From 948d70c418f057b848597d3515721bf56c3058d1 Mon Sep 17 00:00:00 2001
From: Sanket Panda <pandasa123@gmail.com>
Date: Wed, 22 Nov 2023 16:43:57 -0500
Subject: [PATCH 1/7] The transpilation tutorial has been updated to use the
 new runtime transpilation passes, and Batch

---
 docs/tutorials/user-transpiled-circuits.ipynb | 2694 +----------------
 1 file changed, 134 insertions(+), 2560 deletions(-)

diff --git a/docs/tutorials/user-transpiled-circuits.ipynb b/docs/tutorials/user-transpiled-circuits.ipynb
index 5e9350ebe..4a0c1bc74 100644
--- a/docs/tutorials/user-transpiled-circuits.ipynb
+++ b/docs/tutorials/user-transpiled-circuits.ipynb
@@ -4,1075 +4,119 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Submitting user-transpiled circuits using primitives\n",
+    "# Submit pre-transpiled circuits\n",
+    "\n",
+    "## Background\n",
     "\n",
     "To get the best performance from your circuits, the Qiskit Runtime service will pass all circuits through Qiskit's transpiler before running them. While this is usually a good thing, we might sometimes want to disable this by passing the argument `skip_transpilation=True` to the primitive we're using.\n",
     "\n",
     "For example, we may know better than the transpiler in some cases, or want to target a specific subset of qubits on a specific device. In this tutorial, we'll disable automatic transpilation to test the performance of different transpiler settings. This example will take you through the full process of creating, transpiling, and submitting circuits.\n",
     "\n",
-    "## Transpiling circuits for IBM Quantum devices\n",
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create circuit to test transpiler on\n",
+    "from qiskit import QuantumCircuit, transpile\n",
+    "from qiskit.circuit.library import GroverOperator, Diagonal\n",
     "\n",
-    "In the following code cell, we create a small circuit that our transpiler will try to optimize. In this example, we create a circuit that carries out Grover's algorithm, with an oracle that marks the state `111`. We then simulate the ideal distribution (what we'd expect to measure if we ran this on a perfect quantum computer, an infinite number of times) for comparison later. "
+    "# Use Statevector object to calculate the ideal output\n",
+    "from qiskit.quantum_info import Statevector\n",
+    "from qiskit.visualization import plot_histogram\n",
+    "\n",
+    "# Qiskit Runtime\n",
+    "from qiskit_ibm_runtime import QiskitRuntimeService, Batch, Sampler\n",
+    "\n",
+    "# General imports\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
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       "text/plain": [
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+       "'ibm_hanoi'"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Create circuit to test transpiler on\n",
-    "from qiskit import QuantumCircuit\n",
-    "from qiskit.circuit.library import GroverOperator, Diagonal\n",
-    "\n",
-    "oracle = Diagonal([1] * 7 + [-1])\n",
-    "qc = QuantumCircuit(3)\n",
-    "qc.h([0, 1, 2])\n",
-    "qc = qc.compose(GroverOperator(oracle))\n",
-    "\n",
-    "# Use Statevector object to calculate the ideal output\n",
-    "from qiskit.quantum_info import Statevector\n",
-    "\n",
-    "ideal_distribution = Statevector.from_instruction(qc).probabilities_dict()\n",
+    "# To run on hardware, select the backend with the fewest number of jobs in the queue\n",
+    "service = QiskitRuntimeService(channel=\"ibm_quantum\")\n",
+    "backend = service.least_busy(operational=True, simulator=False)\n",
+    "backend.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "\n",
-    "from qiskit.visualization import plot_histogram\n",
+    "## Transpiling circuits for IBM Quantum devices\n",
     "\n",
-    "plot_histogram(ideal_distribution)"
+    "In the following code cell, we create a small circuit that our transpiler will try to optimize. In this example, we create a circuit that carries out Grover's algorithm, with an oracle that marks the state `111`. We then simulate the ideal distribution (what we'd expect to measure if we ran this on a perfect quantum computer, an infinite number of times) for comparison later. "
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 48,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 287.496x284.278 with 1 Axes>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "Next, we need to choose a backend to transpile for. In the following cell, we create a service instance, which we'll use to start a session, and get the backend object, which contains information for the transpiler. Since the transpilation process depends on the device, we'll ask the runtime service for a specific device by name. In this example, we'll use `ibm_algiers`, which is only available through IBM Cloud."
+    "oracle = Diagonal([1] * 7 + [-1])\n",
+    "qc = QuantumCircuit(3)\n",
+    "qc.h([0, 1, 2])\n",
+    "qc = qc.compose(GroverOperator(oracle))\n",
+    "\n",
+    "qc.draw(output='mpl', style='iqp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "ideal_distribution = Statevector.from_instruction(qc).probabilities_dict()\n",
     "\n",
-    "service = QiskitRuntimeService()\n",
-    "backend = service.backend(\"ibm_algiers\")"
+    "plot_histogram(ideal_distribution)"
    ]
   },
   {
@@ -1086,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1094,7 +138,7 @@
      "output_type": "stream",
      "text": [
       "CNOTs (optimization_level=0):  27\n",
-      "CNOTs (optimization_level=3):  12\n"
+      "CNOTs (optimization_level=3):  14\n"
      ]
     }
    ],
@@ -1102,8 +146,6 @@
     "# Need to add measurements to the circuit\n",
     "qc.measure_all()\n",
     "\n",
-    "from qiskit import transpile\n",
-    "\n",
     "circuits = []\n",
     "for optimization_level in [0, 3]:\n",
     "    t_qc = transpile(qc, backend, optimization_level=optimization_level, seed_transpiler=0)\n",
@@ -1117,55 +159,75 @@
    "source": [
     "Since CNOTs usually have a high error rate, the circuit transpiled with `optimization_level=3` should perform much better.\n",
     "\n",
-    "Another way we can improve performance is through [dynamic decoupling](https://qiskit.org/documentation/stubs/qiskit.transpiler.passes.DynamicalDecoupling.html), where we apply a sequence of gates to idling qubits. This cancels out some unwanted interactions with the environment. In the following cell, we add dynamic decoupling to the circuit transpiled with `optimization_level=3`, and add it to our list."
+    "Another way we can improve performance is through [dynamic decoupling](https://docs.quantum-computing.ibm.com/api/qiskit-ibm-provider/qiskit_ibm_provider.transpiler.passes.scheduling.PadDynamicalDecoupling#paddynamicaldecoupling), where we apply a sequence of gates to idling qubits. This cancels out some unwanted interactions with the environment. In the following cell, we add dynamic decoupling to the circuit transpiled with `optimization_level=3`, and add it to our list."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit.transpiler import PassManager, InstructionDurations\n",
-    "from qiskit.transpiler.passes import ASAPSchedule, DynamicalDecoupling\n",
+    "from qiskit.transpiler import PassManager\n",
+    "from qiskit_ibm_runtime.transpiler.passes.scheduling import (\n",
+    "    ASAPScheduleAnalysis,\n",
+    "    PadDynamicalDecoupling,\n",
+    ")\n",
     "from qiskit.circuit.library import XGate\n",
     "\n",
     "# Get gate durations so the transpiler knows how long each operation takes\n",
-    "durations = InstructionDurations.from_backend(backend)\n",
+    "durations = backend.target.durations()\n",
     "\n",
     "# This is the sequence we'll apply to idling qubits\n",
     "dd_sequence = [XGate(), XGate()]\n",
     "\n",
     "# Run scheduling and dynamic decoupling passes on circuit\n",
-    "pm = PassManager([ASAPSchedule(durations), DynamicalDecoupling(durations, dd_sequence)])\n",
+    "pm = PassManager([ASAPScheduleAnalysis(durations), PadDynamicalDecoupling(durations, dd_sequence)])\n",
     "circ_dd = pm.run(circuits[1])\n",
     "\n",
     "# Add this new circuit to our list\n",
     "circuits.append(circ_dd)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2252.61x1204 with 1 Axes>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "circ_dd.draw(output='mpl', style='iqp', idle_wires=False)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Run transpiled circuits using Qiskit Runtime\n",
     "\n",
-    "\n",
-    "## Run user-transpiled circuits using Qiskit Runtime\n",
-    "\n",
-    "At this point, we have a list of circuits (named `circuits`) transpiled for `ibm_algiers`. In the following cell, we create an instance of the sampler primitive, and start a session using the context manager (`with ...:`), which automatically opens and closes the session for us. This is where we pass the `skip_transpilation=True` argument.\n",
+    "At this point, we have a list of circuits transpiled for our system. In the following cell, we create an instance of the sampler primitive, and start a batched job using the context manager (`with ...:`), which automatically opens and closes the Batch for us. This is where we pass the `skip_transpilation=True` argument.\n",
     "\n",
     "Within the context manager, we sample the circuits and store the results to `result`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from qiskit_ibm_runtime import Sampler, Session\n",
-    "\n",
-    "with Session(service=service, backend=backend):\n",
+    "with Batch(service=service, backend=backend):\n",
     "    sampler = Sampler()\n",
     "    job = sampler.run(\n",
     "        circuits=circuits,  # sample all three circuits\n",
@@ -1184,1495 +246,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
