{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "# AuraClima: predicting the CO2" ], "metadata": { "id": "T5UzeL4jY0AC" } }, { "cell_type": "markdown", "source": [ "## Basic Overview and Analysis" ], "metadata": { "id": "CZm9r69dbQWr" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "p6TIsD_dY6EE", "outputId": "243f5ca9-201b-472b-a155-fcb6143064f3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "agri_path = \"/content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv\"\n", "co2_path = \"/content/drive/MyDrive/AuraClima/CO2_Emissions_1960-2018.csv\"\n", "\n", "df_agri = pd.read_csv(agri_path)\n", "df_co2 = pd.read_csv(co2_path)" ], "metadata": { "id": "T1CxxOGcZbqv" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df_agri.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 307 }, "id": "QNXMv2WOa7wR", "outputId": "c971ef85-ca51-4df5-ee3b-6db7e85b1f73" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Area Year Savanna fires Forest fires Crop Residues \\\n", "0 Afghanistan 1990 14.7237 0.0557 205.6077 \n", "1 Afghanistan 1991 14.7237 0.0557 209.4971 \n", "2 Afghanistan 1992 14.7237 0.0557 196.5341 \n", "3 Afghanistan 1993 14.7237 0.0557 230.8175 \n", "4 Afghanistan 1994 14.7237 0.0557 242.0494 \n", "\n", " Rice Cultivation Drained organic soils (CO2) Pesticides Manufacturing \\\n", "0 686.00 0.0 11.807483 \n", "1 678.16 0.0 11.712073 \n", "2 686.00 0.0 11.712073 \n", "3 686.00 0.0 11.712073 \n", "4 705.60 0.0 11.712073 \n", "\n", " Food Transport Forestland ... Manure Management Fires in organic soils \\\n", "0 63.1152 -2388.803 ... 319.1763 0.0 \n", "1 61.2125 -2388.803 ... 342.3079 0.0 \n", "2 53.3170 -2388.803 ... 349.1224 0.0 \n", "3 54.3617 -2388.803 ... 352.2947 0.0 \n", "4 53.9874 -2388.803 ... 367.6784 0.0 \n", "\n", " Fires in humid tropical forests On-farm energy use Rural population \\\n", "0 0.0 NaN 9655167.0 \n", "1 0.0 NaN 10230490.0 \n", "2 0.0 NaN 10995568.0 \n", "3 0.0 NaN 11858090.0 \n", "4 0.0 NaN 12690115.0 \n", "\n", " Urban population Total Population - Male Total Population - Female \\\n", "0 2593947.0 5348387.0 5346409.0 \n", "1 2763167.0 5372959.0 5372208.0 \n", "2 2985663.0 6028494.0 6028939.0 \n", "3 3237009.0 7003641.0 7000119.0 \n", "4 3482604.0 7733458.0 7722096.0 \n", "\n", " total_emission Average Temperature °C \n", "0 2198.963539 0.536167 \n", "1 2323.876629 0.020667 \n", "2 2356.304229 -0.259583 \n", "3 2368.470529 0.101917 \n", "4 2500.768729 0.372250 \n", "\n", "[5 rows x 31 columns]" ], "text/html": [ "\n", "
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AreaYearSavanna firesForest firesCrop ResiduesRice CultivationDrained organic soils (CO2)Pesticides ManufacturingFood TransportForestland...Manure ManagementFires in organic soilsFires in humid tropical forestsOn-farm energy useRural populationUrban populationTotal Population - MaleTotal Population - Femaletotal_emissionAverage Temperature °C
0Afghanistan199014.72370.0557205.6077686.000.011.80748363.1152-2388.803...319.17630.00.0NaN9655167.02593947.05348387.05346409.02198.9635390.536167
1Afghanistan199114.72370.0557209.4971678.160.011.71207361.2125-2388.803...342.30790.00.0NaN10230490.02763167.05372959.05372208.02323.8766290.020667
2Afghanistan199214.72370.0557196.5341686.000.011.71207353.3170-2388.803...349.12240.00.0NaN10995568.02985663.06028494.06028939.02356.304229-0.259583
3Afghanistan199314.72370.0557230.8175686.000.011.71207354.3617-2388.803...352.29470.00.0NaN11858090.03237009.07003641.07000119.02368.4705290.101917
4Afghanistan199414.72370.0557242.0494705.600.011.71207353.9874-2388.803...367.67840.00.0NaN12690115.03482604.07733458.07722096.02500.7687290.372250
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Country Name196019611962196319641965196619671968...2009201020112012201320142015201620172018
0Aruba204.631696208.837879226.081890214.785217207.626699185.213644172.158729210.819017194.917536...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1Africa Eastern and Southern0.9060600.9224740.9308160.9405700.9960331.0472801.0339081.0522041.079727...1.0219541.0488761.0053381.0216461.0318331.0411450.9873930.9710160.9599780.933541
2Afghanistan0.0460570.0535890.0737210.0741610.0861740.1012850.1073990.1234090.115142...0.2113060.2970650.4070740.3353510.2637160.2340370.2321760.2088570.2033280.200151
3Africa Western and Central0.0908800.0952830.0966120.1123760.1332580.1848030.1936760.1893050.143989...0.4267700.4728190.4970230.4908670.5046550.5076710.4807430.4729590.4764380.515544
4Angola0.1008350.0822040.2105330.2027390.2135620.2058910.2689370.1720960.289702...1.2059021.2215151.2163171.2047991.2615421.2853651.2609211.2277031.0343170.887380
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Missing CountMissing %
Crop Residues138919.942570
On-farm energy use95613.725772
Manure applied to Soils92813.323762
Manure Management92813.323762
IPPU74310.667624
Forestland4937.078248
Net Forest conversion4937.078248
Food Household Consumption4736.791098
Fires in humid tropical forests1552.225413
Forest fires931.335248
Savanna fires310.445083
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Missing CountMissing %
19606323.684211
19616223.308271
19626022.556391
19635922.180451
19645319.924812
19655319.924812
19665319.924812
19675319.924812
19685319.924812
19695319.924812
19705219.548872
19715119.172932
19725018.796992
19735018.796992
19745018.796992
19755018.796992
19765018.796992
19775018.796992
19785018.796992
19795018.796992
19805018.796992
19815018.796992
19825018.796992
19835018.796992
19845018.796992
19855018.796992
19865018.796992
19875018.796992
19885018.796992
19895018.796992
19902810.526316
19922810.526316
19932810.526316
19942810.526316
19912710.150376
19952710.150376
19962710.150376
19972710.150376
19982710.150376
19992710.150376
20002710.150376
20012710.150376
20022710.150376
20032710.150376
20042710.150376
20052710.150376
20062710.150376
20072710.150376
20082710.150376
20092710.150376
20102710.150376
20112710.150376
20122710.150376
20132710.150376
20142710.150376
20152710.150376
20162710.150376
20172710.150376
20182710.150376
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"missing_report(df_co2, \\\"CO2 Dataset\\\")\",\n \"rows\": 59,\n \"fields\": [\n {\n \"column\": \"Missing Count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12,\n \"min\": 27,\n \"max\": 63,\n \"num_unique_values\": 10,\n \"samples\": [\n 28,\n 62,\n 52\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Missing %\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.849909261138194,\n \"min\": 10.150375939849624,\n \"max\": 23.684210526315788,\n \"num_unique_values\": 10,\n \"samples\": [\n 10.526315789473683,\n 23.308270676691727,\n 19.548872180451127\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(\"Agri shape:\", df_agri.shape)\n", "print(\"CO2 shape:\", df_co2.shape)\n", "\n", "df_agri.describe(include='all')\n", "df_co2.describe(include='all')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 495 }, "id": "XauQcTypbLdI", "outputId": "e9684ffe-f391-4b2b-8d50-9c1c724e5868" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Agri shape: (6965, 31)\n", "CO2 shape: (266, 60)\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Country Name 1960 1961 1962 1963 \\\n", "count 266 203.000000 204.000000 206.000000 207.000000 \n", "unique 266 NaN NaN NaN NaN \n", "top Zimbabwe NaN NaN NaN NaN \n", "freq 1 NaN NaN NaN NaN \n", "mean NaN 3.160220 3.292338 3.462051 3.906006 \n", "std NaN 14.821169 15.114669 16.286958 16.909771 \n", "min NaN 0.007984 0.007857 0.008435 0.009336 \n", "25% NaN 0.195031 0.185078 0.211355 0.208805 \n", "50% NaN 0.616754 0.662374 0.659399 0.706050 \n", "75% NaN 2.060142 2.420962 2.526705 2.270349 \n", "max NaN 204.631696 208.837879 226.081890 214.785217 \n", "\n", " 1964 1965 1966 1967 1968 ... \\\n", "count 213.000000 213.000000 213.000000 213.000000 213.000000 ... \n", "unique NaN NaN NaN NaN NaN ... \n", "top NaN NaN NaN NaN NaN ... \n", "freq NaN NaN NaN NaN NaN ... \n", "mean 4.007034 4.029132 3.992162 4.249468 4.363587 ... \n", "std 16.325047 15.139695 14.054838 15.943855 14.936638 ... \n", "min 0.011589 0.011851 0.013248 0.011791 -0.020098 ... \n", "25% 0.219304 0.236942 0.258556 0.265284 0.293462 ... \n", "50% 0.793962 0.794770 0.885508 1.032198 1.004645 ... \n", "75% 2.523331 2.630170 3.202366 3.913345 4.072719 ... \n", "max 207.626699 185.213644 172.158729 210.819017 194.917536 ... \n", "\n", " 2009 2010 2011 2012 2013 \\\n", "count 239.000000 239.000000 239.000000 239.000000 239.000000 \n", "unique NaN NaN NaN NaN NaN \n", "top NaN NaN NaN NaN NaN \n", "freq NaN NaN NaN NaN NaN \n", "mean 4.494702 4.329083 4.315602 4.372246 4.280935 \n", "std 5.177846 5.006608 4.912689 4.894707 4.844110 \n", "min 0.000000 0.000000 0.000000 0.035207 0.042976 \n", "25% 0.736515 0.779051 0.798791 0.805751 0.815681 \n", "50% 2.741639 2.667232 2.689378 2.869867 2.695691 \n", "75% 6.524149 6.110808 6.188510 6.315302 6.284435 \n", "max 34.544976 33.544700 32.305726 33.373132 31.927018 \n", "\n", " 2014 2015 2016 2017 2018 \n", "count 239.000000 239.000000 239.000000 239.000000 239.000000 \n", "unique NaN NaN NaN NaN NaN \n", "top NaN NaN NaN NaN NaN \n", "freq NaN NaN NaN NaN NaN \n", "mean 4.195690 4.148874 4.147100 4.154185 4.158613 \n", "std 4.732984 4.654801 4.592901 4.575980 4.547079 \n", "min 0.039617 0.037904 0.026146 0.028010 0.026169 \n", "25% 0.824377 0.806683 0.818367 0.851900 0.827804 \n", "50% 2.698682 2.696240 2.754968 2.667119 2.691814 \n", "75% 6.108660 5.920151 5.836392 6.158644 6.069018 \n", "max 32.693532 32.470570 32.128042 32.179371 32.415639 \n", "\n", "[11 rows x 60 columns]" ], "text/html": [ "\n", "
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Country Name196019611962196319641965196619671968...2009201020112012201320142015201620172018
count266203.000000204.000000206.000000207.000000213.000000213.000000213.000000213.000000213.000000...239.000000239.000000239.000000239.000000239.000000239.000000239.000000239.000000239.000000239.000000
unique266NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
topZimbabweNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
freq1NaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
meanNaN3.1602203.2923383.4620513.9060064.0070344.0291323.9921624.2494684.363587...4.4947024.3290834.3156024.3722464.2809354.1956904.1488744.1471004.1541854.158613
stdNaN14.82116915.11466916.28695816.90977116.32504715.13969514.05483815.94385514.936638...5.1778465.0066084.9126894.8947074.8441104.7329844.6548014.5929014.5759804.547079
minNaN0.0079840.0078570.0084350.0093360.0115890.0118510.0132480.011791-0.020098...0.0000000.0000000.0000000.0352070.0429760.0396170.0379040.0261460.0280100.026169
25%NaN0.1950310.1850780.2113550.2088050.2193040.2369420.2585560.2652840.293462...0.7365150.7790510.7987910.8057510.8156810.8243770.8066830.8183670.8519000.827804
50%NaN0.6167540.6623740.6593990.7060500.7939620.7947700.8855081.0321981.004645...2.7416392.6672322.6893782.8698672.6956912.6986822.6962402.7549682.6671192.691814
75%NaN2.0601422.4209622.5267052.2703492.5233312.6301703.2023663.9133454.072719...6.5241496.1108086.1885106.3153026.2844356.1086605.9201515.8363926.1586446.069018
maxNaN204.631696208.837879226.081890214.785217207.626699185.213644172.158729210.819017194.917536...34.54497633.54470032.30572633.37313231.92701832.69353232.47057032.12804232.17937132.415639
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11 rows × 60 columns

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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe" } }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "markdown", "source": [ "### Agriculture Data" ], "metadata": { "id": "-OiCwU2JqAfG" } }, { "cell_type": "code", "source": [ "df_agri.columns" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zXpQVb21db0P", "outputId": "17cb861d-5245-424b-8097-358f3587431d" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['Area', 'Year', 'Savanna fires', 'Forest fires', 'Crop Residues',\n", " 'Rice Cultivation', 'Drained organic soils (CO2)',\n", " 'Pesticides Manufacturing', 'Food Transport', 'Forestland',\n", " 'Net Forest conversion', 'Food Household Consumption', 'Food Retail',\n", " 'On-farm Electricity Use', 'Food Packaging',\n", " 'Agrifood Systems Waste Disposal', 'Food Processing',\n", " 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils',\n", " 'Manure left on Pasture', 'Manure Management', 'Fires in organic soils',\n", " 'Fires in humid tropical forests', 'On-farm energy use',\n", " 'Rural population', 'Urban population', 'Total Population - Male',\n", " 'Total Population - Female', 'total_emission',\n", " 'Average Temperature °C'],\n", " dtype='object')" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "source": [ "What we observe:\n", "\n", "- Many columns above represent 0 values here which is just no right, there can't ever be 0 rice cultivation for instance, hence we will replace those with the mean of that by the country.\n", "\n", "- However, since the fire columns can absolutely represent 0 values aka absence of the fire, hence we won't touch those." ], "metadata": { "id": "5Iwn2_GzfIRg" } }, { "cell_type": "markdown", "source": [ "**Stage 1 computation**" ], "metadata": { "id": "DAPdVxWYl6DE" } }, { "cell_type": "code", "source": [ "columns_to_clean = [\n", " 'Crop Residues', 'Rice Cultivation', 'Drained organic soils (CO2)',\n", " 'Pesticides Manufacturing', 'Food Transport', 'Forestland',\n", " 'Net Forest conversion', 'Food Household Consumption', 'Food Retail',\n", " 'On-farm Electricity Use', 'Food Packaging',\n", " 'Agrifood Systems Waste Disposal', 'Food Processing',\n", " 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils',\n", " 'Manure left on Pasture', 'Manure Management', 'On-farm energy use',\n", " 'Rural population', 'Urban population', 'Total Population - Male',\n", " 'Total Population - Female', 'total_emission', 'Average Temperature °C'\n", "]" ], "metadata": { "id": "Kx_bEcH6edcc" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df_agri[columns_to_clean] = df_agri[columns_to_clean].replace(0, pd.NA)" ], "metadata": { "id": "n8FuqY8wmEIU" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Fill NaNs (former 0s) with country-level mean\n", "df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QHmHV3TvmN3d", "outputId": "72004a0f-d587-4683-b8e4-f058e8e48a90" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n", ":2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))\n" ] } ] }, { "cell_type": "code", "source": [ "missing = df_agri[columns_to_clean].isna().sum()\n", "print(\"Remaining missing values after replacement:\")\n", "print(missing[missing > 0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eQ05zjuYmjsn", "outputId": "0f47af7e-f783-4ef1-ff0f-255f90deb967" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Remaining missing values after replacement:\n", "Crop Residues 1227\n", "Rice Cultivation 31\n", "Drained organic soils (CO2) 3822\n", "Pesticides Manufacturing 217\n", "Forestland 1732\n", "Net Forest conversion 2337\n", "Food Household Consumption 445\n", "IPPU 896\n", "Manure applied to Soils 918\n", "Manure Management 918\n", "On-farm energy use 922\n", "Rural population 248\n", "Urban population 62\n", "dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "# Fill all remaining NaNs (again) using country-wise means\n", "df_agri[columns_to_clean] = df_agri.groupby(\"Area\")[columns_to_clean].transform(lambda x: x.fillna(x.mean()))" ], "metadata": { "id": "YmJHIqvwmnrC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Final check: Should show all zeroes (i.e., no missing values)\n", "missing_check = df_agri[columns_to_clean].isna().sum()\n", "print(\"Missing values after second groupby fill:\")\n", "print(missing_check[missing_check > 0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bXUzLTYfnSmQ", "outputId": "ba1c627f-e1f1-48af-e5af-42c75b0bb527" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Missing values after second groupby fill:\n", "Crop Residues 1227\n", "Rice Cultivation 31\n", "Drained organic soils (CO2) 3822\n", "Pesticides Manufacturing 217\n", "Forestland 1732\n", "Net Forest conversion 2337\n", "Food Household Consumption 445\n", "IPPU 896\n", "Manure applied to Soils 918\n", "Manure Management 918\n", "On-farm energy use 922\n", "Rural population 248\n", "Urban population 62\n", "dtype: int64\n" ] } ] }, { "cell_type": "markdown", "source": [ "There are still many missing values, let us just fill with global mean" ], "metadata": { "id": "uPEQ65P5nkPe" } }, { "cell_type": "code", "source": [ "# Fill any remaining missing values with global column means\n", "df_agri[columns_to_clean] = df_agri[columns_to_clean].fillna(df_agri[columns_to_clean].mean())" ], "metadata": { "id": "ZoEAUzVInW24" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"✅ Total missing values after global mean fill:\")\n", "print(df_agri[columns_to_clean].isna().sum().sum())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9S2fkzoyntjl", "outputId": "a529e5ab-d7c1-4c89-9661-b0236ee8b372" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Total missing values after global mean fill:\n", "0\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Stage 2: Analyzing the extremes of the data**" ], "metadata": { "id": "xa0uBENqtvOd" } }, { "cell_type": "code", "source": [ "columns_to_measure = [\n", " 'Year','Crop Residues', 'Savanna fires', 'Forest fires', 'Rice Cultivation', 'Drained organic soils (CO2)',\n", " 'Pesticides Manufacturing', 'Food Transport', 'Forestland',\n", " 'Net Forest conversion', 'Food Household Consumption', 'Food Retail',\n", " 'On-farm Electricity Use', 'Food Packaging',\n", " 'Agrifood Systems Waste Disposal', 'Food Processing',\n", " 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils',\n", " 'Manure left on Pasture', 'Manure Management', 'On-farm energy use',\n", " 'Rural population', 'Urban population', 'Fires in organic soils',\n", " 'Fires in humid tropical forests','Total Population - Male',\n", " 'Total Population - Female', 'total_emission', 'Average Temperature °C'\n", "]" ], "metadata": { "id": "4H04J--3nvvm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Handle outliers using IQR method\n", "for col in columns_to_measure:\n", " Q1 = df_agri[col].quantile(0.25)\n", " Q3 = df_agri[col].quantile(0.75)\n", " IQR = Q3 - Q1\n", "\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", "\n", " median = df_agri[col].median()\n", "\n", " # Replace outliers with median\n", " df_agri[col] = df_agri[col].apply(lambda x: median if x < lower_bound or x > upper_bound else x)" ], "metadata": { "id": "iBj7-XfDuLYD" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "for col in columns_to_clean:\n", " Q1 = df_agri[col].quantile(0.25)\n", " Q3 = df_agri[col].quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower = Q1 - 1.5 * IQR\n", " upper = Q3 + 1.5 * IQR\n", " outliers = df_agri[(df_agri[col] < lower) | (df_agri[col] > upper)]\n", " print(f\"{col}: {len(outliers)} outliers replaced\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3caxMAMWuZYQ", "outputId": "16f40884-6308-4faf-e61f-a1360ccc7969" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Crop Residues: 99 outliers replaced\n", "Rice Cultivation: 533 outliers replaced\n", "Drained organic soils (CO2): 0 outliers replaced\n", "Pesticides Manufacturing: 1161 outliers replaced\n", "Food Transport: 916 outliers replaced\n", "Forestland: 0 outliers replaced\n", "Net Forest conversion: 0 outliers replaced\n", "Food Household Consumption: 1083 outliers replaced\n", "Food Retail: 933 outliers replaced\n", "On-farm Electricity Use: 1161 outliers replaced\n", "Food Packaging: 2991 outliers replaced\n", "Agrifood Systems Waste Disposal: 517 outliers replaced\n", "Food Processing: 1081 outliers replaced\n", "Fertilizers Manufacturing: 32 outliers replaced\n", "IPPU: 289 outliers replaced\n", "Manure applied to Soils: 208 outliers replaced\n", "Manure left on Pasture: 157 outliers replaced\n", "Manure Management: 222 outliers replaced\n", "On-farm energy use: 246 outliers replaced\n", "Rural population: 704 outliers replaced\n", "Urban population: 625 outliers replaced\n", "Total Population - Male: 646 outliers replaced\n", "Total Population - Female: 627 outliers replaced\n", "total_emission: 863 outliers replaced\n", "Average Temperature °C: 34 outliers replaced\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Stage 3: Data Normalization**" ], "metadata": { "id": "0yUEH_9zvfnQ" } }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import StandardScaler\n", "import pandas as pd\n", "\n", "# Step 1: One-hot encode 'Area'\n", "df_encoded = pd.get_dummies(df_agri, columns=['Area'], drop_first=True) # drop_first to avoid dummy trap\n", "\n", "# Step 2: Normalize only the numerical columns\n", "scaler = StandardScaler()\n", "df_encoded[columns_to_measure] = scaler.fit_transform(df_encoded[columns_to_measure])" ], "metadata": { "id": "lrfGFzbeuh2g" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df_encoded.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8FmTa5-Uvr3w", "outputId": "c6fa62d7-f358-4c95-c228-7ba12541ca9d" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(6965, 265)" ] }, "metadata": {}, "execution_count": 33 } ] }, { "cell_type": "code", "source": [ "df_encoded.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 307 }, "id": "yTkZxUxhvuIV", "outputId": "0d7859a5-ac9d-4d92-b765-b3c8873726aa" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Year Savanna fires Forest fires Crop Residues Rice Cultivation \\\n", "0 -1.700569 0.002847 -0.350999 -0.358933 0.015239 \n", "1 -1.588134 0.002847 -0.350999 -0.350862 0.004759 \n", "2 -1.475699 0.002847 -0.350999 -0.377764 0.015239 \n", "3 -1.363264 0.002847 -0.350999 -0.306615 0.015239 \n", "4 -1.250829 0.002847 -0.350999 -0.283305 0.041439 \n", "\n", " Drained organic soils (CO2) Pesticides Manufacturing Food Transport \\\n", "0 0.635421 -0.473601 -0.570823 \n", "1 0.635421 -0.474986 -0.573847 \n", "2 0.635421 -0.474986 -0.586398 \n", "3 0.635421 -0.474986 -0.584737 \n", "4 0.635421 -0.474986 -0.585332 \n", "\n", " Forestland Net Forest conversion ... Area_Uzbekistan Area_Vanuatu \\\n", "0 0.567415 0.857852 ... False False \n", "1 0.567415 0.857852 ... False False \n", "2 0.567415 0.857852 ... False False \n", "3 0.567415 0.857852 ... False False \n", "4 0.567415 0.857852 ... False False \n", "\n", " Area_Venezuela (Bolivarian Republic of) Area_Viet Nam \\\n", "0 False False \n", "1 False False \n", "2 False False \n", "3 False False \n", "4 False False \n", "\n", " Area_Wallis and Futuna Islands Area_Western Sahara Area_Yemen \\\n", "0 False False False \n", "1 False False False \n", "2 False False False \n", "3 False False False \n", "4 False False False \n", "\n", " Area_Yugoslav SFR Area_Zambia Area_Zimbabwe \n", "0 False False False \n", "1 False False False \n", "2 False False False \n", "3 False False False \n", "4 False False False \n", "\n", "[5 rows x 265 columns]" ], "text/html": [ "\n", "
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YearSavanna firesForest firesCrop ResiduesRice CultivationDrained organic soils (CO2)Pesticides ManufacturingFood TransportForestlandNet Forest conversion...Area_UzbekistanArea_VanuatuArea_Venezuela (Bolivarian Republic of)Area_Viet NamArea_Wallis and Futuna IslandsArea_Western SaharaArea_YemenArea_Yugoslav SFRArea_ZambiaArea_Zimbabwe
0-1.7005690.002847-0.350999-0.3589330.0152390.635421-0.473601-0.5708230.5674150.857852...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1-1.5881340.002847-0.350999-0.3508620.0047590.635421-0.474986-0.5738470.5674150.857852...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2-1.4756990.002847-0.350999-0.3777640.0152390.635421-0.474986-0.5863980.5674150.857852...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3-1.3632640.002847-0.350999-0.3066150.0152390.635421-0.474986-0.5847370.5674150.857852...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
4-1.2508290.002847-0.350999-0.2833050.0414390.635421-0.474986-0.5853320.5674150.857852...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_encoded" } }, "metadata": {}, "execution_count": 34 } ] }, { "cell_type": "markdown", "source": [ "**Stage 4: Data Analysis through feature analysis**" ], "metadata": { "id": "tXXK3McmwS-8" } }, { "cell_type": "code", "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "# === CORRELATION MATRIX ===\n", "plt.figure(figsize=(18, 14))\n", "corr_matrix = df_encoded[columns_to_measure].corr()\n", "sns.heatmap(corr_matrix, annot=False, cmap='coolwarm', center=0)\n", "plt.title(\"📈 Correlation Matrix (Pearson)\", fontsize=16)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "P_Yw8vQRvzZ0", "outputId": "8da55e5a-74cb-4cd0-ecc9-077106ceb3cb" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Split features and target\n", "X = df_encoded.drop(columns=['total_emission'])\n", "y = df_encoded['total_emission']\n", "\n", "# Fit Random Forest\n", "rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", "rf.fit(X, y)\n", "\n", "# Feature importance\n", "importances = rf.feature_importances_\n", "feature_names = X.columns\n", "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})\n", "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n", "\n", "# Plot top 20 important features\n", "plt.figure(figsize=(12, 8))\n", "sns.barplot(data=importance_df.head(20), x='Importance', y='Feature', palette='viridis')\n", "plt.title(\"🌲 Random Forest Feature Importance (Top 20)\", fontsize=16)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 785 }, "id": "cMcbvbZ9waj5", "outputId": "940be531-a091-4f1f-9cbd-d7ab62429fbc" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Optional: subset just the numeric columns if needed\n", "numeric_df = df_encoded[columns_to_measure]\n", "\n", "# Compute correlation matrix\n", "corr_matrix = numeric_df.corr()\n", "\n", "# Plot heatmap\n", "plt.figure(figsize=(20, 16))\n", "sns.heatmap(\n", " corr_matrix,\n", " cmap=\"coolwarm\",\n", " annot=False, # Set to True if you want actual values shown\n", " fmt=\".2f\",\n", " linewidths=0.5,\n", " cbar_kws={'label': 'Correlation Coefficient'}\n", ")\n", "plt.title(\"📊 Correlation Heatmap of Agri-Climate Features\", fontsize=18)\n", "plt.xticks(rotation=45, ha='right')\n", "plt.yticks(rotation=0)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "X5UKpGo2wjAN", "outputId": "74182543-934e-40be-94f9-38e21b4a63c6" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Save to CSV in your Google Drive\n", "df_encoded.to_csv('/content/drive/My Drive/AuraClima/cleaned_agriculture_data.csv', index=False)" ], "metadata": { "id": "-_ZnLKHvyXn1" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### CO2 Dataset" ], "metadata": { "id": "S14Wi3U8zj7h" } }, { "cell_type": "markdown", "source": [ "**Stage 1: Filling missing values**" ], "metadata": { "id": "7eF8X6q81vb4" } }, { "cell_type": "code", "source": [ "df_co2.columns" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9IeoKQ6xyvcy", "outputId": "175e8f86-3534-4d5c-aae3-07edb81ff832" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['Country Name', '1960', '1961', '1962', '1963', '1964', '1965', '1966',\n", " '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n", " '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984',\n", " '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993',\n", " '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002',\n", " '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011',\n", " '2012', '2013', '2014', '2015', '2016', '2017', '2018'],\n", " dtype='object')" ] }, "metadata": {}, "execution_count": 40 } ] }, { "cell_type": "code", "source": [ "# Replace empty strings with NaN\n", "df_co2.replace(\"\", np.nan, inplace=True)\n", "\n", "# Count missing values per column\n", "missing_summary = df_co2.isna().sum()\n", "missing_summary = missing_summary[missing_summary > 0].sort_values(ascending=False)\n", "print(\"📉 Missing Values per Column:\\n\")\n", "print(missing_summary)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k8QOQVd_z5PY", "outputId": "a85e4127-7b08-4ac4-f344-c7b7d85b482c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "📉 Missing Values per Column:\n", "\n", "1960 63\n", "1961 62\n", "1962 60\n", "1963 59\n", "1964 53\n", "1965 53\n", "1966 53\n", "1967 53\n", "1968 53\n", "1969 53\n", "1970 52\n", "1971 51\n", "1972 50\n", "1973 50\n", "1974 50\n", "1975 50\n", "1976 50\n", "1977 50\n", "1978 50\n", "1979 50\n", "1980 50\n", "1981 50\n", "1982 50\n", "1983 50\n", "1984 50\n", "1985 50\n", "1986 50\n", "1987 50\n", "1988 50\n", "1989 50\n", "1990 28\n", "1992 28\n", "1993 28\n", "1994 28\n", "1991 27\n", "1995 27\n", "1996 27\n", "1997 27\n", "1998 27\n", "1999 27\n", "2000 27\n", "2001 27\n", "2002 27\n", "2003 27\n", "2004 27\n", "2005 27\n", "2006 27\n", "2007 27\n", "2008 27\n", "2009 27\n", "2010 27\n", "2011 27\n", "2012 27\n", "2013 27\n", "2014 27\n", "2015 27\n", "2016 27\n", "2017 27\n", "2018 27\n", "dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "# Convert year columns to numeric (they're likely strings due to NaNs)\n", "year_cols = [col for col in df_co2.columns if col != 'Country Name']\n", "df_co2[year_cols] = df_co2[year_cols].apply(pd.to_numeric, errors='coerce')" ], "metadata": { "id": "nlBJodWa0pwv" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Fill missing values per country with that country’s mean across years\n", "df_co2[year_cols] = df_co2.groupby('Country Name')[year_cols].transform(\n", " lambda x: x.fillna(x.mean())\n", ")" ], "metadata": { "id": "jmQDyxWv065l" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"Remaining missing values:\\n\", df_co2.isna().sum().sum())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rGwy2MCY09Pf", "outputId": "9d677aab-1a63-4931-a17d-954fb541a786" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Remaining missing values:\n", " 2352\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Stage 2: Using a regressor to fill in the missing values**" ], "metadata": { "id": "IKOVYSQR11w2" } }, { "cell_type": "code", "source": [ "df_long = df_co2.melt(id_vars='Country Name', var_name='Year', value_name='CO2')\n", "df_long['Year'] = df_long['Year'].astype(int)" ], "metadata": { "id": "vjEEXvFA1Dom" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Known and unknown CO2 values\n", "train_df = df_long[df_long['CO2'].notna()]\n", "missing_df = df_long[df_long['CO2'].isna()]" ], "metadata": { "id": "w5rv9RG918xS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import OneHotEncoder\n", "\n", "# One-hot encode country names\n", "encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n", "country_encoded = encoder.fit_transform(train_df[['Country Name']])\n", "country_encoded_missing = encoder.transform(missing_df[['Country Name']])" ], "metadata": { "id": "9sWUM42X1_g9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Input features: country + year\n", "X_train = np.hstack([country_encoded, train_df[['Year']].values])\n", "y_train = train_df['CO2'].values" ], "metadata": { "id": "I2rDC8os2CFw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "X_pred = np.hstack([country_encoded_missing, missing_df[['Year']].values])" ], "metadata": { "id": "4tDzUJr92Wvt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "\n", "model = RandomForestRegressor(n_estimators=100, random_state=42)\n", "model.fit(X_train, y_train)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 80 }, "id": "IuTj_4aL2lrK", "outputId": "2706aff9-533f-4587-fd18-f4921af9621c" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "RandomForestRegressor(random_state=42)" ], "text/html": [ "
RandomForestRegressor(random_state=42)
