ4La5oKeb9+H/Wmkyr1zNAkLonOYXHkJEJG0Knd7bCwV1vBbm0Ze7UVGBwn9wkJErKCY5nTW1ZODxx54LggCjSaOzhurKOkq5Y6k4YOmwG93ThgCftUeEu88PUa3X7oEl2YXbbh7wj4eSkI9wkmyjeUzMAEJgUnkhkYj98oCKnBburz3pX3a2SSIiFbGc/ssEzmhWeSEjB8xcAlChTqGljfepQvmos9z+cj9ebi6BxuSMwnK3jkpXOdzcRHdUd4t+4gLWZ3Y4wEmB+Rxk3JM5kfkTomJU4QRb5qqeSViv3saz+ZGFEQHs8dGQUsiU4bd4Wvy2blg+pi3qw4Ql33ySlc8yITuSVjGhfFpp43Pn5RFDnc3sLHVSV8VnPc4weVSiQsik1iVXouS+KTz5tF/YUKq9PBnuZGtjbUsqW+hsbu/lm7yUoVSxKSWBKfwoyomPOi+XUwTBDPswyHy8VBTTNf1dewpaGGKn3/MWYxgUGeFcvsmLhvpJH4XMPqdPB5XQXvlRezV9PouT5M4cd30rK5ISPnaw12PxWiKLK3rYFXjh9gc1OVh6bE+gfz3YypXJ+Sg0ox+vGFw6Gws5lXyvfxZVOZRwFODQrjjvR8rkzIHvO+WKxr5uXKXWxuKfOQrghFINcnTee6xOmEK0bekFBh1PBe/T7WNRdh7c3w9Pf2YWVMHtcn5JMcOHwHvSAKHNRVs6b5ANvaSnD067xWUBCazpzwDGaHZRAiH9m2mZxWyoz1lBrrOG6s47ixnh7nwFOA3B3swYT1qo9uVdJ9OcwnGLUihGCZ/5h8oRaXDa2tiw6rng5bVz8lVdurrA62XVIkJPpHkRWcSFZQIpOCEonxHZmFxym4ONbV4FGLa3pOmdjlH8HK2JlcHDUV5QgWBBaXnS+ai3mvfl8/28QUZSw3JBSwLGryiKOZXKLANk0lb9ccYFd7tef6eP8Qbk2dxTXxU8ccf3ZU18qrFftZ11DqiXtKDFBxR0Y+VyeOXLkfKQRRZEdrLW+UH+53bIjxD+J7mTO4PjVnVF7osw2L08EXtRW8W1HEPs3J6ka4r7/72Js+haTz6Nh7oeJEw/EJEeugprlfdmiATM78uAS3iJWQjPo8G+k5QTzPArpsVrY11LGproqvGmrp7tMR6CWRMD0yhiUJbrKZ9g0xC58PKOls493yYj6pLu236l4Yk8SqjBwuik85r1bdDsHFuroyXj6+v185fWF0MrdmTGNRdPK45m6C28y+sbmCV8r3cbjz5IlhXkQSd2QUMD8yeUzqjSiK7G6v4T8VO9mnrfNcXxCWyE3JM1kSlTlihcYhONmsKeW9uv0c0Z9UmFIC1NyQWMBlMbn4ew+vDmosBta1HGRt80E0VoPn+kR/NXN7fYo5ygS8h9knRFGkydJBaZebZJYY66g3aU4rj/tIZaQHxpMVlEB6YBwRChXhPkqU8oCvtdnI4rR5yvv1Zg3Hu9yEud2mP+2+wTJ/JgUlkBWURFZQIulBcSNSYlstevZoy9nVUcZBXZUnUspb4sUCdRYrY2YyMzR1WE+oKIoU6Rt5r34fG1tLPB7TELkfV8dN5zvx+UT7KUf82mu7O3m39gCrGwo9fk2V3I9bUgu4MWkmwfKx+WNbzUbeqDzIO9VHMPY2OSnlvtzY6wNV+45/ibmxx8BbFYW8V1WEvjeSKUAmZ1VqLrdnziA24PyKzavp0vFeeTEfVZX0qzbNiozjhowcLk1MnxBaxglGm40dTXVsqa9hW0MtWkv/rOA8dRRLE1NYmphCxnnQHzJBPMcJjUYDm+pq2FRXxb7Wpn6rD5XCl0XxbkPwgrjE8yKS55sCo93GZ9WlvFtRzFHtSfIWGxDE9ek5XJeWTXTA+aOAg3th8k5VIa+VHUJjdvt6FV7eXJsyhTsyZ5wVH5fN5WR13VH+U7aXuh636i6TSrkiPps7MvLJVEaM6XldosCG5uP8p2Inx7vcSpW3RMrKuBy+lzabtKCRZ3q2WY18VH+AjxsPerx/3hIpiyMncUNCAdNVicMeMO2Ckx3tpaxpPsD+zioPOQzwVrA8Ko8rYmaSETR8+dbmsnNIX8HOjmL260rp6lOSP4FIhYpJQYlkBSWQFZxEsn/0sCT2fILW1tVrDXAT6sruxn5qMIAUKZOCEpgXnsO8sBwifYdXq7odFja0FrKm+SDl3SczOCMUwVwWPYPLY6YTNYLn6bT18HHDQT5sOEBbbzSTFAkLIjK4PqGAWWHJIyb0Zqed1Q2F/Ldyt6cM7+8t54akGdyWMmvMRNHksPNRXRGvlu+nwWQA3NaYqxOncFfmbJICx1/dszodrK4t4eXSA1Qb3bYTL4mEi+MzuCsrn7yw6HH/n2eCwfz1QXIfrkzJYlV6DtlhYzv+TOB0CKLI0Q4NW+pr+Kq+huKO/tWI2MAgliamsiwxhfyoWGRfQ0l+gniOEYIoUtSuYVNdFZvqqk8LhU0NUXk+3Dx11LgrV99miKLIwbZm3q0oZm1NmceEL5NKWZGQxqqMXOaeh52VDd0G/lt2gPerijE73WXjMIU/t2dO56a0vLNSTu922Hin+jCvlu+n3eomc8FyBTenTueW1OljPuHaXU4+aSjilcrdnjgkXy8Z1yVO47bU2UT7jVx9Ketq5fWanaxvPebJxQz3CeSa+BlcGz8DtWL473N1t4Y1zQf4svUIXX0mA00PSWZl7EwWqofvcjY7rezXHWdnRzH7OkuxCie7zOVSGemBsZ6SdFZQIqpRBq6Dm6jr7V202zppt+rosOlot3XSYdVhE0bX1S6RSFDJglErQgn3UaH2UXkuK0bpF4UTzURNHDfW9VoI6umwGfrdJy0glrnhOcwPyyHef3iyUGFsYU3zAda3FtLtPBGcLmGGKoWVMTNZoM4atoTuFFxsby/nvbp97Ous8Vyf5B/GLclzuSwmd8RleIfg4svmEv5TsYtKYzsAMqkXV8XnckfaHBIDxrbocwkCm1oqeLnsZCVBAlwSN4m7M2cz5Sw0IgmiyLaWGl4pPdAvE3RGeCx3Zs1kWWzaeXfeaekx8kHlMd6vKKap52TOa3ZoBDdm5HBFStZ55bn/JqDd1MPmercotrOpoV8GeKDch0XxSSxNTGFRfNI5E8UmiOco4BIE9rU0saaqjI11Vf3kbC+JhJlRsR45OzF4bJ2UExgcJoedd8uLeauskOquk17ZNGUoN6TncG3a5LNC3s4UZfp2nivexfrGk1mYmcpwvp81kysSs8YcTTQUuh02/lO2h9crD3pKjJG+gdyZMYvrk/PGHM9iF1y8U3OAVyp30XGCyMp8+W5KPjcn5xPiM/L3v0jfwD8rtrBPe9KHN02VwA0JBSyJzBrWFuEQnGzUFPFRw15KjSe9vOE+QVwWPZ3LYmYQ6zc0kbC7HOzpPMbmtsMc1JXhEE8elMN9lMwNm8K8sByyghNHnDfpEBw0mFvRWDvQWLVoLFo30bR1orXpT4soOhsIkgWg9glF7aMiQhFGZO9PrF8UKvnIFwVtVh17tCXs1BZz1FDdr1Eq3i+CheF5LIucQZTv0NNsbC4H29pLWNN8kIO6qj7b6cfFUVO5IX4u0SMI9K/t6eD9+v181nQEk9O9X4f6BHBL0lxWJRaMOEJJFEW2tVXycsUuDnU2AG6iuCImi/snLSYpcOzTeQ52NPJi2W62tJx8nXMjknhwykLyQseWMzocSnVtvHL8AJ/VlXpyNuMDlPxgcgHXp+acVxYj6B0c0VLPu+VFbKiv8sTEKby8uTw5k+9PnkFW6NgmoE1gcJgddnY2NbCprorNddV09gmv95ZKyY+K5ZLkNC5NySDU9+ydSyeI5zAQRJEjbS2sqSpjXXUFHeaTJbcAmZyF8Uks610tTIS4nx1oLSZeKz3M66VHPJ3eft4yViZnckN6DtPUI+8+Ppdo6unimaIdrK455jldL4xO5s5JM5kXNXzZeCxwCQIf1BbxzNFtdNrc+2pKYCh3T5rNFfHZY+50FEWRLa3l/PXYRo/CGekbxO2ps/lO4jT8R9FU0WDq5O9lG9mkKQHcUUjLoiZzW/I8JgUPXyZ0iQLrW4/wSvVmWiw6z3PMD5/EypiZFISlD+klFEWR8u4GNmgO8FX74X7NN9G+YcwPy2FeeA4ZgfEj+owEUaDe1ExRVzlFhjJKjVXYBceg95ci7W0yciuUah8V4T4q/EaZx+kSXXTaujyktsOmo93aidk19NjMGN8IcpWZ5AZnkh2cNuL/a7D3sFt7jJ3aYo7oK/oR6JzgFJZH5rMgPBffYfy3LWYda1sOsq75EO02d9nbSyLl8pgZ3JF80YhC6nscVj5uPMRbtbs9ZfhIRTD3ZSzlkpicUXlqD2kbeLlyF1s1FYDb3nFT8kzuyVw4Zg8oQLmhnZfK9rCmocSTyXxF/GQeyV1C9DhngZ5Au7mH/5Uf4s2Kk8fK5CAVD+ct4JL4jPPyOKmzmlldVco75UVU9slVXhiTxA9z8pkdNbLv4QRGB5cgUNinatt39LaXRMLc2AQuT81gRVLauCuhE8RzAIiiyDFtG2uryllbVdZvalCwj4JLk9O5JCWdWdFx53VkwYWOBqOBl44d4P2Ko57yQFJQCHdmz+CqlCwCztOSjN5m4YWju3m9/LBnJX9ZQib3T5lLRsjYsjBHgt1tdfzhyEbKutwlxMQAFT/LXcyymIwzsh1UdLXxp6Pr2dtRC0CYjz/3TVrMVQl5oxplabCbealyK+/X78cpupAi4cq4adyZupAYv+ErBKIosrX9GC9VbaTO5H6NKnkANyTM4/LoGcPOWjc7rXzeupcvWvfSYD7pewr3UbI0YgaL1VNJ9I8a0UlOa9NTZChz/3SVYXT09Ls9yDuAaF+1R2lUK8I8pXCVPBgvydk7bpicZtqtnbT3lvHbrFo0Vi2tlg401v4TiqRISQ9MdBNRZSZpAYkj8qn2OCy9SvEhDusrPF5ahVTO/PBcro6dT1pg3JDP4RIF9ndW8m79TvZ3VgIgl3pzTdwsbk1aNKKEAYfgYl1zEf+q2OwhoJOConlw0gpmhiUP+/i+qOhq49nSLR4CGizz5b6sRdyQOOOMoouaTAb+fmwHH9cVI+KOzbsrYxZ3Z84+a8HwZoedd6uKeP7obs9s+NzQKB6dtog5kQln5X+eKU7EMr1acoh1deWe6lBuWCQ/zClgRcL5Zx34JqG+y8CG2ko+qyrjaB9fqFzqxYL4RFamZrI0MWVc9tkJ4tkHFTota6rKWFNVRl2XwXO9v0zG8qQ0VqZmMC82cYJsnmUc07bx4tF9rK29sA4+Zoed/5Yd5MWSfZ7y9qyIeH4+bdFZNfzXdnfy58LNbGpxn7yDZArunzyfm1Onn9G+arRbeb7sK96uOYBLFJFLvbg9dTZ3p8/DXzZy0m9zOXinbh8vV22jpzercW54Gg9kLictKHLYx4uiyN7OCl6q2kCZ0d2sEujty61Ji/hO/GwUXkMfCI0OE6ubtvNJ8w6PuukjlTEvPIflkfnkKVOHVcjMTgtHuyoo7lU1my39DfsKqZzJwenkKjPIVWYS5zsyAnuu0eM0c6yrwkOaW60d/W739VKQHZTmIaIxvhHDvo4Oq4FNbQfZoNlPk+Xk881UZXJj/FKmKFOG3a4ifR3/rvySQkMdAH5ecq5PmMdNCfMJlA2vOlpdDt6u3cMr1ds9JfgF6gx+krmclBFEbvXFrrZq/nx0PVXd7teSERTBb3IvZXpY/Kie51Qc07Xyh8JN7O9wl/bVigAeylnENYk5Z82P3m238Z/j+3m5dL/HV74wOplHpy4kS3X+NvTUG/W8dPQAH1Qe84gOycEh3J2dzzVpk8+KPWkCJ1Fr0LO2uow1lWVU9FFCFd7eXJSQzMrUTBbFJ405leBbTzxPvMFrq8r7NQj5eHmzNPHM3+AJjAyiKLJf08QLRXvZ1lzruX5hbBI/nHJ+l1scgov3q4r5W/FOT2zIpBA1j05dxMLopLO23d12K38v2cHrlQdxigJeEgk3p07n/snzR+W1PBWCKPJZQxFPlWzylOuXR0/iZ9nLifFXjuJ5BL5sOco/yjfRajEAkB4UyU8zVzA7PHVEz1Gor+XFyvX9CMkNCfO4KWEBAbKhyz9am4EPG7exrmW3p1EozlfNtXELWaSeNuw0H4vTynbtAba276eiuw6Bk0kVUiSkBiaSG+wmmumBSSP2gA4Em8uGTRhZGPwJSJAQ4B1wRvtXu7XTQ0KLu8rpdvbv3g+VKykIzWV5xFwS/If2J4qiyHFjPZ817+Sr9iOe9ys7OIkb45cxU5U55LaKosi+zkperFrfb4Hx3aSFXBc3B98R2Dl0NhMvVX7Fhw0HcIoCUiRcEz+DH6YtJkwx8kY6pyDwft0h/l66xTMTfmVcDg9nL0U9iuc5FaIosqG5nD8XbvZ0wU8OieT/pi5nRvjQCvGZoMNi4h9Hd/F2RSFOUUACXJk0mYdy5xMXqDxr//dMMZDNKtzXnzuzZ/DdzLzztur1TUJ5Zwdrq8tPE+QCZHKWJ6Vy+RgEuW8l8Ww3m1hTWcYnlaX9JGWZVMrC+CRWpmRyUWIKAfILcw7qhQRRFPmqqYYXivZysM19svGSSLg8OZMfTik4rw3moijyeUM5Tx3ZRm3vdJG4gGAeyl3AFUlZZ03FEEWRT+qP8eeizWitbqKwKCqFX+QtJTVo7E0R4J6l/ruizz2jLZMCQvlV7iXMVQ+vWvXFoc46nj7+BaVd7nnOakUQ92Ys5bKY3BHN9y4zNvFi5Qb2drrLnnKpN9fGzeKWpOFLsC0WLe83bGGDZr8nHig1IIabEpYxJ2zKsP+/uqeBDZqdbO84iLUPGYxSqD2K5pTgdPy9R0funYITrU2LxtqGxqrp/e3+0TtOz9QcCXykPkQo1EQqIolURPT+uC/7e48uNFoQBWpNTR4ietxY3a/ZKiMwieWR85gbOg2fYVTmVouW9wb4DG6MX8rc8JxhPbjb2kt4qWoDtX0sFbcnL+HK2HzkIyD49T1anivbwJa24wD4esn5Xso8bkmei+8w294XepuZZ0s382HdYUTcEUz3TlrEzcn5Z9SwY3M5+V/lAV4o3UVPb3Xk6sQp/Dx3CWGjGLAwWtQZ9TxdtJ01de73RSaVcnP6VO6bMpfQ87Ax8wRMDjvvlBfx8rGDtJrc1rdguYLvTZ7G7VnTCZnorzjrOGFBXFNZxtrq8n4jvoN9FFyaks7VaVnMiIoZ9tx33hDP7du38+STT3Lo0CFaW1tZvXo1V1111YgfP9wL6bHbWV9bySeVx9nVVO8p4XpJJMyJTWDlWTLRTmBguASBL+oqeKFoL6U698nFx8uL69Km8IOcfOLP41U4wO7WOv5yZBtFne753qE+ftyXM4eb0qaeVStGqV7DY4fXc0jbSwwDVfxm6nIWRo2OGJ4Kg93C30u38F7tIQRE/Lxk3JO5kFtSZ43Kx6mzmXjm+JesbS4E3FOGvpcyn5uTZo/ohF/b08Z/qjbyVfsxwN10sjJmJt9LXjJs00ltTyvvNmxia/sRj49xSnAyN8YvZcYwapvVZWNHx0E2tO2kqqfBc320Qs3yyLnMDp2KWjGyqB2Ly0Kdqb4fuWy1aOiwdfRTTc82Ar0DTyOjsX6xRPioR6SS2lx2jnVVsLl9D/t1xZ6oK38vXxapC1gROY84v6FjgrS2Lj5q3MraU1TnG+Iv4qKI6UP6SV2iwIbWQl6u3uRpIotUKPl+ylIujpo6Ii/qEV09Tx//kmMG9/clxjeEX2avZK46bdjH9sVRfTO/K/qco3r3Qio1MJzf5F5KfnjiqJ7nVGitJp4+upUPagoRgQCZDw9kL+CW1DPzlQ6Ho50a/npkKzta6wA3ob57cj53Tso/a77T8YDd5eLT6lL+VbzPk2zi5y3j5sw87sqeQcTEbPhzgr5N12uryvsl/MQEBHJlWhZXpU8iXTWwEHLeEM8vvviCXbt2MX36dK655ppxIZ4Ol4sdTXV8UnGcDXVVWJ0nV+9TI6K4Km0Sl6VkEHaejZP6JsPucrG6uoR/Fe2j1uhWefxlMr6bOZXvZ88gwu/srfbHA8d0Gv56ZBvbW9x2AD9vGXdl5XNXVj4Bo/A9jhZddgvPHt3GW9WHEUQRP28Z92bN43vpBWdEdAVR5KP6Izxbshl97zz1S2Oz+Vn2MiJ8R25ZEUSBTxoP87eyDRgdFiRIuDZ+BvekLxm26Qeg3drFi1Xr+bLFTRolSFgRlcf3U5YOG4l03FjPu/Wb2N15zHPdTNUkboy/aFh/Ya2piQ2anWzrOICltxvcW+LFrNA8VkTOY3JQ2ohIWru1g0JDEYWGIsq6y3ENEpkkl8r7EcETlyMUagK8R7fvD6agtlo1GByGQR+n9lGTp8whT5lLRmA63iNQEPX2Lja37WFj227abSc9X5OCUlgeMY85YVORSwe3IxkdJj5p2sEnzTvodpp7tyOE6+IWc0lUwZAKqkNwsqb5IK/WbEFrczcQxfuF86O0FSxUTx728xFFkQ2tx3j2+Ho0VncX/fKobB7OumREGbEncOK78kzJJgx2t1d4LN+VgVDU2cxvD63nqN69kE0PDuexaSsoUJ/dRqCdrXX85fBWjurcwx/CFH7cnzOXVal553Uvg0sQ+LK+kheK9lDS6RYu5FIvrkvP5odTCogPUn69G/gtgksQ2NvSyCeVx/miuoIex8k84qwwNVenTeKKtElE+J88vp03xLPfP5JIxkw8DQYD1RYTn1YeZ01VGbo+OVVJwSFclT6JK9MmTeRsnmNYnA7eLS/mxaP7+5VK7pg8ndsnT0Ppc36XShq6DTxduJ1P60oBd4nqprQ87p0yl3Dfs7dwEUSRj2qL+GvxV+hsvcQwbhK/zFtK1BlGsoyXilPV3cbvj35God6tFGYERfLr7CuYEjK8Z80puHi/YRevVG/C7HIfsBaqJ3NXyjJSAoduPDrWVcPrtV9yxOBuqpIgYX54DjfGLyU1MHbQx9lcdnZpD7G+bRcV3Sf9xJGKMJZHzGOxehZK+dDKiUt0UdVT7Sab+iJarK39bg+ThxHjG+0mlr4nSWaILOSceJWtLittfUr5GquGVmsbjeZGnH3K5wqpginBk8lT5pKjzCFINvTrFkSBQsNxNmh2cUB31KPgBnj7s1hdwPKIucT6Df65mZ1W1rXs4cOmr9DZ3ccBpSyAa+MWcXXM/CEJqNXl4OPGPbxeu9UzJKAgNJ2HMq8gzn94i4nZaeNfFVt4u24vLlEgwNuHezOWcl1C/ojsHycwUHXgR5kLuXWU1YFTcSIK7anir9D3EtuV8ZP5Rd5FRJyFEZwnIIgin9eX8VThdup6LUPxAUoezlvA5YmTzrtBHH0hiiJbm2p5vmhPP6vWyuRJ3JNTQIbq7KWITOB0WJ0ONtVV80nlcbY21HomOEqAOTHxXJk+iUuS0xGttguTeNpsNmy2k/4ro9FIXFwcc176O83Ok9eH+fpxeWomV6dPIic88rxtUPmmwmi38cbxI7xy7CCd1l6lw8+fu7JnclNG7nlvDjfYLPzj6G5eLz/kCWa+IjGLh/LmkxB4dhcvxboWHj+0nkKdmximBYXxf9NWMCci8Yyed7x8axaXnZcqt/JGzS6cooCvl5x70i/ixsSCEZVBD+tqePr4p9SY3D7r7OB4fpq5kqzgoQmrxqLj5Zo1bOsoBNzl+IsiZrAq/iLi/Ab3BOvsXXzSvInNbXswuyyexxaoclkROY/s4PQhu9tNThNHu45RaCii2HAUk6vvAAkv0gPSPEpipO/w3fpfB6wuKyXGUgoNxRQZiuhynJweI0FCSkAyecpccpU5xPnGDnm87LQZ2Ny+h42aXWjtJz2qk4PSuDp2GdOUWYM+3u5ysF6zn/cbt6Cxukumap8Q7ky+nEXqqUP+X5PTypt123mrdhsO0YVM4sXNSQu5LWnRsAkH4J6U9Ydjn3G0t/yeFRzDb6ZcMaIM2b4YLz/0qdDbzDx7dBtvV5/8ft43eT63p888q0HwDsHFu5VFPFe8y+MdzwmN5FfTl1AQcWYd/ecC+zWNPF+0l21NJxeTKxLS+HHuLHLDx39y1ASGht5qYV11OZ9UHOeg5uToXB8vbxaER/HyNasuPOL52GOP8fjjj592fdzTv8M/MICLk9O5Mm0S82ITzqpXZgIDo9Ni5pWSg7xeepjuXuk9LjCYH+UUcG1qNgrv8zsOw+Zy8kb5Yf5xdLenm3J+VCKPTltEturskgq9zczTR7fybvURt+/LW8792Qu4NW3GGZ14BFHkw7rDPFOyydOpe0VcDg9nLyN8lA0NO9or+NOxNbT0dqsvjpjEo5MvI9J3+PDvTls3/6hYx/rWQgCUMn9+nH4Jl0ZPG5L4mZ1W3mnYzEeNW3GITqRIWBFVwM0Jy4hQDD71psvRzeqmjXyh2e4Jdo/wCWVZ5DwuUs9CKR/8wGd0GNml3UOhoYiK7sp+Hk1/L39ylVPIU+aSHZw96mYjcCs2BocOjbUZq8sy/AP6QCqREioPR62IRi4dvS9PEAXqTPUei0C9uaHf7aFyFXnKXPJVM8kITB+UDLpEgSP6UjZodnJIf8zjr80ITOKm+JXkKDMG3Qan4GJL+yFeq/3CM54zKyiRH6VeRWbQ0GXmRpOWp8s+Y19vA1qUIoSfZq5kvjpr2NfuEgU+ajjI38s20uO0IkXCjUmzuCf9IvyHCcDvi4ESIFZEZ/GLnBVnXH4/pmvlscPrOdLpPmmnBoXxf9OWMzci6YyedziYHHb+e/wA/y7Zh8npPnYvi03j59MWkRI8tpGi5xJHtRpeKNrLl3UVnsTaedEJ3Js3m1mRcRPi09eARqOBTyvLWF1RSrVBh2Cx0vjQby484jmY4vnWoX1cOWXsIwEncGbQmLp56eh+3i4vxtKbG5emDOWe3FlckTzpvF8EnOhU/8vhrTT0GAD3eMtfTF/MwujRBVKPFi5B4L2aQp4+utXjIbsyIZuf5y4Z80z1EyjvauOxwrUedWas2YRtViNPlnzumToUqQjm59mXsShi0rCPdQouPmrcw3+qN2Jy2pAg4erYAu5OW06wbHDS5hIFNmoO8N+adegd7vJsnjKVH6ZeRUrA4BE/3Q4Tn7ZsYl3LVk9jS0ZgEtfFXcJU5aQhSW5NTy0b2zazX3egX2k6xjeaPGUuecpcUgKSRxwEL4gCOrsWjbXZ/WNp9ly2CUNPGhoOEiSE+qiJVMR4fqIUMUQoovHxGnmzpM6uo8hwlEJDESVdpTjEk9OX4nxjWRqxhNmhs/AZYg681qZnbctX/Uh+dlAaNyZcTlbQ4BFaVpedjxq38m7DZs9ntTRiBnckXUa4Qjno4050wP+tfA1tvf7NuWGZPJh5xYhGcGqt3Tx1/Au+bDkKuNMXHs26lCWRg6u1A6HbYeUfx7fyds1+XKKIv7ecn2Qt4abkmaMq458KQRT5uLaYvxRv6We1+UXe0rM2/egEOiwmniveyTuVhbhE0R3Xlj6Vn+TMO6874E+g0tDJv4v3sbrq5OSo6eoY7s2bxeLY5AkC+jVAFEVKtO18WHyYx5deeuERz1NxLma1T2BwNBgN/Kt4Hx9WHvNM65kSFsG9ubNZnpB2XvuETuBQexN/OPwVhzvcCoPaN4CH8+ZzbfKUsx5af1jbxOOH13NM7zb5ZwSreWzaCvLVZ1biMjvtvHB8K/+r3otLFPHzlnP/pMXcnJw/qkWASxR4r24fL1RsxuS04SWRcnPSbH6Ythi/EShExYZ6njr+CZXdbi/kpKBYHpl0FZOCB/diAhQZqvh31SdU9bg/k2hFGD9IvYLZodmDnjhMTgtrWrawpmWLZ3xkakA8q+IvH7L86xSc7NcdZFPbZqpNNZ7rk/2TmBM6m1xlDmrF8J6xbkcXtabKU0hmCw7RPuD9pXihVkQQ4D2645ZTdNJu1WB29Qx6H5U8rB8hjfVLIMY3YdjAfJvLxvHuMg7pj7C3cx/2XjLo7+XHgvD5XBSxmHCfwd8Lnb2Lj5vWs16zy0Pc85STuDH+ctIDEwd9nNZm4L81n7Ox7QDgDvq/IX4J18UtGbKMbnHaebV2C+/U7cApupBLvbk1aRHfTVyIzwjmt+/uqOJPx9bQaHaX/ReoM3h08mUjmqjVF2VdGh47so4ifW8ZXxnFY3mXMSXkzGa0G+1Wnj22jTerDiGIIr5eMn6UNYc7M2ad9TD1qi4tfz68lU1N7tnzgTIffpQ9izsyZ1wQ+daN3V28eHQ/71cUY3O5z01ZKjX35s3i4oT083YgyTcZ37jmognieW5hsFl45vAu3jx+xLOqzI+I5d682SyIOTvzyMcbRruV3x3czAfVbtXDz1vGDyYXcNekfPzOsnJuczn5a9EWXqt0n2gDZT48kL2Q76ZOP2N1uETfwgP7P6DJbABgWfQkfplzMZGjLAG2Wbr42ZH3KeptHspRxvGrKSvJCBreN+UQnLxQ8QXvNewCIEjmx4/SVnBFzMxh/JRWXqj82ENA/LwUfDdxOVfGzB80x1EURba07+V/das9QeiJfjGsir+MfFXOoPuiQ3CwvWMHa1o+9+Rpekm8KFDlsyziIpIDhi9tWl0WigwHOajbSXl3iWeEZF94S2SoFVEeRfIEGQxXROAlGRt5EEWRHqcRjbWZ1lPU1G5n14CPCZGFMkM1h5mqeUT5Dk38we1t3dGxk03tX9Fhc0/zkSBhVmgBV0WvHNLPqrXp+aDxSza37/bEMc0Lm84dSdcSIh/cllFubOBfVZ9QYnT79SJ8QvjZpJvJGSaloM7UzlPHP+WQrhpwd7//IfcmUgOH31etLgevVG3j1eqdOEUXCi8Zv86+gstj84Z9bF8IosgHdYd4pmQzRocVCfDDjAX8eNLCM1I/AcoMbTx2eD0HOhoBSAkM5W+zryIr5Ox7indr6vnjoS0c07k92XEBwTw55zJmXQD+T4A2cw8vHzvAm8cLPVOcMkPC+e2sJcyJPj/HiH5Tcd4Qz56eHqqq3CuqqVOn8swzz7B48WJUKhXx8cPv2BPE89zCKQi8VVbIM4d3YrC5VaWFMUncmzeL/MizN4FjvLGztY6f7f6cFrMRCXBDai4P5s5HfQ5inWqMnfxkz2pKDe4D+XeScngkZwlhijPrkhdFkffrDvGH4i9xCC6ifIP5Te6lLI5KH/VzHdDW8LMj76O3mwjw9uEnmcu5Nn7GsIoZQItZx6+L3+a40a3+rIyZwT1pl6CUD/36jhqq+WvZ22isOiRIuCx6NrclXoJyiOD4RnMr/65+l1Kj+xgS6xvJqvjLmB2aN+i2OgUnO7S7+KxlLTq7W+lSyoJZol7MIvUCgmVD+1Vdoosy41EO6HZSbDjUT9GMVsQR4xffR22MJdQn/KzOaD8VJmc3GmuLh4y2WpuoM1X1K+3H+iYyUzWX6arZBMuGVvcEUaDIUMzGts2UGN3pDhIkzA2bw5XRK4dUg9usWt5v/IKt7ft6O8F9uSXhCpZHzhv08xFFke0dhbxUvYZ2mx4JEq6LW8xtSZcMGSIviiKb24p5rnwtWls3cqk3P81cyZUx+SNaCNf2dPD7o59xSFcHwPUJ+TySdcmoJ1NprT389dgG1jS6F7Szw5N5auY1qHzO/Pu9pqGEPxZuosNqQi714ue5F3Fr2oyzvtAXRJFPa0t48sh2WszuxrQ7Mmfws6kLLwj1E9xNL6+WHuLVkkMY7W673sWJ6fwqf9F5nx/9TcF5Qzy3bt3K4sWLT7v+tttu47XXXhv28RPE89xhZ0s9j+/ZTIXBPWI0IySM3866iLkX0KrR7LDz5yNbeb38MAAJgUqemnMZM9VnnzSLoshHdcU8fng9ZqcDlY8ff82/nMXRowu1Hghmp53HC9fxWWMxAEsiM/jj9CsJlo8urkoURf5Xs4u/l21AQCQ9KJKnp91InP/wvjmArW3H+EPJh/Q4rQR6+/Kb7OuGbfpwCE5er/uS9xq2ICISqVDxaObNZCsH99baXHY+aPqST5s34RRd+EjlrIq/jMujFg/aWe8UnOzu3MOnzWvR2t37sFKm5Iroy1gQPh/ZEHmUoijSYK7hgG4Xh/R76HGe7ApX+0T1krg5hPucn3Ow7YKdkq4jHNDtotRY6MkblSAhI3AyM1TzyFXOQOE19P5Sa6rjk+bPKDQUASBFyrzwuVwRfTnhPoNHG1X3NPDv6nc8If3pAYn8MGUVSQGDf+/MTiv/qvqELzX7AEj2j+YXWd8l0X9oFdNgN/G7Y++zW1sOwLLIXB7NumZEzUMuUeDFyq94qXIr4Fb5n5y+iohR5H6ewGcNxTxWuBaLy0GEIpBn869jauiZH2d0NjM/37+WzS3uOLGl0Wn8Of/yMxqXO1L0OGz84eAW3qlyf/7JQSqennM5U8NHlwzwdUJvtfDM4Z28WVaIIIr4eHlxV3Y+9+QWTPSInGWcN8TzTDFBPM8+GowGfr//K9bXuw90Sh8FD02bz02Zued901BfHGxv4qHda6nvNgBwS/o0fjFt0VkvqwN0O2z838Ev+KzB3ZwzW53I07OuGJecvtpuLT/Z/wGVxnakSPjp5Iv4ftqcUasgJqeN3xZ9zCaNW9W6PCaXX025YkSTh1yiwItVG3ijdivgjkj6Xc5NRPoqh3xck7mDP5T+z+PlXB6Zzz2pVw85T/2ooYIXqt+izeomjzNDpnBn8nWDThgSRZE9nftY3fwp7TZ36HSwLJjLoy5lkXrhkAHoWls7B3W7OKDbRbvtZGZngHcQ00NmM1M1l3i/sTUsOAUHHbYmrH3imUYCqURKmE8M/qP0hp6AydnNYf0+Duh2Umuq9Fwvk8jJUU5npmoemUFThlRpq3tqWN38KUe7Tkya8mJB2DyuirkS5SCldJcosF6znTfr12BxWZEiZWX0Ym5KWDnkZ7Czo5hny9/H6DQhk3hzd8pKroyZP+R7LogCb9ft4N9V63GJAkn+av6Sd+uIcj8BtreV86vCD+l2WlHJ/fnLtBuYGTr6rvJKYzs/2fc+tT2deEukPJy9jFtTCs5YoRRFkdcrD/Lnos3YBReRvoE8PetKZp3l4PkT+Kq5mkf3fEG7pQepRMKPJs/iJznzzuvw+VNRpuvg8b2b2d3qXgxF+AXw85kLuTpldA1mExg5JojnBIaF2WHnhaJ9/OfYfmwuF14SCbdMmspPp80974Pf+8LmcvJM0Q5eKtmHCET5BfLX2ZcyP/rsxpOcQHFnCz/Zs5oGkwEviYQHshfyg8zZ42Ju/6KphF8f+Qyz006Yjz9P53+H/LDEUT9PTXc7Dx16h1qTFm+JFz+bfCnXxc8c0QG4y2Hmt8XvsK/TTWJuTJjHPWmXDJvpubOjmCfL3sHsshLo7cdPM65nfnjuoPd3Ci7ebVzHx00bEBEJlSu5M/k6ClS5g25nh03Lq7X/85SIA70DuTzqEharFw3apS2KIqXGIjZoPqXGVOG5XiaRk6ucwQzVXDKDskflz3QIdtqs9bRYami21NBiqaHNWo+rT+f8aBEsCyPaN7nfT5BsZMr0CQxFrGeHLmJpxOX4DTH7vbK7itXNn3reX38vP25KuJG5obMH/Ux0NgOv1H7I7s4jACT6x/BQ+h1DBtB32rp4uvw9Dujcs8YXqafyYPoN+A6jYhbp6/h18Vtobd0EeCt4bMoq5oZnDvmYE2g06Xjo8DtUGDV4SaT8JHM5tySNYUHnsPHrI2v4stm96FwRncXvp10xLhPPjuvbuH/Pamq6O5EAP86ax32T558TQcBgs/DYgU18Uut+XZNC1Dwz93ImhQyeqXu+QRRF1tdX8vv9X9HY7fZGT1NH88TspUwJOz8zeS9kTBDPCQwKURRZU1PGHw9s9UwbmhudwG8LllxwEyFKdG08tGstZQZ3c8S1ydn8duZSguQjj5sZKwRR5OXyvTxdvBWnKBDjF8zfZl/FtLDhGzuGg11w8dSxDbxRvR+AGaEJPJ1/LWrF6BXUja3H+G3RaswuO+E+gTw1fRW5ISNrHKjqbuXRwjdosejwkcr41eTvsCxqcPII4BJc/Lf2c95v3AJAdnASv8q6lTAf5aCPabd28kzFq5T3ThxaFjGX7yVeg+8gyqggCmxu+4oPmj7CJtiQSWRcEXM5KyKWDRkLVGuq5LPmd6nqKQNGX4oGsLksaKx1tPQSzBZLDe3WxgHntSukfgQM47E8FU7BjsHRMeBtAd7K08ioUhY+ovGSA1kJ/Lz8WR55JQvClyEbIjO0zFjO2w3vejJBpwRn873EWwn1GTz/8YDuKM9XvYnR0YOPVM5dydezRD1r0G0VRZHVTdt5qeYzXKJAgl8E/zf5e8T7D21v0NqM/KroLYoN9UiQcGfKUm5PXjwiv7LFZecPRz9jbbO7tLwsajKP5Vw9qszPE9v+Vs1+/np0Aw5RIDEglOfyryM9+MytGWannScOb+CDWvc2Tg+L5W+zriLaf/hs3fHAF/Vl/HLfevQ2CzKplAdzF3B3Vv4F1TVudTp5peQgzxfuwex0IAFWZeTwyPQFhPqe/xFSFwomiOcEBkRpZzuP7d3EPo27MSQ2IJjfFCxmRcLIZlefL3AKAv8u2ctzxTtxCAKhPn78cdbFrIgffaPNWKC19vDwvjXs0LjjeS6Nm8QfZlw6LoRXYzHy0/0feLI570yby0+yloxa5XAKLv5Rvon/1ewEYIYqib9Mu57QEcxYB9jYWsQfSz7EKjiI9lXx57zvkhY4tNdLb+/mj6WvU2hwNwNdG7uQO5NXDqmO7tEe4fmqtzC7LPh5+XJP6k3MDZs26P1bLRpeqX2Vyh73/0gPTOP7Sd8jUjH4SV5jbWFty/sUGdzd9N4SGQvDl7NYfQnB8uGJYZu1gUO6zVR0H0Frax6wu93PK4ho32Ri+pDCEHnEmL5XVpeZVkttP3LbYWtGHIDc+noFEOeXztSQRWQFFeA9RFkbwCU6OdZ1hHUtH9Jqde9jIbJQLo2+lnzV/CGbtr7QrOeT5s9wik4UUh+uj7uOxeqFgz5GZ+/iuYr/Udzl9mPOD5vBD1NW4ec9OME/Zqjhd6X/Q2c34uvlw8MZq1igzhvyNTkEJ38rW8vHTXsBWBCexf9NuX5IS8cJiKLI+/X7ebL0C5yii+SAcJ6efiNJAaNfhBfqmnhw/we0WowovLx5PG8lV8TnjPp5BsKahhJ+feBzepx2gmQK/px/GStiR6bunik6LCZ+sfcLT/TS9PAYnp5zOYlBF9aIao2pmz8e2Mqn1W5lPUjuw4PT5nHLpKkXlK3sfMUE8ZxAPxhsFp46tJO3eg3XCi9v7smdxQ+mzLxguhZPoMao48FdaynUukdOrohL5w8FKwg7i7PV+2KHpoaH9n5Gp82Ewsub30xdzg3JeeNC3Pd11PLQgY/otJkIlPnw5+lXsyRq8Akxg0Fn6+HRI+9zoNOtIN6aPJf7M5aNaOSlU3Dxr8r1vF2/HYD80DSemLKK4GG61ku6avldyf/otHehkMp5OHMVC9VTB72/zWXnv7UfsaHNTYzTAxJ5MON7RCgG9um5RBdftK7nk+ZPcYyQ+HTZ9Xze+hF7O7chICBBQkHoAi6NupYQ+dDTWmwuC0e7dnNIt4kGc3m/2wK9Vacpj8Gy0LO6eLMLVjSW+n5ktM3agIDLcx8/r0CmhixihmopasXQjS6CKLBft4N1LR9icLi7/6MUsayMvoHs4MHHW7ZYWnml9jWqeol/RmA6dyTdPijxF0SB1c0bebt+LQICkYowHky/g7TAwf2KOpuRP5S+TnGXOz7pO7GLuDP5cryG2X8/azrAU8c/wSG6SPAP5895t5DoP7LScJG+gYcPvUuHrRt/bx+eyLmai6Imj+ixfaG3mXnk4Mfsandv+41JM/h5zsVnNO/9BBp69Dyw5xOKesft3pQyjV/lLT0nx3BRFPmw5iiPH9hEj8OOr5eMX05fzHfThx6Fej7igKaJ/9uziVKd2xOeERLGY7MumohfOkNMEM8JAO6pOe+UF/HkoR2eeKTLkjL45cxFxAaem1LNeEEQRd4oP8yfDn+F1eUkUObD4/nLuDpp8jk58DkEF88e3caLZXsASA8O5++zryYt+MztCaIo8krlbp4t2YyASGZwBM/lX098wOg8fQBH9Y08fPhd2qxG/LzkPJ57Ncuiskf02C67id8Uv8MBnZtU3JK4kB+krRgyp1AURT5t3sG/qz/FJQrE+an57eQ7SBiiRNpgbuHp8ldpMLe4Jx3FLOPG+MsHJcYN5kZernmVenM9MHyp1+w0saltLVvbv/TEIU0Jns7K6OuHzLgURZFmSxUHdZsoNuzEJrgnTUmRkhE0g6khi4j3yyBwlOXzswWn4EBjree4cT+HdFvoduo8t8X7ZTBDtZQpyrnIpYMrf3bBzo6OjWzQfIrZ5c5JTfbP4MqYVSQHDFxBEESBTW1b+KDpI+yCHZlExjWxV3Fx5PJBFwFlxhqeqXiVDpsOL4mUWxKuZGX0kkHv7xJcvFK7jg8avwJgSnAKv866FZXP0OeBkq5GflH4Bh02I35ePjw25YYRjdsE6LT18LPD73kil25Pnse9GUtHtGDrt+2iwD/LtvHPMvfiLTcklr8VXDfqrN2BcDaPQyNBU08Xj+xex542t+1iflQSf519CVH+F9b5ebBz46/yFxMTcGG9lvMFE8RzAt+oVV2Lycgju9exS+MmHvMiE/nrnEuJPkcHuyaTgQf2fOKZr3xzyjR+OU5KQ4/Dxi8Pf8rGFnf558q4XH6bdxm+o3xuURT5sOEAfyn5HKfoItE/jGem30hy4MgUnwpjCz8vfINWqx6FVMavsq9jaeTQZUKL08azFe/zVbs7vmpheB4PZtyA3yAlTlEU2di2m1dqP8AuOFDKAvlJ+m3kKQcezekQHHzWspZ1rV/gEl3DNrc4BDvbOzayQfOZZ/JPsn96L4kaXDm2OHsoNGzjoG4TGmu95/pQeRQzVBcxNWTxeUM2B4NLdFHZfYSDuk2UGw96PKc+Ul9ylPOYoVpKjG/qoIs0N1lf00vW3UHcOcEzWBl9PZG+A0/o6bB18N/a/1FqdO+7Sf5J3Jl0O7F+A5N7k9PMC1Vvs6e38WiqMov7025FKR/cu7yjo5inyt7G7LKhkgfy66zbmDJM4LzO1s2vit6i0FAHwPeTL+KOlItG5Pt0Ci7+XraR12vdwxHyQ5P589TrUI3QotIXWzUVPHpwNUaHFZXcj6fzv8Os8PFpetypqeGhfZ+htborL49NW8F3kgZvxBtPCKLI/8oO8ecjW7G5nATJfXhi5nKuTLrwOsYNNgtPHzoZv+SuBhbwgyn5F1w18OvGBPH8FuOb5GMRRZGPao7x+IFNdDtsKLy8e8s7087ZuM4vG8v4xYF1GB1Wd/l75uVcHDc+3qpKYzv373ufup5OZBIpv8y9hBsSp4/64G11OfjjsTV81uQ+oS+NzOKxnKsJkI3Mc7q+tZA/lXyETXAQ4xvKn/NuITVw6K7PRnM7jx/7L/XmNrwkUu5OvoKrYxcMMfLSzD+r3vZ0O09VTuolHQN/r6t6qnml9jVaLO6y4oyQ6dyScPOAcT6CKHBAt5N1LR+id3QCEKmI4YroG8gOnjbgNomiSK2phIO6TZR07cHZS7a8JTImB89mhmopSf6jU9PtLjMaayUaazmtlnI0lgpswuCjLweCFCmhPglE+mYQpcgg0jeDYFnkqLbD6NBxRL+Vg7pN6Owaz/WRikRmqJaSp1yAr/fARMpg13nsCSJi7zSjhVwSdc2A9gRRFNmu3cm7De9hdlnwknixMvoyVkZdhvcA4ezuxccuXqn9ELvgIEQWxAPpt5GjHPw71Whu54ljr1Jn1iBFyt0pK7kmduGQ74lTcPH3inV80LAbcM96f2zKqhF/Jzb0NuVZXHYiFEE8NW0VU0JGn9PZaNJz/773KOtqO6M4tIGgtfbw0N7P2NnmttRcET+ZJ2ZcQuA4dNSPBFVdnTy0ay1Fne7EhEviM/h9wYoLYub7qfim9D98nZggnt9CDNS5d2NGLg9Pn39Bdu5pLSZ+ue9LNjS6Y3ymhkXz9NzLSQ4affl5LLC5nPzhyEbeqnareVNDY/jb7KuI9VeOy/N/3nSM3xz+DLPLQaRvEH/Lv45c1eg74lvMBh469A7HjS1IkXB/5nJuS547ooOlU3DxQuUXvFvv9lnOCk3n8ZxVBMmG3l92dBTxVNk7vSpUEL/Jum3IQPjy7lqeKX+Vdlunex58/BVcGTOwAmVz2fioaTUb2jYhIhIsC+KWhJuZqZox4HM3mGp4u+E/NFvcpT+lTMVl0d8ZtFHGPalnO1+1fUCn/WTEUKQigRmqZUOSsr5wiU60tjo0FjfJbLWW0WlrgAEaj84Uvl7BRCoyiPJ1E9FIRTp+3sNbZQRRoM5UOiC5zgtZxEURNwwa0aSxNLOm5X2Kuw4CIJPIWBF5FUsjLx8wakpn1/N63ZscMRQCEOsby/eTbh90NGm9qYWny1+h0aJBgoRrYpezKu6yQcvaFpeNv5W/z5YRqusnsK75EH89vhq74CTeL4w/591CUsDIus2re2PI6kxaZFIvfj75cq6NH3g/HApWl4PHC9fxSYO7M31pVCZ/nH4lgSMkwUNBEEVeKtvDM0e34hJFEgJCeG721UxRDT9OdDzgFAT+dWwPzxXvwikKhCn8+NOsS1gWd+aDM841RFFkbW0Zf9h/MvFlXnQCv511EekhI8uI/TZjgnh+y7ChvpIn9m3xZJVNV8fw+OyLLtissi8byvnV3vV02szIpFIeyJnPDyYXnDPFtra7k3t3fUxZl9um8IPM2fx0ykJk49Ag4BQEnjy2gder3RNbZoUn8fTMa8c0cu+AtoZHDr+HwWEmRO7HX6beQH7Y4ASwL7rsJn5V9BaH9O7O/NuSFnNX6rIh/ZwuUeCVmrUe311OcAq/GsJ3J4oinzRv4s36zxAQiPAJ5cGMO0gPTBzw/g3mRv5Z9W9arW6Vbm7oHG5KuIGAAYigU3DyeeuHbGpbi4joiQaaH74M+SDRQDU9R1nX8ioaax0AcqmCXOX8YcvQADaXiTrTIVospbRaymm3VuHsM07zBAK91R6CGKXIIEA2dBPT6a/LTrut2kNoO2w1uHoJY18Ey6KI8nWT0QT/aYT5JA75vGZnN0WG7RzQbaKt104gk8iZG34Fi9TXIpMOrJLV9lTyacs7VPe4G6xifRO5JfGHRPuerv6Josg+3X7erH+bbmcPUqRcE3sVl0ddOuB7624w+5ANbe6ydkZgEg9nfJ8wn4FtDW4/8U5erP4Up+gizk/NY5PvGDZy6XhXE78oeoM2axd+XnL+b8oNLFSPrHGox2Hl/4o+Zkubu4J0TdwMfpF9+aiPBaeOvE3wV/H8rFWkBo2PN/OQtokH9qymxWxEJpXy89yl3HYOxm2ewLFODQ/uWktFl3vww3dSpvDYzKXjkmd6rmF22Pln8T5eOnoy4/rmzDwembGAIPmF93rOFSaI57cEPXYbj+3dzAeV7gkjkX4BPHoBT2cQRJFni3bwj6Pu8limMpxn5l5OlurcjSo8qmvlju3vorOZUfn48XTBFSyIGtpTNlI4BYGfH1rNuib353V3+jzuz1o8JNkbDLs7Knng4NvYBSdZwdE8Pf1GooaZJHQCXQ4z9x38D5Xdrfh6yflN9nUsjpgy5GNcosCTZW+zue0QANfHLeaOpMsG7TQWRZFXaj9gXes2AOaFTeeHKTfiP0iUTpmxnGcr/o5VsBIiC+F7SbeRqxx4m7odRv5b+3eqetxkYHrIbL4TdxsB3gN7BW0uC19qXmd/53rAna+5UH0NBaGX4DNMdqfZaeCw7hOO6D/DJpj63eYjDSDSN50oRWYv2UzH33t8FXmnYKfDVuMu3VvL0VjK0dmbTrtfov90CkJXEes3ZcjvviiK1JuPs771DU+3fpg8mmvj7iPef2AfrCiKHNTt4sOm1zG7TMilPtyS8CPyQmYOeH+jo5s3699in84dXzU/bC7fS7pt0GlJu7SH+WfV25hdFsLkITyR/ROifAcnZKVddfyu5DW09i6CZf48mXsPSQFDR33pbD38uvgtjuhrkSLh8ZxVLI0cOpP2BARR4L/VO3ihfDMiIksjs/jT1OvHtBAt1jXzwP73abUYUcp9eXXerWQGj49A0GW38PP969jQ7P5c70jP5xd5S8+ZLcnqcvJs4Q5eKnUP88hQhvPyomuJu0BnpZ861S82IIhnF15GfuTZH8F8IWKCeH4LcKitmQe2raOh24AE+GFOAffnzT4nIyLPBkwOOw/uWsv6Rvc0mTsnzeSRqQvx8Rr5BJkzxd72en6w4316nHamhETx0vzrUI/D2Etwk86fHfyYL5pL8JZIeXLmNVwcM/q4FoC92mruP/AmdsHJ4ohJ/GnqdSi8RmaE73ZYuP/Qy5QZm1HJA/j79DtJGcbP6RIFni57h41tB/GSSHk082YWRwyetSmIAv+peZ8vNTvcod7J13FJ5OD+z8P6Qv5Z9S8copPMwAzuS72HANnA5e5mcwMv1TyDzt6BQqrg5oQfDkqAwK1yftz4AnqHW73OV61gWeRN+A1CUk/A6GjnYOdHFBu+wCnaAFDKokkMmE607yQiFRmEyKORjGHRcKawunpos1bQaimjyVxCvemwJ+Mz2ncSBaGrSA4YenSje4LTPtY0v0y3U4cEKXPDVrI0ctWg6qfRYeD1un9R3u1eOF0adS0rIq8a0NIgiiJbO7bzv7o3EBGZFjKVH6X8YNDxmW1WLb8r/SfNljZC5Up+l/0TonwHb4zT27v5VfFLVPY0oZQF8FTej0nwH3o/dgou/lz6MetaDuElkfL7nJtYFDGyxAeAbW1lPHz4XRyCi+VR2fwx7zuj7ngH0NlM/HDP2xzVtxAsc5PPScrxIZ+iKPJK+T7+VLQZgGsTc/jjzMvOqb//QHsjP97+Ke2WHkJ8fPn3wqspiBjZ0IrzEbta6vn5zvU0dBvcI0RzCvjptLnjUgH7JmGCeH6D4RQE/lG4m38U7sElit+IVVizqYs7v/qI4/p25FIv/jjrYr6TMrQCN97Y3FzBvbs/xi64KFAn8OK868bNpO8QXPzs4Md82VyKTCLl2fzruCh6bA1K+7U13HfgDWyCk0URmTw57QZkAzRxDIQeh5WfHHqFUmMjITJ/Xph597B+N0EUeKb8PdZr9iNFyq8n3zrk6EtBFHix+l02tO1CgoQfp97MRRGzB73/Tu1uXql5FQGBqco87kn94aDkpMhwgNfr/oVdsBHmE8HdyQ8OGo9kF6ysb32DvZ1fAKCUhXNN3L2kBAy9X+ntzezXvk9J1yYE3CMvIxTpzApdRWrg7K+FaA6HLruGA7oPOWr40lOWD/dJoiB0FelB85EOMZfd4uxhXeurHNF/1fu4GK6Nu484v4GjlFyii0+a3mZrx5cA5Cnz+W7CD/DxGtiveEh/mH9VvYhDdDIpMJOfpN+L7yAqs97exf8d+ztNFg0qeTBPZP+EGN/B989uh5lHi/5FZU8TIbJAnsr78bBld5co8PtjH/Bl6xG8JFL+lPvdEcctgXvO+4OH3sEpurg4egq/z712TOTTaLdy1+43KdY3EyxT8Mq8W5msHD9f5ke1xfziwFpcosjymAz+Nvuqc7qI15i7ueurjziq0+AtkfL7ghWsShuZwnw+4tTqYk5YJM8tupzk4HPTc3AhYIJ4fkNRZ9TzwNa1HOlwN0VclZLF7+Ysu6B9J4fam/jBto/RWs2EKfx4ceE1TFef+djJ0eCTuqP8bP8aXKLI0ug0/j7nmnE7SDsEF48c+Jj1LW7S+VzB9SweQyg8wMHOWu7d/wZWwcECdQZPT181YtJpctp44NArHOtqIFjmx/Mz7h62c10QBf5W8QFftO5FioRfZt0yZCi8IAr8q/odNrXtRoKEe9O+yxL1rEHv/6VmA+80vAfAvLA53JF0+4DlWFEUWa/5hHWtHwKQHjiZO5Lux3+QJqDanhI+bnoenb0NcKucF0fdOmRZvd1azb7O96gw7vCoh3F+ORSEriLBf+DO+OFgdnZicxlH9RiJxIsgWTTSUcyKPwGTU8dB3ccU6tfi6M0gVcqiyQ+9nqzgi/AecizmQT5p+hfdTj0SpMwPv5IlETcMOkpzj3Yr7zX+F5foIsY3nruTH0TlM3B5/LixjL9V/AOrYCXRL4GHMn5KkGxgxdlgN/J/JX+n0dxKiCyYJ7LvH3LOu9Fh4mdF/6K6pxmVPJCn8u4lzm/oCDGXKPD40ffYqCnCW+LFX/JuYc4IZ7wDbG07zsOH3sUpClwWk8sTudeMyS7T7bBy1663KNI3ucnn3FuYHDK0ZWA02NBUzk/2rMYuuJgTkci/516H/zmsiFmcDh7Z/Tlr692WmO9lzuBX00c/he18wrracn6xcz1ddiu+3jL+r2AxN2acmxir8x0TxPMbBlEUeb/yKI/t2YzZ6SBI7sPv5yzjypSRr9TPR3xYfZRf7v0Su+BiUoia/yy6ltiAcxts/0blQR477Pb+XZUwhb/kXz5uB0a74OKh/R+yqbUMmdSL5/KvZ3HU2MZ6HtbVcc/+17G6HMwNT+PZ6TchHyE5tjjtPHj4vxQa6gj09uX5GXeRHjT0CU4URf5e+SFrW3YjRcKjk77LkiHK6y5R4J9Vb7Gl3U1S70u7lUXq/EGf+6Pm1axpWQfAiohlrIq/fpAudytv1b/EEYO7GWth+Aqujr15QIJqF6xsaH2LPZ3u5w2WhXFN7D2kBuYNut3N5hL2db5LTc9+z3XJAQUUhN5AjN/orBAWp55WSyGt5sO0mA/T5WgY1eNPQCb1I9I3j2i/qUT7TkflkzIqpdXq6uaI7jMO6Vdjdbm7cwO8w5gZei1TlJcOGihvdnazruW/FBrcvly1TxzXxt1LrN/AHco1PeW8XPM3up1GAryDuDP5AVIGyUutNdXxdPmzdDt7iFJE8kjGg4MOADDYu3ms5O/Um1tQygJ5IvsB4oYgn132Hh4p+ie1plZC5cE8nfdjYvyGbtpxCi5+e/RdtrQdRS715q95t1IQNvLv5hZNKT87/B5OUWBl7FQey7lqTOSzx2Hjrt1vUqhrIkim4KU5N48p3WIw7G6r44c7P8DktJOniuaVBatQ+gztax5PiKLIP47u5pmiHQAsiE7iH/OvJHgcxgt/XWg1dfPgtnXsbnV/v5fFp/KXeRdfkOkx44kJ4vkNgt5q4ee71vNlndv7WBAZyzMLLrvgJg/1hUsQ+OuRbbxY6iYTK+LSeWbu5ed0NS6KIs+X7uRvx9zTRW5Nm8Fvpi4fNyO+zeXk/n3vs72tEpnUi38U3MDCyLFFjBzR1XPP/texuOzMCU/l2ek34TNCT6fVZeehw69xWF9DgLeCf8y4k8ygoU9soijyQtXHfNq8EwkSfpZ5E0sjB4+RcYkCz1e+ydaOfUiR8ED67cwPH/j+gijwev1bfNW+FYBrY69mZdRlAyoGOruW/1Q/Q5OlHi+JF9fHfY85YYsHfN4603E+bnzeE5E0Q7WUS6JuR+F1+slAFEXqTYfZ2/kOTeajAEiQkhG0gILQGwhXjCwZwObqRmMposV8mFbzYXT26lPuIcFHGsRodimnYMcpWvpd5yMNIspvKtF+04jynYZSnjAihcUuWCjWf8FB3Yf0ON35pr5eQUwLuYqpqitReA2sGJd27efT5n/T4zQgRcp89dUsUV8/4Bz40XxGLZZWnix/Bp1dh0oewiMZDxHtO3B52ejo4bclf6fO1EywLJAnsu8n3m/wxZLB3sMjhS9QZ9YQ7qPk6bwfE+U7dASOU3Dxq+K32N5eilzqzVNTb2dmaOqQj+mLja3H+PmRD3CJAlfFTuP/cq4cUUj9qTA5bNy9+y0O6xrx95bz4uybmR42fp7Ios5m7tj+Hga7hbSgcF5buIpIv3N7Pv28vowHd63F6nKSHKTilcXfIekcReOdDQiiyMvHDvDkwR3YBRfhvv48Of8SFseN7NjxTcQE8fyGYEdzHQ9t/5w2cw/eEikPT5/P3VNm4nUBlyq67TYe2PkZm5vdJ+n7pszhp7nzz1nnJbiJ7++ObOCNKneH9v2T53P/5PnjVi4xO+3cu/c99nTUoPDy5vlZq5irHltnfLG+kR/t/x8mp42CsBSem3HziBuJbC4Hjxz5Hwd0Vfh5+fD36d9nsnLoE5ooivyr6hNWN29HgoSHM1exPHJg5RLcpPMfla+zreMAUqQ8mHE7c8OmD3hfp+DkpZqX2ac7gAQJtyZ+lyXqRQPet7pXTesZRk1ziU42tL7JLu2a3tzPUK6OvYe0wIEtAWZnFxs1z1HZ7Y7wkeJNtnIZM0OvI0Q+8ISeE3AIFtosxbT0KpqdtgpPWf4EQuTJRPtNJ9pvKpG+efh4ja45TRBd6GxVbjJrOYzGXITjFCLq66Vyk1C/aUT7TiNQFj1MmLqd0q5N7O98H4PDTcz9vEK4OPpBkgMG/mzNzm7WtLxMscGtVEUo4rkh/qdEKE6ffHa6Kr28V5U+XZHvtHXyZPkztFo1BHoH8FD6T0kKSBxwG4yOHh4veZ4aUyNBsgCemHw/Cf6Df0Z6ezcPF75Ag7kNtU8IT+fdS6Tv0OTGITj5ReGb7NKW4SOV8cy07zFNNXLysL7lKL848gECItfEzeDXU1aOjXw67fx4zzvs09bh6yXjn7NvHLcpRwAVXR3cvu1t2iw9xPgF89rCG0kOGl3U15nimE7DXV99RKu5m2C5ghcWXMW8qMRzug3jjdLOdu7fuoZKg3thd1vWNH45c+G3curRBPG8wGF1Ovnrwe28UuIOb04JVvHcossv2FzOE2joNnDnVx9S0aXFx8ubv86+lCuTzq1dwOZy8vC+z/i88TgS4NdTl3N7+uBd0aOFyWHjB3ve5lBnA37ecv41+0bywxLH9FxHDU38aN9r9Dht5Icm89zMm/H1GpkqbBecPHrkdfZ2VuDrJefZaXeQGzL0doiiyIvVn/FR01YAHsy4gUuiBvdoukQXz1W8zg6tu9v9wfTvMSds4HK8zWXjH1X/5GjXMbwkXvwg+U4KQgcmPbu1X/F+46u4RBexvgnclfIgKvnp6pXF2cM7DU9R3VMMwPSQi7g0+nYUXgNnotb07OfLlmcwu/RI8WKq6gpmqK4lUDZ0WVZnq+ZI52vU9exAxNXvtmBZnJsA+k0nyjcPX+/xHa0piE601nJazIdosRymzXIU1yn5oUGyWHJUN5EedMmQ3lBBdFFu3M4e7ZueSKZc5WUsjLh70PJ7SddePm36NyaXEblUwQ3xD5IZdLqaPbAP9z78B0gPMDq6eabib9Sa6lBIfXgg/X4mBQ3ssex2mHi85HmqTQ0EeQfwWPZ9JPkPrtjrbEYeLnyBRks7kQoVT+X9mAjF0ORzLN+Vvvi8uYhfF36EgMh18TP5ZfbKMS1irS4H9+19j53t1fhIvfnHrBuYHzFyBXY4NPYYuH3bO9T16AiR+/LKghvIDR16sTXeaLf08IOtH3NE24KXRMJvZy7l1oyBF6oXCqxOB386sI3XSt3DDdKUoTy36HImh567GMDzARPE8wJGma6Dn2xdS5m+A4DvZubx64LFo57dfb5hb1sDP9q2Gr3Ngto3gP8supbcsHMzXeMEuh02frTzQ/a01yGTSnmy4ApWxo8t0mggGO1W7t7tbhYI8PbhpTk3MzV0bGkDJYZmfrDvNXqcVqarEnl+5i34eo+MdPZVcRS9Ks7UYVQcURR5uWYt7zduAeCB9Ou4LHrOoPd3iS7+VvE/dmrd0TQPZ3yfWaF5A963x9nDsxV/p6qnGrlUzv2pP2aK8vQYG0EU+KT5bb5qd3eiT1UWcHPC3QN2TGttLbxR+0e09hbkUgXfibufycEDk2S7YGVb238oMqwFQCWP57KYR4lQDH1S19tqOdz5GrU9WzzXBXhH9lMb/YchreMNl2Cn3VrSq4geod1S4um+D5RFMVV1G6lBK4YkoA7Bxs6OVzmkWw1AiDyGS6N/RpTvwOTP5OzinfqnqTUdQ4KUS6JuY07Y5QOSqyLDQV6v+6cneeCe1EcJ9zn9BGxxWXiu4nmOd5fhLfHmntQfMD1k4EVLj9PM4yXPU9VTT4C3P09Mvo+kgMG/V1pbFw8XvkCzpYMoRShP5f0YtWLoBYHV5eBnfaoDz03/PtnDVAf6Yl1TIb8u+hgRkRsSCvj55IHtI8PB7nLywP4P+EpTgUzqxbMzvzPmBIyB0Gk18f3t73FU34qft4wX5lw7bjnFI4XV5eSXe7/g45oSAL6bPpXfzlx6wccTbW2q4eHtX9BhMSGTSnlkxgLuyp55Tqt5XycmiOcFCEEUebXkEH85uA2by0Wowo8n51/MRfHjt+L9uvBuZRG/3rcepygwRRXJfxZfS6Tf+ORjjhRaaw93bH+PEr0Gf285/5r7HeZGjl8pS28zc+fuNyk1tBIsU/Dy3FvIHmOH6vGuFu7e+yrdTivTVAk8P/MW/LxHllzgFFz8uvhttrWXIJd68/TU25kxjG9NFEVerf2cdxo2AXB/2ndYGTN3yP/xTMWr7Ok8grfEi4czvk9B6MBRKXq7nqfKn6XJ0oy/lx8Ppv+E1MDTt8clunin/mX26dye28uivsOKyKsGPHnX9Bzl7fonsbh6CJaFcUviL4jyHfizbLWU8XnLk+h7Fb5pIVcxX33HoFmVAAZ7PUc6X6O6ezMnRmAmBSwmL/RWQn3Or++jQzBT3rWWQt2bWF16wK2ATg29jZTApUMS0HrTEb5oeYoepxYJUmaH3UxB2KoBS+ROwcGalpc5qNsIwAzVMlZG3zmg77PF0sCL1e6s1SBvJT9Oe5Ro39NJnF1w8K/qFzmsP4IECXck3c6C8HkDbqvJaeaJkheo6KkjwNuPxybfR0rA4MSww2rgocLnabV2Eu0bxtN5PybMRzno/cHth3748Gsc0tfg7+3DP6bfxaTgkTf6fNZ0hN8WrUZE5MbEWfwsa+CJTcPBLrj4WW8Sxplm/g6EHoeNH+/6iJ1ttXhLpPw1fyVXJo48z3Q8IIoi/y7Zx1+PbEUEZkfE88+FVxNyDhufzgY6LWZ+vvNLNjRUATA7Kp5nFlxKdMA3m7/ABPG84NBm6uah7V+wo6UOgCVxyfx1/iWE+45+jOL5BIfg4ncHN/N6ubsEcXnCJJ6cc+k5V28bevTctu0dGnr0qHz8eGXBDeSoxi+2RGvt4fu73qDC2I5K7sd/591KRvDYyizHu1r4wb7XMDos5IbE88/8W/EfBens26n75NTbyA8dvqHpf7Vf8Gb9BgB+nHo1V8UuGPS+DsHJMxWvsrezEG+JNz/LvJOZqoGzMdutHfy1/Ck6bFqUsmAeyXiQWL/TT+QOwc5rtS9Q3HUQKVJuSribgtD5Az7n/s4NrGn+DwIu4vzSuTnhUQJlp6tZguhir/Yd9mjfQkQgwDuMS6IfIsF/8M78LntjL+Hc5PFuJgYsZGro7WMinC7BikMwj+oxEokUuTR41ITFIVg4bviEYv3bWF0GwG0DmBr6PZIDlwya5Wl1dbNJ8zxlxq0ARCoyuDT6Z6h8Tv+cRFFkt3YtX7T+DxGBJP9sbkp4ZMAwfqPDwAuVf6bF2oiflz8/TH2EJP/T90WX6OK12tfZrt0JwI1x13Nx1IoBt9XktPC70hco767F38uXxybfR2rg6Z7TE2i36nmo8Hk0Vh2xvuE8lfdjQn2GbsrsnwCh4B8z7iIjaOTl6E8aD/FY8ScA3Jw0m4cnXTIm8ukUBH55+FPWNBYjRcIfp1/JlfHjl4Npd7n42f41rGlwq46/zlvG9zIG93KfLWxsrOSBnWswOe3EByj5z6JryQg5t1WE8YYoirxbXszj+7Zg6U2h+ePc5axMnvR1b9pZxQTxvIDwRV0FP9/5JQabFYWXN78uWMx3M/Mu+FwwndXMj7d/wp42d+TEg7nzuW/KnHP+ukr1Gr63/V20VhOx/sG8tvAmkgLHr5uyzWLkjp1vUNOjJVwRwKtzbyVljPOXS7ua+cHe1+h2WslRxvHP/FsJkI0sdqRvNqFM4sVf8m5ldvjweaFv1K3n9Tp3GPgPU67k2rhFg97XITh5qvwV9uuKkUm8eTTzLqarBlZK2qzt/Lnsr+jsetQ+an6W+SDhA+Q8Wl0W/lPzLBXdJXhLvLk96T5ylad7CF2iiy9bX2O31h2VlKucz9WxPx4wZ1Jvb2Zd81/QWN2jAzOCFrI08l58vQY+hhjtzRzRvUaVcaPHw5ngP49poXcQqhh5EoEoutDbSmm37KXdvJdOazFibxl8NPDzjkbtOwu13yzUvvnIvUaeYOEQzJQaVlOsexub4M4QVcoT3AQ0YPGgsUzHu75ik+Z5bEIP3hIfFkXcTa5y4HJxufEQ7zU8g02woJJHcmviLwlXnE5UzU4T/65+klpTJXKpD3cl/5TMoNMXKaIo8l7jB3yhcceaXR97LZdFXzrgdlqcVp4ofYGy7hr8vHz57eR7SQ9MHPT90Fh0PFT4PO02PXF+ap7K/TEqn6HPJSanjZ8e/i9HDfUEyfx4YcZdpAaO3Bb0UcNBfnf0U4AzUj5dosBjR9byYf0RJMDjU1dyXeLgC6fRQhBFfn9kI/+rdI82/UHmbB7JWXzOj9Fl+nbu2voRjT1d+HvLeWbu5ayIH1vs3PmE2i4dP9m6liKtBoBrUifzxOylBF7AudtDYYJ4XgBwCgKP793M68ePAJAdGsFziy4nVXluOw3PBpp6urhx49ueA8mz8y5nedy5P5AUd7Zwy9a36HHayQxW8+rCVeM2AhPco+9u3PZfGkw6onyDeHXebSQEjI3UVne3c/vu/9DttJIbEs8LM28ZMekE+HPJx3zavB9viRd/zL15RNNYPm7cxr+qPwHg7uQruC5+4BgccPsvnyx/hb2dhcgk3vxi0g+YGjLw/9DZ9TxR8gf0Dj1Riih+nvkwSrnytPvZBRvPV/6JWlMlPlIFd6U8SEbg6SVFp+DgnfonKet2N9stjbiJReprByFF2/mi5Smcog0fqT9LI+9lUvCSAbfT7jJxsPM/HDd84iGccf5zmB56B2GKkYX8uwQrbZY9tPRsptW8A4cwusD44SFB5ZNNtP9FxARchL9sZKVfu2CmVP8hR/XvYhPcWZ4h8mTmRjxEpG/OgI8xOtr5suVpGsyFAKQGzObSmJ8P2HjUZq3njdo/oXe0o5D6cVvSbwac9W5zWXm55m+UdR/FS+LF3ckPkRU8sHL3SfNnrG52E7ab4lexInLZgPezOK38/vg/KTVW4+el4PdTfjpkw1GrRctDhS/QYTOQ4BfBc9MewN976O+WyWnl/oPuKV9KmT8vF9xDjN/Ij80fNxzkiV7yeWvyXB6cdPGIH9sXgijyh+IveLvGTQ7/MO0KrkkYfIjDaCGKIi+W7eHJYvfkqptSpvHE9IvPOfnU2yzcs221R6j4zYyL+P6k8Wv6/LrgEFz8/chuni/aiyCKxAYE8+rya0kPGTrq60LEBPE8z2FxOrjvqzVsbKhCAr2zX+ch97qwzdUAbeZurt/wFvXdhq+1dNJi6uKaTa/SYTWRHx7PS/OuI3AcQ4udgsBdu99kb0ctMX5K/jfvNmL8lWN6LpPTxs07/02dSTvq8jrAmuYD/LHkI6RI+EPuzSOaP32sq4aHjryAgMAdSZdxY8LSIe//UdN63qz/DJnEm19O+iF5IQOXjZyCkz8e/wvVphqiFVH8fNIjBMtOV+xEUeS1uhc4rN+Dn5c/P0p9lET/05scBNHFBw3PUdy1E2+JnOvi7idbOXDT08HOj9na/iIAcX65XBL9MEGy06fYiKJIXc929rT/DbNLC0Cs3yymhX4Pte/whN0hmNCYdtBi2ozGvBOXaPXcJpMGEu47E7VvAWq/Wfh7x43qJO4UzGgth2i37KPdvBejo382aLA8g5iAi4j2X0KQfPimELurhxLDhxzVv4dd6AEgM/gKZob9cMCoJ1EUOKz/hO3t/8UlOohUpHN13BP4D9Ctb3J28VbdX6g3l+HrFcBdKb8bMG7JITh4ve4FCg0HUEh9eSjzCSIVA1tdPm1ew8fNnyBBwqOZDw/a7W512fh96b8oMVYSpQjnydxH8fce3B/YYtHy4JHn6bR3sUg9lV9OumXYz6XbYeG+gy9T3t1MZlAM/575wxHn50L/svtfpl7PiuixjQEWRZG/Htvw/+yddZQdhfXHPzPz3Pate3bjLoSEQIIEdynuUqQtpfKDUkpbqFBKW6q01JDiDsUtaAiQQNw9m3WX5zrz+2PeJGnePNnsbnZJ9nvOnpOTuSPvvZHv3Hu/38sjWxdjFCUeP+rqfjWZB3h++0p+vPRNZEXhtunHccOE1GNuBwpROc6vlr7Po4nWrF/NOZnLx/UfyR5MfNlSz/c/eoN6Xw8uk5mHTzyP2SX7d0LfQGOYeA5hdIWCfH3BiyxvbcQsSfxl/pmcWv3VLysAdIQCXPzuU2zpaafSkcPzJ1++30VEoDbPX/T+Y2zsaWV8ThHPHX8ljn6au67h92sX8NCWz7BJRp6Zfx1jXenH9KWCoijctuI53m1aS7HFxdNH3kieOfve3k2eBm744h9E5BjfGHMSV4/Sz+7tie6Ij28t/T3tkR6OLZrJ7RMvT/sQXtO9mZ+vuw8ZhRtHX8qJJamFR4/XPMl7rR9gk2z8YvKdFFn0XzreaX6F1xufQ0TiprG3M9aZTGQVReHVxn/zRcc7SIKBy6t/xDhncqlRVuJ81PJvlne9DMAhuWdxbPE3dfsaZSXO4rb7WN/9EqAKceYV3UK5PbVBPqhks9H3Pg3+92kNLkbew9LIaiih3H48ZfbjybNM3adxl6kQjLXS5P+IBv/7tAeX/Y+dk9M4kjL78VQ4TiLHnL4lIBTv4Yu2f7DZo7YqOAylnFLxe9wmfYFOY2A9/63/GcG4hxxjKedV/kq37zMih/jP9l9QG9iE05DLDaPvJs+cbPsWk2P8dcuv2e7fRKG5hB+M/yU2Q/J5rigKD+54mEXtn+EyuPjFlDvJM+kr0j1RHz9Y9Rvawl3MyZvObROuT3ser+vZwc0r/oaMnFFAp6E52M1Vi+/DEw1wTsUcbpv0tYzr7In7Nr7Lw9s+wSaZePLIbzLSsW8v4Yqi8L0vnmdB4waKLU5eOPYGCiz6AwD2FY9s/pK7VryLAPx93vmcVLFvo337AkVR+N2Kj/nHusUIwB/mncG5o/av8GmgcCA/+6F3fO2r60T+FUS9t4fzXn+S5a2NuExmnjjlwgPmxOuJhLjy/WfZ0tNOic3JUydcMiikMy7L/N/il9nY00qBxc4DR13Y76Tz3Yb1PLTlMwB+NfPsfSadAE/VLObdprUYBIl7Z17cK9LpjQb5yaonicgx5hZM4MqR8zOuE1dk7tnwOO2RHiqshXx/3AVpH9Yd4W7+sPlhZBSOKzqcE4pTWyx91r6Y91pV66FvjLouJelc3b2U1xufA+CCyqt0SSfAey1P80XHOwgInF/5XV3SGVdivN5wzy7SeUzRdRxXfKMu6YzJYT5o+tku0jk973LOrXokI+ls8L3PgtpzWNb2M5oDC5GVCA5jFePcX+fY8ic4ZcSbTCv4AQXWQ/qVdAJYDUWMyrmQo8r+xWnVC5hZ+DOKbUciYMAb3cGm7gd5v/5ClrfeRSQxHlMPFimHo0t+xOkVf8VpLMMXa+K12htpDa7TjS+zTeKSqj+RYyylJ9rE0zv/j+bg5qQ4k2jhiuofU2wZgTfWxX92/BJvtCspziAauG7U98g15tMWbuY/O+4jrsST4gRB4Mqqyxlhq8QT83D/1n8Qk/X7ZF1GB7eOvx6DYGBJ5ypeaXw/5ecHmJwzkutGnQHAP7b+l02ezCNNS6xufj71IgQEXq5fwpuNyzKusyduHHc8s/NHEohHuGXZ0wRi4V6tr0EQBH4982xGOQpoCXm5+csXiMly5hV7gavGzuKy0TNRgJsXv8LazqZ+3X42EASBHx5yDFeNV4/jB5+9wVs7N+734xgI5FqsPHXqRZw4YgzheJxvvf8yjyda7Q42DBPP/YQNna2c+/qTbOvppNTu5MUzLuOwkn3zeBxq8EcjXPPB86zrbKHAYuPJEy6m0ukelGP57aoP+KBxK2bJwL+OvIBye/+OFt3qaeP25Wrv1jVjjuDUin23OVnZWcufNqjCnlsmnsK03OzPB1mR+eXa52gIdlJqyeVnU/Vnne+NJ2veZXnXZsyikTsnX4MtTa9bTI7z+00P0RP1Um0r54ZRF6UkqfWBev5T8ygAZ5WdwYxc/T6+xmAdj9X8A4CjCk7gyMLjdeM+bXuNj1pVM/Izy29gmjvZZkdRZN5u/D2bvZ8gCUbOKLud2fn6RDoc9/J2wy3U+D5GFIwcW/pzZhd8A0MaW6VgrJXFzbewpOUHhOLt2AzlTMz9JsdXPM+Jlf9lSv53yLVM3m/9cGYpl2rXOcwr/StnVH/A7KJfU2qbD0CN9yXeqzuXBl968lVqm8FZlf+k0DyRsNzDG/XfY6dvkW5snrmCS6v/TIllHMG4hxfqfkx7uCYpzmZwcvXIO8k1FdMZaeaRHXcRjPuT4pzGHG4YfTMm0cxG71peaXg6xec0c9OYG7FJVrb6tvFM3XMpP89YZxXXjjwPgMdrXmFdz5a0n//8yvnMK5hKVIlz17pH8ESTj3NvHFEwnmtHq+fp79a/zFZv9oTMIEr85pALKTQ72e5r4641r7KvRUaH0cxfD78Im8HEl+07+cO6Bfu0nVQQBIE7Z57MUSWjCMaj3LDoOZoD/d2znN1x/Gz2iVwweiqyovDdRa/yYcPe42i/mrAajPzz+HO4dPx0FOCnny3g3qWf7PM58VXFMPHcD1jUuJMLXn+KloCP8bkF/PfMyw+Y5uJQLMp1H77A8rYGckwWHj/hYkbnDI5A6ulty3loszq2797DzmRGP0/l8EXDfHfJswRiEQ4rqObmyen7ItOhM+zjhyueJabInFI2lYur5/Rq/SdqFrKobQMm0cCvZ1yGy5g8k3xvLO3cuMs26XvjLmCkI71S9/GdLyfUwxZ+OOE6zCmmJgViAe7b8ncicoTJrkl8rfxs3Th/zMu/t/2BsBxirGMS51VeoRu3outD3mz6D6AKiebkJ9vrKIrC+y1/Z4PnQ0Qkziq/gwk583W354u28FrdjTQHV2EU7ZxS/ntGO/UJr7ptmR2eF1lQdx6N/g8QMDDefS0nVr7IxLxvkGMeM+iuE0bJSaXzVI4o/RNHlz2Iw1hFKN7OkpYfsLj5FoKx1pTrWg25nFb5FyrthxNXwrzX+BM2dr+qG2s3uLmw6neUWiYQint5ofbHdEeak+JcxjyuGfkzHAY3zaEaHt9xNxE5ObtXYavmiqpvAvBh61ss7vhYd7/FliJuGHUdAAta3ufzjiUpP8/JJUdxdOFsZGT+sOlhuiI9KWMFQeAH4y+h1JJPS7iLezc+haxkzhxeM+o45uSPIyxHuX3VE/iioYzraMg3O/jdzIuQBJG3Glfz3M4vsl53b4xyFnDPzHMAeGTrYt6oX7vP29KDQRS574ivMcZVQEvQxw2LnicQi2ResZ8hCgK/OfxUzqiaSFSW+ebH/+Xz5p37/TgGAgZR5NfzTuLmmerL9N9Wfc4PPnmLSDy5AnCgYph4DjBe2LKWq95+Hm80wpySCp4//VJK7fu/BD0QiMTj3JiwTHIYTTx2/EVMzN33snNf8GnzDn62TM0efn/K0Zw+on9HccYVmVu+fJEdvg5KrC7+eNj5GMR9u3ziisztK56nNeRhpL2AO6ee3Ssis6xzG//aolrP3DLhLCa4Mjept4a6uGf9EygonF56BCeWpFeMft6+glcb1bL5d8deSalV/3dVe/L+Q0u4hTxTHt8afYNu5jWuxHho+310RNrINxVy7ajv6hqVb/B8yUt19wMwr+BM5hedp7vfT9seY2XXa4DAaWU/ZLRTn7h3hrfxat236I7UYJMKOLPyfspsqS1pvJEaPmm8nhVtvyIm+8g1T+a4iieZnH8TUprsqB4UJU5cDvTyr/el2ALroRxf8Szjc69DwECj/wMW1J3H9p4XUFKQKqNo5cSyexjnOh0FmUWt97Ks/SHdzItJtHLuiLsoMFfji3XwfO2P8EU7kuLyzSVcM/JOLJKdnYGNPL3zXuJKcpl8Ru5hnFpyLgDP1j7Mdl9yCR/gkNwZnFl6OgAP73iE+kC9bpwgCHxr9CVU2krpinr4w6b/6JbxNTiMVu6cfDVGwcDijvU8V/tBylgNoiDy86kXUWJxUx/o4K51z/UqS3VIXhXfn3ASAPeuf4s1XXVZr7s3TiqfyHVj1f7Uny5/hXVdjfu8LT24TBYeOOpC8sw21nU1c8viV4n3c1k/G0iiyJ+OPIMTKsYQjse4NpHgOBAgCALfO2Quvz3yFCRB4IUta7nm3RfwRPatFeOrhmHiOUBQFIW/rPiUWxa+SUyROWvURB4/5UJyzP2nrB5MxGSZ//v0NT5o2IZFMvDQsefv9xGYGjZ0tXDjpy8QVxTOqZrCTZP0p5/0Bb9fu4CFLVvUGcpzLiK/F72Ye+Mfmz9gScd2rJKJ3x96SdZTiQBaQz3csfppZBROLzuUM8szW45E5Rh3r38MT8zPGEc5N45JL5BoCLbw161PAHBO+QkppxIBvNn8Nsu6lmMQDNw05ls4jfovVS/WP8EW33rMooUbRt+iO8O7MbidZ3f+ERmZQ3KP5ZTSq3QJ+dKOF1nc8RQAJ5TclDLT2RhYwet1NxGIteE2VXPWiH+SZ9ZXgstKlI1dD/J+/UW0h5YjCRam5f+A+eWPkmPOvg9bViK0Bz5mfdttLKw9jI9rD+nl3zSWNl5Ibc8jhGMtWe9XEs1Mzvs2x1U8Ra55CjHZx8r2u1nYeB3eSI3uOqJg4Kji2zgk72oAVnQ+wictv0XWIYtWycX5lb/e1fP5Qt1PCMaTy7Al1mqurP4xRsHEZu9y3mh8WHffp5R+jRnu2cSUGA9u/xOdkXbduHMrzmGyaxIROcJft/6dYDyoG2eRzNw2/nosopl1ni08ufM13TgNY5wV3DRWfan5z443WdW1NW08gNtk5+7pl2EUJBa2ruepnZ9kXGdPXD5yLieUTCKmxLl1+bN0RTKX+VPh+5OP46jiMYTiMW5c/AytwdT9vfuCEY5c/jHvfEyixLsNm/jVygWDUg42ihJ/O/oc5pVUEYhFufqD51jXmf11MdRx8fhpPHTiedgMRhY17uT815+k0bf/2xv2N4aJ5wAgEo9z6ydv8cflnwLw7emH85f5Z2CW+ld4MFiQFYXbPn+TN3ZuxCRK/Gv+ucwpzn6ucX+iwd/D1xc+gy8WYU5RFb+evW8zktPhhZrlPLJ1MQD3HHrOPo/CBFjYsokHt6rlxTunns1oZ/YZYm0cZlfEx1hnKT+YqD9Scm88uP011ntqsEsW7px8NaY0ljCheJjfbXyQYDzEJNcYLq86K2XsBs9Gnq97EYDLqi5htEN/Hvyitvf5pG0BAgJXVd9ImTW5l9UT7eSJmnuIKmHGOGbwtYobdTOna7rf4aPWfwNwVOE1zMg9Q3ef270f8HbDLURkH8XWaZxReT8Oo/40qc7QGj6ov5T1nfcjKxGKrHM5ofJFxrgvQ0gx8WdPyEqMzuBnbGj/KYvqjmR16w00+18mrvgyrqsHT2QVW7vu4dP6Y1jWdBn1nieJxPWJ2d7IMY9lfvkjTMu/FUmw0hFawft1F7Kx6wFkJZoULwgChxZcy5FFtyIgstnzBgsabycqJxM8hzGfC0bcg92QR3t4By/V3UlEJ67KPpGLRtyMgMCSjrf5vP3NpBhRELm86puUW0fgjXl4YNufdEvzoiDyzdE3kGfKoznUwoPbH05JgMptxdw09nIA/tuwgCUdq9J+V6eWzuHE4tnIKNy9/jE6wqlL9Bom5VTy/QlnAvCPLW+zvHN7xnU0CILAz6d9jRH2fJpDPfxk5QvEsyjz60ESRP4w+zxGOwtoDXn59uJnCMaSf9++YFZhJb+bo37Wx7Ys5YFNi/t1+9nCIhl4YP55zCqswBMJc+V7z7K1J7vr4auAYytH8fzpl1Jks7Opq51zXnuCdR0HDrnWw7CdUj/DEwnzrfdfZlHjTiRB4FdzT+TSCTMG+7D6DYqicMcX7/LE5hVIgsDfj/7aoE2Z6A4HufD9R9nm7WBcTiHPHnclrn706gT4sn0n1y56jKgi8+0Jx3DTxPn7vK2GQBeXLPoHnmiQi6vm8KMp+qQpFf688TWerf0Uh8HCw4ffRKUtc5/wwtaV3LVeFf38Ysq1zC1IbU2iKAr3bXmMj9q+wG108ocZt5Nn0hdndUa6+NnaX+KJeZiXP5frR31dlwRv9W7gr1vuQSbOmWUXclJJcv9nVA7z4LY7qQ9uodBczjfG/AarlJxR3uT5hNcbfo2CzOy8Czi66Frdfa7tep7FbX8FFKodxzC/5A5dEVFMDrCu83629TwNKJhEN9MKbqXSkXnMoaLIdIeX0ep/k1b/O0Tl3aVnk1hAkf0Uiuyn4jRNAnrj4+mhLbCAFv+b9IT3VFCL5FrmUGQ/jSLbiRglfYuhPeGPNrKy7W5agqoDg8s0hpmFd5Jn0feT3OlbxAdNPyeuhCk0T+Sk8t9i1fHvbAvV8OzOWwjJPqrsM/laxS8w6EyQ+qT1Zd5ufgwBkatG/pSxzhlJMZ3hNu7ddCe+mIdD3HO4ZuR3dL/7bb7t3L3hN8SVOBdVXsBppakN2R/a/gKvN32ITbLy++m3UWpNbWEUikf47vI/s8PfxNSc0dw7/VtIYvqXDUVR+OXa53i7aQV5JgePHvFdCjJMQ9oTWzzNXPHpvwnJUb459li+OS6zBVoq1Po6ufCjB+mJBjm1fDJ/mK0/WKEveGjTEn698j0A/jjn7P0+112DJxLi0gVPs7azhWKrg+dOvowqZ+br4KuCBp+Hq995gc3d7diNRv5+3NnMr9B/mR+KGLZTGiQ0+jxc8PpTLGrcic1g5KETzzvgSOfdyz7gic3qCLfBHG0WikW5/pPn2ObtoMTq5OGjL+530lnn7+K7S54lqsicUj6JGyccs8/bCsej/GDZM3iiQaa6K7hlUu8mmbzXvJpna9UM+h1TLsiKdNYHWvnDpmcAuLDyuLSkE+DdlkV81PYFIiK3jL82JemMyTHu3/oPPDEPldYKrqrW9wHtCLfx0I6/IBNnZu4RnFicnD1VFIUX6/5GfXALVsnBFdU/1iWdNb5lvNHwGxRkprlP1SWdiiKzpO1+FrfdByhMyjmX40p/oUs6m/2LWFB3Htt6ngIUKh2nc+KIFxnhTD3eUFEUekIr2dL5az6tP4YVzZfT4H2KqNyBUXRT5riIQ4ofZV7lQsbl34HbMgtJtCGJ1qz/zIZiKlyXc2jpU8yt+IgxuT/CaZoKyHSFPmdTxx0sqjuSVS3X0+T7LzE5dYnVbixjbunfmF30a0yiG09kKx81XMWq9t8R05khX+U4ktMq/oxZzKEtvIHX6m7EE0nuqSu0VHPeiLsxChZ2+pfzRuNvkHV6Ko8sPJuZucehIPP0zt/TGkru0cwzF3LdqO8jCRIrupfwTvPLup9ltGMUl424BIDn6l5ggye1xc6V1ecw3jmSQDzI7zY9QDieWhxjkUzcOflqbJKZNT3b+M+O5Ozs3hAEgdsmfY1RjmI6Iz7uWPUUMTl7YchYVwk/napeC//a8hGftqZX4qfDCEce9825EIMg8lbDOv6+aeE+bysVrh0/h2vHqT3Ut335Gp827+j3fWQDl8nCY8dfxLgcVfh06YKnqfdlzlJ/VVDucPHCGZcyt3QE/miUr7/7Ik9vSp+1/6pimHj2E9Z1tHDOa0+wsauNIpud506/hGMrvzpvK5mgKAr3LP+QBzeoo9t+ffgpnD1y362E+oK4LPP9xS+zvKMel9HCf465hFJb/2bEfdEw3/r8abojQSa7S/n1zHMQ+5BJ+N36N9ngacRttPG7mRdhFLNvu6jxt3LPOtVa6IrqYzi6KPP3HopH+OW6RwjEw0zNGc3XR+rPvtaw1buTB7er+7i86iym5KQ2JH+m7jm2+rZhk6x8Z+y3MUvJxC4iR3hw+5/xxbxU2kZyWZW+ufcHrc+xpudTRCQuq7qNfHNyn3BDYB0v1/8CmRjjnUdzQklyViyuRPmo+Ves6VKJ9uyCb3JE0feT/DwVRWFtx3181vwdgrFmbIZS5pXez+ziX2GW9MedykqM2p5H+Lz+eJY1X0Sd51Ei8VYMgpMSx7lML36QeZWLmFDwS3Kth2dVns8GFkMpI3KuYXbZCxxR/h6j3LfgME1EIUZHcCEb2n/EJ7VHsKb1uwSj+mIVQRCodJ7KiSNeYoTjdEBhW8/TfFB/Kf5oMqkstk7hzBF/x2EoxROt59W6b9EWSiZ5pdYJnFP5cyTByBbvp7zb9OckIZMgCJxd/g2q7ZMIywEer7kbfyy5f220YzwXVl4DwJtNL7KuZ6XuZzmuaD5z849AQeHvW/9FZyTZLxTAKBr4wfhrcRkc1PgbeGB7ajsmgApbETePvxiAZ+s+4LP2zEpxi2TinumXY5PMrOyu4R8JsV+2OKNiBuePmI2Cwo9XPk9TsLtX6++JwwqruWO6en3/bcNHvN2g783aF/xoxvGcXjmJqCzzrU9fYF1XsrPB/kCexcYTJ1xMtTOXBr+HSxY8RYP/wCGfOWYLj558AeeNmUxcUfjRonf43dKFB5zd0jDx7Ad8XL+DC95Q7ZLGuQt4+cwrmFqQPL3jqwpFUfjtio/593rVBuRXc07mkrEzBu1YfrniXRY0bFb7S4+8gHE5/TuSU1Owb/O2UWhxcP/hF2M1ZD8qb2+8Wr+CF2uXIiDw60MuoNTqznrdQCzMj1c+QSAeYWbuKG4Yc1LGdRRF4b7NL7DD30Su0clPJl2Rtnzojfr53aYHiSkxDsubxjnlqW2iPu9YwoIW1SvyhlHXUWzRH0n5TO1D1AdrcBhcXD/q/zDpZB1Xdy/ig5ZnATi74huMdCQT6rbQdl6qu5OYEqbaPovTyn+YRCYjcT/vNNzKNu8CBCSOKfkJ0/MuSyKnshJlWesdbO5WrZpG51zKCZUvUGxLbYrvi2xmWdPFbO26h1C8AUmwUWw/g2lF/+DIEZ8xqeAe8q1HIQr7fn5kA6uxkmr3DRxW9jJzyt9ipPs72IyjUYjSFniHJY1nUtvzSEoVu1nKZVbxr5hX+nesUjG+6E4+ariK7nAyqXSbRnDWiL+Tbx5LKN7FG3Xfpc6fbGdUZT+EM8p/jIDI2p53+aj1gaQHpEE0cmnVDxMeny08tfN3xOTkXsS5BcdyZMEJKCg8WnM/baFkYiMIAldXX0GltSJhLv/PlObyBeZcbh5/DQIC77d+znstn+nGaTimaAZfKz8agHs3PkVTMHMP4Qh7IXdMuQCAp3Yu5KOW3lkb3TrpVCbllNETDXLr8meJxPU/Sza4cOShXDlazUrevuzlfle6i4LAvXPOZE5RFf5YhGsXPkO9v7tf95EtimwOnj7xEqqcbup8PVzy7tM0+g8cQY5JkvjD0afxvUPU+9L9qxbzvY9fJ9yH82OoYZh49hGfN9Vy7YIX8UejzC0dwQtnXEq546vRj5otntyykn+uUxvL7zrspEGdn/vfmjU8sXWZOk7t8LM5rKj/RU2Pbl28S8F+/+EXU2zd99+zJeTh12tVhe03xs5nbuGYXq3/r63vsMPfSoHZyV3TLsGQof8M4POOdSxo+RIRgR9PuoJ8c3oT/Yd3vEhbuJMSSwHfHZt6hnV3pJtHdqj9omeWns4huTN045Z3LebLzkWIiFwz8iZyTcm+rl2R1l22SUcWnMWsvGSyG477eanuZ4RlH+XWyZxdcQfSXgRPVmIsaPwxjYFlGAUrJ5f/jrGu5DYGRVFY3nYXtb43EJCYWfhzphfcikFM7X/a6n+bpU0X4I2swSA4GZ//C46s/JzJhX+gwHYcoqDvazrQsBtHMdJ9E3PK3mB26cu4LYchK0G2dt3D2rbvEtcpo2soth3B/IrHyDGNJRzv4JPGb+CLJPsj2gwFnF7xV8pss4gpQRY03k5LcE1S3FjnXE4pvRmAZZ0vsa4n2dTcbnBxZfWPMYs2avzrd71s7I3zKq6g2j6GYDzAYzV/1/XXNEtmvjNWM5ffyhtNb6X8rNPdE7hkhGrH9MD252gP62dINVw/+kwmuqrwxYL8fuMzWWWZ5hdP4dIqlbDeve4FunuhVDdLRu6deTEuo5W13fU8uE3f0zRb3DrlpF1K9+9/8TyheP+KjcySgX/OO5/xOUW0hfx8+9MX+316UrYotbt45sRLqXK6qfV18/UPnifUz+KqwYQgCNw880juPepUDILIK9s2cPPHbyAfIJnPYeLZB2zp7uCG9/5LVJY5pXocj558wQFjl6RhXWcLv/xSbSz/4SHHcMX41B6IA43WoJe7VqgPtv+begynVeqPWuwLmgI93L/hIwB+Mv1Upub2zYT+zxveIRSPMj13BDeMnd+rdWv9bbxYpxL+O6ZcSJ45s/9rTI7z4DaV6F5QeSwzctPP8N7mq+WjNjWb9X/jrsZuSE3EXqh/iZAcZpR9JOdWnKMbE46H+G/DkwCcXHoO45z6bQGvNz5EVAlTbZ/EyaX6RvLvt/wdb6yVHGMpX6v8JUYx+dpa2fk4TcHlGAUrp1XeR4X9MN1tbe7+D7Xe1xCQOLzkD1S79E3uQSWpNd3/YG3b95CVEHmWI5lT/gblzouRdI5hsCAIAk7zRA4pfpTxeT9HwEhbYAHLmi8lFEtdCrUaiji67CFyzVOIyh4+a/4eER1rJJNk5+Ty31FlPwpZifJh0y8I64zlnOw+kSMLrwLgg5Z/0KNjMF9kqeS8ypsAWNT+qm6/p0E08PWR38UsWqgJbOOLTn27omJLMVdWqer115veTFlyBziv4mQmukYTkaMZLZaMooGfTLoSk2hkdc82FndkV7L+1tiTGessxRcL8fD29JOj9ka5LXdXv+ej2xbREEhPjtPBIKpK92KLk/pANw9s1p9I1Re4TBYeOvoickwW1nY189AgKd1BJZ9Pn3gpBRYbG7vb+OXS3n33XwVcOG4qD510LkZR5PUdm/jd0v7v4R0MDBPPfUR70L/L8HVmURl/OeZ0TFL/9HYNFfiiYb7zyStE5DjHl4/mW5MPH7RjURSFO5e9jScaYkpuCd+YkLo82hf8Zs07BOJRZuZVcl5V3zK7S9q38VbjagQEbpt8WlYjLffE/ZvfIq7IzC2YwGH56QmkhjebPqcu2Irb6OCSqhPTxiqKwiM71LnlRxfOZpxzZMrY7b4dLGpXy5WXVV2S8rO80/wyPdEuCkxFnFh8pm7Mhp4v2Oj5EhGJs8u/qTtXfbNnEet73kNA5LSyW7FIjqSYluAaVnQ8AsC84lsptEzQ3V+D733Wdf4VgOkFP6TUnlokFpfDrG+/le3dfwagwnkl04r/hdmgb8WUDqHodryhz3r15wt9gaz0zkRaEETKXZcws+RxjGI+vsgGljadjye8OuU6RsnJESV/wmoowRfdyZKWW3XtliTByDGlP03Md2/h09Y/6GYCD8u/iHLrZCJygLea/qBb8p+cczgTnLOIKzFebfi37nZyTfmcWqqay7/a8AyBmH4G8fD8OYxxjCEiR3iu7oWUn1MURK6uVrf3cdsXbPOln89ebMnj3Ao1g/nA9teIZyEaMogS3xmnZlZfqltMrb8t4zp74sSSyczKG0lYjvGbda/3qZ/PabRw+zQ14//A5k+p8SUb/fcVpTYXP52h3lv+vHYh2zyDZ21UZnfxp3lnIgBPbVnJazUbBu1YBgrzK0bx2yPV3/Qfq5fw9MavvuBomHjuA4KxKNcteIk6bw9VTjcPnnAulj70AA5FKIrCT5e8y3ZPJ6U2J7+fd8agjgl8s24DCxo2YxBEfnvYGfs8NSgdPmnZyruNG5AEgTtnnN4nMVE4HuXuRIn9oqrDmJTTu8zp8s7tLGxbjySIfGdcemGQBn8sxOM1qsjhiuqTsaeZww6wtGstaz1bMAoGLhuhTxJBnQv/xM6nUFCYm38EYxz6JuytoSY+aFVVwedVXoFRx2InIod4vfFBQFU9F1mSpy75Y50saL4PgNn5F1BuS86aRuJ+Pmq+CwWZ0c4TGePSJ9ld4fUsbf0pAKNzLmFUzoUpP2ck3sHKlqto8auZ0fH5v2Bc/k8QdSYspYKiyHQHFrC55WLWN81nS+vFvfrb3Ho+axuOoKnnz0Sz9O7UkGM5hFllz2M3jiMSb2N58+W0+lOXoi2GAuaW/AWDYKMt+AUr23+bYnKRjWNL7kRAYrv3fbZ6302KEQWJU8t+gFGwUB9YzbLO/+ru84zy6zAKJnb417KqWz97c0zhyRSby/DGPLzV9JJujCAIXF51CQICn3csZrM3tTJ8nLOaowpmoaDwSM1/MxK7i0ccT47RTl2glTebssvozc4fw9yCCcQVmfs3p/7OU32WH085A4Mg8UnrZj5s6Rt5OqlsIvOKRhOV49y96q0BEaZ8rXoqR5eMIiLHue2L1wdlspGGo8pGcuOUIwC4ffFb7PTue9Z4qOK8sVP4fqLn8yefvcvH9YPjLNBfGCaevURclvneR6+zoq0Jt9nCIyefT74185zsrxqe37aal3esQxIE7jvqLHLN1kE7lo6Qn58vVwnVjZPmMcHd++xTJoTjMX61SiVNl4+aw/icvu3jke2LqPV3UGB28O3xvZvpLisy9216A4Czyw+j2pGdyfyzte/THfVRYS3ktNIj0sbG5DiP1qjk4IyyYymyJPdhavi0/TO2+bdjEc1cVHm+boyiKLxQ/xhxJc4k1wym5Oi3ZHzY8jzd0XbcxkKOLb5AdzvvNv2FYLyHQvNI5hZcrrudz1r/hDfahMNQyryim3VjArEWPm/6HnElRLFtHlPz9eMAfJFNLG28gJ7wCgyii+nFD1HuvDhl/N6IywHavI+xvuk4trdfiy/8GSBhMY7FYhyX9Z9BzCcmt9PU80fWNhzBzo4fEoxsyvo4rIZyDi19mnzrfGQlzNq277Oj+/6U5CPHPI7Zxb8BRGo8L7K15wnduCLrZGbmXw3AZ61/xBtNFq+4TWXML/4GAJ+0/Yf2cE1STK6paNfv/mbTIwTjyRlNg2jg/MorAVjY9i6NQf0s5Uh7NUcXHgXA4zufTDtz/fKqszAKBtb2bGZZV/oSut1g5fKqkwF4rOZt/LHs5rJ/Z9xpSILIwrb1vTKWBxjlLOLq0erEtd+uewN/bN9HJwqCwE+nn4pRlFjUuo13G/s/CygIAnfPOg2HwcSKjgYe27q03/fRG/zf9KOYXVSBLxrh2wtfPqCEOBq+f8g8zk2o3W/84JWvtMn8MPHsBRRF4Y7PF/DOzi2YRIkHTvgao3L0LVi+ytjc3cadX6i9lDdPP5rZRcmTZvYn7lqxgM5wgHE5hXxr4rwB2ceDWz6l1t9FocXRJ5N4gFp/Bw9tVbM5t046Daexd32B7zatYpO3AZtk5trR2ZHW1lAXL9ar4oTrR5+ZUYT0XsunNARbcBkcnFdxcsq4QCzA8/XqdKKzy8/CbXLrxq3pWcYGz2oMgoHzKvR7NltCtSxqexVQM196Sve1Pe+wzbcYSTByWtkPdY3Jt3neY6v3HQRE5pf+FJNOGT4mB/i86XuE4u24jKM5rPg3KTOX7YGPWNZ0MaF4A1ZDFYeWPkueNT1x1xCJNdHQfQ9rG+dQ1/VTwrHtSIKLIuc3mFK2iEml7zOp9L2s/6aWf0F1/l+xmaajEKbD/wwbmk9kS+tl9AQ/TKla3xMG0cG0or9T6VItinZ038f69h+knANfaj+KaQlSvqbjTzT59UUu0/OuoNg6jagc4MOmu3RHa05zn8pI+2HElShvNtxLXKd8P6/gLArNFfhjPSxoflJ3XxNcU5nuno2MzPN1j6UkzudXnItNslIbqOPjttT9b0WWfM4oOxaAR2v+m3aWO8AZZXMptxbSHfVlNcsdoNpRxNnlao/xXze/kZYI6+G6McdQYculJeThn5uz22fqY8nfNc/9ntVv44/2/wzwMnsOt00/HoDfr/6Qnb7ByzQaRJH7jlQTJGs7W7hn+YeDdiwDBUEQ+O2Rp3BE6Qh80QhXvfMCtZ7uwT6sfcIw8ewF/rLiM57cuAoB+PP8MzisZHAJ2UAgGIvy7YWvEIrHOKp0JN+aMnh9nQALGjbxWu06REHgt4edMSB9tLW+Tv69SRUy/GjqyTiM2c9O3xuKonDP2teJyDGOKBjDSaW9m/IRjEX459a3Abhq1LHkmZNJlR4e2fEWETnK1JxRHJGffp++WICnal8H4KIRp2I3pM5mv9L4Gj1RDyWWYk4q1ifBETnCi/Vqpuy4otMpsiRbiSmKwqsN/0YmzkTXYUx0Jc+Y90U7+LDlXwDMLbiCQkuyD6432synrX8AYEbelZRYp+nsS+bLlp/QE9mEWczliNL7MIrJ36OiKNT2PMLq1m8RVwK4LYcxq/RZ7MbM/rv+8Cp2tH+HtY3zaPH8g7jcg9lQRUXuL5hSvoSK3J9gMvRemCYIRvLsZzO++FXGFb+I23oqIOINfcK2tqtY33QCbd4nkHVGVf7vdiTG5v2I8fl3IWCgxf86K5qvIBzT7z8cnXMpI13nAQpftNxOdzg5yyoKEvNLfopRtNMaWsvKzsd19itwcun3sUhOWsNb+bIjuf/SIBo5q/wGAL7oeIf6gH6Z/NzyyzAKRrb6NrC8S7/k7TI6+Vr5OQC8UP9ffLHUY0rPqzgJl8FBfbCZt5vSz1k3iBLXjVIni71Y/xFtoe608RquHX0CNsnMRk8DbzetyGodDRbJyO2T1ZaXp2oWs8nT1Kv198YN44+kwuamJeTl7xv7pphPhYtHH8LhRVWE4jF+/OXgqq5L7S7+MFfttX1k4zLeqd08aMcyUDBJEv86/hwm5hXSFvRzxTvP0R7M3klhqGCYeGaJJzas4E8r1Mkxd809kdNHjh/kIxoY/PzLBWzpaafQaudP887oU59jX9ETCXLnUpWEXT/+cKbl7fuM9FRQFIW7V79NRI5zROEoTi3vmyn+u01r+bx9KybRwO1Tet8X++iOD2kJ9VBqyeXCEdlld7d663mvRS113TD6rIz7fKb2dbwxP5W2Uk4qPiplXGOwcZdn52UjLsGQwvT+/ZbX6Yy04TbmcVKJ/mz3ld0fUeNfj1Ewc3rZ11Ns534icoASyzhm5yeX9GUlzsfNvyIi+yiyTOaQ/Kt0t7Ou8680BT5CFEwcXvpH7Mbk80ZWImzquJOtXfcAMqWOC5hR/FDaUZSKEqcr8CabWs5lU8uZdAVeAWI4zIczquBBJpV+RJHzGiQxefJSbyEIAg7zbEYV/ovJZQspcl6HKDgIx7ZS1/Vj1jQeTkP374ikUa8DlDsvZEbxQxjEHDyRVSxtugBfJNm7UxAEphfcRqH1MOJKkM+bv09Qh6Q6jaXMK7oFgBUdj+haLDmM+RxXfCMAn7c/SWc42dx+lGMKM9zHoKDwSsO/dScf5ZkLd41YfbnhScJx/ZL38cXHUm4twxfz8VL9Kym/C7vBxqVVKpl8uvZ1PNHUJBVgXsFUJrtGEpajWU00Uo/ZwdWj1Mzq/ZvfwhfNrky/a59FYzmhZDJxRebuNa/1Omu6JyySkZ9MPxWAx7YtYbOndZ+3lQqiIPDr2adjlYwsbt3JM9t6R7b7G8dVjOGGSWrW+dbP3zigJhtpyDFbePSk86lw5FDj6ebqd1/AF+n/jPZAYph4ZoG3ajZzx+eqpdD3DpnLFRMHz8dyIPHy9nU8u3U1AvCXI8+iwNr3B2hf8OuV79Ea8jHKmc93J6cmSH3Be00bWdiyBaMgcsf0zDO608EbDXHvevUBde2YoxlhT903qYdafxtP1qjlwu9POAOLlFmwpigK9299CQWFY4tmMsFVlTZ+p7+RtxLZnutGnp+yJK8oCk/ufIa4EmeGezrT3PrzvTvCbSxoVsvnX6u4DLOU3FYQiHl5q1H1/zyu+AJyTck9q5s9i9jiVScYnVz6f7pK91WdT9IcXIVRsDK/5A7d0nmN52U2dz8CwKGFPyffMiMpJhr3sLLlOhp9zwECY3JvZ0L+XWl9OTv9r7Ku8Wh2tH8Tf3gpAkbybOcyoeRNxhU/h9t2Ur9NLNobZsMIKnLvZGr5Eircd2KSKonLXbR4/sa6xnnUdNxCXE5toJ1rPZxZpc9hM1QTjjexrOkS2gMfJcWJgpE5xffiMFYTjDWzuPn/iMvJxGmM60RGO09EQeaj5ruI6PRpTnQdS7X9UOJKlHeb/6LbInBq6VVYJDuNwW180ZEsWAI4vvh0CkxFdEe7Uo7TlASJK6ouA+CD1g+pDehPcQI4oXgeI+0V+OPBjPZKgiDwjTEq8V3Q8iUbPMl+p3q4uOpIRtgK6Yz4eGBbsq9pJtw6+TRskonV3XW8VLes1+vvifkl4zi+dAIxReaulW8OiNCoypHLLdPmA/DbVe/TGBhcM/cfzDiGGQVleCKqK0u0F+NMvyootjt5/JQLyLNYWdPewjfff4VI/KvzOYeJZwYsbqrlex+9hqwoXDp+Ov93yMD0GA42tns6+fESNbv43WnzmFuSnsAMND5p3s4LO1QSfM/s0wfENSAQi3DPavUzf33sXEY6M88/T4f7N71He9hHlT2fa0b1jigrisIfN75KTIkzt2A8RxVOymq9D1qXs7ZnBxbRxPWjUivTtX08tON5ZGQOz5/BNLe+/RDA8u4VrPWswyAYuHREapHNS/VPEFWijHVM4hD3HN2Yd5ufxB/3UGSuZG5B8jGG4j7eb1HN5GfnX6BbYm8Nrmd5x8MAzC2+GZcpuYzdFlzKira7AZiQ+w0qnacmxchKhDVtN9EdWoIk2JhW9E9G5FydZj67TGP3vdR03EQkXock5lLi+g5Tyj+juuDP2Ey9a6XoCyTRSZHrOiaXLWRkwb+wm2ejEKXT/zybWr5GOJbaKshmrObQ0ufItRxBXAmwpvUmukPJpMYkuZhbeh8m0U1XeB1LW+/UJY3zim7GYSjFG23is9Y/JS0XBIETS76LQTBTH1jD6u63k2IcRjcnlaiE8d3mJ/FGk3sEjaKJcytUgdkHrW/SGtIvP090TWB2nqpcf2LnUykJliSIXDdSFTctaPmU7b7UJFXdbhUnFs8C4P4tL2WVgTSKBm6ZqGb+X6j9jC3e3k0RKra4dgkS/7LhXTrD6TOzmXD7tJOxSAaWduzk1brUFlt9wZVjZjEzvwJfLMJPlw4Mwc0WJknir0edhctkZkV7I79fcWB4X+6NUTl5PHLS+dgMRj5prOGWhW9+ZQzmh4lnGqzvaOW6BS8Rjsc5uWosv5p74qBaCg0UQvEY3174MoFYlMOLR/DdqYNLrn3RMD/+UlV1Xzl2NrMKB6aX9p+bFtIU9FBmy+Eb44/u07bWdTfw7E51pOiPp5yJScreggfgo9a1LOnYgkk08H8TMpfLAQKxEP/epmYbL6k6gUKLO2384o6VrOnZjEk0ck3C21APETnCUzvV6TKnlpysOxYTYINnNat7liIickHlVbrHXBfYzNJONetzVvkNGMTkF4iFrQ/hj3WSa6rgiILLdI4nwEfNv0QhzijncYxxJouhfJGdLGn+AQoxKhwnMzH3G0kxiqKwqeNnu0jnzJInKLDNT/k9yHKIHR030exRPUCLXTcytWwxZe5bMUr976yQLQRBItd2KuOLX2Rc8YsYpRJC0S1saj4bXzh1hswo5TC9+AEKbSeiEGVN67cJRJPJqsNYyeElv0fAQIN/ARu6/pkUY5IczC/9KQIiW73vsM3zXlJMjqmEo4quBuDj1gfwRZM9JWfnnUi5dTRhOcBbTY/oHveUnJlMck0nrsR5oT610OiSygsxiSY2eTfzReeXKb+HSTljdtkrPbjj+Ywk6bpRZ2KTzGzy1vJO8xdpYzUclj+WY4unIqPw+w2v9JqIXVR1GBNcpXhjIf64IZm09wblNjc3jle9a+9duwBPpHfl/2wgiSK/Oex0TKLEx03beKkmuQVjf6LS4eZ3R6g2dP9av4QPG7YN6vEMFKYXlvLP48/BIIi8un0Ddy354Csx132YeKZArbebK995Hm80wpySCu6bfybSAHhHDgX8aun7bOhqJd9s4y9HDv7n/OXyd2kMeKi0u7ll6vwB2cfGnmb+s+VzAH4y7dQ+zWKPKzJ3r30VBYXTyqYxp0Df5zIVgrEIf9mkin0urz6GClt2Jfqndi6gM+Kh1JLP+RXz08aG4xH+U6N6Ip5ddkJa+6S3mt6hPdJOrjGXM8tO142JyTFeqHsMgKOLTqLUmuzHGVfivFL/LxQUDsmdrzuLvc6/mtXdanvCSSXf01WxL279C55oA3ZDEfOKfpBEcCPxHnX6jtxDrnkqhxb+XJcE7+z5N02+lwCRyYV/xmlO3c8bjbexufUiugOvI2CkKu8PlLt/hCgOnq2YHhzm2YwvfhWrcQoxuYMtLRfT6U/d5ygKRiYV3IvTNIWo3MXqlhuIxruT4gqshzKz8A4ANnY9QK33jaSYEus0ZuSptkeftv4BbzS53/SQ3LMpsYwjIgd2ZbX/93jUIQICIqu6P2GbL5mwCILAeRVXYhAMbPCsZm3Pct3Plm/O54xSlWw8U/cc4Xjqvrcrq8/BLJrY4NnGovb05ew8s4vLq9WXnYe3v4Evml7YpeF740/HIhpZ3b2Tt5r0jzkVDKLET6aehYDA6w2r+LK9d/ZMe+OqsUcwylFAR9jP79f1vvyfDUa7CvjeFPUF/pcr3h20We4aThkxnqvHHwrAzZ++TtMBNM99TxxTMZLfH61Wdx5et4x/rs7u5WgwcWAyqT6iJxziqneepy3oZ0JuIQ+ccC4WQ+8yWF8VvF27iSc2qw3hfzzyDIptmccyDiTeqd/IizVqif3eOWdiN/b/POy4IvOT5a8SU2ROLJvIcaV9E4o9v/NL1vc04jBYuGVScnk3Ex7Z8cEuQdEVI+dntU59oG2XfdK3xpyDKUM/6CuN79MW7qTAlMt5FSeljOsId/J6k0oELx5xAWZJX+H/cds7tIabcBpcnFZ6nm7MFx3v0BTagVVycErplUnLY3KEd5v/AsA092lU2pMV6ju8H7PZ8yYgML/kp5il/z0/FUVmScsP8UV3YjWUcETJH3XHWrb5F7C9+48AjMv7KQW21NOLwtEaNjWfTSCyAkl0M6boSfIdyZ6jmaAocWTZ17s/JTtSsydMhhLGFb9AjvUkFMLUdHyHZs8/UsZLopVpRf/ALJUSiO1gTdt3UHTEPVWusxjnVi2Zlrf+gs5QMik8JP8qiiyTicg+Pm7+VVJZXhQSPbtIbPF+yhbPp0nbKLeNZk6+Suxebfg3MTnZgqnIUsKxRSqpfKH+caJyRPeznVp6MgWmAjojXbzWlEyWNRSYc3fZiD1a819CaUgqwDnlR1FpK6I76uPxndllIIstbr4+WrUb+ts+CI2muiu4oEp1f7h77WtE5X33pjSJEj8/RBVWPV+znM9b+0ZkU+G68YdzSH45vmiYHyx5ddBLv7cfeixT8orpCgf53qLBP56BwtfGTOaOOaqo7TdLP+alrdmNex0sDBNPHdzz5Uds7+mi3O7isZPPP+Dmr2sIx2PclZhv+83Jh3NMWWYbmYFEVI7zm1Wqf903Js5lduGIAdnP2w3rWd/dhMto4Y7p2U0FSoWYHOc/29QeopvGn0B+lvZHGnzREM/XqqMov5eloAhUi5eYEmdW7gQOz0+vxA/HI7zWqPraXVl9DmYpNZl/v/UDInKEcY6xzMnTn3sek2O836I+1M8suwirlDxAIa7E+LhVzbCeUHwJDoM7KWaD50O6IvXYpFyOLro2abmsxPiyXSVQ03MvpdSWLOpr8C+gLfgFkmBlbslfsBiS+3Sj8R42dvwMUEdgVriSy/m7P1s3W9uuJhKvx2wYyfjiV3BaemcppigyXv/T1DXNoLZxbO/+GkbT1vEtYvH0avW9IYk2RhX8iyKn2mLQ2H0PnX796UEAZkMR04v/hSTY6A59QYP3Gd24yXk3UWY/Dpkoq9rvTSrjiYKB+SV3YBCsNAdXUetPJpaFllG7XAo+bX9ctxR4Ysml2A05tIcbWNfzue6xnFxyNjnGXDojbSzvWqIbYxJNXDJCnU71fssHabOeZ5cfT5E5n45INwvb0hugG0UDN475GgCvN36OJ5qdhY0qNCqgK+LjtcbU5f9UuGn8CeSa7NT423m/eX2v198TswuquGSk2q/65/UDU5I1iCJ/PPxsbAYjX7bVsaAh++EHAwGzZOBvR52D3WDii9Z6Xj8AR2pquG7KbL4xVb1n3/nZApr93kE+otQYJp57YXFTLU9vUhuw/zL/DIrtg5sBHEg8snEZDX4PxVYH3582+KKpl2vWUOvrIs9s49uTBuZ4FEXhwc2LALhyzOEUWnpHFPfGxy0baQl5yDXZObfy0F6v/1bTMoLxCFX2Qo7OUlDkj4V4r1l9UF484viM/aCfd6zAF/NTaM5lboH+RCFQCeUnbSpxOKX0pJTbXdOzDG+sB5fBzWH5R+rGrO3+HG+sE4fBzay8ZP9PRVFY1qkS01n55+rOYt/mfQ9PtAGLlMOM/OSMqazEWN+p9h+Oc19Fjnmc7rFs7/4zUbkDm3E0Y/Ju1Y1RjynKjrZvEo5txyiVMa74OSzG1PPr9RCJbqC57Rw6um5GlvdlhrWCP/gyDc1H4fE+iKJj0p4KgiBRkfsTip3fBGBnx634wqnJjsM0ntG5PwBge9efiOiM6BQEkRkFtyMJFrrCa2gOJPtfukzlTM5VieXyjkd0Cc3s/AswChbawzuoDSRb7lgkO4fnq9WCT9tf092GWbJwVMEJiZj3U36umbmHUGguJBAPpu31NIlGTitVM9/vNH+SkYgdmjue0Y5yInKUd5uzI5FG0cAl1arQ8MXaxb22R3IZrVxYpZKJZ2v6XkK9ccIxmEUDq7sa+KK9ps/b08MIRy7XjFOP+S9rPxn0LGO1K5dvTlaFj/eu/JjQATjVSMNts47mkMJSvNEId36e3Hc9VDBMPPdAKBbjR4vU0YyXT5jB7JLkvrUDBW1BP39do5KMHxxy9KDPmo/E4/xtvUoIvzlxLjZD/5fYQZ3HvrGnBZvBxGWj9DN6vcEzO9XMy9cqD+21oEhRFF6oVY2xz6+cm7VwbUHzl4TkCFW2Yqa5M/eTvt2skoUTi49EElJf8su6VuCJeXAb3cxwT08Z90mbekM7omA+ko6lkaIofNquWtXMyT9FV1C007+c9nANRsHCNHdy1llW4qzsUA3Kp+RehFFMzqrWed/EF63BJLoZ49bPYnrCa2jwPg3A+LyfpbRMUhSF2s6f4A1/hijYGV34n14JiGTZR2f3L2hsOZFw5EsEwUZuzp2MKNvKiPLtWf+VFr2FyTgDRfHR2XMHTa2nEg73rj+wzP0jcqynoBBhe9v1hGOpbYDKnRfjNE0mpnjZ2vk73RiLoYDROaqzwfrOv+uq3KfmXohBsNIR3ky9P9ns3SI5meJWS9tLO/Tnrx+WfxIGwUhDcBu1Af1M2REF8xGR2OHfQn1A/3OJgsj8QrXX8MPW9MbpxxbNwSgY2O6vY4svvV2SIAicVaa+EL/W8GnWJPLkkkNwGCw0BDtY0p56pnwqnDdiFgZBZEXXzj6byhdYHJxbNQOAfydewAcC146fg8NoZlNPK2/XJ/vG7m9cO3E2JTYndb4e/rNhcMd7DiQkUeQ3R56CQRB5Z+cW3qoZmib6w8RzD9y38jN2eLootjm4bXbqHrADAX9c9Qm+aISpeSWcN0rfo3F/4qWa1dT7eyiw2Ll0dOqsXF+h3Wwvqj4Ut6lvQpFt3la+7NiBiLCrF6s3+LJzK7WBNmySmVPLsvvMiqLwWqP6wnBm+ZEZyeoOfz2bvDuQBJETitOPgfyo7SMAjik8CimFJ2VzqJEtvvUICMwtOFY3pi6wmYbgVgyCkcPy9ftJl3aqYzinuk/RzXbu8H5IT7QWs+hkUk6yAl9WomzoUqccjXNfnWIyUZxNHb8AFIrtZ5Jr1bd7Amj1/psO/zOAyMiCv2EzTUwZ+7/7UPAHXqeh5Wg8vn8CcWzW0ygvXkiO81uIoh1RsGb9ZzbNoLTodfLcv0EQXESia2lqO4P2rh8Sl7uzOiZBEKnO/3NCcNTJtravp/T5FASJ8fk/BwSa/a/QFdLPqo11X4VBsNMT2USDPznbaJHcTHSfA8DyTv2s56F55wACO/xf0h5OJnkOg5vpbpUwftb+uu5xuIxuprvVcnG6rOdRhfOQBIlt/u1pfT1dRgfzCtRKxTvN6acZARxbPBO7ZKEx1M6yruwe6laDiTPK1WN+oU6/jSAdiiwujitRqyH9kfW8duw8JEHgs9btrOvqndVTtsgxWfl6Iut539qFxOV9N8LvD9iMJm47RH2m37/2M1qDfbOoGsqYkFfIt6ar97o7PltAT7j/XQz6imHimcCGzlb+lVCD3XXEibhM+z42cahjfWcLz25dBcCds48f1OlEoPaa3r9HtrMvCvN0WN5Ry7KOWoyixNVjspvFnQ7PJeyTjimeQKnV3ev1X6xVH0Knlc3EbsjufFvVvZXaQAsW0cQJCX/BdHi3Wf1e5+RNJ9eUkzKuOdTCes9GBASOLtQvnwN81q724E7OOYQ8k77vqUYapruP1u3tbAvVUONfhoDIzLxzkpYriszKTlUxPyX3QkxS8iCDGs/LBGKNmKUCRuVcqHscjd7n8EbWIAkOxuTelvIzdQfeoaH71wBUuO8gx3p8ytg9EY3V0NpxOW2d1xOPN2GQRlCU/wRF+Q9h2IdxmRoEQcLluIqKkkXYbecDCj7/4zQ0H4nP/1xWvXmSaGN04cO7rJa2t9+YsmzvMk+jzKF+h5s7foGsM1/dLLkZ41b9NDd0/kNXjDQ192IkwUxbaD0NgeRStNtUxljnXIBdbRZ7Y26B6qKwvmcx3RH98Z7zCtXf58vOTwnF9cVYOcYcZrpnAPBRa3ofx5NL1PN9UfsyfLFA2lirZOakEpVQvdqQfcbw3Eq1T/jz9k3UB5JtpTLhoiqVSLzZuApPlqr6VCi3uzmtQk02DGTW85pxh+EyWtjiaefN+sHvrTx75GSm55fii0b448rMLxlfZdw0/QhG5+TRFvTzmy8HZlxqXzBMPIG4LHPbJ28TU2ROqR7HydVjB/uQBgyKonDX0veRFYXTqyYwu2jw582/sGMVjQG11/SSUQM3Ferfm9Sb7NmV0yiy9q131xcN8Vq92qt2UXXqTFoqNAW7WNSm3ozPq8yeBL+ayHaeUDILuyG96C0YC/FRq0qOTy5Jb2j/ceLhPC1nCgVmfUIZkSMs6VDjjizQJ2fdkfZd4hCNROyN5Z2q6GWMcy5uU2nS8hrfQroiOzCKdia5kxXzcTnExq4HAJiQey0GHYujSLyDbQkV++jc/8NsKNQ9lkBkLTUd3wUUChxXUOjUH+e5JxQlTLfnjzQ2H0sw9AFgIsf5f5SVfIQtS9KaDSSpkMK8v1JS+CJGw1hkuYP2ru/R3PY1ItHMog2ToYTRhQ8jCla8oYXUdf0sJWkdnXszRjEXf3QrdZ5HdWPG5lyGSczBG91Bne+tpOU2Qx4Tc1Tj9BUpej1n5am/5/qe9/HHupOWl1irGeWYiozM4o7kfQCMc0yiyFxKWA6xtDNZzKTh2KL5AHzW8XlakdF450iqbWrv5oet+qKlPXFmuVpuX9KxnuZgZ8Z4gEpbAYfnj0NB4aU6/bnz6TAzr4oxziJC8Siv1vd9LOX149TPsKBxA9u9+9KLnBkuk4Vrx6v3xr+u/WTQs56iIHDnbLVH+Nmtq1jb2TsB31cJFoOBe+aprS1PbVrF4qbUwyUGA8PEE3h0/XJWtTfjMpn55eH99+AYilhQv4XPW2oxiRI/mjl/sA+HcDzG39erD49vTpw7YL2mm3pa+LhlCyIC147ru3Dp9YZVBOIRqu0FzMnvvRvAf+uXIKMwK28M1Q59g/a90R7u4dM21dJG6zVLh4XtXxKSw5RZipiaoy+8AYjKUT5pV0n5/KLULSYruhYTiPvJMxUy0ZVsfQSwpOMtZGRG2adQYq1OWu6PdbHeo5ZIZ+Ull9AVRWFFp0p8prjPT7JPAtjueYFQvA2roYRql74R/tau3xOTPThMkyh3XqIbE4k1s63t68hKEKflaCpzf5GxdSEYWkhDy3F0e+5FIYTFfBTlxe+Tm/NDRGFgPD4t5rmUFb9HrusnCIKVcGQJjS0n0Nn9K2Q5fYbOZppCdf5fAYF23+O0eR/WjTNKbsbk/hCAmu77CcWSewmNkpOx7qsA2ND5L93M6NS8S5EEEy2hNTQFkwlSmXUSJZbxxJUoq7r0y+lzC1Tbny87FxDRGdkpCALzEi8+i9rfT0mmJ7omUGguJBgPsqQzdYlaEIRdL2bZiIwqbUXMzFVJ5BtN2ZfOzx+hZntfb/iSUFzfDirdMV5cpWZNn61Z0qcZ7gBjXUUcVzIeBXhoS2ry3ldcNW42bpOVbd4OXq/tmyq/P3BoYTlnVU9CAe5amvrcORAwp7SSyyaovfq3f/oOodjQEVUd9MSz3tvDvcvUtPvts485oFXs4XiMu5eppdIbJs2h0uEe3AMCnt2+guaglxKrk4sGMNv54Gb15npS+SSqHb2bob43FEXhuYSo6KKqOb2eZhWOR3m1Xn0Qnj8i+2znm42fIyMzNWcUIx1lGY9RExWdXHJU2mNc3rUCb8xHrjGX6W59QgnqQx5gXsGxiDoipYgc4svElCKNPOyNlV2vEVeilFomUGZNVvHX+j+lM7wVo2Blcm5yCT0mB9jcpZKnCbnXI+mIhbpDS2n2vQQIjM//ue4M9bgcYFvb14nGm7EYxzKq4O8IOkKpXfuNt9LW8S1a2i8iFtuOJBZRkPcPiguexWgck3K9/oIgmMhx3UR58cdYLScDMTy++2loOYpA8J2067ptJ1Hu/gkA9d130RPU740scZxDjnkmcSXAls57dGNG51yMWcrHH6tnp/fVpOV2QwHjc9TffkXHIzqfQ2BWvvqysKLrVaJyciZyvPNQ8kwlhOJ+VnR9pHscc/KPwigYaQjWUhPYqhsjCiLHJkRGmcrtxxTOxiKaaQi2sM6TWQB0ZuLF7+2mxUSy9Nc8vGAc5dY8vLEQ7zatzGqdPXFa+TQcBjN1gU4Wt/d9Es/149UWg9dqV9McHBhzdafRzHWJrOd96z4hNshZT4DbZh6DWTKwpKWOd+qGpvimv/Cj2fMpstnZ3tPFX1f2vr94oHBQE09ZUbht0dsEYlHmlFRw8fjUSt4DAY9uXMZObzeFVjvfmtI7b8KBQCgW5R/rVQ/Lb0+ah7mXqvBsUefv4s36tcDuElNfsLRjB9t9bVglE2dUzOj1+u81r6YnGqDE4mZeQep56XsiJsd3ZVfOKk/dg6lhs6+GGn8DJtHIsUXpWwE05e/RhUemFBXVBXZQ49+KJEgcnj9fN2ZF18cE4z7yTCWMdyVbS0XlMCsTWa5Z+eclkWFFUVjZofZ2TnKfi0VyJW1jW8/ThOUu7MZKqpzJc99lJZoQFEGZ4wJyzMnXtKLI1HR8j2B0LQYxj9GFDyOJyfvSEI930Nx6Nv7gy4CI03Et5SWf4LCds99H6BoMlRQXPEJR/qNIUgXxeCOtHVfj9ev7cGoocl5Pvv1iQGZH+00EIsk9d4IgMj7/ZwhItAXeoSOQTNYMopXxbrUdYWPnA8R1iOO03EsRMdAUXEFzMHk2+DjnUTgNRQTjPWzo+SBpuSiIHJFo0/is/Q3d7J7d4OCQXPUe9klb6kk8RyXO6UwiI6vBwtGFqkDw7abM/X9H5E+m0OymO+rjk7ZVGeNBnROv9Xq+UPd5r7NtNoOZMyvUl/NnazK3BGTCjLwKZhdUEVVk/rPlsz5vLxWuGDuLPLONGl8nr+xcO2D7yRbl9hy+MUnt07172QeED2B7JZfJzF1HnAjAP1cvYU370GgvOKiJ5+MbVrCocScWycBvjzxl0EU2A4n2oJ+/rlFvLj885JgBmQjUWzy9fQWtIR9lNhfnj5wxYPt5aMunyCgcVTyGSe7knsLeQrNQOqN8Bk5j74cLvJhQtp5TMQeDqE/09san7WvojHjINTqZV5DZhUBT6M4rmInTmCzO0dAcbGaDVxUVHVOYug/049Z3ATjEPQeXMVmkJCsynydERUcUnIaoQ2A39LxPMN6Dy1jMWGfyC0BTcAVt4Q1IgokpuRclLY/EvWzuVsvwE3O/iSgkt2XUe57AH92MUXQzOvdm3c/S6v03PcF3EDAxqvABzIaqlJ9bVkK0dlxDLF6DQaqktOgt8t2/QkxDVPcHbNaTKC/+GKddLX13dN1KMJS6ZCoIAiPy7sZpnous+Nnefj2ynCxScZgmUOFSPVM3d96lSyxHus7DKhUTjLeww/Ni8jaMxYx1nQLA6s6nkpaLgpRQuMPSzpf0FfC5x2EWbbSHG9jiXan7mY4uVB+oy7sW44n26Ma4jC5m5qpk7cPWj3RjNJxSqp7/iztX0h1JnwGURInTS9VqRW9ERqeXz8IkGtjibWJ1d3r7Jj1clPD0XNi6mYZAV6/X3xs3jFNfYp+vWU5XOH3bxr7CYTRz3XiVcP9t/SKicrIwbX/jG5MPp9jqoM7Xw8MHsL0SwCnV4zhj5Hhiisz/ffzGkCi5H7TEc0dPJ/ck1F63HzafkTl5g3xEA4s/rvoEbzTMlLziIWGfFIxF+ecGLdt5JCYpOwLWW7SFfPx350oArh+XOVOYCS3BHj5qUX3pLqruvQ/oup46NnjqMQoSZ1Vkb8GkWSidVnYERjF9Ztgb9fNpu+r9mElU9FGbJiqaSr5ZvwXBG/WwrEsly0cX6dsjbfOtoi3cgFm0MjP3uKTliiKzNKFknpl7ji4xXd35JADjXKdjNeQmLd/a8zhR2YvTOIpKx8lJy0OxZnZ03wfA6NxbMUrJ2wjH6mjsUUVHlXl34TCn/g0URaGj8+aEL6eLooInMJtStyLsb4iijTz3r7FZzwZitHVcSySaukwsCEZGFv4To1RGJFZLk+c+3biR7pswSUUEY7XUeh5IWi6JZibkXQ/Apu6HiOkQ2Gl5lwACtf5P6QrvSFo+1X0KJtFGZ6SWHf7kB79ZsjIrT+3j/DyFtVKVfTTV9jHElfgutwU9HFuo9i1/1r44rchopL2Ccc6RxBWZ91szlyVPLT0cgyCx3lPDVm99xniAHKONk0tnALtfQHuDakchhxeMRkHh+Z19t1aaVzSaiTklBONRntje9yxqKlwx9lDyzDZqfV28XJM8enV/w2408cODxF4J4K65J1JotbOlu4M/Lh84J4NscVASz7gsc8vCtwjGoswtHcGVEweut3AoYENXK89o9kmzThgSmd0nty6jPeSnwp7DeSMH7mH+2NbFROQ4M/IqmJXf9xGcL9R+SVyROTSvmjHO7A3Gd6+vku0TSqaTa8pualKNv4lV3VsREXdlWdLhw9YlROQo1fZyxjmqU8ZF5SiL2lVCe2waUdFnHR8SU6KMsI2i2qbfz/hpghwcmnc8Fp0Rmjv8S+mM1GESbUx1J5PGjtAW6gNfICAyNffipOXheBdbu1ViOinvRt2+za2d9xBXArjMh1Dq0Bcu1XXegaKEcJiPSJSeU6Pb8wf8wf8CBoryH8RkTC3QGiwIgkhB3p8xm2YhKz20tl9BXGcCkQaD6KYy95cAtHj+RTCSrI43iA7G5t0OwM7ufxGIJitiq5xnYTdUEI53sq0nucyfYxpBtUPtr1zdlZz1NEt2prrVrOiyzuSsKcDhBachILLFt5LWkH6Z/JhC9UVoUfv7xFPYRU10TaDYXERIDrE4jcgIdlsrvdO8iHgGAU+e2cWRheq9S3ObyAaai8UHLWtoD/e+t1KzVnq5bjmheLLAqzcQBIEbEr2eT277An+sd6KnbGEzmPjGBPVz/239IiLxwc96fm3UlIPGXinPYuOeeeq18u81X/BlS3YvSgOFg5J4PrN5NctaG3AYTdx71KlDgogNJH6/4uNd9kmHFQ++fVJUjvPvjaqlyE2TjsSYZbm5twjGojyzQ82m3DAus9l6JsQVmZfqlgH7ZqHkjQZ5v1nteeuNqOitJvW7mlswhUKLO2P8e60quT0lg6hoZfeqXaKiaW79LLg6hUgVoxxTqD9GsyPczBbvCgQEjshPnkIEsLzzFQCmuU/FrOPLubb7OQBGOo/FZUoWTm3teYqYEiDHNIEye3JGtTu0jNbA24CYEBQl39o8oY/xhD5AwMiIvLvTfjfB0Mf0eP8AQH7ub7Ba0meOBxOiYKEo/z8YpBHE4jtp7/pe2ni37SRyrCcBMeq779KNKbKdSq5lHjIRtnX9XmefRibmqXPht3Q/mrLXE2CrZwGBWLLt0My8cxAQ2elfQUc4mdzmmYqZ6FIz0ks63tY9zhnuObgMbnqiXazp1p/wJAoixxSpJHhhW3qCMS9/Jg6DjbZwJ2u6M1tWae4SH7QsIxhLnU3dE+Nd5UxzVxFXZF5vWJbVOnvi6OLxlFpz6I4G+jy/HeDEsolUO/LpiYZ4OVEdGghcNuZQCix26v09vF67bsD2ky1EQeDOWWpW/dmtq9jW03t/1a8STqwaywVjp6AAt33y9qCOMj3oiKesKDywRjU3vnnmkVQ4U5tqHwhoC/r5sHE7ALfMOHqQj0bFl211dIT95JltnFM9cGX/xW078MXClFpzOKak79mqjT1NdIR92A1mji3ObrLNnljWuY2oEmeErZBJOdm/AHzZqZb2jy/OPAu+I9xNXaAJEYEjC9LHr+lRG/0Py5uVUlTUFKqnK9KBUTBxSK4+2d7sVR+eI+2TyTOXJC2PyEHqAmrGXcty7QlZiVHrUzNGE3POSVquKAr1PlW5Pc59lS5hbPQ+C0Cp4xycJn3BVrtPFS4VOK/AkkaJrihxOntUgZLTfjVOu/44zkyIR7cQCb7Vq79o6CMUpfdZJ0kqoKjgMcBIMPQBwVB60+iK3DsBAW9oIaFocilcEATG5v0IgPbAe4RjyWbulY5TsUhFROQeWoPJmcQi6yQKzONRiFPnTy4r5xiLqbar5+h2n36Zd2ae+pKxyatPKg2igVl5qk3ROs9K3RiAI/LVc3ebbzuBNCbxZsnE4fkzAFjZndn0fErOKIrNuYTlKGt7tmeM16BNKlvSnpnc7g1JEDmtTBXNLWrtuypbEkQuGqn+Du81Ddx4S6vByOVj1P28NQQM5QEOLarg2PLRKMB/tw8+GR5o3DHnOFwmM9t6Ovmwru/OCPuKg454flS/nR2eLlwmMxePHzr9WgOF12s2ICsKMwrKGOUaGn2s79arN9vjy8YOWLYT4OMW9aZ8TMnYfslqL2pTtzc7f+Q+HffSzm2J9bO332kP91AXaEVEYIY782CDNT3qMY5yVGI3JJe898QGj/o7THKlJtEbPGqGdqxzIkZRX5C21bsqETNDd3mdfxVxJYrLWEyeKZlwtwTXEpa9mEUXxdYpScs9kW34o3WIgokSe3LmMSZ7aQ2oxLTMkSxKAojEGukJqn2AhY4rdGM0+ALPE41uQBRycOf8MG1sKoT9T+JtO55A1w29+vN3XoGv42KUFCMu08FkHI/LcTUAnT136U4X0mA2jMBlUUeetvuSS+EADtM4XOYZKMRp9r+ctFwQJMrs6jYadcZoAoxwqBlB7cVib4x0qNO3dvj0BR6j7FOQBANdkRY6wvqKXM1TdqNndUqleJ4pj2JzEQoKm7zpyZrmebu2J7OtkiAIHJKrxq/szn4O+6y8MYl91BHch/L23EL1XvB529aMLQHZYH7ixXxp+0580ewyt/uCk8rHA7CoeceA7qc3+NrIyQC8UrPugPb1BMgxW3bxnofX9T7b3l846Ijnw2sTpdJx04aEsnug8fIO9S3unJHJnomDAUVRWNCgEp6TKsYP6H4WNqsPgmNK+mcS1cct6nEfU5SdBdLeWNqp+g3Oyhud9TqrutV1xjgrcBgzG5SvSzwsp6QxjAfoCHfSGm5FQGCcM/X3s9GjCgEmuPQz03Elxna/mjkd7dC3I6vxaxnRWbrZylq/2hpQaT8cUcdLUyM1RdbDMYrJZfoW/xvISgi7cQwuHfskgHbfM4CMw3wEFmPq71+WA3R7fgdAjut7SGKyQCkTQr4HCPb8CFAQDZOQTLOz/kNwEo98ia/jYuR4dlNx9kSO8/sIgotodB3+gH7vpIYCh1oK7/Q/j6zok4Ayx/kANHlf1H0olzvUUmWT/2NknR7LEXY1G9kQWEpMpxxfbVeJZ0Nwna5ZvFmyUmlTz+WtvpW6xzjKMR6jYKQ72kVzqEE3BmCCS71uN3jSZ/WmuNTrYYe/Dn+GEZoAM3LV+BVd2RPPcmseJRY3MSXOyu7kjHMmTM+txGW00h0NsLortU1Utqh25FNlzyOmyHzWOnCZsHE5hVQ5conIcT5pzj5DPJA4sXIsdoOJOl8Py9tSnz8HCq6aOBNREFjUuJNNnfpjaQcaBxXx3NzVzieNNYiCwFWTZg724Qw4dng6WdXRhCQInFHV+9LwQGBtVzPNQS82g5F5xSMHbD9bPK00BT2YRQNzCvq+n9aQh/U96k3pqKLel+1bQz3s9LchIjAzL/tJRysTD7Nssp0AazxqNkd7eKbCBq/68K22V2NLkRmNyBG2+dS4CU594lkX2ExEDmGTXJRa9b9nLZtV7dCfLV+nEU/HXN3ljX41U1lu158q1uh9AYBSx/m6xFZRYnT41VJ8gSN92dzje4B4vAlJqsDpuCZtbPJ+FELePxPyqOIds+NbOAvfxlnwUi/+XkAQ84lH1+DruAA53tKrY5CkPNyu7wLQ5fktspJ6rneO9TiMUgkxuZPugL4JfZH9VETBSiC2g55wcrk733IIJtFNRO6mI5Q8qSjfPA6bVEBMCdIcXJm0PNdUjstYTFyJUufX98Mck3ih0TLre8MkmhjtUEnlRm9qxfREjXh60xPPPLObMksRMgrrevTN6ffEdLeavdzqa8Abzc6SSDXSV9db2tF7omcQJeYlsp4ft/RPeVx7Qf+4OXsC3VsIgsCJiazngoahYd5uNRg5eYT62V/eMfjTlQYaFc4cTqlSn2GDlfU8qIjnw+vUB+BJVWOpPMB7O2F3tvPI0pEUWFN7Oe5PvJvIdh5TMnrADONh981zTuFIrP0whvOTRC/VFHcFBZbeT7daliizj3eV4zKmL4HvCa18p2VV0qEt3ElLqB0RkUmu9OV8Lesz0ZU667zdt4moEsVtzKXEUq4bo5GBMY5putOMuiONdEcbEZEYYUvORnoiDXRHdiIgUWFLtqfyRevoiWxGQKLEntyj7I9swxtZg4CBEsdZusfYE/yQaLwJg5iH25asqNcQj7fT4/0bALk5tyMK2Xu0KopCyPNrQglBksX5AyzO23staJOMk3Dkv4AgliDHNuNrPw851jsFqtNxLZJUTjzeiMf7YMo4QTCQb1dbE1KV2w2igyL7qQA0+15OWi4KBkrtqiNCgy+53C4IApUOVUhX60s2KRcEgZGJrGeNjq0SwJhEC8d23xriKdoHtIy8lqHXw0Sneq7XBerxRdPb50zZVW7PTI4KzDlU2tQy/uru7EmkVm7XKiG9xTHF6udZ2Nr7PlHd7SXK7Qtbtgyo8ESrdH3QuGVIqNsBzk6U29/YuWFI+IwONL4+We21/e+29XSGBsa/NR0OGuLZFQry0lb1bebayZlFGl91KIrCK4m3t6FSZgdYUK/eyLW33oHCxy39XWZXidoxRft23NrD5dBelNmbgh00hzqRBJEpOZmztlp/5xjHCKyG1KRJURTW9ajnxiRn6kz47jL7tJQESit/jnHql7h3+NQ36jLbJF01u1ZmL7FO153L3uhL9GVaZ2GW3EnLWwNvAZBnnYdJ0vchbfepNkx59gsQBbNuDEC3548oig+TcSp26zkp4/aGosgEe35C2P9PACyuO7E4v7fPLgqScQyOghcRpRHI8Z14O84jHsu+LCkKFnJdtwHQ4/1rWnulAsfFgIgv/BmhqP4+SuzqhKi2wLu689nLEpnoRv8HKDr9hiPsiT5P/2e65XotE661ZOyNcusorJKDkBygIcV4zIkJ4rnFu4GorG8x5Da5KbOWoaCw3pte3LKrzzOL8ZmwuyLRmz7PQxOVjy3eJnoi/qzX0zC3cCwGQWS7r406f+/bMvbGrIIqbAYT7WE/67ub+ry9VJiRV0aBxY43GuaLtt6b6A8E5pVUU2Cx0RkO8klj71sfvmqYVVzO1IJiwvEYT23MbvJWf+KgIZ5PbVpFOB5jSn4xs4srBvtwBhyrOpqo8XZhlYycVDk0/Ad3eDvZ4mnDIIjML8uegPUW3ZEgKzrUvqf+IJ7BeIQlidnIxxT3vr9TUZRd5bTe9HdqD7GJriqsUmrCpEHLzkzOSf+ZG4KNdEW7MApGxrtSnxsbEmXLVGX2YMxHfUD9XGNS9neqWSwtq7U3NOI5wqFvL6X1d+pZKCmKQov/TQCK7Po2TpFYA57QRwAUOC7RjQGIRrfh9T8OQG7Oz3TtmPSgKDEC3bcQCTwOCFhzfovFcX1W66aDZBiBo+AFRMMYlHgjvvbziUezVwLbbedhMk5BUbx0e/+cMs5kKMdlmQ9Au+9p3Ri35TCMYh5RuZuu0OKk5UXWwzAIdkLxNrrCySMRy22HIgkmfLFmuiLJD/URtumISHRFGuiOJBMeUZAY5VBFZ1t9+g/JUkslLoObqBJhuz91lnKqS81srelJr2CenKNmI2v8DXijmUnhIYmKxMpe9HkWmF2MtKuZ0mWdve93dBmtzMyrBuDj1r6X202ixNxClQx/3DxwZXBJFDm+TP2+NKHpYMMgipxRrb6E/3fHga9uFwSBr09W78mPbVix3zPPBwXxjMpxHluv9id9ffKh+32+8mBAK7OfVDl2yIioNFHR4UVV5JgyC2X2FZ+2bEVGYYyzkHKbu8/b+6J9O2E5Rqk1h7H7YBpfH+igNdyDUZCYnlud9Xq96e9UFGUX8ZyaQVi0NmGjNME1HlMKpXpPtIvGYC0CAuNdyUpzgO3+tSjIFJrLyTEVJC2PK1FqEzZKmm3OnojE/TQHVgK7RSh7IhhrpTO8BhAoTain94Q/uoVAdBsCRgpt+v2fu0VFc7EYU/fWdnl+DcSwWk7Aakke56kHRYkQ6Po20eALgITN/RfM9kuzWjcbiFIpjvznkQyTUeQ2fO0XEouszGpdQRDJzbkTAK/vUaKx1FmcTCIjUTBQZFfNp1v9byUtl0QzJXbVhLzBnzxByBNI6fIAAQAASURBVCBaKLOpPfW1/mR1u1myU2ZTqzIpy+2OGUDqPk9BELIqt0/NUc/ltT1r0yqYc005VFhLUFBYl0XWU+vzrAk00xXxZozXoPV5aq04vcXRiXJ7v/d5tgxcnyfsLre/17h5UP0k98TXRqrnxoK6LUNGcT+QOGPkBAqtdloCPt6s2b8vAAcF8Xxrx2aaAz4KrXbOGLVviuSvEmKyzOs1anbknFGTB/lodmN3mX1gM7D9X2ZXL8qjiybs00vLl4ky+xT3CCxSdi8BiqL0qr+zNdxBW7gLSRCZ4EovXtKyPVNyUp8bmzwqOa20jcRh0O9p1WZoa6RgbzQE1hOVg9gkN0WW5ExvQ+ALZGLkGCvJMSVPlWr0fwhAnmUaVkNh0vLWRLYz33oUBjH5GP9XVJSaEIbCXxAIvgmI5Ob8NGXc/247iL/zOqKhNwETttx/YrJ9Lat1ewNRKsBe8AyScSaK0o2v4xJi4eSsox6slqOwmo8FYnT1/DplXFYiI5uaUW4LLEDW8RndVW73faBL6HaV23X6PGF3RjyVrZLWylEX2EQort+TNjEL4jneNQ6jYKQz0kVjKH05uTd9ni6jndEOtQ+6N1lPrQKyz32eCYeNFZ078URTC8myxdGJe+aarkbaQwM3RvKIomocBhMtQR9rOhsHbD+9wbT8EkY6cwnFY7xbN7DEeyjAJElckZja+PDapfvVSuqgIJ6aqOiKiTMGVNAyVLCoqYb2UIA8s5UjS6sH+3AAaAv6WNGhiiROGEDiGVdkPmlRb+L9YRovKzILE2UsrZm/t9D8OzUxQTaoDbTSGfFiEo1MdFVnjNf6O8c6qrFKqfs7I3Jkl4+hlv3Rw8YMZXZQ57NDmv5Ovzqoodp+qG7pujZrNbt+mV3LvqUqs/cEPyAab04rKlIUha4edXqPw34xJmPm31iRffg7riIW/hAEC/a8hzFZk43x+wui6MaR/yQG0xGg+PB1XkE00T6QCbnuOwCRQPB1QmF9UqeKjNTxoVo/7N5wW2ZhkgqJyR46g8lm8CW2eYiCCX+sDk8k+aFdaVdbKVpD6wjGupKWa32etf6VxORkYptnKibPVIKMzA5fcjkfYLxTPZ/rgzV4oz26MSbRxHinel/QBiikwtREy0o2fp6wb32eh+SOQkSgNtBOa0j/mNOh0p7HKEehaoPU1neyVGRxMsldCrDrPjoQMEsGjilV74fvDhF1uyAIu0RGLx8E5XaAyyZMxyxJrGpvZnnr/nsBOOCJ5/LWRla0NWESJS6dMGOwD2e/4JXERXNG9cQBNWjvDd5r3IwCTM8ro8TmGrD9rO5soDsSxGW0cEhe38eDru9ppD3swyaZmJXXe1smWZFZrhHPXhjHaw+vKTkjMYmZX5ay7e/c5NlMVImSZ8qlzFKa8pg3erRyvD7x7Ag30xlpQRIMjLTrZ0617NVIx2ydfeyeZqNlw/ZEON5FezAhTNIhnr7IRgKxGkTBTIEtuQwPu5Xa6URFgeAbhCNLEQQrbtetujH/c9xyN76OS4lFPgfBgSPvCYyW1HPu+wuC6MCe/ygG83GghPB3XkskqD9Gck+YjBNx2FTlelfPL1NmNXaLjD4nFE0u+wqCRFGCvGuZ5j1hEG0UW1VyqVdudxiLyTePBRTqA8lTiorMo7BLeUSVEA1B/Ye+9oKTqs/TacyhwloNwCZvalI5NZHpX5uxz1O9lnYGGumJZi6f7+7zzJ6wOY1WJrhUzcGXHftG9I4u0srt/aRuLx54WyWAEyvUF4AFQ6TPE+CcBPFc1FRDW7D3gq+vGgqsds4apba5aAm6/YEDnnj+J/Flnj16IoVDxFJoIBGIRninTiUh2tvbUIDWRD7gZfZEU/y8otEYxL6f3gsTN/MjCsdg2ods+RZvEz3RADbJxCRX9qK23vd3qvGZ+jtXJ7I8U3KmpGwbaAzW4Y31YBLNjLTr7197+FfaxmGWkvt1vdE22sM7EBB1+zvbQhsIxXswiQ5KrMnktsm/EIU4Oabx2I3J35umZs+3HoNBdCQtV0VFaqk+VZldUaKJ3k5wOb6JQUoe97knZLkbX/tFxKMrEAQ3jvynMZj1x4gOBATBij3vAYyW04EIga5vEgm+lnE9d86tCIKFcORLAqHkHk0Ak6Fsj0lG+iIjzVapLfBeinK7+oLQqEM8ASoTfbz6tkoi1Q71PNnh+1J3/Ux+nrD7RWlDmnL7lESmf6NnExGd7KqGHKOTEbYyYPdghnSYmjMKEZHGUDstoexV5ofm97HcnhA8ftq2hVg/WAFpLUqftm4bUGuh+SWjMYoi27wdbPOkdl7Yn6h25TI9vxRZUXa1qx3o0KyV3qrZTKOv9xPT9gUHNPEMx2O8XaPeMA4Gw3iAL9vqCcSilNtdzCwoG+zDASAuyyxpqwXg+AEmnks71P0cVZx9djHt9jpVUca+mMYDrOtR1fVT3dUYepF93uBRbUamuTOr4LsiPXREuhERmeBM39+51ac+3CanGZO53a+S7TGOCRhSZFtr/Wr7wWiH/tjZ+oBKcIstY7AakjPcTUHVbLzcNkt3WlFbUM2Kldrn626/M7gIgELbSbrLu4PvAEpiUpF+pjoYWkgstgNRzCfHeaNuzJ4Ief+MHFuPIBbiKHgOg2lGxnX6G4Jgwpb7N4zW84E4ge4fIcvdadcxSKW4HDcAqtAoFTTVf3dAn5zmmGdikgqJKz56wsnkT/XzFPBEthCKJROvSvvhwO7ffm9ofZ51AX3SOMqhksr2SCP+mP4DcrxTfdlOp2wvt5bhNuYQVaLU+NPb+UxO+OFu9takjQOwGSyMc6ovSdr1mw0OzVWvWe1e0VtMS0wx8kSDbPbqjxXtDabmluM2WfHFwmzq6d0Ag97AabIwp7AKgM9aagZsP73FWQn7wQ8aBm+W+f7EpPwiDi+pJK4o+01kdEATzw2dbUTkOLlmK1Pye69G/ipiU5c6AmtGQdmQUe83BHoIx2OYRInRTn2vxf7CTp/6wBvnKuqf7fk7ABjrTJ8NS4WGgHo8VfZkcUwqROJROiJqv1eFNfN6LWH1GAvMuZgziJdaQq0AlFlTv5S0h9WYYkvqmK6I+kAqNOsby3dFGhLHpE/6eiLqQzbPrE+sPQnbHbc5uedSUWT8iXKwy6zfChCIqMTXkSYjGQovBMBmPQVRJ2u6J+R4ExH/E2q8+09IxsGbBCYIBmzuPyAaJoDiIez7d8Z17LbzAAiFl6ScZuSwqAb+kXgdMR0yKwgiTpP6UA5EkrNzJikHm0Ft3/BFk1X0uWaVYIXi3UTiycKVfHM1AN2RBt2WAKtkx2lQR5hq59/e0M7ZznA7cooZ5oIgUGZVj7M1nH5kYJm1KBGXXQaz3KZer829yHiOSNwbmoJd+zR3XRJERjvU49zp6+j1+ntDFARGO9Vj2tkP/qDpMDFXfS5v9/b9uPsLMxIJm83dgzNOcjBwZHk1AKvaBs6/dU8c0MRT+xKnF5YOGRI20Njco5YsxruzJzoDDe2mUuXIQ+qH8ncq+GMR2sPqA63Sntfn7fmiIToS2xth3zfC3BRUb9yl1uxnfreGVfGFRTThMmZuD2lLPBQLzek/sz8WwB9X+5aKzKnPj47EwzjflJq8dyYe/LkpYrqjjYnl+uTVE1WJqUunjK4oCr6omjFy6mQrQ7EGZCWEiAmLQb99IRhR+/dsptQCqmD4EwCs5uSJSEn79N4PhJFMh2HIIn6gIQgiFufNAIT9DyPLyYKdPWE0jEUSS4Aw4RQiI4PoxiSpfdHBiP7oQHtizr1fpw8UwGmsBsAbTc74mUQbVkk9R7Xff0+4TerLXVj2E4rr91TmmVSi0pmCeOYYczEIBmTidEdSk5lCs3retoXSkwvtmmoLZ0eMSixqfG9K7UWWHCRBJKbEaQ/vW6lTuz/VBvqHwFUl7p+1voElnqMSiYgd3oHdT28wLke1hmsJ+ugJhwb5aPYPZhSqL2Kr2vqeMc8GBwnx3Lds1VcR2lvaOHeyr+JgYYdHvamMcvWdDKaDNr0j12TDZcp+3GHK7QW07dlxGvdte/VB9UFQYcueuGrZkhJLXlYvTM0h9WWj2JJ+H20JQukyuLCkUb63R9SMZ4FZn1RG5TDehDI5z6R/bXVHVOLpTkU8I6rDgcuUTByD8VbiShABA3ZjckZVIz0240jdMr2shAlG1RYbm0m/zzkebyOaMGS3mNP7dipKnGjwFTXWse8TifobRsspiIbxoPiJhhakjRUEAYvlKGB3plcP1sT3FYjoC29sRrX07I/q9yM6TNUA+CI1usu131vLeO8Jo2jBYVDvW9qLy97ITRDPrsQ5ujdEQSTPpL5UtaeIgd0vXq3h1DEAJRb1eJpD2RG64l3EM/2LwJ6QBHHXi2n9PhLHERpR9PcP8Rzh0La3f4jnds/QyXg6TWbK7Wp70OaegyPrOa1AvY/v9HbTFeq7LVcmHNDEc6VGPAv01bsHGmRFYXP30Mt47khkPEcNcJldezsf0Q/ZTth9E9/XbKeiKNT51d+j0pb9i0Br4qGlPcQyoSmo3hxLLel/c414FqWJUxSFzsTDOD8F8eyOqNsxi1askn6JuisN8YzIAYJx9bdy6RBLjbTYjeWIgjFpuUZ6bCb9Pt5QdBMQQxLdGCV94hsMqz2iJuMUpBSjNjXEo6tRlG4QnBjM+hOWBgOCIGC0qD2usfDHGeOtZtXkPRhalDJGI+rBiL4q3G5KTzx3ZzxrdJdrv7cnqj9/XjtftPNnb2gZ9lSldoD8BKnsSFNG166B1nB6UUtxgnj6Yn58scwzrYv3IeMJu+8Ptf59E9nsynj2F/HcTxnPkU51P42BHkIx/VGng4Fxiefnpu6hIXoaaOSYLYx0qS8/q9oHvtx+wBJPTyTM9h71ojlYMp51vm5C8RhmycAIh3uwD2cXtifKKNXOgc14av1I2tt6X9FX4tkW9hCSo0iCSFkvSu1axjNb4tmcKBeWZCCerYn+zsI0ZXZ/3EdIVstL+TrTiAA6E5mkXFORbvYvHPcTjKs9qm5j8kuflu20SDm689k10uIwVukfYyLjqZV990ZgjzJ7quxkKKSW2S3mo3SX7wmN1BnN8xB0iPBgwmhWrZxi4U9QlPQKZC3jGYmuIp5CkKS1JgSiKYhn4juPxNuIxpN9J52JjGcq4plj1DKe+sQz16gSz+6MxDN1plJrEelIE6NdA20ZejwtkplcYw4AzcHM2a89S+29MeSuTFRE6gJDhHjup4xnntmGy2hBAXb6ss8SDzS0iuHB1Oc5LcGT9ke5/YAlnmvam1GACoeLgoPARglgU+IiGZOTP6C9lL3F/s54VtmzJ3npoAmLRtj2jcjWJx4iJZZcjFl4cWpo2UU8s/scGvEszSBE0rI76fs71Yd1jjEXY4pxml27+jv1BXsaabBJbsxS8rWnZbtcRn2fVV+CtDhN+sIkTdhiN+pnPDXiaTXql9kVRSGYIJNWS2biGU2Upg3mgffr7C0k00wQHChyF/EUZFGDQSrFaFC9NEPh5NGVsPs7C0W3IcvJJTeD6MCcsJ0K6PR5ai8LgWgjcR3LJZdJ/c31ejxhd8YzFfHUWjvSEU+tRaQ9TRlduwZ6oj2E4+nHI5ZaVRLSlKEfFKDQ7EZAICxH6clixruGikTGs35fiWeCuPZEg/REMmdmM24vcQ9tD/vxD+D4SEEQGJVISAwlgZHW57npICKeMwq0Ps/hjOc+Y3WCtU87SMrswB5l9qHT3+mNhGgOqkKBMa6BPa5av/rGPFQynnWJfq3KXvR3Qu8ynsF4iO6EubXWj5YKWj9bOuKpPazzTaljujIIi7qiWfZ36pTZAbwRTViUnPFUFGVXmTcV8dTKxKmERbHYDuLxRsCE2ZTeh1ORPcQjy4GhSTwFwYgx0aOaTbndYlGFUVrGd28YpWIMYgEgE4zqz/+27+rzTCaeFqkQg2BHIY4/mtzHubvUrm8dtKvUnrLHUz3nuqNtyCkyvNq52xFJTRrsBjt2yQZkznpqlYTmLIinUTSQb1L7A3tTbh+RIJ51+9jjaTWYKDSr1YP+yHo6jRbyTLbE9gY2EzkmQfK2DhEvT9jdqra5u32/jpIcTExLCIxWtzcP+Gc+YInnyvaDT1i0aZewaOj0d25O3ExKrM5+Efykg1YW6u8ezyr7vhFmrWxW2cv1NWFCSRbEUxMWOQx27AZb2tjWkNbjmVqtrj2sU/V3wu5sU16GjGcq4tmTyHjm6AiLALwJKx5Hol9wT4TjzcSVAAIGrMbk+e6KEieYEA2lEhZpanaLeRaimP47i4Y/BeKI0igkQ98nYQ0ENEIczarPU83waj2ue0MQhF3fWyBln6embE/u8xQEAYdJfWHQXiD2hCYuCsV7COso13MzZDxdxjwkwUBcieGJ6hM77dztyCAcKtwlMMpEPDWBUXbESHth7I2lktbj2RDo2CdLJdh9n9r5FSu3j8tRf6+hJOQZk5OPAHSFg7SFDvwJRgBT8ouQBIG2oJ9Gf+ZJXX3BAUs8V+9hpXSwYMsQFBZt7lFv/uNyBvaYQvEozUHViqQ/iKcnGqQ7GujT9nYRz15kPPf08CzKotSuPQxLM2Q7Y3KMjoS9TKE5daz2sC7IykopPfHU+vX2RjorpZgcJBhTqxVav+Ce0MiO1VilKzwKxbYjK0FEwYrZoF+qj0RWAmA2JY/yTDoercy+H8Zi7is04hmPLEeR0z8wtM8ci20jLuvPBrdmIJ67lO06Xp6w2wJLz8szo6VSoic4GO8hpOP1KQoSbqN6L0lVbtdK7d6Yh3A8tR2O9gKWmXgmfDazyHiq8RrxzJ4AFlvdGAWJqBKnNdSd9Xp7or/7PCv3k8BIezYMJeJpMRipdqr3380HicDIYjAyPlf9LQa63H5AEs/WgI9GvxcBmHqQGMeH4zG29qg3nKFFPBNZ2Jz+MXRPhbpEOchpNOM2JY9w7C20rEGh2YnNoD/nOxPq/VqpPfvfoymRJbFJZtzG9KbmsFvwkElY1BHpQEHBKBhxG90p4zQLmvw05fiu6G5xkR76YqXki6qTp0xiDmYpmXj7I5qwKFWZXevvnIQg6E+KCkdXq/swTdddrkFRlD2ERUOXeEqGEYjSSCCWyNCmiZXyMCS8OiMR/QlBWotCMKpvqZTRy1PLeOp4ecLu392jIzAySTZsid+9O6L/8MukbLdKNmyJ3uJ05fZdGc8MhHJXqT2YHQEptaoEsCmYPQGUBJGyRC/5vpbb+9/LU/0dBtpEXiOeO7wdhOOxAd1Xb6CRsA1dAze9aahht5/nMPHsNVa3qxmTse4CHKZ9Iw1fNWzubiemyOSarZTakpXCg4VNu4jnwJLhPa2U+sNnsa/9nbIi7/Lw7E3GszFBJMusBVl6eGapaE9kdQothWm32xlOX2oPxnyEEib0+9LjGYn701spZVS0a/2dmRXtepCVINGoOhbObExPPOX4DuR4HWDCYBo6Nkp60DKy2fR5aoQ7EtWfeW41JohnZBOKkmxxo3334XgTMTk5K6m1SKTy8tylbE/R57mr3J5CgKSdd6lM5CG7cntRlsp2TbTXFe0hlEGIBOq1C9CQhQp+T2jl9rp9tlT6anp5Flkc5JgsxBVlSPl5Tk5MVVrXmb5l40CCViFe1T6wyvYDkngejMbx6zvVm/Dk3OIhY3ANsGV/Ec8B6u/cV+LZGvIQkWMYBIliizvr9RoTWRXt4ZUJWqm9JEN8WxaK9rgSpzOixqUqtWsPe4fBjUlM7tmNyCH8Mc14P5l4auXVlFZKmrAolaI9k7AokaWzpujvjEY2AHFEsQBJSt+Gs6vMbpqNkKEXdLCx21YptTm8BpNxGgDhyGrd5WbDCETBgUKYkE5W0yi5MUnqeRSIbk9anrWXZwpLpUxennkZTORh9/mb1kR+l5dnemLhMNhwJPqns+nzLE8Q1YYsM6Qa+tNSqT/EIdq9dOcAl9oFQRiS5fbJeRrxPHgynhpnWtPejDyAAqMDknh2Jpz3yx2uQT6S/YfOsPqZS+xDJ9sJ4ImoPVaFloG1tPImLD/6o8wO4IuFEtvbN8Khre80WjGI+iVfPQQSPWnZjMpU49Xf3WlIHx9MxDnSxMXkKDKqsMGWIi6csNix6tgkAURlzcpFwKJDLCOymi01S+4U63sTy/X7W6Oy2sdrSmH6Hot3JZbrk8q4rD5EDVJ5xhc0Oa4+cERj8rz4oQbJMAHYfczpYEiMGZVlfUIhCCKmhGVSLEWMSVJfdKI6faLmRA9nNEW/qSXx20dkfdsfq6TetyPxVMvV8yqUYn3Yff6G4qmnsDgMaitLME2MBmciNhDLHKtdu/5Y78Ytuk3qPny9XE9DnsmeWL9/7I9yE/c+X3Tgx0YWWtTP3h0Z+Kk52aI0Mb2oK9x3e6qvCiocqmetLxohMoBtDwck8dQgMHQyf/sLQ/UT768sbH/vp69b29f1e3vuZh/fX3Hpl2c6nr7/Sn07vt4dwVC9qvZEP3+ejNdRuuUZ1u3TtrNDf9/798fZMvDXRC+3th8rZ0PxWT30jmjgsb8+8wFNPIcxjGEMYxjDGMYwhjF0sF+I5/333091dTUWi4U5c+bwxRdf7I/dDmMYwxjGMIYxjGEMYwhhwInns88+y80338zPfvYzli9fzvTp0zn55JNpbT14lGLDGMYwhjGMYQxjGMPYD8Tzj3/8I9dffz3XXHMNkyZN4p///Cc2m42HH344KTYcDuPxeP7nbxjDGMYwhjGMYQxjGAcGBpR4RiIRli1bxgknnLB7h6LICSecwOeff54Uf88995CTk7Prr7JyaI6oG8YwhjGMYQxjGMMYRu8xoMSzvb2deDxOcfH/Tg8qLi6muTnZoPT222+np6dn119dnb7B8DCGMYxhDGMYwxjGML56MAz2AewJs9mM2XxwTBoaxjCGMYxhDGMYwzjYMKAZz4KCAiRJoqXlf02NW1paKCk5eKYKDWMYwxjGMIYxjGEMY4CJp8lk4tBDD+X999/f9X+yLPP+++9zxBFDe/bxMIYxjGEMYxjDGMYw+hcDXmq/+eabueqqq5g1axaHHXYYf/7zn/H7/VxzzTUDvethDGMYwxjGMIYxjGEMIQw48bzoootoa2vjzjvvpLm5mRkzZvD2228nCY76E2Ji1Fd4AGeNDjVIic8ciccH+Uj+F6IggDLwx6X95jG5f/Yjom1P7tvxKL1bX0gUIWJKdp9DGzUXzxCfTdyeI/LiSgwwpdyOnHI76vEryMhKHFH43zn1mdbXPr+sRNMvJ8VyQVs/1bxqIXF82cyzThy74s8idnChKD71H3t93/qx2mdPXfDa/f3pb09J/D6CzjZ2/zb625eVWGJd/QF9u5anGNkoE0+7Puw+z/sakxSbxRhJ7ZoXezlyMtaLfaRdv58GH2r30v0xOjMiq795b7+zgUQ48cwShYNnwGNoD840kL/7fvlGb7rpJnbu3Ek4HGbJkiXMmTNnQPc3xp0PwLrOg8ekfqQrD4BNPW2DfCT/i0q7G4Ad3o4B3U+J1QVAY6CnX7ZXbM1Rtxfs2rf1Ler6nmgAfyyU9XoFZnW9llBnVvH55lwAWsPp43NNalxHJPXvYBLNWCUbAN0R/e25jOq15Yl2oChK0nKb5EISjImY5HPRYSwCwBdr0SWfNmMpAP5og+7+rYYKAILRnbrLzYaRAISim3SXG41jAYhGt+5BwPRhME0HIBZOtn4baohF1GM0GGdkjI1G1wO7v4u9EZf9RGKqo4jFODJpuaLECcZq1eWGZMs7f1Rd124o092+N9qkLk+cC3vDE1U1AU5Doe7ynkg7sPtc1ENX4jx3m/JSxnQkrpm8NDGgks72iHofKEhcb+mgXbuFZnfG2D2h3WtKLL1bT0NDILG+1dUvpKEh0A1AaeJeOJDY4VW/sypH5u93f2FTt3r/GuVKf34cSFjfoXKmKqcbszRweckDkspPL1QfXqvbmnUfjgciJuepGeSt3R1DKtM7Lkd9eGweYEI8wqHeHGr92RG2jNuzqQ+1nf59I8x2g4VckwOAukB71uuVWwsAaAxmt06pRf1+W0Lpv98isxrXFkq/3XyTSgY6IvovbW5TAQIiUSWCL9adtFwQRHIS5LE7kkwe7YZiRAzIShR/LHkfDmM1AL4UxNJmHAOAP7pNf7lpCgCB6Drd5QapElFwA1Ei0Y26MbtiTXMBI3J8J/FYTdrYwUY09DEABvMxGWPDkVUAmI3TdJcHoxsABaNUhFFKJofBWAOyEkYUzFgN5UnLtd/OYarS3b4nUg9AjrFCd3lXtBEAt0mfuHZGVGKaZ9InrrD7/M1PE9MaVmMKzfoEV0N7uIu4ImMUDOSb3GljARqD6rVYbk2/3b1R51evzQpbQa/W06Ddq7R7V1+x06+RwYElXuF4bFdiQnteDAWs71TPM+3ZejBgVbtqczm9cGDF3wck8ZyYV4hRFOkKB6nz9k8GbKij1OYk12wlpshs6c6e6Aw09hfxrLKrN8eGQDfRfii3VznUm3+dvxO5l+VyDZWJB0BdL8hrWYJ4toa6iMqZXyBKLGp8cwZCqRHPrmgXETmSMi4/Edce1ieekmAgx6h+1xoB2Bu5CcLQnSAQe0IUJJxGdblHh5g6jSpZ8UXrdMvtdpNGPLfq7ttqmgxAMKJPPAVBwGRSCVckQcBSQRDtGEyzAIiFP04bO5hQlMjujKclPfFUlDiR6BoATImM7t7QvjurcYru8kDiu7cZRyHolPa9kR0AOBMvEXvDk8hmu0zJxFNRZHoiakY0NwXx7EqQylyTPiGQFZnORFa0wJyaeLaF1XtSkSU92dGurWJLQVZl14bES6N2LWcL7QW1ch+JY61GPO37RlyTtufrTGxvYInnDm8HcUXBZbRQbHUO6L56g/Vd6nk2KTf1OXSgYVWbeu1pybuBwgFJPM2SgUl56smyqr1pkI9m/0AQBMa7NZI3lIin+jsMNPEstDixSAbiikJTP5Tby6xuJEEkJEdpC3n3aRuVicxFbzKeeSYXZtGIjEJbuDtjfEniodkUTP/9OgwOLKIFgPZw6uMp2JXxTL29XJP6NtyVgnhqmaquSDLxhN2EoyeaPCDCaihGEiwoxPDrEFe7cTQAgUiKjGeCLIVjNcRl/d/NnCCe4chK3eV7wmA+GoDoECaescgyUPwIYgGSYWLa2GhsK4oSQBCsGA1jdGMCCeJpS5D4vaGRfu232BteLeOpQzwVRcYTVTOeLp2Mpy/WQUyJICLhMuoTS+28S0U8u6OdxJU4kiCRY0xdum1NVAmKMmQ8tWtLe8nLhOZEqb3Umj2B9MdCdEbUPt3Kfcx47iae/ZPxrPOrpfsRA5zx1J4N43IK90s/aTZQFIXNiVL7hIOEeCqKwsph4tk3TEt8cdoXeTBgvFu9YWm9KUMBWsZzi6cNeQDbHkRBoNLef+V2oyhRZnUntrdv5fZ9IZ6CIFBkUR+WzVn0eWoPw9ZwB/E0mVl1u+pv0ZqmLJ+XeAh3pMh4AuQmyGlXqnJ8IqPZnYJ45hjV8qxHp49TEEQcu7KeNUnLbcZRAETkdqLx5P5bg5SLUVL3H4is192/2aT2mIfCn+ou/5/tJUrXsfBnKErqTPFgQsvGGsxH7xJXpUIovAgAs2mWbrYSIBBZC+zOHu8Nf4L02436xNUXUYmn01SdvO1YO3ElgoCE05hcztNeVlzG4iRhGkA4HsQf9wCpS+3auZtnKkybodyV8UyTFQVoTlwvpRkyoxq0Hs9iS/aErT6g3mNyjXacRmvW6+2J2sQ2qvqJeGr30YHOeO5JPIcK2oJ+uiMhREFgdE7/fJ9DHc0BH21BP5IgMCV/YNsLDljiOWOPPs+DBeO0jOcQKrWPcORiEiVC8Rh1/u6B3VfiBrmzv/o8EzfwnYH9RzwBShIPrGwERvnmXAyCgZgSpyOcXgilZXZaw6mJp5bxTFVqh8zEc1epPWXGUxWkaL1+e8OZ6A306vR5GkQ7Fkklrqn7PLVy+1rd5Rbz4YCBWLyWaEy/l1SDZJyMIOaD4iceWZY2drCgEU9jFv2dwdAnAFjMR+kul5XILmGW1i+7N3ZnPJOJZ1yJ4I+pLxR6pfaeRLbTaSxFFJLFC9o5k6q/UzvnrJIDi2TXjdHO3XRldlUwpF7XmXo8tVJ7SRalc0VRdl23Jb0gnto9omIfy+SKolDXj0QxrsjUJjKeVQNMPLckiOfYnP5pEegPaMmbamculgEU2QwlaGX28bmFWA3GAd3XAUs8pxWob9NrOlr22RLnq4ZdpfYhlPE0iCLVTvXGtd0zsMr2KruaKdR6k/oKjXjuc8YzsX5dL4lrcS+IpySIFFvU/TRnFBipD+LWNKRSe1h3RNpSCvPyEiXOVD2e7l09nk0oOllYrcSqkZC9oZVotV7BvWE3qSXeVMTTalSJZyqBkSg6MJtmAhBKELFUEARxSJfb5Xgb8ahKsLXjTAVFiREKfwaA1aJPPEPRLShEkQQXJilZsa4oMoHodgBspuRSu6polzGIDsxScqZIe9nQK7PD7r7g1P2dWpk9nbBIvQ7yTakJZWeki7gSxyAYyM0gGNKuq5IsMp7eWIBAXHVLKOqFqr2v/Z1tYS/BeAQRgXJb35XhLUEvUTmOURApsbn6vL102JYQFo12DT3iqT1TDwZoxFPjTgOJA5Z4js7Jw2E0EYxFh5TYZiAxNlFqb/B78Eay8SncPxjlVG+mA22ptFvZvm8WSHujqo/EsyKRIfFEA/REA1mvpxHPbErtsLvc3pSBeBYmHpxtaXo8c00FCAhE5DC+mCdFjEo8U/V4uoxFiEjElSjeWPK+ckxqxtIbbdS1VHJmq2yP6AuMtExdKoERgNWikrRgOD3xhN2ZxFh4YcbY/Y1Y4vgl4xREKf2DOxxZhaJ4EQU3JuNU3ZhdwiLTZN1+u1C8ibgSQMCI1TAiafluYVGV7vray0aOjrAIdpfaU2Y8o1oZPXUpUCu152chLCo0pxcMKYqyO+OZBfHUXhZzjU7MUrIPbipoAsS+9neW2dwYxb5n6LQye4U9F2kAfSyjcpw6Xzew+zkxFKBVDbVn6sEArS1xxgD3d8IBTDwlUWRKgXpzWnWQlNtzTBZKbKoqcCgJjEZqGc+BJp5aj+cQyXhaDSYKzGq2oN6f/e+hlehaQ9kRaO2BmK2yvTWUOuNpFI27BBmpBEZatqkn2pEwmv9fiIJETkKApFduz2iplOgN9EVqdPeviVoyWSoFo5uRFX0PVa3UHAp/opuV3RNaJjEeXYMcHzrXFezOwmZjoxRKEGeLZV7G/s5UwqJARFO0j9QtlWvtESkV7bsynsk2TLBHqd2YwkopnF5YBNllPLVrIFOZvSvqISxHEBEpNGcuObckrtne9HfCHqX2ISIs2l+K9jpfNzFFxmYwUjKEFO0HW8ZTVhTWJKyUpg2wlRIcwMQTYEaBytxXHUQCo3GJN7ShVG7X3mS3e/uHEKaCdpOsC3SlFdpki6pEv1V9H7a3y1KpF+X2fc54ZlC2aw/ZtnB7WouoTJZKTkMuBsGEgrzLzHtvpBMY7Wmp1KPT56lZKoXlLiLxZIcCrbcwkMJSySiVIom5QJxgRN9I3mw6BEGwI8tdRKL6IqRdxysVIiUyhJHAM2lj9yfkeAfR0NtAdv2dmrAoVX8nQCCqCYtS9XdqwiJ9Rbv2suDQERYBuxXtJr0yvrLrfOlLqT2bHs/WRNY/k6K9OahlRnOzyiTuS38n7BYX9dVKqaqfrJS0PvkRA2zoriUjRjryhoyiXVYUtiQSN+MPkozn9p5OvNEIFsnAuNyB/8wHNPHUlO0Hi6US7NnnOXQyMyO1UvsA93iW2FwYBZGoHKc5qF8m7tX2rDkYRYmIHKM5uG8WTeWJB0lDsBfEMzEdpSPck5WXZ3GCeLZlmF6Ub8pDRCSqROmOpv48u0zkUxBPQRDITWSTUirbNUslHUsk2G2p5NHp8zSINqySmtHy6ijbtR7PcLyFmI5lkiAIuwVGKfo8BcGIxXwEoGY9M8Fsv07dp++fKHLfz63+QNj3D1ACSMapSKbD08bKcoBQeCkA1hTEU1FkgpENANiMGayUTPqKdu330st4qlZKqvBIzzw+EO8iqoQQEFNbKUU1D099UhmOh/DG1HM7K/P4DOXz1rB63RZZsiOE2sui5kyRDfyxMN1RdSxrxT4Sz/42j99fGU+NeFYPoTJ7g7+HQCyKSZSocg6dSUoDCU2EPTm/GKOYeexuX3FAE0+tV2FjZxvBmP5s5wMN44agpZI2cqw15MMbHbjeU0kQqUgIjHb2Q7ldEkQqrAnB0j6W28sS6zf1YvRmrsmJKeHl2Z6Fl2dRogSoPSRTwSAayDerN/h05fZdGc8UpFI9xvQCo0zKdo14pFK2a1NvvDrldoPoxJwgpv4Ufp67BEYplO2wO/MXDH2UMkaD0Xo2omEsitJD0PuHQZ+IFo9uIRx4FACL89aM2aJQ+HMggiSVYzAkj8EECMd2ICt+BMGMJUVGU+ur1ct4KoqyywJLz8PTH2vbZaXk0CGWXRFNDV+IQUzuj1QUZdeLTp5JvxyoGcdbJRs2g77qHaBtl4dneislbRRtNmV22F1q703Gsymo7sNltGE3WLJeb0/0e6l9P1kpacmIoTSWclNXYlRmTt5+IWFDASva1Pv0jP1QZocDnHiW2Z1UOHKIKwqvbU8/Hu9AwbQ8lWwvba3HE8l+RvhAIsdkpSyhjPyyrXZA9zXWpT5IVnQmm5PvC8a41Afkiq70tjupUGVTj2eDR59g6UEQBCoS4/a2eDOvV2wpQETEFwvQGkpPPsutKiHc7tdXjAOUWlRSWOvfnjKmyKzG1Af1y90F5moAGgMbdHso88yqH2dLcI3u+m7TeADag0t1lztNkwDoDOl7cTrMswHoDryDojMBCcBmOR4QCIUXZjSTFwQJi/OHAET8DxPy/GrQyGcsuhZfx/mghJBMczCY56eNVxSFHu9fAbBZTkxJUrsDbwFgNx2CoNO/GY134U2IjxyJ739P9EQ2EZV9SIIFx/+zd9bRVZxbG//NcYu7QUIgCe5avGipl7pSd9dbvXXXW3cDWkpLHQoUd3cCIQlx1+My8/0xCZevZ05yAgnS22etLNZi3pl555wzM8+7936erfMXHjV/1+G6zor1ocV2ueSh+bfzV1S6inCLTjSCjnCtcqTyoF1eiMQblGtIAVw+F0UOmeQmGVsWUuRaC5vGte5rKEkSOVb5fk02BW863vxs6Gw+snrCWreNPKtMlpqfV0eDereDA43Nx+s483RJkljb9D7oHn7itKVcWiI/95rfpX93OL1efsuTS5KGxPuXwHQE/tbEUxAELu/eD4BPdm087lGKY4GM8Gi6hUXjFn3ML9h3vKdzCKcmZgCwsEi55q69MDJOTgEuL9vfPseLkee9rPzIFi79I+XoUq61nBpX8B2Q+obL17G1TpnYHQ6DWk/XEDlCuKO+5e+8Z2j3pnGBI4EZIT0QEChxFlLvVo7UdrHI3X9yGrcq3leJxh5oBQM2Xw0VLn8Cm2yWTdwrXXuwe/3JcoJ5LACl9mWKrTNjTJMAqLD9rji/UONYNKpovGIl9Y7FimO02nTMpukA1NQ92erzQWecgjH03wC4bB/gqP9Xq8Kk9obXvQlr1YVIYg1qbR/MkR+2Gu10OBfgcq9DwEBYyG2KYyRJpMo6C4Ao8/mKYyrti5DwYtFmYtL692Evtsmfc5xpBGrBP2JZYJOtnFLMwxWPn2eTFxmp5oGK2/c3bm3a3h2NStlncE/DdgAyQ5RrVAH2NO7FK3mJ1kW3GPEUJZFdDfJzpFdYt4DjmlHsqKTSVYdW0NAjVLlPvRI21chkeWBEl6D3ORwrK/YhIpERGk+c4eitj1ZWHMAnSaSHxJBoCjvq4wXC/oYqCqy16FRqRsYd2bW3N9w+H78elJ/1Z6S23AXs74Kfc/dQ5bSTYA7h1E7KmY72xt+aeAJclNkHo0bLnppK1pa1TxTsRIYgCJydJkcj5uUFtpM51piULEewFpXsx9eBvqpj4uQXxI7aYqpdtqM+3qjYDAQEshvKjqjOM0JnoVuIvHLeVBM4gvhX9I+Qr2NrbXAEunfTi3Fnfcvje4XJL+R9jftx+ZTLHsyaEFJMMmHe26gckUyz9EAtaKjzVFLt9q+h1qh0dDL3AyDPusFvu0kTTbQ+C4BC21q/7VGGvujVkXjERiod/sbt0aYJCGixefZjdftfs0rQEWW5AIAq69eK1wAQEXo/oMflXoPDuSjguGboLVdjDH8JEHDbv8JedzeSgrK/I+BxrcJafQlIDah1g7FEzUalarkGTZK81NQ/DUBoyHVoNMqinQbncty+QtRCKBGmMxTHlDeR/FjzaYrbS6x/ApBoHu+3TZS8FDV9z50sI/y2u3w2Suzy86qLZYji8XOs2wDoalHuMS9KInsb5AVV91BluyiAnfXyeXqHKVtGNeOgrRir145BpSfd4h/B/Su2NN2rPcNSg7ZSkiSJjdUy8RwUpVw32xqWlcuL+TGxmUe0v9/xmhbtY+JbJ9tHgz+aghAj49Mwa4O3nupILC/JpdblIMZoZkR88IuHkxWSJPHJLvn5emX3AcestOBvTzzD9UbO6yrXe32yUzlt93fDmU3Ec03ZQcrsR9ZnvL0xOCaFMJ2BGpedzdXBp53bilhjCN3D4pGAFe0Q9YzUW+gTIacfllccWbR2UKS8itxY03r0shl9wtNRIVDoqAiqzrNXmByZ3Vm/r8XIXYIhnihdJF7Jy97GwNfT/OLe26BMPHUqA51NMnHMadymOCbNIqe7lYgn/JeAFFj90+WCoCbRPA6AEps/IdSqQ4k0jgQCRz2jLRcDMqlyeZVLPDSaZEJDrgOgtv6poEik3nQRpoi3AA0ex1zstTcjSR3rm+txLsZWfSVIdjT6UVgiv0JQtW4/02j7Gq/3ACpVJGEhtwYcV2WdCUCk+TxUKv86Q7evhjqnTBxjzVP9z+POo9GTi4CGeJO/kX25YycusRG9KpRYg3+avsC2FREfEbokwnX+KU6v6CHPKhPGriHKxLPIno/dZ8WgMtLZHDhy0xztb16EBR4nPz96hKWjDmA/dTi21snj+4UHT9jybRVUuxvRqTT0Cmud3P4VHtHLmir5uTI6LqvN+/8VPklkRfmxIZ4Li+Xnz8Sk9iHM7YF5eXK5xxmdu6NW/e3pEevKCtldU4FBreHirD7H7Lx//08WmNFTTt0sLMjhYEP7mIufyEixhDMoJhkJ+Dm/ZauYYwWtSs24BPlB9kcHp9ubH5jLytsn3d4cSTjSdPvASDmSsbFGWQijBLPGSEaITHi3BBH1zArpgkZQU+WubdHPUxCEQy/cltLtWSFNxLNxZ0DrpWYC0ByJ+itSzYMAKHHsweXzjz53MsvEs9i+Ea/oT9wSzafK+9uWIikYzcc1EaAK22+KZFuv6UyIYRQgUWUNbIMUHnIbKlUEHu9+rLZZAccdDp3xLMyR7wM6PM7fsdVcgyQ6gtq3rXA7fsZWcy3gQmOYhDnyUwSVqdX9RNFKXcPLAISH3oNKpZyGdXvLqHcsBCDaconimEr7H0j4CNH1VEyzl9jkaGescQg6tT8h/m+afZhifWdzmj3NPFjx/AX2bDySC7MmjDiDciRqT1N0PiOkJ2qFc4BsJVbmLEeFih6hLRO1nU1lK71CM1ocB3K0dVtTWUy/iOAJW/NitE94Knp129sUbqzOx+Z1Ea230DNMOZrdFmyvKabO7SBEq6d/ZMfV+5XY6tlZW4ZKEBif2LEEN1hYPS4WFcnP2rO7KLs6/N3QHO08r1tPwvXGY3be/wni2TU8ijHJaUjA57s3H+/pHBOcdSjdfmIQT4BJyU11nsUtR+WOFmPi5fOsLD+AR/QnLG3F6DiZeG6ozsPhdbd5/34RaagFFSWOGkrswavtm19gQdd5WuQX8s5W6jx7h/VsGhe4FCPV3A29yoDV20CxQzlamN6U8sy17lA0kg/XxROhS0ZCpMC21W97lD4Dkzoar+Sg1OG/PcY4CK0qBJevmmrndr/t0aZTUaHD7s3D5lFezERbLgWg2vpNQJGRShVKeMjdANQ2vIQoBleioTVMwhz1GQhGvK5lWGsuR1KwdzoauOzfYK+9FfCiNZ6NOeI9BEEf1L71je8gilVoNF0IMV8ecFy1bQ7gw6wfhFGnHH2qOJRm9492AhQ3Ec9Ey6mK2w8RT4U0uyRJ5NvkF2CqRbm+879p9j4BOw01R+ezWkyzy4utdEsXTJrA5N0niexqkO+73mGtE898Wxn1HhsGlY7MkOAjl4fS7JFHVlvXnIUZFZvZYgemYNG8WD8ltmuHpl0XFsvPqIHRyUQbArsPHEssKNiH0+elS2gkvSOPjbr7eKKgoY4/Dsrf91U9lO+7jsL/BPEEuKanHH35Zt+OE6qdZEfh9NTuaAQVu2rKyTlBuhiNiu+CXq2h0FZHdn1gq56jRe+IRCJ0JqxeF1uqj76uN90SS5IxAvdhaa22wKzR0yNMjh60Jd3enLLbWrs/KKLe/ILc2dByhLRHaA9UqCh1llEVoH2mRqWhW4i8eAmUbk80pmFSh+ASHRTZlc+Z1hT1bI5oHQ5BEA6l2wutq/22qwQtCU1p2xKbv0BIo7IQ2bS9PEC6Pdw4EY0qBq9YSZ0jcA1niOUKNOpURLGSBut7Acf9FVr9KCyRX4MQgs+9Dmv1xYhi+2RVXLbPcNTdC4joTBdjCn8dQQguKub1lR26jojQhwLuJ0k+qptERc0k/a9w+6qoda4HINY0xW+73VNKnWs3IJBgGuu3vd5dRL37IAJqkk3+9Zs17kIaPOWoBS3JJuV0X3M5R1dLP8XtTp+DPJtMZloinjsO1Xe2nGbPtxVh9zkwqQ2kWVqP/DXXYvcOT0cTJGHzij4218p130dS3ylJ0qEszJi49klXLz9G9Z3NxPNETLOfldbjhDGz70h8tnszEjAmKe2YmMYfjv8Z4jk6KZWu4VFYPW7m7FN+kf6dEKE3MjZJVgrOyz0xop4mjY5R8fKcOjLdrhZUjGpSty8rO3plvyAIjGmqn2ou5G8r/lvnGXy6vWdYGhpBTYWrltJWbJIg+DpPs8ZEF0uXprGBo57/Tbcr3y8qQU26RR4TqM6zOYKVb9ukOKeUpnR7gW21sjre0pxu/1Nxe5xJFroESrcLgjYokZEg6IgI+xcgRwq9vuAXRhq9LPQRVBH4PNuwVl2A6Ds6H11n49s46h8FZPN6Y9gLAdtcKqGu4SUkyYFeNxiTcVrAcQ3OFbh9RahVYUQEGFdhWwCIhOr6YNT6k7AS2xIAog39MWj8/RgLbWsAiDf2Ra+Qhm+OdiabeqFTqC+1exspccj3TXpIAGJq3YNP8hGtiyVGr2zN45N87GmQDfJ7hbWcSm12h+gR2jWoXuVbmuo7+7ehvnNfYwlWrxOLxkBWaGD7p0DIaSynxFGHXqVhaPTRq5HLHQ3sqS9DAEbHHZnQKRjUuuysr5Tt6SYmtR5NPhaocFhZVZYPwFmpf/80e6PbxTf75CzS1b2ObbQT/oeIpyAIzOgxAIBPd2/uUGX1iYKz0uQbaF7erhPGSqr5QdO84u0otHudZ1NEYUVFdovtJgNhUFOd56aaA0F/Fwa1ju6hqUBw6vbMkDQ0goYadz0lLRjEw39tlfY0tC4wyrVm4/Ipe8I2p9sD1Xkmm/qgFrQ0eMqpcftHn5NMA1ELOqzeMmrd/qr/WOMw1IIBu7eUOrd/jW2UaSxqwYTDW0C9y1/9Dv8VGTU6l+PyBvZjNRlPR6cbgCTZqWt4KeA4JWh0fbBEzUFQxSJ692Ktmo7oUzbPbwmSJOFoeBFn4/MA6C13Ygh9rE0RGLcnG6tNrmmNCHu0xX2byXggURFAqfV7AGLNysS0ORrdXJP7VxTYZPFYJ0sAGyVrs43SIMXtB6w7kJCI1acQplU2SA8mzX7QVtAUxTSSZk4NOA4Oq+8MIs3uE31sr5OJcb+I4Alb8yJ0QESXoMjtX7GsKc0+JLoLxiBV9C0eryna2TsiiUh9x6W/l5Tk4JMkssJiO7wlZ7D4JX8PoiTRLzqR1NATY04die/278TqcZMeFsnoJOWGEh2J/xniCXBu156E6QwUNNbxZ2HwkaeTFROSu2LW6Ciy1bOpsvh4TweA8YndUAkCu+vKKbTWddh5RsZ2RS0IHGisosh29KnPAZGdsWj01Lht7Kxr+2fZK7wTepWWGreVXKtytx8l9G96kTVHVFqCXq0jM0R+iLRW59m9SVixp3FvQCIco48nUheDV/KSY1UWVjULjIrs+3EoCIh0KgPJJjmtma+QbteoDCSa5BV3cx3g/99uJM50CvBfu57/v91MrFlO/5Za5yrOUa/pRIhBTsm3JDISBIHIsMcAsNpm4g5QNxoIam0mlujvENRJiL5cGqvOw+Nahde9Jeg/R8PjuKyy2bsh9GGMofe0Oe1XW/8UIGIynoZBryzWgWZRkVx+EEhUZHXvpdG9EwEt8ZYz/bY7vdVUObcAHHIh+H/n8NkotW8FoJP5FL/tHtFFkV2OvKRZlIlnjlXeP5CaHWDPIeIZWJm7p1H+DWeEZLRYD+mTfOxukN8PwdR37rcWYfc5CdGY6GIJPnK5sVouuxkUdYT1nYdslI5ezS4f7xjZKDWr2ZNPjGgn/Nd6sNmK8O8MUZL4tElUNKPnQFTHoazgf4p4mrQ6LsmSH14f71KOjvydYNRomdxJvrlPFE/PSL2JwTFy8f2iDox6huoM9I+Uz7OsHWyVtCoNI2Kao6htV7frVBr6RaQCHV3nGZyfZ7olHa2god5TT6mzTHGMIAj/tVUKkG6P0MUSrUtERCTXqjym2RA8z6p8zzWr2wsU6jwBkg6p25WN4BMssgl8hW0+XtGqOOa/IqNvA4qMAAz6oZgMUwCR2vpnA44LBLUmjZCouajUaUi+ImzVF2GtOjPoP7ftUwCMYU9jsNzY5vM7nCtxOBcDGiJCH25xbLXtW2RR0WCMWmUSUNIok/lo03h0av80eql9GSARru+BSetvg1RsX4+EjzBtCmE6/zR9sX0nXsmNRRNNlM5frS5J0mH1ncrEs8ZVSYWrFBUqMkICE4c9DfJ9270VNfsBayEOnxOLxkSquXUi2ew60Sc8PejIpcvnYVtdPvDfbEhbUOOysqNOtqUb1Q71nW6flzUVcsahWZzZEXB4Pawok88z6QSp78xtqGF7dRlqQeD0zn9/0/g/Cw9wsLGOUJ3+kNXkscb/FPEEuKJ7f9SCwJrSAvbUdJzA5UTB2U3p9l8P7m0XhXd74L/p9pPTVmll5ZER5kFHYKuUFdoZvUpLncfKQbsyQTwcPYOs89SptKRb5EhL8wtZ8fzNdZ4BBEYQjK2SHHUrsm/Ho2Cb1NzJpsK5C4fXPzodbxqJgIZGTx4NCun4MP0ATJo0fJI9oKdnuHHCYSKjhQGvBSAi7GFAjcP5B06XMhluCSpNEpbo79Dox6NSp7TtT5OBKfxN9OYr23xeSRKprX8SgBDzFWi1gbvByKIiOfobSFQkSm7KbT8BkGA5T3FMsVVeDCQpmMZDy2p2ONxGaZBiZLfaXUqdpxK1oCHNovyS3NsoK9VTzV0xqpWV6l7Ry75G+TnQPSQ4G6Weod2CUoofiX/nzvoC3KKXaH0Iqea2t6VcVbkfCYnuoYnt0q1oQ9VB7D4PMQYLPcI6TtG9sjwXp89LkinshGmT2RyUGZmQRrTxxFDYdyQ+bvIzvzizL6bjZNz/P0c8Ey2hTE2VX84vb1pxwtQ+dhRGxHcmxmim1uXg1/wTo19980p3Q1UhRba6DjvP2KaV+7rKPGraoYvRsBiZOGY3lFHqqGvz/s0ptc01B4K2ZdKqNPQKkwnE+uo9rY7PDElFp9JS52kkz9ayUX9z5Kfl9pk9ERAocxZTGSAy2hyJym7YhKjgtxmt74xFE41Xch8SkhwOizaOKH03QCLfusxvu1YdQqxJbrFZ0PiL33ZBEEgIkYlRYcMXiArWTrLI6EIAyhvea7HVpVbb9ZD9UFXtffiOQKWuUsdiifqc0LjVbfuLXYzOdE6bzwdQ3/gWbs8OBMFCeOhdLY6ttf9ymKhIuRNRqXUeHrEOvTqOqCaz/sNh95ZT6ZDV7kr1nV7ReSiKrZRmlySRnEZZeBTIRmlvg9x8oJMpS1F4BLCjXrbIywwNrFTfb83BJbowq82kmJIDjgPYUiuLMXsG0SbT5nWwsz4PaFt95+pKedE9MDL9iBTUzTZKw2PaRwQ0v1gmX6PjunWoontevvysmZSceUIox51eD9/lyIvqc9L+/qKi1SUHWV1agFoQuLJJ83I88D9HPAFu7zcCrUrFooIDfJ9zYqSgOwoalYqrMuWH+otbluHwBk4zHiskmcMYGZeGKEm8vWtlh50nPSSaXuGJuEUfXx5Yd9THi9JbGNTUe31uQdu7YGWEJJJkjMLuc/NH2dag9xsd0w+AX0vXtCps0qq0DIyQX8ALy/07Ah2OQRHy72Jb3Xbq3HWKY0wa8yHBxooqZTuiriF9Maot1Huq2NPg36VIEAS6h8n1f5trflA8RrdQuU5zR+03iuQ1LVQmlgfqZ+P0+nuhJlqmo1GFY/Pso7jhK8VzxIZciUqwYHdvbVHhDhAeei9qdRJeby6V1dciSW33bz2WsNl/oq5BFiRFhj2KWh3YHsUr1lNUK0dGY0OuC9ip6EDtKwCkhF6tqKjPrv0QCS/RhgGE6FL9tu9r+A2X2ECINoF4o7/o54B1HfWeUvQqy6EuV4dDlHysrZ4PQJ9wf+IKUOWqYFe9XGM6IEJZvASwvHIFAAMjB7QYxSx2lLOzYT8CAkMjW+/ksrBsI27RQydTHJ1NwUUKnT4Pv5bIz4/xcYHFUIFQ6WxkSZm8CJ2U0LItVDCocDTyY6FcZ3t2p8B1tEeL7LoK5hfJwY/paceuS05L+HjPBkrsDSSYQg6Vpf1d0eh2cd8KOSN0cWZfkixHHyk/UvxPEs/MyBjuGiCv4J9Yu5gSa8NxnlHH4prug0kyh1Jib+DD3euP93QAuL2XLPaYm7+dg9aO6SYlCALXZcgvrJm5G7B5jt6/9cJU2YdwbsFG3L629ehWCSrOSxkGwHcFyvZBShgX1x+z2kCJo4pNta2n+afEy7/tpRXrcXiV1egAyaYkulm6IiKyvCrwAmBMzGQA1lYvU1S3a1V6hkROAmB1lX9EEmBAxFmoUFNo3065w7/0ITPsdPSqEBo8RRy0rvDbnmAaS7i+Bz7Jwb66T/3noI4gPeIeAHLr3sTl9RdwadVxJIbfC0BJ3Qt4WrBMUqujiIv+EkGw4HStpqr2vhM2O+J0baKy5nYAQi3XEWK5osXxJXUv4hUr0WvSiQu9QXHMgdpX8Yp1mLUZJIde5rfd5ikiv+FHAHpE3uK3XZS8bK+RU/m9Iy5S7Fa0sUZWy/eNOA2dyr9ryt6GjdS6yzGqLfSLGKs4z5WVi5CQyArpRbxBuXOP1WNlQ41M9MbFjFEc04w/yuT7YEBET2INygr6ZkiSxM8l8uLuzKSRQUfwFpdto95jJ94Qzikxba8pnFuwAa8k0jeiE1lh/nW1bcXnOWvwiD4GRKYwKLrj+pO/uUu+r09L6U7WCZBmr7BbeXunHHF/cMBYjJq2d446mfD0uiUUWRtICQnjocEt3wcdjf9J4glwQ+8h9I9JoMHt4v4V80/Yl0p7wKDR8uAAOeL07s61J0T/9oHRyYyO74Kvg6OeExK7k2aJosHj5Jv8oxeUjY3rTqwhlFq3jYVlbY+WT0saiF6lJcdadkhc0BqMaj2T4mXC+3Nxy1FMgN5hmSQaYnGKLlZUtRyZHRsrP4CWVSwPGE3tHtqHGH08Dp+dDTXK39XQqCmoUJNv202Jw78OM0QbQ2aovNhoJhyHQ6sy0T38XAC218z0ux8FQaBnE8HJbZiDw+tPGhMt0wnV9cUn2dhf+4LiPGMsV2LS9cYnNXCw+p4We7PrtN2JiXofUGOzf0t945sBxx4veLwFVFRfCbgwGiYREfZ4i+PrHYuossoR4U6Rz6JS6IJU79xKqXUOAJlRjyuSxj21HyDhJdY4gmijf8our3EpVm8pBnUYGaH+qfwyx36K7NtRoaZ/hL9aHv67iBkcOQmdyn+ebtHFmuqlAIxuWhwpYWX1ajySl06mlBZtlFw+N39WyJmRKfGjAo5rxra6HArs5RjVeibEKSvy/wpJkphTIJOdc1OGtdlGySP6DmVbLuo8tE37KqHe7WB203Pxukz/cor2wu7aMuYX7UUAbuvZ+md7LPDi1mXYvR76RydyZurfW83+Z+EBZu/bjgC8PGoqFl1w3c86Cv+zxFOjUvHK6NPQqzWsKMlnZrayMOLvgtM7ZzEwJgmHz8OLW/zr6I4H7myKev5wcAd5jcG3kmwLVILAtU1Rz89y1rQ5SvlXaFVqpndq6oKV3/b0fajWxOSEfgB81/QCCgZnJMnXsK56F+XOlj8rQRCY1BT1nF/Wch3zkMhBmNUmqtzVAc3kVYKK0TETAVhW+Yfi8cJ00fQKlwUkq6t+VTzOwEg5XZ7dsIxGj7/Bes/w81ALOipdeyh1+Le2jTUOJ8rQH1Fysbf2I7/tgqAiM+oJQEWF7VdqHP7CIEFQ0ynyJQTBQINz2aGUcyCYDOOJDH8GgLqG56msuR2f7/h3ApMkCavtO0orpiGK1ei0vYiJfKdFk3m7ezd5VbcCEtGWKwgx+KemRclLds0TACRYziXc4E+oGty5FDTK33GPyJsV57atVi5l6Bk+HY1CKn9TjayWzwwdQ4g2xm97iSOXPNsuVKgYFuXfLQlgQ80q7D4bUboYeob1UxwjSRJLK5YDMC52bItRydXVW7B6bcToI+kf0ToR+akp2jkhbhBmjXL96V+xq76Q7MZidCoNZyQFtroKhCXle6h0NRKltzAh4ejJ0szcDdi9bjJCYxkT13E2Sm80RTundepBRpj/932ssaO6jO8OyLWdjw2acELUm3YU6l1OHlgpl6xc3XMQwxKCb+naUfifJZ4A6eFRPDBIJj/PrV9Kme34RwI7CoIg8NigCQB8n7uTbVWlx3lG0DcqiXEJXRElif90YNTz9JQ+xBtDqXRamVdw9AuMc1MGoRHUbK8rZE99203Cz0uRX/hLK3ZS6QyuzCPFFMuAiAxEJH4paZ2wjosdilbQkGcrYr81P+A4nUrHKdEyYVxSGXhBMjRqNDqVnjJncUBPzxHRpwOwvW4FjR4FdbqxG8mmPoj42FL7k992oyaCzDD5GFuqv/DbLgjCIaKT3/ADNo+/n2qIvgfJIbJKe1/1vxEVajNNuh6kRr0BQKX1MyoaP1O8nmaEWq4kLOQuQMBmn0Nx2SgarV+0KFDqSLg9+yivmk5V7W2IYhVaTTdio79ApQqsyPX4yjlQOQNRshNiGElKhHJktLhxJlb3HjSqMNIj7lMcs6fmfUAkwTSWSIO/IKPAtpoaVw5awXgoin04GjwVZDfIZHBQpP92+O/ipVfYCMJ0/vWqkiSxslKuOR4VMzFg3WZ24z5KnaXoVXqGR7UcIVxQJpOjSXGntBqJrHLVsapSJi5nJCor9pUwt1C+dyfG9yVc13YFdfNi99yUgWhV/pHotsDh9Ryqfb8uI/hSgbZiR00pi4r3oRIEbj8Bop2SJPHkRvm3c05aT/rHKJdo/F3w3IalVNhtdAmL4P5Bx//zh/9x4glwVY8B9I9JoNHj5rE1gXs5/x3QNzqBc7vIxehPblx0QpQX3N5LvhF+KtjJgYaOiSTpVGqu6iqTvY/3r8J7lF2rog0hh6INRxL1zAhNpG94Kj5J5Mei4Pc/I1GOes4vXYtbbDlyG6q1cEq0LB5aUNYyqW9Ot2+t3UatW7ne1qg2MThSPv/KKmU/zRRTNzqZMvFJXtZXL1Ac00w0ttX+hlt0+G3vE3EJKjSUOjZTZvdfJMQYBxFrHIaEl721Hyqeo0vEHehU0di9+RTUf6I4JsI0lcTwBwEoqn2CescSxXGHxofdT0LML+i0vRClOqrrHqC04nRc7mPXflcU7dTWP0tJ+QScrtUIGAgPfZDEuEVo1IFr/UTRwYHKa/D4StFrupIW/a5i73aXt4Lc2tcBSI+4W9G3s86VTbHtD0AIGO3cUv0ZAD3Cz8Wg9hcwbKn5CREfKaY+xBn9o2xWTx3b62QSOCLmdMVrOmg/QJHjIBpBy7Co0YEunaVNi6lhUUMwqv3rSJuRZysiuzEPtaDi1LjAIqVm/FayFhGR3mFdSLMER1xqXI0sLpNFPM2Lz7Zgf0MZm2ryUQsqpndqe7T0r/ju4GZq3XZSzBFMSeo4Rfebu+RFxhmdepIeemx7givht4JsNlQUYVBruL//8a117GisLS1gVrb8m3th5BQMJ0gd6/888VSrVDw/cgoaQcWCg/v5Pb9jWzkeb9zffwxGtZZNlcX8nN+6PU9Ho09kIhMSuyFKEm91YNTz/NQBhOuMFNhqWVBy9L3rL2yqr/q9ZDv1bnub929+8cwrWo+nFRLZjOFRPYnRh1PnsbKisvXI7eSmdPvKqk00egLbSSUZE8mwdJNFRpWBv4NTomXLnG1162nw1CvPMVpuq7iuZgFe0d9BId0ylAhdEi7Rys66P/y2W7RxZITJNYFbaj5XPEcz4TnY+DON7ny/7RpVCF0jZVKZX/8uDo+yrVRcyE1EmS8ERPKqbsHhbtlXVq8fQELs70SGPYUgWHB7tlBaMYXqukcQxY4VKNodf1BcPob6xrcAD0bDRBLjlxEeegeCENiLT5JE8qvvxO7ejloVQdeYT9CowhTH5tS+gE+yEarrQ6LlfMUxu2veASDZMokwvT9pLLKvo8q1F41goFfEhX7b3T472+t+A2BQpLI36LqaBfgkLymmbqSYlJXGKyvlxU//iKGYNf7936FZVCTXL46LGas4phnN0c5hkf2I0Cl/Ps3wij5+LZUjl2cmBV8X+VPxBjySj55hKXQPa9nSSQnfHpSFoePisogztjzH1uAWfXy6Xy5FubrbCDSqjqEC26qL+bMkB5UgcFvPjqshDRZOn5dnN8kd0G7sOYwE8/FTdnc0nF4vD66UAwCXZvVlSLx/A4fjhf954gmQFRnDTX1lIvHYmoXUuwIrgU92xJtCuLmXrKx+fvNSnCeAvVKzwv2Xgl3sr/ev/WsPmDQ6LkuXv+OP9q086mhvv4hOZITG4xK9/Fi0pc37j43rSZQuhGp3I0vLgxMpqVVqpiXIhPWn4tZJemZIGqnmJNyih6WVLUdWD4mMKgOLjFJMqaSa0vFJPtZWK6fle4YNI0wbhc1bz/Y6/zkKgoqBkbJP5aaaHxStk/pGXoaAmmL7Bioc/p9NpKE38abRgMie2vcU5xFnPp1ww1BEycn+mmcUxwiCQErkM1j0wxElKwcqZ+Dxtfz7EwQNoSHXkhS/ErPxbECk0foxxWWjsNrntXsWwestpLzqKiqqr8TnK0KtTiI26lNioz5Hq2m9Vquk/iXqHL8joCU9+gP02lTFcTWONZTbfgEEMqIeV6wVrXHuoMy+HFDRPcK/q9Lh0c7u4Wdj1Pj3vN5Z/wcu0UaELpkuliH+1yt6WNdkoTQi+gzFudq8VjbXysRvVPQExTEAK6tW4ZW8dDZ1Js2SGnCcw+tkWYVsAzY5CFHRqqod1LgbiNCGcEp0cHZIXtHHD4XyPTg9JfjUfDMaPU5+KZYXmxe2g6jo18IdlDoaiNZbOKdTv6M+XiA013ae3bk3aSEtuwQcC3y8ewPFNtk+6YaeR/85nsh4c+tq8hpqiTWZeXDw2OM9nf+Hf4hnE27tO5z0sEgq7Dae33BiiG86Ctf1GEKi6cSxV+oZEc+kpEwk/mu50RG4tMsQTBode+vLWVEefNtKJQiCcEhV+u3Bda36a/4VWpWGs5ObrJkKgxcZTU0YhkZQs7shn5zGlg3iBUFgcpz8Il1Q1jLZHhw5ELPaRLW7hh0BREYAp8TIUc9VVX8qXrNa0DAsaiogq5KVztkjbCIGdQj1ntJDBuKHI0SbQLdQWaW8peYzxXk0Rz2LrAuod/lnKQRBIDPycQQ0VDn+pNKuXB6gEnR0iX4fvSYNt6+IA5XXIoqtLzw16jhiot4lLvobNJp0fGIFVTU3UV51ER5P8J2pAkGS3NQ3/Ifi8jE4nAsADaEht5IUtxyTcUpQ9XjV1jmUN7wNQKfIF7AYlF+0ouRmX/W/AUgKuYRQvbI3ZHO0s3PIGYq+nSX2TVQ4d6EWdPSOuEjhPD42Nfm4Dow8B0GhjnJ73Ups3npCtZH0DBumOI/1NSvwSB4SjZ1INSsbqEuSxNLKZlFR4FQ8wLLKDThFF0nGOHoFYRrfbKF0WuLwoOssV1buocJVT4TWzPj4tnt3/ly0BYfPTRdLDIOi0tq8/+EQJYmP9snXcGXXYejVR1crGgibq4pYVnoAtSBw6wkQ7aywW3mnyT7pgf5/b/ukPTUVvL9dfrc/NXwiocdZxf5X/EM8m2DQaHjuFPllNzN7G+tKC4/zjDoOsr3SWADe2bmW8hPAXumOplrP3wv3kF3XMa1Mw3VGLkyV6x4/2Hf0af2pSX0I0RgosteyqrLtRPas5KGoBRXb6vLZ1xCcSClSH8rIGNl8uVlV2xLGxAzGoNLLxtgt9G+XRUZyDefSiqUBxw2IGIZRbaLGXcmehu2KYwZFTkQr6Ch15pFv8y9r0KkM9A2XU/KbFKyVAPpGXo6AikLbWiqd/mKmcH0mSWbZO3R3gKinWZdOp7CrAdhf/Qw+hZpSAI06nPSYz1CrwrC7t3Cw5p6ghUNGw2iS4hYTHno/AgacruUUl4+ntv4FREn5fK3B6VpNSflEahueQZIc6HXDSIxbRGTYw6hUyi0h/4pG51oKauRyg/jQ24hq6mevhIL6T7F789CqougSfqfimErHRiocaxHQkBVxveKY5kVCVthZmDT+0a2cxtXUe8owqEPoGeYfqZQk6ZCF0rCo01Ar2DjJoiJ5ETEy+tSABFwWFZVhUOkZ1oKoSJIkFpTLi93J8a0LbPJtpWyry0GF6lD2IRh8VyCntc9MHoKujaIgSZIOpdkv7Dz0qEVAi0r2kmutIkSr56K04GygjgTNQYRzU/vQ2eIf/T7WeHnrcmxeN/2jEzkr7e9rn+QTRR5YMR+vJDK5czempJ54xvj/EM/DMDQhhUsy5c4ND66aj9N7dNY7JzLOSO3OgBPIXikrPI7TUrp3eNTzyq7D0AoqNlUXsLm64KiOZVTrOCtF9jA8EpFRjCGUsbFydKktUc+zEuXowZ/lm7B6WiY3Ro2BMbGyEKG5ji0QmiNDW+u2UxNAZKRT6RnaJOYIJDIyaULoHyH7xq6q+llxTP+IM1Ghodixi1KHP7EM0yWTHiKTk63VyrWe3SNvAFSU2pZQ41SO0qaG3YRenYjTV0x+vTJBBTBo0+gS/QECWmrtP1Na/1rAsX+FIOgJD72LxPilGA3jATf1ja9TUjaWuoZXqWt4I+i/yuqbKas8D493HypVFNERbxAf8z06bWbQ83F68situh4JD+GmaSSE3RNwrMNbTH69HMnsGnk/WgUxkCRJh6KdqaHnYNb6i2lK7Vsoc2xDJWjpE3Gx4rma/Vv7hZ+OVsFiKd+2m1JnHlpBx+DIiYrH2G/dTYWrFL3KcEjspoQlFc2iomEtior2WfPJtxWjU2kZG9N66vXnYplAjojuRYwhvNXxALnWcjbV5qJC4JyUtqd311Xnkm+rwqzRc3pyvzbvfzgkSeLDpkX3JWmDsWg7JhK2sbKQFWW5aAQVt/Q4/tHOnTVlzDkgL5QfHRR4wfJ3wGe7N7OtqowQrY6nhgcuRTme+Id4/gUPDh5DrMlMbn0tr29pPaJ0skK2V5LTpt/n7mRPbcdEGduC23qOQgDmF+1lZ03H2D3FGUM5q6kt3PvZRx/1vKCznC5fVbmfAlt1m/ef3kmOmiwo3Uq9O7h+8j3D0kgzJ+ASPSwoa53wNtetra3ZSo1bWRQEkGhMJDMkAxGRZU1pSiU0i4x21W+hxqVcE9ksMtrbsIFql3+Pd4s26lAbzQ3V3ykeo1/UFYDAQdtKqpz+wp9QXRc6WWQh0q6aNxXT+mqViYzIhwEoqP8Yqztw1DfEMJxOkc8BUNbwBlXWmQHHKkGr6Uxs1FfERH2EWp2I11dAXcNL1DU8H/SfzfEDIBBivoKk+BVYzBe06SXp9pZxoHIGPrEOk64fqZGvKaa0QSYh+6qfQpSchOsHE28+S3FcmX0l1c4tqAQ9WRHXKh5nc7XcTSoz9HTMSr6c9t2UOHajFrT0j1Q2jG9epPSLGIspgGCoOdo5KHIEhgCEssHTyMbaJlFRbMuq5fml8mJsZPRAQrQt2xvZvE4Wlcu1oM2+usGgOdo5OrYHcUGS1cMxO39t0zn7YdYcHVFcXZnLzroS9CoNl3dVLmU4WkiSxGs7ZeJ/XlofUizhHXKetsznhc1LkYCzUnswICbpuM6nI5HfUMvLm+Tf9ENDxhJnVr6Pjjf+IZ5/QZjewJPD5dX2u9vXMTtbOZ34d0C/6ESmdc5CAu5a+fNxFxplhMVweifZ1uPBDb/gOkqz90C4JuMU1ILA8vL9LCpR9qQMFp3MUYyMyUBC4rU9yhZCLaFveCrdQmQS+dGB4Oy8BEHgrCY17eyCxa1GPdPMyWSFdMEniXxToGzu3oxxsWMB+KNsIQ0e5RKMeEMiGSE9kZD4pXSO4phYQzIZIQOQkJhfqhyxbLZW2te4ggKbv0o/XNeZ9BCZ5C4vfx6f6O/J2T3yBgQ0VDrWs6f2fcXzRJtOJco4FgkP2ytuwu0LbMAfZbmAuNCbACioeZCSupfa5NcpCAJm4zSS4pYTHvogFvMlbfoLMc8gIfZXoiJeQK1qW3rS7t5JdvmZuLy5aNWJpMd8pNiHvRl5dW9R7ViCgKZJUORPcK2eAjZVPApAl9DzMWpi/cZk1/9MqWMLKkFL38hL/baLko/F5e8C0D10HGaNv01Tvm03exrWIyAwomnR8lcctB1ga52cch7Zgqjox+Kf8Epe0syppJoDt4A8aCtmeWXwoqJZBxdi97lIMcXSPzw4s/V8WwU/FcvnOL9T20VFG6rzWFq+FwHh0CL3SOHyeXlmm9yr+/zUAUTp2+4jGgy+yd3K2oqD6FTqEyLa+dW+LawozUerUnFPv5brfU9mVDpsXDF/DnavhyFxyVzclL09EfEP8VTA1NQMbukrrwYfWrWAhQcDR0lOdjw+eALRBjN76yr590bl1OmxxMP9JhCpN7GnruLQqrm9kWqJ4upu8kvg31t/pcF9dC4Gd3afhEZQsaR8D0vL22ZRJQgCt2XIL9rvC9cGXes5KX4IKcZY6jxWvsif3+r4yzvL0ayF5as5YA1cYjA0cjCdTZ2w+xx8VzQ34LizEi9CQGBDzSoOWJVtiCbHX44KFbsb1pHd4N+uNMbQhb7hcsRyYdkbeESX/3xibsGgDqPGlcOG6g/8tpu1yfSPkSOae2vfp7Dxd78xgiDQPfo5DJoUnN5CdlTcjE/hXM1IDHuA+NDbAChreIu86luCEhwdDpXKTHjoHURHvNKmv6iIZ9Hr+rfpXAB19oXsK5+Ox1eGQdOVjLhv0Kr9SWIzyqw/kl8vC48yo57AovMnUm5fA6tL78At1hOh76no21nnPsiaSrmV6KCo67Fo/Xtwb675kXLnPvQqMyNjrvLb7pO8/FgkLxoGRU4g1uBv+yJKInMKP0dCYnDkKSSblAllgb2QxRWyJ+sFKYHrWiVJ4qO87xARGRbVj4yQ1IBjAYrsFcwtkp9H13Y5I6gotCRJvLrnJ3ySyCnRWQyITG91n8PhEb08u0NutDC90yC6hAT+PoPBu3uXkWetJlpv4dbuY4/qWIGQ31jD01sWAnB377EkmY/O9ulosaumnKea3msPDhhHp5Dw4zqfjoLV7eKqBd9xsLGOZEsY/xl/JqoTuJzgH+IZAPcNHMX53XojShK3LPmZDeUtK4hPVsQaLbw+8gwEYNb+rfyUd/Qel0eDGKOFZwfLZOTj7HVsrfbvTtMeuCVrLGmWKKpcVt7c8+dRHatrSByXd5FTby/s+hWH1z8y1xIGR3VlQnwfRCRe3vtjUAp5rUrDzd1kW6Ifi1eSZ225NKFHWFdGRQ9CQuKj3DkBFe4qQcVlnS8BYHnlSvICdD3qZO7C8KixAMwp/ExxzvHGzoe6Gf1c8hFuBbI3OvZazJpIat3FrK362m+7SRPNqDhZJLOz9huKbBv8xqSGnk1G+FUAbKp8gmrnVr8xOnUkfWPfR6MKpd61hb3V/wr4GQiCisTw++gc+QoCWursv7Kv4sJWrZaOByRJorzhI3Krrm3qSjSKjPgf0GsCR/rqnBvZUyWT9U6h15EY4u/ZKUoe1pXfh9WTj1ETz/D419Go/n9q2yd5WFL6JD7JRaJpIL0jLvA/l7uMlZWfATAm9losWn/R0arKn6lwFWJWhzIp/jLFOa+rXs5B+wH0KgNnJSnXkEqSxNcHZ8nkNGIgPUK7B/wM1lZvZWf9PnQqLTNSlbsnHX7cd3Lm4ZV8DI7MYnhUcGbrf5bvYENNDjqVhruylK2hWsLnuavIs1URqTNzW5ZyzWuwyG2s4pMm387H+p1GmC5w3euRQpQkHtrwKw6fh2Gxnbkm8/jaFVk9Lm5dPg+36GNCcleuzuo4IdXxhNvn44bF89hZXU6kwciXU84nzmQ53tNqEf8QzwAQBIHnR07m1JR0XD4vV/8xl321x79Hc0dgZEIqt/aWI4D/Wjef/AZlYcmxwsSkTM7u3AtRknhwfcek3PVqDY/2lQnurNyN7Ko7uprS67uOJcEYTqmjng9ylrZ5/9sypmFU69hRd5DfS/z7lCthUGQWp0T3RkTknZzvW/WQvCL1bPQqHXsbc1lRtTHguIyQboyIGoaExJcHvw5IhM9IuhCT2kyxoyCg0Gh83IWEaaOodZezrMI/gqpXm5kQL0cX11fPocLpb0XU2TKSrDA5Yru87Bmcvjq/MT0jbyPBPA5RcrO29G5sHv/IsVmXTq+YNxHQUG77hX01TyJKgX9bUZbz6Rr7NWpVOHb3FvaWnU5l45eIAdTxxxo211byqm6kuO5J5P7rl9I15rOABvEA9c7NbK+4BQkPMabJpEfc7TdGkiS2Vr1ApWM9asHI8PjXMWj8O85sqvqYatc+9KpQxsQ97FdLKkkSi8rexCu5SDb1oXe4f8/1Onclf5Z/C8CUhCsVazvtXhs/lXwDwNSEcwnTKpcgrK/ZwN7GbHQqHRd18ifBzXD53HyaLwudzk6aQKyhZX/JtdW72FCzB42g5uau5wQV7bR7XbyRLSv0L08dQ5KpbR6WRfYaPty/FIB7ekwlVHvkRFGSJJ7a9hseSWRsfAYTErKO+FgtYWbOJtZXFmDSaHlhyOnHNeImSRIPr11AXmMtiaZQXh4x7W8pKBIliXuW/8rKkoOYNFo+mzSdLmH+pSwnGv4hni1Ao1Lx9vgzGRCbSIPbxRUL5lBi7dgOJccLd/QZyZDYFKweN7esmNdh9ZXB4pH+E4nSm9nfUMU7uztG5DU8tgunJfdCROLJrb8iHoX5t1Gj48Gecsr8y9xV5DSWt2n/WEMYV3eR6xnf3vc7ja3UbTbjhvSz0Km0bK3LYXkr3Yyi9RGclyxbhn2RPw+HL3D6+IKU6RhUeg7YclldvVZxjEUTwrREOVr2a8kcrF7/mlC92si0xGsAWFE5j0qnfwS7W8gIuoWcgoTIgtLXFU3lh8XcSpiuM3ZfNSvKXvAj2YKgYnDsM4TpsnCJtawpvR2PaPU7TqRxOFlRTwFyX/LtFTfiFQPbiYUYhpEZ9yN6TRoeXymFtQ+zo2QoxXUv4Pb6i6Y6GpLkpdb+K9ll55BdfiZ1jt8BgaTwR0mJeFaxFWYzyqw/srnsCrxiHaH6/vSIflFReHSgfib5DXMBgSFxzxGu91fUl9g3s71WFl+NintAUVC0p+FP8m2bUAtaJsXfoXiuX0o+xiO5SDX3oH/EWMV5/1Y6F6u3gXhDImNjJyuOcflczC6UCey0hKlE6wO3ZpxXvIhKVw3RugjOTZoUcByA2+fh3Zx5AJybPIZkU3Dp7k9z/6TS1UCiMZLL0sYGtU8zJEniuZ2/4BK9DInqwmmJfdq0/1/xW9FO1lbmoVdpeLhPcB6wbUWxrZ4Xt8slDvf1GUeyObzdz9EWfJuznR/zd6MWBN4adSbh+vaP8B5vSJLEk2v/5KfcvWgEFe+fejZ9YwK3zj2R8A/xbAVGjZZPJ51H1/AoSm2NXL5gDrXOEyPi0Z7QqFS8OepMIvRGdtWU89ymlntXdzQi9CaeHChHSN7bs5rdtR3zkn+g1yTMGh3ba4v5Lj+4SGMgjInLYlxcd7ySyDM7fm6zqfyFnU8h1RxLrcfGBzn+7SSVkGCM4oIUWR3+Ue7PrfZwPyvpVOL0UVS765hbFPgcEboIzkyS04PfFn6Hw6f8mx8ZfSpJxk7YfTZ+KflWcUyP0KFkhAzAJ3n5qeQDxcjsqXG3oFeZKXfuY3PNj37bNSoD4+IfQ4WGg7aVZNf72zRpVEaGJ7yOQR1Ng+cA68sfVIxoJoScS6+YN1EJBmocK9hUehEOT2DfXoM2jaz430gOfxydOgWfWEd5w9vsLBlBXtUd2N0dL0D0iQ2UN3zIrpLR5FXdhM29CQEtkebzyIr/nbjQ6wISCkkSOVD7Grur7m+KdE6if9wnqBWER6W25WyvfgWA3lF3kWD2V4U7fQ0sLXsakMgMO4PUEH/Bht1bx5Jy2b5qRPRlROr9W0TubdjInob1qFBzZtL1ivMvthewvFL+nZ6XfKWityfAL6W/UeOuJVoXzWkJ/pHVZlQ4q/m+WD7eVWnnoFcHbjcK8EPxckqd1UTqQrm0c3Dp7nxrBbMOysriu7LOwKBum1H54rLdrKrcj1al5l+9Tj8qotjocfL8Dvl6b8gcRbK5/f00JUni4Y2/YfO6GRSdwmVdj29KO7u2ksc3yHWm9/Ybw8DYtrcnPRnw7vZ1fLpbrp1/ZcxpjE4+usYCxxL/EM8gEK438uXk80kwh5BTV82MP+Zi97Stju9kQLwphFdPkWvyPsvexPyClntXdzSmpGQxNTkLryTy4Ppf8Ij+kbCjRawxhNu6y8Tt1V2LqXW1ve/64bi/52kY1Tq21B7kpza20tSqNNyTJVvNfF+4lgONwZHtCzudSpQujDJnDT8Wt+zVqVNpmZEm98j+sXgxpY7AdYuT4iYQp4+j3lPPj8XKfpwqQcX5KVcBsLpqCQW2XL8xgiBwRuK1aAQdudYdbK/zn6NFG8WYWNmqZ2XlZ9S5/Usfog0ZDIqWzcvXVL5Jnfug3xiTJo7h8a+jFgyU21exo/pVxXnHmiczMH4mOnUsNk8OG0vPp84ZuPxArTITG3oNPROX0yX6Ayz6IYCXWvsP7C07nX3l51NnX4CkEK09Gri8BymsfYIdxcMornsKt68ItSqC+NDb6ZW0mtSo1zDpApth+0Q7Oyvv5GCTh2nnsBvpFfMGagUj+nrXPjaUPwRIpIacS9cw/3pLSZJYWf4idm8lYdoUhsXcpnjeJeXv4fA1EKNPY1CUv8jHLbr4pfgjAE6JOYM4g3/7T0mS+K5IFhT1Cx9MVqhyR6UKZyW/l8oCu4s7XYhOFZhMfpE/D7fooWdoN0ZEDQg4DqDBY2PWQdlp4uq0aZg0gR0CDsfr2b8cEhSNjAlcZ6oEm9fFC7tk54kZXUaRavGPJLcFb+1ZSpXLSmdzJNd0a7uqPhjMzdvOirJc9GoNzw+ZdlxT7PamjJ3T52VMYpe/bVvMb/ft4IWNsuXdY0PHc3b6yWWI/w/xDBKJllC+mHw+YToDWypLuGXJT3jFtkW0TgaMS0o/dLPet/o3Chvrjut8nhg4mXCdkV115Xy4Vznle7S4tMsQMkPjqPc4eGVXcJZGgZBgDOemjPEAvLZnAbVBenM2Y1BUV8bFyun//+z7Lah9DGodV6XJbSpnHlxIg6flcw6J7EPfsCy8kpevDvpHF5uhVWm5tLMs5PijfBElDuU62HRLJoMjT0FCCig0itTHMzZWJry/lX6G0+c/x97hU0g29cEruZhf8rJiyr13xIUkmgbik1xNwhZ/C7AIQ08Gxcrp9AP1szhQrxyJDdH3ZHDCd4ToeuIRa9lSdhWl1h8CfBoyBEFNuGkKGXHfkRX/CxGmcwANVtc6cquuY1fpGCoaP8GnkOYPFpIkYXWu40Dl9ewqGUNl4yeIkhWDthudIp+nd+I6EsPvRav2V5AfDpe3nM1ll1FpX4CAlu7RL5AecZdiytvprWJ12R14JTsxxiH0i3lQMdK2v+E38q3LEFAzNuExtCr/FOa+hhXsaViCgIpJCXcpRimXVnxHraeCMG004+OU6zE3164lx7oXraDjnCR/m6ZmzCqYjUfy0jO0BwMjAjsC7Gk4wKrqzagQuKbL9FYjiV/l/4HN56SLOZEJ8cFF8dZW7WNd9T40gpo7sk4Pap/D8c6+xVS6GkkxRXJ116Oz/tlTV8bXB2T7qUf7noauA1pjljsaeXqrHF28q9eY496P/fENC8mprybOaOHVU45vnWlHYXHBAR5cKS+0buw9hGt6nXyiqX+IZxuQERHNJ5POw6DW8GdhLo+uXtiqoONkxL39RtM/OpFGj4tbV/yI29f+kcZgEW2w8Gh/OcX11q4V7K9vf2WxRqXi8X5yfebcg1vYUn107VIvTh1GRohMZF/fE1zK/HDcnDEVjaBmbfU+1lX59yFXwsT4waSZE7B6Hcw8uLDFsYIgMCPtXFQIrK7ewt4G/yhlM/qG96ZfeF98ko+ZBbMD/t7PSroYvcpAvv0A62uUo66jYs4mWp+E1VvHwjJ/c3ZBUDEl4W60KiNFjp2K7TQFQcWYuIfRq0Kpdu1jU9XHiudKskygZ+StAGyvepFyu3JnKL0mjgHxXxNjmoyEhz1VD3Kg9pWgvDtNuj6kRb9Br8TVxIXejFoVhttbQFHtE+woHkpR7VM0OJbR4Fge9F+1dQ7Z5Wewr+J86h3zAZFQwxi6xnxJ9/hFRFsuadGbsxkNrp1sKJ1Oo3sXWlUE/eM/I8FytuJYn+hkTdndOLxlWLSdGRr3EiqFWtF6dxGrK94AYFD0tcQY/EUqNm8NC8tke6WhUReSYPSvD61wFrGyUl7wnJ54DTqF63H5nPxQLLscTIo/k0i9cuRvR91ONtdtRS2ouazzxS2UG0h8lif/nk6NG0GaueX0a7G9kp9K5AYT16efiTqACf/h8Ekib+2To5XTOw0nxRS4zlQJe+tLmJUnL64f6nV6m1P0h0OUJJ7c9isiElOSenBKXNusnIKBJEk8uvF3Gj0u+kQmMCPj6HxGjxbf5+5kzoEdqASBN0adSZQhuPayJxN2VJVxy5Kf8EkS07v14sHBLTdIOFHxD/FsIwbFJfHWONl+aGb2Nt7fsf54T6ndoVWpeWvUWYTq9GyrLuWlrce3peZZnXsxLqErbtHHg+t/wdcBkeb+USmc26kfAE9u+/WootlalZqHe8sp8x+LNrO5Jr9N+yebog51NHpr32/4giBBakHF9elN5yxeSYmjZQeGzuYkxsfK5/g0v2VF/CWdLkQjaNhRv5MtdcoCpjBtBFMTZFuan4pnY/f6RzQ1Ki1nJl4HwLrqBRTb/RXs4boExsXeAMDKys+pdOb5jTFrYxgV9wAA22tnUmJXrs3NCL+aTiFnIOFjXfn9NLj9zwegVhnpFfM6ncNuBOBg/QfsrLwdnxhc2YVOE09S+IP0SlxHSsQz6DVdEKVGKho/JKfycnIqLwv672DNPdjd2xEEPVHmi+kev5CusV8SahwTdK1fhW0Bm8suxe2rwKztxqCEOYQblKMikiSxqfJxal070KnCGBH/BjqFtpmi5GVp6ZN4JQcJxv70VmiLKUkSC0pfw+FrIFbfleEx/lFKSZL4qfgDfJKXzJBBdA9VJivzy+ZR76klWhfLqXHKhvJe0cvXBbMAmBA7nkSjfyvPZqyq2sw+az4GlY6LOikf73B8nPcrPklkcGQWAyODa1f6S/FGcq3lhGiMXNVlfFD7NMMniTy982dEJCYn9GJETHAG9YHww8GtbK0pwqTW8kBvZUHW0eLngt0sLtmPVqXi+cGno1EdPzqRU1/NI+vkBh539DmFYXH+pRsnO4oa65nxx1wcXg+jk1J5fuTkk1ap/w/xPAJM6tyNx4bKD5bnNizj17yj635zIiLZEsZLw+UH9Ie717O4KOe4zUUQBJ4aNBWLVs/WmhI+2+/v5dgeuKfXBMK0BvbWlzMz9+gWFH0jOnFuivyyf2bHz3haEf38FVd1GU+IxsgBaxm/FgeuPTwcgyKzGBSRhVfy8Uluyx2KAC7ufDp6lY59jXmsrg5cjxpniGNKvKz+nVkwG7eo3OFqTMxk4vSJNHob+L3UP1oJkB7Shz7ho5AQ+bH4feV0evgUuliG4pM8/FbyIl6FjkWpIaPJDDsDkFha9jROn7/bhCAI9I95hChDf7yildWld+AK0LVIEFSkR9wlK73RUmlfyKayS3C2QbmuVpmICbmcHgl/kh7zKaGG8Ri1Pdr0Z9L1ISHsHnolrqVz1AsYdcH3aJckify6d9lZeTui5CTKOJqBCbMxav0N2Zuxp/Z9iqx/IKBhaPzLWHTK/p+bqz+l0rUHncrCmPiHUQlqvzE76n4n17oetaDltKT7UCtETbfVLSfPthOtoOP0pGsUX5zlzhKWVMhlJucmX442QM3mwvLFlDrLCNWEcnaSchtOAI/o4cumkpKzkyYSqWvZ1HxXfR4rKrehQuC6LoGPezhsXtchQeDV6acSpm1btO37go3srCvCrNFzT4+pbdr3r6h12Xl5l5z1uLX7WOKN/guJo0WV08aTm2Wid0uPkWSGH525/dHA6fVw64p52L0eRsR35tZeHVPLejzR4HYx44+5VDpsZEXE8M74s9Cq/O/BkwX/EM8jxNW9BjGjx0AA7lr2K5vKO8bo/HhicqcMZjSZ7t6z6hdKbMfPSirBFMq/+sp2Q6/sWEp+Y+C2h0eKSL2Zu3rK53hzzxIqnIFtdoLBHVkTidCZOGCtYGZe2+pTw7Qmrk6X5/JBzkLs3sCddg7HdelnICCwrHIru+vzWxwbqQvjnCS59eCX+fPwBCCUAGckTiNcG06lq5KFZcqpfI1Kw/SUKwBYXvkHJQ7lkoXTEq5CrzJR7MhhQ43/sQRBYHLCnRjVoVS6cllT9ZXicYbF3EaYNgW7t5KV5S8q92oXdAyLfwWzJhm7t5i1ZffikwILA+MtZzEg/gu0qkis7j1sLJ1Og6ttqnVBUBFmPJWusZ/RPWF+m/6y4n8hIewOtOq21cr5RBe7q+4nt+51AJJDrqB37LtoVIGNpAsbf2dvU5vR/jGPEGNUjoqW2LewteZLAEbG3a/YnajOXcqScvlYI2OuIlqf6jfG4bPxW+lnAIyNm06kzv84kiQxt+hLfJKPHqH96BWmXLPZ4GngxxJZ8HZ+yrmYNIGJ3q+ly6hwVROpC+OspFMDjms+/wcH5G5Bk+KHkGYJzp7m6/xl1LitJJuiOC+lbT3Qa9023tzbRBQzJhBrODqi+PruxdS5HXQNieGy9I4R1/x78wJq3Q66h8dyY/fjS/Se3LiYvbWVRBtMvH7KGaiPY+S1I+ARfdy0eB776qqINZn5dNJ5hOj0x3taR4W/1zd0jPHo0HFM7NQVl8/HjD/msr7s6GoDT0Q8OGAsvSPjqXM7uX7pXOpdR9de8mhwQZd+jIhLxeXzctfaedjb2CEoGExPHUCfiCRsXjePbv45qDR3IITpTNyRJUcKP8hZSqGtbWT5vJRhJJuiqHY38n5OcH3gu1gSmRwvpy//s39uEPZKE4jQhlHuqua7osDnMKgNXJAii4N+KvmFModyJDArtDf9wgcjIjLr4IeKZDZEG8HEeLk70oLSrxS9Pc2aSCbG3wHIxvI5jav9xmhVRsYmPIaAmnzrMlZVvKIYQdWrIxie8AZalYVq5xbWld2LtwUT+DDDAAYlzMGszcDtq2RT6SXk172LqCBkOhHQ4NrOxtLplNt+QkBNZtS/yYh6GFUA6yGAYutCNlU8DkBG+FWkhp6lOK7MsZ3FJQ8DEt1Cp9IlZJzfGJfPxs/FT+ORnCSb+jAo0r8TkCRJ/Fz8ATZvPTH6JEZGK59vfc1K9jRsRyNoOC/5csWIqCRJfHVwFg6fg1RTZ0ZGnxLwOsudVcwplIUYl3Q6A4O65Rf2grL17G6QU/JXpgUXecyzljMzX65rvqXbVLSq4EU8kiTxyu7fafQ6yQiN54LUo6uTXFmew5wmW7jH+03rkKjYt7lb+a1wD2pB4PnBpx/XyNvnezcxc/9WBOC1U84g9gTv2NNWOLwebl784yGD+E8nTifR0v4R7GONf4jnUUCtUvHm2NMZGJtEvdvJpb9/y8+5bevVfaJDr9bwn9FnEaU3sbOmnMsXz6b+KHubHykEQeC5wdMI1xnZXlPK7at/aHdnAbWg4ol+p6NXaVhevp9Xdx1d//ozk/vTN6ITNq+LezfPwukLnrxoVRruymzy0ixYzeKy4CJvV6ZNJURjYr+1iPdzAqvWAQxqPTPSZKIwp3A+W2oDt0wdHjWMzJAMnKKLt3LeweVTjsKek3wZRrWJfPsBvi9SjlYOjZpMZ1MWLtHOl/nPYlcwn88IHUmf8NOQEPm5+FkKbFv9xsQYsprqPQX21v/I4pJH8Cr0VQ/VdWFo3MuoBD1l9hWsKLkBpzfwQsCoTWZgwiyijaci4SG37nU2lJxDYcPnuLxtaw7QERAlD9X25eyuvJ+NpRdi8+xDq4qgb9xHJIVcFHA/SZLIqZvJuvIHEPGQZJ5Ez0hlS6T8xuX8XnQXLrGRWEMvTon173LkEV38UPQE5c4cjOowpibco6iaX1oxl211K1Ch4uzkm9Co/NPwhfZ8vimQxWKT4s8i1hCvOK9FFX+yrmY9KlRcnnopqgDCH7fo4cW9H2H3OciwpDI2tuXoX661hP/sl7trXdp5ItH61vuM27wuHtr2FS7Rw5CoboyJDa6dZjPmFm7kl2I5rf9Qz9ODEjEFwoGGSu5a/x0ScGHqQAZFB26beqRYUrKfRzbKZRC39RxFr8jjZ1j+RfamQ36dd/YdyajEk8fHMhhUOWxc9Nts/ijIQa9W8/b4M+kV3bKTxcmCf4jnUcKk1TFz6gVM7twNt+jj1iU/8+62dX8rtXvnkAi+nngRkXoj26vLuGLRN8eNfCabw/lw1AXo1RqWlObw6Kbf2/2z7h4ezzMD5NquT/av5oeDW4/4WCpBxYv9LyBCZyK7oeyQR1+wGBGTxeWpsnLx2V3fkW+raHWfaH0YD3SXhR0/lazkz/KWjfFHxQxiUpxsh/Tavs+odCkTMpWg4ub0GwjThlLkKObT/C8UP/tIXTRXpt6CgMDKqkWsqVqqcCw1l6TeT7g2mmp3KbMOvoRPwex9QvytdAs5BZ/k4YeiJyh1+HvLZoRN5dSEp1ALOg7aVvJb0V04ffV+42JNQxmV+B46VRi1rh0sKjyPgsZfA/5+NCoLvWPfpkf0S2hV4dg8+9lf8yyrisawufRyihpm4vZVK+7bEZAkHzWONeytepRVhSPZVnEdZbYfAZE48xkMTfqNSGPgtKfNU8Kq0lvZXv0SINEl9AKGxD2rSBR31/3AotJH8EluOplHclrya2j+oj73SV5+KX6WIvt2dCoT01OeIUznTxZ31q9hUbnsYnBG0nWkmv09B23eRj7OfR2P5KFHaF8mx5+teA05jTnMKpDbZ16YMp2ulsBq7Y9z55BrKyREY+berGtaJHU2r5Mnd32GS/QwKCKLCzq1Lg6SJInnds3loK2SGH0oj/e6sE1ij931xYeeB7dlTaB/5JETxVqXnZvXzsLqdTEwqhP/6hPYRP9Isa26mNtW/4BPkjg3tQ+39hjZ7ucIFl9mb+ax9TLpvLHnMG7vHTjqfTLiQF01Z//8FVsrSwnXG/h66oWMT2l/Z4LjhX+IZzvAoNHy7vizuKanXPP5/MZlPLJ64d/K5zMrIpavJ15MhN7ItupSrv5zDrbjZKI/IDqZN4afjUoQ+DZ3K2/tatk0/UgwLaU3N2aOAuDxLT+zqargiI8VZwzjuf4XICDwQ+Em5hW2rUPS9V0nMSCiC3afm39t/Sqoes+hUT24pJNsQ/Va9jcctLUcpbumy/l0MafQ6LXx8t6PA4qhwnXh3Jx+IypUrKley+IK5Q5XPcP6cVqCnJr/tvBTcq3+tlAWTTiXpz6MTmUg17aTn4s/9COBKkHNtMQH6WTqh0d0MLfwYapc/sbxaSFjmJr8GjqVhQrnTn4uuJlGj7/vaJShH2OSPiVU1w23WMfGikdYVXorNo9yjbYgCMRbzmRY0ny6RT5MmL4/IFHnWs++mn+zqnAUW8qupqRxDh6FHvJHC0kSqXNuJLv6SVYVjmZr+VWUWL/FI9ahVUWRFHIJA+Nn0zPmZXRq5R7NkuRjf91XLCo8jwrHalRo6R11F32jH0T4i0hIkiQ2VL3P6opXAYmssDOZkPiUH+mUJJEFJa9ywLoWjaDjnJQniTP6K7GL7Qf4rkC2YBoeNY0hUf4Ka5/k5ePcN6l2VxKti+WK1JsVo5gNngb+k/MePsnH4IiBTI4P3O5yScU6/ihfhYDAXRlXEaMP3L9akiReyZ5NsaOSGH04D3YPHEU9HN8WrGJx+XbUgopn+l5KpD74NG+92869m2bjEX2Mjcviqi6jgt73r3CLPu5cP4cCWy1JpnDeHHpBu3t25jfWcO2Kb3H4PIyK78Kzg087borqb3O28ej6pm5MPYbyQP/gHR9OBqwvK+TcX76msLGeTiHh/HDGZQyO+3t1X/qHeLYT1CoVjw07lSeGnYoAfLV3K9ct+v64kbOOQPeIWL6ecBGhOj2bKou5bulcnMepp/vEpEz+PUBe1b+xawXfHGhbl6BgcFv3cUxK7I5HErlt3TcU2+qO+FjDotO5uclY/rmdP7O3XtmMXQkalZqn+lxMtD6EPFsFz+9u2f6oGVekTaFfeDecopsnd32KowXCqlNpuT/rWiwaE/us+XyWr6xKB8gKzeSCFLkbzcyC2eRYlW2KJsWfRb/wwXglLx/lvk6t2z86GG/szIWd7kZAYEPNQtZU+0eENSodZyc/TrwhE6evke8KHqLO7V9jGm/swxkp72DWxFLvKeCnghupdu73GxeiS2N88tf0iLwVlaCjwrGaRYXT2V/3VcDOQ1p1BCmhVzAwYTYjkpfQNeJ+QnS9kPBR61zF3upHWFk4km3lN1BqnYf3KA3k613b2F/zHKuLxrK57FKKG7/GLVahUYWTaLmAfnGfcUrKcjKjHifMENg0vd61j6XFV7Kj+hV8kpNowwBOTfmWbuFX+L2sRcnL8vJn2VYjl0cMjLqGU2Lv9asVlSSJxeXvsLthMSrUnJH0CCmm3n7nbvDU8GX+c3gkN91C+jM18SrFOc4t+or91t3oVQauS78bs8afwPkkH+/kvE+tp5YEQwLXdJkRkGwctBXz3gHZZumClKn0j2i5q8sPxctZUbkNjaDmkR5XEqZrnUBuq83nraYGD3dkTqN3ePDRSlESeWTbXEocdSSbIniy77lHTJwkSeKZbb+xviofk0bHO8MvJlJvPqJjBUKV08qMZbOocdnpFRHP2yPOO251nT/n7+GBNb8DcHXWIB4cMPZvRTp/zt3Dpb9/S53LSf+YBOadcRldwgIvmk5WCNIJnBNuaGggLCyM+vp6QkNPnoLa+fn7uH3pL7h8XnpFxfHJpPOI+xsVPW+pLOGyRbOxed2MT0rnvTHnolMfnwfRqzuW8vbuVagFgfdGns/4xKPzv/sr7F43ly3/lD31ZXQLjWXW6Ksxa49MUShKIrdv+JqVlftIMUXy9cgbCdX6d34JhG21+dyy8QN8ksg9WWcd8vpsCbXuRm7c+DI17gZOjR3IA90vbfFBvbFmJ8/seReAuzNmMComsP/j2znvsqF2ExHaCJ7s9RihWv971OVz8tq+f1PsKCDZmMpdmY+hU/l/fisrf+T30s8RUHF56r/IDPVvZ+jwNTD74L1Uuw4Srk3gos6vYNH6q79tnkrmF99LrTsXrcrEhMRnSDIpX0ejO58tlU9R5ZSj0BH6nvSPeZRwfXA2RnbPQSpsv1Nh+w2r579lACp0RJpGE6LrAQT/YvSK9VTaF+L0/jcCqxYsxJgmEmeeSoRxhKK5+1/hE13srf2QfXWfI+FFq7LQK+pOUkPOUUytu0U7f5Y8SpF9PQJqRsbdR2aYst/lyorPWVs9ExCYlvgA3cP8BUdu0cWHBx6hxHGAWH0KN3R9FoPanxCtrFzMN4WfICBwXZe76B0+UPGc3xZ+x6+lv2NQ6Xm85yMBPTttXgf3bXuRUmcF/cK780iPm1tMse+uz+furW/hk0Ru6XoOZye33i2oxmXlyrVvUuVqYGJ8X/7d+6I2kZ+Pcpbxn+xF6FQavhhxHVlhgf1HW8OXB9bx7Pb5CMDbwy5mXELGER9LCTaPm0uWfMnO2jJSzOF8N+FKog3H5122qHA/Ny77Aa8kcnG3fjw79OT1sfwrJEni/R3reW6D7Jk9uXM33hh7OkbNkTcRONZoC1/7h3h2ELZUlHDNwu+pdtpJMofy2eTpZES0rZPFiYy15QVcufhbXD4v0zpn8ebIM4+LjYUkSTyw/hfm5m/HoNYwc9xl9I1KatdzlNrruWDph1S5bIyLz+CtYRcesQig3m3nopXvUuqoY2xcFq8NvKRND89ZB1fwZvavaAQ17w6+gV7hrRsl76g7wL1b30FE5PZu0zkjqeV6qK8O/sTcogUYVDpe7Hs/KSZlAYHD5+Dfu56h1FlKj9As7s28G7WCv2ONq5KXsh/D6m2gf/hQZqTd5nfNkiTxQ9E7bKpdjF5l5Lr0p0kw+osFrJ5qZh28h3pPKdH6NC7s/CJGBcNzl6+RRSUPU+rYggoNo+MfomuocmpWkkTyG+exs/o1PKIVAQ3dwq+ge8R1qIPoEtQMm/sA5bbfqLD/ht0TuBtUMFALJqJN44k1TyXSMAq1AlkPhErHRrZUPoXVI5eHJJrH0zf6AYwaZa9Fu7eGP4rvp8qVjUYwcGrCk6RYlBc1G6vnsrTiAwAmxN9Gvwj/tpCi5GN2wSvsql+LSR3KTV2fJ1LvX/uZ07iHt/Y/h4iP0xMvYHK8stJ9U+1m3tz/NgC3dL2RIZGDFcdJksSL2R+xtnor0boIXun3IKHawCSpzm3l5k2vUOmqY0xMPx7u4R8F/iu8oo87N33MptpcUs2xfDz0Fkya4L+bdVUHuGnd54hIPNHnbM5OUSbawWBleQ43rJ6JiMS9PSdwTUb71jl6RZHrV37LstIDROpNfHvqlaSFHJ/o28rSfK7+cw5u0cfZaT15ZcS0v41tklcUeXzNIr7auxWAq3sO5JEh40666/uHeJ4gONhQy5ULviOvoZZQnZ73Tz2bEYntrzQ8XlhanMt1S7/DI4pMT+/Ni8NPOy69cT2ij+tXfMvyslyiDWZ+mDCDRHPritS2YFtNEVes+Ay36OPabqdwT68JR3ysXXXFXLXmQzyijzuyJjEjPfj6LkmSeHj7TJaU7yBWH8Znw28jIojU4JyCJXyQ+xNaQc1r/W8nMzQwYfVJIv/e9RY76veRbIznxb73YVQrE7ASRwlP7Hoal+hiWsJphyyX/ooc617e2vesTDISzmdywtl+Y7yih8/yniTPtguLJpzr058hSu9Peuvcpcw6eDc2bw0Jxu6c3+k5dAo9w32im6Vlz5Bn/ROAIdE30TsicFtFh7eSbVUvUGKTnQws2k70j3k0oL9lIEiShNWTTaVtQZvFR4KgJsIwhCjjWNQK19QS3L5Gdla/Tn6jXCZhUEfTN/ohkiyBhTL17kLmF99Do6cUgzqMSYkvEmtUTk3vqFvAgtJXARgVM4Oh0f7qeUmSmFf8HhtrFqIWNFzd5QlFMVG1q5KXsx/F6m1kQMQwrkq9VfF7KXOW88Sup3D4HEyOm8glnQMr9n8sXsxn+d+jEdQ80/suMkICq5x9ksjD2z9gU202KcZY/jPwLkya1hcZ7+6fzxd5SzGqdXwy9FZSLcEbp5c7G7hoxTvUum2cnTyAJ/qeE/S+f0VeYxUXLvuIRo+Lczr145kBZ7Z79O+JTfP5MmcTBrWGr8ddRr92XtAHi40VRVy+6BscPg+TUzJ4e/TZx7VLUnvC5nFz65Kf+LMwFwF4bOh4rj4Je6/DP8TzhEKt08G1i75nY3kxWpWKV0dP48z07sd7Wu2G+QXZ3LJ8Hj5J4orMAfx78MTjkv6welxcuPgL9tZXkBUWyzenXoHlCFPigfBL4Q7u2yi/1N8Ycj6TklquHWsJ3x3cwNM7f0KFwAfDZjAoKngrEJvXydVr/0OBvYohUd14dcCMViOwkiTx712fsqpqB3H6CN4ZdA+h2sC1YHXuRu7Z9hw17npGRg/k7ozANXXrqjfwzoH3ALij260MiFCuOVxV9Sezm+xyrutyF33C/R+wDp+Njw48SpkznwhtLNd3fZZQrX+UpdKZzzcH78EpWulsHsA5yf9Go9DhRpJE1lX+h511cwDoGX4+w2JuVUw3N6PY+ifbqp7D6ZPbjqaGnEOvqDsVW0meKCi2LmZr1fO4muacFnoePSPvQKcOCbhPhWM3f5Tcj9NXT4g2kSlJrxCmUxYxZDes4JfiZ5EQGRQ5nTGx1yr+HhaUfsXyyu8RUHFR53voFeYfOXX5nLya/QQlzkJSTGncmfGoYvmFy+fiyd3PUOQoJsPSjQey7kUTwCNzd0MOj+54AxGR67qcz2kJYwNeN8CX+fP5In8BepWWtwbcFZRR/IqK3dy/9QsAnupzMRPi+7a6TzM8oo9r137CttoCMkPj+XzE9Ufci93mcXHhso850FjJgMgUPh15RbuLiT7ft4Ent/whp/BPOY/JyVntevxgsaO6jEsWzqLR42J0Yhofjj0PfTtf6/FClcPGjD/msr2qDL1aw5tjT2dKavuWShxLtIWv/T2WDScwIgxGvp5yIaelZuIRRW5b+jMf7eyYlo/HA1M6ZfLyiGkIwBfZm3lhy7LjYiVl0er5cNQFRBvM7K2v4M4189q9p/vpKb2Z0VV+kT60+UcONFQe8bHO6zSI05P6ISLxwJZvqWxDlySzxsCzfS/DoNKyvno/Hx9Y1Oo+giBwb+bFJBiiKHfV8tLemYgtmOOH60K4N1O2oFlZtYnfy5YFHDs0ajCT4mQF/Qe5H1PmVFbQnxI9ntExcrr78/x3KHH4OwUY1WauSnuUSF08tZ4KPst7UtHjM8aQynmdnkErGDho28wvxc8qttYUBBXDYm9nSPQtAOyqm8OfpU/gFQMLrZIs45mY8j1pobKAKr/xBxYWnkdh4/wTzkTe6ilkTdndrCu/F5evCos2ldGJH9E/5pEWSWeBdTW/Fd2B01dPtD6LM1LeDUg6DzSu5dfi55EQ6R0+JSDpXFE5j+WV8sLsrKQbFEmnKIl8kf8uJc5CQjRhXNflLkXSKUkSn+Z/QZGjmDBtGLd0vTEg6axzN/Dy3k8QERkZPZCp8WMCXjfAxpq9fJkvq6LvyDg/KNJZZK/myZ3fAnBBpxFtIp0Ar+9dwLbaAiwaAy8PuPiISackSTy8+ScONFYSY7Dwegco2JeW5vD0Vtmq6L4+448b6cyureTyRbNp9LgYEpvC+2PO/duQzoMNtZz3y9dsryojQm9k1tQLT2rS2Vb8QzyPAQwaDf8ZdwZX9ZAFE0+tW8Iz65cgnrjB5jbhnC69eGaorDB/b9da3t655rjMI9Ecxgcjzz/k8fn8tqMzf1fC3T0nMCQ6FbvXzW3rvsHqCa6V5V8hCAIP9z6DbiFxVLusPLjlW7yisqJaCekh8TzYUzZ+/zT3T1ZX7m11H4vWyGM9r0IraFhbvZtvC5WtkJrRPTSdKzrL6cBP874nuzEv4NgLU6aTYemGw+fgrf2BzeXPTb6UjJCeuEUX7x94FasCqQzRRjCjy+OEaCIpdxbwZf6zuBVM4ROMWZyd8gRqQUuOdQ3fFTyEQ6FnO0CfyIsYF/84KjTkWZcwv/heXL7AZF+rDqF/zMOMTvwYizYVl6+KDRUP8Vv+RDZWPE6pbXmLrTc7Eo3uPPbWfsyfhZfwR8GZlNqWIKAhM+JaTk2eTbSx5brBvfU/s7DkX3glJ8mmoUxLeQOTRrl2b1vtb8wr+jciXjJCRjEx/nZF0rmpZjHzS+Vo4OT4yxgcNVHxeL+Xfs/2+o1oBA3XdbmLCJ1ya9DFFUtYU70WFSpu6Xoj4bpwxXE+yccr+z6l1lNPsjGem7u2XDNd6azj+T1fISExLWE4E+OV60UPh9Pn4V/bvsLqddIrrBO3ZpzW6j6H44/SnXydJz8Tn+53LinmI6+T/DRnDQtKdqMVVLwx5AJi2lnok11XwR2rf0CUJM5P68v1WW1r/9leyGuo4bLFs6lzO+kblcAn46efVEKblrCjqoxzf/6a/IY6ki1hzD39EgbGHZ8yhuOFf4jnMYJapeKJYafy4CB5Nf7Bjg3ctexX3L7gycaJjEsy+vHIQLmW7OWty/l4z/GJ6vaNSuKlIXK3n0/2rWdmTts8M1uDRqXi1SHTiTeGkmet5qFN8444wmtU63h5wEWYNXo21eTzVnbrkcvDMTmhP+c29YV+Ysc3lDpab8nZNSSZW7vJdZif5v7KtrqcFsefkTiO4VH98Uo+Xt77MQ0eZZsgjUrDLV1vbDKXL+KzAObyakHD1Wm3Ea2Po8Zdyce5bygax0fq4pjR5VGMagsF9my+zn8Rr0L7zc7m/pyX8jQ6lYkix05m5d9DvUc54poeOoEpyS+jVZkoc2zlu/zLyG38s8XvL9o4gFOTZ5MVcT16VQRusZ6Cxp9YU3YHv+aNZ335gxRbF7bYgvNoIUkSda5sdte8w8KC81hYeC67a/5DnXsPoCLWOIzxyTPpGXlLiyIkq6echcX/kvva46Nb6FQmJT2PVuXf51ySJFZWfM7CsjeQEOkZNpFpSQ+iUhCP7apfyw9FshPCqJizGR3r3zITYHPtWuaX/QDARZ2uIc2i7ECRYz3AzILZgLygyQwJHAmaVfALO+v3YVDpuD/ruoC1yCALg57e/Tn1HhtdLUnc3DW4GstX9v7I/sZSwrVmnul7aZtaYuZbK3lim3zNM9JHMTbuyMus1lbm8cpO+RnxUJ8p9I9KOeJjKaHKaeW6Fd9i9boZGtOJJwdOPS5lU8W2ei5dOJtKh42siBg+P/WCdi+bOl5YXpTHhb/Nosppp0dkLN+fcSnp4cqLr78z/iGexxCCIHBT36G8NnoaGkHFvAO7mfHHd9Q6O+6ldSxxbY8h3N1XFso8tXExX2a3L+kLFtM69eCuXjLBf2LzfJaVKvtMHimi9GZeH3I+WpWaRaV7eX/fkRvYd7ZE82Qf+QX4ee5KFpXuatP+d2SeTo/QFBq9Dh7eNhNXEC05pyYMZWLcIEQkntv9FbXuwJE/QRC4teulJBpiqXLX8uq+T/EF8Lo83Fx+dfVaFlX8qTjOrAnh+i53Y1AZyLHuYU7h54rkL87QmStSH0Yr6MmxbmVO4RuK5LOTuR8Xd34FiyaaGncBM/PvpNShHAFONA3k9JS3CdOm4PDV8Gfp4ywovp8aV2AVulqlp0fkTZyWupBRiR+SHnYRBnUsXslGkXUB68rv59f88awtu4e8hu+xeUoCHitYeEUH5fbV7Kh6lT8KzuLPoovYW/shjZ5cBDTEGUfQP+ZRpqUuZGTiu4TpA9uIeUUn22tmMTf/cg7aViCgZmDUNYyOe0ixn7tHdPJ76ctNlkkwPPpSpiTcg1ph7P7GrXxT8CoSIgMjTmVy/OWKcyiw5fJV/vsAjI89jaFRyrZFde56/rP/3aBM4tdXb2dukZwyv7nrpaSYlFtsNuPj3F/Y3ZCPWW3gsZ5XoQsi3f1z8QZ+Kd6ICoEn+1xMrCF40aLN6+KeTbOx+9wMikzjloxTg973ryi213H3+u8QkTi7U18uSmtfAYrd6+bGld9RbK8n1RLJ26ecd1ws8kpsDVy6cDYl9ga6hEby5akXEa5vm8juRIQkSXyzbzsz/piLzePhlMTOfDvt4r+VzWJb8I+46DhhWVEeNy6eh93rIdZk5pVRpzE6+eTvNStJEs9vWcr7u9YBMCNrEA8PHH/MVYiSJHHvup+Yd3AnOpWaV4adxWkp7Svq+jZvE49v/QWA+3pN5OpugdsVtoZXd8/ni7xVaFVqXhlwMaPjgvORBChz1HHl2jdp8NgZHp3J8/0uR9dKVMbhc3HrptcosJeTak7gpb43E96COv6grYT7t7+IW/QwPKo/d2fMQBPARPr30gXMLvwWAYGr065kdIyyan9H3WY+zH0VCYkRUeO4sNPVih1j9jdu5cv8Z/FJXtLMvbik832YNP71i42eSuYWPkKVKx8BFcOjL2VY9MWKUTqv6GJbzddsq/2qqW5ToEvIOPpHziBCnxrwc2iGJInUunZRbFtMiXUxNm/R/9tu1qQQaxqKRduJtvl42qhybqbasQWR/5JslaAnzjSCJPOpxJtGt1i/efg17q3/iW01X+HwydHwWEMvRsbdR6S+i+I+pY5sfit5kVp3EQIqJsbfTp+IqYpj11f/wc/FHyLio0foUC7qfK+indYBazbv5byEU3TQPbQPN6bfp/g917nreH7vy5Q6S0kwJPB4z4cxqpVJx+baXTy/5wM8kpfTEsZwXZcLWvwsZh9cxMd5cnOCx3rOYFRMnxbHAywt38kj22fik0Su7zqRGV2CJ45Wj5NbNnzJttoCYvQhzBp5E9GG1r8zJRRYa5ix6gtK7PV0D4tn5pirj7hGVAk1LjvXLv+GbTUlhOkMzJ1wFWkhxz4Kt7GiiBuX/UCV00ayOYw5ky8lwXzyv/frXA4eXvUHv+TJPr9npXfn5VGnHTfv647CP6r2kwS7qyu4dclPHKiXXwpX9xzIA4PGYNCc3AXUkiTxxvZVvL59JQCnxHfmP6PPJuIYr1xdPi93r/2R+UV7EYAnBkzmsm7tFymQJIk39yzhvWw54nl793HclNW6AbUSvKKPB7fMYVHZLjSCmlcGXsSYuOCL+rfU5HLX5k9xiR5GxnTn2SBSgsX2Su7Z+jbV7nq6mBN5qd/NLSrdN9bs5IW9H+KVvAyN7Ms9mVcrnkOSJL48OJPFTRHPK1MvZ3zsWMVjrqlayqyCj5CQ6B8+lCtSb1YUkWQ3bOabgldwiQ4idfFckfovYgz+YhiXz8bCsjfZ27AUgARDFqcl3U+ETrmGqs59kI1VH5JvbRZPCaSHTKB/1FWE61r3SG2+3nr3PkptS6lwrKPGuR2Joy+hMWriiTUOJd50CnGmkWiCtFfyiW6yG35ha/WX2JtU7hZNPP2jriIjdKqiol+UfKytmsWaqq+RELFoopiaeB+dzf4OBaLk4/fSz1hdJRO5vuGjODf5VjQqfzK0q34rH+e+gUdyk27J5Ib0ezGq/VP71a4aXsp+hVJnGZG6CB7Mup84g7JV0aaanTwfxO+wGc1WYgAz0k7jks7K9aeHY0XFbv617Wu8ko8pCf15tNf5QbXRBGj0OLll/RdsryskVGvkvaFX0iPsyGr48hqruGrlF1Q4G0m1RPHZyCuIM7bfu7DYVs9Vy2aR21hNuM7IR6MupH/0sa83nL1/G4+uX4BHFMmKiOGjsdNJtrSvJd7xwKqSg9y97FfK7FY0goo7B5zCLX2HHRfbwY7GP8TzJILD6+GZ9Uv5co/c8jEjPJo3x51O98jg/eFOVMwvyObuVb9g93roZAnn43HT6RZ+bE30faLIE5sXMPOAnPa/tcdI7uw1ul1rl97LXs4bu2Whzg2Zo7ij+7gjOr5H9PGvrXNYWCqTz1cHti3yubE6h3u2fIZb9DImtidP97kkYFSyGYX2Cu7d+h9q3I2kW5J4se9NLZLPTU3k0yN5GRLZh3szrwlIPr8umMXCclngNT35XE5PUO7vvKV2HZ/nv41P8tE9tA/XpN2BXqFWr9x5kC/znqPWU4FBZeLizvfSNaSf4jz31C9hUdlbuEQbGkHPuLgb6BMeuL90tSuHzVWfcNAmLyIEVKSHTKR/1FUB1d6B4BGtVDk2UeFYj7uN/dsFQU2EvgexxmFYtJ3b9DvySR721f/K1povsXkrADBrYukfeSXdwqaiDtDxqNZdzG/FL1LqlMsTMkPHMCH+VkVjfqfPzjcFr7KvUb6fJsRdwtjY8wIIjtbwRf67TRHRflzT5XZFBXupo4wXs1+hxl1DlC6SB7PuJ9YQozjXDTU7eHHvR3glL8Oi+nFPxtUt/sa/L1zGuwfmAXBF6hQuT/XvFf9XrKnM5oGtX+CRfEyM78vjvYNvGGH1OLn5MNL5/tCr6H6EnYn2NVRwzcovqHLZ6BoSwycjr2hXMVF2XQUzls+i3GElwRTKZ2MupmvosX0+e0WRpzcu5rPsTQCc1uSSYtL6W6OdTHD5vLy0cQUfNjnYpIVG8MbY0+kb07qDwsmKf4jnSYg/Cw9w3/LfqXLa0anU3D9oNNf0GnTSr4z21lZw3dK5FFrrsWh1vDXqLMYlpR/TOUiSxFu7VvDGLplUXNSlP08OnNKunSE+3b+aF3fKFiRXdR3G/b0mHRH59Io+Ht76HQtKd6JVqXlt4CWMjA3eZmNd1T7u3/oFbtHL+Lje/Lv3Ra2SzwJbOfdufZtaTyPdLMm80PcmQrT+UalmbKndzXN73scjeRkU0Yv7s65FqxDtkiSJuUU/8HOpHBmbEj+Ji1IuUPxc9jRs56Pc13GLLtLM3bgh/R7MCul0m7eer/Nf4KB9LypUTEu8hmHRyqngBk8F80teocC+FYA08xCmJN6FOYCCG6DKmc3m6k8psK0CQEBNt9DJ9Iu8klDdkbc27EiIkpd99b+xteYLrF5ZWGXSxNAv8nIyQ6ehVvA3Bfn72V73G0vK38crudCrzEyIv5XuYcqG8zXucr7Me5YKVyFaQcf0lNvpFa5cXrKichFzCj9DQmJAxHAu76xsh5RvO8jL2a/S6LUSb4jj/sx7iNIrp3nXVW/j5eyP8Uo+RkT1564Wyj0A5hUt5+0cWdhzWedJXJmm/Dv5f+c47P4ZF9ebJ4O4f5ph87q4ef0XbKstIFRr5IOhVx1xO8zddaVcs+pL6twOuofF8/EplxOhD3xPthUbKwu5bsW3NHicdAuN5tMxF5NgOrbv2HqXk1tXzGNFaT4Ad/cdxW29R5z0bTD31VZx+9Kf2VMj2+1dktmXR4eOO+nJdGv4h3iepKhy2Hhg5XwWFchimBEJnXh1zDQSzEdWG3SioMZp58ZlP7C+ohAB+NfAcVzbfcgxf8DMzNnEY5vmIwGTkjJ5ffjZ7eoL93Xuep7e9jsAF6cN4pG+R9bJ6fC0u06l4fVBlzIipmvQ+6+u3MuDW7/EI/mYFN+Px3pf0GrEJt9Wyn1b36HOYyUzpBMv9L0RsyZwandr3R6e2/M+btHDwIie3J91HToF8gkwv3QBswplD8SR0adwddqVirWAedb9vHvgRRw+O9G6WK5Pv4cEo3+00St6mFf8LltqlwIwNGoq0xKvVjymJIlsqpnHispP8EkejOowJiXcSbeQlutxK5172Fz9CYW2tYBMQLuGTqSTeSQJpn4Y1Mc3DShKXqpd+ymxb2Jv/U80ekoBMKmj6Bt5GZlhZ6BpQeFu89awoPQ1cq3rAUgx9WVq4r2EapUzLfm2PXyd/wJ2XwMhmkguS32QZJP/b9Inefm+6GuWV8qin5HRp3J+ylWKaeo9DXt5fd9bOEUnnU2duTfzLkK1ys+6tdVbeTn7Y3yS7NV5Z4byb6gZPxWv5K39cwG4uNMEZqQFjnY34/CMwejYHjzT59KgSafd6+KW9V+ypfYgIRoDHwybccSRzm01RVy/+msaPE76RCTxwYhLCdO1X5nSouJ93L7mB1w+LwOikvlw1AXHXMCT21DDtUu+I7ehBqNay2sjT2dKp+CzOyciREni892beW7DUlw+H5EGIy+OnMLEzoGFf38n/EM8T2JIksTM7G08ufZPnD4vYToDz42czLS0k/umdPt8PLb+D2bnbAPg/PTePD108jE3BJ5fuJe71s7DLfoYEtOJ90eeT6gu+H7creG7/M08tuVnJODczv14sv8ZR9TX3SP6eGDzN/xZvge9SsMbgy9jWHTwkeIVFbt5aNtX+CSRqQkDeLjX9FbnkWct4d6t79DgtdE9tDPP9bkRcwttBLfX7eWZPe/hFj30D+/Bg92vD0g+V1Su5JO8zxERGRDej5u63qg4ttRRxPsHXqbaXYleZeDK1FvoHT7Ab5wkSayonMcfZbInYxdzL85LuY1wnXKKttKZz28lL1DZpF7vFTaJsXHXY2hFpFPh2MXm6k8osq8/7H8FovRdSTANINHYn3hjP3TqwOUJ7QFJEqlx51Jq30yJfTOljq14RNuh7UZ1JH0jLyUr7KwWCackSexvXMnCsrdw+OpRC1pGxVzNwMizA9R+iqyt/o35pV/gk7wkGtO5LPVBwrT+UUmbt5FP8t5iX6PszDAtYTqT489WJHyba7fwTs57eCQvWSGZ3JlxW0Ah0ZqqLbyy7xN8ksjo6EHcnnFFi6Tz15LVvL5P7lR1Qcp4ru1yequkc0tNLndv/hSn6GFEdBbP97ssaNskh9fNrRu+ZFNNPhaNgfeHXkXP8COrk9xUVcANa77G5nUzIDKF90dc2q5WQt/mbuXhjb8hShLjE7vy5vBzj7k/5oqSPG5ZMY8Gt4tEUygfjjuPnpFxx3QO7Y1yu5X7lv/OsmLZ63hschdeGjWF2P8h1fo/xPNvgAN11dy57Fe2V5UBcH63Xjwx7FQsupPXz0ySJD7bu4mnNi1GlCQGxSTz3phziDZ27Ev7r1hbcZAbVs7B6nGRFRbLZ2MuJsbYfg+Inwq289CmeYhInJ7cm+cGHllvYY/o5b7N37C0fC8GlZY3B1/GkGhlNbISlpTv4NHts/BJImckDeLBHue2KpA4YC3mvq3v0Oi10yM0lef63NBiD+sddft4Zs+7uEQ3fcOzeCjrBvRq5ZRSsGTD6m3kk9w32G/dg4DAtMTzmRSn3It6d/065hS+gVt0olcZmZpwFYMiJyiO9YpuVld9yfrqOYCETmWiX8QZDIw8B7MmosXPpdyxkwONCym1b6HW/f+N9AXURBsySTT2J8E0kHhjbzSqo1vMSJJEvaeAEvtmSuybKLVvxSXW/78xOpWFBGM/ksxDyAid2uI5JUlkf+Nq1lXPpty5H4AYfRdOS3yAGEOq4j7VrlK+L3qbfNtuAHqFDee8lEB1mkV8cOAVqtwV6FR6rki9mb4KLVEBVlau4uO8z1pdhACsqtrEq9ny2DExg7mt2xUtLqDml67jlWzZA/S85LHckN56D/NttfnctfkTHD43Q6MyeKHf5eiDVI07fG5u3/AVG6rzsGj0vDv0KnqHt60muBlrKnK5Ze1sHD4PQ6NTeXv4xZg17ZeefX/Pal7cLteiT0/rwzODph1TtxFJkvgiezNPblyET5IYGJPEe2POJeYYP//bGwvy9/PAyvnUuhzo1Rr+NWQsV3bvf9KXDLQV/xDPvwk8oo/XNq/inW1rkYBOIeG8NmYag07yLgfLSnK5dfmPNHpcJJlD+WjcdLpHHFsx1e7aMmYsn02V00ZnSwRfjL2EZHN4ux1/fvEu7tvwPV5JZFJid14afB66INN2h8Pt83LP5lmsqNiHQaXlP0Mub1Nf90Vl23h8+2xEJM5JHsp93ZUjUIcjp7GI+7a9g9XroFdYGs/2vgGjJvCCZ2f9fp7Z/Q5O0U3vsAwe7n5TQPJ5eHo11dSZewKkV32Sl7mFX7KiSjbMHhAxjEs7X69IeqpcJcwtfIsCu2xX0s3Sj3OSbyZMpyyUKLTvYHHZf6hy5QOgEXT0Dp/CoKjphGlbj7zYvdWU2rdQ6thCiX0zDZ7/b6WkQkOssScJxgFtrgv1iA7KHTsotW/G7qv+f9s0gpF4Y18STf1JNA0kUt9V0SrqcPgkL3vrl7Cu+ltq3AVNx9EzMPIchkdfqtjfXpRE1lX/zoLSr/BILnQqA1MSrmRIpHLd8o66zXye/zYu0UmULobr0+8m0ajsCBBs2QXAisqNvL7vM0QkxsUO5Zaul7VIOv8oW8/Le2cjIXFO0mhu6tr6b31XXQG3b/oYu8/F4MiuvNj/yqCtipw+D3ds/Jp1VQcwqXW8O/RK+kYE54TwVywv289t677BLfoYFdeVN4de0G6WSZIk8eL2JXywV+6edEPWcO7rc2QCyCOFR/Tx+PqFzNy/FYDzuvTi2WFTTuoWmDaPmyfX/snsfdsB6BEZyxtjTycj4tgKtE4U/EM8/2ZYX1bIXct+pcjagEoQuK3fcG7vN+KYe2O2J3Lqq7l2yXfkN9Zi0mh5feQZTEo5tr1q8xtruHLZTIps9cQZLXw+5hK6hSmnao8Ef5Zmc+f6OXhEH6cmZPLqkPOPmHzetWkmqyr3Y1TreHvI5QyITA16/wWlW/j3jm+RkJieMpy7s1qPAmU3FPDAtnex+Zz0CUvn6T7XYVQHJp+7G3J4atc7OEUXvUK78XCPmzAEGJ9ny+eV7NeCEpSsrFzMnMLPEfGRYkzluvS7FdssipKP1VW/sLBsJl7Jg15lYlriDAZEjFe8VkkSOWBdx9qqWZQ5ZcKqQk33sPEMibqAKH3wBMLqKW8ioZsosW8+pCg/WqgFHXGGXiSYBpJo6k+Mobui4bsSPKKLnXV/sKFmDg1NnZz0KjP9I85kQOTZmDThivvVuMr4vuht8mxyuryLuRfnpNxCpM6fkEuSxMLyn/ilZA4SEt0s3bm6yx1YFERhSkKzC1MCWxQtrVjPW/u/QERifOwwbu56aYukc1HZRl7cOxMJiTMTR3Jrt3Nb/Y3vqS/i9k0fYfU6GRDRhVcGXIUhwILpr3D5PNy1aSarK3MwqnW8M+QK+kd2Dmrfv2JJ6T7uWP8tHtHH+PhMXhsyvd36r/tEkUc2/c63uVsBeLDvqVx3jNtg1roc3LTsB9aWFyAADw0Yx3U9jn2Nf3tia2Updyz9mfyGOgTg+t5DuGfgyJOaSB8t/iGef0M0uF08tnohPxyQ0179YxJ4fezppIa2nCI8kVHncnDL8nmsKjuIANzbbww39xp2TB9IZfYGrlo2i/0NVYTrjHw6+iL6RLWfenlFeQ63rp2NW/QxLj6D14ecf0QvFZfPw50bZ7KmKgeTWsfbbXzR/Vq8iWd2fYeExEWdR3J7xrRWP+e9DQd5YNt72H1O+oV35ane17X4Yt7bkMuTu9/G4XPSI7Qrj/S4KWALw1JHGS9lv0K1u4ZIXQT3Zd5NolH5c89p3MPHeW9g9TYSognjui53BWy3WOksYm7RWxTa5XRyRsgAzk6+SbEmEWRCVGjfxtqqWYfU7yCQETKSoVEXEmdsmzBAkiQaPSVyetyxFWcb7ZRUgppofSYJpoHEGnq0WK+pBJfPxra6X9lY/T12Xy0AJnU4AyPPpV/E6egD1KKKksj66gUsKPsSt+hEK+iZknAFQ6ImK5JDt+ji64MfsrlWjqKNip7AeSmXK3Y3EiWRL/K/Ykml7JXakrUWwJKKdby1/0skJCbEjeCm9ItbLBH5s3wzL+z5Si5tSRzB7d2mB7GwKua2jR/R6HXQNzyVVwfMwNRCVP//XbvPy92bZrGych8GtZZ3hlzRpoXg4VhSms0d677FI4lMTuzBS4PPRXsEi1MluHxe7l33E78V7kElCDwz6DQu6NKvXY4dLPbXVXHNku8osNZh0ep4Y+SZnJocvFDyRINPFHl721pe37IKnySRYA7h1dGnMSLxyBYdfyf8Qzz/xvjpwB4eXv0HDW4XJo2WJ4afygXdep+0q0eP6OOpjYv5oqm95tlpPXl++FQMx3DlWOuyc01T5w6zRsf7I89neFxqux1/VfkBblk7G5foZWx8Bm8cIfk8PLVn1uh5d8iV9IkIvl/zT0UbeG63rPS9LHUMN3eb0urvZnd9Pg9ufw+Hz8WAiAye7HVNwDQ6QHZjHk/u+g92n5Puoek82v1mjAFqRGXT8FcpdZZi0Vi4J+NOuliUywiqXZV8kPsqJY4CNIKGCztdzbCoMYpjRcnHysqfWFw+G6/kwaAyMS3xGvpHjG3xeksde1lXNZsc65pD/5dqHsjQ6ItJMfUOuN+JALu3ns2189hS8xMu0QpAiCaWIVHn0yt8MtoWCGytu4Lvi94m17oDgDRzT85NvoVIvXILylp3NR/mvkahPQ8Vas5PuYKRMRMUx3pFL+/nfsT6mg0ICFyZejnjYpW/N4DF5Wt4O+drJCQmxY3khvQLWySdyyq28OzuLxGRmJowjDszWjd6z2ks49aNH1DvsdMrrBOvD7wGcxtI572bZ7O8IhuDSstbQy5ncBtKXw7H4pK93LV+Dh5JZEpST14cdE67kU67183Nq+ayoiwXrUrFa8POZmo7d25rDUuKD3Dbih+xetykWML4eNx0MsLbL6N0rFHQWMddy35lY3kxAGekZfHMKZMI07efOPVkxj/E82+OYmsDdy/7lbVlhQBMSc3g+VMmE2E4eXvafpm9mSc2LMQnSfSNSuDDsecdU0Wg1ePixpXfsaYiH51KzZsjzmFiUvs5CayuyOXmNbNwiV5Gx3XjzaEXHFFa5mjFDN8XruWlPfMAuCptHNd3bd1vdGd9Lg9tex+n6GZQRBb/7nV1i32u9zXm8+9d/8Huc5AV0oVHe9yMKYA1U6OnkVf2vUGeLQ+DSs/t3W6lZ1gPxbEun5Mv8t9le/1GAMbFTuWspIsD1ghWOAuZW/gfihxy9DMzZBBnJ99IqDawjyfICvj11d+wt2EpEiIAScaeDI6aTifzAHRHKRxqL0iSRI27kO11v7Ot9le8kguASF0yQ6IuonvYOMUI5OH7b6j5g99LPz8U5ZyccBlDo6YGJG951v18mPsajd56zGoL13S5k24hyoTG6XPy1v532NmwC7Wg5sb06xgSOTjgfBaVr+adHDldPiV+FNd1uaBFErmichtP7/oCEZHJ8UO4O7NlkirPv5xbNnxArcdG99Bk3hx4LRZtcN/n4WI/vUrDW4Mvb5PY73AsLNnD3eu/wyuJnJbcixcGntNupVP1bgfXLv+WzdVFGNVa3hs5nZHxRzbPI4EkSXy0Zz3PblqCBAyNS+Hd0ecQaWg/H9JjCUmS+CFnN4+uWYjV48ai1fHU8Imc07XHSRvw6Qj8Qzz/B+ATRT7YsYFXNq/AI4rEGM3cP2g007v1OmlN51eX5nPT8nnUu53EGi08P2wK449hWsbl83LHmh9YWLwPtSBwb59xXJvZfu3N1lTkcvPaWTh9XoZEp/LqkOlE6duu6Pz/9i16nu9/QZtM5ucUrOLVvT8DcEGnU7gt47RW/Qq31x3g4e0fNAmI0nmkxxVE6gPfkzmNB3li11vYfA5SzUncm3ENSSZl4Y7D5+DN/W+zu2EPGkHDJZ0uYnyscnRSlETml/7A72XfA9DVksUlna4jxqAcnfNJPlZW/sji8tn4JC8GlYmxsdMZFn0a2gDG6s2oc5ewoXoOO+sX4pPkvukCKmL0acQbM0kwZhJvyCRK36lVkU97wO6to9SRTZkz+9C/Tl/joe2x+q4Mi76IriEjWp1PgS2b+aVfcNC+B4BUcw/OTb6VqABRTp/k48/y3/it9Du8kpdEQwrXpd9NtF5ZFFjmLOednPc5aD+ITqXj9m630DusV8Bjf1PwG3OK5gNwWsIYrk07P+BLXZIkfixeybs58xARmRg3iHuyLm7VLmxbbT4PbfuKWreVzJAk3hx0DaEtNEo4HA0eBw9tmcOqyv3oVBreGHQpw9vgrXv43GfnbeTZ7fPxSiLTknvxfDuSzn31ldy++nv2N1QRqjXw8egLGRB9ZCr7I0G1086j6xbwW4FcN31x1778e8ikk7YveW59DU+vW8LiQtlbe1BcEq+NmUankPDjO7ETEP8Qz/8h7Kgq446lvxzq9947Oo4nhk04aZXv+Q21XLd0Lvvr5R7TF3btwyMDTyXkGNlIeUWRhzf+ynd5slJxTEI6Lw05gyhD+1h+rKvM4+Y1s7D7PMQZQnh96AX0i2z7i8HudXHbhq/YVJOPgMAtmadyTXrwrUBnHVzBm9my0OP/2Hvv8Mju+m77npGmanrRqPe2Tdpe3e21De4YsE0vgYQ3JCRAAkmAQAJJqE/y8JBAqIFgbFPcjfu6bu9NvdfpvZfz/nFGI2lX0kq72vVqPfd1zXXOnBlJo9HonM/v823rjXV8ve19GOXzO8zHfb18+cSPiaTjmOQ6vrzqw6zWz+2k9IaG+KfTPyCQDKGUyvlk/f1cX7xl1ucmM0l+1PtjDnjF0XmrdCv5aO2HsSpmrxA94t3H/w7+iEQmjkwi551l93Kt9ZZZJygB2GND/H74+4xGxQuIQWZhu+UO1hmvQz1LMcx0Qkk3Bz2/pzPwGsGU86zHZRIlNlUTpcrmnCDVFlovyA1JZmLYYz1MRDsZj3YwHuvMFQlNp1Aip0K9hg2me6gp2jjvz8wIGfpDJ9nr/iOnA/uyr13OzaUfYKv5nXO6hQPhXn47/AuGImIP1Fb9Rj5U86lZx5pmhAzP21/k9yOPksgk0BRq+Oumv6RBM3sPWmfcw/c6f05HUPzed5bdwEdq5i4Miqbj/HvnI7zsEFNzbs46nfOJTkEQeHRkH/+n40lSQppGbSnf3/An6OUL+5/uCdr564MPMhzxoJAW8n82vo/t1sU3BY+mknzt6NM8Piz2Mr6jspV/WX/XkohOQRB4uO8o/3zkeWLpFMVKDT+/9n5aDJeuP+YLw9383V5x+l6BRMKXNtzIR1o2LEtXMJCI83+P7Obnpw6REjIUSqR8Zt12/r+2rcu6qPdikheebzMS6TS/OH2I/3tkN8FkAoB76lfyd5uuxbYMpx7FUkm+c/Q1ftp+AAGoKNLznR23sdV2fq1KFosgCPym9whfP/oC8exJ/Htb71qyvM+egJO/3Pcw/SE3MomUv2u9lftr5xcNs5FIp/jW6Wf43ZA4D/h62wr+ue1dCw4dvmI/yT+ffIRIOoFNqeff1n6QFt38Ing44uBrJ3/GYMROgUTKJ+vu5J6KuQWvJ+7j/3T/Dyf9XQBca93En9bdP2vep1gl/SKPDP+epJBEIVXwnsp7ubH4+llFkSvu4DdDP8k1LDfJLbyz9N1sMu2Y9fkZIc1R72u8aH8Qf1JsVVQgKWSVbisbTTdRq1k9b6hWEASCKScT0a5prmMXyUz0rOeqC4yUqprRFJqBhf9dU0IcR6wXV3wgF+afjklelXNaS1XNWJW1c85gn8SfdHPYs4tD3pfwJkTxKkHCeuMN3Gi7b862U47YOE+OPcJRn9g8X1Wg5t6KD7LZdPWsf+/R6Bg/7fs5vWFRRK7UtfCJuo9jks+e2rDXfZQf9PyaUCqCukDJp+rfx1XWDXP+HiMRB187+XMGIhNIkfLJ+jt4V8W18/7fxNNJvtvxOE+OiukZN9pa+YdV70a1wP6Yz42d4KvHHyOaTlCmMvC9DQ+c1xjMoZCHv9z3CJ0BO1IkfHbVjXyscWnGQwaTcf7hwDM8PSwWnl5dUsd3ttyBZQnnus9HIBHjnw6+xO96xRzhZoOV726/jdXm2d3zy5mMIPD77pN88+BrOKPigIbrK+r48pbrqTfMXqCYRyQvPN+mOKNhvn3wNR7pOoEAFMlk/OXa7Xx01YZl2eZhr32Iz7/5NCNhPxLg4ys28fl1116ywqNOn4O/3PMoPQEXEuDTq67i0yuvXpIVbygZ5x8OP87zY2Ko887KVr669vbzmiLy6NAh/uXUkyQzaaqLzHx3wwM0aBfmdAyEHHzh6C8ZiriQSwv52xX3cFv53Bd/gGgqzve6HuYVxxEArrWu5bPN983ZaD4tZPjDyPM8NPQ0GTKUKq18rvlj1GtmX0hMxOz8tP/ndAXF3MxGTQMfr/0opaqzL2SCILDP8xpPj/0WX1Ks4i5TVnJn+f2s1LXNemFPZuIc9u7ioOdFxqJ9ueNGuY0NxhtZb7p+zir4M8kIaTyJkZwzORHrxBnrJ0N6QV8/H5pCM6WqlpzItCkb56xKP5O0kKYrcIgDnhfpCh7OiVilVE2b8Rq2mG/Fppz9/fcnvTw7/ii7XbvIkEGChM2mq7mj7L3o5Wd30UhlUjw9/keeGHuKlJBCVaDi/sr3cK119gVJIpPk5/1/4NmJ1wBo1FTz2eaPUaKcu//h687jfKfjwazbruVLKz/MGsP8k7wcMT9/d/R/OR0YRoqETzXeyvtrFhYVSGbS/HvHc/y6Xyw022Kp55vr3otBvvg8xV3jXXzh0B8IJuOYFUV8d9O9bLGeX0HSmRz3jPGZ3Y8yFPZRKJHy2TXX8YmWpUsPOhe7Jwb5m91PMxoOiG2FVm3hs21XL8vrzVHnOP+450WOOsXxs7U6I1/ZegM3VC58YtzbmbzwfJtzLPsPdGTaP9A/br2R6ysvXYL5UhFKxvn6wZdzozYb9Ga+t+N2Ws2ll+TnR1IJ/vnw8zzSL/78zdYqvrf1LkrVF/55FASBX/Ts4bunxEkezTob/3fLe6nSzF/8MhsnfSN8/tBDTMT8KAtkfK31Hm4pW1gldigZ42snH+YNpyiC3125jc803z5v3qcgCDw2+jo/6n2ctJChSm3jK6s+SnXR3IK3PdDL9zp/jivhpVBSwAer7+aOstkbWWeEDLscr/LI8G+JZeLIJIXcXX4X7yi9ZdZiokQmwWuO53je/gTRdAQQ8z/vLL+f2qK5w6KjkV4Oel/kmPd14hnx6yRIadauZ6PpJpp0G+YdzzgbyUwcZ6yXiVgXsXRoUV8rkUixyKspUTWjlS2+EbU7Ps4hz0sc9u4imPLmjtcUrWSj6UZW6bfP2oQfIJqO8JL9KXY5niWREQuVVuvWcUf5e+dsCN8fHuCnfT9nOCo20V9raOPDNR+Y0+Ucjkzwvc6fMRARK4PvKd/JA1W3zzmeMp1J89P+p/ntsDhxZ42+jn9Y+SHMCv2878MRTx//cPxBvIkQOpmaf259gM3mhYXHXbEgf3vkYQ57BgH4WP01/HnzjYsefZsWMvyg/VX+q1MU2GtNFfz75vdgUy3NuePnXfv51vGXSWYylKv1/Me2e1hnuTQpVtFUkm8eeYVfdIipMVUaA9/dcRubihfeZeNywRkN862sYQNThs3HVm1ctrmpbwV54ZmHjCDwh55T/NuBV3Mhgxsr6/nK1huWZe/Pl0d6+MLeP+KMhimQSPj0mu18es32JWs/ci6eGDzFlw8+QyiVwCBX8c3Nt3NT+dI0vN/vHOCzB36HOx5GK1PwzQ33cH3p4ivqPfEwf3fkEfa5RRfvA7Xb+UzLzQt6jzJChp/3vcxPesVJQWsNNXyj7f2YFPOnapz09/H1U7/EnfCjKlDwueb7uLZ43ZzPDybD/KDn1+zziEJ+g3EVf9H4QfSzTC8CcMfd/Hzgl5zwnwSgWl3Nn9R9lCr17Be4SCrMC/YneMXxHKlsQVCbYRN3lL0Xm3LuEGkiE+eUfzcHPS/lRkQCaAuNrDdezwbTjZgVl2axs1iSmQSn/Xs56HmRvvDJ3PGiAh3rTTewwXgjVuXcgiSZSfK680Wen3iMcFYo1xY1cmf5/TRoWmb9mkQmwWOjT/DH8efIkEFTqOEDVQ+w1bxljob9Ai859vCTvt8SzyTQy7R8pvFDrDPO3sEAwBMP8PXTv+SEX8zLfXfFdXy87twLot8N7+E/Op8iLWRo1Jbyb20fpEy9sMXcEc8gf3v4YZzxIJpCBf/U9i5uKJn7Nc6FNx7hbw7+gTcd4mv/QN1m/mbNzec1QOJM3LEwf7v/SV4ZF7/3rRUt/Oum29DJL023haOuMT775lP0BcS6gvc3rePv119PkWzpxnteCpKZNP9z+jD/fvjNXIrauxpW8cVN12J7G81YXyrywjNPjjOTpOXSAv5k9UY+vXbbsjtReONRvrTvOZ4e7ABgjamE7+24nUbDpRlRNhD08Jk9j3LSOwHABxs28ndrb1ySsJI9GuCv9/+OIx6xRdafNl/NX6y47vxcls6X+Fmv6LKsN1XzrXX3YVEuLNf3dcdpvnbyYcKpOFaFjn9d+0FW6ed3MbyJIN84/UuO+XoAeFfFNXyi7s45BYIgCDw38To/6/89SSGFUabnr5s+whrD7EJeEATedO/hwcHfEE5HKJAUcFvpO7iz7PY5i4m8CTfPjP+efe7XEBCQImWb5TreUfKuWcPF03HGRjjoeYkj3l2E04Hc8Wp1C1VFLZSp6ihT1WGSl5yzfc/FIJoKMRbrZyzay1i0n+7gEaJZwShBQoN2LRtNN9Gi3UjhHO8PiIuNg543eXr8d3gSYjGfTVHGHeX30aqfuyikK9jNT/t/zkRMzBfdYtrEB6rfh042+zk6nIryw97f8IZLdMfa9C18pulDGOVzu5YnfX388+n/wZMIoCpQ8Pnm+7mmeO2870ssneTb7Y/yzJhYeLSzpI2/X3XvgqYRCYLAbwb28r12sdq8XlPM9zY8QLVm8eeWk94xPrP/EcYiflQFMr627nbuqGxd9PeZjd32AT6393EcsRByaQFfWreT99WvvyQFPIl0mu+feJP/PLmHtCBgU2n45rZ3cl358oukvTE6wD/ufYken5jrvdps42vblm9R7uVAXnjmOYsen5uv7X2J10YHALCpNfz95uu4q27Fsqs6fKL/NF/e/zz+RAy5tIC/XXctH1ux6ZLkNSXSab5zYhc/7RSrglcYivmPbfdQr7tw8ZvIpPn2ief53z6xmGOrtZbvbLr3vFouvTxxmi8f+0NWQGr51vr7FjzpaDDs5AtHf8lg2IlMUsDfrLybO8rn7r8IYkj05/3P8PDwywCs1tfypZUfnjckOhAe5budP2MkOoEECe+uuIX7qt45Z2jbl/Dzq8FfczBb+V6mKuPjtR+Zs2IaYDw6wpNjD3PCL4oRmUTOdcW3cpPtdtSF87+vqUySjuBBDnpepCd4FIGZp0qFVEWpqjYnRMtUdVgU5YsOzc9HOOVnLNqXu41G+3IFQtPRyyxsMN3IBuMNGOTzN+kWBIHTgWM8MfYwY9Gh7NcbeWfpvWwxXzPn64+lY/x2+Pe85NiFgIBBpufDNR9kvXFuh7srOMD3On+GPe6mQCLlfVV3cHf5TXMKdrEC/TX+u+8J0kKGarWNf1z9MSrVs7dtmmQi6uPvjv2KjsAoUiR8uumd3F991YLObdFUgn868Th/HBM7WdxatoavrLlrwZOMpr/2hwcO8S/HnyWZSVNVZOL7W95Lk/7CK8uTmTT/cfI1fti+GwFo0Fn4v9vuodkw//uyVHR6nXx291Oc8oifvTtrVvJPm3diUCyv3tFDQR9f37eL5wbF/HGTUsXfbryG9zauoSBfrX5B5IVnnlkRBIEXh3r4p327GAr6ANhkq+CrW29kteXStd1YCuyRIH+754+8OiaGlbfYKvnO9tuo1Bguyc9/ZbyHv9n3JJ54BFWBjK+sv5n31M5ezLJYnh4+wZePPEk023Lpe5vfzXrz4iv6B0Mu/vrQg/SFnBRKpHxuxTu4v2b2UOiZhFMx/unkI7zmEMPO76rYyl+1zJ2LN8mbzhN8q+NBIukYRpmWL636MK3zFIHE0nF+2vc7XnTsBqBFW8dnmz+KVTF3aPSA5yC/HPg1gVQACRJutt3EvRX3oJhnlnxfqJPHRx+iLyxW16sLNGy3XMcm04458xen40s46Q4eZSzWx3i0j/HoICkhcdbzZBI5JaqarBCtR1u4uLSWlJDAHhvKCs3eXPX9mRjlNsqyordS3Uxt0cpz9u6MpiMc8x1gj+uV3PugKlCz03Yn1xbfPGfuJ8AJ/0l+0f9LXAnx9VxjuYr7q95L0RziPSNkeHz0JX49JArIYoWZzzZ/lGbt3EU1kVSM73U+zKvOowBcV7yOzzbdh+ocAvCwp5d/OPYgvmQYvUzN11vfx0bzwnpsDoXdfO7Qb+gO2imUSPnrFbfyvprFj+2NpBJ89ejTPDksitcbS1v41w13oV1gh4n5GAn7+Ks9j3HELebF3le3li+vu/m8ChEXSzqT4SftB/ju0ddIZNIY5Eq+sfVWbquePQXjciWaSvJfx/fxw+P7iadTFEgkfGjFev56/Y785KElIi8888xLLJXixycP8INje4mmkkiA+5tb+ZsN12BWLZ/pEoIg8GD3Ub5x6GUiqSRFhXK+vPFG7mtovSQuriMa5PP7nuRNez8At1Wu5Bsb34F2CXKtegJOPrPvEfpCLlE0rr6JD9efzwUxzteOP8Zz42Lu3zvLWvnymrsW1E4mI2T4n/5d/LjnRQQEWg3V/EvbBzCfI+9zJOLga6d+wUB4HClSPlL7Dt5bdcO8aQNvOA/xX72iYC0qUPGR2ndxQ/HWOZ2xUDLEg0MP86ZbFKwmuZE7y27nastVFM4hjgVB4KT/ME+MPcxEbDR3vExVxSbTDjYat2OYoyjmTNJCGld8ZJob2c9YtI9EJragr18oEiSYFWVZIVtLmaqeMmUtqsKF5aClhRTtgeMc8LzJCd8hktm810KJjGutN7Oz5E6K5vleg+EhHht9nMO+owBY5GY+WvthVutXzfk1Y1EHP+z9DSeyLbS2m9fx/zW8j6LCuc8tPcFR/qX9lwxHHBRIpPxp/V3cXT5726ZJBEHgkaE3+X7XM6SFDE3aMv5t7QcpVS1M7L9q7+Afjv6eUCqGRaHhW+vvO6+Z631BF5/Z9wg9QScFEgmfW3UTH2nYtiTnoGeG2/n7A08TTMbRyhT8y6bbeOclGn05GPTy+d1Pc8AhFo7dUF7Pv219xyWdKHehCILAk30d/MuBVxgPi8MWtpVW8bWtN9JsWr7jOy9H8sIzz4IYCwX41wOv8ESfmDOpkyv4zLrtfGjF+mVVzXfmCfK6sjq+ue0d2NQXv4dpRhD47449fO/EK6QFgcoiA/++7W7Wmi88VyicSvCVI0/yzIgoGm8uW8E31t+FRrb4EOCvB/bwf9qfyxZc2PjuhgeoKlpYy6A3nR189cRD2Qu0lq+tuZ/1pvlbjETTcf6j87e85BDD4qv1tXyh5QOUqOYWdhMxF9/r/BndIbGaeIWunj+rv58q9dxFQcd8J/jFwC/xJMRCB4vczJ3ld7DDvG1OAZoRMhz3HeKA5w1OBY6QFsTWRxIkNGpXssm0gzbDJlQFi1uEZYQMnsQEo5FexqJ9jMf6iabDi/oeUqRYleW50H2pshZFweLCmYIgMBDp4YDnTY549xJKTU03sinK2GTawWbz1Rjlc//9RyIjPDr6RC6tQYKEG4uv5z2V96KcpXE8iEVKfxh5gd+PPEdSSCGXyvh47XvYaZu7X2VayPC74V38ov+PpIQ0ZrmeL6360LyDCUB05L95+lFemBCL1G4tXccXVr4L5TyjXKf/zB92vcyPe14FYK2xim+vvx/rAvOgp/PHkVN86cgTRFIJrEoN39v0bjZaFpbSMh/RVJJ/PvI8D/cdBWCduZx/33Y3FUWGC/7e50IQBP636wj/cmgX0bS4oP/Kxht57yVa0C8VJ1wTfHXvS7nZ6hUaHX+/+XreWdO0rH6P5UJeeOZZFPsnhvnq3pc45XYAUK838ZWtN3BdxfJJGk9nMvys4wDfPiKGhPRyJf+0eSd31lyaebpHXKP81d5HGQn7s/30ruUTLdsuOO9UEAQe7D/AN48/R1LIUF1k4j+2vJfm88gbO+Qe4G+PPIw7HkJTqOQba+/lWtvCQmbDYRdfPPor+sJiA+yP1d/IR+rmdzHFIqL9/GfPo0TTcdQFCv688V3stG2aR4ikeWpsF78Zepp4JkGBRMrd5Tfxnop3oJijSCSRSfKK41WeGn8Gf9IPgFVh5e6yO9hm2TpvzmU4FeKobz8HPG/QG+rMHZdJZKwxbGCTaQcrdK3zzjy/XHDGJjjgfZODnjdxxqfyQLWFejaYtrHJdBWVqpp5/x/GomM8NvoE+z0HERCyPTw3cXf5HZSp5l4AnPR38cPehxiNij93rWEFn6y7j1LV3K6SPebhm+0P5qrWt5tX89fN92E4xwStzsAoXzr+ICMRMW/0L5reyXurdizo/9yXiPB3R37LHpdYCPdAzVY+u+LWRXfHSGTSfOfk8/yqV8zH3myp4Tub7sW6BE3bz+wf/GcrtvOZ1ddckg4eY+EAX9jzDK+PDwCw1VbFt7e/85KlMC0FrmiYbx98nYe7jiMAyoJC/r+2rfzpmk0oL0F6wtuVvPDMs2jSmQyPdJ/g2wdfxx0T+xneUFnHFzdeu6xCEt0+F5998ylOeMTK83dUNfO1TTsvSXgokIjxDwef4ZlhsR/mDlst39x8+5L0/DzuGeWv9v+W8agfhbSQL7W9g3ur1y1aVDtiAf7m8MMc84pFJR+rv4ZPNV1/ztxNEIswvtvxOE+PiS7YRlMDX179HoqV8/dUHI+6+Gb7g5wKiCkJV1vb+IvGezHK53aYnHEPP+57hAMesbeeTWHm43XvYaNx9Zy/czwdZ5fjFZ4a/yPBrMtnU9i4s+w2tpg3z1kBP4k77uSgdzcH3G9gj4/ljmsKtawzbqVVv5FyVSWaQt1l4ZgkM0mc8XG6g+0c9O5mINyTe0wuVdBm2Mgm01U0aVeds+BpODLC0+N/ZK97X66IapNxA3eX30mFeu5pVp64j/8deoJdDrHYziDT8rHad3OVZe6qeHFC1QF+0P0okXQMpVTO/9d4D7eWzJ9/nBEy/H54L9/vfJqkkKZEaeCfWh9gjWFhDuNx7zBfOPII41EfygIZX1lzF+8sb1vQ105nJOzl8wf+wDGvGGH5ZNNV/MWK6y94sEQ6k+HXvYf416MvkcikKVZq+M7WO9lhW5pm8/OREQR+13ucfz74MsFkHEVBIV9cdx0fbtlwyZrRXyjRVJJfth/h+9Mm+N1Vv4IvbryWMk1eP1xs8sIzz3lzZvslgFuqG/l021ZarZdnH8MzSWbS/OfJPXz/+G5SQgZVgYw/XbWFT6zcfNFbSAmCwCN9R/mn7MxkVYGMP1+5g481b7ngtkveeIQvHHqU1+2iwNhqreUrbe+kVru4ivpkJsX32p/jNwN7AajTWPnSmjsXnN/2zNghvn36MWKZJOoCOR+vv4n3Vu2Yt79iWsjw8NBL/HLgWdJCBnWBggeqd/Ku8muQzxMe3ec+xo/7HsGd8AHQpK3lgarbaNO3zCtAX3S8zDPjzxJKiW2GdIU6ri++luuLr8V4jnZKgiAwHB3ggOcNDnn2EEz5ZzxeVKChRFVOiXLyVkGpshydzHBRBGkiE8ceG2ciNspEdFTcxkZxxidmVNpLkNCiW8Mm01W06jfMOkt9OmkhzRHvUV6wv0RHcMrtXW9cxz3ld83ZKxXAlwjyh9HneW7idRKZJBIk3FJyFR+ovnPeXM7T/gF+2PsY7QExnWKlroYvrHg/Zar5P8OdgVG+3f4Yp/xiu7GrrCv40ur3oJedOx0imIzx/zpf5JHB/QgIVKpNfG/DAzTqFjfSMZlJ84uePfxnx6vE0il0MiX/tuEeri+98H6+R92jfPXQc5zwikM/riut51ub78CsXHxHi8Wye2KQfzn0MiezFetrLWV8d/tt1OuXx4jIQCLOL08f5qenDuKJieNrV5ttfHXbjWyyzT8COM/ScVkIz2984xs8/fTTHD16FLlcjs/nW/T3yAvPt45en5vvHHqDPw505i5t15TX8Om2bWwpXR7TKU66J/jKgRc47BRzfIpVGj7XdjXvrr/4rTN6Ai7+/sDTHHKJrkiNxsRX1t/MtaUXNn4tIwj8rHs3/6/9FeKZFDJpAZ9o2sEnmxY/pu758ZP868mn8CbEPMS7K9bzVytuWdBYwIGQg2+c+h0n/aJzWldk43Mr7mK9af70jK7gMP/R9Vu6gqKAKFGa+GT9nVxlmTt/LJqK8duRZ3l6/BUSGbE4ZqWungeq7mC1fu5pNNF0lJfsu3jR/jLe7DjNAkkBG43rucl2I42ahnMKxbSQpit4ioOe3fSFOnEnnGe1VZpEVaCeJkbFm2aO3pZzkcokccYnZojMc/3MclU1rYYNbDBuQycznPNnBJNBXnW+zkuOXbncWClSNhjXcVvZO6ktqpnzawPJEI+Nvsgz468Sz4iuUou2jo/UvmveinVHzMtP+57iZYfY1koplfP+6p28p/J6CuZZsASSEX7U8zyPDotOrLpAwZ813sy7K88951wQBJ4fP8m3Tz+DKy4uQG4vb+MLq25fdLX5QdcgXzv6ND1BJyCG1r+x/k4qii5sGIc7Fubbx3fx2+xkNI1MwefWXMsHGzZedFe9x+/iXw/t4qVRMdVBK1Pw6TXb+fiKTUsyFvhi44lF+OnJQ/yy/TCBhDhpq1Kr5y/XbufdjauXjVN7pXBZCM9//Md/xGAwMDIywk9/+tO88FymdPvc/NexvTzWe5p09qOyyVbBn7dt5bqK2ssi5DgfgiDwx6FO/u3wKwyFfAA0G6z8/Ybrubbs4uawCoLA44Mn+bdjL+GMieJuZ3kTX1q384KLBIbDXv752DM597OqyMSX297BVbaFtZGZxJ+I8B8dL/CH4YMAGGRq/nrFLdxZce4wfkbI8PTYIf6z61l8SfH3u6V0LX/RdNu8le8ZIcNL9kP8tO9p3AnRTVyjr+dTDXfRqJ17UeNN+Pn9yPM8P/EGSSEFQKu+mQeqbqdFN/ffMpVJcdh3hBfsL+Xmv4NYiNRmaGOtoZUWXQvyc4TiYeHu41JTVKChVFUxU9iqytEVLsxlnYhOcNR3jKO+43SFunMFVdpCDddZr+X64uswz9PCKpyK8Pjoyzw1votoWqzcb9BU876q21lrmLsXcDQd55Ghl/nt8C7iWWf05pJNfLT2nfP2eM0IGf44dpgfdP0Rb/aztbOkjb9oug2r8tzXguGwh3899SS7neL/R3WRmS+tvpNNlsX9z3viYb576kX+MHgUAJNczd+uuZk7Ky+s0CaVyfBg7yH+z4nXCCTF9/Pemlb+tu16LEuQJzofzmiYfz/+Bg91HyUtCBRKpLy/aS1/2XoVZuXl39XEHg7y3ycP8OuOY0RT4kK0wWDmz1u3cmf9imUhmq9EHB4PNrP58gi1/+IXv+Cv/uqvFiQ84/E48Xg8dz8QCFBZWcnNv/gR71m7njsbWiguWj6tHK4khoI+fnR8P490nSCRES9aq8zF/HnbNm6tbrzsm+/G0yl+1XmY75/YjT8hnuivLq3h79Zfz0rTxe1hGkzG+f7J1/lF937SgoCioJBPrdjOJ1u2XVD4XRAEnh9r51+OP4sjJuY03lTawhdbb6FcbVjU9zrqGeLrJx+nJygWmG0w1fClNXdSqzl3fq8/GeFH3c/x2IgYyiwqVPCJ+p3cW7lt3vD7pCh5ZHhXLly7EFHiinv5/chzvGjfTSoroNr0LdxSchWbTK3z/szB8BAv2l9ir2c/icxUH06FVMEq/UrWGtpo07dimGeqzmxM5luOTxOjE7FR4unFtVeSSCSY5cW5UH6pUhSb2kU7pym6Qz0c9R3jiPcY9vjMpvPV6mputt3IZvPmeQX3aNTOCxNv8qJ9N+G0GMasLarggarb5823nX1xUcenGu6ed3EB0BMc59vtj3HcJ4bjq4usfL7lrgX15kxmUvyi9w1+0vMq8UwKubSQjzdcw0frrka+iP+1tJDhof6D/N/Tu3LC8D016/nsqpswyC+safpB5zBfPfws7T7xf22VsYSvrr+F9ZaLGxaOppL8tP0A/3VyL+GU+Nm/ubKRL6y7blmE1QcDXn54fD+/6z454xr06bZt3FrTlHc43wJSmQyvDw/wWHc7z7WfpPMvv7j8hOdXv/pVvva1r511vPK7/4xUpUQqkbCjvIq7Gldwa10TGvnyGvl4JWAPB/nxyYP8uuMokexqs15v4lOtW7i7YeUlm51+vvjiUf7fid38T+chkpkMEuCeulX8ddvVF71ys8vv5GuHn2OvQ7ygVhUZ+NK6ndx4gTPfQ8k432/fxa/7RGGrLCjkk01X87HG7YsStslMmv/t382PunYRyyQplBTw0fqr+HjDtQtqU3PaP8x32h+nPSCmFzRoSvj8irtpM9bM+3WzhWFvL9vOvZXXYZlHgDpibn47/CwvO/aSQcxHNsp03GDbxs22HRQr576YxtNxTgfaOeo7zlHfMXxJ34zHa4tqWWtoZZ2hjSp11WXv7IPY2/SY/wTHfMc44T9JJCsUQUwxaNE2s9bQSpuhDZty7ok3yUyKfe5jPGd/g5PZXpwAlepSHqi8jS3mtjn7q6aFDG84j/Hg4Iv0hcUCrYWkU4ivP8aPe1/g98N7SAsZlFIZH6+/ifuqdyyo+O2Qe4BvnHyCvpAYDt9iruPv19xJ9QLbhk1yxD3MPx97hna/WKC4Ql/Cl9veyTrzhaUYOaJBvnlsF48NigVzermSz6+5jvvq1l3UhXsqk+GRnuP8x/E3sEfFlINWcwn/sOEGttgWP5jiUtPpcfKfx/fxRF87mWUYdbvSEASBY44JHus+zZPdHbizebWZaIzhz315+QnPuRzPH+15nefGhzg0MVVpqiws5Kaaeu5pXMk1lTXIllHfySsBbyzKz08f4uenDuXya8qLdHyydTP3N6257NtWDAa9fOvIq7m573JpAe9vWsen12y/qOEmQRB4eridfz36IhNR0aW8vrSBL63bSY12Yc3L56Ir4ODrx57hgGtS2Br5u9Zbua5kccJ2NOLl3049xesOUXRUqk383eo72G49t+OUFjI8MXKA/+p+lmBKPCHtLGnjE/U7qSyav4DkzMITmaSAm0s2c1/VDZTOU3ziiLl53v4GL9n34EuK76kECWsNK7il5Co2mlbPW9UtCAKDkaFsKPoY/eGBGY8bZUbaDK2s0a+iXFWOVWGZs0fopUIQBIKpIBMxO93Bbo76jtMd6pkR7tcWamgztLLW0MZq/SpU5+gHOh518oL9TV5y7CGQnJr9vt64iltKrmK9cdWc7bOSmRQv2Q/x0NBLjEZF4bfQArJkJsXjIwf4ed9LeBLiz73etobPNN+GTWk453vhTYT59/bneXxEXLiY5EV8fuU7eEfZ4sLhrliI75x8kceHxXxLnUzJZ1bewH21G+ZtG3Yukpk0v+o+yH+cfI1QKoEEeG/dWj7fej0mxcU91zw71Mm3j75GX0DM5S0v0vG3667ljpqVl71DeNQ5zg+O7uH5oaluDddW1PLptq1sLlkedQZXEgN+L491tfNY92kG/L7ccbNSxR2NLdxYUsk1jc1LLzy/+MUv8s1vfnPe57S3t9PSMtUbcDHC80zOzPEcCvh4vLudR7va6fN5cs8zKVXcVt/M3U0rWW8rza+ALiGhRJz/7TjKT04exBkVc7EsSjV/snoTH1ixFq18cc3OLzXHXON888gr7J4QxY5GJueTK7fw8RWbLmoFfDiZ4Aen3+BnXftIZjLIpQV8omUrn1qx44JG4QmCwDMjJ/nWyRdy4ffrSpr4+9ZbqVxEIYQgCLw8cZpvnn4GRywAiDOsP7/iHVgW0Gjblwjzn91/5MlRMXe0QCLltrINfKz+xnnFhCAIHPB08ODgC7n2S1KkXG9bx/1VN1JTNHdnhWQmxX7PcZ6feIPj/qkqbbPcwI22bey07cCiOPd74Ev4OOY/wVHvUU4GTs8IyU++HqvCQonShk1ZQqmqhBKljRKlDYPMMKcbeD7E0jHsMTvjsQkmYvbczR6bmOFoTlKpqmBtNm+1TlN3zteSyqTZ7znG8xNvcszfkTtukuu5sXg7O0u2zzu+NJZO8Oz4Xh4Z3oUz7gNAW6jmnoqruav8anSyuauyU5k0z40f4Se9LzIRE7+2Um3hsy13stVy7sWSIAg8PnKEf29/Dl9SbAH37qpN/GXLTnSyhYfDU5kMD/bt5/vtrxBKiYvoe6vX8dlVN2JSXFhV+V7HIF899BzdAVGMt5nK+Or6W2g1z90TdSnYMzHIvx1+hWNusUreqFDxF2u28/6mdRfcXeNiIggCe8aH+MGxvbwxJp6TJcA7apr5/9q2sMayuE4EeS4MdzTCUz0dPNrVzlHHeO64qrCQm2sbubtxBVdVVCMrKLh4xUVOpxO3e/bZwZPU1dUhnxYCX0rhOYkgCJx02Xm0q50nuttxRSO5x6p0eu5qXMEdDS00mRbXZibP+RNLJXmk6yQ/OrGPkZAoVHRyBR9euZ6PrdqA6TJOWhcEgdfHB/i3w7s47RXzrizKIj7TuoP7G9suavpAX8DN1w4/xxvZsZtlah1fWreTm8ubL2gBFU7G+c/O1/hlz15Sgihs/6RpB59oumpBYfPc90nF+UHnSzw0sJdMNn/zo/VX8/7abajmaOg+nc7AKP/d8wK7XaKokUkKuKdyKx+uvR6TYv587eO+Xn4z+CIHvVOCaIdlDQ9U3USzbv4Q4XjUwfP2N3nZvpdAtqWSNOve3XwO9246iUySjkAHR33H6A71YI85iGficz5fLpVnBakoRDULHG05STqTwhF3MZEVmmemAExHggSz3EyFupxW/WraDK1YFAs759ljLtHdPE+XOJyK8sTom/xh5FV8WXfUJNfy7srrua10G+rCuavGM0KGXfaT/Lj3BQbDoiCzKLR8pPYG7qzYtKCw+hHPIN9rf5YTPjGto0lr4x/W3EmbcXGh4/2uAb5+7I90B8T/+9WGMr7c9k5aTRc2eWwiEuBfj73EU0OnATDKVfxN6/W8p27tRXUaT3vsfOvIq7wy1geAqkDGJ1Zu4hMrt1zWJoAgCLw03Mv/O7qXI04xslkgkXBPwyo+1bqFBsPln4N6pRBMxHlxoJcnutt5bXggV1QslUi4qqKae5pWcnNtw1nGzGVR1T7JxRCe00llMuweHeKxrtM829edyzsEaDZZuL2hmdvrW6g1XFjbizwLI5lJ80RvOz84tpdev+hKqwplvK+5lT9ZvemybuSbEQSeHGjnu0dfy1XA12iNfG7tNdxW3XLRLhiCIPD8aCdfP/ICYxFRtF9lq+Uf1u2kSX9hzfv7gi6+fuyP7HGKF6JytYEvrrmFG0sXJ2zb/WN8/cQTnPJnW1Mpdfx5043cXrF2QQLuuG+QH3Y/yxGvKLBVBXLeW7WD99Vcje4cvRi7gsM8NPgib7hO5ELJ641NPFB1E22G+VsiJTNJ9rqP8fzEG5wMTK9oN7LF3EqbYQWrdA2oCxfmkAmCgDfpwx6z58ThRMzOeHQCZ9yZyzVdSrSF2pyjWqKccleLlbYFVeKDKPYGI2Mc83VwxHuaE/6u3HtpkGm50badnbbt2JTzC1dfIsQfRl7l8dE3iGQLp0qUJt5beQO3lGyeN6QuCAJ7XJ38qOd5uoKiuNDJ1Hyw5lreXbUN5QIWMoMhF//e8Ty77OKQBlWBnD9rvJ731W5b1ALREQ3yrZPP83R2HK1epuKzq27k3pp1FxRWj6dT/KxzH//Z/iaRVBKpRML76tfz16uvxaC4sKKk+RgO+vjesdd5rP8UAlAokfJA41r+onU7xarLtyA3lcnwVH8H/3VsHx1ecRGiKCjgvqZWPrlmM5XaxRX55Tk/oskkLw/18WR3B7uG+omnU7nH2opLuKtxBbc3tFCsnjsCcFkIz6GhITweD0888QTf/va3ef311wFoaGhAo1nYP8Ji2ylFkgleGOjlyZ4OXh3qJ5mZugissdq4o6GF2+qbKddevuLnSiEjCDw32M3/O7qHk26xorZQIuWu+hX8WesWmoyXrxudSKf5TfdRvn/iTVzZKU5rTCV8Yf11XFVac9F+bjSV5L/a3+THHXtJZNJIJRLur1vHX62+5oIaSU9Wv3/zxHOMR0Vhe7Wtgb9bcyu12oU7CRkhw7NjJ/h+54uMR30ANOlK+OuWW9i2gPxPMYzeww+7n8sVIGkLlby/5lreW7UDVeH8wmMwbOfhoRd5yX44J/BW6mp4oOomtpjPPRp1NGLnefsbvOzYRyg1NUO9QCKlUVPDWkMLbYYVNGiq562Mn4tUJoUr7sqKUVGUxjKLrGpHgiUbxi9RlmBTFi/aNZ3EFfdyzNfBMV8Hx/2d+JPBGY+3GVq4xXbuTgAgFoD9bvgVnhnfQzzbS7VabeP+qpu4vnjdvL04AY54+vhhz3O5SnUx//Mq7q++Gs0Cemp64mF+1L2L3w8dICVkkCLhXVUb+bPG6xeU+jFJIpPmf3v38YOOV4lM5lvWbuAzK27AeAH5lpO529869jKjEbGKf525nK9tuJVVxosXHnbHInz/xJv8uutI7np3e/UKPr/2Gmp0l6/ZEkkmeLjrBD85eSAXIdPI5HxwxTo+tmrDJZk093Ynnk7x2tAAT/V28kJ/zwzTrs5g4vb6Zu5qXEG9cWG1B5eF8PzIRz7C//zP/5x1fNeuXVx33XUL+h4X0sfTH4/xfH8PT/Z08ObIYM4uBthQUsYdDS28s755XgWf58IRBIHXRgf4r+P72DM+lDt+Y2U9f9a6mU22iss2JzeUjPPT9gP896n9ufYjV5XU8MX117HafPEuJoMhL9889hLPjYh5ihqZgk+vvIoPNW68oPysSCrBf3e+zs969pDMpJFJpHywYSt/1nz1ohpqx9NJfjOwj5/0vEooJQqrHdZG/qrl5gVNgxEEgdecp/nv7ufpC4uLEqNcw0dqr+fuyi3IzxFqnYh6eGT4ZZ4d35fr51mpLuaWks3caNs4byU8iOHzw95THPW1c8zXwUTMNeNxVYGS1fpGUYjqV1CmKr5sP6PTiaSinPR3c8wvis3JuemTKKVyVukbadW3sMm0Zt456gDpTJoDng6en9jPHvepXOuqJm0l76vayTbLqnPmkLb7R/hRz3Psc4tus1xayLsrt/PB2msxyM997o2lk/y6fzc/632dcDb/8priZj7TcjP12rkr889EEARes/fwzRPP0R8S08VajeV8pe2drDJeWL7lcfcYXz/6Qm5YRIlKy9+0Xs+d1ReviXk4meAn7fv58en9hJJT56YvrL+ONRfx3HSheGIRfnn6CL84fRhvXMxRNivVfGTlej68cj16xeIa++dZHMl0mt2jQzzV28mzfd0EE1NpQxVaHXc0tHBHQwsrzNZFn/MuC+G5FCxVA3l3NMIf+7p4sqeD/WMjubpPCbC1rJI7Glt4R10TRuXFC4XkEasUf3h8H88OdOX+BuuLy/hU6xZuqmq4bKssXdEw/+/k7hmuwh01oqtQrb14rsI+xyDfOPICp3yigKjSGPli2w0XnP85EHLzL8efzTWfN8nVfGblDYsOM/oSEf67+xUeGdxPSkgjRcJdlev5VNMNFC+gyXdayPDC+DF+0vsCo1ExLcOm1POxuht5R9n6c+b6eeIBfj/yKk+OvUk0LZ5ApUjYYGrhlpJNbDOvnjf0O4k95uKYr5NjvnaO+7tmuKEghuVbDc2s1jdRpiqmRGlBV6h5S8VoIpPEHnMzEXPSGxrimK+DruDAjFC/FAkNmmraDC20Glpo1tYuKH+yPzTOC/YDvDhxEO80l7TN0MADVTex3th0zt+9JzjBT3tf4BXHKUB0le8q38yH666nWHnu8GlGyPD06DF+0PkSEzHRQVyhK+OzK25ZdBP4noCTb554jjcc4oQek1zN51bfxN1VF5ZvOR4J8J3ju3hsUAzXqwpk/OmKbfxJ89YLKhCcj2QmzUPdx/iP42/iyg6luBTRmAtlJOjnxycP8HDXiVzT90qtnk+u3sx7m1Zf9l1QljMZQWD/+AhPdnfwx76u3FhRAFuRhtvqm7mjoYW1xSUXdE7LC895sIdDPN3byZM9HRyxT1VpyaRStpVXsbOmnhtr6i/rXMTlTp/fw49PHJjRCLhOb+SB5jbubViNWXV5FiINB318N5tHBWLqwLvqVvMnKzfRZLiwXMy5SGcyPDpwgu+c2JWbfrTBUsGnVmznutJzj3yci9kcoAatlT9tvppby1ctavrHUNjN/+14gRcnxPdFWSDj3qqNfKB2O6Uqwzm/PpVJ89TYQX7W+xLOuBh2syh03F6+kdvLNlCunj8dIJyK8qrjGM9P7M9VwgNoClVst6zhKssaNhibFyRCM0KG/vAIR30dHPd10B7ozbmq01EVKLEpzFiVJooVZvGmNGFViPc1heoLOoknM0lccS/OuAdH3IMj5sYRd+f2PQn/rJOSSpVW2gwttBlaWK1vQjPP3PTpDIXtvO46zhvOY/SERnPHDTINN9o2sLNkE/Wa+Qtu4ukkrzlO8+ToAQ54xEWNBAm3lq7j4/U3nvPvKP7eaZ4fP8n/9L5OV1BccJWq9Hy66SbeUd66qI4BfUEXP+1+k8eHjpEWhPN2+M9kPBLg5537+XXvIWLZXLh7atbw+TXXUaK+ONeNSDLB7/tO8pP2/QwGfQBUaw18ru0abq9ZcVku2tOZDG+MDfJQ53GeG+zKRR1XmYv5VOsW3lHTnJ8ydJGIp1PsGR3mxYEeXhzoZSIcyj1mUqp4Z30TdzS0sKm0Ysk+O3nhuUCGA36e7u3k8e522t3OGY+tshRzU009O2saWGVZHqG25YYjEuLnpw7xvx1Hc71AZVIpO6saub+5lavKqi/LiUgnPRN868irvDY2JXKuKavlEys2c1VpzUX5rISScX7UsYefdu7LJX4364v505Zt3Fa18rxP4MlMmof6D/KD9lfwZye0lKsNfKxxO++qXruoCvijniG+1/4sx33iHPYCiZSbS1fzgdrtrDKcu0o4lk7y6PBeHhx8DVd8ymnbYKrnjvJNXFu86pyvZyTi5IWJA7xgP5Br7wOgKlCwxbSSq6ytbDatQFW4sArfeDpBe6CXo752ekJDTMScuBO+c36dqkBJscK04KKlSdKZNO6Eb05hOR2lVEGJykKFqoRWfTNthpZ5m+ZPRxAEekOjvOE6zuvO4wxFpsLyBRIpW82ruLlkM5tNK86Z/9kdHOPJ0YM8N36UQLatkRQJ19lW8/H6m6jTnHsyWCAZ5fHhIzw4sJvxqOhwagoVfLzhGh6o2baoz+Fxzyg/7nqDl8Y7cu/gTaUtfH71Tqo1598rtyfg4scde3h88GQu8rHBUsGX1u68aO2R7JEgv+w8zK+7juDLTlyzKNX8ZesO7m9Yi/wy7F89FgrwSNcJHuk6wWg4kDt+VVk1f9a6havKqvPX04uAJxph11A/Lw708NrwAOHkVM6mVq7g1rpG7mhoYXt51UUR/HnheR70eN28ONDLiwO9HJoYnXHKLynScFNNPTdW17OtvApl4eXbB205EkrEebKvg4e6jnPUOeVCV2h0vKdxDe9pWkP5ZehAH3KM8JP2Azw33JWbqNFssPLxFZu4q3blRemXNxEJ8POuA/ym93Au77RcredPWrbwntq15x3iCyRi/Kb/AL/s2YsnIYoHk1zNhxq28kDtJnTyhTlEgiCw29nD//S9wX53X+74akMF91Vv5ubS1SjOISISmRSvOU7z1OgB9runmqJrC5XcXLqOO8o30qybX8imhQwn/X284TzOm64TM0SoTFLIRlMLa40NrNTVUK8pX1AIepJ4OpFzH50x91mO5JlFPOeLXCrLOajF05xVq9KETWFBL1tcuN8Z83E6MMApfz973CeZiE31Qi6UFLDe2MRV1la2m1ejl89f3BFMRnlh4hhPjh6gIzDlkBYr9NxWvoE7yjdSqjq3yOsMjPPwwH6eGTtGLC1eKE3yIh6o2cp7qzejly/MsRU/d338pOtN9jqnFoQ3lrbwJ007WGs6/3GUR92j/LB9Ny+OTqUIbbFW8ckV27i2pP6iiKjTHjs/aT/AkwOncyK3SmPg4ys28e76NRe1x/D5kEineWm4l4c6j/PqSF/ufdLLldzTsJL7m1tZYVp4Tm6ehdHn8+R0y8GJ0dx1CKBYXcRNNQ3srLk0uiUvPC8QdzTCrsE+Xhzo5bXhgRnVXupCGddU1nBjTT3XVdVizRcnLSntHgcPd57gDz2ncjPVJcA1FbXc39TKTVUNl90qfyjo42cdB3ik53jus2JRFvHh5vW8v2ndRelh6k9E+d+eQ/yi6wCeuCgUDXIV72tYzwcbNlCsWnil73Ri6SR/GDzKz7p3MxrxAVBUKOe+2o18uH7ror5vu3+MX/W9yQsTp0hmUyqMcjV3V27g3VWbKFefOz92POrlmbFDPDV6MNdkHKBJW8Yd5Zu4ubTtnO2YBEGgMzjEG87jvO46zlh0ZjGRXCqjSVvBSl0NK3Q1rNTVYFKc//kmnk5kQ+Ru4unEub9gGhKJBJPcQLHCvGhhOZ1EJkVPcIT2wACnAwO0BwZniG8AhVTGJtMKrra2ssW8kqJzuLOCIHDE28eTowd52X6CREZ03gslBVxdvJI7yjey2dx4zjzhZCbFSxOneXhgP0e8g7njDdpiHqjZxu3lbedcnEySFjK8MNbOj7ve5LRvPPt6pNxR2crHG7dTrzu/FJh0JsOLY138tHNfrmgIYGd5E3/asp11lgvr8zkbGUHg1bE+fnJ6P29OTL0vm4or+PiKTeysaLzsIkA9PjcPdx3n992ncMem+mlvK63igeZWbqluyhs1S0gqk+HQxCgvD/bxwkDvjEE6ACvMVnbW1HNTTQOrrbZLmoKRF55LSCyVYs/okLiqGOzFPi1XQgK0FpdwfVUdN1TXXfI/9JVMLJXiucEuHuo8zu5p1fBmpZp7G1dxX1PrZddU2J+I8Zvuo/yi4xATEdH1UhYU8q661XykZcNFyQONpZL8rv8YP+ncx3DYB4jpCndUreZjTZtZYTx3mHM2UpkMfxw9yY+73sw115ZJC7ijcg3vr9vMSsPcE4TOxBMP8YfhQ/xu8ECuUESKhGtszby3egtbLeeesJMWMhx09/Dk6EFec5wima2wlksLua54NbeWrWO9se6cgkUQBAbC4+xxn+K0XxRlwVTkrOeVKE1ZEVrNiqwrej4tli4Vrrgv+/sM0h4YoDs4clZuqhQp9ZoyVuhqWGtsZJOp5Zy9MwVBYCjiYpf9JE+NHmQ0OjVApK7Ixu0VG7m1dB3GczikAPZYgN8PHuD3wwdxx8XzaKFEyg0lK7mvegvrTQsPwYaScR4bOsqvevczFM72Cy6Q8Z6a9Xy4YRtl6vPr/xhOJvhd/zF+0bWfoWn/T3dWreaTK7bRoFv6NnDhZILH+k/x846D9PjF97dAIuEdVS38ycpNrLVc3ClHiyWSTPDMgHhuPmCfEuXF6iLe07iG+5rWUH0Zt3JabrijEV4Z6uflwT5eGx6YUYkuk0rZWlYpRmRr6ql4C/ue5oXnRWJyYtIL/T28PNjPSdfMdiVWdRHXVdVyQ1UdV1VWX9aTIpYTgwEvD3ed4LfdJ3BEpiqON9kquL+5lXfWNKG+jEJPyUyaZwY7+PHp/Zz0TH1Gttqq+FDzenZWNi75NKS5HJodtlo+1ryZa0rqz2tRJAgCr9q7+UnXmxxyTy0A1poqeF/dJm4pW4l8gSkFqUya1xydPDy4n32u3tzxqiIz763ezJ0V6xY06tCfCPPs+BGeHD1Ib2gid1wplbHR3MB2Sws7rC0Lqp4WBIHRqJPTgYGcEB0IT5yVX6mQyqjTlGFTmLAqDVgUeqwKI1aFHqvCgFGuXdJRmWcSTcVxxH244j6ccR/OuBdn3I8z5mMwMnGWmwmglxXlHNyVuhqadJWoCs59TkpkUhzx9LHb1cGbzs4ZYlNdIGdnyVruKN/ISn3lOYWiIAgcdPfz8OA+dtk7SAti2Niq0HJv1UbeVbVxQR0QJukKOHiwbz9PDh0nkg3N62UqPlC/mffXbT7vXpzjkQC/7D7Ib3oPE0yKF/aliCDMR1/Aw686D/O73hO5n6mRybm/oY2PtGykQnP5NE8XBIGTbjsPdR7n8d7TBLMtnAokEm6orOf+5lauq6jLFwstAYIgcMrl4OXBPl4e7OOYY3zG2cioVHFtZQ07axu4prLmstEZeeF5ibCHQ7mVyBsjM5N5ZVIpm0oruKG6juur6qgzGPMJ1RdIKpPhlZE+Huo8zsvDvbkqSY1Mzl31K7i/qZU1lgtrCbGUCILAXvsQv+g4xAsj3bn8G5tKw/ua1vJAw9qL0ij5qHuUn3bu49mRjtzPbNBZ+FjTZu6qPv/WJYfdQ/y67wDPj54mlRUQZkUR765ex321GyldhMs0EHLy8OB+nhw5kpuPrSyQ8c6yNt5bvZkW/bkdVUEQaA+M8PToIV5znsYVD8x4vEFTwnZrC9stLaw2VC24VVQ4FaMzMMjpwCCnAwN0BAZndUWnUyCRYpHrs6LUQLFC3KoLFlc9nRYyuBP+aQLTjzPmJZyevxG9FAm1mrJsukA1K3U1lKksC/5fcMT87HF1stvZwQFPD9FpKQKFkgLWGmu5pXQtN9jWoF5AYVYoGeOp0WM8MriPvtBU4eYGUw331WzhetuKBS++kpk0L4138OveAxx0T4Wg67UWHqjdxN3Vayk6x/CBuTjpGednXft5emjqM12jMfGx5s3cU7MG9Xl+37lIZzK8NNrDrzoP8/r4QO54tdbAB5vWc19D22UjJEDsh/1Y72ke6jzOaY8jd7xKa+D+5lbe3bAKW9HSi/K3G6FEgjdGBtk12Meuob4ZBgvASksxN1TVcX11HWuLSy67lAvIC8+3hHg6xYFxMfdi12Af/X7vjMerdQauq6rl2qoatpZVXlYO3XLEHg7yu55TPNx5PNdeBKDRYOaehlXcXb/ysipIGgsHeLDrKA/1HM1NQyqUSLm1qpkPNa9nU/HSN9IfCfv4ZddBHu47QihbiGRSqPlAwwbe37ABy3lOQ3LGQvx24BAP9x/CERNTCqRIuL60mffXbWKrtXbBv0skFeeZ0eM8PLiP7uCUO1ynsbKzdDU7S1fRoD13uoAgCHQFx9jt6mC3s5NT/uEZrqVOpmaruYnt1ha2WprQnyMvdDoZIcNwxMlAeAxnXBSFjrgPZ0x0ID2JAJlzVKAvBeoCZU7QWpWGrNtqpFRpplFbMe989DNJCxlO+4fZ7ezgTVcH3cHxGY+b5Vq2WZvZYWlhk7mRogWKzVcdnbwwfpLdzp5cDqiqQM7t5W3cV7NlQX/LSRyxIL/tP8QjA4dzn7MCiYQbSlt4X90mtljOr4NERhB4eaybn3XuY59zysXfYq3i481buL6scclTptyxCA91H+PB7iO5Sm8JcEN5PR9s3sA1ZbWXTZpWIp1m13Afj/ae4qWh3lzLO0VBAbfWNHF/UytbS6sum9e7HBEEgS6Pi9eGB3h1eIB9Y8MzJi2qC2XsqKjOGle1lGguf3GfF56XAf0+ryhCh/rO+lDJpQVsKi3nmsoarq2qpdm0cGciz0wygsDe8SEe6jrOswNdxNPiSVICbCmt5F0Nq3hnTfNl4yLE0ymeHerkl52HOeScqgZuMVr5YNN67q5dteQVq8FEjEeyeWuTs+Dl0gLuql7N+xs2sMa08HzN6SQzaXaNd/Jg3wH2uQZyx+s0Fh6o28hdVW0L7pcoFq4M8vDAPl62t+eKkcTvZ+Xm0tXsLF294Gk13kSIva4udrs62OfqIpiacgylSFhtqKLVUEOzroxmbTnlatN5h8rFFkgBnNlQeC4kHvMRyyTP/Q2mIUWCUa6lOOucWrM3i8JA0SKE5ZkEk1G6gmN0BkbpCIxywN2DLznlqkiQsEJXwXZrMzusLTRpyxb0foRTcV61d/DC+CnedHbnxCaIf7f3VG3m9oq1i/ocHHQP8WDfAV4ca8+5kBZFEe+uWc99tRspUZ3ftSCQiPHY4An+p+sgAyExL7RQIuW2qpV8tGnzef8fzIUgCBx1jfOrrkM8NdCRE3BGhYr3NrTygcZ1VGoNS/ozzxdBEDjsGOMPPad4qr8DX3zq/2WFycp9Ta3c07Dyos6bv9Lxx2O8MTLIq0P9vDY8MKO3Jojm1PXVYr3IlrKKi9IV5WKSF56XGZM2uri66Wc0ODMkaCvScHVFNddW1XJVRXV+gtJ5EkjEeaa/k0d7T7F3fDh3XFFQyM1VDdzTsJJrKmqXPL/yfDnlsfOrzsM81n8q14haK1Pw7vo1fKBpHfX6pS2eSmUyPDvSwU8793LcM+VwrTKWcH/dWu6oXo1Wdn4CvSfg5Df9B3hs6BiRrLuqLpBxR1Ur76lez0pD6YIXV8FkjNfsHTw/fpLdrp4ZIrReU8zO0lXcXLqaugWK0FQmzQnfIHtcnbzp6qAvZD/rOeoCBU26Mpq1ZTTrymnWlVOltlzWBUVz4U2E6AyIIrMzOEZXYDQ3GWo6RYUKtpib2G5pYZulGZNiYWkf4VSc1+ydPD9+8iyxWVNkyS4SRKd6oX9zfyLKMyMneaj/IF2BqZDuelMl76vbxM7ylcjP428hCAKHXCM83HeUZ4ZPz/g/e6B+PR9q3EjpEjd9j6aSPDnQzq86D3PCM5WD3GYu5YPN67m9uuWymdQzGPDyaM9pHu09xUDAlzterC7irrqVvKthFSvN+TZI50M6k+GYYyJ33T/mmJjR7khRUMjWsgquqazluqraZZ+OlxeelzGCINDn8+Y+jHvHhomlpk7ck5Xy11aKYfm24tJ8wvZ5MBoK8Fjvaf7Qc4oe31RxhFmp5o66Ft7VsIrWyyQf1B+P8dve4/xv1xEGglMpGltslTzQsJZbq5tRLuHqd/Ji/L89h3huZMqJURXIuKNqJffXr6PVVHZe700oGeeJ4eM82HeA3uBUbl+91sIdla3cXrmGcrVhwd8vmIzx6qQIdfbk5oWDKEJvLlvNzpJVCxahILZoOuDuFsVZcJTu4PgM8TSJQiqjUVuaFaTl1GtLsCp0mOSat1yQCoJAKBXDHQ8yHHHl3MzOwBiOuH/WrylTmWjWltGkK6c16/gu9PeIpOK85ujk+TFRbManvV/VReacI924CLGZSKd41d7NE0PHedXenVtgqApk3F65hvfVbaJFf35zx33xKI8OnuDh3iN0B6baZzXprTxQv457a9qWPLLQ7nXwUPdR/tB3KlcsJJcWcHvNCj7UvP6yqU73xqI81d/BH3pOcdgxljuuLpRxa00T72pYxfbSqssyj/ByZyIU5LWRAV4dGuCNkUH88Zm52Y1GM9dW1XBNZS2bS8svmwXIUpAXnsuIWCrFwYnRnP3e6ZnZY1AnV7CjooprKmu5prKGcu2V+T5cLCarMR/tOcXjve25/EqAer2Ju+tXck/DKirfwjYUk2QEgdfH+/llxyF2jfXlVscGuZJ76lbzQGPbkrdk8sQjPDogXqB7g1MCfYWhmPvr1nFX9Wq0C2wePx1BEDjgGuSh/oO8PN45Q6hsNFdzZ1Urt5StXHBjehCn20yK0D3O3hkitKbIwmZLHVvMdWyy1C2oOn6SVCbNYNhJZ1AUbl2BUbqCY0Tm6MEpQYJBXoRZrsGs0GFRaDErtOJWrpvaV+gWNXEHxHxSXyKMKx7EHQ/iSgTEbTyIOx7AFQ/iSYRwxQOziuXJ11eptohpBLpymrVlNOrKFpXXmhYydPjH2e/uZZ+rjyOewRl/w6qc2FxFk3bhCzhBEDjsHubJ4eM8O3oqNy0LoEVv4+6qtdxTtXZRn4vp3/uga5iHeo/wzHB7bkGlLCjk9qqV3F+3nrXm81tQzUU4meCpgXYe7D7KMfe04RdFet7ftI77GlovSh/fxRJPp3hpqJdHe06xa6Qvl/ollUi4uqyGexpWckt1Y772YJHEUkn2j4/y2nA/rw0N0OV1z3hcJ1dwVUU111TWcE1VzRU9ijsvPJcx46Egr2cTjmdbMdUZTFxTWcPVFdVsLC1Hrzj/vK+3G6lMhtdHB/hDzymeH+zOhd0AtpRUcE/DKm6pbrwsLhTj4QCP9B7nkZ7jM8bObbCWc39jG7dWLm3e6nwX7dsqV/LeurWst5zfXN9QMs7zY6d5Yug4+10DuTIcubSA60uaub1yDTuK6xc1dSmQjPKKvYPnx06y19WTywUEMU+yRV/GFksdm8x1tBoq0CxyNndGyDAScedc0c7AGINhB55EKNcSaCFIWdz7tdgiJW2hkmKlgSZtWU5oNmrLFlQMNOPnChn6Qk4OuQfY5+rloGeAQDI64zmValPO2WzWLU5s9gSdPDNykqeGTzCSHUwAUKzUcnvlGu6qbKVJf349Z53REE8MnVryxdNcTOZu/rb3OI/3n85NECuUSLm5spEHGteyo7TmLS++SWcyHLSP8njfaZ7s68iNJQZYaSrmXQ2ruLN+BbaL0FnjSiWdydDhcbFndIhXhwfYPzaSG2EMb++IZV54XiGkMxmOO+28NtzP68ODHLGP5VoIgfghX2EpZnNpOZtLK9hUWpGfpLRAQok4zw5284eeU+weG8xd7qUSCRuLy9lZ3cDN1Y3UvMWNkNOZDK+N9/NQ9zFeHOnO/f3l0gKuKavltuoWbqpoXFIR6otHeWzwJA/1HqE7MBUuL1FpubWihZsrmtloqTyvUNx4xM9TIyd4Yug4PdNC8cqCQrZb67m+tInrSpqwKBd+MQwkoxx097PP1cd+Vy/94ZlRAwkSGrXFtBmrWWuqYq2xijKV4TwrojP4EhHcicCUK5l1I0WHcvLY3I7kuZh0VEUHddJNnemkWuRaTArtoh3VSaKpBCd9Ixz1DnHUO8Qx7zCh1MxFrqZQwQZTDVss9Wy21FGvKV7we5bKZDjsHuLl8U5enuhkODyVQqIulHNL2UruqFzDZmvNgttcTWc8EuCF0U6eHelkv2Pq/1ddKOP2qlU8ULeONaaF5xWfi0mx+cxQB88MdsxYDNZojTzQuJZ761ZjUb21599oKslrIwO8MNTNS8O9eGJTi4cStYa768W8zWbT0g+zuBJJpNOccNrZPz7MgfFRDoyPzmjgDuJI7Wsqa7imsoYdb+MajbzwvELxx2O5ldbe0eGzWjYB1OqNWRFazuayCiq1+ssij/FyZjwc5LGe0zzR1z6jVx2I7Zlurm7k5upGWi0lb6mL4YiE+G3vCf7Qd5LewJSzMylC31HVzE0VjUvmgoth0VEe7j3Cc6OdhJJTJ1yTQs1N5U3cUtHMtuKaRVdgCoJAh9/OE8PHeG60nfHozLzEFfoStllr2VZcxwZz9aLcUHsswAFXH/vcfRx2DzAaPfv/xKrQ0qizUauxUquxUpfdGuVLIxwmczDj51HVrpOplyyHNJlJMRh20x9y0h9y0pe99QYdZzm3qgI5awwVbDLXssVSz0p92YJfhyAI9AVd7HH2scfRz37XQK4/K4if0e3F9dxeuYYbSpoX9fecpD/o5vmRTp4b6eSYZ2zGY2tNZdxb28Yd1avOu0DuTDKCwBHnKM8Od50lNtWFMnZWNHJ/YxtbbVVv6TnWFQ3z4lAvLwx18/ro4AwHTi9XsrO6gXsaVrGt5PwWi28nIskEh+3j7B8b4cD4CEcc4zNqMEDsG72hpIyrK2sUjmEhAAEAAElEQVS4trKGBqM5f40lLzzfNjgiYQ6Mj7B/bIT94yN0uJ1nBepKijRsKq1gc2k5m0oraDJZ3vIQ0OXMcNDPi0M9vDDUw77x4Rkh3GJ1ETurGrixsoHtZVXndfFcCgRBoMvn4umhDp4e6JghQgslUraVVHFzZRO3VDYtWYP6eDrF6xN9PDfSyUtjXfgTU+6YplDO9WWN3FLRzDUl9Ysu2hAEgc6AXXTHxjs55ZvZU1ImLWCdqZJtxbVss9ax2li2KJfMGQtyLOvsHfUO0eEfm/F3nY5Bps6J0emCtESlu6iTiS6UUDJGf9iVE5iTt5GId87UAJtSx1pjFWuN1bSZKmnSlixK8DqiwZzQ3OPsy/XanMQgV3FdSRPXlzSzw1a/6CbvgiDQ7rPzXFZsTnffJcB6SwU3lzfzjsoVlBctTY52MpNm78QQzw538fxwF87oVMspdaGMGysauK26hevK6t6ywhBBEOj2uXl5uJcXhno4ZB+dcd6v0Oi5ubqBnVUNbCqpuGy6eFyOeGPRrJMpXkNPOu0zoooAJqUqew2tYHNZOSvMxW+b8PliyAvPtyn+eIyD46PsHx/hwPgox50TpDIzLzp6hZJNJeVsygrRNVYbsoL8iWk2/PEYu0b6eGGwm1dG+gklp4pNFAWFbC+t4vrKOm6orH/LipOmi9Dnh7ro8J1xcbaWc0tlE7dWNVO1RD0Dk5k0+51DPD/SyfMjnThiU/3oFAWFXG2r5eaKFm4sazyvvn+uWIh9zn52O/vY4+hjPDqz/ZhWpmCLRXRDt1nrqNGYFuU4RNMJOvzj9E0TaH0hJ+NR35xfoyyQUaU2YZQXYVQUYZCpc1uDXI1JXoRBrsaQ3V7oxV4QBIKpGL5EBF8igjcRxjttf/p2IubHEQvM+b2KChXThLSFmiIrLfpSSlWGRb2mcDLOftdgVmz2zUiVANHV3GCuZltxLdutdbQYShYdRs8IAoddI6KzOdrBSHjKCS+USNlmq+Hm8mZ2ljdhVS3NoiqaSvLaWD/PDXfy4kjPjFxIrUzBjRUN3FrV9JaKzVgqye7xIXYN9/HycB8joZkRgjUWGzurGrm5uoEWozXvwM3BWCjAgbHJa+TIWcVAAGUabS51bXNped7RXCB54ZkHgGgyyRHHeM4VPWwfI3pG2EBVWMja4lI2lJSzvqSU9bYyDG/THJX5iKdT7B0f5vnBbnYN980Iu4EYkr+hsp4bKuvYYCt/y1yGgYCX54a7eHaokyOumeHIFcZibq0SndBmw9JcnDKCwDH3GM+NdvDcSCdDoamwdoFEwpbiaq4rbWCHrYYmffGi3XZBEBgMe9jj6GO3o499rv5cq5pJSlU6NlqqWWkoZZWhjJX6EorOI9waTSUYOMM17As5GQp7ZlTQLwRNoRKjXL3oHMy0kCGQjOJLROZ0ZefCotDMcGprsvvFCu2i/9YZQWAw5OaUb5yT3jGOe0c54R2d8ZokwCpDWc6JXmeuPK+cU288wl7HIK9P9PHSWDeu2JTLqCwo5JqSem6paOb6sgb08qU5N/kTMXaN9PLccBevjPYRTU+lRFiUanZmIwbbS6qRv0UL89FQgJeHe3l5uJfdY0MziiEVBQVsLa3ipsoGdlY3UJofW3kWqUyGDreTw/YxDk2McXBi9Kwe2gD1BhObyypyKWoVl0GHk+VIXnjmmZVkOs0pl4P92bDCwfHRGRMqJqk3mNhQUsaGknI2lJRRZzDlw/PTEASBLq+Ll4f7eHm4l0OO0RnhGZ1cwTXlNdxQWc91FXWYVW9NlfxEJMjzw108N9TFXvvQjNdYozVyS2UT15bXsdFasSQXV0EQ6PI7eW5EFKEd/pn5smZFEdttNeyw1bDDVkvZeYRH00KGU97xnOt22DM8o8E8iIKoRmNmlaGMVYZSVhpLWakvRXOeuX/JTJrRiIfRiA9fIow3GcEbD+Obtp1yIKMzRnVeKKoCOcask2qUq2fsT24tCi01GsuiWkhNJyMIDITcnPKNcco7zinfOO3+8Vy19nSqioxss9axrbiOLdZaDOchBGOpJIdcI7xp7+dNez+nvBMz3jGtTMGNZY3cnE3dWKqUlqGgj1fH+nhxpIfdEwMzpsmVF+lykYEN1vK3JBcylclw2DGaO690emcWyZUWabkhG2HZXlqVb310Bv54jMMTYzmhedQ+TiQ1M8daKpGwylKcC51vKi1/y87PVxp54ZlnQWQEgR6vm0MTYxyaGOXwxBh9sxQs6RVK1tsmXdEy2opLlrz58nLGH4/x6kg/Lw/38spIP974VCWpBGizlnJ1eQ1XlVWzrrjsLRmF5o1HeXGkm+eGunhtrD/XLgmgqFDOtpIqri2r45qyWqq1S1PJPxjy8tJoF2/Y+9nvGJrhKgHUaEyiCC2pZWtx9Xm5WdFUksPuIY55RzjtE0XTRPRsV2M2MVqrsWBRFC1pGG26Y+lNhGe8zwtBAuhlqlzI/nyr1ucimkoyHPHS4ZsQhaZvnHb/RG7a1HSUBYW06EtYlXWSN1mqqSha/GcjnclwyjfBbvsAb9r7OegcPut9adRZ2WGr4drSerYW1yzJQiiSTLDXPsRrY/28OtZHf3Dmua1Bb+bWymZurWpilWnhje+XkqGgjzdHB3ljbIDXRwdn5E5LJRI2FJfnxGazMT9aeRJBEOj1eTg8IYrMQ/ZRerxnT+fSyuWstZWxwVbG+pIy1tlKL5vxyVcaeeGZ57zxRCMcto+LQtQ+xjHHxFlVfVKJhCaThXXFpbQVl7DWVkqj0ZyvmCQ7Js01kQuRnXLPdP1UhTI2l1RwVVk1V5XV0GKyXnI3OZxM8MpYHy+NdPPa2MCM0CaIbug1ZbVcW1bHVlvVkiwyEuk0R92jOZfruGdmazCpRMIaYynbbTVss9XQZio7b4fSHQ9zyiuKqlO+cU77xs7KE51EWVBIhdpIZZGRiqLsNnu/XG14ywrIzpeMIOCMBRkOexkJexmOeBkO+8T9sBdXPDTr100XmasNZaw0llGnsZxXEUVGEOgLutnvGGK3vZ89jkF8iZk9QW0qDTtsdeyw1bDdVkOx6sJDxYIg0Olz5oTmAcfIDIFbKJGy3lrOtWV13FLVRMMSj6RdCN5YlN3jg7wxOsgbY4MMBX0zHjcolFxXUccNlXVcW1Gbn42exR2NcNQ+zjHHOEcdExxzTJzV4xrEri7rS8rEiJ2tjIb8demSkReeeZaMZDpNu9uZW1UenhhjLBQ863nqQhlrim2sLS5lbXEpbbYSSosWn1t2pWEPB3lltJ83xwZ5c3RwxuQkEPPJtpdVc1W5KETLL/Fki4wgcNprz12sDzlm5vHJpFI2FVfmhGjLEuWGBhMx9jmHeNPez277AD2BM3tvQoPOSpu5lDZTOWvNZTTpz7+a1B0PZx1RMZzc4Z9gLOI/Z8N2q1JDpVoUpaVqPQaZCp1ciS671ctU6GRKdHIV6gLZRfm8JzNpgskY/kSMQDJKIBnDn4jiT0YJJGK446GcuByJeM/psuplSuq01qyTWXpBIhPEBu5HPaMcc49xzDPGcc/4jNZbABqZgq3F1bk0izrt0hRs+OJR3hgf4NWxPl4b68cenSmsK4r0uc/u9pLqS+52xVJJDthHeWN0gDfGBjnlts/4xBVKpKwrLhMXouU1rLOWvu2FUjSZ5KTLzjHHBEcd4xy1jzMyS26moqCQtuISUWhmHc182PytIy8881xUJkJBjmZPCsfs4xx3ThBOnt2v0KouygrREtpspay2FL+tC5cEQaDT6+KNsQHeGB1k38TwWTlItTojO8qq2V5WzZaSikvekDqUjLN7YjAnRIfPqJ61KNVsKq5kU3EFm4srWWEsXpIL5UQkkAvF7ncOMRY5+0KjLChktbGUNlMZbWbxVq4+/z61yUya8Yg/6wp6p1zC7G16H8qFIJNI0cqU6OWqnDBVSBeXVpHJVrMHsiLTn4zNGgafjwKJhDK1IevcGnJObmWRiQq14YIKdMLJBKe8Exz1jHLcPcZRzxjjs/ytVAUyWk2lbMsKzVZT2ZK0oHFGwxxwDLPfMcx++zAdPmdutCyIn5Gttqm0kTrd4joeXCjJTJoTLjt7x4d4Y2yQg/YR4umZC4Fmo4WryqrZUVbDlpIKNG/j0G8qk6Hb6+aEYyJ3Tel0O89qaQTQYDTRljU21tpKaDZZ37LCrzxnkxeeeS4p6UyGXp8nuzqd4JhjnI45Th6VWj1rrDZWW22syd7ermI0kU5z1DnG61k35Jhz/Kz3rF5vYmtpJZtLKtlSUnlJq1cFQWAg6M25SXsmzs7T1MoUrLeWs7m4gs22SlrNpUuSw+qMhjjmER20Y+4xjnvGzqpmB7Fgqc1cykpDCfU6C406C7Va0wW3vREEAX8ylhOiI2EvE7EAgUQs5zROdx8XW4F+PmgKFWe5rDqZEpNCnUsPqCwyUqLSX7DIEwSB8WiQ3oCLnoCLLr+T4+4xugIzhR6I7nSj3spaUxmt5jLWmspp1FuX5DUMh/zsdwyLYtM+fFaeJkCT3sK15XVcU1rLJlslykuYQx1LpTjqHGPfxAj7JoY57BgjesZiskSt4aryGnaUVbOjrPptO6IyJzKdE5xw2DnhtNPuds5oeD9JzrSwicbFGmsJOsXbV6AvB/LCM89bTjSZ5JTLIYpRxzgnHHYGA75Zn1uh1WXFaElOjL4dx44FEnH2ZZ2SvePDdHidZz2nUqtna8mkEK2gSnt+ox/Ph3g6xQn3RM5tOuQcPUsMyqUFrLWUsqm4ks22SjZYy887V3M6GUGgP+jOidCj7jE6/PYZlcmTSIDKIgP1Ogv1OgsNOjMNOisNOvOSzuyeRBAEIukkgURWiE4TpmdW3J8LCRI0MoUoLidD+jIlWpnyojStTmUyDIe99ARc9Phd9ATc9AZc9AXds1a1gzg6tc1cRpupnDZzGauNJUv2N+72ucTPl2OYA44RJiIz03okQLPRypbiSvEzVly5ZEMSFkI4meCQfZR9EyPsnxjmqHP8rNQGg0LJlpJKMYWmrJp6/aV1XS8Hkuk03V43J52iwJxPZGpkclZZi3NpWmttpZQUad5279lyJy8881yW+OMxTjkdHHdOcMJp56RzfjG6ymJjhdlKi9nKCrOVSp3+bdXWyRePcmBihL0Tw+yfGOGk236W21Si1rDBVk6btZQ2SwmrzbZLFrpLZzJ0+Jzst2cdKcfIWYVKUomEZoOVNaYSVplsrDbZaDEWL0nBUjyd4rTXzjHPGJ1+R86dm14ZfCbFSg11OjM1GhOlah02lQabSpu76eXKK+qCF0+ncERD2KNB7NEgjmiIiahYfNQX8DAY8syZE1ookVKtMdKgFwX8GmMpbeYybEtQCJQRBAaDXk557OLNa+eYa/ysv51MKmWNuZTNxZVsLq5gg7ViyUbCLuQ19vk9HHdOcMw1zlHnOCdcE2dFJayqotxicGtpJQ0G89vqPOWPx+h0u+jwOGl3OzntctDudpJIn/250srlrLLYZkS9avTGt9X7daWSF555lg2TYvSEc4ITLlGMDvh9sz63SCaj2TQpRC20ZEXp26U9RjAR57BjjH0Tw+ybGOaYc/wsx08C1BvMtFlKaM2K0RWmYpSFFz/8OBma32+fcqyGQr6znicBanUmVpls4s0obk3KCy8MEAQBdzycc+56srfegOuswpPZUBQUYlNqKM6JUVGYFqu0WJVFaGQKNDI5RYVyigoVqAsvTkHRXCTSacKpOOFUglAyTiiZwJ+IYs+KS0c0yERWYNqjQbxnVJPPhrKgkHqtWXSH9RYask5xtca4JIMQkpk03T4Xp7yiyDztsXPa65gxCWwSdaGM9ZZyNtvEPOK1lrJL0llAEARGQgGOu8azQnOCk64JgrO8xgqNji3Tog41OuMVtViZi3Qmw4DfJwpMl5MOt5N2j3PWpuxwtshstZZQrTfkReYVSl545lnWTIrR024HHW4nHW4XXV7XrCtoEN3RSVe00WihyWSmzmC64hPPY6kkR5xiKsNR1zjHneOMhc/uOFAokdJistJmLWGNpYQ2SylNxvOvYl4M9kiQI66xKWfLY59TAJapdaw0FbPSZKNBZ6ZOb6JOa1qyRtnBRIzeoJvegJuhkBf7GSLtzJY/C0GC2AdVI1OIYlQmCtIimRxNoQL5It/jDAKRVJJQUhSX4WSCUCpOOJkgnEosui8oiOkPk45ucVZIlxfpqdGYaNRbKFMvTSQhIwiMhQP0Btz0+t10+Vyc8tjp9Dlnfd2KgkJaDFZWZhcgbeZSWozWSzL1yxkNc9w5zjHXBMecE5xwTeA+o+MEiKJ8ldnGWqu4kNtYfOVPthEEAUckTJfHRZfHTZfHRbvbQZfXfVZrvUnKNNpcdKrFbGW1xZYXmW8z8sIzzxVHKpOh3+eh3e0SxWh21T0+i9ACsbK3Rm+kyWTOidEmk4UavfGKFqRnXlCPu8bxxM4WVMqCQlaai1lpKmaFyUqLqZgWo+WShOmd0XBOhJ7OumADsxSNTFKm1lGvN1GfFaP1OjP1ejM21dLmgZ0ZlhZvU06iKxYWncZUgnAyvoTziRaPoqAQTVbg6mQKSrKubLFKk9svyd43yFVL+j6Fkwn6Ax56Ax56A276/OK2P+CZMdZxOlqZYsrhzrrc9XrzRV/8pDMZhoI+TnucdHgcdHicnHDbZz1vTF+gtVpKabOW0Gi4NAu0twJBEHBGI3RPCkyvi26Pm26ve9YemSCOWG4yWViRXehPCs1Llf6Q5/IlLzzzvG3wxaJ0eLJi1O2ky+Om2+simJi9MKJQKqV2miBtNJmpN5io0RsvSTj6UiMIAqOhAMdcEzlBesI1MWuYE6BKa2CFyZoToytMVqq0F9+5CCbitHsdnPLY6fA56At46PW78cTndiGLCuXU6UzU6UxU64yUq3WUFukoK9JRptZe1JGCgiAQTSdzjmQo60iGk5P7cUKpBKlZip/mQwKoCuVoso5pUS6sL0crE++rC+UX1RVMZzI4Y2HGwoHcbSTsp8/voS/gmbXV1SQyqZQarYl6vYkGnYVVpmJWmUqo1Jx/26uF4o/HxAWpVxSZ7R4nnV7XWVXmkO0TazDTZi3NRgEuXUrKpUYQBCbCIfp8Hnq87uw5UnQyZxuZDGJudo3OQKPJQrPJzApzMS1mK1U6/du+z2ie2ckLzzxvayZPtF0eF91eN93TVvNzCS4JUKHVU280UWcwUW8w5fatKvUVlcM1WTRxwjVBp9fF6awTZI/MHgJXF8poNopitMloocFgpl5voqRIe9EFqTcepc/vzoZvsw5bwMNg0Dtru67pGBUqStVayop0lBdlRal6at+sVL8l40vfSjKCQCARYyISFEXl5DYcYCzsZywcZCISPGd7KJNCRb3enHWfTdTpxP0KzYW3cjoXoURcdFn9Hrp9bjo8YgRkNDy7IFYUFNJstNBisuYc/ktZhHepiKWS9Pt99Hrd9Pm89Po89GVvs/VZBlFgVusMNJrMNBrNNGUX43UG0xUpwvNcPPLCM0+eWRAEgfFwUFzxe1x0ed30eN30ej0EEnM3C9fKFdQbTNQZjNQbzdTqDdTojVTrDVfUzHpPLEJ79iLe7nHS4XXQ5XWd1QB7ElWhjDq9URQg+qxY15up1RsvekFIIp1mKOSjNytKh0N+xicFVCQw5wLjTIoK5RgVKkxKFUaFGpNClb2vFrcKFUalGqNchUGhRFkoQ1UgQyaVvqWLkYwgEEsliaZThJJxPLEo3ngEbzyKNx7FE4/ijUXEbTy7jUXxJqJndUaYjQKJhJKsaC9Viy5ync6YTXcwY7zIoxwz2f/VPr+HHl920eETw/oTcyyQAMqLdLSYrKzIpZBYqdUZrxiXLp3JMB4OMuj30e/3igLT66bX52E0GJgz/aNAIqFab6TOYKQpm3rUaLJQbzBecM/bPHkgLzzz5FkUgiDgjkbo9XlyLkGvV9wfCQbmvVAXq4uo0Rup0Ruozm5r9EaqdQY08uUvSlOZDP0Bby502eNz0+NzMxjwzeuKVWh01OvFnMxqnYFKrZ5KjZ5Krf6ihsAnCSRiOSdvNBxgPOfsLdzVm48CiQRVoQxlgQxVYSGqApl4P7s/uV2s+5cRBOLpNLF0kmhWWEZTyZzIjKaSxNKpWfshLgaDXCmmJMzhBherNJck/9IeCTEc9DMc8jMc9OWczD6/d9YQ+SQWpTq30Gk2WVlhFEXmlZBrmM5kGAsFGfB7GfD7GPB7GfT7GAh4GfL75y0w08kV1BunIjb1BjN1BiNVOsMVndue560nLzzz5FkiYqkUg35vTpT2ej0M+H0MBnx4ZynamY5FpaY264xW6kTRNbktLtIs64rPZCbNcNBP76Qb5c++P373nHljk5iV6hlCVLyJ4rSsSHdJLpAZQSCYiOOZ4RJG8MaiOZcwdzzrJvoTsXOG998K1IWyrDurxqgUtwaFONHoTPfWpFCjVygvSYqBIAh4YtGcqBwO+qduIR+jocCsAwAmKZRIqdYZcqkddXpRSNXrTcteYIaTCYYDfvEWFLdDAR/9fh/DAd+874tMKqVSZ6BGbxCjMFlxWW8wYb7C0oLyLB/ywjNPnkuAPx4TXQmf6EZMhr8G/b5ZK8mnIy8ooEKjoyIrRKt0WVGava9XLN9G5p6Y6B73+MXQ6FBwSnjMl9IAYs6ZTa2hRK3BVqSd2j/jvkYmv+TvjyAIJDMZoukk8VSK6KQrmd2f7kpO3hYrVCUSseOA6JrKxP3CKVdVWTjTYVUUFL4lC5hkJo0zEmYiEsIeCTERDuKIhHL37ZEQY6EAkXlcSxDFZblGl1uA1OiM2bQNM5Va/SVprXQxSKTTjAYDOVE5uR3Jbs95fpAWUKnT5xauk5GUGr2BMo3uikkdyHPlkBeeefK8xfjjMTE85vcxGPDOcDfGQ8FzChKNTE6ZRkupRkuZVke5Rpe7X67VUVKkXZahM388NiO0OuWAiRfludrxnIm6UJYVo6IotaqKMCnFHE2DcjI3U8zdNChUV2xLnKVCEATCyUQuF9QTy27jEbyxGO5YGEckzEQkiCMSxhUNL7idlE2tyTncVTrDNKfbQIlas+xE1KSTOx4KMhoKMBYMMhYKMBaa2jrCoXO+P3qFMhsFyQpvnYFag5imU6bRLrv3Jc/bm7zwzJPnMiaZTjMRDjIcONsRGQ76cUbC5/weEsCqLhKFqEZHmVZLSZE2J8RsRRpsRUXLqnBA7CsYZjQUyLlm9mmO2uT+uVzT2dDLlWIBkVKVKybSyBRo5GLroqLsTTPtlrsvv/QTihZLPJ0SWzsls03nE9P2c9t4bt8Xj+GJRfHFp0TmYpvTy6RSsSH95GdOraFErc19/krUGso1+mVVHZ3J5nvbwyHxFgkxEQoxHg4yHgoyFgwwGgouKMdWWVg4I72mUqenYtp9neLKqqrP8/YmLzzz5FnGRJPJnHuSc1WyFz3x/sIufCC6KiVFGoqLNJTkRGmRGLYuKsKqLsKsWl5thSLJBI5oOCdGJyIh3NFsTmZsKidTFFbz55suFLG/pgx5QYF4k05uC5EVSHNbxbT78oICChYpVgXEhUkikyaR3Z55P5FOk5y2jaVS5zXRaDYUBYWYswI9lzOaFes2tYZi9dTnyKRUL5s85Ywg4I/HcEXC2CPhKWGZvU2EQzgiIRyR8IJ7r1rVRZRNW/iVFuko12op0+go1Wix5PMt87yNyAvPPHmuYCZDfbnwXjbUN3HGhXQxlc9auQKrSo1FXYRFpcasUmNRq7Goiqa22eNFssvb/ZtOKpPBH4/hiUfxZUXpZBFRaJobGExkx1Jmj013CxfSfuhyQVlQiFauoEgmm+bgKnL7k1u9QnSAJwuPJgXmpZiLvlQk02m8sSiuaES8RcIzt9OOe2LRBQtKCWBRF4kObnbRlkt7yaa62Io0y2qxlifPxSYvPPPkeZsjCAKBRHymo5Pd2iNTAtUdjcxbQTsbcmkBBqUoXAyTjtiMm1IUMirxvl6hRCtXLBt3bDqCIBBLpwgm4kRTSRKZDIl0imR2O3l/uguZcyfTaTLnMVhz0k2VSc90VwuQzXJfWVCYSxVYjrmsgiAQT6fwx+Nir9HcLZZzsH3T8k4nb3NNJ5sPvUKJVa2mpEhLsboom54yGQEQhaZFpUa2DPOn8+R5K1mMXssv2fLkuQKRSCToFUr0CiVNJsuczxMmQ5DRCM5IGHfOJYrgioaz2+x+NJIL6zoiYrHJgl8PoJEr0CkU6BVKdNl9nTx7X6FAJ1eiVyhEx25a7qVGLm7VMtklF6+SbL/O5eQEXmri6RThxKRDnCSczS8NJeIEEnH88RiBeJxAPI4/MbUfiMcIJMT9800VkADmSYd+mmM/3aWf3Der1MuyIC9PniuNvPDMk+dtjEQiwZB1LhuM5nmfKwgC0VQy50R5Z3Ghpj82+XgslUJAnMceTMQZDc4963ve1wqoZTI08snQsRhOLpLLUWfFoUpWONVyKNvQXVlYmBOPqux9ZaEMRcFMZzHnMBYULEt3djqCIJDKZKZyRNNp4tNc2Xg6JbaBSiaJpaYa00eTKbEt1OSx1FR7qElRmStYyu4v1jGfC6lEgkGhnOGei666coarblKJnQpMKhU6uSJf/Z0nzzIjLzzz5MmzICQSCWqZHLVMTrl24akv8XSKYDyOPx7POlyx7H52m3O+sm5YIk4oK2rEW5KMICAA4WRyzrnTS0mhVDoV2i4oQC6VIisoQCqRUiCRUCDNbiUSpBIphVIp0uz93GNSKVIWW1wkkBbEWyaTISVkyGQmj2VIZ8RtZtr9VCYzFeafVpB0qXOolIWFUx0Cso71mQ731L7obuuy9/UK5bLKHc6TJ8/5kxeeefLkuagoCgpRqAuxqIvO6+snndbZHLfJIqBIMtvUPefQic5dbNr9aGrmcyaFWjKTPsu1S2UyYjHKORqgLxekEsmUqyuV5lzfnAssm2xWX5hzjhXTmtcrCwtnpD1M35/cLsf80jx58lx68sIzT548lzXTnVbOU7yei4wgTLUomiZIxfB0tqAonRFdRkEgkw1lZ3Iu5JQDmck+J505n9IiznZOJVKkUgmFkqyrKj3TdZVOtXk6I2Vg0rXNi8I8efJcLuSFZ548ed72SCUS0ZnNt8jJkydPnovKsjjLRpJJCi9BXleePHny5MmTJ0+exRFZhEZbFsJz609/iFSpfKtfRp48efLkyZMnT54zyMQWPiUun/iTJ0+ePHny5MmT55KwLCYXjbtc+clFefLkyZMnT548lyGBQIBSi+XKmVyklslQy/KTQ/LkyZMnT548eS43UovQaPlQe548efLkyZMnT55LwrJwPPPkyZNnoWQEQWwUn0wRyzaSjyZTxHLjIcX93KSfyb6d6al+nTP6eU7bTk4VSmemenaK/TyF3FShqePnl8UklZDt0SmlQCrJbaUSydSEpOy2UCqlQCr28ZQVSJEXFCIvmLw/ra9nYcGMY4qCAlQyGUpZdrSobGq0qFJWiEomy/f+zJMnz0UhLzzz5MnzliIIArFUikA8jj8WIxRPEEzECSeyk4qm3ULZY6EzjucmF2XFZp4LRyaVoigURaiysJAiuQyNXEGRXEaRXJ67ac64Lx6To1FkR2YqlWjk8vxM9Tx58gB54ZknT54lIplO44/F8ESj+KIxvNEo3miUQCyOPx4jGIvnxGUgHieQvR+Ix0mm0xflNckKCsQxkLLsiEhZIYrsCEhFYeFMZ/AMx3DmY+L0n0mHUZzRnnUgz5gklJsyJJUgOY9Z7YKA6Kjm3FNhmqs6Na990nlNZTI5l3Y+x3b6fjyVJppKEktmR4omU1nhnsxNW0pmMiSzIn8p0CrEee06hQKdUpETpdrsVqcUt0aVCpNKhUEl7qvzM9zz5LmiyAvPPHnyzEosmcQVieAKR7Lb8FmicuoWIxiPX9DPk0ok6BQKtAqFOP8765qJLppMnBGeddMmj2kUogOnlslywnJ66Djvsi0OQRBIpNNEk+J8+0kXOZpMnuUyhxNTx2Z7LJhdVEw60MF4nGA8zugiX5O8oABjVoQaVSoM2a0pe8ysVmMuUmNRF2EpUqNXKpHmhWqePJcteeGZJ8/biGQ6jTMcwR4K4gyFZwhLd3jm/fB5OF0SyLlWxqxrpVcq0SsVWcdral+vVE65X0olRXln6y1HIpGgyLrBhiX6nvFUilA8gT8em3K5s663PxYnmD3uj8fxZRc2nuyCZtKptYfC2EPhBf28QqkUs1qFOStELWo1liJ17r61qIhiTRE2jQaNXJ7/zOXJc4nJC888ea4AMoKAJxJhIhTCEQrjCIWwZ/ft0/Y9kQiLKXmRFRTkLtwWtRqTWj0jDHrmTa9ULCuXMZXJEI7PdOrC8SkHLxRP5HJNo4nktPD1zEKkRGq2YiQxPL5YCqVS5IVnFgWJaQCyguxj01IAJvMvixTT8yxl09xiee5xZWHhJRdak0LWXKRe1NcJgkA0mcq56qLbPtNl90SjuMJh3NkFUyAeJ5XJLFioqmUyijVFFGs02DRFFBdpKNZoKNFqxONF4nFlvp1fnjxLRl545slzmSMIAoF4nPFAkLFgMLsNMB4IMh4UbxPBEKlMZkHfTyaVYtUUYS0Sb9MdIfM0h8iiVqNVKJaFIyQIAsF4HG8kmwYQyYqT7H1fRBQq3sikeIkRiseJpy5ObunlSoFEQpFCjk6pyIWsjWolJvVkCFuJUa3COO2+Qa1CXlBwyV+rRCJBLZehlsso1y9sgEg8lcITjeIOR3BFwtPSRCK4s/cd4TDOUJhAPE4kmWTA62PA65v3+xqUSkp1Wkq1Wsp0Wsq0Okp1Wsq0Wkp1Woo1mnwXgDx5FsiymFy0kE74efIsVzKCgDMcZsTnZyQQYMTvZzQQyAnNiUCQcDJ5zu8jASxFYghxMpQ43c2xabQUa4owqlTLJgcuIwj4IlHRwQqGcASzju60rT0YwhOJkr6AU5msoCBbtS3POoeyM3JM5ahksrPaEonuo/SsQqRJx7JAusjiomwhUc5VTaVIZjIkUme3dpo8Fkulsq7ttC4A0+6H4mLV/4WgUcixTn6mtEXYJh3BM7aKwuXjZUQSSRzh6RGCcDYyMDNasJAuCVKJhGJN0QxBWq7XUanXU6HXU67T5l3TPFc0i9FreeGZJ89FRhAEvNEYowE/w/6AKDD9foazAnPEHyCxgKpuo0pF2TTXRdzqKNVqKdGKonK5uS6RRFJ8P3x+RvwBRnwBxgMB7EFRVDpDYZILdHIBiuSyGc6dIevo5YpS1OJxvVKJVqnItQOSLyPBdD5kBIFIIkEoniSUEDsKTHeBZ7jD2f3JfMvFpAsYVMqcKLVptVQYdNmbngq9DoumaNksemDKSZ+MLIwFZm7HA0EmgsEFfUatRUVUTBejuX3xf1j2FrjKefIsFXnhmSfPJSYjCDhCIQZ9Pga9vhnbIZ//nIU6UomEUq02d2Eq0+nEkF5OWGpQLUPHJJFKMRYIMuITBfewz89oVmCO+Px4ItEFfR9zkXrKvdVOOWyTIsdcVIRJrbziBeSlJiMIBGJxPJEIzlAYRzDrPIdC4uIgFModW8jiSVFYQJleR4VeT+WkIDXoKNeL+waVclmkdkwnIwi4wuEzhKm4oBQXmIEF/f+X6bRUGwxUGwxUGcVttdFAlV6fd0vzXPbkhWeePBeBdCbDRDDEkG+msBzwehny+c8ZkisuKqIi63BMbifdjxKtZtk6Hsl0mmGfn0GPjwGPj0GPV3xvPD7GAsFzOmY6pUJ8P7JCpEyvxabV5G7mIvUlzTFMpFKi45d1BH2RKL6ImBM6OQFJ3GanIWX3o5P7qan7mfOYXqQoLEApm5ogpJQV5m65+4WFKGVi+yi1XIZBrcKgVmJQTW5FR/dSiThBEPDH4jNSIMQFx9RCY3wBnwWtQkG1yUCNSRRdNSajuG8yYlApL8nvstQIgoAvFsuJ0NHsdsTvz97OHfEo0WqoMRpzwrQ6K0yrDAbU8rwozfPWkxeeefJcALFkkn6vjz6Phx63mz6Pl163m36vd95ilAKJhHK9btqFwSg6FgY9FTrdsnctkuk0g14fPU433U43PS5xO+DxzVvYpJIV5kKt5VmHa0po6tApL76gSGcyOINhxvwBxvxBJvxB3OFsEVJ264lE8ISjRBIXlg95uVAolYqpBUVqjGoVpmzRkLFIRbG2iDK9jlK9llK9liKF/KK/nmQ6zXjW/R6dTDnxBXKpFs5wZN6vtxSpabCaabSYabSac/v6ZSpIJ5nM8R72+c+KmAx4veds4F+i1VBvMlNvNlJvMlNnNtJgMmMpUi879zjP8iUvPPPkWQDeaJRe90xx2efxMuL3z9lySCaVUmnQT4XBprkP5TrdsnUtz8QbidLhcNLpcNFhd9Jhd9Htcs85YUglK6TaOOlOiU5VtclAjdGA+RJcAEPxBOP+AOP+IGO+IGPZ/XF/kDF/EHsgSHoR7mOBVIJedbaDqJbLck6j6EKK4yRV8sJpLuXU8cW2lhIQSKTSOfc0mkwST6ZyDmvOVZ3mtIbiCTEfMxLDl3Voo8nFjQ3Vq5SUZUVoqV4n7hu0OXFq1RQhXWSh1GKJJVMMebOuudebdc99DHi8OOZpjVSi1dBis9JSbKXFZqHFZqXaaFhWbb3mYjI/fNDnPSuFZ9DrwxeLzfm1WoWCOpORBrOZOpORerOZepOJKoP+inhv8lxe5IVnnjzTCMbjdLvcdLlc9LjddDnddLvdOMNzX8z0SsUM96DObKLeZKJcr1t2BTzzkc5kGPD46HA4cwKzw+HEHgzN+vwiuYx6i+g0NVizzpPFRKlOe9HFZTqTYdQXoM/loc/ppd/lodfloc/lwReZ+wI8SaFUik2nocygo1SnwawpyrYSUmMqUmFQqzAVia6gVqG46ELrYhJLpvBFongiWTd3mrvriUSwB0I5YR6InXvilKxASrXJQK3FRJ3FRJ3VSJ3FRK3ZiEapuOi/TyieoNflodflpjvrtPc43YwFgrM+X1lYSKPVPFOQFlvRXoLXeinxRaPiotnjodftptfjpc/tYdjvnzOtQVFYQJ3JRJPFIrrHZjNNFgvlet2yKvzKc3mRF5553pZEEkl6PW66XG66Xa7s1s14cPaLE0C5Tke92USdyURDdltvMmFSq664MFU6k6HX5eH42AQnxu2cHLfT5XTNmT5QadBnL9yii9RcbKHCoL/oF6dIIkm/y0Ofy5vdeuh1ehj0+EjMk+qgVymm3Lpprl2pXnTuLBp13umZhVAszlhWhI5nUxEW4xbbdBrqLMYpUWoxUm81U6wtugROd5yOnCsvOvRdTtecjm+FXkeLzUprWQlrymysKbVdklSPS008lRLTYjwe+tyebNqQuJ0rF10tk9FgFgVpg8WcE6YlGs0Vdy7Ms/TkhWeeKxpBEBgNBGh3OOl0umh3OulwOBny+eYMkds0GposZhotluzWTIPZTJH84ue2vRUIgsCIP8CJsQmOj9k5MT7BqXHHrP0c1TIZTcUWWootNGfdoeZiCxrFxXeHvJEop8cdnB5ziNtxB4Me35zPVxQWUGM2Um81zRA6lSYDmouQpyhkq7od/hDucIRwLNsjM54kFI8TiSfF6UZn3ELxBJF4YsFN/SeRIEE1OYVIMbkVbxqFHHV2O/W4Ar1KgVWnwaJVX5RUj1Q6gz0Yot/lzbrN4mKg3+XBGZo7L1OjkLOitJhVpcWsKitmZWkxNWbjRRf/6UyGIa9/movvpMPhYnwOd7TWZGRNmY3WshJay0pYYbMuq36kiyEjCAz7/HS7sxGgbCSo1+OdM41Gp1DQbLWwothKi9XKimIrjWbzss9Zz7O05IVnniuGaDJJl8uVE5kdTicdThfB+OzhQbNanROWTRYLjWZx/0p0NabjCUc4Pm4X3cwxO8fHJ/DO0qqoSC5jdal4kV1damOFzUqV0XBJQmzOYDgnLk+POzg1ZmfMP7sYMBWpxFCuxUi9xZwL7ZbqtUsmXBKpNM5gCLs/hCMQwhEI4wiEsAfE+3Z/CGcwTGyR+ZJvFRIJmIrU2HQaivUacasTt1ZdUe64bgmr3QPRGP1uL71OT86l7nN5GPL4ZnVJVbJCWkqsrCqzsTIrSuuspkuSG+2Lxuh0ODk14RD/R8YmGPb5z3peoVRKc7GFNaVTYrTeYrqi3fJUJsOg1zcVKcoK0wGPd9bBDFKJhFqjUUxjsFpZUWyh2WrNu6NvY/LCM8+yxBuNctru4JTdwSmHg3aHgwGvb9ZcJZlUSoPZTEuxVVyNW8UT4GLnQS9HBEFgyOvn0PAoB4dHOTQ8Rr/He9bzZFIpzdmwYmv2Ilp7CRwnEHPyjg6Pc2R4jFNjdk6NO3AGZ8+prTYZco7YytJiVpQUYyxSLcnrSGcyjPkC9Du89Dk99Ds99Du9DLi8uOdx685Er1Ji0arRKBSi06iUT81Gn7avye6rFeLkI1nh4gSVIAhEEkkiWdd00l0Nx+JTc+RjopsaToj7/mgMZyC8YHdVJSuk3KSn1mqk1mqizmqirthEjcW4ZNXtiVSafpeHU9MWGu3jjllD4PLCApptFlaV2lhTUcLGqnKqTPpLImA8kSgncwu2CY6P23HPUl2vUchZV17GxsoyNlSW01pWglJ2Zbqi04mnUvR5vHQ4nLQ7nXQ6xe1c/XcNSiUtxVZWFhezylbMapuNmiuk0CvP/OSFZ57LHlc4LApMu4OTdjun7A5GA4FZn2tWq2mxWmmxigUCK6xW6symt2R+9FtBKpOhw+7kUFZkHhoenbX1TL3FlHNp1pSWsMJmuWQN1R3BEIeHxjg0OMqhoTE6JpxnLRikEgl1FmNWYNpYWVbMipKlKfiIJJIMurz0OabEZZ/Tw4DLO29eqKygAJuuCKtumkOon3QKxePFOs1lLzIyGQFPODKnczt53B+dvwirRK+h1mrK3ow5UWpdgnzNdCbDgNubdbunBGkofna7IItGzfqqMjZWlbO+upwWm5XCgosvXgRBYDwQzOVBHx+b4OS4nfAZLbZkUimrS21sqCxjY2U56yrKMKqXZrF0uSNk2z9Npjh1OJ20O1z0ezyzuqNqmYwVxdacEF1lK6bebL6iijTz5IVnnssMeyjEiQk7p+x2TtodnLbbsc/RHqXaYGCVrZiVtmJWFhezwmrFqim6xK/4rSWSSHJsbDwnMo+Ojp994SsooLXMxobKcjZUlLG+ouyS9TMUBIF+tzcnMg8PjTLkOTtkWWHUsb6yjNaKUlaWFtNSYl2SZteRRJL2UTsnRuycHJng5Iid4Vl+/iTywgJqLMacwzcpqsoMOgzq5Tcp50KIJVPY/SGG3L6sQPdkneD5HWCNQs6KsmJWV4qu5JqKEkoNF97JIJMRGPb6OT1u59SYgyPDYxwftZ+Vb6iWy1hbWcqGqnI2VImfqUvVOD2dydDpcHFoeCwbYRidtb1Tg8XEhspyNlaWs6GyjAqD/pK8vsuFeCpFt9tNu8OZi1y1O2d3uRWFBbRYrawqLmZViShGmy2WK6Yd3duRvPDM85YRiMU4MWHn2MQEx8cnODExMavIlAB1JhOrbMWsyq6CVxZbr/hczNlIptMcG5tgT/8QeweHOToyftbsZ51SwfqKMjZkQ31rSm2XrABCEAR6nG529w5xYGCEQ0NjZ+WPSiTQYrOyvqqcDdVlbKgqx6bTXPDPTqbT9NjdnBgWBeaJkQl67O5Z0y+MRaoZ4eNaq5G6YhNlBt2ShPoi8SSBaIxgNE4gGiMQiU+7P3Us93g0Tiq9uOIigCKlHJ1KgU6lRKdWoFUq0KmV4jG12E9Ur558XIlGKV+S388XiTEwzS3ud3roc3gY8fpnzdc0a9SsLrexurKENRU2VleULEmKRDyZ4uSYnUNDoxwaHOPw8BjBM1o+FUqlrCwtZn1VGdvqqthUU3HJhOhk4d7BIVGEHhoZo9flOet5FXodW2uq2F5bydbqSixvswU0iKK9z+PlVDaqddJu57TDOesIUXlBASuLi8W0oJISWktLqDEa8y2elgl54ZnnkhBLJjntcHJiYoJj4xOcmLDT7z0711AqkdBgNrHaZmN1iY1VxcW0FFuv2Iryc5ERBDrsTvYMDLNnYIiDQ6NnVZuX6rQ552RDZTmNVvMlPQE7g2H29A3xZu8ge/qGcJyRn6koLGBNeQkbq8vZUFXO2srSJQmZj3r9HBkYy7mZ7WOOWds9FeuKWFNRwuqKEtZU2mgpvfC80HgyxZg3wKgnwIjbz6jHn90GGPH4CUbP3e/yraBQKqXEqKXCpKfcrKPCpM/ui1tD0YW5uolUin6nl1OjdlH8D0/QNeGaNae0wqhjTaX4d2mtLGF1he2C0z0yGYFupzvrsI9yaHCUicDMPrOyAilrK8vYUV/NjvoqVpYWX9K8Qk8kypGRsVzO9clx+1nvT5PVzLaaKrbVVLKpquKK6ym6UDKCwJDPl021snNyQhSkgVkKRrUKBa0lNtaUlNBWWsKaEhslWu1b8KrznIu88Myz5KQzGXrdnpyTeWx8gi7X7BefSr2e1tKS7KrVxqpi29t6nrAgCAx6fezNCs29gyNnOYZGtYqt1ZVsq6lke20VlYZLU1wxSTSR5ODQKLt7RbHZZXfNeFxRWMDG6gq21VWyobqcVaXFFywoBEFg1BvgQN8IB/pHONA3wpjv7DxfrVLB6gpbVmiKzppNf35uajKVptfupnvcxfA0cTniCeAMhDjX2bBAKhGdRtWUC6md5k7qVMrsfXFfvsjioowgEI4nCERExzQQiRGIxXPu6uSxSZc1uoDxnmqFTBSi04RpbbGJlnIrJs35FePFkik6x51ZJ3qCEyN2BlxnLzqVskLWVpWysbaCzXUVrKksWZK84zFfgENDo+wfGGF37xCjZ3xu9CoFW2ur2F5fxY76aiqMlzbsHU4kODg0mltcttudMx4vkEhYXWoThWhtJesryq7YFk4LQRAEBn2+rIEhXl9OO2ZfdNo0RazJOqJtJaIYfTtGyi438sIzzwXjjkQ4NDLK4bExjo1PcMo+ew9Ik1pFW/Yk0FpSwpqSEkxvkyT7+fBHY7zeN8AbfYPsHRg+a8KKWiZjU1UF22or2VZTRXOx5ZKHlPpdXl7q6OGNnkEODY2dlVe3srSYHfVVbK+vZn1lGYolKLBxBcO80TXAvt5hDvSPMO6b+b4USqWsLC+mtbIk52hWmQ3nNUUomkjSMerg5LCdjlEHnWMueu3uecPfKrmMCrOecpOOiqxjOHm/1KBDrZBdVjmhyVQadzDCiEcU0We6tY7A3NO5QHSOm8qstJRbWVUhhs1thvNriROIxv5/9s46PIqzi+K/uBF3dw/ESNBAAgR3p5Q6pU7dvaXuTr20RYq7BwiQQDxEiLu76+7O98eGQEo2RrB+Oc/Dk3Z3ZnV25rznnntuF1U0NreIqsauCywVRQU8rczws7NgvJMNHubG1zwhShAE8qpqOZuZS1hmLuey869qWLLW02GsvRUTnWwZa2d1w5ruLqGqqZnzufmEZ+cRnpNPbnVNl/tVFBXwsTBjtI0VQQ62OBsZ3FLH2c1Au1hMWkVlJxG9UFxCemX3NhtbXV0pETU1wc/CAmfDG38+/X/HEPEcQr9RVFdHRH4BUQWFRBYWkll5tWdJQ0kJdxNjRpgYM8LUFE8TE8xuwKjE2wW5VTWEpGcRkp5JVF5hlw5PJXl5vCxMO0ttI8xMbriRXhAE0soqOZKczpHkdNLLKrvcb6qtyVg7KdEcY2eJ3iBEU4klEpIKSglNzSY0NYekwtIu9yvKy+NhaYyfrQV+tpZ4WZsOKNJHJJaQWVpJYl4JifmlJOaVkF5S0a03UVNNBSdTA2wMdTtI5WWiqavx35pY1douorCq7irbQEZxBbkVNd3uY6CpjoeVCcOtTPCwNMHd0hht9f4rSoIgkFlWRVSHmh2ZXXBVA5OehhrjnWyY4GLLWEfrQWmQE4klJBSVcDYjl7CsPOILirscBxoqygQ52THVzYEABxvUbkI1pqi2rlMNDc/OuyqlwkxLk0lO9kxytMPfyvyGE+VbFU1t7SSVlXZauy4Ul5Bfe3VjoZaKCr7mZvhZWDDSwhwPE+P/mxSUm4Uh4jmEHiEIAjnVNUQUFHSSze6ijJwM9Blpbo6nmSnDTYyx1/tvhyj3F2KJhLjC4g6ymXVVg4GDgR6BDraMsbHC19L8ptgNBEEgubiMw8npHEnOIKfycjlUUV6eUbaWBDrZMs7eGlsD3UEhXW0iMecz8zmalM6J5MyrVC93cyPGOdrgZ2eBl7XZgD4XsURCSmEZYal5hKflkphfQnPb1d2zl0iUm7kRzuZGuJgZYqp77YslQRBobGmjuqG5418T1Y0t0r8NzVTXN1PT2NxjlFN3kJMDLTVVdIepoTtMDZ2Ov3rD1Dtv09ZQG5RoocaWNtKLK0gtLudiYRlJeaUyybq1gQ7+jpaMcbLG38FywEQ0u7yayOx8wjPyCE/P66JMysvJ4WtjTrCHI1PcHQZsp/g3Glpaicgp4HRGDsdTMrv4ldWUFJngaMs0d0cmONpel+lXvUEQBDIrqgjPyedMVg5hOXldSswaysoE2FkzycmOifa2/zexTX1FVVMzCaVSRTSmsIiYwiIa/1WdU1VUxMvMFD8Lc/wtLfAyNUVtaPLSoGKIeA6hCySCQEZFZSfRjCwopLyxawlOQU4ODxNjRpqb42dpga+5GbpqQye4f6OhtY2z2bkcT8vkVGZOF6+morw8I63MmexoR5CjHVa6OjflNUokAhcKSziSnM7h5PQu/jdlRQXG21sz1c2RIGe7QYtgam0XEZaRy5GEdE5ezKLuii7kYSrKjHOyJsDZlvFONhhqDqy7t7i6jvC0PMJSczmXnkdtU9dMSg0VZdwtjaWRP1YmeFhJvaADIZmCIFBW00BWaRVZJZVklVRRUFHbSTJrGlto7yepHExoqaug20FGDbU1sDXWw85EHzsTPayNdAdsi2huaye1UOrdTMgrITGvhPzKroqSvJwcHpbGjHG2ZqyTNcOtB6bet4vFxOYWEZqazenUHDJKuyrwXtamBLs7MtXDETPdwTn/SyQC8YXFHE5K58jFjC6eYmVFBQIcbJjq5kCQkx1aNyie7N9obm8nPDuP4+lZnEzP6qKGysvJ4W1hymRHe4Ic7bDTH5zF4n8JIomElLJyIgo6KngFhVQ3d138KsnLM6KjLO9vaYGPuRnD/k+bXQcLQ8Tz/xxiiYSU8ooOkin9V/2v4GhlBQU8TU3wt7TAz8ICbzPT/9su895Q29zC8fRMDl9M50x2XhcvpJaqChPtbQlytGOCvfVNM7mLJRJi84s4nJTO0YsZXbp+VZUUmeBowzQ3JyY6DZ6q09zWzum0HI4mpnMqJZvGK9Qr/WHqTHF3INjDgZG2FgMiJo0tbURm5neSzZzyrs0rw1SV8XeQqnB+9hbYGun12y8okQgUV9WRWVJJdmkVWSWXiWZjy9WRL/+GqrJiJwHUHaaGroba5f/WVO83AZRIJNQ2tVDTcIV6eoWqWtvU0msDlLycHBYG2tiZXCajdib62BrroabSf5WnprGZ+NxiwtNyCU/LI6u0q7KvoaKMn4MFY5ysGetsjbWBzoDIUGF1LUcTMzialE5cbnGX+9zNjZk63JFgd0esDXT6/djdQRAEEotKOZKcwZHkdHKrajrvU1KQZ4ydFdPcHJnkYn/TVEaJIJBQVMqJjqpKSlnXJiVbfV2muzgyzcURV2PDIRLaDQRBILOqisj8QiILCjifX0BpQ9dUBAU5OdyNjTqJ6EgLc7SHGpb6hSHi+X8GsUTCxfJyzuXmcz4/n6jCoqtmmasqKuJjZoa/pfSH5Wlq8n/dRdkbqhqbOJqWyeGUdM7l5Hfp3rfW1WGSkx2THO3wsTC7aaHHgiCQUlLOrviLHEhIofwK/5y6shJBznZMdXMkwMFm0Mr8ja1tnErJ5khiGmdSc7qEQxtrDSPYw4FgD0e8rc36bcsQBIG04gpOJmURlpbLhZziLp+7grwcHpYmjO1Q2jysTPpVchYEgezSKqIzCojPKpaSzZIqmbPYFeTlsDLUlaqJpnpYGeqgr6nRpQR+o/2BYomE2saWLmS0pLq+C2HuKfbJVE8LOxM93CyN8XEwx8vWrN9ktKS6vpOEhqflUvMv5dlMV4sxTlaMc7FhvMvAPJSltQ0cS8rgaGI60TmFXRpKnE0NCXZ3YOpwR+yN9Pv92N1BEATSSis6LCnpZJRfJtcK8nKMtbNmnpcrk53tb4on9BIKa+s4kZ7FifQszuXkd8n7tdTRZpqLA9NcnBhhZjxEQmVAEATyamqJ7KgARhQUUFDb1WomB7gYGuJvacFoK0tGWVoMdc73giHi+R+HIAhkVFYRnpfHuTwp2az9V8CyhrIyI83N8LO0wN/CYshc3QeU1jdwNDWDIykZROQVdL3YGRkw1dmB6a6OOBjo39STemldA/sSUtgdf7FL7JGWqgqTXOyZ6ubAODvrQelCB2lJ9HRqDrtjkglNze7iWzTX1SLYQ1oOHW5hMqAO5cySSg7FpXI4Pp3ssq5qmqW+NmOdraWqpoNFv8qfEolAelEFMZkFRGcUEJ1RSHXD1TOmlRQVsDHSvawMmuhJS9aGuv2etX6zIQgClfVNZBVLSeiVSm5V/dWTiRTl5XG1MsLXwQJfBwu87MzQVOt7vqREIpBSVCa1P6TlEZPdNR1BTVmJQDc7pnk5Md7FZkDHZEV9IyHJmRxNTOd8Vn4XD6q9kR5zvF2Z6+02aJ5QgMzyyk4l9GLJZZVRQ0WZ6W6OzPNyY6SV+TV35F8LGlpbOZGezeGUdEIzc2gRXV5AmWppMs3FgakujvhYmA11ePeCS821EQUFROYXXpVHLd+hiI6xsmS0lRUjzW+OZ/9WxhDx/A8iv6a2k2iG5+Vf5dHUUFbG38KcUR2rM1cjo6FZuH1AUW0dR1IzOHQxndiCIq78MbibGDGto4xlq697014jSMvax1Iy2R2fTFhmXicpVlJQYLKLHXM9XRlvb9Pv3MiekFFaya7oJPbEXuzSjWytryMte3o44mZmNCASnltezaG4NA7FpZJRctnbp6yowDhnG8a72jDGyQpLfZ0+P6ZILCG1sIzo9EJiMguIySykrqnrgkxFSYERNmb4OJjjYmGEnYke5vra19ysIwgCtY0tVNY1UlHbSEVtE5V1jbS199MHKgc6w9Qw0NLAQFsDfW119DXVUR6ERUR1QzPZJZVkllQSn1VMdEYBxdVd46zk5eRwtjDE18ECH3tzfBzM0elHMH9TazvRWQWEpeVyIjGTwqrLStIwVWWC3O2Z7uXEGCfrAZH6msZmTlzM4khiOmEZuZ3RWPJycoxztGbBSHeCXO0GtQs8p7KaPfEX2XPhIgXVl9+PmY4W80a4MtfTFVuDm3t+aGprJzQzm0Mp6ZzKyO4yYtdomAZTnB2Y7uLISCvzoetCH1DW0EBkQSHn8/I5l59PVlVXIqokL4+nqSmjrSwZa201VEFkiHj+J1De0EhYXl4n2fx3KUBFUQFfM3Np6LiVJcNNTIZOKH1ERUMjBy6msS8plbjCrl4yL3PTTqXA8ibPWpZIBCJzC9gdf5FDSWk0XXEx8bEyY56nK9PdnQZ1Rnt9SysH41PZEZVEQkFJ5+36w9Q7lCVXnEwGljFYUFnL4fg0DselcbGwrPN2RQV5xjlbM83LmSB3O4b1Y6JLQUUNx+MziEjNJy676CpfprqKEl52ZvjYW+DraI67pXG/SZxYIiG3tJrc0uoOUtlIZV1Tx99GKmubqKhrHNB4zL5CW0MVfS119DsI6aV/+lrqmOhp4mhh2C+l8hKKquqITu9QhDMLyS+vuWobB1N9fB0smDjcDj8nyz5bSwRBIDG/VKpmx6VRWnvZV6eppsJkDwemeznh79j3x7wS9S2tHEvMYGd0EtE5hZ23a6upMtvLhQUj3XE1M+r348qCIAjE5BWxKz6Zg4lpXTryPS1MmOfpxgwPp5vedd7SLuJMVi6HU9MJScvqYrvSU1djuqsTc9yd8bEwGyrH9xEl9fWdok94Xt5Vucyqior4mpszxtqSsVZWuBvf2MlZtwKGiOdtiJb2diILCjmbm8uZnFxSyrtOjlGUl8fT1KRT6vc2M/2/X2H1Bw2trRxNzWRfUgph2XmdGZtywEgrc6a5ODLV2QETrZs/ji27oppdccnsvXCRotrLJzhLXW3meroyz9MVKz2dQXs+iUQgIiufndFJHE1M74xyUZSXZ4KLLQt83QlwthkQOSiprudwvFTZTMy/nOGpIC/HaEcrpnk5M8nDvl/xPDmlVRyNS+d4XAYpBWVd7humpoKPvVln6djFwqhfamZLm4iMwgpS8stIyy8nJb+MjIIKmT7Qf+PfBFFVuZ/NRYJAdX1zp3JaWdvU7XSw7mBhoI2TpSEulkY4WxnibGGEoY5Gv8hFaU0DsZmFHdaEArJKuloftNRVmOhhT7C3I6OdrfpM4iUSgfjcIg7FpXEkPo2KK8r+OuqqTBnhyHQvJ0baWwzogp1bUc2u6GR2xyRTekVjnYupIQt83Znt5dIv5bY3tLSLOJGaya74i5zJyOks/yspyBPoZMc8T1cmONoOagViIGgTiwnPzuNwSjrH0jKpuaLJ1EJbi1nuzsz1cMHR0OAmvsrbC5c8oldWICubutpYdFRVGWNtxXgba8ZbW2Ou/d/mLzBEPG8LSASB1PIKzuTkcCYnl8iCQtr+PTnGyIhx1laMtrZkpLn5UNd5P9EmFhOakcPepBRC0jO7ZOONMDNhrocL010cMdIcPG/YQNHc1s7h5HS2xSQSlXtZvdFUVWGGuxPzvVzxthxchaKwupZd0cnsik7uEivjYKzfebE2GED0UZtIREhCJtvPJ3I+I6+zC1teTg4/ewumeTkxZbgjusP6RgQEQSCjuJJjcekci0sns/hyaV5BXo6RDhZMGG6Hr4MFjmYGfSYutY0tpOaXkZpfTkpeGan5ZeSUVHc7GUVNRQk7Uz0MtYd1Ko362hrXpSR+JSQSgbqmq0v4V6qu+eU1lFTVd7u/rqYazpZGuFga4mxphLOlIVZGun32JlbVNxGTWci5lFxOXMik8grCqKGqzAQPO6Z4OjDWre9NRGKJhJisQg7FpXH0QjrVV+S8GmkPY76fOwv83bHQ73/FQSyREJaex67oJI4nZ3b6TRUV5Jnkas8CX3fGOloPSg7qJVQ0NLI/IZXd8RdJLr68ENLXUGe+lyuLfDywM9AbtOcbKEQSCeHZeexNSuFoakaXcryzkQFzPVyY5eaM2f8BSRpMCIJAemUl5/LyCcuVktGGtq6VF1tdXcbZWDHexoZRlhZoqvS/OnGrY4h43qIobWjgbI5U0Tybm3fVKslEcxjjra0Zb2PNGGsr9NWvfXLM/xskgkBUXiF7Ei9yOCW9S9OVrb4uc91dmO3ugvUgKobXgqSiUrbFJLL3Qkpn6U5eTo4ARxsWeLkR5GQ3aE1CIG0UCknOZGtEAuEZeZ23a6qqMNPTmQW+7nhYDKwjNr24gh3nE9kbfbFLxqaPnTnTPZ0IHuGIgVbfiKwgCFzML+NYXDrH4zPILbsi+F5BnlHOVkzxciRwuH2fCWxNQzPnL+YRnpxLZGo+xZVXD02A7smapZHOLV06u5JEX/qbXVwlk0R72Jgwxt2aMW7WOJob9omIiiUS4rKKOr+TsporIruUFRnvZkuwlyPj3W3RUO3bIlkklhCRkc/huDSOJaRTd0VH/ihHSxaNGs4kD/sB/QZqmlrYH5fCrugkkosuE0IjLQ0WjvRgkZ8HZjqDe11JK61gT/xFdscnd0mZGGltzmIfD6a5Od7UrvhLaGkXEZKeyd6kVEIzsrt0x/tZmTPH3YVpLo433TZwO0IkkXChuIQzHdf6+OLiLlPsFOXl8TI1ZbyNNeNsrP4zNrlbgnjm5OTwzjvvEBISQklJCWZmZtx555288sorKPdRubvdiWdjWxsR+QWczc3jbG4u6RVdA5LVlZQYZWnRcQBaY6+nN+S5GQAEQeBiaTn7klLYl5RKSf3lC6LRMA1pOcndBTeTgTXCDDbqmlvYl5DKtpjELgqJha4Wi32Gs8DLDWOtwVVhC6pq2RaZwI6opM5GITk5GG1vxXxfd6a4O6A6gIt7Y0sbh+JS2XE+kQt5lz2hl5Sr+f5ufW4QEgSB5PxSDsekcSw2naKqruHeY12tmeLlyAQPO7T6UJpvF4m5kFXMueRcwpNzuZhXelUGpoWBdmdZ2tlKWqY20O5fefrfEEskVNc1UV7TSGs305R6gpwc6GqqY6Q7DNUBZG5eiUu2gU5VV4ZtQF9LnVGuVox2s2a0qzUG2r0vDiQSgYTcYo7HZXA0Lp3ibr6rYG8ngkbYo97H3NhLSvmOiETC0y4virTVVZnj68qCUR44mQ6sJJxSXM7OqCT2xV3sjH+Sl5MjwMmGJaOGM8HZdlAXFiKxhND0bLbGJHIqLbtzATBMRZk5I1xY7OOBu5nxoD3ftaC2uYVDKensS0ohIregs8FSSV6eAHsbZrs7M8nRfqiLe4Cob23lXF5+JxHNranpcr+WigpjrCwZay0lotY6A8vBvdm4JYjnoUOH2LJlCytWrMDBwYHExERWr17NqlWr+OSTT/r0GLcj8SysreNYRgbHMjKJKijsspKUA4abGDPexoZxNlZ4m5kNRRxdA2qaW9h5IZktsQlkXTFbXlNFhWkujsz1cMHPyvyWUaryqmr49Ww0u+KTOy/+SgoKBLs6sMTXg1E2loMez3Ihr5j1JyM4lZLVSboMNNVZ6CtVfCz0BtZAVVRVxx+notkZkURzR8lOUV6eie52LBzlwThn6z5/7s1t7eyPvMjmU3FkXFFGV1VSZLy7DVO8nAjoo4pW39zKmYRsTsRmEJaUQ1Nr19F5DuYGjHGzZrSbNR62Jv1uyBEEgbrGFkor6ympqu/4W0dpZT2lVfWUVNZTUdPQ7djJ/kJTXQVjPU2M9TUx0dfCRE+zy/8b6mig2E8PoVgiIaekisjUAsKTcohKK6D5X5+Ri6URgV72TPJ2wN6s9+iwK9Xpo3HpXRqU1JSVmO3vyoqJXtiZ9D1zs6Cyll2RSeyKSOrSlORpbcq9QSMJcrcf0G+lTSTieHIm/5xPICIrv/N2Ux1N7g3wZZHf8AEtwHpCaV0DO2OT2Bab2KUr3sPMmAfGjyTY1eGWOUeV1NWzPzmVvYmpJJdeXhSrKSkyw9WJFT6eQxmh14j8mtrOXo6w3Dzq/pW5bamtzSR7O6Y42DPSwvym5UT3F7cE8ewOH3/8Md9//z1ZWVnd3t/a2krrFV9CXV0dlpaWtzTxFASB5LJyKdlMz+RiedfJEpba2oyztmKsjTVjrCyHxlBeIwRB4EJRCRtjLnAgObXTt6miqECQgx2z3V0IdLAZ1DiVa8XF4jJ+OhPFoaS0TuXD0UifJT4ezPF0HfRyliAIRGUXsv7E+S7l9LEOViwZNYIgV7sBn8zSiiv47UQkB2NTO8mVjaEuC0d5MGeka788oaXV9Ww5Hc/2swmdpXkVJQUmetgz1ceJca42fQo3r6pr4kRcBifiMolIyevSXa6rqcZoV2lJeZSrFYY6fVeSRWIJ6fnlXEgvJD69iIz8Ckoq62jpg4opLyeHvrY6an0sOV+CWCyhsraxz89hoKOBuZE2w+3N8HQ0Y7ijGTp9tB7AZVU4LCmHc8m5pOSXdVGFLY10CPKyJ8jLgRF2pn0ioelFFRyLS+dgdGoXEjrO1YY7Ar0Z42LdZ9IolkgIS81lZ0QiJxKzOhutbI30uC9oJLN8XAactZpTXs3WyAR2RiVR29F0oz9MnXsCfFk2agQagzy3XSIROJ+Tz9boBI5evOw/tdbX4YFxI5nn6XpLnbcyKirZm5jC/uRU8qovj011MzZihe8IZrs7D/UdXCPEEgmJpaWczcnjTG4usYVFXcQqbVUVJtraEuzoQICtzS091vOWJZ6vvvoqhw4dIioqqtv733zzTd56662rbr/ViGe7WExEfgHHMjI5npnZJVpBXk4OX3MzJjvYM8XBHhvdm5vv9l9BY1sbexNT2BRzgYull8m9i5Ehd3ScBIfdYobtqNwC1odGcjojp/O2CY42PDDeDz9r80FXDQRB4Gx6LutPnCcmpwiQqpBzvF25f+JIbA0H3uCQlF/C90fOcSo5u/O20Y5W3DdpJKMdrfr1XhJyivn7RCxH49I6yau5vhYrJnozb7R7n1RIiUQgIiWP7acvcCouq0vnt62pHkFeDgR52eNqZdxnktPQ1EpCZnEn0UzKKrlKDbwEXU01jPW1MNGXqpAmepoYdfy3sZ4m+toaA25gEQSBhqZWyqobKKuSKqslHYpqaWWd9G9Vg8xZ8TameoxwlBLREY7mWBn3vXRXXd9E6IVsTsRlcC45t8uwAGtjXRYGDGf2GLc++WoFQSAqo4CNJ2M5mZDZSWhtjfW4I9CL2X5u/ZqaVFHXyN9nYtl8Np6GjtgsY+1h3DfJj0WjPAbshW5tF7E7JpmfT0VS2KFIaqupsmqcNyvHel2Xme1VjU38dT6OvyPiqO3wtRpqanDvGF+WjRw+6KT3WiAIArGFxWyKucDB5LTOJlgNZWXmDXflDp8ROBkNdcUPBhrb2gjLzeNYRiYhmVldZswrKSgw1sqSKQ4OTHaww2jYzW+KvRK3JPHMyMjA19eXTz75hNWrV3e7za2seDa1tXM6J4cj6RmcyMzqIo+rKSky3tqaKY4OBNnZoTdkyB40pJZVsCk6nt2JKTR2dAqqKCow082ZFT4j8DQzuaXKPoIgcCo9mx9PRxKTJyV/8nJyzPBwYvV4P1xMDK/Lc564mMUPIedJKpRGFikpKLBwpDv3TxyJue7A80iT8kv54cg5TiZLqxRychA8wpH7gvxwt+y7R61dLOZ4XAYbT8ZyIedydqqvgwUrA72ZONyuT+XGqrom9oQlseN0AgUVV6gw1sZM9nEgyMsBG5PeCbYgCBRV1HWSzAvpRWQWVlzlAR2mpsIIR1NGOJjjbmeCqYEWRnrDUL3JfjeJRKCqromyqnoyCys630NOcdVV2+pqqjHc4RIRNcPVpm9Zpk0tbZxNyuFEbAahF7I6bQtKigpM8XFkYcBwfBz7toAqqKhhc2g8O8MTO7NWtdRVWDh2OMsCPDHV6/v5vaGlla3hCfwZGkN5nXSQhpH2MFZP9mfhKPcBq4btYjH741L46WQkORXSZjYNFWXuGOPJXeN80Bs2+M2eja1tbI1O5PfwaEo6YqC01VS4c5Q3d47yuuWae6qbmtlxIZnNMRfIra7pvN3Hwow7fEcwzcVxKOZvkCCWSIgpKuJYeibHMjKv8oZ6mZoy1dGBqU4Ot4TAdV2J54svvsiHH37Y4zYXL17ExcWl8/8LCwuZOHEigYGB/Pzzz31+rpvt8axpbiYkM4sj6RmcycntMpJMX12904cxztoKVaUh4/VgoU0k4uDFdDbFxBNTcJmk2OrpstxnBAtGuKFzHVSIa4FYIuFQUjo/nYkkpWPEnpKCAgu93bh/3MhBzd28BEEQOJOWw1dHwjq7dtWUFFk6agT3BPhidA0NShcLyvjuSDgnk6SEU15Ojlk+LjwYPAobw76f5Goam9lxNoHNp+M7u6CVFBWY4evMHYHeuFj0HvAtkQhEpeWzPTSBE3EZnaX0YWoqzBrlysIJw3E0711xkUgEEjKLOHIulVMxGZRVN1y1jbmhdodaaI6noxm2Zvo3dSxif1HT0ExCehHx6UXEZxRxMauki2oJ0s/fx9mC4FHOBPo6oKXR+2+psaWNQxEpbD+dQEreZe+frakeC8dLVVDtPjxOQ3Mre84ns+lULPkdCwcFeTkmezqyMsibETa9l/MvoU0kYmdEEj8di+j0gRprD2P1FH8W+A+cgIolEo4kpvPjiQjSSqR5yurKSqwa58M9AT7XRQFtE4nZe+EiP52JIqeyuvM5l/oO596xvoPebHitkAgC53Ly2RQTz7HUzM6ubR01VRZ5urPCZwRWujo390X+h3BpTPaxjEyOZWQQX1zS5X5HA32mOjoQ7OiAu9HNaaK9rsSzvLycysrKHrexs7Pr7FwvKioiMDCQ0aNH8/vvvyPfDxP1zSCexfX1HE3P4Gh6BhH5BV1iECy1tQl2dGCqowPeZqa3jCH8v4Ky+gY2xVxgc2wClY3SzmtFeXmmONmzwncEo60tbyl1E6QXv11xF/nlbBS5VTWA9IKxwm8Ed4/xuW4ZoRFZ+Xx1JIzY3KLO51w51uualZnUonK+OxxOSGImICWcM32ceXDKKGyN+l6qr25o5vdjkWwJje9spNLTVGfp+BEsGT8C/T7EKlU3NLM3LIntpxPIL6vpvN3D1oRFASOYOtKp11KtIAik5pZx5HwqxyJSKam8bItRVJDHxdqIEY7mjHAwZYSjOQY6/c8tBalXsqS8jqLyWorKaykuq6W4vE5mqV4W5OXlMNAZhpmRFmZGOpgZamNqpIWWhuqAjv22dhEpuWVSMpohVUWr6i7H/CgqyDN2hC3Bo5wJ8LJDvQ+e1OScErafTuBQZGrn+1NRUmCKrxOLAobjad973qxYIuFMUjZ/n4wlIu1yk4+XnRmPzhqLn5Nl39+jSMT2c4n8HBJJWQcBNdHRlBJQP/cBe0AlEoGTKVn8EHKOpI5JW1qqKtw7YSQrx3pdl3K4WCLh6MUMfjwd2Zl4oaSgwHwvVx4Y54d1P0bI3iiU1jewLS6RLbEJnYkicsAkRztW+XkzxubWO2/f7ihtaOBYeiZH0jM4n5/fxWpkrqVFsKM9wY4OjDS/cc21t0ypvbCwkKCgIHx9ffnrr79Q6GdDw40inpmVVRxJz+BoejoXSkq73OdiaNBBNh1xMRzYqMAh9Iz4whL+jIrlYHJap7HaWHMYK3xGsNjT/ZYIeP83Glvb+Cc6gd/Coimrl5b7tNVUuWu0Nyv9vdDpxySe/iA+r5ivjpzlXKb0Yq2qpMiKMZ7cP8EP3WuYzJJaVM4PR85xLCEDkJbUZ3i5sCZ4FHbGfSec9c2t/BkSzd8nYztLqs4WhtwZ6MM0H6deS7yCIBCdVsD20wmExGZ0+hg1VJWZOcqFRQEjcLLs3a6QXVTJ0fOpHDmfSl7J5QxQDVVlJvo6EOzvhK+rZZ9L5oIgUF7dQHFZLUXldRSV1XT8raW4vJayqvqryvSDCQ01ZcyMtKX/DLUxNbziv420+vU+8kqqCYlK58j5FDILrkgRUFYkwMue4FHOjBlug0ovU5camls5GJHC9tAE0gou+64dzPRZGDCcWaNd0ezD7yCtsJyNJ2M5EJXSqc6OcrLk0dnjGGFr2qf3BVKv5vbzifxyPIKyjhK8ma4Wq6f4M2+k24AJqCAIhCRn8vXRMNJLpZ+XnoYaDwT6s2zUiEHvgr/0nGcycll/OqJzoIS8nBzT3Z14MOD6WHauFSKJhNCMbP6Ojud0Vm7n7Y6G+qwa6cW84a6oDVUGBx21LS2cyMziaHoGoTk5NF8Rl6arpsZkBzumOjoyztrqutogbgniWVhYSGBgINbW1vzxxx9dSKeJiUmfHuN6Es+0igoOpqZxKC29S76mHOBjbkawowPBDg5YD5ULrgvaxWIOp6SzITKuy7x0HwtTVvl5M9XZ4ZaMkahpauGv87H8eT6usxPWWGsY947xZYmvx3VrCrhYVMbXR8M4lSJt7lFUkGeJ33DWBPljeA1luLTiCn44Es7RC5cJ53RPZx6aOgo7477H3zS3trPxVCx/HI+irknqf3axMOLR2WMZ72bTp27ok/GZ/LjvHKn5l0mMm7UxiyYMZ9pI517VuMLyWo6dT+Xw+RQy8i+PnFVRUmC8lz1TRzkzZoRNn0laUVktUUm5RCXlE52UR1VtU4/bqygrYnaJEBpJyeEw9f4dDyKxhLLKeorKOpTT8joqaxp73EdRQR53B1N83S0Z6W6Nu4NJnycoZRZUdBD0FArKLntmNdSUCfJ1JHiUM35uVj02SgmCQGJOCdtDEzgSmdqpcKupKLF0oid3TfVFV7N3Fb6stoFfDkewPSyh004xwcOWR2aN7ZMl4xJa20VsO5fAz8cjOkdzmutpsXqyP3P93AZ8XhFLJBy6kMa3x8LJrawBpGH0D00axQJfj+s2HjMmr5D1pyM5lXa5sW+iky1rAvzwsTK/Ls95rcisqOKvqDh2XkimqV2qimurqrDUezh3+Hr+X4yQvBlobm/nTE4uR9MzCMnMoqbl8iAPDWVlJtvbMcPZiQAb60G3B94SxPP333/n3nvv7fa+vj7lYBJPQRA6yebBtHQyr8h9VJKXZ4y1FVMdHZhsb4/hsIGV24bQOyobm9gSm8DG6HjKGqQXVCV5eWa6O3PXSC+Gm/VtUXKjUd3YzK9h0fwdEUdTR26ltZ4Oq8f7MdfT5brFoGSVVfHtsXAOJaQBUj/cPB83Hp40GjPdgf8mskor+fZwOEfi0wEp4Zzm6cSa4FE4mPS9Q7W1XcS2sxf45UgkVR0XeTsTPR6ZNZZJIxx69UdKJJcJ5yXVTE1FiRn+LiyaMBxXq54bmJpa2th/JpmD4RdJzLy8gFFQkGe0hzXTRrkQ4G2PhlrvBLCiuoHopHyikvOITsqjuLzrZCMFeTmM9bUwNdLG3OiS6qjVSTZ1tdSvS0WkpbWdkoo6CstqOxXWovJL/11HQ1PXHEAVZUU8nc07iaizrVGv5TZBELiYU8qRc1JLwpX+Vx1NNSb7ObEgcAROVj0rbfVNLew/n8KO0AtkFEkX9KrKiiwN9OSu4JHoafVOQAsra/np0Hn2nE/ujB8L9nbkkZljse1D49gltLSL2Bp+gV9CIjtHfZrrabEmeDRzfF0HnDogEkvYE5vMd8fPUVwjtW5Y6Grx8OTRzPYa+OP2hpSScn48Hdklls3fxoLHgsbgb2NxXZ7zWlHX0sL2+GT+jIqloGMsr7ycHFOc7Lnb35uRloOf7jEEKdrFYqIKCjmSnsGR9AxKGy7/pjWUlAjqIKETbG0GRYm+JYjnYOBaiacgCKSUX1I208iqulxyU1JQIMDGmhlOTkx2sENL9dZqVvmvIbmkjA2RsexLSu2M4zDQUGeFzwiW+4y4Zcl+dWMzv4VH89f5y4TTxcSQBwP8mObmeN38M/lVNXx//Dx7Yy8iEQRp6XuEM49OHtOv5p5/o6y2ge+OhLPzfFLnxWuqpyMPBY/GsR9TYdrFYnaHJ/Hj4fOdTUMWBto8PHMM032de/1cJBKBE3EZ/Lj/HOkFHQ0cKkosn+TNnVN8es2iLK2q559jsew8kUBDRxyNnBz4ulgydbQLQb4OaPfyGLUNzcQmFxCVlEd0ch45hV07whUU5PHoUBJ93a3wcDAd9Fnsg4HC0hqik/NkKrPD1FXwdrXA180KX3dL7C17tgxJJALx6YUcOZ/K8cg0auovR7r4u1txxzRfxgzvWcUWBIHTCdms3xvOxY5mJFVlRZZM9OTuqX0joLll1Xx/IJzDMakIQoff2M+Fh2aMxsJAp9f9L6G5rZ2t4Qn8euIyAbU31uOp2QFMcLUdMPFpE4nYGpHIjyfPdyqrtoa6PDZlDFM9nK5bU1puZQ0/n41kV1wy7R3KsL+NBY8HjcHvFiWgYomEkxnZbIiMJTznsqfX1diQu/y8me3uPNQNfx0hEQTiioo5lJbOobS0LhGQ6kpKBNrZMt3ZiUBb2wFPqPq/Jp6XAt0vkc2cKyIflBUUCLC1YYaTI5Md7NG8xXIf/2sQSSQcT8vkj4hYovILO28fbmrM3f7eTHd1umUnN1U3NfNbWFfC6WZqxGOBowlytrtuq/SyugZ+CDnP9sjETsP4ZDd7Hgsei1M/lMh/o6Glld9ORPNnaDTNHeHkkzzseXT62H6NIRRLJByITGH9oXOdcUbGOsN4cPpo5o7uvYx5iXCu33eOjEIp4dRQVWZ5kBcr+0A4U3JK2Xg4mqMRaYg7LrqWxjosnuRF8CgnDHoJiK+obuDw2YscC08lNafrCE05OXCyNmKkhxW+7lZ4Opv3qdmmOwiCQGVNI83NA2gu0hvWq7+yp+fNLqwkOklKRGOT86n/lyKqq6XGWG87Zk5wx8vZokeCJBJLiLqYx57QREIi0zsXK7Zm+qyY5sOMMa49vlZBEDiTmM36vedIzpX65y8R0Lum+vapySy9qIJv94VxMkHa8KYoL8/8Me6snjYKY13NXve/hOa2dv4Ju8BPxyM6Bxb42pnzzJwJDLcaeKWlua2dTefi+flkZKf9xtnUkLVTxzLBeeDEtjcU19bz4+kItsUkdhLQUbaWPBY4+pYloABpZRX8GRXH7oSLnUkxuupqLO8owxvfgp7+/xIEQSC+uIRDaWkcTE2nsO5yZUdNSZGJtrbMcHYi0M62XwMC/u+IpyAIJJaWcjBVyubzai57lZQVFJhoa8MMZyeC7O2GyOYNQF1LC1vjEvkrKp7CWulBrSgvzzQXR+72977lsjevRHVTM7+HxfDn+dhOwulqYshjQWOYdB0JZ21zC7+ciuTvsLhOj9w4R2seDx7LcMuBXxTbRWK2nktg/dFzVDVIlStPa1OemROAt23f/WGCIHAsLp3vD4STVSJVBvU01bl/qh+Lx43oNbxbIhEIiU3nx33nOkuwGqrKrJjkzcopPj1G8UgkAmcvZLPxUBTRKQWdt/s4W3DHdF/Ge9r1SJ5a20ScjsnkYGgS5+JzOskTgI25HiPdpUTT29WiV5X0SgiCQFVNEwXF1eQXV1Nw6V9JDYUlNTS39I90XoKcHBgbaGFuooOlqS4Wnf90MDPW7pfqKpZISMsp6ySi8akFtLRebj4wM9RmRoAbMwLcMDfW6fGxispr2XIslj2nLmdx6mqqsXiyF4sneaLbg4opCAJnE3NYvy+cpJwOAqqkyOKJI7hr6sg+zYdPzC3h231hhKdIG1eUFRVYGuDJfcF+6PXBQ3oJdc0t/HI8kr9Px3ZOPpvm6cTameOw7IeS+m80tLSy4Wwsf5yOpqFV+vl4W5vx1PTx+NpcPy9mUU0dP52J7EJAR9ta8ljQaEZa37oEtKa5ha1xCfwVFU9xhwKnKC/PDFcn7vLzxtP81rRd/ZcgCAIJJaWdJDS/9jJ3UlFU6CShQfZ2vU5N+r8gnoIgkFRaxr6UFA6lpVNQe5m1qygqEHiJtffhAxvC4CC3qobfI2K6GMp11FRZ7j2CO3w9MbnFsuiuRF1zC7+cjb7hhLOlXcSfZ2P49VQUdS1SZcrb2ownp41jpO3ALxqCIHD0QjpfHjhLXkUNIB1t+eSs8UzysO/X+4nJKOCzXadJzJVmx2mpq3DP5JGsmOjdpzij4zFdCecwVWVWTPbmjsk9E862dhH7ziSz6XA0uR2d6Qryckzxd2bFNB/cbHu+MCVnlrDvZCLHwlO6qH7DncyYEeBGgI89Brp9Oybzi6pJSCm8TDBLaigoru6RXMrLy6Gm2r+ylUgkobWHcZny8nIYG2heJqMmOthZGeDpZtEnQtouEnMhtZAjYSkcO5dKU3Nb531eLhbMnOBG8BgXVHv4XhuaWtl9KoHNR2MprZISBmVFBWaOc2PljJFYm8i2glwioD/uO0dijvR4UlVSZNGEEdw73a9PJfjojAK+3RdGTKa0iqKhqsw9k0eyapIvqv1Qikuq6/nmcBh7opIRBCnpWTp2BA8Fj+7TZCZZqGlq4dfQSP46e5nYTnS25cnp46+patEbimrq+PF0JNtjuxLQJyaNuWWbkOByZez3iBii84s6b/c2N+Vuf2+muVw/S9MQLuMSpzqYlsbB1KsFvAkdAl6wg0O35fj/NPGsampmz8WLbEtIJKX8cueqmpIigXZ2THdy7LdEPIRrQ3FdPd+cPseO+KTO3FMnQ33u8vNmrofrdYkbGSwIgsCeCxf56PDpzuxQFxNDHgsczWSX/hG0/iIqu4DXtx/t7JB1MjFg7dRxTHS5tvJceV0Db2893jltSF9TnUemjmbBKI9+dfQ2trTxxe7TbD1zAQA1ZSVWTfJh1STfPo21zC6pYt1fx4hJlxKES4Rz5WSfHkPLxRIJh8NTWL8jjOJK6YJymJoK8wOHs2yKN8b6ssurIrGEk5HpbDkYTWL65WYjY33NTmXPyrT3BhWJRCAls4TTERmcjkgnp+DqiUBwiQhqYWGqg4WpbodCqYOFiS6mRtooKfXPSiIIAtW1TVJiW3RZSS0sqSG/B6KrrqbMaG9bxvs7MMbHFs0+hLm3tLZzKiqDg6FJRCTmdtoOtIapMm/SCBYHe2HU02ctEhMSlc7fh6O5mC1VMRXk5ZgT4MED80dj1EMZXBAEwpJyWL/vHInZUgI6TE2FJxaMY2HAiF79kYIgEJ6Sy9d7z3IxX+ohtTbS5c2VwXjb9Y9kpRaV88X+M5xJyQFAb5gary6aTPAIx349zr9RWtvADyHn2B6ViFgioCAvx/0T/Hho0qgBj/fsC7ojoPM8XXluagAGt6iX/hISi0v5IzKWA0mpndF6job6rJ0wlmDn63s+HsJlXLIsHuogoVdaFjWUlZnl4sSS4R54mV4e+PCfI55V1dVcqKpma0IixzMyOw9IZQUFgh0dmDmInVlD6DsqGhpZHxbJxpgLtHc0DAXY2XD/aN/bIjQ4o6ySN/cd78zJszPQ4+kp45jkbH9dp9U0tLTy2aEzbDkvJXRGWho8PT2AmZ69N+X0BEEQ2B+Twvs7T1DX3Iqigjz3T/LjvqCRqPcz5ul8ah5vbjxKcZWU+C0c68Ejs8Zi0AdPXrtYzJ9Hovlx3znaRGJUlRRZNdW3V8IJEJGUyxebT3XGIRnqaLBqph9zAjx67Eyva2hhz4kEth2JpbQjJF5JUYFJo5yYNdEdXzerXr/TtnYRMYn5nInI4HRkBpXVl2OMFBTkGe5iho2FPpamupib6GJpqoNpP0vf14IrS/sFJdUUFEvJaEJK4VWv1dvdkgB/B8b72WNs0Ls/vqyynkNnk9kTkkBhR6ySgoI8k0Y5sWy6D+4OsvM0BUEgPr2IP/ZHcDZeGvmjoqzIiqk+3DtnVI+quJSA5vLtrrOkdBBIH0dzXl8VjJVx7010EonA4ZhUPtsVSnltI3JycGegD4/MHotaP5skzqfn8cGuk2SUSJX5GV7OvLwwCJ1ryMcFyK2o5rNDZziWJI0sszXU5e2Fwfhcx/I7QGFNHT+cOs+22EQEATRVVXhq8jiWjRx+yyuIl4aJ/BkV11kJGm5qzFOB4xhna3XLX1v+S7icCJTOnosXuyih9vp6LPHwYL67K8pi8X+LePp/+gWV4stj3zyMjVk83J05ri5oD3Wj33DUNrfwy7lo/oiM6Qyr9bey4KnAsfha3rolnUtobG3ju1Pn+CM8FpFEgqqSIo9MHMU9Y3yvWxbfJYSmZPPWrmOUdExYWeI/nGdmBKCpem3e44q6Rt7edpwTSdIGDDcLI95dPq1fneogVTk/3xXKtrMJAJjpafHWyql9niSTml/GW38c7SQRY91teHnlZMz0ez4RVdQ08sXmkxw5lwpI1a+7Z/uxbIp3j2Xfqtom/th1jj0nEzq9i7paaiyc4sWCKZ7o9zKJqKGxlfCYLE5HZHAuNrtL6fmSihjg78DoPqqINwNd1dkMcgq6TpZztjcmwN+BAD8H7Kx67mgXSyScjcliy6EYYpIvdx97OJrywKKxjBph0+NriUsr5Nutp4lPl5ZMTfQ1ee7OSQR42/e4n1gi4Z+T8Xy98wwtbSJUlBR4eO5YVk7x6RNJqmtq4ZMdp9hzPhkAK0Md3rpzar/VzzaRiB+OnOeXkEgkgoC+pjqvLZrM5OEO/Xqc7nAsKYN3dh+nor4JOTlYMdqLJ6eNu27Zv5dwoaCEN/cd75yE5G5mxJuzJzP8NvBQXrrWbIiM7bRv3U7Xmv8aBEEgsqCQbQmJHExL67z+K8rLM9bEmN/uvOO/Qzxt316Hvq4O89xcWeThgavRrTe14f8BTW3t/BEZyy/norqsQp8OHMfY22AVKggCh5PT+eDQKUrqpMRvkrMdr8wMwlzn+gYaVzc288G+k+yLSwHAUk+btxYGM8q+76MBZeFIfBpvbztObVMLigryPBQ8mvsmjex3UPa5lFze2niU4mqpYrgswJO188b3SS1taxfx0/7z/HE4CpFEgpa6Cs8uDWTWaNdeo3t2nUrgm39O09DcirycHIsne7J6/pgeG31a20RsORTNH7sjOsmig5Uhy6b7EDzWpcdO68amVo6EXiQ0Ip3YpHxEosvj5vR1NQjwc2C8vwM+HpYDVjPr61vIya+gsamt942vgIK8HKYmUiVVYYB5kHlFVR2qbSaJqYVdOvfNjLUJ8HNg6kQ3nO16zkhNyyljy6EYjoaldE6QGu1pw+MrJ2JnIXtBIwgCp2Iy+XzjyU6rRJCvA0+vDMJYr+cu9MKKWt758ygRKVLS625jzBt3TcXBvG8LqNNJ2byz+RhlNQ3IycHKQB8eHYD6mZhXwqubD5NZKrVYzPZ14eUFk/pkMekJtc0tfHrgNNujEgEw1dHkzQVTGO9kc02P2xvEEgmbIi/wZUgY9S2tyMnBUt/hPDV5/HWbsjaYqGxsYn1YBBujL3TG8U20t+WpwLG4mfR9uMAQBg/1ra3sT0llW0ISccXFSFpayH79lf8O8dwaFc0czxFDOV83CW1iMf/EJvDdmfNUdPggnQz1eXLiOCY7Xb/Gm8FETmU17+w/wdlMaUesha4Wr8wIIsjZ7ro+ryAIHEpI4709J6hqbEZeTo67xvvw2JQx/b4Y/hv1za18sOsEe6IuAuBiZsi7K6bhbNa/hVlDcyuf7z7N9g6V01xfizfv6LvKeSGrmLc3HCGrWHqRnuzjwAvLJ/XaqZyeX84HfxwjIUPqxXSxNuLFe6b02DQkCAJHw1P4fvMZSiqkpMbZxohHVgTg52Hd47FYWd3I1v3R7Doc3yV03cZCjwB/R8b7OeDqYNIvm0VzSxs5eZVk51aQnVve8beCisqG3nfuASoqithY6mNrbYittQE21gbYWRtgaKDZr99bVU0jYVFZhEakE3Uhl7b2y5Uj3+FW3LlgFCNH9LxorKxp5M+9EWw/EodILEFeTo65k4azevFY9Hr4jptb2/l5dzgbD0Ujlgioqyrx0MJxLJni1aOKKQgCu88m8dm2UBo6LCMPzBzFvdP9+jT2sq6phU93hrL7XBLQoX6unIq3ff8UstZ2Ed8dDuf3k9FIBAFTXU3WrZiOn/21d4qHZ+Tyxo5jFFZLj+G53q68MGviNZf1e0NFQyMfHznN7njpOUNXXY1ng8ezwMv9utqLBgvFdfV8e/oc26/oJ5jt7szaCWOx1tO5uS/u/xjpFZVsi47m5enT/jvE83rPah9C9xBLJOxLSuXL0LDOqROWOtqsnTiGWW7X5ke8UWhpF/Hj6Qh+OhNFu1iMkoICq8eP5MEA/+ve9FRe38jbu44Tkiwtfzsa6/P2oqmMuIZ4pEuIzirg5Y2HKaquQ15Ojvsn+/Fw8Oh+z6MOT8nl7StUzuUTvHhi7rg+qZzNbe18tyuMjSExCII0XunFFUFM8XXqeb/Wdn7aFc6mw10JyeLJXj1OfYlPLeSrv06SnCltRjHSG8ZDywKYNs61x4tmQXE1m/ZEcfBEYifxsrHQY2aQB+P9HbAy673ZSCSWkJNbQXaelFhm50hJZnFprcx9jAw10elDl/aVaBeJKSyq7kIQr4SGujK21obYWOlja2OInbUB9rZGaGv1TliamtuIjM8hJCyVk+FpiCUdjYB2xqyc70fgaKceVdb8kmq+23Sak5HSaVfqqkqsmjuK5TN9ehxDmpFfzvv9XGAAlFU38N7G44RekDbJOZgb8OZdwbjZ9O33cyYpm7evUD/vmOjNY3PG9XvBF5ddxEubDlFQWYucHNwz0ZfHZoy95mllTW3tfHXkLH+FxSIIoD9MnTfmT2ay+7WX9XtDZE4Bb+8PIb1MasvwtjTljdmTb8kZ8N0ht6qGL0PD2JckteYoysuz2NOdRwNGD+WA3iT855qLhojnjYUgCISkZ/H5ybOklUtPTIYa6jwaMJrFXh63bOj7vxGSmsm6Aycp7CDN4x2seXVmEDb6A5/80xcIgsDBC2m8uzuE2mZp+XtN0CgemOh3zR7SdpGYbw6F8dvJKARBOgLw/Tum9yuTE2SonCun4ufYN5UzMjWfdzYc7QyRnzXalWeWTOw1AP50XBaf/BnSrxJsQWkN3206zYkI6dhQNRUl7prnz/IZvj36P1MyS/h7ZwSnzqcj6SBZHs5mrJzvz7iRvTeQicUSEpILOB6aQujZNGpkzGrX09HoVCVtO/7ZWBkwTGNgZVmxWEJRSQ3ZOZeIrpTk5hdWd4bmXwl5eTm8hlsSFODChLFO6Gj3TnaLy2rZsjeKvccSOuObLEx0WD7PjxmB7j1aFeJSCvjqr1NczJIuAIz1NXlo2XimjpW9ALhkqfh262nqmy5bKh5aNI5hPZSvBUHgcGQqH205SU2DtGKwKtiXNXPG9Ck6qa6phc92hrKrQ/20NNTh7QGon40tbXy0+xQ7IqQlcmczQ96/Y3q/PdTdIT6vmNe2HyGzrKOs7+XCS3OCrnsJvF0sZsO5WL49eY6mtnbk5eS4c5QXjweNuWbP+Y1CckkZn58M41RmR1ObogJ3+XmzeowfOmq3voXgv4Qh4jmEASMit4BPT5whtlCqTmipqrB6zEhWjfQe8CitG42C6lreO3iSkFSpUmKiNYyXZgQy1dXhutsCKhuaeGf3cY4mSjtYXc2MWLd4Ks6m164kZJRU8NLfh0gpks40X+DvzgvzAtHo53Sd2KxCXvnjEEUdHev9UTnrm1v5cvtpdpyWElZj3WG8snIK44fb9rhfWXU9n/51ghPR0s/FRF+T51ZNJsBLttWhrqGF33edY+vh2M4S75xAD1YvGSezaUgQBKIT8vhrZwRRF3I7bx/jY8udC0YxwrXn2dCCIJCcWkxI6EVOnE6hsupyt7iGujJ2tkbYdpS9baykJLMvRG8w0N4uJr+wqrOcn50nVV4Li2s6t1FQkMfXy5rJE1wZP8axV/JbU9fE9gOxbD8YS12DdOqOno46S2b5smCal8z9JRKBo2EpfL/ldGeKgKudMU/cGYiXi+xSdGVtI19sOsXhc1Kvs6GOBk+vDGLSSMcev5fq+iY+2nKSw5FShcvKSIfX7wrGx7FvZe9/q5/3TvHj4Zlj+l0hOJ6QwVtbj1Hd2IyyogJrZ47nzgDvay5Tt7aL+O74OX4NjUIiCBhoqvPWgmACXa+vFQigpLaeDw6f4lCSVMk2HKbO89MmMnu4821howKIzCvg0xNniSmQNrVpqqjwwGhf7vL3HopWvEEYIp5D6DeSikv57ORZTmdJL9aqiorc5e/N6tEj0b5NVo5tIhG/no3mh9MRtLSLUJSX554xPjw8cdR17xwFOJyQxju7Q6hubEZRXp41k0axOtCv300+/4ZEIrDxTCyf7z9Dm0iMjroqby4N7nenbbtYzE+HzvPz4QgkgiDtWL+z7yrnmYRs3v3rWOds9sUTRvDEwvE9KlZiiYRtx+P4YXsYjS1tKMjLsWKaL6vnj5EZs9MuErPjaDy/7gzvJEOjRtjw2B0TcLDqnsCLxRJCI9L5a2cEqZmXMyWnjHfljvl+2FvLJv6CIJCeWUpIaAonTqdQUnZ5GMUwDRUCxjoxeYIL3p7WPVoBbhaKS2o4cTqV46EXycgq67xdSVGBUSNtmTTBlbGj7FHrYYHS1NzGvuMJbN4bRVmFlEhqqCszb6onS2f7ygzab2lrZ8vBGDbsudzkNdHPgUeXT8DSVHZl4XxiLh/9eZz80hoAxnna8tydkzAz1O7xvZ6Kz2Td38epqJUuCJYGevLEgvF9Gm1a39zKJztOdXo/3SyNeO/uGdgY9261uBIVdY28/s9RTl+UqmyjHC15d/k0THT6Pr5TFi7kFfPytsNkl0sHJszzcePF2RPRugHn4DMZubxzIKQzV3iUrSWvzQzCwUj/uj/3YEAQBE5mZPPZybOklknj2PQ11Hlk3CiW+Qy/bSp1tyuGiOcQ+ozsymq+OBXGwYvSMqaivDxLvYfzyDh/jG4jr0xYZi5v7z9BTqX0hO1vY8HrsybdkJNmdWMz6/aEcPCC9DN0MjHgvSXTcDW79m7L0toGXtt8mPC0PADGu9jw9rJgDPs5BSqvvIZXNhwkoWNazGw/V15YEtSnLt3m1nY+23aK7aFSldPCUJvXVwUz0rlnwpqeX847vxwhpWNEooe9KS/ePQUnGeQRICw2iy/+PEl+x6QiOwt9Hls5kTGe3SuqYrGEgyeT+GtnBAXF0n1UlBWZM2U4y+aMxNRINpHJzq0gJPQiIaEpFBRVd96upqrEuNEOTJrgip+PzYA625ub28jKLqepsbX3ja+AvII85ua6GBtpDUhtyiuoJCQ0hZDQFHLzL8cqqagoMtbfnkkBrowaaYuKDNIvEok5eiaFjbsiyO7YX0lRgemB7ty9eDQmht2fh6tqG/lpWxh7QhKQCAKKCvIsnurNA4vHysxfbWlr5/e9EWw4EIlILEFVWZE1C8ayfFrPEUr1TS18vu00u85Ky96WRjq8e990htvKzhq9Esfi0nl701HqmlpRVVbk2YUTWTR2eL8+b0EQ2Bp+gY/3hNLSLkJTTYXXFk1mhrdznx9DFlraRXxzNIzfz0QjCGCsNYy3Fk4hwLnnqsJg4NLi/fvQ87SKxJ2L90cCR982FS+JIHAgOZUvToWRVy21Alloa/H4hDHM9XC5LXoTbkcMEc8h9Ip/TxuSA+Z4uPDEhDFY6erc7JfXZ5TWNfDh4VMcSJSSPoNh6rxwA8tEx5MyeGvXcSobmlCQl+OBif48NGnUoOSBHopL5Z1tx6lrbkVVSZFn5kxg2dgR/b5A7j6fzIdbT9Dc1s4wNRVeXTaZ6b59u0Am55Twyq+HyC2VErMVk7x5bEHPDRpiiYS/Dkbx485w2kVihqmp8OiS8SwIlD2RprquiS82nOBImLQEq6ulzoNLxjE70EOmyhiXlM+Xv50gPVuq8mkNU2XRDG8WzvBGV0b5u71dzJGQJLbujiI79/LkM2VlRcb42TNpggujR9qh2sdRl4IgUFFRT2ZWGRkZZWRklpKZWUZRcTXXcmYdNkwFezsj7O2NcbA3wt7eCGsrA5T7OBJSEASycisIOSW1DFxZjldXU2byRFfuWDwKM1OdbveXSATCorP4e1cECSnSAQsqyoqsXODPynl+MolrVkEF3/wdSnhHkLyxviYv3B/MGC/ZpCm7qJIP/jhGbKr0ebyczHnjgWmYG3X/2i7hXHIub204Qml1AwrycqyeNZr7Zvj3SZUurWng9T8PcT5NGtsUONye11dM6dfMd4Cc8mpe+vsgifnSxdVMbxdeWRQ0KAplbG4Rr2w93KlALhrpwXOzJtwQ/2VBdS3rDp7kRIddyVRbk5dnBDLlOk9zG0y0i8Vsi0vk2zPnKWuQKuSOhvo8OXEsU5xun/dxu2CIeA5BJqoam/ghLJKN0fGdeWiTHO14KnAczkbXb47wYEMklvDX+Ti+OhHWaYy/w9+TtZPG3pATc01TC+/vPdGZy2lvpMd7S6bhYXHtHev1za28tzOEfdHSx3a3NOb9O6Zja9S/kmBtYwvvbD7KsTipr9LXwYJ3V03DVK/335JYIuH3Q1Gs3xuOSCLBUEeDt++ZxihX6x73Kyir4a2fDnUGiAd42fHSPcEY9ODJPBqewud/nKCmXto8smyGD/cvkq2UlZTX8d2GU4SEdYTNa6hw9+LRzAv2RF3GPq1tIg4cucDGbecpK5eWkhUV5fH3sWXSRFfG+dujrt7zcSMSicnLryQjo4zMrDIyM0vJyCyjrq652+319Yahq9e/EYXtbSIKi6q7ZItegoKCPNbW+jjYG3eQUikx7a2jXRAEUjNKOBGaQsjplM73ryAvx+RAN+5cOhprS9mVgfiLBfy08QxxyQUAGBto8ujdgQSNcZJ58T4Xn8PHvx6jqFyqOE0f78aTqwLR1uz+tQqCwO5TCXyx+RRNLe2oqSjx1IqJzJvYsxJZ19jC+5tCOr2fI+xMeee+6Vga6vT4mYCUXP99Moav9p6lXSTGQEudt++cxlhXm173vRLtYjE/Hj3PT8cjEEsETHQ0WbdiGv4O157R29zWzpdXdL6baGvyzqJgxjr2/DscLPy7QXOioy2vzgzE8jaKLmpub+evqDh+DIuktiN/eoSZSWf+9BAGB0PEcwhXoV0s5pdz0awPi6Cx7fIEiKeDxuFjYXaTX13/kFZawfM7DpFSIm2y8bQw4Y3Zk3EzvTFBwucz83l+ywEq6puQl5PjvgkjeXTK6GuOVwFpcPXTG/ZRXF2PvJwcD0z256Gpo/rtE72QXcyzv+6jrKYBRXl5Hpk1hrunjOxTmamqronn1u8jNkOqQE3xceSVO6eg3cvknoNhyXzwx3GaW9vRUFXm6ZWBzB7vLpM41DY0s279YU5HS+Om7C0NePnBabjZd0/exWIJm/dG8euWMFrbRMjLyzF3yggeWDFOZmyRWCxh3+F4ft8YRlXHWEk9XQ2WLfBj1tQRaGr2/J7EYgnh5zLYtz+O2Lhc2ruJOZKXl8PKqoMQ2ht1EkMdnYE1HbW3i8nLqyAj87KCmplVRn19S7fbm5npEDzFg1kzPDEw6NlnKJEIxCXksWlbBBExUlVSTg6CAlxYc+9ETGRYEwRBICQslW83nOr0gPp4WPLiI9MwM9bpdp/mlnZ+2naWLQdjkAgCulpqvLJmOuO8ZTfMFJbV8NbPh4lLkx574z3teHvNDIb1sCgQBIGDESl8sDGEhpY21FWUeOPuqQT3Eut1CakF5bz0xwGySqRd5XdN8mXtvPH9LsnG5xbz0t8Hye+IXbovyI8nZowblHzMqOwCXt12hPwqKZFfPtqTF2ZNvO6T1kBKftefjuCXs1G0iyWoKCrwRNBY7h3re1tkf15CXYt0CtLvEZcn7o21teL1aUHY6fdvUT+EqzFEPIfQBReKSnhl/9FOw7W7iRFPB45jvF3Pgdu3IrbHJvHO/hBa2kVoq6nwzJQAFvt43LAT4D8RF1i3+wQiiQQ7Qz3WLZ7KCKu+ect6w/GEDF78+yAt7SIs9LV5f8V0vGz7vyg4mZDJi78doKVdhI2RLu/dPQM3q56n1FxCXlkNj321g4LyWtRVlHhhRRCzR7v12gn+065wft59DgAfZwtef2Baj40imfkVvPDZbgpLa1BSVOCe+aNYNddfZpdxaUUd73x1gLgkqerm5WbB2vsm4Wgre7ERn5jPVz8cJ6OjFG9kqMkdi0cxM3i4zFLxJVRVNbD/YDz79sdR3qEQAqirK3eqjQ72xjjYG2Nj0/cS+EAhCAJl5XVStbVDac3MKqP4ihK6vLwc48c5MW+uD16evU8Su5hWzF9bwjlzTqqIKysrsnLJKJYv9JdpNWhpbefvXRH8vSuStjYRGurKPLN6ClMnuMl8nqSMYt778TBZBZXIycGDS8Zz9zx/ma9PLJGw6XAM32+XKpF25vp8/tQCTHuZOV9UWcdrvx7qXDA9vXgCdwb79rhP5/tqE/HlntNsOhUHwBQvB9bdNQOVfvp7m1rb+GjPKbafk/pPpwx34L07pl/zsAiQ5n5+fugMG8Olr9HPzoIv7ph93UPnLyGroop39ocQniW1J0x0tOWDhdPQVb8xzz9YKG9o5IezEWyOuUC7RIKyggKPBozmgdG+19wI+v+MIeI5BEB6ovriVBgbImORCAI6aqq8NGUi84a7In+bEc6mtnbe3h/CrjjpLObxDtZ8uGA6+sNuTJSNSCzh4wOh/BUWC8BMT2feWTR1UELoBUHgr9OxfLznFIIgbSD6eNVMhg3AMrD9bALrthxHIgiMc7Ph4/tm9SkmCSAxu4S13+6iur4ZM30tvnliATYmPSsBbe0i3v31CIfCpbaAe2b789DCnlWekxHpvP39QZpb2zE11OKDp+bhZCObQJ4IT+WjH45S39CCmqoSa++bxKxJHjKJS1l5Hd//epKQUOlrGqahwn13jmfuDC+UlGRfWARB4EJCPnv2xhJ6OrUzM1NbW42ZMzyZOsUDS0v9W0rlaWhs4fz5LPbsiyEhoaDzdmsrfebM8WZqsAfDelGq0zNL+eanEOISpITC2FCLRx4IYuI42aX0otIa3vnqYKf/c+oEV55+YIrM+KV2kZgvNpxgx7F4AIL8nXj1oWk9dqOn5JTy9Be7qKhpRE9Lnc+enI+bXc9WFrFEwmdbT7EpJA6AOyZ78/TiiX3+zg5Hp/LqX4dpF4nxsTfniwfnojWAPM0DMSm8uvkI7WIxw61M+Oq+uRho9s92IQunUrJ4bvNBGlvbsNTT5tu752F/gzrPBUFga3Qi6w6eoFUkxkRrGJ8tmYWP1e1VNQPIq67hzUMhnOlIcnE2MmDdrGBGmN36M+xvRQwRzyFwJiuX1w8co6C2YySbhwsvT5mInsaNIWqDifSyCp78Zz+Z5VXIy8nxxKSxPDje74YRgNrmFp7duJ+wDGln+WNTxvDQpFGDohaLJRI+3HWKTWfjAFg6ZgQvLQjqd2yPIAj8cPAc6w9KVcf5o915ZfnkPq/gQy9k8eJP+2lpE+FiZcRXj83vdeRlTUMzz3+1h7i0QhQU5HnxrsnMmzhc5vYSicBP28L4fZf0Nfq6W7HuidkyfX9NzW189dsJ9h2XdtO7Opjw+tpZWJp1H9PT2trO5h2R/L31HK2tIuTkYM4ML+6/c3yPWZuNja0cO57E7r0x5ORcbjhydzNn3lwfJgQ4X3dFczCQlV3Gnr2xHD2WRHNHtJGqqhJTJrszb4439vayVW9BEDh5JpXvfjnR6QH1Gm7JEw9Nwd6m+xQCkVjChu3n+H1rOBKJgKmRFq+vncVwF9nh7LtDLvDJb8cRiSXYWxrw4dPzMJdRqgcoraznqc93klFQgYqyIm+vmUGQr2OPn4MgCPx5NJovtp8GINjXkbfvnd5n9TIyLZ+nftpDQ0sbdiZ6fPfIQkx0+x+VFJ1VwNrf9lLb1IK5nhbfPTAfO+PBIYjpJRU8umE3hdV1DFNR5pMVM29I1/slpJaUs/af/eRUVqMgL8fTk8ffdqV3kB4rexJTWHf0JDXNLdKRxn7ePDlx7G3TxX+rYIh4/h+juqmZ94+dYleCdBavmZYmb82YzESHG3dSGkzsjEvi7X0hNLeLMNTU4LPFM/GzufZZyX1FTnk1j27YTU5FNWpKiry3dDpTPXq+8PUVTa1tPP/XAU4lS712z8wO4O5A334TWpFYwrotx9kZLi3vPTh9FA/PHNPnx9lxOoH3/paqpGPcrPlozexeQ+nzS6t58rOd5JfWoKGmzIePzcHfXXbDQ0NTK29+e4CzsdIu2eUzfHn0jgkyCXZKRglvfrGfguJq5OTgzgWjuH/ZWBS7KcULgsDpsHS+/eUEJR0jLEe4W/DEmsk49kC2srPL2b03pluiNne2Nw4OfbMnXIJEIpCfW0FSYgEXEwtJSS6ivr77xiNZkJeXx8bOEDcPc9zcLXB2M0OjnxOQLhHpXXtiyL2ic9/D3YJ5c70JGC+bSLe0tLNp+3k2bougrcNHO2+mF/fdOR4tGQuEhJRC3v5yP8VldSjIy3H3kjHctWi0zO/2QlohL3+xl8qaRjQ1VHn3iVn4D7eR+X4amlt55bv9hCfkICcHTyybyB3TfHo9vg9GpPDG74cRiSX4OJrz2cNz0epF/b2E9KIKHvluB+W1jRjpDOPbhxfgaNb/5suc8moe+Wkn+ZW1aKqp8OU9c/AbhKYjgKqGJp78ex/ROYXIy8nxzIwA7h7f++cyWGhobeONvcfYnyBt7Ap0suWDBdOv+8Sl64GqxibeO3aKPYnSKomFthZvzZhMgL3NzX1htxGGiOf/IQRBYH9yKu8eOUlVUzNyIF25BY69LSc3NLe1886BE+yIlYY9j7W34uOFM25YaR3gbFouz27aT11LKybamnxz19xByeYEKKtt4LFfdnOxsAwVRQXeu2M6Uz371gxxJZrb2nnht/2EJmYjLyfHy0snsXj8iD7tKwgCP+wN56f95wGYM8aNV1dN6VUljUsr5LmvdlPb0IKpvhafPT0fe3PZF+Xcoiqe/3QXecXVKCsp8OIDU5kR0L0nUCIR2Lg7gp82nUUslmCkr8mrT8zAx6P77tOKyno++OIgkTE5ABgaaPLwfYFMmuAi8wKcmFTAz7+c4kJHaRnAylKfuXP7Vpq+hMbGVlKSC0lOLCQ5oYCU5CIaGrpvABoo5OXlsLE1xNXDHHcPC1w9LDC30O0TubhkHdi9J4bTZ9I6rQM6OuosnD+SpUv8ZRLQ4tJavv/lJKfOSkmFlqYqTz4czOSJrt1u39DYyuc/H+dwqNQKM9zFnNfXzpSZo1pWVc9Ln+8hObMEeTk5Hr1jAitmyl50icQSPv4zhJ0nLwCweJInT6/svTIQkZLHs9/vpaGlDVtTPb55YkGfUh0AiqvqePT7nWSVVDFMTYUvVs9lZB8nJV2JqoYm1v62h7icYhQV5Hl7aTBzRsr2xPYHbSIx7+4JYXukdNE539eNN+ZPHpRGx75AEAT+iU5g3cGTtInEmGlr8tmSWXhZDo7v/UbjVEY2bxw8TlGdVPWfN9yVl6dMvO18rDcDQ8Tz/wwldfW8cfA4JzKkypmToT7rZgXjaX57/vgzyyt58p/9pJdVIi8nx+NBY1gT4H/DyjiCIPB3WBwf7j+FRBDwsjbly5VzBs2jlVZcwaM/76Kkph69YWp8dd88PK37/11VNzSzdv1uLuQUo6KkwAf3zCJohH2f9m0Xi1n313H2hEmJ/epZo3hoTu8q6eFzKbz9s9QD52ZrzCdr58uMSgI4E5PJm98eoLG5DSO9YXzw9DxcZfj06htbeOOzfUTE5QAQOMaJ59cEy1TaIqKzWffpfmpqm1BWUmD5In/uWDJK5oSeurpmfvrlJPsPSH2G/WnGEQSBgvwqkjvUzKTEAnKzy6/K6lRVVcLJxRS34Ra4uZtjaNy/81Zri4j01GIpmU0soLSk9qpttHXUcXUz63gOC5xcTVGTESN1CZWVDRw4GM/e/XFUdHSlW1jo8eQTU/HxtpG5X0x8Ll+tP96ZeTpnuiePPzhJZnPWkdMX+fTHozQ2tTFMXYU3nprFGJ/uu9hb20R8/Nsx9p+SHoPTxrny4upgVGWUOAVBYOOhaL76JxRBgLEjbFj38GyZsVuXkFZQzhNf76KspgFDHQ2+fmwBTpZ9G2Fb29jCkz/uJjarCCVFBd67azrB3v1fILa2i3hl02EOx0vzhh+ZOpqHpo4eFHXyep+v+oKLxWU8+c9+cqtqUJSX55ng8dwz5sapr4OJxrY2vjgp7Y0QAD11NV6fFsQMV9me5yEMEc//G0gEgX9iE/jw+Gka29pQUlDgkXH+rB7rd9uOB9sVl8xb+45LS+vD1Plk8UxG2Q5OaaovaBOJWbcnhG3XSUEIS83l6T/20djaho2hLt+tno+lvk6/H6ewspZHvttJblk12uqqfLlmHl52fTP4N7e28/yP+zibmIO8nBwv3TGJRRN6VkkFQeC3vRH8sOMsAIG+Drz94AxUZRAQQRDYfCCarzdKG6Y8nc1578k56MnwjeYVVfHi+7vIK6pCVUWRJ++fLLOBSCQS88ufZ9i4TarUOtga8caLc7Cy6N4/JwgCR48l8cOPIdTUNAEwY/oI7rk7AMNe4ofaWkUcP5rI9s3nyb3C/3kJJqY6uHmYdyqStvZG3doBBorKinqSk6SKanJSIempxbS3dY10UlFRZOqMESxcNgoLy56bwcRiCSdOXuSHH0Oo6phDP2WyOw+tmYSebvffjUgs4Y+NZ/lzSziCALbWBrz54jxsrLr/vItKa3j7ywMkphYhLy/HI6smsmxO92qmIAhsOxLHl3+eQCwRcLUz5pPnFsg8TgBORKXz+o8HaW0T4WhpyGdPzcdYr+fvsaSqnse/3klmUSUaqsp89shc/HqZvHUJLW0iXv7jICEXMpCTgxcWBbF8olef9r0SEonAlwfO8OuJKADmjnTlzSXB/Z4XLwtn03J5ZtN+6q9DhaYvaGhp5fW9xzqHeUxytuO9+dNuy9I7QHxhMS/vP0p6uXSC12Qne96cPgnj22ii343EEPH8P0BOVTWv7j9GRJ60k9Xb3JR1s4JxMLw95ur+G1eV1u2s+GjRdAyG3bhVe1VDE09t3EdUdiFycvDsjAmD6pnafi6Bd7YfRywRGGlvwRf3zEF7ACfllIIyHvt+JxV1TZjqavLtIwuwM+nb917d0Mzar3eRmFOCqpIi76+eyUTPnlVSsURa5txxQlrmXDndl8eWBsjMORRLJHyx4QTbjsQBsGCyJ0/dHSTzAhsZn8Nrn+6lobEVIwNNPnxxgcyYpNKyOt76cA9JKdKA+vmzvHnkgSBUZJSM8/Iq+eKrw8TFSxvDrK0NeGrtNEYM75l01FQ3sndXDHt2RFPTkf+ppKyAk7Mp7sMtcHU3x83DAj39G3sRamsTkZFWysWkgk5VtKKjGUhODkaPc2Tx8tEM97Ts8bhtaGzh199C2b0nBkGQTkpafX8gs2Z6yawsRMXmsO6T/VTVNKKqosRTjwQzfYpHt9u2t4v57Odj7D0mbQybGeTOs2uCZY4fjUnO5+Uv9kjtG4ZafPb8QmzMZR/TSVnFPPPFbqrqmjDW0+SrZxdia9bzb6CusYWnv99DTHohSooKvHvf9D5nfYolEj7YeoKtZ6S/gfuC/Xh8zrgBnRu2nUvg3Y7zgL+DJZ/dPXtA54HukF1exWMb9nR60t9fOp3gQfKk9wWCILA58gLvHz71nyi9t4nFrD8bwQ9nI2iXSNBUUeGFyQEs8ZKdqvH/iiHi+R+GSCLh9/MxfBkaRqtIjJqSIs8EjWelr+dtO4M2o6ySp7ZeLq0/FjiaNRP8b+j7SSup4LErukQ/Xj6TCS6D05AlkQh8fegsPx+PBGC2rwtvLQ0ekIoakZrHUz/vpbGlDUczA755eAHGOn0jP0WVdTz25Q5ySqvR1lDli0fn4Wnfs0ra0tbO6+sPcjJaqvY8szKIpVO8ZW/f2s4b3+wntCMU/omVE1kuw7snCALbDsTyze9StcvD2Yz3np+HnozS/ZnwdD744iD1DS1oqCvz/NoZBI7vfvRna2s7f28KZ/OWc4hEElRUFFl15ziWLPLvMVIpL7eCHVsiOHoogbY2aci0oZEmCxb7M3OuFxrDbi31RhAE4mNz2bb5POfDMjpvd3IxZdGyUUwIculRgU1JLeazLw6RkSEd+ejmasZTa6fJ7ICvrGpg3Sf7iY6XRtBMneTOU48Edzsx6tL3+/XvJ5BIBIY7m7Guh+83v6Sapz/cQUFpDZoaqnz0zDy8XGR7KovKa3ni0x3klVSjpaHCZ08uYIRjz8dza7uIV345SEis9Hh+flkQy4K8etznyvfz85EIvt0XBsAcfzdev6N3T3R3OJuSwzMb9tPY2oatkR7fPTAfC33Zubf9wfVM4egrkovLeOo/UnoHSCur4OX9R7lQVALAaGtL3p015bYaL329MUQ8/6O4WFrOK/uOkFgiDcQeZ2vFOzOnYKEzOCesm4F/l9Y/XjyT0TewtA4QkpzJC1sO0tTWjqWeNt/cNQ+HQYo9aW0X8ermwxyKk5afHgoexSPT+t5xfiUORafy6p+HEIkl+DpY8PnqOX3OGEwvrOCxr3ZQXtOIia4m36xdgJ1p7wrRs1/uJi5NqhC9vWYGk/1kK0RVtU0898lOkjNLUFZS4PWHZzB5dPfEUKqIHWfvMamC1JMi1tYuYv1vp9i2OxoAFycT3nh+rsw545GRWXz5zRGKimoAGOVvzxOPBWMqY/tL5G375vOcu4K8OTqbsHj56F7JW3eQSATyssqplzFOUxbkFeSwsTcaEMHtnjRrsWCJHzPnyCbNYrGE3Xti+PX3UJqa2pCXl2PRQj/uuWt8t95RsVjCxq3n+fXvM0gkAlYWerzxwlwc7LpXqSPicnj90700NLVibKDJBy8twFFGbmt1XRPPf7KLxIxilBQVeP3h6UwZ4yLzPdfUN/P05ztJzCpBRUmBdY/MZoJ37wr+h5tOsC1Ueuw9MHMUD8/t+29yV3gi72w+hlgiMM7Vho/v73tW7pVILSrn0Z93UVrbgN4wdb65fx7DrQYnQ/LfucMzRjjxzqKpgxJk31f8u/Qe5GzH+7dx6V0skbAhMo7PT56lRSRCVVGRtRPHco+/920r+gwmhojnfwxtIhHfnYngx/BIRBIJ2qoqvBQ8kQXDe54ocyujqa2dd/aHsLMjEP5mlNYFQeDnU5F8eeQsggCj7C35bMWsQZsEUtXQxJO/7SU2pwhFeXneXDqFeX7uA3qsP0Ni+HTnKQCCvR15d1Xfcwmj0wp46rs9NDS3Ym+mzzdPLMC4l1zC0qp61n66g6zCSoapqfDJ2rn4uMheEOQVV/H0hzsoLKtFa5gqHz0zH0/n7vMca+qaePXjPcQlFyAvL8fDqyawfM7Ibo/lwuJq3vpgD6kditzSBSN58O6J3aqWlZUNfPfDcU6clEaJGRho8tgjUwgY331TgEgk5uTxZLZvOU9GmvTx+1OuvhLt7WLSkotIjM0lMS6PpPg8Ghta+7TvvyEnB3aOJnh4W+HhZYWHlzV6Bn0v6XdnE1BXV2bGHC/mL/bDRAYBL6+o57vvj3OqI3jf0FCTxx8NZvy47hcb8Yn5vPPRXsorG1BWUuCxBycxd4ZXt59ZXmEVL3ywk/yialRVFHlt7Swmjuq+BNzS2s6b3x3gVKR0EfDoigmsnN398QFSz/LL3+7j7AVpssOL90xhfg95stAxbWv/eX7YGw7AgvEevHTH5D7n54YmZvH8r/tpaRfhZmXMNw/NR0+z/4kbpbUNPPbzLlKKylFRVOD9lTMIHjF4pfGtEQm8uzsEkUSCu7kRX62ai4l2/zNJBwpBENgSdYH3Dv03Su8gDZ5/9cAxzuVIUzGGmxrz3uypOBv1P27rv4Qh4vkfQkxBES/vO0pWpXSO8HQXR16bFoThDSRog42Mskqe/GcfGR2B8DejtN7SLuL17UfZHy+9yC4f7cmLsycO2si07LIqHv15lzS/T1WFz++ZzSjH7iOBeoJEIvDFntNsOC5V+1ZM9OK5hYF97vA/EZfBSz8doE0kxsvejC8enddrlmFWYSVrP91BaVU9hjoafPnMQhx66AKOTy3k+U93UdfQgrmRNp+9sBAr0+6bXDJzy3nxg50Ul9Whoa7Mm0/Nltn1HHI6hY+/PERTcxtamqq8+NRMxo1yuGo7sVjCnr2x/PpbKI1NrcjLy7FwwUjuuWs86t3M+G5paWf39ih2bYvs9Ed2Nugs9cdCRtNMl8dobiMpPr+TaKYkFdLWKuqyjZq6MvqGmv1aHLY0t1FeWnfV7WaWeh0k1IoRPjaYWnQfon8lumuMkpeXIyDQheWrxuLg2L26du58Jl9/c4Tijo76sWMceOzRYEyMr66s1NQ28f5nBzgXJc1nDQpw5tnHp3c7waiuoYU3P9tHRHwOAA8sH8fdi7vv7BZLJHz150n+OSxV7BYGe/L03ZNkniNEIjHv/3GMvaelHvE1C8Zy39zey8vbQi/wwcYQJIJAoKc97z0wE9U+DgtIyCnm8R92UdPYgqWhDt8/sgALA50+7XslGlvaeO6vA5y+KE0leXp2APcMIM9XFqKyC3jy731UNzZjoKnOV3fOxXOQxvz2Ff8uvT89ZTz3jr19S++CILAtPokPjoVS39qKorw8a8b68fA4/xsWZXWrYYh4/gcgCAI/hUfx6YkzCICBhjpvTJ/ENJcbZxS/Hjifnc/DG3fT1NZ+U7rWQaq2PvTbTqJzClGQl+Ol2UGsGOM5aI+fXVbF3d/8Q3Vj8zVPLPlo+0k2npRefNfOG889k2UrP/9G6IUsnv5uDxJBYKKnHe8/MKvXi2p+aTX3v7uZmvpmbEz1+PKZhT3OyL6QVsjj67bR1i7C1c6koyO5e+UnJbOEtW/+Q2NTG+YmOnz40gJsuulEFwSBPzaF8dvf0g764W7mvP78HIwMr34dDY0tvPnWTmJipZ5DFxdTnlo7DUeH7klVTlY5b7+2nfxcaaeqnr4GcxeOZM58H7R6mG4kCAJ52RVEhqUTFZ5JYmwu7e1du8u1ddSl5NBbqlLaO5mgoNj/xVRlRT2JsXkkxuWSGJtHdkbpVbFNZpZ6+I11YOQYB0b42sicrX7ptUedz2Lb5vPEREnJjbyCHPc9GMTSO7onfi0t7fy9MYwtW88jEklQV1fm1ZfnMXrU1WVsiURg665I1v8eilgswcZKn0/fXYqB/tXKmkgs4bsNp/hnn3QhtWC6F08/MFnmMb3pQDRf/30SQYAZAW689tB0mdsKgsAPO8L4ba807eCBeaN5cMFYmZ/LJYTEZvDyz9LF2Vh3G754dF6flc/csmoe/nYHRVV1GGpr8OczKwY05UgklvDR7ssTzB6cMorHZ/T+2vuKgqpaHtuwm/TSSpQVFfhm1TzGOcke+HA98O/S+3wvN96dG9zvKW23EkrrG3jrUAjH0qSedg9TY75bPAcTrRunKt8qGCKetzkaWtt4Zf8RDl5MB6Qhtq8GB6Ktdnt6Yy7hVFo2T2zZS6tIjJ+NBZ8vmXlDS+sgVTof+WMX5zPz0VRV4cs75zDKfvCIb1ltA6u+3kJRdR2uFkZ8/8AC9AdQggPYeDKWj7afBOCtlVOZN7rvZfrU/DLu+/gfmlvbmTXalTfumtrrCb62oZn7391MXkk1LjbGfPXsQnSGybYd5BZV8eCbm6hraGG0pw3vrZ2LmgwClJ1fwWOvbaG2vhlPVwvef2Fet/mcgiDw3S8n+GenNHJmxSJ/Hri7+wlHtbVNPP/SFtLTS1FVVWLN6iBmz/JCQcb7DDmSyOcfHaClpR19g2HcszqQScHuvY7DvBCdw09fHSUtuajL7YbGWozwsZESTW9rLK31r4uC01DfQvKFfBJipOpqalJhZxg8SJXVpXeNY+HKMT0SUICsjFL+/O00Z05Jg+HHBjjx3MtzGKbZ/bklJ7eCTz87SFKydCzqyy/OISiw+xD55JQiXn9vF+WVDZgaa/PZumUyfbh7jl7g4/VHEAS4c4E/D905QeZrPn4ulTe+2Y9YInDP/FGsWTq+x/e4+UgMn208CcBbD05nxtjew9qj0wp4/KudtLSLWDLRkxdXBPX5uyyvbWDNN9vJKqnC0cyA355cyjC1/k2buoQNp2L4eI/UUvPSgiDuGO81oMfpDo2tbTy3+QCnUrJRUVTg+3sWDOq5ry+41PX+7kFpQ+E0N0c+XjQD5UGMILvREASBwynpvH7wODXNLehrqPPVwln4Wd24CXu3AoaI522MrMoqHtu2l4yKKhTl5Xl1aiB3+A6eGnezcDg5nWe3HaBdLCHI2Y4vlszqs0dxsNDaLuLxP/dwNj0XdWUlfrl/ESMGseRU19zCPd9uJb24AmsDHTY8vgy9AU5aOpWQyVM/7UUiCKydO557g/36vG95TQOr3t9EWU0D/i6WfP3Egl4tBO0iMY9/vJ2Y1AJM9DX59bU7egyGr6xpZPUbGykur8PN3oRvXlkqk3QWltTwyKubqKxuxNXRhC/fWCqzC/qz746y50AcAI8/OInF80Z2+5gVFfU89+IWcnMr0NZW48P3l+Eko3Tc1ibih6+PsXenVGXz9rXhpTfnoysjt/IS8rLL+eXrY5w7LVVolJQVGOFjw8gxDowc63DdiGZvaGxoJT4qm8jwDKLCMijrKInrG2py90NBTJnlKZN8g/Rz3rc7hu+/PEp7uxgzc11ee3ehzNK7SCTmg4/2E3IiWZps8NQMZs7o/pxUXFrLM69sobC4BgP9YXz5wQoszLq3Bew+Es/H648CsGZlAKsWjpL5mvecuMD7P0m3ff7+KSyY3PM58et/QvnzQBSKCvJ88/xifJx7JwEhsRk8t34vggDPLQtkxSTZ6Q3/RlFVHXd9uomKuibGudrw5Zq+q6b/xvdHzvHd4XDk5ODjVbOYNoCJZrLQJhLz5F97OZWajZqSIuvvW4ivTfde7OuJYxczeGrrAdrFYiY42vDVsjmo3uDrwWAjv6aWR7buIbWsAgU5OV6YMoG7/bxvWztBfzFEPG9THE3N4Pk9h2lsa8NomAZfLZqNj0XfQsFvZeyKS+blXUeQCAIzPZz4cOH0QfNS9hVtIjFP/r2XUynX54Tb0i5izY87iMkqxEBTnT8fXz7geJSL+aXc+8U/tLSJWDjWg9eWT+nzyau5tZ37P/mHlLwybEz0+OOFZWj20kUqCAJv/XyYA2eT0VBT5udXlmNvIdso39TSxqPv/ENKdinmxjr8+OYKmeX18sp6Hnl1E8VlddhZGfDN28t6VTrl5OD5tTOYGdx9g0hxcQ3PvrCZ4uIaDAw0+fiDZVhbd/96S0tqeee17aReLAZg5d3jWHXfhB6JWVVFA3/+dJJDu2OQiAXkFeSYMd+HVasD0b3BuZ29QSIRCD2WxK/fHqe0o4vfxt6IBx6fwsixDj0eN6kpRbzz6g5KS2pRUlbg8aenM2O2V7fbisUSvvz6CPv2xwHwyMOTWbyw+8VQZVUDz7z6D9m5FRgbavHNx3d0a5MA2Lg7ku82SBW+px6YzKIZssneT9vO8uuOc8jLyfHhM/MY7yO7e10iEXj5u32ERKWjpaHKr6+twMqkd1/sH4ej+HLHaeTl5PjskblMGNG9/7g7JOWVcP8XW6Wq6fgRvLx00oBIhyAIrNsRwpawCygpKPDDgwvwH6T57iBdgD/25x7C0nPRUFHm5/sWDuoCvK84k5HLY5v30NIuwt/Ggu/umMewAaQD3Epobm/n1f3H2Jsk7R2Y7e7MuzODUb+BaQI3C0PE8zaDWCLhq9Bwvj8bAcBIS3O+XDjrtm4guoQ/wmN4/5D0wrLI252350654dETIrGEZzbt51hSBiqKCnx3z3xG2/e/0UcWxBIJz2zYz/GEDIapKvP7o0txNuvbSL5/o6iqjrs/3Ux5XSOjXaz4+qH5fSbpYomE537Yx8n4THSGqbHhxeVYGOr0ut8ve86xfkcYCvJyfP70AkZ72MjcViSW8PwnuwiPz0ZHU40f31qBpYwLek1dE4+9tpmcgirMTXT49t3lGOh2T9x++/sMv2+U5iM+v3Y6s6Z2P0kpN7eCZ1/YTGVlA6amOnz60XJMTLp/jxHhGXzwzh7q65rR1FLjxdfm4j/m6uakS2hpbmPbX+Fs/fMsLc3tAIye4Mz9j0/ByubW7lhtaxOxd2skG38NpaFOOi/e29+O1U8EY+8sO6Knrq6ZD9/ZTUS41KM2beYIHn9mercjMQVBYP1PJ/hnq/Q8dc9d41l1Z/ch6lXVjTzxwkbyC6uxMNPlqw9XoK/X/Xf/06Yz/LHtHACvPj6D6YHdW0oEQWDdj4fZfyoJVRVFvnllKe4OsglTS1s7D3+wlaSsEiyNdfjltRU9WkcuPce7fx1j55lE1FSU+PW5pThb9n36T0h8Bs/8IlVN184bz71T+l6puBJiiYTn/tzP0QsZaKgo89sjS3C1GLwpRM1t7Tzyx24isqSWo18fWISbeffZrdcTUbkFrPl7N42tbXiYGfPTnQvQHaRUkZsFQRD4MyqOD46FIpJIcDYy4JtFc7DW07nZL+26Yoh43kaobmrm2d0HOZ0lbY6428+b5ycH3HBFcLAhCAJfhoTxQ6j0InXXaG9enDbxhs1bvwSxRMILWw5x8EIqSgoKfHPXXMY72Qza4wuCwNvbjrPtXAJKCgqsf3ABfgNUJ2obW7jn8y1kl1bhYKrPb08tQ7MfXrHPt4Xy59FolBUVWP/04l7D4UE6e/21Hw4A8OLdU1gYJHt0piAIfPDzUfacSEBFWZFvX5V94W9uaeOJN//hYnoJRvqafPvuckyNuleAN2+P4PtfTwLw+JrJLJ7r2+12aeklvPDSFmprm7G2NuDjD5Zh0M3IS7FYwp+/nWbjH2cQBGmY+uvvLsLYpPvnF4slHNkbx4b1J6iqaADAyc2MB9dOZbhP/xow2lrbuRCVQ0RoKlFn06ntiDPqKxQU5HEZYcmoCc74T3DCUAaploW62mY2/3aaPf9E0N4uRk4OJs/05O6HgjCS8f4lEoFNf55lwy+hSCQCdg5GvP7uIswtrk4mEASBvzaG8dvvpwFYssiPh9Z0r+yVldfx+PMbKSmrw87GkC8/WC5T7f7y1xNsOxCDgrwcH7y0QGbSgUgk5tlPdnH+Qg66Wmr8+NYdWBjryPw8Kmsbue/tTRRX1uHpZM63zy2SOT3pEtrFYh77cieRqfkY6Qzjz5dWYNjHIQ3QNfps3V3TmeXXvSe2N7S2i3jop51EZRagr6nOn48vG9B4XVlobG1jzW87ic0tQltNld9WL8bZdGAL5mtBQmEJq//aSU1TC7YGuvyyaiFmOrf/9T4qv5C12/dR3tiEpooKn8ybTpBj3xX02w1DxPM2QVJxKY9v30dBbR2qioqsmxXMHA/ZYcm3C8QSCW/vD2FLlHRc3pOTxrJmgv8N97pIJAKvbj/C7phkFOXl+fLOOQS6Du4P/7vD4Xx/5BxycvDpXbMHnMHX0iZizTfbiM8uxlhnGBueXt5r1uaV2BZ6gff+Pg7Ae/fPYLp/78dRfHohj3y4jXaRmJXTfVm7fGKP2/+6I5yftoUhLyfHB0/PJcC3e/VQJJbw8oe7CIvOQmuYKt+tW9Ft9zrA7gOxfPat1Lv3wF0BrFo2ptvtEhLzefmVbTQ2teLkaMKH7y9Fu5vyfk11I++/vZuYSGn39pz5Pjz0RHC3DUSCIBAZlsHPXx0lN6scAGMzHe57dDITprj3eZFUWVZHxOk0IkJTiT2XSUtzW5/26wtsnUzwD3Bi1EQXnIdb9GgRuBIlhdX89l0IJ48kAqCsosiC5aNYds94mUHyMVHZvP/mLmpqmlDXUOH5V+YwbkL3AwC274jk2++lx9vMGZ48tXZat6+tsLiax5/fSGVVIy5OJny2bhka3URcSSQC674+yOHQZFRVFPn67WW4yljUNDa38cg7W0jLKcPCWIcf31qBrpZsL3VWYSX3v7uJxuY2Zoxx5c0HZXfGX0JdYwv3fLSFnJIqXKyM+OXZpah1owLLwic7TvHXiRipx/Sh+Yx2GVgHeX1zK/d+t5XUonKsDHTY8NiyATcrdoeGllYe+GUHCQUl6Gmo8dvqJYM2PKM/yCqv4r4N2ympa8BEaxi/3LUQ+9t0/POVKK1vYO2OfcQUSK0+jwWM5rGA0cj/B32fQ8TzNsCOC0m8cfA4rSIxVrrafLNoDi7GN361OdhoE4l4bvshDienIycHb8yaxHK/G98cJZEIvLXrGNsiE1GQl+PTFbMGfWbxP2HxvLM9BIDXFk1i6diBvU+xRMKzv+zjxIVMNNVU+O2ppTiY9r20G56cyxNf70QsEXh47hhWzxrd6z4FZTXc984mauqbCfR14INH5/RItPaHJvHuD4cAeO7eySwM9up2O0EQ+OC7w+wPSURFWZEv31yKh3P3yuuRkCTe+2w/ggArl4ziwXu6J76RUdm8/uZ2WltFjBhuybp3FqPRTU5kcmIB77y2g4ryelRVlXjy+ZlMntr9LPH0lGJ+/vIocR3xQsO0VLnjvgnMWeLXa5e7RCIhPamI86EpRISmkXGxa7e7vpEm/gHO+E9wxsLGoF8LrqbGVmLCM4gITSXlQj4SyeXTs5aOOiPHO+If4IzvOEc0tXovSaYmFfLTl0dJ6Iib0tZRZ+UDE5i5cGS3IfwV5XW8+/pOkhIKAFiyYjT3rwnqNhbq4KF4Pv38EBKJQFCgKy+9MLvbCU85eRU88cImauua8fSw4KO3lnTbfd/eLuaF93cSEZ+DjpYa36+7A0sZjUkV1Q2sfmMTJRV1eDiY8vWrS1DtwUcXkZTL2s92IhZL+hyzVFBew10fbKamoZlAT3s+fmh2n21CEonAS38c4HBMGuoqSvy6diku/SjZX4nyOmlSRmFVHW4WRvz68BI0VAfPC1nb3ML9P2/nYlEZBprqbHhwKdYGvfthBxvFtfXcv2EHWRVV6Kir8tOdCxhuPjiTnG4m2sRi3j96ir+j4wGYaG/LJ/Om3/YpNf/GEPG8hdEmFvPe0VNs/A8ehI2tbTy+ZS9hmXkoKcjz0cLpzPDoXjG5nhAEgXV7TrDpXLy0EWHZDGZ6Du7rOHohnWc27EMQ4OGpo3lkWvdKXV9e6/tbT/DP6XiUFRX4/tGF+Dr0PYYjNb+M+z/+h6bWdmaOcuGde3tXc2oamnmgIzbJ1daY9S8uRbUHNScyMZenPtyBWCxh1Rw/HlkhO/rmkl9PXl6O956fx3i/7lXR0LA03nx/N2KJwILZPqx9qPssx9NnUnn3vT20t4vx97PjzdcXXEVaBEFg59ZIfvz2OGKxBEsrfV5/dxE2dlcv5MRiCX/9eJJNv51GEEBJSYG5S/1ZcV9Aj0SupbmNqDPpnA9NIfJ0GjVVl0vocnJyOHuY4zfBmVETnLF3MR0Udb+2upGos+lEhKYSfTadhvqWzvvkFeRx97bCP8CZMUGuWPTgQRUEgXOhafzyzTHyO4LkbR2MeO2jZZhbXl1OF4nE/Pz9CbZvkeZhDve05JW3FqDfja3h5KkU3vtgDyKRhNGj7Xnj1fnd+kPTMkp48qXNNDa14e9ry7rXFnRb8m5qbuOx17eQllWKqZE269+/Q+Zs95zCSh58YxP1Ta0E+jny7tqeieGuUwm895tUXX9j9XRmjes9Zik+s4g1n22jTSRmWaAXzy8P7PN329Yu4tHvdxGZno+Bljp/PL0c8wE2HOaWV3PXN1uoamhmtKMV3z0wH6VBjCCqaWzm3p+3kVZSgYn2MP54cCkWejd+FHN1YzMP/r2ThMJS1JWV+HbFXMbYDZ4f/2Zi54VkXj94jFaRGEsdbb5ZPAfX/4DYdAlDxPMWRUmdVHaPLZTK7o8HjObR/4jsXt3UzJq/dnGhsAR1ZSW+Xj6HcfY3NqAYpBfZjw+E8seZGOTkYN3iaczz6f0C0x9EZuSz5sedtIvFLB49nNcXyw7A7g0/H47gm31nkZODj+6dRbB336NTSqrqufvDTZTXNDLSyYJvnuj+Yn4lWttEPPbJduLTCjtik1Zg0IN/LSOvnIfe2kxjcxvBY5x589FZMpXRXYfj+OTHYwA8/9BU5gZ37xeNiM7mpbe3IxJJmD7FgxfWzuj2MY8cTeSjT/YjkQhMCHDmlZfmXqXStbWK+Pi9vZw8Lh29OnGSK0+/OKvbiUV1tc188Op2os9JG2kCp3pw7yOTMDGXre5IJBJC9sXz21dHqCyr77xdXUMFn7EO+E9wxm+803XvdheLxCTH5XE+NJWI02nkZZZ13icnJ8eUuV7c83gw+kayz5NikYRDe2L444cT1FY3oTFMhRfeWcio8d0fc6dPpvDJe3tpampDV0+DN99bjJvH1Yui8xGZvPHWTtraRHh5WfHuW4u6/fwTkgt49tWttLS2EzDGkTdf6j5yqKqmkYde3khRaS1OdsZ88/aybuO3AGIvFrD2faldZPkMX9auCpT5/gG++ec0Gw5Eoqggz1fPLmSka++k5khUKi/+JPVBP714AncGd+9B7g71za3c98U/pBdVYGOky+9PLxvwSN6k/BLu/W4bzW3tzPB25oM7uv/dDBSVDU3c8+NWssqrMNPR4o81S26K17KhtY3HN+8hPCsfJQUFPlk8g2lut/fglEtILinjsW17O+11786awlyPgXmAbzUMEc9bEJF5BazdsZ+Kxia0VFX4eO5/x2hcUlvP/X/uILO8Cm01VX68cz6eFjc+nkMQBL48cpafTkYC8OaCKSzx73lmc3+RWlTOPd/+Q0NLG5OHO/DpXbMG3KW/+1wSb/x9BIAXFgeyYmLfcwPrm1u5/6MtZBRVYmeqx2/P9x6bJJEIvL7+AEfOp0pjk15djr25bKWsvLqB1a9vpLSyHi8Xc758abFMYnvqfDqvfbIHiUTgvmVjuW9p96XM+MR8nnt9K62tIgLHO/P683O69Qbu3hPDl19LP5tpU4fz7NMzrtquuamNN17aSmx0DgoK8qx5bArzF3c/2SkjtZh3nv+HkqIaVFQUWfvyHCbPlN1IBZAUm8sPHx0gPakQAEMTbcYHuzNqgjPuPtYo3cTcweKCKiJCUzl/KoWYjo50VTVllj8wgQWrxqHSQ5B8RVkd7764lYsd5fQ7V09k5QPdN/4V5FXy9ms7yM4sQ1VNibfeX4LPSNurtouPz+OV17fR1NSGq4sZ769bglY3CnJUbA4vvrmddpGYqZPceempmd0+b35RNQ+/spGaumb8PW348KUF3VoDAI6EXeSNbzqI4d1BLJnmI/O9SyQCr/6wn2MRaQxTU+HnV5djZ967l3DDkSi+2C5tqPrwwVkE+/Z9gVha08A9n22muLqeEbamrH9sEWoDjNcJS83l0Z93IZJIuDPAm+fnTRxU73xZXQN3/7iVvMoaLPW02bBmKUZaNz5CrE0k4tntBzmSnIG8nBxvzp7M0pGDey6/WahpbuGZXQc6G4pXjfTixSkTbvuG4iHieQtBEAR+i4jh4+OnEQsCzkYGfLt4Dla6Ojf7pQ0KsiqqeGDDDopq6zHWGsYvqxbiYHRzTOHfHT/Ht8fCAXhlbhB3jPEa1McvqKxl1debqahvwtfOnPUPLhxwCP7Z5ByeWL8LsUTg3ikjWTsvoM/7tovEPP71TiJS8jHQ1uCPF5djqtf77+O7bWf4fV8ECgryfPnMQvzdZKs9jc1tPPz2ZtJzy7Ey1eXHt1agLSOKJv5iAU+9tZW2djFzg0fw3Jrgbi+GF9OKefrlLTQ1tzHGz453XumeTGzcFM7Pv0q7ghfO9+WRh6dcRU4a6lt45fktJCcUoKqmxNsfLMXb16bb13d0fzxfvb+PtlYRpua6vPbRUuydZHvHSouq+fWLI5w6JG2OU9dQYcXqQObdOaZX/+fNwMX4fNZ/tJ+UDiJpZKbDA09NI2Cqh0xS0t4uZv1nh9i7TTohyn+cI8+/vaBbu0FzcxtvvbKd6IgslJQUePWdhYztRiVNSS3mxZe2UFffgp2tIR99sAy9biKUzoSn8/p70mN/7kwvnn5ExvGSUczjr2+hpVXEtAluvPK4bIXvj93n+WHLGeTk4IOn5jFhpOzYrNY2EY9+tI0LGUWY6mvxy2srehyWANLz+EebT7LlZBzKigr88NQivBz6ngOcVVLJPZ9voa6plcDhdnxy/5wBB8zvj0nhxb8PArB25jgemOw/oMeRheKaeu758R8KquuwM9Tjt9WLMdC88dF+YomEt/aF8E+09Hf49JRxrB7v958IZBdLJHwdeo7vzkrtLD4WpnyxYDYmN4HkDxaGiOctgub2dp7fc4jDKRkAzHF34Z2ZU/4zYbIXi8u4b8MOqpuasdHX5Ze7FmJ+k2IwfjkVyWeHzgDw3MwJ3BPQ93JYX1BR18iqb7ZQUFmLo6kBvz+6BK0B+nIv5pdy3xdbaW5rZ5afC+/cOb3PJTNBEHjzjyPsDU9GTUWJX55diotV700Lu08lsK7D3/b6A9OYPV72+E2xRMJzH0uzOnW11PnprRWYy4isySmo5KGXN9LQ2Mp4P3vefa778ml2bgWPP7+R+oYWvEdY8eGbi7r1Av6+4TQb/pTOaL9z5VjuvTvgqgtNXW0Tzz+5kcz0UoYNU2XdJ8tx87iaBIjFEtZ/fpjdW6SRXn5jHXjhnYUyvZyidjGbfz7FP7+G0tYqQk5OjmkLfLj78eB+l9IFQSDrYjFV5XX92k9BQR5Xb2vUumme6gkSiYSTBy/w6+dHqCiTPqe7tzVr35iHlZ3s4+Povji++mB/Jyl/+/MVWNle7TtraxPx3pu7OBuairyCHC+9Pp/AyVdbWLKzy3nuxc1UVTViYa7LF5+t7JZ8HjuZzLufSD3SKxb589B9gd2+vnOx2bzwvrQpaOV8fx5e1b2/WBAEPvz5KLs7or6+e20ZbvayFxc19c3c/+4m8ktrpD7nl5b22JwEHU2AP+zlVHwW2hqq/PHCcqyM+96EE5tZyEPfbqe1XcyS8SN4ZdnkPu/7b1w5WvNaGhtlobC6lrvWb6Wkth4HY31+X73kpuRrCoLAF8fDWH9a+hu+b6wvz029+pxwu+JYaibP7z1EQ2sbeupqfL1o9m07anOIeN4CaG5vZ80/uzmXk4+SvDwvBwdyh++I/8wPprSugcU/bqS8vhF3MyN+unMBehqDF/PRH4QkZ/L4n3sAWDt1HA8GDa4CIJZIWLN+B+cz8rHU1+aPx5ZiOMCVaUubiKUf/EleeY00IH5N/5oEDkem8tLPB1CQl+OLR+cxzuPqsue/kV9azR2vbqC1Xdynjt7NB6P58s+TvV7ARSIxq1/8m/TsMoY7m/H5G0u6bVJqaWnnwSc3kJtfiZuLGZ++s6RbD+DZsDRee2MHAA+uDmL50qtHKIpFEl56ZhOx0Tno6Grw4ecrsHO4OvhaIhH4Yt0eDu+JA2DlAxO4c3WgTIJfXFDFhy/806kaDh9pw0PPz8LepX+WkcrSWkJ2x3J0RxT5V/gw+wNVdWXGTRtO8EJfhvvbId8PK0dLUxvb/jjD1t9O09rSjoqqEg89P5Ppi7q3IIDUhvD28/9QWlSDrp4GH62/p9vAfLFIwifv7+PY4QSUlBT49JtVuLpfTfgLC6t59oVNlJbW4e1lzUcfLOvWTrHvcDwff3UYgHdemc+Esd2Xrw+eSGTdN9JEhc9eW4y/l02324nEEp77ZCfn4nOwNNFlwwereiSTeSXV3P/uJmobWrhzxkieWCa7ae4SmtvaWfPpNhJzShhua8qvzy/tl9XmyoD5T+6fzRSvgXsXvzp4lp+ORaAoL8+Gx5cx3GpwO8BzK2q456d/KKtrZIyDFevvXXDDh39cwm9h0Xx4OBSAl6dP5K4xsu0Utxtyq2p4fPs+UsrKUVVUZP2yeYyxuf0aqoaI501GU1s7D/2zm3O5+WgoK/Hjsvm37SqmO7S0i1j12z8kFJbiYKjHpgeWo6naP4VmsJBbUcPSb/6mobWNlWO8eHlu0KA/x8/HI/jywFnUlBXZ8tRKbI2u7gTuKz7bGcqGkGgMtTXY/vJdaPXiy7wS1Q3NLHrjD2oamlkzezRr5vTeSS8IAo98uJXolAL83a34+tlFPS5+ispqWfnC77S0injh/mDmT5btg/ztnzB+2RKGtqYaf35xj8zu48++PcLuA3Ho62nw6zf3otNN/mZZWR2rH/qV+voWlizy4+GHuleD1n9zjG2bz6OqpsRXP9yDrf3Vat7/2jvL+KbONg5fSdqk7g4tFHd3d7cxYMiQOYwpG4O5u4/BfDA23G24u7u0tFDaUndvI+f9cJLSl5ykSYV2kOv36wfIk5Mnev7nlv+t0wnM+2wz/647g1wuY86Ho+llwlYJYM+Wc/z40SbycgtxcXXgubdG0HNQc8u7lwvVHN11mV1rT3PmUHixBZJSZUdwXT+rLjaz0nNJ0o++BPCr4UnfUW3o91A7gmpZXsKSnJDBt++uK67/7NavKS++OxJXE6NNMzPymPvsYm5cT8TLx4WvfplGjRDjx9PpBD54czWHD4bj7ePCgj+ewEsiGhwdk8r0ZxdRUKDmicd6MGmi9MXOT3/uY/maE7i5OvDnj4/hK9E5D/Dt77tZs/Usfj6u/P3tNEkvUIDs3AImzl5ESkYuk0d04Nnx5ktYDp67wSvfrUcuk/HnOxNoElq6eEtMz2bMe4vJLSji1XE9mdjXOhH04+bD/L79BF6uTqx5YwqepUxTMoUgCLz69xZ2nL9OsLc7q2Y9WqE2SwDXE1KYsGAZ+WoNT/fqwIsDu1bo8a3hz8On+WLHAeQyGb88Ooru9WpX2V4qmgK1hufXbGZ/5E0c7Oz4edxIuoT+t8SnTXhWIXlFap5ZuZ7jt2JxVir5Y8JD98W8dQOCIPDqmq1suRiGu6MDq56eQEgVjQLLL1Iz8aflhCek0LpWEH8+OQZlBVqMAFyMTmDKvBVodDo+GNefhzqaFjClcf5mHNO+XYEgwA/PjKRHM+uay978YytbT1yjXpA3S96cZFGkdP2+C3yyaBcOSjuWfTSFGn4eJtcKgsCLn67m5KVo2jQJ5sc3x5oUTdejknjytX/QanW89/Iw+nWTNqw/fDyCNz4Qo5hffTSO9q1rG63RanXMenUpFy/F0rBBAD98N1my9nPPjkt8+sEGAN7+cDQ9eht3gwqCwPwv/mXT6lPI5TJmv/8QfQZJNyXk5RYy/+NN7N58DoBmbWrx2qdj8Qv0kFx/9+NcOxfNrrWn2L/lPLklrI6atK1N/9Ft6T64Bc4Sk3pKO+7Vs7fYufYUB7ZcIC/nznGbtQul3+h2dB/cHCcTJvAl0el0rF18hEU/7ESj0eLj78acT8fSXKI5CETx+dr0v4iKTMLHz5Uvf5lGkMT0ory8Ql54ehG3olJo2rwmX/7wqOT7tX3HRT7/cgtyuYzvvp5EM4mOeLVay7Ov/EN4ZCJtW9biq4/GSUal8wuKmDrrL+ISMxnerwVzZgww+bz3n7zO3G83opDL+OOjSTSsbX4U5Fs/bWHH8TDqBfuw+N1Jkl6kd2MY2OCgtGPVu1Oo4WO59VCRWsOEL5cSGZ/KoLYN+WzaEIvvezdZ+QWM+fof4tOzGd62MZ9MHFTmY5liy7lrvLZCrCmdN3kEfZrUrfDHsARBEHhzw07Wnr2Mq4OKFU+Np45P2YMA1Y0ijYbn1mxmX8RNVHYKfh43kq6h994ZpqxYo9eqJm5+n5JXpObpFXdE55/3megE+PnACbZcDBMnAT0yrMpEpyAIvL9uF+EJKXi7OPHNxKEVLjpzCgp57Z9/0eh0DGzZgFEdTNdFlkZBkYZ3/9mBIMCwDo2tFp0HL95g64lryGUy3p06wCLRmZyeww8rxE7cZ0Z3NSs6Abbsv8zJS9Eo7e2Y+6R0wweIKfZPftyGVqujR8f69O0q7ZGampbD59+JJ6xxD7WTFJ0Ai/8+xMVLsTg5KXnrzZGSIiYiPIGvP9sCwPjJXUyKzp+/2c6m1aeQyWDWOyNNis6wi7HMHDef3ZvPIZfLmPxsHz7//fFSRWdyfAYrft7D04O+Yta4+fy7/Di52QX4Bnow4dm+/L5zNl8vf5ZB4zpaLTpBtEZq0qY2L340hqVH32bONxNo060BMpmMS6du8t0bq5jY+UO+fHU5Z49cR6fTmTyWXC5nzLRufPvP09So5U1KYhZznvyTxfN3odVojda7ezjx+YIphIT6kJKUzZwZi0koEX014OSk4r1Px+LsouLyxVgWfL9D8vEH9G9Gv75N0ekEPvp0I1lZ+UZr7O0VvDV7GCqVHafP32LlupOSx3J0UPL6TFFUbdp1gRPnokw+757t69OnYwO0OoFPft2BRmv6NQKYNak3bs4ORMSk8PfWU2bXGhjdrTltG9SkoEjDR//swpr4jdLejvcnDUAuk7HtdBh7zkdYfN+7cXN04PNJg5HLZGw6fZVNp6+W+VimGNqqEY92ER03Xl+5jVsp6RX+GJYgk8l4b1gfWgcHkl1QyIylG0jPM/5M/VdR2tnx48PD6F0vlEKNlukrN3BY3/l+v2ETnhVEblERT61Yx4noWFxUShZOHE3r+0x0brpwle/3HAHgzcG96BRatpnkFcGyY+fZdO4aCrmMbyYOrRTLj4/X7iE2NZMgTzfeGVt2r06An7ceJSopHV83Z157uJdV983JLywehzmpXxua1i49HSgIAl/8vZuc/EKahPozfoB5q6bUjFx++GcfAE+N6UJwgOmmib/XHuf6zSTcXBx49el+kq+LIAh88f02MrPyqRvqy1NTpevnzp67xT9Lxc/UrJcGUUNiUk1mRh7vvbGaoiIN7TvVZdqTxhOOBEHgt+93sn652CX68lsj6D9UuuFiz5ZzzJr6K/ExafgFuvPlwieZNL0PCjNi/vyxCN587Hem9vyURV9vI/ZGMioHe/qOasMnfz3Fon1zmfLyQGrUrjhDaJWDPb2Gt+bjhU/y1/7XeezVwdSs40thgZo9G87wxtTfmNbrM5bM20lRodrkceo3qcGPK56l/8jW6HQCS3/Zx5vT/6Igz3i0p4eXM58vmErNWt4kJWTy2oy/SErINFpXM9iL198dhUwGm9ef4d+NZ43WyGQyXnphADWCPElKyuKrb7ZKCrRawd4891QfAH5ffJDIm9K1sa2bBvPwYPFz/NlP28nNKzT5nF+Z1gdXZwfCo5JYtsW8mPRyc2LWxF4A/LHhGFFxaWbXA8jlMt6e3B+VvYLjV6PZcORyqfcpSbNaAUzr1w6Aj1fsJjO3oJR7mKZ1aA2m9xfroT9as5uYlIwyH8sUrw7pTutaQeQUFvHSks3kFZn+vFUmSjs75o0fTpC7K7dSM5i5bCOFak2V7KUyUNrZMe/hYfSpX4dCjZZnVm7g0H0oPm3CswLILSriqeXrORl9WxSdE0bTqsa997GsTI7fjOGN9WJk47EubZnQ4d6PwTRw7lYcn28ROzpnDepOu9CKr5/ddOoKm0+LEcbPJg0qcwc7wMWoeBbvPg3Am+P7WlXXCfDD2kMkpudQ09ed6SMsm5C059R19p+JRKGQ8+bjA0ptCvh60W6y8wppFOrP+CGmHQGuRyWxaPUxAF5+sq/Jus5N285z7NQN7O0UvPXqMEn/z4yMPD75VGy0GDKoBX16G3dJazU6Pnp3HYkJmQTV9OT1d0YaNaoIgsDC+btZs0S00nrh9aEMHCEttPf+e56v3lyDVqOjW/+mLFj1HE1bm05naTVaFn29lden/MaZQ+EIgkCz9qG89MlYlhx5m1e/HE/rLvWtagAqC76BHox7pje/bnuVb1bOZMiETji7OpAcn8E/P+zkpTE/mm1mcnRS8cqHDzP383E4Oas4d+IG773wj+RceS8fF774aSpBwV4kxmXw2oy/ijvlS9Kxcz2mPdULgHnfbOPKpVijNU5OKt5+ayR2dnIOHQ5n4yZjgQowfFBLunSoi1qj5aOvtlBkQkxMf7Q7Qf7uJKVkM3/xfpPP18vdudhM/vc1R4mONy8mB3dpTOfmtSnSaPl40c7/G1VqihA/D2aMEGtXv1l1gOSMnFLvU5JnBneiToAXqdl5fLFmr1X3vZun+nWkTWgQeYVq5izZilprHNEuD/YKBd9MHIq3ixPhCSl8sM66KG9F4uPizC+PjsLVQcWZ6Djmrttu0fv1X0FpZ8cPDw+jb/06FGnFyOfByKiq3laFYhOe5SSnsIgnl6/jVMxtXFUqFk54mJb3meiMTE7l+eWbUGt1DGxSn9n9LfecrGhSc/KYtXQLGq2OAc3qM7VbxXc3Rqdk8NFacQb7jIGdaB1quV/f3RSqNby7ZAc6QWBIu0b0am5dfdTp8FhWH7gAwNuT+1tkPJ2Zk8+Xf4v7nzqkPfWDzUfh9p24zt4T11Eo5Lzx9ACT/oIajZZPS6TYTdV1xsalM/838UT61LQe1JGIAup0Ap99sZnUtBxqhXgz89l+ksf67afdnDsdhaOjkvc/GStphbT4l32s+Eu0YJo5ezBDR7eTPNaBHZf48s016HQCgx9uxxtfPoKLmTGZSXHpvDbpZ1b8vBdBEBg0rgN/7JrDl0tnMHBse5xd7/2YW5lMRuPWtXj+g9EsPfo2s78aj4e3CzevxfP8Q9+zc6356F6vwS346OepODopOXfiBh+8tEQyWurt68rnC6YQEORBfGw6r834i9SUbKN1EyZ3oXuvRmg0Ot5/cw0pEmsa1A/gmafEpr8FP+8mMjJR8nnNfmEQ7m6O3IhK5s+/D0nu39FBydxnBwKwcecFTp6PMvlch3RvQsfmtShSa/j0N/NiUiaTMXdqPxxV9pwPv83avedNri3JxL5taFLLn5z8Qj5btscqMaYqkXLfcvIa+y9GWnzfu7FTyPls0mBcHVVcjE5g/rajZT6WKfzcXPhm4lAUchmbzl1j2THLXqPKoL6fD/MeGYa9Qs7Wy+F8s0v68/JfRalQ8P3Dw+jboC5FWi0zVm3kwH0kPm3CsxwYROfpmDhRdE4cTcsaFWtpUdUkZ+fy1D/rySoopHVwIJ+PttxzsqLRaHW8uuxfErNyqOPrxUdjBlS4PZVao2XOP/+SV6imbZ0aPFVOc+af/z3KjYQ0vF2deG1ML6vuW1Ck4YO/Re/N0d2b076hZaUNP6w4QFpWHrUDvXh8hLElUUmycgr4apGYxn90WHvq1zLt+fjPuhOE61PsrzwlnWLXaHV8/PUWCgrVtG4RwtiR0iJwzdqTnDgpGpK//dZIHCVGIu7cdpE1eg/O2W8Nl5y9vn3jWZb+IdqsPPPyQEaMk36/Du26zGdzVqLT6hgwqg3Pvz3CbJTy6K7LzBzxHVfO3MLJxYE3fniUFz8eY1VneWWjVNnTZ2Qb5m98iZad61GYr+abOSv5avZy8nNNp6GbtAzhwwVTcHBUcuZoJB+8tJSiIuMIo1+AO1/8PBX/QHduR6fx3ivLKSz4f5Eqk8mY/cZwaof6kpaaw4dvrZE81uiH2tGpU13Uai0ffLyBfKlIq6czs18Q6ziXrz3B+Usxkvtv0yzkTsp9gemUu0wmY86T/XFU2XPuWiwb9l4w+ZoABPq48eyYbgD8uOogianGIvpu7BRy3pnSHzu5nL3nItl95nqp9ylJ89qBTO4jXjx/tHw3Gbllr1kM9HTj/XH9Afhz70mOX48u87FM0S60JrMGiYGHz7fs59ytuAp/DEvpVCeED0eIz/f3w6dYfrLqhHBloFQo+H70UPo3rFcsPvdH3KzqbVUINuFZRnIKC3ly+VrOxMbh5qBi0cTRtAi6v0RnXpGa6UvXE5eRRS1vDxZMGIlDFY4J/H7HYU7ciMFJac93jw7DWVWx1iEAP247wqWYRNwcVXw2cXC5fOtOXY9l0W4xAvXmI32tntH8y6ajxCRl4OfhwosPWxZlPn7pFpsOXkYmgzcf71/q7PYfl+4nNSOXkEBPHnuok8l1YopdjKK89GRfvD2lU+zLVh3nyrU4nJ2UJschhoXF89sf+wB4dkZf6oQai93rYfF894U4BnHilK5072kcXb18PpofPt0MiD6doydK7//o3qt8+toKdFodfYe14sV3R5kUnUWFGn76YAMfzPiLnMx8GrQI5scNL9J9sPnxmlLERCRy/sh1q/4uHY+kqMC6+jkvPzc+XvgkU14aiFwuY/f6Mzz/0PdEXjEtCpq1qc0H8yejcrDn1OHrfDRrGWqJ9LZ/oAefLZiCq7sj4Vfi+P6TzUZRPUcnJe99OgYXFweuXLrN/G+3Gx1HJpMx59WheHu7EBOTxo8Ldknuq3vn+gzu3xxBgE+++dekqHxmUncC/dxJTMnmx79Mp9wDfd15ZpxeTC49QFIpYnJM35Y0rxdIXoGazxZblk5uUNOXxwa3B+CzZXutrtecMaQLof5eJGfl8tHy8qWw+7eoz8OdmiEI8PrSbaTnVHzzzdRubRjQrD4arY6Xl24mJTu3wh/DUka1asLzvcXyow+27GVf+I0q20tloFQo+O6hIQxoWA+1Vsuzqzex7z4QnzY7pTKQU1jIE8vWcfZ2vF50PkyzQPOWHf81NFodzy3fyL7wm3g6ObL8yfHU8vaosv3svHSdl5aIIuObiUMZ2NzyWcmWciw8mqd/XYMgwLdTh9GvRdnNnbPyChj32T8kpGczqlNT3ptk2v5FiitRCUz5bDk6QeDbZ0fQs2XpKfr8QjXj3/yL+JQsxvZtxezJfcyuP3npFi98shqAn995hJaNpGtl1WotT875h8hbyXTvUI9PXhspGe0Mu57AjFdEi6U3XhnKwD7GLgC5uYU88+xC4uIy6N6tAe+985DRsTLSc5n55J8kJWbRsUs93v90rFFdZ1JCJs9P/Y2MtFy69WnMm5+OlRS5xw+E8eFLS9FotPQe0oJXPx4jaWYOcDsqmU9fXFIs2EY/0YNpswZhb8WYzLycAvZvPMO2pUcJP1+2iJOLuxN9Rrdj0ITOhDa2rkHx0smbfD5rKSkJmdgr7Xjq9WEMm9TZZGbg3PFI3nnub4oKNXTu3Zg3vxqPnYSrwLmTN3n9+b/RaQWefKEfYycb+zmePBbJm7OXIwjw4quDGTbKuAzm/PloXnltGTqdwJuvD6ev1Gckr5DHn1tEQmImQ/o3Z85LgyX3fvZyDM+/swKAr956mE6tpW2itDod099bzqWIeLq1qcMXr4wymym5cTuVye/+g1qj5cPpQxjYSbqkpCRFag0TP17Cjfg0hnVqzAePWWdrdCU6kSlfL0ej0/HexP6M6lx227a8QjXjv1vKzaQ0ejWpww+Pj6jwzFBuYRHj5y/jRnIa7evU5PfHHy7zCNDyIggCb6zfwbpzV3BS2rP4sbE0C7q/zsdqrZZZ6/9l+7UI7BUKsfu9vnXOKJWNzU6pEskqKOBxveh0v09FpyAIfLJ1H/vCRT+xBRNHVKnovJmcxpurxcamad3bVoroTMvJ441l2xAEGNOpeblEpyAIfLJiDwnp2QT7elidYldrtLy3eCc6QWBQ+4YWiU6An9ceJj4lC38v1+KUoSnyC9R8/rs+jd+/pUnRCbBo9VEibyXj7upocg57YaGaj77ajFaro2fXhgyQaBQSBIFvv99OXFwGfn5uvDpriNGxNBotH72zjqTELGrU9GLu28bNRAUFat6fvYKMtFxC6/vz6rujJEXnqcPX+ehlUXT2GNiMVz962KTo3LPhDM+PEqOEbp5OvP/rYzw1d5hFolMQBK6eieK72cuY1PZtfpizgvDz0djZKwhpEEAtK/48/dzIycxj48IDPDvgc14a/g3blh39Pz9PczRrH8r8jS/RsU9j1EUaFry/no9m/k12Zp7k+lYd6/LeD49ir7Tj6N6rfDZnJRq1cWNKq/ahzJgliqk/5u3ixGHjlHL7TnV5/GmxlnP+d9u5dME4Vd6yZQiPThIbcr79fju3bxtb8zg7qXhj1hBkMvh350UOHZVOX7duGsyYIaK4/XzBdrJNRBoVcjmv62uXD525we5jYZLrDNSp4c3jw8USla+X7CUju/SoodLejnemDBC7/I9d5fClqFLvU5ImIf7MHCa+Lp+v3kd0coZV9y+Jk8qeLycPwV6hYN+VGyw/XPEpaGeVku8eHYaT0p6TN2L5fsfhCn8MS5HJZHwwoh9d6oaQV6RmxpL13M6wblRtdcdeoeCbUUMY1Kg+aq2W51ZvYmdY2W24qhpbxNMK0vLyeXzpWq4kJhWLzqb3megE+OPwKb7ccRCZDL4bN4yBTcouwspLbmERExYsIzIpjXahNfjjiTEVfmUtCALP/7mB/VduUsffi+UvTbSoiccUW05e5c3F21DIZSx6+RGa17au2ez3LcdZsPEIHs4OrHl/Kp6upY8ivXwjnic+FCOk3816iC4tzI/S/P7vfSzfehp/b1eWfD7V5CSYqxHxTH99KVqdwAevDKdPF2nPznm/7mb1htN4ezmzcP7juEs07WzddoEvv/5XNBT/ZhLNmhqL3Z/n7WTNihM4OiqZ9+s0at01N1wQBD59cw37d17G3cOJH/56ioAgD6PjnDkWwbvP/YO6SEO3fk2Z+/k4yUheQV4RCz5Yz841YklE8w51eO3rCfgElG4Inp2ey+61J9m+7BhRYfHF/1+zrh8Dx3em35j2eJiYxGMKrVbHuYNhbF12lGM7LqLViB6Ujs4qeo5ow6AJnWnQKqTUCJYgCKxfdIg/v/wXjVqLX5AHc7+bRGMTHfynDoXz/otLUKtFkT7n07FG9lKCIPD9J5vZuv4MTs4qvl/0pNFoTUEQ+OiddRzYexVPL2d++vMJvO96DbRaHa/MXsaFizE0qB/AvO+lBwb8snA/S1cfx8PdiYXzH8NLoryjoFDNtFcWExufzqBeTXjredNm7H+sPcrvq4/g6ebIki+m4elm+nul1miZ8t4/RMamMqhzIz54xjKT969W7mPp7rMEeLmy6t0pVk0S0up0PD1vDacjYmlWK4CFL4/DXlF2b+IlB8/y2fp9KO0ULHtpIg0CjceglpftF8OZtVT01/1u0jD6N6u6c0VOQSET/1xJeGIK9Xy9WPrEI+VyI6mOqLVaXt2wja1Xw1HIZHw1ajBDm0j/Jt9rbBHPSiCvSM20JWu4kpiEt7MTfz869r4UnQevR/HlDtF0fM6AnlUqOgE+3bSPyKQ0fF2d+WrC0EpJ56w9fon9V26itFPwxaNDyiU6kzNz+GSl2FH+zOBOVovOqIQ0ft0i2hXNHt/bItGp0wl8snCXGCHt3KhU0XklMoEV20R7p9ce72dSdGq1Oj6dvx2tTqBv14YmRefFK7Gs3qA/3ouDJUVnQmIm8+aLEdbHp/WQFJ2nT94obiaa8/YII9EJsGHFCfbvvIxCIeetz8ZKis7rV+J4/4UlqIvE9LEp0ZmRmsOsR+azc80pZDIZk57vx6eLny5VdGakZPPbB+t4tP27/PLeOqLC4lGq7Ok7pj1frn6BX/e+wZjpfawWnQAKhZy2vRrz1i+P88/JD3jizRHUqONHfm4h25Yd5aUR3zBr1HecOXDNbC2gTCbjoce68/WKZwkM8SYpLoNXJ/zEng1nJNe369aAt76diJ2dggPbL/HjJ5skjznztSE0bRVCXm4h77+6nKJCjdGaV18fRu06vqSn5Rab/t/9HN98fThurg6EX09gsYkO9sce7UrdUF8yMvP47ifpmlAHlT1vPjcImQy27bvC8bOm69+mjOhAnWAf0rPy+f7vfSbXAaIN2ON6k/ej1zhx2TIvxZkjuxLk7UZCWjbz11sXBVTI5Xw0eSAujiou3Upg0S7LzOxNMbFbK3o0DqVI3zBZmpF+WRjYvAHTuov2a2+u3kFcFUYaXRxU/DppFH6uzkQkpzFr9b9VZvlUWdgrFHw9ajAjmzdGKwi8sn4ru8LK7oZQVdiEpwUIgsC723ZzLSkZH2cn/nl0LI38K84ourqQW1jEu5vEH/jx7VowtbN50/HK5lJsAutOi8bMX08Ygq+rdENLeShQa5i/Xe//OLgrDYPK977+ufMkuQVFNA3x5/H+1nfE/7zpKBqtjq7NajOovWVXsvvPRnA9JhlnRyWzJpY+q/631YcRBBjYtTFdWpuuE9p7NJwb0Sm4ujgw6ylpuyOAP/8RhcOQ/s3p1E76eH8tPkRBgZrmzWsy/hHjJiCtVsfP88TP3siH29G1h/FzT03J5q+f9aL+5QG0aFvbaE1BXhGfzVlJYYGadl3r88ZXj0iKzvSUbOY8+gs3r8Xj6ePCZ38/zaMvDDCZigdxjvqfn25kWpcPWPvbPooK1dRpWoOZH41hyekPePXbR2nWsW6F1dN5+LgyZnpfftv3Bl+sep6+D7dHqbLn2pko3pz0E6+NmceFo+bTbQ2aBzNv/Yv0GNICnVbHV7NXmLRc6tijIW9+PR6ZTMbW1ac4uOOS0Rp7ewXvfD4OLx8XYm+lsnrJEaM1jk5K3vpgNHZ2ck4ei+TUCeOGD19fN2a9LNZurlpzUtKGSWlvx5uvDEUmg/2Hw4i4Ie1T2rxRjeKU++8rDpsUG/Z2Ct56RrRi2nHkKjdiUyTXGWhaJ5CH+4h+xb9tOGaRiHFU2fP2ZPG7svrABeJSjM33zRHo5cZcfWnOX7tOlctYXiaT8eH4Abg7ORCRkMqWMxU/1Qjg5YHdaBUSSG5hET9sr7qUO0CAuyu/TBqFyk7BoYhbbLxQOc+5KrGTy/l8+EDGtGyGThCYs2k70ekZVb0tq7AJTwtYcfYiGy5eRSGT8f3oodS9j+bDlmTe3qPEZWYT5OHGawN7VHhBujUIgsBX/4qR1+GtGtG2EkziAVYcOU9yVi6Bnq5M6FY+U/z4tCxWH74IwIsjulkdnQ2PTWbHqXBkMnhhdHeLXn+dTuC39aJwHtevNR6ljGm8HBHPsfNRKOQynhzTxexxF60Sj/vI8La4mzjuxSuxnDkfjZ2dnGkTjRtOAKKjU9m5SxQx05/qI1mPuXPbRaJuJOPq6sDUJ6SnHP35427ycoto2LQGw8dKi/qfv9jC7Vsp+Pi58dqnY7GXMq5PzWHOo78QHZGIt787Xy6bQYuO5utoN/91kGmd32fVgt0U5hfRoGUIH/09nR+3zmbY1O64uJcemS4rMpmM5p3q8ep3j7LwyDuMfKIn9io7Lp2IZM64ebwxcQEZqabNy51dHZjz7USGTOgk1tnOXWVSfHbu3Zhxj4sOCt+/v57khAyjNR5ezjz1otgst3zhIcnJRrVq+zDyYdFK67cFuyU9NLt3a0DTJjUoKtKwZKm072TdUD96dxebexYvN+1NOXl0R1RKO65eTzA7TrNxnQB6ta+PIMDCtcdMrjMwdVh77O0UnA+/zcmr0vZOd9OxcS06NQ5Bo9Xx27/HLbpPSYa0a0yDIB9yCor4a3f5op5eLk483lt8H37acQy1xKjU8mKnkPP68F4AbDp3jcu3jX1a7yWNA/2Y0VO8uP1s2wHSy2FRVV2Ry2S8P7gPbWoGkV1YyHOrN1PwH5rgZBOepXApPpEPd+wD4JXe3WgfUjkCqKq5HJfI4mPiVJH3hvXBqRzp5opg79UbnLwZi8pOwQsDpQVNecktKOKP3eJs6Bn9O6G0K59V1O87TqDWaGlXvyYdGoZYff+fN4on1gHtGlK/hmX1WHtPXyciJgVnRyUTB5Zupv/HGvExBnVvQk1/D5Pr9h8PJyo2FRcnFWMGmz7u4mX64/Vthr+fdF3PosUH0ekEunSuR2OJLu2CAjV//S5a4kyY0lXSJP7y+Rh2bRGbJJ6dPVhSvB7aeZlta0+L3pKfjMHNw1gM6nQ6Pp+1lJjIJHwC3PliyTOljrlcOX8n899aTX5uIXWb1eS9hU/x3aZZtO3V+J5fnHn5uTH9vdH8cfBthk3php29grMHw3j9kR/JkIgaGpDL5Tz3/kMMm9QZQRD44a01hEk0/wBMfrYvDZrVICe7gC9eX41WIkXbe2AzmrUOobBAzW/f75Q8zsQpXXF2UXEjIondEtFTmUzGE4+LFxlbtp4jQULkAkwZL14g7T8cxs1b0lFKLw9nRg0QLxwXrTpqNjr5+GhRlOw+HsbN2FST6wD8PF15qFdzAH5de8Ti1O2MkeKeNx+9wq1E62aby+UyZgwV7790/1nSsqUbwyxlQrdWeLs6cTsti3UnrRvtaSnNagYwrJV4gfDllgNVnuJ+vEtb6vt5k56Xzxc7DlTpXioLe4WC7x4aipeTI9eSkvlge/mmX91LbMLTDBn5BbywZjNqrZa+DeryRCfTowT/y2i0Ot7ZJNYIDmnWgB71zdcIVjZqrZavt4rRzind2hDkUTmNZUsOnSU9N58QHw+GtzPuwraG2JQMNhwVf9RnDjUdSTTF5agE9p2PRC6T8cww036aJSkZ7ZwwoA3uLqVHO4+ev4lCLmPaKNOPIUY7xWjQ2KFtcHE20XgUFs+JM+LxJo2VPl5ERCL79l9DJoPHpklHMtetOkFKcjb+Ae6MlJg6pNXqWPCl6Ok5cEQrGjU1niSVnJDBd++vF/f8eHdadpBO+a/6dR/njkSgcrTn44VPElTLvMBf9sMOFn4m2ng9OmsQ8/59lY79mlVpNgDEEZozPx7L/O2v4eXnRlRYPHMe+ZH0ZNM1djKZjGffHUXXgc3RqLV89tIScrONU7l29grmfjYORyclF09HsfIP4xO3TCbj2VfFC4ADuy5z7pRxbaWbuxPjHxW/Cwt/3WdUDwrQqmUt2rapjUaj46+/pdO0obV86NlVdLL4e4XpqOeEUe1R2iu4GBbHGRPm8wD1a/ndiXquK33Cz9ShHVDZK7gQEcdxC2s9m4cG0qNFHbQ6gV83lx5ZvZtezevQJMSfgiINC3eetPr+JXFU2hcPwvhl5/FKm23+4sCuqOwUnLwZy96rVeunqbRT8MGIfshksO7cFY7dtCxa/V8jwM2Fb0YNQS6Tsfr8Jdacr5wLi4rGJjxNoBME5mzcRmxmFsEe7nw+vOKn5FQXlpw4x+W4JNwcVLw+qFdVb4clR84RlZKOt4sTT/ZsXymPkZ6Tz6K9YkPMswM7l7tp6Zetx9HodHRpXIvWda0fsblgg1grN7RTY2oHWFbKsftkODdup+LiqGLCACuind3MRzsPn4ok8lYyTo5Kxg41fdy/lot77t+7KUGB0sf7c5EoWnr3akLdOsZG8RnpuSz/WzzOY0/3Qqkyjjpv23CGiLAEnF1UPD7TuNZUq9XxxRurycnKp0GzGkx5tq/kXq6ciWLxd6It18x3HyKknvnmwCXfbWPxl2JzzJTZQ5n08uBq9xsQUj+Az1c9j7e/O9HhCcx55EfSJOaqG5DJZLz48cP41fAkISaNeW+vkYxOBYV4M/ON4QD8/dMerklER+s2CGDoaPFi/KevthV34JfkobHt8fVzJTkpi/VrpAXU44+JFyQ7d13ilomI5uRHRJPwPQeuEm0iSunj6cLwfqLR/8JVxrWn//eY+qjnrmOlRz19PV14qLcYTbUq6jlC3PO2k9eIuG2+nvRuZDJZ8QXsykPnScq0bg783Yzt3JwAD1eSMnNYduhcuY5liiAPN6boRxh/sWV/pQlcS2kdHMT4duLn4d1Nu6p8P5VFl9AQXuwhftbe27abq4nJVbyj0rEJTxP8euQkeyNuolQomPfwMNwc7i9bBgNxGVl8v0f8kX6lf7dKaeCxhsTMHObvEgXSiwO64uIgHW0rLz/tOEZ2QSENg3wZ1Kp8vqBRiWlsOSkWsc8YYn2088z1WI5euYWdXM5TQ82PuDSg1emKo52TBrXF1dn857NktHPqKNOPIQgCC/W1nQ8Pbo2bidrO8MhEjp6IRC6X8ahEsxDA5Su3OXZcXDNtirSv6JK/DpOXV0S9Bv707mdsJp6Vmc+iBWJD0ZRneuPhZfz5XPXnAS6eisLBUcncz6Q72LMz8vj85aXotDp6j2hNv9GmsxeCIPDPN1v55+utADw2dxgTXrBuAMC9pGYdP75Y9Tw+gR7EXE9kzrh5pErUXRpwdXdi7rcTkSvk7N9ynh2rpQVh3+Gt6DVYbEr6fO5KciW8RKdM74ObuyNRkUlskjiOSmXPtKd6AbB08WGyJPxEGzcKomuX+mKkfbF0h3v9uv507VgPQYB/VpqOIE56qAP2dgrOXY7l3GXzUc+e7etZHPWcMqQ9KqUdl24kcOSCZZNjGgb70a+NGFn9eZP1s9O7NK5Fy9BACtVa/th+wur7l0RpZ8ezA0Vx8uuuE5Uy0QjgqV4d8HNzJiYtkz/2ly9SWxHM6iee026lZvDLwfK9htWZZ7p2oGfdUAo1Wl5Ys5nsAtMjc6sDNuEpwbGoGL7bL4qxdwf1oUmA6fnV/2UEQeDDf/eSV6SmTUgQY9s0r+ot8cWW/eQVqWkVEshDbY2FSEVwIzGVlUfFesHZI3qUaywmwM9bj6ETBHo2q0Pz2taNTRUEoTjaObJbU2r6elh0v53Hw4iKT8PNWcUjA0p3H/hz7Z1oZ3CAp8l1x87cJPxGIo4O9jwy3LQ4W7xM3HOfHo0IriEdof1joVi3OWhAc2rWNF5zOzaNTevEqPNTz/aVrNtc/PMesjLzqV3Xj+FjjKPfN8MT+PsnUZjOfGMYQSHGs9QFQeC7N1aTFJdBUC1vnnvfeFpSybV/f/UvS77dBsATb45k3Mz+kmvNERuRyLcvLuaNMd9a9ffOhHnsWnFUsq7SHEGhvnyx6nn8angSG5nEnHHzSInPMLm+cetaTH1Z7PD+6YMN3Lpu3BAik8l4/q0R+Ad5EB+bzh8SozDd3B15TB9hXvzLXjLSjMcn9h3QjDp1/cjNKWTpYulI5GPTuovd6weucT0iQXLNlAmicNq19wq346XrJv28XRnaR5z6s2i1+RT34w/pj3csjKjb5qOePh7OjNV3uP+63nwNaUmmD++MTAZ7zkZw5ZZ1TTcymYznhon17WuPXiI+rXxWRSPaNaZRkC/ZBYUs2GG9ELYEZ5WS14b2BOC3/SeJTs2olMexFFcHFW8O7iXu59BJIpLMv8//VeQyGV+OHEQNdzdupWcwd/OOKq+zNYdNeN5FYnYOL6//F50gMLpFE8a0rBzxUx3491I4e8NuYK+Q88HwfpIn/nvJ0YhbbLsYjlwm462R0t3PFcHXmw6i1Qn0alqHjvWtbwIqSURcCtvPiJNQZgztbPX9T1yL5sz12yjtFDw5xLJop0ar4/cN4kl10qB2uDiajwpfjojnyDnrop0PDWyFhwmD7cioZA4evY5MdicFejdnzkZx7lw09vYKJkuMVwSx7k+r1dG+Yx3atDOuK44MT2DLWn05xOzBKOz+/+dKp9Mx7+ONaDU6uvRpTL8R0gJ8y9KjHNl5CTt7Ba9/PwknF+nosCAI/PX5Zpb9IKbjn35nFGOmmx87ejdJsal8++Jinu76LtuXHOLMvqtW/Z3YeZGvZi5kRo/3ObzlrFUnj8BaPqL4rOnF7ZvJzBk3j2Qz4nPMUz1p3bU+hQVqPntpCYUSM+KdXR149aOHAdi6+pRkyn3gyNbUaxRIbk4hf8439ttUKOQ8+az4Om5ce4r4OOM91Qn1o3cvsc564V8HJffbqH4gHduJdZNLzEQ9H32oAwqFnFMXbnHx2m2T6xrUvhP1/HNd6XWYjw5pj4PSjqs3Ezl0zrIaxjpB3gzuIDbdGJoHraF9g2Da1w9GrdHy23brO+RLopDLmT1SFIWrjl7gRmLliLBBzRvQuV4IRRotn27aV+UCaGCT+vRqEIpaq+OtjTvR6irez7Q64OHowPejh2KvULAzLII/j0t79lYHbMKzBGqtlpfWbSE1N4+Gfj68O6hPtavpqiiSs3P5YIsYKXq6ewfq+RlHiu4lRRoNH20Qu/Imdm5J46DKiTIfCbvFgas3sZPLeWW4dLOLNfz071EEAfq1qk+jmtbtWRAE5uujnQ/3aIG/p2WG49uPXSM6IR13FwfG9bM82jmwlGjnyfO3uHI9HpXSjvEjjJt8DPyjb/Do2bUhtUOMm3MEQeDPhWJt57ChrfD3MzZkv3r5Nvv3XEUmgydnGIs7QRCY/+VWdDqBHv2a0lLCs3PXxrNcORuNg6OSGXOHSn5Xb1yN49dPxOagJ14bSj0J4/riPX+ykRV64fTMew/x0FOle6IayEjO4pc3V/BEh7fZvuQQOq2OjgNb8OqCx3jtpycs/ps8dwQuHk5Eh8Xz4dSfeHHAp5zZf8XiffgHe/PFqucJCPEmLiqF18b8QNLtNMm1crmc2V+Nx9PHhajwBH6VMI0HaN4ulH4jWiMIAvM+2oj2LksehULOzNmiJ+f2jee4dtlY7LXrIF5cqNVaFv22T/Jxpk3phlwu49ixSK5ckRaMU8br6yZ3XyYhSbqcIMDPncG9xIDBotXmxV5x1PPotVKjnl5uTsXfN2uink8P64xCLuPQpZucj4yz6D4leXaYuMcNxy4TU45RmgAd6gXTu2ldtDqBrzZJC/zyIpPJeHNEb+wUcg6E3WTPlao1OJfJZLw7rC8uKiXnYuJZeOR0le6nMmkRFMCb/cWLi6/2HORkdGwV70gam/AswTf7DnM6Jg4XlZJ5Dw/D0b5qLYUqC0EQeGfTLjLzC2gS6Mf0HtYbnVc0iw6eISolHR9XJ57rb32dpCVodTq+3Cimf8d3bUltX9MizBKuxiSy+3wEMhlMH2JZJ3pJDl26yaWbCTgo7XhskGVNVBqtjj82iCfTyUPa4+xofiRfyWjnNAujnSMHtMTLQ7rWNyo6lb0HrwF3RMDdHDseyZWrcahUdkyaYLxGEAR+06fH+w9uQR2JJp+92y5y+Vw0Kgd7nnrRONWdlZHH79+Iqd9HZ/TBN8DDaE1BXhGfviROMOrQuzEjp0pHXgVB4PcP17Nab04/48OHGfVEL8m1d5OblcfiTzfwWLs3WffLbtRFGpp3acA3/87h/SXP0W9cZ/qM7Wjx36RXh7Ho9CeMnzUElZOS8LNRvPHwd8x96BuunrIsyuZf04vPVz5HQIg3CdGpzBn3I4mx0uLT08eVV78cD8C/y45xcOsFyXVPzhqEi5sjkdfi2bTCuFauSYtg+g0VU9Hzv/jXyLdTJpPxlD7quWfnZcKvxRsdo2ZNLwYNEMt9/lgobYHTrHEN2raqhVarY8kq0xHAyaM7opDLOH42iqsRxo9loEFtP3q0syLqObgdTg72hN1KYv8ZywRViJ8HI7qIQvinjeabnqRoXacGXRvXRqsT+GWb9R3yd/PK8O7YyeUcvHqTI2GWdelbS6ivF4/pJxp9unkfeUXG0fR7SaC7K68PEgXZ93uOcj3Jumav/xIT2rRgRLNGaAWBl9f9S0qOcflLVWMTnnp2XIvgj2PildCnwwZQ26t8oqQ6s/78leIU+6cPDSzXPOCK4HZ6Jr/sFU8iswf3wLWSGorWHr9EREIqbo4qpg+wXijezYIt+rrJto2oZ+UcZJ1O4Ce9gBzfuxU+7pY1dW09coXYpEw8XR0Z27dVqestjXaevRTDxWu3UdormDjStAj+Z6UY4e3WuT51Q40jvDqdwEJ9J/tDI9vi5eVitObY4etcPBeNUmnH1Cd6Gt2el1vIbz+I3pATHu+On8QIy4Xf7yArI49adf0YNUlaAC94fz2xN5Lx9ndn1mfjJCOigiDwy/vrWKuPws38eCwjTNg+laQwv4hV87Yzre2bLP16C/m5hdRvWYuPV73IFxteoUkH84b05nBxd2LaG6NYdOoTRj7dB3ulHecOXuPlQZ/x/uT5RF01nT424FfDiy9Wv0BQbR8SolN5bew8EqKlI3ptujVg3DNidPf7N1eTEGMsUj28nHlMfwGw+MddpEp0zj/xXD+cnJWEX4lj5+ZzRrfXaxBA34Fi/eVvC3ZLRgwffbQrdnZyzp67xZmzUZL7nTpBvDDduuMiSSa8S2sEeDCgh5i6N1iDmaK4w92CqKeHqyOP9Bc7t39df0TSGF+KJ4d2xN5OwYlrMZwMs97ax1DG8+/Ja9xMkL6IsJRavp6M1w/L+HLj/koZpQnwdO+OBHq4Ep+Rza97y1cmUBGMbt2UnvVDUWu1zF23HbW24s30qwMymYwPBvejno8XSTm5zFq/FU01Ky+wCU/gVloGczeL0ZPHO7ZlYKOqnU9emSRkZvPJVjHq93zvzjT0t04wVQafbtpHgVpD+zo1Gao3Ia5ocgoK+XGbKMKmD+iEu1P5XAou3Izn4GUxkjh9sPUidu+5CK7FJOHsoGTKANNp7ZJoNFr+2Cj+gE8Z0h5HlfmI/JXIBIuinUBxtHN4vxb4SIhFgNjbaezeL3bvTzFR23ngYBgRkUk4O6l4RGo0pkbH7z+LJRWjx3XAz9/Yo3XpnwdIS8khsKYnD0uIyqvnY9i6Rpzo8vxbIyS72PdsOMPOtaeQy2W89vV43CW64QVB4Od317LhD/334bNHGGai+77kfbb+fZDH27/FH++vITs9l+D6Aby18Bl+2PUGbXs3rbDyHE8/N2Z8Mp7fj3/IgIldkMtlHN16nhk9PuCLGX+QYqLBxoBvoAefr3yeGnX8SIpNY864ecSbsit6cQCNWoWQm13AZy8vRaM2PikPfrgdDZvVJC+3kF+/2mp0u5ePC4/qO9j/+HEXORIeoY891Qt7ewXnztzi5DHjiGGAvzvDhrYC4M9FByXFactmwbRsVhO1Rsuy1Waing93RC6XcfhUJOE3TDf2NKztXxz1XGhB1HPiwDY4OyqJiElh7+nrpa4HcRTm6G5iNPenDZZbMhloViuAXs3rohMEftla/qjn9P6dcHNUEZGQyroTxub+FYGT0p7Xh/UCYOHB09xIKp9gLi8ymYwPRvTD3VHF5bgkfjtY9V33lYWT0p55Dw/Hyd6eY7di+H6/9ZH2ysQmPIH3t+8hp7CItsFBvNK7cqbkVBe+23OE7IJCWtYM4PEulgmeyuRCTAJ7r97ATi7nrRGVV1O7+uhF0nLyqOXjwfgu5RuNCbB4jxgdH9ahCbX8rI+OL94h3n9i39Z4lGL8bmDfmUjikjPxcnMqniFtjhVbxccY0LWx2WhnRFQyZy/HYGcnZ+Io09HOtZvPotMJdGpXh4b1pbv3V+jTn2PGtMddYgLRkUPhREel4OrmyCMSojIzI4/1y8RjTJ81CKXS2Ndzkb75p//I1jSTqP3Myyng5482AjDxuX4mx2FuX36MjQsPIJPJeOnL8QyZZL7EIzs9l/cnL+D7l/8mNSEDv2BvZs2bxs8H36Xb8LaV9tn1D/Zm1g/T+PnQe3Qb0QZBENiz6jgze33Iqd3mRYNPoAdfrHyO4Hr+JN1O56Nn/kRdZOxnaGevYO63E3FxcyTsfDRbVxgLOrlcznNvjUAul7F/20UiJdLlIx/pQEioD5npeWyQOIZ/gDuj9O4Ey/6WPhk+OrELKpUdV67c5tJl6Rq1qRPE3+ktOy6Ql18kuSYkyIu+XcUL2VVbzDdaGKKeO49eIyXdvGemu4tjsW/u0u2W1ws+PqQ9KnsF5yLjuHDDdPrfFM/qo57bz4aVu8Pd3cmh2F7pl53HK63hpk+TuvRsGIpGq2PB7srppLcGfzcX3hoilnz8dOAESdnl80etztT18eLjYWKW4tcjJwmrRuUFD7zwPHErlkM3bmEvl/P58KpPO1cmCZnZbL4g1ue9MbhXuU3TK4KVx8WasiEtG1LPv3IanARBYL1+VNzUXm2xtyvfe5xbUMTBy2K93fge1ovYuJRMLt6MRyaDsT0tv/+uE2L3/PDuTXEoJdpZWKTh0Flxjw/1Nf8Y+46Kx+3cug7+PtJTonQ6gf2HxXUjhrSSXBMbm0ZYWDxyuYyRw6WN53dvF2fZDxneChdX46jzzs3nUKu11GsUSKfuxv6q0TeSOH/yJnK5jMkmjOI3LzlKdkYeNUJ9GC/RuAQQfyuFX99fB8Bjrw9noIl6VQPhZ6OY2ftDjm07j73SjqfeH8Pvxz5gwIQuKMr5ebKUkAaBvPXndH7Y+QZ1mweTmZrDW4/8wJ8frkVnRjh4+bvz6bKZuHk6c+Py7WKrqLvxr+nFFL3F0qpf90kK1PpNgujWX6xX3LzSWFja2SmY8Jg4633r+jOStlCjx7VHJoNLF2JIkOi69/JyoVcPUTDu2XNVcq9tWoZQI9CDwkINR0+YrrUc0V80ED90KhKNmTnlDWv707ReIIIA+09FmFxn4KFeLZDJ4GJEPImppkeVlsTX3YU+rcVs2s7T4RbdpyQNavjStl4NBAF2nLX+/ncztnNz3BxVJGbmcPx65Uz2kclkPD9AvKDbeTmC1Jzyjf+sCIY1b0jr4EDUWi1/HztX1dupVIY2acjgxvURgO/2SU8GqwqqXnlUITpB4PPdYj3a2FbNCPH0qNoNVTJ/HTuLRqejfe2atKwZWNXbITO/gK0XRDHzSMcWlfY4l2ISiUxMQ2WnYFCrhuU+3v5LNyhUawnx9bC6kx1gpz4917Z+TYtrO/ML1RzWG1f3bV+64f2Ji7fIyy/C18uFpvXMv9f7j4v76dnJdInJxSuxpKTm4OykpH2b2pJr9uwVu6/btQ3FQ2JOelZWPif06dW+A5oZ3S4IAts2nAVg6EPSHqKbV4qNLR17NsJPYlpSQV4Ra/8Uv9PjZ/SVFIVarY6vXv6H/NxCmnWsy+inzXevpydl8faEeSTFphEY6ss3W+fw8MwBKEsR/5VFg9a1+XbrXIbrG6BWfr+N9b/sNnsf7wB3nv9sHACrFuziisSIS4CBY9vj5edKcnwGu9ZJR/OGPSKWbezZfF5y5Ga3Pk1wdXckOTGLMxLpdB9fN1rqP0N7dkqP+OvTRxS3+w5clRSMMpmMXt314lTf7CZFi0Y18HR3IjungLNmDOUBencQP/97j5cu6nw9XWhZX5xQtvuU5SJwQDvxu7vr9HWL60NLMrCN+Pu1/Uz5hafSzo4hbcTXcP2Jyhu12DjIj+Y1A9Bodaw7XfUjHWUyGU92E6Puy09dIKdQOmJ+v/BCjy7IZTJ2X79RbbrcH2jhufnyNS7GJ+KstOe57uVvNqnOZOUXsPK0GG16smvVp9gBNp25SoFaQ4MAH1qGVJ4QNvyo9m1eH9dSPC8tYftpUSwPbNOwTOlVQ7TDcBKyhMPnb1JYpKGGrzsNa5UudvedFMVk7/b1zfqhRt9O42ZMKgqFnK7tTTfE7NonisqeXRuitDdOfwuCwG698OzTu7HkMQ7uvYparaVOXT9C6xo/h6sXY4mJSkHlYE9PCWGan1fIro2iMB32iLQTw7/Lj5GZlktAsBe9h7eSXvP3Ya6cvImji4pXv52EwkzkXxAEvn3xLzJTsqndpAY/7nmL+i1rmVx/r1A62DPz84lM//gRABZ+uI6bV8yfVLoNaUXfh9uj0wl8++pSI1skAKXKnjFP9gJgxc97JGs9m7etTUhdPwoL1OzadFbiGHb0HSTWM26XaDKCOxceu3dckqx3bNO6Fp4eTmRm5nP6TJTkMfr1FD9nx0/eIFtCAINo9dS9Qz0A9h0zX4/Zu4P4fTx7NZb0rNIjc4YLwN0nLReBnZvUwsVBSVJGDudvWG+t1K9VfeQyGVeiE8ttrQQwqr3YgLX7UgSZedKvYUVgCCysOn6hTIK7oundoA6hPp5kFxSySn9evF+p6+PFuFbi9+3TXQfQVQNj+QdWeBaoNXy9Vww9P9OlAz4uVTsqsrJZceoiuYVF1Pfzpkf92lW9HQRBYMUJMc0+rkOLSquPK1Br2HpWFIqjOjQp9/Gy8go4fDUKgIFtrB+1GZucwZVbichlsuK0myUY0ux92zco9bVSa7Qc1KcLe3Uwv8d9x8STZtvmISbHbqrVWvYdEh+/Xy/p1zAiMpGYmDSUSju6dpF+zN366FYfCVEJsF0vKnv0bYKzi/EFwt5/L5CXU0hQiDetOxmL5MICNat/19tlzegjGe0syC9i2TyxRvSxOcPxDzZf3rFl4X5O7LyIvcqOOT8/gbOJEaJVxcin+9BxQAvURRo+f+Z3iiRM4Esy/f3RuHk5ExuZxJ61pyTXDB7fEQ9vFxJj09m70VhYymSyYuG/ecUJSeE4QG/mf3TfNTIzjEVc954NUSrtiI5KIVJiapJCIadnT31Ec6+0j2md2r7Uqe2LWqPlwBHT4q9nR/F7duD4dbMToYL83GlY2w+dIHDAgnR7n3b1rU63K+3t6NVK/OzutCJSasDL1Yn2DYKBiol6NqnpT70Ab4o0WrafCyv38UwxqEUDXB1UxKZncSSiciycrEEul/F4FzGr8tfRM/dth7uBF3p0xlmp5FJ8Ipsvm84Q3CseWOG58MQZ4rOyCXJzZVoH6Xq0+4UijYbFx8Ti+se7Vl4ThDWcjrrNjaQ0HJX2DG9dOZ3sAHsvRZJdUEigpysd65VvShHA3guRaLQ66gZ6Uy/IekeAXfo0e7uGNfEyMRnobv4vJT5H/QAAOPpJREFUzV6KkAQ4dTma7LxCvNydaNEwyOxaQ5q9VyfTxz159iZZ2QV4eTrTqnmw5Jrde0Rx0KljXZydjUVjYkImF89FI5MhOZO9IL+I/XphOlBiApEgCGxaLtYUDh3XAbnEmNNtK0+QnpyNX5AHfUZKf6e3LD5EelIW/sFeDJpovq4z5no8v727CoDH3x5NaBNp8/mqRCaT8fL3U/DwdSXqahwLP1pndr2LuxNjZ4i1sUu/3y4Z0XRwVDL6cdFSavlPuyUjo32HtcLBUUnMzWQunDRO29dtEEC9RoFoNDr2bjOOKDm7ONCpqygId22XbpDq01u8yDl0+DoFJgS1Ieq5y4zJfptmIbg4q0jPzONSmPkoY++O4vegMtPt/Q3p9jPXy9TUM6itmG7fcab8QlEmkzGqg/h9XH/S8kEF1uKotGdEa/G9WnWiekQYR7RojK+LEwlZOfx7qfJEd3XAx8WZZ7qI5QVf7z1Mgdq4fvte8kAKz5ScXH49ItaKzerdFQeJ1OH9xKYL10jOycPfzYWhzSpP5FnDCn1T0dCWDXGpJN9OuJNmH9GuSYWM4DSMxyxLtBNgxylDmt3yWtOSafZGlqTZT+jFZPv6ZufQxydlEhaZiFwuo7sZ38ld+8QGj749GkmmpXU6gb2GNX2kI6J79aKyRatakhZKB3dfIT+viKBgL5q1Nr5AuHo+hpvhCShVdgwYaSxMiwo1rPp1HwDjnumNvUQ3fH5uISsXiJOJJrwwUHKNATGC+AeF+Wpa92zMyKetG515L/HwdePl76YCsO7nXaVOOho2tTuevq4kRKeyQ6JBCGDYpM64eToRdyuV/VvOG93u7OJA32GtANgk0b0OMFBf6rBto/ToT4On595dlyUjkU2b1CAgwJ38/CKOHZduIOqjF55nL0STYiLqaG+voGs78fNtiPCbwlDneepKDJk5+WbXQtnS7Z0a18LVSUVKZi7nIqxPt/dpUQ87uZzwuJRye3oCDGvTGDu5nIvRCUQmVN4s83EdxfKLvVcjScys+m5ylb0dj3YUf0v+OHy6ykd7VjbTOrQhyM2V+KxsFp6o2nGaD6Tw/P7AUXKL1DQP9GdY0+ohxCoLnU7gj8Nik8CUTq1R3qMOXHOk5eSx85KYyhpXiU1FCenZHL0upnVGti9/mj09J5/jYdHAnSJ/a4hOTOdaTBIKuYzeretZfD/DSc2SNLtGq2O/vr6zVwfzqfwD+mhni8Y18DTR5JSXX8ThY+J71ddEmv3ipRiSk7NxdlLRUULACoLA7h1iVMsgNu5m+8ZzAAwY3kryORrETa/BLXB1N44U71xzktTETLz93en/sLQl1MaFB8hKyyWotg/9xpifFPXPF5uIuBCNq6czr/74mGSEtTQ0ag1ZqdlW/eVlly52pOg4sAXDHhPN+L+euYisNNMndgdHJeNm9gNg+Q/bKSo0jn44OqsYNU3sTl++YI+kMByqT7cf2XNV0lC+18Dm2CsV3LyeSERYgtHt7TvWxdXNkbTUHM6fMU6/ymQyevcSheXuvdJNKYH+7jRvInZ67zlgOoVoiOjvP37drMAICfSibrAPWq2OQ6dLn0xUlnS7vZ2C3q3E7/+OMqTb3Z0d6NRIrDPeXgFRT29XJ7o1rg3AhpOV1/xTz9+HtrVroNUJrDlVOd6h1jK+fQuclPaEJ6ZwqBqUAFQmDvZ2vNJb9Cn+9cgJkqtwotEDJzzDk1JYdU780L/evyfyapB2rkz2hd/gRkoaLiolj7RtXtXbAWD9mSuotVqa1vCnaQ3jcYkVxcbTVxAEaFe3JsHeHuU+3o4z4Wh1Ag1r+pbJu9PQzd6+UQieFnp35heqOXRetEWyJM1+7losmTkFuLs40LqxdFrcgKHZwlya/fDxCAoK1dQI8qCRCe/OPfo0e/fuDSR9N29EJBF1Mxl7ewXdexpf6N2OTuXi2VvI5TL6DTG2fspIy+WQXrgaOqpLoi7SsPIX0ZR+7NO9UKqM95CblV88EnPSy4PMWiBdOnqdld+LlkMvfP0o3hLd86VxfNs5JtZ7kbEhM636Gx04na+n/0ZBbqHVj/nk+2OoWc+f1IQMfnjlH7MCa8ikrngHuJMcl8G2ZdJ+miMmd8HFzZGYG0kc3m6cHq3TIICmrWuh0+qKDf1L4ubuSFe9cNwuUStqb6+gZx+9sNwpLUQMEfQTJ26QkyPd/NK3p7hm135p6yWADi1r4ehgT1JKNtcijUVwSYq720+Ubg5f5nR7W32ktMzpdvH+/566ViGRulHtxXT7ptNXK22SEdwJNKw5ebFSH8dS3B0dGKc/L/5+WLrm+X5iaNOGNA/0J7dIzQ8Hqs5X9YETnp/vFru6BjaqR7vgGlW9nUrnD/2XaXy7FpWa0rYUnU4o9u6sTAslQRDYoK9ZGlUB0U6ADcfFaMDwMjYp7dB3w5elmz3IwjS7oTatZ/v6Zn1aU9JzuBQmjl7s0dF0ZHS3vpu9X88mkpFItVrLfn2kyVCTZ3QMvWjs1LW+pHfnDn3nc9tOdfGVSMPvWH8atVpLg2Y1aNDU+Du7Z8MZkuIy8PRxYdA46W739X/sIyczj+B6/vQcKW3VBOLs9S+e/RNBEOg/oQvdR5heK4VGreH3t1bwzsPfkGlinKM5BEFgx98HeaHne0RfK300ZkkcnFTM+flJFHZyDm06w64Vpk8sSgd7xj8/AIAV83ZSKGHC7uzqWDzfftn83ZJeoYYmo61rTkrWiw4Y0QqAvdsuSkZW+/YXI+CH9l2TrOOsE+pH7do+qNVaDh6Sju717t4QhVxG2PUEYm5Lp55VKns6tQ4FYN/RUrrb9XWeJy7eIjev9AuAsqTbOzQOxs1JRVp2HmeuW/c+A/RpWQ9HpT3RyRmcu2l9uv5uejQJxcvFkZTsPA6HRZX7eKYY0KweHk4OJGTmcDBc2tLrXjO1cxvs5HKO34zh4m3zFyX/deQyGa/3FzMjq85dIryKTOUfKOF5MDKKg3qz+Nl9ulf1diqdM9FxnI6Ow14hZ3In47q4quBYZDQxaZm4qJQMbll+T01TnLl5m+iUDByV9vRvUf4RqBFxKVyJTsROLmdIO+vLM24mpHE9NgU7ubw4zWYJd9Ls9UtNs2t1umLz69LS7AePRyAI0KR+IH7erpJrMjLzOKG3sunbU9oi6fQZsfHI09OZ1q2MbYa0Wh179NGsPv2Nm4q0Wh07N4s1hFJNRVqtji16706paKdWo2X5T2Ikc8xTvVA5GHtrZqfnFs9in/TyILP2SfPnLCMpJpXA2j7M+GS8yXVSpMSl8dqQz1j17RYARs7oz6bU39mavcjivy+2vo6Xvzu3rt7m+R7vsXuZdabP9VvVYvLckQAsmLOM+Khkk2sHju+EX00v0pKy2PK39OOMnNoNR2cVUeEJHN1lXDvatV9TPLycSU3K5th+41R3q3ah+AW4k5NdwBGJ25s0r0lAoDt5eUUcOywtCPvqL2gMDWx34+HuRLs2oqjcbSbq2auzPt1+LNxslDC0hjchgZ6oNdriIQzmKFO6XaG4YyZfhnS7k0rJAH2d+cZj5W8KslcoGNpG/I5Xpqen0s6Oh9qKvwMrj1ePJqNAd1eGNhfPRX8etnwS1X+VdsE1GNio3v/5mN9rHhjhqdHp+Ez/Ik9q1+q+N4sH+FMf7RzeojH+btLzt+81hqai4a0b46SsPANuQ7RzYMsGOKmU5T/eMfHHuEezOni5WtaNXpJdeu/Ojk1CcDdhW3Q3/5dmt8A0/mJ4HKkZubg6qWjX1HwH/179tCJzpvH7D4eh1epoUNefWiZsh/bsFU/0vXpKNx5dOBdNakoOLi4OdOhsLLjPHIskNTkbN3dHOkpMKjp1+DqJcRm4uDnSc6Bxqcix3VdIiEnDzdOJIeOlvXjX/LqXvOwCajcKpLu+IUaKfWtPsGfVceRyGbMXPIGTRHTWFKd2XuDZzm9z+Ug4Tm6OvPXPczz71WSUDkrkcrnFfy17NGbB0Y9o1asJBbmFfPHkL3z33J+SEUlTjH1+IE071SM/t5Avn/1TsisdwF5px8QXxajnygW7yJdI77u6OzFisjh5Zt1C45OUUmnHoNGiL/BmiSYjhUJO/2Fi+YRUul0ul9Gn/x1PTyl664XnufPRpKZK164Wd7fvu2JSVHZuUwelvYLYhAwibpkW5DKZjD6G7vYTlnW3tyhHd/vus9fLlHYe2VF8XbafCSO/0LyNlkXH02eG9l25QboFjVVlZWwH8Xt8MPwmt9MzK+1xrMFgrbT9ynVi0jKqdjP3gNl9umMvl3Pwxi0OREbd88d/YITnugtXuJ6cioejAzO7GUdO7jeiUtPZHSYWxxu+VFVNcnYue6+Ke6rMpqK8QjXbz4sngIrw7lRrtWw5JUZrRnYq2/EMUQ1DbZclHLlwJ83euHbptbCGmrRubeuaHQuakp5TPMWld2fT+zFEj/r1ko52FhSoOXRYH5E1kWbfoxcTPXo3kqz/NBiM9xncQvJ2Q7Rz4Kg2ktHMTf+I9YmDH+mIg5PxBUZWei4b/hS9PR+dNdhkk1B6UhY/zl4KwPiXh9DETJf/3Wz8ZRdvPfQ1mSnZ1GtZi/mHP6D7Q9Ipf0vw9Hfnk42vMfnNh5DJZGxduI9Z/T4iN8syMaBQyJm94HGcXB24ciKSNQt2mlzb9+EOBNbyITM1h82LD0muGTapCwo7OZdPRXHjqnFad/CYdsjlMs4dv0HMTWNBN0Av9s+euEFSgrHQMPi6njwWKen5GRToQZPGQeh0AvtMRDS7daqHSmVHzO10wiOMfUEBnByVdGpTB4BdZqYdwR0z+aPnosgvxRsVoF8Z0u3tGwbj4exAenY+p8OtH1nZum4Ngn09yCtUs/Nc+T09Gwb50rimHxqtjn/PVp7XYy0fTzrXC0EQYPXJ6tFk1DDAl+71aqMTBBYdNb5Aut8I8fTg0fatgDvlh/eSB0Z4rjkvfsCf6twOd0fLIxn/VXZfjUQQoGvdWtTzq5wZ6NZy9PottDqBpjX8aBBgvQempVy4FU9eoZoAD1fahJa/jjcsNpm07DzcnFR0aVLb6vtn5OQTESfalPRsabmgOXNNnETTo1Udi7xXz+nXd2tj/jHOXY5FEKBBHX+C/D0k1xQWabikFxk9TBjCh4XFU1CgxtvbhcaNpf1Cz56OEo/Rx1i86nRC8UjF3oOMo5lFhWrOHhVLBwZIjNDMycrn4gkxIjzYRLTz2I6LFOQVUatBAF0Gmb7Y+XfxAXIy86jbPJiJrw41ue5uzh+4yk+v/o0gCAx9sg/f7nmboDrlb5hTKOQ8+sZDfLJxNu4+rkSci+Lr6b9Z3EgSEOJTPNVo7U87JeeuA9jZK3jkObHDfc/ak5JrfALc6aCfRnVst3Fa1z/Ik9adxGj2yUPGAiighidNWwYjCKL4vJtatX2oHeqLVqvj4vloyT106yamQs+ZuN3JSUV7fbr97AXpNQC99BH+M5dNrwGoX8sXPy8XitQart2UFrIl6dFa/M5duZFAQZFl0Uc7hZzuLUQhfCrM+lGGMpmMwXpPz6PXzD8fSxmq91Q+Ela5Hd6j9On2g5VYT2otU/TlaDuumnc+uF94tmtHnOztuZ6cysW40j/jFckDITyTsnM4GxsPwPD73D7JwNGb4g9RdZhSZODsLVHItK9jvtu6vFyMFgvEW9UOrBCz/As3xc9Oi9pB2Cust6MKi0kCoKavu8VpdoDwGDF61Di0dCGj1mi5GSuK20alrL8aIT6fpg1Mjym9cTMZrVaHh7sTAf7u0vuLEF/nRg2lX+eM9FwSEzKRyaCRhDCNvZVCbk4hKpUd9Rsa7yX88m3Uai2e3i6E1PE1uv3yqZvodAI1Qn3wryHtMnBCXxfYZXBLk58FrVbHtr8PAjB6Rn/sLPT1TYlL45Mp89HpBPpN7Mrz301F6VD+so6StOnTjA9Wz8LOXsHhDadY/f1Wi+/bZ2xHPP3cyEjO5ujWcybXdRrQHJlMRtS1eJJMNOe06SpefFyQEI4ALTuIou+yhC0SQNOWYunHtUvSAqtRU/HzEXYtXvL2xvrPx3WJKUfFa/Sf57AI0w0iTeqLayJuJqOWaIYyIJPJaKj/Hl2/lWRynYEAb1c8XB3R6gRu3LbcC7NxLfExwmJMp/7N0aaueGF9oQIajABahYrvw8XohEoVX+1DxWEMYfHJ5FaTWekdatdEaacgOTuXmynpVb2dSsfd0YGe9cTv7c7w0id1VSQPhPDcHR6JALQMCiDATbqR4n6iSKPl9C2xU7JTaOWKPGswCM/WtcxP0ykvF6LFk1fzCpr/Xiw8Q6XthErjWrR4UmkUXHpXugGdTiBCf78GIaXf71ZcGmqNFmdHJYG+xp3hJQmLFE/ejeuafj7Xrosn7wb1/E0KNoMIaGDCZskgImqGeOPsYiy4r14URUiDJjVQ2Bn/FF0+K4qYpq1rSe7BIIJamEiLq4s0nNF33Hfsa9zYZOD0nssk307H1dPZ4i52dZGGjyfPJyM5i9BmwTz//bRKmwjWqH1dpn/5KAB/vrOS8wdMN9CUxM7ejoGPir59WxcfNLnO3cuFxm1rA3DSRANPi45iZO7qmSjJ7vSmrcXGsstnb0kKlsbNRaFx9ZJ0B3fDRuJvQrgJ4VmvnijQkpKzyJBIxwM01K8xJzxrBHjg6uKAWqPlRoz5jt76tcSLnXALhKdMJqNBiH59tOUisrH+u30tpvTHkKJZrQBkMridmkVqVvl9GRsF+WKnkJOem09sWuXVX/q7uxDk4YZOELgQI/2e32tU9na0CRY/h0dvVEwEubozoJHeT/bavY3yPhDCc0eYqOb7N7S8m/i/zIXb8eSrNXg5O1Lfr/JS2taQlV9ARJIYCWhVQYJQCkEQiiOezUMqxiP0QpQh4lm2fRsino0sEJAG4lIyyS0oQmmnoFZA6Z6h1/XNEvVr+ZkVQFqtjrAbomBsVM+08DREMxuaWxMurqlvSnheES80DKLibgzRr0bNpcshLp8Vf/wNouZuLuqn2RhEkdH9T94gP6cQDx8X6rc0fQH2719i00y/RzqjlKgjleK3N5Zx5dh1nN2deGfZCzg4Va5V2bAn+9B3Qld0Wh2fTJlPSpxlE2sGT+6OTCbj7P6rxN0wLW7aG/wyTQjP4Lp+eHi7UFSoIfyCcT1i/aY1sFfakZGWS1y0ccSvYTPxPb4VmUSeRBNTw8bidyv8ajw6nfEJ0NlZRc2aXuKa69LCsoH+s3o7LoMcEz6oMpmMRnXF34WrZgQqiN8luPPdKo0GwaLwvB5tuYisX8MHmQxSMnNJybReOLo4qqgbIJZSXYgqvxWQyt6ORkHi87h4q3KthQwBiLNRFROtrQg61REj88duWl9z+1+kZ93a2CsURKVlEJFSeVOr7ua+F56Z+QUcvyWe4B4U4Xn0hvil6RQaXCFjIiuC89HxCAKEeHvg4yo9JaciSMjIJjU7Dzu5nMY1yy88U7JyiUvLQiYTowtl4Zr+RNTQiohnmD7KUjfYBzsLpk0Z0oGGKI0pbt1OI79AjaODPbVqeJlcZ2jQaGhCVOblFRKjT+03aCC95toVMbrVqIm08DREvxo3M56BrtPpuGKIeLYxFp45WflE6oVt8w7SwvPkbtGJoF2vJiabipLj0jmxQ3RaGDLVMou1vSuPsuEnsWHntd+fqZCaztKQyWS88MM0QpsFk5GcxceT55us2yyJf7A37fTR3q1/m456dtCvOX8oXLKDXiaTFQv8CxLjK5VKu2KPVUOkuiTePq74B7qj0wmESzQohdb1Q6m0IyengNux0qLaEFk3XPDcjbubY3FZSLgZUdlIH+m/Vprw1Ecwb8amojHhDPB/+9ML1TALhSqAk4OyeBhFWBmjni1CRdFeUel2Q6boYsw9Ep63qo/w7KwvAzsRFVMmY///Gi4qFV1DRbG949q9S7ff98Jzb8QNNDodDXy9CfW2ftrMf5HjNw3C07ylzr3kTNS9SrPro3BBPjhYWKtn9nj6NHvdAG9cHK2PauUVFHErSawXahRiXhSWxJCuM0RRSl1fHPE0vz5MP7WlQai/ST/LwkI1N/XHM6Qv7ybyRhKCAD4+rnh5Gl9ICILAtaviayclPPPzirgVqY8ESwjP6MhkcrILcHBUUrehsbAtWd/pbaIG1RC969DXtBPB9n8OodMJNO/SgOD6pUe0oy7H8u3MPwCY8NoIOg25d/64Dk4q3ln2As7uTlw5dp3f31hu0f2GTOkBwI5lR0yK1dDGQfgEelBYoObCMekTUHN9SYOpOk/DBcIlCeEJ0LCp+D5fu2hc52lnp6Ce/n2+dkVaiDSor6+5NCMYLUm3GyL9pU0wCvR1x9lRiVqjJcqCCLOhJOZ6TLJk1NbkfgzpditS9CUxZGIMmZnyYsgUVXrEs7b4u3A+JqHaiLymgf64qJRk5hdyNaFs78d/jQH6gNzOsNJHxFYU973wNKj4ByXamVek5ry+kapTJTfxWMO9qu80/Fi2CClbdPJuitPsoWVLs1+/nSIKNHdnvN0sj/SG66OkltR3CoJAhD7i2aCU6UaG9GIjE4ISIPJmMlqdgIe7E74+0jXRhqiTQQzcTdztdLKz8rFXKqgj8VjhV+PQ6QR8/d3w9jV+DEPUrHHLYMnxlqXVd8ZFpRAbmYTCTk6bHtINhVqNlm3/iBZCQ6b2kFxTktzMPD6c9AOFeUW06dOMyW+NLvU+FU1QHX9e+/0ZANb/tIO9K0sfe9dhQHN8Aj3ITMnmyBZpqxiZTFacbj8p0bkOVtR5npGuj2usL6kwVefZWH+BYoiU342hpMNUqh3upNtNWSoBNNavuRmdQoEZ/0u5XFZ8IWdJuj0kwBOVvYL8QjWxSRmlrjdgyISUN+J5+VYiam3pkdnSaF5LPN7V20moLYj0lpX6/t64qJTkFhYRnlA1E3Tuxk4hp31t8QLp2ANS59mnQV3kMhlXEpOIybg3vqr3tfDMK1Jz8EYU8OAIz9O3bqPW6gjycCPYUzoSdK9Ra7VcjBVPFpUuPIsbiypIeBY3FpVt31f1AtKaxiK409DQoJQIJkByWg6ZOQUoFHJq1zBvnXVVH+VpZKaxKEx/0jbXWGQ4+ZtsLNJHrerVD8De3lg4GqJejZobRzvhTtSsrPWdJ/eIafam7evg7OYovWbXJVLi0nH3dqHrMPORS0EQ+Hr6b8ReT8C3phdzF84wOwGpMuk0pDXjZw8H4NuZfxB12bwVj8JOwcBJYpORoZ5Vig7FdZ6XJRsNguv64eljus6zSSsxw3L7VgoZEkbvhsj2tUuxksdv1EQUpqYinvX1FzmJiVlkmvA0tSTi6evlgpeHE1qdwPWb5sXenTrP0kWhnUJO3ZpiTX2YFXWehkzINSvuU5Lafl64OqooUGuIiCu/gKvl44Gbo4oijZbw+MqL+inkclro0/rVKt2ub8h9UOo8vZwcaR8ifvd2ht2bdHul/nKOGDGCkJAQHBwcCAwMZPLkycTF3bsP2MEbURRqtNT0cKOxv+Vpzv8yhm68zqHBldZlay1X45IpUGtwc1RRx9d0XWF5UWu1XIkVf7wroqNdrdVyJVoUYS1ql03IlqWxKC0rj+SMXGQyqFez9M+tQaTWCvJCJWHCbkCt1hKpH6HY2EzTkOGk3ciEqAQI13e0m2osumpoLDLh73lV31gkVd8J/9/RfjeW1Hee0Eft2vcx3c1e3FQ0vgtKlfmmojU/bOPwxtPYK+14a8nzuJuIBN8rprz9MK17N6Uwr4gPJ/1AgYlmGgODJndDLpdx/lAYsSaiga26NcBeZUdiTBrRErZFMpmM5h316XaJOk9XN0dq64Xf5XPG0aJ6DQOxs5OTkZZLYlyG0e2Gz8qNiETJiKqLswM1gsRyKVN1niUbjLJzCiTXiA1GhnS7ef/CeoZO9SjLRGFDg1C1Im1uiHjGpmSSnSe9Z3PI5TKa63+fzt8sf7pdJpMVX7hfuFcNRtVIeBoajE7fuk1RJUZ8qxOGwNy9qvOsVOHZu3dvVq5cSVhYGGvWrCEyMpIxY8ZU5kP+Hwb1PqBh6XOu7xcMV2nVMs0eElSpzU4R8akUqDW4Oqio7Vv+et7rt1PE4zmqqO1XNsF8+ab4w924lBT4/z2u/qRV088DZ8fSfSGv6bvUS0uz34hJoUitxcVZRY0AD5PrDI0ZDUyI0/z8ImJi9I1FJoSnIV3aWKK+UxCEOxFPCeGZnJBBUlwGcoWcRi2Mby+tvjM/t5ALx8QpTqbqO5NiUzm1WxwqMXiy+aaigtxCln6+AYBnvphEo3aWDwGoLBQKOXMXzsAnyJPY6wnsXGK6cQjAt4YX7fuJJv1bF0tHPR2cVLQwGMHvkZ7X3UIv9E3WeZawVbobpcqOuno/zqsSfp4Bge54eDih0eiIMOHXaWhkM1XnWbLB6LoZUWm48CqtwahhqD4NHpVkUd1mff0FZrgV0UsPF0eCvEULtCu3ymbkXVznWQHCE+5kjAwOIZVFm2ooPOv7eePt7ES+WlNctna/YxCeZ2PjSM4pvy1XaVSq8Hz55Zfp1KkTtWrVokuXLsydO5djx46hVkvX1RQWFpKVlfV/f2WlSKtlz3Xxx7F/w6o/UdwLMvIKuJog/uB1rFb+naIIMRSTVxaGNHvTYP8KEbh3jOMDy3S8nPxCIuNFgdbcihrRMCvqOwGu3hBPDk3MpM+hRH1n3QCTF2KFhWqibonpOnONRTqdgLeXC97eLka3q9VaIvXCoVFTY6ukxPhM0tNysbOTFzeUlMRgo1S3USCOEjZFpdV3njsUjqZIS0CIN8EmnsO2v8WmopbdGlLTTL0rwN5VR8nNzCMw1I+hT/Q2u/Ze4uHrxtiXxSlLG3/dXaoP3+AposDeufwIRSbGQHboJ0aIT5is8xRfc5N1nm3EaJEpI/nGelslqQYjmUxW3Ihmss5T/16ZiniChQ1G+u9KaZZKdWv6oLS3IyevkNjE0k3FDV6e1nS2AzSvo+8kv1E2oWcoBaqwBiN9nWdlC8/mwQEo5DLiM7JJyMyu1MeyFJlMVnz+fFDS7YFurjQP9EdA9D2vbO5ZkVJaWhpLliyhS5cu2NtLp7U+/fRT3N3di/+Cg8suno5FxZBTWISvsxOta1au4KkuHI+KQRCgnq8Xfq7GgqAqEAThHhrH6/07y2h7ZHQ8/Y948zKm2S9HJSIIEOTtho+79Y1FDS2IkgqCwJVIy4SnoaPdXJo9wpLGIn19Z/0G0oLtRkQiarUWN3dHAoM8jG43+HfWaRAgOX/dEC1rVsb6zhP6aF2Hvk0lBbZWo2X7ksNA6U1FgiCw6ZddAAx9so9JW6aqov+kbjg4q4i+epsLpcwfb9+vGT5BnmSl5XJ48xnJNR30pQmXT94gJ9PYqL1mHV+zdZ6GiGfEtTgK8oxtmQw1vdcumzCSLxaepjrb9RFPCxqMwsysMXS2R8elkZtnukzBzk5Bg9qimLxaSloeoH6wLzIZpGbmkpJheeSouX44RVkjls30E5BiUzJJy5Y22LeG5sHifqKS08ksQ/rfUpxVShoGiK/vmWrk52mwVXpQGoyghJn8PajzrPRf0Tlz5uDs7Iy3tzfR0dFs2LDB5NrXX3+dzMzM4r+YmLJfbeyPuAlAX33H1oOAYVpRx2pkoxSTlklKdh72CgXNalaMIDRFcX1ncMU8zmVDfWcZO9qv6u/fzMqJR5GxYsSxfnDp5v9JadlkZOejUMiL69FMEa5vpDAYaEs+tn6NucaiG5GGNdLPKzxMPHk2bCQ9SjNcLyoaNZM2jr96XvzeG5pVSlKQV1RqfefZg2EAtO8tnWY/fziM1IQM3H1c6TLUfFNR1OVYIi9Eo3SwZ2ApKfmqwNndiT7juwCwc8khs2sVdgoG6ScZ7Vl9XHJNQIg3wfX90Wl1nD9y3ej2knWel0/fNLrdL9ADH383tBod1yWilo30lkoR1+LRaowtdAwNRtfDpAWYoaY4PiGTXBN1rYaIZ4QZw3xPdycC9BO+SmswalJHL2SjSheejip7QvzFMh/D99gSmul/Y66WMdXu5uRAnQCxHMjwu1UePF0cCfYWSxauxpat6clS2oaK7/mZKOmLkaqgs77O83xsAkWa0v1y7wcGNKwPiEG7AnXlPmerhefcuXORyWRm/65du3PlPXv2bM6ePcuOHTtQKBRMmTLFZEpIpVLh5ub2f39lxWAL0DSw8s2dqwsJWWKqoo5P9fErTcwUu1treLpViK+mOVL1V/pBXmX/3JQkRT+CroZ32dwBDJNIDPVblpKu79j18yq9gSVNH5Xydncy21gEkJYu7sffx/R+DGv8zDTPpOkjOb4mRnNmpOkfJ9BD8vZ0fcezqdtTk8USm6AQ4w79tOQsdDoBByelZH2nVqsjWd+4EmqisSlGn6Zt2rEe9qW8ZreuiifD+q1r4+ZdPcfttunTDIDoa6WfuFvpraVizEQDQxuKr1tynHRquUYt8YIoNdG4FEomkxEYLAqg1GTj1KlfgPieaTQ6srONO9P99benp0tHC11dHXDQR8kzJCKyAL76z3dGpnTne/Fe9J/xNBPHMRCgP15p64of31PMNmXkmH/8khh+I9Ky88rsaRnkJb52FTE6EyDIU9xTak75I6jmCNXX4ydmGTshVBU1PNywV8jR6HSk5lr+Pv6Xqe3lgZO9PRqdjoTsyi17sFoJvPLKK0ybNs3smjp17kQifHx88PHxoUGDBjRu3Jjg4GCOHTtG586drd6sNSRl60+gLpU3Jae6kab/gng5S9vHVAVpueKPlpdL5e5JEASy9CkhdyfjueDWotZqyS0QU4XuzmU7Xlaufj9Wvh85+tSfqwVjGLP0nbuuFuwxW78fV4m56cWPnVP6mtwc/f5cpfeXlaU/hqv0MbL1wtpVwuZIEASyM8Tb3TyMb8/Ui1p3L+nvdVZaDjqtDplMhoePdLlJanwGAH41S28Yi9NHw+7FdKKyYthbfCmROxCbjEB8DQRBkIxIe/rpBVmSdI294bXPTJMWCm7uTgBkSQg1hZ0cZxcVuTmFZGfm43HX8AHDZyY3pxCtRofCzjg24urqQEGBuvhzaHS7i/i5zMktQKcTTNZnG74zprrf716XVcq64vX67222mRT+3Rh+YwQBcvKLyvSb46G/T0Wlxt2cKvZ4pvByFj8vaZUscK1BJpPh6eRIUnYuabl5BLpXz4vOikQmk+Hn6kxUWgZJ2bnU9qq8AJbVwtPX1xdf37JZE+n0V3KFhZZ/IctKYo4+qlJNah3vBakGkaf/IlcHDFfLlb2nvEI1Gv3nqyKEZ8mThmsZJhYBZBQLT8v3U1ikKbbwsGRSkkFMupkRiiA2/BTom0FcnU0fN1ufvjQnPA0nahcTz8sQyXJ1lRbcxcLT3fj2vNzC4vGEru7GnxmD2HH3kv5ep+ujbO7ezpLG8wCpCRkAJicelSRe7xgQWMc6H9Z7SWCo+HuclZpDTkYuLh6mL7Y9/cQolrpIQ3Z6Lm4Sr6OXfk16knTUw83TIDylI2tunnrhmS4tJFzdHEXhKeHFWfIzk5NTgLuH8WfAxdmB5ORsk4LRxeWOiMvLL8LFxOfdIFAN3yFTGL5bpa0rfnwny45bEns7BU4qe/IK1WTm5JdJeLoZhKcVj2sO93slPF3E97iyI6vW4u3spBeeD0bEE0S9FJWWQWJ25UafK63G8/jx4/z444+cO3eOW7dusWfPHiZMmEDdunUrPdqp1mqLRdgDJTyLRV41injq9+TtUrnCMytf/HG0VygqJKWfof+xcXVUoShjQ4kh4ulmxUnEcB+FXGaRlZIhClOa8DScBGUycDYTSTVEPF1czK0p1K8pLaIpfXtWpumIZ3aG+HlROdjjIPH8M9PE2w3i5m7S9VE6TxNlAHBHeHoGlC484/R1goGh1Vd4Ork64qF/vqVFPZUqe9z0EUvD63A3htcuPbmUiKeJdLi5iCfcueDIkkiFK+zkOOmFopQwhTufzRwTwlOltEOpL6EwF828E/E0HwgxfLeyLBR0hu+7NcKz5P3KKvQqWii66S98Db+tlYW3IeJZzQSeIViSklv59kLVBT8XUS8l5fxHhaeTkxNr166lb9++NGzYkCeeeIIWLVqwf/9+VKqyRZAsxeBDZS+X4+lUfURYZVKo1pBVIP6A+rpUH7F9r9L/mcVpdlWFeLYajudRxjQ7lEy1Wy883ZwdLHoeWYYIZSmPYTgBOzupzE7cybEg4lksTk2m0vWpdhMTgwyCwk0iolksSiWioVB6xDNNH/E0JzzTEsT6b28zXqYG4vXCM8hMQ1Z1wBCRjTPTUGPAS/+8UxOkx+PdEZ7SEU/Da28y4qmPUhouIoxu17/vpoSlId1uKNm4G8MFj1lRaYhmml1jmUB0tVJIuuvLiqyNPHrofyMtFbhGj1tJEc+sSo94is87t7Co0ptarMFHHyxJqQCXgP8K/q7iRWVlRzwrrdujefPm7Nmzp7IOb5YkvfD0dXF+YDraDWJbaafAvYyp4cqgOApbyRHPzAqs74QSArAcxytLqj3Tyihpll7Yl7Y+20qBakp4qtVaCvQekKZSmDnZpoWnTieQY6bGM0sfRXP3lE4XG6Jspmo805NEMWVIF0uRlmiZ8CzMLyI1XmywCarGEU8Q93f1eESxUDaHd4AHUVduF78Od+Plr290SZK+3fDaZ6XnStaJGoRnpgnhaXjfs000/7i6OZCYkEmORPMRlIx4mo5Uujg7kJqWazIqCpbXeBZHPHMKTNbF/t96Q02o1RFP8XllWFhLejfFEc//WKrd1UGFnUKORqsjLTePII+KaQ4tL356EXYvDNWrCwYbxsTsyn3O1cuUroIwqPUHKc2eohd4Pi7O1WpKk6G5qLJT7RUtPMsiGkui0wllinhm6jthS0udG7C0uag0QXn3OlP1myUtbJxNCE9zqfbcnAIMphYuErcb0rOmI57mhWlxxNNPuhmgIK+w2J/Su5RUuyFt7ezuhKuJCGt1wRCRtaTByPC800ym2sXXLjM1F63EyEBDjadWoyu+iPi/20uJeBo+F6Yjnnphmi0teAyf4RwTt//fGjPi1FDrnF3KuFHDOq1OIC/f2Jv0bsoqPA1NiGWNMLpXcHPRvRKeMpnsTrq9GtV5+ugbk5MrWYRVJ/yLhed/NNVelTyIwjO5OMpbfRqLANJyDKn2eyM8yxOhLElxh3wZSwRyCwrR6RWWVTWehppNC+9jOGlaWuNprrEI7pyoXU3UeJaWshcEgazi5iLjPRnEhoOjfXEdXkkMDSmmhGVWcVe7ieYifY2nqYinIcqncrTHyUTzkwGDiAsM9atWF3NSGGpQ4ywQnl76pipTNZ5uXi7IFXIEQSAz1fgEpFTZ4aj/HGVJ1Hm66ZubSo14mhKepdxuSLXnmBF2hmh8RaTaHZT2KPV141mliFSwvibUgEE4WmPDJHX/zAqqlbwjPCu/GdiQEUsr43OvDHwfxIinXmz/Z2s8qxKD8DSEyh8EUrLvlBdUJ9Jy7o2dUkVHPMt7PEO6y0Fph8qKZidrG5Isbi6yIOKp0eqKIzqmGofu1HdKC9O8vCJ0WlFwSzYPmWksgpIRT+kLlTupdunb00ppLjLUd3oFeJQqJuP0k2qC6lbvNDuUqPG0YLpOaTWeCoUcD/0o1NItlaSEp144mhKe7qUJT/MRUUNtsamIKJSIeJoRf64W1IoauCMmSxdGBgFoqf2S0f0sELeS96/o5qJ7FPGEOz0Ahqbg6kBxxPMBEp6GYF1Sdm6pI3jLw30pPA01ng9kxLMaie0ijba44cm7kiOeWfn6yJ9jBQnP4hrPstXLZpYxVW9tev6OUDW/T0tqPEum0Uur3zRppaQXC0qlHSqV8ThMcx6eUCLiKWGjA5bbKZkUnob6Tn8PydtLcifiWb0biwCC9HtMjUunqMB8OthQ22qqxhPu2C6lmepsN2OpZEjF5+cVUVRk3CxSeo2nPuVsqrlI/9k0NbkISjYglT/VDne+X5aIybKn2ssXsTQIxYIiDYUV0KTjrv8tzcovqFQRAndKsapTqt0QxEmpRnuqbAwRzyKtloxKdDO4L4VnccSzGnV3VzYG4elTjSKe6fofUIVcVmGC0BQVaR4Pd4SjRxlT7XeEp3X3LxaSFtZ4Fvt4lvI4dyKeZjw89WscHe2xM+GBWdz1Xqo5fCm3m6jhLI54mhSepTQX6YWSoUHmboo9PK2wUgqqxh6eBtx9XXF0cUAQBBKiks2uLa3GE0r38jRnIu/soio2bZeKepaWSncrrvE03/VuPuJpeardYDRvjjtenpaIVHFtoVpLQZG61PUG3F3K1xwkWr+Jr3tFRCkNQlaj1ZFXaPnzKAvFqfZqZKlkKFvLLSwiz4r38b+M0s6u2AmoMus8K3eGYTkxXGVlZUlfdZsiLikFbUEBLghW3/e/SlxSCtrCApyr0XOOTkhGW1iAh4sjOTmVO4IrMTUNbWEB9oKmQp5/cmoa2qKyHy8+MQVtUQEOCp1V909KFh/XDsseNy0tA41aA7pCs+uTU9LQqAuwk2lNrktITEajLsDB3c7kmqSkFDSaAuzspY8TH5eMRlOASuUieXtifDIabQH2KunPaXJiKhptIQql8fELC9TFnyOZnfHtBflFZGWKUTyFSvp3I/ZWHBpdEY4e9qW+vrciYtAIRbj6OVWb75Q5fELcuXk5i+uXb+ARZHrSitJZjkZXRGJ8MhkZGcglfGod3RRodIXERsdLPneVsxyNrpD420mStzs4y8nKyCM2NhH7u65BZAoNGm0BqSnpkveV22nQaApISU6Vft0FNRpNAalp0vcHkMs1aNQFJKekmVwjaMU1APGJyWazASqFDo26gITEFLKyAkyuA/G8JWiL0Gl1xMYlWTT6FkChU6MtKiAl1fSeS8NJIZBRUMDthGQcZGUbvWlAEAQUWjVFGi0xCYnFIzkrA0e0aAsLiE9KrjbfNUEQUGrV5Gu03IiLJ8TLo6q3dE/wVMhJKSjgRlw8QVY45BjeN0ui4zKhsmPo5SA2Npbg4OCq3oYNGzZs2LBhw4aNUoiJiaFmzZpm11Rr4anT6YiLi8PV1dWqrtKsrCyCg4OJiYnBza16eII9qNjei+qD7b2oHtjeh+qD7b2oPtjei+pBWd8HQRDIzs4mKChIMotSkmqdapfL5aUqZ3O4ubnZPsDVBNt7UX2wvRfVA9v7UH2wvRfVB9t7UT0oy/vg7m5ZOcZ92Vxkw4YNGzZs2LBho/phE542bNiwYcOGDRs27gn3pfBUqVS8++67qFTVZ2b5g4rtvag+2N6L6oHtfag+2N6L6oPtvage3Iv3oVo3F9mwYcOGDRs2bNi4f7gvI542bNiwYcOGDRs2qh824WnDhg0bNmzYsGHjnmATnjZs2LBhw4YNGzbuCTbhacOGDRs2bNiwYeOeYBOeNmzYsGHDhg0bNu4J973wHDFiBCEhITg4OBAYGMjkyZOJi4ur6m09cERFRfHEE08QGhqKo6MjdevW5d1336WoqKiqt/ZA8vHHH9OlSxecnJzw8PCo6u08UMyfP5/atWvj4OBAx44dOXHiRFVv6YHjwIEDDB8+nKCgIGQyGevXr6/qLT2QfPrpp7Rv3x5XV1f8/PwYNWoUYWFhVb2tB5KffvqJFi1aFE8s6ty5M1u3bq2Ux7rvhWfv3r1ZuXIlYWFhrFmzhsjISMaMGVPV23rguHbtGjqdjl9++YXLly/z7bff8vPPP/PGG29U9dYeSIqKihg7diwzZsyo6q08UKxYsYJZs2bx7rvvcubMGVq2bMnAgQNJSkqq6q09UOTm5tKyZUvmz59f1Vt5oNm/fz8zZ87k2LFj7Ny5E7VazYABA8jNza3qrT1w1KxZk88++4zTp09z6tQp+vTpw8iRI7l8+XKFP9YD5+O5ceNGRo0aRWFhIfb29lW9nQeaL7/8kp9++okbN25U9VYeWBYtWsRLL71ERkZGVW/lgaBjx460b9+eH3/8EQCdTkdwcDDPP/88c+fOreLdPZjIZDLWrVvHqFGjqnorDzzJycn4+fmxf/9+evToUdXbeeDx8vLiyy+/5IknnqjQ4973Ec+SpKWlsWTJErp06WITndWAzMxMvLy8qnobNmzcE4qKijh9+jT9+vUr/j+5XE6/fv04evRoFe7Mho3qQWZmJoDtvFDFaLVali9fTm5uLp07d67w4z8QwnPOnDk4Ozvj7e1NdHQ0GzZsqOotPfBEREQwb948nnnmmareig0b94SUlBS0Wi3+/v7/9//+/v4kJCRU0a5s2Kge6HQ6XnrpJbp27UqzZs2qejsPJBcvXsTFxQWVSsX06dNZt24dTZo0qfDH+U8Kz7lz5yKTycz+Xbt2rXj97NmzOXv2LDt27EChUDBlyhQesAqDSsPa9wLg9u3bDBo0iLFjx/LUU09V0c7vP8ryXtiwYcNGdWDmzJlcunSJ5cuXV/VWHlgaNmzIuXPnOH78ODNmzGDq1KlcuXKlwh/nP1njmZycTGpqqtk1derUQalUGv1/bGwswcHBHDlypFJCyA8a1r4XcXFx9OrVi06dOrFo0SLk8v/ktU+1pCzfC1uN572jqKgIJycnVq9e/X/1hFOnTiUjI8OWiakibDWeVc9zzz3Hhg0bOHDgAKGhoVW9HRt6+vXrR926dfnll18q9Lh2FXq0e4Svry++vr5luq9OpwOgsLCwIrf0wGLNe3H79m169+5N27ZtWbhwoU10VjDl+V7YqHyUSiVt27Zl9+7dxSJHp9Oxe/dunnvuuardnA0bVYAgCDz//POsW7eOffv22URnNUOn01WKVvpPCk9LOX78OCdPnqRbt254enoSGRnJ22+/Td26dW3RznvM7du36dWrF7Vq1eKrr74iOTm5+LaAgIAq3NmDSXR0NGlpaURHR6PVajl37hwA9erVw8XFpWo3dx8za9Yspk6dSrt27ejQoQPfffcdubm5PPbYY1W9tQeKnJwcIiIiiv998+ZNzp07h5eXFyEhIVW4sweLmTNnsnTpUjZs2ICrq2txrbO7uzuOjo5VvLsHi9dff53BgwcTEhJCdnY2S5cuZd++fWzfvr3iH0y4j7lw4YLQu3dvwcvLS1CpVELt2rWF6dOnC7GxsVW9tQeOhQsXCoDkn417z9SpUyXfi71791b11u575s2bJ4SEhAhKpVLo0KGDcOzYsare0gPH3r17JT//U6dOreqtPVCYOicsXLiwqrf2wPH4448LtWrVEpRKpeDr6yv07dtX2LFjR6U81n+yxtOGDRs2bNiwYcPGfw9bkZ0NGzZs2LBhw4aNe4JNeNqwYcOGDRs2bNi4J9iEpw0bNmzYsGHDho17gk142rBhw4YNGzZs2Lgn2ISnDRs2bNiwYcOGjXuCTXjasGHDhg0bNmzYuCfYhKcNGzZs2LBhw4aNe4JNeNqwYcOGDRs2bNi4J9iEpw0bNmzYsGHDho17gk142rBhw4YNGzZs2Lgn2ISnDRs2bNiwYcOGjXvC/wD73NqfkBTFzgAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -2297,7 +2446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 58,
    "id": "abd902e8",
    "metadata": {
     "execution": {
@@ -2311,7 +2460,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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K3uSjeBik15Qb3pQcQ9cV6aOoYO/8GC6OF0X2UcB5SGQy8Q0AgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoZmy7xdvouOJ0HItHx7HSVQd2N7LHkP0xsFdwfye3aToef3ab3Pheb5icoGBNHd8fbcmjKfmwHA5zJ7ZNntaz2+TW1PX4iIjIbpM97ouisz25/4I5RrH/zt/hxzBCdH3/ZQ7ZJ5cAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADV9Fd6Aa+q1579Ghfd9sGPZI62KTif2TWN5DwljyO7pux9V3DMzURyjuSaetn9R0SvN0zOkRvf7+fGR0RMTgxycyTXNJE85tJtMgbD/M/82W2WBrnxi4OJ1PiIiKWl3BzD5JqGBedpOEi+WIe58W12/xER2Zdqck3Z/bejeNvNzlHy1tV2/Yba7e6L5ih5j89IvA/55BIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoZny7xZs4/87mEfRZF/VyZ41jj3fWCHq5u56jSe6/SfZZn90m2RWe7LMu6fHu93M93tne71WTS6nxERGr+rltVvcXU+Onevk19TvuFl8q6MxeGOYe5S8vTabGzy3l3yrmFnPbZPvLl5ZG0HeevBYl3eJtslO9zd5+6e7y5PiiObIP5eT+I6JNl6qnJ+hc5/3oEbnjSCzHJ5cAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANRdFt3hR73fXlZ2j6P3OHnfJmrLbjKBbPNv9nV1Tr+Pe77NzdNsVPpXs5I6ImE52f182mevxXjO5kBofEbG2P5+bo5+bY01y/xERk02uUz1rsc13Zp9Zmk6On0qNfzG5/4iIM/3cHC8t5vrO53v5t6+F5DZLS7lnwbDJf16TrZIfZvvLkz3eRXXW2bfg7BwlPd7pbbrvO0/XnWff44uiUOJAdIsDALAShEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgmv5KL+DVtL022l5JW/15KimdT+2/qEG+4/EjWFPymjUF1zi7TTORG9/rDVPjJ5L7j4jo9wep8VP9pdT41ZO58RERayYXkuPnU+OvmHo5NT4i4vLJl1Lj1/fnUuPXTeTGR0RM9xbT22TMDyfT25zur0qNP7WUG39i8bLU+IiIfrM6OT73ujtT8Dxrkts0Te4tcqnkfWWQPI4m95lQO0guqskfRDvs+A21JAq0yTW1yUmK4knXwSOvTbwmMpnMJ5cAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANWPbLR5NjFcNZ7bHtmTtXXeFF/woke7+znb3FvRyZ9fUdVd4tic8It8VvirZFZ7tCT+7Tbdd4RumzqTGR0S8bvLF1Pgr+rk5Lp/Ir2lV0223+Fyb7xY/MViTGr+qlxvfK+jxHkfjeRQTyfG559kw++Afxftutva7pLt8mL3aozjwbH95thc+NzwtsX+fXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVjG+3eNe67gov6hbvuCu8pB842xWe7P1Od5fH+HWFT06UdIvntlmV7CJf3c/3X69NdotfPvlSany2Jzwi4nX906nxv9Q/lRp/5UR+Taua3LXImmvzj+Vs3/lEsp+6xDDZi7zY5h5ow+T4iIil9By5Y2izXdBFxqyLPCJfmZ29/UpOay+50aXYRd4hn1wCAFCNcAkAQDWpcLl3795417veFevWrYvXv/71ceutt8bhw4fPGTM3Nxe7du2KK6+8MtauXRs7d+6M48ePV100AADjKRUu9+/fH7t27YqDBw/GE088EYuLi/Ge97wnzpw5szzm3nvvjccffzweffTR2L9/fxw9ejRuu+226gsHAGD8pP7l+Ne+9rVz/vsLX/hCvP71r49Dhw7Fr/3ar8XJkyfj85//fDz88MNx4403RkTEQw89FG9961vj4MGDccMNN9RbOQAAY+c1/ZvLkydPRkTEhg0bIiLi0KFDsbi4GDt27Fgec/XVV8e2bdviwIEDr7iP+fn5OHXq1DlfAABcmIrD5XA4jHvuuSfe/e53x9vf/vaIiDh27FhMTU3F5Zdffs7YjRs3xrFjx15xP3v37o2ZmZnlr61bt5YuCQCAFVYcLnft2hXf+9734pFHHnlNC9izZ0+cPHly+evIkSOvaX8AAKycov8T9bvuuiu+8pWvxLe//e3YsmXL8vc3bdoUCwsLceLEiXM+vTx+/Hhs2rTpFfc1PT0d09PTJcsAAGDMpD65bNs27rrrrnjsscfiG9/4Rlx11VXn/Pn27dtjcnIy9u3bt/y9w4cPx/PPPx+zs7N1VgwAwNhKfXK5a9euePjhh+PLX/5yrFu3bvnfUc7MzMTq1atjZmYm7rzzzti9e3ds2LAh1q9fH3fffXfMzs76TXEAgEtAKlw++OCDERHxG7/xG+d8/6GHHorf+73fi4iIT37yk9Hr9WLnzp0xPz8fN998c3zmM5+pslgAAMZbKly27S8uUV+1alU88MAD8cADDxQvKq3JFs5HvnM+Pb5gTdlfr0rO0fTya8pu0ySPoVdwnnrJNfV6w9z45Jr6E7n9R0RMJtc0PbGUGr+qv5gaHxGxtr+QHD+fGj8z8VJqfETElf0XU+NfP3E6NX6mlzuGiIg1yWuXdWaYu9YRERORu2eHyYfNYjuRGh8R8VJ/KjX+5WFu/MIw/ysDC8PccSwOcuMHBc+CwTB3LbLPs7ZNPpTP433+pw172TfIgvfH7AzZS5E9hmHJMWTPU1bBmtpu1qRbHACAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKgmX846Kk17/t3ZJdWYXXeFd10hGhFN1/3oJdukz1NB33lyTdku8olkd292fETE5MQgNb6fnGOql++nXt3LdYtflh6f7/Fe13s5OT63pssLrt1lvXzPdsZk5O6NiIhh5I77TJs7r6d6q1LjI/L3R/b+m+rlusgj8q+j7Ot0MdkTHpF/fgyTXdDD7C1e8EzOP/eTD/GC967sFOlK9aL3+JFMkpRYU+I6++QSAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqGZ8u8UvdEU93t32cjcj6PFOV5EXdcZ2e9zZ8RMF57WX3Kbf5DqOJ5t8Z3a2d3m6t5gavyo5PiJiTZPsL0+ep5Ke8MuafKd1SrJjOyJirs0dd/a8lly77P2R7v0uuceT90f2dVryLBi351lT8FBOb5F+o8if12QF+wgOIvLV4hcwn1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABU01/pBVwwCjrqL0VN047dHNlL1xvBte5F7hh6yWPOjo+ImIhhcnxujqlmkBofETGZ3GYyee0mYyK3QURMNvltUvtvS9aUPU+58SXXLnt/ZO+/kns8/TpKHkOJ7PMm+3gax2dy216ib6jZw+7+0nXGJ5cAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANf2VXsAFo02ObzpZxdhr29yBN+kTm58jO8Mwv6S0YfIGGSaPOTs+ImKQ/FlzkDyGhXYiNT4iYjG5zWLy2i3GILdBRCy2+W1S+y9aU3Z87ryWXLvs/ZG9/0ru8fTraAQP8uzzJvt4yj4vS4xijovCCN5bxoVPLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKrpr/QCLlpFBfVNp3O0JWvKbpQ9hII1tW1ukq7HD5LjIyKGyW2W2onU+MU2/3Pj0jC3zfxwMjV+Ljk+IuJMO5Ua/1K7kBq/ajhIjY+IiF5ujqyXCtb0UvL+yJ7XkmuXvT+y91/RPZ48T9nXacmzYNyeZ0XP5Oz49HtX/rxGdpv0+NzwS41PLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqxrdbvG0SXZ+jKPnMdpsWrKnjXu6mqFs8O77jPteIaJMHPhwm+4Gb3M9cg2QnckTE4iDXcbw0kZtjYZh/ab88TPZ4p8dPp8ZHRJwerk6NX9Pker97MZ8aHxGxGAV95AlnCu6n08lrkT2vJdcue39k77+SezzdX558nZY8C7LbZJ9n6a7wkfR4Z/efHB9lHem5CUq2Gb/zlFpTYqxPLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqxrdbPKOkC7XzPvKCNQ2Ta+olO2aHud2XSfZ+NyXnKbdNk+wKH/Zyx7A0KOgW7+W2mR/kXqpzS5Op8RERL/Zy3c6re7m+6ZO9y1LjIyImm1yPdy9yN/mg4HX6UruU3iZjrs0/lv/fwdrc+KXc+JOD/LV7cSl3f7y4lLv/Su7x7OtoMdn7XfIsGCbfv4Ydd5Fn1xORf29ps/3oyfFnN0puk31/LOpg73p8SRbqhk8uAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqumv9AJWTLrgPdsgXyK5pmFyTb2CUvvkFO0wuf+CJQ3TPxNlFzWRHJ/XNLkT20uO7zfZY47oJbeZTI7PHkOJQfLemGsn03OsahbT22SUrOnEYE1q/P9ayo3/8eLa1PiIiBOLl6XGv7g4nRr/8lL+PM0t5d7yFpZyz4LFQf7ZsZScYzDIPTSHw9xroh3mH8rpbbLvv+n368g/9tNrSu6/ZJuS4x4TPrkEAKCaVLh88MEH45prron169fH+vXrY3Z2Nr761a8u//nc3Fzs2rUrrrzyyli7dm3s3Lkzjh8/Xn3RAACMp1S43LJlS9x3331x6NCh+O53vxs33nhjvP/974/vf//7ERFx7733xuOPPx6PPvpo7N+/P44ePRq33XZbJwsHAGD8pP4Byvve975z/vsv//Iv48EHH4yDBw/Gli1b4vOf/3w8/PDDceONN0ZExEMPPRRvfetb4+DBg3HDDTfUWzUAAGOp+N9cDgaDeOSRR+LMmTMxOzsbhw4disXFxdixY8fymKuvvjq2bdsWBw4ceNX9zM/Px6lTp875AgDgwpQOl88++2ysXbs2pqen44/+6I/isccei7e97W1x7NixmJqaissvv/yc8Rs3boxjx4696v727t0bMzMzy19bt25NHwQAAOMhHS7f8pa3xDPPPBNPPvlk/PEf/3Hcfvvt8W//9m/FC9izZ0+cPHly+evIkSPF+wIAYGWl/38up6am4k1velNERGzfvj2eeuqp+PSnPx0f+MAHYmFhIU6cOHHOp5fHjx+PTZs2ver+pqenY3o69/9vBgDAeHrN/z+Xw+Ew5ufnY/v27TE5ORn79u1b/rPDhw/H888/H7Ozs691GgAALgCpTy737NkTt9xyS2zbti1Onz4dDz/8cHzrW9+Kr3/96zEzMxN33nln7N69OzZs2BDr16+Pu+++O2ZnZ/2mOADAJSIVLl944YX43d/93fjRj34UMzMzcc0118TXv/71+K3f+q2IiPjkJz8ZvV4vdu7cGfPz83HzzTfHZz7zmU4WDgDA+Gnath1FafZ5O3XqVMzMzMSWT/5f0Vu9qruJuq7sLOlRzq4pPX4Ea+rl5miS40u2aSaSPd69XCntRHL/ERH9/iA1fqq/lBq/ejI3PiJizeRCcvx8avwVUy+nxkdEXD75Umr8+v5cavy6idz4iIjpXrfd4vPDfGf26UHuWXlqKTc+2xMeEfG/Flanxp9JdoufWZxKjY+IeHkx2y2eG5/tCY8YQVd4cv8j6RZPd5Hnhp/dpuM5RrGm9P673f3w5bn4z3s/EidPnoz169f/3LG6xQEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqcsWpI9QMm2jOs3+0LenM7lxBh2j2MLLH3RSsqesq1JKu1TZ33G1yfExkO2lzXeQR6UNIn6eS8zpIbrPU5n42XWrzvcsvD3I926f6uT7rNf1cP3pExGST64XPWiw4T2eWkr3cS7le7heT+4/Id3+/tJi71vPJnvCIkq7w3D0+HOQ/r8l2hQ+TXeHZHu+SbvH0e9dIusWz4zt+s4vovPu7ZP9N4rjPN5NF+OQSAICKhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgmv5KL+BVtXHeJexNFBTOJwve2ya7QW54RET+MJIblKwpKztHyaXLzjHMTTIc5iZoJ1LDz87RZtfU7fiIiKVh7mfNhaXcgc8t5R83Z/pTqfGr+4up8VO9pdT4iIh+b5jeJiN7HSIiFoa5c/vy0mRqfMm1m1vMbbM4yN1PS8n77+w2uXM7TF6LdpB/3bWD5BzZ13bHz8uRzdG17DGM4P20Sb5PFMkcR2KsTy4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAai6KbvGRdHw2I+j47HiKdD96RH5No+iMzVY7Z6t4kz9ytclO5IiIZiJ3LYa9ZCfyIH+tlxZzx7EwkbsQ84u5PuuIiJcmBqnx/eSaJgp6wku2yRgUdItnt1lK9llne78jCnq8k2vK9n6fnSP5MEg+z0q6xTvv5U53Zo9Zn3XJ+BhBL/cIcsdI5sjQLQ4AwEoQLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgGuESAIBqhEsAAKoRLgEAqEa4BACgmvHtFh823fZUZ3u2R9Hx2XEValMyQdc1syVVvNltxrAfvV1K3lDJHwMHvfwNO0j2lzfJORYL1jSXPO5etvc7+xyIiKbryuKSZ03yRZHt5W4L6tTbbC939nVX8jpNz5Hc/0XQy13Uyd31++O4dWxHjOmaOr7/Eq8fn1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1Yxtt3gTRbW/528UHbBj5mI55K67nTvvU4/o/iCKOttzL7js/TSK+2+QfWhcJK+JfN+0DuxOjKJvuvNjuDheFJ3mh0tU5s7wySUAANUIlwAAVCNcAgBQjXAJAEA1wiUAANUIlwAAVCNcAgBQjXAJAEA1wiUAANUIlwAAVCNcAgBQjXAJAEA1/ZVewKsa/vcX1WRK58db7kiatqNljLOiYx7DO6QdwzVdii6WF9EleDt5CVFNIpP55BIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoZmy7xZu24zrbi6QqN2UEJbMjqSDueo7k/ovOavYYsmsqOUfZORI9s2fHF5ypjtdUdC91ff+V3FDJbdrsxwpNflFtL3ei8mtKjo+CR2DX46PgdkrOkV7SCLrIR9J3PpI3ozHT8XnNnFKfXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVCJcAAFQjXAIAUI1wCQBANcIlAADVjG23eAybs19jYixrSsewxzt9xUbRgd1xj3fJMWR7tvM93rnxERHNoNs5svsv2abrY4jo/llQ0ruc7eVuJ7odf3ab3IF0fQxFc6TH52+OdG17tkd+BP3onfeRF+2/6xL27qWvXdeZIPG+5ZNLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAavorvYBX07Rnv85L12XtJQrWlO2oTys5T8ltzvuaFe6/ZJtmmDuzzTC7/9z4iIjoeI7eIDc+IqJZSs6RHJ/df0TExGLuYqePoeA8df68KXgQDCdy49vkk38wmV9Udo5hcnx2/xEF5yn78Uuv4Dwl58ivKfvATO6/ZJuux5dskzxNRY+B5JrS76clEmvKrMcnlwAAVPOawuV9990XTdPEPffcs/y9ubm52LVrV1x55ZWxdu3a2LlzZxw/fvy1rhMAgAtAcbh86qmn4nOf+1xcc80153z/3nvvjccffzweffTR2L9/fxw9ejRuu+2217xQAADGX1G4fPHFF+ODH/xg/M3f/E1cccUVy98/efJkfP7zn49PfOITceONN8b27dvjoYcein/913+NgwcPVls0AADjqShc7tq1K9773vfGjh07zvn+oUOHYnFx8ZzvX3311bFt27Y4cODAK+5rfn4+Tp06dc4XAAAXpvTv2z3yyCPx9NNPx1NPPfUzf3bs2LGYmpqKyy+//Jzvb9y4MY4dO/aK+9u7d2/8xV/8RXYZAACModQnl0eOHIkPfehD8Q//8A+xatWqKgvYs2dPnDx5cvnryJEjVfYLAMDopcLloUOH4oUXXoh3vOMd0e/3o9/vx/79++P++++Pfr8fGzdujIWFhThx4sQ52x0/fjw2bdr0ivucnp6O9evXn/MFAMCFKfXX4jfddFM8++yz53zvjjvuiKuvvjr+7M/+LLZu3RqTk5Oxb9++2LlzZ0REHD58OJ5//vmYnZ2tt2oAAMZSKlyuW7cu3v72t5/zvTVr1sSVV165/P0777wzdu/eHRs2bIj169fH3XffHbOzs3HDDTfUWzUAAGOpev3jJz/5yej1erFz586Yn5+Pm2++OT7zmc/UngYAgDHUtG07Vs3cp06dipmZmXjjX/xl9Cr90tAruRh6vLOKekqzHdjpbvH8lei8+3sE3eLZTuuue78jInrJHu/eYnb/ufERERMLuTVlu8h7S/kXRVPSR57QJvuvIyKG/dzrKNsVPpjKv06Hk12PL1hTx/3l2e7yiJKu8G73n15PRPrBn37sF6yp4K0lZxR950ldh7nh3Fz8j4/+n3Hy5Mlf+PsxusUBAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAapLNqaPTRMc1nOkO7Nzwoh7vrOyaCjqwswWt6TkKzlO6Wzzb493x/iPy3d/p8cmO7YiI3kJufLb3uz+fX9NEcpveQu7iTSTHR0Q0w25f3G0v/+QbTOU+J5hIjh9M59e0lNxmMMg+awp64Ye5OYbZ26PgHTXbJZ/u/s72nZfc3k3y/sgeQ1uwqOwc2Vu8YEnpvvPk+OxlyMrs3ieXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1fRXegGvqo3zL4YvKJDParJzlKwpuU12Tc2woNU+PUdy/9nxBXOkxw9y43tLufEREb3F7PjchcjuPyKiP5+bYyI5vv9y/mJPzOW2mZjLXYzeQvJiR0SzVHDTJrT9/M/8vamJ1PjBqtyjvxkUfA7R5rbJPs+WouR5lpykLZgjqdu7qUDJe1fXH1M1JdchdyDpS12wpOwm6UtRcu0yi0rs3yeXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDUXRbd4uve7xAi6xbPd36PoO++6Kzy9/4Jtuu4KL+oWX0p2hSfnmFjIX+zsNv1k73f/5XyP98TLya7wlxZS45u5fAl7s5Q/joy2n+sJj4hoVk3mxg9z17ppC94q0l3Nuc86imq/sx3V2YdsUQd2zth1kUf+rSXdsV3wMVj2/TS7qLbXffDo/m5KXjvd4gAArAThEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqGdtu8abtuDO8417udK9pdN8VXtLj3XVX+Ci6xXvJKuhsF3kzyN+o6b7zZAX2RL4yOybmk33n87kL0ZvPd3Knu8Jfms+Nn8vtPyKiXSw4uQnNZK4nPCIihslrkdx928s/z3oTuW0mknOUrCnbUZ0d3xT0TTfJPvLsYRe8FXWu6y7yiIJrl52g4MR23kdecKIyuSMz1ieXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1QiXAABUI1wCAFCNcAkAQDXCJQAA1fRXegFVlHTBJ7fJFLYXy87R9fjo/rhL9t8Mkxskx2f33wxy40u26Q1yJ6pJjo+IaJZy20ws5k5Ub34pNT4ioplbzI1/eT41vp2bS42PiGgXcmtKmyo4T23yevdynyv0Jidy+4+IicncHMPk+JJ7vJd83Q0HTWp8U/BxTXabtuPnWeQOOSLyby3Z53729j67UcE2GQXnKX3c+SnyCo7jfPjkEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhmfLvF2zj/Ys1RdGa3yY7Zcew7H8Wasj22I+gWz56nZpjs8c4ec8E22fHZLvKSbbJd5M1ivoS9Wcz1bGd7v9u5XBd5yRxpw4Ibqpfr/m6mJnPjS65d8v7I3n8l9/hwmHyOd93jHQXPm+R7USTX1Jb0o3c9xwje47vuRz87R/L+S66q8y7yxAQ+uQQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqGd9u8Qtd5yWf+TmK+s67NobnqfPx0X1ncZOvgo4m2y0+yC0qu/+IiFhKHshSsos8Of7sNh13i/eS3dER0WSPI3leS65d1/dH0T3edVd4yfNsBM+bsdp/gaIe73RZeHaC5PiSOS5gPrkEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCopr/SC4C0dqUXQJF2BBeuHebGDwvW1PVxFK0pedzp/XvRXbBcOlaATy4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoBrhEgCAaoRLAACqES4BAKhGuAQAoJqxq39s/7tmbDg/l9ioZKLs+CY1vOm4ja1kjqI1Jc9Tek2D3PiIiHap2znapdxBt4u5/UdExEJy/GLyQiwWXOyl5Ilayh14O5jP7T8immFum7bNndhhwcVriy74+WuSz5qIiF47kZsje14Huf1HRAySr9Ol5P23tJhf01I/93nKoMldi4LHWbrtczjMrSl5a8Sw5JmcnKPNfqyVf0mk50ivqUD+uLNvwMn9J7f5SS5rz6MOtmnPZ9QI/ed//mds3bp1pZcBAMBPOXLkSGzZsuXnjhm7cDkcDuPo0aOxbt26aH7qp8ZTp07F1q1b48iRI7F+/foVWiGj4FpfOlzrS4drfelwrS8+bdvG6dOnY/PmzdHr/fyPYcfur8V7vd4vTMTr1693s14iXOtLh2t96XCtLx2u9cVlZmbmvMb5hR4AAKoRLgEAqOaCCpfT09Px0Y9+NKanp1d6KXTMtb50uNaXDtf60uFaX9rG7hd6AAC4cF1Qn1wCADDehEsAAKoRLgEAqEa4BACgGuESAIBqLphw+cADD8Qb3/jGWLVqVVx//fXxne98Z6WXxGv07W9/O973vvfF5s2bo2ma+NKXvnTOn7dtGx/5yEfiDW94Q6xevTp27NgRP/jBD1Zmsbwme/fujXe9612xbt26eP3rXx+33nprHD58+Jwxc3NzsWvXrrjyyitj7dq1sXPnzjh+/PgKrZhSDz74YFxzzTXLzSyzs7Px1a9+dfnPXeeL13333RdN08Q999yz/D3X+9J0QYTLL37xi7F79+746Ec/Gk8//XRce+21cfPNN8cLL7yw0kvjNThz5kxce+218cADD7zin3/84x+P+++/Pz772c/Gk08+GWvWrImbb7455ubmRrxSXqv9+/fHrl274uDBg/HEE0/E4uJivOc974kzZ84sj7n33nvj8ccfj0cffTT2798fR48ejdtuu20FV02JLVu2xH333ReHDh2K7373u3HjjTfG+9///vj+978fEa7zxeqpp56Kz33uc3HNNdec833X+xLVXgCuu+66dteuXcv/PRgM2s2bN7d79+5dwVVRU0S0jz322PJ/D4fDdtOmTe1f/dVfLX/vxIkT7fT0dPuP//iPK7BCanrhhRfaiGj379/ftu3Zazs5Odk++uijy2P+/d//vY2I9sCBAyu1TCq54oor2r/92791nS9Sp0+fbt/85je3TzzxRPvrv/7r7Yc+9KG2bb2uL2Vj/8nlwsJCHDp0KHbs2LH8vV6vFzt27IgDBw6s4Mro0nPPPRfHjh0757rPzMzE9ddf77pfBE6ePBkRERs2bIiIiEOHDsXi4uI51/vqq6+Obdu2ud4XsMFgEI888kicOXMmZmdnXeeL1K5du+K9733vOdc1wuv6UtZf6QX8Ij/+8Y9jMBjExo0bz/n+xo0b4z/+4z9WaFV07dixYxERr3jdf/JnXJiGw2Hcc8898e53vzve/va3R8TZ6z01NRWXX375OWNd7wvTs88+G7OzszE3Nxdr166Nxx57LN72trfFM8884zpfZB555JF4+umn46mnnvqZP/O6vnSNfbgELi67du2K733ve/Ev//IvK70UOvKWt7wlnnnmmTh58mT80z/9U9x+++2xf//+lV4WlR05ciQ+9KEPxRNPPBGrVq1a6eUwRsb+r8Vf97rXxcTExM/8dtnx48dj06ZNK7QquvaTa+u6X1zuuuuu+MpXvhLf/OY3Y8uWLcvf37RpUywsLMSJEyfOGe96X5impqbiTW96U2zfvj327t0b1157bXz60592nS8yhw4dihdeeCHe8Y53RL/fj36/H/v374/7778/+v1+bNy40fW+RI19uJyamort27fHvn37lr83HA5j3759MTs7u4Iro0tXXXVVbNq06ZzrfurUqXjyySdd9wtQ27Zx1113xWOPPRbf+MY34qqrrjrnz7dv3x6Tk5PnXO/Dhw/H888/73pfBIbDYczPz7vOF5mbbropnn322XjmmWeWv975znfGBz/4weX/7Xpfmi6IvxbfvXt33H777fHOd74zrrvuuvjUpz4VZ86ciTvuuGOll8Zr8OKLL8YPf/jD5f9+7rnn4plnnokNGzbEtm3b4p577omPfexj8eY3vzmuuuqq+PCHPxybN2+OW2+9deUWTZFdu3bFww8/HF/+8pdj3bp1y//eamZmJlavXh0zMzNx5513xu7du2PDhg2xfv36uPvuu2N2djZuuOGGFV49GXv27Ilbbrkltm3bFqdPn46HH344vvWtb8XXv/511/kis27duuV/N/0Ta9asiSuvvHL5+673JWqlf139fP31X/91u23btnZqaqq97rrr2oMHD670kniNvvnNb7YR8TNft99+e9u2Z//viD784Q+3GzdubKenp9ubbrqpPXz48MoumiKvdJ0jon3ooYeWx7z88svtn/zJn7RXXHFFe9lll7W//du/3f7oRz9auUVT5Pd///fbX/7lX26npqbaX/qlX2pvuumm9p//+Z+X/9x1vrj97/9XRG3rel+qmrZt2xXKtQAAXGTG/t9cAgBw4RAuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKAa4RIAgGqESwAAqhEuAQCoRrgEAKCa/w/1ugUagpwzWAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -3307,7 +3456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 62,
    "id": "d13a6775",
    "metadata": {
     "execution": {
@@ -3320,6 +3469,668 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "weight",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "2e9790b3-da72-498b-a031-742722360a67",
+       "rows": [
+        [
+         "0",
+         "18.0",
+         "8",
+         "307.0",
+         "130",
+         "3504",
+         "12.0",
+         "70",
+         "1",
+         "chevrolet chevelle malibu"
+        ],
+        [
+         "1",
+         "15.0",
+         "8",
+         "350.0",
+         "165",
+         "3693",
+         "11.5",
+         "70",
+         "1",
+         "buick skylark 320"
+        ],
+        [
+         "2",
+         "18.0",
+         "8",
+         "318.0",
+         "150",
+         "3436",
+         "11.0",
+         "70",
+         "1",
+         "plymouth satellite"
+        ],
+        [
+         "3",
+         "16.0",
+         "8",
+         "304.0",
+         "150",
+         "3433",
+         "12.0",
+         "70",
+         "1",
+         "amc rebel sst"
+        ],
+        [
+         "4",
+         "17.0",
+         "8",
+         "302.0",
+         "140",
+         "3449",
+         "10.5",
+         "70",
+         "1",
+         "ford torino"
+        ],
+        [
+         "5",
+         "15.0",
+         "8",
+         "429.0",
+         "198",
+         "4341",
+         "10.0",
+         "70",
+         "1",
+         "ford galaxie 500"
+        ],
+        [
+         "6",
+         "14.0",
+         "8",
+         "454.0",
+         "220",
+         "4354",
+         "9.0",
+         "70",
+         "1",
+         "chevrolet impala"
+        ],
+        [
+         "7",
+         "14.0",
+         "8",
+         "440.0",
+         "215",
+         "4312",
+         "8.5",
+         "70",
+         "1",
+         "plymouth fury iii"
+        ],
+        [
+         "8",
+         "14.0",
+         "8",
+         "455.0",
+         "225",
+         "4425",
+         "10.0",
+         "70",
+         "1",
+         "pontiac catalina"
+        ],
+        [
+         "9",
+         "15.0",
+         "8",
+         "390.0",
+         "190",
+         "3850",
+         "8.5",
+         "70",
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+         "8.0",
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+         "plymouth 'cuda 340"
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+         "15.0",
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+         "toyota corona mark ii"
+        ],
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+         "15.5",
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+         "plymouth duster"
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+         "200.0",
+         "85",
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+         "16.0",
+         "70",
+         "1",
+         "ford maverick"
+        ],
+        [
+         "18",
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+         "4",
+         "97.0",
+         "88",
+         "2130",
+         "14.5",
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+         "3",
+         "datsun pl510"
+        ],
+        [
+         "19",
+         "26.0",
+         "4",
+         "97.0",
+         "46",
+         "1835",
+         "20.5",
+         "70",
+         "2",
+         "volkswagen 1131 deluxe sedan"
+        ],
+        [
+         "20",
+         "25.0",
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+         "110.0",
+         "87",
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+         "17.5",
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+         "peugeot 504"
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+        [
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+         "107.0",
+         "90",
+         "2430",
+         "14.5",
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+         "2",
+         "audi 100 ls"
+        ],
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+         "17.5",
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+         "saab 99e"
+        ],
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+         "26.0",
+         "4",
+         "121.0",
+         "113",
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+         "12.5",
+         "70",
+         "2",
+         "bmw 2002"
+        ],
+        [
+         "24",
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+         "6",
+         "199.0",
+         "90",
+         "2648",
+         "15.0",
+         "70",
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+         "amc gremlin"
+        ],
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+         "215",
+         "4615",
+         "14.0",
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+         "1",
+         "ford f250"
+        ],
+        [
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+         "307.0",
+         "200",
+         "4376",
+         "15.0",
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+         "chevy c20"
+        ],
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+         "11.0",
+         "8",
+         "318.0",
+         "210",
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+         "13.5",
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+        ],
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+        ],
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+         "15.5",
+         "71",
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+         "chevrolet vega 2300"
+        ],
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+         "95",
+         "2228",
+         "14.0",
+         "71",
+         "3",
+         "toyota corona"
+        ],
+        [
+         "32",
+         "19.0",
+         "6",
+         "232.0",
+         "100",
+         "2634",
+         "13.0",
+         "71",
+         "1",
+         "amc gremlin"
+        ],
+        [
+         "33",
+         "16.0",
+         "6",
+         "225.0",
+         "105",
+         "3439",
+         "15.5",
+         "71",
+         "1",
+         "plymouth satellite custom"
+        ],
+        [
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+         "17.0",
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+         "250.0",
+         "100",
+         "3329",
+         "15.5",
+         "71",
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+         "chevrolet chevelle malibu"
+        ],
+        [
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+         "19.0",
+         "6",
+         "250.0",
+         "88",
+         "3302",
+         "15.5",
+         "71",
+         "1",
+         "ford torino 500"
+        ],
+        [
+         "36",
+         "18.0",
+         "6",
+         "232.0",
+         "100",
+         "3288",
+         "15.5",
+         "71",
+         "1",
+         "amc matador"
+        ],
+        [
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+         "14.0",
+         "8",
+         "350.0",
+         "165",
+         "4209",
+         "12.0",
+         "71",
+         "1",
+         "chevrolet impala"
+        ],
+        [
+         "38",
+         "14.0",
+         "8",
+         "400.0",
+         "175",
+         "4464",
+         "11.5",
+         "71",
+         "1",
+         "pontiac catalina brougham"
+        ],
+        [
+         "39",
+         "14.0",
+         "8",
+         "351.0",
+         "153",
+         "4154",
+         "13.5",
+         "71",
+         "1",
+         "ford galaxie 500"
+        ],
+        [
+         "40",
+         "14.0",
+         "8",
+         "318.0",
+         "150",
+         "4096",
+         "13.0",
+         "71",
+         "1",
+         "plymouth fury iii"
+        ],
+        [
+         "41",
+         "12.0",
+         "8",
+         "383.0",
+         "180",
+         "4955",
+         "11.5",
+         "71",
+         "1",
+         "dodge monaco (sw)"
+        ],
+        [
+         "42",
+         "13.0",
+         "8",
+         "400.0",
+         "170",
+         "4746",
+         "12.0",
+         "71",
+         "1",
+         "ford country squire (sw)"
+        ],
+        [
+         "43",
+         "13.0",
+         "8",
+         "400.0",
+         "175",
+         "5140",
+         "12.0",
+         "71",
+         "1",
+         "pontiac safari (sw)"
+        ],
+        [
+         "44",
+         "18.0",
+         "6",
+         "258.0",
+         "110",
+         "2962",
+         "13.5",
+         "71",
+         "1",
+         "amc hornet sportabout (sw)"
+        ],
+        [
+         "45",
+         "22.0",
+         "4",
+         "140.0",
+         "72",
+         "2408",
+         "19.0",
+         "71",
+         "1",
+         "chevrolet vega (sw)"
+        ],
+        [
+         "46",
+         "19.0",
+         "6",
+         "250.0",
+         "100",
+         "3282",
+         "15.0",
+         "71",
+         "1",
+         "pontiac firebird"
+        ],
+        [
+         "47",
+         "18.0",
+         "6",
+         "250.0",
+         "88",
+         "3139",
+         "14.5",
+         "71",
+         "1",
+         "ford mustang"
+        ],
+        [
+         "48",
+         "23.0",
+         "4",
+         "122.0",
+         "86",
+         "2220",
+         "14.0",
+         "71",
+         "1",
+         "mercury capri 2000"
+        ],
+        [
+         "49",
+         "28.0",
+         "4",
+         "116.0",
+         "90",
+         "2123",
+         "14.0",
+         "71",
+         "2",
+         "opel 1900"
+        ]
+       ],
+       "shape": {
+        "columns": 9,
+        "rows": 392
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3518,7 +4329,7 @@
        "[392 rows x 9 columns]"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3539,7 +4350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 64,
    "id": "f7857be6",
    "metadata": {
     "execution": {
@@ -3550,322 +4361,671 @@
     },
     "lines_to_next_cell": 0
    },
-   "outputs": [],
-   "source": [
-    "Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a373fcb4",
-   "metadata": {},
-   "source": [
-    " Both `Auto.csv` and `Auto.data` are simply text\n",
-    "files. Before loading data into `Python`, it is a good idea to view it using\n",
-    "a text editor or other software, such as Microsoft Excel.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bba17c66",
-   "metadata": {},
-   "source": [
-    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "7912c27e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.328873Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.328782Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.331187Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.330966Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      130.0\n",
-       "1      165.0\n",
-       "2      150.0\n",
-       "3      150.0\n",
-       "4      140.0\n",
-       "       ...  \n",
-       "392    86.00\n",
-       "393    52.00\n",
-       "394    84.00\n",
-       "395    79.00\n",
-       "396    82.00\n",
-       "Name: horsepower, Length: 397, dtype: object"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto['horsepower']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af443415",
-   "metadata": {},
-   "source": [
-    "We see that the `dtype` of this column is `object`. \n",
-    "It turns out that all values of the `horsepower` column were interpreted as strings when reading\n",
-    "in the data. \n",
-    "We can find out why by looking at the unique values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "f8078632",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.332465Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.332343Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.334500Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.334302Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['100.0', '102.0', '103.0', '105.0', '107.0', '108.0', '110.0',\n",
-       "       '112.0', '113.0', '115.0', '116.0', '120.0', '122.0', '125.0',\n",
-       "       '129.0', '130.0', '132.0', '133.0', '135.0', '137.0', '138.0',\n",
-       "       '139.0', '140.0', '142.0', '145.0', '148.0', '149.0', '150.0',\n",
-       "       '152.0', '153.0', '155.0', '158.0', '160.0', '165.0', '167.0',\n",
-       "       '170.0', '175.0', '180.0', '190.0', '193.0', '198.0', '200.0',\n",
-       "       '208.0', '210.0', '215.0', '220.0', '225.0', '230.0', '46.00',\n",
-       "       '48.00', '49.00', '52.00', '53.00', '54.00', '58.00', '60.00',\n",
-       "       '61.00', '62.00', '63.00', '64.00', '65.00', '66.00', '67.00',\n",
-       "       '68.00', '69.00', '70.00', '71.00', '72.00', '74.00', '75.00',\n",
-       "       '76.00', '77.00', '78.00', '79.00', '80.00', '81.00', '82.00',\n",
-       "       '83.00', '84.00', '85.00', '86.00', '87.00', '88.00', '89.00',\n",
-       "       '90.00', '91.00', '92.00', '93.00', '94.00', '95.00', '96.00',\n",
-       "       '97.00', '98.00', '?'], dtype=object)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.unique(Auto['horsepower'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46fb50cb",
-   "metadata": {},
-   "source": [
-    "We see the culprit is the value `?`, which is being used to encode missing values.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71e7a9fe",
-   "metadata": {},
-   "source": [
-    "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n",
-    "Now,  each instance of  `?` in the file is replaced with the\n",
-    "value `np.nan`, which means *not a number*:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "aad623ae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.335798Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.335712Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.338898Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.338695Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "40952.0"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto = pd.read_csv('Auto.data',\n",
-    "                   na_values=['?'],\n",
-    "                   delim_whitespace=True)\n",
-    "Auto['horsepower'].sum()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8aa30cf6",
-   "metadata": {},
-   "source": [
-    "The `Auto.shape`  attribute tells us that the data has 397\n",
-    "observations, or rows, and nine variables, or columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "ae1cad4e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.340150Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.340081Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.341961Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.341744Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(397, 9)"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a699c56e",
-   "metadata": {},
-   "source": [
-    "There are\n",
-    "various ways to deal with  missing data. \n",
-    "In this case, since only five of the rows contain missing\n",
-    "observations,  we choose to use the `Auto.dropna()` method to simply remove these rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "52eb7971",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.343152Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.343088Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.345337Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.345111Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(392, 9)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto_new = Auto.dropna()\n",
-    "Auto_new.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55a60e42",
-   "metadata": {},
-   "source": [
-    "### Basics of Selecting Rows and Columns\n",
-    " \n",
-    "We can use `Auto.columns`  to check the variable names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "44829ab6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.346569Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.346495Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.348509Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.348265Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
-       "       'acceleration', 'year', 'origin', 'name'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto = Auto_new # overwrite the previous value\n",
-    "Auto.columns\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a397a994",
-   "metadata": {},
-   "source": [
-    "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n",
-    "Recall that the first argument to the `[]` method\n",
-    "is always applied to the rows of the array.  \n",
-    "Similarly, \n",
-    "passing in a slice to the `[]` method creates a data frame whose *rows* are determined by the slice:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "3aad1bdb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.349852Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.349784Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.353582Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.353356Z"
-    },
-    "lines_to_next_cell": 0
-   },
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
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@@ -3933,637 +5093,1000 @@
        "      <td>1</td>\n",
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-       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
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-       "1  15.0          8         350.0       165.0  3693.0          11.5    70   \n",
-       "2  18.0          8         318.0       150.0  3436.0          11.0    70   \n",
-       "\n",
-       "   origin                       name  \n",
-       "0       1  chevrolet chevelle malibu  \n",
-       "1       1          buick skylark 320  \n",
-       "2       1         plymouth satellite  "
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        "      <td>1</td>\n",
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-       "      <td>toyota starlet</td>\n",
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+       "      <td>2790.0</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>plymouth champ</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>345</th>\n",
-       "      <td>35.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1760.0</td>\n",
-       "      <td>16.1</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic 1300</td>\n",
+       "      <td>ford mustang gl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>346</th>\n",
-       "      <td>32.3</td>\n",
+       "      <th>393</th>\n",
+       "      <td>44.0</td>\n",
        "      <td>4</td>\n",
        "      <td>97.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>2065.0</td>\n",
-       "      <td>17.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>subaru</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>347</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>1975.0</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 210 mpg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>348</th>\n",
-       "      <td>37.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>2050.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota tercel</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>349</th>\n",
-       "      <td>34.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>1985.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc 4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>350</th>\n",
-       "      <td>34.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>2215.0</td>\n",
-       "      <td>14.9</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>plymouth horizon 4</td>\n",
+       "      <td>52.00</td>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw pickup</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>34.4</td>\n",
+       "      <th>394</th>\n",
+       "      <td>32.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>2045.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>81</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.00</td>\n",
+       "      <td>2295.0</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>ford escort 4w</td>\n",
+       "      <td>dodge rampage</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>352</th>\n",
-       "      <td>29.9</td>\n",
+       "      <th>395</th>\n",
+       "      <td>28.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>2380.0</td>\n",
-       "      <td>20.7</td>\n",
-       "      <td>81</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79.00</td>\n",
+       "      <td>2625.0</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>ford escort 2h</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>353</th>\n",
-       "      <td>33.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>2190.0</td>\n",
-       "      <td>14.2</td>\n",
-       "      <td>81</td>\n",
-       "      <td>2</td>\n",
-       "      <td>volkswagen jetta</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>355</th>\n",
-       "      <td>33.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>2210.0</td>\n",
-       "      <td>14.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda prelude</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>356</th>\n",
-       "      <td>32.4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>2350.0</td>\n",
-       "      <td>16.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota corolla</td>\n",
+       "      <td>ford ranger</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>357</th>\n",
-       "      <td>32.9</td>\n",
+       "      <th>396</th>\n",
+       "      <td>31.0</td>\n",
        "      <td>4</td>\n",
        "      <td>119.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>2615.0</td>\n",
-       "      <td>14.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 200sx</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>358</th>\n",
-       "      <td>31.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>2635.0</td>\n",
-       "      <td>18.3</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda 626</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>359</th>\n",
-       "      <td>28.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>141.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>3230.0</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>2</td>\n",
-       "      <td>peugeot 505s turbo diesel</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>360</th>\n",
-       "      <td>30.7</td>\n",
-       "      <td>6</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>3160.0</td>\n",
-       "      <td>19.6</td>\n",
-       "      <td>81</td>\n",
-       "      <td>2</td>\n",
-       "      <td>volvo diesel</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>361</th>\n",
-       "      <td>25.4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>168.0</td>\n",
-       "      <td>116.0</td>\n",
-       "      <td>2900.0</td>\n",
-       "      <td>12.6</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota cressida</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>362</th>\n",
-       "      <td>24.2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>146.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>2930.0</td>\n",
-       "      <td>13.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 810 maxima</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>363</th>\n",
-       "      <td>22.4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>231.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>3415.0</td>\n",
-       "      <td>15.8</td>\n",
-       "      <td>81</td>\n",
+       "      <td>82.00</td>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>buick century</td>\n",
+       "      <td>chevy s-10</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>364</th>\n",
-       "      <td>26.6</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>3725.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>oldsmobile cutlass ls</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>365</th>\n",
-       "      <td>20.2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>3060.0</td>\n",
-       "      <td>17.1</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford granada gl</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>366</th>\n",
-       "      <td>17.6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>225.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>3465.0</td>\n",
-       "      <td>16.6</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chrysler lebaron salon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>367</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2605.0</td>\n",
-       "      <td>19.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet cavalier</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>368</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2640.0</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet cavalier wagon</td>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>397 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
+       "0    18.0          8         307.0      130.0  3504.0          12.0    70   \n",
+       "1    15.0          8         350.0      165.0  3693.0          11.5    70   \n",
+       "2    18.0          8         318.0      150.0  3436.0          11.0    70   \n",
+       "3    16.0          8         304.0      150.0  3433.0          12.0    70   \n",
+       "4    17.0          8         302.0      140.0  3449.0          10.5    70   \n",
+       "..    ...        ...           ...        ...     ...           ...   ...   \n",
+       "392  27.0          4         140.0      86.00  2790.0          15.6    82   \n",
+       "393  44.0          4          97.0      52.00  2130.0          24.6    82   \n",
+       "394  32.0          4         135.0      84.00  2295.0          11.6    82   \n",
+       "395  28.0          4         120.0      79.00  2625.0          18.6    82   \n",
+       "396  31.0          4         119.0      82.00  2720.0          19.4    82   \n",
+       "\n",
+       "     origin                       name  \n",
+       "0         1  chevrolet chevelle malibu  \n",
+       "1         1          buick skylark 320  \n",
+       "2         1         plymouth satellite  \n",
+       "3         1              amc rebel sst  \n",
+       "4         1                ford torino  \n",
+       "..      ...                        ...  \n",
+       "392       1            ford mustang gl  \n",
+       "393       2                  vw pickup  \n",
+       "394       1              dodge rampage  \n",
+       "395       1                ford ranger  \n",
+       "396       1                 chevy s-10  \n",
+       "\n",
+       "[397 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto = pd.read_csv('Auto.data', sep = '\\s+')#delim_whitespace=True)\n",
+    "Auto"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a373fcb4",
+   "metadata": {},
+   "source": [
+    " Both `Auto.csv` and `Auto.data` are simply text\n",
+    "files. Before loading data into `Python`, it is a good idea to view it using\n",
+    "a text editor or other software, such as Microsoft Excel.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bba17c66",
+   "metadata": {},
+   "source": [
+    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "7912c27e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.328873Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.328782Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.331187Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.330966Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      130.0\n",
+       "1      165.0\n",
+       "2      150.0\n",
+       "3      150.0\n",
+       "4      140.0\n",
+       "       ...  \n",
+       "392    86.00\n",
+       "393    52.00\n",
+       "394    84.00\n",
+       "395    79.00\n",
+       "396    82.00\n",
+       "Name: horsepower, Length: 397, dtype: object"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto['horsepower']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af443415",
+   "metadata": {},
+   "source": [
+    "We see that the `dtype` of this column is `object`. \n",
+    "It turns out that all values of the `horsepower` column were interpreted as strings when reading\n",
+    "in the data. \n",
+    "We can find out why by looking at the unique values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "f8078632",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.332465Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.332343Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.334500Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.334302Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['100.0', '102.0', '103.0', '105.0', '107.0', '108.0', '110.0',\n",
+       "       '112.0', '113.0', '115.0', '116.0', '120.0', '122.0', '125.0',\n",
+       "       '129.0', '130.0', '132.0', '133.0', '135.0', '137.0', '138.0',\n",
+       "       '139.0', '140.0', '142.0', '145.0', '148.0', '149.0', '150.0',\n",
+       "       '152.0', '153.0', '155.0', '158.0', '160.0', '165.0', '167.0',\n",
+       "       '170.0', '175.0', '180.0', '190.0', '193.0', '198.0', '200.0',\n",
+       "       '208.0', '210.0', '215.0', '220.0', '225.0', '230.0', '46.00',\n",
+       "       '48.00', '49.00', '52.00', '53.00', '54.00', '58.00', '60.00',\n",
+       "       '61.00', '62.00', '63.00', '64.00', '65.00', '66.00', '67.00',\n",
+       "       '68.00', '69.00', '70.00', '71.00', '72.00', '74.00', '75.00',\n",
+       "       '76.00', '77.00', '78.00', '79.00', '80.00', '81.00', '82.00',\n",
+       "       '83.00', '84.00', '85.00', '86.00', '87.00', '88.00', '89.00',\n",
+       "       '90.00', '91.00', '92.00', '93.00', '94.00', '95.00', '96.00',\n",
+       "       '97.00', '98.00', '?'], dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.unique(Auto['horsepower'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46fb50cb",
+   "metadata": {},
+   "source": [
+    "We see the culprit is the value `?`, which is being used to encode missing values.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71e7a9fe",
+   "metadata": {},
+   "source": [
+    "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n",
+    "Now,  each instance of  `?` in the file is replaced with the\n",
+    "value `np.nan`, which means *not a number*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "aad623ae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.335798Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.335712Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.338898Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.338695Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_208199/134681464.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
+      "  Auto = pd.read_csv('Auto.data',\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "40952.0"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto = pd.read_csv('Auto.data',\n",
+    "                   na_values=['?'],\n",
+    "                   delim_whitespace=True)\n",
+    "Auto['horsepower'].sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8aa30cf6",
+   "metadata": {},
+   "source": [
+    "The `Auto.shape`  attribute tells us that the data has 397\n",
+    "observations, or rows, and nine variables, or columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "ae1cad4e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.340150Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.340081Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.341961Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.341744Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(397, 9)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto.shape\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a699c56e",
+   "metadata": {},
+   "source": [
+    "There are\n",
+    "various ways to deal with  missing data. \n",
+    "In this case, since only five of the rows contain missing\n",
+    "observations,  we choose to use the `Auto.dropna()` method to simply remove these rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "52eb7971",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.343152Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.343088Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.345337Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.345111Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(392, 9)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto_new = Auto.dropna()\n",
+    "Auto_new.shape\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55a60e42",
+   "metadata": {},
+   "source": [
+    "### Basics of Selecting Rows and Columns\n",
+    " \n",
+    "We can use `Auto.columns`  to check the variable names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "44829ab6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.346569Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.346495Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.348509Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.348265Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
+       "       'acceleration', 'year', 'origin', 'name'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto = Auto_new # overwrite the previous value\n",
+    "Auto.columns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a397a994",
+   "metadata": {},
+   "source": [
+    "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n",
+    "Recall that the first argument to the `[]` method\n",
+    "is always applied to the rows of the array.  \n",
+    "Similarly, \n",
+    "passing in a slice to the `[]` method creates a data frame whose *rows* are determined by the slice:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "3aad1bdb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.349852Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.349784Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.353582Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.353356Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "f6b9d6fd-efe8-4a86-aeef-c4d14915c790",
+       "rows": [
+        [
+         "0",
+         "18.0",
+         "8",
+         "307.0",
+         "130.0",
+         "3504.0",
+         "12.0",
+         "70",
+         "1",
+         "chevrolet chevelle malibu"
+        ],
+        [
+         "1",
+         "15.0",
+         "8",
+         "350.0",
+         "165.0",
+         "3693.0",
+         "11.5",
+         "70",
+         "1",
+         "buick skylark 320"
+        ],
+        [
+         "2",
+         "18.0",
+         "8",
+         "318.0",
+         "150.0",
+         "3436.0",
+         "11.0",
+         "70",
+         "1",
+         "plymouth satellite"
+        ]
+       ],
+       "shape": {
+        "columns": 9,
+        "rows": 3
+       }
+      },
+      "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>369</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2395.0</td>\n",
+       "      <th>0</th>\n",
        "      <td>18.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet cavalier 2-door</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>370</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>2575.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>82</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
-       "      <td>pontiac j2000 se hatchback</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>371</th>\n",
-       "      <td>29.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>2525.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>82</td>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
-       "      <td>dodge aries se</td>\n",
+       "      <td>buick skylark 320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>372</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>151.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>2735.0</td>\n",
+       "      <th>2</th>\n",
        "      <td>18.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>pontiac phoenix</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>373</th>\n",
-       "      <td>24.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>2865.0</td>\n",
-       "      <td>16.4</td>\n",
-       "      <td>82</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
-       "      <td>ford fairmont futura</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>374</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>15.3</td>\n",
-       "      <td>82</td>\n",
-       "      <td>2</td>\n",
-       "      <td>volkswagen rabbit l</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>375</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>2025.0</td>\n",
-       "      <td>18.2</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc custom l</td>\n",
+       "      <td>plymouth satellite</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>376</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>1970.0</td>\n",
-       "      <td>17.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc custom</td>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
+       "0  18.0          8         307.0       130.0  3504.0          12.0    70   \n",
+       "1  15.0          8         350.0       165.0  3693.0          11.5    70   \n",
+       "2  18.0          8         318.0       150.0  3436.0          11.0    70   \n",
+       "\n",
+       "   origin                       name  \n",
+       "0       1  chevrolet chevelle malibu  \n",
+       "1       1          buick skylark 320  \n",
+       "2       1         plymouth satellite  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto[:3]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b42c876",
+   "metadata": {},
+   "source": [
+    "Similarly, an array of Booleans can be used to subset the rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "ae1e9a3a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.354951Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.354871Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.366222Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.365980Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "4ba65194-8830-4f75-8274-90a324cadf17",
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+         "3160.0",
+         "19.6",
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+         "volvo diesel"
+        ],
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+         "116.0",
+         "2900.0",
+         "12.6",
+         "81",
+         "3",
+         "toyota cressida"
+        ],
+        [
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+         "24.2",
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+         "120.0",
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+         "13.8",
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+         "datsun 810 maxima"
+        ],
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+         "231.0",
+         "110.0",
+         "3415.0",
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+         "1",
+         "buick century"
+        ],
+        [
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+         "26.6",
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+         "350.0",
+         "105.0",
+         "3725.0",
+         "19.0",
+         "81",
+         "1",
+         "oldsmobile cutlass ls"
+        ],
+        [
+         "365",
+         "20.2",
+         "6",
+         "200.0",
+         "88.0",
+         "3060.0",
+         "17.1",
+         "81",
+         "1",
+         "ford granada gl"
+        ],
+        [
+         "366",
+         "17.6",
+         "6",
+         "225.0",
+         "85.0",
+         "3465.0",
+         "16.6",
+         "81",
+         "1",
+         "chrysler lebaron salon"
+        ],
+        [
+         "385",
+         "25.0",
+         "6",
+         "181.0",
+         "110.0",
+         "2945.0",
+         "16.4",
+         "82",
+         "1",
+         "buick century limited"
+        ],
+        [
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+         "38.0",
+         "6",
+         "262.0",
+         "85.0",
+         "3015.0",
+         "17.0",
+         "82",
+         "1",
+         "oldsmobile cutlass ciera (diesel)"
+        ],
+        [
+         "388",
+         "22.0",
+         "6",
+         "232.0",
+         "112.0",
+         "2835.0",
+         "14.7",
+         "82",
+         "1",
+         "ford granada l"
+        ]
+       ],
+       "shape": {
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+        "rows": 11
+       }
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+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe tbody tr th {\n",
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+       "\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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>377</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
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-       "      <td>2125.0</td>\n",
-       "      <td>14.7</td>\n",
-       "      <td>82</td>\n",
+       "      <th>341</th>\n",
+       "      <td>23.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>173.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>2725.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>81</td>\n",
        "      <td>1</td>\n",
-       "      <td>plymouth horizon miser</td>\n",
+       "      <td>chevrolet citation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>378</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2125.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>mercury lynx l</td>\n",
+       "      <th>360</th>\n",
+       "      <td>30.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>3160.0</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>volvo diesel</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>379</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2160.0</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>82</td>\n",
+       "      <th>361</th>\n",
+       "      <td>25.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>81</td>\n",
        "      <td>3</td>\n",
-       "      <td>nissan stanza xe</td>\n",
+       "      <td>toyota cressida</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>380</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>2205.0</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>82</td>\n",
+       "      <th>362</th>\n",
+       "      <td>24.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>2930.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>81</td>\n",
        "      <td>3</td>\n",
-       "      <td>honda accord</td>\n",
+       "      <td>datsun 810 maxima</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>381</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2245.0</td>\n",
-       "      <td>16.9</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota corolla</td>\n",
+       "      <th>363</th>\n",
+       "      <td>22.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>231.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>3415.0</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick century</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>382</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1965.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic</td>\n",
+       "      <th>364</th>\n",
+       "      <td>26.6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3725.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>oldsmobile cutlass ls</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>383</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1965.0</td>\n",
-       "      <td>15.7</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic (auto)</td>\n",
+       "      <th>365</th>\n",
+       "      <td>20.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>3060.0</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford granada gl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>384</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1995.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 310 gx</td>\n",
+       "      <th>366</th>\n",
+       "      <td>17.6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3465.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chrysler lebaron salon</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>385</th>\n",
@@ -4590,18 +6113,6 @@
        "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>387</th>\n",
-       "      <td>26.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>2585.0</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chrysler lebaron medallion</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>388</th>\n",
        "      <td>22.0</td>\n",
        "      <td>6</td>\n",
@@ -4613,129 +6124,13 @@
        "      <td>1</td>\n",
        "      <td>ford granada l</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>389</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>144.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>2665.0</td>\n",
-       "      <td>13.9</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota celica gt</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>390</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>2370.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>dodge charger 2.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>391</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>151.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>2950.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet camaro</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>392</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>2790.0</td>\n",
-       "      <td>15.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford mustang gl</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>393</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>2130.0</td>\n",
-       "      <td>24.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>2</td>\n",
-       "      <td>vw pickup</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>394</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>2295.0</td>\n",
-       "      <td>11.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>dodge rampage</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>2625.0</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford ranger</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>396</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>119.0</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>2720.0</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevy s-10</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "      mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
-       "338  27.2          4         135.0        84.0  2490.0          15.7    81   \n",
-       "339  26.6          4         151.0        84.0  2635.0          16.4    81   \n",
-       "340  25.8          4         156.0        92.0  2620.0          14.4    81   \n",
        "341  23.5          6         173.0       110.0  2725.0          12.6    81   \n",
-       "342  30.0          4         135.0        84.0  2385.0          12.9    81   \n",
-       "343  39.1          4          79.0        58.0  1755.0          16.9    81   \n",
-       "344  39.0          4          86.0        64.0  1875.0          16.4    81   \n",
-       "345  35.1          4          81.0        60.0  1760.0          16.1    81   \n",
-       "346  32.3          4          97.0        67.0  2065.0          17.8    81   \n",
-       "347  37.0          4          85.0        65.0  1975.0          19.4    81   \n",
-       "348  37.7          4          89.0        62.0  2050.0          17.3    81   \n",
-       "349  34.1          4          91.0        68.0  1985.0          16.0    81   \n",
-       "350  34.7          4         105.0        63.0  2215.0          14.9    81   \n",
-       "351  34.4          4          98.0        65.0  2045.0          16.2    81   \n",
-       "352  29.9          4          98.0        65.0  2380.0          20.7    81   \n",
-       "353  33.0          4         105.0        74.0  2190.0          14.2    81   \n",
-       "355  33.7          4         107.0        75.0  2210.0          14.4    81   \n",
-       "356  32.4          4         108.0        75.0  2350.0          16.8    81   \n",
-       "357  32.9          4         119.0       100.0  2615.0          14.8    81   \n",
-       "358  31.6          4         120.0        74.0  2635.0          18.3    81   \n",
-       "359  28.1          4         141.0        80.0  3230.0          20.4    81   \n",
        "360  30.7          6         145.0        76.0  3160.0          19.6    81   \n",
        "361  25.4          6         168.0       116.0  2900.0          12.6    81   \n",
        "362  24.2          6         146.0       120.0  2930.0          13.8    81   \n",
@@ -4743,59 +6138,12 @@
        "364  26.6          8         350.0       105.0  3725.0          19.0    81   \n",
        "365  20.2          6         200.0        88.0  3060.0          17.1    81   \n",
        "366  17.6          6         225.0        85.0  3465.0          16.6    81   \n",
-       "367  28.0          4         112.0        88.0  2605.0          19.6    82   \n",
-       "368  27.0          4         112.0        88.0  2640.0          18.6    82   \n",
-       "369  34.0          4         112.0        88.0  2395.0          18.0    82   \n",
-       "370  31.0          4         112.0        85.0  2575.0          16.2    82   \n",
-       "371  29.0          4         135.0        84.0  2525.0          16.0    82   \n",
-       "372  27.0          4         151.0        90.0  2735.0          18.0    82   \n",
-       "373  24.0          4         140.0        92.0  2865.0          16.4    82   \n",
-       "374  36.0          4         105.0        74.0  1980.0          15.3    82   \n",
-       "375  37.0          4          91.0        68.0  2025.0          18.2    82   \n",
-       "376  31.0          4          91.0        68.0  1970.0          17.6    82   \n",
-       "377  38.0          4         105.0        63.0  2125.0          14.7    82   \n",
-       "378  36.0          4          98.0        70.0  2125.0          17.3    82   \n",
-       "379  36.0          4         120.0        88.0  2160.0          14.5    82   \n",
-       "380  36.0          4         107.0        75.0  2205.0          14.5    82   \n",
-       "381  34.0          4         108.0        70.0  2245.0          16.9    82   \n",
-       "382  38.0          4          91.0        67.0  1965.0          15.0    82   \n",
-       "383  32.0          4          91.0        67.0  1965.0          15.7    82   \n",
-       "384  38.0          4          91.0        67.0  1995.0          16.2    82   \n",
        "385  25.0          6         181.0       110.0  2945.0          16.4    82   \n",
        "386  38.0          6         262.0        85.0  3015.0          17.0    82   \n",
-       "387  26.0          4         156.0        92.0  2585.0          14.5    82   \n",
        "388  22.0          6         232.0       112.0  2835.0          14.7    82   \n",
-       "389  32.0          4         144.0        96.0  2665.0          13.9    82   \n",
-       "390  36.0          4         135.0        84.0  2370.0          13.0    82   \n",
-       "391  27.0          4         151.0        90.0  2950.0          17.3    82   \n",
-       "392  27.0          4         140.0        86.0  2790.0          15.6    82   \n",
-       "393  44.0          4          97.0        52.0  2130.0          24.6    82   \n",
-       "394  32.0          4         135.0        84.0  2295.0          11.6    82   \n",
-       "395  28.0          4         120.0        79.0  2625.0          18.6    82   \n",
-       "396  31.0          4         119.0        82.0  2720.0          19.4    82   \n",
