From 395fc15f22299cfeca9796a86fa5de80616934e0 Mon Sep 17 00:00:00 2001
From: AlexisRalli <rallilex@hotmail.co.uk>
Date: Fri, 5 Jan 2024 16:18:45 +0000
Subject: [PATCH 1/3] R_LCU as sequence of rotations improved and graph
 functionality of base PauliwordOp improved

---
 symmer/operators/anticommuting_op.py | 39 ++++++++++++++--------------
 symmer/operators/base.py             | 29 +++++++++++++++++----
 2 files changed, 44 insertions(+), 24 deletions(-)

diff --git a/symmer/operators/anticommuting_op.py b/symmer/operators/anticommuting_op.py
index b2ae581a..3790d880 100644
--- a/symmer/operators/anticommuting_op.py
+++ b/symmer/operators/anticommuting_op.py
@@ -287,11 +287,13 @@ def generate_LCU_operator(self, AC_op) -> PauliwordOp:
 
         return Ps_LCU
 
-def LCU_as_seq_rot(R_LCU: PauliwordOp):
+def LCU_as_seq_rot(R_LCU: PauliwordOp) -> List[Tuple[PauliwordOp, float]]:
     """
     Convert a unitary composed of a
     See equations 18 and 19 of https://arxiv.org/pdf/1907.09040.pdf
 
+    number of rotations is 2*(R_LCU.n_terms-1), which can at most be 4*n_qubits
+
     Args:
         R_LCU (PauliwordOp): unitary composed as a normalized linear combination of imaginary anticommuting Pauli operators (excluding identity)
     Returns:
@@ -302,20 +304,18 @@ def LCU_as_seq_rot(R_LCU: PauliwordOp):
     from symmer.utils import random_anitcomm_2n_1_PauliwordOp
     from symmer.operators import AntiCommutingOp
     from symmer.evolution.exponentiation import exponentiate_single_Pop
-    from functools import reduce
+    from symmer.utils import product_list
 
     nq = 3 # change (do not make too large as has exp checking cost)
 
     AC_2n1 = random_anitcomm_2n_1_PauliwordOp(nq)
     AC_op = AntiCommutingOp.from_PauliwordOp(AC_2n1)
     Ps_LCU, rotations_LCU, gamma_l, AC_normed = AC_op.unitary_partitioning(s_index=0, up_method= 'LCU')
-    exp_terms = LCU_as_seq_rot(rotations_LCU, include_global_phase_correction=True)
-    print(AC_normed.perform_rotations(exp_terms) == Ps_LCU)
+    print(AC_normed.perform_rotations(rotations_LCU) == Ps_LCU)
 
-    # needs global phase correction here!
-    ## This is expensive operation!
-    check = reduce(lambda a,b: a*b, [exponentiate_single_Pop(x.multiply_by_constant(1j*y/2)) for x, y in exp_terms])
-    print(check == rotations_LCU)
+    ## expensive check to see if operation is identical! should NOT do this when using
+    a2 = product_list([exponentiate_single_Pop(P.multiply_by_constant(1j*angle/2)) for P, angle in rotations_LCU])
+    print(AC_op.R_LCU == a2)
     """
     if isinstance(R_LCU, list) and len(R_LCU)==0:
         # case where there are no rotations
@@ -326,24 +326,25 @@ def LCU_as_seq_rot(R_LCU: PauliwordOp):
 
     expon_p_terms = []
 
-    # IF imaginary components the this makes real (but need phase correction later!)
+    # # IF imaginary components the this makes real (but need phase correction later!)
     coeff_vec = R_LCU.coeff_vec.real + R_LCU.coeff_vec.imag
-    for k, c_k in enumerate(coeff_vec):
+
+    # for k, c_k in enumerate(coeff_vec):
+    #     P_k = R_LCU[k]
+    #     theta_k = np.arcsin(c_k / np.linalg.norm(coeff_vec[:(k + 1)]))
+    #     P_k.coeff_vec[0] = 1
+    #     expon_p_terms.append(tuple((P_k, theta_k)))
+    # ## phase correction - change angle by -pi in first rotation!
+    # expon_p_terms[0] = (expon_p_terms[0][0], expon_p_terms[0][1]-np.pi)
+
+    for k in range(1, R_LCU.n_terms):
         P_k = R_LCU[k]
+        c_k = coeff_vec[k]
         theta_k = np.arcsin(c_k / np.linalg.norm(coeff_vec[:(k + 1)]))
         P_k.coeff_vec[0] = 1
         expon_p_terms.append(tuple((P_k, theta_k)))
 
     expon_p_terms = [*expon_p_terms, *expon_p_terms[::-1]]
-
-    ##### manual phase correction with a rotation!
-    # if include_global_phase_correction:
-    #     ## multiply by -1j Identity term!
-    #     phase_rot = (PauliwordOp.from_dictionary({'I' * R_LCU.n_qubits: 1}), -np.pi)
-    #     expon_p_terms.append(phase_rot)
-    
-    ## phase correction - change angle by -pi in first rotation!
-    expon_p_terms[0] = (expon_p_terms[0][0], expon_p_terms[0][1]-np.pi)
     
     return expon_p_terms
 
diff --git a/symmer/operators/base.py b/symmer/operators/base.py
index a5c643c1..e7b7d4f2 100644
--- a/symmer/operators/base.py
+++ b/symmer/operators/base.py
@@ -1227,15 +1227,28 @@ def tensor(self, right_op: "PauliwordOp") -> "PauliwordOp":
         return left_factor * right_factor
 
     def get_graph(self, 
-            edge_relation = 'C'
+            edge_relation: Optional[str]='C',
+             label_nodes: Optional[bool]=False
         ) -> nx.graph:
-        """ 
-        Build a graph based on edge relation C (commuting), 
-        AC (anticommuting) or QWC (qubitwise commuting).
+        """
+        Build a graph based on edge relation C (commuting), AC (anticommuting) or QWC (qubitwise commuting).
+        Note if label_nodes set to True then node names are pauli operators.
+
+        To draw:
+        import networkx as nx
+        H = PauliwordOp.random(3, 10)
+        graph = H.get_graph(edge_relation='C', label_nodes=True)
+        nx.draw(graph,
+                with_labels = True,
+                alpha=0.75,
+                node_color="skyblue",
+                width=0.1,
+                node_size=750
+        )
 
         Args:
             edge_relation (str): The edge relation to consider. Options are 'C' for commuting, 'AC' for anticommuting, and 'QWC' for qubitwise commuting. Defaults to 'C'.
-
+            label_nodes (bool): flag to label nodes of graph
         Returns:
             nx.Graph: The graph representing the edge relation.
         """
@@ -1251,6 +1264,12 @@ def get_graph(self,
         np.fill_diagonal(adjmat,False) # avoids self-adjacency
         # convert to a networkx graph and perform colouring on complement
         graph = nx.from_numpy_array(adjmat)
+
+        if label_nodes:
+            node_list = np.apply_along_axis(symplectic_to_string, 1, self.symp_matrix).tolist()
+            mapping = dict(zip(range(len(node_list)), node_list))
+            graph = nx.relabel_nodes(graph, mapping)
+
         return graph
 
     def largest_clique(self,

From 328b179cff5ab932edcb501ea8fa74842806b250 Mon Sep 17 00:00:00 2001
From: AlexisRalli <rallilex@hotmail.co.uk>
Date: Fri, 5 Jan 2024 17:03:30 +0000
Subject: [PATCH 2/3] docs updated and poetry lock updated

---
 docs/source/HPC.rst                           |  32 +-
 .../1_Basic_Usage/1.1 PauliwordOp Usage.ipynb | 242 +++++++++------
 .../1.2 QuantumState Usage.ipynb              | 281 +++++++++---------
 3 files changed, 317 insertions(+), 238 deletions(-)

diff --git a/docs/source/HPC.rst b/docs/source/HPC.rst
index 77f6740b..98d8979e 100644
--- a/docs/source/HPC.rst
+++ b/docs/source/HPC.rst
@@ -2,14 +2,40 @@
 Multiprocessing and HPC
 =======================
 
-Currently `symmer <https://github.com/UCL-CCS/symmer>`_ uses `ray <https://github.com/ray-project/ray>`_ to accelerate
+Currently `symmer <https://github.com/UCL-CCS/symmer>`_ uses `ray <https://github.com/ray-project/ray>`_ by default to accelerate
 the codebase by distributing problems over multiple cores if available. For standard use this works seamlessly and the user
 doesn't need to do anything. However, when deploying on HPC systems a couple of things
 need to be set. For further information see `link <https://docs.ray.io/en/latest/cluster/vms/user-guides/community/slurm.html>`_.
-Below we comment on how to implement these tools on a SLURM system.
+Below we comment on how to implement these tools on a SLURM system when ray is used. However, first we show an easier fix that will work on all linux based systems,
+but can have issues with windows OS.
 
 +++++
-SLURM
+Easy fix
++++++
+Note these fixes can be implemented for local use on a laptop/desktop too. When importing symmer the following code
+should be run before any other symmer functionality is used:
+
+.. code-block:: bash
+
+    from symmer import process
+    process.method = 'mp' # for multiprocessing to be used instead of ray
+
+An alternate fix is to turn multiprocessing off via:
+
+.. code-block:: bash
+
+    from symmer import process
+    process.method = 'single_thread'  # stops all multiprocessing
+
+Finally, ray can be turned back by doing
+
+.. code-block:: bash
+
+    from symmer import process
+    process.method = 'ray'  # makes ray the multiprocessing library (set to this by default)
+
++++++
+Ray and SLURM
 +++++
 Note these steps are **NOT** required for local use on a laptop/desktop.
 
