GPU_circuit_cut_SPSA_CE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:19.320003Z",
     "start_time": "2024-02-15T18:09:19.308526Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_55040/4071736489.py:9: DeprecationWarning: ``qiskit.algorithms`` has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. The ``qiskit.algorithms`` import path is deprecated as of qiskit-terra 0.25.0 and will be removed in Qiskit 1.0. Please run ``pip install qiskit_algorithms`` and use ``import qiskit_algorithms`` instead.\n",
      "  from qiskit.algorithms.optimizers import ADAM, SPSA\n"
     ]
    }
   ],
   "source": [
    "from circuit_knitting.cutting import partition_problem\n",
    "from qiskit.circuit import QuantumCircuit, ParameterVector\n",
    "from qiskit.quantum_info import SparsePauliOp, PauliList\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from qiskit_algorithms.optimizers.cobyla import COBYLA\n",
    "from qiskit.algorithms.optimizers import ADAM, SPSA\n",
    "from qiskit_algorithms.utils import algorithm_globals\n",
    "from IPython.display import clear_output\n",
    "from accuracy_score import encode_y\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b94d0bd-445c-48c7-949c-11d240fce1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c00f7c-def5-4106-bd98-522927f57892",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "84e0ff14-514d-440f-851a-63c136d80243",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:20.482075Z",
     "start_time": "2024-02-15T18:09:20.472994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.923998</td>\n",
       "      <td>1.531329</td>\n",
       "      <td>0.566517</td>\n",
       "      <td>-0.920265</td>\n",
       "      <td>-3.141593</td>\n",
       "      <td>0.004682</td>\n",
       "      <td>-1.66872</td>\n",
       "      <td>-0.104720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.771994</td>\n",
       "      <td>-0.457820</td>\n",
       "      <td>0.257508</td>\n",
       "      <td>-1.301064</td>\n",
       "      <td>-3.141593</td>\n",
       "      <td>-0.650792</td>\n",
       "      <td>-2.40918</td>\n",
       "      <td>-2.094395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0    -0.923998  1.531329       0.566517      -0.920265 -3.141593  0.004682   \n",
       "1    -2.771994 -0.457820       0.257508      -1.301064 -3.141593 -0.650792   \n",
       "\n",
       "   DiabetesPedigreeFunction       Age  Outcome  \n",
       "0                  -1.66872 -0.104720        1  \n",
       "1                  -2.40918 -2.094395        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "data = pd.read_csv(\"diabetes_normalized.csv\")\n",
    "data = data.drop([\"Unnamed: 0\"], axis=1)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92a101c1-41f3-4fbe-b1c4-78eee1e9d524",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:20.926480Z",
     "start_time": "2024-02-15T18:09:20.920864Z"
    }
   },
   "outputs": [],
   "source": [
    "y = data[\"Outcome\"]\n",
    "x = data.drop([\"Outcome\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8ef2ba7-dda4-41ea-944f-c710c9fc9528",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:21.304727Z",
     "start_time": "2024-02-15T18:09:21.299733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(537, 8) (537,) (231, 8) (231,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
    "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "505092d4-3e8d-4853-9f59-3391739bae30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:21.800966Z",
     "start_time": "2024-02-15T18:09:21.796485Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train_A = x_train.iloc[:, :4]\n",
    "x_train_B = x_train.iloc[:, 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea2b1465-28bb-4db5-9541-4ba60e770021",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:22.250366Z",
     "start_time": "2024-02-15T18:09:22.235445Z"
    }
   },
   "outputs": [],
   "source": [
    "x_test_A = x_test.iloc[:, :4]\n",
    "x_test_B = x_test.iloc[:, 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "878cdeed-18a8-4f22-bea4-2031a7be0a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_y_train = encode_y(y_train)\n",
    "new_y_test = encode_y(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202a0b22-ad7d-468f-878a-e4569cc6d0fa",
   "metadata": {},
   "source": [
    "## Data Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23a08b2f-5a0b-4aa5-96e7-5cd2a4655aaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:23.793347Z",
     "start_time": "2024-02-15T18:09:23.786288Z"
    }
   },
   "outputs": [],
   "source": [
    "# Data Embedding - Angle Encoding\n",
    "def angle_encoding(feature_dims: int):\n",
    "    embedding = QuantumCircuit(feature_dims)\n",
    "    feature_param = ParameterVector(\"Theta\", feature_dims)\n",
    "    for qubit in range(feature_dims):\n",
    "        embedding.ry(feature_param[qubit], qubit)\n",
    "    return embedding, feature_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cbfc86a-2a27-4381-84ce-2a469fb57661",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:24.329495Z",
     "start_time": "2024-02-15T18:09:24.321044Z"
    }
   },
   "outputs": [],
   "source": [
    "embedding, feature_params = angle_encoding(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09640b63-6b10-40b0-837f-7acbc1f88c47",
   "metadata": {},
   "source": [
    "## Tensor Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06ed2511-a0bb-4f5b-85b9-85940a954de3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:25.102164Z",
     "start_time": "2024-02-15T18:09:25.070178Z"
    }
   },
   "outputs": [],
   "source": [
    "from ttn import TTN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c2d9089-9292-4580-98e4-6f6287a59473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 705.552x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttn = TTN(num_qubits=8).ttn_simple(complex_structure=False)\n",
    "ttn.draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ebafa996-adaf-4060-9f32-21cdb706a932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ttn.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f81b56c-c727-47cb-94a6-e2b5359d8a19",
   "metadata": {},
   "source": [
    "## Circuit Cutting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6cda3f8fc3c6b2d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:34:50.371027Z",
     "start_time": "2024-02-15T03:34:50.309487Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "observables = PauliList([\"ZIIIIIII\"])\n",
    "partitioned_problem = partition_problem(circuit=ttn, partition_labels=\"AAAABBBB\", observables=observables)\n",
    "sub_circuits = partitioned_problem.subcircuits\n",
    "sub_observables = partitioned_problem.subobservables\n",
    "bases = partitioned_problem.bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a43241fdcd1decfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:34:50.869863Z",
     "start_time": "2024-02-15T03:34:50.865500Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': PauliList(['IIII']), 'B': PauliList(['ZIII'])}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_observables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81331ea7-8123-4e9b-867d-54fd786e3fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 723.581x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_circuits[\"A\"].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2af67465-17d0-4791-8e1c-279835483dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 807.394x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_circuits[\"B\"].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77655d0a6a0d1c81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:00.944952Z",
     "start_time": "2024-02-15T03:35:00.935773Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling overhead: 9.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df0c32d-bfd9-407f-b867-d08632f53764",
   "metadata": {},
   "source": [
    "### Sub Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a3327e421fea3182",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:05.890185Z",
     "start_time": "2024-02-15T03:35:05.693811Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "from circuit_knitting.cutting import generate_cutting_experiments\n",
    "\n",
    "subexperiments, coefficients = generate_cutting_experiments(\n",
    "    circuits=sub_circuits, observables=sub_observables, num_samples=np.inf\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "560e4327-900d-4758-b747-7ae311fe2902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subexperiments\n",
    "len(subexperiments[\"A\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cb2b2aa32d6d16b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:14.153045Z",
     "start_time": "2024-02-15T03:35:14.062981Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0QAAAFvCAYAAABq7QvyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABq9klEQVR4nO3deXwM9/8H8Nfu5pZEJEFCyClIJISIO4464qbOlhZV1VZLVcWXtlS1jrpbiqq6qxR1n3VGRImIMxFyIJEgJOS+dn5/5JetbXZzbrLJzuv5ePRRO/OZmffszLKvnc98RiIIggAiIiIiIiIRkmq7ACIiIiIiIm1hICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItFiICIiIiIiItHS03YBRESkGYIgIDcjS9tlaIyesSEkEom2y6BqShAE5OXlabuMUpHJZDznibSAgYiISEfkZmRhu/NobZehMaMit0HfxEjbZVA1lZeXhz179mi7jFIZMmQI9PT41YyosrHLHBERERERiRYDERERERERiRYDERERERERiRYDERERERERiRYDERERERERiRYDERERERERiRYDERERERERiRYHuyciIgWbdu7w2ztXaVpOWgZeRcUjcvd5hG04AiFPrqXqiIiINI+BiIiIConaG4DY0yGARALj2hZwGdYZPnPHomaj+giavk7b5REREWkMAxERERXy/GY0ovYEKF7f3XQcgwNWwvXtNxCycAeynr/SYnVERESaw3uIiIioWLkZWXgWcg8SqRTm9nW1XQ5RtRQXF4dXr/hjAlFVwytERETFOHHiBJYtW4YrV64gIyMDzs7OePvttzFt2jQYGBhou7xKY+aQH4SyklO1XAlR5YmOjkZ4eDiio6MRExOD1NRUyOVy6Ovro27dunBycoKTkxM8PT1hYmKidj2PHj3Cd999h5o1a+Krr76Cubl5Je4FERWFgYiIqAhLly7FF198AQCwt7dHw4YNcevWLcyaNQuHDh3C33//DWNjYy1XqXl6xgYwtDRT3EPU+N2esPJwwrOQe3gVFa/t8ogqVHZ2Ni5evIgTJ04gKipKbbtnz57h1q1bAABDQ0N07NgRPXv2hL29vVK7gjD08uVLvHz5Etu2bcPHH39coftARCXHQEREpMbly5cxffp0SCQSbNy4EWPGjAEAREZGws/PDxcvXsTMmTOxYsUK7RZaAbz8R8LLf6TStJjDl/DPzF+1VJH2hUUl41xwPFLScmBWQx+dvW3R1MlC22WRht26dQvr1q3Ds2fPCs2TyWQwNzeHVCpFZmYm0tLSFPOysrJw6tQpnDp1Ct26dcPo0aNhYmKiFIYAwMnJSfF3CRFVDQxERERqzJs3D4Ig4L333lP6AuPs7IwNGzagc+fOWLNmDWbNmoU6deposVLNu7v1BGIOBkGqr4daTRqi2aRBqGFrhbysbEWbzmumAlIJzk1cpphmYGGKQWeXI/jbLYjaG6Bq1dXO4fMPsWTzLZy9UvjKWJfWtvhiTDP09W2ohcpIk3JycrB161acOHFCabqjoyM6d+6MRo0aoUGDBkrdZF+8eIHo6GiEhoYiICAAmZmZAIDTp0/j+vXrGDp0KP744w+lMPTll1+iRo0albdjRFQsDqpARKKRmJgIf39/uLi4wMjICA0aNMCUKVOQlpaG8ePHQyKRYNWqVQCAlJQUxRejCRMmFFqXr68vXF1dkZ2djQMHDlTqflSGV1EJiA+4ibjT13Dr5/04NWYhrFs4o92iiYo2QTPXo07rxnAc1EExre389/H0crjOhKEFv15Hv09OqgxDAHD2Sjz6fXISC369XsmVkSZlZmbihx9+UApDTZs2xbx58zB//nz4+fnB2dm50D2DlpaWaNWqFcaPH481a9bgnXfegaGhIQDg+fPnWLduHcMQUTXAQEREohAaGgoPDw8sXrwYCQkJcHNzQ05ODn788UeMGDECYWFhAIAWLVoAAK5du4bs7GwYGhrC29tb5To7duwIALh06VKl7IM2PQu+i8jd5+E4qANqezcGAGQnp+LitDVo8/37MK5bC/Z928KmvTuCZujGc4rW7w7HrB+DS9R21o/BWL87vIIrooqQk5ODZcuW4ebNmwAAAwMDjB07Fl9//TUaNWoEiURSovUYGxujb9++WLx4MZydnZXm2djYMAwRVWEMRESk8xITE9G/f38kJCRg2rRpiI+PR0hICBISErBo0SIcPnwYV65cgUQigaenJwAgIiICQP5ACnp6qnsXF3zpKWir664v3w15bh68po9QTIs7E4qYgxfhu2oy2i6cgIvT1iArqfqPQpeZlYuZJQxDBWb+GIzMrNwKqogqyq5du3Djxg0A+aHmyy+/hJ+fH6TSsn1FysrKKnT/UXJyMjIyMspdKxFVDAYiItJ5kydPRmxsLD755BMsWbIEZmZminn+/v5o3rw5cnNz4eDgoBgKNykpCQBQq1YttestmFfQVtelxCQgen8g6vl6ok6bporpwXO3wMzRBnGnryH2VIgWK9ScP09E43lyVqmWeZ6chT9PRFdQRVQR7t27h0OHDgEA9PT0MGPGDDRu3LjM6ysYQKHgWUMFV4QyMzOxbt06CIJQ/qKJSOMYiIhIp4WFhWHnzp2wtrbGggULVLZp1aoVAKB58+aKaQU3Rxf1nKGCewX++8tvdHQ0BgwYADMzM9SqVQvvvvsunj9/Xq79qCpurNwDeZ7yVaLcjCykPniKpLCHWqxMs/44pn6o5aLsOFq25ajyyeVyrF27VhFShg0bhiZNmpR5fapGk1u0aBEsLS0BADdv3sT58+fLXzgRaRxHmSMinbZjxw7I5XKMGjUKpqamKtsUPEfo9UBkZGQEIP95JOpkZWUpLQ/kD8bQtWtXWFpaYseOHcjIyIC/vz/69euHwMDAMnXD8fb2RkJCQrHt9AUp5sCn1Ot/XULQbWyyHap2/st7cdhiN0LtfE1ybeSKHIm8Urb1X0/NPwD06pd6ub/PXoad3fgKqIhKy8DAQO2PIED+fYJxcXEA8ru/9uvXr8zbUhWGCu4Z+uCDD7Bw4UIAwKFDh+Dr66v2vqSCgVqIqPRsbGwQHFy6rs4FGIiISKedPn0aANC1a1e1bWJjYwEoB6KSdIdT1a3ul19+QVxcHM6fP4+GDfOHYrazs0P79u1x4MABDBo0qNT7kJCQoPjiVhQDiQyoW+rVV1mP4x8jW8jTzsaNMsv0L2ROdkaJjhVVvIIruOq8PqLc0KFDIZPJyrSdosIQkD9Qi6urKyIiIvDo0SOEh4ejadOmKtf1+PFjxQ8tRFR5GIiISKc9ePAAAAo9Ob5Abm4uAgMDASgHIldXV8Xyubm5KgdWiIyMVGoL5P8C3LFjR0UYAoB27drByckJBw8eLFMgsrGxKVE7fUEKaOeCSoWoZ1tPa1eIXshSUJZb4I1lKbCsX/orS6R5RXV3ffHiBa5fzx8qvXbt2kqf/dIoLgwV6Nmzp2LwlbNnz6oNRPXq1eMVIqIyKum/laowEBGRTit4kry6EZ527tyJxMREmJmZwdHRUTHdy8sLBgYGyMrKQnBwMNq2bVto2QsXLgAA2rRpo5h2584dDBs2rFBbd3d33Llzp0z7UNIuADnpmdjuPLpM2yivY0PmaHydEfcioG9ipPH1lkTA1QT4jjtc6uWO//4lOrX6qQIqotLKzc3Fnj17VM67f/++4s/t27cvU1fWkoYhAPDx8YGenh5yc3Nx7949teuMiIhQO6olEVUcDqpARDqt4BejkJDCo5/Fx8dj+vTpAABPT0+lfv1mZmbo0aMHAGD9+vWFlj1//jwiIiJgYGCAgQMHKqYnJSXBwsKiUHtLS0u8ePGiXPtCladjy7rwaKR+hEFVmrnUQseWOtRnUYdFRf07+MV/nxlUEqUJQ0D+1aqCq8bx8fEcgpuoimEgIiKd1r17dwDAokWLlJ4XdOXKFXTt2hWJiYkA/n0g6+u++uorSCQSbNy4EZs3b1ZMj4yMxPjx+TfOT5w4EXXq1KnAPSBtkEgk2PydL0xNSvZrvamJHrZ8r/5meapaHj78d0TE168Ml0Rpw1ABJycnAIAgCErbJyLtYyAiIp3m7+8PKysrPHr0CO7u7vDw8ECjRo3g4+MDJycndOvWDQBU3kPQtm1bLFy4EIIgYOzYsXBwcICXlxeaNGmC+/fvo02bNorRowrUqlULycnJhdb14sULxfC7VD14NbXGyXW9YV2r6G571rWMcGKdH7yaWldSZVRe6enpij+ruqKrTlnDEADUrFlT8eeCYf2JqGpgR1Ui0ml2dnYICAjA9OnTce7cOcTExMDNzQ3r1q3DhAkTFN1l1N1UXfDg1qVLl+LKlSt48uQJXF1dMWrUKEybNq3QSFZNmzZVea/QnTt34Ovrq/kdpArVtnkd3D0wFJsP3MOaXWG49+CVYp5MJsHiz30wZkAjWNYsekQzqlqmTZuGzMxM5OTklOqencePHyseulqaMAQAvXr1QqdOnWBgYKD0cGgi0j6JwMcmE5FIpaamwtzcHBKJBCkpKTAxMSn3OpcsWYJZs2YhKioKdnZ2AIB//vkHbdu2xd69ezF48OByb0MdbQ6qUBFGRW7T2qAKqsjlAuKfpaPliH14+iIT9WobI+7U29oui9QoalCF8rh48SKOHj2K//3vfyUOQyU1ZMgQDqpApAXsMkdEonX79m0IgoBGjRppJAwBwAcffABbW1sMHDgQhw4dwu7du/HWW2/Bx8dHafAFqn6kUgnq160Bfb38fzp5v5A4tW/fHnPnztV4GCIi7WEgIiLRunnzJgD13eXKwtzcHKdPn4atrS1GjhyJ999/H+3bt8ehQ4fKNLRvebWYNhwyQ30AQMcVk+A2oW+p19HQrzVqt2xUorYuw7vg7bub0e/YIsU0Iytz9Pj9S7wZ+BMGnlmGum3/fQZLp9VTMOL6evh8O7bUdRFpizY+y0RUcfiJJiLRqohABOQP43vo0CGkpqYiOTkZ27ZtQ+3atTW6jZJq8cW/gaisGvr5oHYr1+Ib/r+Ei7dxyG+G4nWrL0fj2dV72NvhU1z4bDV8V38GiZ4MABAwaSXubjlZrvqIiIjKgx1ViUi0KioQVRXtFn0AAOi9bx6EPDnSnyShZqP66LlrDmrUs0Ly3Uc49+FyyHNyIdGToaX/SNh0bAaZvh5eRsUjyH8dardyRYOe3rD19YTL8C4I23gMsSevwnfNZ9A3M4bM0AAJgbfwz1e/AWpuSXUY0A57230KAHh+PRLpT17App0b4gNuVtp7QUREpA4DERGJ1unTp7VdQoUKmvELGr/bE0cHfY3sV+nouGISLN0dcWzoHMizcuH317ew79sG0fsC0ezjgcjJyMLhPjMBAJ5Th8Jrxlv4Z9aveHQiGC9ux+DO+sMAAJmhPk69uxC56ZmQSKXotmkGHAe0R/T+wEI1GNYyhVRPDxnPkhXTUh89Q4362rliRkRE9F8MREREIvLw6D/Iy8gGACSG3oeZgw2A/PuEDMxM4NCnDQBAaqCH1EfPVK9EKkGrr0ajrk8TQCKBsbU5ksMfqgxEREREVR0DERGRiORl5Sj+LOTJIZXl38sjkUjwz1e/4fG568Wuw31ifxhb18ThvjORl5WD1t+MgcxI9X1KWUmpkOflwbi2heIqkWmD2kiLUxO2iIiIKhkHVSAi0mHZKenQNy9+SPGHxy7D7YN+kBkbAABkxgawcLX7/3VkQN/s33UY1KyBjKdJyMvKgXFtCzj0a1fkuh8cDELjd3sCAKyaO8PExhIJQYUfXktERKQNvEJERKTDbq89iJ5/zEZeRhbSnySpbXdz1T40/1wf/Q4vUIyNcGv1PiRHxCJy9zl0XPkJGvr5IHzTMYT9egRd1k/DwLPLkZHwAo8DbhRZQ/B329Bp1WS8GfgT8nJyEfDJjxBy8zS5m0RERGXGQEREpMOuL/sT15f9qXJe8LdbFH8W8uQIXbwToYt3Fmr3/Hok9neZqjStYPCFkshMfImTI+eVuD0REVFlYpc5IiLSmNzMbFi6Oyg9mLUonVZPgdOQTshOyajgyoiIiFTjFSIiItKYmAMXEXPgYonbB0xaWYHVEBERFY9XiIiIiIiISLQYiIiIiIiISLTYZY6ISEfoGRtiVOQ2bZehMXrGhtougaoxmUyGIUOGaGx9i9ftREpaGsxq1MD0iSMKvdYE2f8/F4yIKhcDERGRjpBIJNA3MdJ2GURVgkQigZ6e5r7mCADkQv7/9fT0Cr0mouqLXeaIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi09LRdABERaYYgCMjNyNJ2GRqjZ2wIiUSi7TKqDR5/IqKyYSAiItIRuRlZ2O48WttlaMyoyG3QNzHSdhnVBo8/EVHZsMscERERERGJFgMRERERERGJFgMRERERERGJFgMRERERERGJFgMRERERERGJFkeZIyIiUuNVajaCrj9F8O1EXA1LREJiBp6+yAQAPH+Zhe9/CUUrN2u0b1EH5qYGWq6WiIjKgoGIiIjoP25EvMDPO8Ow7dB9pGXkqmyTmZWHr1ZdBQDUMNbD6H4u+HhEU3i6WlZmqUREVE4MRERERP8vMSkTny4Iwh/Hokq1XFpGLtb9GY51f4ZjhJ8jVs1sD+tafIYOEVF1wEBEREQKNu3c4bd3rtK0nLQMvIqKR+Tu8wjbcARCnlxL1VWsfadjMPHbQEWXuLLaeSwaZy7HY93sDhjUzUEzxVUiMZ8DRCRODERERFRI1N4AxJ4OASQSGNe2gMuwzvCZOxY1G9VH0PR12i5P4376/TYmL7yksfU9fZGJwZ+dwsoZbTF5lLvG1luZxHYOEJF4MRAREVEhz29GI2pPgOL13U3HMThgJVzffgMhC3cg6/krLVanWT//cUejYeh1UxZdgp5Mgo9HulXI+iuSmM4BIhI3DrtNRFSMEydOwM/PD1ZWVjAxMYGHhwcWLFiA7OxsbZdWaXIzsvAs5B4kUinM7etquxyNCbiagE8WBFXoNj5ZEITzwfEVuo3KoKvnABERAxERURGWLl2KXr164fjx4zAzM0Pjxo0RHh6OWbNmoWvXrsjIyNB2iZXGzCH/S3BWcqqWK9GMtPQcjJt9HoJQuuWu7BiARydH4sqOASVqLwjAe3MCkJaeU4YqqxZdOweIiAAGIiIitS5fvozp06dDIpFg06ZNiImJwbVr1xAeHg4XFxdcvHgRM2fO1HaZFULP2ACGlmYwtDKHRZOGaDP/fVh5OOFZyD28iqr+VzsAYPbPIYh8lFLq5WysTWBXtwZsrE1KvEzkoxR8vfpqqbelTWI4B6j0cnLkeJ6ciYzMXAil/TWBqIriPURERGrMmzcPgiDgvffew5gxYxTTnZ2dsWHDBnTu3Blr1qzBrFmzUKdOHS1Wqnle/iPh5T9SaVrM4Uv4Z+avWqpIs5JfZWHNrrBK3ebaP8Mxe6IXLMwNK3W7ZaXr5wCVXF6eHIfPP8LPO8Nw/GKcYnpTJwt8PKIp3unngppmfDAxVV+8QkREopGYmAh/f3+4uLjAyMgIDRo0wJQpU5CWlobx48dDIpFg1apVAICUlBScOHECADBhwoRC6/L19YWrqyuys7Nx4MCBSt2PynB36wkcHz4XJ0d9j+B5W5H5IgU1bK2Ql/XvfVOd10xF53WfKy1nYGGK4aHr4fRmp8ouuVQ2H7iHjMy8St1mRmYeNh+4V6nbLA9dPweoZB4/TYPP2wcwcMrfSmEIAMKikvHpgiA4+O3EmcuPtVQhUfkxEBGRKISGhsLDwwOLFy9GQkIC3NzckJOTgx9//BEjRoxAWFj+1YIWLVoAAK5du4bs7GwYGhrC29tb5To7duwIALh0qWJGKNOmV1EJiA+4ibjT13Dr5/04NWYhrFs4o92iiYo2QTPXo07rxnAc1EExre389/H0cjii9gaoWm2V8eveCK1sd/2eu1rZblno+jlAxUtMykSX8UcQEva8yHbJKdnw++i4TgweQuLEQEREOi8xMRH9+/dHQkICpk2bhvj4eISEhCAhIQGLFi3C4cOHceXKFUgkEnh6egIAIiLyvzDb29tDT09172JnZ2eltrrsWfBdRO4+D8dBHVDbuzEAIDs5FRenrUGb79+Hcd1asO/bFjbt3RE0o2o/oyb5VRZu3U/SyrZvRyYj+VWWVrZdXrp0DlDJTFl0CfcelGx49ewcOYZPP4Os7Mq98kqkCQxERKTzJk+ejNjYWHzyySdYsmQJzMzMFPP8/f3RvHlz5ObmwsHBAebm5gCApKT8L8y1atVSu96CeQVtdd315bshz82D1/QRimlxZ0IRc/AifFdNRtuFE3Bx2hpkJVXtEciK+7Vb17dfHrpyDlDxnjzPwJ8noku9zN6/YyqmIKIKxEBERDotLCwMO3fuhLW1NRYsWKCyTatWrQAAzZs3V0zLzMwEABgYqL9R2NAw/+b414feLghePj4+MDQ0hEQiKfc+VBUpMQmI3h+Ier6eqNOmqWJ68NwtMHO0Qdzpa4g9FaLFCkvmWrh2A4m2t18eunIOUPE27Y9ATq681Mut/TO8AqohqlgcZY6IdNqOHTsgl8sxatQomJqaqmxjbGwMQDkQGRkZAUCRD1/NyspSWh4A7t+/jz179qB169YwMDBAYGBguffB29sbCQkJxbbTF6SYA59yb68oN1bugeOgDvCaPgLHh34DIP+BnakPniIp7KFGt+XayBU5ktJ/ISvOK+NugHFnlfOu7BhQ7HDaNtbGiv8/OjlSbbuExHS0fqvwgBtzv1uM5V+eLkXFJVMZxx+ovHOgoo5/WQ0e9xlqmJojPiEednZ2hV7rmhc1BgOGLUq93IUrkTr5flDVZ2Njg+Dg4DIty0BERDrt9On8L55du3ZV2yY2NhaAciAqSXc4Vd3qfH19ER+ff2PxN998o5FAlJCQgLi4uGLbGUhkQN1ybivoNjbZDlU7/+W9OGyxG6F2viY9jn+MbKEC7keomwYYq55V8IyhktCTSUvc9nUpKelIeVL88SwtTRx/oOqcAxV2/MtInpen+H9cXFyh1zqnQQ5QhhHi5YJUN98P0mkMRESk0x48eAAgf3AEVXJzcxWh5fVA5Orqqlg+NzdX5cAKkZGRSm0BQCrVfE9kGxubErXTF6RA1flBvdzq2darmCtERkZQ9zjWhMT0Ype3sTaGnkyK3Dw5EhIz1LZTty4zUyOY69UvSamlwuNfsaQymeL/9evXL/Ra1yQbCUgrw3IyZMBGB98PqvpK+m+lKgxERKTT0tLy/0l//T6f1+3cuROJiYkwMzODo6OjYrqXlxcMDAyQlZWF4OBgtG3bttCyFy5cAAC0adOmAir/V0m7AOSkZ2K78+gKraUyRdyLgL6JkcbX+/vhSIyaeVblPFVd3P7r0cmRsKtbAwmJGWjQ449Sb3/tyrl4u69zqZcrDo9/xZq/ejtepabB1sYWsbGxhV7rmjOXH6Pb+0dLvdzn4zvjh8+nV0BFRBWHgyoQkU4r+MUoJKTwjd7x8fGYPj3/H25PT0+lARDMzMzQo0cPAMD69esLLXv+/HlERETAwMAAAwcOrIjSq5VjQ+bg9trq8YDaVm5Wot5+RalO5wAVr0trWzRxrFmqZSQS4MPhTSqoIqKKw0BERDqte/fuAIBFixYpPS/oypUr6Nq1KxITEwH8+0DW13311VeQSCTYuHEjNm/erJgeGRmJ8ePHAwAmTpyIOnXqVOAekKY1sq8Jsxr6Wtm2WQ19NLIv3ZdMIm2QSCSYP1n1Q6nV+WBoEzjZmVdQRUQVh4GIiHSav78/rKys8OjRI7i7u8PDwwONGjWCj48PnJyc0K1bNwDK9w8VaNu2LRYuXAhBEDB27Fg4ODjAy8sLTZo0wf3799GmTRssXLiwsneJykkqlWBIdwetbHtIdwdIpbozFDvptsFvOGD1rHYoydMD3nzDAT/9r13FF0VUARiIiEin2dnZISAgAH379oWRkRFiYmJgaWmJdevW4fDhw4qrRqoCEZAfqI4dO4YePXrg5cuXCA8Ph6urK77//nucO3cOJiZFD9FMVdPHI5oW30iHtktUVh+PdMOR1b3Q0Uv1EIb29UyxZJoPdi3pCn19fq2k6omDKhCRzmvatCkOHTpUaHpqaipiYmIglUrRrFkztcv36tULvXr1qsgSqZK1blYbrZtZ48qtxErbpre7NVo3q11p2yPSFL+OdvDraIcbES9w+PwjzP81FKnpubCqaYjIw8MgkzEIUfXGQEREonX79m0IggBXV1eNXunZvXs3AODOnTtKrx0cHODtXbo++VRxfvpfO7R/9xDkcqHCtyWVSrBqJrsTUfXm6WoJT1dLrP7jDlLTc2FkKGMYIp3As5iIROvmzZsA1HeXK6thw4Zh2LBh+PPPP5Ver1q1SqPbKYkW04ZDZpg/gEDHFZPgNqFvqdfR0K81ardsVKK2LsO74O27m9Hv2CLFNI/Jb2JwwEqMiduFhn6tldp3Wj0FI66vh8+3Y0tdV3m18ayDL8aovzKoSV+MaYY2npU/+EZVOP7WLVzQ99B89D+xGIPOr0Czj/8dldH763cwNHgNum30L3VdRESawkBERKJVUYFIEASV/23atEmj2ymJFl/8+4W4rBr6+aB2K9fiG/6/hIu3cchvhuJ1/PkbODnqezy5FFaobcCklbi75WS56iuPuR+3ROtm1qVaJiExHbFP0kr0EFcAaN3MGnM/blmW8sqtKhz/9osn4saPe3Gw53QcGfAV3D8agJqudgCA4HlbEfrDznLVR0RUXuwyR0SiVVGBqKpot+gDAEDvffMg5MmR/iQJNRvVR89dc1CjnhWS7z7CuQ+XQ56TC4meDC39R8KmYzPI9PXwMioeQf7rULuVKxr09IatrydchndB2MZjiD15Fb5rPoO+mTFkhgZICLyFf776DRBUdz1LDL1fmbtdKkaGejj6cy90HX8EN+8llWiZkjy8tYBHo1o4+nMvGBlW/j+3VeX4CwJgULMGAEDPxBDy7FxkJ6VW2vtARFQcBiIiEq3Tp09ru4QKFTTjFzR+tyeODvoa2a/S0XHFJFi6O+LY0DmQZ+XC769vYd+3DaL3BaLZxwORk5GFw31mAgA8pw6F14y38M+sX/HoRDBe3I7BnfWHAQAyQ32cenchctMzIZFK0W3TDDgOaI/o/YHa3N0ys7Iwwtnf+mLwZ3/j/NUEja23U8u62LeyByxrGmpsnaVRVY5/4NTV6LZpBrxmjISRpTmCZvyCjGfJlfU2EBEVi4GIiEhEHh79B3kZ2QDyr9yYOdgAyL9PxMDMBA592gAApAZ6SH30TPVKpBK0+mo06vo0ASQSGFubIzn8YbUNRABgWdMQp3/tjZXbb+PLn64iMyuvzOsyMpTh+09bYcoo9yp3w7k2jr/HJ4Nxdf52RP91AaYN66D33m+ReD0SLyNiNb+DRERlwEBERCQieVk5ij8LeXJIZTIA+U+l/+er3/D43PVi1+E+sT+MrWvicN+ZyMvKQetvxkBmVL77VKoCmUyKz9/1QD/fhpj3yzXsOh6N7Bx5iZc30JdieC9HfDWhBRo7WlRcoeVQ2cff0NIMDXv74NxHywEAqQ+f4lnIPdRp3YSBiIiqjKr10xUREWlUdko69M2LH1L84bHLcPugH2TGBgAAmbEBLP7/xvfslAzom/27DoOaNZDxNAl5WTkwrm0Bh366NZy0q0NNbJ3fBbEnR2LhZ954o009WJgZqGxrYWaAN9rUw8LPvBF7ciS2zu9SpcKQto9/dnIactMzYdMhfzQ/Q0szWLd0QXL4w/LsFhGRRvEKERGRDru99iB6/jEbeRlZSH+iftCAm6v2ofnn+uh3eIHi3vhbq/chOSIWkbvPoePKT9DQzwfhm44h7Ncj6LJ+GgaeXY6MhBd4HHCjyBo8PxuCxu/0hJGVOdo3+Qhtvn8fB3pOR9bzV5rcVY2rbWmMGe81x4z3mkMQBETHpSD+WQaysvNgaCCDbW1jONY3g0Qi0Xapamn7+AtyOc5OXAbv2e9AKpNBqi/DnfWH8exqhKZ3lYiozBiIiIh02PVlf+L6sj9Vzgv+doviz0KeHKGLdyJ0ceEhkJ9fj8T+LlOVphXcfF8SN1bswY0Ve0rcviqSSCRwsjOHk525tksplapw/OMDbuJQrxnFNyQi0hJ2mSMiIo3JzcyGpbuD0oM5i9Jp9RQ4DemE7JSMCq6MKkNpj7/31+/A49PByErmMNxEpD28QkRERBoTc+AiYg5cLHH7gEkrK7AaqmylPf7B87YieN7WCqyIiKh4vEJERERERESixUBERERERESixS5zREQ6Qs/YEKMit2m7DI3RMzbUdgnVCo8/EVHZMBAREekIiUQCfRMjbZdBWsLjT0RUNuwyR0REREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREosVAREREREREoqWn7QKIiEgzBEFAbkaWtsvQGD1jQ0gkEm2XQUREOo6BiIhIR+RmZGG782htl6ExoyK3Qd/ESNtlEBGRjmOXOSIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0+h4iIiBRs2rnDb+9cpWk5aRl4FRWPyN3nEbbhCIQ8uZaqIyIi0jwGIiIiKiRqbwBiT4cAEgmMa1vAZVhn+Mwdi5qN6iNo+jptl0dERKQxDERERFTI85vRiNoToHh9d9NxDA5YCde330DIwh3Iev5Ki9URERFpDu8hIiIqxokTJ+Dn5wcrKyuYmJjAw8MDCxYsQHZ2trZLqzS5GVl4FnIPEqkU5vZ1tV0OERGRxjAQEREVYenSpejVqxeOHz8OMzMzNG7cGOHh4Zg1axa6du2KjIwMbZdYacwc8oNQVnKqlishIiLSHAYiIiI1Ll++jOnTp0MikWDTpk2IiYnBtWvXEB4eDhcXF1y8eBEzZ87UdpkVQs/YAIaWZjC0ModFk4ZoM/99WHk44VnIPbyKitd2eURERBrDe4iIiNSYN28eBEHAe++9hzFjxiimOzs7Y8OGDejcuTPWrFmDWbNmoU6dOlqsVPO8/EfCy3+k0rSYw5fwz8xftVSRdqWm5+D3I5E4eyUeqem5MDXRQ5fWtni7jzNMTfS1XR4REZUDrxARkWgkJibC398fLi4uMDIyQoMGDTBlyhSkpaVh/PjxkEgkWLVqFQAgJSUFJ06cAABMmDCh0Lp8fX3h6uqK7OxsHDhwoFL3ozLc3XoCx4fPxclR3yN43lZkvkhBDVsr5GX9e99U5zVT0Xnd50rLGViYYnjoeji92amyS64QublyzFx5BfW778DEbwOx42gUDp57iB1HozDx20DU774DM1deQW4uhyInIqquGIiISBRCQ0Ph4eGBxYsXIyEhAW5ubsjJycGPP/6IESNGICwsDADQokULAMC1a9eQnZ0NQ0NDeHt7q1xnx44dAQCXLl2qlH2oTK+iEhAfcBNxp6/h1s/7cWrMQli3cEa7RRMVbYJmrked1o3hOKiDYlrb+e/j6eVwRO0NULXaaiUnR443p57Cwg038Co1R2WbV6k5WLjhBt6cego5OQxFRETVEQMREem8xMRE9O/fHwkJCZg2bRri4+MREhKChIQELFq0CIcPH8aVK1cgkUjg6ekJAIiIiAAA2NvbQ09Pde9iZ2dnpba67FnwXUTuPg/HQR1Q27sxACA7ORUXp61Bm+/fh3HdWrDv2xY27d0RNEM3nlM0fdllHDz3sERtD557iC+W/lPBFRERUUVgICIinTd58mTExsbik08+wZIlS2BmZqaY5+/vj+bNmyM3NxcODg4wNzcHACQlJQEAatWqpXa9BfMK2uq668t3Q56bB6/pIxTT4s6EIubgRfiumoy2Cyfg4rQ1yEqq/qPQJSZlYs2usFIts/bPcCQmZVZQRUREVFEYiIhIp4WFhWHnzp2wtrbGggULVLZp1aoVAKB58+aKaZmZ+V9sDQwM1K7b0NAQAJSG3t69ezeGDBkCe3t7mJiYoEmTJvjyyy+Rmlr9Q0JKTAKi9weinq8n6rRpqpgePHcLzBxtEHf6GmJPhWixQs357a8IZJeyC1x2jhy//aX7VwuJiHQNAxER6bQdO3ZALpdj1KhRMDU1VdnG2NgYgHIgMjIyAoAiH76alZWltDwALFmyBDKZDPPnz8fRo0fx0UcfYc2aNfDz84NcXv3vMbmxcg/kecpXiXIzspD64CmSwkrWvaw6OBoYW6nLERGR9nDYbSLSaadPnwYAdO3aVW2b2Nj8L7GvB6KSdIdT1a3u4MGDqF27tuJ1586dUbt2bYwaNQoXLlyAr69vqffB29sbCQkJxbbTF6SYA59Sr/91CUG3scl2qNr5L+/FYYvdCLXzNcm1kStyJNoJkU/NJwJ69Uq9XGBQCOzsPqiAikjbBo/7DDVMzRGfEA87O7tCr8Uk3uJzQFpTlPtOVZeNjQ2Cg4PLtCwDERHptAcPHgDIHxxBldzcXAQGBgJQDkSurq6K5XNzc1UOrBAZGanUFoBSGCpQMEpdXFxcWXYBCQkJJVrWQCID6pZpE1XS4/jHyBbytLNxw9Qy/QuZk5VS5uNMVZs8L0/x/7i4uEKvRcUsD5CKdN9JJzEQEZFOS0tLA6B8n8/rdu7cicTERJiZmcHR0VEx3cvLCwYGBsjKykJwcDDatm1baNkLFy4AANq0aVNkDWfOnAEANG3atMh26tjY2JSonb4gBap/rzyFerb1tHaF6KU0EalwLb7hf5hKE1Gzfv0KqIi0TSqTKf5fv379Qq/FJF4mgxz5+24rsn2nqquk/1aqwkBERDrNxsYGSUlJCAkJQbt27ZTmxcfHY/r06QAAT09PSCQSxTwzMzP06NEDhw8fxvr16wsFovPnzyMiIgIGBgYYOHCg2u3HxcXh66+/hp+fn+IZR6VV0i4AOemZ2O48ukzbKK9jQ+ZofJ0R9yKgb2Kk8fWWxL0HL+Haf3eplwv5exka2desgIpI2+av3o5XqWmwtbFFbGxsoddiYtd9B+Kepufv+y1x7TvpJg6qQEQ6rXv37gCARYsWKT0v6MqVK+jatSsSExMBQGVY+eqrryCRSLBx40Zs3rxZMT0yMhLjx48HAEycOBF16tRRue3U1FQMHDgQBgYG+O233zS1S1QJGtnXRF/fBqVapq9vA4YhIqJqiIGIiHSav78/rKys8OjRI7i7u8PDwwONGjWCj48PnJyc0K1bNwDK9w8VaNu2LRYuXAhBEDB27Fg4ODjAy8sLTZo0wf3799GmTRssXLhQ5XYzMjLQv39/REdH48SJE7C1ta3Q/STN2zTPF64lDDiu9jWxaV7pB8wgIiLtYyAiIp1mZ2eHgIAA9O3bF0ZGRoiJiYGlpSXWrVuHw4cPK64aqQpEQH6gOnbsGHr06IGXL18iPDwcrq6u+P7773Hu3DmYmJgUWiYnJwdDhw5FcHAwjh49Cjc3twrdR6oY1rWMELCpL7q0LjrMdva2QcCmvrCupZ3ufUREVD68h4iIdF7Tpk1x6NChQtNTU1MRExMDqVSKZs2aqV2+V69e6NWrV4m2VfDMo1OnTuHIkSPw8SnfMNikXXWsjHFmQx8E336GNbvCceZyPB48ToFcAEyMZDj7W1+0blZ4ZEEiIqo+GIiISLRu374NQRDg6uqq8kpPWUyaNAl//vkn/ve//8HExASXLl1SzHN2dlY5LDdVfd7utbFhbv6xK7ihvJa5IcMQEZEOYJc5IhKtmzdvAlDfXa4sjh49CgBYuHAh2rVrp/Tf4cOHNbYdIiIi0gxeISIi0aqIQBQTE6OxdWlCi2nDcXPVX8jLykHHFZPw4nYM7qwvXTBr6NcaGU+T8SzkXrFtXYZ3gc+8cXgVnYBDfjMAAB2Wf4w6rZsgLzMbOWmZuDx7I55fz3+orffX78BhYHu8uBmN0+N+KP0OEhERlROvEBGRaFVEIKpqWnwxHDJD/XKto6GfD2q3KvlDShMu3laEIQB4ePQy9nX+DAe6f4GbP/2FruunKeYFz9uK0B92lqs+IiKi8uAVIiISrdOnT2u7hArVbtEHAIDe++ZByJMj/UkSajaqj5675qBGPSsk332Ecx8uhzwnFxI9GVr6j4RNx2aQ6evhZVQ8gvzXoXYrVzTo6Q1bX0+4DO+CsI3HEHvyKnzXfAZ9M2PIDA2QEHgL/3z1GyAIKut4dOLfB8s+C4mAiY0lJDIphDx5pbwPRERERWEgIiLSUUEzfkHjd3vi6KCvkf0qHR1XTIKluyOODZ0DeVYu/P76FvZ92yB6XyCafTwQORlZONxnJgDAc+pQeM14C//M+hWPTgQrdbWTGerj1LsLkZueCYlUim6bZsBxQHtE7w8stia39/si9tQ1hiEiIqoyGIiIiETk4dF/kJeRDQBIDL0PMwcbAPn3CRmYmcChTxsAgNRAD6mPnqleiVSCVl+NRl2fJoBEAmNrcySHPyw2EDkN6QSH/u1wdPBsze0QERFROTEQERGJSF5WjuLPQp4cUpkMACCRSPDPV7/h8bnrxa7DfWJ/GFvXxOG+M5GXlYPW34yBzKjo+5QcBrRHi8+H4/jwuchMfFm+nSAiItIgDqpARKTDslPSoW9e/DOWHh67DLcP+kFmbAAAkBkbwMLV7v/XkQF9s3/XYVCzBjKeJiEvKwfGtS3g0K9dket26N8OLWe8heMj5iItLrEce0NERKR5vEJERKTDbq89iJ5/zEZeRhbSnySpbXdz1T40/1wf/Q4vUIyNcGv1PiRHxCJy9zl0XPkJGvr5IHzTMYT9egRd1k/DwLPLkZHwAo8DbhRZg+/qKch4mow3Nv478tzx4XORlZSqkX0kIiIqDwYiIiIddn3Zn7i+7E+V84K/3aL4s5AnR+jinQhdXHgI7OfXI7G/y1SlaQWDL5TEloYjS9yWiIiosrHLHBERaUxuZjYs3R3Q79iiErX3/vodeHw6GFnJvFpERETawStERESkMTEHLiLmwMUStw+etxXB87ZWYEVERERF4xUiIiIiIiISLQYiIiIiIiISLXaZIyLSEXrGhhgVuU3bZWiMnrGhtksgIiIRYCAiItIREokE+iZG2i6DiIioWmGXOSIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi0GIiIiIiIiEi09bRdARESaIQgCcjOytF2GxugZG0IikWi7jGqDx5/ESNfO+6KU9jMhCEBmXgUWVAGMZIA2PvYMREREOiI3IwvbnUdruwyNGRW5DfomRtouo9rg8Scx0rXzviil/Uxk5gGdjlRgQRUgoA9grIV0wi5zREREREQkWgxEREREREQkWgxEREREREQkWgxEREREREQkWgxEREREJZSbK4dcELRdBpHW5OX9+xkQ+FkgHcFR5oiIiFQQBAEBVxNwIigOwbcTcTXsORKTMhXz45+lw+/DY2jlZo2e7evDt5UNh4kmnRMa/hwHzz3E1TuJCL6diLin6Yp58YkZ6Dr+CFo1tUKX1rbo3dEOMhl/a6fqh4GIiIjoNekZudi4PwI/7wzDnchkte3kAnD8YhyOX4zD/F+vw83ZAh8Nb4r3BrnCRBvjxhJpSG6uHH8ci8LPO8MQdP2p2naCAJy9Eo+zV+KxdMstNLStgYlDm2DisCawsuCQ6VR98G9sIiJSsGnnDr+9c5Wm5aRl4FVUPCJ3n0fYhiMQ8uRaqq7iBVxNwLjZ5xH5KKXUy96JTManC4KwYtstbPzWF51a2VRAhRVP7OeA2N2JTMLYr8/jyq3EUi/7MD4NX/50FSu23caar9pjSA/HCqiw4vEzID4MREREVEjU3gDEng4BJBIY17aAy7DO8Jk7FjUb1UfQ9HXaLk/j8vLk+N+KYCzdchPlvS0i8lEKOr93GJ+/0wyLprautl2IxHYOELBy2y34L7+C7Jzyfdl/lpSJodNOY6SfEzbM7VRtr5jyMyAe1fMMJSKiCvX8ZjSi9gQoXt/ddByDA1bC9e03ELJwB7Kev9JidZqVmyvHO7PO4Y9jURpbpyAAS7fcQuzTNGyb3wV6etUvFInpHBA7QRAw68dgLNxwQ6Pr/eNYFGKfpOHw6p4wNzXQ6LorAz8D4lH9/oYmIqJKl5uRhWch9yCRSmFuX1fb5WiMIAgYPydAo2HodTuPRWP8nACdGI1LV88BAr77JVTjYajAhWtPMGDySWRm5VbI+isTPwO6i4GIiKgYJ06cgJ+fH6ysrGBiYgIPDw8sWLAA2dnZ2i6tUpk55H8ByEpO1XIlmrNhbwS2HLxfodvYcvA+NuyNqNBtVBZdPAfE7vQ/jzF7dUiFbuNccAK+WXOtQrdRWfgZKFriqU24+2UXbZdRauwyR0RUhKVLl+KLL74AANjb26Nhw4a4desWZs2ahUOHDuHvv/+GsbGxlqvUPD1jAxhamin6zjd+tyesPJzwLOQeXkXFa7s8jXgYn4rPl/xT6uWu7BgAG2sTJCSmo/VbB0q0zOdL/kHP9vXR0Na01NvTFjGcA2KXmp6D8XMCim/4H2X5DCzedBODu9mjjWedUm9PW/gZEA8GIiIiNS5fvozp06dDIpFg48aNGDNmDAAgMjISfn5+uHjxImbOnIkVK1Zot9AK4OU/El7+I5WmxRy+hH9m/qqlijRvyqJLSEnLKfVyNtYmsKtbo1TLpKTlYMqiS/hrRfdSb09bxHAOiN3cNdcQ87j0VzrK8hmQywVMmHsB13cPrjbP6+JnoORifnofSRf+gJCXCyEvB9dG5P/40+L3l5DIZFqurnjsMkdEpMa8efMgCALGjRunCEMA4OzsjA0bNgAA1qxZg6dP1T+no7q6u/UEjg+fi5OjvkfwvK3IfJGCGrZWyMv6t5tg5zVT0Xnd50rLGViYYnjoeji92amySy6V6NgU7D/zoFK3uf/MA0THln44b23R9XNA7NLSc7B+791K3ebNe0k4c7n6XFkR82cgKyEKD1Z/gFsfN0HIMBOEvl0Ltyc1RfSKMUi5caZQe4dPf4XXzlQ0/GgtTN06wWtnKrx2plaLMAQwEBGRiCQmJsLf3x8uLi4wMjJCgwYNMGXKFKSlpWH8+PGQSCRYtWoVACAlJQUnTpwAAEyYMKHQunx9feHq6ors7GwcOFCyLiPVyauoBMQH3ETc6Wu49fN+nBqzENYtnNFu0URFm6CZ61GndWM4DuqgmNZ2/vt4ejkcUXtL3w2nMq3bHV7u4bVLSxDyt1td6Po5IHY7jkbhZUrl3we5+o+wSt9mWYn1M5B2Lxh3JnsgKXAXzFv0RIPxy2E7cg7MPLohLfwikoL2aLtEjWOXOSIShdDQUPTu3RsJCQmoUaMG3Nzc8PjxY/z444+IjIzEixcvAAAtWrQAAFy7dg3Z2dkwNDSEt7e3ynV27NgRERERuHTpEt5///3K2hWteBZ8F5G7z8NleBfc2XAEz4LvIjs5FRenrUGnVVOQEHQHdbwbw6a9O/Z3nartcoskCAK2HqrYgRTU2XLwPhZM8a42XYZep0vnAEFrn4H9Zx/gZUo2appVv2G4xfIZiN85F/KsdDRdFAoTx+aF5uckJWihqorFK0REpPMSExPRv39/JCQkYNq0aYiPj0dISAgSEhKwaNEiHD58GFeuXIFEIoGnpycAICIif1Qwe3t76Omp/u3I2dlZqa2uu758N+S5efCaPkIxLe5MKGIOXoTvqslou3ACLk5bg6ykqj360uOn6Xj8NF0r245/pr1ta4KunANil5cnR/DtRC1tW8C18Oda2bYmiOEzkPn4HmRmVirDEADo17Kp5IoqHgMREem8yZMnIzY2Fp988gmWLFkCMzMzxTx/f380b94cubm5cHBwgLm5OQAgKSkJAFCrVi216y2YV9BW16XEJCB6fyDq+XqiTpumiunBc7fAzNEGcaevIfZUxQ7fqwnBd7TzRbCqbL88dOUcELvw6JdIz9Tec4Gu8jNQpRnaOCMv5TmSgvZqu5RKw0BERDotLCwMO3fuhLW1NRYsWKCyTatWrQAAzZv/+2tYZmYmAMDAQH23DkNDQwBARkaGYlpAQAC6d+8OW1tbGBoaws7ODiNGjEBYWPXpN1+UGyv3QJ6n/OtobkYWUh88RVLYQy1WVnK372s3wGp7++WlC+eA2N2O1PJnQMvbLy9d/wzYDv8KEj19RC0cglsfuSLmx/fw7OgaZDzSjX/HVOE9RESk03bs2AG5XI5Ro0bB1FT1M2AKniP0eiAyMjICgCIfvpqVlaW0PJB/tcjDwwMTJ05EnTp1EBsbiwULFqBdu3a4desW7OzsSr0P3t7eSEgovs+2viDFHPiUev2vSwi6jU22Q9XOf3kvDlvsRqidr0mujVyRI5FrfL0vjd8AjH1Vzit4vkpRbKyNFf9/dHKk2nbqntGy4Ifl+HnuqVJUXDKaOP5A1TkHKur4l9XgcZ+hhqk54hPiYWdnV+h1dZJm4AWYDlI5rzI+A3/s/Asnfnu75AUXQVPn/euqymfgv0r7mZAYGKPuinul3o5pk3ZouvQqnuxfipdXj+L5qY14fmpj/jy3TnCYsgmGNk6qtynTh9Sg7M/mc3VtBCE7o/iGKtjY2CA4OLhMyzIQEZFOO336NACga9euatvExsYCUA5EJekOp6pb3YABAzBgwACldq1bt0bjxo2xZ88eTJkypZR7ACQkJCAuLq7YdgYSGVC31Kuvsh7HP0a2kKf5FddNAdT8e12a56voyaSlfhYLAKSmpCD1SfHHs7R4/CuWPC9P8f+4uLhCr6uVWg6AmmcEV8ZnICMjQ2Pvma6d90Up7WdCamhS5rfG2MEDDlM2AQCynj5A6q1zSDz5K1LvBOD+/IFouvQqpPqFe1BYdRkFqy6jyrhV4PHjx5BnVf59lgxERKTTHjzIf9aMvb29yvm5ubkIDAwEoByIXF1dFcvn5uaqHFghMjJSqa06VlZWAKB2cIbi2NiU7AZWfUEKVJ0f1Mutnm29CrlCkGJkiFdq5iUkFv8PsY21MfRkUuTmyZGQqP6XTHXrMjc1hJle/ZKUWio8/hVL+v/PU5HKZKhfv36h19VJhr4JXqiZVxmfARNjGWpp6D3TtfO+KKX9TEjKcaXmdYZ17GHY7V1Ydn0Hd2d2QlpYINLvXYapW0eNrP919erVK9cVorJiICIinZaWlgZA+T6f1+3cuROJiYkwMzODo6OjYrqXlxcMDAyQlZWF4OBgtG3bttCyFy5cAAC0adOm0Ly8vDzI5XI8ePAAM2fOhI2NDYYPH16mfShpF4Cc9Exsdx5dpm2U17EhczS+zoh7EdA3MdL4eg+ceYCBU/5WOU9V957/enRyJOzq1kBCYgYa9Pij1Nvfun4hBnRVHdDLQ5vHH9D8OVBRx7+s5q/ejlepabC1sUVsbGyh19XJncgkuA9WfcN8ZXwG5n35IT5/d3Wpl1NF2+f96yri78HXlfYzkZELdDqiue1LJBLUcG2DtLBAZD+vmKuiERH3YKyFdMJBFYhIpxX8YhQSUnjUn/j4eEyfPh0A4OnpqfRsGDMzM/To0QMAsH79+kLLnj9/HhERETAwMMDAgQMLze/cuTMMDAzQqFEjhIaG4vTp06hdu7ZG9onKp5Wbtai3T9TYoSZqaONb5//jZ6BqexV6EkJe4VEI5VkZeBWa/8By4wZulV1WhWIgIiKd1r17dwDAokWLlJ4XdOXKFXTt2hWJifnDvxY8kPV1X331FSQSCTZu3IjNmzcrpkdGRmL8+PEAoBg84b82bNiAS5cuYceOHTA3N0fPnj3x8GH1H31IF9SrY4L6dYq+abwit11PS9smKiCTSeHtrp1QIpNJ4NXESivbppJ5tGEqboxvgAc/T8TTw6uQ+PdvePzHXNz5rAUyH9yCZdd3Yezgoe0yNYqBiIh0mr+/P6ysrPDo0SO4u7vDw8MDjRo1go+PD5ycnNCtWzcAyvcPFWjbti0WLlwIQRAwduxYODg4wMvLC02aNMH9+/fRpk0bLFy4UOV2GzdujDZt2mDkyJE4deoUUlJS8MMPP1TovlLJSCQSvNu/kVa2PaZ/I6UrkUTaoq3PwOBu9jA3Vf84A9K+Bu8tQ622byLt7iU83vENHqz+AE8P/Qh9y3qw/3QDHCZv1HaJGsd7iIhIp9nZ2SEgIADTp0/HuXPnEBMTAzc3N6xbtw4TJkyAs7MzANWBCPj3wa1Lly7FlStX8OTJE7i6umLUqFGYNm2a4llERbGwsICLiwvu37+v0X2jsps4rDEW/nYdglB525RI8rdLVBWM9HPCtCX/IDlF/aMFKsLHI5oW34i0ytyrJ8y9emq7jErFQEREOq9p06Y4dOhQoempqamIiYmBVCpFs2bN1C7fq1cv9OrVq8zbf/r0Ke7evaty8AXSDvt6ZnjzDQfs+Tum0rb55hsOsK9nVmnbIyqKibEeJg5rgkW/3ai0bTZvbIkurW0rbXtEJcVARESidfv2bQiCAFdXV5iYaOa+jtGjR8PFxQUtWrSAhYUF7t27h+XLl0NPTw9Tp07VyDZIM1bOaIu//3mMl5XwC3lNMwOsnFF4pEIibfr6gxbYfTIakY9SKnxbMpkEv37TkV1GqUriPUREJFo3b94EoL67XFm0bdsWR44cwbhx49C7d28sXrwYnTp1QmhoKFxcXDS2nZJqMW04ZIb6AICOKybBbULfUq+joV9r1G5ZsvsNXIZ3wdt3N6PfsUWKaR2Wf4wBp5ZiwMnF6Hd0IWw7/nszbqfVUzDi+nr4fDu21HWVV/26NbDCv/RX7RIS0xH7JK1Ez2spsMK/DeqX4QGW5VUVjr9Vc2f03v8dBvy9BANOLoZNh3+vxnp//Q6GBq9Bt43+pa6Lyq+GiT5+m9up1MuV5TMwY5wnvN0rZ6TNqnDee0x+E4MDVmJM3C409Gut1L7D8o8x+MKPGPD3EvTe/x2smjsr5vEzoR28QkREolURgeiTTz7BJ598orH1lVeLL4bjzvpDyMvKKfM6Gvr54MXtGDwLuVei9gkXb+P0uH8HkLgyZxOyX+V/cbJs5oheu2Zjh/t7gCAgYNJKtJg2HAY1tTPy2pgBjRAQ8gS//RVRfOP/V5LntLzuvcGuGDNAOzewV4Xj3+03f1z4bBXiA27C3MkWPXfNxl8dpyAvMxvB87Yi+e4jNOztU+b6qHx8vW0xf7I3Zv1YsuedAaX/DLzRph5mf+hV2tLKrCqc9/HnbyB63wV0XD6pUNuHRy/j4hdrIeTJYde9Fbqun4bdPh8DAD8TWsJARESiVRGBqCppt+gDAEDvffMg5MmR/iQJNRvVR89dc1CjnhWS7z7CuQ+XQ56TC4meDC39R8KmYzPI9PXwMioeQf7rULuVKxr09IatrydchndB2MZjiD15Fb5rPoO+mTFkhgZICLyFf776DepGKCgIQwBgYFa1hpyWSCT4ZXYHZGXnYfvhSI2vf1RfZ/wyu4NWuglVheNvaGkGIytzxAfkf9ZeRcUj+2U66nfzwsMj/1Tq+0Hq/W+8J9Izc/HdL6EaX3dnbxvsW9kdhgYyja9blapw3gNAYqj6QXQenfg3fD4LiYCJjSUkMimEPLlm3wwqMQYiIhKt06dPa7uEChU04xc0frcnjg76Gtmv0tFxxSRYujvi2NA5kGflwu+vb2Hftw2i9wWi2ccDkZORhcN9ZgIAPKcOhdeMt/DPrF/x6EQwXtyOwZ31hwEAMkN9nHp3IXLTMyGRStFt0ww4DmiP6P2BamtpNWsU7Pu3g2HNGjjz/hK1XyK0QSaTYsv3ndHApgZ+2HgTcnn5a5NKJfAf54HvP/WGVKqdeyaqwvHPepGCjKdJcOjfDjEHg2DV3Bk1nevBtAEfUlyVSCQSzPukFWysjfHF0svIzMrTyHrf6eeCdbM7wNio8r5uVoXzvjTc3u+L2FPXGIa0jIGIiEhEHh79B3kZ+YMIJIbeh5mDDYD8/vIGZiZw6JN/T43UQA+pj56pXolUglZfjUZdnyaARAJja3Mkhz8s8ovB1fnbcXX+dth28oD31+/gyICvIM8p/CR0bZFKJVgwpTUGdGmIcV8H4G7MyzKvq7FDTWyc1wntmtfVYIWaoY3jf2rsInh/NRoen76J5IhHeHI5HEIuv/xVRZNGuqF7m3oYNzsAQdeflnk9NtbGWPd1Bwzoaq/B6spOW3/vFcdpSCc49G+Ho4Nnl3kdpBkMREREIvJ6n3ohTw6pLL8bi0QiwT9f/YbH564Xuw73if1hbF0Th/vORF5WDlp/MwYyI/0SbT8+4Cb0TY1Rq2lDPL8RVbadqEDtmtfFtV2DsPXQfaz+Iww3Il6UeFlPV0tMGtkU7/RzqdRfxEtDG8c/6c4DnHz7e8XrQedXIPnuo3LsBVWkxo4WCNjUF7tPxuDnnWE4fzWhxMs61jfDR8Ob4P0hjVHLvPhntFUWbf+9p4rDgPZo8flwHB8+F5mJZf8BhjSjav6NTUREGpGdkg59cxOl+3hUeXjsMtw+6Icnl8OQl5ENmbEBzBrUQXJELLJTMqD/2r0/BjVrIONpEvKycmBc2wIO/drhwZFLKtcr0ZPB1K42UmLyv1RZt3CBkZU5Uh480dxOapixkR4+GNoEE4Y0RtD1pzgZFIerd57jalgi4p+lQxDyH7JqW9sErZpao5WbFXq0q492zetUuSGFtX38AcC4jgUyniYDABqN6o7c9CzEX7ipkf2jiiGTSTHCzwkj/Jxw694LHDr/CFfvJOLqned4EJ+q6FZqXcsILZtaoWVTK3TxtkWPdvW11kX0dVXhvC+KQ/92aDnjLRwfMRdpcYllWgdpFgMREZEOu732IHr+MRt5GVlIf5Kktt3NVfvQ/HN99Du8QHF7z63V+5AcEYvI3efQceUnaOjng/BNxxD26xF0WT8NA88uR0bCCzwOUP9gR6m+DB1XfgIDcxMIuXnISc/CmQlLkf0yTdO7qnESiQTtW9RF+xb/dn0TBAG5uQL09CRVLvyoou3jDwCuo3vA6c1OkEgkSL4Xi9Pv/VBke6pamjWyRLNGlorX1eEzUBXOe8/PhqDxOz1hZGWO9k0+Qpvv38eBntOR9fwVfFdPQcbTZLyxcYai/fHhc5GVlKqR/afSkwhCFbqzlYiIyiwnPRPbnUdrtQaX4V3QsLeP0vCzxSkYdvvy7E1K00dFboO+iZGGK9Rd1fX4q1umqh3/+au341VqGsxNa2DWpFGFXpN2VNfzvizrK+1nIiMX6HREIyVVmoA+gLEWLtfwwaxERKQxuZnZsHR3UHpAYVE6rZ4CpyGdkJ2SUcGVUWUo7fH3/vodeHw6GFnJ/GWcqq/SnvdF4WdCO9hljoiINCbmwEXEHLhY4vYBk1ZWYDVU2Up7/IPnbUXwvK0VWBFRxSvteV8Ufia0g1eIiIiIiIhItBiIiIiIiIhItNhljohIR+gZG2JU5DZtl6ExesZV5zkm1QGPP4mRrp33RSntZ8JIlj9IQXViJNPOdhmIiIh0hEQiqVKjclHl4vEnMeJ5r55Eop0R26ojdpkjIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiItIB58+fx8CBA2Fvbw+JRILvvvtO2yURUTGOHDmCFi1awNDQEA4ODli2bJm2SyISJQYiIiIdkJqaCjc3N/zwww+wsbHRdjlEVIzg4GAMHDgQvXv3RmhoKL755hvMmjULa9eu1XZpRKKjp+0CiIio/Pr06YM+ffoAAGbMmKHlaoioOMuWLUPr1q2xYMECAEDTpk1x+/ZtLFy4EB9++KGWqyMSFwYiIiIiov8nl8sR+eAxhP9Mz83LU/w/Ijq20OvX1appitqWFkVuJzAwEOPHj1ea5ufnhyVLliA2NhZ2dnbl2g8iKjkGIiIiIqL/J5VKcef+AwSF3FY5Pz0jE7/tOqL2tb6+HqaMG1LsduLj4wt1by14HR8fz0BEVIl4DxERERHRa3p3aYPaljXLtGy/bm1hXatsyxKRdjAQEREREb3GQF8Pw/t1hVQiKdVyjZ0awKd50xK1tbW1RUJCgtK0J0+eKOYRUeVhICIiIiL6jwa2ddCtQ8sStzcxNsTQ3p0hKWGI6tChA44fP6407dixY7C3t2d3OaJKJqpAFBMTA4lEAgcHB22XUm188803kEgk+Oabb0q9rEQiKfE/DERUPqmpqQgNDUVoaCiys7ORkJCA0NBQ3L9/X9ulEVVbXdt5oYFt7RK1fbOXL8xMTUq87qlTp+Ly5cv48ssvER4ejs2bN+Onn37C//73v7KWS0RlJKpARERUVitWrMA333yD5ORkbZeiUnBwMLy8vODl5YX4+HisXr0aXl5eeP/997VdGlG1JZNKMbxfV+jryYps17JZIzRr7Fiqdbdu3Rr79u3DoUOH0Lx5c8yePRvff/89h9wm0gKOMkdEVAIrVqzAgwcPMHbsWFhYWGi7nEK6dOkCQfjvQMGFCYKA+w/i4Gxfv9T3RxCJUW1LC/Tp2hb7TwaqnG9hbooB3TuUad19+/ZF3759y1MeEWkArxAREYlIRHQsNuw8grXbDkBeggBFREBbLze4Oha+r0cCYFjfLjAyNKj8oohIYxiIiIhEQhAE/H3hKgCgYf06vEJEVEISiQRDe3eGiZGh0vSOrT3h3LCelqoiIk2p9oHo5cuX+Prrr9GsWTOYmJjAzMwM3t7eWLp0KbKysopcdv369WjVqhVq1KgBS0tL9O/fH1evXlXZNj09HfPnz4eXlxdMTU1haGiI+vXro127dvj666/x/PnzQsvI5XJs3rwZ3bp1g5WVFQwNDeHg4IAPP/wQDx8+VLmd1wciOHjwILp37w4rKytIJBKcPXsW7du3h0QiwcaNG9XuV0BAACQSCerVq4e8/3+SNgD8/fff+PTTT+Hl5QVra2sYGhrC3t4e48aNw927d4t8rwDg+fPnmDRpEho2bAhDQ0M4Ojpi+vTpePXqVbHLqrJ//3707dsXderUgYGBAerXr4/Ro0fj9m3VD8MrjbFjx0IikWDTpk2IjY3F2LFjYWtrCxMTE7Rs2RJ//vmnou3Dhw8xfvx41K9fH0ZGRvD09MT27duLXP/58+cxbNgw1KtXDwYGBqhTpw4GDx6MixcvqmyfkJCAVatWoXfv3nBycoKRkREsLCzQoUMHrF27FnK5XOVysbGx+Pjjj+Hi4gIjIyPUqFEDDg4O8PPzw48//qjUdtOmTZBIJBg7dqzautUNdFHceVcgKysLK1euRPv27WFhYQEjIyM0btwY06dPR2JiYqH1/ncgk02bNsHb2xs1atSAjY0N3nvvPTx9+lTRfsuWLWjdujVMTU1hZWWFd955p9CwtK979eoV5s2bh5YtW8LMzAwmJibw9PTEvHnzkJaWVqj92bNnIZFIFN3L1q5dCy8vL5iYmMDS0hKDBg0qdP4VvK8PHjwAADg6Oirer4JzrEBYWBjeffdd2Nvbw8DAAObm5nBycsKgQYOwbds2tftRWSKiY/Eo/in09WTo7NNc2+UQVSvmZjUwqFcnxeu61rXQ09dbixURkcYI1Vh0dLTg5OQkABBkMpnQvHlzwc3NTZBIJAIAwcfHR0hKSlJqD0Cwt7cXpkyZIgAQ6tWrJ3h7ewvm5uYCAEFfX184cOCA0nZycnKEdu3aCQAEAIKzs7PQunVroWHDhoKenp4AQAgKClJaJiUlRejRo4cAQJBIJEL9+vWFFi1aCDVq1BAACLVq1RIuX75caJ8KtrFkyRIBgGBtbS20bt1aqF+/vnDmzBlh1apVAgChe/fuat+XiRMnCgCEqVOnKk2XyWQCAKF27dqCp6en4OnpqdhvU1NT4cKFC4XWNWfOHAGA8OmnnwpOTk6CVCoVmjVrJjRr1kzxPru7uwuJiYlq9+W/cnNzhXfeeUcxv06dOoKXl5dQs2ZNAYBgZGQkHDx4UO3+lcSYMWMEAMKcOXOEOnXqCMbGxkKrVq0EGxsbxXa3b98uhIeHC3Xr1lU5f8uWLSrXPXPmTEUbS0tLwcvLS6hdu7YAQJBKpcK6desKLTNv3jzFvjk5OQmtW7cWHB0dFe/hm2++KcjlcqVlYmJiFOs1MDAQmjZtKrRs2VKoW7euIJFIBENDQ6X2GzduFAAIY8aMUfu+qDsmxZ13giAICQkJgpeXl2I/7e3tBQ8PD8HQ0FAAIDRs2FCIjIxUWu/rn7np06cLAARHR0fB09NTMDAwEAAIHh4eQmZmpvDZZ58JAAQHBwel+W5ubkJmZmahmu/evSs4ODgIAAQ9PT3BxcVFcHNzE/T19QUAgqenp/D8+XOlZc6cOSMAEDp37qw4Bx0cHIQWLVoIRkZGAgChZs2awr179xTLHDlyROjQoYNiP729vYUOHToo/jty5IggCIJw5coVwcTERAAgmJiYCM2aNRNatGghWFlZCQCExo0bqz0ulUEulwurNv8lzFi4Tjh46qJWayGqznYcOCXM+mG9EJfwTNulEJGGSASh+nYib9++PYKCguDt7Y09e/agYcOGAIAbN25g4MCBiImJwejRo7F161YA+b9WOzo6Qk8vfyyJX3/9Fe+++y4kEgkyMzMxefJkrF+/HrVq1UJ4eDjq1KkDANi7dy+GDBmCBg0a4NixY3Bzc1PUkJKSgt27d6NLly5wdPx3hJl3330XW7duRdu2bfHLL7/Aw8MDAJCTk4Nvv/0W3333HRwcHBAeHg5Dw38vwRf8Sm9gYIBly5bho48+glQqhSAIyM7ORkpKCmxtbSGXyxEbG1vo4W05OTmwsbHBixcvEBwcjFatWinmrVu3Dn379lV6vkFOTg42btyISZMmwcnJCeHh4UpXEL755hvMnTsX+vr6cHFxwb59++Dq6goACA8Px4ABA3Dv3j28/fbbha6qFKznv6fY7NmzMW/ePDRu3Bi//vorOnbsqGj3888/Y8qUKTA1NUVERITiGJTW2LFjsXnzZujr62PQoEFYv349atbMf3L4vHnzMHv2bNjZ2cHGxgaOjo5K8+fOnYtvvvkG9erVw6NHjyCV/nsh9bfffsP48eNha2uLtWvXYsCAAYp5u3btwvjx45GVlYWQkBA0a9ZMMe/ChQvIzs6Gr6+v4vwDgPv372PcuHG4cOECtm3bhlGjRinmTZ48GT/99BN69uyJHTt2wNLSUjHv0aNH2LVrF6ZNm6aYtmnTJowbNw5jxoxRumrxOnXHpLjzzsDAAF27dsW5c+fQt29frFq1SnHVJzU1FVOmTMFvv/2Gdu3aKV0le/0zZ2Jigp07d8LPzw8A8ODBA3Tr1g1RUVEYPHgw/v77b+zatUsxPyYmBl27dkVMTAzWrFmjNPJSRkYGvLy8cPfuXYwdOxY//PADatfOHxr36dOnGD9+PA4dOoS33noLv//+u2K5s2fPomvXrtDX10etWrXw559/wtfXFwDw4sULDBo0CAEBASrPZwcHBzx48ADR0dEqh+4fMGAADh48iDFjxmDVqlUwNTVVzLt79y5Onz6Njz76SOVxKc5Pm/ciJTWjTMsWyM3LRXpG/lVzsxomHBKfqIwEQUBObi4M9PW1XQoRvcbM1BifjnmzTMtW20B05swZdOvWDTKZDBEREXByclI5XyqV4v79+3B0dFR8OQOASZMmYdWqVUrLyOVyNG3aFBEREZg7dy5mz54NAFi4cCFmzpyJqVOnYtmyZcXWdvv2bTRr1gw2Nja4efMmrK2tC7UZNGgQ9u/fX+hLcMGXFFX1FejXrx8OHz6MZcuWYerUqUrzDh48iAEDBqBJkyYICwsrttYC77zzDrZt24agoCC0bdtWMb0gEAHAlStX4O2t3D0gMDAQHTt2hFQqRWRkpNIXRVVfvhMTE9GwYUNIJBKEhoaiUaNGhWr57LPPsHLlSnz33Xf48ssvS7wPrysIRDY2Nrh//z5q1KihmJeXlwd7e3vExcWpnJ+bmwt7e3s8fvwYISEh8PLyApAfHh0dHREXF4eAgABFkHvdihUrMHXqVLz//vtYv359iWqNjIyEi4sLevXqhWPHjimm+/n54fjx49i/f79S8FJHE4FI3Xl35MgR9O3bF82aNcOVK1dgZGSkND8vLw8+Pj4ICQnBhQsX0KFD/ohLr3/mli5dis8//1xpubVr1ypCgqr5a9aswccff4wBAwZg//79iuk///wzJk2ahJ49e+LYsWOFvtynpqaiadOmePz4MWJiYtCgQQMA/wYiAPjjjz8wYsQIpeWuXbuGli1bwsLCAklJSUrzigtETZo0wd27d3H9+nV4enoWml8e81dvx6vUwl0AiYiIKJ+5aQ3MmjSq+IYqVNthtwu+OA4YMKBQGAKArl27onnz5rh+/TpOnDiBiRMnKs3/9NNPCy0jlUrxySefYPLkyTh69KgiEBV8mTp16hRevHih9Eu9Knv37gUADBkyRGUYAoDBgwdj//79OHv2rFIgKlDUfSCjRo3C4cOHsX379kKBqOBXbVXrBIDQ0FD8+eefuH37NpKTk5GbmwsAinuaQkNDlQJRgbZt2xYKQ0D+k7ZbtmyJkJAQHD9+vND7/F9Hjx5FRkYG+vbtqzIMAfnvzcqVK3H27NkyB6ICb731llLYAQCZTAZPT0/ExcWpnK+npwdPT088fvwYUVFRikB06dIlxMXFwd3dXWUYKqh96tSpSvfdFEhLS8OuXbtw/vx5xMXFISMjQymYhIaGKrUvOO/++usv9OnTR+nKUkVRd97t2bMHADBmzJhCYQjIf08HDBiAkJAQnD17VhGIXjd+/PhC01q2bFmi+VFRUSrrmTBhgsorHaampujevTs2bdqE8+fPF/o8WFhYFApDAODl5QVDQ0MkJyfj+fPnsLKyKtRGnQYNGuDu3bvYvXs3PDw8NHoFxszUuFzL8+oQERHpuvL8W1ltA1FERAQAKHVL+i8PDw9cv3690IAB+vr6ar+MF3SHe32ZwYMHw8nJCTdu3ICdnR169OgBX19fdO7cGa1atSr05eLGjRsA8r/8q/viXPBwx7i4OJXzmzZtqna/Bg4cCFNTU1y9ehV3795F48aNAeT/Kn7w4EEAqgPR1KlTsWLFCrXrBaBycAgASt0EVc0LCQkp0cAMBe9NaGio2vcmMzMTgPr3pjScnZ1VTi/oXqVufkFXvdTUVMW0gtoTEhLU1l4QcP5b+82bN9G3b188evRIba3/fe8//fRTbNmyBZs2bcKRI0fQu3dvdOzYEV27dlVbd3mpO+8K9n3jxo3Yt2+fyjZPnjwBoPq4WVtbK7okvq7gOKibr+o4vF7P/Pnz1Z7TBYMgqKrHxcVF5TIF23z06BFSU1NLFYg+//xznDp1CvPmzcPmzZvh5+eHTp06oWvXrqhfv36J16NKWbsAAPnn5Jpt+/Ew4yk6tvZAv27tylULERGRrqm2gSglJQUAULduXbVtCuYVtC1gZWWldF9IccuYmJjgwoULmD17Nv78808cOHAABw4cAADY2dlh9uzZmDBhgqJ9QdiJiooq9Mv2f6Wnp6uc/t+rFq8zMTHB4MGDsXXrVmzfvh3ffvstgPwrCenp6Wjfvr3S/UxA/pWjFStWwMTEBIsWLULPnj1hZ2cHY2NjSCQSxX09OTk5KrdZ1L086t5nVV4PgsUFHnXvTWmoex8LQmxx81+/glNQ+/PnzxEYqPoBfQUyMv693yMvLw/Dhg3Do0eP0KtXL8yYMQMeHh6wsLCAnp4e5HI5ZDKZ4mpdAU9PT1y4cAHffPMNTp48ic2bN2Pz5s0A8p9wvnTpUnTq1AmapO79KNj3O3fuFLsOVcdNk8fh9XquXbum0XoAKP5uKG1v4t69e+PYsWP47rvvEBgYiF9++QW//PILJBIJunXrhhUrVhT5A05RynMP0etXh67ficSNsKL/TiIiIqqOynMPUbUNRGZmZgD+/VValYJ5BW0LPH/+HHK5XGUoUreMra0t1q9fj7Vr1yI0NBTnz5/Hvn37cP78eXzwwQcwNjbG6NGjAUBxM/WPP/6osmueJowaNQpbt27F77//rghERXWXKxhYYsmSJSpv7FZ3ZajA60Mj/5e690yVgvfm888/x9KlS4ttX5UU1P7mm28qumyVxJUrV3D37l00bNgQ+/btK9TlrKj3vnXr1jh8+DDS09MRFBSEc+fOYefOnbhy5Qp69eqF69evK652qgsPBVQNQ11SBft+4MAB9O/fv8zr0RRTU1MkJyfjxo0bigFLqoKePXuiZ8+eePnyJQIDA3H27Fn8/vvvOHXqFLp3747bt2+X6qpTgZTUDI3cQ5SSVv4fGYiIiHRNtQ1EBSOd3bp1S22bgnkFXcoK5OTk4N69e4WmA//+Aq5qHpB/r0SrVq3QqlUrTJ06Ff7+/li8eDHWr1+vCETu7u7Yt29fkbWVV/fu3VG3bl1ERkbi0qVLcHJywqlTp6Cvr6/y3ojo6GgAUNvV69KlS0Vur6grA8W9Z69zd3cHUPRxq6rKWnvBe+/t7a3y/pvi3nsg/6rgG2+8gTfeeAOzZ8+Gr68vgoKCsG3bNsWgFwVXPdT9SHDv3r1S1f06d3d3hIaG4tatW1UiELm7uyMwMBC3bt2qtEBUmvtuatasiT59+qBPnz6YPXs2mjdvjqioKBw4cADjxo0r9bbL2i+a9w4REZFYiPIeot69e+OHH37AgQMHEBUVVWhghXPnziE0NBRSqRQ9e/YstPzq1asLPdhSLpdj9erVAKAY+rc4HTp0wOLFi/H48WPFtKFDh+L777/HH3/8gTlz5qBePc0/xVomk2HkyJFYuXIltm/fDldXV+Tm5qJfv34qf4E2Ns4/SRISEgp9gTx37hxCQkKK3N6lS5dw9epVpWG8AeDixYsICQmBRCJR+T7/V79+/WBoaIhTp07h+vXraN68+jwcslOnTqhbty4iIiJw6NAh9OvXr0TLvf7eq7J8+fJS1aGnp4c2bdogKChI6bwruC8mNDRUMVT269asWVOq7bxu6NCh2L59O3755Rd8+umnSkNKa8PQoUMRGBiIlStXYtiwYZUy4ETBcXy9O2RJmJqawtPTE1FRUUrHqzTK0gWA9w4RERGVjOobaaqBLl26oH379sjLy8OIESMUo6QB+b/gv/feewCAt99+u9D9NHp6eli7dq2iGxmQfyP/pEmTcPfuXVhYWCg982TZsmVYtmxZoS8zz549w8qVKwFAKSi0aNEC77zzDl69eoU33nhD5f0m169fh7+/f7H3ohSloGvczp07FfuibnS5gntNvvzyS6UrCIGBgRgxYoTKKxev09fXx5gxY3D//n3FtIiICMWv3SNGjCj0PqtiY2MDf39/5OXloU+fPjh8+HChLl737t3DvHnzStUtrTIYGhpi/vz5AP7tspiXl6fUJjY2FsuXL8fatWsV09q1awc9PT1cvHgRv/32m2J6WloaJk6ciKCgIJXbmzhxInbs2KFyQIFdu3YBUD7vPD090aBBAzx58gT/+9//IJfLAeQH/TVr1mDLli1l3veBAweic+fOiImJQc+ePQtdJZPL5QgKCsKHH35Y7H1zmvDBBx+gcePG+Oeff/Dmm28iJiZGaX5OTg5OnTqFt956C1lZWRrZZsFAFqdPn1Y5f8SIEThw4ECh7Z05cwZ///03ABT6QaEi3YuOxcPHT6GvJ0Nnn+rzwwMREVGlq9THwGpYdHS04OjoKAAQZDKZ0Lx5c8Hd3V2QSCQCAMHHx0dISkpSag9AsLe3F6ZMmSIAEOrXry+0bt1aMDc3Vzzx/q+//lLaTkFbAEKDBg0EHx8fwc3NTdDX1xcACLa2tkJkZKTSMunp6cLAgQMVy9na2go+Pj5CixYthJo1ayqmHz16VGm5gukl5erqqljGzMxMSE9PV9nu4cOHgrW1tQBAMDIyEpo3by64uLgIAAR3d3dh2rRpAgBhzpw5SsvNmTNHACB8+umngpOTkyCVSgUPDw/Bw8NDkEqlAgChadOmwrNnhZ/YrW5f8vLyhIkTJyrmW1lZCa1btxZatmypqBGAsGbNmhK/D/81ZswYAYCwceNGjc//7rvvFOeYubm50KpVK8Hb21uoV6+eovYZM2YoLTNjxgzFPDs7O8Hb21swNTUVJBKJsGHDBpXvVfPmzRXntqurq9CmTRvB2dlZ0bZTp05CVlaW0jI7d+5U1FarVi3B29tbqF27tiCRSIRff/1V7TEpyXn37NkzoX379oq29vb2Qps2bQRPT0+hRo0aiulhYWGKZV7/zKlSnvmRkZFCkyZNFNt1cXER2rZtK7i7uwuGhoaK6RkZGYplzpw5IwAQOnfurHY/7e3tBQBCdHS00vTt27cr1unm5iZ07txZ6Ny5s+IzXPC5NjAwENzc3AQfHx+hQYMGimVGjhypdpuaJpfLhdVb/hJmLFwnHDx1sdK2S0REVB1V2ytEQP6DEkNCQvDll1+icePGiIiIQExMDLy8vLB48WKcO3cOFhYWKpddsWIFfvnlF9SpUwe3b9+GVCpF3759ERgYiEGDBim1/fDDD/Htt9+ic+fOAPKv7sTExKBx48bw9/fH9evXC3XZMzY2xl9//YW9e/diwIABEAQB165dw71791CvXj188MEHOHr0KN54441yvQevXxEaPHiwolvPfzVo0ABBQUEYNmwYTExMEB4eDgDw9/dHUFBQsV2gLC0tcfnyZUycOBFJSUm4e/cu7OzsMG3aNAQFBal93pIqUqkUa9euxenTpzFixAgYGxvj+vXrCAsLg4WFBUaPHo09e/bg3XffLfE6K9OXX36J4OBgjBs3DlZWVrh16xZu3LgBfX19DBkyBFu2bMHMmTOVllmwYAFWrVoFNzc3PH36FFFRUWjXrh2OHz+uuJr5X8uXL8fUqVPRokULvHz5ElevXkViYiLat2+PH3/8EX///XehbnHDhw/H/v370a5dO2RmZuLu3bto1qwZTpw4ofI5P6VhbW2Nc+fOYdOmTejRowfS0tIQHByM6OhouLi4YMqUKTh37pzi/r6K5uTkhJCQEKxcuRKdOnXC8+fPceXKFcTFxcHDwwMzZ85U+RDZsnr77bexevVqeHl5ISYmBufOncO5c+cUXSE3b96MDz/8EE2aNMHTp08REhKCtLQ0dOvWDVu2bMHvv/+ukTpKIjc3D3Wsa8HQQJ9Xh4iIiIohEYRSji1LRETVQmZWNowMDYpvSEREJGIMREREREREJFrVusscERERERFReVTbYbdJHBISEjB06NASt+/Tpw9mzZpVgRURERERkS5hIKIqLTMzs1RDkxc8i4eIiIiIqCR4DxEREREREYkW7yEiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLRYiAiIiIiIiLR+j/L9JvUTJlNnQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1060.9x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[\"A\"][0].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a0e854a-f742-4b19-8367-8155a54ab2b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1311.74x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[\"B\"][3].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cce6334-c721-40fc-b46e-f720eae969c0",