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" ] }, "metadata": {}, "execution_count": 52 } ] }, { "cell_type": "code", "source": [ "# Predict missing CO2 values\n", "y_pred = model.predict(X_pred)" ], "metadata": { "id": "4wCV5RI12oST" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Insert predictions into missing_df\n", "missing_df['CO2'] = y_pred" ], "metadata": { "id": "7k8PL9ig3Kt-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Combine back with original\n", "df_filled = pd.concat([train_df, missing_df], ignore_index=True)" ], "metadata": { "id": "-31GhfPF3NTq" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Pivot back to wide format\n", "df_co2_filled = df_filled.pivot(index='Country Name', columns='Year', values='CO2').reset_index()" ], "metadata": { "id": "voGX68SA3QkI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Count of missing values in the entire DataFrame\n", "total_missing = df_co2_filled.isna().sum().sum()\n", "print(f\"🔍 Total remaining missing values: {total_missing}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "z6bwLvGP3S3U", "outputId": "2e71908c-58be-483c-d646-50e8f2fa96c2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🔍 Total remaining missing values: 0\n" ] } ] }, { "cell_type": "code", "source": [ "df_co2_filled.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "kAwKunsn3f48", "outputId": "48af5c2d-553e-4764-d24e-8eecfbe6d7f1" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Year Country Name 1960 1961 1962 1963 \\\n", "0 Afghanistan 0.046057 0.053589 0.073721 0.074161 \n", "1 Africa Eastern and Southern 0.906060 0.922474 0.930816 0.940570 \n", "2 Africa Western and Central 0.090880 0.095283 0.096612 0.112376 \n", "3 Albania 1.258195 1.374186 1.439956 1.181681 \n", "4 Algeria 0.557120 0.535025 0.487889 0.455574 \n", "\n", "Year 1964 1965 1966 1967 1968 ... 2009 \\\n", "0 0.086174 0.101285 0.107399 0.123409 0.115142 ... 0.211306 \n", "1 0.996033 1.047280 1.033908 1.052204 1.079727 ... 1.021954 \n", "2 0.133258 0.184803 0.193676 0.189305 0.143989 ... 0.426770 \n", "3 1.111742 1.166099 1.333055 1.363746 1.519551 ... 1.475652 \n", "4 0.462363 0.525615 0.653389 0.635889 0.663161 ... 3.191554 \n", "\n", "Year 2010 2011 2012 2013 2014 2015 2016 \\\n", "0 0.297065 0.407074 0.335351 0.263716 0.234037 0.232176 0.208857 \n", "1 1.048876 1.005338 1.021646 1.031833 1.041145 0.987393 0.971016 \n", "2 0.472819 0.497023 0.490867 0.504655 0.507671 0.480743 0.472959 \n", "3 1.572251 1.734823 1.579092 1.654524 1.806789 1.759987 1.714126 \n", "4 3.144748 3.222460 3.387555 3.406123 3.566209 3.674233 3.535020 \n", "\n", "Year 2017 2018 \n", "0 0.203328 0.200151 \n", "1 0.959978 0.933541 \n", "2 0.476438 0.515544 \n", "3 1.948872 1.939732 \n", "4 3.505748 3.591657 \n", "\n", "[5 rows x 60 columns]" ], "text/html": [ "\n", "
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YearCountry Name196019611962196319641965196619671968...2009201020112012201320142015201620172018
0Afghanistan0.0460570.0535890.0737210.0741610.0861740.1012850.1073990.1234090.115142...0.2113060.2970650.4070740.3353510.2637160.2340370.2321760.2088570.2033280.200151
1Africa Eastern and Southern0.9060600.9224740.9308160.9405700.9960331.0472801.0339081.0522041.079727...1.0219541.0488761.0053381.0216461.0318331.0411450.9873930.9710160.9599780.933541
2Africa Western and Central0.0908800.0952830.0966120.1123760.1332580.1848030.1936760.1893050.143989...0.4267700.4728190.4970230.4908670.5046550.5076710.4807430.4729590.4764380.515544
3Albania1.2581951.3741861.4399561.1816811.1117421.1660991.3330551.3637461.519551...1.4756521.5722511.7348231.5790921.6545241.8067891.7599871.7141261.9488721.939732
4Algeria0.5571200.5350250.4878890.4555740.4623630.5256150.6533890.6358890.663161...3.1915543.1447483.2224603.3875553.4061233.5662093.6742333.5350203.5057483.591657
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_co2_filled" } }, "metadata": {}, "execution_count": 60 } ] }, { "cell_type": "markdown", "source": [ "**Stage 3: Analyzing the column extereme values:**" ], "metadata": { "id": "TcLqhTjV6IzL" } }, { "cell_type": "code", "source": [ "year_columns = df_co2_filled.columns[df_co2_filled.columns != 'Country Name']" ], "metadata": { "id": "b2lk_zTf3jLB" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "for col in year_columns:\n", " Q1 = df_co2_filled[col].quantile(0.25)\n", " Q3 = df_co2_filled[col].quantile(0.75)\n", " IQR = Q3 - Q1\n", "\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", "\n", " # Replace outliers with median\n", " median = df_co2_filled[col].median()\n", " df_co2_filled[col] = df_co2_filled[col].apply(\n", " lambda x: median if x < lower_bound or x > upper_bound else x\n", " )" ], "metadata": { "id": "tD_aKCZ76N80" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"✅ Outlier cleaning complete.\")\n", "print(\"Remaining missing values (should be 0):\", df_co2_filled.isna().sum().sum())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "D7rQ6wIZ6QYL", "outputId": "3c678a77-f6f3-4bf8-a3bf-d64bd286577b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Outlier cleaning complete.\n", "Remaining missing values (should be 0): 0\n" ] } ] }, { "cell_type": "code", "source": [ "outlier_summary = {}\n", "\n", "for col in year_columns:\n", " Q1 = df_co2_filled[col].quantile(0.25)\n", " Q3 = df_co2_filled[col].quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", "\n", " # Count how many were outside the bounds\n", " outliers = df_co2_filled[(df_co2_filled[col] < lower_bound) | (df_co2_filled[col] > upper_bound)]\n", " outlier_summary[col] = {\n", " \"count\": outliers.shape[0],\n", " \"min_outlier\": outliers[col].min() if not outliers.empty else None,\n", " \"max_outlier\": outliers[col].max() if not outliers.empty else None\n", " }" ], "metadata": { "id": "pfxnSzGM6S1C" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "outlier_df = pd.DataFrame(outlier_summary).T\n", "outlier_df_sorted = outlier_df.sort_values(by=\"count\", ascending=False)\n", "\n", "print(outlier_df_sorted.head(10)) # Top 10 years with most replaced values" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Dpn0oEvm6cNx", "outputId": "56086e4b-e25f-45b9-ac9c-36892790d5d9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " count min_outlier max_outlier\n", "1970 32.0 3.301400 8.066821\n", "1971 31.0 4.231154 8.560478\n", "1965 29.0 1.827946 4.772848\n", "1962 29.0 1.567864 3.868626\n", "1969 28.0 3.076165 6.665435\n", "1967 28.0 2.644873 5.758242\n", "1966 28.0 2.053282 5.172018\n", "1973 28.0 6.957304 12.172175\n", "1974 28.0 7.071735 11.864913\n", "1972 27.0 5.103338 8.874687\n" ] } ] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "\n", "outlier_df_sorted['count'].plot(kind='bar', figsize=(14,5), title=\"Outlier Replacements per Year\")\n", "plt.ylabel(\"Number of Outliers Replaced\")\n", "plt.xlabel(\"Year\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 527 }, "id": "19zhPb8z6eH9", "outputId": "59544dc7-1136-4cb8-ec00-e4301b9804ce" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Stage 4: Graph Analysis**" ], "metadata": { "id": "JLyxOucT6qpc" } }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Drop Country Name for aggregation\n", "co2_means = df_co2_filled[year_columns].mean()\n", "\n", "plt.figure(figsize=(14, 6))\n", "plt.plot(co2_means.index.astype(int), co2_means.values, marker='o')\n", "plt.title(\"📈 Global Mean CO₂ Emissions Over Time\")\n", "plt.xlabel(\"Year\")\n", "plt.ylabel(\"Mean Emissions\")\n", "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 509 }, "id": "B2xqqnRI6i29", "outputId": "20810509-6a17-4a9e-a7c9-fbff754c0458" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import seaborn as sns\n", "\n", "# Compute correlations\n", "correlation_matrix = df_co2_filled[year_columns].astype(float).corr()\n", "\n", "plt.figure(figsize=(18, 12))\n", "sns.heatmap(correlation_matrix, cmap='coolwarm', annot=False)\n", "plt.title(\"🔗 Correlation Heatmap of CO₂ Emissions by Year\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 866 }, "id": "S2VjWiFb6xYV", "outputId": "57471725-0094-42d2-b20d-7d27855b6c34" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "df_co2_filled.columns" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-sP2JGbB6-HV", "outputId": "c2593462-ea0c-4569-84dd-5b7c930b9aed" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['Country Name', 1960, 1961, 1962,\n", " 1963, 1964, 1965, 1966,\n", " 1967, 1968, 1969, 1970,\n", " 1971, 1972, 1973, 1974,\n", " 1975, 1976, 1977, 1978,\n", " 1979, 1980, 1981, 1982,\n", " 1983, 1984, 1985, 1986,\n", " 1987, 1988, 1989, 1990,\n", " 1991, 1992, 1993, 1994,\n", " 1995, 1996, 1997, 1998,\n", " 1999, 2000, 2001, 2002,\n", " 2003, 2004, 2005, 2006,\n", " 2007, 2008, 2009, 2010,\n", " 2011, 2012, 2013, 2014,\n", " 2015, 2016, 2017, 2018],\n", " dtype='object', name='Year')" ] }, "metadata": {}, "execution_count": 71 } ] }, { "cell_type": "markdown", "source": [ "**Stage 5: Data Normalization**" ], "metadata": { "id": "UXEs5NG-7gKN" } }, { "cell_type": "code", "source": [ "from sklearn.preprocessing import MinMaxScaler\n", "\n", "# Copy dataset to preserve original\n", "df_minmax_scaled = df_co2_filled.copy()\n", "\n", "# Get all year columns (exclude 'Country Name')\n", "year_columns = df_minmax_scaled.columns.drop('Country Name')\n", "\n", "# Initialize MinMaxScaler\n", "scaler = MinMaxScaler()\n", "\n", "# Apply Min-Max scaling to year columns\n", "df_minmax_scaled[year_columns] = scaler.fit_transform(df_minmax_scaled[year_columns])\n", "\n", "# Show preview\n", "df_minmax_scaled.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "Fn6w5ucJ7Iff", "outputId": "d22d5730-2fca-4ea0-d6a1-6de580392de2" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Year Country Name 1960 1961 1962 1963 \\\n", "0 Afghanistan 0.010176 0.012215 0.016912 0.015899 \n", "1 Africa Eastern and Southern 0.240022 0.244291 0.238947 0.228401 \n", "2 Africa Western and Central 0.022155 0.023351 0.022843 0.025272 \n", "3 Albania 0.334134 0.364941 0.370842 0.287538 \n", "4 Algeria 0.146764 0.140804 0.124205 0.109448 \n", "\n", "Year 1964 1965 1966 1967 1968 ... 2009 \\\n", "0 0.017065 0.018785 0.018251 0.019424 0.020448 ... 0.013644 \n", "1 0.225235 0.217482 0.197850 0.181053 0.166294 ... 0.065989 \n", "2 0.027837 0.036327 0.034975 0.030891 0.024810 ... 0.027557 \n", "3 0.251709 0.242438 0.255838 0.235268 0.232796 ... 0.095285 \n", "4 0.103134 0.107911 0.124088 0.108606 0.103309 ... 0.206084 \n", "\n", "Year 2010 2011 2012 2013 2014 2015 2016 \\\n", "0 0.020950 0.027829 0.020922 0.015206 0.014384 0.015044 0.013834 \n", "1 0.073970 0.068729 0.068761 0.068117 0.074097 0.073525 0.071541 \n", "2 0.033345 0.033979 0.031762 0.031803 0.034629 0.034292 0.033831 \n", "3 0.110880 0.118600 0.107618 0.111011 0.130743 0.133352 0.127806 \n", "4 0.221777 0.220302 0.233679 0.231670 0.260912 0.281584 0.265675 \n", "\n", "Year 2017 2018 \n", "0 0.013368 0.013643 \n", "1 0.071064 0.071154 \n", "2 0.034193 0.038376 \n", "3 0.146469 0.150057 \n", "4 0.265183 0.279596 \n", "\n", "[5 rows x 60 columns]" ], "text/html": [ "\n", "
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YearCountry Name196019611962196319641965196619671968...2009201020112012201320142015201620172018
0Afghanistan0.0101760.0122150.0169120.0158990.0170650.0187850.0182510.0194240.020448...0.0136440.0209500.0278290.0209220.0152060.0143840.0150440.0138340.0133680.013643
1Africa Eastern and Southern0.2400220.2442910.2389470.2284010.2252350.2174820.1978500.1810530.166294...0.0659890.0739700.0687290.0687610.0681170.0740970.0735250.0715410.0710640.071154
2Africa Western and Central0.0221550.0233510.0228430.0252720.0278370.0363270.0349750.0308910.024810...0.0275570.0333450.0339790.0317620.0318030.0346290.0342920.0338310.0341930.038376
3Albania0.3341340.3649410.3708420.2875380.2517090.2424380.2558380.2352680.232796...0.0952850.1108800.1186000.1076180.1110110.1307430.1333520.1278060.1464690.150057
4Algeria0.1467640.1408040.1242050.1094480.1031340.1079110.1240880.1086060.103309...0.2060840.2217770.2203020.2336790.2316700.2609120.2815840.2656750.2651830.279596
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_minmax_scaled" } }, "metadata": {}, "execution_count": 74 } ] }, { "cell_type": "code", "source": [ "# Define save path\n", "save_path = \"/content/drive/MyDrive/AuraClima/CO2_Emissions_MinMaxScaled.csv\"\n", "\n", "# Save as CSV\n", "df_minmax_scaled.to_csv(save_path, index=False)\n", "\n", "print(\"Dataset saved successfully to:\", save_path)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yaNOuXX-7sUT", "outputId": "5de41dd7-2c27-4d0e-ff5a-c832255cafed" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset saved successfully to: /content/drive/MyDrive/AuraClima/CO2_Emissions_MinMaxScaled.csv\n" ] } ] }, { "cell_type": "markdown", "source": [ "_________________________________________________________________________________________________________________________________________" ], "metadata": { "id": "WPN3LjaNVB-n" } }, { "cell_type": "markdown", "source": [ "## Predictive Modeling" ], "metadata": { "id": "YxCL6om_Z0D5" } }, { "cell_type": "markdown", "source": [ "### Agricultural Data" ], "metadata": { "id": "zBBJ4z8XZ2rs" } }, { "cell_type": "markdown", "source": [ "**1. Forecasting Future total_emission per Country via LSTM**" ], "metadata": { "id": "aZJ8FSmaaEuH" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rxgVq9OA71P_", "outputId": "7b731d9f-ad40-4700-9924-0e1e7672067f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "csv_path = '/content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv'\n", "df = pd.read_csv(csv_path)" ], "metadata": { "id": "WdTKmSmyaLos" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "columns_to_measure = [\n", " 'Area', 'Year', 'Crop Residues', 'Savanna fires', 'Forest fires', 'Rice Cultivation',\n", " 'Drained organic soils (CO2)', 'Pesticides Manufacturing', 'Food Transport', 'Forestland',\n", " 'Net Forest conversion', 'Food Household Consumption', 'Food Retail',\n", " 'On-farm Electricity Use', 'Food Packaging', 'Agrifood Systems Waste Disposal',\n", " 'Food Processing', 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils',\n", " 'Manure left on Pasture', 'Manure Management', 'On-farm energy use',\n", " 'Rural population', 'Urban population', 'Fires in organic soils',\n", " 'Fires in humid tropical forests', 'Total Population - Male',\n", " 'Total Population - Female', 'total_emission', 'Average Temperature °C'\n", "]\n", "missing_cols = [col for col in columns_to_measure if col not in df.columns]\n", "if missing_cols:\n", " print(f\"Warning: These columns are missing from the dataset: {missing_cols}\")" ], "metadata": { "id": "BXKGmSxobbWQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Drop rows with missing total_emission or Year/Area\n", "df_ts = df[['Area', 'Year', 'total_emission']].dropna(subset=['Area', 'Year', 'total_emission']).copy()\n", "df_ts['Year'] = df_ts['Year'].astype(int)\n", "df_ts = df_ts.sort_values(['Area', 'Year'])" ], "metadata": { "id": "MP4QEyKMbfdk" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Scale total_emission globally to [0,1] for LSTM training\n", "scaler = MinMaxScaler()\n", "df_ts['total_scaled'] = scaler.fit_transform(df_ts[['total_emission']])" ], "metadata": { "id": "J-S57lV4bj1M" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Create sliding windows per country\n", "window_size = 29 # number of past years to use\n", "X_list, y_list = [], []\n", "end_years = [] # track the end year for each sequence\n", "for country, grp in df_ts.groupby('Area'):\n", " grp = grp.sort_values('Year').reset_index(drop=True)\n", " values = grp['total_scaled'].values\n", " years = grp['Year'].values\n", " for i in range(len(values) - window_size):\n", " # Ensure consecutive years: year difference equals window_size\n", " if years[i + window_size] - years[i] != window_size:\n", " continue\n", " seq_x = values[i : i + window_size]\n", " seq_y = values[i + window_size]\n", " X_list.append(seq_x)\n", " y_list.append(seq_y)\n", " end_years.append(years[i + window_size])" ], "metadata": { "id": "rEImPs8Xbnys" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Convert to numpy arrays\n", "X = np.array(X_list) # shape (samples, window_size)\n", "y = np.array(y_list) # shape (samples,)\n", "end_years = np.array(end_years)" ], "metadata": { "id": "cc8wMBSKb7Pz" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Reshape X for LSTM: (samples, timesteps, features=1)\n", "X = X.reshape((X.shape[0], X.shape[1], 1))" ], "metadata": { "id": "oD1c6lhob9ZO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "# Simple shuffle split; for time-based split, adjust as needed\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=42, shuffle=True\n", ")" ], "metadata": { "id": "L_KR8kLPb_ZS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import tensorflow as tf\n", "from keras import Sequential\n", "from keras.layers import LSTM, Dense, Dropout\n", "\n", "# Build LSTM model\n", "tf.random.set_seed(42)\n", "model = Sequential([\n", " LSTM(64, input_shape=(window_size, 1), return_sequences=False),\n", " Dropout(0.2),\n", " Dense(32, activation='relu'),\n", " Dropout(0.2),\n", " Dense(1, activation='linear')\n", "])" ], "metadata": { "id": "onP7CnWjcCa7" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model.compile(optimizer='adam', loss='mse', metrics=['mae'])" ], "metadata": { "id": "D2ller6qcc7W" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 289 }, "id": "UOc5ADOLctTC", "outputId": "e265e652-bace-4b20-f31c-fc8d42d7ec13" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"sequential\"\u001b[0m\n" ], "text/html": [ "
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              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm (LSTM)                     │ (None, 64)             │        16,896 │\n",
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              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (Dense)                 │ (None, 1)              │            33 │\n",
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val_loss: 3.0072e-04 - val_mae: 0.0119\n", "Epoch 4/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 0.0011 - mae: 0.0256 - val_loss: 2.8104e-04 - val_mae: 0.0151\n", "Epoch 5/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0013 - mae: 0.0235 - val_loss: 5.9391e-04 - val_mae: 0.0089\n", "Epoch 6/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 6.6188e-04 - mae: 0.0204 - val_loss: 0.0023 - val_mae: 0.0140\n", "Epoch 7/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 56ms/step - loss: 0.0018 - mae: 0.0238 - val_loss: 9.6851e-04 - val_mae: 0.0248\n", "Epoch 8/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 53ms/step - loss: 7.9409e-04 - mae: 0.0221 - val_loss: 2.2910e-04 - val_mae: 0.0110\n", "Epoch 9/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 50ms/step - loss: 0.0012 - mae: 0.0179 - val_loss: 2.8743e-04 - val_mae: 0.0055\n", "Epoch 10/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 58ms/step - loss: 8.5593e-04 - mae: 0.0191 - val_loss: 1.1776e-04 - val_mae: 0.0067\n", "Epoch 11/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 9.0911e-04 - mae: 0.0187 - val_loss: 8.4364e-05 - val_mae: 0.0029\n", "Epoch 12/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 49ms/step - loss: 0.0013 - mae: 0.0183 - val_loss: 1.1124e-04 - val_mae: 0.0040\n", "Epoch 13/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 4.7526e-04 - mae: 0.0142 - val_loss: 1.3878e-04 - val_mae: 0.0084\n", "Epoch 14/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 62ms/step - loss: 8.2944e-04 - mae: 0.0157 - val_loss: 1.5159e-04 - val_mae: 0.0094\n", "Epoch 15/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 120ms/step - loss: 9.2724e-04 - mae: 0.0178 - val_loss: 1.9224e-04 - val_mae: 0.0122\n", "Epoch 16/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 86ms/step - loss: 4.1700e-04 - mae: 0.0154 - val_loss: 7.8943e-05 - val_mae: 0.0041\n", "Epoch 17/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 100ms/step - loss: 4.4576e-04 - mae: 0.0145 - val_loss: 7.6974e-05 - val_mae: 0.0040\n", "Epoch 18/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 66ms/step - loss: 4.7390e-04 - mae: 0.0139 - val_loss: 7.6368e-05 - val_mae: 0.0022\n", "Epoch 19/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 5.0270e-04 - mae: 0.0131 - val_loss: 1.9456e-04 - val_mae: 0.0099\n", "Epoch 20/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 4.2210e-04 - mae: 0.0145 - val_loss: 1.3377e-04 - val_mae: 0.0059\n", "Epoch 21/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 71ms/step - loss: 6.7242e-04 - mae: 0.0134 - val_loss: 1.6332e-04 - val_mae: 0.0044\n", "Epoch 22/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 4.0974e-04 - mae: 0.0132 - val_loss: 7.0382e-05 - val_mae: 0.0041\n", "Epoch 23/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 87ms/step - loss: 7.2073e-04 - mae: 0.0145 - val_loss: 2.2019e-04 - val_mae: 0.0096\n", "Epoch 24/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 64ms/step - loss: 7.3721e-04 - mae: 0.0136 - val_loss: 2.2803e-04 - val_mae: 0.0101\n", "Epoch 25/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 61ms/step - loss: 3.5764e-04 - mae: 0.0127 - val_loss: 2.4646e-04 - val_mae: 0.0114\n", "Epoch 26/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 76ms/step - loss: 3.5532e-04 - mae: 0.0140 - val_loss: 3.2368e-04 - val_mae: 0.0113\n", "Epoch 27/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - loss: 8.2147e-04 - mae: 0.0154 - val_loss: 1.3662e-04 - val_mae: 0.0054\n", "Epoch 28/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 3.0444e-04 - mae: 0.0113 - val_loss: 8.4438e-05 - val_mae: 0.0052\n", "Epoch 29/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 3.6416e-04 - mae: 0.0109 - val_loss: 7.6011e-05 - val_mae: 0.0030\n", "Epoch 30/30\n", "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.4136e-04 - mae: 0.0101 - val_loss: 7.5911e-05 - val_mae: 0.0031\n" ] } ] }, { "cell_type": "code", "source": [ "loss, mae = model.evaluate(X_test, y_test)\n", "print(f\"Test MAE (scaled): {mae}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lJjt90M8czLK", "outputId": "2959b3cc-6ad8-49af-a8c3-c665397fa322" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 4.0942e-05 - mae: 0.0024 \n", "Test MAE (scaled): 0.003083903342485428\n" ] } ] }, { "cell_type": "markdown", "source": [ "- Scaled MAE ≈ 0.0031 means on average the prediction is off by ~0.0031 of the full [0,1] range." ], "metadata": { "id": "i2J-12_KefD4" } }, { "cell_type": "markdown", "source": [], "metadata": { "id": "PxN_tO7veL8Y" } }, { "cell_type": "code", "source": [ "def forecast_next(country, recent_years):\n", " \"\"\"\n", " Forecast next year's total_emission for a country.\n", " recent_years: list or array of the last `window_size` total_emission values (unscaled).\n", " Returns: predicted emission (original scale).\n", " \"\"\"\n", " if len(recent_years) != window_size:\n", " raise ValueError(f\"Need exactly {window_size} recent years of total_emission values.\")\n", " # Scale recent values using the same scaler\n", " scaled = scaler.transform(np.array(recent_years).reshape(-1,1)).flatten()\n", " inp = scaled.reshape((1, window_size, 1))\n", " scaled_pred = model.predict(inp)[0,0]\n", " # Inverse scale to original emission value\n", " pred = scaler.inverse_transform([[scaled_pred]])[0,0]\n", " return pred" ], "metadata": { "id": "ijYSMBhBdnZh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Overall MAE\n", "mae_orig = mean_absolute_error(y_test_orig, y_pred_orig)\n", "print(f\"Original-scale MAE: {mae_orig:.3f}\")\n", "\n", "# Error distribution\n", "errors = y_pred_orig - y_test_orig\n", "plt.figure(figsize=(8,4))\n", "plt.hist(errors, bins=30, edgecolor='k')\n", "plt.title(\"Error Distribution (Predicted − True)\")\n", "plt.xlabel(\"Error\")\n", "plt.ylabel(\"Frequency\")\n", "plt.axvline(0, color='red', linestyle='--')\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Scatter plot: predicted vs actual\n", "plt.figure(figsize=(6,6))\n", "plt.scatter(y_test_orig, y_pred_orig, alpha=0.6)\n", "plt.plot([y_test_orig.min(), y_test_orig.max()], [y_test_orig.min(), y_test_orig.max()], 'r--')\n", "plt.title(\"Predicted vs Actual total_emission\")\n", "plt.xlabel(\"Actual total_emission\")\n", "plt.ylabel(\"Predicted total_emission\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "XObQhuR-eQUb", "outputId": "f0892d1c-96be-45f0-cdd8-93c195162d57" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Original-scale MAE: 10815.242\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "MAE Interpretation (with 0-1 scaling): Since the target variable is scaled between 0 and 1, an MAE of 0.00308... is an excellent result. It means, on average, the model's predictions are off by only about 0.31% of the total possible range of the scaled data (which is 1)." ], "metadata": { "id": "u1Ox-dXigC78" } }, { "cell_type": "code", "source": [ "save_path = '/content/drive/MyDrive/AuraClima/Agri_TimeSeries.keras'\n", "model.save(save_path)\n", "print(f\"Model successfully saved to: {save_path}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GpD8yOFUe4Ui", "outputId": "925fabbb-ec2e-4136-87ba-239ed89c1713" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model successfully saved to: /content/drive/MyDrive/AuraClima/Agri_TimeSeries.keras\n" ] } ] }, { "cell_type": "markdown", "source": [ "**2. Regression Neural Network for total_emission with Slider-Ready Features**" ], "metadata": { "id": "jwaUkJE7htsb" } }, { "cell_type": "code", "source": [ "import os\n", "import pickle\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Dropout\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import mean_absolute_error" ], "metadata": { "id": "CITaeAgbgkes" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Load dataset\n", "csv_path = '/content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv'\n", "df = pd.read_csv(csv_path)" ], "metadata": { "id": "x0wZP15-hy2_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Define column sets\n", "required_cols = ['Area', 'Year', 'total_emission']\n", "numeric_cols = [\n", " 'Year', 'Crop Residues', 'Savanna fires', 'Forest fires', 'Rice Cultivation',\n", " 'Drained organic soils (CO2)', 'Pesticides Manufacturing', 'Food Transport', 'Forestland',\n", " 'Net Forest conversion', 'Food Household Consumption', 'Food Retail',\n", " 'On-farm Electricity Use', 'Food Packaging', 'Agrifood Systems Waste Disposal',\n", " 'Food Processing', 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils',\n", " 'Manure left on Pasture', 'Manure Management', 'On-farm energy use',\n", " 'Rural population', 'Urban population', 'Fires in organic soils',\n", " 'Fires in humid tropical forests', 'Total Population - Male',\n", " 'Total Population - Female', 'Average Temperature °C'\n", "]" ], "metadata": { "id": "Bb6rAOzgh3t1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Check for any missing required columns\n", "missing = [col for col in required_cols + numeric_cols if col not in df.columns]\n", "if missing:\n", " print(f\"⚠️ Missing columns in dataset: {missing}\")" ], "metadata": { "id": "PdUMVCLhh6S4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Drop incomplete rows\n", "df.dropna(subset=required_cols, inplace=True)" ], "metadata": { "id": "qHc-8awVh8_j" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"\\n--- Converting general numeric columns to numeric (coercing errors) ---\")\n", "for col in numeric_cols:\n", " if col in df.columns:\n", " df[col] = pd.to_numeric(df[col], errors='coerce')\n", " else:\n", " print(f\"Warning: Column '{col}' in numeric_cols not found in DataFrame.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cND2foEPlykD", "outputId": "fbe7eb92-edbe-4723-a639-9bd00e11049e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "--- Converting general numeric columns to numeric (coercing errors) ---\n" ] } ] }, { "cell_type": "code", "source": [ "# Fill NaNs created by the coercion for these columns\n", "print(\"\\n--- Handling NaNs in general numeric columns ---\")\n", "for col in numeric_cols:\n", " if col in df.columns and df[col].isnull().any():\n", " df[col].fillna(df[col].mean(), inplace=True) # Fill NaNs with mean\n", " print(f\"Filled NaNs in '{col}'.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rhMdQI8Il2MH", "outputId": "7a95dd15-b509-49bf-b30a-aba203dd211e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "--- Handling NaNs in general numeric columns ---\n", "Filled NaNs in 'Crop Residues'.\n", "Filled NaNs in 'Savanna fires'.\n", "Filled NaNs in 'Forest fires'.\n", "Filled NaNs in 'Forestland'.\n", "Filled NaNs in 'Net Forest conversion'.\n", "Filled NaNs in 'Food Household Consumption'.\n", "Filled NaNs in 'IPPU'.\n", "Filled NaNs in 'Manure applied to Soils'.\n", "Filled NaNs in 'Manure Management'.\n", "Filled NaNs in 'On-farm energy use'.\n", "Filled NaNs in 'Fires in humid tropical forests'.\n" ] } ] }, { "cell_type": "code", "source": [ "df_encoded = pd.get_dummies(df, columns=['Area'], drop_first=True) # Get dummies first\n", "print(\"\\n--- df_encoded dtypes immediately after get_dummies ---\")\n", "print(df_encoded.dtypes) # Check what dtypes these new columns have initially" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8E75Monwl8xV", "outputId": "6ad9bd5d-8565-476f-fed2-2fef7f3fabfc" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "--- df_encoded dtypes immediately after get_dummies ---\n", "Year int64\n", "Savanna fires float64\n", "Forest fires float64\n", "Crop Residues float64\n", "Rice Cultivation float64\n", " ... \n", "Area_Western Sahara bool\n", "Area_Yemen bool\n", "Area_Yugoslav SFR bool\n", "Area_Zambia bool\n", "Area_Zimbabwe bool\n", "Length: 265, dtype: object\n" ] } ] }, { "cell_type": "code", "source": [ "# Identify the newly created one-hot encoded columns\n", "area_ohe_cols = [col for col in df_encoded.columns if col.startswith('Area_')]" ], "metadata": { "id": "xoyFh5jBmAK4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# One-hot encode categorical column\n", "df_encoded = pd.get_dummies(df, columns=['Area'], drop_first=True)" ], "metadata": { "id": "HxOEGKYWh_0L" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"\\n--- Converting one-hot encoded 'Area_X' columns from 'TRUE'/'FALSE' strings to numeric 0/1 ---\")\n", "for col in area_ohe_cols:\n", " if col in df_encoded.columns and df_encoded[col].dtype == 'object':\n", " # Replace 'TRUE' with 1, 'FALSE' with 0, and then convert to numeric\n", " df_encoded[col] = df_encoded[col].replace({'TRUE': 1, 'FALSE': 0}).astype(float)\n", " print(f\"Converted '{col}' from object to float (0/1).\")\n", " elif col in df_encoded.columns and df_encoded[col].dtype == 'bool':\n", " # If they came as booleans, convert to float (0.0/1.0) for consistency\n", " df_encoded[col] = df_encoded[col].astype(float)\n", " print(f\"Converted '{col}' from bool to float (0.0/1.0).\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9ZRkMPRqmDIE", "outputId": "091f43d6-bf81-4b38-c54a-d1ed26733711" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "--- Converting one-hot encoded 'Area_X' columns from 'TRUE'/'FALSE' strings to numeric 0/1 ---\n", "Converted 'Area_Albania' from bool to float (0.0/1.0).\n", "Converted 'Area_Algeria' from bool to float (0.0/1.0).\n", "Converted 'Area_American Samoa' from bool to float (0.0/1.0).\n", "Converted 'Area_Andorra' from bool to float (0.0/1.0).\n", "Converted 'Area_Angola' from bool to float (0.0/1.0).\n", "Converted 'Area_Anguilla' from bool to float (0.0/1.0).\n", "Converted 'Area_Antigua and Barbuda' from bool to float (0.0/1.0).\n", "Converted 'Area_Argentina' from bool to float (0.0/1.0).\n", "Converted 'Area_Armenia' from bool to float (0.0/1.0).