diff --git a/docs/source/notebooks/1_Basic_Usage/1.1 PauliwordOp Usage.ipynb b/docs/source/notebooks/1_Basic_Usage/1.1 PauliwordOp Usage.ipynb
index dcde1e53..187fc1c4 100644
--- a/docs/source/notebooks/1_Basic_Usage/1.1 PauliwordOp Usage.ipynb	
+++ b/docs/source/notebooks/1_Basic_Usage/1.1 PauliwordOp Usage.ipynb	
@@ -95,7 +95,16 @@
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lex/anaconda3/envs/symmer_github/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:34: UserWarning: Couldn't import `kahypar` - skipping from default hyper optimizer and using basic `labels` method instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from symmer import PauliwordOp"
    ]
@@ -269,7 +278,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f4c3995bc8848f59c68b1e029a0168f",
+       "model_id": "a051bf4d42e54228b8a0136036e7d79a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -337,14 +346,14 @@
     {
      "data": {
       "text/plain": [
-       "-0.820+0.000j IIX +\n",
-       " 1.366+0.000j ZZI +\n",
-       "-0.235+0.000j YII +\n",
-       " 0.592+0.000j IZX +\n",
-       " 0.716+0.000j XIX +\n",
-       " 1.592+0.000j IXZ +\n",
-       "-0.621+0.000j ZZZ +\n",
-       " 0.397+0.000j III"
+       "-0.464+0.000j ZIX +\n",
+       "-0.060+0.000j IXI +\n",
+       "-0.156+0.000j III +\n",
+       " 0.081+0.000j XXI +\n",
+       " 0.726+0.000j IZI +\n",
+       " 0.420+0.000j YIZ +\n",
+       "-0.646+0.000j ZXX +\n",
+       "-0.328+0.000j IZI"
       ]
      },
      "execution_count": 6,
@@ -414,22 +423,22 @@
     {
      "data": {
       "text/plain": [
-       "-0.373+0.044j II +\n",
-       " 0.080-0.072j IZ +\n",
-       "-0.035-0.157j ZI +\n",
-       "-0.255-0.073j ZZ +\n",
-       "-0.177+0.269j IX +\n",
-       "-0.089-0.031j IY +\n",
-       "-0.200-0.091j ZX +\n",
-       " 0.032-0.025j ZY +\n",
-       " 0.127-0.077j XI +\n",
-       " 0.012-0.034j XZ +\n",
-       "-0.396+0.089j YI +\n",
-       " 0.010+0.140j YZ +\n",
-       "-0.106+0.169j XX +\n",
-       " 0.092+0.274j XY +\n",
-       " 0.074-0.484j YX +\n",
-       " 0.035-0.128j YY"
+       "-0.037+0.168j II +\n",
+       "-0.069+0.104j IZ +\n",
+       " 0.316+0.283j ZI +\n",
+       " 0.157-0.049j ZZ +\n",
+       " 0.006+0.177j IX +\n",
+       "-0.117+0.191j IY +\n",
+       " 0.440-0.095j ZX +\n",
+       "-0.080-0.079j ZY +\n",
+       " 0.057+0.300j XI +\n",
+       "-0.106-0.162j XZ +\n",
+       "-0.009+0.353j YI +\n",
+       " 0.159+0.002j YZ +\n",
+       " 0.213-0.168j XX +\n",
+       " 0.059-0.017j XY +\n",
+       " 0.064-0.292j YX +\n",
+       " 0.040+0.037j YY"
       ]
      },
      "execution_count": 7,
@@ -466,8 +475,8 @@
      "output_type": "stream",
      "text": [
       "IBM op:\n",
-      "1.0 * XX\n",
-      "+ 2.0 * YY \n",
+      "SparsePauliOp(['XX', 'YY'],\n",
+      "              coeffs=[1.+0.j, 2.+0.j]) \n",
       "\n",
       "symmer op:\n",
       " 1.000+0.000j XX +\n",
@@ -476,9 +485,10 @@
     }
    ],
    "source": [
-    "from qiskit.opflow import PauliSumOp \n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
-    "ibm_op = PauliSumOp.from_list([('XX', 1), ('YY', 2)])\n",
+    "ibm_op = SparsePauliOp(['XX', 'YY'],\n",
+    "              coeffs=[1,2])\n",
     "\n",
     "P = PauliwordOp.from_qiskit(ibm_op)\n",
     "\n",
@@ -607,7 +617,7 @@
     }
    },
    "source": [
-    "This can be useful if we want to add terms to a given variable (without having to define it during)."
+    "This can be useful if we want to add terms to a given variable (without having to define it during a loop)."
    ]
   },
   {
@@ -677,8 +687,8 @@
       "text/plain": [
        " 1.000+0.000j ZI +\n",
        " 2.000+0.000j ZZ +\n",
-       " 1.000+0.000j XX +\n",
-       " 2.000+0.000j YY"
+       " 3.000+0.000j XX +\n",
+       " 4.000+0.000j YY"
       ]
      },
      "execution_count": 11,
@@ -693,8 +703,8 @@
     "                                )\n",
     "\n",
     "P2 = PauliwordOp.from_dictionary({\n",
-    "                                 'XX': (1+0j),\n",
-    "                                 'YY': (2+0j)}\n",
+    "                                 'XX': (3+0j),\n",
+    "                                 'YY': (4+0j)}\n",
     "                                )\n",
     "\n",
     "P_add = P1 + P2\n",
@@ -784,10 +794,10 @@
     {
      "data": {
       "text/plain": [
-       "-4.000+0.000j XX +\n",
-       " 0.000-2.000j XY +\n",
-       " 0.000+1.000j YX +\n",
-       "-2.000+0.000j YY"
+       "-8.000+0.000j XX +\n",
+       " 0.000-4.000j XY +\n",
+       " 0.000+3.000j YX +\n",
+       "-6.000+0.000j YY"
       ]
      },
      "execution_count": 12,
@@ -867,8 +877,8 @@
     {
      "data": {
       "text/plain": [
-       " 1.000+0.000j ZI +\n",
-       " 2.000+0.000j ZZ"
+       " 4.000+0.000j ZI +\n",
+       " 8.000+0.000j ZZ"
       ]
      },
      "execution_count": 13,
@@ -877,8 +887,37 @@
     }
    ],
    "source": [
-    "P1.multiply_by_constant(4)\n",
-    "P1"
+    "P1.multiply_by_constant(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a638571e-a386-41e9-ace6-e5c59c82549d",
+   "metadata": {},
+   "source": [
+    "rather than:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f59d88c3-882b-4c11-8e32-090e88ef7c6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       " 4.000+0.000j ZI +\n",
+       " 8.000+0.000j ZZ"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "P1 * 4"
    ]
   },
   {
@@ -915,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "3cda049b",
    "metadata": {
     "collapsed": false,
@@ -976,7 +1015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "fb71d0cf",
    "metadata": {
     "collapsed": false,
@@ -1042,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "93f40034",
    "metadata": {
     "collapsed": false,
@@ -1060,7 +1099,7 @@
        "True"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1075,7 +1114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "62fb5e44",
    "metadata": {
     "collapsed": false,
@@ -1093,7 +1132,7 @@
        "False"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1106,20 +1145,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "```{note}\n",
-    "Note the difference between: commutes and commutes_termwise\n",
-    "```"
-   ],
+   "cell_type": "markdown",
+   "id": "bb54fe82-d92c-454f-b8a2-d1fad5b650d9",
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "source": [
+    "```{note}\n",
+    "Note the difference between: commutes and commutes_termwise\n",
+    "```"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1141,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "ea7a9f6b",
    "metadata": {
     "collapsed": false,
@@ -1186,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "31d68929",
    "metadata": {
     "collapsed": false,
@@ -1250,7 +1291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "628f3886",
    "metadata": {
     "collapsed": false,
@@ -1301,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "b31c365b",
    "metadata": {
     "collapsed": false,
@@ -1354,7 +1395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "86b0ed7a",
    "metadata": {
     "collapsed": false,
@@ -1399,7 +1440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "6bd2f698",
    "metadata": {
     "collapsed": false,
@@ -1467,7 +1508,7 @@
        "2          XX                  3.0                       0.0"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1495,7 +1536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "85549b95",
    "metadata": {
     "collapsed": false,
@@ -1515,7 +1556,7 @@
        "(2+1j) [Z0 Z1]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1542,7 +1583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "6113600d",
    "metadata": {
     "collapsed": false,
@@ -1557,11 +1598,11 @@
     {
      "data": {
       "text/plain": [
-       "PauliSumOp(SparsePauliOp(['II', 'ZZ', 'XX'],\n",
-       "              coeffs=[3.+0.j, 2.+1.j, 1.+0.j]), coeff=1.0)"
+       "SparsePauliOp(['XX', 'ZZ', 'II'],\n",
+       "              coeffs=[1.+0.j, 2.+1.j, 3.+0.j])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1588,7 +1629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "3d787c24",
    "metadata": {
     "collapsed": false,
@@ -1606,7 +1647,7 @@
        "{'II': (3+0j), 'ZZ': (2+1j), 'XX': (1+0j)}"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1618,14 +1659,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [],
+   "id": "722a8c60",
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -1643,7 +1688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "id": "a2231fb5",
    "metadata": {
     "collapsed": false,
@@ -1669,7 +1714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "36aa0dac",
    "metadata": {
     "collapsed": false,
@@ -1683,7 +1728,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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CBQvA5/MRHx/P2UHIGzZswJo1axAUFIQuXbpwkoEQoviKioqKd7j/buQzPz//m+dqaGj89hrNr1dp/nvU02zbHcRl5ErsNTWprQH/xb0l1j5XqKAkhEP79u2Dra0t9u/fj7lz5/728enp6dDR0cGff/6J1atXSyHhjxUVFcHY2BgCgQBPnjyBmpoaZ1kIIYQxhi9fvpSo8ExLS8O/Sx8ej4c6der8U1w20kFM+zmABK/L5AEIXzNQ4a5pVKxXQ4gcuXbtGhYuXIhFixaVqJgEgAMHDkAkEmHevHkSTvdrqqqqOHLkCLp06YKNGzdi7dq1nOYhhFRsPB4P1atXR/Xq1aGvr//LxxYVFeHjx48/nHJ/k1Eo0WISABiAd+k5aN2wukT7kTYaoSSEA+Hh4TAxMYGZmRm8vLxKdLRGQUEBmjRpAnNzcxw4cEAKKX9v9erV2LRpE548eYL27dtzHYcQQsolNP4TRrk8lHg/F+eaoKNOTYn3I010LD4hUpaSkoJhw4ahadOmOHXqVInPaTt58iRSU1Nhb28v4YQlt2LFChgYGGDatGnf3dBBCCHyRk1FOmWRtPqRJsV7RYTIsLy8PIwcORIFBQXw9fVF1aolOzqCMQYnJycMHz78t9M50qSmpoYjR47g+fPn2Lp1K9dxCCGkXHRra0KyE97/rKHUra14tw1RQUmIlIhEIkybNg3Pnz/HpUuXfnu0xr9du3YNkZGRWLx4sQQTlk2XLl2wZMkSrFu3DhEREVzHIYSQMtNUV4GOhG+y0amtoXAbcgAqKAmRmjVr1sDT0xMeHh6lPmpn+/btMDIyQo8ePSSUrnxWr16NZs2aYdq0aRAIBFzHIYSQMuutrwVlJcmMUyor8dC75a/PGpZXVFASIgUnTpzA+vXrsXnzZlhYWJTquSEhIbh9+zYcHR3LdEivNFSqVAnu7u548uQJdu7cyXUcQggpswnGOhI51BwAhCKGiX/oSKRtrlFBSYiE3b9/HzNmzMD06dPx559/lvr5Tk5O0NXVLXUhKm3dunWDvb09Vq5ciejoaK7jEEJImbwOuQ8kvwQTCcXarrISD6Z6dRTy2kWACkpCJCo2Nhbm5uYwMTGBi4tLqUcYExIS4OnpCXt7e4nfXysO69evh7a2NqZPnw6hULxvxoQQIkkJCQkYPXo0hgwZAt3UQKirivc9V0WJh02j2oq1TVlCBSUhEvLp0ycMGzYMtWrVwvnz58t0m8yuXbtQtWpVTJ8+XQIJxU9DQwNubm54+PAh9u7dy3UcQgj5raKiIjg5OaFVq1YIDAwEn8/HHd9zWDeyjVj7WTeiNbQlvOGHS1RQEiIBRUVFGDNmDFJTU3H58mXUqlWr1G18/vwZhw4dwpw5c1ClShUJpJSMnj17wtbWFsuWLUNsbCzXcQgh5KcePHiAzp07488//4SNjQ1evnwJKysr8Hg8WHfVweIBLcXSz5IB+rDqqphrJ7+igpIQMWOMYf78+bh37x4uXLiAFi1alKkdV1dX5OfnY8GCBWJOKHl///036tWrBxsbG4hEIq7jEELIN9LS0jB9+nT06NEDlStXxuPHj7Fr1y5Ur/7tdYi2vVtgs0VbqKsolXrnt7ISD+oqSthi0Rbze+uJM75MooKSEDHbsWMHDh8+jEOHDsHMzKxMbRQVFcHZ2RkTJkxAw4YNxZxQ8qpUqQJXV1f4+/vj4MGDXMchhBAA/5wH7OrqCn19fVy8eBEHDhxAYGAgOnXq9NPnWHfVwU17M5g0q/3Pf2C//pD8tfA0aVYbN+3NFH5k8iu6y5sQMfLy8oKFhQWWLl2KTZs2lbmdkydPYuLEiXj+/DnatpXfRdyzZ8/GqVOnEB4ejiZNmnAdhxBSgYWFhWHu3LkIDAzElClTsHXrVmhple5MyOCoeAxZuAk6fwxGZpEK/l1A8fDPoeW9W2ph4h86Crub+2eooCRETEJCQmBqaoohQ4bA09MTSkplmwBgjKFTp06oV68erl27JuaU0vXlyxe0adMGBgYGuH79usyeo0kIUVxZWVlYvXo1du/eDX19fbi4uKBnz55lauvIkSOwsbFBcnIyNKvXwrv0HBQKRFBTUYJubU2FvAGnpKigJEQMPnz4ACMjIzRq1Ah3796FhkbZd/Ldvn0bffv2hZ+fH/r16yfGlNy4du0aBg8eDFdXV9jY2HAdhxBSQTDGcO7cOSxatAiZmZlYvXo17O3toaqqWuY2R44cifT0dNy/f1+MSRUDFZSElFN2djZ69uyJtLQ0BAcHo0GDBuVqb8iQIfjw4QOePXumMCN606ZNw4ULFxAREYHGjRtzHYcQouBev34NW1tbXL9+HSNHjsSuXbvKvewmNzcXderUwbp167B48WIxJVUctCmHkHIQCoWYOHEiYmJi4OvrW+5iMiIiAlevXsXixYsVppgE/tmopKmpidmzZ4M+wxJCJCU/Px9r165FmzZtEBUVBR8fH3h5eYllDbefnx/y8vIwcuRIMSRVPFRQElIOS5cuxaVLl8Dn89GuXbtyt7djxw40atQIVlZWYkgnO2rWrIkDBw7gypUrOHHiBNdxCCEKyM/PD23btsXGjRvh4OCAyMhIDB8+XGzte3t7o1WrVmU+Ck7RUUFJSBkdPnwY27dvx44dOzB06NByt5eUlAQPDw/Y2dmV6VYdWTdixAiMHz8ednZ2SEpK4joOIURBJCYmwsrKCgMGDEDjxo0RFhaGTZs2lWst+38JhUJcunSJRid/gQpKQsrg1q1bmDdvHubNm4eFCxeKpc29e/dCTU0NM2fOFEt7smj37t1QU1PDvHnzaOqbEFIuAoEAu3btgoGBAe7evYsTJ07g9u3baNWqldj7evjwIdLS0mBubi72thUFFZSElFJUVBRGjx6Nvn37YteuXWJZ65iTkwMXFxfMnDkTNWrUKH9IGVW7dm3s378fXl5e8PT05DoOIUROBQUFoWvXrrC3t8ekSZMQHR2NiRMnSmztube3Nxo0aICuXbtKpH1FQAUlIaWQlpaGoUOHolGjRvD09ISKinjOHDty5Ai+fPkCOzs7sbQny0aPHg1LS0ssWLAAHz9+5DoOIUSOZGRkYPbs2TAxMYGysjKCg4Oxb98+iX4QZ4zBy8sLw4cPL/P5whUB/c0QUkIFBQUYNWoUsrOzcfny5e/ufC0roVCIHTt2wNLSssLcJrN3714wxmBra8t1FEKIHGCM4ejRo9DX1wefz8eePXsQHBwslRHDyMhIxMbG0vrJ36CCkpASYIxhxowZePz4Mby8vKCrqyu2ti9evIi3b9/C0dFRbG3KOi0tLezZswdnzpzBhQsXuI5DCJFh4eHhMDMzw7Rp0zBgwABER0dj/vz5UFZWlkr/3t7eqFKlCvr06SOV/uQVHWxOSAls2LABK1euxOnTp2FtbS22dhlj6NatGypXrow7d+6IrV15wBjDqFGjEBgYiMjISNSuXZvrSIQQGZKdnY1169Zh586daN68Ofbv389JUWdsbAwdHR2cPXtW6n3LExqhJOQ3PD09sXLlSqxbt06sxSTwz87B4ODgCnnrAo/Hg4uLCwoLC7Fo0SKu4xBCZARjDBcvXoShoSH27NmDtWvXIiwsjJNiMjExEY8ePaLp7hKggpKQXwgKCsKUKVMwceJE/O9//xN7+9u3b4eBgQEGDx4s9rblQYMGDeDs7AwPDw/4+vpyHYcQwrG3b99i+PDhsLCwQLt27RAZGYnly5dDXV2dkzw+Pj5QVlbGkCFDOOlfntCUNyE/8e7dOxgbG6NFixa4deuW2N/QYmJioK+vj0OHDmHGjBlibVueMMYwdOhQhIWFISIiQqGPTSKE/FhBQQGcnJywYcMG1KlTB7t378bIkSM5v4J28ODBKCwsxK1btzjNIQ9ohJKQH/j8+TOGDRuGKlWq4OLFixL5dLxz507UrVsXEydOFHvb8oTH4+HQoUPIzs6uUBuTCCH/uH37Ntq3b4/Vq1fD1tYWkZGRMDc357yYzMrKwu3bt2m6u4SooCTkPwQCAaysrPD+/Xv4+vqibt26Yu/j48ePOHLkCGxtbVGpUiWxty9vGjdujO3bt8Pd3R3Xr1/nOg4hRAqSk5MxceJE9O3bF1paWggNDcXWrVtRpUoVrqMBAK5du4bCwkIqKEuICkpC/mPRokW4efMmzp07J5ErvADAxcUFPB4Pc+fOlUj78mjGjBno168fZs6ciS9fvnAdhxAiIUKhEPv27YOBgQGuX7+OI0eOwN/fH23atOE62je8vLzQvn37CnM+cHlRQUnIv+zZswf79u3D/v370a9fP4n0kZ+fj71792LatGmoU6eORPqQRzweD4cPH0ZGRgb+/PNPruMQQiTgyZMnMDY2hq2tLaysrBAVFYWpU6dyPr39X0VFRbhy5Qrd3V0KVFAS8n8uX76MRYsWwdHREbNmzZJYPydOnEBaWhrs7e0l1oe80tXVxZYtW3Dw4EHcvn2b6ziEEDHJzMzE/PnzYWRkBIFAgMDAQBw8eFBmz58NCAhAZmYmTXeXAu3yJgTA8+fP0b17d/Tp0wcXLlyQ2A0MIpEIhoaGMDQ0pBtifkIkEqFPnz6Ij4/H8+fPZWY9FSGk9BhjOHnyJBwdHZGXl4f169dj/vz5UFFR4TraLy1cuBDe3t549+6dzI2eyioaoSQVXnJyMoYNGwY9PT2cPHlSotd5XblyBdHR0RXyIPOSUlJSgqurK5KTk7F8+XKu4xBCyujly5fo27cvJk2ahN69e+Ply5ews7OT+WKSMQYvLy+MGDGCislSoIKSVGi5ubkYOXIkhEIhLl26JPHRsO3bt6Nbt24wMTGRaD/yTk9PD5s2bcKePXtw7949ruMQQkohNzcXy5cvR/v27ZGQkIDr16+Dz+ejUaNGXEcrkWfPniEhIYHWT5YSFZSkwhKJRJgyZQrCw8Nx6dIlNG7cWKL9PXnyBP7+/nTWYgktWLAA3bp1w/Tp05Gbm8t1HEJICfj6+qJ169bYsWMHVqxYgRcvXmDAgAFcxyoVb29v1KhRAz179uQ6ilyhgpJUWCtXrsT58+dx8uRJdOrUSeL9OTk5oVmzZvSpt4SUlZXh7u6OhIQErFq1ius4hJBfiI+Px6hRozB8+HDo6+sjPDwcq1evlstzdr29vTFkyBCoqqpyHUWuUEFJKqRjx45h06ZN2LJli1QKvHfv3uHs2bNwcHCQ6BpNRWNgYIB169Zh586dCAoK4joOIeQ/ioqKsHXrVrRq1QqPHj3C2bNncfXqVejp6XEdrUzevXuHZ8+e0e7uMqBd3qTC8ff3R//+/TFlyhQcOnRIKouu7e3tcezYMSQkJEBTU1Pi/SkSgUAAExMTZGdnIyQkRC5HPAhRRAEBAZg7dy6io6OxcOFCrF27FlWrVuU6Vrns3r0bS5YswcePH1GtWjWu48gVGqEkFUpMTAwsLCxgamqK/fv3S6WYzMzMhKurK+bNm0fFZBmoqKjA3d0dr1+/xrp167iOQ0iF9/HjR0ydOhVmZmaoXr06nj59ih07dsh9MQn8M93dp08fKibLgApKUmFkZGRg2LBhqFu3Ls6dOye19TGHDh1CYWEhbG1tpdKfImrTpg1WrVqFrVu34unTp1zHIaRCEolEOHjwIPT19XHp0iUcPnwY9+/fR/v27bmOJhafPn2Cv78/TXeXEU15kwqhsLAQgwYNwvPnzxEUFCS19T2FhYVo2rQpBg0aBDc3N6n0qaiKiopgZGQEoVCIJ0+eQE1NjetIhFQYoaGhmDNnDh49eoTp06djy5YtCnd1rIeHByZNmoQPHz6gYcOGXMeROzRCSRQeYwxz587F/fv3cfHiRakuFufz+UhMTISDg4PU+lRUqqqqOHLkCF6+fIlNmzZxHYeQCuHLly+ws7NDly5dkJubi3v37sHNzU3hikngn+luIyMjKibLiApKovC2bdsGd3d3uLq6wtTUVGr9Msbg5OSEIUOGoHXr1lLrV5F16NABy5Ytw8aNGxEWFsZ1HEIUFmMMnp6eMDAwgJubG7Zu3YqQkBD06NGD62gSUVBQgGvXrtF0dznQlDdRaBcuXMCYMWOwfPlybNiwQap9+/n5YcCAAbh9+zZ69+4t1b4VWWFhITp37gxVVVUEBwfTWXGEiNmrV68wf/583Lx5ExYWFnB2doa2tjbXsSTq6tWrGDJkCMLDw2kAoIxohJIorCdPnmDixImwtLTkZHfw9u3b0bFjR/Tq1UvqfSsyNTU1HDlyBGFhYdi2bRvXcQhRGHl5eVi1ahXatm2L2NhYXL58GefPn1f4YhIAvLy80Lx5cxgaGnIdRW5RQUkU0vv37zFixAi0a9cOR48ehZKSdL/Vnz9/jhs3bmDx4sVSOZqoounSpQuWLFmCtWvXIiIigus4hMi9a9euoW3btti8eTP+/PNPREREYMiQIVzHkgqRSAQfHx+Ym5vT+3U50JQ3UTjZ2dno0aMHPn36hEePHqFevXpSzzB16lTcvn0bsbGxNCUrIfn5+ejYsSOqVauGBw8eQEVFhetIhMid9+/fw97eHufOnUPfvn2xb98+6Ovrcx1LqoKDg/HHH38gICBAquvsFQ2NUBKFIhQKMW7cOLx58wa+vr6cFJMfPnzAqVOnYGdnR8WkBFWqVAnu7u54/Pgxdu7cyXUcQuSKQCDAjh070KpVK9y/fx+nTp2Cn59fhSsmgX92d9epUwcmJiZcR5FrVFAShbJkyRJcuXIFZ86cQdu2bTnJsGfPHlSuXBkzZ87kpP+KpFu3bli0aBFWrlyJ6OhoruMQIhcePnyIzp07Y8mSJZg2bRqioqIwbty4Cjvd6+XlhWHDhkFZWZnrKHKNCkqiMA4ePIidO3di165dGDRoECcZsrKycPDgQcyaNYuu7pKSDRs2QFtbGzY2NhAKhVzHIURmpaenY8aMGejevTvU1dXx6NEj7N69G9WrV+c6GmdiYmLw8uVLmJubcx1F7lFBSRSCn58f5s+fjwULFnB6xaG7uzuys7OxcOFCzjJUNBoaGnBzc8ODBw+wd+9eruMQInNEIhHc3Nygr6+P8+fPw8XFBYGBgejcuTPX0Tjn7e2NypUro3///lxHkXu0KYfIvcjISHTr1g3du3eHj48PZ5szBAIB9PT00KNHD3h4eHCSoSJbsGAB3Nzc8OLFCzRv3pzrOITIhOfPn2Pu3Ll4+PAhJk2ahG3btnGytlxWmZqaolatWvD29uY6ityjEUoi1z5+/Ihhw4ZBR0cHfD6f052+Fy5cQFxcHBwdHTnLUJH9/fffqFevHmbMmAGRSMR1HEI4lZWVBUdHR3Tq1AmfPn3C3bt3cfz4cSom/yU1NRUPHjyg23HEhApKIrfy8/Nhbm6O3Nxc+Pr6crpmkTGG7du3o2/fvujYsSNnOSqyKlWqwNXVFXfv3sXBgwe5jkMIJxhjOHfuHFq1agUXFxds3LgRz549g5mZGdfRZI6vry94PB6GDx/OdRSFQAUlkUuMMUyfPh0hISHw9vZGkyZNOM1z7949PH78mEYnOda3b1/MmjULf/75J+Li4riOQ4hUxcbGYsiQIbC0tETnzp3x8uVL/PXXX1BTU+M6mkzy9vaGiYkJ6taty3UUhUAFJZFL69atw+nTp3H8+HEYGxtzHQfbt2+HoaEhZ7vLyf+3detW1KhRAzNnzgQtEScVQUFBAdavX482bdogMjIS3t7eMvFBW5bl5ubCz8+PprvFiApKIndOnTqFNWvWYMOGDbC0tOQ6DqKjo3Hp0iW6ZlFGVK9eHYcPH4afnx/c3d25jkOIRN28eRNt27bFunXrsGjRIkRGRmLEiBFcx5J5N27cQF5eHhWUYkS7vIlcefjwIXr37g1ra2scPXpUJgq42bNnw8fHB+/evYO6ujrXccj/mTZtGi5cuICIiAg0btyY6ziEiFViYiIcHR3B5/NhZmaG/fv3w9DQkOtYcmPatGl49OgRIiIiuI6iMGiEksiNt2/fwtzcHMbGxjh06JBMFJOpqak4duwYFixYQMWkjNmxYwc0NTUxe/ZsmvomCkMgEGD37t0wMDDArVu3cPz4cdy5c4eKyVIQCoXw9fWl0Ukxo4KSyIXPnz9j2LBhqFatGi5cuCAzxdv+/fuhrKyMOXPmcB2F/EfNmjVx4MABXLlyhc4FJQohODgYRkZGWLRoESZMmIDo6GhMmjRJJj5cy5OHDx8iLS2NCkoxo4KSyLyioiJYWloiMTERly9fRp06dbiOBOCfRd379u2DjY0NatWqxXUc8gMjRozA+PHjYWdnh6SkJK7jEFImnz59wpw5c9CtWzfweDwEBQXBxcUFNWvW5DqaXPLy8kKDBg3QtWtXrqMoFCooiUxjjGHhwoW4c+cOzp8/D319fa4jFTt+/DgyMjKwaNEirqOQX9i9ezdUVVUxb948mvomcoUxhmPHjkFfXx+nT5/G7t278ejRIxgZGXEdTW4xxuDt7Y0RI0ZASYlKIHGiv00i03bt2oUDBw7gwIED6NOnD9dxiolEIuzYsQMWFhZo1qwZ13HIL9SuXRv79u2Dl5cXzpw5w3UcQkokIiICvXr1wtSpU9GvXz9ERUXB1tYWysrKXEeTa5GRkYiNjaXpbgmggpLIrEuXLsHBwQFLliyBjY0N13G+cenSJcTExGDx4sVcRyElMGbMGIwZMwa2trb4+PEj13EI+amcnBwsXboUHTp0QHJyMm7evIlTp06hQYMGXEdTCN7e3qhSpYpMDVAoCjo2iMikZ8+eoUePHujfvz/Onz8vc1MTpqamYIzh/v37XEchJZSamgpDQ0P07dsXnp6eXMch5Dve3t5YuHAhUlNTsWLFCixZskRmNiAqCiMjIzRp0gRnz57lOorCka3f0oQASEpKwvDhw6Gvrw8PDw+ZKyaDg4Nx//59Gp2UM1paWtizZw/OnDmDCxcucB2HkGLv3r3DiBEjYG5ujjZt2iAiIgL/+9//qJgUs8TERDx+/Bjm5uZcR1FIsvWbmlR4ubm5GDFiBBhjuHTpEjQ1NbmO9B0nJye0aNECw4cP5zoKKSVra2uMGDEC8+bNQ3p6OtdxSAVXWFiIv//+G4aGhggNDcX58+fh6+tL67IlxMfHB8rKyhgyZAjXURQSFZREZohEIkyaNAkvX77EpUuX0LBhQ64jfefNmzc4f/487O3taXG8HOLxeDhw4AAKCgpodz7h1J07d9C+fXusXLkS8+fPx8uXL2FhYUFnSkqQt7c3zMzM6LglCaGCksiM5cuX4+LFizh9+jQ6duzIdZwfcnZ2Rs2aNTFlyhSuo5AyatCgAZydneHh4QFfX1+u45AKJiUlBZMmTUKfPn1Qu3ZthIaGYtu2bahSpQrX0RTaly9fcOvWLdrdLUFUUBKZcOTIEWzZsgXbt2+X2ankjIwMuLm5Yf78+dDQ0OA6DimHyZMnY/DgwZg9ezYyMzO5jkMqAKFQiP3790NfXx9Xr16Fu7s7AgIC0LZtW66jVQjXrl1DUVERFZQSRAUl4dydO3cwa9YszJo1C/b29lzH+amDBw9CKBRi/vz5XEch5cTj8XDw4EFkZWXB0dGR6zhEwT19+hTdunXD/PnzMXbsWERHR2PatGkyt+FQkXl7e6NDhw5o0qQJ11EUFn03E069evUKo0ePRq9evbB3716ZXT9UUFCA3bt3Y/LkydDS0uI6DhEDbW1tODk5wd3dHdevX+c6DlFAmZmZsLW1RdeuXVFYWIiHDx/i0KFDqF27NtfRKpSioiJcvnyZRicljM6hJJxJT0/HH3/8AVVVVTx8+BA1atTgOtJPHTlyBNOnT8fLly9hYGDAdRwiJowx9O/fH69evUJ4eDiqVavGdSSiABhjOH36NBwcHJCTk4N169ZhwYIFUFFR4TpahXTz5k30798fISEhMrs+XxHQCCXhRGFhISwsLJCZmQlfX1+ZLiYZY3BycsLw4cOpmFQwPB4Prq6uyMjIwF9//cV1HKIAoqKi0K9fP0yYMAE9e/ZEVFQU7O3tqZjkkLe3N3R0dNChQweuoyg0KiiJ1DHGMGvWLAQFBcHLy0vmz1y7fv06IiIiaK2dgtLV1cWWLVtw4MAB3L59m+s4RE7l5uZixYoVaNeuHeLi4nD16lWcOXMGjRo14jpahcYYg7e3N0aOHCmzS6oUBU15E6n7+++/sXz5cnh4eGDChAlcx/mtfv364fPnz3j06BG9ISkokUiE3r17IyEhAc+fP6cjXEipXL58Gba2tkhMTMSyZcuwdOlSVKpUietYBEBoaCg6deqEmzdvom/fvlzHUWg0Qkmk6ty5c1i+fDlWrVolF8Xks2fPcOvWLSxevJiKSQWmpKQENzc3JCcnY8WKFVzHIXIiISEBFhYWGDZsGFq0aIHw8HCsWbOGikkZ4uXlhRo1aqBnz55cR1F4NEJJpObRo0cwMzODubk5Tp06JRcF2qRJk3Dv3j28fv2a1kBVADt37oSjoyMCAgLQo0cPruMQGVVUVARnZ2esXbsW1apVg7OzMywtLeXiPa2i6dChA9q0aQMPDw+uoyg8KiiJVMTHx8PIyAjNmjXD7du35eITfEJCApo1a4Zt27bRNX0VhFAohKmpKdLS0vDs2TM6wJ585969e5g7dy5evnyJhQsXFheVRPa8e/cOTZs2xZkzZ2Bpacl1HIVHU95E4rKysjBs2DBUrlwZXl5eclFMAsDu3buhqakJGxsbrqMQKVFWVoa7uzvi4+OxevVqruMQGfLx40dMmzYNPXv2RNWqVfH06VPs3LmTikkZ5uPjAzU1NQwaNIjrKBUCFZREogQCAaytrREXFwdfX1+5ORT8y5cvOHToEObMmYOqVatyHYdIkYGBAdauXYsdO3YgODiY6ziEYyKRCIcOHYK+vj68vb1x6NAhPHjwgI6gkQNeXl7o06cPvYdLCRWURKIcHR1x/fp1nDlzBq1bt+Y6Tom5uroiNzcXCxYs4DoK4YCjoyM6d+6MadOmIT8/n+s4hCPPnj1D9+7dMXv2bIwcORLR0dGYOXMmXZkoBzIyMhAQEABzc3Ouo1QY9FNBJGb//v3YvXs39uzZg4EDB3Idp8S+LrgfP348nSFXQamoqMDd3R2vX7/GunXruI5DpOzLly9YtGgROnfujKysLAQEBODIkSOoW7cu19FICV25cgVCoRDDhw/nOkqFQQUlkYjr169j4cKFsLOzw9y5c7mO80M5BQJEJH5GaPwnRCR+Rk6BAMA/RxslJCTQQeYVXJs2bbBy5Ups3boVT58+5ToOkQLGGM6cOYNWrVrh8OHD2Lx5M0JDQ2Fqasp1NFJK3t7eMDIyQsOGDbmOUmHQLm8iduHh4ejevTtMTU3h7e0NZWVlriMVi0nJwsngeNyJTkV8Ri7+/c3PA6BdSwOpobdRLzsG/j58rmISGVFUVAQjIyMIhUI8efIEampqXEciEhITEwNbW1vcuHEDo0aNgrOzM3R0dLiORcogPz8fderUwfLly7F8+XKu41QYVFASsUpJSYGxsTGqV6+O+/fvy8xi6ISMXCy/+AL3XqdBWYkHoejn3/ZMJARPSRmmenWwaVRbaNeio2MqsmfPnqFr165YsWIF1qxZw3UcImb5+fnYvHkzNm/ejAYNGmDv3r0YOnQo17FIOVy5cgVDhw5FREQEDA0NuY5TYVBBScQmLy8Pffr0wbt37/Do0SNoa2tzHQkAwH8cj9U+ERCI2C8Lyf9SVuJBRYmHtSNaw7orjVRUZKtWrcLff/+NJ0+eoH379lzHIWJy/fp1zJ8/H/Hx8fjzzz+xfPlyOntUAcyePRu3b9/Gq1ev6LB5KaKCkoiFSCTC+PHj4ePjA39/f3Tt2pXrSACAvXdisP3Gq3K3s3hAS9j2biGGREQeFRQUoHPnzlBTU0NwcDBUVVW5jkTK4cOHD7C3t8fZs2fRp08f7Nu3DwYGBlzHImIgEonQqFEjTJgwAdu3b+c6ToVCm3KIWKxZswaenp44ceKEzBST/MfxYikmAWD7jVfwfBwvlraI/FFXV8eRI0cQFhaGbdu2cR2HlJFAIMDOnTthYGCAgIAAnDx5Ejdv3qRiUoE8evQIycnJGDlyJNdRKhwqKEm5eXh4YP369fj7778xevRoruMA+GfN5GqfCLG2uconAgkZuWJtk8iPrl27YsmSJVi7di0iIsT7vUUkLzAwEF26dIGjoyOmTp2KqKgojB8/nqZEFYy3tzfq1KkDExMTrqNUOFRQknK5f/8+bGxsMG3aNPz1119cxym2/OILCEqxXrIkBCKG5RdfiLVNIl/WrFmDpk2bYvr06RAKhVzHISWQnp6OmTNnwsTEBKqqqnj8+DH27NmDGjVqcB2NSIC3tzeGDx8uU6eLVBRUUJIyi42Nhbm5Obp164YDBw7IzCf9mJQs3HudVqoNOCUhFDHce52G16lZYm2XyI9KlSrB3d0djx8/xs6dO7mOQ35BJBLhyJEjMDAwwNmzZ7F//34EBQWhc+fOXEcjEvLq1Su8fPmSprs5QgUlKZPMzEwMGzYMtWrVwoULF2TqfL6TwfFQVpJMcausxINHEK2lrMhMTEywaNEirFy5Eq9eiWeNLhGvFy9eoGfPnpg+fToGDRqE6OhozJ07l0atFJy3tzcqV66M/v37cx2lQqKCkpRaUVERxowZg5SUFFy+fBm1atXiOtI37kSnin108iuhiOHOq1SJtE3kx4YNG9CoUSOa+pYx2dnZWLJkCTp27Ij09HTcvn0bJ06cQL169biORqTA29sbAwYMoKOfOEIFJSkVxhjmz5+PgIAAXLx4ES1ayNZROtkFAsRLeONMfHpu8TWNpGLS0NCAm5sbHjx4gH379nEdp8JjjOHChQto1aoV9u3bh/Xr1yMsLAy9e/fmOhqRktTUVDx8+JCmuzlEBSUplR07duDw4cM4dOgQzMzMuI7znbj0HEj6YFUG4F16joR7IbLOzMwM8+fPx7JlyxAbG8t1nArrzZs3GDZsGEaPHo2OHTsiMjISy5Ytk6llOETyfH19wePxMGzYMK6jVFhUUJIS8/b2xpIlS7B06VJMnTqV6zg/VCgQKVQ/RLZt3rwZWlpamDFjBkQi+p6QpoKCAmzYsAGtW7dGeHg4vLy84OPjA11dXa6jEQ54eXnBxMQEdevW5TpKhUUFJSmRkJAQjB8/HhYWFti4cSPXcX5KTUU639LS6ofItipVqsDV1RV3797FoUOHuI5TYdy6dQvt2rXD2rVrYWdnh8jISJrqrMBycnLg5+cHc3NzrqNUaPRbkfzWhw8fMHz4cBgaGuL48eNQUpLdbxvd2pqQxuFFTWrRom/yj759+2LmzJlYsmQJ4uLiuI6j0JKSkjB+/Hj069cP9evXx7Nnz7B582ZoampyHY1wyM/PD/n5+fShgmOyWxkQmZCTk1N8SKyPj4/M757TVFeBjoSLvaKMRDTVbggLCws4OzsjJCSEdvpWcNu2bUONGjUwc+ZMMCbpVbwVj1AoxJ49e2BgYICbN2/i2LFjuHv3Llq3bs11NCIDvL29YWhoCD09Pa6jVGhUUJKfEgqFmDBhAmJiYnDp0iU0aNCA60gl0ltfS2LnUDKREEiKgJ6eHuLi4rB06VJ07twZtWrVwpAhQ7B582Y8fPgQhYWFEumfyKbq1avj0KFD8PPzg7u7O9dxFMrjx49hZGQEOzs7jBs3DlFRUZg8ebLMXKRAuCUQCHDp0iUanZQBVFCSn1q6dCkuXboEPp+P9u3bcx2nxCYY60jsHEqekjL66arjzZs3CAkJQcOGDTFp0iRMmTIFIpEIGzduRPfu3VGjRg306dMHa9aswe3bt5GbS3eAK7rBgwdjypQpcHBwwIcPH7iOI/c+ffqEefPmwdjYGIwxBAYG4sCBAzJ37i3h1sOHD5Genk7rJ2UAj9H8DPkBV1dXzJw5E87OzrCzs+M6TqkwxtB77Xm8zVMDT0l8N2MoK/Fg0qw2TtgYQyAQwN/fH3w+H+fPn8enT59gYGCAsWPHom3btoiPj0dAQADu3buHjIwMqKqqokuXLjAzM0PPnj1hYmKC6tWriy0bkQ2fPn1C69at0alTJ1y6dIlG0cqAMQYPDw8sXrwYeXl52LBhA+bNmwcVFRWuoxEZ5OjoiNOnT+P9+/cyvb6/IqCCknzn1q1bGDRoEGbOnIl9+/bJ1S/FpKQk2NjYwO9hCLTnHIKIJ76CUl1FCTftzaD9nzWahYWFuHnzJvh8Pry8vJCVlYX27dvD2toalpaWyMvLQ0BAAAICAuDv74/k5GQoKSmhQ4cO6NmzJ3r27AlTU1PUqVNHbFkJd3x8fDBy5EgcP34ckyZN4jqOXImMjMS8efPg7+8Pa2tr7NixQ26W2hDpY4yhRYsW6NevHw4cOMB1nAqPCkryjaioKHTr1g1GRka4fPmyXI0KXLhwAbNmzYKKigrc3d3xpW4bLL3wQmztb7FoC6uuOr98TF5eHq5duwY+n49Lly4hLy8PxsbGsLKywtixY9GwYUPExsYWF5gBAQF4+/YtAMDQ0LC4wOzZsycaNWoktuxEuiZMmICrV68iMjIS9evX5zqOzMvJycGGDRuwfft2NG3aFPv27aP7mMlvhYeHo23btrhy5QoGDx7MdZwKjwpKUiwtLQ3GxsaoVKkSHj58KDdTsl++fIGdnR2OHj2KUaNG4eDBg8WH2+69E4PtN16Vu48lA/Qxv3fpdhBmZ2fD19cXfD4fV69eRVFREUxNTWFlZYUxY8ZAS0sLAJCQkPBNgRkVFQUAaNasWXFxaWZmhqZNm8rVaHFFlpaWhtatW6N79+44f/48/X/7BR8fHyxcuBDJyclYsWIFlixZgkqVKnEdi8iBjRs3YvPmzUhLS4O6ujrXcSo8KigJgH9unejXrx+io6MRHByMpk2bch2pRO7du4dJkyYhIyMDu3fvxpQpU7775c1/HI/VPhEQiFipNusoK/GgosTDuhGtfzsy+TuZmZnw8vKCp6cn/Pz8wBhD3759YWVlBQsLC9SsWbP4sampqbh3715xgRkWFgbGGBo1avTNCGarVq2oUJFh586dg6WlJfh8PqysrLiOI3Pi4uKwcOFC+Pj4YNCgQdi7dy+aN2/OdSwiR4yMjKCrq4szZ85wHYWACkqCf9ahTJkyBWfOnMGdO3fQrVs3riP9VkFBAVavXo2tW7eie/fuOH78+C+L4ISMXCy/+AL3XqdBWYn3y8KSiYTgKSnDVK8ONo1q+92ayfJKS0vD+fPnwefz4e/vDxUVFQwcOBDW1tYYMWIEqlat+s3jMzMz8eDBg+IC88mTJxAIBKhTpw5MTU2LC8z27dtDWVl8a0ZJ+VlaWuLu3buIjIykK+H+T2FhIXbs2IF169ahVq1a2LVrFywsLOjDESmVDx8+oHHjxvDw8MCECRO4jkNABSUBsGHDBqxcuRKnTp3CuHHjuI7zW+Hh4Zg4cSIiIyOxfv16LF68uMSFVExKFk4Gx+POq1TEp+fi39/8PAA6tTVQPec9brqsxqsnAWjcuLFEXsNXiYmJOHfuHPh8PgIDA1GpUiUMHToU1tbWGDJkyA8Pks/JyUFQUFDxJp+goCAUFBSgWrVq6N69e3GB2aVLF6ipqUk0P/m1lJQUtG7dGv369QOfz+c6Duf8/f0xb948REdHY9GiRVi9evV3H6AIKQkXFxcsWLAAHz9+/GaGh3CIkQrN09OTAWBr167lOspvCYVCtmPHDqaurs4MDQ1ZSEhIudrLzi9i4R8yWUhcBgv/kMmy84sYY4x9+fKF1axZky1atEgcsUvs3bt3bOvWraxTp04MANPU1GTjx49nPj4+LD8//6fPy8/PZ/fu3WMbN25kAwcOZFWqVGEAWOXKlVmfPn3YmjVr2O3bt1lOTo4UXw356uTJkwwAu3DhAtdROJOSksImTZrEADATExMWFhbGdSQi5wYOHMj69u3LdQzyL1RQVmBBQUGsUqVKbMKECUwkEnEd55fi4+NZnz59GAC2aNEilpeXJ9H+Vq1axTQ0NFhqaqpE+/mZV69esfXr17PWrVszAKx69eps2rRp7Pr166yoqOiXzy0qKmKPHz9mTk5ObOTIkaxWrVoMAFNVVWUmJiZs6dKl7MqVKywzM1NKr6ZiE4lEbMSIEaxevXosLS2N6zhSJRAImIuLC6tRowarXbs2c3NzY0KhkOtYRM59/vyZqaqqst27d3MdhfwLFZQV1Nu3b5mWlhbr3r27xIuz8jp58iSrXr06a9y4Mbt586ZU+kxLS2Oamprsf//7n1T6+5UXL16w//3vf0xPT48BYHXq1GFz5sxhd+/eZQKB4LfPFwqF7MWLF2zfvn1s7NixrH79+gwAU1JSYp06dWKLFi1iFy5cYB8/fpTCq6mYPnz4wGrUqMEmTZrEdRSpefr0KTMyMmIAmI2NDX1/EbH5OrP27t07rqOQf6GCsgL6/Pkza9OmDWvWrBlnI3AlkZGRwaytrRkANm7cOJaRkSHV/u3t7VmNGjXY58+fpdrvz4hEIvb06VO2ZMkSpqOjwwCwhg0bMjs7OxYYGFjiUWaRSMRevXrFXF1d2eTJk5muri4DwAAwQ0NDNmfOHHbq1Cn2/v17Cb+iiuXIkSMMAPP19eU6ikRlZmYyW1tbpqSkxNq2bcvu37/PdSSiYMaNG8c6dOjAdQzyH1RQVjBFRUVs0KBBrHr16iwyMpLrOD/l5+fHGjVqxGrUqMFOnTrFSYb3798zVVVVtmXLFk76/xWhUMgePHjAFi5cWDziqKury/78808WEhJS6iUMcXFxzMPDg82aNYsZGBgUF5jNmzdn06ZNY0eOHGGxsbEyvzRClolEIjZ48GDWsGFD9unTJ67jiJ1IJGKnTp1i9evXZ1WqVGFOTk6/XZ5BSGkVFhay6tWrszVr1nAdhfwHFZQVjK2tLVNWVmZ+fn5cR/mh3NxcZmdnxwCwvn37svj4eE7zzJgxg9WrV0+mlwUIBAJ2+/ZtNnv2bFa7dm0GgLVs2ZKtWrWKRURElKnN5ORkdu7cObZw4ULWvn17xuPxGADWqFEjNm7cOObi4sIiIiKowCyl+Ph4VrVqVTZ9+nSuo4hVVFQU69u3LwPAxowZwxISEriORBSUn58fA8BCQ0O5jkL+gwrKCmT37t0MADt48CDXUX4oJCSEGRoaMnV1debs7CwTi/dfvXrFlJSU2P79+7mOUiKFhYXs6tWrbOrUqaxatWoMAGvbti3buHEje/36dZnbzcjIYJcuXWJLlixhxsbGTFlZuXg956hRo9jOnTtZSEhIidZ0VnQHDx5kANj169e5jlJuubm57H//+x9TU1NjzZo1Y1euXOE6ElFwtra2rEmTJvRhVgZRQVlBXL58mSkpKTEHBweuo3xHIBCwTZs2MVVVVdahQwcWHh7OdaRvWFtbM11dXbmbvsvLy2NeXl7M2tqaaWhoMACsS5cubPv27eUe+c3KymJ+fn5s5cqVzMzMjKmrqzMArFq1amzIkCFs8+bN7OHDh6ygoEBMr0ZxiEQi1rdvX6ajo8O+fPnCdZwyu3z5MmvatClTU1Njq1atYrm5uVxHIgpOJBIxbW1ttmDBAq6jkB+ggrICCAsLY1WqVGEjRoyQuRGk2NhY1r17d8bj8djSpUtlsgB59uwZA8BOnDjBdZQyy87OZp6enmzUqFHFxV/37t3Znj17WFJSUrnbp7MwS+ft27dMU1OTzZkzh+sopRYfH88sLCwYANavXz8WHR3NdSRSQTx9+pQBYLdu3eI6CvkBKigVXFJSEtPW1mYdOnRgWVlZXMcpJhKJmJubG6tSpQrT1dVl9+7d4zrSLw0dOpQZGhrKxDR8eX3+/JkdP36cDRkyhKmoqDAlJSXWp08fdujQIbGdk1hUVMQePXrEtm/fzkaMGMFq1qz5w7MwZWUHPRf27t3LALDbt29zHaVECgsL2bZt25impiZr0KAB4/P5NO1IpGrVqlWsRo0arLCwkOso5Afo6kUFlpeXh169euH9+/cIDg6W+DWCJfXx40fMmjULXl5emDZtGpydnVGtWjWuY/3SgwcP0KNHD3h5eWHkyJFcxxGb9PR0XLx4EXw+H3fu3IGSkhL69+8Pa2trjBw5EtWrVxdLPyKRCBEREcX3kQcEBCA5ORlKSkro0KEDevbsCTMzM/To0QN16tQRS5+yTiQSoXfv3nj//j2eP38OTU1NriP91IMHDzBnzhxERkbC1tYW69atE9v3BiEl1aFDB7Rp0wYeHh5cRyE/wnVFSyRDKBQyS0tLpqGhwZ48ecJ1nGK+vr6sXr16rE6dOnJ3FV3Pnj2ZkZGRwo7KJCcns71797IePXowAExdXZ2Zm5szPp/PsrOzxdrXf8/CbNKkSfFRRa1bt2Zz585lp0+fZh8+fBBrv7ImJiaGVa5cmS1cuJDrKD/08eNHNm3aNAaAGRsbl/u6U0LK6s2bNwwAO3PmDNdRyE9QQamgVqxYwXg8nswUbdnZ2Wz27NkMABsyZIhY1u1J27Vr1xgAqd3Ww6X4+Hjm5OTEunbtygAwDQ0NZmVlxS5evCixI5TevXvHTpw4wWbOnMn09fUrzFmYO3bsYDweT6aWfQiFQnb48GFWq1YtVqNGDXbgwAGFWO5B5JezszNTU1OT641sio6mvBXQsWPHMHXqVGzduhVLlizhOg6Cg4MxceJEJCYmwsnJCbNnzwaPx+M6VqkxxtC5c2fUqlULN2/e5DqO1MTGxuLMmTPg8/l4/vw5qlWrhlGjRsHKygr9+vWDqqqqRPpNSUnBvXv3iqfInz9/DsYYGjVqhJ49exZPkxsYGMjl99NXQqEQpqamSEtLQ1hYGCpXrsxpnrCwMMydOxeBgYGYMmUKtm7dCi0tLU4zEdKnTx9UqlQJV65c4ToK+Rlu61kibv7+/kxVVZXZ2NhwPpJTWFjIVq1axZSVlZmRkZFC7AY9e/YsA8CCgoK4jsKJyMhItnr16uIRxNq1a7NZs2axW7duSfwEgV+dhWlhYcGcnZ3l9izMly9fMnV1dbZ48WLOMnz58oXZ29szZWVlZmhoyPz9/TnLQsi/paenM2VlZXbgwAGuo5BfoIJSgcTExLBatWqx3r17c378TlRUFOvatStTVlZma9askbszHH9GIBAwfX19NnLkSK6jcEokErHQ0FC2dOnS4rvA69evzxYsWMDu378vlenRr2dh/u9//2M9e/aU+7MwN2/ezJSUlKT+YUUkErEzZ86whg0bMg0NDbZlyxbaRUtkyvHjxxkAhV9TLe9oyltBZGRkoFu3buDxeAgMDETNmjU5ycEYw4EDB+Do6IjGjRvDw8MDRkZGnGSRlCNHjmD69OkIDw9H69atuY7DOcYYHj16BD6fjzNnziAxMRHa2tqwsrKCtbU1OnXqJJUp6fz8fDx+/Lh4ivzBgwfIyclB5cqV0a1bt+Jp8j/++IPzaeUfEQgE6NatG3JychAaGgp1dfVvvp5TIMC79BwUCkRQU1GCbm1NaKqrlKvP169fw9bWFtevX4e5uTmcnZ3RpEmTcrVJiLiNGTMG79+/R1BQENdRyC9QQakACgsLMWjQIISFhSE4OBh6enqc5EhKSoKNjQ2uXr2KuXPnYtu2bTJ9FEpZFRYWQk9PD2ZmZjhx4gTXcWSKSCTC/fv3wefzce7cOXz8+BHNmzeHtbU1rK2t0aZNG6llEQgECA0NLS4w7927h0+fPkFVVRVGRkbFBaaJiYnMHFsVHh6OTp06YcmSJdi4cSNiUrJwMjged6JTEZ+Ri3+/WfMA6NTSQG99LUww1kGLelVL3E9+fj62bNmCv//+Gw0aNMCePXswbNgwsb8eQsorPz8fderUwYoVK7Bs2TKu45BfoIJSzjHGMHPmTBw/fhw3b95Ez549Oclx4cIFzJo1C6qqqnB3d8fgwYM5ySEtu3fvhoODA2JiYtC0aVOu48gkgUCAO3fugM/n48KFC8jMzIShoSGsra1hZWWFli1bSjXPf8/C9Pf3R0pKCpSUlNCxY8fiApPrszDXr1+PDc4uGPS/YwhLLYSyEg9C0c/fpr9+3VSvDjaNagvtWhq/bN/Pzw/z5s1DXFwclixZghUrVkBD49fPIYQrV65cwdChQxEREQFDQ0Ou45BfoIJSzm3btg1//vknjh07hsmTJ0u9/y9fvmDhwoU4duwYRo0ahUOHDlWIg6lzc3PRpEkTWFpaYv/+/VzHkXmFhYW4ceMG+Hw+vL29kZ2djY4dOxYXl1xMszLGEBMT881h63FxcQCA1q1bFxeYPXv2RMOGDaWW62TQW6y4EAbwlAAl5RI/T1mJBxUlHtaOaA3rrjrffT0xMRH29vY4c+YMevXqhf3796NVq1bijE6I2M2aNQt37tzBq1ev5Po0h4qACko5dvHiRYwePRrLly/Hhg0bpN5/QEAAJk+ejIyMDOzZsweTJ0+uUD/wmzZtwrp16/Du3TvUr1+f6zhyIy8vD1euXAGfz4evry/y8/PRrVs3WFlZwdLSUqrF23/FxcV9c1RRdHQ0AKB58+bfHFWkq6srke/1vXdisP3Gq3K3s3hAS9j2bgHgn5Hiffv2YeXKlahcuTJ27NiB8ePHV6ifVSKfRCIRGjVqhAkTJmD79u1cxyG/QQWlnHr69ClMTU0xbNgw8Pl8KCkpSa3vgoICrFq1Ctu2bUP37t1x/PjxCjntm5mZCR0dHcydOxdbtmzhOo5cysrKwqVLl8Dn83Ht2jUIBAKYmZnBysoKo0ePRt26dTnN9++zMP39/fHixQswxtC4ceNvRjDFcRYm/3E8ll54IabkwBaLtmgiTMTcuXOLz5bcuHEjatSoIbY+CJGkoKAgdOvWDffu3UOPHj24jkN+gwpKOfT+/XsYGRlBW1sbd+/eleqO1fDwcEycOBGRkZFYv349Fi9eDGXlkk/LKZply5Zh7969iI+P52xnvaL49OkTvLy8wOfzcevWLQBAv379YGVlhVGjRslEIZSRkYEHDx4Uj2A+ffoUQqEQdevWhampaXGB2a5du1L9XCRk5KLfTn8UCERiy6rEhEg4OBvtmzeCi4sLunbtKra2CZGGZcuWwc3NDUlJSRX694y8oIJSzmRnZ8PU1BQZGRkIDg6W2lSrSCTCrl27sGzZMujp6cHDwwMdOnSQSt+yLCUlBbq6uli+fDlWrlzJdRyFkZqaivPnz8PT0xMBAQFQVVXFoEGDYG1tjeHDh6NKlSpcRwTwz89jYGBgcYEZHByMgoICVKtWDT169CguMDt37gw1NbWftjPJLRgP36T/cvNNaTGRELqVC3F75Sj6ZUzkUqtWrdCtWze4u7tzHYWUABWUckQoFGLUqFG4e/cuHjx4gLZt20ql34SEBEyZMgV37tyBvb09Nm3ahEqVKkmlb3lga2sLPp+PuLg4hTwmiWsfPnzA2bNn4enpiaCgIFSuXBnDhg2DtbU1Bg8eLFNnSv7sLEwNDY1vzsI0NjYuzh2TkoX+zgESy3TTvif0tEp+pBAhsuDVq1fQ19eHl5cXRo4cyXUcUgJUUMoRR0dHODs7w9fXV2rH8pw6dQrz5s1D1apVcfToUfTt21cq/cqTuLg46OnpYdu2bVi0aBHXcRTa27dvcebMGXh6eiI0NBRVqlSBubk5rK2t0b9//1+OAnKhqKjou7MwMzMzvzkLM6lhDwQkMggl8E6srMTDJOMmWDOCDuAn8mXbtm1YvXo10tLS6FgrOUEFpZw4ePAg5syZgz179sDW1lbi/WVkZGD+/Png8/kYP3489u7dS2sEf2HKlCm4desW3rx5I3NFjaKKjo6Gp6cn+Hw+Xr58iZo1a8LCwgLW1tbo1asXVFTKd4uMJIhEIoSHh39zVJGy+Xqo1pTczvYmtTXgv7i3xNonRBK+ngfr5eXFdRRSQlRQygE/Pz8MHjwYc+fOxZ49eyTe382bNzF16lTk5OTAxcUF1tbWEu9T3r18+RKtW7fG4cOHYWNjw3WcCoUxhvDwcPD5fHh6eiI2NhZaWloYM2YMrK2t0b17d6meglAaWflFaLv2hkT74AEIXzOw3Nc0EiItKSkpaNCgAdzc3DBt2jSu45ASks13WVLs5cuXsLS0xIABA7Bz506J9pWXlwc7Ozv0798fBgYGePHiBRWTJdSqVSuMGjUKW7ZsgVAo5DpOhcLj8dC2bdt/riqMicHjx48xadIk+Pj4oGfPntDR0YGDgwMePXoEWfv8HJ+RK/E+GIB36TkS74cQcfH19QWPx6PrQOUMFZQy7OPHjxg6dCi0tbXB5/MlOoUXEhKCzp074+DBg3B2dsaNGzfQuHFjifWniJYtW4aYmBicP3+e6ygVFo/HQ5cuXbB9+3bExcXh/v37GDVqFE6ePAljY2M0b94cy5YtQ1hYmEwUl4ViPCZIFvohRBy8vb3RvXt3zs+hJaVDBaWMys/Ph7m5OXJycuDr64tq1apJpB+hUIi///4bxsbGUFdXR0hICOzs7GR2ilCWdenSBf3798emTZtkolip6JSUlNC9e3fs2bMHiYmJuHXrFvr164dDhw6hQ4cOMDQ0xNq1axEVFSX1bLm5uXj27Bnu3PKTSn9qKvTzTORDTk4O/Pz8aGe3HKI1lDKIMYaJEyfiwoULuHv3LoyNjSXSz5s3bzB58mQEBgbir7/+wpo1a2hDSTndvXsXvXv3xpUrV6S2E5+UTlFREW7evAk+n4+LFy8iKysL7du3L75XXFy3PjHGkJSUhKioKERHRyMqKqr4n/j4eAAAT7UStB3OSvQaRFpDSeTJxYsXYWFhgZiYGOjp6XEdh5QCFZQyaN26dVi9ejU8PT0xduxYsbfPGMORI0dgZ2eHunXr4vjx43StlZgwxmBiYgIVFRXcu3eP6zjkN/Lz83H16lV4enrCx8cHeXl5MDIygrW1NSwtLUu07KOgoACvX78uLhb/XTxmZWUBAFRUVNC8eXMYGBh884++vj7MXZ8hToJrKWmXN5EnU6dOxePHjxEREcF1FFJKVFDKmNOnT2P8+PHYsGEDVqxYIfb2P378iFmzZsHLywvTp0/Hzp07JTadXlFdunQJI0aMQEBAAExNTbmOQ0ooOzsbvr6+8PT0xJUrV1BYWAhTU1NYW1tj9OjRUFJS+m6kMTo6Gm/evIFI9M8axRo1aqBVq1bFxeLXwrFZs2ZQVVX9Yb9rfCJwIjhOrLfkfMUDw9iODbBlbGext02IuAkEAtSvXx+zZ8/Gxo0buY5DSokKShny8OFD9OnTB1ZWVjh69KjYp8F8fX1hY2MDkUiEQ4cOYdSoUWJtn/xDJBKhffv20NbWxpUrV7iOQ0pBIBDgzZs3ePr0Kby8vPDw4UN8+PDhmzWxPB4PTZs2/W6k0cDAAHXr1i31z62kb8pJP74Ik80HwN7enqYQiUwLCAiAmZkZgoODYWRkxHUcUkpUUMqIt2/fwtjYGAYGBvDz84O6urrY2s7OzoajoyMOHTqEoUOHwtXVVWp3gFdUp06dwoQJExASEoKOHTtyHYf8R2Zm5g9HG1+/fo2ioiIAQJUqVWBgYIAmTZqgqKgIb968QUREBFRUVDBgwABYW1tjxIgRYhnhl8Rd3spKPHRpXBWtP97B3r17kZaWhpEjR8LR0RHdu3eX6LpNQsrCwcEBfD4f79+/p42hcogKShnw+fNnmJiYoKCgAEFBQahTp47Y2g4KCsKkSZOQmJiIHTt2YNasWfSLRAoEAgH09fXRuXNnnDlzhus4FZJQKER8fPwPN8WkpKQUP05bW/uHo40NGzb87mclKSkJ586dA5/Px8OHD1GpUiUMGTIE1tbWGDp0aJmviEvIyEW/nf4oEOPxPuoqSrhpbwbtWhrIz8+Hh4cHduzYgZcvX8LIyAgODg4YPXq0TN4oRCoexhj09PTQv39/HDhwgOs4pAyooOSYQCDA0KFD8ejRIwQFBUFfX18s7RYVFWHDhg3YuHEjunTpghMnTqBFixZiaZuUzMGDBzF37lxERUWhZcuWXMdRWNnZ2Xj16tV3m2JevXqF/Px8AEClSpXQsmXL7zbFtGzZEpqammXqNz4+HmfOnAGfz8fTp0+hqamJkSNHwsrKCgMHDiz1LAP/cTyWXnhRpiw/ssWiLay66nzz30QiEa5duwYnJyfcvn0bTZo0gZ2dHWbMmIGqVauKrW9CSis8PBxt27bF1atXMWjQIK7jkDKggpJDjDHMnz8fhw8fxvXr19GnTx+xtBsdHY2JEyciNDQUq1atwvLly2kUggP5+flo1qwZBg8eDDc3N67jyDXGGBITE78ZZfxaPCYkJBQ/rn79+t+NNBoYGEBHR0eiU2ivX78uvlc8PDwc1atXh4WFBaysrNCnT5+fbsj5r713YrD9xiswxso1k7BkgD7m9/71eslnz55hx44dOH36NDQ0NDBr1iwsXLgQ2traZe6XkLLauHEjtmzZgo8fP4p1yReRHiooObRr1y4sWrQIhw8fxowZM8rdHmMMLi4uWLx4MRo3bgwPDw9a2MwxJycnLFu2DLGxsfSLugTy8/MRExPz3TR1dHQ0srOzAQCqqqrQ09P74RE81atX5/gVABEREcXFZUxMDOrUqYMxY8bAysoKpqamUFZW/ulzhUIhjMc74KNOL6ioqkFYindnZSUeVJR4WDei9Xcjk7/y4cMH7NmzBwcPHkR2djbGjh0LR0dHdOrUqeSdE1JOXbt2RdOmTWmJkByjgpIjvr6+GDFiBBYvXoytW7eWu72kpCRMnz4d165dw7x587B169YyT+UR8cnKykKTJk0wefJkODs7cx1HJjDG8PHjxx+ONr59+7Z4R3WtWrXQqlWrb0YaDQwM0LRpU7kYcWeMITQ0tLi4jI+PR4MGDTB27FhYW1vD2Nj4u1HIv//+GytWrAD/kh8uJVfBvddpYCIheEo/L0KVlXgQihhM9epg06i20K5VtnWc2dnZcHd3h7OzM96+fYtevXrBwcEBQ4cOpQ0SRKI+fPhQPAgyYcIEruOQMqKCkgNhYWHo3r07+vfvj/Pnz5f7zfr8+fOYNWsW1NTU4O7uTje0yJg1a9Zg69atiIuLq1B30xYVFSE2NvaHm2IyMzMB/HM9YrNmzX64KUacm9O4xhhDUFAQPD09cebMGSQlJaFJkyawsrKCtbU1OnTogODgYPTo0QNLly7Fhg0bAABd+gyDqHl3VNX/A/Hpufj3mzUPgE5tDfRuqYWJf+hAT0s8ayCFQiEuXrwIJyen4nXd9vb2mDx5MipXriyWPgj5NxcXFyxcuBCpqamoWbMm13FIGVFBKWVJSUkwMjKClpYWAgICyjWK+PnzZyxcuBDHjx+HhYUFDh48qFC/hBVFeno6mjRpAnt7e6xfv57rOGL36dOn70Yao6KiEBsbC4FAAACoVq3ad+saDQwM0Lx58wq3XkooFOLevXvw9PTE2bNnkZ6ejubNmyMtLQ26urp4/PgxVFVVERUVhVatWuHs2bMYM2YMcgoEeJeeg0KBCGoqStCtrSnx6xQDAwPh5OSEixcvolatWpg3bx7mz58PLS0tifZLKpZBgwZBIBDg5s2bXEch5UAFpRTl5ubCzMwMSUlJCA4ORqNGjcrcVkBAACZPnoyMjAzs2bMHkydPpuOAZJijoyPc3NwQHx8vlzcTCYVCxMXF/XCaOjU1tfhxTZo0+eGmmPr169P35w8UFRXh1q1bmDdvHt6+fQsAaNOmDaytrZGQkFA8msl10R0bG4tdu3bB3d0dAoEAkyZNgoODA1q1asVpLiL/Pn/+jLp168LJyQkLFizgOg4pByoopUQkEsHS0hLXrl3D/fv3y3zYdUFBAVauXInt27ejR48eOH78OHR1dcUblojdhw8f0KxZM6xbtw5//fUX13F+Kisrq3iE8d/T1DExMSgoKAAAaGho/PAInhYtWpT5HMaK7MiRI5g+fTpOnDiBatWqgc/nw8fHBzk5Oahbty7+/PNPjB07Fjo6Jd9oIymfPn3CwYMHsWfPHiQmJmLw4MFwdHREnz596AMDKRNPT09YW1vj3bt3aNKkCddxSDlQQSkly5Ytw5YtW+Dl5YURI0aUqY3w8HBMnDgRkZGR2LBhAxwdHX+5Y5TIllmzZsHHxwdv377ldC2aSCTChw8ffjja+OHDh+LHNWzY8IejjY0bN6ZNGmISHR2NTp06Ydy4cXB1dS3+7z4+Phg5ciR69eqFwMBAFBQUoHv37rCysoKlpSXnN10VFhaCz+fDyckJz58/R4cOHeDg4AArKyuoqalxmo3Il/HjxyMqKgohISFcRyHlRAWlFHwdgXBycoKDg0Opny8SieDs7Ixly5ahRYsW8PDwQIcOHcQflEjU69evoa+vjz179mDevHkS7y8vLw+vXr364RWDubm5AAA1NTW0aNHiu00x+vr6cjk1L08KCgrwxx9/IC8vr/hg9K8mTJiA0NBQREREICsrCz4+PuDz+bhx4waEQiF69eoFKysrjB49GrVr1+bsNTDGcPv2bTg5OeHq1ato2LAhFi5ciFmzZtHmCvJbhYWF0NLSgr29PVavXs11HFJejEjUnTt3mIqKCps1axYTiUSlfn5cXBzr3bs3A8AcHBxYXl6eBFISabG2tma6urqssLBQLO2JRCKWlJTE7ty5w1xcXJidnR0bNGgQ09XVZTwejwFgAFjdunWZqakpmzFjBtu+fTvz9fVlr1+/ZgKBQCw5SOktWrSIqampsdDQ0G/+e2ZmJqtUqRLbvHnzd89JT09nrq6urF+/fkxJSYmpqKiwwYMHs6NHj7LMzEwpJf+x8PBwZmNjw9TU1JimpiZbuHAhe/PmDaeZiGy7ceMGA/DdzwCRTzRCKUGvXr3CH3/8gc6dO+PKlSslvi0D+OeT/6lTpzB//nxUrVoVx44dE9tNOoQ7z58/R/v27XH8+HFMmjSpxM8rLCwsPoLnv6ONnz9/BgAoKyujefPmPxxt5HIUi3zv8uXLGDZsGHbt2oWFCxd+8zVXV1fMnj0b8fHxv9y4l5KSgvPnz4PP5+PevXtQU1Mrvld82LBhnJ1Dm5KSgn379mH//v349OkTRo0aBUdHR3Tr1o2TPER22drawtfXF2/fvqU1uAqACkoJSU9PR7du3aCsrIzAwEDUqFGjxM/NyMjAvHnz4OnpiQkTJmDv3r2lej6RbcOHD0dsbCzCw8O/W4uYnp7+3fE7UVFRePPmDYRCIQCgevXqPzzwu1mzZrR+TQ4kJSWhXbt2MDY2xqVLl777RdqjRw9UqVIF165dK3Gb79+/x9mzZ8Hn8/Ho0SNoaGhg+PDhsLa2xqBBg1CpUiVxv4zfys3NxfHjx7Fz5068evUK3bp1g6OjI8zNzWntNwFjDE2aNMGoUaOwa9curuMQMaCCUgIKCwsxYMAAREREICgoCM2bNy/xc/38/DB16lTk5ubiwIEDsLKykmBSwoWAgACYmZlh+fLlqFmz5jfFY1paGgCAx+NBV1f3h5titLS06NO8nBKJRBgwYAAiIyMRFhb23UH3r1+/RosWLXDq1CmMGzeuTH28efMGZ86cAZ/PR1hYGKpVqwZzc3NYW1ujX79+pZopEQeRSITLly/DyckJ/v7+aNasGRYtWoRp06ahSpUqUs1CZEdISAg6d+6MW7du0eybgqCCUswYY5g+fTpOnTqF27dvo3v37iV6Xl5eHpYuXYrdu3ejb9++OHr0KBo3bizhtESSPn/+jOjo6O82xcTExKCoqAjAP0fw/Pf4HQMDA+jp6dGtJApoy5YtWLZsGfz8/NC3b9/vvr5q1Srs2rULycnJYvn/HxUVBU9PT5w+fRrR0dGoVasWRo8eDWtra5iZmUl9pPDJkyfYsWMHzpw5g6pVq2LOnDlYsGABGjZsKNUchHurVq3Cnj17kJqaKvUPOUQyqKAUs82bN2PZsmWlupM0JCQEEydOxNu3b7FlyxbY2trSsSxyQiQSISEh4YfT1ElJScWPa9y48TcjjdnZ2cWFRb9+/Th8BURavl6tuGTJEmzatOm7r4tEIjRt2hQDBw7EoUOHxNo3YwzPnz8Hn8+Hp6cn3r59i3r16sHS0hLW1tbo1q2bVN9z4uPjsXv3bhw+fBh5eXkYN24cHBwc0L59e6llINxq37492rZtCw8PD66jEDGhglKMzp07B0tLS6xatQpr16797eOFQiG2bNmC1atXF/9gGRoaSiEpKa3c3Fy8evXqu00xr169Ql5eHgBAXV39uwO/9fX10bJlS1St+u09y4wxdOnSBTVq1MCtW7e4eElEij5//oyOHTtCS0sL9+7d++GIzJ07d9CnTx88ePAAJiYmEsvCGMPjx4/B5/Nx5swZfPjwAY0bNy6+V7xz585SW1Lx5csXuLq6YteuXYiPj0ffvn3h6OiIQYMG0bIOBfb27Vs0a9as+FpRohiooBSTx48fo2fPnjA3N8epU6d++2b45s0bTJo0CUFBQfjrr7+wZs0a2lDBMcYYkpKSfjjaGB8fX/y4evXq/fBeah0dnVJNIZ4/fx5jxoxBUFAQjI2NJfGSiAxgjGHChAm4fPkynj17hqZNm/7wcVOmTEFgYCCio6OlVkyJRCI8ePAAfD4f586dQ2pqKpo3b15cXLZp00YqWQQCAc6fPw8nJyc8fvwYhoaGcHBwwIQJEzjZUEQka9euXfjzzz+Rlpb23YdtIr+ooBSD+Ph4GBsbQ1dXF3fu3PnlGyBjDO7u7li0aBHq1q2L48ePo0ePHlJMSwoKCvD69evvjt+JiopCVlYWAEBFRQV6enrfbYrR19cX24HNIpEIhoaGMDAwgJeXl1jaJLLn2LFjmDp16i832mRnZ6N+/fpYunQp/ve//0k54T8EAgHu3r0LPp+P8+fPIzMzE4aGhrC2toaVlRVatmwp8QyMMdy/fx9OTk7w8fFB3bp1YWtri7lz56JOnToS759IR+/evVG5cmVcuXKF6yhEjKigLKesrCz06NEDnz9/RnBwMOrVq/fTx6ampmLWrFnw9vbG9OnT4ezsTJ/OJIQxhrS0tO9GGqOiovD27VuIRCIAQM2aNX+4KaZp06ZSWSh+9OhRTJs2DS9evECbNm0k3h+RrlevXqFTp04YO3Ys3N3df/q4o0ePYvr06Xj37p1M3NldWFgIPz8/8Pl8eHl5ITs7Gx07doS1tTXGjh0LXV1diWd49eoVnJ2dcfToUQD/jODa29tLpbAlkpORkQEtLS3s27cPs2fP5joOESMqKMtBKBRi5MiRuHfvHh4+fIjWrVv/9LG+vr6wsbGBSCTC4cOHYW5uLr2gCkwgEODNmzc/nKbOyMgAACgpKaFp06Y/PIKnTp06nK7VKioqgp6eHkxNTWlxuoIpKChAt27dkJOTg6dPn/7yiJzevXtDSUlJJtfT5uXl4erVq+Dz+fD19UVeXh7++OMPWFtbw9LSUuI7tNPT03HgwIHiHcHDhg2Do6MjevbsSess5dCJEycwefJkJCYmokGDBlzHIeIkhdt4FJadnR1TVlZm165d++ljsrKy2KxZsxgANnToUJaUlCTFhIrj06dPLCgoiB09epQtXbqUmZubMwMDA6aqqlp8vWDVqlVZly5d2MSJE9mGDRvYuXPn2IsXL2T+usrdu3czZWVlFhsby3UUIkYODg5MTU2NhYSE/PJxb968YQDY8ePHpZSs7LKystipU6fYiBEjmKqqKuPxeMzMzIy5uLiw1NRUifadn5/P3N3dWevWrRkA1rlzZ3bq1CmxXWNKpGP06NHM2NiY6xhEAqigLKN9+/YxAGzfvn0/fUxgYCDT09NjGhoa7ODBg2W6y7siEQgE7M2bN+zKlStsx44dbPbs2czMzIzVq1evuGgEwHR0dNiAAQPYggUL2L59+9itW7fYhw8f5PbvNycnh9WtW5fNmTOH6yhETK5cucIAsJ07d/72sWvXrmVVqlRh2dnZkg8mRhkZGczd3Z0NHDiQKSsrM2VlZTZgwADm7u7OPn36JLF+RSIRu3btGuvfvz8DwLS1tdn27ds5v8uc/F5eXh7T1NRkmzZt4joKkQCa8i6D69evY+jQobC1tYWzs/N3Xy8qKsL69euxceNGdO3aFSdOnECLFi2kH1RGZWdnf3cET3R0NF69eoX8/HwAQKVKlb7bRf31CB6u7iiWpL///htr167F27dvaRpIziUnJ6Ndu3bo2rUrfH19fzktyxiDnp4ezMzMfrnGUtZ9/PgRFy5cAJ/Ph7+/P1RUVDBo0CBYW1tjxIgRErsR5/nz59ixYwdOnTqFSpUqYcaMGbCzs0OTJk0k0h8pn6932EdERNAReQqICspSioiIgImJCUxNTeHt7f3dMTHR0dGYOHEiQkNDsXr1aixbtgwqKiocpeUOYwwfPnz44aaY9+/fFz+uQYMGPzyCR1tbu0Id7v7582fo6Ohg9uzZ2Lp1K9dxSBmJRCIMGjQIL168QFhYGLS0tH75+Hv37qFnz564e/cuzMzMpJRSshITE3H27Fl4enoiMDAQlStXxtChQ2FtbY0hQ4ZI5AaoxMRE7Nu3Dy4uLvjy5QvGjBkDR0dHdO3aVex9kbKbNWsW7t69K9WjsYj0UEFZCqmpqTA2Nka1atVw//79b3ZoM8awf/9+LFmyBNra2vDw8KgQb2b5+fmIiYn5blNMdHQ0srOzAQCqqqpo0aLFD4/gqV69OsevQHYsX74ce/bsQXx8vNiOJiLStW3bNvz111+4ceNGiW5AmjFjBm7fvo3Xr18r5Aeod+/e4cyZM/D09ERISAiqVKkCc3NzWFlZYcCAAWI/ezcnJwdHjx7Fzp07ERsbC1NTUzg6OmL48OEK+fcrT0QiERo2bIhJkyZh27ZtXMchElDhC8qcAgHepeegUCCCmooSdGtrQlP9+xHFvLw89OnTB+/evUNwcPA3R3skJiZi+vTpuH79OubNm4dt27ZBQ0NDmi9DohhjSE1N/W6kMTo6Gm/fvsXXb6E6dep8N9JoYGAAXV3dCjlKW1qpqalo0qQJli9fjpUrV3Idh5TS48ePYWJiAkdHR2zevPm3j8/NzUX9+vXh4OCANWvWSD4gx169egVPT0/w+XxERkaiZs2asLCwgJWVFXr37i3W9wihUAgfHx84OTnhwYMHaNGiBezt7TFlyhSFem+WJ4GBgTAxMcG9e/fo7GUFVSELypiULJwMjsed6FTEZ+Ti338BPAA6tTTQW18LE4x10KJeVTDGMH78eHh7e8Pf3/+bkcfz589j1qxZUFNTg7u7OwYPHiz11yMuRUVFiI2N/eE0dWZmJgBAWVkZzZo1++FoIx08XH4LFizAqVOnEBcXJ7F1Z0T8vnz5gk6dOqF27dq4f/9+ic4wPXnyJCZOnIjY2Fg0a9ZMCillR3h4OPh8Pvh8PmJjY1G3bl1YWlrCysoKPXr0EOtoYnBwMJycnHD+/HnUrFkTc+fOxfz581G/fn2x9UF+b+nSpXB3d0dSUlKpbhQj8qNCFZQJGblYfvEF7r1Og7ISD0LRz1/616+b6tVB3bib2Ln+f9/cO/r582csXLgQx48fh4WFBQ4ePCg3BVVGRsYPRxtjY2MhEAgAANWqVftupFFfXx/NmzeHuro6x69AccXFxUFPTw9bt26Fvb0913FICU2aNAne3t4IDQ1F8+bNS/Sc/v37o7CwEP7+/hJOJ7sYYwgJCQGfz4enpycSEhLQqFEjjB07FtbW1ujatavY1tq9ffsWu3btgpubGwoLCzFhwgQ4ODjQhQJS0qpVK5iYmMDNzY3rKERCKkxByX8cj9U+ERCI2C8Lyf9SAoOgqBC9qn7E8dX/nOofEBCAyZMnIyMjA3v27MHkyZNlboGxUCjEu3fvfjja+PHjRwAAj8dDkyZNfrgppl69ejL3miqKqVOn4ubNm4iNjaXiXQ58Paj55MmTGD9+fImek5CQgCZNmsDV1RXTp0+XcEL5IBKJEBQUBD6fjzNnziAlJQVNmzaFlZUVrKys0L59e7G8J2VmZuLw4cPYtWsXPnz4gIEDB8LR0RH9+vWj9zwJiY6OhoGBAby9vTFixAiu4xAJqRAF5d47Mdh+41XZG2AM4PGwqE9zJPq5Y/v27ejRoweOHz8ulSvIfiUrK+ubgvHrn2NiYlBQUAAA0NDQ+OERPC1atKD1RDLo5cuXaN26NQ4dOoQZM2ZwHYf8QkxMDDp16oTRo0cXXxFYEps2bcLGjRuRnJxM16/+gFAoREBAAPh8Ps6dO4eMjAzo6+sX3yveqlWrcvdRVFSEM2fOwMnJCaGhoWjXrh0cHBwwbtw4sW8Wqui2bt2KNWvWIC0tjX7nKDCFLyj5j+Ox9MILsbWXeX0vllv3hqOjo9TWgYhEIrx///6H09QfPnwoflyjRo1+uCmmUaNGtMNRzowZMwZhYWGIioqi9UYyqrCwECYmJvjy5QuePn1a4sKQMQYDAwMYGRnhxIkTEk4p/4qKinDz5k14enri4sWL+PLlC9q1a1dcXJZ3/SljDHfv3oWTkxMuX76MBg0aYMGCBZg9ezZq1aolpldRsXXv3h1169aFl5cX11GIBCl0QZmQkYt+O/1RIBCJp0HGoKbCwy2H3tCuJf5PWXl5ecUHfv931DE3NxcAoKamhpYtW/5wUwyNdCiOp0+fokuXLuDz+bCysuI6DvmBJUuWYNeuXQgMDETnzp1L/LygoCB069YNN2/eRN++fSWYUPHk5+fj2rVr8PT0hI+PD3Jzc2FkZAQrKyuMHTsWjRs3Llf7L1++xM6dO3H8+HEoKytj2rRpsLe3L/G6WPK9lJQUNGjQAO7u7pg6dSrXcYgEKXRBOcktGA/fpJdqzeTvKCvxYNKsNk7YGJfp+YwxpKSkfDPS+LVojIuLKz6CR0tL64fT1Lq6ujRiVUEMHDgQKSkpCA0NpbVdMub69esYNGgQnJyc4ODgUKrnzpkzB1euXMG7d+9o5qAccnJy4OvrC09PT1y5cgUFBQUwNTWFlZUVxowZg3r16pW57dTUVLi4uGDfvn1IS0uDubk5HB0dYWJiQj+LpeTm5oZZs2YhJSVFbjaukrJR2IIyJiUL/Z0DJNb+Tfue0NP6+YhgYWEhXr9+/d00dVRUFL58+QLgnyN49PT0vtsUo6+vT1MtBHfv3kXv3r1x+fJlDBkyhOs45P+kpKSgXbt26NSpEy5fvlyqojA/Px/169eHra0tNmzYIMGUFcvnz5/h7e0NPp8PPz8/iEQi9OnTB1ZWVrCwsCjz+2leXh48PDywY8cOREVFwdjYGI6Ojhg1ahSdrVtCw4cPx+fPnxEQILnfx0Q2KGxBucYnAieC48Q6OvmVshIPk4ybYM2I1khPT/9upDEqKgpv3ryBUCgEANSoUeOHR/A0a9aMFn+Tn2KMoXv37lBSUsL9+/e5jkPwz3rmIUOG4NmzZwgLCyv1KJinpyesra0RHR2Nli1bSihlxZaenl58r/jdu3ehpKSEgQMHFt8rXq1atVK3KRKJcPXqVTg5OeHOnTvQ1dXFokWLMH36dFpq9As5OTmoU6cONmzYAEdHR67jEAlT2ILSbNsdxGXkSqx95bwMfDnpgLS0NAD/HMHTtGnTHx7BU7duXZomIWXi6+uL4cOHIyAgAKamplzHqfCcnJywePFiXL9+HQMGDCj184cMGYLMzEw8fPhQAunIfyUnJ+PcuXPg8/l48OAB1NXVMXToUFhZWWHYsGFl2nEcEhKCHTt2wNPTE5qampg1axYWLlxY7vWbiujixYuwsLBATEwM9PT0uI5DJEwhC8rsAgHarrkOib4wxjBF8znaGPxzR3WLFi1QqVIlSfZIKiDGGNq3b49GjRrh6tWrXMep0J48eQITExMsWrQIW7duLfXzExMToa2tDRcXF8yaNUsCCcmvJCQk4MyZM+Dz+Xjy5Ak0NTUxYsQIWFtbY+DAgaU+8/X9+/fYs2cPDh48iJycHFhZWcHR0REdO3aU0CuQP1OnTsWTJ08QHh7OdRQiBQpZUEYkfsbQPZKfIry8oAdaN6wu8X5IxXb69GmMHz8eT58+RadOnbiOUyFlZWWhU6dOqFGjBh48eFCmpSrbtm3DypUrkZycjBo1aog/JCmx169fFxeXL168QPXq1TFq1ChYW1ujT58+Jbo686usrCy4u7vD2dkZ7969Q+/e/xwrN3jw4Aq96UogEKBevXqYM2cONm7cyHUcIgUK+d1eKK5jgmSkH1KxWVpaonnz5ti8eTPXUSosW1tbJCcn4/Tp02UqJhljOHr0KEaNGkXFpAzQ09PD8uXL8fz5c0RERMDOzg4PHz7EoEGD0LBhQ8yZMwd3794tXgf/K1WrVoWdnR1iYmJw5swZ5ObmYtiwYcWXE+Tl5UnhFcmeBw8eICMjAyNHjuQ6CpEShSwo1VSk87Kk1Q+p2FRUVPDnn3/i3LlziI6O5jpOhePh4YHjx4/DxcWlzOvAnj59isjISEyZMkXM6Uh5GRoaYu3atYiKikJISAhsbGxw7do19O7dG9ra2rCzs0NgYCB+N5mnoqICS0tLBAYG4v79+zA0NMScOXPQpEkTrFmzBqmpqVJ6RbLB29sbDRs2RJcuXbiOQqREIae8cwoEaCPhNZQ8AOFrBkJTnY6OIJJXUFCApk2bYtCgQXB3d+c6ToURGxuLDh06YNSoUTh+/HiZ21mwYAEuXLiA+Ph4OkdWDjDGEBwcDE9PT3h6eiIpKQlNmjTB2LFjYW1tjY4dO5Zoo2VsbCycnZ3h7u4OoVCIyZMnw8HBAQYGBlJ4FdxhjEFPTw8DBgyAi4sL13GIlCjkEJumugp0JHCTzb/p1NagYpJIjbq6OhwdHXHixAnEx8dzHadCKCwsxLhx41C/fn3s27evzO0UFBTg1KlTmDhxIhWTcoLH4+GPP/7Azp07kZCQgLt372LIkCE4cuQIOnfuDH19faxcuRIRERG/bKd58+bYs2cPEhISsHr1avj6+qJVq1YYNmwY7ty589tRT3kVHh6ON2/e0HR3BaOQBSUA9NbXgrKSZI7qYSIhPoXfh4eHR4VdH0Okb/bs2ahatSqcnJy4jlIhrFy5EqGhoTh9+nS5zhq8fPkyMjIyaLpbTikrK8PMzAz79+9HUlISrl+/jh49emDPnj1o06YN2rZtiw0bNiAmJuanbdSqVQvLli3D27dvcfToUcTHx6NPnz7o3LkzTp48iaKiIim+Isnz9vZG1apV0bt3b66jEClS2IJygrGORA41BwCekjKqpYZh0qRJaNCgAebPn4/Q0FCJ9EXIV1WqVIGdnR0OHz6Mjx8/ch1Hod24cQNbt27F33//Xe41YEePHkXXrl1haGgopnSEKyoqKhgwYADc3d2RkpICHx8ftGvXDps3b0bLli3RpUsXbN++/aezCOrq6pgyZQrCwsJw48YNaGlpYeLEiWjatCm2bt2KzMxM6b4gCfH29sbgwYNLfRQTkW8KW1C2qFcVpnp1xD5KqazEg6leHdy/cg6vX7/G/PnzcfHiRXTq1AmdOnXC/v37FeZNgcieBQsWQElJCbt27eI6isJKTU3F5MmTMWDAgFLf0/1fKSkpuHLlCo1OKiB1dXUMHz4cJ0+eRGpqKs6ePQtdXV2sXLkSTZo0Qffu3bFnzx4kJyd/91wej4f+/fvj2rVrePHiBQYOHIiVK1dCW1sbixYtwtu3bzl4ReLx/v17PHnyhKa7KyCF3JTzVUJGLvrt9EeBuI73YQzqKkq46dAL2v9aoykQCHDt2jW4urrC19cXqqqqGDNmDGbMmIGePXvSLTlErBYvXgxXV1fEx8eX6Ro58nMikQhDhw5FSEgIwsLCUL9+/XK1t3PnTixduhSJiYmoXbu2mFISWZaVlQUfHx/w+Xxcv34dAoEAvXr1grW1NSwsLFCnTp0fPi85ORn79u0rHpQYPXo0HB0dYWxsLOVXUD779++HnZ0dPn78SEdkVTAKO0IJANq1NLB2RGvxNcjjoeDhCWQkfLtWRkVFBcOGDYOXlxcSEhKwZs0aBAUFoVevXmjZsiU2b96MpKQk8eUgFZqDgwPy8vJo96QE7Nq1C9euXcOxY8fKXUwCwLFjxzB8+HAqJiuQqlWrYsKECbh06RJSUlLg6uoKFRUVzJs3Dw0aNMDgwYNx7NgxfP78+Zvn1a9fH+vXr0dCQgL27t2LZ8+e4Y8//kCPHj1w4cKFEp2JKQu8vb1hZmZGxWQFpNAFJQBYd9XB4gEtxdLWTOP6qP35FUxMTHD+/PkfPqZBgwb466+/8OrVK9y9exfdunXD2rVroa2tjZEjR+LSpUsQCARiyUMqpoYNG2Lq1KnYsWMHbQoTo5CQEPz1119wdHTEoEGDyt3es2fPEBYWhqlTp5Y/HJFLNWvWxPTp03Hjxg0kJiZi9+7dyM3NxbRp06ClpQVzc3Pw+Xzk5OQUP0dDQwNz585FVFQUvLy8oKSkhNGjR0NfXx979+795rGy5vPnz7hz5w7Mzc25jkI4oNBT3v/GfxyP1T4REIhYqTbrKCvxoKLEw7oRrWHVVQe5ubmYPn06PD09sXr1aqxateq312tlZmbi1KlTcHNzQ0hICBo0aICpU6di+vTpZT4omVRssbGxaNmyJXbv3o358+dzHUfuZWVloXPnzqhWrRoePnxYpttw/sve3h6nTp3C+/fvS3WVH1F8Hz58wNmzZ8Hn8xEcHAwNDQ0MGzYM1tbWGDx4MCpVqvTN4x8/fgwnJyecO3cO1apVw5w5c7BgwQI0aNCAo1fwY56enrC2tkZcXBx0dHS4jkOkrMIUlMA/ayqXX3yBe6/ToKzE+2Vh+fXrpnp1sGlU22/WTDLG8Pfff2PFihWwsLDAsWPHUKVKlRJlCA0NhZubGzw8PPD582f06tULM2bMgIWFBSpXrlzu10gqjvHjx+Phw4eIiYmhgqWcpk6dinPnziE0NBQtWrQod3tFRUVo1KgRJk2aRMc8kV96+/Zt8QHqz549Q9WqVWFubg5ra2v069fvmw83cXFx2L17Nw4fPoz8/HyMHz8eDg4OaNeuHYev4P8bN24coqOjERISwnUUwoEKVVB+FZOShZPB8bjzKhXx6bnf3KjDwz+HlvduqYWJf+hAT+vn5895e3tj4sSJaNasGby9vaGrq1viDHl5eTh//jzc3Nxw9+5d1KhRAxMmTICNjQ06duxY5tdGKo7nz5+jffv2OHbsGCZPnsx1HLl18uRJTJw4Uax/jz4+Phg5ciTCwsJk5pc9kX1RUVHw9PQEn89HVFQUatWqBQsLC1hbW6NXr17FB+N//vwZrq6u2LVrFxISEtC/f384OjpiwIABnG0CLSwsRN26deHg4IDVq1dzkoFwq0IWlP+WUyDAu/QcFApEUFNRgm5tzVLdgBMeHo6RI0fiy5cvOH/+PHr27FnqDK9fv4a7uzuOHDmC5ORkdOrUCTNmzMC4ceNoYTP5pREjRuD169cIDw//7dIL8r3Y2Fh07NgRI0aMwIkTJ8T2y3j06NF48+YNnU9LyoQxhhcvXoDP58PT0xNv3rxBvXr1YGlpCSsrK5iYmEBJSQlFRUU4d+4cnJyc8PTpU7Ru3RoODg6YMGGCRM+A/NHvzYcBdzBgwAA8e/YM7du3l1jfRHZV+IJSHNLT02FpaYl79+5h7969mD17dpnaEQgEuHLlCtzc3HD58mWoqqrC0tISNjY2dPwQ+aHAwECYmJjgwoULGDVqFNdx5EphYSF69OiB9PR0hIaGiu0IpvT0dDRo0ADbtm2DnZ2dWNokFRdjDE+ePCkuLj98+IDGjRvDysoKVlZWxQfv37t3D05OTrh06RK0tLRga2uLuXPniu2EgeKZvehUxGd8P7NXWZSD3JjHuLF/JVrWp+PMKiIqKMWkqKgIDg4O2Lt3L+bNmwdnZ+dyrWtLSkrCsWPH4OrqitjYWLRo0QI2NjaYMmWKWI4zIYqjd+/eyM7OxqNHj+hDRyksXboUTk5OePDgAYyMjMTW7t69e2Fvb4/ExETUrVtXbO0SIhKJ8PDhQ/D5fJw9exapqalo1qwZrK2tYWVlhbZt2yImJgY7d+7E0aNHwePxMHXqVNjb25d5bXBp9h7wmAiMp/TDvQdE8VFBKWaHDx/G/Pnz0b17d5w9e/anh9iWFGMM/v7+cHNzw7lz51BUVIRhw4bBxsYGgwcPhopKyafniWK6ceMGBg4cCD8/P/Tr14/rOHLBz88PAwYMwJYtW/Dnn3+Kte0uXbqgcePG8PLyEmu7hPybQCCAv78/+Hw+zp8/j0+fPqFVq1bFxWXt2rXh4uKCvXv34uPHjxg+fDgcHR1hampa4g+e5T0dZe2I1rDuSru9KwoqKCXg3r17sLCwQNWqVeHt7Y22bduKpd2vxw+5uroiNDQUDRo0wLRp0zB9+nQ0b95cLH0Q+cMYQ9euXVGtWjXcvn2b6zgyLzU1Fe3bt0ebNm1w/fp1sa49jYiIQJs2bWgJApGqwsJC3Lx5E3w+H15eXsjKykKHDh1gbW0Nc3Nz3L9/Hzt27EBkZCS6dOkCR0dHjBkz5pcDEnvvxGD7jVflzrZ4QEvY9i7/yQlE9lFBKSFxcXEYOXIkXr9+DQ8PD7Ef9BoSEgI3NzecPHkSnz9/Ru/evWFjY0PHD1VQFy5cwOjRoxEYGIg//viD6zgySyQSYdiwYXjy5AnCwsLEfo7fn3/+CXd3dyQmJorlLEtCSisvLw/Xrl0Dn8/HpUuXkJeXB2NjY1hZWaFOnTo4fvw4bt68CR0dHdjZ2WHGjBnfrR/mP47H0gsvxJZpi0VbWNFIpcKjglKCcnJyMGXKFJw/fx7r16/HihUrxL7GLTc3FxcuXICrqyv8/f1Ro0YNTJw4ETY2NujQoYNY+yKySyQSoXXr1mjZsiW8vb25jiOznJ2dYW9vj8uXL2PIkCFibVsgEEBbWxuWlpbYvXu3WNsmpCyys7Ph6+sLPp+Pq1evoqioCKampujRowdev36NixcvonLlypg5cyYWLlwIHR0dJGTkot9OfxQIRGLLoa6ihJv2ZrSmUsFRQSlhIpEIGzZswOrVq2FpaYkjR45AU1NTIn3FxMTA3d0dR48eRXJyMjp37gwbGxuMHz8e1atXl0ifRHYcO3YMU6dOxYsXL9CmTRuu48ickJAQ/PHHH7C1tcWOHTvE3v7Vq1cxZMgQPH36FJ06dRJ7+4SUR2ZmJry8vODp6Qk/Pz8AQI8ePaChoYEHDx4gJycHY8eORXbXaYhIKyrVmsnfUVbiwaRZbZywMRZbm0T2UEEpJRcuXMDkyZPRsmVLeHl5SfRaqqKiIly9ehWurq64cuUK1NTUio8fKs2CbCJfioqKoKenB1NTU3h4eHAdR6ZkZ2ejc+fO0NTURGBgoETO6LOyskJkZCSeP39OP2NEpqWlpeH8+fPw9PTE3bt3oaKiAn19faQWKKPy6I0S6/emfc9fXhZC5BudhCwlFhYWePjwITIyMtC1a1fcv39fYn2pqqpixIgR8PHxQXx8PFauXIkHDx7AzMwMBgYG2LJlC5KTkyXWP+GGqqoqlixZgtOnT+PNmzdcx5EpCxcuxIcPH8Dn8yVSTH769Ane3t6YMmUKFZNE5tWpUwezZ8/G7du38eHDB2zfvh3VqlVDURNjMJFQIn0qK/HgERQvkbaJbKARSin7+PEjxowZg8DAQLi4uMDGxkYq/YpEIgQEBMDV1RXnz59HUVERhg8fDhsbGwwaNIiOH1IQeXl50NXVxahRo3DgwAGu48iE06dPY/z48Thy5AimTp0qkT4OHDgAW1tbJCQkiH2jDyHSYvK3HxK/FEqs/Sa1NeC/uLfE2ifcohFKKatbty78/PxgY2ODGTNmwM7ODgKBQOL9KikpoVevXvDw8EBiYiJ27dqFd+/eYfjw4WjSpAlWrFiB2NhYiecgklW5cmUsWrQIR44cQVJSEtdxOPfmzRvMmTMH48aNw5QpUyTWz7FjxzBw4EAqJoncyi4QIEmCxSQAxKfnIqdA8r/vCDeooOSAmpoaXFxcsH//fuzfvx+DBw9GRkaG1PqvWbMm5s+fj9DQUDx9+hQjR47Evn37oKenh759++LUqVPIz8+XWh4iXvPmzUOlSpUksvFEnhQVFWH8+PHFBzxLaio6OjoaQUFBEhv9JEQa4tJzIOnpSgbgXXqOhHshXKGCkkNz586Fn58fQkNDYWRkhMjISKln6NSpE/bv34/ExEQcP34cAoEAEyZMQMOGDbFgwQKEhYVJPRMpn+rVq8PW1hYHDhyQ6gcVWbN69Wo8ffoUp0+flugpB8eOHUONGjUwfPhwifVBiKQVivGYIFnoh0gfFZQc69WrFx4/fozKlSvjjz/+gK+vLyc5NDQ0MGnSJPj7+yM6OhqzZs3C2bNn0aFDB3Tt2hUHDhzA58+fOclGSu/rUoq9e/dyHYUTt27dwubNm7F+/XoYG0vuqBKhUIgTJ05g3LhxqFSpksT6IUTS1FSkUw5Iqx8ifbQpR0ZkZWVh8uTJ8Pb2xqZNm/DXX39xvlu0qKgIV65cKT5+SF1dHZaWlpgxYwZ69OjBeT7yawsXLsTJkycRFxeHKlWqcB1Haj5+/Ij27dvD0NAQN27cEOvViv/19U7woKAgiRauhEhKeno6Hj9+jPtBj+GR3xGQ4Ps6D0D4moHQVKdNoIqICkoZIhKJsGbNGqxfvx7jxo2Dm5ubzFyj+OHDBxw7dgxubm548+YNWrZsCRsbG0yZMgX16tXjOh75gfj4eDRv3hxbt26Fvb0913GkgjGG4cOHIzg4GGFhYWjYsKFE+5s4cSKePHmCly9f0gcsIvPy8vLw7NkzPHr0CI8ePUJwcHDxZsxatWqh1pTdKFKvIbH+aZe3YqOCUgadPXsWU6ZMgaGhIby8vNC4cWOuIxUTiUTw9/cvPn5IKBRi2LBhmDFjBgYOHEjHD8mYadOm4caNG3jz5o1Ezl+UNbt374adnR18fX0xdOhQifb15csX1K9fH6tWrcLSpUsl2hchpSUSiRAdHY3g4ODiAjIsLAwCgQDq6uro1KkTjIyMYGRkBGNjYzRr1gxrL0XiRHCcWG/J+UpZiYdJxk2wZkRrsbdNZAMVlDIqNDQUI0eORGFhIS5evIhu3bpxHek7nz59wsmTJ+Hq6oqwsDA0atQIU6dOxfTp09GsWTOu4xEAUVFRMDQ0xMGDBzFz5kyu40jUs2fPYGxsjLlz58LZ2Vni/bm5uWHmzJmIj4+XqQ99pGJKTEz8ZuTxyZMn+PLlC3g8HgwMDIoLRyMjI7Rt2xZqamrftRGTkoX+zgESy0g35Sg2KihlWEpKCkaPHo3Hjx/j0KFDEj1HrzwYYwgJCYGrqytOnTqFL1++oE+fPpgxYwZGjRpFmxU4NmbMGDx79gxRUVEKO4Kck5ODzp07o3LlyggKCpLKaKypqSk0NDRw/fp1ifdFyL9lZWXh6dOn34w+vn//HgDQoEGD4sLRyMgIXbp0KfEpB8nJyRi4+RIy1bTAUxbfewXd5V0xUEEp4woLCzFv3jy4ubnBwcEBW7ZskemiIDc3F+fOnYObmxsCAgJQs2ZNTJw4ETNmzEC7du24jlchPX36FF26dMHp06dhbW3NdRyJmDFjBk6fPo2nT5/CwMBA4v3FxsZCT08PJ0+exPjx4yXeH6m4ioqKEB4eXjzy+OjRI0RGRoIxhipVqqBLly7fFJCNGjUq9XpeoVAIFxcXrFixAuq1GqLauG0QMPGtCVZXUcJNezNo19IQW5tE9lBBKQcYY9i7dy/s7e3Rt29f8Pl81KxZk+tYvxUdHQ13d3ccO3YMKSkp6NKlC2bMmIFx48ahWrVqXMerUAYNGoSkpCQ8e/ZM4TaPeHp6wtraGm5ubpg+fbpU+ly9ejWcnZ2RlJQEDQ36JUnEgzGGt2/fFo86Pnr0CCEhIcjLy/t/7d15XM1p/z/w16kIiRlrDFkGlZDtJEvIzpdUg7gpWsa+b922EWYyDMY0ZCs6tokkZR0iUdFiiChryU7Zkvbz+f1xj35jFiNn+Zxzej0fj/uvxud6cc+cec11Ptf7gr6+Plq1avXee4/m5ubQ19dXaM2EhASMHz8ev/32G8aOHYvly5fj+O03+O/+K0r6XQErnFrCWWqqtOeRZmKh1CInT57E0KFDUbNmTYSHh8PMzEzsSB+lsLAQhw8fRkBAAI4cOYIKFSqUjB/q3LmzzhUcTRQVFYXu3bur5bCKOqWlpaF169bo378/fvnlF7X8vSSXy9G4cWP07t0bW7ZsUfl6pLvejez541fXmZmZAIBGjRq9t/PYpk0bpf7Hy4sXL7BgwQJs3LgRVlZW2LBhA2xsbEp+vi7yJlYdv6HwOnP6mGGSXROFn0Oaj4VSy9y+fRv29va4f/8+goKC0L9/f7EjlcqDBw8QGBiIrVu34s6dOzAzM4OHhwdcXV05fkiFBEFAly5dAADR0dE6UeILCwvRtWtXPH78GBcvXsRnn32mlnVPnz4NOzs7REdHo3PnzmpZk7RfXl4eLl68+I8je/648yiVSlGzZk2V5BAEATt37sTs2bORm5uLZcuWYdKkSX/7KlVQQgYWh19FkVwo1clvfT0JDPQkWGpvyZ3JMoSFUgu9fv0aI0eOxOHDh7Fy5UrMmjVL6wqCXC7H6dOnERAQUDJ+aNCgQSXjhxT9Gof+6vDhwxg4cCCioqLQtWtXseMobOHChfj+++9x9uxZtU5BGDNmDGJiYnDjxg2t++eO1OPdyJ4/vvf4x5E9bdq0eW/38csvv1TL30spKSmYOHEiTp8+DWdnZ6xZs+ZfZ7Xee/4W80Ov4OytTOjrST5YLN/93LZJDfg4tuQ7k2UMC6WWKi4uxqJFi7B8+XK4uLhg8+bNWnua+vnz5yXjhy5fvowvvvgCbm5ucHd3R6NGjcSOpzMEQUDr1q1Rp04dHDt2TOw4ComMjETPnj3x7bffYv78+Wpb982bNzAxMYGXlxcWLVqktnVJs/3TyB4AsLCw+KiRPar09u1bLFu2DKtXr0aDBg2wfv169OnTp1TPuPkkG7viMhB54ykyst7ij8VBAsC0eiXYNauFUTamHA1URrFQarlffvkF7u7uaNWqFUJDQ1V+M4gqCYKACxcuICAgoGT8UM+ePeHp6QkHBwetLcyaJCgoCCNGjMCFCxfQtm1bseN8kszMTFhZWcHMzAwnTpxQ6262TCbDmDFjkJ6ejgYNGqhtXdIcqhrZoyoHDx7ElClT8PjxY8ybNw9eXl4Kf5bm5BchPSsHBUVylDfQQ8PqRrxOkVgodUFiYiIcHBwgl8tx4MABWFtbix1JYTk5OSXjh86ePYvPP/8cLi4u8PDw4PghBRQVFcHc3Bxt2rRBcHCw2HFKTRAEDB48GLGxsSXD9NWpR48eAIBTp06pdV0Sxx9H9rzbfVT2yB5VuXv3LqZOnYrw8HD07dsX69atQ5MmPBxDKiSQTnj06JFgY2MjGBoaCjt27BA7jlKlpqYKc+fOFWrVqiUAEKRSqbBp0ybh1atXYkfTSps3bxYkEomQkpIidpRS+/nnnwUAQnh4uNrXTktLEwAIMplM7WuT6snlcuHOnTvCL7/8IsyYMUPo3LmzULFiRQGAoK+vL7Rp00YYN26cEBAQICQnJwtFRUViR/5b+fn5wvfffy9UqlRJqFu3rhAcHCzI5XKxY1EZwEKpQ/Ly8oQxY8YIAIS5c+dq7AfepyooKBD2798v/N///Z+gp6cnVKpUSRgzZowQHR3ND8xSyMvLE+rWrSu4ubmJHaVULl26JBgaGgpTpkwRZf2lS5cKRkZGQnZ2tijrk3JlZmYKR48eFZYsWSIMGDBAqFGjhgBAACA0atRIcHZ2FtasWSNER0cLOTk5Ysf9KKdPnxaaN28u6OvrCzNmzBBev34tdiQqQ/iVt44RBAFr167F7Nmz0a9fP+zevVv0d3hU4f79+5DJZAgICEBaWhrMzMzg6ekJV1dX1KpVS+x4Gm/NmjXw8vLC7du3YWqq+WM9cnJy0L59e5QvXx5xcXFqf59WEAQ0bdoUtra22LZtm1rXJsV97Mied/9T1cgeVXn69CnmzJmD7du3o2PHjtiwYQOsrKzEjkVlDAuljjp+/DicnZ1hYmKCsLAwNGvWTOxIKiGXyxEZGVkyfkgul8Pe3h6enp7o06cPxw/9gzdv3qBBgwYYOXIkfH19xY7zr8aOHYudO3fiwoULsLCwUPv60dHRsLW1RWRkJLp376729enjaerIHlWQy+XYvHkz5s2bBz09PaxYsQLu7u7Q09MTOxqVQSyUOuzGjRuwt7fHkydPsGfPnlKPidA278YPbdmyBVeuXEG9evXg5uYGNzc3jh/6G0uWLMGKFSuQnp6u0bu6wcHBGDZsGLZs2QJPT09RMnz99deIiIjA7du3+S9rDfPo0aP3TlwnJCT8ZWTPu7E9YozsUZWLFy9i/PjxiI+Ph5ubG1asWKF1O6ukY8T7tp3U4eXLl0L//v0FPT094ccffywT7xrK5XIhPj5eGDdunGBsbCwAEHr16iUEBQUJeXl5YsfTGFlZWYKRkZEwf/58saP8o/T0dKFq1arC0KFDRft7NycnRzA2Nha++eYbUdan/+/169dCZGSk8P333wtOTk5CvXr1St57rFOnjuDg4CD4+PgIERERwsuXL8WOqxIvX74Upk6dKujp6QktWrQQzp49K3YkIkEQ+A5lmVBcXIz58+dj5cqVGDNmDDZu3AhDQ0OxY6nFu/FD/v7+iI6ORrVq1UrGD7Vs2VLseKKbM2cOtmzZgrt372rcu7ZFRUXo1q0bHjx4gEuXLqntasU/2717N0aOHIlbt27hyy+/FCVDWfQxI3v+ODBck0b2qIIgCNizZw9mzpyJ169fw9vbG9OmTUO5cuXEjkYEgF95lyk7d+6Ep6cn2rZti/3798PExETsSGqVmpqKrVu3QiaT4enTp7C2toanpyecnZ1RpUoVseOJ4uHDh2jUqBGWLFmC//73v2LHec8333wDHx8fnDlzBp06dRItR58+fZCXl4czZ86IlkHXCYKA9PT09766/u2335Cbmwt9fX20atXqvUMzFhYWZer96Bs3bmDSpEmIiIiAk5MT1q5di/r164sdi+g9LJRlTHx8PBwcHKCvr48DBw6gXbt2YkdSu8LCQhw6dAj+/v44duwYKlSoAGdnZ3h4eKBTp046vcvxd8aPH4/Q0FCkp6ejYsWKYscBAJw+fRo9evTA0qVLsXDhQtFy3L9/H6amptiyZQs8PDxEy6FrsrKykJCQ8N7BmczMTABAo0aN3tt5bNOmDSpVKpt3Qufm5mL58uVYsWIFvvjiC6xbtw4DBgwQOxbR32KhLIMePnwIBwcHXLlyBdu2bcPw4cPFjiSa+/fvIzAwEAEBAUhPT4e5uTk8PT3h4uKi0QdVlOnOnTto2rQpfH19MWnSJLHjICsrC1ZWVmjSpAlOnjwp6k7U8uXLsWzZMjx+/LjM7mIrKi8vD5cuXXpv9/HWrVsAdGNkj6ocPXoUkydPxr179zB37lzMnz+/zBZr0g4slGVUbm5uySiW+fPnY9myZWX69Oq78UP+/v7Yv38/5HI5Bg8eDA8PjzIxfmjkyJGIjo7GrVu3RH0nSxAEODo64uzZs0hKSkK9evVEzWJhYYH27dtj586douXQJh8zsuePu4/aPLJHVe7fv4/p06cjJCQEPXr0gJ+fH8zMzMSORfTvRDgIRBpCLpcLP/zwgyCRSIRBgwbxKsPfZWZmCj/99JPQsmVLAYBQr1494ZtvvhHS0tLEjqYyly9fFgAIgYGBouZYv369AEA4cOCAqDkEQRDOnz8vABBOnDghdhSN9fDhQyE0NFSYN2+e0LNnT6FKlSolp64tLCyE0aNHC+vXrxcSExOF/Px8seNqtMLCQmH16tVC5cqVBRMTE2H37t1lYioH6Q7uUBKOHDmCESNGoF69eggPD+dJ1t8JgoDExET4+/tj9+7dyMnJQa9eveDh4QEHBwedOylvb2+Pmzdv4urVq6LsVl+5cgVSqRSenp5Yt26d2tf/swkTJuDQoUNIT0/X+R3qj5GdnY0LFy6899X1/fv3AQB16tR5b1h4+/btNW5qgCaLjY3FhAkTkJycjIkTJ+Lbb7/lnx9pHRZKAvC/E9D29vbIzMxEcHAwevbsKXYkjfLmzRsEBwcjICAAMTExqF69esn4oRYtWogdTynOnz+Pjh07IiQkBE5OTmpd++3bt5BKpdDX10dcXJzoh4Py8vJQp04dTJgwAT4+PqJmEQNH9qhHVlYWvLy8EBAQgPbt22Pjxo1l8qAk6QYWSirx4sULDB8+HCdPnsSPP/6IyZMn818SfyMlJaVk/NCzZ8/QoUMHeHh4YPjw4TA2NhY7nkJ69OiB7OxsxMfHq/X/+/Hjx2P79u1ITExE8+bN1bbuP9m7dy+cnZ2Rmpqq8++vCb+P7Pnje49/HNnTsmXL93Yfy9rIHlWQy+XYtm0bvLy8UFRUhOXLl2Ps2LH8cyXtJtqX7aSRCgsLhZkzZwoABE9PT7739AH5+fnCvn37hP79+wsSiUQwMjIS3N3dhZiYGK199+n48eMCAOH48eNqW3Pfvn0CAGHTpk1qW/PfDBgwQLCxsRE7hkpkZmYKR48eFZYsWSIMGDBAqFGjRsl7j40aNRKcnZ2F1atXC9HR0UJOTo7YcXVOUlKS0KlTJwGA4OLiIjx+/FjsSERKwR1K+luBgYEYN24crK2tERISUmZG6Hyqe/fulYwfunv3LiwsLODh4QFXV1etGoMiCAKsra1RuXJlREZGqny9jIwMWFlZoWfPnggODtaIHfFHjx6hXr168PPzw7hx48SOoxCO7NEc2dnZ8Pb2xk8//YRmzZrBz88P3bt3FzsWkdKwUNI/OnfuHBwdHWFoaIiwsDC0bt1a7EgaTy6X4+TJkwgICEBoaCgEQSgZP9S7d2+t+EorNDQUTk5OiI2NRceOHVW2TlFREezs7HD37l0kJSXh888/V9lapbFq1SosXLgQjx8/Fu26x0/BkT2aSRAE7N+/H9OmTcPz58+xaNEizJo1C+XLlxc7GpFSsVDSB92/fx8ODg5ISUlBYGAghg4dKnYkrZGVlYWdO3fC398fycnJqF+/Ptzc3ODm5oaGDRuKHe8fyeVytGjRAk2aNEF4eLjK1vH29sayZcsQFRWFLl26qGyd0hAEAa1atYKlpSWCgoLEjvNBjx49em/nMSEhAa9fvwYAWFhYlOw6dujQAS1btmSBEcHt27cxZcoUHD16FIMGDYKvr69G/7NPpBCxvmsn7ZGTkyMMHz5cACB88803QnFxsdiRtIpcLhfOnz8vfP3110LlypUFiUQi9O7dW9izZ4+Ql5cndry/JZPJBADC5cuXVfL8qKgoQU9PT1iyZIlKnv+pEhMTBQDCkSNHxI7yntevXwuRkZHCihUrBCcnJ6FevXol7z2amJgIgwcPFnx8fISIiAjh5cuXYsct8/Ly8oSlS5cKFSpUEExNTTViriqRqnGHkj6KIAhYsWIF5s+fDwcHB2zfvh2VK1cWO5bWeTd+yN/fH7GxsRo7fqiwsBBNmzZF586dsWvXLqU++/nz57CyskLjxo1x6tQpjXoNYOrUqdi3bx8yMjJgYGAgSoY/j+yJj4/H1atX/zKy593uI0f2aJaIiAhMnDgRaWlpmDVrFhYtWgQjIyOxYxGpHAsllcrBgwcxcuRINGzYEGFhYWjUqJHYkbRWSkoKAgICIJPJkJmZiQ4dOsDT0xPOzs4aMX5o/fr1mDp1Km7cuKG0YfeCIOCrr77C6dOnkZSUhPr16yvlucpQUFCAunXrwt3dHStXrlTLmgJH9uiMR48eYebMmQgKCkLXrl3h5+cHS0tLsWMRqQ0LJZXatWvXYG9vj5cvX2Lfvn08qaiggoICHDx4EP7+/vj1119RqVIlODs7w9PTEzY2NqLtPuXm5qJhw4ZwdHTExo0blfLMjRs3YsKECdi/fz8cHR2V8kxleXcYKTk5WWVFICsrCwkJCe8VyMzMTABAo0aN3tt5bNOmDSpVqqSSHKQ8xcXF8PPzw8KFC2FoaIhVq1bBxcWFu8ZU5rBQ0id5/vw5hg0bhqioKPj6+mLChAliR9IJGRkZCAwMxNatW0vGD3l6esLFxUWUkS7ff/89Fi9ejLS0NNStW1ehZyUnJ0MqlcLNzQ1+fn5KSqg8gwcPxsOHD5GQkKCU531oZM/nn3/+3olrqVTK0VxaKD4+HuPHj8elS5cwduxY+Pj4oFq1amLHIhIFCyV9sqKiIsyaNQu+vr4YP348fH19Ua5cObFj6YR344f8/f0RGhoK4H+Fx9PTE7169VLb156vXr1CgwYN4OnpiVWrVn3yc3JzcyGVSgEACQkJol+t+GdPnz7FF198UXJDVGn9cWTPu91HjuzRXS9evMD8+fOxadMmWFlZYePGjejQoYPYsYhExUJJCvP398fEiRPRqVMnBAcHcziykmVmZpaMH7p69Srq168Pd3d3uLm5oUGDBipff+HChVi7di0yMjI+efdl4sSJ2LZtGxISEjTq8NE7a9euxdy5c/Ho0SNUr179X//6D43sMTc3f++9x1atWnFkj44QBAE7duzA7NmzkZeXh2+//RYTJ04U7QAXkSZhoSSliI6OhpOTE4yMjBAWFoZWrVqJHUnnCIKA+Ph4+Pv7IygoCDk5Oejduzc8PT1hb28PQ0NDlaz77NkzNGjQAF5eXli8eHGpf/27dxM3bNiA8ePHqyCh4tq0aYPGjRsjJCTkLz/Lzs7GhQsX3nvv8f79+wAAExMTdOjQoaRAtm/fHlWrVlV3fFKDa9euYeLEiYiKisLw4cOxZs0a1KlTR+xYRBqDhZKUJiMjA4MHD8bNmzexY8cOjTt0oUvevHmDvXv3wt/fH+fOnUP16tXh6uoKDw8PlRwomTZtGnbu3Im7d++WjIvKyS9CelYOCorkKG+gh4bVjWBk+P5Ozb1792BlZYXu3bsjJCREI7/mTUpKQuvWrREeHo5+/fr948geIyMjSKVSjuwpY3JycrBs2TKsXr0ajRo1wvr169G7d2+xYxFpHBZKUqqcnBy4ubkhODgYS5YswcKFC6Gnpyd2LJ127do1BAQEYPv27cjMzISNjU3J+CFlzQrNyMjAl19+ibnf/Yhy5naIvP4UGc/f4o8fHhIAptUqwc6sFkZ2MEXjGpVgZ2eHtLQ0JCUladxhhXcjeyZPnozIyEi0adMGFy9efG9kzx/fe+TInrInPDwcU6dOxePHj7FgwQLMmTMHFSpUEDsWkUZioSSlEwQB3333HRYtWoQhQ4YgMDCQg33VoKCgAOHh4fD398fx48dRqVIlDB8+HJ6enujQoYNCO2n3nr+F/bdBeGFYG/oSCYo/8LGhrydBsVxAXb1XSNwwGxEH/jeXT2zPnz9/b+cxPj4ez549AwBUqVIF/fv358geAgCkp6dj2rRpJbvW69atU9osViJdxUJJKhMaGgoXFxc0adIEYWFhajlAQv+TkZGBbdu2YevWrcjIyEDz5s1Lxg/VqFGjVM8KSsjA4vCrKCqWo7gUnxZCcREM9PXwnZMVhktNS/k7UMzHjuwRBAHLli3DpUuXYGVlpdaMpHkKCgqwZs0aLF26FNWqVcNPP/0EJycnvtZA9BFYKEmlrly5Ant7e+Tk5CAkJAS2trZiRypTiouLS8YPHThwAADg4OBQMn7o315HWBd5E6uO31A4x+w+zTDZrqnCz/k7Hzuy512J/OPIniFDhuDWrVu4dOmSSrKR9jh9+jQmTpyIGzduYNq0afD29taIG6uItAULJalcZmYmhg4dipiYGKxfvx5ff/212JHKpGfPnpWMH7p27RpMTU1Lxg+Zmv51BzEoIQP/3X9FaeuvcGoJZyXsVCprZE9WVhbq1KmDlStXYvr06QrnIu309OlTzJ49Gzt27EDHjh2xceNGTqkg+gQslKQWhYWFmD59Ovz8/DB58mSsWbOGQ9BFIggC4uLiSsYPvX37Fn369CkZP1S+fHnce/4WvX6MQn6RXGnrGhroIWJGN9Sv9vHvJn7MyJ53O4+lHdmzfv16TJ8+HQ8ePOAtNWVQcXExtmzZgnnz5kFPTw8rV66Em5sbDxESfSIWSlKrjRs3YsqUKejatSv27t37UUOkSXWys7NLxg+dP38eNWrUgKurK+40GIDLT/JRLFfex4O+ngSdGlfHDo+/v1GkqKgIycnJ7+0+Xrt2DXK5XCUje6RSKerWrYuwsLBPfgZpp99++w0TJkxAfHw83N3dsWLFilK/W0xE72OhJLWLiorCkCFDUKVKFYSHh6tkbiKV3tWrVxEQEIAd4REwGrpcZetEzOiKL2tWRnp6+nvvPf72229qG9lz7do1WFpaIiQkBE5OTkp7Lmm2V69eYeHChfDz84OlpSU2bNiAzp07ix2LSCewUJIo0tPTMXjwYNy5cwe7du2Cvb292JHod4tCL2NXfAbkUP7JVgkEfJ55BffDfiwZ2dOwYcP33nts27atykf2eHl5ISAgAA8fPuS1iGWAIAgICgrCzJkzkZ2djaVLl2LKlCl87YZIiVgoSTRv3ryBq6srDhw4gG+//Rbz5s3jeA4N0O2HSNx9/lZlzy+X9xL/qXID1tbWkEqlan9/saioCKampvjqq6/w888/q3VtUr/r169j0qRJOHnyJL766iusXbsW9erVEzsWkc7hjfYkmsqVK2Pfvn1YunQpFixYgMuXL2Pr1q0cKC2iN/lFyFBhmQSAogqfYe78RX+5plFdIiIi8OjRI4wePVqU9Uk9cnNz4ePjg5UrV6JevXo4cuQI+vfvL3YsIp3F42wkKj09PXh7eyM4OBgHDx6Era0t7t27J3asMutuVg5U/ZWFACA9K0fFq/yzwMBAWFpaol27dqJlINU6evQoWrRogZUrV8LLywvJycksk0QqxkJJGmHIkCGIiYlBZmYmpFIpYmNjxY5UJhUocUyQJqzzZy9fvsSBAwcwevRovl6hg+7du4chQ4ZgwIABaNSoES5fvoylS5eiYsWKYkcj0nkslKQxWrdujYSEBDRr1gx2dnbYtm2b2JHKnPIG6vlIUNc6f7Znzx4UFhZi1KhRoqxPqlFYWIjVq1fDwsICMTEx+OWXX3DixAmYmZmJHY2ozGChJI1Sq1YtREREYPTo0XB3d8eMGTNQVFQkdqwyo2F1IxWc7X6f5Pd1xCCTydC3b1/UqVNHlPVJ+WJiYtCuXTvMnTsX7u7uSE1NxfDhw7kDTaRmLJSkccqXL49NmzZh3bp1+PnnnzFgwAC8ePFC7FhlgpGhAUxLcZPNpzCtXkmUAzk3btzAuXPnMGbMGLWvTcqXmZkJDw8PdOnSBRUrVkRCQgJ8fX1LdVsSESkPCyVpJIlEgkmTJuH48eO4cOECrK2tkZKSInasMsHOrBb09VSzu6OvJ4FdM3GuOZTJZPjss88481TLyeVyBAQEwMzMDPv378eGDRsQGxuLtm3bih2NqExjoSSN1qNHDyQkJMDQ0BA2NjY4cuSI2JF03sgOpkq9cvGPiuUCRtmYquTZH1y3uBjbt2/H8OHDUaFCBbWvT8px+fJl2NrawtPTEwMHDkRqairGjx+v1FuUiOjTsFCSxmvcuDHOnTuH7t27Y+DAgVi5ciU4j191mtY2hm2TGkrfpdTXk8C2SQ00qWWs1Od+jMjISNy/f5+zJ7VUdnY2Zs6cibZt2+LFixc4ffo0ZDIZateuLXY0IvodCyVpBWNjY4SGhmL+/Pnw8vKCi4sLcnNzxY6ls3wcW8JAyYXSQE8CH8eWSn3mx5LJZGjWrBk6dOggyvr0aQRBwL59+2BhYYGNGzfiu+++w6VLl9CtWzexoxHRn7BQktbQ09PDt99+i6CgIOzfvx/dunXDgwcPxI6lk+pXq4Ql9pZKfeZSe0vUV/GBn7/z+vVrhISEYMyYMTz5q0Vu376NAQMGYOjQoWjXrh1SUlLg5eXFu9eJNBQLJWkdZ2dnREdH49GjR5BKpYiLixM7kk4aLjXF7D7NlPKsOX3M4CxV/7uTALBv3z7k5eXBxcVFlPWpdPLz87F06VJYWlri2rVrCAsLQ1hYGBo0aCB2NCL6AInAl9FISz158gROTk64cOECNm/eDFdXV7Ej6aSghAwsDr+KIrlQqsM6+noSGOhJsNTeUrQyCQBdu3ZFhQoVcPz4cdEy0MeJiIjAxIkTkZaWhtmzZ2PhwoUwMhJnZikRlQ53KElr1a5dG6dOncJ//vMfjB49GnPmzEFxcbHYsXTOcKkpImZ0Q6fG1QHgXw/rvPt5p8bVETGjm6hl8s6dOzh79iwP42i4hw8fYvjw4ejduzfq1q2LpKQkLF++nGWSSItwh5K0niAI8PX1xcyZM9GnTx/88ssv+Oyzz8SOpZNuPsnGrrgMRN54ioyst/jjh4cE/xtabtesFkbZmIpymvvPvL29sWbNGjx+/BiVKqn//U36sKKiIqxfvx6LFi1ChQoVsHr1aowaNYrvuhJpIRZK0hknTpzAsGHDULt2bYSHh6NZM+W8/0d/Lye/COlZOdi5Owg///QjHt+8gsoVyokdq4RcLseXX36Jnj17wt/fX+w49CdxcXGYMGECLl26hHHjxsHHxweff/652LGI6BPxK2/SGb1790Z8fDwkEgmsra3x66+/ih1JpxkZGsCyblW0Mf0cOfevozj/rdiR3nP27Fmkp6fzqkUN8+LFC4wfPx4dO3aERCLB+fPnsWHDBpZJIi3HQkk6pWnTpjh//jy6dOmCAQMGYM2aNRyCrmImJiYAgMePH4uc5H2BgYH48ssv0blzZ7GjEP73aopMJoOZmRl++eUX+Pr6Ij4+HtbW1mJHIyIlYKEknVO1alWEhYVhzpw5mDVrFtzc3JCXlyd2LJ2liYXyzZs3CA4OhqurK9/H0wBXr15F9+7dMWbMGPTq1QupqamYPHkyr0wk0iEslKST9PX18f3332Pnzp3Ys2cP7Ozs8OjRI7Fj6SRNLJT79+9HTk4OR0mJLCcnB15eXmjdujUeP36MEydOYPfu3ahTp47Y0YhIyVgoSaeNHDkSZ86cQUZGBqRSKRITE8WOpHOMjY1RsWJFjSqUMpkM3bt3R8OGDcWOUmaFhYWhefPm8PX1xeLFi3H58mX06tVL7FhEpCIslKTz3hXJevXqwdbWFrt37xY7kk6RSCSoXbu2xhTKu3fv4tSpUzyMI5L09HTY29vDwcEBlpaWuHr1KhYuXAhDQ0OxoxGRCrFQUplQp04dnD59GsOGDcPIkSMxb948DkFXIhMTEzx58kTsGACAHTt2wMjICF999ZXYUcqUgoICLF++HM2bN8fFixcREhKCw4cPo3HjxmJHIyI1MBA7AJG6VKhQAYGBgbCyssKcOXNw5coV7N69G1WqVBE7mtYzMTHRiB3KdyeJhwwZgsqVK4sdp8yIjIzExIkTcfPmTUyfPh3e3t788ycqY7hDSWWKRCLBzJkzcfjwYURHR8PGxga3bt0SO5bW05RCGRsbi1u3bvGqRTV58uQJXFxc0KNHD1SvXh0XL17EqlWrWCaJyiAWSiqT+vXrh7i4OBQXF8Pa2hoRERFiR9JqmlIoZTIZGjRogG7duokdRacVFxfDz88PZmZmOHr0KAICAnDmzBm0bNlS7GhEJBIWSiqzzMzMEBcXB2tra/Tr1w++vr4cgv6JTExM8PTpU1HfS83NzcWePXvg6uoKPT1+tKnKhQsX0LFjR0yaNAlDhw7F9evX4e7uzj9zojKOnwBUpn322Wc4fPgwpk+fjmnTpuHrr79Gfn6+2LG0Tu3atVFcXIysrCzRMhw4cACvX7/m7EkVefXqFaZMmQJra2vk5+cjJiYGW7ZsQfXq1cWORkQagIWSyjx9fX2sWrUKMpkMO3bsQM+ePTXmxLK2eDfcXMw/t8DAQHTp0gVNmjQRLYMuEgQBu3fvhpmZGQIDA7Fq1SpcuHABnTp1EjsaEWkQFkqi37m6uiIqKgq3b9+GVCrFb7/9JnYkrSH2bTkPHjxAREQED+Mo2fXr19G7d2+MHDkStra2SElJwYwZM2BgwAEhRPQ+FkqiP7CxsUFiYiJq166NLl26YO/evWJH0gq1a9cGIF6h3LlzJwwNDTF06FBR1tc1ubm5WLhwIVq2bIm0tDQcPXoUwcHBqFevntjRiEhDsVAS/ckXX3yBM2fOwNHREc7Ozli0aBHkcrnYsTRaxYoVUbVqVVEKpSAICAwMhKOjI6pWrar29XXNkSNHYGlpiR9++AHz5s1DcnIy+vXrJ3YsItJw/N6C6G9UrFgRO3fuRKtWrTBv3jxcuXIFO3bsgLGxsdjRNJZY1y8mJCQgNTUVvr6+al9bl9y7dw/Tpk1DaGgoevXqhWPHjqFZs2ZixyIiLcEdSqJ/IJFI4OXlhfDwcJw6dQodO3bEnTt3xI6lscS6fjEwMBBffPEFevToofa1dUFhYSFWrVoFCwsLnD9/HkFBQTh+/DjLJBGVCgsl0b8YOHAg4uLikJ+fD6lUisjISLEjaSQxhpvn5eUhKCgILi4u0NfXV+vauiA6Ohpt27aFl5cXPD09kZqaCmdnZ0gkErGjEZGWYaEk+ggWFhaIi4tD27Zt0bt3b/j5+XEI+p+IUSgPHjyIFy9e8HR3KT179gzu7u6wtbWFkZEREhMTsXbtWt5rT0SfjIWS6CNVq1YNR48exeTJkzFp0iRMmDABBQUFYsfSGGIUSplMhg4dOsDc3Fyt62oruVyOLVu2wNzcHKGhodi4cSNiY2PRpk0bsaMRkZZjoSQqBQMDA6xduxYBAQHYunUrevXqhWfPnokdSyOYmJggKytLbSX78ePHOHbsGMaMGaOW9bRdUlISunTpgrFjx2LQoEG4fv06xo0bxysTiUgp+ElC9Anc3d0RGRmJ69evQyqVIikpSexIons3i/Lp06dqWW/Xrl0wMDCAs7OzWtbTVtnZ2Zg5cybatWuHV69eISoqCoGBgahVq5bY0YhIh7BQEn2izp07IyEhAdWqVUOnTp0QEhIidiRRqfP6xXezJwcPHozPP/9c5etpI0EQEBwcDHNzc2zatAk+Pj64dOkSunbtKnY0ItJBLJRECjA1NUV0dDQGDhyIIUOGwNvbu8wOQVfn9YsXL15EcnIyD+P8g1u3bqF///4YNmwYpFIprl27hrlz56JcuXJiRyMiHcXB5kQKqlSpEoKCgtCqVSssXLgQV65cgUwmQ+XKlcWOplY1a9aERCJRS6GUyWQwMTFBnz59VL6WNsnLy8OKFSuwfPlymJiYIDw8HIMGDRI7FhGVAdyhJFICiUSCBQsW4MCBAzh+/Dg6d+6M9PR0sWOpVbly5VCjRg2VF8qCggLs2rULo0aNgoEB/5v4nRMnTqBly5b47rvvMHPmTFy7do1lkojUhoWSSIkGDx6Mc+fOITs7G1KpFGfOnBE7klqp4/rFI0eOICsri193/+7hw4dwdnZGnz59UK9ePSQlJcHHxweVKlUSOxoRlSEslERK1qJFCyQkJKBly5bo2bMnNm/eLHYktVHH9YuBgYFo164dWrRoodJ1NF1RURF++uknmJub4/Tp09ixYwdOnToFCwsLsaMRURnEQkmkAtWrV8evv/6K8ePHY9y4cZg8eTIKCwvFjqVyqh5u/uzZMxw+fLjM706eP38eUqkUM2bMgIuLC1JTUzFq1ChemUhEomGhJFKRcuXK4eeff8amTZuwadMm9O3bF5mZmWLHUilVF8rdu3dDIpFgxIgRKltDkz1//hzjxo1Dp06doK+vj7i4OKxfv56jk4hIdCyURCo2duxYnDx5EleuXIG1tTWSk5PFjqQyqi6UMpkMAwcORI0aNVS2hiZ6N3fTzMwMQUFB+PnnnxEXFwepVCp2NCIiACyURGrRtWtXJCYmwtjYGB07dkRYWJjYkVTCxMQE2dnZyMnJUfqzL1++jIsXL5a5qxavXr2Kbt26wc3NDX369MH169cxadIk6Ovrix2NiKgECyWRmjRo0AAxMTHo27cvHBwc8O2330IQBLFjKdW76xdVcTBHJpOhZs2a6N+/v9KfrYlycnLg5eWF1q1b4+nTpzh58iR27dpVMkCeiEiTsFASqVHlypWxd+9eeHt7Y9GiRRg+fDjevn0rdiylUdX1i4WFhdi1axdGjhyp87e9CIKAAwcOwMLCAr6+vvD29kZSUhJ69OghdjQion/EQkmkZnp6eli8eDFCQkJw6NAhdOnSBRkZGWLHUgpVXb/466+/4smTJzp/ujstLQ2DBg2Co6MjWrZsiatXr2LBggUwNDQUOxoR0QexUBKJxMnJCbGxsXj+/DmkUiliYmLEjqSwatWqwcDAQOmFUiaToVWrVmjdurVSn6spCgoK4OPjA0tLSyQlJWH//v04dOgQGjduLHY0IqKPwkJJJCIrKyskJCTA3NwcdnZ22Lp1q9iRFKKnp6f023KeP3+O8PBwnT2MExkZCSsrKyxevBiTJ09GSkoKHB0dOVOSiLQKCyWRyGrWrIkTJ07A3d0dHh4emDZtGoqKisSO9cmUXSiDgoJQXFyM//znP0p7piZ48uQJRo0ahR49eqBGjRq4ePEiVq5cicqVK4sdjYio1FgoiTRA+fLlsXHjRvj5+cHPzw/9+/fH8+fPxY71SZQ9izIwMBADBgwoOUGu7YqLi+Hn5wczMzMcO3YMW7duRVRUVJm/SpKItBsLJZEGmTBhAo4fP46LFy/C2toa165dEztSqSnzPu+UlBQkJCTozGGcxMRE2NjYYNKkSRg2bBiuX78ONzc36Onxo5iItBs/xYg0jJ2dHeLj41GxYkXY2Njg0KFDYkcqFWXuUMpkMlSrVg0DBw5UyvPE8vLlS0yePBnW1tYoLCxEbGwsNm/ejOrVq4sdjYhIKVgoiTRQ48aNERsbi549e8Le3h4rVqzQmiHo7wqlonmLi4uxY8cOjBgxQmvH5giCgF27dsHc3BwymQxr1qxBYmIiOnbsKHY0IiKlYqEk0lDGxsYICQnBwoUL8d///hejRo1Cbm6u2LH+lYmJCfLz8/Hq1SuFnhMREYGHDx9q7dfdqamp6NWrF0aNGoWuXbsiNTUV06dPh4GBgdjRiIiUjoWSSIPp6elh6dKl2LNnD0JDQ2Fra4v79++LHeuD3h2eUfRr78DAQDRv3hzt27dXRiy1efv2LRYsWIBWrVrh7t27OHbsGPbu3YsvvvhC7GhERCrDQkmkBYYNG4aYmBg8ffoUUqkU58+fFzvSP1LG9YsvX77EgQMHMHr0aK2ax3j48GFYWlpi1apVmD9/PpKTk9G3b1+xYxERqRwLJZGWaNOmDRISEvDll1+iW7dukMlkYkf6W8q4fnHv3r0oKCjAqFGjlBVLpTIyMuDo6IiBAweiWbNmSE5Ohre3NypUqCB2NCIitWChJNIitWvXxsmTJ+Hi4oIxY8Zg1qxZGjcE3djYGBUrVlSoUMpkMvTp0wd169ZVYjLlKywsxA8//AALCwvExcVhz549OHbsGJo2bSp2NCIiteLb4URaxtDQEFu2bIGVlRVmzJiBq1evIigoCJ999pnY0QAAEolEodFBN2/eRGxsLIKCgpScTLnOnj2LCRMmICUlBVOnTsWSJUtQpUoVsWMREYmCO5REWkgikWDKlCk4duwY4uPj0aFDB1y/fl3sWCUUuX5RJpOhatWqGDx4sJJTKcezZ8/g5uaGrl27wtjYGBcuXMCPP/7IMklEZRoLJZEW69WrF+Lj42FgYIAOHTrg6NGjYkcC8OnDzeVyObZv347hw4dr3PuHcrkcmzdvhpmZGcLCwrBp0ybExMSgdevWYkcjIhIdCyWRlmvSpAnOnTsHW1tbDBw4EKtXrxZ9CPqnXr8YGRmJe/fuadzsyUuXLqFz584YN24cBg8ejOvXr2Ps2LG8MpGI6Hf8NCTSAVWqVMGBAwfg5eWF2bNnY/To0cjLyxMtz6fuUMpkMjRt2hQ2NjYqSFV6r1+/xvTp09GuXTtkZ2fjzJkz2LZtG2rWrCl2NCIijcJCSaQj9PX14ePjg927dyM4OBjdunXDw4cPRcliYmKCp0+fori4+KN/TXZ2NkJCQjBmzBjRZ08KgoC9e/fCwsICW7Zswffff4+LFy/C1tZW1FxERJqKhZJIx4wYMQJnz57FgwcPIJVKkZCQoPYMJiYmKC4uRlZW1kf/mn379iE3NxcuLi4qTPbvbt26hX79+sHZ2RkdOnRASkoK5syZg3Llyomai4hIk7FQEumg9u3bIyEhAaamprC1tcXOnTvVuv6nXL8YGBiInj17on79+qqK9UF5eXnw9vZGixYtcOPGDRw8eBD79++HqampKHmIiLQJCyWRjqpTpw4iIyMxYsQIuLi4wMvLq1RfQSuitNcvpqWl4cyZM6Idxvn111/RokUL+Pj4YNasWbh69SoGDhwoShYiIm3EQkmkwypUqICtW7dizZo1WLVqFezt7fHq1SuVr1vaHcrt27fD2NgYjo6Oqoz1Fw8ePMCwYcPQr18/mJqa4vLly/juu+9QqVIlteYgItJ2LJREOk4ikWDGjBk4cuQIYmJiYGNjg5s3b6p0zYoVK6Jq1aofVSjlcjlkMhmGDh0KIyMjleZ6p6ioCGvXroW5uTnOnDmDnTt34uTJkzA3N1fL+kREuoaFkqiM6Nu3L+Lj4yEIAqytrXHixAmVrvexo4Oio6ORlpaGMWPGqDTPO+fOnUP79u0xc+ZMjB49GqmpqRg5cqToJ8uJiLQZCyVRGdKsWTPExcWhY8eO6NevH9auXauyIegfWygDAwPRuHFjdOnSRSU53nn+/DnGjh2LTp06wcDAAPHx8Vi3bp3G3IFORKTNWCiJypiqVavi4MGDmDVrFmbMmAEPDw/k5+crfZ2Puc87JycHwcHBcHV1VdkOoVwux7Zt22BmZoa9e/di/fr1iIuLQ/v27VWyHhFRWcRCSVQG6evrY+XKldixYwd2794NOzu7T7rZ5kM+5vrF/fv3482bN3B1dVXq2u8kJyejW7ducHd3R79+/ZCamoqJEydCX19fJesREZVVLJREZdioUaNw5swZpKenQyqV4sKFC0p79sd85S2TydCtWzc0atRIaesCwJs3bzBnzhy0bt0amZmZOHXqFHbs2FEyzoiIiJSLhZKojLO2tkZiYiLq1KmDLl26ICgoSCnPNTExQVZWFgoKCv725xkZGTh16pRSD+MIgoDQ0FA0b94c69atw9KlS5GUlAQ7OzulrUFERH/FQklEqFu3LqKiojBkyBCMGDECCxYsgFwuV+iZ73YDnz59+rc/37FjBypWrIivvvpKoXXeSUtLw6BBg+Dk5IRWrVrh2rVrmD9/PsqXL6+U5xMR0T9joSQiAP+bHbl9+3asXLkSy5cvh4ODA16/fv3Jz/vQcHNBECCTyTBkyBAYGxt/8hoAkJ+fj++++w7NmzfH5cuXERoaioMHDyr9a3QiIvpnLJREVEIikWDOnDk4dOgQoqKi0KlTJ9y+ffuTnvWh6xfPnTuHmzdvKnzV4qlTp2BlZQVvb29MnToV165dg4ODA2dKEhGpGQslEf3FgAEDEBcXh4KCAlhbW+PkyZOlfkbNmjUhkUj+dodSJpPB1NQU3bt3/6R8jx8/xsiRI9GzZ0/UqlULFy9exIoVK1C5cuVPeh4RESmGhZKI/pa5uTni4uLQrl079O3bF+vWrSvVEPRy5cqhhskXuPrgFS5mvMDVh6+Qk1+E3NxcBAUFwdXVFXp6pfsIKi4uxvr162Fubo7jx48jMDAQUVFRaNGiRWl/e0REpEQSQVXXZBCRTigqKsLcuXPx448/4uuvv8a6des+eNDl5pNs7IrLQOT1p7iblQP84etnCYDPy8uRHnsIe7+bgl7WH18EExMTMX78eFy4cAFjx47F8uXLUa1aNUV+a0REpCQslET0UbZt24bx48fD2toaISEhqFWr1ns/v/f8LeaHXsHZW5nQ15OgWP6BjxZBDkj0YNukBnwcW6J+tUr/+Je+fPkSCxYswIYNG9CqVSts3LgRNjY2yvptERGRErBQEtFHi42NhZOTEwwNDREWFobWrVsDAIISMrA4/CqK5MKHi+Sf6OtJYKAnwRJ7SwyXmr73M0EQsGvXLsyaNQu5ublYtmwZJk2aBAMDA2X+loiISAlYKImoVO7duwdHR0ekpKRAJpPhcXUrrDp+Q+Hnzu7TDJPtmgIAUlJSMHHiRJw+fRrOzs5Ys2YN6tatq/AaRESkGiyURFRqb9++hYeHBw5de47qA6Yq7bnLBpoh5fBWrFq1Cg0aNMD69evRp08fpT2fiIhUg4WSiD5JRlYO7FadQpEgUdLcRwEoKsQz2TTMneQBLy8vVKhQQQnPJSIiVWOhJKJP4hIQh9g7WaV6Z/JfCXK0q1cZIZN59zYRkTbhHEoiKrWbT7Jx9lamcsskAEj0cOHBW9x6mq3c5xIRkUqxUBJRqe2Ky4C+nmquN9TXk2Dn+QyVPJuIiFSDhZKISi3y+lPl707+rlguIPLGU5U8m4iIVIOFkohK5U1+ETKev1XpGhlZb5GTX6TSNYiISHlYKImoVO5m5UDVJ/kEAOlZOSpehYiIlIWFkohKpaBIrlPrEBGR4lgoiahUyhuo52NDXesQEZHi+IlNRKXSsLoRVHO++/+T/L4OERFpBxZKIioVI0MDmFarpNI1TKtXgpGhgUrXICIi5WGhJKJSszOrpdI5lHbNaqnk2UREpBoslERUaiM7mKp0DuUoG1OVPJuIiFSDhZKISq1pbWPYNqmh9F1KfT0JbJvUQJNaxkp9LhERqRYLJRF9Eh/HljBQcqE00JPAx7GlUp9JRESqx0JJRJ+kfrVKWGJvqdRnLrW3RH0VH/ghIiLlY6Ekok82XGqK2X2aKeVZc/qYwVnKdyeJiLSRRBAEVd+iRkQ6LighA4vDr6JILpTqsI6+ngQGehIstbdkmSQi0mIslESkFPeev8X80Cs4eysT+nqSDxbLdz+3bVIDPo4t+TU3EZGWY6EkIqW6+SQbu+IyEHnjKTKy3uKPHzAS/G9ouV2zWhhlY8rT3EREOoKFkohUJie/COlZOSgokqO8gR4aVjfiDThERDqIhZKIiIiIFMJT3kRERESkEBZKIiIiIlIICyURERERKYSFkoiIiIgUwkJJRERERAphoSQiIiIihbBQEhEREZFCWCiJiIiISCEslERERESkEBZKIiIiIlIICyURERERKYSFkoiIiIgUwkJJRERERAphoSQiIiIihbBQEhEREZFCWCiJiIiISCEslERERESkEBZKIiIiIlIICyURERERKYSFkoiIiIgUwkJJRERERAphoSQiIiIihbBQEhEREZFCWCiJiIiISCEslERERESkEBZKIiIiIlIICyURERERKYSFkoiIiIgUwkJJRERERAphoSQiIiIihbBQEhEREZFCWCiJiIiISCEslERERESkEBZKIiIiIlIICyURERERKYSFkoiIiIgUwkJJRERERAr5fx91dCeelviRAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1695,14 +1740,21 @@