   "metadata": {},
   "source": [
    "## Neural Network Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2504dedb-a5f4-4d4e-b75d-1b0bed7615e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.neural_networks import SamplerQNN, NeuralNetwork\n",
    "from qiskit_aer.primitives import Sampler\n",
    "from qiskit_aer import AerSimulator\n",
    "from typing import Callable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ddea651-ceb3-4bab-aad4-9883ce9b69db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('CPU', 'GPU')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpu_sim = AerSimulator(device=\"GPU\", cuStateVec_enable=False)\n",
    "# gpu_sim = AerSimulator(device=\"GPU\", cuStateVec_enable=True)\n",
    "gpu_sim.available_devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "874404ef-4077-42c4-9db0-159fb621b542",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = Sampler(backend_options={\"simulator\": gpu_sim})\n",
    "# sampler = Sampler(backend_options={\"simulator\": gpu_sim}, transpile_options={\"optimization_level\": 3}, run_options={\"shots\": 1024})\n",
    "# sampler = Sampler(backend_options={\"simulator\": gpu_sim})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c252b4-15d2-44e7-a577-329c684c5c78",
   "metadata": {},
   "source": [
    "### Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0e55d30c-e279-4f58-bfe6-e9a6d9c7d446",
   "metadata": {},
   "outputs": [],
   "source": [
    "from custom_sampler_qnn import CustomSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5926c303-49fe-4052-b910-9e0763014904",
   "metadata": {},
   "source": [
    "#### For subexperiments[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5aefce4e-f3fb-4694-91a3-a5161d74aee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"A\"]]\n",
    "# final_circuits[0].draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8853b9e4-a866-43f5-a908-e1f0d968c765",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler_qcnn = CustomSampler(\n",
    "    circuits=final_circuits, \n",
    "    sampler=sampler,\n",
    "    input_params=feature_params.params,\n",
    "    weight_params=sub_circuits[\"A\"].parameters,\n",
    "    input_gradients=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f60076a5-2776-4eb9-b316-5e0b71ace948",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_A = algorithm_globals.random.random(7)\n",
    "forward_output = sampler_qcnn.forward(\n",
    "    input_data=x_train_A,\n",
    "    weights=weights_A,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f534f8b7-9104-4101-9646-8e0760fea432",
   "metadata": {},
   "outputs": [],
   "source": [
    "# forward output is a dictionary of 6 subex_circ items.\n",
    "print(f\"Output shape for {len(x_train_A)} samples: {forward_output[0].shape}\")\n",
    "print(len(forward_output[0]))\n",
    "print(f\"Output of the forward pass for first sample: \\n{np.array([forward_output[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d59acee2-9e90-4105-92bd-ee4801a2db86",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_grad, weights_grad = sampler_qcnn.backward(\n",
    "    input_data=x_train_A,\n",
    "    weights=weights_A\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b081ccf-6d7b-4d8b-bece-61518575b4cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Output shape for {len(x_train_A)} samples: {weights_grad[0].shape}\")\n",
    "print(f\"Output of the backward pass for first sample for first subexperiment circuit: \\n{np.array([weights_grad[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7a3765-0f3f-434b-bbad-adb88c863fc7",
   "metadata": {},
   "source": [
    "#### For subexperiments[\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "476fd0f1-1156-4a1a-96a8-17a2c1812c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits2 = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c4cb1766-d548-4c35-b0c5-c612372e0d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler_qcnn2 = CustomSampler(\n",
    "    circuits=final_circuits2, \n",
    "    sampler=sampler,\n",
    "    input_params=feature_params.params,\n",
    "    weight_params=sub_circuits[\"B\"].parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35b3ceb5-5e16-4f30-b89d-56a5651c7f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_B = algorithm_globals.random.random(8)\n",
    "forward_output2 = sampler_qcnn2._forward(\n",
    "    input_data=x_train_B,\n",
    "    weights=weights_B,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc2aef81-e8c6-4b1b-a563-2dd41b3e27d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Output shape for {len(x_train_A)} samples: {forward_output2[0].shape}\")\n",
    "print(len(forward_output2[0]))\n",
    "print(f\"Output of the forward pass for first sample: \\n{np.array([forward_output2[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "734f3787-0de1-44f5-8c66-c7f1425d9f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_grad2, weights_grad2 = sampler_qcnn2._backward(\n",
    "    input_data=x_train_B,\n",
    "    weights=weights_B\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e91322c9-7a6e-4b7c-8c5d-b298dba19b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Output shape for {len(x_train_B)} samples: {weights_grad2[0].shape}\")\n",
    "print(f\"Output of the backward pass for first sample for first subexperiment circuit: \\n{np.array([weights_grad2[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49fdb89d-3094-44a5-8aae-078836e29602",
   "metadata": {},
   "source": [
    "## Loss and Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73d6684f-d62d-452e-a503-5676541f9a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.utils.loss_functions import L2Loss\n",
    "from qiskit_algorithms.optimizers import COBYLA, SPSA, GradientDescent\n",
    "from custom_cross_entropy_loss import CustomCrossEntropyLoss\n",
    "from objective_func import CustomMultiClassObjectiveFunction\n",
    "# from loss_optimization.callback import callback\n",
    "from optimization import create_objective, minimizer, print_optimizer_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a795c0c0-88f0-44f3-9de9-dc333d2d6466",
   "metadata": {},
   "outputs": [],
   "source": [
    "def callback_spsa(nfev=None, params=None, fval=None, stepsize=None, accepted=None):\n",
    "    \"\"\"\n",
    "    nfev: the number of function evals\n",
    "    params: the current parameters\n",
    "    fval: the current function value\n",
    "    stepsize: size of the update step\n",
    "    accepted: whether the step was accepted (not used for )\n",
    "    \"\"\"\n",
    "    # objective_func_vals = loss\n",
    "    global objective_func_vals\n",
    "\n",
    "    if (nfev % 3) == 0:\n",
    "        objective_func_vals.append(fval)\n",
    "        print(f\"SPSA Epoch {len(objective_func_vals)}: {fval:.5f}\")\n",
    "\n",
    "def callback_cobyla(weights, obj_func_eval):\n",
    "    # clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    print(f\"COBYLA Epoch {len(objective_func_vals)}: {obj_func_eval:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "00fcbea5-d74a-4227-aa21-ae9fe3892fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_func_vals = []\n",
    "# loss = L2Loss()\n",
    "loss = CustomCrossEntropyLoss()\n",
    "# optimizer = COBYLA(maxiter=5)\n",
    "optimizer = SPSA(maxiter=50, callback=callback_spsa)\n",
    "# optimizer = GradientDescent(maxiter=2) # This doesn't work yet. The gradient shape doesn't match."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba6ba3e-791d-4f6a-92d8-116d7e03d4d6",
   "metadata": {},
   "source": [
    "#### Optimizer Result for sub-circuits[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bc3ab8f7-1902-4c28-bcd3-13035cd4c0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point = np.random.random((7,))\n",
    "function = CustomMultiClassObjectiveFunction(x_train_A, y_train.values, sampler_qcnn, loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8269a9e8-beae-4ee3-b9da-2f727af3b1f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 4.550413608551025\n",
      "Running Time for Forward Pass: %s seconds 3.526160478591919\n",
      "Running Time for Forward Pass: %s seconds 4.077063798904419\n",
      "Running Time for Forward Pass: %s seconds 3.7208340167999268\n",
      "Running Time for Forward Pass: %s seconds 3.924011707305908\n",
      "Running Time for Forward Pass: %s seconds 3.5359976291656494\n",
      "Running Time for Forward Pass: %s seconds 3.921128749847412\n",
      "Running Time for Forward Pass: %s seconds 3.479431390762329\n",
      "Running Time for Forward Pass: %s seconds 4.113738536834717\n",
      "Running Time for Forward Pass: %s seconds 4.06843376159668\n",
      "Running Time for Forward Pass: %s seconds 3.912919759750366\n",
      "Running Time for Forward Pass: %s seconds 4.198209524154663\n",
      "Running Time for Forward Pass: %s seconds 3.6995625495910645\n",
      "Running Time for Forward Pass: %s seconds 4.10276198387146\n",
      "Running Time for Forward Pass: %s seconds 3.9062325954437256\n",
      "Running Time for Forward Pass: %s seconds 4.1113057136535645\n",
      "Running Time for Forward Pass: %s seconds 3.7392706871032715\n",
      "Running Time for Forward Pass: %s seconds 4.01592755317688\n",
      "Running Time for Forward Pass: %s seconds 3.6546332836151123\n",
      "Running Time for Forward Pass: %s seconds 3.758474349975586\n",
      "Running Time for Forward Pass: %s seconds 3.801577091217041\n",
      "Running Time for Forward Pass: %s seconds 3.8890814781188965\n",
      "Running Time for Forward Pass: %s seconds 4.018622875213623\n",
      "Running Time for Forward Pass: %s seconds 3.9542057514190674\n",
      "Running Time for Forward Pass: %s seconds 4.1514787673950195\n",
      "Running Time for Forward Pass: %s seconds 3.9497170448303223\n",
      "Running Time for Forward Pass: %s seconds 3.70400333404541\n",
      "Running Time for Forward Pass: %s seconds 3.912961006164551\n",
      "Running Time for Forward Pass: %s seconds 3.7912023067474365\n",
      "Running Time for Forward Pass: %s seconds 4.0159912109375\n",
      "Running Time for Forward Pass: %s seconds 3.752734661102295\n",
      "Running Time for Forward Pass: %s seconds 3.9436776638031006\n",
      "Running Time for Forward Pass: %s seconds 3.6101760864257812\n",
      "Running Time for Forward Pass: %s seconds 3.5843403339385986\n",
      "Running Time for Forward Pass: %s seconds 3.6346349716186523\n",
      "Running Time for Forward Pass: %s seconds 4.102117538452148\n",
      "Running Time for Forward Pass: %s seconds 4.339747905731201\n",
      "Running Time for Forward Pass: %s seconds 3.79359769821167\n",
      "Running Time for Forward Pass: %s seconds 3.773332357406616\n",
      "Running Time for Forward Pass: %s seconds 3.5882580280303955\n",
      "Running Time for Forward Pass: %s seconds 3.6670994758605957\n",
      "Running Time for Forward Pass: %s seconds 3.9319028854370117\n",
      "Running Time for Forward Pass: %s seconds 3.9083974361419678\n",
      "Running Time for Forward Pass: %s seconds 3.761951446533203\n",
      "Running Time for Forward Pass: %s seconds 3.645817995071411\n",
      "Running Time for Forward Pass: %s seconds 3.5631754398345947\n",
      "Running Time for Forward Pass: %s seconds 3.725170135498047\n",
      "Running Time for Forward Pass: %s seconds 3.46421217918396\n",
      "Running Time for Forward Pass: %s seconds 3.637681007385254\n",
      "Running Time for Forward Pass: %s seconds 3.876412868499756\n",
      "Running Time for Forward Pass: %s seconds 3.6216964721679688\n",
      "Running Time for Forward Pass: %s seconds 4.391788482666016\n",
      "Running Time for Forward Pass: %s seconds 3.9917373657226562\n",
      "SPSA Epoch 1: 4.05446\n",
      "Running Time for Forward Pass: %s seconds 4.094750165939331\n",
      "Running Time for Forward Pass: %s seconds 3.5604348182678223\n",
      "Running Time for Forward Pass: %s seconds 3.8570632934570312\n",
      "SPSA Epoch 2: 3.49660\n",
      "Running Time for Forward Pass: %s seconds 3.6679840087890625\n",
      "Running Time for Forward Pass: %s seconds 4.0525102615356445\n",
      "Running Time for Forward Pass: %s seconds 3.709628105163574\n",
      "SPSA Epoch 3: 3.28931\n",
      "Running Time for Forward Pass: %s seconds 3.77874755859375\n",
      "Running Time for Forward Pass: %s seconds 3.812209129333496\n",
      "Running Time for Forward Pass: %s seconds 3.9980461597442627\n",
      "SPSA Epoch 4: 3.19161\n",
      "Running Time for Forward Pass: %s seconds 3.710792064666748\n",
      "Running Time for Forward Pass: %s seconds 4.127798318862915\n",
      "Running Time for Forward Pass: %s seconds 3.801650285720825\n",
      "SPSA Epoch 5: 3.15510\n",
      "Running Time for Forward Pass: %s seconds 3.712573528289795\n",
      "Running Time for Forward Pass: %s seconds 3.9694533348083496\n",
      "Running Time for Forward Pass: %s seconds 4.0587122440338135\n",
      "SPSA Epoch 6: 3.12777\n",
      "Running Time for Forward Pass: %s seconds 3.6916708946228027\n",
      "Running Time for Forward Pass: %s seconds 3.588801145553589\n",
      "Running Time for Forward Pass: %s seconds 3.5664355754852295\n",
      "SPSA Epoch 7: 3.11168\n",
      "Running Time for Forward Pass: %s seconds 3.745659351348877\n",
      "Running Time for Forward Pass: %s seconds 3.949026346206665\n",
      "Running Time for Forward Pass: %s seconds 3.7946131229400635\n",
      "SPSA Epoch 8: 3.11264\n",
      "Running Time for Forward Pass: %s seconds 3.8949522972106934\n",
      "Running Time for Forward Pass: %s seconds 3.6919500827789307\n",
      "Running Time for Forward Pass: %s seconds 3.7936527729034424\n",
      "SPSA Epoch 9: 3.10521\n",
      "Running Time for Forward Pass: %s seconds 3.789555549621582\n",
      "Running Time for Forward Pass: %s seconds 3.813908338546753\n",
      "Running Time for Forward Pass: %s seconds 4.337029218673706\n",
      "SPSA Epoch 10: 3.09075\n",
      "Running Time for Forward Pass: %s seconds 3.969581127166748\n",
      "Running Time for Forward Pass: %s seconds 3.5926592350006104\n",
      "Running Time for Forward Pass: %s seconds 3.744138479232788\n",
      "SPSA Epoch 11: 3.09513\n",
      "Running Time for Forward Pass: %s seconds 3.8811137676239014\n",
      "Running Time for Forward Pass: %s seconds 3.8466415405273438\n",
      "Running Time for Forward Pass: %s seconds 3.756108522415161\n",
      "SPSA Epoch 12: 3.09700\n",
      "Running Time for Forward Pass: %s seconds 3.7235443592071533\n",
      "Running Time for Forward Pass: %s seconds 3.8783440589904785\n",
      "Running Time for Forward Pass: %s seconds 3.7456955909729004\n",
      "SPSA Epoch 13: 3.08523\n",
      "Running Time for Forward Pass: %s seconds 3.8024449348449707\n",
      "Running Time for Forward Pass: %s seconds 3.5112643241882324\n",
      "Running Time for Forward Pass: %s seconds 3.8888208866119385\n",
      "SPSA Epoch 14: 3.08295\n",
      "Running Time for Forward Pass: %s seconds 3.6505560874938965\n",
      "Running Time for Forward Pass: %s seconds 3.6303040981292725\n",
      "Running Time for Forward Pass: %s seconds 3.731004238128662\n",
      "SPSA Epoch 15: 3.09808\n",
      "Running Time for Forward Pass: %s seconds 3.8188071250915527\n",
      "Running Time for Forward Pass: %s seconds 3.5586886405944824\n",
      "Running Time for Forward Pass: %s seconds 3.8855087757110596\n",
      "SPSA Epoch 16: 3.09513\n",
      "Running Time for Forward Pass: %s seconds 3.896285057067871\n",
      "Running Time for Forward Pass: %s seconds 3.9483284950256348\n",
      "Running Time for Forward Pass: %s seconds 3.796158790588379\n",
      "SPSA Epoch 17: 3.10070\n",
      "Running Time for Forward Pass: %s seconds 3.714885950088501\n",
      "Running Time for Forward Pass: %s seconds 3.7401576042175293\n",
      "Running Time for Forward Pass: %s seconds 3.8399863243103027\n",
      "SPSA Epoch 18: 3.09653\n",
      "Running Time for Forward Pass: %s seconds 3.75475811958313\n",
      "Running Time for Forward Pass: %s seconds 3.775336742401123\n",
      "Running Time for Forward Pass: %s seconds 3.7660715579986572\n",
      "SPSA Epoch 19: 3.09000\n",
      "Running Time for Forward Pass: %s seconds 3.5306241512298584\n",
      "Running Time for Forward Pass: %s seconds 4.2532947063446045\n",
      "Running Time for Forward Pass: %s seconds 3.9173743724823\n",
      "SPSA Epoch 20: 3.10364\n",
      "Running Time for Forward Pass: %s seconds 4.171210050582886\n",
      "Running Time for Forward Pass: %s seconds 3.7457215785980225\n",
      "Running Time for Forward Pass: %s seconds 4.088801145553589\n",
      "SPSA Epoch 21: 3.09724\n",
      "Running Time for Forward Pass: %s seconds 3.8344695568084717\n",
      "Running Time for Forward Pass: %s seconds 3.8597447872161865\n",
      "Running Time for Forward Pass: %s seconds 3.9757423400878906\n",
      "SPSA Epoch 22: 3.09395\n",
      "Running Time for Forward Pass: %s seconds 3.811455249786377\n",
      "Running Time for Forward Pass: %s seconds 3.9442732334136963\n",
      "Running Time for Forward Pass: %s seconds 3.677311897277832\n",
      "SPSA Epoch 23: 3.08692\n",
      "Running Time for Forward Pass: %s seconds 3.8746657371520996\n",
      "Running Time for Forward Pass: %s seconds 3.619342565536499\n",
      "Running Time for Forward Pass: %s seconds 4.084164381027222\n",
      "SPSA Epoch 24: 3.09105\n",
      "Running Time for Forward Pass: %s seconds 3.7835676670074463\n",
      "Running Time for Forward Pass: %s seconds 3.947674036026001\n",
      "Running Time for Forward Pass: %s seconds 3.9104037284851074\n",
      "SPSA Epoch 25: 3.09135\n",
      "Running Time for Forward Pass: %s seconds 3.7444655895233154\n",
      "Running Time for Forward Pass: %s seconds 3.7197179794311523\n",
      "Running Time for Forward Pass: %s seconds 3.72287654876709\n",
      "SPSA Epoch 26: 3.09883\n",
      "Running Time for Forward Pass: %s seconds 3.789577007293701\n",
      "Running Time for Forward Pass: %s seconds 3.8704819679260254\n",
      "Running Time for Forward Pass: %s seconds 3.785409688949585\n",
      "SPSA Epoch 27: 3.10229\n",
      "Running Time for Forward Pass: %s seconds 3.7582311630249023\n",
      "Running Time for Forward Pass: %s seconds 3.588059902191162\n",
      "Running Time for Forward Pass: %s seconds 3.55244779586792\n",
      "SPSA Epoch 28: 3.08450\n",
      "Running Time for Forward Pass: %s seconds 3.839470386505127\n",
      "Running Time for Forward Pass: %s seconds 3.6780483722686768\n",
      "Running Time for Forward Pass: %s seconds 3.6119940280914307\n",
      "SPSA Epoch 29: 3.09157\n",
      "Running Time for Forward Pass: %s seconds 3.762310743331909\n",
      "Running Time for Forward Pass: %s seconds 3.710073471069336\n",
      "Running Time for Forward Pass: %s seconds 3.550532102584839\n",
      "SPSA Epoch 30: 3.08776\n",
      "Running Time for Forward Pass: %s seconds 3.928203821182251\n",
      "Running Time for Forward Pass: %s seconds 3.603121280670166\n",
      "Running Time for Forward Pass: %s seconds 3.7832577228546143\n",
      "SPSA Epoch 31: 3.09131\n",
      "Running Time for Forward Pass: %s seconds 3.867100715637207\n",
      "Running Time for Forward Pass: %s seconds 3.871535062789917\n",
      "Running Time for Forward Pass: %s seconds 3.7052106857299805\n",
      "SPSA Epoch 32: 3.08980\n",
      "Running Time for Forward Pass: %s seconds 3.756056070327759\n",
      "Running Time for Forward Pass: %s seconds 3.7225546836853027\n",
      "Running Time for Forward Pass: %s seconds 3.950597047805786\n",
      "SPSA Epoch 33: 3.08703\n",
      "Running Time for Forward Pass: %s seconds 3.8512566089630127\n",
      "Running Time for Forward Pass: %s seconds 3.7984976768493652\n",
      "Running Time for Forward Pass: %s seconds 3.858928680419922\n",
      "SPSA Epoch 34: 3.09588\n",
      "Running Time for Forward Pass: %s seconds 3.7064826488494873\n",
      "Running Time for Forward Pass: %s seconds 3.9005887508392334\n",
      "Running Time for Forward Pass: %s seconds 3.624483108520508\n",
      "SPSA Epoch 35: 3.09614\n",
      "Running Time for Forward Pass: %s seconds 3.7938404083251953\n",
      "Running Time for Forward Pass: %s seconds 3.8634982109069824\n",
      "Running Time for Forward Pass: %s seconds 3.8631467819213867\n",
      "SPSA Epoch 36: 3.08175\n",
      "Running Time for Forward Pass: %s seconds 3.749650001525879\n",
      "Running Time for Forward Pass: %s seconds 3.8923301696777344\n",
      "Running Time for Forward Pass: %s seconds 3.769329071044922\n",
      "SPSA Epoch 37: 3.08864\n",
      "Running Time for Forward Pass: %s seconds 3.70497465133667\n",
      "Running Time for Forward Pass: %s seconds 3.6641712188720703\n",
      "Running Time for Forward Pass: %s seconds 3.7105443477630615\n",
      "SPSA Epoch 38: 3.09608\n",
      "Running Time for Forward Pass: %s seconds 3.951032876968384\n",
      "Running Time for Forward Pass: %s seconds 3.647439479827881\n",
      "Running Time for Forward Pass: %s seconds 3.681513786315918\n",
      "SPSA Epoch 39: 3.08927\n",
      "Running Time for Forward Pass: %s seconds 3.7516369819641113\n",
      "Running Time for Forward Pass: %s seconds 4.089674711227417\n",
      "Running Time for Forward Pass: %s seconds 3.5658371448516846\n",
      "SPSA Epoch 40: 3.08372\n",
      "Running Time for Forward Pass: %s seconds 3.8582217693328857\n",
      "Running Time for Forward Pass: %s seconds 3.702868700027466\n",
      "Running Time for Forward Pass: %s seconds 3.9237847328186035\n",
      "SPSA Epoch 41: 3.08246\n",
      "Running Time for Forward Pass: %s seconds 3.6899983882904053\n",
      "Running Time for Forward Pass: %s seconds 3.884336471557617\n",
      "Running Time for Forward Pass: %s seconds 3.6189377307891846\n",
      "SPSA Epoch 42: 3.09868\n",
      "Running Time for Forward Pass: %s seconds 3.7021241188049316\n",
      "Running Time for Forward Pass: %s seconds 3.807757616043091\n",
      "Running Time for Forward Pass: %s seconds 3.7946765422821045\n",
      "SPSA Epoch 43: 3.08054\n",
      "Running Time for Forward Pass: %s seconds 4.12717080116272\n",
      "Running Time for Forward Pass: %s seconds 3.7062618732452393\n",
      "Running Time for Forward Pass: %s seconds 3.7728335857391357\n",
      "SPSA Epoch 44: 3.09032\n",
      "Running Time for Forward Pass: %s seconds 3.462036609649658\n",
      "Running Time for Forward Pass: %s seconds 3.8685429096221924\n",
      "Running Time for Forward Pass: %s seconds 3.5364041328430176\n",
      "SPSA Epoch 45: 3.10212\n",
      "Running Time for Forward Pass: %s seconds 3.728184700012207\n",
      "Running Time for Forward Pass: %s seconds 3.7685348987579346\n",
      "Running Time for Forward Pass: %s seconds 3.920828342437744\n",
      "SPSA Epoch 46: 3.08785\n",
      "Running Time for Forward Pass: %s seconds 3.7865898609161377\n",
      "Running Time for Forward Pass: %s seconds 3.862643241882324\n",
      "Running Time for Forward Pass: %s seconds 3.756549119949341\n",
      "SPSA Epoch 47: 3.09251\n",
      "Running Time for Forward Pass: %s seconds 3.8699028491973877\n",
      "Running Time for Forward Pass: %s seconds 3.7538745403289795\n",
      "Running Time for Forward Pass: %s seconds 3.7223527431488037\n",
      "SPSA Epoch 48: 3.09006\n",
      "Running Time for Forward Pass: %s seconds 3.722985029220581\n",
      "Running Time for Forward Pass: %s seconds 3.7616240978240967\n",
      "Running Time for Forward Pass: %s seconds 3.7962028980255127\n",
      "SPSA Epoch 49: 3.09200\n",
      "Running Time for Forward Pass: %s seconds 3.6118571758270264\n",
      "Running Time for Forward Pass: %s seconds 3.929281234741211\n",
      "Running Time for Forward Pass: %s seconds 3.5410802364349365\n",
      "SPSA Epoch 50: 3.09133\n",
      "{   'fun': 3.0913295865871553,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.11014823,  0.92306064,  1.23722863, -0.80420931, -1.29109566,\n",
      "       -0.03841404,  1.40905931])}\n",
      "Running Time for optimizing Subex-A1: %s seconds 765.0334956645966\n",
      "Running Time for Forward Pass: %s seconds 3.614060640335083\n",
      "Running Time for Forward Pass: %s seconds 3.531407356262207\n",
      "Running Time for Forward Pass: %s seconds 3.772066831588745\n",
      "Running Time for Forward Pass: %s seconds 3.62074613571167\n",
      "Running Time for Forward Pass: %s seconds 3.7597100734710693\n",
      "Running Time for Forward Pass: %s seconds 3.5802533626556396\n",
      "Running Time for Forward Pass: %s seconds 3.8743231296539307\n",
      "Running Time for Forward Pass: %s seconds 3.929410934448242\n",
      "Running Time for Forward Pass: %s seconds 3.982938289642334\n",
      "Running Time for Forward Pass: %s seconds 3.7089431285858154\n",
      "Running Time for Forward Pass: %s seconds 3.8436803817749023\n",
      "Running Time for Forward Pass: %s seconds 3.716299295425415\n",
      "Running Time for Forward Pass: %s seconds 3.8011796474456787\n",
      "Running Time for Forward Pass: %s seconds 3.5913755893707275\n",
      "Running Time for Forward Pass: %s seconds 3.6514406204223633\n",
      "Running Time for Forward Pass: %s seconds 3.789912462234497\n",
      "Running Time for Forward Pass: %s seconds 3.5394067764282227\n",
      "Running Time for Forward Pass: %s seconds 3.620616912841797\n",
      "Running Time for Forward Pass: %s seconds 3.7524688243865967\n",
      "Running Time for Forward Pass: %s seconds 3.6689953804016113\n",
      "Running Time for Forward Pass: %s seconds 3.5543415546417236\n",
      "Running Time for Forward Pass: %s seconds 3.8957135677337646\n",
      "Running Time for Forward Pass: %s seconds 3.7678685188293457\n",
      "Running Time for Forward Pass: %s seconds 3.9218764305114746\n",
      "Running Time for Forward Pass: %s seconds 4.128463268280029\n",
      "Running Time for Forward Pass: %s seconds 4.154904365539551\n",
      "Running Time for Forward Pass: %s seconds 4.0004212856292725\n",
      "Running Time for Forward Pass: %s seconds 4.327851057052612\n",
      "Running Time for Forward Pass: %s seconds 3.867595672607422\n",
      "Running Time for Forward Pass: %s seconds 3.6452157497406006\n",
      "Running Time for Forward Pass: %s seconds 3.7992632389068604\n",
      "Running Time for Forward Pass: %s seconds 3.6830461025238037\n",
      "Running Time for Forward Pass: %s seconds 3.8767952919006348\n",
      "Running Time for Forward Pass: %s seconds 3.7901458740234375\n",
      "Running Time for Forward Pass: %s seconds 4.117069959640503\n",
      "Running Time for Forward Pass: %s seconds 3.5934808254241943\n",
      "Running Time for Forward Pass: %s seconds 3.7634963989257812\n",
      "Running Time for Forward Pass: %s seconds 3.6192948818206787\n",
      "Running Time for Forward Pass: %s seconds 3.830979585647583\n",
      "Running Time for Forward Pass: %s seconds 3.568115472793579\n",
      "Running Time for Forward Pass: %s seconds 3.675004720687866\n",
      "Running Time for Forward Pass: %s seconds 4.26059103012085\n",
      "Running Time for Forward Pass: %s seconds 4.037418365478516\n",
      "Running Time for Forward Pass: %s seconds 4.00754451751709\n",
      "Running Time for Forward Pass: %s seconds 3.895008087158203\n",
      "Running Time for Forward Pass: %s seconds 3.923892021179199\n",
      "Running Time for Forward Pass: %s seconds 3.7402286529541016\n",
      "Running Time for Forward Pass: %s seconds 3.747840642929077\n",
      "Running Time for Forward Pass: %s seconds 3.645007610321045\n",
      "Running Time for Forward Pass: %s seconds 3.6993014812469482\n",
      "Running Time for Forward Pass: %s seconds 3.613187313079834\n",
      "Running Time for Forward Pass: %s seconds 3.9674012660980225\n",
      "Running Time for Forward Pass: %s seconds 4.100275278091431\n",
      "SPSA Epoch 51: 4.08330\n",
      "Running Time for Forward Pass: %s seconds 3.8242459297180176\n",
      "Running Time for Forward Pass: %s seconds 4.148166179656982\n",
      "Running Time for Forward Pass: %s seconds 3.930304765701294\n",
      "SPSA Epoch 52: 3.49415\n",
      "Running Time for Forward Pass: %s seconds 3.564850091934204\n",
      "Running Time for Forward Pass: %s seconds 4.103741645812988\n",
      "Running Time for Forward Pass: %s seconds 3.7524666786193848\n",
      "SPSA Epoch 53: 3.28579\n",
      "Running Time for Forward Pass: %s seconds 3.66591477394104\n",
      "Running Time for Forward Pass: %s seconds 4.011284828186035\n",
      "Running Time for Forward Pass: %s seconds 3.813582181930542\n",
      "SPSA Epoch 54: 3.19159\n",
      "Running Time for Forward Pass: %s seconds 3.917595386505127\n",
      "Running Time for Forward Pass: %s seconds 3.6172754764556885\n",
      "Running Time for Forward Pass: %s seconds 4.0029990673065186\n",
      "SPSA Epoch 55: 3.14582\n",
      "Running Time for Forward Pass: %s seconds 3.754699945449829\n",
      "Running Time for Forward Pass: %s seconds 3.9040987491607666\n",
      "Running Time for Forward Pass: %s seconds 3.6411197185516357\n",
      "SPSA Epoch 56: 3.13155\n",
      "Running Time for Forward Pass: %s seconds 3.848292827606201\n",
      "Running Time for Forward Pass: %s seconds 4.2015156745910645\n",
      "Running Time for Forward Pass: %s seconds 3.8315024375915527\n",
      "SPSA Epoch 57: 3.11600\n",
      "Running Time for Forward Pass: %s seconds 4.066387414932251\n",
      "Running Time for Forward Pass: %s seconds 3.6195759773254395\n",
      "Running Time for Forward Pass: %s seconds 3.8694651126861572\n",
      "SPSA Epoch 58: 3.09305\n",
      "Running Time for Forward Pass: %s seconds 3.7361199855804443\n",
      "Running Time for Forward Pass: %s seconds 3.8879787921905518\n",
      "Running Time for Forward Pass: %s seconds 3.864413261413574\n",
      "SPSA Epoch 59: 3.10628\n",
      "Running Time for Forward Pass: %s seconds 4.488385200500488\n",
      "Running Time for Forward Pass: %s seconds 3.786389112472534\n",
      "Running Time for Forward Pass: %s seconds 3.7992770671844482\n",
      "SPSA Epoch 60: 3.09883\n",
      "Running Time for Forward Pass: %s seconds 3.8737893104553223\n",
      "Running Time for Forward Pass: %s seconds 3.7950477600097656\n",
      "Running Time for Forward Pass: %s seconds 4.184650421142578\n",
      "SPSA Epoch 61: 3.10025\n",
      "Running Time for Forward Pass: %s seconds 3.6494295597076416\n",
      "Running Time for Forward Pass: %s seconds 3.7522661685943604\n",
      "Running Time for Forward Pass: %s seconds 3.8618972301483154\n",
      "SPSA Epoch 62: 3.09580\n",
      "Running Time for Forward Pass: %s seconds 3.7377817630767822\n",
      "Running Time for Forward Pass: %s seconds 3.835956573486328\n",
      "Running Time for Forward Pass: %s seconds 4.0799171924591064\n",
      "SPSA Epoch 63: 3.09363\n",
      "Running Time for Forward Pass: %s seconds 3.8131749629974365\n",
      "Running Time for Forward Pass: %s seconds 4.0957090854644775\n",
      "Running Time for Forward Pass: %s seconds 4.1977293491363525\n",
      "SPSA Epoch 64: 3.08037\n",
      "Running Time for Forward Pass: %s seconds 3.8126258850097656\n",
      "Running Time for Forward Pass: %s seconds 3.691793918609619\n",
      "Running Time for Forward Pass: %s seconds 4.175069808959961\n",
      "SPSA Epoch 65: 3.09894\n",
      "Running Time for Forward Pass: %s seconds 3.7925162315368652\n",
      "Running Time for Forward Pass: %s seconds 3.687145233154297\n",
      "Running Time for Forward Pass: %s seconds 3.5367257595062256\n",
      "SPSA Epoch 66: 3.09870\n",
      "Running Time for Forward Pass: %s seconds 4.300232410430908\n",
      "Running Time for Forward Pass: %s seconds 3.9217138290405273\n",
      "Running Time for Forward Pass: %s seconds 3.875035047531128\n",
      "SPSA Epoch 67: 3.09060\n",
      "Running Time for Forward Pass: %s seconds 3.723219871520996\n",
      "Running Time for Forward Pass: %s seconds 4.426958799362183\n",
      "Running Time for Forward Pass: %s seconds 4.092517614364624\n",
      "SPSA Epoch 68: 3.10173\n",
      "Running Time for Forward Pass: %s seconds 3.7793352603912354\n",
      "Running Time for Forward Pass: %s seconds 3.9088003635406494\n",
      "Running Time for Forward Pass: %s seconds 3.7500085830688477\n",
      "SPSA Epoch 69: 3.09464\n",
      "Running Time for Forward Pass: %s seconds 4.123461008071899\n",
      "Running Time for Forward Pass: %s seconds 3.7546262741088867\n",
      "Running Time for Forward Pass: %s seconds 4.132763147354126\n",
      "SPSA Epoch 70: 3.09449\n",
      "Running Time for Forward Pass: %s seconds 3.680698871612549\n",
      "Running Time for Forward Pass: %s seconds 3.760470390319824\n",
      "Running Time for Forward Pass: %s seconds 3.680736541748047\n",
      "SPSA Epoch 71: 3.09432\n",
      "Running Time for Forward Pass: %s seconds 3.9985995292663574\n",
      "Running Time for Forward Pass: %s seconds 3.8471338748931885\n",
      "Running Time for Forward Pass: %s seconds 3.8288161754608154\n",
      "SPSA Epoch 72: 3.10108\n",
      "Running Time for Forward Pass: %s seconds 3.7915472984313965\n",
      "Running Time for Forward Pass: %s seconds 3.606633186340332\n",
      "Running Time for Forward Pass: %s seconds 3.588075876235962\n",
      "SPSA Epoch 73: 3.09144\n",
      "Running Time for Forward Pass: %s seconds 3.7431178092956543\n",
      "Running Time for Forward Pass: %s seconds 3.9328362941741943\n",
      "Running Time for Forward Pass: %s seconds 3.8020260334014893\n",
      "SPSA Epoch 74: 3.09389\n",
      "Running Time for Forward Pass: %s seconds 3.668269634246826\n",
      "Running Time for Forward Pass: %s seconds 3.6216461658477783\n",
      "Running Time for Forward Pass: %s seconds 3.7054522037506104\n",
      "SPSA Epoch 75: 3.09561\n",
      "Running Time for Forward Pass: %s seconds 3.6928391456604004\n",
      "Running Time for Forward Pass: %s seconds 3.754476308822632\n",
      "Running Time for Forward Pass: %s seconds 3.822495222091675\n",
      "SPSA Epoch 76: 3.09610\n",
      "Running Time for Forward Pass: %s seconds 3.9881937503814697\n",
      "Running Time for Forward Pass: %s seconds 3.6538186073303223\n",
      "Running Time for Forward Pass: %s seconds 3.767258882522583\n",
      "SPSA Epoch 77: 3.09090\n",
      "Running Time for Forward Pass: %s seconds 3.6700780391693115\n",
      "Running Time for Forward Pass: %s seconds 3.845672607421875\n",
      "Running Time for Forward Pass: %s seconds 3.6457762718200684\n",
      "SPSA Epoch 78: 3.09449\n",
      "Running Time for Forward Pass: %s seconds 4.048641204833984\n",
      "Running Time for Forward Pass: %s seconds 3.6929688453674316\n",
      "Running Time for Forward Pass: %s seconds 3.8766326904296875\n",
      "SPSA Epoch 79: 3.09588\n",
      "Running Time for Forward Pass: %s seconds 3.7363197803497314\n",
      "Running Time for Forward Pass: %s seconds 3.749453067779541\n",
      "Running Time for Forward Pass: %s seconds 3.6348743438720703\n",
      "SPSA Epoch 80: 3.09193\n",
      "Running Time for Forward Pass: %s seconds 3.924285650253296\n",
      "Running Time for Forward Pass: %s seconds 4.013496160507202\n",
      "Running Time for Forward Pass: %s seconds 3.8593358993530273\n",
      "SPSA Epoch 81: 3.09232\n",
      "Running Time for Forward Pass: %s seconds 3.417663097381592\n",
      "Running Time for Forward Pass: %s seconds 3.8051960468292236\n",
      "Running Time for Forward Pass: %s seconds 3.7980551719665527\n",
      "SPSA Epoch 82: 3.09187\n",
      "Running Time for Forward Pass: %s seconds 3.754096508026123\n",
      "Running Time for Forward Pass: %s seconds 3.718884229660034\n",
      "Running Time for Forward Pass: %s seconds 3.5853166580200195\n",
      "SPSA Epoch 83: 3.09202\n",
      "Running Time for Forward Pass: %s seconds 3.71802020072937\n",
      "Running Time for Forward Pass: %s seconds 3.7884531021118164\n",
      "Running Time for Forward Pass: %s seconds 3.9249165058135986\n",
      "SPSA Epoch 84: 3.08703\n",
      "Running Time for Forward Pass: %s seconds 3.6511647701263428\n",
      "Running Time for Forward Pass: %s seconds 3.6869747638702393\n",
      "Running Time for Forward Pass: %s seconds 3.6110589504241943\n",
      "SPSA Epoch 85: 3.10016\n",
      "Running Time for Forward Pass: %s seconds 3.7884538173675537\n",
      "Running Time for Forward Pass: %s seconds 3.744022846221924\n",
      "Running Time for Forward Pass: %s seconds 3.811985731124878\n",
      "SPSA Epoch 86: 3.08933\n",
      "Running Time for Forward Pass: %s seconds 3.6926486492156982\n",
      "Running Time for Forward Pass: %s seconds 4.06903338432312\n",
      "Running Time for Forward Pass: %s seconds 3.686824083328247\n",
      "SPSA Epoch 87: 3.10613\n",
      "Running Time for Forward Pass: %s seconds 3.5870203971862793\n",
      "Running Time for Forward Pass: %s seconds 3.614896774291992\n",
      "Running Time for Forward Pass: %s seconds 3.5045723915100098\n",
      "SPSA Epoch 88: 3.09105\n",
      "Running Time for Forward Pass: %s seconds 3.6062207221984863\n",
      "Running Time for Forward Pass: %s seconds 3.5024569034576416\n",
      "Running Time for Forward Pass: %s seconds 3.874779462814331\n",
      "SPSA Epoch 89: 3.08948\n",
      "Running Time for Forward Pass: %s seconds 3.552919626235962\n",
      "Running Time for Forward Pass: %s seconds 3.7235231399536133\n",
      "Running Time for Forward Pass: %s seconds 3.7643492221832275\n",
      "SPSA Epoch 90: 3.07822\n",
      "Running Time for Forward Pass: %s seconds 3.768109083175659\n",
      "Running Time for Forward Pass: %s seconds 3.6544370651245117\n",
      "Running Time for Forward Pass: %s seconds 3.7914741039276123\n",
      "SPSA Epoch 91: 3.09060\n",
      "Running Time for Forward Pass: %s seconds 3.6965839862823486\n",
      "Running Time for Forward Pass: %s seconds 3.8817930221557617\n",
      "Running Time for Forward Pass: %s seconds 3.784029245376587\n",
      "SPSA Epoch 92: 3.09797\n",
      "Running Time for Forward Pass: %s seconds 3.5723724365234375\n",
      "Running Time for Forward Pass: %s seconds 3.6471331119537354\n",
      "Running Time for Forward Pass: %s seconds 3.403489351272583\n",
      "SPSA Epoch 93: 3.08867\n",
      "Running Time for Forward Pass: %s seconds 3.734372138977051\n",
      "Running Time for Forward Pass: %s seconds 3.695497751235962\n",
      "Running Time for Forward Pass: %s seconds 3.6022326946258545\n",
      "SPSA Epoch 94: 3.09410\n",
      "Running Time for Forward Pass: %s seconds 3.4705820083618164\n",
      "Running Time for Forward Pass: %s seconds 3.8026669025421143\n",
      "Running Time for Forward Pass: %s seconds 3.7363476753234863\n",
      "SPSA Epoch 95: 3.09260\n",
      "Running Time for Forward Pass: %s seconds 4.030623912811279\n",
      "Running Time for Forward Pass: %s seconds 3.807393789291382\n",
      "Running Time for Forward Pass: %s seconds 3.7172224521636963\n",
      "SPSA Epoch 96: 3.08536\n",
      "Running Time for Forward Pass: %s seconds 3.721101760864258\n",
      "Running Time for Forward Pass: %s seconds 3.8325881958007812\n",
      "Running Time for Forward Pass: %s seconds 3.4896721839904785\n",
      "SPSA Epoch 97: 3.09064\n",
      "Running Time for Forward Pass: %s seconds 3.863070011138916\n",
      "Running Time for Forward Pass: %s seconds 3.4359521865844727\n",
      "Running Time for Forward Pass: %s seconds 3.937685012817383\n",
      "SPSA Epoch 98: 3.10149\n",
      "Running Time for Forward Pass: %s seconds 3.8189074993133545\n",
      "Running Time for Forward Pass: %s seconds 3.6718590259552\n",
      "Running Time for Forward Pass: %s seconds 3.861638069152832\n",
      "SPSA Epoch 99: 3.09610\n",
      "Running Time for Forward Pass: %s seconds 3.5987837314605713\n",
      "Running Time for Forward Pass: %s seconds 3.934391736984253\n",
      "Running Time for Forward Pass: %s seconds 3.716618776321411\n",
      "SPSA Epoch 100: 3.08920\n",
      "{   'fun': 3.0892023770989225,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.10533164,  0.24947969, -0.88592139,  1.45771135, -0.23893304,\n",
      "       -0.71210416,  0.13935035])}\n",
      "Running Time for optimizing Subex-A2: %s seconds 762.6490080356598\n",
      "Running Time for Forward Pass: %s seconds 4.175903558731079\n",
      "Running Time for Forward Pass: %s seconds 3.633274793624878\n",
      "Running Time for Forward Pass: %s seconds 3.7108473777770996\n",
      "Running Time for Forward Pass: %s seconds 3.936915874481201\n",
      "Running Time for Forward Pass: %s seconds 3.8442702293395996\n",
      "Running Time for Forward Pass: %s seconds 3.561444044113159\n",
      "Running Time for Forward Pass: %s seconds 3.8288767337799072\n",
      "Running Time for Forward Pass: %s seconds 3.6422202587127686\n",
      "Running Time for Forward Pass: %s seconds 3.942718267440796\n",
      "Running Time for Forward Pass: %s seconds 3.6980438232421875\n",
      "Running Time for Forward Pass: %s seconds 4.190257787704468\n",
      "Running Time for Forward Pass: %s seconds 3.910430669784546\n",
      "Running Time for Forward Pass: %s seconds 3.8285183906555176\n",
      "Running Time for Forward Pass: %s seconds 3.5215506553649902\n",
      "Running Time for Forward Pass: %s seconds 3.67771053314209\n",
      "Running Time for Forward Pass: %s seconds 3.747438907623291\n",
      "Running Time for Forward Pass: %s seconds 3.7882373332977295\n",
      "Running Time for Forward Pass: %s seconds 3.6180942058563232\n",
      "Running Time for Forward Pass: %s seconds 3.626312494277954\n",
      "Running Time for Forward Pass: %s seconds 3.920869827270508\n",
      "Running Time for Forward Pass: %s seconds 3.8918302059173584\n",
      "Running Time for Forward Pass: %s seconds 3.819150686264038\n",
      "Running Time for Forward Pass: %s seconds 4.113255262374878\n",
      "Running Time for Forward Pass: %s seconds 3.597019672393799\n",
      "Running Time for Forward Pass: %s seconds 3.6612958908081055\n",
      "Running Time for Forward Pass: %s seconds 3.828519821166992\n",
      "Running Time for Forward Pass: %s seconds 3.6979291439056396\n",
      "Running Time for Forward Pass: %s seconds 3.7856600284576416\n",
      "Running Time for Forward Pass: %s seconds 3.486670970916748\n",
      "Running Time for Forward Pass: %s seconds 3.8457143306732178\n",
      "Running Time for Forward Pass: %s seconds 3.6253397464752197\n",
      "Running Time for Forward Pass: %s seconds 3.6864025592803955\n",
      "Running Time for Forward Pass: %s seconds 3.4152016639709473\n",
      "Running Time for Forward Pass: %s seconds 3.6055874824523926\n",
      "Running Time for Forward Pass: %s seconds 3.883502960205078\n",
      "Running Time for Forward Pass: %s seconds 3.670970916748047\n",
      "Running Time for Forward Pass: %s seconds 3.7464804649353027\n",
      "Running Time for Forward Pass: %s seconds 3.876741647720337\n",
      "Running Time for Forward Pass: %s seconds 3.9087331295013428\n",
      "Running Time for Forward Pass: %s seconds 3.682121992111206\n",
      "Running Time for Forward Pass: %s seconds 3.888223648071289\n",
      "Running Time for Forward Pass: %s seconds 3.7458431720733643\n",
      "Running Time for Forward Pass: %s seconds 3.7015974521636963\n",
      "Running Time for Forward Pass: %s seconds 3.675673246383667\n",
      "Running Time for Forward Pass: %s seconds 4.040863990783691\n",
      "Running Time for Forward Pass: %s seconds 3.7290918827056885\n",
      "Running Time for Forward Pass: %s seconds 4.038406848907471\n",
      "Running Time for Forward Pass: %s seconds 3.6482107639312744\n",
      "Running Time for Forward Pass: %s seconds 3.79982852935791\n",
      "Running Time for Forward Pass: %s seconds 3.816986560821533\n",
      "Running Time for Forward Pass: %s seconds 3.9574618339538574\n",
      "Running Time for Forward Pass: %s seconds 4.110409498214722\n",
      "Running Time for Forward Pass: %s seconds 3.7764086723327637\n",
      "SPSA Epoch 101: 1.54256\n",
      "Running Time for Forward Pass: %s seconds 3.8724520206451416\n",
      "Running Time for Forward Pass: %s seconds 3.708212375640869\n",
      "Running Time for Forward Pass: %s seconds 3.9419875144958496\n",
      "SPSA Epoch 102: 1.54460\n",
      "Running Time for Forward Pass: %s seconds 4.168219089508057\n",
      "Running Time for Forward Pass: %s seconds 3.7558035850524902\n",
      "Running Time for Forward Pass: %s seconds 3.7478621006011963\n",
      "SPSA Epoch 103: 1.53257\n",
      "Running Time for Forward Pass: %s seconds 3.8219316005706787\n",
      "Running Time for Forward Pass: %s seconds 3.6980557441711426\n",
      "Running Time for Forward Pass: %s seconds 3.7298173904418945\n",
      "SPSA Epoch 104: 1.52565\n",
      "Running Time for Forward Pass: %s seconds 3.7021641731262207\n",
      "Running Time for Forward Pass: %s seconds 3.9075067043304443\n",
      "Running Time for Forward Pass: %s seconds 4.013920307159424\n",
      "SPSA Epoch 105: 1.41947\n",
      "Running Time for Forward Pass: %s seconds 3.758697748184204\n",
      "Running Time for Forward Pass: %s seconds 3.988248348236084\n",
      "Running Time for Forward Pass: %s seconds 3.7600178718566895\n",
      "SPSA Epoch 106: 1.20179\n",
      "Running Time for Forward Pass: %s seconds 3.8271944522857666\n",
      "Running Time for Forward Pass: %s seconds 3.618936538696289\n",
      "Running Time for Forward Pass: %s seconds 3.7297167778015137\n",
      "SPSA Epoch 107: 1.17753\n",
      "Running Time for Forward Pass: %s seconds 3.7125277519226074\n",
      "Running Time for Forward Pass: %s seconds 3.9951281547546387\n",
      "Running Time for Forward Pass: %s seconds 3.7197959423065186\n",
      "SPSA Epoch 108: 1.08911\n",
      "Running Time for Forward Pass: %s seconds 3.7546567916870117\n",
      "Running Time for Forward Pass: %s seconds 3.6068227291107178\n",
      "Running Time for Forward Pass: %s seconds 3.406184196472168\n",
      "SPSA Epoch 109: 1.07810\n",
      "Running Time for Forward Pass: %s seconds 3.591172456741333\n",
      "Running Time for Forward Pass: %s seconds 3.5236902236938477\n",
      "Running Time for Forward Pass: %s seconds 3.656949043273926\n",
      "SPSA Epoch 110: 1.04329\n",
      "Running Time for Forward Pass: %s seconds 3.5545618534088135\n",
      "Running Time for Forward Pass: %s seconds 3.589186668395996\n",
      "Running Time for Forward Pass: %s seconds 3.45670223236084\n",
      "SPSA Epoch 111: 1.03699\n",
      "Running Time for Forward Pass: %s seconds 4.251825571060181\n",
      "Running Time for Forward Pass: %s seconds 3.8115978240966797\n",
      "Running Time for Forward Pass: %s seconds 4.001863241195679\n",
      "SPSA Epoch 112: 0.99152\n",
      "Running Time for Forward Pass: %s seconds 3.912367820739746\n",
      "Running Time for Forward Pass: %s seconds 3.5322251319885254\n",
      "Running Time for Forward Pass: %s seconds 3.8876469135284424\n",
      "SPSA Epoch 113: 0.96692\n",
      "Running Time for Forward Pass: %s seconds 3.789414167404175\n",
      "Running Time for Forward Pass: %s seconds 3.9938504695892334\n",
      "Running Time for Forward Pass: %s seconds 3.635277032852173\n",
      "SPSA Epoch 114: 0.95351\n",
      "Running Time for Forward Pass: %s seconds 3.8387625217437744\n",
      "Running Time for Forward Pass: %s seconds 3.723525047302246\n",
      "Running Time for Forward Pass: %s seconds 3.911620616912842\n",
      "SPSA Epoch 115: 0.94954\n",
      "Running Time for Forward Pass: %s seconds 3.7816481590270996\n",
      "Running Time for Forward Pass: %s seconds 4.472214937210083\n",
      "Running Time for Forward Pass: %s seconds 3.6720259189605713\n",
      "SPSA Epoch 116: 0.93749\n",
      "Running Time for Forward Pass: %s seconds 3.7472105026245117\n",
      "Running Time for Forward Pass: %s seconds 3.8642287254333496\n",
      "Running Time for Forward Pass: %s seconds 3.5928118228912354\n",
      "SPSA Epoch 117: 0.94010\n",
      "Running Time for Forward Pass: %s seconds 3.784255266189575\n",
      "Running Time for Forward Pass: %s seconds 4.213103532791138\n",
      "Running Time for Forward Pass: %s seconds 4.07403039932251\n",
      "SPSA Epoch 118: 0.88295\n",
      "Running Time for Forward Pass: %s seconds 3.906543016433716\n",
      "Running Time for Forward Pass: %s seconds 3.9669156074523926\n",
      "Running Time for Forward Pass: %s seconds 4.050437688827515\n",
      "SPSA Epoch 119: 0.85563\n",
      "Running Time for Forward Pass: %s seconds 3.9076790809631348\n",
      "Running Time for Forward Pass: %s seconds 3.7098581790924072\n",
      "Running Time for Forward Pass: %s seconds 3.9717907905578613\n",
      "SPSA Epoch 120: 0.86268\n",
      "Running Time for Forward Pass: %s seconds 3.762101411819458\n",
      "Running Time for Forward Pass: %s seconds 3.6441447734832764\n",
      "Running Time for Forward Pass: %s seconds 3.482152223587036\n",
      "SPSA Epoch 121: 0.85230\n",
      "Running Time for Forward Pass: %s seconds 3.764618158340454\n",
      "Running Time for Forward Pass: %s seconds 3.5634799003601074\n",
      "Running Time for Forward Pass: %s seconds 3.9393773078918457\n",
      "SPSA Epoch 122: 0.84324\n",
      "Running Time for Forward Pass: %s seconds 3.6535768508911133\n",
      "Running Time for Forward Pass: %s seconds 4.243650197982788\n",
      "Running Time for Forward Pass: %s seconds 3.638835906982422\n",
      "SPSA Epoch 123: 0.84030\n",
      "Running Time for Forward Pass: %s seconds 3.903928518295288\n",
      "Running Time for Forward Pass: %s seconds 3.8852124214172363\n",
      "Running Time for Forward Pass: %s seconds 3.959737539291382\n",
      "SPSA Epoch 124: 0.82389\n",
      "Running Time for Forward Pass: %s seconds 3.617938995361328\n",
      "Running Time for Forward Pass: %s seconds 3.599292278289795\n",
      "Running Time for Forward Pass: %s seconds 3.6285781860351562\n",
      "SPSA Epoch 125: 0.81078\n",
      "Running Time for Forward Pass: %s seconds 3.878392219543457\n",
      "Running Time for Forward Pass: %s seconds 3.5234122276306152\n",
      "Running Time for Forward Pass: %s seconds 3.6540911197662354\n",
      "SPSA Epoch 126: 0.76544\n",
      "Running Time for Forward Pass: %s seconds 3.56441068649292\n",
      "Running Time for Forward Pass: %s seconds 3.5158088207244873\n",
      "Running Time for Forward Pass: %s seconds 3.646016836166382\n",
      "SPSA Epoch 127: 0.70661\n",
      "Running Time for Forward Pass: %s seconds 3.421647310256958\n",
      "Running Time for Forward Pass: %s seconds 3.6209681034088135\n",
      "Running Time for Forward Pass: %s seconds 3.733436107635498\n",
      "SPSA Epoch 128: 0.70064\n",
      "Running Time for Forward Pass: %s seconds 3.9270853996276855\n",
      "Running Time for Forward Pass: %s seconds 3.777115821838379\n",
      "Running Time for Forward Pass: %s seconds 3.694394588470459\n",
      "SPSA Epoch 129: 0.67205\n",
      "Running Time for Forward Pass: %s seconds 3.5508198738098145\n",
      "Running Time for Forward Pass: %s seconds 3.7455227375030518\n",
      "Running Time for Forward Pass: %s seconds 3.9755699634552\n",
      "SPSA Epoch 130: 0.67148\n",
      "Running Time for Forward Pass: %s seconds 3.609898328781128\n",
      "Running Time for Forward Pass: %s seconds 3.8606395721435547\n",
      "Running Time for Forward Pass: %s seconds 4.036689519882202\n",
      "SPSA Epoch 131: 0.64975\n",
      "Running Time for Forward Pass: %s seconds 4.082605361938477\n",
      "Running Time for Forward Pass: %s seconds 3.6421732902526855\n",
      "Running Time for Forward Pass: %s seconds 3.8424980640411377\n",
      "SPSA Epoch 132: 0.64701\n",
      "Running Time for Forward Pass: %s seconds 3.730592727661133\n",
      "Running Time for Forward Pass: %s seconds 4.0336291790008545\n",
      "Running Time for Forward Pass: %s seconds 3.6518754959106445\n",
      "SPSA Epoch 133: 0.60673\n",
      "Running Time for Forward Pass: %s seconds 3.8390188217163086\n",
      "Running Time for Forward Pass: %s seconds 3.735714912414551\n",
      "Running Time for Forward Pass: %s seconds 3.8829286098480225\n",
      "SPSA Epoch 134: 0.59845\n",
      "Running Time for Forward Pass: %s seconds 3.6631369590759277\n",
      "Running Time for Forward Pass: %s seconds 3.874335765838623\n",
      "Running Time for Forward Pass: %s seconds 3.707805633544922\n",
      "SPSA Epoch 135: 0.60056\n",
      "Running Time for Forward Pass: %s seconds 3.9536569118499756\n",
      "Running Time for Forward Pass: %s seconds 4.192197561264038\n",
      "Running Time for Forward Pass: %s seconds 3.6931283473968506\n",
      "SPSA Epoch 136: 0.56760\n",
      "Running Time for Forward Pass: %s seconds 3.7833454608917236\n",
      "Running Time for Forward Pass: %s seconds 3.516374111175537\n",
      "Running Time for Forward Pass: %s seconds 3.714057683944702\n",
      "SPSA Epoch 137: 0.54552\n",
      "Running Time for Forward Pass: %s seconds 3.6441354751586914\n",
      "Running Time for Forward Pass: %s seconds 3.922816038131714\n",
      "Running Time for Forward Pass: %s seconds 3.760124921798706\n",
      "SPSA Epoch 138: 0.52090\n",
      "Running Time for Forward Pass: %s seconds 3.8490350246429443\n",
      "Running Time for Forward Pass: %s seconds 3.7305190563201904\n",
      "Running Time for Forward Pass: %s seconds 3.8155720233917236\n",
      "SPSA Epoch 139: 0.51433\n",
      "Running Time for Forward Pass: %s seconds 3.5419821739196777\n",
      "Running Time for Forward Pass: %s seconds 4.010470628738403\n",
      "Running Time for Forward Pass: %s seconds 3.629600763320923\n",
      "SPSA Epoch 140: 0.51210\n",
      "Running Time for Forward Pass: %s seconds 4.115622282028198\n",
      "Running Time for Forward Pass: %s seconds 3.825670003890991\n",
      "Running Time for Forward Pass: %s seconds 3.9558982849121094\n",
      "SPSA Epoch 141: 0.49973\n",
      "Running Time for Forward Pass: %s seconds 4.124451637268066\n",
      "Running Time for Forward Pass: %s seconds 3.8089852333068848\n",
      "Running Time for Forward Pass: %s seconds 3.7198565006256104\n",
      "SPSA Epoch 142: 0.47251\n",
      "Running Time for Forward Pass: %s seconds 3.6350765228271484\n",
      "Running Time for Forward Pass: %s seconds 3.758678913116455\n",
      "Running Time for Forward Pass: %s seconds 3.968719005584717\n",
      "SPSA Epoch 143: 0.46103\n",
      "Running Time for Forward Pass: %s seconds 3.849958658218384\n",
      "Running Time for Forward Pass: %s seconds 3.8071446418762207\n",
      "Running Time for Forward Pass: %s seconds 3.8455708026885986\n",
      "SPSA Epoch 144: 0.44129\n",
      "Running Time for Forward Pass: %s seconds 3.83553147315979\n",
      "Running Time for Forward Pass: %s seconds 4.117368459701538\n",
      "Running Time for Forward Pass: %s seconds 3.7402477264404297\n",
      "SPSA Epoch 145: 0.44266\n",
      "Running Time for Forward Pass: %s seconds 3.9864542484283447\n",
      "Running Time for Forward Pass: %s seconds 3.6509509086608887\n",
      "Running Time for Forward Pass: %s seconds 3.9408934116363525\n",
      "SPSA Epoch 146: 0.44306\n",
      "Running Time for Forward Pass: %s seconds 3.737837791442871\n",
      "Running Time for Forward Pass: %s seconds 3.9669320583343506\n",
      "Running Time for Forward Pass: %s seconds 4.1852922439575195\n",
      "SPSA Epoch 147: 0.43608\n",
      "Running Time for Forward Pass: %s seconds 3.783156156539917\n",
      "Running Time for Forward Pass: %s seconds 3.670489549636841\n",
      "Running Time for Forward Pass: %s seconds 3.7821340560913086\n",
      "SPSA Epoch 148: 0.42125\n",
      "Running Time for Forward Pass: %s seconds 4.014232158660889\n",
      "Running Time for Forward Pass: %s seconds 3.5792808532714844\n",
      "Running Time for Forward Pass: %s seconds 3.914034366607666\n",
      "SPSA Epoch 149: 0.40023\n",
      "Running Time for Forward Pass: %s seconds 4.059790849685669\n",
      "Running Time for Forward Pass: %s seconds 4.233880996704102\n",
      "Running Time for Forward Pass: %s seconds 3.8414440155029297\n",
      "SPSA Epoch 150: 0.39621\n",
      "{   'fun': 0.39620590757628343,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.12018245,  0.02255229,  1.62924039,  0.45615453,  0.57422933,\n",
      "       -0.07941653, -1.28125282])}\n",
      "Running Time for optimizing Subex-A3: %s seconds 762.6013209819794\n",
      "Running Time for Forward Pass: %s seconds 3.7290570735931396\n",
      "Running Time for Forward Pass: %s seconds 3.5895752906799316\n",
      "Running Time for Forward Pass: %s seconds 3.7376205921173096\n",
      "Running Time for Forward Pass: %s seconds 3.5249791145324707\n",
      "Running Time for Forward Pass: %s seconds 3.519585132598877\n",
      "Running Time for Forward Pass: %s seconds 3.5159103870391846\n",
      "Running Time for Forward Pass: %s seconds 3.887427806854248\n",
      "Running Time for Forward Pass: %s seconds 3.5873353481292725\n",
      "Running Time for Forward Pass: %s seconds 3.939990282058716\n",
      "Running Time for Forward Pass: %s seconds 3.8977112770080566\n",
      "Running Time for Forward Pass: %s seconds 3.639134407043457\n",
      "Running Time for Forward Pass: %s seconds 4.2086169719696045\n",
      "Running Time for Forward Pass: %s seconds 3.7571518421173096\n",
      "Running Time for Forward Pass: %s seconds 3.866608142852783\n",
      "Running Time for Forward Pass: %s seconds 3.657160520553589\n",
      "Running Time for Forward Pass: %s seconds 3.9784114360809326\n",
      "Running Time for Forward Pass: %s seconds 3.975910186767578\n",
      "Running Time for Forward Pass: %s seconds 3.782527446746826\n",
      "Running Time for Forward Pass: %s seconds 3.791585683822632\n",
      "Running Time for Forward Pass: %s seconds 3.646735906600952\n",
      "Running Time for Forward Pass: %s seconds 3.7323157787323\n",
      "Running Time for Forward Pass: %s seconds 3.9596519470214844\n",
      "Running Time for Forward Pass: %s seconds 3.5318636894226074\n",
      "Running Time for Forward Pass: %s seconds 3.895565986633301\n",
      "Running Time for Forward Pass: %s seconds 4.075002431869507\n",
      "Running Time for Forward Pass: %s seconds 3.9630842208862305\n",
      "Running Time for Forward Pass: %s seconds 3.6797378063201904\n",
      "Running Time for Forward Pass: %s seconds 3.863393783569336\n",
      "Running Time for Forward Pass: %s seconds 3.6587812900543213\n",
      "Running Time for Forward Pass: %s seconds 3.674395799636841\n",
      "Running Time for Forward Pass: %s seconds 3.8202240467071533\n",
      "Running Time for Forward Pass: %s seconds 3.924243688583374\n",
      "Running Time for Forward Pass: %s seconds 3.833677053451538\n",
      "Running Time for Forward Pass: %s seconds 3.3858437538146973\n",
      "Running Time for Forward Pass: %s seconds 3.6554062366485596\n",
      "Running Time for Forward Pass: %s seconds 3.3710150718688965\n",
      "Running Time for Forward Pass: %s seconds 3.568699598312378\n",
      "Running Time for Forward Pass: %s seconds 3.64825701713562\n",
      "Running Time for Forward Pass: %s seconds 3.778090000152588\n",
      "Running Time for Forward Pass: %s seconds 3.6773438453674316\n",
      "Running Time for Forward Pass: %s seconds 3.751760482788086\n",
      "Running Time for Forward Pass: %s seconds 3.9193215370178223\n",
      "Running Time for Forward Pass: %s seconds 3.82236909866333\n",
      "Running Time for Forward Pass: %s seconds 3.508068561553955\n",
      "Running Time for Forward Pass: %s seconds 4.0178868770599365\n",
      "Running Time for Forward Pass: %s seconds 3.74396014213562\n",
      "Running Time for Forward Pass: %s seconds 3.686159372329712\n",
      "Running Time for Forward Pass: %s seconds 3.737434148788452\n",
      "Running Time for Forward Pass: %s seconds 3.9226272106170654\n",
      "Running Time for Forward Pass: %s seconds 3.621962547302246\n",
      "Running Time for Forward Pass: %s seconds 3.970388650894165\n",
      "Running Time for Forward Pass: %s seconds 4.019900560379028\n",
      "Running Time for Forward Pass: %s seconds 3.8898744583129883\n",
      "SPSA Epoch 151: 2.91482\n",
      "Running Time for Forward Pass: %s seconds 3.6350488662719727\n",
      "Running Time for Forward Pass: %s seconds 4.0791685581207275\n",
      "Running Time for Forward Pass: %s seconds 3.830037832260132\n",
      "SPSA Epoch 152: 2.79926\n",
      "Running Time for Forward Pass: %s seconds 3.686596155166626\n",
      "Running Time for Forward Pass: %s seconds 3.713911771774292\n",
      "Running Time for Forward Pass: %s seconds 3.7045652866363525\n",
      "SPSA Epoch 153: 2.11581\n",
      "Running Time for Forward Pass: %s seconds 3.903046131134033\n",
      "Running Time for Forward Pass: %s seconds 3.46481990814209\n",
      "Running Time for Forward Pass: %s seconds 3.6519651412963867\n",
      "SPSA Epoch 154: 1.43115\n",
      "Running Time for Forward Pass: %s seconds 3.7693140506744385\n",
      "Running Time for Forward Pass: %s seconds 3.7478408813476562\n",
      "Running Time for Forward Pass: %s seconds 3.5446951389312744\n",
      "SPSA Epoch 155: 1.23552\n",
      "Running Time for Forward Pass: %s seconds 3.8172872066497803\n",
      "Running Time for Forward Pass: %s seconds 3.7084391117095947\n",
      "Running Time for Forward Pass: %s seconds 3.6412301063537598\n",
      "SPSA Epoch 156: 1.05607\n",
      "Running Time for Forward Pass: %s seconds 3.8871262073516846\n",
      "Running Time for Forward Pass: %s seconds 3.6792473793029785\n",
      "Running Time for Forward Pass: %s seconds 3.8292782306671143\n",
      "SPSA Epoch 157: 1.02849\n",
      "Running Time for Forward Pass: %s seconds 3.843611001968384\n",
      "Running Time for Forward Pass: %s seconds 3.7688989639282227\n",
      "Running Time for Forward Pass: %s seconds 3.9242501258850098\n",
      "SPSA Epoch 158: 0.93111\n",
      "Running Time for Forward Pass: %s seconds 3.9243857860565186\n",
      "Running Time for Forward Pass: %s seconds 3.717531204223633\n",
      "Running Time for Forward Pass: %s seconds 3.792695999145508\n",
      "SPSA Epoch 159: 0.92722\n",
      "Running Time for Forward Pass: %s seconds 3.484825849533081\n",
      "Running Time for Forward Pass: %s seconds 3.8154609203338623\n",
      "Running Time for Forward Pass: %s seconds 3.708307981491089\n",
      "SPSA Epoch 160: 0.74767\n",
      "Running Time for Forward Pass: %s seconds 3.7646398544311523\n",
      "Running Time for Forward Pass: %s seconds 3.5493555068969727\n",
      "Running Time for Forward Pass: %s seconds 3.7169997692108154\n",
      "SPSA Epoch 161: 0.72403\n",
      "Running Time for Forward Pass: %s seconds 3.7997467517852783\n",
      "Running Time for Forward Pass: %s seconds 3.683194160461426\n",
      "Running Time for Forward Pass: %s seconds 3.923116445541382\n",
      "SPSA Epoch 162: 0.70151\n",
      "Running Time for Forward Pass: %s seconds 3.7755351066589355\n",
      "Running Time for Forward Pass: %s seconds 4.015077114105225\n",
      "Running Time for Forward Pass: %s seconds 3.8243582248687744\n",
      "SPSA Epoch 163: 0.57314\n",
      "Running Time for Forward Pass: %s seconds 3.6583590507507324\n",
      "Running Time for Forward Pass: %s seconds 3.6453585624694824\n",
      "Running Time for Forward Pass: %s seconds 3.8341217041015625\n",
      "SPSA Epoch 164: 0.50045\n",
      "Running Time for Forward Pass: %s seconds 3.779378890991211\n",
      "Running Time for Forward Pass: %s seconds 3.8345272541046143\n",
      "Running Time for Forward Pass: %s seconds 3.6133947372436523\n",
      "SPSA Epoch 165: 0.48760\n",
      "Running Time for Forward Pass: %s seconds 4.314266681671143\n",
      "Running Time for Forward Pass: %s seconds 3.604931354522705\n",
      "Running Time for Forward Pass: %s seconds 3.873880386352539\n",
      "SPSA Epoch 166: 0.45844\n",
      "Running Time for Forward Pass: %s seconds 3.646256923675537\n",
      "Running Time for Forward Pass: %s seconds 3.8383357524871826\n",
      "Running Time for Forward Pass: %s seconds 3.5559616088867188\n",
      "SPSA Epoch 167: 0.44474\n",
      "Running Time for Forward Pass: %s seconds 3.877199649810791\n",
      "Running Time for Forward Pass: %s seconds 3.6215081214904785\n",
      "Running Time for Forward Pass: %s seconds 3.4944329261779785\n",
      "SPSA Epoch 168: 0.43217\n",
      "Running Time for Forward Pass: %s seconds 3.8800787925720215\n",
      "Running Time for Forward Pass: %s seconds 3.8710567951202393\n",
      "Running Time for Forward Pass: %s seconds 3.4171664714813232\n",
      "SPSA Epoch 169: 0.42926\n",
      "Running Time for Forward Pass: %s seconds 3.66847825050354\n",
      "Running Time for Forward Pass: %s seconds 3.7815330028533936\n",
      "Running Time for Forward Pass: %s seconds 3.76008677482605\n",
      "SPSA Epoch 170: 0.42727\n",
      "Running Time for Forward Pass: %s seconds 3.7021431922912598\n",
      "Running Time for Forward Pass: %s seconds 3.731559991836548\n",
      "Running Time for Forward Pass: %s seconds 3.6028175354003906\n",
      "SPSA Epoch 171: 0.42165\n",
      "Running Time for Forward Pass: %s seconds 4.221213102340698\n",
      "Running Time for Forward Pass: %s seconds 3.912137746810913\n",
      "Running Time for Forward Pass: %s seconds 3.6915314197540283\n",
      "SPSA Epoch 172: 0.42454\n",
      "Running Time for Forward Pass: %s seconds 3.794271945953369\n",
      "Running Time for Forward Pass: %s seconds 3.6447792053222656\n",
      "Running Time for Forward Pass: %s seconds 4.100738286972046\n",
      "SPSA Epoch 173: 0.41221\n",
      "Running Time for Forward Pass: %s seconds 3.7506263256073\n",
      "Running Time for Forward Pass: %s seconds 3.8981857299804688\n",
      "Running Time for Forward Pass: %s seconds 3.6654770374298096\n",
      "SPSA Epoch 174: 0.41576\n",
      "Running Time for Forward Pass: %s seconds 4.191698789596558\n",
      "Running Time for Forward Pass: %s seconds 3.501662492752075\n",
      "Running Time for Forward Pass: %s seconds 4.353177070617676\n",
      "SPSA Epoch 175: 0.41711\n",
      "Running Time for Forward Pass: %s seconds 3.666090250015259\n",
      "Running Time for Forward Pass: %s seconds 3.485929012298584\n",
      "Running Time for Forward Pass: %s seconds 3.576765298843384\n",
      "SPSA Epoch 176: 0.41329\n",
      "Running Time for Forward Pass: %s seconds 3.542137622833252\n",
      "Running Time for Forward Pass: %s seconds 3.678577184677124\n",
      "Running Time for Forward Pass: %s seconds 3.6661536693573\n",
      "SPSA Epoch 177: 0.40548\n",
      "Running Time for Forward Pass: %s seconds 3.810088634490967\n",
      "Running Time for Forward Pass: %s seconds 3.4679174423217773\n",
      "Running Time for Forward Pass: %s seconds 4.063844919204712\n",
      "SPSA Epoch 178: 0.39641\n",
      "Running Time for Forward Pass: %s seconds 3.5803914070129395\n",
      "Running Time for Forward Pass: %s seconds 3.791797161102295\n",
      "Running Time for Forward Pass: %s seconds 3.4182441234588623\n",
      "SPSA Epoch 179: 0.39285\n",
      "Running Time for Forward Pass: %s seconds 3.850797176361084\n",
      "Running Time for Forward Pass: %s seconds 3.6986706256866455\n",
      "Running Time for Forward Pass: %s seconds 3.5747241973876953\n",
      "SPSA Epoch 180: 0.38466\n",
      "Running Time for Forward Pass: %s seconds 3.4327898025512695\n",
      "Running Time for Forward Pass: %s seconds 3.7061290740966797\n",
      "Running Time for Forward Pass: %s seconds 3.7695560455322266\n",
      "SPSA Epoch 181: 0.37408\n",
      "Running Time for Forward Pass: %s seconds 3.9057867527008057\n",
      "Running Time for Forward Pass: %s seconds 3.6244633197784424\n",
      "Running Time for Forward Pass: %s seconds 3.7162601947784424\n",
      "SPSA Epoch 182: 0.36872\n",
      "Running Time for Forward Pass: %s seconds 3.837651252746582\n",
      "Running Time for Forward Pass: %s seconds 3.6124305725097656\n",
      "Running Time for Forward Pass: %s seconds 3.8229849338531494\n",
      "SPSA Epoch 183: 0.36029\n",
      "Running Time for Forward Pass: %s seconds 3.519286870956421\n",
      "Running Time for Forward Pass: %s seconds 3.6352932453155518\n",
      "Running Time for Forward Pass: %s seconds 3.6290903091430664\n",
      "SPSA Epoch 184: 0.36578\n",
      "Running Time for Forward Pass: %s seconds 4.046075344085693\n",
      "Running Time for Forward Pass: %s seconds 3.568429470062256\n",
      "Running Time for Forward Pass: %s seconds 4.086497068405151\n",
      "SPSA Epoch 185: 0.35733\n",
      "Running Time for Forward Pass: %s seconds 3.732100486755371\n",
      "Running Time for Forward Pass: %s seconds 4.115960359573364\n",
      "Running Time for Forward Pass: %s seconds 3.6664187908172607\n",
      "SPSA Epoch 186: 0.35126\n",
      "Running Time for Forward Pass: %s seconds 3.932403802871704\n",
      "Running Time for Forward Pass: %s seconds 3.6217174530029297\n",
      "Running Time for Forward Pass: %s seconds 3.715480089187622\n",
      "SPSA Epoch 187: 0.35923\n",
      "Running Time for Forward Pass: %s seconds 3.7221620082855225\n",
      "Running Time for Forward Pass: %s seconds 3.9151182174682617\n",
      "Running Time for Forward Pass: %s seconds 3.7190299034118652\n",
      "SPSA Epoch 188: 0.34463\n",
      "Running Time for Forward Pass: %s seconds 3.5145199298858643\n",
      "Running Time for Forward Pass: %s seconds 3.807791233062744\n",
      "Running Time for Forward Pass: %s seconds 3.633817434310913\n",
      "SPSA Epoch 189: 0.34619\n",
      "Running Time for Forward Pass: %s seconds 3.5494067668914795\n",
      "Running Time for Forward Pass: %s seconds 3.9394781589508057\n",
      "Running Time for Forward Pass: %s seconds 3.684343099594116\n",
      "SPSA Epoch 190: 0.34936\n",
      "Running Time for Forward Pass: %s seconds 3.7348952293395996\n",
      "Running Time for Forward Pass: %s seconds 4.142238140106201\n",
      "Running Time for Forward Pass: %s seconds 3.8331797122955322\n",
      "SPSA Epoch 191: 0.34689\n",
      "Running Time for Forward Pass: %s seconds 3.812429904937744\n",
      "Running Time for Forward Pass: %s seconds 3.5987930297851562\n",
      "Running Time for Forward Pass: %s seconds 3.80521821975708\n",
      "SPSA Epoch 192: 0.34222\n",
      "Running Time for Forward Pass: %s seconds 3.5905773639678955\n",
      "Running Time for Forward Pass: %s seconds 3.7256131172180176\n",
      "Running Time for Forward Pass: %s seconds 3.663693428039551\n",
      "SPSA Epoch 193: 0.34136\n",
      "Running Time for Forward Pass: %s seconds 3.8281638622283936\n",
      "Running Time for Forward Pass: %s seconds 3.6934211254119873\n",
      "Running Time for Forward Pass: %s seconds 3.9168567657470703\n",
      "SPSA Epoch 194: 0.34122\n",
      "Running Time for Forward Pass: %s seconds 3.6012675762176514\n",
      "Running Time for Forward Pass: %s seconds 3.667273998260498\n",
      "Running Time for Forward Pass: %s seconds 3.5278139114379883\n",
      "SPSA Epoch 195: 0.33396\n",
      "Running Time for Forward Pass: %s seconds 3.7392706871032715\n",
      "Running Time for Forward Pass: %s seconds 3.649522542953491\n",
      "Running Time for Forward Pass: %s seconds 3.8115170001983643\n",
      "SPSA Epoch 196: 0.33413\n",
      "Running Time for Forward Pass: %s seconds 3.947087049484253\n",
      "Running Time for Forward Pass: %s seconds 3.715820074081421\n",
      "Running Time for Forward Pass: %s seconds 3.681154727935791\n",
      "SPSA Epoch 197: 0.33605\n",
      "Running Time for Forward Pass: %s seconds 3.5872819423675537\n",
      "Running Time for Forward Pass: %s seconds 3.7640864849090576\n",
      "Running Time for Forward Pass: %s seconds 3.793499231338501\n",
      "SPSA Epoch 198: 0.33055\n",
      "Running Time for Forward Pass: %s seconds 3.9584505558013916\n",
      "Running Time for Forward Pass: %s seconds 4.005445957183838\n",
      "Running Time for Forward Pass: %s seconds 3.76574444770813\n",
      "SPSA Epoch 199: 0.32863\n",
      "Running Time for Forward Pass: %s seconds 3.4944708347320557\n",
      "Running Time for Forward Pass: %s seconds 3.7475452423095703\n",
      "Running Time for Forward Pass: %s seconds 3.4812848567962646\n",
      "SPSA Epoch 200: 0.33412\n",
      "{   'fun': 0.33412415155746217,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.109673  ,  0.80042893, -0.09484683,  0.6863628 ,  0.51014943,\n",
      "       -0.35056261, -1.46389595])}\n",
      "Running Time for optimizing Subex-A4: %s seconds 753.2597916126251\n",
      "Running Time for Forward Pass: %s seconds 3.6563491821289062\n",
      "Running Time for Forward Pass: %s seconds 3.5615360736846924\n",
      "Running Time for Forward Pass: %s seconds 3.7656989097595215\n",
      "Running Time for Forward Pass: %s seconds 3.7983620166778564\n",
      "Running Time for Forward Pass: %s seconds 3.9237849712371826\n",
      "Running Time for Forward Pass: %s seconds 3.9931187629699707\n",
      "Running Time for Forward Pass: %s seconds 4.087742805480957\n",
      "Running Time for Forward Pass: %s seconds 3.606710433959961\n",
      "Running Time for Forward Pass: %s seconds 3.716750144958496\n",
      "Running Time for Forward Pass: %s seconds 3.7379696369171143\n",
      "Running Time for Forward Pass: %s seconds 3.9597253799438477\n",
      "Running Time for Forward Pass: %s seconds 3.5606446266174316\n",
      "Running Time for Forward Pass: %s seconds 3.803535223007202\n",
      "Running Time for Forward Pass: %s seconds 3.7544493675231934\n",
      "Running Time for Forward Pass: %s seconds 3.6073670387268066\n",
      "Running Time for Forward Pass: %s seconds 3.8931703567504883\n",
      "Running Time for Forward Pass: %s seconds 3.647371768951416\n",
