analisis_sentimen.ipynb

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   "source": [
    "# Analysis Sentimen Terhadap Aplikasi Udemy"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "35xSqG_yU4jt"
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   "source": [
    "**Submission Proyek Analisis Sentimen**\n",
    "\n",
    "**DBS Foundation Coding Camp**\n",
    "\n",
    "\n",
    "*   **Nama**            : Roni Antonius Sinabutar\n",
    "*   **Username**   : aantoniusron\n",
    "*   **Domisili**  : Cianjur, Jawa Barat\n",
    "*   **email**      : aantoniusron@gmail.com\n",
    "*   **Kelas**      : Belajar Pengembangan Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6qZ3-x7xU4jv"
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   "source": [
    "**📃 Data:**\n",
    "\n",
    "\n",
    "| Kolom                | Deskripsi Singkat                                     |\n",
    "|----------------------|-------------------------------------------------------|\n",
    "| reviewId             | ID unik review                           |\n",
    "| userName             | Nama pengguna                  |\n",
    "| userImage            | URL gambar profil                            |\n",
    "| content              | Isi atau teks ulasan                  |\n",
    "| score                | Skor ulasan (misalnya 1-5)|\n",
    "| thumbsUpCount        | Jumlah suka         |\n",
    "| reviewCreatedVersion | Versi aplikasi                     |\n",
    "| at                   | Tanggal dan waktu                      |\n",
    "| replyContent         | Isi balasan dari pengembang aplikasi                  |\n",
    "| repliedAt            | Tanggal dan waktu balasan diberikan                   |\n",
    "| appVersion           | Versi aplikasi yang digunakan  |\n"
   ]
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   "source": [
    "# !pip install accelerate==0.22.0\n",
    "# !pip install pyforest\n",
    "# !pip install inflect\n",
    "# !pip install contractions\n",
    "# !pip install beautifulsoup4\n",
    "# !pip install transformers==4.41.2 accelerate==0.30.1\n",
    "# !pip install nltk\n",
    "# !python -m nltk.downloader punkt\n",
    "# !python -m nltk.downloader stopwords\n",
    "# !python -m nltk.downloader wordnet\n",
    "# !python -m nltk.downloader omw-1.4\n",
    "# !python -m nltk.downloader vader_lexicon\n",
    "# !pip install transformers[torch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import   warnings\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import   gc\n",
    "import   nltk\n",
    "import   torch\n",
    "import   random\n",
    "import   pickle\n",
    "import   inflect\n",
    "import   re, string\n",
    "import   unicodedata\n",
    "import   contractions\n",
    "import   tensorflow.keras.backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyforest                          import *\n",
    "from keras.optimizers                  import AdamW\n",
    "from wordcloud                         import WordCloud\n",
    "from nltk.corpus                       import stopwords\n",
    "from keras.models                      import Sequential\n",
    "from nltk.stem.porter                  import PorterStemmer\n",
    "from bs4                               import BeautifulSoup\n",
    "from sklearn.feature_extraction.text   import TfidfVectorizer\n",
    "from sklearn.model_selection           import train_test_split\n",
    "from nltk.sentiment.vader              import SentimentIntensityAnalyzer\n",
    "from sklearn.metrics.pairwise          import cosine_similarity, linear_kernel\n",
    "from sklearn.metrics                   import classification_report, accuracy_score\n",
    "from keras.layers                      import Embedding, GRU, Dense, Dropout, Flatten\n",
    "from sklearn.metrics                   import precision_score, recall_score, f1_score\n",
    "from nltk.tokenize                     import word_tokenize, sent_tokenize, regexp_tokenize\n",
    "from nltk.stem                         import LancasterStemmer, WordNetLemmatizer, SnowballStemmer\n",
    "from transformers                      import (DistilBertTokenizer,\n",
    "                                               DistilBertForSequenceClassification,\n",
    "                                               Trainer,\n",
    "                                               TrainingArguments,\n",
    "                                               RobertaTokenizer,\n",
    "                                               RobertaForSequenceClassification,\n",
    "                                               AutoModelForSequenceClassification,\n",
    "                                               AutoTokenizer)"
   ]
  },
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   "source": [
    "# nltk.download('punkt')\n",
    "# nltk.download('stopwords')\n",
    "# nltk.download('wordnet')\n",
    "# nltk.download('omw-1.4')\n",
    "# nltk.download('vader_lexicon')\n",
    "# nltk.download('sentiwordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    "execution": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    },
    {
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       "<IPython.core.display.Javascript object>"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20001 entries, 0 to 20000\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   reviewId              20001 non-null  object\n",
      " 1   userName              20001 non-null  object\n",
      " 2   userImage             20001 non-null  object\n",
      " 3   content               20001 non-null  object\n",
      " 4   score                 20001 non-null  int64 \n",
      " 5   thumbsUpCount         20001 non-null  int64 \n",
      " 6   reviewCreatedVersion  18117 non-null  object\n",
      " 7   at                    20001 non-null  object\n",
      " 8   replyContent          9888 non-null   object\n",
      " 9   repliedAt             9888 non-null   object\n",
      " 10  appVersion            18117 non-null  object\n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 1.7+ MB\n"
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   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/20rbudemy/udemy_review_app20rb.csv')\n",
    "df.info()"
   ]
  },
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       "type": "string"
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      "text/plain": [
       "\"Too much bad support My account has hacked and support didn't respond me. Even after 72 hours after send inquire I have 2 paid course in my udemy account\""
      ]
     },
     "execution_count": 6,
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   "source": [
    "df['content'][20000]"
   ]
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       "      <th>19996</th>\n",
       "      <td>e277685c-38f8-4087-b18a-8a8905130e1e</td>\n",
       "      <td>A Google user</td>\n",
       "      <td>https://play-lh.googleusercontent.com/EGemoI2N...</td>\n",
       "      <td>Can't open this app. It's crashing.</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.13.3</td>\n",
       "      <td>2020-02-22 22:45:57</td>\n",
       "      <td>Hi Atul,\\n\\nI'm sorry that you had issues with...</td>\n",
       "      <td>2020-02-23 02:25:07</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9.23.0</td>\n",
       "      <td>2024-01-11 01:08:03</td>\n",
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       "      <td>A Google user</td>\n",
       "      <td>https://play-lh.googleusercontent.com/EGemoI2N...</td>\n",
       "      <td>Nice and wonderfull app, I would really recome...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0.1</td>\n",
       "      <td>2022-07-19 06:27:07</td>\n",
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       "      <td>Helpful and consolidated content.</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2.3</td>\n",
       "      <td>2018-11-21 18:20:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.2.3</td>\n",
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       "      <th>20000</th>\n",
       "      <td>301cc673-34c9-4fd8-b9a0-b2da94ca5179</td>\n",
       "      <td>A Google user</td>\n",
       "      <td>https://play-lh.googleusercontent.com/EGemoI2N...</td>\n",
       "      <td>Too much bad support My account has hacked and...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.13.0</td>\n",
       "      <td>2023-09-04 23:06:07</td>\n",
       "      <td>Hi there.\\n\\nThanks for reaching out.\\n\\nPleas...</td>\n",
       "      <td>2023-09-04 23:11:00</td>\n",
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       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-4713d4b2-6696-4171-b226-0d7e6856bf14 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                   reviewId       userName  \\\n",
       "19996  e277685c-38f8-4087-b18a-8a8905130e1e  A Google user   \n",
       "19997  71a2b2c0-d4c7-4829-8c04-c821890f4a0b  A Google user   \n",
       "19998  1c81b972-7cba-46da-b6ad-58b40f289a0f  A Google user   \n",
       "19999  6563f17b-9dbe-4a9f-9c1c-e70819bc1c3a  A Google user   \n",
       "20000  301cc673-34c9-4fd8-b9a0-b2da94ca5179  A Google user   \n",
       "\n",
       "                                               userImage  \\\n",
       "19996  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
       "19997  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
       "19998  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
       "19999  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
       "20000  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
       "\n",
       "                                                 content  score  \\\n",
       "19996                Can't open this app. It's crashing.      1   \n",
       "19997                            Best in UI and features      5   \n",
       "19998  Nice and wonderfull app, I would really recome...      5   \n",
       "19999                  Helpful and consolidated content.      5   \n",
       "20000  Too much bad support My account has hacked and...      1   \n",
       "\n",
       "       thumbsUpCount reviewCreatedVersion                   at  \\\n",
       "19996              0               5.13.3  2020-02-22 22:45:57   \n",
       "19997              0               9.23.0  2024-01-11 01:08:03   \n",
       "19998              0                8.0.1  2022-07-19 06:27:07   \n",
       "19999              0                5.2.3  2018-11-21 18:20:20   \n",
       "20000              0               9.13.0  2023-09-04 23:06:07   \n",
       "\n",
       "                                            replyContent            repliedAt  \\\n",
       "19996  Hi Atul,\\n\\nI'm sorry that you had issues with...  2020-02-23 02:25:07   \n",
       "19997                                                NaN                  NaN   \n",
       "19998                                                NaN                  NaN   \n",
       "19999                                                NaN                  NaN   \n",
       "20000  Hi there.\\n\\nThanks for reaching out.\\n\\nPleas...  2023-09-04 23:11:00   \n",
       "\n",
       "      appVersion  \n",
       "19996     5.13.3  \n",
       "19997     9.23.0  \n",
       "19998      8.0.1  \n",
       "19999      5.2.3  \n",
       "20000     9.13.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:32.629246Z",
     "iopub.status.busy": "2024-07-08T11:47:32.628431Z",
     "iopub.status.idle": "2024-07-08T11:47:32.656250Z",
     "shell.execute_reply": "2024-07-08T11:47:32.654876Z",
     "shell.execute_reply.started": "2024-07-08T11:47:32.629198Z"
    },
    "id": "hwBmcgq7U4j5",
    "outputId": "988d3b43-c220-4598-b973-68866cbadc63"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reviewId                    0\n",
       "userName                    0\n",
       "userImage                   0\n",
       "content                     0\n",
       "score                       0\n",
       "thumbsUpCount               0\n",
       "reviewCreatedVersion     1884\n",
       "at                          0\n",
       "replyContent            10113\n",
       "repliedAt               10113\n",
       "appVersion               1884\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:34.343968Z",
     "iopub.status.busy": "2024-07-08T11:47:34.343270Z",
     "iopub.status.idle": "2024-07-08T11:47:34.391256Z",
     "shell.execute_reply": "2024-07-08T11:47:34.390462Z",
     "shell.execute_reply.started": "2024-07-08T11:47:34.343933Z"
    },
    "id": "AtPI2b6IU4j5",
    "outputId": "73b8b063-d325-4889-98a2-fc8089be9e19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:36.430806Z",
     "iopub.status.busy": "2024-07-08T11:47:36.430403Z",
     "iopub.status.idle": "2024-07-08T11:47:36.438721Z",
     "shell.execute_reply": "2024-07-08T11:47:36.437758Z",
     "shell.execute_reply.started": "2024-07-08T11:47:36.430773Z"
    },
    "id": "oyJCGmAoU4j6"
   },
   "outputs": [],
   "source": [
    "df2 = df[['content']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:39.237887Z",
     "iopub.status.busy": "2024-07-08T11:47:39.237526Z",
     "iopub.status.idle": "2024-07-08T11:47:39.246469Z",
     "shell.execute_reply": "2024-07-08T11:47:39.245593Z",
     "shell.execute_reply.started": "2024-07-08T11:47:39.237858Z"
    },
    "id": "oKbN5pLuU4j6",
    "outputId": "2307c51c-0456-45ea-e090-6c8872d80461"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['content'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:40.825069Z",
     "iopub.status.busy": "2024-07-08T11:47:40.824412Z",
     "iopub.status.idle": "2024-07-08T11:47:40.831393Z",
     "shell.execute_reply": "2024-07-08T11:47:40.830424Z",
     "shell.execute_reply.started": "2024-07-08T11:47:40.825038Z"
    },
    "id": "sPyMwHUuU4j6",
    "outputId": "1a48178a-4426-43bf-8e32-c39dbb0c900f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'I have opted for the course but im unable to login to the app also when im browsing the website, there is a pop to continue in the app. Please help'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['content'][10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:48.684901Z",
     "iopub.status.busy": "2024-07-08T11:47:48.684515Z",
     "iopub.status.idle": "2024-07-08T11:47:48.694964Z",
     "shell.execute_reply": "2024-07-08T11:47:48.694016Z",
     "shell.execute_reply.started": "2024-07-08T11:47:48.684869Z"
    },
    "id": "LTmo_M-aU4j6"
   },
   "outputs": [],
   "source": [
    "def cleaningText(text):\n",
    "    text = re.sub(r'@[A-Za-z0-9]+', '', text)                         # hapus mentions\n",
    "    text = re.sub(r'#[A-Za-z0-9]+', '', text)                         # hapus hashtag\n",
    "    text = re.sub(r'RT[\\s]', '', text)                                # hapus RT\n",
    "    text = re.sub(r\"http\\S+\", '', text)                               # hapus link\n",
    "    text = re.sub(r'[0-9]+', '', text)                                # hapus angka\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)                               # hapus special karakter\n",
    "    text = text.replace('\\n', ' ')                                    # hapus enter\n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))  # hapus punctuations\n",
    "    text = text.strip()                                               # hapus teks kosong\n",
    "    return text\n",
    "\n",
    "def casefoldingText(text):\n",
    "    text = text.lower() \n",
    "    return text\n",
    "\n",
    "def tokenizingText(text):\n",
    "    text = word_tokenize(text)\n",
    "    return text\n",
    "\n",
    "def filteringText(text):\n",
    "    stop_words_eng = set(stopwords.words('english'))\n",
    "    filtered_text = [word for word in text if word.lower() not in stop_words_eng]  \n",
    "    return filtered_text\n",
    "\n",
    "def stemmingTextSnowball(text):\n",
    "    snowball = SnowballStemmer('english')\n",
    "    stemmed_words = [snowball.stem(word) for word in text]\n",
    "    stemmed_text = ' '.join(stemmed_words)\n",
    "    return stemmed_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:52.633009Z",
     "iopub.status.busy": "2024-07-08T11:47:52.632158Z",
     "iopub.status.idle": "2024-07-08T11:47:52.636824Z",
     "shell.execute_reply": "2024-07-08T11:47:52.635932Z",
     "shell.execute_reply.started": "2024-07-08T11:47:52.632976Z"
    },
    "id": "bi0b4m8HU4j7"
   },
   "outputs": [],
   "source": [
    "# membutuhkan waktu sekitar 30 menit\n",
    "df2['text_clean']           = df2['content'].apply(cleaningText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:53.539026Z",
     "iopub.status.busy": "2024-07-08T11:47:53.538131Z",
     "iopub.status.idle": "2024-07-08T11:47:53.542657Z",
     "shell.execute_reply": "2024-07-08T11:47:53.541910Z",
     "shell.execute_reply.started": "2024-07-08T11:47:53.538994Z"
    },
    "id": "e2dNM2zVU4j7"
   },
   "outputs": [],
   "source": [
    "df2['text_casefoldingText'] = df2['text_clean'].apply(casefoldingText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:54.817950Z",
     "iopub.status.busy": "2024-07-08T11:47:54.817570Z",
     "iopub.status.idle": "2024-07-08T11:47:54.822163Z",
     "shell.execute_reply": "2024-07-08T11:47:54.821185Z",
     "shell.execute_reply.started": "2024-07-08T11:47:54.817920Z"
    },
    "id": "sHKx43M0U4j7"
   },
   "outputs": [],
   "source": [
    "df2['text_tokenizingText']  = df2['text_casefoldingText'].apply(tokenizingText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:56.042543Z",
     "iopub.status.busy": "2024-07-08T11:47:56.042189Z",
     "iopub.status.idle": "2024-07-08T11:47:56.046505Z",
     "shell.execute_reply": "2024-07-08T11:47:56.045662Z",
     "shell.execute_reply.started": "2024-07-08T11:47:56.042513Z"
    },
    "id": "E6W-c8P8U4j7"
   },
   "outputs": [],
   "source": [
    "df2['text_stopword']        = df2['text_tokenizingText'].apply(filteringText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:57.332356Z",
     "iopub.status.busy": "2024-07-08T11:47:57.331471Z",
     "iopub.status.idle": "2024-07-08T11:47:57.336129Z",
     "shell.execute_reply": "2024-07-08T11:47:57.335178Z",
     "shell.execute_reply.started": "2024-07-08T11:47:57.332324Z"
    },
    "id": "_I_wCLmlU4j7"
   },
   "outputs": [],
   "source": [
    "df2['text_stem_snow']       = df2['text_stopword'].apply(stemmingTextSnowball)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:47:58.659454Z",
     "iopub.status.busy": "2024-07-08T11:47:58.658755Z",
     "iopub.status.idle": "2024-07-08T11:47:58.664870Z",
     "shell.execute_reply": "2024-07-08T11:47:58.664092Z",
     "shell.execute_reply.started": "2024-07-08T11:47:58.659422Z"
    },
    "id": "6fAREpq2U4j8",
    "outputId": "c6e285bc-8640-4e8d-9bbd-d5c8e5cd776d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Great app. Full access to courses including download and watch offline. Works well so far!'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['content'][9413]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:48:00.961848Z",
     "iopub.status.busy": "2024-07-08T11:48:00.961450Z",
     "iopub.status.idle": "2024-07-08T11:48:00.965945Z",
     "shell.execute_reply": "2024-07-08T11:48:00.964902Z",
     "shell.execute_reply.started": "2024-07-08T11:48:00.961815Z"
    },
    "id": "-3tntdqGU4j8"
   },
   "outputs": [],
   "source": [
    "df4 = df2[['text_stem_snow']]\n",
    "# df4 = pd.read_csv('/kaggle/input/20rbudemy/preprocess_text_20.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:48:06.088495Z",
     "iopub.status.busy": "2024-07-08T11:48:06.087875Z",
     "iopub.status.idle": "2024-07-08T11:48:06.098137Z",
     "shell.execute_reply": "2024-07-08T11:48:06.097121Z",
     "shell.execute_reply.started": "2024-07-08T11:48:06.088464Z"
    },
    "id": "pqzpSC_4U4j8",
    "outputId": "24829381-a8e4-41e8-b4b7-060d3f94bcb5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 20001,\n  \"fields\": [\n    {\n      \"column\": \"text_stem_snow\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19982,\n        \"samples\": [\n          \"use app anymor latest updat result error make app crash immedi tri log log email address\",\n          \"app work proper instal applic uninstal app multipl time crash dont what wrong app work well previous face issu quit lot past week could peopl pleas look resolv\",\n          \"well explor get interest topic learn growgood job udemi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df4"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-d145ce86-b54d-464c-beb5-927e7d5d4320\" class=\"colab-df-container\">\n",
       "    <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>text_stem_snow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>thought id tri subscript plan sign month free ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>star cours awesom feel like ticket get promot ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>frequent failur reload thing everi week reques...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>terribl terribl player freez refus play past c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>app absolut junk id give zero star could conte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>cant open app crash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>best ui featur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>nice wonderful app would realli recomend anyon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>help consolid content</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000</th>\n",
       "      <td>much bad support account hack support didnt re...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20001 rows × 1 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d145ce86-b54d-464c-beb5-927e7d5d4320')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-d145ce86-b54d-464c-beb5-927e7d5d4320 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-d145ce86-b54d-464c-beb5-927e7d5d4320');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-491897d1-3e00-4908-8694-9495f8602d1b\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-491897d1-3e00-4908-8694-9495f8602d1b')\"\n",
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       "\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-491897d1-3e00-4908-8694-9495f8602d1b button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "  <div id=\"id_44664855-3548-4c48-8058-404be1d7983e\">\n",
       "    <style>\n",
       "      .colab-df-generate {\n",
       "        background-color: #E8F0FE;\n",
       "        border: none;\n",
       "        border-radius: 50%;\n",
       "        cursor: pointer;\n",
       "        display: none;\n",
       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate {\n",
       "        background-color: #3B4455;\n",
       "        fill: #D2E3FC;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
       "        background-color: #434B5C;\n",
       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "        fill: #FFFFFF;\n",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df4')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_44664855-3548-4c48-8058-404be1d7983e button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('df4');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                                          text_stem_snow\n",
       "0      thought id tri subscript plan sign month free ...\n",
       "1      star cours awesom feel like ticket get promot ...\n",
       "2      frequent failur reload thing everi week reques...\n",
       "3      terribl terribl player freez refus play past c...\n",
       "4      app absolut junk id give zero star could conte...\n",
       "...                                                  ...\n",
       "19996                                cant open app crash\n",
       "19997                                     best ui featur\n",
       "19998  nice wonderful app would realli recomend anyon...\n",
       "19999                              help consolid content\n",
       "20000  much bad support account hack support didnt re...\n",
       "\n",
       "[20001 rows x 1 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_5xPc9PdU4j9"
   },
   "source": [
    "### Percobaan 1\n",
    "- Pelabelan: **Vander Sentiment**\n",
    "- Pembagian Data: 80% training, 20% testing\n",
    "- Ekstraksi Fitur: DistilBERT Tokenizer / WordPiece\n",
    "- Algoritma Pelatihan: DistilBERT (Distilled BERT)\n",
    "\n",
    "https://huggingface.co/docs/transformers/model_doc/distilbert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:48:14.561704Z",
     "iopub.status.busy": "2024-07-08T11:48:14.561324Z",
     "iopub.status.idle": "2024-07-08T11:48:14.567241Z",
     "shell.execute_reply": "2024-07-08T11:48:14.566274Z",
     "shell.execute_reply.started": "2024-07-08T11:48:14.561674Z"
    },
    "id": "2uzn32aUU4j9"
   },
   "outputs": [],
   "source": [
    "def get_sentiment_label(text):\n",
    "\n",
    "    analyzer          = SentimentIntensityAnalyzer()\n",
    "    scores            = analyzer.polarity_scores(text)\n",
    "    compound_score    = scores['compound']\n",
    "\n",
    "    if compound_score >= 0.05:\n",
    "        return 'positive'\n",
    "    elif compound_score <= -0.05:\n",
    "        return 'negative'\n",
    "    else:\n",
    "        return 'neutral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:48:20.428194Z",
     "iopub.status.busy": "2024-07-08T11:48:20.427314Z",
     "iopub.status.idle": "2024-07-08T11:51:01.650678Z",
     "shell.execute_reply": "2024-07-08T11:51:01.649870Z",
     "shell.execute_reply.started": "2024-07-08T11:48:20.428158Z"
    },
    "id": "qlgYSxtOU4j9"
   },
   "outputs": [],
   "source": [
    "df4['kelas'] = df4['text_stem_snow'].apply(get_sentiment_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:51:01.652677Z",
     "iopub.status.busy": "2024-07-08T11:51:01.652364Z",
     "iopub.status.idle": "2024-07-08T11:51:01.662495Z",
     "shell.execute_reply": "2024-07-08T11:51:01.661502Z",
     "shell.execute_reply.started": "2024-07-08T11:51:01.652647Z"
    },
    "id": "LAZCkYKoU4j9",
    "outputId": "8c2a25d2-d228-4dbf-cae6-c7b7e1b49f0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kelas\n",
       "positive    12161\n",
       "negative     4616\n",
       "neutral      3224\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kelas_counts = df4['kelas'].value_counts()\n",
    "kelas_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:55:14.173304Z",
     "iopub.status.busy": "2024-07-08T11:55:14.172626Z",
     "iopub.status.idle": "2024-07-08T11:55:14.204149Z",
     "shell.execute_reply": "2024-07-08T11:55:14.203419Z",
     "shell.execute_reply.started": "2024-07-08T11:55:14.173270Z"
    },
    "id": "0s_RXqIGU4j9",
    "outputId": "339b807a-7ae6-4b4c-a382-036e3d79a998"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indeks_positif       = df4[df4['kelas'] == 'positive'].index\n",
    "jumlah_baris_dihapus = int(0.48 * 12161)\n",
    "indeks_dihapus       = np.random.choice(indeks_positif, size=jumlah_baris_dihapus, replace=False)\n",
    "\n",
    "df4.drop(indeks_dihapus, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:55:23.632792Z",
     "iopub.status.busy": "2024-07-08T11:55:23.632119Z",
     "iopub.status.idle": "2024-07-08T11:55:23.642143Z",
     "shell.execute_reply": "2024-07-08T11:55:23.641252Z",
     "shell.execute_reply.started": "2024-07-08T11:55:23.632759Z"
    },
    "id": "02uWs1iXU4j-",
    "outputId": "2a8b057a-0363-4fc7-a84f-0c4dd97dc458"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kelas\n",
       "positive    6324\n",
       "negative    4616\n",
       "neutral     3224\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kelas_counts2 = df4['kelas'].value_counts()\n",
    "kelas_counts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:55:31.525972Z",
     "iopub.status.busy": "2024-07-08T11:55:31.525589Z",
     "iopub.status.idle": "2024-07-08T11:55:31.860772Z",
     "shell.execute_reply": "2024-07-08T11:55:31.859829Z",
     "shell.execute_reply.started": "2024-07-08T11:55:31.525942Z"
    },
    "id": "Fuzc2e7_U4j-",
    "outputId": "e0fbe98e-ad66-494f-a0ed-ecc8bbcbcad1"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels   = kelas_counts2.index\n",
    "sizes    = kelas_counts2.values\n",
    "colors   = ['gold', 'yellowgreen', 'lightcoral']\n",
    "explode  = (0.1, 0, 0)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.pie(sizes,\n",
    "        explode=explode,\n",
    "        labels=labels,\n",
    "        colors=colors,\n",
    "        autopct='%1.1f%%',\n",
    "        shadow=True,\n",
    "        startangle=140)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.title('Distribusi Sentimen Review')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:55:46.008810Z",
     "iopub.status.busy": "2024-07-08T11:55:46.008436Z",
     "iopub.status.idle": "2024-07-08T11:55:46.015322Z",
     "shell.execute_reply": "2024-07-08T11:55:46.014432Z",
     "shell.execute_reply.started": "2024-07-08T11:55:46.008781Z"
    },
    "id": "W_1JT0rbU4j-"
   },
   "outputs": [],
   "source": [
    "def visualisasi_wordcloud(teks, judul):\n",
    "    wc = WordCloud(background_color='white').generate(teks)\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.imshow(wc, interpolation='bilinear')\n",
    "    plt.title(judul)\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "def buat_vis_wordcloud(df, text_column, sentiment_column, sentiment_class):\n",
    "    teks = ' '.join(df[df[sentiment_column] == sentiment_class][text_column])\n",
    "    visualisasi_wordcloud(teks, f'WordCloud Sentimen {sentiment_class.capitalize()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:55:49.815332Z",
     "iopub.status.busy": "2024-07-08T11:55:49.814997Z",
     "iopub.status.idle": "2024-07-08T11:55:51.318336Z",
     "shell.execute_reply": "2024-07-08T11:55:51.317528Z",
     "shell.execute_reply.started": "2024-07-08T11:55:49.815307Z"
    },
    "id": "OS2q6V24U4j-",
    "outputId": "a2fb2bde-269c-4278-d142-165ef9db9b69"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Bhh6TcrnMd37nd97Wti9luwbi7TIwTC3L2tH23ypzc3N88pOfpN1ub/FqPPPMM8PvB0gpee1rX8trX/ta3vOe9/DLv/zL/OzP/iyf+cxnrnksN3tef/iHf5j3v//9fOpTn+Lpp59Gaz0Mm4Ln3vkbMWLENzej0KkRI0bsKC9+8YtxXZff+73fY35+fotHw3EcXvSiF/Fbv/VbdLvdLfUzBkbHb/zGb2zZ3nve8x6Abakvffd3fzcLCwt8+MMfHi7r9Xr81//6X69Y9+d//udZX1/nHe94B0mSXPH9Jz7xCf6//+//u+p+ZmZmePjhh3n/+9+/xYh44okn+MQnPrHFgDp48CDNZpOvf/3rw2WLi4tXqPu84Q1vAOA3f/M3tyy//Hxciy984QtXDdf64he/yPr6+tB4eeSRRzh48CC/9mu/dtVq4bcq+1ooFK5qUO00k5OTfNu3fRvve9/7hsbTpdyObO2t8N3f/d2kacp//s//ecvy9773vQghhv26sbFxxW8HHqWryS8PGBis2z233/md38nY2Bgf/OAH+eAHP8hLX/rSLWGGz7XzN2LEiG9uRh6NESNG7Ci2bfOSl7yEv/iLv8BxnGEs+4BHH32UX//1Xwe2Fup76KGH+LEf+zH+63/9rzQaDb71W7+VL37xi7z//e/n+77v+/j2b//2G+77x3/8x/nP//k/86M/+qN8+ctfZmZmhg984AP4vn/Fuj/8wz/MN77xDX7pl36Jr371q7z1rW9lbm6O9fV1/uRP/oRPfepT160M/qu/+qu84Q1v4BWveAV//+//ffr9Pv/H//F/UKlUhrUNAH7kR36Ef/Wv/hXf//3fz0/91E/R6/X4nd/5HQ4fPsxXvvKV4XoPP/wwb33rW/nt3/5tms0mjz76KJ/61Kc4ceLEDY8bMvnT3/u93+P7v//7eeSRR7Btm6effpr/8T/+B67rDus1SCn5b//tv/GGN7yB+++/n7/39/4eu3btYn5+ns985jOUy2X+8A//cFv7vJRHHnmET37yk7znPe8ZFm8cJHLvNL/1W7/Fq171Kh588EF+/Md/nAMHDrC8vMznPvc5Lly4wOOPP35H9ns13vzmN/Pt3/7t/OzP/ixnzpzhoYce4hOf+AQf/ehH+ef//J8PPUi/8Au/wJ//+Z/zxje+kbm5OVZWVvjt3/5tdu/efd2ClYP756d+6qd4/etfj2EYV5V7HmBZFj/wAz/A//V//V90u11+7dd+7Yp1nkvnb8SIEd/k3DW9qxEjRnzT8q//9b/WgH700Uev+O4jH/mIBnSpVNoiw6l1Jof78z//83r//v3asiy9Z88e/a//9b/eIsOpdSaP+sY3vvGq+z579qz+nu/5Hu37vh4fH9f/7J/9M/0nf/InV0ikDvjUpz6lv/d7v1dPTk5q0zT1xMSEfvOb36w/+tGPDte5muyo1lp/8pOf1K985Su153m6XC7rN7/5zfqpp566Yh+f+MQn9AMPPKBt29ZHjhzR/+t//a8r5G211rrf7+uf+qmf0vV6XRcKBf3mN79Znz9/flvytl//+tf1u971Lv2iF71Ij42NadM09czMjP7BH/xB/ZWvfOWK9b/61a/qH/iBH9D1el07jqPn5ub0D/3QD+lPfepTw3UGbbxckvVqkrXPPPOMfs1rXqM9z9PAUOr2WvK2V+s/QP/jf/yPtywbnPtf/dVf3bL85MmT+kd/9Ef19PS0tixL79q1S7/pTW/SH/7wh69o55e+9KUtv/3MZz5zzevhUq51/JfTbrf1O9/5Tj07O6sty9KHDh3Sv/qrv7pFUndwnc3OzmrbtvXs7Kx+61vfqo8dO3bFsV56nSVJov/pP/2nemJiQgshtlwz17ou/vRP/1QDWgihz58/f9U2b+f8jRgxYsTtIrS+jWy+ESNGjBgxYsSIESNGjLgKoxyNESNGjBgxYsSIESNG7DgjQ2PEiBEjRowYMWLEiBE7zsjQGDFixIgRI0aMGDFixI4zMjRGjBgxYsSIESNGjBix44wMjREjRowYMWLEiBEjRuw4I0NjxIgRI0aMGDFixIgRO87I0BgxYsSIESNGjBgxYsSOM6oMPuJvHJeXjhFC3KWWjBjxN5vRvTji+YRSmihKSJIUxzGxrNEQasT2yZ53Gq0jNCFgIDARIvsM1tHEaJ0ACgAhbARmvjzNlwuksC/5XZpvVyOEgcAADGCwHAQChInAGj5rlY7QOsq/lXlbrB097tFdMuJvJIMBzmhgM2LE3WV0L454vqC0ptnq0e2GjNdLI0NjxE2iAUWq2yTpGlK4SFFAygKGMIfrKN1Hqc7QcLCMOkJ4KNVF6R5KhwhhYso6hjAAgdYRidpA6xQpXaTwkKKA0gGpaqBRCAykLGKIMjAwNLqkqpEbJiaGLGN8sxoaSisiFaK1wpI2ptx6oKlOiFVEkPbopR0c6VK2xrCkfcNta62JVECoAlKdoLTCN0t4hn+nDmfEDpMkKUmUkKYKpTSmZWA7FlpplFJIQ2IYEq3zGQGlUVpjSImQkCYKlSoMywANnVafJE5xCzaOY2GYEinvXiShJhtsKZ2S6phUxSQ6zv6/jkl1QqpjlE6zDylKKzQKrRU6/99gS9kjRGQzGGSDOIFEIJHCGP5rCAMpTIzhx8IQFqaw8/9vIMRzO8Lyb9KseNb/CZEKiHVAomISHV1yfSQonWZXw5brIiPrd3FJ/5tIYWEIE1PYmNLGEi6WdDCEmc9w3bnzGcYJrV6IFALfsbBMAykEGo1SGiklhhQX7+tLfiuAVGu01phSIqXI1tEgBNl/8psh+/P5e12keb/GKiBWAYmOLnk+JFf0u8qfBZDPYiKQQmZzllvu90G/O1jSwRLu8PnwfD5fd4o0SVlaarK82sKyTMpl7243acTzihSlI5TqoVSHhA20jnHMfUjho3WfVHdJ0g1S1QAhkZgkQiLpkagNUtVBCIkUBZCVfHsdUtUiUU20DiBVSFnClBNo3SNRa4CBFF7mARFllOqjdI9ErZKk6wjhIaWP1O6OH/VzxtBIdUInaZKoiIpVv8LQSFRMJ2myGi4w3ztNzZ7gSPlbtmVoAHSSFpvxCr2kS6JjdnsHRobG84g4TGg3e4RBTBwlFEoe1XqBNFFEYYLjWhi+jVaKNFHEcUqaKmzHxDQNgl5IFKb4JQetYWVhk04rYGKmSrVewPXtu2poDEh1TD9p009b9NIm/bRNkHYIVIcw7RKp/nCwEatwONjQueExMD6ygYJEDv7NB5WmsIcfSzrY0sOWPq5RwDGKuEYR36hQMKu4RhEpfJ4vw43hAPObmETHxGmfVrJKM16hl2TXSZC282ukR5xPqAw+mhTIjIxLDUpbujhGAVfm/W5WKJg1SmadkjWBIz1M4dzR4+n0I84ubWIZkumxEgXXxrIMUqWJkxTbMvBsC6U1SZrmBkfWzwKIkpRUKTzHxpEGSaouTjCIzDgRQiCleN5cx1cjVTGRyvq9Fa/SS5r0kiaByp4PoeoRqSCfqEjyZ0IedjHs98ygtKWX93sh7/cqRbNGyRqnZI5jSw9TbO+9+jeNJFGcO7/BsRNLTE6U2bN77G43acTzCK0TtO6jCdGkRMk5wvgkwpXY5hyJ2iROl4jTZVLVwJRjICvodBWtU+J0Ba37mHIMYTj5xEqPMDlDkm6gSTODI13FkGUcaz9axyTpOlL6mHIMKYogNYlqEqfzJOkaqdrAkGOY1NGiuuPH/ZwxNBIdsxYu0k+7WNLBMwtbvlcoEh3RT7u04g1s6aJ0uu3tm9LEkR7ayIyayw2ZEc9tkiSl1wkJehFJkhIFMZ1WZngE3YiJ2SrTe8aIg4QwiGmsd2g1ehTKLr7vZIMPQ+D6FlpDp9lnfaUFgEoVY5MlZDGbFb2TM3lKK1IdEauQSPXzT2446IAw7Q0Ni+EAIu3l6/Vy4yIiUdFwJluReekuzmDnhgZi6MmQ+czIwGNhCDOfvXawpJsZHIaPIz1co4hrlHCNIo70caSfDU6kj20UcuPE2+ItuZto4PjiGudWGxyaGWffZO266yulSZSi2QtY2GhiGgZ7x6uUvGxQ3UuatOJV+mmLUPVQWm27LYYwGbN3UbYmMKV9iTv8Jo9JZ/0YqX52PaTtodHZT9t0kg266SZB0iFQ3a1GqA6Hnq+UBK2zeN5sNnvgxTAuMTSzj2uU8IwSBbOKb9TwjBJebny6RglXFnCMQn5d7YxRHsUJjXYvMyAA37YwDYnKPRrlgossSdq9gEa7P/yd0tn3/TAmVYpdE1XqZZ9mt08viDFNiW2aeI6FY5tIzQ2t0G7SoBWv0E87hKp7hafsepjSZsyepWxOYEjrtvs9VD3CtEM/7+9B/3eTDbpJg36aPR8i1cs/mZfjRv0+uO8Hfe5If9jvvlmlYNayPpdFPKOEa5RwjAKOLAyfJ7dDtxuyvtGhH8QkcUqSpMS5tzlVimrFp14vUSjY+J7N8kqLlZUWnmfjOCZBEBOGmWfbMCTT0xXGagWEEKSpot3u0+4EdLohUZigAcs0cF2LcsmlVitg21nfNJs91tY7BEFMHKeo3AvuuRaeZ1Or+VQrPlGcEoUxjWaf5eUmJ0+tcObsGs8cXUTm58N1LaamKpRL2WzwzZ6nzc0ua+sdwighjhO0yjxznmfj+za1WoFS0SUIY/q9KAvf6kUIMu9fkqRIIfA8m1IxO07DkARhTKcT0Gz2SZIUhECliiRJsW0Tz7OpVn1q1QJSbqfNl98Tz/6zX+sUdA90C9LlbKG5DyEHz/3n7pSC0gGJ2iBJN0lVkyRdI04XSXUb0KSqTZwsARpDVjFkDUMWcg9HO3uXyxqGMYYhywhhoVSHOF0mSdcQws68JbqH1B4g0CiU7qNVDFphyDLkeRxSuNl+dQ+UBDSmrO/4cT9nDI1IhSwF52jG69SdKepM7ej2C0YZVxayUBM01mjG5nlFkij6vYgwiABorHdobnTotAK67T73PTxHfbJMrxvSafU5c3yJC6fXqI0XqdaLVMYKVMYKKJWNaMIgpt3okcQpcZTguCaOYyEsA2HcuQeV1ilB2qWTbNCMV2jFKzTjFdrxGp1kgzDtEusoC4UgzcMgFFqn+b9ZjGcWIrE1XOrSl4DOgyfQAkgRGiC6ZLAgLjFGLno+BiE1UhhYIhuIlqw6VWuaqj1DzZ6hYk1hWvYwNvRuo5Tmc8fO8rGvPMPfec2LbmhopFrRj2LOrW3yqW+coODYvOmR+4aGRite5XjnC6wEp9iMFkh0vO22ONLnwcp3ck/pZXiUMIxbHHCiUDqlk2ywHp5nJTzNanCGTrJBJ9lE5d6K4bUxCKPLr5GrXRea7PpLdYxAEKkePZqX9L+BZGCImBTMCiWzzrizlwlnH2PObupyEFqzMySpohfE9MOIIEow8sGOlAJDSqbrZRzLZGm9zamFdYTIvovjlDBJ6fRClMo8GL5jsbTRZr3ZwzIlJd9heqyMbRrobQyiGvESJ9pfZDU8w2a0QKqTbR+Hb1R4sPpaDhRevDP9Hq+zFp1nNTjDSniabrJJN2lcDJO6qX4XW/pdKLGl32Xe7zLv96JZpWTVGXfmmHD2Ubd3YzkOkkGC6a2z2ejyxJPzrKy06PbC7NONCMOYKEq498gMDz80x+5dNXzP5uSpFT7/hZNMTpQZGyuwtt5hY6NDECY4tslrXnWYUsnFMg3CMObC/CZnz61x7vwGjWZmwBYKNhPjJQ7sn+B+d9fQ0FhZbfOVr51hda1Dpx0QxwlJqpgYLzE9VeEFL9hFteLT70dsbnZ55ugizxxb4viJJVZWW3z5K2e4cGETgInxIq989NDQ0LhZFhYbfOVrZ2k0erQ7AUmSkqaa6akyszNV7n/BLgq+Q6uVGTtHjy+xsNBAGhKlFL1ehGFIpibL7N83zv337cLzbDY2upw9t8ax40t0uxHSEIRhQq8XUq36TE2WuffwDJWyh5Tb7dvBe+ZuPfsTUBvo5AQ6+itAI70fAHn95/5zAaX7xOkqqdogTZukqonSYZ7gDalqk6hVHPMAtrkXQ5YAiNMVUt3CMfdjm7uQooiUPgKTlA3SdDP3WhTJwiQLGEYNU9ZJVZNYiCy/gy6mMU5myJSRwiFVTUgukOoWSvexzd07ftx3zdDQWhPriEgFtOMGq+ECi8FZ2nGDs91jRGkAgGv41J3pS36nSHRMM17nXO84jvRQOkWKzD1cscao2RPDGbde0qabtOilHfppD0e6uEaBslWjmHs1lE6JVEiQ9mgnDcI0yO4hrUl1ihACQ5gUzTJVewJbDmY+O7TiDSIVkuqYZPDyz18AFWuMilXHMXxc487GcqYqmxHSOpvtM6XENLJzcDdnnJVWbEZdNqMuzbhPkEYUTY+K5VF3ipSsreeln0Zshl0acY9W3EMgKFkeRgTCAhEK+t2QsB8RRykCsB0LyzaRMvtubalBc6NLrxPgehauZ2PbJrZjUq74GJaBYRqYlolhZucoTRVJooZ/3y5aaxIdEqtwi4ein7azsIe0QTdpDAcQ/bRFP20TqxBFypUzR7fcki3/1Rf/uHyVK5AYGNKmlzbpJps042XWw/OUrHHK5ng2821W8Y0KvlnZ0Znum0GjaXQDzm80affDG64/MLaCOGF+o0XBtQnii4PKREf00syrsRktkuho221xZIFWsko/bWJJB4fthWcOhoZR2qOXtujE67STdRrREo04/0RLuZeru+32XH0/l10H1+j/IG3RSxr00zbteJ316AKr9jQlc5ySWcczy7hGERjkAdw8UggsUxInBoYUKKUJkwTy8KhayUOjCaKYRqePaWTPNaU1qdIYUmCZBqYhEVJk+R1a0wsitNaMV4p52NSN25eokG7ayPv95gyN0OjSiteG/W6zvef9oN/DtHux3+M1GvHysM8b8RJh7t28NfTV7/9r9nt76DVpx2usWxeohlNZv1t1PKOEc5P9Psid6XRCzp1fJ4oSpibLjI0V6fVCLixssrLaIopTigVnaAz0ehHr6x0cx8L3bYpFh2LBIYoSLNukVPayELooodnqc2F+g5XVNuWSS7Wa3XthmLC52WXRtZmZqWJZBo5j4XkWExNlHNuiH0QkiSJNFa12n2eOLTI5WYYjM5imxPMs6mNFZqYrLCxuYm4Y1OtF5vZms7+Viofn3frkpV+wmZosUyw49IN4+D5qtXocPbbE9HSVubnxbMKtH7O21mFtvc3c3nHGxgqEYUK/H9Fo9jh/YYPZmRqafEKtHbC41MQ0DfbNjWMYkjCM6XZDzl/YoFrx2bu3TsF3sCzjuuMFrUNIjoNqgnkQYQzGZs/mGEMBMeguqHVAg77xc/+5gNYBqdokVW2UDtAkcMn73pAehqyidECUXsBQxVwBKjMelOoQJReQooghy5hGnSyBu4LSPTRZnhb5pOFgEgJNbtD08vV0PlER5qG1WQI6uoPSwY4f990zNFCEaT83GI5xvneSpf5ZemkX2ZYs9E8DMOHM8oDhUjIrQBZCFauIbpKFWWXhBSGmsHENj4PFByhbY5j5gKcZrzPfO8NSeI71cImqPUHdnuJA8QUUzXK2TZ3SS9ushUuc6x5nM1pFCJEbIBFSCBzDY7d3gHsNf2hotOINTnaepB1v0lc9grRHmPaIdESsIu4pPsA9xQeoO1N33NBIUkUYJ7nBofFsaxinfDdJtWa+t8kzrQVOdlZYC1vsLYxzsDjJC6t7rzA0uknIyc4yx1pLnOwsY0qDuUKdOWOcfYU6aZDS74akqcIrOBTLHqZlUBvPFED63ZCVhQZBL8QwswRxIUT2YG0HxFGKZZu4rkWx7OEVbPyig0CQJila78wtodGEaY9Ossl6dJ718Dzr0YV8sNgmUv1Lkrq3zkw+V1CkaBXQVjHdpMGaOIcUBo7M4von3f1MeQeZdu/BMQpZeMbzoDSPEGAZmTenF2ZGRKp25rxnz7Uu3aSBb1Ru+te9tMVycJL53jPM95+ik2wSqR6JivN8i2fv+ohUn1hHdJMmK8HpLITGKLDbewF7/Bcw4ezDNQrcziDDNCS+a2NIA9+1iOKEtKey3IskmzyRQqC0JkoSIDMqbNPENCWW4ePZFtWSh20ZFH2HXhiz3uzSD+PhPu40g37vJU18s3rTv+6lDZb6p1joP82F3lP00iah6pOqeEu+xbNBpPokOqSbNFgWJ3GMIp5RZLd/P7u9+5h09+HcQr9rrej2QpaWmkxMlHjxI/uplD2CKOFLf32KtbU2taqfeTP8SwbtuRfLtk0O7J9g967aME/HdS2klPT7IZuNHhfmN+n1I17zyiPs3zcOwOmza/z5Xx5lZbXF6lobz7UZswwmxkuUii4qD5HTGrTSfOozT/GVr57l/vt2AeC5No5tUSx6TE9XWF1r02r1eeiFe3nFyw4CYEiJ6956OPbMVJVaxb9oA2pI4pSPffzr/NXnTvDQC/cODUOlNP1+RKo0hw9N89CDu9EaLsxv8OnPPs3iYoONjQ6WleUsJUlKtxuye9cYL3lkH/V6Ea01f/3lM3z6s0+zvNKi0eghpcSybuDVUF10+Bg6OY703wLGJHevHJu47PPcR+mIVLVQqgeorKOHCAxZxzZDwvgE/egbGLKEIatYxhSWMUEQHyeJ1zFEGducxeNBpPQzL4TQRMk8WkcILLSOM6NCh3mAXb5v3SczLHok6VreFoHWQRZidRMTa9vlLoZOCUxp4RkFxuwpgrTPZrxKqlPGnRkmnVkAStYYjsxizQBSnRKqAFNajDsz2NIh1QmteJPNaJV2skk/7eBSwBI2rixQs8dzj0Y2E5g9wC/OVmkGXo2AZrxON20x485RMMukuRHSiFZpxpv0kg6u9DGlRTdpsxJcwJI2U85uUp0Qqj7LwXkW43MIISiYFSx55xIqk1QRpynLjQ4LG02kEJgyexHbpsF4pUC9lM3s3A3PhhSCiu0z49VoxD36aYTSml4akVwl9t2WJuNOiabXZyPqEKmEME1IbYXnOfjSydzFqc7CKwyJYRpU60WEISnXfGbnxhmfqhDHCX7RxfMdpCGwHRPHszAtk/p0BbfgYNkGjmfhF1ws20RsK051Kzo3Evp5HHUvadJNGnmYywbtZI12PkPdSxokOryp2dK7SRaMkYVrJPkzMVJ9+qKFIslnu9fYCC9QsaepWFN4RjEfiNzBdl2iQJTF91+cs1XXia3XWhPGCRudPuudHkmqtj3jvd12hapHL21uyxOS6oRERXSTTTbjRdbD86yGZ1kPz7MRzROp/l27VrJZrywHKNYQ64B+2sbAIFI9GvEym9ECVXuWijWJKa2blkX0XZtd4xWSVGFbJkma0g9jkjxmf7xSwLFMJmtF7t07iWkYWKbEkBLDGBgdBiXfxTQkZd9FAL5jIQQUXPtZee4prQhV5pXYTqjdoN87yTqb0SLr0XlWg7OsRxfYjBaJdXAX+12RakU67PeQIG0jhUGYdmnGy9SjBar2DGVz+7lIQggsy6BQcIiTlLPn1vF9myQX79i7e4zJiTKua2GaWwe8vm9THytSrfpUKlu9hFGU0OtHNBpdNja7NFt9Tp5eIQhjhICl5RadToAouYRBFqKVJopuN2RpuUkUp1vUyVZW27TbAWGUoHVm5GTvGou46OLYJqYhKfg21crtCcroPNeo2eqxtNwkTfWwLUppVtfatLsBUZRkYbNaZxMltomf52MMzke7EwzPZz+I6fVC0ly5zXEsCgWbctkbtrlUcnFdC6U07U5AoeDAtUK/tCbzInTQ6QKk59Gqc5eG95lqkjDmwH40WyTHeT4YG4as4JgH8sG8xjSmcdQBHHMu/74ITKN1kgmxCBcpC1jGOAIbTYqp6kjhYsgaUnoI4WIaUwhhIWUZrWMEZm6k1NGiiBA2pppCmT1sY08mcyscDFnCMmeQws2NkhjTmNzx475rhoZA4EgP23YomhXKZo31aBmJwaHiCzlYvD9fT2JJi1Bl7pyBtGPFGuNI+SGq1gQSwdH241kyedKlk7SyGHPDpmLXKVplTGnhGA69pIO6xuyQ0in9tINGs7dwiL3+IQSSheAMX938C/pph27SwjeKeKJAkHZZj5aZ8w9zf+UlmMIi1hFfb3yetWiJklllwplBituLa70ecZrSDSLOrmzytdMLOJZJyXWyWGYheXDfNGOlu6ccJBHMejWqlo8hJL5hE6trJ/EXTYeDpSl800EiWAvbxDrFtAz8okup4lKfKGcvhcFBaZBG5r0Zn65SHS9l3lSyh2ymPpM9hgYytlO7aiiVPbSFEIg8CfxWxiQKhdIJrXiNtfAsS8FJVoLTdJJ1uknjknjqdJgj9HxmEB44GBQtBcfxjDL7C9/CXOEh6s6eO29ocFHWdhCWQa5IdK0k3sF6vTBmfqPJ0maLVGlsw9gxz59GZ7kPaWtbhobSCWHaYSU4xbHO51kNztBO1nOPV/KculYyNaOUlfAM69EFSv3jVO1pDpVegS19PIoYxs0ZGkXPxp0ZywdQYmg0aq1RGkwpMA2D3RNVpsfKw34Sg1FhzkDetlJ0KfnO8BowngVvBuQeTNWjn7ZI1XYMzJgg7bAUnOR4+/Oshedox2t5Iv9zrd8z2eTl4BTr4XkWg3Fq1iyHy6/ALni428xJEULguVm+xMpaiy/99amhCECt6nPgwCTTU5WrJCULSkWXycnSVk/HoH0qMxoajT7NVp+FxQZRlGQDZ7Kw2MHfSaJIckXCpZUWX/7qWZqtLFF6kBd06swqYZSQJIOcuFt7L2wHpTRpqphfaPDXXzlDkCfJSyOToT57bp04SklSNTxXUgp8z0alCsu+OLYwTUmx6BInijA3vizTwLIMikUH33O2KCs6jkWl4mOYkk4nJKxez7DNw5NUG3QDrRoIdn7me3uYIOsgKwjzQLboDqvj7RSWMYFp1HKhBkX+JkPk7ZeigDQ8TKOeF+yD7PrLpMYtY4qBiiDCQGIBEmlMYhp1bL0/2x4iD58y0TrBYjafTJN5KJYx3Jchx9BmhEDmy3c+f3lHDI31fo/FTjv/dEjyMIS95Qr7qzXGPI+auzVEJlPDEWgtsKTENtw8CVFiSwf3GtKzhjBxDR/PLOIZBbxcBcUxXMxc6jZR0VCRyhAGEoGVS3pKYVxTRUYIiW14mNq5ZNsSV3pYwkEgh3kYILCkTcEoEaqAc73jGMIgzQeUs+7+q8r0DujFMd9YXeZ0Y5MoTYfn7EbsLpW5b3yCMc/DMy2SVBFEMQIouoPjzxLJsnCDi7M1l9NNQk62l+mmIfcUs8H9Qm+TZtxDAxXLY5dfo5dEnOwsUzBdDhYnSbRiI+ywGXdpRj1SnSUe+4ZN0XSZ8ipMuZVhP1vCwDUsHMPElibpdWacpZDYQuJIC8ewsKRBkqZAPquUh0NtOZ5Ljs8w5TDPYjCTqfMndFYxMzMozG0nvl3JQEKylzRzr0XmrWjFKzSjVVrJCq14LU/s3vl4x+cCGp0NPElQqSJWARf6TxOqHpPxCpPuPqrWFEUri2HeSY8BwImlNZ6ZX6UfxfSjmKcuLBOnKV8+deG6akFaQz+OWWt16QQRE5UCB6bG8J2debhmITQ9ekmT+DoDzkRFBKpDI1pkOTjJUj8zTtvxeqZ49BwKoduKHtZ26STrJDrGki5B2mbS3c+4vSdXKtreTK+UEvsqtsDAYzW4aizTwLpslntoaHKxToYhBM+SbXFZW9Qwz+J6BuYgZ2szWsj6PTjBSnCGbrJBqHrP2X7Xw35PIF4nVQlmx6GXtJhy9zNm78YzStjXCBG+1KuUKpWpOlUL+H6WP1cpe9SqPhPjpauqH0kpMPMaK1d8JwSOY+IXbDw38zIcvmeayckSebIlWkO16rNrtoZtmzSaPdbXOzSaPXzfZnZmIqvDpKDR7KPU0lWeI5nBMcgPUkrfdrHJfhDRavVZX+/QbPao14vMzlRBZwbSymqLhYUGw5kUQGkIw5heP1NgHJ7XVNPvZ6pclmng2NnYI8vriAjCGHXJOCOOE7rdkLFaAc+ztoZNDSpYE0NyDJ2cBNWCdAmdnMmMjfDPUOkqW17GxhTCehhhTOWbiUH3IV1EJ6cyZSjzIKhNdHoOdBt0D/JiccI6jDAPAVaWlDlsS4RWbXRyFNILwKBqtgQ5jrBeCMbMLfXBs0lmMJhokYm6DMK+Lr4fB38baOFwaeK9yH+/Vfhl8NvMSLi0ovfFbRpIzNyQuLQ+TvY7iUBjZeIwd2hSfEcMjdVuly8vLvDFhQt8YeECQZJZYt8+t5/vOnCIw/XxKwyNW8WUNgWzjG8UMYQ1PJkSA1s6SCGzwc9NSFIOMDDw8thTSzp5x2SDX0va+SMrU//JjBufql2nk7R5svml4QVQtcbZ6x+iYl1bJqwTRfzZ2dP88anjtMNweM5uxCv37OVt5kNYhoFtGKSpoh8l2JbBdLVEP4oJooRYJ8RJuuXBckUbkoAvrp9kOWhS2OUw6ZZ5onmes901lNbMFcYpWx4rQYvPLD/FtJsZEEEac6y9xMn2Mic7y0Qqm4GbdCrMeFVeNLZ/aGjcEa6c8NrWb3ZqsJsVjwzZjBZYCI6x1D/OYnBsKFOrh/kWz51ZyTvJYACy2D/OaniWRrxMN9ngQPERitYYO+3S1sBT51f40Oe+znq7R7PXJ0pS4lTxF0+f4fPHzm9jC7BrrMKjR+Y4MjtBYccMjTx0Krl+6FSsQ1rxKvP9Zzja+ivWw/N5uMxOCgHcWcJcmrmftljun+RQ6eXIosw9Wrdfo2i7V81zIWBCowjyHI3rhU7FKqCVrHCh/zTHWo+xGS1m9S92VADiTpJd37EK6KUNVoJTHC69AoFEOnuuaWgMSFNFGCYUCy73v2AX01NlymUPyzQu1ju53qD9Kl9JQ1IoOFQrPqWii20ZvOwlBzh8eDrzROjMcyCkwDQlzWaPxcUmGxsd+v2IfXPjPPryezBNgyhKWFpuXn3XeRFInXshbkb++Fr0ehHLyy02G13CMGbXTJXXvOrI0Dg4fXaNJ5+e33Jl6FxlqtXuE0XpMMw/SVLanYB+P8K2TXzfJgzjbHk7oNsL83Zn64dhQqPZY2a6QrHo4jiXDwcV6AAdfQkd/DFabYJq5IZBgu7/MVp8cus5sr4FURwHYwKQoKPMqIgfz7Zh3oMQHiQn0NGn0cl5UCuZR0L4SO8HEMYsiAJgbWkHahkd/Ck6+vPsbxLARJj3Q7GGeB4YGlvZzozIte6Fm1kuENcd6svhWPdOsSOGhtKaOE3pJzGdOBoOmvtJQqzS68ZMw8VTo4cFxy5Ws718cHjRBrtYJwAGHpLBhob/uTlEtu2BlXip5XdJlM7F/2pFolN8o0DJqmFLN/NymOUsHMyqXmdnmlSr4XnrxtuT0OwnSeZByM+pY5nUil6myiL7JKmiG0b4js14uUDRu7FLUWlNolPCNKGThLTiPkorOklAoGISnfVhrFK6Sch62OZkexkpBC+tHxzOMq2FbRb6m+wKaqyFbXzDwTd31g13KzNHOx2j3U0arIfnWegfZb7/DM14mV7SGkrS/s1EZ7H8SrEZLaC1QgqDWAXUnT2UrCwxc6eMvQNTY7z+ocM0egHNbp8nLyxzYmmdwzPj7Ju4tsyhEFl4hGubTJaLHJkdZ+94Fc/eobo6l+ZoXMWjkahM0Wo9vMD53pMs9o/RjJezUKnn3bWTPadjFdBlk/n+MyQ6Yk/aZso9SMGs5cniN8927tm7Xb/lUgY5Gv20ddV+j1VIL22yFp7jfO9JlvrHacWrz9t+V2Q5jZ1kgwu9p4hVSF+1mVIHKJi1KwxNpTRKqaygotJsNro8c3SB+YVNvDyJWinN9HSFPbvH8FxrqDx1Iwb1I8ZqBfbuGWNtrcOZs2usb3SG14hSmvF6kf37xrEtk3LJxbZNwjDhwoUN/vrLZ/KBtqDZ6lGpeLjOlc8EQ0rGagXGagVOnV4ljtOhR2bv3jr1seJNnUkn/61lGvT7MWfOreN86RQyV2DrdkMqZQ/n0nMhstzEJE45fmKZMMzqgDSbfbTWTEyUqNV8Cr5DEmeqmaZp0Njs8dXHz+F7NnGcsrjUpFb1GR8vUS37VzleAdgI8wXgalCrkC6g4ydANRH2i8E8wKXJ2MLYBXISLh0xaQW6j1YbiOQkOvpiNnNuHkYY+0DHQJL9a+wFPLYOTQUIG+QYwn4ZyAqoNVCZd0XrPuImaqpdTq+dSeT7RZfCDlV6Xz6/ztGvnaVU8Tn88NwV273+e1Bc8t9rfXu15Vf/5kaPyWfjOfocqaORh7ighwo811t38L870Q5xyU1zIxQpsQoZs6c4ULyfilXDN8r5wPv6bRR50raVx2LeKq5t4tomrV4WohMlCZ1+SL3ks3u8kiVHXmPzEoEtDWxpoLQmVDFhGhOkMalWBGlElCYorbFl9hDuJgHLQZNTnWVeWNvL62YepGhmCZh/uvQETzQusBw0WQlaTLrlHTc0ngt0kg3mc3WYC/2n8lC658Ns5J1Ho2jGK3STBrEO6adtTOkMDY2dQAAP7Jnivt2TdIOIVi/gf/3FV7mw0eJF+3fx2gcPXvOXUghsy6DsuRRdG8+2stnUHWpbpoI3SAa/UnIx1iHNeIWF/lGOtT/Henguzxl7/l4/g8KCC/1naESLudy4kYe53tlcnecKgyJ71xIBiFVAM1pmofcMx1p/xWa08E3R76HqcqH/NI14CUWaFwS0rjA0tNZZgn+aorVmba3N4mIjT7aGVGmSOOWRF+2jWHAQteL2DQ0p8FyLsVqBfbkE7FNPL7Cx2UXkUbZaw71HZqjVsvCsWq2I59mEUcLJ06vMLzTwvKxQX6PZo1b18byrGBqmZHy8xMR4ieMnlnn86+cpFh327qnj+85NGxqua1Ovl3Aci34Qc+z4UpYk79m4rkWz1aNWK2xRtBJk+RhJonjm6CLHTyzT7WYqizPTFWanq9RrBQoFm04nQMosCX+j0eWLXzpFHKd0uiFjNZ/p6QpTk2UqFe8qilMShAv2SxD2Q4h0CZ0cA9VGcw7hvgHhfhdbZ+Zl/tn6RNU6Ad1Dpx2IBFgPIeyXIOREbjhsoNUqwtibezMuRQAuyCmE+zqEfhSS05mXRPW5XTqtPssX1pncNbZjhsbCmTU++f/7IrP7xpndP7Fj232+cvcNjfx6NIVFxRqjm7Q41ztGO97ElDZlq8a0u+emE6qzCEPFSrDISjDPRrTMRrRCkPZISTEw6KZtxuwJPKNIchMKH8P4dJ1pFjfiNU52vpFXUHZyg0kx6e5i0pnFkg6W3Drg9k2LV+zay7hXoBNH9OOYRCkSpYhVykbQ51Rjk5Vuh1YYEt8gh6Na8Dg4Pc5UrUQvjKn4LtWiR/E6qiuGkJQtn04S0k4CQpWQ6BRTGkgtSLTKkrFVSsl08QyLThKyGXVpRD2OtZb4tPEkbp4AeqK9jNKDY8g8L9+M+GaFKfcgzXgF0c+qaY64lCyeuxWv5vVnxpBIqvYMhZuW/tzKpR5MyUBdSFAteIwVPCarRWbHrh22JxCYhsCxMlU2U1497vtWyWb4I6K0S6wCEhUN88KCtM1GNM+Z7teY7z9NN9l8Hs5mX5vBYHsxOI4iHYoGXG2G+462Q1+sFiK5QSjOTu0TTaLCPKwozPvdROlMmW09PM/p7tdY6D9DL21+k/V7Sph2WewfHfZ7qhMKRm0YStXtZQpPGxtdymWPStmjWvWHdRtWVlvML2wipWBtrYNtm5TLLgf2TyAE7JqtUa14w7yDSxn0r+OYzMxUcRyT6ekKvV54MU8PzeR4KfMeWAZSSvbvG+c7vvVe4jjFMDM1M8syCMKEIIg4dHB6KBAy2I5lGuzfN4FtGaystFhfadPc6EA/hvQWwrUNieOY3HNwEikFOl9mWVl7+kFMmij27xu/2BYpsB2LsbEih+6ZZHKiTBQlSCkpl13qY0WKRZc8jx3DkHml8wKH75nCtk2iKMHzbMpll+mpylAG/pKTmv2TtRKtLRAmWS7FwJAw85yAKw2Lq10lkICoIqwjWS6GsRtkCfDAMBGikBkdl9+vw7Zk8SzDtuxQwdiV+Q2+/thxHnz5PUztHrvt7QFZeF2SbglVu1MMPIXzy00ePzrPRnNrnaXJsRJzs2NM1ouMV4vPmkDGpdx1Q2Mw629Ki6o9TiNe42z3GCfUN/CMAru8A5TMCiWrxvCiuqY6UO5FEECuXLISXODJ1pfoxk26aZsk16LvJx02ozXuKT3AlLuHVCeILS26eksFmZt88BLVaDajVVbD+eF6aZ4s/EDlpRTNMj7iCkOjYNu8as8cr9y9NwtdUop+EtNPEvpJzPGNdT555hRfV5ogSa5raCitqRY8qgVveF5ULpsnpbhmwpohJBXLo5306cQBaW4k2MJAi6wo1mrYxhSSkuVhGybdJKQR9WjG2WclaGFctl2dh1ndKGTu+UrBqGF6Fivh6VwAQPyNycfYLhpFJ1knUj0c6WcqcNId1pbYqcGfbWYCEhXfZazkM1EuMF0t7ci2b5VUR8P8hURHmDikOqadrLMcnOJU58ssBScvqod8E5HokOXgBJ14HUs4ONLDlDa2zAacN9vvtxIHn8mV5y94CTLfxJ01ODSJjojSfmZg6hhLCBId0U7WWQpOcqrz16yGZ56zCd+3Q6T7LAbH6SSbWMLBFi6m62DJTC6114s4d36DZqNHueSxe1eNe4/MDJWhnjm6gBBgmgbrGx0qFQ+t4eCBSQ4e2J7cpuNYzM5Us2TqG2BZsH/fBPv3TdzUcVqWwYH9E+zZVWN1qcn82XWe/OpZojBG3IKhYZoS05QcPjTN4UPT1103SdJhSLdjm4yNFXjhg3uG9T4up93OohxMQ+K5Frt3j/Hylx28aa/LzqBBpwijhrAeQJhHwJi9JHnZy5Skno2WXPpM0VmY09ceO8b4bJUHXn4PkA8hr/a40Fv+uWLdaz1jLt3noAbM1Ve8+W0rpQnjlFMX1vjIJ7/G8bOrW75/8NAsr3nxPTx4eJZqyf+baWgMsKXDLu8ARbNCkGbSjqawKVlVCmYZS1gUzQqz7j58o4RvFPP6Ghl1e5oHKy/DM4tUrTqO4SGEZCr3hsQqJFZRJjWKxpEenlGgZk9QNMsoneJID0tkYUAl82Kcd8mscrj0EABVu47SipVwnm7SpmzWGLdnKFlVjNzrsh4usxIuIBA0ojUMx8Dn2jf3MG7ctDClxDVNaq5HwbKwjOtX6uxHMe1+mOvOa8q+Q8V3Wd7ssLjZYnaszHTt6gMvQ0oqtk8j7rEatgnTCMcwmfGqKHRebG8D33So2QWqlk/Z8qjaPiXLYzZP/HalxSDrTmnNjFdl2q1SNK+hyf08x5QmUKBsjjPuzNGMl+kkG3e7WZeQZ0A+B0h1zHp0AUNYWTVpo4xrFLDEzl0bUggenJvGdywOTe9ciNbtoHU2u99NGjjSI1AdzvW+zrnuN+gkG9+Ug81LyUKpjjLQjpKugWMUsG5ShrIbx7TjcPii9kwLL39OSiHoxCG9JMaRJraRPX+DJOFcu0E3jpjwCoy5PiXbwTXv/OtOkRKqDr2kgS09ummDs92vca73BN1085t+QiJUPeb7z5BJdmaz5K4s4roW05Nl4ijh/PkNNhtdlleaWJaZRQU0ejSaffbsGmPPnjFqtcJdLzY74g4gXISogyhytwr9RUFMvxuyurDJ8vl1nv7r06wvNnjyi6eGg3DDNDh4/272HJoa/ubc8SWWz28Q9iPiMCZNNZZtUK4VqE9Xmdk3TvEadVWiMCbohiyeXefCyWUmdtWYOzyDV3CwXYv1pSbrSw02V1o01jvD+i3Fik+lXmR23zhjU3dQYOcO8xwyNFxmvX3MevuuuU5R2hTNCtPe3iu+qztT1J2pK5ZPuruYdK9u8V9OCRh3rlQuKFlVSpckdq+GC2yEy/TTHkWzzKS7m93+Qaxcf/h092liHSEQtOMGpeuEiwyS2AeF9gZdUnVdPDOTd5XXcQ8GUcJGu08/igmTBK3LlD2X5WabJ88tYxkGU9XSVR/appBULJ+C6XKivUw77nOoNM2YU0RpxVrY4WRnmarls8evM+GWKZgOVbtAxfI5WJzi1RNHKFoeEpHXk9AYwsDMXzJa50nvKpPwTXReDEorEpUSqxQpxPAYNWTf6ZREpfn6mlSnxPkyKS6ekbuREGoIC8OwKFnjTDhzpDqik2xyZwb3l+ckDbx5V/O/iYuy0QykFy+ZSRn+f52Hl+hLlt+ZAVCqEzajBRIVMe7spWJP5XHczlXaf2sIAS/YNcmR2QkMKYf1MrY7qBNsDY/YCTKZ2w7dZJPECGnH65zrfoNzva/nycI3f74HPtXMa3vxmrj8GtgqXnGxb7f0tx5IbtyZvo91wFJwklD1KJljFMxq1u/5M/JG51rp7BrtxCFL3XYugAF1z8/6GI0hBM0wpBH2KVg2nmlhCEE7Cnl6Y5XVXocjtWzG2jEN3Gfhdad1SpB26SYbxEaBRrTI2e7jXOg/vSP9LsQlibeXPBO4bMnd6vdI9VjqHydWASWzjm+UMW0b17GZnCizudml0ezRaPY4c3YtjxDQWJaB41h4vs3uXWNXzY8YFLfTKj+iQf0cQMjs+If1UwbqVSKbQR7UTLo0REjlRSEFl6hd5SdQ5/K1Sl12jsSVtVmydmUysnFe5A8YhjkNajlly27vGSPEQOpXXlPu99J1hRBII1vXvDw86m4gHJA1hCiyE2FPt0IUJrQbPc4dW+LJL57k9NMLbKy0OPH1c/RaWc6H7Vr4RZc990yBgDhKOHdsiae+dIrWZpdeOyCJUxzPYnLXGAcf2E15rHAxF+PSXHitCXoRGyttTjxxnq/82TPc98g+xiYrGKbEckzWlxoc+9o5zp9YZvHMKkprpCEZn64yu38C17epTpSG8vzPN54zhsbzCUd61Owp4vACK8EFOkmL9XAZKbIXYCduEKRd6vYUk95ufPPOhXIYUuJYBq1ewFqzi29bjJV8ukFEJwiJ02uHZxjCoGz5lCyXVtxnM+pSMB1mvRqpVvTSiM2wiyEkJcul7hRxpcWMV+Xe8gyhSvjTpScwRDa7qPKX2H3lXdxbziq7p1pxsr3Mqc4K57rrLAVNYpVgGyZBGrPQbzBXGGfcKWFJg1bc40R7mbPdNc5112nGPWKdshl1aMUB+wrjzBXGKVoujrTu0qMqo2TW2e3fl8l7Bqd29LUthYklbGyjgCsLef6PN/yYed6PIaxhfRjJwPslIffcDUL8VB43Hak+oeoRpl2CtEM/bROkbQLVJd1GReNbJVIBS8EJbOlhFV08s7yj25dSQi47GeTKa/0oJlXqysHCZfiORb3o4+6U8hSD4p8t1sPzhKqX1ViJl0l0jLrJKyWT0i7iGaXhx5GFoYfAlA6msDCEmc0iXzJTqLRCkVWhjnVIrPoEaaaONKhgH6oeqY52fLZdo+inLc71nkQDB4svwZIuUpg3NDA3wz4rvQ7dOKKfJKz3e6wFXR6oTzPpF1nudVjv90jycM/FbhulNXtKFUwhidKESKXZwFDIOyQeciVKp/SSBmvhOULVpxEv0opXSXV80+dXIHCNYu4FvLTf/cv63cr7/eIxKp2iSPN+D4hUQJh26F3S75Hq5VK8O9vvCkUvaXK29w0UmnuERdmYplCw2bdvAtsxCYMkq5eRGwJSCkxDMjlZxnVMDHnlbLdKFedPr7F4YQOlNEmc0GkFJElKrV7EtEw219tIKdl7YILaeBEpJZ1Wn6X5TYoll4P3zVLKB4NL85uceHqRYslleneNUsWjUPLodQJazR4LZ9dZOL8xzI1QqcK0DA4/sJtde+vDOhpxlLKx1ubrXzrF+dOrxFGW72k7FhPTFQ4cmaZcvf0cJSkF5bKHkHKoHDU5ce3nqG2b1MeymPyxWoFiyb2qAffsIrLcCrGdvI47g+NaVOrF3DgoIg3J0vl1Dj20l5d95wNA5tHYc2hq2ETbsThwf2ZMxFFCEqWoVNFYb3PqyXkunFrhvhfvH+5DkL2T4jBmc7XF6sImp5/Owutf/roHmD0wSaWeGVudRo/l8xucObrAzNw4D7w8EzOJo4QLJ1ZYnd9kdWGTsckyftnD9Z5/AjsjQ+MWyAyNCZrxBr20SyNeZz1aymdRFZa0saSNbxYZd6YxxJ27uQ1D4NoWSmtavZBmN6DVC+iFMXGShVNdPxncpWL5Q89D2fKZ9qqkWrEatkl05qUoWx41u4ApDKbdCkfKMxxrL/HVjTMkOs1n0bNZs6Lpcbg8g9aaUCWc663ztc2zbEZd2nkuiCklcZrQT6JhoT+Nphn3OdZe5ERrmc2omylgoegnEd0kRGlFzS7iGBZOroR1tyiaY8y4h1kJzgwTP29lMJEVyskGiDIfMFjSxTWKFMwxSmadolnDNyr4ZgXfqOAY2UAzMzxcDGFjDurK5GFsWmdx44mKSHSUa9+36CWNvMDgKq14Jf93lUB1s7wjrXY8WTXRASvBaaQwmXQPUNe7MynpHZqd0VqTKkW7H9Lqh2x0ejT7AXGSkqTX75OJcoGCY++soUFKJ9nM64os0oiy8Dq1TRnG7FrIlJsMYVKyJqhYU1SsSSrWJEVzjKJZwzWKOLKIbXhY0kFiDIUztM5kSLPcgR6B6uZFJtdpxstsRAtshPN0kg2CtEuiw2H1+p1BE6ZdFvpHUTql7uymbI1jSx8prh820Y5CzrYaxEohBSz22pxubrKrWEEKwUqvy7HNVaqOh2eazHdatOMQ37IZcz1ilXlCpRC3rex3M6Q6oZNsIsIzNKJFmvEK7WR92/0u86JZWb9blK/W71YNRxZxZRHLcDPjLb9eIJeJR5HoiDDt5RMKTdrJOs1oiY1okfXwAt10kzDtkuhox/u9n7aY7z2N1ppxZy6TOvZ99hTG2HOLCbep0iyc3+CJr5xBpYooTFhdbhIGMbvnxnF9m/kzaximQRyn7AnHsR2T1aUmT37lLBPTFWb31oeGxupSk69+/gQT05VhoVe/6NJq9LhwZo2j37jAsSfnkTLzeCRJmg1SawWmd9WGHpYoSmhudDn5zCKmtULQj/Oq3SYH751hYrpCsexdtRChvsQrA5fE5Oce2Uvf3UIIfN/B9x2mp8r57y96ay7HsgwqFY9KxWNub/0OzIRfklCw7U0LLiaU3x1s18J2LYoVnz33TLN4do0v/9nT7L9vF4++4aEt6w48ZKZlMDM3zuSuGiL3fmmlOf30PE996TTddp+gd4nKYG6cBv2I1fkNWps9jn3tHIce2su3vOZeyrUCtmvRafZoN3qszG9w/sQyhx+e45Fvuw8B9DohG8stzh1fYmOlRXO9g2WbI0PjbwqmNPEosMvbh28USHWSezMArbPZZSGp2RP54O/O3VS2aVL2YKzk0+6HhHHK0flVpBC8YM8UE+Vry0sKMq/GlFvhDbseop/GHCpNU7ZclIZ7y7P84N6X4pkOk045nxmEiuVzoDTFmFPkSHkWpdVwNk0Dewt1zHwgIaXgvvIsY3aBII2JVCZvKIXANSxKlse0W6FgOphSMu6UeMnYQQ6VpgnSXNlLayxp4hoWdadE3Snm3oy760K0pEPBrFG2JqjZs3STTfppa5u/FnmRSZeCWcXPPwUjMyRco3iJIeENZzCzfweeDAuZD0QvzmhuDaMwhIWQElPbWNLFlh4Fo0rFniZK+0SqRz9t0Y7XaMYrbMaLtOIV2vH6dQvO3SyDOgOdeJ1GtEjVmtpRNaK1Vo/lZpsnzi1zdHE1DyVMEduw+47smmCmVqJa2DkJwkTFrIXn6MTrBKpLqHpXlbu9HIFA5kpdVXuaqjVNzZ7BNyt4RgVH+jjSx5IulnQwRXYtGMLMPFqXPWsEMnsGGQUMYeNIn6JZo2bPMO0eyvt+YHjMsx6ep5928r6//ZnubMAb0knWuNB7CikMZt0jFOX1B5uuYTLmeix1Oyx0OyQqZcIvULLtoceiHUfsKVXZW6qggbV+D0lW+V0KQcGyGXN9ao6HLe9MxdvLiXXIaniWZrxMqLqE6cBrcH0GhmXJrFO1Z4Z9n00slHOPpp/fw27e54N+v7zgltjS76awcI0CBbNGzZ5lOu3QT9q0k7Ws38MLrEXnh0bHTqBRxDqknaxxofcEApjxDt+26hxkM8Wlio9tGxRKLkE/wvNtbMfi0P27hjUm1lZajE9sP5ogizTVnD+zxl99+ikmpiq85vUPYDsWtmOilMYwJHsOTGBaRia0ojRhP0IYgsMP7GbvgQnSVLG+2ubYE/N02gHNRpdKzccrOJiXVbdPkpQ4TBhYG4ZpYJiSJFakSYrtmJi2mYV5JSlpovJQMxMhBUmcoFU2eJaGRCVZAT9hDEIrs8zjqxk5N8/AWw4DXc+Ln+dfOM92UUoT9EJOP73A8vl1VJqFIAopWF3YZG2xQanqk6bZ5N7ACElTxcqFTb7yZ8/gFhxKVZ9qvYjj2Ri5lHDYj2mud2hudGltdHnyCyfpd7L3RBKnXDi5DBpUokji9Ibe+ecqI0PjFjCEiWGYuMb28z/uBFpnihKWYVP1XXrlAqutLkubXaarRabrJUq+c81JYyEEphDUnSKvnDhyxfcly2V/8UpVjqLlUrRc9vg3VokwMdhfnGR/cXvKIbZtUrOfH9r7pswG/2VrkrqzB63VdQwNMZxtNoSBISws6eAZFWr2TDa4sKao2tNUrCl8o5yHmdzGAEkIDCSGuOQ2N6588UYqoJc0WI/Os9g/xlJwEqXVsLr1Tsx0DmotdJNNNqNFqvY0tvR2xNDQGlZaHZ46v8znj5/j8bOLGEaW82RKA+MGL9my7xLE25e33g6KhGa8THOb62fDAgNbOjhGgXFnLzPeYWbce5j27sGRBUzh3NSs5CBfBySGsIbKTwOyPB1FJ9lgPTzPfP8oGmhGS8OCg7fv2cqkjrtJg8X+UUxpU7Gm8MzyJaF+V2IbBiXbYaHTZrXXwbUsxhwX37Qx8jDNOE2xDYOq41F1PMI0QYpMltsQEtcwcQ0T2zCfRY9GTCNe3Pb6F/vdxTEKTDhzzHiHmfYOMe3ek4VJimtLlF91m9vs93a8zlp0jnnzGRSaVrxCL2mS6vi2+13n/d5J1pnvP40pbar2NK5RvG6/bwdpCMoVj3KtQKHk0uuGxGGCaRlMzFQxpGDh3AaN9Q6V7YQsDZSEdDY4XF7Y5Ot/fZq/9b0v4sWvPITnO7j+1lnkOEqyASWaJE2xLIP9h6d4+KUHkYbkzPFlzp9aJehFdFsBvV6E49pXjLjiMKHT7JEmWRFe2zaxXWsYnlOoeEjTIOhFhP2QJM5CQW3HRBqSoJsNSstjRWzHJOhHqFRhmAbSkEhDZjkll+Sf3DrykrAnDTpG6wCEk0/oDAyOgVf9+YPWA+/SZQqdmixEr9nn7NFFTnzjPIYhkVIiDUFro0u70cUrOOhUDxWltNaoVNNu9pg/tcrErhpTe+o4A29Efs3FUUKvE9LPP/Ons2vm0hyPYsXDcky2zCM+zxgZGs9TUqWIkgSd1wEwTUnJdzENg1rRoxtEnF3exDFNxq/j1Rhx+5Sscfb49xOlPdaj81d8LxAYwsrDHuqUzQnK1jhFcwzfrObhL1ns9WDG2hT2s+axMYSJZ5QYt/fiGWVq9i7G7b0sB6dYDk7QTzsodmYgHuuQjegCxWAsCwXh9nXLNZpzqw3++tQ8jmXwyiNz7B2vMl0tXUzyvA4T5QKTd/keGUj/jjtzzHqHqdm7qFiTFMwqtixsNRZ3EIHElUXGnN3Y0qdu72IpOMlC/yjNeIlWvLYjhmasQzbjJbygzKa7gGeU8I3KMDn8crTOkoQjldJPEixjkPiaqeGVHZeZQpmNoM8Xly+w2usSpgnjXoGq42IbBs0o4ExrE4DdpQpV57mngmdJD9+oMOnuZ8Y7TM2eyfrdqGJL7872u1Fk3NmLI33G7T0sBSeyfo+WaSdrO5KzE6mAzWiRglFj013Aln7e77cepijIQllc1yIKY6w4zXI8LBPLMrJBXnYB3VAe+VJRjCRO6XcjVKqHielewcG8opDdRQxDUix5GIaB69pIQwwTtm3HIhYJSmXeiKu1pdPoMX9yOQu96Ua4vo1XdIeJ3BO7sufj4plVVuc3M+MhrxqexAm9VoDtWtzzwr2UagXWFjbpd0Ms28Qvu1THS7i+swM+BwHCQ4gyGgd0AukipKdBTmf1MHScr+fyfBhaDsLUBjUvlNJXOGeUUrQ2Oqxc2KC92cUwJIcfmmNq7xhporhwcpml8+tbrqPBxoWAXfsneMl33E8cJcyfXmHh9CqFssfUnjoTM1Ucz6Y6XqRc8ylWfV7w4gO88BX3DEOvlNIYpqQ+VaFaLz0vw6bgDl8NWUfmITV5rYhYKaI0HaoQqcFDYfgbgSEFxrBytoFjmJgyC9vZiTjDrM5DpoQUp2kWz5vH9G5tS6YGZUiZtUUaOIYxlFa8m9n/qdIEUUIiFVII4nxGxHNMfNdis9Pn/FqTmbEbJ92mShEkCUGaECbJMLbZNU1KtjOUjUyUIk5TIpUSpenwnA0KZA36x5QSQ0gcM6s6bkp51eS+bxYGuRqrwVkMYV6ifJIlapvCxjUK1OxZxuxdjDt7GbN3U7Em85ndLEfjbjHw0DlGgQpTWUy4OYEjfSLVR0SLO1ZkLFEhjWiRolkjTF+A1oqBWtatojUsNto8Pb/CIwd28y37Z3lg7xQHp+o79sy4U4jc41Qwa9Ttvez1H+BA6RFK5ji29G6Yy3Bb+87Pi2142IZHyawz6e6nYI5lnre+SZwXobtdoYDMq7HJRrTAWnieglHDcl1MBgPOrX0Uq5RuHKO1xrcsHMPAEDKLiU8TSpbD7mKZ1aDLardLP46xpMQzLWqOR9VxaUchnThiPegx6T+3JltE7m0ommOMO3vY6z/IgeIjFM0xbOne0efBsMCd4eMYPiWzzpR7EN+sDq/HSAfEKtiRfu8kG8N+z8L/PMzh0OMW7s18Ys1yDCzLwDQlRm5omKbMi6RtNTIG44zBzDVAmiriOB16E1SqiOMErXWm0mQaWLa55flxubEgpcAvOEP1IJm/50RejVvnYhQqb9PlBL2QjeUmm6ttOo0ubsHBL3nZ9mwTt+DglVw2llssnF4Zht2EvYigG9LrhBTKHjP7J3A8m9Zml9ZGByEF1fEShZKH69+cpPRVTzkCcNGiDLIGwkerZYifRpg9tKqSFeTzsgreO2gga61AB0CQGTO6jU5XQDWAEBCgVtHphez/Y2XtEA5ZUcFr3EsiOy7TlNiORdSPWFtqIGWWr+MWHExTEvQiOs0e/U6AUorqeJHdByaJo5SwH+EX3SvUoLL3DozPVPmWVx9hY6XF2uImrc0uZ48uIoSgVPUxLYPyWIHqRIn6VJnZ/ePsv28WITNLRSmV5eYUXVzPzpY/D7mzZqcYijECsN7vsdztcL7dYr7dohkGtMOIME1IVObutg1JwbKpOA4ThQK7imX2VWuMuR62YVxRHO5WSLVmtddlqdNmvtNmudthM+jTDiOiNCFRGinAMgw806TiuNQ9n12lMnOVKmOeR8GyMe/iACZKEtJQQZ6QNohRNw0DxzJY2Gix3u7SC2/8oujGEc+sr3FsY52Tmxu0o5CCZXOkPs637d3HbCkzVtpRyEKnzYVWi/OtJptBn1YUEqUJcaqwDQPbMBhzfeqex1ylylylQtX1qDwHZxN3CtcoIoVBxZqgaNZJdRZzW7Vn8rCoacrWBL5RyRVkirh5/oXcVlXVZxdbelTtGRCZ2tH53jc43f0qQdq57dntVCd00gatZC1Xukq2pUJ0fTRRktCPYupFjyOzE4wV/OfYWb06tvSoWJPMeIfY6z/EuLOXojmGJZ89j9aAzPNmMmbPYko7m3mWNuvhBTajhR3xbASqw2L/KI70KFl1POPq9YWaUcCp5ga+afFtew7QDAM2gh79NGE96OOaJvsrNaYLJYIkJtUaQwomvSK+ZfFAfYq5chVLGvimRdF6bs0EOkYh63f3MHOFh6g7eyiYVUxp82w/Dwb9Xrd3YZUcfLOCIWw2oqzfd4J+2mS+/zS2dKlYE7cfMinEDe8PIcQwlyKOEuIwQecx7kppwiCmudmlVPYy48IycH0bKQVhEBP0Y4JehGWb2M7WodLlJsONW3N1TMvALTgUwiST3zUNpCFQqSIM8jAoQ+L4NoWynw1+LSPz3tgmxVqBUtWnWPFxfZtKvUiaKpprbfrdcCjHe/tDFQHCAllBWC8EEtCb6PBT6MgFHBAuwjyIcL4d5E4a9jEkR9HJMXS6AGoZVAOtViGdBwS6/wfo6HN5G2ezooDGXjAmgeuMPQRUxovsOzLDyoUNPvaBv8TNz/V9j+xn332zFCs+tfESQgo2llt87bFjnDu+jGkZtDY7mfFRL13m+RK5V0IgpGBmrs7L/9aDnD26xJlnshprfsmlXCtQrPjM7pvgngf3sL7U5BMf/EKecJ55W7yCwwMvO8jcvbMYSITxfHizbeUOezQyz0Q3jlnqdjixuc7xjQ2Ob65zanOD9X6PzaBPP8kGqoYUOIZJxXGoez67S2UO1sboJQn7KlUm/AIFy8KQN6ciMphxT5WiHUU0wj6nNjc41djkZGODc80mq70um0FAP4lJVOYlcAyDgmVT93xmiiUO1sZohSF7KxWmC0VKtoNnWc9a/O/WY8qOJ00VcaLohRGdfoRtGaCtzOUmJdsxgIMk5XyryVeWFvjiQhaGUMlnBB+emqbmenTiiHOtJkfX1zi2kRklq90uG0GPIEkI0xTXzGKipwpFposl7quP0woD5ipVdpUq+Jb1rBTNerax8wTNmj3DpLN/qDAz4e5j0t3PuLOHqjU9TOJ+rmNJJ/9kieqKhM1okQZLBGn7tkIqFGkms5k06adtQtXDkYXbnrkftKjg2oyXfDzbeo57Mi4mfU+5B9nt389c4UEK5hgX6yU8y23KxR6K1hhFawyJJKteLuilTWLVz4zo2yBKe6yGZ/HNKrv8eymoGoa48hmaKEUvial7PvvKVRY6bbpxlqgcqZSS7VBzXDTZbLUUYss29parwxAaDXflGX01Bh6DkjnGtHsPe/z7mfMfxDMrd73fS9Y4JWs8r3+kMuWbpEGio9vu90B1WQ3OUDTH2O2/ANcoZcnqt3O8N4hZz8KXTKIwe+f0exFrK638N5rGRoegH5EkKehsQG47Fl7BoVTxCIOY+XPruJ6N42bbEEJQKLoYlsw9FGIof3sr2K5NZSyT5i2U3WGMf5Jk8qm2a2FaBsWKz9h0BdezMS2TfjckiRMs26RQ8SiUvaEHxPVs2jLP0pFZnsZtIwRgZgaEdW+WZh5/Da1OgVoDJEKUQFaASwUFJAgLIatg7gNjKjMGbsosU6BbeajWGXS6mO1Dx5l3BdBqDXQz25cRgzELchK0uuauBtfe2FSFww/Psb7cZP7USm4AFJk7MoPMvQnV8RK18TKrpU16rYAkSnF8G601E7M1JmZreAVnuCu3YDO5a4yxqTKWbVKbKFMeKxIFCWefWcgK+PUiyrUCXsFhas/YsCjg/KnVYeic1ppixaPX2T0UDHg+ckdHfYYQ2IbBqcYGX1leGA5SW2FIOwoJkjTzIOQvBJVmMw2JSunGEWv9HmebDZ5cW+H+8UleuXsv+6s1ynkM7s0QpymdOOJLi/N8dWmRc60G8+0W7SiiG2Va7VGakCqNQiPIBvJxqugnMRv9HudbTR5fXuSeWp0XTk5zpD7OobH6XRk8u7ZJyXfQShOninLBYdd4BdOQ2KbBeqvHRqe3rdCpy0mUohNFdKLM2zTfafHVpUWeWlvh6PoaK70erTDIDYwkL6inCXKDMdEtNoI+C+0WX1te5KHJaR6cnObe+jj7qrU7cDaeG0y6+4cqUIaw8IwSrlHClQVM6dxR9bE7gSltfKpMOvu5p/gSzvef5ELvqdtWptFoYpUVsWsn6xiWdUkIza0gKDo246UCSapYbnaykAbnuWvUmcKmYNaYcg9woPgIU+5BHKN415XULqVsTbK/8CI0EKQtGnns/u2Q6Jh+2qIVr7ARLuDKEiVrHHlZxfC66/NgfYogTTi6sUaiFcVcunbKL+AY+cCPzIi41lkbFER9rpxVSzoUjCpT7kEOFl/MhLMP2/CfU/1etaYwizYaRS9t0YpX6STrt7XNVMX0aOb9Po8tPUrm+DVzdHYC0zQoljyklBRKLs3NLn/16adyhSqTjdU2jmvj+c4wVEVKwe5947z82+4j6IV89o+/nodnZbkflmPx4CNzzN0ztSMKQMWKx66DUyRxQppc9BgqpdBKU6wWcDybyV01KmNFDFMipMhyClI9zE3xiw5JrIj6EVprapNlSlU/SyLeUSyEsQtEEcwDCN0GnU1GZAZFLRvgDxA2yDGE/RIwprLvRY1M4nb7+8Q8gpDjoNoI3QNU/hnuiMyokSBKCDmZGT3buL5m903g+g5hPyIKYkzLwHJMxmdqIDKvU7le5OHXHOHA/bu2JtkDUZTg+Tb16cowtGl23wTf+YMvxS04FCs+QmbCLHNHpvGKDl7BoVIv4RWy9o1NVrBdm+m9dXovDbYk01u2wcSuGoZpjEKnrkaQJmwGfU5ubnBsY53TzU3Ot5q4poljmHimSdHOTvTFHI4s+a/X77Pe77Mo2pxqbtIMQ4q2jSEl99RMrEEs5DYqzCqtWe52ONdq8sWFCzx24Rwr3S6bQT/PvZC5J8XNKooi8pmyzNCI0pTlqMtCp40QgqVul24cZS8/22bc9ylY9rM6a2YZBkXPRiJIUoXO3YODmHTfsagWPaqFmw9ZSrUe9t2FVguN5nPz53hmfY35dotUa1zDxLMsiradCdxpTZznbvTimEYQsNhpY0lJL47pJQm2YVBxXXzTwnkOeDayhPqUIE7ohhFxmiKEwDENfNvGsUxsw9i2NGCmFlXBku5NV75OlSJOUsh1/58LOS2D3I2aPYNGZ7OS4Vl0qm57hjPREd10g26yQckcA27d1S4E1Ioeu+sVelHM0YVV4iQlVTrPD7r+TLFpSFzr6kXC7gwiV5baw7R3iBn3EBV7esuMttIJWidkM6YD+dJM7SULYRr8O6gbn32vSdA6hfw3Ig/N0/mL+dLq8Nn3xjVjmH2zhG+W6KctukkDNPTSRl5z4dYGWQP1sU6ywXp0noJZxTPLWGw1NLJnsWSp22ah08I1LaqOR83xKNtbn2k3MjKeKwgEjiww7swx4x5m2j2UeRDukifjWvhmVq+nmzToJg0Egl7SQG+5fm4ORUqk+rTjDdai83h5PSCT7RsaQggKJZf6RIlSxcPzHdIkCy3SCkwrq4OhUkWtXsRxLYr5TP/snsxT2O+GhP0YxzMxLYO9+yeY3l3DLzhYdvasn5yp8uCLBcefnOfE0wuZVyBPwHZci6AfQ17N3C86jE2U8irRF4/FdkzqEyWiOKFQdLFs86rvEdd3rppDofXFxGQhBNbYlSGGw3XI1kvTCGlm4V+OZw+9HDuJECaIscx4YP821rfycKb9CPPG619zn8Y0GNN35H6ujpeojl9bBtkwDfyiwdzhmdvapjAE9ekq9enqFesXyplXilusMfNc546O9hbabb64cIFzrSbnWg2CJME1DHaXyuwqlak4LqXc0EiUohWFrPd6nG01WOv1SJQi1Vmi8vlWgz87e5pEKSb8AkXb3tZFlyhFmCR8eWmBz547zcnNDRbbbYJcCrFoZ7Nks8VSHpplYxmSVGt6ccxm0Gep2+FCq0UnClFas9Lt8MXFeSKVuZfvH5/kcH38pr0sO4EQWUXVNFUkaWYYJSoLpzKlvOUX2MA4++y57Jw/uboyzN0Y9312FcuUHAfPtPLCcIq1fo/VbpfFTpulbgfIzv/pZoN+kuCZ2cD9YG2MmeKdq5a+XaIkZbnZ4fx6g2eWVtno9LEMyVSlxOHpcXbVytSLfl4Y8MYMalbciiRtmKSst7sIIRgv+hj23Tc0BrhGiXFnDxvRPBVrCrSmmza5nToLqU7oJS26SXNbdQauhwBma2VesHuSM6ubHFtY5fDsBPsnapR9F9+xsur11zAYy57DrrEKBffOx/FfGjK1138he7z78czyFQZpqgOitJkXb/MwhIMhHBQxqQpRZIaIGBZ48zCERaw6JKqLzNc3pIvAQOkwq/6tUzSZoWAIG0sWb+hpq9mzHCq+nFTHrEUXiFX/tr1aYdplNThLwRyj7uzFu0x22ZIGJcvBKkrqrp8Jcogs2fv5yKDfy9Y4+woPscu/D9coPKc8GZdTd3ZjCItEx6yHF4jza+h2CFSbleA0RaPGhLMX5yYmGAxDsPfABPWJEq5vY9smyWSZNM1rR0iBZWViHPWpMoaUWbXvosO3vPwe7n1hSJqoTBVqEE6kwS86lCo+jpuFcpXKHpZlUCp7HHrB7JZ3qDRktn/PznM6HCq1Akop6pdU6a6OFXj45QdRSlMqe3i+jWHe5HthO5fGJetYjkl9qkKa53WYuSdmxIi7zR01NNb6PZ5aW6UR9mmFIVOFIjPFIvurY8xVqtRcj3JuaMRK0QpDlrsdxjyPM80Gi502rTAkUYr1fp8oXaXuF3jJzC6qjotvWTdMDt8M+ix1Onx9ZZkvzF+gHYX04pii7TBVcNlTLrOnVGFPucJUoUjBsrENg1QrenHMWr/HfLtFzfWYb7dY6XboJTGdVoQtDWwpsQ2D2VI5KyZ1G4P7W0GrzBPU6gU0Ov1hTDJkRoh3G+Ejm0Gfry8vIwR0o4ia6zFXrrK3kp2vsuNSMC0UWRtWel2Wux1ObGzgmRYbQZ9mGOR5ODHjq/7QCzLhF66Iq362iZKUxUaLU6sbLG626YQRRcem4sckqdpyLrdDVqjx1h7s/TDm7HoDQ8gdr1J9uwxyNqrWNBPOXlId0Utbt5eroRP6aYt+2rxt7wjkuv1KsdrscGxxnTBO2Oz0KPsuBSfzhF6rnsausQq1ovesGBpZSF2Zmj3DlHuAMWcXlnSHz4xUhSS6R6zaRHlNFiEMHFnFMetEaYsw3RhuL6tGr3CMCqbwCdMGieqAEBjCwTMmMaRDkG6QqC4g0Dol1QGm9BGmgSUNrq78lf3tG1Uct8B6dJ66vZtmfPshVKHqsxEtUI2mCdMuqVlBclHhx8yV/lzTpMrOFVK8WxjCwjcq1OxZJt0DjNmzW/r9uUXWpoJRw3GLrIZnWHZ27UgIVZj22IjmqdmzhGkP1yhtu66GlJJavUitfnUBgUu5fB2/uH3PvuNawwrg18OyTTzfoTp25Xqe77B7362rPW3nfFy+jmkamOXn/70y4puPO2potKOQKE1xTYMJv8Br9u7jlbv3Uvd8armK1CAESuWSs904YrU3y9Nrq3zi1AmeWV8bqlK1o4iVboezzSZV12NXqXzDcIdTm5t8bv4cT6+t0ggCYpViSYN9lSr3jU/w4plZjoxNDBOVB9K1Os87CNMkDwPq8NXlBT595hTz7VYWTtXt8MWFC9RcjxeMT2IIQclxntU5qjBJ6PQjTi6tc2xhFd+2KfvuUF7NNg3qpVtT+OgnCYudNnXfZ1+1xgsnp3jZ7B6mC0V828KSBqaQDBSkBzkbxzfWObq+zufnz/HlpQU0WY7M8Y1Mb3p/pcY9tTqOYSDvghdoQBgnnN9ost7pcWByjIlygYlSgVrBo+y6eI6FtROJdNugFQQ8eWEZyzDYU69Q28Eq1TtF0Rxjl3cfQdplLTxHqm9dhSgdGhqt25bQVBpOL2/wl8+codHto9GstDq0gxDLMDLVFa6dsPng3mmOzE4wcfPpTDeNJV0mnDmm3IMUzLGsKOMlHoVINenEZ0h1iMAkVm3CdJOSNYcpfXrJIs3oGJYsYcsSQbpGmDZwjXEco4bSSe4N2QQEY84D2EaFdnSKMN3EkmU0Cf1kGduoYsoChnCQ10nQlEIihM2YPcv+4rdwpvv47edqqDCvTL1CN21QUDUc6WM8D/T3bwVHFjJxCOcARbP2vMjZkkJiCpu6s5v9hW/hbPfx2zY0YhXQildpxat00waeyqqei5uK2R8xYsQQkT+5xfacYHeDO/pUj9IsZn+mWOfe+gSPTM/y0tndeOa11YcSpdhdKuOZFmcaDTaDgOXcixClKc0wZL7dYrZUYrJQwL3GIcQqJUkVZ5ubfGlhnvOtBkGa4BgGFcflSH2cV+zawwsnp9lfrQ1zG65GkCTsKpUBzflWk1gplrsdOnFEN4443dzk5OYGppRZzsmzOEuVKj3MMWh2s4GVbRqZezivAXLr21YEJPimxYMTU7xkZjcvmp4ZStVe63wVLJsx12cjyMLgOlFMP8m8Q6aUnG83Wel2GPcLWM+ioaG1JkmzULpmP+DsWoNTKxustbuUPXdYG8WUEtc2ccysSNjg/HbDiH4UkyoNaGzTwDFNyp5L2XOGKhGb3T6NXkCiVJ4/k4kLuJaFb1uUfQfXtIjTlH6c0O4HHF9a59jSGqaUzI1X6YZZiELJdaiXfCSCKE3pBBHtIKTiOdSLhWE40HonS/4vuQ5F18Y2s2rIa50urX6Ia5oYhiCMs9oxSmlsy2CiVKDg2NuqC+ObZSbd/ayGZzGERapTbjV8SumUMO0RpN2hStetIgR4js14uXBLxSnLnoP5rBiUF2P0J5z9eGb5ioJsierTi5cQwsQ3p0gQJKpLrDrEqkOYbtCPl5CWhTTGUTohShtonZKobq5frwnTBgCx7iF15tGI0sYwVCrVAYnqkOoARYLAvE6uQ6ZKVLYm2OXfRzNeZrF/FKXTW66tksXs9+glDVrxCkWzhmU5Ny5Qp7N7D1LIc1Syu2uQHGrlg1ad/z24PuWgLBvZ6/jS+gh3+nktcI0Ck85+Jtw5XKN0xwrxXUqaqkzSNZduHRxvFMT0On1M08Ave8PvLr//hZAYZLlnu/yERrzEUnACpRPULUodX+z3TVrxapbTZrm37Al+rjKomREnKUGUEIQx/SAmihOiOKtBpdJBbY+dKI0ItmVQKXqUCy4F38a2bu0aS5Js7NQPYvphRBAlhFFCmqpMLEepoTqWlAIpJZYhsW0T17bwHAvHMXEsc5gw/Wyg8vdtGCX0g5ggigmjhDhJSdKs3UppyMdGUmbjI9sysC0Tz83bbpnYO5zbci2CMCYIY3r59RHHCVFePFApfXEcZ0hM08C1s3b6bhb1kJ1fMVzvuVqR/Y6fTQEcqY/zxnuOcKBao2DZ1w13kkLgmRaTfoEXTEzQigJ6SUQvyWY9+0kmlbvez3I4rkWQJLTDkPPtJkc3VunFSfaydBx2lys8PDXNy3btobIND4QlJVXHZV+lxotndpEqTSsMiNIUDax2uzy+skTBsthTrjzrczNCCMqey57xCrvqFfZNjiFldu5959bDQYw8dGF3uczLdu3m/vFJ/G3o0Y97Pp5pcrIxzrlmk9PNTebbManKwtHOt5qc2NzANgzKzu0XE9ouSmt6Ucxqu8OTF5Z5an6Fo0trtPshvSjm9OomBcfi4OQYLzu4h5lqGcswaPYCTq1ucHZtk/PrTcIke3mPFTzGy0VeMDvJ/bunMsk/rTm5ssHj5xZpByHdMMprxAimKiX21ivcv3uKqXKJdhCy0GjzzMIKT86vcGplAyGy5OSqn3k0jsyM88rD+zClZKPb49TKBkcX17h/1ySvODSHLbOr7djSGl86eZ7DMxMcmqpTL/rYpsHXzy3x1Pwyk+UivmOx1u7R7PUJ4oTxUoFXH9nPwckxTEPeMAzRkQWqdlZkzBQ2CfEtVwwfJIdGaf+2DQ0pBC85uJv9E1dRNLvRza2h6DmMl+58QTcpJK5RYMLdy4Q7hyOv9DQqHROrDq5Rx7d2YcoCKQlSOERpi1RlHhvbqFCy5lA6ItV9EhUQpGuY0kcKGyksDOEgMLLkcK0whINvzmJIm1QHgMjq8OgYQ9jcSAnGN6tY0mXeehrPKBGqHpHq39Y5CVWPjWiBojlG0axfkRR+JblBofugw1wq08gLeiUgKmSViRMgzioYI8iKd2nQXTJ1mgLcltLZdhF5v5eYcOcYd/Ziy2fHW5nECa2NDmmS4ud5B1rBxkqT88cXKZQ85u6dpVDxs1j+a9wrBbOGKWwuWE/l/d4lUsFttS1QPTbCCxSMKiWrzrPTF88uWmt6/Yil9RYLqy0WV5qsNbo0Wj16QUwYxsRpSpKozNC4TWtjrOLz4KFZ7j0wxYE947dsaARRQqcXMr/cYH65wcpGh7XNDt0gph9ERHFCkipMQ2JZBq5tUfId6rUiU/USuyYrTNVL1GtFvGfR0EhSRacXsrbZZX65wdJai5WNDs1Of3i+ozhFyOwd6+SGUb1aoF4tsGuywuxklfFqgbFnydBodvosrra4kJ/rzVaPRrtPGGYGqWFILFPiuTblgsP0eJnZyQpzs2NMjpXwXDsrcD0oIv2stPrmuaNn0zVNfNNirlzlgYlJyo5zw4RpKQTSMCg5DnvKFc63Wjy1tjr8PvNqBHSiKJ9ZvjqtMOR8q8FSp8NGkD0UDSGY8Ascqo0xV6kNE5Jv1DlGXtl63Pe5tz7BUqfD4ytLZFUps1yGYxtrHKzVtlQWfzbIZuCzaupZhfCUThBmOsxkN9St5mk4hsG45zNbLLO3XGXcL2BsY+bbsyxsw2CuXOW+8XFaUcB8u4UGwjRlqdPhTHOTPeXKLbXrdhAi60/bzGZcBlXLHdPEs018O1PEknkV4lAnrHe6nFheY7MbZANymSXAd8KYxvIaFc9lb72Ka5lZqI7MqqInljVUU0tSxcJmi24YsatWYbpcGnpPbNPEMjJddsFA9SrrMyf3TCRK0Yti1to9zq5tMlMtbak02+j2Ob22Sa3os6tWppwnBK61u5xZ2yRVmvGSD2g8O6tb4FomZl4YaDsPKENaGGSyvZ5RJtUJobo1Q0NrRawCYh3khsYl8ik3iRCC6WqJqUoRlXut+lFMkL8QpRBU8qTwi/u/JJfpOmFVO4UUJp5RpGTVKVkTFIwq8iqz2lIYGCLzGMZph1h1UToGobPChvnMr9IR8cAjoVOkMBC4mRaVDhFCDA2O7DcCIQxMWcCSPqYsZAbIcOb/xgxydSrWJHV7N5vx4m0bGpHqsxktUrWmmXJvHEKndRdUGwgzI0KWQHig25nxYThozEzbX7Wy4xOSrKCYkUlxCif7zbOAIUzcvN8HRTsv7feBclAcJURhnM2ymkb+kahUkcTp8G+tNVpdrHCdRJksqmFKDCv7nZQiy92LUoJuQJIonDx5GQFxmNBc65AmijCIcQtpJpt6jXvPkpmnqWJNMmbvYjNavG1DI0y7WY6OPXPViYY0F4LpJhG9JMo89dLM8yLNu57fdy3SXIil0w1Y2+ywuNZifrnBwmqLpbUWm80ezU6ffpDPtqdpXsn89vc9VS8xVvGZmShntUG2ycCr0mwHNNo9VtbbLK+3WVxtsrDaYm2zw0ajRy+MCPLB+iDh3DINHNuk6NuMVQpM1IrML2eD4dnJChNjJcbKPp5jYZg3V//sRgyqvwdRQqPVZ73RZWWjzeJaZtQtr7dZ2+zS7gb0wpj/P3t/9iRHluf3Yp9zfHePPTJyRWKvvbp7uqdnSA51L6lLyUQzmR71pv9NetWDZKYH2jWZXV7ycmbI4Sw9011dGwqFHbnHHuG7H9fD8YjMBBJAbqiqbs23DQ1UZiwe7h7u5/f7fZc0zUmzXH/HDIljmTi2Sbvh02n67ByMWV8ZsbXWYrPXpN30adU9fc+/poJJZ5+VjCYhR6M5z/eGPNsZsHM4ZvdwwmgaMZlFJGlOmikMQyeXe45F3XdZ7YzZORxzMJhxY63FRq9Bu+HjOTaG8WbDkzO3pax0qNW1ZLEWeh94r4VGw3bYqOnwtq7nY17gQ1hS0nY9up53il6TV13xOM/fKkbtRyHf9I84CsPlzwwh2ao3+Ky3Rte7uG4hsGy2GzqszzrxWSapDrPrR9Gpxd8PAaNynUrzgv40ZDiLeHowXI4Hf357g+Yl+f6+ZXGj3mSzXsc7h/D+JIQQrNVqfLKyysPhsXi1UIrDaM6L6YQwu5qDyUUhhcC3baymQd11WG3U9PZMQ36xvc6dXodu3afte9Q93VmdJyn74xnf7w/YaNX5iw/uUnf17/7hyUv+6sET9sZT9iczVmo+7cDj7mqbXt1fismV0nSq//l33/Jwv8+f3b2hHVFcB8c06NQ8Asfi+WCMYxr8m0/ucqenbe5826Lm2kyj5ErdCin11OvWSouVeoAqS2037LuY0rjQQts2fBrWCnmZkKj5pbanRJGVCZmKq1C460GWF4Rpxs5gwsF4xjzJsAzJp9ureHazordBrsrld1VKgVHRg94XTGHRMHu0rA1cWXtjGrohPTxzjUxNGCVfkqopqRpj2T6O0SYqDhBCEOX7FComLg5JiiGBtY1rrhAXR6TFCCFM7d0ubQzh6WLjilqYBRpWj03/Y7J5yjg7uNJrpSpklO4ydbbPZwqgBlA8RRekhp5oCFcXGmpcBXhZlPljnRosbMAEYeriQrQQssOV28fnhClsmtYqLWsdRwYVZer4uJdlSVmURLOY4eGkSop28WsOXuCSJhnRLMYLXAzTQeWKPC8oVUmRK6bDOXGY4NVc3MDBr7kI29AuhFlOlha6gVE5MklDLEPgBFR5DOqdu0MgqgT7j0hVzCQ/fPsT3oFUhQyzHab57TMLjUwVREXGs/mQF+GIluXRtD06jk/L9rDk5Y033ieKQhcZj1/2+dvfPeW7Z4ccDmfM5glJlpPnBdmCGrOgTf3IOWyl0s2ZZ7sDfvdgh4fPDnn0/Kii9eSkeU6W63BgtSh00dSePC9I0px5lDAYhzzbHeJYJp2mz9Z6i0/urvOrT26w1m0QSBuuMdVahxaXjCYRX3y3w9eP9nj47Iij4Yw40YVcmuuiaLntZUmh9PQjywrCOGUWJuwdTXj47AjXMbmx1mJ7o8OvPrnBZ/c38D372iYzea6I04xvnuzzd1885dnukJf7oyU9LS/093uxrUoJ8rwgTQvmYcpgPOfJzoAvHuywudriTz7e4pO769zZ6mJWTc6FFfO7UCgd3VAorUf2TBPvD7HQqNsOW/UGHde7cKidISU1y6ZmO5gnfN5VWZIWOZkq3rqoHycxT8cjRslx50UIQcf1udlo0jinPe5J2IZBx/VoOM6pQiPOM/pRyDTV2RqqLN+q+bhOGIbAtS1agcdmt0FeqGWCpBBCJ4VfEo5h0gt8up6HLc/nDLKAANqux81Gk7p9TIcoSsUojjmcz4nyq7sNXQTaClgXZp5tkeQ5Dc8lSnO6NZ+NVp3VZo2goptNo4RJlHA0C9kfTzGkoD8LSfMCIWASxUSp5lfO4pS661CUJVGaMY5OdvwEYZoxDmOG84gk05/bMiSWYePZFr16gGdZuLbJerPOzW7r4h/wDfN3KQSBY9Op+Wy0Gmy0LmctvFgY29KjZq3obIVLQnfec4oy11z/UlVWrZd4rbIkyQuiNOPp4ZDnRyMOxnOOpnPmcUrg2qy2amy2G3rqVxQMZiHTOCFOc3zbYqPTWB739wFT2DSsVdr2Bo7035iEbgof39ogLdzK3tbCkgGuuYIpAy3aLkFW1rQ6d6VBYG7hGiuY0iOVDYQwsWQNS9YxhYdvbqLKpKJWObhGj5ICU9bfKgQ/icXxD6o0837y/Mr7JVMps3zAPB+SqoiizE65T72OsqKCxUCm04iFC2pCqQaIMgbha1pVWU1bhNKPL3OQJpT+WxODrxOmPC40bOm9llmiCj2ViMOEaBaTRBl5lrO63WXr7irzUcjBiwGd9SaGaTAZzJiN5ng1F9uxiMOE6ShkeDjBsk227q3R6tWh1BON6XCOKhS1po/rOxiWddz5rDRl72qOLSIPa2aXNfcOR8nTK++XTMVMsz5hPiJVEU4ZnHafqqasRamIi5yDYkY/mXMQOzQtjxtBizXvx7dIX0CpkrwoGIxDHjw95KuHu/zuux2e7w6ZhTFZFcZnVTx729bddFnldimlF8NRkhMl6VIP8SqkFJiGQT1w8F0by9Tcfcs06LVrbG+0aTd9rHPQpopKs7Dfn7JzMObLh7v87sFLXuyP2DkYn7lYXWgbhNDTNVWWZHlBlkOc5CxYHqNpxHgWkyRa23H/Zo/bWx0agYttnZ0pcl7oYqFkHiXsHIx59KLPFw9e8t3TQ17sj5jO49eKt2Otg6HNa5R2KcwLSLPThW4YpYym0XJacnuzw9ZqE9s2sS5pF6yUPp77/QlPdgb80zcv+N2DHT0tGoevPX6RDL7QTiqliIuCKMmYzGIMQzKdJ1CWmg4mYDQNKQHTMMjKt6+P9X7U6+lRHDOKYywpsQytO/VMk8C2cE3rWtay77XQqNm2tn29xA3cEALPsvBM85QtZVlZqRbq7QKqWZqyM5syTZPlz6TQGo3VQC/qLgpTSnzLwjOtJSdOFz6FXmBmOvRNmQrjDQuJ64ZpGEghubXaYq1Vq9aa1Z4RYknBuQxsw9AWxI6LeYkLQ812WA1qBCf2dVGWTJKEfhSS/MCFxkWRFQWTKF6KuydRwixOsauLzSxJyQs94s8KLbLOi4KH+31++2y3+l2JUQnKn/dHJ87d8lo99MuF99cZXwpDStq+x1qjhndJ3u5J6FTfLiNjF70cuGx4W1kVGbrQ0BOzy+2TeZywP57xl18/5r8+eEqWFyRZQZxl9Bo1fnl3E1WWyKoQfNEf8/RwyO5oxmoz4H907fdaaBjComH1qgXnm602Tenjiw1cY4WyzJZTW0O6GMKuzpkS31yj7f4cSkWJWjpHueYqZSV4FhiY0kNg0HI+BhSG9BBI6vYdoESiqVUXcf0JzBYCgW+2EMhqGy93DhRlSlSMCfMxSTEjU9o2/I3fDeGB7EDxHNQRpWwjlEepRqBGVQECQniUsqGLDoyq6FBQhpVOI2eZiPYeYQmHprVG017DOuO4q6LQicRJRpErDl/0ef5wn49/fYeNWyuMjqY8/vIFRaHwax67Tw/Ze3LI1r01epsdikKRhAnPv9sjS3P8uke710AIQZ7lDPbHpHFKvR3g1RxNn7oUBDWrTYnCN5tXPu55maKKMWExJi7meEa90q5U9sZC4poWdculbXvshGP2ogmZUtjS4N+uf/CTKjQKpQjjjJf7Y/76N9/zxYMd+uM5YZRSnNCSeo7JSrtGpxnQbvrYlu5CJ5VgfH8wZf9oqouN5PX7o2lIar6tF79rLQLfoebZ1HyHTjNgc7VBr1M7F126KBRJVvDgyQH/5R8eLrvrSZK/scjQlCPt5JdVovEc9drj4zTjYDAlilN2DkbsfXwDQwpubnRoNyRSXr4BWpYlWVZwNJzzN797wm+/0cXRcBySZPkbigyJZWm6VFEVhXmuKc2vYjSLiJKM6Tzmycs+/+bX9/Eci3bDu3ShURQlSZbz8NkR//G/f8vTlwN2Dyek2dlroMW+tk29r5NMT5UWQvyiUIymEb/7bofJPEYIyCqTAdsyKIqC4l1TSqEjW/tRyIP+0XK6seL7rAaBjnyomXAN2o/3Wmh4pknH9S4VsiQQGJVr0smPWZYsc3HfhijPGUQ6v2GBQpU8GQ356xfPqNsO/iXDn/5xf4d5li4rxhJN6UqLgqQotPj3BxJBaU2LwDTsKwm/z4KxLKzMN3Zg3wbHMKhZNo5pYgqJKpXuEuQZ8ywjU2q5D3+KfvJSCM0NNrSnv+/YbLTquLZ1vLwu4dZKi3bgocqSw+mc/fGMvfGMtUaNXk3rIqZRgmUapPHrnYbFJ1elHkef7DIu9os44SCWVefYIpG9LCHJCiZxQnIGN3dhc+xcU/q1ISxco4Ypri7k1/nWixC5y9GXyhJ2hhO+fL5PfxrimCYbrTqmYfDl833GYbzsWpVUx9U0yJTieX9EkufE6fstek1pUzc1T998S6EhhYkUJmXpclK3ssyXEAGu2cMxujhGC05cHUvAwOP04k93TO1XAvHO0oecF7Z0EaYWh/tmk7QIycrk3U88AyVlpfUJmeXDyvLXgTdZvwofIVc03U44mgYlfJ0cLINKs2GD0UMIpxKBCz3hQOnXlXX0re/9X3NM6VTHfQVTvH59TpOcyXDOpD9nNpoz6s/o7w6Zj9cpS7Aci6DpkcYZe0+POHo5ZLA/Jmj4OK7NdDhnOpwzPJiQpRlpnOppQJXnVKrymDpSnN2IOC8s4eljbrTwzQZJEZFf9bgXc+b5EM+o61yR6veq0rbFRcY0i0lVsVwTaNrU68futevqOe8pl33eSSRpzrPdAV8/3uPRiyN2D8ekFcVNSkGr4bGx2mSj12B9pUm74VEPXKzK2TCrKEiDcUh/PF8KscezmFl4vI/LUueJrK00+OjOGu2GR6Pm4bsWgWdTD1w818I033ydX3zeo9GcpzsDfv/dDt883mc4DpnMjifxgWcT+A7tuker7uG5Nq5jVcWR0PSjylUrjFKm85jxLGY4CYmTYyrQPE5xbL19SZrz6f0Nap7WFFxkXy+2O05znu4M+PrRPl891Pt7MotJqmu4ZRoEvk0zcGk1fGq+nv7YtnbkXOj4dHGXMw1jprOE0VQLsYtca4OyvCCMM9oND8OQfHpvg1u2hWFc3M1zNA15vjfim8d7PHx6SH80J4yPqeMLN6luK6DTDPA9G9+xME29r9O8IM0KojhjFiYMJ/pYzaKEF/sjfvftS4QQTGax1vycY5uk0NMxz7SoOw5H85BxkqBKRZRnJHnBJE1ouR4Nx8Y2zAvJH07ivRYatmFQtx2cy1qYvqmSKnmtan0VaZEzSU8vvDJV8N93XvD7owOMK4jJwixjlMSvHcyiLInznEwpnJ8effTCMITAMUxsw+Qyk05DCG17ZxjYhkGqdEGWFQVxni0Xyz+9EkPDMg2ankPL92h4DndXu/z7n39EK/CQgopDCU7lvLE/nvJiMGUYRhRK8cF6l1/fuUGSF+yNpny31+dRPHj9jYTQhp1K6XF2+XqvVUpd9JSURJm27IPj8ec8SelPQ6LsDTz8hfPdNexsQxhLgejl5xkLlKhSVRONy75CyeODIX/z3XPagcuf37/B59vrNHyX/8d//ge+2z2d+WCbBr1GwO7QZRYnmFKSFtenE3kVAp1HEFhtalYXS563IfD6DtFuU7exqyLjvM+7TkhMTOESGE0aVo8Jh2T55RacC2QqYZb3aRQ9ArP1ZvtXEYDhImQLyNAibxMhm4CqCgsTjBsgi0oIjqZKLc5UYQA/RAr84rhrRy3zjOOexhmjwwmjwwmzUchsOCeJUopcC/VbvTp3PrtBf3fEk29eMu7PiGYJk8EM0zKYjkLGR1PCqaaJqapDKw2BaZu4vg2U19L4MoSBLVwCs0Xd7FGWh+TF1Y671nscEZhtArO91F3kpSIqMvrxnBfzMY5h0HNruIZF3XJo22frDheNl4tQcxZiWG3xe7nPMY9Svnm0z2+/ecFBf0qS6YaSlALTNLix3uZ//PV9Pry9yvpKQ4ujZdVAENU2qJK00nD8/ZfP+PvfP+X7Z0enCo28KHQKeSvg3vYKvU6NZs1FClmJnEWVe/OOzwy82BvxX/7+IQ+eHLB/ND3VXRdAu+FzY73Fp3fX+fD2Kt1WQKvhV3Qevc1ZXjCLEg4HMx6/6PPd00O+frRHls00979QJKrk6e6AWZiQF4rN1Ra2ZeDKi1Nly2pff/Fgh7/94imPXhwxHIenp0auxcZKg/s3e3xyd52ttRYr7QCnomwtGoTzKGEWJTx5OeDxiz7fPNpnGiYUKEqlKWGTWcw/ffOS4STCd23WunVcx+KiX6f9wZS/+/1Tvvp+j6PhnDg9fZ+u+Q6r3Tp/8tEWn3+4Sa9do93wl+ekUpBmOYPxnJf7Y756tMd3zw55sTdkMov54rsdQNPA8uL1CdNZMCrjpbVaDdsweCgGRHnOPMsYxwkH8zmNscuH3S63Wy0ajvhpFhqGlDiVQOXyuNw3P1eKOM9PWeCWwCRNmKRXuzi+CaosKUr1WofkDxXLqdIlVqiLToVEYAo9EcirgDfF8b4qlKqmVj89WIak7jmsNWvc6XVwLZOn/REHk5kOdUQXGludBjddB9fSzlVlWTKLU14OpzS8I8oSxqG2Ja259mshgALtLrVSD5gnKV883+NwOsc2DFbqPtudpi7aXYeaY+OYBnvjKX/94AlORS08mMzwbOvSF4KLQAoDSzgY1yDG1OQLVf25JAWrhOE84sVgzI1uk89urHN3rYtpyDOpg4bUP3cskyTT2o7zXJgvA4HEki6uUcORgZ4GnIOm9KZOnyXr+NYmtmy8Ri/6ob5DeuQu8YwGTWuNpAiZM7zSa2rO/oDInlK+JQhSO2jJytZ2UY6LSvR9Eq/YtQpYnG3HP3h/e0wgsaWHa9RwZYAlnVPBjAtkScZsFDIfR5U+o0AVx9kKru/QXm1y8GLA4cshhiEJmj5FrpgMtBA8S7TzlJTieMGaZMwnEdPRnPk4wg10WGpRKCbDOeOjGYYV4dccSlVibBrYjoWQ4sxzT1T3ACmM6rivkqg5YZXXcllkKmaWHRFb65Qn3M8W/P+iuk+AiSFkdT86uxOeZQWTcUSeF/iBg+Mc5zjoTAJRddH14/NKWB/HGVGU4nk29bq7zBRZ8OTfhkU20yzUneVnu0NmYbJcAwSew+Zqk4/vrPHRnTVubbZp1t5MwVmIgD+5s4YhBHmuOBhMlwvIstRC5vEsYjgJ6XVqBN7FJsu6Ix7x6MUR3z09ZO9oQpJmy+Ztp+nTbQXLbb653mZrrUWj5uJ79inOvtaVZLTrPo3ApdXwaNZdHj0/4unOgOk80bbyUUqeFzx6fsTvHrzk4ztr3NnqXohCVRSK4STiyU6fh8+OePKyz3gaLelPrmPRbfrc3Ohoi98bXbbX26y0a9Rr7vK+u9j2ONVi98DVtLPF9j/bGfBif7TUn4wmEVIO+ebxPo2ay50bXVY756PtpVlOlGTsHIz59skBOwdjkhP3G8+x8F2LT+6t89n9De5tr3Bnq0uj5lLzT0++86KgWdcTrMC36TR9ap7Ns90ho0l44al8rhSpKpgkMYM4wrNM7rbbS8c3wXFhMUkSzUy6hOQA3nehUVFPLuJWdF0o1EI7cfn04otCU1l+KD+T94+FgOpK/szVa5hSIovjV1FQ3UQ0b/6nGDRjGQaGK9lo1fl4s8fjwyH/8cuHZEVRFRr6RvNvPr7LZruBb9usNTRlZxzF/OOTHb7f7+PZ2mEoSjPagXemv7lvW2x3mny3f8RffvsYKSV11+YXNzfp1LQTlhkYNH2PuuvwrD/i8eEQ1zLxHYtZnNIJfLwraHLOC4mBKe0r0W+OUaKq/13lNeZxynAa0fJdPt5apeZpp66zICrqlGlIcqVDRd+XLbUUElcG+EYTW7rIK15yLRlUuosfO1Va4Bl1WtY642z/yq+WlXqiERWTC4QAXuaa8cNcZ6QwcI0A32hU6e9vCKjNCuaTkChMUMXrTSrLsTAtkyJTDPfHrN7s0l1vEkcps/G8oloupjXHi7/5RLtYHe0MGR1NSZOMJNJ03+H+hMOdoQ44y3JKoN4OMEwD850LP4FvNmjaa4yu4binKmaa94mK6elGQzWBNaTAluYpYbghUnL1+jmSpjl7eyPCMGVtvUmz6WPbWmCeVZkErmstneeyLGc+TxgO5wz6M1ZW6riuiWEYyMq95123JVWJcWdhzN7RmN3DMcUJcnyz7vLZvQ0+/2CTm+ttWnX/lOb0VSwW8bc2O/Q6dfb7U755vM90npAXmmqTZQU7+2MePjvUuQq9i9nEj6YRD54e8PDZEc/3hszD5BRDZH2lwecfbPLrz7b5+Udb2KaBWdkmv7rlUgp811raxG6tNbmz2eEfv3nJPEwJ44yy0EVSoRTP9ob8zW8fY0jBjbUW1gU0Q1mueHkw4ptH+zx52We/Pz01yaj5Nh/cWuVPPr7Bn362zfpKA6vSOJy1z+3Kjt5zLLbWmmytNvngVo//+Dffsnc0Ja+0t2meM55GfPlwFymFnj6cs9CIk5x+levx6PkR/dH8lMi/5jusdev8+rOb/E//8iMdFGyfLZY3DUmz5hJ4Nhu9BtvrbT1dMSTfPr44/TcpCmapDsB+PBxyt93h89VVHEPvl7LSH+/PZkyShLZ7eUvw95xKsjgxf/hFpKg68Se/GlKIpT2tfOV314H77Q6eZZ1yyfpDxoKWs7BpvdyLVE79ZXnqRiI4UcT8SEVG4Nh8urXGdqfJdrdF/ZWE6MU51PI97q91CRybbt0nL9QpytBWu6FzTCw9XfhkYxUBmFIvZh1T37jurHZwTIONVv2U9gJ0OvXnN9bo1nwmkXbNcC2T7W6zSnoXmEKw1W7wL+5ta8ekrMA2JLZlLjugt7otOjUf19YXiw/Wu3i2ye2VNnXXuZYkdiEMDGFfn71kueBCXn6xr2/Q+t/qHdV+rpQW9kfpMkPlfXnySwxco45vtjCFc+Vz/Szh9jITZEn/qBacSueJ5IWqbqpyaWIBWusTpzr1fhonOJZJO9AOgeY7+NMCcI0aTat3ZvDgRZGrhHk+JC5mb51oHL/7ZfDDXWd0fsbiuNtv3Jd+3WPz3hpxmICC1Rsdtu6vcfPDjWN3KClY3e7y6b+4T2ulTr0TkEYpaZX6nWcFva0O0pB01poIKXBci3avwb2f3yScRvh1j0antiwoPvrTO5RKUWv5dNaaWG9Y3LwKIQSurNMwezjXEDyYlTGzfECsTh93Uxh4hsWaWydv6O6qFAJbGnimTd16XedUFCXzMGU0nCOEIEky6nWXPFP0BzM8z2Z7u0NZwmgY6qwJAXGUEYYpg+EcBLRaAe32+QI8i0IxnccMxiHzKF26Sy0QuDY3N1rcWGvhe/ap+8tZWJwnjq2nMb1Ojc3VJi8PxktOf6EUw4nOu4ji81tWL4rY4Tjkm0d7PN8dECfZcuHr2CaeY3Fve4Vff7bN7c0uzdrbj/HieiOlphov0ranYcrL/RElJTuHE+JET0zG04jHL/rc2uwwnIT6fHLe5jJ3vN1plvN8V08W+qP5cpJhSKHpUr0mn3+wwaf31lnr1qn5b5/0SCHAqATumKx26zi2ybPdIQf9GTuHYw6HM8pSU5J2jyb4ns2nd9e5uaEX+e8Sh8/CmCc7fXYOJoRx+pr4fHO1yZ9+us297RXaDW/pNPWGva31IVXY4Fq3zmf31kmznMPBrCpGi3PbJZtVIHPPD6CE9VqNpuNiGwamlFp7XOlB4zy/sHPsqfe69DN/4jCEwDaMU5WsKSUfdVf4F5s3MIW8dmeoe+0ONevdF5M/FJSUS93AVRaBhVLkSp3qGmtalqZU/VizjIbr8Gd3t1AlVVjN2V/yhufgOza3VtrVRfmVrmOlQQHN///l7U0+315bFtkVDVfbHguhC4dX3qPpu/zy1iY/21bLhaNAW/HaJy5m290m681apeN43UZ5Ebqz6D79fHuDT7fWXltkXgUSiSksJDoI7kozvPKVf19q87SozbFMLT5MEry3JLtmecFgGjKax5jSwK/40u8DQhh4Rp3AaJ3J0b8uLKgmBsfnQ64UozAmznKankvgWnrBVolh0rxgFEbsjqa8GIxp+i4fbawgA/+YP/7mT4Zr1GhYPezrKDTKhFkxJC6mV5xu/TQg0RQj32hiiDdPGRvdGkHT03QptXCOKzFtE+Pk9/7DdTbu9JBSII0TwX1VHgMlOiujWqD6DRc3sOltdShLhRASaQik1M+9cW9NGyNUr2eY5xPmimqS1bBWriXhPFcJs3xIUsxPUadsaWBJgxt+i7btU1SNKt+08QwL64zJS1kqkiRjMolI05wwTMhW6szDhO8f7tPuBKys1MmygidPDnVh1g60e1KuGA7mjMcht29Dp1M715C9KBTjqQ6Li89wifJci83VJqvd2oWt5qUQtBs+NzfaTOcxB/2pfk9VMplFHI3mJOnFsnHKEgbjkK++3+PF/ujUwtd3LVZaAR/c6vGnn928lMOSbRm0Gz63Ntv87MNN0qxgOImIE72dszAhijNeHow5Gs5xbD0NeaempFrsP9vThcZoehwUapkGzZrH9nqLn324yYe3V7HeIoZ/E2qeg2tbfHh7lXmUkivF4XAGaLra0XCGbRrs9aeMpjEdKd+5jyazhO+f99k9HL9WhAoB2xtt/nd/eo/VTu3C9+bAd/j4zhpZrvjq4R77RxPCRNMjzwOnMroJLJubzdYJ86Vq+9Brm14QUJbllSQQf7SFhmUY+JbFJDk+ESSCFc/nfruDb1o4l3SdehM6rqu7gX8kE41ClcRFTnIJasli4ZOqYvmnqPJFzCo52zyxmJmMQp482EMpxZ0P15FSsvdiSDiPKVVJq1tjY7vDsD/j6Xf7dFYb3P5gjWF/xt6zPnGUkcbZMgipVnepN316G02a3Rqj/ozpKCRNct0p2mjSaAe4UhLNE/Z2B0TzFMsxCeourW4Nx9Xnh5QSWwLn4dajJxHuBW1kF+nz72LbWoZxoamEY5nvfM2LYzEN/GnQ3YSA1WbA/fUug3nEX33zhHvrXVzTZFZ1kUZhzMvBhCTLOZzM+eblITvDCd26z+1e+71RziQSx/BxzRqGMCmU0laFhS6+tZvZsbYmLbRmBKqJWJViu3AkS/OCNC/wbAvfsSgK7Xa3N54ymEV0ah4tz8WtPs/i8SXHYteiSqofzEIe7B0xiRJtm+k6leZHnovKaEufwOy81a73vMjLjLiYkqro1ILzDxVSGDgywDVqGPLN1wKjSh1eiJgFVLSh0/vfsk2sV4rnk2Fvr+oJdJdZ8qZb3KuvdV4IBI7hE5htTHn1K0tepsTFpDru5YmfK1KVsxONeTEfYVWFhy1NXNNkzW3QdXSBu/jci8FoXiiSJGc6i0EI4jgjjnPtusUitE1/B+ZhQloVCCV6KrJw6TKMd0/bVQlJxcM/SeNZwDAMfNfGc+0LCdT1d7WsUrfdU7kYZVmSpNqB6Cx71jchTnNmYcJhld0QRinlCRrPSqvGx3fXWO81cR3rcsREobvujZrHve0eR8M5Xz/aYzyLKpqfPj6D8ZzvXxxi2wathvdWv8GyLJmGKYfDGf3RnPE0OiVcdx2Lmxsd7myt0Kr7l84Ok1LrSTd6TZLK2cqQC7dMrfMJK7veZ7sDHMsg8N7ePJpHCS/3RxwOZ6cKAN+1qQUOq50aq5261r5csNAwpMB1LFoNj83Vpk5FPxwTnvOcyCqn1J3phN3pVFPZq3PYMgzudzrcaDQxr6E5+cdbaEhdqdknFmVCQMtxudlo0XLdU0Fy14EFHeiPBUWpiPKcuMgvxWFfWP4u/oAu9mzDxDWtUxOl8WDG3/1v35BnBc12Dcs2+Oo3TzjYGaGU4u4nG7RXauw87fOf/8Nv+fjn22ze7LL3bMDf/m/fMjycMh2FZJlOut242WXr9go///O71Joeh7sjnn9/yGwSYdkGln2XRjUeD2cJ33+1S/9gTNDwWL/RwfPtZaHxzziN5aTmR96OBQSw0Wrw8dYqD/eO+PrFPqMwZqPVYBLF5Eovqp8cDulPQ54eDvnN4x3KsuTX925wb6177dbQy20TBrb0cWUdQ1jkSjFPUmZJSpLluJaFaUgMW99qkyxnMNOUAs/SgnW7NJbe75NYh0j26gGuZZLmBbMk4bu9Pg/3+9xb7XB7pU23pm+4eVEsbx5GpbUqypIkzzmczvny5T5SCD67scZKPcB3NL3uPMfWlh6B2TozH+KiKFRGXM70gvOPwExDCkMXmEYNg/NdRy5z6/jhbzcCW/r4ZgtLXEOBqTIidIF5kvORFjmzPOH7yRG/GbygYbnULX2/dgyTX3YkHcc/8zxduCEVc0Wa5lpgrxRSSkAsi7A01RqNolBLLeECRVEghMG7mrhlpdFI0kUhcxqLxaBjvbtrfxYs08B3rVMGImWpGw5Jmp8Z6vcmxEnG4WDG0XDGZBa/xulf6dT4/INN1jr1K1/ba57D3Rtd9o4mBJ6NISWlKpal5GgS8d3TQ7pN7Z71NhunQpWMpxF7R5MlRe0kfNfm9laXu9sr71z4vwsCtLOUbfL3Xz7DNCV5rrWkoKcqOwdjHr84Yn2lwWr37VqNeaQpZP3R/FShEfg2GysNeu0azbp76WmBlILAs9laa7Hf1/snPCedLi0KpknClwcH/M2LFyRFvlyn1Wyb/8uHH3OjcTH9z5vwR1toLFLJR3EM6JFjWWrXqf35DNc0ryRu+f8HpEXBMIqYxDH5JVx5ZmnKwXxOeMJyVUpB3bbpeB6OaZ42hhECSijyHFUowlnMfBppZ4tZolNGF3bF1RNX1ht89qtbRGFKGme6G5UX7Dwb8P1XO9y8t4qUkla3RhxmHO2P6e9H3P9065j7mWQc7o+YjiI2bnVZWWtgOX+0X40/PgjBZqeBKkscy8CzLUbzmIPRjP40JExS/unxDvuj2TINd6NdZ61Z4+c317m92n4r1eoqkJX7kIHLeJ4yzCb0Zzqs0ja1I19ZwjxJl2nl8yQjzjLiLNdBi80a4yjmYDLXqa22uaThaXpjSZxlhIke9x9PLkr684hplGifdstC2Po7pkr9vLIsEVJiGlpPJMX551SGMDGFgyVdbOmTqxTF5fJIShRFmZGphExF5CrFENZPMl/nPFi4TjmGfy4t02U+54+1b6QwsYSDLV1s6ZGr7ArHvaBQmkKVlhGWcjGERVZ5+VvSoOsEGEJP9fKqu5yXxWtMS9e1uHmzS7Op7+u6oBB6EpjmNJsenmdh2yY3bnSqYqJKuD4xjWg2/UoQfh7NihbpWqaBOOPxZanpVUqVSOPizZlF5sNrtGMpqu/r+V8xSnSI3mgakZ9h592sudza6NCse1c+t6QU2JZBzXdYadfoj+eMJscOUfMoZfdwwngWvVNToFTJeBZVmpT0td/btsFat856t/5Ovcd5YBpSTwrqHr12jdE0WloM62lMyMFgtqSDnYW80LkX8ygljFPS7HSztuY5bPSaNGrepXWqSz2PZS7zNy5CGUuLnGma4FsWdzttpknCNE2hLPEsG7fKHHmVnn0Z/NGupuqOw416g+eT8fJnCh23/nI6ZcW7Oq/4jx1pUTCII0ZJcsom+DwogWlV1M3T44uDFIKG49L1/FPiIikltm3qgKlcoVRBEmfEUYoqFEmsqVFlCY5jYVUdorUbHXrrLYTU3PNSaXHk//v//lf85q8e8Mt/fR9pCFbWmxiG5Iu/e8T+yxHRPFlqAtI0p78/IU1yuqtN1rc7p/jR/4yfNgTaYni1WdOuHabJbx7v8NWLfcbzmDBJ+YdHL7BNg6bvcnOlza/v3eDntzb4eKtHt/b+rgVCCO02VboM5ynj6Yj9yYyyLFltBNRdR9shJylPDodM4wQhBP1ZyM5owmargRDwYjjhm91DtjtN7q92dYFAtZBRJVmulplBRsXFL5SiPw05nM1pBx5Nz1sGhJWV+H4RBGlKeWEba4HElBa28HCkR1kqVHnZBWdJQU5WJqQqIi/TKkvjD7PQkKIqNGTw5kyQP1AsNFr68/mU5ezajrsra9XkryAsMjzTYstvEhYpUV7Rk6pCWT//+AxxXYvbt1eqhWtFRTtFJzt+31rNOZGfcfmFlEDnZCyE0K99vqpQyIsCWxoXHkEVhdJT+pONvqq4Mc2LFRpxrCcao2l05iSkEbhsb7Tw3atPd4XQtMDAt+l1auz3J8zmybLQCKOUvf6E8Sx+5wRTlSWTaczBYHpmt962THqdWiXmvjoLYVEktRoeq906aZYvC42iUAwmIYfD2VtdnvJcEUYp8yghSrLXTQJ8h41eg0bN5arXOMsy6DZ92g3/QrqaJNcTjZbr8rmzxuF8ziAKUaXWb/iWrY/NVVxHK/xxXQFPoOv5fNTt8XB4HJCmypLnkzG/PdhjPQi43WovnY/+Ga8jzDPN35tNmWe6W2qcu/ou2Z/P+ProkH58LNwypaTn+9yoNwhOeDKblkGjHTCbRExGIVmqL66mZaKkIksL+vsTkiil2Qnw6y5lCXvPB7x4fEheBSQJoTtYzx8dkCQZea5dGKQQ2I5Jp1cnnMbEUcru8wGmKQmnMbWGhxDg+TbSuHiQ0D/jx8PifDSkYL1Vx5CSXiPgl3c2idOMpLLwNKTAty1agcdmR4vqffvi3NgLbRuyylCwmUQZYapYbdSouzYt36Ppu9iWQTrPGcyjyv1JWwsGtl1pfQQ1x2ar1eBGu8lWu0nDcxBCmw8Ejk6R9R2Lpu/SDXxc2yJOMxzLxKvc0BbKe0MKXMvSz7NtXNukW9OhlOY5z/2la1opsKSNIwPyMiUr4nc88+0oyoxYzclVgi29125wJxclP+Vph55ouLrIPIdmL0lyojhdhs3ZtnmqO5skmU5azhVKKdzKUlQpvZBN0xylSgJf50eAXhSFUUpRlJim7rpblrH8OVTdeMvAskyU0ovaQukOf1kZTriOhevoa/VynwuBJR0c6esp1CUTwhcoyoykmJMZDWw8fNOm59aIi4x+EpIUOfM8pWG5dByfuuW8phI7Ph8qK43lL8Vik19B+Zqu5aKQhqDm2TRr7inTjgXiVC/uBysh3VaAY5+/41yWMJnF7ByOT4X2LSxW2w1t33tepFnBeBppO9sThcbiHPBcG9fWadRXxWJf2pVQu+a7FXVNI8lyxpOIeZQuJz5vKvhKVTKPEkaT09qMhbGK79p4VWL5VddyJ90gfdem3fA5HMyWvy+U1vWMp1GVh7GYIJ9+3yxXTMOEMD47o8mxTZ227ly9MDINSeA7mqJ2ASOiwLZZC2okRUFS5GSVw1SUZ0iEnm5fQ5EBf9SFhsfH3RX+ducFhhAVVUDxfDJGIPj56hppUWBJiZCng1zehZM3u5PxTwv8lG+AF0GUZbycTdmdTZimCWlR4BjG0sH/TZ+zrGgZe/MZX/cPGUTh8nemkPT8oCo0jjsnpmXQ6ATaxWMYkkQpSpVYtkmpFFmWc7g3plQlzU5AUHNRhWLnWZ9/+KsHVZquqooEwcsnR8sgq2pjsWyT7lqDOEyJo4zd531sxyIJU2oND9e3cf2LfVl/OFQdvDObP6+fj2f97qLv96ZnKgpUWVw6YO99wZCS9Vad9Vadn91c1+dhRYtYdD4XovsfCkJITOEgS4dZHJFkilvdFhutOoFja8oFFU0xjJjFCTXXQQpB4OoiQAqouQ5bbcFWp8FWp7G8oS64375j4dsWTc+lFWjqSFYUuJaBa5nVFAMoS6QQVf6KjWdb1FybduDT8C7DuReYwsExAmI1e/fD34FC5XrBWSZnnl8LUbyU4rV8poU4uqT8UW2z4fi4W8Kt3NnOxuJeEqcZw3FIWeqFQ73m4tgmi+9vnGRMpnFVbBS0mv4yHyJNc6aV1sAw9GQY9MJy8RzXMfFdGykdkjRnNIkoVal/7juYpkGWKe22kxVVArU21mg1/GpbTizEquNuywBDTK+8v3KVERczcpVQGiWeYeEZFkfxDFUqkiInylM2vAabfoua6bxxUXne434d54chJTXfoVl3z8xHSpKcg8GEo5HuXi+EyucJAlRlyWgasXM4YRYeswIMKakHLu0Tx+U8SDOdBzGv7q0LmCcW6wtb3euCZRk06x4138Ewjj9zkuYkaU4YabbEokl4FlRZMo9SRtOQ5MQUQUqB45j4roXrmGfu/8tCCAg8m1bDO7WPldLbMpnFS42McUbeSl4UzOaJLvTfUGg0a54u4K94GpqGJPDspRbmXViGSVoWrmEQ5Rlhli21tKosyVXxDqvdC27jtbzKTxCeadHx4GazyScrq+zPpxyGIWGesR/O+O8vXyCAT1ZWudVs4hjmudX1RVmSFQWjJOYwnONbFl3Xx7NMHOOPZ5eq6nO+nE756xfPSIqcT1dWqdvOW78bR1HI/nzGN/1Dvu0fMYj0RMMQAt+yuNFocL/doeEci/FNy6DZDginMQc7Q/KsIKg5BHVX06kKxcvHhwQN7QglpeBwd8zoaEYSZWxsd7n94br+kmQ5UZhwsDM8tV2mZbCx3dHUlIMJ/YMJjmvhuhbdtQadXgPPf38WpNeBoswpypS8zMhVSlFmFGVGXv2tSl0ELIoBhaIsC+2cUSVwL/9dlsep3KXS2dzl8X8v0rrVqZ+VxMWUsJhwlDz7yTsESSGWjYTFf/+Q0AsyC9/yWG9YDGc5zwdjdkYTao5Nt+az0WrgWTqw8XA6J0w1RWSxrUt6iHhz9k9Z6mLl5XCig508l6wo2J/M2BtPtQNcXnBrpU3HuN5iy5K60DDyq3fnFDmZSshVxllF8vdPDvn+8QEf3lvn3u3eqd8lSc53jw8YTyLu3+mxvno9QsbLYEErM6WNeItGI4ozxpOI0ThkOAqxLOPEVKJkNk+ZTGOGozmjcYjjmDiOxWyekKQ5h0dTxpOIWuDQqHv6PEh1SNhkGhHF2TL52rIM2q0AKQVxkhFFGVGc0uvWuXNrhdE45MWOTh93HZPJLGY8ibh3u4dfdUsXi0VBNdEwfOR1HvdSU2PiIicqUvajKS/nY025tT2KUjFMQlac8+VcvG9IKfA9m3YjYLVbY6UdMK2ODehwvC8e7GKZBo3AxTAaOJbxzkJjv68dhJ687HM4mC4zNEAvLNd7De7c6FIPzt8cKFSpHe/y002ihSbBvIQl7LtgSIljGdhv+Mx5UepC2LY0tewNyIuCpJq2LSCFpjjZtnlqWnI90JQ417bOjCtYpIanWYFji9dcs5TSv8+L4szuoGlIHMfEMq9u778MoH2DTuisbS+UIi+182FeNZ8ajs55yoqCUXze0NTz4Y9nVfwKXFPb4N1qtPi8t0pZlvSjSLuthHP+Ye8l01Tzoeu2Tc228Uzr2F5wefgXXbLjfydFTphlPJuMeTjs0/MDjI5ACA/bMM914pzFSzz5k+OQvFceV3U6Tv6pNvkUrqNbU6Kdo/bmM/5m5zmmlKz5NcxF/sUrYzVV7Z/d2ZSv+4d80z/i+9FguX2OYdJwXLbrTW632jrSvoJlGTQ7PoPDCYe7I5QqufvxBvWmR1GUHO6MeP7ogPUbHW7dX8O0DIb9KZPhnDhM6a41+MW/ukeeFURhyjf/9Py1L51pGaxutpGGZOdpnxePDrFdi95Gk09vr7B1ewUvuH4z2LdhecFf8Io59tJf/vvEz1IVk6hQ/ylCMhWRqrj6W3PbFwVIXuZVIZKjyhxFTlEW+t+lQpEfFyZVcVIsi5T89aLllef9VCYar36XTo6/Fwv0HwtauGnjWz5rDShVyFcvDxiFMXXXZrvbouV7+LbFVruJEIIXgzFZ1Z025bFg3KxyUF7/NGIZQHo0naPKks2KQjYOYwazSBc80qBbD2h4zrLjbxlXy7LRr2tfmxZBlQWZiilKrcd6dcOePO/zX/7mOzzXXhYaJ6cC3z7c4/nOkGbD+5ELDYEpbB3S+JYU9zjJ6A9mHA1mDEchvmdRqpJ6pSGYzRN298fLQqPbqWGaBlGUMpspnjzTC9G7N3s06nqSlWYFewdjDo9mSCkIo5T9gwlCQG/l2EpzMo3Yr8LUVlfqHA1mPHxySODZ9Lo19g4mPH85oB643NzqYJ/qdgvMqtC43uOuF+hJkTNOYwZJyEE8Y92r07G1lmqeJ+Tl9S6ELgtDyopi47G+0mCtq3M6FoXGZBbx9aM9XMfk1lYXz7Wo+y7midySJamx0p6UZcnLgxFffrfL45d9+qP58v2kELi2xWavyZ2tLvV3hNKdhFKKNCt0oXHikmkaUge8vgddoiEFTvXaZ12Gi0LpKV1RvNGWtiwrDVqanxLtC6FduRzrfML9i2Dx2u4bJjylKskyRZoXWoD9ymOOCw11JgvBkLIqYq6+z2VVaGj93bsfrwP4MtJCFxsLeJaFZRgczGbk6nhteR34oy00FrjbbvPvuEtZlgyikEmaEOU5/Sji28ERqSr43cE+q0FA1/U0L9pYcJoFqlQ6QbcoiLKMWZYySRLGccwwjhjGEb9a32SjVr+wXa5mMmgnDR1qV+oKUykGUcQ8TfUo68SCLqmcoPpRqC86hv4iLBb/UrxOKbgsbMOgZtkYQvBiMuGvnz9lEEXcqDdYC2pVcWYuk7/HScwgivlueMSDQZ8no+OJgmUYfNDp8idrG2w3mrimeWo7FxoNz3cYD0KEgHrTZ22rRVGUjAczBgcTmp2ARjug1tCJr7tP+8RRyne/f6kzMqoL+Hg4p9Wt4Z6wuxNCYNkGjmvjeBaWo9NwpRS4nqZNyR+JNlWUKYkKiYs5STEjVnPiYk5aFRWZinUhoRJdTJQ5ReX2oqcc+XFxsJhinJhMqGoacXqSUVZpvMc/1+fa649Zlj0n/v1TQ6Vb+4lBYAgLx3SRnp5ueJZFnOVYhkHDcwhcGykEa40avmOx2qjp8L1KU1JznGqcraifIdQ0pODeaoe652AbmipVr4qJP7+rU+Q926LuOnRrnravFYK27/LzmxtYhtZ5XBaGMLHk9STFq7IgKxOK8uyJxlufq0riJGMeJjr1+UeEEPq4m9J+q0bDcyx6K3XyXDGeRIRRRl5MK7FySRSl9AczfM/m/p1VGnWPILAJo5TZLKbV9LBtg831JqsrdTzPIooyptUUxHEsVKlwXZOiKJnNYtI01/qOssT3bBzbqpyXdABgo+5ya7uLNCRxnOE4JkmWn1pw6c+nXcfOo0F5F4qyIK2Ou8580Qvwlu1xu9bmVq3DVtDS9Ech6fxEJhoL+J7Nx3fWieKMKM6YVSYmeaHpaA+fHfE//5cvubHeZmOlQbPuEXg2ZmXOkBeKLC8YTSKGk5AnL/s8edln53CyfA9DClbaNW5utLmz1WVrrXUhO1ftYKUpcSev30Jc3MHqvNC5GosA2ddfv1D6cxdnWAO/+rjFtp967YoK+z4u+3Kx7WfpRtD7Ms8LVPn6MnphAlAU6sx7pZTH++WqWOxjvR/e/XqH8zmPR8MzDX6KsuTJeMQoik+5hV4Vf/SFxnajyaofsD+f8Xwy5sV0wt5sxjRNGCcxT8cjTeepN9mo1ei4PnVbp3sLBLkqiPOcWZYyimMGUUg/ihhEIWl1oBqOw1/cuFkduDPacBx33VRZEuc5SZGjSp1VcTJrYvHn5XTCKIl1hsWJL1eYZezNpviWRZRleJaFXSVTL0KNrKrwMKRc/u4ycA2T1SCgKEv2ZlMGUcj3owHbjRb32x26nk/DdnTQUanYmU55OZ3waDTgyXi0fB1LSuq2w8fdFf58c4vNeuM1iplpGtQb/pIqJU1JqxPQ22ihipKdp0dagyEljbZPsx1Q5Ipaw8OyDPr7EyajOa7v4Hq2tjDd7hI0jkfLQghMy8R2dPiVZRma0+yYWp9xRQ/ut+G4614uC4BjelNBWoTM8iGzfMAs6zPNB8zyPvN8RFiMCfMJcTHV9Ch+Gt28nxLCRNvB+o71zvC9RfJ6USiSPEdUmoX3od1YLMhsw8Y1JHXHY61ZP5H+fhy25tkWnZrH4ut+PF19+4TSkJLtbosbneZyymkI7Ty12qi98XkNz72kLuM0Fnanb9MinBeK4hSFRlV2xHleNXziTDu6xCmTKh140bCZTGPCMCVL8zMFmD8kBBJDWNqi9y03f9s2aTU8JpMIKSRhHDOdxayu1Ks0ZB0812p4rK02qsLA1IJuKWnUXOqBy+pKg25HH+v5PCFJMsJQc/FNU+s28rwgSXKyvBLfSqFF5Y6pT8JK3xIELpvrbZ3qPJpjWYYWidun9+m1FpicnmiAXujVLIdVr86G32Q7aC8F4Gclg/+Y8Byb+zdXyPKCZ7tD+qOQKEnJ8oIoyXi6O+Dl4Yi1Tp1bm11WOzU6zaDSRAiStCBOMnYPx7zcH7M/mHI0PNY8GYbEc3TK+Ed3Vrm11WHtHRkOr6Isy0p4rU7V8ELw/jRNQiDFm4uYxYJclW+n4JaqRBXqtem1FLpR+D62XTchzzbHKEt9bSoKdWY/RNuOqzdeh4Sg0nZcR6Gx0B6Kc+k9xknMk9GIkhJDnJ40FUoxjCLCLLuw0+jb8EdfaBhCB8T9cm0D37L4ze4Ovz3YZ3c25SgKdcpmnrM/nzFLE1xzjG0cuxcs+Gyp0ouSuAqwy09QlqTQ1pD6OW8+0rlSTNOUv3z+hN/s7ZCrkqJUFKU+IYsT4tVJmvByOqEfRaTqeGG5O5vyn5495jf7u3imdWqKIYU+2Qwh2KjV2ajV+ay3ysfd3hu36W3oeB5/trEFwFdHhxzMZ0zTlKfjEeM4xjVNHMOoyD0wT1OmacokOXaeMYTgTrPNh90V/nRji09XVumckV+iiwDJ5q0V/k//118jhODG3R61huYdf/yLm3i+Q3ulRqMVYDkmplVy/7MtvJqeJEkpMC0DwzSWmRrbd1eXuQILlGW5/H1vo8X6jQ7uD6DNKFHkZcosHzLPB8yyAbN8SFiMiIopSTE/Mb2IyVRMVibLKYYuMn7amogfA6os+W/fPeXvv3/J//6zu/yLD26+8/FJlrMznPLbpzu4psmf3d9mtfnmRflloft4EokOC1v+fGHec9Zz3vK7d76fEBj8kIYUAgMTQ1rX0tlWZUFeJqgyp6RkHqaMJxHPdgY8ezHgm+92ebk75L/+7fe8qDRYi5t+livm84ROp7Z0XvqxIJZH/u33hDTVhcQ8TClL3d2dzRPiRH/+es1je7NNGKf89vfPCXwH39cmAoaxSHAvT72FbZv0VhpkuWI2T0izQk9tHZtOK9Bd9nlCkallZ9VzLSzbOC6K3nn6LCYa9lupYeeFKgtylSwLDUNILGkQFxmH8YykyHkx18fblib3GyvcCNpXft/rgmnoou/WRpt/9Ys71H2Hrx/vc9Cfate7qlgeTiIKdcTe0WRJyxFCUCynHwmzMD2VF2FIwcZKg+2NNr/8+Aaff7DB+krjwtsohVyeN6/eD3P17sX+ZVBWU5RCnT0Dl7Lapnc0eQxDYpnm6QlAqRfGizyg64ZOiS/OLBb0FMGoKGFnkVmFtgx/A0NikU6vrmExv2i0nJfqtOIHfNZb1fqZV+hyqiw5mM+ZpylrwfXdD//oCw0pJJYs+bC7wnajWXXSBU7V5c9UQVYowixlmiZLjuTJQ6aLCVF1JwWGkASWhSUNbEPSdl1c08J8R5dFlSXzNOVvd17w//rmSzKllomT58VRFHJ0wsXpVSxuax93e3y60qPlupcuNBqOyyfdHo6pu73fDUwejYZM04RRHOmi6MT+WnReDSHxTBPbMAgsi09WevzZ5hY/761xp3X2zUFIgSENehtNehuvc6uD+jq3P1x/7ec37va4cfftn68sdbZGnhVkWcF4MCcKtT6ns1pndat97dOMRU6BqoLICpWRlSlxMWWY7jBIdxikLxmlu4yzfcJiQq7Sn7y4+qcIVZb8/tk+/5+//5LtbvNchUaaF+yNpvy3b59Rc20+3Oy9h0JjsdQUiNcW4Wf3ua9iJ/hjaFL0tdGoFpxX7zKXpSJXWbXg1ELRySzm5e6Ir77dYWdvzGQa8/jZ0XHHt9TTKdM0uLnVYXOthe/9sFqrk1iWGEKecdxPoygUcaxtMh3HxLFN4iRbUk3qNYfN9RbfPzlkd3+sbSx9h95KjVbTxy5MqGh2C1imQa9bQ6mSvYMxYZToiXHg0u3USNOcPTUmywpMy8CrphqeaxMEDo5tLjnqnmtrbYYUp/jf+ribGOI6C8ysyuMoMYTUekchyJTiKJlzFOvj7ZoWq97FuvnvG4YhCaoQNiF0GnicauH1YDQnTnWxMQuTU1a1J3FysmAYUieCmwauY3Hv5gqf3l3nl59u8/GdtUtRbqQUWJZZ6QKOn68nhsWZ7khXRamOKURniRWMKrPibU5XC2qXZRmnRN+LqUFeFO+n0KhoXWct4IUA05RagH1WoSEXdKazpxaLKe216CBK/XoL1713oeW6uNX6bJFltihWirKk5weEWUbbu75A6z/6QmMBQ0hc0+Sznu6o/3x1nefTMS+rnIh+FDJOEuI8Iykqez8qoY00cE0D37So2Q4t16XreawFNdZrdT7qrLBRq+Fb1lsXCRX7nVwpMnVNJ9kZ7wGaynTV91joR24GNdaCGp+trPJgcMTT8ZgX0zGjOK5sb7WoyJb65tB2PTqex+1mi9vNFh90utxtdVj1fxxebZErDnfHHOwM2X3W53B/TDxPqTc9uqsNOr069nvogGZlSlLMGaQvGVYFxTQ/IszHxMVU6zFUSKpCCpX9JHUPf4wQCKxqahmmKULom8p1Q2IghfHOxeYfNIQuNHSK99U/p1YG5cuCu+Y7yFW9GLm51eGffv+cL75+yecfb/LRvbXqOSzzJxo1l2bDY6Xz43H4BQZSnI8v7Tgm7XaA41q0Wj5ZVpDnik47QBp6wSpagg/urrLaayzzMDzPwnEsilwnZAfBaQe/VsvHcUy6nYAsL6oQMhPPtfQkd6WOUpqKWq872JbJWq/OL3+2TaPuYUhBtxNgmut4lXWmeUqsK5AYGMK8lomG1pHlWjNWarptIGzu1VfoOH5F66o+n5Ssutc/fbwOWJZBtx3wwa2enkgFDn/3+2ccDKZVVoRYFoWLxd3C0njh/uQ5Js26R6vus9FrsNFrsLnaYnO1Sa9Tq4rQi0M7QL0uzM4LRZzk70XXVCgt4tbJ2Gdsk6G36Sxnp5MwTUPTzE4UGjrxvVjazF4nyspVKk7yZcjgSUihMzxs0zhV5J/8vWUZmIZx5rEqlCLJzn7ti2LhgKVF/u/eD6bUa+HFviyqiVac6bWvIQRNx1k2468D17K6sg2DhuOwFtS41WyR5Hr8uRYE1Gz7UhoBIQSmkAS2xWa9zjTVXYC267Feq9F03XOLnhedPtswud1ss11vcr8TcTCf82DQ59FowF5FpZqlKWGekVYHTUpZpSRaNByHluOxGgRs1mrcbLa40+rQdBwCy36nmEqgcyTarsd24/06oqzX6nQ871T69kWhOwYlLcfldqvN/XaXzXqd7wZ9Hgx8DuYzBlFEXBRkqsAxTDzTZL0qwD7rrfJJd4WeH9D5AZPYX/2ylWVJFCYMD6fsPO3TP5zQXWuystGi3atTb17Pti2E0jrAKibMx8zyAbvRd+zH39NPnzPODioR9z/rLK6C147xiSLtXRfbBVUgzQviNMc0jPdQ9OtOlnzN+PBiSLOCNMv137neVsuUOLaFewEP/fcHUS2sjWtccC7smHXas+tadFoB9+9oilScZPzqZzf501/cOrkZy+v8j5tjpCmsQhjnKjQsy8SyTHzfpt3ylwLUZQilrfUVtcBlc+O4Y7nQ9ZyFRQ5HLXBYeWU0v/RSLI9/tvir1fRpnbgWNure0snqLGju/XUVGqXWrFV2FKY0MDHY9Jts+j+ee9hFYUitpWhVLlQ7h+PlAtkyBZ5jUa+5y4X1yULDNk0C36bm2/Q6dVY7Ne7cWOH2VodG4F7IyvYsmKamyOmJ1fG5k+eKKElJK/tYIctrE4bnhdZTJWl+6rqsz/Nqsf4OfZwQ2rnKd+1TFrxlRYHVRdLbQ/8ugzTLda7MKwWYYWiXJ9vU1+Kz3k+Homod6Fm/z6p9nmXFG1S954eqbIuT7Ozpy+vbpvW7qppgTJOEaZoS5zlZkeNbNoFlYZsGr1wmLo1ruVP1goBfb2xxu9Xmf7h5e9kdXA9qbNYbNC7oxgSa21+zbT7p9vi/ff4LRrHm/TuGSc22WfUDapd4XdAnYs22dSFjWdxrtwmzjDjPl65PixudtqfURY9lGDiGgWta+JZF3bap29rp5TwHwpSSluvy7+99wGe91Utt+3lRtx3qjsNW7fIj5rISqxcVDajhONxrdVnxAj5bWSXKc5I8X2pLFvvJsyx806LtutWY7uo+6xff9hJVHjtH9NabeL7Nxq0uSZTheDa1hkurc32dMUVBrlIOksccxk/opy8ZpjuE+ZiwGJMUs6rI+Gd61HVgabCgqnZnubhxn32xXXS+wzRjZzBhdzihUOqUJus6IZZExsu/9mA859nekJ3DMTuHYzrNgNVOnTtbHe5sdq9tW6+C6yioFlgQDt803VvvNfnFpzdY6zVO2BhfwxtfMy7ac5Za0fnO5130s77p8dexz44pYtfwYuUir+cPe6qbZjnDccjD50f8zW8f8/WjfQbjOY5t0m743L+5ws8/2qJV97SdbCWxWYiazWrxqicbFvXApR441xJGZ9smnaZPLXBOdeGzoqCMS8I4I04zXKw3Ws1eFFleMJnFzMLk1NTYtg0Cz8b3tPHO26hgUujJZqfpn2quKKWplVGcEiUZSZbrCYNx9fOxLCGsQgLjEyGBhhQEnq3DF6v8jrNOf9M0CAKnCsl8/QFJqsMT4yR7k3/QuVEoRRhpXY96h3vXqW2oNMffDfo8HPQJLO0iOktTirLkF+vrfNBdufyGncClzt5XLwZNx6HpONxtd65lo+C46tqqN9iqX1z49DZIIfBMnYrYdA3K0kKVEZobWkfK6+OmnYQQGa6Z8vmKy+crLlL6SHF2sVSWBWXlvCKE9dbQp/cFHdZW3QBKPblq2Q5t20U29YXxpFvFSWFWWZaYleVaocrlhIiSJXdRvweV1/QizVe/t0Asv6DLrBAhqhvS8SJz4T9uVDa1ZeVFPq8uPp6tec9+w6PRDti8xbUjVzpALyxGzPMRL8KveRl9TT95zjDd/WfdxTVicdxHYcxwFlbnVs5gHmoh22TOg92js59bPX8aJTw7GvG8P8K1TNqBdy1+5mfjaje9NC+Yhgk7h2O+eXJAr11jHqW06x5sXtMmXhELDcr1dBKPAyLPQqcdYJqSZsO7du/864A48f8Xep54uy25WFiQXfA13xf0K8tr0WfAIimogPcgSP4hsLguRXHGs70hXz7c5R+/fsHzvSGqLFnr1rm3vcIvP9nmX/7iNr12Ddsyf9Bz2LVNuq2ARs09pXVQqiRVBWGUMJ5GiDpXLjQWk5o4yRlOQibzWOs0ltti0WkGBJ7zTtcoKSX1wKHbDnSS9uI90GuHKMmZzmPmYYIM3Cslmy+OY6EU0zChP5rrYqCCYUhqvkuzrhPD33T8LNOg5ju6kKqoaif7X3GS0R+FzE+I/i+LPC8q7U96IaeoXCnCLOMoDHk+HrNRr2NJn/35nHESc6vV1ontcOXOxBXK5OsaqvyYKCiKAVmxT5J/T1nmBM6f48jb7+G9FHlxSFYcoNSEEoVrfYht3jj70WVCXhwihMSUKwjxfoqf86Cs3B3COGU8j3R+R2Uj6jrHupQkywljTTvLi4Jm4FLzHOI0I0oqOhrQDFwC10agLxTjeUSWFzrAphrp6RAhCyhJskKLrwyjcpfRQTiLokYBgWMRuDZZUZBkOY92+zw/GLHRbbDeqdMKvAuFG10EYTFmnB2wE33DTvgtk/yIWdYnVvN/LjLeA0rgq+f7/OevHjGNEqZxwpODIUlW8JdfP+bx/uCtz82LgjDNcEyTrU6Dj7d61M7Ip/gpoNP0+eTOWtX1u67F/PVi0dm+jnvBMr+lXFiFn4ZtG9SuqcP7z7gKREVVu57jflxg/uFONApVMpyE/OPXL/iHr54xGGvTFts02Fpt8a9/eYeP767TbvhvDLB7n/Aci9VOnXbdP1NXMJ7FPNsdIuDKNK2Fbe08SjgYTBlNo1OFhu/arHXqNAL3ndc0KQWtus96t4F3xnU6zXIOBlP2+1NsSzcWr4JCKdIsZzSJOOhPCePThUa77tFr16r1ydkwDUnNcwg8G9fWAvzsBAVrFibsHk6YzGLKK4400rxgOA0ZTcML6WwW2UwN2+FWq82tZpO1Wg1FiZwInIo6dR2n6eUmGmWBKo8dciQmUtg/yZvg26D5wCG5OiTOHlKWGa718ft7vzKnLGNUGVY30/yNj1VlSJo/BWEgrRqSH6nQKBdBP4oiU4znMXmhEELQbQSsW6bOAskLxvOI0SyqCoCKMy0Eo1nENNSOXgs/cLcSic3jlPE8JsnyZWiQKksC16bXrFGWJeN5hBCCwHVIskx3RyrxV5rrwmK1pb/4eZU0uj+c8XCnj5SCmmcTXONCcnEzTIuIRIUcJk/Zj7/nefglL8OvKivaNx/bnwaOA5QEi47S4mcnfnfi56IieS8Wg4vU8R8DUZYznEUMZiGjMGYWJ0DJcB69Q9itJ1+WIenUfD7cWOHeehf/HdkbV8UiKTbJ9Kg/y9VyXypVarGuaeI5FoFnL7tyNc+h5jmEccpoprt2J0f5r75HnGbESbZ0vAF9DO1KTBl49nKRnqQ50zCpxJQLz/cS0zCwLQPP0SLgd3ddr0YPO43yrcX5PEwZDOesdGq47g9PybwsMlUQ5ilRkRIXGQKBY5j4pk1gOO8lw+WimGUxkyzCknq7LGlgibM55hoCxNlBbBdFWZHm/lALDaVKkiRjMAl59OKIxy/6xGmOFALHMVlpB3xwe5WbG+1lSN8PDdex6HVqdJo+nmtr/cGJxf9oEvL9s0PqvsPWWgu4/FQszQqm85jBeM5wEjGP0lOC7Vqg36Pd9N+5L6TQJg+r3TqNwMG2DJ29Ub1enOTsHIx5uT9ipV2jXhkjXHbb51FKfzRnMJkznkWnwgQtw2ClXWN9pYH3luuPYcjKicymWfPwvTnTebzc5nmUsns4ZjSJyLICTC48iVlMX9I052gwoz+ak2YX136ahnYJ1QYpcpnFNk9TDuYzXMPErX5vXvI6dalCoygT0mJIUcaUZYEtGzjmyrWIwn48vO8vvsQ0ekgZVJSoEkO+WeRWqDFh+juEsLHNbUzOtoX9IaBUSZzlqEyLw/dHM3b6E+5vrrDS9ImSjKNJyNF4Tn8yp+bZ1D2H0SxiFiUcjUMmYUzdc2jVdCBflhU8PxwxmIRIKUhzxWgWEaf6JrzeqVNzHYpS8eJwjGFI1tt1htOIJ/sDDCmpew7DWcTheMYnN1fp1H2dMlot6tIspyw5d2LmRTHO9tmNHrATPWAn/pYwH5Gp5A9girFwjDEQQl9cFg5Jy7+FeeLfpx+TlymZSgjzEfNi9CNsPXy+vUan5jENE8ZhzP/y+4f89++e8as7W/zyzps5RVJox6nAtWgHHmutOp3Af2fI32VwgtxHVuiu097RhEcv+wwnEYbUicBhRfFbadW4vdnhw1s9gkvYsxZKsXs04fnekJ3DCf3xHNAd1U4zYKvX5INbPXptrUsaTkK+eLjL3tGEWZySZTlFoWjUXHqtGndvrPDhrR62fNdt4vqF9G/Cd48P+Lt/fMJf/NldOu2fVjo0HB/zVxfMszzh0fSAp7M+z8MBhpBs+i3u1Hp82FgjkD+eHe8Cz8MB/9B/yopT405thRW3Tsd+2z4uT/y5Kv6wmpSvIi8U41lMf6QXlEmao5TCtnXjIPDspbvSj/VJHduk0wxYadfotgKiOGU6T5bU5MPhjN8/3GVjtcnnXO2IzKOERy/6PNsZEkbpa3kRrbrH/Vs9Vjs1xDsaGVIK6oFDr11jpVWjVfeYzJMlpSlKUp68HNBpBty/1aOkdultL0s46E958OSAo+GcvDit+bMsg43VJre3utTOwZAIPJvN1QajaUgUp8tMtHmYsKsUR8MZk3msaVbGxZuhJTqs9sX+mJ2DMUl6/jTvRY5GlOfszWaMkxjbMJgm2hDp2/4R+/M5m7U6a7UaXc+j7lzuOnW5GVOpUGVGWkzI1IhczkEYGELvKCkcDPHukdh5UKiQvDgAIbFkDyjJ1ZiyTIESKWuYskWhpuTqEEM0MY0eqoxR5RxVhigVLV9PCgchXAzZQIjKQ7gsKcscVc7Jih3izKMsi6rT6yJlgCEbyOrzKRVRlFNUGVGW8TIzQQgTgYEhWxiyjRCiEiXPUWqmt6WMq23wOPlV1o5FGWUZU6gpSfaQOPsagYVj3aUs42r765hGFykWC6P3f9nKioJZlFKkinmc0p+EPN0f0qp5FErrIfYHU8IkrbQZBp5jMY9T5nFGUnVgbdPAsy1MQ5IXiv445GA0ZbVVx5SSKM0YTkOKQmFbBlmhKVKjWYRlGrQCj2mUsD+cUvdcmoFLXhSMZjGzKCXNckAci8uEfk/fOe1WcVksFg9JMScsJuzFD3k6/y0HyRMOkydXfv3zQiArS1ETQ5jVRPFEUbC019Q+/pKFn/+xv79xqoh4pcjAxFgUGydeb/HfUTFhno+A8gcvNBbXlPVWnfVWnSjNmMUJ3+/3+er5Ph+sd/lXH56do6G/z1SFho1nW5U94ftokJzs0OqpRRRnHI3mPHx+xHASsdapYZkGSZrpicU0QkrBRq+BvViUnOMaugh/iuKMncMx3z8/IkyOv3dJqt1TkixnpR1Q9x0sy1jaIiZZvrSgLApF3J9xMJhh2yZ3b3RZsJTetCmVJP8a9hmcnKqdheFwzqOnh3zy4cY1vd/1o9SuBKeW39qjXjHPE/ajMQr93w3LI1fXI7i8KkZpxMPJPnGQ0XECata76DOvZ05dFgvNx6sNoUXS/UIvuHisISXGT8g2euGGFkbaYWkxKSjR0/0kzRlNI5p1D8+xTlAh9fN/CDaIZeqAuZV2wK2NNlGSMY+OBcSjacTjl32e7w7ZPZzQCJwLU6iKQlObD4dzHjw54PHLPmGcLvUJpiGxLT3hubnRptMM3u3WKQSubdGsuWz0GtxYa/N0d7AsNJI0Z+9owpOdPi/3RzRqLo3AvTC9Mq2ug893h/z+u10OB7NlkSEE2JZJs+ZVdsPNM2lcryLwHbY32hwN5+weTaCaOCRZTpoX7B5N+P75EdvrbbxKf3LecyEvFLMwYb8/ZfdwfOGJxsLmtmbZNByHEk3/C2yLwLaXGpiiikm4yvf8UoWGEBamDEiKI+bZCxLZpygTDOEAAtfs4ZsbcA0BTnlxwCT6XxDCouH9O0oUYfIb8qIPlDjWBwTOr0myh0zi/xXP/jkN738iLw6I84ek+TPS/DkLab9l9LCMTTz7c2xzu3oXRVlGZMUes/i/EqZfoNQcISwscx3HvI9vf440tMtLrvpE2Vek+XOyYl8XPWWJlD6GrOHbf0rg/DkLS4k0f0mSPSTJH5MVu1jGOrZ5A9/+BYa8C0BJTqEmutBJvyJMvyDJHgICEVqY1Xu71sfU3f8BaVyf8P5dSLOCYRySxjmTMGEyj4lTbSlXliVRknE4ntGp+9xaa1P3HQLH5rudI/qTkPVOjbVWbUkDsS2TME4Jk5QkL2jXPRq+S1EqJDCaR68klx4LqRZuUnXf4fZ6B9s0CeMM1zZJsgLPsTTlw7HxHZtmzaXb8LGuodBYYJTu8Tz8PS+jb9iJviUuptf22u/CohhwjRquUceTdVyjhm34ONLHlh629LCkgykcLGljShtDWKeKkqWAd0mfkq/TpJY87BM/F4JhustR8pSkmHOYPOP6O9rnh20a1FyHhu/Srvn0mjXWW292Wlt8BtMQGFK+VwrDUm9w4mdFoZhHKbZl8IuPtrix1qIoFI9e9vnb3z9l52jMYBziORY1zzmXg0pJJbqchbzYH7HXn/LLj29wb1svYIeTkH/4+jn7/Sn7gymNwKVV92jVPX7x4SYf3VrVCzql/3zxcIe//MdHbPSm5/R5V5Ut6dXPA+1gZVQi49c/u2UZeJ59rd/n60ZZHheYC9RMh/v1NRzDwpCSURr+pBbKF0W5LDLO1tJcHPJMi2SdB1WQKm2hDtqFyDccvJ/aOXBGfZxlBaNJxIv9EV8+3CXPC7ZWW9QDB8fR58IPzaLqNgM+/2CTMM7YORgtv+NRnJHnigdPD+k0n/LpvfULFxpZXjAYhzzbGfDFgx2+f35EdELj4Ls23XbA+kqTtW6dmu+c+/NblsmtrS6DSlzeH+mJbZ4rxrOIF3sjfvvtDlJKPr27jt282PJ2QZl68PSAf/rmBYPJcTCyISWtusfGaoP1boNO0z9XIdMIXO7eWGHvcMJX3+8RofdFWRnaPNsd8Ff/8D1/8cu7bKw0Krr5+bY3jFK+f37Et4/32e9PK3ra+Rs+lpRIy+Zep8Narcbp73FV+gu0cVIV8HdZXKrQkEJiCBcpHKQwKUtFUUaoMkcIUGXz2kQkWqvwDCFslJqjyEjyp2TFLpRFRUWKydWQOHuopxkqIit2idMvUWWMQC4v/FmxT6EmmMYKpuwsaUyKRE8oUBUXXaLKiCR7BIBtbiFlDYGl3yv9kqw4AgrARAoLnYz7+g1ysZhTZVgVSAoQONb9Vz7tSTvMkpL8xH/LU6/1QyLNC9IwI4lzwiQlroJmFh0mKXWIVImu1M1Eam2HKrEMSakgzvLqoiqWizvL1Jy/LC+I0ow8L7RntmVSlnA4nutO0Cyi5jnHHtElOJZJK/CYRYn21zYMCqU0NcY0ME3tbJXlBfM4Jagec6X9UERExYT9+BHP5l9wlD5jkh2+F6qUpjZZWNLBkq7+W+h/29JbFhqurOEaNV1kGN5rhYYpbUxhV9MPazm9gMufSyWKuJhiS7c6U388GFJim3Cr1+JXdzfZ7japuT8+DUWjXE47l9C0dhzbYrPX5N4NXQzEif5+xInWILVjH9+1z9eqqYr90STicDjT08U4pVQliCp9OsmYRSmzMGEep3rsL6pEWbTjmzQFlLqLdTQ+ySl+u/GHXnSenfx7cYgqg+LsRaTrWrQaPnmuODyaYtuVV/3JV6jCsq7iPnN5LCZZp68JtmHSMUxSlXPgTigpiYo30xy0XiImKlKSIqekRCBwDQvPtKmbLjVLn+e5KhikIdMsIleKotRhs4bQjw9Mh6bl4Ri6YxrlKZMsYp5rvQjoQNu9aERcZBTndH5aiPavpcBEnJi8ak1LrhSDZM4gCU9lgJhCsupp58irICsK0rygqFwSbcPAsY5ZDifvVe+ClNqSNvBsXEcnei8MS+I0Y78/5fcPd5nOY3YOx9R9tyo0Fs2c13aIfl0hELJKxjb19i3fx7WWzo4XQbPu8cHtVQ6HM75/fkR/PCes9Bp5oXi2O8AytSmLFJq2FPi6Qbgo8Bddd1WWFIUWT4dRytForicCD3d5stOnP5pTFEqvYqSg0wr4+O4atzY71AP3QsJty5RsrTYJo4Tnu0MOhzPCKK1o0gX90ZyvH+3pa5kQbK01qQcurqPtehfHcumSWX3eKE4J44zne0Oe7gx48OSAnYPx0sFpkfR+c6PNx3fWWO3W8c+p+Qw8mxtrLTZXW3SaPllRECfZ8lJ5OJjxZblLq+HRrnu0Gh6Nmlft6+o+XZ0LSpXkhc5+mocJOwdjvnjwkm8e7TOcaCH4Rb6JhpQYQMfTAcvvE5eU52sahW20qNt3KcuismnV1ZhedF8PBAZCOAhhUFJUVKWIsgwrUXqMKrUQVAoXUBTlmDR/QZR+Tc39l9TcvwBAlSmz+C+J0q/I8l1Mo6dfi5KyzJDCIXB+hWf/DMqCJH/CNPpPpNkzcusQU7YwZJNCjYmzh0hZJ3D+DNvYqKhS2obWEE2OL4sCy9xEyjpCOBiiVlnpvvo5zYqeZWHKDlLWSPPnCOHQ8P4PONY9AAwRIOXlszEug7woiOOMrErJfDWnoO47bK+22RtM+MeHO/hVN7buOWyuNNkbTHi016fmOrRrHve3VmjVPDp1nzQveHk0IUozxvMIVZY0fZeyLPn2+SGTeczheMZmt/lad1WINy199JonLxS7/SlSCO6sd/CcN2tizoNZ3mcnesCL6Ev24odExfS96TFMaeMbLRpWj5a9RsPqUTdXCMwWvtHElHZFcTpBmzqRRL24aQsWFKrFnOIn1gW8Bkgh+NnNdbY6TVYbP53EYD3NOP19MaRcLkZO8rVty6BZBXlFcUaUpJTl+YIkyxLCOGU4iRjNYg6GM/72y2c82dHuW2leMJyEBJ6tFxOVK9xkFvPb73Y4HM60I0xlsfroZZ9pGJPlhS5Eyre5G5YnJjfXuOB8Qxlc8x1WV+rMw4Svv9ul267RbHqnHm+aOrDO834cJ7HFMb/K/tiPJ3w93mU3GrEfTXTjTgg2vSabfpuPGuvLQiMucr4Z7/DNeI9ZHhPmKXmpcKXFmtfgTm2Fz1tby0JjmIZ8Nd7h2bzPXjQBwDMsRmnIKItIi5zztA/0Ub+eAlN3co3lcYyKnGkW8814n2/GB7Rtj4atu+u2NPFMm45zNY1OlGlb7CQryIqCTuDp60epJykXmXiapu549zp12g0f37MJo4Qs19+fwTjkiwc7PHx6iOtoF6JlwvcZ77EIntMFhknNt5c6hbVug9tbXTZXm/iejWFf7Jquczna7B+t8HRngPFc8CwZLoXK+0cTZvOEeZiydzjmw9tr3Nteod30adTcU99LpXSTYzCa82x3yMNnR3z1/S7PdoccjeYUSk90DakbgDfWWvzFL+5w/1ZvuZA+L0zDoNepUSjF/Z0Bw2nI890hw4leT82ihO+fHTIPEwbjOR/c6vHh7TU2eg3aTR/7lfdL84IoTnm+N+LZzoCvHu3x9aM9joZzshPrHNOQ1HyHT+9v8Oc/u83qBbK3PMditVvnxlqLW5sdvSY5miwduKZzbcbx979/xmgS8dn9DT65u0a7GdCsnV78K6WYhSmHwxmPnh/y4MkhX36/y8v9EWGcIqVcUvXOkxD+Q+KShUZ1ES2VXuxTIMoCRY5SMaasVR2Ya4DQC3AoKNQUVc6W+RKAnmYUA0pyDNlEICmKEXmxT1a8JC/6FGoKCMoypVAzVDnXGgs1g2qCIYSJFDUsY2O5qC8pEMKhJKVQEwo1Q4oaUviYxrp22kKiyhRRzjBEEynqCHF8g9P+6DWkcMiNHoUakKujyr7xxMcUEoFNKSykrGEVe0gZIIWHbd7Arbbpx4Btmnh1E+HrE7jmaarKZreBaUh812a1VVsmaTq2iVPxGVs1Tycb54XmxFv6Amsakm4jWLpSCam/0JYhadf1Amsa6jR405SstmtL56ibqy1Wmj5SCgLXZr1Tp13zcG1Tp+oi6NQ9bq21l5OMq/iV50qLnwfpDs/DL9mPHzMvhtfquGQIE1v6uEYNz6gTGG1qVpeGtVIVGV1qZgfPaOAatRP+9X/YIsrrwko9oFPzodRc27QoyCpnkndddC3TwHcsrGvO0qiY5cuO/2LhuaAondysspoAlpJlR+4iEGKRa6Of71YOUwgIQNMTA5deu4ZlGYRxxuFozsuDsc7laHhLKoA0xHIbj8W+b94eRUFR5tdSdIvFROMNtKJClWRZzmF/ynA0ZzAKadTdpf4GwHNt7tzq/oiFxrFFr77Ovz0n4CTSIidWGbvRmIfTA4pSEVjHE7pxFhFOUpqWx4pTw5Gm1h1Vi289nZIUFd3o6bxPUSpu11ZolwGZKugnMx5ODxgmcywpcaSFa1hMsoioSElVfq7aQZUKVebXWGAeT7KKUul9UeTERYYUAfVKM2JJA0te/rs6S1JGYcTBZMbeZLac0N9f7dKt+RzNQnZGEzabDbba58vwkpWOoNsM+PD2KrMw4fHLPqNJRF4UJGm+1EydFwtLd8s08D2rCq0L6LVrHAxmbG+02FptsdqpUQuct9qtnoRtmdiWyc2NNr/85AZSCObVtDNKMsJY/xECpmHMZBZzOJzRbnjUq5wKWelPs7wgjFIGk5CX+2Oe7Q54/KLPcBJqGk+pxdzths9Gr8nHd9e4d1MbUlx0EiOlwHdtVlo1Prq9SpxkVZdfLWlf45l2xUwyna0xmkasdeu0Gv4yoX2R87XQqOwejHl5MObRiz5PXvZ1cVRNcyzLYKPX5O52lw9vadewt7lNvQrTNDBNg41eg88/2KBQisk8Xk6Qsrwgywue7w2ZRylxkjGeRbQbPo2at5x4LVwLJ/OY/nDO050Bz3YHPN8bkuUFa90GUgoms3ipEzpPSvgPhcsXGmVOqsbMsqeAxDVWyNSUuDhACoe6dfdaNlAKG9PootSUXB2hlOblCWwQClUmZMWOLnCMHgKbXA2qPyPmyd+TqcNqsxW5GulbZ5lXgnIJSAxRQ0gLUU1mAISwMWSrEpVHqHJOSYFtbtH0/o8k+VPi7CFKTVEkuOY9XPszbHMb2/jpOaJcFs3AZWu1jmtYS+FpXih819YCsxKsusR3LG6utpeLncX4r+7Z3NtcQYrKSq0aLa80A5qBq3MxlKp0LgLT0DSsRUdVlSWOpQW8hfJZbQXYld1atxEcb4dpVF9MuLXWpteqLYWD/hWsMFMVMckOOYgf8yL8iml+RFFe3EbubbCER8feoufcYs27T9taJzDb2NJbaixMYSGF+Uc5lbgqZEU/SKrR8iiMmMYJWa7eyVtt+i5bnea1FxpwHESmKi57XuggqMUNpqzW8GmmKYJ138VzbTzHPnexIYS+AbcbuphYaQX8+tNt/uSjGyfG7mAa+nHaiGHO4XDGPEpp1j3+/PNbuimQFyil+N2DHc7V1S5BlQW5ylDXELYmhNRGBG/IZ5jOYnb2RpWtKuzsjzFfoUi1WwHNhsfqyvUGvV4Ei26/Qr3xs5yFeZFyFE/ZjcbsRCP+pL3Nv+rdW9p+/7fD7/nd8AWbXos1t0HXCQhMl48a62z77WPiVlnydNbn/7vzBU9nfcI8IVN6SnAYT3k+H9B1Av7N2sesOFqQ+9cHD/luekB2rmtbiSpz8vKajjuycsGTS5qUEIKW7XG71uFufYVbNa1LlELgGZe/nvdnc373co+n/RG742kVJqsbDh+tr/Dd/hH/6zeP+Lcf3Tl3oQG6MGg3PP78Z7fwHIs0K0iSnDAuz6l1Og09lS8oquyoWRUg93RnwFff77HarfOrT2/w+Qeb3N3qnrvQWGB9pbEUII+mES/3R0QnwukWtrT7R1N+8/ULHNvAtnWRYkq5XCgviqgkzXVmVpxRFK9MMtZb/MWf3OWz+xustAJc27q0NsVzLT6+u47rWCRJTpLk7B5NmFWNyTjJ2DuaMppEfPfsEMe29LZbugGqypK8olsdb3dOnKTkRbEssrU9rcPPPtjgz352i7vbK9p6/BLmIWsrDf7857fJcsWL/TFKlczDZHmFnc4T4kQbBnzx3a5u1tomdkUBzbLi1L6OEm1jnqQ5vU6NX358A9OUPHhywO7hRFPbL5Cp8b5xqUJDlUVlbZujXZY8bKOFIkPlme5ynOjgXcVRQQgbU3bJypQs36UsE6TwEIapO2hlQZI/RggHy1jBkM0q02PhAFXHMlZZXOgtNhBYOOZtDNmkLBMK9PSiJOO0e8rCCaqo9BcGIDBEA8e8g8BEUJKrYeV6NWae/HegxDZucOVu8zIh99086fcJyzRoBC41y9bd4eomf/K4GobENk0a/vHYbpFmq3mvxx3cxVjYsCWOpYsKKk7skvuplB4vVh2RxXsqFKZpIU0torNN7fmsCkWRFShDghQ4UmI5FsKQSONyCbYLfv28GLEbPeAgfsQs75Oq8J3PfTcEhjBxZEDDWqFlbbDibNN1tllxblIzOzhGgCH+OZjsPJjFKZMo4UV/xM5gyiSOmSfpic78m3Gz16Llu+8ltE+7DRWoMlueT2VZMgtTHr/sLx1qnu8PEULQCFyadc0rLkvoj+YcjmY82RksFwJJlvN4R/Ob2w2fVt3DMvUkcGOloZ1kVMnhcLb8PpVlqY0SXBvHPub/Jqnu/B0N5+R5gSpLPX10LBzbPJeOpygL8hO5SleBQC4L6rPQafncu7P61tdo1NwLdR3fB3RGU05RZghhn/uqHRcZ/WRGlKeYQtKyfW4GXaQQFKXi6/EuUkjmRUo/meGZFo5hMcsTxunxdUkAszxmmIZY0iCrxNTTLGGeJ6hSEZgOW36LVbcBlHSdGr5hY4l3F9wl1XFXGSVXX9DoYFobQ+h7rERgVGnzRamYZQmH8QwAUwi6bg3HuNy1cZqkPDkaMk8z1ho1+rOIndGEMNUpzdM45cVwzCRO3vlai3tdlmsqYn80Z/9ownASkqTZsjt+WSxoMEoVZLnWckHCUIZMZjGGFBSFwjIklqUdFq1zpnr7VZicng7kPHh6gOOYjCYR41m8bIbMI71fpKwyiCqnvqJQS53Dq91z05A4tkWn6bPRa/DZ/Q0++2CDG2stPMe6kn7KrELzyo0Ov/hoC9OUfPf0kJcHI8bT+DhLKMkYTaPlemPRjFzoShbb/ipc28RzbdZ7DbbX2/z8oy0+vLVKp+lfmO61gO/abPaafHRnlcE45Pvnhzx5OSCMj13KdMp5Rp85UmqzEq011UVdURQURbnUzniuxeZqkw9vrfKzDzcplGI8jZjMY6bzmPMb3b5/XC6wj4JcRWiHqVUco01g3URkBlG+X90k3j1yPw8EeqKRqz5J9hgA27yBEC5QkBd94uw7bPMGtv0JpuxgyAaGbGPIBr7zK5r+/5ljy0QFpUBKDyEMsuIAij2UmlGUs0rvocfdZZlRqBFlmSOFiyFrFcVJ07kc6wNs6xZlGaHKiEn0nxiF/wFDNqk5/+pKnxoq6hYLvux1yeuvhvMUjW96yBt/fsYvyxJU5WqlqgJECEGWZCRhguVaGA1P82kLRRpnpHGKZVtYjkkaZ+Rphhu4mP5lU1h1V3KaHfE0/C37ySPy8t03n/NAIrGFR9ve4G7t12x6H9K0VvGMBqZ0Koeo6++w/zGiBI6mcx7vD/jrb5/yD49ekiudp6JpSG+/qf3qziaf3Vhj7S1uVVfZOlVm5GWmjSaq8Lz+bM5vvnnOFw93mEcZhhQEns1Gr0Gn4eM5JoVSPNsb8vdfPePl4ZiDwUznxFS2hjsHY37+wSafVt29VsPj5kaHOCt4tDPgnx7sIKvLXlnCRrfB//in99hea9OqedR9h7x6j8k8JnBtHMekP5rjezaB5/Du+rzqbKsMdQ1TPikMTKHP/7OKnPt3VtmowsTeBMMQBOfwuH+f0AVmRlFmVbPgfN/lpMgYZZpz3nPq1MxjTrxAUDMdVhwdZNpP53ScAMew+Ga8w7eTfXJVUFJiCMkwnbMfT1hzG7qNpgqmWUymCuqWS910McVCJ6SF423Hxzedc1zny6qQSq9lomGwOO66QJRCYEmDuMg4iGcM04gHkwME4BoWv1rZpmVfTsAapRl7kxmbrQb/7uN7fLN3yNFsrulbFf3wTIH2G1CW2rHp6c6Ab5/s89X3+zx52edgMCWq6D3XDaVKZlHC14/2mEeJbgT6DusrjXMXGlIIhGFw58YKq5066yt16r7DN4/3mUUp6pUCQqmSUpXaDrdyASk5TQFdwLZMuq2Aj++s8Wc/u8m97RU2ek0C174WkwbDkLQaHn/62U3Wew06zYDfP9zh28cHpKMCVRy7/S2KtTQ7DjF9m46hFrisd+v82c9u8Sef3GCz12SlHVxp4q0LBosPbq7SrHn87Rc+aVaw35+cSanT1Nqi0s6h93VlMSuE/vydps8vP9nmFx9t8um9DcbTiMcv+uweTn4kI4w343L2thiY0qMoXYSakKk5YbZDrkJso4EpA7gmesdioiGFR1bsIYSLJ3+GaXSgVBRqTJo/x5QtTLmCaawghY9tbFWuToIkf3RcaJQlCANH3MQUHRYuUUJYlKogyb7Xb1zmpMUuJQrD6GAYHaSoITDIiz5J/qhytDIoySnLDKVmGCJAoG9yCx1LVuxoi9v8EUn+lKIYAyVR+iWUBabRw5CtKl/DqLbHwTRWUGpOmP6WvDjS+8LoYRvbSHkx27nrORZvv/Qe//qsx5393De9ZhqljI+m5FmOWXHHVaHIs4I8zah3ang1l+lwzmB3RDiNCGcxQcMjaPhIQ2BYBqZt4orLLToyFRMWY4bpLsN0h3k+uvJiSiAwhYNvNll17rDu3eeG/wldexvPqGNK3VVPVcq8CJFC4hle1em7HiwusAsnmx/Cw/19oixLXg4m/NOTXQbziJpn0w48Gp5TCTrffi26t97Bd95PB7ykpKg62wotMHVti0bNY63bwHdt4jTT/O6Gz4211olkcK2HWl9p4DoWa90GqkrwrvuOnn7UvFO0wc1eAyGg4TsMJuFyqkhZ0m1pmqFpSjxhsd5t8LN7G4ymEaZZudrYJqvtGtvrbT642cM2Td7V4CjKjLyMr6fQwMCS7nLB+SoC38H3bOIkJ6moA3musG297a5jYV/Ayeb9QR/3XGXVAvp8zzKExKkCEqMirQqH4yOQqWLpFGUJQz8mVuzHE0bpnDW3QcPyQWjSnimkVgmV+rtuSZ30naqCrCxOmS+rUhcjmhb6jsVxqY97phLUNUw0pDCxqgYLQF4q4iLDEJKm7eEZ1pIuZRsG/hWoU4XSUztLSnr1gN3R9JimWB77hr1tDyyuoXGS8fJgzLPdIQ+e7PP98yOe7Q4ZTULKUlvJtps+dd/Bda3lNOCtRiYsphiaOpXnBWGs9QSD8ZzxLF5O/KfzhL2jKd89PaRR86j558++WFipBp6N79rcv9nDkJJ202d9pcFoGjGeRYSRdmXKMm0iURQKVTENTCm1ttIy8Bz9OjXfptsKtLbhxgof312j1w7wPec1muNlsLhfWaZBq+EhK3fJwLNZadfZO5ownkZM58d6hSwvjtPEhW5GmFLTkjzHwnW1gU3Nd9jsNdlca/LxnTXu3ugSeOfXv7wJUggwBM26nkRHSYYQghf7Q17uj5nOY2ahDiGM01xnV6hyqb2zDAPbMgiq636nqe8Vn97f4O6NLt1WgFIlrbpH4DvvKRvq8rjUFdkQDtLoUKiIokxI8j0m5QMsWccxOliyjniDD/pFIYWNIbsI4ZKpI0zZwDTa2OY2JQVx/oCs2EWV9zGNLqZcQQob27yJb/+CtHjBcPb/5Nh2tkTKgKb37wmcP0V/rQ0t4mZAlP6WKP29FooLgSHbOOZ2VQzUAEFaPGUU/gdydVRRqnRBIWUNz/4M29xcvldJQZR9zSz+K7LigLzoVwUKFGpKmr8kcP4U1/oQIVvoID90sWTeJE4fMIn+E0KYGKKO7/wKw2sj+eELjR8S8Txh7+khyTzBDVyU0lOLstJxSMOgtdqkvzPk6799yKQ/Yzae01lt0Vlv0uw1aPXq+PXL27bFKqSfPGeQvmCej8hUdGXhoxAGjhHQsW/wYeNfccP/FM9oYEuv4nJrJEXCUXKELW1sx8a4Zv2AqmguP4Zd8nWjLOHZ4Yi/f/SCmyst/vVHt/lse427ax3tX/SOj+iYJrVLJHGfc+uqBWdKWWoRpeuY1HyHf/n5LbZWW8tR+OKG7Vh6MShMwfaGFnvm1Y1Hv6K2LjUMseQdLxYNa506nUbAhzdXj7th1ZMMQ2uVTNPAMrQDTLcVLB+3sPPUOqwS1zZxHfMd+0937lMVo67BHEEKo7Jmtt56bs7DhP5wzmgcEoYpzYa2hlzp1n4ShcbJAvMilDLXsOjYPi/mAw7jGdM8rq45+rjM8oSjeMp20KZpe4R5RpjPGKcRpjD4rHWDj5rrpEXON5NdvhztHLvnSIPAcjClwTSLmWbx0hUIIFYZozQkzLMzu9Svf76MTF1TgSkM7KrAFAiSImeSxriGyc2gzYbfYM3VE0dt83v5YyyWf8SysFh8pkWnuzwZ3vQGaApkwj9984K///IZT3cGHA5mpFmBaUjqgcv2RovP7m9wc6NDr12j5ju4tqkdp878YpVLx8SF9epCJ7FzMOKL73aJ4kPSnKVzURSnfPfsEN+zuXOjy0bv4u6KQsBGr0m3pdO1h5OIxy+OePxywM7BWDtRhQnzKFlSfSzTwLZ1OG8jcOl16jpQb73NjbUWN9ZatOre0mHvKoYsb0PNd/jozho31tr87MNNdg4nPH7R5/nekN2DMcNJuBS6J2mOIQWmaeA6Fp5j0WvX6HVq3Fhvsb3WZmu9xWavievoa6u8xkW7aRoEUvLJ3XVub3Z5tjvgycv+UtzdH4cMxiFxmpOm+bIB5Hs2rZrHVuVedX97hRvrbTpNn5rvYJqaZlWvJlvnyV/6IXHJwD5tmWkZDXxzC0vWKcoIU9awjTa2bHEcAHZVGEjpYps3abj/Fil9bGNbZ2CgcK1PaXr/Htf+qNJnOICBaazh2Z9j5G1M2eX48qJtcE1jBYGFIRvY5hYlv6oC/ARQVMWAiWmsYJs3tKOV0EWFIVt49scUasLx5Eb/3JRdbPM4mVggMGUP1/oYy7iBKkOobsiG1CnmemLjQjXNADBlE9/+BabsavoWpXagMraWCeXXBdc0ud1skyvFahAwjPTofrPe4KOVHl3Pu5LLx2WgCkUSpUyHc6ajOZZt4QY2eZoTzWKCpk9ZOQqpQmGYBkHdQxqSLMkxbZNaK8B2rUufh0kx4zB5yjDdIVXxlYsMiYEn62x6H3HD/5RV5w51c2VpUXsSqUoZpkMcwyEwAwp0Ur0hDExhLj9TqlJSlSIrMeXiT6pSsjLTdAAhsYSFIQyKsiBWMaNsRK5yamYNz/BwpIMpz3c5KEuuLaDtujCNE/rTkJ/d3ODz7TXurHbYbDde0xL90CjLkkwl1ULcqey/dTpuvepMLR736t4UgGtbF+qmaUeZN2/LUuklWAoOr4ISyMuUVIXX4sJmiGqiIa0zuZaD0ZzBcM7+4YSDo6nurqqS/nCG41is9xqs9up0WgH12o/XjFGlWh53SwWEechOOOLZvM+j2SHDNCQtcrKiwBCSLb/NmlvHEJIVt07XqdGyPQ7jKX+1/x1C6InDYTylaXt0nRqrbp240GLsolSMs4hncx1kC3AY6069b9iYQmJJg7rp0rF9Ok5AmKf8Xf8JLdvHkgbP5wOkkJhSvrNHWFKSlcm1HXcpTk+yTClxDJNhGnEYz/AMC9/QWhed4yWwL1ls+I7FRqtBVhT83ZMXPDzoMwwjHh4M+C/fPeZoFnKz06Lpv7lJlReK/nDO45d9Hjw54PtnR3qBmGTYlsFqt85n99b54NYqt7Y6rHbqNGvuMmNjaW37ChZ6xaLSliVZTpxkdFsBq50asuLtP98bcjTUxjhpXnA0nLF/NGFeuRlpCtj5zSTg+HpgWSaNwMW1TTrNgDtbXUaTsFqoZzp7pFDLhoVt6YVws+bSbvistGt0mj6dpo9zxUnAebbbMPQ5blsmvmfhezZ132FztclgPGcepkRJRprly6wuw5DY1v+Pvf/qkiPbrzzB3znHtOvQERCJBFJfwSvIIllV06unp2em+2FWz1N/hPl68zD9VFM1VdVd5CqSdXl16kxohI5w7SaOmIdj7gggI4BQQOYlue9CrotAuPsxO+Zmf7X39gTxdtN3hld6DVa6Dc97a59PWvyimHc2siRaeHFkScRKr8ntraW6s1FS1t0jJf1ak8gf13InY3WpyeZqh147I4kCgpo30sxi3r25jJSC1V6TycyPed9c7/Le7RVcBJ8e7XFc5BzMJouxuLvdJe51l8iCkDT0++WLE3N+8InU/JLP0is9ZULZoR016oqNBSTPzZau6+EuEIQk4XtEaguEQIps0UloxP+KLPoJQkRIkTEP/EO1SiB7JOGHuJdvhEIgRYIgJBDLKNmtk4N6fg8W1Qx/PCFi0coXRMEdQrW+kC6kfpVPRFT9u/OfB6TRxyThvfr3T1a36vMlIgQBJ8fNlOzRiH5JFv0Zbl4xEhLJXBnr+tCIQn60usb7S8tU1iwqp4GUxEFAKC9uCnRVOMBZx2xSMBlM6aw06a23sWbK8HBEd62Ns5YgCshaflwqCAOMMTjnaPUaLG/1rlSNmJkxe8UDjspnaFde6Xh8khDSCLq80/gpd5t/7v0wXrhWnqO0Jf2qT2QiWkEL7TRKKGIZo5RaVHtzkzPUQyIREcmIRCVIIclNzsRMfOAgApqBl8StbMWoGvFg8oCpnrKZbLISr9CLegTnvB14ouvVSI7XC0epNbOyYrmV8uHWKs0k/kH0aiy2Dshy7Cvm9OfjEifHZN5EgvRcqOG63ttR2ZLCTDDu6vTD5yM0pwcou/tDPv9yh2e7ffYORvQ6GY0sWgR5m2sd3rm5xCcfbn2viYbDUrmcws6IbMV+PuXvD77ly+EO+8WYqS7QzrI9G7A96/Pj7g1+uXyH5bjJatxiM+uyNe2yMxvwh/7Txb6tJi1uZD220i4bSYfczIsJgv18xN8dfMsf+09JVUjl/L2wE2UEUhEIRTtM6/fo8mB8yH/Y+ZRAKFphQmErUuWlbl/f6XRoW1DY6bV0spQITiQagliFdOKUb0YHfDXcozCaqS49x0kq7jSXXpD9vQhaccy7yz2+PTji3336NXvDEXujCbOy4ml/wJ3lHh9trLLaPFs5ci5J+sevt/nm8QE7B0OMtQRK0Uhj3tlc4v/8lx/w0bvrpMk8uZAL/sdrzweA88F/q5a13VrtEEcBaeLVluaJhtaeBLww3tPG8zQu+R2PQkUYSNIk5OZG1xOQa1K7dQ7jLM75rqoQsi6eiAWBWSlvlivfMk9ASUEa+4RjudPAWLsgT7vFn+enZc7DkdJ3W1RtfqjeUOflNHjJ4JjbW71F0cS65521+ejrgjckxULJK1Avck/bjYSP3l3n3q3VhbAHePJ8FAZ8drzH328/4Td72/x2bwdtfVz5v7z/Cf/P9z9mvdFcJBpALfJgwEszcJoZ9XlxqUTDuBJjp/VCJEpEKJFS2RGFPiKsOxu4Of/hKqpTc6pajFTfvbH4mc7Tss8AcS7FHk/u5gLBuxQRnLerIECQgLjYQ88nIG8mq34ZSkhUIEmC73/kYI6kEbP+zipZK6WYFqTNhKXNLq1ekziNWNroIgNFe6nJrY+2kEKgAuVl9ayls9pecDsuijm5dWaGjKoDZmZ45fEAJUKW4htsJh+wFN2kEXQJRHSmX4AfTTAUtmCsx0zNlNzk9KIeG8kGla2YmAkzM2NmZmirMc7QDbu0whaDasBUT4lVTKYyEpUQEtaBb8XUTJmYCdUlgkP/wLke34Trgdewb9fjT5O8JA4DsjOC1bcJh0XbAityGlmXG2stZP0wTtOQQnu98/nDMNeacVGSRSFLaYaULEaZtLW+wiUkxjmss4TSczMcvuKtjXc6jpRakBetc5TGMC5K9sZeuWer3aIRRV49yFoqYz0Bt9bIn/tSnCTFnnYf9x2NgtxO0NeQaMyV2EKZnBrs9vtTHjw5pNfJ+PlPejQz75A8mhSMxjlHxxOebPe5c2vlymu5ChyWyuZUdgZYWmHCvdYazTBmoktf0HGORAU0g4QbWY9WmBDLgEAq1pM2P+nd5EYx4aicLBLQpajBctxgLWkvusxLcYOf9m7RjTIC4T0mIunV/D5ob9CLGizHzYUE9FLU4EedG6zGbfrlBCE8ERzAOMdm2mY9adMIzn4mzjsahb2eBFOJkEQ2CKXvWighiGVAL864kXVphwmqlr6dX5+XRSdNeH99mTQKaCUxa60G/emMLA7ppAnvLPW4s9JjtXX287fShqd7A756tM/RYLpQL0piH5zfvbXM+nKbTitd+E5cBOLF//gypBSsLre4MZrRyF6MP4x1VNpSlF6VzgfNF/rIBaT/4qOkXHRHXc3fmZmKw3xCbko2kw7tKPLS9MJzhlTNCbLOUdiq5iR6Izltre+uS/Wd8zEvslRWU1pDKBWBkBjnCemhrN/beWeik692gLa+UxFKtRgjus6x4HmStbhX8zzktrViZihrlTRr64TMoYTnV3zneHEL2XWh/O+EoXr+uroj5d8LQilxwPFsRllqOjIlUxLhnvcblPLPleSsr60QTHVFv8jZn00WzufjsqCq1+z3ocK5Am2PsHaIcxYhQkK1SaC68ze70Pm7nLytzcnNAdZVCKGIZAepYgpzzKD4gizcIpBNlIguvKB/wb8AIGsl3PpgA2u80oWQAhVInHVs3Vv3X6pQ0V1t01pqzttQ1JndlVQXfIA/YWYGTHSf3Ex4LTnyNQhExGp8h9uNH9MN14nE+bgj2momesLUTDkoDriZ3aQX9RhWQ3bzXd9ZwHFcHXNcHrOZbLIarzLSI2ZmRjtoI/EGXih/g7POLhKT+WjVxWCxzvyAEg1oxhHLrQbaWHaHY5IooJN9/zwm/4DOsaKk04podpd5Z3Np8cDMKz8+M68+Hc1mPBuOWGlktOKYSCiMcxRGk1eaUCniIKAy3s04i0DJWhveWqbav18rjheJhrGWvNLsjcf8dnsH8OOSkarfWxumVUkgJY2wNris+RpKeP7I2cUiPxpWmOsanQpJVJNIxKcnGqMZT54d8fH7G/x3f/3BYm3WOY4HU/7df/qUJ8+OmM6u1oG8Klw9OlXZHCkcq0mLXpRh3AmacX27mku5Kuk90QHWkzYrcbMO2p5/z6SQi98Hb17XjTL+auUuf758Z3HOTlBzFgpO85/14gbtMMXgnhsKnvh9Jfz7vyo4no8E5te070G976FMoD6+UEg26nGyedcGfKc9uEKnupN6KevNTov31pY5msw4msxoJRG9LGWpkdLLsldWtrW2PNsf8O3jA0aTfPHzLAm5c2OZe7dW6TSTayE/zyGloNNMWFtukZ4ixW2tXfgsXJW8/DIc/h50XEz4bLDNcTnlz5claRCSG+07A7VamXWWshYtkHUSa5wjNxWRVPXevRx4+8+YmYpJVZAFEYkKKa1BO0ODGKl84qGdfWEk1tTCAQJBI4guPVL3Ohjn0NZQ1mpc82uwNAYpBM0oQgLaWkpjKI0hUupUh3ljrefdnUhahBDeaNYaYuXPU641xjoaUYR1jmfjEeOy4F0hiGrZfsXpRaDLwXnLBzug1I+o9JPaUiJDxhGB6nCZmP5COzLXga/smEn1mMoOa2WLBpHqUJo+udkjUh2ey8n+C/4FF4dvZ0pc8FKAr0CdkO+TtU+GJ++xILVeBdoWjPQhY31cj0xdLcmYB0/ekO8OadB+7RrnwUikInpRj8hETGsVqsL4LsdxeVxXUsK6uh0Sq5hUpUzMBO00EzMBAZ3IfydDERLLmEhGGGdoBA0ylRFcwK/DOEPl8toN+vuHELDRbfHRjVWOJzP+90+/5b2NFW6vdL0b/WIm+vRznkUhvWZa+71cLxyW0s4orTf7nOuwj4qCJ4Mh48JfX40oYilLF34Jpk48Dqczng2HDPOCSVmy1myy1W6xN56wN5nw3vIS73S7HE1nHM9mddVQeGWe+bwt/uE373QoKX2VTUovsmA0R9MZAphGFcY6ZlVFL0vZbLVODbicc1jMIpiu3NXI4ALp5/RF7Een5OkjhXMnYKXkd4KtJAqQYi4NeemlXAssltJOKewE6zwP41XFDx/wzzuFGpAEUuGcxQqDFCGSAO/3ZDDOdzGVSJAiJBC2TgoVXglx3oGdX/XPfZhet5ZXYd7N1K7e9yuSwef7Hsi4Hp2q3ekRdRKVEr4UOEoEaXB5nuKkLL2crRC04pgkDFlqZMSBIov8nHqoTueYzsdvtPEGeoPRjLJ6ft0HgaLbSr23TS3qcL141fsJ3y2tVa2uE75z6YP8oObIHNS+JpFUdWIQUeqSZ7M+R8WUiS5QQpAFMcZZJrpgLW5xt7VKqqJ69Mov9LicsJ+PUbUc+SjPKa2pHbolK3GTVhhzXE6Z6rJOnAWF1YvObiOIr2Tk+CpY55hWJYM8Z2cyZlyUJGFIWCcbzShis9nC4tgejZhWvsvXTVK2pCQNghcKNkf5jO3RaFFgmpcH5s/9eaEnUookCIkDL8pRGs2wKHg6HFIYzWrWpBldJ2dX4L9hEudKb1LtvFdOaZ4hRIiSy7Uw0vlxwSerq1vCIybVY2Z6B+MKf6OTGdQPn4a7BdekOvUv+BecG9d0uVWuYFTtM9ZHVx4L8K3jiES16MWbLMe3XlCXehUcjkQlrMQrZCYjNzmRjMhtzkiPOK6OiWREFmRIIWkEDVpBi1bQYqiHWGcXCUdhCgSCSEakKiVRvtrfCls0g4vdNIzTlHZ2LaMy1wEhBJu9Fp/cXON//+w+v3+0w9PDIfc2lllqZrSzmOAVPKPlVuYN6t5EouF8opHbMYbnAcm4LPnm8IiDyYRIKbbabZaydNFmn1/K+5MJv3m2zcFkyiAv+GR9lTQM+OrwkM/3DsjCkFudDgeTCQ+O+ygpycKQbpJAOl+DW7TJW3FMFCiS2t13PlZ1NPVJSjOqGJUF++MJd5eWWG+efW0YWy2CzcrmZ/7eeeCN+iIfcIoEeTX64PcO6wyFndbclfMkYLWDvCvQLvdJhYiwaKwrCWSGFEHtXVFQ2THG5cSqS0CGrQ0ThcjqIMF3HAXzWX1fhRE8Fxy5LIzTVHZGafMr+wrNfVNCEROIeLHvPtBSdKKUNt/t/l7lCAaznK/3Dv0I1doK3Sypg/PzDds459DaMp2VjKYvHr9Xm4ppN5NLm7ud/cE+ibbGncqPE8J/fnBGknRVGGcxWBpBRGE1+8WYQTVjNW6xlghMaBnrgi8HezyYHDKqcpQQtMIUXXu4fNhZZy1tE0n1ghntQT7hs8EOK3GDlbjJ9mzAfjEiEOp5UgJsTwccFhMaQYQQgkE1wzlYTZqLsa03AZ9oVOxOJvxxb4+d8ZhWHNMIQ6IgYL3RoBXHlNrw5eEhgyInUopb7Q69JCGsizvzXTmYTvnD3u5CcXCOsO5kGOs9l1azBmuNJu04JpTSO4qXJbOqYlJVNMOY1rUmGiAWPGO78JVzzlHpp4AlCWMUbzTR8FXBSHVpR++TBGtYV6FE7BMNBM5ZsnCrXuz5L3bnDNZNMHa4kIE1dlBnVCUOXd84JYIQKTOkaBKoJQK5XEvbLtWEbL/W18G6glI/odJPKPVjtD0CQIoGYXCDKLhJHNxGyfbFTtOpx1cxLf9AXn2BtSOs88pOUjZJw4+Jg3e9apa8mByrdTOsnaLtAZXZw5i+P2+UOFdCrQ4kCJEiQoomSrbr8+XP2/Ps9Hz7pc0Rldmh0A8psJ9iLQABAABJREFU9QOcqwBBFv+cZvyXzB3UrZti3YTK7FDpPYwdYt3I7+c8SBUCQYSUKUq0/X6qdQK5hJRN5Dnn7K/zxmpcxdSMyM34GnwzFM1giaXoBqlsfUdd6uzXeYWpmZnxdPaUmZlxVB6xEq+QqpRu2GU9WacwBaUt/WgBEu00pX1uolWaEmMN2ukXzpHDUdiCvXwPgaAbdhfJx+tgXEVhJmj7/Y6nzOGc49FBn7//+jHbx0MqbdjuDym0JosjL1EozlZheW9jmY1u642MWlkMuZ0w0yOMfZ6YzSUsi5q0N60qJmXFuCg5nubEyhv2KSFIgpBemtKKY5SQPBuOGBfFomcshB+FakYRgzwnryryzvN7lqzb7GkQkNXVWiXkYqRqVJQcz2b1rDFUxvo6uPDBhUOdemco7YyJPr5ykgEQyHpkSqY1wfT0vWo1YzbWOuzsDfn//R+fkWURURQwnZYMxzl5UbG+2iJNv19+jnWGwkyYmdG5Eo3CHDPRzyjNkMpN/DNVZPU+CLJgAxlEzDsTlR2Rm0MKc1yrEDqkCEnVCkrE5OYYY2d1ETAllC2Ca/BecjhKO2WiB2h7dfNSJUJS1SSUKeJExbc0msJqnkz6PJ0O/MhMnSxHSvFhe43bzaVLfWapDYNZztP+kC92DmilMb0sZbXZYK3dIIsi0tcVHc543Mz5VFd1BD8N1jr6oyk7B0Om+Yv3Xs+n8HLUURRcmBNyHgRSkqiQNIhITUVhDZEM6EQp3bhBJAMkJbFSdMLEJwN4JTmHoxnGLMdNYhmcOq4rgMJqhlVedyqgcgZBhal5EBZHYb0S1zypaIUJ3Sjz6mniuVDKdcI4x/Fsxu5kzEzrBceiNN6LptBmMb7tx6v0wpcGeKF4BL7DPNP+vhDNk1whqIxZdKUTqejEMStZRhoE6Dr5mO+t53H6TvXL73/Fo60FlCRSRDhXIUREqDa8h5s4WyThLFyibCSJVI9u/DG21ghXIiGQTQS+ZSzq1stF4NAYO6TUj5mWvyevPvczYma3DsxzvFN2iBQZgVohDDZIgvdIw48W8rbiAm7KzpUU1TeM879lXPwX8uoLAAK1TjP+K5rxvyaQnWtKNDTT8lccT/7fVPoZ2h4AEKpNlhr/KzKNESJEnlK9eRWszdH2kFn5GdPy95T6AaV+iLGjOqj3rXYpGkjRJAo2iYKbJOFHpOFHCBFduA2m7RGz8g+M8v/McPYfsG6GQLDa+n/RiH6x8FCxboI2u0yL3zIrf0ehH1HpZ1g3T7RqDxPZrGWBb5CEH5BFP4PwPUIRw/dA6NXOE8GvI9GQQi4SjVid/zzPuRMTPeGxeczMzhhVI9phm1SliMjfVvaKPcbFeCF5q62msAXGmUUy4Y/pxWDHOUducnbyHSyWRCUXSjQ8+ffqY2XXAecc9/eO+ZsvHi5+tjecsDecnOv1eVXxl+/feiNrs85SmDEzM3yxA1STCudzurOqYlQUDPOCo9mUZuxJloGUNOOIRhSSBAHjsqwTjXJB3AbIoohOktSdj5xCP99vUbfg0zAkC0OvsCIEpv7cYV7Qn/lkIVYBjudkxDn5UZ0SuPhEo091xao2zMcLW0Qye2Wg0G6mbK532N7t84fPn7Ky1KTdStk/HDGZlmyutXnn1vJCOvL7gnWG3IzrROP1nb/cHHGU/5GZ2aeyI5RIiWQbJROCejwqVks8TzTGTKudmiflk4xQtpAEBDKrk5Y+SkTEqkczDFEk1xCMOAo7vbZ9991e7yN0MmYorGZUFXza3+G/7j+gqBMPgFYQE98OLp1oVMYwzAs+fbbHt/tHdLOEW0tdfrS1zk/FOistXp1oiLrkesrJdM77Wxhz/eN7xln6oxnbB4MXOEgn5VrjOCSOgmsPtb3ZY0CqovpPBVTe9yXOWIo8cV4JSaoi71gvAyyO0ui62BGwlrRIVLjgGM0hhecGVdYwrGZU1iCBylmc1fUIlT+qyhoG5QztDL0oI1UhS3GDXvTmxHOMtRznOXuTCaXRNT/DoZ3Fai/C4ZVHxYL4Ph/zPMknmcNaS6k1UkrCmsMhhfAjrsaQhn5cqpukrGb+uMZl+QJXSVB3maxFqOuaIKo7q1R1HB8zt4Twxfd3L/U5F0o05ifLWU1lpxg3wzlNKNsEMqMwfSb6GbFaIgs2qUXaTn1wzGfRrMspq2/Jq28p9NeU+iGV2f1uR2OhcmOweI1h6yYYc0yhvyWqfk8U3PFJR/gxUqYIoldWugUhYeAdxGfl7xY/t3ZGoR8Sqi0adm7q519xUfiZzhxtj6nMHpXZwbhJfVZilOwQqg1CtYk8B0HY1bSpSu94p/HqG/Lqayqzgza7aOs7Gv6clYsHkWWKc5pSa4wdUOkd8vIzovAOcXCPNPqYsPboOJ9aF8wpXNQ6ENbN0PYY66YYO2RW/oFZ9SmV3qYyu34/7bjutlT1GdX+C+k0zs3Q5ohCPyAO7pJGPyIJ79VO6G9HgQu86lRuJl668YqEZ4EkVW1a4QrRGUo6p6EVtrjbuLvoVsw7FUvREpGMkEhEJEhUwnK0XGvLKzKVkaqUSEZ0wg6V9Q6kS9Hzh3KiEu407rAarxLK0KtSXaDaWdqcSXVMaaYXPh9vAlJI/uLezUu7e2/2Wqy0L16lOQ8sPuCcmD7alYv7nqoTiC3RYrXRYLmR0UtTltKUZuyThiQIWc4yPlxdwTmHkpKqJhkW2qCt4Wa3g6iJiKLVJAoUhdasNJ5/X+azvu0kXjzQ4jDwYw1JzO1uhywKEbDgdVTW0k0SolMUUzwcuRkxqPYoruE6CEVMM+iRquYrxQk21zsI4WVu9w/Hi0pgq5kQBpL1tTYbq226ncsbdV4HLIaZGTE1gxf2/azv/3z8OGGZWHURKKQI6iJeihRhXVkMUSJEEiJFsHguWqfrMQfnkwvZAWep7Bhtp/VnX31syhN2RwyrvWv5/ocyphUukarmC+emNIZRVdCJUj7pbjCsckaVP75URWSBJ8eeFsS9DivNBr+4fYNbvS7HkymlMZ6XVFb8/YMnREoRBQEfb67x8ebqd17vA3tBGoc00mhhYAeQlxVP9/ps7LZZXWrQvgaJZesc01nJUX/CF/d3+d0XzzjsPy+iRKFibanF1mqHLA7fyMC6wHMxCCLW0zbtMFnwNZpBstiDLIh4p7lMYarF99jU5G0lJK0weUGYYI7luMmHnY1FIK2tWVTrBV7WuRFE3Mp6dMK09o9xJEFIK0gWo1RvCqFS3O50aEQRpdFMq2rRBV7NGqw3m7TjhFFZ0IpiYhWwmjXYaDbJovA799DNVou/vHkLAV58A39dGef9U0LlVUB7afrc5yRQ3Gy36dSyUkkQ0Inrsb9r23U/XikJ8XIlBQ6NdTmVfopAEag1lGhd6F0vpzrlNMZNqewY6wqEUDi3RGGOGJSf0QrfJQ3WeKWfhvNdDGtHTMs/MM7/hmn5G0r9gBcD+xdJ5Q6LcznW5WAPKHkAgJLLhMEmnfT/5k3wWEHJqNYiPn0NQoREagsbjlCyi9cJtjg3o9KPKYMtjJvULdDXOwyffb5mGHuENntos1f/VCFlhpI9wmCDMFg/+1ydhLM4NKV5zDj/GybFr5gWv8GR87K5ygsvcxWOEmtGVOZZffwxYblJGv0IIRRKthG0WSSIFzpgh3H1CJfeptCPGOX/iXH+tzxPSE5ZFxZXj1j5Ls+3UEIU3MHYA3AVKukhnFcjeRvma3PVqdLOapLm5SGEJFUtWsEy4QUkjptBk2bQrOXmnpfG5sc/52YsuaXnQcz83DhoBS0sdmHgdxKJSrid3a4Jvf74zsMbmY8CVHbGWB9R2B9IoiEFv7h7g1/cvfF9L+U7cHVle1qPmjjnEHgX8FYc00kSPlhZppMmi6