    "source": [
     "import networkx as nx\n",
     "\n",
-    "Graph = op.get_graph(edge_relation='C')\n",
+    "Graph = op.get_graph(edge_relation='C', # <- can change to ['AC', 'QWC']\n",
+    "                     label_nodes=True)\n",
     "\n",
-    "nx.draw(Graph)"
+    "nx.draw(Graph,\n",
+    "        with_labels = True,\n",
+    "        alpha=0.75,\n",
+    "        node_color=\"skyblue\",\n",
+    "        width=0.1,\n",
+    "        node_size=750\n",
+    "        )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "a15395bf",
    "metadata": {
     "collapsed": false,
@@ -1785,7 +1837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "id": "93d0bcb0",
    "metadata": {
     "collapsed": false,
@@ -1834,7 +1886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "id": "9d7d0292",
    "metadata": {
     "collapsed": false,
@@ -1857,7 +1909,7 @@
        " 1.000+0.000j XII"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1884,7 +1936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "id": "9d436161",
    "metadata": {
     "collapsed": false,
@@ -1941,7 +1993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "id": "6fc9dc51",
    "metadata": {
     "collapsed": false,
@@ -1959,7 +2011,7 @@
        " 1.000+0.000j II"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1986,7 +2038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "id": "bec8807c-e91d-4ce7-9ffd-0af842a77a18",
    "metadata": {
     "pycharm": {
@@ -2035,7 +2087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "id": "149a6df1-8d99-40a3-a655-34d7ae893c64",
    "metadata": {
     "pycharm": {
@@ -2049,7 +2101,7 @@
        " 1.000+0.000j II"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2088,7 +2140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "id": "9aea1941-e1f3-4d90-92de-66c2c6b2ef84",
    "metadata": {
     "pycharm": {
@@ -2129,7 +2181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "id": "7cf332a5-cf05-4098-9403-1d55c1a933a7",
    "metadata": {
     "pycharm": {
@@ -2165,7 +2217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "id": "2fe1b460-74f5-428a-a41a-f49b4f85153d",
    "metadata": {
     "pycharm": {
@@ -2230,7 +2282,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/1_Basic_Usage/1.2 QuantumState Usage.ipynb b/docs/source/notebooks/1_Basic_Usage/1.2 QuantumState Usage.ipynb
index 15aff273..600abe5b 100644
--- a/docs/source/notebooks/1_Basic_Usage/1.2 QuantumState Usage.ipynb	
+++ b/docs/source/notebooks/1_Basic_Usage/1.2 QuantumState Usage.ipynb	
@@ -93,7 +93,16 @@
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lex/anaconda3/envs/symmer_github/lib/python3.9/site-packages/cotengra/hyperoptimizers/hyper.py:34: UserWarning: Couldn't import `kahypar` - skipping from default hyper optimizer and using basic `labels` method instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "from symmer import QuantumState"
    ]
@@ -244,7 +253,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lex/anaconda3/envs/sym/lib/python3.8/site-packages/symmer/operators/base.py:2091: UserWarning: statevector is not normalized\n",
+      "/Users/lex/anaconda3/envs/symmer_github/lib/python3.9/site-packages/symmer/operators/base.py:2184: UserWarning: statevector is not normalized\n",
       "  warnings.warn(f'statevector is not normalized')\n"
      ]
     },
@@ -463,7 +472,7 @@
     }
    },
    "source": [
-    "Note this method doesn't allow bras to be defined."
+    "Note this method does NOT allow bras to be defined."
    ]
   },
   {
@@ -490,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "2837e728",
    "metadata": {
     "collapsed": false,
@@ -511,7 +520,7 @@
        " 100.000+0.000j |11>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "ef807a93",
    "metadata": {
     "collapsed": false,
@@ -582,7 +591,7 @@
        " 0.500+0.000j |11>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,12 +630,14 @@
     }
    },
    "source": [
-    "Importantly this is **NOT** the same as `normalize`"
+    "```{note}\n",
+    "Importantly this is **NOT** the same as `normalize`\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "e3dafd8d",
    "metadata": {
     "collapsed": false,
@@ -647,7 +658,7 @@
        " 0.426+0.000j |11>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -777,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "1617baa1",
    "metadata": {
     "collapsed": false,
@@ -792,13 +803,13 @@
     {
      "data": {
       "text/plain": [
-       " 0.320+0.454j |00> +\n",
-       " 0.093+0.268j |01> +\n",
-       " 0.095+0.071j |10> +\n",
-       " 0.568+0.524j |11>"
+       " 0.245+0.112j |00> +\n",
+       " 0.339+0.740j |01> +\n",
+       " 0.138+0.025j |10> +\n",
+       " 0.242+0.432j |11>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -834,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "30693c48",
    "metadata": {
     "collapsed": false,
@@ -849,17 +860,17 @@
     {
      "data": {
       "text/plain": [
-       " 0.281-0.316j |000> +\n",
-       " 0.230+0.351j |001> +\n",
-       "-0.064-0.030j |010> +\n",
-       "-0.256+0.232j |011> +\n",
-       "-0.274-0.343j |100> +\n",
-       " 0.031+0.348j |101> +\n",
-       " 0.297+0.290j |110> +\n",
-       " 0.029-0.179j |111>"
+       " 0.196-0.670j |000> +\n",
+       " 0.093+0.326j |001> +\n",
+       "-0.009-0.058j |010> +\n",
+       " 0.311-0.164j |011> +\n",
+       "-0.055-0.199j |100> +\n",
+       " 0.023-0.147j |101> +\n",
+       " 0.159-0.367j |110> +\n",
+       " 0.006-0.212j |111>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -889,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "ef61051e",
    "metadata": {
     "collapsed": false,
@@ -907,7 +918,7 @@
        " 1.000+0.000j |000>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -952,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "5912abec",
    "metadata": {
     "collapsed": false,
@@ -968,13 +979,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 0.263+0.296j |01> +\n",
-      " 0.633+0.473j |10> +\n",
-      " 0.310+0.351j |11>\n",
+      " 0.265+0.270j |01> +\n",
+      " 0.633+0.410j |10> +\n",
+      " 0.227+0.486j |11>\n",
       "\n",
-      " 0.263-0.296j <01| +\n",
-      " 0.633-0.473j <10| +\n",
-      " 0.310-0.351j <11|\n"
+      " 0.265-0.270j <01| +\n",
+      " 0.633-0.410j <10| +\n",
+      " 0.227-0.486j <11|\n"
      ]
     }
    ],
@@ -1008,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "4229cfaa",
    "metadata": {
     "collapsed": false,
@@ -1023,10 +1034,10 @@
     {
      "data": {
       "text/plain": [
-       "(0.9999999999999998+0j)"
+       "(1+0j)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1053,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "86ba0e52",
    "metadata": {
     "collapsed": false,
@@ -1069,7 +1080,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "overalp unormalized: (30+0j)\n",
+      "overlap unormalized: (30+0j)\n",
       "using overlap to renormalize: True\n"
      ]
     },
@@ -1077,7 +1088,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/lex/anaconda3/envs/sym/lib/python3.8/site-packages/symmer/operators/base.py:2091: UserWarning: statevector is not normalized\n",
+      "/Users/lex/anaconda3/envs/symmer_github/lib/python3.9/site-packages/symmer/operators/base.py:2184: UserWarning: statevector is not normalized\n",
       "  warnings.warn(f'statevector is not normalized')\n"
      ]
     },
@@ -1090,7 +1101,7 @@
        " 0.730+0.000j |11>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1104,7 +1115,7 @@
     "psi_ket = QuantumState.from_array(ket_unormalized)\n",
     "\n",
     "overlap = psi_ket.dagger * psi_ket\n",
-    "print('overalp unormalized:', overlap)\n",
+    "print('overlap unormalized:', overlap)\n",
     "\n",
     "# This can however be used to re-normalize a quantum state\n",
     "psi_ket_normed = psi_ket * (1/np.sqrt(overlap))\n",
@@ -1132,7 +1143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "d0b0ee1a",
    "metadata": {
     "collapsed": false,
@@ -1147,10 +1158,10 @@
     {
      "data": {
       "text/plain": [
-       "(0.2874816901032643-0.05516269673460425j)"
+       "(0.5068453030977333-0.0423240580868338j)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1214,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "ff5380fc",
    "metadata": {
     "collapsed": false,
@@ -1232,18 +1243,18 @@
      "text": [
       "Generate a random Hermitian operator:\n",
       "\n",
-      " 1.077+0.000j IXIZ +\n",
-      "-0.104+0.000j IIYY +\n",
-      " 1.422+0.000j ZIII +\n",
-      "-1.211+0.000j XIYX +\n",
-      " 0.724+0.000j XIXI +\n",
-      " 0.953+0.000j IIXI +\n",
-      "-0.818+0.000j IZII +\n",
-      "-0.411+0.000j XZXI +\n",
-      " 1.194+0.000j IZYZ +\n",
-      "-0.286+0.000j IIZX\n",
+      " 0.899+0.000j IIII +\n",
+      "-0.063+0.000j IZII +\n",
+      "-0.052+0.000j XXII +\n",
+      "-0.460+0.000j ZZIZ +\n",
+      " 0.744+0.000j XIIZ +\n",
+      " 0.716+0.000j XXZI +\n",
+      " 0.101+0.000j IZXY +\n",
+      "-0.438+0.000j XYII +\n",
+      "-0.849+0.000j YXIX +\n",
+      "-1.849+0.000j IXZZ\n",
       "\n",
-      "Expectation value <psi|op|psi> = -1.0369313079409164\n"
+      "Expectation value <psi|op|psi> = 1.0628769377122658\n"
      ]
     }
    ],
@@ -1266,23 +1277,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72ef5f18",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "id": "b6072492-9baf-4dd0-8dbc-a1f0885cbe1c",
+   "metadata": {},
    "source": [
-    "Note here that psi is the same on both sides. This means we can use the `PauliwordOp.expval` for a faster measurement:"
+    "```{note}\n",
+    "Note here that psi is the same on both sides. This means we can use the `PauliwordOp.expval` for a faster measurement:\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "de05e626",
    "metadata": {
     "collapsed": false,
@@ -1294,18 +1299,11 @@
     }
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-08-25 14:56:15,843\tINFO worker.py:1621 -- Started a local Ray instance.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Expectation value <psi|op|psi> = (-1.036931307940916+0j)\n"
+      "Expectation value <psi|op|psi> = 1.0628769377122658\n"
      ]
     }
    ],
@@ -1336,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "id": "1ff45d13",
    "metadata": {
     "collapsed": false,
@@ -1352,7 +1350,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Expectation value <psi_1|op|psi_2> = -0.2929390180505392\n"
+      "Expectation value <psi_1|op|psi_2> = 0.4508779352637781\n"
      ]
     }
    ],
@@ -1401,7 +1399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 29,
    "id": "e4bae4c6",
    "metadata": {
     "collapsed": false,
@@ -1421,7 +1419,7 @@
       "\n",
       "op =   1.000+0.000j XXI\n",
       "\n",
-      "psi' = op * psi =   1.000-0.000j |110>\n"
+      "psi' = op * psi =  1.000-0.000j |110>\n"
      ]
     }
    ],
@@ -1434,7 +1432,7 @@
     "\n",
     "print()\n",
     "psi_prime = op * psi\n",
-    "print('psi\\' = op * psi = ', psi_prime)"
+    "print('psi\\' = op * psi =', psi_prime)"
    ]
   },
   {
@@ -1457,7 +1455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 30,
    "id": "40ea4ac0",
    "metadata": {
     "collapsed": false,
@@ -1521,7 +1519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 31,
    "id": "bbe16a57",
    "metadata": {
     "collapsed": false,
@@ -1542,7 +1540,7 @@
        " 16.000+0.000j |1111>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1577,7 +1575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 32,
    "id": "744d4afe",
    "metadata": {
     "collapsed": false,
@@ -1601,7 +1599,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 320x240 with 1 Axes>"
       ]
@@ -1633,7 +1631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 33,
    "id": "31bff1fa",
    "metadata": {
     "collapsed": false,
@@ -1657,7 +1655,7 @@
     },
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 320x240 with 1 Axes>"
       ]
@@ -1693,7 +1691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 34,
    "id": "a92ee070",
    "metadata": {
     "collapsed": false,
@@ -1714,7 +1712,7 @@
        " 0.548+0.000j |1111>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1742,7 +1740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 35,
    "id": "91a92536",
    "metadata": {
     "collapsed": false,
@@ -1759,20 +1757,20 @@
      "output_type": "stream",
      "text": [
       "With n_samples=1         , <psi_sampled|psi_true> = (0.565685424949238+0j)\n",
-      "With n_samples=4         , <psi_sampled|psi_true> = (0.939834563766817+0j)\n",
-      "With n_samples=16        , <psi_sampled|psi_true> = (0.9920789746996936+0j)\n",
-      "With n_samples=64        , <psi_sampled|psi_true> = (0.9980780654610442+0j)\n",
-      "With n_samples=256       , <psi_sampled|psi_true> = (0.9998086542387652+0j)\n",
-      "With n_samples=1024      , <psi_sampled|psi_true> = (0.9998076901702848+0j)\n",
-      "With n_samples=4096      , <psi_sampled|psi_true> = (0.99989709263263+0j)\n",
-      "With n_samples=16384     , <psi_sampled|psi_true> = (0.9999938107198831+0j)\n",
-      "With n_samples=65536     , <psi_sampled|psi_true> = (0.9999994619112661+0j)\n",
-      "With n_samples=262144    , <psi_sampled|psi_true> = (0.9999992264077784+0j)\n",
-      "With n_samples=1048576   , <psi_sampled|psi_true> = (0.9999992217148499+0j)\n",
-      "With n_samples=4194304   , <psi_sampled|psi_true> = (0.9999999281758942+0j)\n",
-      "With n_samples=16777216  , <psi_sampled|psi_true> = (0.9999999698532791+0j)\n",
-      "With n_samples=67108864  , <psi_sampled|psi_true> = (0.9999999957903308+0j)\n",
-      "With n_samples=268435456 , <psi_sampled|psi_true> = (0.9999999978895546+0j)\n"
+      "With n_samples=4         , <psi_sampled|psi_true> = (0.799070478491457+0j)\n",
+      "With n_samples=16        , <psi_sampled|psi_true> = (0.9274672813718126+0j)\n",
+      "With n_samples=64        , <psi_sampled|psi_true> = (0.9998560311499007+0j)\n",
+      "With n_samples=256       , <psi_sampled|psi_true> = (0.9995171613100531+0j)\n",
+      "With n_samples=1024      , <psi_sampled|psi_true> = (0.9998474493807042+0j)\n",
+      "With n_samples=4096      , <psi_sampled|psi_true> = (0.9998306893940146+0j)\n",
+      "With n_samples=16384     , <psi_sampled|psi_true> = (0.9999579174180465+0j)\n",
+      "With n_samples=65536     , <psi_sampled|psi_true> = (0.9999857884510102+0j)\n",
+      "With n_samples=262144    , <psi_sampled|psi_true> = (0.9999988550667571+0j)\n",
+      "With n_samples=1048576   , <psi_sampled|psi_true> = (0.9999996869616397+0j)\n",
+      "With n_samples=4194304   , <psi_sampled|psi_true> = (0.9999999782683482+0j)\n",
+      "With n_samples=16777216  , <psi_sampled|psi_true> = (0.9999999574731135+0j)\n",
+      "With n_samples=67108864  , <psi_sampled|psi_true> = (0.9999999945852163+0j)\n",
+      "With n_samples=268435456 , <psi_sampled|psi_true> = (0.9999999993936086+0j)\n"
      ]
     }
    ],
@@ -1800,7 +1798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 36,
    "id": "0507a7e8",
    "metadata": {
     "collapsed": false,
@@ -1816,14 +1814,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 0.380-0.321j |000> +\n",
-      "-0.026+0.389j |001> +\n",
-      " 0.414+0.032j |010> +\n",
-      " 0.434-0.036j |011> +\n",
-      " 0.003+0.187j |100> +\n",
-      "-0.004+0.170j |101> +\n",
-      " 0.233-0.089j |110> +\n",
-      "-0.224+0.251j |111>\n"
+      " 0.422-0.152j |000> +\n",
+      "-0.018-0.111j |001> +\n",
+      " 0.174+0.075j |010> +\n",
+      "-0.208+0.157j |011> +\n",
+      "-0.159-0.257j |100> +\n",
+      "-0.130+0.190j |101> +\n",
+      "-0.015+0.476j |110> +\n",
+      "-0.339+0.442j |111>\n"
      ]
     }
    ],
@@ -1835,7 +1833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 37,
    "id": "3753c2b5",
    "metadata": {
     "collapsed": false,
@@ -1849,7 +1847,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1864,7 +1862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 38,
    "id": "2390a4c1",
    "metadata": {
     "collapsed": false,
@@ -1878,7 +1876,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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iqWv69Ok4efKk678nnnjCq+dKEETdRFD8gyDMhR4v8/C7gjRnzhyMGjUKI0aMQIcOHbBgwQJERkZi4cKFzPKvv/46+vfvj/Hjx6N9+/Z48cUX0b17d7z55psAqqxHc+fOxeTJk3HnnXeic+fO+Pjjj3HixAmsWLFCUleDBg0QGxvr+q9evXrePl2CCHgozsgcxNeRrilBWA+/KkhlZWXYsmULUlNTXdvsdjtSU1ORlZXFPCYrK0tSHgDS0tJc5Q8dOoTc3FxJmaioKCQnJ7vV+corr6Bp06bo1q0bXn31VVRUKGf/LS0tRVFRkeQ/giAI3di0ixAE4X+C/dn4mTNnUFlZiZiYGMn2mJgY7N27l3lMbm4us3xubq5rv3ObUhkAePLJJ9G9e3c0adIEGzZswMSJE3Hy5EnMmTOH2e7MmTPxwgsv6DtBgqiFUOyMfgTGnH7JxDa6pgRhOQwpSAcPHsSiRYtw8OBBvP7662jWrBl++OEHtGrVCldddZXZMnqFjIwM1+/OnTsjNDQUDz/8MGbOnImwsDC38hMnTpQcU1RUhPj4eJ/IShD+hnIfEUSAQK+qaeh2sf3yyy/o1KkTsrOzsXz5cly4cAEAsGPHDkybNk1XXdHR0QgKCkJeXp5ke15eHmJjY5nHxMbGqpZ3/qunTgBITk5GRUUFDh8+zNwfFhaGhg0bSv4jCIIgCKJ2oltBmjBhAmbMmIHVq1cjNDTUtf3GG2/EH3/8oauu0NBQ9OjRA5mZma5tDocDmZmZSElJYR6TkpIiKQ8Aq1evdpVPTExEbGyspExRURGys7MV6wSA7du3w263o1mzZrrOgSDqAjTN3zNYQdiUSZsgrI1uF9vOnTuxePFit+3NmjXDmTNndAuQkZGB4cOHo2fPnujVqxfmzp2L4uJijBgxAgAwbNgwtGjRAjNnzgQAjB07Fv369cPs2bMxYMAALFmyBJs3b8a7774LoKojHzduHGbMmIGkpCQkJiZiypQpiIuLQ3p6OoCqQO/s7GzccMMNaNCgAbKysvDUU0/hvvvuQ+PGjXWfA0EQBEEQtQvdClKjRo1w8uRJJCYmSrZv27YNLVq00C3AoEGDcPr0aUydOhW5ubno2rUrVq1a5QqyzsnJgd1eY+jq06cPFi9ejMmTJ2PSpElISkrCihUr0LFjR1eZZ599FsXFxRg9ejQKCgrQt29frFq1CuHh4QCq3GVLlizB888/j9LSUiQmJuKpp56SxBgRBMGGpqSbgziui2K8CMJ66FaQBg8ejOeeew7Lli2DzWaDw+HA+vXr8cwzz2DYsGGGhBgzZgzGjBnD3Ldu3Tq3bQMHDsTAgQMV67PZbJg+fTqmT5/O3N+9e3fd7kCCIKqgsVw/dM0IIvDQHYP08ssvo127doiPj8eFCxfQoUMHXHfddejTpw8mT57sDRkJgiBqD7TUCOFFyMJrHrotSKGhoXjvvfcwZcoU7Nq1CxcuXEC3bt2QlJTkDfkIgrAY1P16COMCkrJEENbDcKLIVq1aoVWrVmbKQhAEQRAEYQm4FCQ9wctKmagJ63GhtAJ5RSW4/LL6/haFCCAooNhDaKkRgggIuBSkbdu2Sf7eunUrKioq0LZtWwDA33//jaCgIPTo0cN8CQmvcd2stcgvLsO3Y/qiU8sof4tDELUW1lIjBEFYGy4Fae3ata7fc+bMQYMGDfDRRx+5cgadO3cOI0aMwLXXXusdKQmvkF9cBgDI3JtHChLBDY3v5kCGOIKwNrpnsc2ePRszZ86UJFRs3LgxZsyYgdmzZ5sqHEEQ1oMGdg9huNjomhJmQc+SeehWkIqKinD69Gm37adPn8b58+dNEYogCKI2IZl6LTC2EQRhOXQrSHfddRdGjBiB5cuX49ixYzh27Bi+/PJLjBw5Enfffbc3ZCQIwkrQuG46pCwRhPXQPc1/wYIFeOaZZ/Cf//wH5eXlVZUEB2PkyJF49dVXTReQIAiiVkGz2AgiINCtIEVGRuKtt97Cq6++ioMHDwIALr/8ctSrV8904QiCsB5WsHa89+s/yNybh0UP9EJEaJC/xdGENYuNYkUIb0CPlXkYThRZr149dO7c2UxZCIIIAKwwsL+0cg8AYPHGHIzsm6hR2vpY4ZoSBCFFt4J0ww03wGZTthGvWbPGI4EIgiB4KSmv9LcIXEj0HxtjG0EQlkO3gtS1a1fJ3+Xl5di+fTt27dqF4cOHmyUX4UPo65XQQvxJZKXHpbZk9a4dZ0FYDUEQVA0ahDq6FaTXXnuNuf3555/HhQsXPBaIIAjrYdUBPJD1o9qi3BFEbUX3NH8l7rvvPixcuNCs6giCsCg0sOtH65rRNSW8AT1WnmGagpSVlYXw8HCzqiMIwqJQn0sQ1oWUIvPQ7WKTJ4MUBAEnT57E5s2bMWXKFNMEIwiC0CKQx4JAlp0IDOgZ8wzdClLDhg0lQV92ux1t27bF9OnTcfPNN5sqHEEQ1kDsAqIvVP1oXTK6pARhPXQrSB9++KEXxCAIgtBPICtrgSw7ERhUfdjQLDaj6I5BatOmDc6ePeu2vaCgAG3atDFFKIIgrIU0EbTyyE6DvjHouhGE9dCtIB0+fBiVle7J2UpLS3H8+HFThCIIwsJYaDC3wrInPLAVoMCQnQhc6AnzDG4X2zfffOP6/eOPPyIqKsr1d2VlJTIzM5GQkGCqcARBWAOycBBEYGD2R8PR/IsY9E4WHuybiIeurVteIm4FKT09HQBgs9ncMmaHhIQgISEBs2fPNlU4giCsgsD45X9qj+JWa06EsBBmvB8vr9yDE4UlmPH9HlKQlHA4HACAxMREbNq0CdHR0V4TiiAIolbBGKhqj3JH1GYqHHX3QdU9i+3QoUPekIMgCAsjHsytNLBbSBSPsNI1JWoPZrjb6vKzyaUgzZs3D6NHj0Z4eDjmzZunWvbJJ580RTCCIIjaAmugqsPjDkEEBFwK0muvvYahQ4ciPDxccbFaoCo+iRQkgqh98E7z9zl1+fOWIDQw5/Wou+8Yl4IkdquRi40g6jZW0kksJIpuJG5L/4lB1DLMfj+t9L77GtMWqyUIovZi1U7SqnLJCRQ5CUJOXX50uSxIGRkZ3BXOmTPHsDAEQVifutxhegtSoAjCenApSNu2beOqTLyILUEQtQfBoiO4peKhdBLIshOBgRmvrVXffV/ApSCtXbvW23IQRJ2g4GIZjp27hI4torQLW5S63GEaha5Y4LHzWCGO5Bfjts5x/haF8BO68yCJOXr0KAAgPj7eFGEIorbTe2YmSsod+PLRPujRurG/xeFGMovNQqO9lWTRizRIO4BPpJZy+5u/AwBaNIpAt1aB866KMSUPkglyBCq6g7QrKiowZcoUREVFISEhAQkJCYiKisLkyZNRXl7uDRkJotZQUl6Vkf6Xfaf8LIk+AlkRIQhPOHDqgr9F0IXZr2pdfvd1W5CeeOIJLF++HLNmzUJKSgoAICsrC88//zzOnj2Lt99+23QhCe9Sh59/v0HX3BwC5Tqy3JJWzU5O1B5MiUHyvIqARbeCtHjxYixZsgS33HKLa1vnzp0RHx+PIUOGkIJEELUQcgERdRWafFR30e1iCwsLQ0JCgtv2xMREhIaGmiETQfiUtXtP4a8ThT5tM5AtBlaS3UqyqMESk5TOwCCQJyUEruTWQLeCNGbMGLz44osoLS11bSstLcVLL72EMWPGGBJi/vz5SEhIQHh4OJKTk7Fx40bV8suWLUO7du0QHh6OTp06YeXKlZL9giBg6tSpaN68OSIiIpCamor9+/cz6yotLUXXrl1hs9mwfft2Q/ITgcv+vPMY8eEmDJj3u79FsTYUUOxVTp0vxeiPN+O3/af9LYppXCqrxHd/nkBRCcWm+hSTFbpAVhA9RbeCtG3bNnz33Xdo2bIlUlNTkZqaipYtW+Lbb7/Fjh07cPfdd7v+42Hp0qXIyMjAtGnTsHXrVnTp0gVpaWk4dYodxLphwwYMGTIEI0eOxLZt25Ceno709HTs2rXLVWbWrFmYN28eFixYgOzsbNSrVw9paWkoKSlxq+/ZZ59FXBxN46yrHDxd7Jd2Sckwh9pyHaes2IWfdufh/g/UPw4DickrdmHM4m145JMt/halzlKXlRsz0B2D1KhRI9xzzz2SbZ5M858zZw5GjRqFESNGAAAWLFiA77//HgsXLsSECRPcyr/++uvo378/xo8fDwB48cUXsXr1arz55ptYsGABBEHA3LlzMXnyZNx5550AgI8//hgxMTFYsWIFBg8e7Krrhx9+wE8//YQvv/wSP/zwg6qcpaWlEqtZUVGR4XMmiEDrt3in+QfYafkMyTUT3LedLLzkU3l8wZdbjwEANhw862dJCE8ItL7KTHQrSIsWLTKt8bKyMmzZsgUTJ050bbPb7UhNTUVWVhbzmKysLLelT9LS0rBixQoAVYvp5ubmIjU11bU/KioKycnJyMrKcilIeXl5GDVqFFasWIHIyEhNWWfOnIkXXnhB7ykSBEEQhM+ow/qM6fh1sdozZ86gsrISMTExku0xMTHIzc1lHpObm6ta3vmvWhlBEPDAAw/gkUceQc+ePblknThxIgoLC13/OZNkEoQRAq0Ts+zK85YShhBTWyZ/BfIjZobstcWNbQTdFqSzZ89i6tSpWLt2LU6dOgWHwyHZn5+fb5pw3uKNN97A+fPnJZYrLcLCwhAWFuZFqWonB09fwNq9p3Bf79YIDwnytziWIdDM1uJOkuIa9CO5fiYOOMWlFQgLtiM4yK/fukxsCGzlgiB0K0j3338/Dhw4gJEjRyImJsajHBHR0dEICgpCXl6eZHteXh5iY2OZx8TGxqqWd/6bl5eH5s2bS8p07doVALBmzRpkZWW5KTw9e/bE0KFD8dFHHxk+J0LKTbN/AQCcu1iG8Wnt/CwNUdsI5AHYUz0zv7gM3V9cjStj6uOnp/pplp+/9gASmtbDgM7NNcuagc1mC7wvAQaBbAgzZ7Faz+sIVHQrSL/99ht+//13dOnSxePGQ0ND0aNHD2RmZiI9PR0A4HA4kJmZqZgyICUlBZmZmRg3bpxr2+rVq11ZvRMTExEbG4vMzEyXQlRUVITs7Gw8+uijAIB58+ZhxowZruNPnDiBtLQ0LF26FMnJyR6fF+HOliPn/C2CpVCzIlRUOvCf97JxebN6mHl3Zx9KpYxVXWyBaM0yS2RnSoC/87SXwtiacw6v/rgPADCg8wBzBNAgkBULMYH3hJlLAL5ipqFbQWrXrh0uXTJvxkVGRgaGDx+Onj17olevXpg7dy6Ki4tds9qGDRuGFi1aYObMmQCAsWPHol+/fpg9ezYGDBiAJUuWYPPmzXj33XcBVH21jBs3DjNmzEBSUhISExMxZcoUxMXFuZSwVq1aSWSoX78+AODyyy9Hy5YtTTs3glBEpdPZeCgfGw9X/WcVBcmqBEzn7Wc5z5wv1S5kMrUlBimgMWWpkUB5ycxHt4L01ltvYcKECZg6dSo6duyIkJAQyf6GDRvqqm/QoEE4ffo0pk6ditzcXHTt2hWrVq1yBVnn5OTAbq/xr/fp0weLFy/G5MmTMWnSJCQlJWHFihXo2LGjq8yzzz6L4uJijB49GgUFBejbty9WrVqF8PBwvadLmISt+nvSai+bFTvxSguO+rzT/Al+rPYumE1V+EXtPkcrQu+neRjKg1RUVIQbb7xRsl0QBNhsNlRWVuoWYsyYMYoutXXr1rltGzhwIAYOHKhYn81mw/Tp0zF9+nSu9hMSEgLSVE94jr9uu1qz9CjyE4iXyh8y+2M9MQt+e9Q5zFDC63J/pFtBGjp0KEJCQrB48WKPg7QJoq6ippBbsT8SWJkOCW6Ya7HRZQwYcgtL8OSSbRiekuCzIHfC/+hWkHbt2oVt27ahbdu23pCHIHyKv/T7QB4crSS7lWSxMv54zO216OP5+W/+qooNPJTvsyB3MzBlFpvnVQQsupNn9OzZk5IkEoSHqLvYrNclWU+iwEIyC9C51Igv2/dhW05qkX6Egktl/hbBf9Thl1+3BemJJ57A2LFjMX78eHTq1MktSLtzZ5p1QxC1DqtO87eUNL7Fgnq0hFqkH1n+WisRoGJbBt0K0qBBgwAADz74oGubzWbzKEibqP1Y9WvSb0Ha1HMRPsQfr1+tiU8NsHfVbAt0Xf4I0a0gHTp0yBtyEESdQq3TsWJ3JF1qxI+CyLCSLGqwlhrxdCCzuv5hcfHqBFZ01wcSuhWk1q1be0MOwp/U4ZfI6oOMFanLX5RWwvKvLb1btQLLP2deRLeC5GT37t3IyclBWZk0eO2OO+7wWCii9kGKiJRA63QCTd5AwJeX1B/vH73y/oGVkONk4SXsOFqImzvEwG7Xd2fq8quvW0H6559/cNddd2Hnzp2u2COgxt9MMUgE4SEW75FIWdIPaxabx3Va/EGpNTFIsPwrqUnKzDUAgDn3dsHd3fUtp1WX3XS6p/mPHTsWiYmJOHXqFCIjI/HXX3/h119/Rc+ePZlZrwkCoEE10LHq/VNNuCkIlu7cLSyaKdQi/SigUFPGfz9wxrfCBDi6FaSsrCxMnz4d0dHRsNvtsNvt6Nu3L2bOnIknn3zSGzIShIuS8tphobTywK2FlURXEkUQBAxbuBH/eS87oK+1GjYdTixysdUdtJ72/OIy/PRXLioqHabUV5vRrSBVVlaiQYMGAIDo6GicOHECQFXw9r59+8yVjqg1mNFB7887j3ZTVuG5//vT88r8jGqiSAt2SdK4BuvIp6T7FF2qwG/7zyDrn7PIK/L9SvZyvLFQi5XuA4va5GKz+KWWIP4gkD8jNtiQPn89Rn+yBe/8+g9nfaaKF1DoVpA6duyIHTt2AACSk5Mxa9YsrF+/HtOnT0ebNm1MF5AgnLz9y0EAwNLNgZ/JvS53OoST2v0Q1Bb1yOqKqF5y8i8CAFbuPMlVvnadvT50B2lPnjwZxcXFAIDp06fjtttuw7XXXoumTZti6dKlpgtIEC7q8pvqZyRfpSr3wdfurEAZvGqrm0+NWmVBCtRTkT12temW+ALdClJaWprr9xVXXIG9e/ciPz8fjRs3rl0vBGEqeuIlzKK80oF1+07j6oTGaBQZ6vP2jWLFsdSCIqliacVJEMT/1Fpq1XAQQPeK97nivj+1/UFVQbeLjUWTJk1IOSIsx4J1BzHq483494IsxTL+emotPYAHEHW479aFPz5QaETwP+qLYvtMjIDFFAWJIKzINzuqJhAcOHVBsYy/+gjezskqrhmLiMGNPxQCNVjXz9NLavSe+OqZqk3fzIH0QWO2rIFz5uZDChLhE8zoLOvKi2p1ZcRK8ilO87fw0+JvyXx1/6ygpB46U4zSitqRGoQXtTxIRu6Ild53X0MKEuET6vJLxoL3cljnuilPHbY6tcmSYRjRNfDV3VO67psO5+NEwSWvt//b/tO44X/rMFDFxV6XsU7fYl10K0jOGWyENTiafxG3vfEbVmw77m9RCB1wu9i8KwbhR/w1QPnMxcbY9uexAgxckIU+r6zxevtfbD5W3Wah19uyEmo5y4x8LATaB5GZ6FaQYmJi8OCDD+L333/3hjyETp7/5i/sOl6EcUu3+1sUVfySyZejTXERy8T76Cy/4cAZ7Dru3UHAG2uJmQGPLFaSF1CIR/KyjJLn3LtN1bTJeAG3HDnno9brLqbPYqvD6FaQPv30U+Tn5+PGG2/ElVdeiVdeecWVTZvwPedLK/wtgmXhiYHw39jJ17KW0nay8BL+8342bnvDGh8sNJuVH38p5P5UGB0WU1Z5sJqCrQf1nGWe11Hb0a0gpaenY8WKFTh+/DgeeeQRLF68GK1bt8Ztt92G5cuXo6KCBmx/8b8f9yG/uMzfYngNbw8ovuwIzHKxHT/n/VgOuRzW6i+1pbGCzqZ1v60go9nUpnOygpKw/WgBZv+0T3M9SjWXmJHAeSucu78wHKR92WWXISMjA3/++SfmzJmDn3/+Gf/+978RFxeHqVOn4uLFi2bKSXDw5toDGL9sh7/FCCj84XoArJWNmgepi8168smxsojO6+e3FBM+atnO0JAC4dmxKunz1+ONNQfwnsYaapJ31YR26/Id051J20leXh4++ugjfPjhhzhy5Aj+/e9/Y+TIkTh27Bj+3//7f/jjjz/w008/mSkrwcGmw/n+FoHggHeQCjTLg8+XGlFori506sbzIJkrhxJWezY9wUrP098qed30YKVzsiq6FaTly5dj0aJF+PHHH9GhQwc89thjuO+++9CoUSNXmT59+qB9+/ZmykkQXqdqcLdWr26VGSRiObwp0YaDZ1B0qQL9O8Z6sRXfo3Ufa6NhhfUmBcp51hZLl/w8DM1iqyXXwgi6FaQRI0Zg8ODBWL9+Pa6++mpmmbi4OPz3v//1WDiCg7r77JqOZVxsumqylkLnKf95LxsAsGHCjYhrFKFZXtGCZOFO3SmZL0UUB8/7zoLEcLEFYIdltc8mrWfbys9+oKFbQTp58iQiIyNVy0RERGDatGmGhSKI2o43EkUKguC1WWS+nuZ/9kIZn4KkcCWtNkRYyVXqKyXF3xYkTxQF+aFWe554IV3JM3QHaTdo0ACnTp1y23727FkEBQWZIhThWwLlHdIrp95BJ9A7E9/J7/2GPI3RCoR76S9ris+uDeP9C4DbAkAqp5WsR4B2Kg21+2vMxab/mNqCbgVJSSsvLS1FaGioxwIRhBKevKgVlQ4s23wUR84qZ4L35YBlRn4SQNrhObzYk/m6jzTzVKzWwZslj55q/JIokrHNX25FT7DY46OJUXkFQUAlI1FVILpFzYLbxTZv3jwAVQ/d+++/j/r167v2VVZW4tdff0W7du3Ml5CoFfg7geDnm45iyopdAIDDrwzwqyyAebPYpHX6BispHEqiSIPK/S8wUwI/ZSf32VIjARyDJL9GVorrMUsWeT1D38/GwdMX8Mv4GxAeQt4gQIeC9NprrwGouqgLFiyQuNNCQ0ORkJCABQsWmC8hYSl+2HkSL363G28O7Y7urRr7WxxutnCkP7BQH8iNeAjyqgXJBxfH1DYC8F56E7GuUlcsSFZSanyJeryg8ofqhoNnAQBbj5xDnyuizRcsAOFWkA4dOgQAuOGGG7B8+XI0bhw4gyNhHo9+thUAMPLDTdg29WY/S8NPaLDhnKjeQbXvNmb98NV44ItmPA1iFzjK+AuWON42sPrjGgRyHiSLPTK68NRKJz/am8/OqaISvLxyD+5PaY0erZt4ryGD6B411q5dS8oRgfLKwJqMHhJkLQXJG7PYfIW3ZDIzW7fZ2YQ9hXU+Zsql53r5bJo/4633pVXHE7e+Fd87I8iVJUNB2ibJwmLC8p1Ysf0E7nk7y4utGIfLgpSRkYEXX3wR9erVQ0ZGhmrZOXPmmCIYYQx/x/p4E09eVB4FKRDN/+L77V0Xm9eqrmnD0DHaR9VVV4sYmx+itFldUSDeikCT2VN53VIcePECHD6jPGnGCnApSNu2bUN5ebnrtxK1eXAOFGgwqEH8PIZZzcWmglHrh89cbF5qiKfeSoeAJz9X7oNcdVnCbsTGtRabiUHagsBvHfBZHiRmkLbv8CgPkkxS6z5N7njqXrbyu+NruBSktWvXMn8TdRe9qrC/dWcuC5Ivp/lz7tPq5H1lGLBKp7lu3yl8v/NkzQaOPEhW+GbwhghGFQDfudj817aniOX0d9+lG5WLzHMqtSVJphlY4rN6/vz5SEhIQHh4OJKTk7Fx40bV8suWLUO7du0QHh6OTp06YeXKlZL9giBg6tSpaN68OSIiIpCamor9+/dLytxxxx1o1aoVwsPD0bx5c9x///04ceKE6efma6xqxfN3x6ikIPljCQaltj794wjunL8e+cVlhur0potNjLdaERR+i7lYVqm7LqsiVjo9fW21zlccD+SzWWzMRJGBEYMkxt99lycEsOiWgMuCdPfdd3NXuHz5cl0CLF26FBkZGViwYAGSk5Mxd+5cpKWlYd++fWjWrJlb+Q0bNmDIkCGYOXMmbrvtNixevBjp6enYunUrOnbsCACYNWsW5s2bh48++giJiYmYMmUK0tLSsHv3boSHhwOomo03adIkNG/eHMePH8czzzyDf//739iwYYMu+ess1tTDFAkJZgtsJZfk5Oo8TcfPXXJts4qLzScxSAasPkrFrHRffYGehZZ9lweJ1bZPmq7TeHqJ3Y6vw/eMy4IUFRXF/Z9e5syZg1GjRmHEiBHo0KEDFixYgMjISCxcuJBZ/vXXX0f//v0xfvx4tG/fHi+++CK6d++ON998E0DVyz937lxMnjwZd955Jzp37oyPP/4YJ06cwIoVK1z1PPXUU+jduzdat26NPn36YMKECfjjjz9csVaE9fCkYw8VWZCU6lGrvbzSgV//Po3i0grDMvByvqTmGdSzhpevBj6vzWLzUk9sVN5Vu3IxbOFGnD5f6jcZvIHPLEisWWw+ahuovUqy1lmpzQa12iw2q8NlQVq0aJFXGi8rK8OWLVswceJE1za73Y7U1FRkZbGn/WVlZbnNpEtLS3MpP4cOHUJubi5SU1Nd+6OiopCcnIysrCwMHjzYrc78/Hx89tln6NOnD0JCQpjtlpaWorS0pqMsKiriPk8r4mnnEcgxSGWVDoQFVyU6lbrYlK/Ja6v/xlvrDiKlTVN8Prq3x/KoxiAZvDW+siBZSZFRVHZNkPeRT7cAAF76fjfmDu5mqA41TA3SNtiuN2G+8wGitLjP5PJuew6HgIc/3YI20fUw8db23m1MAytnEfc1fo1BOnPmDCorKxETEyPZHhMTg9zcXOYxubm5quWd//LU+dxzz6FevXpo2rQpcnJy8PXXXyvKOnPmTImlLD4+nu8kCUsgUZAqHLqPX7wxBwCQ9c9ZU+RR63QqdUxj80dsiW/gOxueUp7272cNxoTJpKj5ZdKNMlqNz2axMdv2HR7lQfLx27T5yDms3p2Hd379x+O6pMvsqJRTmuDgsQS1By4FqXv37jh37hwAoFu3bujevbvif4HE+PHjsW3bNvz0008ICgrCsGHDFAeuiRMnorCw0PXf0aNHfSwtG6Mvcl34KBB3j8FBNX8pKUg+Nf+r7BMvGKnn/sqDtAVBwIhFG/FotSXEEwTFP7wDdwwSxyw2q2KmiFrnK3mO/BilHQj3RY4vlCU9H21aap/aNWa5Pd0rUP2zTsHlYrvzzjsRFhYGAEhPTzet8ejoaAQFBSEvL0+yPS8vD7GxscxjYmNjVcs7/83Ly0Pz5s0lZbp27erWfnR0NK688kq0b98e8fHx+OOPP5CSkuLWblhYmOsaEPq/zvzhYVMSsaxSQUFS6QlM79hNsyIoWyZOFpZg7b7TAICLZRWIDOVeWci9HZ+sxWZiXZxf0YGMNMWD+ln6ZakRxjafptLwJA8SKQkuAlGpNQuuHnPatGnM354SGhqKHj16IDMz06V4ORwOZGZmYsyYMcxjUlJSkJmZiXHjxrm2rV692qXUJCYmIjY2FpmZmS6FqKioCNnZ2Xj00UcVZXE4qgZNcZxRbcbTZ97fMUWeYMTF5i80LQMqsTZ6p/0LgoD//bQPVzSrj7u6tVQup6tWHe17LbbJ/z086z6J5fJl8Kxfp/n7/1ZwIRaTy+ricXv8F0YzSFv820hcn1uSzAC5aV7A8Cfl5s2bsWfPHgBAhw4d0KNHD0P1ZGRkYPjw4ejZsyd69eqFuXPnori4GCNGjAAADBs2DC1atMDMmTMBAGPHjkW/fv0we/ZsDBgwAEuWLMHmzZvx7rvvAqiybIwbNw4zZsxAUlKSa5p/XFycSwnLzs7Gpk2b0LdvXzRu3BgHDx7ElClTcPnllzOtR4Q18Og1FR2sqCD5sB/g7XS83RmKyT6Uj/lrDwIA7urWElNW7EL98GA817+dqe3wwNuEUrlAGYidmL08hNt+E9vixe7nTNpmIUAIqKwmqi42Hg+bT/tBa6NbQTp27BiGDBmC9evXo1GjRgCAgoIC9OnTB0uWLEHLlspfniwGDRqE06dPY+rUqcjNzUXXrl2xatUqV5B1Tk4O7PaaUKk+ffpg8eLFmDx5MiZNmoSkpCSsWLHClQMJAJ599lkUFxdj9OjRKCgoQN++fbFq1SpXDqTIyEgsX74c06ZNQ3FxMZo3b47+/ftj8uTJdcaNZoWvat14ILJYISk1YEEy22JmWqCuqCJP6zwnCkY+mn8Rn/xxBADwzM1t+eXxoH1vzeqy2pNu2qunox7Jc+LPIG0f3gzPFquVW1ECFc4PMRP7kdqEbgXpoYceQnl5Ofbs2YO2bas6zn379mHEiBF46KGHsGrVKt1CjBkzRtGltm7dOrdtAwcOxMCBAxXrs9lsmD59OqZPn87c36lTJ6xZs0a3nIGAw+G/zs/K8Ay+VjQlaymy4r1mZtIWK5HbjxZIGvKaK8zIMYrT/K3V4bNE8JdY