      "Running Time for Forward Pass: %s seconds 3.8100829124450684\n",
      "Running Time for Forward Pass: %s seconds 3.6902682781219482\n",
      "Running Time for Forward Pass: %s seconds 3.759822368621826\n",
      "Running Time for Forward Pass: %s seconds 3.7120585441589355\n",
      "Running Time for Forward Pass: %s seconds 4.158390760421753\n",
      "Running Time for Forward Pass: %s seconds 3.798151731491089\n",
      "Running Time for Forward Pass: %s seconds 3.8754024505615234\n",
      "Running Time for Forward Pass: %s seconds 3.5757384300231934\n",
      "Running Time for Forward Pass: %s seconds 3.74478816986084\n",
      "Running Time for Forward Pass: %s seconds 3.7968432903289795\n",
      "Running Time for Forward Pass: %s seconds 3.9364840984344482\n",
      "Running Time for Forward Pass: %s seconds 3.6628963947296143\n",
      "Running Time for Forward Pass: %s seconds 3.8669192790985107\n",
      "Running Time for Forward Pass: %s seconds 3.612461566925049\n",
      "Running Time for Forward Pass: %s seconds 3.6869871616363525\n",
      "Running Time for Forward Pass: %s seconds 3.9745280742645264\n",
      "Running Time for Forward Pass: %s seconds 3.6708104610443115\n",
      "Running Time for Forward Pass: %s seconds 3.8570146560668945\n",
      "Running Time for Forward Pass: %s seconds 3.7003543376922607\n",
      "Running Time for Forward Pass: %s seconds 4.31529688835144\n",
      "Running Time for Forward Pass: %s seconds 3.5271706581115723\n",
      "Running Time for Forward Pass: %s seconds 3.540956735610962\n",
      "Running Time for Forward Pass: %s seconds 3.792387008666992\n",
      "Running Time for Forward Pass: %s seconds 3.6749491691589355\n",
      "Running Time for Forward Pass: %s seconds 3.5996804237365723\n",
      "Running Time for Forward Pass: %s seconds 3.847465753555298\n",
      "Running Time for Forward Pass: %s seconds 3.7772836685180664\n",
      "Running Time for Forward Pass: %s seconds 3.850902557373047\n",
      "Running Time for Forward Pass: %s seconds 3.943490743637085\n",
      "Running Time for Forward Pass: %s seconds 3.6911063194274902\n",
      "Running Time for Forward Pass: %s seconds 3.913112163543701\n",
      "Running Time for Forward Pass: %s seconds 3.652200698852539\n",
      "Running Time for Forward Pass: %s seconds 3.8246564865112305\n",
      "Running Time for Forward Pass: %s seconds 3.593268632888794\n",
      "Running Time for Forward Pass: %s seconds 3.629927635192871\n",
      "Running Time for Forward Pass: %s seconds 3.675539493560791\n",
      "SPSA Epoch 201: 4.07429\n",
      "Running Time for Forward Pass: %s seconds 3.728353500366211\n",
      "Running Time for Forward Pass: %s seconds 3.374638319015503\n",
      "Running Time for Forward Pass: %s seconds 3.994805097579956\n",
      "SPSA Epoch 202: 3.50760\n",
      "Running Time for Forward Pass: %s seconds 3.6601266860961914\n",
      "Running Time for Forward Pass: %s seconds 3.7478904724121094\n",
      "Running Time for Forward Pass: %s seconds 3.7172627449035645\n",
      "SPSA Epoch 203: 3.29614\n",
      "Running Time for Forward Pass: %s seconds 3.9864234924316406\n",
      "Running Time for Forward Pass: %s seconds 3.706406593322754\n",
      "Running Time for Forward Pass: %s seconds 3.8346078395843506\n",
      "SPSA Epoch 204: 3.19221\n",
      "Running Time for Forward Pass: %s seconds 3.6559536457061768\n",
      "Running Time for Forward Pass: %s seconds 3.974538564682007\n",
      "Running Time for Forward Pass: %s seconds 3.8164453506469727\n",
      "SPSA Epoch 205: 3.15126\n",
      "Running Time for Forward Pass: %s seconds 3.907749891281128\n",
      "Running Time for Forward Pass: %s seconds 3.836245536804199\n",
      "Running Time for Forward Pass: %s seconds 3.8211684226989746\n",
      "SPSA Epoch 206: 3.12923\n",
      "Running Time for Forward Pass: %s seconds 3.8751425743103027\n",
      "Running Time for Forward Pass: %s seconds 3.762462615966797\n",
      "Running Time for Forward Pass: %s seconds 3.548464298248291\n",
      "SPSA Epoch 207: 3.12908\n",
      "Running Time for Forward Pass: %s seconds 4.013483285903931\n",
      "Running Time for Forward Pass: %s seconds 3.756585121154785\n",
      "Running Time for Forward Pass: %s seconds 3.772876262664795\n",
      "SPSA Epoch 208: 3.11286\n",
      "Running Time for Forward Pass: %s seconds 3.7864365577697754\n",
      "Running Time for Forward Pass: %s seconds 4.069889545440674\n",
      "Running Time for Forward Pass: %s seconds 3.6706364154815674\n",
      "SPSA Epoch 209: 3.10738\n",
      "Running Time for Forward Pass: %s seconds 3.557906150817871\n",
      "Running Time for Forward Pass: %s seconds 4.098142623901367\n",
      "Running Time for Forward Pass: %s seconds 3.993027925491333\n",
      "SPSA Epoch 210: 3.09640\n",
      "Running Time for Forward Pass: %s seconds 3.9538443088531494\n",
      "Running Time for Forward Pass: %s seconds 3.5934698581695557\n",
      "Running Time for Forward Pass: %s seconds 3.913566827774048\n",
      "SPSA Epoch 211: 3.09556\n",
      "Running Time for Forward Pass: %s seconds 3.6186492443084717\n",
      "Running Time for Forward Pass: %s seconds 3.7069296836853027\n",
      "Running Time for Forward Pass: %s seconds 3.6867618560791016\n",
      "SPSA Epoch 212: 3.09309\n",
      "Running Time for Forward Pass: %s seconds 3.848140001296997\n",
      "Running Time for Forward Pass: %s seconds 3.841454267501831\n",
      "Running Time for Forward Pass: %s seconds 3.646399974822998\n",
      "SPSA Epoch 213: 3.09445\n",
      "Running Time for Forward Pass: %s seconds 4.2085120677948\n",
      "Running Time for Forward Pass: %s seconds 3.8180463314056396\n",
      "Running Time for Forward Pass: %s seconds 4.116548538208008\n",
      "SPSA Epoch 214: 3.10680\n",
      "Running Time for Forward Pass: %s seconds 3.6966583728790283\n",
      "Running Time for Forward Pass: %s seconds 3.7100679874420166\n",
      "Running Time for Forward Pass: %s seconds 3.4010705947875977\n",
      "SPSA Epoch 215: 3.09215\n",
      "Running Time for Forward Pass: %s seconds 3.806732654571533\n",
      "Running Time for Forward Pass: %s seconds 3.8763434886932373\n",
      "Running Time for Forward Pass: %s seconds 4.0766003131866455\n",
      "SPSA Epoch 216: 3.07872\n",
      "Running Time for Forward Pass: %s seconds 3.985055923461914\n",
      "Running Time for Forward Pass: %s seconds 3.519416570663452\n",
      "Running Time for Forward Pass: %s seconds 3.7142534255981445\n",
      "SPSA Epoch 217: 3.10020\n",
      "Running Time for Forward Pass: %s seconds 3.664274215698242\n",
      "Running Time for Forward Pass: %s seconds 3.805330991744995\n",
      "Running Time for Forward Pass: %s seconds 3.653256893157959\n",
      "SPSA Epoch 218: 3.08273\n",
      "Running Time for Forward Pass: %s seconds 3.889496088027954\n",
      "Running Time for Forward Pass: %s seconds 3.818264961242676\n",
      "Running Time for Forward Pass: %s seconds 3.7098822593688965\n",
      "SPSA Epoch 219: 3.08828\n",
      "Running Time for Forward Pass: %s seconds 3.6330459117889404\n",
      "Running Time for Forward Pass: %s seconds 3.7581989765167236\n",
      "Running Time for Forward Pass: %s seconds 3.8615880012512207\n",
      "SPSA Epoch 220: 3.09812\n",
      "Running Time for Forward Pass: %s seconds 3.5393500328063965\n",
      "Running Time for Forward Pass: %s seconds 3.835139036178589\n",
      "Running Time for Forward Pass: %s seconds 3.985718011856079\n",
      "SPSA Epoch 221: 3.10182\n",
      "Running Time for Forward Pass: %s seconds 3.890745162963867\n",
      "Running Time for Forward Pass: %s seconds 3.731625556945801\n",
      "Running Time for Forward Pass: %s seconds 3.863898754119873\n",
      "SPSA Epoch 222: 3.09275\n",
      "Running Time for Forward Pass: %s seconds 3.5581648349761963\n",
      "Running Time for Forward Pass: %s seconds 3.7206943035125732\n",
      "Running Time for Forward Pass: %s seconds 3.8008217811584473\n",
      "SPSA Epoch 223: 3.08727\n",
      "Running Time for Forward Pass: %s seconds 3.8222198486328125\n",
      "Running Time for Forward Pass: %s seconds 3.7388434410095215\n",
      "Running Time for Forward Pass: %s seconds 3.8669769763946533\n",
      "SPSA Epoch 224: 3.09204\n",
      "Running Time for Forward Pass: %s seconds 3.7655882835388184\n",
      "Running Time for Forward Pass: %s seconds 3.627819061279297\n",
      "Running Time for Forward Pass: %s seconds 3.704153537750244\n",
      "SPSA Epoch 225: 3.08871\n",
      "Running Time for Forward Pass: %s seconds 3.992788791656494\n",
      "Running Time for Forward Pass: %s seconds 3.743515729904175\n",
      "Running Time for Forward Pass: %s seconds 3.8468544483184814\n",
      "SPSA Epoch 226: 3.09283\n",
      "Running Time for Forward Pass: %s seconds 3.9795610904693604\n",
      "Running Time for Forward Pass: %s seconds 3.5961437225341797\n",
      "Running Time for Forward Pass: %s seconds 3.88217830657959\n",
      "SPSA Epoch 227: 3.09496\n",
      "Running Time for Forward Pass: %s seconds 3.951972246170044\n",
      "Running Time for Forward Pass: %s seconds 3.8731095790863037\n",
      "Running Time for Forward Pass: %s seconds 3.6869847774505615\n",
      "SPSA Epoch 228: 3.10366\n",
      "Running Time for Forward Pass: %s seconds 3.7344558238983154\n",
      "Running Time for Forward Pass: %s seconds 3.7660911083221436\n",
      "Running Time for Forward Pass: %s seconds 3.815316915512085\n",
      "SPSA Epoch 229: 3.09084\n",
      "Running Time for Forward Pass: %s seconds 3.70993709564209\n",
      "Running Time for Forward Pass: %s seconds 3.8681797981262207\n",
      "Running Time for Forward Pass: %s seconds 3.6403489112854004\n",
      "SPSA Epoch 230: 3.09047\n",
      "Running Time for Forward Pass: %s seconds 4.0598859786987305\n",
      "Running Time for Forward Pass: %s seconds 3.917383909225464\n",
      "Running Time for Forward Pass: %s seconds 3.8377387523651123\n",
      "SPSA Epoch 231: 3.09021\n",
      "Running Time for Forward Pass: %s seconds 3.9439096450805664\n",
      "Running Time for Forward Pass: %s seconds 3.699122667312622\n",
      "Running Time for Forward Pass: %s seconds 3.922877788543701\n",
      "SPSA Epoch 232: 3.07945\n",
      "Running Time for Forward Pass: %s seconds 3.5243306159973145\n",
      "Running Time for Forward Pass: %s seconds 3.89871883392334\n",
      "Running Time for Forward Pass: %s seconds 3.7690117359161377\n",
      "SPSA Epoch 233: 3.08772\n",
      "Running Time for Forward Pass: %s seconds 3.7321529388427734\n",
      "Running Time for Forward Pass: %s seconds 3.5726847648620605\n",
      "Running Time for Forward Pass: %s seconds 3.9836854934692383\n",
      "SPSA Epoch 234: 3.09251\n",
      "Running Time for Forward Pass: %s seconds 3.741246461868286\n",
      "Running Time for Forward Pass: %s seconds 3.6656923294067383\n",
      "Running Time for Forward Pass: %s seconds 3.658055067062378\n",
      "SPSA Epoch 235: 3.09191\n",
      "Running Time for Forward Pass: %s seconds 3.73740291595459\n",
      "Running Time for Forward Pass: %s seconds 3.787172794342041\n",
      "Running Time for Forward Pass: %s seconds 3.7678890228271484\n",
      "SPSA Epoch 236: 3.08525\n",
      "Running Time for Forward Pass: %s seconds 3.612302541732788\n",
      "Running Time for Forward Pass: %s seconds 3.797586679458618\n",
      "Running Time for Forward Pass: %s seconds 3.6189963817596436\n",
      "SPSA Epoch 237: 3.08338\n",
      "Running Time for Forward Pass: %s seconds 3.9344770908355713\n",
      "Running Time for Forward Pass: %s seconds 4.124384880065918\n",
      "Running Time for Forward Pass: %s seconds 3.75846529006958\n",
      "SPSA Epoch 238: 3.08836\n",
      "Running Time for Forward Pass: %s seconds 4.076010465621948\n",
      "Running Time for Forward Pass: %s seconds 3.8414626121520996\n",
      "Running Time for Forward Pass: %s seconds 4.151020050048828\n",
      "SPSA Epoch 239: 3.09294\n",
      "Running Time for Forward Pass: %s seconds 3.759681463241577\n",
      "Running Time for Forward Pass: %s seconds 4.069513320922852\n",
      "Running Time for Forward Pass: %s seconds 3.7064201831817627\n",
      "SPSA Epoch 240: 3.09565\n",
      "Running Time for Forward Pass: %s seconds 3.7792370319366455\n",
      "Running Time for Forward Pass: %s seconds 3.7669003009796143\n",
      "Running Time for Forward Pass: %s seconds 3.7071573734283447\n",
      "SPSA Epoch 241: 3.08783\n",
      "Running Time for Forward Pass: %s seconds 3.7108564376831055\n",
      "Running Time for Forward Pass: %s seconds 3.9121968746185303\n",
      "Running Time for Forward Pass: %s seconds 3.930155038833618\n",
      "SPSA Epoch 242: 3.09301\n",
      "Running Time for Forward Pass: %s seconds 3.7391302585601807\n",
      "Running Time for Forward Pass: %s seconds 3.658764123916626\n",
      "Running Time for Forward Pass: %s seconds 3.4802608489990234\n",
      "SPSA Epoch 243: 3.09247\n",
      "Running Time for Forward Pass: %s seconds 3.6876649856567383\n",
      "Running Time for Forward Pass: %s seconds 3.4990551471710205\n",
      "Running Time for Forward Pass: %s seconds 3.7080276012420654\n",
      "SPSA Epoch 244: 3.09006\n",
      "Running Time for Forward Pass: %s seconds 3.5962142944335938\n",
      "Running Time for Forward Pass: %s seconds 3.8895721435546875\n",
      "Running Time for Forward Pass: %s seconds 3.665407419204712\n",
      "SPSA Epoch 245: 3.08467\n",
      "Running Time for Forward Pass: %s seconds 3.931164503097534\n",
      "Running Time for Forward Pass: %s seconds 3.6291422843933105\n",
      "Running Time for Forward Pass: %s seconds 3.6922857761383057\n",
      "SPSA Epoch 246: 3.09032\n",
      "Running Time for Forward Pass: %s seconds 3.6071596145629883\n",
      "Running Time for Forward Pass: %s seconds 3.9696383476257324\n",
      "Running Time for Forward Pass: %s seconds 3.6814424991607666\n",
      "SPSA Epoch 247: 3.09318\n",
      "Running Time for Forward Pass: %s seconds 3.8539516925811768\n",
      "Running Time for Forward Pass: %s seconds 3.593777656555176\n",
      "Running Time for Forward Pass: %s seconds 3.580364465713501\n",
      "SPSA Epoch 248: 3.09689\n",
      "Running Time for Forward Pass: %s seconds 3.4819037914276123\n",
      "Running Time for Forward Pass: %s seconds 3.6814064979553223\n",
      "Running Time for Forward Pass: %s seconds 3.6608383655548096\n",
      "SPSA Epoch 249: 3.09752\n",
      "Running Time for Forward Pass: %s seconds 3.580941677093506\n",
      "Running Time for Forward Pass: %s seconds 3.771702766418457\n",
      "Running Time for Forward Pass: %s seconds 3.7448322772979736\n",
      "SPSA Epoch 250: 3.09142\n",
      "{   'fun': 3.091415534445266,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.10833522,  0.50224113,  0.69999132,  1.10236594, -1.10923757,\n",
      "        0.17336111, -0.75555267])}\n",
      "Running Time for optimizing Subex-A5: %s seconds 758.1974244117737\n",
      "Running Time for Forward Pass: %s seconds 3.9048752784729004\n",
      "Running Time for Forward Pass: %s seconds 3.515754461288452\n",
      "Running Time for Forward Pass: %s seconds 3.898674964904785\n",
      "Running Time for Forward Pass: %s seconds 3.7473461627960205\n",
      "Running Time for Forward Pass: %s seconds 3.905531883239746\n",
      "Running Time for Forward Pass: %s seconds 3.982137441635132\n",
      "Running Time for Forward Pass: %s seconds 3.889739513397217\n",
      "Running Time for Forward Pass: %s seconds 3.7871861457824707\n",
      "Running Time for Forward Pass: %s seconds 3.8178625106811523\n",
      "Running Time for Forward Pass: %s seconds 3.495431900024414\n",
      "Running Time for Forward Pass: %s seconds 3.8766348361968994\n",
      "Running Time for Forward Pass: %s seconds 3.8017702102661133\n",
      "Running Time for Forward Pass: %s seconds 3.870403528213501\n",
      "Running Time for Forward Pass: %s seconds 3.9215877056121826\n",
      "Running Time for Forward Pass: %s seconds 3.495999336242676\n",
      "Running Time for Forward Pass: %s seconds 3.964869737625122\n",
      "Running Time for Forward Pass: %s seconds 3.6954421997070312\n",
      "Running Time for Forward Pass: %s seconds 3.903733730316162\n",
      "Running Time for Forward Pass: %s seconds 3.707209348678589\n",
      "Running Time for Forward Pass: %s seconds 3.662156343460083\n",
      "Running Time for Forward Pass: %s seconds 3.5028936862945557\n",
      "Running Time for Forward Pass: %s seconds 3.7697737216949463\n",
      "Running Time for Forward Pass: %s seconds 3.8159806728363037\n",
      "Running Time for Forward Pass: %s seconds 4.009709119796753\n",
      "Running Time for Forward Pass: %s seconds 3.4863693714141846\n",
      "Running Time for Forward Pass: %s seconds 3.7333083152770996\n",
      "Running Time for Forward Pass: %s seconds 3.7132861614227295\n",
      "Running Time for Forward Pass: %s seconds 3.9860689640045166\n",
      "Running Time for Forward Pass: %s seconds 3.7786593437194824\n",
      "Running Time for Forward Pass: %s seconds 3.9073359966278076\n",
      "Running Time for Forward Pass: %s seconds 3.6829471588134766\n",
      "Running Time for Forward Pass: %s seconds 3.7801339626312256\n",
      "Running Time for Forward Pass: %s seconds 3.7557671070098877\n",
      "Running Time for Forward Pass: %s seconds 4.052571535110474\n",
      "Running Time for Forward Pass: %s seconds 3.463503360748291\n",
      "Running Time for Forward Pass: %s seconds 3.592193126678467\n",
      "Running Time for Forward Pass: %s seconds 3.8318581581115723\n",
      "Running Time for Forward Pass: %s seconds 4.042343378067017\n",
      "Running Time for Forward Pass: %s seconds 4.1357927322387695\n",
      "Running Time for Forward Pass: %s seconds 3.9008498191833496\n",
      "Running Time for Forward Pass: %s seconds 3.6710681915283203\n",
      "Running Time for Forward Pass: %s seconds 3.8814327716827393\n",
      "Running Time for Forward Pass: %s seconds 3.7726943492889404\n",
      "Running Time for Forward Pass: %s seconds 3.8336522579193115\n",
      "Running Time for Forward Pass: %s seconds 3.794581890106201\n",
      "Running Time for Forward Pass: %s seconds 3.8444089889526367\n",
      "Running Time for Forward Pass: %s seconds 3.8877546787261963\n",
      "Running Time for Forward Pass: %s seconds 3.7160818576812744\n",
      "Running Time for Forward Pass: %s seconds 3.7355427742004395\n",
      "Running Time for Forward Pass: %s seconds 3.5308499336242676\n",
      "Running Time for Forward Pass: %s seconds 3.7221932411193848\n",
      "Running Time for Forward Pass: %s seconds 4.3232622146606445\n",
      "Running Time for Forward Pass: %s seconds 3.8092057704925537\n",
      "SPSA Epoch 251: 4.09496\n",
      "Running Time for Forward Pass: %s seconds 3.575148344039917\n",
      "Running Time for Forward Pass: %s seconds 3.711433172225952\n",
      "Running Time for Forward Pass: %s seconds 3.7094438076019287\n",
      "SPSA Epoch 252: 3.50629\n",
      "Running Time for Forward Pass: %s seconds 3.6684508323669434\n",
      "Running Time for Forward Pass: %s seconds 3.4769368171691895\n",
      "Running Time for Forward Pass: %s seconds 4.13964319229126\n",
      "SPSA Epoch 253: 3.28338\n",
      "Running Time for Forward Pass: %s seconds 3.7450528144836426\n",
      "Running Time for Forward Pass: %s seconds 3.978424072265625\n",
      "Running Time for Forward Pass: %s seconds 3.8035168647766113\n",
      "SPSA Epoch 254: 3.18800\n",
      "Running Time for Forward Pass: %s seconds 3.8857510089874268\n",
      "Running Time for Forward Pass: %s seconds 4.006571292877197\n",
      "Running Time for Forward Pass: %s seconds 4.017819166183472\n",
      "SPSA Epoch 255: 3.15564\n",
      "Running Time for Forward Pass: %s seconds 3.793555974960327\n",
      "Running Time for Forward Pass: %s seconds 3.6713736057281494\n",
      "Running Time for Forward Pass: %s seconds 3.7693614959716797\n",
      "SPSA Epoch 256: 3.13596\n",
      "Running Time for Forward Pass: %s seconds 3.5753860473632812\n",
      "Running Time for Forward Pass: %s seconds 3.834857225418091\n",
      "Running Time for Forward Pass: %s seconds 3.716486692428589\n",
      "SPSA Epoch 257: 3.10667\n",
      "Running Time for Forward Pass: %s seconds 4.067874431610107\n",
      "Running Time for Forward Pass: %s seconds 4.167677402496338\n",
      "Running Time for Forward Pass: %s seconds 4.0818657875061035\n",
      "SPSA Epoch 258: 3.11011\n",
      "Running Time for Forward Pass: %s seconds 3.5458457469940186\n",
      "Running Time for Forward Pass: %s seconds 3.843683958053589\n",
      "Running Time for Forward Pass: %s seconds 3.602166175842285\n",
      "SPSA Epoch 259: 3.10347\n",
      "Running Time for Forward Pass: %s seconds 3.880103826522827\n",
      "Running Time for Forward Pass: %s seconds 3.5774729251861572\n",
      "Running Time for Forward Pass: %s seconds 3.75465726852417\n",
      "SPSA Epoch 260: 3.10010\n",
      "Running Time for Forward Pass: %s seconds 3.7342376708984375\n",
      "Running Time for Forward Pass: %s seconds 3.748793601989746\n",
      "Running Time for Forward Pass: %s seconds 3.621936321258545\n",
      "SPSA Epoch 261: 3.09861\n",
      "Running Time for Forward Pass: %s seconds 3.8085451126098633\n",
      "Running Time for Forward Pass: %s seconds 3.5107858180999756\n",
      "Running Time for Forward Pass: %s seconds 3.6697304248809814\n",
      "SPSA Epoch 262: 3.09077\n",
      "Running Time for Forward Pass: %s seconds 3.5355968475341797\n",
      "Running Time for Forward Pass: %s seconds 3.614842414855957\n",
      "Running Time for Forward Pass: %s seconds 3.7845921516418457\n",
      "SPSA Epoch 263: 3.09724\n",
      "Running Time for Forward Pass: %s seconds 3.544588804244995\n",
      "Running Time for Forward Pass: %s seconds 3.778414487838745\n",
      "Running Time for Forward Pass: %s seconds 3.492208242416382\n",
      "SPSA Epoch 264: 3.10476\n",
      "Running Time for Forward Pass: %s seconds 3.880204200744629\n",
      "Running Time for Forward Pass: %s seconds 3.912274122238159\n",
      "Running Time for Forward Pass: %s seconds 3.792407751083374\n",
      "SPSA Epoch 265: 3.08738\n",
      "Running Time for Forward Pass: %s seconds 3.7091987133026123\n",
      "Running Time for Forward Pass: %s seconds 3.6872944831848145\n",
      "Running Time for Forward Pass: %s seconds 3.6870763301849365\n",
      "SPSA Epoch 266: 3.09919\n",
      "Running Time for Forward Pass: %s seconds 3.8489201068878174\n",
      "Running Time for Forward Pass: %s seconds 3.6931912899017334\n",
      "Running Time for Forward Pass: %s seconds 4.023319482803345\n",
      "SPSA Epoch 267: 3.09270\n",
      "Running Time for Forward Pass: %s seconds 3.961834669113159\n",
      "Running Time for Forward Pass: %s seconds 3.8727705478668213\n",
      "Running Time for Forward Pass: %s seconds 3.8083770275115967\n",
      "SPSA Epoch 268: 3.08314\n",
      "Running Time for Forward Pass: %s seconds 3.9138200283050537\n",
      "Running Time for Forward Pass: %s seconds 3.789077043533325\n",
      "Running Time for Forward Pass: %s seconds 4.037074327468872\n",
      "SPSA Epoch 269: 3.08499\n",
      "Running Time for Forward Pass: %s seconds 3.9505720138549805\n",
      "Running Time for Forward Pass: %s seconds 3.8425753116607666\n",
      "Running Time for Forward Pass: %s seconds 3.818547248840332\n",
      "SPSA Epoch 270: 3.09103\n",
      "Running Time for Forward Pass: %s seconds 3.511003255844116\n",
      "Running Time for Forward Pass: %s seconds 3.870762348175049\n",
      "Running Time for Forward Pass: %s seconds 3.701436996459961\n",
      "SPSA Epoch 271: 3.09316\n",
      "Running Time for Forward Pass: %s seconds 3.8953654766082764\n",
      "Running Time for Forward Pass: %s seconds 3.8767361640930176\n",
      "Running Time for Forward Pass: %s seconds 3.672532796859741\n",
      "SPSA Epoch 272: 3.09485\n",
      "Running Time for Forward Pass: %s seconds 3.6774256229400635\n",
      "Running Time for Forward Pass: %s seconds 3.826892614364624\n",
      "Running Time for Forward Pass: %s seconds 3.80787992477417\n",
      "SPSA Epoch 273: 3.08755\n",
      "Running Time for Forward Pass: %s seconds 3.770591974258423\n",
      "Running Time for Forward Pass: %s seconds 3.8914103507995605\n",
      "Running Time for Forward Pass: %s seconds 3.7300422191619873\n",
      "SPSA Epoch 274: 3.09249\n",
      "Running Time for Forward Pass: %s seconds 3.9326419830322266\n",
      "Running Time for Forward Pass: %s seconds 4.11155891418457\n",
      "Running Time for Forward Pass: %s seconds 3.997276782989502\n",
      "SPSA Epoch 275: 3.08968\n",
      "Running Time for Forward Pass: %s seconds 3.753232955932617\n",
      "Running Time for Forward Pass: %s seconds 3.969346046447754\n",
      "Running Time for Forward Pass: %s seconds 3.787625551223755\n",
      "SPSA Epoch 276: 3.08531\n",
      "Running Time for Forward Pass: %s seconds 3.8861799240112305\n",
      "Running Time for Forward Pass: %s seconds 3.5768637657165527\n",
      "Running Time for Forward Pass: %s seconds 3.8157694339752197\n",
      "SPSA Epoch 277: 3.09711\n",
      "Running Time for Forward Pass: %s seconds 3.91636323928833\n",
      "Running Time for Forward Pass: %s seconds 3.857957601547241\n",
      "Running Time for Forward Pass: %s seconds 3.7004153728485107\n",
      "SPSA Epoch 278: 3.08559\n",
      "Running Time for Forward Pass: %s seconds 3.7174031734466553\n",
      "Running Time for Forward Pass: %s seconds 3.693787097930908\n",
      "Running Time for Forward Pass: %s seconds 3.75630259513855\n",
      "SPSA Epoch 279: 3.09612\n",
      "Running Time for Forward Pass: %s seconds 4.051669120788574\n",
      "Running Time for Forward Pass: %s seconds 3.91286301612854\n",
      "Running Time for Forward Pass: %s seconds 3.729905128479004\n",
      "SPSA Epoch 280: 3.09475\n",
      "Running Time for Forward Pass: %s seconds 3.9365122318267822\n",
      "Running Time for Forward Pass: %s seconds 3.8991858959198\n",
      "Running Time for Forward Pass: %s seconds 3.696160316467285\n",
      "SPSA Epoch 281: 3.09616\n",
      "Running Time for Forward Pass: %s seconds 3.7991714477539062\n",
      "Running Time for Forward Pass: %s seconds 3.713251829147339\n",
      "Running Time for Forward Pass: %s seconds 3.8974852561950684\n",
      "SPSA Epoch 282: 3.08658\n",
      "Running Time for Forward Pass: %s seconds 3.530820846557617\n",
      "Running Time for Forward Pass: %s seconds 4.04815149307251\n",
      "Running Time for Forward Pass: %s seconds 3.614957094192505\n",
      "SPSA Epoch 283: 3.08591\n",
      "Running Time for Forward Pass: %s seconds 3.9564146995544434\n",
      "Running Time for Forward Pass: %s seconds 3.4925343990325928\n",
      "Running Time for Forward Pass: %s seconds 3.760040283203125\n",
      "SPSA Epoch 284: 3.09266\n",
      "Running Time for Forward Pass: %s seconds 3.8335936069488525\n",
      "Running Time for Forward Pass: %s seconds 3.449207067489624\n",
      "Running Time for Forward Pass: %s seconds 3.832960605621338\n",
      "SPSA Epoch 285: 3.09500\n",
      "Running Time for Forward Pass: %s seconds 3.814410924911499\n",
      "Running Time for Forward Pass: %s seconds 3.937056303024292\n",
      "Running Time for Forward Pass: %s seconds 3.63869309425354\n",
      "SPSA Epoch 286: 3.09410\n",
      "Running Time for Forward Pass: %s seconds 4.052218437194824\n",
      "Running Time for Forward Pass: %s seconds 3.7809791564941406\n",
      "Running Time for Forward Pass: %s seconds 3.8936574459075928\n",
      "SPSA Epoch 287: 3.08761\n",
      "Running Time for Forward Pass: %s seconds 3.8559038639068604\n",
      "Running Time for Forward Pass: %s seconds 3.904350757598877\n",
      "Running Time for Forward Pass: %s seconds 3.7276771068573\n",
      "SPSA Epoch 288: 3.09273\n",
      "Running Time for Forward Pass: %s seconds 3.788522720336914\n",
      "Running Time for Forward Pass: %s seconds 3.489971876144409\n",
      "Running Time for Forward Pass: %s seconds 3.485182762145996\n",
      "SPSA Epoch 289: 3.08508\n",
      "Running Time for Forward Pass: %s seconds 3.9140207767486572\n",
      "Running Time for Forward Pass: %s seconds 3.8803865909576416\n",
      "Running Time for Forward Pass: %s seconds 3.8757872581481934\n",
      "SPSA Epoch 290: 3.08783\n",
      "Running Time for Forward Pass: %s seconds 3.609208106994629\n",
      "Running Time for Forward Pass: %s seconds 3.597036123275757\n",
      "Running Time for Forward Pass: %s seconds 3.534045457839966\n",
      "SPSA Epoch 291: 3.10005\n",
      "Running Time for Forward Pass: %s seconds 4.02286958694458\n",
      "Running Time for Forward Pass: %s seconds 3.976245880126953\n",
      "Running Time for Forward Pass: %s seconds 4.084081172943115\n",
      "SPSA Epoch 292: 3.09922\n",
      "Running Time for Forward Pass: %s seconds 3.694035053253174\n",
      "Running Time for Forward Pass: %s seconds 3.667076826095581\n",
      "Running Time for Forward Pass: %s seconds 3.626767158508301\n",
      "SPSA Epoch 293: 3.09292\n",
      "Running Time for Forward Pass: %s seconds 3.805643081665039\n",
      "Running Time for Forward Pass: %s seconds 3.633852958679199\n",
      "Running Time for Forward Pass: %s seconds 3.909203052520752\n",
      "SPSA Epoch 294: 3.09885\n",
      "Running Time for Forward Pass: %s seconds 3.8212344646453857\n",
      "Running Time for Forward Pass: %s seconds 3.9850480556488037\n",
      "Running Time for Forward Pass: %s seconds 3.9985485076904297\n",
      "SPSA Epoch 295: 3.08720\n",
      "Running Time for Forward Pass: %s seconds 3.740576982498169\n",
      "Running Time for Forward Pass: %s seconds 3.813308000564575\n",
      "Running Time for Forward Pass: %s seconds 3.7867672443389893\n",
      "SPSA Epoch 296: 3.09588\n",
      "Running Time for Forward Pass: %s seconds 4.088976144790649\n",
      "Running Time for Forward Pass: %s seconds 3.8409647941589355\n",
      "Running Time for Forward Pass: %s seconds 3.730191230773926\n",
      "SPSA Epoch 297: 3.07870\n",
      "Running Time for Forward Pass: %s seconds 3.787325382232666\n",
      "Running Time for Forward Pass: %s seconds 3.889693260192871\n",
      "Running Time for Forward Pass: %s seconds 3.7338943481445312\n",
      "SPSA Epoch 298: 3.08622\n",
      "Running Time for Forward Pass: %s seconds 3.902963399887085\n",
      "Running Time for Forward Pass: %s seconds 3.5892908573150635\n",
      "Running Time for Forward Pass: %s seconds 3.8202085494995117\n",
      "SPSA Epoch 299: 3.10023\n",
      "Running Time for Forward Pass: %s seconds 3.801161289215088\n",
      "Running Time for Forward Pass: %s seconds 3.736041784286499\n",
      "Running Time for Forward Pass: %s seconds 3.7078568935394287\n",
      "SPSA Epoch 300: 3.09487\n",
      "{   'fun': 3.094874935734211,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.10817189, -0.31117326,  0.86747737, -0.14543593, -0.00674705,\n",
      "        1.23057636, -0.84427576])}\n",
      "Running Time for optimizing Subex-A6: %s seconds 761.5903267860413\n"
     ]
    }
   ],
   "source": [
    "# Optimizer result for 0th circuit for sub-circuit-A\n",
    "start_time = time.time()\n",
    "optimizer_result_A0 = minimizer(function, function.objective0, function.gradient0, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A1 = minimizer(function, function.objective1, function.gradient1, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A2: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A2 = minimizer(function, function.objective2, function.gradient2, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A3: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A3 = minimizer(function, function.objective3, function.gradient3, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A4: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A4 = minimizer(function, function.objective4, function.gradient4, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A5: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A5 = minimizer(function, function.objective5, function.gradient5, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A6: %s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3d06fd73-6d5f-4251-b91f-7803a4eb8098",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_results_A = [optimizer_result_A0, optimizer_result_A1, optimizer_result_A2, optimizer_result_A3, optimizer_result_A4, optimizer_result_A5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "62002702-1c92-4bec-942b-7a6cfa2ee79d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New params (The final point of the minimization): [-1.11014823  0.92306064  1.23722863 -0.80420931 -1.29109566 -0.03841404\n",
      "  1.40905931]\n",
      "The final value of the minimization: 3.0913295865871553\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.10533164  0.24947969 -0.88592139  1.45771135 -0.23893304 -0.71210416\n",
      "  0.13935035]\n",
      "The final value of the minimization: 3.0892023770989225\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.12018245  0.02255229  1.62924039  0.45615453  0.57422933 -0.07941653\n",
      " -1.28125282]\n",
      "The final value of the minimization: 0.39620590757628343\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.109673    0.80042893 -0.09484683  0.6863628   0.51014943 -0.35056261\n",
      " -1.46389595]\n",
      "The final value of the minimization: 0.33412415155746217\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.10833522  0.50224113  0.69999132  1.10236594 -1.10923757  0.17336111\n",
      " -0.75555267]\n",
      "The final value of the minimization: 3.091415534445266\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.10817189 -0.31117326  0.86747737 -0.14543593 -0.00674705  1.23057636\n",
      " -0.84427576]\n",
      "The final value of the minimization: 3.094874935734211\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n"
     ]
    }
   ],
   "source": [
    "# Print results from 6 sub-experiments of sub-circuit-A\n",
    "for opt_result in optimizer_results_A:\n",
