Part_3_Qermit.ipynb

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    "# Developing and Executing Error-Mitigated NISQ Algorithms Across Devices and Simulators\n",
    "\n",
    "Cristina Cirstoiu &rarr; <cristina.cirstoiu@quantinuum.com>, \n",
    "\n",
    "Dan Mills &rarr; <daniel.mills@quantinuum.com>\n",
    "\n",
    "Quantinuum\n",
    "\n",
    "Notebooks and slides &rarr; https://github.com/CQCL/IEEE_Quantum_Week"
   ]
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    "## Part Three\n",
    "\n",
    "We have covered:\n",
    "1. Explore the tools available in TKET, providing a comprehensive, feature-focused tour of the platform.\n",
    "2. Introduce noise, error mitigation and Qermit.\n",
    "\n",
    "We will cover:\n",
    "1. Introduction and getting started with Qermit.\n",
    "2. Out of the box error-mitigation with Qermit; `MitRes` and `MitEx`.\n",
    "3. Advanced use of Qermit to fine tune and combine error-mitigation schemes.\n",
    "4. Developing new error-mitigation schemes."
   ]
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    "# Error-Mitigation & NISQ Devices\n",
    "\n",
    "Noise:\n",
    "- Noisy Intermediate-Scale Quantum &rarr; low numbers of qubits and high error rates.\n",
    "- Error correction, break encryption, Grover's search &rarr; ❌\n",
    "\n",
    "Error-Mitigation:\n",
    "- Trade reduced noise for increased circuit shots.\n",
    "- Moderate to no increase in qubit requirements, unlike with error-correction."
   ]
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   "source": [
    "## What is Qermit?\n",
    "\n",
    "An open-source python package for the design and execution of digital error-mitigation."
   ]
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   "source": [
    "By being implemented using TKET, Qermit is platform-agnostic, so may be used:\n",
    "- With a wide range of quantum hardware.\n",
    "- In conjunction with several common quantum software development kits."
   ]
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    "A common interface to the following protocols, and several variations upon them, is presently available: \n",
    "- ZNE, CDR, and PEC; which mitigate for errors in expectation value calculations.\n",
    "- Error-mitigation based on frame randomisation, and correction through characterisation of State Preparation And Measurement (SPAM) errors. "
   ]
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    "Graph based architecture takes advantage of the modularity of error-mitigation schemes:\n",
    "- Sub-processes: circuit execution, circuit modification, model fitting, etc. \n",
    "- Vertices may be amended to adapt the protocol. \n",
    "- Sub-graphs and graphs may be reused and combined. "
   ]
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    "## Getting Started\n",
    "<p><center> <code> pip install qermit </code> </center></p>\n",
    "<p>Documentation and examples &rarr; <a href=\"www.qerm.it\">www.qerm.it</a>.</p>\n",
    "<p>Repository and manual &rarr; <a href=\"https://github.com/CQCL/qermit\">https://github.com/CQCL/Qermit</a>.</p>"
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    "# Shot Count Experiment\n",
    "\n",
    "There are two types of error mitigation methods in Qermit: \n",
    "- `MitRes` &rarr; modify the distribution of shots retrieved from a backend.\n",
    "- `MitEx` &rarr; return a modified expectation value estimator of some observable.\n",
    "\n",
    "MitRes and MitEx object may perform any modification of this form, or none.\n",
    "\n",
    "- Now &rarr; experiments where the output is a collection of shots.\n",
    "- Later &rarr; experiments where the output is an expectation value."
   ]
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    "We will see experiments:\n",
    "- Conducted in raw TKET, and the equivalent in Qermit.\n",
    "- With and without errors.\n",
    "- Where errors have been mitigated by a Qermit `MitRes`."
   ]
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    "## Ideal Shot Count\n",
    "\n",
    "Consider again the circuit to generate the bell pair:\n",
    "\\\\[ \\left| \\psi \\right\\rangle = \\frac{1}{\\sqrt{2}} \\left( \\left| 00 \\right\\rangle + \\left| 11 \\right\\rangle\\right) \\\\]"
   ]
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    "from pytket import Circuit\n",
    "from pytket.circuit.display import render_circuit_jupyter\n",
    "\n",
    "circ = Circuit(2,2).H(0).CX(0,1).measure_all()\n",
    "render_circuit_jupyter(circ)"
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   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_state_probs(state):\n",
    "    state_dict = {'State':[i for i in range(len(result_state))], 'Probability':abs(state)**2}\n",
    "    state_df = pd.DataFrame(state_dict)\n",
    "    sns.catplot(x='State', y='Probability', kind='bar', data=state_df, aspect = 3, height=2)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_counts(counts):\n",
    "    counts_record = [{\"State\":state, \"Count\":count} for state, count in counts.items()]\n",
    "    count_df = pd.DataFrame().from_records(counts_record)\n",
    "    sns.catplot(x='State', y='Count', kind='bar', data=count_df, aspect = 3, height=2)\n",
    "    plt.show()"
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bUTN16lTV1dUpOztbDodDY8aM0fr169t1PAAAoHNr13Ocznbs2DF9++238ng8uvbaa9WnT5+O7u2KwnOcAP/iOU5A5xVIz3Fq1xWns/Xp00c33nhjR/YCAAAQ0Hz6yRUAAICuiOAEAABgEsEJAADAJIITAACASQQnAAAAkwhOAAAAJhGcAAAATCI4AQAAmERwAgAAMIngBAAAYBLBCQAAwCSCEwAAgEkEJwAAAJMITgAAACYRnAAAAEwiOAEAAJhEcAIAADCJ4AQAAGASwQkAAMAkghMAAIBJBCcAAACTCE4AAAAmEZwAAABMIjgBAACYRHACAAAwieAEAABgEsEJAADAJIITAACASQQnAAAAkwhOAAAAJhGcAAAATCI4AQAAmERwAgAAMIngBAAAYBLBCQAAwCSCEwAAgEkEp7Pk5+fLarUqIiJC48eP1/bt2/3dEgAACCAEp/9Zu3atsrKylJOTo/Lyco0ePVo2m021tbX+bg0AAAQIgtP/LFu2TLNnz9bMmTMVHx+vgoIC9ejRQ6+//rq/WwMAAAGC4CTJ5XKprKxMKSkpxlhwcLBSUlJUWlrqx84AAEAg6ebvBgLB4cOH5Xa7FRMT4zUeExOjr776qs1tmpqa1NTUZCzX19dLkpxO56VrVJK7qfGSzg9cqS71uXc5nDjl9ncLQEC6XOd3r169FBQUdN4agpOP8vLy9Mwzz7Qaj4uL80M3AKJWzvF3CwAulbyoy7Kb+vp6WSyW89YQnCRFR0crJCRENTU1XuM1NTWKjY1tc5tFixYpKyvLWG5ubtbRo0fVt2/fC6ZVXPmcTqfi4uK0f//+C55kAK48nONdU69evS5YQ3CSFBYWpsTERNntdk2ZMkXSmSBkt9uVkZHR5jbh4eEKDw/3Guvdu/cl7hSBxmKx8EcV6MQ4x/FTBKf/ycrK0owZMzRu3DglJSVpxYoVamho0MyZM/3dGgAACBAEp/+ZOnWq6urqlJ2dLYfDoTFjxmj9+vWtbhgHAABdF8HpLBkZGef8aA44W3h4uHJyclp9XAugc+Acx7kEeTwej7+bAAAAuBLwAEwAAACTCE4AAAAmEZzQZR05ckT9+/fXvn37Lut+XS6XrFarvvjii8u6X6Ar8df5LUnTpk3T0qVLL/t+cXkQnNBlPfvss7rvvvtktVqNserqaqWmpqpHjx7q37+/FixYoB9//LHdc+fn58tqtSoiIkLjx4/X9u3bjXVhYWF68skntXDhwo44DABtaOv8fvzxx5WYmKjw8HCNGTPGp3n37NmjtLQ0Wa1WBQUFacWKFa1qFi9erGeffdb4KS50LgQndEk//PCDXnvtNT366KPGmNvtVmpqqlwul0pKSrRmzRoVFhYqOzu7XXOvXbtWWVlZysnJUXl5uUaPHi2bzaba2lqj5qGHHtLWrVu1Z8+eDjsmAGe0dX63eOSRRzR16tSLmnvIkCF67rnnzvnLEiNGjNDQoUP1xhtv+LwfBDAP0AWtW7fO069fP6+xjz/+2BMcHOxxOBzG2EsvveSxWCyepqYm03MnJSV50tPTjWW32+0ZMGCAJy8vz6tu4sSJnsWLF/t4BADOpa3z+2w5OTme0aNHX/R+rrnmGs/y5cvbXPfMM894fvnLX170PhB4uOKELumzzz5TYmKi11hpaalGjhzp9dBTm80mp9Np+sqQy+VSWVmZUlJSjLHg4GClpKSotLTUqzYpKUmfffbZRRwFgLa0dX5fbklJSdq+fbuampr82gc6HsEJXdL333+vAQMGeI05HI5WT4pvWXY4HKbmPXz4sNxud5vz/HSOAQMG6Pvvv29v6wAuoK3z+3IbMGCAXC6X6b8duHIQnNAlNTY2KiIiwq89dO/eXT/88INfewA6o0A5vyVxjndCBCd0SdHR0Tp27JjXWGxsrGpqarzGWpbPdRNoW/OGhIS0Oc9P5zh69Kj69evX3tYBXEBb5/fldvToUUniHO+ECE7oksaOHau9e/d6jSUnJ2v37t1e337buHGjLBaL4uPjTc0bFhamxMRE2e12Y6y5uVl2u13JycletZWVlRo7duxFHAWAtrR1fl9ulZWVGjhwoKKjo/3aBzoewQldks1m0549e7z+Vzpp0iTFx8dr+vTp2rlzpzZs2KDFixcrPT29XT/0mZWVpVdeeUVr1qzRl19+qblz56qhoUEzZ870qvvss880adKkDjsmAGe0dX5L0rfffquKigo5HA41NjaqoqJCFRUVcrlcpud2uVxe2x04cEAVFRX69ttvveo4vzsxf3+tD/CXpKQkT0FBgdfYvn37PHfddZene/funujoaM/vfvc7z+nTp431VVVVHkmezZs3n3fulStXegYNGuQJCwvzJCUlebZt2+a1vqSkxNO7d2/PDz/80GHHA+D/a+v8njBhgkdSq1dVVZVRI8mzevXqc87b8jfgp68JEyYYNY2NjZ6oqChPaWlpBx8VAkGQx+Px+CeyAf5VVFSkBQsWqLKyUsHB5i6+bt68WQ888ID++9//qk+fPj7ve+rUqRo9erSefvppn+cAcG6+nN9VVVW67rrrtHfvXl177bU+7/ull17Su+++q08++cTnORC4uvm7AcBfUlNT9c033+jAgQOKi4sztc3HH3+sp59++qJCk8vl0siRIzV//nyf5wBwfr6e37/97W8vKjRJUmhoqFauXHlRcyBwccUJAADAJG4OBwAAMIngBAAAYBLBCQAAwCSCEwAAgEkEJwAAAJMITgAAACYRnAAAAEwiOAHoEurq6jR37lwNGjRI4eHhio2Nlc1m0+effy5JCgoK0nvvvdfuea1Wq1asWNGxzQIIWDw5HECXkJaWJpfLpTVr1mjIkCGqqamR3W7XkSNH/N0agCsITw4H0OkdP35cffr00ZYtWzRhwoRW661Wq77//ntj+ZprrtG+ffv03XffKSsrS9u2bVNDQ4OGDx+uvLw8paSkSJJuu+02FRcXe83V8id169atWrRokb744gtFR0fr/vvvV15ennr27HkJjxTApcZHdQA6vcjISEVGRuq9995TU1NTq/U7duyQJK1evVqHDh0ylk+ePKm7775bdrtd//73vzV58mTde++9qq6uliS98847GjhwoHJzc3Xo0CEdOnRIkvTdd99p8uTJSktL065du7R27Vpt3bpVGRkZl+mIAVwqXHEC0CX84x//0OzZs9XY2KiEhARNmDBB06ZN06hRoySducfp3Xff1ZQpU847z4gRIzRnzhwjBFmtVmVmZiozM9OomTVrlkJCQvTyyy8bY1u3btWECRPU0NCgiIiIDj8+AJcHV5wAdAlpaWk6ePCgPvjgA02ePFlbtmxRQkKCCgsLz7nNyZMn9eSTT2r48OHq3bu3IiMj9eWXXxpXnM5l586dKiwsNK50RUZGymazqbm5WVVVVR18ZAAuJ24OB9BlRERE6M4779Sdd96pJUuWaNasWcrJydHDDz/cZv2TTz6pjRs36s9//rOGDRum7t2768EHH5TL5Trvfk6ePKnHHntMjz/+eKt1gwYN6ohDAeAnBCcAXVZ8fLzxCILQ0FC53W6v9Z9//rkefvhh3X///ZLOBKJ9+/Z51YSFhbXaLiEhQXv37tWwYcMuWe8A/IOP6gB0ekeOHNHtt9+uN954Q7t27VJVVZXWrVun559/Xvfdd5+kM/cq2e12ORwOHTt2TJJ07bXX6p133lFFRYV27typX/3qV2pubvaa22q16l//+pcOHDigw4cPS5IWLlyokpISZWRkqKKiQt98843ef/99bg4HOgGCE4BOLzIyUuPHj9fy5ct16623asSIEVqyZIlmz56tVatWSZKWLl2qjRs3Ki4uTmPHjpUkLVu2TH369NHNN9+se++9VzabTQkJCV5z5+bmat++fRo6dKj69esnSRo1apSKi4v1n//8R7fccovGjh2r7OxsDRgw4PIeOIAOx7fqAAAATOKKEwAAgEkEJwAAAJMITgAAACYRnAAAAEwiOAEAAJhEcAIAADCJ4AQAAGASwQkAAMAkghMAAIBJBCcAAACTCE4AAAAmEZwAAABM+n+QEchjQp8fhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pytket.extensions.qiskit import AerBackend\n",
    "\n",
    "n_shots = 100000\n",
    "ideal_backend = AerBackend()\n",
    "result = ideal_backend.run_circuit(circ, n_shots=n_shots)\n",
    "plot_counts(result.get_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d1fde4de",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qermit import MitRes, CircuitShots\n",
    "\n",
    "ideal_mitres = MitRes(ideal_backend)\n",
    "\n",
    "circ_shots_list = [CircuitShots(circ, n_shots)]\n",
    "\n",
    "result_list = ideal_mitres.run(circ_shots_list)\n",
    "result_counts = result_list[0].get_counts()\n",
    "plot_counts(result_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a405ed2",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## MitRes TaskGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a623e12e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.44.0 (20200408.0750)\n",
       " -->\n",
       "<!-- Title: MitRes Pages: 1 -->\n",
       "<svg width=\"137pt\" height=\"292pt\"\n",
       " viewBox=\"0.00 0.00 137.00 292.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 288)\">\n",
       "<title>MitRes</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-288 133,-288 133,4 -4,4\"/>\n",
       "<!-- Inputs -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>Inputs</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"37,-232 37,-284 92,-284 92,-232 37,-232\"/>\n",
       "<text text-anchor=\"start\" x=\"44.5\" y=\"-269.2\" font-family=\"monospace\" font-weight=\"bold\" font-size=\"11.00\">Inputs</text>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"43.5,-238 43.5,-255 86.5,-255 86.5,-238 43.5,-238\"/>\n",
       "<polygon fill=\"none\" stroke=\"#6bb24d\" points=\"43.5,-238 43.5,-255 86.5,-255 86.5,-238 43.5,-238\"/>\n",
       "<text text-anchor=\"start\" x=\"61.5\" y=\"-244\" font-family=\"monospace\" font-size=\"10.00\">0</text>\n",
       "<polygon fill=\"none\" stroke=\"white\" points=\"37,-232 37,-284 92,-284 92,-232 37,-232\"/>\n",
       "</g>\n",
       "<!-- CircuitsToHandles -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>CircuitsToHandles</title>\n",
       "<polygon fill=\"#c0dd8e\" stroke=\"transparent\" points=\"0,-146 0,-224 129,-224 129,-146 0,-146\"/>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"6.5,-201 6.5,-218 123.5,-218 123.5,-201 6.5,-201\"/>\n",
       "<polygon fill=\"none\" stroke=\"#6bb24d\" points=\"6.5,-201 6.5,-218 123.5,-218 123.5,-201 6.5,-201\"/>\n",
       "<text text-anchor=\"start\" x=\"61.5\" y=\"-207\" font-family=\"monospace\" font-size=\"10.00\">0</text>\n",
       "<text text-anchor=\"start\" x=\"7.5\" y=\"-183.2\" font-family=\"monospace\" font-weight=\"bold\" font-size=\"11.00\">CircuitsToHandles</text>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"6.5,-152 6.5,-169 123.5,-169 123.5,-152 6.5,-152\"/>\n",
       "<polygon fill=\"none\" stroke=\"#6bb24d\" points=\"6.5,-152 6.5,-169 123.5,-169 123.5,-152 6.5,-152\"/>\n",
       "<text text-anchor=\"start\" x=\"61.5\" y=\"-158\" font-family=\"monospace\" font-size=\"10.00\">0</text>\n",
       "<polygon fill=\"none\" stroke=\"white\" points=\"0,-146 0,-224 129,-224 129,-146 0,-146\"/>\n",
       "</g>\n",
       "<!-- Inputs&#45;&gt;CircuitsToHandles -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>Inputs:out_0&#45;&gt;CircuitsToHandles:in_0</title>\n",
       "<path fill=\"none\" stroke=\"#6bb24d\" stroke-width=\"1.5\" d=\"M65.5,-237C65.5,-229 65.5,-227 65.5,-219\"/>\n",
       "</g>\n",
       "<!-- HandlesToResults -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>HandlesToResults</title>\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_mitres.get_task_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641abff0",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`MitRes` and `MitEx` objects are constructed as dataflow graphs, called a `TaskGraph`. \n",
    "- Each node of a `TaskGraph` is a `MitTask` object; itself a function that computes some step or sub-process of an error mitigation protocol. \n",
    "- Edges of the graph move data between `MitTask` objects. \n",
    "- When `run` is called, `MitTask`s are ordered and run sequentially."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "707d85e2",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## SPAM + Depolarising Noise Shot Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e0234a1",
   "metadata": {
    "code_folding": [],
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import qiskit.providers.aer.noise as noise\n",
    "\n",
    "def depolarizing_noise_model(n_qubits, prob_1, prob_2, prob_ro):\n",
    "\n",
    "    noise_model = noise.NoiseModel()\n",
    "\n",
    "    error_2 = noise.depolarizing_error(prob_2, 2)\n",
    "    for edge in [[i,j] for i in range(n_qubits) for j in range(i)]:\n",
    "        noise_model.add_quantum_error(error_2, ['cx'], [edge[0], edge[1]])\n",
    "        noise_model.add_quantum_error(error_2, ['cx'], [edge[1], edge[0]])\n",
    "\n",
    "    error_1 = noise.depolarizing_error(prob_1, 1)\n",
    "    for node in range(n_qubits):\n",
    "        noise_model.add_quantum_error(error_1, ['h', 'rx', 'rz', 'u'], [node])\n",
    "        \n",
    "    probabilities = [[1-prob_ro, prob_ro],[prob_ro, 1-prob_ro]]\n",
    "    error_ro = noise.ReadoutError(probabilities)\n",
    "    for i in range(n_qubits):\n",
    "        noise_model.add_readout_error(error_ro, [i])\n",
    "        \n",
    "    return noise_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c188c0d2",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qermit.taskgraph.mitex import MitEx, gen_compiled_MitRes\n",
    "from pytket.extensions.qiskit import AerBackend\n",
    "\n",
    "noisy_backend = AerBackend(\n",
    "    depolarizing_noise_model(5, 0.001, 0.01, 0.05)\n",
    ")\n",
    "noisy_mitres = gen_compiled_MitRes(noisy_backend, optimisation_level=0)\n",
    "\n",
    "noisy_result_list = noisy_mitres.run(circ_shots_list)\n",
    "noisy_result_counts = noisy_result_list[0].get_counts()\n",
    "plot_counts(noisy_result_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73acc194",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## SPAM Error-Mitigation with Qermit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e6abd41",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from qermit.spam import gen_UnCorrelated_SPAM_MitRes\n",
    "\n",
    "spam_mr = gen_UnCorrelated_SPAM_MitRes(noisy_backend, n_shots)\n",
    "spam_result_list = spam_mr.run(circ_shots_list)\n",
    "spam_result_counts = spam_result_list[0].get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9a71b8a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_counts(noisy_result_counts)\n",
    "plot_counts(spam_result_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14513e2b",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## SPAM MitRes Task Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a45cab57",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.44.0 (20200408.0750)\n",
       " -->\n",
       "<!-- Title: SPAMCorrection Pages: 1 -->\n",
       "<svg width=\"291pt\" height=\"636pt\"\n",
       " viewBox=\"0.00 0.00 290.50 636.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 632)\">\n",
       "<title>SPAMCorrection</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-632 286.5,-632 286.5,4 -4,4\"/>\n",
       "<!-- Inputs -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>Inputs</title>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"159,-576 159,-628 214,-628 214,-576 159,-576\"/>\n",
       "<text text-anchor=\"start\" x=\"166.5\" y=\"-613.2\" font-family=\"monospace\" font-weight=\"bold\" font-size=\"11.00\">Inputs</text>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"165.5,-582 165.5,-599 208.5,-599 208.5,-582 165.5,-582\"/>\n",
       "<polygon fill=\"none\" stroke=\"#6bb24d\" points=\"165.5,-582 165.5,-599 208.5,-599 208.5,-582 165.5,-582\"/>\n",
       "<text text-anchor=\"start\" x=\"183.5\" y=\"-588\" font-family=\"monospace\" font-size=\"10.00\">0</text>\n",
       "<polygon fill=\"none\" stroke=\"white\" points=\"159,-576 159,-628 214,-628 214,-576 159,-576\"/>\n",
       "</g>\n",
       "<!-- SPAMFullTomographyCircuits -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>SPAMFullTomographyCircuits</title>\n",
       "<polygon fill=\"#c0dd8e\" stroke=\"transparent\" points=\"92.5,-490 92.5,-568 282.5,-568 282.5,-490 92.5,-490\"/>\n",
       "<polygon fill=\"white\" stroke=\"transparent\" points=\"98.5,-545 98.5,-562 276.5,-562 276.5,-545 98.5,-545\"/>\n",
       "<polygon fill=\"none\" stroke=\"#6bb24d\" points=\"98.5,-545 98.5,-562 276.5,-562 276.5,-545 98.5,-545\"/>\n",
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    "# Expectation Value Experiment\n",
    "\n",
    "Experiments where the output is an expectation value.\n",
    "\n",
    "We will see experiments:\n",
    "- Conducted in raw TKET, and the equivalent in Qermit.\n",
    "- With and without errors.\n",
    "- Where errors have been mitigated by a Qermit `MitEx`."
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    "## Ideal Expectation Value"
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    "import numpy as np\n",
    "from scipy.stats import unitary_group\n",
    "from pytket.circuit import Unitary2qBox\n",
    "\n",
    "def random_circ(n_qubits: int, depth: int, seed:int = None) -> Circuit:\n",
    "    \n",
    "    np.random.seed(seed)\n",
    "\n",
    "    c = Circuit(n_qubits)\n",
    "\n",
    "    for _ in range(depth):\n",
    "\n",
    "        qubits = np.random.permutation([i for i in range(n_qubits)])\n",
    "        qubit_pairs = [[qubits[i], qubits[i + 1]] for i in range(0, n_qubits - 1, 2)]\n",
    "\n",
    "        for pair in qubit_pairs:\n",
    "\n",
    "            # Generate random 4x4 unitary matrix.\n",
    "            SU4 = unitary_group.rvs(4)  # random unitary in SU4\n",
    "            SU4 = SU4 / (np.linalg.det(SU4) ** 0.25)\n",
    "            SU4 = np.matrix(SU4)\n",
    "\n",
    "            # Add gate corresponding to unitary.\n",
    "            c.add_unitary2qbox(Unitary2qBox(SU4), *pair)\n",
    "\n",
    "    return c"
   ]
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[[[&#34;q&#34;, [0]], [&#34;q&#34;, [0]]], [[&#34;q&#34;, [1]], [&#34;q&#34;, [1]]], [[&#34;q&#34;, [2]], [&#34;q&#34;, [2]]], [[&#34;q&#34;, [3]], [&#34;q&#34;, [3]]]], &#34;phase&#34;: &#34;0.0&#34;, &#34;qubits&#34;: [[&#34;q&#34;, [0]], [&#34;q&#34;, [1]], [&#34;q&#34;, [2]], [&#34;q&#34;, [3]]]}&lt;/div&gt;\n",
       "        &lt;/div&gt;\n",
       "        &lt;circuit-display-container :circuit-element-str=&#34;&#39;#circuit-json-to-display&#39;&#34;&gt;&lt;/circuit-display-container&gt;\n",
       "    &lt;/div&gt;\n",
       "    &lt;script type=&#34;application/javascript&#34;&gt;\n",
       "        const { createApp } = Vue;\n",
       "        const circuitDisplayContainer = window[&#34;pytket-circuit-renderer&#34;].default;\n",
       "        // Init variables to be shared between circuit display instances\n",
       "        if (typeof window.pytketCircuitDisplays === &#34;undefined&#34;) {\n",
       "            window.pytketCircuitDisplays = {};\n",
       "        }\n",
       "        const uid = &#34;f0dfe4d5-85ba-44d8-9e52-3ccf01905335&#34;;\n",
       "        // Create the root Vue component\n",
       "        window.pytketCircuitDisplays[uid] = createApp({\n",
       "            delimiters: [&#39;[[#&#39;, &#39;#]]&#39;],\n",
       "            components: { circuitDisplayContainer },\n",
       "        }).mount(&#34;#circuit-display-vue-container-&#34;+uid);\n",
       "    &lt;/script&gt;\n",
       "&lt;/body&gt;\n",
       "&lt;/html&gt;\n",
       "\"\n",
       "        width=\"100%\" height=\"200px\"\n",
       "        style=\"border: none; outline: none; resize: vertical; overflow: auto\"></iframe>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_qubits = 4\n",
    "rand_circ = random_circ(n_qubits,n_qubits,seed=23126)\n",
    "render_circuit_jupyter(rand_circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70ac2a9f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ideal expectation: 0.5549662007585134\n"
     ]
    }
   ],
   "source": [
    "from pytket.pauli import Pauli, QubitPauliString\n",
    "from pytket import Qubit\n",
    "\n",
    "ideal_circuit = rand_circ.copy()\n",
    "ideal_circuit = ideal_backend.get_compiled_circuit(ideal_circuit)\n",
    "\n",
    "qps = QubitPauliString(\n",
    "    [Qubit(i) for i in range(n_qubits)], \n",
    "    [Pauli.Z for i in range(n_qubits)]\n",
    ")\n",
    "ideal_result = ideal_backend.get_pauli_expectation_value(\n",
    "    ideal_circuit, \n",
    "    qps\n",
    ")\n",
    "print(f\"Ideal expectation: {ideal_result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d4386620",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ideal expectation: {(Zq[0], Zq[1], Zq[2], Zq[3]): 0.557980000000000}\n"
     ]
    }
   ],
   "source": [
    "from qermit import MitEx\n",
    "from pytket.utils import QubitPauliOperator\n",
    "from qermit import (\n",
    "    AnsatzCircuit, \n",
    "    SymbolsDict, \n",
    "    ObservableExperiment, \n",
    "    ObservableTracker\n",
    ")\n",
    "\n",
    "ideal_mitex = MitEx(ideal_backend)\n",
    "\n",
    "qpo = QubitPauliOperator({qps:1})\n",
    "obs_exp = ObservableExperiment(\n",
    "    AnsatzCircuit(rand_circ, n_shots, SymbolsDict()),\n",
    "    ObservableTracker(qpo)\n",
    ")\n",
    "obs_exp_list = [obs_exp]\n",
    "\n",
    "ideal_expectation = ideal_mitex.run(obs_exp_list)\n",
    "print(f\"Ideal expectation: {ideal_expectation[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38c01cf",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## MitEx Task Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6bdba4b8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
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      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7ffcb8a00bb0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_mitex.get_task_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47243a12",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Noisy Expectation Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1a219713",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noisy expectation: {(Zq[0], Zq[1], Zq[2], Zq[3]): 0.274160000000000}\n"
     ]
    }
   ],
   "source": [
    "noisy_mitex = MitEx(noisy_backend)\n",
    "\n",
    "noisy_expectation = noisy_mitex.run(obs_exp_list)\n",
    "print(f\"Noisy expectation: {noisy_expectation[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ada38b5",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## ZNE Error-Mitigation with Qermit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0a43d474",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from qermit.zero_noise_extrapolation import (\n",
    "    gen_ZNE_MitEx, Fit, Folding\n",
    ")\n",
    "\n",
    "zne_me = gen_ZNE_MitEx(backend=noisy_backend, \n",
    "                       noise_scaling_list=[9,7,5,3], \n",
    "                       fit_type=Fit.exponential, \n",
    "                       folding_type=Folding.circuit,\n",
    "                       show_fit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "020d7f20",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns \n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f7adc781",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{(Zq[0], Zq[1], Zq[2], Zq[3]): 0.359250338709462}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zne_me.run(obs_exp_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b32f06",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## ZNE MitEx Task Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93743d7f",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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       " -->\n",
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    "zne_me.get_task_graph()"
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    "## ZNE Options and Design\n",
    "\n",
    "There are several alternatives when building a ZNE task graph. "
   ]
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   "execution_count": 21,
   "id": "9b066ef6",
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   "source": [
    "alt_zne_me = gen_ZNE_MitEx(backend=noisy_backend, \n",
    "                       noise_scaling_list=[5,4,3,2], \n",
    "                       fit_type=Fit.richardson, \n",
    "                       folding_type=Folding.gate,\n",
    "                       show_fit=True)"
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    "Your favourites are available:\n",
    "- Folding: `circuit`, `gate`, `odd_gate`\n",
    "- Fit: `poly_exponential`, `exponential`, `polynomial`, `linear`, `richardson`\n",
    "\n",
    "For many `MitEx` objects there are several options to personalise, without interacting directly with the `TaskGraph`."
