Repository: Qiskit-Extensions/circuit-knitting-toolbox
Pull request #586 (2024-05-10 17:55:23)

Provide options in the cut-finder API to turn LO gate and wire cut finding off or on, expose min-reached flag.
Add fields `gate_lo` and `wire_lo` to `OptimizationParameters` in `cutting.automated_cut_finding.py` to allow the user to turn LO gate and wire cutting off or on. The default is to have them both on.

To do:

- [x] Enabling only wire cutting causes multiple tests to fail. The same thing will likely happen if only gate cutting is enabled. Test both of these out and fix these tests.
- [x] Add tests for the `CutBothWires` action. While running the cut-finder on some circuits with `gate_lo` set to `False` (so only wire cut finding enabled), this action was implemented and we realized that there were some new bugs that were not covered by the existing tests.
- [x] Decide if pieces of the code that track the cost of LOCC wire cuts should be removed completely (cf https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/pull/471#discussion_r1475017958).
- [x] Remove any pieces of the code that were excluded from coverage because they were tied to the rarely invoked `CutBothWires` instances.
- [x] Expose metadata field to check if minimum was reached, update tutorial, update tests, and clarify in release note that if the search is terminated early, this flag can give a false negative.

garrison: >  ibrahim-shehzad added this to the 0.7.2 milestone

This adds a feature, so it (as a whole) will have to wait for 0.8.0.  It might be that some of the fixes are worth backporting, though.
coveralls: ## Pull Request Test Coverage Report for [Build 9085894792](https://coveralls.io/builds/67489454)


### Details

* **30** of **30**   **(100.0%)**  changed or added relevant lines in **5** files are covered.
* No unchanged relevant lines lost coverage.
* Overall coverage increased (+**0.01%**) to **95.51%**

---



|  Totals | [![Coverage Status](https://coveralls.io/builds/67489454/badge)](https://coveralls.io/builds/67489454) |
| :-- | --: |
| Change from base [Build 9084779766](https://coveralls.io/builds/67487410): |  0.01% |
| Covered Lines: | 3510 |
| Relevant Lines: | 3675 |

---
##### 💛  - [Coveralls](https://coveralls.io)

ibrahim-shehzad: Instances of the greedy search (warm start) algorithm still had access to the LOCC wire-cut cost function and did, in certain instances, output costs that were inconsistent with the LO-only scenario that we currently support. This has now been changed to make things consistent and cost functions that track LOCC wire cuts are no longer exposed anywhere.

Patch:
diff --git a/circuit_knitting/cutting/automated_cut_finding.py b/circuit_knitting/cutting/automated_cut_finding.py
index 51505e0..aa15a80 100644
--- a/circuit_knitting/cutting/automated_cut_finding.py
+++ b/circuit_knitting/cutting/automated_cut_finding.py
@@ -52,6 +52,10 @@ def find_cuts(
               ``data`` field.
             - sampling_overhead: The sampling overhead incurred from cutting the specified
               gates and wires.
+            - minimum_reached: A bool indicating whether or not the search conclusively found
+              the minimum of cost function. ``minimum_reached = False`` could also mean that the
+              cost returned was actually the lowest possible cost but that the search was
+              not allowed to run long enough to prove that this was the case.
 
     Raises:
         ValueError: The input circuit contains a gate acting on more than 2 qubits.
@@ -63,6 +67,8 @@ def find_cuts(
         seed=optimization.seed,
         max_gamma=optimization.max_gamma,
         max_backjumps=optimization.max_backjumps,
+        gate_lo=optimization.gate_lo,
+        wire_lo=optimization.wire_lo,
     )
 
     # Hard-code the optimizer to an LO-only optimizer
@@ -106,7 +112,7 @@ def find_cuts(
         )
         counter += 1
 
-        if action.action.get_name() == "CutBothWires":  # pragma: no cover
+        if action.action.get_name() == "CutBothWires":
             # There should be two wires specified in the action in this case
             assert len(action.args) == 2
             qubit_id2 = action.args[1][0] - 1
@@ -126,6 +132,7 @@ def find_cuts(
         elif inst.operation.name == "cut_wire":
             metadata["cuts"].append(("Wire Cut", i))
     metadata["sampling_overhead"] = opt_out.upper_bound_gamma() ** 2
+    metadata["minimum_reached"] = optimizer.minimum_reached()
 
     return circ_out, metadata
 
@@ -137,6 +144,8 @@ class OptimizationParameters:
     seed: int | None = OptimizationSettings().seed
     max_gamma: float = OptimizationSettings().max_gamma
     max_backjumps: None | int = OptimizationSettings().max_backjumps
+    gate_lo: bool = OptimizationSettings().gate_lo
+    wire_lo: bool = OptimizationSettings().wire_lo
 
 
 @dataclass
diff --git a/circuit_knitting/cutting/cut_finding/cut_optimization.py b/circuit_knitting/cutting/cut_finding/cut_optimization.py
index aabe5ac..791233b 100644
--- a/circuit_knitting/cutting/cut_finding/cut_optimization.py
+++ b/circuit_knitting/cutting/cut_finding/cut_optimization.py
@@ -60,11 +60,16 @@ def cut_optimization_cost_func(
 
 
 def cut_optimization_upper_bound_cost_func(
-    goal_state, func_args: CutOptimizationFuncArgs
+    goal_state: DisjointSubcircuitsState, func_args: CutOptimizationFuncArgs
 ) -> tuple[float, float]:
     """Return the value of :math:`gamma` computed assuming all LO cuts."""
     # pylint: disable=unused-argument
-    return (goal_state.upper_bound_gamma(), np.inf)
+    if goal_state is not None:
+        return (goal_state.upper_bound_gamma(), np.inf)
+    else:
+        raise ValueError(
+            "None state encountered: no cut state satisfying the specified constraints and settings could be found."
+        )
 
 
 def cut_optimization_min_cost_bound_func(
@@ -125,7 +130,7 @@ def cut_optimization_goal_state_func(
 # Global variable that holds the search-space functions for generating
 # the cut optimization search space.
 cut_optimization_search_funcs = SearchFunctions(
-    cost_func=cut_optimization_cost_func,
+    cost_func=cut_optimization_upper_bound_cost_func,  # valid choice when considering only LO cuts.
     upperbound_cost_func=cut_optimization_upper_bound_cost_func,
     next_state_func=cut_optimization_next_state_func,
     goal_state_func=cut_optimization_goal_state_func,
diff --git a/circuit_knitting/cutting/cut_finding/disjoint_subcircuits_state.py b/circuit_knitting/cutting/cut_finding/disjoint_subcircuits_state.py
index 38fa96a..e5d30c4 100644
--- a/circuit_knitting/cutting/cut_finding/disjoint_subcircuits_state.py
+++ b/circuit_knitting/cutting/cut_finding/disjoint_subcircuits_state.py
@@ -40,8 +40,12 @@ class Action(NamedTuple):
     args: list | tuple
 
 
-class GateCutLocation(NamedTuple):
-    """Named tuple for specification of gate cut location."""
+class CutLocation(NamedTuple):
+    """Named tuple for specifying cut locations.
+
+    This is used to specify instances of both :class:`CutTwoQubitGate` and :class:`CutBothWires`.
+    Both of these instances are fully specified by a gate reference.
+    """
 
     instruction_id: int
     gate_name: str
@@ -49,7 +53,7 @@ class GateCutLocation(NamedTuple):
 
 
 class WireCutLocation(NamedTuple):
-    """Named tuple for specification of wire cut location.
+    """Named tuple for specification of (single) wire cut locations.
 
     Wire cuts are identified through the gates whose input wires are cut.
     """
@@ -64,10 +68,10 @@ class CutIdentifier(NamedTuple):
     """Named tuple for specification of location of :class:`CutTwoQubitGate` or :class:`CutBothWires` instances."""
 
     cut_action: DisjointSearchAction
-    gate_cut_location: GateCutLocation
+    cut_location: CutLocation
 
 
-class OneWireCutIdentifier(NamedTuple):
+class SingleWireCutIdentifier(NamedTuple):
     """Named tuple for specification of location of :class:`CutLeftWire` or :class:`CutRightWire` instances."""
 
     cut_action: DisjointSearchAction
@@ -130,15 +134,13 @@ class DisjointSubcircuitsState:
         if not (
             num_qubits is None or (isinstance(num_qubits, int) and num_qubits >= 0)
         ):
-            raise ValueError("num_qubits must be either be None or a positive integer.")
+            raise ValueError("num_qubits must either be None or a positive integer.")
 
         if not (
             max_wire_cuts is None
             or (isinstance(max_wire_cuts, int) and max_wire_cuts >= 0)
         ):
-            raise ValueError(
-                "max_wire_cuts must be either be None or a positive integer."
-            )
+            raise ValueError("max_wire_cuts must either be None or a positive integer.")
 
         if num_qubits is None or max_wire_cuts is None:
             self.wiremap: NDArray[np.int_] | None = None
@@ -213,7 +215,7 @@ class DisjointSubcircuitsState:
         for i in range(len(cut_actions)):
             if cut_actions[i].action.get_name() in ("CutLeftWire", "CutRightWire"):
                 self.cut_actions_list.append(
-                    OneWireCutIdentifier(
+                    SingleWireCutIdentifier(
                         cut_actions[i].action.get_name(),
                         WireCutLocation(
                             cut_actions[i].gate_spec.instruction_id,
@@ -231,7 +233,7 @@ class DisjointSubcircuitsState:
                 self.cut_actions_list.append(
                     CutIdentifier(
                         cut_actions[i].action.get_name(),
-                        GateCutLocation(
+                        CutLocation(
                             cut_actions[i].gate_spec.instruction_id,
                             cut_actions[i].gate_spec.gate.name,
                             cut_actions[i].gate_spec.gate.qubits,
diff --git a/circuit_knitting/cutting/cut_finding/optimization_settings.py b/circuit_knitting/cutting/cut_finding/optimization_settings.py
index bf8f3c8..25a8142 100644
--- a/circuit_knitting/cutting/cut_finding/optimization_settings.py
+++ b/circuit_knitting/cutting/cut_finding/optimization_settings.py
@@ -45,9 +45,11 @@ class OptimizationSettings:
     max_gamma: float = 1024
     max_backjumps: None | int = 10000
     seed: int | None = None
-    LO: bool = True
-    LOCC_ancillas: bool = False
-    LOCC_no_ancillas: bool = False
+    gate_lo: bool = True
+    wire_lo: bool = True
+    gate_locc_ancillas: bool = False
+    wire_locc_ancillas: bool = False
+    wire_locc_no_ancillas: bool = False
     engine_selections: dict[str, str] | None = None
 
     def __post_init__(self):
@@ -57,12 +59,12 @@ class OptimizationSettings:
         if self.max_backjumps is not None and self.max_backjumps < 0:
             raise ValueError("max_backjumps must be a positive semi-definite integer.")
 
-        self.gate_cut_LO = self.LO
-        self.gate_cut_LOCC_with_ancillas = self.LOCC_ancillas
+        self.gate_cut_lo = self.gate_lo
+        self.gate_cut_locc_with_ancillas = self.gate_locc_ancillas
 
-        self.wire_cut_LO = self.LO
-        self.wire_cut_LOCC_with_ancillas = self.LOCC_ancillas
-        self.wire_cut_LOCC_no_ancillas = self.LOCC_no_ancillas
+        self.wire_cut_lo = self.wire_lo
+        self.wire_cut_locc_with_ancillas = self.wire_locc_ancillas
+        self.wire_cut_locc_no_ancillas = self.wire_locc_no_ancillas
         if self.engine_selections is None:
             self.engine_selections = {"CutOptimization": "BestFirst"}
 
@@ -102,8 +104,8 @@ class OptimizationSettings:
         The default is to only include LO gate cuts, which are the
         only cut types supported in this release.
         """
-        self.gate_cut_LO = self.LO
-        self.gate_cut_LOCC_with_ancillas = self.LOCC_ancillas
+        self.gate_cut_lo = self.gate_lo
+        self.gate_cut_locc_with_ancillas = self.gate_locc_ancillas
 
     def set_wire_cut_types(self) -> None:
         """Select which wire-cut types to include in the optimization.
@@ -111,22 +113,22 @@ class OptimizationSettings:
         The default is to only include LO wire cuts, which are the
         only cut types supported in this release.
         """
-        self.wire_cut_LO = self.LO
-        self.wire_cut_LOCC_with_ancillas = self.LOCC_ancillas
-        self.wire_cut_LOCC_no_ancillas = self.LOCC_no_ancillas
+        self.wire_cut_lo = self.wire_lo
+        self.wire_cut_locc_with_ancillas = self.wire_locc_ancillas
+        self.wire_cut_locc_no_ancillas = self.wire_locc_no_ancillas
 
     def get_cut_search_groups(self) -> list[None | str]:
         """Return a list of action groups to include in the optimization."""
         out: list
         out = [None]
 
-        if self.gate_cut_LO or self.gate_cut_LOCC_with_ancillas:
+        if self.gate_cut_lo or self.gate_cut_locc_with_ancillas:
             out.append("GateCut")
 
         if (
-            self.wire_cut_LO
-            or self.wire_cut_LOCC_with_ancillas
-            or self.wire_cut_LOCC_no_ancillas
+            self.wire_cut_lo
+            or self.wire_cut_locc_with_ancillas
+            or self.wire_cut_locc_no_ancillas
         ):
             out.append("WireCut")
 