    "    print_optimizer_results(opt_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8be438f3-e234-4217-bd1c-7e313b393e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAHHCAYAAABtF1i4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACM1klEQVR4nOzdeXwV1dnA8d/M3bMnQEJCwhJAwqaIyqpVFAQFlLqLCLZqC0XcRal9xbAUqVBbRC3WAFI3RBFbW1GqBVQQBAwKSFRMCEtCWJKb9a5z3j9uuBATIIEkl4Tn+3E+c+/MmZlnJph5cuacM5pSSiGEEEIIESJ6qAMQQgghxLlNkhEhhBBChJQkI0IIIYQIKUlGhBBCCBFSkowIIYQQIqQkGRFCCCFESEkyIoQQQoiQkmRECCGEECElyYgQQgghQkqSESHECd111120b9/+tLZ9+umn0TStfgOqpTOJWwjR+CQZEaIJ0jStVtPq1atDHaoQQpySJu+mEaLpee2116p8X7JkCatWreIf//hHleVDhgwhISHhtI/j9XoxDAObzVbnbX0+Hz6fD7vdftrHP1133XUXq1evJicnp9GPLYSoO3OoAxBC1N2YMWOqfP/yyy9ZtWpVteU/V15eTlhYWK2PY7FYTis+ALPZjNksv2KEEKcmj2mEaKauuOIKevTowebNm/nFL35BWFgYv//97wF4//33GT58OElJSdhsNjp27Mj06dPx+/1V9vHzthc5OTlomsacOXN4+eWX6dixIzabjUsuuYSvvvqqyrY1tRnRNI377ruPFStW0KNHD2w2G927d2flypXV4l+9ejUXX3wxdrudjh07smDBgjNqh1JWVsYjjzxCSkoKNpuNLl26MGfOHH5eObxq1SouvfRSYmJiiIiIoEuXLsHrdtTzzz9P9+7dCQsLIzY2losvvpg33njjtOISQkjNiBDN2uHDh7nmmmu47bbbGDNmTPCRzeLFi4mIiODhhx8mIiKCTz/9lKeeeori4mKeffbZU+73jTfeoKSkhN/+9rdomsaf/vQnbrjhBn766adT1qZ8/vnnLF++nN/97ndERkYyb948brzxRnJzc2nRogUAX3/9NcOGDSMxMZH09HT8fj/Tpk2jVatWp3UdlFJcd911/O9//+Puu++mV69efPTRRzz22GPs27eP5557DoDt27czYsQIzj//fKZNm4bNZuPHH3/kiy++CO7r73//O/fffz833XQTDzzwAC6Xi2+++YYNGzYwevTo04pPiHOeEkI0eRMnTlQ//9/58ssvV4D629/+Vq18eXl5tWW//e1vVVhYmHK5XMFl48aNU+3atQt+z87OVoBq0aKFOnLkSHD5+++/rwD1r3/9K7hs6tSp1WIClNVqVT/++GNw2datWxWgnn/++eCykSNHqrCwMLVv377gsh9++EGZzeZq+6zJz+NesWKFAtSMGTOqlLvpppuUpmnBeJ577jkFqIMHD55w39dff73q3r37KWMQQtSePKYRohmz2Wz86le/qrbc4XAEP5eUlHDo0CEuu+wyysvL2blz5yn3e+uttxIbGxv8ftlllwHw008/nXLbwYMH07Fjx+D3888/n6ioqOC2fr+f//73v4waNYqkpKRguU6dOnHNNdeccv81+c9//oPJZOL++++vsvyRRx5BKcWHH34IQExMDBB4jGUYRo37iomJYe/evdUeSwkhTp8kI0I0Y23atMFqtVZbvn37dn75y18SHR1NVFQUrVq1CjZ+dTqdp9xv27Ztq3w/mpgUFhbWeduj2x/dtqCggIqKCjp16lStXE3LamP37t0kJSURGRlZZXnXrl2D6yGQZA0cOJB77rmHhIQEbrvtNt5+++0qicnjjz9OREQEffr0oXPnzkycOLHKYxwhRN1JMiJEM3Z8DchRRUVFXH755WzdupVp06bxr3/9i1WrVjF79myAE9YIHM9kMtW4XNVipIAz2bahORwO1q5dy3//+1/uvPNOvvnmG2699VaGDBkSbNzbtWtXsrKyeOutt7j00kt59913ufTSS5k6dWqIoxei6ZJkRIhzzOrVqzl8+DCLFy/mgQceYMSIEQwePLjKY5dQio+Px2638+OPP1ZbV9Oy2mjXrh379++npKSkyvKjj6TatWsXXKbrOldddRV//vOf2bFjBzNnzuTTTz/lf//7X7BMeHg4t956K4sWLSI3N5fhw4czc+ZMXC7XacUnxLlOkhEhzjFHayaOr4nweDy8+OKLoQqpCpPJxODBg1mxYgX79+8PLv/xxx+DbTvq6tprr8Xv9zN//vwqy5977jk0TQu2RTly5Ei1bXv16gWA2+0GAj2Ujme1WunWrRtKKbxe72nFJ8S5Trr2CnGOGTBgALGxsYwbN477778fTdP4xz/+cVY8Jjnq6aef5uOPP2bgwIFMmDAhmEj06NGDzMzMOu9v5MiRDBo0iCeffJKcnBwuuOACPv74Y95//30efPDBYIPaadOmsXbtWoYPH067du0oKCjgxRdfJDk5mUsvvRSAq6++mtatWzNw4EASEhL47rvvmD9/PsOHD6/WJkUIUTuSjAhxjmnRogUffPABjzzyCH/4wx+IjY1lzJgxXHXVVQwdOjTU4QFw0UUX8eGHH/Loo4/yf//3f6SkpDBt2jS+++67WvX2+Tld1/nnP//JU089xdKlS1m0aBHt27fn2Wef5ZFHHgmWu+6668jJyWHhwoUcOnSIli1bcvnll5Oenk50dDQAv/3tb3n99df585//TGlpKcnJydx///384Q9/qLfzF+JcI++mEUI0GaNGjWL79u388MMPoQ5FCFGPpM2IEOKsVFFRUeX7Dz/8wH/+8x+uuOKK0AQkhGgwUjMihDgrJSYmctddd5Gamsru3bt56aWXcLvdfP3113Tu3DnU4Qkh6pG0GRFCnJWGDRvGm2++SX5+Pjabjf79+/PHP/5REhEhmiGpGRFCCCFESEmbESGEEEKElCQjQgghhAipJtFmxDAM9u/fT2RkJJqmhTocIYQQQtSCUoqSkhKSkpLQ9RPXfzSJZGT//v2kpKSEOgwhhBBCnIY9e/aQnJx8wvVNIhk5OsTynj17iIqKCnE0QgghhKiN4uJiUlJSTvmqhCaRjBx9NBMVFSXJiBBCCNHEnKqJhTRgFUIIIURISTIihBBCiJCSZEQIIYQQISXJiBBCCCFCSpIRIYQQQoSUJCNCCCGECClJRoQQQggRUpKMCCGEECKkJBkRQgghREhJMiKEEEKIkJJkRAghhBAhJcmIEEIIIULqnE5GfF4vB3dn43W7Qh2KEEIIcc46p5ORJY9OZMnkSeTv3BbqUIQQQohz1jmdjES69gGQ9/XnIY5ECCGEOHed08nIYd8FAOzN+inEkQghhBDnrnM6GbFHRABw6MDhEEcihBBCnLvO6WTkiCUcgLLyUpRhhDgaIYQQ4tx0RsnIM888g6ZpPPjggyctt2zZMtLS0rDb7fTs2ZP//Oc/Z3LYeqPatAN0DOWn+GBBqMMRQgghzkmnnYx89dVXLFiwgPPPP/+k5datW8ftt9/O3Xffzddff82oUaMYNWoU27aFvgdLXNfz0ExxABTs2BTiaIQQQohz02klI6Wlpdxxxx38/e9/JzY29qRl//rXvzJs2DAee+wxunbtyvTp0+nduzfz588/rYDrU1rneEymQPx7MjNDG4wQQghxjjqtZGTixIkMHz6cwYMHn7Ls+vXrq5UbOnQo69evP+E2breb4uLiKlND6JYYjcliByAvZ2+DHEMIIYQQJ2eu6wZvvfUWW7Zs4auvvqpV+fz8fBISEqosS0hIID8//4TbzJo1i/T09LqGVmfRYRaUIwzKoLCwsMGPJ4QQQojq6lQzsmfPHh544AFef/117HZ7Q8XElClTcDqdwWnPnj0NdizVIh4At7scv8/bYMcRQgghRM3qVDOyefNmCgoK6N27d3CZ3+9n7dq1zJ8/H7fbjclkqrJN69atOXDgQJVlBw4coHXr1ic8js1mw2az1SW002br2Anv9zZQbg7nZhOfel6jHFcIIYQQAXWqGbnqqqv49ttvyczMDE4XX3wxd9xxB5mZmdUSEYD+/fvzySefVFm2atUq+vfvf2aR15OUnl3QTS0A2P/1xhBHI4QQQpx76lQzEhkZSY8ePaosCw8Pp0WLFsHlY8eOpU2bNsyaNQuABx54gMsvv5y5c+cyfPhw3nrrLTZt2sTLL79cT6dwZrq3i2WXORyPD/Zu/45eN4Y6IiGEEOLcUu8jsObm5pKXlxf8PmDAAN544w1efvllLrjgAt555x1WrFhRLakJlZTYMHRroP3LgX0nblQrhBBCiIZR5940P7d69eqTfge4+eabufnmm8/0UA1C1zWIjIRiKC11hjocIYQQ4pxzTr+b5ihzclsAfD4X7vKyEEcjhBBCnFskGQFiunUHLfAG34Lvt4c4GiGEEOLcIskIcF639pjMgWHhd9dyMDchhBBC1A9JRoC01lFYKoeF3/vDrhBHI4QQQpxbJBkBHFYTeuWIsoUHD4c4GiGEEOLcIslIJT0uMPCZq6IYpVSIoxFCCCHOHZKMVArrdB6gYSgvJYcPhTocIYQQ4pwhyUil5AvPR9MDjVj3ZW4JcTRCCCHEuUOSkUrdUltjMQe69+7O/DrE0QghhBDnDklGKsVH2jBbA28KzsvZE+JohBBCiHOHJCOVNE3DFBGoGSl1FoU2GCGEEOIcIsnIcWxJrQHweEow/P4QRyOEEEKcGyQZOU6L888HLIDB4dzcUIcjhBBCnBMkGTlOas/u6KYYAHI2bghtMEIIIcQ5QpKR43RqHYPV4gAgZ9uOEEcjhBBCnBskGTmO1axjsQd61BzJzw9xNEIIIcS5QZKRn7HExgBQUeYMbSBCCCHEOUKSkZ+J6tgBAL+/DE9FRYijEUIIIZo/SUZ+Jvnii0ALtBvZt+O7EEcjhBBCNH+SjPxMl/M6YTFHAfDjl9KjRgghhGhokoz8TEy4DWvlsPD7f/wpxNEIIYQQzZ8kIzWwhtsBKCk8HOJIhBBCiOZPkpEaOBLiAfC4i0MciRBCCNH8STJSg4TzuwGgDBfFh6R2RAghhGhIkozUoMNFfdD1QCPWXRu3hDgaIYQQonmTZKQG7RITsJoD3Xt/2vJ1iKMRQgghmjdJRmqg6xo2uxWAw/v2hTgaIYQQonmTZOQEbDGRAFSUFoU2ECGEEKKZk2TkBKLbtQHA53Fi+P0hjkYIIYRoviQZOYF2fS8BTICPvF17Qh2OEEII0WxJMnICnXtciNkUeFSz84svQxyNEEII0XxJMnICYQ4Htsph4ffu3BniaIQQQojmS5KRk7CHBZKR0kMFIY5ECCGEaL4kGTmJ8FaxAHgqnCGORAghhGi+6pSMvPTSS5x//vlERUURFRVF//79+fDDD09YfvHixWiaVmWy2+1nHHRjSejeGQDDX4yr3BXiaIQQQojmqU7JSHJyMs888wybN29m06ZNXHnllVx//fVs3779hNtERUWRl5cXnHbv3n3GQTeWzn37o2k2QPHDphOfoxBCCCFOn7kuhUeOHFnl+8yZM3nppZf48ssv6d69e43baJpG69atTz/CEGqd0hmrOQy31833G76i5y8uCnVIQgghRLNz2m1G/H4/b731FmVlZfTv3/+E5UpLS2nXrh0pKSmnrEU5yu12U1xcXGUKBU3XcdgsAORJjxohhBCiQdQ5Gfn222+JiIjAZrMxfvx43nvvPbp161Zj2S5durBw4ULef/99XnvtNQzDYMCAAezdu/ekx5g1axbR0dHBKSUlpa5h1pu2HQLJiLt0F7u27ApZHEIIIURzpSmlVF028Hg85Obm4nQ6eeedd3jllVdYs2bNCROS43m9Xrp27crtt9/O9OnTT1jO7XbjdruD34uLi0lJScHpdBIVFVWXcM/YgR2fs2LWC5R6SghvdRHj56c36vGFEEKIpqq4uJjo6OhT3r/rXDNitVrp1KkTF110EbNmzeKCCy7gr3/9a622tVgsXHjhhfz4448nLWez2YI9do5OoZLQ7VI6JXsBKDu4lb075S2+QgghRH0643FGDMOoUotxMn6/n2+//ZbExMQzPWyj6n7HJOzmMMDHhwv+EepwhBBCiGalTsnIlClTWLt2LTk5OXz77bdMmTKF1atXc8cddwAwduxYpkyZEiw/bdo0Pv74Y3766Se2bNnCmDFj2L17N/fcc0/9nkUDa93jclITDQCK8zZxIOdwiCMSQgghmo86JSMFBQWMHTuWLl26cNVVV/HVV1/x0UcfMWTIEAByc3PJy8sLli8sLOTee++la9euXHvttRQXF7Nu3bpatS852/S6cwIWkw2Ui3+/9FqowxFCCCGajTo3YA2F2jaAaWj/fOhGftjvRtOjuGvuAuKSIkMWixBCCHG2a7AGrOeyi8b8BpNuQRnF/Oult0IdjhBCCNEsSDJSB20uGkpKi8AlO7xrHc6D5SGOSAghhGj6JBmpo4vHjENDR/kP8q+/vRfqcIQQQogmT5KROmrXdyRJcYFX+hzc+TmlhfI2XyGEEOJMSDJSV5rGxbfeBmgYvj38+5WVoY5ICCGEaNIkGTkNnS6/mfioyhfofbOGMmftBn0TQgghRHWSjJwOTeOiG0YB4Pf8yIeL/xfaeIQQQogmTJKR09Rt2J3EhlsAxd7Na6go9YQ6JCGEEKJJkmTkdGkaFw4fBoDfvYOP/rEuxAEJIYQQTZMkI2eg1y/vJcJuBvzs3rAGV5k31CEJIYQQTY4kI2dA03UuGHw5AD7XN3z0j40hjkgIIYRoeiQZOUN9Rk/CbjGBcpPz5Wd88cGuUIckhBBCNCmSjJwh3WSm11WXAuCr+JxN7/6Tz/75Q4ijEkIIIZoOSUbqwYBxj3Bejw4A+Mo/Ycu777D6vR0hjkoIIYRoGiQZqQearjPiD/Po2f9CAHyuL8h87y0+eWdriCMTQgghzn6SjNQTTdO4+sHpXDJ0EAB+9xa+ef91Vr0ljVqFEEKIk5FkpJ794teP8IubbwTA8Oxg2wevs/Ifa0IclRBCCHH2kmSkAVxy06+4+p570NAwvLvYsfJN/v33D0MdlhBCCHFWkmSkgfQcMoqRDz2Mruko316y/reMf72wLNRhCSGEEGcdSUYaUOd+g7jx/9Ix6WaUv4AfPn+fpdNe4vCh4lCHJoQQQpw1NKWUCnUQp1JcXEx0dDROp5OoqKhQh1NnB3J+5M3fP47f7wY0dHMi9qh4Wg8awjW/HIjdYg51iEIIIUS9q+39W5KRRlKUn8cbT06morSwynLdHIeK70T3Edcy+IremExSWSWEEKJ5kGTkLFW4bzdfvPoKuTt/osJdDBy7/IYpAtqez0VDLqXvxRcQHh0dukCFEEKIMyTJyNlOKQ5/9R++WPpv9hR4cHkOAv6qRSwR2BLaktqjK+f1SCOhQ0ciW7RC07TQxCyEEELUgSQjTYgqyGLvB/9g41dF7C214vcdQBnOGsuazBZaJMTTtut5tL/kctp0vwCzxdLIEQshhBCnJslIU1R2GM+GJeTv2E3uPo28IgcHXXbcvlKUvwDlPwIYP9vIgsmSjMnaPjCZY0HTQNPQdY2oVg5iW4cT2zosOI+Od2C2mEJxhkIIIc4hkow0B4Yf5dzLoR9/5LvMPeTnlFF8xEuFy4/PdwDDuxtUWZVNND0W3dIB3dIe3ZwMmAgkMD5QPlB+FD6wKwhTKAdYwh04IiOIiIkmJi6amBgHsdF2YqNt2BxmrA4zZosuj4eEEELUiSQjzViB08WWnQc5uH83pT9txrf3O1RhAd4KT5VyGqqyeWxdkwgLaDY0zQa6HU2zoWkmzLqB2aSwmMFm0wgLsxAWbsURYScsJhx7uB1dU+ga6LrCpIOOgR6oqEHHwGTSsUdF44iKwx4VC7ZwfCYHfnMYHpMDv+5AmazEhFnR9epxK6Xwedy4ykqpKCnBWVBI0YEjOAsKKT3ipKzISUVJMe7yUryuMnSznfDoRKIT2tCqXXsSO7Ynrk0M0a0cmHQ/h3Zvw1WUj1nXsFjMWHQNs0nHatIx6RqusgpKiktwuXxYo1tCVALesFZ4zA7KfVDq9lFc5sWZX0b5ETe2SAvhCQ4iwq04LDrhqowozwHC3QdxuPKxug5itTrQw2LBEQOOWLBXzh0xYI0IXKyfnfORMg+5R8rJPVLOnoJS9ueX4a7wEWEyEW7WCTeZcOg6dl3DpulYNbAoDYtJJzzKSmS0lagwHxHWChyWUmwUo7mc4CoCTxk44iAiHiISAlNYHOiNV3umlKLU7aOozENRuYdilw+Xz8DtM3B5/bi8Bm5fYO7y+nF7fPhdZSQnxNEpNpJkhxWzV1FR6qWixFM5eako9WILNxPVwk5kCzuRcQ6iWtgJi7KiVf77UkqBUmj6mfVkU0ag1vJM93NGMVT+OlfuUlThbijKRfdVoEUlQmTlZLGfaidQegAO7oSDWcemotzAv5EWnaBFx8AUVzm3RQaP39B/tHjdLkoOH6L40EHKnUWB32564I8lTdPRdR10Dc3vpazUibOkhBbxCcS3aYc9MgZbWBgWu+OUcSrDwF1RjqeiHHd5Oe7yMswWK5EtWhIWFY2m67h9fvKdLvYXudhfVEGeswKPX5ES66B9y3DaxYXRKtJWf9fEXQLFeajiffgL8/E5D2IoHWWNBEs4yhoOlgiUJQy330xJSTnOwmKUrhEWHYEjMgJbWBhWhwOrIwyr3YHZVo/x1UCSkXNQ3sFCMr/cyO7Mryjf9S2qoqxaGbPmx6QpdE0HzYSh2fAbCsPwYSh/DXttSBqaZkXXrZg0CyY9MOmaCUP58St/ZVw+DOVFGV4M5aX6o6o6HlOPRjO1wGoOJ8qiiDCXoqvDYBTj81fg8fsp85ko9dnwqxPfWDRNA80GOECrTNr0cNDDsOgQZSmnheUIrawFJNj20dqSi9XkrrIPvzLjUQ48Rhhe5aDCiKBUj6PYF0OJN5wybxjlPjtuvw2334LHMGMoA6U8cPRaKD+qch64Nsc+B5Ybx5Yf913Dh4YfDQOTrgcSMl3DqitsJgOLRcditWBzWLGFh2EoEy63H4/XwOs18HgN/D6Fzx+Y/JU3Y11Tlf/GAnOz5sesGZg0PxbNh0kDTTMDZtAsKGyAFaXZUNgxsOPDGkhGDC9evxef34XP78IwyjGMcpRRTqAnmgZaOJoeWTlFVPuslAdllIFRilKllZ9L0FURyijFb7iDafvRn6uGVvkLunKuaWgEbrYKdeymr45+Pra92WTCYtGxWTQcNgO7Dew2DZtFD8ytOmaThq5pgaRd0zBVZuyGYcfrs+PzWynzWin2mCjzmCh3g8urcHv9eL1+/D4vyu9FU15MyoeGD1350fGj4QdlHBenQkOhaQodFbhkmoZu0jGZdHSzGZNJx6Z7sVOOzV+MjQrsug+bqXLSfVh1Pz6l4zVMuPwWXIYdl9+K27BSYoRT4ndQZlgxNAu6yYZusldODnQ9DN0cHphr4aDb8WMGdAwFGAbK70f5vRg+H36/D5QLk7kcjRKUUYLhc+J1FeHzVP+9djp03YLJZEM32TCZbSjlx/C7Mfwe/H4PhuE74bYaYDXrWMxgNSssZgOLxY/Z7MfQNYp9kZT57Lh9FvyGCRMaujLQ/B7wusDwYXLEYrLHYbLGoJujMZmj0LRwMBT4A5NuKHQFGDqGYcKvLPiUFT82lPKCqkD5izEMJ8ooQhlOlD8wR1XU8kpoaLoZTbfQb/Rv6D98cH1c3iBJRs5xSilKCw+j6yZMFgtmHUwHd6Dt+wr2bIS9X0HxvirbGJoZd3hb3OHJlNtaU6zH4TSiOOwJo8jppqyoBE+5C7/Li+E10AwFhkbgD0steJNTqONugOrYXAUeFynDBXh+HnIdaZUJgB2THqjRsJn8OExerGYvmlnht5gwvF6Uy6DUo1PmDSQ4dWXSLZh1HZ+h4VcGqNON3YSm2wOxV97UqClZEEKcggVNjwok/0Dg94w6llQGE8Sj/495UMoNyk3d/x8zgWZF06wo5av2aLx+mdBMsWh6CzRTLOAHw4VSFaBcKOVCGYHPP+99WRNNc6DpkaAdTV48lX/IeKjpd3Cva2/jqnFj6vWManv/lqE/mylN04iMa1l1Yds+gan/xMB35z7I/yZQxRrTFj0yCYfJjAOIAZJOcQxnhZfcw+XkHC5j98FSSsq9hFnMOCw6drMpOLebdRwWE3azCatJwwQojw9fRSne0hK8ZaV4y0vxlBXjLS3CXV6CoZnwaWa8mHAbOhV+qPBDud9PqcfA7fUSYz5MomkPKfo+UrSDpGgFxGmlNcbqRyfflES+rTOF5raUEUdFmU75kcN4KtyYLFFABD5fGF63A02LhKN/XWuBxxWWykk3KVokGMTFlBEVXkSE5SBm30HcZcWU+20Uu60UFRuUlHqoKC3FU1GC4Q/88lBGHX6RaTq6yYbFasPmsGN32HGE2XBE2HGE27FYLJhMJkxmHV03Vf6lG5jruo7JbELXzShbOIY1HL8lEp9uw+NXuCr8VJQbuCv8uMp8lJeU4CopxVNeir+iFOUqRfOWga8C/C4wPIHaAc0Cujnwl5TJDCYzutmCbrGgmy1g0lF+FZgMo8qE4UcZflB+NM2HpryAD035UEZlzZfPg+H3AmB1RBAWEUVYeARh4WGEO2xEhFkJt+tE2DXCLT7QfXgNH0VlPvIKXRwsclFY7KK8tAKfy4XJ68HQTbhNNnxWB4YtHOwRWGxRWG3R2CzRWE3R6IYJ5feifB7wB2odMHxgBOaa4UOrfJSjaRq6ZkLTdXRNR9N0THpgrpSG1+XH5/Lg83gw/G6UUY5SZWCUVX4+WqujjrtpcmzZ0Ukzo+nWQI2hyYzZFPj/x2pS2E0Gdt0XaIhui0KzRoI9CqxRgcd9thg0qwNdM+FyGxQVuygpceMpK8df4QaPD92v0JWGpkyVf0C4gzfswNx17LvhJnDzMqNpVtAsgRu0bkU3WTGZLIEaIRNoBG6YhlGB4Xfh97vwV9Y2+P1elDo+GVCYtGO1acdq1AwsusJhArvZhMVkw6QHbqwuvRUlqhXlKha3EYZPU/h0hW41YbFbsYfbiYiOILZFNPGtogkLs+D1+Tlw+DCHDx2k5MhBKooP4Ss7gtlbit0ox6rcKEz4NTtezYFXd+AjDGUORzeHoZvsmEw2LLoZswLNW4HhLsfnKQ1M3lL8vhKUURqosdQd6LoDk8mG2WTBopuw6GDV/YTrXqy6C4+vgnK/hwqfB5fXg8vrxlB+lP8Qyn8IvKf+FaE0E0ZYNEZEHES1QItqiRbVAlN0S8wxrbDYbYQpF1ZfCVZXCabyUnRXGWZ3BVS4wFWBcnkw3F4Mj48OF3Sv/e+neiY1I6JJcvv8uH0GXp+B16/w+g28fgNfRQmaMxfduRtz8R5sYZHEduiNLbEbWMNqtW+/z6DkiIuSQy6KD1dQfKgCj8tPy+QI4ttHEZcUXueRcn0eD0f2F3Ag+wCaBha7BVuYFZvDitVhxWy1oJtMwclss2G2WM/JRsPKMDAMA5P5zP9WKixzYzLpRFjNNbZBamhet5+yIjdlTjdlRW5Ki9y4SrxYHSbsEVYcERYckRbs4VYckRZsYWb0RhqF2TAUBSVusg+VcrDIRYTFTKTVRITVTITVRJjZhA4YfoXhDzzyMVtMWOwmLLbA/HRGjPZ5PCjDQDeb0XUdzVMCxftRxfvxFu7FW7gXf9E+DK8bT0QS7rAkKsKTcIcnUe5Iwq/b8CuF31CYdZ32LcNIinac1s/3cKmbrAMl/FhQisWk0yrCRqtIG/FRNlqE27Caa39+ht/A6/ZjGAqrzYzJcoJtlQokuqaqQzIow6D4UAGH9uRyeG8uRQfysFht2CMjcUREYY+IwB4ZhSMiEkdk4Htt2r6EmjymEUIIIURI1fb+Xae09qWXXuL8888nKiqKqKgo+vfvz4cffnjSbZYtW0ZaWhp2u52ePXvyn//8py6HFEIIIUQzV6dkJDk5mWeeeYbNmzezadMmrrzySq6//nq2b99eY/l169Zx++23c/fdd/P1118zatQoRo0axbZt2+oleCGEEEI0fWf8mCYuLo5nn32Wu+++u9q6W2+9lbKyMj744IPgsn79+tGrVy/+9re/1foY8phGCCGEaHoa5DHN8fx+P2+99RZlZWX079+/xjLr169n8OCqfZaHDh3K+vXrT/ewQgghhGhm6txc/dtvv6V///64XC4iIiJ477336NatW41l8/PzSUhIqLIsISGB/Pz8kx7D7Xbjdh8bHKq4uLiuYQohhBCiiahzzUiXLl3IzMxkw4YNTJgwgXHjxrFjx456DWrWrFlER0cHp5SUlHrdvxBCCCHOHnVORqxWK506deKiiy5i1qxZXHDBBfz1r3+tsWzr1q05cOBAlWUHDhygdevWJz3GlClTcDqdwWnPnj11DVMIIYQQTcQZj65jGEaVRyrH69+/P5988kmVZatWrTphG5OjbDZbsPvw0UkIIYQQzVOd2oxMmTKFa665hrZt21JSUsIbb7zB6tWr+eijjwAYO3Ysbdq0YdasWQA88MADXH755cydO5fhw4fz1ltvsWnTJl5++eX6PxMhhBBCNEl1SkYKCgoYO3YseXl5REdHc/755/PRRx8xZMgQAHJzcwOvb640YMAA3njjDf7whz/w+9//ns6dO7NixQp69OhRv2chhBDirOf3+/F6a/HSFdFkHH1H1pk654eD9xUWYo6Nrdd9CiGEOEYpRX5+PkVFRaEORTSAmJgYWrduXeN7cuStvaeglOLgvHkULXuH9m++gVV67AghRIM4mojEx8cTFhZ21r/cTdSOUory8nIKCgoASExMPO19nbvJSEUFpf9bjf/QIfbccy/t3npTakiEEKKe+f3+YCLSokWLUIcj6pnD4QACzTji4+NP+5FN47yr+iykh4WRsmABlqQkPLt3s2f8eIzy8lCHJYQQzcrRNiJhYWEhjkQ0lKM/2zNpD3TOJiMAloR4Ul75O6boaFxbv2Hfw4+gfL5QhyWEEM2OPJppvurjZ3tOJyMAttRUkl96Cc1mo3T1avLT02kCbXqFEEKIZuOcT0YAwnpfSJu5c0DXKVr2DodeeDHUIQkhhDiHLV68mJiYmFCH0WgkGakUOXgwrZ/6PwAOzZ9P4dtvhzgiIYQQZ4P169djMpkYPnx4rcpnZ2czevRokpKSsNvtJCcnc/3117Nz584GjrR+zJw5kwEDBhAWFtZoCZEkI8eJve02WkwYD0D+0+mU/O9/IY5ICCFEqGVkZDBp0iTWrl3L/v37T1rW6/UyZMgQnE4ny5cvJysri6VLl9KzZ88mM86Kx+Ph5ptvZsKECY12TElGfqbV/fcT/ctfgmGw76GHqcjMDHVIQgghQqS0tJSlS5cyYcIEhg8fzuLFi09afvv27ezatYsXX3yRfv360a5dOwYOHMiMGTPo168fAKtXr0bTtCrJSWZmJpqmkZOTU2V/K1asoHPnztjtdoYOHVrtxbHvv/8+vXv3xm63k5qaSnp6Or7KjhjTpk0jKSmJw4cPB8sPHz6cQYMGYRjGCc8hPT2dhx56iJ49e9biCtUPSUZ+RtM0EqelE/6Ly1AuF3vGT8CdnR3qsIQQotlQSlHu8YVkqmsHhbfffpu0tDS6dOnCmDFjWLhw4Un30apVK3Rd55133sHv95/RdSovL2fmzJksWbKEL774gqKiIm677bbg+s8++4yxY8fywAMPsGPHDhYsWMDixYuZOXMmAE8++STt27fnnnvuAeCFF15g3bp1vPrqq1Ve3XI2OGcHPTsZzWIh+bnn2D3uLlzbtrHnnntp//ZSzDJgjxBCnLEKr59uT30UkmPvmDaUMGvtb30ZGRmMGTMGgGHDhuF0OlmzZg1XXHFFjeXbtGnDvHnzmDx5Munp6Vx88cUMGjSIO+64g9TU1DrF6vV6mT9/Pn379gXg1VdfpWvXrmzcuJE+ffqQnp7OE088wbhx4wBITU1l+vTpTJ48malTp2IymXjttdfo1asXTzzxBPPmzeOVV16hbdu2dYqjMZxdqdFZRA8PJ2XB37C0bYt33z4K33gz1CEJIYRoRFlZWWzcuJHbb78dALPZzK233kpGRsZJt5s4cSL5+fm8/vrr9O/fn2XLltG9e3dWrVpVp+ObzWYuueSS4Pe0tDRiYmL47rvvANi6dSvTpk0jIiIiON17773k5eVRXjmIZ2pqKnPmzGH27Nlcd911jB49Ori/8ePHV9k2lKRm5CTMLVoQO/p2Cp6ZjfuHH0IdjhBCNAsOi4kd04aG7Ni1lZGRgc/nIykpKbhMKYXNZmP+/PlER0efcNvIyEhGjhzJyJEjmTFjBkOHDmXGjBkMGTIk+Ijk+Mc9pzN6aWlpKenp6dxwww3V1tnt9uDntWvXYjKZyMnJwefzYTYHbv3Tpk3j0UcfrfNxG4IkI6dg69ABAI+0GxFCiHqhaVqdHpWEgs/nY8mSJcydO5err766yrpRo0bx5ptvMn78+FrtS9M00tLSWLduHRBoVwKQl5dHbOU70TJr6Czh8/nYtGkTffr0AQI1NUVFRXTt2hWA3r17k5WVRadOnU547KVLl7J8+XJWr17NLbfcwvTp00lPTwcgPj6e+Pj4Wp1DQzu7/zWcBaxHk5Hdu1GGgXaWNfoRQghR/z744AMKCwu5++67q9WA3HjjjWRkZNSYjGRmZjJ16lTuvPNOunXrhtVqZc2aNSxcuJDHH38cgE6dOpGSksLTTz/NzJkz+f7775k7d261fVksFiZNmsS8efMwm83cd9999OvXL5icPPXUU4wYMYK2bdty0003oes6W7duZdu2bcyYMYO9e/cyYcIEZs+ezaWXXsqiRYsYMWIE11xzTbBnT01yc3M5cuQIubm5+P3+YKLUqVOnhnuco5oAp9OpAOV0Ohv92IbXq3b06Kl2dElT7j17G/34QgjRlFVUVKgdO3aoioqKUIdSJyNGjFDXXnttjes2bNigALV169Zq6w4ePKjuv/9+1aNHDxUREaEiIyNVz5491Zw5c5Tf7w+W+/zzz1XPnj2V3W5Xl112mVq2bJkCVHZ2tlJKqUWLFqno6Gj17rvvqtTUVGWz2dTgwYPV7t27qxxv5cqVasCAAcrhcKioqCjVp08f9fLLLyvDMNRVV12lhg4dqgzDCJafNGmS6tixoyopKTnhuY8bN04B1ab//e9/NZY/2c+4tvdvTamz/0UsxcXFREdH43Q6iYqKavTj7xo+As+uXaS88goRlw5s9OMLIURT5XK5yM7OpkOHDlXaMYjm42Q/49rev+WZQy1YO7QHpN2IEEII0RAkGakFW/v2gCQjQgghREOQZKQWgo1YfzZMrxBCCCHOnCQjtXA0GXHnSM2IEEIIUd8kGakFa+VjGt/+PIyKitAGI4QQQjQzkozUgik2Fr2yn7knNzfE0QghhBDNiyQjtaBpGtb27QBpxCqEEELUN0lGasnWXoaFF0IIIRqCJCO1JD1qhBBCiIYhyUgtHW3E6s7OCWkcQgghmr/FixcTExMT6jAajSQjtXR8zUgTGEFfCCFEPVm/fj0mk4nhw4fXqnx2djajR48mKSkJu91OcnIy119/PTt37mzgSM9cTk4Od999Nx06dMDhcNCxY0emTp2Kx+Np0OPKW3trydquLWgaRnEx/iNHMLdoEeqQhBBCNIKMjAwmTZpERkYG+/fvJykp6YRlvV4vQ4YMoUuXLixfvpzExET27t3Lhx9+SFFRUeMFfZp27tyJYRgsWLCATp06sW3bNu69917KysqYM2dOgx1XakZqSbfbsSQmAtKIVQghzhWlpaUsXbqUCRMmMHz4cBYvXnzS8tu3b2fXrl28+OKL9OvXj3bt2jFw4EBmzJhBv379AFi9ejWaplVJTjIzM9E0jZyftUtcsWIFnTt3xm63M3ToUPbs2VNl/fvvv0/v3r2x2+2kpqaSnp6Oz+cDYNq0aSQlJXH48OFg+eHDhzNo0CAMw6gx/mHDhrFo0SKuvvpqUlNTue6663j00UdZvnx5La/Y6ZFkpA6kEasQQtQDpcBTFpqpjo/Z3377bdLS0ujSpQtjxoxh4cKFJ31U36pVK3Rd55133sHv95/RZSovL2fmzJksWbKEL774gqKiIm677bbg+s8++4yxY8fywAMPsGPHDhYsWMDixYuZOXMmAE8++STt27fnnnvuAeCFF15g3bp1vPrqq+h67W//TqeTuLi4MzqXU5HHNHVg7dCBsi++wC01I0IIcfq85fDHEz/qaFC/3w/W8FoXz8jIYMyYMUCg1sDpdLJmzRquuOKKGsu3adOGefPmMXnyZNLT07n44osZNGgQd9xxB6mpqXUK1ev1Mn/+fPr27QvAq6++SteuXdm4cSN9+vQhPT2dJ554gnHjxgGQmprK9OnTmTx5MlOnTsVkMvHaa6/Rq1cvnnjiCebNm8crr7xC27Ztax3Djz/+yPPPP9+gj2hAakbqxBp8e29OSOMQQgjR8LKysti4cSO33347AGazmVtvvZWMjIyTbjdx4kTy8/N5/fXX6d+/P8uWLaN79+6sWrWqTsc3m81ccsklwe9paWnExMTw3XffAbB161amTZtGREREcLr33nvJy8ujvLwcCCQoc+bMYfbs2Vx33XWMHj06uL/x48dX2fbn9u3bx7Bhw7j55pu599576xR7XUnNSB1YO7QH5DGNEEKcEUtYoIYiVMeupYyMDHw+X5UGq0opbDYb8+fPJ7ryNSE1iYyMZOTIkYwcOZIZM2YwdOhQZsyYwZAhQ4KPSI5/3OP1eut8KqWlpaSnp3PDDTdUW2e324Of165di8lkIicnB5/Ph9kcuPVPmzaNRx99tMZ979+/n0GDBjFgwABefvnlOsdWV3WqGZk1axaXXHIJkZGRxMfHM2rUKLKysk66zeLFi9E0rcp0/EVqSmxHa0b27EFVNhASQghRR5oWeFQSiknTahWiz+djyZIlzJ07l8zMzOC0detWkpKSePPNN+twuhppaWmUlZUBgXYlAHl5ecEymZmZNcawadOm4PesrCyKioro2rUrAL179yYrK4tOnTpVm44mPEuXLmX58uWsXr2a3Nxcpk+fHtxffHx8lW2O2rdvH1dccQUXXXQRixYtqlP7ktNVp5qRNWvWMHHiRC655BJ8Ph+///3vufrqq9mxYwfh4Sd+BhcVFVUladFq+Y/hbGNOTESz21EuF969e4OPbYQQQjQvH3zwAYWFhdx9993VakBuvPFGMjIyGD9+fLXtMjMzmTp1KnfeeSfdunXDarWyZs0aFi5cyOOPPw5Ap06dSElJ4emnn2bmzJl8//33zJ07t9q+LBYLkyZNYt68eZjNZu677z769etHnz59AHjqqacYMWIEbdu25aabbkLXdbZu3cq2bduYMWMGe/fuZcKECcyePZtLL72URYsWMWLECK655ppgz56fO5qItGvXjjlz5nDw4MHgutatW5/29TwldQYKCgoUoNasWXPCMosWLVLR0dFnchjldDoVoJxO5xntpz7suu56taNLmir+3/9CHYoQQpz1Kioq1I4dO1RFRUWoQ6mTESNGqGuvvbbGdRs2bFCA2rp1a7V1Bw8eVPfff7/q0aOHioiIUJGRkapnz55qzpw5yu/3B8t9/vnnqmfPnsput6vLLrtMLVu2TAEqOztbKXXs3vnuu++q1NRUZbPZ1ODBg9Xu3burHG/lypVqwIAByuFwqKioKNWnTx/18ssvK8Mw1FVXXaWGDh2qDMMIlp80aZLq2LGjKikpqfHcFi1apIAapxM52c+4tvdvTanTH070xx9/pHPnznz77bf06NGjxjKLFy/mnnvuoU2bNhiGQe/evfnjH/9I9+7dT7hft9uN2+0Ofi8uLiYlJQWn00lUVNTphlsv9j7wICUffUT844/T4ld3hTQWIYQ427lcLrKzs+nQoUOTfUQvTu5kP+Pi4mKio6NPef8+7QdBhmHw4IMPMnDgwBMmIgBdunRh4cKFvP/++7z22msYhsGAAQPYu3fvCbeZNWsW0dHRwSklJeV0w6x3wUas0r1XCCGEqBennYxMnDiRbdu28dZbb520XP/+/Rk7diy9evXi8ssvZ/ny5bRq1YoFCxaccJspU6bgdDqD089HnAslmwx8JoQQQtSr0+rae9999/HBBx+wdu1akpOT67StxWLhwgsv5McffzxhGZvNhs1mO53QGtyxsUakZkQIIYSoD3WqGVFKcd999/Hee+/x6aef0qGylqAu/H4/3377LYmV73lpao4OCe87eBB/aVmIoxFCCCGavjolIxMnTuS1117jjTfeIDIykvz8fPLz86moqAiWGTt2LFOmTAl+nzZtGh9//DE//fQTW7ZsYcyYMezevTs4Vn5TY4qKwlT5xl55VCOEEEKcuTolIy+99BJOp5MrrriCxMTE4LR06dJgmdzc3CoDuRQWFnLvvffStWtXrr32WoqLi1m3bh3dunWrv7NoZPKoRgghhKg/dWozUptewKtXr67y/bnnnuO5556rU1BnO2uH9lRs3iw1I0IIIUQ9kBflnQab1IwIIYQQ9UaSkdNwtBGrO0eSESGEEOJMSTJyGqzBsUZ21+rRlRBCCFEXixcvJiYmJtRhNBpJRk6DNTkZTCZUeTm+goJQhyOEEKIBrV+/HpPJxPDhw2tVPjs7m9GjR5OUlITdbic5OZnrr7+enTt3NnCk9eO6666jbdu22O12EhMTufPOO9m/f3+DHlOSkdOgWa1YktsA0m5ECCGau4yMDCZNmsTatWtPeVP2er0MGTIEp9PJ8uXLycrKYunSpfTs2ZOioqLGCfgMDRo0iLfffpusrCzeffdddu3axU033dSgx5Rk5DTZ2suw8EII0dyVlpaydOlSJkyYwPDhw1m8ePFJy2/fvp1du3bx4osv0q9fP9q1a8fAgQOZMWMG/fr1AwK9TjVNq5KcZGZmomkaOT+7p6xYsYLOnTtjt9sZOnRotdejvP/++/Tu3Ru73U5qairp6en4fD4gMM5XUlIShw8fDpYfPnw4gwYNwjCME57DQw89FIx9wIABPPHEE3z55Zd4vd5aXLHTI8nIaZKxRoQQ4vQopSj3lodkqms7v7fffpu0tDS6dOnCmDFjWLhw4Un30apVK3Rd55133sHv95/RdSovL2fmzJksWbKEL774gqKiIm677bbg+s8++4yxY8fywAMPsGPHDhYsWMDixYuZOXMmAE8++STt27cPDjL6wgsvsG7dOl599VV0vXa3/yNHjvD6668zYMAALBbLGZ3PyZzWu2nEcT1qJBkRQog6qfBV0PeNviE59obRGwizhNW6fEZGBmPGjAFg2LBhOJ1O1qxZwxVXXFFj+TZt2jBv3jwmT55Meno6F198MYMGDeKOO+4gNTW1TrF6vV7mz59P376Ba/Xqq6/StWtXNm7cSJ8+fUhPT+eJJ55g3LhxAKSmpjJ9+nQmT57M1KlTMZlMvPbaa/Tq1YsnnniCefPm8corr9C2bdtTHvvxxx9n/vz5lJeX069fPz744IM6xV5XUjNymo7vUSOEEKL5ycrKYuPGjdx+++0AmM1mbr31VjIyMk663cSJE8nPz+f111+nf//+LFu2jO7du7Nq1ao6Hd9sNnPJJZcEv6elpRETE8N3330HwNatW5k2bRoRERHB6d577yUvL4/y8nIgkKDMmTOH2bNnc9111zF69Ojg/saPH19l2+M99thjfP3113z88ceYTCbGjh3boL1HpWbkNB19TOPduxfD40G3WkMbkBBCNBEOs4MNozeE7Ni1lZGRgc/nIykpKbhMKYXNZmP+/PlER0efcNvIyEhGjhzJyJEjmTFjBkOHDmXGjBkMGTIk+Ijk+Jv76bTHKC0tJT09nRtuuKHaOrvdHvy8du1aTCYTOTk5+Hw+zObArX/atGk8+uijNe67ZcuWtGzZkvPOO4+uXbuSkpLCl19+Sf/+/escZ21IMnKazPGt0MPCMMrL8e7Zg61jx1CHJIQQTYKmaXV6VBIKPp+PJUuWMHfuXK6++uoq60aNGsWbb77J+PHja7UvTdNIS0tj3bp1QKBdCUBeXh6xsbFAoAFrTTFs2rSJPn36AIGamqKiIrp27QpA7969ycrKolOnTic89tKlS1m+fDmrV6/mlltuYfr06aSnpwMQHx9PfHz8KeM/2tjV7XbX6nxPi2oCnE6nApTT6Qx1KFX8dMONakeXNFW8alWoQxFCiLNSRUWF2rFjh6qoqAh1KHXy3nvvKavVqoqKiqqtmzx5srr44otr3O7rr79W1113nVq2bJnavn27+uGHH9Qrr7yiwsPD1bRp05RSSnk8HpWSkqJuvvlm9f3336sPPvhAdenSRQEqOztbKaXUokWLlMViUX369FFffvml2rRpk+rXr5/q169f8FgrV65UZrNZPf3002rbtm1qx44d6s0331RPPvmkUkqpPXv2qNjYWDVv3rwq5devX3/C8/7yyy/V888/r77++muVk5OjPvnkEzVgwADVsWNH5XK5atzmZD/j2t6/JRk5A3sffkTt6JKmDr78cqhDEUKIs1JTTUZGjBihrr322hrXbdiwQQFq69at1dYdPHhQ3X///apHjx4qIiJCRUZGqp49e6o5c+Yov98fLPf555+rnj17Krvdri677DK1bNmyaslIdHS0evfdd1Vqaqqy2Wxq8ODBavfu3VWOt3LlSjVgwADlcDhUVFSU6tOnj3r55ZeVYRjqqquuUkOHDlWGYQTLT5o0SXXs2FGVlJTUeG7ffPONGjRokIqLi1M2m021b99ejR8/Xu3du/eE16o+khFNqbN/PPPi4mKio6NxOp1ERUWFOpygg/Nf4ND8+UTfeANJlV2phBBCHONyucjOzqZDhw5V2jGI5uNkP+Pa3r+lN80ZODbWSE5I4xBCCCGaMklGzoC1Q3tABj4TQgghzoQkI2fAVlkz4i8sxN9E3jkghBBCnG0kGTkDeng45spuUfKOGiGEEOL0SDJyho4NC58T2kCEEEKIJkqSkTMUbDciNSNCCCHEaZFk5AzJ23uFEEKIMyPJyBmyBV+YlxPaQIQQQogmSpKRMxR8e+/u3ajK8fuFEEIIUXuSjJwhS1ISWCwotxvv/rxQhyOEEKIZWLx4MTExMaEOo9FIMnKGNLMZa9u2gDyqEUKI5mj9+vWYTCaGDx9eq/LZ2dmMHj2apKQk7HY7ycnJXH/99ezcubOBI61fbrebXr16oWlajW8Vrk+SjNQDacQqhBDNV0ZGBpMmTWLt2rXs37//pGW9Xi9DhgzB6XSyfPlysrKyWLp0KT179qSoiQ2OOXnyZJKSkhrlWJKM1AObDAsvhBDNUmlpKUuXLmXChAkMHz6cxYsXn7T89u3b2bVrFy+++CL9+vWjXbt2DBw4kBkzZtCvXz8AVq9ejaZpVZKTzMxMNE0j52c17CtWrKBz587Y7XaGDh3Knj17qqx///336d27N3a7ndTUVNLT0/H5fABMmzaNpKQkDh8+HCw/fPhwBg0ahHGKNo4ffvghH3/8MXPmzDnFFaofkozUg2Aj1hxJRoQQ4lSUUhjl5SGZ6vqi+rfffpu0tDS6dOnCmDFjWLhw4Un30apVK3Rd55133sHv95/RdSovL2fmzJksWbKEL774gqKiIm677bbg+s8++4yxY8fywAMPsGPHDhYsWMDixYuZWfkW+SeffJL27dtzzz33APDCCy+wbt06Xn31VXT9xLf/AwcOcO+99/KPf/yDsLCwMzqH2jI3ylGauaOPadzSZkQIIU5JVVSQ1fuikBy7y5bNaHW4wWZkZDBmzBgAhg0bhtPpZM2aNVxxxRU1lm/Tpg3z5s1j8uTJpKenc/HFFzNo0CDuuOMOUlNT6xSr1+tl/vz59O3bF4BXX32Vrl27snHjRvr06UN6ejpPPPEE48aNAyA1NZXp06czefJkpk6dislk4rXXXqNXr1488cQTzJs3j1deeYW2le0ca6KU4q677mL8+PFcfPHF1WpqGorUjNSDozUjvv15GC5XiKMRQghRH7Kysti4cSO33347AGazmVtvvZWMjIyTbjdx4kTy8/N5/fXX6d+/P8uWLaN79+6sWrWqTsc3m81ccsklwe9paWnExMTw3XffAbB161amTZtGREREcLr33nvJy8ujvLwcCCQoc+bMYfbs2Vx33XWMHj06uL/x48dX2Rbg+eefp6SkhClTptQp1jMlNSP1wBQbix4djeF04tm9G3uXLqEOSQghzlqaw0GXLZtDduzaysjIwOfzVWnEqZTCZrMxf/58oqOjT7htZGQkI0eOZOTIkcyYMYOhQ4cyY8YMhgwZEnxEcvzjHq/XW+dzKS0tJT09nRtuuKHaOrvdHvy8du1aTCYTOTk5+Hw+zObArX/atGk8+uijVbb79NNPWb9+PTabrcryiy++mDvuuINXX321znHWhiQj9UDTNKzt2+Ha+g2e7GxJRoQQ4iQ0TavTo5JQ8Pl8LFmyhLlz53L11VdXWTdq1CjefPNNxo8fX6t9aZpGWloa69atAwLtSgDy8vKIjY0FqLHrrM/nY9OmTfTp0wcI1NQUFRXRtWtXAHr37k1WVhadOnU64bGXLl3K8uXLWb16NbfccgvTp08nPT0dgPj4eOIr3zx/1Lx585gxY0bw+/79+xk6dChLly4NPi5qCJKM1BNb+w6BZETajQghRJP3wQcfUFhYyN13312tBuTGG28kIyOjxmQkMzOTqVOncuedd9KtWzesVitr1qxh4cKFPP744wB06tSJlJQUnn76aWbOnMn333/P3Llzq+3LYrEwadIk5s2bh9ls5r777qNfv37B5OSpp55ixIgRtG3blptuugld19m6dSvbtm1jxowZ7N27lwkTJjB79mwuvfRSFi1axIgRI7jmmmuCPXt+7uftSY4+vunYsSPJycl1v5C1JG1G6olVuvcKIUSzkZGRweDBg2t8FHPjjTeyadMmvvnmm2rrkpOTad++Penp6fTt25fevXvz17/+lfT0dJ588kkgkGS8+eab7Ny5k/PPP5/Zs2dXqY04KiwsjMcff5zRo0czcOBAIiIiWLp0aXD90KFD+eCDD/j444+55JJL6NevH8899xzt2rULNkTt06cP9913X7D8hAkTGDNmDKWlpfV1qeqFpurQz2nWrFksX76cnTt34nA4GDBgALNnz6bLKR5LLFu2jP/7v/8jJyeHzp07M3v2bK699tpaB1lcXEx0dDROp5OoqKhab9eYild+xL4HH8R+wfl0OO4fixBCnMtcLhfZ2dl06NChSjsG0Xyc7Gdc2/t3nWpG1qxZw8SJE/nyyy9ZtWoVXq+Xq6++mrKyshNus27dOm6//Xbuvvtuvv76a0aNGsWoUaPYtm1bXQ591jtWM5JT537sQgghxLmsTjUjP3fw4EHi4+NZs2YNv/jFL2osc+utt1JWVsYHH3wQXNavXz969erF3/72t1odpynUjBguF1kX9gal6LzuC8xxcaEOSQghQk5qRpq/Rq8Z+Tmn0wlA3EluvOvXr2fw4MFVlg0dOpT169efcBu3201xcXGV6Wyn2+1YEhMBaTcihBBC1MVpJyOGYfDggw8ycOBAevToccJy+fn5JCQkVFmWkJBAfn7+CbeZNWsW0dHRwSklJeV0w2xU8sI8IYQQou5OOxmZOHEi27Zt46233qrPeACYMmUKTqczOP38xUBnq2PvqMkJbSBCCCFEE3Ja44zcd999fPDBB6xdu/aU/Y5bt27NgQMHqiw7cOAArVu3PuE2Nput2uhvTUHwHTXZOSGNQwghhGhK6lQzopTivvvu47333uPTTz+lQ2VNwMn079+fTz75pMqyVatW0b9//7pF2gRIzYgQQghRd3WqGZk4cSJvvPEG77//PpGRkcF2H9HR0Tgqx/sfO3Ysbdq0YdasWQA88MADXH755cydO5fhw4fz1ltvsWnTJl5++eV6PpXQC7YZyc1F+XxoZhngVgghhDiVOtWMvPTSSzidTq644goSExOD0/EjwuXm5pKXlxf8PmDAAN544w1efvllLrjgAt555x1WrFhx0kavTZUlKRHNZgOvF+/+/aEORwghhGgS6vSne22GJFm9enW1ZTfffDM333xzXQ7VJGm6jrVdO9zff48nOxvrz8b4F0IIIWpj8eLFPPjggxQVFYU6lEYh76apZ8casUr3XiGEaA7Wr1+PyWRi+PDhtSqfnZ3N6NGjSUpKwm63k5yczPXXX8/OnTsbONL60b59+8CblY+bnnnmmQY9pjRqqGfSiFUIIZqXjIwMJk2aREZGBvv37ycpKemEZb1eL0OGDKFLly4sX76cxMRE9u7dy4cfftikajmmTZvGvffeG/weGRnZoMeTmpF6dmzgs5yQxiGEEGcrpRRetz8kU13fgFJaWsrSpUuZMGECw4cPZ/HixSctv337dnbt2sWLL75Iv379aNeuHQMHDmTGjBn069cPCDRn0DStSnKSmZmJpmnk/OwP2RUrVtC5c2fsdjtDhw6tNu7W+++/T+/evbHb7aSmppKeno7P5wMCCUVSUhKHDx8Olh8+fDiDBg3CMIyTnkdkZCStW7cOTuHh4ae4UmdGakbqme3oC/OkZkQIIWrk8xi8/MCakBz7N3+9HIvNVOvyb7/9NmlpaXTp0oUxY8bw4IMPMmXKFDRNq7F8q1at0HWdd955hwcffBCTqfbH+rny8nJmzpzJkiVLsFqt/O53v+O2227jiy++AOCzzz5j7NixzJs3j8suu4xdu3bxm9/8BoCpU6fy5JNPsnLlSu655x7ee+89XnjhBdatW8fWrVvR9ZPXRTzzzDNMnz6dtm3bMnr0aB566CHMDdhDVGpG6tnRmhHfgQMYJ3mbsRBCiLNfRkYGY8aMAWDYsGE4nU7WrDlxItWmTRvmzZvHU089RWxsLFdeeSXTp0/np59+qvOxvV4v8+fPp3///lx00UW8+uqrrFu3jo0bNwKQnp7OE088wbhx40hNTWXIkCFMnz6dBQsWAGAymXjttdf45JNPeOKJJ3jsscd44YUXaHuKzhX3338/b731Fv/73//47W9/yx//+EcmT55c5/jrRDUBTqdTAcrpdIY6lFrJ6tdf7eiSpiq2bw91KEIIEVIVFRVqx44dqqKiIrjMMAzlcflCMhmGUevYd+7cqcxmszpw4EBw2cSJE9WYMWNOuW1xcbH65z//qX7/+9+rnj17Krvdrj7++GOllFL/+9//FKAKCwuD5b/++msFqOzsbKWUUosWLVJms1n5/f4q+42JiVGLFy9WSinVsmVLZbfbVXh4eHCy2+0KUGVlZcFtFixYoAB16623VtnXb3/72yrbnkhGRoYym83K5XLVuL6mn/FRtb1/y2OaBmDt0IGKwkLc2dnYu3ULdThCCHFW0TStTo9KQiUjIwOfz1elwapSCpvNxvz584mOjj7htpGRkYwcOZKRI0cyY8YMhg4dyowZMxgyZEjwEYk6rv2K1+utc3ylpaWkp6dzww03VFtnt9uDn9euXYvJZCInJwefzxd83DJt2jQeffTRUx6nb9+++Hw+cnJy6NKlS53jrA15TNMApBGrEEI0bT6fjyVLljB37lwyMzOD09atW0lKSuLNN9+s9b40TSMtLY2yykf3rVq1AqgyQGhmZmaNMWzatCn4PSsri6KiIrp27QpA7969ycrKolOnTtWmownP0qVLWb58OatXryY3N5fp06cH9xcfH19lmxPJzMxE13Xi4+Nrfc51JTUjDcAqjViFEKJJ++CDDygsLOTuu++uVgNy4403kpGRwfjx46ttl5mZydSpU7nzzjvp1q0bVquVNWvWsHDhQh5//HEAOnXqREpKCk8//TQzZ87k+++/Z+7cudX2ZbFYmDRpEvPmzcNsNnPffffRr18/+vTpA8BTTz3FiBEjaNu2LTfddBO6rrN161a2bdvGjBkz2Lt3LxMmTGD27NlceumlLFq0iBEjRnDNNdcEe/b83Pr169mwYQODBg0iMjKS9evX89BDDzFmzBhiY2PP9LKe2Ekf4pwlmlqbEefHH6sdXdLUTzfcGOpQhBAipE7WnuBsNmLECHXttdfWuG7Dhg0KUFu3bq227uDBg+r+++9XPXr0UBERESoyMlL17NlTzZkzp0r7j88//zzYluSyyy5Ty5Ytq9ZmJDo6Wr377rsqNTVV2Ww2NXjwYLV79+4qx1u5cqUaMGCAcjgcKioqSvXp00e9/PLLyjAMddVVV6mhQ4dWaSczadIk1bFjR1VSUlLjuW3evFn17dtXRUdHK7vdrrp27ar++Mc/nrC9iFL102ZEU6qOna5DoLi4mOjoaJxOJ1FRUaEO55TcP/7ITyNGooeHc96mr07YBUwIIZo7l8tFdnY2HTp0qNKOQTQfJ/sZ1/b+LW1GGoClbVvQdYyyMnwHD4Y6HCGEEOKsJslIA9CtVixt2gDSbkQIIYQ4FUlGGkiwEav0qBFCCCFOSpKRBnKse6+8vVcIIYQ4GUlGGohN3t4rhBBC1IokIw1EakaEEEKI2pFkpIFYj9aM7N2LOo1hfoUQQohzhSQjDcQcH4/mcIDfj2fP3lCHI4QQQpy1JBlpIJquH3tUI+1GhBBC1MHixYuJiYkJdRiNRpKRBmQLdu+VdiNCCNFUrV+/HpPJxPDhw2tVPjs7m9GjR5OUlITdbic5OZnrr7+enTt3NnCk9eff//43ffv2xeFwEBsby6hRoxr0ePKivAYkNSNCCNH0ZWRkMGnSJDIyMti/fz9JSUknLOv1ehkyZAhdunRh+fLlJCYmsnfvXj788EOKiooaL+gz8O6773Lvvffyxz/+kSuvvBKfz8e2bdsa9JhSM9KAgo1YpWZECCGapNLSUpYuXcqECRMYPnw4ixcvPmn57du3s2vXLl588UX69etHu3btGDhwIDNmzAi+KXf16tVomlYlOcnMzETTNHJ+9sfrihUr6Ny5M3a7naFDh7Jnz54q699//3169+6N3W4nNTWV9PR0fD4fANOmTSMpKYnDhw8Hyw8fPpxBgwZhGEaN8ft8Ph544AGeffZZxo8fz3nnnUe3bt245ZZbannFTo8kIw3oaM2IW2pGhBAiSCmF1+UKyVTXd8O+/fbbpKWl0aVLF8aMGcPChQtPuo9WrVqh6zrvvPMOfr//jK5TeXk5M2fOZMmSJXzxxRcUFRVx2223Bdd/9tlnjB07lgceeIAdO3awYMECFi9ezMyZMwF48sknad++Pffccw8AL7zwAuvWrePVV19F12u+/W/ZsoV9+/ah6zoXXnghiYmJXHPNNQ1eMyKPaRrQ0WTEf+gQ/pISTJGRoQ1ICCHOAj63m3njbgrJse9/9R0sdXh7cEZGBmPGjAFg2LBhOJ1O1qxZwxVXXFFj+TZt2jBv3jwmT55Meno6F198MYMGDeKOO+4gNTW1TrF6vV7mz59P3759AXj11Vfp2rUrGzdupE+fPqSnp/PEE08wbtw4AFJTU5k+fTqTJ09m6tSpmEwmXnvtNXr16sUTTzzBvHnzeOWVV2jbtu0Jj/nTTz8B8PTTT/PnP/+Z9u3bM3fuXK644gq+//574uLi6nQOtSU1Iw3IFBmJqWVLQNqNCCFEU5OVlcXGjRu5/fbbATCbzdx6661kZGScdLuJEyeSn5/P66+/Tv/+/Vm2bBndu3dn1apVdTq+2WzmkksuCX5PS0sjJiaG7777DoCtW7cybdo0IiIigtO9995LXl4e5eXlQCBBmTNnDrNnz+a6665j9OjRwf2NHz++yrZA8PHNk08+yY033shFF13EokWL0DSNZcuW1Sn+Op1rg+1ZAGBr357yQ4fw5OTg6Nkz1OEIIUTImW027n/1nZAdu7YyMjLw+XxVGqwqpbDZbMyfP5/o6OgTbhsZGcnIkSMZOXIkM2bMYOjQocyYMYMhQ4YEH5Ec/7jHexqDY5aWlpKens4NN9xQbZ39uNqftWvXYjKZyMnJwefzYTYHbv3Tpk3j0UcfrbJdYmIiAN26dQsus9lspKamkpubW+cYa0tqRhqYNGIVQoiqNE3DYreHZNI0rVYx+nw+lixZwty5c8nMzAxOW7duJSkpiTfffLNO55uWlkZZWRkQaFcCkJeXFyyTmZlZYwybNm0Kfs/KyqKoqIiuXbsC0Lt3b7KysujUqVO16WjCs3TpUpYvX87q1avJzc1l+vTpwf3Fx8dX2QbgoosuwmazkZWVFSzn9XrJycmhXbt2tT7nupKakQYm3XuFEKLp+eCDDygsLOTuu++uVgNy4403kpGRwfjx46ttl5mZydSpU7nzzjvp1q0bVquVNWvWsHDhQh5//HEAOnXqREpKCk8//TQzZ87k+++/Z+7cudX2ZbFYmDRpEvPmzcNsNnPffffRr18/+vTpA8BTTz3FiBEjaNu2LTfddBO6rrN161a2bdvGjBkz2Lt3LxMmTGD27NlceumlLFq0iBEjRnDNNdcEe/b8XFRUFOPHj2fq1KmkpKTQrl07nn32WQBuvvnmM7qmJ6WaAKfTqQDldDpDHUqdFX/yqdrRJU3tGvXLUIcihBCNrqKiQu3YsUNVVFSEOpQ6GTFihLr22mtrXLdhwwYFqK1bt1Zbd/DgQXX//ferHj16qIiICBUZGal69uyp5syZo/x+f7Dc559/rnr27Knsdru67LLL1LJlyxSgsrOzlVJKLVq0SEVHR6t3331XpaamKpvNpgYPHqx2795d5XgrV65UAwYMUA6HQ0VFRak+ffqol19+WRmGoa666io1dOhQZRhGsPykSZNUx44dVUlJyQnP3ePxqEceeUTFx8eryMhINXjwYLVt27YTlj/Zz7i2929NqTr2cwqB4uJioqOjcTqdREVFhTqcOnH/lM1P116L5nDQZfMmtBN0pxJCiObI5XKRnZ1Nhw4dqrRjEM3HyX7Gtb1/y52xgVlTksFkQlVU4CsoCHU4QgghxFnnnG4z8t/d/+VA+QF8hu/YpHxVvnuNQAvnVo5WtA5vTWJ4IonhiSSEJ2A1WU95DM1iwZqcjGf3bjzZ2Vhat27o0xJCCCGalDonI2vXruXZZ59l8+bN5OXl8d577530BTqrV69m0KBB1Zbn5eXROsQ35kXbFvHNoW9Oe/uWjpYkhifSOrw1rcNbE2ePI8oadWyyBeZau2TYvRtPTg7h/fvX4xkIIYQQTV+dk5GysjIuuOACfv3rX9fYt/lEsrKyqjwvio+Pr+uh613fxL60Dm+NWTdj1s1YdEvws1kzBz8rFAfLD5JXlkd+WT75Zfm4/C4OVRziUMUhvj307UmPc6fLz0jgHytnc7jdTv7Q7w9YdEvjnKQQQghxlqtzMnLNNddwzTXX1PlA8fHxxMTE1Hm7hnR/7/tPazulFEXuIvLK8qokKEXuIordxRR7ApPT7aTYU8z+uDJA0eKgh7//sJwSTwl/+sWfMOvn9FMyIYQQAmjENiO9evXC7XbTo0cPnn76aQYOHHjCsm63G7fbHfxeXFzcGCHWmqZpxNpjibXH0q1Ft1OWL+64nn0rf835FXFY9HJW7V7Fk58/yR8v/SMm3dQIEQshRGg1gY6b4jTVx8+2wXvTJCYm8re//Y13332Xd999l5SUFK644gq2bNlywm1mzZpFdHR0cEpJSWnoMBuUI7UjAOYDhczt/wxmzcx/sv/D0+ufxlA1v8ZZCCGaA4sl8Ej66LtSRPNz9Gd79Gd9Os5onBFN007ZgLUml19+OW3btuUf//hHjetrqhlJSUlpkuOMQCBr/P7iSzDKykj94F+sMf/E5LWT8Ss/t5x3C3/o94daD1EshBBNTV5eHkVFRcTHxxMWFia/75oJpRTl5eUUFBQQExMTfK/N8Wo7zkhIGi306dOHzz///ITrbTYbtjq8zOhsp2ka1vbtcW3fjjs7m6uHXI3X8DLlsym8/f3bWEwWHr/kcfkfVAjRLB3tOVkgYy01SzExMWfcOzYkyUhmZmaNGVRzZu3QAdf27cF31AxPHY7H7+GpdU/x+nevY9WtPHTRQ5KQCCGaHU3TSExMJD4+/rTeTivOXhaLBZPpzNs+1jkZKS0t5ccffwx+z87OJjMzk7i4ONq2bcuUKVPYt28fS5YsAeAvf/kLHTp0oHv37rhcLl555RU+/fRTPv744zMOvimxdmgPgCc7J7jsl51/idfwMv3L6Szavgirycp9F94XmgCFEKKBmUymerlxieanzsnIpk2bqgxi9vDDDwMwbtw4Fi9eTF5eHrm5ucH1Ho+HRx55hH379hEWFsb555/Pf//73xoHQmvOTvT23lu63ILX8PLMxmdY8M0CrCYrvzn/N40foBBCCBEi8qK8RuLauZPsUb9Eczjo9OknmGNjq6xfvG0xczcHXiH9yEWPcFePu0IQpRBCCFF/5EV5Zxnbeedh69YVVVHB4Zf/Xm39XT3u4r5egUc0czfPJetIVmOHKIQQQoSEJCONRNN14h98EIDC11/Hm59frcxvL/gtQ9oNASDj24zGDE8IIYQIGUlGGlH4ZZfhuPgilMfDoRderLHM0fYiH+3+iNzi3BrLCCGEEM2JJCONSNM04isb/BYtX447O7tambS4NC5rcxmGMli4bWFjhyiEEEI0OklGGllY795EXH45+P0cev75Gsvc0/MeAP65658UlMsgQUIIIZo3SUZCoNVDDwJQ/J8PcX33XbX1vRN60zu+N17Dy5LtSxo5OiGEEKJxSTISAva0NKKGDweg4C9/qbHM0dqRt79/myJXUSNFJoQQQjQ+SUZCpNX9k8BspmzNWso3b662/tI2l5IWl0aFr4I3d74ZggiFEEKIxiHJSIhY27Uj5sYbASj483P8fOw5TdO4u8fdALy+83XKvfL6bSGEEM2TJCMh1PJ3E9BsNio2b6Zs7dpq64e0G0LbyLY43U7e+f6dEEQohBBCNDxJRkLIkpBA7B13AFDw3F9QhlFlvUk38esevwbg1e2v4vF7Gj1GIYQQoqFJMhJiLe69Bz0iAvfOnRR/+GG19SM7jiTeEU9BRQH/2vWvEEQohBBCNCxJRkLMHBtL3K9/BcDBefNQXm+V9VaTlbHdxwKwaPsi/Ia/0WMUQgghGpIkI2eBuLHjMMXF4d2dS9Hy96qtv/m8m4m2RbO7eDercleFIEIhhBCi4UgychYwRYTTcvxvATj0wgsYLleV9WGWMO5IC7QteeWbV6r1vBFCCCGaMklGzhIxt92GOSkRX0EBha+/UW396K6jcZgdZBVm8fm+z0MQoRBCCNEwJBk5S+hWK60m3gfA4Zdfxl9cXGV9tC2am8+7GYBXvn2l0eMTQgghGookI2eR6Ouvw9qpI36nk0MvvFht/dhuY7HoFrYUbGHLgS0hiFAIIYSof5KMnEU0s5mEx58A4Mjrr+P+KbvK+oTwBK7reB0gtSNCCCGaD0lGzjIRl11KxOWXg89HwezZ1db/usev0TWdz/Z9xrgPx/HXLX/ls72fUewprmFvQgghxNlPU02ga0ZxcTHR0dE4nU6ioqJCHU6Dc2dn89PI68DnI+XvfyfiskurrJ/z1Rxe3fFqlWUaGp1jO3Nh/IX0ju9N74TetA5v3ZhhCyGEEFXU9v4tychZ6sAzszmyeDHWjh1JXfEemsUSXKeUYnfxbr4u+JotBVv4uuBrdhfvrraPNhFteOzix7iq3VWNGboQQggBSDLS5PmLi9k1dBj+wkISfv974sbeedLyhyoOBZKTA1vYUrCFnUd2YigDDY2HL3qYcd3HoWlaI0UvhBBCSDLSLBS+tZT8p59Gj46m48oPMcfG1nrbMm8Zz21+jqVZSwG46byb+H3f32PRLafYUgghhKgftb1/SwPWs1jMzTdh69IFw+nk0PPz67RtuCWcJ/s+yeRLJqOh8c7373DfJ/dR4ilpoGiFEEKI0yPJyFlMM5lImDIFgMKlS3H/8EPdttc07ux2J38Z9BccZgfr9q9j7Idj2V+6vyHCFUIIIU6LJCNnufB+fYkcMhj8fg7Meua03ktzZdsrWTRsEa0crfix6EdG/3s02w5ta4BohRBCiLqTZKQJiJ88Gc1ioWzdOkr/t/q09tG9RXfeGP4GnWM7c9h1mF+t/BX/3f3f+g1UCCGEOA2SjDQB1pQU4u66C4ADs59BeTyntZ/W4a1ZMmwJA9sMxOV38fDqh1m8bbG8BVgIIURISW+aJsJfWsaua4bhP3iI+Mceo8Xdvz7tffkMH89sfCbY0yYhLIGE8ITAPCyB+LD4qvPweGwmW32dihBCiHOEdO1thoreXU7ek0+iR0TQ8aOVmFu0OO19KaX4x45/8Nzm5/Ap3ynLd4zuyND2QxnaYSip0amnfVwhhBDnDklGmiFlGOTcfAuu7duJuflmEqdPO+N9FrmK2FOyhwPlBzhQfoCC8oJj87LA3OV3Vdmmc2xnhrUfxtD2Q2kX1e6MYxBCCNE8STLSTJVv3szuO8aAphHevz/hA/oTPmAAtrQ0NL3+mwAppSh0F/L5vs9Zmb2S9fvXV6lJSYtLC9SYtB9KSmRKvR9fCCFE0yXJSDOWP206hW+8UWWZKTaW8P79COvfn4gBA7C0adMgx3a6nXya+ykf5XzEl3lf4lf+4LrkiGSibdFEWiODU4Qlosr3hLAELk64GJNuapD4hBBCnD0aLBlZu3Ytzz77LJs3byYvL4/33nuPUaNGnXSb1atX8/DDD7N9+3ZSUlL4wx/+wF2VvUNqQ5KRqpRSeH78kbL16ylbt57yjRsxysurlLG0a0vEwIG0+O1vsSQkNEgcRa4iPsn9hJU5K9mYvxFDGbXark1EG+7oege/7PRLIqwRDRKbEEKI0GuwZOTDDz/kiy++4KKLLuKGG244ZTKSnZ1Njx49GD9+PPfccw+ffPIJDz74IP/+978ZOnRovZ7MuUp5vVR88w1l69ZTtm4dFd98A/5AjYUlKYm2ixZibdewbTsKXYX85PyJUk8pJd4SSjyBqdRTSrGnOPDZW8r2w9txup1AYMj6GzrfwOi00SRHJjdofEIIIRpfozym0TTtlMnI448/zr///W+2bTs24udtt91GUVERK1eurNVxJBmpG39pKeUbN1Iw+094du/G1KolbTMysJ93XqhDo8JXwb92/YvXvnuNbGc2ALqmc2XKldzZ7U4ujL9Q3i4shBDNxFnzorz169czePDgKsuGDh3K+vXrT7iN2+2muLi4yiRqzxQRQeSVV9Lu9dewnXce/oOHyL1zLBXffhvq0HCYHdzS5RZWXL+CF696kQFJAzCUwX9z/8u4leO4/d+388FPH8gL/YQQ4hzS4MlIfn4+CT9rs5CQkEBxcTEVFRU1bjNr1iyio6ODU0qK9NI4HeaWLWm35FXsF5yP3+kk965fUf7VV6EOCwjUhlyWfBkLhizgveve48bON2LVrWw/vJ0pn01h4JsDuemfNzFrwyw+yvmIQxWHQh2yEEKIBnJWDgc/ZcoUnE5ncNqzZ0+oQ2qyTDExtM1YSFjfvhhlZeTecy+la9aEOqwqOsV24ukBT7Pq5lVM7DWRlMgUFIqswize2PkGj655lEFvD2L48uH83xf/x3s/vEducW6tG8wKIYQ4u5kb+gCtW7fmwIEDVZYdOHCAqKgoHA5HjdvYbDZsNhl+vL6YIsJJeXkB+x54kNLVq9kz8T7azHmWqGHDQh1aFXH2OMZfMJ7xF4ynoLyALQVb2HIgMH1f+D25JbnkluSy4scVANhMNpIikmgT0YbkiGSSI5MDnyvnkdbI0J6QEEKIWmnwZKR///785z//qbJs1apV9O/fv6EPLY6j22wkPz+P/Y8/QfF//sO+hx/BKCsj5sYbQx1ajeLD4hnWfhjD2gcSpmJPMZkFmYHkpGAL2w5tw+13k+3MDjaE/bkoaxRx9jjsZjt2kz0wN9txmBzBz3aznShrFK3DW5MYnkhieCKtwlph0S2NebpCCHFOq3MyUlpayo8//hj8np2dTWZmJnFxcbRt25YpU6awb98+lixZAsD48eOZP38+kydP5te//jWffvopb7/9Nv/+97/r7yxErWgWC0nP/gk9PIyiZe+Q9+QfMMrKiBs7NtShnVKUNYpfJP+CXyT/AgCv4SW/LJ+9JXvZV7qPfaX7qnw+4jpCsaeYYk/dGz/rmk5LR8tgcpIYnkicPQ6f8uH1e/EaXjx+D16j6meTZmJExxEMSBpQ36cvhBDNWp279q5evZpBgwZVWz5u3DgWL17MXXfdRU5ODqtXr66yzUMPPcSOHTtITk7m//7v/2TQsxBSSlEw+08cWbwYAGuHDugOB5rdjm63ozkc6DYbmsOObnegO+yEX3oZ4f36hjbwOijzlrGvdB/F7mJcfhcun+vYvPJzha8Ct89NobuQ/LJ88sryyC/Lx2t4z+jYA9sM5JGLHqFzbOd6OhshhGiaZDh4cVJKKQ69+CKHnp9fq/Ka3U7HD/+DJTGxgSMLLUMZHHEdIa80j/zyfPJK88gry6PQXYhFt2DRLVhN1uBni8mCVQ98zy3J5d0f3sVn+NA1nV92+iUTe02kVVirUJ+WEEKEhCQjolY8ubl48/NRLheGy1V1XuFCuV2UfPIp7qwsooYPp83cOaEO+ayWW5zLX7b8hVW7VwGBcVV+1f1XjOs+jjBLWIijE0KIxiXJiKg3rh07yL7xJlCKdm+8QVjvC0Md0lkvsyCTZzc9yzcHvwGgpaMl9/W6j1GdRslLAoUQ5wxJRkS92v+HP+B8513sPXrQ/u2laPpZOUTNWUUpxce7P+Yvm//C3tK9AHSK6cTtabfTP6k/KZEymJ8QonmTZETUK9+hQ+waOgyjrIzEWbOI+eWoUIfUZHj8Ht7a+RYLvllQpXdPm4g29EvsR7+kfvRt3ZdYe2wIoxRCiPonyYiod4dfeYWCOXMxt2pF6ocfYooID3VITYrT7WRp1lK+2PcF3xz8Bp/yVVmfFpcWSE4S+9EqrBVOtzPQPdkd6KJ89PvRuaEM4sPiaeloSXxYPK0crYLfW4W1wmaSgQOFEKElyYiod4bHw08jRuLNzaXFb35D/MMPhTqkJqvcW87mA5v5Mu9Lvsz7ku8Lv6/3Y0TbomnlaEULewvi7HG0cPxsbm9BnCOOWFssdrMdXZNHb0KI+iXJiGgQJZ98wt6J96FZraT+599Yk5NDHVKzcKjiEBvzNrIhfwMb8jZQ4asgyhpFtC36hHOF4mD5QQ5WHAzOC8oLOFh+EI/hqXMMZt2MzWQLdFWu7LJsM9mwmqxYTVZah7cmNTqV1OhUOkR3oF1UO+xmewNcDSFEcyHJiGgQSilyf/1rytd/SeTVV5M876+hDkn8jFKKYk8xB8sPUlBRwBHXEY5UHOGw6zBHXEc4XFE5dx3mcMXh0x7kTUOjTUQbUmNS6RDVgdSYVNpGtiU5MplWjlbSa0gIIcmIaDiurO/J/uUvwTBou+RVwvv0CXVI4jQppSj3leP2u/H4PcHJbbjx+gND3bv9blx+F3tL9pLtzOYn50/85PyJEk/JCfdr1s0khifSJqJNcDr6UsPUmFSirPL/sRDnAklGRIPKS0+n6M23sKWl0eHdd9BM8lfwuUQpxWHX4UByUvQT2cWB+Z6SPeSX5VdrnHs8i27hmg7XMLbbWLrEdWnEqIUQjU2SEdGgfIWFga6+xcW0npZO7C23hDokcZbwG34KyguCLy3cX7qfvaV7g/P8svxg2T6t+zCm6xh+kfwLeawjRDMkyYhocEdefZUDs57BFBdHx49WYoqMDHVIogn45uA3vLbjNT7e/TF+5QcgJTKFO7rewahOowi3SJdxIZoLSUZEg1NeLz9ddz2e7Gzifv1rEiY/FuqQRBOSX5bPmzvf5J3v3wkOBhdpieSGzjdwe9fbaRPRJsQRCiHOlCQjolGUrlnDnt+OB4uFjv/6J9b27UMdkmhiyr3l/GvXv3jtu9fIKc4BwKSZuKXLLfzugt8RY48JaXxCiNMnyYhoNLn3/oayzz4jrF8/4h9+CHvXrmgWS6jDEk2MoQw+3/c5S3YsYUPeBgAirZH87oLfcWvarVh0+TclRFMjyYhoNO5du/jpuuvBH3j+r9ntOHr2xNG7N44LexHWqxemmJjQBimalA15G/jTV38KjkzbPqo9j13yGJe1uQxN00IcnRCitiQZEY2qZPVqit58i/LMTAyns9p6a6eOhF14IY4Le+Po1Qtr+3by5l9xUn7Dz3s/vsfzXz/PEdcRAAYkDeCxix+jU2ynEEcnhKgNSUZESCjDwJOdTfmWLVR8nUnFli14cnKqldOjonCcfz6OCy7A0esCHD17Su2JqFGJp4S/f/t3XtvxGl7Di67p3HzezUzsNVHedCzEWU6SEXHW8B05QkVmIDEp/zoT1/btKJerWjlrhw6B5OSi3kRffz261RqCaMXZak/xHv68+c/8N/e/AIRbwukQ1YEIawSR1kgiLBGEW8KDnyOtkURaI+kS14WUyJQQRy/EuUmSEXHWUl4vru+/p2LrVlxbt1KRuRXP7t1VykRccQXJL74gj3JENV/lf8WfvvoTO4/srPU2KZEpDEgawMCkgfRJ7CNjmQjRSCQZEU2Kr7AQ1zffUP711xxZuAjl8dDyd7+j1f2TQh2aOAv5DT/bDm+j0FVIiaeEUm8ppZ5SSrwllHpKg98LXYXsPLKzyvD0Zt1Mr1a9GNhmIAOSBpAWl4auSdIrREOQZEQ0WUUrVpD3xBQA2jw/j6ghQ0IckWjKSj2lbMzfyLr96/hi3xfsLd1bZX2cPY4O0R3Q0NA0DR0dtMBbiXVNRwt8IcoaRY8WPejRsgddW3TFYXaE6IyEaDokGRFNWv4f/0jhkn+gh4XR/u2l2DpJ7wlRP3KLc/li/xes27eOjfkbKfeV13kfJs1Ep5hO9GjZg54te9KjZQ86xnTErJsbIGIhmi5JRkSTprxecu+5l/ING7C0a0uHZcswyc9e1DOv38s3h77hUMUhFApUYPA1hcJQBnDse0F5AdsObePbQ99yqOJQtX05zA46xXTCYXZgMVmw6lasJisW3VJlHmYO49I2l3JBqwtkzBTR7EkyIpo835EjZN90E779eYT/4jJSXnoJzSRvdhWhpZTiQPmBYGKy7dA2th/eTpm3rE776RjdkRs638DIjiOli7JotiQZEc2Ca8cOckbfgXK5aPGb3xD/8EOhDkmIagxlkOPMIduZjdvvxmN48BpePH4PXr8Xj+EJfDa85Jfl82nup7j8ge7tZt3MlSlXcmPnG+mX1E8a04pmRZIR0Ww4//UB+x8LvBG4zV+eI2rYsBBHJMSZKfGU8GH2hyz/YTnbD28PLk8KT2JU51H8stMvaR3eOoQRClE/JBkRzcqBPz3LkYUL0RwO2r/1JvYuXUIdkhD1YueRnSz/YTkf/PQBJZ4SINCTJzE8kVh7LDH2GOJscYG5PY4YWwyx9lhibbFYTVZ8hg+v4a0yPzp5DS8xthj6JPaR3j8iJCQZEc2K8vnY85vfULZuPZbkZDq8s0yGjxfNisvn4r+5/2X5D8v5Kv+ret233WRnYJuBXNX2Ki5PuZwoq/weFY1DkhHR7PgKC8m5+Ra8e/cSPmAAKS8vQDNLV0rR/BSUF5BXlkehqzAwuQspchVxxHWEIncRha5CjriO4FM+zJoZi8mCWTcf+6yZseiBZTnFOewr3Rfct1kz0yexD1e1vYor215JS0fLEJ6paO4kGRHNkisri5zbbkdVVBB9/XW0njoVPSws1GEJcdZSSpFVmMV/d/+XT3I/4ceiH4PrNDR6xffiypQruTDhQtLi0rCZbCGMVjQ3koyIZqt45Ur2PfQwKIW1XTuS5jyLo2fPUIclRJOQ48zhk9xP+CT3E7499G2VdWbdTFpsGj1a9uD8VufTs2VP2kW1k/FQxGmTZEQ0a2Vffsn+J6bgy88Hs5lWE39Hi3vvlcc2QtRBflk+n+R+wvr96/n20LcccR2pVibKGkXPlj3p2aonF7a6kAviL5AXDYpaa9Bk5IUXXuDZZ58lPz+fCy64gOeff54+ffrUWHbx4sX86le/qrLMZrPhquEV8iciyYioid/pJO/ppyn5cCUAjt69SfrTbKzJySGOTIimRynFvtJ9fHvoW745+A3fHvqW7w5/h8fwVCln0kx0ietC7/jeXJRwERfGX0gLR4sQRS3Odg2WjCxdupSxY8fyt7/9jb59+/KXv/yFZcuWkZWVRXx8fLXyixcv5oEHHiArK+vYQTWNhISEej8Zce5RSlH8r3+RP206Rmkpeng4CX/4A9GjrpeqZSHOkNfv5fui7/n24LdsPbiVrwu+rtIY9qj2Ue25KOEieif0JiUyhUhLJFG2KCKtkdhNdvl/8RzWYMlI3759ueSSS5g/fz4AhmGQkpLCpEmTeOKJJ6qVX7x4MQ8++CBFRUV1O4PjSDIiTsWzdx/7H3+cis2bAYgcNozEp6dK918h6ll+WT5bDmxhS8EWNh/YXKVBbE0suoVIayRR1iiirIEExWF2YNaP9fipNjdZCDeH0zqiNUnhSSSGJ9LC0UJGp22Canv/rtMDdo/Hw+bNm5kyZUpwma7rDB48mPXr159wu9LSUtq1a4dhGPTu3Zs//vGPdO/evS6HFuKkrMltaLfkVQ7//RUOzp9PycqVVGzZQsvfTcDavj2WNm2wtG6NZrGEOlQhmrTW4a25NvVark29FgCn28nXBV+z5cAWMg9mcqjiECWeEko8JfiVH6/h5YjrSI3tUerColtIDE8kMSKRxPBEksKTSI5M5uKEi0mMSKyPUxMhVKdk5NChQ/j9/mqPWBISEti5c2eN23Tp0oWFCxdy/vnn43Q6mTNnDgMGDGD79u0kn+DZvtvtxu12B78XFxfXJUxxjtJMJlqO/y3hAwey/7HH8OTkkP90+rECuo45IQFLmySsbdpgTgrMrR074ujZUxq/CnEaom3RXJFyBVekXFFluVKKcl85xe5iij3FlHhKKPYEPrt9bnzKh9fvDc6PH0XWa3gp8ZSQX5bP/rL9FJQX4DW85JbkkluSWy2GtpFt6ZfYj76JfenTug8x9pjGOXlRbxr8t2///v3p379/8PuAAQPo2rUrCxYsYPr06TVuM2vWLNLT02tcJ8SpOHr2oMPydzn8SgYV33yDd98+vPv2oTwefHl5+PLyqNi0uco2elQU4QMGEHHZpYRfehmWhOrtn4QQtadpGuGWcMIt4SRyZjUXXsMbGAiuNI+8sjz2l+4nryyPH4p+YPuh7cEk5e3v30ZDIy0ujX5J/ejXuh8XJlwoQ+E3AXVqM+LxeAgLC+Odd95h1KhRweXjxo2jqKiI999/v1b7ufnmmzGbzbz55ps1rq+pZiQlJUXajIjTpgwD/+HDePfvx7tvH57KBMW7dx+ub7/F73RWKW/r0iWQmFz2C8Iu7IVmtYYociHEyZR6Stl0YBNf5n3JhrwN1dqwmHUzieGJJIQl0Dq8dZV5Qnjgc6wtVhrZNpAGbcDap08fnn/+eSDQgLVt27bcd999NTZg/Tm/30/37t259tpr+fOf/1yrY0oDVtGQlN+P69tvKV37GaWff47r22/huP8t9LAwwi65BGuHDlhSkrGmpGBJTsGS3AZdkhQhzioHyw+yIX8DX+7/kg35G8gvyz/lNlbdSofoDqTFpdG1RVfS4tLoEtuFCGtEI0TcvDVo195x48axYMEC+vTpw1/+8hfefvttdu7cSUJCAmPHjqVNmzbMmjULgGnTptGvXz86depEUVERzz77LCtWrGDz5s1069atXk9GiPrgKyyk7It1lH22ltLPv8B/+HDNBTUNc0IC1uRkLCmB5MTSpg2WpCQsSW2wtE6QdihChJBSKtju5EDZAfLL8wPzsnwOlAfmh10n+P+bQFuU4xOUVo5W+JUfQxn4DB+GMvArf2CZYeBTPpRSKNSxOYrAf8eW65pOpDWSaFt0sJdRuCW8WdbONEhvGoBbb72VgwcP8tRTT5Gfn0+vXr1YuXJlsFFrbm4uun6s+1VhYSH33nsv+fn5xMbGctFFF7Fu3bpaJyJCNDZzbCzRI4YTPWI4yjBwffcdFZmZePfsxbN3D949e/Hu2YNRXo4vPz8wCuymTdV3pOuYWydUJifHJlNEBJrVimazoVmsgc9WK7rt2GfNYgkkMmYLmsWMZjajmUyNfzGEaMI0TQv0vjlJbxuv30t+WT7fF33PziM72Xl4J98d+Y4D5QeCbVE+3v1xg8dq0kyBxMQWRbQ1mihbFG0i2tA2si3totrRNqotyRHJWEzNs0egDAcvxGlQSuEvLMS7Zw+ePXvx7t2DZ8+eQJuU/fvx7c9Deb31e1BNCyQolYmKZrNiCo9Ajzg6hVf7rtvsKK8X5XFjeDwojwfl8QbmbndgbhiYW7XCkphYmTAF5qYWLer9LzVlGIHzaIZ/AYrmpdBVyHdHvquSoJR4SjBpJky6CV3Tg59NWmDSNR1d09HQAmOiaIGXEWoE/s1rBP7dG8qgxFOC0+Ok2F1cbZTbE9E1ncTwxEByUpmkdI7tTLcW3Yi0Rjbk5Tht8m4aIUJIGQa+g4fw7t8XTFCOJilGeTnK48HwelDuownCseTA8HqhvhOZ06BZrYEEpU0S5taJaCYdw+VGuVwYblcgdpcLw125zOMOJGB+A+X3g98fSD58vsDc7w+2xdFsNjS7Hd1uR7Pb0O2OwNxmR3PY0R1hmGKiMcXEYI6NxRQTg+novPKzHhEhSY1oFlw+F063k2JPcXBe6Cpkb+ledhfvJrc4UENT4as44T7aR7WnW4tudG/Rne4tu9M1rithltC/0VySESGaMKVU4CZ+3FTlu9eHcrswysrwl5ZilJZhlJZilJVW/e6qQA8++rEeezxktQQeDVmtoOn4Dhbg3bcfb15eIGkqKKjSiPesZDYHEpWWLTHHxWFu2QJTXIvAvEULzC1aYIqLwxQejma3o9lsgeTHZkPTZSRP0bQopThUcSiQnJTksrt4N7uLd7PzyM4ah+jX0EiNTqV7y+4kRyYTZg7DbrJjNwcmh9lR9bvJQXx4PDaTrV7jlmRECHHalMeDt+BogrI/0C4G0Ky2YzUY9qM3d3ugvYvdHhjhVtcDj5F0HY7OTaZAmxeTCQwjWKNiVFSgfj53uQNJVlFR5VSIv6gIX1ER/sLAMlVx4r8Qa0OzWALxVp6LHhmJOTYGU0xsIIGJDdS+mOPiKpfFBpKb2FhJZMRZp9BVyI7DO9h+eDvbD21n++HtHCg/UOf9LLlmCRfGX1ivsTVYA1YhRPOnWa1Yk5PP2jcgGy4X/sJCfIeP4D9yGN+hw8G578hh/IcO4zsc+KzKKzDcbvD5gtsrrzfwSKmkBH/lMnfNh6rKbMbcqhXm+FZY4uMxxydgjo+vnFphbtkKU2QEeng4eni49KYSjSLWHsvANgMZ2GZgcNnB8oPBBOVQxSFcPhcuv4sKXwUu37H58cvsJnvIzkFqRoQQ5wTl8wXavHgq27i43IFHXS4XRmkp/iNH8BUWBmpfjhzBX1T4s+9FdX50pdntgcQkIpCcmMLC0SMiAu1f4n5W8xIXV1krE4ceHnjWryoqqj6KKyvDKCsNxFtWhmY2H7ddYB96VJS0pRFnDakZEUKI42hmM6YIMxB+WtsrrzdQ21JQgPfAAXwFBfgKDgbmBw7gO1iA79BhjLIylCfQO0K5XPhdrhOPVXOiWC2WQCNgw6h7oGYzptgYzLGVSUpMTKC2JiISPTICU0QkemRklWW6zYbf6Qw8Fis8LgkrLAxMRUX4S0uwxCdgadcWa9t2WNu1xdquHZbkZHRb/bYzEOceqRkRQoh6pjwe/GVHazIqp9LSQC1HSclxN/oj+I4UVtbKHMF/pBDlclXdmaYFumofX8MSHviuvN7gdv4jRzDKyhr/ZDUNc2JrrO3aYU1pi+6wo3x+lN8X6FHl84Phr1zmB78P5fEGel+5j/Yic2O4j+tu7najDCPQtufn09FxeCo/6w4HelgYWlhgrjvCKucO9PCwQNsgkxlMeqC9j6aDrgU/ayYddB3QqpzTsc/HPuoOB6boaEzR0eiRkdJ+qBakZkQIIUJEs1oxW60QG1vnbY3ycvyFhWCxBAbIczhq/djFcLsDSc6RyiSn8Egg8SktwSgpxSgtwV9SilFScmxZSQmGx3OsK3VMbOAx0tEpJjowDw/Hm38Az+7deHJ3492di2f3boyyMnz78/Dtz6N8/Zd1Pt+TqZaYnU00DVNUFHpMNKbomGNJSlhld9rg3/nH/t4P/u1vqEBS5j2uh5zfB8d9x+8PJFjhx40fFB5+3JhCgc+a1RpoMG42BxqMHzdhMqNZzCivL/B4rzIp9pce/Xx8olxKq/vvx9KmTeNex0qSjAghxFlEDws7dkOr67Y2G3rr1lhat67nqGqmlMJ/5AieysTEu2cPyudDM5sqe1BV1kiYzMctMwW6mdtsaLZA9/JAd/PK7zYbms0WqMHweYONjZXHc+yz1xsYxM/tQbkqMMrLK6fKzxXlwWWqvCIwzk3lpJSq4bO/yjkd+1LlZDFcFRhFTozyclAq8GjL6cRLbqNc74YWc9ttkowIIYRoWjRNw1w5pktY7/rtEno2Ux4P/uLiYDLiL6qcO4swysuP1WQFa7SqPwLSzIFaC8zmymTNHHz1AyZzYJDBygbMRxssHxtP6OiyUpTHe/IxiXw+NLO5smYlrMZaFj08HFNERKMlsTWRZEQIIYSoA81qxdyyJeaWLUMdSrMhrW+EEEIIEVKSjAghhBAipCQZEUIIIURISTIihBBCiJCSZEQIIYQQISXJiBBCCCFCSpIRIYQQQoSUJCNCCCGECClJRoQQQggRUpKMCCGEECKkJBkRQgghREhJMiKEEEKIkJJkRAghhBAhJcmIEEIIIULKHOoAakMpBUBxcXGIIxFCCCFEbR29bx+9j59Ik0hGSkpKAEhJSQlxJEIIIYSoq5KSEqKjo0+4XlOnSlfOAoZhsH//fiIjI9E0rd72W1xcTEpKCnv27CEqKqre9itqJte7ccn1blxyvRuXXO/GdbrXWylFSUkJSUlJ6PqJW4Y0iZoRXddJTk5usP1HRUXJP+ZGJNe7ccn1blxyvRuXXO/GdTrX+2Q1IkdJA1YhhBBChJQkI0IIIYQIqXM6GbHZbEydOhWbzRbqUM4Jcr0bl1zvxiXXu3HJ9W5cDX29m0QDViGEEEI0X+d0zYgQQgghQk+SESGEEEKElCQjQgghhAgpSUaEEEIIEVLndDLywgsv0L59e+x2O3379mXjxo2hDqlZWLt2LSNHjiQpKQlN01ixYkWV9UopnnrqKRITE3E4HAwePJgffvghNME2A7NmzeKSSy4hMjKS+Ph4Ro0aRVZWVpUyLpeLiRMn0qJFCyIiIrjxxhs5cOBAiCJu2l566SXOP//84OBP/fv358MPPwyul2vdcJ555hk0TePBBx8MLpPrXb+efvppNE2rMqWlpQXXN9T1PmeTkaVLl/Lwww8zdepUtmzZwgUXXMDQoUMpKCgIdWhNXllZGRdccAEvvPBCjev/9Kc/MW/ePP72t7+xYcMGwsPDGTp0KC6Xq5EjbR7WrFnDxIkT+fLLL1m1ahVer5err76asrKyYJmHHnqIf/3rXyxbtow1a9awf/9+brjhhhBG3XQlJyfzzDPPsHnzZjZt2sSVV17J9ddfz/bt2wG51g3lq6++YsGCBf/f3v3HRHFtcQD/rou7gIgLQhYRBVoF+bkoKKxo1YJS01q1NVClFUN/WSD1B9LSGLqIqSxt0YrV2tQGNGncWhtaLZFI5UcjRQtbELBAxVDRCFINoiiKsuf9YZzXKfge6pJBOJ9kkpl7Z++cexjCycwsA39/f1E759v8fHx80NLSIizHjx8X+gYs3zRMzZgxg+Lj44Xtnp4ecnZ2pvT0dAmjGnoAUG5urrBtMpnIycmJPvnkE6Ht6tWrpFQqaf/+/RJEOPS0tbURACopKSGie/kdOXIkfffdd8I+dXV1BIDKysqkCnNIsbOzoz179nCuB8j169dp8uTJVFBQQHPmzKE1a9YQEZ/bA0Gn05FGo+mzbyDzPSyvjHR3d8NoNCI8PFxoGzFiBMLDw1FWViZhZENfU1MTWltbRbkfM2YMgoODOfdm0tHRAQCwt7cHABiNRty5c0eU8ylTpmDixImc88fU09MDg8GAGzduQKvVcq4HSHx8PJ5//nlRXgE+twfKmTNn4OzsjKeeegrR0dFobm4GMLD5fiJelGduly9fRk9PD9RqtahdrVajvr5eoqiGh9bWVgDoM/f3+9ijM5lMWLt2LUJDQ+Hr6wvgXs4VCgVUKpVoX875o6upqYFWq8WtW7dgY2OD3NxceHt7o6qqinNtZgaDAb///jvKy8t79fG5bX7BwcHIycmBp6cnWlpasGnTJsyePRu1tbUDmu9hWYwwNlTFx8ejtrZWdI+XmZ+npyeqqqrQ0dGBgwcPIiYmBiUlJVKHNeScP38ea9asQUFBASwtLaUOZ1hYuHChsO7v74/g4GC4urriwIEDsLKyGrDjDsvbNA4ODpDL5b2eAL506RKcnJwkimp4uJ9fzr35JSQk4KeffkJRURFcXFyEdicnJ3R3d+Pq1aui/Tnnj06hUGDSpEkIDAxEeno6NBoNtm/fzrk2M6PRiLa2NkybNg0WFhawsLBASUkJsrKyYGFhAbVazfkeYCqVCh4eHmhsbBzQ83tYFiMKhQKBgYE4duyY0GYymXDs2DFotVoJIxv63N3d4eTkJMr9tWvXcPLkSc79IyIiJCQkIDc3F4WFhXB3dxf1BwYGYuTIkaKcNzQ0oLm5mXNuJiaTCbdv3+Zcm1lYWBhqampQVVUlLEFBQYiOjhbWOd8Dq7OzE2fPnsW4ceMG9vx+rMdfn2AGg4GUSiXl5OTQH3/8QW+99RapVCpqbW2VOrQn3vXr16myspIqKysJAG3dupUqKyvp3LlzRESk1+tJpVLRjz/+SNXV1bR48WJyd3enrq4uiSN/Mr3zzjs0ZswYKi4uppaWFmG5efOmsM/q1atp4sSJVFhYSBUVFaTVakmr1UoY9ZMrOTmZSkpKqKmpiaqrqyk5OZlkMhkdPXqUiDjXA+2f36Yh4nybW2JiIhUXF1NTUxOVlpZSeHg4OTg4UFtbGxENXL6HbTFCRLRjxw6aOHEiKRQKmjFjBp04cULqkIaEoqIiAtBriYmJIaJ7X+9NSUkhtVpNSqWSwsLCqKGhQdqgn2B95RoAZWdnC/t0dXVRXFwc2dnZkbW1NS1dupRaWlqkC/oJFhsbS66urqRQKMjR0ZHCwsKEQoSIcz3Q/l2McL7NKyoqisaNG0cKhYLGjx9PUVFR1NjYKPQPVL5lRESPd22FMcYYY+zRDctnRhhjjDE2eHAxwhhjjDFJcTHCGGOMMUlxMcIYY4wxSXExwhhjjDFJcTHCGGOMMUlxMcIYY4wxSXExwhgDAPz111+QyWSoqqqSOhRBfX09QkJCYGlpiYCAAKnDeaDi4mLIZLJe7+xgjPUPFyOMDRKrVq2CTCaDXq8Xtf/www+QyWQSRSUtnU6HUaNGoaGhQfQ+DMbY0MLFCGODiKWlJTIyMtDe3i51KGbT3d39yJ89e/YsZs2aBVdXV4wdO9aMUTHGBhMuRhgbRMLDw+Hk5IT09PQH7pOamtrrlsVnn30GNzc3YXvVqlVYsmQJtmzZArVaDZVKhbS0NNy9exdJSUmwt7eHi4sLsrOze41fX1+PmTNnwtLSEr6+vigpKRH119bWYuHChbCxsYFarcZrr72Gy5cvC/1z585FQkIC1q5dCwcHB0RERPQ5D5PJhLS0NLi4uECpVCIgIAD5+flCv0wmg9FoRFpaGmQyGVJTUx84Tnp6Otzd3WFlZQWNRoODBw8K/fdvoeTl5cHf3x+WlpYICQlBbW2taJzvv/8ePj4+UCqVcHNzQ2Zmpqj/9u3beP/99zFhwgQolUpMmjQJX3/9tWgfo9GIoKAgWFtbY+bMmWhoaBD6Tp06hXnz5mH06NGwtbVFYGAgKioq+pwTY8MNFyOMDSJyuRxbtmzBjh07cOHChccaq7CwEBcvXsQvv/yCrVu3QqfT4YUXXoCdnR1OnjyJ1atX4+233+51nKSkJCQmJqKyshJarRaLFi3ClStXAABXr17Fs88+i6lTp6KiogL5+fm4dOkSIiMjRWPs3bsXCoUCpaWl2L17d5/xbd++HZmZmfj0009RXV2NiIgIvPjiizhz5gwAoKWlBT4+PkhMTERLSws2bNjQ5zjp6enYt28fdu/ejdOnT2PdunV49dVXexVRSUlJyMzMRHl5ORwdHbFo0SLcuXMHwL0iIjIyEq+88gpqamqQmpqKlJQU5OTkCJ9fuXIl9u/fj6ysLNTV1eHLL7+EjY2N6BgbN25EZmYmKioqYGFhgdjYWKEvOjoaLi4uKC8vh9FoRHJyMkaOHPmgHx9jw8tjv2qPMWYWMTExtHjxYiIiCgkJodjYWCIiys3NpX/+qup0OtJoNKLPbtu2jVxdXUVjubq6Uk9Pj9Dm6elJs2fPFrbv3r1Lo0aNov379xMRUVNTEwEgvV4v7HPnzh1ycXGhjIwMIiLavHkzLViwQHTs8+fPEwDhzctz5syhqVOn/t/5Ojs700cffSRqmz59OsXFxQnbGo2GdDrdA8e4desWWVtb06+//ipqf/3112n58uVE9N+3SBsMBqH/ypUrZGVlRd9++y0REa1YsYLmz58vGiMpKYm8vb2JiKihoYEAUEFBQZ9x3D/Gzz//LLTl5eURAOrq6iIiotGjR1NOTs4D58LYcMZXRhgbhDIyMrB3717U1dU98hg+Pj4YMeK/v+JqtRp+fn7Ctlwux9ixY9HW1ib6nFarFdYtLCwQFBQkxHHq1CkUFRXBxsZGWKZMmQLg3vMd9wUGBv7P2K5du4aLFy8iNDRU1B4aGvpQc25sbMTNmzcxf/58UUz79u0TxfPvednb28PT01M4Vl1dXZ+xnDlzBj09PaiqqoJcLsecOXP+Zzz+/v7C+rhx4wBAyO/69evxxhtvIDw8HHq9vld8jA1nFlIHwBjr7ZlnnkFERAQ++OADrFq1StQ3YsQIEJGo7f7thn/69y0AmUzWZ5vJZOp3XJ2dnVi0aBEyMjJ69d3/4wsAo0aN6veYj6OzsxMAkJeXh/Hjx4v6lEql2Y5jZWXVr/3+md/734C6n9/U1FSsWLECeXl5OHLkCHQ6HQwGA5YuXWq2OBl7UvGVEcYGKb1ej8OHD6OsrEzU7ujoiNbWVlFBYs7/DXLixAlh/e7duzAajfDy8gIATJs2DadPn4abmxsmTZokWh6mALG1tYWzszNKS0tF7aWlpfD29u73ON7e3lAqlWhubu4Vz4QJEx44r/b2dvz555/CvLy8vPqMxcPDA3K5HH5+fjCZTL2eQ3lYHh4eWLduHY4ePYqXXnqpzweIGRuO+MoIY4OUn58foqOjkZWVJWqfO3cu/v77b3z88cdYtmwZ8vPzceTIEdja2prluDt37sTkyZPh5eWFbdu2ob29XXgQMz4+Hl999RWWL1+O9957D/b29mhsbITBYMCePXsgl8v7fZykpCTodDo8/fTTCAgIQHZ2NqqqqvDNN9/0e4zRo0djw4YNWLduHUwmE2bNmoWOjg6UlpbC1tYWMTExwr5paWkYO3Ys1Go1Nm7cCAcHByxZsgQAkJiYiOnTp2Pz5s2IiopCWVkZPv/8c+zatQsA4ObmhpiYGMTGxiIrKwsajQbnzp1DW1tbr4d3+9LV1YWkpCQsW7YM7u7uuHDhAsrLy/Hyyy/3e66MDWV8ZYSxQSwtLa3XbRQvLy/s2rULO3fuhEajwW+//fbAb5o8Cr1eD71eD41Gg+PHj+PQoUNwcHAAAOFqRk9PDxYsWAA/Pz+sXbsWKpVK9HxKf7z77rtYv349EhMT4efnh/z8fBw6dAiTJ09+qHE2b96MlJQUpKenw8vLC8899xzy8vLg7u7ea15r1qxBYGAgWltbcfjwYSgUCgD3rvgcOHAABoMBvr6++PDDD5GWlia6RfbFF19g2bJliIuLw5QpU/Dmm2/ixo0b/YpRLpfjypUrWLlyJTw8PBAZGYmFCxdi06ZNDzVXxoYqGf375jNjjA0hxcXFmDdvHtrb26FSqaQOhzHWB74ywhhjjDFJcTHCGGOMMUnxbRrGGGOMSYqvjDDGGGNMUlyMMMYYY0xSXIwwxhhjTFJcjDDGGGNMUlyMMMYYY0xSXIwwxhhjTFJcjDDGGGNMUlyMMMYYY0xSXIwwxhhjTFL/AZ3qXPK11gWIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(objective_func_vals[:50], label=\"A Subex-1\")\n",