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   "id": "d87d839c",
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    "# Advanced Qermit Use\n",
    "\n",
    "We have seen:\n",
    "- `MitRes` and `MitEx` objects.\n",
    "- Use of predefined `MitRes` and `MitEx` objects, such as SPAM and ZNE.\n",
    "- Personalised use of predefined objects.\n",
    "\n",
    "We'll now look at some more advanced use cases:\n",
    "- Combining error-mitigation protocols.\n",
    "- Constructing original protocols."
   ]
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   "id": "fcd9ff2b",
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    "## Combining Error-Mitigation Schemes"
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    "zne_spam_me = gen_ZNE_MitEx(backend=noisy_backend, \n",
    "                       noise_scaling_list=[9,7,5,3], \n",
    "                       fit_type=Fit.exponential, \n",
    "                       show_fit=True,\n",
    "                       experiment_mitres=spam_mr)"
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    "## Combined ZNE SPAM Task Graph"
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   "id": "c2b10b8c",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7ffcd5981250>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zne_spam_me.get_task_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "862d7d76",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{(Zq[0], Zq[1], Zq[2], Zq[3]): 0.559749211780641}]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zne_spam_me.run(obs_exp_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75163c0c",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Clifford Data Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0844b95",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from qermit.clifford_noise_characterisation import gen_CDR_MitEx\n",
    "\n",
    "cdr_mitex = gen_CDR_MitEx(device_backend = noisy_backend,\n",
    "                      simulator_backend = ideal_backend,\n",
    "                      n_non_cliffords = 5,\n",
    "                      n_pairs = 3,\n",
    "                      total_state_circuits = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8bfd781",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## CDR Task Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae0ca84b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
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      "/Users/danielmills/qermit/qermit/clifford_noise_characterisation/cdr_post.py:136: UserWarning: Training data regularly differs significantly from original circuit, fit and results may be poor.\n",
      "  warnings.warn(\n"
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       "            window.pytketCircuitDisplays = {};\n",
       "        }\n",
       "        const uid = &#34;d8d0a795-164f-491d-a925-fe7fa3c7d159&#34;;\n",
       "        // Create the root Vue component\n",
       "        window.pytketCircuitDisplays[uid] = createApp({\n",
       "            delimiters: [&#39;[[#&#39;, &#39;#]]&#39;],\n",
       "            components: { circuitDisplayContainer },\n",
       "        }).mount(&#34;#circuit-display-vue-container-&#34;+uid);\n",
       "    &lt;/script&gt;\n",
       "&lt;/body&gt;\n",
       "&lt;/html&gt;\n",
       "\"\n",
       "        width=\"100%\" height=\"200px\"\n",
       "        style=\"border: none; outline: none; resize: vertical; overflow: auto\"></iframe>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "circ = Circuit(2)\n",
    "circ.X(1).measure_all()\n",
    "render_circuit_jupyter(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9eae7c3c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noisy_backend = AerBackend(depolarizing_noise_model(5, 0, 0, 0.25))\n",
    "noisy_mitres = gen_compiled_MitRes(noisy_backend, optimisation_level=0)\n",
    "\n",
    "circ_shots_list = [CircuitShots(circ, n_shots)]\n",
    "\n",
    "result_list = noisy_mitres.run(circ_shots_list)\n",
    "result_counts = result_list[0].get_counts()\n",
    "plot_counts(result_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe64de3",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Find Zero MitRes TaskGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c8d829c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from pytket import OpType\n",
    "\n",
    "def gen_find_zeros_task():\n",
    "    \n",
    "    def task(obj, circ_shots_list):\n",
    "        zero_loc_list = []\n",
    "        circ_size_list = []\n",
    "        for circ_shot in circ_shots_list:\n",
    "            not_zero_loc = []\n",
    "            for command in circ_shot.Circuit.get_commands():\n",
    "                if not command.op.type == OpType.Measure:\n",
    "                    not_zero_loc.extend(command.qubits)\n",
    "            zero_loc = [loc for loc in circ_shot.Circuit.qubits if loc not in not_zero_loc]\n",
    "            zero_loc_list.append(zero_loc)\n",
    "            circ_size_list.append(circ_shot.Circuit.n_qubits)\n",
    "        return (circ_shots_list, zero_loc_list, circ_size_list, )\n",
    "    \n",
    "    return MitTask(\n",
    "        _label=\"FindZeros\", \n",
    "        _n_out_wires=3, \n",
    "        _n_in_wires=1, \n",
    "        _method=task\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b5a9b7c5",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from pytket.backends.backendresult import BackendResult\n",
    "\n",
    "def gen_remove_ones_task():\n",
    "    \n",
    "    def task(obj, result_list, zero_loc_list, circ_size_list) -> Tuple[List[BackendResult]]:\n",
    "        \n",
    "        mitigated_result_list = []\n",
    "        for result, zero_loc, circ_size in zip(result_list, zero_loc_list, circ_size_list):\n",
    "            \n",
    "            result_dict = result.to_dict()\n",
    "            result_shots_array = result_dict['shots']['array']\n",
    "            \n",
    "            mitigated_result_shots_array = []\n",
    "            for result_shots in result_shots_array:\n",
    "                result_shot_binary = bin(result_shots[0])[2:].zfill(8)[:circ_size][::-1]\n",
    "                delete_shot = False\n",
    "                for zero in zero_loc:\n",
    "                    if result_shot_binary[zero.index[0]] == \"1\": delete_shot = True\n",
    "                if not delete_shot:\n",
    "                    mitigated_result_shots_array.append(result_shots)\n",
    "            \n",
    "            result_dict['shots']['array'] = mitigated_result_shots_array\n",
    "            mitigated_result_list.append(BackendResult.from_dict(result_dict))\n",
    "\n",
    "        return (mitigated_result_list, )\n",
    "    \n",
    "    return MitTask(_label=\"RemoveOnes\", _n_out_wires=1, _n_in_wires=3, _method=task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dcceaaa9",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from qermit.taskgraph.task_graph import TaskGraph\n",
    "from typing import Tuple, List\n",
    "from qermit.taskgraph.mittask import MitTask\n",
    "\n",
    "taskgraph = TaskGraph().from_TaskGraph(noisy_mitres)\n",
    "\n",
    "taskgraph.add_wire()\n",
    "taskgraph.add_wire()\n",
    "\n",
    "taskgraph.prepend(gen_find_zeros_task())\n",
    "taskgraph.append(gen_remove_ones_task())\n",
    "\n",
    "find_zero_mitres = MitRes(noisy_mitres).from_TaskGraph(taskgraph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f44277",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Find Zero MitRes TaskGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d51fdac5",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
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    "find_zero_mitres.get_task_graph()"
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   "execution_count": 34,
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     "data": {
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"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\n",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_list = find_zero_mitres.run(circ_shots_list)\n",
    "result_counts = result_list[0].get_counts()\n",
    "plot_counts(result_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe07cbc8",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Spectral Analysis MitEx TaskGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "76c77461",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from qermit.taskgraph.mittask import MitTask\n",
    "import numpy as np\n",
    "\n",
    "def gen_param_grid_gen_task(n_vals):\n",
    "    \n",
    "    def task(obj, obs_exp_list):\n",
    "        \n",
    "        # A parameter grid is generated for each ObservableExperiment in obs_exp_list\n",
    "        param_grid_list = []\n",
    "        for obs_exp in obs_exp_list:\n",
    "                        \n",
    "            # List of symbols used in circuit\n",
    "            sym_list = obs_exp.AnsatzCircuit.SymbolsDict.symbols_list\n",
    "            \n",
    "            # Lists of values taken by parameters, in half rotations\n",
    "            param_val_list = [np.linspace(0, 2, n_vals, endpoint=False) for _ in sym_list]\n",
    "            \n",
    "            # Grids corresponding to paramert values\n",
    "            param_grid = np.meshgrid(*param_val_list)\n",
    "            param_grid_list.append(param_grid)\n",
    "                    \n",
    "        return (obs_exp_list, param_grid_list,)\n",
    "    \n",
    "    return MitTask(_label=\"ParamGridGen\", _n_out_wires=2, _n_in_wires=1, _method=task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1b207dac",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "from itertools import product\n",
    "\n",
    "def empty_array(n_vals, dim):\n",
    "    \n",
    "    empty = None\n",
    "    for _ in range(dim):\n",
    "        empty = [empty] * n_vals\n",
    "        \n",
    "    return np.array(empty)\n",
    "\n",
    "def gen_obs_exp_grid_gen_task(n_vals):\n",
    "    \n",
    "    def task(obj, obs_exp_list, param_grid_list):\n",
    "        \n",
    "        # A grid of ObservableExperiment is generated for each ObservableExperiment in obs_exp_list\n",
    "        obs_exp_grid_list = []\n",
    "        for obs_exp, param_grid in zip(obs_exp_list, param_grid_list):\n",
    "                      \n",
    "            # List of symbols in circuit\n",
    "            sym_list = list(obs_exp.AnsatzCircuit.SymbolsDict.symbols_list)\n",
    "            # TODO: Remove n_vals as an input here. It should be recoverable from param_grid\n",
    "            obs_exp_grid = empty_array(n_vals, len(sym_list))\n",
    "            shots = obs_exp.AnsatzCircuit.Shots\n",
    "                        \n",
    "            # Generate an ObservableExperiment for every parameter value in the grid\n",
    "            for grid_point in product([i for i in range(n_vals)],repeat=len(sym_list)):\n",
    "                                                \n",
    "                sym_map = {sym:param[grid_point] for param, sym in zip(param_grid, sym_list)}  \n",
    "                sym_dict = SymbolsDict().symbols_from_dict(sym_map)\n",
    "                \n",
    "                circ = obs_exp.AnsatzCircuit.Circuit.copy()\n",
    "                \n",
    "                anz_circ = AnsatzCircuit(circ, shots, sym_dict)\n",
    "                obs = copy.deepcopy(obs_exp.ObservableTracker)\n",
    "                \n",
    "                obs_exp_grid[grid_point] = ObservableExperiment(anz_circ, obs)\n",
    "                                \n",
    "            obs_exp_grid_list.append(obs_exp_grid)\n",
    "        \n",
    "        return (obs_exp_grid_list, obs_exp_list, param_grid_list, )\n",
    "    \n",
    "    return MitTask(_label=\"ObsExpGridGen\", _n_out_wires=3, _n_in_wires=2, _method=task)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "56ad459d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from pytket.utils import QubitPauliOperator\n",
    "\n",
    "def gen_flatten_task(n_vals):\n",
    "    \n",
    "    def task(obj, obs_exp_grid_list, obs_exp_list):\n",
    "        \n",
    "        # ObservableExperiments, currently stored in a grid, are flattned to a single list.\n",
    "        # TODO: Is there a way of going this with the array flatten function\n",
    "        flattened_obs_exp_list = []\n",
    "        # Store strcuture of flattned grid as list of dictionaries.\n",
    "        struct_list = []\n",
    "                \n",
    "        for i, (obs_exp, obs_exp_grid) in enumerate(zip(obs_exp_list, obs_exp_grid_list)):\n",
    "            \n",
    "            sym_list = list(obs_exp.AnsatzCircuit.SymbolsDict.symbols_list)\n",
    "                        \n",
    "            # Iterate through each grid point, and add ObservableExperiment to list\n",
    "            for grid_point in product([i for i in range(n_vals)],repeat=len(sym_list)):\n",
    "                flattened_obs_exp_list.append(obs_exp_grid[grid_point])\n",
    "                struct_list.append({\"experiment\":i, \"grid point\":grid_point})\n",
    "                                            \n",
    "        return (flattened_obs_exp_list, struct_list, obs_exp_list, )\n",
    "    \n",
    "    return MitTask(_label=\"Flatten\", _n_out_wires=3, _n_in_wires=2, _method=task)\n",
    "\n",
    "def gen_unflatten_task(n_vals):\n",
    "    \n",
    "    def task(obj, result_list, struct_list, obs_exp_list) -> Tuple[List[QubitPauliOperator]]:\n",
    "        \n",
    "        result_grid_list = []\n",
    "        \n",
    "        # For each ObservableExperiment, create appropriatly sizes result grid\n",
    "        for obs_exp in obs_exp_list:\n",
    "            sym_list = list(obs_exp.AnsatzCircuit.SymbolsDict.symbols_list)\n",
    "            result_grid_list.append(empty_array(n_vals, len(sym_list)))\n",
    "        \n",
    "        # For all results, add to the appropriate grid and grid point\n",
    "        for result, struct in zip(result_list, struct_list):\n",
    "            result_grid_list[struct[\"experiment\"]][struct[\"grid point\"]] = result\n",
    "                \n",
    "        return (result_grid_list, obs_exp_list, )\n",
    "    \n",
    "    return MitTask(_label=\"Unflatten\", _n_out_wires=2, _n_in_wires=3, _method=task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f243c361",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def my_plot(z, *axis):\n",
    "    \n",
    "    if len(axis)==2:\n",
    "        plot_3d(*axis, z)\n",
    "    elif len(axis)==1:\n",
    "        plot_2d(*axis, z)\n",
    "\n",
    "def plot_3d(x,y,z):\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    \n",