\n", "Converted 'Area_Aruba' from bool to float (0.0/1.0).\n", "Converted 'Area_Australia' from bool to float (0.0/1.0).\n", "Converted 'Area_Austria' from bool to float (0.0/1.0).\n", "Converted 'Area_Azerbaijan' from bool to float (0.0/1.0).\n", "Converted 'Area_Bahamas' from bool to float (0.0/1.0).\n", "Converted 'Area_Bahrain' from bool to float (0.0/1.0).\n", "Converted 'Area_Bangladesh' from bool to float (0.0/1.0).\n", "Converted 'Area_Barbados' from bool to float (0.0/1.0).\n", "Converted 'Area_Belarus' from bool to float (0.0/1.0).\n", "Converted 'Area_Belgium' from bool to float (0.0/1.0).\n", "Converted 'Area_Belgium-Luxembourg' from bool to float (0.0/1.0).\n", "Converted 'Area_Belize' from bool to float (0.0/1.0).\n", "Converted 'Area_Benin' from bool to float (0.0/1.0).\n", "Converted 'Area_Bermuda' from bool to float (0.0/1.0).\n", "Converted 'Area_Bhutan' from bool to float (0.0/1.0).\n", "Converted 'Area_Bolivia (Plurinational State of)' from bool to float (0.0/1.0).\n", "Converted 'Area_Bosnia and Herzegovina' from bool to float (0.0/1.0).\n", "Converted 'Area_Botswana' from bool to float (0.0/1.0).\n", "Converted 'Area_Brazil' from bool to float (0.0/1.0).\n", "Converted 'Area_British Virgin Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Brunei Darussalam' from bool to float (0.0/1.0).\n", "Converted 'Area_Bulgaria' from bool to float (0.0/1.0).\n", "Converted 'Area_Burkina Faso' from bool to float (0.0/1.0).\n", "Converted 'Area_Burundi' from bool to float (0.0/1.0).\n", "Converted 'Area_Cabo Verde' from bool to float (0.0/1.0).\n", "Converted 'Area_Cambodia' from bool to float (0.0/1.0).\n", "Converted 'Area_Cameroon' from bool to float (0.0/1.0).\n", "Converted 'Area_Canada' from bool to float (0.0/1.0).\n", "Converted 'Area_Cayman Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Central African Republic' from bool to float (0.0/1.0).\n", "Converted 'Area_Chad' from bool to float (0.0/1.0).\n", "Converted 'Area_Channel Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Chile' from bool to float (0.0/1.0).\n", "Converted 'Area_China' from bool to float (0.0/1.0).\n", "Converted 'Area_China, Hong Kong SAR' from bool to float (0.0/1.0).\n", "Converted 'Area_China, Macao SAR' from bool to float (0.0/1.0).\n", "Converted 'Area_China, Taiwan Province of' from bool to float (0.0/1.0).\n", "Converted 'Area_China, mainland' from bool to float (0.0/1.0).\n", "Converted 'Area_Colombia' from bool to float (0.0/1.0).\n", "Converted 'Area_Comoros' from bool to float (0.0/1.0).\n", "Converted 'Area_Congo' from bool to float (0.0/1.0).\n", "Converted 'Area_Cook Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Costa Rica' from bool to float (0.0/1.0).\n", "Converted 'Area_Croatia' from bool to float (0.0/1.0).\n", "Converted 'Area_Cuba' from bool to float (0.0/1.0).\n", "Converted 'Area_Cyprus' from bool to float (0.0/1.0).\n", "Converted 'Area_Czechia' from bool to float (0.0/1.0).\n", "Converted 'Area_Czechoslovakia' from bool to float (0.0/1.0).\n", "Converted 'Area_Democratic People's Republic of Korea' from bool to float (0.0/1.0).\n", "Converted 'Area_Democratic Republic of the Congo' from bool to float (0.0/1.0).\n", "Converted 'Area_Denmark' from bool to float (0.0/1.0).\n", "Converted 'Area_Djibouti' from bool to float (0.0/1.0).\n", "Converted 'Area_Dominica' from bool to float (0.0/1.0).\n", "Converted 'Area_Dominican Republic' from bool to float (0.0/1.0).\n", "Converted 'Area_Ecuador' from bool to float (0.0/1.0).\n", "Converted 'Area_Egypt' from bool to float (0.0/1.0).\n", "Converted 'Area_El Salvador' from bool to float (0.0/1.0).\n", "Converted 'Area_Equatorial Guinea' from bool to float (0.0/1.0).\n", "Converted 'Area_Eritrea' from bool to float (0.0/1.0).\n", "Converted 'Area_Estonia' from bool to float (0.0/1.0).\n", "Converted 'Area_Eswatini' from bool to float (0.0/1.0).\n", "Converted 'Area_Ethiopia' from bool to float (0.0/1.0).\n", "Converted 'Area_Ethiopia PDR' from bool to float (0.0/1.0).\n", "Converted 'Area_Falkland Islands (Malvinas)' from bool to float (0.0/1.0).\n", "Converted 'Area_Faroe Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Fiji' from bool to float (0.0/1.0).\n", "Converted 'Area_Finland' from bool to float (0.0/1.0).\n", "Converted 'Area_France' from bool to float (0.0/1.0).\n", "Converted 'Area_French Polynesia' from bool to float (0.0/1.0).\n", "Converted 'Area_Gabon' from bool to float (0.0/1.0).\n", "Converted 'Area_Gambia' from bool to float (0.0/1.0).\n", "Converted 'Area_Georgia' from bool to float (0.0/1.0).\n", "Converted 'Area_Germany' from bool to float (0.0/1.0).\n", "Converted 'Area_Ghana' from bool to float (0.0/1.0).\n", "Converted 'Area_Gibraltar' from bool to float (0.0/1.0).\n", "Converted 'Area_Greece' from bool to float (0.0/1.0).\n", "Converted 'Area_Greenland' from bool to float (0.0/1.0).\n", "Converted 'Area_Grenada' from bool to float (0.0/1.0).\n", "Converted 'Area_Guadeloupe' from bool to float (0.0/1.0).\n", "Converted 'Area_Guam' from bool to float (0.0/1.0).\n", "Converted 'Area_Guatemala' from bool to float (0.0/1.0).\n", "Converted 'Area_Guinea' from bool to float (0.0/1.0).\n", "Converted 'Area_Guinea-Bissau' from bool to float (0.0/1.0).\n", "Converted 'Area_Guyana' from bool to float (0.0/1.0).\n", "Converted 'Area_Haiti' from bool to float (0.0/1.0).\n", "Converted 'Area_Holy See' from bool to float (0.0/1.0).\n", "Converted 'Area_Honduras' from bool to float (0.0/1.0).\n", "Converted 'Area_Hungary' from bool to float (0.0/1.0).\n", "Converted 'Area_Iceland' from bool to float (0.0/1.0).\n", "Converted 'Area_India' from bool to float (0.0/1.0).\n", "Converted 'Area_Indonesia' from bool to float (0.0/1.0).\n", "Converted 'Area_Iran (Islamic Republic of)' from bool to float (0.0/1.0).\n", "Converted 'Area_Iraq' from bool to float (0.0/1.0).\n", "Converted 'Area_Ireland' from bool to float (0.0/1.0).\n", "Converted 'Area_Isle of Man' from bool to float (0.0/1.0).\n", "Converted 'Area_Israel' from bool to float (0.0/1.0).\n", "Converted 'Area_Italy' from bool to float (0.0/1.0).\n", "Converted 'Area_Jamaica' from bool to float (0.0/1.0).\n", "Converted 'Area_Japan' from bool to float (0.0/1.0).\n", "Converted 'Area_Jordan' from bool to float (0.0/1.0).\n", "Converted 'Area_Kazakhstan' from bool to float (0.0/1.0).\n", "Converted 'Area_Kenya' from bool to float (0.0/1.0).\n", "Converted 'Area_Kiribati' from bool to float (0.0/1.0).\n", "Converted 'Area_Kuwait' from bool to float (0.0/1.0).\n", "Converted 'Area_Kyrgyzstan' from bool to float (0.0/1.0).\n", "Converted 'Area_Lao People's Democratic Republic' from bool to float (0.0/1.0).\n", "Converted 'Area_Latvia' from bool to float (0.0/1.0).\n", "Converted 'Area_Lebanon' from bool to float (0.0/1.0).\n", "Converted 'Area_Lesotho' from bool to float (0.0/1.0).\n", "Converted 'Area_Liberia' from bool to float (0.0/1.0).\n", "Converted 'Area_Libya' from bool to float (0.0/1.0).\n", "Converted 'Area_Liechtenstein' from bool to float (0.0/1.0).\n", "Converted 'Area_Lithuania' from bool to float (0.0/1.0).\n", "Converted 'Area_Luxembourg' from bool to float (0.0/1.0).\n", "Converted 'Area_Madagascar' from bool to float (0.0/1.0).\n", "Converted 'Area_Malawi' from bool to float (0.0/1.0).\n", "Converted 'Area_Malaysia' from bool to float (0.0/1.0).\n", "Converted 'Area_Maldives' from bool to float (0.0/1.0).\n", "Converted 'Area_Mali' from bool to float (0.0/1.0).\n", "Converted 'Area_Malta' from bool to float (0.0/1.0).\n", "Converted 'Area_Marshall Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Martinique' from bool to float (0.0/1.0).\n", "Converted 'Area_Mauritania' from bool to float (0.0/1.0).\n", "Converted 'Area_Mauritius' from bool to float (0.0/1.0).\n", "Converted 'Area_Mayotte' from bool to float (0.0/1.0).\n", "Converted 'Area_Mexico' from bool to float (0.0/1.0).\n", "Converted 'Area_Micronesia (Federated States of)' from bool to float (0.0/1.0).\n", "Converted 'Area_Monaco' from bool to float (0.0/1.0).\n", "Converted 'Area_Mongolia' from bool to float (0.0/1.0).\n", "Converted 'Area_Montenegro' from bool to float (0.0/1.0).\n", "Converted 'Area_Montserrat' from bool to float (0.0/1.0).\n", "Converted 'Area_Morocco' from bool to float (0.0/1.0).\n", "Converted 'Area_Mozambique' from bool to float (0.0/1.0).\n", "Converted 'Area_Myanmar' from bool to float (0.0/1.0).\n", "Converted 'Area_Namibia' from bool to float (0.0/1.0).\n", "Converted 'Area_Nauru' from bool to float (0.0/1.0).\n", "Converted 'Area_Nepal' from bool to float (0.0/1.0).\n", "Converted 'Area_Netherlands (Kingdom of the)' from bool to float (0.0/1.0).\n", "Converted 'Area_Netherlands Antilles (former)' from bool to float (0.0/1.0).\n", "Converted 'Area_New Caledonia' from bool to float (0.0/1.0).\n", "Converted 'Area_New Zealand' from bool to float (0.0/1.0).\n", "Converted 'Area_Nicaragua' from bool to float (0.0/1.0).\n", "Converted 'Area_Niger' from bool to float (0.0/1.0).\n", "Converted 'Area_Nigeria' from bool to float (0.0/1.0).\n", "Converted 'Area_Niue' from bool to float (0.0/1.0).\n", "Converted 'Area_North Macedonia' from bool to float (0.0/1.0).\n", "Converted 'Area_Northern Mariana Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Norway' from bool to float (0.0/1.0).\n", "Converted 'Area_Oman' from bool to float (0.0/1.0).\n", "Converted 'Area_Pacific Islands Trust Territory' from bool to float (0.0/1.0).\n", "Converted 'Area_Pakistan' from bool to float (0.0/1.0).\n", "Converted 'Area_Palau' from bool to float (0.0/1.0).\n", "Converted 'Area_Palestine' from bool to float (0.0/1.0).\n", "Converted 'Area_Panama' from bool to float (0.0/1.0).\n", "Converted 'Area_Papua New Guinea' from bool to float (0.0/1.0).\n", "Converted 'Area_Paraguay' from bool to float (0.0/1.0).\n", "Converted 'Area_Peru' from bool to float (0.0/1.0).\n", "Converted 'Area_Philippines' from bool to float (0.0/1.0).\n", "Converted 'Area_Poland' from bool to float (0.0/1.0).\n", "Converted 'Area_Portugal' from bool to float (0.0/1.0).\n", "Converted 'Area_Puerto Rico' from bool to float (0.0/1.0).\n", "Converted 'Area_Qatar' from bool to float (0.0/1.0).\n", "Converted 'Area_Republic of Korea' from bool to float (0.0/1.0).\n", "Converted 'Area_Republic of Moldova' from bool to float (0.0/1.0).\n", "Converted 'Area_Romania' from bool to float (0.0/1.0).\n", "Converted 'Area_Russian Federation' from bool to float (0.0/1.0).\n", "Converted 'Area_Rwanda' from bool to float (0.0/1.0).\n", "Converted 'Area_Saint Helena, Ascension and Tristan da Cunha' from bool to float (0.0/1.0).\n", "Converted 'Area_Saint Kitts and Nevis' from bool to float (0.0/1.0).\n", "Converted 'Area_Saint Lucia' from bool to float (0.0/1.0).\n", "Converted 'Area_Saint Pierre and Miquelon' from bool to float (0.0/1.0).\n", "Converted 'Area_Saint Vincent and the Grenadines' from bool to float (0.0/1.0).\n", "Converted 'Area_Samoa' from bool to float (0.0/1.0).\n", "Converted 'Area_San Marino' from bool to float (0.0/1.0).\n", "Converted 'Area_Sao Tome and Principe' from bool to float (0.0/1.0).\n", "Converted 'Area_Saudi Arabia' from bool to float (0.0/1.0).\n", "Converted 'Area_Senegal' from bool to float (0.0/1.0).\n", "Converted 'Area_Serbia' from bool to float (0.0/1.0).\n", "Converted 'Area_Serbia and Montenegro' from bool to float (0.0/1.0).\n", "Converted 'Area_Seychelles' from bool to float (0.0/1.0).\n", "Converted 'Area_Sierra Leone' from bool to float (0.0/1.0).\n", "Converted 'Area_Singapore' from bool to float (0.0/1.0).\n", "Converted 'Area_Slovakia' from bool to float (0.0/1.0).\n", "Converted 'Area_Slovenia' from bool to float (0.0/1.0).\n", "Converted 'Area_Solomon Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Somalia' from bool to float (0.0/1.0).\n", "Converted 'Area_South Africa' from bool to float (0.0/1.0).\n", "Converted 'Area_South Sudan' from bool to float (0.0/1.0).\n", "Converted 'Area_Spain' from bool to float (0.0/1.0).\n", "Converted 'Area_Sri Lanka' from bool to float (0.0/1.0).\n", "Converted 'Area_Sudan' from bool to float (0.0/1.0).\n", "Converted 'Area_Sudan (former)' from bool to float (0.0/1.0).\n", "Converted 'Area_Suriname' from bool to float (0.0/1.0).\n", "Converted 'Area_Sweden' from bool to float (0.0/1.0).\n", "Converted 'Area_Switzerland' from bool to float (0.0/1.0).\n", "Converted 'Area_Syrian Arab Republic' from bool to float (0.0/1.0).\n", "Converted 'Area_Tajikistan' from bool to float (0.0/1.0).\n", "Converted 'Area_Thailand' from bool to float (0.0/1.0).\n", "Converted 'Area_Timor-Leste' from bool to float (0.0/1.0).\n", "Converted 'Area_Togo' from bool to float (0.0/1.0).\n", "Converted 'Area_Tokelau' from bool to float (0.0/1.0).\n", "Converted 'Area_Tonga' from bool to float (0.0/1.0).\n", "Converted 'Area_Trinidad and Tobago' from bool to float (0.0/1.0).\n", "Converted 'Area_Tunisia' from bool to float (0.0/1.0).\n", "Converted 'Area_Turkmenistan' from bool to float (0.0/1.0).\n", "Converted 'Area_Turks and Caicos Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Tuvalu' from bool to float (0.0/1.0).\n", "Converted 'Area_USSR' from bool to float (0.0/1.0).\n", "Converted 'Area_Uganda' from bool to float (0.0/1.0).\n", "Converted 'Area_Ukraine' from bool to float (0.0/1.0).\n", "Converted 'Area_United Arab Emirates' from bool to float (0.0/1.0).\n", "Converted 'Area_United Kingdom of Great Britain and Northern Ireland' from bool to float (0.0/1.0).\n", "Converted 'Area_United Republic of Tanzania' from bool to float (0.0/1.0).\n", "Converted 'Area_United States Virgin Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_United States of America' from bool to float (0.0/1.0).\n", "Converted 'Area_Uruguay' from bool to float (0.0/1.0).\n", "Converted 'Area_Uzbekistan' from bool to float (0.0/1.0).\n", "Converted 'Area_Vanuatu' from bool to float (0.0/1.0).\n", "Converted 'Area_Venezuela (Bolivarian Republic of)' from bool to float (0.0/1.0).\n", "Converted 'Area_Viet Nam' from bool to float (0.0/1.0).\n", "Converted 'Area_Wallis and Futuna Islands' from bool to float (0.0/1.0).\n", "Converted 'Area_Western Sahara' from bool to float (0.0/1.0).\n", "Converted 'Area_Yemen' from bool to float (0.0/1.0).\n", "Converted 'Area_Yugoslav SFR' from bool to float (0.0/1.0).\n", "Converted 'Area_Zambia' from bool to float (0.0/1.0).\n", "Converted 'Area_Zimbabwe' from bool to float (0.0/1.0).\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"\\n--- Dtypes of 'df_encoded' AFTER ALL conversions ---\")\n", "print(df_encoded.dtypes)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JN3gYqvDmHAD", "outputId": "968d3f68-9c8e-4620-945c-3ae829830426" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "--- Dtypes of 'df_encoded' AFTER ALL conversions ---\n", "Year int64\n", "Savanna fires float64\n", "Forest fires float64\n", "Crop Residues float64\n", "Rice Cultivation float64\n", " ... \n", "Area_Western Sahara float64\n", "Area_Yemen float64\n", "Area_Yugoslav SFR float64\n", "Area_Zambia float64\n", "Area_Zimbabwe float64\n", "Length: 265, dtype: object\n" ] } ] }, { "cell_type": "code", "source": [ "# --- Prepare features and target ---\n", "feature_cols = [col for col in df_encoded.columns if col != 'total_emission']\n", "X = df_encoded[feature_cols].copy()\n", "y = df_encoded['total_emission'].values.reshape(-1, 1)" ], "metadata": { "id": "fa6AD_5hmKLY" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(\"\\n--- Dtypes of X DataFrame (before scaling and to_numpy) ---\")\n", "print(X.dtypes)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TLwGPIW6mNnR", "outputId": "99f2350d-613b-4fed-8bad-38fc9bca2c40" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "--- Dtypes of X DataFrame (before scaling and to_numpy) ---\n", "Year int64\n", "Savanna fires float64\n", "Forest fires float64\n", "Crop Residues float64\n", "Rice Cultivation float64\n", " ... \n", "Area_Western Sahara float64\n", "Area_Yemen float64\n", "Area_Yugoslav SFR float64\n", "Area_Zambia float64\n", "Area_Zimbabwe float64\n", "Length: 264, dtype: object\n" ] } ] }, { "cell_type": "code", "source": [ "# Normalize numeric features only\n", "num_idx = [X.columns.get_loc(col) for col in numeric_cols if col in X.columns and col != 'total_emission']\n", "scaler_X = StandardScaler()\n", "X_scaled = X.values.copy()\n", "X_scaled[:, num_idx] = scaler_X.fit_transform(X_scaled[:, num_idx])" ], "metadata": { "id": "HEcq4JDNmP0W" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Prepare features and target\n", "feature_cols = [col for col in df_encoded.columns if col != 'total_emission']\n", "X = df_encoded[feature_cols].copy()\n", "y = df_encoded['total_emission'].values.reshape(-1, 1)" ], "metadata": { "id": "wHLlvBqViCHl" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Normalize target\n", "scaler_y = StandardScaler()\n", "y_scaled = scaler_y.fit_transform(y)" ], "metadata": { "id": "fzpglxaRmU_q" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Split dataset\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X_scaled, y_scaled, test_size=0.2, random_state=42, shuffle=True\n", ")" ], "metadata": { "id": "hOXqQZp_mYYK" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# --- Final check of NumPy array dtypes ---\n", "print(\"\\n--- Final NumPy Array dtypes for model input ---\")\n", "print(\"X_train dtype:\", X_train.dtype)\n", "print(\"y_train dtype:\", y_train.dtype)\n", "print(\"X_train shape:\", X_train.shape)\n", "print(\"y_train shape:\", y_train.shape)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8me0b6Xpma6y", "outputId": "c70c0b2c-f7c8-4225-bdde-daa3a68bfa13" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "--- Final NumPy Array dtypes for model input ---\n", "X_train dtype: float64\n", "y_train dtype: float64\n", "X_train shape: (5572, 264)\n", "y_train shape: (5572, 1)\n" ] } ] }, { "cell_type": "code", "source": [ "# --- Build and compile model ---\n", "tf.random.set_seed(42)\n", "model = Sequential([\n", " Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n", " Dropout(0.2),\n", " Dense(32, activation='relu'),\n", " Dropout(0.2),\n", " Dense(1, activation='linear')\n", "])\n", "\n", "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n", "model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 289 }, "id": "kc-NuBSOiJJu", "outputId": "2094f255-74b6-436d-c9f3-73c3af4f015c" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"sequential_2\"\u001b[0m\n" ], "text/html": [ "
Model: \"sequential_2\"\n",
              "
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ dense_5 (Dense)                 │ (None, 64)             │        16,960 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_4 (Dropout)             │ (None, 64)             │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_6 (Dense)                 │ (None, 32)             │         2,080 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_5 (Dropout)             │ (None, 32)             │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_7 (Dense)                 │ (None, 1)              │            33 │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,073\u001b[0m (74.50 KB)\n" ], "text/html": [ "
 Total params: 19,073 (74.50 KB)\n",
              "
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 Trainable params: 19,073 (74.50 KB)\n",
              "
\n" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ], "text/html": [ "
 Non-trainable params: 0 (0.00 B)\n",
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\n" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# --- Train model ---\n", "history = model.fit(\n", " X_train, y_train,\n", " validation_data=(X_test, y_test),\n", " epochs=50,\n", " batch_size=32\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Op7RcE0QiM_p", "outputId": "66d1d6ef-9ee0-475e-aeaf-1c5b4c6e604f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 7ms/step - loss: 0.5021 - mae: 0.2308 - val_loss: 0.0861 - val_mae: 0.0912\n", "Epoch 2/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1248 - mae: 0.1270 - val_loss: 0.0161 - val_mae: 0.0602\n", "Epoch 3/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.1008 - mae: 0.1090 - val_loss: 0.0521 - val_mae: 0.0679\n", "Epoch 4/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0667 - mae: 0.0930 - val_loss: 0.0105 - val_mae: 0.0408\n", "Epoch 5/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0931 - mae: 0.0958 - val_loss: 0.0075 - val_mae: 0.0398\n", "Epoch 6/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.0567 - mae: 0.0832 - val_loss: 0.0137 - val_mae: 0.0468\n", "Epoch 7/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0890 - mae: 0.0896 - val_loss: 0.0139 - val_mae: 0.0431\n", "Epoch 8/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0632 - mae: 0.0801 - val_loss: 0.0050 - val_mae: 0.0378\n", "Epoch 9/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0566 - mae: 0.0787 - val_loss: 0.0099 - val_mae: 0.0416\n", "Epoch 10/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0791 - mae: 0.0802 - val_loss: 0.0555 - val_mae: 0.0599\n", "Epoch 11/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0482 - mae: 0.0749 - val_loss: 0.0132 - val_mae: 0.0439\n", "Epoch 12/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0470 - mae: 0.0744 - val_loss: 0.0176 - val_mae: 0.0436\n", "Epoch 13/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0468 - mae: 0.0716 - val_loss: 0.0124 - val_mae: 0.0383\n", "Epoch 14/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0509 - mae: 0.0718 - val_loss: 0.0350 - val_mae: 0.0589\n", "Epoch 15/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0290 - mae: 0.0653 - val_loss: 0.0098 - val_mae: 0.0390\n", "Epoch 16/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0550 - mae: 0.0720 - val_loss: 0.0150 - val_mae: 0.0473\n", "Epoch 17/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.0408 - mae: 0.0682 - val_loss: 0.0180 - val_mae: 0.0381\n", "Epoch 18/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0474 - mae: 0.0694 - val_loss: 0.0125 - val_mae: 0.0399\n", "Epoch 19/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0384 - mae: 0.0650 - val_loss: 0.0247 - val_mae: 0.0468\n", "Epoch 20/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0473 - mae: 0.0701 - val_loss: 0.0103 - val_mae: 0.0428\n", "Epoch 21/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0404 - mae: 0.0637 - val_loss: 0.0053 - val_mae: 0.0327\n", "Epoch 22/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0477 - mae: 0.0683 - val_loss: 0.0353 - val_mae: 0.0472\n", "Epoch 23/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0383 - mae: 0.0641 - val_loss: 0.0073 - val_mae: 0.0353\n", "Epoch 24/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0297 - mae: 0.0613 - val_loss: 0.0071 - val_mae: 0.0326\n", "Epoch 25/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0378 - mae: 0.0664 - val_loss: 0.0102 - val_mae: 0.0407\n", "Epoch 26/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0455 - mae: 0.0678 - val_loss: 0.0091 - val_mae: 0.0346\n", "Epoch 27/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - loss: 0.0403 - mae: 0.0646 - val_loss: 0.0084 - val_mae: 0.0278\n", "Epoch 28/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.0354 - mae: 0.0620 - val_loss: 0.0074 - val_mae: 0.0298\n", "Epoch 29/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0581 - mae: 0.0713 - val_loss: 0.0157 - val_mae: 0.0419\n", "Epoch 30/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0460 - mae: 0.0696 - val_loss: 0.0042 - val_mae: 0.0276\n", "Epoch 31/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0424 - mae: 0.0661 - val_loss: 0.0066 - val_mae: 0.0256\n", "Epoch 32/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0496 - mae: 0.0663 - val_loss: 0.0126 - val_mae: 0.0393\n", "Epoch 33/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0296 - mae: 0.0625 - val_loss: 0.0059 - val_mae: 0.0291\n", "Epoch 34/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0508 - mae: 0.0679 - val_loss: 0.0293 - val_mae: 0.0472\n", "Epoch 35/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0545 - mae: 0.0653 - val_loss: 0.0084 - val_mae: 0.0326\n", "Epoch 36/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0319 - mae: 0.0614 - val_loss: 0.0130 - val_mae: 0.0298\n", "Epoch 37/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - loss: 0.0592 - mae: 0.0708 - val_loss: 0.0038 - val_mae: 0.0215\n", "Epoch 38/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0341 - mae: 0.0631 - val_loss: 0.0211 - val_mae: 0.0329\n", "Epoch 39/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0551 - mae: 0.0661 - val_loss: 0.0032 - val_mae: 0.0225\n", "Epoch 40/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0388 - mae: 0.0651 - val_loss: 0.0108 - val_mae: 0.0282\n", "Epoch 41/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0311 - mae: 0.0619 - val_loss: 0.0078 - val_mae: 0.0265\n", "Epoch 42/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0423 - mae: 0.0650 - val_loss: 0.0076 - val_mae: 0.0269\n", "Epoch 43/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0474 - mae: 0.0633 - val_loss: 0.0079 - val_mae: 0.0250\n", "Epoch 44/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0362 - mae: 0.0656 - val_loss: 0.0039 - val_mae: 0.0212\n", "Epoch 45/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0329 - mae: 0.0647 - val_loss: 0.0035 - val_mae: 0.0230\n", "Epoch 46/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0453 - mae: 0.0671 - val_loss: 0.0028 - val_mae: 0.0236\n", "Epoch 47/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0344 - mae: 0.0665 - val_loss: 0.0120 - val_mae: 0.0299\n", "Epoch 48/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0275 - mae: 0.0610 - val_loss: 0.0043 - val_mae: 0.0230\n", "Epoch 49/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0344 - mae: 0.0622 - val_loss: 0.0194 - val_mae: 0.0384\n", "Epoch 50/50\n", "\u001b[1m175/175\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0464 - mae: 0.0662 - val_loss: 0.0030 - val_mae: 0.0246\n" ] } ] }, { "cell_type": "code", "source": [ "y_pred_scaled = model.predict(X_test)\n", "y_pred_orig = scaler_y.inverse_transform(y_pred_scaled).flatten()\n", "y_test_orig = scaler_y.inverse_transform(y_test).flatten()\n", "mae_orig = mean_absolute_error(y_test_orig, y_pred_orig)\n", "print(f\"\\n✅ Test MAE (original scale): {mae_orig:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aMjfyQDAiPIG", "outputId": "b5115d5c-4cf2-4a4f-8468-418458bfea53" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step\n", "\n", "✅ Test MAE (original scale): 5609.6419\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"Original total_emission min:\", df['total_emission'].min())\n", "print(\"Original total_emission max:\", df['total_emission'].max())\n", "print(\"Original total_emission mean:\", df['total_emission'].mean())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lGmdb6OJiShp", "outputId": "182ed2e8-152e-4ce8-cad8-3ad902c76b4c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Original total_emission min: -391884.0563\n", "Original total_emission max: 3115113.7483999995\n", "Original total_emission mean: 64091.24414739476\n" ] } ] }, { "cell_type": "markdown", "source": [ "- An average absolute error of 5609.6419 is quite small relative to the mean value of total_emission (around 8.75% of the mean) and exceptionally small relative to the overall range (0.16% of the range).\n", "- This indicates that, on average, your model's predictions are quite close to the actual total_emission values, even though these values can fluctuate significantly." ], "metadata": { "id": "_eh70zyInquv" } }, { "cell_type": "code", "source": [ "# 1. Make predictions on the test set\n", "y_pred_scaled = model.predict(X_test)\n", "\n", "# 2. Inverse transform predictions and actual values back to original scale\n", "# y_test and y_pred_scaled are 2D arrays because of reshape(-1,1) in scaling.\n", "y_test_original_scale = scaler_y.inverse_transform(y_test)\n", "y_pred_original_scale = scaler_y.inverse_transform(y_pred_scaled)\n", "\n", "# Flatten arrays for easier plotting if they are 2D with a single column\n", "y_test_original_scale = y_test_original_scale.flatten()\n", "y_pred_original_scale = y_pred_original_scale.flatten()\n", "\n", "# 3. Calculate residuals\n", "residuals = y_test_original_scale - y_pred_original_scale\n", "\n", "print(f\"Shape of y_test_original_scale: {y_test_original_scale.shape}\")\n", "print(f\"Shape of y_pred_original_scale: {y_pred_original_scale.shape}\")\n", "print(f\"Shape of residuals: {residuals.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wnAa_HzMjSTw", "outputId": "c00b0cbe-b843-4b35-9be3-3512f5f1bbb5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n", "Shape of y_test_original_scale: (1393,)\n", "Shape of y_pred_original_scale: (1393,)\n", "Shape of residuals: (1393,)\n" ] } ] }, { "cell_type": "code", "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# --- Graph 1: Predicted vs. Actual Values Plot ---\n", "plt.figure(figsize=(10, 8))\n", "sns.scatterplot(x=y_test_original_scale, y=y_pred_original_scale, alpha=0.6)\n", "plt.plot([min(y_test_original_scale), max(y_test_original_scale)],\n", " [min(y_test_original_scale), max(y_test_original_scale)],\n", " color='red', linestyle='--', label='Perfect Prediction (y=x)')\n", "plt.title('Actual vs. Predicted Total Emission (Original Scale)')\n", "plt.xlabel('Actual Total Emission')\n", "plt.ylabel('Predicted Total Emission')\n", "plt.grid(True, linestyle='--', alpha=0.7)\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 807 }, "id": "-HgIg5G1n8zW", "outputId": "a1125a56-e32f-4505-f405-053b5ddaa1ff" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "- Observation: The scatter points are tightly clustered around the red diagonal line (the line of perfect prediction, y=x). This is a very strong indicator of good model performance.\n", "- Interpretation: It shows that your model's predictions are, for the most part, very close to the actual total emission values across the entire range of your data, from negative values up to around 3 million. The linearity of the scatter confirms that the model has captured the underlying relationship well. There are no obvious signs of the model systematically over- or under-predicting across different ranges of emissions." ], "metadata": { "id": "zujsI7k5o2Py" } }, { "cell_type": "code", "source": [ "# --- Graph 2: Residuals Plot (vs. Actual Values) ---\n", "plt.figure(figsize=(10, 6))\n", "sns.scatterplot(x=y_test_original_scale, y=residuals, alpha=0.6)\n", "plt.axhline(y=0, color='red', linestyle='--', label='Zero Error Line')\n", "plt.title('Residuals Plot (Errors vs. Actual Values)')\n", "plt.xlabel('Actual Total Emission')\n", "plt.ylabel('Residuals (Actual - Predicted)')\n", "plt.grid(True, linestyle='--', alpha=0.7)\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "dr7Vvrx3n_m1", "outputId": "a53d9093-222c-44e5-9578-f01dc30f0363" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "- Observation: Most of the residuals (errors) are clustered around the red dashed \"Zero Error Line\".\n", "For lower actual emission values (e.g., up to ~0.5 million), the errors are generally small and tightly clustered around zero.\n", "As actual emission values increase, the spread of the residuals seems to slightly increase, forming a very subtle \"fanning out\" or megaphone shape. This is common in regression and indicates heteroscedasticity, meaning the model's error magnitude increases with the magnitude of the predicted value.\n", "There are a few outliers (points far from zero) especially at higher actual emission values (e.g., around 1.4 million and 3 million), where the model has larger prediction errors (both positive and negative).