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vv/8iICAAzZs3R1ZWFvbs2YPU1FSMHj26yOvPi5OVlQV7e3tkZmaqXVZBREREVBqK+uzx+PFjXL9+HXXr1pVvQVDZFN6Q/ty5c2ojWkT08rR5jzG6OZIFBQUYN24cFAoFYmNj8fXXX+PTTz/FH3/8gQYNGiAiIkJj2eKiLFmyBBkZGfjss89w6NAhLFy4EKtWrcLly5dRu3ZtbNiwoUT7ISIiIiIiInVGV0geO3YMCQkJGDp0qNpKSPb29oiIiEBeXh42btz4wv0U3kT16ZW7AMDBwQE+Pj4AoHYzUyIiIiIiUpeeno7U1NRi/9y9e9fQIZKBGF0hefz4cQCAv7+/xrbCVZROnDjxwv00bdoUAHDw4EG19vv37+P06dOoXr262jK+RERERESkrk2bNnBzcyv2z4ABAwwdIhmI0S22Ex8fDwCoX7++xrbq1avDxsZG7vM8U6ZMwb59+/DBBx/gp59+UpsjaWVlhd27dz/3HjS5ubnIzc2VH2dlZQEA8vPz5aWmFQoFTExMoFQqoVKp5L6F7QUFBWrLLpuYmEChUBTb/vQS1gBgavrk9BQUFJSo3czMDCqVSm0JaUmSYGpqWmx7cbEzJ+bEnJgTc2JOzMnwOZGm0aNHy/f5o9K3efNm5OTkFLvd0dGxDKMhY2J0hWRmZiaAJ5eyFsXOzk7u8zyurq44e/Yshg8fjkOHDuGnn34CAFSpUgXjx49HixYtnvv8+fPnY86cORrthw8fhpWVFQCgdu3aaNWqFS5evIgbN27Ifby9vdGwYUP8+uuv8opdwJOblnp4eCA2NhYPHjyQ2zt06AAXFxccPnxY7ZeGr68vqlSpojGq2rt3b+Tk5CAmJkZuMzU1RZ8+fZCRkYGzZ8/K7ba2tujevTuSk5Nx4cIFud3Z2RkdO3ZEfHy82o1/mRNzYk7MiTkxJ+ZkHDn99ttvIDK00loNlco/o1u11d/fH0eOHEF8fDy8vLw0tru7uyM7O/uFxeQ///yDfv36wcbGBp999hlatmyJ+/fvY9OmTZgxYwbatm2LkydPFnsLkKJGJGvVqoWMjAx55bTK+u0oc2JOzIk5MSfmxJxKP6e7d++iatWqXLWViMqMNu8xRjciWTgSWVyhmJWVVaIh9NGjRyMpKQnXrl1D9erVAQA2Njb4+OOPcefOHSxbtgxbt27FsGHDiny+hYUFLCwsNNrNzMw07mtjYmJSZEFa+MunpO3F3ddHm3aFQgGFQnPqa3HtxcXOnJiTtu3MiTkBzKm4GLVtZ07MCSg+diIiY2B0i+0Uzo0sah5kamoqsrOzi5w/+bQHDx7g9OnTaNSokVxEPs3X1xcAcP78eT1ETEREREREVLkYXSHZtWtXAE/mIj4rKipKrU9x8vLyABR/e4/CORRFjTgSERERERHR8xldIenn5wdPT09s2bJFbaJ6ZmYmIiMjYW5ujpEjR8rtKSkpuHLlitqlsFWrVoW3tzdu3LiBtWvXqu3//v37WLJkCYD/G5kkIiIiIiKikjO6QtLU1BRr166FSqVCly5dEBoaikmTJqFFixa4evUqIiMjUadOHbn/tGnT0KhRI+zevVttP5999hlMTU0xbtw49OjRA1OmTMHYsWPRoEEDXLlyBW+88QZ69OhRxtkRERERERGVf0Y5i9vX1xenTp3CrFmzsG3bNuTn56NZs2ZYuHAhQkJCSrSPXr164cyZM1i8eDFOnTqFEydOwNLSEo0aNcLMmTPx9ttvl3IWREREREREFZPR3f7DWGVlZcHe3l5tCW4iIiKi0lLUZw/e/kM/jh8/Dl9fX8TExKBbt2562eeGDRswZswYXL9+Xe3qudJmqOM+q/B1PH78uMFiKGRMsZQ35fr2H0RERERlbq+k/XNeM77v4hO8Zxs6BNSLM3wMhVauXAkrKyuMHj3a0KG8tMjISDRu3Bj9+/c3dCiVxu3btzF16lScO3cOt2/fhomJCRo0aIAJEyZg5MiRkCQd3jcqEKObI0lEREREpA8rV67Ehg0bNNq7dOmCnJwcdOnSRW/HGjFiBHJycuDh4aG3fT4tMjISe/bsKfPjVmYZGRm4efMmBg4ciCVLlmDevHlwc3PD6NGjMX36dEOHZ3AckSQiIiKiSkWhUOj90mATExOYmJjodZ/GfNzKoHnz5hqXx4aHh6Nfv374/PPPMXfu3Er92nNEkoiIiIiMxvnz59GrVy/Y2dnBxsYGfn5++Pnnn+XtGzZsgCRJiI2NRVhYGKpWrQo7OzuMHDkS9+7dk/vVqVMHly5dwokTJyBJEiRJUps7J0mSWpHQrVs3NG3aFBcvXkTXrl1hZWUFLy8v7Ny5EwBw4sQJtGvXDlWqVIG3tzeio6PV4i6MKzExEQAwe/Zs+bjP/nn6UtslS5agY8eOqFq1KqpUqYLWrVvLxywkSRIePnyIjRs3auzj2eMWWrlyJZo0aQILCwvUqFEDEyZMwP3799X6FOb8999/w9fXF1ZWVnB3d8eiRYtKeLaeLzc3F7NmzYKXlxcsLCxQq1YtTJ06Fbm5uXKfpk2bFnlLPpVKBXd3dwwcOFCtbdmyZWjSpAksLS3h6uqKsLAwtfNeFurUqYNHjx7J966vrDgiSURERERG4dKlS+jcuTPs7OwwdepUmJmZYfXq1ejWrZtcyBUKDw+Hg4MDZs+ejbi4OKxatQpJSUlykbhs2TK8++67sLGxkS9DdHV1fe7x7927h759+2Lw4MEYNGgQVq1ahcGDB2Pz5s2YOHEixo8fj6FDh2Lx4sUYOHAgkpOTYWtrW+S+BgwYAC8vL7W23377DcuWLYOLi4vctnz5cgQFBWHYsGHIy8vD1q1bMWjQIOzfvx99+vQBAHz33XcYO3Ys2rZti9DQUABAvXr1is1j9uzZmDNnDnr06IG3335bfn3OnTuH06dPw8zMTC3nwMBADBgwAMHBwdi5cyc++ugjNGvWDL169Xru6/U8KpUKQUFBOHXqFEJDQ9GoUSP8+eef+Oyzz3D16lX5Mt2QkBDMnj0bqampqF69uvz8U6dO4fbt2xg8eLDcFhYWJi8u9N577+H69etYsWIFzp8/r5HXszIyMkoUt62tLSwsLNTacnJy8PDhQ2RnZ+PEiRNYv349OnTogCpVqmjxilQ8LCSJiIiIyCjMmDED+fn5OHXqFDw9PQEAI0eOhLe3N6ZOnYoTJ07Ifc3NzXH06FG5ePDw8MDUqVOxb98+BAUFoX///pgxYwaqVauG4cOHl+j4t2/fxpYtWzBkyBAAQM+ePdGwYUMMHToUZ86ckQvZRo0aISAgALt27Sp2IZ/mzZujefPm8uOMjAxMnz4dzZo1w6xZs+T2q1evqhUk4eHheOWVV7B06VK5kBw+fDjGjx8PT0/PF+aSnp6O+fPnw9/fH4cOHYJC8eQCxIYNGyI8PBybNm3CmDFj1HL+9ttvMWLECADAW2+9BQ8PD6xbt+6lCsktW7YgOjoaJ06cgI+Pj9zetGlTjB8/HmfOnEHHjh0REhKCmTNnYufOnQgPD5f7bdu2DTY2NvJrcOrUKaxduxabN2/G0KFD5X6+vr4IDAzEjh071Nqf5ezsXKK4169fr3FOly9fjmnTpsmP/fz8sH79+hLtryJjIUlEREREBqdUKnH48GH0799fLiIBwM3NDUOHDsWaNWuQlZUlt4eGhqqNQL399tuIiIjAwYMHERQUpFMMNjY2aiNg3t7ecHBwgLu7u9poaOG/r127VuLchgwZggcPHuDYsWOwtraWtz1dRN67dw9KpRKdO3fG999/r1MO0dHRyMvLw8SJE+UiEgDGjRuHiIgIHDhwQK2QtLGxUStOzc3N0bZt2xLnVpwdO3agUaNGaNiwodpoYPfu3QEAMTEx6NixIxo0aICWLVti27ZtciGpVCqxc+dO9OvXT359duzYAXt7e/Ts2VNtf61bt4aNjQ1iYmKeW0geOXKkRHE3adJEo23IkCF49dVXkZ6ejv379+POnTvIyckp0f4qMhaSRERERGRw6enpePToEby9vTW2NWrUCCqVCsnJyXJb/fr11frY2NjAzc1NY66gNmrWrKlxSwd7e3vUqlVLow1AiefmzZgxA8eOHcOBAwc0Lkndv38/5s2bhwsXLqjNHdT11hJJSUkAoPE6mpubw9PTU95eqKicHR0dcfHiRZ2OXyg+Ph6XL18udiQwLS1N/ndISAgiIiJw69YtuLu74/jx40hLS0NISIja/jIzM9UuCy5uf0Xp0aOHDlk84eHhIa+KO2TIEISGhqJHjx6Ii4ur1Je3spAkIiIiIgKKXYGzuHYhXnwv0T179mDhwoWYO3cuAgMD1badPHkSQUFB6NKlC1auXAk3NzeYmZlh/fr12LJli/YJ6OBlcnselUqFZs2aYenSpUVuf7o4DwkJwbRp07Bjxw5MnDgR27dvh729vdrrpVKp4OLigs2bNxe5vxddupqamlqiuO3t7V9YHA4cOBBr1qxBbGwsAgICSrTfioiFJBEREREZnLOzM6ysrBAXF6ex7cqVK1AoFKhVqxbOnTsH4MkI1dOrfWZnZyMlJQW9e/eW2wx9w/irV69i1KhR6N+/PyIiIjS279q1C5aWloiKilJb4KWo+XclzaVw5CwuLk7tEuG8vDxcv379pUbmtFGvXj388ccf8PPze2HsdevWRdu2beXLW3/44Qf0799f7TWpV68eoqOj0alTJ51GAd3c3ErUr6g5ks8qvKw1MzNT6zgqEt7+g4iIiIgMzsTEBP7+/ti7d6/a5al37tzBli1b4OPjAzs7O7n966+/Rn5+vvx41apVKCgoUFsgxtraWuOWF2UlOzsbr7/+Otzd3eXbdjzLxMQEkiRBqVTKbYmJifKKpk8raS49evSAubk5Pv/8c7VRxXXr1iEzM1NevKa0BQcH49atW1izZo3GtsJVUJ8WEhKCn3/+Gd988w0yMjLULmst3J9SqcTcuXM19ldQUPDC1+bIkSMl+vP0CGN6enqR+1q3bh0kScIrr7zy3GNWdByRJCIiIiKjMG/ePBw5cgQ+Pj545513YGpqitWrVyM3N1fj3oZ5eXnw8/NDcHAw4uLisHLlSvj4+KgttNO6dWusWrUK8+bNg5eXF1xcXOTFXkrbnDlz8Pfff2PGjBnYu3ev2rZ69eqhQ4cO6NOnD5YuXYrAwEAMHToUaWlp+PLLL+Hl5aUxR7F169aIjo7G0qVLUaNGDdStW1dtAaBCzs7OmDZtGubMmYPAwEAEBQXJr0+bNm1KvILtyxoxYgS2b9+O8ePHIyYmBp06dYJSqcSVK1ewfft2REVF4dVXX5X7BwcHY/LkyZg8eTKcnJw0Rk67du2KsLAwzJ8/HxcuXIC/vz/MzMwQHx+PHTt2YPny5Wr3nHyWLiOx//3vf3H69GkEBgaidu3auHv3Lnbt2oVz587h3Xff1bi9S2XDQpKIiIiogqgXN9vQIbyUJk2a4OTJk5g2bRrmz58PlUqFdu3aYdOmTRpF04oVK7B582bMnDkT+fn5GDJkCD7//HO1kb+ZM2ciKSkJixYtwoMHD9C1a9cyKyQLR7PmzZunsW3UqFHo0KEDunfvjnXr1mHBggWYOHEi6tati4ULFyIxMVGjkFy6dClCQ0MxY8YM5OTkYNSoUUUWksCT+0g6OztjxYoV+OCDD+Dk5ITQ0FBERkY+916L+qRQKLBnzx589tln+Pbbb7F7925YWVnB09MT77//Pho0aKDWv2bNmujYsSNOnz6NsWPHFhnnV199hdatW2P16tWIiIiAqakp6tSpg+HDh6NTp056z6FPnz5ISEjAN998g/T0dFhaWqJ58+ZYv349Ro0apffjlTeSeNmZtJVEVlYW7O3tkZmZqXZZBREREVUAe3WYS/da6X6EKuqzx+PHj3H9+nXUrVsXlpaWpXp8Y1V4Q/pz586pjWgR0cvT5j2GcySJiIiIiIhIK7y0lYiIiIiIipSenq62GNCzzM3N4eTkVIYRkbFgIUlEREREREVq06YNkpKSit3etWtXHD9+vOwCIqPBQpKIiIiIyo3Ro0e/8D5/pD+bN2+W75tYFEdHxzKMhowJC0kiIiIiIipSaayGShUDF9shIiIiIiIirbCQJCIiIiIiIq2wkCQiIiIiIiKtsJAkIiIiIiIirbCQJCIiIiIiIq2wkCQiIiIiIiKtsJAkIiIiokrl+PHjkCQJx48f19s+N2zYAEmSkJiYqLd9GvNxn9WtWzd069bNoDEUMqZYSps2579OnTp6vQcrC0kiIiIiqpBWrlyJDRs2GDoMvYiMjMSePXsMHUalcvv2bQwfPhze3t6wtbWFg4MD2rZti40bN0IIYejwDM7U0AEQERERkX5MG5Vp6BAwf6O9oUOQrVy5EtWqVdMYhenSpQtycnJgbm6ut2ONGDECgwcPhoWFhd72+bTIyEgMHDgQ/fv3L9PjVmYZGRm4efMmBg4ciNq1ayM/Px9HjhzB6NGjERcXh8jISEOHaFAsJImIiIioUlEoFLC0tNTrPk1MTGBiYqLXfRrzcSuD5s2ba1z+HB4ejn79+uHzzz/H3LlztX7tu3Xrhjp16lSIkXJe2kpERERERuH8+fPo1asX7OzsYGNjAz8/P/z8889qfQrnhMXGxiIsLAxVq1aFnZ0dRo4ciXv37sn96tSpg0uXLuHEiROQJAmSJMnz5oqaI9mtWzc0bdoUFy9eRNeuXWFlZQUvLy/s3LkTAHDixAm0a9cOVapUgbe3N6Kjo4uMq3Cu2uzZs+XjPvvn6RHSJUuWoGPHjqhatSqqVKmC1q1by8csJEkSHj58iI0bN2rso7g5citXrkSTJk1gYWGBGjVqYMKECbh//75an8Kc//77b/j6+sLKygru7u5YtGhRCc7Wi+Xm5mLWrFnw8vKChYUFatWqhalTpyI3N1fu07RpU/j6+mo8V6VSwd3dHQMHDlRrW7ZsGZo0aQJLS0u4uroiLCxM7byXhTp16uDRo0fIy8sr0+NeunQJ3bt3R5UqVVCzZk3MmzcPKpVKo58QAvPmzUPNmjVhZWUFX19fXLp0Se/xcESSiIiIiAzu0qVL6Ny5M+zs7DB16lSYmZlh9erV6Natm1zEPS08PBwODg6YPXs24uLisGrVKiQlJclF4rJly/Duu+/CxsYG06dPBwC4uro+N4Z79+6hb9++GDx4MAYNGoRVq1Zh8ODB2Lx5MyZOnIjx48dj6NChWLx4MQYOHIjk5GTY2toWua8BAwbAy8tLre23337DsmXL4OLiIrctX74cQUFBGDZsGPLy8rB161YMGjQI+/fvR58+fQAA3333HcaOHYu2bdsiNDQUAFCvXr1i85g9ezbmzJmDHj164O2335Zfn3PnzuH06dMwMzNTyzkwMBADBgxAcHAwdu7ciY8++gjNmjVDr169nvt6PY9KpUJQUBBOnTqF0NBQNGrUCH/++Sc+++wzXL16VZ7vGRISgtmzZyM1NRXVq1eXn3/q1Cncvn0bgwcPltvCwsKwYcMGjBkzBu+99x6uX7+OFStW4Pz58xp5PSsjI6NEcdva2mpcJpyTk4OHDx8iOzsbJ06cwPr169GhQwdUqVJFi1fk5aSmpsLX1xcFBQX4+OOPYW1tja+//rrIGGbOnIl58+ahd+/e6N27N37//Xf4+/vrvfBlIUlEREREBjdjxgzk5+fj1KlT8PT0BACMHDkS3t7emDp1Kk6cOKHW39zcHEePHpWLBw8PD0ydOhX79u1DUFAQ+vfvjxkzZqBatWoYPnx4iWK4ffs2tmzZgiFDhgAAevbsiYYNG2Lo0KE4c+aMXMw2atQIAQEB2LVrV7GrYDZv3hzNmzeXH2dkZGD69Olo1qwZZs2aJbdfvXpVrRgIDw/HK6+8gqVLl8qF5PDhwzF+/Hh4enq+MJf09HTMnz8f/v7+OHToEBSKJxcgNmzYEOHh4di0aRPGjBmjlvO3336LESNGAADeeusteHh4YN26dS9VSG7ZsgXR0dE4ceIEfHx85PamTZti/PjxOHPmDDp27IiQkBDMnDkTO3fuRHh4uNxv27ZtsLGxkV+DU6dOYe3atdi8eTOGDh0q9/P19UVgYCB27Nih1v4sZ2fnEsW9fv16jXO6fPlyTJs2TX7s5+eH9evXl2h/+rJw4UKkp6fjl19+Qdu2bQEAo0aNQv369dX6paenY9GiRejTpw/27dsHSZIAANOnT9f7nE5e2kpEREREBqVUKnH48GH0799fLiIBwM3NDUOHDsWpU6eQlZWl9pzQ0FC1Eai3334bpqamOHjwoM5x2NjYqI2AeXt7w8HBAY0aNVIbES3897Vr10q0X6VSiSFDhuDBgwfYvXs3rK2t5W1PF5H37t1DZmYmOnfujN9//12nHKKjo5GXl4eJEyfKRSQAjBs3DnZ2djhw4IBafxsbG7Xi1NzcHG3bti1xbsXZsWMHGjVqhIYNGyIjI0P+0717dwBATEwMAKBBgwZo2bIltm3bJj9XqVRi586d6Nevn/z67NixA/b29ujZs6fa/lq3bg0bGxt5f8U5cuRIif4EBARoPHfIkCE4cuQItmzZIherOTk5L3wN8vPz1WLNyMhAfn4+cnNzNdqLukT1aQcPHkT79u3lIhJ4UhwPGzZMrV/h+X/33XflIhIAJk6c+MJ4tcURSSIiIiIyqPT0dDx69Aje3t4a2xo1agSVSoXk5GQ0adJEbn92JMbGxgZubm4vdT/FmjVrqn34BgB7e3vUqlVLow1AiefmzZgxA8eOHcOBAwc0Lkndv38/5s2bhwsXLqjNHXw2jpJKSkoCAI3X0tzcHJ6envL2QkXl7OjoiIsXL+p0/ELx8fG4fPlysSOBaWlp8r9DQkIQERGBW7duwd3dHcePH0daWhpCQkLU9peZmal2WXBx+ytKjx49dMjiCQ8PD3h4eAB4UlSGhoaiR48eiIuLe+7lradPny5y/ueZM2ewdetWtbbr16+jTp06xe4rKSlJ4/JuQPM8F57fZ38+nJ2d4ejoWOz+dcFCkoiIiIgIKHYFzuLaS3IvwT179mDhwoWYO3cuAgMD1badPHkSQUFB6NKlC1auXAk3NzeYmZlh/fr12LJli/YJ6OBlcnselUqFZs2aYenSpUVuf7o4DwkJwbRp07Bjxw5MnDgR27dvh729vdrrpVKp4OLigs2bNxe5vxddupqamlqiuO3t7V8493HgwIFYs2YNYmNjixzBLNSiRQscOXJErW3SpEmoXr06pkyZotb+9PzQ8oKFJBEREREZlLOzM6ysrBAXF6ex7cqVK1AoFBqjgvHx8WqjPdnZ2UhJSUHv3r3lNl1H9fTl6tWrGDVqFPr374+IiAiN7bt27YKlpSWioqLUFngpav5dSXMpHDmLi4tTu0w4Ly8P169ff6mROW3Uq1cPf/zxB/z8/F4Ye926ddG2bVts27YN4eHh+OGHH9C/f3+116RevXqIjo5Gp06ddFrkxs3NrUT9ipoj+azCy1ozM59/31ZHR0eN19vR0RFubm5anwcPDw/Ex8drtD/7M1N4/uPj49XOf3p6ut5Xt+UcSSIiIiIyKBMTE/j7+2Pv3r1ql6beuXMHW7ZsgY+PD+zs7NSe8/XXXyM/P19+vGrVKhQUFKgtEGNtba1xy4uykp2djddffx3u7u7ybTueZWJiAkmSoFQq5bbExER5RdOnlTSXHj16wNzcHJ9//rnaqOK6deuQmZkpL15T2oKDg3Hr1i2sWbNGY1vhKqhPCwkJwc8//4xvvvkGGRkZape1Fu5PqVRi7ty5GvsrKCh44WujyxzJ9PT0Ive1bt06SJKEV1555bnH1KfevXvj559/xq+//iq3paena4zQ9ujRA2ZmZvjiiy/Uzv+yZcv0HhNHJImIiIjI4ObNm4cjR47Ax8cH77zzDkxNTbF69Wrk5uYWeV/DvLw8+Pn5ITg4GHFxcVi5ciV8fHwQFBQk92ndujVWrVqFefPmwcvLCy4uLvJiL6Vtzpw5+PvvvzFjxgzs3btXbVu9evXQoUMH9OnTB0uXLkVgYCCGDh2KtLQ0fPnll/Dy8tKYo9i6dWtER0dj6dKlqFGjBurWrVvknDlnZ2dMmzYNc+bMQWBgIIKCguTXp02bNiVewfZljRgxAtu3b8f48eMRExODTp06QalU4sqVK9i+fTuioqLw6quvyv2Dg4MxefJkTJ48GU5OThojdl27dkVYWBjmz5+PCxcuwN/fH2ZmZoiPj8eOHTuwfPlytXtOPkuXkdj//ve/OH36NAIDA1G7dm3cvXsXu3btwrlz5/Duu+9q3N6lNE2dOhXfffcdAgMD8f7778u3//Dw8FD7v+Ls7IzJkydj/vz56Nu3L3r37o3z58/j0KFDqFatml5jYiFJRERERAbXpEkTnDx5EtOmTcP8+fOhUqnQrl07bNq0qciCacWKFdi8eTNmzpyJ/Px8DBkyBJ9//rnayN/MmTORlJSERYsW4cGDB+jatWuZFZKFo1nz5s3T2DZq1Ch06NAB3bt3x7p167BgwQJMnDgRdevWxcKFC5GYmKhRSC5duhShoaGYMWMGcnJyMGrUqCJfF+DJfSSdnZ2xYsUKfPDBB3ByckJoaCgiIyOfe69FfVIoFNizZw8+++wzfPvtt9i9ezesrKzg6emJ999/Hw0aNFDrX7NmTXTs2BGnT5/G2LFji4zzq6++QuvWrbF69WpERETA1NQUderUwfDhw9GpUye959CnTx8kJCTgm2++QXp6OiwtLdG8eXOsX78eo0aN0vvxnsfNzQ0xMTF49913sWDBAlStWhXjx49HjRo18NZbb6n1nTdvHiwtLfHVV18hJiYG7dq1w+HDh/U+Gi2Jl51JW0lkZWXB3t4emZmZGpdWEBERUTm3V4e5dK+V7keooj57PH78GNevX0fdunVhaWlZqsc3VoU3pD937pzaiBYRvTxt3mM4R5KIiIiIiIi0wktbiYiIiIioSOnp6WqLAT3L3NwcTk5OZRgRGQsWkkREREREVKQ2bdrIN7kvSteuXXH8+PGyC4iMBgtJIiIiIio3Ro8e/cL7/JH+bN68Wb5vYlEcHR3LMBoyJiwkiYiIiIioSKWxGipVDFxsh4iIiIiIiLTCQpKIiIiIiIi0wkKSiIiIiIiItMJCkoiIiIiIiLTCQpKIiIiIiIi0wkKSiIiIiIiItMJCkoiIiIgMbsOGDZAkCYmJiS/sW6dOnVK/l+TLHCMxMRGSJGHDhg1y2+zZsyFJkn6Ce4Fu3bqhW7du8uPjx49DkiTs3LmzTI4/evRo1KlTp0yORYbDQpKIiIiIyAjdvn0bs2fPxoULFwwdigZjjo3KBgtJIiIiIjK4ESNGICcnBx4eHoYOpVTMmDEDOTk5Wj3n9u3bmDNnjtbF2uHDh3H48GGtnqOt58W2Zs0axMXFlerxyfBMDR0AEREREenJ3rK5dPK5XhM6Pc3ExAQmJiZ6DsZ4mJqawtS0dD96P3r0CFZWVjA3Ny/V47yImZmZQY9PZYMjkkRERERkcEXNkRRCYN68eahZsyasrKzg6+uLS5cuFfn8+/fvY+LEiahVqxYsLCzg5eWFhQsXQqVSqfVbsmQJOnbsiKpVq6JKlSpo3br1S80dvH//PkaPHg17e3s4ODhg1KhRuH//vka/ouZIHjlyBD4+PnBwcICNjQ28vb0REREB4Mm8xjZt2gAAxowZA0mS1OZdduvWDU2bNsVvv/2GLl26wMrKSn7us3MkCymVSkRERKB69eqwtrZGUFAQkpOT1foUNzf06X2+KLai5kg+fPgQkyZNks+Pt7c3lixZAiHUv3iQJAnh4eHYs2cPmjZtCgsLCzRp0gQ//fSTRkxkWByRJCIiIiKjNHPmTMybNw+9e/dG79698fvvv8Pf3x95eXlq/R49eoSuXbvi1q1bCAsLQ+3atXHmzBlMmzYNKSkpWLZsmdx3+fLlCAoKwrBhw5CXl4etW7di0KBB2L9/P/r06aNVfEIIvPbaazh16hTGjx+PRo0aYffu3Rg1atQLn3vp0iX07dsXzZs3xyeffAILCwv8888/OH36NACgUaNG+OSTTzBz5kyEhoaic+fOAICOHTvK+/j333/Rq1cvDB48GMOHD4erq+tzj/nf//4XkiTho48+QlpaGpYtW4YePXrgwoULqFKlSonzLklsTxNCICgoCDExMXjrrbfQsmVLREVFYcqUKbh16xY+++wztf6nTp3CDz/8gHfeeQe2trb4/PPP8cYbb+DGjRuoWrVqieOk0sVCkoiIiIiMTnp6OhYtWoQ+ffpg37598mje9OnTERkZqdZ36dKlSEhIwPnz51G/fn0AQFhYGGrUqIHFixfLI2EAcPXqVbWiKTw8HK+88gqWLl2qdSH5448/IjY2FosWLcKUKVMAAG+//TZ8fX1f+NwjR44gLy8Phw4dQrVq1TS2u7q6olevXpg5cyY6dOiA4cOHa/RJTU3FV199hbCwsBLFe/fuXVy+fBm2trYAgFdeeQXBwcFYs2YN3nvvvRLto6SxPe3HH3/EsWPHMG/ePEyfPh0AMGHCBAwaNAjLly9HeHg46tWrJ/e/fPky/v77b7nN19cXLVq0wPfff4/w8PASx0mli5e2EhEREZHRiY6ORl5eHt599121S0InTpyo0XfHjh3o3LkzHB0dkZGRIf/p0aMHlEolYmNj5b5PF5H37t1DZmYmOnfujN9//13rGA8ePAhTU1O8/fbbcpuJiQnefffdFz7XwcEBALB3716Ny29LysLCAmPGjClx/5EjR8pFJAAMHDgQbm5uOHjwoE7HL6mDBw/CxMREo1idNGkShBA4dOiQWnuPHj3UCsvmzZvDzs4O165dK9U4STsckSQiIiIio5OUlAQA8ghjIWdnZzg6Oqq1xcfH4+LFi3B2di5yX2lpafK/9+/fj3nz5uHChQvIzc2V23W5x2NSUhLc3NxgY2Oj1u7t7f3C54aEhGDt2rUYO3YsPv74Y/j5+WHAgAEYOHAgFIqSjfW4u7trtbDOs6+lJEnw8vIq0b07X0ZSUhJq1KihVsQCTy6RLdz+tNq1a2vsw9HREffu3Su9IElrLCSJiIiIqFxTqVTo2bMnpk6dWuT2Bg0aAABOnjyJoKAgdOnSBStXroSbmxvMzMywfv16bNmypSxDRpUqVRAbG4uYmBgcOHAAP/30E7Zt24bu3bvj8OHDJVrBVpt5jSVVXEGtVCrLbFXd4o7z7MI8ZFgsJImIiIjI6BTeTzI+Ph6enp5ye3p6usbIVL169ZCdnY0ePXo8d5+7du2CpaUloqKiYGFhIbevX79e5xiPHj2K7OxstVHJkt5DUaFQwM/PD35+fli6dCkiIyMxffp0xMTEoEePHjqNkj5PfHy82mMhBP755x80b95cbnN0dCxy1dmkpCS186BNbB4eHoiOjsaDBw/URiWvXLkib6fyh3MkiYiIiMjo9OjRA2ZmZvjiiy/URqKeXoG1UHBwMM6ePYuoqCiNbffv30dBQQGAJyNdkiRBqVTK2xMTE7Fnzx6dYuzduzcKCgqwatUquU2pVOKLL7544XPv3r2r0dayZUsAkC+5tba2lnPQh2+//RYPHjyQH+/cuRMpKSno1auX3FavXj38/PPPaivj7t+/X+M2IdrE1rt3byiVSqxYsUKt/bPPPoMkSWrHp/KDI5JEREREZHScnZ0xefJkzJ8/H3379kXv3r1x/vz5Ilc5nTJlCn788Uf07dsXo0ePRuvWrfHw4UP8+eef2LlzJxITE1GtWjX06dMHS5cuRWBgIIYOHYq0tDR8+eWX8PLywsWLF7WOsV+/fujUqRM+/vhjJCYmonHjxvjhhx+QmZn5wud+8skniI2NRZ8+feDh4YG0tDSsXLkSNWvWhI+PD4AnRZ2DgwO++uor2NrawtraGu3atUPdunW1jhUAnJyc4OPjgzFjxuDOnTtYtmwZvLy8MG7cOLnP2LFjsXPnTgQGBiI4OBgJCQnYtGmT2uI32sbWr18/+Pr6Yvr06UhMTESLFi1w+PBh7N27FxMnTtTYN5UPHJEkIiIiIqM0b948zJkzB+fPn8eUKVOQkJCAw4cPy6NhhaysrHDixAlMmTIFx48fx/vvv48FCxYgPj4ec+bMgb29PQCge/fuWLduHVJTUzFx4kR8//33WLhwIV5//XWd4lMoFPjxxx8xbNgwbNq0CdOnT4e7uzs2btz4wucGBQWhdu3a+OabbzBhwgR8+eWX6NKlC44dOybHa2Zmho0bN8LExATjx4/HkCFDcOLECZ1iBYCIiAj06dMH8+fPx/Lly+Hn54ejR4/CyspK7hMQEIBPP/0UV69excSJE3H27Fns378fNWvWVNuXNrEVvk4TJ07E/v37MXHiRPz9999YvHgxli5dqnM+ZFiS4KzVEsnKyoK9vT0yMzNhZ2dn6HCIiIhIn/bqMBfttdL9CFXUZ4/Hjx/j+vXrqFu3LiwtLUv1+ERU+WjzHsNLW4mIiKjCmTbqxZcWPm3+gFIKhIioguKlrURERERERKQVFpJERERERESkFRaSREREREREpBUWkkRERERERKQVFpJERERERESkFRaSREREREREpBUWkkRERETlDG8DTkSlQZv3FhaSREREROWEmZkZAODRo0cGjoSIKqLC95bC95rnMS3tYHR17tw5zJo1C2fOnEF+fj6aNWuGDz/8EMHBwVrtJy0tDfPnz8f+/fuRnJwMa2trNGjQACNHjsTbb79dStETERER6Z+JiQkcHByQlpYGALCysoIkSQaOiojKOyEEHj16hLS0NDg4OMDExOSFzzHKQjImJgYBAQGwtLTE4MGDYWtri127diEkJATJycmYNGlSifZz4cIF+Pv74969e+jTpw8GDhyI7OxsXL58Gfv27WMhSUREVA4keM/W/kntP9B7HMaievXqACAXk0RE+uLg4CC/x7yI0RWSBQUFGDduHBQKBWJjY9GyZUsAwMyZM9G2bVtERERg4MCB8PDweO5+srKy8NprrwEAfvvtNzRv3lzjOERERETljSRJcHNzg4uLC/Lz8w0dDhFVEGZmZiUaiSykcyHZuHFjjBs3DiNHjkTVqlV13Y2GY8eOISEhAWPGjJGLSACwt7dHREQERo8ejY0bN2LmzJnP3c/KlStx48YNrFu3TqOIBABTU6OroYmIiCq8ccu0f87Heo+iYjAxMdHqQx8RkT7pvNjOjRs3MHnyZNSsWRNDhgzBsWPH9BLQ8ePHAQD+/v4a2wICAgAAJ06ceOF+tm3bBkmS8MYbbyAuLg5ffPEFFi1ahB9//BF5eXl6iZWIiIiIiKgy0nlYLjU1FZs3b8batWuxbds2bN++HXXr1sXYsWMxevToEl9b+6z4+HgAQP369TW2Va9eHTY2NnKf4uTl5eHPP/+Es7MzvvjiC8yaNQsqlUre7unpiT179qBZs2bF7iM3Nxe5ubny46ysLABAfn6+fBmJQqGAiYkJlEql2v4L2wsKCtSW0DUxMYFCoSi2/dnLUwpHTZ+9DLe4djMzM6hUKiiVSrlNkiSYmpoW215c7MyJOTEn5sScmFPp5PTilQDLi7I4T0RExkrnQtLGxgZhYWEICwvDn3/+ia+//hpbtmxBREQEZs6cib59+2LcuHEIDAzUajWxzMxMAE8uZS2KnZ2d3Kc4d+/ehVKpxL///otPPvkEixYtwogRI5Cfn4/Vq1dj3rx56NevH65cuQJLS8si9zF//nzMmTNHo/3w4cOwsrICANSuXRutWrXCxYsXcePGDbmPt7c3GjZsiF9//RXp6elye8uWLeHh4YHY2Fg8ePBAbu/QoQNcXFxw+PBhtV8avr6+qFKlCg4ePKgWQ+/evZGTk4OYmBi5zdTUFH369EFGRgbOnj0rt9va2qJ79+5ITk7GhQsX5HZnZ2d07NgR8fHxiIuLk9uZE3NiTsyJOTGn0s3pNVQUpX2efvvttzLJg4hIF5LQ4x1tc3NzsXPnTqxbt06+/NTd3R1vvfUWxo4dC3d39xfuw9/fH0eOHEF8fDy8vLw0tru7uyM7O/u5xeTt27flY73//vtYtmyZ2vaQkBBs374d3333HYYPH15sLs+OSNaqVQsZGRmws7MDYOzf+D5Rsb7FZk7MiTkxJ+ZU3nN650vtRyQ/XjVb6+es1XLV1vkDHLQ+hqqfslTP0927d1G1alVkZmbKnz2IiIyFXlecsbCwQEBAAFJSUhAXF4eUlBTcvHkTc+bMQWRkJMaOHYvFixfLI3pFKRyJLK5QzMrKgqOj43PjeHo0MygoSGN7UFAQtm/fjv/973/FFpIWFhawsLDQaDczM9O4QWdxk92LW9CnuPbibvypTbtCoYBCoTn1tbj24mJnTsxJ23bmxJwA5lRcjNq2V6acyjNDnSciImOgt3f0w4cPIzg4GDVr1sRHH30ESZLwn//8B//88w+2b9+OV155BV999RUmTJjw3P0Uzo0sah5kamoqsrOzi5w/+TRra2t5RNLBwUFje2FbTk5OCTIjIiIiIiKip71UIXnr1i3MnTsXnp6e6NWrF3bt2gVfX1/s2rULSUlJmDNnDjw9PTFw4ECcPXsWvXv3xt69e5+7z65duwJ4Upg+KyoqSq3P83Tv3h0A8Pfff2tsK2yrU6fOC/dDRERERERE6nQuJPv27Ys6depg1qxZyMnJwUcffYSEhAQcOnQI/fv3L/ISjY4dO75woRw/Pz94enpiy5YtahPVMzMzERkZCXNzc4wcOVJuT0lJwZUrVzT2O378eADAggULcP/+fbk9NTUVy5cvh0KhwBtvvKFD5kRERERERJWbzhffHzx4EN27d0dYWBhef/31El3H369fP9SoUeP5AZmaYu3atQgICECXLl0wePBg2NrayqOcS5YsURtJnDZtGjZu3Ij169dj9OjRcnvHjh3x4YcfYunSpWjevDn69euH/Px87N27F2lpaYiMjESDBg10TZ+IiIiIiKjS0rmQvHr1apGrqj5P06ZN0bRp0xf28/X1xalTpzBr1ixs27YN+fn5aNasGRYuXIiQkJASH+/TTz9Fs2bN8OWXX2LDhg2QJAmtWrXCV199hddff12r2ImIiIiIiOgJnW//8eabb6J///5FropaaP/+/fjhhx/wzTff6BygscjKyoK9vT2X4CYiInoJ45Zp/xxjvf0HXtPbHdSKxM8eRGTMdJ4juWHDBrU5jEX5448/sHHjRl0PQUREREREREaoVG/o9PjxY94DiYiIiIiIqIJ5qSpPkqQi24UQSE5OxqFDh164uA4RERERERGVL1qNSCoUCpiYmMi39pg9e7b8+Ok/pqamqFu3Ln7//XcMHjy4VAInIiIiIiIiw9BqRLJLly7yKGRsbCxq166tdiuOQiYmJnByckL37t0xbtw4vQRKRERERERExkGrQvL48ePyvxUKBcaMGYOZM2fqOyYiIiIiIiIyYjrPkVSpVPqMg4iIiIiIiMqJUl21lYiIiIiIiCqeEo9Ivvnmm5AkCZGRkXB1dcWbb75ZoudJkoR169bpHCAREREREREZlxIXkhs2bIAkSfjoo4/g6uqKDRs2lOh5LCSJiIiIiIgqlhIXktevXwcAuLu7qz0mIiIiIiKiyqXEhaSHh8dzHxMREREREVHlwMV2iIiIiIiISCslHpG8ceOGzgepXbu2zs8lIiIiIiIi41LiQrJOnTqQJEnrA0iShIKCAq2fR0RERERERMapxIXkyJEjdSokiYiIiIiIqGLR6vYfRERERERERFxsh4iIiIiIiLTCQpKIiIiIiIi0UuJLW998801IkoTIyEi4urrizTffLNHzJEnCunXrdA6QiIiIiIiIjItWcyQlScJHH30EV1fXEs+ZZCFJRERERERUsZS4kLx+/ToAwN3dXe0xERERERERVS4lLiQ9PDye+5iIiIiIiIgqBy62Q0RERERERFp56UJy9+7deO2111C7dm3Y29ujdu3a6N+/P/bs2aOH8IiIiIiIiMjYlPjS1mcVFBRg6NCh2LVrF4QQMDU1RdWqVZGamooff/wR+/btwxtvvIEtW7bA1FTnwxAREREREZGR0XlEcv78+di5cyc6d+6MkydP4vHjx0hJScHjx48RGxsLHx8f7Nq1CwsWLNBnvERERERERGRgOheS69evR8OGDREdHY1OnTpBoXiyK4VCAR8fH0RHR6NBgwb45ptv9BYsERERERERGZ7OhWRKSgr69etX7GWrZmZm6NevH1JSUnQOjoiIiIiIiIyPzoVkrVq1kJ2d/dw+Dx8+RO3atXU9BBERERERERkhnQvJsWPHYvv27cWOON66dQvbtm3D2LFjdQ6OiIiIiIiIjE+Jl1O9ceOG2uPg4GCcPn0arVq1wsSJE+Hj4wNXV1fcuXMHJ0+exPLly+Hj44NBgwbpPWgiIiIiIiIynBIXknXq1IEkSRrtQghMnz69yPYff/wR+/fvR0FBwctFSUREREREREajxIXkyJEjiywkiYiIiIiIqHIpcSG5YcOGUgyDiIiIiIiIygudF9shIiIiIiKiyomFJBEREREREWmlxJe2FuXBgwdYsWIFoqOjcfv2beTm5mr0kSQJCQkJL3MYIiIiIiIiMiI6F5Lp6eno2LEjEhISYGdnh6ysLNjb2yMvLw85OTkAgBo1asDMzExvwRIREREREZHh6Xxp6+zZs5GQkIBvv/0W9+7dAwB88MEHePjwIX755Re0bdsWderUwaVLl/QWLBERERERERmezoXkwYMH4efnh+HDh2vcFqRNmzY4dOgQEhMTMWfOnJcOkoiIiIiIiIyHzoVkSkoKWrVqJT82MTGRL2kFAEdHR/Tq1Qvbt29/uQiJiIiIiIjIqOhcSNrb2yM/P19+7OjoiJs3b6r1sbOzw507d3SPjoiIiIiIiIyOzoWkp6cnEhMT5cetWrXCkSNH8O+//wIAcnJysG/fPtSuXfulgyQiIiIiIiLjoXMh6e/vj6NHj+LRo0cAgLCwMKSlpaFFixYYNGgQmjZtioSEBIwePVpfsRIREREREZER0LmQHD9+PNasWSMXkgMGDMDixYvx8OFD7Nq1C6mpqfjwww8xZcoUvQVLREREREREhqfzfSTd3NwQEhKi1jZp0iRMnDgRGRkZcHFx0VjNlYiIiIiIiMo/nQvJ4piYmMDV1VXfuyUiIiIiIiIj8dKFZEpKCrZu3Yrz588jMzMT9vb2aNWqFQYPHgw3Nzd9xEhERERERERG5KUKyS+//BJTpkxBbm4uhBBy+6ZNmzB9+nQsWbIE77zzzksHSURERERERMZD50Jy69atePfdd1GtWjVMnz4dnTt3hqurK+7cuYPY2FgsX75c3h4cHKzPmImIiIiIiMiAdC4kFy1ahGrVquHChQuoUaOG3O7t7Y0uXbpg9OjRaNWqFRYuXMhCkoiIiIiIqALR+fYfly9fRnBwsFoR+bSaNWti0KBBuHz5ss7BERERERERkfHRuZB0cHCAtbX1c/vY2NjAwcFB10MQERERERGREdK5kAwKCsK+fftQUFBQ5Pb8/Hzs27cPr732ms7BERERERERkfHRuZBctGgRrK2t4e/vj59//llt29mzZ+Hv7w9bW1ssWLDgpYMkIiIiIiIi41HixXY8PT012vLy8vD777+jU6dOMDU1RbVq1ZCRkSGPUrq5ueGVV15BQkKC/iImIiIiIiIigypxIalSqSBJklqbmZkZateurdb27OI7KpXqJcIjIiIiIiIiY1PiQjIxMbEUwyAiIiIiIqLyQuc5kkRERERERFQ5lXhE8nkKCgoQFxeHrKws2NnZwdvbG6ametk1ERERERERGZmXGpG8e/cuxo0bB3t7ezRv3hw+Pj5o3rw5HBwcEBoain///VdfcRIREREREZGR0HnY8O7du2jfvj3++ecfODk5oXPnznBzc0Nqair+97//Ye3atThx4gTOnj0LJycnfcZMREREREREBqTziOTcuXPxzz//YMqUKUhKSsJPP/2E9evX49ChQ0hKSsJHH32E+Ph4/Pe//9VnvERERERERGRgOheSe/fuRbdu3bBw4UJYW1urbbOyssL8+fPRrVs37N69+6WDJCIiIiIiIuOhcyF5+/ZtdOjQ4bl9OnTogNu3b+t6CCIiIiIiIjJCOheS9vb2SEpKem6fpKQk2Nvb63oIIiIiIiIiMkI6F5Jdu3bFjh07EB0dXeT2o0ePYseOHejWrZuuhyAiIiIiIiIjpPOqrbNmzcKBAwcQEBCA3r17o2vXrnB1dcWdO3dw/PhxHDp0CFZWVpg5c6Y+4yUiIiIiIiID07mQbNKkCaKiojB69GgcOHAABw4cgCRJEEIAAOrVq4cNGzagSZMmeguWiIiIiIiIDE/nQhIAfHx8EB8fj9OnT+P8+fPIysqCnZ0dWrVqhU6dOkGSJH3FSUREREREREZC50LyzTffRLNmzfDBBx/Ax8cHPj4++oyLiIiIiIiIjJTOi+1s2bIFaWlp+oyFiIiIiIiIygGdC8l69eohJSVFn7EQERERERFROaBzIfnmm2/iwIEDuHXrlj7jkZ07dw69e/eGg4MDrK2t0b59e2zfvl3n/d27dw/u7u6QJAmBgYF6jJSIiIiIiKhy0XmO5BtvvIGYmBh07NgRU6dORZs2beDq6lrkAju1a9fWat8xMTEICAiApaUlBg8eDFtbW+zatQshISFITk7GpEmTtI43PDwcmZmZWj+PiIiIiIiI1OlcSHp6esq3+3jvvfeK7SdJEgoKCkq834KCAowbNw4KhQKxsbFo2bIlAGDmzJlo27YtIiIiMHDgQHh4eJR4n7t27cKWLVuwYsUKhIeHl/h5REREREREpEnnQnLkyJGlcnuPY8eOISEhAWPGjJGLSACwt7dHREQERo8ejY0bN2LmzJkl2l96ejrefvttjBgxAn369GEhSURERERE9JJ0LiQ3bNigxzD+z/HjxwEA/v7+GtsCAgIAACdOnCjx/saPHw8TExMsX76cl7YSERERERHpgc6FZGmJj48HANSvX19jW/Xq1WFjYyP3eZFNmzbhhx9+wJ49e+Do6MhCkoiIiIiISA9eupDMzc3FwYMHcf78eWRmZsLe3h6tWrVC7969YWFhofX+Cos9e3v7Irfb2dmVqCC8ffs23nvvPQwZMgSvvfaa1nHk5uYiNzdXfpyVlQUAyM/PR35+PgBAoVDAxMQESqUSKpVK7lvYXlBQACGE3G5iYgKFQlFse+F+C5maPjk9z84xLa7dzMwMKpUKSqVSbpMkCaampsW2Fxc7c2JOzIk5MSfmVDo5maGiKIvzRERkrF6qkPzxxx8RGhqK9PR0tV88kiTBxcUFX3/9Nfr16/fSQepi7