/pzmHzAxSCbKwdWel+LveMv5NP4ygNA9zf+XX37B22+/7VKOAKBt27Z444038Ouvv5oqHKGf86UV2Jd7XrOcFZ55X1qxpBYFpUHVN7LoaUvTxeaDyUn3vO2b7PJmPg+15ftfFUmSUPWi/rCoMWN3fND4xbIKrNp1kntZGi4CSUtQ+RhU+rC1ssXVn+hWkOLj45nZpisrKxEXF2eKUIRnzFq11+tt6J7F5oeobpvCAGKFvs68L2nxV6BynfrPWTlQ0zfLjpgTo2VFBLcfBl24Bk/eZx8m/tGPMOHLnXjk061Ys/eU4Tqs0Ef4C/dEkX4SxALoVpBeffVVPPHEE9i8ebNr2+bNmzF27Fj873//M1U4wntYQWHQ264nSoX4WCVXlFrtZl+jH//K4xqodM1iE1T26ZBNq11vPS5mfsV68/l+8bvdeGrpdl2Khp77aATNd8MP7zs7Bsn7jX+zw9xFxwNNQVB7j5Q+VKUxaoQTrhikxo0bSy5scXExkpOTERxcdXhFRQWCg4Px4IMPmponiTCGL4w1AR2DpFjGt13D9qMF6NaqsWoZzfw24t9eFN83ViMjB2nvMFv0D34/BAC4q1sLXHflZYbr8Wk8jh+GvUCe5u/ry2Vmc0b6MfWPw0C5aebDpSDNnTvXy2IQ5qKtvlghINmXEvhKkdCDkdl0csTnohakrbeT80uHaWagqpfqEp/7sIUbcfiVAXx1eNlFqadO31mQAneav/h+WS7nmxfutVGLa+HFcoz4cCPSu7XAsJQE/gMDBC4Fafjw4d6WgzARy73QsJrFSb+LzRuEBPEkJdHYzWka99hl5eHxutvjDmL33E3Jy/mScoSHBJlTmcnoCeb3FWZbkI7mX0R4SBAua+DbVCxW+aDixZBSpOKYU6vurXUHsDWnAFtzCuqugqRESUkJysqkCzk2bNjQI4EIz+FRRjx96c1QwqoGd++pTjaFaT5KFgFvd4Ryy0uwXTsEUHPgU6lf2rZmU+rteBDPxN2GxC3mWSvS7t5ziU+dL0GvlzLRLraBx3U5EV9TY5m0jcVA+SwPkuT1E2Cz2Qy3XXixHNfOWgsA3FY7T/D1UiPemsFp5HqrxTLKMXWmoAXRHaRdXFyMMWPGoFmzZqhXrx4aN24s+Y+oK1jLJqSF+B13mDTYnyy8hPd+/QeFl9xndTJlkDUWZDd+DY8XXML3f56U5L1STYGl80RV+2tvedgM1KuY08rkoOTMPVUzovZypNDQlIdxAT1XYM0LGDcL8QeKs02jbR89d9H12xe53nxuMRX/9tEN4g07sEI4hr/QbUF69tlnsXbtWrz99tu4//77MX/+fBw/fhzvvPMOXnnlFW/ISOjEii42M/Ck31CyGknK6OwI/v12Fo4XXMLO44WYN6SbbplCgjgsSAoiXfNKVaLT9K7i1BoqFiSd5+aP9ZhMDVQ1WV61V8rhEGA3oOx6bMUVKyBabSn89iYSC5KHdYUF17wrpRUORIT61tXJc6+e/mIH8otLsfCBqz3M4q3Rh2tUrfZxwLXUiHaROoNuC9K3336Lt956C/fccw+Cg4Nx7bXXYvLkyXj55Zfx2WefeUNGQie+WFzRDLz9IspN/Kx2BQ9GjuMFlwAAvx84w1VeXj2HfqQ50Ivb9lU2XF7lY9jCjXhr3QGjjfAV85EFSYmNh/LRZfpP+HLLMdPq5M8BpcdqJEh+//hXLm6avQ5/nSjULR8vYiWhstrqY9Q6EhZcoxCVlHvfraN7UoMg4Mutx7B232kcOHXBQIPMn5pl2bv5ZBePEpRJm41uBSk/Px9t2rQBUBVvlJ+fDwDo27cvZdK2CFZbkNAoB09fwMLfD6G0wvMOkdecbIRQHk0H7p2uGXJIZ7GplDOxHV5+/fs0Zq3ap6MNthKregxPkLYXVfGRH23C+ZIKPL1sh7oMDIVNSSozXY3MsgAe/mQLDp4uxqOfbtXfGCeSAbj6bI3eCXG4XokJ/YEeBGj3qeLrX+nhi21qPBJnn+DrRLCBgm4FqU2bNjh06BAAoF27dvjiiy8AVFmWnIvXEr6D9TB76mIrrahERaX6FHS9bRiZ0XLT7F8w/bvdeHvdQX2NMeCZsWG0XwgJNnbBedrTTDAo+n3ojPKXq/4vYuMyGcVItTwWJG9iVjvi98PMNfWcKH0gXCyrML0tZvsexiCJjysp9zw9hmZ7Ku2zEN8zh4fimf0xoz/Fh9y9XnfRrSCNGDECO3ZUfS1NmDAB8+fPR3h4OJ566imMHz/edAEJ76D0VV1SXomuL6xG6pxfVI83qoMZ6SC3HDlnsDVRuxwyGO28uS1IBtrTjC0RVfKIF60Bvugkxdfj5z15OHjagKuimpOFl5j1GkXpg8CjBVGVZlYaqsxoYd+6482w5vnGxaazvOi3EQXXWxYcAYJ0tqTC/VZzSddli5LuIO2nnnrK9Ts1NRV79uzB1q1bccUVV6Bz586mCkcYQysGSRAExYd+X+55XCqvxOGzF9kFTMRfK3ubHZ/CE2zNasuM81erwZNB198xCYvWH8ai9Yc1p3QriTLyo80Ke8yFd0KV0Sn53Mdo1O+rmCwxrLUQzbEg+XhqOYfQZlr9tLPn67UI1cATl0nU4FEeJABISEhAQkKCCaIQpqGiHwmCgH8vyMKFEt+Y1mvw/EvVrD5Imm+HvV0P4hk2uuTgsSBpFOKdnqv32rl1+D7oTL2lMHtTIfCOO4yvTn1N+3cw9DQGSXxNxC62SoeA9QfOoEvLRoiKDPFERMX2BHAoLSYqoGamfBAEPiunurJUd9UnQz17ZmYmbrvtNlx++eW4/PLLcdttt+Hnn382WzbCIGqqSMHFcmw5cg778tj5XCSxECqfx+YkitRZ3qQX1fQgbU4Fyc23z6Ugae1XLqCVe0dPu55cMu4vfiOWEz/33R6lnjCxTrVj/jxW4FX3Kw+mWpBEQdqLs49g2MKNuHP+7x5Ix2pQX3Gxovx33nnu3Giu5kx1q6n/7YRn4dq6jm4F6a233kL//v3RoEEDjB07FmPHjkXDhg1x6623Yv78+d6QkdCJWg4OLcVG7J7zdDZG4UV9nYQSRsVQmsaqFI9ktB1eF5scr7vYeAsyj1VTvPRVdvdbGzjbNKeM+zGeX2cltzWvBUn6nLkf483cZXJ3o3/HP88bLxUp3N/uOAkAXg0J0Psh8/SyHUiZmem19rRDKKR/Kz2j3MsU1WF9SbeL7eWXX8Zrr72GMWPGuLY9+eSTuOaaa/Dyyy/j8ccfN1VAwly0EpjxzqbRekkrKh3oMv0nXbIpYY4iUVOHYoehdrzKteC2IHkj+FE1Vsi4W8VMWXefLDLUJrsM7/exvnqN4omLTUk5Nxbkq8wl2XIQ/siM7GxRriiq9UdH8y8iOMiGsgoHBr/7h2u7r2exTfvmL83y8nvmyRIcZt8fTy3V6h9htVt70v3pW1BQgP79+7ttv/nmm1FY6L2kYwQ/qklYdXypqk1X1arnvM9jnBiIhJR0AB64nlgYtSCZAa8FyeMYJM42FY8xSUvxtJpdxwsx9+e/9Qf6Ks1i80wcdp2GXGz8Fj+fBWkzZOBt+0JpBa6dtRYpM9dg3NLtyC0qce0T3zurxK15uvqJHku2niBueV2K3bZK+3XZ5aa7Z7/jjjvw1VdfuW3/+uuvcdttt5kiFOEZZpnrPXGx8XwF645BMineQ6kDMdoR8FqQ3GSycAySvMP3dFHfl1fu0SzDI6P8ueK6hqLft73xO+b+vB/v//aP9oEccnkWL6TfkmmknHL7vsNlQRJJ/eXW427lthw5h+e/+Qv7cmusjqeKSiVlyjRytPkFL8YQ6T5exfqrtPyLmQtF1ya4XGzz5s1z/e7QoQNeeuklrFu3DikpKQCAP/74A+vXr8fTTz/tHSkJXajpR5r+bdHBlR58Fpm5nqQZHTrPF5rRdsK8OM1f+2uRb69+ZdTgxVA47L3fDuG/AzpotMlRvUnWlYOni/VX5COMXHs9R/hqALQxLLjiU3tm2Q60bhqJqxOauLbd83ZVvJraEihmzhhTbEP3B4V5gpiaSbv6f/ral9dRd+FSkF577TXJ340bN8bu3buxe/du17ZGjRph4cKFmDx5srkSErpRjTPSUpAkK3CbZ0FiZtL24aunaDUyWp+oEm/OYtOWg2+f3qbcLEgqf5mFUq3FpRXYm1uEbvGN3S1IBusND2Evdrp6dx7W7D2F5+9QV+b0oudee/ut8IvHRJD84+LQ6WKJguRk78maWbbuLkLrDdl6FaTf959B4aVyDOjcHIDcmuMZ8uOVRONdi60uw6UgOZcWIQIDVQuSjtdPzYKk5cXzRn4YT/A0CFaO2Mxv2MXGU0ajkNmxQjXtWuP+CYKAwe/+gZ3HC5HxrysRGxXOdYwWEQwFqazCgVEfV834ahNdT7+wnLjcTUqWTE4PkhmWFG/OnhPjyoMkl1Oh/QpR3+M24Cv8NhPdFled9d/3QTYAoHPLGxDfJNJjF7ZEFtlzwTMpRfJbxUVX1/AourQqI3MdvnoBiJ4AQLUYJK3ZcDzuOd5Hx3Dnr1SfpG5jHVN5ZU3hYLvRWWw8Ljbj+z2Jr5Lfe5+4NRgVOwRg5/Eqd8uc1X9jD8eMOPmzx5I3IlR6z0rKK9FzxmrX3+KgYMC3C3JwJ4rUiBtxXk/5u+qfIO3qf2VyKl1X8fNnJO7MU/RbXI0JdbKwxG3bwt8PebTMjhyxZDy5j9wtUHV3jDekIH388cfo1KkTIiIiEBERgc6dO+OTTz4xWzbCKDo9bEruJ08WXZQf69sVn9Qx8r7LOxZxh8j7Fa72JWwYNRebzhgkaZJQ5WZ8+dVeIRPkq23SwF5W5+2u2AtuZSNDpcbzLUfOoUhl5qXWB4EWTAVGqawJF7ii0oHb3/wdj3/mniDSn9P85U0rXVexkqsWE2OVwduoGGUVVc+3+PDXM/fjptnqa2FqSCP5JQnS1jyC4dL0QJJAR3cepDlz5mDKlCkYM2YMrrnmGgDA77//jkceeQRnzpyRrNVGeB/Ww6uWo0irQ3GofLnpgXVsRaUDe3NFsQWcdXm6TAEgP28lk7OKu8oLX7F8QcnqhcycYWW2G9IM5NYgeXZ3psKvILrYLaoUg6RVhxloW3EN1CM7aNvRAuw6XoRdx4tQP8zjFaUMwVr3i9PDJlWQZPt8oRTpbcOoSKUV5q8rp/B9wCioazMTrXx4gY7uN+eNN97A22+/jWHDhrm23XHHHbjqqqvw/PPPk4JkcbQUKvH448ksNpZ77ullO1zuEl+j5CIy7DoycGkMJTnUrJNvn94O3N3FZswV6SkVGgoRSxYlF9vF0prBKDJUXUHyBcqzKXldbPrr1trnLZxNGpqhp2ol9Q56xTT6QeG0IHkLQeCbxcbx/Vgn0e1iO3nyJPr06eO2vU+fPjh58qQpQtV1Ssor8UnWYRzN106fz9LfVSexaTz8plmQGMrV19tPyGThHAgYxXR/4Yl+m5GCgNdNsedkEQoulrHrMEUOtX0C8zdXvV4YRY/mX3TL6ixt031bZaVsI4dYbspd9b/FZTUuNLuPP3z1WI24n0+VuBEPJrKaBktBl7fNE77nr0SXSnz/p/s4Z7SvLDWgIGk+S/JXRqm8JCeSSr/vo+t92xu/4dUf9/qmMU50K0hXXHEFvvjiC7ftS5cuRVJSkilC1XXm/rwfU77+C/96TdsPrffZ1V6Vuma/6iw2jQHG03XctPDEfaSkOPBW+c/pC/hmR42yp3Qpdh4rxC2v/4ZeL2cy6+dpT7szdC+w42gBcs5e9MiCpBZ/ZjSG5dpZa3H9/9bqqlduQXI/hrFNHj/ltCCJlDMtJcSbcTpqAdVG29Zzf30VtyM1SlS72GRN87ho5NKKB3B/KEuPL97qvnyLwevvikEy8TzkfZqi8qZgNTLST5nBruNFmL/2oI9a40O3i+2FF17AoEGD8Ouvv7pikNavX4/MzEym4kToZ/2BMwCMrzmk2uVoWpDYv/VSIf/y1y+Kx8faFL6Q1N0PQvWxylfxRs4Ayl/3nwag3AnydYr6FISj+Rdx5/z1AIDvnujLWUsVkiShMuHMGpTyZFmRxXC5yzjaUFLOy0UxSHrPwVODE1ORU1LOOWVTK6ae6sMPKFiQeGLfrRg0XFbhQITITatPQar5Xep6Jr2pkOvDfdagFa64f9BtQbrnnnuwceNGREdHY8WKFVixYgWio6OxceNG3HXXXd6QkdCJJ+Z1sWtMzWyspQDxJIrkhTm4cLy0SonQBIXtDkHAve9kYcSHmzyWj+s4EzoeeRX7T51n7uNKKSC+FhxT5X2BfBYbT+ftHoPkbr3Qcom4WTq87JJTej75j1Gx9qoc583TYk0dN/LMuy97I/ptQC4etF2iyh8QmnWLfpeWV+Kz7CMY/39/ch+v9SzKLcdck0EUjq/r6LIglZeX4+GHH8aUKVPw6aefekumOo+n5n31WWzu277feRJd4hvhXx1iuIO0c4tK8Oz/7cCsf3dh7jczD5LR8pJjJfWwrSGHzxRj0+FzAKqsDbyL0HLLJe/ozay7GkkmdA+eI/ntc/hiUGJs48lp5FYPh8XBzY3gw0HBFY+j5PngtSCpFFRac0tpi1obggDYPQzaUopB4kmf4H4/vX+zvLnUiMTFVunAjK+01ymUHq+x3+1vtqVSSSmyosXOX+iyIIWEhODLL7/0liyESSj1OcWlFcwX+dCZYoz6eDPKKx3cMUgA8MXmY4r7PJkBp4yCS4LrUG0XkZrMau0ZSeynJgdvuyzW7jvl+v3GmgO66hGXcQ909sGgxLgg7rPYtK+hkotNr0XNTPS0ZyTg1/0Q8cxU49bAxxdvxfX/W4eScv3T0aWDMVtD4lG7VF3TFjF36JFCakEyfxab9JIIkgaVnkMjsZjy4wouluF8SbmOo62Pbhdbeno6VqxY4QVRCCfiZ3j6t7vx9Xb3Va/1cqLgEq6a9iPur05xz8IhCFJLgQd9D5eCZNDyAhiwPol+z/h+NxatP+S23WicjdHr5A2l4+OsI67fq3fn1bTFZXkR/1YeVL2lXBixILGuodIxavfXV0tuiFG6ikZeC1UFnsNyqXRPV+7MRU7+Rfz692lOqdjt1liQpO3YRRdeqc/whwqk9YjLrfR63glxUXFuLrNQswDxWC2Nute7Tl+NTs//xFc4QNAdpJ2UlITp06dj/fr16NGjB+rVk65Z9OSTT5omHAEsrB7I7+zagvsYm63qJRGbr50ZiLVWMRcPIp7MRJN3dmYnFPNEuThzoQwvfLsbI65JlGyXrv9kfrfsdjl1Ki0ets5RoqaMVQI1NRUkDuWZlWjUn4GnWi17kv7CiSQruoqya7MBa/eewtPLduB/AzvjxnYx7La4JFLGebyaYlquoCyoLVbrrbuot149BnPxe+aNPEjyDwG9Fkn3bkrn8bKxJ5DRrSB98MEHaNSoEbZs2YItW7ZI9tlsNlKQTEDpeT5VVIJf/j6N27vEqWYC/nzjUew8XogVj12D4Oo4mlCOeBr5y2Rmokj2UgucAwHrWL0WJJYrxiFdS1BteQP1unnPQ/1v9jHmDAF6LUjyscorHlOV9mvkUH+OeI5xeXdEhTWn+csGGU/7e11uC87CvAqfVnXOSQkPfrgZh18Z4JFMSu2yAuUBqYtNKaWD23vjP922RgYD7nJWWa9k0lZpj8dqqRYUz9W+Ce+LVdCtIB06dMgbchAc3PbG7zh1vhQHTl/AxFvaq5bddbwIO44VokfrxgCA4CCeYEiZqdVEC5IZeBak7X7wxbIKRRebvroNyuTDjp6nKUmSUPkSHwbdj/pwr9gtBknBOiRGMQZJtSXf46kVSzVIW1LQ3HaNoORik1iQFKwpbhYwRr1mo7nEj2y30b7DG/2kfEKF0vWSfgSwfxtrX4C9lixBYmixWidVMxys0NXUDU6dr8ohs2bPKY2S7gRzWJCqYpCUB0k98LjYuL+UOdwoLMRmXlb5S7LAU7HVRO1L14gsVeW0rWpG6za7Hr+soM60BkkHTR5rglIMhnjz2QulHlldPELjYpoRpC1+9tUUDLW8VJ7Cur5qp1aukJ2Ux61qNlpNyPfrm8VW83vjoXzu41hsOXIOvV76Gd+KEtequceV+hxWvFjNMfrwhbXZVxhSkD744AN07NgR4eHhCA8PR8eOHfH+++8bFmL+/PlISEhAeHg4kpOTsXHjRtXyy5YtQ7t27RAeHo5OnTph5cqVkv2CIGDq1Klo3rw5IiIikJqaiv3797v2Hz58GCNHjkRiYiIiIiJw+eWXY9q0aSgrYy8JYQUGvZPl0fGhPBYkyNZis4gF6TRjMNPtF2dsu1RWKdnhMBiDZNQNptft5Ql6lTHVtdi8pD6wapXn2+KZgix3D7qsF6Jj31p3EFO+3sUtm6cxdEZdMGZgjovKU2uX0p6a61qukFvNTX54/1nUwpPlT8SKoFZMqBYPf7IZp86X4onPtzFlk3sFuPocN5OjPpl09Z0WN7DoVpCmTp2KsWPH4vbbb8eyZcuwbNky3H777XjqqacwdepU3QIsXboUGRkZmDZtGrZu3YouXbogLS0Np06xrSQbNmzAkCFDMHLkSGzbtg3p6elIT0/Hrl01nd2sWbMwb948LFiwANnZ2ahXrx7S0tJQUlICANi7dy8cDgfeeecd/PXXX3jttdewYMECTJo0Sbf83oD1yGSLvjSM+HeDORY9crcg6W/HCVceJM66jpy9iGeWSROpmRGDJLcgSdeh86xuZjmNv72J3o5RIYzH52hm0mZanfgG2k//yKnZZ6F+mlcU1SBt0W93C5LvT1apTXFfVqE0o0vFguQ9F5vGfp3lnRw5W4zOJs70YsVteXpN3N99nR+jFnqXPEW3gvT222/jvffew8yZM3HHHXfgjjvuwMyZM/Huu+/irbfe0i3AnDlzMGrUKIwYMQIdOnTAggULEBkZiYULFzLLv/766+jfvz/Gjx+P9u3b48UXX0T37t3x5ptvAqjSSOfOnYvJkyfjzjvvROfOnfHxxx/jxIkTrvQE/fv3x6JFi3DzzTejTZs2uOOOO/DMM89g+fLlinKWlpaiqKhI8p+/4H0AxZ1PSDBfkLY0kNUDC5LsWE+D9r7cKs25xCOZJJM2MwapUrK9UvLlpW2p0CNLVZ3yv3mUSN/1NpL4Mz/EILHqNRKDtPHQWWYZf1pxJHVrtMEf9M93T9z2+eiRYiky8qbF0/wVZ7HJA6LNEM5D5H0jb1/59jrP1xoTN2VndKwOWT+m11Lt6bNfpxWk8vJy9OzZ0217jx49UFFRwThCmbKyMmzZsgWpqak1AtntSE1NRVYW26WUlZUlKQ8AaWlprvKHDh1Cbm6upExUVBSSk5MV6wSAwsJCNGnSRHH/zJkzERUV5fovPj6e6xyN4A2zYwhHJlxBEHzqYntjzX58+scRzXIsdF8jRvES2YKTUhebHln0iaKnDV/GIKllUffJVzsr4FrjOZLv3nIkH89/u1taL8PFphfPZ+XocDt42hQ8W26IeYyng6ZCPVJLF2fbPlDWNa+SbLcnH5OewLrN7lnwlT/8XNsl1mPPzsXT463kdtOtIN1///14++233ba/++67GDp0qK66zpw5g8rKSsTESHNvxMTEIDc3l3lMbm6uannnv3rqPHDgAN544w08/PDDirJOnDgRhYWFrv+OHj2qfnI+gvdh4gnSFgT1mUx64FGQFq0/jMkr+ONABIXfeo91crGsUhp3Y3CaP78M/vNb8cUgKXeS/prmrzT1u+YY6f4//nEPehVk/7Kw0rTkminxAp78fBteXsleikKitMpnh+lcbsgbSC1c2i42JbnUgsy9hW4XG2e9Zj9nrHxD8jxRSv2m5LceC6QGnitYHh1uKrqn+QNVQdo//fQTevfuDQDIzs5GTk4Ohg0bhoyMDFe5OXPmmCOlFzl+/Dj69++PgQMHYtSoUYrlwsLCEBYW5kPJPEf86gRzWJDkmbQ9yoMkn8Vmcseg9x1kncul8kpJJ1Gp1HtoS2OomL/cZywmfbUTi7OVY3KMLkXgKZrTrXWU99dXPqDkblL4mq/e/NeJInxTPTtp0q3uaT14Bzj343xzHaQKHBu1hJZKx/LU622MuMurMLcjZPWrarJxWZI5+v2Jy3di94lCtG/e0L197SZUqXQICPJw7T+z0K0g7dq1C927dwcAHDxY5U+Njo5GdHS0JFCaJ5NmdHQ0goKCkJeXJ9mel5eH2NhY5jGxsbGq5Z3/5uXloXnz5pIyXbt2lRx34sQJ3HDDDejTpw/effddTXmtgvjSmpmxVIDcimC8Lj2LjBrKvMohm1bne0nVxabDJWLUxcZxnK/GdLFyBLBmsXlfBlYb2gkdeep1aSQBgVNM8TIUrHfEm8+dkkxGqWlT+blSjsmSy6IszU9/sb0EetE6X7kMvrR6SPp/xn55vKD0u09bCeWxjn2+saq/OFvsPvNb8DA5uD8/ZOToVpDWrl1rWuOhoaHo0aMHMjMzkZ6eDgBwOBzIzMzEmDFjmMekpKQgMzMT48aNc21bvXo1UlJSAACJiYmIjY1FZmamSyEqKipCdnY2Hn30Udcxx48fxw033IAePXpg0aJFsHPM8vIV+oJJ+QrzDPhus9hMDNJWLesQuBJZijFjZkVZpUO6FpTB2Abesmpfwt7GU4ubRHlkVLbreKEJ2abd6+W1IJVXOnCxtJJ5njwuNh5ZzIZXIQCqBmC1V0R+iNmDjPxjggfmvXCzboh+c15zqVIlPWbmD3t5xeNug4XRbNN63pFf/z6N3m2aIlQ2wUY7SFtZNpacp86X4EeRYqlnmSGWtcnTdyegFSSzycjIwPDhw9GzZ0/06tULc+fORXFxMUaMGAEAGDZsGFq0aIGZM2cCAMaOHYt+/fph9uzZGDBgAJYsWYLNmze7LEA2mw3jxo3DjBkzkJSUhMTEREyZMgVxcXEuJez48eO4/vrr0bp1a/zvf//D6dM1CzEqWa4CHa5nTpBO7ffExaYnfslIM7pjtBkHqA0quhK/GejYWe17UrfZ9agueCkrW1xagdve+B0AEN8kwoB0zjbdt2k+G9UH3fL6bzhw6gL+k9xKV/2+gucrXr5fLG+Fw4EgexCzXFVZ5fulJgsvTy/bgfRuLTxwfQjMtiXXxeCHhpiScvOX7mDK4Bajxye8nqs3bOFGjLo2Ef8d0EGxDOt2aPd0Um6Z+5vEEqSnn2JZ/j21pgV8DJKZDBo0CKdPn8bUqVORm5uLrl27YtWqVa4g65ycHIl1p0+fPli8eDEmT56MSZMmISkpCStWrEDHjh1dZZ599lkUFxdj9OjRKCgoQN++fbFq1SqEh4cDqLI4HThwAAcOHEDLli0l8lghgl5fkkLOchwFHYJxJUGOPLhW7cuJtx1P7g3zpRMECCK5KkVBSG7FVZo27urQPtCsx1G/BUn6t9o9OnuhpnOVJ3b0FG1loooDpy4AUMgyX11I7RzULBuAua5spTbUtrNykmm5rY3sUyt3oaQCUZEhXMcCcgWOXaeR9BFq5eS5zYyi+dzJdnvL6vHJH0dUFSTtIG1pXClLSrmbzHn8z7vzUFRSrk9g6LsWLPm9sfyKUfyuIAHAmDFjFF1q69atc9s2cOBADBw4ULE+m82G6dOnY/r06cz9DzzwAB544AEjoloOM766nDgEac4MX63FZuQc9ErGOhdB1rZYqftm+wkcPluMabdfpfnVzK2kyuMvOI8zA0+vl6By8StEo7fZaoQpMUgK1gtezB77tN037gWqrrHyAtVqlhmtsspyyI/Ta4V0b9NNTgP9jZocRlyBpsAQyYxV7bUuCat6tXeXK+YLVbI/9PFmDvkY/aqnFiQLKUjWCbwhXJgVgyRdi4zDWgHpy6WU2JYHPQqSkXxLeq1JLHHkL6JY5unf7cbHWUfw3Z8n5IcxZNEliq7jvNlV/J13HuOWbMOhM+7LHbgvpaAsifi6edK3sWNWtCxI2pZKJeuFLzEW0yZ+F40rJ+47eesw73o5q3I/DRWrrWJl7vU6KVVY8FYvWqfubkHiq8MMQ6T4mWcqSA5JYdk7IhXqYpl77kKHIHh0Hc1Y7NYqWMKCRBiH33qhjcMhNcd6lAdJ9pCr5WXhd7GJfquU+/NYAT74/RBOFJTUtMEMJpTWw8q5c7p6gWA104jRGCSeu2LWIOWs59T5EoQFBSEqMgR3v7UBF0orsP1ogVt5t1lskt/SfeUS16RxeVnHaj0bbq4w1fr1yCL+rW9R7uLSCtQLq+laHQ4B07/7S7c84iZZCpL69G3l2rmfVxV59B5fE1fFtm5cKqvUMeHE++i18ilZqOXoXdNP61xZQdpulmoVhfIUY7FiQah6ho3i6f3xJEGx2ZCCZEF0deTcX4N85SQWJA8e1KP5l7jL8k4LlXS4KqLd8eZ6t23MDky2ibUWFJfyxnsPNNr3JgKA8yXl6PVSJgDg8CsDcKG6Ezx89qJbebUYJLncZlmQWGitB8h1e5xlVMqaFWK0YttxjFu6HZMHtMdD17YBAKz6Kxe7jtcsTcSrGImvK1tBkvylsk/tuBrk10BPsK4WglD1sXHsnLRfEABsP1qA9Pnr0SW+EWdd5ijknuD2LjPKOAQBQbDh9/1ncEWz+oiNCtf/nLGsUCIlS2sWmwD1Z6Gkwt0l6RCqkugyxeFwG3qeSdujw02FFKQARPyCqD2MsqwpmvVWxSAJkr+12J93HkkxDVx/z197AHtOFuG7P09qHqunHcCzjpFpAq+q1PU3y4LE42Y0+j7zHGdWXyEIVYv+8pfnH3DFq5N70rkxXWxax8j+ZgatVv+ra2aiwfMYt3Q7AGDG93tcCtKZC+5f6VVtsBtxPttipYi5KKmKHKr7FHbKB1v59dIdPCuzXFz90s9MWV79sWpq/g6GJZNZrQ8GUO0gben+F7/bzSgD/PL3aQxfuBFA1UeJ2bBUFfcZjcofN6xJFQIEFDNcb7x4+pFkpSBtikEKQHgXqRTD88wJAnS72P712q8uN9SBU+fx6o/7dClHgEFLld6+mmlBknaDrAGUZ1Dlm43m7qbx7ZeSNABfS2a3PEiyr1Klsp64BJW+wlWP8YHS4+mxeo0GzstZoWFBEuNm7VGzICls15KTda0rHQLuXZCFjGrFULlNZWXQI9edF94hQRCw8PfD3DIANbMopWUEbDhwRrJNvwFJ/QTZQdqi4wWZi01WH1PxFoDiUiULkqo41WU8uylWikEiBSnA4X2UeJ45eaJIrXWwnDiDfM+XGPvq4H0hJG4enW2wysubLWd8TfF8zfDI8teJIobLQluxMKuveG31fok5XTvGwk0SxbIVaukRFPh9/xmMXbIN5yT5V9hKrB7YQdqCpmyaQbkmunKcbSnV6JRXn4uNXYeefXILkpsrlXHc9qMF2Hg4H8u3HXdvh+NDTj6A82Doe0rHQWv3ncKXW495XJ8gwONpnWyras1GrWn+8jrkyhIzrMAhMIO3q47RxtM+S8ut7ktIQbIgel7mSs6niWuxUkgHRt6cNs53NIRjQVxmu5ynqxZsqAU7iFL69cq6lq6BSXUw0m7fmUhR73FmOdl+P3AG89cecP2tpZS6BaGKLo2bmd4hXRKDh/s+yMbX20/gJYWFWGvkUK9H3hxTQeKQzf16mGjt0xl44mxKYkFSeH7lxyj9zYVMTJ4Fi9UVsZrfSh8aRuQ0si6gnvvH44r2lZFDqxlWBhLxpf5wwyHV66XkulWyIAHa77in18ZKQdqkIAU4+Yy1cMScPl+KSgefGVvuBirnVL6c76haviA1Bc3YLDZ9LxFzmr/bQO9eiMvFxinD1pxzisf5ok/Ye/I8s20W7i429hEOhyC5bnrDB04U1ATt8lj53Nrn/ZJXqF+tbT1ymImzLbHCrmVBMsPFVlbhwNNf7HBZD+TlPJnVWqHQl1S5ufXV6+17wZMtnEcE1rNpNC+SklLCmhUnLvvznlNYvTtPtE9alvUR7BAEXCpXsCAJ2oknPXWRWcnFRkHaFkTr8XC+FBWVDuZigU625pzDnfPX47orL8O/e7RULOdqV5A+nOUV+ixIwWoKkkpVvEF5RmKvnCjNYhN3Jiw5uFxsnMKMXbJd8TilGszsK8TXT68FSaLMVf915kIp+s/9FRGhNQkMPYpBYiqxGl+ruupXs3aoW4zM7LJd90Gx0qodYsWTHUzL0QZrn8qBX249hhaNwpFxc1u3a3JB59Rv8dEs97UTz3Jn8fYd/PAoMXwfnDoaNViXVgwSwI6PcsJSXNVikKr26+s79EKJIglTyC8uU30JP/njCICqRQ95OpKqpUZq/lb66nOn6i21G1ynifd9+uOf/JpjdLbBs6gicxabBxYkfQHE6i4Is5Mc6o1BYk3zX/j7IZy5UCZJ6eBZECdLidUy5/PkKBI0ZdO2IPmu03Zee2n6BC1FUY9Gp17XicISZqlbXv+Ne6aZHKUPDYcg6H6ZjS2cy99IEI+CxBmyIEevAUlLbp48SNLYTVmfx1K8BeVEkQI43N7quzWxkH5ECpIl4XxATp1nTx92EqozJkgepF2mM5W2etCocudg5IvjQkkFNhw44+p4dx0vxMwf9uC8wtpBihYk0d+VLHMz1/Q/LpENV1FSXonUOb/g6S92eN5QNZoWJPd1Jhh1uG/juRRKl5RtQVKvS95hMwcMLYON5k7PUBoTFWd2VW8WD17MoGE1i5iKPLxuS1Z+stcz96vUK7fC1fxdrtCXVL2D+i7+0s1H8fw3VYk3eY/U0wLPdx7Pt6MZSrVDAF5b/bei/HZGFy+XTfIOiX4LgnIMklpsq94UCHqhaf6EKRReUl9IMDS45vbymoTF5fQGaau6MCAoDhRG3oeh7/+B/7yfjYW/HwJQFQT9zi//YNaqfdxtVFkfav42ngeJfQJapnqeoPOj+RfRbfpqHDxdrDmzRos8UdZcrWsut5yxZhAanXVmhmVNLIy4Q1XNpK1qQZIP7m7NmIfWKVU3Lj6vResPq1ZjRgyS/FjWc6320aV2zRSDtA1e2A83HHbbtmj9IWOVyeCxhPModaxT1ptJG6hSSvXEILlPsHB/d52wvAQOQVCcwSwfI5TKeIKVYpBIQbIgvI+HlqYt7sz4XmhB8jKxpoCqofXVyvq6B4x9MZypXkFerjTsOVnEKq4wmMvlMJZJW6mInnXElO5Pxhc7TFuhXIwnMUhqdXhicGO3oV0Xb5yS0bgcSSUGUNKTFZ+b6n+10myoBml7cK7O95FVTvzR5VavSp1KMUgChzyqiI594Vv3ZI2uYjra4HKx8dQnuCswRjO2KzWntvagE6l7XLqT7WJjW9NZ9bH3q+7WxEoKEgVpBzBa8THiafe8z5z44S7jtSC5jlUuX1rhUPkqMf5C8M4KUXIHiTezLUjGFSQ9x+mp49T5Eu1CWm1r6L5yF5vUTC+4b3OV41PEmTIZqE+QuYXV6lW7lVoKhi+XtHDKwpvCQ60OFv+cVg7aBUQuNsY+NQXJubQGSwa1WWysxZJ5eO7//sQ/nMfquX88s9h42zTruVEO0taOQVLrxtkzd5WVc3lqFCPvrBbOPvf93/7B6fOluPfqeFx+WX2P6jQKKUgWhFdh0IqPEXdmPFq9W6JITguS8yVV689/+fu0SrtczTDh7ct4smQzZ7FxBWkbOwEj/cgHvx9iLmugF61OTP7Fz5pxx7tAp1sZHRY3Dg8bh5XJaREx/lXsSZ8vtyJoGquqG1Ob+VVVj7IFUu3I2av/1mi/6l/WNVG1IKk0qvSh8deJItWZuGos3XyUu6x0UK9aT+x8STkW/HIQAzrFoUNcQ9d+Lhcbj6XUSzq1uF6WpG7hg4x31wm7jxdUPwy9+a4AwN955/FZdg5W7crFhdIK9E2K9puCRC62AEbLuiG2IPG6ingCK+U4X1KjioInXxy8JmtWE/LzZV1PniBtwxYkA8eYoRwB2tf8+53S5WLExR0qLhiek9JjMdAOCJXeNyU3rnY7nu03s03ndr3Li4jx5J1yWZAYVajGIKkoaUrKXubePOZ2b/HU0u24afYvuFRWiVd+2Iv5aw/i1nm/ScrwuNiMWEq3HDmHD6pjJvUivrZ550td/RY7UaTyh59cbKYFyaGcA08QpPeV9X56akF67sud+L8tx1xpJcyy6BmBFCQLovV4Od9frQcxNLjmwSrliGOpsiDV/F3OadqpCdLmKu6GJ7MWeAdEHmsHq4wnsmkHaSt3XN5Gb3OsJWiMrl13y+u/MbezjuSxDomV2Lwid/ejK+hYpS6tIGePLEi6p3ZX/asZg8Q4hrlTJzUuNg8tSBzWaB5lxEy+2nYc/5wpxqq/TmLn8UJmGZ7xmMtSCqn18J63N/AJyapL1OCOowWYvGIXAKWlRqR/SxQkWTnWfREgqMYgabnnzSaYNVXPR5CCFMBoDR7irz2eQN8qd4V2pybH2QmYEYujF94YJKaiI2uYVcSbeZC0vsS8id6vPIkFyRWDpK10Gm1D3pbaMeIyxYwcOc696nWpDAgm3xrX2nAK9QoQ8MPOk/h/q/Yyj/3kjyPYpTC4i+swimt1Hb0WJJUmlZQ9oxY/vchlO19Sofg88CWK9J5lmYfPsnMAsJU5uWyqQdo6Y5AA7WfL7CBrf1qQKAbJgmjGXVTv12PduMiRXE2eRl4rBkKO0RfDkxeK991hD77sgV9SxgMXm+Zpifb73IKksz22BUm9HA/isYjV8fK8C7xrN+myIMn3cygcSgkMda/gLgCPfrbVbfv3f57EV9uO4+c9VW6pR/pdrlqHUdQUOK0gbUk9ot9KH1tGk8vqRX7/LpRWeLQoKp8FybwgbSXY0/yV/5ZLw8yDJCiPLWWVDuzLPc/cp9S+p6it0OBtSEEKYLQGI7F+w2VBEtRjkLyVYn7zkXPYf+oC13IocnhfHa3FPgElCxJP7exC2voR2/TtC/S6DsXFneZ3I0HVeuHKAK8x0LkGfLUybseo/83iX6/9ol1I0ia7UqX36PHFUqVJbekdT+5DzfrM7pUEB6mtt6iMsgVJj2TmcUHFgmRaLi8T3wWl5thLjShbkOQwXWyCch6kBxZuxNacAsX6AKDgYhmOnC1G66b1mHWXVjgQHhLEOJINWZAIXThfCq1BTvxi8KTnd8jcFe4Kkno7RvsDZ+BxYnQ99GjdWNexchO9kgzMpUYE+SBjrgVJz2wPXy5lAbAtFGpIgtk1XESGUbDyqR4iaE/zF5dV3icXRdkaosSxc5eY2+WDmKZVjKMtrYKe3Bq1IG11K1zNzrIKB/48VuMGVLJG+8vFdqG0QuW91V+fUj1GEkMy21O4o2qZ48VyiCqSoDeTtpZyBAAPLNoEANgw4UbENYqQ7Hvss634eU8efnv2RsRGhWvWBagr5d6GYpACGE0FSbSfx10mD9KWJxFTqsG53dNB/ngBe4BRw5MgbbX1xpy4kuap1K2olOkYCH1tQdK7ppZYvkqXi81zqSVKImM/TxJI3tle6vfQTUNiV+ILDDTlngncuLzOy6l30oJ419vrDkr2seKpAN9ZBwRI+0NVCxLHDeC5uma615RuJyt+2S0GSRKkLd3HShSplknbvazyPrGC7OSHXbkorxTwf1v4UzT408VGCpIF4X2xtPpAyWKXnJYQiQVJdsyKbccVjnMOmJpNqBKhw+zqhPcDlKUfypOescIkuIK0DQYhSS1Ims34Fd4YJE9QihPTOob32qnnQZL/rd+CJEbcpytZEdSCtHmQKNga+p0e1GKQVJVR0a41+05xteUzBUk26F8qr1RUkPRakBQXmjbx/VCqii8GSaQg8ViQBH4XvNo79cinW1CmsOht/TB+51UQzWIjjKA1eItfDN6BXlysXPZwT1j+p0I7zuM1m1DFiILEa0FivsiCfJBhfE3xKJYK23kTGAYCkmB2Ey1ImrdPENC0XqjibocgaD7bNYki1ZpRV4j0nqrac6lVlRkKnye3xmiSQPE+rkWewT8L1Qwka/bZVBRUnR9Fyoqu9+GJQVLNg8T4KnQIAvc6nFqXKie/JueZuK364SFc9QNkQSJk8HZuWlq+eD/XkhmQWwqkL09kKFvrP3j6gq5YECXCQ/Q/jrwfF4bzIHF1lgrbNbpIyXEW15VYFiTTA7KZSee0jjHHxaZ0jFHsNhsOnSnGmr15ijMJjCrWruPFVgzu2rVRS+Ogdq0FznJifDX2CZD2ZzbYVCxI2rKrzQxzlTHRxKq4WK1IQ1KajKAapK0Qg6S0NIwcPX1+UUmF63e9UP6PYX/GIFGQdgCjZxYbT3yPfKkRedxSZGgQCi+Vux337P/9ibIKB9pc5j5rQQ/Od/1SWSV+/CuX7xjOIEiW+8zhkFrMmLPYvGpBEv+2toYkdUWaZ0FSasOJQ1C/MlUuNs+/drVcVHpjeux24Ib/rQMA3N29ha5jR328mauc1nUxippFWH09OwGnzpfg2x0nce4i3/IhvkoUKQ8fgE35XHh0A/FT6YvFVZVaECuYn/xxBMNSEtyeVWmiyJrfxwsuobSCkTdMUF9qhEcuFuKxQ89tp0zahASzLEjiL5iNh/I161u5M1dSp/MroqzCgX9OX0Ckitb/1toDHltBftpdld/l+W/+wril27mOcZ8hpPBVqPClJCnDsjJxnJNim1oHc5jprYK4Y1Vb7d2zNtyRz6xkHaXpYnNZkNSsH9J9Wi43LcQutu2ygHhX1SZeQE/lZdXFvh/K7Zy7WI5eL2Xixe9242Qh34LKrDxIXtGZBKm1xAbl54pL4RFbkJQsyCa+H0p1iS/VzJVVgfDua7Ep1/P5RvdgaUHQzuLupOCi+wezkoRiBYm3fsC/mbTJghSA7M09j7IKh65p/jx8vjFHenz1l9ToTzZj3T7lxWZr2tPVnBvv/PIPHr7ucny59Rj3MR4tNSIbfFlKlHO/oT5blwXJ2oi/qtWWGjEbeSC9236Bw8VW/a+69UPf31r4Yvq6movNHAuSeyXyay1+ZyZ8yY5RVIUhZ5DNhgovPFuSSSuC8nOlUz/yaDacp4ifsybVsXpuMUjiDzGOOh06LEh6OF9SoyDpqZ/yIBG6+XDDIU33kqcPufN4HuUIMGfALLxUrqtb4X13WFaGhesPSf5Wywyt151xsaxC8+tK+mVnbRVJ4lLwlgVJQYlVDUbmkMN1vI6gZiN5kMRInkulgVhnne7H81vE9FATg+S+T96niP/afOSc4bbEVA365j5cAqSz2CodgvLsM4621WaG1ZQxT0k6e6GUuV2shzeuF8KUh0dWMYLAnv5vBLF8Eu+Ejvopkzahm7/zLuCKZvVVy3iqsOher8uj1kT16GiXfxabdhnDLjaGe+aqaT/yD9ywvgVJ3KF5y4LEdOk4tGKQzImXkO+Tx6Hoj0FSm8VmznVTc52YYUFiXTE3F5uHp8L6cLHbAWjntdWFIECyAGulQyUGiVOJcP1WuJ+CIJgWqH3jbKUs7TXPWeNItgVJ+izzKH+ef1y7Wquu5ti5i5j7837X9ho3vXY7ZEEiJPA8NDwuNk8f8qrpnvwLFpk1YOqpRT5NWOlYrmBrRhHeLy4xpRUO3WZ6edZyqyGWz5ll13QzPNOCx94uPoQnmSSgfi/dY2s8Ozex4u4LV6QcT1rUkwfJU8vn/rwLbtu8EbgtQDozSz4hRQzP/RKXUHoNBLAnh5hJiGiGV8vGkVXtuinLeu+RwD2LTQvntRz0zh+SiUK86ycCZEEiDFBaUan5deLp+FXpEHC2mG82CmCem0hPNdyJIjkuBjvbtoDcwhJcKK1gHFGF/LBilbKs477efhxjl2znOsZflIm/vqt/eiNOQY7Wcp9us5PYlQDQKGeiBQaQxqwpDQa8bYQG2xUT7olqk9Xt+b1hWvSq6z11vgS7jhciJMizAFrWe+Wt+C152hPPLEg1hZTXdPO+ciy+Vg3Cg5nySGKQuCzp+oKo1XBec/ksaj31kwWJkMDz6JRVOHQlijRCpUPARY413JyY0RfofRV43x2uRU9ZLjYAM77frV637I7xXjPnUVZXjgC2BUmead1TWKqQZgySIHB/pavrRzK3hMSF4qHLys1dp+/4+mHByK9w/1DhSVboCcwPhupzueHVdSguq8Q93fUvMK2FmnvSKPJM2lVB2squMc36JOWVS3lLQXJeIp7YQJ6cTWIEgT9RpBZKH1GVnC9tkN3m02Sicmiaf4BSVFKB9QfOqJbx9CHXnmLtXt7X8M9i0y7DzJUkCDh9nh0g6UR+ibgVJIsHZouRKkhVcutxvyohnmjAuhyaeZDA42ITXGUVy7jtlG44eu6iahtyxI+lJ6vGA8ouBn3nox+mi616Y3H1M76Oc0kRPXjDYlDl7pJbkBRcbFzueIH5+/XBXWvqEbxnZXUIwIYDZ5h53NxjkNiyqtVtltxK15jXguRP6xFACpIl4encthw5h02Hz6mWMSNImzfIkGeg8gb8a7Hp6/ScOISqdZtUj5P9XVzG52ILJMTLztQoSGa4cdStIGrTsasr0HxGncdrzYaTtlvz+0JJBT7OOqLahjvmxSDxxGC4y2+8zZrr5b5Pfq298XHvjTFx5sq9WPj7IdffnrvY2OVv6xznWhrH2y62/7yfLU1TohA7pneavwD+TNpa3PHmehw4dd5tO68CFuJnBYlcbLUYM1xseoLp/GEQ4TW/GnWxca16L7cglfJakLiKWYIyxiy2cpM6USesyyEI6gHTArSVXz63gvzvmg08WejlSKc3G5cLAIIUllrw3iw2QfKvGD39gVG8EYMkz62mFqSt93kRPyt2W829FyBwZeX2BJYcamux8dVpbh6kZ//PPT8WWZAIw5g1DdiMWWx6XnBvBYaqwduZ8gVp62y8Gvn94rUgWX15ETFsF5v35XcIGrFDgrIlwL0uNUVLkP1dg6fvkafvhdKsLi3F0Shq5+u+z/wBzBeDYqVsmaFvd5xw/dYbgyS+JFUfbLbqeryvUAqM3/IW1RRpZp2C+zJTnlBSzlgQl/OdCvZwEoCn+F1Bmj9/PhISEhAeHo7k5GRs3LhRtfyyZcvQrl07hIeHo1OnTli5cqVkvyAImDp1Kpo3b46IiAikpqZi//79kjIvvfQS+vTpg8jISDRq1MjsU7IMZsxi47VCVZmTPWsP0G/14k4UaXAWGw/uMUj6ZrEFAmUMF5vZqQnYiSI5YpC4XWzaZVh/G3E38Mxi40VJYVCyYgBVeWeMouSqce4zI/ZMDV9kIa+Uub+e+Hyb6zfXNH+J26rqt1Ns5+3SE6JgFLGoH2cdwerdeaoWJB5pHIKAMhPvMatNsiBxsHTpUmRkZGDatGnYunUrunTpgrS0NJw6xQ7827BhA4YMGYKRI0di27ZtSE9PR3p6Onbt2uUqM2vWLMybNw8LFixAdnY26tWrh7S0NJSU1KwNVFZWhoEDB+LRRx/1+jn6E89jkPR9PZthEdH7tS1/fbblFCjUq12XWZ3ZB6JYBzUulFZwL0zqb8TKkLNzM2MqsCRIm7FfPNuINW6quUqcnC8px8yVe7DrRJFiGbW1zIxYyiRB2gq5g3gfdb1rUS3bfFSSlE8vRZcq8P2fJ5mKfqVDwNPLdrj+9koMkg9GJYdakDZPX8GwyjgVO+c1KbxY7gOXpLT+UR9vVo9B4lL+wJFWwjN4xxV/5kAC/KwgzZkzB6NGjcKIESPQoUMHLFiwAJGRkVi4cCGz/Ouvv47+/ftj/PjxaN++PV588UV0794db775JoCqmz937lxMnjwZd955Jzp37oyPP/4YJ06cwIoVK1z1vPDCC3jqqafQqVMnX5ym3zDDj8w7COYWleCSjpQASugVmdvFxjl7wwjyw46d44tZmb/2AFZXL9BrdSRTpE2cxSaGdYsEkYuN1VkKHEr8rB/34Z1f/5G4Udzqcau3ZosRRdAmCdLWfbiL0de1UbYgKfye/dPfxhtE1bv8+OKtmPTVTrd9DgH4envNdfTG8OULC1IFI0jbmb+M78NS9D44Ffjqv533/j/vZ+PQmWJPRVWXgkNUiQWJS/kTUFphXipzllJGFiQNysrKsGXLFqSmptYIY7cjNTUVWVlZzGOysrIk5QEgLS3NVf7QoUPIzc2VlImKikJycrJinbyUlpaiqKhI8p+3MOujwwyLiJ5BcDwjGE8vepU63iBtnk7PuItNehxv8jx/pEUwA1eQtq9ikKp/szpLAdr3zUgWdU/fQa5p/hz1PH7DFQhWCNIuEc2ulM6qMue+HDztPrjLU154Q5fxRiZtOQ7xg1XNVdN+xM5jhbqz5ytZkADgz2OFHkqqDs+9lrrYON4FmGtBYtVVyem2rrMWpDNnzqCyshIxMTGS7TExMcjNzWUek5ubq1re+a+eOnmZOXMmoqKiXP/Fx8d7VJ8vMGMA9sUgKMZbMUg876PRcUV+mLfjDvyN8x6ZNRW4BoUYpOr2mK4mwZzlHORKrqdKRm5RjUtfbYFXLew2IDw4iLlv+dbjzO1mWHKV2M4zq9NDvJEoUk6lgmv29cz9fHmQRL9dFiRXDJLvBnW9TynPK1tR6TD1440Vz0QWpFrGxIkTUVhY6Prv6NGjXmvLrGfTDP+3r9cI85bL3rsWJOnfvpgK7U+cMTlmz2JTCgp2bmZZUgST1o1yd7F5WB/DwuBeRrsRm82GpvVDtdurPoN9uedxnnOpGzOwecHJ5osxUSkGqbSikjuQ2Ynzpz8SPut9TnksSKxZZ57A6ie4Z7H5IiBNBb+1Hh0djaCgIOTlSWMw8vLyEBsbyzwmNjZWtbzzXz118hIWFoaGDRtK/rM6ZsQg+VpB0quk8Jb2qoIk+9sXa5T5E6/NYlPYphWDZErCSlkVZib480RhttuAJvW0FSSgypXx1roDhtsygthSZha+mcXGTg9RWu7gnMVWXY9DcOX5YbnYvI3eJ4vnUSwxMf4IYFuayYKkQWhoKHr06IHMzEzXNofDgczMTKSkpDCPSUlJkZQHgNWrV7vKJyYmIjY2VlKmqKgI2dnZinVaEbP6Zl8GaZuFlswxDcMkf/MOZHpnpuhC7p6p5QqSUzHyxbMhbkIpBsmT5zy/ejFmsy1IYuTPqJ667TYb1/n99FceOk77URJAHaj44rlSuqYlFZV8mbSr//1h10lk/XMWgJ8UJL0flBzFS022ILHCNJzXX0ucEIX4O1/hV/tVRkYG3nvvPXz00UfYs2cPHn30URQXF2PEiBEAgGHDhmHixImu8mPHjsWqVaswe/Zs7N27F88//zw2b96MMWPGAKgyR48bNw4zZszAN998g507d2LYsGGIi4tDenq6q56cnBxs374dOTk5qKysxPbt27F9+3ZcuHDBp+fP4kJpBc5cUF/7ixczvoJ9bUHSCg5MatZA8rcVvFluFiQrCOVF/jlTjJOFl3zjYhONVixzuyAIHmX0fuSTLa56xJh5D+VV7TlZhJyzfHmKbDZwucy+3XHC1Nw1/uSiD1yESh8xpeUOzqnwVWXOXSx3bZPPYvMFevt4nvJmzmAD2BN9nEqwljj+tiD5damRQYMG4fTp05g6dSpyc3PRtWtXrFq1yhVknZOTA7uoU+zTpw8WL16MyZMnY9KkSUhKSsKKFSvQsWNHV5lnn30WxcXFGD16NAoKCtC3b1+sWrUK4eHhrjJTp07FRx995Pq7W7duAIC1a9fi+uuv9/JZqzPaxLw4ZlgyvJ0PQ45WUHhosHSQ1OrMbDbvK1Fu7pnaMU6psut4kReWGmHEKogurjcsSBsP51fV40UXm5zNR87hulfXYuIt7TTL2mDDsN6t8f2fJ1XL8c6cDAR8FaTNorSiUte9F/c/8kSRvkDvRwpPabNjkNQtSOoS+TsGye9rsY0ZM8ZlAZKzbt06t20DBw7EwIEDFeuz2WyYPn06pk+frljmww8/xIcffqhXVJ8QEcKesWIEM76Cfe1iK6tU/3oJlQ0EJwpLVAfIYLvN6zPx5C95bbYgNQgPxvmSCpRVOExXPFn1VUosSF6MQZLdQ1889rtPaqcKsduA5DZNFffXCw1CcZm+Qd3q+CQGSUEHKK3gm8HlWq/OIVaQbJJ/fYHe+76H45kz24LE+pByjita31j+tiDVns+OWkJ4qHkKkjnT/K3lYgsLkT6yGw/l46GPNimW98UL5jaLrRbHINULrfqmEufg8QQtd7KWBQkwJ92AuxXQ+/dw53HtHDlayoLT2lJ7nzjvoKRYlJTzKZvOIuKSzsfTl0O63r7mIkcKCLM/KNVc51rvrlIOMF9BCpLFMNOCZEYn7+s8SKUqCtIHw3syTa5r951WPMYXJlpnB7BqVy76z/3V6+35k/BqBdWsmS57c89j/toD+NecX5gWFbEFU2nhSjOeUXkNvrDI8LivtYwRTqWxFhmQfLKIs2KQdrmDS9t0Xm9xNb7Mf+QkUD/Gvt95EkfzL2rmMPO3BcnvLjZCiuVcbBayIAXZbbpnNfjEglT97yOfbvF6W/4mvPr5NDNO4dUf9wEA9p9ynyQh7v+Vsup6Z5q/x1VqwjO4ablrgl0KUmAOlCx8cSqKQdqcMUjOEqwYJF/i6xAIMxn6frZmRu06m0mbYBNhoovNjI7G5y42lfZsNptuhccXL1htGpy0CHMpSN7L1ixGPJAp3XsznlG51cIX99SMwc1ptfC2tHd3a+HlFmrwxeuk9PHoEIAlm7STALMWHLb56F6ICVQLEgDk5F/UfAf8bUEiBclihOu0IE2/8yovSVKFr11sWm4HvbN1/O3Drm2EV88iLPWRgiQeAJSsh2bMpvPlLDYnZgxuzgHE25beob1bebV+X6N27c+XaKcZqHGx1dTjHMt9+cEUyAoSoC2/v2exkYJkMfS42Gw2YFhKgveEgTfW21JHLQbJBv1fFPIXbNrtHfDkjVcYEU2ROmRAqnGx+Sj9QyVHkHZ5hRkuNtlMRB8MPGYoNU4LkqfyPte/HSbdqpx2wJcDlS+UU71tfPmoNNGwAAGHzxRj5g97Xduc+Y98qbME+oxZreeWLEiEhIgQa90Sq1mQ9LrM5C/YZQ3CENcoQrdcavgiqNQqOIO0fWVBEisuSoO0KS422S30xXNvhovN+XyXe1jXo9dfjsG9lK1Etc0Sq1ehDLLL868B1/9vnWSbP8ZybyryvghP0LQg0Sw2QoyRGKSmnGs1GcHXiSLV2rPZ9L8w8pfcBpvpwZQB/hGnC6cFSc3SZyaVHDFI3lis1hexd2YoSM7n2wx5g1ReDF8movRJkLbONuT9iNrxvvxgMjubvRh5Ul5voPUOUJA2IUHPl6uzI/nowV7o0jIKD/RJMF0eb7rY7mIEfmotlyD/ktNCPqjabOYvBVBX9CO7rWag9JmCxDGLrcwUF5u8TvXzG9k3EbENw1XLaGGKi83Eaf5q7gxfDlRmuNhaKFiJf87oZ6g++bVh9YtOqX0ZlXC84JLX6jaiFN/ZNQ7Nozx7L8To7e/NhhQki3HqvP512Dq2iMLXY/qiz+XKGXeN8ukfOabX6YTV6apakGBDiIcuNpvr/8yjrsxiCw6yu+6ZlWaxmWNBkt5DHlevp+Z/PVYMJQVFzeqjF7U8Pj61IJlQx43tmrltax4VjssahDFKayO//qznozZlMweM3XOzc0GRBYmQcFvn5m7bQoPt6NC8oeaxgfaChjBMuFqWiSCdg5L8JbfZzH+JBVQtMlzbCbHXpFnwlQVJnG1aSSHxRh6kUg3rTnCQzadKg5JyaOa6ZaoWJB/GgnjLGvbesJ6Gg37l15n1/LuyawdYP6xEqIF7bgMwoJP7GGYUCtImJFwZ0wA/jL1Wsm3p6N4Y2LOl5rEnC0u8JZZXYFmD1GIpbDYgxEMXmzfW2v7ndDE6TvvR5FqtR3CQXeRi840FSYySud3TVey35Zxzs1poW5DsPv26VRoozNTR1E7Ht9OtzQted3JVXEN0bBFl+J7Jj2NNUnBK7cu12LyJUuZ6VWzAM2lt0TDcnBzUZEEi3JDPsgoJsnNp0oH24cL6AtccmDwM0o4IDfJLxtvaQEhQjQXJ18H7gFombc9kueutDfjpr1zdsljCgmTiw6w2sOvNYO8JZkzMcnvvqycXyBe75kV+ndUsSLUFI/fcbrMhPCQI6SYlFtXrMTAbUpAsiLyfCg6ycXWEg66O95JE3oH1haIeg1SzWCov8oGlfliwX9ZMqg2IrSa+crFJ21eKQfJ8ZNqbe15XeZvNt0rDPd3ZFmRfSWDImmAQM1xU8vfeuci1UZekvMtgP/9CdRuGmvAqRlxVRj4AnK2Y1ceSBYlwQ/5IhATZuR64emGBtbQea4DRcpdEhulLgyC3ODUMDyYLkkGCRRakUhPXYuNvn91d+UNZs9lsPlUaJtyinMTRF/h7oNKLXN6wYM+WcHKLUWO4mJ16uhU/wMJVpuxHKqSWMTLN33nqZj0vNIuNcENu6g6x202NNbAKrLgGVdeNTb8SKH/BGoSH6Dq+LqE1ESAkSGxB8n0MklKnu/FQvo8lqcKXSoPiEkQ+GowDbRab/L0PMzmnD+sDwWn5sp56pK7sKCl0xixIVXWxJuAYwd+KeS0cdgMf+SPB62ILNEKCbRhzg3TZj0sq08dtsOl2sclfsPrhwbUmiNIIcSo5Sr59oi/u7BqnuD/YbnMNPP6w2vh7RosYm803ifQ08VHgS5Dd/ASrSjhMyTAu/dtTBYlrFlv1v1bsX9QsaEriGpkV7dRL9aZjUYIyaRNusGKQeAcHo+/mktG9MeW2DsYONkh0/TA8k9ZWkpvkUpm6ZaKeTheb+LrZbUC90CC/LAlgFdQG9SC7TTWvTnCQ3dVh+UNBstJHgg02wzl1zMSXa3HpnUFqFO9YkIy72O7sGuf2YcF0sTnYFqQmXlzpgJcwlSWs7Db2+GIsx1bVMTzuZ57q/TEZRAwpSBZEPhG9ysXG97AamaWx+qnr0LtNU1wZU1/3sUZ4+a5OuLt7C9xdPdNBfGZqCQgbhAfrtiCJ45zqh1VZj8yf6B84aFo9VC5NiCQGyR/T/H3epCI2GzDp1vZIauabd0YJby41IcdnX/NemMWmpiBo8frgbm5WIdVFtWWXSW698scHmtq4YLOxrbNJMQ10t+OKQeJ4VhpHaiuOhZfKdctgJhbqcggnLAsSr9nWiILkfBGMToHVy8CeLTHn3q7MrwyWi236nVfh4X5t0LFFlO4YJLHVoX71sRYyRPgcLQVJTXkMtttcA0+xhqXPG5iZFJGXQT3jMevfnd1lsVVZQD8blcw8rj1HYlcz0JsDypP8NL6KBzEj4a3bLDYDLrZguw2fj+rN3MeMQar+V27plLftD1exmoIoCGxrUZAd+HDE1bracdaiZm1sVm15ZVmH5g3pJvm78CIpSIQGIUF2bnOnJ8Fx4sHTkynM8U3Y6yABwIvpHVWD/1gK0rCUBEy8pT0A/S42cWcVWa0g1WkXm4YSrPaYBXPm4/IWZi6rwcuDfRNxb0/39BlORVJJofSVtUuvBalbq8b48/mb8cnIXrrb4g3atYLrUW7BUAxyV2Hm3Z2QorB8EyvTulOvc7cgSdv2lat49HVtXL/V7l1pRSVT+Q2y2dDvysvw2qAu3G06z01t/HCePktBahNdT/J3AVmQCDnyF6gqUSTfsZ5YgcQvsieZc/tfFau47/7erVWPvVhtmVDqQyJ0dnTi975mOqt75VaIE/AFWrEBavpPSJB/nZP+UM6UXGjO51PpOfXV9GStJJnya1ZaUYmGKjM535B9wYvhdbG1i9XvmhFjTgyS5xYkVaWClUm7WkOS99/hIf6xID3XvyY1RIGKJaa0wsFMyGi3V3ku7urGzsE1gLEsVo2LTXrO4o9vZy/Csn7Kr93ll9VzK+NLSEGyIPJON8jOP4stJNj4yyd+iD0xp+v9QhJ3iM6vCiVFyGaz4bOHkjE0uRVX3WLXpLNOlnh6FS+zeebmK/HW0O5eb0d8W69gDP7qLja7X9ec83WQdsPwYE23ntJeX4XrlGvM+JJb3UqqXUNKh3VuGaVYF8/1796qES6/zLO4LDNcbGbkQVJTCJ0xSB1b1LhSHS4LknrbvrKEihWxYpX3VhDYH8Racv6rfYzbNpeLTW7BEytIKtWKxbipXTM8eVOSqgzehhQkCyJ+fpwPGu/g4JkFqeZYT1K8mzHNVU1hueaKaNysYqWSylLz22lBYl1Lf08ht9lsuNXERR6VEJ97W8aXvtqtCwmy+TVo0tfT6uVL/ohxPuNKz7qv1i7TsiDJxXBOghBnq375rk6u32quKJ4ZRclt2C4pPfDqR9dcodyWWxyQyIrDG4eldg+dFqSWjSJd2wSwZ7HJ43/8sXyG1uxg1rCh1SdGRbpbIp3vg7sVrea5UqtVrJRNuKWd3/PWkYJkQcSdrjMomXcAn3r7VYbbDZNYkIw/GkqiXpsUzV2HVswArz4jflEjqhUk1qGBFri9SGfwpBPxeTYIC0a6St4jOcF2u6qpnhetASoyNAg3tWvmtt3sZH9a9ExorLhP63ExonCPuCZB9zFaMUjuFqSqgVJspREvhB3OsLTM/0+VZZNnSRczArl57Uep7WOw8slrmfvk1p/rr6x5npY/1oerfrU4mvPVFhlxFmrnJZV3nfKPVk8sSGK3mR4uasw6ZfX3WtbTRhEsBanqX/nzL1GQVM5fvM8KfTIpSBZE/Fw4Z17JH9aBPVri+yf7uh3b78rLDFtDzPpCZ9Wz+qnr8MFw9qDO+mKMUEh/74Q3GkZcKiKk9sxii+R0Cf5voDTAUqwwCgLQpJ40oFZ1wdJgu6pVhYfurRphfFpb1TKdW0bhdUYsjN5A23u6t8QnI3vh2f5tsfihZF2D95BerTB5gHJeMFcMksJ+I++gEReiltIi7zdcLjaRMUg8YLNmOzljTXgmbthtJiSU5NSQbAA6xLFnC4pjwBY+0BOtmtZYeq5o1gDdWzXSrF8tBul8SZWCJO6nnPdP3jfJ74HRdAnfPdEXD/ZNMHRspZYrlvG8aj3CLRtHum1zXgN3BYlvbBEfZoWEm6QgWRDxc+FUkORfHa8O7IKr4tjxAkpr62gh9pUrxQHwrAnFUpCSYhqoKGDubWnFBBmxIDmvC+u90+pAvI3evoA33cFN7ZopxjYJENw6azU5OreIksyMMUJwkF2z46t0CExlRq8FKSLUjmuTLsNj11+BPldE4537e2DhAz3x3rCemsfOvLuTqkLmlI51Kg3Dgw0pSOdL9FvnKhzqbi/5dXTOEhU/7eJzCA8JwrAU9kQKnllsZliQ2nAG5qo9R2I5WjVxr4/HQs6jyIj7KacSIL8E8sV3jSjCY29KQscWUV6Lw2Odq1aXyJqt6HxHVV1sKqcgfm/8rx6RgmRJxC9+fQNT03l8+M0YD7dYgRErDOIM211aNtKs24x8SloKEu/XhbgfrFGQGJ0Bh4L08YP6p0Z7C/G9UpsZWLVMTc3fckVQPpCrXdXkNk1QLyxYcyaiGoIgaCoPFQ6BORgbmaot5qb2MbixXQz+1cE9uFQvTllYlsyfnupnSFE4p+G+HM5QXMo1XGzy2ZnODNDiDyD5+zD9zo7Mmag8CoMZuareub8HVzm1psTPGMt6wXMuPAqh+GPU+U7Kr6fcyufJFdKrIKVWB1KznvlWTWosQKznlSdY/sdx10n+dvbbbgoSZ5C8+DgrZM4nBcni1A9nu9jU0LKG/Ce5FbrGN3LbLu5UWohcKdH1azpZ+dcQizATZoRpLaHA/+6IO0rlGCSeJRu8uWAna6CtHxaMGxmxOIC08+qm4i6okrmm7g0Hz0r3yxUklesaWZ3FPFJnLioxSknpxFQ62EqUXhcwjxt21LWJuL7tZbrq7d2mCdKrs8DLm0hqVh+xUeGGLEgXy9RnCE67/SpdcXwAcG3SZZJp907rmdZ7zLJQ8lqQeN3fT9x4BXO7eOBWReU5Eg+uLMWa5/7wKLnhIgXJaYGXiyXvj424jpyH6H2sXhvUBbMHdsHse91zGX0wvCdS2jTF0tG9mcoIz0dj29gGSBC5L53XWv6ohHN6NcTjnAX0I1KQrE59nUHagLrm361VI7x8VyfFwea3Z2/Aj+OuQ1ORUnRb5zg8eeMV+HRksqbZFQDCdCoSvRkzX7TcKTxfF//u0ZKZB4nVQfEkJBZ/db5ydyfcoHNg1cvmyamYw+jYAGmnq7bsQbDdhsJLZcx9ggAMrE6CeN2VVeeiNrg54y30LvciDgQXoN3x1WcMzp+OTPbKLLb+HWN1uaTbXFYPS0an1CjbsnNxvqcsC4X6rCvgQom6gmS32/DJSHbmbjEdRFm87TYbvh5zDfa+2B8HX74V1yZV3Wet95iVkJVnLTbefuqD4T1xT3d2fh1eBUKtVKXI9chSkHiUPS4LUoi7BUneN/G67wf2YF8PMXqVqwbhIbinR0tm7qukmAb4fHRvJLdpiuj67h4F3iTt4Uw3o1TOpiJLpuo0f5v4t/81JFKQLE4DpwVJx8Oi9nHorEdpsIlvEom2sQ0k7QXZbci4uS36JkVzmV31DmQvpXeS/P1Iv8vRv6P6NH6tfrhn68b438AukqnJV1Z/SbOO5TkvSVxD00h8MPxq7nxMYlidkfNyL3rgathtwAt3XIXwkCDFDlEQamaD9VHI9gtU3btTRaXsOlB1v/96IQ0fPnC1RA4WTvN5z9aNlQsxkFs/tQZR+fV5/IbL0TcpWrfizYPNZtPnEpY9JvIzCWak5Xjl7k7YMfVmNGsQDiVsNptrZpQn3NElDnMHd3X97RAEhAUHITwkSHLdtR73pvXcn9HJt7XXbD/IzhekfVP7GC6X6UN9E5kJCQH1Z7VM5HoMZ/RH4nd5rEKuHa4YJLGLLYgdgyTvD5X0TLX3QvzhcpvC9fCEmXd3kngNAP58VOJ8amEuC5L0XMSuXrWPMLF12QL6ESlIVsf5tX6VwmwNFmoPttO0rm2h0V+3E73BtPJ8GhNuaac5aPHmxzh9oUY5uF7FSsLzlScO7GwcGQq73YaX7uqkcgSbqIhgtzWOnBLd0K4Zdk/vj+F9ElTrqHQI2DDxJvz27A1o3VQ5qNVms+HfPdW/TOuF1SREVFPEnQpSnyuU3Tytm7q7R3qIFCpBEDSVfXnwZ0L1+Xmy4KgSESFBuhR6+VMiV2Cds6fEA/DgXq0QFRmimovGbquZGeUJ84Z045ppqPUeP9g3AV1aRuHZ/jUzDq9NugzLHklx/f0A4xllDfKLH6qxel2bFI0Phle5+bRmqgLAje2budIMyHG+x4/fcLnbPnF+KFb2eLHy05AxXR3gsyBFiKypzudT3L+8MaSbm4VSSUHgDaN4U+F6eEJ8k0i39Ae8CtKVokVtnUqv/FzEHgml199mk0/z97+GRAqSxXHGIIWHBHHnwFBVkKr/1VJAlAYxnnfGDFeI1iDKuzSI2G1Rk9zPvRyPv13cqTZiJEnjJdhuV5yeDEhN1kpTqysFAfXDghHPEa/RPCpCcs7OmDK9wcpaiu+1SdH4VOYCanNZPQy+usbK5hC0B4LEaLbCFxpkXrbzp/91Je7v3RrtYtVmV7ojj92Rn0l09XPJOseSCmUFyWazabrYeBE3rRRrpDX4NQgPwddj+uKx66VxQuLcN0/ffKXbcUF29+Ff3NLtneNwU3XgsHgiRoNqtyorEF0J5zPNitPTyg8l/thRehx5XIricwhzBWnX7L+9SxzKKuQxSOy61F4LX+gKWrFTSogtT87rIY8zbBQhtiBJCbbb0KlFFP7vkRTLzWIzvrQz4RPE8Ri8C8iqPdfOfvHG9jH4KOuIYp1Kg5jAkaTEiIJ0e5c4fLvjBIb0qhpMtTqExpwKCmtpDFbVWvlkOrZo6EqyV9W+8bXbgoNsbnE2SucbGRqMRQ9cjdV78rA4O8e1nSdYXkyDsGAUVQ/Aq5/qh7/zzqNXYhO3curLAKjfFFZ8zL0946WuHWgHaTsXhw0PsaOk3OGKUVN7ruKiwnGisESyTa2ZJ0RuFT2Kl7sFqeZ3k3qheDG9IwB2gO9FFQvShyOuxlNLtzMXa9aL+ONC6bE2YTUPprWIdW/FbYknQ4jv5+Be8bi/d4LbQtdq7hjnHtaU/XKt9Aeifk/JtaV3mr8zSFv+cVcuC+ZRqlXtvfCFsiC/1rwWpMb1avpiZwySu4utpozcMpRyeVNX31FwsSZe0r+JV6ogC5LFEQ+kZnRqzir6XXkZlozujfUTbmSWc34djuybKNnOE7hnZN2jWfd0xocjrsa026tSCmhZkLQWXXXCVJBYQdoaF/eLh1MkHbFW/ESnFlGIbRgucS84Cbbb3NIYqA0EN7RrhpfSO+Jd0fTny+orx7OwELskG9cLRXKbpszrcM3l+mZJaeHWgiCoLryc3jXONXBu+m8qfn/uBpeVjKUgRYQE4b7erTwyx+uzICnv+/LRPi73Fmux2hIF5efjB3uhz+XReH/Y1ejWqhFaNvYsGackGajCMMPzoaOnHSesGCRxW0qDbkm5A62aRuq6j872WYqM1oK5wRyWCh4FSbxcTyjDggRIc1W9P6wn8xzn3NtF9QOE97KwXNy8yJvX0DFdiD8WwxnT/B+9/nJXugGAsRSL6P0TXwO9H4HegCxIFqc+57pB3IgeOtbsMSftYhti34z+bsoOb5D28sf64O63NnCLFREahOvb1pjKPV3Kyvl+shZpZH0xarnYIkOD0bFFQ4zsmyiZ1upsS35Zbu3UHI9e7x4b4Wxf74Bus9lw81WxWDK6N/KLyySZgXl4tn9bjF2yHYOqrTNK3NSenVZATvOocJwsLEGH5g3RtH4ohiazXSPugyXQlBGkDlTFHj0rciM3CA+RKHZiRaZXQhPce3U87uneAjabDfe8vQHHCy5xyS5Hj4Ikf/7Fiq34sbqvdyt8vjHHNTsQUFaQnBMJOrWMwlePXYPlW48h44sd3DLJET/fSlYJozOExIexqmC9W+JLpvSeKV0bVUWuuilWrFCP1k2w4L7uSFBw14oVWKV3UcvFlvGvKyXxcs6ZWm4WJJGLLbVDDF5eucetrru7t8RfJ4oU20rh/HD5YHhPvLxyr2LguR54Up8AMgWperxoHlXzAecWGiK73I+K3LjyTP/+hhQkiyOxIPnY6MiyBCk9tItHJeM/72UDqIpvEsfYyGdH8MCTS+XBaxKxcP0h1TIsCxIrfoinM7DZbJKkmU42/TcVPWf8LNmmNnWc5RLgHa+UlNpPRvbC/R9sVDzuzq4t0KN1Y8RFqd8Lm82G7q0aYWtOgWq5Lx5OwdJNRzG8TwIzo66TS2XSz1BBUJ4Ft+6Z61UzhMdFhSM5sQmCg2z4dGSyZGB77d6umPL1Ljx8XRv85/1sVdnlnCtmp0FgwTNDFACuiovC1in/ksTsKLnYushykqV3bYGIkCB0ZuQq40GsoygtgJx2VSw6NG/IdLPywlK+WMp/gmgSgVJcSwnHQrhyalxs7Jenf0fl2V7i0AKld0/LguSMffp/93TCkk1HXUvoyOsb2rsVNh7Od802VbIUsZTLtc9cj4KLZejWiv3OOBmXmoRrrojGFc0aYOEDxtZpbBwZig7NG2L3ySpFjdvFJlKQnF1bQnQ9vD64KzNWVHyW9/VuJZnE4ck6dd7AEi62+fPnIyEhAeHh4UhOTsbGjcodPQAsW7YM7dq1Q3h4ODp16oSVK1dK9guCgKlTp6J58+aIiIhAamoq9u/fLymTn5+PoUOHomHDhmjUqBFGjhyJCxcumH5uRundpgma1gtFcmLNgGimi80oSi+NOPeKs2P58tEUXHNFU0MLq/K8J1Nv74CN/72Juc85I4iVaVick+P6tpchLNiOhQrrxPHAmravpiDdpyMQlZdrky6TzDhi0bJxJNdMGZ5nJL5JJJ5Ja6uqHAHAmepZhM5BrFdiE9hsNuaSJVrT/202G5aM7u2mHAFVaRc+erCXZIYdb1frVFCa1gvFuNQkplvUidzsL/5okT+zTeqFSq73w/2qLIriadoDOjV3u4Z2uw23dGpu6MOiSg4bPhxxNd4a2h3JCgp1eEgQVo69Fs/foXdxa+kso6Wje0v2yu/h0tG9JdZOJUOtkgUpnrHel7h9wFgC10jR7DMla5q83vSucUhqVh//SW6FF+64Ch1bVC31NOjqVvjqsWtcllH5s3lHlzj89NR1rn5w7qCukj6o5nzcZWhSL1RTObqne0s8eWMSrk5QV3afYQTVi7HbbfjuiZr1PVnWvlHXJuKquIaS9RQbhIvjZGuu2Z1dW7jybinRXPbBJr4GZEECsHTpUmRkZGDBggVITk7G3LlzkZaWhn379qFZM3dz/4YNGzBkyBDMnDkTt912GxYvXoz09HRs3boVHTtWBUjOmjUL8+bNw0cffYTExERMmTIFaWlp2L17N8LDq0x/Q4cOxcmTJ7F69WqUl5djxIgRGD16NBYvXuzT81di8UO9USlIl1y4u3tL/O+nfbihLZ8bBKhStLblFLiSCXr60IkVpEUPXI0nl2zDq//uzLQ29WjdBJ891NttOw+8LgCleCenu2XMDVfgzbUHJCuli794/tUhBu8P6ynp2CcPaI+zxWXIKyzB8m3HDeU6YllCnv7XlUjrGIskUd4QJ/KOwgijr22D7H/y8cvfpz2qhzWI1TO4vt/p81UKUubT/bB6d57LFdeQ4Trmued6XJO8bonbuzRHREgQ+rW9jJmkUoz80oifmwZh6hMH7ktuhd6JTZAYXQ/f/XkSANCsobqCyaJRZAgKqpcl+TnjOqTO+dWtzPU6+gg9tImuh5Q2TdEoMgRBdhuS2zTFuNQkzP256gM0WDaLTa6gKccgSRWkb8f0xbmLZaqzNJ3ttGgUgZ6tG2PzkXPc5/HwdW2QuScPd3aNk1jc+l8Vi1V/5QJwV5DmDu4GQRA0n0H5XpvNJpkK37FFFDZPTsWkr3bh8401Ey/E1pNhKa1hAxClkILAyRXN6jOzZLMYc2MSGtcLxX+/2qVYRqzQsyYCxjQMx/dPXut2zPO3d8DJwhLJeSpxVVwUDp4uBqB+fr72mLDwu4I0Z84cjBo1CiNGjAAALFiwAN9//z0WLlyICRMmuJV//fXX0b9/f4wfPx4A8OKLL2L16tV48803sWDBAgiCgLlz52Ly5Mm48847AQAff/wxYmJisGLFCgwePBh79uzBqlWrsGnTJvTsWZWT44033sCtt96K//3vf4iLi3Nr19fY7TbYZa/aZQ3CsOuFNF2J7ZaMTkGlQ8Dlk6qsbFcwBmc9iIMfb2jXDDum3gy73SYxnRtZZkEO7zgoHrgbhAe7LEfOfzP+dSVu6RSLdrGi7MIi+QoulrsFfHdqEYXkNk1RVuHA4F6tVJfyUKIHw43UrGGYpANxurI6t4zCLRqJMXkIDrLjowd74ae/cjH6ky3G7wNjEFs7/nruwyfc0g6v/LAXQE0eqtZN6+Gha2usRg/2TcS2nAJk7j3l2sY7S1OLrIk3Ys/JIu4PibDgIMVkhHLklyYsOAjv3t8D5ZWCWz4vOTabDUkcA4gWV1xW36UMXNGsAeYO6opxS7fjZhPWmNPCbrfhc5nVSBwYrKXkKlk5SsulLrZOLaMkfy9+KBmTv96Fl+/qhMHv/lEtS41Myx5JwX9X7JLM9FSjcb1QrM7oBwD481iBa/uj11/uUpBY7w+Pgt4rsYnmR0qV0iTti8XtTb+zo2Y7gP7ZbVpWJjEsC1KkQhb9B65JZG4X890TffH9zpP4T69W+GbHCQBA++bSdCfisU0tsaqv8KuCVFZWhi1btmDixImubXa7HampqcjKymIek5WVhYyMDMm2tLQ0rFixAgBw6NAh5ObmIjU11bU/KioKycnJyMrKwuDBg5GVlYVGjRq5lCMASE1Nhd1uR3Z2Nu666y63dktLS1FaWpN0sKhIOaDOm/DMEAsJsklcS0F2G1Y8fg3+b8tRPP0vdTeMFlc0a4DPR/VGTPWXr1PZCLLb8OA1iTh3sQxtFAIj9cC7nEVwkB1bp/wLlQ4B4SF2pL32K04UlrhiK+x2G66Ki1I8nhWPJJ41xRuj4ZySDlQt7hvTsObljooIQeGlcvSVmZs/H90bJwpKFPP+GOXmq2Kx7JEUSeyHHqbefhXuebsmwH7Sre10dVaP9LvcpSApyRAZGowPHrgaCRO+BwAsf6yPaYnhmkdFmGKRYxHXyP063MxY2FWLLi2jsONYIe7upr28hJw593bFgx9tcrkp07u1QJf4Roj3cPabUTq1aOT6XS8s2C2mCgCyJ92EY+cuMfcB2rOv+lwRjTVPXy/ZJrY+2Gw2DOjUHIuzcxDbUN/A2rllIyx8oCdaNIpE66aRaBgeLHl/9TLq2jaoHxasuW7ef5JbYcPBs67YpAYGJuTwJNsUc2VMA7x451VopnJ+t3SMxQ+7cvGgaAaz8+NT71qAYjq2iELHFlGSiTPy2YZ2uw1bJqei0iHoPjevIPiR48ePCwCEDRs2SLaPHz9e6NWrF/OYkJAQYfHixZJt8+fPF5o1ayYIgiCsX79eACCcOHFCUmbgwIHCvffeKwiCILz00kvClVde6Vb3ZZddJrz11lvMdqdNmyagysIu+a+wsJDvZH3I5sNnhf5zfxX+OHjG36IYpqLSITzyyWbhjcy/dR1XUFwmvPPLASG38JJquXX7TgnPf7NLKCmvcG37O7dI2HjorCF5t+ecE+6a/7uw+bD78UWXyoTj5y4aqtdflJZXCqXllcLOYwWCw+HQffyek4XCC9/8JZw5X6JabsfRc8Kvf58yKqbP+OPgGWHkhxuFnLPFptRXUl4RcM+EGj/vzhXeXLNfKC2vFBwOh/DllqPC37lFmsdtPZIvTFr+p5B/oZS7rc+zjwj//epPobLS/bn863ihUHSpTJfsckrKK4QKRt3epri0XBj8Tpbw/m//aJZdvvWo0G/WGmEfxzXWS2Wlw+1+FF0qE46Z+Lz+su+UkOXH8amwsJBr/Pa7iy1QmDhxosRyVVRUhPh49SnT/qJH6yb4Yey12gUtTJDdhrfv66FdUEZUZAhGX8eeXi+m35WXod+VUouOJ+6PLvGNsPyxa5j75NPVAwHn1HdnIKpe2sU2xNTb3Wf8yencspGh+n1NcpumigHPRggLDuJaEiRQuKl9jCtDNlAVL8lDt1aNNYOQ5QzupRwTqJahnhcjedzMIDI02M19qcRd3VriLgPWRx7sdhsay4LIze7DrrtSPXjbKvh1Flt0dDSCgoKQl5cn2Z6Xl4fYWLbZOjY2VrW881+tMqdOnZLsr6ioQH5+vmK7YWFhaNiwoeQ/giAIgiBqJ35VkEJDQ9GjRw9kZma6tjkcDmRmZiIlJYV5TEpKiqQ8AKxevdpVPjExEbGxsZIyRUVFyM7OdpVJSUlBQUEBtmzZ4iqzZs0aOBwOJCcrT/ElCIIgCKJu4HcXW0ZGBoYPH46ePXuiV69emDt3LoqLi12z2oYNG4YWLVpg5syZAICxY8eiX79+mD17NgYMGIAlS5Zg8+bNePfddwFUBeuNGzcOM2bMQFJSkmuaf1xcHNLT0wEA7du3R//+/TFq1CgsWLAA5eXlGDNmDAYPHmyJGWwEQRAEQfgXvytIgwYNwunTpzF16lTk5uaia9euWLVqFWJiqvzZOTk5sIsyD/fp0weLFy/G5MmTMWnSJCQlJWHFihWuHEgA8Oyzz6K4uBijR49GQUEB+vbti1WrVrlyIAHAZ599hjFjxuCmm26C3W7HPffcg3nz5vnuxAmCIAiCsCw2QbBCvsrAo6ioCFFRUSgsLKR4JIIgCIIIEHjHb0ssNUIQBEEQBGElSEEiCIIgCIKQQQoSQRAEQRCEDFKQCIIgCIIgZJCCRBAEQRAEIYMUJIIgCIIgCBmkIBEEQRAEQcggBYkgCIIgCEIGKUgEQRAEQRAy/L7USKDiTEBeVFTkZ0kIgiAIguDFOW5rLSRCCpJBzp8/DwCIj4/3syQEQRAEQejl/PnziIqKUtxPa7EZxOFw4MSJE2jQoAFsNptp9RYVFSE+Ph5Hjx6lNd68AF1f70PX2LvQ9fUudH29ixWuryAIOH/+POLi4mC3K0cakQXJIHa7HS1btvRa/Q0bNqSX04vQ9fU+dI29C11f70LX17v4+/qqWY6cUJA2QRAEQRCEDFKQCIIgCIIgZJCCZDHCwsIwbdo0hIWF+VuUWgldX+9D19i70PX1LnR9vUsgXV8K0iYIgiAIgpBBFiSCIAiCIAgZpCARBEEQBEHIIAWJIAiCIAhCBilIBEEQBEEQMkhBshjz589HQkICwsPDkZycjI0bN/pbJMszc+ZMXH311WjQoAGaNWuG9PR07Nu3T1KmpKQEjz/+OJo2bYr69evjnnvuQV5enqRMTk4OBgwYgMjISDRr1gzjx49HRUWFL08lIHjllVdgs9kwbtw41za6vp5z/Phx3HfffWjatCkiIiLQqVMnbN682bVfEARMnToVzZs3R0REBFJTU7F//35JHfn5+Rg6dCgaNmyIRo0aYeTIkbhw4YKvT8VyVFZWYsqUKUhMTERERAQuv/xyvPjii5K1uOj68vPrr7/i9ttvR1xcHGw2G1asWCHZb9a1/PPPP3HttdciPDwc8fHxmDVrlrdPTYpAWIYlS5YIoaGhwsKFC4W//vpLGDVqlNCoUSMhLy/P36JZmrS0NGHRokXCrl27hO3btwu33nqr0KpVK+HChQuuMo888ogQHx8vZGZmCps3bxZ69+4t9OnTx7W/oqJC6Nixo5Camips27ZNWLlypRAdHS1MnDjRH6dkWTZu3CgkJCQInTt3FsaOHevaTtfXM/Lz84XWrVsLDzzwgJCdnS38888/wo8//igcOHDAVeaVV14RoqKihBUrVgg7duwQ7rjjDiExMVG4dOmSq0z//v2FLl26CH/88Yfw22+/CVdccYUwZMgQf5ySpXjppZeEpk2bCt99951w6NAhYdmyZUL9+vWF119/3VWGri8/K1euFP773/8Ky5cvFwAIX331lWS/GdeysLBQiImJEYYOHSrs2rVL+Pzzz4WIiAjhnXfe8dVpCqQgWYhevXoJjz/+uOvvyspKIS4uTpg5c6YfpQo8Tp06JQAQfvnlF0EQBKGgoEAICQkRli1b5iqzZ88eAYCQlZUlCELVC2+324Xc3FxXmbffflto2LChUFpa6tsTsCjnz58XkpKShNWrVwv9+vVzKUh0fT3nueeeE/r27au43+FwCLGxscKrr77q2lZQUCCEhYUJn3/+uSAIgrB7924BgLBp0yZXmR9++EGw2WzC8ePHvSd8ADBgwADhwQcflGy7++67haFDhwqCQNfXE+QKklnX8q233hIaN24s6R+ee+45oW3btl4+oxrIxWYRysrKsGXLFqSmprq22e12pKamIisry4+SBR6FhYUAgCZNmgAAtmzZgvLycsm1bdeuHVq1auW6tllZWejUqRNiYmJcZdLS0lBUVIS//vrLh9Jbl8cffxwDBgyQXEeArq8ZfPPNN+jZsycGDhyIZs2aoVu3bnjvvfdc+w8dOoTc3FzJNY6KikJycrLkGjdq1Ag9e/Z0lUlNTYXdbkd2drbvTsaC9OnTB5mZmfj7778BADt27MDvv/+OW265BQBdXzMx61pmZWXhuuuuQ2hoqKtMWloa9u3bh3PnzvnkXGixWotw5swZVFZWSgYQAIiJicHevXv9JFXg4XA4MG7cOFxzzTXo2LEjACA3NxehoaFo1KiRpGxMTAxyc3NdZVjX3rmvrrNkyRJs3boVmzZtcttH19dz/vnnH7z99tvIyMjApEmTsGnTJjz55JMIDQ3F8OHDXdeIdQ3F17hZs2aS/cHBwWjSpEmdv8YTJkxAUVER2rVrh6CgIFRWVuKll17C0KFDAYCur4mYdS1zc3ORmJjoVodzX+PGjb0iv0Qmr7dAED7k8ccfx65du/D777/7W5Raw9GjRzF27FisXr0a4eHh/hanVuJwONCzZ0+8/PLLAIBu3bph165dWLBgAYYPH+5n6QKfL774Ap999hkWL16Mq666Ctu3b8e4ceMQFxdH15dQhFxsFiE6OhpBQUFuM3/y8vIQGxvrJ6kCizFjxuC7777D2rVr0bJlS9f22NhYlJWVoaCgQFJefG1jY2OZ1965ry6zZcsWnDp1Ct27d0dwcDCCg4Pxyy+/YN68eQgODkZMTAxdXw9p3rw5OnToINnWvn175OTkAKi5Rmr9Q2xsLE6dOiXZX1FRgfz8/Dp/jcePH48JEyZg8ODB6NSpE+6//3489dRTmDlzJgC6vmZi1rW0Qp9BCpJFCA0NRY8ePZCZmena5nA4kJmZiZSUFD9KZn0EQcCYMWPw1VdfYc2aNW5m2R49eiAkJERybfft24ecnBzXtU1JScHOnTslL+3q1avRsGFDt4GrrnHTTTdh586d2L59u+u/nj17YujQoa7fdH0945prrnFLTfH333+jdevWAIDExETExsZKrnFRURGys7Ml17igoABbtmxxlVmzZg0cDgeSk5N9cBbW5eLFi7DbpcNdUFAQHA4HALq+ZmLWtUxJScGvv/6K8vJyV5nVq1ejbdu2PnGvAaBp/lZiyZIlQlhYmPDhhx8Ku3fvFkaPHi00atRIMvOHcOfRRx8VoqKihHXr1gknT550/Xfx4kVXmUceeURo1aqVsGbNGmHz5s1CSkqKkJKS4trvnIZ+8803C9u3bxdWrVolXHbZZTQNXQHxLDZBoOvrKRs3bhSCg4OFl156Sdi/f7/w2WefCZGRkcKnn37qKvPKK68IjRo1Er7++mvhzz//FO68807m1Olu3boJ2dnZwu+//y4kJSXVyWnocoYPHy60aNHCNc1/+fLlQnR0tPDss8+6ytD15ef8+fPCtm3bhG3btgkAhDlz5gjbtm0Tjhw5IgiCOdeyoKBAiImJEe6//35h165dwpIlS4TIyEia5l+XeeONN4RWrVoJoaGhQq9evYQ//vjD3yJZHgDM/xYtWuQqc+nSJeGxxx4TGjduLERGRgp33XWXcPLkSUk9hw8fFm655RYhIiJCiI6OFp5++mmhvLzcx2cTGMgVJLq+nvPtt98KHTt2FMLCwoR27doJ7777rmS/w+EQpkyZIsTExAhhYWHCTTfdJOzbt09S5uzZs8KQIUOE+vXrCw0bNhRGjBghnD9/3penYUmKioqEsWPHCq1atRLCw8OFNm3aCP/9738lU8jp+vKzdu1aZp87fPhwQRDMu5Y7duwQ+vbtK4SFhQktWrQQXnnlFV+doiAIgmATBFEqUYIgCIIgCIJikAiCIAiCIOSQgkQQBEEQBCGDFCSCIAiCIAgZpCARBEEQBEHIIAWJIAiCIAhCBilIBEEQBEEQMkhBIgiCIAiCkEEKEkEQBEEQhAxSkAiC8CrXX389xo0bp1omISEBc+fO9Yk8BEEQPJCCRBCE39m0aRNGjx7tbzG8is1mw4oVK7xSNymYBGE+wf4WgCAI4rLLLvN6G2VlZQgNDfV6OwRB1A7IgkQQhNepqKjAmDFjEBUVhejoaEyZMgXiZSDlFhCbzYb3338fd911FyIjI5GUlIRvvvnGtb+yshIjR45EYmIiIiIi0LZtW7z++uuSNh944AGkp6fjpZdeQlxcHNq2bYvp06ejY8eObvJ17doVU6ZMUZT/l19+Qa9evRAWFobmzZtjwoQJqKioUJTfWefzzz/v2g8Ad911F2w2m+vv559/Hl27dsU777yD+Ph4REZG4t5770VhYaGrHpaLMj09HQ888IBr/5EjR/DUU0/BZrPBZrMpngdBEPyQgkQQhNf56KOPEBwcjI0bN+L111/HnDlz8P7776se88ILL+Dee+/Fn3/+iVtvvRVDhw5Ffn4+AMDhcKBly5ZYtmwZdu/ejalTp2LSpEn44osvJHVkZmZi3759WL16Nb777js8+OCD2LNnDzZt2uQqs23bNvz5558YMWIEU47jx4/j1ltvxdVXX40dO3bg7bffxgcffIAZM2Zwn7+zvUWLFuHkyZOS9g8cOIAvvvgC3377LVatWoVt27bhscce4657+fLlaNmyJaZPn46TJ0/i5MmT3McSBKEMudgIgvA68fHxeO2112Cz2dC2bVvs3LkTr732GkaNGqV4zAMPPIAhQ4YAAF5++WXMmzcPGzduRP/+/RESEoIXXnjBVTYxMRFZWVn44osvcO+997q216tXD++//77EtZaWloZFixbh6quvBlCltPTr1w9t2rRhyvHWW28hPj4eb775Jmw2G9q1a4cTJ07gueeew9SpU2G3a39nOl2IjRo1QmxsrGRfSUkJPv74Y7Ro0QIA8MYbb2DAgAGYPXu2W1kWTZo0QVBQEBo0aMBVniAIPsiCRBCE1+ndu7fE9ZOSkoL9+/ejsrJS8ZjOnTu7fterVw8NGzbEqVOnXNvmz5+PHj164LLLLkP9+vXx7rvvIicnR1JHp06d3OKORo0ahc8//xwlJSUoKyvD4sWL8eCDDyrKsWfPHqSkpEjkv+aaa3DhwgUcO3ZM++Q1aNWqlUs5AqqujcPhwL59+zyumyAI45AFiSAISxISEiL522azweFwAACWLFmCZ555BrNnz0ZKSgoaNGiAV199FdnZ2ZJj6tWr51bv7bffjrCwMHz11VcIDQ1FeXk5/v3vf3skq91ul8RUAUB5eblHdfqiboIglCEFiSAIryNXXP744w8kJSUhKCjIUH3r169Hnz59JLE6Bw8e5Do2ODgYw4cPx6JFixAaGorBgwcjIiJCsXz79u3x5ZdfQhAElxVp/fr1aNCgAVq2bAmgyoUmjv0pKirCoUOHJPWEhIQwLWY5OTk4ceIE4uLiAFRdG7vdjrZt2zLrrqysxK5du3DDDTe4toWGhqpa4wiC0A+52AiC8Do5OTnIyMjAvn378Pnnn+ONN97A2LFjDdeXlJSEzZs348cff8Tff/+NKVOmSAKftXjooYewZs0arFq1StW9BgCPPfYYjh49iieeeAJ79+7F119/jWnTpiEjI8MVf3TjjTfik08+wW+//YadO3di+PDhbspfQkICMjMzkZubi3Pnzrm2h4eHY/jw4dixYwd+++03PPnkk7j33ntd8UQ33ngjvv/+e3z//ffYu3cvHn30URQUFLjV/euvv+L48eM4c+YM93UgCEIZsiARBOF1hg0bhkuXLqFXr14ICgrC2LFjPUoM+fDDD2Pbtm0YNGgQbDYbhgwZgsceeww//PAD1/FJSUno06cP8vPzkZycrFq2RYsWWLlyJcaPH48uXbqgSZMmGDlyJCZPnuwqM3HiRBw6dAi33XYboqKi8OKLL7pZkGbPno2MjAy89957aNGiBQ4fPgwAuOKKK3D33Xfj1ltvRX5+Pm677Ta89dZbruMefPBB7NixA8OGDUNwcDCeeuopifUIAKZPn46HH34Yl19+OUpLS91ccgRB6Mcm0JtEEEQdQxAEJCUl4bHHHkNGRobf5Hj++eexYsUKbN++3W8yEATBhixIBEHUKU6fPo0lS5YgNzdXMfcRQRAEKUgEQdQpmjVrhujoaLz77rto3Lixv8UhCMKikIuNIAiCIAhCBs1iIwiCIAiCkEEKEkEQBEEQhAxSkAiCIAiCIGSQgkQQBEEQBCGDFCSCIAiCIAgZpCARBEEQBEHIIAWJIAiCIAhCBilIBEEQBEEQMv4/4+5jrMb0yjQAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1895,7 +1893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 39,
    "id": "2870f1a5",
    "metadata": {
     "collapsed": false,
@@ -1909,7 +1907,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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8WrgLDg7WgQMHNGnSJB09elRr164td60pU6ZY3e4gJyeHkAQAQDV10zeK9PLyUocOHSqzl0o3ZswYHTx4UF9//bXV+OjRoy0/t2/fXkFBQerdu7eOHz+u5s2bl7mWyWSSyWRyaL8AAKBqsDsgPfDAAzIYDOW+v3nz5gqt4+fnJ3d3d2VnZ1uNZ2dn23VuUHni4uK0fv16bd++XY0aNbru3PDwcEnSsWPHyg1IAADgzmH3OUidOnVSx44dLVvbtm1VUFCgjIwMtW/fvsLrGI1GhYWFKTU11TJWUlKi1NRURURE2NuWhdlsVlxcnD799FNt3rxZTZs2veFn9u/fL0kKCgq66boAAKD6sHsP0vz588scnzFjhnJzc+1aKz4+XjExMeratau6d++uBQsWKC8vz3JV2/Dhw9WwYUMlJiZK+vXE7sOHD1t+PnXqlPbv36/atWurRYsWkn49rJacnKzPPvtM3t7eysrKkiT5+PioZs2aOn78uJKTk/XQQw+pfv36OnDggCZMmKA//OEPVf6QIQAAcI6bPgfJ1lNPPaXu3btr7ty5Ff7M4MGDde7cOU2fPl1ZWVnq1KmTNmzYYDlxOzMzU25uv+3kOn36tDp37mx5PXfuXM2dO1e9evXS1q1bJUlLliyR9OvNIH9v5cqVeuaZZ2Q0GrVp0yZLGAsJCdGAAQM0derUm/zmAACguqm0gJSWliZPT0+7PxcXF6e4uLgy37sWeq4JDQ2V2Wy+7no3ej8kJETbtm2zq0cAAHBnsTsg2d4M0mw268yZM9q7d6+mTZtWaY0BAAC4it0BqU6dOlZXsbm5ualVq1aaOXOm+vTpU6nNAQAAuILdAen99993QBsAAABVh92X+Tdr1kwXLlwoNX7p0iU1a9asUpoCAABwJbsD0o8//qji4uJS4/n5+Tp16lSlNAUAAOBKFT7E9ve//93y88aNG+Xj42N5XVxcrNTUVIWGhlZqcwAAAK5Q4YAUHR0tSTIYDIqJibF6r0aNGgoNDdW8efMqtTkAAABXqHBAKikpkSQ1bdpUe/bskZ+fn8OaAgAAcCW7r2I7ceKEI/oAAACoMioUkBYuXKjRo0fL09NTCxcuvO7ccePGVUpjAABcT0FRiYwedl9rBFRIhQLS/PnzNWzYMHl6epb7sFrp1/OTCEgAAGcweripx+xU5eYXOa2mv7dJm1+632n14DoVCki/P6zGITYAQFWRm1/k1IDkZXJ3Wi24FvsmAQAAbFRoD1J8fHyFF0xKSrrpZgAAAKqCCgWkffv2VWix3z/EFgAA4HZVoYC0ZcsWR/cBAABQZdzSOUgnT57UyZMnK6sXAACAKsHugFRUVKRp06bJx8dHoaGhCg0NlY+Pj6ZOnarCwkJH9AgAAOBUdt9Je+zYsVq7dq3eeOMNRURESJLS0tI0Y8YMXbhwQUuWLKn0JgEAAJzJ7oCUnJysVatWqV+/fpaxDh06KCQkREOHDiUgAQCA257dh9hMJpNCQ0NLjTdt2lRGo7EyegIAAHApuwNSXFycZs2apfz8fMtYfn6+XnvtNcXFxVVqcwAAAK5g9yG2ffv2KTU1VY0aNVLHjh0lSd9++60KCgrUu3dv9e/f3zJ37dq1ldcpAACAk9gdkHx9fTVgwACrsZCQkEprCAAAwNXsDkgrV650RB8AAABVBg+rBQAAsGH3HqQLFy5o+vTp2rJli86ePauSkhKr9y9evFhpzQEAALiC3QHp6aef1rFjxzRy5EgFBATwgFoAAFDt2B2QvvrqK3399deWK9gAAACqG7vPQWrdurX++9//OqIXAACAKsHugLR48WL95S9/0bZt23ThwgXl5ORYbQAAALe7m7oPUk5Ojh588EGrcbPZLIPBoOLi4kprDgAAwBXsDkjDhg1TjRo1lJyczEnaAACgWrI7IB08eFD79u1Tq1atHNEPAACAy9l9DlLXrl118uRJR/QCAABQJdi9B2ns2LEaP368Jk6cqPbt26tGjRpW73fo0KHSmgMAAHAFuwPS4MGDJUkjRoywjBkMBk7SBgAA1Ybdh9hOnDhRavv3v/9t+V97LVq0SKGhofL09FR4eLh2795d7txDhw5pwIABCg0NlcFg0IIFC25qzatXr2rMmDGqX7++ateurQEDBig7O9vu3gEAQPVkd0Bq0qTJdTd7rF69WvHx8UpISFBGRoY6duyoqKgonT17tsz5V65cUbNmzTRnzhwFBgbe9JoTJkzQ559/rk8++UTbtm3T6dOn1b9/f7t6BwAA1Zfdh9iuOXz4sDIzM1VQUGA1/thjj1V4jaSkJI0aNUqxsbGSpKVLl+qLL77QihUrNHny5FLzu3Xrpm7duklSme9XZM3Lly9r+fLlSk5OttzLaeXKlWrTpo127typHj16VLh/AABQPdkdkP7973/riSee0HfffWc590iS5X5IFT0HqaCgQOnp6ZoyZYplzM3NTZGRkUpLS7O3rQqvmZ6ersLCQkVGRlrmtG7dWo0bN1ZaWlq5ASk/P1/5+fmW19w1HACA6svuQ2zjx49X06ZNdfbsWdWqVUuHDh3S9u3b1bVrV23durXC65w/f17FxcUKCAiwGg8ICFBWVpa9bVV4zaysLBmNRvn6+tpVNzExUT4+PpYtJCTkpnoEAABVn90BKS0tTTNnzpSfn5/c3Nzk5uame++9V4mJiRo3bpwjeqwSpkyZosuXL1s27gUFAED1ZXdAKi4ulre3tyTJz89Pp0+flvTrydtHjx6t8Dp+fn5yd3cvdfVYdnZ2uSdgV8aagYGBKigo0KVLl+yqazKZVKdOHasNAABUT3YHpHbt2unbb7+VJIWHh+uNN97Qjh07NHPmTDVr1qzC6xiNRoWFhSk1NdUyVlJSotTUVEVERNjbVoXXDAsLU40aNazmHD16VJmZmTddFwAAVC92n6Q9depU5eXlSZJmzpypRx55RPfdd5/q16+v1atX27VWfHy8YmJi1LVrV3Xv3l0LFixQXl6e5Qq04cOHq2HDhkpMTJT060nYhw8ftvx86tQp7d+/X7Vr11aLFi0qtKaPj49Gjhyp+Ph41atXT3Xq1NHYsWMVERHBFWwAcBMKikpk9LD7v7eBKs3ugBQVFWX5uUWLFvr+++918eJF1a1b13IlW0UNHjxY586d0/Tp05WVlaVOnTppw4YNlpOsMzMz5eb22790p0+fVufOnS2v586dq7lz56pXr16WE8RvtKYkzZ8/X25ubhowYIDy8/MVFRWlxYsX2/tHAQCQZPRwU4/ZqcrNL3JaTX9vkza/dL/T6uHOc9P3Qfq9evXq3fRn4+LiFBcXV+Z7tlfFhYaGWm4rcLNrSpKnp6cWLVqkRYsW2dUrAKBsuflFTg1IXiZ3p9XCnYl9ogAAADYISAAAADYISAAAADbsDkjXrmADAACoruwOSAEBARoxYoS+/vprR/QDAADgcnYHpL/97W+6ePGiHnzwQd11112aM2eO5W7aAAAA1YHdASk6Olrr1q3TqVOn9Pzzzys5OVlNmjTRI488orVr16qoyHmXeQIAADjCTZ+k3aBBA8XHx+vAgQNKSkrSpk2b9OSTTyo4OFjTp0/XlStXKrNPAAAAp7npG0VmZ2frgw8+0Pvvv6+ffvpJTz75pEaOHKmff/5Zr7/+unbu3Kl//vOfldkrAACAU9gdkNauXauVK1dq48aNatu2rV544QU99dRT8vX1tczp2bOn2rRpU5l9AgAAOI3dASk2NlZDhgzRjh071K1btzLnBAcH6y9/+cstNwcAAOAKdgekM2fOqFatWtedU7NmTSUkJNx0UwAAAK5k90na3t7eOnv2bKnxCxcuyN2dhwcCAIDbn90ByWw2lzmen58vo9F4yw0BAAC4WoUPsS1cuFCSZDAY9N5776l27dqW94qLi7V9+3a1bt268jsEAABwsgoHpPnz50v6dQ/S0qVLrQ6nGY1GhYaGaunSpZXfIQAAgJNVOCCdOHFCkvTAAw9o7dq1qlu3rsOaAgAAcCW7r2LbsmWLI/oAAACoMioUkOLj4zVr1ix5eXkpPj7+unOTkpIqpTEAAABXqVBA2rdvnwoLCy0/l8dgMFROVwAAAC5UoYD0+8NqHGIDAADVnd33QQIAAKjuKrQHqX///hVecO3atTfdDAAAQFVQoYDk4+Pj6D4AAACqjAoFpJUrVzq6DwAAgCqDc5AAAABsVGgPUpcuXZSamqq6deuqc+fO172cPyMjo9KaAwAAcIUKBaTHH39cJpNJkhQdHe3IfgAAAFyuQgEpISGhzJ8BAACqI7ufxXbN3r17deTIEUlS27ZtFRYWVmlNAQAAuJLdAennn3/W0KFDtWPHDvn6+kqSLl26pJ49e2rVqlVq1KhRZfcIAKiAgqISGT249gaoDHYHpGeffVaFhYU6cuSIWrVqJUk6evSoYmNj9eyzz2rDhg2V3iQA4MaMHm7qMTtVuflFTqvp723S5pfud1o9wFnsDkjbtm3TN998YwlHktSqVSu9/fbbuu+++yq1OQCAfXLzi5wakLxM7k6rBTiT3ftiQ0JCVFhYWGq8uLhYwcHBldIUAACAK9kdkN58802NHTtWe/futYzt3btX48eP19y5cyu1OQAAAFeo0CG2unXrWt0cMi8vT+Hh4fLw+PXjRUVF8vDw0IgRI7hPEgAAuO1VKCAtWLDAwW0AAABUHRUKSDExMY7uAwAAoMq4pRtmXL16VTk5OVbbzVi0aJFCQ0Pl6emp8PBw7d69+7rzP/nkE7Vu3Vqenp5q3769vvzyS6v3DQZDmdubb75pmRMaGlrq/Tlz5txU/wAAoHqxOyDl5eUpLi5O/v7+8vLyUt26da02e61evVrx8fFKSEhQRkaGOnbsqKioKJ09e7bM+d98842GDh2qkSNHat++fYqOjlZ0dLQOHjxomXPmzBmrbcWKFTIYDBowYIDVWjNnzrSaN3bsWLv7BwAA1Y/dAenPf/6zNm/erCVLlshkMum9997TK6+8ouDgYH344Yd2N5CUlKRRo0YpNjZWbdu21dKlS1WrVi2tWLGizPlvvfWW+vbtq4kTJ6pNmzaaNWuWunTponfeeccyJzAw0Gr77LPP9MADD6hZs2ZWa3l7e1vN8/LyKrfP/Pz8StlbBgAAqj67A9Lnn3+uxYsXa8CAAfLw8NB9992nqVOnavbs2froo4/sWqugoEDp6emKjIz8rSE3N0VGRiotLa3Mz6SlpVnNl6SoqKhy52dnZ+uLL77QyJEjS703Z84c1a9fX507d9abb76poqLyb66WmJgoHx8fyxYSElKRrwgAAG5DdgekixcvWvbE1KlTRxcvXpQk3Xvvvdq+fbtda50/f17FxcUKCAiwGg8ICFBWVlaZn8nKyrJr/gcffCBvb2/179/fanzcuHFatWqVtmzZoueee06zZ8/Wn//853J7nTJlii5fvmzZTp48WZGvCAAAbkN2P2qkWbNmOnHihBo3bqzWrVtrzZo16t69uz7//HPLw2urkhUrVmjYsGHy9PS0Go+Pj7f83KFDBxmNRj333HNKTEyUyWQqtY7JZCpzHAAAVD9270GKjY3Vt99+K0maPHmyFi1aJE9PT02YMEETJ060ay0/Pz+5u7srOzvbajw7O1uBgYFlfiYwMLDC87/66isdPXpUzz777A17CQ8PV1FRkX788ceKfwEAAFAt2R2QJkyYoHHjxkmSIiMjdeTIESUnJ2vfvn0aP368XWsZjUaFhYUpNTXVMlZSUqLU1FRFRESU+ZmIiAir+ZKUkpJS5vzly5crLCxMHTt2vGEv+/fvl5ubm/z9/e36DgAAoPqx+xCbrdDQUIWGht705+Pj4xUTE6OuXbuqe/fuWrBggfLy8hQbGytJGj58uBo2bKjExERJ0vjx49WrVy/NmzdPDz/8sFatWqW9e/dq2bJlVuvm5OTok08+0bx580rVTEtL065du/TAAw/I29tbaWlpmjBhgp566qmbulUBAACoXm4qIKWmpmr+/Pk6cuSIJKlNmzZ68cUXS11dVhGDBw/WuXPnNH36dGVlZalTp07asGGD5UTszMxMubn9tqOrZ8+eSk5O1tSpU/Xyyy+rZcuWWrdundq1a2e17qpVq2Q2mzV06NBSNU0mk1atWqUZM2YoPz9fTZs21YQJE6zOSwIAAHcuuwPS4sWLNX78eD355JOWQ2o7d+7UQw89pPnz52vMmDF2NxEXF6e4uLgy39u6dWupsYEDB2rgwIHXXXP06NEaPXp0me916dJFO3futLtPAABwZ7A7IM2ePVvz58+3CjTjxo3TPffco9mzZ99UQAIAAKhK7D5J+9KlS+rbt2+p8T59+ujy5cuV0hQAAIAr2R2QHnvsMX366aelxj/77DM98sgjldIUAACAK1XoENvChQstP7dt21avvfaatm7darm0fufOndqxY4f+53/+xzFdAgAAOFGFAtL8+fOtXtetW1eHDx/W4cOHLWO+vr5asWKFpk6dWrkdAgAAOFmFAtKJEycc3QcAAECVYfc5SL9nNptlNpsrqxcAAIAq4aYC0ocffqj27durZs2aqlmzpjp06KC//vWvld0bAACAS9h9H6SkpCRNmzZNcXFxuueeeyRJX3/9tZ5//nmdP39eEyZMqPQmAQAAnMnugPT2229ryZIlGj58uGXsscce0913360ZM2YQkAAAwG3P7kNsZ86cUc+ePUuN9+zZU2fOnKmUpgAAAFzJ7oDUokULrVmzptT46tWr1bJly0ppCgBuZwVFJa5uAcAtsvsQ2yuvvKLBgwdr+/btlnOQduzYodTU1DKDEwDcaYwebuoxO1W5+UVOq+nvbdLml+53Wj2gurM7IA0YMEC7d+9WUlKS1q1bJ0lq06aNdu/erc6dO1d2fwBwW8rNL3JqQPIyuTutFnAnsCsgFRYW6rnnntO0adP0t7/9zVE9AQAAuJRd5yDVqFFD//d//+eoXgAAAKoEu0/Sjo6OthxaAwAAqI7sPgepZcuWmjlzpnbs2KGwsDB5eXlZvT9u3LhKaw4AAMAV7A5Iy5cvl6+vr9LT05Wenm71nsFgICABAIDbnt0B6cSJE47oAwAAoMq4qYfVXmM2m2U2myurFwAAgCrhpgLS8uXL1a5dO3l6esrT01Pt2rXTe++9V9m9AQAAuITdh9imT5+upKQkjR07VhEREZKktLQ0TZgwQZmZmZo5c2alNwkAAOBMdgekJUuW6N1339XQoUMtY4899pg6dOigsWPHEpAAAMBtz+5DbIWFheratWup8bCwMBUVOe+2+gAAAI5id0B6+umntWTJklLjy5Yt07BhwyqlKQAAAFey+xCb9OtJ2v/85z/Vo0cPSdKuXbuUmZmp4cOHKz4+3jIvKSmpcroEAABwIrsD0sGDB9WlSxdJ0vHjxyVJfn5+8vPz08GDBy3zDAZDJbUIAADgXHYHpC1btjiiDwAAgCrjlm4UCQAAUB0RkAAAAGwQkAAAAGwQkAAAAGwQkAAAAGwQkAAAAGwQkAAAAGwQkAAAAGxUiYC0aNEihYaGytPTU+Hh4dq9e/d153/yySdq3bq1PD091b59e3355ZdW7z/zzDMyGAxWW9++fa3mXLx4UcOGDVOdOnXk6+urkSNHKjc3t9K/GwAAuP24PCCtXr1a8fHxSkhIUEZGhjp27KioqCidPXu2zPnffPONhg4dqpEjR2rfvn2Kjo5WdHS01WNOJKlv3746c+aMZfv444+t3h82bJgOHTqklJQUrV+/Xtu3b9fo0aMd9j0BOF9BUYmrWwBwm7qph9VWpqSkJI0aNUqxsbGSpKVLl+qLL77QihUrNHny5FLz33rrLfXt21cTJ06UJM2aNUspKSl65513tHTpUss8k8mkwMDAMmseOXJEGzZs0J49e9S1a1dJ0ttvv62HHnpIc+fOVXBwcGV/TQAuYPRwU4/ZqcrNL3JaTX9vkza/dL/T6gFwDJfuQSooKFB6eroiIyMtY25uboqMjFRaWlqZn0lLS7OaL0lRUVGl5m/dulX+/v5q1aqV/vSnP+nChQtWa/j6+lrCkSRFRkbKzc1Nu3btKrNufn6+cnJyrDYAVV9ufpFTt7wC54UxAI7j0oB0/vx5FRcXKyAgwGo8ICBAWVlZZX4mKyvrhvP79u2rDz/8UKmpqXr99de1bds29evXT8XFxZY1/P39rdbw8PBQvXr1yq2bmJgoHx8fyxYSEmL39wUAALcHlx9ic4QhQ4ZYfm7fvr06dOig5s2ba+vWrerdu/dNrTllyhTFx8dbXufk5BCSAACoply6B8nPz0/u7u7Kzs62Gs/Ozi73/KHAwEC75ktSs2bN5Ofnp2PHjlnWsD0JvKioSBcvXix3HZPJpDp16lhtAACgenJpQDIajQoLC1NqaqplrKSkRKmpqYqIiCjzMxEREVbzJSklJaXc+ZL0888/68KFCwoKCrKscenSJaWnp1vmbN68WSUlJQoPD7+VrwQAAKoBl1/mHx8fr3fffVcffPCBjhw5oj/96U/Ky8uzXNU2fPhwTZkyxTJ//Pjx2rBhg+bNm6fvv/9eM2bM0N69exUXFydJys3N1cSJE7Vz5079+OOPSk1N1eOPP64WLVooKipKktSmTRv17dtXo0aN0u7du7Vjxw7FxcVpyJAhXMEGAABcfw7S4MGDde7cOU2fPl1ZWVnq1KmTNmzYYDkROzMzU25uv+W4nj17Kjk5WVOnTtXLL7+sli1bat26dWrXrp0kyd3dXQcOHNAHH3ygS5cuKTg4WH369NGsWbNkMpks63z00UeKi4tT79695ebmpgEDBmjhwoXO/fIAAKBKcnlAkqS4uDjLHiBbW7duLTU2cOBADRw4sMz5NWvW1MaNG29Ys169ekpOTrarTwAAcGdw+SE2AACAqoaABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABAAAYIOABMDhCopKXN0CANjFw9UNAKj+jB5u6jE7Vbn5RU6r6e9t0uaX7ndaPQDVCwEJgFPk5hc5NSB5mdydVgtA9cMhNgAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJOAOUlBU4uoWAOC24OHqBgA4j9HDTT1mpyo3v8hpNf29Tdr80v1OqwcAlYGABNxhcvOLnBqQvEzuTqsFAJWFQ2wAAAA2CEgAAAA2qkRAWrRokUJDQ+Xp6anw8HDt3r37uvM/+eQTtW7dWp6enmrfvr2+/PJLy3uFhYWaNGmS2rdvLy8vLwUHB2v48OE6ffq01RqhoaEyGAxW25w5cxzy/QAAwO3F5QFp9erVio+PV0JCgjIyMtSxY0dFRUXp7NmzZc7/5ptvNHToUI0cOVL79u1TdHS0oqOjdfDgQUnSlStXlJGRoWnTpikjI0Nr167V0aNH9dhjj5Vaa+bMmTpz5oxlGzt2rEO/KwAAuD24/CTtpKQkjRo1SrGxsZKkpUuX6osvvtCKFSs0efLkUvPfeust9e3bVxMnTpQkzZo1SykpKXrnnXe0dOlS+fj4KCUlxeoz77zzjrp3767MzEw1btzYMu7t7a3AwMAK9Zmfn6/8/HzL65ycHLu/KwAAuD24dA9SQUGB0tPTFRkZaRlzc3NTZGSk0tLSyvxMWlqa1XxJioqKKne+JF2+fFkGg0G+vr5W43PmzFH9+vXVuXNnvfnmmyoqKv/KnsTERPn4+Fi2kJCQCnxDAABwO3LpHqTz58+ruLhYAQEBVuMBAQH6/vvvy/xMVlZWmfOzsrLKnH/16lVNmjRJQ4cOVZ06dSzj48aNU5cuXVSvXj198803mjJlis6cOaOkpKQy15kyZYri4+Mtr3NycghJAABUUy4/xOZIhYWFGjRokMxms5YsWWL13u/DTocOHWQ0GvXcc88pMTFRJpOp1Fomk6nMcQAAUP249BCbn5+f3N3dlZ2dbTWenZ1d7rlBgYGBFZp/LRz99NNPSklJsdp7VJbw8HAVFRXpxx9/tP+LAACAasWlAcloNCosLEypqamWsZKSEqWmpioiIqLMz0RERFjNl6SUlBSr+dfC0Q8//KBNmzapfv36N+xl//79cnNzk7+//01+GwAAUF24/BBbfHy8YmJi1LVrV3Xv3l0LFixQXl6e5aq24cOHq2HDhkpMTJQkjR8/Xr169dK8efP08MMPa9WqVdq7d6+WLVsm6ddw9OSTTyojI0Pr169XcXGx5fykevXqyWg0Ki0tTbt27dIDDzwgb29vpaWlacKECXrqqadUt25d1/xBAACAKsPlAWnw4ME6d+6cpk+frqysLHXq1EkbNmywnIidmZkpN7ffdnT17NlTycnJmjp1ql5++WW1bNlS69atU7t27SRJp06d0t///ndJUqdOnaxqbdmyRffff79MJpNWrVqlGTNmKD8/X02bNtWECROszksCAAB3LpcHJEmKi4tTXFxcme9t3bq11NjAgQM1cODAMueHhobKbDZft16XLl20c+dOu/sEAAB3BpffSRsAAKCqISABAADYICABLlBQVOLqFgAA11ElzkEC7jRGDzf1mJ2q3PzyH29T2fy9Tdr80v1OqwcAtzMCEuAiuflFTg1IXiZ3p9UCgNsdh9gAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJAAAABsEJBwRysoKnF1CwCAKoiH1eKOZvRwU4/ZqU59aKy/t0mbX7rfafUAAPYjIOGOl5tf5NSA5GVyd1otAMDN4RAbAACADQISAACADQISAACADQISAACADQISAACADQISAACADQISAACADQISAACADQISXI7HfQAAqhrupA2Xc8XjPiQe+QEAKB8BCVWCsx/3IfHIDwBA+TjEBgAAYIOABAAAYIOABAAAYIOABAAAYIOABAsutwcA4FdcxQYLV1xuz6X2AICqiIAEK86+3J5L7QEAVVGVOMS2aNEihYaGytPTU+Hh4dq9e/d153/yySdq3bq1PD091b59e3355ZdW75vNZk2fPl1BQUGqWbOmIiMj9cMPP1jNuXjxooYNG6Y6derI19dXI0eOVG5ubqV/NwAAcPtxeUBavXq14uPjlZCQoIyMDHXs2FFRUVE6e/ZsmfO/+eYbDR06VCNHjtS+ffsUHR2t6OhoHTx40DLnjTfe0MKFC7V06VLt2rVLXl5eioqK0tWrVy1zhg0bpkOHDiklJUXr16/X9u3bNXr0aId/XwAAUPW5PCAlJSVp1KhRio2NVdu2bbV06VLVqlVLK1asKHP+W2+9pb59+2rixIlq06aNZs2apS5duuidd96R9OveowULFmjq1Kl6/PHH1aFDB3344Yc6ffq01q1bJ0k6cuSINmzYoPfee0/h4eG699579fbbb2vVqlU6ffq0s756uThZGgAA13LpOUgFBQVKT0/XlClTLGNubm6KjIxUWlpamZ9JS0tTfHy81VhUVJQl/Jw4cUJZWVmKjIy0vO/j46Pw8HClpaVpyJAhSktLk6+vr7p27WqZExkZKTc3N+3atUtPPPFEqbr5+fnKz8+3vL58+bIkKScnx/4vXgH/39tfK8+J5wL51TZpzfMR8jTnq1jOq2sq+fXP0Nl1XVmbutSlLnWpe32e5mKH/f16bV2z2Xz9iWYXOnXqlFmS+ZtvvrEanzhxorl79+5lfqZGjRrm5ORkq7FFixaZ/f39zWaz2bxjxw6zJPPp06et5gwcONA8aNAgs9lsNr/22mvmu+66q9TaDRo0MC9evLjMugkJCWZJbGxsbGxsbNVgO3ny5HUzClexVdCUKVOs9lyVlJTo4sWLql+/vgwGgws7+01OTo5CQkJ08uRJ1alTh7rVsDZ1qUtd6lL31pjNZv3yyy8KDg6+7jyXBiQ/Pz+5u7srOzvbajw7O1uBgYFlfiYwMPC686/9b3Z2toKCgqzmdOrUyTLH9iTwoqIiXbx4sdy6JpNJJpPJaszX1/f6X9BF6tSp45J/GO+0uq6sTV3qUpe61L15Pj4+N5zj0pO0jUajwsLClJqaahkrKSlRamqqIiIiyvxMRESE1XxJSklJscxv2rSpAgMDrebk5ORo165dljkRERG6dOmS0tPTLXM2b96skpIShYeHV9r3AwAAtyeXH2KLj49XTEyMunbtqu7du2vBggXKy8tTbGysJGn48OFq2LChEhMTJUnjx49Xr169NG/ePD388MNatWqV9u7dq2XLlkmSDAaDXnzxRb366qtq2bKlmjZtqmnTpik4OFjR0dGSpDZt2qhv374aNWqUli5dqsLCQsXFxWnIkCE33OUGAACqP5cHpMGDB+vcuXOaPn26srKy1KlTJ23YsEEBAQGSpMzMTLm5/bajq2fPnkpOTtbUqVP18ssvq2XLllq3bp3atWtnmfPnP/9ZeXl5Gj16tC5duqR7771XGzZskKenp2XORx99pLi4OPXu3Vtubm4aMGCAFi5c6Lwv7gAmk0kJCQmlDgVSt/rUpi51qUtd6jqHwWy+0XVuAAAAdxaX3ygSAACgqiEgAQAA2CAgAQAA2CAgAQAA2CAgVQPbt2/Xo48+quDgYBkMBstz6RwtMTFR3bp1k7e3t/z9/RUdHa2jR486vO6SJUvUoUMHy83HIiIi9I9//MPhdW3NmTPHclsJR5oxY4YMBoPV1rp1a4fWvObUqVN66qmnVL9+fdWsWVPt27fX3r17HVozNDS01Pc1GAwaM2aMQ+sWFxdr2rRpatq0qWrWrKnmzZtr1qxZN35eUyX45Zdf9OKLL6pJkyaqWbOmevbsqT179lR6nRv9rjCbzZo+fbqCgoJUs2ZNRUZG6ocffnB43bVr16pPnz6WJxPs37//lmveqG5hYaEmTZqk9u3by8vLS8HBwRo+fHilPLD8Rt93xowZat26tby8vFS3bl1FRkZq165dDq/7e88//7wMBoMWLFjg8LrPPPNMqX+f+/bte8t1HY2AVA3k5eWpY8eOWrRokVPrbtu2TWPGjNHOnTuVkpKiwsJC9enTR3l5eQ6t26hRI82ZM0fp6enau3evHnzwQT3++OM6dOiQQ+v+3p49e/S///u/6tChg1Pq3X333Tpz5oxl+/rrrx1e8z//+Y/uuece1ahRQ//4xz90+PBhzZs3T3Xr1nVo3T179lh915SUFEnSwIEDHVr39ddf15IlS/TOO+/oyJEjev311/XGG2/o7bffdmhdSXr22WeVkpKiv/71r/ruu+/Up08fRUZG6tSpU5Va50a/K9544w0tXLhQS5cu1a5du+Tl5aWoqChdvXrVoXXz8vJ077336vXXX7+lOvbUvXLlijIyMjRt2jRlZGRo7dq1Onr0qB577DGH1pWku+66S++8846+++47ff311woNDVWfPn107tw5h9a95tNPP9XOnTsr7b5/Fanbt29fq3+vP/7440qp7VDXfVIbbjuSzJ9++qlLap89e9Ysybxt2zan165bt675vffec0qtX375xdyyZUtzSkqKuVevXubx48c7tF5CQoK5Y8eODq1RlkmTJpnvvfdep9e1NX78eHPz5s3NJSUlDq3z8MMPm0eMGGE11r9/f/OwYcMcWvfKlStmd3d38/r1663Gu3TpYv7LX/7isLq2vytKSkrMgYGB5jfffNMydunSJbPJZDJ//PHHDqv7eydOnDBLMu/bt6/S6lWk7jW7d+82SzL/9NNPTq17+fJlsyTzpk2bHF73559/Njds2NB88OBBc5MmTczz58+vtJrl1Y2JiTE//vjjlVrHGdiDhEpz+fJlSVK9evWcVrO4uFirVq1SXl5euY+nqWxjxozRww8/rMjISKfUk6QffvhBwcHBatasmYYNG6bMzEyH1/z73/+url27auDAgfL391fnzp317rvvOrzu7xUUFOhvf/ubRowY4fCHQvfs2VOpqan617/+JUn69ttv9fXXX6tfv34OrVtUVKTi4mKrG9lKUs2aNZ2yp/CaEydOKCsry+qfax8fH4WHhystLc1pfbjS5cuXZTAYnPqczYKCAi1btkw+Pj7q2LGjQ2uVlJTo6aef1sSJE3X33Xc7tJatrVu3yt/fX61atdKf/vQnXbhwwan1b4bL76SN6qGkpEQvvvii7rnnHqu7mjvKd999p4iICF29elW1a9fWp59+qrZt2zq87qpVq5SRkeGQ80PKEx4ervfff1+tWrXSmTNn9Morr+i+++7TwYMH5e3t7bC6//73v7VkyRLFx8fr5Zdf1p49ezRu3DgZjUbFxMQ4rO7vrVu3TpcuXdIzzzzj8FqTJ09WTk6OWrduLXd3dxUXF+u1117TsGHDHFrX29tbERERmjVrltq0aaOAgAB9/PHHSktLU4sWLRxa+/eysrIkyfIUg2sCAgIs71VnV69e1aRJkzR06FCnPFh1/fr1GjJkiK5cuaKgoCClpKTIz8/PoTVff/11eXh4aNy4cQ6tY6tv377q37+/mjZtquPHj+vll19Wv379lJaWJnd3d6f2Yg8CEirFmDFjdPDgQaf9F2+rVq20f/9+Xb58Wf/v//0/xcTEaNu2bQ4NSSdPntT48eOVkpJS6r/2Hen3ezA6dOig8PBwNWnSRGvWrNHIkSMdVrekpERdu3bV7NmzJUmdO3fWwYMHtXTpUqcFpOXLl6tfv35OeUbimjVr9NFHHyk5OVl333239u/frxdffFHBwcEO/75//etfNWLECDVs2FDu7u7q0qWLhg4davVAbThOYWGhBg0aJLPZrCVLljil5gMPPKD9+/fr/PnzevfddzVo0CDt2rVL/v7+DqmXnp6ut956SxkZGQ7fG2tryJAhlp/bt2+vDh06qHnz5tq6dat69+7t1F7swSE23LK4uDitX79eW7ZsUaNGjZxS02g0qkWLFgoLC1NiYqI6duyot956y6E109PTdfbsWXXp0kUeHh7y8PDQtm3btHDhQnl4eKi4uNih9a/x9fXVXXfdpWPHjjm0TlBQUKnA2aZNG6cc3pOkn376SZs2bdKzzz7rlHoTJ07U5MmTNWTIELVv315PP/20JkyYYHlQtiM1b95c27ZtU25urk6ePKndu3ersLBQzZo1c3jtawIDAyVJ2dnZVuPZ2dmW96qja+Hop59+UkpKilP2HkmSl5eXWrRooR49emj58uXy8PDQ8uXLHVbvq6++0tmzZ9W4cWPL76+ffvpJ//M//6PQ0FCH1S1Ls2bN5Ofn5/DfYbeKgISbZjabFRcXp08//VSbN29W06ZNXdZLSUmJ8vPzHVqjd+/e+u6777R//37L1rVrVw0bNkz79+932q7i3NxcHT9+XEFBQQ6tc88995S6bcO//vUvNWnSxKF1r1m5cqX8/f318MMPO6XelStXrB6MLUnu7u4qKSlxSn3p1780g4KC9J///EcbN27U448/7rTaTZs2VWBgoFJTUy1jOTk52rVrl9PO73O2a+Hohx9+0KZNm1S/fn2X9eLo32FPP/20Dhw4YPX7Kzg4WBMnTtTGjRsdVrcsP//8sy5cuODw32G3ikNs1UBubq5VEj9x4oT279+vevXqqXHjxg6rO2bMGCUnJ+uzzz6Tt7e35TwFHx8f1axZ02F1p0yZon79+qlx48b65ZdflJycrK1btzr8X3Jvb+9S51d5eXmpfv36Dj3v6qWXXtKjjz6qJk2a6PTp00pISJC7u7uGDh3qsJqSNGHCBPXs2VOzZ8/WoEGDtHv3bi1btkzLli1zaF3p178sVq5cqZiYGHl4OOfX1KOPPqrXXntNjRs31t133619+/YpKSlJI0aMcHjtjRs3ymw2q1WrVjp27JgmTpyo1q1bKzY2tlLr3Oh3xYsvvqhXX31VLVu2VNOmTTVt2jQFBwcrOjraoXUvXryozMxMyz2IrgXzwMDAW9p7db26QUFBevLJJ5WRkaH169eruLjY8jusXr16MhqNDqlbv359vfbaa3rssccUFBSk8+fPa9GiRTp16tQt38riRn/OtgGwRo0aCgwMVKtWrRxWt169enrllVc0YMAABQYG6vjx4/rzn/+sFi1aKCoq6pbqOpyLr6JDJdiyZYtZUqktJibGoXXLqinJvHLlSofWHTFihLlJkyZmo9FobtCggbl3797mf/7znw6tWR5nXOY/ePBgc1BQkNloNJobNmxoHjx4sPnYsWMOrXnN559/bm7Xrp3ZZDKZW7dubV62bJlT6m7cuNEsyXz06FGn1DObzeacnBzz+PHjzY0bNzZ7enqamzVrZv7LX/5izs/Pd3jt1atXm5s1a2Y2Go3mwMBA85gxY8yXLl2q9Do3+l1RUlJinjZtmjkgIMBsMpnMvXv3rpT/D25Ud+XKlWW+n5CQ4LC6124pUNa2ZcsWh9X973//a37iiSfMwcHBZqPRaA4KCjI/9thj5t27d99SzRvVLUtlXeZ/vbpXrlwx9+nTx9ygQQNzjRo1zE2aNDGPGjXKnJWVdct1Hc1gNjvhNrEAAAC3Ec5BAgAAsEFAAgAAsEFAAgAAsEFAAgAAsEFAAgAAsEFAAgAAsEFAAgAAsEFAAgAAsEFAAuBQ999/v1588cXrzgkNDdWCBQuc0g8AVAQBCYDL7dmzR6NHj3Z1Gw5lMBi0bt06h6xNwAQqHw+rBeByDRo0cHiNgoKCW3oAKYA7C3uQADhcUVGR4uLi5OPjIz8/P02bNk2/fwyk7R4Qg8Gg9957T0888YRq1aqlli1b6u9//7vl/eLiYo0cOVJNmzZVzZo11apVK7311ltWNZ955hlFR0frtddeU3BwsFq1aqWZM2eqXbt2pfrr1KmTpk2bVm7/27ZtU/fu3WUymRQUFKTJkyerqKio3P6vrTljxgzL+5L0xBNPyGAwWF7PmDFDnTp10v/+7/8qJCREtWrV0qBBg3T58mXLOmUdooyOjtYzzzxjef+nn37ShAkTZDAYZDAYyv0eACqOgATA4T744AN5eHho9+7deuutt5SUlKT33nvvup955ZVXNGjQIB04cEAPPfSQhg0bposXL0qSSkpK1KhRI33yySc6fPiwpk+frpdffllr1qyxWiM1NVVHjx5VSkqK1q9frxEjRujIkSPas2ePZc6+fft04MABxcbGltnHqVOn9NBDD6lbt2769ttvtWTJEi1fvlyvvvpqhb//tXorV67UmTNnrOofO3ZMa9as0eeff64NGzZo3759euGFFyq89tq1a9WoUSPNnDlTZ86c0ZkzZyr8WQDl4xAbAIcLCQnR/PnzZTAY1KpVK3333XeaP3++Ro0aVe5nnnnmGQ0dOlSSNHv2bC1cuFC7d+9W3759VaNGDb3yyiuWuU2bNlVaWprWrFmjQYMGWca9vLz03nvvWR1ai4qK0sqVK9WtWzdJv4aWXr16qVmzZmX2sXjxYoWEhOidd96RwWBQ69atdfr0aU2aNEnTp0+Xm9uN/zvz2iFEX19fBQYGWr139epVffjhh2rYsKEk6e2339bDDz+sefPmlZpblnr16snd3V3e3t4Vmg+gYtiDBMDhevToYXXoJyIiQj/88IOKi4vL/UyHDh0sP3t5ealOnTo6e/asZWzRokUKCwtTgwYNVLt2bS1btkyZmZlWa7Rv377UeUejRo3Sxx9/rKtXr6qgoEDJyckaMWJEuX0cOXJEERERVv3fc889ys3N1c8//3zjL38DjRs3toQj6dc/m5KSEh09evSW1wZw89iDBKBKqlGjhtVrg8GgkpISSdKqVav00ksvad68eYqIiJC3t7fefPNN7dq1y+ozXl5epdZ99NFHZTKZ9Omnn8poNKqwsFBPPvnkLfXq5uZmdU6VJBUWFt7Sms5YG0D5CEgAHM42uOzcuVMtW7aUu7v7Ta23Y8cO9ezZ0+pcnePHj1fosx4eHoqJidHKlStlNBo1ZMgQ1axZs9z5bdq00f/93//JbDZb9iLt2LFD3t7eatSokaRfD6H9/tyfnJwcnThxwmqdGjVqlLnHLDMzU6dPn1ZwcLCkX/9s3Nzc1KpVqzLXLi4u1sGDB/XAAw9YxoxG43X3xgGwH4fYADhcZmam4uPjdfToUX388cd6++23NX78+Jter2XLltq7d682btyof/3rX5o2bZrVic838uyzz2rz5s3asGHDdQ+vSdILL7ygkydPauzYsfr+++/12WefKSEhQfHx8Zbzjx588EH99a9/1VdffaXvvvtOMTExpcJfaGioUlNTlZWVpf/85z+WcU9PT8XExOjbb7/VV199pXHjxmnQoEGW84kefPBBffHFF/riiy/0/fff609/+pMuXbpUau3t27fr1KlTOn/+fIX/HACUjz1IABxu+PDh+u9//6vu3bvL3d1d48ePv6UbQz733HPat2+fBg8eLIPBoKFDh+qFF17QP/7xjwp9vmXLlurZs6cuXryo8PDw685t2LChvvzyS02cOFEdO3ZUvXr1NHLkSE2dOtUyZ8qUKTpx4oQeeeQR+fj4aNasWaX2IM2bN0/x8fF699131bBhQ/3444+SpBYtWqh///566KGHdPHiRT3yyCNavHix5XMjRozQt99+q+HDh8vDw0MTJkyw2nskSTNnztRzzz2n5s2bKz8/v9QhOQD2M5j5NwnAHcZsNqtly5Z64YUXFB8f77I+ZsyYoXXr1mn//v0u6wFA2diDBOCOcu7cOa1atUpZWVnl3vsIAAhIAO4o/v7+8vPz07Jly1S3bl1XtwOgiuIQGwAAgA2uYgMAALBBQAIAALBBQAIAALBBQAIAALBBQAIAALBBQAIAALBBQAIAALBBQAIAALDx/wNBova6VsxEQwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1927,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 40,
    "id": "44e0482f",
    "metadata": {
     "collapsed": false,
@@ -1945,13 +1943,13 @@
        "<Axes: xlabel='binary output', ylabel='probability amplitude'>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1966,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 42,
    "id": "c7091393",
    "metadata": {
     "collapsed": false,
@@ -1984,13 +1982,13 @@
        "<Axes: xlabel='binary output', ylabel='probability amplitude'>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2000,6 +1998,7 @@
     }
    ],
    "source": [
+    "# plot all outputs above probability threshold:\n",
     "psi.plot_state(probability_threshold=0.1)"
    ]
   },
@@ -2020,9 +2019,8 @@
    "source": []
   },
   {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "eecc33f9",
+   "cell_type": "markdown",
+   "id": "0012423e-4419-49ba-8b2f-c304852cc5db",
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -2032,7 +2030,6 @@
      "name": "#%%\n"
     }
    },
-   "outputs": [],
    "source": [
     "## Final comments"
    ]
@@ -2413,16 +2410,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [
-    "## Final comments"
-   ],
+   "id": "79d60b85",
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": [
+    "## Final comments"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2433,7 +2434,7 @@
     }
    },
    "source": [
-    "Please note that `QuantumState`, while efficient, represents a full statevector simulation that is most efficient for states that can be efficiently described in the computational basis (efficiently meaning not too many basis states). For states that are not necessarily entangled, but require all computational basis states, e.g. the plus state on each qubit (act with Hadmard gate on each qubit). `QuantumState` will keep track a vector of size $2^{n}$. In such cases, alternative python libraries can describe these systems more efficiently if this becomes a bottleneck (e.g. qiskit circuit simulation tools). The goal of `QuantumState` is not to compete with these optimized libraries and has slightly different usage. In fact we find they can be used together, for example getting the statevector out of a qiskit statevector simulation and initalizing a `QuantumState` to perform analysis on."
+    "Please note that `QuantumState`, while efficient, represents a full statevector simulation that is most efficient for states that can be efficiently described in the **computational basis** (efficiently meaning not too many basis states). For states that are not necessarily entangled, but require all computational basis states, e.g. the plus state on each qubit (act with Hadmard gate on each qubit). `QuantumState` will keep track a vector of size $2^{n}$. In such cases, alternative python libraries can describe these systems more efficiently if this becomes a bottleneck (e.g. qiskit circuit simulation tools). The goal of `QuantumState` is not to compete with these optimized libraries and has slightly different usage. In fact we find they can be used together, for example getting the statevector out of a qiskit statevector simulation and initalizing a `QuantumState` to perform analysis on."
    ]
   },
   {
@@ -2465,9 +2466,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}