\n", "- Interpretation:\n", "The model is generally unbiased, with errors centered around zero.\n", "The slight fanning out suggests that while the model performs very well overall, its predictions might be less precise for very high emission values compared to lower ones. This is often an inherent characteristic of data with a wide dynamic range, where absolute errors are naturally larger for larger values. Given your large range of total_emission, this slight heteroscedasticity is not necessarily a major flaw but something to be aware of.\n" ], "metadata": { "id": "jss4eOoKpA5R" } }, { "cell_type": "code", "source": [ "# --- Graph 3: Distribution of Residuals (Histogram/KDE) ---\n", "plt.figure(figsize=(10, 6))\n", "sns.histplot(residuals, bins=50, kde=True, color='skyblue')\n", "plt.axvline(x=0, color='red', linestyle='--', label='Mean Residual (ideally 0)')\n", "plt.title('Distribution of Residuals')\n", "plt.xlabel('Residuals (Actual - Predicted)')\n", "plt.ylabel('Frequency')\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "4b9_oyAloH1z", "outputId": "4fd412c9-31ba-4be4-f436-aa54174581c3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "- Observation: The histogram of residuals is sharply peaked around zero, indicating that the majority of the errors are very small. The mean residual (red dashed line) is very close to zero, which is ideal. The distribution appears roughly symmetrical, but with longer tails, especially on the positive side, indicating the presence of some larger positive and negative errors (consistent with the outliers seen in the residuals plot).\n", "- Interpretation:\n", "The model's errors are predominantly small and centered around zero, confirming that it's not systematically over- or under-predicting.\n", "The non-perfect normal distribution (with longer tails than a pure bell curve) suggests that while most predictions are very accurate, there are occasional larger deviations. This is often fine, especially for complex real-world data." ], "metadata": { "id": "_WnTnl4bpJPg" } }, { "cell_type": "code", "source": [ "# --- Graph 4: Actual vs. Predicted Over Time (IMPORTANT for Time Series) ---\n", "\n", "sample_size = min(200, len(y_test_original_scale)) # Plot a manageable subset\n", "plt.figure(figsize=(14, 7))\n", "# Plotting based on a simple index for demonstration.\n", "# Replace `range(sample_size)` with your actual time index if available and un-shuffled.\n", "plt.plot(y_test_original_scale[:sample_size], label='Actual Total Emission', color='blue', alpha=0.8)\n", "plt.plot(y_pred_original_scale[:sample_size], label='Predicted Total Emission', color='orange', linestyle='--', alpha=0.8)\n", "plt.title(f'Actual vs. Predicted Total Emission Over Time (First {sample_size} Samples in Test Set)')\n", "plt.xlabel('Time Step / Sample Index') # Change to 'Year' or 'Date' if applicable\n", "plt.ylabel('Total Emission')\n", "plt.legend()\n", "plt.grid(True, linestyle='--', alpha=0.7)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 591 }, "id": "SKZ4DxzhoLwk", "outputId": "57e4a25a-fcf9-416d-c662-a51f39561b84" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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phBBC0zQRHR0tHnvsMZf9XnnlFQFALFmypMBlaJomhBBi69atAoCYM2dOgX1q1aolhg8fXmB7p06dRKdOnZzf2+12kZWV5bLPxYsXReXKlcWoUaNctgMQEyZMKHZ8s2bNEgDEH3/84bK9UaNGomvXrs7v4+PjRe/evYu9rNIAIO6//36RnJwszp49KzZv3iy6desmAIhp06YJIYRYs2aNACDq1Kkjrly54vxdTdNEvXr1REJCgvO2FUKIK1euiJiYGNGjRw/ntgEDBojAwEBx/Phx57a//vpLWK1Wce3LhWvvg7Lcp7JqvJ7GjRu75OXxxx8XAMT69eud2y5duiRiYmJE7dq1haqqxY7DUfO1Xn/9daEoikvNEyZMKFG9w4cPFwAK/ZeQkODcb86cOc5t+W/Ddu3aCUVRxEMPPeTcZrfbRXR0tMvYhSj4NxAWFibGjh1bZG07duwQAMSiRYuKHcO1WSnp7ezIdFxcnMvf8bvvvlvo32F+KSkpAoDo379/sbX169dPABBpaWlCCCHuvvtuERUVJex2u3OfpKQkYbFYxOTJk53bunXrJpo0aSIyMzOd2zRNE+3btxf16tVzbnPcLzfffLPLZZbEokWLBACxZs2aAj9zXO7Ro0ed22rVqiUAiI0bNzq3/fTTTwKACAoKcsmf47Es/2WXdEyFOXTokAAg/u///q/Az4rKsCN/xY1lxYoVLpf13//+VwAQycnJRdZS3N9nSX3++ecCgJg9e3aBy/3ss88K7D9+/HgBwHnb9e7dW9SpU6fAfunp6QKAeO6554q9/tmzZwsAwt/fX3Tp0kW8/PLLYv369c6/jfyufczJzs4WN910k8tzkhC5f98BAQEut7MjB1WqVHH+DQghxPPPP1/gPunUqZPLc44QQmRlZYlmzZqJqKgokZ2dLYQQ4ujRowVu/5Jm60afOx2PFfnz7Kg3//2VlZUlqlSpIm6//fYSX3ZycnKBx0Z3jmfs2LGleu4aNmyYACAqVKggBg4cKKZOnSr27t1bYL9XX31VhISEiAMHDrhsf+6554TVahUnTpwocnzXuvXWW0VcXFyJayQiIvfjUglEROQW8+bNQ+XKldGlSxcAuYfuDRkyBPPnz4eqqs79vv76a8THxxc6g6u4wwRLy2q1Og811TQNFy5cgN1uR6tWrVwO9y6pQYMGwWazYcGCBc5te/bswV9//YUhQ4Y4t4WHh+PPP//EwYMHyz6IPLNnz0ZkZCSioqLQtm1bbNiwAU8++SQef/xxl/2GDx/usg7kzp07cfDgQQwdOhTnz5/HuXPncO7cOaSnp6Nbt25Yt24dNE2Dqqr46aefMGDAAJfDgOPi4pCQkHDd+spyn3qqxutZvnw52rRp4zITrly5cnjwwQdx7Ngx/PXXX9e9jPy3fXp6Os6dO4f27dtDCFHooeklERgYiJUrVxb498YbbxTY9/7773e5vdu2bQshBO6//37nNqvVilatWuHIkSPFXm94eDg2b96MU6dOFfpzx2zJn376CVeuXCnxeEp7O48cOdLlkHHHLMri6r906RIAIDQ0tNhaHD93LCkyZMgQnD171uWQ68WLF0PTNOff+IULF/DLL79g8ODBuHTpkjOv58+fR0JCAg4ePIh//vnH5XpGjx7tkTVpGzVqhHbt2jm/b9u2LQCga9euLn8zju2O2/BGxpTf+fPnAQAVKlQo9OeFZXjatGnFjiUmJqbA33V4eDiA3MO2HYd7u9u+ffswduxYtGvXDsOHD3dudyx3UtiazY71VR37ZGRklGi/oowaNQorVqxA586d8dtvv+HVV19Fx44dUa9ePWzcuNFl3/yPORcvXkRqaio6duxY6HNct27dULt2bef3jhzcfvvtLn8r1+bDwWazYcyYMc7v/f39MWbMGJw9exbbtm0rdCylyZa7nzvLlSvnsi6tv78/2rRpc93HvuLoOR4AmDNnDt5//33ExMTgm2++wdNPP424uDh069bN5W900aJF6NixIypUqOCs8dy5c+jevTtUVcW6detKfJ2OyyAiIv2YunG7bt069O3bF9WqVYOiKPj2229LfRlCCEydOhX169dHQEAAqlevjtdee839xRIReTFVVTF//nx06dIFR48exaFDh3Do0CG0bdsWZ86cwerVq537Hj582OVM3TJ9+umnaNq0qXN9ucjISPzwww8ua3CWVKVKldCtWzeXtQwXLFgAm83mcrbpyZMnIyUlBfXr10eTJk0wfvx47N69u0zj6N+/P1auXIlVq1Zh8+bNOHfuHKZNm1bgxGMxMTEu3zveMA4fPhyRkZEu/z7++GNkZWUhNTUVycnJyMjIQL169Qpcd4MGDa5bX1nuU0/VeD3Hjx8v9HIchzsfP378updx4sQJjBgxAhEREShXrhwiIyOdh93fSOaA3EZr9+7dC/wrbKmP/M054GpztUaNGgW2X7x4sdjrfeutt7Bnzx7UqFEDbdq0wcSJE10aHjExMXjyySfx8ccfo1KlSkhISMAHH3xw3XGW9na+dkyO5mBx9TuaUI4GblGubfA61ljO/+HMggUL0KxZM9SvXx9A7nIEQgi8/PLLBfLqWEbl2hNIXft3KUtp7n/g6m14I2MqjLhm3WSHwjLcsmXLYi+rsNtsyJAh6NChAx544AFUrlwZd911FxYuXOi2Ju7p06fRu3dvhIWFYfHixS7NdkeDNCsrq8DvZWZmuuwTFBRUov2Kk5CQgJ9++gkpKSlYt24dxo4di+PHj6NPnz4u98WyZcvwr3/9C4GBgYiIiHAe/l7Y3+GN5sOhWrVqBU6s5/i7uHZNXIfSZMvdz53R0dEFPjisUKHCdR/7iqPneIDcE7GNHTsW27Ztw7lz5/Ddd9+hV69e+OWXX3DXXXc59zt48CBWrFhRoEbHmrWlOcmdEMKtH6oTEVHpmXqN2/T0dMTHx2PUqFEub7pL47HHHsPPP/+MqVOnokmTJrhw4QIuXLjg5kqJiLzbL7/8gqSkJMyfPx/z588v8PN58+bh1ltvdct1FfUGQlVVlzfaX3zxBUaMGIEBAwZg/PjxiIqKgtVqxeuvv47Dhw/f0HXfddddGDlyJHbu3IlmzZph4cKF6NatGypVquTc55ZbbsHhw4fx3Xff4eeff8bHH3+M//73v5g5cyYeeOCBG7re6Oho5xuu4lzbEHA0NN5+++0i1/QtV65coU0GT/GFGktCVVX06NEDFy5cwLPPPouGDRsiJCQE//zzD0aMGCFthmB+Rc3qLGx7UU02h8GDB6Njx4745ptv8PPPP+Ptt9/Gm2++iSVLljjXh5w2bRpGjBjhzPq4cePw+uuv4/fff3fLSbiKqv169YeFhaFq1arXbZLs3r0b1atXd64VHBAQgAEDBuCbb77BjBkzcObMGWzYsAFTpkxx/o7jfnz66aeLnOl97drTJWnUuUNp7n/g6m14I2PKr2LFigCKb6aXVmG3WVBQENatW4c1a9bghx9+wIoVK7BgwQJ07doVP//8c5lmNaempqJXr15ISUnB+vXrUa1aNZefV61aFQCQlJRU4HeTkpIQERHhnGVbtWpVrFmzpkDDy/G71152cYKDg9GxY0d07NgRlSpVwqRJk/Djjz9i+PDhWL9+Pfr164dbbrkFM2bMQNWqVeHn54c5c+bgyy+/LHBZN5qPsihNttz93CljXHqO51oVK1ZEv3790K9fP3Tu3Bm//vorjh8/jlq1akHTNPTo0QPPPPNMob/raLiXxMWLF11e4xARkeeZunHbq1evYhenz8rKwosvvoivvvoKKSkpuOmmm/Dmm286z1q+d+9efPjhh9izZ49z9oinZlUQEXmTefPmISoqynmG5PyWLFmCb775BjNnzkRQUBBiY2Ndzs5emOJmd1SoUAEpKSkFth8/fhx16tRxfr948WLUqVMHS5Yscbm8a08uVhoDBgzAmDFjnDPyDhw4gOeff77AfhERERg5ciRGjhyJy5cv45ZbbsHEiRPd8matNGJjYwEA5cuXL7bxGxkZiaCgoEIP6dy/f3+JrudG71NP1Xg9tWrVKvRy9u3b5/w5UPQ4/vjjDxw4cACffvqpy0m/rndGdG9WtWpVPPLII3jkkUdw9uxZtGjRAq+99prLa6cmTZqgSZMmeOmll7Bx40Z06NABM2fOxH/+859CL7Okt3NZ9enTB//73//w22+/FXoiqPXr1+PYsWMuh34DubM6P/30U6xevRp79+6FEMJlKRTHY4yfn1+JPkzxBWUdU82aNREUFISjR4+6u7QCLBYLunXrhm7duuGdd97BlClT8OKLL2LNmjXo3r37Dc0MzMzMRN++fXHgwAGsWrUKjRo1KrBP9erVERkZicTExAI/u/Zkl82aNcPHH3+MvXv3ulyW44R6pTkxZn6OE6M5GsBff/01AgMD8dNPP7kszTBnzpwbuvzrOXXqFNLT011m3R44cAAAXJZgyK+02fKW586iuHs87prJ2qpVK/z6669ISkpCrVq1EBsbi8uXL1+3xpJc/9GjRxEfH++WOomI6MaYeqmE63n00UexadMmzJ8/H7t378add96Jnj17Ot80fv/996hTpw6WLVuGmJgY1K5dGw888ABn3BKRqWRkZGDJkiXo06cP7rjjjgL/Hn30UVy6dAlLly4FkLue3q5du/DNN98UuCzHTBjHG8PCGrSxsbH4/fffkZ2d7dy2bNky/P333y77OWbb5J9ds3nzZmzatOmGxxoeHo6EhAQsXLgQ8+fPh7+/PwYMGOCyj2O9R4dy5cqhbt26LjNGU1NTsW/fvhs+fL6kWrZsidjYWEydOhWXL18u8PPk5GQAubdVQkICvv32W5w4ccL587179+Knn3667vWU5T71VI3Xc9ttt2HLli0u+UhPT8dHH32E2rVrOxswRY2jsLwJIfDuu++WuTZPU1W1QDajoqJQrVo1Z47T0tJgt9td9mnSpAksFkuxs6NLejuX1fjx4xEUFIQxY8YU+Ju8cOECHnroIQQHB2P8+PEuP+vevTsiIiKwYMECLFiwAG3atHH5UD4qKgqdO3fGrFmzCp196cirLynrmPz8/NCqVatCm5ruVNjra0cT1JG54p47CqOqKoYMGYJNmzZh0aJFLmsEX+v2228v8FyzevVqHDhwAHfeeadzW//+/eHn54cZM2Y4twkhMHPmTFSvXh3t27cvtqb8Swvlt3z5cgBXl4axWq1QFMVlDfljx47d0NJvJWG32zFr1izn99nZ2Zg1axYiIyOLXPqiNNkqyXOn3tw9ntLk9fTp04WutZ6dnY3Vq1fDYrE4Z/sOHjwYmzZtKvS5MSUlxfnYHRwcXOz1p6am4vDhw9fNLBERyWXqGbfFOXHiBObMmYMTJ044D2l6+umnsWLFCsyZMwdTpkzBkSNHcPz4cSxatAifffYZVFXFE088gTvuuAO//PKLziMgIvKMpUuX4tKlS+jXr1+hP//Xv/6FyMhIzJs3D0OGDMH48eOxePFi3HnnnRg1ahRatmyJCxcuYOnSpZg5cybi4+MRGxuL8PBwzJw5E6GhoQgJCUHbtm0RExODBx54AIsXL0bPnj0xePBgHD58GF988YVz5qZDnz59sGTJEgwcOBC9e/fG0aNHMXPmTDRq1KjQBmFJDRkyBPfeey9mzJiBhIQE5wlzHBo1aoTOnTujZcuWiIiIQGJiIhYvXoxHH33Uuc8333yDkSNHYs6cORgxYsQN13I9FosFH3/8MXr16oXGjRtj5MiRqF69Ov755x+sWbMG5cuXx/fffw8AmDRpElasWIGOHTvikUcegd1ux//93/+hcePG1z3kvKz3qSdqvJ7nnnsOX331FXr16oVx48YhIiICn376KY4ePYqvv/7auZ5wUeNo2LAhYmNj8fTTT+Off/5B+fLl8fXXX5f58HG73Y4vvvii0J8NHDiwwJqT7nDp0iVER0fjjjvuQHx8PMqVK4dVq1Zh69atzpNK/fLLL3j00Udx5513on79+rDb7fj8889htVpx++23F3nZJb2dy6pevXr49NNPcc8996BJkya4//77ERMTg2PHjmH27Nk4d+4cvvrqqwKPG35+fhg0aBDmz5+P9PR0TJ06tcBlf/DBB7j55pvRpEkTjB49GnXq1MGZM2ewadMmnDx5Ert27XLLGDyprGPq378/XnzxRaSlpTmXnnC3yZMnY926dejduzdq1aqFs2fPYsaMGYiOjnbOqi7ucaYwTz31FJYuXYq+ffviwoULBf7W8p/c6oUXXsCiRYvQpUsXPPbYY7h8+TLefvttNGnSBCNHjnTuFx0djccffxxvv/02cnJy0Lp1a3z77bdYv3495s2bd90lHfr374+YmBj07dsXsbGxSE9Px6pVq/D999+jdevW6Nu3LwCgd+/eeOedd9CzZ08MHToUZ8+exQcffIC6deuW+fGwMNWqVcObb76JY8eOoX79+liwYAF27tyJjz76CH5+fkX+XkmzVZLnTm/gzvE4Gt7jxo1DQkICrFary1q1+Z08eRJt2rRB165d0a1bN1SpUgVnz57FV199hV27duHxxx93Lmkwfvx4LF26FH369MGIESPQsmVLpKen448//sDixYtx7NgxVKpUCUFBQWjUqBEWLFiA+vXrIyIiAjfddJNzzfpVq1ZBCIH+/fvLvEmJiOh6BAkhhAAgvvnmG+f3y5YtEwBESEiIyz+bzSYGDx4shBBi9OjRAoDYv3+/8/e2bdsmAIh9+/Z5eghERLro27evCAwMFOnp6UXuM2LECOHn5yfOnTsnhBDi/Pnz4tFHHxXVq1cX/v7+Ijo6WgwfPtz5cyGE+O6770SjRo2EzWYTAMScOXOcP5s2bZqoXr26CAgIEB06dBCJiYmiU6dOolOnTs59NE0TU6ZMEbVq1RIBAQGiefPmYtmyZWL48OGiVq1aLvUBEBMmTCjReNPS0kRQUJAAIL744osCP//Pf/4j2rRpI8LDw0VQUJBo2LCheO2110R2drZznzlz5hQYU1EAiLFjxxa7z5o1awQAsWjRokJ/vmPHDjFo0CBRsWJFERAQIGrVqiUGDx4sVq9e7bLfr7/+Klq2bCn8/f1FnTp1xMyZM8WECRPEtS8XatWqJYYPH+6yraz3qbtrvJ7GjRu75EUIIQ4fPizuuOMOER4eLgIDA0WbNm3EsmXLCvxuUeP466+/RPfu3UW5cuVEpUqVxOjRo8WuXbsKjLWk9Q4fPlwAKPLf0aNHhRBX87R161aX33dcT3JycoHLDQkJcdmW/28gKytLjB8/XsTHx4vQ0FAREhIi4uPjxYwZM5z7HzlyRIwaNUrExsaKwMBAERERIbp06SJWrVrlcrmFZaUkt3NRmT569GiJ/3aEEGL37t3i7rvvFlWrVhV+fn6iSpUq4u677xZ//PFHkb+zcuVKAUAoiiL+/vvvQvc5fPiwGDZsmKhSpYrw8/MT1atXF3369BGLFy927lPU/VISixYtEgDEmjVrCvzMcbmO+1+I3Nu5d+/eBfYt7PHDcRu+/fbbpR5TUc6cOSNsNpv4/PPPXbYXlrUbHcvq1atF//79RbVq1YS/v7+oVq2auPvuu8WBAwdc9ivuceZanTp1KvZv7Fp79uwRt956qwgODhbh4eHinnvuEadPny6wn6qqzucff39/0bhx40KfLwrz1VdfibvuukvExsaKoKAgERgYKBo1aiRefPFFkZaW5rLv7NmzRb169URAQIBo2LChmDNnTqGPL6XJQWF/e506dRKNGzcWiYmJol27diIwMFDUqlVLvP/++4Ve5rW3eUmyVZLnzsI46s3/t+Ko91qFPf8XJzk5udDXB+4aj91uF//+979FZGSkUBSl2OeFtLQ08e6774qEhAQRHR0t/Pz8RGhoqGjXrp343//+JzRNc9n/0qVL4vnnnxd169YV/v7+olKlSqJ9+/Zi6tSpLjVs3LjR+Zx67ViHDBkibr755hLfXkREJIcihBtWnjcARVHwzTffOA95XbBgAe655x78+eefBT4ZL1euHKpUqYIJEyZgypQpyMnJcf4sIyMDwcHB+Pnnn9GjRw9PDoGIiIiISBf3338/Dhw4gPXr1+tdCrlZ586dce7cueuuZU7Gcfr0acTExGD+/PmccUtEpDMulVCE5s2bQ1VVnD17Fh07dix0nw4dOsBut+Pw4cPOQ+0ci/S76+QaRERERETebsKECahfvz42bNiADh066F0OEZXB9OnT0aRJEzZtiYi8gKln3F6+fBmHDh0CkNuofeedd9ClSxdERESgZs2auPfee7FhwwZMmzYNzZs3R3JyMlavXo2mTZuid+/e0DQNrVu3Rrly5TB9+nRomoaxY8eifPny+Pnnn3UeHRERERERUdlwxi0REZF+3HMGCB+VmJiI5s2bo3nz5gCAJ598Es2bN8crr7wCAJgzZw6GDRuGp556Cg0aNMCAAQOwdetW1KxZE0DuSV++//57VKpUCbfccgt69+6NuLg4zJ8/X7cxERERERERERERke8z9YxbIiIiIiIiIiIiIm9k6hm3RERERERERERERN6IjVsiIiIiIiIiIiIiL2PTuwBP0zQNp06dQmhoKBRF0bscIiIiIiIiIiIiMgkhBC5duoRq1arBYil+Tq3pGrenTp1CjRo19C6DiIiIiIiIiIiITOrvv/9GdHR0sfuYrnEbGhoKIPfGKV++vM7VyGe327Fjxw40b94cNpvp7m6SiNkiGZgrkoXZIlmYLZKBuSJZmC2SgbkiWYyarbS0NNSoUcPZoyyOcUZdQo7lEcqXL2+axm1ISAjKly9vqJCT/pgtkoG5IlmYLZKF2SIZmCuShdkiGZgrksXo2SrJEq48ORkRERERERERERGRl2Hj1uAURUFQUBBPxEZux2yRDMwVycJskSzMFsnAXJEszBbJwFyRLMwWoAghhF5Xvm7dOrz99tvYtm0bkpKS8M0332DAgAFF7r9kyRJ8+OGH2LlzJ7KystC4cWNMnDgRCQkJJb7OtLQ0hIWFITU11RRLJRAREREREREREZF3KE1vUtcFItLT0xEfH49Ro0Zh0KBB191/3bp16NGjB6ZMmYLw8HDMmTMHffv2xebNm9G8eXMPVOx7NE3DuXPnUKlSJVgsnGBN7sNskQzMFcnCbJEszBbJwFyRLMwWXUtVVeTk5JTpMjRNw8WLF1GhQgXmitzKV7Pl5+cHq9XqlsvStXHbq1cv9OrVq8T7T58+3eX7KVOm4LvvvsP333/Pxm0RNE3DkSNHEBER4VMhJ+/HbJEMzBXJwmyRLMwWycBckSzMFjkIIXD69GmkpKS45bKys7ORmppq6kPayf18OVvh4eGoUqVKmev26VOyaZqGS5cuISIiQu9SiIiIiIiIiIh8gqNpGxUVheDg4DI1l4QQuHLlSpkvh+havpgtR81nz54FAFStWrVMl+fTjdupU6fi8uXLGDx4cJH7ZGVlISsry/l9WloaAMBut8NutwMALBYLLBYLNE2DpmnOfR3bVVVF/qWAi9putVqhKIrzcvNvB3IPQSjJdpvNBiGEy3ZFUWC1WgvUWNT2/GPKf1lGGZMR7ydfG5NjH03TXK7Xl8dkxPvJF8fk+L+RxnS92jkm+WMSQhSo3dfHZMT7yRfH5Ph5URnzxTFdr3aOSf6YABS4Xl8fkxHvJ18cU/56jTKm4mrnmAqvXVVVpKSkIDIyssBEOEVRXC63JNs1TYOqqggICHA2127kckqzvTRk18IxyRuT4+8rf7Y8VXtZxhQYGAghBJKTkxEZGVngckvDZxu3X375JSZNmoTvvvsOUVFRRe73+uuvY9KkSQW279ixAyEhIQCAyMhIxMbG4ujRo0hOTnbuEx0djejoaBw4cACpqanO7XXq1EFUVBT27NmDjIwM5/aGDRsiPDwcO3bscHlQbNq0Kfz9/ZGYmOhSQ6tWrZCdnY3du3c7t1mtVrRu3RqpqanYt2+fc3tQUBDi4+Nx7tw5HDlyxLk9LCwMcXFxOHXqFE6ePOnc7hjTiRMnkJ6eju3bt0NRFEOMyYj3ky+OqXz58ggLC8Pp06dx6tQpQ4zJiPeTr41p7969zses4OBgQ4zJiPeTL46pRYsWCAkJcT4fGmFMRryffHFMQgikp6cjKysLiqIYYkxGvJ98bUwVK1ZEVlaWy2OWr4/JiPeTL45JCAGLxQJFUQwzJsB495MnxlS+fHkEBgYiPT3duU1RFISEhEBVVWRmZjq3WywWBAcHw263u0yMs1qtCAoKQk5ODjRNw5UrVwDkNrQDAwORlZXl0kj29/eHv78/MjMzXWoMCAiAn58fMjIyXJrXgYGBsNlsuHLliksDLSgoCBaLxaV2AAgJCYGmaS63S1nGlJ2d7dzOMek3JqvVClVVfXJMAHDhwgWXx4KgoCDExMSgpBRR1pa4myiKgm+++QYDBgy47r7z58/HqFGjsGjRIvTu3bvYfQubcVujRg2cP3/eeeY2fmrHMXFMHBPHxDFxTBwTx8QxcUwcE8fEMXFMHJMZxpSZmYm///4btWvXRmBgoMv+3jZjk7NTC+dttXNMBWVmZuLYsWOoXbs2/Pz8XC43PT0dYWFhSE1NdfYmi+JzjduvvvoKo0aNwvz589G/f/9SX09aWlqJbxwj0DQNp06dQrVq1WCxcPF5ch9mi2RgrkgWZotkYbZIBuaKZGG2CMhtKB09ehQxMTEFGrc3QgiBnJwc+Pn5lfowcKLi+HK2ivs7K01vUtdH6suXL2Pnzp3YuXMnAODo0aPYuXMnTpw4AQB4/vnnMWzYMOf+X375JYYNG4Zp06ahbdu2OH36NE6fPu1y2AC50jQNJ0+edPnkjsgdmC2SgbkiWZgtkoXZIhmYK5KF2SJZ8h+ublaKouDbb7/VuwwXetU0ceJENGvWrMyXM3fu3GKXRzUDXRu3iYmJaN68OZo3bw4AePLJJ9G8eXO88sorAICkpCRnExcAPvroI9jtdowdOxZVq1Z1/nvsscd0qZ+IiIiIiIiIiDxn06ZNsFqt1106szC1a9fG9OnT3V/UdSiKUuy/iRMnFvm7x44dg6IozkmP7jRixIhC6+nZs2eZLvfpp5/G6tWry1zfkCFDsGPHjjJfji/T9eRknTt3Lna9iLlz57p8v3btWrkFERERERERERGR15o9ezb+/e9/Y/bs2c6lP7xdUlKS8+sFCxbglVdewf79+53bypUrp0dZAICePXtizpw5LtsCAgLKdJnlypVzy5iCgoIQGRlZ5svxZVzUxuAsFgsiIyO5fhG5HbNFMjBXJAuzRbIwWyQDc0WyMFski83mmXmBly9fxoIFC/Dwww+jd+/eBSb8AcD333+P1q1bIzAwEJUqVcLAgQMB5E4ePH78OJ544gnnzFKg8MP6p0+fjtq1azu/37p1K3r06IFKlSohLCwMnTp1wvbt20tcd5UqVZz/wsLCoCiK8/uoqCi88847iI6ORkBAAJo1a4YVK1Y4fzcmJgYA0Lx5cyiKgs6dO7ulJoeAgACX+qpUqYIKFSo4f64oCmbNmoU+ffogODgYcXFx2LRpEw4dOoTOnTsjJCQE7du3x+HDh52/c+1tunbtWrRp0wYhISEIDw9Hhw4dcPz4cQDArl270KVLF4SGhqJ8+fJo2bIlEhMTAeRO6IyOjnap98MPP0RsbCz8/f3RoEEDfP755y4/VxQFH3/8MQYOHIjg4GDUq1cPS5cuLfXt4i34aG1wFosFsbGxfGImt2O2SAbmimRhtkgWZotkYK5IFmaLCiMEkJFx4/8yMxUIEYjMTKXUv1vMQdiFWrhwIRo2bIgGDRrg3nvvxSeffOJyJPcPP/yAgQMH4rbbbsOOHTuwevVqtGnTBgCwZMkSREdHY/LkyUhKSnKZBXs9ly5dwvDhw/Hbb7/h999/R7169XDbbbfh0qVLpRtAId59911MmzYNU6dOxe7du5GQkIB+/frh4MGDAIAtW7YAAFatWoWkpCQsWbJEek3XevXVVzFs2DDs3LkTDRs2xNChQzFmzBg8//zzSExMhBACjz76aKG/a7fbMWDAAHTq1Am7d+/Gpk2b8OCDDzob5/fccw+io6OxdetWbNu2Dc899xz8/PwAwLmP4//ffPMNHnvsMTz11FPYs2cPxowZg5EjR2LNmjUu1zlp0iQMHjwYu3fvxm233YZ77rkHFy5ccPvt4gm6LpVA8mma5jyLHZ+cyZ2YLZKBuSJZmC2ShdkiGZgrkoXZosJkZgIdO5blEgSEEHnNNaVUv7l+PRAUVPL9Z8+ejXvvvRdA7iH+qamp+PXXX52zUF977TXcddddmDRpkvN34uPjAQARERGwWq0IDQ1FlSpVSlVn165dXb7/6KOPEB4ejl9//RV9+vQp1WVda+rUqXj22Wdx1113AQDefPNNrFmzBtOnT8cHH3zgXCqgYsWKLnW7q6Zly5YVWNbghRdewAsvvOD8fuTIkRg8eDAA4Nlnn0W7du3w8ssvIyEhAQDw2GOPYeTIkYVeflpaGlJTU9GnTx/ExsYCAOLi4pw/P3HiBMaPH4+GDRsCAOrVq+f8maMp78jX1KlTMWLECDzyyCMAcs+V9fvvv2Pq1Kno0qWL8/dGjBiBu+++GwAwZcoUvPfee9iyZUuZ1+7VAx+pDU7TNCQnJ/OsoeR2zBbJwFyRLMwWycJskQzMFcnCbJEsxZ2/yF3279+PLVu2OBtyNpsNQ4YMwezZs5377Ny5E926dXP7dZ85cwajR49GvXr1EBYWhvLly+Py5cs4ceJEmS43LS0Np06dQocOHVy2d+jQAXv37vVITV26dMHOnTtd/j300EMu+zRt2tT5deXKlQEATZo0cdmWmZmJtLS0ApcfERGBESNGICEhAX379sW7777rMtv5ySefxAMPPIDu3bvjjTfecFly4Vp79+4t0W2Vv96QkBCUL18eZ8+eLe5m8FqccUtEREREREREZFKBgbkzX2+UEEB6egZCQkKglG7CLQIDS77v7NmzYbfbXU5GJoRAQEAA3n//fYSFhSGoNNN381gslgKN55ycHJfvhw8fjvPnz+Pdd99FrVq1EBAQgHbt2iE7O7vU1+cu7qopJCQEdevWLXYfx9IFwNVlCwrbVtSHQnPmzMG4ceOwYsUKLFiwAC+99BJWrlyJf/3rX5g4cSKGDh2KH374AT/++CMmTJiA+fPnO9cmvhH5a3PU56sfWHHGLRERERERERGRSSlK7nIFevwraaPXbrfjs88+w7Rp01xmhu7atQvVqlXDV199BSB3puXq1auLvBx/f3+oquqyLTIyEqdPn3Zp3u7cudNlnw0bNmDcuHG47bbb0LhxYwQEBODcuXMlK74Y5cuXR7Vq1bBhw4YC19eoUSNnzQAK1C2rJlmaN2+O559/Hhs3bsRNN92EL7/80vmz+vXr44knnsDPP/+MQYMGYc6cOYVeRlxcXLG3lRFxxq3BWSwWREdHc/0icjtmi2RgrkgWZotkYbZIBuaKZGG2SBZHc1GWZcuW4eLFi7j//vsRFhbm8rPbb78ds2fPxkMPPYQJEyagW7duiI2NxV133QW73Y7ly5fj2WefBQDUrl0b69atw1133YWAgABUqlQJnTt3RnJyMt566y3ccccdWLFiBX788UeUL1/eeR316tXD559/jlatWiEtLQ3jx4+/odm9hRk/fjwmTJiA2NhYNGvWDHPmzMHOnTsxb948AEBUVBSCgoKwYsUKREdHIzAwEGFhYW6rKSsrC6dPn3bZZrPZUKlSJbeM7+jRo/joo4/Qr18/VKtWDfv378fBgwcxbNgwZGRkYPz48bjjjjsQExODkydPYuvWrbj99tsLvazx48dj8ODBaN68Obp3747vv/8eS5YswapVq9xSqzfio7XB8YmZZGG2SAbmimRhtkgWZotkYK5IFmaLZFAUBf7+/s7D5WWYPXs2unfvXqBpC+Q2bhMTE7F792507twZixYtwtKlS9GsWTN07doVW7Zsce47efJkHDt2DLGxsc6TfsXFxWHGjBn44IMPEB8fjy1btuDpp58ucP0XL15EixYtcN9992HcuHGIiopyy9jGjRuHJ598Ek899RSaNGmCFStWYOnSpc6TdNlsNrz33nuYNWsWqlWrhv79+7u1phUrVqBq1aou/26++Wa3jA0AgoODsW/fPtx+++2oX78+HnzwQYwdOxZjxoyB1WrF+fPnMWzYMNSvXx+DBw9Gr169nCeXc2TK8f8BAwbg3XffxdSpU9G4cWPMmjULc+bMcZ6czogU4YkVpL1IWloawsLCkJqa6vLpiVGpqooDBw6gfv36sFqtepdDBsJskQzMFcnCbJEszBbJwFyRLMwWAUBmZiaOHj2KmJgYBJZmkdkiCCGQmZmJwMBAqc1bMh9fzlZxf2el6U3yYzaDE0IgNTXVI2d4JHNhtkgG5opkYbZIFmaLZGCuSBZmi2S5dv1VIncxe7bYuCUiKgl7OpB1Qe8qiIiIiIiIiMgkeHIyIqKSWNUp9//d1gB+ofrWQkRERERERESGxxm3BmexWFCnTh0uPk9uZ9psXT6qdwWGZtpckXTMFsnCbJEMzBXJwmyRLAEBAXqXQAZl9mxxxq3BWSwWt53pkCg/ZotkYK5IFmaLZGG2SAbmimRhtkgGRVHg5+endxlkQMwWZ9wanqqq2LVrl+kXcyb3M1W2hHb1ax87k6WvMVWuyKOYLZKF2SIZmCuShdkiGYQQuHLlCk96R27HbLFxa3hCCGRkZJg65CSHqbKVv3EbUku/OkzAVLkij2K2SBZmi2RgrkgWZotk0TTt+jsR3QCzZ4tLJRARXY9iAVpMB6AB1mC9qyEiIiIiIiIiE2DjlojoehQLEHWz3lUQERERERERkYlwqQSDs1qtaNiwIaxWq96lkMGYLlt/vQXsfAG4ckrvSgzNdLkij2G2SBZmi2RgrkgWZotkCQwM1LsEtxoxYgQGDBjg/L5z5854/PHHPV7H2rVroSgKUlJSPH7dRfF0Tfmz5a774dr715uxcWtwiqIgPDwcCk+oRG5mqmyp2cCJhcDpn4GcFL2rMTRT5Yo8itkiWZgtkoG5IlmYLZJBURTYbDbpuRoxYgQURYGiKPD390fdunUxefJk2O12qdcLAEuWLMGrr75aon091dh0XE9x/9auXVvk78+dOxfh4eFSaqtdu3ah9bzxxhulupxrs1Wa+6E47777LubOnVvmy/EENm4Nzm63Y+vWrR55ICNzMVW27Jevfs0TOUhlqlyRRzFbJAuzRTIwVyQLs0UyCCGQnp7ukZPe9ezZE0lJSTh48CCeeuopTJw4EW+//Xah+2ZnZ7vteiMiIhAaGuq2y3OH9u3bIykpyflv8ODBztvH8a99+/a61Td58mSXWpKSkvDvf/+7VJdxbbbcdT+EhYVJa1q7Gxu3JqCqqt4lkEGZJlsi31kshUnGrCPT5Io8jtkiWZgtkoG5IlmYLZLBE01bAAgICECVKlVQq1YtPPzww+jevTuWLl0K4Orh76+99hqqVauGBg0aAAD+/vtvDB48GOHh4YiIiED//v1x7Ngx52Wqqoonn3wS4eHhqFixIp555pkC47n2EP2srCw8++yzqFGjBgICAlC3bl3Mnj0bx44dQ5cuXQAAFSpUgKIoGDFiBABA0zS8/vrriImJQVBQEOLj47F48WKX61m+fDnq16+PoKAgdOnSxaXOa/n7+6NKlSrOf0FBQc7bp0qVKggICMADDzyAChUqIDg4GL169cLBgwcB5M7WHTlyJFJTU52zYSdOnAgA+Pzzz9GqVSuEhoaiSpUqGDp0KM6ePVvau8r5+/n/hYSEOK9fURT89NNPaN68OYKCgtC1a1ecPXsWP/74I+Li4lC+fHncc889SE9PL/J+mDFjBurVq4fAwEBUrlwZd9xxh/NnixcvRpMmTRAUFISKFSuie/fuzsu6dqmErKwsjBs3DlFRUQgMDMTNN9+MrVu3On/uqHf16tVo1aoVgoOD0b59e+zfv7/Ut0tpsXFLRHRdWhFfExERERERGYQ9o+h/anbx+6r5v84q2eW6QVBQkMvM2tWrV2P//v1YuXIlli1bhpycHCQkJCA0NBTr16/Hhg0bUK5cOfTs2dP5e9OmTcPcuXPxySef4LfffsOFCxfwzTffFHu9w4YNw1dffYX33nsPe/fuxaxZs1CuXDnUqFEDX3/9NQBg//79SEpKwrvvvgsAeP311/HZZ59h5syZ+PPPP/HEE0/g3nvvxa+//gogt8E8aNAg9O3bFzt37sQDDzyA55577oZvmxEjRiAxMRFLly7Fpk2bIITAbbfdhpycHLRv3x7Tp09H+fLlnbNhn376aQBATk4OXn31VezatQvffvstjh075mw+u9vEiRPx/vvvY+PGjc4G+/Tp0/Hll1/ihx9+wM8//4yZM2cW+ruJiYkYN24cJk+ejP3792PFihW45ZZbAABJSUm4++67MWrUKOzduxdr167FoEGDivyA4ZlnnsHXX3+NTz/9FNu3b0fdunWRkJCACxcuuOz34osvYtq0aUhMTITNZsOoUaPce4MUwib9GoiIfF3+WbaCjVsiIiIiIjKgVR2L/lmlDkCrd69+v6YHoGY6vw1SNcCaNzewQgug7UdX9/21b+HnCumZeMOlCiGwevVq/PTTTy6H34eEhODjjz+Gv78/AOCLL76Apmn4+OOPneukzpkzB+Hh4Vi7di1uvfVWTJ8+Hc8//zwGDRoEAJg5cyZ++umnIq/7wIEDWLhwIVauXInu3bsDAOrUqeP8eUREBAAgKirKeTh+VlYWpkyZglWrVqFdu3bO3/ntt98wa9YsdOrUCR9++CFiY2Mxbdo0AECDBg3wxx9/4M033yz17XPw4EEsXboUGzZscC6XMG/ePNSoUQPffvst7rzzToSFhUFRFFSpUsXld/M3I+vUqYP33nsPrVu3xuXLl1GuXLkS1/Dss8/ipZdectn2448/omPHqzn7z3/+gw4dOgAA7r//fjz//PM4fPiw8/a8/fbbsX79+kIv/8SJEwgJCUGfPn0QGhqKWrVqoXnz5gByG7d2ux2DBg1CrVq1AABNmjQp9HLS09Px4YcfYu7cuejVqxcA4H//+x9WrlyJ2bNnY/z48c59X3vtNXTq1AkA8Nxzz6F3797IzMyUenI+Nm4Nzmq1omnTpjxrKLmdqbLl0rjloWUymSpX5FHMFsnCbJEMzBXJwmyRLBaLZw7oXrZsGcqVK4ecnBxomoahQ4c6D/EHcptzjqYtAOzatQuHDh0qsC5qZmYmDh8+jNTUVCQlJaFt27bOn9lsNrRq1arI2Zk7d+6E1Wp1NvBK4tChQ7hy5Qp69Ojhsj07O9vZbNy7d69LHQCcTd7S2rt3L2w2m8vlVaxYEQ0aNMDevXuL/d1t27Zh4sSJ2LVrFy5evAhNy528dOLECTRq1KjENYwfP77ATN3q1au7fN+0aVPn15UrV0ZwcLBLE7xy5couSxbk16NHD9SqVQt16tRBz5490bNnTwwcOBDBwcGIj49Ht27d0KRJEyQkJODWW2/FHXfcgQoVKhS4nMOHDyMnJ8fZQAYAPz8/tGnTpsBtlb/eqlWrAgDOnj2LmjVrXufWuHFs3JpA/gctIncyTbZc1rjlyclkM02uyOOYLZKF2SIZmCuShdmiInUvfGYjAEC5ptnfZeXVr/O/R1IUQLmmidvp+7LX5rjaLl3w4Ycfwt/fH9WqVYPN5trWcqyh6nD58mW0bNkS8+bNK3BZkZGRN1RDUFBQqX/n8uXcE17/8MMPBZqXAQEBN1SHDOnp6UhISEBCQgLmzZuHyMhInDhxAgkJCaU+2VulSpVQt27dYvfx8/Nzfq0oisv3jm2OxvG1QkNDsX37dqxduxY///wzXnnlFUycOBFbt25FeHg4Vq5ciY0bN+Lnn3/G//3f/+HFF1/E5s2bERMTU6pxFFcvgCLrcxeucWtwqqoiMTGRC9CT25kqW45ZtooNiGipby0GZ6pckUcxWyQLs0UyMFckC7NFxbIFFf3P6l/svulZWr59A0p2uTcgJCQEdevWRc2aNQs0bQvTokULHDx4EFFRUahbt67Lv7CwMISFhaFq1arYvHmz83fsdju2bdtW5GU2adIEmqY516a9luPDkfx/Z40aNUJAQABOnDhRoI4aNWoAAOLi4rBlyxaXy/r999+vO8bCxMXFwW63u4zr/Pnz2L9/v3PWrL+/f4HHgn379uH8+fN444030LFjRzRs2PCGTkzmTsU1Rm02G7p374633noLu3fvxrFjx/DLL78AyG2sdujQAZMmTcKOHTvg7+9f6NrFsbGx8Pf3x4YNG5zbcnJysHXr1lLNMJaFM26JiK4nMAqIfz33U2YLDysjIiIiIiLyBffccw/efvtt9O/fH5MnT0Z0dDSOHz+OJUuW4JlnnkF0dDQee+wxvPHGG6hXrx4aNmyId955BykpKUVeZu3atTF8+HCMGjUK7733HuLj43H8+HGcPXsWgwcPRq1ataAoCpYtW4bbbrsNQUFBCA0NxdNPP40nnngCmqbh5ptvRmpqKjZs2IDy5ctj+PDheOihhzBt2jSMHz8eDzzwALZt24a5c+fe0Ljr1auH/v37Y/To0Zg1axZCQ0Px3HPPoXr16ujfv79zHJcvX8bq1asRHx+P4OBg1KxZE/7+/vi///s/PPTQQ9izZw9effXVG6rh0qVLOH36tMu24OBglC9f/oYu71rLli3DkSNHcMstt6BChQpYvnw5NE1DgwYNsHnzZqxevRq33noroqKisHnzZiQnJyMuLq7A5YSEhODhhx/G+PHjERERgZo1a+Ktt97ClStXcP/997ul1rLgjFsiouuxhQBVewBVuupdCREREREREZVQcHAw1q1bh5o1a2LQoEGIi4vD/fffj8zMTGcD8amnnsJ9992H4cOHo127dggNDcXAgQOLvdwPP/wQd9xxBx555BE0bNgQo0ePRnp6OoDcdVwnTZqE5557DpUrV8ajjz4KAHj11Vfx8ssv4/XXX0dcXBx69uyJH374wXnofs2aNfH111/j22+/RXx8PGbOnIkpU6bc8NjnzJmDli1bok+fPmjXrh2EEFi+fLnzcP/27dvjoYcewpAhQxAZGYm33noLkZGRmDt3LhYtWoRGjRrhjTfewNSpU2/o+l955RVUrVrV5d8zzzxzw+O5Vnh4OJYsWYKuXbsiLi4OM2fOxFdffYXGjRujfPnyWLduHW677TbUr18fL730EqZNm+Y8+di13njjDdx+++2477770KJFCxw6dAg//fRToWviepoiilpt2aDS0tIQFhaG1NRUt3X5vZndbkdiYiJatWpVosMIiErKdNk68imQfhyoORgIa6h3NYZlulyRxzBbJAuzRTIwVyQLs0VA7om5jh49ipiYGAQGBpb58oQQSE9PR0hIiHPdTyJ38OVsFfd3VpreJBu3BieEgKqqsFqtPhdy8m6mylZ2KvBLt9yvm73FmbcSmSpX5FHMFsnCbJEMzBXJwmwRIKdx68BckTv5crbc1bjlUgkmUNoz/xGVlGmylX786teCJ3KQzTS5Io9jtkgWZotkYK5IFmaLZCjuBFJEZWH2bLFxa3CqqmL37t08ayi5namylb9ZK8z9pCGbqXJFHsVskSzMFsnAXJEszBbJkpGRoXcJZFBmzxYbt0RE15WvWcsZt0RERERERETkAWzcEhFdj5a/WcsZt0REREREREQkHxu3JmC1WvUugQzKNNniUgkeZZpckccxWyQLs0UyMFckC7NFDu5cO9TXThxFvsNXs+Wuvy9F5D9FmwmU5sxtREQAgOQNwLbHcr9u/CJQY6C+9RAREREREd0gTdNw8OBBWK1WREZGwt/f32ebY0TeRgiB7OxsJCcnQ1VV1KtXDxaL67zZ0vQmbTKLJf0JIZCamoqwsDA+EJNbmSpbQoWqAqp/ZfhX66V3NYZmqlyRRzFbJAuzRTIwVyQLs0UAYLFYEBMTg6SkJJw6dcotl6lpWoHmFJE7+Gq2goODUbNmzTLXzsatwamqin379qFVq1aw2Xh3k/uYKluhDTB1xSs4mRyOtzsGIpBHl0ljqlyRRzFbJAuzRTIwVyQLs0UO/v7+qFmzJux2O1S1bCdgttvt2LNnD2666SbmitzKV7NltVphs9nc8gGZ74yaiEgvQZXx9eZ+0DTg4kWgalW9CyIiIiIiIiobRVHg5+cHPz+/Ml2O3W4HAAQGBvpUc428H7PFxi0R0XUJAbSrtRx1Kv4Ja2oXoGorvUsiIiIiIiIiIoNj49bgFEVBUFAQ1y8itzNTtkTGWdzTchrK+afCcrkqADZuZTFTrsizmC2ShdkiGZgrkoXZIhmYK5KF2QIUIYTQuwhPKs2Z24iIAMB+YjkOLH4FABB58zhEthmmc0VERERERERE5ItK05v0vdOyUalomoazZ89C0zS9SyGDMVO2tLx1dQBAaGVbuJ+KZ6ZckWcxWyQLs0UyMFckC7NFMjBXJAuzxcat4WmahiNHjpg65CSHmbIl8o1RmGC8ejJTrsizmC2ShdkiGZgrkoXZIhmYK5KF2WLjlojoulQ1f+OWM26JiIiIiIiISD42bomIriN/s5YzbomIiIiIiIjIE9i4NThFURAWFmbqM/CRHGbKlqZebdya+RANTzBTrsizmC2ShdkiGZgrkoXZIhmYK5KF2QIUIYTQuwhPKs2Z24iIACB195f4Z9U7OHz+JtS+/T00acHHDiIiIiIiIiIqvdL0Jjnj1uA0TcPJkyc5S5DczkzZyi7XCp8lPoMlu8cgB2zaymSmXJFnMVskC7NFMjBXJAuzRTIwVyQLs8XGreEx5CSLmbKVHVAfqw4Mxh9J7WCC4erKTLkiz2K2SBZmi2RgrkgWZotkYK5IFmYLsOldABGRt1NVoHGVzbipyhaEpMcDuEXvkoiIiIiIiIjI4Ni4JSK6noxTGNDkYzSI3IHgK4PBxi0RERERERERycalEgzOYrEgMjISFgvvanIvM2Ur6Pz3aBC5AwAghHkP0fAEM+WKPIvZIlmYLZKBuSJZmC2SgbkiWZgtzrg1PIvFgtjYWL3LIAMyU7Y0TXV+LUy8to4nmClX5FnMFsnCbJEMzBXJwmyRDMwVycJsccat4WmahsOHD5t6IWeSw0zZEvkatxBq0TtSmZkpV+RZzBbJwmyRDN6Wqy1bgIcfBv7+W+9KqKy8LVtkDMwVycJssXFreJqmITk52dQhJznMlK38s2y5VIJcZsoVeRazRbIwWySDt+Vq2TJg61Zg/Xq9K6Gy8rZskTEwVyQLs8XGLRHRdbnMuNU445aIiIjMxW53/T8RERF5Bhu3RETXkb9xyxm3REREZDZq3kshNm6JiIg8i41bg7NYLIiOjjb1GfhIDjNly7FUwraTnXHU7986V2NsZsoVeRazRbIwWySDt+XKcYSqygOPfJ63ZYuMgbkiWZgtwKZ3ASSXI+RE7mambKUGdsHCHVVx8FxTDO1aWe9yDM1MuSLPYrZIFmaLZPC2XDkatiZeYtAwvC1bZAzMFcnCbHHGreGpqoq9e/dC5cfj5GZmylaaXxv8sHcYDiQ34xsWycyUK/IsZotkYbZIBm/LFWfcGoe3ZYuMgbkiWZgtzrg1PCEEUlNTIYTQuxQyGDNlS1WBmuEH0Dx6HSpm1wRwq94lGZaZckWexWyRLMwWyeBtuXK8Xzbx+2bD8LZskTEwVyQLs8XGLRHRddmyT6Jrva/Rtd7X8FNvBhu3REREZCZs3BIREemDjVsiouuocmkmutZbkfsN10ogIiIik3G8/OHLICIiIs/iGrcGZ7FYUKdOHVOfgY/kMFO2hNAK/Zrcz0y5Is9itkgWZotk8LZcORq2dru+dVDZeVu2yBiYK5KF2eKMW8OzWCyIiorSuwwyIFNlS8v3LoWNW6lMlSvyKGaLZGG2SAZvy5VjiQTOuPV93pYtMgbmimRhtjjj1vBUVcWuXbtMfQY+ksNM2XKZZSuMP149mSlX5FnMFsnCbJEM3pYrR8PWS8qhMvC2bJExMFckC7PFxq3hCSGQkZFh6jPwkRymypZL45ZTTWQyVa7Io5gtkoXZIhm8LVc8OZlxeFu2yBiYK5KF2WLjlojouoSW710KZ9wSERGRyXDGLRERkT64xi0R0fXkNWtXH7wDkeUHo77O5RARERF5Ehu3RERE+mDj1uCsVisaNmwIq9WqdylkMGbK1incht/23IStJ7oioVkdvcsxNDPlijyL2SJZmC2SwdtyZc87TytPTub7vC1bZAzMFcnCbLFxa3iKoiA8PFzvMsiAzJStU7gNX+/O/ZozTeQyU67Is5gtkoXZIhm8LVeOhq2jgUu+y9uyRcbAXJEszBbXuDU8u92OrVu3ws5XWeRmZsqWqgIVg0+jZ8N5qGldpnc5hmamXJFnMVskC7NFMnhbrhyNW8649X3eli0yBuaKZGG2OOPWFFROESRJzJKtAPUkmkdvxNAW/4Wffx0AffQuydDMkivyPGaLZGG2SAZvypWjFC8qicrAm7JFxsFckSxmzxYbt0RE1xGvvIi4Vn/mfiM41YSIiIjMhScnIyIi0geXSiAiuh6XZi0bt0RERGQunHFLRESkDzZuDc5qtaJp06amPgMfyWGqbIl871I441YqU+WKPIrZIlmYLZLB23LFGbfG4W3ZImNgrkgWZouNW1Pw9/fXuwQyKNNki41bjzJNrsjjmC2ShdkiGbwpVzw5mbF4U7bIOJgrksXs2WLj1uBUVUViYqLpF3Mm9zNVtvI1axWYYLw6MlWuyKOYLZKF2SIZvC1XjjJMfFJvw/C2bJExMFckC7PFxi0R0XUp+Wfcco1bIiIiMhnOuCUiItKHTe8CiIi8X27jdskfY1CpfhvU1bkaIiIiIk/yVy4jvtou2LW24FtIIiIiz9F1xu26devQt29fVKtWDYqi4Ntvv73u76xduxYtWrRAQEAA6tati7lz50qvk4jM7WDmQPy4916sP9IHpzLi9S6HiIiIyKP+3WEcnur8GDpV/5/epRAREZmKro3b9PR0xMfH44MPPijR/kePHkXv3r3RpUsX7Ny5E48//jgeeOAB/PTTT5Ir9V1WqxWtWrUy9Rn4SA4zZWv3pRH4asfjOJ9elYcISmamXJFnMVskC7NFMnhbrrJygnL/bw/QuRIqK2/LFhkDc0WyMFs6H+fSq1cv9OrVq8T7z5w5EzExMZg2bRoAIC4uDr/99hv++9//IiEhQVaZPi87OxtBQUF6l0EGZJZsaRoQaEtHu9o/oUGIBcAAvUsyNLPkijyP2SJZmC2SwZtydTkrFACQkVNO50rIHbwpW2QczBXJYvZs+dTJyTZt2oTu3bu7bEtISMCmTZt0qsj7qaqK3bt3m/oMfCSHmbIVhCTEVNyLkW2m4OaK0/Qux9DMlCvyLGaLZGG2SAZvy5WSd3JWu2reGU9G4W3ZImNgrkgWZsvHVpY/ffo0Kleu7LKtcuXKSEtLQ0ZGRqEd+KysLGRlZTm/T0tLAwDY7XbY7XYAgMVigcVigaZp0PIdB+3YrqoqhBDX3W61WqEoivNy828HUCBoRW232WwQQrhsVxQFVqu1QI1Fbc8/pvyXZZQxGfF+8rUxOfbRNM3len15TEVt71fpHvToluYYeYHHDl8ckzffT47/G2lM16udY5I/JiFEgdp9fUxGvJ98cUyOnxeVMV8c0/Vq55jkjwlAgevVa0yKYkWrGr8AAOpV2AC7vT/vJx8eU/56jTKm4mrnmDw3psL6Dr4+JiPeT742pqL6Dr48JkVRUBo+1bi9Ea+//jomTZpUYPuOHTsQEhICAIiMjERsbCyOHj2K5ORk5z7R0dGIjo7GgQMHkJqa6txep04dREVFYc+ePcjIyHBub9iwIcLDw7Fjxw6XO6Vp06bw9/dHYmKiSw2tWrVCdnY2du/e7dxmtVrRunVrpKamYt++fc7tQUFBiI+Px7lz53DkyBHn9rCwMMTFxeHUqVM4efKkc7tjTMePH0dKSgq2b98ORVEMMSYj3k++OKbQ0NxD5pKSkpCUlGSIMRV1P1W0ZwPIfQNlz8lyXrcvj8lb76e9e/c6H7OCg4MNMSYj3k++OKbmzZtD0zTn86ERxmTE+8kXxySEQEpKCjIzMxEUFGSIMRnxfvK1MUVERODSpUsuj1l6jalevTjnBxNVg3YhMTGR95MPj0kIgczMTAAwzJgA491PvjampKQkl76DEcZkxPvJF8cUEJC7tvr58+dx/PhxQ4wpKCgIMTExKClF5G8p60hRFHzzzTcYMGBAkfvccsstaNGiBaZPn+7cNmfOHDz++OMuN3x+hc24rVGjBs6fP4/y5csD8J2O/I18cpKdnY0dO3agWbNmsFqthhiTEe8nXxyTquYeshAfH++cGeLrYypq+8k5XXHl0hUAQEg5C6qP2ujzY/LW+yk7Oxs7d+5Es2bNYLPZDDEmI95PvjgmIQR27NiB+Ph45z6+PiYj3k++OCZVVbFz5060aNECNpvNEGO6Xu0ck/wxOT5oyv+YpdeYVNWKQ++3AgBkaBGIf3w57ycfHpOqqti1axdatGgBRVEMMabiaueYPDOmovoOvjwmI95PvjimovoOvjwmRVGQnp6OsLAwpKamOnuTRfGpxu2zzz6L5cuX448//nBuGzp0KC5cuIAVK1aU6HrS0tJKfOMQEQHA8Y/bIz0td9ZtuXJAzdFbgVIe3kBERETkizIygKMf5jZu03Kq4l/Pfq9zRURERL6tNL1JXU9OdvnyZezcuRM7d+4EABw9ehQ7d+7EiRMnAADPP/88hg0b5tz/oYcewpEjR/DMM89g3759mDFjBhYuXIgnnnhCj/J9guPwPS/pz5OBmClbirj6SV3ucI0/Zr2YKVfkWcwWycJskQzelCtVBb7/cyQAYOepzvoWQ2XmTdki42CuSBZmS+fGbWJiIpo3b47mzZsDAJ588kk0b94cr7zyCoDctTMdTVwAiImJwQ8//ICVK1ciPj4e06ZNw8cff4yEhARd6vcFqqpi3759BaZrE5WVubKluX4rzDBmfZgrV+RJzBbJwmyRDN6UK00DNJF7CKgC/euhsvGmbJFxMFckC7Ol88nJOnfuXGzXfO7cuYX+zo4dOyRWRUSUjxBQ8hq3sze/hMo1K+IZxXqdXyIiIiIyBlUFVOF47WPeN85ERER60LVxS0Tk/QS2nx+Mv4+r+P34rWhYLhjg8rZERERkEpoGDGoyCwBQr+J2nashIiIyFzZuDU5RFAQFBUHhiZTIzUyTLcWCVf88g01bc7/VtOJ3p7IxTa7I45gtkoXZIhm8KVeqCpxLr4ZKIaewfO8wdNW7ICoTb8oWGQdzRbIwW2zcGp7VakV8fLzeZZABmSlbqgoo0NA+ZgVqRWiAeitg9de7LEMyU67Is5gtkoXZIhm8KVeaBqRnh6JSCHAxvaLe5VAZeVO2yDiYK5KF2dL55GQkn6ZpOHv2LDROEyQ3M022hIYA5QJCA1Mwpt0rGFB3IqBe0bsqwzJNrsjjmC2ShdkiGbwpV5oGWJXctW3tmo1HH/k4b8oWGQdzRbIwW2zcGp6maThy5IipQ05ymCZb9st4rMmteH/QrbnfCwDC4GPWkWlyRR7HbJEszBbJ4E25UlUgOvwQAKBd7RVs3Po4b8oWGQdzRbIwW2zcEhEVL1+TVkCBAADBMyoTERGROeR/r9y8+jo2bomIiDyIjVsiouIIDULkfqkJK2fcEhERkamo+T6vVhQBu12/WoiIiMyGjVuDUxQFYWFhpj4DH8lhmmwJOwRym7aasOQ2cTnjVhrT5Io8jtkiWZgtksGbcpW/cSuEwhm3Ps6bskXGwVyRLMwWG7eGZ7VaERcXB6vVqncpZDCmyZbQAAGowgpNc4yV71hkMU2uyOOYLZKF2SIZvClXmgbM2jQZAHD8YkOXRi75Hm/KFhkHc0WyMFts3Bqepmk4efKkqRdyJjlMk628pRKEsECDJW+NW4OPWUemyRV5HLNFsjBbJIM35UpVATXvw2uLorJx6+O8KVtkHMwVycJssXFreAw5yWKabOUti6AJC2b//jI+3TEFCKioc1HGZZpckccxWyQLs0UyeFOuNC1vnX+wcWsE3pQtMg7mimRhtgCb3gUQEXk1WzC2nOqLCxf9sOVEd1S6Aj5yEhERkWloGjD25ucBAJVDT7JxS0RE5EGccUtEVJyAivjqjwmYs+UFAOAJOYiIiMhU8jdqP9n8Il8LEREReRDnjRmcxWJBZGQkLBb26Mm9zJQtxxuUplU3IiIsA8hpDfiV17cogzJTrsizmC2ShdkiGbwpV5oGZNmDEGi7gtNpNWG3610RlYU3ZYuMg7kiWZgtNm4Nz2KxIDY2Vu8yyIBMky2hwYZM2Cw23P+v/6BSubPAlc+AsEZ6V2ZIpskVeRyzRbIwWySDN+VKVYEAJXfarSqsnHHr47wpW2QczBXJwmxxqQTD0zQNhw8fNvVCziSHabKVtg9v3XoL3u43CKpmhRAAhMHHrCPT5Io8jtkiWZgtksGbcqWqgJ81CwDQue63XOPWx3lTtsg4mCuShdli49bwNE1DcnKyqUNOcpgmW0KFEIAQFgiR95DJxq00pskVeRyzRbIwWySDN+Uqfwk9Gixg49bHeVO2yDiYK5KF2eJSCURExctr0qrCCiEUCAAQfMdCRERE5qDahfNrBYKNWyIiIg9i45aIqDh5M241zZr3PQCY99M+IiIiMhdNE9d8r1MhREREJsSlEgzOYrEgOjra1GfgIzlMk6282bWasEATlry+LaeayGKaXJHHMVskC7NFMnhTrjRhwSsrPgcAZOSU44xbH+dN2SLjYK5IFmaLM24NzxFyInczTbaEljvjVlydcSuEgKJvVYZlmlyRxzFbJAuzRTJ4U65U9eqRR4qiwW7XuSAqE2/KFhkHc0WyMFuccWt4qqpi7969UPnROLmZabKVb8btkj8exMe/vwItOEbnoozLNLkij2O2SBZmi2TwplxpGqCK3Pk+VkXlUgk+zpuyRcbBXJEszBZn3BqeEAKpqakQQlx/Z6JSMEu2hH9FbD7eHcnp1bDt7y4AANUPsOpcl1GZJVfkecwWycJskQzelCthv4LHbnkaAOBnzUKOed87G4I3ZYuMg7kiWZgtNm6JiIqllWuA9397w3UbZ5oQERGRWWjZqBJ6AgDw5uoZeOxmneshIiIyETZuiYiKkf+IjDoV/0RoQAq0jAZAYCX9iiIiIiLyEJHvE+vD5xvz5GREREQexMatwVksFtSpU8fUZ+AjOcySLU11HJKhYGiL/6J+5E7g4ptAhW56lmVYZskVeR6zRbIwWySDN+VKted2agUsABQeeeTjvClbZBzMFcnCbLFxa3gWiwVRUVF6l0EGZJpsnf4Znw19EX+ebgNN5D5ZCI1TTWQxTa7I45gtkoXZIhm8KVeOGbcKNPSK+wKqeq/OFVFZeFO2yDiYK5KF2QLM27I2CVVVsWvXLlOfgY/kMEu2NOcsEwWayD0lmaZyqoksZskVeR6zRbIwWySDN+Uq/wfWdzefDtXO10G+zJuyRcbBXJEszBZn3BqeEAIZGRmmPgMfyWGWbGl5s0w0YYVyzTZyP7PkijyP2SJZmC2SwZtyJa55s5z7ATbn//gqb8oWGQdzRbIwW3zGJSIqlqNJK4QFArkzbrlUAhEREZmFEK4fWGsmnvVERETkaWzcEhEVQ6h2AIAGa95JOQBVNe+nfURERGQuaWpNPLjwV+f3bNwSERF5DpdKMDir1YqGDRvCarXqXQoZjFmy5VjPVsAKKLkNW864lccsuSLPY7ZIFmaLZPCmXGmaghw14Or3bNz6NG/KFhkHc0WyMFuccWt4iqIgPDwciqJcf2eiUjBLtoRzPVsLfjs2EJ8njkdWcLyuNRmZWXJFnsdskSzMFsngTbnSNEATV9828gNs3+ZN2SLjYK5IFmaLjVvDs9vt2Lp1K+x2u96lkMGYJVs5tirYdaoD/k5tiD+TO2LlgSHI9qujd1mGZZZckecxWyQLs0UyeFOuArWTeLjDy87vOePWt3lTtsg4mCuShdniUgmmoPLFFUlihmxdKXcLpq29BeXLA5a8j7o0rfjfobIxQ65IH8wWycJskQzekiubdhH/qvUTAOCN1R+i9z2hOldEZeUt2SJjYa5IFrNnizNuiYiK4XiOsFqByqEn0CBqO5B5Rt+iiIiIiDzEsWzUmcs18NeZ1sjR/HWuiIiIyDw445aIqBiO2bVWK9C7wcdoFrUcQSmPAbhP17qIiIiIPMGxpq3IW+eWRx4RERF5DmfcGpzVakXTpk1NfQY+ksMs2Sp3dg5m39Ued970NjTkjlUIvmORxSy5Is9jtkgWZotk8KpcidzGbZXQ4+hWbzEUe5rOBVFZeFW2yDCYK5KF2WLj1hT8/Xk4E8lhhmwJNQd+lmzYLCoUxZK3zdxr7MhmhlyRPpgtkoXZIhm8JVdavim2w1u/AT/tnI7VkDt4S7bIWJgrksXs2WLj1uBUVUViYqLpF3