    "    ax = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "    ax.plot_surface(x,y, np.real(z))\n",
    "    ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "    ax.plot_surface(x,y, np.imag(z))\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "def plot_2d(x,y):\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    \n",
    "    ax = fig.add_subplot(1, 2, 1)\n",
    "    ax.scatter(x, np.real(y))\n",
    "    ax = fig.add_subplot(1, 2, 2)\n",
    "    ax.scatter(x, np.imag(y))\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "47449e9d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from numpy.fft import fftn, fftfreq, ifftn\n",
    "\n",
    "def gen_fft_task(n_vals):\n",
    "    \n",
    "    def task(obj, result_grid_list, obs_exp_list, param_grid_list):\n",
    "        \n",
    "        fft_result_grid_list = []\n",
    "        \n",
    "        for qpo_result_grid, obs_exp, param_grid in zip(result_grid_list, obs_exp_list, param_grid_list):\n",
    "                        \n",
    "            sym_list = list(obs_exp.AnsatzCircuit.SymbolsDict.symbols_list)\n",
    "            \n",
    "            # for each point in qpo_result_grid, extract expectation value\n",
    "            result_grid = empty_array(n_vals, len(sym_list))                  \n",
    "            for grid_point in product([i for i in range(n_vals)],repeat=len(sym_list)):\n",
    "                qpo_result_dict = qpo_result_grid[grid_point]._dict\n",
    "                result = qpo_result_dict[list(qpo_result_dict.keys())[0]]\n",
    "                result_grid[grid_point] = result\n",
    "                                      \n",
    "            # Perform FFT on grid of results.\n",
    "            fft_result_grid = np.fft.fftn(result_grid) \n",
    "            fft_result_grid_list.append(fft_result_grid)\n",
    "                        \n",
    "            my_plot(result_grid, *param_grid)\n",
    "            my_plot(fft_result_grid, *param_grid)\n",
    "            \n",
    "        return (fft_result_grid_list, param_grid_list, obs_exp_list, )\n",
    "    \n",
    "    return MitTask(_label=\"FFT\", _n_out_wires=3, _n_in_wires=3, _method=task)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f64d6c11",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def gen_mitigation_task(tol):\n",
    "    \n",
    "    # TODO: Note that obs_exp_list is not used here\n",
    "    def task(obj, fft_result_val_grid_list, param_grid_list, obs_exp_list):\n",
    "        \n",
    "        # Iterate through results grids and set values close to 0 to 0\n",
    "        mitigated_fft_result_val_grid_list = []\n",
    "        for fft_result_val_grid, param_grid in zip(fft_result_val_grid_list, param_grid_list):\n",
    "            \n",
    "            fft_result_val_grid[np.abs(fft_result_val_grid) < tol] = 0.0\n",
    "            mitigated_fft_result_val_grid_list.append(fft_result_val_grid)\n",
    "            \n",
    "            my_plot(fft_result_val_grid, *param_grid)\n",
    "        \n",
    "        return (mitigated_fft_result_val_grid_list, param_grid_list, obs_exp_list)\n",
    "    \n",
    "    return MitTask(_label=\"Mitigation\", _n_out_wires=3, _n_in_wires=3, _method=task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "28e062d9",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def gen_inv_fft_task():\n",
    "    \n",
    "    # TODO: Note that obs_exp_list is not used here\n",
    "    def task(obj, mitigated_fft_result_val_grid_list, param_grid_list, obs_exp_list):\n",
    "        \n",
    "        # Iterate through results and invert FFT\n",
    "        mitigated_result_val_grid_list = []\n",
    "        for mitigated_fft_result_val_grid, param_grid in zip(mitigated_fft_result_val_grid_list, param_grid_list):\n",
    "                        \n",
    "            mitigated_result_val_grid = np.fft.ifftn(mitigated_fft_result_val_grid)\n",
    "            mitigated_result_val_grid_list.append(mitigated_result_val_grid)\n",
    "                \n",
    "            my_plot(mitigated_result_val_grid, *param_grid)\n",
    "            \n",
    "        return (mitigated_result_val_grid_list, )\n",
    "    \n",
    "    return MitTask(_label=\"InvFFT\", _n_out_wires=1, _n_in_wires=3, _method=task)"
   ]
  },
  {
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   "execution_count": 42,
   "id": "959a8f03",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def gen_result_extraction_task():\n",
    "    \n",
    "    def task(obj, results) -> Tuple[List[QubitPauliOperator]]:\n",
    "        return (results, )\n",
    "    \n",
    "    return MitTask(_label=\"ResultExtraction\", _n_out_wires=1, _n_in_wires=1, _method=task)"
   ]
  },
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   "execution_count": 43,
   "id": "e0bab58e",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
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       "<title>FFT:out_2&#45;&gt;Mitigation:in_2</title>\n",
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       "<!-- InvFFT -->\n",
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       "<!-- Mitigation&#45;&gt;InvFFT -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>Mitigation:out_0&#45;&gt;InvFFT:in_0</title>\n",
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       "<!-- Mitigation&#45;&gt;InvFFT -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>Mitigation:out_1&#45;&gt;InvFFT:in_1</title>\n",
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       "<!-- Mitigation&#45;&gt;InvFFT -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>Mitigation:out_2&#45;&gt;InvFFT:in_2</title>\n",
       "<path fill=\"none\" stroke=\"#6bb24d\" stroke-width=\"1.5\" d=\"M286.5,-237C286.5,-228.25 278.5,-227.75 278.5,-219\"/>\n",
       "</g>\n",
       "<!-- ResultExtraction -->\n",
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       "<title>InvFFT:out_0&#45;&gt;ResultExtraction:in_0</title>\n",
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       "</g>\n",
       "<!-- ResultExtraction&#45;&gt;Outputs -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>ResultExtraction:out_0&#45;&gt;Outputs:in_0</title>\n",
       "<path fill=\"none\" stroke=\"#6bb24d\" stroke-width=\"1.5\" d=\"M262.5,-65C262.5,-57 262.5,-55 262.5,-47\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7ffcb9da0640>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qermit.taskgraph.task_graph import TaskGraph\n",
    "from typing import Tuple, List\n",
    "from qermit.taskgraph.mittask import (\n",
    "    MitTask,\n",
    "    CircuitShots,\n",
    "    Wire,\n",
    ")\n",
    "\n",
    "n_vals=8\n",
    "\n",
    "experiment_taskgraph = TaskGraph().from_TaskGraph(noisy_mitex)\n",
    "experiment_taskgraph.add_wire()\n",
    "experiment_taskgraph.add_wire()\n",
    "experiment_taskgraph.prepend(gen_flatten_task(n_vals=n_vals))\n",
    "experiment_taskgraph.append(gen_unflatten_task(n_vals=n_vals))\n",
    "\n",
    "experiment_taskgraph.add_wire()\n",
    "experiment_taskgraph.prepend(gen_obs_exp_grid_gen_task(n_vals=n_vals))\n",
    "\n",
    "experiment_taskgraph.prepend(gen_param_grid_gen_task(n_vals=n_vals))\n",
    "\n",
    "experiment_taskgraph.append(gen_fft_task(n_vals=n_vals))\n",
    "\n",
    "experiment_taskgraph.append(gen_mitigation_task(tol=5))\n",
    "\n",
    "experiment_taskgraph.append(gen_inv_fft_task())\n",
    "experiment_taskgraph.append(gen_result_extraction_task())\n",
    "\n",
    "experiment_taskgraph = MitEx(noisy_mitex).from_TaskGraph(experiment_taskgraph)\n",
    "experiment_taskgraph.get_task_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a369fefb",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<iframe srcdoc=\"\n",
       "&lt;!DOCTYPE html&gt;\n",
       "&lt;html lang=&#34;en&#34;&gt;\n",
       "&lt;head&gt;\n",
       "    &lt;meta charset=&#34;UTF-8&#34;&gt;\n",
       "    &lt;script type=&#34;application/javascript&#34; src=&#34;https://cdn.jsdelivr.net/npm/vue@3&#34;&gt;&lt;/script&gt;\n",
       "    &lt;script type=&#34;application/javascript&#34; src=&#34;https://unpkg.com/pytket-circuit-renderer@0.2/dist/pytket-circuit-renderer.umd.js&#34;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&#34;stylesheet&#34; href=&#34;https://unpkg.com/pytket-circuit-renderer@0.2/dist/pytket-circuit-renderer.css&#34;&gt;\n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    &lt;div id=&#34;circuit-display-vue-container-9a25547c-490d-4da6-8a0e-64eacc969cd4&#34; class=&#34;pytket-circuit-display-container&#34;&gt;\n",
       "        &lt;div style=&#34;display: none&#34;&gt;\n",
       "            &lt;div id=&#34;circuit-json-to-display&#34;&gt;{&#34;bits&#34;: [], &#34;commands&#34;: [{&#34;args&#34;: [[&#34;q&#34;, [0]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [0]]], &#34;op&#34;: {&#34;params&#34;: [&#34;alpha&#34;], &#34;type&#34;: &#34;Rz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]]], &#34;op&#34;: {&#34;params&#34;: [&#34;beta&#34;], &#34;type&#34;: &#34;Rz&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [0]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}, {&#34;args&#34;: [[&#34;q&#34;, [1]]], &#34;op&#34;: {&#34;type&#34;: &#34;H&#34;}}], &#34;implicit_permutation&#34;: [[[&#34;q&#34;, [0]], [&#34;q&#34;, [0]]], [[&#34;q&#34;, [1]], [&#34;q&#34;, [1]]]], &#34;phase&#34;: &#34;0.0&#34;, &#34;qubits&#34;: [[&#34;q&#34;, [0]], [&#34;q&#34;, [1]]]}&lt;/div&gt;\n",
       "        &lt;/div&gt;\n",
       "        &lt;circuit-display-container :circuit-element-str=&#34;&#39;#circuit-json-to-display&#39;&#34;&gt;&lt;/circuit-display-container&gt;\n",
       "    &lt;/div&gt;\n",
       "    &lt;script type=&#34;application/javascript&#34;&gt;\n",
       "        const { createApp } = Vue;\n",
       "        const circuitDisplayContainer = window[&#34;pytket-circuit-renderer&#34;].default;\n",
       "        // Init variables to be shared between circuit display instances\n",
       "        if (typeof window.pytketCircuitDisplays === &#34;undefined&#34;) {\n",
       "            window.pytketCircuitDisplays = {};\n",
       "        }\n",
       "        const uid = &#34;9a25547c-490d-4da6-8a0e-64eacc969cd4&#34;;\n",
       "        // Create the root Vue component\n",
       "        window.pytketCircuitDisplays[uid] = createApp({\n",
       "            delimiters: [&#39;[[#&#39;, &#39;#]]&#39;],\n",
       "            components: { circuitDisplayContainer },\n",
       "        }).mount(&#34;#circuit-display-vue-container-&#34;+uid);\n",
       "    &lt;/script&gt;\n",
       "&lt;/body&gt;\n",
       "&lt;/html&gt;\n",
       "\"\n",
       "        width=\"100%\" height=\"200px\"\n",
       "        style=\"border: none; outline: none; resize: vertical; overflow: auto\"></iframe>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sympy import Symbol\n",
    "\n",
    "a = Symbol(\"alpha\")\n",
    "b = Symbol(\"beta\")\n",
    "\n",
    "circ = Circuit(2)\n",
    "circ.H(0).H(1).Rz(a, 0).Rz(b, 1).H(0).H(1)\n",
    "render_circuit_jupyter(circ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a8e3709f",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DlZWVSJKkBBc9iefi4Lmgd0a4Hmqn00lTUxNms5nMzEwSEhK6fEzPY/tCkiSMFlcbq2fmAuBocTXDB3T+8pLFobGxse6dm1xCMZvNnDx50h3dR0VF9VjtsrfoznY2z24UpTRyduDtXRFM54+8MamurqaqqorU1FSSkpLO+tT195VANyXd4bEhn787nmvPdcPpdJ6VZWyZ71Vw4c+7IlDCEVxYrVZKSkowm81otdqwBhYd1XtVkqy5cLlzepJXVotakrD6+uYOzqXRaNBoNO6ac1xcnHsYUU1NjXsXIOs1uqt22dtlke48vxJcnB0EWwbxRU1NDQ6Hg4EDB7p3xqEQyjoUyLX2dKB+NmwMgiUcHhvy+bt73ZLfcWcr35vgIhyLQ1eDi5aWFkpKSoiKiiI+Pt7dFtXdyDeYvfXaPX0uwFUWkaTgjH68kYVSWq2WxMTENrXLhoYGKioq3LVL+X/hiqp7M7jo7u6Os32B+CHQUXt6IOj1eoxGo7sbpKv3fajPaWeakB8S4Xq5h+KxIZfIwjUfxR9Op7Pbz9EVzt4rCwJZWdvVgWOhPtROp5Pq6mqqq6tJT08nISGB5ubmHjPCkSQJg9mKVu1a1GqbjW2+LneMiKLoDr6Cwdc5vWuXsjjUYDBQW1tLeXl5SOlEX+fuTc1FsOcOVrfTE4uQgm+8dVnBrh2ez71GoyE+Pj4sAbWiuTh7zxuIx4ZOp8Nms7nF8N1xHXJZ5GzemJzTq1pXaqS+COWhttlslJSUYLFYyM7ORqfTuY8VbvwdUxRFjpQ2kBoXSZPBjM3e9uVWVteMwWwNObjo6Nwy3uJQ73SiPKNDDjbkCL8zeju1GszDKwiuabZGozHg3/PZvkB8X/H2rgg2QyWXP61WK9nZ2VRXV4ft2roruPg+BSyd0RObEn8eG7JOrbGxkebm5m7x2JDvW0Vz0Q04nU4MBgNVVVVuQ6qu/tGC3XUaDAaKioqIjIwkJyen3R+6px5mhxOOltQzon8idc0Gn585WV7HkMzEdrNLugvPdKKnOFR+8IA2D50/F73e1lwEc2659zyYv7tSFulZ5Pb0qqoqJEkiNjY26PurpaWF4uJioqOj6d+/v/vvfjZPHv4hlkV6Gs/SscFgICoqyv3/h9tjQwkuugk5W2G1WqmvrycjIyMsxw30xeB0OqmtraWyspK0tDQSExPbPbzdUTf1h0olcby8nqlD0qmobfT5maPF1QzOSAz62OHAUxzqHeF7i0N9PXTnSllEftCDeckomYuew1OXZTAY3CnuYL6/qqqK2tpa0tPTiY+Pd98fPTWX6Fyit7OOvW2+JwcSgXhsyKXjQEuk8tp0NgeN51Rw4atGKteewtVT3NkDYbfbKSkpwWQydagK76nUpiAISKLI8bIGorVqKhpafH6fLOoM9ZzhvIk7Eod6P3QOh6PXdoTB/txOQcAWZNlJ9hBR6F68vSuCDQY8zfCys7PbeQt0hwFfuOmtl/33SXMRKL7Wjo48Nurq6txzUALx2OjJ4MJisXDVVVfx5z//mUmTJgX8fedMcOHL39/TxSychiX+jid71Wu1WnJycjqMMrvLZc8b+XdwvLwBjUqkptF3WeREaS2qENL2PUFH4lCA4uLisIhDgyXYrIJGpcJitQV9DiW46D78tacHUwJtbm6mpKSEmJgYMjIygp4tEixn4zN6rtHbmYtAsp5d8djoqXKq2Wzmt7/9LXl5eUF/71kfXHhaeHt3g3gGA+HAX3DhdIY2IyAU5ExMoIiiSKPBTHWTEbUktjPQkskr61rHSE8+qLI4NCoqioaGBvr16+fWbHRFHBoswZRF5Lq7GORlKJqL7qOj9vRAXuCyC2IgM4EUzcXZRW8HZ6GUOwP12BAEgYKCAlJTU7v175qfn89vf/vbkH+XZ3Vw0Zl3hWcUF64WMPm8Mp6jkoOZEdBTqU1RFDleXIcoglol+Q0uiqoasdjsXeoY6S3UajU6na7L4tBgCWb34z0LINDjK5mL7qEz74rOMhcWi4Xi4mKcTmdAM4HCGVwomYvwcLaVRYLFn8fG4cOHeeihhzAYDEyYMIErrriCyy+/PByX3YYdO3YwadIkfv3rXzNmzJigv/+sDS4C8feX/y2cD7V8PEmSMBqNFBcXo9Fogp4R0FNlEYcTjpTWMSApBlEQ/AYXDqeTgop6BvaJDes1dSe+HDK7Kg4N9vwBW8d7zAwJhnDNIFBw4anL6qg9vaPns6mpiZKSEuLj492daJ1xtpdFeiNg+SELOrvjuZYFyJMnT2bZsmUcP36cmpoad/k43Nx0001d+v6zLrgI1sI73CptcN0YchkkOTmZlJSUHnf7DBS1SsWJ8gYGpccDUO01tMyTI8XVZPUJXB0v01uLRCDnDUYcGqxzaDALhCgG3ykCZ7/L3rmEw+HAZrMF5NLrK3PhcDioqKigoaGBzMzMoDpJzoZuEavVikqlOuuC1d64nt4OLnri/FlZWcyfP79bz9EVzqpVLRQL73CnIwEqKiowGo3079/fLbYJ5VjdXRZxqd4Fjpc1MKp/EtB+aJkneaW1iBMHh/2auptgHtJwOocGukCIoohKOlOiC5RzwWXvXMBTlxWoZbv3umE2mykuLkYQBHJzc9FoNEFdQ29mLuSgqK6uDlEU0el0REVFhdW06Vyjt7Mm3f1c93bwFAhnTXARqr9/OHcMJpMJcO0AcnJywjKIK5w3ga+6Mbg6RRaO6Q90HFycKK1FrZIwh7AQ9tbuo6vn7opzaKALhGcmJJjfqxxEK5qL0PHekATanue5bjQ2NlJaWkpCQgKpqakhvRR6Sycht8g6HA6ysrLc7Y0tLS1u06aoqCh3VuOHwtnw8u3O858LLey9frf58q4IdqcajsxFQ0MDZWVlCIJARkZGlwOLnrixRVGktsVEg95MekIUFquNFqPF7+fzujhjpKfpjsU6GOdQq9Ua0A72jHlWcJ0+8mfP9kXibCUQXZY/5GegtLSUpqYm+vbt6xbOhUJvZC70er3bKTQ9PR273Y7T6WxTIpSzdhaLBZPJhNFobJO16+0XcHfSm+Z73X3+c2EmUa9eXVf9/eXv6cpD7XA4KC8vp6mpiX79+lFaWhrysbyvC4KPoDv7rHdZ5GhpDQApMTrqWkwdfm9BZT02u+OcCy666yHtTBwqBx02m61DcagT1/VZQzDQOttd9s5GgtVl+UJ2+DWZTOTk5ARdBvEm3OXZjo7l2RovOwQD7Z5pURSJiooiKioKu92OJElERESg1+vdEz3l+zoqKirsJZTeLk30VrlR/rm7uyxytpdTeyW46Mi7Ili6krnwrLPKC0y4Folwe3B4HlPG4RQ4VurabSdER3Qo5gSw2h0UVzeSmRhYO61Mbwo6e/LF6y0OLS0tRaVSIUmSX3GoSqVCo1ZhttoRCK1TRAkuAicUXZY39fX1lJeXA5CdnR02A76eyFw4HA5KS0vR6/VtHIIDFT97Zu0sFgt6vd7dZSVJkjvQCEb4HMh5e5reDmy6m3PBH6fHg4twLA6ehBoMeNZZ09LS3NcQTg1Hd6NRSxwvcwUXcToNpytqO/2eI8XVZCYGL1LtrRdgb7945YmH4FscmpKSQnR0NNVNRvrEBLf7PRcWiLMJh8OBxWIJeUNit9spLy+nubmZtLQ0qqqqwnZ/hXvd8HUss9lMUVERkiR1qAmTJAm1Wo3ZbPab/ROEMxM9vUsonsJnOdA410oovam56IlNQ0/74xw/fjzo7+nR4KIrNVJ/BPtQe7ab+aqzhmsHEmrmIhghqyAIHC9vAECrlqj2Y/3tyYnSGuaOyQ7qmnqL3hZlebei+hKHqtVqSmpbiFCL1NbWUl9fH7BzaFezdj8U5DKI3A0Syu/MZDJRXFyMJEnk5ua614FwEe7MhTey94b3ZsgbtVpNREQEDocTjSYCs7njUqmMZwklJSXFLXzuqRJKd9Bb19cTJYtzYWPSI1cnizblXUM4F9RgMhcWi4WCggIMBgO5ubk+BVy9HVx0hnc970RrcKFWSdR0UhYByCutQ6M+u4VAMr2dQepskVCpVKg1EewprCYpRue27jWbzZSWlnLy5ElKS0upr69vs4sM9PhdwWKxcOmll7J9+3b3vxUXF3PrrbcyZswYLr74Yr755ptuOXc4cTqdGAwGiouLgeAznbI+4dSpU8TGxjJw4ED3izFYq335/L7orrKIbEFeUlJCRkYG6enpfn/+iIgIdDod/1mxlsm3/RGjxdrGWTSY65OFz+np6WRnZ9O/f390Oh16vZ7Tp09TUFBARUUFzc3NfvVbvV2a6M3gorvPfS44+3b7W8azDKLXu15+snd6OAg0cxGo697ZEFz4uzE9/10URSobDbSYrAAdzhXx5IRHx8jZPAsBej9zEcj51SoVpXUtiIJrGFZ8fHzAzqHdtfvwNWzI6XTys5/9jMGDB7NixQrWrVvHvffey1dffUVGRkbYr6GreOqybDYb9fX1QV+n3W6nrKwMvV7fzrNG/r0Hc4/J+htPEbpMdwQXNpuNkpISLBaLz0msnoiSCqfTyd0Pv8Tytd8CcOP9z/DZs/d3al0eyPWcayWU3gxsesJ191zIXHRrcOHtXdEdeobOXpJy5F9XV+d23etoIehNM5ygji2KHCmpBiAxOgJVgMHFyfI6HK075kCDi958UM+msog3kiQhigLl9Qb352U6cw796quveO+99xg1ahQLFy5kypQpYQm6/Q0b2rZtG8XFxbz//vtERkaSk5PDd999x4oVK/j5z3/e5fOGE1/t6XKWIdD7QbbuV6vVPq37Q5kuKZ9bpVJhsVjafS2c64bNZuPkyZPuCcz+dqmCICCIElV1jdz0x2c5kHfa/bXvDpzg3ide4aU/3k1CQgKNjY1hub5ASyhdDWq6wvc5cyEH3j/YzIW84/CskXbHbrmjh1o2mLHb7W2GD4mSClEUsFrae0L05o7ebrdTUlKCwWBoo9r2XhidTidOBI61ijlzU102xYFoLkxWG+W1zaTEat2LdyD0luL7bM5cSJKE3mxFb7a6P+8Pb+fQPn36oNPpOHDgAC+++CK7du3igQce6PI1+xs2tH//foYNG+Y+P8D48ePZt29fl88ZTrzb00VR9PAR6XxB9WzTTElJ8WvdH1JmsfV7bPb260M4W1HlFug+ffp0OHrAFdxKfLPvKLc9+E/qGlvafWbZ6q3k9k3jt4t+hNVqDcv1eePtHePZhQJQUFDQLV0onfF91lz8oFtRPbUBnp0Y4X5xi6Lo8yXZ3NxMSUmJu27o+YdwOJ047GdX5sJsNnP69GnUajWpqamYTCbq6+upqKhw21V7pkUj1CqOlzUAuIeRBZK5AFfHyPQR/YP7YXqBs11zIYgiu/OrSY+PwmILzuNCq9UyefJkJk+eTE5OTlcv1Y2/YUPV1dX06dOnzb8lJSWFVdQYLrzV9oFOPw5mgrF8/EDXI0mScDicVDXpSUuIxup0tPnecGRlZZFpU1MTWq223d/L+3pUKhXPv/sFf3/lQ+w+Ah6Zpa99RG6/NC6dNgGzydStmyfPEkpMTIx7NLjBYKCurq7HSijf58yFfI4fbObC18PmLxDoCt4LhNPppKqqitraWtLT00lISGj/PbgEkGZ7+2vpjeBC1oMkJiaSkpKC1WolOjqa5ORkt1bFYDBQVVUFQF1dHbGxse7gol+Sq5bcmc+FzInSWqYNHxDaD9WD9HbmorOyiCCI7MivpG9SFM4QFuyedNkzGo3tjKI0Gk279H5vI5dPPQkkEJBFn1qtNuAJxsE864Ig4sDJu5sO8OMF44mMULcrg0Ho96zVaqWoqAin00mfPn1oaWmfhZCRVCpUksT6HQd54b2vOgws5Gv6ydL/0j89hZE5/TGbTT0SuMvnkEso0NZ+37sLJTIy0u01FK5z9wbdrbmQS4Q/2ODCF92VuZBvJKvVSklJCTabza8AynPIlK/r6cngwjMQkvUg3tcjSZI75Wi1WikoKCA6OhqH00leRQMAmYmu/65vMQZ0XSdKa4jQqLCYA/s5fqiai45eFKIoolZJ7DxVxQWDRiKGcJk9mdqMiIigoaGhzb9ZLJYORYJnE/7WDqfTSW1tLZWVlaSmppKUlBRUO3fA65EAalHiSFE1X+7K46oLh7b9cheCC08b74yMDJqamnw+c4IgIEpSa4ZSYPb5I8n79J/UN+vZeTifd1Z+zRdf7/Z5DpPFyg3/9wybXv4byXExAbeoBkNERASiKLpL4vI1e9JRCSWcRl69nbnozufa0yPqbKbbggtff9ju0lw4HA5aWlooKSkhKiqK/v37dyyA6qBM050iTE9kfYXZbO5UCS4j30wxMTGU1ekxWV03WZ9YHc0GM3ZHYNd9rnSM9PYCAf4fYDlNvq+wmgHJMSHdMz0pykpNTSU/P7/Nv9XU1HSYeu8tfD2Dvu5Vm81GaWkpJpOpjVtlMOcJ5P6XJAmp9T7IL6/DvN3G9ReNaFMaCUXD4RkYyVlWeX3yPo4kSUiSxKaDhfzkn5/TaDCTm57IzFEDmTV6ILMmjmTB5LGYrTaKK2pYu20/L3+8joLSSvcxquubuPYPT7HmX38lIiICsznA3UUniKKIVqtFkiSqGlroE+/a7KgtVndnnq/fc0ddKL5KKFqtNqgX6ve1LCLfG0rmwoPuCi5kvYLnA9rRNQDY/aSueiJzYTKZKCoqQqPRdKgE939ckSOlde7/TorRBqy3ANcCCcH9PX6oFr4diemOl9ejN9tIjY/EYQteLNeTmYvRo0fz3//+F5PJ5A5kd+/ezfjx43vk/F3F+16Vd/uRkZHk5uaGtNAGqpOQ7wGHw0lhZT0FFfXoTRZ0GlXIwYU/G29fyPqKZz/5lqdWfIuj9Rz55XXkl9fx8urdRKglJp3Xl5mjBjJnTA4/uXY+91wzj2aDiYN5hXy47jveXfUNR06VsOQvL7D88d8CdDnAiIiIQJJUCILAH99cx+tr99I3OZYZI7OYMXIg00cOIEoXgdVmx+l04GidCePr9+TZhQJdK6GcDQ6d3X383m737YxzOriw2WzU1dW5yyA6nS7g7913qoKRA9rv2sKp+vYVXMi240lJSfTp0yekG8QpnOkUAYiPjKC4qr6D72hLi9FCVUMLcbquj5TvTnp7gQD/wYUDge15lSTHaNGoJIzW4LULPdmrfv7555Oens7999/PT3/6UzZu3MiBAwd49NFHe+T8XUVeO5xOJ9XV1VRXV7uHdoV6jwS8HgkCDqeT0tomzK3Zwq925XHFBUM8PiIHIJ0fT7bxVqlUPvUh8roht5la7A7u+sfHrNqd7+eIYLba+frQab4+dJqH3t1EemI000dkMWt0NjNGZjNlzFCe+NUSHA4Ha7ft55/LVvKLGy9xd+cEi0qlQlKrEQURo8XGnc9/yuZDhQCU1DTx9sYDvL3xAJIoMHpgGjNGDWT26IGMzk5Dq9VittoQnA5sNptfEy5fJRRP3xi5hCKXUTwDzN7emHTnc/2DDy66uywi71zUajUajSbgwMLucCKK8M3h04zNScdqaT9JMFyiU2+nPVlfEep4Z0EQXJMNPTpFAKIiVFQ2+BeA+eJYSQ1j+ifS0NAQViFVuOntsoi/7FaEWsXOU1X0bZ3TEsp93ZOiLEmS+Ne//sWf/vQnrrrqKgYMGMCLL754Vhpo+SuLWK1WCgsLsVqtQW8mAj2PN3JJpNlk4XjrBGKAL3Yc59qpw7E47GcCgQCOF4iNtyAIREdHI0oqiqsbWfz0R+5sY6CU17Xw/teHeP/rQ4iCwDN3LeDqKcMwWe1cPHU8oihganXwFEUx4AyGIAhoNBFoNGpsdgdltc3c/OSHfq/P7nCy52Q5e06W88zH3xIbGcHUYf2ZPjKLOWOyyUiKxeFwuMfF+zIok88rl1ASEhLcvjF6vb5dV11UVFSPGFn5o7s3ReeCgRacg5kLzzplWloaGo3GPeEwEKRW5d2Ww0X88vIL211Td5RF7HY7xcXFATntdYb8vbLtN4BGJQVVFgE4XlLDhNz0gIRUvbUL6G3Nhb/dgfy72XmyirFZyUBowUV3LxLew4YGDBjA22+/3W3n604cDgdVVVXExMR0qKkKhkDWI0EQOFJSx8CUGPI8SpGbDxZiMFuJUEltxIv+nhVf4m1/REZGkpyczKrdefz8pa9oMXato+fVX17O/PG5bD5YyN3//BwBuGjEAGaMGsicMdmkxkcjqVQ4HU6cTkebwZKeqNVqVGoNKknE4XCy80Qptz/3CQ36wMWhTQYzX+3K46tdLvfYrNR4/nPvZQxvzSJrNBqsNjt4ZDX8lVA8fWO8Syh2u52amhpiYmJ6fPPU3V1g58pMonMquPAl4NLr9UEdUxRFLDY7e06W43C0f3mFW9BpsVjcUXUo+gpvdDoddoeDk5Vn3PbUKpGaAAy0PDlRWos2QkPfvn192vlqtVp3sNGbwUVv0dHOR5IkqpuMlNXruXRclk9Tpc44V1z2ehv5pWw0GomJiaFv375hW1QDetYFgXWHSvj5/JHklZ2ZOmy22lm1O49Lzz+v0+MFY+OtUqmIiIjgvU0H+O2rq+nKIxCr07Dq4cUMTE3gn59v59HlW9x6jS92nOCLHScAGJyZxMxRA5k5aiAXDu1HZGQkVpsdUcD9gpfUaiI8prC+vXE/f/rf+pDufU+WLpnDqIFpfPztEX7/2hpGDkhl+sgsZo3JZsSAPoiCgNlqRWh1bQ20hCK7m/oqofgyJgwn3V0WORcMtOAcKot49rHn5OS4b45gzGvkP0iT3oTRbOV0dQMDUuLa3LDhDC4cDgeVlZUdOgUGi06no6imBYvN9XtUiSJqSaS6KTCPC5m8slr3PAygnZ2v7K1RVlaG3W53/93kiYg9xdnYTuZEYFuey3wqMzHK74LXlXP8kJH/5p4Ou7GxsURERIT1fuhsPZJLIrXNZgRBIL+sber/8+3HuWrysDalEe+1w2g0UlRUFLCNt93hRBCd3DhjFD+6YAjHimv4eNtR3t14AKMl8HLtyAF9WPGnG4hQq/jZv77go2+P+v3sidJaTpTW8p+Vu9BpVFwwpC8zRrqyGtnpia7sq8Pp3jH/9e0N/HeV75bXQInUqFjzyBJy0hN5+qNveeqjrQBsO17CtuMlPP7hNyREa7lo+ACmj8xidmuGxe5wYLfZcTjsbdYlT+R7JDY2Fq1W22kJJdgulM7o7oyrPFX8bKfHMxfBzgjwtPP11ccejABT/oPUtbjSeHtPltM3Kfwj1+V5JhaLxS3cDAeCIKCJ0LLj5JlFLjs1BkEQgi6LdNSOqlKpiIuLIy4uDqfTSXFxMSqViubmZqqqqlCr1e7ySWRkZLfd6GdDWcQXKpXErlMuQ7O+idGEeolK5sI/ssNuTEwMGRkZVFZWdlsbe0dfP1paT2aiK/WeX17b5uubDhRiNFvRtJZGvI9XX19PeXk5KSkpJCcnd+iZIooSpyrqWfzMRzTpzUwbOYCZowYyd2wODy+azYM3zaSmSc/WI8W8sXYvu/LL/F73jdNH8thtc6lvMXLto8vYX1Dp97PeGC02Nh4oZOOBQv76zkYyk2K497JJ3DJztPs5f+CG6dy1YAJbDp/m9TV7OHi6KuDjA/RLjmX13xcTExnBz//9JR9uPeLzc/UtJj7bfpzPtrvKe4MyEpkx0pVhmTysH1FaLRarDVpbgj27UDyf385KKA6HA51O517TulpC6QkTLSW48CJQG1+ZQOx8gwlY5K/XNbtexIdOV3H5pCE4vD7T1dJNSUkJVqsVnU4XdpMirVbLcY9OkZw+rtptsGWR+hYT9S1GItUd36RCa3YjKiqKuDhXlsdoNKLX66mursZms6HVat0PZjh3l73dLeIv+yaJIjtPuhbU/snRhJK7Pld61XuDqqqqNt4P0D3uvp1mPQWBz3YXMuW8NBr0Jupb2moLTFYbq/fkc/HEwW2OJ9t4NzY2tpvG6o3cZvr59uP86r8rMbTOqfnku2N88t0xBAFGDOjjbjG94sIhXD1lGAazlfyyWj7bfpy31u+jqVWX8dQd87hx+kj2nqrgtmc/Cdi11x/zxuZw84xRlNc1s+jpj4jVRTBjlCuTcP20Edw4fSQGk4UTZbV8vv0Eb284cy2+mDy0H2///mqsNjvXLl3GtuMlAV9LXlkdeWWu1luNSmLi4EyuunAo108fgab13WK2WMHpIDo6uoPNQcddKPJ6F2oJpae6Rc52erwsAoEFF/JUQ41G06GdbzCjk524etVrm1xOlodPVyFJInb7mWxFV2YEeKZAs7Oz3Xa+4UIQBLQRmjZizv4prkma1UFmLgBOlNQyLie10895/gySJBEdHe1eMD0fzNraWvcuwd/QtWDo7XYyf2JOk9XGkRJX9igzMTqk6zxXXPZ6g5iYGKKjo9tM1ewud19/x5RLIl/sPc1NUwaRV1rr83Nf7DjBFRcOxeKwIwiC20XX6XSSk5PTznK9zflbB4/97b1N/PvLnT4/43TCwcIqDhZW8cJn24mNjOCi4QOYOSqLuWNzeeCG6dx/3UXUNRtRiQIJMZG8s/EAf3xzXdDzbrx55q4FXH/RCHacKOH25z5xB1c780p5csVWEqN1rcLQLOaMyeHPN07nj9e7rmXH8VLe2bSfjQcK3cdbPGs0Dy+eTVldMzc+/gEFlQ0hX5vFZqfFZOaKC4dgNNu4999fEqVVu1teBw8ejN3uaC2ddFxCCaYLJZASSk+YaJ0Lm5IezVzI6vvORqR7phM70yoEY15jszsRBScN+jPBBbgWGXmxD7Us0tDQQFlZGcnJye5rDvcNJt9QxzzaUPu1tkIGWxYBOFpSzZjszoML8K990Gg0aDQa4uPjcTqdbmGorwcz2CFFvV0W8bWIiKLEnsIabA4nkREqYnUajMbAbNc9kZ+Bc2GR6GkiIyPbZSl6eqKyXBIprG4mPlLD1wdqfH5uw/5TmCw21JLkzljExcW1G5bofWxBlDBarNz5/EdsOXza5+d80WQw8+XOE3y58wSwhvP6JnPz9JHcPn+c20X06ilDGZuTxspdeby2di91zcHdnypR5PMHb2ZMdhpvbdjPH99c51O4Wddi5NNtx/h02zEAhvZLcWU1Rmczb1wOl5w/GJPFRlF1I3qjmdHZaezKK+PWZz9ulwUKlksmDuZfP72E2mYjNzz+gbvMK2tLhvZLcWk1Rg/k/PP6ovUoodhbjbx80VEJpaKiArvd3mkJpSdMtHpqJlFX6NYrDNTGV8Zut1NWVoZer2fAgAEdphM9jweBZUPUKhGb3eHWXNQ2G6lu1JMUows5uJD1FXV1dfTr14+YmBj318LdeSKKIlabncLqJve/pcZHYrLYMJqDN8LJK61FpZICnjHSGYIguB/M5ORkvw578oOpVqsDLmX1NH4XCFFke76rhp2Z4CrThfI37qjVVaE9PZ25kEsiougSH+Z5iTlljBYba/bms2BcLhaLhfj4eDIzMzs8pyhJnCipZfEzH1FS0+T3s4EwMDWeRbPH0GK08NMXv0CSRGaOGsi8cTn89qop/PqKC6nXm9h1oox3Nh5g7b6THR4vLSGa1X9fTHJsJA/8bz2vrtkT8LUcLa7maHE1//5yJ7oINRcOcTmG3jZnLJIk4nA6Oa9vMi/dexkffnOYFd8eIZQ/6c8umcj/XTeNE6U13PTEh1Q2tC/9yNfy0lc70WlUTDqvr1sYOigjyVUO8Qg2gjXyMhgMfksoPZG5OBcynj0e/vh7oE0mE8XFxUiSRE5OTsAdCcE444mCgCgINHhEzftOVTBzVFab4wX6srDZbBQXF2Oz2cjJyWmTxg32WIEgiiKnqprazBBJidEFvTOROVFWi9TaL90dJQh/Q4paWlqorq5GpVK1yWp4B4e9+RD5WiAEQUCjktjtIeaE0DwuZMW3Ely0p7sN+DzP4+u+9yyJjOqXhCSJbdpQvfl8+3F+NGkIiYmJHRp7yfqKFVsP8/tX1wTV/eGL+6+7iJ9ecj4ny+u45akV7kBl7d6T/PHNdWSnJbRqNbKZOSqL+eNzMVttFFU1snpPPq+u2UNF/RnzvUnn9eXdP1yD0+nklqc+bFPSCBaj2cqO46U8sng2kiTyzMffUlHfwqzRA5k2IotpI7J4+s4FVNQ3s/nQaV5fu5cjRdWdHvfJO+Zx0/RRfH2okDtf+BS9qfNNldFiY9PBQjYdLOShdzeRlhDNtBEDmDHSFYRFRLh0cQ6HvUN78kBLKDabDYvFgk6n65b161wRgp8VwYVcBgnVEjsQnYT8x1BJYpvpoQcLK5kxMsv934G+aGV9hU6n82vqE+6XthNXqtaTxOiIoNtQZeRUoiRJHYrlwvEzeD6Y8pAieQdQXV3tFsDKwUZERESvay68Fwb5b7z7lGsRzEiMwtEqJg7l+EpgETg9mbkQBJFjZa6SyM/mDgdo14bqyfr9BZitNlJSUmhubvZ9LkmFSiVhtzuYOmwAT90xn9fW7mV3B10f/q8blt13HVOHD+CrnSf4+UtfuYWgnpyqqOdURT2vrtmDVu1qMZ012tWBcu9lk/jpJefTZDCx91QFJTWN3DBtJBX1Ldz85Id+MzWBktUnnpV/u4VIrYafvPg5n3znKp28tWE/KklkfG46M0a6ruWWmaO5ZeZo9CYLx0pq+Hz7cd7ZeIAWU1th6PL7r+Oi4QN4Z+MB/u+NtSF7bFTUt7B8y2E0KolLJw3mVEUdH209yoxRWUwYlNHlEoqcya6pqQlrF4qMkrmg87KIw+GgvLycpqamdiWFYM8TiNOejKej3OHTVahVEma7rd31+UPWV3TWYgbhFSWKkthmpghAjFZNXknnEb8vqhr0tBgtqHvhRhVFsY0w1Gq1ur016uvr3Z+RdwI9XWP0VRaRJImTFY00tqrh+yZGu9wEQzy+krkInO6cqNz+C/DZ7kIARvZPwmKzd1i+MJqtrNt3irljsnE0Nrb5mqyvaDZauP+NtfTvE8e8sblcfuEQrmrt+jhRUsMn247x1vp9GDrJZiTHRrL674vISIrliQ+/4dlPvgvoZzVZz+ze//L2RvqlxDGzVfw4Z0w2IGB3OBAFgUWzRvPq6j2crm7s9Li+uGh4f/7326sxWW1c88gyduaVtvm6ze5g+/FSth8v5fEPvyEpRse0EVluYeiDN8/kgRumU9dsYNvxEpZtPsRDt8wkNyOJx5Zv4fnPtoV0XZ7cd81Ufv6jSezOK2PR0x/RZDDz/GfbiNKquXBIP7c9eVZqQpsSis1m83sfypnayspKMjMzEQSh0xJKKCj2336QFwmz2UxxcTGCIHSqqg7kmJ29xAVBwGZ3uDIXHmUEuUdbkiTsdnunFr4VFRU0NDQEFAyF+uKQr8Hbz0MtSW1migBo1SpqQsxcgMtMa0T/5ICuqTtRq9XEx8e7haEmk4nKykrMZjOnTp1Co9G4H8zuSjd64iuz4BREtuVXuP87MzEqpDZU+fjnwgJxttBdmQvvZ92zJAKQmxpLYWW929nSH59tO8YlEwe3KefK+opDp6u47dmPKa9zlSD++fkOd9fH7DHZzBt75oVa02Rg65HTvLZmD3tOVrQ5x6Tz+vLu769GEARufeZjVu/xP8isM4qrG3l34wFumDYCURRZ9vVBapqMzB2bw10LJnDn/PE0G80cLKxi+ZZDfLT1KLYAfv+3zRnDQ7fMoqSmiRuf+JDTVQ2dfk9ts5GPvzvKx98dRRBgWL8+bmHoxRMGc8nEwTgcTupbjOgi1PSJi6QqyNZ7T56/eyHXTh3Oyl15/PRfX7iH0QHoTa5Acd2+U/z5rQ30TY5lemvgM31kFjFRUdhaO098lVBkWwRJklCr1e1KKL7E7sGuaUpZxA+iKKLX6ykvL+9weE+wx+xs4XE4oclkJTEqgjqPskhxdSMtJgs6teuP1ZGFr6yvyM7Obqev8EWoZRFfLzb5xvNsQ4VW6+8QOkVkjhRXM6xf58FFTyIIAjqdzj2QLjY21t3uWllZGZBiu6v4evlHqFVufwuA/skxiCGe9lxx2esNekpz4euYgiByvKyBgipXeSMlVsc3hws7Pda6faewWO3uDYesr3h3k6st1PMFBt5dHy4vC7lkccWFQ7l6ynAMJgvHS2v55LujqFUi911zERX1Ldzy1Ap3STNUXMLNRSTHRvHnt9bzymqXcPPh9zeTnhjNjJEDmTV6IDNGDWTKsP48dcd8Kuqb2XigkNfW7OG4j/M/dutcFs0azfbjJdz27Mc0GoJXijudcLioisNFVWw+WMAnf74JgI+/Pcr0kVn88vIL+Pllk9zlnA+2HOLT7ccCFoYu+79rmTYiizfW7eVPb67vNGgsqWninU0HeGfTAURBYEx2WmsXSjZjclwTXu12OyaTqd2MKk88SyjJycnY7Xafa5pcFva3psnBS3euHWazmYceeog1a9ag1Wq5/fbbuf3224M+TreXRTyRMxZWq5V+/fqFNBnU33k6e4k7nE4MrcGF96Cdw6ermDgow++xPPUVAwYMCPgPG07NhSiKmK12imrb1nTVKpHqLkTxeaW1qCSJro1G6h48jaZiYmKIiYnxaXrT0dC1rpzb8+98Rm9xJrjolxQd8gtPyVwEh6dBVbh+bz6fT4+SCLgmDnekt5AxmK2s33+K2aMHuhxbBZE/vLaGtzbsD+haDp2u4tBpl5dFXGQE00ZmMWv0QOaNzeGhW2ZhtdmRJJEDBRXoIrq2bHcm3Cyva+G9zQd5b/NBJFFgXE6GO/BZPHsMi1u7U44UV/HR1qO8//VB3v3DNUwe2p/3Nx/kD6+twdrFmSPzxuXw35//iAa9iRse+4BjJa5W4AF94pkxKotZowa2+n0M5Lm7L6a8tplNBwt5dc0en+JblSiy5pHFDO2XEnJpxeE8M+H12U++445543jwphmoVBJSayuyvB50ttnxXtM8y8Kd+QV1d+biiSee4NChQ7z55puUlZVx3333kZGRwYIFC4I6To9lLiwWC0VFRdjtdhISEsIWWEBguxq1JGKyuuqZDV491vtPVTA2Ow04s+DI2YP6+nrKysro06dPp/oKb8IdXORVNrTJwveJ1SKJXctc5JXWIkkdd4ycTVNRfSm2Oxq6ptVqQ8pqePeSS5JEg95MQbUruJNEgZQYHZYQ+3jPlbrp2YKnWV44j+m5bniXRDISIolQqzrsFPHk8+3HWThhEA3NRm55+qOQxJoAjQYzn28/zsb9BUwYlEliTCRf7cqjb1IsCycM5pLzz3OLHz/+9gjvbDqIKcDOE7lsUdnQwk1PdC7ctDuc7MwrZWdeqz4iNpKZI7OYOXogs0dn89htc3l48WwkUaCwqoE31+/rcmBx1/zx/OWmGZwsr+PGJz5wl5MATlc18Oa6fby5bh9qSWTCoAyXdmRMDkvmjGHJHFfgc7S4mk+3HeXdTQdRSyIbH7udtIRofvPyKt7bfLBL1wfws0vP5/+uncqeI/lYrTYmjBgEnLk/g3m2BUFw+wUlJCR06BdUX1+P1WrttrXDYDDwwQcf8PLLLzN8+HCGDx9OXl4e77zzztkZXDQ1NVFSUkJ8fDwQ/vp9IDMCBEHAandgtFjbOdcdKqpCo1ZhNtvbmHKVl5fT2NgYsOdGd+JE4HBxWzFnbmo8EJqBlsyJsjMzRkIdwNWddHavyCIpX0PXSktdQjI5HRnM0DXvzIIgimzPL3f/d1p8JKIYulW8krnwT1fdfYM5j2ewIggiJ8obOFXlEm9OPS8d6LhTxJM1e/Ox2OzsLagIObCQGdIvmU8fuBFdhJpf/OcrPthyGID4KC3TRgxgVqtW4+HFc3jw5llUN+j5+nAhr67Zy8FC37NEnr5zPjdMG8mOE6Xc/lxoRla1TQY+3HqED7ceISc9kTV/X4RKJXGooIpRWams/Nsidznns+3HeHtD+66Pjli6ZDaLZ4/h2yPF3P78Jx2Om7faHXx3rITvjpWwdPkWkmMjmTEyixmjXCUd+XcjCCCJIs98vDUsgcVfbprO3QsmsH7bPhbd9zg3XTqTC8cMxWI2h62zzl8J5c9//jNFRUWMHz+emTNnsmjRorBmMY4dO4bNZmPs2LHufxs/fjwvvfRS0Buibg0u5Bd0fX09mZmZxMXFUVlZ2eMzAs4sTE4a9e13mocKzzh1ygtboBa+HdHVOSWeSJLEifK2wcWAPq3W312YH1Ba24TJYus0uOiNroZQ2jW9h66ZzWb0en3QQ9e8zy2JEjtPnlm0u2KgBa6XZE9Olz3XaP/i79zdN1i8MxdOr5LI+IEuLdLJinrvb/WJ3mRl4/4CLhjSt0vXdc2UYTx953yaDGZueOJD9p48E9Q26M8M8xIEGDkg1VWyGJfLtVNHcP20kehNrp37x98e5d3NB7HZHHz215sYm5PO2xv388c31nU5uzBjVBav/+pKjBYr1z66nD0ny92Bz8zRA5k7Joe/3jSTP10/ndpmA98eKebN9fvY3sEskXd/fw0zRw9k2deH+N2rq4NuNa3xCHwEAa6dOpyn7pyPzebEKcFvrpzCTy45n4KKelbtzuf1tXuD3pi9cPfFXDN1GB+u2cI9D76AzW5n9Te7eeY+EZVKhdlsDrs5nmcJ5Z133mHLli3U1NRw9OhRjEZjWDe+1dXVJCQktHnnJScnYzabaWhoIDExMeBjdWtwUVVVhV6vb2Mw1aMtZR5fN1ps7TwuZPLKarHaXAI7s9kVfKjVavr27dul3WUoZRFfN6UgCKgksV2nyIBkV3BR24XMhdMJJ8vrGJQeH/IxuoJOp/Nrn93VXYAgCGi1WrRaLUlJSR0OXfMWUXm2ooqiiCSJ7PTQW/RNCt1Ay/v4CoER7rXDswSqUqlQeZREAM5LT6Civjko99vPdxxn/vhcvnroZl5etZuPW/0dAuXRW+ewaNZoDhVWseSZj3y6T8o4nXCgsJIDhZU89+k2EqK1TBvhcqGcMyabR5bM4aFbZgKgkiReX7OHP/5vfVDX44s754/nLzdNp6iqkRuf+JDi1pZVz8AHYFj/FGaNdg1cu2zSeVw5eShGs5X88jq+3HmcN9buo9FgRqMSWfPwYs7rm8JTH23l6Y++7fI1zh49kCdun0dtk5HrHl1GeX0LU4b2d5mKjc3mN1dO5leXX0ij3sSek+W8//VBvthxosNjvvP7q5k1Opv/fvAVf3jqFff6VFJZw/GCErL7pvaIO2dmZiYLFizoUoelP4xGY7vjyv9tsQSnzOvW4CIlJYXExMQ2L+ieainzRBAECqsbidVpKChv369uszvIK6tlcEYihYWFAKSlpXU5bR2q5sJfp8gxr8xFenwUDoezTfdLKBwtriY3PcHv17tLc6HRaFCpVO42YF/nDfcOQPbWkEVU3kPX5KyGZwpQkiSsNgeHis6kxzMSokL2uIBzZ/jQ2US41462Qw9F8sobOFl5Zn3ITIzkeHFwHjJr9uRjtdkZmZXGv352Gc/9eCHFNU2s2pXHK16OmJ6oRNGdXVi+5RB/eG1Nuw6TzqhvMblnfQgCLJo5moeXzMbpdIkRb5s3jmsvGsHR4mo++vYI7246gMUW3O9Tdsj87lgxtz/3CU0ddIQcKarmSFE1//x8B9E6DVOH9WfW6Gzmjs3m/66dxu+vnkp9s5FonQaNSsWv/7uS978+FNT1+OLmGaN49NY5nK5q4LrHlrs1G6v35Lvad99w2aa7hpxlM2VYf2aPycZitVNW18SG/QW8tmZPm4zVVw/dwticdB57eRmPvvx+u3Ou3LKDn954WY9MRIXum0kUERHRLoiQ/zvYCd/dGlxIkhTUbJFQ6eyYDqeTvIpGpgxO8/si3nuqgpy0BAYMGOAOMMJBOF7MkiShN1spr2+boUiN09GgN4VqteDmRGltr9T/A3lAumsX4Cmi8hy6Jlv5ms1mampqMJvNpKWlcbC4FrNHMNE3MfROETh3etXPJroruHA4HDiBTz1KIgAxWk3AYk6ZZqOFzYcKmTgok9uf+4TZY7JZMD6Xn102iZ9ccj6NehM7TpTy9sZ9rNtXAEC/5Fi++tsiEqN1/OWtDby8eneXf7bFs8bw90WzqKxv4eYnVlDTZHDP1pg9JptHb53L3xbNoqpBz+aDhbyyejdHi30PZwOXK+hHf7qRSef1Dckhs8VoYdXufFbtdnlzDMpIZMnsMSyZMxaV5Po7PHrrXO6+eAKrduXz2to9IXXB/e6qyfzqigvZnV/G4qc+8tsOW1DZQMHavby+dq97fLtslX77vHHcNncsLSYLR4uq6d8njrSEGP7w1Cv8Z/mXPo+3eutufrX4KiRJ6vbMRXfOJEpNTaW+vr6NcWF1dTVarTboJowebUWFnp9uCC6Pi1OVTcwclkF9s28R0+HTVdw4bSTR0dFh6/II1w0giiInShva/XtitJba5tBLIjInSmtdLqUd/NzdczO7jM386T160iLbU0QFcOrUKaKjo7FarTgFke35bQ2N+iVFh+xxAYr9d2cEO/Qw1HPIx1VJbUsiWpWITqPyO2q9Iz7bdpw5Y3JoNlr4+3ub+ft7m+mXEsfs1l27POfDZLFRUd9MRlIMZoudGx7/IKgJqf546o553Dh9FDtPlHKbh3BTNqoSBYFRA1OZPSabuWNzuGH6SG6aMYoWo5kjRdWs2HqE978+6M5qxEZGsH7pEvomx3U4Hj4Y+iXHccus0TS0GLntuU+Ii9Iya9RA5o3L5VdXXMgvfnQBjXoTu/LLeG/TQVbuzuv0mM/ctYDrp41g9e58fvriF+7uwM6w2OxsPVLE1iNFPPz+ZlLjo5g+MouF4wcxb1wuouhap361+ComDB/Eqx+tYtv+tuWu7QeO0aw3dIvnjifdPZNo6NChqFQq9u3bx4QJEwDYvXs3I0eODHoDelbMFgnHMTsSiWpUEgXVTagkyT1u3ZtDhVWIouD+w4UruAjHcRxOOFrSXlQWF6mhoLxrZjqAe3fW0x0jTkGADl6yvTlbBHBrMSK1Eew61TY93i8pGr1ej9Fo9Dl0rTOUzEXwdFdwIQgCeRVtSyIXDk5DFAXyyoOfsbF6Tz42u4Nf/GgSd73wGeAy63tj3V7eWLcXnUbFhUP78fdFs+mfEodKEpFEkRd/eglbjxTx2po97MwLvttEJYp8+tebGNeJcNPhdLLvVAX7TlXw9EffkhSjc3dYzB6dzeO3z+PhJbOprG9hx/FS5o7NJkKt4s7nP3WbfnWFW+eM4W+LZnG6soEbHv+A0lbvnvX7TvGn/60nKzXenUWYNmIAc8fmYLbaKK5pYm3rwLVSL7+f9++7lukjs/jf+n3c/8a6Ts2xOqKyQc+mAwU8eNNMbHYHf3p9HSlxUcwenc3V8y/iuoXTMZgs5J8u5fNN23jlg6+oa2ph7bd7WDh1Qrdmgbu77KLT6bjiiit48MEHWbp0KVVVVbz22ms8+uijQR/rexFcdCTolP8QBZVNqCXRb/vVkeIqHB4pp3BcY7iCC5VK4nh5++AiKkJNVQeCr0A5XdXgFrT2VHAhiiJqScThcOJ0+A8uemt3Lz/EcgCwy8OZE1yai5amBr9D1zqbN6O0ogZPONcOp9NJZWWlK2uh1vDprrY70fNzUoHA21A9aTKY+fpQIVOG9ff5daPFxp3zxzMwNZ61e0/y9EffMnlYf+aPy+HS88/jiguHuts5P/r2KG9v3N+pj0WfuEjWPLKElNiooEsrtc1GVmw9woqtR9wulLNGZ3Pj9BH86IIhqCQRo9nKnQvGkRCt5f3NhwKyAvfFQzfP5I7549hxvJRbn/3Yp2ajsLKB11tLFhFqiUnnuUa3zxubw08uOZ+7L55Is8HMvlMVfPjNYX5yyUSG9e8T1KyVjshOTWDVw4sQBYHrH1vOttYOl2c/+c5t2z5jZBZzxmbzp7tv5P67rqe+qQWz1YpOp+3WOUiyELw718X777+fBx98kCVLlhAdHc3Pf/5z5s2bF/RxvhdlkY4EnfI1lDcaUEmiX82F3mSlpKaJvkkxAc0qCYRwBBeCICCJIse8OkUANF20/paxO5wUVjW4u0+86a5x7AAtRhM6jf/bsDeDC0EQkCSJ4tpmaj2C0vioCLQaFc6oKAYOHNhu6JpniSUqKsrnYtPdmYu1a9dy7733tvm3+fPn88ILL3TbOcOJv7UjHMGvp5V/eno6Kknky71tyxHD+yXQYrSE3Ob92fbjzBw1kGH9U9qMEk+I1rLm4cX0TY7jmY+/5amPtrq7Pl76aicxOo2742P+uBz+vmgWf71pBlUNLWw6WMjLq3dzzEsbMXFwBu/fdy1OJyx6agUbDhSEdM1wxoXy/PMy6RMfTWFlPf/6cqd7DsoFd/Rj6ZI5VNS3Xs+q3QHrUt78zVXMG5fDR1uP8OuXV7XzG/KF2Wrn60On+frQaR56dxN9k2JdDp2js5k+MouLhvfH7nDSYjKTHBtJTlpCwK3DvhidncrHf7oRo8XG9Y8t59DptpsKb9v23PRE9xyUC4f2AyA7JxdRcJUw5O7DcNET5ns6nY7HH3+cxx9/vEvH+V5kLjo6piAINBktxOlc7TTe7pye7DtZTnpC+DQXEPyLuaWlxe3IFh0d7RbReM8UAdf4+FDHrXtzpKia/snhc03tDPnFWl7bxMA0373TvZm5kHcITgS+O9HWlKhvYpT7M+B76Jper6exsZHKysp2Q9fk+6s7F4n8/HxmzpzJ3//+d/e/BTIP52wmHEG/wWCguLgYnU5H//79kSSJ/IpG8ivbdpENTIkhP4SSiMzq3fnY73Dyix9dwD3//ByAcbnpLPu/61CJot8SQ7PR4n55yT4Ws8dkM29cW22EPFBMF6HioZtdjps3P7HCbYrXFZ778QKunTqCb46c5s7nP6XZaOG9zQcRBYGxOeluW/JFs0azaNZomo1mDp+uYvmWw3yw5XC7rIZKFFn190UMH9CH5z/9jsc++CbkayupbeLtjQfYsP8U65feikoUeWfTAS4Y0veMENNo4dDpKlZsPcKyrwPPsswYOYA3fnMVtc1GrnnkfQoqGzr9nvzyOvLL63hl9R53lsU1Sj6b3Iwk1Go1Vpsds6lrHX0y55Kzb68EFz0yI8DjawVVTfRPdvkS+PK5kDl0uoqLJw7uNUFnbW0tFRUVJCQkYLfbqa6uxul0YhNUVDe1vW6tSkSjkqjpwlwRT06U1rJwQq7fr4f/Je86Xk1jCwNSfbfB9qbtOLSWbtSqNvNEADITo9t8zhN56JpOpwNoN6DIZrPx7rvvEh8fzxVXXMGIESO6JYA6efIkgwcPJiUlJezH7i26ujGpq6ujvLyc1NRUkpKSXJkplZpPd7f3okiMimD70aKQz9WgN/HN4dNMGz4AgNvmjuGhm2dR3ahn0dMftclm+MPTx+LZT75zayNmj3b5WFw4dKFr5ogo8s3hooAFjP4QRfjszzczflAGb67fx5/eXIfdceYedzid7M4vY3d+GU+u2EpybGSr9XY2s0YN5Jm7+vH4bXMpr29m4/5CXlm9i9pmIxsevY2UuEh+/+pq3t54oEvXCDBiQAqf/vkm7A4n1yw9M9K9T3wUM0a6shqzRg3kwqH9ePTWOVQ26Nl8sIDX1u71+3u/8sIhPH/3xZyubnT5YtT5bhnuCM8sy9/e28Qd88a5rNE7Ga8QDOdSObVXyiLQvTMCPLE7nORVNJAe7+oC6Chzceh0JSrJ5XXQk2URT6vxfv36oVKpcDgcJCQkEBUVxYGShnbfk50aB0B1GMoi4BJ1alQqLGHM2nSIXK6qa0QUB/j8SG9lLuSfX61WIwpCG/MscGUu7A5HQL8n7wFFZrOZ3NxcNmzYwAcffEBqaiqfffaZOxgJFydPnmTy5MlhPWZP4m/tsFoDN7SScTgclJeX09TU1MbKX5IknyURgMgINfldFEt/tv0400dm8dZvr2TW6Bx2nCjhjuc+DdmXRtZGfPrdMbcnxteHT9M3KZYbpo/k+mkjaDKY2Z1fxrubDgYlvoyP0rJ+6RLSEmN48J2N/Gflrk6/p6bJwAffHOaDbw4jia6sxuzR2cwd55rzsXj2aBxOJ5Io8vbG/WGx3p45KovXf30lDXoT1z26vM102KoGPcu3HGb5Ftf1jMlOZ+YoVxbhxhmjuHnmaFpMFo4VV/PJd0fdM1numDeWB2+exeGiKm58/IOQbNG9uXbqcB66eSafb9zGZTMvQJKksDhTK5mLDuiJGQEyDocDUYKCqibSW+2aO85cuKLamJiYHhN02u12ioqKsNlsDBgwwF2fd0/YEySOlLZPzw7s4yphhENzAbgf0p4QdcqtfwAl1Q2oVBL+KpO9EVzIf3uVSkWLyUJeRUObr2cmRmML0nwIzjiGXn/99Zx//vkMHjyY4uLioM1pOsPpdFJQUMA333zDf/7zH+x2OwsWLOAXv/hFt7j69RShlEUsFgvFxcUA7az8RVHkZGUjeRWNbb5naEY8Kkkkz8dzFwyrdufx5B3zmDM2l9fW7OGv72wM2tLaG3/CzfTE6NaMRg7TRg5g1uhszFYbp6sa+WrnCV5ds8fvWnFe32Q+/8tNqFUSdzz3iduLIhjsDie78srYlVfG4x9+484EGCxWJFHklpmjuf6iEZTVNbNhfwGvrNrNqcrgtBE3TB/JE7fNbWeO5e965CzLUx9tJSlGx7QRroFrc8Zku+eOGMxWorVqjhRXc9XD72MIwo3VH4tnj+Hx2+byv0/X8stHX+Lk6jeI0obnuVOCCw96Y0YAgNVqpaysjMGDB3OqqokZQzMB2o1b96S6UU9ts4HIyEiamto7eQZLZ8GF2Wzm9OnTaDQa+vfvjyCcaYWVAy+NWuVTb5GV4gouasNUFimoqMfu8O05Ee5MhqfAsaC8BrGDVtTezFyIosTOvMp2JmWZiVEIQui/E/lejYyMZNiwYSEfxx9lZWVuG9/nnnuOkpISHn74YUwmEw888EDYz9dTBFsWaWlpobi4mNjYWNLT09styg5ne+MsgCnnuSYkdzVzUd9i4tsjxUwYnMGfwmC7PXFwBu//4VoQhHbCzfK6Ft7eeIC3Nx5Ao5K4YEhfZo/OZv542TdiEvUtJrYdL+HNdXvZcthV8pFHmzcbzFyzdBkH/Aw9C4ZbZo5i6ZI5FNc0ccPjH1BW28T43Ay3r8Ztc8dy65wxNBvNHCioZPmWw6z49jAd/Wl/e9Vkfn3FhezJL2fRUyv8mmP5o7bZ6Pb5kLUs//rZpQxMS0AUBEYMSOXgv35GXmktn+84zlvr99HUwdA0f/zkkon85cYZNDbr+cXSf+N0OjmcX8ikkUOCPpYvZKv6c4Feucru6Ff3PJ5er6eoqIj0dNdUw4KqJq4+P4cWk6VNDdEX+09VMHVYv24vDcgLX1xcHCkpKe5amufL1BVsCO1mioDrBQfhy1xYbHZKaprc5SNvwm3DDWB3ODhV5lK+txsi1YseF3JQI0oSO7xaUAGykmPoii1qd7vsZWZmsn37duLi4hAEgaFDh+JwOPj973/P/ffff074a3SlW8TpdFJTU0N1dTXp6ekkJLTX9LhLInval0TGDEjGZndwuqqx3deC5dNtx5g6vD+nXvsVu/LKeHfTAT4Jct4IwOJZo3l48WyqGvTc9OSHbcoB3lhsZ2r/f31nIwNT490v9nljc7hk4mCMZiv1LSZS46NaR5t/SFlds99jBsqfb5jO3RdPYHd+GUue/ti9mdtxopQdJ0p5dPkW+sRHuX0sZowayNThA3jqzvmU1Taxfv8pXlm9h0IPMeXTd87nhukjWbv3JPf84/Mua0ucTvjl5ReQk57IOxsP8MiyzUwe2t81cG1sDg/cMJ37r72I2mYj244X89b6/XxzpHP9ze+umsJvr5pMS0sLcTHRZGWkUlBawcEThUwccV6XrllGyVx0QnfY+MreAXV1dVRUVJCenk5SUhLgCi4SoiJo7CBrIXPodBVThw/oVs2FLCxLS0tzd4P4cl1TqVxK42Ol7dOHafGRGMzWLj9onhwpqiY9IStsx/OP6+esbmihqnXWgvfPLv/eeqssotPpUEliOzEnQEZi1zQ53e2yBxAfH9/mv3NycjCbzTQ2NgY12fBsIpCyiN1up7S0FKPRyMCBA/1qWURR5FRVEycq2gcQuWlxFNc0drmEAa7SyOO3z0WnUTMuO5WLhl/Gc3ctoKimia925vHfVbuoa+5Yg/HkHfO4cfpIdueXcdsznwSt2SiobOCV1Xt4ZfUeIiPUTB3en6fumE9aYjSiIJCdnsiXD93CN4dP88qa3ew/FVr24tVfXs7CCYP4fPtxfvGfr/zORqlq0LPs60Ms+/oQKklkfK6s1cjljnnjuX3uOJoMZg4UVhIfGcHIgWm8tWE/97+xttPNYSB8/MANXDCkH//4fDtLl30N0Ka9dEjfZHf55OIJg/nRpCEYLVZOVdSzclcer63dS73X3+zBm2dy98IJVFdXYzQaiYyMZO6Ucfx3+VccOXkajUaN1dL1tlRF0OlBT9j4yjua0tJSWlpayMrKIioqClEUqW0xoTfbiI3UdPoQAxwsrESjksKyu/P+2Z1OJxUVFTQ0NNCvXz/3wue9i5VV7FaHk3te2Uijj/RccowuoJ8nGPLKapk1emBYj+kTWcxZ20R1o+/g4sxHe6csEhMTg93hYG9hW08BrVoiIUrrd5JrIHT37mPLli387ne/Y9OmTe577OjRo8THx5+zgQV0vm6YTCaKi4tRqVTk5OR0mD52OOHTXb79INLiItlxvLjL1wuudPy2YyUMyYjnvIvv4MIxQ5k/dQKXTDufX15+Afdedj71zUa+PVbMa2v2thlJrhJFPvnLjYzPzeC9zQe577U1XR6VbrJa+fUVk0mJi+L1tXt4e+OB1tbSXK6cPJRrpg5Hb7K4bcDf29z5cDNRhFUPLWbkwFRe/GIHjyzbHHBiz2Z3sP14KduPl7J0+RbSEqJbRZg5LJwwCACr3c5Fw/vz15tm8PKq3RTXhFayFkVY98itDO2Xwt/f28y/vtzh83PHSmo4VlLDv7/cSZRWzZRh/VtNvHL53VVT+PUVF9LQYmJXXhnvbNrP3LG5LJo1GpPJRF1dHTqdDqvNxsKpE1uDiyLE1pJ3V99755Kz7/cicyGrcM1mMzk5OajVavfXTrbuTKIj1JRWdS4gkk1TwuEJ4Blc2O12iouLsVqtbYSb3i8ZURSRVGpqmk0senGtT/MsgISoCKoagm+X6ogTpbVEqFVYvBI84e7sUUkiNruDirpGmg0mrDb7WZW5cDqdREVFcbS0HqOXM6IsDO7K76S7yyJjx44lIiKCBx54gJ/97GcUFxfzxBNPcOedd3bL+boDX78bf9NzARobGyktLSUpKYk+ffp0+LsVJRWiIPCZD70FQFSEivww+EXIfLbtGBfeNpf+6Sl8vesgX+86yJ+ee53sfunMnzKei6edz8Xjh/GjSUMwmCwcK6nhy115/Hj+eFLiowLu3uiMxBgd6x9ZQp/4aB7433peXbMHwD25NC4ygukjs5gzNoe5Y3N47La5/H3RLCrqW9h4oICXV+0i38spOC4ygg2P3kpaQgz3v7GON9bt7dI1VtS38Pn24/z2ysk4HE6eXPENkVoN88flcteCCdw5fzxNre6cy74+yKfbj3Wo1ZCJ1KjY9PjtZCbF8puXVwXcuaI3WVmz5yRr9pzk/jfWkZOe4Gq/HX1mRoyjdeihzWY744/jcDB1/HAitREcO+UKVMPx3lMyF50QzuBC1i4AbV7aADaHk5OVruBCq5YCSieermrAYLaGNbiwWCycPn0atVrdTrjpiSRJqNRqDhXXsuRf66nxM2QNIEan5lhReAy0ZE6Uunbp9fX1qFQqd/YnnMiBX02zkYo61w6kvtlAXGRbNXVvai4cDgcRWh3b951q9zVvA61Qj9+dC0R0dDSvvvoqS5cu5eqrryYqKoobbrjhnAoufOErCyrbeNfV1dG3b98OJzcKgoAgSpisdn7y2sZ2xlkASdEu99W8EGy//fHlrjwevXUuv7/9Wu556IxD6qnicv79/hf8+/0viInSMfP80cybMoEfzbqA+6+9CIBmg5kBfeLJ6hNPYVVDyNcwrH8Kn/75RiRJZMkzH7HOx73daDDz2fbjfLb9OIIAowemMWdMDvPH5bB49phWwywLBwoqeG/zQQ4UVPDFg7eg1ai4/blPXOPMu0h6YjTrl95KlFbDnS98yspdroFlS5d9TXpiNLNGuaa6Th+ZxfSRWTx398WU1DSydu8pXl3tO6uREK1l8+O3Ex+l5a4XPuWrXZ0PQfPHyfJ6TpbX87/1+3n5Fz9izpgcLBaze4MrbxxsNhvRWi3TJ45i5ZadlFTW0CchtsvtqIrmwoPusgB3Op3U1tZSWVlJamoqFRUV7c4lCgKnqlw3W0dzRdoe1zUhdczAPmEJgJxOJydPnuxQuAmuDgqNRsNnuwr4zVvfYPJTr5SJUEkhWxP7Q96VaLVaKisrKS8vR6fT4XA4sNlsYeneEEWRykYDkihQUesK/Kobm4mPSm7zud7MXAiCQKROy04fYs6+idFufU+o9MQCMWjQIF5//fVuPUdP471ueNp45+TkdLghEEURUZLIr2zi9pc2UlTrO+s39TyXCDzYUesdUdtkYPvxEuZOHu/3M816I59t3EZiXAzXXzyDyvoWPtx6mDke3RWyh8VbG/YH1S56ycTBvPjTS2jUm7n5yQ/bWVr7wunEPdzsqY+2khIXxcxRWcwdm8PMVhGmzW5HJUms3JXHgcKKTo/ZGcP6p/DZX27C4XRy3aPL2H68tM3Xy+taeGfTAd7ZdAC1JDJxcCazRrvcS+9eOIG7Foyn2WBm76ly3t90iE+3H6NfcixrH1mCRi1x85MrwjJ1VqdR8cZvrmTKsP6YTMY22TR5fXc6nZgtFuZPGc/KLTs5cPwUcy4Y06XzyuuOUhbpgK56KTgcDsrKymhpaXGLtioqKtrVo1SSSEFrcKGSxA4NtDw5UFjJqIGpIV+fTHNzM3a7nfT0dOLi4twvFe8XplqtRq1W88yX+3jmy30BHVsthWeuiCdGs5Wy2mYSYmLcKWij0YjRaKSiogKVSuWelxEZGRnSC9KJwKGiWmaP7OfOXFTWNTM407eTZG8EF3J2xZeYMzMxCmsAMxE64lzafZxNSJLkdvc1mUwUFRURGRnptvHu6PtUKhWf7irg9+9ua1fq8mRCtus+PNkF629ffLr9GEuXzGFg33QKSsp9fua5++9hyeVz2Z1fzq3PfExdi5HHPviG1Pgod7fH9FYHSrPVxqmKej7ffpzX1uzx25r568sv5DdXTeZEaQ03P7mCivrQSqnVjWcMqm6eMYpHb5tDZb0es9XGwgmDmD8ul0aDS4fw1vr9rN13MqjjzxiZxRu/uZJGvZnrHlvO8ZKaDj9vtTv49mgx3x4t5uH3N5ORGOOa6jomm+kjspgxciAv3LMQlSTiBO58/tOwBBbROg3v/P5qxuVkYDaZfLbtu6ftAgunnc+vHnuJw/mnmXPB2C6f/1xaO3otuAh152exWCgqKkIUxXb6Cs9djXsaqpy5UIkdGmh5cqhV1GmxhPYS8UzVCoLgdmf0mbFQa0AUufe1zXziR2DmC41KCntwAXC0uJqLhvV1X2tkZCSCIJCa6gq2jEYjVVVV2O12d6ARFRXV5u/QEYIgUN7gum45uKiqb243Irk354poNBoq6vXu6/QkszVz0RXOpbppb+Hrby//W11dnTtjKdt4+0OUVAiiyEMrdvHKxqOdnndoZgI1TQaaQ/A46IivduaxdMkcfnfr1fzs4X+2vUZRZM3LS5k48jyfws3KBj3vbjrIu5sOEqGWuHBIP2aPyWbhhEH84Zqp/PaqydQ0GvjmSBEvr97l7vb4988u5fILhrBu3ynu+efnYTGIuu/aqfz8sknsP1XBoqc+oq7FSN/kWFfwMybHPSLdZLFRUFnPFzuO8/ravR1mja+fNoInb59HUXUj1z+2vN049UAoq2t2+3yoJZEfL5zA/107FZvDiUYl8eqvrqBJ78pqvLfpIJ/vOB70OeKjtCz7v2sZ1j8Fs8noM7Mtay7AlVlLS05gxKAsjp4sQqNRYzZ3zf1TyVx4EM6yiKc3RFpaWpsF2jtgEQQBh9PJ6Zpm4iI1SKLYoYGWJ4dOV7l1EcFep91up6SkBLPZTN++fSkpKeHUqVNER0cTExPj3vELgoBK7bqu3QVV7vJNIKTHRyKKQtjLIuASdU4d3p+IiAgsFgvl5eVERES4x4hrtVri4+PdWY3m5maqqqrcw7mioqLcw7m8kcWcVa1zUspbg4vqxhac+BZ09gaSSs22Y2U+v9YvKRqxizHPuaT47k38tXJXV1e3sfH2972CKKG32Pjxy5v5Li+w9sp+SdFu7VE4qW7Us+tEKfOnTmjz7ymJcWx56xlSkxMCEm6arXY2HSxk08FC/vzWBgZlJDJnbA4LxuVyxYVDuHrKMPQmCw6nkxhdBK+u3s1f3t7YLngPhZfuvYwfTTqPlbvy+Nm/vnS3wZfUNPHmun28uW4fWrWKyUNdwc+C8bn8/uqp/ObKydQ1G/n2aDGvrdnDjhNnyh2/vvxCfnvVZPYXVHDzkysCXqM7Ys6YHP5wzVRKa5u5ZukycOLOalw0YgAzRw3EbLVRUtPE6j35vLJ6d6ezRJJjI1nxx+sZmJaA2WTy+17w3DjY7XZsNjvzp4zny69dnSkdiZI7I9wzubqbXstcBDMjwFNfkZGR4dMUxzsQEEWRqkYjJqud89LjAdr1JvvjRGktNrsj6OBCFm6qVCoGDBiAIAjk5uZiNBrd005tNhspKSmkpWdQXNNITaOB8YMy+Or/LqPFZOFwcT3Lt+XxwfZ8vyroQWmuuSK13ZC5qGxoQaOSMNkEqqqqUKvVZGRkAGdubvkGV6lU7qyMyWTCaDRSXl6Ow+FwBxqeI8fl7EZlY9vMRU1jC5Io4nlH9GbmIiJCwy4fJRGA/sldt4Z3OBznjMve2YKnjXe/fv06DCxkfcWx0gZu/+9GyuoDf07idOoODaq6wifbjvHwotn0T0+hqLyaiSMG8+mLDyGIEouf/oj1PkSWnZFXVkdeWR3//nIncZERXDl5KH9fNNttr7949hhmjclmzZ58/rNyV0gDuUQRPv/LzYzLzeC/K3fx0Lub/AYrJquNDQcK2HCggD/9bz2DMhKZNTqb+eNyuWTiYC6/YAgGs5UTpbWYrDbOH5zJur0nueefn3dYrgqUm2eO4rFb55JXWst1jy13Z3f/t2E//9uwH43KNbl01uiBzBuXw08vOZ97Fk6kyWBid345721uP5MlIzGGjx64gYzEGCxm/4EFtF+3HA47C6dN5Pm3PsFqs3VJEuA5luBc4KwvizgcDkpLS9Hr9QwcOJDISP8Okt7HzG+dCZGZ1DoRNcCo2GKzk19Wy+DMpIA+D2dcQWNjY+nTp4/7mgRBIDo6mujoM+l0nS6Srw8X8uMXPqPZaCE+SsuMUVnMGeNy0Ht60VQevfFCSuv0rN5fxMsbDlPZeCYwygrzXBGZhRMG8cfrpmEwW5EkNYMHD8ZitSHgxG63t9lxOxwO7Ha7O+CQJ4EmJCRgs9kwGo3ukeMRERFERUWRmZlJRYMejUrEZLFR3+y6/prGFtQqCe+/Tm8EF5IkIYmiTzGnKAj0idVhs3YtZa6URYLDM2NpNpsD0lcs33aSPy3b3qkw2hOVKKLVqMgPY6eIJ1/tPMHDi2fzu9uuZdeRPJ75w90BOW4GSv8+8fzphulYbXZue9bljjl3bA4Lxg/i7oUTuWv+BBoNJnYcL+WN9XvZdKCw02PG6jSsf/RWMpJi28wyCRQ5+PnPyl1E6zRMH5HF7DHZXHfRcCTR1ZI+KiuVR5bM4ZXVuwOaFuuPX/xoEn+4eiq7T5Zxy5MrfJa2LDY7Ww6fZsvh0zz07ib6Jscyq3XK7EXDBzB7jEvPUlzdyOrd+azec5KXfn4ZybE6zCZjp+8tz7KI/N/jhw0iJjqSU8XlDMwMXcvnOa35XOCsLot46ityc3M7jNjaKcntTk62lhoyAxha5s3+gkqy03yPAvemvr6esrIyUlNTiY+P9yvc1Gg0qNVqXl+7hz+/tcHtNtegN/HJd8f45LtjSKLA+NwM5ozNYeH4QdwzdwQ/nj2cBoOZ7fmVvLH5KH1bR35Xh2muCMCtc8byyJLZlNU2Mev+N7DaHFw4tC+zx+SwYHwumUmx2B0OHHYHTqfD7TIJrgfI6TwTgMgC1djYWJytPeBGoxEnAnsKqkmNi6Sy/kwZqKbhjJGW/AD1VuZCkiSMFhvHytp7ovSJdbl2Wsxdz1ycKwtEb1NdXd3GxlsWSftCEATXRGGnk1idmkvHDWD9oVLq9YE5I07MSUESxbB2inhS2aBnT34Z1y2czqIfzWZ3fjm3PfsxtWEww7v8gvN44e5LqGsxctMTH3K02PWS3pVXxqPLt5CZFMPs1s2L7M9gsljJK6vjk++O8ea6vei9NBkDUuJY9fBiIjVqfvzCZ0FNWfVFi9HCyt0n+OXlFyCJIv/6cgfNBjPzxuVy/bQR3Dh9JC1GC4eKKln+9WE+2HIYW4BZwr/dMpM75o1nw4FT3PX8ZwE7F5fUNPG/9fv43/p9RKhdWY3ZrR0oP7tsEj+59HzMJjPFp0+7N0kdvYe8Nw42mw2tVsucC8ay//gpsjJCDy7k99u5snactd0inruV9PT0Tl803pkLSRI51drHnhrnynYEWhYB1/j1a6YOw+bwf53ePfaRkZEdCjclSeJPb67jtbX+jWbsDqfbh3/psq/pmxzr7jWfPaI/C8e4WsDAJYR6c/0+TF1IJwoC3H/dNH5+2SQOFFSw8K9vucsxGw8UsvFAIQ/8bz256YnMHpPN/HE5TBzcF41G02lWQw46IiMjiYyMRJIkDhTVkpsWR0WtR3DhQzvSW8GFIIrsOVXt02Y4M6nrBlry9yuai45xOp0UFRVhMBja2HhLktRhvdtisSCKIrOHZzJ/VD+cTthTWMOq/UWsPVjSobbpglzXwt9dmQtRhLhILWqVCofTpcMYmJrQ5eDi91dP4Zc/uoCjJdXc/OQKqhraP0+ltc3ul6hOo2LKsP7MGZPDggm5/PnG6dx/3UVUN+rZdLCQ/67aRYxOw7L7rsNis3P10vfZledbgxQM0VoNGx9zZUHue30t/1u/D4DnPt1GYrSOGaNcWY05Y3J45q5+PH7bXMpqm1m3/yQvr9zN6Wrfs15e/MklXDl5KB99e4Rf/XdVyLbtZuuZmSwfbj3Mij/dQIRKwmDQI0lSu0xsZGRkG32Zr/Z5p9OJ2Wxh/tTxHMk/zZVzpoR0bXAmK9Jb5eJgOevKIoEMHfJ3TM9FRxIFCqpdC0lKrA6Hw0mTMXBv90OFVUiiiMNPlsVTuDlgwAD3KGfvqFK28rbZHdz27CdtJhkGQklNE2+s28sb6/aii1Dz5m+uZPLQfpitNh68eSYP3DCNino9G/ef4j8rd3GyIvAxxmpJ5LkfL+SqKcNYtSuP2577xO9n88vryC93pTdjdBqmtaY354/LITEmEpvdjrM1mLC11hbhTFZDEARUksiBolqmDE6novZM+lO2AK+srHTrNXoLQRDZke9b/JeZ4MoY/ZBc9noLQRBISEggPT29zU6xs0nDcgYNwGJ3PY9j+icyNiuJB64cz+maZlbucwUauwvaBpEj+yditFgpr+/6AC9vkmMjWfvIYvrER/O/9fsYlJHEvHG5XDxxMHqThcNFVSz/+jDLvj4U8G4d4OVf/IiLJw5mzZ6T/PRfX2AMoCPEaLGxbt8p1u07xf+9sZah/VKYOzab+eMHuTMIdocDAYHnPv2OPSe7HlikJUSzYemtROk03PV8eyOruhYjH317lI++PYooCIzLTWfOmGzmec0b2XOynHc3HeCLHa4synv3XcOMkQN5edUu/vrOxq7ME3QzYVAG7/3hWjQqAbPJNV04KSmJpKQk7HY7er0evV5PeXm5e/MUFRWFVqsFfGXrncyfMoGP1m5FrVJh7uQe9oe/jPjZylnl0OlwOCgpKel06JAvPCejuttQW905k6K1NBvNQd14h4uq3Mf1Ri7XSJJEVlaW+3PtTLxarbyrmwzc9MSHnfZud8Z/7r2Mi4YP4NPvjvKr/65iUGaia/cxPpdFs8dwy6zRNBnN7D1ZztsbDnSYxozWaXjj11dy4ZC+/GflLh58Z2PA19FstLgH/QgCjMpK5YEbZnDBkL6oJRG12jVwDafDLQCVxZwHimpJjdNxOO/MDrK2NbhISkqitraW2tpaLBYLgiBQV1dHVFQUGo2m2x8qQRBQqyS/Ys6+iVFhGWaldIsERmxsbLt1IlhBnOv7HWAHhyjSNyGSO2YM4Z45w2k0WFh7sJg1B0v4+mgZA/vEUlBRH5YXlCfjctNZ/n/XIQoCS54+444Zo9MwfWQW88blMm9sDk/dOZ+lt86hpKaRla1Dzar8lD41KpGvHlrE8AF9+NeXO3j4/cDneXhztLiao8XVvPDZdv568wzumj+e0pomEmMjuf+6afz2yikU1TSyalceL3dwTf4Y0i+ZL/56M04n3PDYcr47VtLh5x1OJ7vyytiVV8ZjH3xDWkI0s1onlk4fmeXu9nA4neg0al76aicPvbsptB/eiynD+vPW765CAsym9ho9SZKIjY11l3xNJhMGg8Gd1QBXq3R0dDRardbt1hkbHUWkzmXyFqqo81wrp/aa5sL7l2s2mykqKgpo6JAvPLMhoihidzgobnXhi4/SBDQR1ZNmo4WSmkYyEmPaXKvBYKCoqIiYmBj69OnToeOmJKk4UFjJ4qc/6pL4UqMSWf3wYob0TeH5T7/j8Q+/wemEg4VVHCys4tlPviMlLqp1AJHLQW/GSNcDWFjZwOc7jvPKqt1uo53U+CiW/d915KQn8pe3N/DqmtDnATidMH1kFhcO7cu+UxX88j9fMS43gzljspk1KptonRarzY5KEimv19OgN5MWH+nuFAGXyKrFaEatVhMfH098fDwtLS3U19djNBqpra1FkiR390moBl6dIUkSDqeTPQW+RWUZiVFdMn+DMy5759Ii0VuE291XDnQBLA47URqRy8b155pJOVjtDpwOBwcK9aQnRofUVeGLm2eM4tFb51DdqOemJ1e02WA0Gy18seMEX+w4gSi4tFZzx+Vw8YRBrnr/JROpazHx7dEiXl65m135rgxCn7hI1i29lcQYHX94bQ1vbdgflmv9xz0Xc/WUYazZe5Kf/PNzrHYHEwed0X/de9kkfnrJROpbTHx3rJjX1+7l26MdD3i7aHh//vfbq2k2mrnu0eUcC2GDVVHf4vb50Kgkpg7rz6u/ugKdxrVh+fGC8Vw7dTg7TpTw5rp9bD4UmlnWnDHZvPqrK8DhCMiPQhAEt5A9KSnJbexms9koLXW128pZDbVGw/CcAeiNJlQhtqP6e9ecrZwVZZHm5mZKSkqIj48