diff --git a/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb b/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
index e9d3ac2..d0988c9 100644
--- a/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
+++ b/docs/circuit_cutting/tutorials/01_gate_cutting_to_reduce_circuit_width.ipynb
@@ -421,7 +421,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb b/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb
index cb59434..c77bb83 100644
--- a/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb
+++ b/docs/circuit_cutting/tutorials/04_automatic_cut_finding.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -26,7 +26,7 @@
        "<Figure size 1300.22x494.978 with 1 Axes>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -60,6 +60,7 @@
      "output_type": "stream",
      "text": [
       "Found solution using 2 cuts with a sampling overhead of 127.06026169907257.\n",
+      "Lowest cost solution found: True.\n",
       "Wire Cut at circuit instruction index 19\n",
       "Gate Cut at circuit instruction index 28\n"
      ]
@@ -71,7 +72,7 @@
        "<Figure size 1367.11x494.978 with 1 Axes>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -92,7 +93,8 @@
     "cut_circuit, metadata = find_cuts(circuit, optimization_settings, device_constraints)\n",
     "print(\n",
     "    f'Found solution using {len(metadata[\"cuts\"])} cuts with a sampling '\n",
-    "    f'overhead of {metadata[\"sampling_overhead\"]}.'\n",
+    "    f'overhead of {metadata[\"sampling_overhead\"]}.\\n'\n",
+    "    f'Lowest cost solution found: {metadata[\"minimum_reached\"]}.'\n",
     ")\n",
     "for cut in metadata[\"cuts\"]:\n",
     "    print(f\"{cut[0]} at circuit instruction index {cut[1]}\")\n",
@@ -108,17 +110,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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4cPIKrizZiZyHabrpmny13jKISsLrX6nKM3ewQV5qpu77/IxsmNtZAyioDls628HKxQHyapZwbdsIFg42SL55HzW7+AEA3Lv6wcrJHgBw/0AQdneegn8GzYUioAncOvgafXuIiIjIMBybeOGVP79G26/fQezJ0CL7NB3fH5F/nYVWpTZyOiLxKGlfcWzshZijwTgwaA6cfOtAEdBEN01maQ6/6UNw/ef9xo5MJooFDKry8lIzYW5XTfe9mY0V8tKydN+f+eQndPphEjqvmoKUsChkxSUh5shlpN15gJ4758L9xRZIenSfX15aFrQaDTT5Ktzbfw6Ovt5G3x4iIiIyjKRrkdj/6mcIHDMfbee9XWi6d7/2cPL1xuUFWwRIRyQeJe0rOUlpiDkWAmi1eHA8BNUb1wYASGRSdFo5CddW7dUbf46oJCxgUJWXcOkWXNs1gkQmha2XArlJacATl4HGnb2BfwbPxfH3F0NubYGEi7cAAMGLtuHAwNmI+ucClP9eAwCY2Vrr5lMENEb63VjjbgwREREZhNT88Z3W+WlZUGfrD8pds0tz1B/+Ik5OXK533kBU1ZS2r8SdvQGnZnUAAE7N6iDt0flx++8+wINjIbh/4LzxwpLJ4xgYVOXlpWTg1qZA9Nr1JbRaDc7+72e4d/WDuYMN7u46hVazR8HJtw40KjUufbMJmnwVLBxt0XXNNGhUamTGJOLcZ78AAJq8/yrcu/hBq9EgMfg2D8hEREQmyqV1Q/hNGwKtWgOJRIKgOev0zg86Lp2ArLhkvLz5cwDA8fcXIzshRdjQRAIobV+5OG8D2i/6ADJLc6SERSHmyGW4d/WDV98XYOPpAu9+7ZF07S6CZq0TelPIBLCAQQQgfMNhhG84rPs++frjRz9dmPt7of65Sek4MHB2ofbghVsRvHBrxYQkIiIio1GevooDTzw14Wlbm79rxDRE4lXavpIZnYiDw77Ua4s5GowNdd6o6GhUCfEWEiIiIiIiIiISPRYwiIiIiIiIiEj0eAsJVRm2Xornml+jUiPtTsGgQ3Z13CCVywTJYYhlGGpbDJGFiIjIWMTy/sn3TjIl3G/KT0xZxZTFECRaLYdNJiqLzAcPsb3lewCAwRdXo1pNJ4ETPbvKtC1ExsL9hoh4HCAqP+43ZEi8hYSIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj05EIHIPEKHD0f6ZFKoWMAAGy9FOj22wyhYxAREZWbmN5PTQXf90snpt8rU/p5TTkHxGQJnQJwtwYWtxU6BZHpYQGDipUeqURKeLTQMYiIiEwa30+pIvD36tnEZAF30oVOQUTPireQEBEREREREZHosYBBRERERERERKLHAgYRERERERERiR7HwBCB2IQsHAl6gAvXEnHzbiqyc1Uwk0tRx8MWLRs7o3NLBRp4Owgdk4iIiIiIiEgwLGAI6ExIHBavv4ZdRyKhUmmL6RUGAOjcSoGJrzfBa91qQyKRGC8kERERERERkQiwgCGAzKx8zFh6ASs2Xy/zPMcvKHH8ghK9O3li9eft4e5arQITll+HJeNRb2hXAIBGrUZ2XApiT1/FpXkbkaVMEjgdERERUdXF8zQiqiw4BoaRxcRlos0be8tVvHjSXyei0GzQLpy7Em/gZM9PefY6tjZ7BztafYAT45fAqakXuvw0VehYRERERFUez9OIqDJgAcOI4h5mo8vb+3H9dkqxfWQyCdxdreHuag2ZrOhbRZJSc9H9vQO4eD2xgpI+G02eCtkJKchSJiHu7A2EbTgMl9YNYGZjJXQ0IiIioiqN52lEVBmwgGEkWq0WIz89hoj7aSX2UzhbIfrQcEQfGg6Fc/FvKOmZ+Rj4USDSM/MMHdUgrFyrw6tPO2hUamjVGqHjEBEREdEjPE8jIlPFAoaR/LwzDIfOPDDoMu89yMDH35836DKfh+KFJngjYj1G3NmIocFroAhogutr/oIqOxcAYK1wxKALq2DpZAcAkFmZY8Dp5XBoWEvI2ERERESVXmnnaV3WTIXPiJd0/R2beqP/iSWQWZgJFdmkhL7rJXQEoirBpAsYISEh6NevH+zt7WFnZ4f+/fsjNjYWtra2GDZsmNDxdHLz1Phs+cUKWfaP22/i1r3UCll2eSVcuoW9L03Hvl4zEPz9dsSfD8PlBZt107OUSbi+eh9azx0DAPCbOgT3/j6HlJv3BUpMREQVJSMf2HYXmHAGeOskMC0ICHwAqPjHXiJBlHaeFvT5Wvh++BosHG0BiQQB89/FuU9/gTo3X8DURET6TLaAERgYiHbt2iEsLAwzZ87EvHnzEB0djV69eiEjIwN+fn5CR9TZeSgSCck5Fbb8H7ffrLBll4c6Jw/pkUqkhEUheOFWpEfFo+3Xb+v1ufHL33Dw8USjd15B7VfaIuS77QKlJSKiinJcCfQ6CHwbCpxNAEKTgWNK4JMLwIAjwJ10oROKh2u7Rnhx7ScYdH4VxsTuQLPJA4WORJVUaedpWcokXFu9D60+H4kGI7sj9U4sYk+FCpjYNET9PAXXJ/shP+kBrk/2w51vhwodiahSM8kCRkJCAoYOHQp/f39cvnwZ06dPx4QJExAYGIj79wv+mi+mAsbvf96q4OVHQKvVVug6nkXwoq2oN7QrnJrX1bVpNRqcn70Obb98Cxe+XK+7bJGIiCqHs/HA9CAgR/247cl3qAdZwHungdgso0cTJbm1JVJuReHCl+uRFZcsdByqQoo6T7u59gAcGnjCd0J/nJ/7m4DphKfJzUbMxs9x9f36uDTYCsFvOOLG1NaI/3OZXj/Pdxaj8ZJgmDnWROMlwajz8VaBEhNVDSZZwFiwYAGSk5Oxdu1aWFk9HujS3t4e/v7+AMRTwNBqtQi6mlCh60hMzsHdGPH9OSv9rhJRhy7Af8ZwvXb3bi2QpUxCdY59QURUqWi1wJJrBQWLksrqyXnAuoqt7ZuMmCOXcWneJkTu/ReaPF6qT8ZT5HmaVouw3w8hOvASch+WPPB8ZXf/xw+QdPR3eIxZiCYrrsPnq6Oo8cp4qDJThI5GVKXJhQ7wLLZs2YKOHTvCx8enyOmurq5QKBQAgG3btmHZsmUIDg6Gs7MzIiMjy7UulUoFpVL5zFnvK7OQnKb/pBCZTFLsE0bcnmh3K6aPMjEbarX+qeGhU+Ho3UHxzDmLkp+veu5lXF25F73//BqKgCZQnrkGh4a1UKtnG+zrNQOv/Pk1bu88gYz78WXKEh0d/dx5nkdOXIru69jYWFhqsoUL85wq07YQGQv3m9LdyDBHRLpLGXpqsS9Ki4EOsbCWie8KQkMzxPtpVSOG9/2iiOk4UBHnaQAAjQZaTfn2S7H+vIqSn+8KoPSBSVPO7UbNN76CQ7v+ujZr7+YGzJGP6Og4gy1PzMS035C4KBQKyOXlK0mYXAFDqVQiJiYGQ4cWvr9Mo9EgNDQULVq00LVVr14dEyZMQFxcHBYvXvxM6/P09Hz2wFbeQL3P9Jr+e1Rqac5v7l9ku0f3zYiJ07/29v3x04GkY8+askhfOXWHu5ldmfqemvxDke0JF8Kwzm2Q7vuABWNxfvY6ZCmTcPnbLWj79dsIHPlNqcsPDw/HkOf5ORhAdakVvnd5BQDQpk0bJJvwwbcybQuRsXC/KZ1Ln4nwfHdpGXpKkKuRoMVL/ZEVcaHCcwmtPO+nVEAM7/tFEdNxoCLO056VWH9eRWm8/CqsajUptZ9ZdTekXToAx06vQ27raPAc4eHh8OzR1ODLFSMx7TckLlFRUfDw8CjXPCZ3C0lmZiYAQCKRFJq2Z88exMfH690+0r17dwwbNgy1a9c2VsSnFM5p2ut5dvXfeAk5iamIDrwEALi9/TjMqlmi1ittBU5GREQGIS3faYVEKqugIEREz6f2hJ+RfS8UIaNq4PrEZrj3w1iknN0tynHniKoSk7sCw9PTEzKZDMePH9drv3fvHj788EMAhh3/QqFQICoq6pnnvx2diS5jT+m1KROz4dF9c5H93ZytdFdetB6+G7GJhSuUyiLali9dgP5d3J45Z1HODJmPzLvPfvvM025tPIxbGw/rtR0YMLtM8/r4+CBq268Gy/IscuJScKrPHABAUFAQLF0dBM3zPCrTthAZC/eb0l1KtcAXt8vWVwYtgv75A3byyv9cVUO/n1YFYnjfL4qYjgMV9XsVse0YIrYdK9c8Yv15FeXD666IKsPDAW0atUfT1beRGR6EzLAzSL92ArcXDIJ9y16o+9neQn9MtfRsXK4cPj4++Oc5PmOYEjHtNyQu/w37UB4mV8AwNzfHqFGjsHbtWvTr1w+9e/dGVFQU1qxZA1dXV8TExBi0gCGXy8t9WcuT3Nw0qGZ1FpnZj+9TVKu1hW4BKUpsYnaZ+gFAtxfqw8PD4VljFsnMTDy/HmZmz/dzMIRM6RPjk7i5oVpNJwHTPJ/KtC1ExsL9pnRu7sCaB4Ayu+RBPAHgJXcJGnvVNEouoYnp/dRUiOF9vyhiOg6I6fdKrD+vopjdAlCGAgYASGRy2DR6ATaNXoBr/6l4eGwDIhePRMa1E7Bt2lmvb/1Z+8uXw8zMZF6z5yWm/YZMn8ndQgIAy5Ytw9ixY3Hu3DlMnToV586dw65du1CzZk1YW1sXO7inEGQyKfwbVexOamNtBp/avLeWiIiEJZMAHzQsuXghAWAhBd6sb6xU4ia3toRjEy84NvGC1EwOqxoOcGziBVsvww7MTUTPz9KjEQBAlVr6APREVDHEU7otBxsbG6xevRqrV6/Wa7969Sp8fX0hLec9uBVteK+6OHmp4kYZHtrDGzKZuLaZiIiqplc8gbR84LurRRcyrOTA922Aeqy7AwCcm9dFzz/m6r5v9FYvNHqrF5T/XsOBgWW7zZKIDC/s085w7Dgc1vVaQW5fA7mxEYhZ/ylk1Rxg69tV6HhEVZZJFjCKkpKSgujoaPTu3VuvXa1WIz8/H/n5+dBqtcjJyYFEIoGFhYXRso3oUxcfLz6PjKyKeb77uKGNKmS5REREz2JYHSDABVgfAey+X9BW2wbo6wn0rQVUN95bsOgpz1wzyFMgiMiw7P17IenERjzYPAvqrDTI7V1g26QTvCauhdzOWeh4RFVWpfmzfWhoKIDCA3iuX78eVlZWGDJkCO7fvw8rKys0aNDAqNlsq5njk7d8K2TZ/V+sDf/Gwh9E7eq4YdT9Lajhr39NsN/UIRh0fhW6b3r8KFmZlTle+fNrvH7zN3j3a2/sqEREZAS1bYB3n3i7XRkAjK7P4gWREIo7T/tPz51zEbBgbLnmqewUg2agwTcn0fz3ePjvyEGzX+7D+6MNsKpVvsE6iciwKn0BY8yYMdBqtXr/IiMjjZ7vkzebo0VDw46F4WhvgVUzXzDoMp9V8ymDoDxzvVB72PqDhS6B1eSqcPSthbi+5i9jxSMiIiKqsoo7TwMAj5daIj+j8BPuSpqHiEgolaaAMW7cOGi1WrRr107oKEUyM5Ni84IucK5uWWK//x6x6tF9c5GPS/2PXCbB+nmdoXC2NnTUcnNuUR/Z8SnIin1YaFp2fAqg0b8LWqvRIDshxTjhiIiIiKqwks7TIJGg4Zs9cXPdgbLPQ0QkoEpTwDAFDbwdcPinnnBxLL6I8d8jVmPisqBWFz2Ou7mZFNsWvYhXOnpWVNRyaTZpAEJX7BI6BhERERE9paTztHpDuuDe/nNQ5+SXeR4iIiGxgGFkzRs44dLW/ujV4dme++xbvzrOrH8Vr3XzMmywZ+TRzR8PQ24jNzlD6ChERERE9ISSztNkFmaoM6AjIrYcKfM8RERCqzRPITEl7q7V8NcPL2Pz/jtY9FsoLt8s/fK8Wm7VMH5oY0we2QTmZjIjpCwbx6ZeULzQBC6tG8ChYS3Y1a2Jo28vLLh1hIiIiIgEU9J5mk0tF5jbV8NL6/8HcwcbWLk4oO7gzqhW04nndkQkWixgCEQikeD13nUx/JU6CApNwD//xuDCtUSE3kpC5IOCineHFq54wc8FnVu5occL7pDJxHfBzJWlf+DK0j8AAB2WjEfY7wfh2MQL5u1tcHfXKfiMeAl1B3eGfT13vLx1Fk5OXI7suGR0+XkanJp6Q5WVA2f/+jg/e52wG0JERERUyZR2nrav5ycAAEVAE3j3b4/b24/r5ntyHhYviEgsWMAQmEQiQdtmLmjbzAUAEK3MhOfLWwAAmxd0hYeimpDxyuXU5B8KtYVvOIzwDYcLtR97Z5ExIhERERERij5P+4/yzDUoz1wr1zxEREIQ35/0iYiIiIiIiIiewgIGEREREREREYkeCxhEREREREREJHocA4OKZeulEDqCjpiyEBEREQlNTOdGYspSGndroRMUEEsOIlPDAgYVq9tvM4SOQERERERF4Hnas1ncVugERPQ8eAsJEREREREREYkeCxhEREREREREJHosYBARERERERGR6LGAQURERERERESixwIGEREREREREYkeCxhEREREREREJHosYBARERERERGR6LGAQURERERERESixwIGEREREREREYkeCxhEREREREREJHosYBARERERERGR6LGAQURERERERESixwIGEREREREREYkeCxhEREREREREJHosYBARERERERGR6MmFDkDiNeUcEJMldIoC7tbA4rZCpyCq3AJHz0d6pFLoGGVm66VAt99mCB2DiEgQYjpmV8TxWEzbZyr4vige/BxVcVjAoGLFZAF30oVOQUTGkh6pREp4tNAxiIioDCr7Mbuybx9VbvwcVXF4CwkRERERERERiR4LGEREREREREQkeixgEBEREREREZHosYBBRERERERERKLHAgYRERERUSXWYcl4jIndgTGxOzAqeisGX1yNDss+hLXCUehootVz51y8sOj9Qu02HjUwJnYHXNo0FCAVVUZhn3VB5PJ3CrXnxkXiYj8JMq6fEiCVeLGAQURERERUySnPXsfWZu9gR6sPcGL8Ejg19UKXn6YKHYuIqFxYwCAiIiIiquQ0eSpkJ6QgS5mEuLM3ELbhMFxaN4CZjZXQ0YiIyowFDCIiIiKiKsTKtTq8+rSDRqWGVq0ROg4RUZnJhQ5AREREREQVS/FCE7wRsR4SqRRyKwsAwNVVe6HKzgUAdFkzFQ+OhyB8w2EAgGNTb3RaOQl/dp8OdW6+YLnFrueuL2BuYwWJmRzx527g7P9+hlbDohAZ3t0lo5F26W/I7V3QZPlVoeMIxqSvwAgJCUG/fv1gb28POzs79O/fH7GxsbC1tcWwYcOEjkdElYg6Lx/xF8Px4MQVJN+4B61WK3QkIiKiMku4dAt7X5qOfb1mIPj77Yg/H4bLCzbrpgd9vha+H74GC0dbQCJBwPx3ce7TX1i8KEXgyG+wt/t07OkyBRZOdvB6NUDoSFRJOb/0FurPPiB0DMGZ7BUYgYGB6NOnD2rXro2ZM2fCysoK69atQ69evZCRkQE/Pz+hI1ZpmtxsxO6Yh+STW5D3MBpScytYKOrCqctIuLw6Ueh4RGWmys5F6IrdCFt/EDkJqbr26o1ro+n7fVFnUCdIJBIBE1Y813aN0OS9vnBs6gUbjxq4tGAzrizZKXQsIiIqB3VOHtIjlQCA4IVbYeulQNuv38a/034EAGQpk3Bt9T60+nwkEi9HIPVOLGJPhQoZWVB5aVkwt6tWqN3cvqDtv8JOfkY2AEAil0FmJucfOKjcZNb2UGelFmpXZ6YAACRmlgAA26adkRsXacRk4mSSBYyEhAQMHToU/v7+OHz4MKysCgYfGjlyJLy9vQGABQyB3f/xA6SHHoXnO0th5d0c6qw0ZN25jLyE+0JHIyqz/KwcHBr2JeLPhwFP1SiSb9zDyYnLkXQ9Eq1mjarURQy5tSVSbkXhzq6TaPPFm0LHISIiAwhetBWvnViKsPWH8DDkNgDg5toD6P3XPLi1b4o/e80QOKGwUiNi4PVqACRSqd4tIc4t6kGjUiP9bqyurceOOXBq6o3owEu4t++sEHHJhFl6NETy6e3QqtWQyGS69sxbQYBUBgu3egKmEx+TvIVkwYIFSE5Oxtq1a3XFCwCwt7eHv78/ABYwhJZybjdcX5sOh3b9YeHqDWvv5nDuNgY1h80SOhpRmZ2fta6geAEAT/9B5dH31378E5F7/zVqLmOLOXIZl+ZtQuTef6HJ46XERESVQfpdJaIOXYD/jOGPG7VahP1+CNGBl5D7ME24cCJw87cDsKxhj/ZLxsOpWR3Y1naFd//2aPHxMERsPYq8tCxd338GzcFWv3chszKHokNTAVOTKarRaxxUKXGIXPYmMiMuIjf2NpJObMaDjZ/DudubkNs4CB1RVEyygLFlyxZ07NgRPj4+RU53dXWFQqFAbm4u3n33XdSpUwe2trbw8fHB8uXLjZy2ajKr7oa0SwegSk8SOgrRM8lJSkfE9mOld5QA13/aV+F5iIiIDO3qyr1w7+IHRUCTx40aDbQa3gaRGZ2I/a9+Bgv7auj22wz0PfIdmk0cgKsr9+LMjDWF+qtz8nD/7yDU6tFagLRkyixcaqPBgn+hzkzG7a9exfVJzRC7Yx5cX5uOWu+vFDqe6JjcLSRKpRIxMTEYOnRooWkajQahoaFo0aIFAEClUkGhUODgwYOoU6cOrly5gh49esDV1RVDhgwp0/pUKhWUSqVBt6EksYk5j79WxgIqS6Ot+2n5+a4AzJ5p3toTfsbd715HyKgasPJsgmoN2sG+5Suwb9vvmS61z8/PR3R03DNlMZScuBTd17GxsbDUZAsX5jlVpm2pKDG7z0CTpyq9o7ZgYLRb50Jg5e5U8cEqUH5+GbZXRPLzVYiOjjba+rjflF9ingyAG4CC1yzfXC1sIIGY2r4lBsbev8tKTMeB8vxenZr8Q5HtCRfCsM5tkEGyGPrnJYb9Jvn6PQSOnl/sdDNba0jN5ch9mAaJTArP7q2g/PeaERPq434jHuX9HGXt3Rz1Zv5ZQVmE/xxVHIVCAbm8fCUJkytgZGZmAkCRH4L37NmD+Ph43e0j1apVw5dffqmb7ufnh759++LUqVNlLmAolUp4eno+f/CyklcHGi0EALRp3QZQJRtv3U9pvPwqrGo1Kb1jEWwatUfT1beRGR6EzLAzSL92ArcXDIJ9y16o+9nechcxwsPD4dlD2Evyqkut8L3LKwCANm3aINmED76VaVsqSu9qDTDItuy/cz06dsXdfOH2V0P4yqk73M3shI5RZuHh4RhixOMz95vyM3NyR7NfC06m27RpjfyHMQInEoap7VtiYOz9u6zEdBwQ0+9VRfy8xLR9xTG3t0bXn6dDaiaHRCZF7IkQhK0/KFge7jfi8Tyfo4pze8FgZNw4BVVaIq685QHFoE/h8sq4UucTw+eo4kRFRcHDw6Nc85hcAcPT0xMymQzHjx/Xa7937x4+/PBDAMWPf5Gfn4+TJ09i2rRpFR2TAEhkctg0egE2jV6Aa/+peHhsAyIXj0TGtROwbdpZ6HhEJcrWlm+sh2yN8H8pIiIiel4R244hYtsxoWOYhMzoROzr+YnQMaiKqPvJdqEjiILJFTDMzc0xatQorF27Fv369UPv3r0RFRWFNWvWwNXVFTExMcUWMCZMmABbW1uMGjWqzOtTKBSIiooyUPrSxSbmoM2oguJM0PkguDkLdwvJh9ddEZVTer+ysvRoBABQpcaXe14fHx/8Y8SfQ1Fy4lJwqs8cAEBQUBAsXR0EzfM8KtO2VJQcZTJO9f0CKO1xaBLAupYLgoKumfyTSM4MmY/Mu8a7Ze55+fj4IGrbr0ZbH/eb8kvMk+GdqwVfBwWdh3MVvYXE1PYtMTD2/l1WYjoOiOn3qiJ+XmLaPlPB/UY8DP056nmI4XNUcRQKRbnnMbkCBgAsW7YMZmZm2LNnD44cOYKAgADs2rULX3zxBSIiIooc3POjjz7CmTNncOTIEZibm5d5XXK5vNyXtTwXeabuSzeFGzwUhZ8/bSxmtwA8444X9mlnOHYcDut6rSC3r4Hc2AjErP8UsmoOsPXtWv4sZmbG/TkUIVP6+Ik3bm5uqFbTdMc7qEzbUmE8PHC/Z2vc/zuo5H5awHfsq8a91ayCmJkV/ZYgt7aEnXfBG4zUTA6rGg5wbOKF/MwcpEcKd3JpZmbc4zP3m/IzywbwqIDh5uYGV6sSu1daxe1bVDxj799lJabjgJh+ryri5yWm7TMV3G/E43k+RxmaGD5HGZJJHhlsbGywevVqrF69Wq/96tWr8PX1hVSq/3CVyZMnIzAwEEeOHIGzs7Mxo1ZZ9v69kHRiIx5sngV1Vhrk9i6wbdIJXhPXQm7HnwGZhoAFY5F0LRIZ94u/aqhWrzZoMPplI6YyPufmddHzj7m67xu91QuN3uoF5b/XcGDgbAGTEREREVFVYpIFjKKkpKQgOjoavXv31mufOHEijhw5gqNHj6JGjRoCpat6FINmQDFohtAxiJ6LVQ0H9N43D0Gz1iLyzzPQqjW6aeZ21mj4Zi/4TRsCqUwmYMqKpzxzzSCj1BMRERERPY9KU8AIDQ0FoD+A571797B8+XJYWFjA29tb196xY0f8/fffxo5IRCbIqoYDOq+agqYf9MOfPT4GAAQsfA91B3aC3MpC4HRERERERFVHpS5g1K5dG9rSBuAjIioDS2d73dceL/qzeEFERCbFxqMGOq2cDI1KBYlMhrMz1iD5xj3d9I4rJsK2liskMilurjuA29uPl7A04djVcUP/Y4vxd//PkXDplt40m1ouaP/9OEjN5Lj/dxCu/bgXMitz9Ng2Gw71PXDmk59wd8/pEpdv4WSHdl+/DUsnO6iy8xA46hu96Y3f7Q3v1zpAk69GUugdnJtZ8qCZzacMQs0uzaHOycepySuQFZtU6vqkZnJ0+mESrFwcIJFJce6zX/Dwyh00nzIIbh18AQC23gpc/WEPbvyyv6wvHYnQpcHWqObTBgDg0mcSqge8VqhP2GddYOneELXH/ahry4kJx7UPm6DBNydh06Cd0fKKQaUpYIwbNw7jxpX+HFwiIiIioqomM/Yh9vebCWi1ULRvimYTB+D4B4t104O/24b0u0pIzeXod+R73N19Gpp88T0ivPmUQVCeuV7ktFYzR+LSN5uQcDEcPf+Yi3t/nUVmTCKOvrUQDUaVbbyq1rNHI3jRVqRGPChyetShi7i+5i8AQOdVU+Aa0BhxxeRx8PGAS5uG+Lvf53Dr1Az+nwzHqck/lLo+t46+yEvPwrGx38G5RX00mzQQR99eiJDFOxCyeAcA4NWD3+LeX2fLtE0kXuY1aqHB18eKnZ5yfh9kVraF2mO3fQnbJp0rMJl4SUvvQkREREREpkyr1ugeDW5ua4Wk65F609MfPbJUk6cCtFpRXsXs3KI+suNTkBX7sMjp9vXdkXAxHAAQffgSXNs1glajQXZCSpmWL5FK4dDAA74TXkPPP+ai/uvdCvV58ulbGpVKb3ysp7m2a4yoQxcBALEnrsCpWZ0yrS89UgmZhRkAwNzeGjkPU/Xmc/DxQF5qJrKU+ldzkOnJT3qAsE87487CYchP0R80XqvRIGH/D6jxyni99sywczBzUMDcufI8WaQ8WMAgIiIiIqoCHJt44ZU/v0bbr99B7MnQIvs0Hd8fkX+dhValNnK60jWbNAChK3YVO10ilei+zk3NhEX1wn+5Lomlsx0cG3vh6qq9ODjsS9Qf9iJsa7sW2delTUNYKxwRH3Sz2OWZO9ggLzXjcT6Z/kev4taXEZ0AuZUFXju5FO2/H4cbP+vfJlJnYCfc2XWqXNtG4uT70x00mHccDm36InrtVL1pD4/8BoeAAZCaWeq1x27/GoqBVfdhCSxgEBERERFVAUnXIrH/1c8QOGY+2s57u9B0737t4eTrjcsLtgiQrmQe3fzxMOQ2cpMziu3z5EUj5nbWyE1OL9c68lIzkfkgESlhUdDkqRB39jocGngW6mdf3x2tZo7Esfe+L3l5KRkwt6v2ON9TV2sUt756Q7ogIyoeuzpOwt99Z6L99/q3ydd+pS3u7TtTrm0jcZLbOQMAqncYgqw7l3XtmrwcJB3fCOdub+r1T73wF6zrtYLczsmoOcWEBQwiIjKYDkvGY0zsDoyJ3YFR0Vsx+OJqdFj2IawVjkJHIyKq0qTmj4e+y0/Lgjo7T296zS7NUX/4izg5cbl+JUAkHJt6QfFCE3Tf9BncOjVD67ljYOXioNcnNTwazn71ABQUPOLO3Sh2efJqljC3s9ZrU+fmIzM6Ufee5disDtKeuGUEAKq5O6PD0gk4MX4pcpMeF0isFY6QSPU/WsWdvQ73F1sAABTtm+LhlTtlW59EgpxHy85NzYTZEzld2jREyq1o5KVlFbttZBrUOZnQqguudEq/dgIWbvV003Lj7kKdmYKIL/sg+rePkXpxPx4e+R1Zd4KRcfUYbs3pibTgQ4j+ZQryk2KF2gRBVJpBPImISByUZ6/j+NjvIZFJYevlinbz3kGXn6Zif9/PhI5GRFRlubRuCL9pQ6BVayCRSBA0Zx3cu/rB3MEGd3edQselE5AVl4yXN38OADj+/uIyjx1hDFeW/oErS/8AUFAsD/v9ILLjU/S24eK8jWj/3QeQyGWI+uc8Mu4XjCnQ5edpcGrqDVVWDpz96+P87HXw7t8BckvzQk/xCJq9Dp1WToJULkf00ctIDY+GVQ0HNH6vDy5+tQGtZo6EpaMdOiwpGJcgdMUuxBwNRqdVk3Fk9Hy9wkJKeDQeBt9Grz1fQp2rwukpBQN41hvSBRkxiVCevlrk+jKjEtBp5WT0/GMu5FYWuLxgs26ZdQZ0xJ0/ePtIZZATfRP3fngXMksbSORmqDVuNVIvHYA6PQmOnV9Ho+8vAADSQ48h6eQWOL04CgDgNqTgfCpy6Rg493wfZo5uQm2CIFjAICIig9LkqXQnvVnKJIRtOIx2X78NMxsr5GdkCxuOiKiKUp6+igOnrxY7fWvzd42Y5vk8+SSPmKPBuq/TI5U4MHB2of7H3llUqK16Q0+ELNlZqD3p6l0cGKC/jOyEFFz8agMA6D255T8SuQwZ9+OLvCoi+LttCP5um15bxLZjJa5PlZ2LI28uKLQsADg7Y02R7WR6qtVricaLL+m1WT5xFcZ/bH27wNa3S6F2r0nrKiiZuLGAQUREFcbKtTq8+rSDRqUucaR2IiIiYwr6fK3BlqVVqXFq0gqDLY+IiscxMIiIyKAULzTBGxHrMeLORgwNXgNFQBNcX/MXVNm5AAruEx50YRUsnewAADIrcww4vRwODWuVOI2IiIiIqjZegUHFcrcuvY+xiCkLEZUs4dItnJq0AjILM3j1fQE1OzbTu383S5mE66v3ofXcMTg5YRn8pg7Bvb/PIeXmfQAocRoRERWw9VI81/walRppdwoG/7Or4wapXCZYFmMts7LjayYeYvrsIqYshsACBhVrcVuhExCRKVLn5CH90ajtwQu3wtZLgbZfv41/p/2o63Pjl7/R58ACNHrnFdR+pS32dptWpmlERFSg228znmv+zAcPsb3lewCAHtvnoFpNcT2W8Xm3j0hI/BxVcXgLCRERVajgRVtRb2hXODWvq2vTajQ4P3sd2n75Fi58uV53e0lp04iIiIio6mIBg4iIKlT6XSWiDl2A/4zheu3u3VogS5mE6kWMb1HSNCIiIiKqmljAICKiCnd15V64d/GDIqAJAMChYS3U6tkG+3rNQP3Xu8Gmlouub0nTiIiIiKjqYgGDiIgM5tTkH3Bw6BeF2hMuhGGd2yAoz1wDAAQsGIvzs9chS5mEy99uQduv39b1LWkaEREREVVdLGAQEZFR1X/jJeQkpiI68BIA4Pb24zCrZolar7QtcRoRERERVW18CgkRERnVrY2HcWvjYb22AwNm600vbhoRERERVV28AoOIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItFjAYOIiIiIiIiIRI8FDCIiIiIiIiISPT5GlYiIDM7BxwMBC9+DVqOFVqXG6amrkHE/Xjfdb+oQ1BvWFam3onHo9a/LNA8RERERVW28AoOIiAwu52EaDo/4Bgdem4WrK/eg+ZRBetPD1h/EgYGzyzUPEREREVVtLGAQEZHB5TxMQ356FgBAk6+GVq3Rm54dnwJotOWah4iIiIiqNt5CQkRVRuDo+UiPVD7TvBqVWvf1P4PnQCqXPXMOWy8Fuv0245nnNyUyS3P4TR+CM5+sqdB5iIiIiAzlec4ZKxuxnbeygEFEVUZ6pBIp4dHPvZy0O7EGSFP5SWRSdFo5CddW7UXKzfsVNg8RERGRIRnqnJEMj7eQEBFRhWj/3Qd4cCwE9w+cr9B5iIiIiKhq4BUYRERkcO5d/eDV9wXYeLrAu197JF27i5ijwTB3sMHdXafgM+Il1B3cGfb13PHy1lk4OXE5HBvXLjRP0Kx1Qm8KEREREYkECxhERGRwMUeDsaHOG8VOD99wGOEbDuvPE5dc4jxEREREVLXxFhIiIiIiIiIiEj0WMIiIiIiIiIhI9HgLCRHRUzosGY96Q7sCADRqNbLjUhB7+iouzduILGWSwOmIiIiIiKomXoFBRFQE5dnr2NrsHexo9QFOjF8Cp6Ze6PLTVKFjERERERFVWSxgEBEVQZOnQnZCCrKUSYg7ewNhGw7DpXUDmNlYCR2NiIiIiKhKYgGDiKgUVq7V4dWnHTQqNbRqjdBxiIiIiIiqJI6BQURUBMULTfBGxHpIpFLIrSwAAFdX7YUqOxcAUKtXG/h9NFhvHnsfDwR9vhZhvx80el4iIiIiosrOpK/ACAkJQb9+/WBvbw87Ozv0798fsbGxsLW1xbBhw4SOR5WERqXGvb/O4t/pP+rabm8/jvyMbAFTUUVLuHQLe1+ajn29ZiD4++2IPx+Gyws266bf/zsIe7tP1/0L/m4b0iOViNh+TLjQRERUITRqNaIOXsCZT37Std3acgR5aZkCpiISN41ajajDF3FmxhP7zaZA5KZyv6FnZ7JXYAQGBqJPnz6oXbs2Zs6cCSsrK6xbtw69evVCRkYG/Pz8hI5IlUBKeDQOj/wGGffj9Novzd+E0BW70GnlJHh2byVQOqpI6pw8pEcqAQDBC7fC1kuBtl+/jX+n/Vior7WbI9rOeweH35gHdXaesaMahfuLLdDyf6/Dvr4HsuOTcf2X/bi+ep/QsYiIKlza3VgEjpqP1IgYvfbghVsRumI3Oi4dD69XXxAoHZE4pd+Pw+GR3yA1PFqvPfi7bQj9YTfaLx6HOv07CJTu2bm2a4Qm7/WFY1Mv2HjUwKUFm3FlyU6hY1UpJnkFRkJCAoYOHQp/f39cvnwZ06dPx4QJExAYGIj79+8DAAsY9NwyohNwYODsQsWL/+Rn5uDIm98i9lSokZOREIIXbUW9oV3h1Lyu/gSJBJ1WTELoit1IvnFPmHAVzKl5XXRb9wmij17G3u7TELxoG1rOeB0NRr0sdDQiogqVFZ+MAwNnFype/Eedk4dj7y9G1KELRk5GJF7ZiakF+81TxYv/qHPzcWLcUtw/EGTkZM9Pbm2JlFtRuPDlemTFJQsdp0oyyQLGggULkJycjLVr18LK6vETAezt7eHv7w+ABQx6fleW/YGcxNTiO2i10Gq0CJrzG7RarfGCkSDS7yoRdegC/GcM12tvPnkg8tKzcPPXvwVKVvGajO2DxODbuDRvE1JvxSBi2zHc+PVv+E7oL3Q0IqIKdW3lXmTFJhXfQasFtFoEzV4HrYaDPBMBwLXVfyIzOrH4Do/Om01xv4k5chmX5m1C5N5/ocnLFzpOlWSSt5Bs2bIFHTt2hI+PT5HTXV1doVAoAADjxo3Dn3/+idTUVNja2mLw4MH49ttvYW5uXqZ1qVQqKJVKg2UvTWxizuOvlbGAytJo66bHVBk5ZRvLQKtF8rVIXPv7FByaeVd4LkPJiUvRfR0bGwtLTdUYzyM/X/Vc819duRe9//waioAmUJ65BpfWDVD/9W748+Xp5c4RHV30XyWEVNzr49KmIW5tCtRrizkajKbj+sHazbHkk/sKZOzXsaruN88jMU8GwA1AwWuWb64WNpBAnvfYUxWJ4TipzslD2KbDpXfUFhS5r+w6Cqe2DSo+mIHwmEYVQZOnQtj6g4AEQEl/39NqkXE/HiHbD8O5fWNjxSszHrcfq8jjsUKhgFxevpKEyRUwlEolYmJiMHTo0ELTNBoNQkND0aJFC13bhAkTsHDhQlSrVg2JiYkYPHgw5s2bhzlz5pR5fZ6enoaKXzp5daDRQgBAm9ZtABUvTRKCt1l1zHJ6scz9Jw9+E4FZtyswkWFVl1rhe5dXAABt2rRBchU5afnKqTvczexK7Xdq8g9FtidcCMM6t0EAAHM7a3RcPhGnJq1AbnJGuXKEh4djiDGPK2VU3Otj5eKA7IQUvbbs+ORH06oLVsAw9utYVfeb52Hm5I5mvxac9LRp0xr5D4u+DL+yK+uxhx4Tw3GyptwWXzuX/Va5T0ePw/7M8ApMZFg8plFFcJXZYH6NHmXuP+vtSfgz82YFJno2PG4/VpHH46ioKHh4eJRrHpMrYGRmFoxaK5FICk3bs2cP4uPj9W4fadz4cUVPq9VCKpXi1q1bFZ6TTJsUhX+/DNmfTF+D0T1g5eKANnPH6LVHbD+O6z9xcEsiIlNX3vd2mWnemU1kUOU+hy7iMx1RSUyugOHp6QmZTIbjx4/rtd+7dw8ffvghgMLjX8yfPx9fffUVMjMz4eTkhPnz55d5fQqFAlFRUc+du6xiE3PQZlTBtgWdD4KbM28hEUJecgZO9poFrbps9+UtXLcSTgGNKjiV4eTEpeBUnzkAgKCgIFi6Ogiax1jODJmPzLuGuSUsdPkuhC7f9Uzz+vj4IGrbrwbJYUjFvT7Z8SmwquGg12b56Pv/rsQQgrFfx6q63zyPxDwZ3rla8HVQ0Hk4V9FbSAxx7JFbW+K1U0tx5M1v8TDEdK74K468miUG/rscB4d/heTrhQdAFsNxUpWRjRM9Pocmr2yXks9ZuQgrX2xewakMh8c0qgiqrFyc6DETmpyyjQ8xc+l8rHjZv4JTlZ8hzxlNXUUej/8b9qE8TK6AYW5ujlGjRmHt2rXo168fevfujaioKKxZswaurq6IiYkpVMCYMWMGZsyYgRs3bmDjxo1wc3Mr8/rkcnm5L2t5LvLHz0V2U7jBQ1HNeOumxzyA+73bIXLvvyX3k0hg4+EM3wEvQiqTGSebAWRKHw9+6+bmhmo1nQRMYzxmZuI45JmZGfm4UkbFvT7xQTdRs4sfQhbv0LW5d/VDRlS8YLePAMZ/HavqfvM8zLIBPCpguLm5wdWqxO6VliGOPb4T+uNhyB08DLkN+3o18erBhTj3+Vrc2vh4jAYbjxroG7gIwd9vx/XV+6AIaIKXt83C4RHz8OB4iK6fs189vLL3Kxx7fzHu7z9Xrhze/dujw5IJ2PfKDL3Cg0QmxSt7v0ZOUhqkMinMbKzxd//P9Qboc/T1Ru9983Bi/DLc23cG11bvQ+vZo3Fw6BeF1iOW42T0gI6I2HK05E4SwNLZHn7De0AqkveZsuAxjSrKg0FdEL7hUKn9LBxt0eKNXpBZmBkhVfmI5ZxRDMRyPP6PSV7rtmzZMowdOxbnzp3D1KlTce7cOezatQs1a9aEtbV1sYN7NmrUCM2bN8fIkSONnJhMUbOJAyCzNAeKu7RNAkCrRYuPh5tU8YKovK79tA81WtRDixnDYV+vJuoO7oxGb/VC6IrdQkcjqhJkFmZoMPrlgoHxAKRGPMCFL9ajzdzRsPUq+OuVRCpFxx8mIjHkDq6vLriNTXnmGq7/tA/tF4+DRXUbAIDcygKdfpiE2ztOFFu8UAQ0waCglUVOu7v7NCL3nUGnHybpfehoPnkQbDxr4PTkH3Bq8g+wq6OA78TXHm+DpTk6rZiIO3+cxL19ZwAAEVuPQhHQGA4NxDcm0H+ajusPeTXL4s8FAEALtJg21KSKF0QVqem4vjCzsSp5vwHgN22IKIsXJZFbW8KxiRccm3hBaiaHVQ0HODbx0h2LqeKZZAHDxsYGq1evhlKpRHp6Og4ePIiAgABcvXoVvr6+kEqL36z8/HyEh5vOAEskHMcmXuj22wzIrS0KGgodgyVoPXcM6g7qZOxoREb1MOQ2jrz5LTxfaom+h79Di4+H4dKCzQj7/aDQ0YiqBPeufpBZmutdRXFz3QHEnb2BTismQiKTwnfia3Dw8cSpScv15r00fzNyk9IR8O17AIA2X74JiUyKc58/++XAZ//3M8yqWcL/0zcAFFzR4TvxNZyeshI5D9OQHZ+Cf6f9iOZTBsGpeV0AQMvPRkBqboZzMx+vN+dhGuIvhKHuQPG+jzrUd0f3DZ8WfBgDijgXAPxnDEeDUWUf7JOosrPzdkP3zTNhbmdd0FDEfuM3fSgajulp3GAG4Ny8LvoeXoS+hxfBWuGIRm/1Qt/Di9D+uw+EjlZlVJpScUpKCqKjo9G7d29dW2pqKnbt2oX+/fvD3t4eoaGh+Oqrr9CjR9lHxqWqrWanZhh4ZgVubT6C2ztPIDs+BWY2VqjVozUajO4Bh/ruQkckMorowEuIDrwkdAyiKsk1oAmSrt4tNC7T6Skr0e/od+i4fCK8+rTDyQ+XF7qtS5OvwonxS9Hn7/nouPxDePfvgAMDZkGVmYNnlZ+ehRMfLkfP7bOhPH0VrT4fifCNgXrHiPsHziNi2zF0WjERF75cjwajuuPAgNmF1ptw6RYU7Zs+cxZjcG3XGAPPrEDE1qOI2HYMWXHJMLO2gOfLrdBgdA9Ub1hL6IhEouPSqgEG/LsCEduOImLrMWTHJUFuZQGPl1qiweiX4djYS+iIz0R55pruiXQkjEpTwAgNDQWgP4CnRCLBhg0b8NFHHyEvLw8uLi4YMGAA5s6dK1BKMkVWNRzQbOIANJs4QOgoZAQj7mxE4uUIAMD1n//C/b+DdNM6rpgI21qukMikuLnuAG5vPw4HHw8ELHwPWo0WWpUap6euQsb9eKHiE1ElZFvLpcjxZrITUnDxm81ov+h9RO47g7t7Thc5f0pYFK79tA/NJw3E1VV7EX8+7LkzxZ+7gdCVe9D11+lIuxOLC1/8XqjP+Vnr8Oqhhej663RcWbwTCRcLXwGbFZsE29ouz52nolk62aHpuH5oOq6f0FGITIaloy2avt8XTd/vK3QUqkQqdQHDzs4Ohw8fLmYOIqLCMmMScWDg7CKnBX+3Del3lZCay9HvyPe4u/s0ch6m4fCIb5CfngX3rn5oPmUQTk8p+t5xIqJnIbM0R15aVqF2iUyK+sO6Ij8zG06+dSCvZlnklRXyapao078D8jOz4dK6ASRSqd7gmtXcndH/+OLHy5VKIbMwwxsR63VtGdGJ2NNlit5ygxdtKyiKrNgNdU5eofWqsnNxddVeBMx/FyFLdhSaDgDq3LyC8aaIiIjKoNIUMMaNG4dx48YJHYOITJyVa3X0/GMusuNScG7mL8h5mKablv7ocVqaPBWg1UKr1epN1+Sry/zoXSKissp5mAYLB5tC7c0nD4JdHTf82eMTvLx5JtrMHYN/p/1YqF+7r9+GRqXGvl4z0PvPefCd+BquLNmpm56lTMLel6brvq/hXx8tPxuhV8zVqAo/SlSrKngsrkZd/ONxtfkF8xV3bLRwsNE7jhIREZXEJAfxJCKqKDvbjceBAbNx/+B5tJ4zusg+Tcf3R+RfZ3Un70DBX0j9pg/B9Z/3GysqEVURD0PvFHpSh3OL+mg2aQD+nb4aabcf4OSkFag3rCs8urfU61e7d1vUGdARJycsQ+qtGJyd+QuaTxkER19vXR+tWoP0SKXuX1ZsErRqtV5bZnRihWybQ6PaeBhyp0KWTURElQ8LGERET8hNSgcARO79F45NvQtN9+7XHk6+3ri8YIuuTSKTotPKSbi2ai9Sbt43WlYiqhpijlyGbW1XWNd0AvDoUagrJuL2zsePQo07cx3XV+9D+0Xvw8LJDgBg5eKAgG/fQ8iSnUgMLhjb586OE4j65wI6Lp8oiscXKto2QvThi0LHICIiE8ECBhHRI3IrC0gePYbZtV1jpEcq9abX7NIc9Ye/iJMTlwNara69/Xcf4MGxENw/cN6oeYmoaki9FYPY01dRd1BnAEDrL8ZAIpfqPZIUAC4t2IzsxDS8sLDgkakdlk5AemQcrizdqdfv349Xw8K+mu4xqEJRvNAE8mqWuPvnv4LmICIi01FpxsAgInpe9vXd8cKi95GfmQNNvhpnPl4N965+MHewwd1dp9Bx6QRkxSXj5c2fAwCOv78Yjk294NX3Bdh4usC7X3skXbuLoFnrhN0QIqp0Li/cis6rJuP6T/twZvrqIvto8lTY222q7vtDw78qsl9eSga2tRhb7LqUZ65hR5uyjStW2uMEI7YdQ8S2Y0VOazquH0JX7IY6u/AAoEREREVhAYOI6JGHV+7gz5c/1mt78iqMrc3fLTRPzNFgbKgj7F8xiajyiz93AyHfb4dtLRekhEcLHee5yatZIv5iOK7/tE/oKEREZEJYwCAiIiIyAeEbKs+j4VWZObiyuOhHqxIRERWHY2AQERERERERkeixgEFEREREREREosdbSIioyrD1UggdAYB4cjxNrLmKY2p5iYiIiOj5sIBBRFVGt99mCB1B1Pj6EBEREZGY8RYSIiIiIiIiIhI9FjCIiIiIiIiIROLlrbPQYcl4oWOIEgsYRERERERERFWI1Mw0R5MwzdREREREREREItVwTE80fLMHbGsrkJeehbhzN3DsnUUYFLQS4ZsCcWXJTl3fFxa9DztvNxwYOBsdloxHzU7NAAD1hnYFABwYMBvKM9dKXJ9EJkWzSQNRd3BnVHNzQk5SGu7vP4dzM38FAIyJ3YFzM39FDf/68HjJHzFHg6HOydOt40nBi7Yh+LtthnopDIoFDCIiIiIiIiID8Zs2BE3efxUXv96IB8dDIK9mCY8XW5Rp3nOfr4VNbVdkxyUj6PO1AIDclIxS52v//Ti4v9gC5+f+hoTzYbB0skONVg30+jT/aDCCF23F5W+3AFIJchLTcPHrjbrpnj1aod037yLu3I1ybK1xsYBBREREREREZAByKws0HdcPl7/diptrD+jak0Lvlmn+/PQsaPJUUOfkITshpUzz2HopUG9IFxx9ZxHu/XUWAJB+Lw4Jl27p9bt/IEgv03/rAwDHJl5oPWc0zs38FbGnQsu0XiFwDAwiIiIiIiIiA3Bo4Am5lQUeHA8x2jqdfL0BoNR1JgZHFNlu5eKAbr/NwK1NgQj77R+D5zMkFjCIiIiIiIiIjECr0QISiV6bsQbUVGXlFGqTWZmj228z8PDqXQTN/s0oOZ4HCxhEREREREREBpASHg1Vdi5qdm5e5PScxFRYu1bXa3Ns6q33vSZfBYms7B/VHz66PaW4dZak47IPIZHJcOKDJYBWW+75jY1jYBAREREREREZgCorB9dW/wm/aYOhzsnDgxMhkFmaw6ObP0KX78KDk1fQcHQP3P87CBnRCWgw6mXYeDgj6YmBOtPvx8OtfRPY1nZFXnoW8tKyoFWpi11neqQSt3eeQLv570JmaYaEC+Ewd7CBS+sGuPHz/mLn85s6BG7tm+LgsC9hZmMFMxsrAEB+Zk6RV2uIAQsYRERERERERAZyecEW5DxMQ6O3e6H13NHIS81E3NmCJ3uErtgNG48a6