        "\n",
        "     origin                               name  \n",
-       "338       1                   plymouth reliant  \n",
-       "339       1                      buick skylark  \n",
-       "340       1             dodge aries wagon (sw)  \n",
        "341       1                 chevrolet citation  \n",
-       "342       1                   plymouth reliant  \n",
-       "343       3                     toyota starlet  \n",
-       "344       1                     plymouth champ  \n",
-       "345       3                   honda civic 1300  \n",
-       "346       3                             subaru  \n",
-       "347       3                     datsun 210 mpg  \n",
-       "348       3                      toyota tercel  \n",
-       "349       3                        mazda glc 4  \n",
-       "350       1                 plymouth horizon 4  \n",
-       "351       1                     ford escort 4w  \n",
-       "352       1                     ford escort 2h  \n",
-       "353       2                   volkswagen jetta  \n",
-       "355       3                      honda prelude  \n",
-       "356       3                     toyota corolla  \n",
-       "357       3                       datsun 200sx  \n",
-       "358       3                          mazda 626  \n",
-       "359       2          peugeot 505s turbo diesel  \n",
        "360       2                       volvo diesel  \n",
        "361       3                    toyota cressida  \n",
        "362       3                  datsun 810 maxima  \n",
@@ -4803,45 +6151,18 @@
        "364       1              oldsmobile cutlass ls  \n",
        "365       1                    ford granada gl  \n",
        "366       1             chrysler lebaron salon  \n",
-       "367       1                 chevrolet cavalier  \n",
-       "368       1           chevrolet cavalier wagon  \n",
-       "369       1          chevrolet cavalier 2-door  \n",
-       "370       1         pontiac j2000 se hatchback  \n",
-       "371       1                     dodge aries se  \n",
-       "372       1                    pontiac phoenix  \n",
-       "373       1               ford fairmont futura  \n",
-       "374       2                volkswagen rabbit l  \n",
-       "375       3                 mazda glc custom l  \n",
-       "376       3                   mazda glc custom  \n",
-       "377       1             plymouth horizon miser  \n",
-       "378       1                     mercury lynx l  \n",
-       "379       3                   nissan stanza xe  \n",
-       "380       3                       honda accord  \n",
-       "381       3                     toyota corolla  \n",
-       "382       3                        honda civic  \n",
-       "383       3                 honda civic (auto)  \n",
-       "384       3                      datsun 310 gx  \n",
        "385       1              buick century limited  \n",
        "386       1  oldsmobile cutlass ciera (diesel)  \n",
-       "387       1         chrysler lebaron medallion  \n",
-       "388       1                     ford granada l  \n",
-       "389       3                   toyota celica gt  \n",
-       "390       1                  dodge charger 2.2  \n",
-       "391       1                   chevrolet camaro  \n",
-       "392       1                    ford mustang gl  \n",
-       "393       2                          vw pickup  \n",
-       "394       1                      dodge rampage  \n",
-       "395       1                        ford ranger  \n",
-       "396       1                         chevy s-10  "
+       "388       1                     ford granada l  "
       ]
      },
-     "execution_count": 83,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "idx_80 = Auto['year'] > 80\n",
+    "idx_80 = (Auto['year'] > 80) & (Auto['cylinders'] > 4)\n",
     "Auto[idx_80]\n"
    ]
   },
@@ -4855,7 +6176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 77,
    "id": "2b543957",
    "metadata": {
     "execution": {
@@ -4869,6 +6190,283 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "72c170a4-6cab-41bd-9a00-bf37fb78ffa2",
+       "rows": [
+        [
+         "0",
+         "18.0",
+         "130.0"
+        ],
+        [
+         "1",
+         "15.0",
+         "165.0"
+        ],
+        [
+         "2",
+         "18.0",
+         "150.0"
+        ],
+        [
+         "3",
+         "16.0",
+         "150.0"
+        ],
+        [
+         "4",
+         "17.0",
+         "140.0"
+        ],
+        [
+         "5",
+         "15.0",
+         "198.0"
+        ],
+        [
+         "6",
+         "14.0",
+         "220.0"
+        ],
+        [
+         "7",
+         "14.0",
+         "215.0"
+        ],
+        [
+         "8",
+         "14.0",
+         "225.0"
+        ],
+        [
+         "9",
+         "15.0",
+         "190.0"
+        ],
+        [
+         "10",
+         "15.0",
+         "170.0"
+        ],
+        [
+         "11",
+         "14.0",
+         "160.0"
+        ],
+        [
+         "12",
+         "15.0",
+         "150.0"
+        ],
+        [
+         "13",
+         "14.0",
+         "225.0"
+        ],
+        [
+         "14",
+         "24.0",
+         "95.0"
+        ],
+        [
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@@ -5031,7 +6629,7 @@
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@@ -5044,6 +6642,613 @@
    "outputs": [
     {
      "data": {
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+         "71",
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+         "100.0",
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+         "19.0",
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+         "250.0",
+         "88.0",
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+         "15.5",
+         "71",
+         "1"
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+         "18.0",
+         "6",
+         "232.0",
+         "100.0",
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+         "350.0",
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@@ -5243,7 +7448,7 @@
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@@ -5255,7 +7460,7 @@
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@@ -5275,7 +7480,7 @@
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@@ -5298,7 +7503,7 @@
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-   "execution_count": 88,
+   "execution_count": 84,
    "id": "04e94eee",
    "metadata": {
     "execution": {
@@ -5312,6 +7517,85 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
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+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
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+        {
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+         "type": "float"
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        "<div>\n",
        "<style scoped>\n",
@@ -5391,7 +7675,7 @@
        "ford torino            10.5    70       1  "
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      },
-     "execution_count": 88,
+     "execution_count": 84,
      "metadata": {},
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@@ -5411,7 +7695,7 @@
   },
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+   "execution_count": 85,
    "id": "5cd1291d",
    "metadata": {
     "execution": {
@@ -5425,6 +7709,85 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
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+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
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+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
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+        {
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+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        }
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+       "conversionMethod": "pd.DataFrame",
+       "ref": "a75c0323-38b8-4e30-a24c-6361e02c515e",
+       "rows": [
+        [
+         "amc rebel sst",
+         "16.0",
+         "8",
+         "304.0",
+         "150.0",
+         "3433.0",
+         "12.0",
+         "70",
+         "1"
+        ],
+        [
+         "ford torino",
+         "17.0",
+         "8",
+         "302.0",
+         "140.0",
+         "3449.0",
+         "10.5",
+         "70",
+         "1"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 2
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5504,7 +7867,7 @@
        "ford torino            10.5    70       1  "
       ]
      },
-     "execution_count": 89,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5523,7 +7886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 86,
    "id": "5f1f3970",
    "metadata": {
     "execution": {
@@ -5537,6 +7900,338 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "224e617e-6a30-4f51-be0d-2e7cd609674b",
+       "rows": [
+        [
+         "chevrolet chevelle malibu",
+         "18.0",
+         "307.0",
+         "130.0"
+        ],
+        [
+         "buick skylark 320",
+         "15.0",
+         "350.0",
+         "165.0"
+        ],
+        [
+         "plymouth satellite",
+         "18.0",
+         "318.0",
+         "150.0"
+        ],
+        [
+         "amc rebel sst",
+         "16.0",
+         "304.0",
+         "150.0"
+        ],
+        [
+         "ford torino",
+         "17.0",
+         "302.0",
+         "140.0"
+        ],
+        [
+         "ford galaxie 500",
+         "15.0",
+         "429.0",
+         "198.0"
+        ],
+        [
+         "chevrolet impala",
+         "14.0",
+         "454.0",
+         "220.0"
+        ],
+        [
+         "plymouth fury iii",
+         "14.0",
+         "440.0",
+         "215.0"
+        ],
+        [
+         "pontiac catalina",
+         "14.0",
+         "455.0",
+         "225.0"
+        ],
+        [
+         "amc ambassador dpl",
+         "15.0",
+         "390.0",
+         "190.0"
+        ],
+        [
+         "dodge challenger se",
+         "15.0",
+         "383.0",
+         "170.0"
+        ],
+        [
+         "plymouth 'cuda 340",
+         "14.0",
+         "340.0",
+         "160.0"
+        ],
+        [
+         "chevrolet monte carlo",
+         "15.0",
+         "400.0",
+         "150.0"
+        ],
+        [
+         "buick estate wagon (sw)",
+         "14.0",
+         "455.0",
+         "225.0"
+        ],
+        [
+         "toyota corona mark ii",
+         "24.0",
+         "113.0",
+         "95.0"
+        ],
+        [
+         "plymouth duster",
+         "22.0",
+         "198.0",
+         "95.0"
+        ],
+        [
+         "amc hornet",
+         "18.0",
+         "199.0",
+         "97.0"
+        ],
+        [
+         "ford maverick",
+         "21.0",
+         "200.0",
+         "85.0"
+        ],
+        [
+         "datsun pl510",
+         "27.0",
+         "97.0",
+         "88.0"
+        ],
+        [
+         "volkswagen 1131 deluxe sedan",
+         "26.0",
+         "97.0",
+         "46.0"
+        ],
+        [
+         "peugeot 504",
+         "25.0",
+         "110.0",
+         "87.0"
+        ],
+        [
+         "audi 100 ls",
+         "24.0",
+         "107.0",
+         "90.0"
+        ],
+        [
+         "saab 99e",
+         "25.0",
+         "104.0",
+         "95.0"
+        ],
+        [
+         "bmw 2002",
+         "26.0",
+         "121.0",
+         "113.0"
+        ],
+        [
+         "amc gremlin",
+         "21.0",
+         "199.0",
+         "90.0"
+        ],
+        [
+         "ford f250",
+         "10.0",
+         "360.0",
+         "215.0"
+        ],
+        [
+         "chevy c20",
+         "10.0",
+         "307.0",
+         "200.0"
+        ],
+        [
+         "dodge d200",
+         "11.0",
+         "318.0",
+         "210.0"
+        ],
+        [
+         "hi 1200d",
+         "9.0",
+         "304.0",
+         "193.0"
+        ],
+        [
+         "datsun pl510",
+         "27.0",
+         "97.0",
+         "88.0"
+        ],
+        [
+         "chevrolet vega 2300",
+         "28.0",
+         "140.0",
+         "90.0"
+        ],
+        [
+         "toyota corona",
+         "25.0",
+         "113.0",
+         "95.0"
+        ],
+        [
+         "amc gremlin",
+         "19.0",
+         "232.0",
+         "100.0"
+        ],
+        [
+         "plymouth satellite custom",
+         "16.0",
+         "225.0",
+         "105.0"
+        ],
+        [
+         "chevrolet chevelle malibu",
+         "17.0",
+         "250.0",
+         "100.0"
+        ],
+        [
+         "ford torino 500",
+         "19.0",
+         "250.0",
+         "88.0"
+        ],
+        [
+         "amc matador",
+         "18.0",
+         "232.0",
+         "100.0"
+        ],
+        [
+         "chevrolet impala",
+         "14.0",
+         "350.0",
+         "165.0"
+        ],
+        [
+         "pontiac catalina brougham",
+         "14.0",
+         "400.0",
+         "175.0"
+        ],
+        [
+         "ford galaxie 500",
+         "14.0",
+         "351.0",
+         "153.0"
+        ],
+        [
+         "plymouth fury iii",
+         "14.0",
+         "318.0",
+         "150.0"
+        ],
+        [
+         "dodge monaco (sw)",
+         "12.0",
+         "383.0",
+         "180.0"
+        ],
+        [
+         "ford country squire (sw)",
+         "13.0",
+         "400.0",
+         "170.0"
+        ],
+        [
+         "pontiac safari (sw)",
+         "13.0",
+         "400.0",
+         "175.0"
+        ],
+        [
+         "amc hornet sportabout (sw)",
+         "18.0",
+         "258.0",
+         "110.0"
+        ],
+        [
+         "chevrolet vega (sw)",
+         "22.0",
+         "140.0",
+         "72.0"
+        ],
+        [
+         "pontiac firebird",
+         "19.0",
+         "250.0",
+         "100.0"
+        ],
+        [
+         "ford mustang",
+         "18.0",
+         "250.0",
+         "88.0"
+        ],
+        [
+         "mercury capri 2000",
+         "23.0",
+         "122.0",
+         "86.0"
+        ],
+        [
+         "opel 1900",
+         "28.0",
+         "116.0",
+         "90.0"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 392
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5657,7 +8352,7 @@
        "[392 rows x 3 columns]"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5677,7 +8372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 93,
    "id": "a9429e4b",
    "metadata": {
     "execution": {
@@ -5691,6 +8386,50 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "2780b563-67f0-49b3-b7f1-01d97e5677fc",
+       "rows": [
+        [
+         "amc rebel sst",
+         "16.0",
+         "304.0",
+         "150.0"
+        ],
+        [
+         "ford torino",
+         "17.0",
+         "302.0",
+         "140.0"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 2
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5745,13 +8484,13 @@
        "ford torino    17.0         302.0       140.0"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Auto_re.iloc[[3,4],[0,2,3]]\n"
+    "Auto_re.iloc[[3,4],[0,2,3]]"
    ]
   },
   {
@@ -5764,7 +8503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 97,
    "id": "1fd20b7d",
    "metadata": {
     "execution": {
@@ -5778,6 +8517,96 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "15820715-16dc-47fb-8462-a192331665cd",
+       "rows": [
+        [
+         "ford galaxie 500",
+         "15.0",
+         "8",
+         "429.0",
+         "198.0",
+         "4341.0",
+         "10.0",
+         "70",
+         "1"
+        ],
+        [
+         "ford galaxie 500",
+         "14.0",
+         "8",
+         "351.0",
+         "153.0",
+         "4154.0",
+         "13.5",
+         "71",
+         "1"
+        ],
+        [
+         "ford galaxie 500",
+         "14.0",
+         "8",
+         "351.0",
+         "153.0",
+         "4129.0",
+         "13.0",
+         "72",
+         "1"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 3
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5798,28 +8627,58 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
        "      <th>origin</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>429.0</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>4341.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>351.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>4154.0</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>71</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>351.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>4129.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>72</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -5827,20 +8686,26 @@
        "</div>"
       ],
       "text/plain": [
-       "                   mpg  origin\n",
-       "name                          \n",
-       "ford galaxie 500  15.0       1\n",
-       "ford galaxie 500  14.0       1\n",
-       "ford galaxie 500  14.0       1"
+       "                   mpg  cylinders  displacement  horsepower  weight  \\\n",
+       "name                                                                  \n",
+       "ford galaxie 500  15.0          8         429.0       198.0  4341.0   \n",
+       "ford galaxie 500  14.0          8         351.0       153.0  4154.0   \n",
+       "ford galaxie 500  14.0          8         351.0       153.0  4129.0   \n",
+       "\n",
+       "                  acceleration  year  origin  \n",
+       "name                                          \n",
+       "ford galaxie 500          10.0    70       1  \n",
+       "ford galaxie 500          13.5    71       1  \n",
+       "ford galaxie 500          13.0    72       1  "
       ]
      },
-     "execution_count": 92,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]\n"
+    "Auto_re.loc[['ford galaxie 500']]#, ['mpg', 'origin']]\n"
    ]
   },
   {
@@ -5857,7 +8722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 98,
    "id": "df6fa9de",
    "metadata": {
     "execution": {
@@ -5871,6 +8736,283 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "7e7491e7-3a1c-4dc0-9d8e-ba727e37b618",
+       "rows": [
+        [
+         "plymouth reliant",
+         "2490.0",
+         "1"
+        ],
+        [
+         "buick skylark",
+         "2635.0",
+         "1"
+        ],
+        [
+         "dodge aries wagon (sw)",
+         "2620.0",
+         "1"
+        ],
+        [
+         "chevrolet citation",
+         "2725.0",
+         "1"
+        ],
+        [
+         "plymouth reliant",
+         "2385.0",
+         "1"
+        ],
+        [
+         "toyota starlet",
+         "1755.0",
+         "3"
+        ],
+        [
+         "plymouth champ",
+         "1875.0",
+         "1"
+        ],
+        [
+         "honda civic 1300",
+         "1760.0",
+         "3"
+        ],
+        [
+         "subaru",
+         "2065.0",
+         "3"
+        ],
+        [
+         "datsun 210 mpg",
+         "1975.0",
+         "3"
+        ],
+        [
+         "toyota tercel",
+         "2050.0",
+         "3"
+        ],
+        [
+         "mazda glc 4",
+         "1985.0",
+         "3"
+        ],
+        [
+         "plymouth horizon 4",
+         "2215.0",
+         "1"
+        ],
+        [
+         "ford escort 4w",
+         "2045.0",
+         "1"
+        ],
+        [
+         "ford escort 2h",
+         "2380.0",
+         "1"
+        ],
+        [
+         "volkswagen jetta",
+         "2190.0",
+         "2"
+        ],
+        [
+         "honda prelude",
+         "2210.0",
+         "3"
+        ],
+        [
+         "toyota corolla",
+         "2350.0",
+         "3"
+        ],
+        [
+         "datsun 200sx",
+         "2615.0",
+         "3"
+        ],
+        [
+         "mazda 626",
+         "2635.0",
+         "3"
+        ],
+        [
+         "peugeot 505s turbo diesel",
+         "3230.0",
+         "2"
+        ],
+        [
+         "volvo diesel",
+         "3160.0",
+         "2"
+        ],
+        [
+         "toyota cressida",
+         "2900.0",
+         "3"
+        ],
+        [
+         "datsun 810 maxima",
+         "2930.0",
+         "3"
+        ],
+        [
+         "buick century",
+         "3415.0",
+         "1"
+        ],
+        [
+         "oldsmobile cutlass ls",
+         "3725.0",
+         "1"
+        ],
+        [
+         "ford granada gl",
+         "3060.0",
+         "1"
+        ],
+        [
+         "chrysler lebaron salon",
+         "3465.0",
+         "1"
+        ],
+        [
+         "chevrolet cavalier",
+         "2605.0",
+         "1"
+        ],
+        [
+         "chevrolet cavalier wagon",
+         "2640.0",
+         "1"
+        ],
+        [
+         "chevrolet cavalier 2-door",
+         "2395.0",
+         "1"
+        ],
+        [
+         "pontiac j2000 se hatchback",
+         "2575.0",
+         "1"
+        ],
+        [
+         "dodge aries se",
+         "2525.0",
+         "1"
+        ],
+        [
+         "pontiac phoenix",
+         "2735.0",
+         "1"
+        ],
+        [
+         "ford fairmont futura",
+         "2865.0",
+         "1"
+        ],
+        [
+         "volkswagen rabbit l",
+         "1980.0",
+         "2"
+        ],
+        [
+         "mazda glc custom l",
+         "2025.0",
+         "3"
+        ],
+        [
+         "mazda glc custom",
+         "1970.0",
+         "3"
+        ],
+        [
+         "plymouth horizon miser",
+         "2125.0",
+         "1"
+        ],
+        [
+         "mercury lynx l",
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+         "1"
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+         "2160.0",
+         "3"
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+         "3"
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+         "3"
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+        [
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+         "3"
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+         "3"
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+        [
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+         "1"
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@@ -6257,7 +9399,7 @@
        "chevy s-10                         2720.0       1"
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      },
-     "execution_count": 93,
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@@ -6277,7 +9419,7 @@
   },
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    "id": "b57f8503",
    "metadata": {
     "execution": {
@@ -6291,6 +9433,283 @@
    "outputs": [
     {
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+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
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+         "rawType": "object",
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+        {
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+         "3"
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+        [
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+         "3"
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+         "2160.0",
+         "3"
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+        [
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+         "3"
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+        [
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+         "3"
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+         "1"
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+        [
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+         "3015.0",
+         "1"
+        ],
+        [
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+         "1"
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@@ -6677,7 +10096,7 @@
        "chevy s-10                         2720.0       1"
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      },
-     "execution_count": 94,
+     "execution_count": 99,
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@@ -6701,7 +10120,7 @@
   },
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    "id": "965b5b74",
    "metadata": {
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@@ -6715,6 +10134,203 @@
    "outputs": [
     {
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+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
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+        {
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+         "1975.0",
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@@ -6957,7 +10573,7 @@
        "chevy s-10                         2720.0       1"
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@@ -6981,7 +10597,7 @@
   },
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    "id": "37ba9d44",
    "metadata": {
     "execution": {
@@ -6995,6 +10611,283 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
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+         "3"
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+        [
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+         "3"
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+        [
+         "ford pinto (sw)",
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+         "1"
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+        [
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+         "2019.0",
+         "3"
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+        [
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+        [
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@@ -7339,7 +11232,7 @@
        "ford ranger            2625.0       1"
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+     "execution_count": 103,
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@@ -7523,7 +11416,7 @@
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    "id": "35700d0c",
    "metadata": {
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@@ -7537,6 +11430,72 @@
    "outputs": [
     {
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@@ -7599,7 +11558,7 @@
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@@ -7619,7 +11578,7 @@
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@@ -7676,7 +11635,7 @@
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@@ -7693,15 +11652,15 @@
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      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[102], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mhorsepower\u001b[49m, mpg, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'horsepower' is not defined"
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[109]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m fig, ax = subplots(figsize=(\u001b[32m8\u001b[39m, \u001b[32m8\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m ax.plot(\u001b[43mhorsepower\u001b[49m, mpg, \u001b[33m'\u001b[39m\u001b[33mo\u001b[39m\u001b[33m'\u001b[39m);\n",
+      "\u001b[31mNameError\u001b[39m: name 'horsepower' is not defined"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7725,7 +11684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 110,
    "id": "dae69229",
    "metadata": {
     "execution": {
@@ -7739,7 +11698,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7767,7 +11726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 111,
    "id": "9fbba9c1",
    "metadata": {
     "execution": {
@@ -7781,7 +11740,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7838,7 +11797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 112,
    "id": "c0e67313",
    "metadata": {
     "execution": {
@@ -7851,7 +11810,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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9L/xr1671d6gAgDDH+wwAwBWvZpL17dtXM2bM0ObNm21t7e3t2rx5s+bMmePwmubm5i6/OKKjoyVJhmF4Gy8AoBc5cuSIfvSjH+mFF15Qv379/NZvb/sX/jJLo7YUV6m8uinUoQBASPE+AwBwxauZZJK0evVqXX/99TrnnHM0a9YsPfbYY2pqatKyZcskSUuXLtUZZ5yhdevWSZIuu+wyPfroo8rMzNTs2bNVWlqqe++9V5dddpntlwsAAI7s3btXVVVVmj59uq2tra1N27Zt0+OPP653331XLS0tqqurs5tN5mpGgNR7/oW/rrlFq/KKtK3EYmvLzkhWbk4mO2AC6LV4nwEAOON1kuzqq6+WxWLRfffdp8rKSk2bNk3vvPOOrfjl4cOH7f6l5Z577pHJZNI999yjzz//XMnJybrsssv0y1/+0n9PAQDokS644ALt37/frm3ZsmWaMGGC7rzzTqWkpCgmJkabN2/WVVddJUkqLi7W4cOHnc4I6E1W5RWpoLTarq2gtFor8wq1afmsEEUFAKHF+wwAwBmTEQFzhBsaGpSQkKD6+nrFx8eHOhwAiHiR/Lm6YMECTZs2TY899pgk6ZZbbtHbb7+t5557TvHx8Vq5cqUkaefOnR73GW7jUWZp1KHjzUpNGqA08wCf+zj/kXynx7fcscDnvgHAnXD7XA01xgMA/CtQn6tezyQDACCc/Pa3v1VUVJSuuuoqnTx5UosXL9YTTzwR6rB84u3ySFfJtEPHm13eq6KmiSQZAAAA0AFJMgBARNm6davd9/369dOGDRu0YcOG0ATkR54uj/QkmTZmSJzLe6UmkSADAAAAOvJqd0sAABAYZZZGbSuxqK1TFYQ2w9C2EovdzpSukmlWY5MHKjsjWdEmk9150SaTsjOSmUUGAAAAdEKSDACAMODJ8kjJu2Rabk6mstLNdudlpZuVm5Ppp6gBAACAnoPllgAAhAFPl0d6U2ssIS5Gm5bPUnl1kypqmrq1EQAAAADQ05EkAwAgDFiXRxaUVtvNEos2mZSVbrYlt3ypNZZmJjkGAAAAuMNySwAAwoQnyyOpNQYAAAAEBjPJAAAIE54uj8zNydTKvEK73S2pNQYAAAB0D0kyAADCTMflkWWWRh063myXMKPWGAAAAOB/JMkAAAhDdc0tWpVXZDdbLDsjWbk5mappOmlLnC0cP9TuuvziKhUdrdP00Ymal5EctHgdJfMAAACASEKSDACAMLQqr0gFpdV2bTtKLFrw8BbVNrfa2qyJs7qvWrRkQ4HdscS4GL2x4jylJLku9t8drpJ5CXExAbsvAAAA4G8U7gcAIMyUWRq1rcRit8ulJLVLdkkwSSoordbKvMIuCTL9+9zLN+wIaKyOknnWmAAAAIBIQpIMAIAwc+h4s8fnthmGtpVYuiTIrGqbW7W9wywvf3KWzLPGVF7dFJD7AgAAAIFAkgwAgDAzZoh/l0fuO1zr1/6s3CXzKmpIkgEAACBykCQDACDMjE0eqOyMZEWbTH7pb/roRL/005m7ZF5qEgX8AQAAEDlIkgEAEIZyczKVlW726NyZqYlKdFIkPzEuJmC7XDpL5kWbTMrOSGaXSwAAAEQUkmQAAIShhLgYbVo+S1vuWKDbv5Xh8tzr56bqjRXndUmUxffrozWXTAhobTBHybysdLNyczIDdk8AAAAgEPqEOgAAAOBcmnmALpsyUr99v8TpOfH9+qi0ulGv/jBLR2ubtbO0WjtKqrX/iwb99E/7JUnZGcnKzclUgpMZZ76yJvPKq5tUUdOk1KQBzCADAABARCJJBgBAmLMuayworbbbSTLaJMX3j9HSZ/bY2rIzktXa1q5Pvzxh10dBabVW5hVq0/JZAYkxzUxyDAAAAJGN5ZYAAEQAR8sa4/vHqL651a5tR6lFu8pq7JJpktRmGNpWYgno0ktvlFkataW4KmziAQAAAJhJBgBABOi8rDHaJLsZZFbthoOLO6ioaQrpjK+65hatyivSthKLrS1QS0EBAAAAbzCTDACACJJmHqCF44eqzU0yzJnUpNAuiVyVV6SC0mq7NutSUAAAACCUSJIBABCBfPkFPnNMYkhnkZVZGrWtxBL2S0EBAADQO5EkAwAgArX7cM0Nc1P9HYZXDh1vdnm8ooYkGQAAAEKHJBkAABFozJA4r6+ZeEZCACLxnLuYQ70UFAAAAL0bSTIAACLQ2OSBys5IVrTJ5PbcaJNJ2RnJIV1qKTmPOVziAwAAQO9GkgwAgAiVm5OprHSz2/Oy0s3KzckMQkTuOYo5nOIDAABA79Un1AEAAADnyiyNOnS8WalJA7rMtEqIi9Gm5bO07bMqLX1mj9M+1l5xthLiYgIdqkesMZdXN6mipsnhcwEAAAChQJIMAIAwU2Zp1D+/bNCmnRXaU1Fra8/OSFZuTqZdwquuuUUPvVPssr8PymqcJqSsSbhok0lthuFT0spVIs/ZsTQzyTEAAACEF5JkAACEibrmFq3KK9K2EovD4wWl1VqZV6hNy2fZ2lblFenTLxpc9rvm1f22P1sTbYYMp/dylIzzNF5X/XvaLwAAABAK1CQDACBMrMorUkFptdPjbYahbSUWlVc3STo9S2tbiUXtXtzDmmhzdS/rOb7E66p/T/sFAAAAQoGZZAAAhAFrwssTFTVNSjMP0KHjzV7fx5po8+Sc8uom25LIzssmncXrqn9H/QIAAADhgiQZAABhwJuEV2rS6QTTmCFxgQpH0ulkXGJcjMNlk1efM6pb/ZIkAwAAQLhhuSUAAGHAk4RXtMmk7IxkW4JpbPJAZWckK9pk6nLeOWMSux1TatIAp8smn91Z0a1+AQAAgHBDkgwAgDDgLOHVUVa6Wbk5mXZtuTmZyko3dzlv4/UznSbQsjOSXd7Leo7x7+WRbYZhd7zNMPTRoVrNTE30qv/OST4AAAAgnLDcEgCAMJGbk6mVeYV2SxtnjknUDXNTNfGMBIfJpYS4GG1aPkvl1U2qqGmy1Qxz1l/HRFvnY53P2Xek1mW8189NVf+Yox737yjJBwAAAIQLkmQAAIQJRwkvwzA8qleWZh7QJYnmKoEmye5YnyiTTrUbdue4WwJ69sgEbVo+0qP+Ox8DAAAAwg1JMgBAWHvyySf15JNPqqKiQpJ09tln67777tPFF18sSVqwYIHy8/Ptrvn+97+vp556Ktih+k2aeYDTgvm5OZlKiIvxqJ+OO1IuHD/U6b2cJa+sS0ALSqvtllxGm0zKSjfbrnPVh9FpqSYAAAAQrkiSAQDC2qhRo7R+/XplZGTIMAw9//zzuuKKK1RYWKizzz5bknTTTTfpgQcesF0TFxfYXR+DwVnB/JV5hdq0fJbLa+uaW7qdYLNyt2QzGDEAAAAAwUCSDAAQ1i677DK773/5y1/qySef1AcffGBLksXFxWn48OGhCC8gyiyNDmuFtf27kH55dZPLpYvdSbB15m7JZjBiAAAAAIKB3S0BABGjra1NL730kpqamjRnzhxb+wsvvCCz2axJkyZpzZo1am52XcPr5MmTamhosPsKJ+5qkH1QVqMtxVUqr27qcsyaYHO0I6U1weaLNPPpJZueJMgCFQMAAAAQSMwkAwCEvf3792vOnDn6+uuvNXDgQL322muaOHGiJOmaa67RmDFjNHLkSH388ce68847VVxcrFdffdVpf+vWrdPatWuDFb7X3BXMX/PqftufOy9hdJdgq6hxPQvNH8IhBgAAAMBbJMkAAGFv/PjxKioqUn19vf70pz/p+uuvV35+viZOnKibb77Zdt7kyZM1YsQIXXDBBTp48KDGjRvnsL81a9Zo9erVtu8bGhqUkpIS8OfwlLOC+Y50XsLoLsGWmhT45FQ4xAAAAAB4i+WWAICw17dvX6Wnp2vGjBlat26dpk6dqv/+7/92eO7s2bMlSaWlpU77i42NVXx8vN1XuMnNyVRWutntedYljC/tPqzy6iZbgi3aZHJ4/v1/+afqm1v9Ha4dZzFEm0zKzkhmFhkAAADCEkkyAEDEaW9v18mTJx0eKyoqkiSNGDEiiBH5n7Vg/pY7FujZZTO1/srJLs+/69X9WvjwVi3duFu/XDLJaYLNOvPME2WWRqe1z9xxlOTzZFdMAAAAIFRYbgkACGtr1qzRxRdfrNGjR+vEiRN68cUXtXXrVr377rs6ePCgXnzxRV1yySVKSkrSxx9/rNtvv13Z2dmaMmVKqEP3izTz6d0kyyyNHp1fUFqtu1//RD+/fKLOfyS/y3FPdsisa27Rqrwiux02O9c+c8fXXTEBAACAUGEmGQAgrFVVVWnp0qUaP368LrjgAu3Zs0fvvvuuvvWtb6lv377629/+pgsvvFATJkzQj3/8Y1111VX661//Guqw/c7dMkoraxLsrf1fujyvosb57LBVeUUqKK22a/NmBlpH3uyKCQAAAIQSM8kAAGFt48aNTo+lpKQoP7/rbKmeKjcnUyvzCu1meDnzyHufuTzurHh+maXRYf+ezEBz1t+h483MJAMAAEDYI0kGAECE6LiE8YOyGq15db/f73HoeLPL4xU1niXJ/LFkEwAAAAgmllsCABBh0swDNDttSLf66Lzc0lqkP9r1ak6nM9A68+eSTQAAACAYmEkGAEAEcjfjy50+UaezYY5mfCXGxai+uVXtHc6PNpmUlW72aBaZv5dsAgAAAMHATDIAACLQmCFx3br+VLshyfGMr4avWrssicxKNys3J9OuzTr7rLzaflaauwTeh2U1voYNAAAABAwzyQAAiEDW3S4LSqvVZhheX5+aNMDFjC+ptrlV/7t8lk61G12K7rurN+YugXfXq/v19v5K6pMBAAAgrDCTDACACJWbk6msdLPX1yXGxSjNPMDtjK9T7YYWjh/aZWmku3pj1gRetMl5gTPqkwEAACDckCQDACBCWXe73HLHAk06I15RboruW9U2t6q8usntjC9HRfqts886z17rWG9Mcp/A63w+AAAAEGokyQAAiHBp5gF6Yfm5Oi892eNrKmqanM74ijaZlJ2R7LC4vrvZZ9ZdM60JvHVXTvbofAAAACDUqEkGAEAQ5RdXqehonaaPTtS8DM+TWu5Yk1Ll1U2qqGlStEla+swep+dbZ4n9+MIMHW8+qU8+b7Adc1Sk38rb2Wez04Z4dT4AAAAQKiTJAAAIgkM1TVqyoUC1za22tsS4GL2x4jylJHVvp8qO0szfFNl3VNg/2mRSVrpZiXExWrpxt13x/Ukj4/Wrb0/WlJTBTvt3tmGAtd/Os8+8Pd+dMkujDh1v7rKZAAAAANBdLLcEACAIOifIpNO1wS7fsCNg93RUF8w6S8xR8f0DX57Qw+991q1+/XG+I3XNLVq6cbfOfyRfy57do4UPb9XSjbtV32lMAQAAAF8xkwwAgAB76+MvuiTIrGqbW7W9xOLXpZdWnZdgWmdfWYvvd9axmL6rWVrO+vXX+Y642lFz0/JZXvUVjpghBwAAEHokyQAACLCH3i12eXzf4dqAJMmsOi7BlDwrvu9JoqZzv/4+36q7Sb1wVtfcolV5RXbPl52RrNycTCXExYQwMgAAgN6H5ZYAAARQmaVRh2pcJ6Wmj04MUjSn46ms/9rlOeFWTN/THTUjkasZcgAAAAguZpIBABBA7hI8A2Oj/TqLzNmyPUczlpy5/y//DKuZTN7uqBkpevIMOQAAgEhEkgwAgAByl+D5n+vO8ct93C3bczRjyZlwq/Xl7x0yw4W/lr0CAADAP1huCQBAAFkTPNEmk117lOl0Emtup10ffeVq2V5+cZW2lVjsEkyudJzJFC78sUNmuOmpM+QAAAAiFTPJAAAIsNycTK3MK7Sb5XVeerJXCR5Xux9ak2CdWZNdniyxdMTTmUz+3pnRUX/+2CEz3PTUGXIAAACRyqeZZBs2bFBqaqr69eun2bNna/fu3S7Pr6ur04oVKzRixAjFxsbqzDPP1Ntvv+1TwAAARBprgmfLHQv07LKZ2nLHAm1aPsujml91zS1aunG3zn8kX8ue3aOFD2/V0o27Vd/cajt2/bN7AhJ3nyiTy+OuYvOFJ/2lmQdo4fihPSaB1BNnyAGRgPcZAIAjXs8ke/nll7V69Wo99dRTmj17th577DEtXrxYxcXFGjp0aJfzW1pa9K1vfUtDhw7Vn/70J51xxhk6dOiQBg8e7I/4AQCIGGlm72c/udv90NM6Y45EmaR2FyswT7k66CY2X+qZ+bu/SNATZ8gB4Y73GQCAM14nyR599FHddNNNWrZsmSTpqaee0ltvvaVnnnlGd911V5fzn3nmGR0/flw7d+5UTMzpfzFPTU3tXtQAAPQC7nY/7K6BsX3U8PUpp8dd1cTy986MvX2nR18SqAB8w/sMAMAZr5ZbtrS0aO/evVq0aNE3HURFadGiRdq1a5fDa9544w3NmTNHK1as0LBhwzRp0iT96le/Ultbm9P7nDx5Ug0NDXZfAAD0Nu52P+yuhq9PafIZ8V3+YyDaZFJ2RrLLpI0nOzN6w9/9AYAjvM8AAFzxKklWXV2ttrY2DRs2zK592LBhqqysdHhNWVmZ/vSnP6mtrU1vv/227r33Xj3yyCN68MEHnd5n3bp1SkhIsH2lpKR4EyYAAD2Cu90P/WH/5w1q79TmSU0sf+/MyE6PAIKB9xkAgCs+Fe73Rnt7u4YOHar/+Z//0YwZM3T11Vfr7rvv1lNPPeX0mjVr1qi+vt72deTIkUCHCQBA2LHufhhtsi+gH20yadLIeL/fL8okzUxN9GhTAVexuZuFFoz+AMBfeJ8BgN7DqySZ2WxWdHS0jh07Ztd+7NgxDR8+3OE1I0aM0Jlnnqno6Ghb21lnnaXKykq1tLQ4vCY2Nlbx8fF2XwAA9EbOdj/85bcn+/1e7Ya0p6JW5dWeLW30986M7PQIINB4nwEAuOJV4f6+fftqxowZ2rx5s5YsWSLp9L+sbN68WbfeeqvDa7KysvTiiy+qvb1dUVGnc3KfffaZRowYob59+3YvegAAejhXux9mZySroLRabYbrXSi9tfLFfXrhxnPdzibz986M7PQIINB4nwEAuOL1csvVq1fr6aef1vPPP68DBw7olltuUVNTk213mKVLl2rNmjW282+55RYdP35cP/rRj/TZZ5/prbfe0q9+9SutWLHCf08BAEAEKLM0aktxlccztTpKMw/QwvFD7ZJGjmZe+cOnXzRoZV6h0+OdnyPNPEBjhsSpoqbJp2frzNGzAoC/8D4DAHDGq5lkknT11VfLYrHovvvuU2VlpaZNm6Z33nnHVvzy8OHDtn9hkaSUlBS9++67uv322zVlyhSdccYZ+tGPfqQ777zTf08BAEAYq2tu0aq8Im0rsdjasjOSlZuT6Xa2liuG/DuDzKpd0rYSi8qrm+wSVY6eY+64JBmGtKusxtbmj2cDgEDhfQYA4IzJMPy8RiMAGhoalJCQoPr6etbzA4Af8LlqL9DjsXTj7i7LIqNNJmWlm7Vp+Sy/9uupKElxsdFqPNnm9Jxnl83UwvFDvb6fP54NQGTj94w9xgMA/CtQn6sB390SAIDerMzSqG0lli6JpTbDsM3W8me/njovI1kv3Hiuy3NSk76ZRebN/br7bAAAAEAoeL3cEgAAeO7Q8WaXxytqmnyqveWuX0fWXzlZwxL62Qril1kaXZ5/4uvWbt3P12cDAAAAQoEkGQAAATRmSJzL4x1na3mqzNKoyvqvvL5u9tgku6SVu8TXj//4D/3s0rOUmjTA7XM44suzAQAAAKFCkgwAgAAamzxQ2RnJTmuSeTPTylHhfE84u5e7xFdJVaOWPbtH0uli/HPHJenDsuMe1yRjFhkAAAAiCTXJAAAIsNycTGWlm+3astLNys3J9KqfVXlFKiit9vr+zu41NnmgJo30rNBpQWm1DENdnmPuuCTNGZvk0f0AAACAcMZMMgAAAiwhLkabls9SeXWTKmqabDXBvGEtnO/Muisn69yxSTpa26x9h2s1MqG/zINi3d7rl9+erCs2FLi9f5thaFdZjbbcsUCSujxHd54NAAAACAckyQAACJI0s+8JJHf1w4Yn9LP1Py8j2eN+p6YMVnZGsnaUWtTuwUaZFTVNWjh+aJfn6M6zAQAAAOGA5ZYAgLD25JNPasqUKYqPj1d8fLzmzJmj//f//p/t+Ndff60VK1YoKSlJAwcO1FVXXaVjx46FMOLA8HQDgPziKv335s/0xz1HtKW4SuXVTSqzNNr+7EhuTqbOS/cssdYnytSlzV3/AAAAQCRgJhkAIKyNGjVK69evV0ZGhgzD0PPPP68rrrhChYWFOvvss3X77bfrrbfe0iuvvKKEhATdeuutuvLKK1VQ4H4JYSRxtQFA5ujB2l1eoyse36GGr0+57Cc7I1m5OZlKiIuxtRnyYArZv123cbetD0NGl40EHPUPAAAARAKTYbjZoioMNDQ0KCEhQfX19YqP96zAMADAuUj/XB0yZIh+85vf6Dvf+Y6Sk5P14osv6jvf+Y4k6V//+pfOOuss7dq1S+eee65H/UXKeNQ3t2r583v00aFaW1tiXIxqm1s97sO68+Sm5bNsbUs37u6SfPOkD0lOd+3s2L9VmaVRh443U7cM6AUi5XM1WBgPAPCvQH2uMpMMABAx2tra9Morr6ipqUlz5szR3r171draqkWLFtnOmTBhgkaPHu0ySXby5EmdPHnS9n1DQ0PAY++uuuYWrcorskuQxffro4avPE+QSacL8G8rsai8uklp5gFuNwRw1Ycn/XeMnRlnAAAACGfUJAMAhL39+/dr4MCBio2N1Q9+8AO99tprmjhxoiorK9W3b18NHjzY7vxhw4apsrLSaX/r1q1TQkKC7SslJSXAT9B9q/KKVFBabdfW8PUptfk4H7yi5nT9MHcbAvjK2r/kOPaC0mqtzCsMyL0BAAAAX5AkAwCEvfHjx6uoqEgffvihbrnlFl1//fX69NNPfe5vzZo1qq+vt30dOXLEj9H6n3W2l6fLIT3xxJZS1Te3ut0QwFfWjQScxd5xxhkAAAAQDkiSAQDCXt++fZWenq4ZM2Zo3bp1mjp1qv77v/9bw4cPV0tLi+rq6uzOP3bsmIYPH+60v9jYWNtumdavcBaI2V77DtVpZV6hbUOAaJP9rpXRJpOyM5J9PmZdauku9o4zzgAAAIBQIkkGAIg47e3tOnnypGbMmKGYmBht3rzZdqy4uFiHDx/WnDlzQhihfwVitlfHmVy5OZm2QvxWWelm5eZk+nzM09itM84AAACAUKNwPwAgrK1Zs0YXX3yxRo8erRMnTujFF1/U1q1b9e677yohIUHLly/X6tWrNWTIEMXHx2vlypWaM2eOxztbRgLrbK/OO0lGSUrwcnfLzipqThfY37R8lsqrm1RR09Rl90lfj7mK3boLJrtcAgAAIFyQJAMAhLWqqiotXbpUX375pRISEjRlyhS9++67+ta3viVJ+u1vf6uoqChdddVVOnnypBYvXqwnnngixFGfrsV16HizXeLIUZuncnMytTKv0G6HyPP+vUPk8eYWVdQ0KdokLX1mj1f9dqwd5i7ezjFbj3deculJ7J1nnAEAAAChZjIMP1YBDpCGhgYlJCSovr4+7OvGAEAk4HPVnj/Ho665RavyiuwSQnPGJslkknYerLG1Zf87wZUQF+NV/65mbUnS0o27u8zacsQ6k+t3OdO8jtfRM3ryXO5iB9Bz8HvGHuMBAP4VqM9VapIBAOBHq/KKVFBabde2q6zGLuEkSQWl1VqZV+h1/2nmAVo4fqjTJJOjOmGOWGdy+RKvo2scnedt7OGizNKoLcVV7LwJAADQy7DcEgAAPymzNDqcXeVIx8L5/kwaJcTFaNPyWXp592Hd+ep+p+fdlJ2mmqaTXse77bMql9cE6rmCwdEMOV9n/AEAACDyMJMMAAA/OXS82etrKmoCM1up8sTXLo/vO1zrU7yFR+o8Oi9QzxVIjmbI+TrjDwAAAJGHmWQAgIBITEyUyUFBd5PJpH79+ik9PV033HCDli1bFoLoAmPMkDivr7EWzve3aaMGuzw+fXSizhjc3+t+M1Nc92sVqOcKFGezACN5ZhwAAAC8w0wyAEBA3HfffYqKitKll16qtWvXau3atbr00ksVFRWlFStW6Mwzz9Qtt9yip59+OtSh+s3Y5IHKzkh2u9ujdLpwfnZGcsASL/PHD1WikyWCiXExmpeR7FO82WcOdXmNq+cK51pf7mbVReLMOAAAAHiHmWQAgIDYsWOHHnzwQf3gBz+wa//973+v9957T3/+8581ZcoU/e53v9NNN90Uoij9LzcnUyvzCt3uFmktnB9Ib6w4T5dv2KHa5lZbW2JcjN5YcV634nV0jaPzrCKh1pe7WYCRNjMOAAAA3jMZhps94sMAWyYDgH8F43N14MCBKioqUnp6ul17aWmppk2bpsbGRh08eFBTpkxRU1NoZ+kEYjzKq5tUUdOk1KQBtllVjtqCYXuJRfsO12r66ETNy0j2KV7DMHToeLPD432iTDrVbjh9rqUbd6ugtFptHf6TI9pkUla6WZuWzwrAE/smUuIEIgH//W6P8QAA/wrU5yozyQAAATFkyBD99a9/1e23327X/te//lVDhgyRJDU1NWnQoEGhCC/g0sxdE0aO2oJhXkay0+SY1eGaJn18tE59oky2GNPMA9TwVYtWvrhPn3zRYDvXOgvM0fOUWRrtkmn5xY53w7TW+tpeYnEbW7A4miEXjBl/AAAACA8kyQAAAXHvvffqlltu0ZYtWzRr1ulZOHv27NHbb7+tp556SpL0/vvva/78+aEMs9c7VNOkJRsKuizJ/L//mq1fv1vsMMG1rcSiW17YqxdvOtfW5mhJZWJcjF2/jly3cbdfl152TtJ5IyEuRpuWzwrZjD8AAACEFsstAaAXCtbnakFBgR5//HEVFxdLksaPH6+VK1dq7ty5AbunL3rz75nMB95zmMjqE2VSu2Go3cV/JWy5Y4EtieRoqaKn/LGkMRLqngG9SW/+XHWE8QAA/2K5JQAg4mRlZSkrKyvUYcCJ/OIqpzO9TrnKjv3bh2U1SjMPUJml0eGMM09Zl16WVzf5PHNrVV6RCkqr7doKSqu1Mq+QemIAAADwCEkyAEDAtLW16bXXXtOBAwckSRMnTtQVV1yhPn349RMOio7Wdet6axrt0PHmbsciSRU1viXJnCXp/JF8AwAAQO/BWwoAICD++c9/6vLLL1dlZaXGjx8vSfr1r3+t5ORk/fWvf9WkSZNCHCGmjRrcrevPHZskSRozJM4P0ZxedrmluMrrWmDuknS+Jt8AAADQu5AkAwAExI033qizzz5bH330kRITEyVJtbW1uuGGG3TzzTdr586dIY4Q88cPdVpcv0+USYYhpzXG5oxNsiWexiYPVHZGss81yaJ0umj+0md229q8qSfmLkmXmkSCDAAAAO5FhToAAEDPVFRUpHXr1tkSZJKUmJioX/7ylyosLAxhZOjojRXnKbFTIioxLkZvrMhSVrrZ4TWJcTF66Kopdm25OZldzu/crzMJcTFq+Mo+UWetJ+YJa5Iu2mSya482mZSdkcwsMgAAAHiEmWQAgIA488wzdezYMZ199tl27VVVVUpPTw9RVL1bmaVRh4432y1nTEmKU+F9F2p7iUX7Dtdq+uhEzctIliRtWj5L331yp/YeqlV7h34avjqlu1//xFYQ39rv2itO/6wrapps9yivbrJ9/1HFcRUcrFbWOLPOSR2iipomRZtMdjPIrKz1xF7afVizO8xacyY3J1Mr8wrtapNlpZuVm5PZnSEDAABAL0KSDAAQEOvWrdOqVav085//XOeee64k6YMPPtADDzygX//612poaLCd689tm9FVXXOLVuUV2SWQOi9nnJeRbEuOWZVZGrXnUG2X/qwJrH8cqdMj733mst808zcJuTTzAH33nBTbuWnmAdpSXOUy9rte3e+w384S4mK0afksu6QcM8gAAADgDZNh+FA8JMgaGhqUkJCg+vp6XqQAwA+C8bkaFfXNin7Tv5fBWX/ldPzeZDKpra0tIDF4Kpx/zzia/eWtpRt3d6kXFm0yKSvdbJsN5siW4iote3aP0+MZQwfqoKVR7R3+SyJK0sSR8cq9ZrrSzAP0+OYSFRys1ryMZJ09Ml5FR+vsZquVWRp1/iP5bp/Bk3gBhI9w/lwNBcYDAPwrUJ+rzCQDAATEli1bQh1CRPNk9pcnyiyNdn1YWWeDlVc73/nRXUH8kqrGLm3tkj75okELH95q176r7Ljd96frnp3ncdF/T+IFAAAAuoPC/QCAgJg/f75mz56t/v3768SJE6qvr7f7mj9/vu0LXa3KK1JBabVdmzfF7K0OHW92ebyipsnpMWcF8f2htrlVl2/YIclx0X9nXMXrL2WWRm0prlJ5deDvBQAAgPDBTDIAQEC88847Wrp0qaqrq7scC4clluGsO7O/OnM3Gyw1yfuC+P5S29yq7SUWzctIttUT+6CsRmv+XYfMEXfxdmd5qr9m7wEAACAyMZMMABAQK1eu1He/+119+eWXam9vt/siQeZad2Z/deZsNliU6XQCyF0iyVoQf8sdC/Tssplad+Vkj+/tiX2Hv9kYIM08QDmzRjuMN9pkchlvXXOLlm7crfMfydeyZ/do4cNbtXTjbtU3t3oci79m7wEAACAykSQDAATEsWPHtHr1ag0bNizUoUSc7s7+6iw3J1Ozxw6xa2s3pNa2do+TSGnmAVo4fqhmpw1xf7IXpo9O7NLmaPllVrpZuTmZTvvpboLLOnuvc120jrP3AAAA0LOx3BIAEBDf+c53tHXrVo0bNy7UoUQcZ8XsrTs8eruMMCEuRn2iohSl04X1rXaXH9fKvEKvdoy0xrajxGLXly8S42Jsu1x2jte6/LKipsnt0kl/LE/1ZPYeGwYAAAD0bCTJAAAB8fjjj+u73/2utm/frsmTJysmxr6m06pVq0IUWWRwVAvM3WwqZ/xZ48wa27V/+ECffNHgdSxW1t0tXUkze1ZXzB8JLn/P3gMAAEDkIUkGAAiIvLw8vffee+rXr5+2bt0qU4caUyaTiSSZG97OpnLF37OkEuJi9LucTJ3/SL5P8fzmO1P03XNSfLrWEX8kuPw9ew8AAACRhyQZACAg7r77bq1du1Z33XWXoqJ6dgnM/OIqFR2t0/TRiQ6XD3aHp7OprDru7mgYhg4db1ZVw9cur0lNGuDwGcosjfqw/LhMkkYO7qc2Q7Zknauk0lkjB+mTz53PMmtrN2xxRptk168v/JHgKrM06nszR+mr1lPaU/HNZgK+zt4DAABA5CFJBgAIiJaWFl199dU9OkF2qKZJSzYUqLZD8XvrMsKUJNezm/ytrrlFq/KKHC6rdCbaZNL0MYN15RP2zzC4f4xSk+JUdLTe4XXZGcnKzcl0uiT0xxdm6IoNO53e965X97vsNyEuxuFxV3xdnupo3GaOSdQNc1M18YwEZpABAAD0IibD6LSNUxhqaGhQQkKC6uvrFR8fH+pwACDiBeNz9fbbb1dycrJ+9rOfBaR/f/J1PDIfeM8uuWSVGBejwvsu9GeIbi3duLvLTCp3sjOS9Y+jdar/yrMdLq2sM7SsBf8dLQn1JZ7O/frC2+WpjuL0RxxAb8d/v9tjPADAvwL1ucpMMgBAQLS1temhhx7Su+++qylTpnQp3P/oo4+GKDL/yC+ucpggk6Ta5lZtL7H4femlM84K87vyv8tnqb3d0PXP7vH6fp0L/jtaEupoZpe3/frCm+Wp/t7QAAAAAJGNJBkAICD279+vzMzTS90++eQTu2Mdi/hHqqKjdS6P7ztcG7QkmbvC/I6cajf0sZtncMdVwX/rxgN5uw9rjZPllb7060/+3tAAAAAAkY0kGQAgILZs2RLqEAJq2qjBLo9PH50YnEDkfndHR1KTBqi71eI82TVydtoQr/uNNpm0pbiqW8X8PeGPXTEBAADQc5AkAwDAB/PHD1ViXIzTmmTBmkUmOd/d0ZGOOz6mmQc4fQZP+3ClrrlFP3/jU4/7NUkaHBejpc/strV1p5i/O+52xTQMIyjJOgAAAISHnrvlGAAAAfbGivOU2Cl5Y93dMthyczKVlW52e17nHR8dPYO3fTizKq9IBaXVHvc7qF8fNXTaRKCgtFor8wq9is8bjsZtVtoQnWpv1/mP5GvZs3u08OGtWrpxt+q9TCYCAAAgsrC7JQD0Qnyu2uvueGwvsWjf4VpNH50Y1BlkjnTc3VE6XVerT5RJp9oNlzOitpdYdM/rn+jI8Wa1d/gvA5NJSk8eqPsum+i2j47KLI06/5F8j2KOMklnj4zX/s8bnJ6z5Y4FAZ3N1XHc7v/LP9nxEugmfs/YYzwAwL/Y3RIAgDA1LyM55Mkxq867O3b8c5ml0enywfZ2Q4dquhayNwyppKpR1n9TO3K82ZZMcpW02lJ8zOOYz0tP1vdmjtKtLzqfMebPIvpllkYdOt5s9wzWcWPHSwAAgN6LJBkAIKytW7dOr776qv71r3+pf//+mjt3rn79619r/PjxtnMWLFig/Hz7WUvf//739dRTTwU73LBU19yiVXlFdskfa60vQ0aXY44sfWZPlzZH9cIc3cudr1tP6a4/f+zyHH8U0Xc1DglxMf8+7nppJzteAgAA9FzUJAMAhLX8/HytWLFCH3zwgd5//321trbqwgsvVFNTk915N910k7788kvb10MPPRSiiMOPo9pg1lpf3tYNc9RH53vt8CJBJkm7K2rVeLLN4bFok0nZGcl+SUy5Ggfr8U+/cL7kU2LHSwAAgJ6MmWQAgLD2zjvv2H3/3HPPaejQodq7d6+ys7Nt7XFxcRo+fLhHfZ48eVInT560fd/Q4DoxEsncLR/sjs5LEJ3dqzsmjhzk0SYB7rgbh22fVbmMPcp0elkos8gAAAB6LmaSAQAiSn19vSRpyJAhdu0vvPCCzGazJk2apDVr1qi5uWt9Lat169YpISHB9pWSkhLQmEPp0HHn4+AvFTVNAbvXoonD7JZz+spdbIVH6lwenzgy3i/JOgAAAIQvkmQAgIjR3t6u2267TVlZWZo0aZKt/ZprrtH//d//acuWLVqzZo3+93//V//5n//ptJ81a9aovr7e9nXkyJFghO8Ta7H98uom9yc7MGZInM/3fuiqyR6dZ12C6O5eUSbvY5g+OtH7ixxwF1tmymCXx3NzpvslWQcAAIDwxXJLAEDEWLFihT755BPt2LHDrv3mm2+2/Xny5MkaMWKELrjgAh08eFDjxo3r0k9sbKxiY2MDHm93uCsy76mxyQOVnZGsgtJqtf17h0rpdK2vrHSzJDk99r2Zo/Xmx5Vdjnc+z7oE0dm9rBL6xyg9eaD2Ha5zeLyzxLgYv+0a6m4css8c6vI4yywBAAB6PmaSAQAiwq233qo333xTW7Zs0ahRo1yeO3v2bElSaWlpMEILCHdF5r2Rm5NpS4hZZaWblZuT6fKYs2sdnefqXlb1za1qPHlK00cPtmuflTpECf3t/90uMS5Gb6w4z+2zecOXZ3X0jAAAAOiZmEkGAAhrhmFo5cqVeu2117R161alpaW5vaaoqEiSNGLEiABHFxjuisxbC+V7KiEuRpuWz1J5dZMqapqUmjTA7npXxzpf2yfKpFPtRpfzOp7/88sn6vxH8rsca5d0oPKEJGlmaqKun5uqs0cm2PrZXmLRvsO1mj460W8zyDrH5s2zOntG6fTP6NDxZpfnAAAAILKQJAMAhLUVK1boxRdf1F/+8hcNGjRIlZWVkqSEhAT1799fBw8e1IsvvqhLLrlESUlJ+vjjj3X77bcrOztbU6ZMCXH0vnFXZL6ixrskmVWa2XlCx9UxT4535EkB/32H6tQ/5qg2LR9pa5uXkRyQ5Fhn3XlWfy2DBQAAQPghSQYACGtPPvmkJGnBggV27c8++6xuuOEG9e3bV3/729/02GOPqampSSkpKbrqqqt0zz33hCBa/3BXZN5aKN+f8ourVHS0zu0sLk9mUHmyWYB1Vtz2EovtftYYRiT0U/Kgfqpq+FqVDV/7fWZZd2aBuVoGu2n5LL/FCAAAgOAjSQYACGuGmwLvKSkpys/vurQvkrkrMu/P5X2Hapq0ZEOBaptbbW3WemApSd8ku9zNoOqYeHJXwL+j6zbu1qzURBUfO6H6r045Pc9RTN7q7iwwfy+DBQAAQHihcD8AAGEoWEXkOyfIJKm2uVWXb7DfQdTZDKof/N9eLd24W+c/kq9lz+7Rwoe3aunG3frlkklOC/h3trui1mWCzBrT//f4dpVZGrWluErl1U0e9e3JM3i6GYIny2ABAAAQuXxKkm3YsEGpqanq16+fZs+erd27d3t03UsvvSSTyaQlS5b4clsAAHoNaxH5LXcs0LPLZmrLHQu0afksv9a9yi+u6pIgs6ptbtX2f8+ass6g6jwrrM0wtKusRjtK7WdXFZRW6+7XP9Gm5bO06b9m+i3e+q9OdUnG1TuJvzNXz2CdBeZOKJbBAggc3mkAAJ15nSR7+eWXtXr1at1///3at2+fpk6dqsWLF6uqqsrldRUVFbrjjjs0b948n4MFAKC3STMP0MLxQwOyjK/oaJ3L4/sO10pyP4OqvdOKyo6Jp8/rvu5OiC4FexaYdRlptMlk1x5tMik7I5mllkAE4Z0GAOCI10myRx99VDfddJOWLVumiRMn6qmnnlJcXJyeeeYZp9e0tbXp2muv1dq1azV27NhuBQwAAPxj2qjBLo9PH50oybNC/I6cTjy5rknWHaGYBRasZbAAAot3GgCAI14V7m9padHevXu1Zs0aW1tUVJQWLVqkXbt2Ob3ugQce0NChQ7V8+XJt377d7X1OnjypkydP2r5vaGjwJkwAAOCB+eOHKjEuxuGSy8S4GNuOks4K8UdJanfRf2rSAJ8TbN6oqHFfMH9s8kCXz+rpLDDrMtjy6iZV1DT5tEMmgNAKxjsN7zMAEJm8mklWXV2ttrY2DRs2zK592LBhqqysdHjNjh07tHHjRj399NMe32fdunVKSEiwfaWkpHgTJgAA8NAbK85TYqc6Z9adJDtyNIPqvIxkzR2X5HL54djkgZo7LsllDLNShyihv+8bbnecBeassH+ZpdFl/TVvNwII5DJYAIEVjHca3mcAIDL5/l+kHjhx4oSuu+46Pf300zKbPdvhSpLWrFmj1atX275vaGjgFwsAAAGQkhSnwvsu1PYSi/YdrtX00Ym2GWQdOZtBVd/cqpV5hdpW8k3x/s7LD5+8dkaXc2aOSdQNc1M18YwEW6LJGsPg/jF6Ze9RffL5NzMvEuNi1PBVq9o6rN6MNpmUlW5WmnmA6ppbtCqvyO4e2RnJys3JVEJcjEc1yUh4AXDEl3ca3mcAIDJ5lSQzm82Kjo7WsWPH7NqPHTum4cOHdzn/4MGDqqio0GWXXWZra28/vTCjT58+Ki4u1rhx47pcFxsbq9jYWG9CAwAgopRZGnXoeHPYLNebl5HcJTnmKMY08zd/th5fe8XZkk4nmqJNUpshHW9uUUJcjMNznD1z+793ABibPFBvrpynbZ9VqfBInaaPTtSUMwa7TMatyitSQWm1XX/Wwv6bls8K+s6U4fbzBfCNYLzT8D4DAJHJqyRZ3759NWPGDG3evNm25XF7e7s2b96sW2+9tcv5EyZM0P79++3a7rnnHp04cUL//d//zb+mAAB6HXcznsKBuxgdHZ87LkmGIe0qq7G1da4B5uw5D9U0acmGArtz+0SZdKrDtpnWa483t3RJtJVZGu1isepY2N9ZXbWOs9H8IRJ+vkBvxzsNAMAZr3e3XL16tZ5++mk9//zzOnDggG655RY1NTVp2bJlkqSlS5faimD269dPkyZNsvsaPHiwBg0apEmTJqlv377+fRoAAMKcqxlP4cJdjI6O7zxYY5cgk9SlBpiz5+ycIJNklyDreK2jWmCeLKWUgrMzZST8fAHwTgMAcMzrmmRXX321LBaL7rvvPlVWVmratGl65513bIUvDx8+rKgor3NvAAD0eJ7MeAr10jx3MW77rMrhcU84es784iqnBfXdXWvl6VLKQO9MGQk/XwCn8U4DAHDEp8L9t956q8OpyJK0detWl9c+99xzvtwSAICIFwnF493FWHikrtv36PicRUe968/RGHm7lLJjXTV/ioSfL4Bv8E4DAOiMfx4BACBIgl083hfuYsxMGdzte/SJMtn+PG2Ud/05GyNvllKWWRq1pbhK5dVNXt3bnUj4+QIAAMA5n2aSAQAA7wWreHx3uIsx+8yhDo9747qNu23F7OePH9qlwL8j7sbIk6WUgS6qHwk/XwAAADjHTDIAAIIoGMXju8tdjI6Ozx2XpDljk+zaOs4Y66xjMfs3VpynxE5Jqs7XejpGjgr7WwWjqH4k/HwBAADgmMkwfPxn4CBqaGhQQkKC6uvrFR8fH+pwACDi8blqLxTjEaji8f7kLkZHx61tT2wp1b5DdW5nm225Y4Ht2u0lFu07XKvpoxM1LyPZr2NUZmnU+Y/kexSHP0TCzxcIJH7P2GM8AMC/AvW5ynJLAABCIFDF490pszTq0PFmh8mbzsfcxejouGEYqqz/Wnsqaj2Kx1rMvszSqKO1zRo6qJ9GJcZ16OsrVdZ/bbufq/hdPZ+7ovoflNX49ecRqp8vAAAAfEeSDACAXsBVPS5DRrdrdTnq3xND4mJ0zdMfaOfBGrv2+H591PD1Kbu2hP59VP/VN20dY3RXb8xdUf01r+7X/9tf6bf6ZAAAAIg81CQDAKAXcFWPyx+1uhz14Uq0yaTsjGQ98l5JlwSZpC4JMkl2CbLOMbp7BmtR/WiTZ3XSAAAA0PuQJAMAoIcrszRqW4mlS32wNsPQthKLy2Pl1U0+9+9KVrpZP74ww+uZZ45i3PaZZ8/gqKi+q/MBAADQu7DcEgCAHs5dPS5XrDXD/NH/zNREXT83VWePTFCaeYC2FFf5HFdHhUdc1z+zPkNCXIw2LZ+ll3Yf1l2v7nd6/gdl1RTdBwAA6IVIkgEA0MO5q8flSmqS+ySRu/7XXzlZs8cmdUk4dSeujjJTEl0e7/wMzhdcnrbm1U9sf/a2NhsAAAAiF8stAQDo4ZzV47LWBXN1zJOZVO76/49Zox32Y73OV7b4z/TsGQ7VNCnzgfd0p4tZZJ1tK7HoB/+31+cYAQAAEDlIkgEA0As4qseVlW5Wbk6my2P+6N/ddXPHJXVpj+/XdbJ7Qn/7to79e3L/JRsKVNvc6vpBHNhVVkOdMgAAgF6A5ZYAAPQC1npc5dVNDuttuTrmj/5dXffiTeeqvLpJH5bVyJB07r+XZjprc9S/u/vnF1e5TJBdNf0M/Xnf506Pf1BWQ30yAACAHo4kGQAAvUia2XnyytUxf/Tv7jrDMOw2AXDUl7v+nR0vOlrn8v71X7meYeaujhkAAAAiH0kyAAB6kTJLow4db/b7zo3WfqNNJrUZhu1/O97H2b3rmlu0Kq9I20ostjZ/Fcy33nN4fD+X51149jD97YDz3TZnj+26JBQAAAA9C0kyAAB6gUAlohz129nccUkyjNO1vRzde1VekQpKq+2uKSit1sq8Qm1aPstvcfWJMulUu9Hl3MH9Y/S9c0br9cIvtPNgTZfjc8d13ZkTAAAAPQ+F+wEA6AVcJaK645b/2+cyQSZJOw/W2CXIOt67zNKobSUWtRn2yas2w9C2EovPBfMdPa+jBJkkZQwdKEl68toZXXbbzM5I1pPXzvApBgAAAEQWZpIBANDDWRNRnXVMRPkyU6rM0tgl+eUp6713lx93eV5FjXexlVka9WF5jdvEXUd7DtXaxqC7GxgAAAAgcpEkAwCgh+tYDN8RbxNRVh+6SXB5wvHcrm+kJtnH5U1dM290HAN/bGAAAACAyEOSDACAHm7MkDiXxzsnojznLsXl3rljkzR3XJLbWmDuaqr98IV9DvvwlO9jAAAAgJ6CmmQAAPRwY5MHKjsjWdEmk117tMmk7Ixkn2dNzU7zfcfHjvc2nOTaOra7qqmWX1zVrQQZAAAAIJEkAwCgV8jNyVRWutmuLSvdrNycTJ/7HJs8UHPHuU+UnTV8kKalDHZ4b1d1zXaV1ai8usltcf/rn93j8zNYVdT4tkFAqJRZGrWluMrnjQ0AAADQFcstAQDoBRLiYgJSlH79lZN1xYYC1Ta32toGxkbrzovG6+WPjuqTzxt0oPKE0+s9qZfWXTfMHaPzJwzT0md2Oz0nUpZbult2CgAAAN8xkwwAENbWrVunmTNnatCgQRo6dKiWLFmi4uJiu3O+/vprrVixQklJSRo4cKCuuuoqHTt2LEQRh7c08wAtHD/Ub4Xp73n9n2r46pRd21ct7Xr0/RId+MJ5cmxHqUU3btrjUb00d+e4c/3cNGWfmRyQJadScGd1uVp2CgAAgO4hSQYACGv5+flasWKFPvjgA73//vtqbW3VhRdeqKambxISt99+u/7617/qlVdeUX5+vr744gtdeeWVIYy6d3C1DLK2ubVLe0fthrSnolZ3/uljzUod4vAca+H+sckDNSs10acY54z9pvj/g0vOVnx/+0n08f376JdLJvnUd11zi5Zu3K3zH8nXsmf3aOHDW7V0427Vd5hV50/ulp2y9BIAAKB7SJIBAMLaO++8oxtuuEFnn322pk6dqueee06HDx/W3r17JUn19fXauHGjHn30UZ1//vmaMWOGnn32We3cuVMffPBBiKPv2dwtlfTE3kO1Kj7W4PBYx1xQ8THns9Kcyc5I1lP/OcP2/Z1/3m+3LFSSaptb9dM/f+x131LwZ3UFY2kqAABAb0ZNMgBARKmvr5ckDRlyevbR3r171draqkWLFtnOmTBhgkaPHq1du3bp3HPP7dLHyZMndfLkSdv3DQ2OkzRwrbvLICWpXVJ9p+WaVtbC/Ydrmpye01mUSZo4Ml65OdPtllB6skGAN0surbO6Ous4q8tfS1qtPFmaCgAAAN8xkwwAEDHa29t12223KSsrS5MmnV4iV1lZqb59+2rw4MF25w4bNkyVlZUO+1m3bp0SEhJsXykpKYEOvUcamzxQiQEuFl9R06Sio3Uenz9xZLxeWH5ulwTVh+XHXV73gZMEmjOhmNU1NnlgwOqqAQAAgCQZACCCrFixQp988oleeumlbvWzZs0a1dfX276OHDnipwh7lzJLY5fli/6WmjRA00YN9vj83JzpTnZ5dF4fTZJMLo92FapZXbk5mcpKN9u1TR8zWLk5mQG5HwAAQG9CkgwAEBFuvfVWvfnmm9qyZYtGjRplax8+fLhaWlpUV1dnd/6xY8c0fPhwh33FxsYqPj7e7gveczebypP/yIg2mZQYF+NydtT88UPdzlhzN5tqdlqSy+tnj3V9vLNQzepKiIvR73KmaeaYbzYy2FNRq5V5hQHbMAAAAKC3IEkGAAhrhmHo1ltv1Wuvvaa///3vSktLszs+Y8YMxcTEaPPmzba24uJiHT58WHPmzAl2uL2Ku9lUM8a435EyK92sN1ac12V2VFa62W521BsrznOZKLOeX2Zp1Jbiqi47PY5NHqi54xwnwqy7aHrL0awud3H4w6q8Iu07XGfX5mzDgEDGAQAA0NNQuB8AENZWrFihF198UX/5y180aNAgW52xhIQE9e/fXwkJCVq+fLlWr16tIUOGKD4+XitXrtScOXMcFu2H/1hnUxWUVqutw1aU0SaTstLN2rR8lrZ9ZtHSZ3Y77WPtFWcrJSlOm5bPUnl1kypqmpSaNKBL0iolKU6F912o7SUW7Ttcq+mjEzUqMc52fmJcjFbmFdoV08/OSFZuTqZt+eWT185weo4vEuJiusTtSRzd4emGAXXNLVqVVxSwOAAAAHoikmQAgLD25JNPSpIWLFhg1/7ss8/qhhtukCT99re/VVRUlK666iqdPHlSixcv1hNPPBHkSHun3JzMLkmhjrPAio7Uury+ouabXSANw75uWJmlUYeON9slzf5xuE4fHKxRTFSU5nVY1rh0424VlFbbXW+dXfXzyyfa+umc1DIMQ/uO1DpMzHkqzTzAozg2LZ/lU/8debJhQJp5gFblFQU0DgAAgJ6IJBkAIKx1Tpw40q9fP23YsEEbNmwIQkToyHBSEP/I8SYteHi328L+qUmOZz0lxsXYXTtl5CB9/MUJ2/e7yo7roXeL9fJN5yo5Ptbl7KrzH8m3tVlnUyXGxfh9ppWns7y6w5MNA4IRBwAAQE9ETTIAAOAzZzOWljyx0+OdLx310fnajgmyjq5++gO3s6s6x7Yyr9DlTCtfeTLLq7s82TAgGHEAAAD0RCTJAACAT6wzlto6zfZrMwydanc/A1CS3vzH5w778EZBSbX7k/7NOpvKWdzWmVa+8GSWlz+42jAgmHEAAAD0NCy3BAAAPvFmBpczNU0t3e7jn1/UO9xAwFcd66R5w91GBv5a4uhow4COfQcrDgAAgJ6GmWQAAMAn7mYseeL8CUO73ce8f9cS6zy7ylfdmWnlbpaXP6WZB2jh+KEOk16exlFmadSW4iqfZ88BAAD0JMwkAwAAPnE1Y8lkktsll3PGJin7zKHdngX2w4XpktRldtX9f/mnV/36Y6aVu1leweIuDkebJXR34wIAAIBIx0wyAADgM2czlt5YkaVEF8mW7IxkPfWfM5z20SfK5OiyLl6+6Vy77zvOrvJ2dpk/Z3y5muUVTM7iCMTGBQAAAJGOmWQAAMBnrmYsFd53obaXWLTvcK2mj07UqMQ4h7OaOvcRbTJp6TO7nd5zWkqCLpw43DaDzJPYnt9Zrud2HnJ67h0Xnqlbz8/w8ukDq8zSqEPHm/0+G8264UJnHTcuCHVyDwAAIBRIkgEAAIe8SdKkmR2f095hyaXhZtmj9fgXdV+5PK9/TLSSB8U6PPby7sPaVV6jrHFmffecFFts44fHu+zTW91NYLm63pOlkN25v7sNF3zduAAAACDSkSQDAAB2/FGv6lBNk5ZsKFBtc6vD4x37c3Q/V3aVHdeusuNa8+p+vbEiSxPPSND+o3X69hM7bXXQXi/8wu747LQhLvt8+L3PtLu81u0zdndsPLne1VLI3+VM6/bPxt2GC93ZuAAAACCSUZMMAADY8Ue9KlcJss79ObqfJ061G7p8Q4Ek2SXIHB0fmzxQCf1d/9ugJ8/Y3bFxd711KWTnzQasSyFvev6jbv9sxiYPdFovLjEuhllkAACg1yJJBgAAbNwlacqrm9z2kV9c5TJB1rG/bZ9ZHN7PU6faDd3xx3843UnzVLuhVz46ojJLo+q/OuVRTM6esbtj48n17pZC7jlU262fjTUOZz+f2uZWj/vxRpmlUVuKqwLSNwAAgL+w3BIAANj4Wq+qY42soqN1Ht+v8EittyF28UG561loBQerZXZSw8wRZ8/Y3Vpenlzvbilkd+7vTRz+mk3mj6W7AAAAwUKSDAAA2Hhbr8pREmTSGZ4Xyc9MSfQuQAfOTTPrT7VHnR7PGmf2KvnkrCZXd2t5JfZ3nRSyFuHPzkhWQWm13YyxaJNJ08cM1p4K50lFR/d3VOA/mDXJXC0v3bR8lt/uAwAA4A8stwQAADZjkwcqOyNZ0SaTXXu0yaTsjOQuM4wcJUEOfHFCfaLsr+/M2l/2mcmaMzbJ53j7RJn08PemOr1fnyiTvntOisYmD3R7H2fPaOXt2HT26PslTo91vD43J1NZ6Wa741npZv1h6UxlZyQ7/I+3PlEm9ekQV11zi5Zu3K3zH8nXsmf3aOHDW7V0427VN7d2+zk85Y+luwAAAMFEkgwAANhxlqTJzcm0a3OVBDnVbii+n/MJ6x37M7nOp0mSBsX2UXSn8/pEmfTGiixJ0hsrsrokyjoe9+Q+jp6xM0/HpjPrWDlzx4Vn2v6cEBejTctnacsdC/TsspnacscCbVo+SwlxMcrNyVSUg4Tg6U0Kdti+d7dBgK/P4Q1PlnUCAACEE5ZbAgAAO9YkTXl1kypqmuyW6nXkLgny3zmZ6hNl0r7DtZo+OlGjEuO69FdmadTOgzVO+7hh7hhdcNYwzctIliS98tERFRysVtY4s757TortvIlnJKj0V5c4Pe7uPv+7fJbtHq54OjaduRurmuaWLm1p5q59Fx2pdbpJQW1zq7aXWHTG4P4OE3IdZ3ClmQf49BzeCOayTgAAAH8gSQYAABxylKTpyJMkSJp5gF3yqXN/7pJH88cPtbv+u+ek2CW/OnN23N19nCWenHE3Np35K2HkblOEfYedJ9GsOhbm9/Y5vGFd1umovlpWujlg9wUAAPAVyy0BAIBP/FHbytvkUZmlUVuKq7yuZxXsWU2d43Q2VlEmeVUHbNqowS6PTx+d6NWz+jqengrGsk4AAAB/YSYZAADwWW5OplbmFdot7/MmCeLpbCNHu2hmZyQrNydTCXGud4203mdWaqJ2O9gdclbqEL/NanIVZ25Opm55Ya/dss92Q2pta1d9c6tHzzF//FAN7h+juq9auxwb3D/GNuvO3Zh6M56Odsj0lK/LUwEAAEKBmWQAAMBnrorMe8qT2UbuCtF7ovjYCSftDR734Y6rOBPiYtQnKqrLf3ztLj/u1XNkDB3ott3dmHoynq52yPRWmnmAFo4fSoIMAACENWaSAQCAbutObSt3s42c7QzZuRC9K/nFVar/6pTDY/VfndL2EotHhftdcRfnts8s3X6OMkuj9hzqOhtOkvYcqrX14WpMPR1PV4m0TctnuYwTAAAgEjGTDAAAhAVns43cFd2vqHFfT8uTgvfecFTLy12chUdc38OT5/iw/LhXfTgaU0/G05pI67hcU7JPpIWbQNdXAwAAPR8zyQAAQFjzR9F9Twree8JVLS93cWamuL6Hq+dwdF9v+7DyZDzdJew67pAZat2tVwcAAGDFTDIAABDW/LGL5vzxQ5XoJGGSGBfj8VJLV0sQ3cWZfWayz8/h6L7e9mHlyXgGezfQ7vBHvToAAACJJBkAAIgAnhT3d+eNFed1SZQlxsXojRXneXS9J0sQ3cXp7rijJYPO7uusD0+4i8MficlgiMRloQAAIHyx3BIAAIQ9d8X9PZGSFKfC+y7U9hKL9h2u1fTRiV4V6/eklleaeYDLOJ09h3UnSUdLBt3dd/2Vk/Ufs0Z7/Byu4ugoNydTK/MK7WLyNhkXaJ7+TAAAADxBkgwAAESMwzVN+vhonfpEmRwmP/KLq1R0tM5lAuyMwf11qt3QqETXSwo782YJorvdPjsfd7Vk8OeXT3R53zMS+7s87kyZpVH/u+uQjtQ2a/HZw7vE64/EpDexHDre7PU9ImlZaKTx9WcCAEAkI0kGAADC3qGaJi3ZUKDa5lZbm3WpZEpSnNvjUvcLvFuXIBaUVtst74s2mZSVbvY5kWBdMtiZdcmg6d9LHDvf1+q6jbu9eo665hYte26PCg/X2dr+dqBKd/35Y/311vM08YwEu/PdJfy6wx8/kzljk7SrrKbLsTljk0ju+ICNEAAAvRk1yQAAQNjrnACTpNrmVl2+YYdHxyX/FHj3R220zjxZMujovh158xyr8orsEmRWbYZ0+YYCj/rwF3/8TDqVTXPbDtfYCAEA0JsxkwwAAIS1/OKqLgkwq9rmVm34e4nL49tLLDpjcH+Xs7XKqz2rXRWIJYieLBm03nfbZ1Va+syeLud4+hzOZq1ZnWo39MpHR/Tdc1I8fwAfuZtB58nPpMzSqJ0Hu84ik6SdB2s8/rniNH/8TAAAiGTMJAMAAGGt6Gidy+M7Os166Wzf4VqPZmt5I808QAvHD/VLwsCbnSTbnG9wKcn9c7gbB0kqOOh6PP3FHz8Tf/9cezvGEwDQ25EkAwAAYW3aqMEuj5/nYhmiJE0fnej3Au9llkZtKa5SebV/kgaeLuN09xzH6r+2i6lznO6ul6Ssca7H01/88TMJ959rpGEjBABAb+dTkmzDhg1KTU1Vv379NHv2bO3evdvpuU8//bTmzZunxMREJSYmatGiRS7PBwAA6Gj++KFKdFIwPDEuRivOz3B5fF5GsleztVypa27R0o27df4j+Vr27B4tfHirlm7crXonyz09ZV1OueWOBXp22UxtuWOBNi2f1aVQurPnsLrr1f1a+PBWXfP0B8r5nw+6xJk0IFbZTnb9lKQ+UaagLLWUvim674inRffD/ecaafw1npGCdxoAQGdeJ8lefvllrV69Wvfff7/27dunqVOnavHixaqqqnJ4/tatW5WTk6MtW7Zo165dSklJ0YUXXqjPP/+828EDAIDe4Y0V53VJhFl3r/TkuOSfovuBLmruyTJOd0X8pdP1uDrv+GiNMzcnU9NHD+5yTbRJemNFlk9x+8ofRfcj4ecaSQKxOUU44p0GAOCIyTAc7CXuwuzZszVz5kw9/vjjkqT29nalpKRo5cqVuuuuu9xe39bWpsTERD3++ONaunSpR/dsaGhQQkKC6uvrFR8f7024AAAH+Fy1x3hEju0lFu07XKvpoxM1z8GMKHfHJflcdL/M0qjzH8l3enzLHQuCOtOmvLpJH5TVaM2r+726zhpneXWT/m9XhQ4db9bis4cHbQaZlb/Hs6f8XMNFdzenCPfP1WC/04T7eABApAnU56pXu1u2tLRo7969WrNmja0tKipKixYt0q5duzzqo7m5Wa2trRoyZIjTc06ePKmTJ0/avm9oaPAmTAAA4ER+cZWKjta5TCKFszMG99epdkOjEh3XTpqXkez2ubz890EbT4qae5pMKLM06tDx5m7tjplmHuBTIXVrnGnmATIM6Whts4orT/gUg6ccPa8/x1OS7Zm81d04/PGzDEe+jmckCMY7De8zABCZvEqSVVdXq62tTcOGDbNrHzZsmP71r3951Medd96pkSNHatGiRU7PWbdundauXetNaACAHmrbtm36zW9+o7179+rLL7/Ua6+9piVLltiO33DDDXr++eftrlm8eLHeeeedIEca3g7VNGnJhgLVdqixZF2OmJLkvph7qNU1t2hVXpG2lVhsbdkZycrNyexStytQffijqLk/nsObmBxJTRqg9z75Ujf/3z5b278qG/WHHeXauPQcXTBxmIurvePqecOlSLyvcfj7Z4ngCcY7De8zABCZgrq75fr16/XSSy/ptddeU79+/Zyet2bNGtXX19u+jhw5EsQoAQDhpKmpSVOnTtWGDRucnnPRRRfpyy+/tH3l5eUFMcLI0DlBJkm1za26fMOOEEXkHX/UjOpuH/4oau7v2lfuCvl31ifKpDTzALsEWUfLN33kUxzOuHresckDXW64EKxZTL7GQR2z3suTdxreZwAgMnmVJDObzYqOjtaxY8fs2o8dO6bhw4e7vPbhhx/W+vXr9d5772nKlCkuz42NjVV8fLzdFwCgd7r44ov14IMP6tvf/rbTc2JjYzV8+HDbV2Jioss+T548qYaGBruvniy/uKpLgsyqtrlV2zvMhAlHZZZGbSuxqK3TMsk2w9C2EovKq90vOfRHH1L3ipr7KwZPYnLmVLuh7z/vOhH24Juf+hRHZ+6ed9tnFpd/L30dD2+VWRq9jiNQP0sERzDeaXifAYDI5FWSrG/fvpoxY4Y2b95sa2tvb9fmzZs1Z84cp9c99NBD+sUvfqF33nlH55xzju/RAgDgwNatWzV06FCNHz9et9xyi2pqalyev27dOiUkJNi+UlKCW7A82IqO1rk8vu9wbXAC8ZEnNaOC0YckJcTFaNPyWdpyxwI9u2ymttyxQJuWz/JoeZ2/YnAX0w1zx7g8/6Mjx10e31Hqn6Spu+ctPOL6752v4+EtX34ugfpZIjh4pwEAOOP1csvVq1fr6aef1vPPP68DBw7olltuUVNTk5YtWyZJWrp0qV0RzF//+te699579cwzzyg1NVWVlZWqrKxUY2Oj/54CANBrXXTRRdq0aZM2b96sX//618rPz9fFF1+strY2p9f0tmUw00YNdnl8+mjXM+9CzR+1q/xd/yrNPEALxw/1aklgoGtwWWNaOH6oy/POSXG+eZIkjRkywC8zodw9b2aK67934VyTLFzqqcF3vNMAABzxOkl29dVX6+GHH9Z9992nadOmqaioSO+8846t8OXhw4f15Zdf2s5/8skn1dLSou985zsaMWKE7evhhx/231MAAHqt//iP/9Dll1+uyZMna8mSJXrzzTe1Z88ebd261ek1vW0ZzPzxQ13WXAr3XS79UQvMH310V7BicPfz/v31rmfAvPvpMS18eKuWbtyteifLED3h7nmzz0wO+c/EkzgdxREOf5/QPbzTAAAcMRm+7oMeRA0NDUpISFB9fX2Pf5EBgGCI1M9Vk8nUZXdLR5KTk/Xggw/q+9//vkf9Rup4eONITbMu37AjYne3rG9u1cq8wm7tJOiPProrWDG4+3lv/vSY2yL90SaTstLN2rR8ls9xuHvecPiZeBKnv67pTXrD56o3GA8A8K9Afa6SJAOAXihSP1c9SZIdPXpUo0eP1uuvv67LL7/co34jdTx8sb3Eon2HazV9dGLYzyBzpLy6SRU1TUpNGuDzbB1/9NFdwYrB3c/7wTc/1d//dUxl1c5rbG25Y0G3Y3T3vOHwM/E1jnCJPdz0ps9VTzAeAOBfgfpc7eO3ngAACIDGxkaVlpbavi8vL1dRUZGGDBmiIUOGaO3atbrqqqs0fPhwHTx4UD/96U+Vnp6uxYsXhzDq8DUvIzkik2NWaebuJyL80Ud3BSsGdz/ve/6/icrKMGvZs3ucnlNR0xTwMQ+Hn4mvcYRL7AAAoPtIkgEAwtpHH32khQsX2r5fvXq1JOn666/Xk08+qY8//ljPP/+86urqNHLkSF144YX6xS9+odjY2FCFDPhdmaVRh443281WctTmbV+Pby7RjoOud7N0V4Tekzh8jdUqv7hKRUfrInYGpL+5G09PxpsxBQCgK5JkAICwtmDBArmqDPDuu+8GMRoguOqaW7Qqr8iu7tWcsUkymaSdB2tsbZ7UwnLUlzt9okzq06k4vav+OsfhyTmuHKpp0pINBRFbS8/f3I2nJ+PNmAIA4JzXu1sCAAAgOFblFamgtNqubVdZjV2CTJIKSqu1Mq/Q677cOdVu6PINOzzur3McnpzjSudkjiTVNrc6jamnczeenow3YwoAgHMkyQAAAMJQmaVR20osavNgj6U2w9C2EovKq5u63Vdntc2t2t5p9pmz/jrG4ck5ruQXV3VJ5riKqadzN57bPqtyO96MKQAArpEkAwAACEOHjjvfcdKZihrHiSdf+upo3+Far/qrqGny6BxXio7WeRVTT+duPAuP1Lk8XlHTxJgCAOAGNckAAADC0Jgh3teHclZk35e+Opo+OtGr/lKTBrisJWg9x5VpowZ7FVNP527MM1MGuzyemjTA7b+O97YxBQCgM2aSAQAAhKGxyQOVnZGsaCeF8zuKNpmUnZHsdCdDb/rqLDEupsvuh8766xiHJ+e4Mn/8UCU6Ke7vKKaezt14Zp851O14M6YAALhGkgwAACBM5eZkKivdbNc2Z2yS5o5LsmvLSjcrNyfT6746i+u0xsC666Gn/XWOw5NzXHljxXldkjquYurp3I2nJ+PNmAIA4JzJcDcXPgw0NDQoISFB9fX1io+PD3U4ABDx+Fy1x3gg3JVXN6mipkmpSQNsM7ActXnb1xNbSrWrrFpzxpr1m+9OlSRtL7Fo3+FaTR+d6NHMIk/i8DVWK29j6uncjacn4x3oMeVz1R7jAQD+FajPVWqSAQAABFGZpVFvfvyFjje16oKzhnqUoEgzd012ePLvnPnFVSo6WmeXCOnY15ghcTpaG2fX97yMZLuYyiyNOnS82S7h0rnNXeLLXayO4uzoi9qvVF7dpJEJ/d0+c3c4etZw5G48PfmZdP45AwAAkmQAAABBUdfcops2faQ9Fd/sIPjczgol9I/Rm7eep5Qkz4rr1zW3aFVekbaVWGxt2RnJys3JVMK/l9EdqmnSkg0Fqm1utZ1jXVKXkhSnnaUWXfOH3bZju8qO66F3i/XyTedq9r+Xcjq6z5yxSTKZpJ0Ha5ze25tY3cW5/2idvv3ETp1qP50Uer3wC615db/eWJGliWckeDRenvBkTMNBpMQJAEC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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -7862,7 +11821,7 @@
    ],
    "source": [
     "fig, axes = subplots(ncols=3, figsize=(15, 5))\n",
-    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);\n"
+    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[-1]);\n"
    ]
   },
   {
@@ -7886,7 +11845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 113,
    "id": "660482a9",
    "metadata": {
     "execution": {
@@ -7904,7 +11863,7 @@
        "CategoricalDtype(categories=[3, 4, 5, 6, 8], ordered=False, categories_dtype=int64)"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7925,7 +11884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 114,
    "id": "9eb5befc",
    "metadata": {
     "execution": {
@@ -7938,7 +11897,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7962,7 +11921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 116,
    "id": "f529de12",
    "metadata": {
     "execution": {
@@ -7976,7 +11935,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7986,8 +11945,8 @@
     }
    ],
    "source": [
-    "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.hist('mpg', ax=ax);\n"
+    "fig, axx = subplots(figsize=(8, 8))\n",
+    "Auto.hist('mpg', ax=axx);\n"
    ]
   },
   {
@@ -8000,7 +11959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 117,
    "id": "9a897cee",
    "metadata": {
     "execution": {
@@ -8014,7 +11973,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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SEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMkMLvcCgF6Wy5V7BQAcRjyzCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyRxSbN56662Ry+ViwYIFhW27d++O5ubmGDlyZAwbNiyampqira3tUNcJAEA/dNCx+cwzz8Q3v/nNmDp1apftCxcujMceeyxWrFgRa9asiS1btsS8efMOeaEAAPQ/BxWbb775ZlxyySVx3333xVFHHVXY3t7eHvfff3984xvfiJkzZ8a0adNi+fLl8dRTT8W6det6bNEAAPQPBxWbzc3N8dGPfjQaGxu7bF+/fn3k8/ku26dMmRITJkyItWvXHtpKAQDodwaXeoWHH344nnvuuXjmmWe6XbZ169YYMmRIjBgxosv20aNHx9atW/d5e52dndHZ2Vk439HRERER+Xw+8vl8qcvrdXvXWLa1VleX535LlP/zOvP9ZL3lZFbF61ezKvP3s7J/r+pHzKp4ZlW8gTarUh5HSbG5efPmuPbaa6O1tTWqqqpKXti+3HLLLbF06dJu23/yk5/E0KFDe+Q+ekNra2t57vi73y3P/R6k1gceKPcS+g2zKl6/mNWPflTuFUREGb9X9UNmVTyzKt5AmdXOnTuL3jeXZVlW7M6PPvpofOITn4gjjjiisO3tt9+OXC4XgwYNiieeeCIaGxvjjTfe6PLs5sSJE2PBggWxcOHCbre5r2c2x48fH3/84x+jpqam6AdSLvl8PlpbW2P27NlRUVHR+wuore39+zwI+erqaH3ggZh92WVRsWtXuZfTp5lV8frVrNrby3r3Zf9e1Y+YVfHMqngDbVYdHR0xatSoaG9vP2CvlfTM5qxZs+L555/vsm3+/PkxZcqUuO6662L8+PFRUVERq1atiqampoiI2LhxY2zatCkaGhr2eZuVlZVRWVnZbXtFRUW/+mKUbb19/Qfsu1Ts2tX3o6CPMKvi9YtZ9ZHvZ/3te2s5mVXxzKp4A2VWpTyGkmJz+PDhcfLJJ3fZduSRR8bIkSML2y+//PJYtGhR1NXVRU1NTVx99dXR0NAQp59+eil3BQDAAFDyG4QO5I477ohBgwZFU1NTdHZ2xty5c+Puu+/u6bsBAKAfOOTYXL16dZfzVVVVsWzZsli2bNmh3jQAAP2cz0YHACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBekMuV95Tbe0766itLf26AIdAbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQzOByLwCAPq4/f4pQlpV7BXDY88wmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyZQUm/fcc09MnTo1ampqoqamJhoaGuLHP/5x4fLdu3dHc3NzjBw5MoYNGxZNTU3R1tbW44sGAKB/KCk2x40bF7feemusX78+nn322Zg5c2acf/758etf/zoiIhYuXBiPPfZYrFixItasWRNbtmyJefPmJVk4AAB93+BSdj7vvPO6nP/a174W99xzT6xbty7GjRsX999/f7S0tMTMmTMjImL58uVx4oknxrp16+L000/vuVUDANAvHPRrNt9+++14+OGHY8eOHdHQ0BDr16+PfD4fjY2NhX2mTJkSEyZMiLVr1/bIYgEA6F9KemYzIuL555+PhoaG2L17dwwbNixWrlwZJ510UmzYsCGGDBkSI0aM6LL/6NGjY+vWre95e52dndHZ2Vk439HRERER+Xw+8vl8qcvrdXvXWLa1VleX535LlP/zOvP9ZL3lZFbFM6viHbazOojvzWX/vt6PmFXxBtqsSnkcuSzLslJu/K233opNmzZFe3t7fO9734tvf/vbsWbNmtiwYUPMnz+/SzhGREyfPj3OOeecuO222/Z5e0uWLImlS5d2297S0hJDhw4tZWkAAPSCnTt3xsUXXxzt7e1RU1Oz331Ljs13a2xsjOOOOy4+9alPxaxZs+KNN97o8uzmxIkTY8GCBbFw4cJ9Xn9fz2yOHz8+/vjHPx5w8X1BPp+P1tbWmD17dlRUVPT+Ampre/8+D0K+ujpaH3ggZl92WVTs2lXu5fRpZlU8syreYTur9vaSr1L27+v9iFkVb6DNqqOjI0aNGlVUbJb8a/R327NnT3R2dsa0adOioqIiVq1aFU1NTRERsXHjxti0aVM0NDS85/UrKyujsrKy2/aKiop+9cUo23r72Q+Nil27Dq8fdIfArIpnVsU77GZ1CN+X+9vPoXIyq+INlFmV8hhKis3FixfHueeeGxMmTIjt27dHS0tLrF69Op544omora2Nyy+/PBYtWhR1dXVRU1MTV199dTQ0NHgnOgDAYaqk2Hz11Vfjc5/7XLzyyitRW1sbU6dOjSeeeCJmz54dERF33HFHDBo0KJqamqKzszPmzp0bd999d5KFAwDQ95UUm/fff/9+L6+qqoply5bFsmXLDmlRAAAMDD4bHQCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgADVy5X+qm29p3r1tYe3PV76gQDhNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACCZkmLzlltuidNOOy2GDx8exxxzTFxwwQWxcePGLvvs3r07mpubY+TIkTFs2LBoamqKtra2Hl00AAD9Q0mxuWbNmmhubo5169ZFa2tr5PP5mDNnTuzYsaOwz8KFC+Oxxx6LFStWxJo1a2LLli0xb968Hl84AAB93+BSdn788ce7nH/wwQfjmGOOifXr18dZZ50V7e3tcf/990dLS0vMnDkzIiKWL18eJ554Yqxbty5OP/30nls5AAB9Xkmx+W7t7e0REVFXVxcREevXr498Ph+NjY2FfaZMmRITJkyItWvX7jM2Ozs7o7Ozs3C+o6MjIiLy+Xzk8/lDWV6v2LvGsq21uro891ui/J/Xme8n6y0nsyqeWRXPrIrXZ2blZ+CAMtBmVcrjyGVZlh3MnezZsyc+/vGPx7Zt2+LnP/95RES0tLTE/Pnzu8RjRMT06dPjnHPOidtuu63b7SxZsiSWLl3abXtLS0sMHTr0YJYGAEBCO3fujIsvvjja29ujpqZmv/se9DObzc3N8atf/aoQmgdr8eLFsWjRosL5jo6OGD9+fMyZM+eAi+8L8vl8tLa2xuzZs6OioqL3F1Bb2/v3eRDy1dXR+sADMfuyy6Ji165yL6dPM6vimVXxzKp4fWZWf/7tYV9W9p+B/chAm9Xe30QX46Bi86qrroof/vCH8eSTT8a4ceMK2+vr6+Ott96Kbdu2xYgRIwrb29raor6+fp+3VVlZGZWVld22V1RU9KsvRtnW289+aFTs2uUHXZHMqnhmVTyzKl7ZZ+Vn4IA0UGZVymMo6d3oWZbFVVddFStXroyf/vSnMWnSpC6XT5s2LSoqKmLVqlWFbRs3boxNmzZFQ0NDKXcFAMAAUNIzm83NzdHS0hLf//73Y/jw4bF169aIiKitrY3q6uqora2Nyy+/PBYtWhR1dXVRU1MTV199dTQ0NHgnOgDAYaik2LznnnsiIuLss8/usn358uXx+c9/PiIi7rjjjhg0aFA0NTVFZ2dnzJ07N+6+++4eWSwAAP1LSbFZzBvXq6qqYtmyZbFs2bKDXhQAAAODz0YHACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzY3CuXO7hTbe0716+tPfjbOJQTAEAfJjYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQB9US7X90+1te+stba263b4P8QmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJBMybH55JNPxnnnnRdjx46NXC4Xjz76aJfLsyyLG264IcaMGRPV1dXR2NgYL7zwQk+tFwCAfqTk2NyxY0eccsopsWzZsn1efvvtt8ddd90V9957bzz99NNx5JFHxty5c2P37t2HvFgAAPqXwaVe4dxzz41zzz13n5dlWRZ33nlnfPWrX43zzz8/IiIeeuihGD16dDz66KPx6U9/+tBWCwBAv1JybO7Pyy+/HFu3bo3GxsbCttra2pgxY0asXbt2n7HZ2dkZnZ2dhfMdHR0REZHP5yOfz/fk8vavuvqgrpb/8/XyB3n9w4U5Fc+simdWxTOr4plV8d5zVr3587uf2Ns0vdo2CZXyOHJZlmUHe0e5XC5WrlwZF1xwQUREPPXUU3HGGWfEli1bYsyYMYX9LrroosjlcvHII490u40lS5bE0qVLu21vaWmJoUOHHuzSAABIZOfOnXHxxRdHe3t71NTU7HffHn1m82AsXrw4Fi1aVDjf0dER48ePjzlz5hxw8T1q70dulShfXR2tDzwQsy+7LCp27erhRQ0c5lQ8syqeWRXPrIpnVsV7z1m1t5dvUX1UPp+P1tbWmD17dlRUVJR7OYds72+ii9GjsVlfXx8REW1tbV2e2Wxra4sPfvCD+7xOZWVlVFZWdtteUVHRu1+MQ/yGUrFrl29KRTCn4plV8cyqeGZVPLMqXrdZDYCYSqXX+yaRUh5Dj/6dzUmTJkV9fX2sWrWqsK2joyOefvrpaGho6Mm7AgCgHyj5mc0333wzXnzxxcL5l19+OTZs2BB1dXUxYcKEWLBgQdx8881x/PHHx6RJk+L666+PsWPHFl7XCQDA4aPk2Hz22WfjnHPOKZzf+3rLSy+9NB588MH48pe/HDt27Igrr7wytm3bFmeeeWY8/vjjUVVV1XOrBgCgXyg5Ns8+++zY3xvYc7lc3HTTTXHTTTcd0sIAAOj/fDY6AADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQD0rFzO6d2n2tp3ZlNbm+4++iixCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAyYhNAACSEZsAACQjNgEASEZsAgCQjNgEACAZsQkAQDJiEwCAZMQmAADJiE0AAJIRmwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgGbEJAEAyYhMAgGTEJgAAySSLzWXLlsWxxx4bVVVVMWPGjPjFL36R6q4AAOijksTmI488EosWLYobb7wxnnvuuTjllFNi7ty58eqrr6a4OwAA+qgksfmNb3wjrrjiipg/f36cdNJJce+998bQoUPjgQceSHF3AAD0UYN7+gbfeuutWL9+fSxevLiwbdCgQdHY2Bhr167ttn9nZ2d0dnYWzre3t0dExOuvvx75fL6nl/feqqoO6mr5qqrYuXNnvFZVFRVZ1sOLGjjMqXhmVTyzKp5ZFc+simdWxeuVWb32Wprb3Yft27dHRERWzGPJetgf/vCHLCKyp556qsv2L33pS9n06dO77X/jjTdmEeHk5OTk5OTk5NTPTps3bz5gG/b4M5ulWrx4cSxatKhwfs+ePfH666/HyJEjI5fLlXFlxeno6Ijx48fH5s2bo6amptzL6bPMqXhmVTyzKp5ZFc+simdWxRtos8qyLLZv3x5jx4494L49HpujRo2KI444Itra2rpsb2tri/r6+m77V1ZWRmVlZZdtI0aM6OllJVdTUzMgDp7UzKl4ZlU8syqeWRXPrIpnVsUbSLOqra0tar8ef4PQkCFDYtq0abFq1arCtj179sSqVauioaGhp+8OAIA+LMmv0RctWhSXXnppnHrqqTF9+vS48847Y8eOHTF//vwUdwcAQB+VJDY/9alPxf/+7//GDTfcEFu3bo0PfvCD8fjjj8fo0aNT3F1ZVVZWxo033tjtpQB0ZU7FM6vimVXxzKp4ZlU8syre4TyrXJb5WwUAAKThs9EBAEhGbAIAkIzYBAAgGbEJAEAyYrMITz75ZJx33nkxduzYyOVy8eijj3a5PMuyuOGGG2LMmDFRXV0djY2N8cILL5RnsWV2oFl9/vOfj1wu1+X0kY98pDyLLbNbbrklTjvttBg+fHgcc8wxccEFF8TGjRu77LN79+5obm6OkSNHxrBhw6KpqanbByYcDoqZ1dlnn93t2PqHf/iHMq24fO65556YOnVq4Q9HNzQ0xI9//OPC5Y6pvzjQrBxT+3brrbdGLpeLBQsWFLY5rvZtX7M6HI8rsVmEHTt2xCmnnBLLli3b5+W333573HXXXXHvvffG008/HUceeWTMnTs3du/e3csrLb8DzSoi4iMf+Ui88sorhdN3v/vdXlxh37FmzZpobm6OdevWRWtra+Tz+ZgzZ07s2LGjsM/ChQvjscceixUrVsSaNWtiy5YtMW/evDKuujyKmVVExBVXXNHl2Lr99tvLtOLyGTduXNx6662xfv36ePbZZ2PmzJlx/vnnx69//euIcEz9XweaVYRj6t2eeeaZ+OY3vxlTp07tst1x1d17zSriMDyuDvjp6XQREdnKlSsL5/fs2ZPV19dn//Iv/1LYtm3btqyysjL77ne/W4YV9h3vnlWWZdmll16anX/++WVZT1/36quvZhGRrVmzJsuyd46jioqKbMWKFYV9fvOb32QRka1du7Zcy+wT3j2rLMuy//f//l927bXXlm9RfdhRRx2Vffvb33ZMFWHvrLLMMfVu27dvz44//vistbW1y2wcV92916yy7PA8rjyzeYhefvnl2Lp1azQ2Nha21dbWxowZM2Lt2rVlXFnftXr16jjmmGPihBNOiH/8x3+M1157rdxL6hPa29sjIqKuri4iItavXx/5fL7LsTVlypSYMGHCYX9svXtWe33nO9+JUaNGxcknnxyLFy+OnTt3lmN5fcbbb78dDz/8cOzYsSMaGhocU/vx7lnt5Zj6i+bm5vjoRz/a5fiJ8L1qX95rVnsdbsdVkk8QOpxs3bo1IqLbpyONHj26cBl/8ZGPfCTmzZsXkyZNipdeeim+8pWvxLnnnhtr166NI444otzLK5s9e/bEggUL4owzzoiTTz45It45toYMGRIjRozosu/hfmzta1YRERdffHFMnDgxxo4dG7/85S/juuuui40bN8a///u/l3G15fH8889HQ0ND7N69O4YNGxYrV66Mk046KTZs2OCYepf3mlWEY+r/evjhh+O5556LZ555pttlvld1tb9ZRRyex5XYpFd9+tOfLvz7Ax/4QEydOjWOO+64WL16dcyaNauMKyuv5ubm+NWvfhU///nPy72UPu+9ZnXllVcW/v2BD3wgxowZE7NmzYqXXnopjjvuuN5eZlmdcMIJsWHDhmhvb4/vfe97cemll8aaNWvKvaw+6b1mddJJJzmm/mzz5s1x7bXXRmtra1RVVZV7OX1aMbM6HI8rv0Y/RPX19RER3d5119bWVriM9/b+978/Ro0aFS+++GK5l1I2V111Vfzwhz+Mn/3sZzFu3LjC9vr6+njrrbdi27ZtXfY/nI+t95rVvsyYMSMi4rA8toYMGRKTJ0+OadOmxS233BKnnHJK/Ou//qtjah/ea1b7crgeU+vXr49XX301PvShD8XgwYNj8ODBsWbNmrjrrrti8ODBMXr0aMfVnx1oVm+//Xa36xwOx5XYPESTJk2K+vr6WLVqVWFbR0dHPP30011e98O+/f73v4/XXnstxowZU+6l9Losy+Kqq66KlStXxk9/+tOYNGlSl8unTZsWFRUVXY6tjRs3xqZNmw67Y+tAs9qXDRs2REQclsfWu+3Zsyc6OzsdU0XYO6t9OVyPqVmzZsXzzz8fGzZsKJxOPfXUuOSSSwr/dly940Cz2tfLxQ6H48qv0Yvw5ptvdvk/jpdffjk2bNgQdXV1MWHChFiwYEHcfPPNcfzxx8ekSZPi+uuvj7Fjx8YFF1xQvkWXyf5mVVdXF0uXLo2mpqaor6+Pl156Kb785S/H5MmTY+7cuWVcdXk0NzdHS0tLfP/734/hw4cXXttUW1sb1dXVUVtbG5dffnksWrQo6urqoqamJq6++upoaGiI008/vcyr710HmtVLL70ULS0t8bd/+7cxcuTI+OUvfxkLFy6Ms846a59/dmQgW7x4cZx77rkxYcKE2L59e7S0tMTq1avjiSeecEy9y/5m5Zj6i+HDh3d5fXRExJFHHhkjR44sbHdcveNAszpsj6tyvx2+P/jZz36WRUS306WXXppl2Tt//uj666/PRo8enVVWVmazZs3KNm7cWN5Fl8n+ZrVz585szpw52dFHH51VVFRkEydOzK644ops69at5V52WexrThGRLV++vLDPrl27si984QvZUUcdlQ0dOjT7xCc+kb3yyivlW3SZHGhWmzZtys4666ysrq4uq6yszCZPnpx96Utfytrb28u78DK47LLLsokTJ2ZDhgzJjj766GzWrFnZT37yk8Lljqm/2N+sHFP79+4/3+O4em//d1aH63GVy7Is6824BQDg8OE1mwAAJCM2AQBIRmwCAJCM2AQAIBmxCQBAMmITAIBkxCYAAMmITQAAkhGbAAAkIzYBAEhGbAIAkIzYBAAgmf8P4biV52JAgFEAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -8042,7 +12001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 118,
    "id": "5e003b22",
    "metadata": {
     "execution": {
@@ -8056,7 +12015,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 49 Axes>"
       ]
@@ -8080,7 +12039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 120,
    "id": "87f2d391",
    "metadata": {
     "execution": {
@@ -8094,7 +12053,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 9 Axes>"
       ]
@@ -8106,7 +12065,7 @@
    "source": [
     "pd.plotting.scatter_matrix(Auto[['mpg',\n",
     "                                 'displacement',\n",
-    "                                 'weight']]);\n"
+    "                                 'year']]);\n"
    ]
   },
   {
@@ -8119,7 +12078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 121,
    "id": "33cc3b75",
    "metadata": {
     "execution": {
@@ -8133,6 +12092,73 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "fe8466e3-65e8-4969-bedb-aeb41c1969c9",
+       "rows": [
+        [
+         "count",
+         "392.0",
+         "392.0"
+        ],
+        [
+         "mean",
+         "23.445918367346938",
+         "2977.5841836734694"
+        ],
+        [
+         "std",
+         "7.8050074865717995",
+         "849.4025600429492"
+        ],
+        [
+         "min",
+         "9.0",
+         "1613.0"
+        ],
+        [
+         "25%",
+         "17.0",
+         "2225.25"
+        ],
+        [
+         "50%",
+         "22.75",
+         "2803.5"
+        ],
+        [
+         "75%",
+         "29.0",
+         "3614.75"
+        ],
+        [
+         "max",
+         "46.6",
+         "5140.0"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 8
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -8213,7 +12239,7 @@
        "max     46.600000  5140.000000"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8232,7 +12258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 122,
    "id": "398d24d3",
    "metadata": {
     "execution": {
@@ -8258,7 +12284,7 @@
        "Name: mpg, dtype: float64"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8286,6 +12312,11 @@
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -8296,7 +12327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/Ch03-linreg-lab.ipynb b/Ch03-linreg-lab.ipynb
index 2720fcde..8011ba21 100644
--- a/Ch03-linreg-lab.ipynb
+++ b/Ch03-linreg-lab.ipynb
@@ -185,6 +185,7 @@
        " '__name__',\n",
        " '__package__',\n",
        " '__spec__',\n",
+       " '__vsc_ipynb_file__',\n",
        " '_dh',\n",
        " '_i',\n",
        " '_i1',\n",
@@ -487,7 +488,7 @@
     "matrices (also called design matrices) using the `ModelSpec()`  transform from `ISLP.models`.\n",
     "\n",
     "We  will use the `Boston` housing data set, which is contained in the `ISLP` package.  The `Boston` dataset records  `medv`  (median house value) for $506$ neighborhoods\n",
-    "around Boston.  We will build a regression model to predict  `medv`  using $13$\n",
+    "around Boston.  We will build a regression model to predict  `medv`  using $13$ (should be $12$!)\n",
     "predictors such as  `rmvar`  (average number of rooms per house),\n",
     " `age`  (proportion of owner-occupied units built prior to 1940), and  `lstat`  (percent of\n",
     "households with low socioeconomic status).  We will use `statsmodels` for this\n",
@@ -500,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "d720e2c5",
    "metadata": {
     "execution": {
@@ -519,7 +520,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -529,6 +530,27 @@
     "Boston.columns\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c7636471",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(506, 13)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Boston.shape"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "27b9c8f5",
@@ -2965,6 +2987,11 @@
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -2975,7 +3002,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
diff --git a/Ch05-resample-lab.ipynb b/Ch05-resample-lab.ipynb
index 9fd3ada3..1e2f36cc 100644
--- a/Ch05-resample-lab.ipynb
+++ b/Ch05-resample-lab.ipynb
@@ -1,1314 +1,1859 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dc2d635a",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6dde3cef",
-   "metadata": {},
-   "source": [
-    "# Cross-Validation and the Bootstrap\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb\">\n",
-    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>\n",
-    "\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch05-resample-lab.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9fd4324",
-   "metadata": {},
-   "source": [
-    "In this lab, we explore the resampling techniques covered in this\n",
-    "chapter. Some of the commands in this lab may take a while to run on\n",
-    "your computer.\n",
-    "\n",
-    "We again begin by placing most of our imports at this top level."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f1deb5cc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:13.493284Z",
-     "iopub.status.busy": "2024-06-04T23:19:13.492950Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.143174Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.142882Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import statsmodels.api as sm\n",
-    "from ISLP import load_data\n",
-    "from ISLP.models import (ModelSpec as MS,\n",
-    "                         summarize,\n",
-    "                         poly)\n",
-    "from sklearn.model_selection import train_test_split\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afa08b62",
-   "metadata": {},
-   "source": [
-    "There are several new imports needed for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "268c41b3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.144884Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.144773Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.146541Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.146330Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "from functools import partial\n",
-    "from sklearn.model_selection import \\\n",
-    "     (cross_validate,\n",
-    "      KFold,\n",
-    "      ShuffleSplit)\n",
-    "from sklearn.base import clone\n",
-    "from ISLP.models import sklearn_sm\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c04f8e4",
-   "metadata": {},
-   "source": [
-    "## The Validation Set Approach\n",
-    "We explore the use of the validation set approach in order to estimate\n",
-    "the test error rates that result from fitting various linear models on\n",
-    "the  `Auto`  data set.\n",
-    "\n",
-    "We use the function `train_test_split()` to split\n",
-    "the data into training and validation sets. As there are 392 observations,\n",
-    "we split into two equal sets of size 196 using the\n",
-    "argument `test_size=196`. It is generally a good idea to set a random seed\n",
-    "when performing operations like this that contain an\n",
-    "element of randomness, so that the results obtained can be reproduced\n",
-    "precisely at a later time. We set the random seed of the splitter\n",
-    "with the argument `random_state=0`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "22f44ae0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.147809Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.147730Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.152606Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.152414Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "Auto = load_data('Auto')\n",
-    "Auto_train, Auto_valid = train_test_split(Auto,\n",
-    "                                         test_size=196,\n",
-    "                                         random_state=0)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "318fe69f",
-   "metadata": {},
-   "source": [
-    "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0c32e917",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.153847Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.153757Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.157537Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.157339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "hp_mm = MS(['horsepower'])\n",
-    "X_train = hp_mm.fit_transform(Auto_train)\n",
-    "y_train = Auto_train['mpg']\n",
-    "model = sm.OLS(y_train, X_train)\n",
-    "results = model.fit()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e883b8f",
-   "metadata": {},
-   "source": [
-    "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n",
-    "created using the validation data set. We also calculate the validation MSE of our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "86ce4f85",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.158717Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.158637Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.162177Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.161910Z"
-    }
-   },
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "dc2d635a",
+      "metadata": {
+        "id": "dc2d635a"
+      },
+      "source": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "23.61661706966988"
+      "cell_type": "markdown",
+      "id": "6dde3cef",
+      "metadata": {
+        "id": "6dde3cef"
+      },
+      "source": [
+        "# Cross-Validation and the Bootstrap\n",
+        "\n",
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb\">\n",
+        "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+        "</a>\n",
+        "\n",
+        "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch05-resample-lab.ipynb)"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_valid = hp_mm.transform(Auto_valid)\n",
-    "y_valid = Auto_valid['mpg']\n",
-    "valid_pred = results.predict(X_valid)\n",
-    "np.mean((y_valid - valid_pred)**2)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2ecdee6",
-   "metadata": {},
-   "source": [
-    "Hence our estimate for the validation MSE of  the linear regression\n",
-    "fit is $23.62$.\n",
-    "\n",
-    "We can also estimate the validation error for\n",
-    "higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n",
-    "as a training and test set and returns the MSE on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "50a66a97",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.163466Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.163397Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.165323Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.165076Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def evalMSE(terms,\n",
-    "            response,\n",
-    "            train,\n",
-    "            test):\n",
-    "\n",
-    "   mm = MS(terms)\n",
-    "   X_train = mm.fit_transform(train)\n",
-    "   y_train = train[response]\n",
-    "\n",
-    "   X_test = mm.transform(test)\n",
-    "   y_test = test[response]\n",
-    "\n",
-    "   results = sm.OLS(y_train, X_train).fit()\n",
-    "   test_pred = results.predict(X_test)\n",
-    "\n",
-    "   return np.mean((y_test - test_pred)**2)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a255779c",
-   "metadata": {},
-   "source": [
-    "Let’s use this function to estimate the validation MSE\n",
-    "using linear, quadratic and cubic fits. We use the `enumerate()`  function\n",
-    "here, which gives both the values and indices of objects as one iterates\n",
-    "over a for loop."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d49b6999",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.166563Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.166497Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.177198Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.176975Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([23.61661707, 18.76303135, 18.79694163])"
+      "cell_type": "markdown",
+      "id": "a9fd4324",
+      "metadata": {
+        "id": "a9fd4324"
+      },
+      "source": [
+        "In this lab, we explore the resampling techniques covered in this\n",
+        "chapter. Some of the commands in this lab may take a while to run on\n",
+        "your computer.\n",
+        "\n",
+        "We again begin by placing most of our imports at this top level."
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "MSE = np.zeros(3)\n",
-    "for idx, degree in enumerate(range(1, 4)):\n",
-    "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
-    "                       'mpg',\n",
-    "                       Auto_train,\n",
-    "                       Auto_valid)\n",
-    "MSE\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d7b8fc1",
-   "metadata": {},
-   "source": [
-    "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n",
-    "choose a different training/validation split instead, then we\n",
-    "can expect somewhat different errors on the validation set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dac8bd54",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.178405Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.178321Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.188650Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.188432Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([20.75540796, 16.94510676, 16.97437833])"