NixMDMzw+eff67T8+fPn485c+ZotB8+fBhWVlYAnqxG26pVK1y8eBE3btyQ+3h7e6Nhw4b49ddfkZ6eLre3bNkSHh4eiI2NxYMHD+T2Dh06wMXFBYcPH1b7peHr64sqVarg4MGDajH07t0bOTk5iImJkdtMTU3Rp08fZGRk4OzZs3K7ra0tunfvjuTkZFy4cEFud3Z2RseOHREfH4+4uDi5nTkxJ+bEnJgTcyrdnLT/ctdYlfZ5+u2338okDyIiXUji6QpQC0ePHkVgYCBMTEwwYsQIdO7cGa6urrhz5w5iY2OxadMmKJVKREVFoXv37iXe76BBg7Bz507873//Q+vWrTW229rawtHRUe0N91kbN27E6NGjsWPHDgwcOFBuT0xMRN26dREQEICffvrpuXEUNSJZq1YtZGRkwM7ODoCxf+P7RMX6Fps5MSfmxJyYU3nP6Z0vtR+R/HjVbK2fs7b9B1r1nz/AQetjqPopS/U83b17F1WrVkVmZqb82YOIyFjoPCI5a9YsVKlSBWfOnEHTpk3Vto0cORLvvfceOnXqhFmzZmlVSBbOjYyPj9coJFNTU5GdnY22bds+dx/nz58H8KQoLUpUVBQkSUKLFi3UvjF8moWFRZGX5pqZmcHMTP2XoImJCUxMTDT6Fv5CLWn7s/vVpV2hUEChUJS4vbjYmRNz0radOTEngDkVF6O27ZUpp/LMUOeJiMgY6PwOdf78eQwdOlSjiCzUvHlzBAcHY+vWrVrtt2vXrpg/fz4OHz6MwYMHq22LioqS+zxPhw4dkJ2drdGenZ2Nbdu2oWbNmggICEDt2rW1io2IiIiIiIheopC0srKCs7Pzc/u4uLjI8wlLys/PD56entiyZQvee+89+V6SmZmZiIyMhLm5OUaOHCn3T0lJQWZmJtzc3OQFekJCQhASEqKx78TERGzbtg1NmjTB2rVrtYqLiIiIiIiIntD5GpMePXogOjr6uX2io6PRs2dPrfZramqKtWvXQqVSoUuXLggNDcWkSZPQokULXL16FZGRkahTp47cf9q0aWjUqBF2796tSxpERERERESkJZ0LySVLliAtLQ0jR45EcnKy2rbk5GSMGDECGRkZWLJkidb79vX1xalTp9CpUyds27YNq1atgqurK7Zu3YpJkybpGjIRERERERHpgc6rtnbv3h337t3DxYsXYWJigtq1a8urtt64cQNKpRLNmzeHo6Oj+gElCUePHtVL8GUpKysL9vb2XDmNiIjoJYxbpv1zjHXVVrym00eoEuNnDyIyZjrPkTx+/Lj874KCAly7dg3Xrl1T6/PHH39oPE+SJF0PSUREREREREZA50Ly6fsdERERERERUeVRsW7oRERERERERKVOb4XkjRs3EBsbq6/dERERERERkZHSWyG5fv16+Pr66mt3REREREREZKR4aSsRERERERFphYUkERERERERaYWFJBEREREREWlFb4Wkvb09ateura/dERERERERkZHSWyE5ceJEXL9+XV+7IyIiIiIiIiPFS1uJiIiIiIhIK6Yl7Vh4j8i2bdvC0tJSq3tGdunSRfvIiIiIiIiIyCiVuJDs1q0bJEnC5cuX0aBBA/lxSSiVSp0DJCIiIiIiIuNS4kJy5syZkCQJ1apVU3tMRERERERElUuJC8nZs2c/9zERERERERFVDlxsh4iIiIiIiLSicyH54MEDXLt2Dfn5+Wrt27Ztw7Bhw/DWW2/h999/f+kAiYiIiIiIyLiU+NLWZ02dOhWbNm3CnTt3YGZmBgBYtWoVwsPDIYQAAGzduhW//fYbGjZsqJ9oiYiIiIiIyOB0HpE8ceIEevToASsrK7ltwYIFcHd3R2xsLLZv3w4hBBYvXqyXQImIiIiIiMg46DwimZKSgsDAQPnx5cuXkZycjEWLFsHHxwcAsHPnTq3uN0lERERERETGT+cRydzcXJibm8uPT5w4AUmS4O/vL7d5enri1q1bLxchERERERERGRWdC8maNWvi4sWL8uP9+/fDyckJzZs3l9v+/fdf2NjYvFyEREREREREZFR0vrS1V69e+PLLLzF58mRYWlrip59+wsiRI9X6XL16FbVr137pIImIiIiIiMh46FxITps2Dfv27cPSpUsBAG5ubvjkk0/k7WlpaTh9+jTCw8NfPkoiIiIiIiIyGjoXktWrV8elS5dw9OhRAECXLl1gZ2cnb8/IyMDixYsREBDw8lESERERERGR0dC5kASAKlWqoG/fvkVua9y4MRo3bvwyuyciIiIiIiIjpPNiO0RERERERFQ5vdSIpFKpxPbt2xEdHY3bt28jNzdXo48kSfLlr0RERERERFT+6VxIPnz4EP7+/vj5558hhIAkSRBCyNsLH0uSpJdAiYiIiIiIyDjofGnrvHnzcPbsWcyZMwcZGRkQQmD27NlISUnBtm3b4OnpiUGDBhU5SklERERERETll86F5A8//ID27dtjxowZcHJykttdXV0xaNAgxMTEIDo6GosXL9ZLoERERERERGQcdC4kb9y4gfbt2//fjhQKtdHHmjVrok+fPti4cePLRUhERERERERGRedC0traGgrF/z3d3t4eKSkpan2qV6+OGzdu6B4dERERERERGR2dC0kPDw+1IrFp06Y4duyYPCophMDRo0fh5ub28lESERERERGR0dC5kPTz80NMTAwKCgoAAKNGjcKNGzfQoUMHTJkyBT4+Prhw4QLeeOMNvQVLREREREREhqfz7T/GjRuHqlWrIj09HW5ubnjzzTdx/vx5rFy5EhcuXAAAvPHGG5g9e7aeQiUiIiIiIiJjoHMhWb9+fXz00UdqbV988QVmzpyJa9euwcPDA9WrV3/pAImIiIiIiMi46FxIFsfZ2RnOzs763i0REREREREZCZ3nSBIREREREVHlpPOIpKenZ4n6SZKEhIQEXQ9DRERERERERkbnQlKlUkGSJI32zMxM3L9/HwDg5uYGc3NznYMjIiIiIiIi46NzIZmYmPjcbR9++CHu3LmDI0eO6HoIIiIiIiIiMkKlMkeyTp062LZtG+7du4fp06eXxiGIiIiIiIjIQEptsR0zMzP07NkT27dvL61DEBERERERkQGU6qqtjx49wt27d0vzEERERERERFTGSq2QPHnyJL7//nt4e3uX1iGIiIiIiIjIAHRebKd79+5FthcUFODWrVvyYjwzZ87U9RBERERERERkhHQuJI8fP15kuyRJcHR0hL+/Pz788EP07NlT10MQERERERGREXqp+0gSERERERFR5aNzIVkoLS0Nt27dgkqlgru7O6pXr66PuIiIiIiIiMhI6bTYTm5uLhYtWoT69evDzc0Nr776Ktq2bQt3d3dUq1YNH3zwgTxHkoiIiIiIiCoWrQvJ5ORktGnTBtOmTUNCQgLc3NzQtm1btG3bFm5ubrh79y6WL1+OV199FdHR0fLzUlJSeE9JIiIiIiKiCkCrQjI/Px+9e/fGX3/9hSFDhuDy5cu4efMmzp49i7Nnz+LmzZu4fPkyhg0bhrt376J///5ITExEQkICfHx8cOXKldLKg4iIiIiIiMqIVnMkV69ejUuXLmHWrFmYNWtWkX28vb3x3XffoUGDBpg1axaGDRuGxMREZGRkoHXr1noJmoiIiIiIiAxHqxHJ7du3w8vLq0T3hpwxYwbq16+Ps2fP4vHjx4iKikKfPn10DpSIiIiIiIiMg1aF5N9//w1/f39IkvTCvpIkyX1/+eUXdOvWTdcYiYiIiIiIyIhoVUhmZ2fD3t6+xP3t7OxgamoKLy8vrQMjIiIiIiIi46RVIeni4oJ//vmnxP0TEhLg4uKidVBERERERERkvLQqJDt06IBDhw4hNTX1hX1TU1Nx4MAB+Pj46BwcERERERERGR+tCsnx48cjOzsbr7/+OjIyMort9++//+L111/Ho0ePEBYW9tJBEhERERERkfHQ6vYfvr6+GDduHNasWYNGjRohLCwM3bt3R61atQAAycnJOHr0KNasWYOMjAyEhoZykR0iIiIiIqIKRqtCEgBWrlwJOzs7fPbZZ5g/fz7mz5+vtl0IAYVCgcmTJ2tsIyIiIiIiovJP60LSxMQEixcvRmhoKDZs2ICzZ8/KcyarV6+Ojh07YtSoUahfv77egyUiIiIiIiLD07qQLFS/fn3897//1WcsREREREREVA5otdgOEREREREREQtJIiIiIiIi0goLSSIiIiIiItIKC0kiIiIiIiLSCgtJIiIiIiIi0goLSSIiIiIiItIKC0kiIiIiIiLSCgtJIiIiIiIi0goLSSIiIiIiItIKC0kiIiIiIiLSCgtJIiIiIiIi0orRFpLnzp1D79694eDgAGtra7Rv3x7bt28v0XOFEDh06BDefvttNG/eHPb29rCyskKLFi0QGRmJx48fl3L0REREREREFZepoQMoSkxMDAICAmBpaYnBgwfD1tYWu3btQkhICJKTkzFp0qTnPj83Nxe9e/eGhYUFunXrhoCAADx+/BhRUVGYPn069uzZg+PHj8PKyqqMMiIiIiIiIqo4jK6QLCgowLhx46BQKBAbG4uWLVsCAGbOnIm2bdsiIiICAwcOhIeHR7H7MDExwbx58/DOO+/A0dFRbs/Pz8cbb7yBffv24csvv8SUKVNKOx0iIiIiIqIKx+gubT127BgSEhIwdOhQuYgEAHt7e0RERCAvLw8bN2587j7MzMwwffp0tSKysH3atGkAgBMnTug9diIiIiIiosrA6ArJ48ePAwD8/f01tgUEBAB4uSLQzMwMAGBqanSDsUREREREROWC0VVT8fHxAID69etrbKtevTpsbGzkPrr45ptvABRdqD4tNzcXubm58uOsrCwATy6Pzc/PBwAoFAqYmJhAqVRCpVLJfQvbCwoKIISQ201MTKBQKIptL9xvocJit6CgoETtZmZmUKlUUCqVcpskSTA1NS22vbjYmRNzYk7MiTkxp9LJyQwVRVmcJyIiY2V0hWRmZiaAJ5eyFsXOzk7uo61Dhw5h9erVaNSoEd56663n9p0/fz7mzJmj0X748GF5kZ7atWujVatWuHjxIm7cuCH38fb2RsOGDfHrr78iPT1dbm/ZsiU8PDwQGxuLBw8eyO0dOnSAi4sLDh8+rPZLw9fXF1WqVMHBgwfVYujduzdycnIQExMjt5mamqJPnz7IyMjA2bNn5XZbW1t0794dycnJuHDhgtzu7OyMjh07Ij4+HnFxcXI7c2JOzIk5MSfmVLo5vYaKorTP02+//VYmeRAR6UIST3/1aAT8/f1x5MgRxMfHw8vLS2O7u7s7srOztS4mz507Bz8/P5iamuLkyZNo0qTJc/sXNSJZq1YtZGRkwM7ODoCxf+P7RMX6Fps5MSfmxJyYU3nP6Z0vtR+R/HjVbK2fs7b9B1r1nz/AQetjqPopS/U83b17F1WrVkVmZqb82YOIyFgY3Yhk4UhkcYViVlaWxiI6L/K///0P/v7+UCgUiIqKemERCQAWFhawsLDQaDczM5PnWRYyMTGBiYmJRt/i5mEW1/7sfnVpVygUUCg0p74W115c7MyJOWnbzpyYE8CciotR2/bKlFN5ZqjzRERkDIzuHb1wbmRR8yBTU1ORnZ1d5PzJ4vzvf/9Dz549oVKpEBUVhTZt2ugtViIiIiIiosrI6ArJrl27AngyF/FZUVFRan1epLCIVCqV+Omnn9CuXTv9BUpERERERFRJGV0h6efnB09PT2zZskVtonpmZiYiIyNhbm6OkSNHyu0pKSm4cuWKxqWwv/32G3r27ImCggIcOnQIHTp0KKsUiIiIiIiIKjSju/je1NQUa9euRUBAALp06YLBgwfD1tYWu3btQlJSEpYsWYI6derI/adNm4aNGzdi/fr1GD16NADg7t276NmzJ+7fv4/AwEAcOXIER44cUTuOg4MDJk6cWHaJERERERERVRBGV0gCT5YVP3XqFGbNmoVt27YhPz8fzZo1w8KFCxESEvLC52dlZeHevXsAgJ9++gk//fSTRh8PDw8WkkRERERERDowykISANq2bYtDhw69sN+GDRuwYcMGtbY6derAyO5qQkREREREVGEY3RxJIiIiIiIiMm4sJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrLCSJiIiIiIhIKywkiYiIiIiISCssJImIiIiIiEgrpoYOgIiIqKQSvGdr1b9enHb9iYiIqGQ4IklERERERERaYSFJREREREREWmEhSURERERERFrhHEkiIjKIccu0f87Heo+CiIiIdMFCshKaNipT6+fM32hfCpEQkbbF1JqJpREFlQdcaIiIiIwJC0kjww+VRET6wy/OiIiISgfnSBIREREREZFWWEgSERERERGRVnhpKxERURkri4WGeFkvERGVJo5IEhERERERkVZYSBIREREREZFWeGkrEVE5ou0tIABgbfsPtH4OL3EkIiKi5+GIJBEREREREWmFI5JUrvEG3drRdvENjkoRERERUVE4IklERERERERa4YhkOafLfCnoMF+KyJjo8v++so9GExEREekTC0mqVHhfNSIiIiKil8dCkogMqixuzE5ERERE+sU5kkRERERERKQVjkgSGQFdRuXWTNR3FEREREREJcNCkowGL3EkIiIiIiofWEgSlVNcsZeIiIiIDIWFJJXMXkm7/q+J0onDELTNHag4+TN37VSU3AH+zGurouRfmXMnIiKtcLEdIiIiIiIi0goLSSIiIiIiItIKC0kiIiIiIiLSCgtJIiIiIiIi0goLSSIiIiIiItIKV20lokph2qhMrfrPH1BKgRARERFVAByRJCIiIiIiIq0YbSF57tw59O7dGw4ODrC2tkb79u2xfft2rfaRm5uLTz75BPXr14elpSVq1KiB0NBQpKWllVLUREREREREFZ9RXtoaExODgIAAWFpaYvDgwbC1tcWuXbsQEhKC5ORkTJo06YX7UKlUeO211xAVFYX27dvjjTfeQHx8PNauXYujR4/i559/hrOzcxlkQ0REREREVLEY3YhkQUEBxo0bB4VCgdjYWHz99df49NNP8ccff6BBgwaIiIhAUlLSC/ezceNGREVFYciQIThz5gwWLFiAXbt2YeXKlbh27RpmzJhRBtkQERERERFVPEZXSB47dgwJCQkYOnQoWrZsKbfb29sjIiICeXl52Lhx4wv3s2bNGgDA/PnzIUmS3B4WFgZPT09s3rwZOTk5eo+fiIiIiIioojO6QvL48eMAAH9/f41tAQEBAIATJ048dx+PHz/GL7/8Am9vb3h4eKhtkyQJPXv2xMOHD/G///1PP0ETERERERFVIkY3RzI+Ph4AUL9+fY1t1atXh42NjdynOAkJCVCpVEXu4+l9x8fHo3PnzkX2yc3NRW5urvw4M/PJrQPu3r2L/Px8AIBCoYCJiQmUSiVUKpXct7C9oKAAQgi53cTEBAqFotj2/Px85D02e25uz3qgzH1xp2dzy8vS+jlZj7Trr7p/X87paaamT/7LFRQUaLTnPZagLW3zL4vcASD/33+LzdXMzAwqlQpKpVJu0/a8A2Vz7nXNHXjypY2pqalGroXthT83FSl35b17Rb4XPO89Iu+xdm/Dxvozr7x3T+v3vbzH2n+Xacw/80+/lz+tqPeCsvh/X5a5FyrJ+15F+plX3b9f5Pvbi973Cr3oPeLu3bsAoPazQ0RkLCRhZO9O/v7+OHLkCOLj4+Hl5aWx3d3dHdnZ2XJhV5QzZ86gU6dOGDZsGDZt2qSxfc2aNQgNDcXSpUvxwQcfFLmP2bNnY86cObonQkRERKQHycnJqFmzpqHDICJSY3QjksZi2rRp+PDDD+XHKpUKd+/eRdWqVdXmXJY3WVlZqFWrFpKTk2FnZ2focMpUZc4dqNz5M3fmztwrl4qSvxACDx48QI0aNQwdChGRBqMrJO3t7QGg2BHHrKwsODo6vvQ+nu5XFAsLC1hYWKi1OTg4PPe45YmdnV25/uX6Mipz7kDlzp+5M/fKpjLnDlSM/J/3WYWIyJCMbrGdp+cvPis1NRXZ2dnFzn0s5OnpCYVCUexcyufNwyQiIiIiIqLnM7pCsmvXrgCAw4cPa2yLiopS61OcKlWqoG3btoiLi9O456QQAkeOHIG1tTVeffVVPUVNRERERERUeRhdIenn5wdPT09s2bIFFy5ckNszMzMRGRkJc3NzjBw5Um5PSUnBlStXNC5jDQ0NBfBkruPT6wmtXr0a165dw7Bhw1ClSpXSTcYIWVhYYNasWRqX7VYGlTl3oHLnz9yZe2VTmXMHmD8RUVkwulVbASAmJgYBAQGwtLTE4MGDYWtri127diEpKQlLlizBpEmT5L6jR4/Gxo0bsX79eowePVpuV6lU6N27N6KiotC+fXt07doV//zzD3744QfUqVMHv/zyC5ydnQ2QHRERERERUflmdCOSAODr64tTp06hU6dO2LZtG1atWgVXV1ds3bpVrYh8HoVCgb1792L27NlIT0/HZ599htOnT+Ott97C2bNnWUQSERERERHpyChHJImIiIiIiMh4GeWIJBERERERERkvFpJERERERESkFRaSREREREREpBUWkkRERERERKQVFpKVSHHrKnG9JSIiIiIi0gYLyUpEkiTcunULAJCXl4dHjx7J7VSx8UsEosqHP99ERFSaePuPSkAIgf3792PdunX4888/kZ2djebNm6NZs2Zo3bo1WrZsCS8vL1hYWEAIwcKygoqPj4eLiwsePHiAKlWqoGrVqoYOiQyIP+uVE887ERHpCwvJSmDmzJlYsmQJrKysUKtWLeTn5yMvLw/JyckQQqBFixYYOHAgRo4cCVdXV0OHWyqUSiUUCkWl+wCVm5uLHTt2YOXKlTh//jwUCgXq168PLy8vtG7dGh07dsQrr7wCW1tbQ4daKoQQUCqVMDExqXTnvlBaWhrS09NRtWpVPHjwAM7OznBwcDB0WFSKlEol4uPjce/ePQBPfg7q1atXYd/fiYjIMFhIVnCJiYlo0qQJunXrhk8//RQNGzZERkYGkpOTkZCQgNjYWERFRSE+Ph4tWrRAZGQkevXqBZVKBYWi/F/5nJycjFq1asmPVSoVhBAwMTExYFRlZ9KkSVi+fDk8PDxQv359mJmZ4f79+/jzzz+RlZWFWrVqoW/fvnjzzTfRunVrQ4erVwkJCahXr578WKVSQaVSwdTU1IBRlZ2UlBRMnz4dR44cwa1bt2Bra4u6deuiUaNGaNeuHXx8fNCsWTNYWFgYOlS9Khxxq2znu1BcXBymTZuGgwcPIi8vDxYWFnB0dESdOnXQoUMHBAYGomPHjrC2tjZ0qEREVM6xkKzg5s6di2XLlmH79u3w8/NDQUGB2gerrKwsXLp0Cdu3b8fy5cvh6uqKQ4cOoWXLloYLWk8SExPh6ekJf39/DBkyBH379lW7nFOpVEKSJCgUCvnDZ15eHszNzQ0Ytf5cv34djRs3Rr9+/fDll1/C2dkZDx48wP3795GSkoLY2Fjs3r0b586dg7u7O+bMmYORI0dWiEvfEhISUL9+fTRs2BBvvvkmhg8fjurVq8vblUolAMDExETO9+HDh8jOzkbVqlXLffGRmpqK119/Hb/88gsCAwNhY2MDhUKBpKQkXLx4ETk5OWjcuDGCg4Mxbtw4uLm5GTpkvcnKysL9+/dRu3Ztue3p812R3b59G4GBgbh06RJGjBiBatWqwdTUFJcuXcLJkyeRlZUFBwcHvP766wgLC0Pbtm0NHbJe3bt3DxcvXkT79u0r3BckRERGSVCFNnLkSOHm5iZSU1OFEEKoVCq1v5+2detWYW9vL9q3b1+mMZaWyMhIIUmS/KdatWpi1KhR4sCBAyI/P1+tb25urhBCiK+++kr4+fmJuLg4Q4SsV//973+Fk5OTOHr0qBBCiIKCArXt+fn54tq1a2LZsmXC2dlZSJIkfvrpJ0OEqncLFy5UO/eSJIlu3bqJTZs2iby8PLW+T5/7tm3bit9//90QIevVzJkzhb29vVi2bJncdu/ePZGcnCxiY2PFjBkzROPGjYVCoRAdOnQQp06dEkIU/b5Q3nz44YdCkiTRuXNn8c0334iHDx+qbc/PzxdKpVKtLSUlRdy5c6fc5z9jxgzh6Ogo1q5dK7fl5uaKvLw8cePGDbF69WrRqVMnoVAoROPGjcX+/fuFEBXjvAshxOTJk4UkSeKVV14Rc+fOFX/99VexfQtzvnr1qrhw4YLG+wIREb0YC8kKbsmSJUKSJLFjxw657dkPUU9/iBgzZoyoVq2auHLlSpnFWFr69u0rbG1txdq1a8WoUaOElZWVXFR4eXmJyZMni19//VXtOQMGDBCSJIns7GwDRa0/77zzjnBwcBDJyclCiOd/WDx8+LBwc3MT3t7e4vHjx2UVYql54403RJUqVcSWLVvEzJkzRePGjeVzb2ZmJgYPHiwX2IUq0rlv3Lix6Nu3r0hLSxNCaJ77x48fiz/++EMuuho2bCju3LljiFD1rlmzZhpfIrz++uviwIEDav0KX5OsrCwxbNgwERAQoPEFU3nTokULERgYKJ/Lon7m09PTxRdffCGcnJyEra2t+Pvvv8s6zFLTsmVLoVAohJOTk3zufX19xerVq8XNmzc1+mdnZ4shQ4aI9u3bs5AkItIBC8kKLjY2VtjY2IiGDRuKc+fOqW1TqVRyUVn4d2RkpLC2ttYosMqbtLQ00bZtW+Hu7i635eTkiM2bNws/Pz+1D5lt2rQRn3/+udi+fbtwc3MT/fr1M2Dk+rNu3TohSZL48ssv5Q+UBQUFxRaU06ZNEzY2NuV+RC49PV107NhRVK9eXW7Lzc0Vhw4dEm+99ZZwc3OTz72zs7P4+OOPxaZNmyrMuU9NTRWNGjUSPXv2fGHf/Px88fnnnwtJksRHH31UBtGVroSEBOHs7Cy6du0qYmNjxdtvvy1q164tn29HR0fxzjvvqP0fP3/+vHB0dBRdu3Y1XOB6kJGRIV599dUSXVGSn58vtm7dKiRJEmFhYWUQXem7fv26qFGjhmjfvr24cOGCmDt3rujSpYuwtLQUkiQJW1tbERwcLHbv3i3+/fdfIYQQv/76q3BychK+vr4Gjp6IqHxiIVmBFRYMa9asESYmJkKSJBEaGiqio6NFVlaWRv9Hjx6JIUOGiKpVq5Z1qHp348YN0blzZ9GnTx8hxP9dvljo9u3bYsmSJWqjF4UfOJ4duSiv/vzzT+Hu7i6cnJzEvn371LapVCr5UtfCLxGWLl0qLC0txZkzZ8o8Vn1KTU0VgYGBomfPniI/P19jpCE9PV18++23IigoSFhbW6t9qVDez33hl0MDBw4UdnZ24pdffpHbn720+WnNmjUT3bt3Fw8ePCirUEvF4cOHhSRJYtKkSXLb/fv3xbZt28SgQYOEvb29fK7r1asnFixYID766CMhSZJ8mWd5VPheP27cOCFJkti3b5/8pdHzRlk7deok2rRpIxdW5dmxY8eEQqEQ7733ntz24MEDERUVJT744APRvHlz+dy7u7uLiRMnirCwsHJ/7omIDImFZCWQnZ0tVq1aJVxcXIQkScLFxUW89tprIjIyUkRHR4u7d++KX375RYSFhQlzc3O1D2HlVV5enjh27Jg4c+aMXCgVfph+9tLeuLg4MWHCBCFJknBycjJEuHpX+MHy0KFDombNmkKSJBEQECC2b98u7t69q9E/OztbBAcHV4gvEYQQIj4+Xvz1118a5/7Z0dgbN26ITz75RFhZWQlHR0dDhFoqvv76a3me4LPzxJRKpdprkZmZKXr37i2aNm1qiFD16syZM6JWrVri66+/FkIIjSIqKSlJfPHFF6Jbt25qXyBUlHN/8OBBIUmSaNCggYiKilLbVlBQoHbe79+/L/r37y8aNGhgiFD17sKFC6J+/fri888/F0JozglPSUkR33//vRg1apSoW7duhTv3RESGwEKyAnv2Q3N2drZYtmyZ6NChgzA1NZV/kSoUCmFubi4kSRJjxowpci5JefVs0Vio8Jv6wg8bv/76q7CyshKhoaFlGV6py8/PFzt37lT7Nr5FixZiwoQJYteuXeLy5cvihx9+ECEhIcLExER8/PHHhg651BUWlYXn/uzZsxXy3C9YsEAoFAohSZIYNWqUiIqKEjk5OfL2wveH6Oho4e7uLsaNG2eoUPUmLy9PXLp0SV5cTIjiR2OvXr0qhg8fLiRJEhMmTCjLMEvV5s2bhaurqzw/cNu2bWrzfgvP+4EDB0SNGjUqxHkvlJWVpfFFWVG/A27duiXCw8OFJEninXfeKavwiIgqHN7+oxLKyMjA1atX8fPPP+PkyZNQKpVo0KABGjVqhLfeesvQ4elF4U3ohRBQqVQvXPb/3XffxZdffolz585VuPspFtqzZw/Wrl2LqKgo+XYIkiRBCAEzMzNMmDABH330UaW7aXl4eDhWrlxZYc69+P+3M7l//z6++eYbLFy4EOnp6TAxMUHr1q3RqVMn+Pr6wt7eHufOncOKFSvw4MEDHDt2DM2aNTN0+KXq2feDTz75BLNnz64w5x4AcnNzsWvXLixduhS///47AMDFxQVdu3ZFz549YWFhgb/++gvr1q2DhYUFDh8+jCZNmhg46tL37LmfNWsW5s6dW6HOPRFRWWMhWUGlpaXhzz//xNWrV5GdnY22bduiYcOGqFatmkZRlZubq3bPLVEB7iOojaysLISFhSEmJgapqamGDkeviiqkU1NTERMTg9OnT8PMzAw1atRAw4YN0a9fPwNGahgPHz7EhAkTcPDgQaSlpRk6HL149uf38ePH2LhxI7799lucPXtWo3/jxo0xbdo0DBs2rCzDLBUqlQoKhUL+Iqkoha/P1atX0a9fPxQUFCAhIaGMIy19Qgjs27cPX3/9NQ4fPoyCggK17R06dMCMGTPQq1cvA0VoONeuXUP//v3x4MEDXL9+3dDhEBGVWywkK6BDhw5h3rx5Gh8anZyc4Ofnh5CQEPTr1w9mZmbytsIPYBVBcUX00zeaf/aDZm5uLtLS0lCrVi1Dha032pzLZ1+H8v4lgq7/j7OysmBnZ1cKERmXGzduIDo6Gn/99ReqV68OFxcX+Pj4wMvLy9Chlbm4uDj0798f/fr1w6JFiwwdjt6IJ1NW1H4OMjMzcfz4cVy7dg01atSAjY0N2rRpAxcXFwNGajjXr19HWFgYunbtiunTpxs6HCKicouFZAWTnJyMbt264eHDhxg9ejR8fX1x7do1nD9/Hn/88QcuXryI3NxcNG7cGBERERg4cCDMzc3LfQFR6HlFdI8ePeQiurCgrOiKK6yUSiUUCgUkSUJBQUGFfD1KUlQWFBRAkqQXXvpcXvz000/466+/cOHCBbi6uuLVV1+Fl5cXatWqhapVq6p9eVTRPJ27i4sL2rRpAy8vL3h4eKBq1arype7Pvs9VhP//xY3AKpVKSJJUYb4kLM7zRqBL43lERPQEC8kKZsaMGVi5ciXWrl2LAQMGqG27efMmzpw5gx9//BFbtmwBACxYsABTp041RKh6V9IiukmTJpg2bZpcRFeU0dg7d+5g0aJF8Pf3R7t27eDg4CBvK/wxrwhfFhSlMucOAPfv38f8+fOxePFimJiYyHNggSdfonTq1Amvv/46goKC4OTkJG+rCF8glTT3/v37q/2/qAhFxLPvXUWNRj7bLoSAUqks98UzUPL8n5WXlwdzc/PSDo+IqOIr5cV8qIy1a9dOdOvWTaSnpwshhNrKpE87duyYaNWqlbCwsBDr1q0r6zBLxfTp04Wjo6PYtWuXxrbk5GSxbds2MWzYMHn10oULFxogytIzc+ZMIUmSqFu3rujTp49YvHix+PXXX8Xjx4/V+hXe/kEIIWJiYsShQ4cMEa5eVebchRBi0aJFwsrKSrz++usiJiZGxMXFia1bt4o5c+aIvn37CmdnZyFJknjllVfE7t27DR2uXlXm3FeuXCmCg4PF/v37Ne4BqlQqi121uqKo7PkTERkaC8kK5MGDB6JHjx6iYcOG4uHDh0II9aXPC29WXuj3338Xjo6OIigoSN5enlXmIloIIVq2bCnMzc1F+/bt5du51KlTRwwbNkysXbtWXL58Wa3/w4cPRVBQkFAoFGq3hSiPKnPuQgjh4eEh+vTpIzIyMjS23bp1S+zfv1+EhobKt/1Zs2aNAaIsHZU59zp16ghJkoSlpaVo166d+M9//iPOnj2r8V5eeD/Nhw8fis8++0wcO3bMEOHqXWXPn4jI0FhIVjAfffSRkCSpyALp6V+uhQXla6+9Jho0aCASExPLLMbSUNmL6Bs3bog6deqI1q1bi7y8PHH27Fnxn//8R7Ro0UJIkiRMTExE8+bNRXh4uNi+fbvIzMwUv/76q6hevbro16+focN/KZU5dyGEuHz5srCxsRERERFyW1GjMbm5ueLAgQPC09NTODk5iTNnzpR1qHpXmXP/66+/hCRJ4tVXXxU9e/aUr7SwsbERAQEBYvny5RpfoJw8eVJIkiQ6depkoKj1p7LnT0RkDFhIVjA3b94UzZo1E5IkiXfffVf89ttvGiMuhd/OZmZmikGDBonatWsbIlS9q6xFtBBC/PLLL8LJyUmMGjVKCCFEQUGBUCqV4s6dO+LQoUNi/PjxwsPDQ0iSJKysrESXLl2En5+fkCRJ7Nu3z7DBv6TKnLsQQvz999+iZs2aIiQkRAjx5Of72S9Rnv7/v2fPngpzaXdlzv37778XkiSJpUuXCiGEiIuLEwsXLhQtW7aUiyo3NzcxZMgQ8e2334q7d++KTz/9VEiSJPbv32/g6F9eZc+fiMgYsJCsgHbv3i3q1q0rf1s7d+5cERMTIxITE9WKyk2bNglnZ2cRFhZmwGj1pzIX0fHx8WLAgAFi8+bNRW7Py8sTiYmJ4rvvvhPBwcHCyclJSJIkHB0dyzhS/avMuRdq166dsLW1FQcPHtTYVlhIFRZY//77r6hbt64YOHBgmcZYWipr7qtXrxaSJBWZ96+//io++OADUatWLbmoatCggahevbqwt7cv+2BLQWXPn4jIGLCQrCCevTTz33//FZMnTxa1a9cWkiQJFxcX0b17dzF8+HARGhoqRowYISwsLETDhg3FlStXDBS1/lXWIloIIe7fv1/kPLGnFX6g/uqrr4QkSeKdd94pi9BKXWXNvfDn/pdffhHu7u5CkiQxceJE8csvv2h8iVK48NCZM2dEjRo1xHvvvVfm8epTZc/97Nmz4oMPPhD//POPWvvTcnJyxP79+8WoUaOEvb29kCRJhIeHl3W4elfZ8yciMhYsJCuQwl+iycnJ8ofmP//8U8yfP18EBATIRaUkScLJyUl0795d/PXXX4YMWS8qexFd1PzOwss7izNlyhQhSZL47bffSjO0UleZc39aQUGB2LBhg3BzcxOSJIkmTZqIDz74QOzYsUNcunRJfj1u3rwphgwZIkxNTStM/pU59wcPHojc3Nwitz37szFhwgQhSZI4f/58GURWNip7/kREhsb7SFYABQUFOH36NL755htcvXoVkiTBysoKbdq0QXBwMFq1agUhBJKTk5GTk4Nr166hYcOGqFWrFkxNTSvEveQKc7h58yZq1KgBhUKBv/76C/v378fx48dx+fJlJCcnAwAcHR3RsmVLfP7552jSpImBI9ePwvxTU1Ph4uKidh81pVIJhUIhn+ObN2+iT58+uH37NtLT0w0Vst5U5tyflZ6ejhUrVmD79u24evUqrKys4O7uDhsbGzg5OeHKlStIT0/HmDFjsHLlSkOHq1eVOffiFP5sJCQkICQkBJmZmYiPjzd0WGWmsudPRFTaWEhWAEuWLMHcuXPx4MEDeHl5wcTEBHFxcfL2xo0b45133sHAgQPh4uJiwEj1r7IX0c/mr1AoUKVKFbRo0QJvvPEGOnbsqPGcjIwMfPfdd6hRowZCQkIMELV+VObcnyWEgEqlgomJCXJychAfH49z587h9OnT+OWXX3DlyhU4OzujVq1aGDt2LIYPHw5ra2tDh60XlTn3ktq/fz+CgoIwZcoULFy40NDhlLnKnj8RUWlhIVnOXb9+Hc2aNcMrr7yCjRs3wtzcHK6urkhNTcW+ffuwY8cOHD9+HADg6+uLhQsX4tVXXzVs0HpUmYto4MX5N2zYEOPGjcOQIUNQvXp1uT0vLw+mpqZqo3flTWXOvSRUKhUeP34Mc3NzZGZmIjU1tcKMwL9IZcm9pF+E3blzBz/99BP69esHJyenMoisbFT2/ImIDK6ML6UlPfvPf/4jXFxcRHR0tNz27NyQixcvipEjRwpLS0vh7e0t/ve//5V1mKXi2rVrwtraWnTu3Flcu3ZN3Lx5U+Tn54vk5GSxcuVK4evrK88J7d69uzh37pyhQ9YrbfL38/OrMPPChKjcuQshxKNHj8SVK1fEo0ePNLYplUq194Bn3w+eN3+0PGDuRef+IgUFBaUQUdmq7PkTERkbFpLl3IABA4Snp6dISkoSQvzf7S1UKpXGL85ly5YJSZLE6NGjyzzO0lCZi2ghXi7/ohapKU8qc+5CCDF//nzx6quvisjISHHs2DFx69YtjZ/3Z++hmJaWJr8/lGfM/fm5P6ui5C4E8yciMjYsJMu5uXPnCkmSxKVLl4rt8/QHqjfeeEPUrl1bJCQklEV4paoyF9FCVO78K3PuQgj5dhempqaiatWqol+/fuKLL74Qv/76a5G3QcnOzhaTJ08WY8aMKfejcsxdt9wrwohcZc+fiMjYmBr60lp6Ob6+vgCAYcOG4dNPP4WPjw/Mzc01+imVSpiYmMDb2xuHDh1CdnZ2WYeqd61atcLu3bvlXExNn/x3liQJJiYmAP5vDs3777+PkydP4tixY7h27Ro8PT0NFre+VOb8K3PuV69eRWZmJjp06IChQ4fiyJEjOHv2LPbv34/atWujW7du6NGjB1q1agV3d3c4ODjgr7/+wpo1a9CtW7dyPTeUueuee+HPRXlV2fMnIjJGLCTLufbt2+PDDz/E0qVLER4ejgkTJmDgwIFwdXWV+xR+uL537x5u3rwJa2trNG/e3IBR60dlLqKByp1/Zc796tWrePz4Mfz9/TFhwgT07dsXcXFxOHv2LI4dO4Zdu3Zh8+bNaNy4Mbp3747AwEAcPXoUWVlZGDdunKHDfynMvXLmDjB/IiKjZOARUdKTr776StSrV09IkiTc3d1FeHi4OHDggLh48aK4dOmSuHXrlvj444+FpaWl+PDDDw0drl4UFBSISZMmCUmSRKNGjcSKFStEampqkX3v3r0rRo4cKZydncs4ytJTmfOvzLnv2LFDSJIktm3bptael5cn4uPjxc6dO8X7778vWrRoIczNzYW1tbWwsrISjo6OBopYf5h75cxdCOZPRGSMWEhWECqVSly9elVMmTJF1KpVS16x0tXVVdSsWVOYmJgISZLE0KFDRXJysqHD1avKWEQ/rTLnXxlzV6lU4u+//xbXrl2THz8rOztb/P777+L7778X/v7+QpIk8e6775Z1qHrH3Ctn7kIwfyIiY8T7SFZADx8+xK+//ooff/wRt2/fRlpaGuzs7BAcHIw33ngDlpaWhg5Rr4QQ+Oeff7BmzRps3boVN2/eBAC4uLjAzMwMKSkpUKlUGDJkCBYuXIiaNWsaOGL9qsz5V+bciyKKuK/ee++9hxUrVuC3335Dq1atDBRZ6WPulTN3gPkTERkKC8kKLj8/H2ZmZoYOo8xUtiL6WZU5/8qc+7NUKhUUCgUSExPx2muv4d69e7hx44ahwyoTzL1y5g4wfyKissbFdiq4ylREAoC1tTV8fX3h6+tb6YpooHLnX5lzf1bh6qS3bt1Cfn4+3nnnHQNHVHaYe+XMHWD+RERljSOSREQVlBACN2/ehJOTE6ytrQ0dTpli7pUzd4D5ExGVFRaSREREREREpJXye3dmIiIiIiIiMggWkkRERERERKQVFpJERERERESkFRaSREREREREpBUWkkRERERERKQVFpJERERERESkFRaSREREREREpBUWkkRERERERKQVFpJERERERESklf8HFQ3iRGMwM58AAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 504x360 with 1 Axes>"
+       "<Figure size 700x500 with 1 Axes>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from qiskit.visualization import plot_histogram\n",
-    "\n",
     "binary_prob = [quasi_dist.binary_probabilities() for quasi_dist in result.quasi_dists]\n",
     "plot_histogram(\n",
     "    binary_prob + [ideal_distribution],\n",
@@ -2690,21 +279,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can confirm this by computing the [Hellinger fidelity](https://qiskit.org/documentation/stubs/qiskit.quantum_info.hellinger_fidelity.html) between each set of results and the ideal distribution (higher is better, and 1 is perfect fidelity)."
+    "We can confirm this by computing the [Hellinger fidelity](https://docs.quantum-computing.ibm.com/api/qiskit/quantum_info) between each set of results and the ideal distribution (higher is better, and 1 is perfect fidelity)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.927\n",
-      "0.938\n",
-      "0.951\n"
+      "0.958\n",
+      "0.961\n",
+      "0.963\n"
      ]
     }
    ],
@@ -2717,16 +306,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0.4.0'"
+       "'0.15.0'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2739,47 +328,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.20.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.9.1</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.18.3</td></tr><tr><td><code>qiskit-aqua</code></td><td>0.9.5</td></tr><tr><td><code>qiskit</code></td><td>0.34.0</td></tr><tr><td><code>qiskit-nature</code></td><td>0.3.0</td></tr><tr><td><code>qiskit-finance</code></td><td>0.2.1</td></tr><tr><td><code>qiskit-optimization</code></td><td>0.2.3</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.2.1</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.10</td></tr><tr><td>Python compiler</td><td>Clang 13.0.0 (clang-1300.0.29.3)</td></tr><tr><td>Python build</td><td>main, Jan 15 2022 11:48:00</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Apr 13 19:59:49 2022 BST</td></tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
-      ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "'0.45.0'"
       ]
      },
+     "execution_count": 30,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from qiskit.tools.jupyter import *\n",
+    "import qiskit\n",
     "\n",
-    "%qiskit_version_table\n",
-    "%qiskit_copyright"
+    "qiskit.version.get_version_info()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "primitives",
+   "display_name": "env",
    "language": "python",
-   "name": "primitives"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2791,7 +365,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,