PzjFGhUaoSt+weRf56Bnbebbv5rP+5F9Ua10DdwEcyqWZXpMaqnJv8Av48Gw/+T4bByrY6cxDTc++tMifMoXmgCi+q2ePWfb/Xa+RhVIiIiIiIioirixs/7i7z6QZWZg5MfLi9x3oz78Tjw2qxyrU+rUuPyt1sKHpFahHVugwq1HRg4u1zrEAOOgUFEREREREREoscrMIiIiIiIiIhEynfiADSb+Fqx0zfWG2nENMJiAYOIiIiIiIhIpMJ+P4jIvf8KHUMUWMCgKmPKOSAmS+gUgLs1sLit0CnEKXD0fKRHKoWOYfJsvRTo9tsMgyyr74eHcDs6zSDLeh51Peywd3l3oWMQkYkTy/uMIY/TRFT55aVkIO+Jp5RUZSxgUJURkwXcSRc6BZUkPVKJlPBooWPQE25Hp+H67RShYxARGQTfZ4iITBsH8SQiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9FjCIiIiIiIiISPRYwCAiIiIiIiIi0WMBg4ioBD13zsULi94v1G7jUQNjYnfApU1DAVIREREREVU9LGAQERERERERkeixgEFEREREREREoscCBhERERERERGJnkkXMEJCQtCvXz/Y29vDzs4O/fv3R2xsLGxtbTFs2DCh41EVEfqul9ARiIiIiIhEKeVWDGJPhSL+YjjUeflCxyETJxc6wLMKDAxEnz59ULt2bcycORNWVlZYt24devXqhYyMDPj5+QkdkYioyjq8phfM5FJ0eesvaLWP23cvfQnuLtYIGPknVCpt8QsgIhKR7ps+g5mNNf7u/zm0Go2u3dHXG733zcOJ8ctwb98ZARMSic/dvf/i6qo9eBh8W9dm4WSHBiO6w3fiazCzthQw3bNzf7EFWv7vddjX90B2fDKu/7If11fvEzpWlWGSV2AkJCRg6NCh8Pf3x+XLlzF9+nRMmDABgYGBuH//PgCwgEEVLurnKbg+2Q/5SQ9wfbIf7nw7VOhIVAHy0rJgbletULu5fUGbOpd/SSjK6JnH0bRedXzyVjNd29hBDdC9nTtG/O84ixdEZFJOTf4BdnUU8J34mq5NZmmOTism4s4fJ1m8IHpK8HfbcPy97/Ew5LZee25SGq4s3Yl/Bs5BXnqWQOmenVPzuui27hNEH72Mvd2nIXjRNrSc8ToajHpZ6GhVhklegbFgwQIkJydj7dq1sLKy0rXb29vD398fgYGBLGDQM7vYT1LidHOX2vBdEwnPdxYDKLiFpPGSYCMkIyGkRsTA69UASKRSvb+6ObeoB41KjfS7sQKmE6+YuCx88NVprJ/XGQdOxyArR4Xvp7fF9O+DEBaZKnQ8IqJyyY5Pwb/TfkTn1R8h5mgwHobcRsvPRkBqboZzM38VOh6RqEQduoDgRdsKvnn67xWPvk8MjsDZ//2MTismGjXb82oytg8Sg2/j0rxNAIDUWzFwaOAJ3wn9Efb7QYHTVQ0mWcDYsmULOnbsCB8fnyKnu7q6QqFQ6LVlZ2fD19cXSqUSGRkZxohJJqrZuscfSDNu/os78wei0eJLMKvuVtAolQmUjIRw87cDaPhWT7RfMh43fv4LeamZcG5RDy0+HoaIrUeRl2Z6fz0wlm3/3MWrnWth4zedkZWjwomLSqzcekPoWEREz+T+gfOI2HYMnVZMxIUv16PBqO44MGA2VJk5QkcjEpXra/4qU7+7u0+h1ecjYe1avYITGY5Lm4a4tSlQry3maDCajusHazdHZMUmCZSs6jC5AoZSqURMTAyGDi18ub5Go0FoaChatGhRaNqsWbNQu3ZtKJXKcq1PpVKVe57nEZv4+E0wVhkLqEzz3jAxys93BWBWaj+z6o+LX3Ibx4L/7WrotT9fjnxER8cZZFnPKicuRfd1bGwsLDXZwoV5Qn6+SugIhWRGJ2L/q5/B/5Ph6PbbDJjZWSPjXhyurtyL6z+X7Q3a2PLzVYiOjjbIslT5z3eLzIRvziDm8DBoNFr0mXDouXIYapuelVj3GzFLzJMBKCj+xsbGIt9cLWwggYjx2CZ2hjyOPb3c53F+1jq8emghuv46HVcW70TCxfBnzsFjGlVGuYlpiD0ZWqa+WrUGwev3o9awzhWcqvyKO1ZYuTggOyFFry07PvnRtOqVsoBRkccrhUIBubx8JQmTK2BkZmYCACSSwpf579mzB/Hx8YVuH7l48SIOHDiA7777DgMGDCjX+pRKJTw9PZ85b7nJqwONFgIA2rRuA6iSjbfuSq7x8quwqtVE6BgIDw+HZ4+mgmaoLrXC9y6vAADatGmDZJGctHzl1B3uZnZCxygk+fo9BI6eL3SMMgsPD8cQQx236s8FLN2fefYRvetCAgmsLWVo2dgZ+09GPdNywsPD4ek5/JlzGIJY9xsxM3NyR7NfC0562rRpjfyHMQInEkZFHtscfDwQsPA9aDVaaFVqnJ66Chn34/X6dFwxEba1XCGRSXFz3QHc3n4cNh410GnlZGhUKkhkMpydsQbJN+6VuC6JXIbXji/Brc2BCF2xW29a43d7w/u1DtDkq5EUekd3W4WFkx3aff02LJ3soMrOQ+Cob8q0XQY9jj3heX8WquxcXF21FwHz30XIkh3PvJyK2r7y4DGNKoK73A5fOXcvc/9Fc+dh1/QRFZjo2Yj1nFQIFXm8ioqKgoeHR7nmMbkChqenJ2QyGY4fP67Xfu/ePXz44YcA9AfwVKlUePfdd/HDDz9A88T960REVLEaetvj2yltMOnbs2hcxwE/z+kA34F/4GFKrtDRiCqNnIdpODziG+SnZ8G9qx+aTxmE01NW6vUJ/m4b0u8qITWXo9+R73F392lkxj7E/n4zAa0WivZN0WziABz/YHGJ62owsjtSI4ouQkUduqi7bLzzqilwDWiMuDPX0Xr2aAQv2orUiAeG2WAR0D76y6xWzfNKoqdla8p35WaO1rSuUMuOT4FVDQe9NstH3/93JQZVLJMrYJibm2PUqFFYu3Yt+vXrh969eyMqKgpr1qyBq6srYmJi9AoYCxcuRIsWLdCpUyccO3as3OtTKBSIinq2vxg+i9jEHLQZVVCcCTofBDdn3kJiKB9ed0VUBdymaunZuFz9fXx88I8Rf6eKkhOXglN95gAAgoKCYOnqIGie/5wZMh+Zd413y1Zl5ePjg6hthhlUrtv7pxB+P7Pc88nlEmz4pgsOn4vBzzvDYGEuQ/cAd6ye1R6DPjpS7uX5+Pgg8Aj3G1OTmCfDO1cLvg4KOg/nKnoLSUUe23Iepum+1uSri/xQnf5o3Zo8FaDVQqvV6vUzt7VC0vXIEtcjt7aE+4stcO/PM7BycSi8jsjH26dRqaBVayCRSuHQwAO+E16DTS0X3N5xotC948Ux5HHsSWJ5n6mo7SsPHtOoImi1WgSNWIT0WzGFB/AswuIDm/CTl2vFByun4o4V8UE3UbOLH0IWP74Cy72rHzKi4ivl7SNAxR6vnh63sixMroABAMuWLYOZmRn27NmDI0eOICAgALt27cIXX3yBiIgI3eCeERER+PHHH3H58uVnXpdcLi/3ZS3PRf74g4Kbwg0eisKPb6RnY3YLQAUUMOrP2l++HGZmxv2dKkKm9PHTe9zc3FCtppOAaR4zMzPJQ5LomJkZ7rglNyt93JiifDGuJTxcq6HXuH8AALl5aoz43zEEbeqLka/Ww/o/I8qdg/uN6THLBvCogOHm5gZXqxK7V1rGOLbJLM3hN30Iznyyptg+Tcf3R+RfZ6FVFRSSHJt4od38d1GtpjOOvr2wxOU3HdcX19f8hWoKxxL7ubRpCGuFI+KDbsLKxQGOjb1wauIKpN2NRc8dc6E8fRXp90ofB8qQx7GnlysGFbV95cFjGlWUnPf74vRHq0rtV7NTMzTo0NIIicqvuGPFtZ/2ofefX6PFjOG4s+M4nFvUR6O3euH8nN+MnNB4xHC8epJU6ADPwsbGBqtXr4ZSqUR6ejoOHjyIgIAAXL16Fb6+vpBKCzbr1KlTiIuLg4+PD5ydndGvXz9kZmbC2dkZJ06cEHgriIgqp/YtXDF9jC/emXMSCUmPq4YhYUmYvfISln3SDp4szhIZjEQmRaeVk3Bt1V6k3LxfZB/vfu3h5OuNywu26NqSrkVi/6ufIXDMfLSd93axy7d0todjU2/EnrhSYg77+u5oNXMkjr33PQAgLzUTmQ8SkRIWBU2eCnFnr8OhgbDjPhBRxas3tCu8+7cv+KbwsIUAAGuFI9p/P854oQzkYchtHHnzW3i+1BJ9D3+HFh8Pw6UFm/kIVSMSRxnaAFJSUhAdHY3evXvr2oYMGYKXXnpJ9/2ZM2cwZswYBAcHo0aNGkLEJCKq9E5fjoOZ/9oip83/5Qrm/1LyhyAiKp/2332AB8dCcP/A+SKn1+zSHPWHv4jDo74BtAXXdEvN5QW3lADIT8uCOjsPACCvZgmpTKr3iOjqjWrB0skO3Td9BmuFI6Rmcjy8ehcPjoXo+lRzd0aHpRNw/L3FyE1KBwCoc/ORGZ0Ia4UjspRJcGxWBxE79McwM0UR244hYtsxoWMQiZZEKkXHFRNhV7cmbvy8H3mpT9yKKpGgVs/WaPvV2yZ71U904CVEB14SOkaVVWkKGKGhBY/reXL8C2tra1hbW+u+r1GjBiQSiagugSEiIiJ6Vu5d/eDV9wXYeLrAu197JF27i6BZ6+De1Q/mDja4u+sUOi6dgKy4ZLy8+XMAwPH3F8PexwN+04YUjFUhkSBozjoAgHf/DpBbmuPGL49vj4w9Gap7LGK9IV1g5eKAB8dCYFXDAY3f64OLX21Aq5kjYelohw5LxgMAQlfsQszRYATNXodOKydBKpcj+uhlpIYL++hQIjIOqUyGFtOGwnd8f9zecQJnPl4NAOi9bx5q+NcXOB2ZskpdwHhaly5dkJGRYaREVBnY+nZByz1lGIGIDE5mZY4e22bDob4HznzyE+7uOV2oj9/UIag3rCtSb0Xj0Otfl3m+J72w6H14vNQSUf+cx5lPfiqyj++E/nDr2AxSuQyXFmxGfNDNcj0a0KK6DTos+xDmttZIDI4odJ+k4oUm8P/f69Dkq6DKysWJCcuQl/L4WNVh6QRY1bDXbePAsz8gMyYRAHB3z2letkhUhcUcDcaGOm8U2f6frc3fLTQ9OyEFB05fLdRevaEnQpbsLHZ9T155kJ2QgotfbQCAYp9gknT1Lg4MmF3s8oiocpNbWcCjm7/ue+tSxtEhKk2lKWCMGzcO48aZ3n1URFQ0Ta4KR99aiAajXi62T9j6g4jYfgwB898t13xPCl60DXd2nnx8r+ZT3F9sAZmVBQ4O/UKvvTyPBvSd8Bru7DyBu7tPo+MPk6AIaALlmWu66WmRSvwzaA7UufloMOplNHqrF0K+3w4AqN6oNszt9MeL0OSrcGAgPxAQkeEFfV707V9ERERiYJKDeBJR5afVaJCdkFJin+z4FECjf4VMWeZ7Upay5Edeeb0aALm1BV7eNhsdloyHvJql3qMBe/4xF/Vf71biMlzbNkLUoYsAgKgDQXAN0H/0btaDh1DnFjw3XZOvglbz+PGGzacMwpVlf+j1l0il6LFjDrr9NgO2XuV//BQRERERkSliAYOIqATWCkdo89U4OGQukq5Foun7fWHpbAfHxl64umovDg77EvWHvQjb2sU/w9zM1gqqzIKnceSmZsKiuk2R/Syc7NBgTA/c2hQIAFAENEHqnQfIeaog89ern+KfQXMQunIP2n//gWE2lIiIiIhI5FjAICIqQW5yhu5e8pijl1G9ce1yPxowPyMHcmtLAIC5XTXkJhcei0dubYkuqz/C2Rk/F1xZAsD3w/64tnJP4UyPRviPP3cDVjUcnm8DiYiIiIhMBAsYRFQlyKtZwtzOuvSOT1GeuQan5nUBAE7N6yLtbqzeowEBwLFZHaRFKiGRSWHl4lBoGXFnr8OjWwsAgOfLrRB35rredKmZHF3WTMW1H/9E4uVburxWNRzQ+ccp6LBsApya1UGTD/pCai6HzMIMAGBXxw35Gdnl3iYiIiIiIlNUaQbxJKLKp8vP0+DU1BuqrBw4+9fH+dn6jwb0GfES6g7uDPt67nh56yycnLgc2XHJRc5X1KMBgYIxJjx7toaVswNe3joLB4d9CStne92jASO2HkX77z5Ajx0Fg2yenLgcAIp8NKCttwKtZo7E0bcX6q0jdOUedFw6AY3efgUPr9zWDeDZYdmHODVxOeoPfxE1WtSD3LIvmn7QFzFHLyN0xW7s7T4dAGDjUQMB347FtVV7YeVaHS+t/x9UWbmABDgzY40RfhJERERERMJjAYOIROvYO4sKtT35aMDwDYcRvuFwmeYr7tGAIYt3IGTxDr22Jx8NqMlT4eSHywvNV9SjAWu0qI9bm48U6pv7MA2HR8wr1H7qUTEk7PeDJT4KNSM6QfcI1ey4ZPz58sfF9iUiIiIiqqxYwCCiKsEYjwa888fJCl8HEREREVFVxTEwiIiIiIiIiEj0eAUGVRnu5R+/sUKIJYcY2XophI5QKRjydazrYWewZT0PseQgItMmlvcZseQgoqJxH31MbK8FCxhUZSxuK3QCKk2332YIHYGesnd5d6EjEBEZDN9niKgseKwQL95CQkRERERERESixwIGEREREREREYkeCxhEREQkqJ9++gldunTR/XNzc8Nnn31WbPuTTp8+ja+/LnjMcFZWFgICAuDg4IAtW7YUWo9Wq8W7776LTp06oUePHoiKigIABAUF6dbRsmVL+Pv7AwCSkpIwYsSICt56IiIiKiuOgUFERESCGjt2LMaOHQsAuH37Nvr3749p06ahevXqRbY/acGCBVi7tuAxyRYWFti1axd+/PHHItezZ88eWFhY4MSJE7h48SJmzJiBjRs3ok2bNjh27BgAYMmSJcjOzgYAODo6wt7eHlevXkXTpk0rYtOJiIioHHgFBhEREYlCfn4+RowYgVWrVqF69eqltqelpSE1NRVOTk4AAJlMBoWi+NHSw8PD0apVKwCAv78/Tp48WajPpk2bMHz4cN33vXr1wo4dO55724iIiOj5sYBBREREojBjxgz07t0bHTp0KFN7WFgYvL29y7x8X19f/PPPP9Bqtfjnn38QHx+vNz08PBzm5ubw8vLStdWtWxehoaHl3xgiIiIyON5CQkRERILbv38/QkJCcPDgwTK1P4tevXrh7Nmz6Nq1K5o3b45mzZrpTd+4cSNef/31514PERERVQwWMIiIiEhQsbGxmD59Og4fPgypVFpq+398fHxw586dcq1r7ty5AIDAwEBYWFjoTdu2bVuh20pu377N8S+IiIhEggUMIiIiEtRXX32FtLQ0vbEnXnzxRcTFxRXZPmvWLACAvb097O3t8fDhQ904GAMHDsTly5dRrVo1nDt3DosXLwYAjBo1Ct9//z0GDRoEuVyOWrVqYfny5brlnjt3DnXq1IGzs7Netr///hvvv/9+hW07ERERlR0LGERERCSoH374AT/88EOx00ryySef4Mcff9Q9XnXnzp1F9vv9998BQPe0kae1bdsWf/31l15bUlISUlNT4evrW2IGIiIiMg4WMIiIiMhkdejQodDgnobi6OiIDRs2VMiyiYiIqPz4FBIiIiIiIiIiEj0WMIiIiIiIiIhI9FjAICIiIiIiIiLRYwGDiIiIiIiIiESPg3hSsaacA2KyhE5RwN0aWNxW6BRERERkygJHz0d6pFLoGLD1UqDbbzOEjkFEZHJYwKBixWQBd9KFTkFERERkGOmRSqSERwsdg4iInhFvISEiIiIiIiIi0WMBg4iIiIiIiIhEjwUMIiIiIiIiIhI9joFBREREREREJKC8fDWOX1DiwrVEXL75EMlpuZBIABdHK/g3ckJbXxcENHeBVCoROqqgWMAgIiIiIiIiEkBCUjaWbbqONTvDEPcwu8g+G/+6DQCo62mLD4Y0wvuDG6KatZkxY4oGbyEhIiIiIiIiMrJt/9xB49f+wFc/BRdbvHjS7ah0TPsuCM0G7cLxC7FGSCg+LGAQERERERERGYlGo8X4r//F0OlHkZicU+7570Sno8tb+7FoXWgFpBM33kJCREREREREZARarRYffHUaP+0IK7aPTCaBwtkKAKBMzIZarS2y3/TvgwAA08b4Gj6oSPEKDCIiIiIiIiIj+HVXeInFCwBQOFsh+tBwRB8aritkFGf690E4dr7q3E7CAgYZXei7XkJHICIiIiIiMqooZQY+WnTO4Mt9a9ZJZGTlG3y5YmTSBYyQkBD069cP9vb2sLOzQ//+/REbGwtbW1sMGzZM6HjlolJpsPtIJN7/8rSubeNfEcisIr+IRERUuWSpgL+jH38/LwQ4FguoNMJlIiIiEtIXP15GWobhP9/djUnHyi03DL5cMTLZAkZgYCDatWuHsLAwzJw5E/PmzUN0dDR69eqFjIwM+Pn5CR2xzK7fTkaDvjvw2uRA/HUyStc+Y+kFuL+0GX8/0WbKon6eguuT/ZCf9ADXJ/vhzrdDhY5EREQV4FQc0OsgsOKJc6nT8cC088Cgo0BkunDZxMb9xRboe2ghRkZuxqCglWj8Xh+hI1ERum/6DK/s/RoSqf6ps6OvN0be24zafQIESkZEpiIlLRcb99+usOX/uP0mNJqix8qoTExyEM+EhAQMHToU/v7+OHz4MKysCu4LGjlyJLy9vQHAZAoY9x6ko+vb+xGfVPTos2mZ+eg76RAOre6FLq3djJyubC72k5Q43dylNnzXRMLzncUACm4habwk2AjJiIjI2M4nAFODgOLOoaIzgbH/Ar93AhQl39Zb6Tk1r4tu6z7B1R/34vi4JajRoj4CFoyFOjsPYb8fFDoePeHU5B/Q78h38J34Gq4s2QkAkFmao9OKibjzx0nc23dG4IREJHY7D0ciO0ddYcu/G5OOk5eU6NxKnJ8ZDcUkr8BYsGABkpOTsXbtWl3xAgDs7e3h7+8PwHQKGPN+Dim2eAEAWi2gVmsxddE5aLXirKg1Wxer+1dnRsGbeqPFl3RtDRedFzghEREZg1YLfHetoHhR0jtWUi6w7pbRYolWk7F9kBh8G5fmbULqrRhEbDuGG7/+Dd8J/YWORk/Jjk/Bv9N+RPMpg+DUvC4AoOVnIyA1N8O5mb8KnI6ITMG50IQKX0eQEdYhNJO8AmPLli3o2LEjfHx8ipzu6uoKhUIBABgzZgw2bdoEc3Nz3fQdO3agZ8+eZVqXSqWCUql8/tBFSMvMx+97Sz+D02qBSzceYt+Ra2jRwKFCshQlP98VgFmp/cyqK3Rfy20cC/63q6HX/vxZ8hEdHWew5VV1OXEpuq9jY2NhqckWLgyRieB+U7qbGeaISHMpQ08t/ryvxUD7WFjJxFmcN6T8fFWR7S5tGuLWpkC9tpijwWg6rh+s3RyRFZtkjHiilJ+vQnR0dOkdn2G5z+r+gfOI2HYMnVZMxIUv16PBqO44MGA2VJnF/yGqpBwVsX3lwWMaGQt/1wqcDdF/UsiTj0p9mtsT7W4lPIXk6UesnroUheHdHZ8zqfEoFArI5eUrSZhcAUOpVCImJgZDhxYeP0Gj0SA0NBQtWrTQax87dixWrFjxzOvz9PR8pnlLZVUHqPdpmbv3HTIRSDpaMVmK0Hj5VVjVamK09ZUkPDwcnj2aCh2j0qgutcL3Lq8AANq0aYPkKvpGQlQe3G9K59JnIjzfXVqGnhLkaiTwe6k/siIuVHguoX3l1B3uZnaF2q1cHJCdkKLXlh2f/Gha9SpdwAgPD8eQCjj/Ku5nUVbnZ63Dq4cWouuv03Fl8U4kXAx/puVU1PaVB49pZCz8XXukwbeA+ePiwn+PSi3N+c39i53m0X0zYuKydN/v/eso9q4wnbGUoqKi4OHhUa55TO4WkszMTACARFJ43IU9e/YgPj7eZG4fgaScL395+xMRERmTVFau7pJy9icSmio7F1dX7QW0QMiSHULHISJTUvKwgaa0EkGZ3BUYnp6ekMlkOH78uF77vXv38OGHHwIoPP7Fxo0bsWnTJri6umLEiBH45JNPynypikKhQFRUxTwFJCE5F61GHoOmjI+U2/DrInT2d66QLEX58Lorosp/VWSpLD0bl3seHx8f/FNBP4eqKCcuBaf6zAEABAUFwdLVQdA8RKaA+03pLqZa4ssyDrAuhRbnDvwBe7PK/1zVM0PmI/Nu4dtRs+NTYFXDQa/N8tH3/12JUVX5+Pggapvhx5Yo7mdRHtpHt6Fo1c/+u1tR21cePKaRsfB3rUC3D04j/F6G7ntlYjY8um8usq+bs5XuyovWw3cjNrHoq1aUT7X3fLkT1sycZJjARvDfsA/lYXIFDHNzc4waNQpr165Fv3790Lt3b0RFRWHNmjVwdXVFTEyMXgFj4sSJ+Pbbb+Hs7IxLly5h+PDhyMnJwZdfflmm9cnl8nJf1lJWHh7Aay9GYufhyBL7SSSAV00bDO/THFKp8apqZrcAVEABo/6s/eXPYmZWYT+HqihT+sR9dW5uqFbTScA0RKaB+03p3NyBNQ+AuOySB/EEgG41JWjiXdMouYRmZlb06VZ80E3U7OKHkMWP/5Lv3tUPGVHxVfr2EaDgNauI9/3ifhbGVlHbVx48ppGx8HetQBtfV70Chlqt1bv9ozixidll6gcAAX4egh9bKppJ3pOwbNkyjB07FufOncPUqVNx7tw57Nq1CzVr1oS1tbXe4J7+/v5wcXGBVCpFq1atMHfuXGzZskXA9Po+fac5LMylKOKOGAAFFwFptcCXE1oatXhBRERUXjIJ8H6DkosXEgDmUuDN+sZKJV7XftqHGi3qocWM4bCvVxN1B3dGo7d6IXTFbqGjERGRgbVqXPFX0rdqYryr9YVikgUMGxsbrF69GkqlEunp6Th48CACAgJw9epV+Pr6QiotfrOkUqmoHkfq39gZe5Z2h5VFwV8EChUyJMCSj9vijd71jB+OiIionPrUAiYXMf7zf29vljLg+zaAj71RY4nSw5DbOPLmt/B8qSX6Hv4OLT4ehksLNiPs94NCRyMiIgMb+JI3ZLKK+4O0c3VLvNjGrcKWLxbiuI7OAFJSUhAdHY3evXvrtW/duhU9e/aEnZ0dQkNDMXfuXAwePFiglEXr0d4DEX8Nxi+7wrD+zwjEJ+XAtpoZ+r9YGx8MaYRGdRyEjkhERFRmI+oCL7gAOyKB47FApgpwsgRe8QD61QKcLYVOKB7RgZcQHXhJ6BhUDhHbjiFi2zGhYxCRifFQVEO/LrXxR2BkhSz/7dd8YGlRaT7eF6vSbGFoaCiAwgN4rly5Eu+//z7y8/Ph5uaGkSNH4n//+58ACUvmVsMaM8e2wMyxLUrvLGK2vl3Qco94rnAhIiJh1LEFPvYt+EdERETAzLHNsefYPajVhv28VN3OHJNHFHH5YyVU6QsYTz+thIiIiIiIiMjYWjRyxqfvNMeXq4MNutxlMwKgcLY26DLFyiTHwCjKuHHjoNVq0a5dO6GjEBERERERERUyc6wfurUt+Slc/z1i1aP75kKPSn3aW6/54I3edQ0ZUdQqTQGDiIiIiIiISMzMzWTYvfSlEosY/z1iNSYuq8TbTUb3rY/Vn7eHpLhHWlZCLGAQERERERERGYmNtRn2r3wZn7/n90xPJqlmJcfKz17Ar190hFxetT7SV62tJSIiIiIiIhKYuZkMX4xviaCNfdH/xdqQSksvZFiYyzC6b32E7hyAD4Y2KtM8lU2lGcSTiIiIiIiIyJT4N3bGriUvIUqZge0H7+LCtUScv5qIiKg0AECjOg5o61sDbZrWwJAe3nByqNrPImcBg4iIiIiIiEhAngobfDSq4Nnj0cpMeL68BQBw8Mee8FBUEzKaqPAWEiIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItHjGBhULHdroRM8JqYsRERE5WHrpRA6gsmpqNdMLD8LseQgIjI1LGBQsRa3FToBERGR6ev22wyhI9Aj/FkQEZk23kJCRERERERERKLHAgYRERERERERiR4LGEREREREREQkeixgEBEREREREZHosYBBRERERERERKLHAgYRERERERERiR4LGEREREREREQkeixgEBEREREREZHosYBBRERERERERKLHAgYRERERERERiR4LGEREREREREQkeixgEBEREREREZHosYBBRERERERERKLHAgYRERERERERiR4LGEREREREREQkenKhAxAZy5RzQEyW0CkAd2tgcVuhUxBVPYGj5yM9UvnM82tUat3X/wyeA6lc9szLsvVSoNtvM555fiKiyuR5j89UPL7fUGXDAgZVGTFZwJ10oVMQkVDSI5VICY82yLLS7sQaZDlERGTY4zMRVW68hYSIiIiIiIiIRI8FDCIiIiIiIiISPRYwiIiIiIiIiEj0WMAgIiIiIiIiItHjIJ5ERERP6bBkPOoN7QoA0KjVyI5LQezpq7g0byOylEkCpyMiIiKqmngFBhERURGUZ69ja7N3sKPVBzgxfgmcmnqhy09ThY5FREREVGWxgEFERFQETZ4K2QkpyFImIe7sDYRtOAyX1g1gZmMldDQiIiKiKokFDCIiolJYuVaHV5920KjU0Ko1QschIiIiqpIq9RgYSUlJmDdvHnbv3o3o6GjY2tqiadOm+OKLL9CxY0eh4xERkYgpXmiCNyLWQyKVQm5lAQC4umovVNm5AIBavdrA76PBevPY+3gg6PO1CPv9oNHzEhEREVV2lbaAce/ePXTp0gUZGRl4++234ePjg9TUVFy5cgUxMTFCxyMSVH5Gtu7r7IQUVKvpJGAaInFKuHQLpyatgMzCDF59X0DNjs1wecFm3fT7fwfh/t9Buu9r9WwN//+9jojtxwRIS0RUPqqsHN3XWXHJPBcgIpNQaQsYI0aMgEqlwpUrV+Dm5iZ0HDJhmtxsxO6Yh+STW5D3MBpScytYKOrCqctIuLw6Ueh45ZIZk4iQJTtxe8dxXdu+XjPg+XJLNJs0EDVa1BcwHZG4qHPykB6pBAAEL9wKWy8F2n79Nv6d9mOhvtZujmg77x0cfmMe1Nl5xo5KRFRmWXHJuLJ0JyK2HNW1/dV7Bjxe9Ifvh6/BtW0jAdMV5v5iC7T83+uwr++B7PhkXP9lP66v3id0LCISSKUsYJw4cQKnTp3CsmXL4Obmhvz8fOTn58Pa2lroaGSC7v/4AdJDj8LznaWw8m4OdVYasu5cRl7CfaGjlUvq7Qc48NosZCek6E/QahH1zwXEHLmMrj9Ph+fLrQTJRyR2wYu24rUTSxG2/hAehtx+PEEiQacVkxC6YjeSb9wTLiARUSnSo+Lxd//PkfXgof4ELRAdeAkxx4LRaeVkePd9QZiAT3FqXhfd1n2Cqz/uxfFxS1CjRX0ELBgLdXYeb9UjqqIq5SCe+/fvBwDUqlULr776KqysrFCtWjX4+Phgw4YNAqcjU5NybjdcX5sOh3b9YeHqDWvv5nDuNgY1h80SOlqZadRqBI6eX7h48WQflQbHxn6HzJhE4wUjMiHpd5WIOnQB/jOG67U3nzwQeelZuPnr3wIlIyIqnVarxdG3vi1cvHiyj0aLkxOWIvX2AyMmK16TsX2QGHwbl+ZtQuqtGERsO4Ybv/4N3wn9hY5GRAKplAWMsLAwAMC7776LpKQk/Pbbb/j1119hbm6OkSNHYu3atQInJFNiVt0NaZcOQJWeJHSUZ/bgWAjSSjsZ0Wqhzs1H2IZDxglFZIKurtwL9y5+UAQ0AQC4tG6A+q93w+kpPwicjIioZHFnriPpamTJnbRaaPLVCPvtH6NkKo1Lm4aIOXpZry3maDBsPF1g7eYoUCoiElKlvIUkPT0dAGBra4ujR4/C3NwcANC/f3/UqVMHn376KUaPHg2ptPT6jUqlglKprNC8ZBz5+a4AzMo9X+0JP+Pud68jZFQNWHk2QbUG7WDf8hXYt+0HiUTyDDnyER0dV+75nsfV9WU/EQnfehQub/ApPVT55Oerytz31OSiCxIJF8Kwzm0QAMDczhodl0/EqUkrkJucUe4s0dHR5ZqHiOh53Fh/oMx9b20/hppju1dgGn3FHZ+tXBwKXT2aHZ/8aFp1ZMWa7h+XjEUM7zc5cSm6r2NjY2GpyS6+MwEAYhMfD7Ibq4wFVJYCpqk4CoUCcnn5ShKVsoBhZWUFABg+fLiueAEA1atXR9++ffH7778jLCwMjRqVPkiRUqmEp6dnhWUl42m8/CqsajUp93w2jdqj6erbyAwPQmbYGaRfO4HbCwbBvmUv1P1sb7mLGOHh4fDs0bTcOZ7H1Ood0MTcpUxZU2Pi+TtPldJXTt3hbmZnsOU1GN0DVi4OaDN3jF57xPbjuP5TyQPMhYeHYwj3MyIyogkO7eBvUbNM5wL5KZlGPRcw9PGZHhPD+011qRW+d3kFANCmTRsks4BROnl1oNFCAECb1m0AVbLAgSpGVFQUPDw8yjVPpSxg/PciKBSKQtP+eyJJcnLl/CWgiiGRyWHT6AXYNHoBrv2n4uGxDYhcPBIZ107AtmlnoeOVKkdbtr88a7XaMvclqupCl+9C6PJdQscgIiqTHK2qTMULrVaLXK3aCIlKlx2fAqsaDnptlo++/+9KDCKqWiplAaNNmzb48ccfi7xc6r82FxeXMi1LoVAgKirKoPlIGB9ed0VUTun9ysLSo+DqHVVqfLnn9fHxwT9G/p168FcQrs/ZVGo/iUSCxkNeRNSMwo+JJDJ1Z4bMR+ZdcdwS6OPjg6htvwodg4iqkLgjIQj9pPRx4CQSCbxeaYeoL1YYIVWB4o7P8UE3UbOLH0IW79C1uXf1Q0ZUPG8fKSMxvN/kxKXgVJ85AICgoCBYujoImscUxCbmoM2o4wCAoPNBcHOuvLeQlFelLGD0798fkyZNwoYNGzBz5kzY2NgAKLjnavfu3fDx8UG9evXKtCy5XF7uy1pInMxuAXiGAkbYp53h2HE4rOu1gty+BnJjIxCz/lPIqjnA1rdr+XOYmRn9d0ox2gW3l+5FbkomoNUW3UkCQAu0HDcAjvydp0rIzEw8b3lmZnxvISLjqjlcgduL9yArLgko5lTgP/7jB8DFiMeo4o7P137ah95/fo0WM4bjzo7jcG5RH43e6oXzc34zWjZTJ4b3m0yple5rNzc3VKvpJGAaEyHP1H3ppnCDh6KagGHEpVI+haR69epYtGgRYmJi0K5dO3z//feYP38+2rVrh7y8PCxfvlzoiGRC7P17IenERkR8+QqujWuAyGVvwrJmfTSYfxpyO2eh45WJ3NIcXX6aCqmZrKBQ8TSJBNACrWaNhGNjL2PHIyIiogomNZOjy08fQWZhXsy5QMF/ftOGwKWlj1GzFedhyG0cefNbeL7UEn0Pf4cWHw/DpQWbEfb7QaGjEZFAxPPnKAMbO3YsnJ2d8e233+Lzzz+HVCpFQEAANm3ahPbt2wsdj0yIYtAMKAbNEDrGc3Pr4IueO+bg/JzfkHDplt40Gw9n+E0binpDuggTjoiIiCqcS+uG6LXrCwTNXof4oJt606wVTmg+ZRAajDTe00fKIjrwEqIDLwkdg4hEotIWMABgwIABGDBggNAxiETDpXVD9P7rGzy8cgcJl29Bq1LDrm5N1OzUDJIyPFaYiIiITJuzXz28sucrJF2PRPz5MGhVath6KVCzS3NIZTKh4xERlahSFzCIqGhOzerAqVkdoWMQiY6DjwcCFr4HrUYLrUqN01NXIeN+4cF6e+6ci9SIGJz55CfIrMzRY9tsONT3wJlPfsLdPacFSE5EVD6Ojb142ygRmRwWMIiIiB7JeZiGwyO+QX56Fty7+qH5lEE4PWWlXh+Pl1oiP+PxM+w1uSocfWshGox62dhxiYiIiKoUXjNORET0SM7DNOSnZwEANPlqaNUa/Q4SCRq+2RM31x3QNWk1GmQnpBgxJREREVHVxAIGERHRU2SW5vCbPgTXf96v115vSBfc238O6px8gZIRERERVV0sYBARET1BIpOi08pJuLZqL1Ju3te1yyzMUGdAR0RsOSJgOiIiIqKqi2NgEBERPaH9dx/gwbEQ3D9wXq/dppYLzO2r4aX1/4O5gw2sXBxQd3Bn3N5+XKCkRERERFULCxhERESPuHf1g1ffF2Dj6QLvfu2RdO0uYo4Gw9zBBnd3ncK+np8AABQBTeDdv72ueNHl52lwauoNVVYOnP3r4/zsdQJuBREREVHlxAIGERHRIzFHg7Ghzhul9lOeuQblmWu674+9s6giYxEREREROAYGEREREREREZkAFjCIiIiIiIiISPR4CwlVGe7WQicoIJYcRFWNrZdC6Ag6YspCREREZCpYwKAqY3FboRMQkZC6/TZD6AhERERE9Bx4CwkRERERERERiR4LGEREREREREQkeixgEBERERFRlfby1lnosGS80DGIqBQsYBARERERERGR6HEQTyIiIiIiMnkNx/REwzd7wLa2AnnpWYg7dwPH3lmEQUErEb4pEFeW7NT1fWHR+7DzdsOBgbPRYcl41OzUDABQb2hXAMCBAbOhPHOtxPUNClqJ2ztOwMLRFnX6d4A6X4WQ77cjfONhtJ41CnUGdoIqOxehy3fh5toDuvmsXBzQZu6bcO/qB6m5HImXI3D+i9/xMOQ2IJFg0PmVCPv9EEKX/aGbR2oux9CQn3Hhy/W4tSmwYHvf6oVGb/aEjUcNZD54iIhtRxG6Yje0ao3BXlMisWEBg4iIiIiITJrftCFo8v6ruPj1Rjw4HgJ5NUt4vNiiTPOe+3wtbGq7IjsuGUGfrwUA5KZklGneRm/1QvDi7fiz5yfw7t8e7ea9A49u/nhw8gr29ZoBr1cD0PartxB7+ipSw6MBAC+u/QQyczkOj/oGeWlZaD55IF7e8jn+aP8hcpPScWfnSdQd1EmvgFGrR2vILMwQ+eeZgu2dOgT1hnVF0Ky1SLoaCfv67gj4dixkFua4/O2W8rx0RCaFt5AQEREREZHJkltZoOm4fghetB031x5A2p1YJIXexZWlf5Q+M4D89Cxo8lRQ5+QhOyEF2Qkp0OSryjSv8sw1XF+9D+mRSlxZ+gfy0rOgVWt0baErdiMvLQtu7ZsCANw6+KKGf30cH78U8UE3kXLzPk5OXA51bj4aju4BALi9/Rgc6nvAqXld3XrqDu6C+wfOIz89CzIrczQd3w9nPl6N+38HISMqHjFHLuPygi1o9Favcr56RKaFV2AQEREREZHJcmjgCbmVBR4cDzH6upOuRT7+RqtFzsM0JN24p9+WmApLZ3sABVlzktJ0V2MAgCZPhcTLt+DQwBMAkBrxAAmXbqHuoM54GHIblk52cO/SHIFjFhQsw6dge7v8PA3QanXLkUilkFtZwMLJDrkP0ypuo4kExAIGERERERFVWlqNFpBI9NqkZob5GKRRqZ9amRbafHWhfhKppFBbSW5vP47mUwfj/NzfUGdAR+QkpePBsZBHyyq4iP7Yu98h7U5soXnzkst2+wuRKeItJEREREREZLJSwqOhys5Fzc7Ni5yek5gKa9fqem2OTb31vtfkqyCRVfxHo5SwKFg62sHex0PXJjWXw7lFfSSHRena7uw+BXNba7h39UPdwZ1x54+T0Go0umWosnNhW9sV6ZHKQv/+60dUGfEKDCIiIiIiMlmqrBxcW/0n/KYNhjonDw9OhEBmaQ6Pbv4IXb4LD05eQcPRPQrGi4hOQINRL8PGwxlJTwzUmX4/Hm7tm8C2tivy0rOQl5YF7dNXVxhA7KlQJFy6hc4/TMLZT38uGMRzyiDILMwQ9ts/un55KRmIDryEFtOHwcnXGycnrtDb3ivLd8H/f68DWuDBySuQyqSo3qg2HJt64+LXGwyem0gsWMAgIiIiIiKTdnnBFuQ8TEOjt3uh9dzRyEvNRNzZGwCA0BW7YeNRA51/nAKNSo2wdf8g8s8zsPN2081/7ce9qN6oFvoGLoJZNasyPUb1WR15cwHazH0T/2/v3uOqrA84jn+5JgRimHJU0KNzpIKmqHktc5bJCxdeSqzUlVuW9vJSpPnqFV3Wwli2aa+lMbdwZvNSamlu5CW11CILQdBCGSoXOd6AieAFOGd/uBFMS5QDz3Pw8/7v/H4Ph+/D+et8+f1+zz3vPX/pMapp2do0/lVdKCqtdV326u0atvQ5nc44rJLvc2vN7fvjhzp3vFhdHxuhvi9NUuX5izqTU6jsVdsaJDNgFm4OR42TXwAAAACgEX00ZJZKahxqCedpERqsUTsWGJqh7NhpfdD7CUnSg98m6ua2LQ3N4wrybWUKGX7pcbh5m8Yr2HKzwYnMgzMwAAAAAACA6bGFBAAAAABq6D5jjHrMGP2j8+93ntiIaQD8DwUGAAAAANSQtWyTjqzfbXQMU9j6q9dVesR23T9f81Gznz74stw9Pa7rffytFg3729zrzoGmgQIDAAAAAGq4WHJWF2s8peRGVnrE5rQzSs7kFDrlfXDj4gwMAAAAAABgehQYAAAAAADA9CgwAAAAAACA6VFgAAAAAAAA06PAAAAAAAAApsdTSAAAAAAA9TZ4wVPqHDNUkmSvqtK54yUq3JWp1Pj3VW4rMjgdmgJWYAAAAAAAnML21QGt6vEbfdhnqj5/aoFahlt1959jjY6FJoICAwAAAADgFPaLlTp3skTltiId/+o7ZS3fotZ9b5OXn4/R0dAEUGAAAAAAAJzOJ+gWWUf2l72ySo4qu9Fx0ARwBgYAAAAAwCksA8P0SPZ7cnN3l6fPTZKkzMXrVXnugiTp7iWxOrYjXQeXb5EkBYZ31F2LZmrDvbNVdaHCsNxwDS69AiM9PV3R0dEKCAhQ8+bNNWrUKBUWFsrf31/jx483Oh4AAAAA3FBOph7S+ntm65PIuUr7wwc6sSdLexNWVM9/HZek7tNH66ZAf8nNTQNef1wpz/+V8uK/Mg8V6cW3v61+nZC0T7mFZw1MZC4uuwJj69atGjlypDp06KAXXnhBPj4+Wrp0qSIjI3X27Fn17NnT6IgAAAAA6iGof1eFPXG/AsOt8gtupdSEFdq3YI3RsfATqs5fVOkRmyQp7Y1V8rda1O+1X2v3s+9IksptRdqf+In6xE3Uqb3Z+ndOoQp3ZhgZ2RTOX6jU5Be/0Ip/5tQa/9OKA1q08oCefbS75s3sK3d3N4MSmoNLFhgnT55UTEyMIiIitGXLFvn4XDoQZuLEierYsaMkUWAAAAAALs7Tt5lKDuUpZ90XuuO3jxkdB9chbf4qjf58obLe26zT6f+SJH2flKyojfFqMyhcGyLnGpzQeHa7Qw89t10ffXb0yvMO6fdJGbLbpTdi72jkdObikltIEhISVFxcrKSkpOryQpICAgIUEREhiQIDAAAAcHUFn+1VavzfdWT9btkvssXAFZUetilv8zeKmPvQD4MOh7KWbVb+1lRdOH3GuHAmsTXl2I+WFzW9uSxDOfk39t/LJVdgrFy5UnfeeadCQ0OvOB8UFCSLxVL9euPGjYqLi1NWVpb8/f0VGxur2bNn1+l3VVZWymazOSU3AAAAgNoqKiqNjtBkVVRUKj8/v97vUV+Zi9YrasNrsgwIk+3L/ZcG7XY57I5rylHfezGrN5furdN1Doc0/909en7ybQ2cqHFYLBZ5el5bJeFyBYbNZlNBQYFiYmIum7Pb7crIyFCvXr2qxzZt2qQpU6Zo2bJlGjJkiMrLy5Wbm3tNvy8kJMQp2QEAAADU9ruW96qdV3OjYzRJBw8e1Lh6fpe5ls9n56y3rzh+8pssLW3zQL1yOONeTKvLfMmrxdWvczi0eGmyFr90T4NHagx5eXkKDg6+pp9xuQKjrKxMkuTmdvnhJR9//LFOnDhRa/tIXFyc4uLiNGzYMElS8+bNFR4e3ihZAQAAAAD4aXU92cEhubnkKRBO43IFRkhIiDw8PLRjx45a40ePHtX06dMl/XD+RVlZmfbs2aPIyEh16dJFxcXF6tevnxYuXFh92OfVWCwW5eXlOfUeAAAAAFzy5bjXVXaYLdsNITQ0VHmr363XezTU55O9eruyV2+v8/XOuBezevC5r5WSUayrbqhxc9fDY3+hhBnTGyNWg6t57ENduVyB4e3trUmTJikpKUnR0dGKiopSXl6elixZoqCgIBUUFFQXGMXFxXI4HFqzZo2Sk5PVunVrzZo1S2PGjFFqauoVV3H8P09Pz2te1gIAAACgbry8XO4ricvw8qr/dxmzfD7OuBezmvHIBT08d3udrn3m0d4KDr61YQOZmEuuP3nrrbc0ZcoUpaSkKDY2VikpKVq3bp3atm0rX1/f6sM9/f39JUkzZ86U1WqVr6+v4uPjlZaWxqoKAAAAwOQ8fZspMMyqwDCr3L085dOqhQLDrPK3Xvt/bgGzGnuvVT1CA696XfTQ9urd7cYtLyQXXIEhSX5+fkpMTFRiYmKt8czMTHXv3l3u7pd6mYCAAHXo0KFOKy0AAAAAmMutt/9MI9a+Uv266+RIdZ0cKdvu/Uoe+5KByQDn8fbyUPLi+zRi6qfad7BIbtJl20nuG9hOy+fdbUA6c3HJAuNKSkpKlJ+fr6ioqFrjTz75pBYuXKjhw4erVatWiouLU+/evdW+fXuDkgIAAACoC9uX++v99ArAFbRp5auU93+pDzcf0Turv9OBnBJ5uLupb3grTYvpqsjBwfLwcMkNFE7VZAqMjIwMSar1BBJJmjNnjoqLixURESG73a7Bgwdr7dq1BiQEAAAAgKanRWiwBrzxhBx2hxyVVdoVu1hnc09Uz3v4eKvfq5Pl1z5I7h7u2jIhXi1uC1GfuImSJE+/ZnJzc9OG4XOMugVTaHaTpyaM7KwJIzsbHcW0mnyB4e7uroSEBCUkJBiQCgAAAACatvOnz2jLhHmqKC1Xu6E9dfvTD2jX04uq53s+M04563bKtiuzeuxUWnb1NqBuj0fJo5l3o+eG62kya1CmTZsmh8Oh/v37Gx0FAAAAAG4Y50+fUUVpuSTJXlElR5W91rxlUJja39dHI9a8oh6zxl728x1HD9bhdTsbJStcW5MpMAAAAAAAxvFo5q2es8fpwF/+UWs8sJtVBdvSlPzAy2rZvZMsA8Kq55p3aiN7RaXO5p9s7LhwQRQYAAAAAIB6cfNw112LZmr/4vUq+T631tz5ojMq2J4uORw6tiNdt3TrUD3XacydylnL6gvUDQUGAAAAAKBeBr05Vce2pys3ec9lc8e/+k4te3SSJLXs0UlnDhdWz1nvH6gjG3Y3Wk64tiZziCcAAAAAoPG1G9pT1vsHyi+ktTpGD1LR/sMq2JYm7xZ+Orxup76NX65B86fKo5m3SrLyVPDZXknSrb1+rtKjx3WhqNTgO4CroMAAAAAAAFy3gm1pWt7pkR+dL8s/pU3jX71s/NTeQ9o6cV5DRkMTwxYSAAAAAABgehQYAAAAAADA9CgwAAAAAACA6XEGBgAAAADD+FstRkdospzxtzXL52OWHDCWm8PhcBgdAgAAAAAA4KewhQQAAAAAAJgeBQYAAAAAADA9CgwAAAAAAGB6FBgAAAAAAMD0KDAAAAAAAIDpUWAAAAAAAADTo8AAAAAAAACmR4EBAAAAAABMjwIDAAAAAACYHgUGAAAAAAAwPQoMAAAAAABgehQYAAAAAADA9CgwAAAAAACA6VFgAAAAAAAA06PAAAAAAAAApkeBAQAAAAAATI8CAwAAAAAAmB4FBgAAAAAAMD0KDAAAAAAAYHoUGAAAAAAAwPQoMAAAAAAAgOlRYAAAAAAAANP7D+sMRNnv+h+5AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1367.11x561.867 with 1 Axes>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -140,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -166,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -176,7 +178,7 @@