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "9d98bbe0",
+        "outputId": "8e06e4f8-d5e6-4001-b6c2-0287941db6f6"
+      },
+      "source": [
+        "%pip install ISLP"
+      ],
+      "id": "9d98bbe0",
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: ISLP in /usr/local/lib/python3.12/dist-packages (0.4.0)\n",
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+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (0.3.2)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (1.20.1)\n",
+            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.12/dist-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (3.10)\n"
+          ]
+        }
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto_train, Auto_valid = train_test_split(Auto,\n",
-    "                                          test_size=196,\n",
-    "                                          random_state=3)\n",
-    "MSE = np.zeros(3)\n",
-    "for idx, degree in enumerate(range(1, 4)):\n",
-    "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
-    "                       'mpg',\n",
-    "                       Auto_train,\n",
-    "                       Auto_valid)\n",
-    "MSE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61f2c12d",
-   "metadata": {},
-   "source": [
-    "Using this split of the observations into a training set and a validation set,\n",
-    "we find that the validation set error rates for the models with linear, quadratic, and cubic terms are $20.76$, $16.95$, and $16.97$, respectively.\n",
-    "\n",
-    "These results are consistent with our previous findings: a model that\n",
-    "predicts `mpg` using a quadratic function of `horsepower`\n",
-    "performs better than a model that involves only a linear function of\n",
-    "`horsepower`, and there is no evidence of an improvement in using a cubic function of `horsepower`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f22daa51",
-   "metadata": {},
-   "source": [
-    "## Cross-Validation\n",
-    "In theory, the cross-validation estimate can be computed for any generalized\n",
-    "linear model.  {}\n",
-    "In practice, however, the simplest way to cross-validate in\n",
-    "Python is to use `sklearn`, which has a different interface or API\n",
-    "than `statsmodels`, the code we have been using to fit GLMs.\n",
-    "\n",
-    "This is a problem which often confronts data scientists: \"I have a function to do task $A$, and need to feed it into something that performs task $B$, so that I can compute $B(A(D))$, where $D$ is my data.\" When $A$ and $B$ don’t naturally speak to each other, this\n",
-    "requires the use of a *wrapper*.\n",
-    "In the `ISLP` package,\n",
-    "we provide \n",
-    "a wrapper, `sklearn_sm()`, that enables us to easily use the cross-validation tools of `sklearn` with\n",
-    "models fit by `statsmodels`.\n",
-    "\n",
-    "The class `sklearn_sm()` \n",
-    "has  as its first argument\n",
-    "a model from `statsmodels`. It can take two additional\n",
-    "optional arguments: `model_str` which can be\n",
-    "used to specify a formula, and `model_args` which should\n",
-    "be a dictionary of additional arguments used when fitting\n",
-    "the model. For example, to fit a logistic regression model\n",
-    "we have to specify a `family` argument. This\n",
-    "is passed as `model_args={'family':sm.families.Binomial()}`.\n",
-    "\n",
-    "Here is our wrapper in action:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "601ae443",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.189993Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.189906Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.876368Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.876129Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24.23151351792924"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "f1deb5cc",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:13.493284Z",
+          "iopub.status.busy": "2024-06-04T23:19:13.492950Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.143174Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.142882Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "f1deb5cc"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import statsmodels.api as sm\n",
+        "from ISLP import load_data\n",
+        "from ISLP.models import (ModelSpec as MS,\n",
+        "                         summarize,\n",
+        "                         poly)\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hp_model = sklearn_sm(sm.OLS,\n",
-    "                      MS(['horsepower']))\n",
-    "X, Y = Auto.drop(columns=['mpg']), Auto['mpg']\n",
-    "cv_results = cross_validate(hp_model,\n",
-    "                            X,\n",
-    "                            Y,\n",
-    "                            cv=Auto.shape[0])\n",
-    "cv_err = np.mean(cv_results['test_score'])\n",
-    "cv_err\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebadc35f",
-   "metadata": {},
-   "source": [
-    "The arguments to `cross_validate()` are as follows: an\n",
-    "object with the appropriate `fit()`, `predict()`,\n",
-    "and `score()` methods,  an\n",
-    "array of features `X` and a response `Y`. \n",
-    "We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n",
-    "$K$ results in $K$-fold cross-validation. We have provided a value \n",
-    "corresponding to the total number of observations, which results in\n",
-    "leave-one-out cross-validation (LOOCV). The `cross_validate()`  function produces a dictionary with several components;\n",
-    "we simply want the cross-validated test score here (MSE), which is estimated to be 24.23."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25f47b99",
-   "metadata": {},
-   "source": [
-    "We can repeat this procedure for increasingly complex polynomial fits.\n",
-    "To automate the process, we again\n",
-    "use a for loop which iteratively fits polynomial\n",
-    "regressions of degree 1 to 5, computes the\n",
-    "associated cross-validation error, and stores it in the $i$th element\n",
-    "of the vector `cv_error`. The variable `d` in the for loop\n",
-    "corresponds to the degree of the polynomial. We begin by initializing the\n",
-    "vector. This command may take a couple of seconds to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "11226c85",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.877800Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.877726Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.384419Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.384193Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([24.23151352, 19.24821312, 19.33498406, 19.42443033, 19.03323827])"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "afa08b62",
+      "metadata": {
+        "id": "afa08b62"
+      },
+      "source": [
+        "There are several new imports needed for this lab."
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv_error = np.zeros(5)\n",
-    "H = np.array(Auto['horsepower'])\n",
-    "M = sklearn_sm(sm.OLS)\n",
-    "for i, d in enumerate(range(1,6)):\n",
-    "    X = np.power.outer(H, np.arange(d+1))\n",
-    "    M_CV = cross_validate(M,\n",
-    "                          X,\n",
-    "                          Y,\n",
-    "                          cv=Auto.shape[0])\n",
-    "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3a920ae",
-   "metadata": {},
-   "source": [
-    "As in Figure~\\ref{Ch5:cvplot}, we see a sharp drop in the estimated test MSE between the linear and\n",
-    "quadratic fits, but then no clear improvement from using higher-degree polynomials.\n",
-    "\n",
-    "Above we introduced the `outer()`  method of the `np.power()`\n",
-    "function.  The `outer()` method is applied to an operation\n",
-    "that has two arguments, such as `add()`, `min()`, or\n",
-    "`power()`.\n",
-    "It has two arrays as\n",
-    "arguments, and then forms a larger\n",
-    "array where the operation is applied to each pair of elements of the\n",
-    "two arrays. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "64b64d97",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.385768Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.385690Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.387686Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.387484Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 5,  7],\n",
-       "       [ 7,  9],\n",
-       "       [11, 13]])"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "268c41b3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.144884Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.144773Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.146541Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.146330Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "268c41b3"
+      },
+      "outputs": [],
+      "source": [
+        "from functools import partial\n",
+        "from sklearn.model_selection import \\\n",
+        "     (cross_validate,\n",
+        "      KFold,\n",
+        "      ShuffleSplit)\n",
+        "from sklearn.base import clone\n",
+        "from ISLP.models import sklearn_sm\n"
       ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = np.array([3, 5, 9])\n",
-    "B = np.array([2, 4])\n",
-    "np.add.outer(A, B)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71385c1b",
-   "metadata": {},
-   "source": [
-    "In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n",
-    "to the above (and is significantly faster). Here we use `KFold()` to partition the data into $K=10$ random groups. We use `random_state` to set a random seed and initialize a vector `cv_error` in which we will store the CV errors corresponding to the\n",
-    "polynomial fits of degrees one to five."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ca0f972f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.389014Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.388934Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.407438Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.407194Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([24.20766449, 19.18533142, 19.27626666, 19.47848403, 19.13720581])"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1c04f8e4",
+      "metadata": {
+        "id": "1c04f8e4"
+      },
+      "source": [
+        "## The Validation Set Approach\n",
+        "We explore the use of the validation set approach in order to estimate\n",
+        "the test error rates that result from fitting various linear models on\n",
+        "the  `Auto`  data set.\n",
+        "\n",
+        "We use the function `train_test_split()` to split\n",
+        "the data into training and validation sets. As there are 392 observations,\n",
+        "we split into two equal sets of size 196 using the\n",
+        "argument `test_size=196`. It is generally a good idea to set a random seed\n",
+        "when performing operations like this that contain an\n",
+        "element of randomness, so that the results obtained can be reproduced\n",
+        "precisely at a later time. We set the random seed of the splitter\n",
+        "with the argument `random_state=0`."
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv_error = np.zeros(5)\n",
-    "cv = KFold(n_splits=10,\n",
-    "           shuffle=True,\n",
-    "           random_state=0) # use same splits for each degree\n",
-    "for i, d in enumerate(range(1,6)):\n",
-    "    X = np.power.outer(H, np.arange(d+1))\n",
-    "    M_CV = cross_validate(M,\n",
-    "                          X,\n",
-    "                          Y,\n",
-    "                          cv=cv)\n",
-    "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b234093",
-   "metadata": {},
-   "source": [
-    "Notice that the computation time is much shorter than that of LOOCV.\n",
-    "(In principle, the computation time for LOOCV for a least squares\n",
-    "linear model should be faster than for $K$-fold CV, due to the\n",
-    "availability of the formula~(\\ref{Ch5:eq:LOOCVform})  for LOOCV;\n",
-    "however, the generic `cross_validate()`  function does not make\n",
-    "use of this formula.)  We still see little evidence that using cubic\n",
-    "or higher-degree polynomial terms leads to a lower test error than simply\n",
-    "using a quadratic fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb4487a4",
-   "metadata": {},
-   "source": [
-    "The `cross_validate()` function is flexible and can take\n",
-    "different splitting mechanisms as an argument. For instance, one can use the `ShuffleSplit()` funtion to implement\n",
-    "the validation set approach just as easily as K-fold cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "080cdb29",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.408750Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.408677Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.413979Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.413762Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([23.61661707])"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "22f44ae0",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.147809Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.147730Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.152606Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.152414Z"
+        },
+        "id": "22f44ae0"
+      },
+      "outputs": [],
+      "source": [
+        "Auto = load_data('Auto')\n",
+        "Auto_train, Auto_valid = train_test_split(Auto,\n",
+        "                                         test_size=196,\n",
+        "                                         random_state=0)\n"
       ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation = ShuffleSplit(n_splits=1,\n",
-    "                          test_size=196,\n",
-    "                          random_state=0)\n",
-    "results = cross_validate(hp_model,\n",
-    "                         Auto.drop(['mpg'], axis=1),\n",
-    "                         Auto['mpg'],\n",
-    "                         cv=validation);\n",
-    "results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2f4b4cf",
-   "metadata": {},
-   "source": [
-    "One can estimate the variability in the test error by running the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "7c46de2b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.415225Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.415158Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.437526Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.437302Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(23.802232661034168, 1.421845094109185)"
+      "cell_type": "markdown",
+      "id": "318fe69f",
+      "metadata": {
+        "id": "318fe69f"
+      },
+      "source": [
+        "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`."
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation = ShuffleSplit(n_splits=10,\n",
-    "                          test_size=196,\n",
-    "                          random_state=0)\n",
-    "results = cross_validate(hp_model,\n",
-    "                         Auto.drop(['mpg'], axis=1),\n",
-    "                         Auto['mpg'],\n",
-    "                         cv=validation)\n",
-    "results['test_score'].mean(), results['test_score'].std()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07165f0e",
-   "metadata": {},
-   "source": [
-    "Note that this standard deviation is not a valid estimate of the\n",
-    "sampling variability of the mean test score or the individual scores, since the randomly-selected training\n",
-    "samples overlap and hence introduce correlations. But it does give an\n",
-    "idea of the Monte Carlo variation\n",
-    "incurred by picking different random folds.\n",
-    "\n",
-    "## The Bootstrap\n",
-    "We illustrate the use of the bootstrap in the simple example\n",
-    " {of Section~\\ref{Ch5:sec:bootstrap},}  as well as on an example involving\n",
-    "estimating the accuracy of the linear regression model on the  `Auto`\n",
-    "data set.\n",
-    "### Estimating the Accuracy of a Statistic of Interest\n",
-    "One of the great advantages of the bootstrap approach is that it can\n",
-    "be applied in almost all situations. No complicated mathematical\n",
-    "calculations are required. While there are several implementations\n",
-    "of the bootstrap in Python, its use for estimating\n",
-    "standard error is simple enough that we write our own function\n",
-    "below for the case when our data is stored\n",
-    "in a dataframe.\n",
-    "\n",
-    "To illustrate the bootstrap, we\n",
-    "start with a simple example.\n",
-    "The  `Portfolio`  data set in the `ISLP` package is described\n",
-    "in Section~\\ref{Ch5:sec:bootstrap}. The goal is to estimate the\n",
-    "sampling variance of the parameter $\\alpha$ given in formula~(\\ref{Ch5:min.var}).  We will\n",
-    "create a function\n",
-    "`alpha_func()`, which takes as input a dataframe `D` assumed\n",
-    "to have columns `X` and `Y`, as well as a\n",
-    "vector `idx` indicating which observations should be used to\n",
-    "estimate \n",
-    "$\\alpha$. The function then outputs the estimate for $\\alpha$ based on\n",
-    "the selected observations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a4b6d9b3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.438786Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.438714Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.441484Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.441268Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "Portfolio = load_data('Portfolio')\n",
-    "def alpha_func(D, idx):\n",
-    "   cov_ = np.cov(D[['X','Y']].loc[idx], rowvar=False)\n",
-    "   return ((cov_[1,1] - cov_[0,1]) /\n",
-    "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d50058e",
-   "metadata": {},
-   "source": [
-    "This function returns an estimate for $\\alpha$\n",
-    "based on applying the minimum\n",
-    "    variance formula (\\ref{Ch5:min.var}) to the observations indexed by\n",
-    "the argument `idx`.  For instance, the following command\n",
-    "estimates $\\alpha$ using all 100 observations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "81498a11",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.442843Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.442765Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.445171Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.444944Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0.57583207459283"
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "0c32e917",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.153847Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.153757Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.157537Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.157339Z"
+        },
+        "id": "0c32e917"
+      },
+      "outputs": [],
+      "source": [
+        "hp_mm = MS(['horsepower'])\n",
+        "X_train = hp_mm.fit_transform(Auto_train)\n",
+        "y_train = Auto_train['mpg']\n",
+        "model = sm.OLS(y_train, X_train)\n",
+        "results = model.fit()\n"
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha_func(Portfolio, range(100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f5d0aab",
-   "metadata": {},
-   "source": [
-    "Next we randomly select\n",
-    "100 observations from `range(100)`, with replacement. This is equivalent\n",
-    "to constructing a new bootstrap data set and recomputing $\\hat{\\alpha}$\n",
-    "based on the new data set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "64fe1cb6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.446422Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.446354Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.448793Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.448579Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6074452469619004"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7e883b8f",
+      "metadata": {
+        "id": "7e883b8f"
+      },
+      "source": [
+        "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n",
+        "created using the validation data set. We also calculate the validation MSE of our model."
       ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rng = np.random.default_rng(0)\n",
-    "alpha_func(Portfolio,\n",
-    "           rng.choice(100,\n",
-    "                      100,\n",
-    "                      replace=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91a635fe",
-   "metadata": {},
-   "source": [
-    "This process can be generalized to create a simple function `boot_SE()` for\n",
-    "computing the bootstrap standard error for arbitrary\n",
-    "functions that take only a data frame as an argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "dd16bbae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.450062Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.449992Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.451958Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.451742Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "def boot_SE(func,\n",
-    "            D,\n",
-    "            n=None,\n",
-    "            B=1000,\n",
-    "            seed=0):\n",
-    "    rng = np.random.default_rng(seed)\n",
-    "    first_, second_ = 0, 0\n",
-    "    n = n or D.shape[0]\n",
-    "    for _ in range(B):\n",
-    "        idx = rng.choice(D.index,\n",
-    "                         n,\n",
-    "                         replace=True)\n",
-    "        value = func(D, idx)\n",
-    "        first_ += value\n",
-    "        second_ += value**2\n",
-    "    return np.sqrt(second_ / B - (first_ / B)**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac4e17ed",
-   "metadata": {},
-   "source": [
-    "Notice the use of `_` as a loop variable in `for _ in range(B)`. This is often used if the value of the counter is\n",
-    "unimportant and simply makes sure  the loop is executed `B` times.\n",
-    "\n",
-    "Let’s use our function to evaluate the accuracy of our\n",
-    "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b42b4585",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.453190Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.453118Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.631597Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.631370Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0.09118176521277699"
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "86ce4f85",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.158717Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.158637Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.162177Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.161910Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "86ce4f85",
+        "outputId": "a3836524-6514-4704-c570-1edf94e8e6fb"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(23.61661706966988)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ],
+      "source": [
+        "X_valid = hp_mm.transform(Auto_valid)\n",
+        "y_valid = Auto_valid['mpg']\n",
+        "valid_pred = results.predict(X_valid)\n",
+        "np.mean((y_valid - valid_pred)**2)\n"
       ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha_SE = boot_SE(alpha_func,\n",
-    "                   Portfolio,\n",
-    "                   B=1000,\n",
-    "                   seed=0)\n",
-    "alpha_SE\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c5464d7",
-   "metadata": {},
-   "source": [
-    "The final output shows that the bootstrap estimate for ${\\rm SE}(\\hat{\\alpha})$ is $0.0912$.\n",
-    "\n",
-    "### Estimating the Accuracy of a Linear Regression Model\n",
-    "The bootstrap approach can be used to assess the variability of the\n",
-    "coefficient estimates and predictions from a statistical learning\n",
-    "method. Here we use the bootstrap approach in order to assess the\n",
-    "variability of the estimates for $\\beta_0$ and $\\beta_1$, the\n",
-    "intercept and slope terms for the linear regression model that uses\n",
-    "`horsepower` to predict `mpg` in the  `Auto`  data set. We\n",
-    "will compare the estimates obtained using the bootstrap to those\n",
-    "obtained using the formulas for ${\\rm SE}(\\hat{\\beta}_0)$ and\n",
-    "${\\rm SE}(\\hat{\\beta}_1)$ described in Section~\\ref{Ch3:secoefsec}.\n",
-    "\n",
-    "To use our `boot_SE()` function, we must write a function (its\n",
-    "first argument)\n",
-    "that takes a data frame `D` and indices `idx`\n",
-    "as its only arguments. But here we want to bootstrap a specific\n",
-    "regression model, specified by a model formula and data. We show how\n",
-    "to do this in a few simple steps.\n",
-    "\n",
-    "We start by writing a generic\n",
-    "function `boot_OLS()` for bootstrapping a regression model that takes a formula to\n",
-    "define the corresponding regression. We use the `clone()` function to\n",
-    "make a copy of the formula that can be refit to the new dataframe. This means\n",
-    "that any derived features such as those defined by `poly()`\n",
-    "(which we will see shortly),\n",
-    "will be re-fit on the resampled data frame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "6bc11784",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.632802Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.632725Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.634450Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.634222Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "def boot_OLS(model_matrix, response, D, idx):\n",
-    "    D_ = D.loc[idx]\n",
-    "    Y_ = D_[response]\n",
-    "    X_ = clone(model_matrix).fit_transform(D_)\n",
-    "    return sm.OLS(Y_, X_).fit().params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a6ea3ce",
-   "metadata": {},
-   "source": [
-    "This is not quite what is needed as the first argument to\n",
-    "`boot_SE()`. The first two arguments which specify the model will not change in the\n",
-    "bootstrap process, and we would like to *freeze* them.   The\n",
-    "function `partial()` from the `functools` module  does precisely this: it takes a function\n",
-    "as an argument, and freezes some of its arguments, starting from the\n",
-    "left. We use it to freeze the first two model-formula arguments of `boot_OLS()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "740cd50c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.635644Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.635575Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.637097Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.636867Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed6d19e2",
-   "metadata": {},
-   "source": [
-    "Typing `hp_func?` will show that it has two arguments `D`\n",
-    "and `idx` --- it is a version of `boot_OLS()` with the first\n",
-    "two arguments frozen --- and hence is ideal as the first argument for `boot_SE()`.\n",
-    "\n",
-    "The `hp_func()` function can now be used in order to create\n",
-    "bootstrap estimates for the intercept and slope terms by randomly\n",
-    "sampling from among the observations with replacement. We first\n",
-    "demonstrate its utility on 10 bootstrap samples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "ffb3ec50",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.638287Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.638220Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.656475Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.656261Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[39.12226577, -0.1555926 ],\n",
-       "       [37.18648613, -0.13915813],\n",
-       "       [37.46989244, -0.14112749],\n",
-       "       [38.56723252, -0.14830116],\n",
-       "       [38.95495707, -0.15315141],\n",
-       "       [39.12563927, -0.15261044],\n",
-       "       [38.45763251, -0.14767251],\n",
-       "       [38.43372587, -0.15019447],\n",
-       "       [37.87581142, -0.1409544 ],\n",
-       "       [37.95949036, -0.1451333 ]])"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f2ecdee6",
+      "metadata": {
+        "id": "f2ecdee6"
+      },
+      "source": [
+        "Hence our estimate for the validation MSE of  the linear regression\n",
+        "fit is $23.62$.\n",
+        "\n",
+        "We can also estimate the validation error for\n",
+        "higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n",
+        "as a training and test set and returns the MSE on the test set."
       ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rng = np.random.default_rng(0)\n",
-    "np.array([hp_func(Auto,\n",
-    "          rng.choice(Auto.index,\n",
-    "                     392,\n",
-    "                     replace=True)) for _ in range(10)])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6d09d96",
-   "metadata": {},
-   "source": [
-    "Next, we use the `boot_SE()` {}  function to compute the standard\n",
-    "errors of 1,000 bootstrap estimates for the intercept and slope terms."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "7d561f70",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.657733Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.657659Z",
-     "iopub.status.idle": "2024-06-04T23:19:17.204871Z",
-     "shell.execute_reply": "2024-06-04T23:19:17.204614Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept     0.731176\n",
-       "horsepower    0.006092\n",
-       "dtype: float64"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "50a66a97",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.163466Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.163397Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.165323Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.165076Z"
+        },
+        "id": "50a66a97"
+      },
+      "outputs": [],
+      "source": [
+        "def evalMSE(terms,\n",
+        "            response,\n",
+        "            train,\n",
+        "            test):\n",
+        "\n",
+        "   mm = MS(terms)\n",
+        "   X_train = mm.fit_transform(train)\n",
+        "   y_train = train[response]\n",
+        "\n",
+        "   X_test = mm.transform(test)\n",
+        "   y_test = test[response]\n",
+        "\n",
+        "   results = sm.OLS(y_train, X_train).fit()\n",
+        "   test_pred = results.predict(X_test)\n",
+        "\n",
+        "   return np.mean((y_test - test_pred)**2)\n"
       ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hp_se = boot_SE(hp_func,\n",
-    "                Auto,\n",
-    "                B=1000,\n",
-    "                seed=10)\n",
-    "hp_se\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a834f240",
-   "metadata": {},
-   "source": [
-    "This indicates that the bootstrap estimate for ${\\rm SE}(\\hat{\\beta}_0)$ is\n",
-    "0.85, and that the bootstrap\n",
-    "estimate for ${\\rm SE}(\\hat{\\beta}_1)$ is\n",
-    "0.0074.  As discussed in\n",
-    "Section~\\ref{Ch3:secoefsec}, standard formulas can be used to compute\n",
-    "the standard errors for the regression coefficients in a linear\n",
-    "model. These can be obtained using the `summarize()`  function\n",
-    "from `ISLP.sm`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "3888aa0a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:17.206302Z",
-     "iopub.status.busy": "2024-06-04T23:19:17.206223Z",
-     "iopub.status.idle": "2024-06-04T23:19:17.221631Z",
-     "shell.execute_reply": "2024-06-04T23:19:17.221444Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept     0.717\n",
-       "horsepower    0.006\n",
-       "Name: std err, dtype: float64"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a255779c",
+      "metadata": {
+        "id": "a255779c"
+      },
+      "source": [
+        "Let’s use this function to estimate the validation MSE\n",
+        "using linear, quadratic and cubic fits. We use the `enumerate()`  function\n",
+        "here, which gives both the values and indices of objects as one iterates\n",
+        "over a for loop."
       ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hp_model.fit(Auto, Auto['mpg'])\n",
-    "model_se = summarize(hp_model.results_)['std err']\n",
-    "model_se\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aefc0575",
-   "metadata": {},
-   "source": [
-    "The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n",
-    "obtained using the formulas  from Section~\\ref{Ch3:secoefsec}  are\n",
-    "0.717 for the\n",
-    "intercept and\n",
-    "0.006 for the\n",
-    "slope. Interestingly, these are somewhat different from the estimates\n",
-    "obtained using the bootstrap.  Does this indicate a problem with the\n",
-    "bootstrap? In fact, it suggests the opposite.  Recall that the\n",
-    "standard formulas given in\n",
-    " {Equation~\\ref{Ch3:se.eqn} on page~\\pageref{Ch3:se.eqn}}\n",
-    "rely on certain assumptions. For example,\n",
-    "they depend on the unknown parameter $\\sigma^2$, the noise\n",
-    "variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
-    "formula for the standard errors do not rely on the linear model being\n",
-    "correct, the estimate for $\\sigma^2$ does.  We see\n",
-    " {in Figure~\\ref{Ch3:polyplot} on page~\\pageref{Ch3:polyplot}}  that there is\n",
-    "a non-linear relationship in the data, and so the residuals from a\n",
-    "linear fit will be inflated, and so will $\\hat{\\sigma}^2$.  Secondly,\n",
-    "the standard formulas assume (somewhat unrealistically) that the $x_i$\n",
-    "are fixed, and all the variability comes from the variation in the\n",
-    "errors $\\epsilon_i$.  The bootstrap approach does not rely on any of\n",
-    "these assumptions, and so it is likely giving a more accurate estimate\n",
-    "of the standard errors of $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$ than\n",
-    "the results from `sm.OLS`.\n",
-    "\n",
-    "Below we compute the bootstrap standard error estimates and the\n",
-    "standard linear regression estimates that result from fitting the\n",
-    "quadratic model to the data. Since this model provides a good fit to\n",
-    "the data (Figure~\\ref{Ch3:polyplot}), there is now a better\n",
-    "correspondence between the bootstrap estimates and the standard\n",
-    "estimates of ${\\rm SE}(\\hat{\\beta}_0)$, ${\\rm SE}(\\hat{\\beta}_1)$ and\n",
-    "${\\rm SE}(\\hat{\\beta}_2)$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "acc3e32c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:17.222887Z",
-     "iopub.status.busy": "2024-06-04T23:19:17.222785Z",
-     "iopub.status.idle": "2024-06-04T23:19:19.351574Z",
-     "shell.execute_reply": "2024-06-04T23:19:19.351317Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept                                  1.538641\n",
-       "poly(horsepower, degree=2, raw=True)[0]    0.024696\n",
-       "poly(horsepower, degree=2, raw=True)[1]    0.000090\n",
-       "dtype: float64"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "d49b6999",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.166563Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.166497Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.177198Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.176975Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d49b6999",
+        "outputId": "2ca4750b-f5ba-46e6-f8f5-ec36dde48552"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([23.61661707, 18.76303135, 18.79694163])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ],
+      "source": [
+        "MSE = np.zeros(3)\n",
+        "for idx, degree in enumerate(range(1, 4)):\n",
+        "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
+        "                       'mpg',\n",
+        "                       Auto_train,\n",
+        "                       Auto_valid)\n",
+        "MSE\n"
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "quad_model = MS([poly('horsepower', 2, raw=True)])\n",
-    "quad_func = partial(boot_OLS,\n",
-    "                    quad_model,\n",
-    "                    'mpg')\n",
-    "boot_SE(quad_func, Auto, B=1000)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8a2fd2b",
-   "metadata": {},
-   "source": [
-    "We  compare the results to the standard errors computed using `sm.OLS()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "dca5340c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:19.352904Z",
-     "iopub.status.busy": "2024-06-04T23:19:19.352827Z",
-     "iopub.status.idle": "2024-06-04T23:19:19.360147Z",
-     "shell.execute_reply": "2024-06-04T23:19:19.359948Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept                                  1.800\n",
-       "poly(horsepower, degree=2, raw=True)[0]    0.031\n",
-       "poly(horsepower, degree=2, raw=True)[1]    0.000\n",
-       "Name: std err, dtype: float64"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9d7b8fc1",
+      "metadata": {
+        "id": "9d7b8fc1"
+      },
+      "source": [
+        "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n",
+        "choose a different training/validation split instead, then we\n",
+        "can expect somewhat different errors on the validation set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "dac8bd54",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.178405Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.178321Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.188650Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.188432Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dac8bd54",
+        "outputId": "ed13c196-a490-4e99-dd84-52295eca6e9e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([20.75540796, 16.94510676, 16.97437833])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "Auto_train, Auto_valid = train_test_split(Auto,\n",
+        "                                          test_size=196,\n",
+        "                                          random_state=3)\n",
+        "MSE = np.zeros(3)\n",
+        "for idx, degree in enumerate(range(1, 4)):\n",
+        "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
+        "                       'mpg',\n",
+        "                       Auto_train,\n",
+        "                       Auto_valid)\n",
+        "MSE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "61f2c12d",
+      "metadata": {
+        "id": "61f2c12d"
+      },
+      "source": [
+        "Using this split of the observations into a training set and a validation set,\n",
+        "we find that the validation set error rates for the models with linear, quadratic, and cubic terms are $20.76$, $16.95$, and $16.97$, respectively.\n",
+        "\n",
+        "These results are consistent with our previous findings: a model that\n",
+        "predicts `mpg` using a quadratic function of `horsepower`\n",
+        "performs better than a model that involves only a linear function of\n",
+        "`horsepower`, and there is no evidence of an improvement in using a cubic function of `horsepower`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f22daa51",
+      "metadata": {
+        "id": "f22daa51"
+      },
+      "source": [
+        "## Cross-Validation\n",
+        "In theory, the cross-validation estimate can be computed for any generalized\n",
+        "linear model.  {}\n",
+        "In practice, however, the simplest way to cross-validate in\n",
+        "Python is to use `sklearn`, which has a different interface or API\n",
+        "than `statsmodels`, the code we have been using to fit GLMs.\n",
+        "\n",
+        "This is a problem which often confronts data scientists: \"I have a function to do task $A$, and need to feed it into something that performs task $B$, so that I can compute $B(A(D))$, where $D$ is my data.\" When $A$ and $B$ don’t naturally speak to each other, this\n",
+        "requires the use of a *wrapper*.\n",
+        "In the `ISLP` package,\n",
+        "we provide\n",
+        "a wrapper, `sklearn_sm()`, that enables us to easily use the cross-validation tools of `sklearn` with\n",
+        "models fit by `statsmodels`.\n",
+        "\n",
+        "The class `sklearn_sm()`\n",
+        "has  as its first argument\n",
+        "a model from `statsmodels`. It can take two additional\n",
+        "optional arguments: `model_str` which can be\n",
+        "used to specify a formula, and `model_args` which should\n",
+        "be a dictionary of additional arguments used when fitting\n",
+        "the model. For example, to fit a logistic regression model\n",
+        "we have to specify a `family` argument. This\n",
+        "is passed as `model_args={'family':sm.families.Binomial()}`.\n",
+        "\n",
+        "Here is our wrapper in action:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "601ae443",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.189993Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.189906Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.876368Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.876129Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "601ae443",
+        "outputId": "970d021e-04c2-454d-9672-6f1d554c5395"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(24.23151351792922)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "hp_model = sklearn_sm(sm.OLS,\n",
+        "                      MS(['horsepower']))\n",
+        "X, Y = Auto.drop(columns=['mpg']), Auto['mpg']\n",
+        "cv_results = cross_validate(hp_model,\n",
+        "                            X,\n",
+        "                            Y,\n",
+        "                            cv=Auto.shape[0])\n",
+        "cv_err = np.mean(cv_results['test_score'])\n",
+        "cv_err\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(cv_results.keys())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pOpPEckGFmw7",
+        "outputId": "b6fc592b-ef26-47c0-ee26-bb092f69e9fa"
+      },
+      "id": "pOpPEckGFmw7",
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dict_keys(['fit_time', 'score_time', 'test_score'])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ebadc35f",
+      "metadata": {
+        "id": "ebadc35f"
+      },
+      "source": [
+        "The arguments to `cross_validate()` are as follows: an\n",
+        "object with the appropriate `fit()`, `predict()`,\n",
+        "and `score()` methods,  an\n",
+        "array of features `X` and a response `Y`.\n",
+        "We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n",
+        "$K$ results in $K$-fold cross-validation. We have provided a value\n",
+        "corresponding to the total number of observations, which results in\n",
+        "leave-one-out cross-validation (LOOCV). The `cross_validate()`  function produces a dictionary with several components;\n",
+        "we simply want the cross-validated test score here (MSE), which is estimated to be 24.23."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "25f47b99",
+      "metadata": {
+        "id": "25f47b99"
+      },
+      "source": [
+        "We can repeat this procedure for increasingly complex polynomial fits.\n",
+        "To automate the process, we again\n",
+        "use a for loop which iteratively fits polynomial\n",
+        "regressions of degree 1 to 5, computes the\n",
+        "associated cross-validation error, and stores it in the $i$th element\n",
+        "of the vector `cv_error`. The variable `d` in the for loop\n",
+        "corresponds to the degree of the polynomial. We begin by initializing the\n",
+        "vector. This command may take a couple of seconds to run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "11226c85",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.877800Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.877726Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.384419Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.384193Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "11226c85",
+        "outputId": "524948b7-9546-4dca-d81c-1abae32d36bc"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "max_degree = 5\n",
+        "cv_error = np.zeros(max_degree)\n",
+        "H = np.array(Auto['horsepower'])\n",
+        "M = sklearn_sm(sm.OLS)\n",
+        "for i, d in enumerate(range(1,max_degree+1)):\n",
+        "    X = np.power.outer(H, np.arange(d+1))# creates a polynomial input with\n",
+        "    #powers of horsepower extending to d incl constant 1 too!\n",
+        "    M_CV = cross_validate(M,\n",
+        "                          X,\n",
+        "                          Y,\n",
+        "                          cv=Auto.shape[0])\n",
+        "    cv_error[i] = np.mean(M_CV['test_score'])\n",
+        "cv_error\n",
+        "from matplotlib import pyplot as plt\n",
+        "plt.plot(range(1, max_degree + 1), cv_error, 'o-')\n",
+        "plt.xlabel('degree'); plt.ylabel('CV_error')\n",
+        "plt.xticks(range(1, max_degree + 1))\n",
+        "plt.grid()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cv_error"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IWj_rOzIK_4i",
+        "outputId": "7a2d696b-4705-451f-afb7-3e0ce96ddab4"
+      },
+      "id": "IWj_rOzIK_4i",
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([24.23151352, 19.24821312, 19.33498406, 19.42443029, 19.03320648])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a3a920ae",
+      "metadata": {
+        "id": "a3a920ae"
+      },
+      "source": [
+        "As in Figure~\\ref{Ch5:cvplot}, we see a sharp drop in the estimated test MSE between the linear and\n",
+        "quadratic fits, but then no clear improvement from using higher-degree polynomials.\n",
+        "\n",
+        "Above we introduced the `outer()`  method of the `np.power()`\n",
+        "function.  The `outer()` method is applied to an operation\n",
+        "that has two arguments, such as `add()`, `min()`, or\n",
+        "`power()`.\n",
+        "It has two arrays as\n",
+        "arguments, and then forms a larger\n",
+        "array where the operation is applied to each pair of elements of the\n",
+        "two arrays."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "64b64d97",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.385768Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.385690Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.387686Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.387484Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "64b64d97",
+        "outputId": "bc99d234-b96a-405e-908e-395d228e6be6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(392, 6)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "source": [
+        "A = np.array([3, 5, 9])\n",
+        "B = np.array([2, 4])\n",
+        "np.power.outer(A, B)\n",
+        "np.zeros(H.shape)\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "71385c1b",
+      "metadata": {
+        "id": "71385c1b"
+      },
+      "source": [
+        "In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n",
+        "to the above (and is significantly faster). Here we use `KFold()` to partition the data into $K=10$ random groups. We use `random_state` to set a random seed and initialize a vector `cv_error` in which we will store the CV errors corresponding to the\n",
+        "polynomial fits of degrees one to five."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "ca0f972f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.389014Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.388934Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.407438Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.407194Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "ca0f972f",
+        "outputId": "21148ee0-c8b5-4510-9a16-ac279fb7803c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "cv = KFold(n_splits=10,\n",
+        "           shuffle=True,\n",
+        "           random_state=0) # use same splits for each degree\n",
+        "cv_error = np.zeros(max_degree)\n",
+        "H = np.array(Auto['horsepower'])\n",
+        "M = sklearn_sm(sm.OLS)\n",
+        "for i, d in enumerate(range(1,max_degree+1)):\n",
+        "    X = np.power.outer(H, np.arange(d+1))# creates a polynomial input with\n",
+        "    #powers of horsepower extending to d incl constant 1 too!\n",
+        "    M_CV = cross_validate(M,\n",
+        "                          X,\n",
+        "                          Y,\n",
+        "                          cv=Auto.shape[0])\n",
+        "    cv_error[i] = np.mean(M_CV['test_score'])\n",
+        "cv_error\n",
+        "from matplotlib import pyplot as plt\n",
+        "plt.plot(range(1, max_degree + 1), cv_error, 'o-')\n",
+        "plt.xlabel('degree'); plt.ylabel('CV_error')\n",
+        "plt.xticks(range(1, max_degree + 1))\n",
+        "plt.grid()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8b234093",
+      "metadata": {
+        "id": "8b234093"
+      },
+      "source": [
+        "Notice that the computation time is much shorter than that of LOOCV.\n",
+        "(In principle, the computation time for LOOCV for a least squares\n",
+        "linear model should be faster than for $K$-fold CV, due to the\n",
+        "availability of the formula~(\\ref{Ch5:eq:LOOCVform})  for LOOCV;\n",
+        "however, the generic `cross_validate()`  function does not make\n",
+        "use of this formula.)  We still see little evidence that using cubic\n",
+        "or higher-degree polynomial terms leads to a lower test error than simply\n",
+        "using a quadratic fit."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fb4487a4",
+      "metadata": {
+        "id": "fb4487a4"
+      },
+      "source": [
+        "The `cross_validate()` function is flexible and can take\n",
+        "different splitting mechanisms as an argument. For instance, one can use the `ShuffleSplit()` funtion to implement\n",
+        "the validation set approach just as easily as K-fold cross-validation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "080cdb29",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.408750Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.408677Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.413979Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.413762Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "080cdb29",
+        "outputId": "b10724e1-4d9b-4973-fa80-2eb2f726e74e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([23.61661707])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "validation = ShuffleSplit(n_splits=1,\n",
+        "                          test_size=196,\n",
+        "                          random_state=0)\n",
+        "results = cross_validate(hp_model,\n",
+        "                         Auto.drop(['mpg'], axis=1),\n",
+        "                         Auto['mpg'],\n",
+        "                         cv=validation);\n",
+        "results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b2f4b4cf",
+      "metadata": {
+        "id": "b2f4b4cf"
+      },
+      "source": [
+        "One can estimate the variability in the test error by running the following:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "7c46de2b",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.415225Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.415158Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.437526Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.437302Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7c46de2b",
+        "outputId": "4a4b0f0c-a859-468e-e8dc-1ac1e0293475"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(np.float64(23.802232661034168), np.float64(1.4218450941091842))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "validation = ShuffleSplit(n_splits=10,\n",
+        "                          test_size=196,\n",
+        "                          random_state=0)\n",
+        "results = cross_validate(hp_model,\n",
+        "                         Auto.drop(['mpg'], axis=1),\n",
+        "                         Auto['mpg'],\n",
+        "                         cv=validation)\n",
+        "results['test_score'].mean(), results['test_score'].std()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results['test_score']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DbgSLDjbMmQQ",
+        "outputId": "16093914-0735-49f1-c1c3-4b312002e2e4"
+      },
+      "id": "DbgSLDjbMmQQ",
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([23.61661707, 22.96552529, 23.43853845, 21.72781699, 22.79416823,\n",
+              "       23.09191932, 23.69196999, 23.90184611, 26.53545818, 26.258467  ])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "07165f0e",
+      "metadata": {
+        "id": "07165f0e"
+      },
+      "source": [
+        "Note that this standard deviation is not a valid estimate of the\n",
+        "sampling variability of the mean test score or the individual scores, since the randomly-selected training\n",
+        "samples overlap and hence introduce correlations. But it does give an\n",
+        "idea of the Monte Carlo variation\n",
+        "incurred by picking different random folds.\n",
+        "\n",
+        "## The Bootstrap\n",
+        "We illustrate the use of the bootstrap in the simple example\n",
+        " {of Section~\\ref{Ch5:sec:bootstrap},}  as well as on an example involving\n",
+        "estimating the accuracy of the linear regression model on the  `Auto`\n",
+        "data set.\n",
+        "### Estimating the Accuracy of a Statistic of Interest\n",
+        "One of the great advantages of the bootstrap approach is that it can\n",
+        "be applied in almost all situations. No complicated mathematical\n",
+        "calculations are required. While there are several implementations\n",
+        "of the bootstrap in Python, its use for estimating\n",
+        "standard error is simple enough that we write our own function\n",
+        "below for the case when our data is stored\n",
+        "in a dataframe.\n",
+        "\n",
+        "To illustrate the bootstrap, we\n",
+        "start with a simple example.\n",
+        "The  `Portfolio`  data set in the `ISLP` package is described\n",
+        "in Section~\\ref{Ch5:sec:bootstrap}. The goal is to estimate the\n",
+        "sampling variance of the parameter $\\alpha$ given in formula~(\\ref{Ch5:min.var}).  We will\n",
+        "create a function\n",
+        "`alpha_func()`, which takes as input a dataframe `D` assumed\n",
+        "to have columns `X` and `Y`, as well as a\n",
+        "vector `idx` indicating which observations should be used to\n",
+        "estimate\n",
+        "$\\alpha$. The function then outputs the estimate for $\\alpha$ based on\n",
+        "the selected observations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "a4b6d9b3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.438786Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.438714Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.441484Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.441268Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "a4b6d9b3"
+      },
+      "outputs": [],
+      "source": [
+        "Portfolio = load_data('Portfolio')\n",
+        "def alpha_func(D, idx):\n",
+        "   cov_ = np.cov(D[['X','Y']].loc[idx], rowvar=False)\n",
+        "   return ((cov_[1,1] - cov_[0,1]) /\n",
+        "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Portfolio.shape\n",
+        "Portfolio.index"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "phfGMU7ImR8l",
+        "outputId": "b4a9b81d-008a-4922-98ea-59f18065ca80"
+      },
+      "id": "phfGMU7ImR8l",
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=100, step=1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9d50058e",
+      "metadata": {
+        "id": "9d50058e"
+      },
+      "source": [
+        "This function returns an estimate for $\\alpha$\n",
+        "based on applying the minimum\n",
+        "    variance formula (\\ref{Ch5:min.var}) to the observations indexed by\n",
+        "the argument `idx`.  For instance, the following command\n",
+        "estimates $\\alpha$ using all 100 observations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "81498a11",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.442843Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.442765Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.445171Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.444944Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "81498a11",
+        "outputId": "c5c8c84c-7819-490e-d913-12f4d9175680"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(0.57583207459283)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "alpha_func(Portfolio, range(100))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4f5d0aab",
+      "metadata": {
+        "id": "4f5d0aab"
+      },
+      "source": [
+        "Next we randomly select\n",
+        "100 observations from `range(100)`, with replacement. This is equivalent\n",
+        "to constructing a new bootstrap data set and recomputing $\\hat{\\alpha}$\n",
+        "based on the new data set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "64fe1cb6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.446422Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.446354Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.448793Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.448579Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "64fe1cb6",
+        "outputId": "3fb7207a-f76b-4a6a-8900-ec4a19a882ae"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(0.6074452469619004)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "rng = np.random.default_rng(0)\n",
+        "alpha_func(Portfolio,\n",
+        "           rng.choice(a = 100,\n",
+        "                      size = 100,\n",
+        "                      replace = True))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rng.choice?"
+      ],
+      "metadata": {
+        "id": "DSLHu7A8mprs"
+      },
+      "id": "DSLHu7A8mprs",
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "91a635fe",
+      "metadata": {
+        "id": "91a635fe"
+      },
+      "source": [
+        "This process can be generalized to create a simple function `boot_SE()` for\n",
+        "computing the bootstrap standard error for arbitrary\n",
+        "functions that take only a data frame as an argument."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "dd16bbae",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.450062Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.449992Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.451958Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.451742Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "dd16bbae"
+      },
+      "outputs": [],
+      "source": [
+        "def boot_SE(func,\n",
+        "            D,\n",
+        "            n=None,\n",
+        "            B=1000,\n",
+        "            seed=0):\n",
+        "    rng = np.random.default_rng(seed)\n",
+        "    first_, second_ = 0, 0\n",
+        "    n = n or D.shape[0]\n",
+        "    for _ in range(B):\n",
+        "        idx = rng.choice(a = D.index,\n",
+        "                         size = n,\n",
+        "                         replace=True)\n",
+        "        value = func(D, idx)\n",
+        "        first_ += value\n",
+        "        second_ += value**2\n",
+        "    return np.sqrt(second_ / B - (first_ / B)**2)# E[X^2] - (E[X])^2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ac4e17ed",
+      "metadata": {
+        "id": "ac4e17ed"
+      },
+      "source": [
+        "Notice the use of `_` as a loop variable in `for _ in range(B)`. This is often used if the value of the counter is\n",
+        "unimportant and simply makes sure  the loop is executed `B` times.\n",
+        "\n",
+        "Let’s use our function to evaluate the accuracy of our\n",
+        "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "b42b4585",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.453190Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.453118Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.631597Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.631370Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b42b4585",
+        "outputId": "7f9f9640-8fc1-4b60-d987-a98ef08f7824"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(0.09118176521277699)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "alpha_SE = boot_SE(alpha_func,\n",
+        "                   Portfolio,\n",
+        "                   B=1000,\n",
+        "                   seed=0)\n",
+        "alpha_SE\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6c5464d7",
+      "metadata": {
+        "id": "6c5464d7"
+      },
+      "source": [
+        "The final output shows that the bootstrap estimate for ${\\rm SE}(\\hat{\\alpha})$ is $0.0912$.\n",
+        "\n",
+        "### Estimating the Accuracy of a Linear Regression Model\n",
+        "The bootstrap approach can be used to assess the variability of the\n",
+        "coefficient estimates and predictions from a statistical learning\n",
+        "method. Here we use the bootstrap approach in order to assess the\n",
+        "variability of the estimates for $\\beta_0$ and $\\beta_1$, the\n",
+        "intercept and slope terms for the linear regression model that uses\n",
+        "`horsepower` to predict `mpg` in the  `Auto`  data set. We\n",
+        "will compare the estimates obtained using the bootstrap to those\n",
+        "obtained using the formulas for ${\\rm SE}(\\hat{\\beta}_0)$ and\n",
+        "${\\rm SE}(\\hat{\\beta}_1)$ described in Section~\\ref{Ch3:secoefsec}.\n",
+        "\n",
+        "To use our `boot_SE()` function, we must write a function (its\n",
+        "first argument)\n",
+        "that takes a data frame `D` and indices `idx`\n",
+        "as its only arguments. But here we want to bootstrap a specific\n",
+        "regression model, specified by a model formula and data. We show how\n",
+        "to do this in a few simple steps.\n",
+        "\n",
+        "We start by writing a generic\n",
+        "function `boot_OLS()` for bootstrapping a regression model that takes a formula to\n",
+        "define the corresponding regression. We use the `clone()` function to\n",
+        "make a copy of the formula that can be refit to the new dataframe. This means\n",
+        "that any derived features such as those defined by `poly()`\n",
+        "(which we will see shortly),\n",
+        "will be re-fit on the resampled data frame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "6bc11784",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.632802Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.632725Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.634450Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.634222Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "6bc11784"
+      },
+      "outputs": [],
+      "source": [
+        "def boot_OLS(model_matrix, response, D, idx):\n",
+        "    D_ = D.loc[idx]\n",
+        "    Y_ = D_[response]\n",
+        "    X_ = clone(model_matrix).fit_transform(D_)\n",
+        "    return sm.OLS(Y_, X_).fit().params"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2a6ea3ce",
+      "metadata": {
+        "id": "2a6ea3ce"
+      },
+      "source": [
+        "This is not quite what is needed as the first argument to\n",
+        "`boot_SE()`. The first two arguments which specify the model will not change in the\n",
+        "bootstrap process, and we would like to *freeze* them.   The\n",
+        "function `partial()` from the `functools` module  does precisely this: it takes a function\n",
+        "as an argument, and freezes some of its arguments, starting from the\n",
+        "left. We use it to freeze the first two model-formula arguments of `boot_OLS()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "740cd50c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.635644Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.635575Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.637097Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.636867Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "740cd50c"
+      },
+      "outputs": [],
+      "source": [
+        "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ed6d19e2",
+      "metadata": {
+        "id": "ed6d19e2"
+      },
+      "source": [
+        "Typing `hp_func?` will show that it has two arguments `D`\n",
+        "and `idx` --- it is a version of `boot_OLS()` with the first\n",
+        "two arguments frozen --- and hence is ideal as the first argument for `boot_SE()`.\n",
+        "\n",
+        "The `hp_func()` function can now be used in order to create\n",
+        "bootstrap estimates for the intercept and slope terms by randomly\n",
+        "sampling from among the observations with replacement. We first\n",
+        "demonstrate its utility on 10 bootstrap samples."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "ffb3ec50",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.638287Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.638220Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.656475Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.656261Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ffb3ec50",
+        "outputId": "fdf48a72-69ab-4885-94cd-5234fc87534f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[39.12226577, -0.1555926 ],\n",
+              "       [37.18648613, -0.13915813],\n",
+              "       [37.46989244, -0.14112749],\n",
+              "       [38.56723252, -0.14830116],\n",
+              "       [38.95495707, -0.15315141],\n",
+              "       [39.12563927, -0.15261044],\n",
+              "       [38.45763251, -0.14767251],\n",
+              "       [38.43372587, -0.15019447],\n",
+              "       [37.87581142, -0.1409544 ],\n",
+              "       [37.95949036, -0.1451333 ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "rng = np.random.default_rng(0)\n",
+        "np.array([hp_func(Auto,\n",
+        "          rng.choice(Auto.index,\n",
+        "                     392,\n",
+        "                     replace=True)) for _ in range(10)])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c6d09d96",
+      "metadata": {
+        "id": "c6d09d96"
+      },
+      "source": [
+        "Next, we use the `boot_SE()` {}  function to compute the standard\n",
+        "errors of 1,000 bootstrap estimates for the intercept and slope terms."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "7d561f70",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.657733Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.657659Z",
+          "iopub.status.idle": "2024-06-04T23:19:17.204871Z",
+          "shell.execute_reply": "2024-06-04T23:19:17.204614Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "7d561f70",
+        "outputId": "5b458383-be94-4ffe-8e5b-09d49a96e6d5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept     0.731176\n",
+              "horsepower    0.006092\n",
+              "dtype: float64"
+            ],
+            "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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>0.731176</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>horsepower</th>\n",
+              "      <td>0.006092</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "hp_se = boot_SE(hp_func,\n",
+        "                Auto,\n",
+        "                B=1000,\n",
+        "                seed=10)\n",
+        "hp_se\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a834f240",
+      "metadata": {
+        "id": "a834f240"
+      },
+      "source": [
+        "This indicates that the bootstrap estimate for ${\\rm SE}(\\hat{\\beta}_0)$ is\n",
+        "0.85, and that the bootstrap\n",
+        "estimate for ${\\rm SE}(\\hat{\\beta}_1)$ is\n",
+        "0.0074.  As discussed in\n",
+        "Section~\\ref{Ch3:secoefsec}, standard formulas can be used to compute\n",
+        "the standard errors for the regression coefficients in a linear\n",
+        "model. These can be obtained using the `summarize()`  function\n",
+        "from `ISLP.sm`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "3888aa0a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:17.206302Z",
+          "iopub.status.busy": "2024-06-04T23:19:17.206223Z",
+          "iopub.status.idle": "2024-06-04T23:19:17.221631Z",
+          "shell.execute_reply": "2024-06-04T23:19:17.221444Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "3888aa0a",
+        "outputId": "36c5f66b-6474-4146-b329-2cb7f318ea21"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept     0.717\n",
+              "horsepower    0.006\n",
+              "Name: std err, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\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>std err</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>0.717</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>horsepower</th>\n",
+              "      <td>0.006</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ],
+      "source": [
+        "hp_model.fit(Auto, Auto['mpg'])\n",
+        "model_se = summarize(hp_model.results_)['std err']\n",
+        "model_se\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "aefc0575",
+      "metadata": {
+        "id": "aefc0575"
+      },
+      "source": [
+        "The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n",
+        "obtained using the formulas  from Section~\\ref{Ch3:secoefsec}  are\n",
+        "0.717 for the\n",
+        "intercept and\n",
+        "0.006 for the\n",
+        "slope. Interestingly, these are somewhat different from the estimates\n",
+        "obtained using the bootstrap.  Does this indicate a problem with the\n",
+        "bootstrap? In fact, it suggests the opposite.  Recall that the\n",
+        "standard formulas given in\n",
+        " {Equation~\\ref{Ch3:se.eqn} on page~\\pageref{Ch3:se.eqn}}\n",
+        "rely on certain assumptions. For example,\n",
+        "they depend on the unknown parameter $\\sigma^2$, the noise\n",
+        "variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
+        "formula for the standard errors do not rely on the linear model being\n",
+        "correct, the estimate for $\\sigma^2$ does.  We see\n",
+        " {in Figure~\\ref{Ch3:polyplot} on page~\\pageref{Ch3:polyplot}}  that there is\n",
+        "a non-linear relationship in the data, and so the residuals from a\n",
+        "linear fit will be inflated, and so will $\\hat{\\sigma}^2$.  Secondly,\n",
+        "the standard formulas assume (somewhat unrealistically) that the $x_i$\n",
+        "are fixed, and all the variability comes from the variation in the\n",
+        "errors $\\epsilon_i$.  The bootstrap approach does not rely on any of\n",
+        "these assumptions, and so it is likely giving a more accurate estimate\n",
+        "of the standard errors of $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$ than\n",
+        "the results from `sm.OLS`.\n",
+        "\n",
+        "Below we compute the bootstrap standard error estimates and the\n",
+        "standard linear regression estimates that result from fitting the\n",
+        "quadratic model to the data. Since this model provides a good fit to\n",
+        "the data (Figure~\\ref{Ch3:polyplot}), there is now a better\n",
+        "correspondence between the bootstrap estimates and the standard\n",
+        "estimates of ${\\rm SE}(\\hat{\\beta}_0)$, ${\\rm SE}(\\hat{\\beta}_1)$ and\n",
+        "${\\rm SE}(\\hat{\\beta}_2)$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "acc3e32c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:17.222887Z",
+          "iopub.status.busy": "2024-06-04T23:19:17.222785Z",
+          "iopub.status.idle": "2024-06-04T23:19:19.351574Z",
+          "shell.execute_reply": "2024-06-04T23:19:19.351317Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "acc3e32c",
+        "outputId": "447a8b9f-5b79-4968-c3cd-5349df859ad2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept                                  1.538641\n",
+              "poly(horsepower, degree=2, raw=True)[0]    0.024696\n",
+              "poly(horsepower, degree=2, raw=True)[1]    0.000090\n",
+              "dtype: float64"
+            ],
+            "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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>1.538641</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[0]</th>\n",
+              "      <td>0.024696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[1]</th>\n",
+              "      <td>0.000090</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ],
+      "source": [
+        "quad_model = MS([poly('horsepower', 2, raw=True)])\n",
+        "quad_func = partial(boot_OLS,\n",
+        "                    quad_model,\n",
+        "                    'mpg')\n",
+        "boot_SE(quad_func, Auto, B=1000)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e8a2fd2b",
+      "metadata": {
+        "id": "e8a2fd2b"
+      },
+      "source": [
+        "We  compare the results to the standard errors computed using `sm.OLS()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "id": "dca5340c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:19.352904Z",
+          "iopub.status.busy": "2024-06-04T23:19:19.352827Z",
+          "iopub.status.idle": "2024-06-04T23:19:19.360147Z",
+          "shell.execute_reply": "2024-06-04T23:19:19.359948Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "dca5340c",
+        "outputId": "626e2e1e-48b2-45c6-cb1e-0614ecba7bde"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept                                  1.800\n",
+              "poly(horsepower, degree=2, raw=True)[0]    0.031\n",
+              "poly(horsepower, degree=2, raw=True)[1]    0.000\n",
+              "Name: std err, dtype: float64"
+            ],
+            "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>std err</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>1.800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[0]</th>\n",
+              "      <td>0.031</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[1]</th>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ],
+      "source": [
+        "M = sm.OLS(Auto['mpg'],\n",
+        "           quad_model.fit_transform(Auto))\n",
+        "summarize(M.fit())['std err']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e98297be",
+      "metadata": {
+        "id": "e98297be"
+      },
+      "source": [
+        "\n",
+        "\n"
       ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "M = sm.OLS(Auto['mpg'],\n",
-    "           quad_model.fit_transform(Auto))\n",
-    "summarize(M.fit())['std err']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e98297be",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "jupytext": {
+      "cell_metadata_filter": "-all",
+      "main_language": "python",
+      "notebook_metadata_filter": "-all"
+    },
+    "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": "3.12.3"
+    },
+    "colab": {
+      "provenance": []
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/Ch06-varselect-lab.ipynb b/Ch06-varselect-lab.ipynb
index 765a7fa8..171b42c6 100644
--- a/Ch06-varselect-lab.ipynb
+++ b/Ch06-varselect-lab.ipynb
@@ -1,6412 +1,6830 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e8a7df77",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Linear Models and Regularization Methods\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch06-varselect-lab.ipynb\">\n",
-    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>\n",
-    "\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch06-varselect-lab.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d12a766",
-   "metadata": {},
-   "source": [
-    "In this lab we implement many of the techniques discussed in this chapter.\n",
-    "We import some of our libraries at this top\n",
-    "level. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b59f6bc4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:20.427195Z",
-     "iopub.status.busy": "2024-06-04T23:19:20.426849Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.241188Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.240924Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from matplotlib.pyplot import subplots\n",
-    "from statsmodels.api import OLS\n",
-    "import sklearn.model_selection as skm\n",
-    "import sklearn.linear_model as skl\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from ISLP import load_data\n",
-    "from ISLP.models import ModelSpec as MS\n",
-    "from functools import partial\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c16c47d1",
-   "metadata": {},
-   "source": [
-    "We again collect the new imports\n",
-    "needed for this lab. Readers will also have to have installed `l0bnb` using `pip install l0bnb`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8440e64a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.242868Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.242739Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.482878Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.482642Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.cross_decomposition import PLSRegression\n",
-    "from ISLP.models import \\\n",
-    "     (Stepwise,\n",
-    "      sklearn_selected,\n",
-    "      sklearn_selection_path)\n",
-    "from l0bnb import fit_path\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfbf09cb",
-   "metadata": {},
-   "source": [
-    "## Subset Selection Methods\n",
-    "Here we implement methods that reduce the number of parameters in a\n",
-    "model by restricting the model to a subset of the input variables.\n",
-    "\n",
-    "### Forward Selection\n",
-    " \n",
-    "We will  apply the forward-selection approach to the  `Hitters` \n",
-    "data.  We wish to predict a baseball player’s `Salary` on the\n",
-    "basis of various statistics associated with performance in the\n",
-    "previous year.\n",
-    "\n",
-    "First of all, we note that the `Salary` variable is missing for\n",
-    "some of the players.  The `np.isnan()`  function can be used to\n",
-    "identify the missing observations. It returns an array\n",
-    "of the same shape as the input vector, with a `True` for any elements that\n",
-    "are missing, and a `False` for non-missing elements.  The\n",
-    "`sum()`  method can then be used to count all of the\n",
-    "missing elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "63d45749",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.484407Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.484321Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.489374Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.489194Z"
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "59"
+      "cell_type": "markdown",
+      "id": "e8a7df77",
+      "metadata": {
+        "id": "e8a7df77"
+      },
+      "source": [
+        "\n",
+        "# Linear Models and Regularization Methods\n",
+        "\n",
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch06-varselect-lab.ipynb\">\n",
+        "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+        "</a>\n",
+        "\n",
+        "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch06-varselect-lab.ipynb)"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Hitters = load_data('Hitters')\n",
-    "np.isnan(Hitters['Salary']).sum()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25acaae1",
-   "metadata": {},
-   "source": [
-    " We see that `Salary` is missing for 59 players. The\n",
-    "`dropna()`  method of data frames removes all of the rows that have missing\n",
-    "values in any variable (by default --- see  `Hitters.dropna?`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "47262cbe",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.490561Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.490484Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.493093Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.492903Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(263, 20)"
+      "cell_type": "markdown",
+      "id": "0d12a766",
+      "metadata": {
+        "id": "0d12a766"
+      },
+      "source": [
+        "In this lab we implement many of the techniques discussed in this chapter.\n",
+        "We import some of our libraries at this top\n",
+        "level."