From 82a2b97e55a9f90c392b8669c1ed8774df0fdf67 Mon Sep 17 00:00:00 2001
From: AlexisRalli <rallilex@hotmail.co.uk>
Date: Fri, 5 Jan 2024 17:35:40 +0000
Subject: [PATCH 3/3] poetry updates allow for python 3.11

---
 poetry.lock    | 309 +++++++++++++++++++++++++------------------------
 pyproject.toml |   7 +-
 2 files changed, 163 insertions(+), 153 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index ba3eb81e..013cb063 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -403,6 +403,64 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
 [[package]]
 name = "contourpy"
 version = "1.1.1"
@@ -1182,27 +1240,6 @@ qtconsole = ["qtconsole"]
 test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
 test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
 
-[[package]]
-name = "ipywidgets"
-version = "8.1.1"
-description = "Jupyter interactive widgets"
-optional = false
-python-versions = ">=3.7"
-files = [
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-
-[package.dependencies]
-comm = ">=0.1.3"
-ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0.9,<3.1.0"
-traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.9,<4.1.0"
-
-[package.extras]
-test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
-
 [[package]]
 name = "isort"
 version = "5.13.2"
@@ -1361,17 +1398,6 @@ traitlets = ">=5.3"
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
 test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
 
-[[package]]
-name = "jupyterlab-widgets"
-version = "3.0.9"
-description = "Jupyter interactive widgets for JupyterLab"
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
 [[package]]
 name = "kiwisolver"
 version = "1.4.5"
@@ -1487,39 +1513,35 @@ files = [
 