        " 1: PauliList(['ZIII', 'IIII', 'IIII'])}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,17 +189,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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EF2esEyJ8omqPicFrDA0NERAQwHkLKlDtNo2Q/FcElPkFeH7vIYysLQCRCFAqYWxtDvnj58iXvZhs7ln0Q9T2aAyLBnXw5OY9AMCTv+/DrkNT1foa+XRH3V5t8eD4VUSsPyTINhFR+b0+DrrU3ARpkbFq9Rn/DKuoyM0HlEoolUpthkeVpLT9bt+5BQyMDOH+6VA8PP83/l65D/Inz1X1irwCjqFPVInEpTep+iZOnAilUglPT0+hQ6F/kVqZIfdZlup1XmY2pBYvZpuWP3kOYxsLmNhawbCGMew6NIORlRme3n6AOt3cAQCO3d1h8s94+Q+OB+NA1xn4Y8hC2HdsAYe33bS+PURUcaxbOKPf71+jw9e+SDofVmSblpO8EXvkMpT5ujtZG6krab9bN3dG4plQHB+yALXcGsC+YwtVnYGxFO6fD0PkpqPaDplIb1SLxICqrtxnWZBa1FC9lpiZqA2zeGnmBnRZOw1d189AelQ8ZI/SkHg6BM/vP0SffQvh2KMN0v7pf5r7XAalQgFFXj7ijl6BtZuL1reHiCpOWkQsjr7/FYLGLEUH/08K1bsM7Ixabi4IWbZLgOiospS03+Vpz5F49iagVOLhuZuo2bw+AEBkIEaXddMQsf6Q2nNqRFSxmBhQpUq9cRd2ns0gMhDD3NkeOWnPgde6BDy6fAt/DF2Ic+NXwNDUCKnX7wIAQr/fg+MfzEf8H9eQ/FcEAEBibqpazr5jc2TEJGl3Y4iowoilr3qy5j2XoSBbfSCBOt1ao/GHPXB+6mq1cwbpttL2+6PLt1CrVQMAQK1WDfD8n/N85+UT8PDsTTw4flV7wRLpoWrxjAFVXbnpmbj7SxD67l8MpVKBy19ugmN3d0itzBCz/wLemj8KtdwaQJFfgBvf/AJFXj6MrM3RfeNnUOQXICvxMa589RMAoMX49+HYzR1KhQKPQ+/xHwSRDrNt1xTunw2DskABkUiE4AVb1M4NXqsmQ/boKXrvnAsAODd+BbJT04UNmsqttP1+3X8HOn8/AQbGUqRHxSPxdAgcu7vDeUAnmDnZwmVgZ6RFxCB43hahN4WoWmJiQJXuzo5TuLPjlOr108hXQ9NdW7itUPuctAwc/2B+ofLQ73Yj9LvdlRMkEWlV8sVwHH9t5Jl/2916rBajIW0pbb9nJTzGieGL1coSz4RiR4ORlR0aEYFdiYiIiIiICEwMiIiIiIgI7EpEFcTc2b5cyyvyC/D8/ouHzCwaOEBs+OazTZc3luqG+4a0paocazzOhFOevz3PNZWrqvxNqkocVDSRkrPGUBWQ9fAJ9rb9LwBg6PVA1KhTS+CI6CXum6qruu2b6rY9VDbc/0TCY1ciIiIiIiJiYkBEREREREwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgITAyIiIiIiAhMDIiIiIiICEwMiIiIiIgIgKHQAeizGVeARJnQUQCOpsCKDkJHQVRY0OilyIhNFjoMjZk726Pn1llCh0EVoCodezyu9E9VOv50QWV9RvTxOo2JgYASZcD9DKGjIKq6MmKTkX4nQegwSA/x2CMh8firGvTxOo1diYiIiIiIiIkBERERERExMSAiIiIiIjAxICIiIiIiMDEgIiIiIiIwMSAiIiqXt1dOwpikXzEm6VeMStiNodcD8XbAFJjaWwsdGlGZ9dm3EJ2+H1+o3KxubYxJ+hW27ZsKEFXVF/VVN8Su9i1UnvMoFtcHipAZeUGAqMqOiQEREVE5JV+OxO5Wvvj1rQn4c9JK1GrpjG4b/IQOi4ioTJgYEBERlZMiNx/ZqemQJafh0eVbiNpxCrbtmkBiZiJ0aEREGtOLxEChUGDFihVo2rQpjI2N4eTkBD8/P2RlZQkdGhERVTMmdjXh/J4nFPkFUBYohA6HiEhjejHz8YwZMxAQEIBBgwbBz88Pt27dQkBAAEJCQnDq1CmIxXqRHxERUSWx79QCI6O3QyQWw9DECAAQvv4Q8rNzAADdNvrh4bmbuLPjFADAuqULuqybht97fY6CnDzB4iZ6U332L4LUzAQiiSFSrtzC5S83QalgIlyamJWj8fzGMRha2qLF6nChwymk2icGERERWL16NQYPHox9+/apyl1cXDB16lTs2rULI0aMEDBCqk6UCgWSLoQjevcZZD18AgNjKRy7tkYjn24wqmkudHhEVElSb9zFhWlrYGAkgfOATqjj1Qohy3aq6oPnbkbfg4sRd/QKcp5mouPSsbgy+ycmBaSzgj7+BnmZ2QCAbps+g/P7HRFz8KLAUVV9Nu/8H+zen4aYlaOEDqVIOv1V+c2bNzFw4EBYWlrCwsIC3t7eSEpKgrm5OYYPHw4A2LlzJ5RKJaZPn6627NixY2FqaoodO3YIEHn5KHKykfjzXISPb4wbQ00QOtIat/zaIeX3AKFD02tZSU/w+7szccJnEe7/dh6PrtzCw7OhuLpwK3a3GYfoPWeFDrHasPNshh6bZ2LI1fUYk/QrWk3/QOiQSM8VyHOREZuM9Kh4hH63GxnxKejw9SeqellyGiICD+OtuR+jyce98Ox+EpIuhAkYMVHRcp/LILWoUahcavmi7GUy+zIpEBkawEBiCKVSqb0gqyADU0sUyJ4VKi/ISgcAiCTGAADzll1hYFZ1RyzT2cQgKCgInp6eiIqKwpw5c+Dv74+EhAT07dsXmZmZcHd3BwBcvXoVYrEY7du3V1ve2NgY7u7uuHr1qgDRl8+DHycg7cw21B3zHVqsiYTrkjOo3W8S8v85+Ej7ctIz8ceQBUgLj3lV+NpJUpGbjwvT1vDblApiaGqM9LvxuLZ4O2SPngodDlEhod/vRiOf7qjVuqGq7Pbm47Bq4gS3yd64unCrgNERFe9ZdCJqtWoA0b+6Wdu0aQRFfgEyYpJUZe/+ugAfhv8PeZnZiDt8WduhVinGdZtCdu86lAUFauVZd4MBsQGMHBoJFFnZ6GRikJqaCh8fH3h4eCAkJASff/45Jk+ejKCgIDx48AAAVInBw4cPYWNjAyMjo0LrcXR0xOPHj5Gbm6vN8Mst/coB2A36HFae3jCyc4GpS2vY9ByDOsPnCR2a3rq9+Tie308qvoFSCYiA4HlboMjL115g1VTi6RDc8P8FsYf+giKXXTGo6smISUb8yWvwmPXhq0KlElHbTiIh6AZynjwXLjiiEtzeehzGtS3ReeUk1GrVAOb17eDi3RltvhiO6N1nkPtcpmr7x5AF2O0+FgYmUti/3VLAqIVXu+9E5Kc/QmzAf5AVfR05SfeQ9udOPPx5Lmx6/geGZlZCh6gRnXzGYNmyZXj69Ck2b94ME5NXQ8FZWlrCw8MDQUFBqsRAJpMVmRQAL+4avGwjlUrLHVd+fj6Sk5M1bp+XZwdAUub3kdR0wPMbx2HdZQQMzct/OyovLw8JCY/KvZ7ykD9KV/2elJQEY0W2cMGUkbJAgcjNxwARgJLupCqB7JSnCNn5B+x6tNZWeOUm5L7J07EkKi8vHwkJCVp7P13+3BSlKm1PRRx74esOof/vX8O+YwskX4p4UahQQKkoW5cLbR9XQqlK+19oQp77shIe4+j7X8Fj5ofouXUWJBamyIx7hPB1hxC56Uih9gXyXDw4Fox677ZD0p9/CxBx5X1GynKdZmRbH02W/YWHP8/BvSXvo0D2DFL7BrAb9Dns3p9Wzjje7DrN3t4ehoZlu9TXycRg165d8PLygqura5H1dnZ2sLe3BwCYmpoiJSWlyHZyuVzV5qU9e/YgICAAoaGhsLGxQWxsrMZxJScnw8nJSeP2zVeHw6ReC43bv1R/8ibELB+Bm6Nqw8SpBWo08YRl236w7DAQIpGozOu7c+cOnN4VNtOvKTbBD7b9AADt27fHUx36h2AlNsYK2/4at186aRZ+zax6IxEUR8h9s6RWLzhKLLT2fuV1584dDCvDOaC8dPlzU5SqtD1lOfYuTF9bZHnqtShscRhS7li0fVwJpSrtf6EJfe57GhmHoNFLi62XmJtCLDVEzpPnEBmI4dTrLST/FaHFCNVV1mekrNdppi6t0WjO7xUex5tep8XHx6Nu3bplWkbnEoPk5GQkJibCx8enUJ1CoUBYWBjatGmjKqtTpw4iIyORk5NT6M5BYmIibGxs1O4W1KxZE5MnT8ajR4+wYsWKytuQcjBr1hktA+8h604wsqIuISPiT9xbNgSWbfui4VeH3ig5oDcnRtn+3mLuHyIi0mFSS1N03/Q5xBJDiAzESPrzJqK2nxA6LJ1wb9lQZN66gPznj/H3/9WF/ZDZsO03UeiwVHQuMXg5KVlRF78HDx5ESkqKqhsRALRr1w4nTpxAcHAwvLy8VOVyuRyhoaHo0qWL2jp69eoFADhw4ECZY7O3t0d8fLzG7adE2iFeXua3AQCIDAxh1qwTzJp1gp23H56c3YHYFR8jM+JPmLfsWqZ1ubq64o8yxF0Z5I/SceG9BQCA4OBgGNtZCRpPWShy8/Hnu3OQn6nZzvz0m3lYPtCzkqOqOELum0vDliIrRvPueUJzdXVF/J7/ae39dPlzU5SqtD2VdexF7zlb5hHKtH1cCaUq7X+hVfVzX1bCYxzuM1PoMFQq6zNSnuu04jScubfMy7zpddrL3jNloXOJgZOTEwwMDHDu3Dm18ri4OEyZMgUA1BIDHx8f+Pv7Y+XKlWqJwcaNGyGTyTBy5MgKi83Q0LBMt2wkdwFU0AFnXLcZACD/WdHdpkqMQyIp862mipYlfvWsiIODA2rUqSVgNGXnOuIdRG44XGo7iZkJPP7zHiSmxlqIqmIIuW8kEt06RUkkZTsHlJeuf27+rSptT1U69rR9XAmlKu1/oVWl408XVNZnpCKv08pDm9dpOnfkSaVSjBo1Cps3b8bAgQPRv39/xMfHY+PGjbCzs0NiYqJaYuDm5oZJkyZhzZo1GDx4MPr166ea+bhr1646OblZ1OyusPb6EKaN3oKhZW3kJEUjcftsGNSwgrlbd6HD00vNffsjes9Z5D7LLPEBZLepg3UqKaiqDE2NYeHy4psQscQQJrWtYN3CGXlZcmTEVt1v2YiIiKoynUsMACAgIAASiQQHDx7E6dOn0bFjR+zfvx+LFi1CdHR0oYeSV65cCWdnZ2zYsAFHjhyBjY0NpkyZgkWLFkEs1r0RWy09+iLtz5/xcOc8FMiew9DSFuYtusB56mYYWtgIHZ5eMnOqjd475+DkyK+Rk5ZR5AhFLScOhNtkbyHCq3ZsWjdEn98Wql43+7++aPZ/fZH8VwSOfzBfwMiIiIh0l04mBmZmZggMDERgYKBaeXh4ONzc3Apd7BsYGMDPzw9+fn7aDLPS2A+ZBfshs4QOg/7Fxr0RBl9cjeg9Z3Fn+0k8i04EADgP6ISWEwbAxl03JjfRBcmXIipktBciIiJ6RScTg6Kkp6cjISEB/ftrPmxkUQoKCpCXl4e8vDwolUrI5XKIRKJi50Igep2RlRlajHsPzu91xN62/wUAtJs/Wq/7yhLpA7O6tdFl3XQo8vMhMjDA5Vkb8fRWnKrea81UmNezg8hAjNtbjuPe3nMlrI2oMIsGDvA+uwLHvOci9cZdtTqzerbo/MNEiCWGeHAsGBE/HoKBiRTv7pkPq8Z1cWnmBsQcvFji+o1qWcDz609gXMsC+dm5CBr1jVp987H94TLobSjyCpAWdh9X5pT8sG/rGUNQp1trFMjzcGH6GsiS0kp9P7HEEF3WToOJrRVEBmJc+eonPPn7PlrPGAKHt90AAOYu9ghfexC3fjqq6Z+u0t0Yaooaru0BALbvTUPNjoMKtYn6qhuMHZui/sQfVWXyxDuImNICTb45D7MmVWNQkmqTGISFhQFQf/D4TWzfvh3/+c9/VK9NTExQv379Ms1nQERE+iUr6QmODpwDKJWw79wSraYOxrkJr4a8Dl2+BxkxyRBLDTHw9A+IOXCRs6BTmbSeMQTJlyKLrHtrzse48c0vSL1+B31+W4i4I5eRlfgYZ/7vOzQZ1Vuj9bebPxqh3+/Gs+iHRdbHn7yOyI0vJjjrun4G7Do2x6Ni4rFyrQvb9k1xbOBcOHRpBY+ZHxaa76Oo93PwckNuhgxnxy2HTZvGaDXtA5z55DvcXPErbq74FQDw/olvEXfkskbbpC3S2vXQ5OuzxdanXz0MAxPzQuVJexbDvEXZRpKsbLrXwb4YFZUYjBkzBkqlUu2HSQEREZVEWaAAlC8eLJKamyAtMlatPuOfoScVufnAP/9biDRl06YxslPSIUt6UmS9ZWNHpF6/AwBIOHUDdp7NoFQokJ2artH6RWIxrJrUhdvkQejz20I0HtGzUJvXB3ZQ5Oe/OOaLYefZHPEnrwMAkv78G7VaNdDo/TJik2Fg9GKmYamlKeRPnqktZ+VaF7nPsiBLVr/7ILS8tIeImt0V978bjrx09dEhlQoFUo+uRe1+k9TKs6KuQGJlD6lN1RpxrNokBhMnToRSqYSnZ9W4FUNERPrFuoUz+v3+NTp87Yuk82FFtmk5yRuxRy5DmV+g5ehIl7WaNhhha/YXWy8Sv5rbKedZFoxqFv52uiTGNhawbu6M8PWHcGL4YjQe3gPm9e2KbGvbvilM7a2REny72PVJrcxejNL3Mj4D9cvN4t4vMyEVhiZGGHR+FTr/MBG3Nql3F2rwQRfc33+hTNumDW4b7qOJ/zlYtR+AhM3qz7M+Ob0VVh0HQyxRH5Ewae/XsP+g6j0vWm0SAyIiIiGlRcTi6PtfIWjMUnTw/6RQvcvAzqjl5oKQZbsEiI50Vd2eHnhy8x5ynmYW2+b1G1BSC1PkPM0o03vkPstC1sPHSI+KhyI3H48uR8KqiVOhdpaNHfHWnI9x9r8/lLy+9ExILWq8iu9fdxeKe79Gw7ohMz4F+72m4diAOej8g/qMwPX7dUDc4Utl2jZteDkiZM23h0F2P0RVrsiVI+3cz7Dp+R+19s+uHXkx5LxF1Xv+kIkBEVV7b6+chDFJv2JM0q8YlbAbQ68H4u2AKTC1txY6NKomxNJXj+zlPZehIDtXrb5Ot9Zo/GEPnJ+6Wv0qjqgU1i2dYd+pBXr98hUcurRCu4VjYGJrpdbm2Z0E1ch3dXt64NGVW8Wuz7CGMaQWpmplBTl5yEp4rDonWrdqgOf/mhOmhqMN3l41GX9OWvViWO5/mNpbQ/Sv0SAfXY6EY482AAD7zi3x5O/7mr2fSAT5P+vOeZYFyWtx2rZvivS7Cch9Lit224RQIM+CsuDFHcCMiD9h5PBqBMKcRzEoyEpH9OL3kLD1Czy7fhRPTm+D7H4oMsPP4u6CPngeehIJP81AXlqSUJugpto8fExEVJLky5E4N+4HiAzEMHe2g6e/L7pt8MPRAV8JHRpVA7btmsL9s2FQFiggEokQvGALHLu7Q2plhpj9F+C1ajJkj56i9865AIBz41do3P+b9Nvfq37D36t+A/DiS46obSeQnZKudnxd9/8ZnZdPgMjQAPF/XEXmgxf93Ltt+gy1WrogXyaHjUdjXJ2/BS7eb8PQWFpoVJ/g+VvQZd00iA0NkXAmBM/uJMCkthWa//c9XF+yA2/N+RjG1hZ4e+WLvvJha/Yj8UwouqyfjtOjl6pdsKffScCT0Hvoe3AxCnLycXHGiwePGw3rhszEx0i+GF7k+2XFp6LLuuno89tCGJoYIWTZTtU6Gwz2wv3fql43InnCbcStHQsDYzOIDCWoNzEQz24cR0FGGqy7jkCzH64BADLCziLt/C7U6jEKAOAw7MX/nthVY2DTZzwk1g5CbYIaJgZEpBcUufmqCzFZchqidpyC59efQGJmgrzMbGGDI52XfDEcxy+GF1u/u/VYLUZD1dXrI/sknglV/Z4Rm1zk5I5nfb8vVFazqRNurtxXqDwtPAbHB6uvIzs1HdeX7AAAtVG2XhIZGiDzQUqR3+KHLt+D0OV71Mqi95wt8f3ys3Nw+j/LCq0LAC7P2lhkudBqNGqL5ituqJUZOxSet8jcrRvM3boVKneetqWSInszTAyISO+Y2NWE83ueUOQXlDiyBhFRdRM8d3OFrUuZX4AL09ZU2PpIeHzGgIj0gn2nFhgZvR0f3f8ZPqEbYd+xBSI3HkF+dg6AF/1kh1xbD+NaFgAAAxMpBl9cDaum9UqsIyIiqi6YGBCRXki9cReH3vkch/vOQugPe5FyNUqt/6osOQ2RgYfRbuEYAIC73zDEHbuC9NsPSqwjIiKqLtiVSECOpqW30YaqEgdRZSqQ56om6An9bjfMne3R4etP8Ndnr6anv/XTMbx3fBma+fZD/X4dcKjnZxrVUfVj7mxfruUV+QV4fv/FKCMWDRwgNjQQLBbSPdznZVNZf6+qcn2kzTiYGAhoRQehIyDSX6Hf78agP1chavtJPLl5D8CLGSqvzt+CPr8txOn/+1bVzai0Oqp+em4t38RDWQ+fYG/b/wIA3t27ADXqVL3xyqnqKu/xRxVDH6/T2JWIiPRSRkwy4k9eg8esD9XKHXu2gSw5DTWLeH6gpDoiIiJdx8SAiPRW+LpDcOzmDvuOLQAAVk3roV6f9jjcdxYaj+gJs3q2qrYl1REREVUHTAyIqNq7MH0tTvgsKlSeei0KWxyGIPlSBACg47JxuDp/C2TJaQj5dhc6fP2Jqm1JdURERNUBEwMiIgCNR74D+eNnSAh6MVHNvb3nIKlhjHr9OpRYR0REVF3w4WMiIgB3fz6Fuz+fUit7fVbOkuqIiIiqA94xICIiIiIiJgZERERERMTEgIiIiIiIwMSAiIiIiIjAxICIiIiIiMDEgIiIiIiIwMSAiIiIiIjAeQyISE9YudZFx+/+C6VCCWV+AS76rUfmgxRVvbvfMDQa3h3P7ibg5IivNVqGiEjXvPPOO6hbty62bNkidCjFksvlmDBhAkJDQxEREYF69eohOjpa6LD0Au8YEJFekD95jlMffYPjg+YhfN1BtJ4xRK0+avsJHP9gfpmWISKiwnJzc8u1fEFBAaRSKcaNG4fhw4dXUFSkCSYGRKQX5E+eIy9DBgBQ5BVAWaBQq89OSQcUyjItQ0QkhLVr16J58+YwMjKCra0tPvjgAwCAs7MzlixZotbW19cX3bp1AwCMGTMGQUFB2Lp1K0QiEUQiEc6ePVvq++Xn52PhwoVo2LAhjIyM4OjoiClTpqjqRSIRAgICMGLECFhaWuLjjz/GmDFjVO/x+s+CBQtKfb8aNWogMDAQEyZMQIMGDTT+u1D5sSsRURGCRi9FRmzyGy+vyC9Q/f7H0AUQGxq80XrMne3Rc+usN46DCjMwlsL982G4NHNjpS5DRFQZ5s+fj+XLl2Pp0qXo3bs3MjMzcezYMY2WXbVqFe7fvw8HBwesWrUKAGBtbV3qcp988gmOHTuG5cuXo1OnTkhNTcWlS5fU2ixcuBALFy7E4sWLoVAoYGtri6VLl6rqDx06hIkTJ8LLy6sMW0vaxsSAqAgZsclIv5NQIet6fj+pQtZD5ScyEKPLummIWH8I6bcfVNoyRESVISsrC99++y0WL16MyZMnq8o9PDw0Wt7S0hJSqRQmJiawt7fXaJno6Ghs27YNe/fuxZAhL7pTNmzYEJ6enmrtvL291WJ6+X4AEBoaik8//RQBAQHo2bOnRu9LwmBXIiLSG52XT8DDszfx4PjVSl2GiKgyREREQC6Xo3fv3lp7zxs3bgBAqe/Zvn37IsuTkpLw/vvvw9fXFxMnTqzw+Khi8Y4BEekFx+7ucB7QCWZOtnAZ2BlpETFIPBMKqZUZYvZfgOtH76Dh0K6wbOSI3rvn4fzU1bBuXr/QMsHztgi9KURERRKLxVAq1Z+VysvL08p716hRo1CZTCbDgAED0KZNG/zwww9aiYPKh4kBEemFxDOh2NFgZLH1d3acwp0dp9SXefS0xGWIiLSpefPmMDY2xokTJ9CqVatC9ba2tnj48KFaWUhIiNpzBFKpFAUFBf9etFgvuymdOHFC1ZVIE0qlEqNGjUJ+fj527twJsZidVHQBEwMiIiIiHWBmZgY/Pz8sWLAAJiYm6NWrF7Kzs3H06FF8+eWXeOedd7Bu3ToMGjQI9evXx48//oi4uDi1xMDFxQVnzpzBvXv3YGlpCUtLS0gkkmLfs1GjRhg5ciQmTpwIuVyOjh07Ii0tDX/99RemTZtW7HILFy7E6dOncfLkSWRkZCAjI0O1DWZmZqVua2RkJHJzc5GcnIzc3FyEhoYCeJEcSaVSDf9iVFZMDIjK4e2Vk9DIpzsAQFFQgOxH6Ui6GI4b/j9DlpwmcHRERFTdLF68GLVr10ZAQABmzJiBmjVrokuXLgCAmTNnIi4uDj4+PpBIJJg4cSKGDh2qNjmYn58fwsLC0Lp1a2RlZeHMmTOq4UyLs3nzZixatAhz5szBw4cPYWtrW+rdg7Nnz+Lp06d466231Mrnz5+v0ZCl/fr1Q1xcnOp1mzZtAAAxMTFwdnYudXl6M0wMiMop+XIkzo37ASIDMcyd7eDp74tuG/xwdMBXQodGRETVjEgkwrRp04r8tt7c3Bzbt28vcfkGDRrgzz//LNN7SiQSLF68GIsXLy6y/t/PNQDQaH6EksTGxpZreXoz7PBFVE6K3Hxkp6ZDlpyGR5dvIWrHKdi2awKJmYnQoRERERFpjIkBUQUysasJ5/c8ocjnLLlERFT1+fv7q/r9F/VTGUp6P39//0p5T9KMXnQlUigUWLVqFQIDAxEbG4vatWtj2LBhWLRoUZHDaxGVhX2nFhgZvR0isRiGJkYAgPD1h5CfnQMAqNe3Pdw/Haq2jKVrXQTP3YyobSe0Hi8REdFL48ePR9OmTYutv3r11Rwuubm52LJlC8aMGVPqA8Dt2rUrtu7lg8RF0WQmZqo8epEYzJgxAwEBARg0aBD8/Pxw69YtBAQEICQkBKdOneIQWlVAfnau6vespCeoUaeWgNGUTeqNu7gwbQ0MjCRwHtAJdbxaIWTZTlX9g2PBeHAsWPW6Xp928PhyBKL3nhUgWqKqKzMhVfV7QY52xl6nqkOpeHWXNf1uAkwdrCESiQSMSD9YW1vDyclJo7a5ubnYtGkTRowYUa6RgRo1avTGy1LlqvaJQUREBFavXo3Bgwdj3759qnIXFxdMnToVu3btwogRIwSMUL/JnzzH36v24e7O06qyo+/NhkOXVmg1ZRAc3nYTMDrNFMhzkRGbDAAI/W43zJ3t0eHrT/DXZz8WamvqYI0O/r44NdIfBa8lQ1R2jj3aoO2XI2DZuC6yU54i8qejiAw8LHRY9AbiT11H+NoDeHT5lqrscN+ZcP24F1pNGQSpBe/sVmdKhQK3Nx9HxIZXn9+TwxfDokEdNPPti6aj34WIX+ARaYVOf9Ju3ryJgQMHwtLSEhYWFvD29kZSUhLMzc0xfPhwAMDOnTuhVCoxffp0tWXHjh0LU1NT7NixQ4DICQBkj57iyHuzEbnxCPIys9Xqkv78G3/4LMLdXaeLWbrqCv1+Nxr5dEet1g3VK0QidFkzDWFrDuDprbiiFyaN1GrdED23zETCmRAc6vUZQr/fg7azRqDJqN5Ch0ZlFLnxCII+/kYtKQCA3OdZCF9zAEcHzoE8LUOg6KiyKQoKcG7iSlyZ8z9kxqeo1T2PeYgrs3/C+ckBancTiKjy6GxiEBQUBE9PT0RFRWHOnDnw9/dHQkIC+vbti8zMTLi7uwN40TdOLBajffv2assbGxvD3d1dre8cade5CStU37QXSQn85fcjnoTHaC+oCpARk4z4k9fgMetDtfLW0z9AboYMt/93TKDIqo8W497D49B7uOH/C57dTUT0nrO49b9jcJvsLXRoVAaPLkcieN5moKjeIv+Mfph+Ox4XZ6zValykPeHrDiH24F8vXvx7xMt/Xt/ffwERP/6u1bioaIaGhhgwYAAMDat9hxO9pZOJQWpqKnx8fODh4YGQkBB8/vnnmDx5MoKCgvDgwQMAUCUGDx8+hI2NDYyMjAqtx9HREY8fP0ZuLrt0aNuTv+/j0aXIkhsplapbzLomfN0hOHZzh33HFgAA23ZN0HhET17gVBDb9k2ReCZErSzxTCjMnGxh6sAH13RF5KajL34pPAS6mvgT1/A8JqnyAyKtUuTl49amo0Unhq8TiRC56SgU+QVaiYuKZ2xsjDlz5sDY2FjoUKiS6GTKt2zZMjx9+hSbN2+GicmrseItLS3h4eGBoKAgVWIgk8mKTAoAqA5smUxWIdNr5+fnIzm5hG/ASeXONs2/Nb+37xzqTeuv1T6meXn5GrW7ML3oC/3Ua1HY4vBiVkiphSm8Vk/FhWlrkPM0s8xxJCQklGmZiiZ/lK76PSkpCcaK7OIbV7Di9oOJrRWyU9PVyrJTnv5TVxOyJGFmndb2/hJy35RXQXYO4o5d0bh96NajaOD7biVGVLF0ed9oy5MrUarPbYmUSsiSniD8yJ+wbtu48gPTQ1lZWRq1y8nJQUBAAKZOnVrstdVLQv/vIsDe3r7Md3d0MjHYtWsXvLy84OrqWmS9nZ0d7O3tAQCmpqZISUkpsp1cLle1AV4c8C/vPKSmpsLBwQFTpkzBlClTNIorOTlZ4yf79Z2v5VvobFJfo7aKnHy4OjeEXKnZxXpFWFKrFxwlFhWyriaj34WJrRXaLxyjVh699xwiN5T8sOydO3cwTOBjqqbYBD/Y9gMAtG/fHk+1eIFTkftBG7S9v4TcN+X1euya2PDDavy80LcSI6pYurxvtKWDcV2Mt+qgcXvf4R/jqjyxEiPSX76+mn22cnNzcfToUdjY2JT6heqmTZsqIjQqh/j4eNStW7dMy+hcYpCcnIzExET4+PgUqlMoFAgLC0ObNm1UZXXq1EFkZCRycnIKZbeJiYlqB3d+fj7s7e1x4sQJNGjQAH///Tfeffdd2NnZYdiwYZW7YXpGrtD8Il+hVCJHi0lBRQtbvR9hq/cLHUa1kp2SDpPaVmplxv+81ugbSBJcWRN9bX4xQNpR5mOgDP83iOjN6Fxi8PJ2V1FjGx88eBApKSmqbkTAiwk2Tpw4geDgYHh5eanK5XI5QkND0aVLF1VZjRo1sHjxYtVrd3d3DBgwABcuXNAoMbC3t0d8fPybbJbeeXIlCiGT12vU1q6bGx58v6qSI1J3adhSZMUI3y3M1dUV8Xv+J2gM8kfpuPDeAgBAcHAwjO2stPbexe2HlODbqNPNHTdX/Koqc+zujsz4FMG6EQHa319C7puKcO2/q5F+455GbRf+GoiVLTS7y1gV6Pq+0YZ8WQ7O95mHgn8mgyyJQQ1j/H7+LxgYl7/bLxUWHR2tUbusrCxs27YNw4YNK3WC2Pnz51dEaFQOL3vPlIXOJQZOTk4wMDDAuXPn1Mrj4uJUXX5eTwx8fHzg7++PlStXqiUGGzduhEwmw8iRI4t9r7y8PJw/fx6fffaZRrEZGhqW+ZaNvnKsUwf3fjiI5zEPS33wsM2EQaij5b+rRFI1PhoSifDHVJb41XM8Dg4OWp18rrj9ELHhMPr//jXazPoQ9389B5s2jdHs//ri6oKtWoutKNreX0Lum4qQP8EbZ8cuL7VdrdYN0bx3J52a7ErX9422PBrRE7d+OlpquyYj30H9Rg20EJF+SkrS7OF+iUQCX19fWFlZldqVSOj/XfRmdG5UIqlUilGjRuHatWsYOHAgNmzYgLlz56JDhw6oVevFiff1xMDNzQ2TJk3Cb7/9hsGDB2PTpk3w8/PDp59+iq5du5Y4udnkyZNhbm6OUaNGVfZm6R2RWIyugTNgWKOYkQ3++f/fYvz7qNO1tfYCI53w5OY9nP7Pt3B6py0GnFqONl8Mx41lOxG17YTQoVEZ1O/vicYjer54Ucw1v1FNM3RZM1WnkgLSnMesD1HLreQL/lqtG6LNF4W7D5P2SaVSjBs3rkIGbKGqqWp8LVpGAQEBkEgkOHjwIE6fPo2OHTti//79WLRoEaKjows9lLxy5Uo4Oztjw4YNOHLkCGxsbDBlyhQsWrQI4mJGuvn0009x6dIlnD59mh+ASlKrpQv6H/oawfM2I+lCuFqdSe2acJvijWafaP5wIumXhKAbSAi6IXQYVA4ikQidvvsvzJ3tEfHjIeT8ayIzx+7u6LDkE1g0cBAoQqpsEjMTvLtvAa4u2Ip7v/4JRW6eqk5sJEGjIV3RbsFoSGqYlLAW0pbs7Gx88cUX+Pbbb9VGhaTqQycTAzMzMwQGBiIwMFCtPDw8HG5uboUu9g0MDODn5wc/Pz+N1j99+nQEBQXh9OnTsLGxqbC4qbCazerj3b0LkH4nAY8uR6IgNw/m9ezg2N0d4irSnYeIKo9ILEarKYPQYtx7SAi6gayHj2FgJIWDlxssnMveP5Z0j9TcFJ2XT0Dbrz5CwqnryH2WBallDTj1agujmuZCh0evKSgowJUrV1BQwDklqqtqc+WVnp6OhIQE9O/fv1zrmTp1Kk6fPo0zZ86gdu3aFRQdlcbKtS6sXHWvP+JH93/G45AXD21FbjqCB8eCVXVea6bCvJ4dRAZi3N5yHPf2noOVa110/O6/UCqUUOYX4KLfemQ+KHo4XSJ9YmAkQf1+mg9dSdWPsbU5Gg3rJnQYRHqt2iQGYWFhANSfLyiruLg4rF69GkZGRnBxcVGVe3l54dgxzSfkIv2RlfgYxz8oeuSF0OV7kBGTDLHUEANP/4CYAxchf/Icpz76BnkZMjh2d0frGUNwccY6LUdNREREVBgTg9fUr18fSmUpQ+QQvcbErib6/LYQ2Y/ScWXOT5A/ea6qy/hnmE1Fbj6gVEKpVKrVK/IKoCxQaD1mIiKiN2FkZITZs2eXOusx6S6dG5WoOBMnToRSqYSnp6fQoZAe2ec5CccHz8eDE1fRbsHoItu0nOSN2COXocx/1SfTwFgK98+HIXJT6cP0ERERVQUSiQTe3t6QSCRCh0KVpNokBkRCeDmKSuyhv2Dd0qVQvcvAzqjl5oKQZbtUZSIDMbqsm4aI9YeQfvuB1mIlIiIqD5lMBh8fH8hkMqFDoUrCxIDoDRmaGEH0zwhYdp7NkRGrPkNvnW6t0fjDHjg/dTXwWhe1zssn4OHZm3hw/KpW4yUiIioPhUKBmJgYKBTsBltdVZtnDIi0zbKxIzp9Px55WXIo8gpw6YtAOHZ3h9TKDDH7L8Br1WTIHj1F751zAQDnxq+AdUtnOA/oBDMnW7gM7Iy0iBgEz9si7IYQERERgYkB0Rt78vd9/N77C7Wy1+8a7G49ttAyiWdCsaPByEqPjYiIiKis2JWIiIiIiEplbGyMVatWwdjYWOhQqJLwjgERERERlcrQ0BAdO3YUOgyqREwMiIpg7mwvdAgAqk4cQtG17de1eImIyiIzMxPvv/8+fv/9d5iZmQkdDlUCJgZERei5dZbQIRC4H4iIqpqsrCyhQ6BKxGcMiIiIiIiIiQERERERETExICIiIiINmJiYYOfOnTAxMRE6FKokTAyIiIiIqFRisRh2dnYQi3n5WF1xzxIRERFRqbKystCjRw8+gFyNMTEgIiIiIiImBkRERERExHkMiIiIiPRau3btNGqXk5OD+fPno1OnTjAyMqrkqEgITAyIiIiIqFRGRkZYsGCB0GFQJWJXIiIiIiIiYmJARERERERMDIiIiIiICEwMiIiIiIgITAyIiIhIQ++88w7GjBkjdBhadeXKFXTq1AnGxsZwcHDAl19+iYKCAqHDIqoUTAyIiIiIihAfH49evXqhSZMmuH79OtavX4/AwEB89dVXQodGVCk4XClViBlXgESZ0FG84GgKrOggdBREpQsavRQZsclvvLwi/9W3ln8MXQCxocEbr8vc2R49t8564+VJd6xduxZr167FvXv3YGlpCS8vL+zbtw/Ozs7w9fXFnDlzVG19fX0RHR2Ns2fPYsyYMQgKCgIAbN26FQBw5swZdOvWrcT3c3Z2xscff4zHjx9j586dkEqlmDdvHsaOHYvPPvsMO3bsgKmpKb788ktMnjxZtVxSUhJmzJiB48ePIycnBx06dMD333+Pt956CwqFAs7Ozhg/fjxmz56tWiYnJwf29vb47rvv4OvrCwBYvXo11q5di9jYWDg5OWHMmDGYOXMmDA1LvwRav349LCws8NNPP0EsFqNFixZITEzEF198gblz56JGjRoa/92JdAETA6oQiTLgfobQURDplozYZKTfSaiQdT2/n1Qh66Hqbf78+Vi+fDmWLl2K3r17IzMzE8eOHdNo2VWrVuH+/ftwcHDAqlWrAADW1tYaLbt69WrMmzcP165dw65duzBlyhQcPXoU77zzDq5evYq9e/di6tSp6NGjB5o3bw6lUglvb2/k5OTg8OHDsLS0xJIlS9CrVy/cvXsXNjY2+Oijj7B9+3a1xODgwYOQy+UYOnQoAGDBggXYvHkzVq5cCXd3d9y6dQvjx4+HXC7H4sWLS4374sWL6N27N8TiVx0s+vTpg8mTJyMkJARvv/22RttPpCvYlYiIiEgPZGVl4dtvv8WCBQswefJkuLq6wsPDQ+NuMZaWlpBKpTAxMYG9vT3s7e0hlUo1WrZbt2749NNP0ahRI8yePRvm5uYwMDBQlc2cOROWlpY4ffo0AOD06dMIDg7GL7/8grfffhtubm7Ytm0bjI2NsW7dOgDAqFGjcPv2bVy9elX1Ptu2bYO3tzcsLS0hk8nw7bffIjAwEIMGDYKLiwv69euHJUuWYPXq1RrFnZSUBHt7e7Wyl6+TkpiMU/XDOwZERER6ICIiAnK5HL1799b6e7du3Vr1u1gsRu3atdGqVSu1MltbW6SkpKhirVWrFpo3b65qY2RkhA4dOiAiIgIA0LRpU7Rv3x7bt29Hu3btkJKSgj/++AOHDh1SrSM7OxsffPABRCKRaj0FBQWQy+VITU1F7dq1K3W7iXQNEwMiIiKCWCyGUqlUK8vLy6uQdUskErXXIpGoyDKFQlGm9Y4aNQoLFy7E8uXL8csvv8DGxkaV+Lxc1969e+Hq6lpoWU26QTk4OCA5Wf05oEePHqnqiKobdiUiIiLSA82bN4exsTFOnDhRZL2trS0ePnyoVhYSEqL2WiqVamWozhYtWuDJkyeIjIxUleXk5ODKlSto2bKlquzDDz/Es2fPcPz4cWzbtg0jR46EgYGBah3Gxsa4f/8+GjVqVOjnZbuSdO7cGSdPnlRLWI4fPw5TU1O0adOmAreYqGpgYkBERKQHzMzM4OfnhwULFmDt2rW4c+cObt68iW+++QbAizkKdu/ejRMnTiAqKgozZsxAXFyc2jpcXFxw/fp13Lt3D48fP66wOwr/1qNHD7Rv3x4jRozAxYsXER4ejlGjRkEul2PChAmqdtbW1ujfvz/mzZuHkJAQjB49Wm17Z8+ejdmzZ2Pt2rWIiopCREQEdu3ahZkzZ2oUx4QJE/Ds2TOMHTsWEREROHToEObOnYspU6ZwRCKqltiViIhIB7y9chIa+XQHACgKCpD9KB1JF8Nxw/9nyJLTBI6OdMXixYtRu3ZtBAQEYMaMGahZsya6dOkCAJg5cybi4uLg4+MDiUSCiRMnYujQoYiOjlYt7+fnh7CwMLRu3RpZWVkaDVf6JkQiEQ4cOIAZM2agf//+yMnJQfv27XHy5EnY2NiotR09ejS8vb3h7u4ONzc3tbq5c+fCwcEBa9asgZ+fH0xMTODq6qrxJG1OTk44ceIEPv30U7Rt2xZWVlYYN24clixZUlGbSlSliJT/7lBI9AaGnak6w5U2MAf2dBc6iuoj6+ET7G37XwDA0OuBqFGnlsARVR8Huk7XeLjSt1dOgll9O5wb9wNEBmKYO9vB098XeZlyHB1Q/smWrFzrwvvcynKvh17g54aIdBG7EhER6QhFbj6yU9MhS07Do8u3ELXjFGzbNYHEzETo0IiIqBrQi65ECoUCq1atQmBgIGJjY1G7dm0MGzYMixYtYh9BItJJJnY14fyeJxT5BVAWlG0kF6KK4u/vD39//2LrMzMzVb+/Pt9AcXJzc7FlyxaMGTNGozkS2rVrp1mgRTh//jz69u1bbP2xY8fg5eX1xusn0kV6kRjMmDEDAQEBGDRoEPz8/HDr1i0EBAQgJCQEp06dUpvRkIioqrLv1AIjo7dDJBbD0MQIABC+/hDys3MAAPX6tof7p0PVlrF0rYvguZsRta3okWiIymP8+PEYNmxYha0vNzcXmzZtwogRIzSePO1NvfXWWwgNDS223tHRsVLfn6gqqvaJQUREBFavXo3Bgwdj3759qnIXFxdMnToVu3btwogRIwSMkKjqkj95jttb/1C9vrpoG5p/0he132qiNmEQaUfqjbu4MG0NDIwkcB7QCXW8WiFk2U5V/YNjwXhwLFj1ul6fdvD4cgSi954VIFr9JU/LUEvEri7Ygmaf9INt+6bV7nNjbW2t0XwAVZGJiQkaNWokdBhEVYpOf1V+8+ZNDBw4EJaWlrCwsIC3tzeSkpJgbm6O4cOHAwB27twJpVKJ6dOnqy07duxYmJqaYseOHQJETq9T5GQj8ee5CB/fGDeGmiB0pDVu+bVDyu8BQoem125vOY49HuMQFvCbqiz24EUcHTAHf3ywAPK0KvK0uR4pkOciIzYZ6VHxCP1uNzLiU9Dh60+KbGvqYI0O/r44N34lCrJztRyp/orafhJ72ozF36tefREV+/slHPOei2OD5kH++JmA0RERlUxnE4OgoCB4enoiKioKc+bMgb+/PxISEtC3b19kZmbC3d0dwIs+jWKxGO3bt1db3tjYGO7u7hr1eaTK9eDHCUg7sw11x3yHFmsi4brkDGr3m4T8rHShQ9NbUdtP4vKXm6DIyy+yPvlSBE5+uBj5shwtR0avC/1+Nxr5dEet1g3VK0QidFkzDWFrDuDprbiiF6YKd3fXaVz6IrDYz03KlVv4Y/hi5GVlazky3WFoaIgBAwbA0LDad2ggqpJ08pOXmpoKHx8feHh44NSpUzAxeTEix8cffwwXFxcAUCUGDx8+hI2NDYyMjAqtx9HREX/99Rdyc3MrvS8jFS/9ygHUGbkEVp7eqjJTl9bCBaTn8mRyXFu8DRABKGEw4yd/30f03rNoOvpdrcVG6jJikhF/8ho8Zn2Ikx++Gle99fQPkJshw+3/HRMwOv2Sn52Dq/O3lPq5eRoRi7s7T6O5b39thaZTjI2NMWfOHKHDINJbOpkYLFu2DE+fPsXmzZtVSQEAWFpawsPDA0FBQarEQCaTFZkUAC9OQC/bVERikJ+fj+Tk5HKvRxfl5dkBkLzRspKaDnh+4zisu4yAoXn5+6rm5eUhIeFRudejrxIPXEJehgbfaIqAsA2/w6xni8oPqprKK+ab5bIIX3cI/X//GvYdWyD5UgRs2zVB4xE98Xvvz8scS0KCZnMqUGEPDwcj97ms9IYiIHzjYZi/26raPW9QmqysrFLb5OTkICAgAFOnTi32f/freMwSFc/e3r7Md990MjHYtWsXvLy84OrqWmS9nZ0d7O3tAQCmpqZISUkpsp1cLle1eWnixIn4/fff8ezZM5ibm2Po0KH49ttvNUockpOT4eTkVNbNqRaarw6HSb03u0CsP3kTYpaPwM1RtWHi1AI1mnjCsm0/WHYY+Eb/OO/cuQOnd1u+USwEjLbwQDdTl9IbKoGs+8lo4FQfeeBwmW9iSa1ecJRYaNT2wvS1RZanXovCFochAACphSm8Vk/FhWlrkPM0s8j2xblz5w6G6en5qyJ8ZO6OnjUalt5QCcgepMK1fgPIleVPDHWJr69vqW1yc3Nx9OhR2NjYaPR/d9OmTRURGlG1FB8fj7p165ZpGZ17xiA5ORmJiYlo27ZtoTqFQoGwsDDV3QIAqFOnDh4/foycnMJ9oRMTEwudfCZPnozbt2/j+fPnuHnzJm7evFniGM1UfmbNOqNl4D008T+PWj1GIy/9Ee4tG4J7Xw8AJ+bWvrKeFAxEOncaqbaajH4XJrZWaL9wDAac/E7103zce0KHVu2Jy/glhhj6dbeAiHSDzt0xeHkrsqhvkg8ePIiUlBS1xKBdu3Y4ceIEgoOD1SYqkcvlCA0NRZcuXdTW0bx5c9XvSqUSYrEYd+/e1Sg2e3t7xMfHl2Vzqo0pkXaIl7/58iIDQ5g16wSzZp1g5+2HJ2d3IHbFx8iM+BPmLbuWaV2urq74Q0/3Q0WI3X4a0QGHSm8oAqS1LHAn+L7edYmoKJeGLUVWTMV1PwxbvR9hq/e/0bKurq6I3/O/CotF3zz45SzurDhQekMRILEyw+0r0RDp2Rw60dHRpbbJysrCtm3bMGzYMI0mIJ0/f35FhEZULb3sPVMWOpcYODk5wcDAAOfOnVMrj4uLw5QpUwBALTHw8fGBv78/Vq5cqZYYbNy4ETKZDCNHjiz0HkuXLsWSJUuQlZWFWrVqYenSpRrFZmhoWOZbNtWF5C6AciQG/2ZctxkAIP9Z0d3ASoxFItHb/VARao0diPvrj0CRV1ByQyXQ4j999bb7XEWQSKrOKVgi0d/zV0Ww8R2I6LWHocgtpXuQEmg+pg+c6tXTTmBVSFJSUqltJBIJfH19YWVlpVFXIh6zRBVL576ukEqlGDVqFK5du4aBAwdiw4YNmDt3Ljp06IBatWoBUE8M3NzcMGnSJPz2228YPHgwNm3aBD8/P3z66afo2rVrkZObzZo1C5mZmYiMjMT48ePh4OCgrc3TS1GzuyL12I/IunsNOSlxeH4zCA9+nAiDGlYwd+sudHh6x8TGEs00GDHFxNYKrqN6ayEioqrP2NocLca9X3IjEWBsY4kmYziSV3GkUinGjRvHkQKJBKJziQEABAQEYNy4cbhy5Qr8/Pxw5coV7N+/H3Xq1IGpqWmhh5JXrlyJ77//HhEREZg0aRJ27dqFKVOm4PDhwxCXcCu3WbNmaN26NT7++OPK3iS9ZunRF2l//ozoxf0QMbEJYgP+A+M6jdFk6UUYWtgIHZ5eavvVSDQe0bNwxT9dhkztrdF7z3yY2FhqOTKiqsvjyw/RpKhk+Z+edia1rdB791yY2tbUbmA6JDs7G1OmTEF2Nud6IBKCSFmNnu50cnKCo6MjLl++XGHr/OWXX/D5558jMTGxwtZZHQ07A9yvIhPhNjAH9vBGQ7kplUqkXLmF21v/QNKFcBTk5MGsni1cR/ZEwyFdITU3LX0lVKIDXacj/U7VGG7RyrUuvM+tFDoMnadUKpFyNQpRW47j4YUwFMjzYOZUG64jeqLh0K6QWpTeb7660mRC0czMTPTo0QOnT5+GmZlZqe3btWtXEaER0T+qTgfXckpPT0dCQgL693/zSWOePXuG/fv3w9vbG5aWlggLC8OSJUvw7ru87Uv6RyQSwc6zOew8m5femCqNlWtddPzuv1AqlFDmF+Ci33pkPij87E2ffQvxLDoRl2ZugIGJFO/umQ+rxnVxaeYGxBy8KEDk+kkkEsGufVPYtW8qdChERGWmk12JihIWFgZA/fmCshKJRNixYwcaNGgAc3NzeHt7o1+/fli9enUFRUlEVDbyJ89x6qNvcHzQPISvO4jWM4YUalP3nbbIy3zV9UKRk48z//cdIjce0WaoRESk46rNHYOKSAwsLCxw6tSpCoqIiKj85E+eq35X5BVAWfCvyeREIjT9Tx/c2nQE9fq0BwAoFQpkp6ZrMUqiimFkZITZs2drNOsxEVW8anPHYOLEiVAqlfD09BQ6FCKiCmdgLIX758MQuemoWnmjYd0Qd/QKCuR5AkVGVHEkEgm8vb0hkUiEDoVIL1WbxICIqLoSGYjRZd00RKw/hPTbD1TlBkYSNBjshehdpwWMjqjiyGQy+Pj4QCaTCR0KkV6qNl2JiIiqq87LJ+Dh2Zt4cFx9VBezeraQWtbAO9u/hNTKDCa2Vmg4tCvu7T1XzJqIqjaFQoGYmBgoFIrSGxNRhWNiQERUhTl2d4fzgE4wc7KFy8DOSIuIQeKZUEitzBCz/wIO95kJALDv2AIu3p1VSUG3TZ+hVksX5MvksPFojKvztwi4FUREpAuYGBARVWGJZ0Kxo8HIUtslX4pA8qUI1euzvt9XZlhERFQN8RkDIiIiqhKMjY2xatUqGBsbCx0KkV7iHQMiIiKqEgwNDdGxY0ehwyDSW7xjQERERFVCZmYmunfvjszMTKFDIdJLvGNAFcLRVOgIXqlKsRCVxNzZXugQVKpSLKTfsrKyhA6BSG8xMaAKsaKD0BEQ6Z6eW2cJHQIREZEKuxIRERERERETAyIiIqoaTExMsHPnTpiYmAgdCpFeYmJAREREVYJYLIadnR3EYl6eEAmBnzwiIiKqErKystCjRw8+gEwkECYGRERERETExICIiIiIiJgYEBERERERAJFSqVQKHQQRERGRUqlERkYGzM3NIRKJhA6HSO8wMSAiIiIiInYlIiIiIiIiJgZERERERAQmBkREREREBCYGREREREQEJgZERERERAQmBkREREREBCYGREREREQEJgZERERERAQmBkREREREBCYGREREREQEJgZERERERAQmBkREREREBCYGREREREQE4P8BK0zHs9vKHGAAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 980.198x294.311 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -208,17 +210,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1114.14x294.311 with 1 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -236,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
diff --git a/releasenotes/notes/min-reached-finder-flag-aa6dd9021e165f80.yaml b/releasenotes/notes/min-reached-finder-flag-aa6dd9021e165f80.yaml
new file mode 100644
index 0000000..3cbd9cf
--- /dev/null
+++ b/releasenotes/notes/min-reached-finder-flag-aa6dd9021e165f80.yaml
@@ -0,0 +1,10 @@
+---
+features:
+  - |
+    A new ``minimum_reached`` field has been added to the metadata outputted by :func:`circuit_knitting.cutting.find_cuts` to check if the cut-finder found
+    a cut scheme that minimized the sampling overhead. Note that the search algorithm employed by the cut-finder is *guaranteed* to find
+    the optimal solution, that is, the solution with the minimum sampling overhead, provided it is allowed to run long enough.
+    The user is free to time-restrict the search by passing in suitable values for ``max_backjumps`` and/or ``max_gamma`` to
+    :class:`.OptimizationParameters`. If the search is terminated prematurely in this way, the metadata may indicate that the minimum
+    was not reached, even though the returned solution `was` actually the optimal solution. This would mean that the search that was performed was not
+    exhaustive enough to prove that the returned solution was optimal.
diff --git a/releasenotes/notes/new-flags-to-control-cut-finder-search-e499e1ea49abb0bc.yaml b/releasenotes/notes/new-flags-to-control-cut-finder-search-e499e1ea49abb0bc.yaml
new file mode 100644
index 0000000..9872748
--- /dev/null
+++ b/releasenotes/notes/new-flags-to-control-cut-finder-search-e499e1ea49abb0bc.yaml
@@ -0,0 +1,6 @@
+---
+features:
+  - |
+    When specifying instances of :class:`.OptimizationParameters` that are inputted to :meth:`circuit_knitting.cutting.find_cuts()`, the user can now control whether the 
+    cut-finder looks only for gate cuts, only for wire cuts, or both, by setting the bools ``gate_lo`` and ``wire_lo`` appropriately. The default value
+    of both of these is set to ``True`` and so the default search considers the possibility of both gate and wire cuts.