Dpdq+7eH0SBvSy9EWzPahHDp9j3vWYa/+f/DcpBFEQEAUBrfhF7cNQKdpxzK3uxdyEHTCzY4bV3rVcB4GMaQZLpKr1yq70+mqbtZUWu/tDdvdH9IdTJtOSXidjuddgbaVFt/P2ihJnwTpDbuedrHKx72fd4ucdiUBmKBHViUPlOxrzn6EJCBEiQIoAL8suAI1zuu6+CqSIiFQb6ypK00e7vP6Mq3MGnKsTDb1PYWdXfr9QJjSDJRLVfEHqu7JeCa0TpjS7MQf5mMNiinVuQT6e6/Vc9Imw3MxYbmYLOc+n/SH3D474fGefT7f3yCuDNr6b+HKiMedxKOnFCFpZvKhcAxSF5unugLXlYz54Z42Vrq2T4eevfxVOjlvNvRSM9T4Zz/YHfHF/lz9+vf0CAT0KFesrLbbWO2TJmwm4hRCEtXTyq2SPsyDidrD03fsVL0V1L61xKW6wFDdqFSb/XRGnvCYLYrZeeu+3EROESnGr0+VWpwt4MY8vDw/IteaD5WVWsgYCnyC344Q4UHywtEIrjk9d40azxUbzxWD95XP28msiFXCjffXpmtfDczT8+Z97y2kquwtGIGUDxVtINISYz25ZCnMMSGLVQ7txTVKbt/XOvgAcpa94l79nUvyKWfVHjDlaLEvKjFCu1LyFDIFXIXFUWJtjbB9tDrBu6n/mpmi9w6T4e6yd0Uz+kkb81/X86lmHKZAiRcmuD/bVBtoee3kvN6m5CNtoewMlu3Ub6aJwVGaXvPwcvTi+eZJzgyR8HyXOLxlWmWfMyj8yKX/NtPgVpX6Mo2Ju/qfkEqFcqTks2eJ9rZv5c2QO0OYYh+8SGXtMXn3FYPrv0OaAZvzXROEd/xC74C280k+Z5H9HoR9QVN9S6EeAQ8kWSjRRslc7rHtzQkeJtRMqc4CxfZ881oRZY4+ZFL9BEKHUMkloUXIJwZsfiXDOYlyFqUcDrwKBIFQJqWoSyMtV3F/3KD3t32UtCXmZ154FrzZTUtgJuR1f2cTwnwP8HK2msjkzM2RmhsQyIwtDbtfdiDQ836jDdf3O9cAHnP1qh8Keb0TtVQhlTCtYIg1a55JbDpQiiUPSJMI5SJKQKFJv3STsLDgsxlaUdsZMD8nNiEhmtUHndxHJDp3o3oKH6OpOscAnFYFMUSLBuBxtjjEuR8mIWC0RyeaC+J2oFQRqMdYcB0sEsoG8JpNXh2VmhgzKXcprSDAjmdAKlklUayHtC9AIY9az1mJkqjLGS6ZqzwcU57q7XQTPizTUwe6rJp+CQLGx0uKdrSW+fXrIrPAd+ryseLY/IP46oJFEHA+m3Fjv0mklxFFw6gjiy3DAdFYynhYcHE/YPxrx4Nkh958e8fXjg3oqoVYnkoJWI+H926t89O76tXRQrgsvH+n5718nvHvO+d5vG7FS3Gi3MdbSCKPFehpRyJ1uFyV8R+Ki6/y+jwvwhQwyovAeSi35jBeBEAlK+hH8i+JyiQYKVd+4KjtGElCZIdpOsbZcOFmf2nB3DjBYO2ZW/p7h7N+TV1/VVXbJ3OE7UKvEwV2i4AZK9pDCz3D67sCAUj+hECHaHGDs0BuMuBnT4jcU1X2EiEjCT0C2X1A3eOE4hKxPXodQbRIGm9iqqEfCfDJT6mdEwZ4P2i91s3Zos8us+nxBNvfnJliMbUn5+kTDOYvDUOonjPL/zLT4Dbn+qiZ8S6RIkbJBHLxDHN4jkEuLLo0AjB2g7RFF9Q0ga2K2J99bO0WbI4ztE6oNArWKlI0LB/WV2WZc/D1F9Q2F/qZ+SKYEctVzQ9QtgmATKRJA1SNWh+TVN5T6CcYc1GNlFmMHGDvw1bnwDkq2kKLFuY0Sr4B5kODlW682aCsQRCIhVs0z3Y5f+frXPJhO/XdxvgTCV+fOf2vTriI3Ez9WZqbXYtR1nThZDZy3oE+rEklRJ2LnrDJeFV4GNmdqBszMgECEpGGDG53zXw/nWePbqOzBvPJmmekhg2qXwlxHopHQDJdJVfuV3TVtLEb7ijO1sVUQ+L001qErQ7kwLPt+k46T+z41Q5QICTgj0VAtItVajMZ6vPj9dM5RmiGF6WOdIRAZjWCTLNx84TXGznziISSxXCaSrdrT6vKYf7ecs0z10I/MXUOiEcqEVrjsO1knEsxMhURSMa4KRjonDUIy7XlLnhR7+aDMWFv7GPhORF5p/54I4sAX2AJrCV+RtIZKsrHS5t2byxwOJhwc+8mHsjLsHY6oKoNzMJmVWGsxtksziwnnRnIvL75WZ593WQ77/j2/fXzA14/3+eL+LvefHtbcj+dJRhKHLHUy3ru9yvu312g2riehvCouey86j/LX27rPvfCZL/09DgI2m9+t6mdhRBZe7rv2fRzX6VAIkRCqLUJZT9oIgSA4IbZ0MVwq0fBt3qbvYtgVjKsYVQ9wWNrRPdJg/RXtb0upn1Lor5mVf6SovsHYAYIApXpE6iZp/GckwfsEahUl20iR1FVw4WdSXYGxQ7Q9JC8/Y1L+2ncezMHCbCSvvmA0+49k8U9Jox+/5njiWmHqvZobcuhX6iaU+j6l3iJU6xduF3k4KrNHXn1+omNTdzQC39EIzkE2nydX0/J3zMrPqcwOzulaeatHGv6INP4zQrVOKFcRIkHW5kf+JlZg7QwdH1KZbSbFr8mrz9Dm8IQ61FMmxX9DiIA0+ilSfXc+9VXQ5rAO0geAIok+Jgk/JgnuEoXvoESrTmDqvaTCuhmN6JhCP2Ra/oq8+gpt9haqXNoeMyt/TyC7ROom8GZm6U/Ch1GmTpivzuiTQqFEyEUFEn5oyM2Yo/IpE33MD4EEfhocnuy5P5hwOJ4yyUuKWtNcSUkjjmhnCWudJs0keiPqLKfBOM24OmJQ7ZPIFrF689fxm4JxvlI/NQNG1RHlNahOeY5GPTr1iofZ9k6fx8+O2d0fcnA4Jgi8j47W3jRrfa3N5lqHWzeWWFm6eOXtuqFtyag6ZFTtk6oWMecZ6zr7mgxVEylUneRbosWz4/lrpAhJ1DKhbNc8j+jc6n2vgqm7mVPTZ6yPrkVtLBLzjkbzhftj5bzh24PxId+OD3Gudri3nlg8LyBc5tt7OJly/+CYncGIvdEEYy1SSpIw4M/v3CQNA9Io5O7K2WTzMFTc3lyiKDU7B0MOjidM85Ky8kXWaV7yaPuI6azgwdMjeu2UZiP2MtpR4E3cpPDHZT2ZWGtLXlbkRcVkVjKZlfRHM/qjGceDyXeSjHYj4aO76/zkgy1ub/ZoZN5s81/wLzgL3x1e/y7mkyalvk9ltgGJlC2S4H3CYONSn3uFjkZGpDpou0xu9sn1PpFaohG+U7dvX+RnLCoiGEr9uA50v6A0T4BaUlZtkcZ/Rif9n8iin9YJxuk3SE+Cq5gEN3AYpqXDmIEnr7iKovqaMQFKtknCj/Gtn9MDPSEiouAWcXiPvPps8XNrpxT6AZG+SRr9eHEM5808nTM4V1GZPYrqa7Qd1P/iOxCR2iIO3kXKsxOY+Wca22dWfcqs/AOF/gZjjwFBoLrE4T2ayb+hk/1PNc8iOXWNc3t5bQ8QIqnHpyqsGXteitllWv7Gry24RSCXuQgvwrgBRg8QIkbJJmn4I9rp/0gSfkAc3nnF8VlK/bDm+lRYO1kkGsb0PWld3aAR/9WF9+BycFhnrkwEB3wlQHjN8NOuv7MkEH841Y3nyO2Yo/IJY3185ZGy68Q86LDWMStLhrOC+3tHPDroM5jmTHKvjhUqRaeRsNZpoa1lvdMkiyLCQL7G/frq0K5ipA8ZVnssRVtv7HPeBiqbM9NDpnrA1PSv+G5emDmSKU3VI5HNV3Y0dvaH/PbTxxweTRgMZ3TaKY0soj+YMctL9g9HDIYz2q3kh5FouJKR3mdY7bMUvZ5D9Kp6rp+TbxDKVyepQoTEqnfhtb4OpS2YLvZ98PoXvBJejjuSGY1giUS+SAavrGWqK7ZnQ77o79IME5phjHOOWAULo7PLYDDL+XL3gK/2Dnh8NKCdxGx123y4sconm2usNDN6WfrK+0EYKLZWO0gh+ObxATsHI/aORgzHOaZ26N45GLJzMAS2iUNFI4tppDGNNCIMFUoKrANrLEXlHb3H04LxtPDKVfa7x+g7eH5scGOlzZ99eINffnKLG+vdM5XW5k7VlTULiWAlJLFS3ghu/nu1hLCuOz6WE7yBmusqmHucyIVDeyDFucYdz4P581Bbbw5oaqPRufrdy2sRwivoKSkJ6jUtVJmucD8/+VyenxPtnq9lwZ10nHJe/HpOkt6v49ni6s82tWzuyXNzsms/F/xQwitaKSkXI3uv7xc5H4O5CYV+QF59ijcPXSaQK4Ssn/ik8+OSrki+vStQBLJJgiCUbZSIUSJ5RUXKeUlZ/S3T4h+ozA71FDuBWqEZ/zXN5N8QB+8gRAyvlKr1fhpRcId2+n8B8OM3VgMGbQ+YVZ+Smj/DuhFCJAhOn1/0GsGbxMEdlOwAIaCxbkapH1Pqx1g7AWXhAlVpY0c1CXwfbfuLMSclu4Rqs+7YdGoeyVnw51qbA6blb8irL2sug0KKiCT4gE76P5NEHyNFs+aynA2fJLZoRD/3o2h2QmX28eNsM0r9kEKtos0Bodqqx5wudplEwS3S8EOy+Ock4UcEqvuaVwiU7JFFv/SJmd5G20P8EFPufVXsYa2qYnj1dXE9uN4JYLdQ/zrjnz0B7geYXJzEVA/Yzb9lpPcXPh0/FORlxSgv+M39Z/zh8S5H4xmjWUFwQgLWOcf9/WNCtcev7z/jzlqPn9/Z4uayV2sJ32A1ULuSQbnLcfiMrfTDN/Y5bwMT3We/eMBU96/8XmFtZtkIekQqQ8mIVz3ERuOc7d0h791Z5c5fr5DEAWGgyIuK4Tjnm/t77O4Pmc5+GGN9lS3olzu0gzVuZB9/38u5AhwTfeT3/cpJhu9g+X3vEsvsO07w2hpmuiJVIVuNrl+B83KipTWL4Ooyd8yg7mx+srHGL2/foJXEtJOYXiNjqZGShufr/igl6LRS/uLH79BuJvzhq22+eXzA/vH4BflZ8CN/05nveIynBbKWj547jRtrMcZS1WOBpxWgpBQsdRqsLbe4d2uFe7dX+eCdVTZXO6SvUNzbn074/Gifb/vHPB4OCJVkOc34yco6v9zYIlIBzjm2xyOejIbsTEbsTMZMq5JpVfnEw/lCTaIC2nHMUpJyo9lms9liLfOGcteB+fjY9mTE0/GQ3cmE/emEUVkwqUoqa7DWEUhJrLx3UDdJWc+abDZb3Gy1aUfxtXSq593xp+Mh2+MRO9MxB9Mp47JgqitK4yXkA6EWPkZLScp6o8lWs8WNZodG+F3FqctCO8uwKNifTng07LM9GXM8mzGuSspaXjdUikYY0YljNhotbrU6rDUaLKeZHxuWZ/tJeYjFyHsU3EYg0HbgyeHnViP9Li6daNhaVk8IRSAzArJaZq/AnkESda7E2hGlfsis+rSuWkukbBCqDbLoz2jEf4EU8WsPyleHJZHaJJBLlPopSv6XWpFqWs/4D+uRqmOU6tVB8ynvRUCgVomCmwRyGSWyepwoR5sdKvOslozNETW/4Dzw406P0HYf6yb1Z4UEcsl/llo5h4eFd4ut7B559RmlfohzBVIkKNEmDt+jmfxbArXCa+V8hQAUkgZJ7aExLX7DTHyOc1McBZXZ8eaFZq9O0MJ61On8iNQtGvFfkUY/Ig5vv/J3/ZoESrZIo4+xdsRI/R8IHeKocK5Eu6M6Uctx6O90y64f4sSfK8I5nLNYXnx4zP+/NRZdGS9fGiikrOchxYnE4xXqym8jObHOYJ1hoo/ZLx4xqo6uzF25bgxnBU+PBvz24TZ/99XjhdJSt5HQqCt9pbYMpgWTouSb3UP2hmPaSUwaBYSq9UYTDeNKhnqffrlDbsYYp71E8TVVAt8G5tfsWB+xm3/LxBxf+T0DGdMKlmkGPSKZnsmnm2M2K+kPpmystfm3f/ne4ivhgOP+hO3dAXuPDl9Q5fk+oV3JsNpnUO2S1y7hf4r77nCM9CF7+X2m+hoSDZnQDldoBD1CmXxn3+ei6WkQsRI3mOiSiS4XFeyr3PfiIGApS2mnMXdXlmhE0Qtu2rY23JNCIM/g+cxllZtZxCf3NljuNnxRQwrCQHHYn6CNWXQmrPVJRFn54PRkHiHq/8y9OaQQSCWRUqLq6rgKJEkUcHO9x7s3l/jZR7f48N01Os2URvpqTkC/yPnjwR5/v/2EX+9uE9eO1UoIfry6TmUto7Lgq+NDfr+/yzf9I+73jxgUOcOioKg7IYnyhp+rWYOtZosPl1b5cGml7pK0SYPg0vdQ37GwjMuKUVnwxdEBnx/u8+3gmIfDPkezGf18RmE8nyZSXnlsKfWB/budHh/0/FpuNFs0o5i4lua+mHG0F3U11jGpSvp5zueH+3xxdMC3g2MejwYczaYMioJCe1O+qA7ul9KUrWbLr2VpBWMd640mrSheeGVc5rq1znm557Lg8XDAt4Mj/rC/y9fHh2yPxxzlM2baF1biIKAbJ6xlDe51l/hkZY33e8sIBIXRKCFroZizIYRCkBIFNzz31+zgqK4kKHGpRMOLYlZUZshUP8PU8nmVHVGaYzrxR8Rq6aWZdIe2BxT6AZXZ82pRTr+gvhSolbq6f5GbsEKKmEAtEQe361GgHOrRDu+z8YAYSSC7Z7yHqCv9DaLgJlFwm9I8qgnTFdoMKfUjn4yoLYQ4n3yiNvvMyk/R5uDERymiYIsk/LAma78a1k0p9TNK/RhjBs+10mWXJHyPKLiFFNkFkwGBEGHtgL5BFGxSme2aW+F8J8dsE+ldZNi4AAleIlBEwU2y6OeE6iLzfBIhYqRs1UT2jk/u8Emrc6U3IbR5Xf16c/Ba3kEtB3n1QN44jbYlTp7oAtRVrHxScLg7xBhLZ6lBnEYIASpQRJHfU1NLJ0op5i/1Ny35djog3qDrmH61y0Qf1WozP5xEwzrHt7uH/LdvnzIrNe9vrvDexjK3V7rEYUAUKH++naOoNAejCV9vHzIuSj5/tod1jkYckcVvTmjAG/dNGOtjRtUBE31MqtrelPJPCA7HUO/zbPYFY330+he8BpHM6EabtMKV1yYZAFEUeFLtKYZqQgiSKPxBzap74775vu/TCVdJVZvgLYhaXCecswyrPbbzr2qO1tUQq4xetEkzWDpVdjcLQqRoMtHegyQ3FRNdsBI3WUtatML40oy3Tprw/vqKN7GMQtRL99FcV0yKiiwKabzmniCEH2Va6mT8/ONbbK112T0YsnM4Yu9wxNFgwmCcM5kWTGoOh9bmpWSmTiaUd/eOo4AsjWikEe1GQqeVstJtsNJrstxtsNxtsNJt0GmmROHFr/PCGPr5jEFRMNMV9/vH/Nftx9zvH/Nw0GdQ+gSjrD17jPOmogUa4yyVtQyKnO3xmC+ODvh4eZUfrazx09WNUwnS50GuKyZVxX/becqvdp7xdDzk2XjIsCgYlQWFMRRGe8f1+hh8AdYwLAt2J2O+Pj7kt3vbfLy8xl9s3uBWq0MjCgkuSGCeVZpBmfPftp/ym71tno6GbE9GjMqCcVmSa01Zjy45wBk/zlQazbAoeDYe8eXxIb/e3eYnK+v8xeYN1htNGmF0LtWxl1Eaw/3BMV8eHfCbvW2+PDrgcDalX+RMq2qRfAFY7RO2ma44znO+6R/xXm+ZH62sAd4UMXytSIaq4/AOoUrqKR+Lkpcfx7x8L8Q5LBrjijrRcOTmgEn1iFit1CpJL87Ta3NEUd1H28N6jMhX+EO1QRzc9RKyFySt+TEt7wERBptU5qAmsIBPbgZU+jGBWnnFe9SVfpERqVvE4R20PcQwAAzGDWsDv00CuYJ8DaHv+Zzh3oJwvfgsQkK15WVt5eu1662dUZqntaP2mLn8q5Jt4uAuodqoO0Dn/zL54w2QMvUO62oNbY8BX6nynZw9tNknDG6ee1BJECJlQqg2SML3L7SXQkgEUe0t0kHKJsaN63h2Pjc4xbkcTwh/c4GEQKBEgCK48viUw6FdRWVzzAmX8XmSMTgcs//sGGsdSkmvmlNo4jSku9zEOZhN6uQyUKhAEiiFCK7TpOeMtdfX8cyM2C8e0i+3FwZkPyQ4B08OB/zm/jNur3T56MYaf37vBh/fWHuhiuTq2dq94YRmHPHpkz0eHHiu00/fuRzJ7dxrxFK5gqkZ0K926FabhCImlH86iYZ2JZUtGFb7HJSPKM3VfRRimdKLNmkFK+ciLAdKEscBs1nJzt5gQao1xjIYzigrjRCCybTg4Gi8eE2WRUSvcnt+Q/D7njM1ffrVDu1qlVAmBG9Bpvu6oF1JYaYMqj0Or23fG/SiLVrhyqmJRqJCEhXSDmMaQUxufJC7kjTYyto0g/jSXY1GHL0ygRjOCrYHIzY6rfMlGspLzLYaCbc2egzGM/YORzzaPmbnYMhhf0J/NGM4ycmLiqr26TDGLpKMIJAESpIkEVkS0moktBuJTyp6DW6sddlYaZOl0SvHpM6D0mgGRcHedMzj4YDf7G3z7x98w/50Qj/PvWGhUqRBQCOMsPj7ZmUtVV1ZP85nPBkNeTA45mA6YVpVLKcZnTgmUkHtnP16zDsZ+7MpT0YD/u7ZE/79w68Z1AlGpBSRVIRSkkRJrTYmsPguUWktx7MZu5Mx9/vHfBEfsDedeGlZAbdaXRqhOFc3wbtsO/amY+4Pjvnbp4/4P548ZFDkTKuSSHkfkVAp0iB8YS2m5sEczCZsj0fc7x/TimL6+YwsDLE4brW6JCe4Ja+DqzsZ/XzGF4f7/N32E/5h+wnf9I9w+KQhVgFJED7vmADWeV7JzmTEw2Gf/dmE43zGStagG3ujxbMwnzABiRIhjgzFyQTjct+5y5PBZULCKoFImZt6KJFSmRFKxCdm0p83t43tU+knWDt6/l5CoVSPUK2dyxn77DXFXqFqrrRUw7oplTkkPoe5kBAJUXCb2Nwlr76gFpDA2jGFvu9VotwH51hN7d5tDsirL0/I2nr53jDYJA7fO1+i4XK03kXbg4WbNoAUGaHa8F2RS7fhJVJkSNl6gdvhnEbbgSevu/OPICjZ9K6Rsou4cGfKQyCRIvFCAMhF3dwb+5U43vxIhBSKUMYEMrqyUpTDUdmCwk5ekIMt84qdJ0ccbPcZD2aEUcBsUjAZznj26IB2r8FHP3sHoy3bjw7Q2hJGik6vQW+lhVRvScoUy7Da5+Hkd+wXD7EXuB7eJobTgr3BmJ++s8lP39lgtd04dRZVCEEziXhvc4VJWfHFs32OxlMq/XY4J6WZsZc/IFVt74TN2zBguh5M9DHH5TP65Q6FmdQGcVeBIJYNlqIt2uEqwTk6GtaB1pbff/6Ubx8f0Egj4ihkMi0YjXOO+1OMsfz2j4958sxX3pd6DX780RYbaxczJbxOFGbCbv4NiWrRCldI1PdPVD8vxvqQw+Ipg3KXwkyvZd8T2WApuukTzFeEIStxk1gGFNZXkVMV+j/Bm+tqbw9G/P39x/zynRtsdi5oTKYkrUZCoBSdZsq92ysUhaasNKU2GOMDZE/sxQer4nkgHKh5Z0MRBoo4CkhqB/I0DgmCqxfYKmuZVCWfH+7zv33zBQ+HfbbHI6QQrKQZN9ttbrQ6NMKILAgpjefLHM6m7E8nPBkPOZx5aePCaB6NBiRBwM1Wm0QF3G53aMfn8/PwLtuaX+9u8x8efsO3/SOO8xmlsSghWMuabDaarGVNltOUSCmkkJRGMyrLBZ9kdzJmqitGZcnX/UPcfcdxPuN/vHOPm60OcaD4rh/5iyi0YVKV/P32E/7T4/vc7x/Tz2dU1hAqxWazxUajxVrWoBv7tQjhOw6DImdnPGZnMmJ3Ol6MOn1+dEBpDUf5jP/rnYiVLCNW5/PX0M6yOx3z9fERf7f9hL/bfsLB1I/gSyFohBG32x02my1W0ows9KPzhdaMy5LtyYiHgz7TquKPB3t04oRekrI9Hl5QTGEuuX35WOgKJR4/XqJkvHBPDmSDSHVQMuW0zMfYEZXZ8cTqGr7KWKDtgFI/WHAZLorSPMbYSR2MPz+J1uX1fH/x2vfwMrdbVOYdpJg7ZFsvc1s9pAzuYN2slj2VZ2alzpV1V2CfUj+pydui9pToeglatfkaEvj8vYrnJnsngjxvCz9Fmx3y8mx1rlfB2MFCEhiePzwcGmPHWDe5UGDvuTbrKNm6AnHIJ2M+8Tl5YXtHdIefcX2zDA1FJFNCGftZ6itNCTlKO2Wqh7TD550Aoy3DowlHu0PK0ncwxoMp+bRk++EhZaGpSo01lskor7sa/gbTWW7yZjkqHpUrKGpJ2538KwbV7jUEGW8CjrzSjGYFWRyy1WvTTL5b8Vy4qgcBq+0GS80UbS3TosLYt5NoVG7GYfmYLGizntyjEfRQIrwW1+Y3hXl3eljt83T2OYNq98pdLYkiEBFZ0KEdrtMIushz3DOSOKDVShhPfMei2YhJk4jhKGc2K4kiH5wNRzl57hN7bSxF8f0myIWdcVA8phH02EjukapWLTv7w+VqzPd9UO3xbPY5g2rvGve9SydcIws6L1z7cz6IqwUysiAkq5MKB4yrgsJoLhInldowqyqkEKRRiDaWSVme+Z1/cHjMH57tcmupe+HjU0qSKkla+1v8EGGdozCGR8MBudHkNddgo9HkdrvDve4yd7tLtKKYZhiRG820qtipCdrxQYBEMCoLcqM5nE15IBWfHe7TS1KW0+zciUa/KHg6GvDbvW3+y5OHTKuK0uhFUPzB0jL3usvcaLZZbzSIlZcGzrVmUMx4PBz4sSIV8GwyYlwW7EzGDIqCQEre7fRIgoD1rIkKXv1dO879+/16d5u/efJoMZLUjRNWsoz3eyvc6y5xo9lmJc28YpeAXGuOZjMeD/t8OzgmOlLsTidMypInI5+UNcKIe90lL8HdaL6SkL2YiDGWR0N/bv54sMu3fV+wjqSimyTcbLX5ZHmNu90eG80WzdCT4GdaM8hzHg77dKKEh8M+z8ZDRmXBUT5lUlVnJBqeT+pc8XxkvZ4m8eP6LcQlmwGX5GhotM2Z6R3G1UOMm+E9LgyhbBHKRk12Ey+8yroJ2h7VgXf9UzdjWvwjpX6MlI0zCduvg66DZmP7cFJ601U4N8O7Z78aQoQ1KXyLQC2jZANjp358iaeeFO6GOEq8S/jpF4uxfSqzXXNRZnXHRxCoFeLwLoFcrok1r3/IWEqM7XtTwhNBf6kfM5j9O8bFf/VqU5d4YFlX1r4hnmy9QH2xWVdciPTrzQ97V+pMLUjYL38Ra8fWt0ENUEIRyYxQpq+U2jwPrLNMzcDLmsYvyVsKgQN0ZTDacCiHi+RCSZ/IRklId7mFNZbj/RFFXqGUfCsdjYk+4tnsS3ZmXzKqDint7AdHAp9DKU/CNNYyKyvS6OzE2zqfmJSV9144SQR909C2YlQdclQ85bB4QlpXuKMrfWfeLAwG4yqOymc8mPyGQbl75ff0ZOBVOuE6qWr5pP4c37W1lTY/+fgGulbnCQOFUpKqMotRFClFTcz179dppzS+ZyMzbQtG1T6HxRMOiyfEMqMVrl6bY/ebgMFgbMlh8YSHk98yqK6+75FM631f8xwlGb8wAmpqZSljzXdGPIyzPJ70OSwmvN9epXFOY7RhnvP13iFxEPDe2jKDWc5n2/uMi9OTpk+399gdjpmc8e//VDCtKnbGY1bSjI+WV/nzjRv81dYtunHiCcxKEgjlR4qcZVatcFzMuNXq8Lv9XX69+4yHwz4AE+07CStZxic1H+A8eDg45j8+us8fD/aYVD75U0Lwo5U1/tXmTd7rLfNOu0sW+i7WvPNjrKWyho+WV9kej/jd3i6/29/hd/s7HM6m5LriyWjIf91+DALaW3Ftxng2vjo+5D89us8XRwfk2gfjiQr42domv9y4wXu9JW61OqRhSKKCei3eKLQ0mh+trPFo2Od37S6/29/h9/s7jMqSma54MOjzN08fYZxjKUlfO1rmeUmazw/3+dunj9iZjBf/1kkS/vrGLX6+tsV7vWXWG01PxK/ldLVzVMbw4fIKf755g1/tPOU/PfqW3emEQc29OSPNwKGp7A6Vqb/rzmDsECEkSfgjouDmuff2JC6ZaDwnhBs3w9RjSYFsEqlVItlFor7z8LYurwnWxYn3qijNk4WfxnXDV8BLzqP77/0OmjVvYYNAruDcTq1i1acy+1Rmvx5Z6nGWc7a2x954zu4vEhxBQKjWvBGhXDp/xd9pjBt7fsKJYzD2uPbSeBOw9bo1nOO8zSGJ6qz38vPHXoHjVdKybz7QVSIkUQ1imV1ZHcZhmeo+/WqX0kzr7p9ABZJGK6G73CROQ6yxRHFIECpWNrp0V1oEoVehipKAKA5RoaqTDHmmGsp1QNuS0uYcFc94Mv2UvfwBuRn94JzAn0PQiCN6zZRJXnJ/7whtvHtwqNSC7GkdaGMYTAueHPbZG46Jw4BOlhC8wgX4OmExFHbCoNpjN/+GWGW16k546j3z+8Q8qczNiGF1wEHxiIP8IaW7BqM2mdKLtuhFmySqgTpnR3apl/Eeq17N54Siz9wJfP79OIkkCsiSNysg8TpYDPmJfY9kSiRTpFA/2H2f6SEj7fd9v3h4LdysWGUsRTfoRhvEKvuOAMBUl/SrGXqeaJy43RtneTg+Yi8fs5a0uHVOz8tSGw4nflZeW0t/lvP5zj6TsiSLwu90lQ7HUyZlVUuGvhrPHdMdtjbUmysHOuc774vrsTYdtLV8rXPgrFsoDobR270OdO1V0ktTfrG+xV9u3eQvt26hxNkSqLmuyIKQSAXsTEY8Gw8x1lFozdPRkMfDAZOqxNbHftbxVMZQWsPDYZ9/2HnCk+GQ0hgaYUg7ivlkZY1/e/MdbrQ6bDRePWa40WiR1ZyJ7cmIYeFJ4vvTCb/b22U5yfjxyjrNKD5Vsaw0hqomXP/DzhN2J2O0tTTCiLWswY9X1/k3N29zs9VmJX31RTcfYbLO8mQ0WMgD70xG/Hp3m7Wswc/WNusRsLN5I9NKczSb8qAmgc+0N6rMgpDNRoufrW3yVzdusdVondk98smYRQAHswl2b5ejmR8FOxsW63KsHSNQPs63o/p6vvz3/3LO4CgCmdIIbhDJzqJiLwmQIkTJjNOq9c5prMsXnI63A98O4gK6/0KkxME7lOETr3TkpoDF2CFF9S2BXCIJm3BGQO09PD5Dm/0TP1WEapMk+gilznYc/e7qLdZ5Y72LHMNV4I1obH3eLvBCEdRjT2+fdHmdUCIkU516vOFqx2KdYayPOS6fMjMjLNYbVcUhm+8ss7TWxtTytlLJ+oFlidOItBEzGeXk0xIVKtZv9OgsN99okgHeAfywfMyz2ec8m31Ov9rF/EC5GeCbX6vtBvfWl9gbTPgPv/+Gn93Z5KMba3SyhKzublTGMJjmnjj+cJvd/ohuI+X2SveVHZA3gZkZ8nj6RwSCZrC0CDzFW/CIuSiOymfcH/8ju/k3lC6/lvG5WDVYjd9hKb5BcAFCfLOREIUB40nBZFownZVU2hKGAVkW0chikpfIsvOZ9x8CprrPw8nvAEEzXCaQ8Q923w/LJ9wf/yN7+X0qm5+rWPc6JKrJWvIuvWjrVOWt/WLM5/1djLPfCXbnHY1hmTPV5w96QiXpJDFxGKCEYFZWbA+GKCnZaDdJXhIJ2B95adTzEppxfhR2NimwxhGnIVIK71Yv/b1eSIG1Fl1qiryqu9he2lyXmmY3o7vSfKuJRqgkaRDyfm+Z/+Gdu9xstX0g/qrXSMXNdofCGH69+4yvokPGVUFlLf085zifMasqtDUE8mxWxKQqOZhNeToa8mQ4YFD44vN61uT93jIfL63ybneJ7DVdCIBGGPFeb4VxVfGH/V0O6sr9uCp5ODzm8WiJfpGzlHiPlJfXNC4LDmoy+uPhgML4oH6r2ebj5VU+WlrxXZVz8ILaccLHy6sc5zN+t7fLqCgYFAXDouD+4Iin4yHDMicJAtIzjs06x+FswreDY/ZnE6a6wlhHFChutNp8tOxHuLYarVdylUStMHWr3eH/dPMOlbHcHxydnWg4AEkgeohA1ZMpCqMOca5CycvzCS/Z0XA1KVQSyTZChCgRn6P6a2uewckblkTJFlJkwOnuyVeBN8ZbPuc4j78EpUiJwneIzVOK6uvFcq0dUVTfEqlN4uBdvPrRc/hqtUWbA/LqixNqUxIp4lpd6965SOAn3hXQ3+FKCJGiRKs2UrkeGdY5VM0jUbJ9Ia6FqE0UL09O/2EgkCGNoEuq2leenfct0DGj6pCh3mesj0hViyhMaXcbuM6JcbATWzh/4CgliZKQKA6Jk5BGO1nI3F43tC0p7ITD8jFPpp/ybPYlx+U2hb0cb+ptQSDY6rX58a0Nvto+5OnRkC+e+dGITuoTjbn50nBWcDCc8PRwgJSCd1d7vLexTHZFJZeLorQ5x+UzIpnSjTYQSHrRJrFq/GAq3IWZMDVD9vJveTz9I4Ny58pdLYEkEBHNoMdyfJNOuHYhqdey1IwmOUdHE476U8pKo7VhFAWkSchyr0mvm9FsJKTfcxfjNJR2xlH5lEQ16EYbgKMXbfnuxg9k33MzYWYG7Obf8Hj6B4bVHvaKxUGJQomQRrDEcnyLTrh2ahHHOYdxz92rT0IgSFWICx2RPP99OQ4CVloNQiUJlT/HgVS0kogb3TZZ9OL1dzCa0kzic3lCWGOZjn0xSFe+4GqtxRrLcDAlDANWNjsoKf3vzUqqQqO1XXgo6UoTxsGFeCfXgSyMWM8a3Gl3eb+3TCOMXquIJKWkEyesN5qs1CpThdEUxpAbzaTyo0KlMUghz+yMjMqSR8MB25MR/SKnqLtH640mP1ld53any1JyvnHSSClWsowbzRZbrTaPRwOmtVzusZmxP51wMJ2wmmZEgfrOM71f5DwYHLMzGTMsfcITSslWs8WPVte41e7QO+dakiAgCQJuNFvcaLXYm46ZVBW50ZQzzf50wv50ShZGXhnrlPfwicaMh4M+x3lOVXOJIukTjfd6y2y8opMxh/RKA6ykGZ8sr/H18RFZGFHo744lziGQSNlEuBgp09q8L8G5so7RL4dLJRrGFRT6EFN3JyLZIQlWzzFj6214TpbJhYiIg3eJgneQonGlsZvTEKp1IrVBoFbP/RopMuLgDlWw88LJNXZIob8m1rfPIJcbrKuozAFF9U3tSwFSREjZ9GsJb12Qw+Be+uMRyGXS8COU6iJFg+tNNBp1UnTnwnwLcV1Gd98jfCC0RCPooK7cnXEYV5GbEYfFE3bDb1lP7hLJE+f1FacraUSsbXl5Oam8Zvub8s/IzZi9/FuezD7jweQ39MsdKnv1MZk3DSHg9kqPRhITBQHWWR4d9Pntw22vU19zYcAHMlkcstltc29jmZ/d2eL2Spdm8nblRh0W7Ur61TZfjf6O3E54X/wVXREQyeQHUeEeVHs8mf6Rx9M/clQ8prDTK7/n/LvVCTfohps0gqULdQ2f7fb58ptdb8y3P/KytVHAdFpirWVro8utG0t8eG+ddKN75fVeNyy+e3lUPuPL0d9SmAlhK6YdrhHK5I1LVp8H/XKbJ9NPeTL9lKPyCZV9vZDK6xBIv+/dcINuuEF2Bvl/OW7wSXeDUKrvqPNY59jPJ4x1wVp6fjWoNAq50W3XJp6KdhLz3toyS42UjzbWFh3POQ7GU77ePzxXl7MsNTuPjxgcTWh2UtI0QleaUX/Ko693yZoJjVaCVJLtR4fMpoV/Qsr5+KvDWW8S+LbRSxI+XFphs9lejPKcF0oIWnFML0kZFDnjuv5gnGOmNTOtidTZ3+t+MeOr40N2J+MXiMlrjSY/Wl1nNb14UJvUY0UbjRZ70wmTqsLhuyfPxiPWGk26SUr4Uph6NJvxRe1N8fz4JJvNFh8vrbKUXHwtWRhxo9nh6cirYmF85DsqS56MBgt39dPOucVxlE95PBowLp9/9yKluNFsc7fToxmd/3mVBAGrWYOVLKMbJ8yqiklVfmdYxSeYXvXTiah+BgmkyHAiuVJsfrkoyjmvD14b9Gk1BgSqXoiSCUpkp2TGsq6QP99p7ytxgzT6CYFcQr7WKftiULKFEt4E7ryQIiKQq0SBdx2XIqsdz6eU+qk3t3NjlCsRJ7owxk4x9hht9tH2aDHTpmSHKLhBoFbrbsZFHibe42O+6XMEskscvl87jC9xnb4SUkQo0a47QRch5/sk4/t/VF4NSoSkqk2qOkQqQ+mwHh263MPAYSltzmHxaMH9SGSDQMavDbLCMDjVnOw64OrvcWlnTPWAw/IxT6efsz37ksPiMbkdv/5NfiDoZAlZHDLOvYBBunOIkpJS+/lbACUlSRiw0sp4b3OFDzZWuLPapdfIvqM98DbgsLVHyQOkUCSySWmndKMNEtUkEPFbVSWaK/7kZszEHPNs9gWPJr9nv3jE1AyvZXQmkinL8U1W49s0gh6RvJj4x+HRhK++3UMIQauZ0GzGpElIGCimM69EZYxla+P7k7J9Hfy+D9nLH6BERKwarCV36IabnrNzrumAa1zPYt9HTHS979PfcVA8ZmZGr3+DcyCWDVai26zEt2kE3TP3vRFE3qtAev+EkzGEdd4pfKYr2uH5x+1CpV7oTnTShA/WlulkCautxndGp+4sd/nJjQ3WW68ngcw7GsP+BCkFVluMMfQPxxzsDuhWBl0ZQgFVqZmNC8q8Is4imp0MaxxlPUr1tnU22lHM7XaX5TQjeEX34STmv6GEoBGEtKKY4ER3ae5iXVnzSgnVYVnweNjnKJ+98HvdOOGddpfOOVWrTiJUkl6S0k2SF8becqM5mE0ZFjn2lEr+oO5o9PPnFghSCJbSlFvtDq0LBPVzxEqxlKZ04hfXMtUV+7MJo/JsoR3nYFAU7ExGTKrnHeRQKlazBpvN9rnGuE6+LpSKduSTm2GRM6lOGz308Zu3EqiwblwrqPpkJ1DLKHG5+PxyHA0REMoGhdlnop9QmAOMK1DCKzElwTqNIOHl4FeIACFerNYJERCoVeLwHpG6SaCWL3UgZ0Gg6s+9iLqHQskmSi4TBpsE5nGtIFXg7AGV2UPbQwK3jBLP59bMnARu9mpJME/8DdQ6SfhxfWwXjWgUQsQ+mxRicTOSskkU3CaJPiBSt64gJ/td+M6Ud4e82Hn7pwElAmKVkak2DdVlJA/JzehK4wPalRwUDzFO0wyWaQRdmsEy6hVVnzeNeVV9VB3waPp7ns2+ZC//lpE+oDyH78wPCZ74Lbm7tsRap8kv3r3BOC8Y5yV55ccOQ6VoJlH9J6aReOOu73NaxTpNbibsFw8o7JRBtcud5s9Zjm7SDle4inb5ReH5YIbj8hkPp7+tFce+Ijfja0kywM/ob6UfspG+T6wuXikcjXO2dwf85S/f5Rc/fYcw8CTwShsGwxn/9VffsrM/ZDb7oQoXeBhXUdgxe/k35GbEoPqEO42fs1Sb2KnvYd8Pyyc8nPyO7dmX7ORfUZird7DmSFWbrewj1pN7L3ZzX0IgVT1y891/E/hEJFEhwRUSsW6W8NHmKoGUhKeIQNxZ6dHNEjrpeYJd4TvMDvJpSTGryGcF48EMq60nggsIwoBWJ2U2LjgaDJBKkjUTZuOcyTinyMu3ruiXBRHrjSbt6OLmh1II4iAgDcMXAmnnnhvfvep4vFzumGGRM3dbE0AzjFjNGsTnGFt7GUrIhTrVybG70hiGZb4gqb+MUeXdvEfl3ETaH18rjFlJs1d2Zs5CIBWN+XjUiXNbak0/z5lV1Zmjcg7HuCw4nE6Z6ef3saAeW1tOU+JLrCkNApbTjP3ZBDGbLoQMXv50aycYO6jVSI8xto9AkUY/Q12yEXA5wz7hOQdKJAQiRQiJPTG761y1uHhefF2Cki30KS0YQYCSHcILjDi9KfhqkkTJNlFwm1I/xdihHxVzBm2PKfVj3+0IskXipO0xRfUVldnjOQ9FEKp10uhjArly4Xq/V8JqIEVaqwAs/qX+QqR15+FPx2n2hw4hJApJolp0ow0m5pjKzq5Egp1Xr0W5zbPZ5yihWI3f9dVr2SCQb37/5pVLP8o1ZmYGjPQRh8Vjns4+Yz9/yFDv/0mMS70M6xzGOhpJRCuN6797qduidt4MlCSLfPV7rtOP8/KEUn4/nbi5pOBUDyhtzrySMImPWIpv0FA90qBNKCKUiK51hn8eCHiVsSlTPWCkD9mZfc2j6R84Lp8x0cdXns+HOnmXTXrRJivxbbrhOuElihjW+n3ttjPu3Fo+YQcLzcaUNIkwZnTGQ/SHA/891Ez0gNLOFs+FsT5kKbpBFnTJVItAxCgRvqF9LyjsjKk+ZlxLWT+e/oHjcpuJPr6WwFeJkOTEvntuxtn3uvmo42kQQhAKxVWZN3EQvFLqtJ3ENOPzfdeCQNLp+eBLKYmzECVBzadLvapgEhLGAa1eA11ZrLU02intbkYYKYyxpI3krfNz4kDRjROyUwjSr4WoHc2lfKlQ89wD5VUotOa4mPnxJucW8eKj4YC/efIQ8UrlydPRL2Z82z9mezyiMM85rdpaplVFYQynTajlWnOUz14I6o3zxOn/csm1HMwmfNs/Znc6XnTUASprmOpywUk5Dc7BTGuGZfmC8pmSgkYY0grjS6kkRkrRjmPvan6eFwhZF+nraZorXJ+XLKdKpAiIVI9WdBeHQYqQ52Tq6JQD8YZ1SnZfHMdxBmtHGHOMC64+C3qd8FyNd6mCZ5T6AYY+UHM1qm8I5Cqh2oL6gek7Gl+i7Um1KUGoNnxHQ168WyMIUapdj5SdNDbKvSeJHXs1qj/1eaUfIBLVYCW+zUT3GVR7VOZq16fDkdsJjyd/YKKPyRtjrNMsx7feSqLhvWwMuRmzXzxkL/+W7dmXHJVPmZg+pZmif7AStq9GpQ2FNiRhUBP+BEIpGrEgrU+tECyCGG1rl17nUFISh+pKN9KrwmKobM5B8ZhRdcRu9A3L8W02kntspu/TDJbI1PN77PXBUdgJx8UztvOveDL9lOPyGSN9SGXza0kyAEKRsBzfZC25SzfaIFUd1CWEFqJI0cwSwpdVpBxI4Q39sjQieI051w8FFkNpc/aLhwyrfXrRFivxLTbS99hI3qcR9MjUmxgD8yNyx+Uzns2+5MnsU/rlTr3vF/NPehX8qNwt1pJ36YYbpKp9qX1/2zgvXyGMAtZv9lhe95MNznlRGGsdzjqCMCBtxEgp6PSaZI2E1a0eQSCJ0pC2yVhaaRHGwRsT+Thz7XXV/TLdg1dhbrj4qkuotIZxWb6QEFjgb5894oujg0vdirW1zHTFtKoYnxgNMs5RGF2rLX13UXMX73nw74DCGP7jo/v8enfnUmupjGGmn5PjX1iLNmj7Ilf5RTgq693Y56aSUgiUkMQq+E6X5LwIpCINQj+SiDjj8wVCeJsCSUagVrF2iHP2e1CdcgZjZzgsgfRmcUrEaDulNAOsrH0rnL/ZLxR0ZJtIbVLKh8/fC4O2B5TmKYn74NIH8iYwJ4WXwZMThGu3SDSi4Lbv3jgLGLQ5otAP0OYIcAgR174ca4TqxqX4J1IkBHKFQC159+/62jB2TKmfEAd3r22s4V/wIuI60RhW+2zXowRXPdfGVYz1ERaDEiGFnTHShwsDq0Q1iKTXlxevcJ9/FebcC4ulsjmVzSnMlMJ6FaFxdchx+YzD8gmHxWPG+gjj9LmPLZFNsqCLdRrtKko7/V5HrRzw+HDAo4M+690ma+0mzSQiiyPkKQ/RotIMpwXH4xmH4ylZFHJ3fYl2djmz0NMgEIQyRYmAyuYYV702ePN8mSmlnaJdSWGn5GbEWB/RClZohj0imRHLjEDGhMJzfJQIUSJACv8AmV8zvqg/72LpxX5pW1DanNJOye2kvh62OSgesVfcZ2oG6GsLNgVKBLUj9ntsJu/TUD0Cebm6tKhNsp5sH/P3v75PFAYESlKWmuEoZ+9gxCyvePTkeHEe0iRifbVFs3F9+3vm+pBEMkEKRWWLc+y735/CTijsBO0qCjsht2NG1RGtcJlm4Pc9kimhjBddDr/3r9p3i3EG4zTGVVS2oLIzCuvvBaPqoN73h+zm98nNmMoVXAdZQCCQIqAZLLGZvs9G8h5Z0Ln0vr9NXOSeK5UkyWLfQasr+QKxqAecfC+pJEGkSBvxC/WCOIlOrR+83JW77o6HEoJoLuN7nW99jsvHWEeuNZWxL/z64Wz6Ain7WpbjHKb+c9rSKmvJtUafkH21zrE3nbA3vV7FResc2lqss2eeJodPmkpjMPU1IPEdpPmfy0AJQayUf/1ZeQYw/0fnZn6Myk0ACS/wVC92wVwq0bCUlHaAmZNERINY9Zi6bUrTJ5ANrDP1nNz8pAgC1SMKbqOqTxfv5ZymMru+Y/ADI59KmREFt/2aZaMeXTJYOySvviEJP8KhcRicKzD2mNI8xrg+AEp4palQrfpE4RKEbSkSwmCTUK8hRbioLxo3pND3SexH8FZ9Sf75IJFNVuJ36Jc7JLLBVPTriv/VHsQWw1QPa1WXp+yEX/pqb3yX5fgW3dCTgZW4vIKXr5AXXla32qdfbi+Si2G1Xwe0OdqVFya6t8IVttIPPIncjOiX299vouEcnz7Z4z9/+g0/ub3JJzfXeGe1Rxaf3iUqteFgOOHrnUP+8HiX5VbGUiu71kRDCkWjFhMYV0c1qdacO3gvzJgjmzOs9ngy/ZQs6NAMenTCdTrRBs1giabqkagmiWrWPhxJHXSe6HzWo3KlzSnslJkeMtHH/pqodhbdi5kZUpoZlSuwTl9bRVsiazfoFW5kH7GVfkiiLi/44ZxDG8vvP3vK7z59QquZkCYRw+GM8aTAGG/e99s/PuabB76zvL7W5q9/+e5bSTTmSVUoYkb6qOa3nF5JPQ25GVHZnEG1xyPxBxpBl0awRDdapxOu+30PeiSySawar9l3TVknFjM9ZPzCvj9lrI+Y6SGFnaFdUY+GXs++C6EWLuA3s088J0ee02HvTxXinFIo35kpf/WvnzaGfh3wMr8SKeRbl3AxzgfSZ8msXiccPsA/a5rS1msxb0H5a76W1x21T0jMwvjQdzRer+v6KkghCISq44pXrdBgXb4QPnIuR4iEQPYuyTO+YKLh6n6YthOm+hmVHQOOQDYpzTG5OaQwR0RqqV7KiwsK5BJx+C5BsYwQEc55fwhtDin1Y0r9mErfQck28hWEsbcFQYCUTQK1TKg2qdQu2hxhXY62B2h7gLF9z9C3I7Q9wNpJTQSHQK0Qhx8QyNVLcyikzIjUTUK15TsiJgAM1k6o9FNK/YRSPyFUm7XvxZ/GyMCfAqQIiGWDdrjKWvIu1mmOq6t7CQBYNIXV6KqksgW5GTPVA47KJzSCJVLVIpQpgYgIZFibYcrvdDmcsycq1gbjKrSrqOysTgSGTHWfsT5iVB0y0gfMzBDrzh/0zhHJjFS12Eju8U7jzxjrI/rlDoUZM6h2r3xOLgvn4Gg85cH+MXfXl0nCYOEGfhqkFMRhgHWO7f6QUmuK6noNCSUBrXCFTrhOO1hlYo45LreZmeG5Xm8xWGfqcb2R70bp/mKML1WtxTUSyYRQxATSSxJ6paq6KlVXtbUrF50tf631GZtjRtUBhZ3U4zLX+9AXCGLVYD25y83sE3rRDdKghbyCQl63nfHOrWVms5Ki1GRpRBR593c/MqWIQkUUBYSB/5wsCVFvyfldCb/v7WCFlllloo84rnbIz6ne9HzfPU+qsBMmus9EHzMIdknqfY9kQijTet/DM/Zde/6NyynNlNl83/URY31IbifX2Ll6Du930WQtuef3PdwkVc0r7fsPHeftNlykK+GAb8d7PJ0e8W5zjZvZ0mt9Li6CeRfs+xgY9R0v77wFzwPpG602N5qXH9E5DeuNJne7S6xljVNNGOdrEXWVXwpBJBU3W23WX+NKflHcaLW5211iJc0Wx34ahHi+poW5gbuuMsBZ8PYTxg6ozC7aHGDsMc5plHRcxWj7gh2NWtbWjBhXj8j1HtaVtRxsA4fB2ILM3axN206eSEEgVyBQBGrNS8Yyw7kCbY8o9WMK/S2xvk0U3kXy/Sca/hIMalL4O1RmF2PHWDfBWJ8gaXOAEH202acy+3hjPX85BHKNNPoRgVq59AqkyIiCW0TBLZTsIEWMdTOsm1KZglI/oKi+RoigVgT4l0TjuiDqsY9WuMLN7BMqVzLUh9eSaMxhnGaq++RmxFH5rB6FCIhl5iV2gxaJavmAQkSLcQl/hT0fialcTmlzcjMmNxNyM6awkzqhMBjnAxjr9KXn7jPVZjW5w43MK+Qclk8QSI7LZ9d2Pi4Hx6ysGExz4iBgrdP6jgnXSYRK0WumtNPYv26Wo831BtlSKNrhKpvp+4BYzL6fN9F4GZXN0bZkZkYclU8XgaWnKcoThMVaYroOIOZjdG7+P+fb9vOA1tYjc29C8UYIRaba3M5+wp3mz2kHK8gr+tKsrbb46Uc36gqlWzyQrbNYxyIQ815V/vnTbMQ0Gm9HPU+JkE64zkZyDxAMql3KUX7uRONlPN/3IUflk9rUT76w56/d9/kopTN+oLIuSryZfRd1N6/Lneyn3G78lGa4fOV9/+cI6xy/PX7If979jP/HzV9wI+vxT0E+HnwwH0rpeSkWAiGJA8VPVzf472+/e62f1QwjeknKeqNJeIrRo1+LohAGsIRS0ogifrGxxV9t3brWtbQj7z2y0WidLXpQr0mdkHe2zr2yK3MeOPfqzs5cmMTH449qbsbLo5SXu/ou+O33N7VANsiCLQKRYalQIiIQGV6D15EFG6fOl89JJnHwLmn0E4rqWyrzFDBoO2BW/g6BInMTXPgeSi4hZbb45FfB1QGVtWOMG+FciXMVgVq9tJKVqEdXlGgRh3epzDalfox1Y8Bg3JDSPAHnFhkg7qTa1Fota3v5RMPLzKZeuSr8Ec4WFPobrPMcmVI/ZJT/Z4wd4WJDoFa8Z8g5lBJ8h8p4jxA7wrppnTgmBGoNJS/vBPlPAYsZb9VmPbnHRA84Kp6PHl3PQ9otgj7tnhPYcjFiaoZE2lesAxEi63nseWWwnr7HOluPQPkRGc/LKNCnmkpeHKFISFST9bqTsZ7cJVFNmqq38Hz4vmGspaqThUC9mtsihPfUEFKgjUWb6w+45sFfIGI/Zx8uMa4OFxydi46aPd/r8+rtz+l+34/60pz8vZm+z3pyj064TiTTK1djozCg0Xh1d1hKSZqERLU/QhQpkrfo/C6AUCa0gmWyoLPgZI2rIyp3MUW3y+z7/JXfByKZshzdZCv7gLXkLp1wrfYF+acQHr89VNaQm4r9fMjDyQHD6k9Lbvx1CKQkDT0xucCPahrrWEkbfLK85gsF15RShUqRqIBGGJ46NhRKL4tbGk1hWChTrWfNa19LpBRJEHgX9jO/Ez4JS4KAaeULx3NuR2UNpTEoKV6Q8D0PtLPkc1L8qbcHgbd26BIFN7F2gnW5L2XJBkq0eEuJBoAkUj268SdYV+KcQcnYk8KZk7+DM/gIEiFikvA9jP0rrJ3WiQZYN2ZS/CPa7C9Gj5LwI6RMOE+V3vMkSrTZpzAPvRytHZNFP7uyZK6STeLgHmXwBCF+tfi5sSPK6gEOzzPR9qAeP/DclCBYJYk+rDfosvCbH8g1GvEvvWmgeQbO33gK/ahucY0RQpFEn6Bkt96L110UbjH2VZiH6Lpjo2SPhkj/2Scac/hRhZSxPmavuO9Hk8r8Sq3E10G7EmM0hRm/kDS+fMNzi//OKxXz2vX1Vehj1aAXbXIj/Yj3mv+KLOggUcSqQSdcJ75mk83LwLeWfdXHu+y+hng9/926Mn7daqhzboTD0AqWCWXKNBtiXMWT2WdvgdPyfaUYHrFMuZF+xLvNX7Aav0Om2lxtwtjjZKfiLIShotvJaDeT+jW118FbgMOhbQXO+dE5scFUDzFWezK2ftPS0d+vrG8iG9zMPuFO42esxLdJr2nf/7mhtJqJzhnqGf1qSmH/NNUAz0IoJY0gJFaKEV6NCWtpRRHvtLv12NB1jYidGEU6bS1K0QxDb2JXec6GsZZObR54rWsRJ0a1XvF7oVKkQbAY9fLPE0thNIXWJEHARcXC5qpclT19bLpuIxCpTUK56gvPFAgUgrAWRLocLpRozE+2JCSULR9oOefHdl7jZrrYKKeIgps4V1GZPYwdoM0exg3rIHqbSfkrtD1mFn5KqNaQooUUSZ3ACH+KnMGhvYmeK+qRpjHaHKHtQS2xmxAHV2/DCZERBVtEwRZKdnwyRIW1Y0rzFOtmVGZnoTalZBsle4RyDSXaXi3q0p89V+zqkIQf+fk5vUOhv/GjWq7AOE1efQFAXn1DHP4WKVoo2cI7lwcsAlCncVQ4653OjR1j3ABtDheknyS8h71g5e2fMoSQBCKiF21yt/FzglpJKDejF7oQ1w0/2sD3FjtEMiVTHdaTu9zIPmEz+YAsaBPUfKNQJjSCXu1iHdWjGd+HMIEgiyI6Wco4L7i/d/RKMnhRabb7I/YGY0IlaSbxqbO7V4F/MJRoWwKi5rbcZf49DETESB/8SXqWnA1BICKWoi3WknfZSj9iObpFolrIa5I0PTya8OW3e/Xf/NCydY6i0FTakEQBvW6DLIlY6r598rHDoV2JdiUCQababCbvMR873ivuM64Oa3WnfyoQhDJhKdxkPbnHVvohy/FNEtm8tn1/FUbVjD8OnjIoJ7zX2mAj7RLLgNxUbM/67BdDjsoxS1GTTzo36YR+LHsn7/PNeI9EhaxELWIVEgrFfjFkNx8w1SUz89xIL1ERzSBmK+2xnnRIVPQd47SjYsxnw6dMdcmNrIdAcFCMGFUzpsYnC6FULEdNNtIuS1GDTpTVcY3jwXifr0e7DKoZg2rKl8MdClvxj0cP4ES5CaAVJvyke4vbmZfN/1PqGmVhyHqjyXGRc5jPvDIUlqmu6Bc5rSimeQlH7sugGUZsNFtMdbVwKtfWMqkqBmVBK4poBG/Pp0wIaIQRS0nKoHj+fNDWMiwKjvMZy2n2gtv9eVAYTb+YMa2q15XhcOi6WN/HYZEi8sqpzM/DW1CdEqhaC7seir3Qh0pPbBatmvNwzLTUGD0EDMb2mRa/Ylb+ASXbNRF7i0D2TpjWWRwl1uV1onKMtkcYc+SDaAyxuk0cvou1l5uJfmHFIkGqDUK1SSA7VCKpZ949Idu4EZXewboJYFGySxLeq7ko18M18clLwyc1eg9HhbH9esZaU+j7lPpx7Wje8ucs2ESJFElaV7jtgt+h7THaHGHskR+7wqBEkzi8SyA7ON5cAP2nik64Ttpqehfd4inWVWhzdRWqHyoS2WQ5vsWt7Cd80PprGkEXdSJpDkVMoCIS1SSUCZWdK9e8fTSTiOVmxnBa8MX2Pu0sYbN3eidxVmoeHfTZPh4SBgHt9HIGSK+G850vV2AxRDJlrR45mxP4y+nsn1SiIRBEMmEz/ZC7jZ/7kalo/Vor2rsHQ37/2ZPF350DYyzH/SmzWUmnk/HOzSVubHa5udW7ts89N5zvZPl9t8QyYz29R6yyBSeiNNMre/L8kCAQxDLjRvYxdxo/Yz25RztcfWudjGE14z/u/pFvx3v8Lzd/SSNIkFHKsJrxx8ETfnv8iC9Gz/iwvcVG0qUdpgjg0eSQ/+/27+lFDX7UuUknymioiN/1H/Orowfs5wMOilFd7hEsRw020i7/euUDwqWA5Vh8J9E4KEb8u+3fs1+M+NcrHxAIwR8GT3g0OeSg8DydRhDzcfsGf7F8lw9aGzTDBCUkxlk+HTzl//P0HzksxvSrKTNdUlnD3+5/xT8c3n8h0rqZLZHdi7iVLf3JMTcadXC/PRkjEX54yjmGRcH2ZIQQ4u0lGlHMVrPN/nQKDHB4VaxBmbM7HaNEi0b4FhMNBM0wYjnN2J4853YZZxkUOYf5lGYU0eBia8q15mg2Y1KVrzA0dVhXYu2IymxTmadYVyBFihQNAtXljatOPV+KxrjCL8hVaDtF20n9s4I02KQR3j61muGzboWUGWn0CQKJkl1m5RKlfoq2hzi051xYjXM5xo6RIqtNASXzjMs57QNnO8O6CdZNmTtyOyyCoCalXw1+zd65PAruom0fW8vxlvqJ5zi4CbaubgdqhST8hFCtXWPLzY9jRWqLZvLXfmZOtimq+5TmKc4VOAqMtViXY12JtkcIwrqj4sdJrKt8kman9bmb4PAPPbdw6q2dIP8FwPM6UiBChGyyltzlo/a/4ensC57NPqcw02vjQ/wQkMjmQmnrRvox68k9UtVaJBknr2mBIFFemWusD9H67Z8HIeDmcoc/u7PJ/nDCf/vmKZO85PFBn0YSkdSz+sZaJkXF3mDEZ0/3Gc0KbvTavL+5emb347JwzmFshbYl1hmEECgCUtVhI32/9h4K2c/vM6h2Kez1ase/TUgkgYxZjm+zFt/hdvZjVpJ3SIP2tVe0b272+Ktf3F383QHWWqbTktE4Z/9wjDXXPwp3Xjhs7VVS4pz1+4wkC7psJh8gkAQiZL946Ini36Ms9FUhUYQyYSW+zVryLjezH9XjUtfXwToPBN4xXAnJVJeMqhmNIKK0msNizLCaEghFICQz47sUiQqZmYq9fEiqIhphTKYiAhnQDlM20w6baWdx73c4+uWUqSn5dryHdoZfLL1LO3yxkGhxFFazlw/4zfEDlqIm3TBjqdfAAbmpGFUzSqv5h8NvMM6ymrRpBglSCN5trvI/rP+I/WLIQTHi88EzHkwO+GnvFh+3b7xw7+2GWa1E9af3rO7ECe/1lnkyGvpjqr+v25MRv97dRgnJxjUrPp2FpTTlg6UVnowGi58Z63gyHPD7vR2iDcVq9va6o1IIeknKzVabB4P+4uelMTwbj3gw6LOcZiyl5xttL40h15rjfMbBbMr4lR0N4WkPolY3dYX3zxCX88+Y43I+Gq6iMiMqO0LbCYU5YGZ2KU2f0oxYSX9JFt6AV8jZCUKS8GPC4CZKdZGyhcv/BlMO6lal8e7XLgd7vHjVi3Bn/H+veCJE4pONa4ISbZLwHtruUelnGDegNJPvfHYoV8miHxFckRtyGgK1TlOtEahVlOogRIrOj2sVkdJ3dFyFNVMqs81FzlltN4UQ0b/M1Z4CHyRErCZ3aIerBCJmWO3X2v7/dBKNNGizkb7PO9lPebf5M1LVeeX1EMsmnXCNyuZM6L+9hdYQQnB7pYu2lv/tv33GP3z9mCeHfX7f3WGj26LX8CTkvNLsDcfsHA95ejRkpdXgp+9s8tGNVZrJdVesnnc03Amt+FhmbCTv0VAdQuFJ/rmdUNr8Wnk1bxNSBCSqwc30Y+41/4Kl+AbtYIU3Uay4+84qd26/JK5R83L6wxn/6W+/ZHun/72FXnNuzjzBnCORTTZTP3oYygQlQma1Z8b3y6a5PKRQpKrJrexHvNv8BUvRTVrBEm+7SKWEpBWmtIKU0mpGesaybVI5w6CaUJiKbpTRChNmtmSqCyIZUNqKQTXF4uhFTZoqxjrHWtLGOMtm2mUr7SHrbsN/PfiaXx894P5kn6ezI7ayHu+11k9dU7+c8pvyIXeba/z36x/zUXuLtaTDUTHm8+EzfnP8kL87/IZYhfx86Q6JCkllxCedG3zU3mI3H7A966Ot5dnsmH+98gH/6zt/hUSccKoWr/FE+OGil6S831vms4O9F9SXno6G/GrnKTdbbf6MjbeyluUk46OlFX63t7P4mXGWR8MBv97d5na7y8fL1x/LnQUpYClJudnqvNDVKY3h6XjIt4MjPlg6v8BQYTSDIueoTjQm1WumVUSAIKrvSxXUHI2rKJpesqNRz3C5KaU5orJjnHNIERMpUCKuVaAsc8m97xxLregkSYmDuwgREaoNyvghlX5GZff9aJAd12pIBc5p5h0LgfKcDRHXbZ1mPTbUI1A94uAuSfgecXjv0ifnxGoBvGJW+B6lfsxU/IbvDtAHCAICtUoU3CWQvVpa8MSviefkpEutpO7QhGqVLPqzOvl5b2GuYuwxxgzqczZdeJXMP1yIsCb2pEiRomTrOack2CQJ3iMO30fJ7qXW908Z8z0LiEAJL10qYGf2Fdv510x1/9Lypd8nBLI2Gluqq5N3WE/usRzdJJJZrc/vf/M0JKpBJ9xgVB2+vUWfgAA6WcKd1R7/9uM7LDUzxkVBXmrGecGs9LPRzoGzjs1em3sby9xc6vDjW+usd5pEwfVWYOfeJtoWL/BW5pKkiWqylryLEopUtdjL73NQPGJqhhhX/kkEn7HMSFSLteRd1pN32UjepxdtksjmG/PzkfK59v4czjmkgiCQGG3Iiwpjv5+kzZPBS59gnkgc5/vuFezuokRAGrTZr/d9ZkbXYgb65iEW0tvP9/0DuuEmiWx8Lz5O80SjHaZU1jCsZlTOq/GV1oAQdKMGqYoZVTn9akqsQkpr0NYbC7eChFaQ4PCKSO0w9e8Z+CKFcZZb2TKjKuc3xw84LMbk5rnr+8tdhVRF9KIGd5trvNtYYyvt0QwSIqmQwvM2/vHoAVNdclxOaAQxiQqRQiIFtbGaXAThSkhCqRZE4j91NMKIrWaLW+0Ot9sd9qaTRTD81fEhv97dph3F3G532Wy2LhQzeVdtzUxrplVFEgR04+RMTkM7jrlJh1vtDrdabfpFwbgs2J9N+PL4gH/cfUYaBNxud1nNGhdaS2W8StRMV0x1RSOM6MbJK929hRCsZBl3u0uspg0SFXi1KWt4MhrQjmI+7K2wnKZ04oQ0OJ0D7Jwf1nw2HvHr3Wd8cbRPac4x2uw0zhXeFdyOXxr/f4sdjfoxirE5hT3GuKImzGYIMpRM6nlUe4b61HNIERGH7xIFt8min1OZfWbl78mrzym0Tzq0PUDYIZbnSj+CCCmSE0HyBpHaIg7fJQ7uEQU3CdXNayVISdkiCe5RBF8jRcLLJ12KCClSArlKHNxB1KRZV1fcnhvC+PGvi+Dl4wjUSj2i9SHWzcjLz5mVv6fQ9yn0w1qJ6rDmZOT1WqXnm4iMQPZQaplIbREFt4jDu8TBHaLgJlK063XPP/vUFb3057mk51Uxfx8hJDix+OkPZZxr7mWxkb7PanKHhuqiXcU+/EkmGlJIT+aMtni/9ZdspR/SizaJzmmamcgmnXCdffXgzS70DAgh6GQJrSSmkyZ8cnONX99/xmdP9jgaTxnNChxeWrDXzLi90uFnd7Z4d22JXjMlja5f+nRe2a5ceSpvJVYN1lSDdrjCUnyTVrDsPRNcSW40b1LR7DowT5a64Sb3Gn/OvdZfkKgG0RtWqlsUbr7zD/5PWWmfaFyzL8q5UXM0tCuxp7gez53c28EKK9FNHqgehZ1iXIU135eYwvnh973FUrTFe81/xbvNny+c6b8vKCFpBwntsO5oVDMqq7HOUVrjR1GiBlkQMdY5/XLqf9dotLMEQtKsXw8syOInrzPrLDfSHqXV/LH/mOPSd0o4Y6IkDSJuN5Z5v7XBu81VNtIu4PkZK3Gb++N9AqHITclxMWEpatCL/ok7p59AM4poRhG3W13udLpoaxgWOcf5jEGes5SkhNLLlK9mjde6Yp/cq8oYxmXJcZ5zMJvSjWMaYXRmotGKYlpRzO1Wh3faXdywz6gsOJxNmZQl3TglqBO9pSStjQbPuRZrGFclR7MpB7Mpa1mTVhS/MiqWCFbSBgLBapaRhSGTylEYw9PRECUE3/TX2Gq1iaSX7j0N1nmlqqejAf/lyUM+Pzzw0ravhI/tvVjQBGNHCBlw1QLIpRINKSIi2cMGFdpNcViUSKE2A4pkFymCC47fCISICFSPNPrIV+ztT2s1qan3xeBkxUchCJAiRoik5iw0fQAtuyh5ve6SUPuAqA6N+M8RIsTY0Uv/HiAISaOf1kpP/u5TaUtRakbTnFlZsdzOaGUJxniaWRh4Z1s/gmMpKgM4ojAgUPKVFQxv3JUQBbcQIiYO369J4jPvteEq/BjafI11R0MmdUejuUjWlOwArze2CuQSafRjlGyRRj9ZyBGHarNW57p98ZM7f2+1Qiv574iDe2jbrw1jIApukUQfEMjea5PXtwWJBBGymtxBiZCD4iH7xUOOy2ccl9sLb4sfIgSSSKZ0wjW60QZL0U1W4lusxHdoBj2UOP+tIVFNutE6sfx+H5RCQBIFLLca/OT2Bpu9NrOyoqz8zVVJQRIFtNOE9U6TVhoTvjHH6PkIzasdtwMR0Qx6bGUfEamUg+IJR8UTjqttBtWun/X/gYxUKQKUDOmFm3SjLZbjmyxHt1iJbxHL7AWhgDeFbx8e8O3D/Rd+5gBdGSazkv3DMVkaoa65Q3VevDA69YqkIZAxzXCZm9knJKrJQfmYw+IJg3KHfrVbSyP/QPZdhAQipBdt0Ys2WYpusRzfZCW+TSwbF7pXvJH1SeFHp8KEqS44LqcMah5EJBXtMGU5apLIkFE1IxSKRhBjnKEbNhb8CO0s2hm2Z32eTo8YVFOG1QxjLdpZxjrnuJzweHroZbHr/53GkQiFWnRFgpf4KnNDtrnSXeV8wvPPEXe6Xf7vd94nC0KO85yZriiM4fFogHWO/dmE3+5ts5xm9JKUNAiJlKKu2WKs9RwE47sXo7JgUBT08xkORxqEfLy8ylarTRa++v70/tIy//PdD/nPj+8zKHIKbdDW8mBwjLaGnemYf9h5ykqa1Z2EgPDEWvR8LVozqUpGVcEgL+gXMySCNAz5s7UNbrc7vE6bVgpBGgR8tLzKv75xm9/v7/Jw2McB/SLnb5895v/P3n89WZLdeZ7Y5xzX4uq4ITNSl0YBaACNbkz3TPfMrs0u12ZsSeMLH2k0WzP+W+QLjQ80mpE2a7Ojdjjb0woNVVUomZUyIkNe7Vry4fi9GalFRWUlGvjBEpkVca9fv+7H3X/iK8ZJzNv9NS74bTq2jaMZCAF5I2M7iiMOwwWfnp7w5WREXOQMHJcozwiy7NmlgxBI4aJJRWuQ0m1y2leLVys0MJBSp6xTDDlDCB1T61LXasphyDYS/aXGqMrt1Gxcxrvw+vyVXjikMEGYuNYf4Vp/9ELvUcVDSZzmHE8CJosIicDUdbKipKprfMdECk0VGVnBIlbJdcsTSPnA7fVJoYobHVPfwdR3HvvsZVSV8gx4SBNaPNyIWTpHllXduOs2qmJnVmQNSNHD0ntYxru0OV9ZPV0b4Gt/dm7b+zZjaZ43tC4xtC4xSC8wSC9wO/w1eZ0QFwuSKmww+vV3Dod54CIt0dDx9R4b9jV23PfYdT+gY2ygCeMMVOrFwtI82sY6tvZdFxoCxzRwTIOB/916wNQ1K1Lws5S4dGmiSxNH67BuX2WU3mM/+oy96FPyKiFmQdl0x+tl2/41xtnpoildxTFxrnPR/T7r9lWG9qUzr/v24+7+mP/6868f+lld1cRpTllUeK7F9mbn3KFwLxp1w80pHuHmPBqGtDCkhaN12HCuc5LcVuddfkpaRaRVSFHnK4Wy7+K8CyQIgSUdxTGx32LX+z4b9hUG1u7qdd91aEJBndq6wyyLmOYhsyxCCDClRsdw6FuKWHySLtQz17Ap6oqu6apCA0FWFQRFys3FMb+a3OYgnnCUzFawlaWXwSyL8Q2rKTZUPHoUdKk1vJGHYTIriwAh0Zv7bF4pmFcjbvV7FZfaXYaOxyxL+O3omFGslPgOggUHwYLPx6d0bZurnT4X2x26lo1nmkgh1MSqKImKfKXGdBQGnEQhoziibdpc7/XxTYsfvwBk6Fp3wIbrc9pApiZJwiJNubeYsR/M+XR0TN9xudbps9Nq07VsHMNACkHZ7EuYZ8yyhNMo4igKOG32Zc31uN4dsO56zy0ql/mepeu80x+SFiXjOGZvMaeqK2Zpys8P9rg5HXN7NuV6b8Buu0PHtNCEJC5ypmnMzemET0+POYoCZmlCx7LZ9HxGiSTM82coT4EyivbQZR8pPaTwXn+hUZFTVjFJccQiv4VAw9YGgKCmQBMGNcPft2vmqWHqOp5TY+gaZVVzOF4wDWIF5zA01rstbFNnvIiYBTFRmuNYBpap45jGNzqOVTMlGc8jgjCl7dt4tknRYJhdy1jtV5LmjOeRMqvxHExTPaw1KTENVQjFSUZWKDdl1zJoedbvLCHtvMPTughLKClT6yqz/IhZfsQ8P2VRjEjKxXeiMiOQDZ7ex9cHtIwBbX2NtrFOx9ygbQxxtS5SaK+UOJjSxtM7WNLDEBZlXbzxEJBvP2qquqAgp3qBzrRK6yS+3mfbeRdf77PlvM0sP2aeHzPPT5gXJ2TV65PENZs109IHtI0hHWO9+XuTjrmOp3Vfy36cjY1hmw/f23ns53VdI6Wk07Lpd32Ga9/EJPXVY8XNecGJxPK8t4w1Lrjv0zLW2Hbeac77yeq852X82rw3ltyblr5G21hrzru6V3SMddzv4Lw/K85yNLKqJCliTtMFrmbh6Tam1FmzWgR5wiKPScucjuFS1hU9U000hBDcCU/55fg2x8mcaRZxyRvy08E1NKF4FfM8ZpQG/HJ8i0kePnOfRLNfmpBvRDH2poYuNRwDfrC+hUDwq+MDfnN8yDSJmWep8n5IEm7WY07jEEvTFVdFNNdapTwv0oaTEeYZcZ5TVBVC0HBeXuwMGFLimSZ/vHUBU9P59fEBn54eM0ljwixbycNW1YjDMMDSNPTlRKOuKeuavCxJq5I4z9W+FDllXSMb4v6zmsaPHZtGeatYG3IcBVTU3JyOOYlC6romyDJuTsdMkpjPRydYmlqneXM8pknMKI5wDWWCuO23udBu88nJEcdhQPXEQkMhixQf+Sq6to4QRgO5f/V76quRweuCogpIyzFxfoAQGlAhm82V2j8eBZ7zCF2XgI6UgqIsOZ4GFEWJrkt8x8I2DYrK5HC04GQakGY5vZbL9qD9FH7Ei0dRVMRpzskk4GQasFN1kEIogmytiG+aJsmKknmUcjCakxclZVXh2SY1Co6iaTZZXjAPE6IkJ81L+m0X3319+tJvejh6u8Ev71C6BafpXU7TuxylNzlJbjPLj4mKKRXlqlOpTBQbZ2+Wf8PzO5gPO4U/6DwLVv9rHnKaMPD0vpKstS6rCYx9ibaxjiEsNPnNxoeGtJBCx9O7+PqAvE4ao7rHY/kNq7qirCvqRs1CIJ5GBvqdjGXCWdbZMzvby1DnTcPTu3h6l6F1iaLOGGf7HCc3OUpuoqU6YTElKmaKA/fIGqpX0t7LtfO0NXR21Txp3WgIJL7eo20MGdqX2bSvMbAusmbuqknea5QvPRvDgY8UWw//UAgMXcOydLptF899PvzzW4taFZhlnT+Ro/FoLM+7r/fw9R5r1iXKOuc0vcdxqs67TDQiOSMu5tRUzXaX5706c8f4JuddNvcLVex2jCHr9lU27KusWRcZWBdUSfQdnfdnxVmORlGXTLKIURqAqTgRXcNlYPrkzcQCUnpZSE2tJhqGhUBwLxzxX44/QwqJr1tc8tb4s+E7eLqJpRkcRFNuBsfsx+MXKDTEisz9qne134cCRZcSXUreH6xzud3FNQyiPOfefEpZV6oBWuTczzP2F0/GBSzJ2Yoor2BHpqbjGSYt08TR9RdCXRiahi4lP1jf5FK7i63rpGXB3fmMw3pBWdUN7ChVwiLP2JelOpgUElvT8RpOiq3pL3xeNSkZuh6ObrDIMqpaTb+ippDKK6VCtbeYPTRZW36+JtUavNDq8MP1La52++y2OyzSjL+9vwdPaISo46iQO5o8v2bNNwBXSnTp4RrbGNLHM3aRjQ+DKbsvyc/4xx1xmhNEKZNFxHQRo2kSTUqKolL48bpGkwKpJtVKWUU+eAh8kzidhhyM5kyDmCTLKcqKsqpYRCl5UeI5JlIKDk7nHE8CZkFM3kC9DEPD0DR6bYcdKcmLkjDJSbOCsqpUgvymi6R8JyGQQqNlrKFLk7YxZMd5l7icExdzonJGXC5IyqCBSUSKCFylFHVOWeeUdXEmkVTqbbJJBuSZpECXBrow0YW1gmMYwsbWfCzNx9F8bK2Frfk4soWjt3G0Nq6mHL7FOSUOEskl7/s4WpuK8qlwoaIuWeQx42zOfjyiZg1dDDCk/Y/unlHVJWVVvBJkTgiJjqHWkDDomJtcKD4gLuck5YKonBEWU9IqIisjsipW0446XfED6kZ556wyzrJIkEJDFyaGtDGkjSkcLM3D0Xy1PvQOjtbG0Vq4WgdX7678Eb7LBMh3rccVW5p7piblagr7XYUqokvKunyl875M5DvGEEOa9MwtLrrfU/eOckFUzIjKGWkZqnNfxWRVQvEK592UDoa0saUiqLt6G1c7c971Dq6mzrtKXd7M61MKiWdY+IZNVdeERco0j3B1E1+36Vs+bcNhlAUUVUlWFczzuClCPDzdoqYmKBKOkhnX/A0+7F5k1xvg6xa61KjrmrjKmBURWVU8f6e+YQjBQ91vta5qtJc2SP7dCEOTeJj8cH2Lnu1wdz7j7nzKYRhwFCrozzxLycqCrCxX3XhNSkyp4egGnmHQsix6lsPAcbnY7nC9N+Bqt/dShnum1OhYFj/d2mHd9bg7n3FvMeMwWKzUsYIsJS3LBvamMG+6VAWOo6sip21a9GyHNdflUrvb7Ev/pR29LU3jareHpWmsuS43BiPuzqcchAumSUKQZ+SN+IUhJa5hrGBSu60Ob/XXeLe/RtuysTQNzzBee0/vFQsNlQRrwsbUeliyi6NvrgoNicE/xovhVSPPS6IkI8tVgm6bOpZpUJQlmiaawkMd0yWH4jx4D3UN0yDm3tGEvFTqG3lZrgqNOM1Z7/nomuRkGnBwOiNvCFCTeYzUBHajyNNvudR1TZYXZEVBVSlI1nnXGVWj0AX16jh8Ezngs7FUrKmrmrxUhZKhy1Xicl5cE/UdwBZtHKPDwNwFavI6Iy1DZvkRi/yUoBgTlqpDHTewqiU0RhUbKlEtqhIpJJrQ0KTejPI1pNAxlwkDDrpwcHQfV2/RMgb4Rp+Wvoav99Clhf6tkXXVedpy3mbLefuZr0zKjONkyp3wiNP0NlVtoMsOuvzHNhlT3eaqLl9oovFoSCFBSHypOt1LFfe0VPj9WX7ENDskLGZE5ZS4UIloWoVkVbwqVsuVzLiaG0mhK3KvNDAa7L2jtXC0Fp7ewzcGdIwhHWMDS7rowjpXDtY3DampYsI0dAxDoywrykZAIy9UcVuWNYahfQtu7y8WVV1SnZFif5lQUA+JL/v4Rn/187QMSapwRRaPihnhI+f9wX2jUI2K5u4sG/lqTeho0sSUjlK/kuq8+3oXX+/SMdfpGEptThf2ud1zG9puY/wlAONB0XIOnyEROJqJq5kIICkyZllE11STjL6piglDaJR1RVxmzPNYSdBaLp5uAxCXGdMswpQ6l/0ha1YLWzOoqMnKglkecZosiMvn+BCcQ0gUh0Oe4XEkZY4ldXT5gDgpeH6uoEu5klVVHXKV9nUsC6OR2335/RM4ukHHtFhzvJVsat92cQyjyWcevL6qKqqypihKirxENzQsy0BI9SJDahhS493BsDHPm3NvMeOryYibkzGH4YLTOCTMc5IGjrR8n63rq6R+6Hhsej47XotLnS5Xen1apvXM71iWFVVZITWF7jA01WD9YG2Dt3tqX+4upnw1HnFrNuEwDBjFEVGekZbFQ/viGgYt06JvOwxdjx2/zW5bqVld6nQaCeOXO96GpnGh1WHoemz7ba51+3x8csiXkxEHwYJJEpMUqvi1GinfDdfnem/A94brXOn0uNBqU9Y1UZ4zcF02XJ+4UEI1LcvCkM8WHfqm8UqFhuqz61QUZOWErByTlCcs1YA84yIt8xrnaZb3uxyOZSClh65rbK91GpUpSVlVaJqk6zuN/K1Y3T6quiYv1MPT0OU38N0AIQVlVpPXJVlekmYlQZQQxhlpVuBYJkv52GUCbls6tmngWOpPXauuYcu1KIOaSRSRZM8jFL185HnJLEqoq1rxVAwdQ9fOpQKvoZnKZByM5qRZwc6wQ7/lrgq984ggSgniDMcysC0dsznfmtCxNJeu2MTVO/SrC+RVSlGnDyUIy+S0piJKM+ZRgiYlrmVh6cvjIZtOpUogwqggikt8w8U3XFzTwzVcDGmjC1MpZP0hXluo9Ko6d+UgXRoI4SOQuFqnWT8ZRZWtVM5Uklmt4FRnO9vizFRMCqUipQuj6XJbq063JV3kd6wm9KS4szfi69snvH11g8u7AyaziJNRwJdfH3F4PGd7s8OFrR4Xd/qsDV6Ps/DDsQSznW8TRpcmDgJpbuPqPYoqXU2vyrNT0Bc877o00ITZnPsCg1Th30V9zneKCsihGlMXeyAshH4RhM83AlQ8Ekuokin1FZ8izFPclkXHdNGl8qWwNWMlcTuwfLqmR1u30YWGKXUczSSpcu5HY/qmR1lXBEXKNAv5an7IR9O7nCaL5+/QNwxLUw7ltqaaQ6M04FZwzLrdoWu6FLVSkrQ1HeM51+nQ8fiT7Qtc6/b45xevriROd1sdLrTaeKb50m1h1zD43to6Q9flZzsXVyZwPcvhem9A17LR5YPOfRLnLCYRx/cnHNwbsXVxwDvf38W0Hm9+CaBn2xhS0rcd3u2vEeU5caEgQ2VVra4uKQS6kJiahqXpOLqBVtRUYYGTCPT6+c/1cBEzH4e0eh6d3sOCJpqUDBwHU9NYczw+WFsnKnKSQjUAi+oBgErJxMtV8bOcbCj5XBNTvhiE62mhC0nXttFkj45l8b21DcIiJy2KFedWl+pYuLpB17bp2y5ty2pgfOAZgp9tX6RnOZRNA+xqp8+W38J9ih/HecSrXelCIoXRcDIERRWSV0uH7BpDa/O6MDXLRLeq1Wixbtzxvo1Pl0KN51+m8hNCYJk6lqnT9uzVz8/qwQsgjDNsU1+Rs9UkQVBWFXr96rd+09DxHQsBFGWFJmUjbvHgApQCbEvHdZQbpBDgOzaeY2KbBr5jKbyfJvFdizQvmYXPPgarAqSGOEpJooyyKKmqGk2XGKaO41oYlr58GWWpEuvRTJGdei1XTc40CZUifqn9FY+pYS1HzUvs5HKjK/Ry3SgBlRVJmnMwmrOIUlquRdu1EEJfPWCrql4Zfj2YMj34rOXvlxOX5e+XU55ZmHA6C5v9t5XCiCaUPKjQMaWjvFXqM99n+XVW/6d+No8SjrMAQ9fo+Q62aWAZ2urclQ0U7igOKNMA33Toag6W0DGEhgRE/TD/QU1cKup6+f34xlO0uq7JqoKsyika/gU8kHLUhIatPZha1NAYahXM84hxOm8AYmptmpqOoz3oROVVSVbmFHXZcDvUsdOFhi41TGmgC42oTEjLfEUCTcqcvIE6SCGafRDEZUJdg6tbGPLbSajrM/87r9CEgSaMxrdgcG7b/V2Jw6MZv/74Hp2Ww+5On9Ek5ObtEz776oB79yeMpyFxnNFpO99RoQHqxnO+SlGaMNA0A1NzOS/kdF2XQAnViLq8D6ICSsSS67P8PY96GJ0lOJ/9jqJZ61XzR6q/64K6mlOXd0A4CNmn1myURL14wmfJM59ZP/Jn+blypWp5VsnJ1kwMqRMWKWGZ4mgmLd1GF2pyvZx6TPOIrCrpGC6+YaNL5aWxYXeo65q9aEzHcGnpNvM8ZpyF3I8nK6M+XX67EEJTGrQMh47h0jZcpnnE5/P7BEXKut2mqEt0IRlabTrms+9fXduma9vneruwdZ2r3T5Xu/3nvxjI04L5JGTv1gmf/+YuVVlz7b3tJxcaQqy8Lbb8l1/tk9MFdw+OqNMcscZz87X5JOLu18dcuDp8rNCQQtC2bNqWzU7r/C0TnhSPNm+X61uTcnVctv2n78vT3g+qEPlgbZ0P1tbPcY+fH68ob6uBdHCNbXTpUjVeATVqZGxra68Nz1nXyi4+zQvivKAoFfTnoYTznMIxDLquc27SiWeXv2Xq7Kx16Ldd6kopVPmuhalrq/HiS29fwLDrYZs6eSOl23JtBd1qVKT6bRfL1Lmw3qXfdlfwA1PXFaxIk4po2SS3Cm6k0fIsXMtURcAzoshLvvroHl99dI/JyZwoSOkNW2zuDnj/J1fYuTIEoSYN8zBhNI84mizQpMQydGxTp651kqJkHqZIKbANXalOPKgnMHSJqWvkRUXRjHFV8XI2qaaBrenojUJD2RQHuvZgrWRFwTxK1WTH0DFNHUvXQAjqqiaIU+ZRgq6pgtA21X4GScYsiDmaLBjNIjRN4toGlvF4V7uqauJmImSbxmrCVTb7SkNqy4uSvKzQNalMix5ZCmGSMQsTjiYLjicBmiZwLIM0LUAojxbT0NX3MNS6LcuKeZSQFSW2oY6Hpeto2jcoNICDeMTd6IRJtmCeRyotEAJXM+mZLd5t79I31YOjboqMRT7n5+Mv+a1mkVW5ShakyUVvyHvti/iNedYonTdwqzmTbLEq0gZWizWrw647ZN3u8tvZHW4GB/yod52L3gZfB/fZj0+p6xpfd3i7fQFNSH41/pqyLvmj/nU27N4rf+8/xOuNNCtYBAlZVlDXNQeHU76+fcLl3QEfvLPN3f0xR6cL4uS7869ZlRhvOn+tTqjrKZRH1OUBQvZAuCAsVLEUQjWHRtZ99YWEg/JbKlCQqOXPDaBS7yFTrxFG88eCpjFZUyDqEsRZ3fQY6pl6jbCb11tADnWqPqsuAA2EdmYfHoTeeGb4us0sj4iKDF1KrMZx25I6XdPjMJlxlMxJygzfsHE1RQbf9Qb8xca77EVjbgbHHMRT/vr0SwyhY2o6HcPhz4fv8LenX3GSLr5VnLsuJTYGb7U2+dnwOkGe8tcnX2JpBpamY0mDDbvDnw/foWN+t1LeLxffnHf6vFhMI7786B5ey2Z9u4vXsp/5+qP9Cb/+mxvohsbFaxvf6r69aKj7R/3Kzb+6SXjeFNDrSxUaD/CWAomOKTsY8sH0oqwSiipEkw7fFkejqCryoiQtCuKsIC0KkrwgyjKCNCcrCvKiVDf7c4b1bHR8bMNYFRpplBEFMYZl4Po2VVmR5wVxkBItYkzbxHIMbNfCch7GoD+6gAxdY9A5Xx8CIQQd31lNUpb9ICHEQ9MVAKujU7fdx9QLnhS2adDxneee4bKoiMOUW5/f5+/+42852Z+wmEWsb/e4+sEOg80Og60uJSrpnixiTmchJ9MQy9DotVySTHWjk6xgGsbUNZi61iTcolFeAdc28G2LOMuJ07yZGNCowNRNASXY6PsKxtRApZau7TVLs8SCIE6ZBsqoydA1Wo5F11fwqrKqiFK1rwC6ptFyLTqezTSIGc8jTqYhp7OQlmtRtL0nysgVZcU8TCjKil5LYGiSMMlWn6k3ggFZURLFGQIe2k6WF6R5yTRMmDSfeTwJcCxDyRcXqmiRTcHW77gIYZIXJVGaMw1i0rzA0DU826TrOzjCWE2GXjZqag6TCb+d3SYoEuIyRRMSXep4miJpJmewzRU1aZmxKCJOkqmaPlR54+hbUdYlF911TGlQU3OSzvhysc80WxAWyWqdzouQcbbA1lQH8E54xMfTW+y6QzadPvvxKZ/N7lLVNQOrzabTxxAaN4J9irri7faFl/6uL3Vc3qBkc3k/zKqSMFcj96xsJkRLFZVlkYvCBluajmcYD5EpoyInynPShpi5nCLrUsPUNHzDxDb0lerOeUZeKL5bkhVkWcF4GnE6DvjB9y7w1tUNRpOAvftT8vzbJ+y+KVFWFYsiJSoysuoBjMKUGi3DxtVNpS7YTADqWiXvdT2D6pS6OqYuDwEQcl0l/QioptTVCJXcLw1oNYQcUEsN6gDqBDDU7+sM6gYmVUeADtJDyDUEGvXKn2c58aip60xto5pRVxMQEjBU0SP7UC2o60lzIT2YlAg5BNlwQZs1ZkqNTafDpWzAYaLTNV0c7cHE0tFMtt0eizwmKrLVxMLSlIT8pt3hJ4Or6EIyShdMczXF6Boufcvn7dYml/0h8zzmdnhCS1dNkIcahlJn3WqjCUnXcHE084m+RL5uccHts2F3aOm22sczG9KEREjBZX+NgpJPpnt8OT9gmkdUdYWvO2hCklRPL6iTMmeeJSSlmjI/Wc708XA0A1s3cDUT55zhNHVdK+RCkDA+XhDMY6qyRjc0dEPDdkzsRsmyrmuyJCdL1bVeNMI5Qgp0XcMwdWxXmXNWZUWeFaRxxsHdEV9/dh+/7XDxrU3KUn1v2zHwWo5a2mVFmuSkccbe18d88dE91rd7XH5bKdoJAV7LwfHO7ktBlubkWUFRVM2+SHRDYloGtmOiN828NMkJFzF1BbohFQqiqBRXpapxPQuv5SCb5l7ZuHgr5IWS7C3rSsEZm6boar73FDTEEj1QVBVJUSCFwNL11XxwGcvne5CnBEVDsG/gdEII2oaNryvexln42zeJl5xoVKvphRCykfHLWJLekuKEqNjD1XfQTe9bmWrEWc7JPOTOaMrXxyOO5gGjICLJGzWCqjoDoTrf+NGlbTbaLdqO6qQc7434/Bc32dgd8M6PrhIFMaPDGV/96jaf/+ImW5fXuXB9g0vvbbP71tZztv7tx4s88l80LXiR10VBwuh4xsGdEfs3j4mjjCIrOD2cYjoGx/sT1i5OmeU5UaE6lIsoZTSPcG1Fll9EKfunM4pS6WJHSc4sTJQZU+P/UZQVg7bLsOOxiFPmUUqaq5uB75hYhs7JLCTLS4TYZK3jNYTzR/Y3ybh3PGURpwgBcarkfAcdj7cvSCVDXCqlsLKqOZ4sOJwE9FsOw66CaZRlxTRQBdP22tPHm2lecH80J05zhBDomuTe8RQEbPXbtD0LXQjiNOdgPKft2bQ9G02T6JrGeBGxd6KOS1XVzMKYk5kqNAxNrqBosyihqmreu7RO3W1xOFkwC5VTaVaUzMIE1zZ5+8IaWsdXU6xXSg5rTtM5t8Mjrvnb/LB7Fd9w8XQLvYFNrVmd1avLuiIpM0xp8P3uFXbcNeq65l50wq8nX7MoYqZ5SEVNUZUcxxOOkgnbzoA/G36A1qjzfLXY5+vgPut2l7bhkVclpqaKk6zMScucpFJSo0mZkZY5UhOKBFn/fmncK7hazSiO+Pj4iFuzCUdRQJBl5M0kuKgrpBAYUrLptdhtd/hgbZ3vDTfQhKSua25NJ3w2OmFvMWN/MVcTZGDguGz5LX6wvsn1bh/XMLH184elCQRRnDGeKohly7NwbBPTUFyoN4i7/loiLnN+M97jk8kBh/GMaaaaIBtOi58Nr/BOZ4OB5eHoTbFYzanLu9R1AHUF1RSqExAeUFFXY6juAinUGXU1o65mCOGA9EC/ppo0xR2oxqDtIGSned9cTSDqiLqagfTB+FBNI+rq8QdHNaEubqMmIJK6nEM9Be0awnCoy3tUxVcI0UFIl7ocQR2B+X2E9FDOvg03VLf5XneXC+6AuMxwNJN1+8E9uG95/KB7kUveGj/LYwaWj2/Yq11qGTaa6OMOTd5ub5FVJUVVYkodWzNYs1r4hs1/t/19gjzhsj9EPvKF1qwW/3LrQ9KqYGD6dEwXW3v8Gnirtcn/6fI/wW32cWkaeDYEsG53sDWTHafPz9auU9ZKytqQGr5us+M+Hbp0FC/4m+Ob3FycMkpfXC3rWmvIW50h77Q3uN4ePv8NLxFFXhIFCbe/PCTPSsqyIokyugOP/rDN5Xc2ufz2JgBVWXN8MOXg7ojj/SmTkzl1Dbqh0R34rG93ufzOFp2+R7hIOD2ccuerY776+B57N08wbQPTMmh3VQP38jubfPjTqwgBYZCwf+uUu18d8emv7jA6nPHbX9ymyFTCrRsaH/70Km9/eAGE2u/DvTGHd0cc3Z8wH4eq6WkZdAc+m7s9Lr+zRbeBa54cTPn4729SZCXdNZ8szZmNQ5IoI0tz3vn+Rb7/p9ewHANNk8SNq3neuIuHWUZWFgw9n4HjYOsGupTkpTKPTAulaudojW1CVSk0gK4TZBn7izmmprHlt7Abed9HpZa/Xpzwi9E97kczjuI5oAjtPxte4ceDiwxsj47pnMt5f7mJBiVlnVJTImpBUSfkpdL2FkiiYp9FdhMpLFpcO5cdXEZWlERZxv3JnBvHIz4/OOHT/WP2p3NOg5CsKL/17mHLNpX/RBPT0wVf/foOVVlz/fuXmI0Cbn+6x72vDji6c0qW5ESLmFbPO/dCo6pq6qoimCsikxAC3dDw2g5+x3moK/2iHerzfl2W5gSzmNloweTkAYEumMVMjhcspiGzacRBFJHUCsala3LlTl7XEKUZ909naJpkreOTl+XKVFAZDVarSUSS5YrsnpfMo4Q4zdlpyPeLKGURJQRxRsezn6hxn2QFB+M5UZozbIqRSRAjpWARZxRlRV5UBEmqCt5ZyI39UwZtlyjJ6bddur7qmBTVsztISwWvMM6YBjFVVbN/OsM0dIYdX6nPSAXtCuIMTZOqeCoVP2Eeptw5mtByLMUFaYj88yjBmCrIGzWczALyomJnrYPvWByNF0yCmGEzPVtEKUmWs4haDRfH5NXEesSKmKckJVv4unLdtTUTW5qYmkF5Rva2BlzN5pK3wTutCyvFnU9nd8iqnKhIqJtJyCwPScqcnunzvc5lzCVJMpvzySxjlkeMswU1NZ5mU9YViyImrZR6V1UrMYSoTBFCYEgDqyGQ/r5EUVXMs4R78xkfnxxyazYhygs1mahKZknCaRwBCoP9bn8N1zCJ8wJqCIuMeZry5WTELw73GcUR4zimaDpxSxdcvVFxu9zpYjVu8ecltGAaOo5jMJ/H3LpzSl3DxnpHGYdq8gxP6/cn8qrkbjjhl6O73FiccBSre+1Vf8CW02Hb7dAxziQMdURd3oO6VNMJNBQ/orknVjOq8l6TlGjU1QmUJ9Syr85jNaVGoy73oRohZI8aj7o8pq6OlAhMnVBXx1C3EdoVBXd6wlmp60XD3bAQcgPIqMsTEH2oA7WN8i5olwAX6jl1OUJUM+o6beAhqtCwNYMLbp8LT0m+Pd3G0212HyEr1E1j0hQGpmnQNT2u+hskhUJMmJqa1C0VIbuGt5oOLnmDiBopBL5u837nwooHp9Q55cOfBQytNmtmu/FaEKsmQN38WaotupqFI0227S7yUVnn58Q8i/lsdsgvR/fYC6fE5YvBCSdrEVII1u3zN70si5IkzqCG2ThQhUeYsphFHO9PcXyL7UvK/LksSo73J9z89D6LeUwcJFQNr3A2DgkXCd2Bj+2aapqR5ASziPk0IgpSJSwzDqka6dco6Cp1KSkoc4W2mE1CgllMHGUEs4jpSF07hqmTJpmSdShq4khNSm59fkAwj4kj5aUhhWAyWpAmGf31Nq5voRsawSzm5mcHZEnO1sV+8/kJSZyTxBlhkFBV1eqSSAplTJiVJUlZMImVSWDRrLO+DZ5hrBzIZ2lCVhZYmt44k1fYuoEUDnGRcxyGGJoihndsG88wH5suHycBH433+XJ+zM3FKaDEBXqmy7XWGr5xfn5EL1VoVHVBXi0o64S6LsnKKVF5CNRowiYpT0jKU7wqPHd+xCxO+OrwlI/3DvnlnfvsT+bM4oQ4y8mL6juBKORZQTCLSaKMGhgfTvns5zcZ7vT41//Tv+DWb/e48/l9gtn5u0GXRUmWFnz5m3v84j9/hmZIWl2P9398he/9ydU3RpJSNBCnx3/RkLepFWRIEww6HrqUREm24hckWU4QZ3R9h61+i7ZrUZQVYZyRFSVGrYGpvEpuhQmDtku/5ZEVJUmaM2i7XN7skxcVx00CnhblgwfEmSiriijJqGtY7/rKVb0q0TW1T3GqIBtBpDga81BNTsIkYxrG7Aw7XN7sk2S58iixni7Zaho624MOlhkxDWJmQcI0SOi3XUxDwzZVp8LUtYbn8bDyVpoXzMKEtbbHla0+WVEQJillVXM6C3EsQ/FsNA3L0NE0SV6o75fnBb2WqxTEqnoFwwoTJUhg8PLjUoHgur+NJiSLPOLj6S2KukIXGpe9DXbdIUO7i9nAGHSh0TY8LM3AlMaK2KkJiaWZaCspypR5HlFR07d8PP1h2U1PsxmYbahhmgXoQqNn+qRlznEyIStzDKFTNXjwaRaQVwW2ZuJq3x4R/E2MMM/4ajzi09Nj7s1neIbJX+xeYeh61NR8MT7lv+7dJSkL2qbFjza2+ae7l9n2W2hSsjed88nJEb89PebObMq7gyH//NJVzGa8fnc+4zBc8NHxIXvzGf+7a28zsN2H/AC+abR8m62NDkcnc/buT7h+dZ3339mi13VXIg1V9XtWabxk1GTU1RwhfNAvICpHwaBkB5AK9lQeUwsDIVxAguwg9F2EtkNdh1DcaAqJHIGGwFITkvKUWjjqeha2mjoIoC554kmpM+p6oZTU9ItQWgp+JUyoJg2MC5B9hH5Vwb6WRPE6QHE6vllCVDVQFXggKAJwEoQcLQLWfJeB664M3bKiJC/LRkBDvVYTEkt/4MycFSVJUaBLgW9ZmI13QlWr50zR/DE0iaXrVFVNXjVTxbJaGdktITS2bmC/ZKGBaIzj3qCpbVVW5GnB7tUhf/rfvI9lKZTAZ7+8zUd/f5MLB0Nm41ApZeYlB3fHHNwb894PL3LpHTXpCGYxv/3FbU4OppwczvA7Lo5vsXN5SKvjYjoGJwdT/I7Lz/7bD1jf6QLQ7nrYrqm6+5rk2vs7rG93KfKSO18d8faHu/zFv/oBAFJKBhvquZImGbNRwMGdEeOTOe/90SW2L68BMDlZrPbl9HCG69u0ew84M0IKpC7Z2Omzvt1F0yRlWakCyTFX0KmsLAnybLUW47xglqawWJAWBaILpqY1DuTVqimUFoWa8miSNcfFlJKs8fcI8oysLNlutbnc7WJ8h8qTL/2UXSLFlHxeQwQTirWhCRNdeis/jfOIsqrIy5LjecDHe4f84vY+H+8dMYuTh16nS9ng2sUKE3nel5djPlwVVkVFlmSkcUoSpSwmIePDKVc+uMAP/vwdpidzPvnrr8iS89fdzhoVh7tfHfKrv/oC0zYYbHYYbnXfmIesbujYroXXtvE7DllSUJYllmPS6Xt4bRvLMTEMTU0xygcV/hnvWvSmU6nMBmsl1tgYdGlSuQKfTAOOJwH9lstax2UexkyFwHNMBh2PzmRBEKcIFL8hTnPCOGUWJviOIrUrCV9J3ShcVXXN0jcXoZL7aRATxFmDE88brKZShFhyQaq6fqCM9ZQwdI1+26WoKuUIPwubKY0itUspKMuKvOEcxWnOPEywDaVMBjQKYqrDsZRHVgJd6ljVdaFUzwwdQ5NomjqWUkqq5oG25OJ807pUAJtOH0ez+Dq4T1SkZFVGWCXcCY9JqxxLM1bwKa1RiLE1A/2MlrtoFKqkkFR1hUA8wKM2U5OzB1asfqbOl6fbGFIjLlLCIqGoK1qGS1VXGFJnkikdfEsadA3vjS00nqUc8qqRFIXShJ/PiIqcdc/nw+EmV7s9pBT4psVhGDCO4wY65TcQKIMauL+Y8/f39wjyDEc3uNrt87Ptiyt4VH90zCcnkl8e3ef+Ys73hhtc6fTwDRPrnCBU/a7Htcvr7N0fczoKGPQ8Lu708VyTvKhY6/sURYXr/mPzZTnPeDAFUJCmkvqhhFQ2E4iSukn0RaMWJbQt6vzT1UREkcfNhgi+9M/KqWuJgjQZDb8iV5AnclVACLshmMOSJE6d80Ct6jGMFWrqAo+rYH2zSIqCRZI2989GIKSqOFoEHCwWGJrEN03CTBmjRVlOVhaYmr4S8dA1Sc9xcAydqq5Jy4JpHGNoGrZurAqNssHPL9KUWZLQsW02Wj5hnjGNklVhsVS4LJewRNfBbJ6DL3ovcHWTXb9HWGR0TYeoyJXgdsPXSMuCuMwIC/XndcQyye/2fa68s4nXdhAIjvbGpLHiNcynCqFR5CWnRzOO70+4/sEOjmshhMp9iqwgmEcEs4gkSml1HNyuTavrMjld4LZsOn2PS29vsHv1cYUl3dCwHJPBRpvf/vIOhqkx3Orw9oe7D72uLCriKGM2Djk5nHJ6OKcsq9W+BLOYLFU8ksU0Ig6Sh8jnuq7hejbDrQ5X3t3Cdp5yX2pOaVFWjXRuc46KgjDPSIqCvCwVt7EomKYJJ1FI2oj3OIaS000bCGxZK0f1pChomaqhWGvfFnP6+fFSd38pTEytj163qMkxtS6uvqP0uYVBVs1JyxGOtvHgRvYNIytKJlHMrdMJv7izz+cHJ0TZwxeFEALHNOg4jaKSrj+GRzuP2Gh7DytONV35NM6YHs/JswKv7eC2bEzHRG+wcd/GcCEOEo73xpweTJlPQmzXwvEssrR4U+oMXN9isNFm69IaF66tc3owIwoSNncHXH1vh83dAYNhm0VdcjIPuX04ZhGlHE0COp7N7noXx9JZ77VIspwv7p2siNq6JnEtU0EpTHWc4zTH1DXWOh6H48e1zmuaG31WMFnEHE8DBXMqK3RNQ9MEax2PaRDz9f0RUaJgTZsDNUlZRIrHkaS5UrJqtuu7FjtrbeZhwt9/dpejyYJpELPRe/roWUrRTB00wiQjSjMGbY+u76BrGnlREsQpp/OIo/ECKcWqoPAdC8cy2B60CeOMn392j6PJgpNZwMX1HpuDFuN5xDxMGsy6wDR0Wo7FsOtTI9g/na8KJ98xubTRo+3a6C/pWno2LGnQNX3ea1/kkrdBUmaMswW/HH/FXnzCut19iKeharFnXxxmoxQzyReMswVBkTQGeGo/wzLhNJ2xZnXomy3yqiAuU/biU8bpgp7VYsPuUdUVaZVzms7RhOSiN6Rr+qtu/JsWS9KfkqE+n23mVck4jgnyjK7lsO56uIbC/goh8A2THb9NVpachKHqsC0FS+uKg3DBL4/u873hBn+8tcO7gyHrrodspNDeH6zjGRZ351M+jxSH4/ZswuVO79wKjc31Nr5n8e71DdK0oN/z6LQddE1iGjU/+vAicZoz7H9X0rZvfgjhUmubCgKV/4a6GlGXB6DrCCqE7IJ+VfEhqlMUGdxUkwPhKg5GNQG5htCGiruBidA2gZS6PFVFhdBRxUEGdUhdHvMAolWo4kXYCG0L6oQq/3VDCh8jZAshh9RiH6ipy32qOlKk9ToEroLonClWXj3mScrtyUTJX+sGcZ4T5hlBmhFmOXmTuN2bzrg3mxMkyhW67zrYhk6Y5uia5Eq/x6bv4xoGeVkyTRIMqTFwHVQRxgqieHc64+vRmKv9HgPX4TSM+Oz4BABNCEUOrpQcvaUpmXLPXJL6X+yGsGG3+Oebb/NH/V2CPCUpc9JKwSTTsuAgmnE3nPD14pSbi5OV8dy3GYal0+o4OL6FlA/8wWzXpDtQkOHFVME387RgPg4ZHy/4zd9+zeHeWP08KxgfzzFMnaKoKIoXJ7q/bNR1rSBW44D5JOLkYMov/+pL7nx1BEAaZ4xPFvhtR5kRFtVDTSLLMRhstGl3vWcqdNqaMto7LkIWWYoU0LFtXMOgbZpoUpBVapI2z7IVtMrRDVxD/fGahlDZNDrPQvbKqqKqq+8MKvyShYaOFDp1bVJTqktHEw3pW2LINqbsoAnn3MZ1SV5wfzLn1smYu6Mpp4FahIam4Zg6Xdeh7zn0PJe+py58JX96/oXGtfU+rvlAhcEwdfyOSxKm3PjNHdIkZ/PykO5aSy0q8QD/ed4RhSnHe2PGx3OSKENKSZ4WKzzimxCGqeO1HS69vUkwizg9nBEtEjYvDrh4fYPhTo9Wy2ajaCE1yWgWkugFXd+h13JwbRPPUtKv8yhluogwdY2OZ+NYxirhtgx9RYpe7/m0PZth1yPJcnzHWhkNrnW81X/3Wi5VXePaZjMxAcvQWe/6GJrGJIgxdI22Z9PxHFqOkkDs+g62pbp0hq7hWAa7ww4X1rtM5kp1ynNMpVbl2limvnIePxtSCKSu4dom/baDJgXrPZ9hx1/J0ALYht6okdXYpq7cjgX4jsXOsMNoFqrPtE2EaLE1aHNh2ME2dBxTEc0821THyjQYdn2FKw1iirLEd5TiVMu1cCx9lTS+TCxxx9MsYJQt1HdDrPwu0kp1zpbeJMtYXqHP+kRbMzCky2k6QxMasyzgRnAfvSGDT9IFWsMLGVhKXz7IY75Y7HE/HjG0O2zaPcq6YpIF3A2PKeuK661teob/XLOr1x1VpaZ2YZKxCBMcq1F4axTSpBQrTPdKMa2ulb/PyuH+yduua1bdTE0+8H9Z3iuXhEHZFDfLzRRVRVYWTJKY+8GcH29u8+5gyJbfwjMfqLJYnk8NdG21v5Mk4SAM2PDOL+lv+TYt/8lylXVds7XRecht4Q/xhBAOQtsGoauiQNgIOUTIHkI41MJWxYNwoGzI1sIE0W1e2wNtE+SmKi6ED8JAaENUiWytYE1CdhqSOaCtK2Uq4bBKPYSP0C4opapm0iFkXxU7svVIISEQ0ofaUVAuYcM5wEGSomASxZTNdZSVStUyK8qGEK6SxyDLGIcRcdNxNjRN8eLSBE1IBq5LyzSRUpDkCmdvafrKBBfUVHwcxWpaMl/Qdx2quibMMo4WAaBym7xSsuamJnEMg82W/5D31ouEb1j4hqVQIZX6LllVqGKjLPh6cYouJNMs4tZiqWn0muLRj3rov8VDP66VFBNLjXrD1NnY6eG1HXprPnZDqD77zrqqqRsI5fLYP3UStLqPPvu1da22WTdqlgiB5Zhs7fbpDHy6fR/LMZBnCgrd0HA9C8sxnjmJsnWdjm2TNpMLhdZQ5G7X0BuOhUST9Yp3oXw1TLxGdMM3TawGXjVwHDxD5aoty3ql5/p5xis+ZR9Va1H/lsLElN0Gb30+XyxMM74+HnPrZEJ0hojt2yY7vTYfXtjkx5d3WPM9fFtV/FJ+O6hEzzJpOQ9gYY5vs77b5+juiL/9Xz7iyvs7vPfTa6zvDqirmqqsKMtSLfhzjjhION6fMB8Hb1Rx8Wjohsb1D3fZ2B2QJTllUWI7Jo5n0ep5GLrGWtej7dlcGHYU9KjxEWm5FpqUdFsO60VJlndXEnBaA5WTUqAJwfZam/cvb9DxbCxD58pWn81+m5ZjoUvJ9qDNWtvDMDQE0HZtsqLA1BWHwbHMFQyr13LJimLlaWGbOp5jYZsGrm1QlurneVmSFxWOpX6+1W+T5sUq+fNdC69Zk0+Ltmvxg2vb5GWFZah9sRqfkLZrc3V7wHozGTE0qT6r4V94tsl61199ZlXVuLaBbRmsdTyyvERKdax8x8Iw1LSn5VrsFGUDr3pwrKV8VPfkxaOqKz6f3+Pn4y8aWVO5KjSkUPyNtvHyeu+2ZtI1W8zyHlt2n1G64N8f/JKlDZ4pdTbtARt2j3W7S14V6EIjKXNO0hmG1Nl0VKFR1RVBEZOUGbY06Zge+hsGnSrKijjN2TuecmPvlK1Bm/cub6ywvWazRpZ8hCwvKYoS2zKwzWcnXbqU+KaFpemMk4jTOCIpigYiCFGecxgGxEVO17LxTUupk5UF8zQlylV319YNhq6L+8iUQjSf0TJN2pal4CNJTFaWT9yfbyv+UGQ8J4SL0HZADhB6Co2juBAuiFZz/CqEXAP9Ko1wJogWYCCMd1VxIKwGAmWjpGz7COGpCQUFjUhy8/sSTa4DVVO0OA3sqm7UrNYRNAqWda3UqjBYwqiEtoM0PnjA9ZBdngyxevlYyoXO05Q4z5VZqKFT1nXD51OkblvXads2vaZIN85Mf6VQk4i8LInznHmaMo0TXMMgqx7AaeM853ARME2UcqLWQEFVE6CmbPalbMjkGSDFg/e/yrdd7qsmJVat49UK+D7PE/qWh6Mp3sLrqDOytFBk7TBt/K3U1DaJM6ajgLquafcacZOsoNVx6A58PvyTq/zgT64tx+DUVa2aaI0ErWk3DeDme1RlRVVWq2LjiTTRpraql9KzS+i2OPsageNZdPoerY7L2maHH/3ZW7zz/d1VR6eulTyv33KwXRPjUQPFF5hAWbquvFM0nQ2/hWxEAqRQEyxDU8/Uuq5xDZOubVNUDZenkRFf8np8s6ZlWStlTVPTcHT9iTLLryte6SkrHsFJr36O3oxLzy/iPGd/MudgOifNS3VANclWp8WPLm3zRxe3+cHFLTqOSjDPW7f9WdHu+1x5/wKmbaLfPmF9d8DOtQ1aXUWAW9vu8c6PrtDf7Dx/Yy8ZcZBytDdmNgqeKNX6XceyehdC0Ftr0Vt7OozItUwcs36om/Bo9b9U41ia0Dz6+0ePcMdzOGtL4jsWOI/89xPC0DVc6ymf1UwwnrYvS9UQ+YT9f1qYhr6Sxn00NCkxDV0pWT0SuqYKIL82VaIoxGrt13WNb5srTwSav4QQOJZUJojqhS+F+X3ud5E6biPTKIXij+hCo2+12LR7dEwPKSROY+B30V3HkDq29uBc+LrDJU/9vGv6eLqDq1kMrQ7XWlscJ1NO01kzKq8ZWB2GVod1u4erWRRSp6wrLnpD0ipj2xnQN1srKMI1f5u0yhhYbWzNfEUp328vyqomzQuiZqLRciyiJCPNS2ZBzFrXY2vQXvGLgNU9EfPZmve2rrPb7jBNYsZJxEGw4JeH9zkIlEHmndmUaRLjGga7re6KBL5UpVrihnUpcBpuzTIeuDMLpdIjlSJcWj5ZeOHbiDdFAONND7E00UNNSmE5XXz0+Cm52wc/b14j+srj4rFwmqLiUVnv5n2y+5QdsgCfB5nug30RwqOWA4RcR2gbD/3uvEpK1zQY+h6Wris4imHgmiZxrjDuXcfB0nQGrtso1qmErkYoSGahZGM7to1rmpiahmtUrLmuMkqV2mptGppGyzLZbPm0LYt130OTihti6RqmptOyTIIsI0wzfMuiY1v41sNQo5cJIRRd/1E/VrfhyBmvET4qhHquLaYRt744xLRU4258PMeyDbyWTbvrIYSSlB1udZmcBhimThymjXmxOu6mZdDRJZZtrjr2AoWkaDXiELe/PCQMEnRdo933GKy3V14Xy2Njuya9NZ/FLOLTX95WHh2WTn+9Tbvn4rgmnb6/Io5rmiSOspXQTVVVOJ6FbmpYtvG4wbJ4/r1pCYkzNe0hv7MnhQX45rMhg65xvv4n3zTerHbeEyIrSk6DkFEQk5elImZZJtfW+/zzd69xZdij49gK/vKa96233sb2rnLlgwukcY7bsvF7HrquoWmSt//oMluXhrTXzh8vHIUJx/vjh+TbftfjeRfj60wkXmVfnnVz+DbjacX1s3ZFPO8FLxmakLzXucgFd21VgKkbpvKssKSBo1noQtIxPBzNYsPuIYRYGV8BDK0Ofzp4VxVFjSqUJiQDq42r27zVUqZ+y5TEEDqm1HF0q9kPjbbh8ufD7/Gj3lt0TR9ft6lRMKz/3vgJVV3Rs1poQnvjut91XVOUNbqm0W256I0088Foztf7p3zv6hab/Tans5Bb98d0fJuOr2CGzwvfMHl3sEZZVdyaTbg1m6701k1NQ5dKVedyp8dPt3bYabUxpCR9dCDxHFGn3zd52d/teN4V8Cpd0Fe9qp7wPtlDaFfPTDDOP3qOg6MbjeqTghXqDRG7rGpc08DWdSy9w2bLhzPQwqUsLYDRwF2EEHQdm4HnIoXAM83VN+s6Nu9tDFXRXtU4RqMMqOt0LJvNdovLvS53pzPuTWdc6nW52O3gmsYb1xR5lTAtg3bPZTYK+Lv/9ClZWhJHKYahs31pjbWtbqPapORtL1wZkkQZ92+P+OrjPYRUx76uavrrbX76l+/h+jZCNEWYAMez2L444ODemL/9j59i2gauZ/HuDy/y43/6NvpZqWcB3b7Ppbc2OL4/5d/8P/4W17Nod11+/M/eodO/jO0o/sjutXWKvOTm5wf89he31b7QQDYvDvjpX76H7Zgrj6dXjd/9s/x4nGuhoUiaq3rsTNK15ClUjeeGtpKzfF4UZUWQZARpSlFWeJbJetvnQq/Dbr/Dmu+tcMavOyzHxHLMhxVizvyz1fdp9bxz3bciL8nTnGAaMR0FREFy7g7o30X8rhcZL/K+byOeWPC85mO53F7X9Omazy+qTU1iaga+8fikRilRPZ40P+3nj+0Liku2Yfce/1yp4+vnY0D0bcVS+cYydLzm3jKeRZxOQyaLmDjNgZosLwnjjI5v41oGhv58ozopBY5uYGo6RTNl6Fg2LVOpQrVNi67t8E5/jd12l7ZlrUbyTqOco0tJXlUsshRH11H9tQfY5rKqScqikfaUuLqB/h3jg/8QT4vnnZdXOW/nWGQAQnRAu4CQ7W+w7WeHpSsBmSUH4uynnO0uGw3+/dGfPy28J3SdTV3HPAM5XL67ZVlc6HZY9z3WPJe8UoqAW+0WA8/9zppY5xVG4/G1fXFAWZbkaQFCkCU5TqwmBv1hi42dHmajqljqkvWdHlVVc3hvxOh4/lBO2e55mFYzQTgzmW91Ha5/sIPfcZhPGl8gV8G1xRkY8/L1w+0uH/70KrNRwHwaY7smfsvGtBWvQtM1bNdic1dN8Q7vjZmOgwf7Ute0ui6G+YAb7Hccrr67heUYdHoepqU/Pul4JH6Xz+/z4nwLDZaSmpqCUT3224K6zpHCRPBiahFlXasRZq6wxJ5lsNvvstHxMXXtuQ/X1xmKLPSEX4jzu0Vmac58HCrDmllMluTfiYfIH+IP8Yc4/5BSYJk6tqVkjIMo4yhU0syalCt5X8UnkrRdm2HXf6aiyTLKqibMcuZZwiJLGdgO/+r6O1zvDbB1BfHQpSKf+oa5EjGwNB1pCTzDxJSSpCg4jaLHxvc1KDJ+lrLIUkxNdWmNb6Bk9of4PQ/ZQsgHqk3fdjz6nH7ac/u8046+4+CZBkZjDDj0XDq28t94g1KcVw7LNRkYGq2uw8W3NlYFm+IWVgquZOoPyb9KKVjb7NDuuVx+e5Mif9jZXDc0vJazEt5ZRqfv88FPrvDWh7sUjfyrlALHVaTsR2Pr0oDumk+RV5RFidRkw/84I1NraKzv9Oiutbj6/jZl/vCYdyl8s9yX4WaHP/kX7yGaz9VN7TsnZH+Xca6FRlocEeY3sfVNPOPKY5uP83vExT6ecRXX2H3yRh6NhhxVVYr+aWgaHdeiZVsrWcbvKhbTkMnxnMU4YDEJKfKS8oy8mWkZmI7JzrV1ti4PX2ibRVGSRhlJlBKHGWmckaY5WVI0TtsRs1HAVx/dawhVjZpFkjOfhHz50d1GOu7Fj4vXdrj8zhaDzc5LYfYX04jp6YLJqXL+rqsXh3A5vs2Vd7dZ33m88/wqMRsHzEYBkxO1L65v4bUdNnb7DNY7ZGlOHGaMjmbMRgvSJKcsKjRd4ngW7b5Hu+fR6npYDbFMkfkrFtOQ0dGcYBYRBw+O+bJL0ltvM1hvoxkauv5qSVXViAckUcpiGin30SAlS3LyrKBsyG1CCqSUGJaGYRq4voXrK/3wVtdFNGIIf4jfzdCkxDKU2hk1uLbybGl7tlIl6/kgoO3Z7Aw7dFsO1qPkw2dEjfKIyaqSpMi5NZuQlSVmQxaVCOxG5WTD9djy2+hSYkqNoevxVn+NvCr59fEBUgo6lr0SOziNIm7NJozjGCEEQ8dlp9XG1b9dT4vHJrpLfti3+ql/iNcRYuXP8To+6/kr5pvmG097t6lrD0nnL6cs/1hCa5J3yzZovSBlVQiBZRvqefwSNFfD1B8nZD8jHNfCcZ/t/SalwHbMp/tgPBJLtMsfQsW5ruQwv8Vh+G/o2X+Mo19Ee4gYXrPIPmMU/xWb3v/w4oXGMhrijSYltmE004zv9lEyPpzx6d/f4NYne9z5fF8lhmm+kkDrDHy6623++f/xpy9eaGQF09OFcr3cnzA+njMbh8xGAdOmoFlMI8J5TBKmq/fFYUKW5Pztv/+Ej/7mxks9ZHeurvM//l/+Gd2hkuV90eM6Oprxxa/u8OkvbvHpP9x6rMp/VmzsDvg//E9/eW6FxunBlC9+fZff/vwmn/3yNlu7Ay5cXedn/92H9IZtoiDlZH/Cr//6K778zR0mJwvSOMP2LIZbXa5/eIFrH1zg8rvbq0KjLCuSOGP/1ikf/c1X3LtxxOG9MXlWIBAMNjtsXOjzwU+v4vzpNRzPfuVCo64qsjRnfDzn1ucH7N885vDuiOkoIJzHpFFGlhbousSwDfyOQ6vrsnlxwNbFNS6/u4XjWWg6yDfUG+IP8fzQpEAaOrqmlMCqxjxyqYxjGToC6HeUUpvxEutNNNvXpCJT3ppNuB8s0IVsFHAkhiYZuh4X/DZ/sr1Ly7JpmxaGlGz7LX6ytcP9xYK/2ruDaxhseD6ubiCAT0fHfHR8yEkc4egGO60OVzq9laHftxk1Z0G7f4g/xB/iD/GHeFPiXJ8AVZ2QlxPKKuJhOqBoOmmR+n2dPG0Tj4WUAkvXMXWtccmsG6nKl9OU/jYimIbsfXVIWZRc+WCXk/0xB7dO6Kz7DDZ7JFFKGmXKPfoFI4ky9m6dcPuz++zfOmF8PCecx4SLhHARk4QZSZw9RgCvypqqLMjHyuTmZcJ0DJIofWkGZ54VBPOY8fGcw7sjipcoNKQm1WeeU2RpQTiPGR/NOLhzStUc88npgnAec+OTe3z567t8+dE97n51SDCLyLMC0zaYnSquSzhX6/LC1XX8jsNsHHLzs/t89dFdvvjVHY7ujZmcLlRBJQTzScjkZE5ZlqRxxrUPdrjy3s5LcYayNGcxjRgdzTi4PeLgzin375xyejBhfKz2PYlS8rSgyNVYVzc0HM/C8S1GR3MO7ow42huzd/OEnctrbF5aw3F/NzsqkyhmbzpDCknfdfAtUxEhnyER/LQoK+Xim5clnmliGW9+h3A5UZQSDLTHYJHLZfVoB/RZsSxSJmnMb44PuTEZseH6+IaJrTeGfWdenxQFR1HIzdmE7Umb3VaHTc9n22/z060L/EY7JMgz7sym/MfbX6/efxQGjNOYC602Q8fjYruDaxgvpAS4xMcXVdWoW6l7fFkpqU9H11cO5Y+ac62kSYtcfSfDWNGGi2bKupSPfNOias7NIk8YpyGzPGGRJ43SV7F6zulCYmoavm7hGzYDy6VruivFIMVNOuvu/c1jeZQXecIiS5jnMfM8JS4y4jKnrJWLNSjvFUNqWJqOp5u0DJue5dLSLSzNeO6xX06lbi5O+Xx2RFaV1HVNx3Tomg7bboct99lt7TBPuR2MOUoWhEVK3sgqO7pBz3RZd1psOR0c/dkTkrvBmNvBiLD5nq5m4hkWV/wBW06bcRYySkIO4jmTNAIBHcPm3e4mW04HAZR1RVoWLIqUcRqxyBPCPCWtCvKqZMn00IREF5oyXtNNeqZL33JxNAPnW54EvgmxXGNBs+7nWcI8T4iKnKTMKerykTUmMTUdT7fUGjMdWoaNrekPqeC98v4sIV3UhHlKUGQEeUJQqPtL0lyXSwf3mqUMrUAXEkNq2JqBp5u0DZu2YdMxnQfX6Tk0x5f387jM1T0ji9UxK5Vj/XK/NKGuSVc38XWLnuXStzxsqWNI7cw949uPc33yKua/8USi90rl5iU9NjQpcUwdxzDIcuWxkOYlWVF+5yToKEg4vHPK1e9d4J/97/+Yrz++xy/+4ye89cPLfP/P3+HTv7/B5/9wC8t98RtGHKbc+eKQT/7+JvduHDEbK/nauqpUMdGYxvwhnhG1Kthm44BgFjOfhHz8t1/zN//2I6XhHSRKY7uuydOCOEgZHc2YnCwUoQvYvb7B4d0Rf/O/fMSNj/c4uT8hiTLKstF5RzA+njEbBQTTiHtfHQE/5fI72yBf3M05jTMO7pzy+a/v8Kv/8gV7N0+Iw4Q8LZS2d1U1EzI1JSuriiIvyRqo3OnBFE3X+OJXd2j3PX72Lz/kjw2NwWb3d7LQOFoE/G837mBqGu9vDdntKfPBV3mElFXNOFQOqlud1u9EofFtxDKBPwoD/tc7NzkMFvxgY4u3e2tcbHdoWcqMsqhKkqLgF4f3+Y93vmZ/PuOj40OkEKy7HtutFn3HXRmM3Z5N+Ov9u8rQDBg4Djt+mz/d2eWH61tKHvcFL4QatbbTomj8OkryqiIrStKyVL4dhqHWf+NrsIy8LDkIFoyjiK1WizXHRZNKcz7Kc6UC1JDa37So6pqiKjmK5/x2esDNxSm3gzGTLGKeJRSN/4unW/iGyQWvxyWvxwe9bd5ur9MyFI58qbn/bSQN4zTkTjDi5mLE7cWI4yTgNFV+K1mlpru6lHhNorztdrncGvBOZ4OLXo+elOgvoBhVA78e7/N///pvCfKUvKq43h7yVnvIX26+9dxCY5Yn/M3JLf7+5Db3oxnzXDWO1m2f97qb/HhwkdaG/dxC4/PZEf/fex9zEM85iRdsOG0ueF3+9e6HDG2fvXDKR+P7/PXJTT6fHiIEXG2t8X++/jPW7RaaUIIJszzhTjjms+khtxcj7kczpllMWKaNeWmNpRl4msnQabHhtHm3s8F73U3W7dbvRaGxjEkWPbTGjpIFp0lIXGakZdF49Gh4uknHdNh2u1zy+7zb2eCS30cK91wKDWjuRXXFKA3Zj2bcDcfshVNOkoBxGjLPE8IiIysLKuqHCoy2YdO3mmvA73O1tYalGY231PlcnYoLVzHLYr6YHfHl/JjbwZjjeME8j0nKgrKusTQdXzfZbNbvu51NvtfbomeqY7WUxH8dyKBv4clbU9UFZR0jaonAoKagqlPqOj+jTPViYWoaA9+j77lEmSKFny5CplHymNPw646lM69pm3TX2/hdF93UafVc1ncH3Pp0jzwvqF9CftYwdXrDFjtXhuimRrR4ePqzTDJn44Dx8Zyy6dzrhoZpGfTWWww2Xs63Y/PigM7Af+kF1+l5XHl3S3llDFur5LgsSsqyoiiUusTBnVNO7k9fatvfNNIkYzYJuff1EW7L4vDuiCTOaPc8+utt0jQniTLCuSLUF3nJ6HDGVx/dg7omiTMO747Yu3FMMIvw2g6dvo9uaERBwnwSUeSKNzM5VZyPg9unHN4bNVyPZ5vTZUnO9HTB3q0TPv35Tb786C73bhwxOVlQFhWywbPanonr2xiGhtQkVVlR5JWawCxi8qwkjXPytCBcxPz272+SZwXv//gK1z+8gONZv1MFR15WBGmGqcvGLKt+ZbGDrCy5NZ5wvAiwDZ2e+2YrTj0pzuMZUFQV8yzhNFImfXldcbHd5d3BkKHrYusGQqhEPysr9hZzfFNhludpQpznjTmihmFqXOsq9ZXddofDMGgmzdC1bIaux7uDNbb9Fp5hvvA9JcoyRnFM0jjjVo0hY1lVFGW1mkzM0pRRFGFoEl1qZGVBnBcchQFRnq8KIVnXFFXFLE0bkysD5w3Sls+rkqTMuR/NuDE/4ebilFvBiMN4zkkSEObpampQ1TW2ZmBrOvMs4ThecJQE3Fyc8lZ7na7pKJ6N1M7FlEsVeSX3wgl3gjF3wjF3gzHHyYKTOGDedJ+z5nVCKNNUSxqc6iGjNOQwnnMvHHPB7XHZ73PB67HptPGa5Plp66JGnbd5nrLIExzdwJbqez8tlg3HpMw5iGbcCkaMkoCoVAa/ZV3h6ia7Xo+8Kp6+nebvaRZzOxhxmgRMsxjfsM+8RvGbwiJlnIYcJQulMqTbTLKIcRoSFTnHyaJJAEdNkrpgnEZERUZaFVSN8exyCjTOIg7jOadJwK3glLfbG7zVHrJut1izz18e/7uO5Rrbj2bcCcbcDUbcCVWyfJwEzDM1PcuaCdDSC8TSdFzdZJSq47UXTrjg9bjsqTW25bbxG6nzl81nFs26vhdOuBdOOU4WHMcLTs8UGItmspE2U42lb5YuJYbUcTSDo8TiKF6wF064tRhxwz/lamvAtdYarmFhiFfjFi+nZOM04sbihJvzE24GI/ab9TXLE+IiI1/5HUlszWCaxZwkASdJwJ1wzPWWKt6DPMXSdPTXYOR3zvK2Cilb1RllHSJrA00Y1HVOWUVUdUZN4+z5gmEZOustn2HL43geKGfN2YJREFF8x/4RUsiVZ4YUAmrlMCmkxHIM6qomDhLy7Ok3t0fD9iwuvrWB61tcnm6RpQ+/Nw4SZpOQW5/dZzGNKIsMANM2aHVdrn1wgfd/fOW5Umpno91zGWx0lKP6S1wAvaa4uvjWJj/6Z++QxhlZkpMmOWmSkUY5i1nE3/y7j7+DQiOnGofc+nSfLMkZHc0wbYOdq0N6wzbzScj4eMb926pwAwjmMTc+2SMOE+JIOZUe358ggM3Lfdo9H8e3ON6fUHx5QLRQ53v5vQ/3xty7ccTu9Y3nFhppkrN384RPfn6Tn/+nT7n71SFFXq4gcYap0eq6DDY7rO/0cH0b09LJ0oIkSjnam3Bw95RokVAWJXlWUBQln/7iFve+PqKua7prPmtbv5uTjfOIrCi4cTLi69MxVwY9rq49yWTsH38UVckkSRg1TuCWZrDb7nCx3VG6/83rdE1iatC2LHq2rSYKpUoIliGAS+0uO36boq4aMz51P1921Q3tgVvti8Yiy7gznZIUBVIuH9wPHshLuNQ4jrgxHuEaJrauM0+VwlVaFGhSUjT7KoSgrGtmSUJRVQw976mf/V1EVpXMsphPJgf8z3ufcDsYM80i8qqkbIw/ax7Ag8MiJSqyJgke89vpAT3T5V9svc33etskZY6l6efitZBVJUGR8uvxPv9+/zPuRzMOkzlVXa2cq5eGmcr4U/mAZ2VJUCScpAE3FicYI41Np833ezv8eHARd93E1c2nTl0EykRuzfKJioxFkziN0nBVNDwtaiCvKiZZxCgNSc+sWZWchUyzaFWwPiviMuO0KRhA4BsmQ9t/6iRk2WEO8pSjZMFBNOPT6SH/9fhr7gZjsgZmo+CAcBb0XZYNxCpPOYjn3JifoAvJT4eXmWUxfzTY/UdbaARFxsfjff7d/c/ZCyccxLMGjvf0NZY2a/M0Dbm5OMEQGkPHX60xR7+Cq1uv5Lgyy2LuBhP+8+GX/NfjmwruVmSrYr9qrslHz2FV1815fFCA7okJmpC0DZt1u8Wfb1yjYzpsSg39GdfAs0JBLFNuByP+3f5n/HJ0j2kWERdNQ4J6BUEFKMqyWVsJ+9GUL+ZHOJrBn65foaorplmEJV8PpPRcCw1LW6Ntfo+qzjgK/x268NCkS1nFlHVEUYW0ze9hamsvvE3XNLgy7HE8X3DrdMwoiBgFEfuTGbdGEzRN0HNd5Yz7msOwdLyO03hpAEKgG5Kju6f8/D98zN0v7pPF2Wrq8CJhWjqDzQ6OZ5OlOWXxMO9hMY04PVSQHe0MRls3NBzXYuNCn7d/eBEpBUtV8Gct6ppaKUF0XRDqv+u6RvD8okPXVZdd0xVnoMjL5k9BnqvkN5jGfPaLWy/8/c8r6qqmyEtOD2dITbJ1aY3v/+wtNi70aHVdkijjeH/CR397gzufHxAsYoq8JJhFHNwdURQVhqmzudtnfafP2z+4SLuvdLuP98ds7Pa5+ek+Nz/Zp2xgWPNxwP3bJ/SGT3dBLxtlqeO9MZ//6jaf/cMtTg+mZEmOkILOwGf70hqblwZs7g7oDVu0uh6mraPr2mpKNB+HTE4X7N08Xk1CZqNA8Xeqmq8+uoflmPzgn7yF33WRL6FGlRYFYZoxjmKOFgF91+XKoIfdQI/uNddez3XYaKkErqprjhchp2G4WnF5WVLVNS3bouc4bHVaq6lCmGUczgJOw5B5oqAEhqZxMFtwGoYMPPchaOThfMG9yYwwy0nzYnXzb9kWbdtiu91mveUR5wVhlnE0D7g9mvD54Ql70xl/f3uPWay6om3b5upan4Gn9uW7FpX4tkMKga0pFZuqronyjNMoYpzESn5WSgWHynNmacKd2ZR7izkD22Hd8+lY9kM6/oamnbtk7VJWNypyZnFC27Joex5JURCkKalbrCBgaVmSFJEyBqwVhyMrS7TGYK0C0rIkyDKmSdJMatRa/K79CMpaTWf2wgm/GN3jl6N73A7GKjkuFcxLF5K26dC1XGypY2rqvJVNMqu4AxnHyYJfj/cYZxFZVTLPYrLqxXlyj8ayy/zF7IiPJvt8NN7nVnDKLEuIimwFEelYDm3DxtR0TKFRNbzJuMgJi5R5nqiub1lwxIJPxQFls//vdje44q/hGw+r/CzPiasbDJ0W4yxExBAVCoceF9lTz19Z100RFq0gLaCeYWoCUTDJYqZZQlqpe4d8wvMtLwvSqiAsMpJC8QOW04qh3cJ9ho9PXGbcCk7JqoLbDQTofjQjLQts3cRveCtmk9hVdU1elSzyhFmm4C5pVZCjzt/Xi5PVJGtgubQN57Fj9rsYRVWSVSVfzY/5aHKf34z3+XpxoiBlzRrTpaRvurRNB0sq2e2zaywqMmZ5rNYYBXUMn4lDyrqmqCve725yxV+jbdrP36EzoUsNWzdIyoLjZNHwpMoV/6Jt2ni6iaOZq0mAQFDUJWlVEhcZQVMgq+uwXJ3nT6cHdEyH7/d3+KC7haW9hFJgXZPXJZM0au4Zd/lidsRJsiAp1XRMlxotzaRruXi6iSn1hitUE5cZQa7uGdMs4YvZEQKoanXNx8Wzi/jziPMtNPQNuvaPGcV/zXHw7xBINOFQ1jFVnTFw/hkD559ga5svvE3PMrk27HM8D/j7W3sczhaMw5i9yZwbRyM801RSt99RoeH3PCzXbJJzlfDv3zjkzuf7BNOIPM2pyhe/+RumTn+9TW8IT8KMTE4XWI7JvRtHD2nn67qG5RgMt7tc+2Dn4UKjUex69PFa05gsCtAazF6FKopeiCIkVBJjWjqmqa8Ibkvn4LqsCRcxre53002syorJyRwB/PRfvM8//Vc/xPXtlcni4b0xSZQqYnheEMxi4jAlTTJGR3N2rgz54CdX+PBPrvHhz67T6ipzyNPDKZff3UIKwZ3PDxRvA5hPIu7fPuXi209f32VREcxiDu6O+PTnSq2ryAuEUMIHg402P/zzt3j/J1e58t42rY67cjw9G8uC7td//RX/8J8/48ZHe8xGAXVVkyU5Nz7ZI5hFrG12ufreNsIAXhDDmuQFp0HE50cn/GrvgHc21tjqtFaFxq3RhH/76Ze8vb7GDy9sqe9VVfx674BPDo6aLrlgkaQUVcVur8314QBT331QaKQZnx2d8OnBEXcnM5I8x7NMyqomSFN803yI9Ls/nfO/3bjN4SJgGiVkZUlRlez2OlzsdfmTy7sMfW9VZPzi7j6/3jvgy+NTxlGM4DafH50AcKnfxbdN+p7ze6FQpAmJZ5r4hgk1zNOUvcWMLb+ldPp1g6KqGCcRt6YTvhifcmMywhtucqHVZuC454YvflqYmkbHtpmlCZMkwdZ1WpZFXpbM05Sk0dBf8pTmaUqQprQt+wF3A1Xw1k3hschSJklMXdekRaHu0d9xUVnWNUmZr7qSX86PVlwMUKRvS9PZdNtcaw3pmg4dwyGvVXfyIJ5zEM24H804SRZ8PLnPV/MThraPJXWS53T+nxV50y3+eLLP/+v2rzhJAuZZQsXS+Vph5He9Lrten47h4OkmZV2RVAWjJOAonnMnGJM2OPFFnvDlPGWSxUyykKjMWLP8pybNjm42PAj1+7jMqDNFfH1a46yqKxZ5wqSBJpV1hRQSKVgletMsZpbFzX5VjdfXw5FWBfOmqMqayZIUAt+wWLdbz5Rpjoucr2bH7AUTvpwfc5wEK8hW33TZcjvsul3apoOt6eRVRVRm7IUTbi5GTNKIrCpW3ei7wYSDaM7Q9rno97joiX8UhUZelYRFxm+nB/y/7/yag2jOPI9X93pl8mlywety0VdrzNctSiqysuA0DTmOF9wORqs1FhQpNxYnTLKIaRYRFSl9y3vpQsPWDDqmgxCCqClsQZGqbc1gaPtsOm0Glk/XdHA0A4EgKXPmuZrkHURz0jJfTYHTqiDLCj6fHxGXOZqUvNUevlShUaHuZydJwF8f3+S/HN5gniera11rGgB9y+Nae40Nu03bdJCo6eRpGqzuGUGe8vX8lLvhhIHlMbR8wgYV823GuRYauvRxjAv06p9gaB3qulBPBgEg8c3ruMYldNl+4W2amkbXs7m81uVHl7bRpeTOaMLhbMFf37jDIklZJCkXem3W28rE73URXNa2evzwn75Ld9jGtA02Lq7xg3/2HtOTObPTxYo3ceH6ixdWz/Ox0DTtgQTtIy8TQoAEoUFQhQRFiKs7ONJRmFBqLGmiC528zknKhFE2IatyBmYPTWiMswkAQ2sNV3ebyremrEsEAk3IprtQNooZ+mpf5BlxyeUIT2rfna+DkIJOz2fz0oDBZodW18MwdXRDJdxey2br8ho7e2Pm45BgFqt9r2pqKvy2w9X3d9h9axOv5WBa6nJp9zwEipdiWDpFUVKVNUmUMh0FpNHTH/ZpnHHrs/t89otbnB7NyDOVAPkdh50rQ9754SXe/uEldq4O8Tsupv3kcb2mSwxL59Jbm0ghqMua0dFMKZ3FOXGYMjlZcP/2CXe/OmK406M7ePERfNVg3POyVBDFM0l/1ZB0lxML6pqyqlikKZM45sOtTd5aH5AWJWGacX825854ypVBjzVPQcpOg4i7kylxXvD9nU1lTqXr7E9n/PLewSoxXMZ6y+ePdrcJ0pQkL1eqH/cmMz49PObaWh8EOIbBwHN5f3MdQ9MIUvXA+P7OJu9uKonpnuMwcN03ssiYRQkns4C90Yy7p1OyRyaaS1GNH13Z5o+u7rzQNqVQjuCbjTztnfmUvcWMaRLjGMYKo5uWZUPwLfnJ5g4fDje43OnRs5xv/X5q6ToDx0EKaJkWLcukbztYmoZjGAxdDwGsuS7vrg1VN7MoVo7lSaEKka1WC9cwsHT9gVJZDV3b/s6LDFDwjC9nR3wyOeAomRPkKjG2pMKeX2uv8X53ix23w6bTxtFMHM1Qk5C6Yt50wO823ImvF6ccRjPGaYQu5TcqNPajKR+N9/l4cp/TJFDJFjVd06FvebzdXud6e8jQ9pV6jWZgSm3VcV1Cuw6jOXfDMV/MjjiI5yRlwTxPuLUY0W3I4nlVsuG0H0u4XN1k3V4WImJVKERFxiyL1fF4BMJU1AoydZIGJGWOKXX6todEMM0iklLdS5RKjyLZd0wHKR5uvCRlzigNCfNsde9REw2Loe0/s9BIy4K9aIouNOZ5QsuwudIacNkfcMnvM7R9uoaD3Shwlc33mmQRoyTki9kRv50eMEpDJllERb3iyfx6tIetGWw/hwz/uxAH8ZyPxvv8ZryvuEhFSlnXdAyHnuXydnvIW+111myfteUa05Rre1FXq3VwGC/X2DH3oxlJma9gRW3DZtvtUtY1m04LW3sxbpat6XRNh2utNX4yuIilGTi6Qd/y6JkuXdNpphoWjmZgCAmiEdEoC8Ii5TgJuBuOuTk/5cv5MfM8oQYWecq9cMr9aMooCZFC4GovxmHLypIbixM+Gu9zN5wwzxPyqkAXqijbctu8393isj9g02nTMWxsTbmaF3VFmKsp4/1oyt1wws3FKTcXp6rBUakp6bcd51poaMJD0zxsZ5O+86eUlYJLadJFFw5CaPCS+jG6Juk4NpfWevzxlV3KquY0CDmaLzhZhIzDmDQvyC5u0bKVUZ35mpxohxf6rO30lxYfbF4ZMrzQ5+jeiMPbJww2uwy2utje6+tEqAuyZJbPOEyOWbMGCFOQNwQ0YQikppFVObN8wa3wDmER8ZZ/FVOzuBHcQgiwNQtLMxFIyqokqzKEEJjSoKxL0irDlCaapp2rpOJ5hpSC3nqL3Wsb9Nfb2I+of5m2webFATtX17n9xcGZ3wiEFLR7Htc+uMCFq0MM68Gl4vo2jmfRWWthWIbigzQSt/NRQJo8vUOQxBk3P93ntz+/xeRkvnqg+R2Xt394ie//7Dpvf//iM+FXoIpKTRNcuLrO9uUh46M5X328x+RkThrnK1Wq+7dOuP35fWzXfKlC41UiyRXk6tpan//hg3eghtMw4v/5y4/47PCEo0XIZjtGACdhyL3JDE0KfnblIu9uqCLg727f4+P7Rw3U5cG2tzstNlreSilDTeNq/m9/9yv+w+c3+NmViwjAMw0802C95bHTbXNrNCEuCv7k8kX+8q0rzcF7c70WZmHM5/sn/M0Xd/gvn95iET/8EFhKJ/9f/+WfvmShobPtt/gnO7t4psHf3b/HzemEaaoeNrqQtEyLvuPw3mDIn1+4xLuDIVc6vddyP7U0Dctx6DkOV7oPOB89x2Gn1V49kNc9j6HnPWhkAAjxIDE8I9fYsSw2fbXm35TzPUkjfj3e4+PJfUZJSNaQk21NZ2B7/Hhwkf/x4vdXie2T7q1FXXJrMeKL2RH/894n3A+nzLJ4NXl41bgXTvhPB1/y1fyEWZ6sOrp9y+Ot9jr/7fa7/MXmW0oU4CnT0awqmKUxn84OqagJioyiVpOGu0XWdIZbWFKna7qPFxpN59jXLdXkQk2BwiJjmsZISzxeaFQVkyzmNAlIywJL09ly2uhCkjdQnaKZJI3SkHmW4BsWxiO5SFzknKYhYXH2mhO0DIt128d9RsKaVgX70WwlGbrpdvjT4RX+eO0S73QUYf9Jq1Bh/yv+f4c3yOuSL2fHTLOIJTthL5xgSI0rrReHm7/JsR9O+c+HX/H57IhJGq0meT3L4Xp7jX+x/Q7/zdY7mI0E65Mir0pmWcSX82P+P/c+ZlEkFGlFXObcC6cYUmfdVgXGMul+kbCkjmFqvNUeEhUZPdNhYPtc8Qdsu110KRsO1NPuJjXTpgnwV8c3OUrmLJpCIyoyoiLjIFJiD65uriYiz4u0KvhyfszPT++wH05XzYRlYfROe4N/vfsh73c3cTXzKepbNfejGbeCU/7D/S+4G4xXMMfXEecsb9t0sxUCEiltDKEhMBBCp6xTqjpDEzaaePGxVlXXmJrGsOVxcdDl2voATUrGQcTRdME/3N7n/nTOr+4e0HNtOq6NYxg4jf6+JkXDOfhm36/nOuz2O7jWg4RVCKUglEQpRdbwE9ICyzWpqopwHqPpGvZznCfPK+b5gnvRPosqIC4TirqkpiYuY5IqwdJMbM1qJhwVtmZRUzcP6BpHs5tRnEFSppymIxa52pYhdTzdw9JMDGEgdIGjvdx48nWGEALXt+kNW1jO490DTZN4LYdWx0U/I31qmBpe26HVc7FdA9143BxSCIFp6ngtW6lWZYqfEkfpE/1EqqoiiTJlxnh/wunBlDTKEFKg6xqdgc/ld7a4cH0Dx7Oe2+lY/V6q0e5gq8tbH17gq4/vMRsFgOKDnBxOuXvjiAvX1l/lED4xzlD0HgrH0Ok6jpKjbaZchiZpWRYty6IoK6ZxgkAVJa5hYBs6lq6hNVMvy9DoOnbjnSEaL4WKw3nIrdGYtCjVkFSBtblxMlopFS0N22j+VtPB5b/5nXBM92yLi2td0rzAMQ2CJKOoKg7Gc+6eTknyB6pMLxrLteLoOhfaHQxNY9NrMUmUylPVQERMTen5D12Pbb/FmuMpj4zXMAlYfcYj8KZHMfmiEd140vX4xH9/O7v70rHkZkyyiJvBiL1oQlqqIkMAF7wuP1u/yg/7F+g1CfiTeAQAOhpD26eoK77X22aep9wLJpykwSvtW5inTPO4UduZqES3kcd0NIMPe9v8xeZbXG8NV4Tzp0HpDKHhGxYXvR7/dOM6nmbyt43cbEXNOI34eLxPx7C53l7D1U20M1N8BZ1qrZSDVCgOxigLnzhVKOqSaRY1mPUcQ2p0TQdXNwkbCMwsV7CpURowzWM2qtZjPc+4zBmlAWGRrfwRjDOyvdYLJKyebnHR7/FhT2Hxd70erm4+VQ1MFSYal/w+f7H5FmVdcSsYrbwQ5nnCQTxjkauGgHzGsX+TYzntuhepNTZJI2VAKnVs3eCD7hZ/ufk2b7XXGznYp39PXUp8w+aC2+PP1q/haAZ/d3Kbu8GEipppFvHJ9IC26XC1tUbLsB9aY08LIQSihl2vhyl1bM3A1Y2VX83zj72aUmy5Ha611ni3swE1HCWLFXcqLFIO4zldS00Jn/VIqpupV5CnDcTulEVTGAigZzr8ZO0if7x2mW2ng6OZ6PJpIhyCjulwyR/wXmeTw2jOrWDE3XDyzGNyXvGtCMuLBkKjCQuEumEo0l5MXs4wtT4aL5agVnVNXird9J5rs91tcWWtR5TmzKKE40XI8SLko7sCXZMMWx4bnRZ9z6HrOiuzv2XS8U3i2vqAge+uCo268bSIw4TZaUCWZEphqgbD0BVpNwuwHJNW7/XwFOaFKjQqUVJSUdQFNTVRGbEoAjpGG2hR1SoxU4WFhtb4m3i6gy4MTGmSlAn3on2O0xPCIsLWLHpml57RpWd2sbVGRu6NeZw/HEII3JZNd631ROUlVWjY+B1nBacC0E19JVFr2uZDpPvldkHxaVzfJpzFRCjeRBJlTy40SrVOVKExZXQ0U/ugS0zboDvwufT2JheuDJEvoQKxhK2tbXa4/uEFRoczbn+upjNVUXF6MOPujSM+/NPrD3V9n79h9dcynV1OENS/z6qCLF8usA2Djm0/5AStCYlvmrQsS2HrE9UtTPICx9Bp2RaG9gAvbWk6HcdWxUfTqS6qkr3pjL++eZdFklFWFVIKNCm5eTpuZHAVIR/x8Go8S3N6qe//HUXLMbk47NJ2La6s9whTJen9i6/3mMcpkzBaGZG9bNi6wYVWhwutDj/efLFpyOuOFzk3b/L5e1pUTUd9nIYrT4UluVkKwQW3x19uvMXl1oC2aTf34yeHFIK+5eFoBifdbcJcdUtftdAIipT9cNpAO2YETUff0Qz6lsuHvW3+5fZ7L5TkalLiSYsLskfLsHGkwc3FKUexmt6qJPA+G06LeXadvlUihb66Zl3NYLjkcAhWfL+wyBinIWvW41PZsq6YpBEnSUBSFhhSW0FdwkL5METN36dJyDSLV530sxEXOaMkbDDrtVJQk6pw6loKapk+Qx4XwDcs3uts8pPBRd7vbj7X+2M5gdv1eqzZHneCMf+r+GqlcLQoUupYSa8WdYXO+fkxvM4Ii4z70Yx74YT70ZRZrmDKtq7Tt1ze727xL3feWyX0zwqtgQxtux083cTTTO4EY/bDKXVdM8sSfjs5YGB5/Nn6VTbsFlLTXyhLkUJwwetxweu90ve0dQNbN7jsKx+ZRa74SctCIyoyjpIF21nnic26s1GjOBbzPGEvnHI7GK2euQJB13T4yeASfzq8vGpOPCtahk3LsJl0IwW/qhU073W4sr1GB6uaOL9LkH9Fx/o+ptZ9oXfdny74qy9vc/t0QpLnjINYKdMEIdmZB+4SUz6LVeU/CiLVWT0z0fim+XBd1/zRxa3Vfx/dHXHrt/eYHM9ZTJQbt9TkQ4m3kILvybdY2361hfuysewAVlTkVb76E5UxiyIgrRT+VJM6ljSxpIVAYkoLXWhY0kITGhKJJjRszaJttPB1D01omNLE0Ry6RgdPc9/YIgMAAaZl4HjWQ4XE6tdSoBs6uqE/1O3WdQ3Ht7BdC6k9/fstpxGyIeVXpfIQeZKhYllWTE8CjvcnxGcc0Q3LYLDZYW27i+2YSpb4FQ6p27JZ2+rh+A8K+KquCecxk5M5SZhSlUp6+XnPKSmESv4FCpZYlg8VHFGWMwojoixfVSI1NVGWM4li4vwBqbGsFXF3niTomqTr2NSwMmZTjYQH28/KknmS4BiGgkxkGfdnFSdBRJTn7PTavD1ca3wfapK84Obp+HHdhDOyqGWjK/67EJqU2KZBD7ANQ3Fkqor98QzXMgiS1wML/UOcbyw9Mw6iOUmZr4repRv0ptumb3tPhUs9KTSpse12eaezwWezw1fet2kW8+X8mINo9lACvuG0+V5vi22389IOwrpQk4A12+eSP2CURqqzW6r7yTiNuBmcYus6204Hs0mSbM2g1yjnGFJTpNpadYJPk5Bd73EOynJSdJIEpGVOq3FjHtotpQKUJatpx2kaMM2ixm364YjLvJG1VfdnTzcZWN4zJXnPhhQKZnW9PeR6e/hS5G1DSsCkY9qs2R6zRoVpqTaWlDlBnuDp1htpPPm8mGcxN+bH7Ecz8jMSw+t2iw9621zwumrq8BLb1ITEMyzWbJ+LXp/DeM5xvCAuC/KqYJJF3ApG+IbFttt5YQjVeYSl6Qwsn47pPNQ0KKqq8bp4frOorCtOk4C7wZhFA2WsYVVI73hd1myflmG/1JoYWD7vd7e4tRitlNm+7fhGhUZdV40vhkCgofjxTz6AdV0SFXeZJr/E1jZpme++0GcczRf824+/4Be393mWIbYyd6pZJBmLZ2Dkv0kMPGelfgJwsjfml//pU8ZHM7Ikw7AM7EdgL1KT7L714mTwbxqyIWiXKIm/moqyLsmrnKRMKZqOjC40TGlhSxuNBwWGLW2kkM2UQ8PVlDKPLo2G/1Hg6g4do4P1DLm/NyEEShnM8Wx0/QmFhlCFgm5qD/mOaLrE8Wxs13zmdEFKiWZoq2KkqpSvRvWEh1hZlExHC072JyThg/VpmjqD9TZrm12s53zes8L1bSWL7D94uNVVTRQkzEYBcZRSltVzMKbN92pgNFJIsrIkyXOSPCfWFSkvSDOmkVICqqlXW0vynGkcE2QZca7WX5TlBGlGmOUYmrYqNOZJQlqUZGVJmGXEeY5AEGXL61fBpuKsUJ38KCbKc9Z8j59evqC6i0XJJwdHT51WKj6DkvHLypIoz5FCmYupBsSb98Be7pdt6HTODEE/2zvGNo3vRF3vD/HNIykLDuM5h3FTaDQ/d3WTTbfDptOhZ7rPJBw/GroQbDptsqqga7rLAcBLxyyLuTE/4SCeP5SAb9gtfti/wKbTeeku+nKyMbA9Lvo9DuM5szwmKXOqWpGgby5O6Vsu63aL5be2NCXn6xkWllSyvksS8CgNScoH985lsZZXFdM0ZpSEJGVB31LQqXXHJyxSRkmALjWSquAkCZ7qpxGXGadJsFLh8XRLydrqzyftCsBofBOu+AOutAZP5Rg8KXSpKYlSw2ZgeSt1JmimYUVBmGeYUsfhzTGefNGY5wlfL045iGbk9YMccWi3+H5vh223q5ztX2KdaVLiSnO1xu5HU+Z5QlTm5M307FYwYs32GNr+6y00pE7Pcmkb9goWDEq4IGlUqZ53rVaNO/m9cMIiT1evN6XGmu2z7XYZ2N5Lq5H1LRdL6vyDc2eFGvi2S41vVGhk5Zi4uIcuW7jGLmlxSpjfpK4f7zrUVCzSz0nLE8o6evEPqVWi8Kwi47uKIi+IFgkbuwOu//ASjm+j6dpDSY8QggvXN17bPnWMDpe9i1SipKgLWrqPq7tcdAXr1pC+2VPFCAauJpBmj6qusDSrScLURMaUBprQ2HI2yati9aCpasXjeDLh6A2LppAwLR2pPwUjK2m6dWeKQykxTR3jkUnH429eNs4f4Mvrqn7i074qa4JpzPR0sTIIBFXUeG0Hv+08sRh60VA+KuZj26jrmqqoGh5Rhu2aD8kiPykMXaPj2HRdm7ZtcTAP+DeffIFrGhi6xq3RhI5j4ZhLMlsjTahp6FLy5fEpoKYhUZYRpCmX+h02Wt5K3jbOc3Y6bY4WC/7r13f55P4Rtq5zvAipUf45UggcU6er2+zPFiR5wWeHxxRlialpaFJyGoas+e5jrs8C5few2W5xMFvw0f4hh/MAR9dZb/t8sLnORvsfnxHWH+LNjKxSjr7KlO9BkutoBltOi77lvrTZnkD5o/i6Rcuw8HRLSWs+odHxrIiKTBUCWfTQ5K9jOlz0+w2R+dXC1gy2nA4bToubi9PHPnOcPj5dEKhpwtD2OU1D5k13X/lpPLh3KmhJQVQoF/GgSBW8SEo6psO63SIqcg7jOYbUmqIjZJrGT55oFDmnSUiQK46GpytZW+8Fij9DavQsj6HdUlMH8WoiKYbQcDUT44wi1tIQcOkB8rsYcZlzFM+ZZBHlmfXfMmwu+j165qurAJpSZ9Nps+l2uBmMHnxmoT5zlL6YUeN5hhRi5QFydh0s1bOWao3PiqqumecJp2lIfEZNzpAaa5anCO/y5VN4Q2p4holvWLQMW8ELnwMJ/KbxjQqNopoTZl9j6evY+hZpecw0+SVVnSGEzsOd04oov0NeTiirF2e6SymwdA3X/O6reEvXH+rsLF2hB9s9/uS//wFe+7s3AGvpPjvO1kOQH4GgpT+cVEkhMdCxHyFzO9qDh4oBK7L42Xij4VKPxFIG9mndayHEY3AlIQW6qT2RBP7Qe9UGHuIz1HX9xPtHVVUE85jpKHyo0FDwLQ0EhEHCdPRqOOskTMmSYuXpsYpaTVqytCBNsofUs54WS0O2vusw9F32pnN+ee8+hqauwxrYbMz3dE2ZT1GpB7yhaxzOFgSJKjDKquJCr8OVQY9hy6fjqPWWFgUXex3CLOPGySl5WdGyLDQp8C2Lvuti6TquadJ1bDqO4nKcBCHzOMUxDVzToCgrLva6q+3Cw8ZyO502x4uA+9MFXx2PaNsW14d9drvt39tCo6oUnKyoKsqyeuBE3axbIdSDUtckuqYmLI9Khq/4OrWaZKV5iRCqy2gZihf3IpEXJWleUFQVdQ2mrmGbD+6zj5mzNTC4oqz+/+z913Mk2brlif32du2hI6ATKSuzqk6dOnXE1Wpu9/QojpEzD7T5y/jGN76QNCNp5BjNhmSP6HvvdPcVR5Y4pbIqNZCQocO1782H7REAUgKZyMqsOrnMUMgCAh4eEdu3f2J9a1GUlWPvsSrU8XO3pDn/px3ru4TxcogYZvEJ2kRgOaz4DdqPUCxOAyEErmVkceuOR90xnhb5o3vAUzD/DKPK/G+UJSe6sQ3H52LYoem8uOCHb9msBg1WgsaCHgXzwHPyGI3pyLjPJBpxmTPO4mMdjaOASFdzL5M8ZVqkxGWOwASeLTdgya8TFXk1zCsXDuGj3HwG8xmZOcxzTBfUqbrtshI82z9jDltYtN2QnlcjsJ0XpjfZUuI/4e9Lrcj12UQg3gTM11hcZOwnU6OOduwG2XA8NsM2rZdIZj3LYsVvsFqpmc2RlAV78YRBOqPQZ5trO5pHNJ2FhVs5Zp+az9CYxxx5h80xyVPSsnhsFmgx33iKz1FVPjSHlWzzHLaw6Hkhy37tTH4ccziValzd9mi4/iKJfZV4qURDChfHamHLGgJJoabExTaOrBPYFys6lYFGYcsdcuVWMrenQysI+NmlDVrh61c3+vGFVWreUcJjO8YR23FtdGmq2eJ7oGzzhwKBCeSlJc/0uRiForP9zfOgtSbPCtI4O5EMRJOEbz59wGB/wue/vP2YBO9pMXcMv/X59hOfe66Gpk4ZhACsNxv8J9evMk0z47wsxaKTpdH0aiEr9RrDOGFQFHi2UZ366eY6768um9kLran7Hq3Ap1d5aADUPZcP1ldYazWYpWbAey7aoLSm4Xks12vUXAfPsXl/dXkxt2HOwwxqpoXhfF9b6p6QNgUzaPijtWV6tZAozynK0tC3Qp+l+usxkXwTEGc5O8MJDwcTtvoj+pNooW4FpptUDzw2Ok0uLbdZbtZYatQeo6eVShNnOf/hyzv87vY2gefQq4f86Y2LvLuxfKpzeXA44l9u3mdnOCHNC97fXOGv379CMzxJOZhjEqcMZwl39wfcOxgyjhKmSYZSGiEgcB2aocdau8FGt8lmr023/uJBzHmhVIpZJXF5PJh3LZum45+KnvM0SCEILJe64xMV+Ynq57Og0ZRKk5aGlpNWhnFzemFgO9Qd74WCmTlsIc0Qqn1ywD1TBaMsYpqnT5yfqllGfeownZ0YBj/+2kqtGGYxe9X8hcRQPkPbJbRcGrahIbXdAFtYxlOgyJhWiUmqCjx55NEQlzmTPCGpgq6zdDQsKag7Lk3HPxNl6lEIBJJHi3lVUHuKKvibBo35nNKyclwvDd1WIpBSEFgOdftl15gZ2G+4J5kWmSoY5THT3Hh1nBWlVmSq5DCd0U+NiMA4T5hVLttZWZIpk0wUSqE4SkhmRcowjdiOR4/IJZ8eGpMsTfP0RHHCkoKa7dGwX26teZZNy/GJigxesTn4SyYaPq7VNQZ8QqJ0TqknuKKFZ61UXY05FHHxgLT0EWd42lbg8dHFNS50Tm/y96qwFAbopFjIh6ZxhuVI8iynvzsyFWPXnpe6ARO0eoH7RNWjV4nz7Dp8nzoYj0IIs6Gd7R5uuhzyWLfiZaE1JtFI8hOJRhJlPLi1x4Nbe+fzRE957rJQFPmTB9UfxfwlL9VrpwrIs6JkgMCxJHXP5cZKj79+5/Iz/yZ0Xa4tdbl2mhcAXOl1uNI7m6CCa1sv9Hc/VJRKkeYF++MZNx8e8NXWPl9t77MznDCJ0oWaVc1zadUCbqz3GMcJN9aX8B0b37UrkYAjGmWSF3x2b5f/4TdfErgOm70Wa50G11Z7SPl0laJ5J2K7P+bvP7/FtzuHpHlJVpT8/OoGjcAYts1RlCV5qdgdTrmzN+Czezt8/mCX/jRmNEsW1enQd+jWQ66udnl3fQmBSUoDz8Gp/EBeR2Oj1Jq4yIjL/ETQYwtJ3TEGYC+qJiSEoVDVbPdMlXRVGe2lZUF8zM1YYgoKnrQrXf4XnwuyKtWmmuMujCEB4/ieJyccmI/DGPc1uF/Jb0ZFRj+NSIp88VnPE439ZEJc5scSLo/QdqjZLl3PqE+50kJpTapNwDvJE+Iiw3VMAhIXOVGRMTs2qFtz3Mo/4xSJhpCElkvN8U68zhfB0+63368Uw2Au0pOq8sQas4TAkRZe1ZFzXyJgtoQJvOv2yfc+VyWTLK2G6p9fYJt3MTJVLpTKJnnCvdmAB9GQvXjCfjplmBrVprhKnFJVkCsj2lFo4yhfamMo+zLzDxpNWubM8ozieKKBJLBdao575i7ocbjSpu74uOkZRhleEC+VaNiyRmBfQgoHiY1rdag77xE6l2i47yPEUfVfU5IUe2TlAZY8/fBKzXO5vtJjs/P6XTHHOyM+/bsvScdGmm1/a2AGbX97h937fRqVJKqUcmEKJqTk/T++xo2fPTvweotXg+c5rT/5b07kiucDrSkKRZEXqO+YL1o9vXE8/z7erd7iXDCJU24+POSLB3t8evchDwcTsqLAd2yWVmuLQfM4y5kmGTcfHvDgcMTW4YhRlHB1pcPl5aOkzZIC37FZadW4uNSmP404GM84nEQMZzF138V/CuU1K0omccrBZEZ/GgGCzV6L1U4Dv/JQOY7+NGZnOOGXN+/zq2+3iDMjUNCtB2x2W9XQv2aWZiRZwc3tA3YGE3aHU358cZWPrqyz/hqLVQpNrlU1BHp0EVpC4krb+JW84LEFJmFxpFELPC1KrcnKglypx87Jk/ZLB8xgkhZHWsZP69gLLLUiKyvK3BP+rlbRlubdhLQsmOQJUZmRqwJLWJRaM0wj9pIpcZFjV/4ZXTdcvKcNx6c5d+QWkrLqXOwnU1Yqv464MvKbFumJ96Fme+YcTjFsK+av8xTyrH9IUBUtJ1flIpAHQ912z2mNGSPhx997pTXZ4rlPd6xSa+5N+9yaHHJresDd6SGjLGGcx4tuYVYWi05GqaouBmoh+b6gUL+knpPWZjYnq5KX46/XqTr6L7PWZJXsnXU27EXwUomGJQMseYzTb3VouO8TOBcInEvIYx0NrUtC5zJ52TcdkFMicI3x3puAb3enfHpnn8PtIQB5lmPZFtNRzP6DAfV2SGupgWUdqSdIS7J+ZekEp/nEADFH/GhTeX+7SZ03xLlnDWeHBrRSqFKd2PSEFDiOhWVbZt28AupdvRXg+vappG1fBLZlePlzetSjg9lv8XqhtKYsFYNpzOf3d/n1rQd8tbVPUSrWO4ZidKHbWuyzB5MZDw5HPByMubXbJysMn92SgrVOE6eaf7CEwLUtVlp1rix3mCYZo1nM/mjG/niKZTWfmmikecH+aMreaMpoluBYkotLLdbaDTzbWqivqWp+ZHc44dO7D/ndnW1+e2uL9U6DtU6TzV6T1ZbZc0ul2BtOeTgYc2d/wM5gwiQ26mgr7Trdeohjy5eqAr4o5vQcU+U8+rkQonIcNoSZF4MwCnhCPtMA7FGYjoZ6bMBYCoEtzidgFsJQW0widSwIxJiRGQ77Ezoazpy2ZIL8XJdERWa6QkVOYB+nThkOuy0kbTek69Vwpb3wW6g7HjXbxbds4jInqRKNUR6zplskhUk0Znl2NKcE1G2PZa9BcAq1IoFJ0OwzKif90KEwPkiFPplQSiFwhHyqkeFZIDAqgrY4mayryvCueCSRfhpmRcY4T/hytMuvD+/z++E2X4/3DC1KK+wqYXYqZcB5R2ZurLmQlBHVXKQqScqcqHxy1+40KCsfqZOeVfO1ZvHi5YnqOpfn8xk8D+fqo+FaPRreB4uZjZOQ1J0buLKNb68/8e/fdDQ6da5/dJm1S0vAXM7UOEKXRYnt2ni+c9KZVsqFh4ZSGqUUUsqFmpHWmjQrUUrjudZLKQ+9xZsLgUk6LfvkDdzzHXprbdpLdWrNANc9f2ubWjNgdbNnFKdewfpqeB52W1LzXN5bXWa92Tj353iLF0dRlgymCfcOhvz+/i539wb4jsPlC23+4t3LXF5u0wg87IpaFKUZ4zjld7e3+cev7jJNMv7l5n2aoc87qz2aoV9Rm0xxZLVd58bGElv9EQfj2SJBqfkendqT5yNmScbd/SFbh2PSomS1FXBttcdGp7GgOIHxn8nKkm93+/zdZ7eYpRkXek3+8v0r/Mn1Teq+R+iaPVejidOcvdGUf7l5ny+29hhHKbd2D7m926dTC1hp1an5b7Ys91khMJ0DydmSlWd3bs+39XnWo9UsQ1uqOUef1XxWY5BFCFFbOH4fHHME73ghS34db06Tw3DRu16NthuSJ1OSIucgmS6G3+dGivOOhlWpL9Ycl44X4J5G2UfM5bRPKhj+oUNwZEr4KN605vrX4z1+c3iPz4c7fDXapZ9GFFU3xJYWK36dZb/Oklen69Uqip6LJ+1FN8sWJgkZ5wk78Ziboz0+HWwvTDDfJIhqz/guVuu5RjWObODIpwcZnr2Ca/WQZxgGf5NQawZcfHeNPDWTM2Y2SzM3A5zHj7r6j67+0VoyHZyyVOR5SVkanqnj2IBmPEkoS0WnHb5NNH6gmHt2OO5JyVzHc1heb7P5zgrLG23C+vmLHriBw9rFbpVonH/1Yt517B4b9n7VyFVJrsrFBv8WT0dWlOyPp9zdH3Bnr89wlnB9vcePN1f5sxsXubTcNsWPagMrlVF0kkJwMJ7x1fY+3+wc8s5ajwf9EReFOEo0hGC5WePaSpff1bZN92E05fbugCsr3cfc2Of/P0sz7u0P2B6MKUtFK/S5stxmpVU/4RWS5AWjKOHe/oBP7+2w2WtxbbXHz66s81fvXzEzWI9UkA8nEbM0Z5KkfDLd4eFgwr2DIevdJs3Qfy2JxjwZEI/NfelF1+ZloI/99/TnVPnJPBJqaLRRIjunUFDpxw0zBQL5jA5MYDss+3XqtrcQ0NZooiJjmMXUbCPHPsoSDir/DJNQhCx5Ro1nvuY8adPz67TdkEEWkZTFQgGp1EeO7bMiXRiiBZZLzTaSwacNxN6mGI9DYIQFHjV81FqjqhmG88Bcyen40eYKdPI53lFpRYW6Od7jf939lvvTAdvxaNE5aHsBPa/GpXqXS7U260GLlaBByw1o2B6+5eBZzoK+ZUnJfjLl5niPpMz5erz3womGgMcU/6BSw/pOrPbOB9+hMzgonaJ0CgRY4jt96nOBFzh0VloL5R6lFGWhFg7Rx6HRlLnpdniBab2WpSJJcx7ujhiNY3rdOq5r83BnSFEq3ru+Ri08m/nKW3xPIMBxbVzfOeHp4boWneUGl66vcvWDDVq985dctSzj1RGE3itJNF4HJnnCYTKj69Xo+X+4ClKnQZoXPDgccWdvwCzJaAQeP9pc4f3NFZqhMeg8fhsTlTzsWrvBL65dIMpy7uwN2B/P+Pz+Lr5js9E1xRMpBO1ayIVeSa8e4toWB+MZd/b6jOMLaB6/xWttOhr3Dobsj6bYlqRbD1jvNunUgxOJ+HAWc2u3z95oRl6ULDfrfHRlnZVW46nUHte2WO80uLTc4dZun8E0Znc4ZWcw4cqxGZPvEgte9SP+Cgt6h37xsMGoR6mFZOtpYVVa/84jWv+lUmSiOBfvgfnre/TcLCHwLPux92OOwHKQnlg4hBcV5SwqTUej59fxpM0kTxhkEWlZULc9Om5Iz6+d6EIYl+YabS/Anlkk1UzGOE8q123jcWKoU5rQ8Vjy6tRO6Qj+Fk/HfBbDlfaJYNkoOhVnlp19Eo6vsePXkKxmjRxpPZMyPEgjtqIhN8d7fDs+WKhE2cKi4Xj82fIVft67yIWwZQwcLQffcnAtQ6WyKvqRRCwSG1caWtNLpZ6CSl3RPlEM0HM62iOv96yYfwbfhTfLdxrt52pIVhzg2+snZju+L7AdG9s5esvG/Smjwwm1ZkCzU1vQUuZZ+uHDIYO9seHhBy55oUjTgtE4ZvdgQpLmuI7NcBThOPbjHghv8YOBlBIvdKk1fOObUUFIievZ1Nshq5tdljfeKiSdBv10xlejXW60Vt4mGs9BVpTsDids98fEWU6vWePycodLy21Cz3nMlFJWlurdRsiN9SVuPjxACMFgaoL+qyvdE4+vVbK2S80a7VpAlOVsD8YMpzFxluPZ1oKWVSpNmhcMZ2a4e5pkNAOfpWadTj2k5p3sNozjlHsHQ/qTqOJKG7Wq3dEUef/JN/E0LzicRGRFSak0eakYzGL6U/Oz14G5gZdnnfRiOuEU/IIxg9aQq+LMhm5SSBxpJHZdaWFVw9IlR4FbrgoET/cheh7mw9dJWZzwDrCFkaH1LOeJwZhTOWXXbENPSSoWwKzIGKSRCQYtmBYpkzyl1Ka72XFDel79hFzqPNHouCG2lKSqMIlGZtTKEpUzyCKi0hj1hZZbdVNeXHL4LQxM0G3W1/E1Zga1y8WXp9QTK/engcJcQ3HxuKJbUM3mPIsg1M9m3Bzvc3864CA1iqICQdP1uRC2+Gn3An+9co2eXz+1p4ysqJwvozolAEfY+NbJ609pTaoKkjJ/qSRhvvc86vXxKvAdJhrGsG+cfkbX/zM8e+W7e+pXhO1be/zz/+8Trvxogz/9Lz8ieKSrcfv3D/jN333Oz/7VB3zwFzdIM6Pl7/sOYeDwcHdElpVcvNBhc71D7QU9FN7izYeUgnoroL3UwPOPhguLvGQyipkMZxT56wmCvo94EI34x/07BLbLe63V1306bzSKUjGKUgazmKwwrurtWkAr9E/MQzyKwLVZaobUfUMfidKMveGUaZI99th5B+TqSodvdg4Zxyl74xl7oynLzRr16nnSomBvPGVnOGUcpTi2xaXlNhe6Tbwn0EajNGd/NGOaZqDh5vYBg2lM6DkET5lnmvt7TOKUvdEUKQVpXpDmZ6v4nyfsajA5tJwTKi+5KpnkKVGRvVRHI6nkOM8SNMypIZ40Xh7DLGZWGF+Lua/EOE9oOD6hfEF/H6WY5Ma5+7hEp2tZtByfmu0+tTMlOOYQnpgZiqjI6WcRwzSmcIwsbaFL0OBIScczpnnHjdtcadPzKz8NKZll6VFHA+PxMMiixWdQs11Wg6NB9Ld4ccypP55lm3VkOUZuFg2KynAxwZHyVDLCT8JT15i0aLk+ddt7pgBEP424Od7jMJ0tfmYJwaVah592L/BOY5llv3Emrw+lNVlZPqa2dRYIBL5t07C9E/TgUmuiPGOaZy/VdczLklmekpWv1qwPXjLRKFVMoSZonh8gaV0yy28zyb6k7t449XM8+hm9SQWGcX/Knc8fEDZ8yuJxd9P+7ohbn95j4901LkcpcZITx8ZcyrYkWVYSxxlSCnzfwbJOUTWaD5w98j7oqtJ3XpzHtzhfSFvS6tTorbZOeKoUecHwYEJ/b0KW5gvjsTepkpZXuuKmejj3LKgqNmh6Xo2uV1tUJodZvNj052ojEnOzCSyHpmuMhrLSaKtP84RMlQsudqlVZRomaTiGDmHOQzErjNnWN+M9vhjtcKHW4nLdVNiFEPTccOEy+ya9h68TSmmiNGOWGGNE25LUfZea5z6zUu3aNs3QJ3AN7SHNS0ZRQpof3Zjm77FtSVbbda6t9tgeTNgfz9gZmC5KzXOp+yZoS7KCh4MJDwdjpmlK4DhcWemw0W0uDBuPIysKxnFCkpm5uDjL6U9nDGfyqQHqcTQDM5PRrgWE3uOyud8VbGnRdHzqjn9C0nPuVm2q8mcLGuYSmrkqiUpjRHc80Hoe5kO6ge3Q82sMsoi4zCq5Ts20SNlLpkghTuWO/STMDc8GaXTCdMyzHFMhdv0nfo7zdVBzPJb9BlFhAtKo6mj00xlFpeqjtcaTNjXbo+l41B/xE/Esy3Q0vBBHWFVylzAtUpLC+GoMq0QDfeThcRpZ27d4NuZrzLccel6NPcd4nsxnKmaVY/jcs+VFkKuSfhpxmM7ITiSzppPVcoNnJhrTPGU7GjLJj+YopBCsBA1uNFdYDRrUz7gWclUyLZKXUpySwlAHW26AdyzRKHTJqKIMZme43ueYy/DOCwmv2hUcXjLRyMo+k+wLCj177mO1Vkyzm2TlAaWKX+Zp3xhIKbCdSpb0Cb8XUiCkSSgm04TxJGE6SynKkqIa/m42fIpCcTic0Ww8vy0nqueV8mTDWSlNUZRv6VdvKCzLor3UYGWzQ1A72rSyJGd/e8Degz7RNEUpZYK/NyhGnhUZ+/GEr8d7fD7cIVMlUghKbQyK/nrlHf569R1sIdHAzfEenw22meTGLKnUCltIVoMml+odftxep+uF9LMZD2ZDvqwUPiRH7VxDm3D4oL3OnyxdQQDjPOab8T5fjnb5uL/Fbjzm1wf3iQsThLrS4i9XrvHz3sU36e177dBoitIMeANGmtGysK1nyx3bUiJsY9Inxdzwr3xiFc2SkrVOk3fWEj69t8NW38jj3tnrs95psNIys0dxlnNvf8CDgxFJVrDcrHP1WKLxKOYmg/PnXOs0eGeth2dbuM7pbl++Y7PUqHFpqf3apNLnikhtLzjhXhwVOTvxhH4avZB7ca7MjMEkN1r/LxJ41GyP9aDFYTLjIJ1RVrz5cZZwb9rHtxyW/RdTkkvKnJ14xG4yJj12boHlsBY06Xi1ZwaBNduoTx0kUzQQlznDLOYwnZHrkqQsTEHC9el4IYHtYsuT4+2ePEmdmtN2oiJjnMcM04hBGhOXedXROL0j+FucDqHtshY02Y3HHKazRedtnCXcm/Wp2S5Lfv2F9u1UFezGY3biMckx5/jAclgJmnS92jNNJ1NVMMnTEwG3QNB0fFb8xgslQGlZcJAYN/EXpTdJIWg4Pkt+Hf+YxHKuFIfpjL1Kbe2smDuez4qUYWZMMF81XirRyNWQSfY1pY4rSdtnqb8oCjWiVAlanz6DysqCSZwxTVNmqWnZB65jqmSei2tXGt2vo3pZcQqVUuRZQVmUSEsaGdtSoQqjEW5VXgmeZ+YwlLIpPUXgu2itcRwL17Ee40o/CdKS2K7xXTgeJRRFSRKl5FlhjNmkflvRfYNgWYJmp8bSWpt2r05Y90mT3FCnhhEHO0N27h3SXWnSXqrjvQESnItZo2TKZ8NtHsZjUlVU7XCJRmDx5MqyqAbjLCHQCDJVcmd6SFRkbIZtWm5AUhg9+y9Hu0zyhAthG0/aSCGZ5gkPoxF12+f91tpio50bMCrMIJtGLZ7/RTm+fwh4jvDKE6Ef+W4O9OTHSino1gMu9Fp06yag2x/NuLs/5CeX1lGVI32UZjw4HLE7miCFoFMLWO806TXCxRzHs7DcrPHB5gqNwFt0SZ6HeQenWw9fW6LhSZtlv2EoGMdoPVGZ8TAesZ9MiIrUuHuf0othLu+6m4wZZQlJWbwQ+arp+Fxr9NhLJtyaHjAPOw7SGV+Mduh4Ie80lhZSpaeB0opCKcZ5zIPZkIfRiLQKiiQmgNqsdVj2688MAudB/x27DxiFqGFWdTQWRQlJy/HpeTX8Jzisz7tJTcfHtxwsISs38Ix+OmOQGafnpMzNjMbCLPBtR+O80HA8rtS77MRjbk8PSavYu59FfDXao+vWeKexfMY1ZoaiJ1nCVjRkOxotAm+JoO54bIZtloP6M5UJ58PkJ9zDhaE7epZ9Jt+dslr3ozxmOx5xmEzJX3DgXSLouCHrYevEWsyVUU3bicZM8oS0LHDO4Icxm6/7NCIqXm7O47R4qUSjUFPi/C5CSFyrhRTPqsgrpHARQp6J/zRLMr7ZO+CbvUNu7fWRQrDRaXJlqcP1lR6dWkDoOt+Ju+FjqOhKeVYQTxO80MUPPYqsIE0yQ4UpNGHgsrLUpNdRFEVJlpekaV51NxStRkCj4ROeQnHKsiRe4OK4J5UU8jRnMoxI4wxVKqT1Nuh6kyAtSdjw6a40WbnQZWm9zeHuiNk4Jktz+ntjvv74Hn7o8v7Pr7wRiQaAAraiEX+/8w0bYYu/Wb1Oxw0IqmqfRtN1wxPKNdeby6z4DSO/p41U5n4y4d9ufcm3k33+LL9S/a1pMQ+ziMBy+NOly1yqm2H4z4c7/P3OTUZ5TD+NWA0atN2QD1prXKx1SIqcz4e7fNjZ4L+99BFQbcxe+Lab8QiMipRVzWMIo1BUdVUd6+lBbakUWV5QlCVKm66FZ9tPDAylENQ8l14jpNcwcx2DWcz9gxGTOKWsHKCnScbW4YjDSUTNd1lu1ug2zBD4kwotUkgc21pQvDr1gHfWeiy36iw1TienbEzxBI5t4b8mM0nfctgIW6wHTXzbWVAPoyJjKxrxMB4zSCPqtkfd8U6lVlNoxU405uZon2EWvfDQaccLeb+9xv3ZEFtYgCkE7sZjfnt4nyv13hGd8ZRXV6kN9eognnJ3esh2NCItCyTGJKzjBVxr9FgLmtVzPhk1xwT9oe2iNSRFziiLOUwjcm3mK6zKqG/Zr+M/gUdvCYlv2dRsl4bj49sOUZERlTl7yXThCp6VJiCszalTbzsa54aWG/Bea5WtaMSvD+8tfr4XT/hd/wEXax0KrRDicbnlp6HUilmRcZBMuTftszUbkhT5Yo213IAr9R4bQeuZicaRcd2x59Xm3mSGpU+fKBRKMc0T9uMJd6d9dpPJCcrgWWAJybJf53KtQ8P1F3tGpkr2kwkPoiEHyZRZnlJ3PNzTUO85ElLZTSbfmUTuSyUaQlhI6WHLOoG9iSWevvFrFEmxS1YeIsXpN/s4z7nfH/HF9j5fPtxDKc2DwZg0L1lp1Gicsqr1KhA2AtYuL1EWii9+eYt6OySo+aRJRjJLiCYx61eXaS81CHzHKKDkBVGcMZkaCpXWMIuMfrfr2HjPMWyzHIuw7uHXvIUng1KaIldARn9vzNbtfTorTdqvQCr1LV4MQggc16beCrh4fZWDnSFZmhNNElSpmQwivvnsAbZjvDY2r63Q7NZwq9md5yWNSmm00qRJRhrnRNOELM1ptENqjQDHtU+oXZ0GpdbkumScJ+wmEy6Eba7Weyz5NQLrpDHlXF2j1IqsLIjK7MTtIlflImlIVH7MX4HKHMtjI2xxrbGERNBPIyNrqRRRYWYLXGnjew5tL6Tjmap5z6txrbF0ptf1hwZLympOwmU4i8lLxTRJmSUZ9cDDfcr9KSuMh0WUVTx4x6IV+nhPoCwJYQL5muey1m6w3mly/3DI4STioPoCTX8S0Z/GFEpxodVivdOk5rk4T/EPmj+nf+w5NVD3nAUd6/sAR1q03IBlv86KX2cvnjDJk4Vz8dZsyKeDbUqteaexhFcNjT/putfaDH+PMkMl/GywzWEavfC51W2Pi2GHzVqblaABMcyKlHFF+7g53uP3w4es+k2W/foxb4LHMa8yD7KIW5MDPhs+ZDsaMy1SSq2N27ZfZzPssOI3qFd+GM86t3nQr6uOxihLqo5GuXAEPzLqezy2MOdrEdguHS+k4fikpZnN2I5GhttfFguzvtB26Xm1F55LeYvHUbM9NmttNmttVoMmpVKLmb/7swHfVGtsPWiy4tcrj5Vnr7FRHnNrbNbYVjRkUpj5wbCiYV0MO6wEDRqO/8yuhCstGo5/QhJZoxlmMdvRiNWg+dzXN+9k7CdTbk32+WK0y248YZZnqBcM5YUwXZmlas9Y8mpMimQh/LAXT/hytEvLDXinsUzXC5/5vmVlQVIW3Jv2+e3hA7Zmw+9spvelEg1HNqk713GtHk3vJ89JNErSco+s2EeK00vbJnnB7mjCVn/E/nhGlOXsjqfUPZdfXN4wlbLXNP/cXW3x4z+/wTcf3+Xv/h//gpACP3RJ45w0Srn64UU++uv3WLnYAyDPC6bTlDv3DtnaGdKqnKCHwwjHtfjZjy9Srz07cXJcm3ozpN4M8AIX27HIsgJVKnKt2bl3yCf/+A3v/+Ly20TjDYQfulz78QWy1Mxm7D8cogrFbBJz+4tt0jgjS3LGgxnv/ewynZUmvu8grOckGqXplg33Jxzujti6fcDwYMK1H1/g0o1Vmp3amRMNQy8w8neutAlth7rjPlV9wyhtFHw93quCJlVVQiXTPOVBNMAWVlXdNhftXJKy44ZG+rNKT2whCSwHW0oKXVac3ie5MrzF82BLSbvm022E7I2m5EXJYBoznMX4rv3E2QiAOM05mMyYxsYxOfRcllu1ZxreOZZko9vk2mqHw8mMWZqxO5xwd38AwMPhhFma4Ts2V5Y7bC61TiQRjyJ0XZabdUOTEjCOErYPRyw3vztzyPOAJQS+ZdNxQ67Ue/TTGbenBUWldHR/NuB/efg1uSpZ9uu0hcR62nWGNpSkaMBnw21+3b/H4CUSDd9ysH3JZq3DjeYyWmuiWUaiCvJM8elgG4HgL1eu0aqEHJ5mujuvMj+YDflfd7/l14f32E+mi/mTthvw4/Y6N5rLNBy/6oQ+HXPqVGi7aMzMxziPF8PgaVlgS4u2G7Lk1fGf4eLtWzZLXo2OGzBMI6a5mQ84SKaLfcqRxhG87YUnKG5v8XLwpY3t1dgM29xoLFOokniak5YFhVb8fvgQS0j+YuUabTfAkTx1jSmtiEqTJP6HvVv88uAuO/Fkscaajs8HrTXea63QdoLnrrGwUjbbjSfHnkOzE4/5erzHlUbvua9vnjjdnh7y7x7e5JPBFsMseuEkAyp5W2m8PC6Eba40utye9ElKM680zCL+5eAupdYLc0nPenoRYN79+Wq0yz8f3GYvnn5nhn8vmWi0qbvv4VgtXLmEJZ8eJGutqDnX0CrDs5ZP/Rx5WdKfxRzOIqIsJy0MD3VeYTNKJS/zKl4c9XaNi++ukSUZs3FMnhWLDoMqFZfeXePqjzdpLzXQGrKsJIoz8rxEIKgFLr7vcHg4JZsV5HnJo066j0JKgXRtmu0a65eXyJKcg50RqlRopdl7MOCTf/qGLM1JZilu4OJ5jhlMF8KoU5WKsvrSSuP6DisXOjQ7p/MjMNVzdTSLojSq+n997GdloZiNY+LZ466YZVEyHszYfzhcDLdLKc2Qu5RISxz9WwqkVf3ue04Hsx2bpfUWV95b5/qHF4mjjL0HA6ajiNk4ZufeIVIK4lnK8GBCb7VFrRXgeg62bZnPEbMRaqUpKlPINM5I4ozRwZTBwYT9rQHRNKHeDli72D2hinZaLFxJYUGBKucuxse7GdX3qMgYZDMeRmYwb9mv0/Nqi0dZQho32OOmSkhcy8aThgs7/3xFpTolEAt1nePQzNUzeO4184cO17FY6zTZ7LW4tz8kyjLu7A1YatZoBB6+45xQOtOVKsnhNOKrrX12R1OU1rRrPtdWu3RqTy8U2ZZkrdPg6mqXL7b2GffH7AwnNHZNovBwMCbJCmq+u5C1dZ+RADdDj8vLbb7e3gZ0r+AAAQAASURBVMexLPrTiC+39lhtN7i2muNY8qmzHUrrRSFKiiMBjdexTkRFO+p6IR+015nmKYepMZortWKYx9wc71GzXVzLZq0aYp37DyjmnYy8olsNuTvt89Voj2EWG68C1yYqsjOryNhSYiPZDNv8ydJltGYh95qpkq3ZkFIrBIKoyGi5Ac0qSbClRGvjZTDvEuwnE76dHPBxf4t70wFRmeFIi5rlcqne5afdC1xvLhM+Q9p2jtA2akVzGdyiSmT66cx4J5QZjrTpVN0i/wkdjTk8y2bJr9OuVIgmecr96YBhFqO0NhKstkfd9sx+9ILeIa8bi9mqaq9W6IV3haocuc18j1HeenS9zFXMJnnCII0WHaxHTekscWRI97xryjjQSzbCNn+0dImy6hhMKsXB7WhEWakZpkVOywtoVUmCLWW13yuy0gwx7ydT7kwP+V1/i7vTPrMixRaSmu1yqdblo+4FbjRXqDnPX2MdN+Sd5jLb0WjxM6U1B+mMr0d7C3pfwzGeLvMZqlIbk8ykMAIFu8mEr0a7fDrY5jCd0XIDXGkvjCHPivmeEVgu7zSWOExnC4W0uT/NvekAp/Kk2U0m9LyQ0HaNEaYQC+GDpMzZjcdsRUM+HW6zUyVVS169ohE+Lll+nni5RMPq0JABEusUdChBzXkH31rFlq1TP0deKsZxyiQxmsGuZdEOfLr1kE4YEHrPX0ivCkHdw/G61Ns1bvzsCkmcksYZjuvg+Q5hM6DWCLBdC9BkWUGS5IShx4Zjc/FCB8+zGY1jptMUKU1Q9dwboYBWr847P75AEpmANKsUZfa2B4z6U/a3B3zz6X06y03aS42FOlaRl+R5QRpni2HkznKDv/gvfnLqREMrZY6TleRZsfgqMpMsFVlhfpfmTMcx48HjqmR5VrK/NeDuVw8NXcixDb3HtbAdG8e1FjQi83Pz/fseS1q2pN4MWb+8xEd/eR3Lkvzq778gmsRGhnSScO/mLrsPBnz2L9/S7NRoLzWoNQPCuodlmWSjLMz7H00TomnCdBQvkt35l+e73PjpJZNQvkDbb95VsIQxuYrLjKjIqNkuwSN80HmF5cFsyCCLUFrzfmuNn3YvkJUF92YDvh7ts5dMTvydEFTB3+nOaS6Bq6sgUlXyud/zZfFK4Ts2l5db9CczPr3zkK3+mC+29qgHHpeXOrRrwQnlPKWNStXuYMpvb29z72CIUpqlZo0fba6w3Hz6PmFbFmvtBpM4pRl43C2NWaCRlRUcTGZkRcma73Fpqc16p4lnP/021K4FXF9f4pO7D3Fti/3RjCQruLra48NolXrgPTXR0Fob7wylcGwLG4kln2Xd9erRcUN+3tskLjK+Gu0xymLSUpMUxrTvV4f3uD3tc7ne4Uq9R8v1adh+pfBmqBn7yZR7sz7b0YioyJAIlnwzCL01G5JmLyZXeSFsV+Z4BbenB+wnU7Ispp9FTIuUfhrxu/59LtW6XKi1F74ICk2mCgZpzH4y4e6sz048ZpTFlYmaouH4rIct3muu8PPeRa7Ue8/sPszhWw5tT1QBnoWqBsDnPhhRkdHz3WPUqacf05MOS16dlmNol9M85d5ssHBHDi2HZb9OzfZ+EPuJQlNo4+WQqZK8LBYGebkqKwWoqfEPOXZ/SCta3n4yZSsa4lbu2q60cC3z7/mXLc7mfb0eNvkr6x3SsuDu9BClFXkWGxPGPGOYRnzcf8ClepfNao3VbM88rqLkmfU/4GE0YpTFhlqrFTXbYy1ocaO5ws+6m1xvLhOcQjFq2a/zYXudr0a7i/uLQnOYzogrz497swFX6z3Ww9ai056WRq1qLx5zfzbk1mSfnXjCKI9pOj5X6l2meco3432il1AD9Sybd1urgODedMDdaZ+0LBbzjV+OdtlPp2yELa7Weyz7dRqOkY0ulFp8lg+iIfdnfSZ5Sq4Klrw6q36DrXhEFL3BiYYUzhMTDKWzY1959TgXS/jYVogQp3/auflSnOWUSuN7Nq3Qp+F7eM6TBxO/K4hK3rbRDml2auRZQZZk2K6N6zmLajyYLoDr2oShSxRnJGlOfzjDtgynrtnwcU5BbZknIe3lBu///DLxLOVwZ8zwYEI0S8iSnCwxnOrpKKLRrlFvBVi2UbUqC0VZlGRZQZGVaK1M0PsXp/c26e9NeHBrj+HBhMkwWihuFXm5OP680p7EGbsP+o8dI4lTvv3sAUmUYdkSyzaJhWVb2LZV/cwMvgc1j7VLPa68t05YP50z55sKIcyaqbUCLl1fQ5dGTKDWDNh7MGAyikjijHiWMuoLhgdTDnfH+KGLH7jIal6jLM37nMYZSZQRRylJVN0wqoaDbpvuknxBVTYpBDaSJa/G+61VLGHxu/4D6ra3qBxqNBdqbTaCFrJS6Si1aSPvxGNaUx+tzQCawHCuHx3+POuZiUpRZC1scpjO+Iedb3AtC99yuBC2TsWpfRZCq8WG/y6BbLDiX6U8g0re8bO0sKg7PZrO8zu48y5CXpQkeUFRHrlgK60ZTGOyvFjIV0+ilAeHI1NZlFWVUcpFl9eSclGRdSyLXr3GpeU219eXjFP2NObz+7s0A4/twZhG4Jk5CW2UocZxysd3tvnm4SFKad5Z63F1pctmr0UjfPo1KKWg7rv06iHLTTNDN45S8nIIGCps6Dkst8wQeN1/tpeH75jK8tWVLr+4doHt/oi90Yzf39shcB1W22Yo3LYsI7msjIxvVhREWc5wluDaFu9uLLHabuDZAvkUGmLN7nAheI+a1WLFu4o6hT/UoxAIJDYtZ4W68zjlwrNslr0615sr/OnyZQLb4dbkYDGv0U+jShbamMrVK2dsQ0PUjLKYYRYzSGdEZc7FWpv1oEXbDRa/72cvRqMKbRfXsvigvcYwi/hqtMvXoz2mhZGpzlTJMDUKTTvJmNAyNMq5l8ckTxllMXvJhEmeoirKZc+rcbne5cP2Bj/rbbIetE49aG1XhoI126Pl+kzzdOE0nqsSjZExbTp+VUF++j3Ur3wV5h0N43VgDArnalPLfuWZIL6/xYv5sO9BMiVXJkAvtJkFynVp/I2qeYK54tlxis8wi7kzOaRUmp1ojC0tHCmxheku2Mf+3XJ8rjaWWPEb+JZ9Qrr5SQgsFyewF2vsi9EuX492GecJsyIjj8uFCthuPCa0XXzLMRLdlTHfKDcJ7ShLUFrhSIuu1+BirW3WWHeTC2Gb+ilVwxqOz4WK0vWTzgY78Zj9ZGrWtFbcnfaZ5qaL0vNqCyWqvJJIHqQzDpIpD+MxGpO4vNNY5qfdC2xHI/aSKVlSvrADtyUkHTfkcr3Lz3ubpKrg1uSA/WRSvSdGMW1aXX9tNyCw3IUE/fzngyxmkEWs+HXea63ScQOj/qiKE92cV4FXQkIsdUJejijUhEJNsGUDWzZwZBNbNjjLJayUIi0K0rxAaY1rWzQDn7r/+joZc+hKccqypKnCe2bgVgiBeERBRQio1zxsW3I4mDEcRezsjdBa02mFLC818Dz71AFhd7lJWPeJJik7dw9NWz1KF+Yw0TQhic1wuLQMBWWers9bqggju+sHLtkT3H6fhp17B/zH/+8n3Pt6h92tPsWc8rU4NoD5rpUmeUK2HE9TPv/NHb757EElE1wlUeIYvUFAEHo0OiF/9Lfvs7LR+d4nGnO4nl15arjU2yFLG21+9Xdfcu/mjpm3UKV57+KMPC0Qc+6lABBQ3Rz1I7S1OYQQWJZJ2mxbPrYeTwspBBfCNn+9+g43R3v83cOvKdHYwkJjguB/s/4uS+s1QtthJWhgS4tBGvPLg3t8PdrDtx2UVqSqpO0GuKeQMX0WBND1atyo2t3/l9u/ouH4dL2Q/3T93ZdONJrOMoHdoFA5SpcvrMthJH4tfOv5ncI5NW2WZhxMIuI0N86yRUleKB72x0RpTl4q0HAwnvHV1j6ubTwxHNvCc2xaNZ9W4OO7R9QP25I0Q5/NXpuPrqyTlyUf33nI7+/vcjiJ2OgaWtVc+nV/PGPrcMTDwYSd4YR3Vrt8eHmN9zaWWWnVsZ+hbiIAx7aoBy6r7QZLzRrjKOFgMkNr8BybbiNgo9Ok4Zvk5lkr07ZMwnRjvUdRXufvf3+b+4cjfnNrm293+ryz1uXScofQc/BsmyTPmaUZoyjhcBKxM5jQa4T87//iJ7TDAMeSwJPPv+2sEVptSp2j9ItJxVZEEixh4z3hc7eExLcdrjZ6/OfyfRqOt0gy5pVZVRp++GEyW9BUzLloSmVoMJYQ1B2fn3Y2+XnvIqWeD6IevMBZz89NIIXNj9prrAVN/tfdb8nLknuzAbMio1AlM6W4PxvyMB4vaDRmu9eUmC5jocsFX75mu1xrLPGL3kX+ZvUdLtd7ZzY/k4LFUOzcaMwYgQp8yyGs1KQatvfM+6dnOYY65QXYQhrFnSpZgbmsbZ3697yjsR2N+H/e/ZhPBlsUx7q+c8EOXd2b590OI+16tNoPkimjLObW5ABH2kdd5yqGmBOmBIKLtTb/7aWf8mfLV5Be+NxEw6oG899trbIcNFjevUWpSu5M+2aNaVOs3IqG7CaTk2uMir6rTHdvTkcKLZcr9S4/717kb9be4Vpjibpz+jghsB08y+aD9jpxmfNP+7c5SKaA6e4Os7gy9RsZK4Xq9c/vf/NzKZRiLWjyYXuDP1q6xJ8uXeaz4UM+HWwzzhLKF/C8APPee5bNsl/nL1au0nB8/u3WF0zzhEjn5LokU8b4c5wn1XssT5zjnPpoC8H15jL/au0GlpCUWr/yJAPOOdHIyj5psUdS7pKWu8YvowpoBRaevYpvreLZK7jW8wdsgKOFXQWglpQErrmpvO5EY397wN3Pt+iutbnyow0cz3naPQyogolSkSQ5s1kKwlQAZ3GGN0nM7MYpX5Pj2liOxaV31/iTf/MBK5td7n+zw6g/YzKckUY5aZJR5CUqyRfJj2WZ7oHrG3pXvRmwtN4+4Vb9PGRpwXgwo78/5uDh8IX4/0pp4mlKzOPzG8fhhy55VjAdxT8oM0IpJa4nabRrXLhqZIsd1+bi9RUOHg4ZHc6YTcx8SxJlFFlRebCYuRpRzbTYjoXnuHiB+TyDuk9Y92i0a/RWm1z9YINaMzhVt+xRzNdiyw243ljGkRaB7VBohUQunMFXKw6rZRkt7x+1VhcSg4608KRJoC/XutRsl1W/gVNp22+GbcquIrTdEwFIz6vxs94moeWyGjSp2e7CQwNgM2zzl8vXTPUrSwhsh/oxF/GXgS1dbFyeaQt0ziiqzu3d/SG/vbXFYJZQqGqWSinu7g8ZV67cWmvu7A/4h89vYVumwmhZZl80nYcOa+0Gbt1s70beVtAKfd6/sLzYR7cOR8RZzsPBhHGcYlfJaJIVzNKMmu/y4aVVPthc5adX1rm80n6ie/dxzPdpz7HZ6Da52GvxeZwyihK0Nj4YG9W8SOA97nnwxOMJWGrW+dGmIMpyLEvQn5hh9r3xjFmaY1vm5loqVcn3ms6G69i0awGBU6m3PSOEtKWLLV+t0tCcd91wPC7VOiRdU0C7PTlkOx4xzGLGeUxa0VyM/4heUFXanpmPWA2aXAjb/Ly3yfXGctVB0M+cUTjNuQmgUXUsP+psIIE70z53p32GWcQwi4nKjLjISXW5CGCkEDhCEtouNdul6RgDvY2wxTuNZW40V7hY6xgncE4/ayeEQGhh1Kq8OpMsOfod5udzhajnzVR4c6do58g08XgyGVTGhGYf+v6mGllpfBa2ZyMKXZ45YS60uXbiUwTGrmUxLVJyXZ6KnjtfY6YrbvOTzgYCuD095M5ijRl/h6jITq4xWNyD6rZHw/HpuAFrYZPr1Rq7VOvScoMzrTFLSCwBm7UOGiOp3XB8DpIp/TRiVmTEZUZS5pSFRgrzmLnPRtsJaDoBPb/G5VqXDyqxg9WgyXY0YsmrcZBMyV7QgfvIXd1mPWgtRBFWggbb0YiDZMo4T4jLjKwsSavk2RLCzEZVifiyX2c1aPKTzgY/7mwsqF+n7fy8DM410UiLXQbJr5nlt4iLe1iijiPbFGpIrseE9iVC5yod/49OnWgIBJZl6AFFOa8I2c+94X0XeHhrj3/4f/2K9/7oKhvXVkyi8QxkeVlJ2ybM4oxep4bvOxwcTsmygkub3dM/uTDV5ovvrLByocP993e5/eUWd7/e4d7XOwz2Jwz3J2RpQabzowTDs/ECl3orpNWt0Vttcfm9deqt75eKyw8FjmvTWW5Qb4VcvLHK4c6Iezd3eHBrn+07BxxsDzjcGxNNEtI4oyjKyifFJBlh3Ses+7R6ddpLdVYudFnd7HDh6jLrl5dotEPCRvBSSXnNcfFth9Wgwc+7m4/duDxpL7jRnmXzJ0uX+Wn3wolBwfkAuxCmCmkLSderLbTOpRAnVF42whY9r2ZkU4XEqgYR57hU67AWNBdDjvPK7/dVKaYoS2Zpxs2HB/x/fvsVO4PJiaFOQwcqF1XJb3cOuX8wPFZlhMBz+Kv3roCG0HPp1E9e0zXP4cb6Ep16wFKzxpdb+/y+cvHeHU7IyxIQ1D2XVs3n+lqXH22u8u7GEjc2ls7kQeHaFhe6TfaWO9x8eMAszQGNlHU2ey02l1oEp3T2BmjXfBq+u0imPrmzw2f3dtgfT7m1e7hwD/dsG8+xqQcunVrItV6TG+tLrLTr+I59KlPU7wKutOm4NT7qOlyt9/hmYmRqv50ccHt6yLBKoOfr21TtPS7Wulypd/mws8GPWmt0qyD7IJkyyKJnUodOC0tUBYP2GtcaS9yb9rkz7XNzvMc3k312K2pJXBrVIJNkWEatyQ25ELa4XOvxXmuFy5VXRquSD32hCZmqo7HsN9iJxyfO81n+GY/CkzZdL6TlBpXBmThRyQ8rF/LvIvB69XhcQOPVPMvRM53l2ebds3dbK1ypd7k/M+IGX4/3uDneYzcZsxdPictsscbMsLdH2w3YCFtcrnd5t7nK1fkaq4avX3QKazVo0PVClvwa77VW+WywxRfDHR4u1ruhD1pC4gqT8LTdkLWwydV6jw/a64s5jqbj4UgL33YW63bwgpTGo/dM0nSN6eSyV+ejzgU+G2zz1XiP29ND9uLJokih0HjSpu54rAdNLtW7fNBa48PuBVZ98zoHaYwjp/j2q/cWOte7cq7GRPkdpHDp+H+KLevYok6hZxRqSl72ifJb1J3rpz7mvIMRuA55aVp8c7Op70oD+GlQSlHkZj7heTCqU2YY3HNtlro11lfb+L5DkuQU1TFOq6Az/73jmSHptUs9HNeiu9Li0vU1ZuOYaJos5iZEpew0H7D2QjP7UG+GdFcatLqnGwQHWN7o8Ed/+z6X311jPJihXmGnwXFt/JrH1R9tENaffgPoLDe4/uEmnu9w8foqYBSefvzHV2n1GnjBE/TVLYnrO6xd7PFX/9VHXPvRBgCNdsjqxR6b11bwn9Hp2bi6zF/+lz9h3J8xHcU4rukUXX1//dSvT0iBJa3FucwTwvZSg81rK4yHM2aj2AzuZ4VR0Sk1wjLdKdc71slo+DQ7NVrdGp3lBq1e3cy7nNLI52mYV3xOF8SYwc2Q51eG5zMgTxrgnFdwnwbXsnFPEVx8X2BbktB1uLrS4V9/+A6jKHn+Hz0C17a4vrbEZq9F/QkStFJKPCnp1EKurmoC12G5GXI4iZgmGUV1HQeuQ913Wes0uNBtsdKqUfdc5Bnm4dxqKPwnl9ewLMlHV8w1sdSs8eHFNTMEfoZEYz5z0muEeI6FFJKlZsholjCKEvKypFQax5K4toXvOtR9j14jZKVVp1sPsB415XqNmCfGoXDxpG0CA8vmYq3Dj9N1U0UtssX8gG+ZZH7Jq9Hz62yGbdbDFn6l1tZyAy7Xu/ybjfcWvjI9v8YH7TU6bliZNZ4ORxVUx8w91doLD4rL9S6jPDFqQaXh/guMApFn2YtAcMmvsx406Xl1Go73UteqQHCp1uEvV65yud5ZyJDaUrLk1bkQtln1n0+XlELgSpvrzWX+m0sf0U9nJ6Tx32ks8W5zlZ5fe2qoagnJqt/gJ50NPMvmo84FwCQpl+tdrtR7NM5IDTuOS/UOf7t2gx931ulXksWOtPhF7+LC+fx5WPbr/Jv197jRXDmRSL0KdL2Q91trCxWy0+LRNbZRdePabsCleodRFjOuFKkKVS66Zl7lh9Kthv/Xq4JUw/GfKQRwGpjhdvP5mvOy2Qjbx87F3H9lpYjoWjah5dKuFM82am2WvBp1x1/cK1f9RrVuuxykU3penUu1DhfC9mNO3kopvvzNXW59vkWjHdLq1blwdZnljc7iPbMQeJbAFuFCpXE5aPBea5VxHldy9MafzZbGqLLthvS8Ghthi81ah9A2e07DMRTBv1i+uijQ2VLy8+4my36D4BwTEKHPMVrfm/3PbE3+b3T8P2W98d9giQCBRKNQOufh9L/nIPoHNhv/Hav1/+pUx/x8a5f/8z99zC9vP2B/MqPhe9xYXeJv3rvCf/3Reyw3Th8gnzd+9w9f8O/+7//Muz+7wr/+7/6MsPF02UelFAeHU/qDGdMoQynFxQtdPNfmk88fMJkkfPTjTTY3OmeScZ1/fHOuflmaAeDFHIY+qjTM6f2LKqg4kpW1bIk8ZUBaFCVFVi5mA15l4WQ+u2E5sjIofPI5loWiLOfD6Grxt3MVq/lrPY75+1OWqhpor/5uQTGTiyH6J6HIjbrWfD5CGOIqrufgPMd48VEsPkdNJQ+sqs9UP/GzhPnneTTfIqqhYHFMDtg85M0Irt7i6ZjPNhWqJCvKFwoQBGJBpZJSPJVKYhS7jMxlWc4lL4/P91Sd5OoYcr6uzrCO5sPtZWloTHM+tSWkOcdKBOOsa1NV14KhSB27Po6/Dwu6raE4WHP5bPHmXQ/z637Ooy61RmEkeR+tSM+DrXmgY4mj2u3cLHNuAAgmsJ7Lg56FSvIo5nKo5VwadT5/V53h/Khz7vpCCvWYBOrLvOe64sEXSi06PNUTYmHeC7t6nc87DhhqUFoWj11jZtDZMtX2p5zzkcy3maObz6IIYQbXzbnIx4LI0+L4zIQ6dmxHWgvJ0ucly4VSZKpYrINXCZO8GfGJV73G5jOfQhuqsV1RdeUzPq+zYr6XHF2PaiERDDxxvYtqnUshH1vvhZqvE0WJNutVVutEnBRpKfKS/+v/4d/y//4//XsuXl/h2gcX+Kv/zU/56V/eeOwczbkcrcX5ennWnjE/v/muMe9GZWW5cC+frzVbWM805jwrzrUkKISFFB6WDLBEiCV95hPIUjvm59JDPMWI5UnwHYeNdpP1VoNxnJIWBYfTGcMorpy1X5+Gvh969FbbKKW49+U2zV6dsBEsBpkNjImf4zkEgUuzVMRpzmSace9BH8syvhutZoDnnn4YfHH0+c3TEhWd5nxf45NgV8pQbxLmKlWcoZg0/5xsaZ3ZzA5Mx8Q+Q1X2ueeCudCltPhOBwTe4rVjPovgShv3GXKv5/VctiUA+YrkQI5uvpaUuOe4JxlBhKcnUa8e84KARusYpROMGObJoE5gI2UdKZ69Ic2ve0sIrGcN+D0HJsB/dhfwRWGCuVe2VJ4LUVGzXva1LVgAL3EsIcxA7dM6sS+L53VyTwOTMH2/XM2ftMa01kaGP86JZ6lRVpylCCFYvdSjttQ49/OYd1rm3faXxVyl67TI0rxioxirhCexZRaxAgIpwHnBWGF+lMCWBLzawPFcrxQpHBzZwBJPHqaSwsMWDYQ4/UUQuDabnSYPOk3uHg4ZRDH7kxmDWUxevl4Nfb/m0dtok6cF3352n95am5XNHtKWR/J4QtBeauD6DvWah+NY7B9MGI0jHmwP0FqzutxkqVevVKde04t5i7d4i7d4i1NCo/SMUg3QOkNzctBTiABH2PCcROMt3uItngytNcksZXQ4pb87or83YnQ4xbIlfujSfgWJxlu8GpxvSq5Bo5hkX1PqBEuGWCKgVDGFnhHltyn0hEn6e7Q2kqdS+NTcdwjsjSceMnRdrix32ZvM+Hx7j3GSkuQFB5MZt/b72JZkuVE7Ewf1vJDMUg63h0STGCEl/Z0R27f3j6g2wvCif/Qn12j1zEVhWxYry00cxyLNCpTS1EKPRt3Hc503qq0/h9aqor8llCqi0BGlilA6fcQzxfy/JkfpfPF3oNBaUYmtMW/aYcTrEEJW/7YQwvwMUf0/FkLYSOGabpnwkNXX8X9LWX2vHv+6obUmTnKiOGM8TUizgl67Rr3mGa+ENGcySdBAt12jFrggIIozDgdTbNtiuVvH910sKUizgijOsCxppEttC+t74JRuOo4aTVGtnRmlmlGqmFInKJ1S6rRaO6lZN5RoXTKvFJu1M183T1ozVrU+jFePFL5ZG9KvOqs1bFlfUDnFC9Iavkto1YdyB9Q+qAMQdZBdkOtgXTgnmkACagxYIJuI55quvn7M1xPnRJV4kefPipuk+beUakCpxlWH/uS52LKL9AKsR8xpVaWGlWcFaVJg2ZIgcA119XtwPb/F64Oh9ZRonVOoGaWOzJeKKXV8Yk/VuqASHEbr0tx359+BihxZ3W/NHnp0rz1+f/WPvi/20xBL+MCL+TOdHgK7mtHsrrbwQ5dGu2Zk7xs/DJn7PxSca0RmgoKSSfYF/eSfsGUT1+qQlX3ycrBYrKP0E2b5NwA4so0lgqcmGjXP5epyh/3JjE4tYHs4Js5y9iczvtk9pOF7dMLgtSQa8TRh70GfcX/61MdIS9JZaXHjZ1cAsCzB2kqTtRUzvKa1mXnQWr9xdKQjmECxUFPScp+sOCAtD8jVkVdKoacUakKppotNUOnCbHi6QFNU2vTzIFIf29gchHCQOIv/l8Ix9APhGaNHWce2mjiyiSNb2PPvVtP8W7dwZAshPcRra/Ife8e0ZhZlHAymPNgeMJ4mvHdtBSlbRHHGcBzz4OEApTXvXls1QQZwOJjx1be7BL6L7zk4jo0UFklacDic4bk2NAJ8BJb7pq6XR6FROidTA9Jij7TcIysPydWQvByRq3HluzOm0JFJVnWGokDredJqEg+TVMzXhoOoEgxb1iuvnjaObOFabRzZxrWW8ewVfHsdaTmIV9wiPjeUfcg/Q+e/h+JzsNbBuoFw/xisDc6lj6sTUHuACyKA70GicTzhfD29bE2Sfckk/rcoPUbpGClCpDgZ+DjWJq59HezLJ36ulCbLCmaTlPEowvMdpCXxpfPGKGK9xZsLrQtKnZCVB4t9NC0PycpDMtUnL/vVfdgkHIocrTNKnS32UjNDYJ3YQy3pLhKM+b3VkS0cq1XtqW1cq4trdfFYqfZSeJUUXyGMGEytGVBvhWjVJpoamft68+nzsG/x5uFcIzLPWqblfUSorqB0aqqMMkBVmbYQNhJTnZ5XnW1Rw7E6Tz2mFALPNvSp/+S9q9Q9l0/v79CfxfzzrfuUSuHaFhvtBu0w+E65u+tXV/ir/+YXZPHTze6EEGy+u0aWm+6FUieHdbKs4OBwSlGUrK+1aDW/e5nZeaVE6bQK/oZkZZ9CjcnnyUNViV5UUhbVk6OOhp53NaqOBlVHw1RTVDXYdbyjoauuR4HQGWVVbTZVZwuQiw6FFC6yeEo349jPTPW6tjCJnAegi3+LWsWbfvnqYZRkRFGGbUtcx8apzPGKQpGkOfe2+tzdOsT3HZZ7pjuRpAV3t/ocDqb4nkOvVaMReviejUDgVd0KIcyQelGUCKoB/Fzh2phORrXM89yYupWVGMB8Fsz3bLznyC2fJ8zwb4rSCWl58FgisUhI1ZRSm46G0smimzH/rhcdDVVV5NRiHVWOkEZXXeQonSGIQVgUaooUfSyxu6jCWSc6Gk0c2Ti6iVrmxulZy9iygSX8N6ITtoDVBT5ECBstbMxWfc6BqBqi809A1BGyC5xu74nye0T5XZJim6TYOddT8p0N6s47+PY6rrWMrD4TszYysmKLrHyIZ1/CtS6gyECXSBEghIfpgpmqL+iqSyOq/zf0XY1CqQhNhtYFQvjYsnnKz1+jSdA6wnc/xLXfQVQFkuOQsoFtry3+vywVeV4yHMzYvj8gSTJTWNKaB3cPWV5tcunq0lOv2X78zwySX1bXw/mob8yr2Q3v/YVK5A8BWTlgkn1OlN8nV0OUerZX07MghEXX/3Pa/i/4LrtoSpv9LSv7ZOqQvByYf5d9cjWs9tJZ1cmIFh0NVXU05kU9jnU0Ft8BoRWCEkWO0CmlPt7RGC7ur6ZDPO9oBFgiOHFPnZswmyJga1EIPK97rGVLENXsqjZCLVrr51oJvMWbhXO9s7pWj6b3k4qvejQ0d7TYnjS34eJa7aceU0qjarDRafLXNy7jSMnuaMr9/pDf3t1GCFhu1LCEIHCcE/4aCzfsc8LiVVTHX73UY+Vij+fJLmV5SZoWJlh8RAo2ijLubfVJ05xG3X+licZJgbEjBWxzY87I1Ygov18FEreJiwckxY4JGsuBuamfo8TU0Ub4yGm9BGzRxLFa+PYqnrWGb6/h2dV3axXPWsKRLeYUnGPaESzGo56zOSptVKaiOONwOMVzHcLApRa42LZLXpTM4oz7Dwd8fXuPH99YNzQozyHNCu5v99k7nPDjGxssderUQq+izYHrWMZ5WQgjn1wYs6KiKMnywiQkxxR0srxkFqUUpXmsqtR9Oq0Q1z0ybDtPHK2j4yrqikJPycsh0/wms+ybah3dJy13ycp+tS+8nBLKi68ZiSUDPGsF31ondC5Tc6/RcN7FtzfAaiPxFmuCY3vHs0zeXhWE7ILsonEQWoMeg46f+NgjOtHxJB44sa6PKxRVj1V9yD8F2QHnQ7RuHfs769i82MnXH+cP6Mf/xCD5FePsk/N5wRXa3i9Yqf3ngMCxusxvUVrnlGpKUnxLVD2nLXuUeobWqTEtFG5F28zQOkGjsAgBgVIRIBCWbarCakypJyidYssWlghPnWiaYkqC73xAI/jfAqfYM5QiTXP6B1Nu3dyhyEuW11okUcbO9oAkWmX9Quep1+ww+TW3h//Hav2fj5rQvGO8Xv/f0XB//INJNPJySD/+F/rxPxIVdynU+Pl/9BRIXGTHo+X9rKJcvpq94GhPNQW5UkUUakqU32GWf8ssv13dk7dIi71qHbz4zXKho3R8Kz8lBBZSBFWneA3fXse3NwjsTQL7AtgCRzYRwkJr+dj9FU5/T7Is65gapsDxzi6Y8xavH+eaaNiyhm+vneANKp1WWXFQZcQnW8wCC0s8P7j2bIuleo0fX1hFac3vt/b4YnuXw0nE//T5N3y1c8CVpTbLjRq9ekjouoSuW0k0GqWSl12erm1T9xzsiqaVRBmzUfRMHwkNDGYJo9mTux5JkrO9M0QIw8P/LqB1XlWdD0jKXbJyv6qcDCoKy7D6Pqk6GfGJ5PFNhtIJeVlRdcoBcXEfS5gux5yrb4s6jtXGkR08u4dr9XBlD1s2kMLhee3g4Shi72BCmhu37sP+jDjJubjR4eJGh7yiwglhug+WZZybzf5oEm+rcnOWlni2AIAwVZwoztnZH5NmBbWahwgFgSU5GEy5fe8Az7XxPYfhOGIWZ9y4uoLvOdiWxLJexcasyMpDknKPtNip1tEBWXFQtfAHhhKlJpRq9tI3xpeHRqmMjD5KZ+RqQJTfYWT9DtdawrOW8ezVo4TUXkbi8vqkJs4CBeU9KB+AGqD1pPq5axIWawWsTTPnoWegDqG4b5KM4jaIXRD/o5kBAUP3sX8Mp9iXvyvMuxRS+NhyCVmdm1ITCjUy9CV8smKLQhmarhA2hT6oaHclUtSQMjB9VR2hdHzEXz/D2pQ41fNbQMlpaFxlqUniHK01nV4dx7FYv9BhNIyYThJs13rtvlBv8TjMbGJKqSMkXiV0c/5QZCiVEBdbVYHvAXGxRVYOTDejuieXanLm9XremL8nedlH6ZSsPCDK72CLBo7VxJGdqlu8ZO6t1hKu1cWRner+ejYVpizJybMCrRS1Zohfeyuw8H3DOatOediWhS41BTFK55Q6QmirojP41WKzTy1xWyqjw660oUhtdlvUfBfHstgfT7l90Ofm3iG92h6b3RZXlzpcXe7SqYV0wgDXkjjV7MPLZsKt0Mezm0eJxjRhf6tP/kiCoCttd1FJMe7PUvrVIO+j1K40LxgMI3zPfqzb8bI4XnleDIXpwgzmF3eZZd8wyb5iln9LUm1qrz8gfDkoDI2rKCekT/BRFNjYMsSz1gmcTWrONULnKjXnCp61gi3n1Ko5jeto0HO+fkbjmG/v7ePYNs2Gz35/yvbuCNe1WO7V0bqqIVdJhm2bNSirZNeSxu/Amf/8GdzsudRekuXsV7NAq0sNk1hoh8Fwxs3bu/Q6dXqdGlu7Q/YPp3RaIRfW2ibZeUnDvpPVtrKavcmIi/tMsq+ZZF8wyb4kKXbJyr2Xeq5XB40mp1AjCjUiYWvxG1s28Kw16u47NNwPaLjvIoWNLZsVdaeSG36NHY4nYtHJyKC4i85/awbIdb96gIe2LiDs6yBqID2TaJQPIf8EnX9m/i1sdCYRcu5JlIF9HQh4YxItbebEhPCxrR5SBFXld0JR7uNYy2jdIC93yYotHHsNKUJKNUTpGBDYsoejlwGxoPkZcf4zFniEg8RfdEaEsBH60VupRAhncZ/TSpNnBUJAqx1Qq/ssrzaxLMlOw8d1jeLgSQbAW7x+aHM/0TMcITmTfvqzjrrwQjD3ZFOUMZSvYfJbJtmXTLOvF2IqbxbMXpqrIbkanviN6Xa4eNYyoXOlurdeo+ZeQzoeQjSqrvEpnkVDnhXE04TZOCbPC4Q0A+LSEo95as29iMqirK41geNaR3OwpYnLpGU8w6zqe1mUFHm5iNvmHlS2c9RNmV+TWhv6+9zrSpWP+1zNvTWMn5UwXRlp/v/cvD4qv62iKnZCFWu4FlKaouajz6VKRVmqxXetq/2m+v3c1NmyLKQtF55c54FzTTSScrdq722TFNuGa4+DxvCufWsd375AzblC4Gye6pj9WcxnD3bYGU1Ji4IoyxnHKff7Qx4MxkzTHKU0kyTjQX/MJE65ezjEdxw82zZmTZVfwssGCD++sMJ/9ZP3CCph+MHBmK9/c4doWrn4VgsgniVEk4Sg7hHWA1beXee966s4jo3ziF9DnhccrLbMDegVDDhpcpRKicstkmKbON8mKR6Slnuk5T552a+GuqevvVLyXUCjKFVCyj6ljknLPSbZl9UAcWdRgfGsJTxrBc9ewRL1qhJT0ZWKkuksZXXJY3OtjS0lZamwLYvRJKEeeoSBS+C7hL5LqxHQbdcWXQ3fdwmDnFYzpNUIcJ2nyxrPjQZdxyL0HVzHOhH7aQ1KQ7sZcvXiUjWrAY5jkWWFoWGdw7umqw5GWjwkKu5XHP2HJOXDijt8SKmic3iu7x6lSsjYZ5LlpMU+0+wrfHudwLlE6Fyp9q2VN3OQXI+hPAT1EKEG4LxfDYtjqFbFHdPpKDfMwDce2FdABAgZovUARAPh/6cgq5kCa63qZrw5Aa/hjhuOuBIhoBbdjEINKj8LMyRuuh5dLNlCYFOoQ4rykAILRY4laliygS4zCt2n1FFVYDktNIqUKP2PpMXXWLKJFHPaUTUiK9sE7kc4tnGOdhyLVjukLBWjYcTu9pDD/QlaaYLQpdEMsCzrbZLxBkLpjFLNHmNjvAzmCUZcPGCWf0uU3yMu7pEUO6TFnGr6JiYZz8bcnDlTA3SuSMtDptlN2uUvFowBa9ENfDaEANuxcDybJEoZ7I9JZimNzpSl9Q6Nzkmz5rJQJHHGg292+fgfv8ELHD78k2ukac6dLx8yOpwSz1K6K03WLvVY3eyystnhzpc7fPPJfWbThCRK6a22WFpvc+W9dS5cXT6xDRZ5yfBgQn9vzMHDIcODCbNxQhylZGmBViZR8QOXWtNneaPDhavLdJYbNNoh1jkI/qhSkUQZe9sDvvzNHfa3h0gpWNns8v7PL7O03sYL3MeYDMP+lP2tAXsPBuxt9YmmKXHlSyItQb0V0uyEXLi2wvqlJWrNgLB+Pon1uSYaWbHPKPkdcbFNWu5Xg0EdcjUgV0NSa5e03MWW4akTjcEs5pd3tvh8a5coy4nSjCjNifKcODPul1pr87ssZ3f8dAWol0WS5/zVjSusY6Rq42nK3oNDpkMTYOmKuz/uTxkeTPBrHrVmyOrVZa5dXsbz7AUHd448L1lZisgLRaP+8hvZUaWkMB0lNSEvR0yyLxhnv2eSfsksv1WpQr34kNz3F6pqU/fJVZ94UcwU2MJQ/wJnk9C5St19DyFcPMtdDKWCudCzrMSyJK1GQJzkNMcBUgqSJKdR8/B9ZzGQXa/5iySyKEt8z8b3XZp1f/GZz52UC2XkL7XWZHlpKjGVkeO8k5bnZVWROKqmNOo+F9Y6jKcJ01mKY1nmcd6L36jmA9lKp5QqIcrvMs2+YJR+wjD92LTy9au73r4rHK/ORdzBygJs2aThvU/L+zkN9/2qwzE3YDNdrtcdFGo0qAmoHSj3QUdgv4Pw/sb8vtw1qlLFHVC7aNUDawMhe2Cto8kh+yXIJXD/HGFfe62v51kQwkZKH6nrWDpBCKuS0jZy2oYeRWUY28CWHWyriwC0zig4NJLqWiGEjSUbKB2B0ou/PeWZLIa/0+JbVP5bLLmMJdvH5nvAsTZw7E0cTKJh2ZJa3SeapmRJwf7umNkspV73WVlv4QcO8pVQHF8NjICI4lEn4teDuTP0qxGCMdSpGVo3X/pY8z21VDNyNWGSfcUg+adFB0NVIgbfX8wVKo34BzzAzMf5dIM/QekVzrLMLdvCsi2iacLhwyGT4YxoElNrBNTbhj4534fLUpHMUu59s8vf/fe/Jqh5+KFLEmV8/B9u8vDuAcPDKeuXl7jxk4vc+Ogi0pJ88+l9/vF//JTB/oTxYMaFq8tcurGG69msXOhU52CeI88K9reH3PnyIbe/3Obh3QNG/RmzUUQcZahSGcnqmk+zE3L1RxskUcaV99fxQxfxlG7Dqd7ZKr5Mk5z+3pjbn2/zj//2M+5/s4Mferz3s8usXOjQ7NZxPRuqImORmw7P3oM+X398j9tfbHP7i4dMhhHTUYSUxuy5vdygt9rigz++iio1a5d6uN6Tu0dnxbkmGoWaEhdbeNYKvfCvK4Ufv1KXiZlkXxLl92l6px/OSvKc7cGYu4dDoz9emoHqolSUVZLxurB2eYm/+K9/Tp4aRRNdTVelcUYSZdz9cputb3ZwHAvPs59IYbEsQa3moZQ21eqXhqFJxfkD4uIe0+xbZvmtqoOxR14OTAXvrHSBHzw0pU5Jy31KnZAUe5QqxbfWcWQLbQRlAQhDj7WVJlle8OlX2wxHEYeDGWG4TLdTw/fP7spaFIpZnNIfzNg/mFAqkyDM4oyVXoPDwYzdgzGjiUNelBSFwvccymMqZicM6c8BpY7JywGz/FvG2RfE+T3i4gFpsU9ejn6wiarSOYWaMM2+IS9HzLJvGDkf03Dfp+H+CMdqY4s3ZHBWR6CGRprWWq66FnPYIFogm6BT8zi59JpO9OVgBpdDHGsZS9QRwkVgI4SNZ1/GsVYRwsW1L6D08qKbYck2nu1gWS0zD1ipS1nUwFqrjuWfwURW4NiXCL2/ROsYrTOECBYqO3NYsoMlj9QUValI04LJJGbYnxHNUmxLorVmPIppd1JU+f0JMDWauJyRlq+/i2kJm8Cq41rn762gMR2NQs1QMn/p45V6Rlb2mWRfMU4/ZZbfJs7vkak+Sn8/5iDPAomLJWs4soMt21jybHTMIi9Jooxxf8Zgf0xvrY3t2Kei9PR3x/zmH76i3gxodmsopbFsSRKlfP6r2/T3x2zf3ieaJnSXm3i+Q6MdksYZNz+5x/rlHqsXe3SWGzSr7kmW5Dz4do+vP7nH4e6ILMnpLDdY3exi2XJBkZyNY0b9Kd98+oD+nklgag2fTvU8L3KjVqUimqXs3e/zu/9wk68/vsfocMrKhS4//pNr3PjoIuuXewQ190RisP9wwO0vtrn58X2++t1dirzECxxa3WX80KsoYIrJKKK/O+Lj//A1D77d4xd/8x4f/PFVmp0a4Uv6lpxromH0nfvU3fdZCv4WS/qYd1SjdEFWHjJKPkap5NTHzEvFKEoYTB9XXDmLtft5wLasExSX3lqb3lr7sccZ/p4G/Uu2vtnBsuRjnYw5pJQELxCYPvaclV9FqSMKNWOafcko/YxR+gmT7POq+vc2uXgWTGXbyLEKdnCtLqWOeXTzrwUuaystDgdTdvbHxHFGlpe4tkWrEWBbEqU0nutQD90TdDkpBL7nUAtKI91XoVSKNCtIs4KiVGR5yXiSEPgu9dAjy8vqMTnjSUIUZxSlwrYljZqPV3lqOI5F6Lu4jmUqEWeonBx1w3JDLyv3iIv7DJNfcxj/R5Jih1yN+L61888KTUGpC8oiIim2mOW38bIeeTlACItAb+Jba0jhI6sA9bV1N3ReqVFZZg7jOL1LSBA+4Js5BB3DMylCx9Wq3iwIYS9UkjgWwNuPKBZKa+XE/1uibgIdludHwnSjbCRBJSM8//mpzgTHXq8UquaKdU86Xw9LHlXAy6oSGc8y4jijKEpczzZ+OxNDvVClOiJ5v+FQumScHzLIXv9Mlit9VvzNV5JozGc0SjU1HcAXhNIFSqckxR6z/BaD5JccRH9PVh5QLmh/PzxI4S38N+Yy4qeGBq0UZV6SzFJm44T2sjpSoXrOtTIZRdz+YpuL11e59sEFmp0aXuDw4Ns97n79kMHBmP7OiLVLPdavLJGlOUmU8eVv73L/5g5bt/fZfdDHC5xFolGWiuk4ZjKMQEO9HbJWJSNh3TdGvdOE7bsHRNOUw90RW3f28UOXd396Cb/m4Xg21nMuciGq/wgj56tKTRJnHDwccvvLbT7952+4d3OXVrfGpRur/Pyv3+XKjzYIQg+7ijfmsxh7DwZ88o/f8O1nD7h3c5eVCx0uv7vO8kabpfX2YqblzlcPufv1Dve/3ePWF9uEdY9Wr45lyzcr0ZDCqRx4H+d1maFWD1s2zuRA69kWy40aF7ov37Z8WfRq4amMAY38qMnG42lC8R2oSRVqTFrsLyhSSbFFUjwkKw+rJOMs9IA/bEjh4cg2nrWKa/WwZX3RzQCohR4bKy06rZAkyc2AldL0OjVsy7ilWhZcvtCl0wpZ6h5xSX3P4fqVFbK8oHNMytixLRo1n8sXutRrPkqZjkXou4ShSxi4dFoBQhivjWbdx/dsNtc71AKXXqeGELCy1CCopHYD31kIIZwOphuWFDtMsi+YZl8zzW4uZI5LHfFDTzKeBKVj0vKAYfprkvIhdec9Gu6PqLvvUHPe4bVGhjI0alGqokkdl8DVheli6CGIdZAtjkz5RDUIbRSZfvif69M+o7N/dlLUsC2NMQZ98vVlZkqObs5KabK0AAHtbg3HtUEbKuW8Q/l9CjULnXFn9jk3J79+3adC0+nxM/mvaDq9V3J8pVPT0dAvnmiYzvAtxtnvGSUfE+V3yMrBD4Aq9WxYMsS3N/CsZeRZw00BtmPjhS5hw6fW8BGwmIV43qXrBy7L622uvr/OT/7sHbTWHO6McBybh3cPsKTEcW0231nhF3/z3qJ7MhnMuPf1DrNxTH93xMqFo8JGWPf58M+usXHFdIZtxyKoeXiBKShqTNx3YatPb63FzY/v8+Vv7zIdRuzeP6TeCqg3g+cKtMxnM4Uwe8dsErO3NeA3//AlX/3uHqP+jLWLXX7y59d592eXWLvUw/fdE/TLJM4YD2bc/3aXrz++R5mXXP/JJu/97DI//pNr1JoBQeiitBluv/TuGntbA/75f/o9X/zmNne/3sELXMK6x/JG5xln+3yca6IhhIUlfJTOSMqHWCpAYFXzAhmlihDCNkO4RVUJERJb1Kvux+MIPZdry91nS4B+R7jUa+PbR29ZEmXE0/ixlrdSxohuOooocvWYSd95YK6db/ijccWf/5ZB8i8Mkn+ujNFef1v7+whL+At9cHfR7j2q+DuuRc32qDU8hK4kbKWs2uwapU3Q0O6EtDshlhCL+QslYWmpjhSm22C4zhotNJYraTohzXaIFAJbykVHotUIWF9pPXauy906y90jGk+7GdI+oxeLrgzxDFVqyDT7ikHyz4zT3zPNvvmDX0dzQ8ppNmGafUvq7pOrIZocS4SVeVUNzoMnrufeJCmoGag+Wo/NLIZOEGoIah8tQoTwQYRoqwelY5IMdYgut6sT75thcUqQdYTsAMe6p8IC4Zpko9xHiwDTGQlB1E6tDPjm4/ySDDAzH0pF2FbvBD3q2X9kwknbsWk0AzQQzzIoqSSopcn9XvisvluUumAvvcfN6W9f96nQc9d5p/6zV3b8uerUWRKN+eyK1jmFMiqPo/S3DJPfMkx/91LeHt8nWDIksC/g2StnKjAv/t6WeIFLq1ent9YmaPjV3IB8bhfZCxy6K002rixx5b11bMdi5UKXh3cPsW0L27Hwax6rm13e/emlRdfgt//+KxAmvhsPZmRJfuyYLlfe2+Dyu+Z4T0sYuiumw5HMMr757AGzacLBzoiVza5Jkp4FYZgu0pLmnGYpew/6fPv5Fl/8+g73vtllaa3F1R9t8NFfXOfaBxu4nvPYoHkyS9nfGrB9e5+tW/usXepy/cOLfPin1/jJn183xY4KWmsuXFthPJixfWefm5/cY/eBUS98/xdXUKV6KdWs87XCrRymJ9nnxMUDLFnHljUKNaVQU7LykEKNGacfk5WH5gRESMv/BXX3nScecrle42/fv8ovko1zPdUXQScMaIZHCdHO3X0+/+dviKcnqWB5WpClOYO9MbVmgOO9CsdhExwaidEvGKdfMMk+Jyl2ydV44YT7FmeHLeuV7O2lE1VJjbkgJ1nKME1wpMS3bELHIZCSrCxJy4K8LMmVGZQUCOquiystBmlMXBQEtk1gOwS2gyUEmSqJCyNwkFcSx6Hj0PED3FN00F4WZjgxYpbfZpD8ikn2uZnrKfZR+umu9394MElAUmxT6oRcjYjz+7T9X9D0foIU7qmlG58OBZRQ3IXiU3Rx13hekIDO0TpCqF2w3wfnXRAeyHWQG2h5CMVXUHx77Fg5wtoEuWHmMxbdZmGoVtYlUAfo9H+BrI6QIdjvgfNHFRXrLU5CEWefEGe/pu7/LaH3p6f6K8exaLYDiqJkNJhR5oooSvE8m+5yg063hm1bb0RB7S1OYq46dfaOhiYtDxinnzJKP2WcfkyUP6BUTzbe/CHCEiGBvWk6Gi+QaIBJNtavLJsh58DFD13qrecX02zHpt4OCesB0pLYrk2t4eMFDgiBH3qLhABAWgLHtbFdow6qtSbPSpQ66vYKAbZtiorPoiW7nkN7qUG9HWJZkiIriCYJWZKfaq7YsiW2bZFEKTv3D/nV33/Jl7++w3Qcs3F5iZ/91bu8V3UyHNdBPGGMYDZJeHBrj4OHQ4qipNVr8M6HF1je6BwzQTyC41gEoUerW6e70mQyjBjsT5iNY9IkN+/NC84Rn7+PhmySlQfM8tvYsoYtmxRqTF6Oq9/XKNSMOL9rTkA2qat3n3rMRuDxo2Dlqb9/nYinCbv3DpmOTlZ888pkptYM2LyxRrNzfoOjc9WKQk3Iyz7j9DMGyS8r1YqbbylSLw2BLRuEzlUC+yKWPKIBmq5ESZRn9JMYtMYSgpVancB2mGQp+9GMtCzJVInE+GV0VUBoOwyThEmWYklJzXFYqzWoOQ6FUiRFwThNmeYZSVHQ9n1qjvtKEw2ty2rweURcbDNKP6Ef/0em+bfk5eClOMk/ZMwVqko1Iy12QQgsGeJZyziyeyafoMdvOpVrt06qjsQMY3DlmA4EGZQHYE0MNUrUQNbB2kToielmqB1zHOEbqVvrKlirIBsnn0o0Efa76NIBtQ96CrpmEpe3+8hToMmLLeL0d/jOj57/6Ep3X6NxHBvfd/B8B883inS1ukd3qV7J275+JbPTQ2ALF0++nCS7PvavuZKVqjqsT1K0soWDJRxs6WALB4lF213BeyXzGea8DHVqalTLnvtoc85mgHzMLP+WQfIrxuknTLNvKfXsFZ3nmwhRmTiv41rL1VzT2SGlJGwGuL6DkMKoQJ1Ctt12LMK6jxe6C08rL3BxKs8a17dptGtV4mGex7I1ti2RlvHfKPPyMUaKUpoiL0njjCzNKSoVSFWqhRdHmuTMxjHRJKFUZlYiTTJjPHhKgktZKva3h0xGEb//l1vc+nyLzXdWuPLeOj/6oytc/3AT6xldlbjqhAwPppSF8f0o8pLB/pgszZ+412RpQTRNsSyLPDMeHfE0JU2MDxxvQqLhWku0vJ+a7J8CiY0QDlrnKPKq2mchhbXgvFsywFkM5H2/0F5u8u4vrpDGJzcgIcwNo9Gp0axafucFRU6pIsbZ5wzif2Ka3STK75KpYaW7/RYvDoEUNo5sUXOu4NsblZqMQaEUcVGQlqbKsT2dsDUd87OVddZqDR5MRvxu7yGFMlK1oeNQc10maULT9ci1YpZnbE0nSCH483WLmtMGWFCpZnnG7dGA5aDGRr1BnZcXCngajHv6AdP8Jv34PzJOvyAuHlCo8duE9RTI1QiV5xxG/54kf0g3+HPavtGLt8/QDVDzO4+YC3U6YF8CESJ0DBhZVvMYp+pidIyaFDYgzeNlC6EjozAF5uciNImIaD/+xLIL7h8h9HtHfyNsEN1H1Kve4jikcJGydipfFVWp0OS5MQUr8pJmK8DzbJZXm9i2RRC6uJ79PUoyQApJw26x5L0c02BOFFS6ROmSTCWkZUyhM4pHOggCQc1u0bS7tNxlmk4XT4a0nB4dd+2lzuNZWKhOnbrwosnKPqPkNwzT31WS/1s/WJW+J0NWM7sNPGsFV3bOPqMBgEApzehwymBvTJZkWLbFxRtrz42rRGW6Z9kShJl3EPLIhE5aZkbjcW8Lsegs6jnnsYIqFfEsZXg4YeuWGRYfHU6ZjWOS2CQSZSUnm6YF/d0Rs1FMq1s/MvY7BYq8ZDqK+OxfbhHPUrZu7SEtyeX31vngT67SW21hOc82+82zgukoJp6laKW488VD/ofZf8QLHKPc9YS/UcokN4e7I9I4o9YKyfOCLMnxXkK06FwTDcdqU3dvnEk6VQgHR77+Qe8XQaMdcvm9DbIkpyhKM+VflDieY3iFS3VavcbzD3QKzKvPWXlAXDxgmPymUgJ6SK7G/PCHOV89zMbYxLOX8exVXKtzYgg8VyXjLGWcpcyKjL1oys3BIZuNppGpzFIeTMbVsQRdHWBLyShNycoSUSUS9ycj0PDh0krlFG4qEklZEBeFkW1+ZIM7T5i1lJGVh0yyrxilv6Uf/5KouFtRpd6updNA6QSlTZcqLfdMACp8as4VhL2BPOYM/fRjaAptZLtTVeJISWA7SNooq4WkmuWBhWu00fGbS29WbrayjWWfLNiYCvHRd1TJfALA0Pp8pHUJgSFiGnNbQ/d7nirKDwlKabRSZElOEqU4roNf8yoe+KOKYgIpQ2zZNp3l8qCS2n0k6RDGb0MpSNOcaJYRz1LStKAozF4QBG416JlU1KqzzVa9TljYdN11LocfvNRxTiYaBamKzVcZkaqYTCUm+VAxpcqxhI1nBfTcddb8K9SdNnXbfL0qKJ2eekZD6YyiNJ2MYfqbavD7LsUPwG/oLJgnGY5s41htLPmiBqBmUHk2jhnsjcnTHNd3yNPnx5jGiE4unLIXyYYRnjNdDls+HqyLY1Lx1T1YK02eF8wmCdt3Dti6tcf9b3bZ3x6SRClpklNkxSIOLHLTPZiNY+PcfQYbBq000TShvzcmmiTMJgnTcVzJ1orFPJfZsJ+uaT83MMxS00WJpgm7D/pmX3vOOYR1n7Du0+rV8UN3Yd3wojjXRMOWNaSw0fPq21PsfEQlMQjVYniCStX3AV7o0V2TRNOE2TgmniSkcbYYVDrPCpXSKbkaMc4+5yD6B6bZVyTFNoX6w1QCehWwREBoXyKwLx5Tmjr6DOMi5yCacZjEjNKEUZqSlMY0EsCRFqHjYkuBKy3W6w2WgpCkKEiKglSVjNOUWZbhWBZKa6Qwj9UaDuKIQik+WFphNaxTc15NN0PpnLQ8YJp9xX70d4zTT0nKnbdJxgtC6ZSs7DNIfkmmDlgK/jWWNKZ/z+tslFqTFAWDLOYgnlFzXC7UmthSkpcljmXhW86Ctmc6XyYx1VqTqoJclQSWc0Iu+ej4yphAauM5ZAkJaHKlsIRJagQmiS60IlcKV1oEtv1U6dYfGlSpyNOc/a0+D77do7vS5OKNNbzQfaKTr5RNbGuNUg1Jso+xLGPYdxxCuFiiRVnaRLOMweGU/uGM0XDGeBRh2xbtTo0sLegfTrlybZnecuOpMuhvGizpcDF8j563/lLH0YvvRkBF6RJFSVLOiMsp++kW++kDDtIH9NMd4nLKOHdYDxSBVadud2jYHWz5Yvz/02Ahb3uKRKMoJ4zSjxkmv2GUfEJU3K/ka/+wYAkf31rHt1ce85g5C5TSlEVJkRWURUnY8GlUMrXPgzj29bRHnDZGK0vFZBCxdWeff/6ffs/NT+6RJSbpOS5v63oOlmMZ35w459bnW2a4/AxQWjPYn5DGGd2VFisXOggBcZTx1W+ND4ZbeX64voMtn17MOp4grFzo8N7PL+MF7qlfd9jwuHBlGT90sV6Cxn2uu5rhe08XikdaFyeSDiEcpPDwrFU8+9VI0X2XKCpjlsHemP7uiDTKyNIMx7VxPYfZuEUyS6m3Q2pnVAKaw+hvZ6TFjqmSJL9mmPyGtNz5Qetvvw5YIiR0LhPal7BE+JiCUFqU9JOYYRIzy3PiKsmYU18sIXAtywyJ2zYN16Pp+aTljLgoSMqCWZ6RluWiSl1qTVzkjNKE/WiG0pr1Wp3QcRCVKhWcj0/DvJORlodMsi8ZJb9llH5ClN+uqFJv0lqq/A4eu13Ma6DVLMMbgLnvRlTcJVcjnEqprO68g7AvVDMbz9hqBeRlyTCNiYocW0psISmUouF6WK4kLnOmeYYlBLa0yKs5oFmRkZYF62GDFWlVhRvzXimtSas1N8wSsrLAtWwsISiVIrBdZKWINkxjkrKg1IqG62PLEEdW7/5roPQopSnzklLnCylLIxstF8G/1rqqIqqFu+282pdnBarUWJZRb5kPPxoedXXNanNMVfGq41nK4cMBliVZvZRjP5FWYWC6GTuU6hBLLWPJzomARooGnnMdITqVTKVAlYosLYijDN93kJakKBXD/ozJUoOyVOd6vb9KWMKi5S7R4tUYQKZlTKoimk6Pht3GrWblkjJiVo4Z5weM8gPqThtbOucgwvA0zGc0Zs8UxpibfEb5XZNopB8TF/f/YNSlHoUlAnx7Hc9aMSIZL7ieVanIs2JRzPUCF9czw8/nca2IZ2ciC5RFyeHOkDtfbPP1x/e4+9VD1i71WFprc/m9NdYuLdFohQQ1D8e1K9pSRDSNzy4GpM3+J6Rk7VKPjatLtJca7G8PGPdn3PzkPmsXewR1j7WLPRrt2hPdxqUlcD0b27aMrPZSgxsfXaTRruF4T6ZOPQrHs1nZ7OD5DvIJhazT4lwTjbTYYZz9nll+myi/Q6kTc3HOZUFlC9daYqX2b34QiUZ/d8TN391h69s9du7uYzs2Qd0njVKiacLapSXWrixz42eXufbhiyYaKVl5wDj7PfvRv2OafU1a7lWb3psUGH7/YckaoXOV0LmC9QSOeqZKJllKVOSU+nSu9FprsrI8+hvz08Xvk6JgezrhznjIXjSlVBqnolIFtoNzTOL2ZaEoSMtDpvnXHER/zzD9HVmx90YmGQKrMmmzkJU5GphkSVOiKCqK5pxE9PqhtDF8HCS/IisHrNT+MywZYssW9lMSDVtKQlEpkEnJKEuY5AlSSCwhWA3qONKin0RszUZYQuJaFqMsYZQmjHNDy/vj5Qu0vaBaLyY4LrVmlmfsRBNujg4ZpjE1x8W3bGxpsezX8G2buMj5erjPLDdJzkatSd12kY7Aflm53hdEWZQkaUqZFxR5sZB79GsewbFEYzZJiMYxru/g+g5+6CGkYDqISOMMv9K49wLTHcwSQyUoc0MnkJbE8exFADNXVSlyM9z5xHNTA7LiLnM5Y1HcOybdadapba1iyQ6OvUSrU1sMbDquRaPpU2/4bF5a4mB/wnQc44cOINCat8pTgC1dpLBY9a/QdlaoVfSou7Mv2E3ucj/6mrSMca2AptPDEd6i03feKKnkbZ9heFuqmFn2zWImI8q/pVB/SIPfJyFlQGBv4NmrC1PTM0Mb+k+W5IwOJuxv9bEdi3oroCzK71QLOs8Ktu4c8M1nDxjuTwhqHj/583f4yZ9dZ3WzS7NbW8jdSimZjmOKvMCyrTMnQlIKOssNNq+t8LO/fpf3f36Z3Qd97n69w6/+3efs3O/z23//FWmc8ef/xYfUGsGC5nkcjmNTb4aGBioFYd1n5UKXlQsd6q2Q0/hdCynxqwF6+RIG2eeaaGRqwCT7irwcGb+MMiIpd3BkC0e2KdSYUqcUanKeT/vaMO5Puf35FrNRTBB6hM2AWiskmiRISzIZzph9ErP0AmYn85mMtNxjkn7OMPm1obgUu5XD95sRXBmYsoAUDhLHBIjCQjD/LgFpqtPiqFJt5hB0NcRulEbQqqpXm0By3tHRr9RUTCKFi2O1jRSfvfZEOp9v2XT9kMB20Gh826bpeqzW6gghaHk+lxotbGmCwV4Q0nA9un5R8eAhKwt828azLBquhxBmniO0HVbDOkprGq6Ha1kmwTiHjdQolRULj4xh+hvG2e+J87vfeZJhEgcPKXws6VXu2l61dox4xNEaso8lHPPZgtJcG9X60LpAkR/9W6eUOq2+J9UcxXeVlCuUTomLB5Q6xrV6SOFSd99Dio3qeji5WWutKZUiKwviImeSpZRaVQanktB2KVRJrirp5KoaP81TJnlGUuaLOY9HUWrFKE3YjabsRhMGaUzd8QhsG1fauNIiV4qkLDhIIiZZiistGo5XJdK8NlOH2ThmcHAI2kZKQZ6X5GnB8mYXx7NJZinJLGUyjJiNjUuvtCT1dlh1k2PSOEMcTvFrHr21FkIK+jtmyFFIUVGmCuqdGqub3UonvqKlKTW3NHnsPbBkB9e+9MzztypjWmveZQHKUi9otbYjSTNDxWm1Q+p1/4TZ1h86LGFhCQtHutSsJhqFLR3GeZ/99AHTYojWmoN0i563QcvuvTL61Fze9knUKa1LSh2TlDuM089MklHcI1ejV3IulfWx2S+FU+0p833SqsQkJBy71xoTF3V0n61sCMxemqOqr/O8x1pibtS38uKJBiwq9fPORpbmZGn+1CLAq4IqNZNhRH9vQhJneIHD+qUlrv5og1a3hh+ejBeiacJkFDEbx2c/VyHwQ5d2r87qZpdLN9ZodmsENY+Dh0OSKONwZ8RXv7vL8kYb13XorbcIal7152Yf8Wsuyxtt2r06lmWRpTnTUcTKhQ6dpfqiy/ukRGg+uK61iU/EM4bOT4NzTTQKNSXOH1B336UX/pWRXo3/hab3E5reh4zTz4jyOye8Cb7PiCZG3nbznRX++D/7CfVWiOVYhlOYl/z6f/6M3/79F4/J354GisLI42Xfsh/9L4zTz8nK/huYZFRKOcLGlk0c2cKSIZYIsGUNSwRI4VUB5HwztAB5LGjMFhue+Z5R6oRCTynKMbkaUer4lQWMc6Upz1rGs1dwZPuJmt+9IMS3bUqlUBgDvlwpWp5xLN1stOj4gUkeMDQqW0q6frAIEOeceUtIOr6Pa9ls1Bv0/IDrnR6gcaSFZ9kEjs3zx7ZOA0WhpiTFNv3kH+nH/0Ra7r+GToZACte8z9V77VpLuNYSjtXGlvXKvDM85kkhOJoVqJJTjpLTUsWUKjJrRU3Iyj5ZeUha7pGWeyTFXiXV+125X+tKtGFAP/lncjVECBvX6mCJAPGIiliuSkZZymEScZDMSIoC33LQQK6KxVxGYDu0vZB+GjFKIgSCpuPR8QJcadH1wqqbcZTIFEpxmEbsxbPFTIYGCqXR2sx2zEORebKiS5OgGArWd/B2PQXD/QlbH9/FD4xy33B/zMHDIe+XV2n36hxsD9jfGlAWJVlaMNwfE09T2ssNGh1TXSwLxXB/XFXjLmPZFnc+3yKepbR6dbI05+DhkOWNDvVmQJmXT5kqPA6J7/wEx7r4zEcJ4WIfM/NzHIt2N6QsSw4PJuztjNm6P+D/z95/ftmRrWee2G+b8MenTyRcVaHctbxkX3azDWd6ZjQjzWpJa+l/1Ed9UY80WjNLasNmN8kmL68tX/AJpD8+7N6hDzvOyQQKJgFkonDJ+9TCQgHIEydOnDD7fd/HeJ4iaYd0e/EfcjRegI63ghY+u/43JKpLaqbkds5h/pBuuooX+8T6YoxXnoa1BZV4NnXK1iV5dcCs+Iph7qiol9tIdU0xX/bQymnA3PM2QcmwuW+eKT6Wz9mmoKBoCgtXPFV2SmlHlHaIbRo0bw6BfoI69ZoFoADta8LYJ25HTTK4oMwby9m3eK3UC/vlhskgpbPKfZ52YTpOuf/VPo/vH1OcQ7h+Fgu6qmiSwZWSdPoJV26u8ZM/u4Xna379X77m/jf7/OI/fklZGP74zz9eFhoLJJ2InQ/WeXj7AOVJjvfG/O5vbhPGPts3Vp+ZpXEWzhLX2f2qF+hAzoMLLTRcRzFFyxYt70NKM2SiPiPyrtIJfkhu9kmruxf5lt8r6rrGVoa4HbHzwSZJJzptINQ1X/7tbUxlqO35FziLnIzSHDMpPmeY/4JJI/x29nrfV5HhbnBKxI4OImKkDFEidMWEDPFkGy3aKBkhRYSSzb8JD4F32nERrtCg6bS7AuO0M+1uhDmmni01P5WdY5tiwxUii651irEpts7OaFZe7RhJERDqLSJ9tSmUnm1OEGpNqPXS6WfhALToCLR8n5b/3e5N7D3pACSfMgrQ0r8U4bejdrnE71n5LaP8l0zyz0ir+9j68gtWgXaTItlFqy6ebOPJflNkrOGrFXw1wJP9Zbr2okgVDV3qeYLkRcFhbObOAzujqmeUZkhhTyjMEYU5JK8OKMwRpR25PB87orIz6uWU7DJgsXVGVu0CNVF2FSViEu89ArXGWRGiEAIlBInnsxm1qWpL1BQaxhoGYUygTq1PPSkJpEZLR2taFKX9IEKKJ8tSJQQtL2A9Smj7AWlVkpkKKVyRsh61CJUmMxWh0uCHtL2AQeiKlu9TJ5CnBcOjkpX1gCgJGB9Liqwknzv3ppO9MY/vHjqqk2DpWW+tpSoq1q+tELdDhocTirxkPnULqOHhFGstK1s9N01q/PCzeU5RVC9N7RVCoNUArVwRcTYH4hk/fZqjUddorQgjn6QVOL/6Wb7UYS0GJ++6NuP7gBACX4QILWh7AzreCqYuSe2MUXnAYf6Q9WDn0t7fPaPyZed/YRJSU1HaMZPiC4bZ3zErv6Wwx1zEfVWKwD1bZbJ85ioRuV8yxpMdtGw1/xY1z9lwqQdz+6jchG45AS6xdUWN+xzGzjF2RllPqOzE/blOlwVH1fx58ff1curxfAgUSkR4qocv+2dMVV4PSkn8yGftSh/qmiAO3NQy9N7qtSKlJG6FTt/ga4q8ZO/+EXe/fEyvcWaytqbMK9JZzv2v93h454DR0dRNNF5jXxcvEVLgBx6dfsKNj7YwleXkYMzdLx6ze/cQL9AMNjpIJeiutAlCV9hFScjGzoDtm6ts31ijSEtuf75L1AoIIxd6GCWBc+WSAlMZR1lNS8q8RCpJEHqsbffprrxZFtyFFhoLD3hXSbsVd10bBBIlAqgNlZ03Xfnff3i+Ju5EjYr/Gf8eeMSdCM8/f0VfL+kXD5c8+rza+/6LDBSe7BLqLUJ9hUhfIdDrLqRM9fBkB4HvbnRIELI5H1Rj9XhWdXVmpLvoUNdnO9Vugbwouuq6wtQ5pp4vJxxuIXlEZvbIqkfk5gBbHbzWxGchAk+8m40N34vxuguC72Mh4YrWMcPsbzmc/wfm1f3GpvGyzyXnJufJHu3gY9r+RyTee4R6Gy3bpwWo8JpzZkErWlDtznOcBKqhXSkR4TEgVFvLB6nr2s0p7ZB5eZdZ+S2T4nPm5T1KO6SuLzMrpG4shA85yf4aW+eoJMJXgyfczDyp6PohkfbYjNvU1Eghl6NrX7kU+bCuSbTPaphQNdSqRREmhCBQ3zWkDZTmZmfAdtLB1jXjIuPO5ARb19zsDFgLE2LtkZqSjh+wphJutAf0g4hIeRc0TXs9LIrypBux/d46QgjSWY72NLNxyuhowvHeiKgVLEO44laAqQx5Vrp0240upnLWmFVRkWcFVVkRtgLWdwZY4+wf/cAjT0vytDi3z/1Te8p3r6dGkG+hLA21rV3ysKdYXevQbkdYazk5nrH3aMS0KZL+gOdDIIhVm76/ztyMmZkxk/KEk2KP/FLTtu2SlmnrzE3oURibUZgDhtl/5Sj9SwpzyEXdV7XsEKg1Im+HSO8004GNZvHeeaKgOL1viua5e/Y5u0BjoFHb5f87qrJpCimDqecYO6cwx5TmmLTaJTOPyKpdsuqxK0bqF7MzHAV5gKdWULLV0KZen9svpMAPPa7e2mTj2mpjSavww8tzGXsWlJb019ps7Ay4//Ue+w+P+c1ff8tsknHtw026/YSqNIyOZzy+d8Tx/ojR8Yx0lmPr+kImlZ6vWdvuI4RgMpwjhOCb3zzk6988oN2LoYaPf3ZjWWiEkYfe7HLt1iaf/PENvvnNA+59tUc6c2njGzsrbFwd4HkK7SmyecF8mnH0eMT4ZEZnkLB+pc9P//mH71ihIbzG4va0IyyQ5NU+o/xXpNUjbJ29Us7G81AaQ14ZsqIkLSsqYxylpeY1HhbnQyv0WW0lBF5z2ITjBR8+OuGX//Fz4naEF2jK3AWcPPxmD1NaHt875Hd//TXg0irXrgzor3ef2PZiZG/slGn5DcPsF0yKz0nLh9g65e0WGQolfJRs4Tc+2J7s46sVArXeFBjrrhu9pLwk0NA2LsMWcznlsDNKO6WyI0ozorBH5OaQ0hxTmJPG8WzWdGPmVHbSdLDL5xS4wonA9XVi7zpanKPQeK3uxNtftNVUzMv7TIsvmOSfMS/vUNUzLu9cciJuN6lYJdSbhHqbpBHYR3obX602mow3u/Usz7EFFe8Z43nXKS4xNsWTPXy1SqDWmXv3mofnHoU5cKm/l0Ijc9SutLqPFB6R3nG2j3obT7nsICkEUik8pZ6Yap3tcoMTjfNUZ3Bxn3teN1xKSXJmWhZrD1M7Ct96lND23H060T7bSZdQadaixE03nrG9t4nFO2fzguHBmMlwRtYET3mBJmqHtLqxM1tIS0ezCDx8IQgin/k0owamozlVaVBaOrcaJaiKivHxDGMM6SSjtlCVFfm8YHIyRylF0omwdY0XaJQ+tSt3mpoDKnOIsSNsPXb6oKZB4ia2AUr28PVNattvgrwMCMizktHJnKpy+5TOC4YnM9qd6DsJxH/A0xD4MiRSbbTwqGu7dKGq7HnD9F4XtmlcpCAVAsO8vM0o/xXT8isy8/hc9rffhWsZeLKNbrSsnuoT6rXmWbvRPGtXCdRK06RxmRQXfX0uWAKLZl5odsjNAYVppsJm2EyEp81zddy4ceXLScfCVTRUGygZvjRH6EVYfD6lBHE7Ir4cZty5oD3F2naf9z69wtGe09/Mpxm3P9tlfDIjaYfUtRONZ7Mc7Wuuf7RF0onIs+KVLGWfB6kkYezTX2/zwQ93MJVhMpxz9HjE7c8fOaF8L0YIR5tahBFu7Az44c/fxw88pJQNZXREOs05fHSCVE7EXlUGU1aks2JpJaw9/VKK1bmO3xtv4QykCNCyixJhc+N1AuFZ+Q3z6s7SLekiKAt5ZTiZpRxMZhyMp8zygrQsqawTV14Grq30+Nl177TQsGAry+3fPuCbX94j7kS0egmz8ZzJ8ZSqdOEtX/3iDgcPjgAIk5Cf/w8/+k6h4VBT2CEn2V9znP5VIyp9+zkZUmi07BJ712j7H9Pyb5F47+OpPkqc8kCfHNNerjuNQKOERCp3jtVsNg/4U8G4rXPyar+h6O2SmV3m5R3S8kHTjXm60HALYy0SYu8asXftXBON3xfYumBS/I6j+X9kWn5FeemJ345el/jv0w1+Qsf/AS3//eV4fzHxuuxz5SwEunETu06ot+gEP2zS0L9xGrLsb7DlfdcAuYRj40T4I+bc4yT7GwBW4n+xLDRevO/nw3l/LtSaq60eAP4Zzm3LC7jZ6S91Re8KhICT/RFf/F3F6GjK8d6IK++t0+232NhZgRoOd084OZigPdfl7AxaBJHP/oNj5pOM6XBO0o24/uEWcVvi+5rxyYw7nz2kyEtO9sasbPUw1SrZPOfo0ZDZOKUsKmqg00+cqHtpc1tTVHdI878jL7+gqG43Cy03JRR4KDkg8D6im/xfEbbLfFYwm2YYYxkP5zx+NKQsDFHkM5mkHB1MGKy0/lBonANaePjSUYRqaso6pzAp5pJZEi4c0wX3ydrHYBkVv+Iw/fek5f1G2/Dq35+b9WsCtUHL/5C2/zFt/xM81XeBsWfMMeTyWXs5DQCJmy4rHeDXAyJ9tZkOV00B4pgE8+oe8/IO0/Ib0uo+pRliajdRkiIk0luN29TvZz7as6A9zebVAUHoUWQFQehx/xsX2Pfo7iEI8AOP7qDF+pW+S/D+2Q0efLPP5GSG9tSFLNgBwjjgxsdbaF8xOZnxu+oOu3cOKPKS7koL7Smu3drAazJ5Vja7tLoRg/UOm1cH3PniMXe/fMTx/pj7X+9hTTM9Dz2CyFG0Bmsdtm+scf3DTdrdN18TXWihEag1euFPCfXO8uLpBD9eVsKJvomUruJ9VRjrRLQns5TdkzGPRhP2xlOOpynDeUpWlhSVwSw4sRf5wRrkZcUnW2vQZHAl3Ygr768Tt0PSWU4QekStEM9XS6tE7WmCyMNbjLNiH/2MUCZrCwp7yLT4iknxhUsTtVPeTpEhGvpJi1CvE+gtIr1NpHeIvWuE+gqh3nLZEry6XduF7KEQuPHw87rXzlXDkz0Cu06ot8jNDRLvffJqz42D7TGlWfD0J9R1iZYdQr2Fr1YaPunvR2DWi1DXltKOyavHTIsvmRSfU5ijS6MsLlykYn2V2LtOO/iUtv8xsb5GoDebLu/bt0kVS4czCUIjCdF0llNXJSK0SJjqr5iXdynMYTPduNjjtOByz8qvUTIi8nbQsoUn2899GJ/nGnvZzzz9ry6g77vfgxYCLS8nHPJNEcYBg40uSTeiv95hdbuPF3r01tpILYmSgO5qG6UVnq9Juq6TNxnOmU9SemttklZEZ8WF4wkp8XxNd6WF0oruSovOoEV3pYUQ8MFPrjWdwYR2L166RJ2ipjL75NVXCBESeJ9QVHcx9gitNlGyjbFz6mZyr6QgDD2X+VE6EeuqsVhTE4QeSTtAe4qV1dbSneoPeD4WpJ/F/5m6oqrLM3ldl/fOi26/sSmVnTAtvmRW3m4cpl6druvJLoFeJ1RbLr/Ju0HsXSPS1xpb7CcXeHVdO2/G2mIbq3TV6LIWWU5aOuqUaajIyylcvThu7jUL2+rF9pzTnbNSr2uFRWJqha29xoEuxlcdWnoNTw0I9SaRd42setxMPI4p7RBP9om99wj11hsXGqerODezPb0Kn33fU8pNM7dvrPJP/4cfIqXkxsfb9FZbzVTS0Rd33l/nz//Nz1jZ6HDj4236q+1TGqoUvP/pFf783/yM9Z0Bm9dW6K4kCFnjRx691RY3P71CEPtsXF3h5GBMkZVYW+P5mnYvZmWzy/aNVbZvri2nqwjor7VZ2+49M1h18d4f/PAq//r/9iesbvZY3+kz2PhuQ1opd99b3+7zyZ/cJOlE7D04JmqFbF5dIWmHTwjU/cDDDzy2rlv8QNPpJ6xtdZkM50xHKdbWWGvxA4+gCQLsDFpsXV9lY2dA1Hpz86YLXVVF3pXG0tFZr8XeNQK1uuT4OerNCp4avPK2ja3JiorbByf8+8+/5fPHhzw8GTEvSkxjRbigG1wWksAnLU4XIb21Dh/+7AZ5WmKtbWzApAuEsg0vT4hlYBM4F4X+2ne7maZOmRXfMG4C1ApzeMnd51M4y9GYSG/Tj/4J3eBHJN4HS3tO0XQ63mYn+tXhphOOytUm1NucWuTmZJXTcszKb5gV3zIvb1PVU0K9TexdQ8suAudl//uOGkte7TMtvmBafM28utuIvy8HSkT4apVB9M9Yjf/lctTvzp13p0O+gBIRkXcVX63S8m8xyT/nMP33TIrfYcv8GZOvN4etM9LyAUJ4JN77+GqA1NeR6h9O1++iUdcw2Ojy0R/fRGlJbWuCyEdpSbufEHciVrf6mMo4lywpkA3NaTFNpgn580PN4e7Q0Qq6MTc/vUJ/3WlXlFb4vmZlq8fOB5vNawTa02j/6cZKjbEnlNUunfjfEAc/Z5r9O/LyS5Lgz9B6i3n+V9Q2RQgfTys6vYikHVCVlr6xXGmsdJWSpPOc4cmMJAmXHcg/4PmwWBfiuKANNmnil/vkh4Xmyjk0jd2zpPiGvNp7ree0J7sk/vv0gj+iF/4TZ44he0uHxucFEBprKWtDaStsXeNLN+kvraPmRcJHCqiaY6KRmNqSm2pZjPhKoxrDCdv8W26rpblEaS2VNRR1RWErclNiqVkPWvT8Fbfv3GxCCmfkZo+0vM+0+AohFC3/FqHacrrcN8ZZ/dOLn81KK+J2yAc/3GH7xhpCQBD6aF+itQRRIxV8/LPrXLu1idauIez5erlpKQU/+ee3+PCn19CexvMUXqiW++CHHlc/WGfz2gpVWWEquzSQEEIglVg2PfxAE7cD1rZ70LhHeYsAvWdASsHP/vxjPvnjG02TWi3zf56FVjfi0z+5ya0fXaUqDUIK19T29TIP6CxcoKArRn7w8/exxmUFLWxshRTL4FOtJLrZzkU0QC7kzpZnzgnEmsUJUTa/LDWK0rQozCpWdqhkhBQVSrnQkxcdSDgtHo6mc363u8cv7z3mNw/3uH884mSWUl2igE4I0FIReIrY90gCD3XG8zFuh2xeX3PhMeeEVJK4cxoG53y4c3Kzx7j4LeP815fafT4LgYevBgR6g8S7SeK9T8u/1RSIG2iZXPo+XBSWLj5oeIr/X9cGKUI81cVTfSJ9ldx87Lj7qkuorzRj6ne5kHo5Fv7otk6Zld9wnP0NafXghYm2bwLngJLQ8T+lHfyAbvATYu8munFJeddwVtehUEip3cPQd6YDnuwxFr8irR42VtIXyfuusRQU5ohx/muUjBzfWi6mhBd37pWVYe9kwmiWIaUg8j1Wuwmt6PenqImSgNXtmP56h6ThCi+/PuEe5lJJPM9RaE6/W/ebF3jNz57eG7zAo7fWQWlJq+cKlbNFhCfEd/zwnwXXPbco2UKrdZRsI0WEVmt4ahNJQMUEqBHSJbkLIagb85m6btx0fIXnyaUYU32ffsJncNoxr10hJhT6De0tLwI1NamZMi6PGvG3cFSfFyzML+y9a0tW7THOf9fYZj9yuoxzP6dP9WuR3iHx36ftf0jifUDi3WymrOELp5SmthzkY47zGTU1SkjaXoQUgsNsAgKuJ6sE0uMon1DYCi0VprakVUFuKwpT0fEi1sMOuS0ZlSmzKmde5QTSBYcuLiLZmLhUtWmmJDir2jOMAncPi5y1fdNAjvQOnuqdCbF81WO9mFg5i39jZ2jZQoqIhabFWeZKlvlbi6MswY8EXui5ME28xlAmb6zQZ+gwYhD3mvcyCOHWb7Z2OV5xyyNuhc22DZUdkZcpqnH58gNNELp166LArWtD3TxnF46JdV2iA2gHAWesO9yrlqJ80xgmubyouCWJWj5C+C+1BVZaEbcUnFOnrbRCabUUiy8wywvmuSveDRCFPnFwsRPuCyk0slnB4aMRVfnsi66uoabTUChSIENpxdpW76WFBkBta/ZGE/7d57f55b1HPB5NmBflskK/LCghCT1FL45Y77TY6Lbxz1SjYRLghz6vNjYVTwQzuRN5QlY+Ypz/ilH+G2ydXdyHeAGk8In1NbrhT1iJ/oyWfwspone2E/36kEs7wFBtUQdVY/NnoDYIodDyzVwV3hW4m/OcafkFx9l/pjSXFRxFMznaZBD9U9aT/xEtWk1x+m4smF4OiRQBkbeDr9cJ9Ya7uWeyMRW4eIFpacYM81+CULS8DwnUBuqCC9y8rPhm94hvdo/wPcVKJ+HH7239XhUaSTfmys11emsdpFLPP6XEs80nnrVeCyKP9Z0Bsun8vS4FVAjVLKIam9Om8ADdLGxKrJ0/aXrSTNyrqukiauW6pY1g82xB9H3D1paqNlTWYGpDoHz0O/A8qKmZV2NOyj1yO0cASugmRfyym0SWrHoAVKTVI3Kz74Th54RoQvZi7xqr0Z/TCX5I4n+AFkmzoHz5d1/Vlt10yLeTfTypiHXAWtBGCPhi/BiAvp/Q1jW76ZBxmRJITVVb5iZnUmZMyoyNsEMNDIsZ9+dHDIs5kyoj0QFtHRIoj0Bq2l5Iol1GlJbqmascgcaXfTzZIdYuW0YsnCffqPhzC/eiekxh9gkaI5G6OZZCJM21VjXugWeMpmtDTdnoOhU0zbfSHJBXu/hqDem5Zq+t80ab4i9zvCQxUijqxqa8qB5RmiMCfQWh1pAifopibZ3pSD0FQBE32y6gFgjhN40kAU2goiuODNa6gFnZxAQ4e+EMLdvPpIhfBmZZwf54SmUcve5Kv/NuFhrjkxm3P3tINnMVXdUE1i2C64LIww98irygzCu8wKPViVyI0eqLrQSysuJwOuf24Qn3jobsT6akpSsyBNCOAgZJTCvwiX3PjXyk5P7xiDtHJ24kBFwd9Njun3LxKmspjWFelEyzglGaMS9cVRdoxaAVc6XX4f31FTa6LTpRwI2VPq3g9GEtpTxXjPuLYGzGtPiKUf73ZNXjRlR1mXxTZzsaa2fn2g5+4Pj03nU82YPviU//MhyMZtzbO8H3FKsd151NQh95ji7g0/qOmto9Nhv7XOAfSGFVk5YPmZVfMyvvUJqTxsr2YuFuijGd4If0wj+mE/wIXw5YJMJ/37C2Zl6UFFVFoDW+dt1i+dTFutBwgIduXKH64c+bB39NWt1vposXR2FcdOmy8gHj4tdOPO+9dy5x+HlRGsuDwxGf39+n13IP1Lz4/bIUj1oByZU+cStugqueVUy82sLcDzz66x2EEHi+foNCI0TJLkK4JpNAUtcVefU5lXlIWT1sOt1n7uPNqZZnJUcHE/KicgWPr4lin1Y7pNONUfr7KzYqa6hqw6SaMi6nzpxcKLq0Cb8nip+zZK0ZlUccF485yB8wLo8pbIYQklAlJLqDfoP06fPth6UwR5g6p7IjlxR+rue0QgqPUG/T9j+i4/+QbvBjIu8Knuy8YphdTWEr5qZAWzepkAhqYFblhEpT127SESuf1BTMqpyq0a9oofCkK44X1CuBINI+nnT/poXEl4pQeUTKJ1Y+0yojrypK+917yJPP1lf4KC9BaY4ozH7jCjiECoyd4JpDIb5aQwif0hw0C/zTOIVF0aFlF19tNgt9Z0nszDmOlmZFYFGihZJtyub9Iu99Qn2V0hxSmH1Ks4+pU7w6X56Pi89qbUZpDzF2jmhsyQv2m/2w7tgI7eQEeEgZIUWMsRNKe+ImGXgYJpSmbtwxLUJoFG+HUaKVJPQ0u9MJB5MZpTFkZeWc/KSkG4ckb1h4XEihMTqa8s1vHjAZOo/lbF6QzXKytCCbF3QGCZ1+i8nJlNk4pdWLWd3qsXF1wI2Pt1+47XlRcv9oyO2DE/ZGUyZpfsrYE9CPIz7aXGWr12atnRD7PqGn+Q9f3Obe8WiZcvv++oA/++D6stOVVRWzvGR/POXRcMKdw2Oy0l18ke9xfaXHH9+4wr/+5H2ur/TcuF467tpFwtQpk+J3nOT/ldwccNnib4FAy4Ru8ONmivERkXflrThHvQn2jif8p9/eoRX5/OD6BjurXaLgTb3+5aWP3N8uatLynnMsK+8tnUAuGkrEBHqNbvBHbLX+Ly688R1yGLF1zSTNmcwzuklIKwzwPfXSbzpQa/jRACVDF1CVOVH9xeZtuC5ZbvYZZX+PEqFLo+fiCo2qMjw6GvPVgwOurPXoJSHFK9A73wVESUir10PJ4EI86AG84FQfJ16bpiTc1Ff2EPgul0BIaiqy4u9dcKgdoeUaT9/LpRCkacHDB8eu2MhK4iRgZbXFlWsrxK3guULRtwFTGzKTcZQP2U33CFVIS8cEyqfL9+ctaqk5Lh5xZ/Zb9rK7TMpjpz8QHpFKaOse3qWbGdQU9gTskFcJhZVCoURCy/uAzeR/pu1/5PRZIuBVn7eLbBnTpFPb2hnkWGpKa0i0s1D1pKYXJGS25KSYYWpL24vQUiIFhEpjqZFSuCmGiPCloqqdNiPWPpEK6HoRsQ6YVBnTMnM6kLeCmsIcMC+/aFxKS4yZUxjncqlFu1m8h6TltxRmD4TC5Z0ULJqpgbqCkh084SNF7KhpSEp7TG4eu8kkGk+t4lMxL79iXn6BlBGBvkJh9piXX1HXBUKoZ9LkbJ2SlXep7ARfrVFjyKtH1HV+6sgpVBO+GKPrPp6EwjwmLb9Fyx6+WmsCE8e4wtRHy87TbuaXhsDTtKOQyaNDvt47omjWxqL5t5vr/Xej0FjZ7PKjf/oBeea6p/e/3uPb3z7k+rUVNq+tELdCwtgnmxek04yD3ROy+fl441lZ8nA4Znc4Ji3L5eW91W3z4eYqtzZW+GBjhX4S0QoCfK3QSvL5owO0FNS1m2Gsd1p8vLWGVhIpBZWx5JVhmuUM5ym7wwn3joZ8tXfIcJ5xOJnz7f4xVwddPK3Y6raJniPieR3UtXOiSasHzMt7pOVDKvviMJw3g7v4Wt4t2v5H9MM/IfFvLQXf7zpMbcnLisBTTV7KqyeAL/A0n/sfAlzGSMa8use4+C25Obzw93A8Uo+W/wH98Od0gh+gZRvZ3BHToiQtKrxGTOZphZKSoqycK4dWy2tv4QzncgncgkwIgXfmdeBoQPO8aBKZFVq6gt/Wjj+elxVFZUgCn8h306rKGMqqojSGuj7lGj//cz2ZyRHpKwzCP4VaUNkpudmjso5zf1Go7IxZeRtfrdL2f+AS6cWb+c4vUDfHJa8MoadoxwHeBd673gZEk1F0kZQYIQRCvelFL/C99xFC4+mrCBEQeLcQKCp7hK1nCDy0WkOr9SfeWylJ0grY3OohpeDowKWW7z8eEScBV64O4Hus111KvcJiSW2+dDWqntHJNnXFsNhnUg0veC9qqMFgsHVFamZkZsZedo+D/D6j8vCMe5Km562xGuwQqrfR/T3/M0c0C8ZQ79ANfkwv/CmJdxNPLQTfr349CgSB8uh5MYOgRVuHKNl00W1FpHw6XkTdTD6kEAz8Fr5U9PwEKdwkw5eaRAeU1jDwW25yJWWjzamb6YYmVF4zAanp+zEdL3rpPl4Uamyjeyix5K6oF+BSzr2mwK+bEMWU06Bo7eyoGwq4o01l1HVKZYfNlD/DbcxRrCpzTF2XVHbsaJBLSn7t3q8umqnlswutekmdmjUBiCWWElGXgEaiqeqCyg6pFWjZa14pkCLGU6u4qsJS2TGlneDXL27AXySUlPhasdpOuL7aoxdH+NoFwHpaIS+g03MhhcbG1RXWr/SXf/6b/99n7N0/5pM/vsm//J9/4oR7UlDbmjwr+Yv/1y/5/Bd3zjW6TouKhydjdkdjN85pcKXf4b//9AN+sLPBtZUeQfMgXSwa1toJWspGLC5YacV8sLniqBRKPnG7KI1hOMv4au+Qf/vLL/jbOw/ZHY7JypIk9FFS0gmDN67qzsJiKMwRaXmPtLpHbva4zFA+l84e0w1+wlr83xB7Nwn1Jv+gVtv/iGHrgsqOSav7TIrPLiX1WggPLWLa/sdstv5PrkjldPQ/ywqOpymR7xH5HkkokJ4gK1wx0Ip8PBR5WVE2bhfGWsrKLAWyke+hlFw2c7Ki5HgyR0r3b4HnqFDGWkpjGc8zJlnBRrdF6Onl35fGUhlXwDjXt/N/zkCt44cr1LUhre4vE8Yv0qDB1HPm5R18tUJu9gntFlJ5F0Lhq8EVc8aShD69VoT/DBeSP+B1IAj0hwT6A2gS7EPvE3x1ncLcw9gRntpEyVXkGTMNIVy6cKcT4XmaINCYyrL/eMjB3pheP8GY7zcZXCKWou/Cuo6mtoryGfcSU1cc5A94mH5zwXvhHHBKm1PajJNyn5Nin7mZkJnpE+5SSmh6/jrrwVVC9W5p7ARukpF477GR/I9NNkYHwevz7iWCSPmsBC3eb2+wFfVojC1ZCrgRTKqMrCqghrWwTdeLWQlaT2TnnD7360ZDy3e2tfiJjnbFy+XrYM7undMzuJysAiU0ohbuzxTQGEAs/l0K1Qiogya3KUbKGKgx9Rxb55TmmNIeAQItWo0lfumenfUYU8+c49fyczq+oxNsP0WFfGJfXTFm7Zwag6hrRF27YgNLjcDWGZUdOUF7fQ2app1WHUfvQgM1pT2hMIdEl8RGeBakEPhasdlrOQ2yOP3uPa2asNg3w4UUGlIKOHMSG2NJZzmmMuhG6d6cNyhlKIuK+SSjLF++GCqNYTTPGM9zKmsJPU03Drm20uP6ao+1dtJwsJ88GKrpfEohsLWjUxjrOhJPc7XBaT2urvT4s1vXiDzNX317n2Ga8dnuPrHvsdVrE2hNEvhvSJ9yN0pb50zLrxnmv2i6z5dZZCha/ke0/U/ohj8h8q67m15zQc2zgvsHQ47Gc9cJLSvy0lBWhspY+q2Iaxs91roJ3SRiPM94dDTmZJoynmVLUX4nCeklEZuDNht9N2o31vLoaMzj4wnjecYsc5MsrRS9VsRKJ2aj36abOK/myTxn92jM4WjGaJY6Cz+teHQ84Xg8J/AUr+sBkBcVd/ZOeHzsitaiNFhrnQVlErLaTbiy0sX3FNN5ztF4xuOTKXlZOctMYyhKQxR4dJOQzUGbK6td0rxkmubsnUzZO5ksU4SLssJYSycO6bcjrqx2WelcTuctrXaZ5J+RVrvNTfHiz6dIb9Pxf0Db/xRP9lDCfWfH0zlHkznzvGSeF2SFoyC+tzFgo9dejmKPJjPmRUmau8wbN6VwBURlLNMspxdHvLc5QAjBcJYyzXLmeUlalGRFxaAds9lrczydsz+auoIm8Bs76dP05pNpyv5oSuBp4sBzXa5zX7bOIjXyrrIS/SuE8CjtScPLvqgCrm5438eM8982TYAfItWbNzOEEIS+RxT61NCc5//wwuBelox+GVjoeuoabD3F2pkrQOsKKdoo3UWKxPHCz1BjrK0p8orxKOVwf0yWFvQHCZ1ejKkMa+sd/OD7tbe1taW0FYH0WQ8GJDqm67Xped+lTZnacJjvcnv2m4vdiXqx/QpTV2RmRmqmVHWxLDIEkpbusuJvsRZcZeBvEsq3121/MZy7VKA36AU/oxf+MZHeQcuEF2VQLSxGm00883yWQrIedkh0QNeLl1kYpwN69z+B1KyFHSpr8Bq9hRbPmw6KJ4o3nnrfuq6bjvbbbEgKfLUBvsSSU9cFEh8htFvI4+E19umJ/zGB3nYTjSZA2AUcek5LKF2KuqwjQg+kjBEIpPCbqYltiivpgjfrkkDtIJD4agPhazcBETW+2mqcwU6vUykiQn0Vrx40Iu+FzsIZzbB0viqxdYqWA5Ts4AsPKYLGqUuiZAsfJ6QP1Jb7/G/raAtXbASeJgmffP4s2Alviku5s1VFRTrNKPNqmW8hcAsAa2vytGA2SanOIVAsjXWFRppRGUs79Nnqtrm60uXqSo+V1rNtNIUQDSdRLEeClTEY+93OnpKSJPDxlCIJPFqBz92jE3aHYz5/dICvFJ9srbPSigk89UYHfnEzsTZnVnzFMPsFhTl67e2dB0J4tP2PWY//e1r+B0TeTrMvbmdmWcHn9/f54t4+k7RgMs+XRUFWVNzcGvCvfnQTrm8Qhz5Hozm/+vYRtx8f8+BwRNXwv3fWelzfGPDTD7ZZ77kOkzGWe/tDfvnNLg8OhuwPnTNDFHhc3xhwa2eV0PfoNLaSkzTn83v7fHZ/n/v7QypjaIU+xlpmecmgHS/3+1WRlxVf3N/nF18/ZDTLmKY5pbFEvsfV9S4fX1unE4e08Dkcz/jy/gF//81DRvMcrSRpXjGZ56x2Y3bWuvzs1g6bgw6zrODx8YRffrPLL7/dRSv3QJmmOXlp2Fnr8t7WgMDTDNpx851czI17cSzS8iEn2d+QlbtcVtEa6iusxP+Stv8xnmwvb7hH4zlf7h4ghMDWNbvHY6Zp7poCjT5gnhfc3T9hbzhhlpeUlUFKSSv0XTFiDPcPhmx0W6x1E6y1fLt/TNbcIw7Hcx4dj7m21kNLyZ39Yz5/eMB76wPe31pZHgchnDjyZJby8GhMP4not2I8peCc9KHFYjLSV9Bxh6oeM8p/0YzgL3JSVFPaIeP813iyQ+K/h667Z/bh9SCFIAo8ktDH1s5Qw2UNnR6jfyiwtZN1KvEicpzD2fvGojiBVzsep9uwGDuiMgeOw43E866h5Woz+Tr7DmAqS5oWHB9OuPvtPn7gcfODDVbW2rTawXO99d8mDPVpoRGu0PM6rPj9Zx5XW1ccFY+4O/vdW99PJRQdb4WN8Drr4VUGweZb34fnQzTi701W4n9BL/jZGU0Gz3121fUidM+NF55FV1FCsBa0WQueLPyePvMD5bGuzj85edH5/32F8/p6A1+v890MjbN/FmjVO/d2PdUn8m6e+ZunqXBPFlSB3iLQm995z7NQMkLJa02xtph4LJSjz993TZeA0/NWiQQlY3zWnnrN5cMVXgJfuRiHs1BSfqeJ/zq4lEKj1Y3YurHKwe4J/+//+18uNRrpLGc2yTjeH7OxMyBuvzxx0NaWvHJ840V3u59EdMIQ9YKLQAqB1hJZSbB2qcmIvOcvwpQUJIHPaivh+mqf/cmM/fGUcZbz9f4R/SSiG4WE3pvYjllKOyKtHpBVjyjM0SXa2brFUuxdb+z0bjqR0RnUdU1WVuydTJnnJT+4sUm/FTHLC+7vD/n17Ud0k5Arq13aUcBwmvHoeMyj4wmDdswPb2yimxTKveGE3aMRG/0Wm/2244qWhsfHE4bTlFs7a/zZD9yFnpcVj47HPDwcs9lvE3oapSSHoxkPDodAzT//4Q26SYivFXceH/O3Xz6grKrXLjQCT/PR1TW3+C0NpTEYaxlOM755dMi9vRN+dHOLVlPVl8YwSQs6ccBP3t8m9D2K0vDoeMy9fTcZOZnMSXOnTcoKV4j85P1tPrm+QVG6Bfa9/SH394fc3BywOWgT+R6+dzGX3mJ0nFUPmRSfUdiL12Zo2cFXK7S8D0i89/DVCmeFjLO84GA8I9B6OWptR8GyII8Dfxm46Y5niFaSaZoTeJrNZvpVloY48MirillWcDSeU1SGwHN80VbkE/oa5zYXcnWlx85qjysr7twUYkH/cC4aceC9UVNACA8tW8T6BoPwTxkXnzErvr5QFypjZ6TVfeblHfJqHy3aTSLw6y88Pa24utblYDggzUvu7Q/5wY1NN/WRL1+Q/76gNIa/uHePzw4O+BfXr/OTzRcvOE1dU1QVu5MJv9vfpxUE/NHWFv3oVbrhNWV1l6K6S2l2qex+k9OTkMgISUBa/BJTT4j8n+I3lp9KS6LIZ22jg+crlFL0Bglh5J3LOe9tQAmJLz0m1YxhMSYzOZNyRt/v0Pe/m1D8NiFRaOnR9dYY+JvsRLfYjt6n5629/MVvEZ7s0A4+pRf8jFhfQ8k2tpYYY5Y0biVP07p1w4+vjCGvDJM0Y5zm9JKIXhI5VoG1RL4m0BpjHd3U1BZqGk3q91+kXh6evjYu+lo5z/bO+54v29eLfK+Lg7FujX00nXMwni3PzXGaU9c1P7q6SbL2DojBn0ar8UC///Uev/2b23QHCZ1BwvDQuU5t3Vjl2gfrJOcpNGxNXlUUzQLT15p+HNFutBPPg5SOlrHoDFTWOipL8HwerJKS2PcZtGKur/R5NJwwnudMspxv9o/Z6LT4cHP11Q/IGSzoEml5n6zao7Qnb7S9F0Egibyr9MM/oe1/Quxd/87P2BqKsuJwNCMtKm5dWeXT6xvkZcVv7jxm72RCNwnZXumShD67xxP2hlMORlN++v4V/vUf3Vr68/9vf/slv/zmEXsnE/ZPpk6sW1XsDaeM5zn/8ser/POm0NgfTvlf/up33N07Ye/Ebdv3NEfjOY+OxrSTkJ9/fJX3NlcA+Mvf3uaX3+xSGfvK1KlFYeJpxY3NAdfWe0ghl+FZXz484Ld3H3P/YESalTRNZSpjmWcFWysd/uSjq2wNXJH2//3F1/zi64fsn0w5maTL7RfNAnlnrcd/+9MPABjPM/4f/+HXfPPwkMPRjPEsd+KrCyo0nH5gSmYeMSm+5DJcyzzZIfHeI/HfJ/ZufCfIMS1KTqYpSeAvF/eR76GVo7lFgV4W+vO8ZHvgfLqL0uBrxUo7RivJaJZRU1NWlmmaM5y6QM4kcG4qrTAgaILb2pGPFB22B+4XNJNT4a59X7ugTSEE1tpmpP1qHX0pvKX3fT/8OabOmJe3L3SqYerUGUJU98irPQK15pyW3qjQkFxd6zGaZfzi64fsHo4YTVOyslwK7cVZIu5L8Dqd/7eBwlr+8u5d/p9ffMFKFL200LDWklUVd05O+F+//prNVoub/f4rFxpFdZd5/pfO9tK6cFUle4TeRxjRIy3+lrLaxVObp4WGkkSxTxT7rK5fnMPYRUIJia88bG2ZlDOm1ZyhmKCEemahIZHIS7CzflIh4DqungwIZMRmeJ2r8Udcjz9hM7r5QpOHtw+Bp3p0g5/QD/8JkXcVJRKKylBUFUVZQWN4Yeqa0hgiT6OV05JmZcnhZM6D4xHXVnvEvscsL8jKCiEifK0pjWk0bo0tuwiW66B37fp8c1z257m4hb94xrTj1fH9fH+2rikrw3CW8mg4xtYu9PXB8YiiMmz1Olxf7bk9fM1z7FIKjbWtHj/6p++zeW2Fmx9vLzmA1z8EIQWrWz3WtrqsbPZeuq0amireLRSUFASewtf6hQJPLSWh1syk6zaXlcvM6JwjSdxXipVWzGo7wfcU89wFmhxN55RvKNir64q0esik+OJSiwwlEjzZpu19TD/8OaHeeubPLbh5q92EtCj59e1H3D8YOqpSVrDRb3N1vUfoa4rKMJqlGGvpt2LacfBENy4JfdZ6CUIIDidNZWwsnpKsdhMi/2yiqKATh3TikLysOBrPiQKPvKyIQ59OHOAptfyO/YaGc97sjKdR1zVpXvL17hGPjsdLTqwQgsfHY/ZPprTjwI2vzxQm7di9p5anRrqhrxm0YpRSTNJ82TUPfY9eEi277u57kCSRTyeJMLZmmua0zpE+fF4U5pBZeZu82scVGRdNmxIEap1e+Mck3ntP8FMX6Ldibm4MmGcF86JECoGnFL5WeFpxNJ5zPJ3TS0JY71Eay+FkRl5ViAy+eXREXdc8PB7TSyKiDc1at8UsLxnO0qWu50k6gVsoP30PmDev2R9N2T0eU9dQVIbraz1017lWvXL+ghrQ9j9hXt7Fkz1KO8LW+aseyBeitGOm5Vdo1cVTnTdygvOUYmetS01NWpTsHo359e1HHI5nXFvvsdpJiAIfTzeNmJccj8DTRP73qx+4CCzOSyEEs6JgWhTYczwPnoaxQ0qzR+B9iFb/krT4BcYegRCnwVxvndf+5hAItFAM/C5KuGJUCEH3GRoNJTw2wut83P4nl7IvrhGk8EWAJwMi1SbWbbreCh29SsdbeaeKDImPr1ZJvA9oebeaIiNimhU8PB4xywpHrmnMKha8+LVOC60USjg2ha/d+bk49rO84GSaugmt7/PgaMSj4QQlBZHvsd3voFrxa93X/oA/AFyORhz47Kx06cQhde0YJwCHkzmeks1a6Q3e44L29QkMNroMNrps31hjcjJjMpq7/IxuRKsb0+7FJO3o3F7mCyvLmtOHhVbyhReWo094aJVDDUUTzleZl3cjPS0ZJBErSYSvFCeV4Wg652SWnuv1z8Kim2rrkqzaZVp+QWGGr7Wt88CTbUK9Q8v/iG7wE5710HPiWfC1pteK2DuZ8u2jo2WQT68dcW29x/WNPqGvGc9z5lmBtTXtOCAOvCcWf4Gn6SauOzieZcvPrZSkk4QEZ5xvpJTETdR9WRnG82zpQBT6mjjwUWdoL55WJFFAGLxeom9lXJf8691Dfnd3z+2DcNzE0SzjeDIn9J1r0WKprqUkDjxCTz/xOX2taEUBSgrmeUnk66UtaxIF+PqsWMwJc5PQCZbneXmh7jKFOWJSfNZksFx0kSGRaAK9STf4CZF3DfmMW0Y/ibixPuDh0Yi0rNBK4Xua0NN4SjFOM05mKYN2TCcOeXA0YjYrmi5exe7JGGMsw1lGK/SJAo9OEmJrt2guqhHW1mit3LUvJUad2uUusLhBTrOcaVownud4aoanFavtmEHLIsXL8zSehid7eH6HSXENX61i6+rCC43KjpkWXxGodVr+B6jaaZxe51xXUtJtRVTGsr3S4WQy57d39/j83j4/fn+bGxt9uq3IOXydY4Ey6MSEXvJOLGReRJs8D6VSCIGpa+ZFQVaWmFemYdaYekJlD2nr/wNJ+C8x9oi0OGbJ4f49LDLgtCvb0jGROmUbqGdMLZTQbITXLnyisZhmKKFRUhPKhEi1aHt9Et29tCnKm0LKgEhfoe3dIvHfJ2oae/N8zP2jEcNZiqckZWWYFSWqcdFTUrLSjokDj0DrpXX34r9ZXnA4mbHSTjDWsjec8NWjQyJf00siOlFAJwqQQi/va7Y2Tcp11VivPnmOK+GjRMDyaF/AdW0bG1jbGJGcCvcbNyzhoUTARYQBLz9PvbCWtcuAvEWgXv1CK+Kzn1s0pg2uSSAW2VpicSZe7nVcN7qcpZ1v83me/xkW+95MpXEFuTyTxP6q36eWEiUkK62EfuzWb2lZcTCZURiDpxS2MQV43aNxqW2qIPIQokWYBPRW2/iBRnsaayzj4Zwo9gmiF3fuhHCLDU9JSmMx9tQ7/0UPFq0Uke+5agyYFwXHsznr53D9Wdh9LTyETbMgyqtq6bD0OqgpqeoZWfWIeXmPyk5fe1svQ+y9x0r8Zw1d6sUPPls7LYWnJDc311jpJsSBRysMaMcB3SRsbEUreq3IdajHc8bz/Ak3m1lecDCc0o58VjpO9Gys5XA843A0Y56fiv8raxnPMsbzlPV+wqAdEwaeuxFnBZ5WVJVZXmZFU4x4Sr7yd2CscyHaPZ4wmmVoKfn42jqbgw7GWu7unbgpx1OvcxOcjGlaYMxpAZIVFceTOf12RCd2BYe1NVleLjUbdaMFNbVlluaMZilKygvPNMjNIZN8UWhcLLRsE+pNYn0NX62ghXPweBrtyE22OnHAtTWXEeApxWo3wVOSzV57qcuoa1jtJByMptzeO6YGPthapR0FFGVFOwpoR44OsNETJKHHZr/l8jCkIA78pmhzncFOdDodEgLiwGOt2+KP3tvmvY0Boe+cNFba8WkH/5XhbuiB3qQX/oxR/ivK/ISLzdWYMCtvE3vXsTYFaXndAM1ZXvBXn91bGjA8PnEUUIBpVvCb248JGsqGG2i8+Jj82Q9u8N//7BbBBYgCLwJLt6n6dAnlJpTPEdo2v6ZlyaPJhMeTCTXuufI650PTbz6z9bPvspiIXsZ08XJhakNZV+xlR+ym+9BYmu5Em+zET9LSHJ1q49LyKwTOJUkJDy08fBkiUe/UFOMUAi3btIMf0A3/CH+Zk+D+TQp3fi5CMxPfd1PCwNl1G2uZ585m9Xg253gypxeHzLKC0TznZJaSFu6ZEniaThQQBR7tMEAKQWUt3plzPzfHHGa/ZlreJzPHWPtkZtkg/CHbyb/Al20uqiDOzTH76d8xKe9Qmhm2dkwSISRCePSDj9iK/xnBK4i3XwZTFxRmSGaOycwRuRlS2imVTTF1ilkWW+66FItFeUOJ1SJCyQhftvBki0D18GUXX3XxVYu30SywdYmpCzJzSFodkJkTcnNCVacYm2Ka4k00DQzVFGxaJviyTaD6hHqVWK/jyVbTCHy1/c4rQ1aU7J6MeTScIHAl2zQr6MeOofEmRQZccqHhBx5+4AGnHFhrLIePR0yGc1Y3uy8tNJaLfqWorMVYS9pYsL5owelrSSvw8Ruh1CwvOZzOmRflS/dbIFBCooWbmpilTsS8trUq1BibUpohudkjq3Zd5XrBWITWJN4NBuE/I9Drz+0iLKcs1lJUrghY67W4uTlYFhc1ruJFCDztLGkDT5MWJeNZxnCWLm+g41nGNM1cl6aTIHCCTSmEc7KapQynzh96NE0ZzzPy0hAHrjDxtWaW5hjjaFvDWUZnmi5fP8sKWlHwymLwuq6ZZW4EPW2S5Tf7bT6+ukZROevPVhS4cuzMwqMylnleMEkzRvOcwHfHYzzPSHNHEerEoXtQZEUj5nOOXeNZ5i7WNGeSFuSlwV+Gp11AV6fpfhTmiGnxNYU9fuNtPg1Ptom9m0TeVXzZQz3HQjIJnTZjrZM806JxpZOw8tRreknEcJZSGcv1tR5b/SaxedFJEhD5HoNW9MQ2F789a3G8sHUNfY/Bc9zoXgeLzlegNugGP3VTpPy3FywKn5PWD8iqXUo7xq9XkCLkdR52eVHx2zuP+Q+/+pZ5XjptWrMQX1x/r4L1Xov/9qfvv/LrLhKLa35elsyKAlvXzIqCeVli65pxnvN4+vzGja1rTrKMb4+PeTiZ4ClFKwhey1FFoBGE1HWOsSfYOnXBYnaCsSPAInhSZ1M3YQVnDXnPU+S9TSxcp46KId/O7mNqg0AQqZArsbPbPO1QO+enjvf0lf2PDWJpU9oOPqIdfIKWp1QzJQWh53KFSmPQUhH52mUNBR5x4OjETsfh9BemMa8pmiwgY+uGZeCMNQYtNwFJAh9Pq6bgPltojDjKftUs/O9R2dkTe3yjTlmPftYUGheDwow4SP+OvfSvSatDTGNwI9AoGXK19a9ZDX/0RoWG+5yWuq6o6pzCjpmVD5mWD5iU95mXj8jMMYUZuYKjTpvJjsHlgCgEHlqGaBniybZjfqgVIr1CrDebXxvYem05+XEp5BfTHFyuuaiwdUlhxhRmzKS8y7i4w6x6yLTcpWiC+6qm2HDPPIUSAZ5MCFSPUK3R8rZpe9ewfk6k1/FkghIB8ok8kBfDGNdIfzya8tVjR2OWUrDaTtjsJkSefmPDirdOvDXGsnv7gHtf7fHDn79Hd+XFQTtauoIhCXwK4y7Gk1nKJMubXIxnI/Q8BknE45FLCx7OUu4fDbm18fIbo6mdMCtd2EKycHp48oJ+FdRYcrPPrPyawpw0RcbFd7w82SPQ60TeNUK9iRLPFzrWuO+jbJKax/OcX337iG8fHRE0dCFjazYHbT69tkEnCRm0Y7YGbUfJmKb8r3/zhevYAGlesjnosLXScU5CwmljNgcdTiYpXz485P7BsHlfp5nYXumwOWiz3muhpCQrS7ZXuxyOZvyn39zm70Of0Pc4HM1Qwo2bX7ULKRs9yEo7RgrB4WjGX39xnzt7JygpOZnOsXVNvxU94VKkpMDXTqD+l7+9g6cVWVkynees9xM2V9r02xHT1FHKtHIOIrcfHfP/+dsvyIqKrCgZzTKurXdZ77XoxOGFTDSqek5lRuTmgNIeuy74BcOTPdr+h0R6ByHO57R23q8m8BQbvTbW1kS+98LXvStrMafV+JBJ8Ruk8LF1cWHFhhuXlxT2hHl5ByVjIn0FwatrNbSSbPRbfHBltVmoLCgFr7dvV1YdZ//7Rg384tEj/vO9e8zKknGW8dv9fWZFwf/+zTd8c/z8YrsGcmOY5DlaSq71eny8ukr8yg6CAq3W8b33KKo75NUdsvI3VOYR0+zf4amvqYHA+wB5ZiFX1zVVaTHNvVYpge/rC0gqvzgsAvtW/C7vJVeZVDOm1QxP/v7rcy4LUjjKVMv7gEBtoEXyBL20FQa8v7HClUEHY90CTkuXTaCVItDKNfSanC8lV1hrt+jEAZ0oRG2tst1vs9ZJ8LRqpsMBWjqqbqD1M3PE/iGixlCYMak54CT/gnFxm3m1R2aOKc3EPRNt5gxSzlDHFlQuV6xZSmswdUZp50hxTFrtMS5CPJmgZUygegSqT8//gI5/k1hvEOqLKqjdvszLR0zKe4yKbxkXt8nNSTORmVHaWfMZijP0N1zquHC0uMqmZOaEWfmQofqSA/ULEm+Hrv8ebf86LW8HT5yv2eZrRTsKuLnWJwl80qKkNIbVdsJqK6Ydvbmm9ELuIGVRUeQlWiv8wKMsKvKsfGbnucwrHnyzz9e/vs/Oey+3ptNK0o582qHPJMvJq4rj2ZxRk6vxPCeZ2PdYbSfEvg81nMxT7h0NOWk68ItAv2fBGMskK5hkeWNJdxGw5OaAWfktpR1yWWN1Z0P6EZHewVeDF+9RoxlYiG2h5mQy52QyRwhBWRmmacGNzT6DdkwUeAyagL3rG33u7Z9w+/ERVeUuhM1BmxsbfbZW2gwa6lRZGbZXOoxmKbtHzhaWGgJfs73S4dp6j/Vei17LFUSDIub6eo+ycra4xlpakRsRr3UT1roJUeA1ln7ndIQQwtFnOjHdJMLTisPRjMk8Jwo86rpmpZ2wtdImCk4XHlJKAl9RlIYHhyOstUyzgkE74vpGn+2VDu0ooGwmOp6ShL7HeJ5x+9Ex06xoPn+ba+t91nrJdwJxXhfGzsjMYwpzSGknXKzblOOteqpH4r1PqLeQLyk0XrUzG2jNerdFXdeEnn7u69+ljq+WbZSM3IJCdqjsBFPPL2jrNTUVpRkxL+/iq1UCtfFaonCtJFsrHX5w42JCn3bWuu+MBevedMqv9/Y4TlOGacqkKCit5evjYx6Mx899nRCOia2k5L3BgA9XVri1skL0WoXGBoH3IXn5JXn1DcYMG2rMA2xdEno/wPduIc9YidfWPVfyrCRLS4SEIPDQnkIpidISrU/NL76X/AJHWCJUAT2/7Vz5sH8oNF4AKXwifZWW/2HjGPdkYy9uphZnaX7Pm8iCo6FeGTz557NYacescHHT2t8HuGNXUdmUWbXLMP+KvfSvOM4+W9KMzrWdRgtBXWFqgNkzf06LCF91qOI5WsZu6vGdmfyrfwaoqeqU0kwZFd9ymP2S4+x3HOe/c5OXczStXJBlTsmUxY87mmFAy7tCZo6o6gwlPGjOx8Vk43nkJ6UkgRD0kggpJfO8IC8rOlG41BG9KS7kDjI8mHD/mz16q22ufrDB/sMTbn++i6m+u/ipyorbn+9yvD8iS4tnbO1JeErRT2J6ScTBZEZWVhyMZxxN5xSNZuJZHe5W4IL9Wk067snMUXAenow5mEzpRiGt8NmVWlZWPDgZcf94SFqUKCEJPE2gX4/TCw1H0xw32ozJa23jPAj1Jv3oT4j09kt/tqgqHh6OuH8wJPA1H11dZ3uls0zpPhzN+PLBIZ5WDKeO9tRNQlY6MT+6ucXNzYFz02huoo4/79Fvn94IlXRWm60oYJ4VZEVJ3fx9Evq0o4BuHC5DHdtRwKfXN9lZ6zHPi6UgWDQ811YY0G9HxK/gPiVgSfv600+ucWtnFSXk0gIZWLpdrXVPJ2wLN4atQcTPbl0hDj2qyhL4LqNh0I6fuAg9rWnHAT+4sckPb25SNtkvSeDTinxWuhfHZy7tiHl5h8Icc9FFqxPuxfhqhVBv46v+G9mtPgueVi68sAbPe/fEnc+CW6pqfLVGy/+QeXmHtLqoQsPB2Cnz8i6Rt0Ndf/Ja2wg8za0ra2z0LoYa0W9HqHeg0BDgci/CkHGeczSf8+/u3OHXe3v8fGeHH6yvP/d1YhFI5XmsJgnXul3WkoRQv+ojUKDVFpEI0WqTwHxIZY6w9Rwt+yi1hq+uoNUGSrTPvgwhIE0Ldu8fM51mWFsTRh6dTkR/pcXKWhvPe7Gb4mXCYChsyWF+wrfT+/jSI9YR/jmnmf8YIUVA7F2n7X+IJ19sW/z9X0G/n6ixTfd+l8fz/8Jh9kvm1WNyO8TUL19DvipMXVDZOc6UIEQ+w2nx1WGxtWFc3OYw+yXD/EuG+Tdk5qgpMl6/UeimNQVptc9h+vfk5oSsOmAQfsog+BRPvfg5UFaGrKy4vX/MvcPh0phgdzhGCsGnVza41tjbvi4upNCYzzL2HhwjpOCKXeNob8Tnf3cHIQRB5HH2EjOV4ejxkPkkozxHMnigNautmNVWzB2lmGQFwzTjZJ4yznKysiJ6Rke0HQZcGXToRiECJ5CsrOX24QmfPzpgZ9Blo9Ny6eFSNqNLS14aHg7H3Dk8YXc4ISsrN1UJA+LAR75idbcULmJcfkb14FJE4AKFEJpQb9HxPyXQz37onsXCbnU8d3zKdhywNeiw2o2dWLLp/FdmkazuJkjtOKQdn7qSLPjHtq6prcu5KPOysYqFXlOcOPbGk4R7ayxlXpJOMzzfFXNXVjtcqTvfSXumrqkbm+O6hjIrXTewyUt47rERAq0ErSjgw52XT9EWx0NKge9p1nstPr2+wepLCgXdTDSurvf4ow+uvPR93gSVGTMv71Kai7dIlsLHV30CtYavBmj5Ynrj68BNKi/O5vdtYOFKEqgVWt4tKjsmre5f6HtUdkpa3SevDqipnjuxfRE8rdhe6bC98m7mNbwOFp//vcGAm/0+87LkOE15OJlwdzjkRxsb/HfvP19HIoUgaHQZsecRqNejm7h7yQAle2i1irHXMXaItXOU6qNkFymSRl9zun2Bm5DWtmY+Lzg6mDKdpPiBptdPyPPKTV5bIWHoobRrhLzVyUbtRKCzKmU/P2bgd+n6beQ7QJt7FyHQaNki0jvE+gbqBffJd2ky+/uCs06d8/Ixx9nvOEj/jqPs11gMiwab6+h7SOGjhI8UulkPNVbTy+0taFSGujZYTOMgWJ5Z7C9coGqUDPDf0Gp8sY+lnZObE07yz3k8/89MinvMqj0WTATndqVRMkAJ3+lC0GccsJzJhG3cqSwlxhbYOsdSufWlHS9/lWZMjSVUK8RCOBrVc3QmpTHM8oKj6ZxHwwlb/TaBpzkcz5jlBTuD7ms9h87iQgoNP/Do9BPiJHTez5OMx/eOWNnssb4zeKJDY0pL3ApJpznyHKm9oafZ7nXY7LoPv1jwjtOc+8cj2mHAdq+D/9SmunHIdfoMWjGeVs461Rh+df8R0yznB1c2uLWxQi+OiH2f0lSM05wHxyO+2jvi80cHjde/oR36rLUTVpIY7zUeTjUWW5eU9pisevQdcdZFQMnY6TPUBoHeQJ2Dn7fIt5hlBb+7u8edx8fsHo2JfL20Cp2kOZt9p8lY77eeqS+obY21NWVeUmQlpjKYylIWFdZYuqttWr3Y8ZMbD/EF8rTgcPeEMq/orXVIOhHad5zVqilElaeobY2pDFVpqApDVVaURUWrF9Ndbf+ju5GXdsy8vEdhL9b9CFwGS6SvEugNJH/oZj4NT/ZI/PeYlbcvfNumnpNWjyjMEbYucA+iPyz0nkagFC3fpxsErEQR60nCVus5C73GMWVpjS5fbI1+PgiEiFBSI0UCyiCEDyioK2w9Rch4qbERUqC0pNWJuHp9Bd/X7D44JksLTo6mpPOCg70xG1tdtnYGtFohcXIxNMvzQglJIDwiFZLoGC0UpS2x9cWblvy+Q6DwVIdArRPoNTzVe8MF6R/wLNRYTJ1xnH/G7vwvmFW7TxQZAEoETsTtbRDrTULVR8vWUhS93FJdYuqcys4p7ZTcDF333xyRVY5yZOocJUJ82SXW6yTeNvoFOtfzYlY9ZG/+Nxxlv2Fc3KEwY87SnZVwGpGWd5WWt9PoRLpIETQTlRpbO/pYYSek1T7z6jHT8gG5GT6h+S3tlHF5F5VFaJmwYn9AL7j13M9RVK7QiHyPnUGXa6td+q2YWV5gRzWqacS/yT3zQgqNMPYZbHRp9RzHi9otPONWwMbO4Im8jKqo2HtwxGycos8hig09zWa3zWa3Tdz4vjttQcHeaMp2t81GpwVPUTti38fXmvVOwno7YZS6hO97RyOOpynzvORklrLacrz5oqoYzlO+3T/h3tEJD4cTpnnRbMtjp99lq9vGfw0hr60zKjumNENKc3KhbjULaNkm0jsEegNPdhHncEnQStJvRaR5yf2DIaNZxjwrGntWF46YhI7ys9Fr0UvCJ7ItFijykmyWk6fFMwoNQ9gKSGxEOsvI5wVKN25exjKfpBzsnmCKCj/0kEpix6mbcsxyhMA5kwmBqVyhYo2lKhtdkKforFycewaAEoLQdwGBm30n+NYvKIq1kkv9yvZq51I79a4rY6nsmKzapTLP56W/LpRMCPUOgVp/qTbjZTDWMq9K0rIkq5z7UaAUkfZI/FNXuN8neKpLrG/gqz6uY3ZxhZ6pc2p7TGFPqOwULbtPeN7/AYupgiLyPK52u3y6vs6VTofeKyV8v/k+CHwQPnA66bQ2o7R71HWGxxVQbvHpckCdGNj3NUkroD9ImM00Re4mV+k8ZzLOaI3maC2J4tfLDHpdmNpS2BKBIFERUghSk1M17lPPwqnj0cI+9DWpxc01ZGyFqUtMbTC1azSJRa5Gk62xcAD6Pq1uhVB4cuCopXKAlpdj8/uPHabOyc2QcXGbk/xzjM04nWQoPJkQ6XV6wS06/k3a3lVCtYqvOmgRLZ9frtlbYGxOaacUdkxWHZGaQ+bVHmm15xyg7AQtE0LVb4qW3pvtvy2o6jnj4g776d8yLm6TVofLNaASAUqExHqTxNui679Px79JpFYJVB8lQ5Twmv2vKO2M3AyXYnIt46bYOKayaVOY5RiTMy5uOytcEZB4W82k59l6SAGEWtMKfUdVx7k+tqNgGRHxvdvbxu2I7RureL5GKkl3pcV7n25z4+Nt3vv0Cmcnr2Vesf/whMlojh++/O0DrdnoJGx12/TikNBz6dGlMYzTbGl1+DSkFHhCstPv8rMbV/hsd79xqrLM8oIv9w55NJoQaIVWylm8GsM0K5jlBWm58IGGbhTy0eYaN9cGT6RbnxelHZFWDyjtuDnBLl4I7st+I0hb5bynhGx0EldWu0SBxzwrl5aLiwpWSkErDOi1I5SSz9zy5GTG4zsH2IZaJaVEKEFta4R0vxtjOdkbcfRoSBD5SCVJZxnpJGM2nuMHHtZY5uOUR3f2OdkfMxvPkUoStyP80HNmA5FPGC8e3vUTOR4XhcDXrHQSIt9ne6VD5J/aED4LrcilmLeigA+2V19KsXoT1BhsnVPaMbnZp6ovnoanRUzkXSFQ6+d2m3oeSmu4Mxzy7fCYe6Mhs6LkSrvDjV6Pj1fWWEt+/x7QWnaIPIkne0h002G7ONMI17kak1WP0TJBqrVGH/IHnIUnJT/b3uZat8vVXu/73h0AbJ2Sl59hzAkiDFHNQsUaS5oWHB/NePTgmLqGja0eQUOTWpSrRV6RzgvipHwDK/VXQ42jvc7NjL3siJlJSXREZnOm1ZzcvogHX2PqElsbtPRRb7ikyMyMSXXMrBozNxMnUBeSWHVIdIdE94j1xTaWXgcCTag2SPS1S6GW/gELp84TJs1C2tgUe+Y+q2VML/iQQfApK+GPaHtX0TJGyQCJ10wCFhlijTWuNAT1wFGPvNwtyuuUymZNlsUhTpvh0/LenP6c2yHj4ltO8s+ZlPfIl46jDqFaoeVdZS36KavhT/BVB1+2m2mM3xTVyyhGfNkmVAMSvUU3eJ9++TEn+Rc8nv9npuV916hqtp+bE47z3xGoHr3gA4RQBLLL0035JHB610mWM0ozhg113NOKlXbyWk6fT+NCCo0g9AjC0wVJb7XNBz+8ytb1FVY3u09MNMqiYue9daqyotN/+SJjoY9Y77S4sTrA2JrSGK4OugySiMh79kGQbsXMzqDLH9+4QlqU7I2nZE3Y38FkxsHkxRSmQCt6ccT11R4fbAzY7rcJXnmiUVOakes+2wmX5TblqT4t/xa+erkGYQEpnAbB9/TS9el1UBUV82lKkZaURUXSiYk7EVVZUVuLqYzjJo9TTvZGS01FkRXkWUmZldBp9Bp1yehowvHjIek8R0pJPi8Ik4AgCmgJFwRZ25oyr6jKiot+ImulaEWucFjvv/whEnjaBSkl4Ut/9k1h65zKjKnMiMpOLzyhGgRKxoRqi0CtvLEQrq6bqUZZ8ng65ShNKa0h0Irr3d7F7PJbhhIhUnl4qouWbap6jm184y8GlspOyapH+GoFX72a40lZGfZOJgxnF7NPK514aT/9LkEKwU63y3bbUScLY6iMoaoXOq4X3xc8pQibNOYXwdgp9pz37soekZdfYuyQ0P5w+fc1p1MNcM/MlbU2nW6EH7guo7U1w5MZR/sT16x56btdFBaOOIbCFvhSEwV9xuWUuq7Rz5iOLzINSptzXDxmVo2JVItQxQQqwZch+hz5A4vtpGbGrBpxUuxxUjxmUo2YVaPGLUzS0j1aukfPX6frr5GoDpFK3miK8iYQQhPoNSLvKuoP04xLQu3sbKs9CjvF8qSmV4uIrv8eq+GP6YefEJ9Dl/q89wHIqmMXcNikm4dqjdft4y9cprLqmOPsdwzzr8mqw6VDlkA3xcxV1qOfsRr9lNXwx835fM78i9pRxbSMycwRpi5Iq73le1R1SlWljY3ubUfP8tuopwoNIUCK0+uosi5bzNPygmiml5SjMVjvEC46z0/to1KSm59us36lT2/tfJ0JIVx4yJ++d5VPttaIfM1KO2G726afxC+kM13pdYg8j5NZyv5kxu7J+KUFBrjd7sURf3R9iz+5scP1lT69OHThda8I1528HG3GAp7s0/Lef6WJxkXBj3w6gzYHD48Z7o+JWiHtfsLx4yHTYUrZJILXNVSFYTqaU5XGFadSUJXG0axsje9rwjigu9qmJ7tLHZQXaMJWSKsb0+4nzEZzhgcT8vTtdf7eBRg7JzOPm+nYRXOnncGlElHDO+4j3vAW4SvFjV6PyPPQUnJ/PEL/3otLJQKBJ7sEegvMHoW5yEJjYV/8kMhuU9c3X+mSnmYF/+FX3/Jfv3xwIfvyr378Hv/Hn3/8zhUa4IoNISWFMWRVxSjLmBYFpTGYl9wYemHIdqdD/JLPVZmHZMVvOU+hYeyYovq26Z+eNgGUkkSxz5rq0G6HyObP3hkjCykFrXaI72v392/Z6StSAWvBAC0VgQyYVjPG5ZSe9yxTgZqqLplUJ3w2/msezL+k460wCLbYCm+yGmyTqO5LqZGO6lGxl93lm+kvOcx3GRZ7FHWBsWUT/CnQwsOTAX1/g9Vgm/eSH3MlvuXoVBfsiHceCBS+WiXU238oNC4Nzg62MCPMMxpqSgYk+gpt/zqeePPvwJOtRmfjrnNHWX1duMl0ag44zH7DpLyDqU/Doj2ZEKoBg/AHbCf/ilAPmqn1+a95icaXbVreDuvRzwDYm8+ozJN2v1l1xFH6azzhNCA8xVKY5i7M2NqaQRLRb8XEvsf9oxEns5S8rKjrN8uzuhyDbMHyJmltjTzzZyEF/bUOvdX2ucYxi5twJwr4aGsVa2uS0AX4JYH/0oV/O3JuUZ9e2WBeFHy9d8ydwxPSwvHGz6ZuSuksEENP0woDrq/0+JMbO3x6ZZ3Vdkz4yp7rADWVnZBX+5hLKDQc587DUz0CvflEKulFo7A5mZkzN1Pm1YRARbR0F4Ka7mp7KQbvDFq0+wlFVlKbGi/QCCmI2yG99Q7zSUpVVkStCK0laTsniHyiVoAfeAy2erR6CbqxPTWlQXmONhUlAXE7ch3MtCSIXhz29jZxUhwwLA6bLpyi56/S8foX+h6mnpObA6r6STHZRUCgUDJEyzZadpqwx+9eX3lVkZuKUZ4zyhcJsAu6HazFLVYi9x0pKemFEVJI9mdT5mVJVpbf2eYCs6LgOEtJq3LpdmZxuo5Qe3SCgG4QLu8d4zxjnOdkzT4tuKRaKgKlWIli2oF7YJTGMMpzZmVBVlVU1lDTiISlpOUHDKKIQL34trhICteyTag3MfWMwhy+xhF/Pkydklf7lGb0ygVlVRl2j8Z8dm/vxT/YZPhZW1NZi7W2CQ1zGojQ14S+ZjhNXzod+L6QliWTouDRZMLedMowTZkWBcbaZ1Jqz+JGv08/il4a2leaPdLil0gRI19Ck7F2irEjJwyvv6vFs9aS5yVFXnFydEp7bHVCBoMWQeARvICmeZkIpI/wBL70CKRPIJ0wPFbfndTW1KRmxrA84FF2mzvz39HWfaZmRCAjEt0hPMcCPDVTxuURD9OvuT37LcNij2k1PONOeAqBYFIdM6mO8WWEL0N6/hqJ7l7I5z8/BFL4eKpPoNdR4vIn2f8Y4Sh9TsBdP+NaEsgm5yI5lyb1+XDPEiUDFBejr3TakhHz0gm2s+qY+sxEJlB9usEtev77dPwbCKFeWXMkhESJgFAN6AUfkpsRJ/lnZOb4CR1wbkcMi69p+9ebpHSn+VusrY2x5JWTI9i6ph269Pn7xyNGaUZRvbmm+FIKjeHhlAff7LG21WPrxipaK5Q8PREWOoBXQeR5bHbb1DVo5RYw6jyFCi7h+ZOtNTY6CV89PuTr/SPuH494NJwwL0qKyiCFIPQ0vThks9vm5vqAG6t9rg96rLTi19JmLFDZGbk5xJwzWOZVIEWIp7p4souS8RuLd1+EWTXhcXaP+/OvuTv7gvXwCh+0fsTA26S3vkrSidi8vkaYBIRxQBB6lFf6REmI0pK1nRU6Ky1M6UZzynO5JKYyrsPXajp97Qhz1p2qdgWqlAKlFVK7n+2ttZ3e4x3ptN6efc7fn/wFSigCGfHT/j+/hEIjozCHVHZ64Ys/KXw82cdT3eY8evZxnRYFe/Mpvz3Y59f7e81rBZW1KCH4b2+8xz+9ctVZR7/idX4wn/E3jx7ycDJmkufkxlBZy0oUsdVq8+naOj9a31hSMh6Mx/zucJ/HsykH8xl1DVJAyw9YjxP+9MoOHwWOTjgvS748PuSbk2P2ZlMmeU5Nja80bd/n1mCVP72yQxCd77aoZYtQb5BVj1/pM54Hxmbk5rChW75aoSEbE4f+S+iQCypPXjrXkcwYirIi8D1C39k6bw3abPTfTVc3W9fsz2Z8c3zMf75/n189fkxRVe48PMfI/+c7O3yyvs5K/GKHPmOOycsv8fV76JdQU2tRIVhcO2ds3Y0lmxc8enjCF5895PhwSlmYZTH04Sfb/Mk/fZ/W95Aps9BBeNJDidOsqED5KKFQz1jE2doyKY85zHdJqymVddMNrwiYmzGlzRvt0osxLA/5evL33J9/znH+iNzOn1lkgCtu5tUEUxt88WtKm/Nh+4/feqHhaC8hnuzhy8GlPnP/gOdfwwtHqqpO0e9YiGFhZ4zLO0zLh1R29h3aV6w3WI9+Rsu7+sb6Oy1DEr1Ny7uC35iHmDpbNqhKO2VWPlzSqzT2iVwsrVw6/cF4xsF4Ri+JCD3P6ZWzgtK8eUPzUgqN+SRl/8Exk+Gco70RSTsiboUknZC4HeH5Cu292ltrJdHq1e3jRGNtuNKK6cUhgdb0k5gr/S574ynzoqRsCo3AU3SjiPVOwrWVPhvdFt0wIHjFfT2LmnrZ8byMQsMlFa87ez38Z1b2dV2T25TcphTGPQDauk+sX03EVtmCaTXiMH/E/fnX1NRshNfo+SsEoU8Uh7Aw4sHRnRYtZiHcRCNqNR2Dp20MzvxcEPnLbI7Fzzy9aPCDmrgVvlNmPOPymAfpNyihiVXCB9UPX/6iV4SxKYU5amh4F19o+KqPJ7tIvut4syhsHs+m/OLxLkfpHCUEWio8JZtCQ+JLtZTgvfo+uOlCqDWmWTBW1jLKcw7mc9pBwA9W16mbe/Pj2ZRf7rmFvhCOdx8oTaRdJos6Q9NKq5LbwxO+PHLTByUFvtJE2iPUGl/JV+oqadlqUsIv/iFn6sX3PGmcxs4PXytubg3IXpJTVOPyaIqyYp6XjOcZw2nmkmGLinYccHNrhdVu8sZiwMuAqWvuj0b88vFjHo5G5FVFOwhIPO9cjajVOMY/R5NCCI0UIVqt4OnrL/xZaYeU5gF1XSLO6JsWYvD5PCfPnOB7Pi8wlcsdms9yrLVv7Ff/JlBCPnG9KBTqOdQni2FWjRgW+01xYCltQWULlPDwZfRCfUZlS8q64KR4zIP0Sw7yB6RmisUikEQqIVItpFAIJJmZkpkZVV2RVhMOckcLXAt2WAm28ESAlm9jwS/c1Fe10TJByT9MMy4LAlDCbyhN3/1uTZ0zLR8wLm4jfad5OM3P+H5R2RnT8gHz6nFD+zrNDxNIQr1CL7hFpFebv3v9610IjaeSxhK3hycTrDkNAbR1SVFPyM2Iwo7QMkSfoZr5WtEKfALttGLjNF8+v/tJRPiMnLpXxaUUGkVeMRnOuf3ZLsf7Y1a3eqxf6XP9wy2ufrBBuxe/cqFxEZBCsNZO6EQB7631yZvk5iV1SrikaF9rQk8TaP1amoynUdmm0LAXX2i4wKBtfNl77slQYxmXJxwVjzku9iltzq32T1650DgXnt6F552f5/m5l53b797a59JxdqJxGYWGpwZ4svfEIuksLHB7eML/dvsbfry+yf/0wYe0PJ/Y85ZOZf0wXFrkvSpW44Q/3d6hMAYLy+vzP92/y7/9+kve7w+e4N7vz2Z8fnTIz7d3+OOtbQZhRDcMlwVLNzhdCGTGcG884jCd86fbO9warDCIIiLtDCViz6Ptn7+Z4WyANy6Fo23rbFlovOr3HAU+f/LhVT65tvHCnzsNxHLubYfjGQ8ORvz620f87VcPCDzN1bUuK+343Sw0rOXbkxP+bneXjSThz2/c4JP1da51u0678ZJ9bvk+g5dMMwC0XCX0f0Do/5jI/+kLf7ayh1TVLpU9XmZogJtopGmBlIIrVwe0OxGj4XxpbdvpRo3b35txod8W6rpmbiaMykMK2wScCoknA7reCl1/DU8+n4ZS2IxJdcxR/oj97D7j8hiLQSKRQjPwN9mK3seXAUp47GV32MvukZopuZ0zrYZIoTguHjEqduh4K2+l0HB0nVYzyfj9Chz9/YPAky1CtYJ+Bj2ttDOOst8ikHiihSfbaBm+Ew59lZ0zKx+Smn3sGW2GQKGETyC7JHoLX11UqKpoaFQrBKpHaadn3teFFJZ2xrzaR4sYrSMW7lOh59a5K+2ctCiZpDlHkzndOOTKoEvvAkxuLmW1n3RCNq+tIJVESEEQehRZyf5DF1AUt0JanYj1K336628vvVYIQeR7b0SDehW41MkcY2eUdtIEcF0stIgJ1SZadnjeytvWNZNqyF72gJPiAFNXXI1vXfi+nKfqPW9l/LKfexepHG8D1qaN1d/0uRSD14UUHp7somXnidHqAlXtHN8mRc7+bIavFO/3BySeR6Tf7Jpa+PEXpmJcFORV041vvuZxkXM4nzEp8iZx3n32fhhyvdej5fvklWFc5Ji6pheGJJ6HPCOo9aViI0kojEFLxbwsEUJQ+e7ntVSvtMjTIsZXK42W5WJh65LKTqjqGabOUHX8XA/07+xXE8T5qjbLa72EQTsmzUvu7p8ggKPxnM1BfuHn2kXA1jUnacrj6ZSPV1f56dYWH66ucqXTcR25C7pHaLVB6P8UX99Ev8QBTAhF4H+EtkOU7C3/XkpB4Gta7Ygo8p2trZLM5zlVYQiCJ8/V7xOlrVxQX5NG7EsP/yk2QY0lMzOm1ZDS5kgkgYyIdYdItQlkhHzBgi+zM/az+xwVu8yrMVXzbGx5fQb+JleiD9iJbjnLXKGJVYtYddjNvmE/u09Vl6RmyrA44Lh4TKBiYt6G7a1Eixa+GjTajHfjO/uHCIHEVx1ivUGgeigRYetiqT+wNmdePeI412gZk9shkVojUP0mRyNECu+tTjgW98mqTkmrfbLq5AkK4WJC44qi5EJCHhfTENlMNrSMHbPlqVu2a1KOqPT8CUKJaijOvTjC2JrRPCMtStY6CavthNj337j5cSmFxvqVAZ1eQp45cfDR3oijx0P2Hhzz9W8eQA1REvDP/scfvdVC422jrksqO6Oq5w1t6uITVpWMCfQ6nuzwPE59jW0oT7vMzRQtvGUY0h/w+wVT5xTm+FKoU0J4eLKDlq1nUvAqY5iXTmgbau0mAJ4L+HlT1DgqzN5syl89fMDBfEZlrRs0S8GXR4dMi4KqyWpZ3Cg/Wl0j1B53Ryf83eOHpJXrEP9wbYOPV1fZ6XSXRVAnCPjT7atsJEPuDk/45uSYrCpZiWJ+sLbOe/2Be0DJ830eKSM82b8UMWiNcfxjO2+C+xLUG1oNvwxJGLCzpjgYDrjzeIXxPOPvvnpIEvp8fG2d18gqvXTkVUVWlqwlCR+trdELwwtf+mm1hZI9xDm+ZyliQv8n1HWJlqf6LM/XdPsJUeyT5yUI4aYZtmZmsqVu8V3on+SmYFxNm9A8Q8/rfLfQqGsKmzKvJlR1iRSKWHVo6wG+DF9YZADMqhEP0684zHefcONZC67wSedPWQ+vsxbsLLfT99fZjG5iTioO811sDaauGJWHHOYP6fsvnt5dFIQQaNnCU3+YaFw+BIHqUnuGSK8RqC6FGVPVc8DR93IzwtQFmTniKP8NPf8Duv4H9IJbxHodT7S+hwlHjbEZqTmisCPqM2stJQIC1XPFwAXvl5uWhM8NeLV1SWmnGHuWynWKXhySBB5Vv93Y2yo8pVAX0AC5lCdXGPmEkY+1NbV1Y/nZOKUsDMPDCVVpCCOfdHbRGQDvFlzc/bjx2L/4NHBwYnBfraBk8h2e34IaUdeWWTXiqNinxpKozjvZofwDno/F97XI0TD1aULqRUHiNY5TyTMnGgsXKRCY2mKspaotqj4VfT+9R+e9RRXGMM4z9mYzdqcTSmPZbLUae8yaB2PfWYY2/uQLjslKGOENHB9fCsm0yJmVJY+mE6ZlTqA0a7Hr7AdKsdVqO5E6EHsek6KgMIbPDg8orGElioi983WZlAjQsou8FNcZ9zltnVHZMcZ2USrmMjuoCx1ctxWx3m8xnmc8OBhyPJm/1MHp+4KvFLHvo6RcahwuetopZQic9zvWaDmgpkaglqGitjG3UFqirSaOfUw/wdqaNG0m3a9hknKRMLXF1IZRNeFRuk+N020E0qfHkw3BmrpxIZxhbIUUiki1aOkOnvCf+zlsk/g9q0Yc5g8Zl0eY2qCFjy9DBv4WV6IP6HouK2MRHuvJAF+G9P112rpPapwofFaNnqBvXT4kUsZo2XmiG73QFJ4y8fnev8/fewiQ+PiqTSd4n9Vqj2H+FdPywTIktaaitFMqm1LaGZWdk5ljZtUjYr1BqAYEqosnO/iy1eg9NFxS/kpdWzeNrlMqO8PY7Im1lqWksnNGxVfcn/7vF2okkFb7jIpvnOvUMxrJtjZUNnuCyrWAEMIVFpfUTXorQonGDdJdfM064R8D3CJhhLWXV1ApEeKrPvoFPHGLZVpNOCn2iVRCcmG8wD/g7aLG1gVVPbnggDgHITRatlCL0etT8KREaA8lBVlVMa9K5mWJRLyxlimrSh7PpjyeTZkWBVfaHf7VtRt0goC8MkyLkv/y8P53XucC1wStdTfBSMuKw3TOv/3qc/5ub5ebvQGfrrkgJyUliefht9usxwm5MaRVya/39/i3X33OuMj58fomq/H5KEdShHiSMx2ki7+xuRT4EX69einbfxYCT9FrRUgpOZ6kTNPincyqkULQDgJWoojCGPZmM0Ktl3bG3w8EoBHNzK2ua0xlqSpDWRqsdVPtIPTQnqKqDMdH03fikWhqQ24LjvIhd+YP8YVHoiP6z8jRqKmpbEFhM0xd4QmfUMXEqv1CrYSpDblJmZZDTop9ptUQiyFSLXreKgN/k4G/RaCepCNqoQlkTEevMAg2Ocot02rI3EyZVkOqF6aXXyQESoTN1PfJz2ltfVro/qHIuDBIETAIPkGiXSidOQSbnUkJr6mpKMyIkU2ZFPeayUGfxNuk7V2j49+k7V2n7V9DEyMviU7lJtEplU0xtviO21Rl55i6YHf2Fxykv+AiG0eWCmPTJu38u9dDTYWp82af3u4d55LsbScc74+ZnMyYjOZks4J0nhO3Qz748VW0VsTtkNWt3mW8/TsDt0iYYrmsm6BoFjs9lHDdznk1YVaNmTVZF2VdkFYzHqbfMm/G4QLJl5NfMi5PnrnVtu6yHd2krXvuXV5wwxTAvJqynz1s0l3HmLrC1FVjjagJVESoYjp6QEt3kEI990I3dXWa11FNyOyc3GRUdUlVV0sHHiU0WnpEKiFWLVq66zI9BN+Z7NjaMK3GTMoho/KIqi7Ziq7T1r1lJsisGpPblKrpBCihm4dnRKK7dLwBvvSRqAt5gBQ2Z1QcMa5OmFYjoCZRXTpen663QvCUd31dG+q6xNa588K+BBrestAQz59oaCnZSFr8ZH0Ta2v+8v49Qk8TKr3MpLjW7bLV6iCB0lp2J2Puj0d8dXzE7nRMXhmOs5RQe4zznI2kBcJ1p7WU5KbicD7j65MjOn5ADczKgsTzCbXm7M353njItycnS56p05AUlNbSDUJ8pZaTvVlR8M3JMYeNW1aNm6Tsz6b4StHy/XNZZi+PBwopPKQIz/CHL5aS6LQa06awfDsPB2trKmMwxmJqezpFescghWCz1eL9wYDj+Zy/vHuXg9VVrna7BErhK/VCUXikNZ0wfGmo3Kvg9L3c71lWcrg/YTbJsM9IKz8+mjI6mdHphNT2+z3GdWO8YDl1vhLI56rTDYaqLp1TVCME92WEfEGAXmFTTso9huUBmZkvKbyJ6rAR3qDvr+PL8Ama4CK3RgpFqBLause4PMLWlsJmpGZG9Ywu7WXAWQEHKBEjzyyfaltTlRV5WpDNcoQUtHoJnq+wxhUfynMasLquMaWhLExj2S7dtVZZPF/jBbphgtglK0R5Cv0uchcvEUvtAYpIrUJQk5shSgSNo9MepZ0sw/xqjEvFbtw9SzultBNyM2Re7THWd4jzTUI9IFB9QjUgVI76Kl8whXsV1LXBWLfQr5/BYqmx1HVBURcUdvTG7/dq+1Y3ORqXw655ES6l0DjYHfLbv/mG+1/tcf+bfQbrHVY2u1z/cJOrtzbp9BM6vZggenMhzLsMWxfNIuEyCg1381UiRKseSkaAYFydsJve4XF6j8fZPaZmzKyaNG4dKYXNmVVjjot99HPGdtfiD/jz9f8zLd19oe2aaP6blEOOiwMeZ/d4lN4hNTNyk+KrkFBG9P11VoNNbiafEMgQT/rPLTQqWzGrxhzmj3mc3eMo32NUHjIzE3KTLh9MgYyIVIu1cJvN8Co78XskuvPM/TW14Sh/zL3513w9/TWZmfFnq/8T1+Jb7GUPeJzdYze9w0lxQGbnzfZDWrpH319jJ36fm8knSN1zXf0L6ELkJuVB+g23Z5/zcP4tFsOV6D1uJB9zqx0+o9CoMHWKJW+KjItflDh/+AQlY3jG97MQ2N7o9vjvbrzHZ0cH/C9ff4EUEi0Ftnb0m//pvVsMwhglBNOy4Bd7j/i7x7suUC3LMLXTeOzPZuyurPLzKztst9r0w4iO71Maw9cnxxzMZySeT+R5HM5ndIOAViNMW3z63+zv82+//gIlBUq4fRBAP4z4weo6g9B1RmvgJEv5i/t3+dXBY5SQCOEExZH2WE9a3Oz1X1HULhBCIWXgnKdsfeHap7ouMXaKrfPvOEJfFkpjmaZOkK+kdJkU76DoVQrB1W6XYZbxV/fv85/v3+f+eMz7gwH9MKQdBOhm/5+F1TjmA60vtNB4Gtm84P6dQw72x0j53aJndDJn79GIXj/BmHejmPOlR0sneEITqQD9TJvauqGJVA1NTKCFjyeDF3aLMzPnIH/AsNxfCsABEt1jO3qfnrf+XPGuQBDIkFg5elZNTWnz5rnwthZPrtBwgtsz9sW2pshLJiczDh4eI5XkyvuSuB1S5pXLh1IBSIE1ljwtmY9TlKcIYp8iK8nmBa1uhBdojLFUZUVVGKrKECXBP7pC4xQCLRMS4bOdhHT8G+zN/4rD7FdMynsY82zGSFWnmKogM8eMim9QIkDJkJbeoePfoB98wiD4hFAP8C9AlA2LbI+yaQa+G9fzKWzTsLz4JuXLcDnUqbqmtrC61aO70qK70qK32mbj6oD1KwOiVkAUX8x4uzKW0rjAqUmWk5YVeVlRGUtlF72Zmn4c0U8i2mFAErydAsfWRbNIuPhui+um+kgZuGq8+SoFEiUUvgpJdAclNb4MsLkhM3O0UPgyoO31idWz7W27/gqefPkxmpsJe9l9TG2Ymym5SV0AmgzRwsPWhsymHOa7TKqh80A3M7ajm6wEzxbv5TZlL3vgiqXsHrNqsrQ9DFXceP/XzZRiiEkrJuWQwuZQQ9dfpesNnthmTU1VV+Q2ZVaNGZVH3J1/SWbm7Oe7jMojcjNHSU0kEmxtMXXFuDohNVMKm1HYjCvRe2xHN/DfQARY2oJhccjj7D53Zp+zl91b5pqsBdsM/HW8Z2y/psLYOdYWXFZ32S2aI5QInilUWyySBlHErZVVPKVo+25fZTMhUEKy1Wq7gkwIQqW52ulirGXac4nctq7xpKQdBGwkLfphROR5KCG52unxZzvXmBQ5WrqudKAUWVUxWy/4dHUdT55Ola50OvyT7StIRLMPNVJIBlHEWpyw2tiXCiD2fG4NVgj0IpTM0Z0i7TGIYq60O0QvSYl++njUtUsJViLGinzZTbso2MZQwjUrzve9W1uTlRXlORNd66bTnpUV86zkqwcHfPXwkPEsp5uEJKH3Tprr2Lrm8XTK5wcHPJpMGKYpt4+PmeY5ie8Taf3C4L6PV1fZardpXSLVSnuKbj9GSoHn6+84S3W6EXHis7bRQevv15bT2dNq+n6nyTCQeMIVHc+FEIgzU5oXhe2Bc5s6yB9yUuxT1SUCiRSKlu6yFuzQ8novLGqV8Jpixmm3LAbD5Ux4nwd3vUdPFBpCCjxPoz2FNZb5JOPx3QP8wMPaJvNpvYOpLJPhjPk4ZT7J6AxarF7pk81zTvbHSCVo9xNO9sccPjx221aC1a0+2lNIJd+ZgNq3BTdZc8nZoRogUNioItADpsUDZtUjsuqQ3JxQ2PFywrGgVDmGBZTMkVYtRdG5OWFS3iPRm8TeJoneJNIbKOG9tnbCrU0Kt+Z7F/mm31PxcymFhvY1cTvk5ifb7Ly/jh96+IFGKZfqfJHcxUWR8Wg44f7xiKPpnOPZnKysyEq3qKmBjzZXubWxwtVB760WGqeLhIuFEBolIzf245RO4knfib0DiJRbNJc254v6F4zLY3wZ0PUGXEs+ZC248sxtd70B8XOmA2cxKYfcm3/d+JqP6PlrrDb8Wl8GjIojRuURo/KYvfwh02rEsDwgVPFzC43MzHmU3uXu7EsOi0dIJH1/jZbXJVIJQkjq2jIsjzgsHnFUPGY3u01hM2xteC/59DuFxtPIbcrd2ZfsZw8YFkfUWFaCLVb9VQIZNsWA2/eTap9hecRxsUdlS9aCLfwX+MO/DIXN2c3u8M30N9yefU5mZmyFN7iZfMyH7Z+wGmw9013I1lXjXvZsx4iLwMK1Qj6n0FigEwQkns92q83Pt3cWL14GLPpKuWIA9/9/tLHFD9c2lha2ixdI4aYQWp0Ghb3X67PT7rhsm0ZR6egG7ibuNZSYBX60tsFHg9XTfWiwEId7Zx7K/TDkX127QdXw5Bc/L3D7oaR8La2JxEPJqMm8uFg48eD0zMPzHK+pa6ZpzjQ9333H5WhYTiYpeycTfn37Mb+78xhPazYGLVpR8E5ONIy1fHV4yF/cvUtpDKaueTSZsD+bLadvL9rr0hh+vrNzqfsYRj4711awxjpL26eKifk05+R4Rpz4eP7bz5Y6Cy1cwKUf9Fnxe82xE8+ZUAiEkEgkprmqHenKvrCTm5kZB9mDptD4/7P3p02SJVmaHvao6t1tN9/dY8uI3LOquqqrexrTsxIEKYBQKBDhjwS/QYQCEOSAIGRAzPT09FZdXVW5VmbsvrvbbndVVX7QaxYekbG4e7hnRvXM2xKdlZHuZtfMrqnqOeddqvo7GtDwOqwEWzTU6wsNKRRK+Mv1yViNsfq1z3mVcNQpHyniZXMPavviOCBqhChPkc1yJoMZ4IrN3nqbIPTJ5jm794+YDGfkacH23XVWtrrk84LBwYi4GYGF46enfPHX3xHEPnEjxPc9Wt0GXiD4T6zOOAOXFeEcqLr0w0+Y60MmxQOOs98wzL/ClCVal7zc4dNgrHFFSTVgVHyHFF6t4bjLRvKnrMsmyMZbiLRNHWtQvYMTjR8P17KydVaa3Pl4izAKMNowGcwoC00Y+4RxQNIMiS450TDGoq3hZDrnyemIveGEvdHEFRjTOdO8YFYUlJWhqkO/BBD7HivNhLXWq7szWVlyMJ4xmLnHqbRBSUG/kXBrpUsrutg1G1ui7RR7LRMNz/FERYBb9N3iHMmEbrBGw7QpfEezyU3G0/S+G2/LgNhrsh7uvDJLI1IxkYzf2MXMTca4PKUbrLIT32Ul3KQfbODLAE/4pHrKtBzxYPYlT9P75DrlON9nWBwzLUeEKv7e5CRSMZvxLXwZcEPfRQmPptchUgmBXByALdNqzLg65dHsG+7PvmBcDngy/46VcPON7522FZNyAFg2ohv0gnVWwg2aXgdfBGhbMddTDuqpw6ya1MXGIaPyFCk8QhmdQ1D27A2sTMmgPOIwe8r96RccZE/wRUAvWuNe8zNuJu/T9nuvnCQ5Cs0cY/Nr6pTUNCAR1Ivs6zZ7iVScy9ZWAKHncd5vjlTqQna5oeeCNc8DJQTxNezSQvh1gXb1y6m1JdrO3Od+zo0rzUt+9funfPHo8JzP4SYa88ylg++fTshLzfZqh5/f2+bmevdKLA6vGp5S/HRz81nheEHc6/dpX7NwXClBFLtDi+8pEAJjDFVlKIsKIQSdbkIYfn/a8UNDW01pKyqjqawmUgGRDF/aGHQTDw9fhtjaLrMyBaXJMS+hZlSmINUzxuUJ02ro0sStJvba9Px1Ov4qvoxQr0kThzrN/pqoo+dC3V2XwnuuIDLGUqQF0+GcyWDGbDxn0SQpi5JGFtcaHdCVRlcGow1VUZHNc6ajOcPjCb2NDsYYhBR4gSIIPcLId3k/ZYX0JLxGA/NjwRWauub/X9fE3U2hXbiycqnxQqKEjycTWv4t5tUBaXVEpk/J9SmZPq0F2M/Wz0VBjC3RFmbVHtoWGFswr/bpBh/QCd4nUC38C4exugL8VfQ/XzYIZIeGv0XD33mpFvK60PC2aAd3iL11zkYhZDULqNSastLMioJ5XlJUGiEEt1Y6rLXfLtz5WgqN7mqTTr/B4MiJwofHE0YnUzorLbqrTVa3upcvNKyhqDRPTkf8u28e8JsnB3y9f8QsL52NIAubuWc3uxSCzU6T91Z7ZOWrOdRpUfHt4Qlf7x+xN5oyzwtCz+OjzVW6SXThQsMuJhrXIAaX9URjUWgsEKsGkUpYvAPGajI9o+m5QD9P+iSqyWq4xY3k3ksfu5bevfEaSpMzsRXvNT/hT/r/B3rBGi1v0ZFyLe7C5CihmOspJ8UBw+KYYXnMuBrSrbtZZ5GoFncaH3EzeR+FE5Mr6Z3xZXevtbIlpSnwRcBe9oi5nvI0/Y67zU/eeN3OFnFCrJq81/yUD1o/o+11CWudy2I7ezT/hsJkPJ7/npPi0F17cUIkE4LgTffC85tzZUv208d8O/0t380+Z1qNuBE77ceHrT9iPdp5rfe8GwHPa8HxNRUatbhZ8Lzg+j/j1XCbnocU0bVsGua5QuN8mOcF//GLR/y//urLc/7GwgbbFR2epwg9xc5Khz/96Ba31t7NQsOXkn926xZ/dsmphJumXe9GL4QgCJ7n8lelJktLZrMc31e0WhH+O1BoVFaTVhmpycl0TtdvEb1ynRN4NY1J28pNzm1BYXLMS/QSpSkYlycMy2Pmery0o01Ui/XoFh1/Fe8chbrThZydYCz2mh8OAumoPGcOk6bSzCYZo5Mpw+MJ0+GcpBUjlUQXFVXpDm1SCYRy/5RKYipDOsuZDueMjiek0wytnSi82UmIGyFxM0LVDmX+j2wY8GpYbD1d+qGKQIFHINv4skXTv4UxOaWZMa12GeZfMyy+Zph/xbw6xOjypeJswFGu9JBZ+ZTD9G+42fyvUCKiyY2LFxpCIIRX3x/fvy992aYV3GYz+TO2kj+/ksC+80LWDTElgufOd1lRMpxnzPOCaV6wP5pwNJ4xTnM8KQk+uftuFhrD4ynHe0MOn5xy+GSwdNoYnUx5/HvJxo0e6zf6rG136a2dz2pVm7rAGIz43dNDvtg95Mu9I54OxkzSnEK/uqtla9Hnm7zghXBdWm0su4MxB+MpnnSi0Q8310iCgGZ0/oAyQ4mx2Us9jd8WQngoEbvQoDM3tKML1JkG1iljpVDLv1sUAa4T8HabbKQatL0uK8EGvWDVWRsK/7kvmAXafp/VcJNZNWFaDcl0SlpN6+LneUihCGXkHIyQSCHdwv7Cl1ZaiSc8EtWi5XWYViNSPaM0z0RYrxrBK+HR9nqshlv0g3XaXo9QRt+zZUxUk36wwbA4YVAcU9qCuZ6Qm2z53r4MUkjX8RICbSsGxRGH2S73Z5+zlz0kkCE34nvca37GreQDWn7vjWFsboO9elej+oprOoD/ygXyP+NVEAjcAi6uJVBPOwcTe35LQk8ptvotPrq5dq6fF8LR2DwlCX2PbjNmtd3go5tr7Ky0HXXqHbwnhBD4F5yA/dBYvG9auwnGcDBnf3dAnpUoT4G1VJVhdb3NzdsrBKFf/94Pf62OxuQzrmYMihGZzplWKd2gRcd/PnVbCEEoExqqQ2VKSpMzLYeMimNyk2GsC9tcrJGpnrKXfcdh9phCP7PmTlSbzegO3WC97lC//oVrW9VTE728ZnVmf/sh4DrWirMbgFSSuBGystlFCMjTgiD0EVJgKkPSiWm0Y/RCN1VvH9KTBKHPxs0VoiRg7UYfpSS99Y5z+Qs8gtAjaoSEcfA96t27AlvrZSw/HGXo7IQDFEK6z6WJQImAxNugF3xEqo/JqmNSfURaHZHrAbkenSk8XGNR2wxjKk7zz7EYNuM/w5MxnohQ56RLO43s61gBbvYjRUCouj9ooeF0Lot79xmMtVTaMMlyTqZz0twxcEJPEXquAaK1m7LJSy5M11JonB6M+OJv77P74Ii9hyf01tr01lqc7I8YnU7ZvLnCzt01lHfv3IVGZQxpWfLt4Sn/z19/yVd7x4zSlPI1BcZFIYUg8j2kkBxNZvz+4ARwPPNHN4esthIi3zv3xuaszrJrsRNzfPq4FoL/OIeARDVZj26wEmzS8novdbGSCJo1//Ywe8q4GlCYlFRPqcxLgmNq95I3wfHqPSIV0/Q6zunKpOeyOfSETzdYZSPaoeevvlIUH8qIfrDOib+PEh6VKUmrad2Ne/Vi6j4b96XWtuIwe8q309/y7fR3DMtjbiUfLDUZm9GtN14vuLG0ueCB87xwgsygXvTezY3sXYYUPkpeT6FhrcFa140776ceeIo7m32y8nzrjhQCJQVJGNBuhOystLm90aeZhES+906kVf+hQ2tDOi843B/yu394TJFXrKy2yLOSw/0R73+8xfpmZ1lo/BhQQhIoH201w3LMqJzgyQF32Pl+oVGbczS8DnM9JtMzJtWAQXlIbtKlE9UCcz1hN/2Og+zhc+F6Da/NZvQeXX/9HFRUS2VLCpPXNu0CWU+9f7j0ZwGoenr5fKGRtCKSVsT6jf7SIc7yLMVPSMFsnNZnTVdpeJ4ijH16G23ufLqDqKda/c0O/Y3OH9BguZ5ooH80EbQ75EfEXkTsrUHoriPXA2blLoPiS46z3zIqvqXUU/QLFLyF9exp9gXT4jGh7NAO3kMogTon+XdRaLw4NXj2HBZjq/rn4jqD6ceFsdaZtWQFp9P0OS1kEvh4UqKNcTrKd6nQmAznPPnukN5qiw//6DZJKyJOQubTjNk4Zf/RCXsPjnnvk+03Ptbinj2ezPnVw13+5sETng7GzPICbZxgVAlJMwroJBGx7xN5HgeTKXvDi4kzA0+x3mqy1W095z4zL0p2B2N2um1Wmw3i4Hybga2TK68n80DVot0fT0AYyJCW1yVSCeKMTuQ5iAWXN1g6hVTW+a+/6n2xGDI9Z1y6jIm5npDrrN5gKrTV2FrYdZA/5jjfY66nS0mitfa5btqLkEK64EKvgydf7Z8thMSTAUq6KY3BUNoS/Qahl6q7bKPyhIe1huRJ+i2e9LmZvM/7zZ9yu/Gho5md+4vrFvLrcVdZuHrI5dzrqqFrasF4MOdwd+Bscj/cpLfWevMvv+s444py1bC1uNBasxTbvwmBr7i3vUKnEb/5h3F7h6yTYUNf0UpCmnFA4Llp7rs4zTgLWxt+lFpzPJ8zSFPmZUmhXaHlSUns+3TCkJUkIfH91+ZrXNc1amPxfMXKagupJJtbXSbjlKLQRPFiEvxctvQPCmcc4mhQEunWaZ1TnlnvltkGQtLy+qyG20zKU8acUJiMUXnMd9NfU5g5Da+LRDLTI/bTBxxkD5lVI7St3DTE69ALnDYulNEbvz8WZws+q0aUNgcEvgzq3I0fbqolFjz8M9f73L10dut54Tvb7Cbc+nCLPCsw2tLsJsTNCN/3EC/aH1/BLfDyh7Bn/rzdk7jv3iINO0Ob/Jr2qDfj+99n9++eTEj8DYTwiNQa/fBjZtEe4/I+o+I7Sj2lsvPlb7k8jpxx+YCj7O9ZCX9CcM6QY2dWEOEtNXtnv9OgTUauh1S1jf67sLYmQQBNd87Ny4pWHJIEPvPCNW2bUfBa977z4FpOqdPRnN0Hx9z5eJt/9d/+MUqd4YOlBf/m//6XfPu7p8wn50k3dgKqw/GUv/jmIb96tMvBaEpWVQicb3/oeay2Gtzqd+k1ItpRxG+e7LM/nFyo9+srxXq7wVanRRy4zchYS1qW7A4dleqDzdXzP6A19TTjGgqN2t5WiB+PT+/LkKbfIVTxa25CF7TkSR8pXHiathVVzetdYEGvsxgqUzGtxuxlDznInnCc7zEpB85C12RUplyGAmr0c9kFdrmAuud+GVwAUINGTfV6FaSQeMJfjuaNNVS2fKNnu0QhUQyLY7StuD/7kv3sITfie9yI73G3+Rk34ruvfYwXsRBBXpcHthAShLo2zoauDJNRytMHR/zubx6AgE6/8Y+i0Hg2jr76ruqzZsX5p6Kh73F3a4W7WytXfj3vEp6tGVBpJ2J8PBxyfzjkZD5nWjhtXOh5rMQxNzodPCkd3UqeUX1d4p5/Pnjv++vN9x7TgjEG3/dYWWuSNEJ2bq4yOJkyGs6JogCwGGOfKzh+yIOIsW7tNTiLaGNLClNSvWS9kyhafo+VYJs9dR9nIV4wLo/5bvoPjMsT1qKbSKE4zp5wnD/lOH+6zClyzoPb9IJNGl6H4IXcoLN49jlbCuMKjcoUjrQoQ0IZv5F6eqUQAvddf/Nn8+Ln1+wkNDvJ9VzXi8/9ir+/ujLDPZqbtpdok12rK+Jl4ckYT8bE3jp9PqHQIzJ9yt78LyjNnDl7VNX8zG84i9pJ8RBPJCTeBh3Ot18LFJ6Ma/2sj0Q9lw6ubU6hB3WhYZb39o9ZcCShTxR4zIqCotLs9NqstBJOpnPmeUkjDJ47w18GP66f3jmQlhUnkxkPjgc8HY4ZzjNKY2gEPt1GzL21Ph9vr7HRbtFvxMSBT+ApJlnO3z3c/V4S6+sgap5yHHh04pBWFDIrCvKy4mQ6ZzhPqfT5N3yLwVBd6BrOj0VX5cejuqg6k+N1h/Xzwo0UNcPyhEfzr9lLH3FS7FPoDClk3f1aQwmvHrG7Cvu0OOAw3yWtXCDheeASrt8cLnVZFCZjUlnKNEdlHsPyGGMNqZ4xrUbOihfzWvH392BNTZu6escVUW+c4pyb53/G81iKQ6/lvbO1VeKP6LTzDiOtKsZZxm8ODvjy6Iij2YxRni+tbS2AtTwYDPj86Ii/293lbq/HTzc32Ww2iX0f75KbvLFzrJ1jbIq1FVJESJkgRIx4gWohpMDzFMZYRsOUk6Mpg5M5WVowGadUpUZrTbffpNtLaLViGq3oB6WtucaKc1OqrMZat8a/bJ2SQtL2+6xHN+mmaxzlLQqT1flDp1S2ZFydIhCkesK8mlDWtFaJ+907ySdsRDe/p417GSwWbSrmesKkOqUwbl8IZULstc71GP+pwE1cvO/pSJawZpkQ/Sp3pPPCoCn0hFyP0BfI+vkxoUREqHqsRD/BkzH787/iYPaXaJsviwKLIddDptVTSjO9wKM7Qp8nY2K1SqaOyfRwqa00tqI0cwrj3rNQudDlHxsC6CYxvlI0owBPSdpxRBL4ROdk8LwO11JoCFk7KmhDUYve3HTA/bu1FumJJR/xdciKkieDMQ+OB+yPJoxSNwVpRgm3V7r82b2b/Fefvc9qs0Hoe0uxym+eHCCen1q9EVIIpFJuzJ5EtOOQoqrIK83JbM5gnl7IStEukhivZaIhfnThrhQKX4Z48u2nKhbnWnJaHPL5+G95NPuaVE+JVZON6AYdv08/3KChWoQqwhMBnvS5P/uyFh/q8xcaCDzh4Uv/ygsNay2FzSlMzrg8qWleFikUmU4Zl0PSakppcnwRXKATZ+GCne3zw/GOL9qRN6YOT9QGc9YNpaayLJKQz3ZDFs5G1kBZVOTZmWAjIZBSoDy1PGBZ4/ij1lj3p36cxWO7ACuxfNyXXctzj63ks3Wn7jIb4zrJi+sWYnHd7vHf/B1z1sDXcSq0dYH5Y6S5vuuwwKwo2JtM+ItHj/jfHz50rllS0k8SGkGAtZa8qhikKWlVgbV8sr5OUgf6+UpdKDvFNY001lZoM0DrU4wZY8hQsoOyHZRcQUhnQ7o4yAkh8Hx3X6fznOHpnMO9UW2aJMjzkuFgxvpGB32zj5KSRjPkhyz8F98aY+1SA+ELDyW+nwwvkDS9HgC9YIOW32NcuoDTWTViVo3gJUZpSnh4IqDjr3Gz8RFr4Y1zNaqM1VS2YF5NmJQDDBpPBE4notpX0uy6DrzYZHzVWvKyZuSl93YhkXi8yj3QUZ0WxiJvt3dbW1GYMbkeXEte2HVAyRCFS3dvB3cp9YzT9LdY45o6DpbcjJDlPqWZnXvysBCoeyIm9taYVwcUZrpkXVgcm6MwYzJ96vQcr5nm/RBYUE+T0CfyPSzOfCnwJKGn8N5ymgHXVGg02jFbt1YYHI75X//7v6HZjklaEbNxynQ0Z3w6ZevWKknrzRzieVny+HTIk8GIrKxQNZf49mqXf/3JXX6ys0EnjvCVvLIlWSlJM3QTjeE8pTKaaVYwy0v0hezlTP1lvoZDghD1JvaPQ7xb6IzD/Cm76X2GxREgWI9usBXd4nbjI7r+GrFK8GVQTzVcp+0wf3pJ15Hrs0U0GASwEd2iH6zX/vQZp8URp8UB384+xwI3krv0gvM5Ay39469JaCcuMdGYTzOmo5S9xyccPB24Il9AkVXoStPuN+ivtth5b41219EFrLUUecV4MOPv/8Pv+e7LvWWeQBj5bOz0uPvpNs22WxsGxxN2H54wOJ4wOp1htHv9nX5Cd6XJ5s0VNm70mE1SJsOUvUfHHO0NEbiU8LLQWGsJQp/eSpO7n26zutlx1z/LOdobcrw/4vRwTJYWWAtxEtDqJmze6LPz3uobBbpusqiuabq4oF9eR4H5h43KGL4+PuZXe3sUWvPp2hp3+322W05jFyjlynNjyMqSo/mc+6enaGv5zcEBxlr+7MYNonPmsDhYiuoBRXWfsnpKpQ9r+qrEUiGEh69uEHg3Cby7eMp9v5USRKHP6nobpSRZVr6kCWZJGiHNlksLP08j7ipQK9s4yYc8TvdIqwyJxFMegfDxXzEtEAgCGXMr+RiAB7PfLcXe+gWnRSd+9egFa2zGd7mTfEbHXyU4hzYDYK7HnBYHpHpauxvZ5aS7E7gMjncVZ3odr/khlq6YrvZ8tcbwTXBGKcFzwYZn4eg7I0rVQ6rwrXZBXWdPzKpdqnM2+t4VONOWkFD1aAY7TMvd5yhU1la1AYtrFl9kffdkQsPbZubt1zkdz//3rDrhNPscIkGoevyYTIK80mRlyYOjAY+OR1RGL8+5SeDzkxsb3F7rvdVzXEuh0eok3Li7xv7jU/7+331Nd6VJZ6XJ4GjCZDRj48YKO++t0ey8udDIioqngzG7wwlZWeEpSSPwub3S5b+4d4tb/S6ekpe23XoZlHTi8mboRDBZWTHNXIiJvshEwy4EvNdDnWJ5uDnPa7+uG/lqHrewOcf5PgfZE8blACkkG+EOdxuf8kHrj2j53ed+ftFhiOXb8l2vR8ArkGxEN/ig9TMqUzKphmT6bznIHvN4/g1gaXptml7nDB3s9Y+6WPCu/m4Sy/+7CNJZwcnBmG9+84TP/+6Bs2CMfOaTjCIv6a222brZJ26GNNuxmzgYS5GXDE6mmC92Ub4imxfYWij74U9vsn6jR1zn7JweTfj6N4/Zf3zK6eEEXbvMray3WdvqoDzF6maH+STj5GDEl3//iK9/85goDvB9jywrqEqN73ts3uzTXW3SW2shhGA+zXj87SEPvzlg//EJ82mOsZakEdFbaVIWFSsbbTxPfV+o+eL79yqawluiNl680ETDWktZaYy1KOkmPhcRPxtjMdYsp0RSStRrX/+PA20M3w0G/M3Tp2y1Wny2vs6f37rFp+vrz71eW1ubPx2P+YtHj/ji6Iivjo6QwE/W11lJLrKGWMrqEfP8L6n0AdoMUHIFKRIqc4K1OaXarf++tyw0pJQEoaQfNumvvJ0n/dXDTSbH1ZTH8z186dH2msQqIpIh4UsKjcV7G8iQ7fgeoUrIdUqu5zU9ND2z77nMjVBFrEe3+KD5C7biuzS9Hv65bEMtcz1hUByQm3mtiXIawZbXo+OvEf4IhYZbz559LxcGJG5vcmF9RluqsgIEnr+YptZ+VJal2YKxFlOZ5YTV89Wlk+IXB2glwpdSo7TNycwpkVklVB0uEwC42H+1zZiX+0zLJ88Jqv8Q4MxpFIFqEXvr5Hr43H9fUt9ZZIOcX9Xiy4SGv0NSPWHwEhfNVJ9wmn9OpHp0w/eR1ntrGttlUVQV4zTnu8NTfvVgj7wsySsNFrqNiLV2g1urXeDyU7ZrKTRWt7p89k/usb7T5/DpAKPdptXuNfB8xdpOj7WdHv31Nyv5C605naUMZiml1iRhwI1+l81Oi8T33QZ4xdcvhbO0DZaUL0uhXXLixRrKZ2VXVw1x5s/rf+rszyxHtO8YlVJbTapnpNqNGSPVph9s0g1WX8m/tVhKUzCrJs9ZJv6YEELg4RPIkH6wznZ8B2MN82rCtBqha4rXbnqftt9HCMFauE3jJZkiL3n0a7/+yyDPSqbjlPc/2+HTP75DWVSks5zvvthj99EJtw426K22KIsKow15WuL5ik9+cZvtO6sYbTncHfD1PzxmOk4Zn85QSqErzeHTAYe7Q7orTX76Z/fwfbcp7j485sm3R6xudVnb6pDOC5duPc2ZTTI+/OlNPvjpDYy2TMfps8cezBmdzpBScHo45sl3R5RFxc///IPlFOVwd8iDr/fctONgBECzHSPUq99/9726rhKQCxWBeam5v3/KYDJnvduk305oRAGhf77lfp4XDKYpWVFRlJp+O2a920S9Y4WGtZZhlnE4m/GTjQ1+trnJaqPx0s1QCkE7DPl4bY1ZWfLNyQkn6cWosPWzUpkBZbVLFHxK4N1DihghfIxN0eaUrPiCstrFmD+Ug5crynpBm/ebtwHwhCKUAaEKiF9D7XBTjZCuv8bH7T9lPbrBsDhmVg0pTI7F4suQRDVp+j16/jor4TZNr3tu2qgFCp2T6im+DOmHmwgEiddmLbrJarhNqC6a4Px2WExmS+34YY7y6ew/dalBCILQI53mHD45QQhBd61FGAcuxK/SFHlFEPrEzRBdavK0YDKYMT6dsrrdY3X7cl1kKTwC1SFQbeRLjniFnjApHhLKHg1v65LbikXbglyPGJf3GRf3Kc3sUtf7Y8PYktLM0S9Y4wu8Oj5gYZ98/jfKly06wV1m1R6BbFKaCebM42fVMaf2cxreJt3yA0LVJVCdq3pJF0JeaWZZQTeJ+XRnjXGaM8lyrHWOU3HgOyfPd811qrPSpNNv0lttsbLRYTyYMRnOl3/f32jTXT2f20xZaYbzlNE8o9KGxPfZ6rZYbzUJfQ91AX7teeGsHiX+mUKj1IbKXHw6cb3hNeebZCzt+ISjkyzsYd8Fx4MFrDWUJq+TZQ2+CGj5XRpeB/WSjssiuCnVs1rz8O7wQ5XwCGVE2++xFjoL51TPGFen5Drl4fwrTvIDnsx/7zp9MiGUsaODvbarcX10r0Xv/DLQpabMK9a3e/zizz8AYDpO3YH9m32GJ1Mm4zlV4Qr1SmvCyOe9j7f57Jd3kEryzW+f8PDrfbJ5znScIZUkTws3BR3MuHl3jV/+8w9Imu7Q8+//zW/4/G8fMDgac3Iwdp1CC0VRURaarVsr/PE/+wCpJCcHY/Yfn/L0/hHTScpkOEdK4WhZj46J4oCtWyvs3Flh0W388u8fMjieMjyekjScP/4rPxm7IJ9cFy7mX15UFd/tnvDgYMAnt9bxlCTw1PkLjazk4HTCYJoynufc215hrfPDHuTOAwtMi4JhltGJIu72+7TC8HvT7cX6lgQBtzodnoxGZFXFOM8vUWiAMVO0GeB7t2kn/zXPvpOWsnpKUT2grJ5ieXfWpNdhUch2vBZBw6cymspW+MInkO7PK39XSHwR4omAptdhK36PQXHIpDwl1TPAEqqYptel468RqwZKXEYb5/Io2l4fEbn9LFYt1sIbdP21H9Z1Cjf1K2cZ5dxpx4R01E8hBWVR1brUhHSacfDohMXUqNVrEMYBRVYyG6c02jFREmCMoSorxqdT9h4eESbBWxQaLgwukN06OO55FGbMuLhP4m1S2Q8RVp1b77loqBhbkpsh82qPSfGQafn0yjUazqEyx9iyDh1Wtci91j295V5obIWx2omz9RhtnhcVKeHjCeceddFpgycTmv4OTf8GkbdKbsaUerzU6xZmRGHGjIpvGRa/p+XfRooQKbzaFvfysNYs9cGGsp4AejwL7Hv+fTPGUBpNvxnTigKOJ3MGM9eEiXxvqdt4m3f7Wr6dRlt0pQnjgLWdHp3VFmVeEUQeQeQTxedPQzTWkleaoqow1uIpSSsKiEP/SulSL2JJJRFiOZS4OD1eXLgSPj+scyJ6w/FGIJY2rYEI0aZkrieUpqidj67z8Hp+CCHxZUhQO0FpNLlx2RkvIwuNywGH+VOO8l1yk73RcvbHhi8CtuP3ECgKk/M0/Y7jfB9rqRPVPdp+n/CV3UO5dBK5HoKO4bKOVmEc0O42CGN/eStJKUiaEa1OjDGG2ThDKYnnSVqdBGMsYewjlaMYKCkIIh8pnYlENiuYjOZobej0mzTaMfJMUyFuhPTX2wjhCoakGeEFHkkzpLfaJGqESyG3qLuLQeiDdRMYXWpGpzPGgzlHeyP+w//yW9q9BgKnC5lPM3qrTapSY7R57dtia1rb9ehnZP2Zn3+jK+qJxm/u79GKA1Y7DTqN81NL0qLkaDTj/t4pDw8HKCnqguUy13+98IQgqAOlsrIk8V99KNbGkFYVhdYoKfEv6Q0vZYSU7Vek+kqkiJGyyR+AqeNz8KQiJsJI14SSQtbJ2+e79wSu6Oj4K8SqibYlljpHSQSEKkbWrlYXgUDQCzbwZUhhMsr6QOjJgF6w/gMH9jnoUnO8N2SwW5HNcow2JK0IP3R5KEkrwu5Y0nlOOs2WuRllXrJ+c4UiKxkcjQHorLbwfI+kFbk1UFycxnoWSoTE3jqxt/bSezTXQwb518TeGp3gHom3Sai6nO8c4ATTmR5wlP4tR9mvmJa7zsXqilstFs24eMC0fEKkeoSqR+StEKjWOa/19Sj0mFQfMSkfMSt3Kc3Z3DVJoDok3gb+JejZ7uzokXhrrEZ/BBZOzedoe5Z5YRkV3/Jg8j+xHv8xxhb153aBCIWXwE1oZqT6mHm1X1v0rrspl/z+e9eMQpdmX2qysqLUhlJr5kVZu/ddPhF8gWtZCefTjPHJlGY3obPSfKuOuYtH11TGLB1FIt8nrKcN1wW76PDWIyMhLv5mL52hruMgb23NHXz14XDxvkskkYpJvCalKZhXU8bVgFF5UudELG4D95oVikBFP2iXSAmXbZGoZp3CXTAp3TUmqompCwmDwVjNYf6UB7OvOCkO3WtFvGtssOeghMdKsIkvQkbFMfNqwkH+hL3sId1glVBF+DLAkx7yJd2lZxaq17Ch1qFLZ1NsLwLlSYLQQ6lnJ1EhBb6vCALfTTFKvXSJihuOl+0H3rJ4EFLi1x13YwxlWVHkzqEujHwXaCXOPqciStwmmqeF+5nAI4oDmp2EMDz72M5a1PNdjktVasq8JM9KirxkMpyz+/CE4fF0uQY32zGtboIfKKR8vTDTvXfXlJcjnFUlF+ioVdpwMJhwf/+UD2+sMc8KKn3+aysqzXie8fRkxBePDnhvs/e8k9c7AiEEse/TCkPmZcnuZFJbV0vUWY0GrsgY5zn7kwnDLCNUikYQXIIOJgAfKSK0mVFWTwGvbkhVVOawtqEWGDuh1Iv1yUfJBuKlxcl5cJ003AXqfc5KELWVialAPtOQvWovW7zXnvDxZIernH8JIWj5PVr+2wlSrxJGG6bDOcdPZ0yGM6qiImnFhEmA5yk6qy1a3QZlVlJkBbPRnDIr8QJFb6NDNs8ZHo0JQh9rLNKXSKWgXiuNuTzjQAmfSK0Qeav4sokkwFCyuHdKM6Uyc0b5twz8r5ZFgtN1uLXm2T5jlx1yU4ujCzNhVj7lKPsVR+mvyPTJc2Lpq7LitlYzLZ9wlP2KWK0Se+s0zTaRt+amYiwab97S6v9Zx/7se1af5qxbp93rKJmWjxkVDxgXD0j1MWfXb4ETabeCm/jy4llPi+uJ1Cor0U8ozZRZ9ZRMm+cmP9Nyl3nl1giJRzt4D2Mrl8FRF9Avxhgss8KW+/ZieqGxVlOaqbPmLZ8yLu8TqzWEkEjh48vG984Qke8ReIq0KPHzkrzUlHUzBnBGS+9iofHom33+7v/3JZ/88j3++F9+9FYXKYRLWpZCIkT9Flt7rSn32thldWesRQpB6Cl8dcHiRsilI8nVoxYHn0MkKoSk7fdYj3Y4yvaYVSO+mfyGeTWl7feIVYNFjkVlKxqqxc3k3g+6sPsiZCXcYFge8zj9PdNqzP3Zl2R6zqwa0/BaCCSZmTOtxhznuxxkTyh0RsfvM6smzPT4B7vey0AgiFTCnebHKOlhRoajfJeHs6/qtNwIT/pEMnmJXeNCcHxdE7LLTzSyecHwdFq7NrlUdqMts2nGZDR3Vp2tCGued1V53asIQp92r8FklDI8nTIbp88ddtNZzvH+mFYnobPSIgi95WO/SfjseZIgjOn0GsuC4k//1cesb3VZeGIba2m2YrorTRrNyBUbr8SZQu3KcbbA/GEmj4uUcGstaV5SVNdhQPD2kEKw2Wxyp9fj6XjM//ztt/x8c5MPV1ZoRRFx7SZVaM04z3k8HPLr/X32p1NWk4Tb3e4FHadgcdg3NmOe/yV5+QVSNBEixNgx2oyo9BFgmWV/QV5+A4CnNknCP8FXm5d6rQtTgOssNDKdMS5nVFZjrCE3BZnO2YhWWI/+cYc/XhSinsDGrchpBfPSNUOAqqioCue6JZRjRRhtKYqKfF6QTnPnvnkypdlJqCpNVTpd2/B4wuBozOY0d5SsS7hPCeHhywaxWqHp75DrAak+OnPAdevVuHzAo+n/TCd4n274AYm3QeKtu8A5EbEIiNU2ozJzMn3CvDpiUjxkUj5iUj4i06doWyJrmhFAZWbPhdRdFhZLrk8ZFd8yst8ghCSQHQLVJVYrhF6PUHYJVBtfNvFkgifCOsh4sWbaZWFRmRmFmZHpY9LqkHHxkHF5n1m5x4vfKyEUTX+HfvgZkde/9GsIVIdu8AGFHjOvDhgX95mVuyxs6l3hYxnmX1PoIYm3RcPbJPLWiNXK8nXJms1g7cIcRKNtQWXnVGZOaWYU2tkMF2bs/ugJpZnQDz+jFdzGvMK5UNZ7sSednW07DpFS0Gu45mAzPo9hw+txLYXG6cGIL//uASsbnbcuCKQQeJ7EV5LKuAWwqDSVvno3p6WTgjFM84JZXlAZg5KCOHDpiRcpNJ5x464rMfh8XVSBoOuvshPfpTQFeZZylO8yryZ0ghUS1VxOcIytWAm2WAu3f9hCQwZ0/FXWwm36wQaFyRmWLlm7tCWJatRZFK7QyMyM0uSEKqYj3Sb4LhcaZ11a1sMbSDxOiyNSPWVYnmDnln6wTiAj1sKtmpP67MAshKg7ONdxLy2K1svRz6pSk84LRidTDp8OscYVGbOJGxNHSUCzHZPOiqXLyps2zyB0NKjTI+c0NRnNOXgycPQsYHQypSwqwsinv9aiqgx5WuBsn1//2EopkmZId6XJykYHpSRrW13WtrtLrYcxhjgJaXUSlP+mjo6BazoEiucKzB8Wbppsaqe9d6/UkEKw027zydoaX5+c8PXxMZ4QzMuSbhSRBAFYS641wzTl6XjM18fHSCH4aG2Nu70e8WuoVi+HQIoIJVtoM6LU+yjRRooYbQcYmyJFiBAR2hxj7IKOYbAmu7DBz7N8heueZkBpKmZVSmEKKqvJdEFuCjr+xTu6/9ghpaNH9dcDkla0LCyssehSEzVCvNBDSEGr+2y+E9S0cSkEfujj1eYWWhuqQoNwTRbpSWrzqgu3F5zWTxGpPp3gLrkeuYOofTbVAEtaHZFVJ+R6QK4HNP0bNP0dfNnAk8lyAlCZOYWZMq8OmJW7jIpvmZVP64A+QaBa7ndEjLYlxpZn8ijeBpbSuuct9JDSzFxXXjRI/A1ib91NblSfQHXwZRNfxCgZIoTPIrLT2Aptc8o6JG/hkjWtdpmVT3kxm8oTCaHq0vJv0Q3fr2lll4MvE3yZ0NH3yKpjV4ialNJMaxqVKxzm1T7zap9QPiJUPRr+Fom3RSjb+Kq1LJ6wpp5clLVN8ZTSTJe5HFl1RGHGVCZbvq7YW0fb/JX7+2Kdn+Ul0yxH1xIFT0k86c7eb4trKTQ83yNuRJe2ZzsLJSSJ79IJnVbDME5zpnlxwUyL88HiVPin0zkn0zlFpfGVoh1HtMLwQuJzsZhoXNfhkPOFAUqhWAu3lzSpQEZMqxEzPWGeTt20CIknPUIZ01Bt9A/s26+EIlFN1qMbfNL+E9p+n4PsMamesZs+cHxhJL4MCGTIRniDtXDbFZ4mp7QlR/nuD3rNl4FA4Emftt/lg+ZP8aXPl+NfMa1GfD35NaUpXNq6DPCWiyU84+pfB1F+YZ1bXap4V74iCD0efXuA/p8MWVqQpyVlUbF9e5WVjbbr3JXnv6fCOKDdS1gfp2zs9Bmezvi3/+PfL/+70Yb1nR7rOz1Wt7qMBzNXaJwD0pPEzZCVjQ53PtzkaG/Ir//D7wHqQsNNTN/7eIuf/dk9mu0Y5b3OcapO2r2OvBzkswLzB5JSlVozTXOKyo3P3Zr34+u4XoQSgrv9PrHvE3oevzs85NvBgN8cHODJZ5bnTlRqCZWinyTc6/f5+dYWt7vd12o6Xg6B790kCf4JlkW6so9AYXGe+yxyVYTHorLw5CpSXoZQ9Iwecd2QQhJIr879qWh4MV3Zoum9rYX4Pz4oX7Gy1aLXi9FaY4x1x1prMdriBx7NboK17n8XeQVYgsgVJu1+k+56h6QZOvpU4OH5Ci/YZm27R6vffCNl800IvT5r8S8wVKR6n6qc185Hbo1fTGHn1SGVSRkX3z3rnuPxjHJUYaioTFoXHRO0LbAYAtmmH35Kw9/GWk1aHVKaCZW+Dsc1VzSUzJiVe+R6gBIhUoS1wYBff+9epBibmnJdYmyxfB2lmb/keyVo+jfohR/TDT8i8bbwriC5u+Fvsdn4czzZABTj4j7T8tH3nr80cwwVpZkxLZ/WFCq/poUt6OFm2Rw0tsLgijttc7TJam3U+deLtKiYZjnf7B/z4HhAOwppRK4gDn2P2Pdox2/3HlxLoRFEPq2eW5xGJ1OCyF/aUi6+OAJnB6feoDBcRKG3o5BZXpBXFcfTGYNZRlFptDEX8oh/HbSxzIuSwSzlZDpnNM8otSEJAvqNmG4SXShFdnlIuBZevcbYou4cvFkQ3vJ7hDKmsiWe8Dkp9hkWJxQmr0efikCGJKpFw2vjvUSf4UmfhmrVtq3vsRZu0fTa+OLVoT8C18VveG1Wwy2MNfSDDSeAPuNmIoUiVIouK9xufEikYgIZMiiOSPUMYzUCSeI1aXldduL3uNX4gNIUTKsxhcnJ9Zy2//0xp0QSypi25+hjidek668SqQQlXn3/KeERqYSuv8pWfJuW16Xt9YhU/FKucsvrshXdrl2nYhL1/U6gEAKF06NsxbcRQjCrJhxlLniwsmWdYWCf22Rc0epfS16D23AWXahnFKDzaos8XxHGAboyTEYp6SynKjVrW122bvbpr7eJk4AsCWm04mVgXhg9+/y9QJF0Y0ptyLFoCXEzZHWjw63319l7fMrR7hBd+3uvbHZY3+6yvt2j1YmpSk02L+ivtTCVXrpTgaNKdVeblGVFq5sQxQFRHKA8xa33N5BS8OS7I2bTbDkNsZZlvseb379nwU5XjeXn/pIC01pLXjoLWm3MsvEymKbkZYUxlqwoGc9zBtP0jWuktRZtLPsnY3ZPxsyzgiT0Cf3rMCB4e0ghWGs0aAYB87KkMoYvj44YpSmzolg6SikpCZWiF8fc6/f5bH2dD1dX6cfxJV6XwFPrEEiwtce+dQW6qH35XzbFFqKBFBc/sF+v0cDzUELiS5+ZTplXKZEK8eWCkvgu3gE/HqSUxO0Ev/e8Jam1Z3Ru9fQ2acUv/fveC/b+fuAatCub3Su5Rl82aAfvUZgJk+IBFmer6rrdzxKwSzN5QQj9JghnoSs7NP0brEY/penfINfOClwV313J9QNIfDwRUi7XP6dxKGxBYUZX9CxOS+vLBoFq0w8/YS3+BW3/dp0z8vYIZBs/aNVaigolQsBQmEmdPF45HQwFxhSUTN86o1WgkMJHiQiFX+eGfB9VLfwezFIORq7xnIS1luxyss3v4VoKjbgZsrbdI89Kvv71I7qrLborzXoMWFNBlHCJ4c3XFxqh77HebrDWanAynTPPC54MxuyNxkyynF4jIvC8K1kG86ri8emQbw9POJnOnUbDGOLAY6vTYq3VxL+A9cr1UqcqtEmxtuA8t4JEEsiQzegWbb9PsXB0qgMFF+5USvjEqkHL637vMRqqxU78Ht1glXvNz4hUQsvrEqnkla9R4PQhzmt9hUzPaXhtGqpN/JJOmS8Duv4KkYxZC7YoTE612MhZiA19EtUk8VoYa2j7fTp+j3vNz1gJN753LVIoVsINEq/BVnyL0pa0vC4Nr/3aoKdQxqyGW8SqwU5yF18ENLwWsWq+9Ev7XuNjOv6KKyaEem3qt6zF75vRLWLVINMpUkhi1aDru+yQs5u7WzRCBBftwJ4Htu72lGd44BfwDA88mu2YD392k89+eQddGYy1RFFA1AhodxOUkrR7CUHoLdeC3tqzQizqRGx+uslwknJiSuI0Y91YumstPv6jW9z+YJM8LZbpuWHtXrcI/Wy0IvxA0WhF5GnxXEZP3Ij47Jd3yNKyXnNC/NB1EHfurNJbbXLv0x2q6vmVvd1NaLQi5GtHx7buJqVnNu+rw9nP/WWHvdPJnP3BhHlWktX0jeE05XTicoeOhjO+3T1hmuW0k9d3pYqqIs1Lfv/0mH/4dhdtLP12QjuJLuKu+4NCAKHn8cHKCmuNBn+8tcU4z5kWrikF4CtFKwhohyHdOKYTRbSC4NJ7hhIdhAoxZoK2E6ydgi0RIkTKJlK2kN/rgnoIcRmus6kn1z9EoaEIlE+W5TzNDpAoF9bXusNK2L325/9Hg9d13X5ASDw8mdAO3uNm8/9EnK1zMP9r5tU+pZleekom8Qhkl5XoM1ain7AS/ZRQdZkWj5lXh/Uk7yog8GWDSK3U9KCrKiyeh0tRD+lFn9AP3Z9u+CGBPE+21XnhVu+Gv40nYxJvg6a/wzD/ikH+tRPoX3HgoScjQrVC7K0SqA5KRLz0JhSuadNtxNxe7XJnrc+NuoBWStCK3lGNhhN7umnGZDint9qiWx8qFhuWF3jcuLv+XOfxZYh9j+1eh63uiAfHAyZZzmA25+lgzLeHJ/hKstlpEXreM/73BeGsESsOxlO+3Dvi891DTmZzKmMQAhphwM2VDlvdFuFFCg3hPePWXTGM1Wib1p3UN0w06vdECY+W7H4vZftNWHRjlPVJbIeG10EGcpn0LO0zigLgDpp1uKG1FmskgW2yGXbxYoU17vdMYSgokMo5+yxSVgMREciIltcDIVBqkaj6anT8PrwiaF4KScNr0fBarIZb537dvgzwZUDTa7PBjTf+fD/coB9unOuxnXWke/z2ObQwLu219vS+8h1rITCrMLZydBAU5z1dKuVcp9a2u9z7dOeVP7eYJHRfkowcN0K2bq8QTuZM5vmS35xEAUnjzQtdGPmEkU+r8/3iNQg9Nm68XNDX6Xt0+pf3yHHmFCXaZLXb0NVC4KFE9FI/fGthMs/ZOxlzNJxxOkmptGGa5pyMZ5SVZn8wQQjYH4yJgtcXqUXpCo39wYSDwZStlTb3tldY7zav1eHvPEjLkqyqXnncVlKyEsf0oojSGGZFQa41AvClpBEEhKp2cxOCWVlSak3k+3gXfG2WAmMnVOaQSh9jbQ6UCDtC2iaeXQe14rQb8hWL0nmfyxrssgFwvXDUKR8lnT6sMAWpzsh1fuEp53+qeNX548fIqhJCogiI1RpeFCGEwtiKSfmQtDqqQ+qyZZPJ2AUVe/FZL1ycFFK4dciTMYFskXibrMU/pxd+Ssu/iRQ+lTcnUK23zoE4e/2Jt0k//BRPJgSy4+hBtliyORytS9fUX3ftS1em+lUs/D8XlKqFo9MiPT1QHULVYSX6KSvhT2n6OzT8858Tzvda3HWE9XMpERHWdrOeTMj0CVl1Wr++HGOLJQVq4fp1tqF89rNxzSivnl4EyLpwirwesbdON3ifUPVQInru+5uXFVlZMZilDKauMRV6Toes67OvMPKN58vz4FoKjdkk4+DxKWVRIYTg8OkpQeA/V0xFSUgUB688ACyQhAHvrfbYH0349aM95/CgDY9Ph/zbL78jLUv+2Qd36CUCX13uQF9qzf5oyld7R/zHbx/zD0/2GMxShHBK/G4ScW9thZsrHcILuJS4Dz65wgr/GRxdY46x+Zt/+G2fyzpOfFlUFGnh7EwjR5Up8xI/8oiSsHZcs+RpvvQWd24bJdZYeuttGp2GsxctSvK0xBpD3IhQnqQsHUVgQaerSo3yJGHudQ8hAAEAAElEQVQcoOQ7aOL/A0IKH082UDJime1yxXAOGAXGluf2zr8qxIHP9kqH1U6DShtC3zt3wNyPDedqkl0LdUoKD080XpHZ4DaLySzn8dGQB/sDpmnOJM0Z19TS3ZMRp5M5vpJv1Jdpa9HabTBh4HF7o8effXyb97b61xKMehEM0pQno9Ebb3uLEzdqY5bTLyEEnhBIKZ87JvfimBvtNl5wEctZS1E+ICt/S1k9otQHSNlAEGCsS0YOvFuE3vtEwU+R8tWF9/mercLY/FqK2BchEXhC0Q863G3cZFhOGJUTPPmH8T38z3g5pPBrLcUnJN4G0/LJMjtiXu6T6VNyPUTbzImG68OtEiFKhrWgueUckfxtWv5Nmv6NWozdq4XjlRNjy9ZL08gvdd14dMOPiL010uqItDpiXh2Q6mNyfUquR1RmVhdMaV2AVEtXJjeXF3XD18MTEZ6ICVSbQHWIvTUSb5OGt0XD3yKsszquQpPxJoSqi5Khs7+Nf8asdMJ09xoP3eszQyqTLQsPU9tmO2MYHykCPBnjywa+bBGqDlE9wYjVOpHXJ1Td5etSIuDsIXw4z9gfjjkYTTkcTykrF0o9mmd8558Cbl/+yc0N7qxd1pbb4VpWkKQZsbbTcy4Kr0CUBEvHhdch8jw2O01u9TtsddsM04xJmnMynfPbpwd4ShJ6jtrUTSIi3yfwFPO8eO4wVmlDXlVM84LBzFVvReW4acN5yv3jAV/tHfHl3hG7gwnaGiLPY7XV4Fa/y3avTS+J8S6gwBf4Tlx1DXQXax11ypyTOvU2KPOS+TglmxeUeQk4jmqeFWSzjN56h7UbffKsJJ1kVFWFLjVVqakKTVk4HUmzk7CwDi0LzXToBLxhnKKURFe1qE6I5bQkbkb01lwwm5BXo8X5Q4QQzodfipDrm8EbjM0xNl8K616HIFQ02hHr213KsqLTa2CMZTRzidJaGypj3D+1O/j5StJtxrSSkCjwsdYyTQtmmfsjcIdcJZ/vpKR5ySzLKSvngtSKQ9qNaHmvjGaZm4Qoie8pGlFA4Ctm9WPP85KidB1xJQWh75FEAZ0kInxL0wqLm2hchaXji1h87upln7twjZh+O2EjbVGUmuEsxfcURVlRVJrA82qdhff6tUsIpHBd7WYc0G3G/OzuFh/eWKXfTt44Ubxu7E0m/PXTp8viAc5YJNcdOGPtc9kDCxvlZ5botqY1CkLP471ej9Ukce5U54al0ntkxe9AgJJNpOzUuRoKY+tsDWvwvdv4vGWhYXV9+Lv+iYYQEg9F22vixR6xiohVSNN791Lh/1PAwsSg1JpZUTArStKyJK9crtjynsdZ8CvhHIIC5QS8SeDTDEICT6FEQOStEnnOpSn21ph528z9/dp1arjspi866K7jH1DpgFKHnE5a7JVdmv4qnbDLRrPBShKS+AolwZdNArGBtO9TFoJ5UVJoizYe82nAbHJE7NllZIGzU3XJ080goBUGeFIt1ykhJLG3QqR6JN5GbRF7SKaPyfSAYlFo2DmVydxhnOp5B0WxyDLzKLUiLz2yKmKmE8aijydWCeUaoerWbl1TlJjjKUnkeUSeRzMMaQQ+/plre1t4MsIjqh2tLIlao+FtuYJKH5HpUwp9ttAo6/1lEaDnIUWIJ2JXCKomoewSqT6Rt0rsrRHIdi3uf/k+fnZ91MZ9Lp5Sbu/FMklzpnlOWvQvnemyfL2X+q034Ob7G3T6jec2hRehpKTd/z6F4kV4StJJInZ6HT7ZXictS745OCYtKg5GU/76uyc8Ohlye6XHe2t91tsNVptOz3HWGDCvNOM052A0xZOSUZpxOpvz9HTMk8GIJ4MxB6MJw3mGNs43vh1H/GRng892Nug3YoILhgQqEeCJ5iu7kW8Da0uq5UTjeguNdJqxd/+QdJaDgHxWMBvPmY1TZqM59/7oNu2VJid7Q/buH9LoJDQ6CUVWUGRumqE8VQtra9M5Y5iO5gwOR5jKYC1IJTDa2ZRaC77vsbLVrYW7EvVCaNt/ShDLzvYreJZXAGs1xqT1PfXmw0XSjPADn1Y34d6n27S6CdoY7u+d8uXjQ+ZZybwuINK8pCg1zSTg5/e2+fDmOpsrLbS2fLd3yoO9Ex4eDABY77V4f2eVn7y3udREDSZz7u+fMpnnzLOSD2+u8mljs3Z6gUcHA756fEQzDuk0Iu5s9em3Ep4ej3iwP+DJ0ZCT0RyLJfI9VrsNbm30+OzO5lsXGk6jMcfa8q0e52VYTLJeVmAKYGulTacR8dHNdeZ5wXiW8/RkxL/566/46vERH+yscm97hbVO4/V+6PUmE/qKVhzRaUT0WjG9llv3fuyv3YPhkP/vd9/VVrsOQghUne7tDmDVc8WElLJ20ndT60UmUuL7rCQJxlo+XV+/4JVYKjOg1Ls0o39NEv1ZrZ9RWEoqfcA0+7eU+inmCjjXbqJxPbS8FyFrekmsIkIZ0FAxa2GfWF1/h/c/4+XIq4pJXvDdySn3TwfsjycczmbM8oJ5XXRYLIFSdXhlQD9J2G63uN3rcm+1Tz9JCJcZYIJAtVEipOFtuQN6bQTyjH4EYBFIjBUcTlOeDKf81f1dfr17ghIZq41j/sV7d/jFzg43Ox06cYgnWvjiPVT1fySdfcrjwZCj2ZxxVlLpGMnnKBngSRe02QgC1hoNNlpN3l/p88HqCq0wxFMvnpcEnnS0YV+1aNmbjla0pHvp5RTm+9QpMNb9OZjMeTAdszuesTeaM8oMk3xAWY0p9X3nsCcUSeDTCHw2Wk22Wi3uraxwq9ehG0UvubargAsH9GRCw99+Vli8kjq1SI6XtQ2+qoskv/4T1G5cr9cHd5KIKPCIA59GGCzXyI12k1YU8tXeMSfTWd30OzeT+qW4lkKj1U1odRPKonJd7VKjK+3s23yF53vnmmYAS3vFtXaDn93cXEaj7w7GzPOC/dGU01nK6SzleDJjpdWg34h5eDJ81hG1ltPZnG8PT5lk+TOtxzxlfzjhYOweY5Y7e0y/drq6s9bjpzc3+WhrlXZ8MWtbACECPNl8Kb/6beFoLtmyC6Fs5Sr3aziJW2OpKk06zSiygmxekM1y8rSgLDSmTh0usoLx6cx1jZV0//Qk+bx0IUaVo0bpSpOnBdPBjOHheOnsI6Vwns55hVASm4SURUVZVOjKoLyLiZT/MUHgIUWEFEHNgZVcdRK1oaKybgztbDpf//N+4OEHLu9i8V3Lioq90zFfPz6i30poRMHSHeloNGU4VXx8ax1POftRKyy+J5FSUtQhmUII1jqN7x0qPSkZTFLu753QaUR8cstipaPK7A8mfPnokPd3XAde11qFJ0cjHuyfYoylEQdA7ax0OsFa2Oq3SaKA0FcX/n4/S5rN6/fsOjQai0nW9zc4IQSNKFhaERpjSYuSbivm17/fZe9kvNRZ3Fzv0m++xvVIUE+D3ASkEQYo9SzD5cdGJ4q40+2izzSvrLVMi4JJUbgJtdb045h2GBJ6Hn5dhBhr3TS7KBikbpoNb/O6NKDx1CqR/xHPvigWKRKk+Cs0A67i+2ltVd9bV1/Evois1mS4Ys3lahSmxJOKhJcXG9Y6rxysrdOHL9fxXWhAKlM4AxBTUNpieaRydt8BvgyXboX/GPUii7t7mKYM5il7kwm7owkPBkMeDYYcTmeczOfMioK0rJaHw0ApIt+jEQT04ojNVpOn4zH7kyk3um22Wi16cVwf5EOUOp/AVxvDZD7C6GMOxqd8eVAABauNihudLjttdwAH2J9MeDSc8fvjgAeDDk9HgpNZyCTPScuKXJ8A4ElB5LmJy0qSsNZocDidcjCZcrvf5Va3SzMMaNSTRjeddIdo71VCzJeg0JqsLDlJUw4mUx4OLPdPM56OM/bHglFWMsoy8qoir7SbCknppkG+z1qzwXqzyf5kypNRlxvdDtvtFv0kpnnm2t4Gi3tYyRDF24uuL4IFPdklgRvGacY8L0nCgF4jxvck2tjXDgzOi2slX+ZZyXQ0Zz7JmE8zGq2YRjum2Y7PXWgs0Etifn5rCyUE4zTHWsujk2EtaLHsDycMZim+cgne0yx/bqKxO3D/PfAUnlRUxlBpXWdzuADABZIg4L21Hn90c4uf39rm3nr/EsFOuMpSNhDXMdHA1BV9irYzjG2iLmGfeB54gUez22A+zjjZG2KMIUxCGh3nJtTf7BKEPsrzUJ6kyiumozmtXoOklZDNBszGKWXhOo5FVjIbp4xPp0yHc8I4wPMUZaERUhAmIWEcEDVCooY7xGqtsfY6HJf+MOCEX35daAR1B/VqCw1rKyozRZs5Vl38sRfBPyejOQenU/7o3jZ/+vFN0qJk/2TC//qr3zOeZWz222yutAg859V+d2uFJPQJfMXRcEqaf/9Q1WlG3FUrPDke8eBgwPs7q24BtGCs4XQ859HhgE/vbPDeZh+LZTCZ8/hwyPFoxp9/docPbqwBloPBhH//mwccDKYcDKY0k5B+K0EFFyw0MPVBMEeb2ZV/HkAtWkxq56nXb2pCQOR7tJOQTjOmW08k+u2EjW6L1ZcI5c/89tJMQ0n3513CL7a2uNXpPDe7rYzhi6Mjvjk+5mA2I68q/mRnh49WV2kGwTL1uzKGtKp4PBrxd7u7zIuCzWaTG+32hTR3CwgRIWX75S5SQiJE7NLCr2B7tVSYa6LlPXsO992dVlP2siO01QgE02rOpJrxQfP2K0P7LBZtSgwaj+DShcYCqZ4xLo6Z6CHTarh0Q2x5PZpel5bfx5NXYzn6LuPJcMxv9vf59e4+n+8fMitK5mVJoTWl1kva1OIQmFlLoTWzomQwT3kyGvPFwRGtKOKD1RX+9OYOn6yv8f5q/0q68tpYR0NPUyZ5zjjL+LffPuAf9vZ5MhozTN0BvjSO5mXOHFi1EZTaMC9LRmnO4+GYr49PaAYBf37nFn9+5xZ3+t1loXFZpGXJ/mTKP+zt8x8ePObRcMjJbF4XPdUyjHTxPmrcWrGgqo2yjEeDIb/bP6AXx3y2uc7Ptjb54xvbNPqXd6171+ApSeQrDkcVR+MZvUZMHHhMs4JpXlDpt9/XrqXQGA9mDI8nnB6MOTkYPZtoeAo/9OhvdOhvtOmutGh1z3c4Dj1F0Ei4u97nn2Q5jdCnFYXsjyaczlJnzVi+ejFOS8dvfBU8KWmGPiuNhFsrXX56Y5Of3Nhgp9e+tL2XE+u0roU65WBd4qUe4ck2SsS8rA39omvAogp/lZvAi1W6H3q0+010LdZGQNgI8QMPz/forXdQvqLZTVi/uYJUEqUkzV6DpBlRlZog9J1gXAg8Xzm/8K0uQRTUhYZEVwYhhctdCX2CyCduRk4MrtR/srQpeOZaoYQbUVdySmXenKFyESwKjcrMLiVsXrjNLVJF07zkZDynrDR5WdFrxrSTkH4rfubTDbQSRVbE9JoxaeYoVi8i9D08KZ32wlPkZcXxaLZ0yMjLitD3aMYhrSRkMJkzmKQcj6YcDqYcDqd0Wy434XScMq2tcmdZQZaXmMbFF1NrSyozc9SWa+LQO2ey9kvsUr8PIQRKCSLfY63b4OZal5W2o0wlkU8cXtc6dP1YSRJWkuf3iryquD8YMC9LtlstVpOEP9ra4v06xC+ozUG0MeT1tEMAj0cjxnnOKMuem5qdD46yAFBU3zHNAgQhQiiszanMMZU5wNgZefXtslOvZBNPbaPkm+nCZ2GW99j5wigvhwXVRNThre6fvvTwpffS4sFNMjS5TjnIHjAuT4m9Vp3D1CFWDXwZot5ghGKtwWCZVgOGxRGnxT6DYp9ZNWGuJ/W1CBLVpuG16QYbdP01usEaTa9X071+XKOCq8TRdMbBZMrfPnnK3z3d5ZvjE+6fDGothjunuLNKSFhTuUW9BpZak5YVWVUxKwqGIsObzkjrjJlZUaCtYafdpp8kb6U30MYwznKejsYYa5mXJb/a3eOrw2OGWYo2ltBTxJ6/pJwLIdxBvtJkVeXojvW6Ocoy9zkHfl28GjpRROR5F24G5PXrfzgY8pv9Q/5hd59/2NvneDZjXlZQUygj36MZRsv31NYFR1ZV9fU52hqkHNcupFltmV1ozXarRectg+zeBQSeohmFxIGPryTj1E01pRSstRrEgffWk5trKTSOdgd8+bcPeHr/iL2HxyStmEY7YjZKSec527dX2bm3zid/fOfchQa4Q8xmp8W/+DDkRq/NdrfNPzze49dP9hnNM7KivNSxSwpB4ClWmw1+cWubn910RcaNXockvHwXXYqwpk5d3wZvbEahTwlUH+TrbFLtMi/jbDFil38Pz7h/zyMIfTqrLRrtmI1bq4BLV14ILr3AQypZu0oly3Ap5SuUp4ibEboyhLGPlIK4GRFEPu2VJrrSSOlSj22driqUREon/pZSompqzY8tSH0XIEWIr7qUZkjF9Eof21CizQRtZ5c6OAsh8Gqxd7+dcH//lCfHQ6x1FMhOI2J7pU27cfHFWQqBUIpOI2JrpY218PBggMB1YwWCndUOnUaEkoI0rxhMUobTjP3TMX/5u4d89egQgFIbpvOcfjuh0ppKm0vloWmbUZkRxlyf85sUIZ7qoOTLmwgvg+cptvptilKz2k6IA/9Hd426DmhreTwc8rvDQ/6bDz7gn926xWqjQSMIntPSSSEIlWK90eDnW8628n/66isqY/int25d8FktYLA2Y5b9JbP8P6JEtxaDDzB2grUFQvjM878gK34LQODdphn/l5coNHJKM77mQsMh9kLW6CFR+NIj1Rmzak7Xf1megKUyJZNqwBfjv+LB7Hd0gjVWwx1uxh+yHt2i5fVQ6vVHDINB24qD7CFfjv+a4/wpg+KgDi7VLMglUig84dMLN1gLb/Bh60+IkyZID3Udgbg/Eu6fDvj39x/yD3sHfH5wQFpWS7qgoD4UBgHrrSb9OCb0nBVxXlWM85zDyZRhlpFVjiJaas3ueMIwzTidu2ywX+xs0QyDt5psaGsYZhn3Twf8/viUUZbxYDDkdD5HG0Pk+/SThF4c041DAuWhpBOIj7Kc49mMoqqo6te2MHL4+uiYg8mUxA+40e2w2kguXGikZcmT0Zi/e7rH//LNtzw4HTLJc0qtsdaipMSXkl6csNFygZ9J4NcsF8PRbMbxbM4kz6kK14jItebBYMg4y+vXkPGv7t75R1FoRL6HryQrrYSsKJmkBcfjOf2Wa7p3k7ez6Ibrmmicznj49T7Kk9z5eJtGOyZpRszGKfOJSw5+9M0+N+6eX4i3qKgi37kUuIpL0opC1jtNDsczTqdzJ5IqnDCwKDXaLhJzFzoAV70GSjnnmcBNRlaaCVvdFh9urnK3DizpvCHg6k1QIsJX3TOOMVcv2tYmozCnrgu9LBgcncSlylqM1aQ6pTA5kYoJVYQSboGqbEVpSgqTAYKG1yB4gRIglUQqieereipx5j+ekU34oZtEnHV4AZccvfg5IQTKcwVIED1b6F41dTkvsnlONi8Yn06ZDOas3+yztv36fIoiLxmfTN3vDOeOx9+Oafeb9NZahPG71wGWIiJQfQp9TK6PudqJRklpxlRmeuFCY/F5CeE6kJ5y04dWHOL7iiT0Wes0We81Xy9Kfs3jCwG9VsIHO2uUleb3T4/r5FKLUpIPbqzSa8UIIfA9SegrfCWJAp+1bpPt1fZz38JF4dNMQtQlunvaphR6gLbZhX/3zXA+6UrE+LKFtpKsmi4dlCpboa2m4TVo1MGXiyZBoBQ31zo0ooBuM6LTiAkuSFX9Q4C1lllZMkhTAJeV4bnJ11ksxOG+UiS+jxSCcZ7TuuREw5MrBN49jJ1jbY4UDUejEiBt4sThIkCICFHr84SI4A0ubi+DtjmVGWOu5R5bQNQWwIpABuSmIC1zMp2RmZxWHZha/yTgCoS5HjMoDjgt9jjOnzLXUypT0vb6dPxVEvVyutVZzKoRJ8Uej+df8zT9PaPimJke87J1TSAobUGuU2LZRAmP1XCHjr9ype/G22CpWWFBd6X+36/fz0ZZxmCe8sXBIX/7ZJcnoxGD1H3mSgg2W0026j/rzSa9xGmRfOUmUIXWzIuC03nK8WzG/mTKwXTG4XTqqEJVxf3TAZ6U+Eqx0kjYaDVpBSHyEg28UhuOpjOyeoKSVe7MtdpI2Om02Wy1WG826MYRjTAgkE4/6qYNJYfTKbvjCU+GIx6PRhSVRltHx8rKim9PTvjd/iGfba7Ti8930HVTHcP+ZMrfPdnl757u8uB0wOk8xVj7nH5lq91mrZGw0kiIfZ/I8+qzomGY5gxTp4/ZG0/YG085mc9Jy5JjY/j66BglBdvtFs0woBvHl6LWvyuQQiCkoptEWGMZpzlpWbLaatBvJiSh/9ZskuvJ0Rin7D065hf//CP+xf/l53i+QnoSUxnSec7//j/+PV/+6gGzyeUXz24Sk4Q+t1e6/Bf3bvJ0OObh8ZC9kfMFHsycmGph8+i++pagtlPrxBG9JGar22an1+bOapfNTotGGBD5HsEFgvleBSUjArrI5yhNV1tsuMPOMZWZPvfYFou2Gm0qClNwUhwxrsasBKt0RY9QumKjsiVzPWNQ1EItsUMgX+9O89p/X/z1i3fmNQ8j5pOM470B3/72CQ+/2OOf/J9/8sZCI58XPP5mn+9+94SHX+1RlRU79za4+9kNwl++904WGkpEBHIVJXYRiCu9m8zZQuOymRDWOfxU2nBrvcfHt9ZpN0IaUViLjeWl824AVtsNPr29wecP9/nN/X3XcBCCn7y3yce3NlhpN5B10F+vFdNuRKx1m/zZp7f4+fvby/vSGOtsbgMP31OXuiZtUgpzei2FhguWClEyxpNN5toyLIdUVmOsYa5TMp1xM9lZFhoL+L7i9kaPndUOXp2f8Y9xogEsKSNZVZFWFa3XFA6mpj6kNde9OmOHe34IfO8mCRo32bA1dUec+ffF1PjZ9FjJLkpcbJoBi4nGCH2NeUmLSLPFtY7LKQfZCbnOKUxJrEI2oucP88YaJuWAk3yXVM8obcm0GuAJn7meUNp8eeB+HYblMd9MfsWT+dec5HuU5tUuihbLvJpQmgIlPHKT4nX8d6vQwFLVa6cneK3rz1kcT+d8cXjIb/YP+PzgcJlqL3CNg4/WV/mzWzf4eH2NO/0eXv2dXnAQLM8mGAfTKb/dO+BXT/f468clWW3r7br0BZHnsdNpoaR0hfclCuDF8xzP5hhr8aSkFYbcW13hv7x3l0821lhrNtzjS7G8zoUe4mQ+Z38y5X/79gHDr3PGNkPXgZzaWh4ORvz14yesJDEfrJ7v89XGMi8LHg2G/LsHD/nd/iGjLFtqQ/pJzMfrq/xyZ4d/cusGnSgi9j2kdPF37qfc+5jrim+OTvj88Ih/d/8hwywDYyjqCZEQghudDq0w5KP11T/oQmOBThzRDEO0deuYJx0F/ioyta4nGVwKpJIkzYjeehvl1RdqIUpD4maIVPKtqiTHAw+IfJ9OHDkRZBSy02sznKdMs4JZXiwPPW6mYfGVIlCuw9oMQ3qNmJVmwlqrQSeO3E13RWKAhVOQJxM80cTYDMPVjsC1mZNV+1T+iLMLdGlKMp0yKoeMyxFzPaMwBRKJsYZu0KOhmkgUSiiMNRirX7s5nHfKcBk+39tyALU2lHnFfJIxOp2Qp29+nz1f0VlpsbLZ5fRwxHSUUmQl2bxYOmm9a1AiJvBW8comV129GVtQmgGlGWIvKD5d8FsXVKSy0hwOp/ieJAkDwsDDGEsUemyvdOi3Y3xPkeYlu8djnhwNeXTghNuTeU6lDVHgsb3WYa3TIA6dM1QS+Wz0m/x+12M6z51laV0wbPRaJJFLTo9Dn14r4cZat872yPjy0aHb7oQrNFpJyK2NHlFwuY6NtjPy6hBtZhf/5TdAihBfdvFqUfFiEqOtpjQlEkGsIryXcOClEG9MAX8rPH+GvlIsqJznugwh6MYxa40Ge5MJ//7hQ+72+2y3WsvJhsAdXPKq4ng+5/5gwDcnJ8S+z2oc4124wBQo2a8bUWev82XX/OxNkiKqpxoXw6LQMOY6JxoObvKj0FYzq+YUpqQ0FcNiwkF6TNNv0KyLWothpscMyyNynbq/sdoZEqgGiWrjvcZtsTQFuUk5zffYS7/jtNinNJnLhEDS8Lq0vF5t2ymZViPm1YjSFhQmY1AcooTHVnyXlXCLUMb4r2uQXTOMNVRWM69SjvJTSlvR9po0vYSGFy9DD1+cbJg6L+rpeMxfPX7KdyenzIpiSRS40elwu9fllzd2+PnONjvtNmvNBosMhLOwtY4jCXyUcHTj0mi+PjphdzwhqyqqouDhcMRfP36KrxSbreZyAniRPdhZRhsqjKPFRhE/397k5ztb/HRrg5vdDq0oXOqkXkTs+7TDiJPZnOPZjG+OT/ju5HRpTHA8m/Ht8SmDWynGmHqi/frrmxY5Xx+d8MXhEU9HY0ZpRmUMse/TiUI+WV/jn965ySfr69zpdYlq3d/LHncx6Qw9j2mWU1QVT0djTuYphdaczlO+PDyiHYVstJqsNl7+mfwhIC1K5nnp6MMCGuEzJ8OrwrUUGn7g0WhFBJH3AsvGLcZB6NNoRfhv6V8P7suopKDbiGnFIXeMPeMkUD/rmT3A3QeLcCpnZyals868qgLjGSRCeCjRcLx6Pbhyrm1lpmTVLoUeue5c/RIKkzOtJuymT9jLdglkQCADjNUUJieQAbFK8KRHaMNlsfGHjoucgcIk4MYHGyTtCKkkh09OnQXvOwwlY0K1iievp9Ao9AmlHl7KqtUYS1lp1xEqK75+csTXjw/rbrqgMpqVdsI//9ldPvE2aCUBg8mcv/nqMd88OeZkPGOaFuRFyeFwwtFoyqe3N/ije9us95r4XkQU+AQ1fbLSbtyurCT0fXqtuBYdukTTfjvhva0+RaX55skRf/n5w+V33FrL7c0+SejTSkKkFRcuNiozJdeH6CvIS3gRqqbIKdkE4damQPpoqykoaXgNEhV/b5rxjwK2prq+wc1aCsFGo8GtbpeHwyHfnJzwi60tPlxboxdFJIFzhim0ZpCmPBwO+fu9PaZFQT+O2W63CS8xyVKys9RaPPPsf1mhIc8cLCVcQkugjTP7uM6JxgISiS8U1JTayrqMhmE5xs4tN5OtZ4WGNcyrMaPymMI46poTkEd0/BW6wRrqNYVGYTLG5TEnxR6H+WOm5RCDWTa++sEmN5OPCGSEJ32ezn/PXvYd82pCZmfMakeq02KfUXFMN1j7cQsNbM0cGPHV5D4znbIdrbMVr+FL75Xp6qaeQjwejvjLh485ns2epSUIwb3VPv/ivTv8bHuDj9fW8JR87aovgFYY8mHdZfekowyOsnwpZN6fTPiLh4/oJwl/cmOH2Lu82FfUlMTNVpN/de89/smtG/Ti2E0KXvOYSeDoSp9srDnHJ2u4fzpYThiHaYa1MMpyt8bz5kP8OMv5zf4+v9k74HSeUtbFQisMuNPv8fOdLf713fdYbTQI1MsLjAWkEGw0mzSDgLQssVhKbTiZu3t9XpZ8dXRM7Pv8YmcLY4zTkV7w/XsXMM0KjsYzKuO0itu91ltpk1+Ga7W3ffTNAf/b//Arwtg5COXzgvk04+HXe8ynOfe/2MXUGQpRErB9Z43++suEZ6/GUgdQ++y/S3CpxQJPNgnVKsbmVHpypc9RWTfRcF1ovfyipjrltDhhrmdUtkRZhcFQ2ALPZOh6EylNQarnzPVsSbU6bwpkNs9JpxmDowmnB6M61EVQlRVVUZG0Y5qdhNXtLv11Z0mYZwVPfn/A4HBM0orwfEU6dZkcValptGNufrBJZ6WJlJLZJOVkb8h4MGM2TtF1IRA3I1qdhN56m/7GM7vDsqyYT3MefLWHF3iURYXRhkYrptVrsLrdo7PiDglSSoJQEsUBYezjhx7mNdSL6WjO8HjidB2DGUa7EWPcjEiaEavbXVY2uhw+PeV4d+As/bQhnWaUpabVTfBDn8lgBtayfnOF/kaHpBURnPOLrURMUBca7v4616+dC9aWlHpUi0/zmj71+gX5LE7GM/ZPJhhj2F5t004imnHgxIplxdOTMdbCZJYzmMyJAkUjCnlva4UkCpzjVOVsByPfo5mES/F44HnOgaYec9/e7PEvfnbX3XNScHuj+5yLihDOqWp7tY1SkpV2wmiWPUdgXO00aDeiZXFyUVRmSl4dUF3HRENGBGoFT7YQuNTfQAYMixFH+RGhjIhVhBCSpvc8JaesNMfjGeNZjsUSei6gsHVObczRcMqT4xFK1inhjZhuMz7D5RaOf34t22ottj7HVEMJwd1+n1xrvjw64vFoxMFsxjjP8ZVCOb9eTJ2unFcVvlK81+vx0eoqn6ytXcpCs9L7lHoPbU7QeoClOqNpkkgR46lVQv9jfLV54cc/C23n5Pr4WorZFyGFxJMe/aCNbuy4hh2WUAZEKnguuM9iyc2cWT1lkCgi1aTpdQllgif819KGUj1hL7vPSb5LrlNM/f71gnXWo1tsx/fYju/hCR8lPBLVohus8XD2OU/Tb+uDfcaoOOKk2CVWDRrej2d769yxFF7t2mWsITc5uS5em0MwzjMOJjN2R2NO5+nSOTP0FIkf8F6/x8+2N9hut4j81x/XFuu0qhuoa82ETzbWGaQZXx4eMy1yiso5Ux1N5+yOxzwaDrnR6dBL4kt9myPP40a3w4drq9zsdlhJEkLvzZlEUgikEqw1Gny8vsrnB4fI+rsK1HTIkmleMMkLEt8nesVjLr7fgzTlu5MBD4fD5xxGV5KEn29v8vHaKr0kfuP7CM8KqEYQcLvXZV6UfH10gqqvceG6dTRzFLCTeeqYNZewywaYVRnTKmd3PuA4n/BBa5PbTWe8c915MZ6SBL7iZDDneDJbrpVueivoJE5n81bPcTWX+jxsfcD6+teP+Ie/+IZWL6HVSRidzhgPZygl8QKPz//2Pg+/3gegt9biz//rn1240HjXIRAo2SRQaxTmlKt2wdRmRkZFZUZgNQjXBkz1jNPihNxkKPEs0XxBvbBYsJZMZ0yqCZNy7AqPOo3yPNzSdJZztDvgm18/4su/vY81FqkU80nKfJqxdWuF7bvr/PSffvCs0JgXfP5X3/L13z9k42afKAk52h0yOp6QznK276wRJSGNdowfCManU77++4c8/GqP/UfH5JlbQNa3e+zcXefDX9ymu/ZMdFjmFdPxnK9/9YCn3x4wG6cUecXW7VVufrDBT//8w2WhcVGMT6fc/90THnyxy6OvdimKCmNhdavLxk6fn9Sv88nvD/jtX36Drgx5VnC8OySdZtx4f5NmJ+bxN/sYY/nlv/6Ej/74PZS3cu5CQy4nGi2ufKJBiTVjKj1Gmxxrq1rM+ubnsRYOB1O+enKEtZb3Nvt8fGuDO5tOJzOaZfz1V495ejQiK0pOx3NW2gn9dsKffHQDY+xy9L/oZMtlpoNzHDt7Fe/vrHJ7s7f82ZdpLDwl2ey3We82+ezOxrKpsYCUAt9Tl55kajMj1wfXQp1yE40VPNUEXGKtL3xSnbKb7iPrf09UzGb0vKlGWWkeHw55cOCK3XYj4mf+1rkLjb3TCf/+t/cJA0dzu7e14goyznrTLXQJVw3rCtxzTFc9KflwZYXNZpN26GgaX52c8JuDA2ZFsezgelLSCAI2m00+Wl3lpxsb/HJnh61m8xI5GoaiesA8/49k5ecU5Td1UV4CFiF8lFwlCj6j22heUaFxeC3F7IuQCKTwWAv7rATdZ511Fs5Pz/YEay25TplXEypTIoUiUW1aXo9Qxm/cP2bViKfz33OcP0WfCSNcDXf4SefPWQ9vsRJuLR9nJdxiu7xHpmfsZw/RtsKgGZXHHOe7rAY7V/12XAgCgS88IhWSqJjclI5KekZI/zIM0pSvj495Oh6T1Ta0ALHns5LE3Ol1+XRj3RXNF0QrDPlobZXj2ZzVRsLRbLbMiai05mAy5duTU0LPaVYvsw7Gvse9lT6fbqyx3mwQn+MQfxbdOKIR+Kw2Gq7hw7P5YGWsy7NIM5QUrywQdK29Ok1THg6GPB1NnstFW20k/PLGNh+srbySyvUqKCnZ6TiXw5VGgiclZc2YyaqKUZqyN55wMJk6o6JLFhqTMmM3HfAXR1/z2+Fj/m+3/nRZaFw3Qt+jHUd8vX/M7w9OyCvtwquFM1+6I3vvZqHRXWtx7yc3mU9S8rQkrDvGq/OcLC3xPJcQ7ocefn3ztLoJSfMP3yrsZfBkk9BbI60eX/ljL5KJCz0krXYJvQ182aHlddiJb1LagspUeOKZH7onfZpeCyU8QhnS9jrY+AYAiWqcu4JeFJTzScbp/ogbH2zywc9uURYVeVpwvDfk8Tf7bN5eY+u9OWHsHKkW/70qNEE/4L1Pd5BCUBYV7ZUm3bUWRhtGp1OOng7Yf3SMVIJP/8m9Jd1uNppztDugs9Jk/UafbO4oaVWpKbOS9Z0+7//sJkVWkc5zTvaGPPn2kO331lnb7hHGwbmpe1VZUWQlx3tDHny5i5CCn/2zjxBKgIXB4Zj9Ryds3F5lcDR24YR5RZQExM0QrBOre4Fb5NZvriClIM9K9xpWz58ns5ho+KqDEjHW6gvrKV4Ni0UvDzehWSNQfQTnWGQENOKQ1U6Dp8cj9k8nVNpwMJhgrSUrKg7qNO5uM2at2yAKvEsLlX1P4b/BsMHZ7Qp4C7/4l8G954bSjNxEw15HoZEQeZsEslsLwyW+9On4HbbjTVKdU5oSIeT3HIFKbdg7GfPV4yOkgPVei/e3zy+YLbVmlhWcTlJG04wk9Ln73O8LhFDnFrpeBC5XYTEhePNUI/A82kLw/soKse/zXq/H8XxOVlXLBHAlhNPyRRGbzSY77TaribPNvMzhqjLHFNVDAnWD0P+AvPw9lT7E926iZAetBwh8LkOVWsDdYxptZpRmeM2uUw7LjnhNX3rt9dXT8NykaFshhXJTBdXGk/4rp6DaVmhTMq1GnBb7TKohxmoCGRGphH6wyVp40wXziaCe2loi2cD6lk6wSjdYY1oNqUzBXE+YlKcUPwC17PWozVeswZOKSAWEMsCXfn2Afvn7MckLHg9HnM7T51Lv+0nM3ZU+K43k0uYZsu7Kd6KIm90up/OUrKwcawFHNXo4GLHZal3CFMEhqIXlt3suYO+iFCxVayTiOtV8XhTk9ffWWktZh21Wr2EalFpzMptzNJ0xK0oqrbGALyWx77LRVhsNWmF44b1mIcZvBD7dKKKbxIyzbDl5yrXmeDbnaDZjq/1ml7VXYUEfK0xFpovXvt6rhpJyGe9wc7VLN4mWe6unrkZScC2FxspGh09+eaemlrCkJSy7lUCtyWTx/8PYp9X7R8g3RuDJBqFaq73wrxoGaw2lGZBWj2sRaYde0KcbdHl519EuqQ+xSohUTC/o1Vd78c2xzEsmwxnrO33++f/1j5FSoCvDv/nv/j1f/eoB7+0NmAymiBdcV6SStPsNPvyj26zf6LvmdD1SnU8yhkcTDp+ccvjklJ276/z5f/NHS5rU3/3bz/m3//3fcPD4hM3dVZfFAehKUxYVNz/c5F/+t78EBLPRnP/3f/cX/O6vvuVkf8h0OEdKee5Coyw0s3HK0dMBD7/a5ZM/ucu/+G9/SaMVgRD8u//h7/jqVw843h1wvOemF1IK2v0mndUWnZUW80lGWVRIJbjx/iZ+6HH45ISj3QE3Pzh/11OJCKkCfNlFyhhhs0vpKV4HV2gcUOgtfNmGc+TACKDXjKnWOtzfO+Xrx0c82D/F9xSmzkfxfY+tlRarnQY7qx2CC3a/3hW4RPCSyozJ9P7lHbpeAyUTIm8LX/YQQjr+vPToBz086XGcn3JaDPBfIgavtGbvdMLXT44IPIU29qVp66+C09oYDodT5lnB9kq7phS6zcetHY7Lf9WwGCzlue2VBRAqxd1ej/d6vaUf/+KfsLBGFsuOqRRvY/hh0XpAqXdpRv+SRvQvmaT/H/LySxrRv8JX28yy/x1tx2+Vn7RoIFVmTqXHWM7/+f0QcO5KzmpW24pARE4E7rVeq80wVpOblGk1ZFAcMKuGjt4nY7r+Ov1wk5VwC/8Fi3UlPCKZ0PZW6AcbVKZkrDNSPWVSFx0/Joy1lMZZTntCEUlHNQtlgHzNnjrNC54MRwzm6XOH/X4j4f3VPr23zDAQQDMMuN3rcDCZLEXhAJM85/FwxAerK6+ld70OoVJst9vc7HVILmFAIXCNgNDzaIYBldHLQgPcWpZXVR1R8HIUWnM0m3EwmZGVz7LUAk/RiyNW6jyP5JLOUIuctU4csZLE5GfCoYtloTFfOoW9DRbr03XTpc5CCjfZ3+g28c/ko4Fr6F2FJOGtdvoF1WHJ3K0X76QVsb7Tf2OVbGvBnxACz1NEF/DXX9IsFs8vzpYt7xZ82SX2b+Dll69434RSD5gW3+DLDol/q75ZFgeDWgD7QodQLEu9t3vX/NCj2U2ImyF+oJBSIpVxOopugjWW6TAlbkRIVXfNPEW736C/0SFuRngvHPqromI6nFHmJY12TKvbIIyeTSHiRrTUcYyOJ0RJSBD5hHFAq9sgbrjHFAj8yCdpRTTaEbo0TMcpSev807MyL5kMZoxOJoxOpnz3u6ckzV8Txo5WdP+Lpxht3DQlr9DagBD4oUfcCKlKja60Cx5UkjD2UUpijUVX+mLdpPpz9WSb2LsB1pLr/fP//jlQmZS0fErk7RB7N15pfqhtgbElUvhIfMIAui3Fz+6tsdqJUVIhJZRmjrEFvvLoNJqs92I8z1LZCVVlkMJzjyF85Cs6qe49MlQ2pTQzpPBRIkCKAInvuuB1yNeC+ieERIkAgcJQYmyFtYZnKcie+/03dG+///5MyPUxRS2aP69L0vkh8WSTSG3hq97yUK+EIlQhTQye8Oj6HbrB93npxlrmeUmal3SbESvthPACRV3oe3SbkdN5zLNnjiQLCGdycR2JzJYKbdLaNOP17+vZ7ulZaskzCp5d/OAVO8K476AQPkKEy89HiggpG3Uh+nZp8ZWZUegTKjPGnsMm9seAYTF9snUOh48vw9cerDM95zh/yrA4pDTF8rvT8DpsxXfp+utI1HOf1TKjxwpCGdNQHXx5iMVSmJzczNFX3Gy5KIQAVesztNUUpiQ3BaWpMK+5j+dFyeF0xiTPn/upZhCw0WrS8P1L787L7DHPYyVJ6MQR3pnMjFlZcjCZMsqyS2n93AHcox2GtMPoUpOXxTUuaLIvfkedqY957R5ZaMPpPOV0Pqc4U6QEyqMbx/Uk482uVW+6vsjzSHz/uYO3rhPXXer65feBlh+xTY8/W32f24017rU2frBiw1qL1oZ5XjCcpVjrnPomae6MVXyffvPthgBvV2gALxMOx42QuPHmomFZqIiLv6Uv+ny8iwWGgyBQPWJ7E19eX6FRmAHT8hsS/zZvtGu5YgRRQLvXJErq0alwAt24GdLuN7E4IXV3rVUfzsHzJe1+i/5GhyD+fqehKjWT0ZyyqGh1GzS7yTObZCBKQmedrCSj4yl2hWWh0e43CJNnY1ypJEkrotVtoI1hPk4p186vBSqLislwVocBzpiczjh+OkC+QMkx2izF5+41eoRJSJ6VFHlZBxU+m6RYYzGVwb6mW/Mq+LJN4t2sD70HXGU+i7ZzsmqXvDrChq/upLrcjSm+bCCFTxBAxxf8orXOLz+8gRAKaw2ZPqEwE6TwUCIiVBECQ16N0bbAkzGeTGrHm1dtVsbRlfSUuT7EEzG+bBKoFlL4GFtQ1QdUQ4VAoYSPkI52pG2BNhnaFtilu01Yb44X2yBLMyatntQGDFd9CHT6B082ibwtAtmFWh0hEIQyQAlJx2/jCe+lm5E1kBUleVHRikPWu02iCzj8hb6i10rwvSHTNCcrnr8H3ETD422oQa+CtRptz1dovA5nm09XDSFUrV1yBYa7B7QrvvCBEmPmbzXp0mbmbMvNy8Prfnw4O1tdF9oCgScCPBG+tnDPzZyj/CmD8pDqjANj0+uyHd+jG6w9pwV5HoJARiReC18ENX0rX05VfkyIuhGghKSymtwUeEZRmPK1bo5pWXI8mzHJi+cO043AZ63RILmEWcGLCJSin8S0w+g56tC8KDmYTusi52L32KLzHihFMwxpR+FbnTgWhdqLvk3WunyM111fqTWDecppbT27QKAU3TiiFQZvnQUhcFONOHi+0DDGkpYl8+Lt6E5NL6LpRWzHvbe6zstgkUU0SXOOJy64uNCa3YHTutxa6XJ3vf9Wz3GhQqPQ+lnY0Znwo8T36cYxkecRKoWBpTJfG4OqR9XaWrS1BMoJMI9mM6ZFwUoc04qipbxwIQh62fib+u+nec5pmjoO4oJ7Z5/lZFR1uMoCsv79Qmt07a38JuFOqTWzvFxyfQPPuRB4F+R8O159H1/28EQLbbMrH4WXesS8/M6JU22GJEC+MMK+rgq5SAtGJ07MbYxFCYE1lnSaMz6ZuPFtN8EPn3+/nSnMy6/KCzxa3QbDowmT4TGT4WzpOAXO8er0YERvrU17pbksbPN5weh0Sj7PXXgWYqkjmQycEYETmp//cOnX19LuNWm0EzZurvCT/+J9ojiAmkNsrWX9Rp+1rR7Do/Gz13fhd/P1WLxbnmwT+7fIqr0rfgZ3yEmrJ+R6H2OfdR1f/KTS6ohR8R3t4A5t/w65PmVeHRKpPoHsuFRjm5LrUyozx5dNlIwozRhrNZVJKe0cbTIC1aYdvEcou8iXdMvdoSZ3hYLVzPRTcj2iF35EN/iQebnPtHyMkjFS+JRmgrWadnCPUHWZlo/I9RBVZxkUZownEzrBPaJ6anDeDn2pB8yK31Po0yt4t5+HSwLvEsgVlIwQNTVKW01pKw6yQ/azA9p+i47fcV79/vOURBfeZahqy8WL8mylFEsbzaIy39voXT7QopN/tXAF47ReIy93wL5eL3uBFDFK9hAiAIzTydiKtPh7cvE1RfWw/tnLHzxKM2ZePaDQA97NQkM4Sp9QmLqgsnUz4GWf2+LvUj3jKH/CoDigsiUCiRIeDa/DSrBNQ3V43aophXJTSiFZFDvmWqaKF8Oi0Gh6Dd5r3CAzOZ7waHgxoXx1sVAZQ1o6PdHZV7BwPPKvQF/m1VqFyHt+UuSeu6So9IUnGqKmE0Wehyev4mRx+UdYCLOzqnqOAjYtcu6fDtDW8ng4uhS1a4GsrPjm+IQHgyGT/FmBbKw7lBf6YsyEQlcUpuKr8S6fj55QGk1lDRJBoDx+2X+Pz7o3v/c7R/mYQTFjWmbMdU6qCzJdkuqCrp/wfnsTbQyP5yeMypRCl2zFPT5sb9ELGjS88HuFvO8pEuBG32WfWCAvK6QQnEzneKqmP4vLr60XKjRKrRllGdNasDPJcwZpuoyI78YxvpQ1X9GQLywFpXRq/bpAaYYhvpSOVzedIoQg9H28Ra5FfXgra598gROsKCEwuC/IIMt4MBg43l27ja/UMmbeV4pCa2Zlufzwvfr3p6Wz0FyMwl6Gxe8UleZ0NmeWF1gL7Tgk9NSFCg0XgpTg4yhUnmw7yoy92kKjMiNmRR0gZmfuUPgaruxVIs9KxoPZUodglERXhnSaMhnOkVLQ6iQEgU+15DGKZ1XlSxYZP/Bo9xoEkc9s7B4nz0rKwv3+fJoxPJ7Q7jdprzSXVKQ8K5gM5qSznKpwFm1lVjKfpMzGKcqTNDoxXuC5iYIxVJVBa4M580dXBl1phBSu0Og1aPebtHoN7ny8zT/9r39Go50gpFja2HqeQkjBo2/OUJmu6czjJhq3maqveVaaXw20mdeFhitardXuUPnCa8n0CYP8CwLZdIVGNWBaPATfIjyPwowo9JBMD9A2J1IlykZUeo6uu5mlnTMv9wi9PqHq4YukFho/D0NFZVO0zbFopuUug/xLAtmmG3zAvDrkNP+SxFsnUB2m5WNKMydQHaTwmRSPmFf7JN4GCMm0eIwnEyK1SiBbSPFmD/TluqAHTIvfU15ToRF66wSq7w7zdXfYcZdz9rMDfjv6gs1ona14ExnL7xUa4DptLujqmeDyvJBnrMKrSjsq4HNZROq5a7tKWEpXaJiMd/OADVI2UGrFBfBZ66YYArLy83qSMcNTm1zu+mt7TzNiXj6kMIMrvfarxDP3sUUgrqmnOC953dYVG5me1dSpI7SpUMIjUBENr0s/2KThtV/TEBN1ofGsEWExGPQ7QS9TQtKoA/ostp5kiJd20xdryaLQKF7ohi8EyL58+++YqguN0POemxicLXIuCiHcNUa+d+H15aphrCWvdC10f3bvzYqSWTHi0XDEv7v/8DWP8HbPXVaaUusL6VxKq5lVOb8dPub/8fhvmOmcTBcIBIkX0vDC7xcapmI/G/Ld5P/P3n82SZKkeZ7YT5kxp8EjkhenTaZnZmd2bu929/ZuD4AIRHDfDYLX+AAQgYCIQA57d3JYMjusp8l0V3dVVxbJSh7UqVFVxQs194jkEZkRVVm99U+JjMwIdzNzdzUzffT5k4fcz4/YK8eM64JRPeegmnIlW+XfeEfjLP+wf5M7+QHjOucnKzdIVAhxTFWEfOyj0lKiI0lsNFuDwLrJ65rDeU5tLUaFOb18xqLwaXCmQsO2FXBe18u01VnbMnLec8laTL9P7RyzuubeeMyd8ZhYB9svKQRGSi73+6ykaeiQNA13W2u3nV6PlTT4OedNw73plHlVEWtNagzdKKJsGnbncw7mc0ZFwaQsGZdl6LDUNTdWVnh/Y4Pd+Zw74zF5XVM2Ddu9HutZxqgoKJqGrHUheR4ejKf8u9/+ga/2jsB7Pri0yb/9+B22By8jepJEaoXMXGVeW6ydnnkbz0O40DYUzT2Oil/SNW/Tid481308C0pLosRw76td/uP/6+fUlV26NF1+c5P1nRV6qx1Mommmp2txm1gz3OizeXmVratrFNOS//z/+VVwbvIwOZozXO+xdXWNzcurFPOS6dGcJIsZrHW49+Uu/7//x8+XSd+TwxmX3thgfXtId5AhpWB0MOXrT+/yzR8ecPeLhxztjakry97dI+bjnCvvbHH5zS2ybkLaTdi8usrbH1+lKmv+w//zH1HtCpH3YVL3zo+v8cZHV/DfgmOEUX065jqRWkNg2g7Z+ezX0YDPqewBef1N25Fba2khj+MEN57jlW+BJJYDtIhpfEHdzCjtCOHGNG6O8xYlYjwuUKdEikBwHID2KKzLKZp9SntE5cbUdhI0IksevG+FwSt0zRW8byjaQiDsL2hJEr2BlhnO12Hy4xsal2OU5sUUKhsCL+0es/rmhXQ0tOyQ6avEeqOlJwUscjQSmdDRHZRQ1K5eriafxGK10WhFUTaM5gXVGYIoq9qG59SWyGi0Vo/QkKSI0C1d7rwRAuoOsW72na9SPx2CSL+LFF2Mvo4QCbH5ACk7NHYX56cIkaDlJkbtvPReGjdmXn/dFrOv3/sgAC0MkYypnA+0RldRufKpY7L2ZRvwt8usGbcp4I5UDViLdxiadbR8OhXwGL4tZhyPLsEvV6y+Mzgcha0obMHcFuTtV6ZSLqVbdPSTou4Fa6Px7olJ6qLYPw/HHynahVYpH2ETupb10finX3Ofh0W2z4Kt8l1isShduzPqHc9r/4TheJY9R1LRNQk/W32DnknZLcbslmM+nzzgYTF67nNLV3M3P2TWlLzT20ZJyZfThxS25h/2bzIwGW/1tthKB9ydH2KE4vPJfSKlWY976Mfuc1VbpN07mvBwFOalzjumRcVKJ+SOvEqRAWctNLynsJaiCfZoZVso5E1DaS2RUqxnGXnTcFgUfHl4yG8fPKATRXTjmERrulG0/FrQr+5Pp8yqikGSsNp2RypreTCdcpjnpFozTBLodJhWFV8fHTEuinZwBYrArKqYlCVaKd5cXeUwz7l1dMRRnjMpS6xzxEpxkOfM65qNTue5wXTew95kxn/87Ct+/tVdACZFyV++dY3twdm1FkIIIrVGaq5R2X04ZwHvwpq0sPcYFb9Cyy6Zf2O574uE0oo4jTh4MOLXf/0Z80nJfFqyc32Nq+9ss35pSKefhgsroIwiSg3aBOE4Ilws3AlzABNpzKpm/dKQrWtrfPn7u/zibz6jzmuUEKzvrLBzY521yyt0N7pwEJyqusOU9Z0VRvsTfvUfP2U2Dl2WRY7G6s6QrJfS1A2zcc6Xn9zhD7/8mtH+lHxaYK1jcjhjPsmpypruICPJIjr9lM3Lq7z1o6t88dvb/Pqv/0BTN8ehed6TdROuf3AJqULhpY0OmTFGYaIQBqi1QmkFeExsUFo9ofU4DbTsIU1MrNZRMmnpLeflvOJay+RD5vUtjFrByOFTOmRhRdPjsL5sBdetGFtItOxgfMasuY/3DZUvcL5u+fcCLy0SgxIxWqQEzv/TL9eNLyjsPqUdUbsJtZsFWtcjkxqBkV1SvUFpj0IxAVhf4LAIoYjkgFgNKOVhS8+xWF9hvHvhXMX5pnXk2mVWf4W7AEtNJbuk5hqx2lzSpqANUxOaRCV0dQcjzTMpbQKII01sNPOy4nCSU9bNC9vfgQIIedVw1D4niQyRfrTDJIVpC41X548/DucraneE9VPw9rnX6O8Ggki/QaTfWP4kNu9g9BXq5hucH6PVNkquIsXZ7dqDbtFS2yPy5hsqe3SOx36eCJqMSKYhK8IvhNl5CINdCvHDt8qVHNW7HFa7zO2Euu1oZqrLVnyNodloOxXP/5ydd0tdSNj84s93C+c9hS0Z11MO6zFH1YRxM2ElGrAWD+nwaKGxmNq7dpHq8QmyOMHueFWELC/xRBaH856mpbe/zPRctsf4Xb/3vn0dTRsweRKLMFa5YFCcM4wK4YTyjPQxIzVGan60co0frVzj6+kuX852KW39wkKjdpbdcozzjuvdDTbiEOr66fguvzn6hhudDf5k9QZSCDoq5rCa8eVsl9W4y0eDqzyunq6tZV5W3N4f8em9XTweJSXr3YztQY80MifCWl8OZyo0jJT0oojKWo7y0FZJjcFISRZFJFrTOMe4KLg7HjOtKrRSpMbQi2OytiuhlaJuT65FpSRaDUfjW44/YQAVdc2kLKmdY5AkQYTU/q6yNryZxpAZw1qWsdHphJae1nSMWUbDm3af06piVBTkdb1sBz0O78OHWdsnVxpeHpJIbdIxbzGrv+Si3ArL5iFjfk1mbuDiPDijPHUl+vxgIk3WTbnxwSXe/9M3sLVdpnx3Bynrl1dweMZlychXXP7JZdbfWOPy1Q16Kxkm0tTOMq5KrPdk2hArHYK2hhlXP75Ms2oobiRkUnOlO6DXy8j6CXVP8Y97d1kRMas7A943b3LpjeMAsyDO9sG5aiVjdbMf6CRK0V/t8tFfvM2lNzap2mRy531wQMsihus91ndWgrBcCnorHa6/v8NwvcebH195VMTtPZff3sJEmqvvbtMdZvSGGWk3YWWzT101eB/473ESPo/+ag8pBYPVswcIChRSRERqna55i7y504rCzw+1O2Jaf0ak18jMDRSPTpy0SEn0GoU95MH875k1d8ibPVK90a5wzrB+IcD2NC7H40jUGkpE7e8rFg5RWsYt9//Jwsv5mspNsD5/Dg/cMWvuYX3FrLlL4+Zk5hJGBsF4aUdM6q+ZNYZ5fQ8lY3riOkZ2TkUDqtwBeX2L0t5vA9rOu3MlMXJIx7xJoreRJwoN2wpMYxWxFW/QNz36pkfPPLnooZRkpRfCEEezgi/v7XP/YIud1R6dJHqmrXDVWPKy5v7BmM/v7jEtSjaHHQad5JGVUCkilOwgL+C64nzZptNPsb5E0TzS2Xld4L0/MSmWSBGj1RbeD5GygxAxZzUZgJA2X7kDSvuQ2o4vpJg9DwghSVRGRw+CINvlTOpDDquHlC5fCsQXyO2Eu/kXPCxvUbvjTJBM99lObzCINpCneL+sb6h9uRR/S6GCpusCjAnOAoEgkma5CNDTHaZNn0wnRE/RaCzeGSWPOxcn5xqLIuA85h+OdiL+mChdtfMi/RoUC6+EtuB5XEwea00/jujHMb0kPheb1seRGcNmr8ulfp/kJe1zzwolJJmK0UKyGnXYSPpcSlc4rGbslRP6JmUj7qOlYlIXlK7hYD6jtE/XMkVa00sFb2yu0E2ipZ3waidlpZvSS07vBvssnOkKrpWiF8fkdc2BD1XPMEmWtl8Ln+KiaZiUJXjPII5ZSVNW05QsiugYQ6QUvhVuZ61OIlFh1cx5jyQUE6bl/uV1TaI13nt0W0TEWlNbG96ktlsSKcVmW2hkJgSsGKVImyaEyXCssHf+mKTx+Enm8TTW0VjLebFgBJJYrdMxby6dZJ70znp1BEvEKYPmT6jc0XKSdZFQ7cT86jvb/Nm//hDk8UXzuIh0jKuSI1+x/u4G/SgmbQsKgLypGVUFtXNBk9O2ZdN+yqVsi3LTsHdFsZl1+OnGJVKlsd7z6eEev9u/x/srG1zbWGV9e0CkXjyslRb0hhm94bVTv85OP6XTT9m5sQEcu64tBO3h0/RsXVtn+9rTUz1POrVtXj0OQTtp17x43x6xb178e2H1KBQCRaw26EbvYH1x7oVG48ZMqz+Q6qu4uMCLDDjm4xrZIdObFM0hc3uf2k2ChsIHe1nrCxqXE3jKEQ3BFjVr6UvT+i5Na1WrRYISCUo+PYn8mP8dtmVkipE91IlVdY+ntEc0Lqdyo7a9b5auWM7X5HYPAZRuRCrW0CJBy/S5wuZjbcY+k+p3FM19Qkji+Z27oXA0RGpIZq6HjsaJy7PzjsbXGGEYRoPW2naIROK8C6u6YmEUIFnvd9gcdvndrQd8/aDi6wdHbK302Bh06KTxcqUvvL7WYCMvORjPudWmivfSmOubK6z0skcWZKSI0aLXiqHPF56axjc0bkLjpmjZRT0lK+SR55yYjPnFXy9YwDzhfMvSCvcZ9ADvWxVCe47adgKopURL1RqVRGh1+lDEZ6FxE/L6G4rmAdZPz9005LwgEaSqQ0+vMGtG5HbCzB5xVO8ybY4o7GzZobDeMqr3uZt/wW5xm8qVrQhc0dVDNuJrDMzaKWymPY1bZHfY0E8VKrivfUcdr+OxF8ZGeE0ZmU7o6AwjNNFTxu/inqHEYrIvqOyJQqOlNdnnOFadFs57ahtW/E+eKwvXqDAB//6WGguamVGPakUSrVjLMi4P+lwZ9J+ZLP4qCBa6CVcGfdKXTAU/K6QQZDoikYaByVhpi431ohvcq0zCStwlkprDasbDYkTpakpXP/WWpZVEScFmv0s3jpm3OuZeEtOJIyKjHrlevgzOVmhISSeK2O716EQRtu0I6FbsvdBiXB4M6MYxlQ0imaT9uVYKI2XrVCC5OhiwnmXLbfeTBN26Q6XGcH04ZDXLqJqGuPWCFgRf6LwOblBSSuJF+0oIulGEFIKVND0WoHtP2hYi/TimaBo2u91n8s7cgvNnn+/ffDYIjBqQ+B1itYGRQ6yfnfuKlcPifcms/oKD/D/Rjz+mF31wrvt4EWZ1xbSuQmtVCDom2MtN64qDYo5qtToesN6hhGw7SHY5pmRr2aRa0f6iwyEQwUZOC1IVXM60CNuqncPIi+doOjzWeZq262WUJFI6jHdnSVQYa9a75esRiKUTUOWCw0jHGCKpljzd2oaVeiPVstA4XvmST7S+AWK1Ti/+iKJ5wIRPzvV1Nq37VN58Q2HvIYQOAX7tpDxWKwyEpKOLtlsRcipSvUkke3gyjOxhZJeO3sb5kGQdyQFSaBK1jvMVQkiM7GJkh2fd8BK1xmryURtOGLojjc/pmivLxwgkHXOJnrnadkog0zsswim1TOibGyR6FecqlEyI1copVkMDN7xs7nNU/JK8ucN5LxAomRGrLRJ9CS27rX3q8XuhhCKSESM/4ageBZpGM2Fg+vR1D9VOuAAio7ixvcJkXvDgcMJX9w/4699+xd39MTe2V9gcdtvOhkIgqJqGWVFx72DC1w8O+eLuPvOi4sbWCj9+c4erG4NlICaEQsOofuvgdb5GBAEe66YUzd1QfOqEF3UHAgUldLlduwimpQgif/EopX+x2IQQQejowvVeK0XcLmjZ1gFGSkFe1eRVTe3COTqvKsq6YbWbsZKlxFoTvSCl/rSo3AHj6hMKe6/NfHk9IYSip1dZiy9xVO9yVO9SuZJRvcfN6a/I7ZS+WUUimTSH3C++5mHxNTM7wvmGWGX09Sor0RZdPSCSyQsJUB5P6XJmzYjalQgERsREMkV9h10vjye3BbfmdxnVUzKVEEmDRNDRHbrm2RkEWkqyyGAKRWWPP+/KWmZlRW1ffQwstLVl3XCyCR+Ss8O96rVhJr4EpAiBf3GrA14g1pr1Tsa7G+v87PLOuazMPw4lwry3F0d04/NfeHkagpW0wki1nFuEebgibilZkuNwUgjzrLC4/uS1umoseVXz5cMDvtkfkURhTOR1jRSCDy5vcW1t+ErHfOZCQ0tJqvXSaWqxYn0SqQke0As8awx3nuMRHWvNdq/H1omV3sW+hmn6ZGAfj07MQojMkwPr5HE9C75dATirk8DzIIREiy6xcsR6i1htUFp3Aa3xYDE4r7/mIP9btByQ6Tday9DzvRhLJVvaVEJ/tYtONIVtGJUFe8WcSKplIZBqQ9HUjKuypUappa6nY4JeJ2/t6ax3S2tYKSRKcWLlJZw0gvCzBUcSz7m1ml+E2lrm9WLiYdvVlIZ5XVM0DatpylAmwXLPNmihUDJoORrnGFcFzntM2+6d1zXzpmZeh9XLVOvWqcS3xZok1opEPxneZNQaveh9xuVvEOjWFOCcROG+oLIFeX2HeX0LLTphEtxO+iLVI1I9vA9jbkGBOgnvPbEasMiuWFCUni76fjbbOlZBW3EycI92Fd/7EPqnZUqmtxjG7yy3BlDaMVIYjOzSNZfpRdce+f2LVvOcr7E+J2/uMql+d0G2th1Sc41EXwq0pKdYUyuhqH3NpJkyY86o1lhvWzeRDN1ezrVSXF7rU9UNv//mIbd3R3x2Z5dvdo+4vbfKpbU+w25KGhmEgLysOZoV3N494ubdferGEhvN5fUB71/bYnu192RHQ/aQMmnHnOW8aWS1m5A332DUkJit53cnCPaT87YgaJwjMYvFLbmkVTj8Uvi4uIkuLC8XNOBIKWobbrxSBJvfaVExLgrKJqQWH81yJkUZFrqECEForR3wy66sh3PIUtpdptWnbdfs9S00JJKeWWU9vsS9/AskEusbps0ht2a/Y9ocshZfRiI5qO5xUN7noLpP6XIAUtVlM7nKWrwTcjHksyeBi8U+7z2lnR8XGkJiZEyiskdoht8Fat9wUI3YKw/pmw4dlZGomMQnz7WONUrRjWISXTCvjlOty8YyKcpzSZtubKCMz1u6+Ml99+KY+Ftaib8oSCGWjBp1QkugW0r/pX6Pj3e2We+8Wujc64KFfkctuqmEDqMSEiM1Wqh2/rS4n7YW/M9YEKqaUNTeO5rw+YM9tgY9BlnC7YMRZd2wM+xzbW2w2PtLHfNLj7BvswB+ttnd6R53VjgfTs7z1WgECKFI9CW60Xu4sqR2R+e6/QVKu4uvLB3zBh3zBpFaJ1Ir57qPtBOzfmmFKI3YubGBWIm4NR2xX8w5KHIGUcwgTuhFMWnL53beM2tqbO6ZNxWRVLw5WEMKwYP5lMpZulFEogzSmCes2F4HHBYFX40OMTJYEI7LksOiYFqVzJqaH29skayuc382ZW8+w6hQJKwmwf55XJZU1tKPg4j7D4f73JtOmFRlmLjECbEKF5FUa7pRzFqasiEV8rH0VSN7YUypLSK11mYQzM719Zb2IUfFPyBFRKJ3nikKf9YZKI7XV574zdnx9OIgViv0zFVi2X/id1Jo0paupWV25v1Wdp95/SXz+msadzG8eSP7dM3bpPrqU0XWjW+Y25zKVljX4ITE4zmojihtxU66RaqChkZKQZZE7Kz1+csPrpPFEZ98/YAHR1Nu7454eDglbo0KBKH4reqGsg6Tmje2V/ng+hY/eesS262u4+T8WbQUOC0yjOq1rl7FE8f8KqjdiFn1BYnahui95z62sY6v94+4dXDEtKywzrHWCUnIRik6sWGtk1Fbx1f7h+xOZkzLCqNDkFkviciiCNkRdIkYFyW3D0YgwmLGIhMqfA8BXZOy5N54Smkt11aGRFotLdRfBoFmOKGo7zKrblLZPV5Ht6kFpJD0zSq1u8ow2iArexR2jnUNk+YwiL+rPQSCws0o7bwN6BPL517vfMBmfA19CmpvMJxomNsp42afyhUIBInKSFUPLb8dfvzTIBAkMuZqts3Q9JcTvljGdHSKfo5FbRYZtrodjvKCwzxfFlXTquLBdMaV4eCZzz0tShts+sdFMMU5ue/NbodeEr9GZgtnRwgkTFjNUqIT98eqsRzmOePy0df9x4Cn+6yJ1uzjbNvy7fWtnyZcW1/hyuqA1U5YzN+fzom0WjBLXxovVWi8aFCe55B91r4u8sRY2qWdMYRlUTUuV0rF0xxhNIm+RC96j7z5BpqvuYgbSuNGNG7KtP4DaXWFXvT+cjX6tMFkL0KcRsRpxGCtB2/D3dmEO9MRh0XObj4LFCcBq8nxSoL1nlFZcEQI2ekYQ+NsK1wqyZuwul85S+pfz5WWaVVxdzphEMekRnNUFnw5OmRWVeRNzbXeAOsck6rk4XyGFIEuGElFNzKMyoK8qdlpelgpuT+bhOfXFRLaFa7gTz6ME7aAbhQ9dZQomaJIifVOCPCrb2Pt+RYalT1gVP6GWG8ziH8ErRD9WC/y9MveMQTei/Zc8u0F60kViiDYRLb282G73h97eC+qTn8ckriApkcqL6FF94lzTmJI1BqR7C1tdE+DRaemtA8Ylb9mXn9F42atPuO8IBBojFqhE71Jqi8hebLQCJ0+y7GvSvDpnzUzClsyjI4nJFIIksiw1u/w8Rs7ZEkUaIjfSEbTnHlZMcnLRxydlBSkcXjORze2+YsPrnNja4XVXop+rLgVQiK8RskMI4d4b8+90GjsiFn9BZ3onUC5Q7PoYD0O6xz7szlf7R0yq+qlxq5sFh2HmG4cTEzujybcPhwxq2oirZhXFZu9LpHWx6vJdbP0kBdCkBhNFpmWchW6mFW74gyw1slorAvdx5d8vdZNyZtvmDe3yJt7WD95yS19OxBIUtVjJXKsx5fYL+9yWD1k2hyR2xm5nT5heBIE0zGp6rIeXeJS+har0fYLNTgAjasp3JzcTpg3YxwOLSMS1aGrB5gL0AudBovxGKuIjXiNgenTeBs67sIQSfPcVOpOZNjp93gwnXJ7dKxtnBQld8dj3i3XnqrVOw0W53de1zyczjjI80fSqzNj2Op1Gbxiqvd3DaMkq1nGeicjPkFhLG3DwSznKC9agbNbBjf/MeJ0/fkn4Qlug7FR9NIgnO+2iy9THWiiR/OcSGuMlkur5LPg9ZzJfcd4lDp1+uc5HNZbZCt0expCR2MHG80Zlb9sRaoN5+9gE45oUn7a8toFkdpAiWyZjnze6JsY0R2Q1zUP8injqqB2ls20C2mgrNXOcjefUdmGG/0VLnf6dE3Q+6Q6rEq97Krgt4VFHkwvitnu9Mjrhr1oTs8EfdBamoWWuAkdnXFVMqlKpnWJx3NY5BS2obQNRkak2rCWpKwlKbULltELWkasNKtJSj+Kn2t1mOkrrCb/jAPvKOz5poVbP6No7jCrbjKpfkfHvEWqr3BaV51FAGJjHd55lA52gL5V7or24i+EwFpHXTXINijRNo6qrDGRJk6jUHhYR9MEZzMAhMD6COGHCPXk2JZCEash3juUPP3Yd77C+px5/TWHxd8zr786dzqLFDFGDkj0Dpm+RqTXn2reYKQmIyNRwcVmIf4OAlSJfIrORCvZFgobDDsJP3v7MnujGaNZwayoaFr+t9GKLDEMOynrgyAi31rp0U2jJ1JkFxCEzI9IrWPdnJrReb4tIRm7/pqyuY91M4QMIYFPPZZWxzXIEgZZgpaKYZYQKcWkKMnrYMcea00njljtZqzCkhrVjSM2uh0GaRJoaJFho9dhbzpnfzYnNppBmnDQUqZA0E9jsijwslWrBTxLkOvjKOwDDvO/Z1bd5Pxsqi8WoXBIuJZ9gBSazye/pMlrKpfTPBZIK5EoaViNtrmavcf1zgcM9HqrzXjx+za3Yw6qe8ztpC22PRJFRw/oR2E73yVCblCEERqHa3nz4bx82rm5QD+OuToccuto9MiK8f58zud7+/zk0stnsUBYyplWFbeORjyYTB8J5+vGMVcHA1bS9DvPwngVxFqz1e2w3esu6cULOuXebMbDNiZhJUsDvep7/FovAkpKIq3Iq5r7RxPG84JIKUZ5QVE1fHpvl4fjGZuDDhu9DiudlOyMepSXKjSWnMn2r8Xn9rxK0XvPvKqZV6+ni8ZJzKuavcmM0bw4VWqm8+HClzczZnZGJCNilWCEWbZ0F6uoAkWkVnGmJNbbRGqN2o6wfn4Br8RTNHdp3JhEXybR2yT6CrHYfCqf/lVhlKTjQzK79zBtgig8b44/86BTCAnxUojwWAihRa0oc1pXzOqKTBukcFgf8lbypkYJybgqMVKhhFz+fFqXjKtiqQd5Fb70i5BozUqSspKkDOIkBEGW3WCIoFRLkQr81zUbujnzlhMOLNu7QRSvWEnChT6SKqzCFHOs88RasZIkrCThAvm8m0GstxnGPyWvbzGufoP3Tcudf3U4X+J8ybz5mlH5awSGSK23drfPT4X13lNXliKvmLXJ8VESQuC890gZckaAZVFRzCu0UXR6Cc55yqKm042JEkNV1MynBXW1KDTCtci6NjFpaJH9GqUUSofxLYTCiBdrs04eM7QOQM0dpvXnTKvPqN2I814QUCIlNVeWTlNBbP+0xyliJeioDgPTb68mgT4Vxs6TxYmSkm4a00kitle65FXDwXgeNAbzkroN8YuNppNGrHRT1vodIqNOsfIn0LJHrDbbXKBXfisegfVznC0p7D2K5h6JFkTq6YWGFIJeGrPd7xFpRaQUqdFLhzfdarmMkqx20vBvrcB7aucYtgXKQq+hVRDoLvJDEh0KjaJpmJaKJDJoGQxLwpdecqXPCucrrCvI69uMyl+TN7fahaHXG8fOcxEb8RW0jCjtHI9j2hxR2nmbjO2PtRQyYzt9gze7P2YzvkamT0t58uR2xqjeC85rrZ4jUR0GZo0Vs0n0lAWGbxPBwOTs06l+knBjdchnu3sYpbAudHBHRRGKg+mUozwnNYbkjFqK2jnmVc3udMbd8Zj9+ZymdXWUQjBME66tDFjL0u/1Kr+RkpUsZaPbZSVN6EQmJJ47x6QseTid8fXhEd045sqgv7yPfp9f83lCS0lidOhYKEnVWMrahvtHElFbxzgv6CYRVRoMe868j5c5MA8455eCbNm23l/0nK/3j/j8wf7L7PJbRdk0HEzn3Nw9YF69eHXJekvlKh6U97g9/4qeGbASrTE0qwyj1SceL9Fo2aNr3qKI7jGpfodtLqLQCCnPjZ9xVP4C5wvW0n+JTjpIkaCesUL4ssibmoMiBCIKQusyfB3PQmKleHdlAwlM6pKbo4Nluva9+YTDIqf2wZ0pUZpGO/Km5mE+5e5sQiTnOO+53O1zqdPj4XzK3ek4BA95z9uDNbpRcLnSF3QhWUkS3l1dJ21dLtbTjFjp5QW803Y2BlFM0oZYWhc6NlqG3zvvWUlSjJLcGAyXmTB24Urlg8d6qkP2zMKJ6lmI1CpSGFJzlViuU7sRjT/f9Pmiucd+/tdIkZKZN4jUEPWCCbx3nnxecvhwwu0vdxkdzuj0EqIkCJGj2NDtp9jGMj6cMR3lzKYFWSdmbXtIFLXWelt9esMOR/tTvrn5EOc8UgqcdTjryOcVVVlz+cY621fX6A5Ssu6rTD4ceXOH/fzfMyl/g/XzcyvcTsKoIf3oY7rRuyj5bLFiUMFIhtGAWEXgjy2VhYBMPZk8/MjzhSA2itV+RjeNQ7e2bdcqKdBaLdPET7u6qeWAVF8OFNBzRxtA2tznqPhHBokjUms8jRigpWBn0GOYJgixcGAJReZGrxMKkSRGCri+trKkRC32srBGX6CxlllVEWvF1dUBm70u/TQh0or1TgcpQEiBFhKl5AlTirNfb2o7Yt58xbT6lHnzFZU9eK1F4I9DIIlkworZ4P3+P2M7eYOj+iGT+pDKFXg8kYzJdJ++XmNg1hhGG6TqxbbFC3igcgXzZooRMSvRNgJBpvtsxFdZjy+RyNMvJLxOGCQJb6+t8ttBj24UYZ2nbBqKuuHA53x5cMhv7j/gjdUVrgzOpteYFCU39w/4bHefvdmcvKqXtsyJ1mx2O7y9vsZGt/O97mhIKUmMYS1LubG6wt5szjejQI90wP3JlL/+6hYIwVoWFhq+v6/2/LG45r+9tcrWoM308o96hCsRqLVpZF7KJvjlqFO+DcxrHTyc8yDCinbcOn3oxzyNvffc2j/ib764iJvS+aKxlmlRsTeZMS9f3IEJ5peWWTNlr3xI5UIIWaKenDiE90Sh2sla3x1S2oeUS5eR89ZrOJyvmNdfYd0MLYdo2SPVl4nFeqvZOB9rxoUff6rNkj5U2IZUhxW/jonYSDtspBlaKb4cHTBv6mUWRccYameDXe0y58RjvUcL+Qh9yHqHdSGLZRinJEoHi8uWb3iRV5LMRGTmuHXYjWK6URzs407kaiRakz4lxKcfPzoBjlP9hIvaciL02OrBs/ysteygREKqr9KJ3mZW36RpzrfQqO0RjZuRqstMzBt0zFtk5jr4Z3fHnPPMxgX7D0c8uHPA4e6ETj8lTiO0lnR6CVpLmtpxsDthfDijmNfUlSVOY7SRNLUlySKc8+Szkr0Ho0DBOkFVmY5yirwKwYzDTqBZvSSsn1PbQ6bVZxwV/9imgFec77kpkcIQq3W60Xt0zI3nUhpFa2OYqfSFRcXTngvBjUorRedcFn9FS/m6jJZnD5w8HTxl84Cj8pcYtUo3ehdJ9AS1TErJIE3ot/aVS0+yp0ye1rrPPteW2xOy7YqEm+ogTUiNJjWaYfbsbZ8Fzjd4X1PYe4zKXzOpfk/Z7F5QZ/viIIRAC4MSmkR1WI22GNX7y66GxxPLtC00VolVhhL6mZS8p+4D0MKQqg4b8RVS3W01IuH/Xb3yTKryt4WnaTmfN8YWyCJDohVXBgPeWF3h1tGIe+MJtXM0dc2XBwf8wzd38N6Tts5Ki87G49tcHEPoZFTcOjriV3fv8enD3RCw3OozunHMdq/L1cGA7V6XXvz91mgs8kCGacq7G+sc5jlHRUFeBxfLg3nOP91/SDeO2ep2uNTvM0gS4rb7+aKOvOc48qBqLGXTULWZJEYphkl85rC+WVMwqQsKW1PYirv5IXfmh4zqOY2z3MuP+GR0Oyj4pKJvwjW/cee/2LVwk90wXTae3lB/9X287BOFEMecrrxkXlYMsoTNQZdBltDPkkcGr/dw+2DE338PCg3vF2LCZmk5+jwoIYlkRKISMh0upI2rcf7Zg0KKiMxcx1ExKT9hJm7ifHnOQtMFPNbllOxyUPwttTtiPf1vkOInbSDW+VykO8agZZdeFLPT7QVhm/espRlaSi51e6wmKbEKnYCeibHO0THB1WYQJdTWtkGOhl4Ut++tJlrdYKvTQxC6Iok2pCoERV7tDTBSEitNz8TLoMdvHX5RCBx3+s6CZz369GE5gszcYDX9y9aO9faZ9v8ieII5wrT+DD91bGT/mlhvokSMeIqAGQIdanw4Y//BmLpskEoGvUZtkVKgjaY3zPAO5tMc2S5QdHoJg9UO80nB4e6EwWp3WcRJKagbR9PUJGlEkkaBIhMptFHYM5o4PI6yecBh+XOOyp8zr29Ru9EFaDMiIrlKoi+TmWvEavOpblOvLwRGDcnMVbR8Mp38vFDaPVxVkZnrlPYnRHIFqZ6/snuas+55j+kmEVoNEBynJ59l26dBsI4+YFr9gYP8b5hVN1/bFPDTQhAoUgOzRkf1sQRqo0ShpUGLKIT4nfldFKxEW6SqS+0rGh865krotsh4mW2ePxblq0CExcc2B0WKZ6+gC0KhfHVlwH/z5g3+81ffcH88WdL+bu4fMi3rVsQteHN1hUuD/rPvFcCkLLm5d8Av797jP315iy/2D5idoKxv9br8xbWrvLOxRvJY9sT3GYMk5sc7W+R1zdeHR4xaS+p5VXFvPObnt+8yryo+3tniR9vbbPe6rHWyF44c5zylbdif5ezNZtybTDiYB2H9apbyp1cuc2VwtkLj7vyQ3xzd5s58nzv5IeM6Z1Ln7JVjZrbkPz38PV9OH7RFRsaPh9e4kq0xa74f+q3H8dKFhveeom44mhVM8vCBKilJTEFsNP3H/LA8nlFecOdwfC4H/noiuO+c9NN59iODViPzV8nMDeb11xT2Po27mPfH09C4KfP6i7az0UEgAtVGbaJk+soTnUhpIqVJtWHFe1Tr97xAP0ron9hFL3qUujWMn75Su0h630ifbI93W0H2eSC455RtkaAJHjLPLlqC8xHLIBznwlfZNDTO0YkjkhNccdc+fkGxWDiMqEWo2InHLVbB6saSt+Lo2AR6ll8EkqnHj02Q6G08HzOrv2Ba/SFw3c9tAuOhpbM0bkakVon1Nqm+RLIIxhOPOxSBjhRZL0EqQV1ZlA66jCgyDFY79IcdEFCVNf2yRhtNnBrSTowxmjKvSdJAtUqyiNXNPnXZYBtH2olIOzHdQUpdWforGUkaLfUZp3tVC53HnNoeMak+5aj4OZPq91Ruv+1mnC+07NKJ3qRr3iFWm6fuCpz0Q1/qvk45UfCtPWsItwsTFyVD4fYybixa9IjVFkYOkCI+V13QAtZPsc2cWX2TUflretG7KNl5qsbstMf/osctwr8uAt43OF9TNg+YVJ8yKn/NtPoDld27EGret4XFe6rQKKVP6xNx6m1nukemL66gPQ94Qohr6Soab3HeoYWmo9Nn6jcW3e/tbpefXb7EwTznq8NDjvKCaVVxMJszLUt6cXAdPJjnXJ/NQkikWgS2tSvuLmgZH05nfLa7x2/vP+QPe3vszeZ4Qoe9G0e8ubrCn1ze4cbKSqAR/ZEUGqkxXBkMONwoeH9zg8pa7o4mzOuaSVnx9eER06oMmSJVzaV+n81uZ6nfWtxzfcvYsd4H6+8m5Gbtz2Y8nM24N55wlBdoJbmxMuT9zY0zH+uiEG28C66bQtI1CZmO8QQGR+heCBoXdBFCCLomYcN3maY9YqlJlUZLRc+kbCUDZt0ttpIBcet21jcpO+mQWbPNVjJ4qgNa4yqsryndjNoVZHpIqs63tfGSGo0QqtbY0EaKjGa9H3h+RdNQNfY1dgA/f1jvqFxFaQvmzQxjTEhnfEGnQKDaDsD71G6Ey//uwgqNgNDZKPx99ub/gby+zUr6FwziH5PqK0TqfFZUVcuT/r5dvpwvKe0+4NCyhxKL4uvpn+PCV7+2lqbNXHHOsdsaCVxdG7Iz7OGcX2YVWO+JtUZJEcKYPKSRQSkZksNt+BIiUF1GecHdwzGJMWz0W+euxpLFEb30yZa3kUOEVnTM23SjW8G5x94/9/epdpZR+UsaN2M9+2+Wk83Hu2M60uxcW2Nlo4ezwbpWtBx3KSUmUiQtzSlJAz1KEMIglVEMVjps7AyJU4PWipX1Xvu4YB6gdRB9u/b910ZhjMZEZ720eUq7y6j4BUfFPzIqf03Z7LaOcOcNiZErDJM/ZZj85JkC8Kdh4Wy3cLU5DmU6xXOdp6otZd1QNU0o3FrRc6TPPuFQMsUwxMgVjBzSuMkFUX8c0+rzMBH3dpk1chq3otcNzldU9oBJ9Xsezv8XptXvL6Rj9gO+XSzoNbkteFDsM21mgKBvuhi5iZHPvx4N05Aw/2A65WCe88mDh3y6u4cHauv4bHefh9MZv7h9j/VOsHINzwnFRtk0TKuK3emMvdmc/fmcw3nBqCiXc7FhmvDuxjo/vrTNB1sbbHQ6Z7YpfZ2h2pT16ytD/tVbb5Aaw7+/+RXVZBJCOOuahxNHXtV8vndAP47pxjG9OKIXRygl0UJi2/t13tTkdcOsqphXVQjsbBrypkEJwXavx3av+1IZHZfSVTo64Sf2OoWtcb41LvfHujvZOgtqoRhGGZmKcThy22crkYBjJYpJVMSVbJXVqMu7/R1iaeibIPC/JtdZi3t8MLhMT6fETzFfqNyceXPIbvklR9U9rnf+hDT78NU/kBN4hcC+sMK60knRSjLspFjnKOpm6dbxPEgh6CYRnTi4FOnXaMA7H/zS86pmUlSncp4SrXBGCAGt7eTz+hqL7AElMjrmTRo3I2/uULl9rMsviEIVOhvWT5fhY0IonCuoooO22Fhpux3mpV2pXtaB5eKxSJgNuhXnSxo/x7o51s2o3BFl8xAlM3rRu8RqAyGeTONeYDFGRvOCo3nOMEvopwlFFTp9m/3AER3nBdOiao8gdDVqa5mXNUZJdlb6ZJEJN4ui4mie433g706KkoejGYMspp/GNM4xzcPNo5tEj3QNhRAokSCEpBu9RW0PcL48sVp6PuV/oFBZ8uYOjZuiZRctMlJzhUTvIEW87I5JKej2U7r9dHmMz0LcUqCWJHuALKI3OP5/kkUk2aOPe7U0Zod1U0q7x6T6HYfF3zOpfk/R3Mf5/KW2+zxIEWHkCh1zg170Lqm+hpIv1lxY73DektuCuZ0vP8me7tLRQQv2+FlnnaOsLfOi4nA6ZzQrmOYVeVlT1U0b6tdjfdBltZtiRPDxn+Ylh9M8hN0lEUmkQzftsbEm0CiREOsNUn2FvPkGay9GY1DZPVyZk+jLbRftyoku2utz73gWnK9o3Iyyuc+0vslR8Y9Myt+GcUbN6xzO9wOejQVF03rLzM45qicc1WOmzRyJREuFfQ6FeoHEaBKjeXttjbKxaCmpneVoXnBUFBzlOUd5zv3xhCyKWOukDJJk2dWoWgODvdmccVlStotaQgi6UbQsMn52+RIfbW2y1e0uXdb+WCCFQKoQwvnB5gaNdczKis/29rk/njCtKvK6Zl7XPJjOMFISaU03MkubaiVDoWHb7lBeN+R1RdFaZC/QjSLWsuxMCz0n0TUJXfNyYrnEOipbUfsSJSxGKoxM6Zkn7yOR1Et9x7MQuhlzJvUeB9VttpK3X+q4noeXKjSCLalkpZuRxSG4yyjZ0kjcqdpxSkqurAy4sT6klyRk8esz6KvGMs5L7o8m/OHBHkfz518olhoNmZCpDgoVNBqnqHSliEjNFRwNs/pzKrtL0dyjcRcb2ORpqN0Rh8U/MK9v0anepBd9wDD5EzJzvQ33+z5xxk8Pj6V2I0q7S17fDkFZ9dcUzX1qd0THvIGWHbTso3i2E1BZN4zmBV88PODz+/v89MYOl1b67epwmIxZ57h/NOHe0YRBlmCU5OF4xv50zqQo6ScxRis2el2mRcm9owk3H+zTOMdKJwU8s7ImiQIFq6ga9iZzjNZsPmMhXBA6GkIYCnufWX0T6wr84wlarwjr5jhfc1j8HUVzj7XsX7Ca/CWRWnu17tjjl47nEZxfCQ7nS/LmNgfF3zIu/4lJ+fugC7ggvrwSHbrRu/Tjj0n1FYwaIE5xGbauoXQl+9UBu+Uetaux3nGjc21ZaDzxHOs4nMy59fCIX968w827+xyM58zLcG26vD7gz967yofXt+i0K3qNddzeG/Orm3cYdBKub66wOeyyPug89ZouhCJWW/Ti92j8lNI+fOX36KmvxRd41zAqfoF1Uzay/5ZIrbSdjdf/OmVdzry+xbj8Nfv5XzOrQ/r3D0XG9x8eT+Vq9ssjDqojChvykvxLfK6XB30GaUwnMvSTmF/evcdv7z+kdi4sHFhLUxQUTc2DybTVV4g20NNRN3bpwCha97XNbocf72zzJ5d3+NmVS2z3uo8E2/2xIdGajW6HH1/aZpgm/PLuPf76q2/4+vBoafEL0DiHq2tqa5mU1SNBst6HcGHXms48/klKeRzk+TJuc6+C2hU8LL+gtDOGZpueWf9W9/8yeMkcjeBbn5c1h7O8zUSAfpqw1stO1Y7TUnBp2OPjK9usdbOlY8jrgLxu2JvOiI3izuGYo/nzU28FEiWgZwbspFdQQmFETKJenEIshEKLLonaohd/SOOmOFdi3fyCXKgW8G0bf4/GTbFuHr77OUVzl1hvYuQqRg7QMkOK+NzcqS4aYZXJ4X2D9TnWF6Fr4fP2dU6o7D6l3aWw9ymae6GbZPexbo4UcXj/fSsqfsZHOK9qHo5n7I5n7E9nTItq6cQ2L2vqJliIFnXDrKwYdlIiramtZVZW4fcLpxBrOZzn7I6n7E1myzTsRVu8rC2NDcK2g1lOP0uWLlfw+Kq+JFIrAHSj9yiauy2F6nwngYvORtHcpXZjlEzwvqFj3iA1VzGtw9lZVp3Pi2f/rCMGcL6mcTMqe0DR3GXaajJm9RcU9sEFFRkSKSJitUE/+oB+9CGRWju1LmoxcSlswageU7ka6y2H1RFd3SFVKWmbJeCcD+nW0zmffP2AT75+wO9uPeDO3oiytpRVQ17VWOd5Y2ctFB6tcFUgmMwLvrh3QCeJqOpgBbva7zyDqCRJ9DZd8y6z6gsWN+rzR+hC5s1drM/Rso8Qpu1sbKNE8lqJ6UO3rKZxcyq7R97cZlp9xrj8JDhM2V3OPXzkB3xHaLVSiDbvJiImIpYxfdNFnyFfoxuHEMhio1lqE4dJykE+5ygvmFVVyIiwlrKpl/cP2eohjQr5TVlk6CUxK2nKG6srfLS1ybsba1wbDsii1+c8uQgoKUmlZKPbCQF9UiKF5PKgz93xmFFRtu9jTVEHPWVlw714oY9cvJ9aKjITXOiSNjMnMxHrnYx3N9Z4e22VXhxT2hnj+gECSd9s4oG5PaRxVbDQVl06epXcjhnXD8nUgL7ZpLATZs0B1tdY37Dw2jQyJZIpqRoQywzra2pXktsx+9Ut9oovKdyMYbFN3d6vYtmhZzYwbXildQ2NLyjshLkd4dtr/OJe0tNr7bGGnzssjS8Z1fe5l3+K8xaPQwmNEQlds0aiFlrCM+r5XuaDXFCkbu+P+M2t+8yrCiUl713eoJ8lpyo0lJRsD3t8cGmTrX6XYfrdBu6cxLyquTeaUFQNv4henLIcignJSrRGV/VABM97c6owogAtewzinwKCvLlDaR9ifcm3sdrlfEVh71O7EbP6JoneIjNBqNqL3ic1lzFy9Tu3EDwLnK9xPqdo7rfFRPjKmzuUzQMaN6bxs5ZCFWhU3tdn4krPiop7R2OmZYlWkqppOJzmHEznHMxy8rpeuk8lRrPSCdSqw1lOXjUoGQLEekmwxt2fhOdZ79EqpGe71ka6arVPs7a43+h3cM7hpXyqG5Vos1oG0cfHAlS7y0WMJ+tLnG04LP6eaf05g/gnDOIf048+pGPeAmFeKz699QV5cztQpfK/Y1rfpGp2afz0QoTfsNBj9UnMZfrxj+jFH5zJrUkKuZyw1G2R4bzjqB4hcsFOsrUsNKx3jOcFXz845D/+5it+8fltiqpBCsFKN8U6z/3DJzumUgiECo5eh+OcvdGMaV7STWPeubwOT0m+FkhivUXXlxwWf4dAXugCiXVTCl9ykP81ef0N69l/zUryF8RqHXlOGrPzgMfSuDl5c5uj4h8Yl79lWt9srz0zfigy/nggACM1AxMcHAtXooVkaPpkOiN6gT7jadjqdRkkCVeHA/786hU+29vj87197ozG3BtPmZRhsly1DnuRDg6MvThirZNxedDnjdUV3l5f43K/x1onoxvFF2Zy8DoiUgqVxHywtcHV4YB74wnfHI24dTTi1uGIB9MJD6dTZm2QdG0ttbVLClViwnvaT2KGScJmr8t2r8uVwYBLgz47rWtVN4qYNfe4OfkbpDC82/8XeBy35//ErDnCY9mI3+Rq1uWg/IY/TP4jl9IP6eo1Dqs7fDP/FYWdUNoZlgbw9PQmw2iHnfR9ouhqS23a5UHxB+7nf2Cv/IrK5XwhFA+KzwFYja7yVu8vloVG7Qum9R4Pyy+4l/+exhWB1u8dzje80f1zMjVcFiBhjjDjXvEph/U9GldgvSWSKT2zwY3On54oNM6GVxp1UgqMVvgqTM7L2i6deOD5dpxSCLpxzFo3YyVLGWSvT6ERGU3RNPTSGPOUm+vjCALNBuddm4JqMCI606pr8NTfoGveZBD/COcL5vVXbRrxRcPhfIHzBbUbtV9TKntAaR+Q1DvEagMt+8EOV3bQIkOKFCXjF7oznSfCSqE9USCEIsH6AueK8N0XNG5K46ZUdo/K7VM2+1R2j9LuUrXahVelEqWRYaPXoRMZNvtdtgZdIq1Y62V4oJfEKClY6aQoKRikKZ3EsDXoEhuNlpJOEpHFQdy80knx3rPaDbqnbhKH7IiqZthJ6cQRqx3HtbUBq530mbaEC/2P5JiWVzR3qe0odLD8edPygtahsgfUboxAYd2cqtknN7eJ9AaRWsPIPkpkSGG+le5Y6PZ4HDWNm9C4MbU9pGgetCFpnzMpf3eii3ExglyBwqgB/egDhvGfkJpraDloz5vTQQqJlpqe7rKZrLfW0ZCohFSlj6yaNo3jzt6I33+zy529I6racmN7lUtrfdZ6GUfTnPx3T459IQRCgNGSNDYcTObc2RtzOM1xz6wbBFp0idQmib5Eoi9R2UPsOYdFLnDcRbtH46Zt9zEnNddI9WWMWmmNCZ7M27iY42nfGO+wPqdxs3D9tAeU9iHz+mvG1e+Y119SNg+/dzkZP+D5OHbbkiQqpvGW3OaUviG3JUootFCoM94aF0ngaWRY62R0YsNGJ+NhK/aeVzV5U1Nbh8djpCLRgcozTBM2ux12+j2uDgcMk6Q1IDn7Yo8AOrFhu9/lz69eJmvzIpSUdCLDteGQtc6z6cWnxbXhgH/55o22gArXJqMUH21vspqlJPrs57LzExr3AME+mTliq6dJTJe1LObK4DIHecnhfE5eNxRNE4xYnFt2MyId3tNOFMTiq1nKWpax2euylmX042iZnzFuauZ21EYblFhfM6n3mDR7eG/pqBUaX1G5GdPmgNJNCZ2LhEyvoEVMLDs4LNY3FG7Kg+IPDMw2q9FVpFAYmZCqPpkeoIRBippUDejp4HqV6SHqxDVv3hxyN/8dc3tIJFMS2UHKNkfIWxLVa5kGoQvd+Jra52gR0dVrbRelYNYcclTdYx4fUrltVGtTfRa8kkZjtZvx7s46D8czHo6mbWLgKc+oNuAveckT4CIhoLU8O50Ti3UNhcupbEnlSjqmj9FnXV2TKBET621W079EoGjchNqN+XY5vA7rZszrryiae4zLX6Nlj0itkqid5Q090ZeI1QYRayiZfGsr1qHIKKndmNoeUdkjancYCopFIdHsUbkjGjfG+RpP3a7qh5As164avCrWexm9NBQDHh/ce5Sim8a8uelCMaEU28MeG/0ORiuUkFxe7bcJnMFedOHT//bWGs3GSmjfiqD98QRnK60ksVasdDMurw4wWqFfYLoQLJTXECjK+Kc4X3JU/IKmuSj9j8f7JtC0modMyk+I1Ab9+EN60ft0ordI9WXg/LJbXnxEFuvm5PU3zOqbjMtPmLcUqdoeYX2O8zUXVWSAQLTBfKvpX7Ka/iWJ2jpTkQEg20J+LV6lo1sRohAoodFCPVJoVE3DF/f2+e1X95gWFdurPf7Nn7zNT9+6RCeJ+PT2Lp/d2WNePL17k8URW6s9ZkXJnb0R03n53FySIHDvkeprdM27TPg99pzDIh9H6KLVHBR/y6T6lG70Dr3oPfrxx3TNOxg1/FYKjQCPo6Gyh+TNHabVp0zrPzCrvqBs7red07IdZz/gjxEhWC0EEc5szqSeMVJTVqMBl+X2C12nnoWoNcp5e22Vq8MBjXXUzgXXvceCJxdU20ChCnSfSOulC+RLvS4hWElTOlHETq/Hv3337eXPF0F550F7/9HOFtdXhtj2dS320Y0iOlGEPsWC7+Oo7S6T8m+Zlb9iXn2CVlfox3/OWvYRH26/iXM66DQW1vOwzGoClpbfwaZfLIPtFuZFJ+etQki0NAgUjobGlzSupHEFzltqX2J9hQe0iJZ2x0OzQ7rMBQrziNoVfDL6n7k9/yeuZj8OxksyQ5uYVA1IVZ9RfZ/cjnmj82fspO8DoIQhWoa5eibNLl/N/oGeXuet3l/S1WsYmbY5LxDJtD2W8DoaV1G7ktX4Gu/2/itAUNgpn03+A0fVHXI7JrdTUtW72EJjcbMJ+Rk5k6KiaRMSF3aTp53ACcIEKzb6WxfTvAihkFJLR4cXoqVKVb5i1BxR+SoUHKpDpk/XalokhmvRJdM3sHFOYR8CkqK5+62uhC1WDZ0vaKCd0B9QqXaVTn3VrlAPMaqPEh2UzJariBIT3JqERqJAyLYQWTg0iHY/i/HSCue8Xe47fG9wtMXB8qtsNRczGj9ddi4aOzrRjRm1moyLfc8irYlOtKIXQ8U8JrSLzaOnWSINXj9+ngiy+Lg4XWxrkUy6GIWRFnBKt5DgDBShZZ9u9G7LGQ8WpLUb4/zztUcvB7/sji1Wdz01lT1gVn9ForeCHaoaoESGllnb5YjbL9NOENXyNTwPztdLapjz1QlNzgzrZ8uCdKHDCUXQfRo/uTCa1EksXOUG8Y/pRu+R6EsokZ65++cIfuu5LZg1s9YK0TOMhk+IwRvr2B3NeHA4JYsN1zaG3Nha4drmCkYrHh5NibTkWWdHpBX9LEYrybQIjivPwrJ7JmIyc51+/CMqt0fR3OPiijdYdNEaN8K6GbTuYZXdZ6a/INYbobsheijZQYkUJZPlGBNoaG+wz9LRLaZx3jcnxliN80UodPwc63IaP6V2Y6pmj9I+JG++IW/uUjb3X3mhSMs+Rg7bnKOYsrnXUiAvDs6GiWzTOGxjkVIi1eK9AqXD/0NukMPboBVbhG1aGz53bcL9M8wPPM46EIEJIYRALO77rZ/nwkDjhcfnHYWdUbkC8EihSFSHSH53rIilPkMaerqzdJyKZfzU7IJTbbN1b5RCoKPoObYkFwfRzoWMUnQuUNvRi2N68fnqdL2vaOwRlX1A2dxCCI2SUzqRoxslyHMcLxJNIntYGgo7pXI5DtsuCEmsq5g1h1hfk6o+kUiD5s5NGdcPOfa1DS5Qs+aQ3E6oXdBfCNrOmDKkaoAWEVpEpLpP16w9cizOW6yvqWxOaWf0zSY9vUHPbKBF/JRzLPxfS0MkMzI1oKvXQ/EsIiKZEgwHGqwr8fLJPLMX4cxltvOeaVHyzf6IaVHiPYzyYslxOy1Ci16RmNewoyEEkQqrAqepgZSQGBlhfcO4HjFtJhxVB2ynl09daCwgRUSsg0DH+qLtbEwvzDbyNHDUeDfGuhlFcw8hFALdfldo2WtF472WXtVByw5KZG23oy062sfTdj8Cl9sGziANvqVCWVe0N/NFQTFbFhShgCjwNC23MEw4fCtcWhYrf0S+9K9ahksR0TFvoGRC1ewFLU71BdWFFBrH8NTUbsKk+pRZ/QWCkBwfqy0SvU2id0j0NrHaXFKrwhjqtQX+KWiLvsb6GY0N3b/S7rXObUHkX9g7lM1u27kocb5qg+W+jfEh0LLLMPkZa+m/oGNuoEWH07yux9G4hsKW7JcHPCh3qV1N4y1vd9+gbx7VeljnGc0KRrOC965s8OalNVb7ndai9sX70kqSxQYpBUVrhfsiBx0pDJm5gccxqX6LQC2k92d+rWeFp6Fo7lPZfSbV71EyI1abJHqHzFwNlrjtGAv23SEj5+Six/O2vihgw4LGhModtGYSDymbh8e6Lz/FulnbRW3aDsardU5jtU43eo9YrWPUCvv5X194oWGto64ainlFkVeYSGPiMFUQQpCkEZGS2MbS1JamanDOE8UaISVVGTo3aSdGaIW1jqa21FUoWLVRy2JFEAoRKQXilKvWzlsmzSGjeg/wGBGzHl/6TgsNCPSpVCVsxus0UYMUkkhqzLfWWfsB3xWUMKR6QGlnzO2IygULciUMEk/jKyb1QxpXkekVIpXhcBxUt7k1+yXWBaaFFArvw88bH0w6PH5pN3AaOG+pXYHzDUoYjEiJZIZ+rlmGIJIZHb1KJNMlQyUU0AYtYsC3gvWzX9Nfqp+nlaQTh6AnoyTdNCKNDMOWN/50caqgE0ds9DoIYJAFEWyk1WvY0QhhaS+iTi1Seif1mINqj2kzxrVhWl7459INngXRrv5Hakg3erddGR4xqSLKC3PEeRF8O3m34Ksn7p21G1GJA5RMw8qhiJGtC4wU0bIgCXzABScwbDe4Q/mndDBOajAWRUceJornRH16VbyKJOVi3ZUe34ZEiYTIr9NPfozHIVBMK2jcBMdFreyHJHHXdscg7M+6ObU7oLD3iOohRg5Qstd2N9I2C2QxbsKYWaycP1Jcttoi68pHHMXqVosRKHWHNH6C95ZvY9K7gCQmNVeXdJ6OudE6Jb0sZSysAje+YW7nQRDuHIfVEZlK6Z7I0/DeUzeOunEYo+kkEZFWyFNeZ0PWS9Danf4sExjZCw5U0TsUzV3y5i61O3ypV3tWeOrWuWXejrE8XJfsLkZ9dWIhpNsGccYIYU50XI9X1xdjK4yzVg/myuU1KHRSJ9R2TOOOWm3S6MS16dUhMCiR0DFvsZb+VXs91YzLT85l+y+Eh2Jecbg7QchwHaqrBttY1rYH9IcdxkdzZuPjrJlQQIQiIskiosRgG8vBwzH5tAxhnFKg2jBObRRJFpF1klMFbC7up42vuV98ydezT/BAVw2IZELPrF7Uu/HiY8NjvaWwJdNmRuEC3TDVCWuRQvH87tkP+H5DS0OmhlhXMa4fYn1NJFOMiNvrieeguo2RCR09RKKYNQft1yF9s0Ffb7bjqOKwusuEUEg/DWHm6ThpoyyW5YhACo0QMlCH22ujwyIfmfI/um2BQgnTztVOZCYtmShPPufU789ZHhw4bJAYw+agGwRBccS8qjmczekm8XO7E4M04fJKHykE/TRhkIbAmdcNi1yQ0NF4TqHR/jmsD/h8+nuUUGSqi5YaI88umDkJJTp0zA0EAuvaSVox+Y4KjedjsVLcuBEtW/TEQA0TxJPD9lH4J/6mpYU8Qa1afv2Al4GWGYP4xxg5WNoZ502DcxdPIVrAU1O5A2p3hGhunygkFtS6UGgLoduJoG5DE2XrmHGsswkrx7btYrXj5Gldrm+9uyVQMqEff8Ra+lf0ow+J9fYr6ZhkmxALJ1yn8BzVIzyey+mlRyhUC96xEgKtzmbU4JynbizWOaQ8Lb870KciuUovej/QF/PyWys0TmKRk9O4CUVz97ExthhfCiEMSkQ8Ek7qfbvYYfHU7RhzTxlj7Y3et13Zc3baUjLByCHd6D3Wsn+JdTNqe4R+CdrCmSFASEExL9l/OKKuGpqqYTqaU+QVN96/xNaVNe7f2uPgwRgTa5QO528Ua4brPVY3+7i+p8wrbn+xy9H+NFCpWtqUNgoTada2+kRXDNqcbh7g8TSu5Pb8D/z66D8AsBrtsJO+yWXOP2jstFhkaUyaOQ/LA0b1hNrVrER9ujojUa+Pff8POH8oYcj0kHlzxKj8GoFgJb6yLDSmzT775S2G0Q5DswNCMK4eMGsOaXzJSnSFt7p/gfU1uR2zV37NXvUVTxYDIRg6LLLZpy5mS6EwxEhUEHi7gsoVWFcjpeJZc7DFTO0iSuEzzYQXfEGjFd0kDoJVJUkizZCUWOtn3pSkELy/s4F1LrRfjeba2vBbcSo6KxZBN3op+hF4/+Tq8uIjyVTGRryFQBDJOHypmPgVLi6hsxERqXX68cfh4o9kWn9O3ty5IH79y2JRDJz4yQ/1wCPw3oLP8X6Gd2PAIuQaQvYJp6EDP2vbpDG0OoXT5k94N8P7GYgYIWJAI54odCVKpMR6m2H6ZwhhOCp/zqz6MgjnL6yz8TgWhcCJ1d/Hxoug7WYsumGIpTXfSR3P61Z8SgypuU43epth/DO60XtEanUp/ntZtL0/aleT27y9HgWqxlq0urS2hXCtTaPgWJOXNYeTnOo5OovlPtq3cV7W3D8M9t6DLCFLohfefpZaDRmTmTewvmjpTAfYC7QNfjZeNMZEu3KnT3Rb/YmOxuNj7NtCKLYzfY1B/BN60QfBrY2oXXHMuLiskgBbO4q8YjrOGbUFgolCB0I3Gu88dVlT5jXFvKSpG7TR6EgRp4asl9Dtp2ijKOaeprHUZU1TNUSJIe2E++J8UtDppwgpTt9t847GN1SuoHShm1K5/FTp2xeLxSIJ1K6hchXWu6U73A/444YShkwN0DJi1hxgZEyq+nTUEIcltyMm9S4dvdK6TEU4H6hSlcs5qL4hmgcht/U1tS9JZA91ku4kjvfV0WvUruBe8Slze4QWER29wkp0hVh2kELRMatcSj8A4M78N+yqdEmf8nhWokusRFeXGUqvTtJ+Ns589xOtW5RWMgjGnEMJsbTyfFbdIAR8fGWL93c2lv/X8vXrZkAreFOtu4AKTgPe81RamEDQN0O0NFgXLG6DqCYiPgfOqJED+vFHmAWvOI9ae9bXqdD4AS+Gw/sxzj7ANV8DFcp8iBARiBR8jXMHgAMxRJAtxaqngfcTnH2AkAOEHIRtPvX0lkRyyEryz4jUWhDXu5x5U36rnY0X4ZiqV7O8APrj376eCKv6/egD1rJ/QT/6iNRcf6VOxgLB+9xSuop5kwcbbWHo6R5XssvtFKc9CiHoJBGdJGJWVOwezZ4r6H4c07zk9t6IeVmx0kvpJtGpaYKSiMxcRwjDtPoDeXObomm+g0LjRfAEnVdDu0T4yO++K4TJqqJj3mSz89+TmmvBXEOqVswahJkXeZxN3ZBPC6ajnNHBjOF6l34nFDg6qtFGYRuHtRZr3bLUN5EiSQzD1Q7DtS6inQ9IGVxumqrBxIZOL6WuGqZHc+qyQaljsfmLsLD/9P710uAJQAsVOPY4rHdIJEqczrnyB3y/sdBoaJkwt4ck9EhVn360hfOWvfJLJs0ua+46mRoEjYa36CKisnPu559xUH6DkQlaRBR2QqaHaPnkYrUWMX29yaw+4Pbs19zyllh12E7ebQP+OoCgpze43vkZ9/PP+Gr2cxpfLcen9463e/+cnt7EfQuZPmfraLQnTGNdiG3PSw6mc6o2AXm1l7Gz0kO39KnHTzDNLpoHeLcHfgJyBe/WQO4g1NoT+/vuENbvtgdd/vc/eo8fX90BPG9urLGSpcePal+fEREdJbAyCJulUCihXok6dbyPhWZjjX70IeARQjKt/sC8/grrc17fidcPCK3NGu8m2Pomzt4OVCC5AiIDoQmTnhoWtDgBYWm1aJ9/clU1ApEsOx2hkzHF2Xs4ex+Jx4sEQfTEAsVivHofrJQTtc1K+hcomXJU/COz+itqd/ga0vNe9/EtEBg65kboZCR/Ri96v03+Pp+ALCkkSmiGZsCV7DKFLahcjZHmCVcboxWX1vpc2xzy4HDCzXv7/NMX9/Desz7oMCtqGrtwVPJUdcN4XnI4ybl3MOFXN+9yZ3fEoJPw1qV1tlZ7yNMadggQaIwcMEz+BI/lMP87ZvXNVsPwXa88Pw2vw/gKHaFUX6Ubvcsg+RmpvoKRfY5XyzVKZBgVqI8XdZ6aSNPpp1y6sU6cRmTdmKwbBwpV48g6MUorZuM5ddUwXOvRG2ZEcehmdHrpsnBIsphLNzYYrHZx1hHFhu4gwzaW/kqH3jBDm9NPxq23NL7CvYZmH/6x727Bofc/aDP+2LEQTa9El/mg/6/RMmFodkhUH7xjO3mfHw8dq9EVYtVFiwgvHJvJW+A9oi1SlTAooalcjsezGl1hYVqxGEORTNlK3ybVfUo7w3mLkRE9s0ksO8tzKZYZQ7ODRJGpAZZmuQ2PZz2+gZYRCV2k1uyk79E3mwyineXr0jJmM3mHTK8yNNukevBol+WUeOlk8Lys2R1P+Wr3kPG8pKwb3tlZZ73XQUXyKaeVx9s7+OqX0HwC7htQ74B+HxEZeK0KjYBLK33+xz/9aMmDE62f8uNQbWFxkdByQDcKrjxGraBESml3cfb8BIg/4CLgWwH9CNd8hmu+QkY/Rcqd0HkgAhrwFd7XLDjkwe63DM+lZllsiF5LjWq37qd4ey98uft4kcGiiHkuJJFaZTX5S2K1sUwLtfXFTWD+WCGQKBnTiz9kPfuX9KL3yPR1zrMVLYXESM1qtIKWmr3ygIPqEPOUQsYoyeX1AW9sz7j18IhbD4/ophG1tXx4fZtpXmJdmKiFQsMymhbcP5zwj5/d5pNbD7izN2J90OH9a5tcWuuf2Ydfyy7D+Gdo0aVsdinsfbxzrSj/BzyJQOXqRG+w1fkf6Ebvtroedfx7odAyI5JDKu8u7DyNEkOUGHrDjKtvb4VRfLKJIqCuLNPRHGsdN97bYevyKixsa08MlbQTc/WtzUDL8+HJoS4WtPy/M5hqeJy3NK4+Qfd4nXAszF18d/58tTs/4HWFQKJZi66yEl1uS4Nj45vL2cdcyj5sy4XjRZvt5D22knfaLRz/faybOGmeExDJjO3kPbaTd08IwcPfJ7dtZIqRCT2zyeXswydGYVi8EGgVk6gePbO+3MoCWsRsJ+/j8cjWNONl7movVWgIEZwjQFDVlqqxNNZxOMv58uEBG/0O673OYxcQgVDbYH4UuNZNAqIP5yyiOw8sjlsgkKeI9Pw2WqMLDrRWAzq8iU8btOwyqT5lWn1G48Ztd+MHvH7wy1a/F8fc8GObUxkKDfsgPEZ2wTtccwewCJHg/RzvDpHqKsokODfHuz28z8EXOHfQUqe2X3g0JzsbUmhitclq+hcYNSAuN5hVN8mb2z+kGL8AAoOSCd3oPXrR+yErw7xDJFdOra05/b6CT3+m09avP2IlGjKMhk881mjF5fUBVW3ZHU2REnaPZvzNJ19z8+4+87Jm92hKbR2/+uIed/fH9LOESV5yd2+EdY63L6/zwfUt3r60xtqgc+pC4/g2JJEiIdHbrKX/HCkiRuUvyZvbFxyQ+H2EINWX6UbvspL8OZ3orTZo81ERv/fBJMSoVRo3B0YXczTtPp93X9Pas7Y9IOsm9IYdpH46i0G0oWfen0wDOr6fnQVBp2Rf2mLz4rG4rjqst1jvqP2LraEDwmOm5S+YlH/X3iNiuvGf0Il+9OgjfUPjRtT2AfPqE0p7BwCjNuhFf0qsry1d+8LjwzuX158zLf8W52u07JGYd8iiD5Eifmz7gbZaNfeo7D0au0/jDtt8pKrN91BI0UHLAUZvE6lNtFxDndKsYGHaMat+xaT4W6TsYtQaqXmXRL+N8zOsm1A2t6nsXaybYP0M2vuoEBFKdtByjUhtEetraDU81b6ff1we60Y07pB5/XuK+gvAI0VCat4nMW+g5fCJ13nS/EY9hSrbJqKc6ue+NfJw3i9NPyThPFosEEUqhEM677DOYb0PAYoyBAweH5NoDS4klbWUtsHIkBFn2+cqKVHttq33ISSSsP/KWQ6KnMLW9KOErokwUqHPuvB0pke3WATaSSForKe2QfQ0mhd8+eAQIQSr3ewJ3jDqMshNvFCB2vGcifGjavona7FHtvvI4591UounPPZZj3/0QnvaY3kUz7u4nFzFOdsHZmQPI3tEcpWueYe9/D+07ioOa0t+uIG/fli61bSrloEupTm2jRMhQ8Q9ABzCbYLPcfUngEeoTbw7wDZfoUyNVNdx7j6u+RQwCJnh3RHePgQ1O/VxLUIiY71OrNdJ9GWMHCCJqN0R1hb8MJ6ejeAMNGAl/jM2O/+WWG0S64vpzC7SW1OVkqqUIYNnPnZRaKSxYW8yY1pU/ObLe/zmy/ETV6Wj6aPXYAG8dXmdH13b5KPr27x1aZ00PnsOQLBUjonFJqvpP0fLHrUN+RNB0P/DuAoIgV6pucpG9q/pxx/RMW8gn0JPEAiUzIjkGpXY/w6O9RhKK9a3h6d+/HktxgXq1OvZ0VgQXDyEIsM1NM4uk65Pg2n5c+6N/y+tNXwPgSIzH4ftL+YjWBq7x7z6DXuz/xuT8u8AyMyHqH4HozZOZFaFZ4Alr3/Pg8n/FetnxPoKK+n/gcS8tSw0FvOcYIRQUDRfMC1/QV5/RlHfpHFHOD8lLCLEGLVBrK/TjX5KFn1MGiVIn7XHevJdeRqCpf2k/Dl3x/9njNomNe+y1vk/kui3sG5CZe8yKf+WSfkPVM1daruLpwI8SvQwapPMvEcn/ilK9l+50Aiv39O4I/L6Jgfz/zdH+b8DQMsV1rL/UwiUNQbFxbq/We+pnaVyFussWioEUDlL0AIKjFThMTZ8aSlRRiJPMHCgtcVwnqKpGVUFqTb0RLwsPGKliZWmsA21swgitJZY78ibmnvzMUdlzpXuIKTMG4E+o+7wFQoNyUa/w8fXtiibBu/9Mul7pc3TeCX4HNz9MHlyu+DnBOpICnKAkDugrwGtZsI9BHsb7w7AHbGcIIkERIrQ74B+o914Ex5r74bH+lnYNirw3+U66LdBrbfHMgL7Dd7ugj9q6SyA6IFcRaidUET5CvwEb++A/abdlwBq8A2IGEQXod9tj/3lIUWCUSsM45+iZYdJ9SmT8vcUzV0Ke58fJoivC0SgOskuQnbwrouQqwi1GsbmojUqDEKkgAUhlxktQnaR+g28G+B9GbZBFXQZ7hCpriH123hf4X2OkL0XHM+zYeSAXvQhUkREaiV0y+qbNHbUOlK9Xp3H7wpGDjFqlV70Af3oA3rxB8RqAy2/i+zeZyONDB9e2yKLDdc3V7i9e8T+eM54XlBUDY0NHHKjVHAO7Kas9zNubK/y9uV1rm4M0acU6T4LAo2WXTJzg43OvyHS64yKXy2d815Pzca3AYUUhlRfoRe9xyD5Savr2TgxQXwSWnSI1BrqOw6n+67g2kLjdStUQxGo6OqMa9kl1uMVrHd0VEqmT/9ZKdnBqE2sm2LdCOfnQEO4Tyw6FLbNb3mIPWEK46hp3CG1OyASESwchnyF9WOsG+N9gyTByHWU7D1CtfFtaO6s+jWz6tcU9U2K+ksaH45DyQxNj4WVs/Nzivom1k0omq/Jmq/IzHuk5h20WjnT+xeys2bUdo/SfsO0+Fsm5c+p7QMau4cQCqPW8VRt6GqN9aHL4Xx5LtcR6ydYN2JS/i3j4j9T1J8jMGTRB2TRR3TjnxHpq0hxsUWGB/Km4qgsuDMbs1/OyXQoBkDQ0YbL3QFGKr6ZHjGuCgSCQZRwuTugZ+JlVwNgVBY8mE8YVyWzpgqJ9UoTvOv8Cd1GyJDbTnsM44SH+YzdfMZ+Mcf6YPz0sni5QkMKpFSs9TLWek/eXF9l9WLZPfAzfPMlvv4d2C/AHQIeZA/kNpifINQ6nhgQ4Hbx9S/wzddg78Ji4MkBQqyEAuVEoeHtV/jqF2DvgGtXh4QBMQT9dti2Xwv7dEf4+hN881l4/KITIzfbguEnCLkJfhoKnvpXuOpvEeiwTT8HXwTevFwP39UVgrf7y71PSsYoYozs04vfJy2uoEXGqNRteFTNcbDdDxPE7wrhXIjxooMQGUJ2EHIFKUO41HK8Cx0K0aUdZ2gtC9FBqmt40cO5I4TICIXrHO/GCB0h1RW8O8LZg1NoM54No/oY1W+7G5cwagXrc3LvqN24HU+v1w3+20VwBIrUBh3zJuvpv2A9+5cokbRuQK8X0tjw7pUNrm4MeefSOrd3R3x+b4+7+2NG04KybkIuUqQZdBKubobHXdkYsLXaJ9KvrjsTQqFFl8xcw6ghkVqjcVOsn7fWt/8lds0EUkRo2aUbvcNm57+nG71Hqi89tZNx8nlKdlqTgfMbb4vwL+ubRzoFIfir5WWLp9A7ljki3w48jtqV1K7A+tdLlyhEMOHu6JSOfpnPJkwEpOgQyU0KV1C7fZzPW7pSdKIADYVGbXfxvmif6/E+FBqNPcDI486qo6SxR6HQoEaJHlquoWWPk7Qd70saN2Za/iP7s/972JY7QoksUJvkEC2HeBqsz6mbB9TuHmXzNXn92ZLipNUqSvYJ9uyne/WeCuum1PYhRf0l4/JvOJr/O6TMkCLDyFWU6gaXxLYg8e0c51UDfBedDGuPKJpbTIq/4zD/nxCAkl060Y8Zpv+GRL9JpHdetLlXhsczb2p2ixmfHe3y1eSQfhTTMTGx0qzFGf0owUjFl+MDHuZTYqnZ6fRZiTMybdqOVsC4KvlyfMBRVVA04bxRIjjHRm1XpHYOIxWJ1kRSYaTk7mzMN9MjaueIlaZ5hcyCV7JEsc5T2zChl4LA9TqtO8kz4dpuxh7efgnMEdE/a/UcPvy8+QLsPbD3QSkQPbw7xDefho5B9FehIBEdlrkC6sbxLnzTPv8WqMsI8xOQ3XaSFgVHILkGVOBGeHs3dClEgoj/27BdAPcQb78Bezt0NBYUGT8PXRjzp4jozwELvgxFk7sfiiZ/1G7nVVemBAJNaq6x1n7vxz9iVt1kVn9B5Q7bIL0f8Hqiwfs5zu4GQbeft6szDZ6F2PMpV2vRQ+qreD+lKf8Tzt7C2dvIczBVUCIj1Zcg+TMitca0+oxp9Sl5c4eiuf9fnPmAQCNFsG3tmLfoRG/TNW+TmWvBcvqcnKUuCkZLVnsZWiuGvZRpXlLWQVcHoJUkNopelrDSTeml8VNNL14FAoUWGR1zg63O/0CqrzKufktefx1MLf4LMSCQIkbLPh1zg378I3rRh3TM20HX85xOxgJKZqGjcY6FxtxOeFB8zdx+zWH1AIdFIFiJtliLL9HVQ7p6+MhzrK+5V3zJfnnv3I7jRfDeMbNjRvUe4/rgW9vvtwkpMozapHZ7YH2bdH+AkkOkCBTGRUejcYdIkbYdkNCtaOwhjdvH+evLbXpfhaLBT/BYpEwwah0lHu1oFPUXTKt/ZFb9msbto2SPSF+mE31Eat5FiR5SpkE/SEVjDyjtHWbVr6mb+1TNHWbi10RqE7wj0pdRonuq1+18QeOOwr7tPiAYpv+GSF8l0pdQIkGICO9tW2yM8d6i5ZBIXULLZ1NJX7zvGdZNmZR/z7j8a/L6dwgEmVl0Mv6UWF9HvQJb4CzwHo7KgnuzCbltWhfXoKGoCPQmCPQ076FxHnwoFhA8wSYSIhTCUkikDIIGKQTOewob7uVSCKx3lE2D8751OQzU7sY5BPaV6IovdYcMtA6orWVe1u2BQWI0aRzBYxyxs8GGbobbheYrQCDid0G/Awh8/Wuof4d3gVIlRA9UFuhP9gHoNTAfI9RWKBaEIrQcT75UB24E7gBhfoowPwuuV6JPsMRoH+9nodth74PbA3UJEf8VQl0OWyn/GppPgojX3g9dE0RwC3JjhNpBxv8KUIFb6MZ4exP8OOxfmpY+8/JYrDSl+hKpvkTHvkVtDznI/wYhFPP6a/I2Qfm7CaD6PmAh0A4r1ovJoxAv33F62h7AAMljEwobigs/CV9uDEsdhwztbyEIwXWm3YZCyAGSa3h7F2t/EzQaftLS+p50qjgLlExRMsWoNbrR+2T6Olp2EWVE43Ksn7erSd9F6va3hdZSVGi06KBlj370Eavpf0U3epuOefPcBd8XBa0Ug27KoJtydWP4nRyDECFsLhVXidUmib6EkjECifM1tTsKqe+voTnIeWBhAGHkkERfoh//hK3svycz11Gyc2ob5ECdWj1X6tS8mXAv/4qH1SHfzD/FuhohBFez97nhSnbSN54oNBrfcC//ks8nvzy343gxPKUrKN2cSfPtJ85/G1AyCxqLpgN4nJtR24PWaTBMdD2LQuNgWWgENzdPYw+p7UGrZQhwvqJ2h20xYpEiRav10HVoQ1DBUzRfcpT/r5TNVzRuTKyvkUU/Zpj+t/STf444EQLrfYP1U4r6JgIYuzmVvYOvaiK1gRQZWq0gfViUfdFc0PsS647I6zllfYt+8s/ppf81WfQRqVkkvi80s7bt9JTBqVEolDhLEXAs2g+ajBFVc4dp9XMO5/9Ta7zRIYs+Cp0M8xaxvnKG7b8aFh2NUVUggI6Olgv4i2TwUDi0pCcfpq0SUIKl8vPRV9wm9AgRuhlSYp2jafUfC70HLZVKS4mRilgpKtcgBUtB+rfmOmWdp2wajmY5u6MZRV3TOMfWoMf1jSFGvUrLvQE/AXcA/gjvRrjyf0HUvwZoMzhGQIcQ5tVWd+oGJP+7QFFqfotvPgmbU1cR6mrQW4h2tVdEYH7cnrwOX/8d1AJEhlDXQF0KFCdftxO/EmQfIYePvGVCpHi5EehR7hCka7soGaiNoOFgEfkuWmrWohi5GOcVJVKEEgyTnxHrbfLmFvP6G/LmFnlzh9oeULsxf4w385fBIm031hvEaotE79CL3yczN9CyF+hv57KjGGXeR+qrCLVx4hcaKfoIfQMhklAoLMYIFiGHQbshJcp8BCJGyj6eLl4OQK4h/RvgKzx1a5s7DDSsVz1kZOCRmyusiX9Bqq/Rjz5m3nzJrP6Syu5R2e9WlHoxCPz5WG2Smatk+jqZuUFmrpGaaxi5wqsUcv9lQyCEIVE7rCZ/RaJ2mJq3mdY3mdU3qe3hH1k2UDiHIrUWumHmLbrRW2TmBoneQYqE0wc6tmJwtX6uHY1xvc+o+idGTcGkPmwXowR385vkdooWEdvJjUee47xlXB/woPz63I7jNLC+wfqGyv5xOixKETQaC1ejRUdD+2PNw7LQsIftY3uhSPc11k9p3AH+REBmoEQd0LgxvnUxNGq9XaGXOD9fOjwVzZc4N0PJDln0ISvpf0esb7T3wZPjVLZFzjbd+C8AySj/36jdPvPqtyg5II3eQ8u1x573dAQnOk8kV4j1VdLofbLo40coYMcIukclVFssiFN2lRf2r2Gy7nyB9TOm5T8yLv4j8+oTPA2p+YBO9BG9+M9JzFuv1C15GUghuNIdkBpDbS15U7Obzyhtw1bWZTvrsRqn1M7SNTE7HdjOemxlXXomeao+emFHAzCME7azkHenkCgZuh3WO/CwlgT61dXekGGcUtoGKQTrSYdUPZnbdBq8dI5G1VjGecn90YRJXlDWQfV+ZW2AVq9wG/YuFAt+hvez0Emof4cX35x4kGkn9CcKGnUZIbJQYFS/xvuD0FXQB3g/D6uPy0EbIfR7eDmE+lfQ/CEUFEKFbgNVK8wVgcblmzD5ExmcXI0WBiG6eESrw0hAdMKEUa6AzI6P0bfHzWICeDF8dyUTFAlGDelG71E0d8mb24zLf2qpCt8gmvs4qqDjWHY5/lhXpk9iWdcjhG6dPWIitUpm3qBr3qIbhwyERO+cq7hXiAilbzzl5xqERtBBtp2ypz8/gRMXvOX5pRZdBclpLuhnwSIsMtHbJHqbzNygsnuMyl+jZMas+jJ0N32FYzGWvo+0qmAxGHitOogeZZeOeZtB/DG96CP68Yco0UGK6I8+6dd5j3eesm4oqoY40mSxOZfXvRhTC6ezVF8h1VeJyjVAkIvb1HYf265Wfj+7ZscW1kokaNkjMzdYSf4Zg/hHdKN3iZYOOWe0iZQZkVxpC43AzX9VzJoRd6ub5A7qExS22lWM632uZe898RyPY2ZHHFT3X3n/P+AYoaOx2QqOQ0cjUKEuLW1qva+xbtx2Ha5j1FqgHtkR1k1o7H47cQ9wvqKxoaOBd0iRtFa0gTrVuBmVvU9l71E1d1Cig1ZDUvMuveQvHulkLBDO4xij1ulEPw3jofoNpb1N3nyOqTex7gjva4SIePE4tzhvUbJPYt4iNW+f6GQ8isU1JMwDz/LuinY+Fro4QVB/l2n5C47yf4f3DiUysuh9Bum/JjFvf6udjAWkkGxmXTazQDsrm5o/jPYZlTlvDdbYynqhi1SVDOOEXhTz1mCNlTgNS9qPXaeVCFqMRBu0k6wnHa73Vsi0IVLqEXfYhQm1EIJtbdhKu8srzNO2fVq8VKGhpCDSKnjvWodzx/oMIV5xrU9olm5OYgAqQsT/dSueXmzZIUS/7RoMCFSTKHQd9HsgNxB+FlKTm8+g/J9ZOk+FnYTuhdwE86eg3gyFgtsNOg93GH6ndkAOg97CjtoOxfEJjM8DxUsOw5fosOxevCJ95bxg1KANeurSjd6ltA8pmwfMm9sUzR2K5kHbVs2/p5PE0yKsLBo5wKhVErVDondI9Dax2iRSKxi1EhyFZO8FoszXCd/eWFMiI1YbDOKfkOhLFPFdiuYO8/oW8+YbyuYhld373k0OpTAo0WsnvpdI9VVSc70dGxstJz47t5Tv1x1VbZmXFb/7+gG/+PwOH93Y5r/6+I1zEYc/Di07ZOYqSqZk5gbz5hbz+kvy+hvmzTc0bkzjpnx/OhzB1tfIAYm5Qqavkpk3yMw1En2JWG2gXsGwIWg8AldeigjnG3hFxx0lNLFKqWlobLVc+VRCE8kUJc5ub/wDXg7LjoY47mjUdh/nChZOT407wrppEHbLYGdrfQ7etZ2Lp3U0DrFuCgiUyNBqiBQZgfs/pqy/orFB96LVCol+Ay3XntLJeBTBVa6PkesYuYYS3VZvMaayD4nUAVqtIThdhz1SW3SiH2Ee6fqfDwQSSQRYrBszr37NqPj3zKvfYV1OFr1HGn1AL/4LUvMuWg7P/RheBloqLnV6rCVBBL640ydac7U7xOPpaPPMGcAwSnh3ZT3EUODpmYiONmgpeTxa+2nbOI+ZxSvlaMRGE2lNFnukFKSRCbyxx6ue1qozdAemQWDtp+ALBBXeHQXqkUgIapYOyFWQG0CDUG+1jlHHhUawiu20vHURuHouD9tQV8BXCFnim9/j60+DQHx5szqRuiwHwZnKF9BIvPvbINz2MwQGIYchQA2L92OE28O3J553B+H1sNYWRnF43LLYOD/4xxT/p6kshRBo0UXLLonebkVkEyq73wb9/YGZ+oqiuUtjRzQL7r1vcDTfw27HYiVRtasw4btsuxdKpMR6k0RdohO9Rce8SWZuEKsNpDDLgKPvF85/rD0LS6czNaTL21R2n8ruMS4/wVS/Yy6/Jq9TrM/bcKcGz8mu2Xc9WXx0fITP3KBlj1itk5prdM3bdKN36UXvLalzf+wdjMdRN5ZpXvLZ7V3+vz//DCEEf/nBdbiAQmOhB4rUBr3offLmNtPqD0zU71FVh7J5QGn3WgvLOoyppZbju74uLTphejmegu34gERtLYMce9EHJHq77Ya9/HsY+NERSqZokbVdjRz3imnrsUoZig2UrJFIXPu+dvWAnl4lVS+28xQIjIxRwiAfCxo8TzjvcN5S+/K1c546DwSHpQ1kS51yftHRyAGH9TMad4D1U/AWKbtotUnkC5ybUtm7NG4UrKN9A6gg3HaHOD9HCIOUWSvsDvMt6yeU9jaNOwI8Sg6I9DW0HL6QkhS0Vx20HKLVCkp2cTbH+im13QuuVXJwaiqvVmvBHrd1ZTxXCIlAY92csrnFrPoVo/x/w/kCKRMS8w6D5F+RmneI9dXz3/9pD/Ox/2spWUuePAdjpZddj+ehG8V0o5ejUp/XefzyyeAShlnKW9trWOfQStJPk2e4Tlmgxje/g/q3rVPTvfAzVOgm2LsI817QRxCHjoJ+B+wdfP33UP+c48ragX4TYf4sUJRQ0NzEV/+55bgr8MG5Bz9pXaU2jw/HF/j651D/JjxXCPAWTxEoUup6EJmLCOQaQl0CdRnvDnHl/7oU83pfgRwi1OXwGF8HN6kLgvP+pcU4ARIlUiK1Tj82pOYqK25CbUdU7oDK7lE2DyntLpXdp3ZH1G6Mc8VrMlF8HgIlyqgBRg6J1CqRWsXI1RPdigFadkLxpfrtSkyvvZj+lzWZPA8o0SFWikESkZrr1PaI2h1QNPcomnuU9gFls9s6n02+40RogRSm/cxXiNVaW3TuEOtNIrWOkSvB4lcOUSLjNC5Af4ywzlHVlqJqyMuaqrGcLt34VRDOv0it0oveJ9HbDOKfhg6sfUDZPGjH1C6l3cO62XeeXB8Ki5RIrRGr9dAB0zuk+lLoksrjLqkQhvO6xoTMhpAQ7u3+Kzt2rUbbXI7/irGt2CvvtV0SWIm2WI8vsZ288YItgJExW8l1VqNtUtVBn4NG7El4cjtl1ox5UAaHrD82BJvsRcK2wrk8dDR8jsfR2BG1fYhzOQiNlgMitRm6FvaQUDjMQ26Gn6FEFnI03CHel2gZ7nsn73kLxyfX2vYrkWLkCvIMRjVCaJTsoWSfxh629K4jGjcmOgNTQooEJYeBKnwh8JTNF4ybu+T172jciFhfJTFv04v/lE70YSiMfsC54uVcp9q/5IJCJQ1ZbIhNWP3zT7hO+aC9cCO8u9d2AYKIBwg/Fw8Q7jJCOpARXq4i9NtBxNJ83obwyeX2hOiDqTm2lJ3i7e2g7zjxONQ2InoXobZPvAIL7gBvb3Es1vYgkuAopd8MNCxhQBi83wT1BsI7vL2DX+RoqHWEuhbE5nKtdakq2i7JNjxi7SaC5a7aAjnAixi8xDm3TA5diHgWPDkpQsqo856qaZhXNVpJOlG0/J147HmL+PqFO8ECcuFSIGIkMUYdn0zOl1T2kNI+JK9vkzd3KJv7lPYhlTugcTOcK9ouR7N0sGozJ1tB1sl/n5QenczxODlZOV6JF8t/n/i/EO2/wqrhwhHq5L9P/izc9KNAddHrQdittoj1VkhsVusYNVyuaP+AV8dJPVCmb+Cpsa5g3nzNvL5F3nxD0dymaB62mQnz1inkuFsWxkzb8fCO4xT1xbh5PAfm6ePm6eNDndDjmGWqcnA92iE1V+joN4j1Vigu5EVMjr5/WFxvyjp8WesufI1BtOe7Fj207JESuNGVDQsggZ73NXl9h6K5G4pXOzrReT0xpp64HoVrkn/utSh8f3JMyeU4oh1TsuWsa9lFy15Lwdwh0VdI9RUyc41YrS+vUecPSaw26Ji3w4SsidAyDh26M3Q4BQopIvrx26x33mNmLT29isMhEKxF26zFl4lPkRFjRMR6fJlr2Xv09CrJKbogZ4Xzjkm9z0H1gJkdPbPQCOL7VVJzBSFkS707HwRKU0Zu5+RuF+rRY/tWGBljZEIkE9QZ6ZZCRGilWz1Y0uorDnBt3ox1I+rmIc4XrZ6sj1EbOF9SqwcEcXfe0qtGCKnbQmKEp8GoDZTsh7G5LDQqrBsvC1Yh4vCYM9CHg6lKByU6CKGXwnTn52Hud+rXH6MuiLoc3odDKnuPWfUraruL83OkSIn1FSJ9hUhd5iIWHRezodqVVK6gcRXNCR1NJGOMiNEyxsjvC2379Hg5Mbh1FHXD7f0Rv7+zS2QUO8Mem4MuW8Nu6/t7EkFDIcwHwXHHl63rUjtVFjEhzG4t0KbQbWfhGkKuBN3FCbs2IHQyxDBsG4nQbyPS/xFax4wAH7Yrsrbz0UIkiOjPEer6ckJ7fJxp0GKIE1WtHCDMh8HBapnUGbYTtj9sj7kDUiFMglBvhKLixFstzMeh8yGGeNGndhG1a5bexYnRKCGxziKEINEGj6eoG+6Pp3yxf0A/iXlva51EGxpnA4VNa7z3WOeprKW2dml/RvvqYq2fybEWaIwcIEWEkSt0orewLm8pMCWNm9K0nY8FZ7rxs/B7V2B9sDx1rsS2LVtH03aJwo3ftzd+cK2QS56YCD72ffFvoVGtYFuJDCWz5XctOuH/Mgs0gvZ3UiYokYav9t9SpCgRt3zTHzoXFwWBQsmEVF/ByCFd89ZynDRuSm0P265H6JRZN6Nxs5YOMG/HUNVOGkMQk8O2FIDjfZykxi0mfeHzTttx0EG3343shy5X29E6OY60zNCy146R/zL0F6eBc56qCUmwr5DRdC5QskOMQskuqbmGjWdYN6f2Exo3prYjare4Lo3DeHLz9ppUtBS+hfFF88j1aFFsHBcTekmDWgi5gzFAByW7rc1xF6OGy65pKFBThIix3uFROO+xvmqvOedfaAih6cUfI0SHo+om8+Yhg+gqHb15poWU5SKO7CIQpCrmUnod2V53Y5kRqwx1iu1JoenqIavRDn2zSiIvoNDAYaTB4Z5b/Bi1wlr6V/Si99vFjfOjWHkPU+f5Jr/Fw/Iuo/pR171EddmIr7KZXGc7edIW+MUIn0noWIXA4MYdtFQoR+NG1O4h3pdt6GM/JGb7ikquIkUccjPsAbXdQxBh3Tx0QBBouYoS/ce6tUFgvnTwRCHOTCUWhPnT8cLt8Tl3+ovIovi9iG5ybf//7P3Xk2RZnueHfc45V9/rOnSkztLV3dU9PT1iGxhgIWjkA8wIM/KN/N/4gCfCjC8wI2DAcpfLXeyInlbVpboqK3XoCNfuV9/Dh3PdMyIjslJFdlX11HcsO2vS3a8WP/X9fg8Yp/9IWfNHzDHRFNUJcf4Fvv0OmmJZVHgTGGaH7MZ3GGT7DPMjQCORrHrXWHOv0XW26Lobz13O9w0v9XZdvHQqrcmLkuEs5tHJENcyLoS+Y7PaDM9zhxYqAWqrHo16PgTKSNKy8mLfV+tPBfbf8F3hGM7H0in8ed8PjFfHc689ZZIm2QKun/lECGVM/WplIa01VVWQFTnjJCHJC1zLQklBWWkcS9ENTIUtyQuGccLuaMw09eiFAb5tU1QVkeughCQtCmZZTllVlNp0SapKU2qN1pq279H0PCwlzyWCZs7SR+HjqCezkYsKYFnF5NW4DhIHdcIxNQGinpsxhjpQLKv47Et9WWmsllrfZ6vOi5e7+W9dSYocJBaO7WEpkywsX/an/igZYYvFf4eGZCkuUpF+NZRFRZ4XSCmwbAv5GiZmi05TVpaM05RpnlGUJRWghMC3bJqui2/XJK3vGS/AVKRNkugoB0eZxH5xDVU6Iy8HdYX6hHF6xP70gGF6QlqOKapJHSQmaJ3TcBWrgYetNE+cX5+o+QisZZJhuhVhnXw+uT5s2cCWHTNGZ61gLyp5L9nRqqrKqDAlOek8xfUcvNA1Y4wveE28CsfqIkzilMk8eaMdhpPxnIP+hPEsPbfdf2oo4aKUi82Two/WelncyMo+edknq0wSa66j6alkY14nHNmZbuzyWSQWSnRPrqUF32KRZJhxuyaWbGDJZj0q1VuO2gGUVc6k2CMrxxS6QFUZLJ+zTwpfy65KfVgXz0FNtQyIjfrZQuBBc9ZbZKEEt4UQTTI8EGu03PdpO9dR0lkmzVpXdcdnMXK7eOYuKtkllc6JywGz4hBL+ER2xxzz5WiNmTyotDHxe1aHRgqFr0IadofIar+xjkalS5Jyhv0N3UdLhkTO28/8/HWQVynT2af0s0+4P/uc/eTumc8jq0NWJTjSo+e8WKxzGouOlBQRtlqp/TKGtQN4SalHxhWbCiUjpIhQooklM0O6Fi5VZSRx8/IIKQLKarrsgFiydu0+fR714prUp7bi5Z9P4tTv9HJ5T3ekn7MMIesxw8tP0ItqjM7v1ry8JhUulZ5T6Zgkv09aPCAtHmLLHpbqPH+BL4FKlxRVRj/b4/7sEw7TB/TTPTTGHG+UnxAXE5SwaDmrtWfF98On6UXw6mU8AbaliDwjXZYXT5xmf8CLQWDcGcdJyvF0TloUlJXGVpJO4GMrhWdb5FWJkoKW51Fpzd3jPrZSSCFYbYRYUnI8m7MzHBM4Ng3XYZykjOKEWZaRFiU3eh2utlu0fJfIfZkREWEkYGUbSwS4eoXKyp8QM+uK8zKhWFYwqlMv1adHYU6NRS0TA/NiTZOCwXSGlJJOo4nnu08SEmwTZJ76b5OkLKovlxucJ0nG8GSK7Vh0ehHSefXbRQNpWXI0n/Gr3cd8fnzEII7JyorIcbjWavGXW9vcaHVoeS6u+nOqsAskds19MByh43mX//S44JOjhDiX5FWwJPlqXfGLrS3+z+++y3oUIs68qBZE04UmuqwDoMV1YdXVOHONGAlj0xVbjFa9LMqiIk9z9h8cs/v1ARvXV7jyziaWbWHJF09adGWSfinlK1+qD/b7/PbOzhvtNEzilMFkzsPDwXf2mb4gVkscHNnFJ6uTicVz6emRqlMjnecCoPpaEs+4poSNwF6KByy6rKfHOzQVeTUnKUe1ipltzrcol4F/WRPZq3pkEK1R0sWRIUWVkpYjoxAoPCzpooRLqXMj9YuRGDGiFtaprowJSDQmqZDaejKRrAvSakypc5RwsISLJT0W3kCFTkjLEbPiiFl+iKda2NKnFOkZgrkjQ2wZooT9zA6NMTmzsYTzRro4C0ihTDL1BtfxXYDpaKwZudqqvzSnK6pRbdTnGola4dfjVm3suqNR6KLuHh+aREOP0RRIEWLJnuFonKqamukBd0n8Nu/0/KVGnkxqUdTdZ113Ce16md+tc+VZNwmdH5OXByTFXYqyT1EeM0s/RuLR8P6a6JITjbSMmRR9jtJH7MZfMSkGZDoFNEILjtKHzMsxDbvHpn8bSzhY/3ITDfNgLsqKODMmfa5tUdSjVFlZvulR3uciK0qyosBWCsd6Ur25CFWlSXJDdiyqCiEEkevg2mcPS1YUzNKceZoxz3KUlHi2Reg6RN4T18YF5mnGrP5ukhd4toVn20SeQ+jWLyeNsXwvS0ZxwtF0RloUaG06Q45lkRYFdr1sRykansskSRnMDUdESoFrK1ajkFmWcTybsUpIx/fJCrPcUZIS5zkt36PjewTOi0sVLqoTC68HxeWZRD0Lg/mU+eEeSkm2eps03cvzsnhZzKcpj+4fE0YeUcPHfo1Eo6wq+vGce8MBv9nb5Z/3djmZz8nKksh1eLvbw7MsbCnxrO63mmg8r/qelyV5UaGkQCm55P9chGVl7FTXzAZKDfdHAb/bd5jlgqI6S/670ryFb/2Cntcx1dhnLL8sSsqipKqM94PtWCj7iXdNnhmOgVYSLTVGPQ50VRkPEEBKiVX/xiynenIMhKAqK6pKMx/POXh4jB+5bKSrJmGwF8IQmiIz2yKVQEiJUhIhze/ztGA6mlHmJX7