    "plt.plot(objective_func_vals[50:100], label=\"A Subex-2\")\n",
    "plt.plot(objective_func_vals[100:150], label=\"A Subex-3\")\n",
    "plt.plot(objective_func_vals[150:200], label=\"A Subex-4\")\n",
    "plt.plot(objective_func_vals[200:250], label=\"A Subex-5\")\n",
    "plt.plot(objective_func_vals[250:300], label=\"A Subex-6\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "621adb10-e0d3-4016-ad2b-615a998ada42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective_func_vals.clear()\n",
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8310166a-4dc2-4ffd-b1cf-2d8f83aa3548",
   "metadata": {},
   "source": [
    "#### Optimizer Result for sub-circuits[\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2d4424fc-7ce6-41f0-b177-543f3708c0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point2 = np.random.random((8,))\n",
    "function2 = CustomMultiClassObjectiveFunction(x_train_B, y_train, sampler_qcnn2, loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "60edb6f0-937d-4faf-b4f2-24fc1cd5c731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 5.459405422210693\n",
      "Running Time for Forward Pass: %s seconds 6.521866083145142\n",
      "Running Time for Forward Pass: %s seconds 4.806445121765137\n",
      "Running Time for Forward Pass: %s seconds 5.147233963012695\n",
      "Running Time for Forward Pass: %s seconds 4.875425815582275\n",
      "Running Time for Forward Pass: %s seconds 8.724041938781738\n",
      "Running Time for Forward Pass: %s seconds 4.615893602371216\n",
      "Running Time for Forward Pass: %s seconds 4.771284580230713\n",
      "Running Time for Forward Pass: %s seconds 4.6619908809661865\n",
      "Running Time for Forward Pass: %s seconds 4.662001132965088\n",
      "Running Time for Forward Pass: %s seconds 4.802353620529175\n",
      "Running Time for Forward Pass: %s seconds 6.391260623931885\n",
      "Running Time for Forward Pass: %s seconds 5.2342119216918945\n",
      "Running Time for Forward Pass: %s seconds 8.029057502746582\n",
      "Running Time for Forward Pass: %s seconds 4.7944066524505615\n",
      "Running Time for Forward Pass: %s seconds 6.253872394561768\n",
      "Running Time for Forward Pass: %s seconds 4.777674198150635\n",
      "Running Time for Forward Pass: %s seconds 6.252659559249878\n",
      "Running Time for Forward Pass: %s seconds 5.099300384521484\n",
      "Running Time for Forward Pass: %s seconds 4.56593918800354\n",
      "Running Time for Forward Pass: %s seconds 5.517693042755127\n",
      "Running Time for Forward Pass: %s seconds 4.651324987411499\n",
      "Running Time for Forward Pass: %s seconds 4.724934339523315\n",
      "Running Time for Forward Pass: %s seconds 4.701368808746338\n",
      "Running Time for Forward Pass: %s seconds 4.784976959228516\n",
      "Running Time for Forward Pass: %s seconds 4.512478590011597\n",
      "Running Time for Forward Pass: %s seconds 4.853074312210083\n",
      "Running Time for Forward Pass: %s seconds 4.4841086864471436\n",
      "Running Time for Forward Pass: %s seconds 4.919766664505005\n",
      "Running Time for Forward Pass: %s seconds 4.967156648635864\n",
      "Running Time for Forward Pass: %s seconds 4.796545743942261\n",
      "Running Time for Forward Pass: %s seconds 5.0524914264678955\n",
      "Running Time for Forward Pass: %s seconds 4.6917808055877686\n",
      "Running Time for Forward Pass: %s seconds 4.650073289871216\n",
      "Running Time for Forward Pass: %s seconds 4.7743635177612305\n",
      "Running Time for Forward Pass: %s seconds 4.639742851257324\n",
      "Running Time for Forward Pass: %s seconds 4.756632089614868\n",
      "Running Time for Forward Pass: %s seconds 5.254336357116699\n",
      "Running Time for Forward Pass: %s seconds 4.6330039501190186\n",
      "Running Time for Forward Pass: %s seconds 5.333319187164307\n",
      "Running Time for Forward Pass: %s seconds 4.463791847229004\n",
      "Running Time for Forward Pass: %s seconds 4.626779317855835\n",
      "Running Time for Forward Pass: %s seconds 4.508185625076294\n",
      "Running Time for Forward Pass: %s seconds 4.705137729644775\n",
      "Running Time for Forward Pass: %s seconds 4.603672981262207\n",
      "Running Time for Forward Pass: %s seconds 4.6261749267578125\n",
      "Running Time for Forward Pass: %s seconds 5.433171510696411\n",
      "Running Time for Forward Pass: %s seconds 4.7729222774505615\n",
      "Running Time for Forward Pass: %s seconds 4.546594858169556\n",
      "Running Time for Forward Pass: %s seconds 6.79575777053833\n",
      "Running Time for Forward Pass: %s seconds 4.466248512268066\n",
      "Running Time for Forward Pass: %s seconds 4.6376378536224365\n",
      "Running Time for Forward Pass: %s seconds 4.8257598876953125\n",
      "SPSA Epoch 1: 5.49660\n",
      "Running Time for Forward Pass: %s seconds 4.605271339416504\n",
      "Running Time for Forward Pass: %s seconds 4.615781545639038\n",
      "Running Time for Forward Pass: %s seconds 4.61590838432312\n",
      "SPSA Epoch 2: 4.72470\n",
      "Running Time for Forward Pass: %s seconds 4.675351858139038\n",
      "Running Time for Forward Pass: %s seconds 4.719414472579956\n",
      "Running Time for Forward Pass: %s seconds 4.6436052322387695\n",
      "SPSA Epoch 3: 3.83800\n",
      "Running Time for Forward Pass: %s seconds 4.599944829940796\n",
      "Running Time for Forward Pass: %s seconds 4.723836421966553\n",
      "Running Time for Forward Pass: %s seconds 4.572707414627075\n",
      "SPSA Epoch 4: 3.61456\n",
      "Running Time for Forward Pass: %s seconds 4.724782228469849\n",
      "Running Time for Forward Pass: %s seconds 4.619137287139893\n",
      "Running Time for Forward Pass: %s seconds 4.835793495178223\n",
      "SPSA Epoch 5: 3.48003\n",
      "Running Time for Forward Pass: %s seconds 5.232626914978027\n",
      "Running Time for Forward Pass: %s seconds 4.7249321937561035\n",
      "Running Time for Forward Pass: %s seconds 4.5402703285217285\n",
      "SPSA Epoch 6: 3.32227\n",
      "Running Time for Forward Pass: %s seconds 4.6077189445495605\n",
      "Running Time for Forward Pass: %s seconds 6.107028961181641\n",
      "Running Time for Forward Pass: %s seconds 5.462865352630615\n",
      "SPSA Epoch 7: 3.28149\n",
      "Running Time for Forward Pass: %s seconds 4.707953691482544\n",
      "Running Time for Forward Pass: %s seconds 4.519628047943115\n",
      "Running Time for Forward Pass: %s seconds 4.696977615356445\n",
      "SPSA Epoch 8: 3.21393\n",
      "Running Time for Forward Pass: %s seconds 7.278596878051758\n",
      "Running Time for Forward Pass: %s seconds 4.639926433563232\n",
      "Running Time for Forward Pass: %s seconds 4.445276498794556\n",
      "SPSA Epoch 9: 3.03013\n",
      "Running Time for Forward Pass: %s seconds 5.2871410846710205\n",
      "Running Time for Forward Pass: %s seconds 4.528121709823608\n",
      "Running Time for Forward Pass: %s seconds 4.822529315948486\n",
      "SPSA Epoch 10: 3.02397\n",
      "Running Time for Forward Pass: %s seconds 4.769960403442383\n",
      "Running Time for Forward Pass: %s seconds 5.66403603553772\n",
      "Running Time for Forward Pass: %s seconds 4.478107452392578\n",
      "SPSA Epoch 11: 2.99088\n",
      "Running Time for Forward Pass: %s seconds 4.586122035980225\n",
      "Running Time for Forward Pass: %s seconds 4.740006446838379\n",
      "Running Time for Forward Pass: %s seconds 5.638325452804565\n",
      "SPSA Epoch 12: 2.94434\n",
      "Running Time for Forward Pass: %s seconds 5.45409369468689\n",
      "Running Time for Forward Pass: %s seconds 4.758554220199585\n",
      "Running Time for Forward Pass: %s seconds 5.074146509170532\n",
      "SPSA Epoch 13: 2.93892\n",
      "Running Time for Forward Pass: %s seconds 4.473059415817261\n",
      "Running Time for Forward Pass: %s seconds 4.6515724658966064\n",
      "Running Time for Forward Pass: %s seconds 4.453094959259033\n",
      "SPSA Epoch 14: 2.80729\n",
      "Running Time for Forward Pass: %s seconds 4.662920713424683\n",
      "Running Time for Forward Pass: %s seconds 4.434011459350586\n",
      "Running Time for Forward Pass: %s seconds 5.959740877151489\n",
      "SPSA Epoch 15: 2.79199\n",
      "Running Time for Forward Pass: %s seconds 4.456011056900024\n",
      "Running Time for Forward Pass: %s seconds 4.5428993701934814\n",
      "Running Time for Forward Pass: %s seconds 4.438431978225708\n",
      "SPSA Epoch 16: 2.74919\n",
      "Running Time for Forward Pass: %s seconds 4.61801552772522\n",
      "Running Time for Forward Pass: %s seconds 4.5047783851623535\n",
      "Running Time for Forward Pass: %s seconds 4.657826900482178\n",
      "SPSA Epoch 17: 2.74161\n",
      "Running Time for Forward Pass: %s seconds 4.5182578563690186\n",
      "Running Time for Forward Pass: %s seconds 4.9969658851623535\n",
      "Running Time for Forward Pass: %s seconds 4.506237030029297\n",
      "SPSA Epoch 18: 2.72867\n",
      "Running Time for Forward Pass: %s seconds 4.72149395942688\n",
      "Running Time for Forward Pass: %s seconds 4.68960428237915\n",
      "Running Time for Forward Pass: %s seconds 4.978703260421753\n",
      "SPSA Epoch 19: 2.72983\n",
      "Running Time for Forward Pass: %s seconds 5.670884847640991\n",
      "Running Time for Forward Pass: %s seconds 4.671952486038208\n",
      "Running Time for Forward Pass: %s seconds 4.686565637588501\n",
      "SPSA Epoch 20: 2.72283\n",
      "Running Time for Forward Pass: %s seconds 4.547820568084717\n",
      "Running Time for Forward Pass: %s seconds 4.947234392166138\n",
      "Running Time for Forward Pass: %s seconds 4.952495098114014\n",
      "SPSA Epoch 21: 2.71797\n",
      "Running Time for Forward Pass: %s seconds 4.644673109054565\n",
      "Running Time for Forward Pass: %s seconds 4.664783716201782\n",
      "Running Time for Forward Pass: %s seconds 4.6378889083862305\n",
      "SPSA Epoch 22: 2.72414\n",
      "Running Time for Forward Pass: %s seconds 4.465063810348511\n",
      "Running Time for Forward Pass: %s seconds 4.7234110832214355\n",
      "Running Time for Forward Pass: %s seconds 4.4557952880859375\n",
      "SPSA Epoch 23: 2.72281\n",
      "Running Time for Forward Pass: %s seconds 4.648249864578247\n",
      "Running Time for Forward Pass: %s seconds 4.494967699050903\n",
      "Running Time for Forward Pass: %s seconds 4.754856824874878\n",
      "SPSA Epoch 24: 2.71791\n",
      "Running Time for Forward Pass: %s seconds 4.665125370025635\n",
      "Running Time for Forward Pass: %s seconds 4.46938681602478\n",
      "Running Time for Forward Pass: %s seconds 4.574006080627441\n",
      "SPSA Epoch 25: 2.72367\n",
      "Running Time for Forward Pass: %s seconds 4.4415812492370605\n",
      "Running Time for Forward Pass: %s seconds 4.61817193031311\n",
      "Running Time for Forward Pass: %s seconds 4.431287527084351\n",
      "SPSA Epoch 26: 2.72450\n",
      "Running Time for Forward Pass: %s seconds 4.98233962059021\n",
      "Running Time for Forward Pass: %s seconds 4.727658748626709\n",
      "Running Time for Forward Pass: %s seconds 4.568808317184448\n",
      "SPSA Epoch 27: 2.71948\n",
      "Running Time for Forward Pass: %s seconds 4.682420015335083\n",
      "Running Time for Forward Pass: %s seconds 4.629611968994141\n",
      "Running Time for Forward Pass: %s seconds 4.461745977401733\n",
      "SPSA Epoch 28: 2.71284\n",
      "Running Time for Forward Pass: %s seconds 4.572179794311523\n",
      "Running Time for Forward Pass: %s seconds 4.45591926574707\n",
      "Running Time for Forward Pass: %s seconds 4.73581862449646\n",
      "SPSA Epoch 29: 2.70865\n",
      "Running Time for Forward Pass: %s seconds 5.452514171600342\n",
      "Running Time for Forward Pass: %s seconds 5.096721172332764\n",
      "Running Time for Forward Pass: %s seconds 4.469284534454346\n",
      "SPSA Epoch 30: 2.71520\n",
      "Running Time for Forward Pass: %s seconds 4.741372346878052\n",
      "Running Time for Forward Pass: %s seconds 4.436703443527222\n",
      "Running Time for Forward Pass: %s seconds 4.918285131454468\n",
      "SPSA Epoch 31: 2.72440\n",
      "Running Time for Forward Pass: %s seconds 4.702237129211426\n",
      "Running Time for Forward Pass: %s seconds 4.444893836975098\n",
      "Running Time for Forward Pass: %s seconds 4.724046945571899\n",
      "SPSA Epoch 32: 2.71481\n",
      "Running Time for Forward Pass: %s seconds 4.471838712692261\n",
      "Running Time for Forward Pass: %s seconds 4.8959925174713135\n",
      "Running Time for Forward Pass: %s seconds 4.498892307281494\n",
      "SPSA Epoch 33: 2.72034\n",
      "Running Time for Forward Pass: %s seconds 4.658247709274292\n",
      "Running Time for Forward Pass: %s seconds 4.460019588470459\n",
      "Running Time for Forward Pass: %s seconds 4.8061463832855225\n",
      "SPSA Epoch 34: 2.71013\n",
      "Running Time for Forward Pass: %s seconds 4.413415431976318\n",
      "Running Time for Forward Pass: %s seconds 4.924251079559326\n",
      "Running Time for Forward Pass: %s seconds 4.433883905410767\n",
      "SPSA Epoch 35: 2.71490\n",
      "Running Time for Forward Pass: %s seconds 4.5960307121276855\n",
      "Running Time for Forward Pass: %s seconds 5.495417833328247\n",
      "Running Time for Forward Pass: %s seconds 5.234946012496948\n",
      "SPSA Epoch 36: 2.71486\n",
      "Running Time for Forward Pass: %s seconds 4.507460594177246\n",
      "Running Time for Forward Pass: %s seconds 5.324716329574585\n",
      "Running Time for Forward Pass: %s seconds 4.495622873306274\n",
      "SPSA Epoch 37: 2.71176\n",
      "Running Time for Forward Pass: %s seconds 4.581146001815796\n",
      "Running Time for Forward Pass: %s seconds 7.344296932220459\n",
      "Running Time for Forward Pass: %s seconds 4.936712980270386\n",
      "SPSA Epoch 38: 2.71417\n",
      "Running Time for Forward Pass: %s seconds 6.787734031677246\n",
      "Running Time for Forward Pass: %s seconds 4.633845806121826\n",
      "Running Time for Forward Pass: %s seconds 4.719367742538452\n",
      "SPSA Epoch 39: 2.70757\n",
      "Running Time for Forward Pass: %s seconds 4.440360069274902\n",
      "Running Time for Forward Pass: %s seconds 4.663359642028809\n",
      "Running Time for Forward Pass: %s seconds 4.392715692520142\n",
      "SPSA Epoch 40: 2.72193\n",
      "Running Time for Forward Pass: %s seconds 4.493707180023193\n",
      "Running Time for Forward Pass: %s seconds 4.614206790924072\n",
      "Running Time for Forward Pass: %s seconds 4.546948432922363\n",
      "SPSA Epoch 41: 2.72150\n",
      "Running Time for Forward Pass: %s seconds 4.440429210662842\n",
      "Running Time for Forward Pass: %s seconds 4.620981693267822\n",
      "Running Time for Forward Pass: %s seconds 4.673858165740967\n",
      "SPSA Epoch 42: 2.70798\n",
      "Running Time for Forward Pass: %s seconds 4.59281587600708\n",
      "Running Time for Forward Pass: %s seconds 4.463853597640991\n",
      "Running Time for Forward Pass: %s seconds 4.646566390991211\n",
      "SPSA Epoch 43: 2.71524\n",
      "Running Time for Forward Pass: %s seconds 4.581608533859253\n",
      "Running Time for Forward Pass: %s seconds 4.581520080566406\n",
      "Running Time for Forward Pass: %s seconds 4.460097551345825\n",
      "SPSA Epoch 44: 2.71080\n",
      "Running Time for Forward Pass: %s seconds 4.627322435379028\n",
      "Running Time for Forward Pass: %s seconds 4.422425746917725\n",
      "Running Time for Forward Pass: %s seconds 4.666425704956055\n",
      "SPSA Epoch 45: 2.70805\n",
      "Running Time for Forward Pass: %s seconds 4.594640731811523\n",
      "Running Time for Forward Pass: %s seconds 4.645610809326172\n",
      "Running Time for Forward Pass: %s seconds 4.586632490158081\n",
      "SPSA Epoch 46: 2.71191\n",
      "Running Time for Forward Pass: %s seconds 4.440160751342773\n",
      "Running Time for Forward Pass: %s seconds 4.783355712890625\n",
      "Running Time for Forward Pass: %s seconds 4.595264673233032\n",
      "SPSA Epoch 47: 2.71236\n",
      "Running Time for Forward Pass: %s seconds 4.579892635345459\n",
      "Running Time for Forward Pass: %s seconds 4.551831007003784\n",
      "Running Time for Forward Pass: %s seconds 4.757420539855957\n",
      "SPSA Epoch 48: 2.71402\n",
      "Running Time for Forward Pass: %s seconds 4.420851945877075\n",
      "Running Time for Forward Pass: %s seconds 4.58234977722168\n",
      "Running Time for Forward Pass: %s seconds 7.002823829650879\n",
      "SPSA Epoch 49: 2.69670\n",
      "Running Time for Forward Pass: %s seconds 4.626557350158691\n",
      "Running Time for Forward Pass: %s seconds 4.440667152404785\n",
      "Running Time for Forward Pass: %s seconds 5.120182275772095\n",
      "SPSA Epoch 50: 2.71024\n",
      "{   'fun': 2.710236783725476,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([ 0.17846638,  1.88519386,  0.3996674 ,  0.6364267 ,  1.97488688,\n",
      "       -0.03408613, -0.00463664, -1.56876545])}\n",
      "Running Time for optimizing Subex-B1: %s seconds 974.2528741359711\n",
      "Running Time for Forward Pass: %s seconds 4.592390775680542\n",
      "Running Time for Forward Pass: %s seconds 4.6689441204071045\n",
      "Running Time for Forward Pass: %s seconds 4.561198711395264\n",
      "Running Time for Forward Pass: %s seconds 4.683094501495361\n",
      "Running Time for Forward Pass: %s seconds 4.5315821170806885\n",
      "Running Time for Forward Pass: %s seconds 4.696714639663696\n",
      "Running Time for Forward Pass: %s seconds 4.701809644699097\n",
      "Running Time for Forward Pass: %s seconds 6.907078504562378\n",
      "Running Time for Forward Pass: %s seconds 4.665513753890991\n",
      "Running Time for Forward Pass: %s seconds 4.915533065795898\n",
      "Running Time for Forward Pass: %s seconds 4.7454140186309814\n",
      "Running Time for Forward Pass: %s seconds 4.506550073623657\n",
      "Running Time for Forward Pass: %s seconds 4.687506675720215\n",
      "Running Time for Forward Pass: %s seconds 4.62485671043396\n",
      "Running Time for Forward Pass: %s seconds 6.050684928894043\n",
      "Running Time for Forward Pass: %s seconds 4.980281352996826\n",
      "Running Time for Forward Pass: %s seconds 4.689355850219727\n",
      "Running Time for Forward Pass: %s seconds 4.928643226623535\n",
      "Running Time for Forward Pass: %s seconds 4.63942551612854\n",
      "Running Time for Forward Pass: %s seconds 4.523676633834839\n",
      "Running Time for Forward Pass: %s seconds 5.015144109725952\n",
      "Running Time for Forward Pass: %s seconds 4.513995885848999\n",
      "Running Time for Forward Pass: %s seconds 6.171271324157715\n",
      "Running Time for Forward Pass: %s seconds 4.48652195930481\n",
      "Running Time for Forward Pass: %s seconds 4.796365022659302\n",
      "Running Time for Forward Pass: %s seconds 4.699022531509399\n",
      "Running Time for Forward Pass: %s seconds 4.5779712200164795\n",
      "Running Time for Forward Pass: %s seconds 9.508214235305786\n",
      "Running Time for Forward Pass: %s seconds 4.546230316162109\n",
      "Running Time for Forward Pass: %s seconds 5.779149770736694\n",
      "Running Time for Forward Pass: %s seconds 4.743529796600342\n",
      "Running Time for Forward Pass: %s seconds 5.898132562637329\n",
      "Running Time for Forward Pass: %s seconds 4.4491355419158936\n",
      "Running Time for Forward Pass: %s seconds 4.692395925521851\n",
      "Running Time for Forward Pass: %s seconds 4.602669954299927\n",
      "Running Time for Forward Pass: %s seconds 5.154128789901733\n",
      "Running Time for Forward Pass: %s seconds 4.551029443740845\n",
      "Running Time for Forward Pass: %s seconds 4.699871063232422\n",
      "Running Time for Forward Pass: %s seconds 5.760478258132935\n",
      "Running Time for Forward Pass: %s seconds 4.820947647094727\n",
      "Running Time for Forward Pass: %s seconds 4.773983716964722\n",
      "Running Time for Forward Pass: %s seconds 5.168667554855347\n",
      "Running Time for Forward Pass: %s seconds 4.724374532699585\n",
      "Running Time for Forward Pass: %s seconds 4.504642724990845\n",
      "Running Time for Forward Pass: %s seconds 6.202185869216919\n",
      "Running Time for Forward Pass: %s seconds 4.501833438873291\n",
      "Running Time for Forward Pass: %s seconds 4.999044418334961\n",
      "Running Time for Forward Pass: %s seconds 4.545597553253174\n",
      "Running Time for Forward Pass: %s seconds 7.711133241653442\n",
      "Running Time for Forward Pass: %s seconds 6.223987579345703\n",
      "Running Time for Forward Pass: %s seconds 4.790548801422119\n",
      "Running Time for Forward Pass: %s seconds 4.551041126251221\n",
      "Running Time for Forward Pass: %s seconds 6.226335048675537\n",
      "SPSA Epoch 51: 5.61130\n",
      "Running Time for Forward Pass: %s seconds 4.60271692276001\n",
      "Running Time for Forward Pass: %s seconds 5.613260984420776\n",
      "Running Time for Forward Pass: %s seconds 4.5449395179748535\n",
      "SPSA Epoch 52: 5.60485\n",
      "Running Time for Forward Pass: %s seconds 4.686049461364746\n",
      "Running Time for Forward Pass: %s seconds 6.8316473960876465\n",
      "Running Time for Forward Pass: %s seconds 5.366798400878906\n",
      "SPSA Epoch 53: 5.43594\n",
      "Running Time for Forward Pass: %s seconds 4.447629690170288\n",
      "Running Time for Forward Pass: %s seconds 4.74712610244751\n",
      "Running Time for Forward Pass: %s seconds 5.352945804595947\n",
      "SPSA Epoch 54: 5.22832\n",
      "Running Time for Forward Pass: %s seconds 4.468139410018921\n",
      "Running Time for Forward Pass: %s seconds 5.330453157424927\n",
      "Running Time for Forward Pass: %s seconds 6.002733945846558\n",
      "SPSA Epoch 55: 4.80476\n",
      "Running Time for Forward Pass: %s seconds 4.952847957611084\n",
      "Running Time for Forward Pass: %s seconds 4.730317115783691\n",
      "Running Time for Forward Pass: %s seconds 4.6354148387908936\n",
      "SPSA Epoch 56: 4.39325\n",
      "Running Time for Forward Pass: %s seconds 4.718781232833862\n",
      "Running Time for Forward Pass: %s seconds 4.927138805389404\n",
      "Running Time for Forward Pass: %s seconds 4.981319904327393\n",
      "SPSA Epoch 57: 4.41733\n",
      "Running Time for Forward Pass: %s seconds 4.714812517166138\n",
      "Running Time for Forward Pass: %s seconds 4.90975022315979\n",
      "Running Time for Forward Pass: %s seconds 4.948207855224609\n",
      "SPSA Epoch 58: 4.35102\n",
      "Running Time for Forward Pass: %s seconds 4.4650537967681885\n",
      "Running Time for Forward Pass: %s seconds 4.622579336166382\n",
      "Running Time for Forward Pass: %s seconds 4.684107780456543\n",
      "SPSA Epoch 59: 4.22960\n",
      "Running Time for Forward Pass: %s seconds 4.4835896492004395\n",
      "Running Time for Forward Pass: %s seconds 4.647412300109863\n",
      "Running Time for Forward Pass: %s seconds 4.4465131759643555\n",
      "SPSA Epoch 60: 4.00092\n",
      "Running Time for Forward Pass: %s seconds 4.621494770050049\n",
      "Running Time for Forward Pass: %s seconds 4.532576322555542\n",
      "Running Time for Forward Pass: %s seconds 4.6610493659973145\n",
      "SPSA Epoch 61: 3.88684\n",
      "Running Time for Forward Pass: %s seconds 4.515434503555298\n",
      "Running Time for Forward Pass: %s seconds 4.5950117111206055\n",
      "Running Time for Forward Pass: %s seconds 4.642172574996948\n",
      "SPSA Epoch 62: 3.82876\n",
      "Running Time for Forward Pass: %s seconds 4.799292325973511\n",
      "Running Time for Forward Pass: %s seconds 5.213867425918579\n",
      "Running Time for Forward Pass: %s seconds 4.682560682296753\n",
      "SPSA Epoch 63: 3.77416\n",
      "Running Time for Forward Pass: %s seconds 4.504696607589722\n",
      "Running Time for Forward Pass: %s seconds 4.612706422805786\n",
      "Running Time for Forward Pass: %s seconds 4.479146957397461\n",
      "SPSA Epoch 64: 3.47595\n",
      "Running Time for Forward Pass: %s seconds 5.163290500640869\n",
      "Running Time for Forward Pass: %s seconds 4.694406986236572\n",
      "Running Time for Forward Pass: %s seconds 4.44026255607605\n",
      "SPSA Epoch 65: 3.39546\n",
      "Running Time for Forward Pass: %s seconds 6.4658424854278564\n",
      "Running Time for Forward Pass: %s seconds 4.518670082092285\n",
      "Running Time for Forward Pass: %s seconds 4.727090835571289\n",
      "SPSA Epoch 66: 3.19612\n",
      "Running Time for Forward Pass: %s seconds 4.603156328201294\n",
      "Running Time for Forward Pass: %s seconds 5.225969314575195\n",
      "Running Time for Forward Pass: %s seconds 4.46312403678894\n",
      "SPSA Epoch 67: 3.12072\n",
      "Running Time for Forward Pass: %s seconds 4.741595983505249\n",
      "Running Time for Forward Pass: %s seconds 6.506988763809204\n",
      "Running Time for Forward Pass: %s seconds 4.672292947769165\n",
      "SPSA Epoch 68: 2.99371\n",
      "Running Time for Forward Pass: %s seconds 4.515905857086182\n",
      "Running Time for Forward Pass: %s seconds 4.643364667892456\n",
      "Running Time for Forward Pass: %s seconds 4.498157262802124\n",
      "SPSA Epoch 69: 2.89752\n",
      "Running Time for Forward Pass: %s seconds 5.373710870742798\n",
      "Running Time for Forward Pass: %s seconds 4.66811990737915\n",
      "Running Time for Forward Pass: %s seconds 4.4983484745025635\n",
      "SPSA Epoch 70: 2.85547\n",
      "Running Time for Forward Pass: %s seconds 4.8580591678619385\n",
      "Running Time for Forward Pass: %s seconds 4.59522271156311\n",
      "Running Time for Forward Pass: %s seconds 4.4316041469573975\n",
      "SPSA Epoch 71: 2.84967\n",
      "Running Time for Forward Pass: %s seconds 4.5967512130737305\n",
      "Running Time for Forward Pass: %s seconds 4.584742546081543\n",
      "Running Time for Forward Pass: %s seconds 5.48871111869812\n",
      "SPSA Epoch 72: 2.83374\n",
      "Running Time for Forward Pass: %s seconds 4.505173921585083\n",
      "Running Time for Forward Pass: %s seconds 4.639585018157959\n",
      "Running Time for Forward Pass: %s seconds 4.421507358551025\n",
      "SPSA Epoch 73: 2.79354\n",
      "Running Time for Forward Pass: %s seconds 4.8182148933410645\n",
      "Running Time for Forward Pass: %s seconds 4.412397146224976\n",
      "Running Time for Forward Pass: %s seconds 4.595351457595825\n",
      "SPSA Epoch 74: 2.77062\n",
      "Running Time for Forward Pass: %s seconds 4.521522760391235\n",
      "Running Time for Forward Pass: %s seconds 4.715189218521118\n",
      "Running Time for Forward Pass: %s seconds 4.636888265609741\n",
      "SPSA Epoch 75: 2.77566\n",
      "Running Time for Forward Pass: %s seconds 4.406121253967285\n",
      "Running Time for Forward Pass: %s seconds 4.620239734649658\n",
      "Running Time for Forward Pass: %s seconds 4.577653646469116\n",
      "SPSA Epoch 76: 2.76471\n",
      "Running Time for Forward Pass: %s seconds 4.6219398975372314\n",
      "Running Time for Forward Pass: %s seconds 4.456068754196167\n",
      "Running Time for Forward Pass: %s seconds 4.89485764503479\n",
      "SPSA Epoch 77: 2.75016\n",
      "Running Time for Forward Pass: %s seconds 4.557451963424683\n",
      "Running Time for Forward Pass: %s seconds 5.933364391326904\n",
      "Running Time for Forward Pass: %s seconds 4.492078065872192\n",
      "SPSA Epoch 78: 2.76032\n",
      "Running Time for Forward Pass: %s seconds 4.580211162567139\n",
      "Running Time for Forward Pass: %s seconds 4.488558530807495\n",
      "Running Time for Forward Pass: %s seconds 4.601365327835083\n",
      "SPSA Epoch 79: 2.75207\n",
      "Running Time for Forward Pass: %s seconds 4.8712732791900635\n",
      "Running Time for Forward Pass: %s seconds 4.637503623962402\n",
      "Running Time for Forward Pass: %s seconds 4.4324774742126465\n",
      "SPSA Epoch 80: 2.76071\n",
      "Running Time for Forward Pass: %s seconds 4.597028017044067\n",
      "Running Time for Forward Pass: %s seconds 4.408673286437988\n",
      "Running Time for Forward Pass: %s seconds 4.588039398193359\n",
      "SPSA Epoch 81: 2.74101\n",
      "Running Time for Forward Pass: %s seconds 4.7506561279296875\n",
      "Running Time for Forward Pass: %s seconds 4.474967956542969\n",
      "Running Time for Forward Pass: %s seconds 4.44568395614624\n",
      "SPSA Epoch 82: 2.74582\n",
      "Running Time for Forward Pass: %s seconds 4.647120237350464\n",
      "Running Time for Forward Pass: %s seconds 4.61307692527771\n",
      "Running Time for Forward Pass: %s seconds 5.0457117557525635\n",
      "SPSA Epoch 83: 2.73295\n",
      "Running Time for Forward Pass: %s seconds 4.825012683868408\n",
      "Running Time for Forward Pass: %s seconds 4.498015403747559\n",
      "Running Time for Forward Pass: %s seconds 4.5858800411224365\n",
      "SPSA Epoch 84: 2.73520\n",
      "Running Time for Forward Pass: %s seconds 4.49451470375061\n",
      "Running Time for Forward Pass: %s seconds 4.588457822799683\n",
      "Running Time for Forward Pass: %s seconds 4.411974906921387\n",
      "SPSA Epoch 85: 2.74023\n",
      "Running Time for Forward Pass: %s seconds 4.6290552616119385\n",
      "Running Time for Forward Pass: %s seconds 4.515691041946411\n",
      "Running Time for Forward Pass: %s seconds 4.621678352355957\n",
      "SPSA Epoch 86: 2.73639\n",
      "Running Time for Forward Pass: %s seconds 4.458141088485718\n",
      "Running Time for Forward Pass: %s seconds 4.6215293407440186\n",
      "Running Time for Forward Pass: %s seconds 4.431307077407837\n",
      "SPSA Epoch 87: 2.72925\n",
      "Running Time for Forward Pass: %s seconds 4.554833650588989\n",
      "Running Time for Forward Pass: %s seconds 5.448357582092285\n",
      "Running Time for Forward Pass: %s seconds 4.597857713699341\n",
      "SPSA Epoch 88: 2.72642\n",
      "Running Time for Forward Pass: %s seconds 4.468503713607788\n",
      "Running Time for Forward Pass: %s seconds 4.613462209701538\n",
      "Running Time for Forward Pass: %s seconds 4.436784505844116\n",
      "SPSA Epoch 89: 2.72569\n",
      "Running Time for Forward Pass: %s seconds 4.673892974853516\n",
      "Running Time for Forward Pass: %s seconds 4.442924976348877\n",
      "Running Time for Forward Pass: %s seconds 4.5945634841918945\n",
      "SPSA Epoch 90: 2.72042\n",
      "Running Time for Forward Pass: %s seconds 4.637509107589722\n",
      "Running Time for Forward Pass: %s seconds 4.405862808227539\n",
      "Running Time for Forward Pass: %s seconds 4.60920786857605\n",
      "SPSA Epoch 91: 2.72242\n",
      "Running Time for Forward Pass: %s seconds 4.471735239028931\n",
      "Running Time for Forward Pass: %s seconds 4.592021703720093\n",
      "Running Time for Forward Pass: %s seconds 4.8565592765808105\n",
      "SPSA Epoch 92: 2.72652\n",
      "Running Time for Forward Pass: %s seconds 7.106161594390869\n",
      "Running Time for Forward Pass: %s seconds 4.552117824554443\n",
      "Running Time for Forward Pass: %s seconds 4.6314990520477295\n",
      "SPSA Epoch 93: 2.72592\n",
      "Running Time for Forward Pass: %s seconds 4.484069585800171\n",
      "Running Time for Forward Pass: %s seconds 4.710372686386108\n",
      "Running Time for Forward Pass: %s seconds 4.474261999130249\n",
      "SPSA Epoch 94: 2.71273\n",
      "Running Time for Forward Pass: %s seconds 4.599792957305908\n",
      "Running Time for Forward Pass: %s seconds 4.472526550292969\n",
      "Running Time for Forward Pass: %s seconds 4.5782952308654785\n",
      "SPSA Epoch 95: 2.71703\n",
      "Running Time for Forward Pass: %s seconds 4.4437432289123535\n",
      "Running Time for Forward Pass: %s seconds 4.569300889968872\n",
      "Running Time for Forward Pass: %s seconds 4.4279561042785645\n",
      "SPSA Epoch 96: 2.71103\n",
      "Running Time for Forward Pass: %s seconds 4.550326108932495\n",
      "Running Time for Forward Pass: %s seconds 4.566942453384399\n",
      "Running Time for Forward Pass: %s seconds 4.473348617553711\n",
      "SPSA Epoch 97: 2.72702\n",
      "Running Time for Forward Pass: %s seconds 4.607036828994751\n",
      "Running Time for Forward Pass: %s seconds 4.619541168212891\n",
      "Running Time for Forward Pass: %s seconds 4.560244798660278\n",
      "SPSA Epoch 98: 2.71316\n",
      "Running Time for Forward Pass: %s seconds 4.4409027099609375\n",
      "Running Time for Forward Pass: %s seconds 4.54155707359314\n",
      "Running Time for Forward Pass: %s seconds 4.4159650802612305\n",
      "SPSA Epoch 99: 2.72221\n",
      "Running Time for Forward Pass: %s seconds 4.5299201011657715\n",
      "Running Time for Forward Pass: %s seconds 4.607276439666748\n",
      "Running Time for Forward Pass: %s seconds 4.546811103820801\n",
      "SPSA Epoch 100: 2.72685\n",
      "{   'fun': 2.726846207305318,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-1.24827867,  0.96563892,  1.87530222,  0.49709338, -0.58313121,\n",
      "        0.16524993, -0.05259048, -1.59351331])}\n",
      "Running Time for optimizing Subex-B1: %s seconds 966.1431636810303\n",
      "Running Time for Forward Pass: %s seconds 4.444146633148193\n",
      "Running Time for Forward Pass: %s seconds 4.637687921524048\n",
      "Running Time for Forward Pass: %s seconds 4.520363807678223\n",
      "Running Time for Forward Pass: %s seconds 6.075711250305176\n",
      "Running Time for Forward Pass: %s seconds 4.734385967254639\n",
      "Running Time for Forward Pass: %s seconds 4.486140727996826\n",
      "Running Time for Forward Pass: %s seconds 4.596548318862915\n",
      "Running Time for Forward Pass: %s seconds 4.587908029556274\n",
      "Running Time for Forward Pass: %s seconds 4.680410623550415\n",
      "Running Time for Forward Pass: %s seconds 4.471128225326538\n",
      "Running Time for Forward Pass: %s seconds 4.647343635559082\n",
      "Running Time for Forward Pass: %s seconds 4.465257167816162\n",
      "Running Time for Forward Pass: %s seconds 4.627868413925171\n",
      "Running Time for Forward Pass: %s seconds 4.700127124786377\n",
      "Running Time for Forward Pass: %s seconds 4.608323812484741\n",
      "Running Time for Forward Pass: %s seconds 5.387279272079468\n",
      "Running Time for Forward Pass: %s seconds 4.611963748931885\n",
      "Running Time for Forward Pass: %s seconds 4.498680591583252\n",
      "Running Time for Forward Pass: %s seconds 4.631192684173584\n",
      "Running Time for Forward Pass: %s seconds 4.522758960723877\n",
      "Running Time for Forward Pass: %s seconds 4.65905499458313\n",
      "Running Time for Forward Pass: %s seconds 4.461636066436768\n",
      "Running Time for Forward Pass: %s seconds 4.7810423374176025\n",
      "Running Time for Forward Pass: %s seconds 4.553893566131592\n",
      "Running Time for Forward Pass: %s seconds 4.7025158405303955\n",
      "Running Time for Forward Pass: %s seconds 5.983774423599243\n",
      "Running Time for Forward Pass: %s seconds 4.661640167236328\n",
      "Running Time for Forward Pass: %s seconds 4.46054482460022\n",
      "Running Time for Forward Pass: %s seconds 5.096478462219238\n",
      "Running Time for Forward Pass: %s seconds 4.607643365859985\n",
      "Running Time for Forward Pass: %s seconds 4.841615200042725\n",
      "Running Time for Forward Pass: %s seconds 5.626511812210083\n",
      "Running Time for Forward Pass: %s seconds 4.987172603607178\n",
      "Running Time for Forward Pass: %s seconds 5.324833154678345\n",
      "Running Time for Forward Pass: %s seconds 7.892583608627319\n",
      "Running Time for Forward Pass: %s seconds 4.723132371902466\n",
      "Running Time for Forward Pass: %s seconds 4.508073329925537\n",