Test patch:
diff --git a/test/cutting/cut_finding/test_best_first_search.py b/test/cutting/cut_finding/test_best_first_search.py
index 6cf50f8..59a07fa 100644
--- a/test/cutting/cut_finding/test_best_first_search.py
+++ b/test/cutting/cut_finding/test_best_first_search.py
@@ -92,11 +92,10 @@ def test_best_first_search(test_circuit: SimpleGateList):
     op = CutOptimization(test_circuit, settings, constraint_obj)
 
     out, _ = op.optimization_pass()
-
     assert op.search_engine.get_stats(penultimate=True) is not None
     assert op.search_engine.get_stats() is not None
     assert op.get_upperbound_cost() == (27, inf)
-    assert op.minimum_reached() is False
+    assert op.minimum_reached() is True
     assert out is not None
     assert (out.lower_bound_gamma(), out.gamma_UB, out.get_max_width()) == (
         27,
diff --git a/test/cutting/cut_finding/test_cut_finder_results.py b/test/cutting/cut_finding/test_cut_finder_results.py
index 2178917..0ecf9a3 100644
--- a/test/cutting/cut_finding/test_cut_finder_results.py
+++ b/test/cutting/cut_finding/test_cut_finder_results.py
@@ -14,9 +14,9 @@
 from __future__ import annotations
 