nLS0tpF+e56IjCAIldXpsrSnPWJ2G2ubgX+77CypJiz/T8tbQ0EBZWRkpKSnEx8d3auUNMDIrlZV/W8T6fad4aeVOCisbgrqGPnGRrF16K0kxug4H7lQ3nhljrFFJXDCkL3PGZLNwwmD3JL/aJgO788sYn5NBfLSWO57/hDV7gnPS8+bRJXNYPHsMGw8UcNcLn2K02DhZXs8HWw6jkkQmDspg9pgc7po/jj2F1URGqIiNjGgTXADUNelJS4jGZrNht9sxm13BhmyZbrFYMBgMVFdXY7PZ0Ol07mBD/l13FUmSOFnRSLPJd2o5HB4XoGQuukK45hLJ5ROr1cqJY8cYMmQIoiAyLjedPS/8hMNFVXy1M4+1e/M5WNi5XbYvHrt1LotmjQpIuOlwOtmZV8rOPJfWqn9KXGunR6577LfBZOFkRR25GUk4HE5ufmIFmw8VhvgbaMtnf7mJiYMzeX3tHh743wZ3q+l3x0r47lgJf39vMwP6xDNnjKutdP64XC49/zyMZivHSmpY8e0R3tl4oI3+69qpw3n6zvkU1zRx/aPLKakN3MvHH1q1xNN3LUAlifzkxc/Zd6rCNa59bA6zx+SwcMJgjBYrJ8vr+Gzbcd5ct5emAAzRLpt0Hv/66aU47DbM5tBGo8vvgvT0dHdWQ6/X09jYiFarZeFFEzl6sojhOf1C0pQpmYsAkBcIT31FRkYG8fHxIR+zTacBkF9xRvwTFaGmoDx40dShwkrmj8vF6XRSVVVFbW0tmZmZ7nbYjqy8n1yxlTV78t2jg5fMGcPi2aNpNJjZlVfGm+v2sG5fx/qLkVmpfPzADQiCENRCYrGdESb95e2N5Ka7jHbuWTieuWNzUUkiRrOVn15yPskxkSwPQpXtyWu/vJyFEwez7OtD/O7V1e1KBja7g++OlbD9RAl3L5zgKonEukpd5bVtxVlVDc1kJMWiUqmorq7GZDKRmZnpFvDJRl5yq6v84NbU1LiHrMkGXqE+gE5B5Ls8/zMSBiTHhMXc61xbJM4mOhJ0hoIgCFgsFrdOyN6q0zgvI4nzrkji91dPobKhhZW78lizJ5+tR4qxdGL/LorwyQOul/X7mw9y3+trO/0eb4qqG3l1zR5eXbOHaJ2GGSOz+PUVFzK0Xx9UkojVZmfprbNZtSuf/6zcGXSpQiZSo2LDY7cxoE98pyZep6sa3NcUpVW7dVcLxuXy8KLZPHjTTCrqm9mwvwC9ycJdCyZwsLCSm5/8MKC5Tp2RlhDNhkdvJTJCzZJnPmLDftf6KV+TLkLN1GH9mT3aZeD1x+uncd81U6lpcrmXvrp6N3tPtX++r582gmfuXIDNZg05sIC2baieWQ35v4cmJ7Nm626sNhsnT55sk4kNJFPv3eZ6ttNrZRFZDW4ymYLWV/jCc0dj95iGChChFqkLQZF98HSVK51fVk1DQ0OHwk1wdYQ4BYGfvPg5n3x3DHC5fT77yXekxke5uj7G5zJtxADmjMl2tYKV1rHi2yPtuj4umTiYf/30EupaTNz4+AchpRNl8svruGnGSJLjothxvIS3NhxoNZHJ5em7FvDYbXMpqW1i9e58/rsqMKOdL/56E+MHZfLPz7fzyLKvO/zsRcMGuMScp2tIi3eJNb0zFxV1TQiD+uFwuARSffr0cT9wnq2uTqcTtVqNSqVye4eYzWYMBgOVlZUh25IDRKhVfvUW4Bq37nR2XcypdIsEhj8L8HAGF57CY8//H1zncNjtJEVruWnGKG6dMxaj2cqG/QWs3pPP+n2n2k1aTo6NZO3Di+mTEM1D727ipa92dvkaW4wWRgxI5bzMZA4XVfHo8i1MHtafiycM4qeXns89F0+gvsXE1qNFvLJqNzsD7OzITIph7cNLiNZp+PE/PuPz7YFbYutNVlbuymPlrjx+L6xmVFaqK3MwPpfFs8e4NxoGs5XJQ/t3uY01Nz2BlX9bBAhc9+jyNi6fMkazlbV7T7J270n+7421nNc32SUKHZvTxr30WHE1K7Ye4Z1NB7h55miWLpmDxWLpUmABHQu1ZT+gzNRkoiJdnj96vZ76+noqKircHSiyMNTXvd8bmxKn08kdd9zBpZdeylVXXRXU9/ZK5kIeO2u320PSV/jCM3OhkkR3pwi4uiIa9MEHF4dbpwfqdDri4uLcok3vP7zcEaI3W1n89Ec+27YqG/TuiX5atYopw/szrzWr4er6mE5FfTPr9xWgN1v48YIJnCit4aYnPqTSR2tZMLz6y8tZOGEQn247xi//sxKLzc7H3x09U+dtVYrfc/FEfrxgAo16EztOlPL62j3t6pcalciGR28jJz0xILtigHnjcgE4WFzHjKEut89Kr+CiplFPVUMLp6samTg4E0kUAzbw0mq1aLVaEhISQrIlB9zH3uXDPAtcpbWoCDVGY9faBuUXoxJchEaw7r6d4d1G6I1n90lDSzNGo5EZI/txyfmDcTic7M4v5atd+azZk098tNancLOrvP7rK5g/LpeVu/K4999fYrTY2HSwsMPyyeGiapZtOcSyzb67T8blpvPB/ddjtzu41scE0mBwOl0l5P0FlYzJTmNEViqrd+cjigIzRmYxZVj/Nvqv19bsCSqTMS43nQ/vvx6Dxcq1S5e7PTw643hJDcdLanjxix3ERUYwbaQr0zJvbA5Lb53LQ4tmoZYktw+PnIkOOfPZSanDarXRPz0FSRLd61FycjI2mw2DwYBer3drNXxlNXpaq+VwOHjkkUfYunUrl156adDf3yPBheeLv7m52W3jm5mZGTYrU1EU3UGLKAhujwtwBRuhpOUq6luobzGSkJCAwWDwWQYRRRGVWk1hZQM3PvEhJTWd1xVNVhvr951i/b5T3Pf6WkZm9WHu2Fx3+USO+qsbDQzv34fKhuDaV89cG3z54CLGZKfx4hc7eGRZW0V5mzrv8i30TYpl9ths5o/NZebogcwfn4uxNbvy8XdH+HTbMVb+bRFJMZHc++8vWbH1SEDXMT43g9K6Fhr0ZlLjI2kymNCb2tZBaxpbcDjhyoffJzYygukjs9xmNgnRutZWVyeOVt8RfwZe8qA4T1tyg8HQrm3M2wxHkiRqm01+R3FnJka5z9cVzjWXvbMNSZK6vMP0RN4sdGbpXFdXR0VFBRkZGahEEbPZjCiKjM1OY1xuBn+9aQY2uwOVJHLf62vdKfuuoFGJrHl4Cef1TeYfn2/n0eVft+sI8S6fTB+RxbxxOcwbl8tTd8xn6ZI5lNY0snJ3Pv/5ylU++dGk8/jHPZdQ06Tn+sc+ID8ME2BFEb56aBGjB6bx3Ccu0bnrZzij/1owfpCH/svI1iNFvLJmN3vyfU+JBZg1aiCv/foKahoNXL10GaerGkK6vkaDmc+3H+fz7ccRBHjlF5dz8cTBbTIWniJLURTd/wuUzssWTmKizpTUPe28vTtQvLMaBoOBiooKhgwZEvwPHwKVlZX87ne/o6SkhNjY2JCO0WOZC6fTSXV1NTU1NWRkZFBaWhrW4VTyAiHvCNtkLlRSyDW/g4WVTB7az2dgIUkSao2Grw8W8uN/fBbyJMWDhVUcLqpi5qgsAL7YcZxorYZpIwYwfWRW6wu+lhVbj/C/DfsDMs2K1mrY8OitZCbHdmrcJVNSe2YAkVy/lHdFf71pJn+6fprLGvtEGQcKAhsEBdC/TxxbT7g+nxYX2a4kAi6XzuRY14PX5LUQjB6Y1lpSymHEgFScTqe71VXOYPgz8PJUc1utVp+25FFRUWgitOzooJ9fDi66es+eay57vUlPlEWgY98dh8NBeXk5zc3N7cYPuK/Dbschiq7Ax2Ll8dvm8sfrLmLVnnzW7M5n08HCoCeSpiVEs/aRJcRHafntK6t4d5NvIbcnLR4t4rJXhJwdlWdoGMwWIiPUFFU38aO/vRuW4YeRGhUbH7udvimx3P/GOt5Yd2at8dZ/5aQntJoC5nLZpPO4cvJQ9CYLR4qq+fCbw7y76aA703LNlGE8c9cCCisbuPbRZe4MrkYlMmdsDit35QXdfisI8LdbZrkDC5vNhkajabdJ8ewsktf+zrIanWUWbDYbERFn2lF9X98ZrYac1dDr9Wzfvp0XX3wRSZKYNm0ad999N4MHDw7uhw+Cw4cPk56ezvPPP88111wT0jF6JLiw2+0UFxe30VfIO8lwIS8QgiBgsdkpq3fdiH1itYiCEJT1tycHCiq5YEg/v8LN19bs4S9vb/Dp6Bgonv79nqUG2Ulv3rhcFozP5aFbZvHnG2dQXt/Mur2n+M9XO3261mUmxbDukSVEajUh2/Z61i/f3rifT/98ExabjYKKBiYMzmDz47fRbLSw71Q572w8wGd+6rWiCDG6CA6cdmlGUuP9BBeNLUSoVcToNG2CNKcT9p2qYN+pCp76aKu75XbumBxmjs4iUqdzDQRq7Sd3nTNwW3KDwUBDQwOJSUl+zbPAZaDlcDjDElycS+1kZxvh6hbxxF/AYrPZKCoqwuFwkJ2d3WFXkvwyKi0tRafTkZiYyJUXDOH6i0Zgtdn55nARq3bnsXbfyU41TRNyM1h2/3U4HE5ueuJDthwOvrXS0yti6fIt9EuJ453fXUVORhKiIJCVGs+2p+/iSHEVy74+xPt+yiedkRofxfpHbyVWFxHQWnOyvJ6T5bv4z8pd7gylbJb1+O3zeHjxbMrqmimpaWTSef04UOAShMrTUn9+6SR+deWFREao+e5oEb94aWXAXSiiIPDUnfO5YdoId2Dh/lrrmuFvk+LZieivfNJZWcThcGCxWNFo1AhCYJsLlUpFXFwc1113HRdccAGnT58mLy+Ppqaud950xKxZs5g1a1aXjtHtwYXT6aSwsBBBENroK7oyHc4X8qIjCALF1c3uiLZ/cgwQ3FwRTw6drkKjkrCaz9w4mogIVJLE7rwynvpoa5cCi+zUBL762y3oNKp2D6enk94fXlvDqKxU5o5z7URumzuWJXPG0KQ3seNEGa+v28OmA4WMyU5lxZ9uxGZ3cO3SZT6FT8EgpyXrW0xcu3QZ+eV1JMdGtnpq5DJr9ECmjcjihXtsnK5q5Isdx3ll9W53pmjaiCy3MydAaqyOwpL21yS7dCbHRnaYAfJsuVVLIs/+eCFXXDgEvclKbGQEDofDVVYKIKsh25KrVCpUkn/zLIC+SVFYw3C/nmsWvmcb4e4WAd8Bi8lk4vTp0+h0Ovr27RtQpkm+18rLy2lsbCQmJobY2FgiIiKYMqwf00dl8bgwj0OnK/lqZx5r9px0m/XJXHvRcJ66Y77LF+PxDzkRpjknz/94IYMyk3lrw34eeX8zU4cPcJdPnmwtn5TUNrFqVx7/XbWbivrORd3nZSbxxUO3IADXP/4B33XieeGNZ4ZSFATGZKcxd2wOd8wfR7/kOBBcQs4X7rmYI0VV3Dh9FH1aBeGFFXX0S4ljw6O38uC7GzvN7KgkkRd/cgmXTjoPi8XS6bvHsyTivUnxVz4JrJvjzOykYBFFkVGjRnHxxRcH/b3emEwmt+mXNykpKX4HhAZDj3SL9O3bF7Va3eYXH+4diKzrcDjhROs0VMA95KshxLKILOpsaGjAarWSlp6BxWanpKaJ8YMyOPivn1HTZGDzwUL+/dVOjhUH3tXhMpi5CqPFxpUPv8+ek/5rjwAHCis5UFjJ0x99S1pCNHPH5jBvXA4zRroWCrPFikolYbHauerh9zjchQmDANe1LnSnqxq47rHl7h1XTZOB5VsOs3zLYdSSyKQhfZk7xhX0/ObKyfzy8guoazbyzeEi1CrXA3qo2LVIpsVHsv1A+6i7ukEOLqIoCNAL5FdXXMgVFw7h60Onuf3ZT+gTH+UWbE0e1h+d1/wTaJvVkP+nUqkwW+0cKvZfe85MjMZht3VZsa20oQZOT5VFvC3Fm5qaKCkpITk5mZSUlIACQfleSkhIICYmxu1vUFFRgVardeuA1Go1mbEafn3FBfzhmqlU1rfwVWub6/SRWdy1YDwHCipZ9PRH1IZh6rFOo2Ld0iVkpyWydNnX/OPz7QB+yyc/ueR87l44kfoWI98dLebl1bt9blCmDuvPW7+7mhajmesf/6BL00zBlWnZc7Kcm2eOIlqr4YttR9i8/yQ3zxnf6mGRTXm9gYc/2cMDV4zjH598w6ffHuKhxfN5+s4FXDJxML97dbXPrFCEWuLlX1zO7NEDsQYQWHjTWVZDzoDYbDb3/envGZfLMBqNGoslOO1QOLvM9u/fz+LFi31+7cUXX2TOnDldPkePlEW0Wq1PG99wl0UcDgcOp5MCj+FE6a0TUb1bxgLlVEU9JouNPn364HBCbbPR7bSXk57gEmKOz+WaKcO57qIRrf73Zby5bh+rduf7Pe7iWaN5ePFsSmqauOHxDyjyM5THHxX1Lby1YT9vbdiPTqPi2R8v5NKJg12WuBFqVv59ERV1Lazde5KXvtpJcQBCU09++aNJ/P6aqew7VcEtT65wpyW9sdodfHO4iG8OF/HXd9rXVFWS6BJzGlzZiI7KIgDJcYFFzA/ePJO75o9n1e48fvLiF1hsdoqqG3l97V5eX+uawzJlaD+3PXpqQjR2uwOH44zwU14A1Go1+4pqsHZg7d0/KRqjwUBBQUG7VtdgshDnmsve2UZ3Zi48fXcyMzOJi4vr9Hvl1mL5hSWPfNdqtSQlJWGz2WhpaaG5uZmamhqcTidarZaUlBRiYmJIjI7g5hkjuW3uWJeWyO7g3U0HwvIzpiVEs+6RJcRFafnZv77go2+PtvuMr/LJ3Fbx5YIJg7h00nkYTFaOFFexfMth3tt0kMsvGMKzP15AaW0T1z66PCAReyC88esrmT8+l/+t3cXf/reaf/78akYOTEdvtvLsyoO8vvk4v144CpvdwcO3LWTu+MGs2XWcXSdK+MMNM9n46G38+a0NfPDNYfcxdRFq3vrtVVwwpC8WiyUsv1fvrIbD4cBqtdLS0oJWq21TnvUWhdrtdux2O5IkBb3BDme3yKRJkzh+PPDW41DosW4Rb8JdFpFTzWpJbDP5MDXO5Z/h7+XYGQ6nk6PF1YzJTmPfqYo2Vt6u+uFOXvpqJwnRWmaPzmb++Fxmj85mxsiBmCw28spcQkxPH4uHbp7JHfPHsTuvjCXPfBzytcn88fppXHr+YL49Uswdz3/CwNQEd/nk9nnjuHXuWJr0JrYf991e6o3surnhwCl+/MJnGIOYuupdU939/D1uO+34SA1ataqdgRZAQ4sRm93uFnV2xJN3zOOmGaP4+Nsj/PI/K32WpYxma5vhTEP6JjNnbDbzxuYyLje9TasrgsiO/I5dGPsmRaOVnPTr1w+j0RiygZeSuega3am5KCkpaTeFtSM8vVfka/NGpVK53XxbWlpITEzE4XBQVlaG0+kkOjqa6OhoYmNjWzUdAk/eMZ/Hb5vHrrxSd1Yj0GyejGzA53TC9Y8t79SmW6a4upHX1u7ltbV7idKqmTFyIHPHuTYLT9w+j6VLZiMKInqzhduf/SRsgYXsEPr8R18jCAKHXr0PjUri7a15PP3VAepazKglkZun5ILTgVoSmD02l/kTznOVwqvqiUvU8sI9F3PJ+YP5/atrMFmsvPuHaxmTnYY1TIGFN/L9WF5ejkajITU11a3R8MxqeGo1bCEGF+eas2+v+FxA+BcJURTdPgaemYuUWB02u4OWEDs5APYXVDC0XzJXPvweZqvvgKi+xcSHW4/w4dYjaFQSk4f2a516eMbHoryuGYfTwYA+CXy09Qi/fnlV0M593rz5myuZNy6XD785zG9eXoXV7vBZPpk/LpeZo7Jc7aVmK8fl7pP1e7HYzvwdOnPdDAaDyYpWo3LrLdLiXYGDr8yF0+mktlHfaXDx759dyhUXDuXtjfv5w2trAlaLHyup4VhJDf/83NXzPmPUQOaMyebyC4egliS2deDMqVGJJMfo0Ov1bvOuUA28uju4MJvNPPTQQ6xZswatVsvtt9/O7bff3m3n6056qiwCUFVVhUqlIjs7OyDjNfkF0plAV3b3bWxsZMCAAe5adlpaGiaTiebmZmpraykrKyMyMtJdPtFqtYzNTmP8oAwevHkmBRX1fLnzBGv2nGR3fpnbntsXC8cP4t/3Xkp9i4nrH1vOidLQdBt6k7VN+eTt31/N9BFZNOhNJMboWLf0Vur1rvLJK2v2sL2TgWS+UIki65a62m3X7T7B4nkTSYjW8fWxch5asZsTHk7LC0b3IyEqAqPR6A7orLgyjxlJsdjsdlSSxPxxuUwbPoDqRgMZSdHYrN0TWMAZ0a9GoyEzM7PNfeDtxyNvpq0WCxEaTVDrgHwMJbgIgO4KLoA2mYvEKNdE1K5wqLCKiNkqdBq13+DCE4vNzqaDhWw6WMgf31zHiAF9uOT8Qfzisgtaf24nM0Zl8eJPL+GV1XvYfjy0h3Ll329hxIBUXvhsG48u3+Lzc97lk6nDBzBvbA4LJgzi74tm8ZebplNe18LavfmMz8lgTE46//h8O0s7cd0MhFV/u6WtmDPOf3AB0KA3cvu8cZitNt5YuxeDV8bkzd9cxbxxOby8ahd/eTvwKa7eNBrMfLrtGAsm5KKWJF7fdJSNR/wLX9NbRWSSJKHRaLpk4NXdFr5PPPEEhw4d4s0336SsrIz77ruPjIwMFixY0G3n7Ek8697hCNJkAyWtVsvAgQMDFm7KNfeOAgu73U5ZWRkWi4WsrKw23SaeLYd9+vTBYrG4yydym3R0dDQxMTFERUXRLzmGuxdO4N7LJtGgN7Fqt0sQutmrzfWeiyfwp+unk1day41PfNBlAz6Z1351BTNHDXRvOtITY86UT8YP4tLzz8NgtnK0qJrlWw61aSn1R6RGxeYn7qBvcixWm53Z4wZxqqqZX/zvOzYdba8/W3zRIPQGA/qWljbDCz1N1eytZSmVJJKZHIPFbA5r+d0TObCIiIggIyPDpweSL1GoHGQ4nbinPwdi4HWuDTzstbJId8wI0Gq1GC02qpvO6CvioyJC7hSROVRUhSAIzBuXw/Ithzv/Bi9qmgzcMmM0Dic88OY6HE4nCz0eSr3JwqHTVby76QAfbj1MZ7+WWJ2GjY/dRlpCDPe9vpb/rd8X0HUYLTZ3e+kfXm/tPhmbyyUTB3P73HE4nWC22hickcT0EQNCni4oirDqocUMz0oF4JBXcFFZ3+zz+yrqmhiQmsifb5zB/ddNo6qhhU0HC/nvyl08smQOU4b1b2PQ0xVe+cXlXHL+YP67/jB/W9GxRbOnx0VHKnJPAy/A3eoqt13X1tZy7NgxJk6c2OXr94XBYOCDDz7g5ZdfZvjw4QwfPpy8vDzeeeedcza48BZb+rLrDpXGxkZKS0uJiIggJiYmoON5CoE7CiysVivFxcVIkkRWVlanO06NRkNiYiKJiYnY7XZaWlpoaWmhqKgIURTdgUZ0dDRRGjVXXTiUG6aNxGKz883h06zanc+4nHSuu2gEWw6f5o7nP0HvZwBfMHga8XluYnyWT8a6xhvILaWltU2s2p3Pf1buatd9khSjY9Pjt5McG4nd4cBgsfPEF7t5Z2u+zzJnTmosF+SmUllZSUVFBQ6Hg6ioKHcWUe5C9Hx5dyc2m43Tp0+j1Wp9BhbeeItCzRYLQqs3imdWw9NPw/t+7K3MxYYNG0L6vl7LXHTHjACNRtMmawEQo1NT0+D7ZRYox4trsDscTBuRFXRwMTo7lRV/vAGgTc/6m+v2tXHUWzB+EM/ffTFP3j6fouoGPtvetqVTZkBKHKseXkykRsXtz33C6j3+RaMdIVv2FlU3csvMUdgdTv791U6G9Uth5qiBbconH35ziLc27G9TPvGHRiWy8bHbyEpNoLS2CQcija0lqbT4SKobW1wGWD6obmhhd34Zf3l7I3PH5LBgQi43TB/JTTNGYbPbqW7Ud2nGiowsHPv32oM88vHuTj+f2dpx5L0D6kxF7mngZbPZKCsrY/Xq1bz44osMGTKE3/3ud0yZMqXLP4/MsWPHsNlsjB071v1v48eP56WXXvreaD3kRbwra4fnIMJ+/frR1NTU6e7WW7jZUWBhNBopLi4mJiYmpCnPkiQRFxdHXFwcTqcTg8FAc3MzFRUV2Gw29ws1JiaGiIgIpg7rx4xRAxEFAbvDwe78Mgb0ie9yB4c81KxfSlyHRnze5ZOxOenMa9V8ySMF6vVGth0t4ZU1u6moa2b9o7eiUamw2R28svEY/1hziCaj/2Dolim5WG12d4BlNptpaWmhoaGB8vJytFptm99Jd2YHrVYrRUVF6HQ60tPTQ5viLQio1WrstsAMvEDJXARMd8wIEESJvIq2YsGoCDXHu9jSZbLaKKioZ8SAPkF9348mncc/fnIJtU0GbnziQ457vRg9HfUkcRUTBmUwb5wrkyC3dNY0Gth0sIB/f7mTGF0Ey++/DovNzlWPLGN3fmADivzRLzmWtY8sIUKtYvHTK9h4oBBwKawvap19smD8IB5ePIe/3jST8vpm1u5xdZ+U1LYP2GJ1GjY/fjvJcVHc+68vWbpkDl8fO5PeTI2PbDdTxJOaxhZG5fTjSFE1R4qq+ccX29j02O0Mykhi65FiJgzK4KV7L+P5uxdSVNXIlztP8PLq3UENpXv7d1cze0w2L6zazxOfde5aCtA3MSogbUxHWQ2VSsUFF1xAdnY2RqOR0tJSkpKSAr7uQKiuriYhIaFN+j05ORmz2ewyCktMDOv5egN5VxdqcGG32yktLcVkMpGdnY1Wq6W5ubnD4MJbuNmRT0lTUxNlZWWkpKSQmJjY5ZecIAju0pqs8WlpaaG+vp7y8nJ0Op07WxYZGYkgiPziR5P4zZWTqahrbhWEnuTbo0UddkN50ycukg2P3kZsZAT3/PPzgIeaOZxOdueXsTu/jEeXb6Fvcqzb5XfeuBwuOX8wVrsDtSSyan8xD3+yh9M1HXtqaNUSN1zoEnLKvxO5HCm7WMqZnrq6OnemJzo6mqioqLC+kMMRWIDrPlSr1W2urSMDL+9W+nOBXi2LhHNGgCiKCKLYRswJrp10XRjG/e47VcG8sTkBf/7Xl1/Ib66azPGSGm568kOqOql92h1Oth8vZfvxUv7+3mZy0hNcgtAJg7h26giuu2gEDqcTAXh42eYuBxYjs1L55M83YrXZueqR99nr4bFhNFtZs+cka/ac5PevrWHUwDR3H/wd88dz27xxruFmx0t5dc1uthwuIiUukk2P3Ua0NoJbn/2YzQcKeeGeizlQdCagSo3V+ewUkalpaCGptXSiEkU2P+HKgMj2x2pJ5PzzXHMKFo4fxK+uuJCf/2gSdc1Gvj1azCurd/scGiez7P+uZdqILJ79ch9Pf7kv4N9VZmJU0HVbX1mN5uZmDAYDqampjBs3LqjjBYLRaGznIin/t8USuqC5N/Eui0Doei2LxUJRURGSJJGdnd3G0M/f8QLVV8hlr9raWjIzM92lsXDi/VKV2x+bm5uprq5GrVa30WkkxWi5ZeYobp83DoPZwvp9Z6a5dtShNjgziS8fvBlRFLnh8Q8C7jTxRUlNk7s9fO6YHF791eXU6S3c+8Y3bOukQ0vmsnEDiNb6Hxood+TIXTkGg4GWlhYqKyux2WxuoWx0dHRQU5K9sVqtnD59mqioqJAyUp7I7au+1hXvTYrdbqe6uhpRFLt0/T3N96YsIggCGpVEQVXbl5daEkM20PLk0Okqrpw8lHWPLOHDrYd5Y90+vzM+/vmTS7hy8lDW7zvFPf/8POi5AuBq6fz3lzv595c7ue+aqfz8sklUNuhJjNHxyOI5/PmGGZworeXDrYd5K8B5IzK+XDf94XTC/lMV7D9VwZMrtpKRGMOcsTksGOcx3MxsbTXLEtyln7ljctqIOcFVFjl4wn9XRk1jC0kxkURp1Wx67DbSE2O4919f8vF3rv58q93B1iNFbD1SxEPvbiI7LcFtHXzJxMFcfoFrIuSR4ho++OYQ72066N6trfjT9Uwe2p8nP9/D8ysPBPy7AuiXFIMkQFcquUaj0T34Ki0trQtH8k9ERES7IEL+b61W2y3n7A1CWTv0ej1FRUXExcW123H6CmAguMCivLwcvV7PgAEDeux3rVarSUhIICEhAYfD4d69l5S4BOKeba5Wq5XpwzK5bNJ52B0Odp0oY+XuPFbvyafQo8118tB+vPP7a9CbLFz/2PvtHERD5dLzB/PiTy+lrN7Adf9YR1l94NnkJRcNxmK1BRTge2Z6PIWyTU1N7iFg8u+loynJ3siBaTgCC5lAM/dVVVVYrVaGDRumdIsEQri7ReRdSEFV23S9ayJq1wSd4AouJFEkIzGav940kz9eP43yumZW7srnpa92UlHfgijCZ3+5mfG5Gby6Zjd/eWtjhy1jgfDYrXNZNGs0Xx8q5I7nP8XucDBlWH/mj3NNUf3bLbP4843TKa9tZtUe17V0NLvgjOtmI9c9tqzTOQfelNX9f3vnHd9Uvb/xJ3s03SPdezBsoVDgIhWQPUQRwYEKiD9RvFe993JFUVEcCK671IsK4rhyFRBRkD0FQShQaCmzpXSPJG2zmp1zfn+Eczxp0zbpSFM479eLPyinyUlIPuc53+/zeT46fH3gHL4+4BhutnDiELw4Jxd8nkNpb3j+PtQ26mgzGTP1Uh4owd525gBQEeC/vf8Egv0leOLf27DrdHGbx5fWNeGz3Wfw2e4z8JcIMfq2RDq19J3HJuGtRx1zCgR8LqJDArDqpzP4eE/HA6BaEh8q65Lj3GAwoKqqCnK5vNsKkyvkcjmamppgs9no74NSqYRYLO70ZENfxFMzeGNjI2praxEVFeVya8hVLXLXuGmz2VBdXQ2CIJCYmNhrd5ZcLtdpsibVtaRUKukx3iEhIQBJQCQSITslEjnpjjbX0tpG7DhdDE2zCS/MzkVNow5zVm1CpYfBfm0xb/wgrJw3AVdrNXjo4wNo1Lu/Yj0wNhiDEkI7tcrN4XAgEokgEokQGhoKu92O5uZm6HS6VgLMz8+vzQu3xWJBeXk5/P39IZfLvRaARxAE6urqYDQakZaWRg896yvcNN0i1DJSKWPlgsu9sXLRxZAq4PcY8K2/Xca3v5zHlKFpmD4sHU9OzcETk4egUWeEWMSHVCTEq/89iLV7OjYKdsSG5+/DuEHJ+O6X83h+/V46c+JgwXUcLLiOF7/ch6xEOSbdEBqLpuTg/yYNRZPesU2wdvdpnGJsEzx79wgsdSN1012ykyPxt1m3o0FrwP2rNkEqFjiMXEPT0T8+HJUqHbQ3zJxcDgcRbUxEpaBSOoP8RE4eEHfQMfwrHA4wONkRafynGSPA53FBkCQWTxiInOQI/PfoFRwscq/9l8NxeEVIonPrFgaDAZWVlYiIiOjSHq079O/fH3w+H+fOnUNOTg4A4MyZM8jMzOxTe7VM2grgc6d2kCSJuro6qNVqJCQkQCaTuTyO+XieGDfNZjOqqqogFAoRFxfnM+8xh8OBVCql0yIJgkBgYCAMBgOKi4udzI9SqRTx4QF4amoOeFwuuFwOLlUqMTA+HCqtAcZOrLoy+cvMkVgy63acua7C/E8OQW9yf4UVAB4elQarrXs6QHg8XisBptfroVKpUF1dDalUSr8vzO3E3hIW9fX1MBgMSEtLcyvUzde4qbZFtEYLHTMNANFBfuBwOB4Z/tpC3WxCXaMO2SlRePnrAygqV+D9H44hNiwACydmY9GUHPBu3L0/P3sUJgxOxhf7z7YbAd4WfC4Xe1fOQ/+4cHzww3G8/8Mxl8dRHR8F1+vx3pZjiA0NwITsFEzNcYiNGSMcba7ny+thNNkwJjMRhwqv44l//+RR6qYrpgxNxafP3I3aRh1mr9xITyY8V1qHd78/hsufPUMncwJAeIAYPC63fXFxY77Iyk1HPRIWLSFJ4Oy1WqxaMAF8HherNh1Bk96ESUNSMHpgAiZmxsFkseFavQY/nb6Or49ebrPohcrEEPJ5MJs9f7+oroHw8HC32tW6ikQiwcyZM7FixQq8/fbbUCgUWL9+PVatWtWjz+tt3Kkd1CRmq9WK5OTkdu/6qG0RpnmOMm229X/W3NyMqqoqBAcHuz1/xJvY7XZUVVWBIAgkJSXRK1lM82NDQwMdCkd5Evh8PsZlJWHK0DRYbHYcLSrHnvwS7M0v8TgzY+W88VgwMRsHiqqxeP1RmN3oNmPiJ+Jj9vAk2sjZnVACTCqVOm2f6PV62r8ikUig0+kQGBjYK8JCr9f3WWEB9KK46M6VC6oolNY7L+PFhzpMVd2xcgEA567XYVhajNPPkuXBeGziEGgMZjz10XZEBPlhyo0I8NGZiTBZbLharcKmo+61cwZKRTi8+jFEBMnwt3V7sOGw+/6AqgYtvtx/Fl/uPwuZRIixmYmYPCQV997e3zGSnCAQHxGIxdOHYd2efGgNnTPUPjQmE+8snIiS6kbMWb2p1YAlPpcLmViIwsrf/RZ0gFZ7hs4bKxcdmV/dYd/KebgtQY6XvtqPL2600P33YAHEAj5uHxCHidkpmDo0DS/dm4Oldw+BSmfELxdrsPbgRVyuaaIfhxp85+lnlRIWoaGhrZL7epJly5ZhxYoVmD9/PmQyGZ555hlMmjTJK8/tLTqqHWazGeXl5RCJREhOTu5wn5pauWDmlbS3CqFWq1FXV4fIyEgEBQV19mX0GFTGBp/PR0JCgtNrYZofCYKg21xrampAEIRTm6tQKMQdA+Nw56AkvLtwEs6X1WPXaYdPo6M210/+NAN3j8jA93nX8fz/TnRqcvS9OUkQC3gwmbqnfrcHM2eEIAio1WooFI7Vao1GA5vNRhtlKaHWExAEAYVCQQuL7phO2lv0ec8FleseExuHkhbiIjbUcWHoDs8FAJwvU2DcoGT67/PHD8abj45D5Y3hY9Qe5ZZjFyES8DCqfzwmD3WYDal2zqoGLXbmXcV/dp5qtaKSKA/CnjcfhZDPx/y//4D950o7fa56owU/513Fk1OHgcflYv3efFhsdkwflo7n78vFX2feDqWmGQcLr+OTnadQXNO2qZPJ09OHYdn9o3H2Wi0eeX+LS4EybnASeFwuCstdiIt2Vi4MZisMZotb80XagssF9q9cgP5x4Vi6fi/+e7DA6d9NVhu9rbTsy/3oFxtGt8rNGZmKB25Pg85oQWF5A749XkynDHriuTCZTKisrERwcDDi4uK8elcrkUjwzjvv4J133vHac3qb9moHFTwVEhLi9t0mh8OBzWaDxWKBUChs17ipVCrR1NSEuLg4+Pn5del19ARms5k2Hna0Dcds2SRJEiaTiV7RcBVH3i8mBP3jbsfS2bmoa9Rhx425J79dqnRqc9287H7kDkzAukOX8MbWfLcj+luyYHQ6bDZ7l/xOncFqtUKlUiEkJIRu5abaXGtqaiCRSOj3rTszNajPl1ar7fPCAuhFz0V3bItQ8asEQYDL46G03vnCRc2y6I5uEcDhuxDyeRiULMfsUQOxcGI28q5WY8Hft0LT4iJrttpxsPA6DhY6vBGDk6Mw5YbQ+OOMEXhq+jA0aA04fL4M//k5D0EyCb59YTbMFjvufetbnCttu6vCHYR8Lg6uegwpUSF47ZuD+Gy3wwPy+v8OIy06hG5zfXBMJh4akwmtwYzTxTX4cn8+9p+77vIxX3lgNJ6aPgxHi8qx8J8/trm1MvFGy25RpXOniNVmh0rb/qqEWm/E8ofGYObIfvj+1wvYcLjQrfAuwCEsDq9eiJSoEPx17W58+0vH5k1q5siH208iRCbBuEFJmHBjxPOoflGO/V4PPqeUsAgKCvK6sLgZcddzQZIkGhsb6Y6c4OBgtx6fJEkIb8x5KC0tdcqNYLb1UsPGTCYTEhMTfdJcR/l7qIuiJ589Zhx5eHg4rFYrdDod3dLJjCOXSqUI9Rfj0Tuz8PikIWg2WXCgoBR7z5TgqenDcFuCHO9uP4cP93qeZkyRnRiKjOggr6xaMDGZTKioqEBwcDD9HrZ8XyhTqEqlAo/Hc3pfOuu7oSbyajQapKam+qRw9RSvrVy0bPfq6owA6kMgEokQGxsLAY+L60pncUHdLXfXykVReT0AYM3TM5AUGew0LKw9KA/A2Wu1WLXpKBLlQZh8o31y9qiBmJM7kO4qefWbQ10WFsEyMQ6vfgwh/lL8ac0ObDl20enfi2saUVyTh49/zkNogBQTBidjytBUjM1MwvjByTBarLha1YDNjGTOD/5vMh4ak4ntJ6/gT2t2tPuas5OjUK7UQseIH5YHSlHf1H5YEeBY2TBa7EiKDMbbCybi9UfGoeZGJ8ynu9ruhGHmYvz5s13Y3ImY9ka9kR4+x+dxsfS+UXjm7j/AanPPb2E2m1FZWYmAgADEx8f7jMHvZqOluKBWL3U6HZKSkty642MaN/l8PhITE53GoysUCjoWXCKRQKFQ0FHePbks3lmo8C65XO62sGoPgUDgFEdOXVArKiromHvqoirg8zFlSCruHtEPANCoN8FotSMhTNZhQFZbPJrr8Hz01MAxVzCFRXh4uMtjBAJBq20lvV5Pp6cyI8nd7RyiMlIaGxuRmprapvG4r8EhvbTmZLFYnC4sBEHg4sWL6Nevn8dfVp1ORyv00NBQ8Pl8iMViTH57Gy5U/b68/+kTYzHxtlgkPPaPbnsdVz97Fv5SEd7/4Rg++OF4lx/v5QdGY/G0YahT6xEWIIVIwIfRbMWlSiW+/eU8vvulqMMBQEziwwOx9615EAn4WPjPrR4ZI8UCPkYN/L3NNSzAsdpgsdnhJxZiy/GLeHbNzg7bay9/+gwOXazBn774ffjZ+4/cjtRQMaYu/ajd3/3m5QUAT4jH/rmV7viYmpOGjNgwEAQJdbMRJy5XYd2efPx22RHuw+dycfS9hYgPD8Izn+zAD8cvuf2a2+LOrER8+Zd70dysh0qppAtGe+1qFRUVkMlkSExMZIVFN2G32+mx1RQKhQIWiwWxsbFOq5fx8fGtQsRcwZxoCrhO3KTme6jVahgMBnA4HAQFBSEgIMCjfARv0NjYCKVSiejo6B4J72LCjCPX6/X0BZVa7XFsE3DB5XHB43JxrV6LXQWV2He+CufKG9xqzQ+SCnFm5SyAaP1/31NQwoJa9fEUkiSdhs8ZjUZanMpkMojFYpefGWrFTaVSISUl5aZqGe/VbhHAM6Ncy6VPf39/p5WPshYrFyF+YroVsrsoLKvHwPjwbhEW7z8+CQ+NyXJ0cHy4DSBJ3HFbAn1xf+/xyVg5fwIqlBpsO3EZn+063Wr7hUlWkhxbX3GduukOJqsNB86V4sC5UrzwhSOZ89ulsxEsc7iV7/1Df4y9LRG/XqzAZ7tPI7+k9eML+VzIJEIUlDtHncsDpahrbGp1fEuUGj36J8Y6rfa88/2vdCfMlKGpmJidgmnD0mEwW3G5Uon48AAE+0ux+OPt2OZmTHF7jLktEV/+dRZAEiBuxPQy96EpoUHtz1ORwH5+fqyw6Gba2xYxmUwoLy+HRCJBbGysRxNNqRudtn6Hx+OBy+XCZDIhLCyMjgln5iNQBr/e+v9mjnOPj4/3SleBO3Hk1HsjkUgQHyLBE2PT8adJA9HUbMaewkrsO1+No1dqYbS4bi+9b3gS+FwuTJbuS3BuD6PRiIqKCoSFhXU6kr9lpobNZkNzczPtAeJwOK0yNZjCIjk5+aYSFkAvbot4OiOASsGjvkhisZge5MLj8dCgM6K5RbtgoFTYbVsiFIXX6zEkNarLj0NFUX9zqAAvfrGPdlPTsduf78XQ1GhMHpqKu6hZI3f/AUpNM/YXlGLNz6dQWv/7xdqT1E134HCADx6fjGCZBG999wt+OnGZXkWg0jD1JgvOX6/HN4cK6NWCcYOSweNycZ7htwAcAVrHr3c8ZVWl1iMsoHWRZHbC+IkFGHNbIqYNS8O9IweAy+XAarNj2f13IDslyuUURne5Y2ACvl5yL0CSsFmtTmOxqbhlql2tvr4eR48eRf/+/ZGTk+P2yG6WrsHlcmGxWFBaWoqwsDC3W0E9SdxkrgZQRd/f398poIqKl2a2cnorQZHaCjIaja3GuXuLtuLIqewIPp8PgiAgFAodOSNCIWblJOLBkamONtfLddhzvhIHiqqh0P7urVgwOsMrk02B7hEWruDz+U7D51qGmh0+fBgAMGDAAIwcORKBgYHd9ty+Qq9uHrorLmw2GyorK2Gz2Zz2PKlCzuFwcK2+dReCv1iAmnZaHzvDhQoFJEIB+sWG4nJVQ8e/0AIhn4t9K+cjPSYM737/K/7x428ujyNIEqeKq3GquBpvffcLUqNCMCUnFdNy0jF3bBYeHpsFTbMJJ69U40q1EounDe906mZLxEI+Dq96DHERgXj+8z345pCjHZYasewvEdKTUydlp+Djp+/C35+YgrJ6NR1DzkzmBIDIDgK0KFQaPcIC2zczNZusOFx4HW/NGw+CJPHylwcgD/JzmsKobjbit8tVWLfnDE5cdi8wa9SAeHzzt1nggHQZzdsyblkoFEKj0WDNmjUwGo14+umn8cQTT7j1XCydgyRJ6PV6mEwmxMXFuV2UPREWdXV10Ol0SEhIaLUa0DIfwWw2Q6fT0Stb1BZBV+dYtAczw8KXPCDM7we1zUAJweLiYvrOnVr1uyNDjnEDHfkv5ysbsbugEgqtEYnh/l4xclIGWGrIXE/B/MzI5XKYzWYUFhZix44dWL9+PRISErB27VrExMR0/GB9iF79VLrTMcLsWU9MTARJkq32SG0EiZK61iJCIuJDpenaRNSWnC9zmDoPrV6ImgYtdp8pwX925KHaxZTQloT4S3Bw1QKEBUjx5892YeORIreft6S2ER9tz8NH2/MQHuhHryJMGJyMCYOTQYBEs9mCOwYk4PvjF9BZH1SARIgj7z6O0AApFn/keptBZ7Rg28kr2HbyCvg8Loanx2DykFRMG5aOjNgwlCm10DPMnCI+DyH+ErfEhVKjh59YCIlI0GY6oEwsxK/vPY4Qfwke+8dWumW35fbJpOwUTL+xfXKpQolNR4vwv8PnXXpYRvaPw4bn7wPgXuY/9bl97rnnkJKSgqtXr/rUPvzNAvM9JQgC1dXVaG5uhlAodEtYMMOxOhIW1MRUm82GpKSkDsUB8849PDwcFosFOp2OnmMhFoudvAjdQXsZFr4C09gsl8sBgE7DVCqVrdpcRSIR+kUGoH90Jvg8Liw3UkV7Em8JC1cYjUYMGjQI99xzD4RCIfLz82+KicUt6VVx0VEYDrNnPTQ0lN4GaVkcuBxOq04RwHFRUzd377bItdpGWKw2WCxWKJVKLJw0BAsmDEaT3oijFyrwnx2naAHCJDUqGDvfeBR8HhePvu9ZtHVLlJpmbDhciNsSIsDhcPDrhXLUqfWYMjQN/3pqGt57fBKu16ux9beL+HzPWehN7vlOIoNlOLhqAaQigdvx2zY7geOXKnH8UiVe23AIV9c+i7PXnQN2IgIdd3+1jR2vIlFBWqH+ElS5EBcBEiF+ff//EOgnxoK/b8XBQue2WVfbJxOHpGDykFS8s3AS3po3HtUNWuw6XYLPdju2T0b2i8X/np8NLgCrteP3ilpJEwgESElJAY/HQ//+/Tv8PZbOQ/laOBwOoqOjUV/f+jvWkpbGzfaEhcViQWVlJb2E35ntDaFQiNDQUHrPnTL3qVQqCAQCp8yIzghRTzIsegsqPI7ZygmgVRpmSxHG9Gn0NMxI/u7orPEEjUaDuro6JCQk0IJi9OjRXj0Hb+FVz0VL2tsWaWhoQF1dHaKiohAQENDmXQeXywWf13rUOgDweZxuGbfOxE6QuFzdABmPwB0PP4cYeRimjR6Be8aPwozht2HmyP7QGkw4U1KLdbvP4GDh9RuTBu9Ds9mK2Ss3otCF+PCU9c/dg6nD0rHxSBH+9vke2OwEeNzdGJ4RiylDUjF9eDpenDMaf5uVi/omPfadvYb/7MhDpcr16kGyPBh73noUAAf3r9qEvKvVHp+TkM+FVCRAQUVLv0XHAVoUVAR4eKAfqlqca6BUhF/f/z8ESISY9/4P+KWorN3HajZZsfN0MXaeLgaXw0F2SpRj9klOGhZPH4Ynp+agqdkImVjY5lZIS6hYaR6Ph9TU1D41pbCvYjAY6E6c6OhomEymDu9s3TVuUo9fVVWFwMBAREREdMtFu2USJiU0qK0CSmhIpVK3nq8rGRbegopE78i/0FKEUW2ujY2N9HtDGR+7+3VS5yiXy72erqrRaFBbW4v4+Phu9Xf4Kj63LcI0biYkJEAkErW5YsHj8cAXCHD2uhK/XKpBS7pr3HpLCkrrMGtkBgCgul6FtZt3YO3mHQjy98Pk3GG4e9ztmDgqB3dmJcFktkAo4ENntOCuFRucxht3lp9fm4uhaTH4cPtJvL3x93ZPO0Hit0uV+O3GKkK/2DA6uGvBxGzMGz8Y6mYjjl2sxCe7TtHdHoOS5dj68kMwWW2Y8/amTo9Znpid6jBzthAXVJiZu54LAK1SOoNlYhx973H4iYSY+94WHLtY4dG5ESSJMyU1OFNSg1WbjiI2LABL78vFnDsGwmaztxpV7gpKWHC5XFZYeAmbzYaysjJEREQgNDTULSO4u/4K4PeC3135EK5oObGUupgyI7epC6orEdTdGRY9gU6nQ3V1tceR6EzjIzOOvLa2FgRBOHlYuvp9a25uRmVlZa/EtlOvKS4urlOtrn0Rn9oWYQ4bSkhIoPc8XX3h+Hw+hEIhfjpdir9+fQxmm7O7WMjngs/jdXu3COAYv/7wnVnw95NAx9h2UeuasXHXYWzcdRhikRBvPbcQix64CzabHYF+Yvyy+jFcr1fjh+MXsW73GRg8HB7WVupmW1Dpk//86QSiQmR0Kic11ExvsqC0thH9YsPQoDNi9tsbUVrXcbtoW4wblASCJJ2SOQHHykWzyQKtGzNeqDklTHER4i/Br+89DrGQj7nvbsZvbho02yMyWIbpw9Nhsdpgc2MrhDLRAUBqaqrPmOhudgQCAVJTU526IShxQfmvmDBXLDoybqpUKjQ2NiI2NtZrwUXMlkQqcpvqImhpCOXz+XTXSkxMTI9nWHQWSqAxO2s6gztx5C2nlrqLXq9HVVUVoqKivN6ZodfrUV1djdjY2FtGWAA+tC1C7ScKBAIkJCTQv+Pq9wQCAQQCAd7ffhb/3FXQ6t+B3wdOddfQMiZFZfXgcjiYesdwbNr9i8tj7p86Fo/Pnoa6Rh0mvPIV0qJCMSUnDTOGZ2DZ/aPx/H25qGnUYdfpYvzn55NQdGA8DZaJcWj1YwhtI3WzI2ob9fhq/zl8tf8c3e3xzIwRGBAfAT6PixB/CT575m58f+wCvtx/ju768ITslCiUK3WtWoKjgqSob+p41QIA7AQBTbMRqxZMwH2jBuDH3y7h5QfHQMjn4cHVmzu1XdOSoanR2PjCHPC5HI+EBUmSSEtL67EuABbXUKuXFNTNRktxQfkrOlqxaNnG2VtR3sxoaWbnCZUZwefzYbfbvRKO1VmampqgUCi6XaC1F0euUCggFArd9rD4grCIiYnxyem5PYlPtKLq9Xp6HkNYWFi7dx18gRAkh4OnP/8F2864noEBAHHU0LJuGLfekktVKhAkiTv/kO1SXCxbNBfLnpyLS5VKTHr5a9gIAg3aKpy4UoUVGw5hQHw4pgxNw13D0/Hk1Bw8MXkIGnRGHC4sw8c/n8SVauc7/7iwAOxbOR8iAd9to2V76IwWBErF6BcXjstVSryz+VeMzUrCXcPS8drcO/HSA6NR26jDrtMl+GTnKbczI5Ijg/FzfussC3mgBHUN7okLAFA06cDh8jA4ORIjMmIdA320BvyhXywuVijdNqi6IjslCptenAM+j+uWsKA6FAiCQHp6OissfABKXFAiwhPjps1mo1egfKmNE4BTCFNVVRUMBgPEYjGqq6uhUqlapGD27kWKiqxuaGhAXFxcjw/Z6iiOvK1QM2q7pqurKp2hubmZ3irqLi9PX6LXxYXBYEBDQwMiIyPpfTdXxYHD4YAnEEJvsmLBmgPIv97+yN+YGysXjT2wLWI0W1GuUGNwv1Snn/P5PPzrpT/h0Xsm4lDBdcx973uXv3+xQomLFUr8fetxxIUH0gbM+0YNwOzcAdAazDh5pRqf7T4FrcGCH5d3PnXTFc/ePQJLZ+fiTHENHn5/C/RGC/afK8UrXx/AbQkRmDLUEZRFCZ9GnRFHisqxZmceispdv+8/r3gYfB4PhRWtsz/kgRLU1rlvYlU06aA1E5AHyWCy2LDl2EVMzE6hV3wUaj32nyvFmp2nPPKwDE6OxOZl93ssLGw2GyssfAhmuq8nxk2TyYSqqipIJBJERUX5ZBsnM8MiJSWFXr2gDKENDQ3g8/m00OiNKHJmMmhCQgLEYrFXn5/H4zl5WKj5HlSoGbW1BIBOc/a2sKBMwnK5HJGRkbecsAB6cVuEMjaZTKZWiZsuO0IEQpQptXjko/2ociMkip6I2gPbIoAjmnpcZgL9dz+JGP99dxnG/SEbXx84hxe+2OfW41QqNVi75wzW7jmDEJkEE4ekYFpOGsZmJmLSkBTYCQI8Lherf/qtW4TFa3PHYtGUHBwsLMUT/9oGk9V5C6OoXIGicgXe/+EYYsMCbuRXpOGekf0wa9QA6Axm5F+rxZf7z2L3mRJwucC+t+ZjQHwEAOB8harVc8qDpDh70f2VC5VGj6kjBsJktWP2yu9QWFaPF7/ch36xYY5o9GFpmDd+MB4dN4gOEvt87xkcvdC2yXNQkhyblz0AgQfCoqamBlarFenp6b2SgMjioK10X7vdTvu2OjJuUsvTvtxt0VaGBY/HczI9Unft1AoMs7uipwUTFTKm1+t7LRmUCTOOnNnm2tDQAIvFAqFQCIvFArPZTMf19zTMVldfbRn2Br2yckEZNy0WC6RSKa18XXks+Hw++AIBjl6qwZPrDjuFM7UHla3QE90iAHChXIm7R/QDj8dDSKA/tn70BgakJuDtTUfx8c95nXrMRr0RG48UYeORIswbPwgr502AQtMMf7EQL94/Gn+593aU1DRi068X8OW+fLdHkVP868mpmJ07ED8ev4TnPtsFWwfTXKtUWny+Nx+f781HoFSE8YOTMTUnDeMHJWNMZiJMFisADsRCPk4XV2NIajSKqlrHjkcG+rnVKUKh0uhBkCRmvfWdU+cKZVD917YTiAjyw8TBKZiSk4o7sxIxeWgqPfBt49Ei/O/Q72FZmYkR2PzSAxDy3RcWtbW1MJvNrLDwUbhcLmw2Gz0DpL0C3tjYCIVC0St77u7iboYFs4215V273W7v1u6KlpAkierqapjNZiQmJvrcSh4138NsNsNqtSIqyjGmgcoaoVZ8ZDKZ2y3AnmI0GlFVVYXw8HBER0ffssIC6AVxYbFYUF5eDoFAgPDwcNTX16O2thYBAQGt+pop4+aGX6/gpe9O0PM33CHMXwyjxQqLrWcy6ovK68HncTHvngn42+MPQB4agj/+Z0e3DM567u4ReP6+XJwtrcMj732PZrMVIzJiMTXH4dN4/eE78coDo1HVoMXPeVexZkcemjoQUV8vmYWJ2Sn4Yl8+Xv76ADydhasxmPHD8Uv44fglCPk8jB+chE/+OANCgeMjNCQ1GmUKLQwtzJwysQB+YoFH4kKpaYbeaGm3JVahdgSJbThcCIlIgNEDE+gMi3cem4S3Hh2PKpUWR4rKcO/tAyDi8zwSFkajERkZGb1m9mNpG4IgwOfz6boREBDg8v+JJEnU19dDq9UiPj6+x30BnaWzGRYt79p7MoqcIAhUVVXBbrcjISHBp7wqTKjOFabBlMoaoQaJVVdXgyRJJ59Gdwgxk8lE/z/GxMTc0sIC8OLIdZIk0dTUhIqKCgQGBiI8PBwEQdBjarVaLT0EiBIaIrEYQoEAJosNB4qqsONsOQ4UVbbqRnDFlr9ORUKoH7KfWdMjryfUX4KiNX8CQRAwW224f9VmnC5pnbXhKavmT8C88YNxsLAUi/69DUYXXRtZiXJaaKRGh8JOEFBqDDhw7ho++jmvlQ9h+2tzkZMWg3/8eBzvfn+sy+fIjAh/6qPtqG7Q4oeXH8SOc+X481e/Oh2bGhmIw6/eixnL/oMTF8vcevz5U/6A1YtmIn7BBx6LIA4HyE6OwuShqZiTOxBRIf6w2e2wmDuesEgQBOrq6mAwGJCWluaVtECWjrHZbLSvgvJYME19zc3NdAImJTQov4zVakVcXJzPrj71VIYFtT1Ajf9mTiv1VDBTK80cDgexsbE+m+/iSli4gtkCrNfrYTab4efnR78/nRFi1CyV4OBgxMXF+aSfx9t4TX4aDAaUlZU5GTd5PB4dCxseHk4rb6rnWyAQgMvlIi4uDhMzYzF9SCIsNjuOXKrBjrPl2FdYAbXB9d1okFQIdQ+YOSmGpkWDIEkAHJitdiyYmA292YLLla09B+5CpW5+98t5/O3zPW2u1BSW1aOwrB7vfP8rkuRBmJKThruGpeOhsVmYOzYL6mYTjl+qxNrdZ7D6sQnoFxuOFRsO4dNdpzt9bhTBMjGOvvs4/CUizP/gBxwsvA6xkA+hgN8qPAvwLJ2TQqXWg8/jIshP3OGKTEtIEsi/VgujxYZ54wfDZLbAajF3WBAJgkB9fT2am5tZYeGDtDRutpWAWV5eTneQUG3tvnqX3ZMZFi1TMKkLqadR5DabjY4IiImJ8dmLplqtRn19PeLi4uDn1/7gw5YtwEwhVl9fD5FIRK/4uBPVTs1SCQoKYoUFA69968RiMT0MyJVxkzkEiGrFMhqN4PP5KC4uhp+fH11MRveLwvjbYmEnbseJ4jpszy/DnoIKqHS/X4hkYgGu13U8TKwzzBs/GKvmT0Bdow5rdp7G1JxUzBzZD7Nu7w+twYwTl6uwZucpnLziftjTz689jKFp0a1SNzvier0aa3acwpodpxAe6IfJQ1IwLSfdMdhsaBrsBIHqBm2rKO3OEBksw6HVj0Ek4OHBdzfjt0uVAIBJ2SngcTkoKG8tLiJveF889VwAjiAtT8UFAGTEhuGHlx+AkAuUXiuB0Whs986EIAgoFAro9XqkpaX57PL5rUpHg8eYCZhUvDOPx4PFYsH169c9jtruaZjdFvHx8T0uZPl8Pj2ttGUbZ3tR5NQ8F7FY7NP+AWbWRkfCwhVMIcbszKHiyKm6IZVKWwkHyisTEBCA+Ph4Vlgw8Jq40Gq1UKlUdAJbW/8JVB86SZJ0KxalLBsbG1FdXQ2pVIrAwEAEBwdjeEoEbs+IwqqHRuL0NQV2nC3DznPlkAj5aNR170RUAHhxTi6eu2ckisrrMXX5N7ARBNbtOYNgmRgTb0zhvHOQYxy53mjBudJafL43H7vPlLh8PE9TN9tDqWnGN4cKsePUVRx5ZyEC/cTYf64UYzITse65e2Cy2HClSoWNR87jvwcLXU4HbYuE8EDse3s+OOBgztubcIaxBTR+cDIIgsQFV2bOID806Q0ehXIp1b+Li+Ka1o/ZHukxodj6yoOQigSwWS1ITEykt95c3ZkIhUKoVCpotVpWWPgoVOQ6dafdFtQWA3MgFXUhra52hK9RF9KemFvhDiRJoqamhg7w8vZ2Tcs2zpZR5EyPRlVVFWQymU+3UlLCoruyNpidOdT7o9frW8WRS6VSkCSJyspK+Pv7++yE2t7Ea54Ls9lMzwxpKzOeWl6ilLKr/ywqqU2n08FgMEAikdBCQyAQ3nCPc2Cx2nG6pBpL1u3plnkeAh4XHzwxBXNyB2Jv/jXM//sPbR4rFQlwZ1YSpg9Lx6QhKfATC2G0WHGxQokNhwqx8eh5EIRz6uafP9vlceqmK6JCZDi46jGIBXzM//sPOFJUDiGfh9v7x2FqjiO/IjRACovNjgqlBj/9dgmf7ToNrbFts2NadCh2vfEIrHYCc97eiKJyZ6Pl4dWPgcfnY+wbP7b63TfmDMfIlFCMfuYDt1+Dv1SM0m/fwBP//gk/5111+/fSo0OxdflDkIkFbZo3mXcmGzduxM6dOzF06FDcc889GDNmjM/uzd/KqFQqNDQ0OHkrmEv6zECnttIsqc4KqnZQF1JXwUs9BTPDIi4uzqe2a5g+BI1GA5vNBoFAgNDQUPj7+/vUuVJQ20reCPEiSZLettdqtXjyyScRERGBUaNGYc6cOUhMTOzR5++LeE1cUFD7o42NjbSJ08/PD0VFRTh27BieffZZt2NSqb1EytQlFosREBCA4OBgiG+Ey/C4XFypUuGnE5ex83QxrlR57onwEwvwxZ/vxagB8fhifz5e+fqg278r5POQOyAe04alYdqwdATLJDBbbahQaBAd6g8el4uF/9za5dRN4HcRQJAkHnrne6fVBQrK8DglJw13D89AgjwINjsBhboZe/NL8PHPJ1HV8Pt2UmaiHD8tfwgGsxWz3voOV2tab31c+exZ7CmsxF++/rXVv336f2Mh5Vhw/4p1Hr2Wmi2rUFzTiOX/PYDjN7Zf2iM1KgQ/vvoQ/MVCt7pCqDvIY8eOoaSkBL/++isWLlyIefPmeXSeLN6BJEnYbDY0NTWhqamJFhoCgQDr1q3D8OHDMWHCBLcCnagLqVarhU6ng91u73B4WFdhZljExsb67F0uNYE2ODgYPB4POp0OJpMJUqmUFmO+0IJKCQtvbCu1xGq1Ij8/HwUFBTh//jwuXryIQ4cOeT1MzNfxurhgQrUHffPNN1izZg0ef/xxTJ48mf4Q83g8t5fj7Ha7k9AQCAT0ioZYLAaHywOfx0VZvRrbTl7Gjryrbo0+jwjyw3dL5yAtJhRvfnu4S9sWXA4Hw9Nj8OS0HEwYnAI+jwurzY4qlRY/nbiMT3aegsbQcVeDK4akRuH7lx6AwWTF7Lc34rKbIiotOsQRRT4iA1mJchAEiSa9EUcvlOPI+TK8vWAimvRGzFr5ncsVIKmQj+J1f8aK7/Ow/vClVv/+09+m4ur1Cjz7780evZ6iL15BWJAMPC4XBpMVReX1+OZQIbYcv4CWuzkpUcH4aflc+EvcFxbUnW5KSopX0/ssFgtmzZqF5cuXY8SIES6PWbx4MQ4edBawn3zyCe68805vnGKfwGq1oqysDEuXLoXJZMJLL72EmJgYBAQEQCwWu33xpu5IKaFhtVrh5+eHgICAbsuKcDfDoreh2jSZ20pA69Vialuxt6LIGxoaoFKpekVYUAZXkUiElJQUr4nEvlg3ej3+m8fjYfv27Vi7di0GDhxI35nU19c7qeWOhAaPx6MNn5RpSavVori4GHw+HwEBAY7+4xA/LJ42DM/e/QfUNuqw7eQV7Mi7itMl1a3aHlOjQrBx2f0I85dg0b+3YdeZ4i69XoIk6S0TldaApev3YkhKFGaMyMCfZ47En2aMgEKtx578Enz0cx5qGtwzpI7NSsSXf7kXKq0Bs9/e6NE2UHFNI4prTuLD7SfpyanTc9Jx1/AMTB+WDg6Hg5pGHVKjgl0+7sQhqeByOShwkcwJOLpFjnhg5qSoa9LiQGEZDpwrxeShqZgyNA3/fmoa3nt8Esrq1dh6/CI+33sWEUF++PGVhzwSFo2Njb0iLMxmM5YsWYLi4vY/R9euXcN7772HkSNH0j/z1fCn3kIgEODYsWNITk7GihUrYDab0dTURPszqJZ2iUTS7gWAaSSnsiK0Wq1TVgQlNDqzNdDZDAtvQ/lVXEVlt5zr0ZtR5L4gLIRCoVeFRV+tG726ctEW1P5oY2MjNBoNzGazk9Dg8/luf4ip1RGtVgu9Xk8bw0JDQx37dBwuBHweVNpmbD95FTtOXcWJy5UYkhKNb56/D3weF7NXfoezpXVdfl0PjL4N7y2chHKFBvev3ohaRox5SlTwjVHo/egVhEa9EYcKr+Pj7a2HmVHcO7If/vXkNJQrNZjz9ka3h4y1x9Shafj0mRmoVGlwqVKF8YOSIRbyYTTf8I0c/t038u8np+G+UQOQ/pdvYLI6B5ZxOMC1fz2KV9Ztwxe7fvPoHL57dSFMBBf/96+fAAA8rmPVZ/KNuSexYQGw2QnY7AS4HLgtLJqamqBUKpGcnOzVL15JSQmWLFkCkiRx5coVfP311y7vQCwWCwYPHowdO3YgKSnJa+d3s2Cz2aDRaKBWq6HVajt0+7cHZSTXarWd2hroqQyL7oYyRcbExHg02ZQZRa7XO+oOs/Okuy++KpUKjY2N9LgIb2Kz2ehtrdTUVK9lffTluuF7Lh04J8/FxsbCaDSiqamJ7mWWSCROX/L2hEbLuFxKaFy/7pio6u/vj5CQEATKZHj4zkw8NjEbmmYTJEIB7ASB8S99ievdYAh97h5H6ua50jo88t6WVjNPrtU24aPtefhoex6iQ/wxZWgq7hqegftuH4DZowZAYzDjxKVK/GdnHk5ddXgpFk7MxuuPjMPFCgUefGdzp9o2WzJ71AD8/YkpKKlpxJxVG9GgM0Is4OOO2xIwZWgqpg1Lx9+fmILVCybger0aIf4SXKvXtBIWABDiJ4aQz/OoDZVCpdEjMSaS/rudIPHb5Sr8dtkxWXbykFSs//NM8LgcWC0dbyWRJAm1Wg2FQuF1YQEAeXl5GDFiBP7yl79g8ODBbR5XWloKDoeDuLg4753cTQSfz3fKd9BqtVCr1aipcXxn2pqe6QpmiyK1NaDVap1qUEBAgEuhQUWO90SGRXdCrQR0xhTpKopcp9O16qzoju0lpVKJpqamXhEWVIgYj8fzqrAA+nbd8ElxwYTD4dBBW9HR0TCZTLTQUCgUTslzHQ2moUbzymQypy9DRUUFHQcbHByMgIAAcLiAUCDAvpXzsSe/BDvyruJQ4XWXiZkdQaVuHiq8jif+/VOHj1HTqMP6fWexft9ZepjZXcPTMW5wMqbkpEFvskCpbkZ8RCBOXqnCvA9+QLObM1faY964wVg5fzzOl9XjwXc2Q3vD/2Gy2rDv7DXsO3sNz3++F8PSozE1Jx13DU9HkJ8Yhy66HqgmD/I8QItCqWlGTn/XxS4+PBDvPDYRNrvdrRULwJHeV19fj8TERAQFBXl8Pl1l7ty5bh1XWloKmUyGpUuXIi8vD5GRkXjmmWcwZsyYHj7Dmw8+n08v5xMEAY1Gg6amJtTW1tLfdyoNuCOhwdwaYBrJFQoFxGIxfZEVCoVQKpVQq9VISEjw2TA2kiTp8+yOLQbmDaFcLu+2KHKSJKFSqXpdWHC5XK8LC6Bv1w2fFxdMmMlqUVFR9D6rWq2GUqmEWCymC4Y7QoP5ZTAajXQ/fHl5OUiSRFhYGCIjI3HXsHTMun2AI4b8XCl2nLqKfeeuQd9O+ybF+j/PxNScNGw8UoS/fb6nw2FhLWEOM/MTO/wabz06HgkRQeByOchOicLGF+7HN4cKsOnXolZmR3dZPH0YXr5/NE4VV+OR97e0KVYIksTJK9U4eaUa7285hiufPYvCNvwWVIBWbYPG4/NRafQIC2gtLuLCA/Hj8ocQ7C+G3eqeoFKr1airq0NCQgJCQkI8PhdvUlpaCpPJhNzcXCxatAj79u3D4sWLsXHjRmRmZvb26fVZuFwuHSRFEAS0Wi2amppQV1cHgiDomw537rKZoVQ2m432IKhUKrrmREdH+2z3AHOyaUJCQrfPz2H6WMLDw+ntJY1Gg7q6OqeV5/Zav5kCqCfOsyOo1mEASE1N9cl2XApfrBu++251APUBjoqKQmRkJCwWCy00VCqVR45m5uqISCRCbW0t/Pz8aGOXTCZDUFAQAgMDMTE7GdOHp8Nqs+NIURm2513F3vwSl1sSnU3dbItmkxXTctIREeSHLccuYutvlzAtJw3Th2fgH4umYvVjE3GtthGbjl7AFx5MTf3brNvx55kjcbSoHI/948dWY9jbYtKQFHC5HBS6iP0GHGPvSZLEnLFD8PPx87