From 64601835aea5be69ca255774021d71f814907e9e Mon Sep 17 00:00:00 2001
From: Sanket Panda <pandasa123@gmail.com>
Date: Wed, 22 Nov 2023 18:31:52 -0500
Subject: [PATCH 2/7] Fix style checker

---
 docs/tutorials/user-transpiled-circuits.ipynb | 25 ++++++++-----------
 1 file changed, 10 insertions(+), 15 deletions(-)

diff --git a/docs/tutorials/user-transpiled-circuits.ipynb b/docs/tutorials/user-transpiled-circuits.ipynb
index 4a0c1bc74..163d41c9e 100644
--- a/docs/tutorials/user-transpiled-circuits.ipynb
+++ b/docs/tutorials/user-transpiled-circuits.ipynb
@@ -12,7 +12,7 @@
     "\n",
     "For example, we may know better than the transpiler in some cases, or want to target a specific subset of qubits on a specific device. In this tutorial, we'll disable automatic transpilation to test the performance of different transpiler settings. This example will take you through the full process of creating, transpiling, and submitting circuits.\n",
     "\n",
-    "## Setup"
+    "## Setup\n"
    ]
   },
   {
@@ -30,11 +30,7 @@
     "from qiskit.visualization import plot_histogram\n",
     "\n",
     "# Qiskit Runtime\n",
-    "from qiskit_ibm_runtime import QiskitRuntimeService, Batch, Sampler\n",
-    "\n",
-    "# General imports\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")"
+    "from qiskit_ibm_runtime import QiskitRuntimeService, Batch, Sampler"
    ]
   },
   {
@@ -64,10 +60,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "## Transpiling circuits for IBM Quantum devices\n",
     "\n",
-    "In the following code cell, we create a small circuit that our transpiler will try to optimize. In this example, we create a circuit that carries out Grover's algorithm, with an oracle that marks the state `111`. We then simulate the ideal distribution (what we'd expect to measure if we ran this on a perfect quantum computer, an infinite number of times) for comparison later. "
+    "In the following code cell, we create a small circuit that our transpiler will try to optimize. In this example, we create a circuit that carries out Grover's algorithm, with an oracle that marks the state `111`. We then simulate the ideal distribution (what we'd expect to measure if we ran this on a perfect quantum computer, an infinite number of times) for comparison later.\n"
    ]
   },
   {
@@ -93,7 +88,7 @@
     "qc.h([0, 1, 2])\n",
     "qc = qc.compose(GroverOperator(oracle))\n",
     "\n",
-    "qc.draw(output='mpl', style='iqp')"
+    "qc.draw(output=\"mpl\", style=\"iqp\")"
    ]
   },
   {
@@ -125,7 +120,7 @@
    "source": [
     "Next, we transpile the circuits for our backend. We're going to compare the performance of the transpiler with `optimization_level` set to `0` (lowest) against `3` (highest). The lowest optimization level just does the bare minimum needed to get the circuit running on the device; it maps the circuit qubits to the device qubits, and adds swaps gates to allow all 2-qubit operations. The highest optimization level is much smarter and uses lots of tricks to reduce the overall gate count. Since multi-qubit gates have high error rates, and qubits decohere over time, the shorter circuits should give better results.\n",
     "\n",
-    "In the following cell, we transpile `qc` for both values of `optimization_level`, print the number of CNOT gates, and add the transpiled circuits to a list. Some of the transpiler's algorithms are randomized, so we set a seed for reproducibility."
+    "In the following cell, we transpile `qc` for both values of `optimization_level`, print the number of CNOT gates, and add the transpiled circuits to a list. Some of the transpiler's algorithms are randomized, so we set a seed for reproducibility.\n"
    ]
   },
   {
@@ -159,7 +154,7 @@
    "source": [
     "Since CNOTs usually have a high error rate, the circuit transpiled with `optimization_level=3` should perform much better.\n",
     "\n",
-    "Another way we can improve performance is through [dynamic decoupling](https://docs.quantum-computing.ibm.com/api/qiskit-ibm-provider/qiskit_ibm_provider.transpiler.passes.scheduling.PadDynamicalDecoupling#paddynamicaldecoupling), where we apply a sequence of gates to idling qubits. This cancels out some unwanted interactions with the environment. In the following cell, we add dynamic decoupling to the circuit transpiled with `optimization_level=3`, and add it to our list."
+    "Another way we can improve performance is through [dynamic decoupling](https://docs.quantum-computing.ibm.com/api/qiskit-ibm-provider/qiskit_ibm_provider.transpiler.passes.scheduling.PadDynamicalDecoupling#paddynamicaldecoupling), where we apply a sequence of gates to idling qubits. This cancels out some unwanted interactions with the environment. In the following cell, we add dynamic decoupling to the circuit transpiled with `optimization_level=3`, and add it to our list.\n"
    ]
   },
   {
@@ -207,7 +202,7 @@
     }
    ],
    "source": [
-    "circ_dd.draw(output='mpl', style='iqp', idle_wires=False)"
+    "circ_dd.draw(output=\"mpl\", style=\"iqp\", idle_wires=False)"
    ]
   },
   {
@@ -218,7 +213,7 @@
     "\n",
     "At this point, we have a list of circuits transpiled for our system. In the following cell, we create an instance of the sampler primitive, and start a batched job using the context manager (`with ...:`), which automatically opens and closes the Batch for us. This is where we pass the `skip_transpilation=True` argument.\n",
     "\n",
-    "Within the context manager, we sample the circuits and store the results to `result`."
+    "Within the context manager, we sample the circuits and store the results to `result`.\n"
    ]
   },
   {
@@ -241,7 +236,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we can plot the results from the device runs against the ideal distribution. You can see the results with `optimization_level=3` are closer to the ideal distribution due to the lower gate count, and `optimization_level=3 + dd` is even closer due to the dynamic decoupling we applied."
+    "Finally, we can plot the results from the device runs against the ideal distribution. You can see the results with `optimization_level=3` are closer to the ideal distribution due to the lower gate count, and `optimization_level=3 + dd` is even closer due to the dynamic decoupling we applied.\n"
    ]
   },
   {
@@ -279,7 +274,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can confirm this by computing the [Hellinger fidelity](https://docs.quantum-computing.ibm.com/api/qiskit/quantum_info) between each set of results and the ideal distribution (higher is better, and 1 is perfect fidelity)."
+    "We can confirm this by computing the [Hellinger fidelity](https://docs.quantum-computing.ibm.com/api/qiskit/quantum_info) between each set of results and the ideal distribution (higher is better, and 1 is perfect fidelity).\n"
    ]
   },
   {