 import numpy as np
-from pytest import fixture, raises
+import unittest
+from pytest import raises
 from qiskit import QuantumCircuit
-from typing import Callable
 from qiskit.circuit.library import EfficientSU2
 from circuit_knitting.cutting.cut_finding.cco_utils import qc_to_cco_circuit
 from circuit_knitting.cutting.cut_finding.circuit_interface import (
@@ -28,10 +28,10 @@ from circuit_knitting.cutting.cut_finding.optimization_settings import (
 from circuit_knitting.cutting.automated_cut_finding import DeviceConstraints
 from circuit_knitting.cutting.cut_finding.disjoint_subcircuits_state import (
     get_actions_list,
-    OneWireCutIdentifier,
+    SingleWireCutIdentifier,
     WireCutLocation,
     CutIdentifier,
-    GateCutLocation,
+    CutLocation,
 )
 from circuit_knitting.cutting.cut_finding.lo_cuts_optimizer import (
     LOCutsOptimizer,
@@ -39,362 +39,545 @@ from circuit_knitting.cutting.cut_finding.lo_cuts_optimizer import (
 from circuit_knitting.cutting.cut_finding.cut_optimization import CutOptimization
 
 
-@fixture
-def empty_circuit():
-    qc = QuantumCircuit(3)
-    qc.barrier([0])
-    qc.barrier([1])
-    qc.barrier([2])
+class TestCuttingFourQubitCircuit(unittest.TestCase):
+    def setUp(self):
+        qc = EfficientSU2(4, entanglement="linear", reps=2).decompose()
+        qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)
+        self.circuit_internal = qc_to_cco_circuit(qc)
 