k4fgOtmuj1Mu9THZPxvzTF4+oqjeXAKR5yTzL6I9jyqqq6/CaUpuxI1OFF8vigagDc1jU7KtlFR1qEQtMkCifennqZRHiCUwdXS/X9SSo1PW/myqgEhZKfTd4Neb4ZOQ6Jq/m9QjoDCVdXGm6Hkk5pNApQggqXVBWKZb08FSHSuek5RgwgZ+nWviqS1ZNSYrBYs9xZIQtA/IqJlsKLNTHXldooevqaUxWzUirEUWVYlShQkJrFSksCp2SlROSckRc9JkXR2hKbBWxGHspqpRSZzTsTSzp14nNxRCAJSwsYZ87x5cFASihsL63CoEvDiWMbG1aPKLSKVVVd+fKIUU5RErT8ZDCr8eNAkPGFiEIabgc5SFSRpTVxDiCSw9b9UxH41Twb5LncMnJ0DqrR87yZ2zdeWjKekwxQeuynpIIzPZ9RwJmYzC4iu+8Q9P7W7LyACvvMsv+QJYdkxT3605RG0ddMVMSlzQCmFcp02LAKD9mmB+RVWcNJyfFgHk5YVL0yaoEISUWfz6y0i8dzWgNcZZzOJoymsXLqvwsyUiyfEls/rYwTVKOJ3M6ocdK45svkqKqOJrMOBzPmMQptiV5Z2OV9dZZybBxnHLn4IT7RwMenAzxLIutTpO31nu8t7WK75y9kQ7GU+4cnPDgeMjj/ojtTpPtTov3tla5tWZGkyo0aVEyTTKGccI0zfBti8Cx8W2H0LHrDrtedjYC2zado6oiL0uKoqIoK6QwlvGOUjR9l41mRFYUTJIUDdhKYUtJcYp4/l3FdJLw1We7WI5i40qXZvvbSzRGgxmff/yYlbUGV26sEESvHtRkZcm94ZDf7u/xVb/P3nRCVpRUWlPoioejIb/Z28WWirUwoum+KdWN52MhQHBaaOA05mnOYB4TODah6+BYCqm+nRd/GmfMJzF5WlAWFa1eRNQOQRgu2XQ4I5mleKG7TBSrqqq/b5IO27OJWgFSCrIkJ88KirwEYfyBbM/G9Wxs18ayJGgo8gLrVOKptWY2mTMfxziujeM7eIGLZSvytGB4NObeJ4+YT2LWb6yystWlvdpABS93TfUnc77cOaIq39x9XGq9VJ6qqkXqUFFUGbnOsYSFEhalLs3cvHCwhIXGjG3mVVabb5lrIq9yBOCp4BzJc5HAmIRlkZQKyionr3IsaeNKb5nA5FVOqXNs6eK8EWWj14PWFXkVU+qcrJpiC4+ua8Z4htl9smqGqxoUVcK8OEIKh8Dq1Z0GRVZNSIohLecatuczL44YpPepdE5FQWit4lld0nJMVk7Iq/PKW6VOmeR7xGWfShd1p2WIp1q1aIZkmu9TVCkISMsx8/KEihJLeHUHRzIrDpkVByjh0LS3v7FTIYTpaNjSeWOJBoBEYQnrz76jIeXCTyPAJH6G4J2XJxTVCE+tYKvVpUStSTZclGohS59ST8irI1TVptQzNGUtvbxSE72fHD8pfCzVrX01oNJPlK5eFAvpfOM+XmBkmJsoEb0RvsWrwJgLfkTo/ATPfhvXuoZn3wYUafGIspoSZ5/jqE1s1cG330XJFxuvfx5KnZOUc/LKdDHOQ9ccnIy0nGN9b3y8XgyvLG8ra4M+W0mKSlLob7+bAdCfxXyxd8SttS69RnipwzRpUdCfzpfB10ojoPyGyuIszTgcTZcV3yvds87Oxo9E0vI80JiOh+PgWhah4+DVoxlCCBquSxFWOJYizByysqSsKlq+Z7odrsNaFNINfCLXoRP6JEVBKy/IqpK25xHU8/9/CmitmU4SZpOELM3J0gWBXmBZEte1aXVCvMCmyCvSJGM6SXhw54B7Xx2gLMX21R7x3ChhhJFHZyXCrivIs2nCaDA3FevCVJy11rh1UBg1faKGCdbLoqJ/MmE6TnBdC2VJsrQgz0uqssJxbborEZ7vGKGD2GzL/TuH3PvygPFwzta1Hp2eSUAbLZ9uL0LW1egXmbcvqoqj+YyHoyFH8xnT7Im4QVFU9OOE+6MhV5pNkuI8ebEoTXK5GDxbcG0W686KkqwsUUKc6rAZXkhVy8QJjP+Nkk8qzXlpgklZ80IEEOc5o3mClILQNdehY1mm0qw1aVEwiU1l1nfs2h3crKusdD1L/WSdp3lBl6XLnSUZaZIzOp4wPpkApjPhRy5VVRFPE+aTmOlwTjJLkX25GHmnLErytKCqKqRSBA2PMjfdxNloTlkYMYWyqMizgvZqk7WrXaQ0nQqAqtTL/Y6nCfE0YdyfMRuZwM92LFa2OzQ6IXmWE08TDh+fMBnM8Bs+zU5E9QpjSXFa0B/PaYYe3UZgzo19ud2vvChJspzhLKE/KUnKmEF2jNIlaZkQWg1Cq8GsmJJWMW27R2Q1ScqYtErIKyP04eEjkMTF1BQ8pIPN2RdooXPm5QytK6RQLEabCl1QVDmBFeJIl6ScMysn9fJT2vYKlm19I2/g20ClC+Kyz2JkVFmmKllRmI5GFeOqJgJBrmN0NaUkI1ArRNYaOZJcz8mrqelIlGOScrAcUwVNXs2pKJf/9nQgV+qcuDxmmh8suxdJOUQgKCqjXjTNDxBCEKjVOsmpvaGoKKqEUmd1MjOj0sW50VSBxJMBDcuMmERWB08FKGG9wdEpYUan3mDX5LsCI2SxcAivajGWg9oPI1ka9UnhPRklFS6W7KBkSFnNyMsjLNmjqozKp3EE79b+QE+uGSlCHLWJJZuAqL22dimrcc1levY9pnWF1hmVnlBWQ8pqAmjTPZA9I1X7GhP6lwklAhxrA0etY8seCIWt18mcHbLiIXF+h6R4QJx/aUbSsOsEzEP+CYoaywmSSx4B/y7gpa8AISB0Ha50W0ghiLPCJBi2hWdb3zqJdW844Z++foQlJe9vrdaqUhfDkpLVRkjL986MTj2Npu/y7uaqUYWSknGcfuOlsN6MiFwHS54NBk9DCkHo2Ngqoum5FFWFVSdvsg4WbfUk+HOVInBsyqqiWAZzGs+2CRwbWymanodrmVPaDQIC21l+z1YKW0nsP1HlWWvYfzzg3pf7HB+OGZwYiUEhBWHosbLe5Mc/v8HmlQ7TScLR/pCvv9jnq892eHjvCCEEliWWHY1b72zw83/1Nq2O+f8Pdod88puHDPtT5tOEIjfdnZW1JqsbTd75YJvoXaPekGUFX/zhMXc+36W32sQPHAYnU8ajmDTO6K40+MV/9jYb2x3iecbB7pCv/7jHV5/usvvwpN52TRCah817P77KX/6rt/CCF686VFoT5xmTNCUvzxtslbpilmfM8vzC5DUpCkbzpJ7xN9dp5LnL63CaZgznMb5t49uLWVtNVpTkpRm7UMJ43QSOjRAmeRnOE/KixLWtmvcD+6MpX+wdYVuKq90Wa42QXhQghKgTHvNHa42S4kyyY0YoC/KiWhLdmr5L6DjfdCu+NKajOSd7Q/p7QwaHIxqdiGYvoio1ZVGy/+CYw0cnWLaiKjXDoxHT0dyMQdV/lKXwI49GJ2Q2Mklrf2+IsiSNXsR8nHCyN+DqO5s0uiFlcf68aA39/SF7948o85I8zRkcTSiLkvd+cRvHsynysk5uTLcEQKrXMzV7a2uFX7x7lfVOxErzcom3g2nM4WDC777e5Z/++IhxPuDe7EuwUpJyzoZ3lXVvm4Nkh352xO3ofQIVMS6GjLLaXVjaWNLCEhbzckZFRVQ1zglqpGXMSXpAqQts6Zgko8rNOJUAKSSR1WSYn/AovkdSzkjLhJvhe4SqgRIK9R0KOkudMSsO0bqk494mtNawpEdWu5tb0qNpb1HpnFzHdfKRIC1FZG9iy4BK5yjpGpEEnWLU3IzgSlZNicsBobWGp9qk5ficqlKlC5JyQlwOsOvRFUt6WNI3932VklUTXNUisjfM9pVTbBngqQ7z4phZcQhofKuLI0PkU4p9Ukja9ipb/m1A0LS7hFar5nG8uRhALkenvjvn/E3AkMHXUDKo74k+afGoNtUrUaJZJxpPxpkF9lJVKqsek5dgy0HtN6OXiYYSDU6PTlmyjWvfwsp6ABRVnyS/axzGyfkmd3BNYZSwyuO6CzLDqGY1sK1VLNXheaZ/3xYM99MlcD5ACJvB/H8mLXZIiwdU1QRLtXGsDWy1gXzNEU0lbHxliiYX3R8CiRQKR3p4KsSSfz5jU/CSicbixWi8M1yyIiTNC5K8QACd0Ed9wzz1yyDNC0ZxwjTJlnwQMAo9llK0fNd0LATkRcUkSRnNE/64d8RX+8d0Q58r3ebSqXytGbHSWLQGNZMkZRJnTNOUrCiXnAvPtnj6knIsU9GdpxkrUWiqmNmzJfMC1yFwHVYbU3pRQJwX52aqRe2ubitF6Dw/YLUUZ7gji/nyxZG260RkAa9O/L4taK052h/x5We7gJnXtmyF41i4vo3jWkhlyOCynvW3HYVlK3SlEdJUhT3fHBvbsTgt8COl+b7r2VRlRWFXlEXJ8GTK8eGY7kqDm+8YFbKqrBgcT3l8/5iq1KYzocHzjZO965lZebHYFkviOKbzUVUVQoDj2qe25Zu5PxdC1JRTIS4MuM38sTh3/2RFQZwVDOYxJ9M5Ugp828KSksh1maapuZaTjGmaMSJZHn9dq1wVZYWSEte2yIqS0DMBXVonL0m+4AZZdAKfSZKyO5zg2Rad0Kcd+EvysUYTZzn92dwoo4X+soPSn83ZH00N56ksUVLiWIq0CMiDitCxz/GfXhXzcczhoxOKrMDxbPyGR9gKsF2LqtKM+1OOdwesX+3hRx6DwxHxNCVLMsp6LMoLXPzIo6oqZuM580lC/2BI2ArobnZQKiONM+JpQjJLybOFJPdpaMb9GXv3jky3TcB0OKMsSpJ5SlFURrXMsZCWub79yMOPXJT16sFSrxnwzpVVrqy02Og2X+tYPo3j0ZTHkcduf4ySklxnxOUUKQoKnROXM8b5gFE+YJwPyepxgEqX5FVGrjNkpeoXq8O8nFLpitwpznksFbogLuckZYwSsq5YK0pdkFc5vgrRmPGreTElr1KKpTM431hI+jYghMQWHloY8nmpM0qd1d2HCiVsHBlSUeLIgFKnyMpkX1k1Nd2KWlBD1mIXhtti/k6LCUnRJ1SrODIgKQZk5Zi46GPLAL8+HqompFeUoEsEsu4E2EhRompDNLPOKZXOEASGxE5JWo6wZYCrmkhxPuhRwmLVu7J0EfdVSMPqvPFOgxQSJW0c4eJIH9DY0nshCd7vE5QMcdRq3dHQFOUxaX6fshphZGpb2GqN06Z6QtjYqoMlWyTFfSo9JK+O0TqtR5mMJLzhYoiz62ITR21gq3W0TsjLI5LiPnH2Bba1gS1XOd3ZMGIEJWU1Is6/Yp59TlEdAxJHbeJaV+qkxudNScW+Lsy+SBy1jhQ+afGYtHhMVu6RFo+YZZ+gZIvI+dnSH+RVkyZbukRWh6a9QsteZVIMSKuYhaxJaLVpWD2a9kp9PT9ZT1FWjGcxk3lKkhVkRQm1j52lJKHn0GuF+K65T5MsZzrPmCUpsyQ3PkiAYytc26IVerQif6miOZ2nTOKUeZIRpznaiPHhORa+69CKPCL/9RKtVxydAqUk3cgncE2VXWsTCF/sDP7ymKUZX+4dc/ewz/5oyizNAI1rWUSew3tbq/zV7atYUjGaJ9w5OOGznQM+eXzA/nDC7x7ukeS5Ce6k4O/eu0mvcQ2BIebu9MfcOTjh7mGf/ixmoxVxfaXDT69vPiFsf8fx3XrFPgUNJ0cTHnx9yE9+foP3fnKFZjsgapgL3HYsoqaHUpKw4WE7ilY3xA9d9h4PsB2Lv/67d7l6cxUA33cIoicP1bXNFkFgRp20NtKHutL8h//tU/7+//sFH3x09YKNMtdC1PTYvr5CpxehK41lK5otH8e1sF0L17PprTawbMXjByesb7X55X/1PivrZvQtCF1s9+VuHYkgsG0arot9gVKRJRUt16PpumfUJmZpzv5owvF0Tn82x7dt2qFP6LpoNEeTGXcOT0z3TCmOJzP603mt5W9GmQQQeg5Nz2WapASOY7oOpfFKmKUZJ9M5Tc/jR9vrFGVlhAjqjpoSwiRKdbI0TVIe9UcoKelFAVKYzt2j/ojfPdyr5/ohdG0anss0NQ+wrU7z0hKNeGq6DevXVti4vkrYCgiahrBf5iXpPCWdpTS6EWtXe1R1F2nSn5IlOcqWRO2QtSs9bNcmnsWm81BV+JHHxvVVpu2AeJbgBg7JLCVL8vNqTxrmk5j+/pAg8nB9G8tWeIGDUgq0xvFdgoaPH3lGanqlQaMbYT3D1+ZF4NgWjcDBd21c53Jf5J5rE7jOciTLEjaeDPBtCykkWmsG2TFZlaCEqseXBK708KyAeTYjL6dEVpNKF8yKidGk0XmtnnT2yaXRZFVCWiWEqkHXXWVeTJkWY5plB601lrTwVUCgQpSwiKwmSlrfuTEDS3gE7lsIYF4cM8oeYYkAFuaBS98m88cSPp7VQeuK4+QLsmpKWo7pyQBfdQyxvFZasoRr+EFVago0wiXXcybFvhEtoKirog1CaxWtS+bFMUUV1+p8EquWC47sdZJiyHHyBXHZZ14cowVEepNS5xQ6QemFgpSuTfyekP6VsNnwbtJ1NgHTaXgTkrZPw3A0bDwV0bDaaDSB1UD9mVWAlQhArS45Gnl1DIWiqEYIlFGZUmtnOhpS2PXoVBsjSz8hLw8xrt1Rzc04Lx1ruBs2rnWVwH6PpLhHVuwwzz5FCp+m90sst/PUCJThjWTlPtPsHxmn/0heHqNkg8B5n8B+vyadfzeTjNOQIsBWDqHzE7QuGSf/nkn5T8yzTyirKVL4ONYWkuilDRgXcJWPJSzW3Gts+e/U8ra7priJYtO7xdXgfda8a9jSOfNcy4uC+3t97uyccDiYMJomaMz4dOS7XF/v8JfvX10mGpN5yt3dEx7sD3h0OCRJc/PeiXxWOxHvXlvjR4GLrOOQw+GUu7snPDocsns0QmtQUrDaidjsNfnRrY1vJ9EoKzPbbdxjy9pBtqQZuPjO5ZiixVnOvaMB948HKCmM87hVk6Jdh8AxpDMpTFbnOxatwMOzLTNSZFl0wmDJj/DsJw8igXE09x0bIQRZUTCcJzRmc9L88sx93iQua9b9jUFAsxOwebWLFzhkacF0klCVmkbLx7cVUor6j8KyJJ7v0F2JljyL1Y0WW1e7Zxa7rNTXyytPz7kLmI4TBidTkjg7o3wDpgLghy7tbsTaRovVjRZPQwG2rcz3ehGOaxFGLmubbTa2X13yzlKSjajB7U6XvcmEQRKTFAWVBtdSbEQR7/RWuNnu4p+6VmXdwTNdtJy8JpA3PdeYR6UZe8MJm60GK42QcZxQVLUKXGEq97ZUNDAPjyQvSIqCaZKRFSWWkmRFyTTNcJSirIMhJc090nBdPMeuR/jMSJ8Giqqs/zwRGEhzw91AmLHEyHOwlCTNC8ZJysoFI2OvfDwdiyDyqMqK8cmEIivIk5yoG2LZFo5r4wYO6TxleDSuCeO52TdbYTsmofQjD2lJkpkxxEJDnuZMhjPm4zlpnJGnBXlWkMxSJoMZg8MRUdt0PSzHwnYswqZvukRJvtw+21FYjoVlm06GUhJdaqajOdPBjKgd4ryk6lQzcLm62matFdLwPTzn8oo7C9hK4jpmlE4I8FVAz10jdI2/QlalZFWKIz0qSkKrgUDiKp+mblPpkqxK8VSALW2atrlv7AsIjrZ0aFptlFCkZUJghTStNrYwZqWBCmsuQciKs16PzliEVuM7NacvUUZdygZPterkS6F1iVWPSzTsLZR0UNJF6orAWqHSJbb0KKp0KY3ryBDf6poOherQtLfqDoWD1hWOjIisTTzVIrI3EQhc1caREUo4KOHgWz0jlSu9ZaLhWz1s6QOSyFpHCYe4GBjulvSxpE9cDpDCou3cYGFkaAjl05ooboo9UkgCq0FA4092jBfPckvYbHjXyaoEjSa0mkuuyJ8LhLCRBEgZIoVfK0flVDpBygAlm1iyxWn37sXolKU6CBSVnpOXR4aULRu1I7h1LnZY+BK51nUa7t8AkqKWx52mvwagrMZIGZjzrzUVGUU1JC0eME1/R1bsIIWHa10jdH6C77yDktF3P07h9P5fQyApqiOycp+ympLkXzPLPsaShhwuxRWeSKy/OIxCnkXX2eRG+CNado+he6WeclBseDfZ8G7RsldqrtoT5EXF46MRX+8cI6XAc20cS+E6FoFrE/rmPVtWFXlR0h/Pubd7wmASE7g2gWujteHePdgf0Aw9rq61cR0LJQT7/TFfPToCoNusp34qzXiWkGYF652IXis049WvWBx7pUQjL0vG84TBLGYwjRnOY0azlFvrXZqBdykvvjgveHQyZDiP+evb17i93qUb+ni2XZNQreWsuaV8PNviaq9Fkhd8tnPI+9tr/Hd/8f5yVKVxap5dKcmVbouG52Jbknbg1R2TH3BZEEJw+90N/MBh9+EJn/72AUmSo5Tkrfe3uPHWGutbHRz35SpRutKUpWb/8YA//Po+81lKWZrxFKkk978+XCoQPV19VkrSbAX0Vhu43p+2AuZIxY12h0prDqZTBknM0XxOUVV0fZ+3Ol3+eusqP1pbp3Gqo+Y7NhvNiCQznY15ljPLcnqhj0YvRwxvrXa5udKhqjR5WTGaJ0zTFEtKfMdms92k6XvM0pRRnDKYxSR5QejaKCnx69FBJSVKmsS86Xv0ooCG5yy5V0oIXEsROA7OU3wf21IErhEccG2LzVaDTugzS7Ml6fyy0OhEbL+1wd69Q+598oiw6dPoRNz68TU2bqzS6EW0JzHHuwMefbnHZDgji3MT9H+DTLEQMO5P+fr3D0zXZH+IshS6qphPEo4f901SkxpuWqMd0uxFXHl7g+PdAcOjMZZtRuscz8YLXJNgaHPtzicxj7/apyorbnxwBeclr8O1TsRHt7e4tt6hEbiXTgQHM5boWBaWMhXsyGqy5V0n8GxA1EZwtZGcBld5GGfpAEe6ppNRq1EJIYhqU7uL5pN9FbDubVPqgkpXRr5U2jR1m667hi2MZ4IxgVso0AnsC8Z5vk1IYdGwNwn16tI3w1MdNNp0IhA4yiRktjT3bkfcwoyVKipdUuono3mWcFHCIbDWcFWLhYFfVPM7LOkb927VoHTfr0erbCzhGn8RYeGqJg17c2m2J4VdJxqg7A18q0fp5EvzwlH+kJP0KyJrg7XwQ+bFEeNsZ0km91Qbi29PDW8BW7rcjH7Epn8LMEGcr6Ln/Or7BuPOrWSIpVqU1YyiGiKwUCKq/4Q8LVNryQ627CKEQ6XT2h1c4agNLNGCbxgx86wbqKBJpROy4jGFHpNnn5KVe4yT/4StVrBkayllm5eH5OVxvV0K17pO4PyIyP0Zvv32UsXq+wJb9VAiICv3KKoR8+wTkuI+0/TX6CpBhi6OtVF3dl4t4G45q/iqQaE/pNAm5jSJvocjvQvVpvKyZL8/Ye9kzC/eu8Z719foNAICz0ZKgWObKZ+iqJjFGUeDKXf3+vSaAb/88S06DR8B/O7OLv/x47sc9CccDaYEnoNjK3aPx3y9c8wvf3KLv3r/GgBxmvMfP77L1zsnHAymrHUius3wT5towBOOgedY+IVNlpfYtUP4ZcCzLa50mzj1jg1nMUlW0PRdOqG/3HQTGNVtJM+h6btYStLwXTbbjQuTHoEJ4ADavk/fj8kusdr6A0zA1q7HQwwHQzGfpSRxxsHOgHie4no27W545jdQdy2qJ6o+5jPzYZoWzCYxJ0djDveH+IFLb7WBlMbWzXWtWvnofFArBEtex/P8CwQY4ZhKLxWtnt6Wl4GSkpbrcq3V5i+3tml5HoMkoawqmq7L9Vabd3srrIXhGQ8NQwSsKE91DeZZzjwzYzyuZdH0PbKyZG80YTCLmWcZpTa8DEsZ2eMFZyfOc6SAwDFJRaUNt0XW97NjKRylUEKQFgXH0xmWkrR8b0kun6YZk9pkcnEfubUa2NPr9G2LeCl7fXmJRtDwWNnu1qpjJa7v1v4UNtKStFeNss9kMGU2jonaAVWlcVwbq+bf+KFnvqcEjmuhtWbSn2E5Fo1OSNQOCNsBK1sdM2Z1tUf8k2v4kUezGxFEPlJJmr0GQkq80KXZM9e8F3o0OhHKMtUv17fZuLGKF7h4oYsfeq/E0djqtfir965xba1N6DnYr8HzeBbseu736mqbv3h7m1vra4R2hHuBkMTpxEFheAS2dM587nyDi/Oi0ve0lwaAe8q3xxY2NvaZ7z1rbOp1XdiTvGCe5xzNZuxPplxpNbnZrf1cnvEbISSWOBuEPx2UP83+U+pJsrTwE1nqziyr9+qp350dT7JwL4x5pFDopcLXE2PCeulIYaG0e2adeTVnrk6wpFcTzOtOh/BQwq0J4d8+pJCEVovQOt+R/nOBEAK0RInIKEfplKpKsVWjNuYLzvEFhLCwVBtLdpYqSZrMKD3KDkq2+CYFKCUbSOERuR9R6YSkuEda3K9HpHYpqkG93AqtTbIB4KhNHLVJ4LxP6PwYx7qCkn+6TtdlQQoXIW18+220ztA6MWZ+1ZB5/jludgMlm7jWVWy18krrcOqE4mVgKcV6J2K01sa2JONZQlaURL5LO/LMOLIQ5EXBZJ5yMplzPJySFyV7xyPiuojeH82Ik5w4zZklmREgUpKT0YzD4ZT9kzG7xyMAkqxgNE1Ispx5kjFPc5qvYd76Sk8OUyU1Fa9W6LGS5czi7NK6GQDtwOOvbl/j4fGAu0d9vtw7Yp7lrDUjPryyzs21Ll5ddfsB3024nm3ISg2P2+9skiQZJ4cT/v3/8gfufLHH1Rur3Hhrffl9gQBtyNtlaTgXZxjvQDxPOdwfMTieksQ5N95a56/+83cBY862vzPg41/f55nWyUK8GIFUGC3UslYo4rSp+StAYJKCtTDkP792nV9sbVNUlZm1FCYwD2wbp3Z8XyAtSgbzmGmaUmnjxzFNMpLcOLA2fJcrnRbTJOOf7+0wjhMmSWYShmdUHxzLYqPtkhWl6ZKkueFj1J2MNDdJ93CW8OX+MUVZEWwYLtYwTjiazNgbmt/FtYFlO/AoL7Fj8Ty4gYvt2oRNn2vvbS2J/K7nYNkWnbUWzU5EkZtEpGbjm2MrzRiYVAJlmfGgqtQIKdl/cEyjE/LWT68TNo3ii+VYOJ5Na6XBtfe2kMqY8dmujeUoWt2IqGUSkjI30rhSSVzfXgaMfuTx9s9uUNQiEspSuC/poQGwvdKi1wxwbQvHfgVRgheAbSmagcv719fxXZvtldb37jl7NrR+OcyyjP3JhH96vMN/uv+Q//adt7jeaRuu0hvEm+ObPHu5p9fpWR16vENcDuhnd1FYtRJVi8Dq8V3xQ/iXA4GSEbZapagGNcemecpf4+lv22ZMSrWN7C0KTYHExVa9etTqm86hQghJ6PwU17rBLPs9s+wPJPnXJMUDKj2jqE5YuH4bY7t1fPs9Avs9Aud9HGv7e9fJOAuBa93Akm3y8pi02CUv90jLHSbprwBNy/+vXjnReBX4jsVPbm/RaQbc3+vz6z8+Jk5zQt/hgxvr3Nrqsb0qKUvNeJ4wnMSMpgnHwxnzOFvyIpMsJyuMMNHCpLqsNMOp+c3vvtph79iYhlZaM08ylDQjWUX5eh5sr5RoLKqfeVkRpznTJGOWZLjOxZWpV4FrWay3IsOxkIJW4DNNUtK85LOdQ/KyZCUK8J5yutaaZUVba87N6f+AJzDt+nQpjyhQWNJdtv5fFYvq/+7DE3Yf9Y0EqhTkecl0bHgVUdNbujEvIcBxLTorEfNZyh8/2aF/PMV2FJ1exOaVLkpJXM9GSEES55wcTrh/58BUhzVkaWFcvJ8xkvWiV4IQJlHqrTUoipI//OY+jx8cY9mK1fUWG9ud89v/jcszr3RHKRz14g7rlpIEjs1KFCKFoBf5zNOczXZzKR17rddmHCeM44Ru6JOXJY5lZuytmmvRCQMCx/Cc8rLCtRVFqYlchyQvsKSkGwWEjo0E3l5fIS0KAsc42huFColnmZGoH19ZX7rdtwMPS0rWmiFFubJUm+oEPqHrYFtq6Xh/WVB1sL9QcaI2t1y4iMuaG+FoY3wpTsnwPguNdkhvs03UDml0IsKWf+Y3jmsTNM6fO+lIlDbdkjNkZ/Gkmq4sRdDwl906MNv0svAcC+81HOpfBEpKhA2rrRDHUkS+u+ToPA+vGiy/6O9e5HvzPOer4xNmWcZbvR5rkekCvOh7oNQVaVkyTlIOplOmafpCv3sdvIl31POO1dPrtIRnEgqhWKhU2dLDVS3Un5mB2PcDJuhteX+Hb79NXvZx1JqpqFvrF3xdILBx1CYt/1/jWjeAEkdt4Dvv41hXkDz7PC78OAynI0STI0VoRqLKA6pqRkUCdaJhiQaW6uGqKzjWFRxr4cVhtv15+wYS336HXvjfo7W5xwL7wxf47YvBkm0C532kcPGs68YV3H4PW609c4RMCIyHhlCE7keYTt8xlZ7jqA1c63otD/yng1KSbis04+FCEHoOszgjyQxJPMkKmqFRhnJta8mlCDyH6xtdIv/JOdcatlebrLRCirIiyXJc2xQl1zoRb115kkBpzKTC9Y0urdDDeYl452m8smGfUpIsL9kfThnMYqZxipSCrU7zUoQGlJSEro3ba7LZbpCVxkTqN/d3+Z9+8zlpnvPT61v1GNWTS7OqR00WjrbfcW2mbxWaiqycklczAENaFJ3XTjTAVIi//GSH//3ffo5SpsJrjOGMN8atdzZots4HbZ7vsLnd5f6dA371H79EKkkYubz/0TVanZAgdOmuNPB8hzTJufPFHvu7AzzfwfNtBv0pnZUIz3/9oDaMPLav9th5dML//v/5DNuxCCKXn/7iFr21xkslGq8Kz7ZwVEDT99ioHeulELXEtKDle4SuQ1k2lhLQhoj6JJAQNblbCrFUhBLCPHSudlvGBbwmcDs1z6Lhu8t7aCHDjDIyyqHrcHu9h8QUAWxlFK9u9Dpc6bTqdWLkSqVYcjPeaFVcXBxYiVox60XgRR7r1814k+2eJ02+6jac+9orJBh/SizOXSNwCWp+zvepVjNOUv793fvsjsf8X378ISu1KMjLQmA4SfKV06fvF4z8rYUlPUJrlbrv95wq+A94cxAE9nt41k00hktjJFbtM2pTT//Gta6yEv5f0QsOgLDMWJBwELzIe1FATY521CZaF2hqSWmt6+dpfW3UXBIh7G8cy7poHQKLhvsLAvt9Fj1IJSOe5dnxsrDUCg3514TOz5Z+IOY4PH9bBTah81N8+z2gROvq1LH/0/KUpBD4ro1jGVJ2XpSkWcGdnWP+33//OXsn4yVvoxGYcapm4LK10uK//su3WWtHIMTSVNe2FK6tmMUZk3lKK/Rohh4/ub3Ff/2X7yxOrynUY5IN21Kv9f5+pYhSYyrWeVkyz3KqqjKqULb12i+kxXztODGStf1pjJLCsObLkoPRhNBzaAU+1tMvbCHqUZIm/dmcf/PJHeOLYVtc67XZ7phsOy9LHvfHPDoZ8uB4yO5wzCROjeu2YzNJUtaaEU3fw7Yk0yRjpz/iwfGQu4d9hvOYtG49aeBKp8l6q4FrK5SU7A8n7AzG3D3sc+9wQFoY/XgzllKw3o5YiQIcS2H9iQz0LkJRJQyyO0zy3bqatULHdZaEwdeBEIL17Q4/+ovrCCmQ9YWrLEmrHdBdbdDqhGe+DxA1fd750TbtXsR0HC/dvjevdHBqV28/cLh6Y4Vf/PJtyrJCWRLbsXAcxfa1HkmSc+3m6nJKyrIVN95aw/Vstq/3CCP3G5OExba0uiHvf3SVtc0Wk0my9NxY22w/l+NxWZBCIJVCiicGjgtvGGDpBK6VXnbvvjFAPrXbWmszXlXn40uxBGmMHau6M7UU4xRm3MhSEv+CF5Z7gQfN0+u8bHzTvr5souD5DiubHazaz+Vlfv+i3/0+dFfPXlvf8sa8BCqtKcqSSZqyN57weDRmnuUvvRzftlkJA368uY5rKd5dW/lGfsafC4SQ9T4q1J/7zn4PYJ7lPpIXex8vnaWFg1Td53z7m9drlufCG3LEfvKMCVHyzcgiS2GDsF/y9XOqOCcCFN/eGNgiFk6ygq93jjnoT5DS3KN5WXI8qvmTkV/Hkkbudq0TcWOzixSCuzsn7J2MkctEA7ZWmlzf6OK5hky+tdLi1naPJCv4+M5ufd2Z56ljmY7GerfxWuWWV0s09ELitiIrCmOgF7g06hb760ID/WnMf/zjA77YPTSt/DpQDV2HzXaDa7320gV7AQF0Ap/b6z2OxjP+x3/8A5Hn0go8/tsfvcV2p2kkKPOCTx8f8E9fP+ZkOmM4T0gLMz4ymMfsDSf8xc1tbqx0CD2Hg9GEv7/zkK/2jzkaz4mznFJXHIym7PRHfHRtk1/cVrR8I6975+CE//jH+xyMpxxP5sZnBBjMYx71h/zVrat4Vy0awv2WE405x8nnHCWf4KkOHec2ob1BwOvPH0oleO/H29x+b+PMvwvMGJWq5+OfRrPl88FHV3n3R9vLSrgQxkTPrhNZJSW33t3g6s3VJ6MqdQV/MbZlO08q0o5r8d6Pr/D2B1soy8iMyheoKnd6EY2mb5ykTxHBLUthX7J3wfMgBGf8NS7+zqtVbZ/1/FgkF/9S4PqO8UepxzV/wPcLWmuSomCSpgzjhHGSvpLIR+g4+LbNahjyl9vblypy8gN+wA/4AS8KDczTjN98+Zh//uJRHQubOMe1LXqtgFubXYJaSj7yXda7Td6+usbd3RP+w8f3SLO8fo8bOsG/+vFNNntNfNfGcyyurLWZxCkP9gf89sud5TSE1ppW5PHf/uJdVtohQhnlu1fBK3I0TLDXDFyurbQRQhA4FpHnXkpgIoCG7/KjK+usNIJ6Ptgc9MAxhmWb7cYZ86/Ferc6Tf72rWsM5wmTOMG1LSMR2m4sv2crxbWVNlXtTZBkBUVlJFIbnkOvEbLejIxplVK0A5/3t9ZYiQJmaU5eGnFHz7aIXGcplevaFkpJtjpNfnZji1maMavNUsAkSe3AY6vTwLftS9e/f1EYhZOSQifE5Qmz4sC4yOoc9KsrCyzwJMC3X1q+Vir5fG8BYZzC7ZeYVX/Z7YAnPIDvAp53X71SkvEGlvl9hpACdYGZ4g94NhbPtrws2RlP2J9MOJnHjJPUKJ8JSeQ69AKfa+0W3SDAtYyzPcDj0Yjf7+5jKcm1dpskL5a8iHle4CiJZ9lcaTe53m4TOjaB4yzXrTEqUV8eHXO3P2CUJOyMxjwcDhnGCf/2zl0ejUacZphstZr8bGuTldBUK8tKk5UlD4dDvjg6ZpZllFUFmO7hh+trfLS18Y2v2NPbsjsesz+Z0o+NiEOpNZaUNByH1SjkartF2/dxlVq+A+72+/xh74CG63K11WScphxMZ8yyjHmWYyuFaym2mk02mw06vkfL+/alZn/AD/gBbw4CMz799tVVHMs6MxLtWIpW5LPWiYh8d9mJaIQut7a6hL7DeiciL8pl4gBwc7NrZGq1psyNJ9eN1Q5N3/hrLAqMWmt812atY9Rb/+QdDROsC7pRsORILP79skKTXhTwd+/dNPPjZ1bOcjb8ou7Jla7hdCwe/IuKrXUqqHdtiw+313lva7UmkcJifmRxkFVNHhXARititRGaUZLlUurvi3qOV8rldr613uPmamc547b434WDrpTijSuYfDM0lS4oqpi0HJIWQ0q19i1uzw/4AT/g+4pKa9Ky5MvjY371aIcvDo94OByRlSW2VGy3Gry/tsp/efvmMslYPI/v9Qf8Pz/+BM+y+Ne3b9Gfz/ntzh474zHHszmh49ANfP7u5nXctxQbjcYy0QCoqopZlvEPDx/xv351h/48ZpQkJHlBqTX/0+dfYP3xbLHgb69fY6PRoBcGZgyhKplmGb/f2+d//PhT9icTkrxA1mOJ//ef/5Qfb64/t1uvtWaWZ3x2eMivH+/yxdExu+MJWVHi2xbbrSYfbW7wX9y6gaMUluctxzq+PDrhf/jN77nSbvJf3LzBg8GI3+3tsT+ZcjidETo2Tdfjb69f5a+vXeHtld4PicYP+AH/AhB4Dn/57lX+4u0rZz8QLMeoT3fgI98ldB2urRvfrmUgXAevSpnnWp4VZElB5Nh4Ky3eubr6xF+s/v5iguR1O/yvnGgstuWyWYKnl/0sec5vwsJX43mwlMB6QdKROZEvsQ31C+q7ioqSpBwwKw7Iqzm6tt+6LORVSV6W/HZ/jz8cHhhl2MXoEeYctT2Pv9jY4mbb6NNfxsjds5CVJV8P+jwYDjiO54yfUpFpOC432x22m03WwpDQfjGFlaQoSIqcT4+O+Phw/5x+/8sgchy2oibbzSZXmy0i57uj8jLPcyZpSj+OOamP3yQzYylZWRrrNq1rRS2Fb9mEtgkQu15A1/doOO5T51ksE+8/Vcq9ODtZWTBOU4ZJwjCJGaUpkzQlKYr62q0oqmr5kLWlwlaSyHZoOC5tz6Pt+bS9J1Xlb6NssPBYycuSfhIzSBJGScI4S5nnOUmRU1TV0r1dYIj7tlK4yiK0HVqeS8fz6Xo+kePgWS/nNF5pzcPhiPuDAZ8dHLE/mXK13eLdVcNrKCvNPM9IioJfPdqhP4/5q6vbbDYaCCGo6nGn/jzmV4936Hge76yu8PZqzxhPJgn9OGZ3MuF//fIOf3v92jJBAPNs9m2bn2xuYCvF0WzG49GYT/YPmOc5f3Vlmxvdzpnzc73TYTV8MnttSUlg27y7usJ/9/677I4nHM9mPBgOudcfUrzA+JXpiIy4e9Ln84MjBnHCW70uP1pfQwhBUVXMs5xxkvKfHjzkZB7ziyvby30pq4qkKHjQH/IfeEDkOry90uOdFTPGOk5TxknK0WzGv/v6LlII1qMIS8kzRbSLkJRzRvkRo/yQQXZIVsVnPg9Vi5azStteo+2soS5BDOTJumcMswOG+RGj/IisSs58Hllt2vYaLWeVlr16qesudUFepcTllHkxYl5OiMspeZVS6rw2iSyRQtWO8w6WsPGtCF81CK02oWqhhIUlT3XEhaj5LN9OsbDSFVkVk5bzep8mJOWMrIop6v0qdbHk7ElUbYTpYEsPT4b4KiSwWgRWE0vYSC5HKnuUH/Fg9inj/ASNNuuWFivONleCd/GU4WMUVU6hM0b5McPsgLicklQzqIu5jnRxpE/TXqFp9/BVtPztAouYJa8yZsWQaTFgnJ+Q1Oe4osIYZLq40ieyOrTsFXzVwFUB8jmjyN+Exbt+UvR5MPuUYX74yssC4ydkC4ft4F22/NvA2YmClzHKk6b6/VxuymKMfTKYcXI4XiYsQeQRRC6NdkDY8J7P+3wBfDcceH7AnxRal8RFn2m+T17NLjXJACiqilme8+8e3ON/+MPvKatTHAeMs/StdofQdrjWaqHEq8/+vQiysuSTwwP+7f27fH58xKPx6MznVxpN/pubt/nbq9cIbeelEo1BnPDvH9zj//H73yxN9V4Fm1GDX2xt86+uXKPted+pRGOWZexNJ3zVP+GL+vg9noyZpBmzPKsNBSsixwTiPT9gLYx4t9fjnd4Kb4sugW2fOc8ClsT2P3WYnhYlB7Mp94cDvh4MeDAasjMZM4hj5nnOPM+Ji7x2QbfwbZM4bYYR240mNztdbrU73Ox0vvWq8uJeezQecad/wv3RkIejESfzOYMkZl7kpEVBUWmkEAS2Teg4NB2X9SjiRqvD7U6Ht7s9NqOGMWt8ifWXVcX9/oB/ePiIr/sDpmnGX17Z5q+vbhM4NtMs49eP9/h0/4B/evSYx6MxNzpt1qPoTHHhaDpjFCf89bUr/J/efYetVoOm5/Lx3gH/8PAR9/sDfvVoh14Q8NfXrtRSnuYaCh2bv7l+lb/Y3mJvMuHTg0NGccLJfM5/98F7/Fdv3TqT0C5U2RawpMRyJD/Z3ODHG+vsjifc7ffN2NVw/ELHIStK7hyf8M+Pd7hz0kcKyS9vXOOjzQ0C2+ZkPufXj3f57PCIz+4e0p/HvN3r0gn8M93tx+Mxj0YjfnnjOv+Hd65ytd1is9Hgs4NDPjk45B8fPuLvHzzkervDz7Y28YX93EQjLWfsJ3e5N/0D92a/Z5z3z3y+7l3nRvAhN6OPaNjdS040puzGd7g3+wMPZp8yKQZnPt/wb3Ir/Igb4Y9oWJe77lIXxOWUQbbPQXKf43SHfrbHrBiSVnPyKiWvMpS0caSLJ0M8FdFztljxtll3b2B5Nq4MsE6JXxglLvmtjZZqXZGUM0b5EcfpY46zHYbZAZO8T1LOSauYvEqodIUxkjT751smeerYa3ScTVa9qyhhI5RAXpKy2DA74reDf8OD+WfGg0ia4/d+82/oudtPEg2dE5cz9pN73Jv+jpNsj0F2gNam6BlZLRp2l2vBB1wJ3qXnbJ9LNBbIq4R+ts9efIfH8y84yfaYF2NKXSCFIlAtWnaPTf8trgUf0HO3cJTH66pbaSpG+RG/G/5b7k5//1rLcpWPryL+lfjvl4nGG4cAKQXDkyl3P98jiTOKrKC30WJtq82Vm6v4oYuUrz9GfSl3dalzKp0xK46Y5ntmHKccU+qMUme8iCOwFDa+6hLaG7SdmwTWWUKyGYWqGGcP2Y9/Q6kzPNWh6Vyj49wCIK9mzIoDJvkeWTUhK6foWppMCQclXXzVwbd6NOxtAmv1OVu1yJjnxMUJ8+KYeXlMVk7Iq/jJsqWDJVxc1Tb7YK0R2uu19NvFF7PJiCtKnRGXfZJiQFIOSasxeTWj1KbqYuTkpHHQFS6ubOJZbQJrjdBaRwm3fvCdvxC01mTVlKwaM8sPmBdH5NWcrJoyK/aZ5DvMikNKnTLJd9CUzMsjPNW5cJs91Sa0N2nYWzSsbZS8OBi2pMS3LFaDkKvNFsfzGcNkUcnSpIVmmmXsTsc8Ho9ZDUIa7uWrWyzk3NKyYH824d5wwChNzhjPSCHwLIvtZpMrjRa+9XK3xKKaX+qL3chfFEvflyezfN8aFlXyvdmEu4MB94YD7g8HHM5mHM9njNKUcZqSlQVZWVFpI3YwTTOyoiTOcwZJzPF8xpf9E640mlxrtXinu8K1Vouo7m7YSmFLxZvmXRdVyThNOZ7PuTca8HA0Ymc85mA2ZZDEDJOESZYS5wV5WZJXRlGuEIJCm/n9OM9J8pzjeM7D8YiPD/a5UnefbnY63Gi1aTgu4RtOEBdKYDuTCTuTMQ9GQx6OhhzNzbkZJAnjNF0mS0VZUegnHQ3jEVEwyzLGmamQfz044dd7u2w3m1xvtbjeanOt1Sa0HWz57GeLBvKq4uFwxOeHR6yGIR+sr/LWSpfVKMRWisB2+GhzHSUEh7MZ4yTl4XBEN/BZi6Ll8pqey3arye1ej+12k5Way3Gr20EJQVaUfHZ4xNFsxqPhiI7v0/KfJHkCluO0ss5dF/PMTyu1PQuLDv2Lfv/0cUiKgvv9AXf7A9aikBudDre6HVbDEFsZ2eifbW+BEOyNJwzihHuDIZ5tL30+ANqeRy8MeHulx+1eh45vvG1udIz4yb2ayzHJTIexVz+/vnEbqZUiWdyrZ3l4i+DusgtOT9a/XPsF616s9/XXrbVZR1InFyfZLifpDsP8iFkxZF6MicspWZVQ6JxKF5S6pKqqepQ4Xwbqw/yQg+QBjVmXVfcqK+4VWvYKngpNd0A4iEuSYX2R/cp1SlrO6Wd79OugfJwfMyvHxMWk3q+6o1GZroZGI7SgEgWFzsh1RlLOmBVDTrJd9pO7NKwuHWeDrrNBy1mjba8tO82vuLVmQqK+psoqJ8Uc06LKSMuYShfsJfd4OPuMo/Qhx9kOcTEhrebLTsG00GRVSqELhtkhV4L32PRv07JXlo7wWRlznD7mIH3IXvw1x+ljxvkxcTl50tHQEo2u9z9llB+y5b/NteB9mnaP0Gq/VmcDvbh/Xo/bqnVl9v01YoiXhVQSC+iuNbn+Tsnx3pD+0Zh4lrL/aEA8yzjaH9Jba9HuRbie/VK82NN4rURjcVFUOiMtJwyze+zHv2GcPWSS7ZBVUwodnzoRzzqIAkeGdJy3WfN/giub5xKNxQU8zO7xx9H/i6ya0nZucSX8JQ17C60r5uUxR8ln7M9/w7TYY1YcUumMShfYMsCRTTrubbruOyjhPDPRWD5wNUBFVk4YZfc5Sb/kJP2Cab5HXA4oqwxNhS0DXNWk5Vyn49xi1fsRrtVGCQepTSXkohu3oiSv5kyyxwyyu4yy+0zyHeLimKQaU1QxmgqBwpY+rmoQWVu0nBuseh/gyAihFArn1LE9u56smjDJdzmMP+Y4+ZykOCEpB6TVhEI/aaFPi12mxS778W+feb7bzg3W/b9gM/g5gVqt13setlRIS7AehNxstcnL8lSiAaXWzPKcvcmER+MRgW2/kUTDrKsiLnIOplMejIbkp8YgBGbMLXQcrjZbXG02zymZvQhEPSu5uB++5TzhlbHY/qIqmeYZX/f7/Nv7d/nD4QF3+iekzxkhKaqKeZEzTM25/ooTlBB0/YAb7Q7/x1tvGQJsw/CZHCnPOaFf9v5oTEfrOJ7zxckx//7BPX5/sM/BbMo0y563AIqqYjFo14/PjpysBSEbUcS/vnELW0q2G018234jal2Lc1NWFXlV8WA04Fe7O/zz3i6/P9gjK8ulj8o3Ia9K5nkt+Tp78u8CuNJs8eHqGn+9fYXQccwIqO0sw6mn90nXSdjj0Yg/Hp3w9kqPv756hVvdLithuPxON/CRQvJPjx6zN57wcDhiJQxpnuoGNT2Xt3o93lrpst1sENXPgxudDlvNJl8enzDPco5ncx6PxigpzyQa3ybM+FfO/cGQ+/0BH66v8ZdXtrjR7dCrR7QarstqGJIWBf/p/kNGScL9wZC279Hxn/AcO4HPO6srvLPa42anuxwfvtpusd6I+P/dvU+lNbM0oz+P33hi+33Ak+duRalzZsWQR/MveDD/lJ35V0yK/jf+vtTmdxnmuTUujgGQSCxpczP6iJvVTxBC4CgfKRS2sF8vQH3J/UrLOaP8mPuzT7g/+4STdIdxcfJCyyl1ARrSan7m3yUmYdryb3M1/IDr+oO6q6RAm3173edYRUVVpWRlQl5l9chXzM78S34//HdMiwG5Pm+IWZQZ83LCMD9kV35NWsWAxhI2gWoCmqScsRN/xd3p79mJ7zB5xvEoyoy4nDDMD4DPmRVDbOkYESOridZPJG1fDd9P0RQpBVIquqsNXN9BVxWzaUIyS5kM5pwcjvBDl9vvb2HZCqnkt5NoVBSUOqOffsnB/LcMs/tM8sdoKlxlkgUlXcoqo9QpcXFCXPaX2Z8jI1zVxFcrhNYaTecqbecWrmo+Z83G1XpBZp7l+0zzPfbj3zEr9pgXx2gqfNWlqrsqlS7Iqil5NafUCZV+/txtUg4YpF8xyL6mn95hVuyRlENKnWPLEFuG9UxhRV7NGGUPSIoB8+KYYXafFe8DVtz3MU6rZ1uTFTnTfJ9x9oD9+Lf0069Iy9GSM2EJD9sKWDinaEqycsZYPyKtRiTlgGmxz4r7Piveh1jP0rvWFegKKSxsGSAsgSUD7Mp0nbJqSqVzLOnXyVgD9YxlNewtQmsdRzZeyMRpLYp4f3WVkzjmwWh45rOsLHg8HvP1oM9Wo8nGxYt4LVRaczSfc3cwoJ/Ey1n1BRxlsRaGXGu1abneSwe9SghcZTo3b3V6pOWiIl4teSp5PSO/kDj+LqOsvXHuDQf8894Onxwe8vnxEQez6QsFsRehqgmyO+MR/98H9zicz/ib7avGGwdTjX1TL+y8qtifTngwGvLb/T0+PTrgwWjESTwnLV5e9vRpTPOM/emUv3/8kIPZlL/Y3OInaxusBeGZ4PEyUGpNUZV8fnzMJ4cHfHFyzJcnR+xNp2Rl+VrdNDDJ8ShJ+PLkmLQs2J1M+Mn6Bj9b36TteQT2edW2pCiYJKYTVGlNUBO3n66wSyFwlKTpeQyTxHRTkoSifHJN2UrR9FwC2z7zwldS4KDwLaPwV2rNMEnYKIrX2t/LglGayhknhoeigch16Pg+zlPS5UKYsdGW5zJNMyY1N6g8pfTnWRZd3ye0nXP0x4VIiSUlFfpSzvufCyoq5sWInfgOe/Ed9pK7nKQ754Lrl4GphBccJY8oqpy4mDDyjtBoLOki36RBUI15Oa4Tpz+yE39JP91jmB+QvMZ+LaCpKHTOID+gnBUk5YRRfsSGd5N17wZSyHNxy6vCjEpNmJdjDpL77MRfkVQzkwQ9B2WVc5g+RApJYLXwVYO4nHCS7bATf8VR+uilzvMgP+Srya+RQtFxNnGkh3yl7pTAkg5Nu0vHXq+5MWUdFxc1B+j1ux1vCgsy+NH+kOP9EcpSvPXB9pnvCAHKUswmCUHoQvhqBeHXSzR0TlqO6adfcm/6b5gXh5Q6J7I2aTnXCawVArVCrmPycsZAfE2hEwqdonWJIyNCa52e+x5t9xahtUZgreLI51u8a23kWZNywCTf4Tj5jIfTf4+mxJI+rmrhWa26jZiSlAOyanqqffwND2itqSiZl8fsx7/lIP4do+wBhU6wpIcjG3iqgxI2IMiqCXHZZ14cMa4e1dvzBSBoOdex8M7NQFa6JC5OGGR3OUo+4ST9IwKJEi6OinBkA0dGSJSptlQT4uKYeXHEJN9lmu8zyh6Aho57G6UdeGr22MDMEVjCw1UtXNWi0jlxGaCEQ5UX5FQ4MiSwVomsLZxnJHq+6hHZm7iy+dy2sRCC9TDivd4qnx8dnfs8K0seT8bcHQz46frmE7L4JVaCK605ms24O+jTj+NzwbJrKbYaDa41WzRd17hfvwSUlLiWZfZzZaUeVymIC0PEjXNDFk+KYllxNnyG1xuzumwsJUqrknGW8lX/hP/t3tf88fiYQXL+uC0q9ssRE8zc++l+jsYc/0pr4pr3cDCb8ngyxrMsNqMmZVW9kY7GYmQuLnIejIb8894O/+7eXT49Ojz3yL9oX5b3kV6MfTxZ5mnluQWf4zie8+nxEUlR4EiFXBU03afJ76++LwBpWTDNMv5weMD/9OUXPJ6M2Z9Ozn3/NPdFnN4fnpyZxbnRT+3POEsZZyl70wmfHB4yTlN6XoAQ4FrWMuRY3KNpUTBJM9LCJBq+bdP2/DMiHovvWkrRcB18yyauxQVOX1e2UkSug29bZ47ZQtzDtS0C26bSmnGSPLe79qfCwrtjmmXLbQodh5bnnUk0FufCUYqm55GVZS1/nlFWT54FrmXR8X0Cxz5TJ138XtWKXVqb+7W6BDny7zs0FUWVMin63J/9gXuzjxllR4ZcfAEWXu9CLJ5ci+Us/tZQxwiVLulnewyzQ5JyyrQYsOJewZH+pQvhnNknXVFRMS2GHCUPuDP9DXcmv64D2fPX/rP26cl+6eWY2mJUzfxfUQsFHDHJ+5xku2gq2s4atvSw6yLQ6xLfC50yLYdM8z53pr+hn+0vRQlMHHV2v0/PBVSUy6Rxw7tFy15hkB2wn9xlL77LSbaLQNaEdrl81i0maZ6O9cb5EfNiSNtZ51b4U5SwkOIlO4PCHBNburTsVVbcK4bzo9P676wWHSiodEFlZqyoKF8ouXoRnBafuSiaOC2YcRHyrGQ+TTjaHfLw60NuvrvJ1u0VPM/G8WykEhR5xf6jE+bTlLJ89WfNayUaSTngOPmCYXqXrJzgyCahtc6K9z6r3o9wVIQt/HoWMqeV32SU3eco+QOD9Ou601AS2husuO+ZSrsMnjn7fxqlzsnKCYP0Lnk1R6BYD35KaK0R2ZvYIkBJF61LKl2aMa5qjqOa+KpLYPWeuey8mjMpdjhKPqWffklSDnBVi579Lj33XQJrFVtGSGEBgqLmPfTTrzhJvqDQMXF5TD/9En/WoeO+Tce9dWYdUliE9jo93iOvZgTWGoHVw1MdHNnAlgFSOHUAV5GVU+LyhEF6h8PkD5Q6NUlHscsk3yOyNK5q8XQbz1VNhJC4qkm73oayShlm9xhkd8iqKWWR46kOLecGa95PCO2L+wuWMONbjmzW+/5sCKDr+dxot1kJQnzLXs6+m2NcGYWY8YhhYgwTbaUuVfa30pr92ZQvT07OjG4t4CmLa802tzrdVyJf27VazYerazRc18zDVxWFfpJULP4kRcHjyZiHoyFfnhyzN51exi5eKg5nM/5h5xG/2t3h8XjENMsuTIgajkvb91gNQlbrc+tZ1pKvkhQF8zznZD7nJJ7TT+LlsgZxzD/sPKLtevQTQ74uLzlYKrVmbzrh/nDAf3j4gF/v7bA3m557GFtSEto2Hc9nNQxpex6h7eJaCktI4zRdVUxzU30+nM04mE1JivxMoKu1JisKPj06JKu7WJ6y6PgekfN6I4GLpOCPx8f8avcxvz3Y5/F4xOSCsS8phFGQ8n1WgoC25+FZtkl+6tG+tCqZZxnDJOEknnM4n5lzcKrjllcVszzjk6MDiqrib69c5T+7ep2G6z7FYaoTsuUt+83J85KD9IwPK/0Ntb9TP/vueXRfVOC5GIuk9VlHSgrTsXjTAhl/TsiqhJ34Kx7P/8h+co9J3ifXF9wfKGzp4KsmDbuDJyNTzRYKKeSyCh2X0zqpGDEvRnWwWjHKjyl1SVLOcFXIrBhdsDWXg7he/73Zx9ybfsxx+phSl+eq4xKFI11Cq0NgNfFViC09lLBM7KA1hc7JdUJcTJkVQ+JyZvgQTy0rLqecpLs8mH2K1hXbwTtsXhIxeVqMeDT7gnk5ZpgdotG07FUiq0PD6rJwZB7k+wyyA/IqpTh1DjWaoso4yXZQqtSBbAAAddNJREFUM4t+tsdxusO8HGMJh8jqEFltIquNVcd9s3JEP90nLqemuM1i1L+iIGeaDzhKH9LT23Sc9Vfar9Bqcjv6GeveDao6CSzrmLPC/L1QP8uqmL34Lg/nn5/Zt9dBpTVFWZGV5ZmCBYCtJJ5tPTemiucZ/cMxWZLz+O4RypL4ocvbP9pm6/oK3dUGjXaAH7z6u+y1Eo20HNFP/8goe0hWzWg51+h577ER/Jyt4K+eylQ1zeIqLecaaTlkkN41JHIKfNWj5dyAlyAhVTo3QXKeMSsOWfHeZ9P/OT3vXbru26cCYXOzGXJ1Usu+VTgqOrfMZWVXzxmkX3OcfMYwu0epc9rOTda8j7gW/edE9haWcJfjQ4vRsJ3ZP9RB/F1mxQGD9I6Z51QRbeem2Zp6/yQWgbWKEi6Vzmk512k5N4isDWwZYcmzJzWv5iTFgB31D0yLPcbZY+Kqzyw/ZJrvmbEn1TxXBXNUA0c1iOzNM8uSwiKvZgzlfaQY48gmDWubVe/DZULyumh5HpaUrAQBTdc9U8Usqop+ErM3ndCP58zynOiSZIGX8+y64mA65av+CYPk7Hy9wBjhXG21uNnuvNKss60UtlK81e3xVvfixHVx68/yjI8P9vnV7g4n8/l3KtGotCavKnYnY/7+8SN+u7/LwXRGWp6tvFg1p2I9irjWanG70+VWp0vTcWk4bk2Kr5hkKYMk4cFwyIOR+aP1hKw0HZPf7e/hWk8q1OUrjmU9jUUAl5YFj0Yjfru/xz/tPub3B/tnvieFwJaShuOyGgZcbba43emx2WjQ8wNC28ZRirIyQgIncczRbMqdQR9bSU7iOYO6Q1aeIkXf6Z9wMJvS9QPWwhAlu0sFs1ft1OVlSVIU/PHkmP/l6zvsTMYczc9Was1okiKwba42DZn7RrvNVqNJs3a5VnXiNM9z+knMbk0ktwaKo7kxhsvLsh7RMsnxlyfHPBwNsaQ08s808S1reX/J2j9oaahan/8zlbbFnHn92YKULoU8E5wvroPqqRHDxTnVaKpKL6v68huDcPPZn6pnaDpIctmJqb7xOJhE2EiPcqqL9mRZVn1Mf8gzvhkL8ndcTtmd3+Hu9PfL4PM0zPWmcGVAoJp0nA1W3Ss07C6BaqKkjRKKvDJV6HF+wjg/5iTbhZqInVcp05pQnlZzfBkyfwOJhrlONNNiyGH6kAezT/ly8s/nCPNGstbCkT6h1WLFNcFyw+riWw3smqxeUZFVCUk5ZZQf0U/3GOXHjIsT8iqh0MVyuWk1J63mPI4VcTnFUT4r7lWUUK+tBmbG2r4irxJm5YjI6rLibrPqXmXNvQZ1QfVx/CVaV0yKPtPibDBeVDkn6S5pOeck3WNS9Cl0jq8ieu4Wq+5Vo2wlA0pd0M92EQj66T7Tslh2EjSaUpfMiiFHySM8GdJx1niZG25x1/qqwbXw/Wd+r9JVbScwZVaMkCh24zuXkmhojNloWpRMk5SsKM903yPXMcXbZ4RUi+/lWcF8ljI4mZIlhr8XRB7tlYirt9dodsJvV3WqqBLmxTFpNUJT4sqmUYxSKxdWnRzVJNKbuKpdB9iFUXDSMwqdoISDeMFNWjhb2zIksjdoO7foee8RWmsXzhVKYSHwUcK00k4nQWeXa8jfw+xrBunXFFVMaG+wGfwla96P8VS3/u2TsyeEQuHSdK6xHf4NmpJR/pC4POEk/ZJV70dUOj/H1TAazwFt5xalTnFls+5knD8GUti4qkVobdTfz5nkj5fu3mG1zneNhrwICjaiBm93e9wd9JlP8+Xni7GDx3XQc6PdeSUy9kXIypJplnM4my6r86e3y1UWLddjM2qwGTXwrZd3Dn8ZLEZa1CVoUl82ZnnGo/GIz4+PeDga1kH02fa8AK632ny4usY73RXe6nbp+D7tmttiK7WsWGdlSVoWvNdboV/zc+4M+nx8sM+D0XDZ9SiqCq15Zf7HRYjznOP5jI8P9/nfHz1k76nxIgE0XZe3uz3e6a7wTq/HVtSg7fmEtY+EXVeUKzRlpbla5MR5zgera/TjmE+PDvnD4QE7k/GZ5WvMONEnh/uoOmDs+UFdoX61c743nfD58TF/ODxgbzphmp0lTi66aj9Z3+BHq+tcbbbYajRoui6h7RhjuFo5yhxrk7i82+sxSBIOZzPuDE74w8EBj8eGv5LX56Osz9OX/WP+56+/4pdXrtH1/dolFjxL0XJdk8hIwSzLOZnHeJZNdKpOooG8rJZ8jsCxaXruGVnWrKwYJymzLD/TRVskPvPcjCdJIWh5put0EcRCbapesa6Dev2G/FqMj4dFsx77EkIwrYnagW0TnPquESYoGCUJaVHScF0i10W9adm1P1NUlEyLAcfpY47SR2fGcU7DlQFtZ40191qtXLRGaDVxpIclHKRQCMSyGp1VCVkZMyn6TPI+O/FX7CV3SWplp3kxJhPxOU+Qy8Ciq7Kf3OOP43/iOH18PskQFk2rS8dZZ8O7xYp7hbDmLjjSw5K1LwaiDqrNRElaxUtfk5Nsl4PkPvvxPXKdneluzIsxWmv243s07RWjSGU/T6Hzm5FVCeP8CEd6tO11rgTvcCv6iIbVI1ANFg5xgdVk1d3mq8mvuTv7eKletTg2g+yAaTEkKU2xpetssupe5Xr4ASvuFXwVoYQphrTsFRpWj4fzz7kz/Q1xefZdkFRzhvkBK+U2mjeT14vFUJewcaSHkpcXZ2itKUqTZOwNJkyTFA3Ms5zhLGa72+JnNzaf6UeXZyWzSULU8nnrw20mozmzcYLWGs938ANn6cXyunitqK7UKWk5NDKyusKWEQ1rC0+1z83wCyFwRIiwRE04dih0TF7NKaqYQqcI1HNHchZYSKhZwqNhbdOuOwK2PE/CNPOtFjx32ZVR0KrGjLKHTPLHgCZQK6x6P6LnvYsS5/kWUigQyoxsyYBRdh+BXJK74+KEUqcoXKh/a7ZJIYV/4TY/DSVslLIJrBWa9hXi4phZvk+pE5JyWMvtfncSjeVsthRsRBHv9FYYJDG7TwVmcVHweDzi/mjAahjSvSQSbVwUDOKYo/mcw6fGZpSUhI5Dzw9YDyN6QfDGhApPa/ebROPbM3p6GkspwSxbemTsTSfnxnLsWq74rU6X/+L6TT5YWeWtbu+5+v1gugs74zFXj49IipxRauRXF7yVy0SlNcM04dF4xKdHh/z+YO9MEiOFwLcsNsOIn61v8outK/x4bZ3VMHwhpaiy9gtZC0Pset+P57MznJusLPmqf1IH8yu8210xCk4v2alb8EF2JhP+cecRnx0fcjyfnxkzM0mTx0YU8YvNK/yXN26yGTVYCYJnL/gUFrK9nx4fmutSCEPwztLlPlVac384ZJbn9Hyfj9Y38G0bVykcyyJyHJquS+Q6zLKMndGYhuvS8tw6uTHJyrg+70VZ0vI8Or6HJZ88R9Oi4Hg2ZxgnpHmBW3N3FnyOSZqSlgWOpeiGPv4F5HSjIiexFqNi9T4mRYlrAVKaF2c96nQZL1BTtDD8k6br4tsWoyRhbzKl5XmEjlG3qerxyXGSMklShBAmUfe977S563cZpS4Y5cccpg85yXbPqQ4tOhmh1WbDu8WN8ENuRj8hsjovZFCXlHOScoqjPAqdcZLtkmUJaRWT8vpk7IuQV+mSMH1v9nuyKuXpToYnfXruFlf8d7gV/YwN7yaWtF+46zDMDjlKH6GEzTQfMi2HZxK0tJqTVTEH6X3CeRNbODRrFdBXvWcKnVGUGY4y2341eJ+3op/jKvO+X7wTm3aPNfcao/yYx/GXFFVGoU1xsqI8oyAWWm1W3G1zXsOf0HU3z6yzWa7QdtYodM7j+I8k5exMQpVWc4b5EXH15qYLFnGeEhaWcM05uqRCo9b1NEJRMktSxnFCpaE/nfPweITW8MH2Gtq7mP9aVRVlXtJsh4SRx+B4wvBkSllWWLZllKj05Wzu68nbwplse+Eg+LxAypjdSNAmi610idYliJcPlF3Vouu+RWhtvLYKRKkLknJIXByT10Qy41vRwZHhkjPxLFjSqwlCAVIoqnpMK6tmxEUfz+o8UxL2RbFQjzL+GcKQp3SOviSC0WVDCslGGPFub4UvT47PfZ4UBQ/HIzYHAz5YWbuUdWrgaDbly/4x/Xj+VD3IEDKvNJvc6nRous4bSzK+LxinKZ8dHfHHk2NmF8z+r4cR7/R6/Hxzmw9XVlkLz5qtfRMsIekFAe/0ekyy63iWzT/v7pxTIbsMlFrzdf+Ev3/8iEfj0TmVsdC2ebe3wkfrG/xi+wrv9lZouO4Lp31CCBSS250uvmWjgeN4xiA27uJQdzXKkmGacHfY54uTI253emxE50c1vwlZWTDLcx6Ohnx8sM/OeHyG+LvoFv5kbZ1fXr3Oh6trbEWNC9WhngUlBJ5tcaPV4b+5KWm5LkVV8nA0ZJgkSwPKeZ5xONPcHfT5+HCfG+0O15otBGApybVOmw/X1xklKf/48NEyMXUtRVqU3O33+WT/kHGSErkO19otrrRaePaT188oSfjq+IReEHCj22atjAgdmweDIX88OmZ3NCZ0jETs1VabyD3/HBVCEDg2DdfBsSzysmRnPOb+YMBGo0Ho2BRlhZTGiNG6pBe+UZOyuNXrcjKPOZzO+NWjx7jKOMq7lsUkTbnXH/DJwSHzPGer2eBGt8Nmo3FOneoHvBjKKuc4fczO/EtmxfDc55Zwadgdtvzb3I5+yoZ/E09GL6wwZAsboSKuBe/jqwZ/nPzj0nV6EfxeNob5ATvzrzhOH5NVZ9UxJZKG3aHrbHI7+inXgg9o2atY0n6uOMtp+KrBqnuVvErRumIn/ord+M4ZkrlGM8wOeCg+p+NssOndqicyXu+eiawOV4P36LlbFyZGlnTwCGnaK6w42wzzI6ZPmTyCORaeCtnwbrLlv41vnRcQsqSNT3PJ3YjLKWn5hJtSVBnzckxeJXzXpkFeBIvnWDcKsJUirZX4HvfHxFmxFJR4VrIQhC5stMiSnCwtyPOSPCtJ4uzJaOcldVtfK9EwFQNrWeHXujQEb57M/S2wMNyrdG4SCxaVpde7eB0V0XSuEli9Z5rjvSg0BWk5Ii76RmZWV8h6xCqpZXRfaCk64cmccC3DW42wq4Cnc6HFcTl97BYJyoKEVsvfoNFL072KYqkbUeniHLHruwIpBKtByO12h67vY0v5VAXYSGk+GA9NxbOqTjlGvxq01hzOZ/zx5Jh+HJ97hDxJNLo0avO4f4lYcDMGSczXgz4PRiPiU7Khiyr/ehTx881tfrK+zvV257kGYaehpDTSwVJRaZAIHo9H7E4my5n918XSmLEouDsc8Ov9Xfan0zNO7VIImq7HB6tr/GLrCh+urrEZPV/d7jRkPZezGTXYiBrsTMZ8Pezzte4vEw0wo2DTNOPecGA6Zn7w0onGPM/Zn055OB7y9aDP+KmRKa/uJny4usZ/ef0mvcCn7b1cN1BJiUKyEUWsBgGV1uxOJqS1ilJZd5zSsiQtS+6PhvzuYA/PsrjWbAGGt3O93eYnmxt8dnDI3f6A9SjCUQrPtoiLgs8Pj7jXH2ApyVazyZV2i9UoPHOPZ2VZdwImfH3SZ5ymhLbNl8cnfLy3zyTLuNZqsdVsshqFF74xFuabLc+j6/vsWhb74wmfHRwyTTNankdRVYSOzUajgeWY90VaFMR5QVoYeeq9yYTD6WwpPTtMEh4ODVdFYJKZwDaO3AulOmMu2GWSZuY4nJjjAIYLNkoSPj884tFwRODYbDUbXG016YXBpQpg/EvA4p2ZVQkn6S77yX3i8nxV2lUePWeLTf8trgTv0K4Jvy8abyhpI7XFqneNpt1jlB9ynO4wyfsU5eUmGot9GmZHPJh/Sj/bP5fMSGHRstfY8t/mavA+28E7L7U/C7jKx1U+FSVSKLIq5jB9SF4lZ6YiJnmfQhfcyI9IqnlNnH8dcQtBqFpseLdo2+uoC2I/JSyEFLWR4CZJNT+XaAgklnQIVJOeu82Ku31h0qKEhVIWgdUgtFqM8xOy8kk8UOiMuDDmft9HCEwi4FiKyHfwKxOrztKMdugRuEYqfBFrP42FAV+a5KRxRlGUoGE2iSmLCsexLy02eq1Ew5Ievuoykw0SMSSrJoyzx7Xh3tq5Xcvryn5ajiiqGIHAEj62Mp4ULzo2dRpKODiygSX9l8rqL8JCnSqtJlQ6X7p2H8YfExcnxjfjudCMsod18K/r2c+CQqcXytIZab6YtBozzXeZF0ck5bBWyUoodVovq1bPKo2Ublye1K7r320IIHIcVsOQtSCk5weM0pS4MA/RvKoYJqYKeBLPmaQpoeO8dpXvYDbji+PjcyZrYEYdrjRavNXpvjGjwO8DsrLkeD5jbzJhmMTEtfrQAkpKPMtiq9HgJ+sbXGu1X2hc6iLYSrEeRtzsdLjabPF4PKYfz88kNq+DpCjMaN5kwoPh8Mz4lxKCwHZYD0PeX1nl/ZVVmq953gVwtdnib7avkuTFeZ+YquTBcEjT9fjR6ssrmpzEc/5wuM/D0ehCDovhPXW53emyGga46tUf5YvuyGYU8a+uXiOvjPT00xKy+9Mpv9nb5VqzvZxpVkJyvdPGsRQC+OzgiD8enfDZ4WlJa03kuvzV1Su8s9JjLQzPkaDbvsftbhdHKX69s0tWGC5JUpgE4Eanwy+vX+Pd1ZVnPuUF5jrr+B4/3dpACDiazfk3X32Na1l49Z9311b417dvETjmxXw4nfHZwSEPBiN2J2P685hhbJKeeZbzT48eM05SbKVwlOLHm+t8uL7GWhguDfkcpbjV62ArCRq+Punz8d4+v93dO3UUNB3f5z+7eZ13V1do+/4bM6v8c4ZGk5ZzpsWQaTFgVgwvVJnyVYMrwbts+2/hvdC7+2IYtSqXFfcq14MPeDD/7Bzh/HWx4FGM82P243tMy+GZzw2f02XDu8mt6COa9tOGxi+PQLVY925wlD4iVE1mcGaEqtQFWRkzzI44Sh/RdTaw5as9NwXSEPJVQGS18VTwDfGawFMhTbtHP9s99+liHK5lr+DJsFbYevZ7SQkbXzVwZcBUDKHuEhU6J63m5G+oO/WmUVaaJM85mc55eDxkHJuEqSwrHEvR9F1sSz7zGSOkQApwPAtlSSxb0eqEFHlJVVU0O69+zzyN10o0bBEQ2VvMikOmxT5pOWaYfY2jQkP4li5SWMvq/Kw4YJIZ5+tK57UvwzqubNUqSy//0JU8GSV6XRjjvZi8mtVdF9ONmOp95sURvEQis9geo0ZiPelMLNalKwqdklczc/zyPUbZfab57jLRMElGvtwWXTuYFjqheorA9V2FIUvatD2PtShis9GgqKplomEM3YwSzsFsykk8X77QXwVFZaTejmYz7g37S5fqBWwpiRyXrUaDq80W4UuMmvy5IStLDmYzdqcTxmlK9hQB3FUWHc9nK2pwq91hNQhfufpqSUnb89iIzHG/Fw2WviOXgXGWsj+dcjCbcjSfnePkdDyP7UaTG60OV+qxn1fFosqzETX4aH2Dz48OeeJTYVBUFQezKY3hgGGSkJelUUt6wePXj2M+Pz7i8XhEcYH872YU8dH6JtdabZru6zlkLzwaekHAj9fW2RmP+XvnEfM8P5Pk9OM5WVmyP50yz3MjAiAlK2FA6DhM04y8rLhzcsLBaEpelthKsRaFbDYa/HRrk9u9Li3PPXccGq7LjW4HrTUH0yn9OGYwjwkc8+x4q9flb29cZSV4tgKKEAJLCBquy/trqwgh+P3uPg+GRuBASUnDdVlvRGf2Ky1K+nHMo+GQPx6fMIoT4iLHkoobnTZSCO72+zhK4VoWG42IeScn958sw5KS9SjCs2xmmZFsvnPc53g2Iy9L47cThVxpNfn59hbX2m0arrM8Dg3X5UanzXojWpLdn95LIQS9IOB2r8taFBI6Drb8lzd2pamIyxmT4oRZMTpn1iYQWNImtFqseddZca9g12PNL4vFKLjCoetskPkxJ9keB9y/pL0xyHVKXEwYFycMcuNHdhq2MBX8FfcKm/4tHPFq+3Mai85Gy16lYfcodHEm0aioyHXGuDjhON3BV9ErJzjGTd3FlT6+MrLC3wRH+kRWB/uC7ymhCFWThtVdyhN/Eyxh48mwNuYTy3KvMXFOzh3r7wt0LVaS5gWjecJgZs5d4Nh0Q59O6GOrZ3ORqkpTlRVVpdGVRkhZO4AL0GA9g0T+KnitRMOzOqx6H5KWI4bZXeKyz0H8+5qTMMBTbRzVoNTZUvZ1mN1jlD1ACou2e5MN/2eE1stJi52GEBIlnKWnxWtBa3RtsKKpECgs6WHLEEdGqJc1dam3z1ddLOEjTnVsKl0sk4vD5GMG6ddk1YSyypDCWnaLlPBQwjaqWUIt/TTi4oh5cZ7z8F2FkpKtqME73R6TNOU4PvtyiPOch6MR9xumCvwqvhYAcZEzShKO5jMO57MzDtCqHp9ZC43/Q9vzcF6jEvx9R1oW7M8m7E0nJBcE/A3H4Ua7zUbUwLWsS6m+ukqx3WhyrdXmYDo9Jzv8KjCcHDMqd1Jzcp5e59Vmi9vdnjHSe+01GpiAuUXHNw/005wQYxhYMElTRmnCJEsJbeeFVdXGacrdwYCD2exC+d/1MDJE9uDyqk6esoxAQhSyEUXERc4wSZb7lJYlZWrurZ3JmJUgoOv5hhBtKd5e6bISBPz8yhbz3PimLMaZGq7LShjSdN0Lu2KOMgpWV9otfuldq0fhyqWk8koYsBaFL3T8XKW42m7R9Dze6vWY1R4uQoAtFZ3Ap3eKML8WhfzNtatsRBHX2i2O5zGjJOFKq8VbK11sJZfjnEpIVqOQXhAQOmeLFAIIbJv3VlfYaET81dUrJHlOhUYJo2nf8rxlYnaaBP7O6gr/t7/4CM+2WQkCwx166n6TQvC3169yrd1iLQpZCcNz2/AvAVpXzMohg+zgQkdoJWxC1aJp9whVC0f6zw1GnweJILI69NyMQDWRKC4yg3tVxOWEo/QRk2JwbhRaIAntNivuFpHVxhE+8jXHxE8jUA1W3G2yKqnJ1mcLovNizDA7qGVoXw2WsPBVhCv953N4MYmVpwKsC9RBJQpPhQRWE+sFVJyEkNjSQcmzMaLhvJkx9e8jlJD4js16q4Hv2GR1rGNJiWMpglre9lnIkpz5LCWNM5I4ZzyYMRnOieeGo/HeR1e5/vbFnmovi9fraMiIpn2NjnvAMLvPOHvINN+j0gV5NcVVHTzVotSZce7Md5gVewhh0XKus+K9z4r3Ib7VfeXsXCBrnscl+C+w9M0FFsRr417edK7gyJebszbbJ2jaV3FrpS0zi1mS6xmj7D778e84iH/LJH+MLUM81cK3egRqBc/q1uZ9ZjZSCYt5ccwov1+7e58nSX1XYQnJZqPB270V7gz65z6Pi4KHoyHbjQZvdbvAqwVQkzRlZzLmeD4/I2kLLD09rjRadP2AwH49Yv73HWZ0as7RbHah03LoOFxttlgLo6Xp2+vCUcZJfTtq8MkldZO01pzEc+4N+4wuMGa0lWKz0eBGq010iZycwLKxpaLpuAS2cbw+fRwXnbthmjBMEmypnhsol7WHxSg1Dt2jNDnDY1lIJK8EIbfandfuZpzGwhem6wdsNRoME6MUtVj/wl+jH8/ZnYyXiQmAJQRrUcTaS3JRFlBS4tk2280Gb6/08F7j2rCUohsEdIOAm93Oc7/f9FyankvkOHR8n34cM44Tbva6fLi+hvUsIfpTWFxTrqXYbDbYbL4c/2ejEbHR+OZjJ4Xgdq/L7V73pZb954YKE/yO8qMLJWaVsAitNg2rh6dC7BcwAH4+zDiP8d4wMrK5Ti/N5Tkup5xke0uDwLNrNtyGrrNJ+ILB9YtgEXO5KqRtrzPIDs99R6OJywmj/Jj0AungF4UUFp4KsKX7QlxaJSzsCxQ+wSQOrgzwZfhMm4Iz667lZZeKT/XjdOH8XtUSupcl5fqngpQCRyocS9EKXv49kOcl8SxlPJwzGc4ZHk8Z9qfMp4ZjfOXGivEuugSFvtdKNJRwcFWTnvseAovd+T+xM/9PpNWEQXYPJR4jWRBKDHE8tNZpOTdoOTfoue/WkrQvJsf4pmEMlBSidtU0s30duu5bbAV/VXdeXnqpuKppJH2lA+jaf6TPUfIp+/GviIs+lvRZ9T6k57239CJRwl12MkxCJRlnjyi18S+5jOTqTwUzA95g2s34B89fSl8uEBc5D8cjtkYN4vzVW5lHsxmfHx9xEp+vdFnSVNPf7nZp/QvmZiyQVxWDOKGfxOQXJBqBbbMZNZbeCZcBW0q6vnHifh1ewdMYpSm7k8m55NKsU7ESGEK2b19uB0sAgePQ9X1ONOcStqLSDGuZ5Ybr0uCbr7usLBmnKaMkYZ7nZGV5pmbqKouwHkXs+MFSZvcy0XAcrjbbHE5nZnTrqc/HdTJ/md2U7wIanst12Waz2SArSyLHuTTVlR9wedBak1QzpsXwQiKvJRwaVpem1cW6lCTDQCLNGI6KCK0Ws2J0aYlGWsaM8iPicnquRyKEILSatJ113NfgmjwLjnSJrE7dbTgn40NSzgyR+rUSDYUl3RdKDIxxs0QJ60KFMDMa5+CoF+1Uidor5bzNZy2nQ6Uro4b6HZGe/1NAVxVlUTKfJgyPJ2RpgWUpwsjDshWWY1EVJVJJhPoWEw0pFFIoHBnhW10c1UBiIaWFpzq1SZ7xxlDCxlNtPNWj496m7dwisFbxredXnP5UEEiUcLGEV7t+G1UsVzbq5OjVW4cLmLnAKfPikHH+iHG2gxSKyNqi677Lhv9zWs41PNW+8Pd5NUMJj4tMCb/LkELQrg3yekFA03GZF/kywF3wKnanUwZpQlzPgL9ogLt4OB7O60Rjfj7RcJRkq9Hgdqf32mTgNw39lBrTm6i05GXJqK6259X5RMO3LDNi5nqXpoyz4Gp0PP/SZD01Jvjdm06ZXZCkKmmuvZUgxLvE5GYh5+0pi4bjMknPJznGKT1jlCYXJnNPIy1LBklsODNleU6Vy7MsOr5P03VfSsr2ZRDYDptRRNvzLuxijTOT1L3V6b2R9Z9GVpZkRcEsy5mmKdQeGIsxtQXJu+m5hPW4ZVaWnMzm5hliKSSCrCzJa+dx37bphQG+beQf06JklmXM85x5lqNq2ciL7jmtde3NUTBJ0loOeqEwqLGlohcGZkSv3s55lptl5zlFabh6ShrZ27D2IXmRrskPMNBUpOWcWTEir87fc0paBFaLwGpjvaaj9QKLYqmqK/OB1TIV/loG/3WRVXPG+VFtRPfUsx+BpyKaVu+53IZXgSUc/Gd0fjSatIqZl2OKC471i0IisYVTS/E+/10ia/L4RePwAoklHGzhvpBUsWHZPDuJWCQb30eJ29eBshSOayOloKo0fujSbAcUdXLhefalHZFLuQvj8oTD+GMm2UM0FV33Ha6EvzQcDRnVrTKBEg5KODXnIbwUAvdlQqBwVIRbJ0zGuXxMVk2Mz8clQOuKpBwwzXfJq6nRYFdNImuTjnuLlnP9Gw38Kp2TVzPK75n2s8DMYTcch40wYrvRZHc6ZlgHX2VVMcszBvGc/emU4+aclSDAf8mK7dF8zhfHR+c4IGb9FtsN45/xfVCb0rW2z5uqsRRVxThNGafp0g36NBxlnTEduwwoKZcmb/YlBleTNOVgNmWWXxB4CEnouPU6Lz9Bt6TEU9aF3INKa9KyIH6KWP0sJEXBSTxnnCVnzPkWCGyb1SB8o2N/nmXR9QMi5zxpG4zB4/5syvSCY33ZmKYZx7MZd45P+PLohIXx5TzPSIqS9UbIVrPJjzbWuFWPFE3TjF892uHBYMBaFGEryfFszjhJSfKcq+02f3frOtutJpZSDJOEO8cn3OsPeNAfEjg2a1HEB+ur/HhzHfnUNTOMEw6mUz7dP+TO8Ympk9ZJRdvz+LvbN/lgfRVLSuI8515/wP3BkEfDIePEjKJFjsNqFPLWSo8PN9ZoqO/+8+i7gkXwGxeTC5UXVT3D76tXU7L8JggETk1oHr9Qdf7FYJyz+6QXJi4CR3r4VuNSOzQLSGHkYp/VHSh1Tl4llBeoZr74OlS9jhfl0oplIefcJ8JMmyhpv1hHQzwh9T973d+fWOqy4PkOypLEc8PT6Kw0aXVDJqM5WZITNDyUJb99Z3Az31YwL47pp18SlwMc2aDlXGfd/ym+6mDL6Hsz9yaFhSObeKqDJf1aq3tKUo5Iqwl5FdfE81cPVjSavJqTViPKKq0lfoO629PFU62Lf6cNg6SoEpJyQFZNX0t1amGauDgzmoqK/397//UkWZbf+YGfI6507aEjdemqRjcakgPM2C5mbZdLoxmf+T/ydflE49C4wyE5A6BBoBvdXV2iU4uQrv3qc/hwrntEZERWZkZ6VlUD/i3LysgIj6vFT32/X/PebjdRz5aH2mO/1eZet8skzxjWM/UWN8ozzjKejEfcbLdp+QGRfrOHeVbLYB7PZzydTJifC4IWErubcbwkgvsrVFR4H8iqkmmRIYQk1k6zXwpRu4EapJCo2v3Y2IWfikUJgRSSyhqsdSTUV3WFHGHZVVqvIhx7UtLyfaIVEcGh9jpQmqjep8ut+rfDwkMjLQvGWUpRXd6PrHLys//44vlbdcneBMY4I7tpnl/pdG5t7U5dlRd8PV6FwlRM8oykKC51tYClT8TbeJm8LXzpiNmxd7WOelqWDNNkaRD1LuhGET/d3SFQir1Wk0YQXCC6FlXFNM95Mhrzq+cv2G212G+3l9fji/GUUZKy2YjZa7fwlCKvKp6Px3xzfEpeVXTD0Bn1CYFcKH+d2y+BI/vmZcUwSZhmGXlZsd9ucf4ULK61p+Mx//z8kJP5nMpYpHTPNlsTxhcmWUVVMUxSl8AMhuRVVf9cMM9dAuIpxY1OG6+etf5B5W5rZ9PFdfejfW9bS2lyMjO/cnRJCIUvQzdas/LOvxvbCWSEekeC+XkUJiepJrUT+EVYLNNywGH6iFk5IlKrHZ+alkPGxQnTK0wPwcV5pS0wtsTYqh7jfrtro/bHRl0xvvSKX/jOEpsUCoV+wzKcuPDXGg7aU0tJ26o09LZadHoNwsgnTXLiRoBc0bvyHRONgryauDGg/BEIScu7QdPbXwbrf0hwhKUekd7Ekw0EksLMSapT5uUhDb1FqPrvqGBha0+Mhc+GU83SMnxNG9AsfT5m5QFpNbjgGvq2cK1EBcil6Z+xxXuXzPWk5Ga7zccbmzwcDXn80s/nRcHD0ZD9Vqs2BXuzVvE0zziczTiazxjXRlsLKCnZjBvc6XTpR/HKFJTeJyZFzv3xKVoq9hotGtrHV5rKGHJTEShFpDwqayiMGwspTUWgPHzlEq/CVMTa+85Ew42UVFeedS0lkee5MZIV7Zdzk3akYy3lJa7O28JYS2UMhTFkVXXlsoZpyv/07df8l6ePl4He6mA5SRIGaXJB4Wz50/oYZ+XlMairUBlDWpaOm3HFxz0paXree3WT9pSsFbIuS6yCS4bmRXFlF+xtca/X5b//458ihaATuuTmqjGitCgZJil/cfsm//UnH1FZS1IU/MdvH/Cbg0M+mbiEox2edQaEcN2sjUbMnV6XThhSWUukNf04Qiu3f90o5LOdLULPuZmPkpSkuBzAWpz7/FeHx/z/v73Pf3XnJv/u7m0i3yPUGmttTUSPEALmecHxbM7vTwdkZclf373NjU576bPxv99/yPPxhBeTKb5SbMSxk5b8gWCxTuryYh72o4M7D2Ud/F6+BmXtOeGJi0nrKiBwCkqeDN5Zyeo8qqXU6uXrztiSx/MvOcme1aTm1d77pS0obcG8HF+porUgTTuvj9KNwr9lAidq3oVAruTikvXy1nh3tLoxYezj+c5Po9GOiJoB2vuRyNsu3KxdhX6CFs4LYzGxelarXNg7/bjhTHEiQtWj5e0zLZ4zLw9Jq1NOst+hREA/+JiQLkr4V1zoC3WWnMqmtf+FwZPxOcK7QOKhRICog3ynypU4Z3BbLonfcM4F9VxCNy+PnXP5NZOCBRfFk67iY21Jbiak1ZDSzKlsjkS/lxvZU5LdRpN73R7dMEIJUVfkHRaJxs1Wm/lu4WQpeX11bZCkfDs45Wg2v8Q3UMK5H3/U69MLw0sjLovg9OWr9Ies6GVVwVHq2ugWaHgenlAYXGDd9kNUKBnlKYPMVVYNFi0kUgpmRU5pDDcaHbZrxahFcmXr422spbROSeiqAF1KQaA0nny1FvfbYuHZoGqpUC0lRXV9mcjqfLL0ikA+ryoejUcwHr3Dll8PljMlqTdKNGpZ19xcfUy0lITaQ7/Hl6wSzqjReTRcPu9FZUjKkvLcfXZdTlE7DGmHry8mSCFQtZDA3VpJKi1K/unpC0pjmOU5ozQjPEf2V0LSDHw2Gw1udpwb+VUItCbQmmmWsdVoOCOsK5LGRRI4SFIOp1Mavs8nW5t1gnLxVZoWJdO84GQ252A8Ia8MoySlE4YIYJbnzIqCIM+ZZBlpWb7HfvKbIZ1lnI6GxHGTqBEglXuWsDBXrKvM1p5T6BFnPclXjbqsHnZZZb/qHej8VDy08FYvmCLEknP6JvyA18Euu9EVpSmuLB66jsbwlR2H7wNuG12x83oJlkBeoxNy9ZIW1+R3jUKt8abwAw8/8M79e/XvlndLNM79B1Da1Hk8VAPyalwH7le/rH6ccKShQLbpBx+TVWNKMycpT3k+/3uMLfBlXHc+Oq98iBVmyrw8prTO2bvl3VwmGkIIPBkTqA5KBhhbklUj5uXx0qRvkYRAPdJkS2blAS+SX3CY/oqkOqWyGdcdOnFzpg0C2UFJn8oWJOUJs/JFnWwkaBmj3oOqlRKS7UaTO3lGLwwJtCYrz8ZKkrLg8WjE4/aYeT0+8iYPp+Nkzm+PjzieX55x1VKw12zxycYmnVfIgRprsZY6qfnhxwZKa5iVBUlZkFUlSkqEPQu49hptQq15OhvxzejYBe1CkpuKzJRM8ozKWv5yG3pBiCfV8gXhkgxDaR059lUBsBQST8rliNMqIYRAS4kSkkoYqmvGWGUd/JXmxzpj68ZtjDVc2aJ4CZUxZFVJ+ZLa1AJKyHr86/1dn1IIPLnoOF3+eWmqKzknth67eR9KTaHnSPALAvdiOxuBk6S1FiZpRi86u7+1kvTjmM3Gm/lvvA55VTHPnMZ86HnEnkfD964cL6isYZJmnM4TBknK0WzGf/ja0qk7LvOiYJyk9KKQonp1sv99Ynw6JXn2lI2NPls3+vihh/a9Wo1RIJQbPavKClMZp0Yj5bJCI9X399ysqKhe0dGoS2m1guTqt8fJpX63G/Wbwi4mHOo/P2YT3uV49eJF+TYQ1IXL1cQUYsHhWMnS1njfeEfVKY2WEYFqE+sNx2WohpxmX+GJGF810WIxPiXql5ZwylTCw5NR7R3RrRWrVK329MNgoSzhyQY9/0NKk5BVI8bFY5LymOP0tygRMCmeEWlnpieFwlrjHhK2pLIFaTUgKU+RwkPLAF+2aHn7LPY+UB0aeodQddEyxFAwKw85Sn+FtRW+aqNFgMVQ2ZzczBjnjznOfk1SnRKpPhJNek0fDSEUvmoR601C1cOTMZXNmRUvOEz+idImLgkRQa077ZzdtQjxVZtAtvBV+1qVDSkEkXaO07vNJjuNJkfz2VKWtKg9BI7nMw5nM/aare8k8S6q8yfJnN+dHF8igS94IXvNFne7XdpBsKzIGQvW1IGgsRRlRZoVKCVpNQI8rVwQbs9SOlGv0xiLVBIlF7yJc3rTdnGcr//iddwdgRTO1aWsyiUHQApBxw+XHIuTdI6WEi0UBkNlLKUxyzGhqyo/xrI8Bq/eBpYz56vGsqshBVTXZ2oYa9+4W/BDYNnTtW++h6bmAlz1C8ug7z2+YhfX7SsdZe1iG899z1im44Q0LYhinyD0zvEhWD77hXDHwhhDWVQu4dQuYP2u+6UyrnNVnVup5WxsDlxiIesOKbjjFGhFqPVKEjOBWCZRiwS9shZh7XIc5Pwp01KilVwqTPXjiI04Ovustey2Wmw1Y2LP+8HHObN5zvRghM3cM8wPPbzAc7PcWuHHPl6gmY0SklmK9jSerwnjgCDy4YpjbK/46l1hcaIqzmjt8nIXHfD3dY8IIVdWnV90Clw/+sebZJzh/KTKm2PxDlr5GVlnGm+MvCjJ8hKlJJ5WSFlzyr6H584KfDSU88bw7mLst0yL5xzM/y9O069eMtJzOyRRaBnjyyYNb4e2d5PN8Cd0xT20DN969u99wJMR3eAeCEFWjbBUDPP7DLNvmZUHhKpLpPp4soEWAYaS0qQUZk5hZhQ2oTQJLe8WHf82be9MFlegCFWPtpfT0NuEqkNajZiXhzya/kdO0i9p6F08GWNYSOGekFUjCjMlVD02gs+YlYcU6fWk9QTKJTvebr0NPdLqlFl5wMPZ/8ph+isitenUr4SsE6iMWG3RCz6iG3xI7x3cVhduwTdaHe51eyRFsUw0HG+gZFSTwnebTTfG8YpEYxGUHc9nfHV6vCSXL+ApRdP32Gk2ud3uEmhdj7NYytJQli54McYySzKOTib4vsa/0UcpSVUZN7dcV+3E8ncrfE+jAo0xhqI0KCmQStYO86DU9W9iLSVNz0dLSUP7ZFXJ2GTkxo0JVbXr8uIYFKaiEpZQaRq+I1pH2qMXRuhXmO29SWz+vprTi6DSPei4dhxi61f0vyTVkEW17ip8H3tqX/r7ZYiX/gYwleHocMzp0YSt3Q69fhPPVyitludaKYG1LtMospLZPEMKQdwI8DwQ36FClhQFp/O5cxxfrNNaplnOYJ4ghaAdBPhaUZ7jV4hlgevd4WtFA3+5PfPCSeJG3mUelJKSZuj8VTphQCvw+fcffcBHm/2lAIK1Fk+pmge1WpGC66AsStJpyiAdkc4yPF+j60QijAPaG00a7Yijp6ecvhjiBR5xM2Tr5gb+d0hhns08rBavuxfeR6KxlEqtlTTfHbW/+NtUIv5Asc4JflgkWcFgOCcMPJqNAN9TyO9JFOedEo3SpmTViLQaUtnMZeRCYDCUNkPYl6oKdVUrN1MScUpaDeuRoRlpNaAffExD715L1WCVcP4gMU29y1b0U5QMCVSHWXFQd23GFGbuPoeux5uqmlBdooSHJ2Mi1aOpd/FfMtlRwiNQXTbDz6lszih/yKw8pLI5k+IZWTVGyYDFLKqxJVpGtLx9Wt4NOv5dTrLfcZp9da39EwIUHoHq1NtQMMzvMysOKE3CxDwjrYaOh7IkbzniesvewL6DSdHSQVdpbrbbfNjr83g84sVsuvyMxZG7H4yG7LZa7Dabyxf8y5gVBaMs5Wg+Z5AkFwzTpBD0w4hb7S6b0VnV0BhLmhVMZxmnwznGWDb7DaQQ5IUbWTHGkqQFJ4Mpee6W6XmKwNfM5jmjScJWv8mN3S7TecZglKC1RGtJHPpEgeeCqmuioX1uNDqUxhAoTW4q5mVek74Nm2GDQGpCpYm1R8sP6fghgdJLqdVAabp+eKkrsehUqEVFg6vfcbau2FbWold8Py66QKbuLl0XSgi8ReX8CgicJOytdpfN+PsXp4g9n082NrnRbr+R74UUAk+5cbWr3szfRwfHWpZjdVddGEpeHt8y1jKfZZweT0EI8rzE8zRerWzi+4og9LAW5tOM+SxjNsuIYh/fd597EzwbT/j7x08pqoq0KBkkCb0ooheFdKIQX2nmdaJxlhC9Immrj+EgSXg2nvBwMOTByYCTecI4TWkGPnFdpNhsxEvn9BudNp9tbzHNcv7zw8eEWuMrhcXxPW522nSjkNjz2Gw6Ivo4yxgkCb8/HSxLb8ZaNuKYW73OexlPfFtYY6kqQ5rnrpASevihRzJNHV9DCcfdqAsq6TQlT3O6222kelVCt3iHVStLN9zzy/ksuOW+vEbqKYM3G1d8Gyw8F17VTXlbiCV3oU5erupiIuh4WzR1D7kgVH/PCFRM29usp07WJOz3hbIypHmBqWphBtzouO9pPC2xtuaKZQWVsXhKuhHG+vfd7wDWopTE9zQId28XRcU8K2rBie9XTvudEo2sGjPIvmWY3WdauiBViwBfNglUpzbsO7soF2pLuZmSVzOm1VMmxVMmxRNG+UOUCIj1FuCq7j80AtVhO/wZLW+frn+PQf57BtnXTIsXTmK2mmAo63lQDy1CAtUi1ts09Q694GN6wcfE3s5LS3Y8jZ3oT2noPQ6Sf+Qk+5Jp8ZykOmVWHUFpUcLDly0ivUHX/5Dt6Kc0vT181aqN+95Nx9uTDXaiPyHW27yY/wPH8kumxTOSakBaDbBYJBotA6fCJRRKhCjh8671CV8pbrU7HM/n/OL5s0s/nxY594cDbrTb/HTr5eN3hlGWcn844HA2I6uqC3PjUgh2Gk0+6W/QD6OzDoCxzJOco9MJ3zw4AqDdukMzDlDqLMmdTFN+/+iY6SxDSkkj9um0Io5Ppzx6dsrnH+2yt91hNEl58OQEz5MEvmZns03ga+Q7vIiaXsAH7Q0sbqp1Qd421lXAfKnwlSLWPp0g4m6rzwet/gXSt8B1dK46Uwsytqpbp1cF+wa77J6smmm1GHspzbtRYB05Wr+SHC2FpBNE/Ntbt/njnd13WNP14ClJP4zZiONX8oPOQ9UJoievNpgyixGiK2fTVwNjDUVVUb3i3Lhu2UXfEGshz0qmk4TKGCajeZ1gaMLIp9EM6HRjytJw8GzIaDgjTQs2ttpsbDZfq0SjlTvP908GTNKcaS0B3I0i7vQ67LSadMLwrfkhxlqejSb8x/sPeHA65Gg6Y5bntYRvyvPxmD/e3+NPb+zTjUJagc/HWxsYa/n66Jj/8ddfOulpKTHW0o8j/j+ffMTP9naIPOfH8fHWBl8fnfD3j58wzYp6wkhgsfx0b5d2zVX7oUenFqiqCpMapBJ4vmY6mZPOMtr9Jts3BY12hLWWg0cnzIZzTGlQr6iMuvFMl2isMuiXKLTQGFFeQaBefXJzYenWUNmqTjbeDc58TqBwLtiivi7OQwrFTniX2/EXaOmtzITwbaCEz1ZwE/U+CPZrALX1QVkxTwryoiQv3D0jlaTTjFDSw2LJ85LBeE6WlzRCV6gR9Qh3WVYYNxdNGDgFPyEERWUoyoqydGqGSor3wqV7Fa51xRY1d+E0+4oXyT8wLZzsWi/4yFXwVQtPNuvs92xnFlyGwszJqzGD/FvG+ROyasSkeMK0eMa8vF27iseX1iuQNL19bjf/hq3yjwDYCv8I74rPvhvcNkuh8VWzrjZofNkkVluk1SlZNaay2blEQ6NEgBIBoeoQqh6xt0NDb59TnDo3g2xVnYwpDCWR6pEsl5tjMUj0kjje9Pbp+HcciVx4bASf8kHr/4snG7T92zT0zls8AOr9q0eoQGCikkhvkJQnZGaCsTkvy+829C5d/w6B6r6zbKCWko0oZr/VYiOKaHge6TlS+LwoeDwe8WwyWSooLYLi8xikCV+fnnA0n17ygpBCsNts8unGJr0oWv5uWRnGk5Tj0xmTaYbWkiTNkUIwnaX4nqaqb8zpPGMyy/C0xFhDVRlGk4R5WlAU7kWmlSQMNGlWMJvndNuxI2+/Q+CgaxL2y1hwUhZL7gUxH7T6bMdNWn5wITRdfO7l7Vj8W0qBVo7wXVxRJV+o7+RVhSdXI0t4XvFq0Z15l46GI6yr5Xz+YvnL9eGqPvutFp9tbi2DvO8LSjiX6Uh7b0RIVjWHyVfqysNdGOdifZVnx6pQGMO8KJxc8BWBmlfLHl+8Pl1nqqwMaZJTlQbP1xR5xWyakSY5QeAhpOt2ZFlJkZdU9X687tLypaIZ+Oy1W3yw0SctC4qqoh/HbDcb7LSaS1ncyNN8tr3FVrPBrW5nOUp4FQTQCgPu9no0/YBZnlPUBYuG79MOA/ZaLSLPw6tf2tvNxpL/0Ymi5fiftdCsyem6lm9uBj53+z18penFEbMsX95/FsvtbofQ+3EkGe1+k83PdxGlBixe4Doa6Twjm+f0dzv4kUcyz7C1k3AQevjR1X4r4AaDSltQ2WKFQb9z6NbCp+CyYZ+1ppZszd9LorGU1l0Bp+LMcdwZ2hU2p7LFxc8gaegOm8FNQhW/F3fw10EKRVN3647KD3+t/ouEhcPTKb87PHBJQWXwtCTwPdK0oNUMCXxFVVnSrGQ8TTk4nmCsdR0Pz3nxFIWLW7rtiLt6gzBw93OSFhwNplig3QwRUnxv45rXTDSmjPKHHCa/5Mns/0CgaPs32I3+jJuNv8KXre9w5HStx6ya8GD6HzC2qDsEQ6bFc6bF81p69eXkwY1h9fx7NL3dpVO3EkHt1/H+Ln4tQhp6h1ht0g8+weKqGXY5JbogOjnWo2uFKsRC+eIVCYDEGQRuBp/T9z9+abn1XtetUikcgV7UVY/N8Au6/j0QTgFDcp1Kg5PaDVUPP2yyEXxaq0os2sLn9k0sSPy67lS9W8dJS0k3DNlpNNmMY9pB4MZ0ahOwpCh4OhnzbDJmWr/85RV+DoMk4avjY45ml/kqLtFo8enmFr3obGymqiqG4zkngylFWaG1ZJ7k5EXFYDwnDn2KsnLJRlFRVhVKuQCpKGrCuBS12owgCj02+02eH4wYT2aUpUHr93cDnz8GG6GrlOsrSNuvuyMkAl+6cZCrXKudnGdBXpVvNPbzprDWLgnrb+KW/V1QQriRllolqTLmgjHeInnqBCH7rRZKvHrM6n3gjJjKG6134V3ia32ltVVeVUzz7L0mGmXl5GLTqryyCK2Vqg0Xr3gGWCgLgzUlUkmstcwmKWmSs7HVJopqFSO5MM+TbySL6mlF0w/4o90d/l8f36vJ9WeGlOdd5pu+z1/cukllDX4d8L9ytE4I9tstNuKYyppz4g922R1cjEwtltGNIlpBwM1Op5b4PVv2IgFZdBIjz+NOt8uNdpvS7F4SX/DqTs2PIdHo73a5fecjND7WunMkpMBUBmss2tdIKRgcjcnSgma3QdQIiJuvHkl0Y9T5K6VorwN3LyknMSvUpXEjg6GwOYXJXqFKdX24cdKK0uYrXbYUCk8E5CRUvJxoCAIZ0/Y2aOoukWqubL1vDvGDjW39a4HF8uxwyMHBE6wxIKAZBbQaIbN5Rq8T021HjodWGSazlG8fHzOZpTTigE4zpNdukGQFzw5H7G+32ew18bR7Rs6TnGeHY0Cw2W2glSJY3Wv9O3GtRCOvpozyB4yLx2TViJZ3g77/CV3/HrHeQouQRXD6MhZBtBQeDb1DQ++QVsMlkTqvJpgr3T7dspQIUFfMly2csxcvxmXVyJ4FzNdWABLSSb2+46jSxWUuxlvOZEffBlqE6HesbFzehu+vUrJwCo89j5vtDnc6XfLqhLRONCprsVXFME15Mh7TjyJ2Gs1lVXgxRjRIEh4MB5wmyYX3TVgrW203Guw2mhcCZa0Vm33nWJ8XJZ7WbPaaSCm4d3MT31NEoYdSkg9ub5LlJZ5WS0WpvKzI85L9nQ5KOs5HWVSEgUs44sh/Z46Ru24NUGFtgaWq1b8qoEKICCkaKAFKgKV0CWL9MnCfrxDC5+w2v7hNSkoavk/T9ykqQ1VdvO9yUzHKMuZFQfsNxn7eBAujtUWX6l2xIJTHnsdGFDPOUmbFxRe1sZa0KkmLkobvv1ezu3dFoDUbUUzbD65sbSdlwUmSMH9pH1eJtCoZpAmz/OqKcMPz2IobxPrsnlJKsb3bORvbkwLf105QI8nxfE2nF2ONRUixDA6tdcIKZVmhv4OYuEjUQk+/1ndDSUnsvz4gWtyji0TiTaGFk2YO3uDtKXAkch8FfE9v9Su24VXqc7AoKVmUJwliH08EZzWm8x8S7nw1O64IuNDfD6Kr+XPgXKWzak5Wzd/JYPbiDgl8GRKpJplJXrnO1MyvjCXeDZbCpKTV7FLn4V3gyZCG7lDYjLy8KGhisZS2IDcJlg5avvp4r/GHjfMS4bpWv+y03PNuOs8RgFKS8TRlOEkYTRJmieuSdpoRvXZMoygZTRKUknVnxKKVQCmJrrlUC9XJ7wvXSzSMSzSmxXMqmxOpPpvhT+j4t994fl+iCKWTeR3lj8mZUtqUwl734WDriol4Ket233ff++GrRmtcRKg1t9sd7nX7PJ9OOEnOXhzGWiZ5xoPRgK1GTD+KCDhLNEpTMUhTHoyGDLOLD+eG57MZx2zFDTbj+ELgH/iam3s99ne6QC0ZWpMxtzdbCNxNabG0m+GFl+75e3Mx57iYqwxDj24nptVYBdHKJRrGZhgzw9ocKLA2w9gMrbaQKqae7MTaHGsLBC6xsKRYWyBpIl7RXdRS0vID2n7INMvJXooD8qpilKZMi3xl5OPKGGZFwSTLLxkrvgtafsBWo+HM214KwitrmGQZwzTFV07u9MeKSGu24phuGKKuSFbnRcnxfP5eE42sLDlJ5kzzq8970/fZaTRo+OeTd8mN2xvs3egvX2BCutDWGCf9rJRiNJwjRc1eqoUZ8rykLCo3bvgDOmP/S8Z5oevLuEKU4RXtUSEEnY0WrX7zrFv3HbPela1Iqxmpma0s0RBIfBkR69aVJnbGVqRmRlpNqVht589iyW1KWk0pV5ho+DKkpfsk1ZQpg0trzU3CvBzT0v2VrXONHx88TxGGHp6WhL7HzkaLbjtmNE1J0sLxp6xlMssY1iPcec3LiAKPGzsd15FOMnxPY4zjQkop8D1FHPoE3tW8zfeJa71xjS0pzJzSJjUrXuPJECk8Xtc5OO8bXtmC0iRYyno8yHuF4/YZFk7Zjlh+NuKTlANm5RGR6tHy9urPVczKI+blCU29S9PbXrp1S+E5V+xav9rWxLFlO7be2n9JMLYkrw7rPycYk6BVD19tEKg9PNX53rcp0JpbnQ4fzKb88vDFJQWkaV6TwlttPu5vnPt+xvF8zvF8VrsUn1XHBbDdaPDpxiYbcXzFHKJAK8Vi+uv85aouSGxenGF8OeZa/F4jCtjebDmt+bob8q6wdkZZHWPsHGx5buzA+bUslL+sTanshLJ8QVkdIGULKZtulE6ECPXq6q8nJd0woheGHM1nvNSxZ14UvJhNuZ0kVJ3VjAmU1jBKU07TZGXjPwLohAE3Wm0mWeb25fw6K8PxfM6z6Zhm4NMKvl/FjbeBJ92IUDsIaPkB4yy7oKSWVSWjLGWYpQyShFBrohWOtYG7556Mx5zM51cmGi0/YL/VpumfHUfnh6Gw6rsT0jD22Nnv0upEVJUhbgQ0mqEjE1/x3oh9j+1mg5/s7tSu4N133r8FpqM5pwcjBodjhscTvMCj3W+wudtl80bvUofl5MWI04MRw+Mx48Gcdr9BZ6PF1n6X3lb7ynXkWcFkMGM+ScmSnCAO2NzrEq2kGPFmcF0/N/J61TF25p0ZVf1MeV03VqjXD9BYa6ioXIBcTUirOdXKEg1BqBo0dBdPHl76eWnLpZN2aS5zOK6DRRJmrCGtZsyq1S0bIJAxXX+HSTmAl96CFsusHHGav6Djb61snWv8+LBM3oVYqrupZcHGME9d9zfPSyfW4VogrhYqzuIXVY+LWqAoKiZzl5icDGcUpRv33N/uEAYaJeVLcc/qca1EwxnJZa51aI2rlAiNfKu5fUtpk9oNu8ARony0CL9zOc6dssLYgsrkGCrAMi6ecJD8mo3gIyLdd6o2NmOQPeA4/R378Z8QqR6VzTGUeDJ2ZnsYjC0obQbWtfWv6w/xY4e1JUnxiEn+S6b5bymqE2LvI5r+58gw/EESjVDppfpUy/cvJRqTPOP+cMC9bu9CYDrJMh6NhhzNZ+RVeSEgErXa1Oeb22xEqxYKuIxWM6C5CBzEatJTY2cU5UOMndUmlhqBB0v9dlN/LsWYAXn5FWn+a7TaRMlNtNpBq22kvToAAhfU9sKQXhRdmHFfYF4UPJ+4LlP18nD5NVEawzBLOE3mK+UZdIOQm602T8ejSz8rTMXhbMqzyZib7e//Gn8baClp+j7tOtk4Tb0LRP28cuT8YZJwPJ+zEUcrTzQmubu3jpP5JYEFgHYQsN9q0fLffoQjinxu3O6fG3H97sC26fs0fJ+9Vguzv7tSLsN0OOf3v37K1798xO9//YRmJ+b2J3t8/uf36G23LyUaR88G/O4f7vPtr5/w5JtDbn+yy93P9/npv/no1YlGWnD0bMDhk1NGJ1O6my0a7fD7TTRqJ2sl1NVKZlQUNqekWNk4hcFSGjfuk1QT0mq2Mo6GFJJQNWjqHp68fBwrWzAtBkzK05V2HWytZpVWM6bFkMJmK1t2qBr0/B1O8ieX3oHWWmblkNP8OXvVBytb5xp/WFhI7udFuexAvjzaetXTMS9KkizneDjj4GTCaJoyTws8rdjqN/E9wfueJr5WouF8INr4skEmRmRmxCD7GiU8uipeji5dfKi5A2KtYV6dMC2ec5p9zTB/QGFmaBkR600aeqcmd1+NwsxqKd0UC+iaOO5M6Jp4MkIK7RIKW6CEJlBNkmrIYfZrCpNgbUUvuEdDbzEvj8nMFC18PBmjv0eewvcNIRSh3sdSgjWk4ilKxCvTBL8OpBBEnlc7hbfYajQZpmd+GElR8GI65XA2W6rteFIyyjJ+PxxwdEXVdSFr++nGJv3w8rV03VjlVb8nFqZzK4WHlC2sSajMECmaKNXF2pzKjLAyxY08TCjKpxgzqe83p5CmZNuNV4lXCyX4WrHTaLLbaBKqy4+CaZ7zYDTgo2m/Tub8K1Ws3gZ5WfFsMuHxeLSy8R8hBFtxk0/6m3x1cnzp51lV8XA0ZCOK+ai/ya1254IM8I8Ji2PbCUM+7m+QliWzK0aYDmZT/vnogC+2tumvKJlerOtwNuNgNmWcZRfW60mJrzSbccyNVpuWfznIe5Nr422unwWRHrF6wfNGO+L2J7vO5bs25hTCac5fhY2dDh/97HZNlJY0O3FNlH71OrxAs7HTIQh9tvYzwoYzv/s+IYTEEz5aBleSeXOTMilOV5oMlCZjWBxwmr8gM8lK1Z8WKkxdb5vgilihtAXTcsC4OCapxuQmRV+YVLgOLEk1YVgcMq/GKyeDR6rFZnCTF+nvkah65KuOmTBMyyHH2WO3T+UUTwZo+cNwftZ4PxAI9rY63N68gZQCT0tajZAo8GjEAWlWLkVqjHW+GB/e2sIYQxT4bHQbxJGPtZa7NzdQUtBuRm4M3Bg+uLlB4Dvz4yj02Oo38fRFP6T3hWsmGj6B6uLLFgJFWg04yX5HoLq0/Tu1v8Or5e7m5eHScG6Y33cBsOoR6y0a3s53kpxzM2WYPSA3M6TwiHSfplAooQlVF181UMtEo6zlZruk1ZBx8ZTcTJwjq4zwZZNJ8Zx5dUKsNom1xJfvT83lh4ZAE+pbeGoDgULJJtUVZLrvE1IIQqXphCF7zRY7jSZJWZwlGmVJNp1wNJsu1ae0lIyylN8PTjmezy51M6SQbNeJRmPF1d7vC0J4KNmhMgMqM0RIDymaVIwwdoKxCWAxZkJRPcGYKUJohPAQIkDKLkq+2n8EnGniTrPBbqt1pfSq6yYNeT6dkpUV1tp3JrlnVekSjdGIebGa0QMBbDUafGI26D+5HHhkZcmD4ZDI8/jr5DalMSuT631f6IYRH/c3OZ7PeTAaUrzUWXgxm/LLwwO2YjciuAqkZclxMudgNuVgNmOSX6zY+krT9H02owY3Wu0f3Mn6XdHsxjS7MWEcIKRgeDwhm786+d3c77Kx20EqifYUafL669cPPDb3e2zu91a56W8FiUTLAC38K2XJC5MxKU9JqunKEoLCZgzyA06z5+SVe1atClJIGrpLz98hUJfv98oWTMsho+KYWekSDSmvJ7qygMUyr8YMiwOScrLSTglArFsoqWjOerUR4Zn6pMUyLQdUlIyKY5JqihAS/QOJC6zxfiCEYH+rw5/0b772s6YuhryqyNm8opjRaUV8dPuHGb27VqIRqC6bwRcUZs6sPCCvppxmX1GajEnxlEC18UTsxqmErM1zCuefYSZMiwOmpZOy1TKk53/EVvQTWt4NlAhfI50qEELWo1E51pqlz4MnIyQuyZFolAjx6u87Z2tRdzy82o9C17wQ530Rqd4bkdmLakhWHZBXRxTVMaZ+6CjZxJNtQn2TUN921WebMsu/JCkf0vS/IPY+BKCyCdP81xTVKQ3/UyJ9q1YIspRmRFYekpaPKc2ExcympSLUN2n5X6BlG1AU5pS8PCCvjsmr41qVyKJlGy07RN5tQn3DHbmFIsz5Z/4bxFpnyl2L47+6AG2xrFh73O10eTaZcDh31dTl+oFxnvNoPKIVuPnwUZrxcDTiJJlfeIV1g5CtRoOt2DmBv0o//8cOIQKU6uMLhZJdpGyg5CbStlCiiVQ9QKLVDqH/J1ibYW2OFA2kbKDlxmvPU6AUO40WN1ptemFIqDV5TTYDKGqZ02eTMb88fEFebXKz3XkrhZ4FSmOY5jnPpxMejgY8mYxXSmiOPY/NOOZWu8MH3R4nyZxRfQ0tVKcOZzP+7tlTtFR8vrnFdqPxzh2aV+HlsOpt17ARRfzR9jbPJmP+rxfPL5wXgBfTKb88eMHdTpeP+xvLEaNrbWut4PZ8OuE/P33Mb44PycrLghw7jQafbGw6meBrOlkPjsb8/p+fkMwyml2nQjUdJWRpTpmX7N7e5MOf3iRuhkglGR5POHk+ZHA0ZnA4WfqktDoRrV6DvTtbbN1wQXyW5Dz++gUHT07JkpwiKzGVQfuaVq/Bxk6HvbubS9Wk5Xl/wx0RQmAXD8/XjEimSc7xswHHz4ecHowoi4pWr8HmXpf9u1s02t+fS70SHg3doaHbqCuEIUqbk1QTJsUJg/wFxtsmrv2j3hbGmnp0aciz5Buep78nNfNV7MYSAkEoY5pej6buEas2uUkuBP8WS1JOeJp8hSd9bkSfoOXbj00uRskqW3KcPeXB7NeMy5OV7csCSmh8GdH1ttmPPmSQHzIuz7qzFktpcp4mX+OJgDuNn7Ad3kGJd0ugXoWzsRxbj8b/Yb5H/9DwJu+j8wW/142e/lhwzUSjw0b4GWk14Dj9DZPiCcPsPpPiOUfpP9fdiQ2k8JHCo7IZpUlJyhOS6oTSpFQ2QwpNpHpshV+wH/8lTW8ffcXM5XkIRO1NIZbqVEr4WGHwRIySAU7z2Tlaaxk5V+vai2Lh0RGqzpJ87omIUHWJ9HdXnRYPndIMmeVfMcu/Yl58i6lJ8b7aJNB7dMO/JNQ3MDanMhPG2T8ySP4TNMHXrspcVAOGyX9hXvwegcaXGyipsBRk5QHT/EtG2d+RlQe1XKnB2oJO+OeE6gZKNBBCUlSnTPPfMiu+YV58W0uhWgK9Q6j3EUIRqD3eJkF4eU7X8VhMfezlKg1el9sUeZrb3S7PphP++ejgpfW76vrD0ZCNOGYjihllKY9GI06T5EIA1gtD7nW6y0TjDxVSBCAClOyBvlN/d3H+9pb/1mobrbZf+vmbwVeKrUaDvWaLXhQRex7VOT5AZQ1JaXg6GfPLgxdEWrPdaC6DzDe5nhbXUmEqBumcp5MxD0dDnk3GK6txilreVgrB7U6Hj3p9KmOWiYbFcRuO5zN+8fwZQgj6UeSM1cTZMMl1Htjn7xU3w31mSigXpD638Ldabi+MCDc9vjw+JvY95mVxgdNyOJsySBN+suWSkYWp3NskTmdBlHMbfzoZ85+fPObL42Oyl6SOBbDbbPEnu3vsNVvXHjsbnUz5x//0FYPDEbt3NjHGcPxsyGQ4J5ml/OyvP2bvzgZB6CNqz4ZvfvWYR1895/HXB1SV6+xs7nfZvbWB9jQbe12EgCIrefi75/z6b79lXBOwy6IiiHx2bvX58I9u0e43lkH++3xB52nBi4fHfPVPj/j2V49Jk5zd2xt88vM7NU/je0w0pKah28SvSDQqW1JVJePihJPsGVr4hCpG2kUw8+b3ubEVmUkYlyc8T37PQXp/paRptz2SQMU0TZeW7tPUXcZF9VKXwTKvJjyZf4UvQvr+HpFqXhgde5OAbrFPhck4zp7waP4bJsXpSvcHqDk0mq6/zX70EZUtLyQaAIXJeZZ8Q1bNiXWbjr+FL0P0S3pib3tdnz3DFqmF+7+xJQaDFp6T91/jR4H3M6r9fnHN0Snnkr0RfoqhZJjfZ5w/JjMTSpNQmCnjfF5nwQuVKceOD2Sbpt7BUy2aesd5cASf0PZu4svXG9H4sknP/4CmTrEYfNnEl020DJeJxSIZ0YREagNPRBhKjK0QwiUczlTQo6l3iFT3jdZtKahsSlYdMC++QUqfzcZ/XattQVo8ISnvk5XPycoDpIzQsk3D/wRDRmnGHM//p1q+yKBlk274l0TebZRsIIQir44YZb8gK5/T8D6lE/wFUgTLZCPQu3iq5wj5ZkxWPmNWfIWWXbYb/92y8pAUD0iKR2T6Bbk+RMkWWjTe6jxXtqSwOWmVkJQTtPRo6LZ7uAntHkfWYBaJSG1WaGpFMC28+sXmKPyFyalshSd9tNAXHvqB1txotbnd6dD2AzwpnWt0/fNZ4ZRwFh4Dx/MZaVlc8mLYbjT4ydY2m/Hb7euPG6/QmlwBJI7c+/nmFqM05cvj40tSwYezKX/77EntV+Fzq9Nhu9HgTUTyjLXMioIX0wn/5ekT/u75U55Pp++FEaSE4MPeBn91K2delryYTSnPGfhldTCtXzhFjifjER/3N9huNGl43rU6NW65JWlZcpI4kvuL6ZRZnnO32+Nmu0MvDN+626CkM3K70+ny1zdv8+ujQ746OV7ui8VJBf+qVmr78/0b/PHOLr0wemNVrcpaivqY/Pb4iF88f8r94YBhml4wPQy1puF53Ot2+fnOHrvNFRiGCaeosrnX5d7nbia5KCq2b/RodhuYyjAb5Rw+GXD/t8/ob7f59E/ugnBjA0+/PeTw6YCjZwO2b/ZptCP80OODn9yk1WtQ5CVlXmEqw/Bkwv3fPOXptwd8/mf33n3b3wBB5HHjwx2klviB5vRwjHzPyi6vgsSZwUWySVP3iNSx87V4Sfp1mB/w1eTvKEy29Knwv4MveR4GQ1bNGRfHPE2+4UnyOwb5CypbYt7L3Q5a+mwGN7kRf0I1+y1pflFtrjAZw/yQZ/IbIt0iiaZsBjcIZOOVQr8X96misiUH6QNeJPd5mnzNtBhQmNWRwF9Gx9vibuOnJNWU4+zp0uwQXAyVVQnD4pCvJn/HtDxlO7hDP9ijqbsEMuY67wZDRWkKkmpKUk2YlgNm5ZDKllgsN6NP2Y2+n/tmjX+ZuFaiIYWHrzy64gMaeofT7BuO1K8Y5Y+Zls/JqzG5mdRSsgvJWJ9AtQhUh6bepeXdYCP4lK5/D0+16vGm18NXTXzVXGbdDnVGf041adHBcB2SLsCVv9Pw3nxmzdqSykzIygPmxe9p+p/SDf8cT7pOyIn9XxhnvyAvj8irIwKxh1JdYu8jhPAZZ79gOP9bALRo0An/glbwUwJ9oybtVpTViEn2Kyo7pxv9NW3/ZyjZWCYzC5RmQmknpNUL5sW3dMO/oh/9NVI4fsuh+R8ZpX9fS9keO/8J+WbB90JdIzc5mUmYlAMG2RGBdEaMaIFUCqxdJiOFyZG1S3llKypbEal4+e/SFiTVnMLkxLqJVLL2OHcPxkBpdhtNbrTadMOQQGtMUSwDnlle8GQyohuGdMKA42ROWpUXAiKA7UaTzze32Izfv9rUm+BCd8h+96Sy5cxwculF8B5LF4tlt4OAzza2OE0Snk4mjLL0wnYez+eczOf4StGLYizOtM2T6myEZrGZi+3HjeNkVcXRbMa3g1P+zyeP+dtnT5jl+bLyvqj+rwJKSu52e3hK8e1gwO9OjpnmGUk9BpRXFQezKdM8Y5SlHM1mzhgS2IhiIs+5cS8JyIv9Wu5T/Xf9O4uRo3GeMUpTHo6GPBgO+fL4iNM04d/duoOnFKFWb51oaCnRUnKr0+bf3LhJWpY8HA1Jy/Jcx8ny5ckxzyYTLND0A253jPN3ERIpzglyXNgP13nJypJJnvP16Qn/68P7/PPRAY/Ho6Vp5gJN32e30eSDXp8vtrau5PO8LaQUeJ5ia7/HJz+/Q6t38dk0GydMRwlHTwc8+uo5+/e2+LN//wVKSYyx/C+jv+V3//iglpudoD1Fu99g/94m27f6ztW6vr7u/+Ypv/373zMbJ6Tz9xcknkcQ+uzd2aTRCpFC8OzBEYOjyfey7pfhroWAUDVpexsMi0NKk1/ytRgWRyTVFC0Cuv42HW+LlicRtn5Sn7vR7TLhdXLzpckZF6ccpA/5evL3PE2+IqmmVLZCIlkUm1YJJTSbwU0yM+c0e84gf14nNfXkgc1dVyC1SKkwtsKXEW1PuLFp6wqhC7GBhUzo4v+5SclMwtP513w9/QUn2VPm1djFFktp/NUmUW1vg0i3OM6e8lR/zawaUVaLTo2lsCnjIier5pzkz/igMaa0BSYwtLQ710LIc4nUFYI8S2GexX5m5CZhVBwxLA45yh5zkj2lMiVSKGLVXicabwl76aXBd14r5+8mJ85z/Q7VjxHv9MZwgXxMx7+NJyM2ws8pzIzKZJh6hMdikUiEUCjh16NLDXzVJFJ9PNW8sp37ZrjOCbj+STM2p6hGFNUpZTVkmv8WMfVQ0gW1afEIY0ssFYa85ksIPNXFYpjlX7JwfxNC46k+gd5FiQhqyWBjM0fUFhFaNJAyvFIpxNiMohpQVgOKasg0+2cOEIg6IZkXvwdw/iALZ+k3RG5SBvkxhc3whE9RJxy5STEYSlugasJaZhKGxQmn+SFaaLyacCiFou9vIz3FuDhlWozITIrFooUiUvGFMyFwwUfT97nd6fJ8OuXxeMSsJgzPi4Ln0yktP6AVBBzP5xckV7VwgdlW3OCDXp/Oa9yDv09YXGW/ss4856oHziJoreo/7ir5ftD0fD7ubzJMU74+OWGa50zybNktWmztw9GI/3D/W+4PT/n1YZ/tZpPtuEHkeYRaLwPwrCxJypLTZM7xfM6j8ZCHwyHfDE5JywItJb4O8JWiMpZpnl0iO18HAld934pj/mxvn7Kq+MWLZ3xzenLhiOdVxeFsxi/tC07ThL16P3pRRCcIiTyPQCl0rUVurJs9L4yhrAxJWTCv3c2nec44SxllGePMJTCnSYIUgqQszh2962EzavDT7V2O5wmPxyOeTSYXfEKKqmKa5/zi+VNOkjm3O11uttpsxnHNu3H7IqXbj6KqmBU5wzTlcDbj2cRxZr4dnHI0n1GcG8+SQqCE4KPeBv/21m2+2NzGV/pKI8G3hR969LY7dDZbKO9yJylLC8anU8anUyaDOb/7hwdLx1xrLI++fgEWTGUo85KyKElmGQ9++4yDxydY45J2IeHo6YCT50Oa3VolagWiBn+IiFST3fAD0mrGvBxTVBeTLldcSnmWfE1uE/r+Lj1/l1i1iFQLVXeo3ShtVReYMublmFk5ZFgcMcwPOMmfkZl5HdiHtPUGFsO4OFmpJKwSmpbXZ8vcYiu8xaQcMC0H5C8JnKRmxmH6aNnh6PjbtPUGoWoQyLD2GJF1gayoXcVnjIsTxsUxx/kzTrNnJNUUiaTl9QlkvCTQrxIuFhLsRR9S2pz7s1/xeP7bZdEWXMpW2pxJccrD2T8zyF/Q1F1i3SFSLQIV4YnggtLWIoCtqKhMQWEzijrBSKs5mZkv/06qCUk1JZARTd3lh1Kk/JcAl14b5/huEjdGeOXsuUtbS1uQmQQpFBK1pAn8oeOdEw0pNJ6MaHp7r/+FFeI6B/9dT5ilxNg5lZ1R2RllMcHa6kK3QctGPep0ti4pIjzZXXYbXKLhVJ+07CAW1ZHa3dlJlPpIESB5mZy+qEaUVGZOaaZUZkZSPsLY/AJpS8tWPev/dvtZmIxhcURuMlq6R2HyeuyppLIlnvRoeV1KUzArJ5xkL3ia3MeTPqGM8WWALwNCFRPrJpNiyEn2gsLmSKHoehv18bmYtSsETS/gdqfL08mYo/lsmWikZcHRbEakNbHncTSfXZAX9JWrGm/FDW62OysJhl55fKqq9jaox3LqToXl7Gvq7sQiyZgXBadJwiTPLgRzC1TWkpQl4yzjJJnjKRfkypp1Wk9L152As68XP/OVxlfqWuTm2PO40+0yzlI+6PUZpCmFqZjl+fmCDM+nE55PJ9wfnvJl65h73R4f9vq0g4Cm7y/3dZbnjLOMJ5MRj8cj7g8GvJi6qruvFP0ooukHeEqRlgVZVa4m0RCCUGuUiPjZ9g6BUgwzF1AnZUFRVVigMIZBmjBIE74+PaHheWzFDXaaTXYaTTpBSOx5+HWyUdUu9FlZkZUlo2VCMec0TRhnGdP84hx6P4oudQaug17NJTmYucRbIJbX0CIpnZcFvzk+4qvTE2602txstbnb7bLfatP2A2LPQ0mJsZakLBgkKc+mEx6Nhnw7OGWQOPPE8yMuAudS3vR9Ptvc5G/u3GOv2cKTciVBuudrOhsNWt34kl8FQJmXzKcp82lKMk15dv+IPCuWs8kWJ0/r+S7BLYuK2TjhwZfP+OaXj5dmV0IKJoMZ48GMsBFgFgnIH/67+60Rqga74V3m1YiD9CGZmS9N+oB65LXiIHvIYfaIvr/LRrBPx9ui423hyQBP+nVAXpJVziNjVBwyzI8YFUfMqzHgnk5KeDRUh63w5nI05+Xk5l0ghXKBsLVsB3eYFKcYW1Ka7EJnIzcpp/lzxsUJB8kDOv42W8FNmrpPrFpo6aGEorQFhcmZlgOmxYCT/Bmn+TPK+r3npPJjev4uTd2jTIqVJxqyJndvh7eJdYu0mnGaPVsW+hbyw6UtKKuCeTXmeXofT/iEqkHL26ChO4Sy4SRw69jEeZ+5/XCdmjlJNWVejmsX9dmlbel6W8SqvU4zzsHaxXGs6pjNnL3v678X/1pMhrh7Zca8mtbKbpffda7QWNUSykdooZeJvUs24CxeEi/FA6IuTqtl0vxjw7v3wP8VQQofLTto2UXJNoHaoRf9W7RssZxNsBZfb+KrbVTN+8irI7LyKZWdokRcXy+Oj5EU9/HVTp0UhAgRYKmwJqeyc4xNXbLwkhKXFAGe6uKpLlq1aXif0ov+LXKpmuUetL7aJtBbKPE2c9XCcUKsrdU8KjwZ4BPiqwBPBrVD6pxhcUxazVFC4QuXXAQyJJDhcmwK6pvByrq7dTHJOI/Yd7Pgz6Zjvjw5gro4VVlLWpYczdwDcZSlF/gZG3HM3U7XcQfEapWxXsbBbMo3dYA2zjIK4xKP0lSUlaE0htIaisXXxpBVJadJwvF8xsHs8kN9mmd8dXLMME349dEBTd9H1V2a1/3xpOTzzW2+2NpCS4W+xr5LYKfZ5G/u3CPyPP63Rw94Mh6RlBfNEAEmWc5jO2KcpTwYDfCVwpNnlbOiMuRVxTTPmNQVfwsoIelHMX918xZ7zRZH8xmPxiMGSbpSBSolBRtRzMf9Df793Q/ohiH/ePCCB8PBMkA/j6yqOE0Skvr6cgmGqkeQxMVukzFkVUVe8zLSslyp8eCrcK/b57/56GN6YYgU8Gwy4cXsYpBTGcNpnTQcJ3Oax8d4Si2TA6dcY8hK19WYZBnjzCWV55OMxTX12eYWf753gz/ddbyM2F+1uMKrr9Mg9Oj0m7R7DRqdiE9+fps//X9+vuQ5LLoSGzsd2v0maZIxOp4yGc5RWvLxH99m5+YGVVXx5NtD1+V4zejiv3T4MqTn77JdDtmPniFw41KlfZms7YKmWTmisk5S1Zf3XRCDPKuO27KuwM7JKxcILxDImK3wFtvBbXbCu0zKU47zp8tEZKX7pSJuxp/W1XuBsRXzanJpvypbkpmEUX5IZub4IkRLf/lOMtaZARfGVfuTaurGkqxBIOn5u2wGN9gPPyLWbSbFKaf585XvD0AgI4QW3Gv+DF+GPJn/jufpt+Qmu7Rftp4ySM2MqqiYlSN0XQR2Y1SLcc8Fn7Kqz11OaXJK++4FkX8tKGzOSfaUQX7ArByRmtnyeDp1VTc2vkjaDRXWuvNT2oJB/uJKaWSXjMz5dvqPDPNDxyVG1dMh7mshFKruckhxllgEMqKl+3T8Lfr+HqH68fFT14nGW0DgoVUHX23gq01C7xZN/zO06taBuTO+UyJajlNZcrLyObPidxhb4OttXCCvKapT5sW3TspUNpx3gohQMqI0BUV1Sla9QNVSwU5qzkfJCCE8tOziqU18tUnk3aEZfHFGHF9si4xdcoPA2orKppRmSGkmrjNjUoRUlNWIQo7c7wvwpb+sdimhiHWzFgEI3EMQ4fgZJkMISaxaxKpZk8UDfBniCf+Ma7CYfV8mGVcPB0Xa43anw+Nxh4bn16Mrtv5TMUgT5jUJ/HwAvBFFfLa5xVbceO/6/kfzGf908IJHoyEvplPSsqQwJVlZLZ2bc3P29VXB7ctIypLHY9cBgLOxFU8p/OUfja8knjz3vfprTyo+6vfdlfWW+79IyjYiN3IEcDCdUBnD4Wy2PN4LzMuCeVlcGOH5LkghCLSm5Qfc7XT5y/2b3O50+c3RIUlR8kAN3mp7X78+SSd0I1BaSrpBSFEne+M0ZVYUF85JaQzjPGOcv3u1VeAC9bDuMOkL89LXx36rxW6zia27Er5SzmOmKpfStwt1tkme8Xz69nwAJQRaKlqBTzcI+dn2Lv/vex9yq92hH8WrNThcPAZesUw/9Gj1G3Q3W/S32+zf2+bDn95C1V0KUzln3LgZoj1F8ihlMpiRTFKssfS3O9z+dJciL8mzkrgZwuL5UyuDFXlJnhRMh3OSaUo6y8iSgtkkYXw6o9Ex+IGHlAKEIE9z0nnObDwnmWZkqesgT8dzJoMZfuihfe2SOmPIkpzxYMZsnJBMM9J5TjLNmAznRM0QP9AorZZ8kvcN15EI2ApuMo4+XvLrXEJRvDTSaV2l28zgLWoAEoUnfTr+Fjeij9mPPmYzuMlR9gj/O/yx3gWe8NkObuPLgHk5IjcJIn++TJQWFWRbj7AUVca0Gr7h0gWe8AlUzHZwh9uNz9gNP8CXEfenv+QC+WmV+1T7ntyIPqat3QRAZhKm5ZB5NaayRV3Eq5WxqN85Jv3uBb8BlmI6wseXIeo9yOf+oaI0OSf5c57Mf8dJ9pRJOaCqk4jKFlSmrL8uL1x7r8MiMXmWfMOz5Jvl9xdxk8Iljk5gx0PLs45HU3fZCm5zw5a0dH+daPyhQwgPTYtA79P0P6cyM47n/3PNoXAPHIulHfyMTvhnVCalMjPmxbdMst/Q8r+gG/45AJVJmOa/ZZz9qh6haqJEA0916QR/SlI8ZJr/hln+Ze2vIQFD5N2lE/wpWrbQskWob9D0/4jKzDic/v8ubAsIOuGf0gp+BoAxGZN6mUn5mLw6wNoSKUIqMyUun9LwP0bJDfr+Vh3In6mGLbgXWnh40kOKTQLlkiKnJuWhhb/U9vakX5Py1QVyVGVLSlOgpId6qVMTKMVm3GC32WIjinnuT5jl+TIorKwlq6vs5x/vm3GDLza32Wq8/5tsmuc8GY94OBzybDqhNPUYi7HLcSpTV78XSdLbYvE7tqrqKnSJFMWyyu4SEblMSI7mM2dEp67/0lO1TOyH/T7/7UefcLPd4R9ePOPxaMRhvfy3hRSClh+w02jwJ7v7/Hxnlz/a2ib0PA5nMe0gQL8nNR4lBP0o5pMNgZSCD3s9vjw+5tvBKU8nY0ZputIQwSUZiu1Gg7vdHrvNJu3AcVFWsWxZq2pF2uNmu8ONVoevT4/5djAgX8H4WSsI2Gu2+KS/wU+2tvlsc4tb7Q6tIPjep4S9wKPZEeze2eDjP77NdDTjf/4f/vMyKDcWtJL85L/6kA9+cpNGO6K73UJIwenhiH/8T7/j8TcvUFoxPp1ijKWz2UR7CmstVWE5fDLg21894vG3B7x4eMx8mlLmFdPhnOHRhDuf7nH3s338yMPzNPd/85RvfvWEF4+OOXxyQllUCCHIkoyjpwPufrbP7p1NgtAjmWZ89U8Pefi75xw+OWV4PCGZpRw/G5DMM+58ssutj/fobbWIGgHqivGx94Wm7nG38VMCFRPIiIP0Acf5EwqTvxO5WQlNrFrshB+wH33IzfhTNvw9QtVkXByhhcdCPXG1cIFxQ3W41/xjYt3hyfxLDtIHDPIDlyxdb6koodkJ77AT3eNm9Cl70Yc0VNu5jcsAJXTNn1j1Pjn4MqTtbfBh80/o+NscpA84zB4xyJ4zLk/fy7o9GRDImH6wx050j/icyM6/dhgMaTVjUpwyLo6ZlAPMcoTKjVMvlDhXcV7OBGKc1HBlS4TIkEbWXQ9RC++0SKrpJYGHHwvWicZbQAoNQhOobZr+Z867Iv8GYzPOy+5E+mZ94RUYm2JsirUFgd6hHfwccKZ/afmYvDqksinG5igRo2SLpv8FUniMs38ir45hSQY3SBFi/BToooRPqPYw/ufnfDSqs20Rkth8iOMLOF3sojohLZ9SmhG2zrgNBXl1hJIxgdkjUpu0vS4Cb/niWWTW5+HJgIZuL/99VeW2spXrgqgYbUvX+kNiMChrLzU1PKXoKMVmFLPTbPJkMiYrK6pa1//lwH0RaG/GMR9vbLARvX99+qQoOZnPOZzPOJhNr5VIvAneJkkZZZkjkb/DtigpUVKy32zRDcJlgBlrx1eY5jm5qeqRsLqKfk5N43wCpKTAV45Ps91wBP1/d+s2P9/doxdGZFVJP4poB8Fy7GrVUFLSDhxHoReF3Gp3nPyr7xN5HgfTqeOHVIvRN1Mniwv1j7Ow62wWtr7mpETV+6qlRCu53N/b7Q4f9Te42Wq7/VtFolGve6/VYq/VohUE9MOYhuchEIwz18ko6vNTnUt47Uv3i6jP02JEyqs7Y/utFh/1N/j5zh5/sX+DzTim6Qcr7WQ4bkaTqqwI4wCtXYfiZWhPoT3F1o0+H/30Fg+/esGjr54vjwUWvEBz+1PHDYwaAd3NFt2NJgdxwGQ4I0tywtjHWtjY67J9o0/UCFxHwxiSWcrRswGDgzFZkmNKN0A2m6YcPD6hs9EkzwqUp1DKMB7MefHomPFgSllUmMo9v0YnU4LwlM29LmVR4nmKIi8ZHI45ejpgOppT5u75VeQlJ8+HNFoRm/s9Wt14pb5Eb4JQNwl1E0/6rnAkfZzz9YTCZGdjIBgnJoLl/J2w+M8pHCm00GjhEyhnOHe38RNuxJ+wFdyioV2Q6skILYJ6nPbl7sm7wd0bglA12As/WBoTuqKXZlycUtr8QpXZ1B1/iz3bIyGXyYXr3ocEKuZG/Cl3Gj9hJ7jDRuA6vuPiBF8GeCKgIKWyq080hBA1qdtnL/pwKWMbqSaBjPCykNwk9Tmragl/F+RSO4tfPM7i3L6Kuggol6TjBR+goTs0VIfd8AN2w3vLc7iG42jkxnGTZtX4vYwCXrFWN9q6uMZeunUE0vGfarGeHyPWicY1oGWb2PsQX23R8n9yKXMN1G7NlZBIFdAN/w0N/xNCfROBt1xGN/wrmv4fEehtPNlBCB9R8yqkCAnUPsamnGMD48kuntpwSQ8CrXrE4lN8vUcr+GN46cES6n0E2k0oSEkr+GltJphhcKo47oEToGQLX204M8C60/CuYx8SQcfbIFCRm6tGEKoYT/jf6Tba9H0+6PY4ms0YpcklA7EFQuUIq1txg91G89ruyGucQUtJ7Hl81Nug5Qd8sbXNw9GQJ+MxTydjTpI5p8mcpOYoLDgModaE2qMdBHSCkButNjfabe52utxqd2oH8sipTVlDyw9cR+M9j7pJIYi0x26jyV/s3+Ret8dfTKcczKY8n0w4nM84ns8ZZimTLFvyLsqaeyNwScsiMI89n9jznAKa7y9NJLdi50jfiyL6Ucx2o0Gj9oRZNXYaTUKl2W02+bO9fZ6MxzyZjDmYTTmczRhnKdMiJ6tH96wFKZxoQKgVkefRDUJHgm80XQLTbLHfarHTaLIZNwi1Xnkno7vZ4s/+5nOstXS3WsTNCPUdHa3uRhP9xU22bvT5rPbAWPRrlRRs3+yjPYXSks5Gi5//Pz7j3k9uLsngi05BkRWEjYD+TgepBUJqtm/2+bO/+YL5NKXICqrKcey0r/FDj86G44goTyGV5N4X+3Q2GhRZSZGXdQInCEKPsE504maI0opmN+KLv/yAWx/vUGQlZVlhjEUpiR96NDsx3c0WYcNHqh+Gmd7QXW5Gn9L2NrkRf8IwP+A0f1H7KIyc+lI1p7LFBc8NLXw86deBeIOO3qTtb9L1tun623S9HVq6V/s6OCiha5WnuCbEvg9ewFln43b8GV1vi/3oI4bFIcPikHFxwrQcklUzCptRGZd4uGTLwxchngxoeX3aeoOev0vP36Hr79DxtohU8/ya8GVEpBqYqrpAqn8fkELhy4jt8A5N3WU/+pBJMWBUHDMp3X7NyzFJNV26pFf1n8V1upjtdxMJPr6MCGREVKuKNXWXhu7Wf3eIdZtYtQnVCnxz1vhXjXWicQ0oGbvqP7vf/TmhgRCt2lf8LKbhf3zl70nVwVMdIu/Wa7dFiwZaNjhzi/7u7YnkbSLv9ms/+yZ4kyRECEmsm8S83cOq5Qd80t9gXhRU1jBIr549bfk+nSDkbrdLNwzfOz8DoBX43Gp3sPV2vi9DqrfBTqOBrkep3hWLzsZOs8lOs8nNdod73R4PRyPuDwcczCYczqbMCifzuui8xJ5H7Pl0w5CNKOZet8fdTpfbnQ7bcQMpz7avMIZuGHKj1eLj/sal83aj1cZXq3k8SSGWnJZOGHK73WFW5JzM5zyqJWOfTyccJ3NGqSOmZ3WyURg3HqPqZXhK0fKdylY3DOkGITsNd5z2mi5ID7ReybjUVVic3W4Y0gkC9lstsnKLh6Mhj8YjnoxHPJ1MGKQJoyxdJk3GWqQQhEoTeR5N32cjitlrtbjVanO702UrbtANw5V0YF6FRjvi3hc33vjzcSsibkVs3+y/9rPa09z9bP/NFiyh02/S6b/5c2n7Rp/tG6/fDrctipsf7rzxsr9PLJ7boWoQqgYdb5Od8A6D/AVH6ZNl8JqUkyUhuqoJrAKx5HoEMibWbXr+Dj1/lw1/n5buo6V/SbI+lDFbwU0qWzCvJsvlbQT7tHQPX8bvXNRadDYCFROomLa3yUawz6g4dgTe4pBRcURaTcmrdGmGp4TGE8Gyg9H1tun5O2wEN9jw95f7ex5KaLreNnvRhy55MXMAYtWm623VcsCreX4t9ksKn47cpK37lPYWWTVnWBwwLI4YF8dMiwGzakRazc4RvYulL8Oia7HYn1A2iFSDRp1gtL0N2t4msWoT69a1ttUJDuyRVvML3ZSFclmkmrWvyndDC40vXYdsJ7x74WcN1amvmRDJ659VXq1+1vN2SILpJYnlpu698bW3SGR7/i4W+6Po9kSqRc/fIdadlV1zq4aw7zJrscYa7wnzomCYJpwmToY0e4Wyz2L040arze1253shVR7NZjydjJkVuavov/c1vh536oBeC7nyZGvhizHLc6ZFTlIUpGW5VNeiHp/SdYLiS0VQS6M2fZ+G5y+r44vzUxpDWhYM0pRnkzGTl+RhdxoNPuj2iT3XAVzleTXn5GpnRc68KByxetHJsAZjFgIExpGIORsN86TCO0fKj7RH6Gli7fxEFqNV7xuL0a7KGGZFzix33h5JWZCVLkk6PwomcFwVLdyoV6A0sdbE586Rr9RqSd9r/Ojh1HLcSEhqZkvVpQWpdTF/vsB5jX8tPHwVLavjngxqFaeL139SThgUh6R14mLrMZBANWioNpFuEavW0vdhVfvlJGuzWhkrJbcplSlr8q3brwX5WdYSob4M3f7UHJZFJ+A8SlMwLA6ZlgPKWvodnFt5pJrEqk1Ddy4lKKvAQkGqslU9OpUu1agW27LgDbhRmtq7aznydqZapISHFl7dpQrwZOj+La+nMDcvJ5zkT5iXU85PV/gyJFQNN5alu98ZEFtra9+J1PEgitMLP9fSp62djK8vo9dua1bNmVUj5uWEpJpc4jEs1MQEr5fuLk3OuDxlVosOlOZlxbbvH0p4zkpAtWl5/fdyzb0r1onGGmusscYaa6yxxhprrLFy/PicPdZYY4011lhjjTXWWGONP3isE4011lhjjTXWWGONNdZYY+VYJxprrLHGGmusscYaa6yxxsqxTjTWWGONNdZYY4011lhjjZVjnWisscYaa6yxxhprrLHGGivHOtFYY4011lhjjTXWWGONNVaOdaKxxhprrLHGGmusscYaa6wc60RjjTXWWGONNdZYY4011lg51onGGmusscYaa6yxxhprrLFyrBONNdZYY4011lhjjTXWWGPlWCcaa6yxxhprrLHGGmusscbKsU401lhjjTXWWGONNdZYY42V4/8G7JPcUBwQA5oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "buat_vis_wordcloud(df4, 'text_stem_snow', 'kelas', 'positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:55:56.959240Z",