he5/o4V6jUegTe2Fahhs/FhgXgx+UPIdRfArvVCnfsQlRBi4+PR2hoqNvP31s8/fTTePTRR+ltm379+uHChQvYtGkTKy66CS6X6xQdrtPpaC9OXV2dU5qrO0IjKCgI/v7+qKyshM1mg0gkQnV1tUcx296CasE2mUxem2zqKoqcGvPQ1ntECQuNRtMrwoIa1EaSJNLS0nyi/bY9fLFu9FlxwYQatRsZGQm5XA6r1UoLjYaGBre+5FQXAbX/SG2dUMt7CoUCVVVVdAx5YGAg7hiYgHGDkkGQJE5crsK2k1ew63QxNM3GbkvdZPK/pbNxZ1YS1u05g1e/OQiSBA4WXMfS9fswPD0G04alYcaIDKx4+E68/MBoVKo02HbiSrstrq/NHYtFU3KwN78ET3603aMpsndmJcFOELjoIpkTcHSKWO0EXn50Cl6dPw3FVQpsP34eO04UofBadbuPTUWAhwZIUNuoR0yoP35a/hDC/CWw29wTFlqtFrW1tYiPj0dYWJjbr6s34XK5rfwgycnJKClxnfDK0jWo95uad6TX69HU1ASVStVKaLR158rMsEhOTgaXy3Wad1JRUQEej+fVrgpXMIe59dbMFXeiyGUyGf3eJSQkeD3YjhIWBEH0CWEB+GbduCnEBRMOhwOhUAi5XI6IiAg6eEetVtOj3qliQfXDM0czJyQk0MuZLZf3KKGhUqlooREYGIigoCAMS4vCyP5xWLVgAgwmC2QSEd7eeKTbhMWO1x/BkJQovLflGP6+9bjTvxEkiRNXqnDiShVe/eYQshLltNBgtrjuPlOCj34+SXepvPf4JDw8Ngtbj1/Cs5/u9GikPQAMSorEtXqtSzMn4BAXJpMJOo0aoaGhSI4KxbP3jcVf7x+P+kYtth8/j50nL+C3C6WttouUjPkiHHDw4/K5CA+UwubmigUVaRwbG9snViwoXnzxRXA4HKxatYr+2eXLl5Gent6LZ3VrwJxRQnVCNDU1obGxsc3W+LYyLFw9lk6nQ1VVFTgcjtfnnVBL/CRJIiEhwScmmzKjyAmCoD1w1HnKZDKYTCbw+XyvtX1SAsxmsyE9Pb3PJPb6Yt246cQFEw6HA4FAgIiICERERMBqtUKtVkOtVqOiogJ8Ph98Ph9r1qzB6NGjMXHixHY/TCKRCCKRCGFhYbBarfS+bU1NjVMMuVjoGPn80gOjMWNEBradvIydp4pRWtfk8Wvgc7k4uHoB0qJDsfy/B7BuT36Hv0NNTV29+VekRoVgak4a7h6RgYWThmDBhGw06gzQmyxIlAdjw6FCLF2/98aEV8+IDJFhx9mKNv89KkgKPo9Lz/QQCoWQyWQICwtDWKAf5k8Zgf+7axS0BhN2nbyAXScu4NDZKzCYrfTKRXpMKF547g5EBLkvLKgwoJiYGLfTXnsTpVJJi91x48bhr3/9K0aMGIHs7Gxs374dZ86cwRtvvNHbp3lL4aoTorGxkfZpSKVSlJSUYNu2bXj55Zfb/Zy5eiytVkvPO2EaS3vis0q1UfJ4PJ+d2snlcuHn5we9Xg8ej0f74BQKRStDaE+tuBAEgZqaGlit1j4hLHy9bvhkzoU3oAJRlixZAqvVihdffBFRUVFuBe+0pGWCnVgspoWGUCQCl8MFj8dFcXUDfrqRDupOgqZMLMTh1Y8hMkSGv6zdjc1HL3TlJdMtri8/OAZSkWM6rcZgxm+XKrFmRx5OFbeOC2/v3K589iyWbzqJr45cdnnMvpfvQWqEDFar1WkJlJk3EhYWBj8/P9gJEkIBH2arDYfOXsH+M1fw/uJZaNA2I0AqcltYUFMxIyMjfXbgUkZGhlO/ekZGBlatWoVZs2YBADZv3ox169ahpqYGaWlpWLZsGYYNG9abp8xyA5IkYTQasW3bNqxatQoPP/wwZsyY4TRx193PHPVYVDooZSx1t4PFHajJpiKRCDExMT75fQB+N5k2NzcjISGB3oogSdJpJHpPRZETBIHa2lqYTCakp6d73ePhDn2tbtyy4gIANmzYgNOnT+PNN9+E2Wx2Ct6hkvk8DYOh3ONarRbNzc0QiUR0OqhYLAY4HPB5PMfQsBOXsePUVRS4COgK8ZfgyDsLESAVYdGH29qcquopm5fdj9yBCfj45zyU1DTgruEZGH1bAgR8HvRGC/Kv1WDd7nzsO3et3ceZPWoAPlw8HXe/uwP5ZUqXxxS++yBkQi5sNmeDKHMJVKfTgSRJOm8kICAAJAD+jWVnu52AxWJ2S1hQaYhyudynY5ZZ+jYkSeK+++7D008/jVGjRtHbrkajkW6Np/IvPBEa1OAwauYSU2h0ZhvDYrGgoqICUqnUp78PJEmitrYWBoPBSVi4wtWNHLWi0dkockpYGI1GpKen+2yXT1/jlhYXriAIgt460Wod+QyeBO8woaJyKaFBGUtDQ0NvCA1HOmhdkx7bTzqExqmrNYgJ9ce+lfMhFPAw74Mf8OuFtrcePOHnFQ9jaGo0Vm06gn9vO0n/XCYRYlxWEqYNS8ek7BRIRAIYzVZcqFDg64MFLldMPlo8HTNH9kPGXza49FzwuRyUfjgPVosFdnvbJlHq7o0qGNQgu+DgYNpk54mwiIiIQHR0tM8WUpabE0ocNDU1QaPROF343MngaflYzAmc1LwTpt+jI0wmEyoqKhAYGIiIiAif/T5QwsJoNCI+Pt6jlQhmG3Bzc3OnosgJgkBdXR0MBgPS0tJ8NpekL8KKi3Zg9sNrNBraZNSZ6YlM9zi1r0jdrTNjyBt1BkiEAvB4XMxeuRGnitvvqnAHLhfY++Z8DEyIwKv/PYi1e9o2mYoEPOQOTMC0HMcU1yA/McxWG0pqGrHpaBG+3H8WFhuBo+8uBMHhYdybP7p8nKhgKU6tvB8mkwmEm+EbnV0CNRqNqKioQHh4uE8v/bLcGlDigKob1ApmZ4d9UUKD+k4whYYr/wEltKn2T1/9PjDbYj0VFi3pTBQ5QRCor6+HXq9HWlpaj49tv9VgxYWbUP3w1Kj4rsTbMr8IOp2O9h8EBwc7sv1vCA1Nswm7zxTj57yrOFJU7lGbKAWfy8Xhdx5DUmQwXli/F98cKnT7d3lcDoZnxGJajqPzRB4kg9VmR6VSg+jQAPx4uhR/++a4y9/NTgzD9qV3wWg0urXy4Ap3lkCNRiM9GCouLs5nCynLrQlJkrBarWhsbIRaraaFBrNjzZPPLFN8G43GVuKbMjOHh4f7dGAcU1h0d1ssM32Z6WVhhqQRBAGFQgGdTscKix6CFRedgFqFoIQGtT/qbvAOE2reCfVFoJDJZI6LJZcHoYAPg9mKvfkl+DnvKg4WXofR3HE6pVjIx5F3FiI61B/PfboLW45d7NTrpRiUJMe0YelYODEbUrEQL2w4jm+Pu57UN3VwPNYuGgeDwdCl56RouQR66dIlXLhwAVlZWRgxYgQSExNZYcHi01BCQ61Wo6mpid4qpbwVIpGoS0ZygUAAq9WKiIgIn26/JkkS1dXVsFgsiI+P79G8DaaXRa/Xw2KxYNOmTYiKisKgQYMwbNgw+Pn59djz38qw4qKLUEJDrVZDo9HQ+6MdBe+4wmq1ory8HIDjS0EQBPz9/ekEQEpomK02HCy4jh2nHOmgOhcx5P4SIY68+zjCAqR46qPt2HHqare83lcfGoMnpw7DjrNleOaLI7C1kY2xYEw/vDFnBEwmY7c8LxOCIFBQUIAvv/wSZ8+ehUgkwpIlS2jXNAtLX4ApNPR6Pfh8Pi00qAwed2loaIBCoYBIJILZbKaN5NQ2jK/gTWHhCrPZjK+++goHDhzAtWvXMHjwYHz88cc+PbW2r8KKi26EGbyj1WphsVhcBu+4oqWzG3CYsqg2NbvdTseQOwarOYSGzU7g6IVy/Jx3BXvOlKBBZ0SwTIyj7z4Of4kIj/1zKw4WXO+W1/fmo+OwcNIQbDt9Hc99dbTd0K0X7h6CReMHwO7mYDFPoIKLAgICEBMTg/z8fAQGBqJ///7d/lwtsVgsmDVrFpYvX+5y9DEAXLx4Ea+99hquXr2K1NRUvP7667jtttt6/NxY+i42m40WGjqdDjwejxYaHbXGNzY2QqlUIjY21tHWbbfTKxrU6ghTaPTWCh9JkqiqqoLVau0VYUGSJBoaGtDQ0IDU1FSYTCacOnUK48eP94oAu9VqBysueghm8I5Go4HZbHYSGnw+n/6SUxdLf39/yOXyVl9+yiBGCQ1qdYSKIefy+ODzeQAJnLpahf5x4RAK+Hjk/S04drF7Ok1WL5iIeeMHYUveNfz162Mdhm79Y14u7h6a2O3ighJhMpkMiYmJXg0EMpvNWLJkCfbt2+fUb87EYDBg0qRJmDFjBmbPno1vv/0Wu3btwr59+9h9XRa3sNls9ARXypNFrYQyzYnMiaFxcXEuOx2ofBmtVkuvjlBCw5vzTpjJl/Hx8V5PCGUKi5SUFAQEBHj1+W/F2nFTJ3T2Jsypq7GxsTAajXQ/fH19PT0ZsKysDEVFRZg2bRrCwsJcftmZMeQRERG00FAqla3mnQxJiQKPxwOXy8FL99+BbSevYMepq6hSeT72nOL9xydh7tgsbPytBEs3HHcrzTMqyA88DuC5BbVtqEAgPz8/rwuLkpISLFmypENz6s6dOyESibB06VJwOBy8/PLLOHLkCHbv3s1u27C4BZ/Pdxr0xUwCBhx+LKlUik2bNiE7OxtZWVlt3nm3jNimhAZzlge1OtJTQoOa1WG323tNWDQ2NqKhoQHJycleFxa3au3wvRzYmxBq6mpMTAwGDBiAAQMGICgoCCdOnMAzzzwDg8EADocDi8XiVmeFSCRCeHg4kpOTkZKSAj8/PzQ0NODixYsoKSlGbW0NDAYDMhPCsfyhMTj1zyex/+35ePbuEUiN8sxB/q8np2Lu2CxsOHYVz2/oeMWCIjpY2ukuEVdQwkIqlSIpKcnrEcZ5eXkYMWIENm7c2O5xBQUFGDp0KF2oORwOhgwZgnPnznnhLFluNvh8PkJCQpCSkoKsrCwkJCSAJEm89tpr2LJlC6RSKSwWi1vt3pSYiImJQXp6OqKiougLf3FxMWpra6HX67v1e8scAtZbwoKaeJuUlNRquJc3uFVrB7ty4WU4HA4kEgmMRiPefvttvPDCCxg7dizUajWUSqXHwTvMccaUe1yr1aKurg4SiYROB02PCsbz9+Vi2f2jca22kU4HvVjhOl0TAD5ePB2zRg3AV79cwiubTsKTmhMR2H3igopqF4vF9NRJbzN37ly3jlMqlUhNTXX6WWhoKIqLXXfVsLC4C5fLRXBwMDZs2IDa2lp88cUX4PF4qKurc9lu2R4cDoc+ltm6WVtb65Tn05UYcoIgUFlZCZIkERcX1yvCQq1WQ6FQIDk5GUFBQV59fopbtXZ0WVyYzWa8/vrr2Lt3L8RiMRYuXIiFCxe6PPZmMqt0leDgYOzYsQPR0dEAgMjISDqCnBIangbvCAQChISEICQkBDabjTZ11dfXQywWIyAgAMHBwYgP88ezd4/AX++9HZVKDb11cq60lhYQnz4zA3eP6Id1By9ixfd5Hr02iZAPmVgAs9n1mHdPoISFSCRCSkqKTw5dYmI0GlsNPBIKhbBYut/Y2tdha0fnePjhh7Fw4UJ6H57K4GlqaoJCoWg1Kt4doUFt4VIDw6i6QRnJPQ0OpIQFAMTHx/fK91aj0aC+vh6JiYm9Jiw84WarHV0WF++++y6Kiorw1VdfoaamBi+88AKio6MxZcoUp+MMBgMWLVqEGTNmYPXq1fj222/x5JNP9lmzSlfhcDi0sKD+LhaLERkZCblcDqvVSns0Ghoa6Ohwd41YfD4fwcHBCA4OdsqIUCqVEAqFdDpoZJAUi6YMxR/vGo56tR7bT15BWnQoxmQm4pP9RXjrh9MevzZ5oMNY1tWVC0pYCASCPiEsAMeWVctiYLFY2HkFLmBrR+do2TbJ5XIRGBhIx+Xr9Xo0NTVBpVLRQsPd6HBqC1cqlSIiIoLOiFAqlaipqXESGm09lt1uR2VlJTgcTq9NYdVoNKirq0NCQoJPh4kxudlqR5fEhcFgwObNm7F27VoMHDgQAwcORHFxMTZs2NCqQNxsZpWehMPhQCgUQi6XIyIiAjabjRYa5eXldPAOJTQ6+vLy+XwEBQUhKCiInnei0+lQXFxMi5bg4GCE+Ekxb/wgCHg82AkS/mIBxvSPxvGrdbDa3YvwBoDIIEfB74q4oAoUn89Hamqq15dUO4tcLodK5TzxVqVSISIiopfOyDdha0fPwOVyWxk4m5qa0NDQQI+Kd6c1Hvh9C1cikSA8PJyOIW9oaEBtba3LMejU95bL5SI2NrbXhEVtbS3i4+N9OkysJTdb7eiSuLh8+TJsNhuys7Ppnw0dOhSffPIJCIJw+mC1Z1ZhC0TbcDgcCAQCREREICIigg7eUavVqKio8Dh4h8fjtbrL0el0KC0tpeedUI8ze3gyHs7NgN5kwa5zFdh1rhxHLtW4HFTGRB7YNXHBLFB9SVgAwKBBg7B27VqQJAkOhwOSJJGfn4+nnnqqt0/Np2BrR89DGTj9/f2dWuObmpqchEZAQIBbQoPqWGMKjaamJlpo+Pn5QavVgsfj9ZqwoHwjcXFxCAsL8/rzd4WbrXZ0SVwolUoEBwc77ROFhYXR3gHmctTNZlbpLQQCAcLDwxEeHu4UvENdjN0N3gFc3+U0NDSgsbERPB4Pzc3NCAoKgkwmwz1DEzHnD6kwWmzYf74SO86W4+CFKhjMtlaPKw+UwubBSgcTSlgAQGpqqteDdjqDUqmkRdmUKVPwwQcfYOXKlXjwwQfx3XffwWg0YurUqb19mj4FWzu8S0et8VKplK4dzAyethCJRBCJRAgLC4PVaoVGo4FSqQRJkpBIJFCr1e0OG+wJqLkqsbGxfUZY3My1o0uVuy0DCoBWe0c3m1nFF+Dz+QgLC0NYWBgdvKNWq1FVVdVm8E5bcLlcWK1WmM1mxMfHAwDdDw84euuDg4Ph7++PyVlxmDE0CRabHb9crMHPZ8uwv7ASmhsx5JFBUtjdnITKxG63o6qqCgCQlpbm1cLUFXJzc7Fq1SrMmjULMpkMn376KV577TVs2rQJGRkZ+Oyzz25Jb0B7sLWj92D6KqKjo2mhodFooFAo6AweShx0JDS4XC50Oh1tCKVWQykjObU60vL/sDvR6/WoqqpCTEwMwsPD+8ycoZu5dnRJXLRlQAHQyoRys5lVfA1XwTtqtdopeKe91jIqQjguLo7+MPv5+SEyMhJGoxFarZbuV2fGkI/pH4WJWXGw2Qkcv1qLHfnlSJUHwtOvNpXgRxAE0tPTfVpYXLlypd2/Z2VlYevWrd48pT4HWzt8g5ZCw2QyOQmNjlrjmabr2NhYcDgcp441vV5PB/5R3W+U0OguAdDc3Izq6mpERUUhIiLCp4XFrVQ7uiQu5HI5mpqaYLPZ6OVrKquhZQrazWZW8WWo4J2QkBAQBEFHCTN72AMCAmihoVAo0NTUhPj4+FYRwsziI5fLafd4bW0tKisrnYTGH1LluKNfNDgcDmy21tslbcGMBvZ1YcHSPbC1w/dgGjijoqJgNptpoeGqNZ4atCiRSBATE9Pqou7KSK7VajvV/dYWBoMBVVVVkMvliIyM9GlhcavRJcdN//79wefznRLEzpw5g8zMzFZ3x4MGDcLZs2dpkx9lVhk0aFBXToGlA6jgneTkZGRlZSEpKQl8Ph91dXUoLi7GP/7xDyxevBixsbEuZxMwoYpPREQEUlJSkJiYCJFIhPr6ely8eBHXS6+hvr4eRqMRVmvHI+EBh7CoqamB1WpFenp6jy6dsvgObO3wbSgDZ1RUFDIyMpCZmYmwsDAYjUZcv34dZ8+exZw5c3Dy5ElER0d3eFGnjORxcXFIT09HeHg4nbp77ZqjbhgMBo9M4AaDAZWVlYiIiEBUVBQrLHyMLg8ue/XVV5Gfn4+3334bCoUCL7zwAlatWoVJkyY5mVX0ej0mTpyI6dOn02aV3bt3Y+/evX12T6kvQxAEPv74Y3z11Vd45ZVXEB8fTyf2udMP3xLKPa7T6WAymZx669syZRIEgdraWphMJqSnp/vUaGiWnoetHX0PkiRRV1eH+fPnIz4+HosWLYJEIqFXQ0UikUddIpSRnKodzA4XqVTapmAwGo2orKxEWFiYy1UTlt6ny+LCaDRixYoV2Lt3L2QyGR5//HEsWLAAAJCRkUGbVQCgsLAQr732Gq5du4aMjAy8/vrrGDBgQJdfBEvnWL58OR5++GGkp6fTwTsajQY2m80p4c/Tjg2LxUIXC6PR6NRbT215EASBuro6GAwGZGRksMLiFoStHX2T/Px87N27F88//zwIgqA71qipq8ztDk+EBkmSTkIDAP1Yfn5+tIAwmUyoqKhASEgI4uLiWGHho/TpkeuexAcvXrwYBw8edPrZJ598gjvvvNMbp9pnYAbvaDQaWK1Wj4J3WkLNO9HpdDAYDLDb7Th9+jRuu+02BAUFIS0trcPtGBaW7oStGz0D1ZJKjYrn8/nw8/NzuzWeCXPeiU6nA0EQKC0thdVqRVJSEiIjI3st/ZPFPXw/RKAd3I0PBoBr167hvffew8iRI+mf9caEPF+nO4N3gNbzTsrKynD48GF89NFHSEtLw5w5c/DII4/0+OtiLygsFGzd6BkEAoFTazwV9teZ1viW805MJhNOnDiB//3vf9DpdBg7diz++Mc/Ii0trUdfE1s3Ok+fFReexAdbLBZUVVUhMzMT4eHhvXTGfY+Ognc60w8vk8nw8ssvQy6X48SJEygvL/fKa2EvKCwAWze8RVsZPNXV1fRE1oCAALeFBpfLxe23347JkyfDaDTiwIEDUKvVPf462LrRefqsuPAkPri0tJQeosPSOdrqh6dGGlOmrrb64UmShFKphFarRVpaGvz8/DBz5kyvnDt7QWGhYOuG92Fm8DBb46kMHmrrpK0MHovFgsrKSvj7+yMxMRFcLhcDBw7s8fNm60bX6LMbVh3FBzMpLS2FTCbD0qVLkZubi9mzZ+OXX37x8hnfPFAtqdHR0ejfvz8GDBiAoKAgNDc3o7S0FGVlZVCpVDCbzSBJkhYWGo2GFhbepK0LSkFBAYgWSaLsBeXmhq0bvUvL1vjExESn1vjq6mpoNBrY7Y75RVS7qp+fHy0svAVbN7pGnxUXnsQHl5aWwmQyITc3F+vWrcOYMWOwePFinD9/3mvne7PCFBr9+vXDwIEDERISAoPBgNLSUly/fh2VlZVoampCSkqK14UFwF5QWH6HrRu+A5fLRVBQEJKSkpCZmYnk5GQIBAIoFAqUlJSgsrISZWVlkEqlSEpK8rp5k60bXaPPbot4Eh/89NNP49FHH6X3v/r164cLFy5g06ZNyMzM9M4J3wIwg3ciIyNhtVrR2NgIlUqFxMRE+Pv798p5dfaCsmjRIuzbtw+LFy/Gxo0b2c/KTQBbN3wTLpfbalqzSqUCSZJITk7ula4Qtm50jT4rLjyJD6Y+uEySk5NRUlLitfO91eBwOBAKhYiMjERkZGSvngt7QWGhYOuG78Oc1tybsHWja/TZbRFP4oNffPFFLFu2zOlnly9fRnJysjdOlaWXYV5QKDy9oNTX13vlXFl6FrZusLgLWze6Rp8VFxKJBDNnzsSKFStQWFiI/fv3Y/369Zg3bx4Ax4fAZDIBAMaNG4ft27fjxx9/RHl5OT766COcOXPGK/kKLL0Pe0FhoWDrBou7sHWji5B9GIPBQC5dupQcPHgwmZubS37xxRf0v6Wnp5Nbtmyh/75p0yZy0qRJ5G233Ubee++9ZF5eXrecg9lsJqdPn06eOHGizWMuXLhAzp49m8zKyiJnzZpFnj9/vluem8V9li9fTk6fPp0sKCgg9+3bRw4ZMoTcs2cPSZIkqVAoSKPRSJIkSe7Zs4ccOHAguXXrVrKsrIz88MMPyaysLLKysrI3T5+lG/GFukGSbO3oC7B1o/P0aXHR25hMJvKPf/wjmZ6e3maBaG5uJkeNGkWuXr2aLCkpId98803y9ttvJ5ubm718trc2vnJBYWEhSbZ29BXYutF5+vRskd6kpKQES5YsAUmSuHLlCr7++muMGDGi1XHff/891qxZg/3794PD4YAkSUyePBlPPfUUPZSJhYXl1oGtHSy3An3Wc9Hb5OXlYcSIEdi4cWO7xxUUFGDo0KF0YiWHw8GQIUOc9vFYWFhuHdjawXIr0GdbUXubuXPnunWcUqlEamqq089CQ0NRXFzcE6fFwsLi47C1g+VWgF256GHaCmJp2T/NwsLCwoStHSx9GVZc9DBtBbG0DGFhYWFhYcLWDpa+DCsuehi5XA6VSuX0M5VKhYiIiF46IxYWlr4AWztY+jKsuOhhBg0ahLNnz4JqyiFJEvn5+Rg0aFAvnxkLC4svw9YOlr4MKy56AGbK35QpU6DVarFy5UqUlJRg5cqVMBqNmDp1ai+fJQsLi6/B1g6WmwVWXPQAubm52LlzJwBAJpPh008/xZkzZzBr1iwUFBTgs88+g1Qq7eWz7BuYzWa89NJLyMnJQW5uLtavX9/msRcvXsScOXMwaNAg3HfffSgqKvLimbKwdB22dnQfbO3oZXorvYule3AnQvipp54i09PTnf4cPHjQi2fZed544w1yxowZZFFREbl3714yOzub3LVrV6vj2DRDFhbPYGuHA7Z29AysuOjDuBMhTJIkOXHiRPKnn34iFQoF/cdsNnvxTDtHc3MzmZmZ6fTaPv74Y/KRRx5pdezmzZvJcePGkQRBkCRJkgRBkBMnTnSK52VhYXHA1o7fYWtHz8Bui/RRSkpKcP/996OioqLd4ywWC6qqqpCZmYnw8HD6T8v+eV/k8uXLsNlsyM7Opn82dOhQFBQUgCAIp2PZNEMWFvdgawdbO7wBKy76KO5GCJeWloLD4SAuLs5LZ9Z9KJVKBAcHOxWzsLAwmM1mqNXqVse2bNELDQ1FXV2dN06VhaXPwNYOdatj2drR/bDx330UdyOES0tLIZPJsHTpUuTl5SEyMhLPPPMMxowZ08Nn2HXaSigE0CpciE0zZGFxD7Z2sLXDG7ArFzc5paWlMJlMyM3Nxbp16zBmzBgsXrwY58+f7+1T65C2EgoBtEopZNMMWVi6F7Z2sLWjK7ArFzc5Tz/9NB599FEEBgYCAPr164cLFy5g06ZNyMzM7OWzax+5XI6mpibYbDbw+Y6PqlKphFgsRkBAQKtj2TRDFpbug60dbO3oCuzKxU0Ol8uliwNFcnIy6uvre+mM3Kd///7g8/lOxqozZ84gMzMTXK7zR5dNM2Rh6V7Y2sHWjq7AioubnBdffBHLli1z+tnly5eRnJzcS2fkPhKJBDNnzsSKFStQWFiI/fv3Y/369Zg3bx4ANs2QhaUnYWsHWzu6RO92wrJ0By171RUKBWk0GkmSJMk9e/aQAwcOJLdu3UqWlZWRH374IZmVlUVWVlb21ul6hMFgIJcuXUoOHjyYzM3NJb/44gv639LT05160QsKCsiZM2eSmZmZ5OzZs8kLFy70whmzsPQd2NrhgK0d3Q+HJG+sBbH0WTIyMvD1119jxIgR9N9XrVqFWbNmAQA2b96MdevWoaamBmlpaVi2bBmGDRvWm6fMwsLiA7C1g6WnYMUFCwsLCwsLS7fCei5YWFhYWFhYuhVWXLCwsLCwsLB0K6y4YGFhYWFhYelWWHHBwsLCwsLC0q2w4oKFhYWFhYWlW2HFBQsLCwsLC0u3wooLFhYWFhYWlm6FFRcsLCwsLCws3QorLlhYWFhYWFi6FVZcsLCwsLCwsHQrrLhgYWFhYWFh6VZYccHCwsLCwsLSrfw/Msn5PoNgaIYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[array([[ 0.81141747+0.j,  0.55450515+0.j, -0.02722876+0.j,\n",
       "         -0.59301243+0.j, -0.81141747+0.j, -0.55450515+0.j,\n",
       "          0.02722876+0.j,  0.59301243+0.j],\n",
       "        [ 0.53737712+0.j,  0.37047117+0.j, -0.01345176+0.j,\n",
       "         -0.38949484+0.j, -0.53737712+0.j, -0.37047117+0.j,\n",
       "          0.01345176+0.j,  0.38949484+0.j],\n",
       "        [-0.05145146+0.j, -0.03057979+0.j,  0.0082051 +0.j,\n",
       "          0.04218355+0.j,  0.05145146+0.j,  0.03057979+0.j,\n",
       "         -0.0082051 +0.j, -0.04218355+0.j],\n",
       "        [-0.61014047+0.j, -0.41371753+0.j,  0.02505552+0.j,\n",
       "          0.44915139+0.j,  0.61014047+0.j,  0.41371753+0.j,\n",
       "         -0.02505552+0.j, -0.44915139+0.j],\n",
       "        [-0.81141747+0.j, -0.55450515+0.j,  0.02722876+0.j,\n",
       "          0.59301243+0.j,  0.81141747+0.j,  0.55450515+0.j,\n",
       "         -0.02722876+0.j, -0.59301243+0.j],\n",
       "        [-0.53737712+0.j, -0.37047117+0.j,  0.01345176+0.j,\n",
       "          0.38949484+0.j,  0.53737712+0.j,  0.37047117+0.j,\n",
       "         -0.01345176+0.j, -0.38949484+0.j],\n",
       "        [ 0.05145146+0.j,  0.03057979+0.j, -0.0082051 +0.j,\n",
       "         -0.04218355+0.j, -0.05145146+0.j, -0.03057979+0.j,\n",
       "          0.0082051 +0.j,  0.04218355+0.j],\n",
       "        [ 0.61014047+0.j,  0.41371753+0.j, -0.02505552+0.j,\n",
       "         -0.44915139+0.j, -0.61014047+0.j, -0.41371753+0.j,\n",
       "          0.02505552+0.j,  0.44915139+0.j]])]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sym_dict = SymbolsDict().symbols_from_dict({a:0.3, b:1.25})\n",
    "qubit_pauli_string = QubitPauliString(\n",
    "    [Qubit(0), Qubit(1)], [Pauli.Z, Pauli.Z]\n",
    ")\n",
    "ansatz_circuit = AnsatzCircuit(circ, 16, sym_dict)\n",
    "exp = ObservableExperiment(\n",
    "        ansatz_circuit,\n",
    "        ObservableTracker(QubitPauliOperator({qubit_pauli_string: 1.0})),\n",
    "    )\n",
    "\n",
    "experiment_taskgraph.run([exp])"
   ]
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    "# Conclusion\n",
    "\n",
    "In the last hour we have:\n",
    "1. Introduction and getting started with Qermit.\n",
    "2. Out of the box error-mitigation with Qermit; `MitRes` and `MitEx`.\n",
    "3. Advanced use of Qermit to fine tune and combine error-mitigation schemes.\n",
    "4. Developing new error-mitigation schemes.\n",
    "\n",
    "In the last three hours we have:\n",
    "1. Explore the tools available in TKET, providing a comprehensive, feature-focused tour of the platform.\n",
    "2. Introduce noise and error mitigation.\n",
    "2. Exemplify out of the box and advanced usage of Qermit for the composition and design of error-mitigation protocols."
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   "source": [
    "<center> <h1> Questions </h1> </center>"
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