From f5e505b2c78a71bb47ba64b3a978fcab5ac355ac Mon Sep 17 00:00:00 2001
From: Sanket Panda <pandasa123@gmail.com>
Date: Tue, 28 Nov 2023 09:55:42 -0700
Subject: [PATCH 3/7] Updated names

---
 docs/tutorials/chsh_with_estimator.ipynb  | 2 +-
 docs/tutorials/grover_with_sampler.ipynb  | 2 +-
 docs/tutorials/qaoa_with_primitives.ipynb | 6 +++---
 docs/tutorials/vqe_with_estimator.ipynb   | 8 ++++----
 4 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/tutorials/chsh_with_estimator.ipynb b/docs/tutorials/chsh_with_estimator.ipynb
index c1c5f05aa..69b03e9b6 100644
--- a/docs/tutorials/chsh_with_estimator.ipynb
+++ b/docs/tutorials/chsh_with_estimator.ipynb
@@ -322,7 +322,7 @@
    "id": "ace7dc90",
    "metadata": {},
    "source": [
-    "## Analyze the results\n",
+    "## Step 4: Post-process, return result in classical format.\n",
     "\n",
     "After running the circuits, we need to build the CHSH witness functions. We first build the quantities $\\langle AB \\rangle$, $\\langle Ab \\rangle$, $\\langle aB \\rangle$, and $\\langle ab \\rangle$, by looking at the parity of the outcomes for the four families of circuits we built (two measurement bases for each of the two qubits). Then we use those quantities to build the witness functions as defined previously."
    ]
diff --git a/docs/tutorials/grover_with_sampler.ipynb b/docs/tutorials/grover_with_sampler.ipynb
index 3a98086ae..f5d5fa865 100644
--- a/docs/tutorials/grover_with_sampler.ipynb
+++ b/docs/tutorials/grover_with_sampler.ipynb
@@ -295,7 +295,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Analyze the results"
+    "## Step 4: Post-process, return result in classical format."
    ]
   },
   {
diff --git a/docs/tutorials/qaoa_with_primitives.ipynb b/docs/tutorials/qaoa_with_primitives.ipynb
index 38ea936fa..23e1b18f2 100644
--- a/docs/tutorials/qaoa_with_primitives.ipynb
+++ b/docs/tutorials/qaoa_with_primitives.ipynb
@@ -74,7 +74,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Map the problem to a quantum-native format\n",
+    "## Step 1: Generate quantum circuits and operators.\n",
     "\n",
     "To demonstrate max-cut, we'll create a graph using the [rustworkx library](https://github.com/Qiskit/rustworkx), and create Pauli Hamiltonian that encodes the cost in a manner such that the minimum expectation value of the operator corresponds to the maximum number of edges between the nodes in two different groups."
    ]
@@ -179,7 +179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optimize the circuits and operators\n",
+    "## Step 2: Optimize problem for quantum execution.\n",
     "\n",
     "We can schedule a series of [qiskit.transpiler](https://docs.quantum-computing.ibm.com/api/qiskit/transpiler) passes to optimize our circuit for a selected backend. This includes a few components:\n",
     "\n",
@@ -273,7 +273,7 @@
    "id": "b58c33dc",
    "metadata": {},
    "source": [
-    "## Execute using a quantum primitive function"
+    "## Step 3: Execute using Qiskit Primitives."
    ]
   },
   {
diff --git a/docs/tutorials/vqe_with_estimator.ipynb b/docs/tutorials/vqe_with_estimator.ipynb
index 6dc49c243..1aa5a960e 100644
--- a/docs/tutorials/vqe_with_estimator.ipynb
+++ b/docs/tutorials/vqe_with_estimator.ipynb
@@ -94,7 +94,7 @@
    "id": "988ee237",
    "metadata": {},
    "source": [
-    "## Map the problem to a quantum-native format\n",
+    "## Step 1: Generate quantum circuits and operators.\n",
     "\n",
     "Here we define the problem instance for our VQE algorithm. Although the problem in question can come from a variety of domains, the form for execution through Qiskit Runtime is the same. Qiskit provides a convenience class for expressing Hamiltonians in Pauli form, and a collection of widely used ansatz circuits in the [`qiskit.circuit.library`](https://docs.quantum-computing.ibm.com/api/qiskit/circuit_library).\n",
     "\n",
@@ -180,7 +180,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optimize the circuits and operators\n",
+    "## Step 2: Optimize problem for quantum execution.\n",
     "\n",
     "We can schedule a series of [`qiskit.transpiler`](https://docs.quantum-computing.ibm.com/api/qiskit/transpiler) passes to optimize our circuit for a selected backend. This includes a few components:\n",
     "\n",
@@ -274,7 +274,7 @@
    "id": "b4d480b3",
    "metadata": {},
    "source": [
-    "## Execute using a quantum primitive function\n",
+    "## Step 3: Execute using Qiskit Primitives.\n",
     "\n",
     "Like many classical optimization problems, the solution to a VQE problem can be formulated as minimization of a scalar cost function.  By definition, VQE looks to find the ground state solution to a Hamiltonian by optimizing the ansatz circuit parameters to minimize the expectation value (energy) of the Hamiltonian.  With the Qiskit Runtime [`Estimator`](https://docs.quantum-computing.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.Estimator) directly taking a Hamiltonian and parameterized ansatz, and returning the necessary energy, the cost function for a VQE instance is quite simple:"
    ]
@@ -486,7 +486,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Analyze the results"
+    "## Step 4: Post-process, return result in classical format."
    ]
   },
   {