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Hitters = Hitters.dropna();\n",
-    "Hitters.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5199f18e",
-   "metadata": {},
-   "source": [
-    "We first choose the best model using forward selection based on $C_p$ (\\ref{Ch6:eq:cp}). This score\n",
-    "is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use\n",
-    "it as a scorer. By default, `sklearn` tries to maximize a score, hence\n",
-    "  our scoring function  computes the negative $C_p$ statistic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "096fe127",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.494225Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.494154Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.495738Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.495580Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "def nCp(sigma2, estimator, X, Y):\n",
-    "    \"Negative Cp statistic\"\n",
-    "    n, p = X.shape\n",
-    "    Yhat = estimator.predict(X)\n",
-    "    RSS = np.sum((Y - Yhat)**2)\n",
-    "    return -(RSS + 2 * p * sigma2) / n \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf9e3ca3",
-   "metadata": {},
-   "source": [
-    "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n",
-    "We will fit the biggest model, using all the variables, and estimate $\\sigma^2$ based on its MSE."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b697d287",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.496810Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.496736Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.510614Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.510406Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n",
-    "Y = np.array(Hitters['Salary'])\n",
-    "X = design.transform(Hitters)\n",
-    "sigma2 = OLS(Y,X).fit().scale\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc283589",
-   "metadata": {},
-   "source": [
-    "The function `sklearn_selected()` expects a scorer with just three arguments --- the last three in the definition of `nCp()` above. We use the function `partial()` first seen in Section~\\ref{Ch5-resample-lab:the-bootstrap} to freeze the first argument with our estimate of $\\sigma^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2151b133",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.511833Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.511764Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.513251Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.513008Z"
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "b59f6bc4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:20.427195Z",
+          "iopub.status.busy": "2024-06-04T23:19:20.426849Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.241188Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.240924Z"
+        },
+        "collapsed": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b59f6bc4",
+        "outputId": "40086c54-1aad-4ad5-9149-098969bae8b7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ISLP\n",
+            "  Downloading ISLP-0.4.0-py3-none-any.whl.metadata (7.0 kB)\n",
+            "Requirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python3.12/dist-packages (from ISLP) (2.0.2)\n",
+            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.12/dist-packages (from ISLP) (1.16.2)\n",
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+            "Collecting lifelines (from ISLP)\n",
+            "  Downloading lifelines-0.30.0-py3-none-any.whl.metadata (3.2 kB)\n",
+            "Collecting pygam (from ISLP)\n",
+            "  Downloading pygam-0.10.1-py3-none-any.whl.metadata (9.7 kB)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (from ISLP) (2.8.0+cu126)\n",
+            "Collecting pytorch-lightning (from ISLP)\n",
+            "  Downloading pytorch_lightning-2.5.5-py3-none-any.whl.metadata (20 kB)\n",
+            "Collecting torchmetrics (from ISLP)\n",
+            "  Downloading torchmetrics-1.8.2-py3-none-any.whl.metadata (22 kB)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.20->ISLP) (2.9.0.post0)\n",
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+            "Collecting autograd-gamma>=0.3 (from lifelines->ISLP)\n",
+            "  Downloading autograd-gamma-0.5.0.tar.gz (4.0 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting formulaic>=0.2.2 (from lifelines->ISLP)\n",
+            "  Downloading formulaic-1.2.1-py3-none-any.whl.metadata (7.0 kB)\n",
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+            "Requirement already satisfied: typing-extensions>4.5.0 in /usr/local/lib/python3.12/dist-packages (from pytorch-lightning->ISLP) (4.15.0)\n",
+            "Collecting lightning-utilities>=0.10.0 (from pytorch-lightning->ISLP)\n",
+            "  Downloading lightning_utilities-0.15.2-py3-none-any.whl.metadata (5.7 kB)\n",
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+            "Collecting interface-meta>=1.2.0 (from formulaic>=0.2.2->lifelines->ISLP)\n",
+            "  Downloading interface_meta-1.3.0-py3-none-any.whl.metadata (6.7 kB)\n",
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+            "Downloading ISLP-0.4.0-py3-none-any.whl (3.6 MB)\n",
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+            "\u001b[?25hDownloading lightning_utilities-0.15.2-py3-none-any.whl (29 kB)\n",
+            "Downloading interface_meta-1.3.0-py3-none-any.whl (14 kB)\n",
+            "Building wheels for collected packages: autograd-gamma\n",
+            "  Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for autograd-gamma: filename=autograd_gamma-0.5.0-py3-none-any.whl size=4030 sha256=8b1cdd86ff072ff0bf2d180779e58325a480a311f417ff1a32f066d18be3ce1c\n",
+            "  Stored in directory: /root/.cache/pip/wheels/50/37/21/0a719b9d89c635e89ff24bd93b862882ad675279552013b2fb\n",
+            "Successfully built autograd-gamma\n",
+            "Installing collected packages: lightning-utilities, interface-meta, autograd-gamma, pygam, formulaic, torchmetrics, lifelines, pytorch-lightning, ISLP\n",
+            "Successfully installed ISLP-0.4.0 autograd-gamma-0.5.0 formulaic-1.2.1 interface-meta-1.3.0 lifelines-0.30.0 lightning-utilities-0.15.2 pygam-0.10.1 pytorch-lightning-2.5.5 torchmetrics-1.8.2\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install ISLP\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from matplotlib.pyplot import subplots\n",
+        "from statsmodels.api import OLS\n",
+        "import sklearn.model_selection as skm\n",
+        "import sklearn.linear_model as skl\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from ISLP import load_data\n",
+        "from ISLP.models import ModelSpec as MS\n",
+        "from functools import partial\n"
+      ]
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "neg_Cp = partial(nCp, sigma2)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6993ae8",
-   "metadata": {},
-   "source": [
-    "We can now use `neg_Cp()` as a scorer for model selection.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "436a744d",
-   "metadata": {},
-   "source": [
-    "Along with a score we need to specify the search strategy. This is done through the object\n",
-    "`Stepwise()`  in the `ISLP.models` package. The method `Stepwise.first_peak()`\n",
-    "runs forward stepwise until any further additions to the model do not result\n",
-    "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n",
-    "runs a fixed number of steps of stepwise search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ab28d6fd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.514438Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.514363Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.515875Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.515674Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "strategy = Stepwise.first_peak(design,\n",
-    "                               direction='forward',\n",
-    "                               max_terms=len(design.terms))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4bf946d2",
-   "metadata": {},
-   "source": [
-    "We now fit a linear regression model with `Salary` as outcome using forward\n",
-    "selection. To do so, we use the function `sklearn_selected()`  from the `ISLP.models` package. This takes\n",
-    "a model from `statsmodels` along with a search strategy and selects a model with its\n",
-    "`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be\n",
-    "selected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d8b345ec",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.517091Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.517008Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.178273Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.178018Z"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "('Assists',\n",
-       " 'AtBat',\n",
-       " 'CAtBat',\n",
-       " 'CHits',\n",
-       " 'CHmRun',\n",
-       " 'CRBI',\n",
-       " 'CRuns',\n",
-       " 'CWalks',\n",
-       " 'Division',\n",
-       " 'Errors',\n",
-       " 'Hits',\n",
-       " 'HmRun',\n",
-       " 'League',\n",
-       " 'NewLeague',\n",
-       " 'PutOuts',\n",
-       " 'RBI',\n",
-       " 'Runs',\n",
-       " 'Walks',\n",
-       " 'Years')"
+      "cell_type": "markdown",
+      "id": "c16c47d1",
+      "metadata": {
+        "id": "c16c47d1"
+      },
+      "source": [
+        "We again collect the new imports\n",
+        "needed for this lab. Readers will also have to have installed `l0bnb` using `pip install l0bnb`."
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hitters_MSE = sklearn_selected(OLS,\n",
-    "                               strategy)\n",
-    "hitters_MSE.fit(Hitters, Y)\n",
-    "hitters_MSE.selected_state_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1a78607",
-   "metadata": {},
-   "source": [
-    "Using `neg_Cp` results in a smaller model, as expected, with just 10 variables selected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "915cdff6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.179650Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.179571Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.573219Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.572980Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "('Assists',\n",
-       " 'AtBat',\n",
-       " 'CAtBat',\n",
-       " 'CRBI',\n",
-       " 'CRuns',\n",
-       " 'CWalks',\n",
-       " 'Division',\n",
-       " 'Hits',\n",
-       " 'PutOuts',\n",
-       " 'Walks')"
+      "cell_type": "code",
+      "source": [
+        "!pip install l0bnb"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PSFjEqzB8Pzk",
+        "outputId": "3000aaef-0b19-4402-df2f-8b18c6cc65fc"
+      },
+      "id": "PSFjEqzB8Pzk",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting l0bnb\n",
+            "  Downloading l0bnb-1.0.0.tar.gz (79 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/79.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.12/dist-packages (from l0bnb) (2.0.2)\n",
+            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.12/dist-packages (from l0bnb) (1.16.2)\n",
+            "Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.12/dist-packages (from l0bnb) (0.60.0)\n",
+            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.53.1->l0bnb) (0.43.0)\n",
+            "Building wheels for collected packages: l0bnb\n",
+            "  Building wheel for l0bnb (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for l0bnb: filename=l0bnb-1.0.0-py3-none-any.whl size=22296 sha256=50a27114795f3c0e78e206f0c9e02ec12e1a1e7519dbffd6d5ece21d80e5497b\n",
+            "  Stored in directory: /root/.cache/pip/wheels/0e/74/8b/8a39b657554150b98e1520971211118b5137e6a1873a85f66a\n",
+            "Successfully built l0bnb\n",
+            "Installing collected packages: l0bnb\n",
+            "Successfully installed l0bnb-1.0.0\n"
+          ]
+        }
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hitters_Cp = sklearn_selected(OLS,\n",
-    "                               strategy,\n",
-    "                               scoring=neg_Cp)\n",
-    "hitters_Cp.fit(Hitters, Y)\n",
-    "hitters_Cp.selected_state_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5d37f87",
-   "metadata": {},
-   "source": [
-    "### Choosing Among Models Using the Validation Set Approach and Cross-Validation\n",
-    " \n",
-    "As an  alternative to using $C_p$, we might try cross-validation to select a model in forward selection. For this, we need a\n",
-    "method that stores the full path of models found in forward selection, and allows predictions for each of these. This can be done with the `sklearn_selection_path()` \n",
-    "estimator from `ISLP.models`. The function `cross_val_predict()` from `ISLP.models`\n",
-    "computes the cross-validated predictions for each of the models\n",
-    "along the path, which we can use to evaluate the cross-validated MSE\n",
-    "along the path."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6769115d",
-   "metadata": {},
-   "source": [
-    "Here we define a strategy that fits the full forward selection path.\n",
-    "While there are various parameter choices for `sklearn_selection_path()`,\n",
-    "we use the defaults here, which selects the model at each step based on the biggest reduction  in RSS."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "33ae4aa4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.574545Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.574468Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.576122Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.575907Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "strategy = Stepwise.fixed_steps(design,\n",
-    "                                len(design.terms),\n",
-    "                                direction='forward')\n",
-    "full_path = sklearn_selection_path(OLS, strategy)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d397415c",
-   "metadata": {},
-   "source": [
-    "We now fit the full forward-selection path on the `Hitters` data and compute the fitted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f05e1a81",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.577302Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.577233Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.976283Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.976065Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(263, 20)"
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "8440e64a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.242868Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.242739Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.482878Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.482642Z"
+        },
+        "id": "8440e64a"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.decomposition import PCA\n",
+        "from sklearn.cross_decomposition import PLSRegression\n",
+        "from ISLP.models import \\\n",
+        "     (Stepwise,\n",
+        "      sklearn_selected,\n",
+        "      sklearn_selection_path)\n",
+        "from l0bnb import fit_path\n"
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_path.fit(Hitters, Y)\n",
-    "Yhat_in = full_path.predict(Hitters)\n",
-    "Yhat_in.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "767af527",
-   "metadata": {},
-   "source": [
-    "This gives us an array of fitted values --- 20 steps in all, including the fitted mean for the null model --- which we can use to evaluate\n",
-    "in-sample MSE. As expected, the in-sample MSE improves each step we take,\n",
-    "indicating we must use either the validation or cross-validation\n",
-    "approach to select the number of steps. We fix the y-axis to range from\n",
-    "50,000 to 250,000 to compare to the cross-validation and validation\n",
-    "set MSE below, as well as other methods such as ridge regression, lasso and\n",
-    "principal components regression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "0b07ae72",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.977573Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.977503Z",
-     "iopub.status.idle": "2024-06-04T23:19:23.094004Z",
-     "shell.execute_reply": "2024-06-04T23:19:23.091687Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "bfbf09cb",
+      "metadata": {
+        "id": "bfbf09cb"
+      },
+      "source": [
+        "## Subset Selection Methods\n",
+        "Here we implement methods that reduce the number of parameters in a\n",
+        "model by restricting the model to a subset of the input variables.\n",
+        "\n",
+        "### Forward Selection\n",
+        "\n",
+        "We will  apply the forward-selection approach to the  `Hitters`\n",
+        "data.  We wish to predict a baseball player’s `Salary` on the\n",
+        "basis of various statistics associated with performance in the\n",
+        "previous year.\n",
+        "\n",
+        "First of all, we note that the `Salary` variable is missing for\n",
+        "some of the players.  The `np.isnan()`  function can be used to\n",
+        "identify the missing observations. It returns an array\n",
+        "of the same shape as the input vector, with a `True` for any elements that\n",
+        "are missing, and a `False` for non-missing elements.  The\n",
+        "`sum()`  method can then be used to count all of the\n",
+        "missing elements."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mse_fig, ax = subplots(figsize=(8,8))\n",
-    "insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)\n",
-    "n_steps = insample_mse.shape[0]\n",
-    "ax.plot(np.arange(n_steps),\n",
-    "        insample_mse,\n",
-    "        'k', # color black\n",
-    "        label='In-sample')\n",
-    "ax.set_ylabel('MSE',\n",
-    "              fontsize=20)\n",
-    "ax.set_xlabel('# steps of forward stepwise',\n",
-    "              fontsize=20)\n",
-    "ax.set_xticks(np.arange(n_steps)[::2])\n",
-    "ax.legend()\n",
-    "ax.set_ylim([50000,250000]);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f7508e0",
-   "metadata": {},
-   "source": [
-    "Notice the expression `None` in `Y[:,None]` above.\n",
-    "This adds an axis (dimension) to the one-dimensional array `Y`,\n",
-    "which allows it to be recycled when subtracted from the two-dimensional `Yhat_in`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6c9531a",
-   "metadata": {},
-   "source": [
-    "We are now ready to use cross-validation to estimate test error along\n",
-    "the model path. We must use *only the training observations* to perform all aspects of model-fitting --- including\n",
-    "variable selection.  Therefore, the determination of which model of a\n",
-    "given size is best must be made using \\emph{only the training\n",
-    "  observations} in each training fold. This point is subtle but important.  If the full data\n",
-    "set is used to select the best subset at each step, then the validation\n",
-    "set errors and cross-validation errors that we obtain will not be\n",
-    "accurate estimates of the test error.\n",
-    "\n",
-    "We now compute the cross-validated predicted values using 5-fold cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a1654ad8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:23.097519Z",
-     "iopub.status.busy": "2024-06-04T23:19:23.097271Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.139415Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.139180Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(263, 20)"
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "63d45749",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.484407Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.484321Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.489374Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.489194Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "63d45749",
+        "outputId": "b6eaf6d7-9390-41d5-912d-9e3806f32811"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(59)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "Hitters = load_data('Hitters')\n",
+        "np.isnan(Hitters['Salary']).sum()\n"
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "K = 5\n",
-    "kfold = skm.KFold(K,\n",
-    "                  random_state=0,\n",
-    "                  shuffle=True)\n",
-    "Yhat_cv = skm.cross_val_predict(full_path,\n",
-    "                                Hitters,\n",
-    "                                Y,\n",
-    "                                cv=kfold)\n",
-    "Yhat_cv.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98dfabdc",
-   "metadata": {},
-   "source": [
-    "`skm.cross_val_predict()`\n",
-    "The prediction matrix `Yhat_cv` is the same shape as `Yhat_in`; the difference is that the predictions in each row, corresponding to a particular sample index, were made from models fit on a training fold that did not include that row.\n",
-    "\n",
-    "At each model along the path, we compute the MSE in each of the cross-validation folds.\n",
-    "These we will average to get the  mean MSE, and can also use the individual values to compute a crude estimate of the standard error of the mean. {The estimate is crude because the five error estimates  are based on overlapping training sets, and hence are not independent.}\n",
-    "Hence we must know the test indices for each cross-validation\n",
-    "split. This can be found by using the `split()` method of `kfold`. Because\n",
-    "we  fixed the random state above, whenever we split any array with the same\n",
-    "number of rows as $Y$ we recover the same training and test indices, though we simply\n",
-    "ignore the training indices below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ce919bd3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.140773Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.140698Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.143330Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.143113Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(20, 5)"
+      "cell_type": "code",
+      "source": [
+        "# Does the same thing!\n",
+        "Hitters[np.isnan(Hitters['Salary'])].shape[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i3qTR0Lc878_",
+        "outputId": "1f0ab34f-599d-4876-a717-b63cd61e942e"
+      },
+      "id": "i3qTR0Lc878_",
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "59"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv_mse = []\n",
-    "for train_idx, test_idx in kfold.split(Y):\n",
-    "    errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n",
-    "    cv_mse.append(errors.mean(0)) # column means\n",
-    "cv_mse = np.array(cv_mse).T\n",
-    "cv_mse.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a27004be",
-   "metadata": {},
-   "source": [
-    "We now add the cross-validation error estimates to our MSE plot.\n",
-    "We include the mean error across the five folds, and the estimate of the standard error of the mean. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "26520d22",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.144503Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.144435Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.201431Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.201214Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "25acaae1",
+      "metadata": {
+        "id": "25acaae1"
+      },
+      "source": [
+        " We see that `Salary` is missing for 59 players. The\n",
+        "`dropna()`  method of data frames removes all of the rows that have missing\n",
+        "values in any variable (by default --- see  `Hitters.dropna?`)."
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ax.errorbar(np.arange(n_steps), \n",
-    "            cv_mse.mean(1),\n",
-    "            cv_mse.std(1) / np.sqrt(K),\n",
-    "            label='Cross-validated',\n",
-    "            c='r') # color red\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.legend()\n",
-    "mse_fig\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9881566e",
-   "metadata": {},
-   "source": [
-    "To repeat the above using the validation set approach, we simply change our\n",
-    "`cv` argument to a validation set: one random split of the data into a test and training. We choose a test size\n",
-    "of 20%, similar to the size of each test set in 5-fold cross-validation.`skm.ShuffleSplit()`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "f19c9091",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.202701Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.202634Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.603532Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.603301Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "validation = skm.ShuffleSplit(n_splits=1, \n",
-    "                              test_size=0.2,\n",
-    "                              random_state=0)\n",
-    "for train_idx, test_idx in validation.split(Y):\n",
-    "    full_path.fit(Hitters.iloc[train_idx],\n",
-    "                  Y[train_idx])\n",
-    "    Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n",
-    "    errors = (Yhat_val - Y[test_idx,None])**2\n",
-    "    validation_mse = errors.mean(0)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7421fc11",
-   "metadata": {},
-   "source": [
-    " As for the in-sample MSE case, the validation set approach does not provide standard errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "1fca6b08",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.604861Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.604789Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.664722Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.664495Z"
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "47262cbe",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.490561Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.490484Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.493093Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.492903Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "47262cbe",
+        "outputId": "8a65724a-6077-4272-ba94-18b4414639aa"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(263, 20)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "source": [
+        "Hitters = Hitters.dropna();\n",
+        "Hitters.shape\n"
+      ]
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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mIDs7GwCwbNkyvP/++zh+/DhUKhVUKpWu3ke7nZw8eRLPP/88HBwcUKFCBYwYMQKpqam67UOGDEFkZCQ+/fRT+Pr6okKFChg9erTuWJaIZ77J4HJzASEAG767iIhIAc7OhW978UVg27a8+15ewIMHBbd97jlg9+68+wEBBU8kJ0TRa7OxscGgQYOwbNkyvPPOO1CpVACADRs2IDc3F6+88go2bNiAKVOmwNXVFdu2bcPAgQNRo0YNtGjR4qn712g06NGjB7y9vXHw4EEkJyfr9Q/XcnFxwbJly+Dn54eTJ09i+PDhcHFxweTJk9GnTx+cOnUKv/zyC37//XcAgJub22P7SEtLQ0REBMLCwnDo0CHcvHkTr732GsaMGaP34WLXrl3w9fXFrl27cPHiRfTp0weNGjXC8OHDi/7ClSM8800GFR4up5jfsUPpSoiIiEzTq6++ikuXLmHPnj26dUuXLkXPnj1RrVo1vP3222jUqBECAwMxduxYdOzYEevXry/Svn///XecO3cOy5cvR8OGDdGmTRt8/PHHj7WbPn06WrZsiYCAAHTt2hVvv/227hgODg5wdnaGjY0NfHx84OPjAwcHh8f2sXr1amRkZGD58uWoV68enn/+eSxYsAArVqxAYmKirp2HhwcWLFiAWrVqoUuXLujcuTOiLbifKs9NkkHZ2sozAJxinoiIlJKv18NjrK3179+8WXhbq0dOUcbFlbgkPbVq1ULLli2xZMkStG3bFhcvXsQff/yBDz74ALm5ufj444+xfv16/Pvvv8jKykJmZiYcHR2LtO+zZ8/C398ffn5+unVhYWGPtVu3bh2+/PJLXLp0CampqcjJyYGrq2uxnsfZs2fRsGFDODk56da1atUKGo0GsbGx8Pb2BgDUrVsX1vleeF9fX5w8ebJYxypPeOabDIrDDRIRkdKcnApf7O2L3vbRk72FtSuJYcOGYdOmTbh//z6WLl2KGjVq4LnnnsPcuXPxxRdfYMqUKdi1axeOHTuGiIgIZGVllexABYiJicGAAQPw4osvYuvWrTh69Cjeeecdgx4jP1tbW737KpUKGo3GKMcyBwzfZFAM30RERE/Xu3dvWFlZYfXq1Vi+fDleffVVqFQq7Nu3D927d8crr7yChg0bIjAwEOfPny/yfmvXro3r168jPj5et+7AgQN6bfbv349q1arhnXfeQbNmzVCzZk1cvXpVr41arUZubu5Tj3X8+HGk5bvqdN++fbCyskJISEiRa7Y0DN9kUAzfRERET+fs7Iw+ffpg2rRpiI+Px5AhQwAANWvWRFRUFPbv34+zZ8/i9ddf1+s//TTh4eEIDg7G4MGDcfz4cfzxxx9455139NrUrFkT165dw9q1a3Hp0iV8+eWX2Lx5s16bgIAAXLlyBceOHcPt27eRmZn52LEGDBgAe3t7DB48GKdOncKuXbswduxYDBw4UNflhB7H8E0GxfBNRERUNMOGDcO9e/cQERGh66M9ffp0NGnSBBEREWjbti18fHwQGRlZ5H1aWVlh8+bNSE9PR4sWLfDaa6/hP//5j16bbt26YcKECRgzZgwaNWqE/fv3Y8aMGXptevbsiY4dO6Jdu3aoVKlSgcMdOjo64tdff8Xdu3fRvHlz9OrVC+3bt8eCBQuK/2JYEJUQxRkgh5SQkpICNzc3JCcnF/tiiLL2009A9+5AixbAwYNKV0NEROVVRkYGrly5gurVq8P+0Y7cREbypPddUfMaRzshgwoIkNPL162rdCVEREREpofhmwyqQQPg4Xj8RERERPQI9vkmIiIiIiojDN9kFLm5gAUP4UlERERUIIZvMrh69eRMl8eOKV0JERERkWlh+CaDs7aWU8xzuEEiIiIifQzfZHAc65uIiIioYAzfZHDa8H37trJ1EBERFUlaGqBSySXfVOlExsDwTQZXsaL8yTPfRERERPoYvsng2O2EiIiIAGDZsmVwd3fX3Z85cyYaNWr0xMcMGTIEkZGRRq2rMHFxcVCpVDhmxFEjGL7J4Bi+iYiIni4hIQFjx45FYGAg7Ozs4O/vj65duyI6Olrp0ozm7bffNvjzK4vAbEic4ZIMrmZNIDwceMoHWyIiIosVFxeHVq1awd3dHXPnzkX9+vWRnZ2NX3/9FaNHj8a5c+cee0x2djZsbW0VqNZwnJ2d4ezsrHQZiuKZbzK48HAgKgp47z2lKyEiIosihLxgsiSLVkkfL0SxSh01ahRUKhX++usv9OzZE8HBwahbty4mTpyIAwcOAABUKhUWLVqEbt26wcnJCf/5z38AAIsWLUKNGjWgVqsREhKCFStW5HsJBGbOnImqVavCzs4Ofn5+ePPNN3Xbv/76a9SsWRP29vbw9vZGr169CqxPo9GgSpUqWLRokd76o0ePwsrKClevXgUAzJs3D/Xr14eTkxP8/f0xatQopKamFvq8H+12kpubi4kTJ8Ld3R0VKlTA5MmTIR55LX/55Re0bt1a16ZLly64dOmSbnv16tUBAI0bN4ZKpULbtm112/773/+idu3asLe3R61atfD111/r7fuvv/5C48aNYW9vj2bNmuHo0aOF1m4wgkxecnKyACCSk5OVLoWIiMgkpKenizNnzoj09PS8lampQsgYXPZLamqRa79z545QqVTi448/fmI7AMLLy0ssWbJEXLp0SVy9elX88MMPwtbWVixcuFDExsaKzz77TFhbW4udO3cKIYTYsGGDcHV1Fdu3bxdXr14VBw8eFN9++60QQohDhw4Ja2trsXr1ahEXFyeOHDkivvjii0KP//bbb4vWrVvrrXvrrbf01n3++edi586d4sqVKyI6OlqEhISIN954Q7d96dKlws3NTXf/vffeEw0bNtTd/+STT4SHh4fYtGmTOHPmjBg2bJhwcXER3bt317XZuHGj2LRpk7hw4YI4evSo6Nq1q6hfv77Izc0VQgjx119/CQDi999/F/Hx8eLOnTtCCCFWrlwpfH19xaZNm8Tly5fFpk2bhKenp1i2bJkQQoj79++LSpUqif79+4tTp06Jn3/+WQQGBgoA4ujRowW+JgW+7x4qal5j+DYD5hq+s7OF0GiUroKIiMojcw7fBw8eFADEDz/88MR2AMT48eP11rVs2VIMHz5cb93LL78sXnzxRSGEEJ999pkIDg4WWVlZj+1v06ZNwtXVVaSkpBSpzqNHjwqVSiWuXr0qhBAiNzdXVK5cWSxatKjQx2zYsEFUqFBBd/9p4dvX11fMmTNHdz87O1tUqVJFL3w/6tatWwKAOHnypBBCiCtXrhQYmGvUqCFWr16tt+7DDz8UYWFhQgghvvnmG1GhQgW999CiRYuMHr7Z7YQMTqMBvLzkFPOJiUpXQ0REFsPREUhNLf6S/49VYmLJ9uHoWOQyRTG6qDRr1kzv/tmzZ9GqVSu9da1atcLZs2cBAC+//DLS09MRGBiI4cOHY/PmzcjJyQEAvPDCC6hWrRoCAwMxcOBArFq1Cg8ePAAArFq1Stcf29nZGX/88QcaNWqE2rVrY/Xq1QCAPXv24ObNm3j55Zd1x/7999/Rvn17VK5cGS4uLhg4cCDu3Lmj2++TJCcnIz4+HqGhobp1NjY2jz3nCxcuoF+/fggMDISrqysCAgIAANeuXSt032lpabh06RKGDRum97w++ugjXZeVs2fPokGDBrC3t9c9Liws7Kl1lxbDNxmclVVe1zdOtENERGVGpQKcnEq2aJX08SpVkcusWbMmVCpVgRdVPsopf21F4O/vj9jYWHz99ddwcHDAqFGj0KZNG2RnZ8PFxQVHjhzBmjVr4Ovri3fffRcNGzZEUlISunXrhmPHjukWbQAeMGCALnyvXr0aHTt2RIUKFQDIi0a7dOmCBg0aYNOmTTh8+DAWLlwIAMjKyipW3U/StWtX3L17F9999x0OHjyIgwcPPvUY2n7n3333nd7zOnXqlK5PvVIYvskoONwgERFRwTw9PREREYGFCxcirYAZNZOSkgp9bO3atbFv3z69dfv27UOdOnV09x0cHNC1a1d8+eWX2L17N2JiYnDy5EkA8sxyeHg45syZgxMnTiAuLg47d+6Ei4sLgoKCdIuDgwMAoH///jh16hQOHz6MjRs3YsCAAbrjHD58GBqNBp999hmeeeYZBAcH48aNG0V+Hdzc3ODr66sL0wCQk5ODw4cP6+7fuXMHsbGxmD59Otq3b4/atWvj3r17evtRq9UA5MWbWt7e3vDz88Ply5f1nldQUJDuAs3atWvjxIkTyMjI0D2uLII5hxoko+Asl0RERIVbuHAhWrVqhRYtWuCDDz5AgwYNkJOTg6ioKCxatEjXjeRRkyZNQu/evdG4cWOEh4fj559/xg8//IDff/8dgJzUJjc3F6GhoXB0dMTKlSvh4OCAatWqYevWrbh8+TLatGkDDw8PbN++HRqNBiEhIYXWGRAQgJYtW2LYsGHIzc1Ft27ddNuCgoKQnZ2Nr776Cl27dsW+ffuwePHiYr0O48aNw+zZs1GzZk3UqlUL8+bN0/vw4eHhgQoVKuDbb7+Fr68vrl27hqlTp+rtw8vLCw4ODvjll19QpUoV2Nvbw83NDe+//z7efPNNuLm5oWPHjsjMzMTff/+Ne/fuYeLEiejfvz/eeecdDB8+HNOmTUNcXBw+/fTTYtVfIk/sEU4mwRwvuOzRQ15/smCB0pUQEVF59KQL34ot/4WaxbhwsrRu3LghRo8eLapVqybUarWoXLmy6Natm9i1a5cQQl5wuXnz5sce9/XXX4vAwEBha2srgoODxfLly3XbNm/eLEJDQ4Wrq6twcnISzzzzjPj999+FEEL88ccf4rnnnhMeHh7CwcFBNGjQQKxbt+6pdX799dcCgBg0aNBj2+bNmyd8fX2Fg4ODiIiIEMuXLxcAxL1794QQT7/gMjs7W4wbN064uroKd3d3MXHiRDFo0CC9Cy6joqJE7dq1hZ2dnWjQoIHYvXv3Y6/Nd999J/z9/YWVlZV47rnndOtXrVolGjVqJNRqtfDw8BBt2rTRu9A1JiZGNGzYUKjVatGoUSOxadMmo19wqRKimANTUplLSUmBm5sbkpOT4erqqnQ5RTJyJPDNN3Ks75kzla6GiIjKm4yMDFy5cgXVq1fXu2CuRNLSAO3EL6mp+n3AifJ50vuuqHmN3U7IKNjnm4iIzIaTU7EnySEqKV5wSUZRt66c6fIJ3ciIiIiILA7PfJNR9O0rFyIiIiLKwzPfRERERERlhOGbjCrfkJtEREQGx3EjqCwZ4v3G8E1GcfMm4OkJODjI6eaJiIgMydbWFgCKNI05kaFo32/a919JmGWf71mzZuGHH37AuXPn4ODggJYtW+KTTz7RDRJ/9+5dvPfee/jtt99w7do1VKpUCZGRkfjwww/h5uam24+qgKlg16xZg775Oivv3r0bEydOxOnTp+Hv74/p06djyJAheo9ZuHAh5s6di4SEBDRs2BBfffUVWrRooduekZGBt956C2vXrkVmZiYiIiLw9ddfw9vb28CvjAEYaLgld3dAOwFVUpIM4kRERIZibW0Nd3d33Lx5EwDg6OhY4N91IkMQQuDBgwe4efMm3N3dYW1tXeJ9mWX43rNnD0aPHo3mzZsjJycH//d//4cOHTrgzJkzcHJywo0bN3Djxg18+umnqFOnDq5evYqRI0fixo0b2Lhxo96+li5dio4dO+ruu7u7625fuXIFnTt3xsiRI7Fq1SpER0fjtddeg6+vLyIiIgAA69atw8SJE7F48WKEhoZi/vz5iIiIQGxsLLy8vAAAEyZMwLZt27Bhwwa4ublhzJgx6NGjx2PTw5YnajXg5gYkJ8vhBhm+iYjI0Hx8fABAF8CJjM3d3V33viupcjHJzq1bt+Dl5YU9e/agTZs2BbbZsGEDXnnlFaSlpcHGRn7mUKlU2Lx5MyIjIwt8zJQpU7Bt2zacOnVKt65v375ISkrCL7/8AgAIDQ1F8+bNsWDBAgCARqOBv78/xo4di6lTpyI5ORmVKlXC6tWr0atXLwDAuXPnULt2bcTExOCZZ5556vMr00l2DDjRQFAQcOkS8McfQOvWBqqPiIjoEbm5ucjOzla6DCrnbG1tn3jG26Im2UlOTgYAeD7h9Kr2hdAGb63Ro0fjtddeQ2BgIEaOHImhQ4fqvraKiYlBeHi4XvuIiAiMHz8eAJCVlYXDhw9j2rRpuu1WVlYIDw9HTEwMAODw4cPIzs7W20+tWrVQtWrVQsN3ZmYmMjMzdfdTUlKK8jKYnEqVZPjmRDtERGRM1tbWpeoGQFSWzD58azQajB8/Hq1atUK9evUKbHP79m18+OGHGDFihN76Dz74AM8//zwcHR3x22+/YdSoUUhNTcWbb74JAEhISHisX7a3tzdSUlKQnp6Oe/fuITc3t8A2586d0+1DrVbrdWfRtklISCiw3lmzZuH9998v8mtgqjjLJREREZE+sw/fo0ePxqlTp/Dnn38WuD0lJQWdO3dGnTp1MHPmTL1tM2bM0N1u3Lgx0tLSMHfuXF34Vsq0adMwceJE3f2UlBT4+/srWFHJMHwTERER6TProQbHjBmDrVu3YteuXahSpcpj2+/fv4+OHTvCxcUFmzdvfuqwMKGhofjnn390XT58fHyQmJio1yYxMRGurq5wcHBAxYoVYW1tXWAbbWd8Hx8fZGVlISkpqdA2j7Kzs4Orq6veYo4aNQJeeAGoWlXpSoiIiIhMg1mGbyEExowZg82bN2Pnzp2oXr36Y21SUlLQoUMHqNVq/PTTT7C3t3/qfo8dOwYPDw/Y2dkBAMLCwhAdHa3XJioqCmFhYQAAtVqNpk2b6rXRaDSIjo7WtWnatClsbW312sTGxuLatWu6NuXV2LHAb78BAwcqXQkRERGRaTDLbiejR4/G6tWr8eOPP8LFxUXXd9rNzQ0ODg664P3gwQOsXLkSKSkpuosWK1WqBGtra/z8889ITEzEM888A3t7e0RFReHjjz/G22+/rTvOyJEjsWDBAkyePBmvvvoqdu7cifXr12Pbtm26NhMnTsTgwYPRrFkztGjRAvPnz0daWhqGDh2qq2nYsGGYOHEiPD094erqirFjxyIsLKxII50QERERUTkizBCAApelS5cKIYTYtWtXoW2uXLkihBBix44dolGjRsLZ2Vk4OTmJhg0bisWLF4vc3Fy9Y+3atUs0atRIqNVqERgYqDtGfl999ZWoWrWqUKvVokWLFuLAgQN629PT08WoUaOEh4eHcHR0FC+99JKIj48v8vNNTk4WAERycnKxXqcSSU0VApBLaqpBdpmTY5DdEBEREZmsoua1cjHOd3lnruN8nzwJtGkDuLgA164ZqD4iIiIiE2RR43yTaXJ1lVPLp6fLU+mc9ZeIiIgsnVlecEnmoWJF+TMzU55EJyIiIrJ0DN9kNE5OgIODvM2xvomIiIgYvsnIONEOERERUR6GbzIqhm8iIiKiPAzfZFQM30RERER5ONoJGVWLFoBGkxfCiYiIiCwZwzcZ1fvvK10BERERkelgtxMiIiIiojLC8E1lQqNRugIiIiIi5TF8k1H9/jvg7g60bKl0JURERETKY/gmo3J0BJKTgZs3la6EiIiISHkM32RUHGqQiIiIKA/DNxmVNnynpgIZGcrWQkRERKQ0hm8yKjc3wNZW3ubZbyIiIrJ0DN9kVCoVULGivM3wTURERJaO4ZuMjv2+iYiIiCTOcElG16oV4OsLODsrXQkRERGRshi+yei+/lrpCoiIiIhMA7udEBERERGVEYZvKjOcYp6IiIgsHcM3Gd2qVXKK+V69lK6EiIiISFkM32R0dnacYp6IiIgIYPimMsChBomIiIgkhm8yOoZvIiIiIonhm4xOG77v3QOys5WthYiIiEhJDN9kdJ6ecpp5ALhzR9laiIiIiJTE8E1GZ20NVKggb7PrCREREVkyznBJZaJtWyA1VQZxIiIiIkvF8E1lYsMGpSsgIiIiUh67nRARERERlRGGbypTQihdAREREZFyGL6pTHz2GeDmBowdq3QlRERERMph+KYyYWMDpKRwinkiIiKybAzfVCa0E+3cvq1sHURERERKYvimMsEp5omIiIgYvqmMMHwTERERMXxTGcnf7USjUbYWIiIiIqUwfFOZqFhR/szNBZKSFC2FiIiISDGc4ZLKhJ0d8NxzgIMDkJWldDVEREREymD4pjKze7fSFRAREREpi91OiIiIiIjKCMM3lTlOMU9ERESWiuGbysyUKYCrK/Dxx0pXQkRERKQMhm8qU/fvc6xvIiIislwM31RmOMU8ERERWTqGbyoznOWSiIiILB3DN5UZhm8iIiKydAzfVGYYvomIiMjSMXxTmckfvjncIBEREVkiznBJZcbLC2jeHKhYUU4xb2endEVEREREZYvhm8qMoyPw119KV0FERESkHHY7ISIiIiIqIwzfpAj2+SYiIiJLxPBNZWrIEMDFBVi2TOlKiIiIiMoewzeVqdxcIDWVs1wSERGRZWL4Jn1paXm3790z+O451jcRERFZMoZv0ufklHc7Ntbgu2f4JiIiIkvG8E2FO3fO4Ltk+CYiIiJLxvBNhWP4JiIiIjIohm/Sk56errude+aMwffP8E1ERESWjDNckh57e3vdbc3p07A28P79/OQU8wEBBt4xERERkRlg+CY9KpVKd9s2MRFISQFcXQ22/4AATjFPRERElovdTujJjNDvm4iIiMhSMXzTkxmh37cWp5gnIiIiS8PwTU9mhPD94ouAszMQFWXwXRMRERGZNLMM37NmzULz5s3h4uICLy8vREZGIvaRCWEyMjIwevRoVKhQAc7OzujZsycSExP12ly7dg2dO3eGo6MjvLy8MGnSJOTk5Oi12b17N5o0aQI7OzsEBQVh2bJlj9WzcOFCBAQEwN7eHqGhofjrkU7NRanFZBkhfGdmyok0OeIJERERWRqzDN979uzB6NGjceDAAURFRSE7OxsdOnRAWr6p0SdMmICff/4ZGzZswJ49e3Djxg306NFDtz03NxedO3dGVlYW9u/fj++//x7Lli3Du+++q2tz5coVdO7cGe3atcOxY8cwfvx4vPbaa/j11191bdatW4eJEyfivffew5EjR9CwYUNERETg5s2bRa7FlGlOnTL4PjncIBEREVksUQ7cvHlTABB79uwRQgiRlJQkbG1txYYNG3Rtzp49KwCImJgYIYQQ27dvF1ZWViIhIUHXZtGiRcLV1VVkZmYKIYSYPHmyqFu3rt6x+vTpIyIiInT3W7RoIUaPHq27n5ubK/z8/MSsWbOKXMvTJCcnCwAiOTm5SO1LJTVVCNkdWwhAaFQquc6AxoyRu/+//zPobomIiIgUU9S8ZpZnvh+VnJwMAPD09AQAHD58GNnZ2QgPD9e1qVWrFqpWrYqYmBgAQExMDOrXrw9vb29dm4iICKSkpOD06dO6Nvn3oW2j3UdWVhYOHz6s18bKygrh4eG6NkWp5VGZmZlISUnRW5RwC4BKCOCRLj2lxTPfREREZKnMPnxrNBqMHz8erVq1Qr169QAACQkJUKvVcHd312vr7e2NhIQEXZv8wVu7XbvtSW1SUlKQnp6O27dvIzc3t8A2+ffxtFoeNWvWLLi5uekWf3//Ir4ahqUbZNDA/b4ZvomIiMhSmX34Hj16NE6dOoW1a9cqXYrBTJs2DcnJybrl+vXritRh7PB9+7ZBd0tERERk8sx6hssxY8Zg69at2Lt3L6pUqaJb7+Pjg6ysLCQlJemdcU5MTISPj4+uzaOjkmhHIMnf5tFRSRITE+Hq6goHBwdYW1vD2tq6wDb59/G0Wh5lZ2cHOzu7YrwSxmGs8F21KtCiBfDwiwoiIiIii2GWZ76FEBgzZgw2b96MnTt3onr16nrbmzZtCltbW0RHR+vWxcbG4tq1awgLCwMAhIWF4eTJk3qjkkRFRcHV1RV16tTRtcm/D20b7T7UajWaNm2q10aj0SA6OlrXpii1mCpt+BYGDt8tWgAHDwKLFhl0t0REREQmzyzPfI8ePRqrV6/Gjz/+CBcXF13faTc3Nzg4OMDNzQ3Dhg3DxIkT4enpCVdXV4wdOxZhYWF45plnAAAdOnRAnTp1MHDgQMyZMwcJCQmYPn06Ro8erTvrPHLkSCxYsACTJ0/Gq6++ip07d2L9+vXYtm2brpaJEydi8ODBaNasGVq0aIH58+cjLS0NQ4cO1dX0tFpMle7M96VLQEYGYG+vZDlERERE5q9sBl8xLAAFLkuXLtW1SU9PF6NGjRIeHh7C0dFRvPTSSyI+Pl5vP3FxcaJTp07CwcFBVKxYUbz11lsiOztbr82uXbtEo0aNhFqtFoGBgXrH0Prqq69E1apVhVqtFi1atBAHDhzQ216UWp5EqaEGa/v7i7vaYQePHzfK4TQao+yWiIiIqEwVNa+phBBCuehPRZGSkgI3NzckJyfD1dXVuAdLS5NzvwPo9vzzmLJzJ1oBwJo1QN++BjtM06bAuXOy+wn7fhMREZG5K2peM8s+31Q2atasCV1vbwP3+37wQC4cbpCIiIgsCcM3FapGjRpGC98c65uIiIgsEcM3FcqYZ74ZvomIiMgSMXxToYKCgnThW1y4AGRlGWzfDN9ERERkiRi+qVBVq1ZFoo0N7gNQ5eQAFy8abN8M30RERGSJGL6pUNbW1ggyUtcTTjFPRERElojhm54oODjYKOE7MBAIDQWCggy2SyIiIiKTZ5YzXFLZMdZFl126yIWIiIjIkvDMNz2Rsc58ExEREVkihm96Ir3wHRsL5OQoWQ4RERGRWWP4picKDg7GVQAPADnU4OXLBtlvTg4QEAA4OgJ37xpkl0REREQmj+GbnsjHxwdOzs44q11hoK4nNjbAvXtAejqHGyQiIiLLwfBNT6RSqYzW75tjfRMREZGlYfimpzLWiCcM30RERGRpGL7pqXjmm4iIiMgwGL7pqfTC99mzQG6uQfbLWS6JiIjI0jB801MFBwfjCoAMAMjIAK5eNch+eeabiIiILA3DNz1VzZo1kQsgVrvCQF1PgoOBZ54B/P0NsjsiIiIik8fwTU/l4eGBSpUqGbzf96uvAjExwMSJBtkdERERkclj+KYiMdaIJ0RERESWhOGbisRYI54QERERWRKGbyqSx8K3EKXeZ3w8UK0aUKGCQXZHREREZPIYvqlIgoODcRFAjkoFpKUB16+Xep+ursC1a8Ddu3KXREREROUdwzcVSXBwMHIAXFSp5AoDdD1xdATs7eVtDjdIREREloDhm4okKCgIAHBSo5ErDBC+VSqO9U1ERESWheGbisTBwQH+/v4Gv+iS4ZuIiIgsCcM3FZkxRjxh+CYiIiJLwvBNRWaMEU8YvomIiMiSMHxTkQUHB+M8AA0AJCfLsQJLqU4dOcV8xYql3hURERGRyWP4piILDg5GFoBrarVcYYCuJ9OmySnmhw4t9a6IiIiITB7DNxVZcHAwAOBEbq5cwZkuiYiIiIqF4ZuKLCAgADY2NjjJ8E1ERERUIgzfVGQ2NjYIDAw06Ignx48DVasC9eqVeldEREREJo/hm4pFb8ST06dLPeKJo6Ocqf7atVKXRkRERGTyGL6pWIKDgxGLhyOe3L1b6jECtaOc3L8PZGaWtjoiIiIi08bwTcUSHByMdACJjo5yRSm7nri7AzY28jbH+iYiIqLyjuGbikU74slZlUquKGX4Vqnyzn4zfBMREVF5x/BNxVKzZk0AwOEHD+QKA1x0yVkuiYiIyFIwfFOx+Pn5wdHREae0F1oyfBMREREVmY3SBZB5sbKyQs2aNXHm+HG5wgDhu2FDID0dcHEp9a6IiIiITBrDNxVbcHAwdmjDd2IicOcOUKFCifc3b56BCiMiIiIycex2QsUWHByMVAB3nJ3lirNnFa2HiIiIyFwwfFOxaUc8uWRnJ1dwmnkiIiKiImH4pmLTjnhyTDsrTinDd1QU4O8PvPBCaSsjIiIiMm0M31Rs2jPfB1NT5YpShm9bW+Cff+Q080RERETlGcM3FVuFChXg6ekJXeQuZfjmUINERERkKRi+qUSCg4Ohu8zy33+B5OQS70sbvu/eBXJySl0aERERkcli+KYSCQ4ORjKA+9rBuUsx4kmFCnKaeUCOWkhERERUXjF8kz4nJ0AIuTg5FdpM2+/7mna4wVJ0PbG2Bjw95W12PSEiIqLyjOGbSkQ74slp7Qr2+yYiIiJ6KoZvKhHdiCcpKXJFKcN306ZAq1aAWl3ayoiIiIhMF6eXpxIJCgoCAPyVliZXlDJ8r1xZ2oqIiIiITB/PfFOJODs7o3LlynnDDV69CmjH/SYiIiKiAjF8U4kFBwfjLoB0V1e54tw5ReshIiIiMnUM31Ri2n7fCR4eckUpup6sXg1UqQIMGmSIyoiIiIhME8M3lZh2xJPztrZyRSn7ff/7L3D+vBzlkIiIiKg8YvimEtOe+T6akSFXlCJ8N2okJ9o5eBB47z0DFEdERERkghi+qcS04Xvv7dtyRSnCd506wMKF8vaHHwKff17a6oiIiIhMD8M3lVj16tVhbW2Nw9oz35cvA+npJd7fG28A//mPvD1xIrBkiQGKJCIiIjIhDN9UYmq1GtWrV8dNANmurrKzdmxsqfY5bRrw9tvy9ogRwIULpa+TiIiIyFRwkh0qleDgYFy8eBF3vLzgk5ICnD0rO3CXkEoFzJkD3L8PNGkCPLymk4iIiKhcYPimUtGOeBLn6AgfoNQjngAygC9eXOrdEBEREZkcdjuhUtFedHlSo5ErDBC+H3XrFtChA3DihMF3TURERFSmGL6pVLTh+0ByslxhhPA9aRIQFQVERAAXLxp890RERERlhuGbSkUbvqPj4+WKCxeArCyDHuPzz4EGDYCEBOCFF+RkPERERETmiOGbSqVKlSqwt7fH1Zwc5Do7A7m5Bh+ixMMD+PVXICgIiIuTXVDu3DHoIYiIiIjKhFmG771796Jr167w8/ODSqXCli1b9LarVKoCl7lz5+raBAQEPLZ99uzZevs5ceIEnn32Wdjb28Pf3x9z5sx5rJYNGzagVq1asLe3R/369bF9+3a97UIIvPvuu/D19YWDgwPCw8NxoRyNn2dlZaW76PJ+lSpypRG6nvj4yK4nfn5y9506yRFRiIiIiMyJWYbvtLQ0NGzYEAu1UyI+Ij4+Xm9ZsmQJVCoVevbsqdfugw8+0Gs3duxY3baUlBR06NAB1apVw+HDhzF37lzMnDkT3377ra7N/v370a9fPwwbNgxHjx5FZGQkIiMjcerUKV2bOXPm4Msvv8TixYtx8OBBODk5ISIiAhnaiWnKAW34/tfNTa4wQvgGgIAAGcArVAAOHQJGjjTKYYiIiIiMxiyHGuzUqRM6depU6HYfHx+9+z/++CPatWuHwMBAvfUuLi6PtdVatWoVsrKysGTJEqjVatStWxfHjh3DvHnzMGLECADAF198gY4dO2LSpEkAgA8//BBRUVFYsGABFi9eDCEE5s+fj+nTp6N79+4AgOXLl8Pb2xtbtmxB3759S/wamBJtv+9Ya2vUBYwWvgE5Df2OHXICng8/NNphiIiIiIzCLM98F0diYiK2bduGYcOGPbZt9uzZqFChAho3boy5c+ciJydHty0mJgZt2rSBWq3WrYuIiEBsbCzu3bunaxMeHq63z4iICMTExAAArly5goSEBL02bm5uCA0N1bUpSGZmJlJSUvQWU6YN34e1U8sbMXwDQPPmwOHDwCOfpYiIiIhMXrkP399//z1cXFzQo0cPvfVvvvkm1q5di127duH111/Hxx9/jMmTJ+u2JyQkwNvbW+8x2vsJCQlPbJN/e/7HFdSmILNmzYKbm5tu8ff3L85TLnPa8L375k25IjYWyPdBxhis8r1zf/4ZmDxZzm5PREREZMrMsttJcSxZsgQDBgyAvb293vqJEyfqbjdo0ABqtRqvv/46Zs2aBTs7u7IuU8+0adP06ktJSTHpAK4b6/vffyGcnKBKSwMuXQJCQox+7KtXgZ49gexswMkJeO89ox+SiIiIqMTK9ZnvP/74A7GxsXjttdee2jY0NBQ5OTmIi4sDIPuNJyYm6rXR3tf2Ey+sTf7t+R9XUJuC2NnZwdXVVW8xZRUrVoS7uzs0ADICAuRKI3c90apWDZg3T96eORP44osyOSwRERFRiZTr8P2///0PTZs2RcOGDZ/a9tixY7CysoKXlxcAICwsDHv37kV2drauTVRUFEJCQuDh4aFrEx0drbefqKgohIWFAQCqV68OHx8fvTYpKSk4ePCgrk15oFKpdCOe3KpUSa4so/ANAGPGAB98IG+PHw8sX15mhyYiIiIqFrMM36mpqTh27BiOHTsGQF7YeOzYMVy7dk3XJiUlBRs2bCjwrHdMTAzmz5+P48eP4/Lly1i1ahUmTJiAV155RRes+/fvD7VajWHDhuH06dNYt24dvvjiC73uIOPGjcMvv/yCzz77DOfOncPMmTPx999/Y8yYMQBkKB0/fjw++ugj/PTTTzh58iQGDRoEPz8/REZGGu8FUoC268llbfeeMgzfADB9OjBhgrz96qvAjz+W6eGJiIiIikaYoV27dgkAjy2DBw/Wtfnmm2+Eg4ODSEpKeuzxhw8fFqGhocLNzU3Y29uL2rVri48//lhkZGTotTt+/Lho3bq1sLOzE5UrVxazZ89+bF/r168XwcHBQq1Wi7p164pt27bpbddoNGLGjBnC29tb2NnZifbt24vY2NhiPd/k5GQBQCQnJxfrcWXp/fffFwDEF+HhQgBCNGpU5jXk5goxZIg8vFotxLFjZV4CERERWaii5jWVEBwjwtSlpKTAzc0NycnJJtv/e+3atejXrx96N22KdYcPA/b2QGoqYG1dpnXk5AB9+gCVKgELF5b54YmIiMhCFTWvlfvRTqhsaLud7L12TQbvjAwgLg6oUaNM67CxAdaulT9VqjI9NBEREdFTmWWfbzI92gsuE27dQu7D22Xd71vL1jYveGdny77gV64oUgoRERGRHoZvMggXFxfd8IlJfn5ypULhO7+pU4H584HwcCA+XulqiIiIyNIxfJPBaLue/KPt52QC4fvtt+U09JcvAx06AHfvKl0RERERWTKGbzIYbfg+q+3zYQLh29cXiIqSP0+dAjp3lteBEhERESmB4ZsMRhu+D6WlyRVnzwIajYIVSYGBwG+/AR4ewIEDQI8eQGam0lURERGRJWL4JoPRhu8/4+PlVY9pacD16wpXJdWrB+zYATg5yTPhQ4cqXRERERFZIoZvMhhdt5MLFyBCQuRKE+h6ohUaKme+9PSUs2ASERERlTWGbzKYwMBAWFlZ4f79+8gMDJQrTSh8A0D79nL48fBwpSshIiIiS8TwTQZjZ2eHatWqAQASK1SQK00sfAOAi0ve7XPngC+/VK4WIiIisiyc4ZIMKjg4GFeuXMFFOztUA0wyfGvdugU8+yxw+7ZcZs4ErPhxlIiIiIyIUYMMStvv+3hWllxx5gwghIIVFa5SJeDNN+XtDz8E+vQBHjxQtiYiIiIq3xi+yaC04Tvm9m3A2hpISQFu3FC4qsLNmAEsWSIHZ9m4UZ4J/+cfpasiIiKi8orhmwxKG77PXLwI1KwpV5pw1xNADju4cydQsSJw5AjQogXw119KV0VERETlEcM3GVTNh4H70qVLELVry5UmHr4BoHVrGbjr1QPi44FPP1W6IiIiIiqPGL7JoKpWrQq1Wo3MzEwkV64sV5pB+AaA6tWB/fuB8eOB//1P6WqIiIioPGL4JoOytrZGUFAQAOCas7NcaSbhG5DDEH7+ed5whEIAixbJyTqJiIiISovhmwxO2+/7tHaUk9OnTXbEk6f57DNg1Ch5Ieb160pXQ0REROaO4ZsMThu+/05JkQNn37sH3LypcFUlExYmhyQ8elReiHnwoNIVERERkTlj+CaD0535vnwZMNFp5ouqVSt5IWb9+kBCAvDcc8Dq1UpXRUREROaK4ZsMTjviyYULF4A6deRKMw3fABAQAOzbB3TtCmRmAgMGANOnAxqN0pURERGRuWH4JoPTnvmOi4tDzsPb5hy+AXkB5ubNwOTJ8v7s2cCxY4qWRERERGbIRukCqPzx9vaGi4sL7t+/j8SKFVEZMPvwDcgJOz/5RJ7MT08HmjRRuiIiIiIyNzzzTQanUql0Z78v2Dz8fFcOwrfW4MHAyJF598+fBw4cUK4eIiIiMh8M32QU2vB9LCNDrrh5E7h9W8GKjOPePaBLF6BtW2DVKqWrISIiIlPH8E1GoRvxJC5OXrEIAGfPKlaPsdjaArVrywsxX3kFeOcdXohJREREhWP4JqMobyOeFMbZWV6IOXWqvP/xx0CvXkBqqrJ1ERERkWli+Caj0J75Pn/+fLkO34CcR2jWLGD5ckCtlmG8dWvg2jWlKyMiIiJTw/BNRqE98x0fH490M59op6gGDgR27QK8vIDjx4EJE5SuiIiIiEwNwzcZhbu7O7y8vAAAVx0d5cpyHr4BoGVL4NAhoHt3YPFipashIiIiU8PwTUaj7XpyKjdXrrhxA0hKUq6gMlK1KrBlC1CpUt66HTt4ISYRERExfJMRacP3mX/+AapUkSvL4YgnT7NsGfDii0CPHrwQk4iIyNIxfJPRWMqIJ0+jVgN2dsCPPwKtWgFXrypdERERESmF4ZuMxpJGPHmS/v2B3bsBb2/gxAmgRQtg/36lqyIiIiIlMHyT0eQP36J2bbnSAsM3ADzzDPDXX0CjRnKyz3btgBUrlK6KiIiIyhrDNxlNjRo1oFKpkJSUhCQ/P7nSQsM3IC/E/PNP4KWXgKwsYPBg4NgxpasiIiKissTwTUbj4OCAqlWrAgDOW1vLldeuAffvK1iVspycgI0bgZEj5VCE9eopXRERERGVJYZvMirdiCcJCYCPj1x57pyCFSnPygpYtAgYMQKwsVG6GiIiIipLDN9kVBzx5OkyMzkGOBERkaVg+Caj4ognT7ZpE1CrFrB6tdKVEBERUVlg+CajYvh+sgsXgLg44J13gIwMpashIiIiY2P4JqPShu8LFy5AU6uWXMnwrTNunJz889o1YOFCpashIiIiY2P4JqOqVq0abG1tkZGRgRvu7nLllSvAgweK1mUqHByADz6Qt//zH+DePWXrISIiIuNi+CajsrGxQY0aNQAA5+7cASpWBIQAYmMVrsx0DBokhxy8dw+YPVvpaoiIiMiYGL7J6DjiyZNZW+eF7i++kF1QiIiIqHxi+Caj40WXT/fii8Bzz8lhBzdtUroaIiIiMpYSTfFx4sQJAECtWrWgVqtLfPC7d+9i5cqVAIA333yzxPsh06YXvjt2lCsZvvWoVPKs9717QNu2SldDRERExlKiM9+NGjVCkyZNcPHixQK3x8XF4fnnn0f79u2fuJ/4+HiMHz8eEydOLEkZZCZ45rtoGjZk8CYiIirvSjy5tRCi0G1paWnYvXs3VCpVqfdF5k8bvq9cuYKsoCCoAeDiRdnHws5ONkpLA5yd5e3UVMDJSZFaTUVCAnDnDlC3rtKVEBERkSGxzzcZna+vL5ycnJCbm4sr6emAu7ucT/38eaVLM0k7dgBBQcCQIZx2noiIqLxh+CajU6lUeSOeXLzIridP0bSp7AP+99/Ahg1KV0NERESGxPBNZYL9vovOywuYPFne/r//A7KylK2HiIiIDIfhm8oEw3fxTJwI+PgAly8D33yjdDVERERkKAzfVCYYvovHyQmYOVPe/uADICVF0XKIiIjIQBi+qUwUGL7PnweysxWsyrQNGwaEhAC3bwNz5ihdDRERERkCwzeVCe0Fl//++y9S3d3lsII5OXLIQSqQjQ0waxZQinmsiIiIyMSUeJxvQE6S46wdmzmfGzdu6G5fv3690HG887ej8s3T0xMVKlTAnTt3cPHSJTSqUwf46y/Z9aR2baXLM1mRkcClS0CVKkpXQkRERIZQqvDdoUOHQrdpJ9gJCAgozSGoHAkODkZMTAwuXLigH7579lS6NJOlUjF4ExERlScl7nYihDDIQpaDF12WzuHD8uJLIiIiMl8lOvM9ePBgQ9dBFkAvfPfuLVcyfBdJfDzwzDOym/zzzwOtWytdEREREZVEicL30qVLDV0HWYACz3zHxspESU/k6wsMHQp89x0waRKwf7/skkJERETmhaOdUJnRC9/VqgEODkBmJnDlisKVmYeZMwFHR+DAAWDzZqWrISIiopJg+KYyExQUBAC4e/cu7ty7lzfKCbueFImfn5z5EgCmTeMQ6UREROaoTMP3nTt3cPfu3bI8JJkQR0dHVHk4dMeFCxd40WUJTJoEVKwo5yf63/+UroaIiIiKy+jhOzExESNGjEDFihXh5eWFSpUqwcPDA0OGDMG1a9eMfXgyMRzxpHRcXYH33pO3Z84EUlMVLYeIiIiKqUTh+59//oGfnx/8/PywaNGiQttdvnwZTZs2xf/+9z/cvXtXN7xgcnIyVqxYgcaNG+PYsWMlrZ3MEMN36Y0YIUc7mTkTsLNTuhoiIiIqjhKF719++QUJCQm4e/cuemuHjCtA3759cePGDd143v7+/ggNDYWLiwuEELh37x769euHnGKOdrF371507doVfn5+UKlU2LJli972IUOGQKVS6S0dO3bUa3P37l0MGDAArq6ucHd3x7Bhw5D6yGnEEydO4Nlnn4W9vT38/f0xZ86cx2rZsGEDatWqBXt7e9SvXx/bt2/X2y6EwLvvvgtfX184ODggPDxcdrmwUAWG77NnAY1GwarMi1oN7N0LjBwJ2NoqXQ0REREVR4nCd0xMDACgXbt2qFChQoFttm7dir///hsqlQqenp745ZdfcPXqVcTExCAhIQFDhw4FIEPYpk2binX8tLQ0NGzYEAsXLiy0TceOHREfH69b1qxZo7d9wIABOH36NKKiorB161bs3bsXI0aM0G1PSUlBhw4dUK1aNRw+fBhz587FzJkz8e233+ra7N+/H/369cOwYcNw9OhRREZGIjIyEqdOndK1mTNnDr788kssXrwYBw8ehJOTEyIiIpCRkVGs51xe6IXv6tXlqdv0dIBdkIol/zCD/NxCRERkRkQJNG/eXFhZWYnPPvus0DZ9+/YVKpVKWFlZiWXLlj22XaPRiAYNGggrKyvRr1+/kpQhhBACgNi8ebPeusGDB4vu3bsX+pgzZ84IAOLQoUO6dTt27BAqlUr8+++/Qgghvv76a+Hh4SEyMzN1baZMmSJCQkJ093v37i06d+6st+/Q0FDx+uuvCyHkc/Tx8RFz587VbU9KShJ2dnZizZo1RX6OycnJAoBITk4u8mNMVWxsrAAgHB0dhUajEaJBAyEAITZulD8BIVJTlS7TLGg0QqxeLURwsBDnzytdDRERkWUral4r0ZnvuLg4AEDDhg0LbbN7924AgJubG/r37//YdpVKhVdffRVCCBw/frwkZTzR7t274eXlhZCQELzxxhu4c+eObltMTAzc3d3RrFkz3brw8HBYWVnh4MGDujZt2rSBWq3WtYmIiEBsbCzu3bunaxMeHq533IiICN03A1euXEFCQoJeGzc3N4SGhuraFCQzMxMpKSl6S3lRvXp1WFtb48GDB7hx40Ze15Nz55QtzAypVMDKlXLkk//7P6WrISIioqIoUfjWhsGKFSsWuD0uLg6JiYlQqVRo06YNbAvpmNq4cWMAkCHMgDp27Ijly5cjOjoan3zyCfbs2YNOnTohNzcXAJCQkAAvLy+9x9jY2MDT0xMJCQm6Nt7e3npttPef1ib/9vyPK6hNQWbNmgU3Nzfd4u/vX6znb8psbW0RGBgI4JF+3wzfJTJ7tgzhGzcCDz83EhERkQkrUfhWPexwmpWVVeD2v/76S3c7/9nlR7m7uwOQfbgNqW/fvujWrRvq16+PyMhIbN26FYcOHdKdjTd106ZNQ3Jysm65fv260iUZVIEXXTJ8l0j9+sDgwfL25Mmy3w4RERGZrhKFb+1FlufPny9w+/79+3W3mzdvXuh+7t+/DwCwt7cvSRlFFhgYiIoVK+LixYsAAB8fH9y8eVOvTU5ODu7evQsfHx9dm8TERL022vtPa5N/e/7HFdSmIHZ2dnB1ddVbyhOGb8P64APA3l6OgLJtm9LVEBER0ZOUKHxr+3oXNEqJEAI//fQTANmVo1WrVoXu5+rVqwAe75ZhaP/88w/u3LkDX19fAEBYWBiSkpJw+PBhXZudO3dCo9EgNDRU12bv3r3IzjeHd1RUFEJCQuDh4aFrEx0drXesqKgohIWFAZD9m318fPTapKSk4ODBg7o2lkgvfAcFATY2nC2mFPz9gTfflLenTAGKOXInERERlaEShe9u3bpBCIEff/wRK1as0Nv26aefIi4uDiqVCuHh4XB2di50P9qLDkNCQop1/NTUVBw7dkw3Qc+VK1dw7NgxXLt2DampqZg0aRIOHDiAuLg4REdHo3v37ggKCkJERAQAoHbt2ujYsSOGDx+Ov/76C/v27cOYMWPQt29f+Pn5AQD69+8PtVqNYcOG4fTp01i3bh2++OILTJw4UVfHuHHj8Msvv+Czzz7DuXPnMHPmTPz9998YM2YMANk9Z/z48fjoo4/w008/4eTJkxg0aBD8/PwQGRlZrOdcntSsWRPAw/Btaws8DONUclOnAh4ecr6i335TuhoiIiIqVEmGUklLSxNVq1YVVlZWwsrKSrRo0UL0799fNG7cWFhZWemGGPz9998L3YdGoxFVqlQRVlZW4sMPPyzW8Xft2iUAPLYMHjxYPHjwQHTo0EFUqlRJ2NraimrVqonhw4eLhIQEvX3cuXNH9OvXTzg7OwtXV1cxdOhQcf/+fb02x48fF61btxZ2dnaicuXKYvbs2Y/Vsn79ehEcHCzUarWoW7eu2LZt22PPc8aMGcLb21vY2dmJ9u3bi9jY2GI93/I01KAQQly7dk0AEDY2NiI7O1uIXr3yhhnkUIMltmaNEDt3Kl0FERGRZSpqXlMJUbJLtGJiYtCxY0fcv39fdwHmwzAPABg2bBi+++67Qh+/bds2dO3aFSqVCvv27cMzzzxTkjIsQkpKCtzc3JCcnFwu+n9rNBo4OzsjPT0dFy5cQNCKFbLjslZqKuDkpFyBRERERMVU1LxWom4ngOzv/Pfff6Nnz56wt7eHEAJCCFSrVg2ffvqp3kyQBfnwww8ByIsSGbwti5WVlX7XE+1Fl5YiLU2OD6hSydtGkJgIPByOnoiIiEyITWkeXLNmTWzYsAEajQa3bt2CWq3WXYz4NNqLEG1sSlUCmang4GCcOHEC58+fx4vt2ytdTrmyZIm8AHP4cODzz5WuhoiIiPIr8ZlvvZ1YWcHb27vIwRsAnJyc4OTkBDs7O0OUQGZGb8ST4GDAyiBvRQJQpYo8ob5wIXD5stLVEBERUX5MPKQIvW4ndnZAjRoKV1R+dOgAhIcD2dnA9OlKV0NERET5lajPx969ew1dB9q0aWPwfZLp0p75vnDhglwREgJob1OpzZkDNGkCrFkDvPUW0LSp0hURERERUMLw3bZtW70RTkpLpVIhhzODWBRt+L527RrS09PhUKsWsHWrwlWVH40bAwMGAKtWyYl3oqLk9Z1ERESkrFJ1O9GOcGKIhSxLhQoVdNcIXLx4EahdO28jZ7s0iI8+AtRqIDqaE+8QERGZilINNeLg4IDu3bvjhRdegBUvmKNiUKlUCA4OxsGDB3H+/HnUDw/P2zh8OLBlCy/CLKWAAGD0aGDBAuDsWeDhBK9ERESkoBKFbxcXF9y/fx/p6elYt24d9uzZg/79+2PgwIFo0KCBoWukcip/+EbHjnkbfv5ZTrozc6ZxC0hLA5yd5e1yOrHP9OnA2LFA9epKV0JERERACbudJCYmYs2aNXjxxRdhbW2N+Ph4zJs3D40bN0ajRo0wb948xMfHG7pWKmf0Rjx51PvvAxs2lHFF5Y+nJ4M3ERGRKSlR+La3t0efPn2wdetW/Pvvv/j888/RuHFjCCFw4sQJTJo0CVWrVkXHjh2xevVqpKenG7puKgceG/FEa+xY+XPwYODo0TKuqvw6fBj44QelqyAiIrJspe5UW6lSJYwbNw5///03Tp8+jSlTpqBKlSrIzc3Fb7/9hoEDB8Lb2xtDhgzRzWpJBDwy0U5+H34oOyinpwPdu8u50qlUdu8GmjUDXnuN084TEREpyaBXtNWuXRuzZs3C1atXsXPnTgwZMgTOzs5ITU3F8uXL0aFDB/j7++Odd94x5GHJTGm7ndy6dQv38idCGxtg7Vo58+X160DPnkBmpkJVlg/PPgvUqyeD97hxQEaG0hURERFZJqMNJ9G2bVssWbIEiYmJWL16NTp16gRra2tdNxUiZ2dn+Pn5AQAuXbqkv9HdHfjpJ8DNDdi3Dxg1CuCQlCVmbQ3MnStvr1ghxwE/cEDZmoiIiCyR0cdyU6lUsLKygkqlMujEPFQ+FNrvG5CzXq5bJ4ccXLIE+OqrMq6ufOnYUY7g6O0NnDsHtGoFvP227N1DREREZcNo4XvPnj147bXX4OPjg379+mHHjh3Izs6Gr68v3nzzTWMdlsyMtuvJxYsXC24QEQF8+qm8PWGCnKqRSqx7d+DMGWDgQECjAebN4zWtREREZalUk+w86uzZs1ixYgVWr16N69evA5CzYDo6OuKll17CoEGD0L59e07IQzraM9+PdTvJb/x44MQJYNkyoHdv4K+/gIehnYrP0xNYvly+lEePAi1b5m0TgtPQExERGVOpw/fNmzexZs0arFixAkcfnkITQsDKygrt2rXDoEGD0KNHDziVwwlMqPSe2O1ES6UCFi8GYmOBmBigWzfZYdnNrYyqLJ+6dJGL1sWLwEsvyd49bdsqVhYREVG5VqLwnZGRgS1btmDFihWIiopCbm4uxMOL4erWrYtBgwZhwIABuovpiAqjDd+FdjvRsrOTg1Q3by47LPfrJ2fCtLYugyotw4wZwKlTQLt28vrWTz7JmwCUiIiIDEMlRPGHkHB1dUVaWhoAeZZb26974MCBaNSokaFrtHgpKSlwc3NDcnIyXF1dlS7HoLKysuDg4AB7jQZp2pVPmur9yBGgdWt5leCkScCcOSU/uFLTy5votPYpKfIl/fZbeb9aNeC//wXCw5Wti4iIyBwUNa+VKHxrRy+xt7dHt27d0KFDB1iX8gzkoEGDSvX48qw8h28ACAoKQvylS0UL34AcAaVvX3l7+XJ59WBJMHwXKDpaTsYTFyfvDx8ur3kth289IiIigymT8G0oKpUKOTk5BttfeVPew3enTp2w95dfih6+AdlH4qOPZHeUPXuA0NDiH5jhu1CpqcDUqcDChfL+nDnyrDgREREVrKh5rcTDjgghDLqQ5dL2+y6W99+X4+ZlZgKRkcC//xq8Lkvm7AwsWCCnpX/pJTkrJpmotDR5UbJKJW8TEZFJK9EFl7t27TJ0HWTBShS+razkVI0tW8qrBCMjgb17AQcHg9dnyZ57Ti5a2dnypR45EujaVbGyiIiIzFaJwvdz+f8aE5VSicI3ALi4yCnomzcH/v4bGDYMWLWKA1Ub0ddfA9u3y+WVV4AvvpDjhhMREVHRcLYbUlyJwzcAVK8ObNwI2NgAa9bI8fHIaEaMkFPSW1kBK1cCdeoAmzcrXZUJYRcQIiJ6CoZvUpy/vz/s1OqS76BtWzkzDAD83//J8b/JKBwcgLlzgf37gdq1gcREoEcPOfjMrVtKV0dERGT6GL5JcVZWVqhRo0bpdjJypJwZRgigf3/g9GnDFEcFCg2VQ65PmybnOVq3Tp4VJyIioidj+CaTEBQUVPqdzJ8vz4Knpsop6O/cKf0+qVD29sDHHwMHDgAtWpRuviMiIiJLwfBNJsEg4dvWFtiwQfYDv3wZePllOTwHGVWzZjKA16yZt27mTGD1avlFBBEREeVh+CaTUDN/ciuNihXlCCjOzsCuXcCECYbZryGlpytdgcHlH2Dm0CHggw+AAQPksITx8YqVRUREZHIYvskkNG/eXHc7TjuveUnVq5c35ODChcA335Ruf4YghEylb7wB5D/Lv2qVcjUZSaNGcg4kW1v5OahOHeD77wGNRunKiIiIlMfwTSahdu3autsLFiwo/Q67dZPTzwPAmDFyCnol3LoFfP450KCB7Bi9eDGQnJy3fdw44NgxZWozEltbYMYM4PBhoGlTICkJGDIEqFFDdkd58EDhAskwOKwiEVGJMHyTyfn+++9xxxAXS06bJsfAy8kBevYErlwp/T6LIicH2LZNHrNyZWDiRDkLp7297IuxdWte24wM2e7evbKprQzVry/7gs+aBbi6AnFxwP/+B9jZ5bVhn3AyC/ygQUQGxPBNJudBejq+/vrr0u9IpZJpr2lTOfJJ9+5yJBRjOX9eBv6qVYEuXYAffpAXfDZvDixaJDs/r1wpR2TRqlZNXhw6cGC57JdhYwNMnQokJMgeNh99JIcmBORLU7u2HCXyr78YxImIyDIwfJNJ+uqrr5BuiAsTHR2BLVsAHx/g5EnDh9zUVGDpUuDZZ4GQEGD2bBmyK1aUF3ueOCGT5ciRgLv7449fvVqeEd+2DfjPfwxXl4lxcJDDrw8enLfu99+B2FjZJT80VHbV/+wzOXEPERFRecXwTSanqr8/bt26heXLlxtmh1WqyDnQ1WoZxGfOLN3+hAD27QOGDZOh/tVXgT//lHOud+4MbNoE/PsvMG+e7HvxJA0bAtqz/O+9B/z6a+lqMyMREUB0tOyJY28PnDkjp66vUkWOknL8uNIVEhERGR7DN5mcMWPGAAA+++wz5ObmGmanzzwDfPutvP3hh8D69cXfx40b8sx2rVpA69bAkiWy/2fNmrJj8/Xrsj93jx4y6BfV0KFyekjt7JylHe3FTFhZAc8/L3vixMfLa1FbtJBd5n/8Ub8bSlaWcnUSEREZEsM3mZzBgwfDw8MDFy5cwE8//WTIHQNvvSVvDxlStFFGsrJk3+0uXQB/f9mn+/x5wMlJhuY//pB9J6ZOBfz8Sl7bl1/KvuF37wK9eskLMS2Iuzvw+uvAwYPy2tQ5c+SQhVrDh+d9fso/WAwREZG5Yfgmk+Ps7Iw33ngDADBnzhwIQ16J98knQMeOcqKbPn0Kb3fqlBylpHJlORrJtm2yr7j2jHdCgvzZurX+DDMlZWcHbNwIVKggx+gbO7b0+zRTdesCkybl3c/MlL2FDh6UAd3XV3bd37WrXF6jSkRE5RzDN5mksWPHQq1W48CBA9i3b5/hdmxtDaxZIy+O/Pdf/W1JSXl9H+rXl+Nz374t097UqcC5c/JM99ChcgZNQ6taVdamUgH//a9cCHZ28suGTz+VE/akp8uuKs8/L+crMoU5lIiIiIqK4ZtMko+PDwY/HBpj7ty5ht25u7ucejH/6CPDhsmQ/cYbciZKGxvZd3vrVuDaNdmnOyTEsHUU5IUX9CcHOnzYcPs247GKvb1lj6FTp+TY4a+/LscOv3JFfj7Sys6W4ZyIiMhUMXyTyXrrrbegUqnw008/4dy5c4bdeXCwnPNca9062c+6bl05Ssm//8pRSzp3lkG8LE2dCnTtKvtb9OwpxygnAPJzQ2io/IJCO2z6kCF527dskZ+hRo2Sn6E4dng5ZMYfIomIAIZvMhVOTjIpCSFvAwgJCUG3bt0AyJFPDK59+7zbw4bJ8bhPnpTjc3t5Gf54RWVlBSxfLudjv3pVjsVnqFFfyhFHR/nSVK6ct27rVnlB5qJFsvdQzZqyf/hXX8k+4zk5ytVLREQEMHyTiZv08Mq75cuXIz4+3ngH+uILOdqIIS6eNAR3dznKioODHPv7/feVrsgsLF0qJ+/p31/2Fb90SZ4df/NNoFUr+WWC1sGDcmxxXrRJRERlieGbTFqrVq0QFhaGrKwsfPXVV0qXU7YaNNAfm3zrVmXrMQNWVvILjVWr5EyZO3bIzy2dOwNt2+q+VAEAjBsnexm5u8uLN6dOlZ93/vmH3VXIwrFrD5FRMXyTyZs8eTIAYNGiRbh//77C1ZSxV14BRo+WtwcOlKdyqUjc3OSoku++Kz+3/P573jYh5AWbjo7A/fty2MJPPpFd7P39ZZeV/HgRJxERGQrDN5m8bt26ITg4GElJSfjf//6ndDllb948OcNMUpJMhw8eKF2R2VOpgN9+k/3DT5yQozqOGCEn9rG2lqM+agkBVKsmB7vJ33/cwuZBIiIiA2H4JpNnZWWFtx7OTPn5558jOztb4YrKmFoNbNgAVKoEHD8uh0NkvwiDsLGRQ7oPGybHCz96FEhJkQFb6/p14NYtOda4tv/4M8/IM+fNm8vLBYiIiIqK4ZvMwqBBg+Dl5YVr165h/fr1SpdT9qpUkcMhakdCMZeZZcyw76ijI+Dnl3e/alUZvvP3H69USY4p/vff+nM1JSUBzyMaG9GzzOsmIiLzwPBNZsHe3h5jH065PnfuXMNOOW8u2rUDZs+Wt998U/Z9oDJRsaJ+//HERDnBz7p1crhDrb+PWmEXnsfL2IgpM2w5QiRRUZnhB3WikmL4JrMxatQoODo64vjx4/g9/9VzluTtt+XMm9nZQK9e8pQslTmVCggIAHr3Bho2zFtft7bAOMwHAMz5XI2uXeXZcKISYSAlKpcYvslseHp64rXXXgNghCnnzYVKJQezDg6WY+L17cuZY0yIr4/AfEzAWvSBg4PAjh2yf3hsrNKVEVGB+AHH+JR6jU3435bhm8zKhAkTYG1tjaioKBw7dkzpcpTh6ioHpHZyAnbuBGbMULoiekQfrMefURnw95fBOzRU/lMRESnGhMOopWH4JrMSEBCAl19+GYAFn/0G5Oww2mEXZ88GtmxRtBx6XJNGGhw6BLRuLe9XrqxsPUTGlpsL3L0LXL4MHD4sr43QOn8eeOcdYNQoOQNtp05AWBhQqxbg4wMsWW6jt69yP5Qng7BFY/gms6Odcn7dunW4evWqwtUoqE8fYPx4eXvwYPnXjUyKtzcQHQ3s2SPHCdeyxOuFyXxkZgLnURP7EYZtv1hj5UpgwQI50W7+Lxz37JFj4wcEyEmtbGyAChWAGjWAZs30zwlcvw58/DGwaBGwZg3wyy/AgQPym6HERODmLZWu7cbN1qhdW39iLKLyxObpTYhMS5MmTdC+fXtER0dj/vz5+Pzzz5UuSTlz5sjx7v78U07Ac+CA/hzqpDi1Wv+izOhoYPp0OXR7lSrK1UWWLSlJTph76ZIMyoGBcv2GDUCfPo4QePhhvpf+47y8ZOAG5OUmx48/vm9HR8DDQ05YpRUYKAdpcneX2x79WcUjG3gPEAA+/dIWcXHACy8Ar70GfPqpDPdkXu7eld+AXDxtg4v4FHfhCc0INTRWgEYDDBki/40B4NQpOZqURiNPTmg0+reHDQMefumNs2eBkSMLbzt8uJw0zZQxfJNZmjRpEqKjo/Hdd9/h3XffhYeHh9IlKcPWFli/HmjSRP72GjFCzgSjUj39sVTmcnPl1+7nz8vAs3mz/OqdyJguXgSWLcsL25cuyWCk9fXXcu4uQI5xL4QKTkiFNxLh0TgA7p7WuqAcHJz3uMaN5fj3j4ZptfrxGqpXf8qEVA97XqgA/P5zBqZ95IQFC+Tss9u3y6kNunQpzatAhpadDVy9Kt9fFy7In336AC1byu179wIvvQQAdgDkRHlYnff4li3zwvedO/L3YWGefz7vdmqq3HdhXnyxBE+mjDF8k1nq0KEDGjRogBMnTmDx4sWYNm2a0iUpx9dXBvB27YDVq+XwGg/HRLdYaWmAs7O8nZpqMt8GWFvLr9u7dwdOngTatgUWLwaGDlW6MjJHWVlAXJx+qNYukybJM4sAEB8P/Oc/jz/e21t2EXF3z1vXrBmQcCkNXjVcoAKAPwr//+PpKce/NzRnZznLbO/e8oznhQtA165yTP358+W4+1Q2MjPlNxzat8CxY8DUqTJox8XhsbkMKlfOC9/BwUDt2kDNwBwEbfsCXrgJ6/98AJWdHaysgFat8h5Xs6bskmRlJc8dWVnpL02a5LWtUUN+Q5O/Xf7bNWsa8xUxDIZvMksqlQpvv/02Bg0ahC+++AITJkyAvb290mUp59ln5XezEyYAEyfK31T5f7ORyaheHdi/Hxg0SJ7pefVV+dX9p5/KPrNPlJMjZ/eJjQXOnZM/z5zJ296yJdCmjbzKs1Ur9msxY7m5QFoKkAYfJMIbl7ZYI6heXhemAwfkP7FGU/Djz57Nu12rFvD66zK01KgBBAXJbiDaz6f52dnJUG4Knn1W/t947z3gs8+AVavk5S3as6VkGFlZ+mev8y/XrgGffCKnmABkyP3117zHOjjkvaeCguTITlp16jz89ZSWCTg/3MG4dwEnu8dq8POTXUmKwtNTTnNhzhi+yWz17dsX77zzDq5fv46VK1fqxgC3WOPGyb/I69bJU0aHD8thBMjkODsDGzfKC9hmzpRfx586Bfz8s/xjhtu3ZbB+dLl0SX7XW5gTJ+SyYIG8X62aTGitWslAXreufkdcKjEh5FnBtLTHl6pV5YcsQF5MuHx5we3S0uTX9Noz1LGx8vNTWprcN+AEIF5ufAWYMiUvfFepIoO3o2NeqM6/1KuXV2ulSvIbFnPk4CAvbXn5ZfmtUf7gnZNThA+sBEC+V+KuqHAaXXAWtdFklxXCH3bjOXUKaNq08MdeuZJ3u2ZN2RVIG7Z9feXZZioevm3JbNna2mL8+PF466238Omnn+LVV1+FlSX/FlCp5G/Fkyfl6YY+feRwAba2SldGBbDKycJ7vS+hw4NYbP88Fi/ExcI+/GHIvnOn8Ac6OMjvc0NC5BIQIL+bB2TK+/tvYN8+4OhR2SHz6lXZHQmQY8S3bJkXxlu0kOmNCpSRARw5AuzfbYN2aIKmOALg6WedP/ggb/j9xERg8uTCj1G/ft5ttVq/LzYAqKBBBdxBjeae8PPL++Dk5ye7k3h7W8YlHs2by0Xr+nX5Jc/77wMDB1rGa1Ac9+7JD1xnzsjl7FkgPd0RwM8AgFE/ZevCd1CQvKBVG6gfXfJ/E+LomPfrhkqO4ZvM2vDhw/HBBx8gNjYWW7duRbdu3ZQuSVnOzsCmTTJU7d0LTJsm+zOQMoQAbt7MO3Ot7SoSGytPJ+XmIgxAGABcerho+fvnBeyQENl3ICREnvLM/yEzLS3vr2GPHjKJALKv+8GDciScffuAmBggJUWePvzlF9nGxiavi5K2q4qp9DlQwI0b8mXav18uR47Ir+QBO0xHpC5829vrB2+1WvaJ1S75R+bw8pJdjPJvd3KSIcbJCWjQIK9tlSoyKGm3OSEN9pWcZd/rXfp9r62sLPuLrXnzZJ/jwYOBtWvlBZn+/kpXVXZycuR46mfOAKdPy59Nm8peh1r/93/6j7GzE6iVeRy1cRbPtOihW+/qKsM6P8CUHZUQHHHW1KWkpMDNzQ3JyclwdXVVuhyTM3XqVHzyySdo3bo1/vjjj6I/0EQvyjOIH36QQw8C8sqUXr2Ueb5KvcZKHff2bfkdPyCvXLtwAUhOLry9s7N+wA4JgaZmCPrOqIkGYU54550i/EEs6nPNyZHfivz5Z95y48bj7YKC9MN4rVoFF2Hm/7Y5OfKfpkIFeT82Vj7VR1WqBLQMzUH/rf3RGxuA1FRk2Trh9u28IG20rg9m/hob85g5OfK8wsyZsouOiwswd64cZq5YX4Ca+GssRN5/v4wMeXH2mTPy/Sq7JuXp0EG/P/aoUfIDXd26sv91oHcarN1M97mWh+MWNa8xfJsBhu8nu3HjBgICApCdnY39+/cjrKhjt5Xn8A3I77rnzpXP8a+/ZEdUE/+DatbHPXpUTt137pz+epVKdg15JGQjJET2HXgk2P78M6D9Aufll4GlS59SfkmfqxCyS8q+fXlnx0+denwGoAoVZFcVbRhv1kxelWdm/7Z37siz2toz23/9BURGyov4AHkm28tLhpWwMPmUW7YEAqtkQbV/X95YZ7/9BoSHl81pQjN7jZU45rlz8qLlmBh5v1074LvvZL93Yx631B45brbaCRcv6p/JPn1a/prYuFE2E0J+GNT2SnNwkKOJaMN1s2byrVnUY5br95NCxy1yXhNk8pKTkwUAkZycrHQpJuvVV18VAMRLL71U9Aelpgohf5/J2+VNdrYQbdvK51erlhDx8WX/fJV6jcvyuDk5QsyaJYStbd4xASFWrBDi5Ekh0tOLvctvv83bXaNGQsTFPaGxIZ/rvXtCbN8uxP/9nxDPPSeEvb3+cwKEsLMTolUrISZONPl/W41GiNdeEyIk5PGnoX1t80tPF0IkJgqxZYsQkycL0bq1fL6PPrBZMyFWrhQiM9N4z1MIy/j/Y4Bj5uQIMX++EI6O8uFTppTNcUtKo9E/blhozmO/PrRLYKD+Y1esEOLnn4W4dEmI3NxiHtiS3k8KHbeoec0sw/eePXtEly5dhK+vrwAgNm/erNuWlZUlJk+eLOrVqyccHR2Fr6+vGDhwoPj333/19lGtWjUBOZmWbpk1a5Zem+PHj4vWrVsLOzs7UaVKFfHJJ588Vsv69etFSEiIsLOzE/Xq1RPbtm3T267RaMSMGTOEj4+PsLe3F+3btxfnz58v1vNl+H66M2fOCABCpVKJ2NjYoj2ovIdvIYRISBCicmX5HF96yaz+oJrFca9cEeLZZ/OO1a2bwY77xx9CVKokd1WpkhB79hTS0JjPNTNTiAMHhPj0U/n+8fIqOCF4ewvxwgtCvPWWEMuWCXHkSIk+dBRJAc83JUWIqCghPvhAiAkT9Js3apTXvFYtIV59VYjvvhPi9GkhcrNz5Qekb74RYvBgIWrWLPj5VaiQdzv/BxI/PyH+8x8hbt0qs+daJswsfGtduiTE0KFCpKXlrXtqQC2D56rRyLfZ558L0bmzPCeS/7gtmuUIQAhnZyFatBBiyBAh5swRYts2+SvGYCzp/aTQcct1+N6+fbt45513xA8//PBY+E5KShLh4eFi3bp14ty5cyImJka0aNFCNG3aVG8f1apVEx988IGIj4/XLan5/nGSk5OFt7e3GDBggDh16pRYs2aNcHBwEN98842uzb59+4S1tbWYM2eOOHPmjJg+fbqwtbUVJ0+e1LWZPXu2cHNzE1u2bBHHjx8X3bp1E9WrVxfpxfjDxPBdNF26dBEAxOuvv160B1hC+BZCiP37Hz8ra0Z/UE3yuBqNDJkuLkL3V3PpUiHu3zfoca9eFaJxY7k7Gxshli8voFFZvsYajRDnz8vnOnhwwUFVu1hby7Tbu7cQH34oxI8/CnH5cglO1z0iNVVcRoBYjlfEyGFZomFDIays8g6rVuvn/h9/FGLrViFu3xYypf/+u0zpHTsK4eZWcO116woxfLh8nufP6/+7xsUJ8dFHQvj66gfy4cNlojek8vr/p4yOmZMjg+5//iNEVlbZHVcIIa5dE2LJEiH695efTfO/vVQqIe5cyzvuiQNp4urVh2fEjcmS3k8KHbdch+/8Hg3fBfnrr78EAHH16lXdumrVqonPP/+80Md8/fXXwsPDQ2Tm+1pxypQpIiQkRHe/d+/eonPnznqPCw0N1YU/jUYjfHx8xNy5c3Xbk5KShJ2dnVizZk1Rnp4QguG7qPbs2SMACDs7O5GQkPD0B1hK+BZCiAUL9H/779wpxJ07xj9uefyle/u2ED175u2/VSt5ys1Ix01LE6JPH5nvDh0qoIEpvMa7dsm+MmPHyu4qnp6Fh3IXFyHCwoQYMUK+L/fsEeLu3SceSu+tmpoquuLHx3YbECCDzldfPXwZNBp52nDVKiFGj5afYvKndO3i5CTE888LMX26EDt2FFxLQa9xZqbsetK0qf7+OnSQ+ynth4zCjlsWykn43rgxb5eNGwtx9Kjxjnv3rn547tdP/23h4CA/782dK+vITSkfrzGPq4/hO5+oqCihUqn0Xoxq1aoJb29v4enpKRo1aiTmzJkjsrOzddsHDhwounfvrrefnTt3CgDi7sNfzv7+/o8F+HfffVc0aNBACCHEpUuXBABx9JH/8W3atBFvvvlmofVmZGSI5ORk3XL9+nWG7yLQaDSiRYsWAoCYPn360x9gSeFboxGib9/Hg0eFCjIIDR4sz+atXy/EsWOGez3K2y/dX37JO+NpYyPExx/L02tGPq72a+v8dIc1xddYoxHin39kCJ0zR4hXXhGiYcPHv4HJv1SpIkSnTkJMmSIyl6wU+xafEFMnZurO/Ou+fk9NFYvwugjDPvHWm1li0yYhbtwQeV1k5s0Tolcv2SWkoONUqyaT0Vdfye4x+X7vl/i5/vGHED166If7WrWEWLxYvw+EIV9jYyon4VujkX2kPTzy/stOny5ERkbpj5ueLr9EmTpVXgKgUglx6lTe9mXLhHjmGXm83bsfOWYpjlsqlvR+Uui4DN8PpaeniyZNmoj+/fvrrf/ss8/Erl27xPHjx8WiRYuEu7u7mJCvs+ALL7wgRowYofeY06dPCwDizJkzQgghbG1txerVq/XaLFy4UHh5eQkhZLcUAOLGjRt6bV5++WXRu3fvQmt+7733xKP90Rm+i2bjxo0CgPD09NTrRlQgSwrfQghx82be89X2A3/SUqWKPCM4cqQMNFu3yq/gC/3+tgDl5ZduWpoQY8bk7bN2bSEOHzb+cQtx6JDMdseOld0xH1OS42ZlyYSyerUQ06YJ0aWLEFWrFvoezIKNOI76YiX6i+P9ZslOsOfO5bVZt05eXffsswVfHGpjIzvRjh8vP1j+849xn+vly7LTubY7EiC/BZg6tWTHNqd/WxM+Zny8/pdVderIz2nFPW5cnBCzZwsRHl7w223JkmIUVc5eYx5XKmr4LteT7GRnZ6N3794QQmDRokV62ybmG4m+QYMGUKvVeP311zFr1izY2dmVdal6pk2bpldfSkoK/C1p9oBSiIyMRFBQEC5evIglS5Zg7NixSpdkOvLPZBgbK39evAicP6+/XLggx7L65x+57Nypvx8bGyAwUM6y+OhSwNB5Zu/wYeCVV/KGEBw7Fvjkk4fzwCtj8mRZTsuWwPffWKOXYpUUk62tHBetbl1catEPDg7yLYOkJOxecArrZpxEA5xAfZxEA9VJuIoUNMBJNMBJYA3kkl+fPvr3tcMiapfmzcv236l6dTn7y8yZwLJlwBdfyJlQZs+Wg1K//DIwYYL+VI1kdD4+cri+jRuB0aPlMH5vvilnKi3st5UQwKVLcgKlqlXlunPngKlT89r4+cmh/dq3l0vlykZ/KlROlNvwrQ3eV69exc6dO586PnZoaChycnIQFxeHkJAQ+Pj4IDExUa+N9r7Pw2nFCmuTf7t2na+vr16bRo0aFVqLnZ2d4h8AzJW1tTUmTpyIUaNGYd68eXjjjTdgY7QZMMyckxPQsKFcHnXnjgzhjwbz8+eB9PS8249ydARq1swL49WqGf95GEturgxNM2fKGT18feWg2xERSleGTZuAvn3lkNMvD7THDLyPmZiJ4swtUtZu35af437/XS5XrsiX9r33ALi7o+Ho1pj/d2vUewGoFA641BTA9WtyYqATJ/J+xsbKfxtAzorTurUM2q1ayfeeKXz4c3WV6W70aGDrVuDzz4E9e4A1a+TSsiUwfjzw0ktGnKGnGG7dAo4fl6/v8eNyzHqtwEDAwyNvcXcv2m1X12LOdmN8vXrJccAnTpSL9q0iIEN4YiKw82Dee/TaNWDSJGDOHNnu2WflP1m7djJ0Fzb/FNHTmMD/esPTBu8LFy5g165dqKCdwuwJjh07BisrK3h5eQEAwsLC8M477yA7Oxu2trYAgKioKISEhMDDw0PXJjo6GuPHj9ftJyoqSjfJS/Xq1eHj44Po6Ghd2E5JScHBgwfxxhtvGPAZU35DhgzBu+++i7i4OGzatAl9Hj07Rk9XoYJcnnlGf71GI2dFLCiUX74MPHgg/3gfP/74Prt1A157Tc5souBZ4yK5fFlO075/v7zfqxeweHHedIgK8/AAtm0DpkyRJ1o/xLs4isbovswGg4bLs3WAnFfCzk6ecFbC/fvARx/JIHP0qDybqGVrmzdZCCCf05Yt+R+tkh/eqlUDunTJW333bt6/w99/m/bkWNbWQPfucjl6FJg/X4Zv7fz1VavKb1Jee00GVmPLyZEfXvIH7ePHgfj4wh9z86ZcisvKCnBzK3pYt7fPe6xGU/zjFVGFCsD33+uvm46P8C1G4HYN/feSra3+BLWOjnLyYKLSMssZLlNTU3Hx4kUAQOPGjTFv3jy0a9cOnp6e8PX1Ra9evXDkyBFs3boV3t7eusd5enpCrVYjJiYGBw8eRLt27eDi4oKYmBhMmDABnTp1wvcP/1cmJycjJCQEHTp0wJQpU3Dq1Cm8+uqr+PzzzzFixAgAwP79+/Hcc89h9uzZ6Ny5M9auXYuPP/4YR44cQb169QAAn3zyCWbPno3vv/8e1atXx4wZM3DixAmcOXMG9vl/2TwBZ7gsvvfffx8zZ85EkyZN8Pfff0NlStNjK8XYzzc7G4iL0w/kZ8/KM375ubsD/frJaemaNjXOqaPSzPq4bJk8a5maKuesXrBABvGi1KnAe2r5d5kYPgLIgvy2LDs772Rqv37A2rXyJGSFCkDFinmfqypUkCf2tZ+Dzp2Tn5207Rwdn/KUH3muufZOOHZMnkTt2FGuzsmR+9IGmPr15RnDF16QZxG1Dy8WM5p9sUAJCcDXXwOLFsmvAwC5v6FD5fuuZk3DHPfu3bxwrQ3ap08/Pie5Vo0aed+EhYTIr1YAOXVkRgZw7x6QlCR/Pu12RkbxXpNHqdXym6bKlQtf/PwM8iH+euwDBNayRQ7kJ9RGjfK6kjz7rBHfXub+PuZxC1SuZ7jctWtXgRckDh48WFy5cqXAbQDErl27hBBCHD58WISGhgo3Nzdhb28vateuLT7++GOR8cjlyPkn2alcubKYPXv2Y7WsX79eBAcHC7VaLerWrVvoJDve3t7Czs5OtG/fvuiTwDzEoQaL79atW8LBwUEAENHR0QU3srQLLpW+wGfatMcvrqtXT17MefOm8Y5b1Od686b+RETPPlv8GS4Ueo0PoakYhu/Eyy/pj9zRoUPh19OqVPoDffTpo7/dzk4OGFK/vhDt2smhrrWio4VY9k2G+BmdxWKMEL0is3WjC1avrl/el1/KEfni4w33fMvFhWrp6UL897/y/0D+f5SuXeULrNEU7bg5OUKcPSvE2rVyZtLOneXF0oX9wzs5yRGORo4UYtEiIfbtk+OfG/L5pqfLIWjOnJH737pVvgm++kqOsT5xopwNJzJSDsTdsKEQ/v5Pvwj80cXDQ75+ERFy9qQZM+QIMz//LEeySUx8+pCPqanibzQRm/CSuHmlfF8MWG4ufNRo5C+vBw+ESEqSk1z9+6+8Ivb8eTne/tGjchhTE73g0izPfFsanvkumTFjxmDhwoXo2LEjduzY8XgDnvku+2M6OMiOv0uXyu9vtWfIbGyArl3l2fCOHUvfD7a4z3X7dnnsxET5XfOHHwJvvy27DRjzuIbwhGPm5soTkXfuyJOsd+7kLampD/tbPzRihOyefOcOkJWlfwiVSp5R174cffsC69Y9Xoqrq+wPu3q1/vW9BmVir3GpCSH/T3z+uexLpNWgAfDGG3LRHjc7O+8stvbnqVPyOoyCBATknc1u2FDuMzDw6X2xlX6Nz5yRZ9H//TdvuXFD/35hz/lRtrZPPovu4ZF33Us5Pyv72DEdHeXXU1lZ8r2V/2dB60ra5sED+W0PAPTvL9/zRXnsk34WN7qa2Jlvhm8zwPBdMpcvX0bNmjWh0Whw4sQJ1K9fX78Bw7eyx0xKkn0iliwBDh3KW+/jAwwaJL+Gr1XL8MfN78EDGbK1oyHVqQOsXAk0bmzc4xqSgY8phNxl/qCenCy7vWvNmgXsjs7BnehjcEYq2k9vifAX1WjevAyuHywHr3Ghzp+XI6QsWybfm/lVrSqvACyIg4Ps05M/aNevL/tcl4Spv8ZCyN8f+cN4QQH95s3ihTQnJ/kGtrGRnzSfdPtp2592W4i83zuvvy4/EOXkyE/MJf1ZlDZ378pj2trKEFteWFvL7kq2tnk/bW3z/s8wfFNxMXyXXO/evbFhwwYMGjRI15/fYpnyH9RTp+TZ8BUrZKdhrbAwGcL79JGnVQ153EOH5BCC2lFbxo2TqbI0/UhN+TXmcc3nmPfuAf/9L/Dll3K4z/z8/R8/mx0UVPxvaZ6kvLzG2dnyYtInBfR//338g44ls7F5PMSq1UVfV9A2QA61CcgrsJ2dC39cSX7a2hb8bY4J9/lm+DYDDN8ld+jQIbRo0QI2Nja4fPmyZY+Xbg5/ULOyZBeQJUvkT+2Qcg4OcozkoUOBNm1K97V5To4M2R98IG/7+ckzjS+8UKKnWOTjGoslhWCljqvUc01Kkt0iAPn/ITQU8PQ0/nEt6TXWXlgNyOEs1eq8M8UFnT0uyu2itE1PB+bOlcedPFl2Acl/Vv3RM+wF/SzutqwsoFkzecxz5+TF748GWVO6AN4Mj1vUvFYuhxok0mrevDnatm2L3bt344svvsCn2k/fZJrUajkUYWSkHBVixQoZxM+dA5Yvl0tgIDBkCDB4cN7sF0V16ZI8233ggLzfu7f86rcsAg1RceUfI7JNm/LfNU4J+cNm9eplGwy14fvdd8vuA45WlSp8PynItEbAJzKCSZMmAQC+/fZbJOcftJVMm4+PnOHizBk53Nnw4fIM1eXL8o9VQICc8Gbt2qcPbSaE/Bq/YUMZvF1dZbBfu5bBm4iIyhTDN5V7nTp1Qt26dXH//n188803SpdDxaVSycl+vv1W9t9cvhxo21YG6t9+k4NZ+/rK2QQPH378AqubN+WZ9OHD5Zmf556TI0W88gqnpyMiojLH8E3lnkqlwttvvw0A+OKLL5BZ2CQTZPqcnOSEN7t2yS4kM2bIC9CSkuRQVs2ayVkyFi7Me0xoKPDTT/Ir/DlzgOho8572noiIzBrDN1mE/v37w8/PDzdu3MDq1auVLocMITBQXjR55Yo8A963r5xL/cQJOe+61q1bQL16cnSTSZMMOyoEERFRMTF8k0VQq9UYP348AODTTz+FRqNRtiAyHGtrOVLJmjWyW8rChUCTJnnbx46VwVs7kQYREZGCGL7JYowYMQIuLi44c+ZMwTNekvnz8ABGjQL27s1bN2sWYG+vXE1ERET5MHyTxXBzc8Prr78OAJirHeKJiIiIqAwxfJNFGTduHGxsbLBnzx789ddfSpdDREREFobhmyxKlSpVMGDAAAA8+01ERERlj+GbLI522MEffvgBly5dUrgaIiIisiQM32Rx6tWrh06dOkGj0WDevHlKl0NEREQWhOGbLJJ2yvmlS5fi9u3bCldDREREloLhmyxS27Zt0bRpU6Snp2Nh/tkQiYiIiIyI4ZsskkqlwuTJkwEAX331FR48eKBwRURERGQJGL7JYvXo0QPVq1fHnTt3EB4ejpiYGKVLIiIionKO4Zsslo2NDb744gs4OjoiJiYGLVu2xMsvv4yLFy8qXRoRERGVUwzfZNG6du2KCxcu4LXXXoOVlRU2btyIOnXqYNy4cbwQk4gkJydACLk4OSldDRGZOYZvsnh+fn747rvvcPz4cXTq1AnZ2dn48ssvERQUhE8++QTp6elKl2gYDBDlE/9diYjMCsM30UP16tXD9u3b8fvvv6NRo0ZITk7G1KlTERISgpUrV0Kj0ShdovlhMCQiItLD8E30iPbt2+Pw4cP4/vvvUaVKFVy/fh0DBw5E8+bNsXPnTqXLIyIqf/hBnSwIwzdRAaysrDBo0CCcP38es2bNgqurK44cOYL27dujc+fOOH36tNIlEhEZB4MwkVExfBM9gYODA6ZOnYqLFy9izJgxsLGxwfbt29GgQQOMGDEC8fHxSpdIpAylAhqDIREVhQn/rmD4JiqCSpUq4auvvsLp06fRo0cPaDQafPfdd6hZsyZmzpyJ1NRUpUskIiJzoUQw5Admk8HwTVQMwcHB2LRpE/78808888wzSEtLw/vvv4+aNWviu+++Q05OjtIlkpL4R4bIvPD/LCmA4ZuoBFq1aoX9+/dj/fr1CAwMREJCAkaMGIGGDRti27ZtEEIoXSIRERGZIIZvohJSqVR4+eWXcfbsWcyfPx+enp44c+YMunTpgvbt2+PIkSNKl0hE5oxnZYnKJYZvolJSq9UYN24cLl26hEmTJsHOzg67du1C06ZN8corr+Dq1atKl0hEREQmguGbyEDc3d0xZ84cxMbGYsCAAQCAVatWISQkBFOmTEFSUpKyBRIREZHiGL6JDKxatWpYuXIlDh06hLZt2yIzMxNz5sxBUFAQvvjiC2RlZSldIhERESmE4ZvISJo1a4adO3fi559/Ru3atXHnzh2MHz8ederUwcaNG3lRJhERkQVi+CYyIpVKhS5duuDEiRNYvHgxvL29cenSJbz88sto2bIloqKiGMKJiIgsCMM3URmwsbHB66+/jgsXLuDdd9+Fo6MjDhw4gA4dOuDZZ59FdHQ0Q7ghcZQIIiIyUQzfRGXIxcUF77//Pi5evIhx48bBzs4O+/btQ3h4ONq2bYvdu3crXSIREREZEcM3kQJ8fX0xf/58XLp0CWPGjIFarcbevXvRrl07tGvXDnv37lW6RCIiIjIChm8iBVWuXBlfffUVLl26hFGjRkGtVmP37t147rnn0L59e/z5559Kl0hEREQGxPBNZAKqVKmChQsX4uLFixg5ciRsbW2xc+dOPPvss+jQoQNiYmKULpGIiIgMgOGbyIT4+/tj0aJFuHDhAoYPHw4bGxtERUWhZcuW6NixIw4ePKh0iURERFQKDN9EJqhatWr49ttvcf78eQwbNgzW1tb49ddf8cwzz6Bz5844dOiQ0iUSERFRCTB8E5mw6tWr47///S/Onz+PoUOHwtraGtu3b0eLFi3QtWtXHD58WOkSiYiIqBgYvonMQGBgIJYsWYJz585h0KBBsLKywtatW9GsWTN0794dR48eVbpEIiIiKgKGbyIzEhQUhO+//x5nz57FK6+8AisrK/z0009o0qQJevTogRMnTihdIhERET0BwzeRGQoODsaKFStw+vRp9OvXDyqVCps3b0bDhg3Rq1cvnDx5UukSiYiIqAAM30RmrFatWli9ejVOnTqFPn36QKVSYdOmTWjQoAF69+6N06dPK10iERER5cPwTVQO1KlTB2vXrsWJEyfQq1cvAMCGDRtQv3599OvXD2fPnlW4QiIiIgIYvonKlXr16mHDhg04fvw4evToASEE1q5di7p16+KVV17B+fPnlS6RiIjIojF8E5VDDRo0wKZNm3D06FFERkZCCIFVq1ahdu3a6Nq1K5YtW4a7d+8qXSYREZHFUQkhhNJF0JOlpKTAzc0NycnJcHV1VbocMkNHjhzBzJkz8fPPP+vW2djYoF27dujZsyciIyPh7e2tYIVERETmrah5jeHbDDB8k6GcOXMGGzZswKZNm/RGRFGpVHj22WfRs2dP9OjRA1WqVFGwSiIiIvPD8F2OMHyTMVy4cAGbNm3Cpk2b8Pfff+ttCw0NRc+ePdGzZ08EBgYqVCEREZH5YPguRxi+ydiuXbuGH374AZs2bcK+ffuQ/9dCo0aNdEG8du3aClZJRERkuhi+yxGGbypL8fHx2LJlCzZt2oTdu3cjNzdXt61WrVq6IN6oUSOoVCoFKyUiIjIdDN/lCMM3KeX27dv46aefsGnTJkRFRSE7O1u3LTAwED169EDPnj3RokULWFlx8CQiIrJcDN/lCMM3mYLk5GRs3boVmzZtwo4dO5CRkaHbVrlyZV0Qb926NaytrRWslIiIqOwxfJcjDN9katLS0rBjxw5s2rQJW7duRWpqqm6bl5cXIiMj0bNnT7Rr1w62trYKVkpERFQ2GL7LEYZvMmUZGRmIiorCpk2b8OOPPyIpKUm3zcPDA926dUOvXr3QoUMHqNVq5QolIiIyIobvcoThm8xFdnY2du3ahU2bNmHz5s24deuWblulSpUwYMAADB06FA0aNFCwSiIiIsNj+C5HGL7JHOXm5uLPP//Epk2bsH79eiQmJuq2NW7cGEOHDkW/fv1QsWJFBaskIiIyDIbvcoThm8xdTk4Ofv31VyxduhQ//fSTbtQUW1tbdOvWDUOGDEHHjh1hY2OjcKVEREQlw/BdjjB8U3ly584drF69GsuWLcORI0d06729vTFw4EAMHToUderUUbBCIiKi4mP4LkcYvqm8OnHiBJYtW4aVK1fq9Q9v3rw5hg4dir59+8LDw0PBComIiIqG4bscYfim8i4rKws7duzA0qVLsW3bNuTk5AAA7OzsEBkZiSFDhuCFF17g+OFERGSyGL7LEYZvsiQ3b97EqlWrsHTpUpw8eVK33s/PD4MGDcKQIUMQEhKiYIVERESPK2peM8v5oPfu3YuuXbvCz88PKpUKW7Zs0dsuhMC7774LX19fODg4IDw8HBcuXNBrc/fuXQwYMACurq5wd3fHsGHD9CYKAeRX4s8++yzs7e3h7++POXPmPFbLhg0bUKtWLdjb26N+/frYvn17sWshojxeXl6YMGECjh8/jsOHD2Ps2LHw9PTEjRs3MHv2bNSqVQstW7bEd999h+TkZKXLJSIiKhazDN9paWlo2LAhFi5cWOD2OXPm4Msvv8TixYtx8OBBODk5ISIiQm867AEDBuD06dOIiorC1q1bsXfvXowYMUK3PSUlBR06dEC1atVw+PBhzJ07FzNnzsS3336ra7N//37069cPw4YNw9GjRxEZGYnIyEicOnWqWLUQ0eNUKhWaNGmCL7/8Ejdu3MDGjRvRuXNnWFtbIyYmBiNGjICvry9eeeUVREdHQ6PRKF0yERHR0wkzB0Bs3rxZd1+j0QgfHx8xd+5c3bqkpCRhZ2cn1qxZI4QQ4syZMwKAOHTokK7Njh07hEqlEv/++68QQoivv/5aeHh4iMzMTF2bKVOmiJCQEN393r17i86dO+vVExoaKl5//fUi11IUycnJAoBITk4u8mOIyqsbN26IOXPmiNq1awsAuqVq1apixowZ4uLFi0qXSEREFqioec0sz3w/yZUrV5CQkIDw8HDdOjc3N4SGhiImJgYAEBMTA3d3dzRr1kzXJjw8HFZWVjh48KCuTZs2bfSmw46IiEBsbCzu3buna5P/ONo22uMUpZaCZGZmIiUlRW8hIsnX1xeTJk3C6dOncfDgQbzxxhtwd3fHtWvX8OGHHyIoKAjPPfccli5dirt370LwshYiIjIh5W5Gi4SEBAByzOD8vL29ddsSEhLg5eWlt93Gxgaenp56bapXr/7YPrTbPDw8kJCQ8NTjPK2WgsyaNQvvv//+058skQVTqVRo0aIFWrRogXnz5uHHH3/EsmXL8Ntvv2Hv3r3Yu3cvAMDBwQG+vr7w8fGBj4+P3u389728vGBra6vwsyIiovKu3IXv8mDatGmYOHGi7n5KSgr8/f0VrIjItNnb26NPnz7o06cP/v33X6xYsQLLli1DbGws0tPTcfnyZVy+fPmJ+1CpVKhYsWKBIf3R266urlCpVGX07IiIqDwpd+Hbx8cHAJCYmAhfX1/d+sTERDRq1EjX5ubNm3qPy8nJwd27d3WP9/HxQWJiol4b7f2ntcm//Wm1FMTOzg52dnZFer5EpK9y5cqYOnUqpk6digcPHiAhIUG3xMfHF3g7ISEBubm5uHXrFm7duqU3xGFB7O3tn3gWvXnz5rr//0RERPmVu/BdvXp1+Pj4IDo6WhdwU1JSdH1DASAsLAxJSUk4fPgwmjZtCgDYuXMnNBoNQkNDdW3eeecdZGdn676KjoqKQkhIiG7GvbCwMERHR2P8+PG640dFRSEsLKzItRCR8Tg6OiIwMBCBgYFPbKfRaHDnzp0nBnTt7eTkZGRkZCAuLg5xcXEF7k+lUiEsLAyRkZF46aWXEBQUZIRnR0RE5sgsJ9lJTU3FxYsXAQCNGzfGvHnz0K5dO3h6eqJq1ar45JNPMHv2bHz//feoXr06ZsyYgRMnTuDMmTOwt7cHAHTq1AmJiYlYvHgxsrOzMXToUDRr1gyrV68GACQnJyMkJAQdOnTAlClTcOrUKbz66qv4/PPPdUMS7t+/H8899xxmz56Nzp07Y+3atfj4449x5MgR1KtXDwCKVMvTcJIdItORnp7+xLPpV69efezMed26dXVBvEmTJuyyQkRUDhU5r5XF0CuGtmvXLr0hxrTL4MGDhRByiL8ZM2YIb29vYWdnJ9q3by9iY2P19nHnzh3Rr18/4ezsLFxdXcXQoUPF/fv39docP35ctG7dWtjZ2YnKlSuL2bNnP1bL+vXrRXBwsFCr1aJu3bpi27ZtetuLUsvTcKhBIvNy/fp1sXDhQvHCCy8IGxsbvd9T/v7+YsyYMSI6OlpkZWUpXSoRERlIUfOaWZ75tjQ8801kvu7du4ft27djy5Yt2LFjB9LS0nTbPDw80KVLF7z00kvo0KEDnJycFKyUiIhKo6h5jeHbDDB8E5UP6enpiI6OxpYtW/DTTz/h1q1bum329vbo0KEDXnrpJXTp0gUVK1ZUsFIiIiouhu9yhOGbqPzJzc3F/v37sWXLFmzevBlXrlzRbbOyskKbNm0QGRmJ7t27IyAgQLlCiYioSBi+yxGGb6LyTQiBkydP6oL4sWPH9LY3btwYkZGRiIyMRP369XnBJhGRCWL4LkcYvoksS1xcHH788Uds3rwZf/zxBzQajW5bYGCgLoi3bNkS1tbWClZKRERaDN/lCMM3keW6ffs2tm7dis2bN+O3335DRkaGblulSpXQrVs3REZGIjw8vMjDlxIRkeExfJcjDN9EBABpaWn49ddf/7+9Ow+Lqnr8B/4elmEAWRRlUzZ3UVxRckksTTLUSksr84Pa8tGwr0Iftdy1zCxzyUzTTOwxS61cQpPcQE1MAVFwXxAyRVxBUARnzu8Pf3ObYRZA4I6N79fz3IeZe8+995yZO8N7zty5Bxs3bkR8fDxu3rwpLVOpVGjcuDEaNWpkMAUEBEiDhRERUc1g+LYiDN9EVFZpaSn27t2LDRs2YOPGjbh48aLJsjY2NvD39zcazBs1agQXFxcZa05EZJ0Yvq0IwzcRmSOEwNmzZ3Hu3DmD6fz587h7967Z9evVq4eGDRsaDebe3t78gScRUQUwfFsRhm8ielhCCOTm5hoN5ufOncO1a9fMru/o6GgymAcEBECpVMrUEiKiRxvDtxVh+CaimlJQUIDz588bDeY5OTl6V1opy8bGBn5+fggICEBgYKA0ae/7+fnxXHMiemwwfFsRhm8isoSSkhJkZ2cbDefnz5/HnTt3zK5vY2MDX19fo8FcG84dHBxkag0RUc1i+LYiDN9E9KjRns5y4cIFvSk7O1v6q3tZRGMUCgV8fHwMQrn2vr+/PxwdHWVqERFR1TB8WxGGbyL6txFCIC8vz2gw194ur+ccALy9vfWCufZ2nTp14OzsjFq1akl/VSoVfxxKRBbD8G1FGL6JyNoIIXDt2jWDUK57v7CwsFLbtLGxgbOzs14oLxvQzf01tczJyYkjiRJRuRi+rQjDNxE9boQQuHHjhtFgnp2djVu3bqGoqAhFRUXlXkqxOjg6OsLJyQlKpRJKpRL29vawt7eXblf078OuY29vDzs7O9jZ2Um3y/ure5vfCBDVvIrmNTsZ60RERFQhCoUCHh4e8PDwQPv27c2WVavVuHPnDgoLC1FUVGTw19i8iiwrLCyEtn/q7t27soT8mmJra1tuQDcX4is6VXadsuVtbW1hY2MDhUIBhUIh3a7ovKosLztf929F55Vdpp2IdDF8ExHRv5qtrS1cXFyqfaROIQSKi4v1AnlpaSlKS0tRUlJSob/VVfb+/fsGf43NKy0tNdoWtVoNtVqNe/fuVetjRBVjLqxbegJQqftVLVMR1VkuJCQECxYsqND25MLwTUREZIRCoYCjoyMcHR1Rt25dS1enQoQQ0Gg05Qb0ioZ5U1N5y6tSRtsGIUSFblembHnbMPZXe7sqtNsh+ZWUlFi6CgYYvomIiKyEQqGAra0tfyBaA4wFdnNhvbxluh8CLDXptsvY/YqUqcw6FX2cq7Pco/jBmeGbiIiIqBy654cTVQWPICIiIiIimTB8ExERERHJhOGbiIiIiEgmDN9ERERERDJh+CYiIiIikgnDNxERERGRTBi+iYiIiIhkwvBNRERERCQThm8iIiIiIpkwfBMRERERyYThm4iIiIhIJgzfREREREQyYfgmIiIiIpIJwzcRERERkUwYvomIiIiIZMLwTUREREQkE4ZvIiIiIiKZMHwTEREREcmE4ZuIiIiISCYM30REREREMmH4JiIiIiKSCcM3EREREZFMGL6JiIiIiGTC8E1EREREJBOGbyIiIiIimTB8ExERERHJhOGbiIiIiEgmDN9ERERERDJh+CYiIiIikgnDNxERERGRTBi+iYiIiIhkwvBNRERERCQThm8iIiIiIpkwfBMRERERyYThm4iIiIhIJgzfREREREQyYfgmIiIiIpIJwzcRERERkUwYvomIiIiIZMLwTUREREQkE4ZvIiIiIiKZMHwTEREREcmE4ZuIiIiISCYM30REREREMmH4JiIiIiKSCcM3EREREZFMrDZ8BwYGQqFQGEzR0dEAgB49ehgsGzlypN42cnJyEBkZCScnJ3h6emLcuHG4f/++XpnExES0b98eDg4OaNy4MeLi4gzqsnjxYgQGBkKlUiEsLAwHDx6ssXYTERER0aPLasP3oUOHcPnyZWnavn07AODll1+Wyrz11lt6ZT799FNpmVqtRmRkJEpKSrB//36sWrUKcXFxmDp1qlQmKysLkZGReOqpp5Ceno6xY8fizTffREJCglRm7dq1iI2NxbRp05CWloY2bdogIiICeXl5MjwKRERERPQoUQghhKUrIYexY8ciPj4eZ86cgUKhQI8ePdC2bVssWLDAaPnffvsNffv2xaVLl+Dl5QUAWLp0KSZMmICrV69CqVRiwoQJ2LJlCzIzM6X1XnnlFdy6dQvbtm0DAISFhaFjx4748ssvAQAajQZ+fn5499138f7771eo7gUFBXBzc0N+fj5cXV2r8CgQERERUU2oaF6z2p5vXSUlJVi9ejVGjBgBhUIhzf/+++9Rt25dtGrVCh988AHu3LkjLUtOTkZISIgUvAEgIiICBQUFOHbsmFSmV69eevuKiIhAcnKytN/U1FS9MjY2NujVq5dUxph79+6hoKBAbyIiIiKifz87S1dADhs3bsStW7cwbNgwad5rr72GgIAA+Pr64ujRo5gwYQJOnTqFX375BQCQm5urF7wBSPdzc3PNlikoKMDdu3dx8+ZNqNVqo2VOnjxpsr6zZ8/GjBkzHrq9RERERPRoeizC94oVK9CnTx/4+vpK895++23pdkhICHx8fNCzZ0+cO3cOjRo1skQ1JR988AFiY2Ol+wUFBfDz87NgjYiIiIioOlh9+M7OzsaOHTukHm1TwsLCAABnz55Fo0aN4O3tbXBVkitXrgAAvL29pb/aebplXF1d4ejoCFtbW9ja2hoto92GMQ4ODnBwcKhYA4mIiIjoX8Pqz/leuXIlPD09ERkZabZceno6AMDHxwcA0LlzZ2RkZOhdlWT79u1wdXVFcHCwVGbnzp1629m+fTs6d+4MAFAqlejQoYNeGY1Gg507d0pliIiIiOjxYdXhW6PRYOXKlYiKioKd3T+d/OfOncOHH36I1NRUXLhwAZs3b8Z//vMfdO/eHa1btwYA9O7dG8HBwRg6dCiOHDmChIQETJ48GdHR0VKv9MiRI3H+/HmMHz8eJ0+exFdffYV169YhJiZG2ldsbCyWL1+OVatW4cSJExg1ahSKioowfPhweR8MIiIiIrI4qz7tZMeOHcjJycGIESP05iuVSuzYsQMLFixAUVER/Pz8MHDgQEyePFkqY2tri/j4eIwaNQqdO3eGs7MzoqKiMHPmTKlMUFAQtmzZgpiYGCxcuBANGjTAN998g4iICKnM4MGDcfXqVUydOhW5ublo27Yttm3bZvAjTCIiIiKyfo/Ndb7/zXidbyIiIqJHG6/zTURERET0iGH4JiIiIiKSCcM3EREREZFMGL6JiIiIiGTC8E1EREREJBOGbyIiIiIimTB8ExERERHJhOGbiIiIiEgmDN9ERERERDJh+CYiIiIikgnDNxERERGRTBi+iYiIiIhkwvBNRERERCQThm8iIiIiIpkwfBMRERERyYThm4iIiIhIJgzfREREREQyYfgmIiIiIpIJwzcRERERkUwYvomIiIiIZMLwTUREREQkE4ZvIiIiIiKZMHwTEREREcmE4ZuIiIiISCYM30REREREMmH4JiIiIiKSCcM3EREREZFMGL6JiIiIiGTC8E1EREREJBOGbyIiIiIimTB8ExERERHJhOGbiIiIiEgmDN9ERERERDJh+CYiIiIikgnDNxERERGRTBi+iYiIiIhkwvBNRERERCQThm8iIiIiIpkwfBMRERERyYThm4iIiIhIJgzfREREREQyYfgmIiIiIpIJwzcRERERkUwYvomIiIiIZMLwTUREREQkE4ZvIiIiIiKZMHwTEREREcmE4ZuIiIiISCYM30REREREMmH4JiIiIiKSCcM3EREREZFMGL6JiIiIiGTC8E1EREREJBOGbyIiIiIimTB8ExERERHJhOGbiIiIiEgmDN9ERERERDJh+CYiIiIikgnDNxERERGRTBi+iYiIiIhkwvBNRERERCQThm8iIiIiIpkwfBMRERERyYThm4iIiIhIJgzfREREREQyYfgmIiIiIpKJVYbv6dOnQ6FQ6E3NmzeXlhcXFyM6OhoeHh6oVasWBg4ciCtXruhtIycnB5GRkXBycoKnpyfGjRuH+/fv65VJTExE+/bt4eDggMaNGyMuLs6gLosXL0ZgYCBUKhXCwsJw8ODBGmkzERERET36rDJ8A0DLli1x+fJladq3b5+0LCYmBr/++ivWr1+PpKQkXLp0CQMGDJCWq9VqREZGoqSkBPv378eqVasQFxeHqVOnSmWysrIQGRmJp556Cunp6Rg7dizefPNNJCQkSGXWrl2L2NhYTJs2DWlpaWjTpg0iIiKQl5cnz4NARERERI8UhRBCWLoS1W369OnYuHEj0tPTDZbl5+ejXr16WLNmDV566SUAwMmTJ9GiRQskJyfjiSeewG+//Ya+ffvi0qVL8PLyAgAsXboUEyZMwNWrV6FUKjFhwgRs2bIFmZmZ0rZfeeUV3Lp1C9u2bQMAhIWFoWPHjvjyyy8BABqNBn5+fnj33Xfx/vvvV7g9BQUFcHNzQ35+PlxdXR/2YSEiIiKiGlLRvGYnY51kdebMGfj6+kKlUqFz586YPXs2/P39kZqaitLSUvTq1Usq27x5c/j7+0vhOzk5GSEhIVLwBoCIiAiMGjUKx44dQ7t27ZCcnKy3DW2ZsWPHAgBKSkqQmpqKDz74QFpuY2ODXr16ITk52Wzd7927h3v37kn38/PzATx4UomIiIjo0aPNaeX1a1tl+A4LC0NcXByaNWuGy5cvY8aMGXjyySeRmZmJ3NxcKJVKuLu7663j5eWF3NxcAEBubq5e8NYu1y4zV6agoAB3797FzZs3oVarjZY5efKk2frPnj0bM2bMMJjv5+dXfuOJiIiIyGJu374NNzc3k8utMnz36dNHut26dWuEhYUhICAA69atg6OjowVrVjEffPABYmNjpfsajQY3btyAh4cHFApFje+/oKAAfn5++Ouvv6z+NJfHqa3A49VettV6PU7tZVut1+PU3selrUII3L59G76+vmbLWWX4Lsvd3R1NmzbF2bNn8cwzz6CkpAS3bt3S6/2+cuUKvL29AQDe3t4GVyXRXg1Ft0zZK6RcuXIFrq6ucHR0hK2tLWxtbY2W0W7DFAcHBzg4OBi0QW6urq5W/SLR9Ti1FXi82su2Wq/Hqb1sq/V6nNr7OLTVXI+3ltVe7URXYWEhzp07Bx8fH3To0AH29vbYuXOntPzUqVPIyclB586dAQCdO3dGRkaG3lVJtm/fDldXVwQHB0tldLehLaPdhlKpRIcOHfTKaDQa7Ny5UypDRERERI8Xqwzf//vf/5CUlIQLFy5g//79ePHFF2Fra4tXX30Vbm5ueOONNxAbG4vdu3cjNTUVw4cPR+fOnfHEE08AAHr37o3g4GAMHToUR44cQUJCAiZPnozo6GipR3rkyJE4f/48xo8fj5MnT+Krr77CunXrEBMTI9UjNjYWy5cvx6pVq3DixAmMGjUKRUVFGD58uEUeFyIiIiKyLKs87eTixYt49dVXcf36ddSrVw/dunXDgQMHUK9ePQDA/PnzYWNjg4EDB+LevXuIiIjAV199Ja1va2uL+Ph4jBo1Cp07d4azszOioqIwc+ZMqUxQUBC2bNmCmJgYLFy4EA0aNMA333yDiIgIqczgwYNx9epVTJ06Fbm5uWjbti22bdtm8CPMR42DgwOmTZtmcOqLNXqc2go8Xu1lW63X49RettV6PU7tfZzaWhFWeZ1vIiIiIqJHkVWedkJERERE9Chi+CYiIiIikgnDNxERERGRTBi+iYiIiIhkwvBNBhYvXozAwECoVCqEhYUZDDhkDWbPno2OHTvCxcUFnp6eeOGFF3Dq1ClLV0sWn3zyCRQKBcaOHWvpqtSYv//+G6+//jo8PDzg6OiIkJAQpKSkWLpa1U6tVmPKlCkICgqCo6MjGjVqhA8//BDW8Dv6PXv2oF+/fvD19YVCocDGjRv1lgshMHXqVPj4+MDR0RG9evXCmTNnLFPZamCuvaWlpZgwYQJCQkLg7OwMX19f/Oc//8GlS5csV+EqKO+51TVy5EgoFAosWLBAtvpVp4q09cSJE+jfvz/c3Nzg7OyMjh07IicnR/7KVoPy2ltYWIjRo0ejQYMGcHR0RHBwMJYuXWqZyloQwzfpWbt2LWJjYzFt2jSkpaWhTZs2iIiI0BtwyBokJSUhOjoaBw4cwPbt21FaWorevXujqKjI0lWrUYcOHcLXX3+N1q1bW7oqNebmzZvo2rUr7O3t8dtvv+H48eP4/PPPUbt2bUtXrdrNmTMHS5YswZdffokTJ05gzpw5+PTTT7Fo0SJLV63KioqK0KZNGyxevNjo8k8//RRffPEFli5dij///BPOzs6IiIhAcXGxzDWtHubae+fOHaSlpWHKlClIS0vDL7/8glOnTqF///4WqGnVlffcam3YsAEHDhwod6juR1l5bT137hy6deuG5s2bIzExEUePHsWUKVOgUqlkrmn1KK+9sbGx2LZtG1avXo0TJ05g7NixGD16NDZv3ixzTS1MEOno1KmTiI6Olu6r1Wrh6+srZs+ebcFa1by8vDwBQCQlJVm6KjXm9u3bokmTJmL79u0iPDxcjBkzxtJVqhETJkwQ3bp1s3Q1ZBEZGSlGjBihN2/AgAFiyJAhFqpRzQAgNmzYIN3XaDTC29tbfPbZZ9K8W7duCQcHB/HDDz9YoIbVq2x7jTl48KAAILKzs+WpVA0x1daLFy+K+vXri8zMTBEQECDmz58ve92qm7G2Dh48WLz++uuWqVANM9beli1bipkzZ+rNa9++vZg0aZKMNbM89nyTpKSkBKmpqejVq5c0z8bGBr169UJycrIFa1bz8vPzAQB16tSxcE1qTnR0NCIjI/WeX2u0efNmhIaG4uWXX4anpyfatWuH5cuXW7paNaJLly7YuXMnTp8+DQA4cuQI9u3bhz59+li4ZjUrKysLubm5eseym5sbwsLCrP69Sis/Px8KhQLu7u6Wrkq102g0GDp0KMaNG4eWLVtaujo1RqPRYMuWLWjatCkiIiLg6emJsLAws6fh/Nt16dIFmzdvxt9//w0hBHbv3o3Tp0+jd+/elq6arBi+SXLt2jWo1WqDETi9vLyQm5troVrVPI1Gg7Fjx6Jr165o1aqVpatTI3788UekpaVh9uzZlq5KjTt//jyWLFmCJk2aICEhAaNGjcL//d//YdWqVZauWrV7//338corr6B58+awt7dHu3btMHbsWAwZMsTSVatR2vejx+29Squ4uBgTJkzAq6++CldXV0tXp9rNmTMHdnZ2+L//+z9LV6VG5eXlobCwEJ988gmeffZZ/P7773jxxRcxYMAAJCUlWbp6NWLRokUIDg5GgwYNoFQq8eyzz2Lx4sXo3r27pasmK6scXp6oMqKjo5GZmYl9+/ZZuio14q+//sKYMWOwffv2f+15hJWh0WgQGhqKjz/+GADQrl07ZGZmYunSpYiKirJw7arXunXr8P3332PNmjVo2bIl0tPTMXbsWPj6+lpdW+mB0tJSDBo0CEIILFmyxNLVqXapqalYuHAh0tLSoFAoLF2dGqXRaAAAzz//PGJiYgAAbdu2xf79+7F06VKEh4dbsno1YtGiRThw4AA2b96MgIAA7NmzB9HR0fD19bX6b2V1seebJHXr1oWtrS2uXLmiN//KlSvw9va2UK1q1ujRoxEfH4/du3ejQYMGlq5OjUhNTUVeXh7at28POzs72NnZISkpCV988QXs7OygVqstXcVq5ePjg+DgYL15LVq0+NdePcCccePGSb3fISEhGDp0KGJiYqz+Gw7t+9Hj9F4F/BO8s7OzsX37dqvs9d67dy/y8vLg7+8vvV9lZ2fjvffeQ2BgoKWrV63q1q0LOzu7x+b96u7du5g4cSLmzZuHfv36oXXr1hg9ejQGDx6MuXPnWrp6smL4JolSqUSHDh2wc+dOaZ5Go8HOnTvRuXNnC9as+gkhMHr0aGzYsAG7du1CUFCQpatUY3r27ImMjAykp6dLU2hoKIYMGYL09HTY2tpauorVqmvXrgaXjTx9+jQCAgIsVKOac+fOHdjY6L+N29raSj1q1iooKAje3t5671UFBQX4888/re69SksbvM+cOYMdO3bAw8PD0lWqEUOHDsXRo0f13q98fX0xbtw4JCQkWLp61UqpVKJjx46PzftVaWkpSktLH8v3rLJ42gnpiY2NRVRUFEJDQ9GpUycsWLAARUVFGD58uKWrVq2io6OxZs0abNq0CS4uLtJ5om5ubnB0dLRw7aqXi4uLwbnszs7O8PDwsMpz3GNiYtClSxd8/PHHGDRoEA4ePIhly5Zh2bJllq5atevXrx9mzZoFf39/tGzZEocPH8a8efMwYsQIS1etygoLC3H27FnpflZWFtLT01GnTh34+/tj7Nix+Oijj9CkSRMEBQVhypQp8PX1xQsvvGC5SleBufb6+PjgpZdeQlpaGuLj46FWq6X3rDp16kCpVFqq2g+lvOe27AcLe3t7eHt7o1mzZnJXtcrKa+u4ceMwePBgdO/eHU899RS2bduGX3/9FYmJiZardBWU197w8HCMGzcOjo6OCAgIQFJSEr777jvMmzfPgrW2AAtfbYUeQYsWLRL+/v5CqVSKTp06iQMHDli6StUOgNFp5cqVlq6aLKz5UoNCCPHrr7+KVq1aCQcHB9G8eXOxbNkyS1epRhQUFIgxY8YIf39/oVKpRMOGDcWkSZPEvXv3LF21Ktu9e7fR12hUVJQQ4sHlBqdMmSK8vLyEg4OD6Nmzpzh16pRlK10F5tqblZVl8j1r9+7dlq56pZX33Jb1b77UYEXaumLFCtG4cWOhUqlEmzZtxMaNGy1X4Soqr72XL18Ww4YNE76+vkKlUolmzZqJzz//XGg0GstWXGYKIaxgKDQiIiIion8BnvNNRERERCQThm8iIiIiIpkwfBMRERERyYThm4iIiIhIJgzfREREREQyYfgmIiIiIpIJwzcRERERkUwYvomIiIiIZMLwTUT0iFGr1Vi4cCE6deoEV1dXKBQKKBSKhxo6PScnB//973/RqFEjqFQqaVsbN26s9nqTcdOnT5ced7KsxMRE6bn4tw7hTv9+dpauABEZl5ycjC5dusDJyQn5+fmws3vwcr116xY8PDyg0WiQnZ0Nf39/C9eUqturr76K9evXV3k7OTk56NChA65du1YNtSIiourAnm+iR9Qff/wBAAgLC5OCt3a+RqOBn5+fRYN3jx49oFAo0KNHD4vVwRrt379fCt6RkZHYvn07jh49ioyMDHzxxReV2tZHH32Ea9euwc7ODnPmzEFycjIyMjKQkZGBnj171kT16RHDXneiRw97vokeUdrw3a1bN735e/fuNTqfrMOOHTsAALa2tlizZg1cXV2rvK0XXngB48ePr5b6Ef2b9ejRA0IIS1eDHnPs+SZ6RO3fvx+AYcjet2+f0flkHf7++28AgJeXV5WCt+62mjZtWuV6ERFR9WD4JnoEnT17Fnl5ebC1tUXnzp2l+cXFxTh06BAAhm9rde/ePQCAvb19lbdVUlJSbdsiIqLqwfBN9AjSnnLSunVruLi4SPMPHjyIkpISuLu7o1WrVtWyr1u3bmHWrFno3LkzateuDXt7e9SrVw/BwcF48cUXsWTJEly5ckUqP2zYMCgUCiQlJQEAkpKSpHNKtVNgYKDRfeXn52P27Nno2rUr6tWrB6VSCR8fH/Tr1w8//fST2a+DtduePn06gAenVPTv3x8+Pj5QqVRo2LAhRo8eLfX2Vld7H0ZGRgbefvttNGnSBE5OTnBxcUHLli0RExODCxcumG3fqlWrAADZ2dkGj2tFxMXFGZSfMWOG3naGDRtmsN7Vq1cxefJktGvXDu7u7lCpVAgMDMTQoUOlb1tMCQwM1Ntuamoqhg0bhqCgIDg4OEh1GT16NBQKBXx8fIxu58KFC1IdbWxscOPGDYMy9+/fh4uLCxQKBd5//32D5QcOHMDkyZPRo0cPeHt7Q6lUwtXVFcHBwRg1ahSOHz9uti3a41t7DF++fBkTJkxAy5Ytpf2WvUrGxYsXER0djYYNG0KlUsHX1xf9+/eXTvupDmq1GnFxcYiIiJDa5ebmhiZNmqBnz574+OOP9dqmPQ5mzJghzSt7PCkUCqPHo1qtxqpVq9C3b1/4+vrCwcEBHh4e6NatG+bNm4e7d++arGfZ34KcOnUKb7/9NoKCgqBSqeDj44NBgwbhwIEDRtf/6aefpLqdPHnSaBnt8Wbuqj3PPvssFAoFnnjiCb35FbnayenTp/Huu++iVatWcHFxgVKphK+vL9q2bYsRI0Zg7dq10odkY3JzczFp0iSEhoaiTp06cHBwgJ+fHwYNGlStxwT9iwkisqiVK1cKAFWesrKyKr3v48ePC19f33K3vWjRImmdqKiocssHBAQY7GvHjh3Cw8PD7HrPPfecuH37ttG6astMmzZNTJ8+3eQ23NzcxJ49e6qtvZX18ccfCxsbG5PbdnBwEKtWrTLZPnNTRVTkeIqKitJbJyEhQbi6uppdJzo6WqjVaqP7DAgIkLa7ZMkSYWdnZ7Tu69atk+6fOHHCYDtxcXF662zYsMGgzIEDB6Tlv/32W6XbbmtrKxYvXmzy8dMe3wEBASI5OVnUrVvXYBu7d++Wyu/Zs8fsYzd9+nQxbdq0Sj2HZd2+fVs8+eST5bZt4MCBlXosjL1vZGdnizZt2phdp3HjxuLUqVNG6xoeHi4AiPDwcLF161bh7OxsdBs2NjZi/vz5Buvn5eVJZZYsWWKw/MKFC3rbGTNmjEGZ0tJSUatWLQFATJgwQW/Z7t27jT6PWuvWrRNKpbLcxy0jI8No+1evXm2yzdrpjTfeEKWlpUbXp8cDf3BJ9BgbOnQoLl26BHt7e7z11lvo06cPvL29odFocPHiRRw4cAAbNmzQW2fWrFn43//+h+HDhyMlJQWhoaFYuXKlXhmlUql3/48//kCfPn1QWloKLy8vvPvuu2jTpg18fX1x6dIlrF27FqtXr8bWrVsRFRWFn3/+2WSdt2zZgpSUFDRr1gzjx49H69atkZ+fj/Xr12P58uXIz89H3759kZmZCT8/vyq3tzK++uorTJw4EQBQr149TJgwAV27doVarcaOHTvw2WefoaioCMOGDUPdunXx3HPPSetmZGQAACZPnoxNmzbB19cXCQkJla7DCy+8gNDQUABASEgIAGDUqFF45513pDK1a9eWbqenp6Nfv34oKSmBvb09Ro8ejf79+8PZ2RmHDx/GJ598gqysLCxevBjOzs6YM2eOyX0fOnQIq1evhp+fH/73v/8hNDQU9+/fl34kHB4eLpVNTExE8+bN9dYv2xOZmJhocG1zbRk7Ozt07dpVb9n9+/dRu3ZtPP/88+jevTuaNGkCZ2dnXLp0CWlpafjiiy9w7do1jB49Gs2bN8fTTz9tsi2FhYUYOHAgiouLMWnSJDzzzDNwcnJCRkaG1HOfk5ODvn37oqCgADY2Nnj77bfx0ksvwc3NDUePHsUnn3yC6dOnS8/Hw5o+fbr0GPbt2xdDhgyBv78/VCoV8vLycPjwYcTHx+t926E9Dr766issWbIEwD/HmK769etLt69fv45u3brhr7/+goODA9566y2Eh4cjMDAQhYWF+P3337Fw4UKcPXsWffr0QVpaGtzc3IzW+dKlS3jttddgZ2eHjz/+WOoJ3717N+bMmYOCggLExMQgMDBQ7znWfgt1/PhxJCYmYuTIkXrbNXaMlJWamorCwkIA+sdcea5cuYLhw4ejpKQEnp6eGD16NJ544gnUrVsXd+/exdmzZ5GUlGSyt33dunUYOnQohBDSN3HBwcGoV68eLly4gBUrVmDr1q1YsWIFXF1dMW/evArXjayMpdM/0ePu1q1b4sSJE9Kk2zOzb98+af6xY8eESqUSAMRPP/2kt86JEydESUlJpfZ77ty5CvX0ajQacePGDYP5uj1c5pSUlIjAwEABQDz77LOiqKjIaLlly5ZJ9fn9998NlkOn56h9+/ZGe8i/++47qczLL79cre0tT15ennBychIAhK+vr8jJyTEok5aWJvWK1a9f3+hzptvzWlXa9k6bNs1kmY4dO0o9wgkJCQbLb9y4IYKDg6XeyszMTIMy2p5vACIkJETcvHnT5P5atGghAIjBgwcbLAsKChIARL9+/QQA0aZNG4Myffr0EQBEp06dDJZdvHjR5PElxIPXWuvWrQUA0a1bN6NldL/ZqVWrlkhPTze5vZdeekkqu2bNGoPlBQUFBr3ID8PPz08AEC+99JLZctevXzeYV5le99dee0069s6fP2+0jO4xPHHiRIPl2vcF4MG3UMePHzcok5mZKX1bYOx1MGrUKAFAeHt7G6w7fPhwvWNEoVAYtHvOnDnSMV1QUKC3zFzP94oVK8rt2RZCiDt37og7d+7ozbt69apwc3MTAMSIESNM9mxPnDhRei2dPHnS5D7IujF8Ez1itF/NBwUF6c1PT08XAISjo2Olg7Yxf/zxh/SP5siRI5Vev6LhWxuIVSqVyMvLM1u2U6dOAoB47bXXDJbpBpiUlBST29CGMzs7O3H58mVpflXbWx7tP3wA4scffzRZ7qOPPpLKrVu3zmC5nOH7zz//lMqMHDnS5Hb27dsnlXvnnXcMluuGb1On/GiZClbZ2dlSmEpJSTEarO7fvy9cXFwEADFu3Diz+zFl48aNUl2vXbtmsFw3fM+cOdPkdi5fvixsbW0FANG3b1+T5XQf44cN3/b29gKAWLhwYaXXrWj4zsrKktrz66+/mi07fvx46UNmWbrhe+7cuSa3oft6Wb9+vd6ytWvXSsvKnp6k/YC2fv166XbZ05O07wEdO3Y02K+58D1r1iwBQNSuXdts+42ZOXOm9GGiuLjYZLnS0lJRv359kx9e6PHAH1wSPWJMXcdb+yPM0NDQarl6he6P3uLi4qq8PVM2b94M4MHXv/Xq1TNbtnv37gAejO5pSkhICDp06GBy+YgRIwA8OAVB9yvpmm6v9odU7u7uGDBggMlyb775psE6lqK7/zfeeMNkua5du6JFixYG65Tl5+eHJ5980uw+tacB5Obm6v2gTvsD3uDgYHTo0AFBQUEQQmDPnj1SmbS0NNy+fRsAKjS4U1FRES5cuIBjx44hMzMTmZmZeq+dI0eOmF1/yJAhJpft3r0barUaADB8+HCT5Tp16oSWLVuWW1dztMfu2rVrcefOnSpty5QtW7ZArVbDyckJffr0MVtW+zq9dOkScnJyjJZRKBSIiooyuY3hw4dLp8mUPabKnp6k9ddffyErKwsKhQLh4eHSMaBbRq1WS++VlR0ATPs437x5E5s2barUutr3ub59+8LBwcFkOTs7O+kKVube58i6MXwTPWJMXcdbe93vsue5PqygoCApKM2fPx8tW7bE1KlTsWvXrmr9B5+SkgIASEhIMHq1Bd1p7ty5AB4EM1M6duxodn+dOnWSbuue41rT7c3MzAQAtG/f3uyHIy8vL+lKGtp1LEW7f6VSibZt25otGxYWBgA4c+aMdAnDslq3bl3uPk0FK+1tbWAyFqy0t21tbU1eavPatWuYOHEimjVrBhcXFwQFBaFVq1YICQlBSEgIIiMj9cqaUqtWLTRs2NDkct1jqzLH5MPQhtj9+/cjKCgIo0ePxoYNG3D16tUqbVeX9nV6584d2NnZmX2d9u3bV1rP1Gs1KCgIdevWNbm/evXqSa+Dsueie3l5Sb8HMPb8a8+jNnaMpKWloaCgAEDlzvcGgP79+8Pd3R0A8OKLL+Lpp5/G/PnzkZqaKn3QMkatViM9PR0A8PXXX5f7PvfTTz8BMP8+R9aN4ZvoEXL79m0cPXoUgOme7y5dulTb/n744QepF+b48eP48MMP0bNnT7i7u6N79+5YunQpiouLq7SPvLy8Sq9j7lJmnp6eZtf18vKSbpe9VF1Ntle7r/LqBwDe3t5G6yc37f7r1KkDOzvzv7/X1lkIgZs3bxoto/tDTnPbadasGQD90KTt+TYXvrVl2rVrZ3QAotTUVDRv3hyzZ8/G6dOnyx3J0Nxxpg1hpug+d5U5Jh/GlClTMGLECCgUCuTl5WHx4sUYMGAAPD090apVK0ybNq3Kl8d8mNcpAJMfXCvyOtA+LsZeB9rgrH3OdW+XPUaOHj0qbUNbxtbWttxvYcry8PDA5s2bUb9+fQghsHv3bsTGxkqXDBwwYADi4+MN1rtx4wbu379fqX0Bph87sn682gmRBQUGBiI7O9voMlNfVffv31/v/rRp06RrX1dW/fr1sX//fuzcuRO//PILkpKScPz4cZSWlmLv3r3Yu3cv5s6di61btz70KInaHqM+ffrg008/faht6Kro9a6NkaO9VamfpVRXnW1tbStUrkePHjh16pQUlP7++2+cO3dOOp0A+Cd8aYOVu7u79K2QsR7NkpISDBo0CNevX4e9vT3effddPP/882jatClq164tnQpw/vx5NGrUCADMhvOKtgWo+efc3t4eK1aswHvvvYcffvgBu3btQkpKCkpKSnDs2DEcO3YM8+bNw+rVq/H8888/1D60r9O6deti9+7dFV4vKCjI6PyqPiY9evTA119/LZ2e1Lx5c4Pw7e/vj8DAQFy4cAF79uzBCy+8IJVp27btQ40Q++STT+Ls2bP4+eefsXXrVuzZswcXL15EQUEBNmzYgA0bNiAiIgK//PILnJycAECvV/zNN9/EmDFjKrSvsleFoscHwzcRoWfPnujZsyeAB5cb27FjB5YtW4Zdu3bh3LlzGDx4MA4fPvxQ2/bw8MClS5dQUlJSLQMDldfDp7u8Tp06RsvURHvr1KmDy5cvV6gHUvt1s6n6yUW7/+vXr+P+/ftme7+1dVYoFBXq4TYnPDxcL1ilpqYC+Od0AgAICAjQC1Z+fn7Iz88HYPxc3l27duH8+fMAHlzyUffcel3V9W2D7mNw5coVg8ta6qpqr7RWcHAwPvzwQ3z44YcoLi7Gvn37sGbNGnz33XcoLCzEq6++inPnzpkcxMgcDw8PAA++fWvRokWlPnwYU5E2a8sYex2UPT3JxcUFZ8+e1fuABjw4FuLi4pCYmIj+/ftLv5mp7PneulQqFYYMGSKd85+VlYUtW7Zg0aJFOH36NBISEjBp0iTMnz/foP5CiGobAI2sF087IbKg33//HRkZGdKk/SHhpEmT9Oa/+OKLAB78AEx3fkZGht71m6uDh4cHBg8ejJ07d0q97Onp6Thz5oxeuYr2bLVr1w4ApJ66qjp06FCFl1fkn2BF21se7b7S0tLMfgWdl5cnfdth6X/S2v2XlJRI56yacvDgQQBAkyZNqtxjpxuMEhMTDXo0y5bTLWNjY2P0dIJjx45JtwcPHmxy39pzm6tKew11oHLHZHVRqVTo1asXvv32W3z22WcAHpxGU/a0iMq+Tu/du1ctj1FWVhauX79ucvnVq1el0TWNvQ58fHykb590n3/dD2iA/jGSnp4ufUCr7Pne5mjPsz906BAaNGgA4ME1vbWUSqX0TaX29EAicxi+iSyoadOmaNWqFVq1aoVmzZrhxIkTAIABAwZI81u1aoVTp04BePBLet35rVq1qtC5lQ9L2zsMGP44TaVSAYDZYZaBf06Tyc/PNxiM52FkZGSY7ZX+9ttvATw4baCyvV/m2lueXr16AXgwfP0vv/xistyKFSuk0x2061iK7v61j5sxycnJ0tDl1VFnHx8fNGnSBMCD0FT2x5ZausFKW6Zt27ZGB3bR/cBTVFRkdL8ajQbLly+vWuX/v6eeekrqHV61apXJcocOHarxH9ZW5HUKmH+t9uvXTwrqCxYsqHKdhBD47rvvTC6Pi4sr93Wge953ecfI0aNHpdedqQ9oVeXq6ir9uLbs46x9nzt58uRDDY5FjxmLXeSQiPQkJycLAMLFxUXcv39fmn/t2jWhUCgEAPH3339X2/4OHz4sDh8+bHK5RqPRG8ji0qVLesu1g114enoKjUZjcjvFxcXSICG1atUSSUlJZuu1d+9ekZiYaDAfOtdKDg0NFYWFhQZlvv/+e6mM7lDb1dHe8ugOstOgQQNx8eJFgzLp6enSsNePyiA7oaGh0nXRd+zYYbD81q1bIiQkRBoYxNjgI7rDy1fUW2+9JQBIg60oFAqD68BnZWVJy7TX946JiTG6vZ9//llq7+zZs42W0V6fWjutXLnSoExlHv8BAwZI21q7dq3B8tu3b4t27dpV6Trf169fF5s3bzb7Gvvss8+k7f/www96y1atWiUtO3bsmNl9DRo0SCr7+eefmy17/vx5owML6V7nu3bt2kYHkjl+/Lg0II2Pj4+4d++e0X3ovp615cteE1yIf44/bZl27dqZrLe563xv27bN7Ov+1q1b0jW6mzVrprcsNzdXem37+PgYHYxKV3x8fI2MN0D/DgzfRI8I7aATERERevO1g4I0bty4Wve3cuVKaSCKmTNnivj4eJGSkiKSk5PFmjVrxDPPPCP9k3r++ecN1l++fLm0fOzYsSIlJUWcOXNGnDlzRly4cEGvbHJysnBwcBDAg1HnhgwZItavXy9SUlLEwYMHxaZNm8TUqVOlkGdsBErd4A1ANG/eXKxcuVKkpKSInTt3ilGjRgkbGxvpA0xWVla1trciFi9eLG3Dy8tLzJ8/X/z555/ijz/+EDNmzJD+OSsUCrFlyxaj25A7fB8+fFgolUoBQCiVSvHee++JxMREcejQIbFs2TLRsGFDaTvjx483uo2HCd+rV6/WC6UtW7Y0u23ttGnTJqPlCgsLhaenp3SM/fe//xXbtm0TKSkp4scffxQ9e/YUAETXrl2rLXxnZWVJHwpsbW3FO++8I3bt2iVSUlLEt99+K5o2bap3zD5M+NZ+AAkMDBSxsbFi7dq14sCBAyIlJUX8+uuv4u2335aO+/r16xuM/HrmzBlp37179xZJSUni9OnT0mtVdyTG69ev6z3f3bt3F998841ITk4WaWlpYvv27WLu3LmiV69ewsbGxuADrhD/hO/GjRsLNzc34e7uLmbPni2Sk5NFcnKymD17thSSgQej9Zpy8eJFvefe2Ac0IfQHRjL3AU0I8+E7KipK2Nvbi+eee04sWLBA7NixQ6SlpYmkpCSxePFiaXRWAGL+/PkG2/7555+ljhKVSiVGjhwpNm3aJFJTU8WBAwfETz/9JMaPHy89xuUNZkTWi+Gb6BERGRkpAIiPPvpIb/57770ngAdDFlcnbRgtb+rSpYvRkQBv376t949adzIWXJKTk6Ue8PKmVatWGayvGyR1R+0rO7m6uhrtOa9qeytq1qxZUhgyNjk4OBhtn5bc4VsIIRISEqQeaFNTdHS0UKvVRtd/mPBdNlhFR0cbLacbrGxsbMSNGzdMbnPbtm1CpVKZbEOPHj1EZmZmtYVvIR6EOW0ANzZNnTq1UkO8l6UN3+VNPj4+Jkd+1e3RLjuV/ZB6+fJl8eSTT1Zon8OHDzfYl+7It/Hx8dK3QWUnGxsbsyNgajVu3Fhax9QHtLKv7Y0bN5rcXnnhuyLtHjlypMnXwubNm0WdOnXK3YaNjY3YtWtXue0n68TwTfQIUKvVwt3dXQAwOC1DO+R6XFxcte6zuLhYbN26VcTExIhu3bqJoKAg4eTkJJRKpWjQoIHo37+/+P77703+kxHiwVetY8aMES1atND7J2squBQXF4ulS5eKyMhI4evrK5RKpVCpVMLPz0/07t1bzJo1y+jX1EIYBslt27aJyMhI4eXlJZRKpQgMDBTvvPOO+Ouvv2qsvRV15MgR8dZbb4lGjRoJR0dH4ezsLFq0aCHGjBljEHbKskT4FuLBaTMTJ04Ubdu2Fa6ursLBwUH4+/uLIUOGiL1795pd92HCtxBCNGrUSKqjsdMJhNAPVm3bti13m5mZmeL1118Xvr6+wt7eXtSrV0+Eh4eLZcuWCbVarRdmqyN8CyFETk6OGDVqlAgICBBKpVJ4eXmJyMhIsW3bNiFExYd4N0aj0YiDBw+K6dOni969e4tmzZoJd3d3YWdnJ+rWrSu6d+8uPvvsM5Gfn29yGyUlJeLTTz8VnTp1Em5ubnofDk0dj/Hx8WLIkCGiYcOGwsnJSXosu3TpIt577z2Tp4/phm8hHpxiMnz4cOmx8fT0FAMHDhT79++vUPvfeOONcj+g6T6n5X1AMxe+b9y4IVavXi1GjBghQkNDRf369YVSqRSOjo6iadOmIioqqtzXghBC5Ofni7lz54qnn35aeHl5CXt7e+Ho6CiCgoJE3759xbx580ROTk6F2k/WSSFEOaMQEBE9ArQ/BqvKdc2JqGb16NEDSUlJCA8P1xsgiYj+waudEBERERHJhOGbiIiIiEgmDN9ERERERDJh+CYiIiIikgnDNxERERGRTHi1EyIiIiIimbDnm4iIiIhIJgzfREREREQyYfgmIiIiIpIJwzcRERERkUwYvomIiIiIZMLwTUREREQkE4ZvIiIiIiKZMHwTEREREcnk/wHgP43g9UHOvAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "5199f18e",
+      "metadata": {
+        "id": "5199f18e"
+      },
+      "source": [
+        "We first choose the best model using forward selection based on $C_p$ (\\ref{Ch6:eq:cp}). This score\n",
+        "is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use\n",
+        "it as a scorer. By default, `sklearn` tries to maximize a score, hence\n",
+        "  our scoring function  computes the negative $C_p$ statistic."