 [[package]]
 name = "llvmlite"
-version = "0.39.1"
+version = "0.41.1"
 description = "lightweight wrapper around basic LLVM functionality"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
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-numpy = ">=1.18,<1.23"
-setuptools = "*"
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+numpy = ">=1.22,<1.27"
 
 [[package]]
 name = "numpy"
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+    {file = "pandas-1.5.1.tar.gz", hash = "sha256:249cec5f2a5b22096440bd85c33106b6102e0672204abd2d5c014106459804ee"},
+]
+
+[package.dependencies]
+numpy = {version = ">=1.21.0", markers = "python_version >= \"3.10\""}
+python-dateutil = ">=2.8.1"
+pytz = ">=2020.1"
+
+[package.extras]
+test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
+
 [[package]]
 name = "pandas"
 version = "2.0.3"
@@ -2139,7 +2187,7 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\""},
+    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -2469,13 +2517,13 @@ tests = ["pytest"]
 
 [[package]]
 name = "py3dmol"
-version = "1.8.1"
-description = "An IPython interface for embedding 3Dmol.js views"
+version = "2.0.4"
+description = "An IPython interface for embedding 3Dmol.js views in Jupyter notebooks"
 optional = false
 python-versions = "*"
 files = [
-    {file = "py3Dmol-1.8.1-py2.py3-none-any.whl", hash = "sha256:c4741f1bc2e219024b165e36c3e5c172c0550a3a36b81825d2a9d0eff06cd714"},
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 ]
 
 [package.extras]
@@ -2642,7 +2690,6 @@ files = [
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     {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
     {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
     {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
@@ -2650,15 +2697,8 @@ files = [
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     {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
-    {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"},
     {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
-    {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
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-    {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
-    {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
-    {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
     {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@@ -2675,7 +2715,6 @@ files = [
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
-    {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
     {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
     {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
     {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@@ -2683,7 +2722,6 @@ files = [
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
-    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
     {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
     {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@@ -3199,22 +3237,6 @@ dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
 doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
 test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
-[[package]]
-name = "setuptools"
-version = "69.0.3"
-description = "Easily download, build, install, upgrade, and uninstall Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "setuptools-69.0.3-py3-none-any.whl", hash = "sha256:385eb4edd9c9d5c17540511303e39a147ce2fc04bc55289c322b9e5904fe2c05"},
-    {file = "setuptools-69.0.3.tar.gz", hash = "sha256:be1af57fc409f93647f2e8e4573a142ed38724b8cdd389706a867bb4efcf1e78"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -3736,17 +3758,6 @@ files = [
     {file = "wcwidth-0.2.12.tar.gz", hash = "sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02"},
 ]
 
-[[package]]
-name = "widgetsnbextension"
-version = "4.0.9"
-description = "Jupyter interactive widgets for Jupyter Notebook"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"},
-    {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
-]
-
 [[package]]
 name = "zipp"
 version = "3.17.0"
@@ -3764,5 +3775,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8 <3.11"
-content-hash = "13491887272376cff6df68ca63e0acfd9b43f6a25ad30af4d93a985d27ecf19c"
+python-versions = "^3.8"
+content-hash = "d83187c2a41acda864290b9118580c42df1d134f1d3f5e19bac4d155cb0058c2"
diff --git a/pyproject.toml b/pyproject.toml
index 995837b6..9f736033 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ authors = ["AlexisRalli <rallilex@hotmail.co.uk>", "TimWeaving <weaving.timothy@
 license = "MIT"
 
 [tool.poetry.dependencies]
-python = ">=3.8 <3.11"
+python = "^3.8"
 openfermion = "^1.3.0"
 cached-property = "^1.5.2"
 qiskit = "^0.44.0"
@@ -14,17 +14,16 @@ pydocstyle = "^6.1.1"
 isort = "^5.10.1"
 black = "^22.1.0"
 flake8 = "^4.0.1"
-py3Dmol = "^1.8.0"
 ncon = "^1.0.0"
 opt-einsum = "^3.3.0"
 pytest = "^7.2.2"
-numba = "0.56"
+numba = "0.58.1"
 quimb = "^1.5.1"
 ray = "^2.6.3"
 sphinx-design = "^0.5.0"
 myst-nb = "^0.17.2"
 sphinx-copybutton = "^0.5.2"
-ipywidgets = "^8.1.0"
+py3dmol = "^2.0.4"
 
 [tool.poetry.dev-dependencies]