From f81c30fad36492b31eb74e1624a7778eb3b22b53 Mon Sep 17 00:00:00 2001
From: Sanket Panda <pandasa123@gmail.com>
Date: Tue, 28 Nov 2023 09:59:22 -0700
Subject: [PATCH 4/7] Updated section names for transpiled tutorial

---
 docs/tutorials/user-transpiled-circuits.ipynb | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/docs/tutorials/user-transpiled-circuits.ipynb b/docs/tutorials/user-transpiled-circuits.ipynb
index 163d41c9e..297a8da3b 100644
--- a/docs/tutorials/user-transpiled-circuits.ipynb
+++ b/docs/tutorials/user-transpiled-circuits.ipynb
@@ -60,7 +60,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Transpiling circuits for IBM Quantum devices\n",
+    "## Step 1: Generate quantum circuits and operators.\n",
     "\n",
     "In the following code cell, we create a small circuit that our transpiler will try to optimize. In this example, we create a circuit that carries out Grover's algorithm, with an oracle that marks the state `111`. We then simulate the ideal distribution (what we'd expect to measure if we ran this on a perfect quantum computer, an infinite number of times) for comparison later.\n"
    ]
@@ -118,6 +118,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Step 2: Optimize problem for quantum execution.\n",
+    "\n",
     "Next, we transpile the circuits for our backend. We're going to compare the performance of the transpiler with `optimization_level` set to `0` (lowest) against `3` (highest). The lowest optimization level just does the bare minimum needed to get the circuit running on the device; it maps the circuit qubits to the device qubits, and adds swaps gates to allow all 2-qubit operations. The highest optimization level is much smarter and uses lots of tricks to reduce the overall gate count. Since multi-qubit gates have high error rates, and qubits decohere over time, the shorter circuits should give better results.\n",
     "\n",
     "In the following cell, we transpile `qc` for both values of `optimization_level`, print the number of CNOT gates, and add the transpiled circuits to a list. Some of the transpiler's algorithms are randomized, so we set a seed for reproducibility.\n"
@@ -209,7 +211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Run transpiled circuits using Qiskit Runtime\n",
+    "## Step 3: Execute using Qiskit Primitives.\n",
     "\n",
     "At this point, we have a list of circuits transpiled for our system. In the following cell, we create an instance of the sampler primitive, and start a batched job using the context manager (`with ...:`), which automatically opens and closes the Batch for us. This is where we pass the `skip_transpilation=True` argument.\n",
     "\n",
@@ -236,6 +238,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Step 4: Post-process, return result in classical format.\n",
+    "\n",
     "Finally, we can plot the results from the device runs against the ideal distribution. You can see the results with `optimization_level=3` are closer to the ideal distribution due to the lower gate count, and `optimization_level=3 + dd` is even closer due to the dynamic decoupling we applied.\n"
    ]
   },

From a67f469d242e07eaf6519a68a8b89f35c79d29e8 Mon Sep 17 00:00:00 2001
From: Sanket Panda <pandasa123@gmail.com>
Date: Tue, 28 Nov 2023 10:01:09 -0700
Subject: [PATCH 5/7] Updated grover

---
 docs/tutorials/grover_with_sampler.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/tutorials/grover_with_sampler.ipynb b/docs/tutorials/grover_with_sampler.ipynb
index f5d5fa865..4b03c1866 100644
--- a/docs/tutorials/grover_with_sampler.ipynb
+++ b/docs/tutorials/grover_with_sampler.ipynb
@@ -82,7 +82,7 @@
    "id": "d4845f4d",
    "metadata": {},
    "source": [
-    "## Map the problem to a quantum-native format\n",
+    "## Step 1: Generate quantum circuits and operators.\n",
     "\n",
     "Grover's algorithm requires an [oracle](https://learning.quantum-computing.ibm.com/course/fundamentals-of-quantum-algorithms/grovers-algorithm) that specifies one or more marked computational basis states, where \"marked\" means a state with a phase of -1.  A controlled-Z gate, or its multi-controlled generalization over $N$ qubits, marks the $2^{N}-1$ state (`'1'`*$N$ bit-string).  Marking basis states with one or more `'0'` in the binary representation requires applying X-gates on the corresponding qubits before and after the controlled-Z gate; equivalent to having an open-control on that qubit.  In the following code, we define an oracle that does just that, marking one or more input basis states defined through their bit-string representation.  The `MCMT` gate is used to implement the multi-controlled Z-gate."
    ]
@@ -260,7 +260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optimize the circuits and operators\n",
+    "## Step 2: Optimize problem for quantum execution.\n",
     "\n",
     "For this example, the circuit the operators are simple, so no optimizations are needed."
    ]
@@ -271,7 +271,7 @@
    "id": "c5edec73",
    "metadata": {},
    "source": [
-    "## Execute using a quantum primitive function\n",
+    "## Step 3: Execute using Qiskit Primitives.\n",
     "\n",
     "Amplitude amplification is a sampling problem that is suitable for execution with the [`Sampler`](https://docs.quantum-computing.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.Sampler#sampler) runtime primitive. Because we have a single circuit, we instantiate the [`Batch`](https://docs.quantum-computing.ibm.com/run/run-primitives-batch) context manager to run our non-iterative workload."
    ]

From d98a0d5107e0ee04a7a3a743ad773df9790fc11c Mon Sep 17 00:00:00 2001
From: Sanket Panda <pandasa123@gmail.com>
Date: Tue, 28 Nov 2023 10:05:20 -0700
Subject: [PATCH 6/7] Fixed consistency

---
 docs/tutorials/chsh_with_estimator.ipynb  | 13 +++----------
 docs/tutorials/qaoa_with_primitives.ipynb |  2 +-
 2 files changed, 4 insertions(+), 11 deletions(-)

diff --git a/docs/tutorials/chsh_with_estimator.ipynb b/docs/tutorials/chsh_with_estimator.ipynb
index 69b03e9b6..317b2bd19 100644
--- a/docs/tutorials/chsh_with_estimator.ipynb
+++ b/docs/tutorials/chsh_with_estimator.ipynb
@@ -146,7 +146,7 @@
    "id": "66d5e2e2",
    "metadata": {},
    "source": [
-    "## Map the problem to a quantum-native format"
+    "## Step 1: Generate quantum circuits and operators."
    ]
   },
   {
@@ -240,7 +240,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Optimize the circuits and operators\n",
+    "## Step 2: Optimize problem for quantum execution.\n",
     "\n",
     "For this example, the circuit and the operators are simple, so no optimizations are needed."
    ]
@@ -250,7 +250,7 @@
    "id": "b7194399",
    "metadata": {},
    "source": [
-    "## Execute using a quantum primitive function\n",
+    "## Step 3: Execute using Qiskit Primitives.\n",
     "\n",
     "In order to execute the entire experiment in one call to the [`Estimator`](https://docs.quantum-computing.ibm.com/api/qiskit-ibm-runtime/qiskit_ibm_runtime.Estimator#estimator) we need to batch the circuit and operators together, repeating each for the requested `number_of_phases` points."
    ]
@@ -447,13 +447,6 @@
     "\n",
     "qiskit.version.get_version_info()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/tutorials/qaoa_with_primitives.ipynb b/docs/tutorials/qaoa_with_primitives.ipynb
index 23e1b18f2..d89393eb9 100644
--- a/docs/tutorials/qaoa_with_primitives.ipynb
+++ b/docs/tutorials/qaoa_with_primitives.ipynb
@@ -420,7 +420,7 @@
    "id": "d162e690",
    "metadata": {},
    "source": [
-    "## Analyze the results"
+    "## Step 4: Post-process, return result in classical format."
    ]
   },
   {

From 7488f8fb78e084961aa4219caeab0abe27ced624 Mon Sep 17 00:00:00 2001
From: Sanket Panda <pandasa123@gmail.com>
Date: Tue, 28 Nov 2023 19:09:11 -0700
Subject: [PATCH 7/7] Final updates

---
 docs/tutorials/chsh_with_estimator.ipynb      | 2 +-
 docs/tutorials/grover_with_sampler.ipynb      | 2 +-
 docs/tutorials/qaoa_with_primitives.ipynb     | 2 +-
 docs/tutorials/user-transpiled-circuits.ipynb | 2 +-
 docs/tutorials/vqe_with_estimator.ipynb       | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/tutorials/chsh_with_estimator.ipynb b/docs/tutorials/chsh_with_estimator.ipynb
index 317b2bd19..fe4e2e380 100644
--- a/docs/tutorials/chsh_with_estimator.ipynb
+++ b/docs/tutorials/chsh_with_estimator.ipynb
@@ -146,7 +146,7 @@
    "id": "66d5e2e2",
    "metadata": {},
    "source": [
-    "## Step 1: Generate quantum circuits and operators."
+    "## Step 1: Map classical inputs to a quantum problem"
    ]
   },
   {
diff --git a/docs/tutorials/grover_with_sampler.ipynb b/docs/tutorials/grover_with_sampler.ipynb
index 4b03c1866..a1cbd2787 100644
--- a/docs/tutorials/grover_with_sampler.ipynb
+++ b/docs/tutorials/grover_with_sampler.ipynb
@@ -82,7 +82,7 @@
    "id": "d4845f4d",
    "metadata": {},
    "source": [
-    "## Step 1: Generate quantum circuits and operators.\n",
+    "## Step 1: Map classical inputs to a quantum problem\n",
     "\n",
     "Grover's algorithm requires an [oracle](https://learning.quantum-computing.ibm.com/course/fundamentals-of-quantum-algorithms/grovers-algorithm) that specifies one or more marked computational basis states, where \"marked\" means a state with a phase of -1.  A controlled-Z gate, or its multi-controlled generalization over $N$ qubits, marks the $2^{N}-1$ state (`'1'`*$N$ bit-string).  Marking basis states with one or more `'0'` in the binary representation requires applying X-gates on the corresponding qubits before and after the controlled-Z gate; equivalent to having an open-control on that qubit.  In the following code, we define an oracle that does just that, marking one or more input basis states defined through their bit-string representation.  The `MCMT` gate is used to implement the multi-controlled Z-gate."
    ]
diff --git a/docs/tutorials/qaoa_with_primitives.ipynb b/docs/tutorials/qaoa_with_primitives.ipynb
index d89393eb9..835495c25 100644
--- a/docs/tutorials/qaoa_with_primitives.ipynb
+++ b/docs/tutorials/qaoa_with_primitives.ipynb
@@ -74,7 +74,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 1: Generate quantum circuits and operators.\n",
+    "## Step 1: Map classical inputs to a quantum problem\n",
     "\n",
     "To demonstrate max-cut, we'll create a graph using the [rustworkx library](https://github.com/Qiskit/rustworkx), and create Pauli Hamiltonian that encodes the cost in a manner such that the minimum expectation value of the operator corresponds to the maximum number of edges between the nodes in two different groups."
    ]
diff --git a/docs/tutorials/user-transpiled-circuits.ipynb b/docs/tutorials/user-transpiled-circuits.ipynb
index 297a8da3b..fe78ab888 100644
--- a/docs/tutorials/user-transpiled-circuits.ipynb
+++ b/docs/tutorials/user-transpiled-circuits.ipynb
@@ -60,7 +60,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 1: Generate quantum circuits and operators.\n",
+    "## Step 1: Map classical inputs to a quantum problem\n",
     "\n",
     "In the following code cell, we create a small circuit that our transpiler will try to optimize. In this example, we create a circuit that carries out Grover's algorithm, with an oracle that marks the state `111`. We then simulate the ideal distribution (what we'd expect to measure if we ran this on a perfect quantum computer, an infinite number of times) for comparison later.\n"
    ]
diff --git a/docs/tutorials/vqe_with_estimator.ipynb b/docs/tutorials/vqe_with_estimator.ipynb
index 1aa5a960e..57987e522 100644
--- a/docs/tutorials/vqe_with_estimator.ipynb
+++ b/docs/tutorials/vqe_with_estimator.ipynb
@@ -94,7 +94,7 @@
    "id": "988ee237",
    "metadata": {},
    "source": [
-    "## Step 1: Generate quantum circuits and operators.\n",
+    "## Step 1: Map classical inputs to a quantum problem\n",
     "\n",
     "Here we define the problem instance for our VQE algorithm. Although the problem in question can come from a variety of domains, the form for execution through Qiskit Runtime is the same. Qiskit provides a convenience class for expressing Hamiltonians in Pauli form, and a collection of widely used ansatz circuits in the [`qiskit.circuit.library`](https://docs.quantum-computing.ibm.com/api/qiskit/circuit_library).\n",
     "\n",