+    def test_four_qubit_cutting_workflow(self):
 
-@fixture
-def four_qubit_test_setup():
-    qc = EfficientSU2(4, entanglement="linear", reps=2).decompose()
-    qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)
-    circuit_internal = qc_to_cco_circuit(qc)
-    interface = SimpleGateList(circuit_internal)
-    settings = OptimizationSettings(seed=12345)
-    settings.set_engine_selection("CutOptimization", "BestFirst")
-    return interface, settings
+        with self.subTest("No cuts needed"):
 
+            qubits_per_subcircuit = 4
 
-@fixture
-def seven_qubit_test_setup():
-    qc = QuantumCircuit(7)
-    for i in range(7):
-        qc.rx(np.pi / 4, i)
-    qc.cx(0, 3)
-    qc.cx(1, 3)
-    qc.cx(2, 3)
-    qc.cx(3, 4)
-    qc.cx(3, 5)
-    qc.cx(3, 6)
-    circuit_internal = qc_to_cco_circuit(qc)
-    interface = SimpleGateList(circuit_internal)
-    settings = OptimizationSettings(seed=12345)
-    settings.set_engine_selection("CutOptimization", "BestFirst")
-    return interface, settings
+            interface = SimpleGateList(self.circuit_internal)
 
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=True)
 
-@fixture
-def multiqubit_gate_test_setup():
-    qc = QuantumCircuit(3)
-    qc.ccx(0, 1, 2)
-    circuit_internal = qc_to_cco_circuit(qc)
-    interface = SimpleGateList(circuit_internal)
-    settings = OptimizationSettings(seed=12345)
-    settings.set_engine_selection("CutOptimization", "BestFirst")
-    return interface, settings
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
-def test_no_cuts(
-    four_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    # QPU with 4 qubits for a 4 qubit circuit results in no cutting.
-    qubits_per_subcircuit = 4
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
 
-    interface, settings = four_qubit_test_setup
+            output = optimization_pass.optimize(interface, settings, constraint_obj)
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            assert get_actions_list(output.actions) == []  # no cutting.
 