     "iopub.status.busy": "2024-07-08T11:55:56.958880Z",
     "iopub.status.idle": "2024-07-08T11:55:56.968925Z",
     "shell.execute_reply": "2024-07-08T11:55:56.968019Z",
     "shell.execute_reply.started": "2024-07-08T11:55:56.959210Z"
    },
    "id": "fgewSHZVU4j_",
    "outputId": "a5d8f7c8-5239-4b34-b09b-a692b3d43abd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df4\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"text_stem_snow\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"best ui featur\",\n          \"much bad support account hack support didnt respond even hour send inquir paid cours udemi account\",\n          \"nice wonderful app would realli recomend anyon search learn new thing\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kelas\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-6b7e5af2-c030-4b60-9c3d-565618732211\" class=\"colab-df-container\">\n",
       "    <div>\n",
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      "text/plain": [
       "                                          text_stem_snow     kelas\n",
       "19996                                cant open app crash  positive\n",
       "19997                                     best ui featur  positive\n",
       "19998  nice wonderful app would realli recomend anyon...  positive\n",
       "19999                              help consolid content  positive\n",
       "20000  much bad support account hack support didnt re...  positive"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:00.217632Z",
     "iopub.status.busy": "2024-07-08T11:56:00.217252Z",
     "iopub.status.idle": "2024-07-08T11:56:00.222477Z",
     "shell.execute_reply": "2024-07-08T11:56:00.221485Z",
     "shell.execute_reply.started": "2024-07-08T11:56:00.217589Z"
    },
    "id": "sG_8OizHU4kF"
   },
   "outputs": [],
   "source": [
    "X = df4['text_stem_snow']\n",
    "y = df4['kelas']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rMIUKulvU4kF"
   },
   "source": [
    "#### Berdasarkan observasi terhadap kata-kata yang muncul setiap kelasnya. Mungkin teknik ekstraksi fitur yang cocok adalah menggunakan TF-IDF, hal ini dikarenakan teknik tersebut mampu memberikan bobot pada kata yang jarang muncul pada suatu dokumen namun dianggap penting. Mungkin TF-IDF akan cocok untuk model Machine Learning seperti NB, RF, atau DT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:06.617098Z",
     "iopub.status.busy": "2024-07-08T11:56:06.615900Z",
     "iopub.status.idle": "2024-07-08T11:56:06.621189Z",
     "shell.execute_reply": "2024-07-08T11:56:06.620323Z",
     "shell.execute_reply.started": "2024-07-08T11:56:06.617061Z"
    },
    "id": "_bWtHwnqU4kG"
   },
   "outputs": [],
   "source": [
    "tf_idf   = TfidfVectorizer(max_features=500, min_df=10, max_df=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:09.827300Z",
     "iopub.status.busy": "2024-07-08T11:56:09.826659Z",
     "iopub.status.idle": "2024-07-08T11:56:10.171125Z",
     "shell.execute_reply": "2024-07-08T11:56:10.170332Z",
     "shell.execute_reply.started": "2024-07-08T11:56:09.827266Z"
    },
    "id": "tTLBJFvMU4kG"
   },
   "outputs": [],
   "source": [
    "X_tf_idf = tf_idf.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:12.613454Z",
     "iopub.status.busy": "2024-07-08T11:56:12.613091Z",
     "iopub.status.idle": "2024-07-08T11:56:12.619306Z",
     "shell.execute_reply": "2024-07-08T11:56:12.618381Z",
     "shell.execute_reply.started": "2024-07-08T11:56:12.613423Z"
    },
    "id": "3vZ0k0e_U4kG",
    "outputId": "bb820446-3855-4608-f6ed-a07890c79644"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14164, 500)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tf_idf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:16.052433Z",
     "iopub.status.busy": "2024-07-08T11:56:16.052097Z",
     "iopub.status.idle": "2024-07-08T11:56:16.152156Z",
     "shell.execute_reply": "2024-07-08T11:56:16.151221Z",
     "shell.execute_reply.started": "2024-07-08T11:56:16.052408Z"
    },
    "id": "5RzoDK-WU4kH",
    "outputId": "b9320371-3f84-487c-cd72-01dbfadb6e82"
   },
   "outputs": [
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b30858d-ea92-47e8-b98c-309a087af670')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-9b30858d-ea92-47e8-b98c-309a087af670 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-9b30858d-ea92-47e8-b98c-309a087af670');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-65e02dcb-5296-498c-a5ff-3ba35df6e3db\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-65e02dcb-5296-498c-a5ff-3ba35df6e3db')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
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       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
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       "    }\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    60% {\n",
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       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-65e02dcb-5296-498c-a5ff-3ba35df6e3db button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   abl  absolut  access  account    actual   ad       add  address  afford  \\\n",
       "0  0.0      0.0     0.0      0.0  0.235744  0.0  0.000000      0.0     0.0   \n",
       "1  0.0      0.0     0.0      0.0  0.000000  0.0  0.000000      0.0     0.0   \n",
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       "3  0.0      0.0     0.0      0.0  0.000000  0.0  0.000000      0.0     0.0   \n",
       "4  0.0      0.0     0.0      0.0  0.000000  0.0  0.150677      0.0     0.0   \n",
       "\n",
       "   allow  ...      work  worst  worth  would  wrong      year  yet  your  \\\n",
       "0    0.0  ...  0.000000    0.0    0.0    0.0    0.0  0.230071  0.0   0.0   \n",
       "1    0.0  ...  0.000000    0.0    0.0    0.0    0.0  0.000000  0.0   0.0   \n",
       "2    0.0  ...  0.199892    0.0    0.0    0.0    0.0  0.000000  0.0   0.0   \n",
       "3    0.0  ...  0.000000    0.0    0.0    0.0    0.0  0.000000  0.0   0.0   \n",
       "4    0.0  ...  0.000000    0.0    0.0    0.0    0.0  0.000000  0.0   0.0   \n",
       "\n",
       "   youtub  zoom  \n",
       "0     0.0   0.0  \n",
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       "2     0.0   0.0  \n",
       "3     0.0   0.0  \n",
       "4     0.0   0.0  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_df = pd.DataFrame(X_tf_idf.toarray(), columns=tf_idf.get_feature_names_out())\n",
    "\n",
    "features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:21.829104Z",
     "iopub.status.busy": "2024-07-08T11:56:21.828356Z",
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     "shell.execute_reply.started": "2024-07-08T11:56:21.829069Z"
    },
    "id": "6lb4XablU4kH",
    "outputId": "c8a75289-909e-4b39-ccc5-0a8ccb26b27e"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:43.449985Z",
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    },
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    "outputId": "6ac448d1-7955-492f-c8d0-408560421450"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kelas\n",
       "positive    5065\n",
       "negative    3673\n",
       "neutral     2593\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T11:56:50.047203Z",
     "iopub.status.busy": "2024-07-08T11:56:50.046809Z",
     "iopub.status.idle": "2024-07-08T11:56:50.056253Z",
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     "shell.execute_reply.started": "2024-07-08T11:56:50.047175Z"
    },
    "id": "0ox7g2jnU4kH"
   },
   "outputs": [],
   "source": [
    "label_map                 = {'positive': 2, 'negative': 0, 'neutral': 1}\n",
    "y_train_encoded           = y_train.map(label_map)\n",
    "y_test_encoded            = y_test.map(label_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:05:22.488844Z",
     "iopub.status.busy": "2024-07-08T12:05:22.488065Z",
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     "shell.execute_reply.started": "2024-07-08T12:05:22.488811Z"
    },
    "id": "oXSyfYzSU4kH"
   },
   "outputs": [],
   "source": [
    "distilbert_tokenizer      = DistilBertTokenizer.from_pretrained('distilbert-base-uncased-distilled-squad')\n",
    "\n",
    "def preprocess_distilbert_data(examples):\n",
    "    return distilbert_tokenizer(examples.tolist(), truncation=True, padding=True)\n",
    "\n",
    "train_distilbert_encodings = preprocess_distilbert_data(X_train)\n",
    "test_distilbert_encodings  = preprocess_distilbert_data(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:05:36.672462Z",
     "iopub.status.busy": "2024-07-08T12:05:36.671627Z",
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     "shell.execute_reply": "2024-07-08T12:05:36.677486Z",
     "shell.execute_reply.started": "2024-07-08T12:05:36.672425Z"
    },
    "id": "aiqzFIBKU4kH"
   },
   "outputs": [],
   "source": [
    "# ubah data latih dan test menjadi tipe data torch yang diterima oleh model DistilBERT\n",
    "\n",
    "class DistilBertSentimentDataset(torch.utils.data.Dataset):\n",
    "    \n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings  = encodings\n",
    "        self.labels     = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item            = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item['labels']  = torch.tensor(self.labels[idx])\n",
    "        return item\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:05:41.338541Z",
     "iopub.status.busy": "2024-07-08T12:05:41.337836Z",
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     "shell.execute_reply": "2024-07-08T12:05:41.342021Z",
     "shell.execute_reply.started": "2024-07-08T12:05:41.338510Z"
    },
    "id": "fFya2yTGU4kI"
   },
   "outputs": [],
   "source": [
    "train_distilbert_dataset = DistilBertSentimentDataset(train_distilbert_encodings, y_train_encoded.values)\n",
    "test_distilbert_dataset  = DistilBertSentimentDataset(test_distilbert_encodings, y_test_encoded.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:05:44.563347Z",
     "iopub.status.busy": "2024-07-08T12:05:44.562706Z",
     "iopub.status.idle": "2024-07-08T12:05:46.724370Z",
     "shell.execute_reply": "2024-07-08T12:05:46.723544Z",
     "shell.execute_reply.started": "2024-07-08T12:05:44.563312Z"
    },
    "id": "x2759ICpU4kI",
    "outputId": "cc58abe9-09e1-4057-a0b8-df7c97b9c6cf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased-distilled-squad and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "distilbert_model         = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased-distilled-squad',\n",
    "                                                                               num_labels=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:05:50.752438Z",
     "iopub.status.busy": "2024-07-08T12:05:50.751546Z",
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     "shell.execute_reply.started": "2024-07-08T12:05:50.752404Z"
    },
    "id": "Zd3jtiNDU4kI"
   },
   "outputs": [],
   "source": [
    "# latih\n",
    "distilbert_training_config = TrainingArguments(output_dir='./results',\n",
    "                                               per_device_train_batch_size=19,\n",
    "                                               per_device_eval_batch_size=19,\n",
    "                                               num_train_epochs=3,\n",
    "                                               logging_dir='./logs',\n",
    "                                               logging_steps=10,\n",
    "                                               evaluation_strategy='epoch',\n",
    "                                               save_strategy='epoch')\n",
    "\n",
    "distilbert_trainer = Trainer(model = distilbert_model,\n",
    "                             args  = distilbert_training_config,\n",
    "                             train_dataset = train_distilbert_dataset,\n",
    "                             eval_dataset  = test_distilbert_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:05:57.527851Z",
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     "shell.execute_reply": "2024-07-08T12:17:01.303906Z",
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1791' max='1791' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1791/1791 09:40, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.191800</td>\n",
       "      <td>0.274474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.146400</td>\n",
       "      <td>0.253825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.087000</td>\n",
       "      <td>0.248339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1791, training_loss=0.253590343063484, metrics={'train_runtime': 583.4437, 'train_samples_per_second': 58.263, 'train_steps_per_second': 3.07, 'total_flos': 1609486639311438.0, 'train_loss': 0.253590343063484, 'epoch': 3.0})"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distilbert_trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:17:14.522312Z",
     "iopub.status.busy": "2024-07-08T12:17:14.521930Z",
     "iopub.status.idle": "2024-07-08T12:18:27.152192Z",
     "shell.execute_reply": "2024-07-08T12:18:27.151238Z",
     "shell.execute_reply.started": "2024-07-08T12:17:14.522281Z"
    },
    "id": "_Lblyz1RU4kI",
    "outputId": "b40b6502-5c43-41c5-fca7-9d4720f07b6d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    positive       0.97      0.99      0.98      3673\n",
      "    negative       0.98      0.93      0.96      2593\n",
      "     neutral       0.98      0.99      0.98      5065\n",
      "\n",
      "    accuracy                           0.98     11331\n",
      "   macro avg       0.98      0.97      0.97     11331\n",
      "weighted avg       0.98      0.98      0.98     11331\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluasi data latih\n",
    "train_distilbert_predictions = distilbert_trainer.predict(train_distilbert_dataset)\n",
    "train_distilbert_preds       = np.argmax(train_distilbert_predictions.predictions, axis=-1)\n",
    "\n",
    "train_distilbert_report      = classification_report(y_train_encoded,\n",
    "                                                     train_distilbert_preds,\n",
    "                                                     target_names=label_map.keys())\n",
    "\n",
    "print(\"Training Report:\")\n",
    "print(train_distilbert_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:18:31.388326Z",
     "iopub.status.busy": "2024-07-08T12:18:31.387436Z",
     "iopub.status.idle": "2024-07-08T12:18:39.604483Z",
     "shell.execute_reply": "2024-07-08T12:18:39.603529Z",
     "shell.execute_reply.started": "2024-07-08T12:18:31.388291Z"
    },
    "id": "R8tAR6hQU4kJ",
    "outputId": "c368accc-a857-4666-e65e-d025c3da54e9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    positive       0.92      0.94      0.93       943\n",
      "    negative       0.94      0.89      0.91       631\n",
      "     neutral       0.95      0.96      0.95      1259\n",
      "\n",
      "    accuracy                           0.94      2833\n",
      "   macro avg       0.94      0.93      0.93      2833\n",
      "weighted avg       0.94      0.94      0.94      2833\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluasi Data Uji\n",
    "test_distilbert_predictions = distilbert_trainer.predict(test_distilbert_dataset)\n",
    "test_distilbert_preds       = np.argmax(test_distilbert_predictions.predictions, axis=-1)\n",
    "\n",
    "test_distilbert_report      = classification_report(y_test_encoded,\n",
    "                                                    test_distilbert_preds,\n",
    "                                                    target_names=label_map.keys())\n",
    "\n",
    "print(\"Test Report:\")\n",
    "print(test_distilbert_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:23:04.967569Z",
     "iopub.status.busy": "2024-07-08T12:23:04.967209Z",
     "iopub.status.idle": "2024-07-08T12:23:04.992017Z",
     "shell.execute_reply": "2024-07-08T12:23:04.991108Z",
     "shell.execute_reply.started": "2024-07-08T12:23:04.967537Z"
    },
    "id": "phqkE0RqU4kJ",
    "outputId": "29aa07d6-5c3a-4b7d-b587-4395d0e31cdb"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "torch.save(distilbert_tokenizer, '/kaggle/working/distilbert_tokenizer.pth')\n",
    "\n",
    "model_state_dict2 = distilbert_model.state_dict()\n",
    "torch.save(model_state_dict, '/kaggle/working/distilbert_model.pth')\n",
    "\n",
    "optimizer_state_dict2 = distilbert_trainer.optimizer.state_dict()\n",
    "torch.save(optimizer_state_dict2, '/kaggle/working/distilbert_trainer_optimizer.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T10:29:04.297426Z",
     "iopub.status.busy": "2024-07-08T10:29:04.296950Z",
     "iopub.status.idle": "2024-07-08T10:29:05.169354Z",
     "shell.execute_reply": "2024-07-08T10:29:05.168298Z",
     "shell.execute_reply.started": "2024-07-08T10:29:04.297390Z"
    },
    "id": "l2hHokMkU4kK",
    "outputId": "9dea9095-9272-4057-cc0f-0f96d001f763"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hapus memori gpu\n",
    "del distilbert_model\n",
    "del distilbert_trainer\n",
    "del train_distilbert_predictions\n",
    "del test_distilbert_predictions\n",
    "del train_distilbert_dataset\n",
    "del test_distilbert_dataset\n",
    "del train_distilbert_encodings\n",
    "del test_distilbert_encodings\n",
    "del train_distilbert_preds\n",
    "del test_distilbert_preds\n",
    "del distilbert_tokenizer\n",
    "\n",
    "K.clear_session()\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e4H6jyt-U4kK"
   },
   "source": [
    "#### Percobaan Ke-2 dengan TF-IDF dan GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:24:35.421430Z",
     "iopub.status.busy": "2024-07-08T12:24:35.421057Z",
     "iopub.status.idle": "2024-07-08T12:24:35.429043Z",
     "shell.execute_reply": "2024-07-08T12:24:35.427973Z",
     "shell.execute_reply.started": "2024-07-08T12:24:35.421400Z"
    },
    "id": "8bSBI8tcU4kK",
    "outputId": "ce0cb4eb-650a-4a70-caeb-ed4eb1c8a87d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14164, 500)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tf_idf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:24:37.100992Z",
     "iopub.status.busy": "2024-07-08T12:24:37.100295Z",
     "iopub.status.idle": "2024-07-08T12:24:37.114367Z",
     "shell.execute_reply": "2024-07-08T12:24:37.112911Z",
     "shell.execute_reply.started": "2024-07-08T12:24:37.100956Z"
    },
    "id": "VlU1p5P0U4kL"
   },
   "outputs": [],
   "source": [
    "y_encoded = y.map(label_map)\n",
    "\n",
    "X_train_idf, X_test_idf, y_train_idf, y_test_idf = train_test_split(X_tf_idf,\n",
    "                                                                    y_encoded,\n",
    "                                                                    test_size=0.2,\n",
    "                                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:24:39.452656Z",
     "iopub.status.busy": "2024-07-08T12:24:39.451989Z",
     "iopub.status.idle": "2024-07-08T12:24:39.460211Z",
     "shell.execute_reply": "2024-07-08T12:24:39.458923Z",
     "shell.execute_reply.started": "2024-07-08T12:24:39.452607Z"
    },
    "id": "gEX9K4epU4kL",
    "outputId": "43670e9c-d004-4d18-8ef7-d9e865c26137"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11331, 500)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_idf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:24:41.231979Z",
     "iopub.status.busy": "2024-07-08T12:24:41.231567Z",
     "iopub.status.idle": "2024-07-08T12:24:41.239911Z",
     "shell.execute_reply": "2024-07-08T12:24:41.238603Z",
     "shell.execute_reply.started": "2024-07-08T12:24:41.231947Z"
    },
    "id": "6HzxcyUXU4kL",
    "outputId": "2a58fb9f-8841-4b07-87a2-20b201b6aeef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11331,)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_idf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:24:45.412130Z",
     "iopub.status.busy": "2024-07-08T12:24:45.411494Z",
     "iopub.status.idle": "2024-07-08T12:24:45.488883Z",
     "shell.execute_reply": "2024-07-08T12:24:45.487924Z",
     "shell.execute_reply.started": "2024-07-08T12:24:45.412096Z"
    },
    "id": "YNL2bhJ0U4kL"
   },
   "outputs": [],
   "source": [
    "# siapkan data untuk GRU\n",
    "\n",
    "X_train_idf = X_train_idf.toarray().reshape((X_train_idf.shape[0], X_train_idf.shape[1], 1))\n",
    "X_test_idf  = X_test_idf.toarray().reshape((X_test_idf.shape[0], X_test_idf.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:24:48.780944Z",
     "iopub.status.busy": "2024-07-08T12:24:48.780534Z",
     "iopub.status.idle": "2024-07-08T12:24:50.249791Z",
     "shell.execute_reply": "2024-07-08T12:24:50.248698Z",
     "shell.execute_reply.started": "2024-07-08T12:24:48.780914Z"
    },
    "id": "OLqi4SprU4kM"
   },
   "outputs": [],
   "source": [
    "gru_model5  = Sequential()\n",
    "gru_model5.add(GRU(units=128, return_sequences=True, input_shape=(X_train_idf.shape[1], 1)))\n",
    "gru_model5.add(Dropout(0.2))\n",
    "gru_model5.add(Flatten())\n",
    "gru_model5.add(Dense(3, activation='softmax'))\n",
    "\n",
    "gru_model5.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:24:55.242406Z",
     "iopub.status.busy": "2024-07-08T12:24:55.241545Z",
     "iopub.status.idle": "2024-07-08T12:25:50.854648Z",
     "shell.execute_reply": "2024-07-08T12:25:50.853685Z",
     "shell.execute_reply.started": "2024-07-08T12:24:55.242371Z"
    },
    "id": "1ujS5zeVU4kM",
    "outputId": "d672a4e0-71f2-4683-c2df-b5ac89c3114e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "597/597 [==============================] - 17s 20ms/step - loss: 0.7125 - accuracy: 0.6953 - val_loss: 0.5491 - val_accuracy: 0.7956\n",
      "Epoch 2/5\n",
      "597/597 [==============================] - 11s 18ms/step - loss: 0.5104 - accuracy: 0.8140 - val_loss: 0.5287 - val_accuracy: 0.8129\n",
      "Epoch 3/5\n",
      "597/597 [==============================] - 12s 19ms/step - loss: 0.4850 - accuracy: 0.8292 - val_loss: 0.5278 - val_accuracy: 0.8203\n",
      "Epoch 4/5\n",
      "597/597 [==============================] - 11s 19ms/step - loss: 0.4731 - accuracy: 0.8385 - val_loss: 0.5403 - val_accuracy: 0.8207\n",
      "Epoch 5/5\n",
      "597/597 [==============================] - 12s 19ms/step - loss: 0.4604 - accuracy: 0.8445 - val_loss: 0.5247 - val_accuracy: 0.8239\n"
     ]
    }
   ],
   "source": [
    "history5    = gru_model5.fit(X_train_idf,\n",
    "                             y_train_idf,\n",
    "                             epochs=5,\n",
    "                             batch_size=19,\n",
    "                             validation_data=(X_test_idf, y_test_idf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:25:55.104484Z",
     "iopub.status.busy": "2024-07-08T12:25:55.103466Z",
     "iopub.status.idle": "2024-07-08T12:25:55.998179Z",
     "shell.execute_reply": "2024-07-08T12:25:55.997195Z",
     "shell.execute_reply.started": "2024-07-08T12:25:55.104440Z"
    },
    "id": "4oHCRatfU4kM",
    "outputId": "e67b33fa-4d7d-4714-870f-0b11a57003b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 1s 9ms/step - loss: 0.5247 - accuracy: 0.8239\n",
      "Test Accuracy for GRU: 0.824\n"
     ]
    }
   ],
   "source": [
    "# evaluasi\n",
    "gru_loss, gru_acc = gru_model5.evaluate(X_test_idf, y_test_idf)\n",
    "print(f'Test Accuracy for GRU: {gru_acc:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:25:58.113291Z",
     "iopub.status.busy": "2024-07-08T12:25:58.112925Z",
     "iopub.status.idle": "2024-07-08T12:25:59.124677Z",
     "shell.execute_reply": "2024-07-08T12:25:59.123553Z",
     "shell.execute_reply.started": "2024-07-08T12:25:58.113262Z"
    },
    "id": "UPLUx-6IU4kM",
    "outputId": "d565bd2d-8eb2-4c92-a68a-6c498d0178cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/89 [==============================] - 1s 8ms/step\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Report for GRU:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    positive       0.77      0.80      0.79       943\n",
      "    negative       0.79      0.80      0.79       631\n",
      "     neutral       0.88      0.86      0.87      1259\n",
      "\n",
      "    accuracy                           0.82      2833\n",
      "   macro avg       0.81      0.82      0.82      2833\n",
      "weighted avg       0.83      0.82      0.82      2833\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gru_preds       = gru_model5.predict(X_test_idf)\n",
    "gru_preds_class = np.argmax(gru_preds, axis=1)\n",
    "\n",
    "print(\"Test Report for GRU:\")\n",
    "print(classification_report(y_test_idf, gru_preds_class, target_names=label_map.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:26:06.785169Z",
     "iopub.status.busy": "2024-07-08T12:26:06.784790Z",
     "iopub.status.idle": "2024-07-08T12:26:07.568134Z",
     "shell.execute_reply": "2024-07-08T12:26:07.567163Z",
     "shell.execute_reply.started": "2024-07-08T12:26:06.785139Z"
    },
    "id": "0mXa7xPvU4kM",
    "outputId": "1a06a7ef-fe49-43df-cb43-9fbf5dab8d66"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport matplotlib.pyplot as plt\\nimport tensorflow as tf\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4998"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del gru_model5\n",
    "del history5\n",
    "del gru_preds\n",
    "del gru_preds_class\n",
    "del X_train_idf\n",
    "del X_test_idf\n",
    "del y_train_idf\n",
    "del y_test_idf\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:28:32.053098Z",
     "iopub.status.busy": "2024-07-08T12:28:32.052210Z",
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     "shell.execute_reply": "2024-07-08T12:28:32.113346Z",
     "shell.execute_reply.started": "2024-07-08T12:28:32.053063Z"
    },
    "id": "GfWeE3yFU4kN"
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Percobaan Ke-3 ALBERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AlbertTokenizer, AlbertForSequenceClassification, Trainer, TrainingArguments\n",
    "\n",
    "# Load the pre-trained tokenizer and model\n",
    "albert_tokenizer = AlbertTokenizer.from_pretrained('albert-xxlarge-v2')\n",
    "albert_model = AlbertForSequenceClassification.from_pretrained('albert-xxlarge-v2', num_labels=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the tokenize function\n",
    "def albert_tokenize_function(examples):\n",
    "    return albert_tokenizer(examples.tolist(), truncation=True, padding=True)\n",
    "\n",
    "# Tokenize the data\n",
    "X_albert = albert_tokenize_function(X_train)\n",
    "y_albert = y_train_encoded.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a custom dataset class\n",
    "class AlbertSentimentDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item['labels'] = torch.tensor(self.labels[idx])\n",
    "        return item\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the dataset instances\n",
    "train_dataset_albert = AlbertSentimentDataset(X_albert, y_albert)\n",
    "test_dataset_albert = AlbertSentimentDataset(albert_tokenize_function(X_test), y_test_encoded.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the training arguments\n",
    "training_args_albert = TrainingArguments(\n",
    "    output_dir='./results_albert',      # Lokasi di mana hasil pelatihan akan disimpan\n",
    "    per_device_train_batch_size=19,      # Ukuran batch pelatihan\n",
    "    per_device_eval_batch_size=19,       # Ukuran batch evaluasi\n",
    "    num_train_epochs=3,                  # Jumlah epochs\n",
    "    logging_dir='./logs_albert',        # Direktori logging\n",
    "    logging_steps=10,                    # Frekuensi logging\n",
    "    evaluation_strategy='epoch',         # Evaluasi pada akhir setiap epoch\n",
    "    save_strategy='epoch'               # Menyimpan model pada akhir setiap epoch\n",
    ")\n",
    "\n",
    "# Create the trainer instance\n",
    "trainer_albert = Trainer(\n",
    "    model=albert_model,\n",
    "    args=training_args_albert,\n",
    "    train_dataset=train_dataset_albert,\n",
    "    eval_dataset=test_dataset_albert\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer_albert.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictions_albert = trainer_albert.predict(train_dataset_albert)\n",
    "train_preds_albert = np.argmax(train_predictions_albert.predictions, axis=-1)\n",
    "train_report_albert = classification_report(y_train_encoded, train_preds_albert, target_names=label_map.keys())\n",
    "\n",
    "print(\"Training Report for ALBERT:\")\n",
    "print(train_report_albert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions_albert = trainer_albert.predict(test_dataset_albert)\n",
    "test_preds_albert = np.argmax(test_predictions_albert.predictions, axis=-1)\n",
    "test_report_albert = classification_report(y_test_encoded, test_preds_albert, target_names=label_map.keys())\n",
    "\n",
    "print(\"Test Report for ALBERT:\")\n",
    "print(test_report_albert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9UQxwPZkU4kN"
   },
   "source": [
    "#### Percobaan ke-3 RoBERTa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "7ca18e5b-4ffa-465c-8c32-0d7fef4de6f4"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62a86f814c1d4e27aa49eba81f48c61b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
      "text/plain": [
       "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
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     },
     "metadata": {},
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    },
    {
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      "text/plain": [
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# roberta_tokenizer = RobertaTokenizer.from_pretrained('roberta-base')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:26:41.192670Z",
     "iopub.status.busy": "2024-07-08T12:26:41.192036Z",
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     "shell.execute_reply": "2024-07-08T12:26:41.199016Z",
     "shell.execute_reply.started": "2024-07-08T12:26:41.192633Z"
    },
    "id": "Vz3yZKFeU4kN"
   },
   "outputs": [],
   "source": [
    "# def roberta_tokenize_function(examples):\n",
    "#     return roberta_tokenizer(examples.tolist(), truncation=True, padding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:26:49.593182Z",
     "iopub.status.busy": "2024-07-08T12:26:49.592805Z",
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     "shell.execute_reply.started": "2024-07-08T12:26:49.593151Z"
    },
    "id": "8UguHErTU4kN"
   },
   "outputs": [],
   "source": [
    "# train_encodings_roberta = roberta_tokenize_function(X_train)\n",
    "# test_encodings_roberta  = roberta_tokenize_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:26:54.883503Z",
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    },
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   "source": [
    "# class RobertaSentimentDataset(torch.utils.data.Dataset):\n",
    "    \n",
    "#     def __init__(self, encodings, labels):\n",
    "#         self.encodings  = encodings\n",
    "#         self.labels     = labels\n",
    "\n",
    "#     def __getitem__(self, idx):\n",
    "#         item            = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "#         item['labels']  = torch.tensor(self.labels[idx])\n",
    "#         return item\n",
    "\n",
    "#     def __len__(self):\n",
    "#         return len(self.labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:26:58.482163Z",
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    },
    "id": "1SlV5YNFU4kN"
   },
   "outputs": [],
   "source": [
    "# train_dataset_roberta = RobertaSentimentDataset(train_encodings_roberta, y_train_encoded.values)\n",
    "# test_dataset_roberta  = RobertaSentimentDataset(test_encodings_roberta, y_test_encoded.values)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33a62c09efd34a4c8066ec3f00450173",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# # roberta-base-openai-detector\n",
    "# roberta_model         = RobertaForSequenceClassification.from_pretrained('roberta-base',\n",
    "#                                                                          num_labels=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:27:07.306982Z",
     "iopub.status.busy": "2024-07-08T12:27:07.306619Z",
     "iopub.status.idle": "2024-07-08T12:27:07.480907Z",
     "shell.execute_reply": "2024-07-08T12:27:07.479821Z",
     "shell.execute_reply.started": "2024-07-08T12:27:07.306954Z"
    },
    "id": "wXu142XVU4kO"
   },
   "outputs": [],
   "source": [
    "# training_args_roberta = TrainingArguments(output_dir='./results_roberta',\n",
    "#                                           per_device_train_batch_size=21,\n",
    "#                                           per_device_eval_batch_size=21, \n",
    "#                                           num_train_epochs=3,\n",
    "#                                           logging_dir='./logs_roberta',\n",
    "#                                           logging_steps=10,\n",
    "#                                           evaluation_strategy='epoch',\n",
    "#                                           save_strategy='epoch')\n",
    "\n",
    "# trainer_roberta = Trainer(model = roberta_model,\n",
    "#                           args  = training_args_roberta,\n",
    "#                           train_dataset = train_dataset_roberta,\n",
    "#                           eval_dataset  = test_dataset_roberta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "execution": {
     "iopub.execute_input": "2024-07-08T12:29:18.678622Z",
     "iopub.status.busy": "2024-07-08T12:29:18.677846Z",
     "iopub.status.idle": "2024-07-08T12:29:19.949560Z",
     "shell.execute_reply": "2024-07-08T12:29:19.947825Z",
     "shell.execute_reply.started": "2024-07-08T12:29:18.678570Z"
    },
    "id": "A7mnwNv_U4kO",
    "outputId": "0df03bd1-619f-450a-c2ab-279858bc3319"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1620' max='1620' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1620/1620 17:33, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.528400</td>\n",
       "      <td>0.421994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.279300</td>\n",
       "      <td>0.292601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.127800</td>\n",
       "      <td>0.276381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1620, training_loss=0.3763484098661093, metrics={'train_runtime': 1054.4534, 'train_samples_per_second': 32.238, 'train_steps_per_second': 1.536, 'total_flos': 3022098618607866.0, 'train_loss': 0.3763484098661093, 'epoch': 3.0})"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trainer_roberta.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.460113Z",
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    },
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    "outputId": "353ff2ef-e515-4a9c-e86d-40f481a61add"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport matplotlib.pyplot as plt\\nimport tensorflow as tf\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Report for RoBERTa:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    positive       0.94      0.98      0.96      3673\n",
      "    negative       0.96      0.91      0.93      2593\n",
      "     neutral       0.97      0.97      0.97      5065\n",
      "\n",
      "    accuracy                           0.96     11331\n",
      "   macro avg       0.96      0.95      0.95     11331\n",
      "weighted avg       0.96      0.96      0.96     11331\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# train_predictions_roberta = trainer_roberta.predict(train_dataset_roberta)\n",
    "# train_preds_roberta       = np.argmax(train_predictions_roberta.predictions, axis=-1)\n",
    "# train_report_roberta      = classification_report(y_train_encoded, train_preds_roberta, target_names=label_map.keys())\n",
    "\n",
    "# print(\"Training Report for RoBERTa:\")\n",
    "# print(train_report_roberta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 217
    },
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.461715Z",
     "iopub.status.idle": "2024-07-08T10:30:01.462090Z",
     "shell.execute_reply": "2024-07-08T10:30:01.461933Z",
     "shell.execute_reply.started": "2024-07-08T10:30:01.461918Z"
    },
    "id": "N9RKEhvXU4kO",
    "outputId": "ee546c60-1efb-45f9-d31e-b458aca2ea4f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport matplotlib.pyplot as plt\\nimport tensorflow as tf\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Report for RoBERTa:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    positive       0.88      0.94      0.91       943\n",
      "    negative       0.92      0.86      0.89       631\n",
      "     neutral       0.95      0.93      0.94      1259\n",
      "\n",
      "    accuracy                           0.92      2833\n",
      "   macro avg       0.92      0.91      0.91      2833\n",
      "weighted avg       0.92      0.92      0.92      2833\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# test_predictions_roberta = trainer_roberta.predict(test_dataset_roberta)\n",
    "# test_preds_roberta       = np.argmax(test_predictions_roberta.predictions, axis=-1)\n",
    "# test_report_roberta      = classification_report(y_test_encoded, test_preds_roberta, target_names=label_map.keys())\n",
    "\n",
    "# print(\"Test Report for RoBERTa:\")\n",
    "# print(test_report_roberta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.465862Z",
     "iopub.status.idle": "2024-07-08T10:30:01.466170Z",
     "shell.execute_reply": "2024-07-08T10:30:01.466030Z",
     "shell.execute_reply.started": "2024-07-08T10:30:01.466017Z"
    },
    "id": "INDhZJKqU4kP",
    "outputId": "66e787bc-1b3e-4fda-fbe3-17d6f9217e25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del roberta_model\n",
    "del roberta_tokenizer\n",
    "del trainer_roberta\n",
    "del train_predictions_roberta\n",
    "del test_predictions_roberta\n",
    "del train_dataset_roberta\n",
    "del test_dataset_roberta\n",
    "del train_encodings_roberta\n",
    "del test_encodings_roberta\n",
    "del train_preds_roberta\n",
    "del test_preds_roberta\n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zC8h16xtU4kP"
   },
   "source": [
    "#### Percobaan ke-4 Menggunakan Algoritma Pelatihan dari HuggingFace yaitu 'twitter-roberta-base-sentiment'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
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    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.467196Z",
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    },
    "id": "JsjQar5rU4kQ",
    "outputId": "08f726b8-6928-48c2-bcc8-67e25ebe7cb6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bebef83ab354d739f5c50cf4485fc13",
       "version_major": 2,
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      },
      "text/plain": [
       "config.json:   0%|          | 0.00/747 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c08973cabd34e3cb1d401f1cfdf5aa6",
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      "text/plain": [
       "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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     },
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     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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     },
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    },
    {
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      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/499M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter_roberta_tokenizer = AutoTokenizer.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment')\n",
    "twitter_roberta_model     = AutoModelForSequenceClassification.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment',\n",
    "                                                                               num_labels=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.468894Z",
     "iopub.status.idle": "2024-07-08T10:30:01.469194Z",
     "shell.execute_reply": "2024-07-08T10:30:01.469056Z",
     "shell.execute_reply.started": "2024-07-08T10:30:01.469044Z"
    },
    "id": "M-8Y2fjuU4kQ",
    "outputId": "f1ffe01d-5873-4570-cc06-94d88b09815a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    }
   ],
   "source": [
    "def twitter_roberta_tokenize_function(examples):\n",
    "    return twitter_roberta_tokenizer(examples.tolist(), truncation=True, padding=True)\n",
    "\n",
    "X_twitter_roberta = twitter_roberta_tokenize_function(X_train)\n",
    "y_twitter_roberta = y_train_encoded.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.470311Z",
     "iopub.status.idle": "2024-07-08T10:30:01.470667Z",
     "shell.execute_reply": "2024-07-08T10:30:01.470491Z",
     "shell.execute_reply.started": "2024-07-08T10:30:01.470477Z"
    },
    "id": "zmF6RGEsU4kQ"
   },
   "outputs": [],
   "source": [
    "class TwitterRobertaSentimentDataset(torch.utils.data.Dataset):\n",
    "    \n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings  = encodings\n",
    "        self.labels     = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item            = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item['labels']  = torch.tensor(self.labels[idx])\n",
    "        return item\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.472860Z",
     "iopub.status.idle": "2024-07-08T10:30:01.473338Z",
     "shell.execute_reply": "2024-07-08T10:30:01.473091Z",
     "shell.execute_reply.started": "2024-07-08T10:30:01.473072Z"
    },
    "id": "VRGPAnTVU4kR"
   },
   "outputs": [],
   "source": [
    "train_dataset_twitter_roberta = TwitterRobertaSentimentDataset(X_twitter_roberta, y_twitter_roberta)\n",
    "test_dataset_twitter_roberta  = TwitterRobertaSentimentDataset(twitter_roberta_tokenize_function(X_test),\n",
    "                                                               y_test_encoded.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.474772Z",
     "iopub.status.idle": "2024-07-08T10:30:01.475291Z",
     "shell.execute_reply": "2024-07-08T10:30:01.475008Z",
     "shell.execute_reply.started": "2024-07-08T10:30:01.474987Z"
    },
    "id": "UKLU_0tRU4kR"
   },
   "outputs": [],
   "source": [
    "training_args_twitter_roberta = TrainingArguments(output_dir='./results_twitter_roberta',\n",
    "                                                  per_device_train_batch_size=18,\n",
    "                                                  per_device_eval_batch_size=18,\n",
    "                                                  num_train_epochs=3,\n",
    "                                                  logging_dir='./logs_twitter_roberta',\n",
    "                                                  logging_steps=10,\n",
    "                                                  evaluation_strategy='epoch',\n",
    "                                                  save_strategy='epoch')\n",
    "\n",
    "trainer_twitter_roberta = Trainer(model = twitter_roberta_model,\n",
    "                                  args  = training_args_twitter_roberta,\n",
    "                                  train_dataset = train_dataset_twitter_roberta,\n",
    "                                  eval_dataset  = test_dataset_twitter_roberta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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       "      [1890/1890 17:40, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
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       "TrainOutput(global_step=1890, training_loss=0.24160021548233335, metrics={'train_runtime': 1060.931, 'train_samples_per_second': 32.041, 'train_steps_per_second': 1.781, 'total_flos': 3022098618607866.0, 'train_loss': 0.24160021548233335, 'epoch': 3.0})"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer_twitter_roberta.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
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    "execution": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport matplotlib.pyplot as plt\\nimport tensorflow as tf\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
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     "output_type": "stream",
     "text": [
      "Training Report for Twitter RoBERTa:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    positive       0.97      0.99      0.98      3673\n",
      "    negative       0.98      0.93      0.96      2593\n",
      "     neutral       0.98      0.99      0.99      5065\n",
      "\n",
      "    accuracy                           0.98     11331\n",
      "   macro avg       0.98      0.97      0.97     11331\n",
      "weighted avg       0.98      0.98      0.98     11331\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_predictions_twitter_roberta = trainer_twitter_roberta.predict(train_dataset_twitter_roberta)\n",
    "train_preds_twitter_roberta       = np.argmax(train_predictions_twitter_roberta.predictions, axis=-1)\n",
    "\n",
    "train_report_twitter_roberta      = classification_report(y_train_encoded,\n",
    "                                                          train_preds_twitter_roberta,\n",
    "                                                          target_names=label_map.keys())\n",
    "\n",
    "print(\"Training Report for Twitter RoBERTa:\")\n",
    "print(train_report_twitter_roberta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
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    },
    "execution": {
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   "outputs": [
    {
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     "metadata": {},
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    },
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       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport matplotlib.pyplot as plt\\nimport tensorflow as tf\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
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   ],
   "source": [
    "test_predictions_twitter_roberta = trainer_twitter_roberta.predict(test_dataset_twitter_roberta)\n",
    "test_preds_twitter_roberta       = np.argmax(test_predictions_twitter_roberta.predictions, axis=-1)\n",
    "\n",
    "test_report_twitter_roberta      = classification_report(y_test_encoded,\n",
    "                                                         test_preds_twitter_roberta,\n",
    "                                                         target_names=label_map.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.482417Z",
     "iopub.status.idle": "2024-07-08T10:30:01.483019Z",
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    },
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    "outputId": "218c55c6-86d0-467a-f4ff-480e46456280"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Report for Twitter RoBERTa:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    positive       0.92      0.95      0.94       943\n",
      "    negative       0.92      0.89      0.91       631\n",
      "     neutral       0.96      0.95      0.96      1259\n",
      "\n",
      "    accuracy                           0.94      2833\n",
      "   macro avg       0.93      0.93      0.93      2833\n",
      "weighted avg       0.94      0.94      0.94      2833\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Report for Twitter RoBERTa:\")\n",
    "print(test_report_twitter_roberta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.486139Z",
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   "outputs": [],
   "source": [
    "torch.save(twitter_roberta_tokenizer, '/kaggle/working/twitter_roberta_tokenizer.pth')\n",
    "\n",
    "model_state_dict = twitter_roberta_model.state_dict()\n",
    "torch.save(model_state_dict, '/kaggle/working/twitter_roberta_model.pth')\n",
    "\n",
    "optimizer_state_dict = trainer_twitter_roberta.optimizer.state_dict()\n",
    "torch.save(optimizer_state_dict, '/kaggle/working/twitter_roberta_optimizer.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-07-08T10:30:01.484615Z",
     "iopub.status.idle": "2024-07-08T10:30:01.485101Z",
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    },
    "id": "xKK38EkWU4kS"
   },
   "outputs": [],
   "source": [
    "del train_predictions_twitter_roberta\n",
    "del test_predictions_twitter_roberta\n",
    "del train_dataset_twitter_roberta\n",
    "del test_dataset_twitter_roberta\n",
    "del X_twitter_roberta\n",
    "del y_twitter_roberta\n",
    "del train_preds_twitter_roberta\n",
    "del test_preds_twitter_roberta\n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
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    "execution": {
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    "outputId": "3b0a8204-1bb8-44d2-f51a-8559f5e5069c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masukkan kalimat baru: I'm not impressed with Udemy's app. The course I took was riddled with errors, and the interface was clunky. It's frustrating when you're trying to learn, but the app gets in the way. Needs a major overhaul.\n"
     ]
    },
    {
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       "\n",
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    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import train_test_split\\nimport re\\nimport matplotlib.pyplot as plt\\nimport tensorflow as tf\\nimport numpy as np\\nimport pandas as pd\\nimport pickle'); }\n",
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     "text": [
      "Sentimen kalimat baru adalah negative.\n"
     ]
    }
   ],
   "source": [
    "kalimat_baru             = input(\"Masukkan kalimat baru: \")\n",
    "\n",
    "kalimat_baru_cleaned     = cleaningText(kalimat_baru)\n",
    "kalimat_baru_casefolded  = casefoldingText(kalimat_baru_cleaned)\n",
    "kalimat_baru_tokenized   = tokenizingText(kalimat_baru_casefolded)\n",
    "kalimat_baru_filtered    = filteringText(kalimat_baru_tokenized)\n",
    "kalimat_baru_final       = stemmingTextSnowball(kalimat_baru_filtered)\n",
    "\n",
    "inputs                   = twitter_roberta_tokenizer(kalimat_baru_final,\n",
    "                                                     return_tensors='pt',\n",
    "                                                     truncation=True,\n",
    "                                                     padding=True)\n",
    "\n",
    "class TwitterRobertaSentimentDataset(torch.utils.data.Dataset):\n",
    "    \n",
    "    def __init__(self, encodings):\n",
    "        self.encodings = encodings\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item           = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        return item\n",
    "\n",
    "    def __len__(self):\n",
    "        return 1\n",
    "\n",
    "dataset           = TwitterRobertaSentimentDataset(inputs)\n",
    "predictions       = trainer_twitter_roberta.predict(dataset)\n",
    "prediksi_sentimen = np.argmax(predictions.predictions, axis=-1)\n",
    "label_map         = {'positive': 2, 'negative': 0, 'neutral': 1}\n",
    "labels            = list(label_map.keys())\n",
    "predicted_label   = labels[prediksi_sentimen[0]]  # Get the first element of the array\n",
    "\n",
    "print(f\"Sentimen kalimat baru adalah {predicted_label}.\")"
   ]
  }
 ],
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