Mm9zNLthyHB0KxQHDGrXRmyRV5HrNFsjBbJINX5Uq7pgbNvGf2NgKvyhYZBnNFsjBbbNwSERXLMcskt2lrcdlGREREZHjC9c2yuLaRS0RERNKwcUtEVBzHmxOLFchbKoFn5SAiIiKzOJHRDg8tWoP07PIA+AE2ERGRJ7FxS0RUjKuzSizOxi2XSiAiIiKzUDUbruSE4nJ2GACu9U9ERORJNr0LILmsVitatWpl6jPwkRxmyZZwzCpRrNh/8RYc+rsSBrVoqm9RBmaWXJHnMVskC7NFMnhTrhx9Wk3Lq0VwjVtf5k3ZIuNgrkgWZoszbk0hOztb7xLIoMyQrUxLDew72xIp2dVx7FIbLPtrBC75tdC7LEMzQ65IH8wWycJskQzekquK1j9wf9tXUS38GABA44xbn+ct2SJjYa5IFrNni41bg1NVFbt37zb1GfhIDrNk60zgYExZNQs7zw2AJe8R0+BD1pVZckWex2yRLMwWyeBNuQq1/o1Osd8BsOC/v76D89l19S6JysCbskXGwVyRLMwWl0ogIiqW4/nBagXK2ZIRE5EMW04FAFV1rYuIiIjII0Tui6F9yf/Cjn9uQW2ulEBEROQxbNwSERUjf+O2VeWv0bLBx/BPHwzgGV3rIiIiIvIIx0lZLbnrC5p40hMREZHHcakEEzDzIs4klxmyVSv9Tcy4oxtaV14IKLkPmULwHYtMZsgV6YPZIlmYLZLBa3KV97qnfqVEdIhZjiAk6VwQlZXXZIsMhbkiWcyeLTZuDc5ms6F169aw2Ti5mtzLLNlS1Mso558KP2sOoOQ+YQhN07kq4zJLrsjzmC2ShdkiGbwpV47XPYG2DIxp9woq+f2pc0VUFt6ULTIO5opkYbbYuDU8IQRSUlIghNC7FDIY02TLMbtWsThn3IIzbqUxTa7I45gtkoXZIhm8K1e5r3sUJe9bvg7yad6VLTIK5opkYbbYuDU8VVWxb98+U5+Bj+QwTbby1nVTFCsUx1IJnHErjWlyRR7HbJEszBbJ4FW50lwbt8IbaqIb5lXZIsNgrkgWZouNWyKiYjmbtC4zbtm4JSIiInPYeX4gxn3zI5Ku3JS3xbxvnomIiDyNjVsiomI4T0RmseZb45ZvWIiIiMgcMnOCkJIRiQwtAgBfBxEREXkSG7cGpygKgoKCoDgXpSJyD9Nky3l4oBVns27Cd3sewFmlq85FGZdpckUex2yRLMwWyeBNuXIenZr3ATbXuPVt3pQtMg7mimRhtgDznpbNJKxWK+Lj4/UugwzILNlKR00kXWiE7OBwnMluitW7myK2p95VGZdZckWex2yRLMwWyeBNuYoptxGNWm5E/bA1uHQJzg+1yTd5U7bIOJgrkoXZ4oxbw9M0DWfPnoXGkymRm5klW3+qT2LCis9wIvMWWPMmmph4XXTpzJIr8jxmi2RhtkgGb8pVtZA9uLXBfFzIqYcPNkzBodR/6V0SlYE3ZYuMg7kiWZgtNm4NT9M0HDlyxNQhJznMki1Hk9ZqBYJsl1G1/DH4a6f1LcrAzJIr8jxmi2RhtkgGr8qVsAMATme3wObjt+J8Rk2dC6Ky8KpskWEwVyQLs8XGLRFRsRzPD1YrEFvuF7zZ5w7EiTf0LYqIiIjIU0TuiyFL3qFHdruexRAREZkLG7dERMVoaRmP/w7ogxpBm6BY8h4yhXk/7SMiIiKTyXvdUyngEFrV+AUVA47oXBAREZF5sHFrcIqiICwszNRn4CM5zJKtAJxDxeDT8LPmAEruQ6bCsylLY5ZckecxWyQLs0UyeFOuFOS+7qkWsAXjOj6DhuGrda6IysKbskXGwVyRLMwWYNO7AJLLarUiLi5O7zLIgEyTrbwmrWKxAFruIYKCM26lMU2uyOOYLZKF2SIZvClXjsat8z0zP8D2ad6ULTIO5opkYbY449bwNE3DyZMnTb2QM8lhmmzla9xaLJxxK5tpckUex2yRLMwWyeBNuRJ5NVyd7MTXQb7Mm7JFxsFckSzMFhu3hseQkyxmyZaCvDcrFiugcMatbGbJFXkes0WyMFskgzfl6qfDozH++yU4KfoA4AfYvs6bskXGwVyRLMwWl0ogIiqeY8atYgWslrxJJuZ90iAiIiJzuZRVAWcuVYDdEoEAAJxxS0RE5DmccUtEVJx8SyWkixr4ad9QHM9K0LkoIiIiIs9Q8/q0FlvenB+NjVsiIiJP4Yxbg7NYLIiMjHSuzUnkLmbJVqq9JlJTrdBCg3FJxGLe9idxTxzQS+/CDMosuSLPY7ZIFmaLZPCmXMVXXomwKgdQQdmDTFw9WRn5Jm/KFhkHc0WyMFts3BqexWJBbGys3mWQAZklWyvP/xff/AA8/DBgzV3iFiZeXkc6s+SKPI/ZIlmYLZLBm3LVOHIdmlf+EXZrL8ze/DKuWGIwUu+i6IZ5U7bIOJgrkoXZ4lIJhqdpGg4fPmzqhZxJDrNky3l4oAWwWbJRMfg0AsRZfYsyMLPkijyP2SJZmC2SwZty5ZhhmxnQGL8e7o/D55vqXBGVhTdli4yDuSJZmC02bg1P0zQkJyebOuQkh1my5WjcWq1AlN8u/HdAH3QLe1TfogzMLLkiz2O2SBZmi2Twplw5GrcWa+5bR5UrJfg0b8oWGQdzRbIwW1wqgYioWP0q34+OfdJwyToNUHLXSuDabkRERGQeuW+WgyzJaFp1I3KslQDU17ckIiIik+CMWyKiYoTZjqNa+aOwKnYoeQuiCyF0roqIiIjIMxSR+4F1ePY6PN1lHG6tO1ffgoiIiEyEM24NzmKxIDo62tRn4CM5TJMtkTvLxGK15i50C864lck0uSKPY7ZIFmaLZPCmXFmU3Nc9ijUg9/98HeTTvClbZBzMFcnCbLFxa3iOkBO5m1myZYE99/9WCzSLY6kE866vI5tZckWex2yRLMwWyeBNuXI0ahWLn8v35Ju8KVtkHMwVycJscakEw1NVFXv37oXKswiQm5knW3kzbi1W50k5IIw+Zv2YJ1fkacwWycJskQzelKtPNr+Al5Z/iczwzgDYuPV13pQtMg7mimRhtjjj1vCEEEhNTeWanOR2ZsmWY3atYrFAUTjjVjaz5Io8j9kiWZgtksFbciUEkHy5GgBA8T8AIHfpBE1zriBFPsZbskXGwlyRLMwWG7dERMVyzCqxWK1QreFYc2gQqtcO5bmUiYiIyPC0fJ9V2/xyP8Bm45aIiMhz2LglIipGanZVpF2xQ7HaYLdVwJwtL6BzMDBA78KIiIiIJFNV4JY6SxEedA7+am4X15rXuCUiIiL52Lg1OIvFgjp16pj6DHwkh1my9cEf32L7dmBKm6szS/hmRR6z5Io8j9kiWZgtksFbcqVpQNd6X6NOxT9h9XscX2x7GuevVEYvO+Dvr2tpdIO8JVtkLMwVycJssXFreBaLBVFRUXqXQQZklmw5mrQ2G2BRNIT4X4I/BIBwPcsyLLPkijyP2SJZmC2SwVtypWm5r38AwFI+Bj/v7+DcTr7JW7JFxsJckSzMFmDelrVJqKqKXbt2mfoMfCSHWbLlGJ7FAgRbkvDhHd0wun4ffYsyMLPkijyP2SJZmC2SwVtyZbcDFktuDTa/q28dGXff5S3ZImNhrkgWZoszbg1PCIGMjAxTn4GP5DBFttRMjG58Py5Wt8Jm+Rha3uEZCjjNRBZT5Ip0wWyRLMwWyeAtucqdcZv7ZtmKDDSI3IFsNQCq2kjXuujGeUu2yFiYK5KF2WLjloioaJodVUL2I9wCpFoBi9Xq+IGuZRERERF5Qv6lEpRL+/DSrZ/gxMV6UNWvdK6MiIjIHHRfKuGDDz5A7dq1ERgYiLZt22LLli3F7j99+nQ0aNAAQUFBqFGjBp544glkZmZ6qFoiMhcNyPtgT7HYoHDGLREREZmIqubOuFUUQLH4A0ru9yY+YpWIiMijdG3cLliwAE8++SQmTJiA7du3Iz4+HgkJCTh79myh+3/55Zd47rnnMGHCBOzduxezZ8/GggUL8MILL3i4ct9htVrRsGFDWJ0zBYncwxTZ0uxwHJFhtSqujVsTH6ohkylyRbpgtkgWZotk8JZcaRpgVRwL/vtDAWC12HlyMh/mLdkiY2GuSBZmS+fG7TvvvIPRo0dj5MiRaNSoEWbOnIng4GB88sknhe6/ceNGdOjQAUOHDkXt2rVx66234u67777uLF0zUxQF4eHhUBRF71LIYEyRLaFBABCwwGpTnEsliHz/JfcyRa5IF8wWycJskQzekitVBd5b/xZe/2U2UL4+oOQ2cu12XcuiMvCWbJGxMFckC7Ol4xq32dnZ2LZtG55//nnnNovFgu7du2PTpk2F/k779u3xxRdfYMuWLWjTpg2OHDmC5cuX47777ivyerKyspCVleX8Pi0tDQBgt9thz3vFYbFYYLFYoGkatHwfHzu2q6rqshByUdutVisURXFebv7tAAqcBa+o7TabDUIIl+2KosBqtRaosajtjhqzs7OxY8cONGvWDFar1RBjMuL95ItjUlUVu3fvRnx8PCyWq58B+fKYCmy3ZwMC0IQF+T/gEwKw52TBYgvwvTFdU6O33U/Z2dnYuXMnmjVrBpvNZogxGfF+8sUxCSGwY8cOxMfHu3xi78tjMuL95ItjUlUVO3fuRIsWLWCz2QwxpuvVzjHJH5Omac4jEh216TGmnBwFxy82QFCQgF35Awpyl0qw2zUAvJ98cUyOM7S3aNECiqIYYkzF1c4xeWZMRfUdfHlMRryffHFMRfUdfHlMpW1C69a4PXfuHFRVReXKlV22V65cGfv27Sv0d4YOHYpz587h5ptvhhACdrsdDz30ULFLJbz++uuYNGlSge07duxASEgIACAyMhKxsbE4evQokpOTnftER0cjOjoaBw4cQGpqqnN7nTp1EBUVhT179iAjI8O5vWHDhggPD8eOHTtc7pSmTZvC398fiYmJLjW0atUK2dnZ2L17t3Ob1WpF69atkZqa6nI7BAUFIT4+HufOncORI0ec28PCwhAXF4dTp07h5MmTzu2OMR0/fhwXLlzA9u3boSiKIcZkxPvJF8cUGhoKVVWRlJSEpKQkQ4zp2vvJz34OIXY7VNUGqwU4k3wOuw90R2h5DWe3bUN0jdo+NyZvv5/27t2LlJQUbN++HcHBwYYYkxHvJ18cU/PmzZGTk+N8PjTCmIx4P/nimIQQSElJQWZmJoKCggwxJiPeT742poiICOfzoeMxS48xpadXBFAPdns29vy5FwGaCqHl4Nixv1GnTi3T30++OCYhhPMcMUYZE2C8+8nXxpSUlOTSdzDCmIx4P/nimAICAqCqKs6fP4/jx48bYkxBQUGIiYlBSSkif0vZg06dOoXq1atj48aNaNeunXP7M888g19//RWbN28u8Dtr167FXXfdhf/85z9o27YtDh06hMceewyjR4/Gyy+/XOj1FDbjtkaNGjh//jzKly8PwHc68jfyyYljZnOLFi1cPvny5TEZ8X7yxTGpqoodO3agRYsWLp98+fKYCmzPSMLuWaNwOSMQlW7/HikpGv79bwX16gl88YXmm2O6pkZvu5+ys7Oxfft258w1I4zJiPeTL45JCIHExETn86ERxmTE+8kXx6SqKrZv345WrVpxxi3H5LYxaZqGrVu3ujxm6TGmY8cU/O/lbxBaLgvPvVYXR+aPxYXLEQgftAING/J+8sUxOR6zWrduzRm3HJPbxlRU38GXx2TE+8kXx1RU38GXx6QoCtLT0xEWFobU1FRnb7Ious24rVSpEqxWK86cOeOy/cyZM6hSpUqhv/Pyyy/jvvvuwwMPPAAAaNKkCdLT0/Hggw/ixRdfdLkTHQICAhAQEFBgu81mg83mOnzHHXat/G/wSrL92su9ke2KohS6vagai9vuCHT+y/P1MRnxfvLlMZVmf18Zk8ViAUJr4MXVK5GcDMwbDNhsFigKoGkKbLarv+dTYyrhdr3G5HiCtFqtzuvy9TEZ8X7yxTHZ7fZCnw8B3x1Tcds5Js+OSVEU5z+jjOlGt3NM7hmTpmlFPmZ5ckyKAtwRPwNhwWmw2f6HHw6OxZnzIRg+0FLqMRW33Vfvp+K2e/OY8h95UhhfHJMDx6TfmAp7zPL1MRnxfvLlMbljrN40ppLS7eRk/v7+aNmyJVavXu3cpmkaVq9e7TIDN78rV64UuOEdN65OE4e9ntVqRdOmTYsMIdGNMku2HB+MWSyA1SJgs2TDhozchW7J7cySK/I8ZotkYbZIBm/JlaoCFiVvxlJARaw5PhKrDgxGvklM5GO8JVtkLMwVycJs6TjjFgCefPJJDB8+HK1atUKbNm0wffp0pKenY+TIkQCAYcOGoXr16nj99dcBAH379sU777yD5s2bO5dKePnll9G3b19T34nX4+/vr3cJZFBmyJajcWu15jZvP7mrPfz9AeSsAvzD9SzNsMyQK9IHs0WyMFskgzfkKrdxq0JRAChWON5yXXMkKPkYb8gWGQ9zRbKYPVu6zbgFgCFDhmDq1Kl45ZVX0KxZM+zcuRMrVqxwnrDsxIkTLic9eumll/DUU0/hpZdeQqNGjXD//fcjISEBs2bN0msIXk9VVSQmJhZYZ4OorEyRrfQTeLLD/RjX8RlYrYDVlu/sj4JTTWQwRa5IF8wWycJskQzekitNAyyWvBqEhhph+xAT8ReuWfKPfIi3ZIuMhbkiWZgtnWfcAsCjjz6KRx99tNCfrV271uV7m82GCRMmYMKECR6ojIhMz56O2IhdCA+Ics64FbBACA0Q5n3iICIiInNQVcCqqIACICcNj7UejoxMKzK1gieSJiIiIvfTdcYtEZFXy5tVq2rW3DVurblf5/8ZERERkVFpGqAoGhQAsPhBUXKXTlDtXOufiIjIE9i4JSIqilAhBKCJ3DXdrFZACEvueck445aIiIgMTlM1KBC5M24t/rn/B6CqbNwSERF5gu5LJZBcVqsVrVq14snbyO1Mka285qwmLM6lElThGC9n3MpgilyRLpgtkoXZIhm8JVeqquDNX2YgurqG1/zDHH1baKodgLlPFuOrvCVbZCzMFcnCbHHGrSlkZ2frXQIZlNGzJYTmnHFrseStceucccvGrSxGzxXph9kiWZgtksEbcqUJBX+eboMjaf8CLIFQ8jq3msbXQb7MG7JFxsNckSxmzxYbtwanqip2795t6jPwkRxmyJamXp1xa7PlLpWw7WQnbD/VDbAG6lydMZkhV6QPZotkYbZIBm/JlePqLRYAytXZTpqdefdV3pItMhbmimRhtrhUAhFRkTRNQaY9GJk5wc4Ztx9tmoTgYOBu9m2JiIjI4DR7NrrVW4rq1SyA0vfqjFsTv4EmIiLyJDZuiYiKYA9rjQcXrgMA9Hg5d8YtkHuGZSIiIiLDUzMxvPUbCAoCgH7YdGYUDh+1oXWcn96VERERmQIbtyZg5kWcSS6jZyt/g9ZxcrLc7QIQgHPaCbmV0XNF+mG2SBZmi2Twhlw5ZtYqCgDFgg3Jj2DTH0CzO/Sti8rGG7JFxsNckSxmzxYbtwZns9nQunVrvcsgAzJDtvIfBehYKuGd/v0QWe4UkPYZENZIv+IMygy5In0wWyQLs0UyeEuuhPNTbAVQLM6jj7hSgu/ylmyRsTBXJAuzxZOTGZ4QAikpKRBC6F0KGYwpsnVhB8Z3+TeGNH8PFsvVpRIEAAiulyCDKXJFumC2SBZmi2TwllwJLbdDK/LeNkYEnkD1sCPQ7OY+w7cv85ZskbEwVyQLs8XGreGpqop9+/aZ+gx8JIcZsiUyk9Gk6ibEVvzTOeNWExZA5J+BQu5khlyRPpgtkoXZIhm8JVeOpRIEcj+9vrfOfXi992D4a6f1LIvKwFuyRcbCXJEszBYbt0RERXI0Zx1vVqzWvMYtAE0z7xMHERERmYN2zWsh5/9N/AaaiIjIk9i4JSIqwtU3K7kPlbkzbnPfsGgqZ9wSERGRsQnVdakELe8UKRobt0RERB7Bk5MZnKIoCAoKgqIoepdCBmOGbGmqPfcLJbdZ61wqAYBqV+GnV2EGZoZckT6YLZKF2SIZvCVXmaIS/vvrO2gab8G/ASBvxq3GJaN8lrdki4yFuSJZmC02bg3ParUiPj5e7zLIgMyQLcdSCY5ZtrlLJVhdfkbuZYZckT6YLZKF2SIZvCVX2VowdvxzC8Lq5X4v8j7MhmbXrygqE2/JFhkLc0WyMFtcKsHwNE3D2bNn+ak4uZ0ZsqU6l0O4ulTCweR47E5qD9USpl9hBmaGXJE+mC2ShdkiGbwlV44VESzOd415SyZwqQSf5S3ZImNhrkgWZouNW8PTNA1HjhwxdchJDjNkS2gCAgqg5D5UWq3Ap1ufxdQ178EeEqdzdcZkhlyRPpgtkoXZIhm8JVdWNRUdYpajbvm1AADhWOOWJ2n1Wd6SLTIW5opkYba4VAIRUZHSwm7H8C9vR6VKAoMB5F9Wx8TPG0RERGQSgeIkxrR7BZaQqgA643BWP/y1NxWVW0fqXRoREZEpsHFLRFQEx1GAVmtux1ZR8k5Qpl39GREREZFROZZEEHkHav6VOQLzdwCjmutZFRERkXlwqQSDUxQFYWFhpj4DH8lhhmw5ZtVarVe3Pd1lHGYP6QDruV/0KcrgzJAr0gezRbIwWySDt+Tq6slYr56oFeAH2L7MW7JFxsJckSzMFmfcGp7VakVcHNfiJPczQ7aCUn/BuFt+RFJ2WwB3AAD8bVnws2ZBqDybsgxmyBXpg9kiWZgtksFbcuVo3Aolt2MbbLuASiGZgFoBQJCOldGN8pZskbEwVyQLs8UZt4anaRpOnjxp6oWcSQ4zZMuWdQytotegRtg+5zaRN+PEyOPWkxlyRfpgtkgWZotk8JpcibwPqvNO1HpLuafwTv9+qGzdomNRVBZeky0yFOaKZGG22Lg1PIacZDFDthxnTFaU/A+VuV8LA49bT2bIFemD2SJZmC2SwVty5Zxxm/fBtaOB63iNRL7HW7JFxsJckSzMFhu3RERFu/bNCq6enEPj4m5ERERkcFffKOe9bcxbMsE5E5eIiIikYuOWiKgIIm82iWNdN+BqE5czbomIiMjoLuTUwwcbpmB3xiO5G5TcU6TwdRAREZFn8ORkBmexWBAZGQmLhT16ci8zZEsUu1QCZ9zKYIZckT6YLZKF2SIZvCVXV9RIbD5+K+o6XvY4Pszm6yCf5S3ZImNhrkgWZouNW8OzWCyIjY3VuwwyIDNkSwjHbJKrM25Pp8dCUS+hpaWSPkUZnBlyRfpgtkgWZotk8JZcOVaGsjpeCin8ANvXeUu2yFiYK5KF2eJSCYanaRoOHz5s6oWcSQ4zZOvqUglXHyp/PPQIpqz6COkht+hVlqGZIVekD2aLZGG2SAZvyVUgzqBVjV8Q5bcTAKBYuMatr/OWbJGxMFckC7PFxq3haZqG5ORkU4ec5DBDto4FPIZR8zfit9MPObc5jtDgucnkMEOuSB/MFsnCbJEM3pKrSpYdGNfxGdwU+BEA4LylPVYfvAPns2J0rYtunLdki4yFuSJZmC0ulUBEVCRVs8KuWV0+4nIcKmji5w0iIiIyCeeyUXlHH52y3oFPtwJdu+pYFBERkYlwxi0RUREczVnr1SVuMbDh2/jg9h4IvbhQn6KIiIiIPEW4nqiVH2ATERF5FmfcGpzFYkF0dLSpz8BHcpghW5GZ32JM++1ID+0BoCMAINB2BaEBFwH1ir7FGZQZckX6YLZIFmaLZPCaXGkqoABCye3Y+lszEBqQCYsWCCBI39rohnhNtshQmCuShdnijFvDY8hJFjNkq5y6Cx1qL0dU8JGrGx0nKuNUEynMkCvSB7NFsjBbJIO35MqxVIKS17itZ8898qhphQV6lkVl4C3ZImNhrkgWZouNW8NTVRV79+6FyjMpkZuZIlta3tiUqw+VArlvXDTNwOPWkSlyRbpgtkgWZotk8JpcCdfXQo4GLvg6yGd5TbbIUJgrkoXZYuPW8IQQSE1NhRBC71LIYMyQLeGcVZtvkVvOuJXKDLkifTBbJAuzRTJ4S66Eo0Frsbr+X5j3DbSv85ZskbEwVyQLs8XGLRFR0fIOD7RY8z9U5n7tPMsyERERkUGdSG+D2Ztfxj/KQACAkte4VWDXsywiIiLT4MnJiIiKInLflAiXGbe5XwvOuCUiIiKDO5dZB78eroO2eS+FFL4OIiIi8ijOuDU4i8WCOnXqmHohZ5LDDNlynpDDcrVxm5ZTBccuxCFLidSrLEMzQ65IH8wWycJskQzekitHf9bqeCnknHHLpRJ8lbdki4yFuSJZmC02bg3PYrEgKirK1CEnOUyRLWfj9uoYE8/dg1dWfI4kv8F6VWVopsgV6YLZIlmYLZLBW3JV3vo3mlbdiHI4AiDfh9lc49ZneUu2yFiYK5KF2WLj1vBUVcWuXbtMfQY+ksMM2dqa/R888vUqHMno5dzmmHHCIwTlMEOuSB/MFsnCbJEM3pKr+qEr8HSXcagp5gMAsgIa4rejvXEyLU7XuujGeUu2yFiYK5KF2eIat4YnhEBGRoapz8BHcpghW9lqMC5nBSP/EreOD/rYuJXDDLkifTBbJAuzRTJ4T67y3ijnzbRND+2OjzZ1R2ysjiVRmXhPtshImCuShdnijFsioiI5PtSz5mvctor8Cu/074vojBn6FEVERETkKXmfVDtOSsYjj4iIiDyLM26JiIpQ1/YFRrY5geCAgQByDwkMsl1GpaAkQEvRtTYiIiIi6fLWsnWs92+1aPCz5kDRFAD+OhZGRERkDpxxa3BWqxUNGzaENf+UQSI3MEO2qlp/RZe6SxBqTbq6MW/GiRCcaiKDGXJF+mC2SBZmi2Twnlzlvd7Je/0TkTYPs4d0wKAG/9GxJioL78kWGQlzRbIwW5xxa3iKoiA8PFzvMsiATJEtxywTa77PuByL3LJxK4UpckW6YLZIFmaLZPCaXOW9FnI0bhXnG2e+DvJVXpMtMhTmimRhtjjj1vDsdju2bt0Ku92udylkMGbIlnLN4YEAoIBnJ5PJDLkifTBbJAuzRTJ4S66UvJOTWfJeC1ksNpft5Hu8JVtkLMwVycJsccatKagqX1iRHIbPlnA9IUfuN7lvXLhUgjyGzxXphtkiWZgtksEbcrXnXDfsOlgDt8c3AnB1xq3jw23yTd6QLTIe5opkMXu22LglIiqSY8bt1catc/Yt37AQERGRwR252AL797dAn6Dc7x2vgyyKeWc+EREReRKXSiAiKkohSyVkiXCcSotBhhapV1VEREREHuFYGcqxtK3j5DBcKoGIiMgz2Lg1OKvViqZNm5r6DHwkhxmypeSdeMOSb4xHMm/Dc8sW4U/1cZ2qMjYz5Ir0wWyRLMwWyeAtuYoIOIYGkTvgr50FACjWvAM2uWSUz/KWbJGxMFckC7PFpRJMwd/fX+8SyKCMnq2lSbOwZnU27nsgwrnNMfnW5MvsSGX0XJF+mC2ShdkiGbwhV51rfILGTZcjOPMxAPdBBFZB4t9d8c+lBnqXRmXgDdki42GuSBazZ4szbg1OVVUkJiaafjFncj8zZOtyTgTOX6kCi+3qEwUbt3KZIVekD2aLZGG2SAZvyZVzSYS89f5FeAu8t/4tfLfnfh2rorLwlmyRsTBXJAuzdYMzblNSUrBlyxacPXsWmuZ6mMywYcPcUhgRkd4cD2/5lrhF7eBf8XrvD1De1gzAC3qURUREROQZeUsiOE7U6jhS1cTvn4mIiDyq1I3b77//Hvfccw8uX76M8uXLQ1EU588URWHjlogMo2XYR6jUIh1Byj0AogAAAZbLqB52BHalsr7FEREREUnmmHFryfsU27nEoNCgaRaXD7eJiIjI/Ur9VPvUU09h1KhRuHz5MlJSUnDx4kXnvwsXLsiokYhIF43LL0HPhvMQZE1xblMc71AEp5oQERGRsTkat44Zt34pG/HZ0FaY1HM4NJ6fjIiISLpSN27/+ecfjBs3DsHBwTLqITezWq1o1aqVqc/AR3KYIVvXruvm8rUQni/IBMyQK9IHs0WyMFskg/fk6pqlEmy5bx8tFpWNWx/lPdkiI2GuSBZm6wYatwkJCUhMTJRRC0mSnZ2tdwlkUEbPlvPwQOvVh0qLJW95GM64lcbouSL9MFskC7NFMnhDrq7OuLXk/T/3jbNFUWG361YWlZE3ZIuMh7kiWcyerVI3bnv37o3x48dj4sSJ+Prrr7F06VKXf+RdVFXF7t27TX0GPpLDDNlS8maZWPJ9uqc4Z9xymokMZsgV6YPZIlmYLZLBW3K18XhfLNo1FmpIHADAYsubeatwxq2v8pZskbEwVyQLs3UDJycbPXo0AGDy5MkFfqYoiqlvTCIyGsfhgVc/41IUi8vPiIiIiIxq6989cPkyMLJc7veOQ1UtFhV820dERCRfqRu3Gj9aJSKTsCD3GEBLvjVuVSUYF65Uhl9QhF5lEREREXmE462f4zNsx1FIVoWNWyIiIk8odeOWfI+ZF3EmuYyfrYJLJVxQ2uC1b3/AwIFAW73KMjjj54r0wmyRLMwWyeANuYoMPgYEZMKqVQcQCihWKEruGrds3Poub8gWGQ9zRbKYPVulXuMWAH799Vf07dsXdevWRd26ddGvXz+sX7/e3bWRG9hsNrRu3Ro2G3v05F5myNaM3Qvx/A8LofldnV3rmHHCNytymCFXpA9mi2RhtkgGb8nVyFaT8GqvexFwZVvuBr9Q7DndHn+dacXXQj7KW7JFxsJckSzM1g00br/44gt0794dwcHBGDduHMaNG4egoCB069YNX375pYwaqQyEEEhJSYEQQu9SyGDMkK3k9Jr4J7UOLNarTxKOD/u4aowcZsgV6YPZIlmYLZLBW3JlQW531rlsVHA03tv4Hj7aNImvhXyUt2SLjIW5IlmYrRto3L722mt46623sGDBAmfjdsGCBXjjjTfw6quvyqiRykBVVezbt48njSO3M0O2HEPLf2RGuGUfJvUchm4Vn9OnKIMzQ65IH8wWycJskQzekCshAEXJa9zmezHkOPrIbtejKiorb8gWGQ9zRbIwWzfQuD1y5Aj69u1bYHu/fv1w9OhRtxRFRKQ7TUW3GjNwe9OZsFmynZv9LFcQE/EXKvgd1LE4IiIiIrmEACxK7rRaJV/jlkcfEREReU6pF4moUaMGVq9ejbp167psX7VqFWrUqOG2woiIdCVy0KnGJ8iKAjKUYQD8AVw9VFARfLdCRERExqWquSchAwDFMc024zTe7XMnMrP9oKq/6FgdERGROZS6cfvUU09h3Lhx2LlzJ9q3bw8A2LBhA+bOnYt3333X7QVS2SiKgqCgICiKoncpZDCGz5bQ4FhFx5JvIXTFkjteBeY9VEMmw+eKdMNskSzMFsngDbnStKszbp1n9FYsCLBlQGh2npzMR3lDtsh4mCuShdm6gcbtww8/jCpVqmDatGlYuHAhACAuLg4LFixA//793V4glY3VakV8fLzeZZABGT5bwg5H59ZivbqqjOI4OQc441YGw+eKdMNskSzMFsngDblSVcBiuWaNW8UKRQGsFpWNWx/lDdki42GuSBZm6wYatwAwcOBADBw40N21kASapuHcuXOoVKkSLJZSL2lMVCTDZyvfjFurS+M292uFjVspDJ8r0g2zRbIwWySDN+RK04BVB+5EaEAKngyunLtRsQBK7usgTRUAzDsDyld5Q7bIeJgrkoXZuoGTk5Fv0TQNR44cgcazB5CbGT5bQit+xq3gNBMZDJ8r0g2zRbIwWySDN+RK04Cf9g3F4l2PwBpSJXejYnW2alWVmfdF3pAtMh7mimRhtko44zYiIgIHDhxApUqVUKFChWLXlrhw4YLbiiMi0o1QIQSgCSv88z1SKlYbLmeHwWYL0682IiIiIsns9qtfOyc5KTbnJFvVrgKwXvtrRERE5EYlatz+97//RWhoqPNrMy8KTEQmkTejVhVW5D8iI8tWB88sXo02bYAZOpVGREREJJumAZVDT8CiCChaVcDq7zLjVqh2AP56lkhERGR4JWrcDh8+3Pn1iBEjZNVCEiiKgrCwMDbbye0Mn62AinhtzTxcvizw9qCrmx3n5uAJOeQwfK5IN8wWycJskQzekCtNA165dRTKB6YAGQuBcnUAxYrjac2RkmpBDb4W8knekC0yHuaKZGG2bmCN2+3bt+OPP/5wfv/dd99hwIABeOGFF5Cdne3W4qjsrFYr4uLiYLXyMCZyL8Nny+KH4xcb4PjFhrDl+4jLMfvWxEvsSGX4XJFumC2ShdkiGbwhV6oKWBQtb2mEvBdAFhs++fN/mLJqFnJEsG610Y3zhmyR8TBXJAuzdQON2zFjxuDAgQMAgCNHjmDIkCEIDg7GokWL8Mwzz7i9QCobTdNw8uRJUy/kTHKYIVuOoeVfKsEf5/FC9zG4u+5YfYoyODPkivTBbJEszBbJ4A250jTAarHn9m2Vq2+YHa+LePSRb/KGbJHxMFckC7N1A43bAwcOoFmzZgCARYsWoVOnTvjyyy8xd+5cfP311+6uj8qIISdZDJ+t7ItIqPcJetRfgPwf7vlZc9AwahtqhO7QrzYDM3yuSDfMFsnCbJEM3pAr54xbwKVx6zgSiZH3Td6QLTIe5opkYbZKuMZtfkII5w22atUq9OnTBwBQo0YNnDt3zr3VEZF57X8PCKwM1Bqiz/VnnsOgm2YgNSMCVuvVGix500ws4DQTIiIiMi5NAxRFg6IAUK7O93msWV9k1b+C7Ox5AKroVh8REZEZlLpx26pVK/znP/9B9+7d8euvv+LDDz8EABw9ehSVK1d2e4FEZEJpB4Cjn+V+rVfjFhoEAFVYXZZKUCxW58+JiIiIjErTAKui5q5xm69xG2xLhZ//FZxT7foVR0REZBKlXiph+vTp2L59Ox599FG8+OKLqFu3LgBg8eLFaN++vdsLpLKxWCyIjIx0zhIkchep2bKVc/9llpKmqoAANGF1WSrBYs0drwIBCKFTdcbFxyyShdkiWZgtksEbcpW7VIKat8bt1fk+ArkvjDQucuuTvCFbZDzMFcnCbN3AjNumTZvijz/+KLD97bffNvVZ3ryVxWJBbGys3mWQAUnNli3k6tdCc5nl4SmONyNCWFwat44Zt0I4auPjnjvxMYtkYbZIFmaLZPCGXKl2gR/33ovQUA31rYHO7c7GrcbGrS/yhmyR8TBXJAuzdQMzbv/++2+cPHnS+f2WLVvw+OOP47PPPoOfn59bi6Oy0zQNhw8fNvVCziSH1GxZ8j2WaPochifyxqVeM+PWasv3sCn4hsXd+JhFsjBbJAuzRTJ4Q640oeCrHY/ju/1PArZg53aR96G14Ixbn+QN2SLjYa5IFmbrBhq3Q4cOxZo1awAAp0+fRo8ePbBlyxa8+OKLmDx5stsLpLLRNA3JycmmDjnJITVb2SlXvxY57r/8ElDzzbh1XePWghzNH9lqILjOrfvxMYtkYbZIFmaLZPCGXDn6stceVMmlEnybN2SLjIe5IlmYrRto3O7Zswdt2rQBACxcuBA33XQTNm7ciHnz5mHu3LmlLuCDDz5A7dq1ERgYiLZt22LLli3F7p+SkoKxY8eiatWqCAgIQP369bF8+fJSXy8RebHM01e/1mnGrWbPfTOiaq4zbhVbMO6fvxHPrf4NyHfYIBEREZGRaKqGSiGnUCHojMu6/o7GreBSCURERNKVeo3bnJwcBAQEAABWrVqFfv36AQAaNmyIpKSkUl3WggUL8OSTT2LmzJlo27Ytpk+fjoSEBOzfvx9RUVEF9s/OzkaPHj0QFRWFxYsXo3r16jh+/DjCw8NLOwwi8mYi36dpmj4zbu0hDfGflR8jR/VH1/xLJeR9beIP/IiIiMgEhP0K3unfD/7+ALSNgNUfAHA+ux5Sz0fAUj1A3wKJiIhMoNSN28aNG2PmzJno3bs3Vq5ciVdffRUAcOrUKVSsWLFUl/XOO+9g9OjRGDlyJABg5syZ+OGHH/DJJ5/gueeeK7D/J598ggsXLmDjxo3O9XRr165d2iGYisViQXR0tKnPwEdySM2Wo1kbXBMIKN3jirvYEYoDyc0AwGWpBMfXPDpQDj5mkSzMFsnCbJEM3pArTc39lFpR4HIy1hVn38FPPwFP3aRTYVQm3pAtMh7mimRhtm5gqYQ333wTs2bNQufOnXH33XcjPj4eALB06VLnEgolkZ2djW3btqF79+5Xi7FY0L17d2zatKnQ31m6dCnatWuHsWPHonLlyrjpppswZcoU51qUVBBDTrJIzZbIWx7BVi7v3YLnOWbUWiyuJVgtAk92fhxjWo4Dci7pUpuR8TGLZGG2SBZmi2TwhlwJx3JVCgDlah2Oo4/4Fsw3eUO2yHiYK5KF2bqBGbedO3fGuXPnkJaWhgoVKji3P/jggwgODi7mN12dO3cOqqr+P3t3HiZHVbYN/K6q7p59zSzZ94QkhJCEhH3fZRMjAoKCgCAiLwr6qqCsr7K6i7ggCH6KbCogIPtOWBISEkISsk0ymZlMZt97uruqzvdHdffMZHqbmTq9VN+/6/IKqa7uOsd+crrqqVPPQXV19ZDt1dXV2Lx5c8T37NixA6+99houvPBCPP/889i2bRuuuuoqBAIB3HzzzRHf4/P54PP5wn/v6uoCAOi6Dl23TkZUVYWqqjBNc0jB49B2wzAgBtV1irZd0zQoihL+3MHbAQxLMEfb7nK5IIQYsl1RFGiaNqyN0baH2hgIBLBlyxbMmTMnvC3T++TE7ykT+xRa3XH27NlQBmU27eiTEuiHCkBR3Sn7ngJdtTh5v5Xo9FXBNI8f6JMwceDEd+F2CZh6P1R3UVp/T5kWe4FAAFu3bsWcOXOgaZoj+uTE7ykT+wQAW7ZswezZs4ec+GVyn5z4PWVin0zTxNatW7HffvtB0zRH9Cle29kn+X0SQmDz5s3hc/hU9Mnv8yPPahFMIcKLkVnnfSpMU8n67ykT+2SaJrZt24b99tsPABzRp1htZ5+S06doeYdM7pMTv6dM7FO0vEMm92lwPxIx4sRtqEGDk7ZAckoWmKaJqqoq/OlPf4KmaTjooINQX1+Pe+65J2ri9o477sCtt946bPvatWtRUFAAAKisrMSsWbNQU1OD5ubm8D6TJ0/G5MmTsWXLFnR2doa3z5w5E1VVVdiwYQO8Xm94+7x581BaWoq1a9cO+VIWLVoEj8eD1atXD2nDsmXL4Pf7sX79+vA2TdOwfPlydHZ2Dklg5+Xl4cADD0RLSwt27NgR3l5SUoL58+ejoaEBdXV14e2hPu3cuRO1tbXo6uqCoiiO6JMTv6dM7FNRURG6u7vR0NAwpL61HX0q6duIOboBV8c6bFj1MvqVgfEmWd9T7boXcMGSP2FL80LU1MwI9+nTT7sw2wAUGGjcU4+JMyvT+nvKtNjbtGkTOjo60NXVhfz8fEf0yYnfUyb2acmSJWhvb8dHH30UPlnK9D458XvKxD4JIdDR0YGpU6ciLy/PEX1y4veUaX0qLy9HfX19+Bw+FX3assWHZcKEgGtIn44qeAAnnFWHduP/UFNTltXfUyb2SQiB/v5+zJ07F1u3bnVEnwDnfU+Z2KfBeQen9MmJ31Om9SknJwc+nw8tLS3YtWuXI/qUl5eHGTNmIFGKGJxSjmLp0qV49dVXUVZWhiVLlsTMDq9ZsyahA/v9fuTn5+PJJ5/E2WefHd5+8cUXo6OjA08//fSw9xxzzDFwu9145ZVXwtv++9//4rTTToPP54PH4xn2nkgzbqdMmYLW1lYUFxcDyJyM/GjunIRKUixduhSapjmiT078njKxT4ZhYO3atVi6dOmQ2Wt29EnZ81+on94KBYC+/EGgZEFS+jR4e9P6l9H22o+wrW0Jzrjtj+E+1dSY6Pn30chx+zH3689ALZiY1t9TpsWe3+/HmjVrsHTpUrhcLkf0yYnfUyb2SQiB1atXh38PndAnJ35PmdgnwzCwZs0aLFu2DC6XyxF9itd29kl+n0zTxKpVq4aMWcnu0wdvNKBs/Qq4czyY9c13wts3PnAJXL2bsL3klzj9kiOy+nvKxD6Fxqzly5dDURRH9ClW29mn5PQpWt4hk/vkxO8pE/sULe+QyX1SFAW9vb0oKSlBZ2dnODcZTUIzbj//+c8jJ8daNXRwknUsPB4PDjroILz66qvhzzRNE6+++iquvvrqiO854ogj8Mgjj8A0zfAXtmXLFkyYMCFi0hawsvOhtg/mcrngcg3tfugL29fgC7xEtu/7uaPZrihKxO3R2hhreyigB39epvfJid9TJvdpJPsn1KfKQwfaqAKI8Pmy+6TAukEl4Arvo6oqPB4VQqiAAFRlBH0aZdujbXdq7IV+IDVNCx8r0/vkxO8pE/uk63rE30Mgc/sUazv7lNw+KYoS/p9T+jTa7eyTPX0yTTPqmJWsPinB5VDM4LlQuO2qtUi0EEbWf0+xtqdznwY/eRJJJvYphH1KXZ8ijVmZ3icnfk+Z3Cc7+ppOfUpUQu8cXIYgWkmC0bjuuutw8cUXY9myZTj44IPxq1/9Cr29vbjkkksAABdddBEmTZqEO+64AwDwzW9+E/feey++/e1v43/+53+wdetW3H777bjmmmtsa5PTqKqKmTNnRgxYorGQGlu5lUDBdKB358BCZUlmGqHjDu2fpgGmCG4TQ++m0dhxzCJZGFskC2OLZEiHuAqIfLy29YuoGu/GfkNesS5uhcnzoEyUDrFFzsO4IlkYW6OscRvS09MzZOoxgLhTfAc777zz0NzcjJtuugmNjY1YvHgxXnjhhfCCZbW1tUO+nClTpuDFF1/Etddei0WLFmHSpEn49re/jR/84Adj6YajqaqKqqqqVDeDHEh6bCnB4clMTeJWBMc2gaF33lQVEEKF9RSFOfyNNCYcs0gWxhbJwtgiGdIhrnyiHA+tuh5LlwLnDn5BYeI2k6VDbJHzMK5IFsbWvlPJElBTU4PTTz8dBQUFKCkpQVlZGcrKylBaWjpswbJEXH311di1axd8Ph8++OADHHLIIeHX3njjDTz00END9j/ssMPw/vvvo7+/H9u3b8cNN9wQdUozWfU11q1bN6zOBtFYSY2trq3oadwGrxeACNj/+QkI35RShg6TqsoZtzJxzCJZGFskC2OLZEiHuAqdCg271AomblN1c53GJh1ii5yHcUWyMLZGMeP2K1/5CoQQePDBB1FdXR1zoTJKPSEEvF7vkCLLRHaQGVt9te9g927rQmFuqmbcBn8YzH1m3LpcwFVPvgIBFauvTUXLnI1jFsnC2CJZGFskQzrElTB0FOV0I8/tAlA08EJoxq3gk0eZKB1ii5yHcUWyMLZGkbhdt24dPvroI+y3337xdyYiGoX+Ph1CAIaBlM247c47Ave8/lvkFpdhxaDtqgqI0GIdpvV3IiIiIqfJDWzB7754EUROFYDnw9v7MAHdnTPhK89PXeOIiIiyxIhTDsuXL8fu3btltIWICABg6lay9r2dp8CsOCYlbQioVfhkz2Fo7Js3ZPvgRK3JiSZERETkUOEZtcrQp4/W69fj+ucex07v8SloFRERUXYZ8YzbP//5z7jyyitRX1+PhQsXwu12D3l90aJFtjWOxk7TNMybN491gMl2MmPLNKzyCB3eCuimGx7bjxBfqITOvt3TNOCiZXejNK8FZs/VQOnU5DfOwThmkSyMLZKFsUUypENcmUbkhVpdwSvILC43mNHSIbbIeRhXJAtjaxSJ2+bmZmzfvh2XXHJJeJuiKBBCQFGUrC4YnI4URUFpaWmqm0EOJDO2Qolb3XRD1wFPCjK3Lu8WHDv7U2hF0wAsDW9XVWDxpHdQUdAAs/+ryW+Yw3HMIlkYWyQLY4tkSIu4Cq4zIDB8oVaAidtMlRaxRY7DuCJZGFujKJVw6aWXYsmSJXjvvfewY8cO1NTUDPmT0ouu61i1ahV0nau+kr1kxlaoVMIZCx6C2bza9s9PRH7f+7j04J9i+cRnhmzXNMAQwUU5WCvBdhyzSBbGFsnC2CIZ0iGuTDM043boXJ8Frntx1xnnYHbuM5HeRmkuHWKLnIdxRbIwtkYx43bXrl145plnMHv2bBntIQk4C5pkkRVboRm3ACC6tgBYJuU4cRph/akMn2UihLWN/7bk4P+vJAtji2RhbJEMqY6rgRvUQ8+F8tQWFBbvRLvRkfQ2kT1SHVvkTIwrkiXbY2vEM26PP/54rFu3TkZbiIgAAE255yBg5AAAjBTdWRuYZTK0lo6qAoYZnHFrcMYtEREROZOIchM7vFiZyO4LaSIiomQY8YzbM888E9deey0++eQTHHDAAcMWJzvrrLNsaxwRZaceZT98uvNUHDPraZgpStyGLlYUZXjiNjTj1mSpBCIiInIor1mBd2pOx8SZ1Riy/HTo3Mhk4paIiEi2ESdur7zySgDAbbfdNuw1Lk6WfjRNw6JFi7J6BT6SQ2Zs6TpgmNbwlKoZt+FZJPvOMsHAIh0mxzvbccwiWRhbJAtji2RIh7jqMObgT+/dirMqgTMGv6CGLiF5HpSJ0iG2yHkYVyQLY2sUiVvOMMs8Ho8n1U0gh5IVW7neNVg25XUAQ+vdJpNpRC6VYG2zErfCFEltU7bgmEWyMLZIFsYWyZDquApd9qn7VkoIbeCM24yV6tgiZ2JckSzZHlsjrnFLmcUwDKxevZozocl2MmNrgvchFOe2AQBEihK3oRm3yr5XKwDueuPP+Ppj76C/8NBkt8rxOGaRLIwtkoWxRTKkQ1yZhgGP1g+XNvRcbKCMVPau8J3J0iG2yHkYVyQLY2sEidvTTjsNnZ2d4b/feeed6OjoCP+9tbUVCxYssLVxRJSdBidrTT2QkjY0qqfjN2/fjU87zhj2moFc+I1cmIL3voiIiMiZJiiv4s/nHYlTx109ZLuplaCldwK8gaIUtYyIiCh7JJx1ePHFF+Hz+cJ/v/3229HW1hb+u67r+Oyzz+xtHRFlJWFaidvH1l6DvUWXp6QN3ZiF1buPR1tgzrDXwk8IsnIMEREROVWUev978i/FdU//B+/uuSQFjSIiIsouCde4FULE/DsRkW2Elbjd0z0NPrM4JU0IJWUj1UA/db+HMS5nO5SucwEsTGq7iIiIiJIhWr3/0LkRb2ATERHJx+d8HU7TNCxbtiyrV+AjOaTGlmmVRzBMFwKpqZSA/MCnOHTai6jM2zHstYXV7+GI6c9D9TWkoGXOxjGLZGFskSyMLZIhLeIqPON2aBtCTx7pLHGbkdIitshxGFckC2NrBIlbRVGgKMqwbZT+/H5/qptADiUttoKlEr60+Hco7nlezjHimGA8hauO+BHmFL8+7DURHDoFV1OWgmMWycLYIlkYWyRDyuMqSqmESv+zuPXUi3DUhPtT0CiyQ8pjixyJcUWyZHtsJZy4FULga1/7GlasWIEVK1agv78fV155Zfjvl156qcx20igZhoH169dn9Qp8JIfU2BLWNNuppVtQ3P+m/Z+fSBPMyBcrwMAjg8LgM4J245hFsjC2SBbGFsmQDnE1cC40dJaTB22YUb4RJZ76FLSKxiodYouch3FFsjC2RlDj9uKLLx7y96985SvD9rnooovG3iIiynof910Dd8tDmF2xPrxQWdIJKymrqBEeyVA445aIiIgcLngutO9N7PC5keANbCIiItkSTtz+5S9/kdkOIqKwXd6jsXtbp5W4NVKTuBUi8oIcg7eZXJWDiIiIHKojMBmr647H5NIDhmwPJW4VsMgtERGRbAknbilzZXMRZ5JLVmzpurUwGYDwQmVJF5zpq6gRKsqEZ9wycSsDxyyShbFFsjC2SIZUx9Vu7+F46u3DcdXQvC1UzToPUsAnjzJVqmOLnIlxRbJke2wxcetwLpcLy5cvT3UzyIFkxla1+g5KJ3xg/SUNSyVwcTJ5OGaRLIwtkoWxRTKkQ1yF7k/vew9b1axLSCZuM1M6xBY5D+OKZGFsjWBxMspMQgh0dHRACJHqppDDyIytY0tvwJEznrWOk+rEbYTFyf615UZ8658voy3/88luleNxzCJZGFskC2OLZEiHuDIM69jDHj4K3tTmk0eZKR1ii5yHcUWyMLaYuHU8wzCwefPmrF6Bj+SQG1uDkrUiNYnbLb7z8cf3bsNeffjdvYAoQrevDIbwpKBlzsYxi2RhbJEsjC2SIR3ianHRA/jrBcuwSLt9yHZFy0O3rwz9en6KWkZjkQ6xRc7DuCJZGFsslUBEaUgJJmvveu0+nPT5yZidgjbsDSzFuzXA1EOHvxaaeZLFvx1ERETkdOGnj5Qhm/vLTsa3/nkyKiuB81PRLiIioiySUOL2mWeeSfgDzzrrrFE3hogIwoQC60JhZ9t+6AqUpKQZoaf/ItVBX1L9PA4rXocC73EAImR2iYiIiDKdCN6h3qfef+jciDewiYiI5EsocXv22Wcn9GGKomT19OV0pCgK8vLyht0pJxorabFlBhAqX6ObbgQC9n58osqVj7F0cicKtPkAqoa8NrN0NeZNfgaafwKYuLUXxyyShbFFsjC2SIa0iKvgjFsoQxO3fPIos6VFbJHjMK5IFsZWgolbk4XnM5amaTjwwANT3QxyIGmxJfRw4vbzCx/EXHcxgIvsP04cC3P/gKVHr8b2nNsBnDz0xeCCZRwb7ccxi2RhbJEsjC2SIS3iKjjjdt+FWvP61+GGE3+HNt90ADckv100JmkRW+Q4jCuShbHFxckczzRNNDU1McFEtpMWW4Nm3J6x4CHsl/OIvZ+fICU8y2T4MBm+gDE51cRuHLNIFsYWycLYIhnSIq5Cidt9SyWILsyrWoPJxZ+lolU0RmkRW+Q4jCuShbE1ysXJent78eabb6K2thZ+v3/Ia9dcc40tDSN7mKaJHTt2oLy8HKrKPD3ZR1psaXl4ZvuPgO6tOHHu41BEamoliPDFyvC+CVgXMCKLfzxk4ZhFsjC2SBbGFsmQHnEVPM/ZJ3GrBovcKuAN7EyUHrFFTsO4IlkYW6NI3K5duxannXYa+vr60Nvbi/LycrS0tCA/Px9VVVVM3BLR2Gg5eL/+C+jcU2slbqGnpBmhBdJUNcLqZMEZt6HkLhEREZHTNPfNgK/tcMyeNXXIdlWzLiGZuCUiIpJvxOnqa6+9FmeeeSba29uRl5eH999/H7t27cJBBx2En/3sZzLaSERZRtethckAQEGKVieLspIygIHyCYIzbomIiMiZ1raswM9e/w1ac88csl3VrPMgRWHiloiISLYRJ24//vhjfPe734WqqtA0DT6fD1OmTMHdd9+NG25gcfp0oygKSkpKsnoFPpJDWmzpfZhe9AFmlm+0jiNSM+M2lLiN/DhGcMYtSyXYjmMWycLYIlkYWyRDOsSVEbqHvc+pkBYslaApBngqlHnSIbbIeRhXJAtjaxSlEtxudziRUVVVhdraWsyfPx8lJSXYvXu37Q2ksdE0DfPnz091M8iBpMVWXx0uX/wt+Pyh+mmmNbM1wiJhMoVKJSja8Bm3q9ouxZ9WnYerv1OIhUltlfNxzCJZGFskC2OLZEiHuAolbvc9FQqdG6nBxG2WlhzMWOkQW+Q8jCuShbE1ihm3S5YswapVqwAAxxxzDG666Sb8/e9/x3e+8x0sXMgURroxTRN1dXVZvQIfySEttoQOIQCfnmf9VQAwkz/r9oPWy/GXD2+AV50x7DW/KEFL70T4RXHS2+V0HLNIFsYWycLYIhnSIa5Om/ZT3H/uURjf/48h2zWXC34jFwHDwxm3GSgdYouch3FFsjC2RpG4vf322zFhwgQAwE9/+lOUlZXhm9/8Jpqbm/HHP/7R9gbS2DDISRZpsWUGIATQ6y/GrS8+hAc2/Q1QR/xwwJht7Toer29bgYBWNey10MwTg6XdbMcxi2RhbJEsjC2SIR3iyoV+5Li8UNWhbVBKF+Drj72D6597HHqKKlrR6KVDbJHzMK5IFsbWKEolLFu2LPzfVVVVeOGFF2xtEBFlueCMW7+Rg+2tC1HYBSAF5WyiPR4IANMKPsCXl7yHCmMRgOOT2i4iIiKiZFBgnQwp+9RCGHxulMXX0UREREkx4hm3xx9/PDo6OoZt7+rqwvHHM4FBRGNkBiAA6KYbAFI2k2NC7lrsP/5DeNTeYa9NLFiPz83/G8rM91PQMiIiIqJkCNb7V4bexR6cuOXTR0RERHKNeMbtG2+8Ab/fP2x7f38/3n77bVsaRfZRVRWVlZXhBeWI7CIrtoRpzbg1TBdOmfcIpo7vBwLnAu5CW48Tz6kTb8Rp4xqxF38FsGDoi6ELGE4zsR3HLJKFsUWyMLZIhrSIKxF5oVbF14TvHvtTBIwcGMbdqWgZjUFaxBY5DuOKZGFsjSBxu379+vB/b9y4EY2NjeG/G4aBF154AZMmTbK3dTRmqqpi1qxZqW4GOZCs2DL1ABBM3J6z6PcoLvACgVOSnrgNPR6oRqiVoCjBHw3BxK3dOGaRLIwtkoWxRTKkQ1ypih5uy2CK8GPxpHfhDeRzxm0GSofYIudhXJEsjK0RJG4XL14MRVGgKErEkgh5eXn47W9/a2vjaOxM00RNTQ1mzJiR1XcoyH6yYiuQOxt/X3MdOr3j8LWD74QQAMyAbZ+fqIHEbYS+KSogACF4tWI3jlkkC2OLZGFskQxpEVehG9TqPjexFQ1QAE3VmbjNQGkRW+Q4jCuShbE1ghq3NTU12L59O4QQ+PDDD1FTUxP+X319Pbq6unDppZfKbCuNgmmaaG5uzuoV+EgOWbEVcE/Bi5svwPu7ToFuuK3ErUh+oVslWNct0ozbcKkEzri1HccskoWxRbIwtkiGdIirPd2z8FnzEghPxdAXFA0KAE0xWDUqA6VDbJHzMK5IFsbWCGbcTps2DQCy+v8sIpIvMGhyrSFcwRm3KVihLFTXbd9ZJhhcKoHTTIiIiMiZnvnsf7BnD/DQhfu8oLgABVAVI2WLyBIREWWLES9OBgDbt2/Hr371K2zatAkAsGDBAnz729/O+roTRDR2Rl8zZlfsQXd/GQzTBQEAIvmlElRFh4Hhdd2sFzXAAGfcEhERkWOFyiAMOxUKzrgFANMwMYKHOImIiGiERvwr++KLL2LBggX48MMPsWjRIixatAgffPAB9t9/f7z88ssy2khjoKoqJk+enLW1QEgeWbGlNb+Gm06+FOcsvg+6mboZt7FKJezyn4Lrn3scH/dfl+xmOR7HLJKFsUWyMLZIhnSIq9CDlsNOhRQNSjBza+h8+ijTpENskfMwrkgWxtYoZtz+8Ic/xLXXXos777xz2PYf/OAHOOmkk2xrHI1dKMiJ7CYrtszgM3eG6bJm3KZocbIXaq5B814/Tj+4ZNhrulKC+s4S9PDxQNtxzCJZGFskC2OLZEiHuLpi2XcwMX8j8ntvAXD4wAvBklECKkzDAOBORfNolNIhtsh5GFckC2NrFDNuN23ahMsuu2zY9ksvvRQbN260pVFkH8MwsGnTJhhc8pVsJiu2DN1K0uqmG/e/fzN++vL9QMl8W4+RiJV15+K/m74C1VMw7LXQzBOW/LYfxyyShbFFsjC2SIZ0iKsCVweKc9ugqfvcqdby8J0XVuHiRz6ELnJT0zgatXSILXIexhXJwtgaxYzbyspKfPzxx5gzZ86Q7R9//DGqqqpsaxjZQwiBzs5OCCFS3RRyGFmxZQYTt4rqwo7W/a1tWvKrp4WSspGeyCh3bcGKA17HVNdkAKcntV1OxzGLZGFskSyMLZIhHeJKUUILtar7vgBX8CqSN7EzTzrEFjkP44pkYWyNIHF722234Xvf+x4uv/xyXHHFFdixYwcOP9x6ZObdd9/FXXfdheuuY71HIhobI1gqQdUGhiddBzyeJDZCCEwuWAe1QoWq7A9gaHG3cvdWHHTA/fB5DgUTt0RERORECqzZTZHq/Yc2ZfEEKCIioqRIOHF766234sorr8SNN96IoqIi/PznP8f1118PAJg4cSJuueUWXHPNNdIaSkTZQRjWjFvV5cKSSW+hsrABeueh8FROT2YjcNVBX4ffD5jqawCKh74emnkiOM2EiIiInClW4vYrB9yA3hkBmL4bMew8iYiIiGyTcOI2NC1ZURRce+21uPbaa9Hd3Q0AKCoqktM6GjNVVTFz5sysXoGP5JAVW6YRmnHrxkn7PY6F49+HaL8VSHLiNvQghqJFGCYVLbwf2YtjFsnC2CJZGFskQzrElYIopRIAHFD1OvTSAPzG/4KJ28ySDrFFzsO4IlkYWyOscasoypC/M2Gb/lRVZe1hkkJWbLW7jsB/1pfAKDoA++XsADBQPiFphIFQ5lbThv9AhH80srjOjiwcs0gWxhbJwtgiGdIhrsIzbtXhM25FcPWB0A13yhzpEFvkPIwrkoWxNcL1fubOnYvy8vKY/6P0YhgG1q1bl9Ur8JEcsmKrXT0ET2+4DLW9B8MM3lsyAgFbjxGXMMM5WTVC4lZRQtv478puHLNIFsYWycLYIhnSIa7qOmZhZ/s8KJ6CYa+JYP1/k3GfcdIhtsh5GFckC2NrhDNub731VpSUlMhqC0kghIDX683qFfhIDlmxFcrRut2AUIKJ22TPuMVA7VrNNXyWiRKaecIat7bjmEWyMLZIFsYWyZDquDJN4Ddv3w0AeDnC+tOmsM7RmLjNPKmOLXImxhXJwtgaYeL2/PPPz/opykQkl+ZvwNTSHhTlVkPADQAwU1AqITzjNlItneA2hTVuiYiIyIHMQfemI6xNBgENCgBh8lyIiIhIpoQTt/vWtyUikmGq9zf4yWmvYFXP9yF6QrM5kpy4Na3FyQRUaK7hY1+nuhgPvPgwlh+aj8XJbRkRERGRdIMTt5HuYYcSt5xxS0REJFfCidtsnpacyTRNw7x586BFulVONAayYkuYVpJW0VxAsFSCqSe5xq0rH//ecBUCfuCy8yK9XoQdrftjpi+5zcoGHLNIFsYWycLYIhlSHVeGAdx26ldRmNMJzfsboHD6kNdDi5MJLk6WcVIdW+RMjCuShbE1gsStabKWYyZSFAWlpaWpbgY5kKzYEkYwSau68F79eXhj47H43tFTMdH2I8Xgysd/Pr0Ufj9wefRKCeCwaD+OWSQLY4tkYWyRDKmOK9MEKgobUOjphKYNP+G5f/O/8OEqFbfcOqLKe5QGUh1b5EyMK5KFsQVESEmQk+i6jlWrVkFP+uJO5HTSYis849aNVv8srN9zOPow2d5jJCD05F+kG3t5yl6cPv+vmF/07+Q2KgtwzCJZGFskC2OLZEh1XBkGoCnWyZAa4WRIqLnQTQ8Mk5eTmSbVsUXOxLgiWRhbI1ycjDKTwdpTJImU2DKtGbeq5oIrOEIFklwpAYYfU0pqEDDc0LSZw14uUBpw3pLfQM+ZCuALSW6c83HMIlkYWyQLY4tkSGVcmSagKNZM20iJ29Amhn5m4phFMjCuSJZsjy0mbokovYjgjFvVjcklWzBh1ibk9E0DkrgMmNlbj9tOvRC9/mJo2mvDd1CDVyuCtRKIiIjIeUxz0IzbCKuTHVl9HxYevge5ga8BmJXcxhEREWURJm6JKK2EFidTNRcWVLyFg+f8ATm9K5DUxG2weK1huiKupKxpCgBAARO3RERE5Dy6bs24VRQAyvAZt7OK30FOzhbsNU8HE7dERETysCiRw2mahkWLFmX1Cnwkh6zY2t5/Jp7d+DV41SlQVOveUiiZmyymbs0wMaFGrHEbnnHLxK3tOGaRLIwtkoWxRTKkOq5ME9DU4KOpERK3Alpwv+x+fDUTpTq2yJkYVyQLY4szbrOCx+NJdRPIoWTE1saeL+Hxj4FLlwIFqhsAIIzkFrkN1dARInLiVlGC97xYKkEKjlkkC2OLZGFskQypjCvTBBo6p8PtMjA/eD42lHWCJLK87mCm4phFMjCuSJZsjy3OuHU4wzCwevXqrC/mTPaTFVuhxSLdbgApmnErjFCpBC1iqQRVszaqCv9d2Y1jFsnC2CJZGFskQ6rjyjCA6597HDe9/E/AUzLsdRGchSs44zbjpDq2yJkYVyQLY4szbokozeSJ3aguBHLc1UBohkeKZtyaQoMrwiipcHEyIiIicrBguf+IN7ABDJRPENl7IU1ERJQMTNwSUVo5veJynHxWC7a5H0FHqmrchhO3qrUoxz4Mz0Tc/sofMX2mGz9JasuIiIiI5AtNbIpWUjBc4zaLZ0ARERElAxO3RJRWFFizazWXK2WLk+laNZ759FL0BYpxfITELbQ8bG46CGpFUptFRERElBRC9+KuM74Kl1sFjL8B2j71BVkqgYiIKCmYuHU4TdOwbNmyrF6Bj+SQFVuqsBK3qsuNZmMpfvP+3TjlrArMs/UosemeiXhy3VVwu4HbIrUx+NggJ5nYj2MWycLYIlkYWyRDquPKNHRMKN5plYyK8PjRO9134N//FvjKxXnJbxyNSapji5yJcUWyMLa4OFlW8Pv9qW4COZSM2FJgza5VNRf86nis3n08mgOLbD9OLKGEbLS6bm6lDyfOfRzLq59IXqOyCMcskoWxRbIwtkiGVMaVqVsnQ4qCgXq2g+hKEXr9xQgY7iS3jOzAMYtkYFyRLNkeW0zcOpxhGFi/fn1Wr8BHcsiKrVDiVnO74Q5eC+jJrZQA09+H6qJajCtoivi6C924aNndOG3mL5LbsCzAMYtkYWyRLIwtkiHVcWUagxZgVYZfMoYmPjHsM0+qY4uciXFFsjC2WCqBiNKJMAFYFwqa240CdxsOmbYalcgDcFTSmuHqWo17zrwOu7v2B/DwsNc1zbqAUZC9Px5ERETkXKZpQAEgoszzmZ33DC45eAPKlZMALE9q24iIiLIJE7dElD5MHUJY/+lyuzAuZyu+dcQN0HJmIpmJ29Ask9CKyftS1FDi1oz4OhEREVEmMw0DGqKfC01wf4BZs1/ETnU6mLglIiKSh4nbLJDNRZxJLhmxtbL+QrQ2B7D8EA9ULThEieTWSjBNKyFrRrlYUYMzbgVgzRKO8AghjR7HLJKFsUWyMLZIhlTGlQidC4kobQjWvTVNPn2UiThmkQyMK5Il22OLiVuHc7lcWL6cd8HJflJiS/PgP1uvxbZtwKGXAopmFblVkp24NULHi5yQ1VyaNddWgIlbm3HMIlkYWyQLY4tkSHVcGQbQ0TsBntycyDsooZvrfPoo06Q6tsiZGFckC2OLi5M5nhACHR0dEKHnz4lsIiu2AgHrT7cb0FzWRYEiArYeI57QLJN4pRKCOyejSVmDYxbJwtgiWRhbJEOq48qnTsR1T/8Hv1n7ZOQd1OA5EmfcZpxUxxY5E+OKZGFsMXHreIZhYPPmzVm9Ah/JISW2TB0FWiNKclvhdgOqyx18IbkzbkV4JeXIQ6QaTNwKAUDw35adOGaRLIwtkoWxRTKkOq6C97ChRrlaVEJPGyX5qSgau1THFjkT44pkYWyxVAIRpRPvHtx41BfQ7c2H2/0W1OCMWxXJnXEbqtcmlCg1bt25+Pkbv0JhkYpfX+1JZtOIiIiIpAtdH0dL3IZn3PIGNhERkVRM3BJR+hABCAEYpgsul1XPBkh+jdt+1wy8+NmXoefMwBkRXlc1FesajkRxMfjcAhERETmO278Tt516I7SCCgC/HPa6olrnaKHyUkRERCQHE7cOpygK8vLyoChKqptCDiMltkwdQgC66YbbDQhPGf703i2YPdeDq+07Slw92kL8/aOFmDUr8uuhRS15rWI/jlkkC2OLZGFskQwpjyujD9PLN0F3VUd8uUb9Ov701AU47OhCnJnkptHYpDy2yJEYVyQLY4uJW8fTNA0HHnhgqptBDiQltoQ+ZMat6s7DOzVnoH+cvYeJJ5SQ1SJXSoCqAkfOeBb5eQZgnApoUVZcphHjmEWyMLZIFsYWyZDquBLBWgnRFmo1tWK09hWjz5/MVpEdUh1b5EyMK5KFscWHfB3PNE00NTXB5NRAspmU2Npnxm2wUgICyS1xCxHoQ1leE/Ld3RFfV1Xg8sNuw0VL/w8IRN6HRodjFsnC2CJZGFskQ6rjSpixE7d8+ihzpTq2yJkYVyQLY4uJW8czTRM7duzI6iAnOWTElmkMzLh1uwG3y8SSSW9hdtFrgJm8OrelvU/h1184DWfNvivi65oGCKFACADgvy07ccwiWRhbJAtji2RIdVyZhnVcM0ridhw+xAVLf4lZBa8ks1lkg1THFjkT44pkYWylSeL2d7/7HaZPn47c3Fwccsgh+PDDDxN636OPPgpFUXD22WfLbSARJYURnFobmnHrcRu49pjrcN7c7wOGN3kNCc4ygRJ5iFRVwBBcTZmIiIicSYTPbyKfC5XiE5w67++YkvdB8hpFRESUhVKeuH3sscdw3XXX4eabb8aaNWtw4IEH4pRTTkFTU1PM9+3cuRPf+973cNRRRyWppUQkm+6qxOvbVmDV7uPhdgNasFaCEADM5NVLCN3NE0rkMuDWjFvVapfI3jt/RERE5EwiNLNJiVLwP7ydN7CJiIhkSnni9he/+AUuv/xyXHLJJViwYAH+8Ic/ID8/Hw8++GDU9xiGgQsvvBC33norZs6cmcTWZh5FUVBSUpLVK/CRHDJiy++Zib98eAOe+uRyuFyA26PAFFowQZq8Ugmhum7RhkhVBUxhvWYavGCxE8cskoWxRbIwtkiGVMeVYaro9pXBZxZHfF3V+ORRpkp1bJEzMa5IFsYWEHk6WZL4/X589NFHuP7668PbVFXFiSeeiPfeey/q+2677TZUVVXhsssuw9tvvx3zGD6fDz6fL/z3rq4uAICu69B1PXxMVVVhmuaQuhmh7YZhQFjFLGNu1zQNiqKEP3fwdsBKOCey3eVyQQgxZLuiKNA0bVgbo20PtVFRFMyZMwdCCOi67og+OfF7ytQ+zZ8/H6ZpDjnuWPrk9QoIoUFVAUUB3G4FftMFIQzogX7ApSfnezICUAEIKEP+3YT2N03ANK3jmaYJkebfUybFnhAiPGYZhuGIPjnxe8rUPs2bNw+GYQx5T6b3KdJ29in5fZozZw5UVR3WxkzuU6y2s0/y+6RpGubOnRs+h092n5qNpbjryZdw7LEChwHD2o7gBbQi9IjngdnyPWVqn/bbbz/H9cmJ31Mm9Sla3iGT++TE7ylT+xQp75DpfRqJlCZuW1paYBgGqqurh2yvrq7G5s2bI77nnXfewQMPPICPP/44oWPccccduPXWW4dtX7t2LQoKCgAAlZWVmDVrFmpqatDc3BzeZ/LkyZg8eTK2bNmCzs7O8PaZM2eiqqoKGzZsgNc7UHdz3rx5KC0txdq1a4d8KYsWLYLH48Hq1auHtGHZsmXw+/1Yv359eJumaVi+fDk6OzuH/H+Ql5eHAw88EC0tLdixY0d4e0lJCebPn4+GhgbU1dWFt4f6tGPHDtTV1SE3N9cxfXLi95SJfSouLkZxcTFM00RDQ4Mtfaqr9ULRp0KobnR2uuFylaInoCLHZWD9ujXwuxqS8j319vSgyDTR0+vF6tWrh/XJ61WRa1hJRtMwsT2Nv6dMjL3+/n7k5uY6qk9O/J4yrU9Lly5FXV3dkFJMmd4nJ35Pmdqn/v5+LFu2DLm5uY7pE+C87ymT+lRRUYEPPvgAqjrw9E8y+7RlSzn6+iais7MfQMWwPgU6uzEOgN/nHXLcbPueMrVP+fn5WLhwoaP65MTvKZP6VFdXhx07doTzDk7okxO/p0zsU25uLioqKuByubBz505H9CkvLw8zZsxAohQxOKWcZA0NDZg0aRJWrlyJww47LLz9+9//Pt5880188MHQYvfd3d1YtGgR7rvvPnzuc58DAHzta19DR0cHnnrqqYjHiDTjdsqUKWhtbUVxsfXoT6Zk5Edz5yQ0q3np0qXQNM0RfXLi95