      "Running Time for Forward Pass: %s seconds 4.645925760269165\n",
      "Running Time for Forward Pass: %s seconds 4.70733118057251\n",
      "Running Time for Forward Pass: %s seconds 4.497864484786987\n",
      "Running Time for Forward Pass: %s seconds 4.666040897369385\n",
      "Running Time for Forward Pass: %s seconds 4.490484952926636\n",
      "Running Time for Forward Pass: %s seconds 4.6164162158966064\n",
      "Running Time for Forward Pass: %s seconds 4.493431091308594\n",
      "Running Time for Forward Pass: %s seconds 4.630064010620117\n",
      "Running Time for Forward Pass: %s seconds 4.475610017776489\n",
      "Running Time for Forward Pass: %s seconds 4.6461341381073\n",
      "Running Time for Forward Pass: %s seconds 4.491131067276001\n",
      "Running Time for Forward Pass: %s seconds 4.650893449783325\n",
      "Running Time for Forward Pass: %s seconds 4.524552583694458\n",
      "Running Time for Forward Pass: %s seconds 4.79917049407959\n",
      "Running Time for Forward Pass: %s seconds 4.517844915390015\n",
      "SPSA Epoch 101: 5.43919\n",
      "Running Time for Forward Pass: %s seconds 4.65868067741394\n",
      "Running Time for Forward Pass: %s seconds 4.68890643119812\n",
      "Running Time for Forward Pass: %s seconds 4.49751615524292\n",
      "SPSA Epoch 102: 4.48601\n",
      "Running Time for Forward Pass: %s seconds 4.627897500991821\n",
      "Running Time for Forward Pass: %s seconds 4.538646936416626\n",
      "Running Time for Forward Pass: %s seconds 7.201491832733154\n",
      "SPSA Epoch 103: 3.70543\n",
      "Running Time for Forward Pass: %s seconds 4.525863885879517\n",
      "Running Time for Forward Pass: %s seconds 4.651916265487671\n",
      "Running Time for Forward Pass: %s seconds 4.480942487716675\n",
      "SPSA Epoch 104: 3.35308\n",
      "Running Time for Forward Pass: %s seconds 4.622621059417725\n",
      "Running Time for Forward Pass: %s seconds 4.48372483253479\n",
      "Running Time for Forward Pass: %s seconds 4.628829717636108\n",
      "SPSA Epoch 105: 2.84638\n",
      "Running Time for Forward Pass: %s seconds 6.1711037158966064\n",
      "Running Time for Forward Pass: %s seconds 4.660268068313599\n",
      "Running Time for Forward Pass: %s seconds 4.488563299179077\n",
      "SPSA Epoch 106: 2.84285\n",
      "Running Time for Forward Pass: %s seconds 4.659581661224365\n",
      "Running Time for Forward Pass: %s seconds 4.468435525894165\n",
      "Running Time for Forward Pass: %s seconds 4.664658546447754\n",
      "SPSA Epoch 107: 2.79397\n",
      "Running Time for Forward Pass: %s seconds 4.617546319961548\n",
      "Running Time for Forward Pass: %s seconds 4.480739593505859\n",
      "Running Time for Forward Pass: %s seconds 4.8664679527282715\n",
      "SPSA Epoch 108: 2.71976\n",
      "Running Time for Forward Pass: %s seconds 4.519766807556152\n",
      "Running Time for Forward Pass: %s seconds 4.881431818008423\n",
      "Running Time for Forward Pass: %s seconds 5.705485105514526\n",
      "SPSA Epoch 109: 2.72012\n",
      "Running Time for Forward Pass: %s seconds 4.891202926635742\n",
      "Running Time for Forward Pass: %s seconds 4.9303295612335205\n",
      "Running Time for Forward Pass: %s seconds 4.950535535812378\n",
      "SPSA Epoch 110: 2.72749\n",
      "Running Time for Forward Pass: %s seconds 4.983883619308472\n",
      "Running Time for Forward Pass: %s seconds 4.720808982849121\n",
      "Running Time for Forward Pass: %s seconds 7.548874378204346\n",
      "SPSA Epoch 111: 2.72594\n",
      "Running Time for Forward Pass: %s seconds 4.7148802280426025\n",
      "Running Time for Forward Pass: %s seconds 4.476431608200073\n",
      "Running Time for Forward Pass: %s seconds 4.667485237121582\n",
      "SPSA Epoch 112: 2.72642\n",
      "Running Time for Forward Pass: %s seconds 5.589219331741333\n",
      "Running Time for Forward Pass: %s seconds 4.748625040054321\n",
      "Running Time for Forward Pass: %s seconds 4.527677774429321\n",
      "SPSA Epoch 113: 2.71425\n",
      "Running Time for Forward Pass: %s seconds 4.668552875518799\n",
      "Running Time for Forward Pass: %s seconds 4.634493350982666\n",
      "Running Time for Forward Pass: %s seconds 4.978158473968506\n",
      "SPSA Epoch 114: 2.72141\n",
      "Running Time for Forward Pass: %s seconds 5.366036415100098\n",
      "Running Time for Forward Pass: %s seconds 6.987459421157837\n",
      "Running Time for Forward Pass: %s seconds 4.584677457809448\n",
      "SPSA Epoch 115: 2.72143\n",
      "Running Time for Forward Pass: %s seconds 5.136344909667969\n",
      "Running Time for Forward Pass: %s seconds 4.541749000549316\n",
      "Running Time for Forward Pass: %s seconds 4.707313060760498\n",
      "SPSA Epoch 116: 2.71617\n",
      "Running Time for Forward Pass: %s seconds 4.865528583526611\n",
      "Running Time for Forward Pass: %s seconds 4.5308616161346436\n",
      "Running Time for Forward Pass: %s seconds 5.289588689804077\n",
      "SPSA Epoch 117: 2.71404\n",
      "Running Time for Forward Pass: %s seconds 4.600949287414551\n",
      "Running Time for Forward Pass: %s seconds 4.692229270935059\n",
      "Running Time for Forward Pass: %s seconds 4.526044130325317\n",
      "SPSA Epoch 118: 2.72008\n",
      "Running Time for Forward Pass: %s seconds 4.673327684402466\n",
      "Running Time for Forward Pass: %s seconds 6.530879497528076\n",
      "Running Time for Forward Pass: %s seconds 4.681248903274536\n",
      "SPSA Epoch 119: 2.71125\n",
      "Running Time for Forward Pass: %s seconds 4.54557466506958\n",
      "Running Time for Forward Pass: %s seconds 4.676896810531616\n",
      "Running Time for Forward Pass: %s seconds 9.282366275787354\n",
      "SPSA Epoch 120: 2.71451\n",
      "Running Time for Forward Pass: %s seconds 4.919229507446289\n",
      "Running Time for Forward Pass: %s seconds 5.3911426067352295\n",
      "Running Time for Forward Pass: %s seconds 5.993716478347778\n",
      "SPSA Epoch 121: 2.70749\n",
      "Running Time for Forward Pass: %s seconds 4.537724733352661\n",
      "Running Time for Forward Pass: %s seconds 4.680705785751343\n",
      "Running Time for Forward Pass: %s seconds 4.497999429702759\n",
      "SPSA Epoch 122: 2.70424\n",
      "Running Time for Forward Pass: %s seconds 4.672688722610474\n",
      "Running Time for Forward Pass: %s seconds 4.495074510574341\n",
      "Running Time for Forward Pass: %s seconds 4.69644570350647\n",
      "SPSA Epoch 123: 2.70746\n",
      "Running Time for Forward Pass: %s seconds 4.523252010345459\n",
      "Running Time for Forward Pass: %s seconds 4.593566179275513\n",
      "Running Time for Forward Pass: %s seconds 4.508165121078491\n",
      "SPSA Epoch 124: 2.71995\n",
      "Running Time for Forward Pass: %s seconds 5.423105478286743\n",
      "Running Time for Forward Pass: %s seconds 4.514894723892212\n",
      "Running Time for Forward Pass: %s seconds 4.69511604309082\n",
      "SPSA Epoch 125: 2.71236\n",
      "Running Time for Forward Pass: %s seconds 4.487902641296387\n",
      "Running Time for Forward Pass: %s seconds 4.605298042297363\n",
      "Running Time for Forward Pass: %s seconds 4.670540809631348\n",
      "SPSA Epoch 126: 2.71097\n",
      "Running Time for Forward Pass: %s seconds 4.481941223144531\n",
      "Running Time for Forward Pass: %s seconds 4.655183553695679\n",
      "Running Time for Forward Pass: %s seconds 4.485661506652832\n",
      "SPSA Epoch 127: 2.71331\n",
      "Running Time for Forward Pass: %s seconds 4.668481349945068\n",
      "Running Time for Forward Pass: %s seconds 4.511198282241821\n",
      "Running Time for Forward Pass: %s seconds 5.0340659618377686\n",
      "SPSA Epoch 128: 2.70534\n",
      "Running Time for Forward Pass: %s seconds 4.501460313796997\n",
      "Running Time for Forward Pass: %s seconds 5.844907522201538\n",
      "Running Time for Forward Pass: %s seconds 4.5359947681427\n",
      "SPSA Epoch 129: 2.70495\n",
      "Running Time for Forward Pass: %s seconds 4.687880277633667\n",
      "Running Time for Forward Pass: %s seconds 4.505713701248169\n",
      "Running Time for Forward Pass: %s seconds 4.6345155239105225\n",
      "SPSA Epoch 130: 2.71092\n",
      "Running Time for Forward Pass: %s seconds 4.585482597351074\n",
      "Running Time for Forward Pass: %s seconds 4.666054010391235\n",
      "Running Time for Forward Pass: %s seconds 4.507566690444946\n",
      "SPSA Epoch 131: 2.70508\n",
      "Running Time for Forward Pass: %s seconds 4.756752014160156\n",
      "Running Time for Forward Pass: %s seconds 4.605273723602295\n",
      "Running Time for Forward Pass: %s seconds 4.676068067550659\n",
      "SPSA Epoch 132: 2.70098\n",
      "Running Time for Forward Pass: %s seconds 4.522015571594238\n",
      "Running Time for Forward Pass: %s seconds 4.659414529800415\n",
      "Running Time for Forward Pass: %s seconds 4.575766324996948\n",
      "SPSA Epoch 133: 2.70693\n",
      "Running Time for Forward Pass: %s seconds 4.892556428909302\n",
      "Running Time for Forward Pass: %s seconds 4.83141827583313\n",
      "Running Time for Forward Pass: %s seconds 4.634129524230957\n",
      "SPSA Epoch 134: 2.70252\n",
      "Running Time for Forward Pass: %s seconds 4.676306247711182\n",
      "Running Time for Forward Pass: %s seconds 4.4983789920806885\n",
      "Running Time for Forward Pass: %s seconds 7.09332013130188\n",
      "SPSA Epoch 135: 2.70422\n",
      "Running Time for Forward Pass: %s seconds 5.650047302246094\n",
      "Running Time for Forward Pass: %s seconds 4.702661037445068\n",
      "Running Time for Forward Pass: %s seconds 4.525357961654663\n",
      "SPSA Epoch 136: 2.70261\n",
      "Running Time for Forward Pass: %s seconds 4.898425579071045\n",
      "Running Time for Forward Pass: %s seconds 4.4852564334869385\n",
      "Running Time for Forward Pass: %s seconds 4.637286901473999\n",
      "SPSA Epoch 137: 2.70130\n",
      "Running Time for Forward Pass: %s seconds 4.5231614112854\n",
      "Running Time for Forward Pass: %s seconds 4.6253578662872314\n",
      "Running Time for Forward Pass: %s seconds 4.675433874130249\n",
      "SPSA Epoch 138: 2.69113\n",
      "Running Time for Forward Pass: %s seconds 4.560244083404541\n",
      "Running Time for Forward Pass: %s seconds 4.6310646533966064\n",
      "Running Time for Forward Pass: %s seconds 4.535252809524536\n",
      "SPSA Epoch 139: 2.70764\n",
      "Running Time for Forward Pass: %s seconds 4.659440279006958\n",
      "Running Time for Forward Pass: %s seconds 4.924022674560547\n",
      "Running Time for Forward Pass: %s seconds 4.636190176010132\n",
      "SPSA Epoch 140: 2.69709\n",
      "Running Time for Forward Pass: %s seconds 5.525529861450195\n",
      "Running Time for Forward Pass: %s seconds 4.707009553909302\n",
      "Running Time for Forward Pass: %s seconds 4.490339994430542\n",
      "SPSA Epoch 141: 2.70723\n",
      "Running Time for Forward Pass: %s seconds 6.0814971923828125\n",
      "Running Time for Forward Pass: %s seconds 5.205873966217041\n",
      "Running Time for Forward Pass: %s seconds 7.699905872344971\n",
      "SPSA Epoch 142: 2.71187\n",
      "Running Time for Forward Pass: %s seconds 5.36884880065918\n",
      "Running Time for Forward Pass: %s seconds 5.1277015209198\n",
      "Running Time for Forward Pass: %s seconds 4.575315952301025\n",
      "SPSA Epoch 143: 2.70126\n",
      "Running Time for Forward Pass: %s seconds 8.608126163482666\n",
      "Running Time for Forward Pass: %s seconds 4.925914764404297\n",
      "Running Time for Forward Pass: %s seconds 4.7805914878845215\n",
      "SPSA Epoch 144: 2.71346\n",
      "Running Time for Forward Pass: %s seconds 4.546014785766602\n",
      "Running Time for Forward Pass: %s seconds 4.810000658035278\n",
      "Running Time for Forward Pass: %s seconds 4.523254632949829\n",
      "SPSA Epoch 145: 2.71037\n",
      "Running Time for Forward Pass: %s seconds 4.715172290802002\n",
      "Running Time for Forward Pass: %s seconds 4.675457954406738\n",
      "Running Time for Forward Pass: %s seconds 4.746763467788696\n",
      "SPSA Epoch 146: 2.71189\n",
      "Running Time for Forward Pass: %s seconds 4.697670936584473\n",
      "Running Time for Forward Pass: %s seconds 4.496078014373779\n",
      "Running Time for Forward Pass: %s seconds 4.706432819366455\n",
      "SPSA Epoch 147: 2.70817\n",
      "Running Time for Forward Pass: %s seconds 5.082828044891357\n",
      "Running Time for Forward Pass: %s seconds 4.714538097381592\n",
      "Running Time for Forward Pass: %s seconds 4.470601558685303\n",
      "SPSA Epoch 148: 2.71131\n",
      "Running Time for Forward Pass: %s seconds 4.685251712799072\n",
      "Running Time for Forward Pass: %s seconds 4.504605770111084\n",
      "Running Time for Forward Pass: %s seconds 4.6597206592559814\n",
      "SPSA Epoch 149: 2.69812\n",
      "Running Time for Forward Pass: %s seconds 8.727521896362305\n",
      "Running Time for Forward Pass: %s seconds 4.637555122375488\n",
      "Running Time for Forward Pass: %s seconds 4.51005482673645\n",
      "SPSA Epoch 150: 2.69666\n",
      "{   'fun': 2.696657022144027,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-0.17625114, -0.87154108, -0.74224826,  0.48833692,  1.0491028 ,\n",
      "       -0.14682407, -1.24694847, -0.39887112])}\n",
      "Running Time for optimizing Subex-B1: %s seconds 976.1451413631439\n",
      "Running Time for Forward Pass: %s seconds 4.678783893585205\n",
      "Running Time for Forward Pass: %s seconds 4.610512018203735\n",
      "Running Time for Forward Pass: %s seconds 4.845078468322754\n",
      "Running Time for Forward Pass: %s seconds 4.6047821044921875\n",
      "Running Time for Forward Pass: %s seconds 4.6606035232543945\n",
      "Running Time for Forward Pass: %s seconds 4.50229024887085\n",
      "Running Time for Forward Pass: %s seconds 5.174661874771118\n",
      "Running Time for Forward Pass: %s seconds 7.531425714492798\n",
      "Running Time for Forward Pass: %s seconds 4.636538982391357\n",
      "Running Time for Forward Pass: %s seconds 4.547307729721069\n",
      "Running Time for Forward Pass: %s seconds 4.694470405578613\n",
      "Running Time for Forward Pass: %s seconds 4.640638113021851\n",
      "Running Time for Forward Pass: %s seconds 4.705552339553833\n",
      "Running Time for Forward Pass: %s seconds 4.58808708190918\n",
      "Running Time for Forward Pass: %s seconds 5.964986562728882\n",
      "Running Time for Forward Pass: %s seconds 4.673338174819946\n",
      "Running Time for Forward Pass: %s seconds 4.737854957580566\n",
      "Running Time for Forward Pass: %s seconds 5.010638475418091\n",
      "Running Time for Forward Pass: %s seconds 4.628318548202515\n",
      "Running Time for Forward Pass: %s seconds 4.529435634613037\n",
      "Running Time for Forward Pass: %s seconds 4.627402067184448\n",
      "Running Time for Forward Pass: %s seconds 4.958527565002441\n",
      "Running Time for Forward Pass: %s seconds 4.96124529838562\n",
      "Running Time for Forward Pass: %s seconds 4.563734769821167\n",
      "Running Time for Forward Pass: %s seconds 4.5769171714782715\n",
      "Running Time for Forward Pass: %s seconds 5.945551872253418\n",
      "Running Time for Forward Pass: %s seconds 5.4450297355651855\n",
      "Running Time for Forward Pass: %s seconds 4.653048276901245\n",
      "Running Time for Forward Pass: %s seconds 4.493061065673828\n",
      "Running Time for Forward Pass: %s seconds 4.4779698848724365\n",
      "Running Time for Forward Pass: %s seconds 6.176165342330933\n",
      "Running Time for Forward Pass: %s seconds 4.59470009803772\n",
      "Running Time for Forward Pass: %s seconds 4.451428413391113\n",
      "Running Time for Forward Pass: %s seconds 4.595233917236328\n",
      "Running Time for Forward Pass: %s seconds 4.435716152191162\n",
      "Running Time for Forward Pass: %s seconds 4.598349094390869\n",
      "Running Time for Forward Pass: %s seconds 4.520591497421265\n",
      "Running Time for Forward Pass: %s seconds 4.624912738800049\n",
      "Running Time for Forward Pass: %s seconds 4.487643480300903\n",
      "Running Time for Forward Pass: %s seconds 4.57918906211853\n",
      "Running Time for Forward Pass: %s seconds 4.448597192764282\n",
      "Running Time for Forward Pass: %s seconds 4.666322231292725\n",
      "Running Time for Forward Pass: %s seconds 4.460856199264526\n",
      "Running Time for Forward Pass: %s seconds 4.641905069351196\n",
      "Running Time for Forward Pass: %s seconds 4.6365532875061035\n",
      "Running Time for Forward Pass: %s seconds 4.729257583618164\n",
      "Running Time for Forward Pass: %s seconds 4.9838128089904785\n",
      "Running Time for Forward Pass: %s seconds 6.599056005477905\n",
      "Running Time for Forward Pass: %s seconds 4.760531902313232\n",
      "Running Time for Forward Pass: %s seconds 4.52282190322876\n",
      "Running Time for Forward Pass: %s seconds 7.762556076049805\n",
      "Running Time for Forward Pass: %s seconds 5.9445037841796875\n",
      "Running Time for Forward Pass: %s seconds 4.727480888366699\n",
      "SPSA Epoch 151: 6.42267\n",
      "Running Time for Forward Pass: %s seconds 4.602226734161377\n",
      "Running Time for Forward Pass: %s seconds 4.661543130874634\n",
      "Running Time for Forward Pass: %s seconds 4.512753963470459\n",
      "SPSA Epoch 152: 6.24669\n",
      "Running Time for Forward Pass: %s seconds 4.966545820236206\n",
      "Running Time for Forward Pass: %s seconds 4.479997634887695\n",
      "Running Time for Forward Pass: %s seconds 4.708606958389282\n",
      "SPSA Epoch 153: 6.04660\n",
      "Running Time for Forward Pass: %s seconds 5.5815722942352295\n",
      "Running Time for Forward Pass: %s seconds 7.055327892303467\n",
      "Running Time for Forward Pass: %s seconds 5.304747104644775\n",
      "SPSA Epoch 154: 5.83558\n",
      "Running Time for Forward Pass: %s seconds 4.7795186042785645\n",
      "Running Time for Forward Pass: %s seconds 4.4982147216796875\n",
      "Running Time for Forward Pass: %s seconds 4.62099814414978\n",
      "SPSA Epoch 155: 5.62782\n",
      "Running Time for Forward Pass: %s seconds 4.637708425521851\n",
      "Running Time for Forward Pass: %s seconds 4.485575437545776\n",
      "Running Time for Forward Pass: %s seconds 5.322356462478638\n",
      "SPSA Epoch 156: 4.63680\n",
      "Running Time for Forward Pass: %s seconds 5.365555763244629\n",
      "Running Time for Forward Pass: %s seconds 4.73003625869751\n",
      "Running Time for Forward Pass: %s seconds 4.570855140686035\n",
      "SPSA Epoch 157: 4.52619\n",
      "Running Time for Forward Pass: %s seconds 4.8219780921936035\n",
      "Running Time for Forward Pass: %s seconds 4.502613544464111\n",
      "Running Time for Forward Pass: %s seconds 4.707584619522095\n",
      "SPSA Epoch 158: 4.03128\n",
      "Running Time for Forward Pass: %s seconds 4.555985450744629\n",
      "Running Time for Forward Pass: %s seconds 4.7680253982543945\n",
      "Running Time for Forward Pass: %s seconds 4.590097427368164\n",
      "SPSA Epoch 159: 3.98065\n",
      "Running Time for Forward Pass: %s seconds 4.625766038894653\n",
      "Running Time for Forward Pass: %s seconds 4.681561708450317\n",
      "Running Time for Forward Pass: %s seconds 4.70470929145813\n",
      "SPSA Epoch 160: 3.94875\n",
      "Running Time for Forward Pass: %s seconds 4.534228563308716\n",
      "Running Time for Forward Pass: %s seconds 4.7385735511779785\n",
      "Running Time for Forward Pass: %s seconds 4.639634609222412\n",
      "SPSA Epoch 161: 3.93544\n",
      "Running Time for Forward Pass: %s seconds 4.491053581237793\n",
      "Running Time for Forward Pass: %s seconds 4.646726846694946\n",
      "Running Time for Forward Pass: %s seconds 6.032064914703369\n",
      "SPSA Epoch 162: 3.63981\n",
      "Running Time for Forward Pass: %s seconds 4.705438613891602\n",
      "Running Time for Forward Pass: %s seconds 4.961479425430298\n",
      "Running Time for Forward Pass: %s seconds 4.696910381317139\n",
      "SPSA Epoch 163: 3.61976\n",
      "Running Time for Forward Pass: %s seconds 4.582137823104858\n",
      "Running Time for Forward Pass: %s seconds 5.861052751541138\n",
      "Running Time for Forward Pass: %s seconds 4.5928122997283936\n",
      "SPSA Epoch 164: 3.62219\n",
      "Running Time for Forward Pass: %s seconds 4.602451324462891\n",
      "Running Time for Forward Pass: %s seconds 4.545686483383179\n",
      "Running Time for Forward Pass: %s seconds 4.636175155639648\n",
      "SPSA Epoch 165: 3.29084\n",
      "Running Time for Forward Pass: %s seconds 4.489821672439575\n",
      "Running Time for Forward Pass: %s seconds 4.723820447921753\n",
      "Running Time for Forward Pass: %s seconds 5.582252264022827\n",
      "SPSA Epoch 166: 3.27741\n",
      "Running Time for Forward Pass: %s seconds 4.682186126708984\n",
      "Running Time for Forward Pass: %s seconds 4.7821760177612305\n",
      "Running Time for Forward Pass: %s seconds 4.6350321769714355\n",
      "SPSA Epoch 167: 3.26398\n",
      "Running Time for Forward Pass: %s seconds 4.460522174835205\n",
      "Running Time for Forward Pass: %s seconds 5.727821350097656\n",
      "Running Time for Forward Pass: %s seconds 4.640780687332153\n",
      "SPSA Epoch 168: 3.24827\n",
      "Running Time for Forward Pass: %s seconds 4.6424994468688965\n",
      "Running Time for Forward Pass: %s seconds 4.492576599121094\n",
      "Running Time for Forward Pass: %s seconds 4.670861721038818\n",
      "SPSA Epoch 169: 3.23291\n",
      "Running Time for Forward Pass: %s seconds 4.83138632774353\n",
      "Running Time for Forward Pass: %s seconds 4.654989719390869\n",
      "Running Time for Forward Pass: %s seconds 4.998122692108154\n",
      "SPSA Epoch 170: 3.24105\n",
      "Running Time for Forward Pass: %s seconds 4.666712522506714\n",
      "Running Time for Forward Pass: %s seconds 4.49719762802124\n",
      "Running Time for Forward Pass: %s seconds 5.212799787521362\n",
      "SPSA Epoch 171: 3.07169\n",
      "Running Time for Forward Pass: %s seconds 4.502051591873169\n",
      "Running Time for Forward Pass: %s seconds 4.693042993545532\n",
      "Running Time for Forward Pass: %s seconds 4.561331033706665\n",
      "SPSA Epoch 172: 3.06110\n",
      "Running Time for Forward Pass: %s seconds 4.726645231246948\n",
      "Running Time for Forward Pass: %s seconds 4.492995262145996\n",
      "Running Time for Forward Pass: %s seconds 4.700013160705566\n",
      "SPSA Epoch 173: 3.04417\n",
      "Running Time for Forward Pass: %s seconds 4.675816774368286\n",
      "Running Time for Forward Pass: %s seconds 4.55228328704834\n",
      "Running Time for Forward Pass: %s seconds 4.683063507080078\n",
      "SPSA Epoch 174: 3.04503\n",
      "Running Time for Forward Pass: %s seconds 4.550172328948975\n",
      "Running Time for Forward Pass: %s seconds 4.613939523696899\n",
      "Running Time for Forward Pass: %s seconds 6.515071392059326\n",
      "SPSA Epoch 175: 3.00542\n",
      "Running Time for Forward Pass: %s seconds 4.655141830444336\n",
      "Running Time for Forward Pass: %s seconds 4.548745632171631\n",
      "Running Time for Forward Pass: %s seconds 4.79257869720459\n",
      "SPSA Epoch 176: 2.89696\n",
      "Running Time for Forward Pass: %s seconds 4.562696695327759\n",
      "Running Time for Forward Pass: %s seconds 4.679145097732544\n",
      "Running Time for Forward Pass: %s seconds 4.841134786605835\n",
      "SPSA Epoch 177: 2.88132\n",
      "Running Time for Forward Pass: %s seconds 6.1291844844818115\n",
      "Running Time for Forward Pass: %s seconds 4.630543947219849\n",
      "Running Time for Forward Pass: %s seconds 4.850857734680176\n",
      "SPSA Epoch 178: 2.83155\n",
      "Running Time for Forward Pass: %s seconds 5.020424842834473\n",
      "Running Time for Forward Pass: %s seconds 4.6383137702941895\n",
      "Running Time for Forward Pass: %s seconds 4.459368944168091\n",
      "SPSA Epoch 179: 2.83454\n",
      "Running Time for Forward Pass: %s seconds 4.654356479644775\n",
      "Running Time for Forward Pass: %s seconds 5.024864912033081\n",
      "Running Time for Forward Pass: %s seconds 4.709699869155884\n",
      "SPSA Epoch 180: 2.83740\n",
      "Running Time for Forward Pass: %s seconds 4.611464023590088\n",
      "Running Time for Forward Pass: %s seconds 4.480039834976196\n",
      "Running Time for Forward Pass: %s seconds 4.622967481613159\n",
      "SPSA Epoch 181: 2.79502\n",
      "Running Time for Forward Pass: %s seconds 4.516709804534912\n",
      "Running Time for Forward Pass: %s seconds 4.648299217224121\n",
      "Running Time for Forward Pass: %s seconds 4.65519380569458\n",
      "SPSA Epoch 182: 2.78469\n",
      "Running Time for Forward Pass: %s seconds 5.959026575088501\n",
      "Running Time for Forward Pass: %s seconds 4.4730963706970215\n",
      "Running Time for Forward Pass: %s seconds 4.654729843139648\n",
      "SPSA Epoch 183: 2.79552\n",
      "Running Time for Forward Pass: %s seconds 4.682738542556763\n",
      "Running Time for Forward Pass: %s seconds 4.589644193649292\n",
      "Running Time for Forward Pass: %s seconds 4.50086784362793\n",
      "SPSA Epoch 184: 2.79135\n",
      "Running Time for Forward Pass: %s seconds 4.684676170349121\n",
      "Running Time for Forward Pass: %s seconds 4.531659841537476\n",
      "Running Time for Forward Pass: %s seconds 4.604249000549316\n",
      "SPSA Epoch 185: 2.79494\n",
      "Running Time for Forward Pass: %s seconds 4.4987359046936035\n",
      "Running Time for Forward Pass: %s seconds 6.888228178024292\n",
      "Running Time for Forward Pass: %s seconds 4.510818719863892\n",
      "SPSA Epoch 186: 2.77104\n",
      "Running Time for Forward Pass: %s seconds 4.841534852981567\n",
      "Running Time for Forward Pass: %s seconds 4.625166893005371\n",
      "Running Time for Forward Pass: %s seconds 4.493271350860596\n",
      "SPSA Epoch 187: 2.75020\n",
      "Running Time for Forward Pass: %s seconds 4.676809310913086\n",
      "Running Time for Forward Pass: %s seconds 5.6283299922943115\n",
      "Running Time for Forward Pass: %s seconds 4.951185941696167\n",
      "SPSA Epoch 188: 2.75057\n",
      "Running Time for Forward Pass: %s seconds 4.493809700012207\n",
      "Running Time for Forward Pass: %s seconds 4.632724285125732\n",
      "Running Time for Forward Pass: %s seconds 4.493234634399414\n",
      "SPSA Epoch 189: 2.73260\n",
      "Running Time for Forward Pass: %s seconds 4.630482912063599\n",
      "Running Time for Forward Pass: %s seconds 4.5262675285339355\n",
      "Running Time for Forward Pass: %s seconds 4.642816543579102\n",
      "SPSA Epoch 190: 2.73323\n",
      "Running Time for Forward Pass: %s seconds 4.48478102684021\n",
      "Running Time for Forward Pass: %s seconds 4.666276216506958\n",
      "Running Time for Forward Pass: %s seconds 4.478577136993408\n",
      "SPSA Epoch 191: 2.74275\n",
      "Running Time for Forward Pass: %s seconds 4.88762903213501\n",
      "Running Time for Forward Pass: %s seconds 4.519448280334473\n",
      "Running Time for Forward Pass: %s seconds 4.67824912071228\n",
      "SPSA Epoch 192: 2.74135\n",
      "Running Time for Forward Pass: %s seconds 4.4984519481658936\n",
      "Running Time for Forward Pass: %s seconds 4.633796691894531\n",
      "Running Time for Forward Pass: %s seconds 4.648987054824829\n",
      "SPSA Epoch 193: 2.74193\n",
      "Running Time for Forward Pass: %s seconds 4.49267840385437\n",
      "Running Time for Forward Pass: %s seconds 4.640158414840698\n",
      "Running Time for Forward Pass: %s seconds 4.452639818191528\n",
      "SPSA Epoch 194: 2.73211\n",
      "Running Time for Forward Pass: %s seconds 4.676819801330566\n",
      "Running Time for Forward Pass: %s seconds 4.49308967590332\n",
      "Running Time for Forward Pass: %s seconds 4.6407129764556885\n",
      "SPSA Epoch 195: 2.72803\n",
      "Running Time for Forward Pass: %s seconds 4.874125003814697\n",
      "Running Time for Forward Pass: %s seconds 4.701504707336426\n",
      "Running Time for Forward Pass: %s seconds 4.5352888107299805\n",
      "SPSA Epoch 196: 2.73795\n",
      "Running Time for Forward Pass: %s seconds 4.6434736251831055\n",
      "Running Time for Forward Pass: %s seconds 4.496263742446899\n",
      "Running Time for Forward Pass: %s seconds 4.707376718521118\n",
      "SPSA Epoch 197: 2.72889\n",
      "Running Time for Forward Pass: %s seconds 4.5125651359558105\n",
      "Running Time for Forward Pass: %s seconds 4.695141553878784\n",
      "Running Time for Forward Pass: %s seconds 4.73671555519104\n",
      "SPSA Epoch 198: 2.72137\n",
      "Running Time for Forward Pass: %s seconds 4.520552635192871\n",
      "Running Time for Forward Pass: %s seconds 4.712123394012451\n",
      "Running Time for Forward Pass: %s seconds 4.4851975440979\n",
      "SPSA Epoch 199: 2.72006\n",
      "Running Time for Forward Pass: %s seconds 4.65212345123291\n",
      "Running Time for Forward Pass: %s seconds 4.66078782081604\n",
      "Running Time for Forward Pass: %s seconds 4.859400749206543\n",
      "SPSA Epoch 200: 2.73003\n",
      "{   'fun': 2.730026278055404,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-0.03188983,  0.43293543,  0.27713371,  0.69514278,  0.25219255,\n",
      "       -0.12769652,  1.40393153, -0.06835327])}\n",
      "Running Time for optimizing Subex-B1: %s seconds 963.1766777038574\n",
      "Running Time for Forward Pass: %s seconds 4.60044527053833\n",
      "Running Time for Forward Pass: %s seconds 4.782365322113037\n",
      "Running Time for Forward Pass: %s seconds 4.5306079387664795\n",
      "Running Time for Forward Pass: %s seconds 4.7248053550720215\n",
      "Running Time for Forward Pass: %s seconds 4.747076749801636\n",
      "Running Time for Forward Pass: %s seconds 4.633245229721069\n",
      "Running Time for Forward Pass: %s seconds 4.569215536117554\n",
      "Running Time for Forward Pass: %s seconds 4.66972017288208\n",
      "Running Time for Forward Pass: %s seconds 4.508587121963501\n",
      "Running Time for Forward Pass: %s seconds 4.6152520179748535\n",
      "Running Time for Forward Pass: %s seconds 4.443307399749756\n",
      "Running Time for Forward Pass: %s seconds 4.722367286682129\n",
      "Running Time for Forward Pass: %s seconds 4.498692750930786\n",
      "Running Time for Forward Pass: %s seconds 4.639026641845703\n",
      "Running Time for Forward Pass: %s seconds 4.504581451416016\n",
      "Running Time for Forward Pass: %s seconds 4.643901348114014\n",
      "Running Time for Forward Pass: %s seconds 4.7072529792785645\n",
      "Running Time for Forward Pass: %s seconds 5.420947313308716\n",
      "Running Time for Forward Pass: %s seconds 6.15658712387085\n",
      "Running Time for Forward Pass: %s seconds 4.702662467956543\n",
      "Running Time for Forward Pass: %s seconds 4.782816648483276\n",
      "Running Time for Forward Pass: %s seconds 4.4810967445373535\n",
      "Running Time for Forward Pass: %s seconds 4.648205995559692\n",
      "Running Time for Forward Pass: %s seconds 4.467848539352417\n",
      "Running Time for Forward Pass: %s seconds 4.650341033935547\n",
      "Running Time for Forward Pass: %s seconds 4.489687204360962\n",
      "Running Time for Forward Pass: %s seconds 5.057534694671631\n",
      "Running Time for Forward Pass: %s seconds 4.5121870040893555\n",
      "Running Time for Forward Pass: %s seconds 4.673637628555298\n",
      "Running Time for Forward Pass: %s seconds 4.513292551040649\n",
      "Running Time for Forward Pass: %s seconds 4.618202209472656\n",
      "Running Time for Forward Pass: %s seconds 4.486616373062134\n",
      "Running Time for Forward Pass: %s seconds 4.659748077392578\n",
      "Running Time for Forward Pass: %s seconds 4.7035813331604\n",
      "Running Time for Forward Pass: %s seconds 5.320603370666504\n",
      "Running Time for Forward Pass: %s seconds 4.703627109527588\n",
      "Running Time for Forward Pass: %s seconds 4.547328948974609\n",
      "Running Time for Forward Pass: %s seconds 4.7137229442596436\n",
      "Running Time for Forward Pass: %s seconds 4.52457594871521\n",
      "Running Time for Forward Pass: %s seconds 4.66487717628479\n",
      "Running Time for Forward Pass: %s seconds 4.576858758926392\n",
      "Running Time for Forward Pass: %s seconds 4.685624599456787\n",
      "Running Time for Forward Pass: %s seconds 4.571345090866089\n",
      "Running Time for Forward Pass: %s seconds 4.636167764663696\n",
      "Running Time for Forward Pass: %s seconds 4.489108562469482\n",
      "Running Time for Forward Pass: %s seconds 5.787586450576782\n",
      "Running Time for Forward Pass: %s seconds 5.428801774978638\n",
      "Running Time for Forward Pass: %s seconds 4.716962814331055\n",
      "Running Time for Forward Pass: %s seconds 4.456777095794678\n",
      "Running Time for Forward Pass: %s seconds 4.681816101074219\n",
      "Running Time for Forward Pass: %s seconds 4.4925079345703125\n",
      "Running Time for Forward Pass: %s seconds 4.638614654541016\n",
      "SPSA Epoch 201: 0.23018\n",
      "Running Time for Forward Pass: %s seconds 4.613248586654663\n",
      "Running Time for Forward Pass: %s seconds 4.49260950088501\n",
      "Running Time for Forward Pass: %s seconds 4.569921970367432\n",
      "SPSA Epoch 202: -0.19916\n",
      "Running Time for Forward Pass: %s seconds 4.444880723953247\n",
      "Running Time for Forward Pass: %s seconds 6.084014892578125\n",
      "Running Time for Forward Pass: %s seconds 4.397498607635498\n",
      "SPSA Epoch 203: -0.20091\n",
      "Running Time for Forward Pass: %s seconds 4.611585855484009\n",
      "Running Time for Forward Pass: %s seconds 4.450483322143555\n",
      "Running Time for Forward Pass: %s seconds 5.150983095169067\n",
      "SPSA Epoch 204: -0.20166\n",
      "Running Time for Forward Pass: %s seconds 4.465108156204224\n",
      "Running Time for Forward Pass: %s seconds 4.5957558155059814\n",
      "Running Time for Forward Pass: %s seconds 4.843016624450684\n",
      "SPSA Epoch 205: -0.20588\n",
      "Running Time for Forward Pass: %s seconds 5.124582290649414\n",
      "Running Time for Forward Pass: %s seconds 4.589388847351074\n",
      "Running Time for Forward Pass: %s seconds 4.436092138290405\n",
      "SPSA Epoch 206: -0.20609\n",
      "Running Time for Forward Pass: %s seconds 4.61339807510376\n",
      "Running Time for Forward Pass: %s seconds 4.477532148361206\n",
      "Running Time for Forward Pass: %s seconds 4.883671522140503\n",
      "SPSA Epoch 207: -0.20798\n",
      "Running Time for Forward Pass: %s seconds 4.446264028549194\n",
      "Running Time for Forward Pass: %s seconds 4.567007541656494\n",
      "Running Time for Forward Pass: %s seconds 4.563362121582031\n",
      "SPSA Epoch 208: -0.20798\n",
      "Running Time for Forward Pass: %s seconds 4.6322808265686035\n",
      "Running Time for Forward Pass: %s seconds 5.281583070755005\n",
      "Running Time for Forward Pass: %s seconds 4.641544818878174\n",
      "SPSA Epoch 209: -0.20979\n",
      "Running Time for Forward Pass: %s seconds 4.501267671585083\n",
      "Running Time for Forward Pass: %s seconds 4.780894041061401\n",
      "Running Time for Forward Pass: %s seconds 4.384900093078613\n",
      "SPSA Epoch 210: -0.20975\n",
      "Running Time for Forward Pass: %s seconds 7.607141494750977\n",
      "Running Time for Forward Pass: %s seconds 4.557217836380005\n",
      "Running Time for Forward Pass: %s seconds 4.876006364822388\n",
      "SPSA Epoch 211: -0.21151\n",
      "Running Time for Forward Pass: %s seconds 4.483925104141235\n",
      "Running Time for Forward Pass: %s seconds 4.607579708099365\n",
      "Running Time for Forward Pass: %s seconds 4.5520241260528564\n",
      "SPSA Epoch 212: -0.21171\n",
      "Running Time for Forward Pass: %s seconds 4.4155237674713135\n",
      "Running Time for Forward Pass: %s seconds 4.689567804336548\n",
      "Running Time for Forward Pass: %s seconds 5.296602249145508\n",
      "SPSA Epoch 213: -0.21380\n",
      "Running Time for Forward Pass: %s seconds 4.6716461181640625\n",
      "Running Time for Forward Pass: %s seconds 4.488439083099365\n",
      "Running Time for Forward Pass: %s seconds 4.71766996383667\n",
      "SPSA Epoch 214: -0.21420\n",
      "Running Time for Forward Pass: %s seconds 4.541994571685791\n",
      "Running Time for Forward Pass: %s seconds 4.7878663539886475\n",
      "Running Time for Forward Pass: %s seconds 4.411780595779419\n",
      "SPSA Epoch 215: -0.21528\n",
      "Running Time for Forward Pass: %s seconds 4.601467132568359\n",
      "Running Time for Forward Pass: %s seconds 4.57315731048584\n",
      "Running Time for Forward Pass: %s seconds 4.416311025619507\n",
      "SPSA Epoch 216: -0.21629\n",
      "Running Time for Forward Pass: %s seconds 7.049895286560059\n",
      "Running Time for Forward Pass: %s seconds 4.482512474060059\n",
      "Running Time for Forward Pass: %s seconds 4.646583795547485\n",
      "SPSA Epoch 217: -0.21644\n",
      "Running Time for Forward Pass: %s seconds 4.42891263961792\n",
      "Running Time for Forward Pass: %s seconds 4.596712827682495\n",
      "Running Time for Forward Pass: %s seconds 4.480061769485474\n",
      "SPSA Epoch 218: -0.21703\n",
      "Running Time for Forward Pass: %s seconds 6.056992292404175\n",
      "Running Time for Forward Pass: %s seconds 4.509927272796631\n",
      "Running Time for Forward Pass: %s seconds 4.6445472240448\n",
      "SPSA Epoch 219: -0.21699\n",
      "Running Time for Forward Pass: %s seconds 4.461568832397461\n",
      "Running Time for Forward Pass: %s seconds 4.547817230224609\n",
      "Running Time for Forward Pass: %s seconds 4.401066303253174\n",
      "SPSA Epoch 220: -0.21770\n",
      "Running Time for Forward Pass: %s seconds 4.583385944366455\n",
      "Running Time for Forward Pass: %s seconds 4.404299974441528\n",
      "Running Time for Forward Pass: %s seconds 4.5493690967559814\n",
      "SPSA Epoch 221: -0.21741\n",
      "Running Time for Forward Pass: %s seconds 6.359281539916992\n",
      "Running Time for Forward Pass: %s seconds 4.72345232963562\n",
      "Running Time for Forward Pass: %s seconds 4.396744966506958\n",
      "SPSA Epoch 222: -0.21767\n",
      "Running Time for Forward Pass: %s seconds 4.790957927703857\n",
      "Running Time for Forward Pass: %s seconds 5.383939504623413\n",
      "Running Time for Forward Pass: %s seconds 4.574275493621826\n",
      "SPSA Epoch 223: -0.21782\n",
      "Running Time for Forward Pass: %s seconds 4.431716442108154\n",
      "Running Time for Forward Pass: %s seconds 4.572495222091675\n",
      "Running Time for Forward Pass: %s seconds 4.448901653289795\n",
      "SPSA Epoch 224: -0.21832\n",
      "Running Time for Forward Pass: %s seconds 4.571103811264038\n",
      "Running Time for Forward Pass: %s seconds 4.4520509243011475\n",
      "Running Time for Forward Pass: %s seconds 4.608521461486816\n",
      "SPSA Epoch 225: -0.21862\n",
      "Running Time for Forward Pass: %s seconds 4.4343101978302\n",
      "Running Time for Forward Pass: %s seconds 4.5413737297058105\n",
      "Running Time for Forward Pass: %s seconds 4.589721918106079\n",