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ax.plot(np.arange(n_steps), \n",
-    "        validation_mse,\n",
-    "        'b--', # color blue, broken line\n",
-    "        label='Validation')\n",
-    "ax.set_xticks(np.arange(n_steps)[::2])\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.legend()\n",
-    "mse_fig\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4df366a",
-   "metadata": {},
-   "source": [
-    "### Best Subset Selection\n",
-    "Forward stepwise is a *greedy* selection procedure; at each step it augments the current set by including one additional variable.  We now apply best subset selection  to the  `Hitters` \n",
-    "data, which for every subset size, searches for the best set of predictors.  \n",
-    "\n",
-    "We will use a package called `l0bnb` to perform\n",
-    "best subset selection.\n",
-    "Instead of constraining the subset to be a given size,\n",
-    "this package produces a path of solutions using the subset size as a\n",
-    "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6e9dd7d4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.665992Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.665901Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.679390Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.679171Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "D = design.fit_transform(Hitters)\n",
-    "D = D.drop('intercept', axis=1)\n",
-    "X = np.asarray(D)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b87bd74",
-   "metadata": {},
-   "source": [
-    "Here we excluded the first column corresponding to the intercept, as\n",
-    "`l0bnb` will fit the intercept separately. We can find a path using the `fit_path()` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "caed84ae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.680677Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.680609Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.246130Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.245877Z"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Preprocessing Data.\n",
-      "BnB Started.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "096fe127",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.494225Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.494154Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.495738Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.495580Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "096fe127"
+      },
+      "outputs": [],
+      "source": [
+        "def nCp(sigma2, estimator, X, Y):\n",
+        "    \"Negative Cp statistic\"\n",
+        "    n, p = X.shape\n",
+        "    Yhat = estimator.predict(X)\n",
+        "    RSS = np.sum((Y - Yhat)**2)\n",
+        "    return -(RSS + 2 * p * sigma2) / n\n"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 1. Number of non-zeros:  1\n",
-      "Iteration: 2. Number of non-zeros:  2\n",
-      "Iteration: 3. Number of non-zeros:  2\n"
-     ]
+      "cell_type": "markdown",
+      "id": "bf9e3ca3",
+      "metadata": {
+        "id": "bf9e3ca3"
+      },
+      "source": [
+        "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n",
+        "We will fit the biggest model, using all the variables, and estimate $\\sigma^2$ based on its MSE."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 4. Number of non-zeros:  2\n",
-      "Iteration: 5. Number of non-zeros:  3\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b697d287",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.496810Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.496736Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.510614Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.510406Z"
+        },
+        "id": "b697d287"
+      },
+      "outputs": [],
+      "source": [
+        "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n",
+        "Y = np.array(Hitters['Salary'])\n",
+        "X = design.transform(Hitters)\n",
+        "sigma2 = OLS(Y,X).fit().scale\n"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 6. Number of non-zeros:  3\n",
-      "Iteration: 7. Number of non-zeros:  4\n"
-     ]
+      "cell_type": "markdown",
+      "id": "bc283589",
+      "metadata": {
+        "id": "bc283589"
+      },
+      "source": [
+        "The function `sklearn_selected()` expects a scorer with just three arguments --- the last three in the definition of `nCp()` above. We use the function `partial()` first seen in Section~\\ref{Ch5-resample-lab:the-bootstrap} to freeze the first argument with our estimate of $\\sigma^2$."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 8. Number of non-zeros:  9\n",
-      "Iteration: 9. Number of non-zeros:  9\n",
-      "Iteration: 10. Number of non-zeros:  9\n",
-      "Iteration: 11. Number of non-zeros:  9\n",
-      "Iteration: 12. Number of non-zeros:  9\n",
-      "Iteration: 13. Number of non-zeros:  9\n",
-      "Iteration: 14. Number of non-zeros:  9\n",
-      "Iteration: 15. Number of non-zeros:  9\n",
-      "Iteration: 16. Number of non-zeros:  9\n",
-      "Iteration: 17. Number of non-zeros:  9\n",
-      "Iteration: 18. Number of non-zeros:  17\n",
-      "Iteration: 19. Number of non-zeros:  19\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = fit_path(X, \n",
-    "                Y,\n",
-    "                max_nonzeros=X.shape[1])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4e9ca6a",
-   "metadata": {},
-   "source": [
-    "The function `fit_path()` returns a list whose values include the fitted coefficients as `B`, an intercept as `B0`, as well as a few other attributes related to the particular path algorithm used. Such details are beyond the scope of this book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "f2fc2ef8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.247604Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.247525Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.249696Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.249497Z"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2151b133",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.511833Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.511764Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.513251Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.513008Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "2151b133"
+      },
+      "outputs": [],
+      "source": [
+        "neg_Cp = partial(nCp, sigma2)\n"
+      ]
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'B': array([0.        , 3.25484367, 0.        , 0.        , 0.        ,\n",
-       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "        0.        , 0.67775265, 0.        , 0.        , 0.        ,\n",
-       "        0.        , 0.        , 0.        , 0.        ]),\n",
-       " 'B0': -38.98216739555551,\n",
-       " 'lambda_0': 0.011416248027450178,\n",
-       " 'M': 0.5829861733382012,\n",
-       " 'Time_exceeded': False}"
+      "cell_type": "markdown",
+      "id": "f6993ae8",
+      "metadata": {
+        "id": "f6993ae8"
+      },
+      "source": [
+        "We can now use `neg_Cp()` as a scorer for model selection.\n"
       ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "path[3]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4a96126",
-   "metadata": {},
-   "source": [
-    "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.114$ for the penalty parameter `lambda_0`.\n",
-    "We could make predictions using this sequence of fits on a validation set as a function of `lambda_0`, or with more work using cross-validation.\n",
-    "\n",
-    "## Ridge Regression and the Lasso\n",
-    "We will use the `sklearn.linear_model` package (for which\n",
-    "we use `skl` as shorthand below)\n",
-    "to fit ridge and  lasso regularized linear models on the `Hitters` data.\n",
-    "We start with the model matrix `X` (without an intercept) that we computed in the previous section on best subset regression.\n",
-    "\n",
-    "### Ridge Regression\n",
-    "We will use the function `skl.ElasticNet()` to fit both  ridge and the lasso.\n",
-    "To fit a *path* of ridge regressions models, we use\n",
-    "`skl.ElasticNet.path()`, which can fit both ridge and lasso, as well as a hybrid mixture;  ridge regression\n",
-    "corresponds to `l1_ratio=0`.\n",
-    "It is good practice to standardize the columns of `X` in these applications, if the variables are measured in different units. Since `skl.ElasticNet()` does no normalization, we have to take care of that ourselves.\n",
-    "Since we\n",
-    "standardize first, in order to find coefficient\n",
-    "estimates on the original scale, we must *unstandardize*\n",
-    "the coefficient estimates. The parameter\n",
-    "$\\lambda$ in (\\ref{Ch6:ridge}) and (\\ref{Ch6:LASSO}) is called `alphas` in `sklearn`. In order to\n",
-    "be consistent with the rest of this chapter, we use `lambdas`\n",
-    "rather than `alphas` in what follows.  {At the time of publication, ridge fits like the one in code chunk [22] issue unwarranted convergence warning messages; we expect these to disappear as this package matures.}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "4a5d6fc8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.250946Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.250878Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.303537Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.303273Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(19, 100)"
+      "cell_type": "markdown",
+      "id": "436a744d",
+      "metadata": {
+        "id": "436a744d"
+      },
+      "source": [
+        "Along with a score we need to specify the search strategy. This is done through the object\n",
+        "`Stepwise()`  in the `ISLP.models` package. The method `Stepwise.first_peak()`\n",
+        "runs forward stepwise until any further additions to the model do not result\n",
+        "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n",
+        "runs a fixed number of steps of stepwise search."
       ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Xs = X - X.mean(0)[None,:]\n",
-    "X_scale = X.std(0)\n",
-    "Xs = Xs / X_scale[None,:]\n",
-    "lambdas = 10**np.linspace(8, -2, 100) / Y.std()\n",
-    "soln_array = skl.ElasticNet.path(Xs,\n",
-    "                                 Y,\n",
-    "                                 l1_ratio=0.,\n",
-    "                                 alphas=lambdas)[1]\n",
-    "soln_array.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e7770f5",
-   "metadata": {},
-   "source": [
-    "Here we extract the array of coefficients corresponding to the solutions along the regularization path.\n",
-    "By default the `skl.ElasticNet.path` method fits a path along\n",
-    "an automatically selected range of $\\lambda$ values, except for the case when\n",
-    "`l1_ratio=0`, which results in ridge regression (as is the case here). {The reason is rather technical; for all models except ridge, we can find the smallest value of $\\lambda$ for which all coefficients are zero. For ridge this value is $\\infty$.}  So here\n",
-    "we have chosen to implement the function over a grid of values ranging\n",
-    "from $\\lambda=10^{8}$ to $\\lambda=10^{-2}$ scaled by the standard\n",
-    "deviation of $y$, essentially covering the full range of scenarios\n",
-    "from the null model containing only the intercept, to the least\n",
-    "squares fit.\n",
-    "\n",
-    "Associated with each value of $\\lambda$ is a vector of ridge\n",
-    "regression coefficients,   that can be accessed by\n",
-    "a column of `soln_array`. In this case, `soln_array` is a $19 \\times 100$ matrix, with\n",
-    "19 rows (one for each predictor) and 100\n",
-    "columns (one for each value of $\\lambda$).\n",
-    "\n",
-    "We transpose this matrix and turn it into a data frame to facilitate viewing and plotting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "1bca28f6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.304830Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.304756Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.311709Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.311502Z"
-    }
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ab28d6fd",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.514438Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.514363Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.515875Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.515674Z"
+        },
+        "id": "ab28d6fd"
+      },
+      "outputs": [],
+      "source": [
+        "strategy = Stepwise.first_peak(design,\n",
+        "                               direction='forward',\n",
+        "                               max_terms=len(design.terms))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4bf946d2",
+      "metadata": {
+        "id": "4bf946d2"
+      },
+      "source": [
+        "We now fit a linear regression model with `Salary` as outcome using forward\n",
+        "selection. To do so, we use the function `sklearn_selected()`  from the `ISLP.models` package. This takes\n",
+        "a model from `statsmodels` along with a search strategy and selects a model with its\n",
+        "`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be\n",
+        "selected."
+      ]
+    },
     {
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-       "      <td>0.001404</td>\n",
-       "      <td>0.001343</td>\n",
-       "      <td>0.001439</td>\n",
-       "      <td>0.001450</td>\n",
-       "      <td>0.001253</td>\n",
-       "      <td>-0.000037</td>\n",
-       "      <td>-0.000492</td>\n",
-       "      <td>0.000769</td>\n",
-       "      <td>0.000065</td>\n",
-       "      <td>-0.000014</td>\n",
-       "      <td>-0.000007</td>\n",
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-       "      <td>0.001355</td>\n",
-       "      <td>0.001451</td>\n",
-       "      <td>0.001433</td>\n",
-       "      <td>0.001293</td>\n",
-       "      <td>0.001698</td>\n",
-       "      <td>0.001772</td>\n",
-       "      <td>0.001694</td>\n",
-       "      <td>0.001816</td>\n",
-       "      <td>0.001830</td>\n",
-       "      <td>0.001581</td>\n",
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-       "      <td>-0.000621</td>\n",
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-       "      <td>-0.000017</td>\n",
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-       "      <th>-11.613102</th>\n",
-       "      <td>0.001608</td>\n",
-       "      <td>0.001787</td>\n",
-       "      <td>0.001397</td>\n",
-       "      <td>0.001710</td>\n",
-       "      <td>0.001831</td>\n",
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-       "      <td>0.000104</td>\n",
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-       "      <th>9.784658</th>\n",
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-       "      <td>336.929968</td>\n",
-       "      <td>37.322686</td>\n",
-       "      <td>-59.748520</td>\n",
-       "      <td>-26.507086</td>\n",
-       "      <td>134.855915</td>\n",
-       "      <td>-17.216195</td>\n",
-       "      <td>-387.775826</td>\n",
-       "      <td>89.573601</td>\n",
-       "      <td>-12.273926</td>\n",
-       "      <td>476.079273</td>\n",
-       "      <td>257.271255</td>\n",
-       "      <td>-213.124780</td>\n",
-       "      <td>31.258215</td>\n",
-       "      <td>-58.457857</td>\n",
-       "      <td>78.761266</td>\n",
-       "      <td>53.622113</td>\n",
-       "      <td>-22.208456</td>\n",
-       "      <td>-12.402891</td>\n",
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-       "    <tr>\n",
-       "      <th>10.017243</th>\n",
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-       "      <td>-26.606957</td>\n",
-       "      <td>134.900549</td>\n",
-       "      <td>-17.108041</td>\n",
-       "      <td>-388.458404</td>\n",
-       "      <td>89.000707</td>\n",
-       "      <td>-12.661459</td>\n",
-       "      <td>477.031349</td>\n",
-       "      <td>257.966790</td>\n",
-       "      <td>-213.280891</td>\n",
-       "      <td>31.256434</td>\n",
-       "      <td>-58.448850</td>\n",
-       "      <td>78.761240</td>\n",
-       "      <td>53.645147</td>\n",
-       "      <td>-22.198802</td>\n",
-       "      <td>-12.391969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.249827</th>\n",
-       "      <td>-290.923382</td>\n",
-       "      <td>337.260446</td>\n",
-       "      <td>37.518064</td>\n",
-       "      <td>-60.051166</td>\n",
-       "      <td>-26.686604</td>\n",
-       "      <td>134.936136</td>\n",
-       "      <td>-17.022194</td>\n",
-       "      <td>-388.997470</td>\n",
-       "      <td>88.537380</td>\n",
-       "      <td>-12.971603</td>\n",
-       "      <td>477.791860</td>\n",
-       "      <td>258.523025</td>\n",
-       "      <td>-213.405740</td>\n",
-       "      <td>31.254958</td>\n",
-       "      <td>-58.441682</td>\n",
-       "      <td>78.761230</td>\n",
-       "      <td>53.663357</td>\n",
-       "      <td>-22.191071</td>\n",
-       "      <td>-12.383205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.482412</th>\n",
-       "      <td>-290.958537</td>\n",
-       "      <td>337.377455</td>\n",
-       "      <td>37.587122</td>\n",
-       "      <td>-60.158256</td>\n",
-       "      <td>-26.750044</td>\n",
-       "      <td>134.964477</td>\n",
-       "      <td>-16.954081</td>\n",
-       "      <td>-389.423414</td>\n",
-       "      <td>88.164178</td>\n",
-       "      <td>-13.219329</td>\n",
-       "      <td>478.398404</td>\n",
-       "      <td>258.967059</td>\n",
-       "      <td>-213.505412</td>\n",
-       "      <td>31.253747</td>\n",
-       "      <td>-58.435983</td>\n",
-       "      <td>78.761230</td>\n",
-       "      <td>53.677759</td>\n",
-       "      <td>-22.184893</td>\n",
-       "      <td>-12.376191</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.714996</th>\n",
-       "      <td>-290.986528</td>\n",
-       "      <td>337.470648</td>\n",
-       "      <td>37.642077</td>\n",
-       "      <td>-60.243522</td>\n",
-       "      <td>-26.800522</td>\n",
-       "      <td>134.987027</td>\n",
-       "      <td>-16.900054</td>\n",
-       "      <td>-389.760135</td>\n",
-       "      <td>87.864551</td>\n",
-       "      <td>-13.416889</td>\n",
-       "      <td>478.881540</td>\n",
-       "      <td>259.321007</td>\n",
-       "      <td>-213.584869</td>\n",
-       "      <td>31.252760</td>\n",
-       "      <td>-58.431454</td>\n",
-       "      <td>78.761235</td>\n",
-       "      <td>53.689152</td>\n",
-       "      <td>-22.179964</td>\n",
-       "      <td>-12.370587</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 19 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d8b345ec",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.517091Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.517008Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.178273Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.178018Z"
+        },
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+        "outputId": "6165f494-510f-408a-e4ea-2fd2ec0a57aa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "('Assists',\n",
+              " 'AtBat',\n",
+              " 'CAtBat',\n",
+              " 'CHits',\n",
+              " 'CHmRun',\n",
+              " 'CRBI',\n",
+              " 'CRuns',\n",
+              " 'CWalks',\n",
+              " 'Division',\n",
+              " 'Errors',\n",
+              " 'Hits',\n",
+              " 'HmRun',\n",
+              " 'League',\n",
+              " 'NewLeague',\n",
+              " 'PutOuts',\n",
+              " 'RBI',\n",
+              " 'Runs',\n",
+              " 'Walks',\n",
+              " 'Years')"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "                           AtBat        Hits      HmRun       Runs        RBI  \\\n",
-       "negative log(lambda)                                                            \n",
-       "-12.310855              0.000800    0.000889   0.000695   0.000851   0.000911   \n",
-       "-12.078271              0.001010    0.001122   0.000878   0.001074   0.001150   \n",
-       "-11.845686              0.001274    0.001416   0.001107   0.001355   0.001451   \n",
-       "-11.613102              0.001608    0.001787   0.001397   0.001710   0.001831   \n",
-       "-11.380518              0.002029    0.002255   0.001763   0.002158   0.002310   \n",
-       "...                          ...         ...        ...        ...        ...   \n",
-       " 9.784658            -290.823989  336.929968  37.322686 -59.748520 -26.507086   \n",
-       " 10.017243           -290.879272  337.113713  37.431373 -59.916820 -26.606957   \n",
-       " 10.249827           -290.923382  337.260446  37.518064 -60.051166 -26.686604   \n",
-       " 10.482412           -290.958537  337.377455  37.587122 -60.158256 -26.750044   \n",
-       " 10.714996           -290.986528  337.470648  37.642077 -60.243522 -26.800522   \n",
-       "\n",
-       "                           Walks      Years      CAtBat      CHits     CHmRun  \\\n",
-       "negative log(lambda)                                                            \n",
-       "-12.310855              0.000900   0.000812    0.001067   0.001113   0.001064   \n",
-       "-12.078271              0.001135   0.001025    0.001346   0.001404   0.001343   \n",
-       "-11.845686              0.001433   0.001293    0.001698   0.001772   0.001694   \n",
-       "-11.613102              0.001808   0.001632    0.002143   0.002236   0.002138   \n",
-       "-11.380518              0.002281   0.002059    0.002704   0.002821   0.002698   \n",
-       "...                          ...        ...         ...        ...        ...   \n",
-       " 9.784658             134.855915 -17.216195 -387.775826  89.573601 -12.273926   \n",
-       " 10.017243            134.900549 -17.108041 -388.458404  89.000707 -12.661459   \n",
-       " 10.249827            134.936136 -17.022194 -388.997470  88.537380 -12.971603   \n",
-       " 10.482412            134.964477 -16.954081 -389.423414  88.164178 -13.219329   \n",
-       " 10.714996            134.987027 -16.900054 -389.760135  87.864551 -13.416889   \n",
-       "\n",
-       "                           CRuns        CRBI      CWalks  League[N]  \\\n",
-       "negative log(lambda)                                                  \n",
-       "-12.310855              0.001141    0.001149    0.000993  -0.000029   \n",
-       "-12.078271              0.001439    0.001450    0.001253  -0.000037   \n",
-       "-11.845686              0.001816    0.001830    0.001581  -0.000046   \n",
-       "-11.613102              0.002292    0.002309    0.001995  -0.000058   \n",
-       "-11.380518              0.002892    0.002914    0.002517  -0.000073   \n",
-       "...                          ...         ...         ...        ...   \n",
-       " 9.784658             476.079273  257.271255 -213.124780  31.258215   \n",
-       " 10.017243            477.031349  257.966790 -213.280891  31.256434   \n",
-       " 10.249827            477.791860  258.523025 -213.405740  31.254958   \n",
-       " 10.482412            478.398404  258.967059 -213.505412  31.253747   \n",
-       " 10.714996            478.881540  259.321007 -213.584869  31.252760   \n",
-       "\n",
-       "                      Division[W]    PutOuts    Assists     Errors  \\\n",
-       "negative log(lambda)                                                 \n",
-       "-12.310855              -0.000390   0.000609   0.000052  -0.000011   \n",
-       "-12.078271              -0.000492   0.000769   0.000065  -0.000014   \n",
-       "-11.845686              -0.000621   0.000970   0.000082  -0.000017   \n",
-       "-11.613102              -0.000784   0.001224   0.000104  -0.000022   \n",
-       "-11.380518              -0.000990   0.001544   0.000131  -0.000028   \n",
-       "...                           ...        ...        ...        ...   \n",
-       " 9.784658              -58.457857  78.761266  53.622113 -22.208456   \n",
-       " 10.017243             -58.448850  78.761240  53.645147 -22.198802   \n",
-       " 10.249827             -58.441682  78.761230  53.663357 -22.191071   \n",
-       " 10.482412             -58.435983  78.761230  53.677759 -22.184893   \n",
-       " 10.714996             -58.431454  78.761235  53.689152 -22.179964   \n",
-       "\n",
-       "                      NewLeague[N]  \n",
-       "negative log(lambda)                \n",
-       "-12.310855               -0.000006  \n",
-       "-12.078271               -0.000007  \n",
-       "-11.845686               -0.000009  \n",
-       "-11.613102               -0.000012  \n",
-       "-11.380518               -0.000015  \n",
-       "...                            ...  \n",
-       " 9.784658               -12.402891  \n",
-       " 10.017243              -12.391969  \n",
-       " 10.249827              -12.383205  \n",
-       " 10.482412              -12.376191  \n",
-       " 10.714996              -12.370587  \n",
-       "\n",
-       "[100 rows x 19 columns]"
+      "source": [
+        "hitters_MSE = sklearn_selected(OLS,\n",
+        "                               strategy)\n",
+        "hitters_MSE.fit(Hitters, Y)\n",
+        "hitters_MSE.selected_state_\n"
       ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln_path = pd.DataFrame(soln_array.T,\n",
-    "                         columns=D.columns,\n",
-    "                         index=-np.log(lambdas))\n",
-    "soln_path.index.name = 'negative log(lambda)'\n",
-    "soln_path\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2246bb2b",
-   "metadata": {},
-   "source": [
-    "We plot the paths to get a sense of how the coefficients vary with $\\lambda$.\n",
-    "To control the location of the legend we first set `legend` to `False` in the\n",
-    "plot method, adding it afterward with the `legend()` method of `ax`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "08bc997b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.312975Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.312908Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.473880Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.473585Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "c1a78607",
+      "metadata": {
+        "id": "c1a78607"
+      },
+      "source": [
+        "Using `neg_Cp` results in a smaller model, as expected, with just 10 variables selected."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "path_fig, ax = subplots(figsize=(8,8))\n",
-    "soln_path.plot(ax=ax, legend=False)\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Standardized coefficients', fontsize=20)\n",
-    "ax.legend(loc='upper left');\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae73ca09",
-   "metadata": {},
-   "source": [
-    "(We have used `latex` formatting in the horizontal label, in order to format the Greek $\\lambda$ appropriately.) \n",
-    "We expect the coefficient estimates to be much smaller, in terms of\n",
-    "$\\ell_2$ norm, when a large value of $\\lambda$ is used, as compared to\n",
-    "when a small value of $\\lambda$ is used. (Recall that the  $\\ell_2$ norm is the square root of the sum of squared coefficient values.) We display  the coefficients at the $40$th step,\n",
-    "where $\\lambda$ is 25.535."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "0f7359c7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.475363Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.475242Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.477806Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.477534Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(25.53538897200662,\n",
-       " AtBat           5.433750\n",
-       " Hits            6.223582\n",
-       " HmRun           4.585498\n",
-       " Runs            5.880855\n",
-       " RBI             6.195921\n",
-       " Walks           6.277975\n",
-       " Years           5.299767\n",
-       " CAtBat          7.147501\n",
-       " CHits           7.539495\n",
-       " CHmRun          7.182344\n",
-       " CRuns           7.728649\n",
-       " CRBI            7.790702\n",
-       " CWalks          6.592901\n",
-       " League[N]       0.042445\n",
-       " Division[W]    -3.107159\n",
-       " PutOuts         4.605263\n",
-       " Assists         0.378371\n",
-       " Errors         -0.135196\n",
-       " NewLeague[N]    0.150323\n",
-       " Name: -3.240065292879872, dtype: float64)"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "915cdff6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.179650Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.179571Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.573219Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.572980Z"
+        },
+        "id": "915cdff6",
+        "outputId": "b5e9d80d-e824-4895-926e-edc8e23f7a9d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "('Assists',\n",
+              " 'AtBat',\n",
+              " 'CAtBat',\n",
+              " 'CRBI',\n",
+              " 'CRuns',\n",
+              " 'CWalks',\n",
+              " 'Division',\n",
+              " 'Hits',\n",
+              " 'PutOuts',\n",
+              " 'Walks')"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "hitters_Cp = sklearn_selected(OLS,\n",
+        "                               strategy,\n",
+        "                               scoring=neg_Cp)\n",
+        "hitters_Cp.fit(Hitters, Y)\n",
+        "hitters_Cp.selected_state_\n"
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_hat = soln_path.loc[soln_path.index[39]]\n",
-    "lambdas[39], beta_hat\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9baa8b00",
-   "metadata": {},
-   "source": [
-    "Let’s compute the $\\ell_2$ norm of the standardized coefficients."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d2984305",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.479160Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.479064Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.481153Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.480921Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "24.17061720144378"
+      "cell_type": "markdown",
+      "id": "f5d37f87",
+      "metadata": {
+        "id": "f5d37f87"
+      },
+      "source": [
+        "### Choosing Among Models Using the Validation Set Approach and Cross-Validation\n",
+        "\n",
+        "As an  alternative to using $C_p$, we might try cross-validation to select a model in forward selection. For this, we need a\n",
+        "method that stores the full path of models found in forward selection, and allows predictions for each of these. This can be done with the `sklearn_selection_path()`\n",
+        "estimator from `ISLP.models`. The function `cross_val_predict()` from `ISLP.models`\n",
+        "computes the cross-validated predictions for each of the models\n",
+        "along the path, which we can use to evaluate the cross-validated MSE\n",
+        "along the path."
       ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linalg.norm(beta_hat)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5111fc6f",
-   "metadata": {},
-   "source": [
-    "In contrast, here is the $\\ell_2$ norm when $\\lambda$ is 2.44e-01.\n",
-    "Note the much larger $\\ell_2$ norm of the\n",
-    "coefficients associated with this smaller value of $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "9a3424a4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.482508Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.482402Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.484554Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.484320Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(0.24374766133488554, 160.42371017725839)"
+      "cell_type": "markdown",
+      "id": "6769115d",
+      "metadata": {
+        "id": "6769115d"
+      },
+      "source": [
+        "Here we define a strategy that fits the full forward selection path.\n",
+        "While there are various parameter choices for `sklearn_selection_path()`,\n",
+        "we use the defaults here, which selects the model at each step based on the biggest reduction  in RSS."
       ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_hat = soln_path.loc[soln_path.index[59]]\n",
-    "lambdas[59], np.linalg.norm(beta_hat)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50239927",
-   "metadata": {},
-   "source": [
-    "Above we normalized `X` upfront, and fit the ridge model using `Xs`.\n",
-    "The `Pipeline()`  object\n",
-    "in `sklearn` provides a clear way to separate feature\n",
-    "normalization from the fitting of the ridge model itself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "c864d25f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.485813Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.485727Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.495072Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.494829Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.24374766133488554, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "33ae4aa4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.574545Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.574468Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.576122Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.575907Z"
+        },
+        "id": "33ae4aa4"
+      },
+      "outputs": [],
+      "source": [
+        "strategy = Stepwise.fixed_steps(design,\n",
+        "                                len(design.terms),\n",
+        "                                direction='forward')\n",
+        "full_path = sklearn_selection_path(OLS, strategy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d397415c",
+      "metadata": {
+        "id": "d397415c"
+      },
+      "source": [
+        "We now fit the full forward-selection path on the `Hitters` data and compute the fitted values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f05e1a81",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.577302Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.577233Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.976283Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.976065Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "f05e1a81",
+        "outputId": "c0ad0566-b3f6-40c9-bbcc-40ac21b3a1d3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(263, 20)"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])"
+      "source": [
+        "full_path.fit(Hitters, Y)\n",
+        "Yhat_in = full_path.predict(Hitters)\n",
+        "Yhat_in.shape\n"
       ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n",
-    "scaler = StandardScaler(with_mean=True,  with_std=True)\n",
-    "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n",
-    "pipe.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71c8c514",
-   "metadata": {},
-   "source": [
-    "We show that it gives the same $\\ell_2$ norm as in our previous fit on the standardized data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "8a07da6e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.496384Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.496277Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.498333Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.498078Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "160.4237101772591"
+      "cell_type": "markdown",
+      "id": "767af527",
+      "metadata": {
+        "id": "767af527"
+      },
+      "source": [
+        "This gives us an array of fitted values --- 20 steps in all, including the fitted mean for the null model --- which we can use to evaluate\n",
+        "in-sample MSE. As expected, the in-sample MSE improves each step we take,\n",
+        "indicating we must use either the validation or cross-validation\n",
+        "approach to select the number of steps. We fix the y-axis to range from\n",
+        "50,000 to 250,000 to compare to the cross-validation and validation\n",
+        "set MSE below, as well as other methods such as ridge regression, lasso and\n",
+        "principal components regression."
       ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linalg.norm(ridge.coef_)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a20ea7e",
-   "metadata": {},
-   "source": [
-    " Notice that the operation `pipe.fit(X, Y)` above has changed the `ridge` object, and in particular has added attributes such as `coef_` that were not there before. \n",
-    "### Estimating Test Error of Ridge Regression\n",
-    "Choosing an *a priori* value of $\\lambda$ for ridge regression is\n",
-    "difficult if not impossible. We will want to use the validation method\n",
-    "or cross-validation to select the tuning parameter. The reader may not\n",
-    "be surprised that the  `Pipeline()` approach can be used in\n",
-    "`skm.cross_validate()` with either a validation method\n",
-    "(i.e. `validation`) or $k$-fold cross-validation.\n",
-    "\n",
-    "We fix the random state of the splitter\n",
-    "so that the results obtained will be reproducible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "c10e3ae5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.499658Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.499553Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.505630Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.505369Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([134214.00419204])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0b07ae72",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.977573Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.977503Z",
+          "iopub.status.idle": "2024-06-04T23:19:23.094004Z",
+          "shell.execute_reply": "2024-06-04T23:19:23.091687Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "0b07ae72",
+        "outputId": "d1316a68-e99a-49f5-f101-c23a032ceb63"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "mse_fig, ax = subplots(figsize=(8,8))\n",
+        "insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)\n",
+        "n_steps = insample_mse.shape[0]\n",
+        "ax.plot(np.arange(n_steps),\n",
+        "        insample_mse,\n",
+        "        'k', # color black\n",
+        "        label='In-sample')\n",
+        "ax.set_ylabel('MSE',\n",
+        "              fontsize=20)\n",
+        "ax.set_xlabel('# steps of forward stepwise',\n",
+        "              fontsize=20)\n",
+        "ax.set_xticks(np.arange(n_steps)[::2])\n",
+        "ax.legend()\n",
+        "ax.set_ylim([50000,250000]);\n"
       ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation = skm.ShuffleSplit(n_splits=1,\n",
-    "                              test_size=0.5,\n",
-    "                              random_state=0)\n",
-    "ridge.alpha = 0.01\n",
-    "results = skm.cross_validate(ridge,\n",
-    "                             X,\n",
-    "                             Y,\n",
-    "                             scoring='neg_mean_squared_error',\n",
-    "                             cv=validation)\n",
-    "-results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5f962ee",
-   "metadata": {},
-   "source": [
-    "The test MSE is 1.342e+05.  Note\n",
-    "that if we had instead simply fit a model with just an intercept, we\n",
-    "would have predicted each test observation using the mean of the\n",
-    "training observations. We can get the same result by fitting a ridge regression model\n",
-    "with a *very* large value of $\\lambda$. Note that `1e10`\n",
-    "means $10^{10}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "0df2b24d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.507058Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.506964Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.512846Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.512640Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([231788.32155285])"
+      "cell_type": "markdown",
+      "id": "5f7508e0",
+      "metadata": {
+        "id": "5f7508e0"
+      },
+      "source": [
+        "Notice the expression `None` in `Y[:,None]` above.\n",
+        "This adds an axis (dimension) to the one-dimensional array `Y`,\n",
+        "which allows it to be recycled when subtracted from the two-dimensional `Yhat_in`."
       ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ridge.alpha = 1e10\n",
-    "results = skm.cross_validate(ridge,\n",
-    "                             X,\n",
-    "                             Y,\n",
-    "                             scoring='neg_mean_squared_error',\n",
-    "                             cv=validation)\n",
-    "-results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e78a776c",
-   "metadata": {},
-   "source": [
-    "Obviously choosing $\\lambda=0.01$ is arbitrary,  so we will  use cross-validation or the validation-set\n",
-    "approach to choose the tuning parameter $\\lambda$.\n",
-    "The object `GridSearchCV()`  allows exhaustive\n",
-    "grid search to choose such a parameter.\n",
-    "\n",
-    "We first use the validation set method\n",
-    "to choose $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "9b76df85",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.514070Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.514003Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.851311Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.851092Z"
-    }
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f6c9531a",
+      "metadata": {
+        "id": "f6c9531a"
+      },
+      "source": [
+        "We are now ready to use cross-validation to estimate test error along\n",
+        "the model path. We must use *only the training observations* to perform all aspects of model-fitting --- including\n",
+        "variable selection.  Therefore, the determination of which model of a\n",
+        "given size is best must be made using \\emph{only the training\n",
+        "  observations} in each training fold. This point is subtle but important.  If the full data\n",
+        "set is used to select the best subset at each step, then the validation\n",
+        "set errors and cross-validation errors that we obtain will not be\n",
+        "accurate estimates of the test error.\n",
+        "\n",
+        "We now compute the cross-validated predicted values using 5-fold cross-validation."
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.005899006046740856, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a1654ad8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:23.097519Z",
+          "iopub.status.busy": "2024-06-04T23:19:23.097271Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.139415Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.139180Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "a1654ad8",
+        "outputId": "15893cc5-1d2a-4714-f310-91e8385fef21"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(263, 20)"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])"
+      "source": [
+        "K = 5\n",
+        "kfold = skm.KFold(K,\n",
+        "                  random_state=0,\n",
+        "                  shuffle=True)\n",
+        "Yhat_cv = skm.cross_val_predict(full_path,\n",
+        "                                Hitters,\n",
+        "                                Y,\n",
+        "                                cv=kfold)\n",
+        "Yhat_cv.shape\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98dfabdc",
+      "metadata": {
+        "id": "98dfabdc"
+      },
+      "source": [
+        "`skm.cross_val_predict()`\n",
+        "The prediction matrix `Yhat_cv` is the same shape as `Yhat_in`; the difference is that the predictions in each row, corresponding to a particular sample index, were made from models fit on a training fold that did not include that row.\n",
+        "\n",
+        "At each model along the path, we compute the MSE in each of the cross-validation folds.\n",
+        "These we will average to get the  mean MSE, and can also use the individual values to compute a crude estimate of the standard error of the mean. {The estimate is crude because the five error estimates  are based on overlapping training sets, and hence are not independent.}\n",
+        "Hence we must know the test indices for each cross-validation\n",
+        "split. This can be found by using the `split()` method of `kfold`. Because\n",
+        "we  fixed the random state above, whenever we split any array with the same\n",
+        "number of rows as $Y$ we recover the same training and test indices, though we simply\n",
+        "ignore the training indices below."
       ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_grid = {'ridge__alpha': lambdas}\n",
-    "grid = skm.GridSearchCV(pipe,\n",
-    "                        param_grid,\n",
-    "                        cv=validation,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n",
-    "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9befd23e",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can use 5-fold cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "3e4c1b53",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.852563Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.852495Z",
-     "iopub.status.idle": "2024-06-04T23:19:30.080670Z",
-     "shell.execute_reply": "2024-06-04T23:19:30.080412Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ce919bd3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.140773Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.140698Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.143330Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.143113Z"
+        },
+        "id": "ce919bd3",
+        "outputId": "5de1a2ad-412c-4217-d8eb-cd26524c3741"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(20, 5)"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])"
+      "source": [
+        "cv_mse = []\n",
+        "for train_idx, test_idx in kfold.split(Y):\n",
+        "    errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n",
+        "    cv_mse.append(errors.mean(0)) # column means\n",
+        "cv_mse = np.array(cv_mse).T\n",
+        "cv_mse.shape\n"
       ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid = skm.GridSearchCV(pipe, \n",
-    "                        param_grid,\n",
-    "                        cv=kfold,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n",
-    "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cae429da",
-   "metadata": {},
-   "source": [
-    "Recall we set up the `kfold` object for 5-fold cross-validation on page~\\pageref{line:choos-among-models}. We now plot the cross-validated MSE as a function of $-\\log(\\lambda)$, which has shrinkage decreasing from left\n",
-    "to right."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "39ff3251",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:30.082088Z",
-     "iopub.status.busy": "2024-06-04T23:19:30.082012Z",
-     "iopub.status.idle": "2024-06-04T23:19:30.194197Z",
-     "shell.execute_reply": "2024-06-04T23:19:30.193921Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "a27004be",
+      "metadata": {
+        "id": "a27004be"
+      },
+      "source": [
+        "We now add the cross-validation error estimates to our MSE plot.\n",
+        "We include the mean error across the five folds, and the estimate of the standard error of the mean."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ridge_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(lambdas),\n",
-    "            -grid.cv_results_['mean_test_score'],\n",
-    "            yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "854e0c21",
-   "metadata": {},
-   "source": [
-    "One can cross-validate different metrics to choose a parameter. The default\n",
-    "metric for `skl.ElasticNet()` is test $R^2$.\n",
-    "Let’s compare $R^2$ to MSE for cross-validation here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "766ab0b0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:30.195678Z",
-     "iopub.status.busy": "2024-06-04T23:19:30.195576Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.425386Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.425155Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-4 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;ridge&#x27;,\n",
-       "                                        ElasticNet(alpha=10000000000.0,\n",
-       "                                                   l1_ratio=0))]),\n",
-       "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606...\n",
-       "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;ridge&#x27;,\n",
-       "                                        ElasticNet(alpha=10000000000.0,\n",
-       "                                                   l1_ratio=0))]),\n",
-       "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606...\n",
-       "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "26520d22",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.144503Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.144435Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.201431Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.201214Z"
+        },
+        "id": "26520d22",
+        "outputId": "723e53b0-e36b-42e6-8f40-c51d45fc08fa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                                       ('ridge',\n",
-       "                                        ElasticNet(alpha=10000000000.0,\n",
-       "                                                   l1_ratio=0))]),\n",
-       "             param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606...\n",
-       "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})"
+      "source": [
+        "ax.errorbar(np.arange(n_steps),\n",
+        "            cv_mse.mean(1),\n",
+        "            cv_mse.std(1) / np.sqrt(K),\n",
+        "            label='Cross-validated',\n",
+        "            c='r') # color red\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.legend()\n",
+        "mse_fig\n"
       ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid_r2 = skm.GridSearchCV(pipe, \n",
-    "                           param_grid,\n",
-    "                           cv=kfold)\n",
-    "grid_r2.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3887a9c",
-   "metadata": {},
-   "source": [
-    "Finally, let’s plot the results for cross-validated $R^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "642f1de3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.426644Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.426570Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.492503Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.492264Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "9881566e",
+      "metadata": {
+        "id": "9881566e"
+      },
+      "source": [
+        "To repeat the above using the validation set approach, we simply change our\n",
+        "`cv` argument to a validation set: one random split of the data into a test and training. We choose a test size\n",
+        "of 20%, similar to the size of each test set in 5-fold cross-validation.`skm.ShuffleSplit()`"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "r2_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(lambdas),\n",
-    "            grid_r2.cv_results_['mean_test_score'],\n",
-    "            yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0cb6f97b",
-   "metadata": {},
-   "source": [
-    "### Fast Cross-Validation for Solution Paths\n",
-    "The ridge, lasso, and elastic net can be efficiently fit along a sequence of $\\lambda$ values, creating what is known as a *solution path* or *regularization path*. Hence there is specialized code to fit\n",
-    "such paths, and to choose a suitable value of $\\lambda$ using cross-validation. Even with\n",
-    "identical splits the results will not agree *exactly* with our `grid`\n",
-    "above because the standardization of each feature  in `grid` is carried out on each fold,\n",
-    "while in `pipeCV` below it is carried out only once.\n",
-    "Nevertheless, the results are similar as the normalization\n",
-    "is relatively stable across folds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "016ffe7c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.493853Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.493772Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.763066Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.762828Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-5 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;,\n",
-       "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03,...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "                              l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;,\n",
-       "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03,...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "                              l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNetCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNetCV.html\">?<span>Documentation for ElasticNetCV</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03, 3.37562814e+03, 2.67512757e+03,\n",
-       "       2.11999285e+03, 1.680058...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "             cv=KFold(n_splits=5, random_state=0, shuffle=True), l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f19c9091",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.202701Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.202634Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.603532Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.603301Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "f19c9091"
+      },
+      "outputs": [],
+      "source": [
+        "validation = skm.ShuffleSplit(n_splits=1,\n",
+        "                              test_size=0.2,\n",
+        "                              random_state=0)\n",
+        "for train_idx, test_idx in validation.split(Y):\n",
+        "    full_path.fit(Hitters.iloc[train_idx],\n",
+        "                  Y[train_idx])\n",
+        "    Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n",
+        "    errors = (Yhat_val - Y[test_idx,None])**2\n",
+        "    validation_mse = errors.mean(0)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7421fc11",
+      "metadata": {
+        "id": "7421fc11"
+      },
+      "source": [
+        " As for the in-sample MSE case, the validation set approach does not provide standard errors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1fca6b08",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.604861Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.604789Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.664722Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.664495Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "1fca6b08",
+        "outputId": "442fbcef-a7cc-4e3a-fc84-f37a70c41034"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge',\n",
-       "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03,...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "                              l1_ratio=0))])"
+      "source": [
+        "ax.plot(np.arange(n_steps),\n",
+        "        validation_mse,\n",
+        "        'b--', # color blue, broken line\n",
+        "        label='Validation')\n",
+        "ax.set_xticks(np.arange(n_steps)[::2])\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.legend()\n",
+        "mse_fig\n"
       ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ridgeCV = skl.ElasticNetCV(alphas=lambdas, \n",
-    "                           l1_ratio=0,\n",
-    "                           cv=kfold)\n",
-    "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('ridge', ridgeCV)])\n",
-    "pipeCV.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6845efe",
-   "metadata": {},
-   "source": [
-    "Let’s produce a plot again of the cross-validation error to see that\n",
-    "it is similar to using `skm.GridSearchCV`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "52f6cf29",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.764408Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.764316Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.836546Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.836313Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "b4df366a",
+      "metadata": {
+        "id": "b4df366a"
+      },
+      "source": [
+        "### Best Subset Selection\n",
+        "Forward stepwise is a *greedy* selection procedure; at each step it augments the current set by including one additional variable.  We now apply best subset selection  to the  `Hitters`\n",
+        "data, which for every subset size, searches for the best set of predictors.  \n",
+        "\n",
+        "We will use a package called `l0bnb` to perform\n",
+        "best subset selection.\n",
+        "Instead of constraining the subset to be a given size,\n",
+        "this package produces a path of solutions using the subset size as a\n",
+        "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate.\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tuned_ridge = pipeCV.named_steps['ridge']\n",
-    "ridgeCV_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(lambdas),\n",
-    "            tuned_ridge.mse_path_.mean(1),\n",
-    "            yerr=tuned_ridge.mse_path_.std(1) / np.sqrt(K))\n",
-    "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80e07995",
-   "metadata": {},
-   "source": [
-    "We see that the value of $\\lambda$ that results in the\n",
-    "smallest cross-validation error is 1.19e-02, available\n",
-    "as the value `tuned_ridge.alpha_`. What is the test MSE\n",
-    "associated with this value of $\\lambda$?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "2b49c3d7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.838011Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.837899Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.840000Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.839740Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "115526.70630987917"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6e9dd7d4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.665992Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.665901Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.679390Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.679171Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "6e9dd7d4"
+      },
+      "outputs": [],
+      "source": [
+        "D = design.fit_transform(Hitters)\n",
+        "D = D.drop('intercept', axis=1)\n",
+        "X = np.asarray(D)\n"
       ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(tuned_ridge.mse_path_.mean(1))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0961957c",
-   "metadata": {},
-   "source": [
-    "This represents a further improvement over the test MSE that we got\n",
-    "using $\\lambda=4$.  Finally, `tuned_ridge.coef_`\n",
-    "has the coefficients fit on the entire data set\n",
-    "at this value of  $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "973e7e79",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.841440Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.841345Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.843427Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.843185Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([-222.80877051,  238.77246614,    3.21103754,   -2.93050845,\n",
-       "          3.64888723,  108.90953869,  -50.81896152, -105.15731984,\n",
-       "        122.00714801,   57.1859509 ,  210.35170348,  118.05683748,\n",
-       "       -150.21959435,   30.36634231,  -61.62459095,   77.73832472,\n",
-       "         40.07350744,  -25.02151514,  -13.68429544])"
+      "cell_type": "markdown",
+      "id": "4b87bd74",
+      "metadata": {
+        "id": "4b87bd74"
+      },
+      "source": [
+        "Here we excluded the first column corresponding to the intercept, as\n",
+        "`l0bnb` will fit the intercept separately. We can find a path using the `fit_path()` function."
       ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuned_ridge.coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b19ffa3f",
-   "metadata": {},
-   "source": [
-    "As expected, none of the coefficients are zero—ridge regression does\n",
-    "not perform variable selection!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfb6c72e",
-   "metadata": {},
-   "source": [
-    "### Evaluating Test Error of Cross-Validated Ridge\n",
-    "Choosing $\\lambda$ using cross-validation provides a single regression\n",
-    "estimator, similar to fitting a linear regression model as we saw in\n",
-    "Chapter~\\ref{Ch3:linreg}. It is therefore reasonable to estimate what its test error\n",
-    "is. We run into a problem here in that cross-validation will have\n",
-    "*touched* all of its data in choosing $\\lambda$, hence we have no\n",
-    "further data to estimate test error. A compromise is to do an initial\n",
-    "split of the data into two disjoint sets: a training set and a test set.\n",
-    "We then fit a cross-validation\n",
-    "tuned ridge regression on the training set, and evaluate its performance on the test set.\n",
-    "We might call this cross-validation nested\n",
-    "within the validation set approach. A priori there is no reason to use\n",
-    "half of the data for each of the two sets in validation. Below, we use\n",
-    "75% for training and 25% for test, with the estimator being ridge\n",
-    "regression tuned using 5-fold cross-validation.  This can be achieved\n",
-    "in code as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "a9140b3a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.844819Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.844727Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.846512Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.846267Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "outer_valid = skm.ShuffleSplit(n_splits=1, \n",
-    "                               test_size=0.25,\n",
-    "                               random_state=1)\n",
-    "inner_cv = skm.KFold(n_splits=5,\n",
-    "                     shuffle=True,\n",
-    "                     random_state=2)\n",
-    "ridgeCV = skl.ElasticNetCV(alphas=lambdas,\n",
-    "                           l1_ratio=0,\n",
-    "                           cv=inner_cv)\n",
-    "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('ridge', ridgeCV)]);\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "8fc4aea0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.847712Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.847645Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.096196Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.095971Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([132393.84003227])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "caed84ae",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.680677Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.680609Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.246130Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.245877Z"
+        },
+        "id": "caed84ae",
+        "outputId": "62e09d79-a6c0-44f7-a261-e2064a356279"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Preprocessing Data.\n",
+            "BnB Started.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 1. Number of non-zeros:  1\n",
+            "Iteration: 2. Number of non-zeros:  2\n",
+            "Iteration: 3. Number of non-zeros:  2\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 4. Number of non-zeros:  2\n",
+            "Iteration: 5. Number of non-zeros:  3\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 6. Number of non-zeros:  3\n",
+            "Iteration: 7. Number of non-zeros:  4\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 8. Number of non-zeros:  9\n",
+            "Iteration: 9. Number of non-zeros:  9\n",
+            "Iteration: 10. Number of non-zeros:  9\n",
+            "Iteration: 11. Number of non-zeros:  9\n",
+            "Iteration: 12. Number of non-zeros:  9\n",
+            "Iteration: 13. Number of non-zeros:  9\n",
+            "Iteration: 14. Number of non-zeros:  9\n",
+            "Iteration: 15. Number of non-zeros:  9\n",
+            "Iteration: 16. Number of non-zeros:  9\n",
+            "Iteration: 17. Number of non-zeros:  9\n",
+            "Iteration: 18. Number of non-zeros:  17\n",
+            "Iteration: 19. Number of non-zeros:  19\n"
+          ]
+        }
+      ],
+      "source": [
+        "path = fit_path(X,\n",
+        "                Y,\n",
+        "                max_nonzeros=X.shape[1])\n"
       ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = skm.cross_validate(pipeCV, \n",
-    "                             X,\n",
-    "                             Y,\n",
-    "                             cv=outer_valid,\n",
-    "                             scoring='neg_mean_squared_error')\n",
-    "-results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e27147ec",
-   "metadata": {},
-   "source": [
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4f126e9",
-   "metadata": {},
-   "source": [
-    "### The Lasso\n",
-    "We saw that ridge regression with a wise choice of $\\lambda$ can\n",
-    "outperform least squares as well as the null model on the\n",
-    " `Hitters`  data set. We now ask whether the lasso can yield\n",
-    "either a more accurate or a more interpretable model than ridge\n",
-    "regression. In order to fit a lasso model, we once again use the\n",
-    "`ElasticNetCV()`  function; however, this time we use the argument\n",
-    "`l1_ratio=1`. Other than that change, we proceed just as we did in\n",
-    "fitting a ridge model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "008ecc7c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.097496Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.097429Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.139203Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.138992Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "3.1472370031649866"
+      "cell_type": "markdown",
+      "id": "d4e9ca6a",
+      "metadata": {
+        "id": "d4e9ca6a"
+      },
+      "source": [
+        "The function `fit_path()` returns a list whose values include the fitted coefficients as `B`, an intercept as `B0`, as well as a few other attributes related to the particular path algorithm used. Such details are beyond the scope of this book."
       ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lassoCV = skl.ElasticNetCV(n_alphas=100, \n",
-    "                           l1_ratio=1,\n",
-    "                           cv=kfold)\n",
-    "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('lasso', lassoCV)])\n",
-    "pipeCV.fit(X, Y)\n",
-    "tuned_lasso = pipeCV.named_steps['lasso']\n",
-    "tuned_lasso.alpha_\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "1b909861",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.140345Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.140279Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.148367Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.148166Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "lambdas, soln_array = skl.Lasso.path(Xs, \n",
-    "                                    Y,\n",
-    "                                    l1_ratio=1,\n",
-    "                                    n_alphas=100)[:2]\n",
-    "soln_path = pd.DataFrame(soln_array.T,\n",
-    "                         columns=D.columns,\n",
-    "                         index=-np.log(lambdas))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abec44b3",
-   "metadata": {},
-   "source": [
-    "We can see from the coefficient plot of the standardized coefficients that depending on the choice of\n",
-    "tuning parameter, some of the coefficients will be exactly equal to\n",
-    "zero."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "ff719798",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.149542Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.149477Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.268439Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.268204Z"
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f2fc2ef8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.247604Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.247525Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.249696Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.249497Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "f2fc2ef8",
+        "outputId": "162c7db2-c218-49cd-820b-7ee600069214"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'B': array([0.        , 3.25484367, 0.        , 0.        , 0.        ,\n",
+              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+              "        0.        , 0.67775265, 0.        , 0.        , 0.        ,\n",
+              "        0.        , 0.        , 0.        , 0.        ]),\n",
+              " 'B0': -38.98216739555551,\n",
+              " 'lambda_0': 0.011416248027450178,\n",
+              " 'M': 0.5829861733382012,\n",
+              " 'Time_exceeded': False}"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "path[3]\n"
+      ]
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "c4a96126",
+      "metadata": {
+        "id": "c4a96126"
+      },
+      "source": [
+        "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.114$ for the penalty parameter `lambda_0`.\n",
+        "We could make predictions using this sequence of fits on a validation set as a function of `lambda_0`, or with more work using cross-validation.\n",
+        "\n",
+        "## Ridge Regression and the Lasso\n",
+        "We will use the `sklearn.linear_model` package (for which\n",
+        "we use `skl` as shorthand below)\n",
+        "to fit ridge and  lasso regularized linear models on the `Hitters` data.\n",
+        "We start with the model matrix `X` (without an intercept) that we computed in the previous section on best subset regression.\n",
+        "\n",
+        "### Ridge Regression\n",
+        "We will use the function `skl.ElasticNet()` to fit both  ridge and the lasso.\n",
+        "To fit a *path* of ridge regressions models, we use\n",
+        "`skl.ElasticNet.path()`, which can fit both ridge and lasso, as well as a hybrid mixture;  ridge regression\n",
+        "corresponds to `l1_ratio=0`.\n",
+        "It is good practice to standardize the columns of `X` in these applications, if the variables are measured in different units. Since `skl.ElasticNet()` does no normalization, we have to take care of that ourselves.\n",
+        "Since we\n",
+        "standardize first, in order to find coefficient\n",
+        "estimates on the original scale, we must *unstandardize*\n",
+        "the coefficient estimates. The parameter\n",
+        "$\\lambda$ in (\\ref{Ch6:ridge}) and (\\ref{Ch6:LASSO}) is called `alphas` in `sklearn`. In order to\n",
+        "be consistent with the rest of this chapter, we use `lambdas`\n",
+        "rather than `alphas` in what follows.  {At the time of publication, ridge fits like the one in code chunk [22] issue unwarranted convergence warning messages; we expect these to disappear as this package matures.}"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "path_fig, ax = subplots(figsize=(8,8))\n",
-    "soln_path.plot(ax=ax, legend=False)\n",
-    "ax.legend(loc='upper left')\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Standardized coefficiients', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "201ccdfb",
-   "metadata": {},
-   "source": [
-    "The smallest cross-validated error is lower than the test set MSE of the null model\n",
-    "and of least squares, and very similar to the test MSE of 115526.71 of ridge\n",
-    "regression (page~\\pageref{page:MSECVRidge}) with $\\lambda$ chosen by cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "f21ad8dc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.269744Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.269654Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.271587Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.271375Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "114690.73118253727"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "4a5d6fc8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.250946Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.250878Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.303537Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.303273Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "4a5d6fc8",
+        "outputId": "b4e908d9-abb9-4c7a-ec06-d4cfad725a1d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(19, 100)"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "Xs = X - X.mean(0)[None,:]\n",
+        "X_scale = X.std(0)\n",
+        "Xs = Xs / X_scale[None,:]\n",
+        "lambdas = 10**np.linspace(8, -2, 100) / Y.std()\n",
+        "soln_array = skl.ElasticNet.path(Xs,\n",
+        "                                 Y,\n",
+        "                                 l1_ratio=0.,\n",
+        "                                 alphas=lambdas)[1]\n",
+        "soln_array.shape\n"
       ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(tuned_lasso.mse_path_.mean(1))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "136f171f",
-   "metadata": {},
-   "source": [
-    "Let’s again produce a plot of the cross-validation error.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "7624e483",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.272793Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.272706Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.345703Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.345426Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "1e7770f5",
+      "metadata": {
+        "id": "1e7770f5"
+      },
+      "source": [
+        "Here we extract the array of coefficients corresponding to the solutions along the regularization path.\n",
+        "By default the `skl.ElasticNet.path` method fits a path along\n",
+        "an automatically selected range of $\\lambda$ values, except for the case when\n",
+        "`l1_ratio=0`, which results in ridge regression (as is the case here). {The reason is rather technical; for all models except ridge, we can find the smallest value of $\\lambda$ for which all coefficients are zero. For ridge this value is $\\infty$.}  So here\n",
+        "we have chosen to implement the function over a grid of values ranging\n",
+        "from $\\lambda=10^{8}$ to $\\lambda=10^{-2}$ scaled by the standard\n",
+        "deviation of $y$, essentially covering the full range of scenarios\n",
+        "from the null model containing only the intercept, to the least\n",
+        "squares fit.\n",
+        "\n",
+        "Associated with each value of $\\lambda$ is a vector of ridge\n",
+        "regression coefficients,   that can be accessed by\n",
+        "a column of `soln_array`. In this case, `soln_array` is a $19 \\times 100$ matrix, with\n",
+        "19 rows (one for each predictor) and 100\n",
+        "columns (one for each value of $\\lambda$).\n",
+        "\n",
+        "We transpose this matrix and turn it into a data frame to facilitate viewing and plotting."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lassoCV_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(tuned_lasso.alphas_),\n",
-    "            tuned_lasso.mse_path_.mean(1),\n",
-    "            yerr=tuned_lasso.mse_path_.std(1) / np.sqrt(K))\n",
-    "ax.axvline(-np.log(tuned_lasso.alpha_), c='k', ls='--')\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2b85ea4",
-   "metadata": {},
-   "source": [
-    "However, the lasso has a substantial advantage over ridge regression\n",
-    "in that the resulting coefficient estimates are sparse. Here we see\n",
-    "that 6 of the 19 coefficient estimates are exactly zero. So the lasso\n",
-    "model with $\\lambda$ chosen by cross-validation contains only 13\n",
-    "variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "b389c7d4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.347189Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.347081Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.349101Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.348883Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([-210.01008773,  243.4550306 ,    0.        ,    0.        ,\n",
-       "          0.        ,   97.69397357,  -41.52283116,   -0.        ,\n",
-       "          0.        ,   39.62298193,  205.75273856,  124.55456561,\n",
-       "       -126.29986768,   15.70262427,  -59.50157967,   75.24590036,\n",
-       "         21.62698014,  -12.04423675,   -0.        ])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1bca28f6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.304830Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.304756Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.311709Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.311502Z"
+        },
+        "id": "1bca28f6",
+        "outputId": "47d9be67-4f56-4487-cc57-93cafa8b1a7c"
+      },
+      "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>AtBat</th>\n",
+              "      <th>Hits</th>\n",
+              "      <th>HmRun</th>\n",
+              "      <th>Runs</th>\n",
+              "      <th>RBI</th>\n",
+              "      <th>Walks</th>\n",
+              "      <th>Years</th>\n",
+              "      <th>CAtBat</th>\n",
+              "      <th>CHits</th>\n",
+              "      <th>CHmRun</th>\n",
+              "      <th>CRuns</th>\n",
+              "      <th>CRBI</th>\n",
+              "      <th>CWalks</th>\n",
+              "      <th>League[N]</th>\n",
+              "      <th>Division[W]</th>\n",
+              "      <th>PutOuts</th>\n",
+              "      <th>Assists</th>\n",
+              "      <th>Errors</th>\n",
+              "      <th>NewLeague[N]</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>negative log(lambda)</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>-12.310855</th>\n",
+              "      <td>0.000800</td>\n",
+              "      <td>0.000889</td>\n",
+              "      <td>0.000695</td>\n",
+              "      <td>0.000851</td>\n",
+              "      <td>0.000911</td>\n",
+              "      <td>0.000900</td>\n",
+              "      <td>0.000812</td>\n",
+              "      <td>0.001067</td>\n",
+              "      <td>0.001113</td>\n",
+              "      <td>0.001064</td>\n",
+              "      <td>0.001141</td>\n",
+              "      <td>0.001149</td>\n",
+              "      <td>0.000993</td>\n",
+              "      <td>-0.000029</td>\n",
+              "      <td>-0.000390</td>\n",
+              "      <td>0.000609</td>\n",
+              "      <td>0.000052</td>\n",
+              "      <td>-0.000011</td>\n",
+              "      <td>-0.000006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-12.078271</th>\n",
+              "      <td>0.001010</td>\n",
+              "      <td>0.001122</td>\n",
+              "      <td>0.000878</td>\n",
+              "      <td>0.001074</td>\n",
+              "      <td>0.001150</td>\n",
+              "      <td>0.001135</td>\n",
+              "      <td>0.001025</td>\n",
+              "      <td>0.001346</td>\n",
+              "      <td>0.001404</td>\n",
+              "      <td>0.001343</td>\n",
+              "      <td>0.001439</td>\n",
+              "      <td>0.001450</td>\n",
+              "      <td>0.001253</td>\n",
+              "      <td>-0.000037</td>\n",
+              "      <td>-0.000492</td>\n",
+              "      <td>0.000769</td>\n",
+              "      <td>0.000065</td>\n",
+              "      <td>-0.000014</td>\n",
+              "      <td>-0.000007</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-11.845686</th>\n",
+              "      <td>0.001274</td>\n",
+              "      <td>0.001416</td>\n",
+              "      <td>0.001107</td>\n",
+              "      <td>0.001355</td>\n",
+              "      <td>0.001451</td>\n",
+              "      <td>0.001433</td>\n",
+              "      <td>0.001293</td>\n",
+              "      <td>0.001698</td>\n",
+              "      <td>0.001772</td>\n",
+              "      <td>0.001694</td>\n",
+              "      <td>0.001816</td>\n",
+              "      <td>0.001830</td>\n",
+              "      <td>0.001581</td>\n",
+              "      <td>-0.000046</td>\n",
+              "      <td>-0.000621</td>\n",
+              "      <td>0.000970</td>\n",
+              "      <td>0.000082</td>\n",
+              "      <td>-0.000017</td>\n",
+              "      <td>-0.000009</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-11.613102</th>\n",
+              "      <td>0.001608</td>\n",
+              "      <td>0.001787</td>\n",
+              "      <td>0.001397</td>\n",
+              "      <td>0.001710</td>\n",
+              "      <td>0.001831</td>\n",
+              "      <td>0.001808</td>\n",
+              "      <td>0.001632</td>\n",
+              "      <td>0.002143</td>\n",
+              "      <td>0.002236</td>\n",
+              "      <td>0.002138</td>\n",
+              "      <td>0.002292</td>\n",
+              "      <td>0.002309</td>\n",
+              "      <td>0.001995</td>\n",
+              "      <td>-0.000058</td>\n",
+              "      <td>-0.000784</td>\n",
+              "      <td>0.001224</td>\n",
+              "      <td>0.000104</td>\n",
+              "      <td>-0.000022</td>\n",
+              "      <td>-0.000012</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-11.380518</th>\n",
+              "      <td>0.002029</td>\n",
+              "      <td>0.002255</td>\n",
+              "      <td>0.001763</td>\n",
+              "      <td>0.002158</td>\n",
+              "      <td>0.002310</td>\n",
+              "      <td>0.002281</td>\n",
+              "      <td>0.002059</td>\n",
+              "      <td>0.002704</td>\n",
+              "      <td>0.002821</td>\n",
+              "      <td>0.002698</td>\n",
+              "      <td>0.002892</td>\n",
+              "      <td>0.002914</td>\n",
+              "      <td>0.002517</td>\n",
+              "      <td>-0.000073</td>\n",
+              "      <td>-0.000990</td>\n",
+              "      <td>0.001544</td>\n",
+              "      <td>0.000131</td>\n",
+              "      <td>-0.000028</td>\n",
+              "      <td>-0.000015</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",
+              "      <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",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9.784658</th>\n",
+              "      <td>-290.823989</td>\n",
+              "      <td>336.929968</td>\n",
+              "      <td>37.322686</td>\n",
+              "      <td>-59.748520</td>\n",
+              "      <td>-26.507086</td>\n",
+              "      <td>134.855915</td>\n",
+              "      <td>-17.216195</td>\n",
+              "      <td>-387.775826</td>\n",
+              "      <td>89.573601</td>\n",
+              "      <td>-12.273926</td>\n",
+              "      <td>476.079273</td>\n",
+              "      <td>257.271255</td>\n",
+              "      <td>-213.124780</td>\n",
+              "      <td>31.258215</td>\n",
+              "      <td>-58.457857</td>\n",
+              "      <td>78.761266</td>\n",
+              "      <td>53.622113</td>\n",
+              "      <td>-22.208456</td>\n",
+              "      <td>-12.402891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.017243</th>\n",
+              "      <td>-290.879272</td>\n",
+              "      <td>337.113713</td>\n",
+              "      <td>37.431373</td>\n",
+              "      <td>-59.916820</td>\n",
+              "      <td>-26.606957</td>\n",
+              "      <td>134.900549</td>\n",
+              "      <td>-17.108041</td>\n",
+              "      <td>-388.458404</td>\n",
+              "      <td>89.000707</td>\n",
+              "      <td>-12.661459</td>\n",
+              "      <td>477.031349</td>\n",
+              "      <td>257.966790</td>\n",
+              "      <td>-213.280891</td>\n",
+              "      <td>31.256434</td>\n",
+              "      <td>-58.448850</td>\n",
+              "      <td>78.761240</td>\n",
+              "      <td>53.645147</td>\n",
+              "      <td>-22.198802</td>\n",
+              "      <td>-12.391969</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.249827</th>\n",
+              "      <td>-290.923382</td>\n",
+              "      <td>337.260446</td>\n",
+              "      <td>37.518064</td>\n",
+              "      <td>-60.051166</td>\n",
+              "      <td>-26.686604</td>\n",
+              "      <td>134.936136</td>\n",
+              "      <td>-17.022194</td>\n",
+              "      <td>-388.997470</td>\n",
+              "      <td>88.537380</td>\n",
+              "      <td>-12.971603</td>\n",
+              "      <td>477.791860</td>\n",
+              "      <td>258.523025</td>\n",
+              "      <td>-213.405740</td>\n",
+              "      <td>31.254958</td>\n",
+              "      <td>-58.441682</td>\n",
+              "      <td>78.761230</td>\n",
+              "      <td>53.663357</td>\n",
+              "      <td>-22.191071</td>\n",
+              "      <td>-12.383205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.482412</th>\n",
+              "      <td>-290.958537</td>\n",
+              "      <td>337.377455</td>\n",
+              "      <td>37.587122</td>\n",
+              "      <td>-60.158256</td>\n",
+              "      <td>-26.750044</td>\n",
+              "      <td>134.964477</td>\n",
+              "      <td>-16.954081</td>\n",
+              "      <td>-389.423414</td>\n",
+              "      <td>88.164178</td>\n",
+              "      <td>-13.219329</td>\n",
+              "      <td>478.398404</td>\n",
+              "      <td>258.967059</td>\n",
+              "      <td>-213.505412</td>\n",
+              "      <td>31.253747</td>\n",
+              "      <td>-58.435983</td>\n",
+              "      <td>78.761230</td>\n",
+              "      <td>53.677759</td>\n",
+              "      <td>-22.184893</td>\n",
+              "      <td>-12.376191</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.714996</th>\n",
+              "      <td>-290.986528</td>\n",
+              "      <td>337.470648</td>\n",
+              "      <td>37.642077</td>\n",
+              "      <td>-60.243522</td>\n",
+              "      <td>-26.800522</td>\n",
+              "      <td>134.987027</td>\n",
+              "      <td>-16.900054</td>\n",
+              "      <td>-389.760135</td>\n",
+              "      <td>87.864551</td>\n",
+              "      <td>-13.416889</td>\n",
+              "      <td>478.881540</td>\n",
+              "      <td>259.321007</td>\n",
+              "      <td>-213.584869</td>\n",
+              "      <td>31.252760</td>\n",
+              "      <td>-58.431454</td>\n",
+              "      <td>78.761235</td>\n",
+              "      <td>53.689152</td>\n",
+              "      <td>-22.179964</td>\n",
+              "      <td>-12.370587</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100 rows × 19 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                           AtBat        Hits      HmRun       Runs        RBI  \\\n",
+              "negative log(lambda)                                                            \n",
+              "-12.310855              0.000800    0.000889   0.000695   0.000851   0.000911   \n",
+              "-12.078271              0.001010    0.001122   0.000878   0.001074   0.001150   \n",
+              "-11.845686              0.001274    0.001416   0.001107   0.001355   0.001451   \n",
+              "-11.613102              0.001608    0.001787   0.001397   0.001710   0.001831   \n",
+              "-11.380518              0.002029    0.002255   0.001763   0.002158   0.002310   \n",
+              "...                          ...         ...        ...        ...        ...   \n",
+              " 9.784658            -290.823989  336.929968  37.322686 -59.748520 -26.507086   \n",
+              " 10.017243           -290.879272  337.113713  37.431373 -59.916820 -26.606957   \n",
+              " 10.249827           -290.923382  337.260446  37.518064 -60.051166 -26.686604   \n",
+              " 10.482412           -290.958537  337.377455  37.587122 -60.158256 -26.750044   \n",
+              " 10.714996           -290.986528  337.470648  37.642077 -60.243522 -26.800522   \n",
+              "\n",
+              "                           Walks      Years      CAtBat      CHits     CHmRun  \\\n",
+              "negative log(lambda)                                                            \n",
+              "-12.310855              0.000900   0.000812    0.001067   0.001113   0.001064   \n",
+              "-12.078271              0.001135   0.001025    0.001346   0.001404   0.001343   \n",
+              "-11.845686              0.001433   0.001293    0.001698   0.001772   0.001694   \n",
+              "-11.613102              0.001808   0.001632    0.002143   0.002236   0.002138   \n",
+              "-11.380518              0.002281   0.002059    0.002704   0.002821   0.002698   \n",
+              "...                          ...        ...         ...        ...        ...   \n",
+              " 9.784658             134.855915 -17.216195 -387.775826  89.573601 -12.273926   \n",
+              " 10.017243            134.900549 -17.108041 -388.458404  89.000707 -12.661459   \n",
+              " 10.249827            134.936136 -17.022194 -388.997470  88.537380 -12.971603   \n",
+              " 10.482412            134.964477 -16.954081 -389.423414  88.164178 -13.219329   \n",
+              " 10.714996            134.987027 -16.900054 -389.760135  87.864551 -13.416889   \n",
+              "\n",
+              "                           CRuns        CRBI      CWalks  League[N]  \\\n",
+              "negative log(lambda)                                                  \n",
+              "-12.310855              0.001141    0.001149    0.000993  -0.000029   \n",
+              "-12.078271              0.001439    0.001450    0.001253  -0.000037   \n",
+              "-11.845686              0.001816    0.001830    0.001581  -0.000046   \n",
+              "-11.613102              0.002292    0.002309    0.001995  -0.000058   \n",
+              "-11.380518              0.002892    0.002914    0.002517  -0.000073   \n",
+              "...                          ...         ...         ...        ...   \n",
+              " 9.784658             476.079273  257.271255 -213.124780  31.258215   \n",
+              " 10.017243            477.031349  257.966790 -213.280891  31.256434   \n",
+              " 10.249827            477.791860  258.523025 -213.405740  31.254958   \n",
+              " 10.482412            478.398404  258.967059 -213.505412  31.253747   \n",
+              " 10.714996            478.881540  259.321007 -213.584869  31.252760   \n",
+              "\n",
+              "                      Division[W]    PutOuts    Assists     Errors  \\\n",
+              "negative log(lambda)                                                 \n",
+              "-12.310855              -0.000390   0.000609   0.000052  -0.000011   \n",
+              "-12.078271              -0.000492   0.000769   0.000065  -0.000014   \n",
+              "-11.845686              -0.000621   0.000970   0.000082  -0.000017   \n",
+              "-11.613102              -0.000784   0.001224   0.000104  -0.000022   \n",
+              "-11.380518              -0.000990   0.001544   0.000131  -0.000028   \n",
+              "...                           ...        ...        ...        ...   \n",
+              " 9.784658              -58.457857  78.761266  53.622113 -22.208456   \n",
+              " 10.017243             -58.448850  78.761240  53.645147 -22.198802   \n",
+              " 10.249827             -58.441682  78.761230  53.663357 -22.191071   \n",
+              " 10.482412             -58.435983  78.761230  53.677759 -22.184893   \n",
+              " 10.714996             -58.431454  78.761235  53.689152 -22.179964   \n",
+              "\n",
+              "                      NewLeague[N]  \n",
+              "negative log(lambda)                \n",
+              "-12.310855               -0.000006  \n",
+              "-12.078271               -0.000007  \n",
+              "-11.845686               -0.000009  \n",
+              "-11.613102               -0.000012  \n",
+              "-11.380518               -0.000015  \n",
+              "...                            ...  \n",
+              " 9.784658               -12.402891  \n",
+              " 10.017243              -12.391969  \n",
+              " 10.249827              -12.383205  \n",
+              " 10.482412              -12.376191  \n",
+              " 10.714996              -12.370587  \n",
+              "\n",
+              "[100 rows x 19 columns]"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "soln_path = pd.DataFrame(soln_array.T,\n",
+        "                         columns=D.columns,\n",
+        "                         index=-np.log(lambdas))\n",
+        "soln_path.index.name = 'negative log(lambda)'\n",
+        "soln_path\n"
       ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuned_lasso.coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acc5ed38",
-   "metadata": {},
-   "source": [
-    "As in ridge regression, we could evaluate the test error\n",
-    "of cross-validated lasso by first splitting into\n",
-    "test and training sets and internally running\n",
-    "cross-validation on the training set. We leave\n",
-    "this as an exercise."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d0aca26",
-   "metadata": {},
-   "source": [
-    "## PCR and PLS Regression\n",
-    "\n",
-    "### Principal Components Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2120ab5",
-   "metadata": {},
-   "source": [
-    "Principal components regression (PCR) can be performed using\n",
-    "`PCA()`  from the `sklearn.decomposition`\n",
-    "module. We now apply PCR to the  `Hitters`  data, in order to\n",
-    "predict `Salary`. Again, ensure that the missing values have\n",
-    "been removed from the data, as described in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}.\n",
-    "\n",
-    "We use `LinearRegression()`  to fit the regression model\n",
-    "here. Note that it fits an intercept by default, unlike\n",
-    "the `OLS()` function seen earlier in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "cf302d4c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.350411Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.350323Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.353307Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.353084Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([0.09846131, 0.4758765 ])"
+      "cell_type": "markdown",
+      "id": "2246bb2b",
+      "metadata": {
+        "id": "2246bb2b"
+      },
+      "source": [
+        "We plot the paths to get a sense of how the coefficients vary with $\\lambda$.\n",
+        "To control the location of the legend we first set `legend` to `False` in the\n",
+        "plot method, adding it afterward with the `legend()` method of `ax`."
       ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca = PCA(n_components=2)\n",
-    "linreg = skl.LinearRegression()\n",
-    "pipe = Pipeline([('pca', pca),\n",
-    "                 ('linreg', linreg)])\n",
-    "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b909182b",
-   "metadata": {},
-   "source": [
-    "When performing PCA, the results vary depending\n",
-    "on whether the data has been *standardized* or not.\n",
-    "As in the earlier examples, this can be accomplished\n",
-    "by including an additional step in the pipeline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "af4145b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.354462Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.354377Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.357347Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.357140Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([106.36859204,  21.60350456])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "08bc997b",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.312975Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.312908Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.473880Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.473585Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "08bc997b",
+        "outputId": "5be9179e-0523-4bc1-9feb-26cbd4612b31"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "path_fig, ax = subplots(figsize=(8,8))\n",
+        "soln_path.plot(ax=ax, legend=False)\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Standardized coefficients', fontsize=20)\n",
+        "ax.legend(loc='upper left');\n"
       ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipe = Pipeline([('scaler', scaler), \n",
-    "                 ('pca', pca),\n",
-    "                 ('linreg', linreg)])\n",
-    "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0da4ae12",
-   "metadata": {},
-   "source": [
-    "We can of course use CV to choose the number of components, by\n",
-    "using `skm.GridSearchCV`, in this\n",
-    "case fixing the parameters to vary the\n",
-    "`n_components`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "16e047ed",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.358664Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.358558Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.448710Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.448481Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-6 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-6 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
-       "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
-       "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
-       "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
-       "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=17)),\n",
-       "                (&#x27;linreg&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PCA<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=17)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      "cell_type": "markdown",
+      "id": "ae73ca09",
+      "metadata": {
+        "id": "ae73ca09"
+      },
+      "source": [
+        "(We have used `latex` formatting in the horizontal label, in order to format the Greek $\\lambda$ appropriately.)\n",
+        "We expect the coefficient estimates to be much smaller, in terms of\n",
+        "$\\ell_2$ norm, when a large value of $\\lambda$ is used, as compared to\n",
+        "when a small value of $\\lambda$ is used. (Recall that the  $\\ell_2$ norm is the square root of the sum of squared coefficient values.) We display  the coefficients at the $40$th step,\n",
+        "where $\\lambda$ is 25.535."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0f7359c7",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.475363Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.475242Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.477806Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.477534Z"
+        },
+        "id": "0f7359c7",
+        "outputId": "c0e3fdd0-bcc4-4297-8749-166f4e1aeca0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(25.53538897200662,\n",
+              " AtBat           5.433750\n",
+              " Hits            6.223582\n",
+              " HmRun           4.585498\n",
+              " Runs            5.880855\n",
+              " RBI             6.195921\n",
+              " Walks           6.277975\n",
+              " Years           5.299767\n",
+              " CAtBat          7.147501\n",
+              " CHits           7.539495\n",
+              " CHmRun          7.182344\n",
+              " CRuns           7.728649\n",
+              " CRBI            7.790702\n",
+              " CWalks          6.592901\n",
+              " League[N]       0.042445\n",
+              " Division[W]    -3.107159\n",
+              " PutOuts         4.605263\n",
+              " Assists         0.378371\n",
+              " Errors         -0.135196\n",
+              " NewLeague[N]    0.150323\n",
+              " Name: -3.240065292879872, dtype: float64)"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                                       ('pca', PCA(n_components=2)),\n",
-       "                                       ('linreg', LinearRegression())]),\n",
-       "             param_grid={'pca__n_components': range(1, 20)},\n",
-       "             scoring='neg_mean_squared_error')"
+      "source": [
+        "beta_hat = soln_path.loc[soln_path.index[39]]\n",
+        "lambdas[39], beta_hat\n"
       ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_grid = {'pca__n_components': range(1, 20)}\n",
-    "grid = skm.GridSearchCV(pipe,\n",
-    "                        param_grid,\n",
-    "                        cv=kfold,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "148bf752",
-   "metadata": {},
-   "source": [
-    "Let’s plot the results as we have for other methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "7390847f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.450064Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.449993Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.518771Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.518504Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "9baa8b00",
+      "metadata": {
+        "id": "9baa8b00"
+      },
+      "source": [
+        "Let’s compute the $\\ell_2$ norm of the standardized coefficients."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pcr_fig, ax = subplots(figsize=(8,8))\n",
-    "n_comp = param_grid['pca__n_components']\n",
-    "ax.errorbar(n_comp,\n",
-    "            -grid.cv_results_['mean_test_score'],\n",
-    "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
-    "ax.set_xlabel('# principal components', fontsize=20)\n",
-    "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4269a0db",
-   "metadata": {},
-   "source": [
-    "We see that the smallest cross-validation error occurs when\n",
-    "17\n",
-    "components are used. However, from the plot we also see that the\n",
-    "cross-validation error is roughly the same when only one component is\n",
-    "included in the model. This suggests that a model that uses just a\n",
-    "small number of components might suffice.\n",
-    "\n",
-    "The CV score is provided for each possible number of components from\n",
-    "1 to 19 inclusive. The `PCA()` method complains\n",
-    "if we try to fit an intercept only with `n_components=0`\n",
-    "so we also compute the MSE for just the null model with\n",
-    "these splits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "1e084cbb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.520137Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.520059Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.525293Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.525077Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "204139.30692994667"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d2984305",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.479160Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.479064Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.481153Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.480921Z"
+        },
+        "id": "d2984305",
+        "outputId": "5bc092b5-a846-4db1-ff4c-b2f3eb128485"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "24.17061720144378"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.linalg.norm(beta_hat)\n"
       ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Xn = np.zeros((X.shape[0], 1))\n",
-    "cv_null = skm.cross_validate(linreg,\n",
-    "                             Xn,\n",
-    "                             Y,\n",
-    "                             cv=kfold,\n",
-    "                             scoring='neg_mean_squared_error')\n",
-    "-cv_null['test_score'].mean()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c0d69df",
-   "metadata": {},
-   "source": [
-    "The `explained_variance_ratio_`\n",
-    "attribute of our `PCA` object provides the *percentage of variance explained* in the predictors and in the response using\n",
-    "different numbers of components. This concept is discussed in greater\n",
-    "detail in Section~\\ref{Ch10:sec:pca}."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "c7616c02",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.526523Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.526454Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.528563Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.528353Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([0.3831424 , 0.21841076])"
+      "cell_type": "markdown",
+      "id": "5111fc6f",
+      "metadata": {
+        "id": "5111fc6f"
+      },
+      "source": [
+        "In contrast, here is the $\\ell_2$ norm when $\\lambda$ is 2.44e-01.\n",
+        "Note the much larger $\\ell_2$ norm of the\n",
+        "coefficients associated with this smaller value of $\\lambda$."
       ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipe.named_steps['pca'].explained_variance_ratio_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2ee7cb1",
-   "metadata": {},
-   "source": [
-    "Briefly, we can think of\n",
-    "this as the amount of information about the predictors\n",
-    "that is captured using $M$ principal components. For example, setting\n",
-    "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n",
-    "By  $M=6$ it increases to\n",
-    "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance.\n",
-    "\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a0ce7e4",
-   "metadata": {},
-   "source": [
-    "### Partial Least Squares\n",
-    "Partial least squares (PLS) is implemented in the\n",
-    "`PLSRegression()`  function.\n",
-    "\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "9c69429e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.529851Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.529780Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.532907Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.532670Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-7 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-7 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-7 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-7 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-7 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-7 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PLSRegression()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression()</pre></div> </div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9a3424a4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.482508Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.482402Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.484554Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.484320Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "9a3424a4",
+        "outputId": "77bd05db-3f3f-45f3-ebfb-ba01c1928957"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(0.24374766133488554, 160.42371017725839)"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "PLSRegression()"
+      "source": [
+        "beta_hat = soln_path.loc[soln_path.index[59]]\n",
+        "lambdas[59], np.linalg.norm(beta_hat)\n"
       ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pls = PLSRegression(n_components=2, \n",
-    "                    scale=True)\n",
-    "pls.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a00592ba",
-   "metadata": {},
-   "source": [
-    "As was the case in PCR, we will want to\n",
-    "use CV to choose the number of components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "043f27fa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.534240Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.534173Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.610294Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.610076Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-8 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-8 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-8 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-8 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-8 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-8 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=PLSRegression(),\n",
-       "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=PLSRegression(),\n",
-       "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: PLSRegression</label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      "cell_type": "markdown",
+      "id": "50239927",
+      "metadata": {
+        "id": "50239927"
+      },
+      "source": [
+        "Above we normalized `X` upfront, and fit the ridge model using `Xs`.\n",
+        "The `Pipeline()`  object\n",
+        "in `sklearn` provides a clear way to separate feature\n",
+        "normalization from the fitting of the ridge model itself."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c864d25f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.485813Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.485727Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.495072Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.494829Z"
+        },
+        "id": "c864d25f",
+        "outputId": "7726e029-74a4-4bfb-bde6-931af081e9be"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.24374766133488554, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge', ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 28,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=PLSRegression(),\n",
-       "             param_grid={'n_components': range(1, 20)},\n",
-       "             scoring='neg_mean_squared_error')"
+      "source": [
+        "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n",
+        "scaler = StandardScaler(with_mean=True,  with_std=True)\n",
+        "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n",
+        "pipe.fit(X, Y)\n"
       ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_grid = {'n_components':range(1, 20)}\n",
-    "grid = skm.GridSearchCV(pls,\n",
-    "                        param_grid,\n",
-    "                        cv=kfold,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1bc745c",
-   "metadata": {},
-   "source": [
-    "As for our other methods, we plot the MSE."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "b702fb04",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.611587Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.611516Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.678469Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.678245Z"
-    }
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "id": "71c8c514",
+      "metadata": {
+        "id": "71c8c514"
+      },
+      "source": [
+        "We show that it gives the same $\\ell_2$ norm as in our previous fit on the standardized data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8a07da6e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.496384Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.496277Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.498333Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.498078Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "8a07da6e",
+        "outputId": "9f360b73-0606-476f-bddf-4c0b5d4320ff"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "160.4237101772591"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.linalg.norm(ridge.coef_)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6a20ea7e",
+      "metadata": {
+        "id": "6a20ea7e"
+      },
+      "source": [
+        " Notice that the operation `pipe.fit(X, Y)` above has changed the `ridge` object, and in particular has added attributes such as `coef_` that were not there before.\n",
+        "### Estimating Test Error of Ridge Regression\n",
+        "Choosing an *a priori* value of $\\lambda$ for ridge regression is\n",
+        "difficult if not impossible. We will want to use the validation method\n",
+        "or cross-validation to select the tuning parameter. The reader may not\n",
+        "be surprised that the  `Pipeline()` approach can be used in\n",
+        "`skm.cross_validate()` with either a validation method\n",
+        "(i.e. `validation`) or $k$-fold cross-validation.\n",
+        "\n",
+        "We fix the random state of the splitter\n",
+        "so that the results obtained will be reproducible."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c10e3ae5",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.499658Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.499553Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.505630Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.505369Z"
+        },
+        "id": "c10e3ae5",
+        "outputId": "eba67252-e359-4b05-c060-9b4b033dc18d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([134214.00419204])"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "validation = skm.ShuffleSplit(n_splits=1,\n",
+        "                              test_size=0.5,\n",
+        "                              random_state=0)\n",
+        "ridge.alpha = 0.01\n",
+        "results = skm.cross_validate(ridge,\n",
+        "                             X,\n",
+        "                             Y,\n",
+        "                             scoring='neg_mean_squared_error',\n",
+        "                             cv=validation)\n",
+        "-results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c5f962ee",
+      "metadata": {
+        "id": "c5f962ee"
+      },
+      "source": [
+        "The test MSE is 1.342e+05.  Note\n",
+        "that if we had instead simply fit a model with just an intercept, we\n",
+        "would have predicted each test observation using the mean of the\n",
+        "training observations. We can get the same result by fitting a ridge regression model\n",
+        "with a *very* large value of $\\lambda$. Note that `1e10`\n",
+        "means $10^{10}$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0df2b24d",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.507058Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.506964Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.512846Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.512640Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "0df2b24d",
+        "outputId": "fbc9f531-099d-4b62-c33b-6cc043a0dbe6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([231788.32155285])"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ridge.alpha = 1e10\n",
+        "results = skm.cross_validate(ridge,\n",
+        "                             X,\n",
+        "                             Y,\n",
+        "                             scoring='neg_mean_squared_error',\n",
+        "                             cv=validation)\n",
+        "-results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e78a776c",
+      "metadata": {
+        "id": "e78a776c"
+      },
+      "source": [
+        "Obviously choosing $\\lambda=0.01$ is arbitrary,  so we will  use cross-validation or the validation-set\n",
+        "approach to choose the tuning parameter $\\lambda$.\n",
+        "The object `GridSearchCV()`  allows exhaustive\n",
+        "grid search to choose such a parameter.\n",
+        "\n",
+        "We first use the validation set method\n",
+        "to choose $\\lambda$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9b76df85",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.514070Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.514003Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.851311Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.851092Z"
+        },
+        "id": "9b76df85",
+        "outputId": "83b502ba-9d3f-47b5-e3fa-70ddb78e4861"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-2 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-2 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-2 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-2 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.005899006046740856, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "param_grid = {'ridge__alpha': lambdas}\n",
+        "grid = skm.GridSearchCV(pipe,\n",
+        "                        param_grid,\n",
+        "                        cv=validation,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n",
+        "grid.best_params_['ridge__alpha']\n",
+        "grid.best_estimator_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9befd23e",
+      "metadata": {
+        "id": "9befd23e"
+      },
+      "source": [
+        "Alternatively, we can use 5-fold cross-validation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3e4c1b53",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.852563Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.852495Z",
+          "iopub.status.idle": "2024-06-04T23:19:30.080670Z",
+          "shell.execute_reply": "2024-06-04T23:19:30.080412Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "3e4c1b53",
+        "outputId": "0f5bf6c7-be3a-4933-ac2b-eb719ffb345a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-3 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-3 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-3 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-3 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-3 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grid = skm.GridSearchCV(pipe,\n",
+        "                        param_grid,\n",
+        "                        cv=kfold,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n",
+        "grid.best_params_['ridge__alpha']\n",
+        "grid.best_estimator_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cae429da",
+      "metadata": {
+        "id": "cae429da"
+      },
+      "source": [
+        "Recall we set up the `kfold` object for 5-fold cross-validation on page~\\pageref{line:choos-among-models}. We now plot the cross-validated MSE as a function of $-\\log(\\lambda)$, which has shrinkage decreasing from left\n",
+        "to right."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "39ff3251",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:30.082088Z",
+          "iopub.status.busy": "2024-06-04T23:19:30.082012Z",
+          "iopub.status.idle": "2024-06-04T23:19:30.194197Z",
+          "shell.execute_reply": "2024-06-04T23:19:30.193921Z"
+        },
+        "id": "39ff3251",
+        "outputId": "6c94ef4c-3bc6-4482-f6ac-2cae1b4cdbea"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "ridge_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(lambdas),\n",
+        "            -grid.cv_results_['mean_test_score'],\n",
+        "            yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "854e0c21",
+      "metadata": {
+        "id": "854e0c21"
+      },
+      "source": [
+        "One can cross-validate different metrics to choose a parameter. The default\n",
+        "metric for `skl.ElasticNet()` is test $R^2$.\n",
+        "Let’s compare $R^2$ to MSE for cross-validation here."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "766ab0b0",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:30.195678Z",
+          "iopub.status.busy": "2024-06-04T23:19:30.195576Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.425386Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.425155Z"
+        },
+        "id": "766ab0b0",
+        "outputId": "ed4ae437-b07a-4e55-e22c-e0096177c725"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-4 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-4 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-4 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-4 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;ridge&#x27;,\n",
+              "                                        ElasticNet(alpha=10000000000.0,\n",
+              "                                                   l1_ratio=0))]),\n",
+              "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606...\n",
+              "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;ridge&#x27;,\n",
+              "                                        ElasticNet(alpha=10000000000.0,\n",
+              "                                                   l1_ratio=0))]),\n",
+              "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606...\n",
+              "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                                       ('ridge',\n",
+              "                                        ElasticNet(alpha=10000000000.0,\n",
+              "                                                   l1_ratio=0))]),\n",
+              "             param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606...\n",
+              "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grid_r2 = skm.GridSearchCV(pipe,\n",
+        "                           param_grid,\n",
+        "                           cv=kfold)\n",
+        "grid_r2.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f3887a9c",
+      "metadata": {
+        "id": "f3887a9c"
+      },
+      "source": [
+        "Finally, let’s plot the results for cross-validated $R^2$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "642f1de3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.426644Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.426570Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.492503Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.492264Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "642f1de3",
+        "outputId": "908a2d47-9892-44e8-e4b0-0e8c9ac0513d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "r2_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(lambdas),\n",
+        "            grid_r2.cv_results_['mean_test_score'],\n",
+        "            yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0cb6f97b",
+      "metadata": {
+        "id": "0cb6f97b"
+      },
+      "source": [
+        "### Fast Cross-Validation for Solution Paths\n",
+        "The ridge, lasso, and elastic net can be efficiently fit along a sequence of $\\lambda$ values, creating what is known as a *solution path* or *regularization path*. Hence there is specialized code to fit\n",
+        "such paths, and to choose a suitable value of $\\lambda$ using cross-validation. Even with\n",
+        "identical splits the results will not agree *exactly* with our `grid`\n",
+        "above because the standardization of each feature  in `grid` is carried out on each fold,\n",
+        "while in `pipeCV` below it is carried out only once.\n",
+        "Nevertheless, the results are similar as the normalization\n",
+        "is relatively stable across folds."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "016ffe7c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.493853Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.493772Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.763066Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.762828Z"
+        },
+        "id": "016ffe7c",
+        "outputId": "9cf809ce-af23-4a0d-9a29-1bf602c0ad1e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-5 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-5 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-5 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-5 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;,\n",
+              "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03,...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "                              l1_ratio=0))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;,\n",
+              "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03,...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "                              l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNetCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNetCV.html\">?<span>Documentation for ElasticNetCV</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03, 3.37562814e+03, 2.67512757e+03,\n",
+              "       2.11999285e+03, 1.680058...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "             cv=KFold(n_splits=5, random_state=0, shuffle=True), l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge',\n",
+              "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03,...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "                              l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ridgeCV = skl.ElasticNetCV(alphas=lambdas,\n",
+        "                           l1_ratio=0,\n",
+        "                           cv=kfold)\n",
+        "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
+        "                         ('ridge', ridgeCV)])\n",
+        "pipeCV.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b6845efe",
+      "metadata": {
+        "id": "b6845efe"
+      },
+      "source": [
+        "Let’s produce a plot again of the cross-validation error to see that\n",
+        "it is similar to using `skm.GridSearchCV`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "52f6cf29",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.764408Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.764316Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.836546Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.836313Z"
+        },
+        "id": "52f6cf29",
+        "outputId": "d86de2a7-33ab-45a3-87c9-4a206220440e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "tuned_ridge = pipeCV.named_steps['ridge']\n",
+        "ridgeCV_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(lambdas),\n",
+        "            tuned_ridge.mse_path_.mean(1),\n",
+        "            yerr=tuned_ridge.mse_path_.std(1) / np.sqrt(K))\n",
+        "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "80e07995",
+      "metadata": {
+        "id": "80e07995"
+      },
+      "source": [
+        "We see that the value of $\\lambda$ that results in the\n",
+        "smallest cross-validation error is 1.19e-02, available\n",
+        "as the value `tuned_ridge.alpha_`. What is the test MSE\n",
+        "associated with this value of $\\lambda$?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2b49c3d7",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.838011Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.837899Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.840000Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.839740Z"
+        },
+        "id": "2b49c3d7",
+        "outputId": "3c45d687-3289-40af-daf7-abfbc73984dd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "115526.70630987917"
+            ]
+          },
+          "execution_count": 39,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.min(tuned_ridge.mse_path_.mean(1))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0961957c",
+      "metadata": {
+        "id": "0961957c"
+      },
+      "source": [
+        "This represents a further improvement over the test MSE that we got\n",
+        "using $\\lambda=4$.  Finally, `tuned_ridge.coef_`\n",
+        "has the coefficients fit on the entire data set\n",
+        "at this value of  $\\lambda$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "973e7e79",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.841440Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.841345Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.843427Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.843185Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "973e7e79",
+        "outputId": "9448545b-da26-497c-d656-26a3636b1e75"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-222.80877051,  238.77246614,    3.21103754,   -2.93050845,\n",
+              "          3.64888723,  108.90953869,  -50.81896152, -105.15731984,\n",
+              "        122.00714801,   57.1859509 ,  210.35170348,  118.05683748,\n",
+              "       -150.21959435,   30.36634231,  -61.62459095,   77.73832472,\n",
+              "         40.07350744,  -25.02151514,  -13.68429544])"
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tuned_ridge.coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b19ffa3f",
+      "metadata": {
+        "id": "b19ffa3f"
+      },
+      "source": [
+        "As expected, none of the coefficients are zero—ridge regression does\n",
+        "not perform variable selection!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bfb6c72e",
+      "metadata": {
+        "id": "bfb6c72e"
+      },
+      "source": [
+        "### Evaluating Test Error of Cross-Validated Ridge\n",
+        "Choosing $\\lambda$ using cross-validation provides a single regression\n",
+        "estimator, similar to fitting a linear regression model as we saw in\n",
+        "Chapter~\\ref{Ch3:linreg}. It is therefore reasonable to estimate what its test error\n",
+        "is. We run into a problem here in that cross-validation will have\n",
+        "*touched* all of its data in choosing $\\lambda$, hence we have no\n",
+        "further data to estimate test error. A compromise is to do an initial\n",
+        "split of the data into two disjoint sets: a training set and a test set.\n",
+        "We then fit a cross-validation\n",
+        "tuned ridge regression on the training set, and evaluate its performance on the test set.\n",
+        "We might call this cross-validation nested\n",
+        "within the validation set approach. A priori there is no reason to use\n",
+        "half of the data for each of the two sets in validation. Below, we use\n",
+        "75% for training and 25% for test, with the estimator being ridge\n",
+        "regression tuned using 5-fold cross-validation.  This can be achieved\n",
+        "in code as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a9140b3a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.844819Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.844727Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.846512Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.846267Z"
+        },
+        "id": "a9140b3a"
+      },
+      "outputs": [],
+      "source": [
+        "outer_valid = skm.ShuffleSplit(n_splits=1,\n",
+        "                               test_size=0.25,\n",
+        "                               random_state=1)\n",
+        "inner_cv = skm.KFold(n_splits=5,\n",
+        "                     shuffle=True,\n",
+        "                     random_state=2)\n",
+        "ridgeCV = skl.ElasticNetCV(alphas=lambdas,\n",
+        "                           l1_ratio=0,\n",
+        "                           cv=inner_cv)\n",
+        "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
+        "                         ('ridge', ridgeCV)]);\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8fc4aea0",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.847712Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.847645Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.096196Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.095971Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "8fc4aea0",
+        "outputId": "69127d36-95f5-4d15-c283-2a0a2a138b7d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([132393.84003227])"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results = skm.cross_validate(pipeCV,\n",
+        "                             X,\n",
+        "                             Y,\n",
+        "                             cv=outer_valid,\n",
+        "                             scoring='neg_mean_squared_error')\n",
+        "-results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e27147ec",
+      "metadata": {
+        "id": "e27147ec"
+      },
+      "source": [
+        "    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d4f126e9",
+      "metadata": {
+        "id": "d4f126e9"
+      },
+      "source": [
+        "### The Lasso\n",
+        "We saw that ridge regression with a wise choice of $\\lambda$ can\n",
+        "outperform least squares as well as the null model on the\n",
+        " `Hitters`  data set. We now ask whether the lasso can yield\n",
+        "either a more accurate or a more interpretable model than ridge\n",
+        "regression. In order to fit a lasso model, we once again use the\n",
+        "`ElasticNetCV()`  function; however, this time we use the argument\n",
+        "`l1_ratio=1`. Other than that change, we proceed just as we did in\n",
+        "fitting a ridge model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "008ecc7c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.097496Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.097429Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.139203Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.138992Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "008ecc7c",
+        "outputId": "4c193041-9048-4f2e-bdea-7840ec2dab73"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "3.1472370031649866"
+            ]
+          },
+          "execution_count": 43,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "lassoCV = skl.ElasticNetCV(n_alphas=100,\n",
+        "                           l1_ratio=1,\n",
+        "                           cv=kfold)\n",
+        "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
+        "                         ('lasso', lassoCV)])\n",
+        "pipeCV.fit(X, Y)\n",
+        "tuned_lasso = pipeCV.named_steps['lasso']\n",
+        "tuned_lasso.alpha_\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1b909861",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.140345Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.140279Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.148367Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.148166Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "1b909861"
+      },
+      "outputs": [],
+      "source": [
+        "lambdas, soln_array = skl.Lasso.path(Xs,\n",
+        "                                    Y,\n",
+        "                                    l1_ratio=1,\n",
+        "                                    n_alphas=100)[:2]\n",
+        "soln_path = pd.DataFrame(soln_array.T,\n",
+        "                         columns=D.columns,\n",
+        "                         index=-np.log(lambdas))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "abec44b3",
+      "metadata": {
+        "id": "abec44b3"
+      },
+      "source": [
+        "We can see from the coefficient plot of the standardized coefficients that depending on the choice of\n",
+        "tuning parameter, some of the coefficients will be exactly equal to\n",
+        "zero."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ff719798",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.149542Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.149477Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.268439Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.268204Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "ff719798",
+        "outputId": "ef9e0bc3-bbbb-49ae-8b07-a5500a4ec828"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "path_fig, ax = subplots(figsize=(8,8))\n",
+        "soln_path.plot(ax=ax, legend=False)\n",
+        "ax.legend(loc='upper left')\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Standardized coefficiients', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "201ccdfb",
+      "metadata": {
+        "id": "201ccdfb"
+      },
+      "source": [
+        "The smallest cross-validated error is lower than the test set MSE of the null model\n",
+        "and of least squares, and very similar to the test MSE of 115526.71 of ridge\n",
+        "regression (page~\\pageref{page:MSECVRidge}) with $\\lambda$ chosen by cross-validation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f21ad8dc",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.269744Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.269654Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.271587Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.271375Z"
+        },
+        "id": "f21ad8dc",
+        "outputId": "44aeb46c-9962-45e8-8a10-44568fd0cd2c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "114690.73118253727"
+            ]
+          },
+          "execution_count": 46,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.min(tuned_lasso.mse_path_.mean(1))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "136f171f",
+      "metadata": {
+        "id": "136f171f"
+      },
+      "source": [
+        "Let’s again produce a plot of the cross-validation error.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7624e483",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.272793Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.272706Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.345703Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.345426Z"
+        },
+        "id": "7624e483",
+        "outputId": "24ffb2ea-8246-4719-cf03-1b5157f30ee7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "lassoCV_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(tuned_lasso.alphas_),\n",
+        "            tuned_lasso.mse_path_.mean(1),\n",
+        "            yerr=tuned_lasso.mse_path_.std(1) / np.sqrt(K))\n",
+        "ax.axvline(-np.log(tuned_lasso.alpha_), c='k', ls='--')\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "c2b85ea4",
+      "metadata": {
+        "id": "c2b85ea4"
+      },
+      "source": [
+        "However, the lasso has a substantial advantage over ridge regression\n",
+        "in that the resulting coefficient estimates are sparse. Here we see\n",
+        "that 6 of the 19 coefficient estimates are exactly zero. So the lasso\n",
+        "model with $\\lambda$ chosen by cross-validation contains only 13\n",
+        "variables."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b389c7d4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.347189Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.347081Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.349101Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.348883Z"
+        },
+        "id": "b389c7d4",
+        "outputId": "4b34cd62-8b66-4516-9d51-81dae220cbd3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-210.01008773,  243.4550306 ,    0.        ,    0.        ,\n",
+              "          0.        ,   97.69397357,  -41.52283116,   -0.        ,\n",
+              "          0.        ,   39.62298193,  205.75273856,  124.55456561,\n",
+              "       -126.29986768,   15.70262427,  -59.50157967,   75.24590036,\n",
+              "         21.62698014,  -12.04423675,   -0.        ])"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tuned_lasso.coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "acc5ed38",
+      "metadata": {
+        "id": "acc5ed38"
+      },
+      "source": [
+        "As in ridge regression, we could evaluate the test error\n",
+        "of cross-validated lasso by first splitting into\n",
+        "test and training sets and internally running\n",
+        "cross-validation on the training set. We leave\n",
+        "this as an exercise."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0d0aca26",
+      "metadata": {
+        "id": "0d0aca26"
+      },
+      "source": [
+        "## PCR and PLS Regression\n",
+        "\n",
+        "### Principal Components Regression"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c2120ab5",
+      "metadata": {
+        "id": "c2120ab5"
+      },
+      "source": [
+        "Principal components regression (PCR) can be performed using\n",
+        "`PCA()`  from the `sklearn.decomposition`\n",
+        "module. We now apply PCR to the  `Hitters`  data, in order to\n",
+        "predict `Salary`. Again, ensure that the missing values have\n",
+        "been removed from the data, as described in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}.\n",
+        "\n",
+        "We use `LinearRegression()`  to fit the regression model\n",
+        "here. Note that it fits an intercept by default, unlike\n",
+        "the `OLS()` function seen earlier in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cf302d4c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.350411Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.350323Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.353307Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.353084Z"
+        },
+        "id": "cf302d4c",
+        "outputId": "33eeb41a-2071-405c-cdb7-37b52f7d923d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.09846131, 0.4758765 ])"
+            ]
+          },
+          "execution_count": 49,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pca = PCA(n_components=2)\n",
+        "linreg = skl.LinearRegression()\n",
+        "pipe = Pipeline([('pca', pca),\n",
+        "                 ('linreg', linreg)])\n",
+        "pipe.fit(X, Y)\n",
+        "pipe.named_steps['linreg'].coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b909182b",
+      "metadata": {
+        "id": "b909182b"
+      },
+      "source": [
+        "When performing PCA, the results vary depending\n",
+        "on whether the data has been *standardized* or not.\n",
+        "As in the earlier examples, this can be accomplished\n",
+        "by including an additional step in the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "af4145b6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.354462Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.354377Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.357347Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.357140Z"
+        },
+        "id": "af4145b6",
+        "outputId": "e7196f2d-b094-47b5-da24-74d7db3d3c06"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([106.36859204,  21.60350456])"
+            ]
+          },
+          "execution_count": 50,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pipe = Pipeline([('scaler', scaler),\n",
+        "                 ('pca', pca),\n",
+        "                 ('linreg', linreg)])\n",
+        "pipe.fit(X, Y)\n",
+        "pipe.named_steps['linreg'].coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0da4ae12",
+      "metadata": {
+        "id": "0da4ae12"
+      },
+      "source": [
+        "We can of course use CV to choose the number of components, by\n",
+        "using `skm.GridSearchCV`, in this\n",
+        "case fixing the parameters to vary the\n",
+        "`n_components`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "16e047ed",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.358664Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.358558Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.448710Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.448481Z"
+        },
+        "id": "16e047ed",
+        "outputId": "6878bfea-42dd-4798-c89b-1b7a79d9525f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-6 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-6 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-6 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-6 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
+              "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
+              "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
+              "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
+              "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=17)),\n",
+              "                (&#x27;linreg&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PCA<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=17)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                                       ('pca', PCA(n_components=2)),\n",
+              "                                       ('linreg', LinearRegression())]),\n",
+              "             param_grid={'pca__n_components': range(1, 20)},\n",
+              "             scoring='neg_mean_squared_error')"
+            ]
+          },
+          "execution_count": 51,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "param_grid = {'pca__n_components': range(1, 20)}\n",
+        "grid = skm.GridSearchCV(pipe,\n",
+        "                        param_grid,\n",
+        "                        cv=kfold,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "148bf752",
+      "metadata": {
+        "id": "148bf752"
+      },
+      "source": [
+        "Let’s plot the results as we have for other methods."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7390847f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.450064Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.449993Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.518771Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.518504Z"
+        },
+        "id": "7390847f",
+        "outputId": "99379d77-dbbc-4fee-eca5-fb9416973bab"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "pcr_fig, ax = subplots(figsize=(8,8))\n",
+        "n_comp = param_grid['pca__n_components']\n",
+        "ax.errorbar(n_comp,\n",
+        "            -grid.cv_results_['mean_test_score'],\n",
+        "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
+        "ax.set_xlabel('# principal components', fontsize=20)\n",
+        "ax.set_xticks(n_comp[::2])\n",
+        "ax.set_ylim([50000,250000]);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4269a0db",
+      "metadata": {
+        "id": "4269a0db"
+      },
+      "source": [
+        "We see that the smallest cross-validation error occurs when\n",
+        "17\n",
+        "components are used. However, from the plot we also see that the\n",
+        "cross-validation error is roughly the same when only one component is\n",
+        "included in the model. This suggests that a model that uses just a\n",
+        "small number of components might suffice.\n",
+        "\n",
+        "The CV score is provided for each possible number of components from\n",
+        "1 to 19 inclusive. The `PCA()` method complains\n",
+        "if we try to fit an intercept only with `n_components=0`\n",
+        "so we also compute the MSE for just the null model with\n",
+        "these splits."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1e084cbb",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.520137Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.520059Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.525293Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.525077Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "1e084cbb",
+        "outputId": "4dc57561-ca2e-4ab4-e3f4-0538303d6930"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "204139.30692994667"
+            ]
+          },
+          "execution_count": 53,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "Xn = np.zeros((X.shape[0], 1))\n",
+        "cv_null = skm.cross_validate(linreg,\n",
+        "                             Xn,\n",
+        "                             Y,\n",
+        "                             cv=kfold,\n",
+        "                             scoring='neg_mean_squared_error')\n",
+        "-cv_null['test_score'].mean()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0c0d69df",
+      "metadata": {
+        "id": "0c0d69df"
+      },
+      "source": [
+        "The `explained_variance_ratio_`\n",
+        "attribute of our `PCA` object provides the *percentage of variance explained* in the predictors and in the response using\n",
+        "different numbers of components. This concept is discussed in greater\n",
+        "detail in Section~\\ref{Ch10:sec:pca}."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c7616c02",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.526523Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.526454Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.528563Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.528353Z"
+        },
+        "id": "c7616c02",
+        "outputId": "1241a9a4-f174-4408-f68f-be57d5e5ebee"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.3831424 , 0.21841076])"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pipe.named_steps['pca'].explained_variance_ratio_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a2ee7cb1",
+      "metadata": {
+        "id": "a2ee7cb1"
+      },
+      "source": [
+        "Briefly, we can think of\n",
+        "this as the amount of information about the predictors\n",
+        "that is captured using $M$ principal components. For example, setting\n",
+        "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n",
+        "By  $M=6$ it increases to\n",
+        "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance.\n",
+        "\n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2a0ce7e4",
+      "metadata": {
+        "id": "2a0ce7e4"
+      },
+      "source": [
+        "### Partial Least Squares\n",
+        "Partial least squares (PLS) is implemented in the\n",
+        "`PLSRegression()`  function.\n",
+        "\n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9c69429e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.529851Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.529780Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.532907Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.532670Z"
+        },
+        "id": "9c69429e",
+        "outputId": "efd01c81-e588-41e2-9a79-a66777f14ebe"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-7 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-7 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-7 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-7 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-7 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PLSRegression()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "PLSRegression()"
+            ]
+          },
+          "execution_count": 55,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pls = PLSRegression(n_components=2,\n",
+        "                    scale=True)\n",
+        "pls.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a00592ba",
+      "metadata": {
+        "id": "a00592ba"
+      },
+      "source": [
+        "As was the case in PCR, we will want to\n",
+        "use CV to choose the number of components."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "043f27fa",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.534240Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.534173Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.610294Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.610076Z"
+        },
+        "id": "043f27fa",
+        "outputId": "f79cfba6-abc8-4bb4-f66f-8e32a6dfff0e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-8 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-8 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-8 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-8 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-8 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=PLSRegression(),\n",
+              "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=PLSRegression(),\n",
+              "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: PLSRegression</label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div></div></div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=PLSRegression(),\n",
+              "             param_grid={'n_components': range(1, 20)},\n",
+              "             scoring='neg_mean_squared_error')"
+            ]
+          },
+          "execution_count": 56,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "param_grid = {'n_components':range(1, 20)}\n",
+        "grid = skm.GridSearchCV(pls,\n",
+        "                        param_grid,\n",
+        "                        cv=kfold,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c1bc745c",
+      "metadata": {
+        "id": "c1bc745c"
+      },
+      "source": [
+        "As for our other methods, we plot the MSE."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b702fb04",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.611587Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.611516Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.678469Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.678245Z"
+        },
+        "id": "b702fb04",
+        "outputId": "693696fe-2e04-492a-80ed-3ef8c51b5afd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "pls_fig, ax = subplots(figsize=(8,8))\n",
+        "n_comp = param_grid['n_components']\n",
+        "ax.errorbar(n_comp,\n",
+        "            -grid.cv_results_['mean_test_score'],\n",
+        "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
+        "ax.set_xlabel('# principal components', fontsize=20)\n",
+        "ax.set_xticks(n_comp[::2])\n",
+        "ax.set_ylim([50000,250000]);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fc9ec466",
+      "metadata": {
+        "id": "fc9ec466"
+      },
+      "source": [
+        "CV error is minimized at 12,\n",
+        "though there is little noticable difference between this point and a much lower number like 2 or 3 components.\n",
+        "\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "pls_fig, ax = subplots(figsize=(8,8))\n",
-    "n_comp = param_grid['n_components']\n",
-    "ax.errorbar(n_comp,\n",
-    "            -grid.cv_results_['mean_test_score'],\n",
-    "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
-    "ax.set_xlabel('# principal components', fontsize=20)\n",
-    "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc9ec466",
-   "metadata": {},
-   "source": [
-    "CV error is minimized at 12,\n",
-    "though there is little noticable difference between this point and a much lower number like 2 or 3 components.\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+  ],
+  "metadata": {
+    "jupytext": {
+      "cell_metadata_filter": "-all",
+      "main_language": "python",
+      "notebook_metadata_filter": "-all"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
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