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+            assert (
+                interface.export_subcircuits_as_string(name_mapping="default") == "AAAA"
+            )
 
-    output = optimization_pass.optimize(interface, settings, constraint_obj)
+        with self.subTest("No cuts found when all flags set to False"):
 
-    assert get_actions_list(output.actions) == []  # no cutting.
+            qubits_per_subcircuit = 3
 
-    assert interface.export_subcircuits_as_string(name_mapping="default") == "AAAA"
+            interface = SimpleGateList(self.circuit_internal)
 
+            settings = OptimizationSettings(seed=12345, gate_lo=False, wire_lo=False)
 
-def test_four_qubit_circuit_three_qubit_qpu(
-    four_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    # QPU with 3 qubits for a 4 qubit circuit enforces cutting.
-    qubits_per_subcircuit = 3
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
-    interface, settings = four_qubit_test_setup
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
 
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+            with raises(ValueError) as e_info:
+                optimization_pass.optimize(interface, settings, constraint_obj)
+            assert (
+                e_info.value.args[0]
+                == "None state encountered: no cut state satisfying the specified constraints and settings could be found."
+            )
 
-    output = optimization_pass.optimize()
+        with self.subTest(
+            "No separating cuts possible if one qubit per qpu and only wire cuts allowed"
+        ):
 
-    cut_actions_list = output.cut_actions_sublist()
+            settings = OptimizationSettings(seed=12345, gate_lo=False, wire_lo=True)
 
-    assert cut_actions_list == [
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=17, gate_name="cx", qubits=[2, 3]
-            ),
-        ),
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=25, gate_name="cx", qubits=[2, 3]
-            ),
-        ),
-    ]
-    best_result = optimization_pass.get_results()
-
-    assert output.upper_bound_gamma() == best_result.gamma_UB == 9  # 2 LO cnot cuts.
-
-    assert optimization_pass.minimum_reached() is True  # matches optimal solution.
-
-    assert (
-        interface.export_subcircuits_as_string(name_mapping="default") == "AAAB"
-    )  # circuit separated into 2 subcircuits.
-
-
-def test_four_qubit_circuit_two_qubit_qpu(
-    four_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    # QPU with 2 qubits enforces cutting.
-    qubits_per_subcircuit = 2
-
-    interface, settings = four_qubit_test_setup
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            interface = SimpleGateList(self.circuit_internal)
 
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
-
-    output = optimization_pass.optimize()
+            qubits_per_subcircuit = 1
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
-    cut_actions_list = output.cut_actions_sublist()
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
 
-    assert cut_actions_list == [
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=9, gate_name="cx", qubits=[1, 2]
-            ),
-        ),
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=20, gate_name="cx", qubits=[1, 2]
-            ),
-        ),
-    ]
+            with raises(ValueError) as e_info:
+                optimization_pass.optimize(interface, settings, constraint_obj)
+            assert (
+                e_info.value.args[0]
+                == "None state encountered: no cut state satisfying the specified constraints and settings could be found."
+            )
 
-    best_result = optimization_pass.get_results()
+        with self.subTest("Gate cuts to get three qubits per subcircuit"):
+            # QPU with 3 qubits for a 4 qubit circuit enforces cutting.
+            qubits_per_subcircuit = 3
 
-    assert output.upper_bound_gamma() == best_result.gamma_UB == 9  # 2 LO cnot cuts.
+            interface = SimpleGateList(self.circuit_internal)
 
-    assert optimization_pass.minimum_reached() is True  # matches optimal solution.
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=True)
 
-    assert (
-        interface.export_subcircuits_as_string(name_mapping="default") == "AABB"
-    )  # circuit separated into 2 subcircuits.
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
-    assert (
-        optimization_pass.get_stats()["CutOptimization"].backjumps
-        <= settings.max_backjumps
-    )
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
 
-def test_seven_qubit_circuit_two_qubit_qpu(
-    seven_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    # QPU with 2 qubits enforces cutting.
-    qubits_per_subcircuit = 2
+            output = optimization_pass.optimize()
 
-    interface, settings = seven_qubit_test_setup
+            cut_actions_list = output.cut_actions_sublist()
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            assert cut_actions_list == [
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=17, gate_name="cx", qubits=[2, 3]
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=25, gate_name="cx", qubits=[2, 3]
+                    ),
+                ),
+            ]
+            best_result = optimization_pass.get_results()
 
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+            assert (
+                output.upper_bound_gamma() == best_result.gamma_UB == 9
+            )  # 2 LO cnot cuts.
 
-    output = optimization_pass.optimize()
+            assert (
+                optimization_pass.minimum_reached() is True
+            )  # matches optimal solution.
 
-    cut_actions_list = output.cut_actions_sublist()
+            assert (
+                interface.export_subcircuits_as_string(name_mapping="default") == "AAAB"
+            )  # circuit separated into 2 subcircuits.
 
-    assert cut_actions_list == [
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=7, gate_name="cx", qubits=[0, 3]
-            ),
-        ),
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=8, gate_name="cx", qubits=[1, 3]
-            ),
-        ),
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=9, gate_name="cx", qubits=[2, 3]
-            ),
-        ),
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=11, gate_name="cx", qubits=[3, 5]
-            ),
-        ),
-        CutIdentifier(
-            cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
-                instruction_id=12, gate_name="cx", qubits=[3, 6]
-            ),
-        ),
-    ]
+        with self.subTest("Gate cuts to get two qubits per subcircuit"):
 
-    best_result = optimization_pass.get_results()
+            qubits_per_subcircuit = 2
 
-    assert output.upper_bound_gamma() == best_result.gamma_UB == 243  # 5 LO cnot cuts.
+            interface = SimpleGateList(self.circuit_internal)
 
-    assert optimization_pass.minimum_reached() is True  # matches optimal solution.
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=True)
 
-    assert (
-        interface.export_subcircuits_as_string(name_mapping="default") == "ABCDDEF"
-    )  # circuit separated into 2 subcircuits.
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
-def test_one_wire_cut(
-    seven_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    qubits_per_subcircuit = 4
-
-    interface, settings = seven_qubit_test_setup
-
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
-
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
-
-    output = optimization_pass.optimize()
-
-    cut_actions_list = output.cut_actions_sublist()
-
-    assert cut_actions_list == [
-        OneWireCutIdentifier(
-            cut_action="CutLeftWire",
-            wire_cut_location=WireCutLocation(
-                instruction_id=10, gate_name="cx", qubits=[3, 4], input=1
-            ),
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+
+            output = optimization_pass.optimize()
+
+            cut_actions_list = output.cut_actions_sublist()
+
+            assert cut_actions_list == [
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=9, gate_name="cx", qubits=[1, 2]
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=20, gate_name="cx", qubits=[1, 2]
+                    ),
+                ),
+            ]
+
+        best_result = optimization_pass.get_results()
+
+        assert (
+            output.upper_bound_gamma() == best_result.gamma_UB == 9
+        )  # 2 LO cnot cuts.
+
+        assert optimization_pass.minimum_reached() is True  # matches optimal solution.
+
+        assert (
+            interface.export_subcircuits_as_string(name_mapping="default") == "AABB"
+        )  # circuit separated into 2 subcircuits.
+
+        assert (
+            optimization_pass.get_stats()["CutOptimization"].backjumps
+            <= settings.max_backjumps
         )
-    ]
 
-    assert (
-        interface.export_subcircuits_as_string(name_mapping="default") == "AAAABBBB"
-    )  # extra wires because of wire cuts
-    # and not qubit reuse.
+        with self.subTest("Cut both wires instance"):
+
+            qubits_per_subcircuit = 2
+
+            interface = SimpleGateList(self.circuit_internal)
+
+            settings = OptimizationSettings(seed=12345, gate_lo=False, wire_lo=True)
+
+            settings.set_engine_selection("CutOptimization", "BestFirst")
+
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+
+            output = optimization_pass.optimize()
+
+            cut_actions_list = output.cut_actions_sublist()
+
+            assert cut_actions_list == [
+                SingleWireCutIdentifier(
+                    cut_action="CutLeftWire",
+                    wire_cut_location=WireCutLocation(
+                        instruction_id=9, gate_name="cx", qubits=[1, 2], input=1
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutBothWires",
+                    cut_location=CutLocation(
+                        instruction_id=12, gate_name="cx", qubits=[0, 1]
+                    ),
+                ),
+                SingleWireCutIdentifier(
+                    cut_action="CutLeftWire",
+                    wire_cut_location=WireCutLocation(
+                        instruction_id=17, gate_name="cx", qubits=[2, 3], input=1
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutBothWires",
+                    cut_location=CutLocation(
+                        instruction_id=20, gate_name="cx", qubits=[1, 2]
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutBothWires",
+                    cut_location=CutLocation(
+                        instruction_id=25, gate_name="cx", qubits=[2, 3]
+                    ),
+                ),
+            ]
+
+        best_result = optimization_pass.get_results()
+
+        assert output.upper_bound_gamma() == best_result.gamma_UB == 65536
+
+        assert (
+            interface.export_subcircuits_as_string(name_mapping="default")
+            == "ADABDEBCEFCF"
+        )
+
+        with self.subTest("Wire cuts to get to 3 qubits per subcircuit"):
+
+            qubits_per_subcircuit = 3
+
+            interface = SimpleGateList(self.circuit_internal)
+
+            settings = OptimizationSettings(seed=12345, gate_lo=False, wire_lo=True)
+
+            settings.set_engine_selection("CutOptimization", "BestFirst")
+
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
 
-    best_result = optimization_pass.get_results()
+            output = optimization_pass.optimize()
+
+            cut_actions_list = output.cut_actions_sublist()
+
+            assert cut_actions_list == [
+                SingleWireCutIdentifier(
+                    cut_action="CutLeftWire",
+                    wire_cut_location=WireCutLocation(
+                        instruction_id=17, gate_name="cx", qubits=[2, 3], input=1
+                    ),
+                ),
+                SingleWireCutIdentifier(
+                    cut_action="CutLeftWire",
+                    wire_cut_location=WireCutLocation(
+                        instruction_id=20, gate_name="cx", qubits=[1, 2], input=1
+                    ),
+                ),
+            ]
+
+        best_result = optimization_pass.get_results()
+
+        assert (
+            output.upper_bound_gamma() == best_result.gamma_UB == 16
+        )  # 2 LO wire cuts.
+
+        assert (
+            interface.export_subcircuits_as_string(name_mapping="default") == "AABABB"
+        )  # circuit separated into 2 subcircuits.
+
+        with self.subTest("Search engine not supported"):
+            # Check if unspported search engine is flagged
+
+            qubits_per_subcircuit = 4
+
+            interface = SimpleGateList(self.circuit_internal)
+
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=True)
+
+            settings.set_engine_selection("CutOptimization", "BeamSearch")
+
+            search_engine = settings.get_engine_selection("CutOptimization")
+
+        constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+
+        optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+
+        with raises(ValueError) as e_info:
+            _ = optimization_pass.optimize()
+        assert (
+            e_info.value.args[0] == f"Search engine {search_engine} is not supported."
+        )
 
-    assert output.upper_bound_gamma() == best_result.gamma_UB == 4  # One LO wire cut.
+        with self.subTest("Greedy search gate cut warm start test"):
+            # Even if the input cost bounds are too stringent, greedy_cut_optimization
+            # is able to return a solution.
 
-    assert optimization_pass.minimum_reached() is True  # matches optimal solution
+            qubits_per_subcircuit = 3
 
+            interface = SimpleGateList(self.circuit_internal)
 
-def test_two_wire_cuts(
-    seven_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    qubits_per_subcircuit = 3
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=False)
 
-    interface, settings = seven_qubit_test_setup
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+            # Impose a stringent cost upper bound, insist gamma <=2.
+            cut_opt = CutOptimization(interface, settings, constraint_obj)
+            cut_opt.update_upperbound_cost((2, 4))
+            state, cost = cut_opt.optimization_pass()
 
-    output = optimization_pass.optimize()
+            # 2 cnot cuts are still found
+            assert state is not None
+            assert cost[0] == 9
 
-    cut_actions_list = output.cut_actions_sublist()
+        with self.subTest("Greedy search wire cut warm start test"):
+            # Even if the input cost bounds are too stringent, greedy_cut_optimization
+            # is able to return a solution.
 
-    assert cut_actions_list == [
-        OneWireCutIdentifier(
-            cut_action="CutRightWire",
-            wire_cut_location=WireCutLocation(
-                instruction_id=9, gate_name="cx", qubits=[2, 3], input=2
-            ),
-        ),
-        OneWireCutIdentifier(
-            cut_action="CutLeftWire",
-            wire_cut_location=WireCutLocation(
-                instruction_id=11, gate_name="cx", qubits=[3, 5], input=1
-            ),
-        ),
-    ]
+            qubits_per_subcircuit = 3
 
-    assert (
-        interface.export_subcircuits_as_string(name_mapping="default") == "AABABCBCC"
-    )  # extra wires because of wire cuts
-    # and no qubit reuse. In the string above,
-    # {A: wire 0, A:wire 1, B:wire 2, A: wire 3,
-    # B: first cut on wire 3, C: second cut on wire 3,
-    # B: wire 4, C: wire 5, C: wire 6}.
+            interface = SimpleGateList(self.circuit_internal)
 
-    best_result = optimization_pass.get_results()
+            settings = OptimizationSettings(seed=12345, gate_lo=False, wire_lo=True)
 
-    assert output.upper_bound_gamma() == best_result.gamma_UB == 16  # Two LO wire cuts.
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
-    assert optimization_pass.minimum_reached() is True  # matches optimal solution
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
+            # Impose a stringent cost upper bound, insist gamma <=2.
+            cut_opt = CutOptimization(interface, settings, constraint_obj)
+            cut_opt.update_upperbound_cost((2, 4))
+            state, cost = cut_opt.optimization_pass()
 
-# check if unsupported search engine is flagged.
-def test_supported_search_engine(
-    four_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    qubits_per_subcircuit = 4
+            # 2 LO wire cuts are still found
+            assert state is not None
+            assert cost[0] == 16
 
-    interface, settings = four_qubit_test_setup
 
-    settings.set_engine_selection("CutOptimization", "BeamSearch")
+class TestCuttingSevenQubitCircuit(unittest.TestCase):
+    def setUp(self):
+        qc = QuantumCircuit(7)
+        for i in range(7):
+            qc.rx(np.pi / 4, i)
+        qc.cx(0, 3)
+        qc.cx(1, 3)
+        qc.cx(2, 3)
+        qc.cx(3, 4)
+        qc.cx(3, 5)
+        qc.cx(3, 6)
+        self.circuit_internal = qc_to_cco_circuit(qc)
 
-    search_engine = settings.get_engine_selection("CutOptimization")
+    def test_seven_qubit_workflow(self):
+        with self.subTest("Two qubits per subcircuit"):
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            qubits_per_subcircuit = 2
 
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+            interface = SimpleGateList(self.circuit_internal)
 
-    with raises(ValueError) as e_info:
-        _ = optimization_pass.optimize()
-    assert e_info.value.args[0] == f"Search engine {search_engine} is not supported."
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=True)
 
+            settings.set_engine_selection("CutOptimization", "BestFirst")
 
-# The cutting of multiqubit gates is not supported at present.
-def test_multiqubit_cuts(
-    multiqubit_gate_test_setup: Callable[
-        [], tuple[SimpleGateList, OptimizationSettings]
-    ]
-):
-    # QPU with 2 qubits requires cutting.
-    qubits_per_subcircuit = 2
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
 
-    interface, settings = multiqubit_gate_test_setup
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            output = optimization_pass.optimize()
 
-    optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+            cut_actions_list = output.cut_actions_sublist()
 
-    with raises(ValueError) as e_info:
-        _ = optimization_pass.optimize()
-    assert e_info.value.args[0] == (
-        "The input circuit must contain only single and two-qubits gates. "
-        "Found 3-qubit gate: (ccx)."
-    )
+            assert cut_actions_list == [
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=7, gate_name="cx", qubits=[0, 3]
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=8, gate_name="cx", qubits=[1, 3]
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=9, gate_name="cx", qubits=[2, 3]
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=11, gate_name="cx", qubits=[3, 5]
+                    ),
+                ),
+                CutIdentifier(
+                    cut_action="CutTwoQubitGate",
+                    cut_location=CutLocation(
+                        instruction_id=12, gate_name="cx", qubits=[3, 6]
+                    ),
+                ),
+            ]
 
+            best_result = optimization_pass.get_results()
 
-# Even if the input cost bounds are too stringent, greedy_cut_optimization
-# is able to return a solution.
-def test_greedy_search(
-    four_qubit_test_setup: Callable[[], tuple[SimpleGateList, OptimizationSettings]]
-):
-    qubits_per_subcircuit = 3
+            assert (
+                output.upper_bound_gamma() == best_result.gamma_UB == 243
+            )  # 5 LO cnot cuts.
 
-    interface, settings = four_qubit_test_setup
+            assert (
+                optimization_pass.minimum_reached() is True
+            )  # matches optimal solution.
 
-    constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+            assert (
+                interface.export_subcircuits_as_string(name_mapping="default")
+                == "ABCDDEF"
+            )  # circuit separated into 2 subcircuits.
 
-    # Impose a stringent cost upper bound, insist gamma <=2.
-    cut_opt = CutOptimization(interface, settings, constraint_obj)
-    cut_opt.update_upperbound_cost((2, 4))
-    state, cost = cut_opt.optimization_pass()
+        with self.subTest("Single wire cut"):
 
-    # 2 cnot cuts are still found
-    assert state is not None
-    assert cost[0] == 9
+            qubits_per_subcircuit = 4
+
+            interface = SimpleGateList(self.circuit_internal)
+
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=True)
+
+            settings.set_engine_selection("CutOptimization", "BestFirst")
+
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+
+            output = optimization_pass.optimize()
+
+            cut_actions_list = output.cut_actions_sublist()
+
+            assert cut_actions_list == [
+                SingleWireCutIdentifier(
+                    cut_action="CutLeftWire",
+                    wire_cut_location=WireCutLocation(
+                        instruction_id=10, gate_name="cx", qubits=[3, 4], input=1
+                    ),
+                )
+            ]
+
+            assert (
+                interface.export_subcircuits_as_string(name_mapping="default")
+                == "AAAABBBB"
+            )  # extra wires because of wire cuts
+            # and no qubit reuse.
+
+            best_result = optimization_pass.get_results()
+
+            assert (
+                output.upper_bound_gamma() == best_result.gamma_UB == 4
+            )  # One LO wire cut.
+
+            assert (
+                optimization_pass.minimum_reached() is True
+            )  # matches optimal solution
+
+        with self.subTest("Two single wire cuts"):
+
+            qubits_per_subcircuit = 3
+
+            interface = SimpleGateList(self.circuit_internal)
+
+            settings = OptimizationSettings(seed=12345, gate_lo=True, wire_lo=True)
+
+            settings.set_engine_selection("CutOptimization", "BestFirst")
+
+            constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+
+            optimization_pass = LOCutsOptimizer(interface, settings, constraint_obj)
+
+            output = optimization_pass.optimize()
+
+            cut_actions_list = output.cut_actions_sublist()
+
+            assert cut_actions_list == [
+                SingleWireCutIdentifier(
+                    cut_action="CutRightWire",
+                    wire_cut_location=WireCutLocation(
+                        instruction_id=9, gate_name="cx", qubits=[2, 3], input=2
+                    ),
+                ),
+                SingleWireCutIdentifier(
+                    cut_action="CutLeftWire",
+                    wire_cut_location=WireCutLocation(
+                        instruction_id=11, gate_name="cx", qubits=[3, 5], input=1
+                    ),
+                ),
+            ]
+
+            assert (
+                interface.export_subcircuits_as_string(name_mapping="default")
+                == "AABABCBCC"
+            )  # extra wires because of wire cuts
+        # and no qubit reuse. In the string above,
+        # {A: wire 0, A:wire 1, B:wire 2, A: wire 3,
+        # B: first cut on wire 3, C: second cut on wire 3,
+        # B: wire 4, C: wire 5, C: wire 6}.
+
+        best_result = optimization_pass.get_results()
+
+        assert (
+            output.upper_bound_gamma() == best_result.gamma_UB == 16
+        )  # Two LO wire cuts.
+
+        assert optimization_pass.minimum_reached() is True  # matches optimal solution
+
+
+class TestCuttingMultiQubitGates(unittest.TestCase):
+    def setUp(self):
+        qc = QuantumCircuit(3)
+        qc.ccx(0, 1, 2)
+        circuit_internal = qc_to_cco_circuit(qc)
+        self.interface = SimpleGateList(circuit_internal)
+        self.settings = OptimizationSettings(seed=12345)
+        self.settings.set_engine_selection("CutOptimization", "BestFirst")
+
+    def no_cutting_multiqubit_gates(self):
+
+        # The cutting of multiqubit gates is not supported at present.
+        qubits_per_subcircuit = 2
+
+        constraint_obj = DeviceConstraints(qubits_per_subcircuit)
+
+        optimization_pass = LOCutsOptimizer(
+            self.interface, self.settings, constraint_obj
+        )
+
+        with raises(ValueError) as e_info:
+            _ = optimization_pass.optimize()
+        assert e_info.value.args[0] == (
+            "The input circuit must contain only single and two-qubits gates. "
+            "Found 3-qubit gate: (ccx)."
+        )
diff --git a/test/cutting/cut_finding/test_cutting_actions.py b/test/cutting/cut_finding/test_cutting_actions.py
index 04cfd53..3b7fb48 100644
--- a/test/cutting/cut_finding/test_cutting_actions.py
+++ b/test/cutting/cut_finding/test_cutting_actions.py
@@ -30,7 +30,7 @@ from circuit_knitting.cutting.cut_finding.disjoint_subcircuits_state import (
     DisjointSubcircuitsState,
     get_actions_list,
     CutIdentifier,
-    GateCutLocation,
+    CutLocation,
 )
 from circuit_knitting.cutting.cut_finding.search_space_generator import ActionNames
 
@@ -93,7 +93,7 @@ def test_cut_two_qubit_gate(
     assert actions_list == [
         CutIdentifier(
             cut_action="CutTwoQubitGate",
-            gate_cut_location=GateCutLocation(
+            cut_location=CutLocation(
                 instruction_id=2, gate_name="cx", qubits=[0, 1]
             ),  # In renaming qubits here,"q1" -> 0, "q0" -> 1.
         )
diff --git a/test/cutting/cut_finding/test_optimization_settings.py b/test/cutting/cut_finding/test_optimization_settings.py
index cbe92df..251b2ed 100644
--- a/test/cutting/cut_finding/test_optimization_settings.py
+++ b/test/cutting/cut_finding/test_optimization_settings.py
@@ -30,27 +30,33 @@ def test_optimization_parameters(max_gamma: int, max_backjumps: int):
         _ = OptimizationSettings(max_gamma=max_gamma, max_backjumps=max_backjumps)
 
 
-def test_gate_cut_types(LO: bool = True, LOCC_ancillas: bool = False):
+def test_gate_cut_types(gate_lo: bool = True, gate_locc_ancillas: bool = False):
     """Test default gate cut types."""
-    op = OptimizationSettings(LO, LOCC_ancillas)
+    op = OptimizationSettings(gate_lo, gate_locc_ancillas)
     op.set_gate_cut_types()
-    assert op.gate_cut_LO is True
-    assert op.gate_cut_LOCC_with_ancillas is False
+    assert op.gate_cut_lo is True
+    assert op.gate_cut_locc_with_ancillas is False
 
 
 def test_wire_cut_types(
-    LO: bool = True, LOCC_ancillas: bool = False, LOCC_no_ancillas: bool = False
+    wire_lo: bool = True,
+    wire_locc_ancillas: bool = False,
+    wire_locc_no_ancillas: bool = False,
 ):
     """Test default wire cut types."""
-    op = OptimizationSettings(LO, LOCC_ancillas, LOCC_no_ancillas)
+    op = OptimizationSettings(wire_lo, wire_locc_ancillas, wire_locc_no_ancillas)
     op.set_wire_cut_types()
-    assert op.wire_cut_LO
-    assert op.wire_cut_LOCC_with_ancillas is False
-    assert op.wire_cut_LOCC_no_ancillas is False
+    assert op.wire_cut_lo
+    assert op.wire_cut_locc_with_ancillas is False
+    assert op.wire_cut_locc_no_ancillas is False
 
 
 def test_all_cut_search_groups():
     """Test for the existence of all cut search groups."""
     assert OptimizationSettings(
-        LO=True, LOCC_ancillas=True, LOCC_no_ancillas=True
+        gate_lo=True,
+        gate_locc_ancillas=True,
+        wire_lo=True,
+        wire_locc_ancillas=True,
+        wire_locc_no_ancillas=True,
     ).get_cut_search_groups() == [None, "GateCut", "WireCut"]
diff --git a/test/cutting/test_find_cuts.py b/test/cutting/test_find_cuts.py
index 365bfcf..559869b 100644
--- a/test/cutting/test_find_cuts.py
+++ b/test/cutting/test_find_cuts.py
@@ -17,6 +17,7 @@ import pytest
 import os
 import numpy as np
 from qiskit import QuantumCircuit
+from qiskit.circuit.library import EfficientSU2
 
 from circuit_knitting.cutting.automated_cut_finding import (
     find_cuts,
@@ -46,6 +47,24 @@ class TestCuttingDecomposition(unittest.TestCase):
             assert len(metadata["cuts"]) == 2
             assert {"Wire Cut", "Gate Cut"} == cut_types
             assert np.isclose(127.06026169, metadata["sampling_overhead"], atol=1e-8)
+            assert metadata["minimum_reached"] is True
+
+        with self.subTest("Cut both wires instance"):
+            qc = EfficientSU2(4, entanglement="linear", reps=2).decompose()
+            qc.assign_parameters([0.4] * len(qc.parameters), inplace=True)
+            optimization = OptimizationParameters(
+                seed=12345, gate_lo=False, wire_lo=True
+            )
+            constraints = DeviceConstraints(qubits_per_subcircuit=2)
+
+            _, metadata = find_cuts(
+                qc, optimization=optimization, constraints=constraints
+            )
+            cut_types = {cut[0] for cut in metadata["cuts"]}
+
+            assert len(metadata["cuts"]) == 8
+            assert {"Wire Cut"} == cut_types
+            assert np.isclose(65536.0**2, metadata["sampling_overhead"], atol=1e-8)
 
         with self.subTest("3-qubit gate"):
             circuit = QuantumCircuit(3)