      "SPSA Epoch 226: -0.21878\n",
      "Running Time for Forward Pass: %s seconds 4.472604274749756\n",
      "Running Time for Forward Pass: %s seconds 4.609302043914795\n",
      "Running Time for Forward Pass: %s seconds 4.4656782150268555\n",
      "SPSA Epoch 227: -0.22140\n",
      "Running Time for Forward Pass: %s seconds 4.577155828475952\n",
      "Running Time for Forward Pass: %s seconds 4.429131031036377\n",
      "Running Time for Forward Pass: %s seconds 4.592108249664307\n",
      "SPSA Epoch 228: -0.22175\n",
      "Running Time for Forward Pass: %s seconds 4.424882888793945\n",
      "Running Time for Forward Pass: %s seconds 4.608304023742676\n",
      "Running Time for Forward Pass: %s seconds 4.524737119674683\n",
      "SPSA Epoch 229: -0.22298\n",
      "Running Time for Forward Pass: %s seconds 4.612629175186157\n",
      "Running Time for Forward Pass: %s seconds 4.546296119689941\n",
      "Running Time for Forward Pass: %s seconds 4.597139358520508\n",
      "SPSA Epoch 230: -0.22361\n",
      "Running Time for Forward Pass: %s seconds 4.427394151687622\n",
      "Running Time for Forward Pass: %s seconds 4.69210410118103\n",
      "Running Time for Forward Pass: %s seconds 4.41636061668396\n",
      "SPSA Epoch 231: -0.22415\n",
      "Running Time for Forward Pass: %s seconds 4.639331102371216\n",
      "Running Time for Forward Pass: %s seconds 4.4334728717803955\n",
      "Running Time for Forward Pass: %s seconds 4.575214385986328\n",
      "SPSA Epoch 232: -0.22399\n",
      "Running Time for Forward Pass: %s seconds 4.508074522018433\n",
      "Running Time for Forward Pass: %s seconds 4.7846839427948\n",
      "Running Time for Forward Pass: %s seconds 4.538884878158569\n",
      "SPSA Epoch 233: -0.22501\n",
      "Running Time for Forward Pass: %s seconds 4.606537103652954\n",
      "Running Time for Forward Pass: %s seconds 4.6018688678741455\n",
      "Running Time for Forward Pass: %s seconds 4.893802165985107\n",
      "SPSA Epoch 234: -0.22543\n",
      "Running Time for Forward Pass: %s seconds 4.664441108703613\n",
      "Running Time for Forward Pass: %s seconds 4.430448532104492\n",
      "Running Time for Forward Pass: %s seconds 4.640487432479858\n",
      "SPSA Epoch 235: -0.22729\n",
      "Running Time for Forward Pass: %s seconds 4.421102523803711\n",
      "Running Time for Forward Pass: %s seconds 4.630447149276733\n",
      "Running Time for Forward Pass: %s seconds 4.510303735733032\n",
      "SPSA Epoch 236: -0.22769\n",
      "Running Time for Forward Pass: %s seconds 4.593947172164917\n",
      "Running Time for Forward Pass: %s seconds 5.7166428565979\n",
      "Running Time for Forward Pass: %s seconds 8.669936895370483\n",
      "SPSA Epoch 237: -0.22781\n",
      "Running Time for Forward Pass: %s seconds 4.826632261276245\n",
      "Running Time for Forward Pass: %s seconds 4.65767502784729\n",
      "Running Time for Forward Pass: %s seconds 4.4508137702941895\n",
      "SPSA Epoch 238: -0.22835\n",
      "Running Time for Forward Pass: %s seconds 4.663900852203369\n",
      "Running Time for Forward Pass: %s seconds 5.382635116577148\n",
      "Running Time for Forward Pass: %s seconds 4.680347681045532\n",
      "SPSA Epoch 239: -0.22865\n",
      "Running Time for Forward Pass: %s seconds 4.57963228225708\n",
      "Running Time for Forward Pass: %s seconds 4.920002698898315\n",
      "Running Time for Forward Pass: %s seconds 4.648893356323242\n",
      "SPSA Epoch 240: -0.22855\n",
      "Running Time for Forward Pass: %s seconds 4.4175169467926025\n",
      "Running Time for Forward Pass: %s seconds 4.604258298873901\n",
      "Running Time for Forward Pass: %s seconds 4.438916206359863\n",
      "SPSA Epoch 241: -0.22928\n",
      "Running Time for Forward Pass: %s seconds 4.5848002433776855\n",
      "Running Time for Forward Pass: %s seconds 4.503974914550781\n",
      "Running Time for Forward Pass: %s seconds 4.669531345367432\n",
      "SPSA Epoch 242: -0.23001\n",
      "Running Time for Forward Pass: %s seconds 4.495818614959717\n",
      "Running Time for Forward Pass: %s seconds 4.675283193588257\n",
      "Running Time for Forward Pass: %s seconds 4.42624568939209\n",
      "SPSA Epoch 243: -0.22963\n",
      "Running Time for Forward Pass: %s seconds 5.156301498413086\n",
      "Running Time for Forward Pass: %s seconds 4.420850038528442\n",
      "Running Time for Forward Pass: %s seconds 4.5385565757751465\n",
      "SPSA Epoch 244: -0.22991\n",
      "Running Time for Forward Pass: %s seconds 6.7783119678497314\n",
      "Running Time for Forward Pass: %s seconds 4.454122543334961\n",
      "Running Time for Forward Pass: %s seconds 4.587587356567383\n",
      "SPSA Epoch 245: -0.23042\n",
      "Running Time for Forward Pass: %s seconds 4.491359233856201\n",
      "Running Time for Forward Pass: %s seconds 4.582942247390747\n",
      "Running Time for Forward Pass: %s seconds 4.712154865264893\n",
      "SPSA Epoch 246: -0.23190\n",
      "Running Time for Forward Pass: %s seconds 4.546192407608032\n",
      "Running Time for Forward Pass: %s seconds 4.456533432006836\n",
      "Running Time for Forward Pass: %s seconds 4.579548597335815\n",
      "SPSA Epoch 247: -0.23296\n",
      "Running Time for Forward Pass: %s seconds 4.424429178237915\n",
      "Running Time for Forward Pass: %s seconds 4.617267847061157\n",
      "Running Time for Forward Pass: %s seconds 4.449098587036133\n",
      "SPSA Epoch 248: -0.23250\n",
      "Running Time for Forward Pass: %s seconds 4.557804346084595\n",
      "Running Time for Forward Pass: %s seconds 4.425354480743408\n",
      "Running Time for Forward Pass: %s seconds 4.552431583404541\n",
      "SPSA Epoch 249: -0.23311\n",
      "Running Time for Forward Pass: %s seconds 4.65116024017334\n",
      "Running Time for Forward Pass: %s seconds 4.482187032699585\n",
      "Running Time for Forward Pass: %s seconds 4.591105937957764\n",
      "SPSA Epoch 250: -0.23348\n",
      "{   'fun': -0.23348194518952065,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-0.92532075,  0.0124676 ,  0.46061794, -0.2395108 ,  1.56846624,\n",
      "        0.12214397,  0.18665411,  1.55514784])}\n",
      "Running Time for optimizing Subex-B1: %s seconds 940.7963502407074\n",
      "Running Time for Forward Pass: %s seconds 5.704207181930542\n",
      "Running Time for Forward Pass: %s seconds 4.703157663345337\n",
      "Running Time for Forward Pass: %s seconds 4.479731559753418\n",
      "Running Time for Forward Pass: %s seconds 5.033267498016357\n",
      "Running Time for Forward Pass: %s seconds 4.56550931930542\n",
      "Running Time for Forward Pass: %s seconds 4.71432638168335\n",
      "Running Time for Forward Pass: %s seconds 4.522528648376465\n",
      "Running Time for Forward Pass: %s seconds 4.6563780307769775\n",
      "Running Time for Forward Pass: %s seconds 4.522504806518555\n",
      "Running Time for Forward Pass: %s seconds 4.911177396774292\n",
      "Running Time for Forward Pass: %s seconds 4.700538158416748\n",
      "Running Time for Forward Pass: %s seconds 4.689552545547485\n",
      "Running Time for Forward Pass: %s seconds 4.730433225631714\n",
      "Running Time for Forward Pass: %s seconds 4.72271466255188\n",
      "Running Time for Forward Pass: %s seconds 5.02386474609375\n",
      "Running Time for Forward Pass: %s seconds 4.522616147994995\n",
      "Running Time for Forward Pass: %s seconds 7.283442974090576\n",
      "Running Time for Forward Pass: %s seconds 4.733983039855957\n",
      "Running Time for Forward Pass: %s seconds 5.323153018951416\n",
      "Running Time for Forward Pass: %s seconds 4.593105792999268\n",
      "Running Time for Forward Pass: %s seconds 4.780411720275879\n",
      "Running Time for Forward Pass: %s seconds 4.604480504989624\n",
      "Running Time for Forward Pass: %s seconds 4.978348731994629\n",
      "Running Time for Forward Pass: %s seconds 4.567462921142578\n",
      "Running Time for Forward Pass: %s seconds 4.659656286239624\n",
      "Running Time for Forward Pass: %s seconds 4.543553352355957\n",
      "Running Time for Forward Pass: %s seconds 4.619212865829468\n",
      "Running Time for Forward Pass: %s seconds 4.473236083984375\n",
      "Running Time for Forward Pass: %s seconds 4.6481757164001465\n",
      "Running Time for Forward Pass: %s seconds 4.467585802078247\n",
      "Running Time for Forward Pass: %s seconds 4.621192216873169\n",
      "Running Time for Forward Pass: %s seconds 4.526474952697754\n",
      "Running Time for Forward Pass: %s seconds 4.664546728134155\n",
      "Running Time for Forward Pass: %s seconds 4.433541774749756\n",
      "Running Time for Forward Pass: %s seconds 4.638030767440796\n",
      "Running Time for Forward Pass: %s seconds 4.537218809127808\n",
      "Running Time for Forward Pass: %s seconds 4.988718748092651\n",
      "Running Time for Forward Pass: %s seconds 4.736724615097046\n",
      "Running Time for Forward Pass: %s seconds 4.594048261642456\n",
      "Running Time for Forward Pass: %s seconds 4.606437683105469\n",
      "Running Time for Forward Pass: %s seconds 4.480456352233887\n",
      "Running Time for Forward Pass: %s seconds 4.606287002563477\n",
      "Running Time for Forward Pass: %s seconds 4.484586954116821\n",
      "Running Time for Forward Pass: %s seconds 4.604284286499023\n",
      "Running Time for Forward Pass: %s seconds 4.502141237258911\n",
      "Running Time for Forward Pass: %s seconds 4.937702894210815\n",
      "Running Time for Forward Pass: %s seconds 4.548855543136597\n",
      "Running Time for Forward Pass: %s seconds 4.631901264190674\n",
      "Running Time for Forward Pass: %s seconds 4.460423707962036\n",
      "Running Time for Forward Pass: %s seconds 4.650564193725586\n",
      "Running Time for Forward Pass: %s seconds 4.6016998291015625\n",
      "Running Time for Forward Pass: %s seconds 4.735225677490234\n",
      "Running Time for Forward Pass: %s seconds 4.508456707000732\n",
      "SPSA Epoch 251: 0.07856\n",
      "Running Time for Forward Pass: %s seconds 4.702916145324707\n",
      "Running Time for Forward Pass: %s seconds 4.489200830459595\n",
      "Running Time for Forward Pass: %s seconds 4.5965094566345215\n",
      "SPSA Epoch 252: -0.23014\n",
      "Running Time for Forward Pass: %s seconds 4.475787878036499\n",
      "Running Time for Forward Pass: %s seconds 5.552039861679077\n",
      "Running Time for Forward Pass: %s seconds 4.636210680007935\n",
      "SPSA Epoch 253: -0.23230\n",
      "Running Time for Forward Pass: %s seconds 6.126391649246216\n",
      "Running Time for Forward Pass: %s seconds 4.63870644569397\n",
      "Running Time for Forward Pass: %s seconds 4.431898832321167\n",
      "SPSA Epoch 254: -0.23277\n",
      "Running Time for Forward Pass: %s seconds 4.607046127319336\n",
      "Running Time for Forward Pass: %s seconds 4.5004870891571045\n",
      "Running Time for Forward Pass: %s seconds 4.573235034942627\n",
      "SPSA Epoch 255: -0.23303\n",
      "Running Time for Forward Pass: %s seconds 7.14972996711731\n",
      "Running Time for Forward Pass: %s seconds 4.6458985805511475\n",
      "Running Time for Forward Pass: %s seconds 4.395936012268066\n",
      "SPSA Epoch 256: -0.23283\n",
      "Running Time for Forward Pass: %s seconds 4.598889112472534\n",
      "Running Time for Forward Pass: %s seconds 4.4286839962005615\n",
      "Running Time for Forward Pass: %s seconds 5.111026763916016\n",
      "SPSA Epoch 257: -0.23330\n",
      "Running Time for Forward Pass: %s seconds 4.465879440307617\n",
      "Running Time for Forward Pass: %s seconds 5.224698305130005\n",
      "Running Time for Forward Pass: %s seconds 6.86870813369751\n",
      "SPSA Epoch 258: -0.23316\n",
      "Running Time for Forward Pass: %s seconds 4.5677430629730225\n",
      "Running Time for Forward Pass: %s seconds 4.552011251449585\n",
      "Running Time for Forward Pass: %s seconds 4.576311349868774\n",
      "SPSA Epoch 259: -0.23684\n",
      "Running Time for Forward Pass: %s seconds 4.584440469741821\n",
      "Running Time for Forward Pass: %s seconds 4.431427240371704\n",
      "Running Time for Forward Pass: %s seconds 4.536300897598267\n",
      "SPSA Epoch 260: -0.23686\n",
      "Running Time for Forward Pass: %s seconds 4.436460733413696\n",
      "Running Time for Forward Pass: %s seconds 4.437798976898193\n",
      "Running Time for Forward Pass: %s seconds 4.523707628250122\n",
      "SPSA Epoch 261: -0.23665\n",
      "Running Time for Forward Pass: %s seconds 5.354676246643066\n",
      "Running Time for Forward Pass: %s seconds 4.61988639831543\n",
      "Running Time for Forward Pass: %s seconds 4.606341361999512\n",
      "SPSA Epoch 262: -0.23724\n",
      "Running Time for Forward Pass: %s seconds 4.486522197723389\n",
      "Running Time for Forward Pass: %s seconds 4.572006464004517\n",
      "Running Time for Forward Pass: %s seconds 4.38353419303894\n",
      "SPSA Epoch 263: -0.23687\n",
      "Running Time for Forward Pass: %s seconds 4.581550598144531\n",
      "Running Time for Forward Pass: %s seconds 4.394169807434082\n",
      "Running Time for Forward Pass: %s seconds 4.572117328643799\n",
      "SPSA Epoch 264: -0.23772\n",
      "Running Time for Forward Pass: %s seconds 4.433194875717163\n",
      "Running Time for Forward Pass: %s seconds 4.599630117416382\n",
      "Running Time for Forward Pass: %s seconds 4.408754587173462\n",
      "SPSA Epoch 265: -0.23892\n",
      "Running Time for Forward Pass: %s seconds 4.617264986038208\n",
      "Running Time for Forward Pass: %s seconds 4.531182289123535\n",
      "Running Time for Forward Pass: %s seconds 4.55966854095459\n",
      "SPSA Epoch 266: -0.23915\n",
      "Running Time for Forward Pass: %s seconds 4.611962795257568\n",
      "Running Time for Forward Pass: %s seconds 4.420580148696899\n",
      "Running Time for Forward Pass: %s seconds 6.200698137283325\n",
      "SPSA Epoch 267: -0.24107\n",
      "Running Time for Forward Pass: %s seconds 4.370042085647583\n",
      "Running Time for Forward Pass: %s seconds 4.589429140090942\n",
      "Running Time for Forward Pass: %s seconds 4.444390058517456\n",
      "SPSA Epoch 268: -0.24149\n",
      "Running Time for Forward Pass: %s seconds 4.682819128036499\n",
      "Running Time for Forward Pass: %s seconds 4.422256946563721\n",
      "Running Time for Forward Pass: %s seconds 4.596423864364624\n",
      "SPSA Epoch 269: -0.24188\n",
      "Running Time for Forward Pass: %s seconds 4.431415557861328\n",
      "Running Time for Forward Pass: %s seconds 4.651993274688721\n",
      "Running Time for Forward Pass: %s seconds 4.684881210327148\n",
      "SPSA Epoch 270: -0.24230\n",
      "Running Time for Forward Pass: %s seconds 4.542374134063721\n",
      "Running Time for Forward Pass: %s seconds 6.792186498641968\n",
      "Running Time for Forward Pass: %s seconds 5.581857919692993\n",
      "SPSA Epoch 271: -0.24353\n",
      "Running Time for Forward Pass: %s seconds 6.574338436126709\n",
      "Running Time for Forward Pass: %s seconds 4.596654415130615\n",
      "Running Time for Forward Pass: %s seconds 4.414036750793457\n",
      "SPSA Epoch 272: -0.24365\n",
      "Running Time for Forward Pass: %s seconds 5.413525104522705\n",
      "Running Time for Forward Pass: %s seconds 4.4241132736206055\n",
      "Running Time for Forward Pass: %s seconds 4.793197154998779\n",
      "SPSA Epoch 273: -0.24479\n",
      "Running Time for Forward Pass: %s seconds 4.406535625457764\n",
      "Running Time for Forward Pass: %s seconds 4.609025239944458\n",
      "Running Time for Forward Pass: %s seconds 4.55444598197937\n",
      "SPSA Epoch 274: -0.24560\n",
      "Running Time for Forward Pass: %s seconds 4.376308441162109\n",
      "Running Time for Forward Pass: %s seconds 4.591169834136963\n",
      "Running Time for Forward Pass: %s seconds 4.460315704345703\n",
      "SPSA Epoch 275: -0.24581\n",
      "Running Time for Forward Pass: %s seconds 4.610935926437378\n",
      "Running Time for Forward Pass: %s seconds 4.437559127807617\n",
      "Running Time for Forward Pass: %s seconds 4.5974531173706055\n",
      "SPSA Epoch 276: -0.24654\n",
      "Running Time for Forward Pass: %s seconds 4.567187309265137\n",
      "Running Time for Forward Pass: %s seconds 4.563470363616943\n",
      "Running Time for Forward Pass: %s seconds 5.7310168743133545\n",
      "SPSA Epoch 277: -0.24669\n",
      "Running Time for Forward Pass: %s seconds 8.959984302520752\n",
      "Running Time for Forward Pass: %s seconds 4.739597320556641\n",
      "Running Time for Forward Pass: %s seconds 4.653627395629883\n",
      "SPSA Epoch 278: -0.24728\n",
      "Running Time for Forward Pass: %s seconds 4.415652513504028\n",
      "Running Time for Forward Pass: %s seconds 4.854106664657593\n",
      "Running Time for Forward Pass: %s seconds 4.442997932434082\n",
      "SPSA Epoch 279: -0.24762\n",
      "Running Time for Forward Pass: %s seconds 4.582234859466553\n",
      "Running Time for Forward Pass: %s seconds 4.446090221405029\n",
      "Running Time for Forward Pass: %s seconds 4.526370286941528\n",
      "SPSA Epoch 280: -0.24779\n",
      "Running Time for Forward Pass: %s seconds 4.443968296051025\n",
      "Running Time for Forward Pass: %s seconds 5.6629931926727295\n",
      "Running Time for Forward Pass: %s seconds 4.475250005722046\n",
      "SPSA Epoch 281: -0.24846\n",
      "Running Time for Forward Pass: %s seconds 4.710953712463379\n",
      "Running Time for Forward Pass: %s seconds 4.7131006717681885\n",
      "Running Time for Forward Pass: %s seconds 4.388787746429443\n",
      "SPSA Epoch 282: -0.24847\n",
      "Running Time for Forward Pass: %s seconds 4.605250120162964\n",
      "Running Time for Forward Pass: %s seconds 4.537459850311279\n",
      "Running Time for Forward Pass: %s seconds 4.572316646575928\n",
      "SPSA Epoch 283: -0.24930\n",
      "Running Time for Forward Pass: %s seconds 4.4406352043151855\n",
      "Running Time for Forward Pass: %s seconds 4.524085760116577\n",
      "Running Time for Forward Pass: %s seconds 4.673818349838257\n",
      "SPSA Epoch 284: -0.24954\n",
      "Running Time for Forward Pass: %s seconds 4.599249839782715\n",
      "Running Time for Forward Pass: %s seconds 4.4390764236450195\n",
      "Running Time for Forward Pass: %s seconds 4.530087947845459\n",
      "SPSA Epoch 285: -0.25034\n",
      "Running Time for Forward Pass: %s seconds 4.413969278335571\n",
      "Running Time for Forward Pass: %s seconds 4.833842515945435\n",
      "Running Time for Forward Pass: %s seconds 4.554807424545288\n",
      "SPSA Epoch 286: -0.25043\n",
      "Running Time for Forward Pass: %s seconds 5.963872671127319\n",
      "Running Time for Forward Pass: %s seconds 4.575817584991455\n",
      "Running Time for Forward Pass: %s seconds 5.199582099914551\n",
      "SPSA Epoch 287: -0.25086\n",
      "Running Time for Forward Pass: %s seconds 4.634652376174927\n",
      "Running Time for Forward Pass: %s seconds 4.612406492233276\n",
      "Running Time for Forward Pass: %s seconds 4.644654989242554\n",
      "SPSA Epoch 288: -0.25090\n",
      "Running Time for Forward Pass: %s seconds 4.4404590129852295\n",
      "Running Time for Forward Pass: %s seconds 4.699132204055786\n",
      "Running Time for Forward Pass: %s seconds 4.389099597930908\n",
      "SPSA Epoch 289: -0.25124\n",
      "Running Time for Forward Pass: %s seconds 4.600332260131836\n",
      "Running Time for Forward Pass: %s seconds 4.604130744934082\n",
      "Running Time for Forward Pass: %s seconds 5.529218435287476\n",
      "SPSA Epoch 290: -0.25153\n",
      "Running Time for Forward Pass: %s seconds 5.5647828578948975\n",
      "Running Time for Forward Pass: %s seconds 4.575003147125244\n",
      "Running Time for Forward Pass: %s seconds 4.417829513549805\n",
      "SPSA Epoch 291: -0.25201\n",
      "Running Time for Forward Pass: %s seconds 4.515480995178223\n",
      "Running Time for Forward Pass: %s seconds 4.422617435455322\n",
      "Running Time for Forward Pass: %s seconds 4.563724040985107\n",
      "SPSA Epoch 292: -0.25264\n",
      "Running Time for Forward Pass: %s seconds 4.440833806991577\n",
      "Running Time for Forward Pass: %s seconds 4.59822940826416\n",
      "Running Time for Forward Pass: %s seconds 4.409902095794678\n",
      "SPSA Epoch 293: -0.25262\n",
      "Running Time for Forward Pass: %s seconds 4.549528360366821\n",
      "Running Time for Forward Pass: %s seconds 4.774192810058594\n",
      "Running Time for Forward Pass: %s seconds 4.379199504852295\n",
      "SPSA Epoch 294: -0.25264\n",
      "Running Time for Forward Pass: %s seconds 5.70962381362915\n",
      "Running Time for Forward Pass: %s seconds 4.542644500732422\n",
      "Running Time for Forward Pass: %s seconds 4.6385345458984375\n",
      "SPSA Epoch 295: -0.25288\n",
      "Running Time for Forward Pass: %s seconds 4.453750133514404\n",
      "Running Time for Forward Pass: %s seconds 4.532623052597046\n",
      "Running Time for Forward Pass: %s seconds 4.416435956954956\n",
      "SPSA Epoch 296: -0.25273\n",
      "Running Time for Forward Pass: %s seconds 4.674622297286987\n",
      "Running Time for Forward Pass: %s seconds 5.18473744392395\n",
      "Running Time for Forward Pass: %s seconds 4.87527060508728\n",
      "SPSA Epoch 297: -0.25291\n",
      "Running Time for Forward Pass: %s seconds 4.529326915740967\n",
      "Running Time for Forward Pass: %s seconds 4.659771919250488\n",
      "Running Time for Forward Pass: %s seconds 4.490701675415039\n",
      "SPSA Epoch 298: -0.25392\n",
      "Running Time for Forward Pass: %s seconds 4.897554636001587\n",
      "Running Time for Forward Pass: %s seconds 4.811173677444458\n",
      "Running Time for Forward Pass: %s seconds 4.681700944900513\n",
      "SPSA Epoch 299: -0.25366\n",
      "Running Time for Forward Pass: %s seconds 4.433310031890869\n",
      "Running Time for Forward Pass: %s seconds 4.853636741638184\n",
      "Running Time for Forward Pass: %s seconds 6.794150114059448\n",
      "SPSA Epoch 300: -0.25393\n",
      "{   'fun': -0.25392757989067916,\n",
      "    'jac': None,\n",
      "    'nfev': 150,\n",
      "    'nit': 50,\n",
      "    'njev': None,\n",
      "    'x': array([-0.83610491,  0.20311135,  0.37172909,  1.28985268,  0.41459826,\n",
      "        0.09114109,  0.16327256,  1.55466201])}\n",
      "Running Time for optimizing Subex-B1: %s seconds 954.2044591903687\n"
     ]
    }
   ],
   "source": [
    "# Optimizer result for 0th circuit for sub-circuit-B\n",
    "start_time = time.time()\n",
    "optimizer_result_B0 = minimizer(function2, function2.objective0, function2.gradient0, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B1 = minimizer(function2, function2.objective1, function2.gradient1, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B2 = minimizer(function2, function2.objective2, function2.gradient2, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B3 = minimizer(function2, function2.objective3, function2.gradient3, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B4 = minimizer(function2, function2.objective4, function2.gradient4, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B5 = minimizer(function2, function2.objective5, function2.gradient5, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d6607356-2e2a-4e70-85ff-f258c2f5cbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_results_B = [optimizer_result_B0, optimizer_result_B1, optimizer_result_B2, optimizer_result_B3, optimizer_result_B4, optimizer_result_B5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0896372c-0930-4451-b75b-5edb6733efc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New params (The final point of the minimization): [ 0.17846638  1.88519386  0.3996674   0.6364267   1.97488688 -0.03408613\n",
      " -0.00463664 -1.56876545]\n",
      "The final value of the minimization: 2.710236783725476\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-1.24827867  0.96563892  1.87530222  0.49709338 -0.58313121  0.16524993\n",
      " -0.05259048 -1.59351331]\n",
      "The final value of the minimization: 2.726846207305318\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-0.17625114 -0.87154108 -0.74224826  0.48833692  1.0491028  -0.14682407\n",
      " -1.24694847 -0.39887112]\n",
      "The final value of the minimization: 2.696657022144027\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-0.03188983  0.43293543  0.27713371  0.69514278  0.25219255 -0.12769652\n",
      "  1.40393153 -0.06835327]\n",
      "The final value of the minimization: 2.730026278055404\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-0.92532075  0.0124676   0.46061794 -0.2395108   1.56846624  0.12214397\n",
      "  0.18665411  1.55514784]\n",
      "The final value of the minimization: -0.23348194518952065\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n",
      "New params (The final point of the minimization): [-0.83610491  0.20311135  0.37172909  1.28985268  0.41459826  0.09114109\n",
      "  0.16327256  1.55466201]\n",
      "The final value of the minimization: -0.25392757989067916\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 150\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 50\n"
     ]
    }
   ],
   "source": [
    "# Print results from 6 sub-experiments of sub-circuit-B\n",
    "for opt_result in optimizer_results_B:\n",
    "    print_optimizer_results(opt_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d38f5b9e-b92e-429d-b715-f734eae3a76e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(objective_func_vals[:50], label=\"B Subex1\")\n",
    "plt.plot(objective_func_vals[50:100], label=\"B Subex2\")\n",
    "plt.plot(objective_func_vals[100:150], label=\"B Subex3\")\n",
    "plt.plot(objective_func_vals[150:200], label=\"B Subex4\")\n",
    "plt.plot(objective_func_vals[200:250], label=\"B Subex5\")\n",
    "plt.plot(objective_func_vals[250:300], label=\"B Subex6\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed7eacba-18a0-476e-97ba-53acde85b048",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_func_vals.clear()\n",
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8068f82d-6639-4886-a96f-f605d23f34bb",
   "metadata": {},
   "source": [
    "### At this point, the training is complete. \n",
    "We have 12 lists of 7 or 8 parameter values that will now be used to make predictions on the test cases. \n",
    "New subcircuits will be built using these parameter values and then their results will be used to reconstruct expectation values. This process will occur for all the inputs in the test set.\n",
    "\n",
    "\n",
    "Let us think of the training process as a box that we can tune/edit while training on the train set. However, once the training process is complete, the box is locked and provided to the user. The user can now send test data to the box and get its desired output.\n",
    "\n",
    "However, in the case of circuit cutting, instead of having one box, we have multiple boxes. In our case, we have a total of 12 boxes (6 boxes per cut). These 12 boxes are trained independently of each other and locked after the training process is complete. The user, now, instead of sending the test data to one box, splits it into 2 parts that are sent to these 12 boxes. The 12 boxes produce quasi-probability distributions respective to the circuits they implement which are then reconstructed to produce the expectation value according to the test input. This process is repeated for every data point in the test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1fc3ef-8cef-4326-8ff9-bbe594b32a23",
   "metadata": {},
   "source": [
    "## Reconstruct Expectation Values and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8824ed8a-0db9-4133-adf5-41a31171b8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from circuit_knitting.cutting import reconstruct_expectation_values\n",
    "from qiskit_aer.primitives import Sampler\n",
    "from qiskit.primitives import SamplerResult\n",
    "from accuracy_score import get_accuracy_score, predict\n",
    "from reconstruct_exp_val import get_subcircuit_results, get_dict_sampler_results, get_reconstructed_expvals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3f4e61-4039-40d7-8604-bfbad8adbc6a",
   "metadata": {},
   "source": [
    "### Reconstructing for train accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2331db8c-82ab-43a1-b9c7-2f401fc78493",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"A\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed0d1504-3faa-483c-b78e-54ef28d13de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_circuits2 = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "22d2c629-840b-4d59-af33-90cdeb74649b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_results_A = get_subcircuit_results(x_train_A, final_circuits, optimizer_results_A, sampler)\n",
    "train_results_B = get_subcircuit_results(x_train_B, final_circuits2, optimizer_results_B, sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6d812aa1-58ec-4db9-98c5-102d5a7a2611",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict of SamplerResults of 6 sub-circuits A and B for each 537 train data inputs\n",
    "A_dict = get_dict_sampler_results(x_train_A, subexperiments[\"A\"], train_results_A)\n",
    "B_dict = get_dict_sampler_results(x_train_B, subexperiments[\"B\"], train_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "17f6e961-c44f-472c-bbd9-a8989db338ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_expvals = get_reconstructed_expvals(A_dict, B_dict, coefficients, sub_observables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5a7d686f-8dbb-4f07-9ba5-b7dabd1f8eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = predict(reconstructed_expvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "307e1bed-0b68-4578-a52c-78974b35a50c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Train Accuracy of the model is: (0.6517690875232774, 0.6517690875232774)\n"
     ]
    }
   ],
   "source": [
    "# accuracy_score function encodes y_train inside the function. Therefore, pass original y_train here.\n",
    "train_score1 = get_accuracy_score(new_y_train, y_predicted)\n",
    "train_score2 = get_accuracy_score(y_train, y_predicted)\n",
    "print(f\"The Train Accuracy of the model is: {train_score1, train_score2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4897db-5983-4c81-8fdc-7d2fddb947fa",
   "metadata": {},
   "source": [
    "### Reconstructing for test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ff3c0935-3633-4ce9-af6a-99b4e7715944",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results_A = get_subcircuit_results(x_test_A, final_circuits, optimizer_results_A, sampler)\n",
    "test_results_B = get_subcircuit_results(x_test_B, final_circuits2, optimizer_results_B, sampler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ea59d60c-3e63-4210-b67e-3078362bd26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict of SamplerResults of 6 sub-circuits A and B for each 231 test data inputs\n",
    "A_dict = get_dict_sampler_results(x_test_A, subexperiments[\"A\"], test_results_A)\n",
    "B_dict = get_dict_sampler_results(x_test_B, subexperiments[\"B\"], test_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d7c9624b-ab21-4aee-99b7-4749885197dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_expvals = get_reconstructed_expvals(A_dict, B_dict, coefficients, sub_observables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "69dded00-c876-44e4-a143-5f5bb4032dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = predict(reconstructed_expvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f5fd5b7d-5afc-45bc-831b-8e715f7f99af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Test Accuracy of the model is: (0.645021645021645, 0.645021645021645)\n"
     ]
    }
   ],
   "source": [
    "# Pass original y_test here.\n",
    "test_score1 = get_accuracy_score(new_y_test, y_predicted)\n",
    "test_score2 = get_accuracy_score(y_test, y_predicted)\n",
    "print(f\"The Test Accuracy of the model is: {test_score1, test_score2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb5854fa-04cb-4991-8a8a-b17af63d2c5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "059bba06-e0d9-44f7-8a59-0bbdf3eb9dfe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba9466da-de3c-41ae-8656-320b7804ea79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "40b0e231-939a-489f-8567-6b973a87fdb2",
   "metadata": {},
   "source": [
    "## Test original circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "aec16e6c-9909-4fbb-84bb-19daa47e7354",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.neural_networks import EstimatorQNN\n",
    "from qiskit_machine_learning.algorithms import NeuralNetworkClassifier\n",
    "from qiskit.primitives import BackendEstimator\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit_aer.primitives import Estimator\n",
    "from qiskit_machine_learning.utils.loss_functions import CrossEntropyLoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cfb7a3be-ddf5-4d96-9bd0-76d02eac5dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding, features = angle_encoding(8)\n",
    "new_ttn = TTN(num_qubits=8).ttn_simple(complex_structure=False)\n",
    "org_circuit = embedding.compose(new_ttn)\n",
    "# final_circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dfed65af-ebb2-495b-8258-cb0b5af6c914",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpu_simulator = AerSimulator(device='GPU')\n",
    "backend = Estimator(backend_options={\"simulator\": gpu_simulator}, transpile_options={\"optimization_level\": 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f67c702c-de14-48ee-a04a-e5ae82092c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable = SparsePauliOp([\"ZIIIIIII\"])\n",
    "estimator_qnn = EstimatorQNN(\n",
    "    estimator=backend,\n",
    "    circuit=org_circuit,\n",
    "    observables=observable,\n",
    "    input_params=features.params,\n",
    "    weight_params=new_ttn.parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d0b6b000-2d96-4d31-8b4e-525712575671",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = np.random.random(len(new_ttn.parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "268bba60-b00f-4cda-8217-f5ced8f8d27a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 768 samples: (537, 1)\n",
      "Output of the forward pass for first sample: [0.27148438]\n"
     ]
    }
   ],
   "source": [
    "# Forward pass\n",
    "output = estimator_qnn.forward(x_train, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {output.shape}\")\n",
    "print(f\"Output of the forward pass for first sample: {output[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "723e885f-c12c-44a6-8224-f1e80f457434",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backward pass\n",
    "_, weights_grad = estimator_qnn.backward(x_train, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {weights_grad.shape}\")\n",
    "print(f\"Output of the backward pass for first sample: {weights_grad[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c36d86e1-a036-4137-b27d-90ab4849f897",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point = np.random.random((len(new_ttn.parameters),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d77f89c6-a938-4068-bfff-08398099255f",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_func_vals = []\n",
    "classifier = NeuralNetworkClassifier(\n",
    "    estimator_qnn,\n",
    "    loss=CrossEntropyLoss(),\n",
    "    optimizer=SPSA(maxiter=50, callback=callback_spsa),\n",
    "    initial_point=initial_point,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "370ae6ca-1f60-417a-a7b7-5514280c2bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "231 231\n"
     ]
    }
   ],
   "source": [
    "print(len(x_test.values), len(new_y_test.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "be3c666f-dbde-4091-871b-a0afdf836e39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPSA Epoch 1: -2.08949\n",
      "SPSA Epoch 2: -5.34833\n",
      "SPSA Epoch 3: -8.69421\n",
      "SPSA Epoch 4: -8.23833\n",
      "SPSA Epoch 5: -8.86264\n",
      "SPSA Epoch 6: -9.74389\n",
      "SPSA Epoch 7: -9.76869\n",
      "SPSA Epoch 8: -10.77774\n",
      "SPSA Epoch 9: -10.55327\n",
      "SPSA Epoch 10: -10.48699\n",
      "SPSA Epoch 11: -10.65250\n",
      "SPSA Epoch 12: -10.66881\n",
      "SPSA Epoch 13: -10.88160\n",
      "SPSA Epoch 14: -10.78356\n",
      "SPSA Epoch 15: -10.61061\n",
      "SPSA Epoch 16: -10.88819\n",
      "SPSA Epoch 17: -10.88048\n",
      "SPSA Epoch 18: -10.78465\n",
      "SPSA Epoch 19: -10.67821\n",
      "SPSA Epoch 20: -10.86942\n",
      "SPSA Epoch 21: -10.80652\n",
      "SPSA Epoch 22: -10.87844\n",
      "SPSA Epoch 23: -10.76855\n",
      "SPSA Epoch 24: -10.77395\n",
      "SPSA Epoch 25: -10.88340\n",
      "SPSA Epoch 26: -10.79193\n",
      "SPSA Epoch 27: -10.78910\n",
      "SPSA Epoch 28: -10.71993\n",
      "SPSA Epoch 29: -11.04899\n",
      "SPSA Epoch 30: -10.83168\n",
      "SPSA Epoch 31: -10.98989\n",
      "SPSA Epoch 32: -10.83823\n",
      "SPSA Epoch 33: -10.86097\n",
      "SPSA Epoch 34: -11.00730\n",
      "SPSA Epoch 35: -11.01732\n",
      "SPSA Epoch 36: -10.85549\n",
      "SPSA Epoch 37: -10.78672\n",
      "SPSA Epoch 38: -10.85028\n",
      "SPSA Epoch 39: -10.85834\n",
      "SPSA Epoch 40: -10.78813\n",
      "SPSA Epoch 41: -10.83487\n",
      "SPSA Epoch 42: -10.83687\n",
      "SPSA Epoch 43: -10.73414\n",
      "SPSA Epoch 44: -10.83968\n",
      "SPSA Epoch 45: -10.94214\n",
      "SPSA Epoch 46: -10.86640\n",
      "SPSA Epoch 47: -10.91658\n",
      "SPSA Epoch 48: -10.84981\n",
      "SPSA Epoch 49: -10.90850\n",
      "SPSA Epoch 50: -10.95739\n",
      "Running Time%s seconds 1690.425876379013\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "classifier.fit(x_train, new_y_train)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time%s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "065c196d-eab9-4924-85d1-2ca43c50ea58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(objective_func_vals)\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "0b24d698-69c9-4243-8386-c6414619981d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict(x_test)\n",
    "# y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "55fc154f-aa3f-4051-8968-bf5181a6dbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6890130353817505"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(x_train, new_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "058ccbbf-3b51-45fc-9149-7b196c685b0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.658008658008658"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.score(x_test, new_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57ecde04-79db-49d3-9710-421cfef35b7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2cff8e7-8f11-4d34-bf80-31ec886461d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a6fc64f-fd49-4220-9642-8b1f3018718d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52b89a65-71e2-4880-bfb1-83fb8d6ca645",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ab51f72-0540-40a6-8b3e-02c723be785f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e11ca7af-c7c2-4cfa-8903-326f48a56a80",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}