SJfTIMA2vXrsXSpUuHXFSMpU/Na59B21s/xcbmw/HF23+NmhoFzY+diNL8Dsy6+O9A4aykfE/rnvgd3Lv/gi2+c3HG/143rE9eL3Dthe/Cpen45V8PgSc/L22/p0yLPb/fjzVr1mDp0qVwuVyO6JMTv6dM7JMQAqtXrw7/HjqhT078njKxT4ZhYM2aNVi2bBlcLpcj+hSv7eyT/D6ZpolVq1YNGbOS2adHH1Xwy1+qOOkkgTvvHN72z17+F5RNd6PGexxO+cEdCfXJid9TJvYpNGYtX74ciqI4ok+x2s4+JadP0fIOmdwnJ35PmdinaHmHTO6Toijo7e1FSUkJOjs7w7nJaFI647aiogKapmHv3r1Dtu/duxfjx48ftv/27duxc+dOnHnmmeFtoS/C5XLhs88+w6xZs4a8JycnBzk5OcM+y+VyweUa2v3QF7avwRd4iWzf93NHs11RlIjbo7Ux1vZQQA/+vEzvkxO/p0zu00j2j90na6A04YGiKHC7AUO4IATgUgEMOo7MPnUpC7Bh+9lQKhcPeV9o/5wc4OOGY6y2qkCULqXd95QJsRf6gdQ0LXysTO+TE7+nTOyTrusRfw+BzO1TrO3sU3L7pChK+H9O6dNot7NP9vTJNM2oY1Yy+jROWY0fnfQn5FbNA3DdsLarLjcEAAVmxM/Jlu8p1vZ07lPoMV0n9SmEfUpdnyKNWZneJyd+T5ncJzv6mk59SlRKE7cejwcHHXQQXn31VZx99tkArJOUV199FVdfffWw/efNm4dPPvlkyLYf//jH6O7uxq9//WtMmTIlGc0mIknMQPBulmINTS4X8Njaa5CX48OdV1XHeKe9mpTj8MAHx+G44yK/Pnj83+eGGhEREVHGc5utmFa1Bj05kS9kewpOwG3PLsHEqQU4LcltIyIiyiYpTdwCwHXXXYeLL74Yy5Ytw8EHH4xf/epX6O3txSWXXAIAuOiiizBp0iTccccdyM3NxcKFC4e8v7S0FACGbSeLqqqorKyMeKeBaCxkxJahBwAAQnEDANxu4N2a06xEqce2w8RvRzAZG20mraoCB058B3nuXpj9hwDFpUlrm9NxzCJZGFskC2OLZEh1XAnTOhlSlMjHF65i7OkqRr434suUxlIdW+RMjCuShbGVBonb8847D83NzbjpppvQ2NiIxYsX44UXXggvWFZbW5vVX9BYqao6rHwEkR1kxJZpWDNuRXBocruD203rf0kbCnQv8t063FoOImWMVRW4ePndqChoAPoeBFCapIY5H8cskoWxRbIwtkiGlMeVsMrRCUR7dNT6c1D5QMoQKY8tciTGFcnC2ALSIiN69dVXY9euXfD5fPjggw9wyCGHhF9744038NBDD0V970MPPRR1YTKySk9s3759SFFmIjvIiC0zOOMW6kDidkb5Rhww4T0EetttO048s83f4A9fOg7Lyv8SdR9TWBcypsF/W3bimEWyMLZIFsYWyZDquArNuIUSOXGbq+/AFxf9Hsuq/53EVpEdUh1b5EyMK5KFsZUmiVuSxzRNNDc3Z3WQkxwyYssMrtgYKpXgcgFfO/hO/O9x/wOzfYNtx4lLBB8PjDHFVwQfHeS/LXtxzCJZGFskC2OLZEh5XAXPhaI97pRr7MTnFz6AJdXPJbFRZIeUxxY5EuOKZGFspUGpBCKikF51Dj7c+Tn05CwAYCVuDdMapozQwmXJIEI/ClGK3AIQwRm3gquTERERkcMMXCBHPhdSgwsBKOB5EBERkUxM3BJR2mjWTsAfVp6ApUutv6sqYAhrmNIDgaS1QwQvVhQ1RuI2+MACSyUQERGR05imioCRA1OJvDosE7dERETJwVIJDqeqKiZPnswF3sh2MmIrlJt1DbqlZAqrbEKojEJyBI8VZSVli/VauAYc2YJjFsnC2CJZGFskQ6rjaptvBS577F283/eTiK+rwZvbqpLM8zOyQ6pji5yJcUWyMLY449bxQkFOZDcZsaX7/XCpgNvlQjgxqiS/VEIiM27N4KODhimS0qZswTGLZGFskSyMLZIh1XEVul8e7TpZ4YzbjJXq2CJnYlyRLIwtzrh1PMMwsGnTJhisw0k2kxFbM/vvxIPnH47Dxv81vM2EO3i85Ne4jbU42X+3XIE/vncr/J45yWpVVuCYRbIwtkgWxhbJkOq4CpW41aLcw1aDj0cpYMmoTJPq2CJnYlyRLIwtzrh1PCEEOjs7IQRnBZK9ZMSWMHQoAKC6B7YFZ9wms1RCc+BA7NmloGDOtKj7bGw5Ci0twLeiT8qlUeCYRbIwtkgWxhbJkOq4mup+Cd877lmU5xwB4Lxhr4dLJXDGbcZJdWyRMzGuSBbGFhO3RJROhFXkVlEHhqY1jadjfe0ifO3YxUlrxqa+8/DIu+fh4tnR9wlNxs3iG39ERETkUIVqLaZPWIkebXzE183CObjphf8HRcvB0UluGxERUTZh4paI0ocRTNxqAzNut3Ychdpa4EtJLOwS7/FAAJhZ/gkmuduh+OYBqEpKu4iIiIiSIlg2CkrkkyHNk4+dbfOHLChLRERE9uNPrcOpqoqZM2dm9Qp8JIeM2BKmVSph8Izb0AVBIGDbYeK3wwhAUwFN0xCtFPgZc3+HqYWr4en9KYBTktc4h+OYRbIwtkgWxhbJkPK4Mq0SVdHq/YdubvPJo8yT8tgiR2JckSyMLSZuHU9VVVRVcTYg2U9KbJmhGbcDQ1NFQQNclU1Q+qsATLT3eFEcV3ItTj//fdS6bwNwWuSdFOuHQ5i8YrETxyyShbFFsjC2SIbUx1XsGbcuswOnL3gauumGaV6ALL6ezjipjy1yIsYVycLYijaVjBzDMAysW7cuq1fgIzlkxJYIzu5QB5VKOHLKo/jxSV9Hafe/bDtOAg0BEH2WCQCYsC5khOBqynbimEWyMLZIFsYWyZDquBJxSiWoRjvOW/xbnL3wz+ESU5QZUh1b5EyMK5KFscUZt44nhIDX683qFfhIDhmxtde/BHvqiuCaUx3eFiqbYBq6bceJRwmukBz7cYzgjFuDVyt24phFsjC2SBbGFsmQ6rhSROxSCS6XldBVVYOJ2wyT6tgiZ2JckSyMLSZuiSiNfNR1JZ54C7h8v0EbleAwZSSvyO3ALJPoQ6QIzbhlqQQiIiJyGGEKQAWUKDNulWCRW00xoOuAx5PM1hEREWUPlkogorQRWoDM7R60MRUzboWVjFW0GENksMatyWkmRERE5DBvNn0XFz2yCju0KyO+roVm3CqccUtERCQTE7cOp2ka5s2bB02LfLecaLRkxFYgYD3+4Bo80VW1srjCTN6M2xGVSuDViq04ZpEsjC2ShbFFMqQ6rqxSggo0V+RzoVC7NNVAFpcdzEipji1yJsYVycLYYuLW8RRFQWlpKRRFSXVTyGFkxNZ5E1bg4QsORqVr/cBxgjNuRRJn3Irw4mTRfxzWNH8BD6/6IbpcS5PVrKzAMYtkYWyRLIwtkiHVcRW6Lx3tHnboHEmBCUPP3rqDmSjVsUXOxLgiWRhbTNw6nq7rWLVqFXQ9eUkvyg5SYkvoUGCGH78DMGjGbfJieHfvQVhTdwwC6rio+9R0H4ZXt56DXmVW0tqVDThmkSyMLZKFsUUypDquFpU8jv856geoEG9FfF3RXAhdQxtcqDWjpDq2yJkYVyQLY4uLk2UFg88vkSR2x5aKAEwA6qAity36Ijy58SocUjkT+9t6tOje2PMdvPMOcOMx0fcJlXNgpQT7ccwiWRhbJAtji2RIZVyNz9uAacWvwq8sjLyDVoC73vgTfD4Nv/xy9s6CylQcs0gGxhXJku2xxcQtEaUNRVh30bRBRW7bzQV45tMFmBwjiWq3UDI2Vhmdyryd2H98E9yByQAmJqVdREREREkRKhulRDkZUjVsb1sKrxcwWSmBiIhIGiZuiShtKLAWIBucuA1Nvk3mkxGhG3qx1iY7dPxfMWvaM9B93wJwSVLaRURERJQcoXr/0U+GQi9l8dOrRERE0jFx63CapmHRokVZvQIfySEjthQEZ9wOKpWQ7+nB1NIG5BoeANNtO1YsX99vBa6c2YBu3A/ggMg7hWagCNZKsBPHLJKFsUWyMLZIhlTHlSKsu9hRF2oVAifMeRy+fgOm/kUAOclrHI1JqmOLnIlxRbIwtpi4zQoejyfVTSCHsju21AgzbifnfYifnPZ9mJ7FAP5s6/FitUNT9JizTKBYrwkzu+vtyMAxi2RhbJEsjC2SIbVxFTy/iVYqAcD5i+6BrgMicCqYuM0sHLNIBsYVyZLtsRUjK0FOYBgGVq9enfXFnMl+tseWENjafhg2NB4C1ZMb3qyowSSuSN5zeErwYkWNmbi1LmQEVyezFccskoWxRbIwtkiGlMdV8IkiVYtyLqQoMIV1LmTqjP1MkvLYIkdiXJEsjC3OuCWidKEoeODjX6OmBvjjeQObVc0aphQRSF5TELpYifU4RvBChqUSiIiIyGFCN7EVJfpNbBMaACOrL6aJiIhkY+KWiNJGIJibHVTiFmqobIKZzJUvQgtyxEjcBi9kTM64JSIiIof526c/w47tOu45xB11HwENClg2ioiISCYmbokobYRWJR5U4haKFrpgSF7iVg0eK+rjgRg0A4UzbomIiMhhAoYbPt0NLcbVohBW4palEoiIiORh4tbhNE3DsmXLsnoFPpLD9tjyteGuE8+E1+eB2/UaAMU6TjCLq6ZZqYRd3qPwwZYKHD5+QbKalRU4ZpEsjC2ShbFFMqQ6rkIPFMU6vAkNKgBDT+ZTUTRWqY4tcibGFcnC2OLiZFnB7/enugnkULbGlhmAS/HBo/XD7VHCmzVXcMZtEhcn29a+DJ82HgzFlRd1n72Bg/DfTV9Bq7k0ae3KFhyzSBbGFsnC2CIZUhlXx055AFccdgvyA5ui7iMQWqiVM24zDccskoFxRbJke2wxcetwhmFg/fr1XDSAbGd7bAkdQgC66R5S49ZwV+A/n16CNS3n23OcBDy07me467X7YHoqo+6jBkdPlri1F8cskoWxRbIwtkiGVMfVfuXv4sgZz8Ij9kbd5/HNP8Vdr/0O/cqkJLaMxirVsUXOxLgiWRhbLJVAROnCDEAIwDBdQxK3wlOBJ9Z9C4fmARcmqSmh34RYT2MUupswq2IvcoxyALxgISIiIudQYJ0MKWr0eT67epZjcyMQEMlqFRERUfbhjFsiSgtiUOJ28OJkof8OJK/EbUJ13eYXPoObT74Es9SHk9MoIiIioqQJ1vtXo58MhXK6WTwJioiISDrOuM0C2VzEmeSyM7ZCC1vopntI4tbtMlBdVI8SzQAww7bjRSUE7jj+GPj8KlzmUwBKI++nWFcrrOtmP45ZJAtji2RhbJEMqYyrRGbcLqx8DdWzO6AGjgZQkaSWkR04ZpEMjCuSJdtji4lbh3O5XFi+fHmqm0EOZHds6X5rSu2+pRJy1U7cc+YK5OYpgPgQUJQon2ATYcKj9UF1A5orxkMJoRkogkVu7cQxi2RhbJEsjC2SIdVxFUrcxppxe9zkP6Bg0g70BqaBidvMkerYImdiXJEsjC2WSnA8IQQ6OjogBItPkb3sjq2AyMfmpqXY3rr/kMSty+MKHTA5SVIxMINW1aIPkUpwxu3g/WnsOGaRLIwtkoWxRTKkOq7U0IxbV4xZTsFzIZO1EjJKqmOLnIlxRbIwtpi4dTzDMLB58+asXoGP5LA7tgI5s3H7K3/Cfe/ePqS2rOa2ErdCABC6LceKzUToN0GLkbgNl0rgjFtbccwiWRhbJAtji2RIdVwpSqjGbfRzIQHrhE0YyTg/I7ukOrbImRhXJAtji6USiChNBEvcwu0eWg3B5RqUuDUDgJYjtyHCROhenhKjlk74QoaJWyIiInKY2197CL5+HQ+tKIq+k2KdJ5kmz4WIiIhkYeKWiNJCwCpxO6RMAjBQKiFpM26FjlDmVov1eGCw5pvCUglERETkMD2+Ynj7Ac0dfR8RvJTkQq1ERETysFSCwymKgry8PCiyF3SirGN3bKktb+J3XzwRVx3+/SHbXW4VAurAjFvZBs24jVUqod1ciH998g3s6D9ZfpuyCMcskoWxRbIwtkiGVMdV6InUGJUSIMAat5ko1bFFzsS4IlkYW5xx63iapuHAAw9MdTPIgeyOLdPfh6KcDhR4eoZsd7kAr+mCS/iTM+NWUbGlaTEUmDg0RuK2S1mApz5ZgC/Nk9+kbMIxi2RhbJEsjC2SIdVxdc4Bv4Fb9cJlXAKgKvJOwVIJyVmDgOyS6tgiZ2JckSyMLc64dTzTNNHU1MTaU2Q7u2PLCBa5FfvcT3K7gZc/Ow8vbfkKoOXbcqyY3MX4v5f+jNteehCuGLe2QjNQOMnEXhyzSBbGFsnC2CIZUh1XR0z/D06Y8wQ0szvqPh+2fwO/fvsetJmLk9cwGrNUxxY5E+OKZGFsMXHreKZpYseOHVkd5CSH3bFlBlckFsrQYmpuN/Do2m/jH2u/A3hKbDlWzHYM6k6sxwNztG5MLt2GAqVeepuyCccskoWxRbIwtkiGVMaVEICqWHem1RgnQ3v9S/HR7uPQZ0aZkUtpiWMWycC4IlkYW0zcElGaMIOrkwll6DTX0KzXQBLK2wJDZ9BqMdYmm6C9jdtPOx+Hldwhv1FERERESWKagKoGE7cxFmoNnSfx6SMiIiJ5WOOWiNKCEWPGbXFuG9yaD2ZgHFS3R2o7zN4G3Lvia+gLFEHT/hl1PyU0A0Vk750/IiIich7DAFRY5zeqGj1xOzFvHQ6bvgd55gIAU5PUOiIiouzCxK3DKYqCkpKSrF6Bj+SwO7ZEsMYtlOE1bm8++RJUFtZDb38QnqpFthwvGlMPoDi3DZqqxyyVEErcKuA0EztxzCJZGFskC2OLZEhlXA2dcRv9ZGhR8SM4+vBX0Wj+AEzcZg6OWSQD44pkYWwxcet4mqZh/vz5qW4GOZDdsdUvyrG7bQG6lElDtrtcgG5as3B1fwBy59sChm5dqJhCjVkqQQmvpMwZt3bimEWyMLZIFsYWxWX4gfpngNwqYNwhgJYT9y2pjCvTBLRwjdvoJ0MieC4kTN7EziQcs0gGxhXJwthijVvHM00TdXV1WV3ImeSwO7YatTNw8wt/xXvNlw/Z7nIBhmndYzJCs3IlCl18iHiJW864lYJjFsnC2CJZGFsUl7cO/WvuRMdr10HUP5vQW1IZV4YBKIp1XC1GjVvexM5MHLNIBsYVycLYYuLW8RjkJIvdsRXKybqHlriFqgK6CM64DchP3Bq61R9DaIj1NIYSnoHCf1t24phFsjC2SBbGFsXVV4f6BqChAajf2Z3QW1IZV6YJXPfUf3Dd089AyyuLvmPoXMiUf35G9uGYRTIwrkgWxhZLJRBRmggErD/3TdwCgAgOVUYSEremEZpxGy9xy8XJiIiIKL5AZx38Puu/+zq7UtuYBBgG0No3HgCgxJrmEyqVIPj0ERERkSyccUtEaWFK/x/wi8+fhWUVjwx7zYSVzTX0gPR2hBO3cYZHnzYZz226CJ+2nyG9TURERJS5OhrqIIT134avJ7WNSUBoUpOqIuZNbLDGLRERkXSccetwqqqisrISqsocPdnL7tjSjHZUFDSgxegd9lpoxq2ZhBq3BnKwq30/dPsrY+7nc03HY2uvweF5wMXSW5U9OGaRLIwtkoWxRfH0NteF/9vwJTbjNpVxZQT8uHDpvdbiY+ZVgBrhcSggnLjl00eZhWMWycC4IlkYW0zcOp6qqpg1a1aqm0EOZHdsCcOaTauow4elTS1HorZlCk4/frJtx4vGnzMXN/7378jPB86NsV9o4bIsLrUjBccskoWxRbIwtigevXsgcQt/4onbVMWVqQdwyrxHrDIJ4ptR96s1zsCjKxfhgMNnJ69xNGYcs0gGxhXJwthiqQTHM00T27dvz+pCziSH3bElhDWbVtWGJ27fqbsAD626Hr2uhbYcK5ZgpQS44tzW0hQ/KgvrUajtkd6mbMIxi2RhbJEsjC2KSZhwBRoG/q4nViohlXEVKhulAAOzaiPoUvbHyp2fQ6t/TnIaRrbgmEUyMK5IFsYWE7eOZ5ommpubszrISQ7bY8uwErdKhMfxQguWJaFSwpC6brEUi0/x87M+j3Nnfkt+o7IIxyyShbFFsjC2KJ5fvP17vLD5QnzccCR2di1L6D2pjCszVLNWQczVyUJPHxkscZtROGaRDIwrkoWxxVIJRJQuRLBUQoQZt3mefhTm9EP35QDIk9oMrXMNfn7WrWj2zQHws6j7qap1taKAVytEREQUWW+fipWblmAllgAADj8cSPdlTU09dHGsxFydrETbiaWTd6FEnQBgblLaRkRElG0445aI0oIwgzNuteEzbr847w7c98UTUdL5hPyGGH2oLKxHWV5TzN1ULTR8CvltIiIiooy0ffvQv3clVuI2pURwxq2Ic6k4WXsB3zn6u5ib/1QSWkVERJSdmLh1OFVVMXny5KxegY/ksDu2egJV2NM1HYZaPOw1oVjJXNOQXyshdLFiiug13QBACfabM27txTGLZGFskSyMLYqlbctKHDf7X5hZvRMA0NfjA0T8xz1TGVeGHkzcxjsXCq/UynOhTMIxi2RgXJEsjC2WSnC8UJAT2c3u2Hqn/Xr861ngyisjHcwaqkQSErehi5VYNd0AQNVCpRKyt9aODByzSBbGFsnC2KJY8lqfxiUHv4oPu67DAXlfQ1FeD9D/HyBvQsz3pTKuRLCOoIk4iVuFZaMyEccskoFxRbIwtjjj1vEMw8CmTZtgcNUAspndsRVaeMw9vFJCkmfcWhcrIt7FCmfcSsExi2RhbJEsjC2KRfHWAQAqp01GwPDAMADh7477vlTGlV+rwg+ffQK/fO/h2DuGbqxzxm1G4ZhFMjCuSBbGFhO3jieEQGdnJ4RgHU6yl92xFbDWJoMr0nMASvDCQA/YcqxYwqtVcsZtSnDMIlkYWyQLY4uiEgK5ppW4nTpvMnr9xRAC8PXGL3SbyrgyTDcaumag2Tsz5n68iZ2ZOGaRDIwrkoWxxcQtEaWJkyq+hztOPxcVrvXDXlPCMzqSMOM2OKs33oxb01WCV7aciw8bzpbeJiIiIso87U2dcKEXUIA5iyahL1AEAPB2xZ9xm0qhSU1a7FMhKGpwByH//IyIiChbscYtEaWFYq0WnpIdaNT8w19UQ6US5M+41UUB9nZPRZdZHXM/4R6Hv67+PqZNAy6T3ioiIiLKNPVb66AC8JpVyC3IgY5g4ra7G2WpbVpMir8FX1z0BPKLiwB8Jfp+4cQtnz4iIiKShYlbh1NVFTNnzszqFfhIDrtjS4E1W0OLUCuhXZ+Dd+tPw9zx+9tyrFg6co7B9/9zDObPB74UY7/wQsq8VrEVxyyShbFFsjC2KJqW2jpUAQi4rUVVdLUYAODriT/jNpVxpQZa8PmFD6AfVYiVuO1yLcOjH96AymmTcVLymkdjxDGLZGBckSyMLSZuHU9VVVRVVaW6GeRAdseWIkKJ2+Grk+32H4fnVh6Ha5bZdrioQo8HxvtdUBUTJbntKHCZACqltytbcMwiWRhbJAtji6LpabYSt1qhlbg1NWvGbaAvfo3bVMZVaLGxeGWjAjkz8fq2mVhWmoRGkW04ZpEMjCuShbHFGreOZxgG1q1bl9Ur8JEcdseWCqsMguYefj/JHczl6kkooZZoXTePaMZvV5yCHx91pvxGZRGOWSQLY4tkYWxRNM9vOAc/feVP8E84HwDQac7D2vqj0e6fFve9qYwrM3zMOAu1Bl/m00eZhWMWycC4IlkYW5xx63hCCHi93qxegY/ksDu2lHDidviMW5fLhFsLwCpxm2PL8aIp9b6A20//C1qVIwBcE3U/VVVhAFDAqxU7ccwiWRhbJAtjiyIRAtjwWSn6+pZiwn7Wtp365/HSm5/HdQcBh8R9f+riSgQzsfFm3OYordh//DZU5RQBWJCElpEdOGaRDIwrkoWxxRm3RJQm1GCNWzVCjdv5hU/jgfOOwHL39dLboRntmFyyHaW5jTH3U4JTcpm4JSIion01NgJ9fYDLBUydam0rtkrcojt+iduUSrRUQqmxGj84/ls4YfJvktEsIiKirMQZt0SUFjr6q2D4ClDg9gx7TXW5AD8gTPm1EoQRmmUSe3jUtEH3vYQJKLwPRkRERJYd2/px3uL7oRZOhkv7PAA1nLjt6QoAGP6EUboIlUoQceb4KGowsSt4E5uIiEgWJm4dTtM0zJs3D1q8gp1EI2R3bN3z3uOorQX+fEaEF9XgxU0SErdmcJZJvESsEizsJgSYuLURxyyShbFFsjC2KJK9O+px+oKHkVNYDChfAABMKVyHB87/JrTCSQCeiPn+VMZVuFSCEvvYavjpoyQsQkC24ZhFMjCuSBbGFhO3jqcoCkpLS1PdDHIgu2MrtPBYhEoJULXgRpGECwNhJW6VOIlYzTXoh4MzTWzDMYtkYWyRLIwtiqS9oQ7IAUTe5PC23MI8uFv8UEVX3PenMq661ANw1wv/D/vN98SsxauoGgQABdm7YEwm4phFMjCuSBbGFmvcOp6u61i1ahV0nXfCyV52x1bAWpsMEdYmg6JZGxURsOVYsSQ6y2TojFtesNiFYxbJwtgiWRhbFEl/Wx0AwFM6Kbwtr6gQAOAS8YvcpjKu/GYBdrbNR3tgVsz9QjfWmbjNLByzSAbGFcnC2OKM26xgGDyZIjlsiy2jH987/DJ4+11wa/cDGFrnVnMlb8atCJdKiPd4oAtv7zgTqqZiXpx9aWQ4ZpEsjC2ShbFFgxkGgD4rcVs8fmDGbV6wyK0i/IDhB7Thdf2Hfk5q4ip4DxtqnCk+4Rq3XKg143DMIhkYVyRLtscWE7dElHqmH5OLP4NRALjcw68SBmbcyk/cBkQ++r1V8HuKY+6nud24//2b4XIBlzNvS0REREG7dwMV+XVQVaC4eiBxW1iSDx0qDMMEAl2AVpHCVkbnCezEGQveQFXZRAAnR90vVONWZY1bIiIiaVgqgYhSz9StkgMA3O7hWVDTPQ6r645Dbc9y6U3ZYX4F3/7381jVcVXM/UK10U1OMiEiIqJBtm8HqorqkJMDqIUDiduiYhV9/kIYJgA9frmEVMk3tuDcxfdi8bh/xtzPyJ2CR9Zci1d2fC05DSMiIspCnHHrcJqmYdGiRVm9Ah/JYWtsCStxq5tuuD3KsJf9nln4zVv34MgjgTPHfrSYQonYeN1SVSDH1QdVMSHM/HDNWxobjlkkC2OLZGFs0b52bDdwWEEDcnIADFqcrLgY2OovQoGnC/6+bngKo39GSuMqdDIUrxRUbjVe2HwhKivlN4nswzGLZGBckSyMLSZus4LHE7t+FtFo2RVbph6AEIBhuiIuThYqcRuQvzZZwnXdNA3407nHQoEJ0f9fKPm8arELxyyShbFFsjC2aLCt21Q8+f7TuPbKOkzMHTg/yM8HNjUtR13nLIz35qI8zuekKq7MYL1/gdgXyaFr6CwvPZiROGaRDIwrkiXbY4tTxBzOMAysXr0664s5k/3sjC09YNVG0013zMStocuvSzDVfAQ3nXwJ9i/+V8z9VBUwhTWEGoaQ3q5swTGLZGFskSyMLdrX9u0KWvvGo2zWMkAZuNxSVeDxjT/GL9/8BTqMuTE/I6VxJUILtca+VNTgxeyK9Zhc9GkSGkV24ZhFMjCuSBbGFmfcElEa0P3WVFrDdIWTtIMVKTvx8AXnQvUUAnhNalvyUYeqik9Q6z445n6aZiVuNQUws/hHhIiIiAb4fNbiZAAwa9bw14uKgK4uoDt9S9xCBB8/ijfjNsfYjZtOvhQ9gQoALyShZURERNmHiVsiSjldB7r6y9HlK4uYuNXcGhSYUJKwarEQidV1U1XANDVABUyDK5QRERERsHMncMjUFzB7/A6M044CcMCQ14uLgfp6oKvTRLo+/Bg6r1HizLhVVFdwP97AJiIikoWJWyJKOZ9nNq7+10tQVeDkW4e/rgWzuWoSErcwQ48HJpC4DV5wccYtERERAcD27cDyKa/h6HmvQekaB5QNTdyeOvt+fO/Av8Dd8iUA16amkfEESyWIOOdCmst6XQXPg4iIiGRh4tbhNE3DsmXLsnoFPpLDztgKLToWqb4tAGjBF5KRuBXBixUlzupkmhaccQvANDnj1i4cs0gWxhbJwtiiwbZtA+YW1SE3B0D+5GGv5+RqcKt+GL7YtRJSGVcN+rG4/5VZOOTIUhwZYz9Vs86VVCUJN9bJNhyzSAbGFcnC2ErX53PIVn6/P9VNIIeyK7b04Pl+tMStyxO6x2QCQnKSdCSlEkRoxi0Tt3bimEWyMLZIFsYWhWzfLlBVWIecKIlbNacYAGDGSdwCqYurPrMSnzUtRbeYGXM/NXgRrbJUQsbhmEUyMK5IlmyPrbRI3P7ud7/D9OnTkZubi0MOOQQffvhh1H3vv/9+HHXUUSgrK0NZWRlOPPHEmPtnO8MwsH79+qxegY/ksDO21M71+PFJX8dFB90e8fVQqQQhAJiSZ3WIBOu6KcBHdcfh/V0nw1Dy5bYpi3DMIlkYWyQLY4sGa2loR66rD54cBcidMOx1NddK3CLQFfNzUhlXoQeJ4jx8BFULlrJSDPDho8zBMYtkYFyRLIytNEjcPvbYY7juuutw8803Y82aNTjwwANxyimnoKmpKeL+b7zxBr785S/j9ddfx3vvvYcpU6bg5JNPRn19fZJbTkR2MX3tmFv5MaaUbon4uis4FVcIAEJu4jZg5KAvUARTzY2778Orf4T73r0dumu81DYRERFR+hMCcPnrAABqfhWgeYbt48kvtF434s+4TZVisREnzX0Mk3JXx9wvXOOWiVsiIiJpUp64/cUvfoHLL78cl1xyCRYsWIA//OEPyM/Px4MPPhhx/7///e+46qqrsHjxYsybNw9//vOfYZomXn311SS3nIjsYgSsZKyJyLUSXB43NjQeik8aY1Vas8fKrh/hyidexy7znLj7hsrs8GKFiIiIvF6g1GMlbl3Fw8skAIAn35pxq5rpm7itUt/HV5fdg5n5L8TcT8vJxz/XfxNPrrsKpiGS1DoiIqLsktLFyfx+Pz766CNcf/314W2qquLEE0/Ee++9l9Bn9PX1IRAIoLy8XFYzM142F3EmueyKLUMPrk6mRB6SXG4Vd792L/LzgS9KHrVCT2Ak0jVNNaEpJkxDRRrcB3MMjlkkC2OLZGFsEQC0tgKVhfVQVcBdFDlxm1NkJW5diF0qAUhdXAkz9Dhq7HMb1Z2HpzdcBgD4XwMYPr+Y0hXHLJKBcUWyZHtspTRx29LSAsMwUF1dPWR7dXU1Nm/enNBn/OAHP8DEiRNx4oknRnzd5/PB5/OF/97VZZ0k6boOPbgikqqqUFUVpmkOWR0+tN0wDAgh4m7XNA2KooQ/d/B2AMNqckTb7nK5IIQYsl1RFGiaNqyN0baH2qiqKpYsWRLusxP65MTvKVP7tHz5cpimOeS4o+mTqYdm3LrCnzW4T4oCCKEhEAiWS5DYJ8PQAAgIYULXRcw+3f65czAurw6i437o1QfE7StjL36fAITHLMMwHNEnJ35PmdqnZcuWwTCMIe/J9D5F2s4+Jb9PS5YsgaZpw9qYyX2K1Xb2KXLbm5oUPP3JZdjedxruv8wMr746uE/u/AJ81rQY7vwizDEMqJoWsU8ulwtLly6FECLiuZHcPlk31AXUqL/FqqpC01RY50yA32/A48mM78mJsTfSPh100EGO65MTv6dM6lO0vEMm98mJ31Om9ilS3iHT+zQSKU3cjtWdd96JRx99FG+88QZycyPXo7zjjjtw6623Dtu+du1aFBQUAAAqKysxa9Ys1NTUoLm5ObzP5MmTMXnyZGzZsgWdnZ3h7TNnzkRVVRU2bNgAr9cb3j5v3jyUlpZi7dq1Q76URYsWwePxYPXqoXWili1bBr/fj/Xr14e3aZqG5cuXo7Ozc0jyOi8vDwceeCBaWlqwY8eO8PaSkhLMnz8fDQ0NqKurC28f3KeGhga4gzVCndInJ35PmdinCRMmoLu7e0iN6dH0ydQDMIUJb38g3NbBfWprU9DXNw9eL9DXpyInR16flha+hv2OXYfO5pOwevWUmH0yTGtgr6utxe7ugRtE6fY9ZVrsBQIBuN1uR/XJid9TpvXpoIMOQmtrK3bu3OmYPjnxe8rUPgUCASxduhQ5OTmO6RPgvO9Jdp82bZoAgWnoQylWf/oJgPphfaqp68FtT/8KZWUBLL50T9Q+VVZWYs2aNUPanqw+tbe1YUKOiY7OLmzZsiXq9zRj+gyML/gUpuHH6lU6CotERnxPToy9kfaprKwMc+fOdVSfnPg9ZVKf6uvrsXPnznDewQl9cuL3lKl9mjZtGnw+H2pqahzTpxkzZiBRihicUk4yv9+P/Px8PPnkkzj77LPD2y+++GJ0dHTg6aefjvren/3sZ/jJT36CV155BcuWLYu6X6QZt1OmTEFrayuKi4M1pjIkIz+aOyehchRLly4N3wnL9D458XvKxD4ZhoG1a9di6dKlUNWBR+lG06dNL/0Tysa7UOM9Dqf84I5hferqAj793dkozmvD/pf/HVrxdGnf06rfX4ti/0rsqbgRx1xwesw+rbzrfFTk7UTh8fdh4sKlcfvK2IvfJ7/fjzVr1mDp0qVwuVyO6JMTv6dM7JMQAqtXrw7/HjqhT078njKxT4ZhYM2aNVi2bBlcLpcj+hSv7exT5LY/+aSKn/1MxXHHCdxxx8D+g/tUW2vii1/UkJsLvP22iNon0zSxatWqIWNWsvr06n2/xST//0Nr0fk4/NJro35PijCx6deHAgAqz38ZZVVFGfE9OTH2RtKn0Ji1fPlyKIriiD7Fajv7lJw+Rcs7ZHKfnPg9ZWKfouUdMrlPiqKgt7cXJSUl6OzsDOcmo0npjFuPx4ODDjoIr776ajhxa5rWQmNXX3111Pfdfffd+OlPf4oXX3wxZtIWAHJycpCTkzNsu8vlgss1tPuhL2xfgy/wEtm+7+eOZruiKBG3R2tjrO2hgB78eZneJyd+T5ncp5HsH6nthq5C1/MhlLxhn+VyuZCbC7g1P9yqH0ZAhytKG+3pkzWgai533DFCBGu/KVGOm27fUybEXugHUtO08LEyvU9O/J4ysU9W2ZXhv4dA5vYp1nb2Kbl9UhQl/D+n9Gm027O5Tx0dJq447GbMmFYBl3I5oOUO27+8XIWiAD4fYOiA6oncdtM0o45ZsvukKMFSUZo79m+xABTFKmOlYOjnpfP3NNrtTupT6DFdJ/UphH1KXZ8ijVmZ3icnfk+Z3Cc7+ppOfUpUylfTue6663D//ffj4YcfxqZNm/DNb34Tvb29uOSSSwAAF1100ZDFy+666y7ceOONePDBBzF9+nQ0NjaisbERPT09qeoCEY1RvfZFXPH4W3i+7raIr7tcgCGsgU4P6BH3sY2w7sopavyBVUALvoUrKRMREWW7vvZ2HDnjOSwu+SuguCPuU1gI3HDiN/DA+Yejr25Vkls4oK8PWLcOGDQZaUDwXAhK5IvYMEWFdft6+AwjIiIiskdKZ9wCwHnnnYfm5mbcdNNNaGxsxOLFi/HCCy+EFyyrra0dkiX//e9/D7/fj3POOWfI59x888245ZZbktn0jKAoCvLy8kZc/JgoHjtjK/T0gTvyNY6VuDWTk7hVhHXhoUS4O7ev0IxbXqzYh2MWycLYIlkYWxTi62oBKgHTXQ6okZOeqgrkeAy4VT/6u7tQEuWzZMfVz34GPPMM8POfA8ccM/S1jd0r8MjKQ3HqiolxP8cUGhToEDwXyhgcs0gGxhXJwthKg8QtAFx99dVRSyO88cYbQ/4+eFERik/TNBx44IGpbgY5kJ2xFbAWL46auFXVgRm3hj9gyzGjGlHiNjTjNtJ0FRoNjlkkC2OLZGFsUYjRF1wEJaci5n4BWLXs+ru7ou4jO64++MD6s7Z2+Gvt/ulYv2c6jkng2UwTGjTo0HUmbjMFxyySgXFFsjC20qBUAsllmiaampqGFGUmsoOdsVXlfxrfO+4aHDjuqejHE1ZW19All0qA1R81So2bwWo6l2Bt/dEIKOWS25Q9OGaRLIwtkoWxRSGKrwUAoOVXxtzP1AoBAP6+6KXeZMZVczOwd6/1310RcsehybMJ3MOGEMGb2JxxmzE4ZpEMjCuShbHFxK3jmaaJHTt2ZHWQkxx2xlauUYNFE1aiPDfCtI/Q8YIPCMhO3JpCs2b3xqvrBuC5bdfil2/+An3uBVLblE04ZpEsjC2ShbFFgLVAl6pbiVtPUewZt0KzZtwG+qLPuJUZVxs2DPx3pMRttWcNjp75DEq0bXE/yww+fWQycZsxOGaRDIwrkoWxlSalEogouwnDSsYqSvQhaXfXAnR7C7FcFEpty//b8md8+CHwf/8Xf9/QpNws/g0hIiIiAL29QJHbKpWQVxZ7xi3cVuLW6I+euJXJStwKKBDo6ho+j2de4TM47tBn0aX8D4DZMT/rjZoL4e0L4EunFklpKxERUbZj4paIUk6YVt1aRYs+JD2x8Udobgb+fonctoSSsAlUSgg/QshJJkRERNmttRUozWuBqgHugtgzblVPISAAwxe9VIJMGzYAZyx4GF9Y9Cf8u+EhAHOHvB6q3a8o8R/OfGnH19HUBJydvWvGEBERScVSCQ6nKApKSkqyegU+ksPO2ArNuIUaPXEbWrgsIHltslASNpHE7SWLrsFfvnwoinpektuoLMIxi2RhbJEsjC0CrMTtb9+5E3e//yww4aSY++o5k/FZ8xK0eKdH3UdWXJkmsHEjcO7ie+FW/VhY/GiEvUILtcY/GXK5Bj6XMgPHLJKBcUWyMLY449bxNE3D/PnzU90MciBbY0uEZty6o+4SujCQnbg9dcrtOLa4Bfnm1QBmxtxXUw1oig5hyl4wLXtwzCJZGFskC2OLACtxa5huaIXjgeinMwCA7tyjcd/LR+Oss4ATo+wjK662bwe8XuCVLefixLmPo6Vr3LB9FGEAChKq9z8ufw/MIj/MwHgAOba3l+zHMYtkYFyRLIwtzrh1PNM0UVdXl9WFnEkOW2MrmPhUY8y4/fLCm3HfOScgv+P5sR8vhulFq7Bk0lvwKN1x9xXBBTkE/33ZhmMWycLYIlkYWwQAbW3Wn+Xl8fcttkrcRlwYLERWXIUXJvOUAgA0s3P4TiJYKiGBx4+uPOgq3H3mF+Hq+8ymFpJsHLNIBsYVycLYYuLW8RjkJIudsWWaAkDsGrd5bi8KPZ2A3jfm48WiBB8PVLVEhkdrHyZu7cMxi2RhbJEsjC0CgK7WLnzj8Jtw4tR7ASFi7htK3HbHuEcsK64++URgQfUqTJ1pLfbqUTqH1eoPnQslUuN24CY2nz7KFByzSAbGFcnC2GLilojSwKstd+CiR1Zht/LFqPsIxUrqmobsC4PgLJME6roheEFjmlydjIiIKJv5uxpxxPTnsaD4GSBOHb6y/Dbcu+JkfGfh0XGTvHZr37UJPzzhmzh+4i8AAEU5HcMSyEKM4lyIK7USERFJwRq3RJRyVt1aBW53jAudUOJWl1vkdmDGbfyLldAsE67IQURElN30nmagHDA9FXH3LSjJA3LbrAsxwwu48qW3DwB6e4EJyusAgLyiAqhaLwo9nejqAkpLB/Zb2XgJGnacgfMTqCloBi8ns3kmFBERkUyccetwqqqisrISqsqvmuxlZ2yFFhxzx1rMQ03OjFt1JKUSgrNMhOAsE7twzCJZGFskC2OLAED0twAA1Lz4idvC4lyYQrNKFOg9EfeREVefbhBYNuVVuD2Ae9a5aPHORGP31GG1dnd3L8Kq3Scg4J4Y9zOFsG5ic8Zt5uCYRTIwrkgWxhZn3DqeqqqYNWtWqptBDmRnbB1cei/mHVWLcu1iAPtH3kmxsroiSaUSEplx29w/B91tXZgyu0pym7IHxyyShbFFsjC2CAAUfzMAwF1YGXffomIFe/zFKMpph9HfBS13+HmEjLjatbEG+xfVIifXDcz8Gn7/ybfw2WfAb740dL9QDjaRa2ShhJ4+Yo3bTMExi2RgXJEsjC3OuHU80zSxfft2Pr5EtrMztibmrsayKa8h39UafadwjVvZpRJCdd3iD48ftHwdd776e7Rox0ptUzbhmEWyMLZIFsYWCQG4TWvGbU5JAonbIqDXXwQA6OvsiriPjLgy97xmHTP/EMBVgJISa3tn59D9phV+iEOmvYRcNCXwqaxxm2k4ZpEMSYkrwy/vsyltccxi4tbxTNNEc3NzVgc5yWFnbCnCSsaqWvSHAHrMiahpWwCvGf+CaCx+9OoruOQf70PkTYu7byi3y2sV+3DMIlkYWyQLY4u6u4FijzXjNr8sfqkElwvwm8HEbVfkUgl2x5UQQKVpJW5zpx4PACgutl7bt1TCMZP+iG8dcQMKjQ1xP3dzx0l4cfMF8GKyLe0k+ThmkQzS46rlfeDlI4Cav8n5fEpbHLOYuCWiNDCQuI1e5HZDz4W4+YW/osb4UtR97GCYKgzhgqrFXhEaADSuTUZERJT1WluB0rwWaBrgLkjsBnMAVta0vyfyjFu77d3ZgPH5WyCgYuLiowEAX5p+FX674hQo3ZuH7iwSLxu1pu3L+Pua69CtzLG9zUREYf1NAATw2a+AQOQbXpQaus7rYdmYuCWilFNg1UXT3NFn3LqCL+mSS6iNpK7bceN/jj986ThMCvDOLxERUbZqawP+7+U/4+4PngXGLU/oPa2BOfiseQl6+oslt86yfssE/Pj5R/BS/Y/hKSwFABTltKMktxV6X/uQfZXgQq2KEv9kKJTb5dNHRCTVpDOAwpnWf9c+kdq2UJhhAOedB1xwAZO3MjFx63CqqmLy5MlZvQIfyWFnbIUTt67oM27dwZcCckvc4uLFN+CqI26AG/FnwLi1fuS7u6GY/XIblUU4ZpEsjC2ShbFFra2AYbqhFY4HXPkJvWdlyzX46cv3oz5wdMTX7Y6rDZ8qqO2Yi0D1WeFtwl0KANC9Q4vcDtT7jz/jNs/dhbK8JkDvs6WdJB/HLJJBdlztaVTx0FtfQ18fgF3/AAyflOPQyLS0ALt2Adu2AY2Nco7BMYuJW8djkJMsdsaWGp5xGz1xu3/xv/CLz5+J+covxny8qITAQRNfwqHTXoKmxJ/aG5qJInh70TYcs0gWxhbJwtii1uDaquPGJf6eUH3Z7u7Ir9sdV598Yv25cOGgY+RYq5MJX8eQfcMzbhMolXDa5Fvw6y+chnGBl2xpJ8nHMYtkkB1Xbz+/FU+8vBAbtlVB97YBdU9LOQ6NTFubdbNvfNEu1GyXM5mJYxYTt45nGAY2bdoEg88vkc3sjC0V1jTaWKUSclz9qCjYA7doj7rPmOndEML6T8VTGHd3oVgXNELw35ddOGaRLIwtkoWxRf1tdfjG4TfhmMkPJvyeaAuDhdgZV/quZ3FUyU3Yr2rN0MRtrpW4RWDfGbfWMVUt/qViaFauMBn/mYJjFskgO64OwjW458wVWFN7qDWzs+b/AabkGnqjYfiBjfcALR+muiVJ0d4O3H76+bj7zC+io+YTKcfgmMXEreMJIdDZ2QkRykYR2cTO2Lrlzf/i64+9AzNvRtR9FDWY1DUl1krwWVNm+gJF0NyeuLtzxq39OGaRLIwtkiWdYksI4B//ANavT3VLsovorcUR05/HnOJXEn7PgvKX8dsVp+AA4weRP9PGuOra/BwOnfo8Fk7+FFOmDGx35ZUCAFQ9cqkENYFSCQJM3GaadBqzyDmkxpUw4Tat67T/bLgEdXvL0dm0B2h81f5jjVXt40DtY8Dqq1LdkqToaNPR1DMZANDfslXKMThmAdGntxERJYnP74LfcCFWrlTRQolbiXdWfS0QADq8FZicyG2t0KIdgolbIiJKvfXrgZ//HJg9G3j00VS3JnvovS1ACaDkViT8nvx8BSX+Vvj0NoktA+DvgGj9CADQV3Q8FGXgpZzCEvQCcImOIW955rNr0N/dhStPmBb/84NPH4GJWyKSJdAFw7CutyqnTcDjH1+N/C3AtccdjxFUqEmOvtpUtyCpctpfwZJJbwEAlJ5tKW6Nc3HGLRGlXGjBsRglbgcSt0LyjFsBdHrHIYGybuGLFc64JSKidFBfb/3Z0JDadmQdXwsAQMtPPHHrKbBqJahG/MVQx6TpLfR7TdR2zMWU/SYNeSmndDzqO2dib0cFBk9kWt94NN6pOQPCk0BKROGMWyKSzNcKXQd6/cW45tsu7HWfhRc2nIWf3O5G+k3CVAfaZPhT2pJkCPQN3HwsMLem4ffhDEzcOpyqqpg5c2ZWF3ImOWyLLSFw0eIf4YrDboZbib4isZqEGbdmfyuEADr7E0vc9pkTsK3lAPSZ46W1KdtwzCJZGFskSzrFVlOT9Wdfn/U/Sg6X3gwA8BRVJvweT0ERAEATkVcnsy2umt+B1wus3n3ckPq2AJA3/Xhc/9zj+Nvq6+D1DmwPlREc2U3sNKw1SRGl05hFziEzrkQwcdvhrcD48cBtt1kTft5+G3j2PwbSKVvY1aljyxZg716Eb+o5meltDf/3+MLtaNpr/008jllM3DqeqqqoqqrK6iAnOWyLLWFg2cQXceSM5+B2RR/oFc2ajqtA3oxbb5dV462zf1x40ZBYPus/F7e99BdsC5wnrU3ZhmMWycLYIlnSKbZCiVsAaHH+9WJaME0gR1iJ2/yyxGfc5hVZiVs3oidu7Ygrf8cu+P3A9paFwxK3OTkDTzsNXiRtv3HvYfGkt6GJ3rifL9zWAmdGf8eY2knJk05jFjmHzLjq72yFMIGu/nKUlwOzZgFXXgkcPfMZlG/4Alq2fmT7MUfro9YL8NdV1+Enr/0NyJ+Y6ubI5xuYcetW/ajfstv2Q3DMYuLW8QzDwLp167J6BT6Sw7bYMgPhm6QuT/Sy28JVjIauGej0TRjb8WKoz78Klz32Ll7f/Q24EqgAHvrt4D8v+3DMIlkYWyRLOsUWE7fJ19UFFOda/2cXlCc+4zYveIdYhTfi00S2xJUQ8LZZdTM8JROH3ZRWFIS3DU7cXrT4Zlx3zLVw6/FrbhjFS/Dm9rOxtXnR6NtJSZVOYxY5h8y46m2zkoNesxy5uda2r34VOGz/TSjLbcCnz/wF6VK5rq5jJl7cfAHWbJuXThOBhzMNYPW3gU0/G9PHuAxrxm3ourijzv4FyjhmMXHreEIIeL3erF6Bj+SwK7aEqQ8kbmMUue1yH4IfPvsE/lt/65iOF0tLCxAwclBYWpDQ/qFHCNPlRMEJOGaRLIwtkiWdYouJ2+RrawNK81qgaYCrIPEZt4WlhQCCa3oFhte5tSWuDC9ae6oQMHIwcWaEG99GP2458Vzcu+JkdLX7wpsVWCc2mit+rQSl+hg88MGPsXL78aNvJyVVOo1Z5Bwy46o5sBDPfHoptvccG96mqsDhF3wVUFRUqh/g5X9usv24o9EarBzg8wHdkR+oSA8d64GWd4Fdj45pcUm3sJLqe4wj8e9PrsCm2pl2tTCMYxYTt0SUYkZgoPSB2xP9AiGU0w1IXJus2XrSERUJXnfNy3sMv/7CaVjo/rW8RtGoGAbw6aeAzpJ7RJRFBiduQ79pJFdLC/C9p5/CPaueB4rmJvy+4hIV21oOwKbGJTBlzSJy5ePXa/6Jyx57B/MXeoa/ruagunAXinPbwuWiAEBRrMStosZP3I4PlvlvbLSlxUREw+zuORBPrrsK9cbJQ7ZPmjMRZvWpAAD/+l+hvS3Fs2mEwHj/47jskJ/gqiN+hJ6NT6a2PbHog7LKvtGdMAgB5ChW4rZj3BX49ydX4KMts+xoHe2DiVsiSqlAwMqsGcIFt0eJul+odIHMRNyMjh/gisNuwZTqtvg7A/BofSjLa4IHnfF3pqR67DHg4ouBf/wj1S0ZPZ8v/j5ERCG6PjDTB+CM22Rpa7POYVyFVYAWITkaRWEhcNtLf8FPXr4fPXriJRZGatcuAFAwM9IkKEVBAFaN2v6ujvBmVbFOtlQt/qVidTWQ4/LC7d8JQ+cjSERkv9Bv27hxw19bcNYV0Dy5mFX2Ed54+NHkNmxfgS4cVnI3jpn1FA6d9iLM5lWpbU8MBnKxqxbY0wigf3R33rxeYMOeg/Fp48GYu8j6HaupSau14hyDiVuH0zQN8+bNg5bQsrBEibMrtnR/MHFruhCjUgKKlS244/Rz8aXpV43peFGZAVSar+LIGc9i3LgEh0Yl1HdeqNjFrrjauNH6c6v9ZZaSYts24NhjgV9zMrdt+HtIsqRLbLW2Dr1Y4ozb5IiVUIjF47EWBwMiP05rR1zpOtAQLFM7dWqUfdRSAIC/tyO8bSSlEsrLBH7/pRNwx2nnoK2B024zQbqMWeQsMuPKaN+CqsI6VIwbPoNHLbCkc5UAAOF6SURBVJyMnAOvBQDMMu7F1rU7bD9+wvytQyYZ6b1J/CFuXw+s/T6w56WEdt/ReTA+2rEUHe2A3jO6sbutDfjTe7fgN+/dhzkLK1FRuBczi95Ba5N3VJ8XDccsJm4dT1EUlJaWQlGiz2QkGg27YitUKkE33Yi1UKRbC2BSyQ6UeuxfqRIA4G+HrgOm0FBSURx/fwCKYjVYsMitbeyKq9CF6uDHhjPJypVWWZD33091S5yDv4ckS7rE1r7jHWfcJofo2IgrD78RR0x6ZMTvjbQwWIgdcdW1+l7ccvJXcfScF6KWgTK0UgCAv3dQqQQkXipB1RT0GVUAgPaGvaNuKyVPuoxZ5Cwy4+pQ9zX42VlnY0bFtoivTz9yBbo8h8Ol+vHKox+kbv2R/pYhiVvFl8QxsekNYO9rQPM7Ce3e2Ai09o2HEEDX3tG1M7hmHMrKrJuRPz3zEnz32O+gceuWUX1eNByzmLh1PF3XsWrVKugs9Eg2syu2fNoUfOOJN3DDf/+NWGOxy23VSlAhqcitz/qh7ewfh4rKxIZGJZRpFkzc2sWuuKqvt/7M1MTttuB56SjPoygC/h6SLOkSW6HxLvTTxBm3yaH2bsPh0/+LGUUjv9N24ZK78dsVp0Db89Sw1+yIq/6mLZhevgkTq/uj3xx3lwIAzP6O8CZNtY6puRI7H/Kr1QCAnhbOuM0E6TJmkbNIiythhhfAyi+L8miDomDOihvx65X34YFXv4xnn7W3CYkK9LbCMIDG7mkAAE1vTt51YvO71p8VRyS0+95GE0998nX88NknsF0/b1SHbG/ToSoGysqsv/eqcwAAXXX2PvLIMYuJ26xgyFrwgLKeHbEV0FV4A4XoN0tj7qcF6ygokDRgBxO3Hd4KVCZaai4045aJW1uNNa76+4EKbQ3uOP1clGN1RtZZCiVuu7qAvr7UtsVJ+HtIsqRDbIUSt7NnW38ycZscot+a2qzkJbiy6SCF+f0oyW2F3hu5tv5Y40rvsR4/ySmdGHUfJceqcSt81oxb0wQe+vCHeHjVD6C4ixI6juG2Erfedt5tzBTpMGaR80iJq0BnuH52cUVZ1N3GTazE0V84GADw299GLkEjW2+bVTtnZ9t+EFCtdvsSWztlTLx7gZ7tMAwg0N8N9NXHfcvi3vNww4nfgMfVj/rG3FEdVmt5HX/58qG4NFiqAkVW4tbfZn+tumwfs5i4JaKUClZKCC8+Fo3qkjvj1uy3ahJ1esdFfZxwWJuC01cUZPcPSbppaAC+f/zVmFSyA1cf9h309KS6RSOj61Zh/xDOuiWiRDQ1AROLa3D84jWYVLId3j6TN36SQAlYiVtP4cgTtwgmRvV+CRkGIaD5rMRtQWX0xC3yJqG+cyY6egsBAIYBvL5tBV7d+iWo7sQu5tWC8QCAQA9/sIjIZj7rGq3HX4JxFbEvGM87D5gxA3AF6rHhkZsA3d5aq/F4O63EbYe3Eh3eCus615eEx/9a3kUgYE38aHj5TqD1g9j7CwG33oCyvGb0+YvCJeZGKtDTBgUCrhxrYc7cSitx6/Zm6CIjaYyJWyJKKdGzC5cd8hOcteBPMfcLl0pQ5My49ba3QgirVEKiC4z4lTLUdcxGd2C8lDbR6DQ0AF395QCAxz++OuNmndXWDtzQAJi4JaLENDUBx835F74w6QrcdeZ5GFfQyDq3SeAyrP+Tc0tGnrgNzWg1ZCRu/W3QA34IqKiYVB11t56qr+H65x7Ha9vPB4AhtSETXQfGXWydBymjXJmciCgas78NhmFNrol3jeZyAf/7PRPfPfY7KO17Hi0rf5OcRgb5uqzEbUAdh7a+KgT0gacypGpeic4u68ZbXx9g9sYZi/VuCMMHADhq5n8w03fPqA5reIOrc+ZYX8y46VbitkjZxlKCNmPi1uE0TcOiRYuyegU+ksO22Orfi2NmPYWDJr0S+3jhGbdyEre9ndbKIAF1XMIXKnuVk3DD849iZdu1UtqUjeyIq4YGwKVamc+tzYsyrs7ttvC6C1aNByZu7cHfQ5IlXWKrqQmoKqyHy21dvFYWNjBxK5lpAnmKdXewoHzkiVs1x1qdTPiHr0425rjyNsDvB9r7KjF5qjvqbiVWpYTwAmmGbmL/8R9iQfUqqEpiF9755Vbi1q3zBysTpMuYFY9oeBFi91OpbgYlSFZc9ba1hCfXlEWvlBB28CEqNivfgxBAz6YnIJrjzD610Wb/1/Drt+9BX+Ex+OWbv8Alj6xEZ87Rcg9qBoDWD9HVBXxYeyKEAHpb4yRuvY0IBICA6cHnFz6AeXmPAfrIH9ER/VYZCFe+NWFm4uyp0E03XOhDR6N9N/IyZcySiYnbLODxeFLdBHIoO2LLNKxErInoFxUAoLk9aO0bj9a+CZBRtHSrdh0ufXQl1nZcnPB7Qr8dKVu51KHGGld763tRkmvdAW7snpqRiduJxTX407nH4IuLfs/ErY34e0gJ8e4Z8UyRdIitpiagsrAebhfgcgOVBUzcytbRAZTktgAKUFiRaIH8Aa7cYA3ZQOQZt2OJq0BXA/QA0NQzCVOnRt+vKNiEUOLWDPTjB8dfhR+e8E1oSmLlqYonTMeb28/GG1tPH3V7KbnSYcyKReh+1D77I+x89icwejPsRC6LyYirnjYrOehHecKTa875xiF4s+Yc9PUCu9550vY2RVPbNhMf7T4OOeXT4Movh2565J/H+1rRq87GnrZKfFR3rLWpI3bS1PQ2QQ8ADZ0z0Bcogj8AYBRPTKi69d24C6zEbV6BC+2BGQCApu32lktI9zFLNiZuHc4wDKxevTrrizmT/eyKLSMQnEGrxK5ZpOWV4tqnnsWPX3oKUJQxHTOS5mZANz0oGZef8HtCJw/852UfO+Kqv7UWgBUmXzjgfrQ3pWB1gjHYtg346rJ7UJjbh88vfAA23rDOavw9pIQ0vgK8eSaw5d6E35IOsSUE0NwsUFnQAJcLcLuAioI9GVcqJtO0tgiU5LXCpQFa/shn3LrzraypYgz/nYoWV3v3Aq++Gv8edmsrsKdrKlr7p8V8vLjUtQN3nH4uvn/UxQgEANMcOJ7mSuxSsWLKRDzwwY/xrzUXwudL6C2UQukwZsXT1d6L3h7A6wWaankilAlkxVWT7wA88+ml2Ok9JuH3jB8PTDjgCABAW338hbrsErpZWlEBVAcr1EhP3OaNxxO7H8R3n34azT1WPXO9Z0/Mt/S07IUQQFtfNVp7x0MPAIGukf87c5nWRJnckoEfmU+9X8Mf37sNnzXuP+LPiyYTxizZmLglopQy9MRm3LqDL+tyKiWEL24rRzBhpkp5C3efuQLHVdwipU00Okb3LgBAbi5w6ry/o7+9LsUtGplt24B8Tw8KC4GP649yVuK2cyNq17yPuprMSqZTFtl4l/VnzV9T244R6uwEctV25Li84VIJFSyVIF1bu4IrHnsL96x6HsiNXkc2GnfBONR1zkJTT4wpsfu4/XbgBz8AVq6Mvd+W7lPw/f/8C683/Sjm/e6CQhcmle7AxJIadHcjvHo7AKgJTm8rKgLy8qz/5lMiifP5pDxE5gj1zWXY3LQUANDpqBOh1OnoAF57Td61lCy7ew7Ek+uuQoN58ojeN2WulcTMMZMUP6aOycZjOHjqK6gYZ2DBlK244rBbUNzwS+mHfvllawKSV1hla1T/3phPDvW0WAO1T6lGp896z2hKG+TAmnGbXzpQwyIw7mS8W3MaNteMYsFOioqJWyJKKSO4CpOIk7gNlrgdsmiTbYTAYuM7uOKwWzChYniduWg8qhfji2pRqPEqJV0IAdTUl2P17uORF5w8rXdnzgl/Xx/Q0CBQUdCAwkLg6Q2XOeoiuHfj4+h962p4nz0OomV1qptDNFzxPOvP+f+b2naMUKhMgssFqEowccsZt9K1tgKGcMFdVAUoI7+sUkrn44bnHsPfP7094ffU1Fh/rlsXe79a6+GTmGUSAEDNLYWmArmuPnS2ByBCM5oUQEnwCSdFAaZO9GJC8U4013cm9J5sV1sLHH88cOedqW5JempoAFp6JwAAeltGueQ9DfGb3wDf/z7w4oupbsnItAbXv0p08eiQ8klW/LhE16jqt46Yvw1HVdyDbx15A8aNUzCxshtHzngWxf1vyTum3ofdO7rx2WeAqgLHnFIBU2jQAybQH/0EoKVnPDY3LUWfNge627rp2N08susl0wQ+3n0wPm08GIUVAzcuZ1iVEsK/VWQPJm5piL//HTj3XHCGhl28jcDa7wNta1LdkrRlBm/7ijilEtwugVtOuRg3n3QBhM/miwK9F+O1d3DkjGdRXjGC+jnhi7TsfWwj3XR3Ax/tPBi/eftu+EpPtDZ6Yz8ulE62b7cWFyor6EBOnhu72udi717nzMjp3L0BwrRuwPBCjNKSr9X695Y3KdUtGZHwwmQuAIqLi5MlyWgTCiHF1tpk4fqy8ZgmwnXbN2+OvW+iiVu4CqGo1vlMX2cXDMOapSWEOqLSVJcf/APcdcY5MPa8kfB7stnatdaM2/feS3VL0lNjgx+twcRtoIvnC3bYvt36c2AR3MxgdGxBdeFuVI4b2eyd6kn5aOyehm3NC9DXlYQnvXyt0HWgwzsOFZUq8sdZyUyXLvFEvuG/yF15Ai5adjcOPhhYsEDFgx/8CH/f9AvAXRT1bRs6V+D2V/6EBm0FkGvNuI1XF3df3d3An9+/EXe9dh9KqgcStzNnCMyvXo2p5j8Ao390/aJhYmdKKONpmoZly5YlvALfv/8N7Npp4v33VZxxhuTGZYNP7wBa3gX2vgac6qzZZSONrWhMIzjjVolTKsGjYHr5ZqiKAVP3QcsZ02GH8ls/tF69AOWVuQm/TVWDfR/hIjYU3VjjKlTGatw4IK98PHq3A1ogc2bcbtsGzKrYgJxcwJ3jwfKpr6G+Yya6u+eGL/AzVqAb6NkZ/mvn3gYUzkve4e0as8jBhEB3Ux3qdwHbuibjjPMSe1s6xFZTE7C9ZSHebLkeM0834N34IT7cNRnNbQKA/XXhyeLpeg/fPPw5VE5YBuDsEb8/NK53R8gpRIqrtraBx5w3bbJyARFzq8LE54tPw+JTK6FMvhdASfRGKCoCohgKOtDX2QFRUWh9BEYWz6ZnPBAAfJ0OekxEotD5SmOj9Z26knhVng5jVjyz+36MpQtfAwCIDLoBn84agvlvWSVfZcXVUXlX49Cz2rC94hEAcxN+X34+8JM3/4muLmDeJcCsclubNYwIJm47+8dh1jiguLISaAFMww8EugBPjHF4tJrfRXeXiQ5vBU5aAUycCLy14yxM8AL/G2NMCVVFqK4G3Mp4QAeM3pGN3cE141BcPFDSEABmzFTwrSNuQHFuG3oaD0ThpAUj7NRwmTBmycYZt1nA7/cntJ9pAoeN+y3uO+dEtDckr4i3o1UnXkQ9EyUaW7E0Kqfi2/9+Hi/v+XHM/VwuQDetX4WA3+biTL4WBHSg01uBihGU41FU62pJwQgStxt+Arz9peQ8spOhxhJX9XUCZXlNmDRJoGCcNVMjX2mEDaGaFNu2AROLa5CbA6hmL/7n6B9j+dRXHVHnVnR8it7egb/3tyV/Bk3E2DL8/PdIlkAHvN19ME2gpP5uoGdHwm+14/dwLJqbgaaeyWh0fxGYdi78+/8M/1j7HbS0MGkrk6d/Ew6b/gKmFsWpWxBFURHwf5+7ED898WSIvuHJqX3janDpnPZ2RC+F0d8Mj2jB1LItmDg1+qyrkIBiJRT6uzth6NZTRKYY2WWiq9CacWX2OuAHKwlCyTPTREp+41M9ZsXj8g9ci9a2TE9dQxzC67XGDEBe4haQEFfChMvsAAAUlI088zremkyalH9jfe3WEzud3nEoLweqxnvQ7SuFHgDQ32T/AQ0/+hs+RH8/8EnjETj2WCtxC1i/FVFrGQuBpkbrxepqwBx3OK5/7nE8sX1ktXjb23Roio6ysqHbCwqAFt9sAEBLzdYRfWYs6T5mycbErcMZhoH169cntAJfUxPwuf0eRoGnCxO8DyehdVmgfLn1p5rjnGedg0YSW7H49Fy0e6sQUGL/GLtcgGFatw51v72Fbk1vK4zgoy0jWZwMoRm3iZZKECZQ9xTQWwPsfWNkjcwSY42r1j1t+PUXTsONhx2L/LIKKCowrqAxYx4X3rYN+Of6b+Lj0v8C086Hyw1UF9Ul76Iu0AVs/QPQ8F/bx6yOXRsQCAAieOphdCf3BmHE2BICeO+rwFufB3RvUttDaaivHqGfl4k5HwK9tQm9za7fw7EIJfSqqqw/Qzche3ut2tkkic/6cXEVjOTkYUBxMVCS14rinDZ495l2Gymu9q15Hq1cQqCrAQEdaO0djylT41/u6WopAMDf0wFdKcYja67FU59+a0R9ySmxEreqn4nbRAxOniVx0XsA6TFmxZMrrJu71z/3OB764Lspbk3m2zPovpCseJMSV/4OmIYJAQXFFWXx999HKHG7JwmTtvvardo5XrMCHo+VFG33ViGgA0JG4rZ9Lbo7+9HRX4EJ+81FSQlQXg5MKN2D5VNeQvu2DyK/L9CJK2Ychl9/4TSMrzZQNbEY9Z0zsbMuf0SHF3vfwl++fCiuOuSaYa/5PHMAAD177EncZsKYJRsTtxRWv3vgLkZ/D8/ybZE3AYAKmL7wyT0NFVpsLN4jYoNn3BoBe2fc9ra1WHdI+ytQPoKbuWqwJpySaKmE/kFXXAEu3iFDb/Mu6z88pVAKJsPlAsblN2bEAj1CDNQdmzq3EihbCrcLqCqsS+4CZbVPAutvBFZ9M+HEVSLadnwCAFi581QAgOpLg5p1/U1Az3bA3w50xSkYSc5XOAt/WPMg+vV86yaDN3MSUM3NwMFTX8Gs8nWA4Ud+nkBVaTsKPJ0Zc+MqE2m69X+up3h0q2fn5ADegFUvoa8j/nnBvjfxoiVuW+oaAAG0+yYNmw0ViVeZhvrOmejtc8FQCvHC5gvxes0F8d84SGGFlSFxGyyVkIg9DTpWHPBHzKlYh7q6VLcmvQh/NzRh3cho6Z2A5mZkzJNT6Wpw4rKnJ/G62ikXLD/Q4yvFuIqRPyZ/yLTXcM9ZX8C0zhslNG4ob6f1e2C6raLnlZVAe18VhAn0tEgYF5vfRVcXsL7hcJx4ovV0jaoCR857H9864gaYO/4R+X39TdADApqio3q8Fp6lO9Lktq/LSlS7PMPXh1FLrcSt2WnfjNtsx8QthdXvDqDHbz0qFfD2pLg1DuDvAHb9Awg9Rt/Hs7JISgLv4cKDfo79Sl+NuZ+qWis3A4Buc+I2dIc0oI7DSErnCDUfzT2T0OmvSvAdCnQd6O8H0Ld7xO2k+IxOK9Eo8qcBBTPwt60P44bnH01u4nOUWlqAzk4r1mfOBJA/GW43UFUkOXHbvm5gdq27GJj+Zeu/21YD754PbH8QMMf4b04IoHMDAGBbz8nWocxmq0xBKnVvGfhvbxokkim1XHl4/7NFeHPb2TBNwNuWOYnbliY/vnXk9VhmXgboPcDGu/Cbs0/Cyfs9xsStRB5h/Z+bVzq6xC0A9BrWbF1fZ/yr5tBvQX5wYtSmTZH362q0xjPdPSGh9cU+wU24/rnHsbntaJjB01Z1hFeJpROsGbcFWiOE6aynzOzW1wcsqfgXzj7gftx48mVJn3Gb7jr2NECYQJevHNDyoMBE457snWlnh/p64JR5j+DUeX9Hef7ejIm5QG8bDAPo7C8f1SKQZeUqqgt3w+XbZX/j9hHoCf7Y5li/B2430A/rGrGn3f4JO7273oWvH9gQLJMQohVapeL0nsi/KXpPI3QDaOurRnU1MGECcOLcx/H52bejv2VnwsfX+6wit8I9fNZT4XgrcZsb2Oq4p45ThYnbLJBoEedd9QW49+07AQAl7lr4fDJblQW6tkD/9NfYsyeUqHNe4taOAuFFxic4Zb9/YFrBqrj7msHErRG1aM/oeLutGxXCPbILr77cpfjuM0/j8a0/T+wNeePxq1dvQk0N0Nu0c4StzB5jiSul30rcukumApoHgYL90dVfnhEzbrdtA46Z9TRuPuMa5LS9BORPhssNFHo60d5k/8202o07setf16D1+cvw5O9exw03AFdcAaz438vwf28+hUb9EAjDD2y9D1j5FaBjw6iPJQRw5yu/x18+vAHzjjgYdZ2z8GnjwVaCKYmGxVbfoCuXXvkn9SSPzwe8/vrYygL09gIdHUBLnzVzsK8t8TsmqV4ww+xthAIBlycX8JQBuVVwuYHKwgYmbiUxDKBAs35cikdUZ2moLn0qAEDvGn5Dd9+4Cs24PeII689oM269wRriSv6khNpQElwzp6sLMANezK5Yj2llnyX03pBxk6zErQYfutszZTpfatTXAz2+gYWK6uuSv8htqsesWNqCWcVufRKuO/FG/Pn8I9G345UUtyqz7dkDnDb/b7hg6S9Rnr83vFCZ3eyOq+5Wa3JNt28ciuKX6x6msNKaTuox5NdK+KTvCvz67XvQrh0e3ray9Rpc+uhKbNEvsfdgfQ3oa94FARX5Uw4esoBxXrl1DqP0R7753NW0FxBAp68KZWVWyZ6j57yA42b/C227tyfcBNNrfTdq3vCMevXMGRBQoehdgM+ei7B0HrOSgYlbh3O5XFi+fDlcCSxVWlcH7OmaBsCqt9VQzzubY9K7Ex0dViH4rQ1Toyz7m7lGEluxiNBMPjX+5/QEytHZPw66bu//l2v91+PSR1diWyDBJcSDQrNRzATPt30+YPWm6RAC6Gvl7L5IxhJXpgnkCyv5VlhtXQiH6j02SSgtZbdt24D9x3+AAyeutG70uPKheKxnXP2d9t74qf2sES3PXoTenSvR0OjC+g/q8dJLwJo1QG0t8PTLk3HGLffip/+5DY1tJTA6twEfXgH42kZ1vN11CtZun4t3dq3AKZ/z4IbnHsMtz92HgCp5id9BIsbW9POB2d+w/rt3Z9LaQvb772Ofof3FK/Cfv45ukSgA6PzkCZww58lwPfVAd2IzbiPGVqALWPu/QPO7o25PorxeIF+xEh2uoknW+UbeRLhcQEUBE7eydLQLlOS2QFGAopGsbLoPv2sKAEDvGFqaJlJc7d0LnLX/g/jSIY+jKLcDzc1AMK8xhNlrnWPklk9IqA2hi/6uLkDt342bTr4UVx3y7RH1Izffgzd3XYB/rr8Szc3OOue1W3098GHtieESYD0tyZ3db9c5vCy9zdZ4FnBNREGBCrfqR28Lz5vHonlPH8ryrJPh5p6JUmbcyoirwU9FjuZSunSClcT0oB0w+m1rVyQ1LbPx0e7j4CmdPHD8ikLopsf+J+fcRXh49Q149tOLcfQJQzPaxVVWn0WgFwgMnyDR02rFgV+rDv9/GlCt93Q3Jd5QJWBdE7jyh5/LT5/lQUPndAQCQH/ztoQ/M5p0H7OSgYlbhxNCoKOjAyKBKerelp1QFRPXPvUsrnz8ddQ3ZPddjTHr3QmfD3hu48W47fV/AZPOSHWLbDWS2Ir5OYZV5FbR3HH3/fm7D+N//vUivJ4FYzrmvpqbAd30oKwid0TvCyVuE62T3rC9EXu6puO7zzyNf7f+a4StzA5jiauWFqCqoBaKApRMtG5CLax6Excu/QVyej60u6m227YNmF2xATk5AEoWAgCUguDJn80z9uvWrUSuqw+tvul4sedxjFv2VVx3HXD77cC99wLnngvk5yt46sPTcP7v/omHXzkdz2/+GnY3jS7Runq19ecBB1iPZOXmJn8l7Wix1VV4Crpm3A7MuSp5jSHbzfX+EPOq1uBg5Zuj/oyc+r/g4uV3hv8u+hIL0EixFdjwKzSuex19b48s+TUazc1AVWE9VDWYuAWAvIlwu4CKgsyo8Z2J2pu7oak6NA1Qc0fxDG+Qq9hK3Jq9Q2fcRoqrpr0mPn/AnzFf3I25M3oBAJ9FmBjb0DYejd1TUVQ9JaE2TMl9G3ecfi5OqLwJpmHdUBcY+XXAOy3X4ekNX8eeluL4O2ex+nrAFBr6YH0/omdnUp8mtuscXpaG9vFY13AEet2LgHxrxmSgKwmrSzmYP3hjKDcXuPvMc1BXZ/93LyOuGn2L8PSGy7DLe8yo3l89qQhevQC6DujdcmModBNtcEmHautBBNsTtzt2F+HRd1fg359+C8fs839N9cRcdPtKrXVkvMP77OuwGmN6xoe3iVzrv73tiZ+Ya4aVuM0pHv77V1wM/Gfb93DrS39BTefShD8zmnQfs5KBiVuHMwwDmzdvjrsCnxDAyRN/gl+efQaOPGA9BNSMqX2TtoKJ2/rOGdi9O/HkXqZINLbiGcmM29BNttCCZnYJzUYa6YSZAnML/u9zF+L8udcltv+mq/H7c47HuPxG7NjB4TeSscRVfZ2B6qLdcLsBtdCacTu94F2cMu8RFAXW2N1U2+3Z1YaKggbk5CpASfDmxOwr8PM3foX3txyU8MzuRPibrLIHZuXx+N7NU/HtbwMXXACcfDJw6KHA978PvPAC8MMfAlWTSnHf27fipr9ejmuvHd3xlO1/wglznsRhyzqhKMCkYG4pmY+HRoot0wS+dtVUfOGqk9EtpietLWS/li7rwsEIjLJusuG3FqsDsGnvsuAmL2DGH4sixVb9ZzvR1hac7Z/oApaj1NQEVBbWw+UGkB+82ZM3wVqcsaARLc0OOwFJE80dxbjkH+/jZx/9F9CGL86SqLyKaWjsnoY9ndOHbN83rnTdWjDPrfrhyvFgzmwDCsyIdW7ve+dWfP8//0LZ9IUJtaEgz8Ckkh0o0nZDBH9szFEkbkNJimTelMtE9fUCM8o3IrfEeiyozLMzqYtF2XUOL8vqupPw8zd+jfbi8+ApCc4aZx36MVH6rCfS8guAPHcPOpsSeIKq/nng1eOB9vUJHUNGXNV2HYh/rv8m9ionjur9ZeUK2vsmQAigfaSrb42E4cN05VEcMvVljCsf+M2fVNWBKw67GUuR2LViol4JVg457DAMKyExYQLQ2jfeul6OUC7B7LMSt+7CgTVaXEVW4lbvSXzw9ggrU51fGnlSR6D4YGxvOQDbd45sYlQk6T5mJQMzBwTAepy/Iq8WUIAJs6yEBxO3YyN6rMTtnq7p0HVIqyWU8UIzbtX4M27dwV1sTdyaBo7OvxpXHHYzqsaN7BEal9KPaWWfoTIvgXpAwgS81j+qlt4J2LFjNI2lWBob/Hhh8wWo6TseyLWuHnNKrRN+l57eV5GGAbh6rGSqq2QG4C4EAJTMOgyfNB6J9t4ytI2uSkFEef2fAAAKJx8QdZ/8fOCcc4DHHgPuuw8AFOzcaT2WPRJC92Oe50FcvPxOHHSgNUPs+Pkv4t4VJ6O09kej64AdOjei9/VvYEnxA+jsjL7ID2WGv3zwQwBAZ28RDH0UMzK89fAHgH49H91iBr7xxBv41YY3AHV0Tx/1dlsJZJ8PEH1yZ/k0NVkzbt0uAKGapjnjoLncUGDC15UBtWIyUGurNWvSUzT6+rYAUDZ5Kr7/n3/ioY9vj7lfczMwoWgnFAVwKX5cMnsFppdvHlbntr9/oDzQ1KmJtSEvWOTWg45w4nY0l4mTxvdjYnENeptq4++cxToam3HrqRdhWtH7cLmBicU7ed01SOiaaeJEoKDCmnHr1nkhNVp9fUCJthMAUBBc2FDvTKCu/yc3WWV/Pv6hvMbFEZrFOtpqNKoK9MGKoe4midcCvmYcN/5nuPywW1FRMVDTobLagyNnPIdJnrcAfQxF+AcRPbXo3PBvTCndipNOGv76xInWtaauR16gbHfXAmxuWgpXycAPRF6Zdd0UrS5uJJ80HIxPGw9GwbjqiK/PmGH9yWteezBxSwCAhl3dKM5tg9sFzJrUhGuO+j6m+P6U6mZlLr0P/q4mCAE09UzCLadcjKIPT4hYZybbCTOUuI0/4/aLC+7BjSdfBnf3R/Y1wN+KiTnv44gZ/8W4ypHNmFGDF/QKEphN5W2E7g9AN92YWrYVn5v4I+g1LJdgp90NeXhs7TX4wH83oFg/bwXjrDvIediT1oua7t4NTC3ZAFUFcscPzJDStIGTVbtmMPl7u1Gs1QAAJi2IPxtLUYCDDwZO2P8VXHn4jWj65LURHa9+82YIQ0ePvwzzFluJ9NJxOSjObYPoTeGVauenMFs+wuyK9divci0CWx8Benh2mYn6+4HNu6dAQEGeuxt7ajtG/iF99Qj4gaaeyVi2TIE3UIixTM75z2dW6Q3DANpqE1/sYzSamoCqojprxm1eMHGrqBB51r83s5ePGMsQuplWPsZS3ZODk6Tr4lTEaWwEJhTvgtttjcu5ecD86o+GJW5311o/dsXFA4uOxZMf3DFX6wwvACtGcZl4yIQncOcZX8J0/Y8jfm82Mbp2AgA8biCglCNgeOJ+/1lDCLQ3WdOPJ04EyiZaSbc8NEp/esGp9uwBxhfvgqZZpRIAwOXblfiTXLMuk9a2eETnZ6guqkXluNHP2ulV56CmbQFa2vNsbNk+fK3QdaDTOw7jBidux+ejX8+3Jh3ZtEhXy+Z3cfr0n+JLS/6Ao48e/np5OfDatgvwizd+gWbl2GGvP7v127j9lT/BUzlQejBUF9djJnaxEQgAv3/rR7jrtftQXB25lvrMGQaOnf1vzPDebT3VRGPCxK3DKYqCvLw8KHGqebfVWXfG/UoFqir6sWzKa6jAB8loojMFyyR09o9Dt68MlYUNMPo7ba9TmUqJxlZcwrpAULT4idtJxdswp2Id4N9nJQ7DD/TUjO6EztcKPQB0eMehsmpkQ6KiWn1XkMBjG3274fcDzT2TML6oFodMfRHdu1bHf5+pA/6OEbUrk40lrkKzVSYNWkS7KHgyUZbbiI4OGxooybZtwKxxVn1btWxQMlXvwwkLXsLJ+z1qW32sHdsDeH7jV7Bu74monlKW8PuWzNqIw6f/F311I7txUv+pNZO4Sz0Anhzrey0MzqBxBZI3g2ZYbHVvhdcL7GrfD6fMfwTTfb8AWtK/FjIN11BvQAgFrb3WhUfLrgRmEu3LWw+/H2jqnoxlVqWEhBO3kcatVz4+HP/+5Aq8teMs7NojdxG+pibgkY+uxSfGD4HieeHtZvWpeHHzBajbWyr1+NmqoOtFXHXEj7B0wktj+pzQb1Zbm0Bfjx7evm9c7d1rJV9cbgCqB7k5wPzq1dizB+jsHPg877Zn8Lsvnogrjron4TYUlpcCAPLc3ejrsRIkphj5IjC5pdbMKzVg90o8zmGagNu3EwAgKo/G480v4W8ffS+pM25tO4eXwPS24KYjjsd955yASRNNVE6ptm4imH74e2x89CiL1NcDE0t2wu0BXEUToShAVcGu2Av3BrrQ0wvU1AA7/acmdBwZcXVs4f/gnjNXYEp5zag/Yyu+iZtf+Cs+af2cbe3aV6C3FYYBdPRXDJkdXFUFtPVVQw8AZp89T7+0126xjpm7HwoLh7+uKECPZwnW1h+N3c1Vw14PXU+MHyhxi3GTgolb0ZZQkjV0TaWqA4tb7mvGDBUXLP0l5hc8DnjHlgNJ5zErWZi4dThN03DggQdC02I/6tfbZF3k+FxTUTrRmjZfpO1K6xlqaa1wNl7o+hv+sPI2AMDe7snw+zHmQSudJBpb8axs/Tb+9z//QoP6+bj7ClgXEaauD32h+zPgnS8Bb68Y8fHNfuuHtrN/3Igfw1FGMuO2rxY+H9DYPRXtfmvhLF/rzvjvW3Md8PqpQOPIZjlmqrHElbe1DiW5rZg0cWDgcheOh8sFlBfsRXNT+s7U2LYN8Bu50Dx54YXJAACGF+fNuwEXHvQLNO21p0bIxm3l+Mfa7+CdnjtHtEKvu2Q6ACDQsXNEx+vbYyVuPZUD/SoNzqDRjA7bHh2LZ1hsdX0GrxfY3T4HjV3T4OsH0DeKhB+lXOeudXjg/CNQUWBlWrsbR/49mr11CASAvT1W4vaIGc/j8qXXon/b03Hfu29stbVZFzX//uQK/Pn9m/4/e2cdZlW5tvHf2jXd3cHQHaIComCgHruOcdRjx6fYfew8tthdx0LFAhQUkO6GAYbp7tida31/vHuK2TXDgIrc14XgXr33u964n/u5H7aVB+Yz2lfU10N+3REYos+D4M6BLGTktXy26XYKa3MxH5zX7G+FMMc2jspaQGrE3v06T0QEnDf+Y946fzrGLW91fL5vu6qrcytuNUDGOajVMCpjMyrJ1U11a2qsJiKolbiYfeZKPqALjUKlAgkFq77V/Wnvl4kRbtVWkHyYuPUGUUhVWF6EJmR3KK4PJnHbX3P4A4GW6moUBeyuUBISVUTHqNlRN5kNFdP7bR70d0NNDTyx8F3mVH+ClHE2Wq3oS3y2OUMRzc1Q0ZjCnB/DArpOv7crRUajtAIQFtv3ApDtBOWBtLg1NomCKQZ7XDfP2YQEaLUkoChgauof4lZpFRUpdXGDve7jnmb3fGbZSUO9GBu6ErfJGZHcN282V36xDJPVfwZqS7MTtcpBdHRnse59kZMrUavPwu4Ae8v+za//zH3WwcJh4vYQhyzL1NfXI/vJhXC0ub2owrJIyMoECUI1rbQ2HESn/EMJah2bi4aws/ZIBg0SqZc2O2A+dAysAm1b/mCwxVJnyASt/3w+RRImt+0VjzvQ5janDM3q9fWNTY0oCrRZ43ud7qhSiy40EOLW0lSBywV1hgzSh2SLD81lvlXCLhs0rRWq5K33Q/3y3t3gXxD7066mxj3Fq+fMYHDEvM4PgxJQa1SoJSfNNU3eD/6DUVgILy19kZUhv0NEXucGXSySNgQJGWN9/8w4271chw7t3XHhydkAaKylAR+jKBBsFcRt8pBO8iolIxyTPRK748D7f7ajW9tSZGR9IVYrlLYMpkafhc0GsuEwcftXhLG+EgkFp6zl+SWz2FTpIXfQDyxNVSgKNJnTyM6GrIQKxqQtx1S90++x+/ZbNbu2Mj5jCdEhIi2ysLDXt9MrtKumkvaxmQsNFX+gswjnYfQfVHbx+2oj+mi+2AVR0VpCtQaszRUdn+3brjqIWy2QfCJoIogKN/fwuXXoRSaDJjKNgKFSY5MF29BiCOfbbTewtvb8Xj9HTIpgAsLUdciuP2+w9I9EZSWkRJaKQqoR2R2K68rKg6eW6a85/IFAS7VYKxnlNBFMkODbkpeYtfxZqpo8p2Qfhm9UV4PVGYY6dhhEDUenE+p9n8StsVAEtJEIavwpICupfm9X9hZcLhkFFVHxgWeI7YsUd7OpqTlw75i5RQyyDlV8N1GERgMWxOBsaOiHgJbLjs4h7JeiswZ53S0j1cyRWQsJaZjd7XNr5Upe+cck7pw2s9ucITRMwkguNmdoQHV5nDUr+fDCo7nz2Bu97hMTA022LFCgpXL/5td/5j7rYOEwcXuIQ5ZliouL/TZylUUQt0ExWehCQzE5hay+saz0QN/iIYsit6Xd9Oki9dJu45CySgi0bflDu3hWE0BGniJ5Vtwq+t3U18OqnUN6bStgbBZknkOK8xox9AaVO+onSf6tEoz1YjFmUWWQPjANl6LBabOB1ccgrt8DivvcihPKvuJQl8H3tV3Z7RClFf1YdFqXaiwqNVZEf2Zs/PP6PLYTOwPy1B3+vABIEk6tWNU52vqh/1AUbNUbCNKYe03cJmaLwIhOrgtYJVta0EyUrhpJJZEzptNLKzUVGkypyC4wNx2cgFa3tmUqx2a2YXWEYFOl0+rIQlHA0niYuP0rwt4i3o21VWewrWYSheW9X+AVBv+Hxxe+T41zqhgLggUBZW317/e2b78llX/NLcfcxfTB81CrHBhr9grbmwMElbmYyTnzSY3chyFWFLJSWkiJLD1M3B4AqF3iSw2J2n/iVhUhxi2XoTtx27Vd1dbC/fO+YGvIBxA5GGLGEhwMQxM3diuuqLKKVXd4Qu9IrmZ7DpVtA6jXJ/HDjqvY1HBur58jLjUeBRUSMi21hxudJ3RNWycsm9Ehb/DSWaeRxk8H7R76aw7vDYsWwYoVfTvW1Cjar0OT2vFZO/H2dy30/OGHcNJJUN7Hmn/t31taGhCeQ7MylvzaI6iq8r6msDQU4XBAfFg1JyQ9ilK31O91+rtdKdYmXE4wWKOJi+87bZWaaOLZ08/h5iGTD5jXqt0g1pOKtqcyWNaKdYildf89bhVjCQ6bC5M9kqxByV73S0+x8n+T72e49BzInUp1fV2dEBypggjbR0jtVaXrAdY28bxqXbDXfSQJHFqxdjA17D9xeyD7rL8CDhO3hwHA4l0nMy//ckLTxwFgQrxkfUk3PAxw7nmHQUFfEh7UyvTpIvXS6ewn4uUQw6DQHzhv9BvEqAv87tuhuHV2T5Uy1+6msRGim97HtvSKXl2/feDxNND6g6TWoLfGYrD6l+qWmo9lWfEZOMOGkZOrps6Qgc0GmHy8Y21CqUj8JBh4A4x9jl7ltv+NUFttIy60VngtJXcvo73MNIub5yygsPHApiv3FRYL1NaINp2X13O7FCbyKBXT/vcfDn0FF+Vdz+vnnsTQIQF4M3dB1sAoDLaYXqU8FW4uREHCospBG9JpxBUcDCaXmCG21vwBKzFDgbBJaM1j2HAV4YlizHMa68BpOfj3cxj7BcUdFI1KEe9KXxa3ZTWx7G0cTURCAgCacLEgkk19qApoFARqfM4AZp19KteNuAhnW2nvzxMAnE7ICVvGdUc/RKr1k+4bW7fz+PQTuWvazMPEbX+idSfl399BSvA2JAkSMhL2+5QhcRkAaOwVXgO0dXVgskcRkjIK1MEQN4GQYBiavKGb4jZIFn1qTFovFLfAF8UfcP+8ryhtEgNRb4PZAGqNqkP80VJzACu4/4VRV2UmJqQenRYIzyY6wkxcaC2hcokoYPQXR0MD3Hsv3HUXfbJocbSJYK4U2tl+BaGk0FDz9/R8+fFHYcGzZEnfjo+0LuXyI/7L4NgVEJzAtpB3+Wj9fVRVeV9T7HTM5NGFH7K06CxcLjDV7J8lTF9g1Tcjy6C3xu5XEcik1FDiQuvAZUexHJh+yWkW60kpqOd6UgoRfaLVaNrv6+gr9+B0QkXrIAbkef/94lNicMg6HA4FrJ0WDaZmIRhyqJN6HHN03gqunPgkcuUvfu/DYRJ+07LG9w+jihDza5f+MKe0vzhM3B4GJhMs2z2Vr7bcTOJAoYhy6twenM2HX7JeQ3bh3PMBF499nphICzk5YNeIxaS15TBxuy8GRfzCGcM/6Khy7xPtituuVgkuO47mzord1uYKsLcEfH2b0Sj+Edx7xYwSkslNcxby5LKv/e67vuYs3lvzELr4YQwYADX6LOx2fC/mW3dQWwuvfDqGdU1XgaZLNVRr//gkHSpoKhcqJacUiaTrbruhi82lzRpHff2fc8grLoZHZlzOS+ecS6x6V4/tuijRf2gd+99/1O7egaJAjXEQKam984mKiYEGSw4oUF9aGtAxCzdO5LrZv1MU8d8e2/TScPLrjqCu5cAWbvIIp5kWUwzlLYMYMQIyBkRhtEdhtQLmCr+HH8afCzqneDfS81I4KmsBR8a9g8PWO4XrvsUNQ+PcRZbstb3LdJCdBDtLARgwOo9GcyaKAvXFB8YvoakJEsKqkCQIjk3vvjEkBY0G4sJqaWrsXaDmMLzAVIFr5eVYy5eiIGGOmkFczjD/x/lBdEoqCioUhwVsnm19at18Q0d6a/zRqHIvYuHuC6msBIMBzEY7YRqh6krK6p3itr3AjLnNQGZ0AXGhfcuGsEn9mBZ8CKK6WuGzTbdTqbkYtJGEJmSjUkFKZEnHb/xXxq5dost0OBR27+p9poFkEe1OF9WpuB2bupj3L5zMKPnufrvPvwrMZqhwT0u6Bmh6g0T1Wo4f+DWpIZuBznHOl4J5d2EoRY0jWV8+XdxHwwH2/PEAQ5PoC03OOEJC/OzsA4lJksjyksHQcGBesg1tN/HKsucxBk/ssa0p+HSu/HIVixoe2+/rtJQLf1s9gwkK8r5fapoo2OpwANbOZ7a3udePwT2Llg1K3sVxed+hNaz3ex9OsyBupWDfc/iQ+GwANO6CjIfRd/w5V7GH0W+QJImoqCifFfgq3VxATAwdknlVRBYOWYexzXoQ7vLPB6dDZvuqPVjbSb3ewFKFzerE4QoiJjnJvZjKoN6YTpM155BJdQ+kbQUERUzqVAF4JbgIweoMxeXq0nUZ9mK1ujDYYqhqcxdgad0e8OWXtz3GlV+uokZ9Vi9vvFON4gpgPdxe5DwrS1QYbbJk45LVtNR795F2JJ/N/1ZdyfJdR3PTTfD556DICux9SxRja8vv9T3/2dHXdmWoExI7s5TZQ5Wc6J6b+Kye+weiZK+ZjOhCMuPKIKhnACEsUZAxYarK/VbjtJWJd8OkHdlr8bYkgV2bjYKKpmr/lZ1lGTZuFL5qQybk9Nhepr6cZxa9yfbGk3p3I31Et7aVcRZ3/7KQ/228g+HDYeBAqNVnYbUBptKDcj+HKpxOCJDX7xcoCoRJYqGfNSyTayc9zlkj3qG2tBdKbmMxGZZZHJm1sGNBG5EgyCfFYQan77lA17almMpx2J1YnaGk5yVj0Qr1YlvFgVn01tdDQngVGg2owvZRWAbFoVJrhUd2PxVF+dsjLIO1JceypOB0Xt8ym5H/ehJUAXg9+UFahpZGY4ooZGsW41nXdmW1wtDoBVwy/gVSgjaKg8JzCBl7B43SJAD27IGaErFAdxFCRGx0r+6hnbiNYy1PnHoxZw14pE/PUuo8nW+3XU9FS26fjj/UUVoZxoLdF6NPvR0AKSIHrQ5SI0sPWoGyfpvDe0DZnjounfAcb5x3Am35vbd/0LpE3x0a39mfRcVHoVXZ0Tr/fl4JBQUQpDGTGlnSJ+LWaIRYXSkAkclCmJWWBhqVnea6Np/XBahsc6eCGUv92gz0d7syt7jt7FR9L0wGoNOBySUCWa21B8Y2raQxj42VxxEUndpjW0KSDqeso64fYlkraq/lv4vfoDHYd1HvlBQEcesEh77zmWWzuIn2rKKuCIoSnymWAG7UHWBUh/j+bWLSRRak5NT32s6wKw5kn/VXwWHi9hCHWq1m6NChPivw1ZXXMyRxI4NzOiP8tqRzufrLFXybf+fBuM0/HVbN/g71mkuo+Gg6rQuvheJPwFAUGOlqKsVmgxpDFrkDxCuWkBbLnT9+z/z6WYdMqnsgbSsQqBTBRKk1Wr/7Lm28n2tnL6NEuajzQ/0urBYobR5CYeMoQdy2bAv4+g0N4JR1xCX4r6C5L9of3a/djq0Za2MxGpWdrCzRBPJtV3LVlyvZYrzW62E7ao7gy003UtYyFFmGF1+ERx524mrcDE4TrP8/r8qcvyr62q7szaUAuIIye2zLiCniknEvMiz4w/64xX5HS9kuJGRkXSIE90y5DYtPR1JBYnglDftrj+UOaqjjRvbp8GLNTVz15QpWVl3id9/CQtDrISTEcyG0g11Ju2vbMhigrEzCKesYNgwGDYLPN93K479+AgnHHJwbOkTx6bv1vPefz1j0q+2gXK+10UiIpg0kSMhMxyCLPqC5vDTwk7TlMzz0E44b8H1Hu0xMCcFoj3KrVXwvYrq1repCXC6x2M3OkVBFikWvvfnAEbdJEZWiYFXoPsStpMKpFQsxe9uf1+P7Tw1Fgd0vQ81CcNlZvRpu+eg53lv7MNffmYOu91MHj0hLgzpjBg4HyEYhr+varurrYVz6Mk4Z+gVhzu4F84YMEX/v3g01VU521B5JrX18r+ebExJn88xp5zMl+R0AFPo2v2sOPYsfdlxNYe2APh1/qKNdMNPhZBGWjU4LCeHVVFceGP/NfdFfc3hPSDO8wImDviJc14azIfD5OACKwobyY9lWM4mY1M4MgphUQbqFSjW+i/oegtizB66Y+DTPnHY+Efb1mHqZbV9dLYoaqjUQFJsNQKb8Ge9fOJkZ2bNEptG+aMtnsPwsR2QsIm9EAiZ7JDarDCbf2ZH93a5qrKP5YcfVVNiP3e9zOTSiDVmaDgz5325HFO8hgbM9S6I/iNv8vVHsrJ1IQo7vwFhMDLTZkkHprjJW2cVNhMT0VNxGJor5gtblX5WscgoBhzbct+I2PSuYRxd+yJ3zFgZUiNwbDmSf9VfBYeL2EIcsy1RWVvo0cnZVL+f+E67jklGPd3yWmq5DQXXQFtR/Nii1vwPgsMvU5G+iec0slJX/hKoAIsfGUuw2qG7LJtfdp2Zni7/LDiHniUDaVkBoV9yq/StWtG5ut2ttMlfkKL7aeC2rSk9hb8MoRL2v3hG3AAl9sKjTyC08eNJV3DbpGp/7yTWLuGHkBdx8zL1kunnF9OxQXIqGYh9FWjeLjCamT4c77xQK33k/a7nmzZewqnPAaYCGlb2/8a5oWAXbH/3T+Hr2tV0pJqFQUkVk99iWEFHHjCGfMzhyYX/cYr9DbhZexs5wz2SqKmYYn+54kVnLn92/NEqXlRCnkFDE5vaNuE3LjsQp6ygJwNlk14YS/nPi1fzfjLc9Fh9sL4JQW2U7KJkIHW3L5eoo5JOaKia3AwZAcfMotpQMo6ltP/LxDoNBhlu5eNxLuHa+clCuV1tlZ0XJPyhonoIuNBR7eyGM+l4MuOYqHHaoN6Z3kCntahWjJRQc3lVJ0L3fai4XBK1BHkBQEISnCOJWYy3ydYo+o6HORVxYrXjHQjx4mga70+XNfz+lWr/AVAKl/4Mdj2G2qHjySVBQceGFMGpU/10mMREKm8aytXoSzUZRXK9ru6qtheSIcrRakMK6BChddiYP3cCUnLns3g0F1bk8u/h1Vlhe7vU9RIYYSY0sISao3S6mb8vEdpLiUEj7729YLBCn2kpmdAFpKW6SVhcLmggkZPQ1faw+1Uv02xzeAyJcOzr+HebY2qtjnS6Jt5bexfNLZpGU2UkIJWQkCSsRlwO78dASLPjDnj0yk7J/BuDsUe+wZ0/vjq+rthAbWic8lcPE+BgaHY9GrZASWebRLsFZv5FxcbM5MutXTjtNoqJ1oCB4Db59bvu7XZW1jebbbdfToDp+v8+luMdCu/4ABDGdZvK0X3Bk5q9eidsrJz7JORk37LfVXfvvP2iQ7/0kCZw68czmJndnrMhoZXH98PieHrfRKYK4DaFWZHj6gFYW72FIlG/FbUYGFDWOpLQ6FrOl7+K1A9ln/VVwmLg9xBFII3e2m0V3mQi2L1zq6rqTZH8HyE4X0YiJxvflL/LxurtZsGkyZeU6DLoj/J/AVIbVCjX67I5CQ1linBTE7SESKe6vDlSFUNwGYpXgibgtqh/E15uvZUv9qdhDxSrKWrczsAreTjOnJd3IdZMeIj6u9/5/KsnFwPit5Mb6npga6yuQZag3ZXSQVQPcQhSvxG3TBloKVhKm0zN+PFx4Ibz5JkRHw7b8UN776VhMZqBlS6/vuxs2zhQBiZJP/O97ENDXdrWubBq/7L4ETeK4Htuik8VEJDqoBsufg5/ugKJAiF0sckJTvBRP00bSpJ5KjT57v6L1zuZd2O0yLZYEBgzrGWkPBO3BKF8Bh3Y0Fm5jUMIWxmVv9Lg9LdXBrHNOZubQySIIcYDR0bYaV5Oy51QuP+IZRri/8uBgMbmEzvTAw+g9TEaFGK34AjcXHhy1XXldLO+sfpT5dS8DoAoXA66jLXDi1tFWidMJ9YbuxO1jCz/gis+XYQ8f7/P4rv2WtUEQtHKYeP7EXPG3zlUjMiX6GaZmUSFapdGBh6Io6ggx6KjtfyBxW/kT1P3+x11/f9A+xkaN5LU3NNTWioDPDTf072VUKtjYehXPL5lFiWkq0L1d1dUqJEeWiXmQm3wBwFzO8RHXc/kR/6Vgj7OjMF9mz+QTv1CHRIt7cc/LFPpmAZGc6CA1sgSdqZdqy78BqqvhiiOf4unTLybCvkF8KEk4g7MBsLeUHpT7OFAkSEtNPWHqepAACaK15bTUBV53oq5OZLHpdBDXpTuLjlHTahVEU1Pl3yt7oD17RKWCMK2h13YJbdWiU3CqoqG9BkRYFlqtUOJWeiif0FpRhKJAk20AxxwD5S0DcTjA2nBwiVu3xW23ttBXaCPFWKiYD0D7sdRwYvoLXD7xGY/3mpQEI1LWkhO5HtnU97HYVruZo+NmMSJljV/iFkAVuk/GjWxnY9UJ7K4fR0xKT4Y5KUu8YxrJiqHFu5UfQH7tEeyoPYqQ2J6WC10RESHWrtDp1dwXHCZuDxO3hwGoLKJDD4rpnAjGxcElE2bx2MkX01Sw7o+6tT8ExUVOvth4E+sqT+We56cw6rQLeGXFK1z43u/8898pHSpIb3AZSnE4oFrfqbjNyoIZg7/gjrEnIO96+YA/w18JEoJgDcQqYWjkT9w9/SbSXN90fJbvtnkdOhSyhmdiskdiNdnAEAD7YmskM2wd49N/JyGx96kXKrXK/QyyT8WgucE9adJmdtgr5ObCJeNf4NSYy0Df817loo/4R/ItHJW1kHFuLnL8ePj0Uxg8GLaUjaa8DMzVvVMzdIOly+TlL+7ruWj7sXy+6Taissf02BYam4xKBSFaI421ffCtPoBoblJIj9gOEsTneVfB9keaVcPe7SgylOtHkpbet6h3Tg5cfsQz/HvIFdjbvE8+ZRmUFkFIR2R4fq60dC0SCg4HuIwHL71DMuwFaz3hQW0MH975+bDBVk4YNBtN4YuHjBf5wcbereWggEPW8d2602ltPfDXbM8Mag+KBceJuYzaGjhxa2kWJzEqaYSHi88iI0GjE5U/eqMc/K3iFl5f8TRy3BQAcgdH0mJJxOEAc33/q27trWLV7dCmgtRzWt/ut6eT/yCyQ78HdjwKm+8Ex4EP0PQ7msWkr8I0lq/ddUj/8x8IDe3/S7UHDTwSKXWNBGvMqDUqCO1ShC48l6DwKII0FtTGfPYWiDlVeyCqN9CF7ZPG6qE9BYKMmGKeOe18zs75e9qt+UJVpSyU0zogLLvjcyVqJHsbR1Nb30/eG38QanYJsr7RNphWh1gEVWwPnMCvqdATojWSmtrd6UOSwIJQD7bV/n2yBxwO0Dc0oqAiMhLSoosp3tO7eay1qRSgIxsFgLBMdDqICGqhvqonQWdpFGOVKmoAERGwy3Amzy15lV22y/v8LH2BpN9FUkQ58XH7WeABCIrNoKR5GBUtef1wZ92h2JpwOqHNEudRcRsfD83mJBQFDA19V9w27VnNqUM+4djBiwIis23hE3lp6YusbBF9saIK5uXFj/PUb++QlNxz3R0cqsPiEkr3xkrfC453V93Hs4tfIzKpp6fvvhg/uJgLx76Cc88H/m/6MLziMHF7GIQoYnETkdwZnpckyE2pJDO6AEP1wa8i+Udiy/Ygfiu4gHXWx1BrVFx8MXz0kbCPqK+Hj57+marvroX6ZR6PtzWVio7Zld3RqaamAioNYdpWzI37Xxn+UEK7skOt9a/siNZVMiJ5DWGKW+5nrcdQtIzokAaGDYNx41T8XnQWvxRcBtpIv+dzmRtxuqDVEt8nqwSVm4VVuvzXE5x6EWLURneupHJzITt2N4lB+Tjb9pEvKgqWmp3IMtRaR3QEAECowN5/H7QJo1AUsDSV9d3svalLUMb01/XxMBqFlyp0kjddIWlDcSDaQ2vNnyt3s3RPPdHBjWi1KoIShnjdb0TaJs4Y/gFyc9+J+h31x/Hx+nuoVM7os9V2QgIMS9nKgLjtNBR7J6H27IGMiB2o1JA40LOSOD4emi2pKAq0+Cpt3N8w7MVigfKWQd2I2wF5Ki6d8Dyp9s/B1njw7ucQQtPeTQAUNY7AKevYscPPAf2AxppW1CpHB+kVlepOBVUC79OcBjEuayI6rQYkSfS3ADW94Dw3F2SytvxEknIEuRYZCWuqL+CbrTdSWuNhRbef2FU1hOeXzKIu+haP28PSx7Bgz0WsKznmj8k4qFlIayvUmQeANuIPuIH9RMtmZAVe+2wMigJnnAETexYN7xe0+yvXVel7BI/sLaI9OzSpoOqy4JZUaJPGo9XC0MQNXJhzNa+feyKD4vxXBd8XQRHR3T/oI3EbmyoijSHqZhy2g+PZ+ldBU1U1GpUDtUYHIZ1KNdWw23l84fss2j71Lx03NFQKH32zbiSWIJEFZ6gIfN6irfiEt88/jovHvdxjm0MjJnjmxr8PcVtSAtuqJnLTT+sIjooDFAzVvfNKcBrEAKaEZnd+qAnFqRELH2P9PvYciozKLNYl4cmC5IxMG8j2mqPZVeTbz7S/MT1qJs+dfg6ZsaX7fa6wlKE8/MsnfLH1nv2/sX1gbmlEUcR6MtbDV6RWg00S37ehse/ErblWyK1dYYMD2j8qKYnNVVPZUykKBOv1YHOXH0j0knhnlUS/1FLjnbi1WOiYT3h63n2Rl17PqUM/JbRtfkD3fRiecZi4PcShUqlISEhApfL8U9utDqK0QmkSn5XVbZvDHZmzNv91CZ2+YKt7fjFmTOdnQ4bA//4HJ54I2dG7sNdtgppfPR6/Qv09jy74iNDE7A5yRKWiQyFhbT40iFt/bStQvL1xFg/+/D/kMP8R0HYfXKXdBqFhBZNDbufaox9h+HAYOxa+2jyT13+did6Z7uNMAobGRlBAb43rSOPoDdRuxS0KoHixWpBdqGziHQtL6AyOJCZCoyVbkFYVpd2PMVdgMRpwyDoSc/PY9ysODoajp0ZRo8/GbAJa+5iOaO6icrRUBWYvcYDRl3ZVW97MwPitpCe1elVBtSs1jI1/LuJ2z24Xy4rPoMp5PKiDve43POYXzhv9BpG2VX2+1qY9mSzaez66tCl9PockgVWdDUBLZan3a603kx5dRFgoqGM9E7cqFVglsRBrqznwC7H2tmVv2ovTCRWtgxjcZe47YKCOBmOq8HH7Cwcy/kg4G4Q6scGUxVFZC6jdufaAX/Oo4Pv44MLJjEpcBEBijpi7BKuasZsCUHg6zSg2UWQjOLb7uDEmZxe3H3crUWWP+DxFe9uSJFWHjUjXgFup6t/8uPNKdpf5V6b0FmXVkWyrmYQuzXNRvZDUcczJv4MNFdM7iqccNCgKpqKFVFfDY59f2xFg+8vAUgPWWhoaVKzYMYL4eLjttgN3ufR0mdfOOYnTgqeDrbHbeKi4+yQ5JKvngbETCA6GIUkbSQirJiKohaR0/8HrfRES2V1xK/fRKiE6IQqnEgQKNFXtn5/joQZLQykANk1WN2K8PehsMnFQ3pP+msP3OK9eELfahFHokkaLz9oCJ25dBjEv1YT3VFNYgkezoWI6lS198AH5i6Kbn+n4V7nxm0Us2Trec0ExL/hxx7+54ZvFmFJv7Pa5K9htK9S6z3zHXInDZsMh60jNS++8fpf78YZ+bVeyCy2tAITH7r9XQl8CsYHC6PZ0sCpxHbZ++8KlEQEtS0vf+0SVUWRohiQG4JNAZ7/Sro2oq7aglpzExuK1sObvbc9zzexl7G7yXqi3pcmJWuVApxMFiP0hLFG0Na29EuTeWxPCgeuz/kr4+z753wQqlYoBAwZ4beR1pdVIyDiVYKKTug+SUoR4yRTj32gBq8gEN/5ASmQpo0Z2D3mHhsIdd8C6ihOwmMFetRRcPZUEe0siKGoaQVZO9x4xNF4Mfoq58pDwufXXtgJFVWs2ZS1D0AQF0PO7VSbtxK2zeTc2G5Q2D2X4cBH1y84WQhV/lhYAxmYx0Nql+B7kaCCQula29PabWmtx2p04ZB3x6Z3hTUkCxb0AM9Tt84617sBihrLmIYwZ53nhNH48fL31Rl5Y8iJyVE9f14Aw6EY4fjFMfBumLwZV3xZp/Ym+tCtz2WoePOkqZh5zn9d9HBoRQba1/rm80ZauS+W9NQ/RmP60z/20UaL/0Nj7HvhpL8g1dGifTwGAEiYi95bGUq/71OZvFr6bYakQ7F3OLgcJhaOl+eAQtwOy03G4lWvqmEHdJpyDBkGNPgu7vXf+qIchoCgQ6RId75ihddw4+QFi9V8c8OsGy5VIyEQnCzVrTEIYr62ZxV0/zqGyNsz/CSzVOOxgskeSkBLebVNSooMxqSsItmzweYr2fqutZAvHpH/KgPj8jqKkQIfffWE/JzApSmeBTW/qGegsvtm+70FD63bMTTVYnaFsqpjCmjUH+fr7i2ahtt1cNASbM5S77xZ+fQcK6ekqbM4Q7A7AXNFtPFS7A8CaSE/E7XiCg2FE8loig5vRaCAkNqXX1w+L7iRu5+26jFLTcX16DkklYXYJkuLPluXyR8OlLwX2UT8iSJTERNCo7FRW9FJya2uGFRfAntcCPqS/5vDdIDuIkIUiMC53BImDx7CnYSxr9k4MWEUsWUU7D4rpGeQyRp/NrOXPsq78hH675T87CgpkQGHwYIjJGkRQeCSyDHt9W812Q3W1GN8SM7qTn5oo0ZeozN3nO4qhCJsVqtpyGThItI/Bg2FUyioybW8I+xsv6Nd2ZW/B5VRQUBGVEL3fp3OXu0DfJmMx9a9QxaoX60mXxjvBLIWKQdph6CNxa2tG5WhEQSIhd2BAh6Smwpi05QwL+QTM1ahK3uODi47mkiPe8HpMZGIiNmeozwL1tqrVfHjh0Tw447qAMvgSMpJwuIJwOpxCKNQHHJA+6y+Gv++T/00gyzJFRUVejZybK0RnbZAzkVTd37x2nzit/eBUOP0zoLmimLMHPs7jp1zKyBE9I0Lx8aKAUIslEWOrGZp6rkKK3NnD7cWn2hGTmoKCCqfNfkik4fprW4HC4bYtCqA2GVI7segmbg1Vu1AUaHQM7fAAHTdOeJlWb18DDt+yBWub/4HWF9oHD0UBxVsE0VyB3S6K3mRld+9ydbHZ4nHcE/l2yK07MJtFuvHYsZ5PO2QI5DdNZ0XBVArLwj3vFACKyiP5/JfxyPTe4/dAoC/tqj0rwKHzocJwV5N1Gv48i0ibDbYLcQpH+Kl7GJYgiNsQpW/Eras5n2TndyRFlDPEuyNDQAhJyBb/8OKLbLdDsFEog0MyjvJ5Lk2E+F3kg+BxK8sy5flLsJhlDLYYMgd2T1tPTIQWexaK0jk2HkbgqCptQycZQFKRMFH44MVIW5BdBy5Q6bQ7CFOLdL74DPGOSBIYgiZRZ8ikvCKAaW54Lq9un88zi97osFvo2BQvVnoaZ71PlUh7v9W2dwkXjn2FU0b/3E3NkjdAJimiHFXD0n71T9br4eiM75mU/TPxUW1e98tIbiUnNp/mBnO/XTsg1C5Eb4CNlccxOnUloXvu3v+CmgcTxkIsZsivGUtsLBx33IG9XHo61BoycdgBU3m38fDDVTdz05yFSAM8eEyG56IJje74X0UdHpBdVI/TxEbRYkmgsjWP77dfTYHp1D4/i00l3h1D434Ysx+C0NhKgc75XwcUhcdOOp/3L5xCQ2Uv5ykV34gMqpDAyfr+msN3hUHv4oPVd7Ck8BxyR2SQMzyD/y55l8/WXhewyjHIJYK4EQlpPbbtqx78O0CpW8ab5x3PKdnPIUl0zN8CLVCm1ws7MehUnLYjJHUCK0pOY0vJ0G7DUmt1BS4XVOsHkCPi9AweDMfm/cCkpA9w1nm3YenPdiVbm3G5wGCNJi5+/ymriAj4v6mP8N6FU2jb/fN+n68r7AZ3FTWd9/WkJlwQt4qlb8St3LYbqxXqDJnkDQ5A7IR4Z84c8T6nDpiFo3k39rY6JBR0Yd4jkO3zIF/vrLlNZCmptUEB3UdmlooagxBG9DWj7UD0WX81HCZuD3HIskxDQ4PXRl7cMIgP193PHuu/emyLdvvE6ZR6cB7kyf4fhKrtwp+v3jGKsAjPTOK06SrWl0/HYABqf+u+seI7RmmeY1DClh7EbWaWhiZTsui0zH99uwR/bStQHJ/zEacP/xCdyr/ZvqTuoriVHbhahXwpJHFIR8Rv3Dh44MRrOVJ9EzT5VknZDYJAl4L7RtyqNSqszlAsjnAUL4txuyaVLzfewO+FZ/eo8hydJt4xjb2sm2LXWLkDlwsqjSO6pXJ3u7YaRossNDZt6sPNKzKKAvffDy++CL/80odzHAD0pV21ZwVI4R6USG40RFzBzXMWsLD0/7yc5OCbyu3aVE1m5DYS4mW/FcCjUgQpFRNc2asUuXY05y/ksnFPcsbIzzt8FPuKmPRsAIJdpR63b98OZlsQNiWKmIGTfJ4rNF7MECXbgVdCy7KMpX4rFquokDx8ePdgpSSByp1pYqo/TNz2Ftt2RXH914v5cO/XZI8Zg0MOJUhlpCL/wPnkN1bUICHjkIOJTe40WmsvzFQeSNxZUrGnLJGyliE9iNuY5HhkRY3TIfsMuLb3W/Ym97NGdLf+GTjAwbOnn8fp6XegWPsvcFtXB/8c+yo3TX0QnexdTnvdqH/z6MmX4WjqnTfifkGRsZT8is0Ka0pPYlz6MhKUxcg1iw/ePewvBs/ki5p5/LzrEo46ij5l5vQGqalQZ8jA5QJLc0VHu9LrZcxmCb01loR0D/MVSUKXPL7jf+2avllyhEXouO2Hn7l//pfYnKH79byyrj3L5TBx2w5ZhjClFICI5OzuGyWJkBAJCRlDTWngJ1VkqJoLsg3UgZE54l76Zw7fFQVFwSzeey4LKu8nMkpCp+tMsd+5M4ATOM1olFYAYtJ6tmFB3CoiLf0QyFz0B1kGnWkbYTo9Ce7iXKeN/IL7T7gWfYnv9U076ssbuGvazVw5+RWC93Hjih56Iu+ueYTVxdNoaen8fKv+Mv7v21/Z0PZ/HWn/iYnQYB0ofFwrvMt9vbYrczXkPwumwMVgxia3b6w1npiYgA/zidAwDVqVHVNj/845ZYsgbtWh3teTobGCuLXb+lZoraW8AFmGirbBZHlf7nRDVBS02dqL+tUiW0R/rI1I8npMdnwJV0x8ijHBL3rdp52odqoDWz9nZIiMNqfTXZulDzgQfdZfDYeJ27859lYms6TwHEzRPaPqSemRGGwxHZH/vwMsVYIBc0V6Tz2fNg3WlZ+A2QzO6mXd7BKc1b8zMeUr0qKKu/nbAWRlQZ0h/ZAhbvsLpw99m/NHv45O5T840O5xKykOMBZjszgw2SNJG9g5wRs3DvY2jMJmBVudb18tu1k4q6tD+1YwRqXRce3sZVz39e+4JM8puVUtmXy//SqWV1zUowJoUnYqTlmLy24Hi1th4bJjb3J7GKWMQO1DCDtuHAxJ3EhQ2Rtg6GW18o23ov/1apTWncSF1RBW/DBsvL135/iToD0rIDjWO/sZnRRHmzWOunoPw54iw+rLIP85sHtXrfU39Pnf89BJV3LnKU/4TTUKi09HpYJwXRv11b2rKAxgqRHSXnvoyP0mH1LzxPeskdtwmlt7bF+3TnhNf9f6K1Kid48sgMjkDHbVTSC/dsL+3VSAcBLCzsqR7G0Y3a0wWTvCk8RsWDYcJm57i+3bQUFFSl4WKo2aZmUMAPW7Nx6wazZXirHUJKd1yxoallXOGcM/ILx5tt9zyHKngmvfoEZyiopmc5LIDLH6J6DUFtEPhyR0j9xmDwjqIORaK3vZV/tAU52RcF2byFgJ8U7WtRf1sbcdRKsYax31zWGY7JEEpR3JrqapuJxgLF7+hwTK+orFq5JosSRy9NEH/lrBwWBC9K+mhoqOz2vd04PISHqQL+0IHXEV66vPBEDy0RZ8QZLENcJ0epLCKwjTtvg/yAtUYYIYcJn+PFkufzSammDOtquZvXUm0dkje2x3BWcD4Gwt6cVJ14GlGjQRkHy8mM84AvD2PgBoV4F2zeoZNgxCtQaqd/sPGtlbq3A6RVp/SkbPTLKUZJl3LjiWR46d0SG8OJRRUwPZUVuQJIjJEYXeBibuYkjiJlStgRUfbK0qYWTKao7I7llQu92eA+iWFl9QAAZbDMnZ3e3d1FEiPd/a0AufBjcMa5+kZu1sTMtuEtYeAcDYLPazKbEBZWUGAjlIkJi2fh4LVzXfxsvLXsAS4n0uG5QwjCu/XMWrGz7t0zX0VaUAWHWDfa4Nu0KSOq3ijI21qO1iHhMc4524TUkwMS1vDoMif/M6VLvM7t8wKLBidaGhYHC57QFrDs+v+4rDxO3fHO41j0cFVmoqVLTmUdmSjUn/N1DcKgohVkHcRmZ7J27T0kCKGUWLJR5jqxGaOwdPS0MpKGAku0d0MCsL9jaMZkvFRKxy9wIQf1soCmpJRB41Wv+jsqTWoCAhywq07cJihdLmId2Uc4mJ0KqMElHasu0+zze36r9c8eVqDOEz+nT7XQdObwHAdsVXVhY9yLkBeWpq9NlUNqdjN7sJQ5WG9/I/5+3VjzJghO+0t/Hj4ZShnzEs+APkhtWB37jLBs0bsNZuweYMweEKIo150LD8r6euV2RCEV9yZIp34rZ9curR47F5I+h3QfU88SMdpMCKrm0FAKHp/klLSRuKDTFBaq3u5f3JTtRGYXAbktJzsdhbJKUG02JNpcGYSm15U4/t69aJvyccoQKV79llQmYKTy96i3dW9H+VX0/Y3XoMD83/gJ8Lr+uRFQGdRTrV9mqPHuaH4R3tth8j3U1MjhHjqKM+AMPxPsJQL94Fm7r7JGZASjnnjX6DDOb4PYdx6wecPXwWadGlHZY77UhJgUZTMk4nuMy+CSi1y4DaKRYzcdndI7c6HehlocJtLO0/BbKhTjDODikWNF4qMwKK2ypGMe9/jrGiwMcfw4IFfnYMSeHBhd9y/7wvOeFELeFZR+GUtdhaKv4yxf+amwWJAXDkkQfpoqFCLu5o7RRMtFZVcNuxt3PxxHe8HiZFDSIuRaS/Rib1vQheZCRMH/gtz51xNkfHvdrn8zijjuTbbdezserv40fqD1VVsKPmKDa1XIYmoudvpI7MBkAylwZ+0orvxN+ppwrv0ZUXw/ZH9/9m+wBXxXwyovcyeHDnhPjIIbt58/zpHCHd7Ddg0+yOoDVb04jysEyKjlFhdkQD0FT556pXcCBQsNtOduwugoJBHSdS7KLc69MYNndYzflCey0Cm9qzRDMz3UFKZCk1lZ1z//Y+r10t3Y7wVEHcSuaSXhUzVpq30FSwlpYWqNpbTeuGtwI6ztwqxlOnKjByMBC023Mppv5tP4V1A9lUeSxhcd7N5pOS1ThlHfV9tLj9pfphbvvhJ/QRp/fqOClUPLO9rVpkUQMRCd6J27h0QfRGBjXS2uLZIkq2it9GFRJ4xqoSKtqgpeXQf3cPFA4Tt4c4VCoV6enpXo2cE5w/MyhhCxlpPXv/0FB4Z/Mb3DP3GyrNfSx+9BeCtaUcjasZh6wjZ4wHKVYXTJuuYm3pSWyrmQxq92LJZcdlFJOOkPjsHsdERsKSyuv47+I3KDEd29+3f9Dhr20FAtkld8zj1AEQt3WaM7j88/XMrXoeW+Nu7DYobRnCsGHd9wvPEJFpuWWXT/KloQFcspa4BC+lNf2g66O7vNgfGsvWkxxRRlZmzx0SEuDppZ9xxw/fU94qKkYpqFi0NpeVJf9g/HjfMsxhw6CkdQwuF+jLAq/aS8tWFJedyvoEqtpy0FtjqW1NwuFQQB+gcdYBQm/blWJpAJcNWVETn9nTE60dCXF2Lhn3IhcNuRuXY58JZ9VPOBxQUBqJ5ZfTYdtD+/MIAcHcXEeUVICCiuwJvu0EOo5BkFPG+l4St4a9OGx2tzo9o7e32gOSBO/umcPtP/xIQXV39tNkgpqSOkBh4kT/52r3rGtr6/RhO1BQqVTo9RlIkijQ5qmJZQ+K57GFHzDzu19RVH3rF/6OsOpbuHbwGVx79COMHCEW7dE5Yt4Q7tjUN4Vl227Y+oBIr/QCR5t4F5SQ7sRtbLpYIIRR7jelVq6cyz+GfsKA9KYeKpa4OGi1JqMoYKj3TtyqVCoyYhzY7NBoSiUnryeJKoeJd8Vc13/Era1VPL9V5b3vA1C7SSKVbf+J2/Xr4dVX4cEHfXvg1dZCfr5EqzWR446DIyeHsrt+PAYjUN9T/fWnQ+G7tC2+lZEpqxkyRBQ/PRgIihF9tMpagUqSSE9Px9ZUzNi0ZYxJW+7z2BPOyCB5+EQyRvTdyPy8ES9z/ujXxf9Iffe+D0kdww87rmZ9cQADwd8E7apGb3ZFoYnZgHcboh6wNUP977hc8OA7Z/Px5xFgLIL630Ff4PPQ/pjD73svkyMe4olTL2bYoE4SMGtELi5Zg8rZjGz0PXepaU5k0d7zKDZN85iFJElgoT3t+9A3uq0v3I1G5QBtDIS67bIGjEOthpzYHRQX+g8ut9fQcIVke9z+f+Mu5b+nnYet1r2GMJZwbPhMzhr5bg+rtrQByVidoThsDjB7zsT11K7qV76N2QRbqyezpOB0rnz2toDIy2rLaL7ffg2VjuP87xwggqNF+1Hb+7f9NLk1DPtmVnZFe2C4vt672McX9u6VaDKlkJnXu8FIFymI2CBrPrLTiYJEbIr3TFNdeCwqtQYJmfoKzxZMkl08sDY08Hsxh0/llu/ms6CpbwHBfu+z/oL4+z753wS+GrlsN3HewAf5z4lXk5Hq2TQxLU2MnL4qCx4qqNq+CUWBatMIUtJ8L9inT4fPNt3GPbNfwRTkrh5lLsdmUzA7IkjO9GzG0+5JU/bXEJv4RH90oA57Z8BAq/NP3Gp1oj06nbDLeilvr36MYtNJPdTNA0elYbDFYLU4fBKR7erLBO9F731CpYLbj7uVe6bfiGxr7bmD7GKM8yaePf1cBmf3HPwkCXIHiO+vuFh8VloKLS0QFCTIJV/QaECKFVF4a+3WwMmRpjVYLLCx7EhCQyUGDIDi5mGYzUBbIEZkHlDxHSw/Fwz7R0r0tl3pa8qQZag3ppOc4n2RGRev4fhB3zA+fTEttV1mjE4T1C6ivh5emHcbddUWaN0GxtL9eg5/KN+4AkWBKtNIUrKiAzpmi+1u7pv3FfmNU3t1LbllO1YrFDWNYOiwAMq/BoDsHPG+trfbdmza4ODpU8/j7Yv+QXKU/9Ty0FCIiQGt2kZNxYFVe6sUJ4114YDk0SYBIHeARHHzKGqaojyrsw/DIyq2bSY+rJohKfkkJYt3N3fMUByuIDRyK8b6Cj9n8ADZCjULoGahV/K1oG4EK0tPRYke3e3zlAGpuBQNyHYsvjw2FblDhaqL6kl+qlQizdDqDKWtxfsiWaVSEew0Irugsi3Po2d1ULzb97a3tjY+4DSIyZkS7FthGRwlFqtB8v4rXebOVZiWN4fRKcv41FvGp62JpYvF9zVmjCA9J02CTZVTsVrAWuGbgPxToGEFurYVRAa3HBSbhHZEJqeyveZotjefiQon6enpON0BCrvGt7Fh8MBziZ3xBlJa34uKxYV3TUHvO3HbXsG97hC0uLVa4Z13YO8uc6+ylIzVBUzIWMzgTM+kUUyGqAQVrS0NSE1J1VxQXOypG8nPK/J49cMcii0nim1F7/k8tL9JEFv9dmx2qG7LYdCwTpuD7FwdFfqhyDLU7dnm8xxFjUP5eP297HVd6XUfp1b0deamQ1+156zfAoAjbHRHyp4UloFLE4dWZadm1w6/51DbxIJTG5XtcbscLAJF9haxn7luDwMiVzEieS0DB3bfd8hQFZWteVitILd5tkvYt10pTZswVa7HpWiojLyXBdUPU14Vwo030s1X1xNKW0czZ/t1NGmm+33OQBHhzkbQKXX955Ps0DM05HOOyFhEvA/nvfh4OHvUu9w97QZai9b1+jJ73G4j+yqh/SHMXWRV7ajH6YRWSzxJyT7W3JIKC4Jlbq72HLBWu4TiNigycOI2JSOMFksiFRV9W4ccJm4PE7eHPFwuF7t27cLlQQ7YWFYuVCT2WBJSPVcXbFdDVR6czOE/FPoykc5pDRnr128yJwcyMyUcDli50v2hqRSbDarbssnL83yC7Gzxd2WZpX9u+g+Er7YVKLoStxqd1u/+7R5HDgdsLUhjZcmpRGX2VJaMGy+xt2EUVgvYG7zYJdiauSjvRq49+hHi4/rmt6dWw4jktQxPXofitPXcwVqDw+7CIetISPecPtPuhdxOgBnWvciJg77iiDHGjqIAvpA2dChOWYvT1AyWACMsTevQ62Fn7USmTYOjjoKSpnbidldg59gXNQtF+mv+M3073o3etquq1kw+2XA3a2ov8emBpVKrMDjF5KWttstEpHYRDruNgqosNlYcx6q9k7E7gKof9+Mp/MNSLogLS7hvD9iu0MQOoaptADV1vVOCtpXtEAUN9CP8FkELFPu223aUbtlKkMZCbLQTggOLiFw9+Tne/+dknEVf9s/NeYGrbilnhk1m5jF3eSVudbrOfrrAt2DpMLqgrUSMn0bduI7xMyZOy/92vcQt381nR0kvG56pDCKHiYwWR6tX9djve07i7VWPoU7tvrCLjFLTbBEL0oYSH5FSax1OuxOnrCUyyXMfvc18HdfOXsYOy9VeT+NyuSjdIWyTTKo8j313bKYgbkPkot4tGBUFpW65x1oDaqvo81XhvhW3YQliMhem2j+VkdkMVds3csXEp7jt2NspXLOKZk92hXteYWzrCUzOmc+0aeKj2FgwhYn+zlKz9aD6ifcaTgtK225MJthdP/agErepaRqeW/IqX++8Gxcadu3aBe7UeSUswIo0+wFFG935P1Lfl4lJSZAaWUJm2BrMxkPIdkZR+PhjWD9vGfr55+IqeDfgQyOMPzPzmLuZlPq5x+1RKVmoVBAZ1Exthd7/Cdt24nTCx4vP7vjogQ+uErU06hb7DKT3xxy+KxoLt4MC1eZR3VSHKhUY1CKw1lrqOzOs3Ws81Uccqj3t26E/9BW3wVZBdIekjur8UJIwBYlsFku1fxuiUHcxvLBEz32HOlJ8LpnFONlYKgKLba4BREZ23zcrC6oMA5FlaPPi1b5vuype+QtWC6wqO5OrbkrhjTeEdVlpqcK3z76HpXSR13tvdMeQfJGhvUVsSgKyosblcIG1n6Lz5kpOzn6Rf014wafiVqWCwal7GZa0HmNdaa8uYSr8hX8Oupsjsxb2INT9IdodRbNYYH35dLZWT/X7nTo1grg1NXombnc3HMGO2qMIjvFt6dcV7euPgIrGekB/91l/RRwmbg9xKIpCW1ubx4r3LZXizTG4Mr0Wq8lNb+LRky9jsuPUv1Qxib7gi8138cLvL6NK969UkCQ4/njx73XLaqFxDRgFcVujz+5RmKwdOVl2Xj1nBidrjgHHAc4LPsDw1bYCRdeU9UA8bqOk3cw85m6OSXipo0LtvjYJIHwJ6+3C57ap0PNE0WlqYEDMOkamrCY+oa/RP1AU8fK4nB4GElMFdjvUG9LJzPL8kg3Preahk67kaNcFYG8lzvg5l054jtGjA1vYjxmno7R5KGYzKM0B2CXYW1Ha9qDXw46aI5kxQ3jlFjcNw7Q/itthbo/Sli37Vcywt+2qrD6Z3wouoEw+x+++VqnToL8DVT/R2gJLC08HJJYWnUFbK1A1D+QDNDlwWQm3imh7VF7gxG17mlVvFUy2OqHKcIbvf2GydgzJKOeBE6/h5KjLu33uqHF7LccfFfCiPyhcmNnZWg/sQszVUoDdrmC2h3slbgEmj8jnkvEv4Cz0vLg+jJ5QtYkFpDapu61ScNpEWiyJbPMttOoO2QFrr4ZlZ0KQe3XR5Fmd0p4NlOaBtzSrxIK0zVchDHMldjs0mlJIS/PcXpOSherQly2Aoij8tPffPPjzp9Rpz/K4T8bgdByuIFwOOy5D4NHwkkIT382aQ9En/6RuzSfdSF+dS3wBQTFecq/diHYzIdFBtVjMfVcZLV4MWyvG0+bMJiQErjvyfuZ+tU9/77LhqPodp81MnSGd6V049ZFHpFDeOoiixmFg+xMXF2rdjtXqol6fhEVJ6fBtPhhoT6OvrOwcD4OcpQDoog88cYuui7moH49yXwgLg0dOuYq7p91EU8Who/5w7HqTiOL7SYqoIERqQL/t84A9mzW2UgB0sdket0vaUIoNU1hZeiq11Z4zIbth7H/5rPhTVhSeyLBhMGoU7K0ZwKIdx4slW9H7Xg/t9RxedvicE1lrhUjCEdbzZdEmCuJWafE9R7U0lBKiNfokbrVR7o2WQ5u4bW6GbeWj2NMwjoTB3cdVbaL4/yCT78KfisNCqCQmjDEZ2R73CU0QfUqwq11x6yZkI/J67KtWwy7r5dzx4w9sMlzv+Zpd2pUsw32f3sus5c8SNOxKYmOFEv+NN2DGyJ+ZlPAWVT8/hL0h3+O5VMZ8kiPKiI8LRH4eGJJTVGytnsyakun9RgA6jI2i8Kgl3idxCyBrhaDB0tI7o9vWovVMyFjMiKxCwnvW7fOJxNQIXlr6Ivf88DmvrXian8vv878WCBHrJWuLZ+L20w138+zi1whLCNx+LTMTjh3wA6ck34VS13u7pP7gHf7qOEzc/o1hahCdtF3rfSKYmBpJduxutK56sB26eaOyDOu3RLC1egoDxwQ2MZ42DXLjdnJe/GnIm+/H3laFwwHVPojbjCwdEgp2GwetANKfGe2KWxk1KrV/8jRE3cyEjMVMSPiMBNPnZEYXeCRgJAlImsqH6+5nccUNHs+lb2wCBfS2OKKj+3b/kgRWp/AylAw9LRmszRU4nVBnyOiwydgXadmR5MVvI4xilMZ1mM1Qa8hk5LhIzwfsg2HDoLh5NE4ntJQEQNw2rcdkUihtzEMKjmPiRJHKWtYyFLvNnXrrCEDpsQ/aXNmUW6eKxULpZ70+vh2KAm1tAZaPVZSObABPxM2+cLkrq9pa3QyMqQK5eTNNzSpWlpzK1KmwpWoKVQ0xKLYmaFzVhyfwD3PFepx2O42mFIZN9NJZeEBaQhunDfuIcWGzenW9rys/4rklrxKaOtr/zgEiPSecwQmbSdDl47ILNVVTE6TqVoMEicMC8+0F0EaKH082HdiFWGvFXlCg0TGoI43XE4blVDJj8BeEG3/rl+vW10O+53XJIQHFYSJMForYlKFjum1rJ7x2+M/o7ETDCrC3YLcrLNp7Lk4n0LSmx24mg50gVyVqyenx/ZdDRKdrbfJF3Ipxu96Y7rUPSXELSnwRtwDllZGUNg8hMcsz65CapuKbHbfy2rKnqWwIPL3wjXfCWZB/BhazTPPqWez88FoszYKwfX/NPbzw+ysEpR3l8xyh0fEsLr6ILzbfQmND4EVl9sW8eQASe+I/Jzh5FCFaI5lNd2BqM3Xu1LACY6uZRmMKusSR3d61KVPgkV8+4o6vP8IZ4qE64J8FrVswGWFP/VgmTCCg7Jf+Qjtxa2gxYjeIeXeEJKxG2kmWAwl1cCdxK+2H4hbAoohoY2ut58X/Xw4OPW1bv2RM0kIaTBlsrZ5MY4ML184XAjo8jFIAIpKzve6zqPVl3l71GGW13osctaOxEd7+cig2ZwjXXw9PPCEI83cWXy3UirW/gbHY73n8wtYES8+ANZd7rhshu9CaxSDnqQBq3AChGA12FIPD4PkaisJZSZfy9vnHkZPoPfgfHi/6V63z0LZKKCiAebsu49M97xCSPKLbtoRB4zDZI6moi/FaXwPA0NSAwRqJ0R5FcobnotjRaaJPidKU4nSCZBIq7bAkz/1zUnYqDcY09hT47xt+/hmKS1QUtE3n3Es6i2FlZ8Nl95zMroZJ2C02ir6+A6expyJhRtxMnj39XNKj+89fMD4eXl35Iq8se5YGY9+LOHaFoVH4vRrscUR4TmDuRLD4HuyG3hG39iYxx5Iie+9fnpYusblqKuWtg5AVdY8irJ7Q7otrN/b0s1CUTpuL3ni/p6dDTmw+o5KWYKntzaTwMNpxmLj9G8PZ5u4IfaRepaZrqTOki7Sbv0gV4L6gtBQMBggODtw7ZuhQsGiH0GaJxtSqp9x5Mjd++xs7Ws/ukV7SjuxssUC020E2HSZu7UoMjy74iOeXeq+U3BVqjVg9OR3wjwEvMiV3vlcf2JwROSwpPIelG3M8bje6c3BsSvx+qRCXlZwFQHDZq0KR0AVtNW5Vu5xJWJjn47PzwmmzxuGwg373PBwOKGkeEbDCR6cDJVpMivVVAfjLBsWytfY4NlRM48QThf1EZCSkZEZSa8ik0Zbbu/Sh8m9RWvO54w64751/CYP+qp/A7se8al/ITlwFH/DQ/VZuvXUIX33lh8hXZJyrrsGw9SM0KjtDApjLKMFiIuJqryarDmKX9TKWFZ6CNiKRxx+H4BAti3efKmwjDpBdwoayyTyy4EMWVtxFYlLgau+kRCcXjHmNo5L/h+IKXIGwNT+S7TVHM3Co96rzvUVyRgxWZwSKolBXItr5lrWNZEYXEBwkEZYVeAn28ATBjGkcnq0+tm+H3ftbM09RcLgnvrq4QT7tcOKzssV+zv0f86qr4dJ/yVx2Gaxdu9+n+1OiuXgrLqdMoymNvBHdyYaRI+HkIZ9xXMRMZGOAVi6VPwDww4bTeOWzScJruGULuLrb0TQU7eL5M87ipXPOJdRD09ZEuec2Rv+K23qDd+I2NdnB7cfexsmRF/n0s6yqCgbwGrhVqaBMOZ+15SeytyQwyUxBASxZApsqj2O1/n4sjlCk1i0Uf3IhW376jpLaNLZWTyY+zQ/JI6lYXH0HC3ZfTENTd6sVlwu+/x6/qujaWti2WagAZ5yiI+nk57CSSGJoCaU/PtipBK5ZgMEAa8tPZPr07i/akCEQEaXDZIKtvainedDRvBmjCfY0HFybBBCe3zOGzeGt847DvuV5VC4TOqlVbEvvJ68bH9CERHf+z34UJwOwq8WYa246NIhbueRLWpvMVLQO5OgzpzCv+A6sdg36olVQv8LnsVaznWid6AMT2v14PKC9H/JpUec0g9PEhx+C3S6UtkcfLSwG7r0XKloH8svmaZjNClT/3Mun7Aml4A1aahswVO327J1rLMJps2BxhpE6MLvH5sEjY6kzZmCzKTi82Zc5WkG2oCB1VLX3hOi0DDZUTGdN6fF9e5i/CNr9TPctEAaQMjCXO+f/xqvLnvRZM6WqJZMbvlnEUyt+QOfFYSs6NQtJBTEh9dRVNKN1igB6Qq5n4rZ9jdx+f97g0Ffz4XvCFvDyy+lBaA4eoiLvvKepMeSiWBso+vwqFEOXIIPsRIuw0wmP67/KkCqVsGoAMab1B8ytgrh1qOL8Wi3qIoTiVmUpDzyTWXaisYj1XURaLw1uEd99aCiE6dpQS86AiFtH+mVcM3sZn2+6rcc2o94Jsgjg7FtjxheCg8EsiXlZb60iDkPgMHF7iEOlUpGbm+vRyFltE4vt4FjvE8HUVKjVZ+FwgOxr8fNngaLQVtN7QrR54yecP/p1po4r9emT2RWSBMdNU7OhYjoGA9jLF2G0RZOc4V0pmZoKDaZ0ZBkMdX8i4tbeRkNlfWDFENzw1bYCxSf/01LUNAK9OjAloMr947gFfpi1Qz0u2EGk/4NQetk9CAQsrYK4dWr2zzxp/u4r0FtjkawVUDa7+zWa3AV5Qr2nkiQkQIMlG0UBS4UwTLaHjiAoKPB7iBpwJPfNm82723wXowCwhY3nvtnPM2f7dcyY0fn5uHFw39zZfFw4GyICVEKZq2DXsxgWXkZDSTF76seyqWgYLocdyr8O/AHsrcjrbqRm2RsMcT5GUFAQS77ZQMu6t70fU/k9zUVbOCb9E3IyLZx1lv/LaN0RZJVNzNaUoET++91M3ln9KOedJ5QqM2bA0qIzaG0F6pd7V4fsBzZsVFHUOJLQ7N4VGYtPjcXmDAFFxtjoR21iqYP1NyGbajsm2IGQ24FCpZYwKNkANJaWAFCfL1SRtuChoAt8NhedKlaqwdT28P7cuhWuugquvLLvvliAKHJlrcelBBGf43vimzlEvK9apQ2roe8+nEaDwlfPfcX/TbgajcrOU0+JojaHGup3C5uERnkcwcHdtw0cCEdmL2ZI3CoaC3yndQJgrYeGVZgt8NFvZ1Ctz6a8PhHFZRfkbRfo3WO9VfKsmtGkHMt982bz3uaXvF7OaajG6YR6Y5pX4jY5VcvgxM3EqPeC1fNKT2rbwRkD3uKo7IVeiVuAPHf2aWEgNRyrf6H0p4cJ07Vx4okS1zx6Jo6JX1JuHIckW9DtfZI7jruVhOg2r4HBrmj3tGvs4lBgNsncdptQ6t18k4PGCu/9ytqfN/HSWf/gxlM+JSUFVCFx6Ac8j0PWoW1ZhmPX2+A04axdgckEq0tO7maTAGLRPMktxl+70iCUfH82yE5czduxmIXi9mATt5IE6nAxVjlaK4gPD8fqCKHFkkhiSsgBv74uPLrj303y/mVpyO5K9qG1H6FYD7I1hqufO1unCf32L7HbYWHRlVxwgYrT/5nJL7svprER5PwXPatR3agvrURCxi6HER7rPZ86PR3UkpPWOh+KvIrvsC+YgW3XhwBcf31H7SpOOQVOPhm+2XoDj859GVPqjR5PEfAcXr8Hw+4fqakRZLJt98eg716YytG4HZsNihuHM2Roz/MlJ8PvZZfzwdr72Vvn2aDT0lyFywmtlgRS0737+CdmJDBr+bN8sOImj3P7QwX1xcWE6fQeiVuVWiJvoPiefQW1hWewRGyi90ChFBSFUxJqXEPh79is0GqNJ2+oZ4Xu4MEwfeC3TAq7D1p7WqupVCpyc3KoWfAId008g6MHreeCCzxfe/T4MCKmzaLOmInLWEvVnKugeZPYaG/B5VRQUBGdEO39IfsAkUWjUFfdPwVxbXoxjilaPz4JgCp6KAoSUcoOKAxMtISpFIfNgcUZRlpe71XCkgRHDMrnzfOO58OLjmJ8+u9+j0nNDMfmDKWysvucAcBUsY4PLpzEI6dc7TUg4PVewgVx2yEe7AX6g3f4q+Pv++R/E6hUKhITE3s2ckUhVBEvTWSKd+I2KQnqjFkoCpjq/vzE7dqPX6Tqi7NY/9VHvTourPl7Th/+IROG945MnTYN1pUfj8EAoYYlqCQXA3xwXhoNOLQiD07/ZyFuLbXUz72Kre/dxPVX6bF5qLHlCV7bViCwNlL6/d0snCuqmtx9d2CHqfdh1UOTvTNRGRkwIK2WKVnfUr5mfo/tNoN7wajzP9D6glMJZfaWm4T1V9G73Yg+xSiIW12Md+JWksDhtitpaxWfRWWO8Lq/J4wZH0pVWy4bNqr8BnBXrBAFZpKThUqjHePHg0vRsDEAbqUDhe8gyy6W7zqKqrZcVCqJH7b9i+YWBIntCqAxGYpQVl1G9Y5N1DeHsrr8dCaNruW2Y25Gv/ldlIbVPY9xGLBue4OmJvhu+7XcMDMqoMlDcLRQdqodQlG8Y4dIYdfp4Gx3bY8zz4SqtgG8ufRujGO/Aa2/vKfeY72oYcQRR/TuuOAQiVa7YJdaq3z0H/oCWPNvaFpD09LHsVhEpNuHyKdPcAYLNbuhthRFAXWLsJYISusd05GUmYBL0eByuJDNnYtVoxEefFBY2YxNWcjqj19Hkfvgz+nQo+x6AYsFftx5NUNH+v5N4xJDMDiSUBSo3NO3cc9lbmbDO7cwI+M5hiRv4x9j5lNVJSqRH2ooqYqhsjUPJWZcj20aDejVIorWUrzJ/8mq5gEyW8vGUGfIBCQ2VxxFk3MoKN1zQi1N4h1wBnn2d03JiqKqLZeSMu+dQ3nME9z6/Ty21J/mNVMmJQWaTMk4nCCbPRtMG8q2MjXnB47IXOqzAOCgPBujUlYR3DjH+04A9lZMG58nXZrHtIHfc8014uMjj0vl2DvfojLsViQJgjQWJgzY5lflA5CaaCQ7dheWxlJw2TFsep3fX7qLVasU4sJquHvqVdTOuwmcPYunKgoEVb5HuK6No0d39j1T/jGM7wr+g8OpYvP2cKhbilFvp0afhTZuoMfvYsoU+MfQTzhRdQIUf+T/xvsKRREL451PgaUX0ip7C/W2YTSbE1FFZHdYFxxMaKPEnEGyVGHTHsE1s5fxwpovAhYW7A904TG0WBLIrzuCGtex+3WugSdeSoM5HbW9mtp5Mw9abQdT/ldUf34yVQX9YBPQjvJvaG3UU2vIIvuo4wkNFfOGVbVX0WSIpbW6HMq8F9hsrSoFQC9nI6m8v7ADE7bz/oWT+UfCdZ53UBSo/I6meitt5ijGjes5l7j3XlDCclm0bQr/fVYC2QmlnwuiTRZWKYHO4Z3lc6mvU1hTdhIbKo6jvs4Fu7tbQxSZZ/Dfxa+xqPRKjzZEkgRtEWexpPActhd4LlraUiWUnq22NJ8entHRdAQIDxUHDk+YHHYvb543nXGZPW2CoDMQX7Kn1es52ou9tdv9eEO+8Z/M2X4dBXucmO3h1BkHeD0mLw9Gp65iZPyvGKt6qqdVKhVRrlKcDdsI1po44Uzhh+4NE6cmYx3zAYWNo9A3GWhYcCvYW7GbmnG5QG+NIT6hf+mqSbmLeP/CyWQ23tUv53OaxHpSCvK/ngxPzubj9fditYJc+G5ABaGdLXuw2aCseTCDBvftu5iQ05nyFRaAv0FKisiYCtM08cHb3e3zzC1i7a7SBns61CeC48R6V2Wp6F2RVvaTdzhE8Pd98r8JXC4XW7du7WHArSgKL/3+PB+vv5eELO+zUpUK7Fox87b48onzB2s97HxaEDkuG+yZBQ396x9ZtXUlkS1fABBS9TYtFQFO2KyNaB3lKEikDu+dumDMGKi1j8flApuhhduOvd0ncQugjhDft73lT0DcGoux/H4lzRWiIEBtRSsvvohQDfjpUL21Lb8wV2FZehXWssVcc9Sj/PvfcJRve74OqLsYzVmdoaQP8k2Izpi4lX8f8TRK6Rc9798sQojq0P1T3I4cCcuLT2P53hNRRj0Bms4Z55LKG/hu+7VevaLaoYrIFvfkAqesJXd0z6IAvjBihCAfm5qgosLHjsZiVi8WqXonnUQ3i4ixY8XfxcXQ2hLAYGoshuqfaWmGj1beSHw83H8/rC8/nrmbzsA06FlQ+WFT65airLmC6qJqiqrTeOK3j7jw5kmccZGBJYXnYTJBw5LHe6helcL3qK9qpbotG3PceRwTYH2v4JTR3DxnAU8v+wLKv2XlD2uQkDn55M50n2HDRKrzL/kXsGB5/6/YTbtmc0zMk+TG7exQhffqeEXck6nBS//RuE4UdrI1UGvM5YoXHwLgyCPpt8Jk7dBGZQPgbCulshJ+2Hwev+69mNSx03p1nsQkFU3mFOGb1b7SAJ59VqG6GgZn1XH9pEcYGfYhxT883MOSxC+qf0Hf2EJJQy7fbTmXIUP891k2jZhcNpSW9u5aAA0rKf/yQhKlVbjQETrhbo695EwAFv9YzN7dAUbH/iL4ev3F3D//S0LzTvO4XZskOhep1Q9xqyhQ9SNmM8xedSYqlRgb3l/zH97Z/ikkdPdNdrmtF1Thnt/TdtKwuRlMJo+7UFmlotmcRHSiZ3URiLTKJksyiuy9wnJb1V5kRcaszvVJrg3KMXDntJlMinrGd2Br94s017ZS2TYAOeNicro4/oSEqjjpun8RetJsElKjOfv0wLICJqV9xWMnX0qu5WkMv15G7coPyQ5dysS8Tdx0SyjRIY2ozGVUL36+x7GFG7aRGbYORVKTNf3fHZ9rNDD0+FO556eveeKzS5GrF2DQw+rSGT1sEtpx1FEi88hhd2EpX3bgCt+2bofCd6FiDqw4D4o+8KmI7EBwAp/sfodbv5vLUUf/McukyKQUFFQ47TY2rhKRvohY7220PxEUP5BbvvuZZxa9iXr/nBLIHBhLVfJr6K2xGKoKsG14qH9u0geU+hU0Ln+OueuO45Y7o6nvnZWkZ7isGHf8D7MJ5uZfyT//KdqFTgf/+ncYX225mcZGcOq9p4VYGksBsGmyfV4qPiMFleQiXFWF4vTQXlu2YG8ppb4pmNWlM7qpbdsRHg6PPy7G/PnzYfb7BZg2vohj+eUovx0La69F3v0au9bP8zuH/3LL7by0+GnmF83kfxvvZkn+dLbT/XfctTecHTVHoYqf4DWI1F5IeGdPkSYAhnrRn9tUvhWFkgSpqQqRwc3UV/Y9I+bPDIveQLRWrGEzhnnOEBo9qJaXzjqNE4PO9LpeG2y+i9uPvY0h6b7Xw+W6a/l++zW8Nf8Crvt6Cb82Pud1vhgaCgZJqKZbyvf22O5yOtn10ws4nbCx7lxOPcczUd8Vp5wRTdOAN9lQOY0nv7uHJSujO3xj9bYAfGN7ifCYKLQqOypb//gkK+5Cm+ow/+vJceNgQ925fL7+RuYWPwBRXvz+uqClbA+KAnXmQX5JeG/QRnT6I0Qm+vdKkCS494adPHbKv8hq/Q+FezvbmFUviFunqvcWFtGpqThlLU6HvXcBVfaDdziEcJi4PcShKAoWi6VHBb7mFhWbyiaypOg8UtJ8kytKWDYALr0H4laR/acjOfSw4Wao+Jb8b5/h40f/h3XXJ7DjCXB6WU21w9YMVfM9KkC6QrY007r8UZFu7ghHhYPSeU8EFM3Rl2/GbheeUMNG9W50UKlg6rEaag2ZuFyQF7/dZ5okQGi8e5G5v8XJ9l3suKy9WwC1bMO1+mpqSuqpbMnhg/wPqTdm8v0cJxU/3Q3bH+mIzHu+vOe25ROGQuTVV1JbXEVdWxrrTXdzvefCpB7RVXFb2jyE4SN8d2EJA4WkVGfZBav/DdW/dJA+dotYNAdF7p/i9u67QatTcd9XTzNv3ZRuM+jFu07ku+3Xkprte3ALS8ru+Hd562BGjeld7olOB8dNKOaGSf/BvMb7wsi+8y3OTTiTGUM+5+STu2+LiYEBuTL3n3At/DYV/KU17n0Lp1Pm123TKGkexo03whlnQHaOmteXPcRnP4/ruZpoh6JA8Scom++kutzMmoIJPPrrJ9zyQC5TpihER+sJG3cjdcYMWmrqsWzuQiSYyjHs+BKjEb7aeju336kJSG0GEJ+oo80aR2uzFceOlzg27CZy4/L55z8795EkOmwXfvihy/16gyIHpix2Q79rHsflfccRQwp7ZerfDqdbsW9r3af/MBRB+Tew8WYUp5mdNeO54IX3qW1NYtIkeOSR3l/LHyJSsgFRKXv9ethTP46drtsJTuydJ4NKBcWGY1lefDq1TSLwsXL+To6ULychvIa7H0qiNvZeZEWNq+Jn7Ktv7pWFRX3w+Tzyw7O8t+YBTj61iYgI/31WezClV5kmLhvkP0fzr7dgaW2mqm0AxpGfkj7pAqYeK3HjWb/w6Mn/YsuXz9MX4fCfEXZ7Z6rmyFGeX8TEwaNRUKGxV/ueqLftBHMF1fWhrCs/ntNOg8suAwUVixfTowiLyiregZBYzx4H4eFw0oifueaoR2nYvd7jPlVu211fxQ01GrBJYqGjr/d8/87WIlBAFek76JY9KA6jPQqnU8baVOJ5p4ZVWIrnozdIfLDuQa682nNlrKzhuRwz81nGnXaqz2u2Qx0uVnxhto1U7Smk2RjDl3ue44EXxnP6OVEUBD+GgoS18AdcVb92O7Ztk7DhqVWdRmhs95XjmWeKQEd1NSwov4PP1l7HqtKTmeYlfhMRAarEo3DKWlFkzVQa0P33Fq7IUczOv5+yMmhtsuLa8was/GdAooFVq0S7O9g2Ce1ITdNQb0zD7gBrcxWKQkC+hP2ByEgI0+mJCm5Eq/I99w4E512Wzhd7X6WyJZP3fveiIu0vGIpoWnQ/JhM4XVpKq2O49VYwG/tekA+Ayh9obWih0ZRK5OAZHR6ZIOY9RZZ/8MDcT/hq53+8nmJT7anMWv4sDUHn+LxUUkYcFkc4iixjqPewTqj8jsYmWFUyg1HjwhjXM9EBgNGj4eqrxb+//hp+WHUMW3ZGsnunjcINm6hc9hGROx9CafRuvt7aCu+9J7G2/EQuuzaZY2ckMmv5s7zwRmq3aVH7GODLjmnECEiLKiay9Wsw9VQY2FpF0FYJ8V9p9vLxT/LaOSehqvrW7779DkXxuTbqD1TlbwcFWuwZxCR7nijmDE0kVGdAdpiQ2wo83KdMPKsZk7achETfEZj28U8EOSSy83zXQ9BEC+LW3rQPcavIWPd+i860F4esI+2YywMu7PjvK4OojH+WFcX/4IEHYMdGt2+sFBPwHD9QtNdV0Mk97bn6giW19/LS0hdxho/yu290tJiP/7jzSh778GyWLXNv8LHOaKq34pB1uMIG9/m7iInv9N+LSw1MsDR4iIb0xDZGpqxi01dvdXzuMIrfRg7AGmJfZGWpqDNkuGsnlfbq2D7xDocYDhO3f1O0q/KSkvCbYqyLyabemE69eYDo4Oxtwux+639g8Qnw6zGw9UHhd7kvXDbYeBuKsYjy+gRue/1a3ll4MZv3pOEw1gvlrTdY63GtugLT2keQm7f4vMdVv+zCaTVSYxyAafSnWJ2haEz5FG70X8q7YY/w56t3jveaKukL06bBc0teZVvNJF5a+qJf4jY2PYP8uiPYUTc5cKJVkaF+mVCPbLkfVl4Ev5/S/fidT8GvU2D5+bDxVsh/Diq+F4TOvgNTw0qU9TdQV6FnR+VIXlv/Hs+8nMTll8OA+B0YS1dhL50Pm+/uFSnlEy3bYO01NFQ2sbcmj5dWv8/dD6f1KvWvK3Fb0TaEgZ6tsjowbHwKC3ZfjN6opWb3Duwb/gO/nwaF7/Dd3oe44os1OGL2LxUwOxuuc69FXnjB7QXkMKLISocnp6/UWRDFkJrMyeyqm8CvdU969e31hREj4OjsXwjRL/I8qVRk9KXrUWSw6EZ5/O7GjlMREdSK1WQFvY93p2031C2moVHiiw03MGgQnHaaIOCuvVbs8tlnoNfjuY072lBK/kdNtcKcDefz/O+v8Z9Hozi2y09x0b+C+bnyUZwuFc3b54n2Dzh3vkRdnYttNZMYfcIkv99tV7QvtkYn/kZTvZUafRaRmcN7eIidcgqo1WCu3UXL4jtg13OdG5s3gqmLqqZpnXgXd7/iuQ/sClsz6IXcJCRzcuA33hVhgrhNsX3e/fO1V0L+M7icLn7Ln8FVb76K2RHBZZfByy/3LAzRH0jMyabFkkhFQxJr3Nl8Eyf27VybzLfy7pqHKaofRH1pJUE7biUnNp/HL3+b0aPh2EvO4H+7X8FoDaV57wZYe5Xw8fUDWYZHHpVYXjAdXeJI/vGPwArvhSWKhmVr80E0uuzdFXxlX2HI/4q6Oli450LqBnzK0Sd2qu3PuSiWII2D4ZHfsezL/S8Ys19w2UQ7btoAsgNFEf67dpvcq8KChTvrUVx2oqO9k5/DR4VS2jwEmw2sNT5Ut1HD2RP7KbN+uw+nEspVVwkLl+hoQSBsXm/u5okarAhSIzLZuzJ+4oB1HJP7E6YqD5WwjCXk6u/jH8M+9psOrwSJ/F9ri4f2ILvQ2UsBCEvyPQGIjZNosOSBAnVFHoxunWbY+RSNDbBw90UMnDCi3yxOgtyWPVYrrCudzmdls7nj2Wkdv9s5V0/g16IrsFqhaemTYBZEir0xnwjrKhRUxE64osd5g4PhoovEvx9/IZNvtlyDNirT5/h8xNGh5NcdgdGA8BLvZygK/Pe/8OznZ3Puu+t58Jsn2LAtnurCCszLZ6JsvN1z2r4iU1mip6pKjAETJvT7rQWE9HSoM2TgsMOMmEe4ZepdDEzdH5PvwBEZCReNe5lXzzmZgTrvqf+BQqeDK28dzL1zv+bDbwezYUM/3KQn2FuwrrqNxjozu+vHIw+5m9hYCUdjPsUfn4Pc4kXqGQBqVKfz3rKZzN7yf1z8r+6TVq0WrrpaRUnzMD76CCwWxFi/D/ZWJLOhYjpByb4z+3RBEs32bACayroEd1x2qP4FW/lvtLXC74Vn+xU+XHUV/N//QfLgYXxT8hIzv/+Ne+fO5q3lD7CxeAwtjQr6JXeAsbTnwc0b+fBdI0YjHXO8666DkBBhM/XbbwhbptYdZJhnMTx5nU/idtgw0a5Ozf4vloqewRPZJOZPmgj/Hp7qMBHFcOire250mj1+//uFdhHRtodgyQxYOEmstbY+AMWf+Bc69DhfE2x7CHn5BZ2erl3QVirGK73KOxGYk6uiqGkMsgtaijyMq9YGFKcVWVET4ysyCaSlyiRFlDM4QayH/RXpjkoXnbvG0mV92bIF65J/0bjsOWRZYmPDBZx4WuAZjZIE99wjMXWqCAjP/VGIbBRNHxbmfhCTkoiCCtlp730hZQ/YWzeIzVVTCY8L7HmnToWLLxb/fuQRqKtogXXX9vDxb8fP5fdzzVfLscXO8Lg9EGiTjsRgi2Z3/XiSkgNMpYgcTNiRDyJJMCL0A3YuXgyAyyLeLym49+qTjAyoMWRhsgSh2Ft7ffzfHYeJ278DFGcP8sRUuoopOXMZnuu/ynN8aix3/vg93xS/KPybFp8I2x6Eml+EmhYFan6G5ed0V5HKLthyH0rrViprI5j5yas0mVIIjQjhjWUPUlEJctm3ggjZF9YG7Cuuo2xXFVt3x3Lf8xO9Fs5qbYWHX5vMg/M/Q5/zNFNPzmCr/Cj/mf8Zj786wq+yydUgrq+OH+v3u/CEI44As5LG80tmoVeP8VskJDU7hmcWvcm7q+7zrkjsioZVgqjddDsUvg21C8GwF2yNYO8yObFUg2wDUwk0rIDyr2DnE0Jhsmhap2q5bilsvI3WZhvLdk3iuSVv8J9Ho0hIEAUOgpLH8MKSFyiv1KHULxNqaft+pCO57MImY921GFoMrN0ziid/e4fb7o336IXlE2GZlDaLtBJ78FC/QYecHKiJvp1bvpvHe0uvZ+OOeCqLm7Bsfwe1tRiXoiEuIcBwsA/8618wdCgYDDD/rdkoS0/DuP1j8qJWERdW71PNBZAxKJXbvp/L04veIme4f7WBJwwam43ZEYHdYkPRe4i+t+VjajNgdkQwfPIwj01v3DgoaR6G2Sz294q9b2CzwbxNp1DVlsutt3am4U+fLjywVM4Wtn39sggi7AOXOpo31r7Mq7/dxacb7+GxJzQ9ithoNHDZzFH8vOtftLVB68onoHUHzXtWYbOp+aX0dq7oySH4RHAwnDHmK64+6jFaWmBZ0elceGHPLyImBo49FoI1ZmwVS6FqLjSshLXXwrrroPBtfvxRELwrv/0V2a6H0k9h2VnieRtWeY7iN6zAbBLf8YjxfbPocEWOQ1bUFDePZNs2YW1RXw9OXRpmKYN3f7+e+794HJVGx2OPwcyZ/W+R0I7kAZncOXc+zy9+kaDaLxmevI6JR/TSxsCNDsVHZQtVP9xMqKaFVnkIYy8WBtg6HZx33VE8sfA9SqrjsTQUCx9fT229HbWL+eHrFtatg6AgeOQROeDU34gh/+CmOQt5dsETYgxRZDCVQc1C2POqaAuLjoO6JR3HlBvHkF8Yz3OLZ2FIv5NLLu3eQUUPmIglVUigYmqeoqGktOeFnSao/El4c9avcI+x/QSnCSq+p/LbKyl7bzLFn5xD4RfXc9FZNRx1FEyZovD+nc9T//2lnhfyHqDZ9RhvX3Ac50xe7HU4S0iAKst4FAWa9m72fjJJYtbHQ1lVegqnnSbahFot+pQTBs0mJX86FLwBgGw3EySJ8S8+wwfrGiYsL+wtHpTTxiJSVb8yPn2p3z5aHSYGK6fJQ7DAXI7D7sDqDCY5x38eo10nyHxb5UpoWt99fC14HUtLLSW1KczZcX2HYq4/EJoygq82z2TW8mfZHfJfnp8VQ1SX7PuoKEg/9lqKmkbQ0mDEtuE/ILuoXfEeLhdsrT+Z0Ud7/q7PP1+k0LYXC5o+3ff0ZsoU2Fx5DCYzuGoWi/lif8BaDxtmMuezGubMEffwz39KVMknc8f33/L56kspLlGzdGETs+eE9pwfmkoJXT2dR2ZczujRSp+CqP2BduLW7gCnU2JCxu/EJx4Eg1sEcTs190cAotVF/XLO0aPhnHNF5/vkk2Cv3wYl/+uXcwPgsqNsuou60mpq9en83vosN96k5aWX4MyRH6OyVVP+/W0dwYgekJ3CWsPk2Xfxi69DmbvzMuSkGR5JrX/8Q/xmLS3w0+xqWHIyrLtekH3urMRK9/LIX18DYFEJbxRTfWnnh+Vfw7b/0FRvp7BxBEmDh3erU+AJajVccQW8+irMmQMrV6l463+5/POOs9kV/iobyifxyZJzWbU1q/uBtiYsK2/jaOc5JIVXcPvtYh4RFweXXip2qfr1OeSVFyPveIrxcZ8wOWeeT+I2OhoaHIK0binpEkhzv/uba05i8d5z0cb5z9jRRrnJXUv339NYtZP6r06i/quTMC84H3a9KOZjfrI2u8FlB2NJ97G37EvY/hBUzwd7M4oii7VWzQIomAXOLhlA1kZBHvuAtXEvFWvns3dTMfpFNwiRTRdIre7vJ9o7ya/RQIsk/LYM5T2JW8VUhsMB9cZ0UlJ99x1ZCWU8d/o5PHDiNcw85m4GDfS9cE4bmI7DFYTTbutY969dZaVsewGNrWF8veVKhp5xXa/nnmo1PPWUEKI0mZKpbM2j2HZG704SAJJTtbRa4nE4QPHWJ/QC7cW74nsxrb/5ZhHM0Oth6QfvojRvFuuHkv/B3jdhx5Ow8XYoeJ2CApAVNQMH97ISWBckZ4Rz2/dzeWnFm70SqcWPOoW6EMEyq3Y8gqwvRrKLALo6pPeK27Q0eG/NI1z5+XKagwPLFjqMThycWcBh/GHYsEFNRu17KAvuQAkORVKHgCaUbH0z1x6tZ7fqHuB8n+don2RUVQHhAwAZIgaixE9hU8VkCkt0HJ/5JnFxElKoe1KvKLDjMZT6ZVTW6Lj36xepbM3jzjvhmGPg8ssn8PP2czhTO4e07Y8jTfkS1G6Ta2sj1uXXUbmnguqWVJ789R2azGqc98Izj9Sj1amhiwH4rFnQ1gaJA3M43a36OP3qaXw4H6p2wrx5cPrpXh7O3obOXoQTiB/UN+JWqxXRs59/xq/aFiDLPT+qrRXKl32rcHdD0fuw902cTmjSR6IPmkpMZi6xGTmoInO7V24/4k0aK+vYub6K0l1VmBoqGJ6xi9FZO4lKSkDSuN3ho4ZhdCbx3ZqxvL3yIa6/UdOhKtFoxKT64ouP4eG5r/HsP28jTdoEi48HbRSknQFDbgFArVYzLNWJWrYAPuR8Ki0Y9uKwO/l507G8suQJzj4vpJu6MlBotCqe+PVdMmL2cuRx3v1tOy6tEqqbTZti+eSTq7n1+8uZkLGE0akrWbBeTHgS/Nsv+YVaDQ8/LAjcurJa9IOMaPSvcec0WFlzGVrtTJ/Hx8cLRaTBgNe0N38YOUrF91+PYqR2Jc3FW4kbN6zbdmP5OswmyK89gpMu9jybGj8eFn80DKttLraGfII8KaYUBRKPoXBdMXO2XcvUqd1VliqVUGU8+ZCTGP2XOGudaFq3Q0gqGEuwhU/gvvtg2bJhqFTDeOwxOPHEzuPVajVDhgxBrVYzYgQsHHw9VW0r0FYVY9+7nes+/Zqc6C1cfE12nxbVxw2c2/EYBcZTefA4z/udcQbctngsxdVpJCZWodoo2r0iaVmxNorH3lEAiVvffYCzpxzLLWd/TZhltQiaNKyAoERIPRkG3ggqMdSay1dgs8HW6inc1MffOSIll/97+1c0Kjtt3da9nQrchASh/h42rMfh/Qq1WvRnNeV6LhzzImqVTN6AeUDvc3rT0kR67gj9JQTp6mm2ppJz4Suogzp/5AkTYMSkQTzyy0c8qpvJkcOLkQwFEOlhJW0sxrbufjLKwogN/Zyrb0oiN1dNXJxoW/6QNSACqwwJQcWseflxxg3cS7DWgy1Q207s8TP44gv44P0RWCzzGDdezb33eiauRp97Dcte2EKiZj2Vc+8h/oaPkTTuQcDeAr+fhtNhQ5FxpxhKYtyNHQsJx3T6vCqyIBtUWv8BQP1eKP0fcs0i6musuO3JsLuCaTYnYbdYcLkgTGdgcPwaGqtqUc27ivgZL0P0SO/nlV3oTFtxquwkZPuWvivRY3G4ZlNX58Ijb6EobN0msXataFdXXdW56YQT4K3V6RgNTuTGtagUhaZGF99vu5qY0EauSfNeyUYXmwXNIJn3IW4tdVD4DnY7VOuzGedHcRsck4y1LRTZ1HPhJNevxm6DGv0ARgz0P61WRw8EG0QYf4X1v8K4FyFxqiAwghNpbIQP1j3AtBNCO+YL/YGRoyTmZF3GkQPg8ss9B3TOOU/DTT8/RWrURTQXbSMl9mloWIGChC39Sq8L8YgIOO88+OQT8f/7BuL2RW4uVNqnIsvPYanZQfjqS2HiO6D1UZXIH5wW2HQ7+srdKNsfA97kttuEsklRYOvWMH744RYe/e0M7HaZqjYVy5bDEw/ria5/HXIuhebNmEzCQ//oo/s5P7cXSE6G/LqJJEeUMTZzNQ5ZR2RibyPdfYNGI9qGLEOQav/VaO246SZYuhTMzTU0/XIDKYk2aFwNySdA0jTQRXs+UFEEQWcqhbBsMZfo2ucpCuQ/TUvJFhpbw3lt9cu89E4UKhUMHw71pzxM+bIKMimg4odbyTjv/Z5FR3c8DtXzxL9VOgjLgYgBEJaDKeRIvv9eDKb/+pfnW9Ro4JprxDxw65KNnHu+jLp5AzRvgPxnUOImcna2wm/Wc0hL859tI4dkAxCj/wq4CrMZVu86haTSr/lxw6ks2P1P3ni39+1TpRJFh1JSYNy4YB647xl+WxXKNzsl3n4bhg9TxHe7900aasw0mbIZOj6tm/L8X/+Cb7+FivpYWlogzF6AIkOFYaTfzAUpVhCRmqYlsOQUQXjKDjhpDb/uOouCgrN4+RL/zxEenwINoHVWg7WBBkMCn30GP3w/iCdPCic62EpjYwnhZSXExn5OWIQWKWYMDLwBYtxst6kCqn4S/3YaRQaKudxt5yPD6Kcg5SSxPWESjpqVbKs+mtmLj2LzngwGpxUxNG0PuQmFrFqZSWyc6NdOy3gFVdNyyDwfsi6CILcq0WkGTSgGA9zy4FHkKteRGVPAEfISlNVPEGUsgsG3iudz7cAJRGaN8f17xol1q1q/WcwJpM4O2lRfiixDjT6LKX66jsSsNErd/x6dtpIBeb4Z18FDVKz5eQA5mnxaKvby/KeZLFhwJMcPvIeW4BP5v7tUjBrV+8JVINbEL70EV101ifvnT+K88/p0Gp9ISoJNxlRiQuoxN9UQFuNjnrMvKr6D4CQIzxV/21sYEf4LoRnJxMf7Gfi6QKuFZ54R49PL82cyPGMPw1O3wp6Xu+2nONoocOsT/GWZ+kJeHpx5TjA5OYFpxrpi3EW38PvzBeTFbKD25zsJke3IQFBE7xW3Oh1Ex4dhrobychEMChRd14d/Vxwmbg9xvP66xCU5djKjXahUBoKCDOh0YHNnv6tjh/s9RztxW1kJxI6D4+azpyyRl16iI+XpOV5l+BArp58lVGhhcjFK1Twqq1Q8Pvdp9jaN5eGHOwnU556D22fOZEzaCnS6ShKT3oIht4KtGfPSG6gqKKe2LZn3tr3Fdbcm8/zzsGVtE7s+uZ4Rw0F15JsQFEvdr/8hf9UlwCjuu4+OtPv4eOHtNGsWfP/JLo4f1UhoVs8KRo62ChrbImm1xDF8TEyP7YHi0kthzx4491z/+0ZHC0WDzWyhssRB3lDvoS9j+Ino6z/i27Xn8M3mqzDZxb7h4YKUGT5cdOR798LKlVoKCtKB7jMnCZmBmY2cWCQq4KpUCdzw0UcUlMYw5RgVl1/e/ZqpqfCf/8C9947jls/e4Z3/u4/YoHJwtHVU9W5pgYKdekbW34oSDFJ4BkQOhsghYjCrXw6DbwZtJM0tErubb+fnBXZ+XjOOIUOECrAv0GoF2VDUOJJLAiSlJEkQkuPHQ3Gxlk8/PYkPfj6pY3tXr7L9QV4eXHklfPrhlUwf9BNZKWLB0xHM8HOPM2eK9LO+euoFBYE5aDSwkuaircSNc0cxDEVQ8jHWHb+iKNCmnUiGF847Lg6sumGggLkmnyBF6TnCSxLr687nxg/ORVKpPP6Wxx0H72UmsKLoFOLjfiJpz6tgrcVpbubJX95h2cph6HRi0jJ16r6nl4iOju74/+tv1HH7VY+g2VHBjm9OwmiUSMzJ7Eb29gZ2VQogqrjOODPRqwLz6KMhLl7Fgl3nMGLAq0RG67AnnsVj//s3v/wuGs2ZZ8KSJSrmrJjKb9um8uwj5UxI+EYsBmz10LgGBru/IJcdR4VIM2rVHtMnWxaAyZPhyCmRVFZCmBGM7j/tyrFRo+DZZ3sX+d8f5OZCrH0dEjJWTS6a8L4ZMQ5K3sWb5wkpj8keiWXYLNJye87obr0Vzl2ezD1z3uPVtNcYlXJK58amDWIiHZqGvP0Jqquc7KkbTd6IRM47r2fb8gWtVlTmfuuVSKLZTkkhRMUEkThgIJrYQRA5BCV6HEvWZfHyPe3Vm1WMHCm+f2++biq1ipyznqB2zkVEUETjd+dhOmIu27bBli0xTNEOQrEZKG4exsRBO8hLKSeUQiRjobA3aCdurfWw9DRAAnUQqILdfweJIGjKDMh1d+7WGpzl86ishOK6LJYWnknmUSczZHQC8ZESz10qxpSwsEjeee19LE23APmofrme2On/hcQpnh/GsAeH1YLJHkn2cN9Ry8gBk7j25d+ZPEXLuMs87LDh/yj/LZm40Gs55qRkUrtky44fDzX2cdgcWszNtYSbK6isy+TbbdeTmgrX+5jDR6UI4jZELhUEjyQJlfbGW1BsDTTo45m783JO96OC08SP4dp3lzFlCuybPd9qCMIlS2ypOYFTM/2viDQZJ7Bu8Vrim2qIb7Kg10dz9CkQEaGmojac7zZeQH7dUTzWj2pbEG3yiSd876NSwdU3p/LBk/dzytDPSBl4OY999S8GJ6znuiezfR57ySXw008icOQvaCRJMGJCEm+vepQ7op8jPCd5/0hbRYbtD2Op282ekmjeW/MgF1zQaeEgSaKY7JgxYDLl8NNPQoW4Zg188viXXH3ct4RWfIesjcVkgj31Yzm3lxkd/QmtFmpcx7Jgt5aRKWuoM2SQmnzwkiTVajGm6CQPdhJ9RHi4qAlw110pfPj7v7n9jLcJbloLTWtFVlbcBEHiJh4HQbG4XGDa+Tnqsg9RbC24ZAgLBU1oHMSMgZjRkHIy6KJptcRRW6vi1RXPcMEV2d3mONNOCOXLypeJqLocqbaY2l/uIXnIKEj9B4S5d0yaBg3LhTpWtoNhj/gDNDTOJy/qNkwpk3zOz045BT74ABbsOB17+AROHzeXoZE/EamtRmX9ndHJkBxeQnKyf+JWE5MLLaBvczLrHli+HOz2WOA7QOKcc8T8f3+gVks8/mQ4xtvEe3DHbXZm338vkWkDMe75AaMRvthyB0+90b3dhYaK7Lxnnr6MKXkLGZ0rVNlK5Ei/Csu43OGYKiOJsOqRLQ3Y7WCzwzfvWCgrE0Harn2/N0SnpuLaBWGqSgo+vYp/f/A9docK0PJJwSdExgZjKN3AiOQ1jExZTWpsDbEx61GHnojeOEoUq2ysIq3mg465k04n/qjVgDpUkLlAQQF8880Y5s//DGuX2O3qvUms3tu9YKZa5SDpn3sYk2ckqPhDKP0M0k4TwpeKObQO/4L/uyORPXugJOIaxsYolG19n3N5C0X+gmhTKc7My3A5rJgdEWQOzfb5PcTnDsVeHIxsaxPXynHLoc2VyCVfAWCQs/1mJ4aG69BowOkEjU5LUJDv/ePioM4ylBRHKdffmUtRLahUErnHnc9VV9ErCzxPiImBN94Q3szn+LaE7hOCgsDoSgW2oK+vISzQetAum+ircDcadShKUAKnDyyjJimzV8QtiLb+0ENw993B3PDuy3zy0JtkJzdBUByKNoaa5jjW70ynrU2Mzf6Kn/uCSgX33NO3YyOj1NiGPk1T8aVo6soJ0sK6mqNJGdq3SmkZGWLOXFHRWRi7A04z9TuWEDHoVEJCu8+nejOHP1TxpyBuX3/9dZ577jlqa2sZPXo0r776KhN9GOV9/fXXPPjgg5SWljJw4ED++9//cuqph+XWnpCRIfPyqudw2bUEqS0Ea80Ea8wEa80YbVFce7d/9qt9EG1uhooqLR9+mMhPP4k1kE4nrALWr4edu4PZ+Qy88grcc97vjAxRMWvRA2yrPZann4bjj+8859ixcMc94Xzwzv3cEXIr6m0/EJdxLoZld1Czt4QGQyKf7Hqb515LJSlJpCA98YCV1mYHlQU1pCtXo8QegbFwEbdO3cxS6cce0b2LLoLtSzfw7+E30rQknNCLvhGRT0UWXpRhGeysGsENX/9GZkoL3zzc9+950CCYPTvw/S+f/A5HxLxD5M400CeJCYLTKFJZE47BnPcIX34Jn36aics6H7MjgkGDRCRy925B1KxbJ/50hSSJFJPJk4Vyc906+PZbFQXliRS8Du++KyLtZWVxpKbCo496Vt2ccIIYLOfMGcyFr8/hqceM1JVUs2N1GKu2CPV1RnQtt0xJIDGqgeCgCoJDKggJ/o3gYDH4r16WwAfLrqWwEGAEICZ8Tz/t31fZG7qSIX2ZtObmCkXEDTfAd9+JhUTXVNH9xRVXwKJF4Xy58QauPPIpAELiAjNhPfts8Wd/EJY+BkxAy1ZQFBQkdm8oI7xwPgYj7KqbQOIY331l0sBByIoau7EVrLUQ4h6YZQdY65CD03npJVG45fzz8Oi/KElCdfvCo//i2LyfiGsQaVybCzLYuDWCsDARUfekLnY6nWzevJmxY8ei0WgIDYVLbhzGbbeJvkqlEou/vhr0b3fcRklFML/suYIPfLyzarXwdPv4o0uJy8rhgquGcss9CRQVifb7yCNw0klCGXjvvZCfD9ffnsmVV97O9dfchKp5JUhdGCVjMSYztFrjSR062Ot1/SEsTBCDXaEowlfPahUT3v4u5OALx+X9QHr84wC4YvteyScuPQ3zNh1alZ0Vlhe59cxsj/vFxooUs6eeiuSmN+/nm+PdwRfZIQoqWusheiRNhdtoMYTyTf49vP2xhErVs235wxlnwITxcfz03jPMXZpHrT6TuHgVt90mlMYvPg6b3BmKCQnivk4+2b81RdagODbGP8Vg/Y3Uljdy00v6jqDcIt0rmOwRSJLE26sgMriZEyds4dzpW8hLnNjpbyW3e48rgmxwWaGrS0Xs+I5/bq+dzNolF/LbzpOosYzkiSckjukZxwTg1rtjeOHZtzBU3wusQlp8OzFT/gPpXdIV3bYRpt3f4XDA3sYxnOWnUOSIkRpcMmzfDkr5HKTmDRAUL/6odJgr1pGjUyNzE1de2f1YtRqOOTaYvQ2jiY/bQHjjGqqrRb/qT+GVkJWOY6dKqJ3szWAohM13gcuMXZvLwz/PosWa7LfwU0qqeKlqPBSh3lB3Lu8vyKNVSUVRnPibWh93QgT/3fgM7/zaaS0Q9IoYd6uqzmHLFolTT/XvjX6gMG4cfDtkBo8uOAHtEjU2myhE6C+jKC4Ovv++U7HpD5Mnw+3fnkLJV0dxhtnJOETgSSs3Q/3vkHwSGIvBWCjsoQx7ARUc+U7nSXa/LCxMZBv2mnWUlGl56ffnGTwmlTvu8NwXhoXBhRcKBf/dd8PKPZNICt7BCWNWERzUKFRq1jF+fR4PNNLSIEZfiizL1OgzGXuQipMBtNgzCZfKqZeOZT+4gh6YNk38mbPkGhq0M7j94kWoGxehs+zGUbsOx5Z1zCvQ893Wf9PSAtPygvj3ES24FA11hgySIyuIi2kizrgIXd0iSJiCrI3l7vduonrv6aQNyvKo0Pvn5Ym88czLTHJcTWvROrT6dTRESZB3HampEBF/DNK0X0WDMVfh0hdhaSjG2VLI3pIagjQWzrrE99iqUgk/2XvugSVrUliy5hokrmJQ4haOy/uRkcmr2Np4OtMCcOfSJE/mxxVXYrGHs2iXyO7JyIATT5Q46aT9I3Da4XQ62bJlM089NZb/+z8Nqc4fqNuxjBDDMupqYU3ZSYybPtpjP3TGGfDFF1reWv4gj4RficEWRWyWf/Zr8LAQ7nvnc1KiyjHaIjHZIzDbIzDZRUZgaGhgxG1iRgIlzlCCMWNvbSY5rJDY7EFccQVMmpSIJEFFxXS+/no6D/6gEK6qYGTKGko+GUiRO609JTKZEwaJyrQ2Zwi1+kzqDBkYySI6MYaMDImaGtjaxdUhN1fYwkydKtZhTU0iTb6pCRoaYO5cLTd98SUTc5Zx+1kfkxu7HaliDgAOB3z23Hz27Pk3MTHw+uuQlyfxzDNX8+qyHK6f9BCyvBqXfRjvr3mAqDAzD6T7Ub4O1bBtzTjGBq1CcVrpaJ4Og+gXAbMmsGKxWq1Yu1nVAaSOAoXKVZTtSKWpLZzMTHj8cbEuczqdrF8f+DzLG5KTxZzqQKFZGc2GCiujhmfglX7UFwirtCG3iZffaYLk6WJcMpWBy4xLX4aiQKMplZg+6L+mT4cLLoDZsyO4+oW7mTlTzCtXrxbtqh3Dh/d97dwfOPO8GG698nkmxH3K+2sfwOYM5c0L+naurEwnR2j/w5CmMnC+Dxp3Zp0iI299GMOaJcz5pIipV8/sFgTu7Rz+UMQf/tRfffUVt99+O2+99RZHHnkkL7/8MjNmzGDPnj0kepDCrVq1iosuuoinn36a0047jc8//5yzzjqLTZs2MWLEiD/gCf7ceOwxmQ0bKhg9egK1tRpKS+n4o9UKnzF/iIzsTOM+/3zRsYNYoP7f/wkiUK+HuXNF+kxZGTz66RWEaC/ApYrgxRdh0qSe5z3tNCgpmcJna29n89zjuUKOQdqaSkq4gS+L3+bZ19I6OsGJE+HBp9J47P53uf2YG4AKQkJ+xGaDb3ffy4Ov9UzJ0Grh7CvHULEgjyypgLbVzxCVMwHKvsRhMfJt01y+/U6HgorcIXEHlezQRIrVpkNfhaFcVAxu/+OomcftD1/GrgoxeObmRnDDDULFKEni+y8uFurM/HwRDc7IEIugo4+m28AxbpxQgf76K3z5JezaJX4frVZYCPhS/d1xh5iwFBXB9TeFA91XMVLkQO795QuCJMiOKyA7ZjdZsbvJiC6k1pDJjzumUOJOyR00SCySzjwTr2rPQBAZKcjr8HDhX9tXJCZ2FhTrT2i1ghi+8oozOTr7F5IjywkdOLT/L+QFOaOHwSrA1sCPs2v5eHYKFeXHcsn4f7Ki+B9kjBjGk37I4dFjdVSsziMsdI/wuQ1JcVufPA71y/m99QUKCsYRHt5ZiMwTjjkG3ksbwKbKKURErGB37UiemP8i2rAY3n3Nd/ED1z7l4485RpCkCxeKBff+LFqSslN4cc5jnHuuf9L+jDPgo49U/G/BVH5cLfy04+LgxRc7AwepqfDee6IA2OzZQnGzbZuOq6+eRl4eRLefzN6M2QRL9p7DcVf3r3JKksSC54/wY0xM1kKr+9/DPHT0ASIjN5Kb170H6mCefyvXZ3981llC2bd9u1gkT54MaQl6xmjyiHXW4qreSmMjfLP1Rq6dmdRNVb9v2/KH1DSJ6x4+gZGrBGFeWQkPPNC5XacTaeeXXSaKtgSK0y6bwAt3PEK0vJGoMCODR0QyahSMHi3+NhhEgb8ffojl2xXT+XbFdDIyOrNWTKYMHJbfsVtsOGxW1NhISbSSnGglOd5GtDOJJDP88gs8+6wap/NOcnPh03d8E4KSBHfcHcrzz72IvvgJpjAXacVjRE9shVy3VHbDTGhag8tt69fMeL9tb/Bg0T+2tIChfAuRxoXdtjc0wOaqY5h2cqzHhfsJJ8DPrx/JiNQNyA3raKs+kvgwLelpSYB3yW16po71xhQSwqswVW0nrOghcJkhZhwF2udpMkeSmupfIdR+TzU1buGuqRh0cRSURvH006DXj+Loo8sB/yu30FARNL39dpg/XwQRi4uFrROIIEN/etv2BbfcAsuWqUWhJYSHZyDw5/HfFRMnikBMTVMMb38EfCS+mwfOmMXEtLkEBz+FWi0IMbXaTZpJWrdFiPsHa94E+nxcLqHeeXvlgxAzhqefxq+fdV4e/O9/8PjjI3ju11l8syWfc8d/gtmsIi5v3AHzBg8U6emQULsbgDZrYq9SSvcXX5a8j7V6I1PPO5a+h+M84667hKhg+cZMlm+8AriCxPBKjshYzMSs35i7/nia3ULfzVXHYWAQZs1gLDYdFWU2cuN2MjhhK1PG7CUhNYP8XYLoCAnJ4u2HPAcNJAmuvXMwL93/DJOjn2Rz5UB+KxjOVre9ZViYmqQkkY3Y1paB0ZgBHNdxfFycWPP4w/Tp8PHHsGULFBZCYaGK4uJxvL1qXMf2QDDhCBUvvnQjwcFifDnxRNGH9vc6xeVyERoqBDdXXXkuC3YWcqLrW6wOHfMLb+b/27vvsKiu9A/g3ztDZygiXUFUUDEKdtQkatRYY1mNGCsal8QaS0wxa41JzJq4pmwsyRpbys9N1mjU1awx0VjQSLFFRcGCShMVEBCBuef3xw0jIx25DIzfz/PwKGcuw/vOfTlz75lzz/1yack/p9UqV4nNmtUK83dtQp7eBpNfLf/y5cBAIN/CE2eSlGv3HR2Bpi2U4/nGjYHOnctZPu5PzvW02HR6BSzzr0Hy7o2FHzqiTRvjbXx8lD528mQJO3f6YssWX1xNU/oYnQ6wsfdDTO5rsLcH8vTA9bvK+xAA3LgJ/PHHg1x79lTOf9u2fbAPPDyKH4uOGwcsXarBkSM9MHJldwx++gTmDN0E7f1reO/HKdgd0xPu7sps0sJJD/PmAStX9sLbuxuiW9Mfce7Ay4iM06BNm/I/APP3B+ZGzsO55L14uXcbeBVeJGfriZj7b+D/DrrArdUz5b+gAH7NXAm3zHWQn5iP9uVvjk7d3PH++2EYNkyphaL7rbLHWaZwXRqOTQeH49VOQBtAuVm60CvLFjk0U5YqvPINABlwbI5EaSC+/NIFsvw+fHwAnwYF8PNIgJR9CVtiEnEhowdCq3jLlFmzlD7jwgXg7bcftNvaKpPjOndWzoFMycoKGD6xBd54411DW1UGqgGgoY8FmspRsNPfUZYocfrzw4W4z3En9ldk37NEVEIPjC7hg/m6UFtqkoSo6G3t1RESEoKOHTvin//8JwBAlmX4+PhgxowZePPNN4ttP3LkSGRnZ2Pnzp2Gts6dO6NNmzZYs2ZNub8vMzMTTk5OyMjIgGNVr1WtQwoKChAZGYkOHTo80qcTY8cqMz0BZUbE7NlA6xKWhBECiIkBvv9eORF5/fWy1+yUZWWb/fuV77WafDzVIQ1LlntBV8JVc9HRwMI30jCz61Q0cLqE3y4Nhu+AhShrwvWyeecx2G08HHQynJ2V9XBT7+jw/s+rcPl2S8Mg5sOXbKtp08YCHN/xM6y093Ev377Ilw45eTrcuecGHx8JL7+sdNbVcQIhhDLQsWePMpBekUH7S5ceDM61aqXs81atlMsgbW0LcPx4JBo16oDYWAucPasc6MTGKgNiHTooJ2bt21e9cy9JQoJymUt5M6RM6dNPgc2b9BBCwmerNCjjAoJqde8esGvJVHg5XMac7T9CL1vCzk4Z7BkxouTZsQ9LSQHWv7UKvvXi8JdZL8C2YSflRklxnyM9Q4NXv/kYJ290wcyZD25SUZojR4DX5uSgldcxnErsCndPa3z2Wdmz5Errs/LylKVZQkLKPyEvS16ecllg584V+/T6r39VDqgA5cRp5crSl9f43/+UWQeFgx2AMjDh76/kvO0HPSBpsX+/aQZZ1ZB64RRu7XoRFpaA/+QjkCyqPiWg8HWryADohQvK+9LDNxfyczmHAYGbcS9fh+uOb+K9ZRrDidajvh/m5QGbNyuD8/fvKyfy06ej8jdZ/FNWlnK5WOPGpS+tcOeO8oHAli3KB6RV1bOnMku8onUnBPDBBwK48BkGttyEVO/lcAzogRs3ANe0lWgof4/Y5JaIutwWGv8JeH1e+U88YYLygeOTgZFo4R2LenZpcLFPg4NVGm4m38fm6Dfxyfpm8Cph+oteD7w08ixe7ToeDf3sEZscCPvcSCTWX4jeYWXfvGT7glfgXy8Cui5L0KixDZDyK1LcF2DDJit8953yPrVqVdmx378PfDL9EwR5ReDpUYNgl7QB2fnOCPv0M1xJdkOrVjImTIjEU0+1q3RtCaG8Lj/8oKwBOnSoujONKmrDBuCf/1T62z17qvd9vFBmpvI+ceSI0i/fvq3ciG5Em89ga5GNO/fccC09ANfS/ZGSHYDbBQG4fb+JYS3HZi5HUc82CRZyBuKSG+Ha/WewYUPllkASQrkk9x//eDAx4e23UeZxZU3YuBHoeKsDZFlGRPI4hH84u8Z+9/z5yj5fuFD5ALO6/fe/ylJJzs5K//nwl5ubMljq7Pzg+FcI5Rxg0ybg8OEHz1W4Hu+bb6Lc9TCzspRJDJcuKX1vYqJSc6VxcFDqfvLkqg+cyLLye27cUAYuK3PqWdJqVdXl4ffD69eBF18UCND9jLQcTwyb2BojR5Yd25QpD5bM27KlYh+qJyYq9/jw83u0K4Ru3QJycio3GUSWyz6fundP+XD22jXlS6tV3ucrs/SUEMqVBytXKvHZ2irvu7duKbPoV68uPqtYCGDNGmDdugdtoaHK+XF5Ro1SlssDlOMIZ2c8ONdNVa4KmzKl/OdJTgZ++UV5/6nocUJBQfEPPatr3EFtK1cqH46PGQPMnlkA/NLbsDwGIAH4c3jMoycic+bi9QXuZR6DNW2q/A1UVUKCMoBrZaWco3fpotzU0ZSzbB8mhFJPp04p3//vf8o5TmUdPgzc3PlXBPmcQJOh7wLefYGkvSiImoe4eGDNocXoNuq5Ystk1JXaqqzKjE2aNOu8vDxERUVh3rx5hjaNRoPevXsjIiKixJ+JiIjAnDlzjNr69u2Lbdu2lbj9/fv3cb9wQVcoLw6g7PyCP4/QNBoNNBoNZFmGXOQssLBdr9ej6Ph2ae1arRaSJBmet2g7UPxTgtLaLSwsIIQwapckCVqttliMpbUXzanoc1U1p+ef12PbNgkvvCDQq5eAhYUWQpScU9u2QFDQg3alYy89p8WLZYSHAxcuSOjYSYu3/+4Oe3uUmFO7dhosed8Fb879Eg11J2Hp2QnhfWUApec06uUAbHlnIga1XIe4JF/8dP4FHLr8HBr72+DViXr06SPg5AQIUXP7qVdv4EhEH6RnS7C0l2ChFXDWCtTXKp10t24CgwZJkCQ9ZFkYBicetfZatlS+tNqS99/DOfn6Anv2SH9ub5yTXq+HJAGurjLc3QsMl96W9PdUUFB9f08+Pto/n9M0f08V6SPCwzU4elSLxEQBf/8Cw8mg2n2Era0WkXnv4Nqhy/D1AYY9r8fAgYCjo5JTQUH5Obm5aXDk5lR8f1Kg4WA9npS3QXNxLe7ckfDu9rdw8kZn9O8vIzRURkFB2Tl17gz4N7dB5JkeCAgQ+OijAnh4lF97hf8W3R8ajYxOnfDnz1Z9P2k0wnAFgCyXX3sjR0qIidGgZ0+BJUsk2NqWXns9e+rRpAmwdq0GsbFAYqIGt28LHDsGHDsGABq0aiXDzq54jNVVezX9/uTSOBC57abD1sUXQmMBfZHnr2xO1tYVz6lJE2DDBi0OHZKQnKxHaqqElBQgNbUFVh1+F15ewPr1euj1D55fCFHs76aknEqLXaMBJkyQMGSIFhkZMho1Kt6/VWY/6XQaNG2qtJfWRzg4KAfKY8dqsWMHcPKkgI2NMMyw1uk0sLMD9HoZSUlAYqKExEQgKUmDzEwBSQImT5YxfryARiMBqHi/9+qrGqxcOR1v7eqH6+lNUXgiY2sZjjz9K5CFBkIAy0fLKCgoKLf2nnxSj9OnNTh0tj0OnW1vOGEv3PQvf5Hh5lZQ6ntx03YtkJ3niKz0TLhpIpEDwNZFGeUtK6dfbi7Asp8c8KLOEtqzEg4c6ImzZ4UhxsaNZciyVGbtWVjo0dAlCT7OFyGdX4E8rYTjp12RnGaDgAAZH36Yj4sX9aXWWFl/T4BAYKAegYHAW28psVdmP6nVR4SGAklJEvz9NXB2Vuc9195eQr9+WvTpo7w/XbgAREQMx+e/D0FaSi5S7zgip8gN2pWaUU6qhQCuXQsxtOt0Elav1sPFpfS/p9JiHzZMuQpk/nwtsrOBTp30KPojpjiO8PLS4Ju9M9G10XacyhxryKEmzjUmTZLh4yPQvbvyWlZ37fXtqxx7F42xpJwkyfh4IThYufHm1atafPWVMgBcUAB06CAweLAMoOz9ZGMjY8IE4/acHBmJiTJSU5VJAfXqaVCvngY6nQxJepBTaccL5f09yXKBYUAaqNlzjbL2U9F49Xo9PD0FVqwAZszohQYNJAwfjnL7iGnTgIkTtXB0VJYPqkgf4e4OeHs/ek716mlQv3719nuWlg9m/xaNvWhZVmQ/DRqkTFpZulSDmBgN7t0T8PMT+OQTGe7uJR/Dhocrx0CrVmkACAQEyCgoEOXm9PzzGqxcKXDvnvLhcmpq4cxhCUII+Pk9eJ6y+ggPDyA0VGkvzLe82gNkw7ZF20sad6htx7AeHjKE0GDrVsClnozRvRfC4tZ+iJuHIOVnQNh6Q27xGr7a0w2ffSYgywItWwo8+aRAYqIGN25ISEgQhg9+WrYs/ziirJy8vR8sufgotVfSfqrO96fp04HwcC3s7QEnJ6lK+8nLCziV6YvmeScgZ12BlHsLOL0YqanAzjNjkGo5AEOHolhOhf9Xzmerfq5h6tp7eD9VhkkHbtPS0qDX6+Hx0NQ5Dw8PnC+c3vmQ5OTkErdPTk4ucftly5ZhyZIlxdpjYmJg/+c1XW5ubmjatCkuX76Mm4XXSQBo2LAhGjZsiAsXLiAjI8PQ3qRJE7i7u+PMmTO4V2RqVYsWLeDs7IyYmBijnRIUFAQrKytEFn4s+acOHTogLy8Ppwo/uoCyszt27IiMjAyj18DW1hbBwcFIS0vDpUuXDO1OTk4IDAxEYmIirl+/bmgvzOnatWuQZRkxMTGPlFODBtGYPFnJKSqqenPKyUnDSy9dwcWLdmjZMhsJCY5l5uTsfBmTXr6LiIgG6Nv3JJKSPMvMKSPjDO649sHkb59FgYULhv7FBl+9Z4e0tOPQ6/WGTyprej9NmPAgp/j4S8Vqz8KiIc6dqz21d/Nm8ZyCgoKQkpKCGzduFNtP5vj3VJmc1q93x8mTp3HhwoOzzprI6eUZBYiM1KBhwyhIEnDjhhMcHSuXU/v2DXHpUi6i9/6Etm3+hox0DTYdfAkH4oeiW7dE9O+fgD+7lHJzGj06BtHRTggJSceVKzJcXcvPqbDPqon9VF7tOTjo8cEHGtjZyQCCoNeXv5+GD1dyatWqI06evIuDB5Nw/bo1bt2yQv/+WQCamTSnaq8957bwbR2M1NTUGs/ppZeccfx4tFFO/v5B0OmscOKEcU7t27dHs2bNDO+HZeZUTu3l5iYiMrJm99Pzz1vBz6/0/VQ4o74wpxs3MnDhwgXY2sqIiqpav/fqq02Rne2EGzsyodPp4eqah6ZNbdGypRYFBQlwcroFe/v7iIwsP6fg4Gi89ZYFsrK0yM3VwsurKfLzLXHmjBLLU0/dQWSkXGrtPftsR/z48TjI0GNs+88gyxLS/1wktqyc7skCGXdtsXJlASwsLGBtbYO8vPvw88tE+/YZ6NjxDhITvcvdT+l5DpBlGenpebic0hRv7/4nrO3zMXFiFOLi9JBlGXl5eZAkqda/P1W09p55Rqm9ivR71ZVT69ZAz56Fx0bxSE6+iXv3tMjJ0cDRsQFcXDxx6dJlZGU9uHFWgwYN0KFDfVy6dAaRkVXv977/vgOysvIQF2f6/WRvH4A958dia/QwhITcMcRaE8cR9vZpCA6+hHPnqjen6nx/eu21PHTqdBZ//KFDu3aZOHlSqtJ+un07EWlp16HRKOuQCuGGevWaIj6+Fr/nVlPtubu7Q6vV4vz584acFi/WICDADxYW7jh5svycZs2yQ+vWAQAsa0VOtWk/TZ1qi7i4YERHZ+OZZ84hIUGPhITSc+re3Q3u7k2xa1c6nJ0vIjJSLjen4cPd4e9/CunpucjKssDdu1q4uDSFXu+AS5cuwMnpjmFWdE3UXkpKitG4Q23dTy4ucXB3b4QrV2yx/AMNtv7QE+PHt4W3Vz9Yy6m4m++GTX9rgZMngby8fHTpkoKxYxNhaSnw7LMPzt2vXbuF9HQLuLvnITGx9tSemn9PL7+sQ5Mm3tBqXSrURzycU0EBcDXNE/rGMrJTL0Pb0BYRaeNxJfocvjw0CfMXnIVG0wrp6cVzCgoKwp07d3D58uVqzcmU+6lxJdZ+NOlSCYmJiWjQoAGOHDmCLkVu1fn666/jwIEDOKZMUTJiZWWFjRs3YlThrWIBrFq1CkuWLEFKSkqx7Uuacevj44Nbt24ZpiPXlRH5qnxyonxao8yEkSTJLHKqyn6SZYEbN5TLUywtzSMnU++noo+XFGNdzMkc91NVctq1S4PPV17GJyNGwNYW2BXdB2uOLEX4S1q8+KIehkuIVMipoKDgzxm2SnzcT8ypunIqfI7C2MwhJ3PcT6W1S5IF+vUT0ObdwD+GDEW+bIWGEw6gvqtlmTnt3CmwZIkGFhbKsgg9e0p4+mkZzs6Vy2nTezsQYv8O0u+54e3//QvWzg2wZk0B3NyUmSGyLMPS0hKSJD3W+4k5VU9O2dka9OqlHF+NGycwffqDWXN1NSdz3E91NafCqwMsLS0NsyTrek5lxc6caian0sYdamNOsgzs3i1h1SoNbt1SruRo00Zg9GgZq1drcPmyBAsLCXPnyhg61Lz2k6lrb+HkQxjbci48AprDecBXCAsTOHdOwoABAosWySXmVDSHor+ztuRUXntp+yk7O7tuLJXg6uoKrVZbbMA1JSUFnqUsGufp6Vmp7a2trWFtbV2s3cLCotj6GIU77GGFO6Ci7aWtu1GZdkmSSmwvLcbS2oUQiImJKbYeSF3OqSr7Sastvr5nXc+pojFWtr2iOZW31kxdzKmq7eaWU/v2wKi2n+DePeDqzYb44uhizHlVC+XzMnVzkiTJ0GcVvn7cT8yprPaKxl5QUICoqKgS+6y6mlNZ7eaYU8+eEs4fVq7wuHPPG0H1LcuN/bnngBYtlA9uH6ydr/nzq2Kxa7Va3HUYgI3H8nEi8Slo7b2wejXg5aXEWHi348Laetz3E3N69JycnAAHBxnJyTnw9LQt9rvrYk7ltTOnmsuprPfDQnUtp6LMZT8VVRdyKm3cobbmNGSIcvO/TZuATZsknDgh4cQJJU43N+WmtK1bl3y8UFtzqkiMpq49S2dllqkmKxa7dgLnz2tgbw/MmiXBwkJTYk5VHXeoC/upoqrhlkdVZ2Vlhfbt22Pfvn2GNlmWsW/fPqMZuEV16dLFaHsA2Lt3b6nbExFR5Xh5AQeT/opdZ8Ow5H+bMH+hFYpc5EBEZBK9ewOBHlEAABeHu6jI8mAajbJ+aUk3PK0Mn0ZW2HdxBIS1V4k3mCGqbk2aKP/6+Jjs4kgiIrNmZ6fcgHDrVuVmdADQti3w1Vcl34idHp2jZwNk5zkiI8cRX6xRliV46SXlppRUOpPOuAWAOXPmICwsDB06dECnTp3w0UcfITs7GxMnTgQAjB8/Hg0aNMCyZcsAADNnzkT37t2xYsUKDBw4EP/3f/+HyMhIfP7556ZMg4jIbEgS0KHXE/jmmyewcKmyziERkam1awekNzoKAEgXLWv0d/fvD9y/Dzz1FNCwYY3+anpMvfmmjK1bb6Bjx6amDoWIyKx5egLvvAO89hrg6IgKfTBMVePja4Gla9bBUpuPpDt28PMDRo40dVS1n8kHbkeOHImbN29i4cKFSE5ORps2bbBnzx7DDcgSEhKMpjd37doV33zzDebPn4+33noLAQEB2LZtG1q1amWqFIiIzM6kScCLL/LAhYhqD60WOGv7CY6f3gLPkBHoVoO/28YGeOGFGvyF9Nhr3Bjo3v0OSrjKk4iIVODkZOoIzJ+vL5CY+eCmXHPnApaWJgyojjDpzclMITMzs8ILAJuDwkWQCxdWJqourC1SA+uK1MLaMg85OcDu3UDfvo++/EF1YW2RGlhXpBbWFqmBdUUVkZgIDB6s/L9HD+DDD8v/GXOtrcqMTfIz3MdAXl6eqUMgM8XaIjWwrkgtrK26z84OGD689gzaFmJtkRpYV6QW1hapgXVF5fH0BNzdleO5OXMq/nOPe21x4NbM6fV6nDp1Cnq93tShkJlhbZEaWFekFtYWqYW1RWpgXZFaWFukBtYVVYRGA2zeDHz3XcVv9MraqgVr3BIREREREREREZF5q1/f1BHUPZxxS0RERERERERERFTLcOD2MaDVak0dApkp1hapgXVFamFtkVpYW6QG1hWphbVFamBdkVoe99qShBDC1EHUpMrcuY2IiIiIiIiIiIioulRmbJIzbs2cEALp6el4zMbnqQawtkgNrCtSC2uL1MLaIjWwrkgtrC1SA+uK1MLa4sCt2dPr9Th//vxjfQc+Ugdri9TAuiK1sLZILawtUgPritTC2iI1sK5ILawtDtwSERERERERERER1TocuCUiIiIiIiIiIiKqZThwa+YkSYKtrS0kSTJ1KGRmWFukBtYVqYW1RWphbZEaWFekFtYWqYF1RWphbQGSeMxW+K3MnduIiIiIiIiIiIiIqktlxiY549bMybKM1NRUyLJs6lDIzLC2SA2sK1ILa4vUwtoiNbCuSC2sLVID64rUwtriwK3Zk2UZly5deqyLnNTB2iI1sK5ILawtUgtri9TAuiK1sLZIDawrUgtriwO3RERERERERERERLUOB26JiIiIiIiIiIiIahkO3Jo5SZLg5OT0WN+Bj9TB2iI1sK5ILawtUgtri9TAuiK1sLZIDawrUgtrC5CEEMLUQdSkyty5jYiIiIiIiIiIiKi6VGZskjNuzZwsy7h+/fpjvZAzqYO1RWpgXZFaWFukFtYWqYF1RWphbZEaWFekFtYWB27NHouc1MLaIjWwrkgtrC1SC2uL1MC6IrWwtkgNrCtSC2uLA7dEREREREREREREtQ4HbomIiIiIiIiIiIhqGQ7cmjmNRgM3NzdoNNzVVL1YW6QG1hWphbVFamFtkRpYV6QW1hapgXVFamFtAZIQQpg6iJpUmTu3EREREREREREREVWXyoxNPr5D1o8JWZYRHx//WC/kTOpgbZEaWFekFtYWqYW1RWpgXZFaWFukBtYVqYW1xYFbsyfLMm7evPlYFzmpg7VFamBdkVpYW6QW1hapgXVFamFtkRpYV6QW1hYHbomIiIiIiIiIiIhqHQtTB1DTCpf0zczMNHEkNaOgoADZ2dnIzMyEhcVjt7tJRawtUgPritTC2iK1sLZIDawrUgtri9TAuiK1mGttFY5JVuS2Y+aTdQXdvXsXAODj42PiSIiIiIiIiIiIiOhxdPfuXTg5OZW5jSQqMrxrRmRZRmJiIhwcHCBJkqnDUV1mZiZ8fHxw7dq1cu9UR1QZrC1SA+uK1MLaIrWwtkgNrCtSC2uL1MC6IrWYa20JIXD37l14e3tDoyl7FdvHbsatRqNBw4YNTR1GjXN0dDSrIqfag7VFamBdkVpYW6QW1hapgXVFamFtkRpYV6QWc6yt8mbaFuLNyYiIiIiIiIiIiIhqGQ7cEhEREREREREREdUyHLg1c9bW1li0aBGsra1NHQqZGdYWqYF1RWphbZFaWFukBtYVqYW1RWpgXZFaWFuP4c3JiIiIiIiIiIiIiGo7zrglIiIiIiIiIiIiqmU4cEtERERERERERERUy3DgloiIiIiIiIiIiKiW4cCtmfvss8/g5+cHGxsbhISE4Pfffzd1SFSHLFu2DB07doSDgwPc3d0xdOhQxMbGGm3To0cPSJJk9DV58mQTRUx1xeLFi4vVTYsWLQyP5+bmYtq0aahfvz50Oh2GDx+OlJQUE0ZMdYGfn1+xupIkCdOmTQPA/ooq7rfffsOgQYPg7e0NSZKwbds2o8eFEFi4cCG8vLxga2uL3r174+LFi0bb3L59G2PGjIGjoyOcnZ0xadIkZGVl1WAWVBuVVVv5+fl444030Lp1a9jb28Pb2xvjx49HYmKi0XOU1Ne9//77NZwJ1Sbl9VkTJkwoVjP9+vUz2oZ9FpWkvNoq6bhLkiR88MEHhm3YZ9HDKjLOUJHzwYSEBAwcOBB2dnZwd3fHa6+9hoKCgppMpUZw4NaMbdmyBXPmzMGiRYsQHR2N4OBg9O3bF6mpqaYOjeqIAwcOYNq0aTh69Cj27t2L/Px89OnTB9nZ2UbbhYeHIykpyfC1fPlyE0VMdckTTzxhVDeHDh0yPDZ79mzs2LED3333HQ4cOIDExEQMGzbMhNFSXXD8+HGjmtq7dy8AYMSIEYZt2F9RRWRnZyM4OBifffZZiY8vX74cn3zyCdasWYNjx47B3t4effv2RW5urmGbMWPG4I8//sDevXuxc+dO/Pbbb3jppZdqKgWqpcqqrZycHERHR2PBggWIjo7G1q1bERsbi8GDBxfb9u233zbqy2bMmFET4VMtVV6fBQD9+vUzqplvv/3W6HH2WVSS8mqraE0lJSXhyy+/hCRJGD58uNF27LOoqIqMM5R3PqjX6zFw4EDk5eXhyJEj2LhxIzZs2ICFCxeaIiV1CTJbnTp1EtOmTTN8r9frhbe3t1i2bJkJo6K6LDU1VQAQBw4cMLR1795dzJw503RBUZ20aNEiERwcXOJj6enpwtLSUnz33XeGtnPnzgkAIiIiooYiJHMwc+ZM0bRpUyHLshCC/RVVDQDxww8/GL6XZVl4enqKDz74wNCWnp4urK2txbfffiuEEOLs2bMCgDh+/Lhhm927dwtJksSNGzdqLHaq3R6urZL8/vvvAoC4evWqoa1Ro0Zi5cqV6gZHdVZJdRUWFiaGDBlS6s+wz6KKqEifNWTIENGzZ0+jNvZZVJ6Hxxkqcj743//+V2g0GpGcnGzYZvXq1cLR0VHcv3+/ZhNQGWfcmqm8vDxERUWhd+/ehjaNRoPevXsjIiLChJFRXZaRkQEAcHFxMWr/+uuv4erqilatWmHevHnIyckxRXhUx1y8eBHe3t5o0qQJxowZg4SEBABAVFQU8vPzjfqvFi1awNfXl/0XVVheXh6++uorvPjii5AkydDO/ooe1eXLl5GcnGzURzk5OSEkJMTQR0VERMDZ2RkdOnQwbNO7d29oNBocO3asxmOmuisjIwOSJMHZ2dmo/f3330f9+vXRtm1bfPDBB2Z5aShVr/3798Pd3R3NmzfHlClTcOvWLcNj7LOoOqSkpGDXrl2YNGlSscfYZ1FZHh5nqMj5YEREBFq3bg0PDw/DNn379kVmZib++OOPGoxefRamDoDUkZaWBr1eb1TEAODh4YHz58+bKCqqy2RZxqxZs/Dkk0+iVatWhvbRo0ejUaNG8Pb2xqlTp/DGG28gNjYWW7duNWG0VNuFhIRgw4YNaN68OZKSkrBkyRI8/fTTOHPmDJKTk2FlZVXsJNXDwwPJycmmCZjqnG3btiE9PR0TJkwwtLG/oupQ2A+VdIxV+FhycjLc3d2NHrewsICLiwv7Maqw3NxcvPHGGxg1ahQcHR0N7a+88gratWsHFxcXHDlyBPPmzUNSUhL+8Y9/mDBaqs369euHYcOGoXHjxoiPj8dbb72F/v37IyIiAlqtln0WVYuNGzfCwcGh2PJm7LOoLCWNM1TkfDA5ObnEY7HCx8wJB26JqEKmTZuGM2fOGK1DCsBo7avWrVvDy8sLvXr1Qnx8PJo2bVrTYVId0b9/f8P/g4KCEBISgkaNGuHf//43bG1tTRgZmYt169ahf//+8Pb2NrSxvyKiuiI/Px+hoaEQQmD16tVGj82ZM8fw/6CgIFhZWeHll1/GsmXLYG1tXdOhUh3wwgsvGP7funVrBAUFoWnTpti/fz969eplwsjInHz55ZcYM2YMbGxsjNrZZ1FZShtnoAe4VIKZcnV1hVarLXbXvZSUFHh6epooKqqrpk+fjp07d+LXX39Fw4YNy9w2JCQEABAXF1cToZGZcHZ2RrNmzRAXFwdPT0/k5eUhPT3daBv2X1RRV69exc8//4y//vWvZW7H/oqqorAfKusYy9PTs9jNYAsKCnD79m32Y1SuwkHbq1evYu/evUazbUsSEhKCgoICXLlypWYCpDqvSZMmcHV1Nbz/sc+iR3Xw4EHExsaWe+wFsM+iB0obZ6jI+aCnp2eJx2KFj5kTDtyaKSsrK7Rv3x779u0ztMmyjH379qFLly4mjIzqEiEEpk+fjh9++AG//PILGjduXO7PnDhxAgDg5eWlcnRkTrKyshAfHw8vLy+0b98elpaWRv1XbGwsEhIS2H9Rhaxfvx7u7u4YOHBgmduxv6KqaNy4MTw9PY36qMzMTBw7dszQR3Xp0gXp6emIiooybPPLL79AlmXDBwZEJSkctL148SJ+/vln1K9fv9yfOXHiBDQaTbFL3YlKc/36ddy6dcvw/sc+ix7VunXr0L59ewQHB5e7LfssKm+coSLng126dMHp06eNPnQq/LCzZcuWNZNIDeFSCWZszpw5CAsLQ4cOHdCpUyd89NFHyM7OxsSJE00dGtUR06ZNwzfffIPt27fDwcHBsFaMk5MTbG1tER8fj2+++QYDBgxA/fr1cerUKcyePRvdunVDUFCQiaOn2mzu3LkYNGgQGjVqhMTERCxatAharRajRo2Ck5MTJk2ahDlz5sDFxQWOjo6YMWMGunTpgs6dO5s6dKrlZFnG+vXrERYWBguLB4c57K+oMrKysoxmYl++fBknTpyAi4sLfH19MWvWLLzzzjsICAhA48aNsWDBAnh7e2Po0KEAgMDAQPTr1w/h4eFYs2YN8vPzMX36dLzwwgtGy3fQ46es2vLy8sLzzz+P6Oho7Ny5E3q93nDs5eLiAisrK0RERODYsWN45pln4ODggIiICMyePRtjx45FvXr1TJUWmVhZdeXi4oIlS5Zg+PDh8PT0RHx8PF5//XX4+/ujb9++ANhnUenKez8ElA8vv/vuO6xYsaLYz7PPopKUN85QkfPBPn36oGXLlhg3bhyWL1+O5ORkzJ8/H9OmTTO/JTgEmbVPP/1U+Pr6CisrK9GpUydx9OhRU4dEdQiAEr/Wr18vhBAiISFBdOvWTbi4uAhra2vh7+8vXnvtNZGRkWHawKnWGzlypPDy8hJWVlaiQYMGYuTIkSIuLs7w+L1798TUqVNFvXr1hJ2dnfjLX/4ikpKSTBgx1RU//fSTACBiY2ON2tlfUWX8+uuvJb7/hYWFCSGEkGVZLFiwQHh4eAhra2vRq1evYjV369YtMWrUKKHT6YSjo6OYOHGiuHv3rgmyodqkrNq6fPlyqcdev/76qxBCiKioKBESEiKcnJyEjY2NCAwMFO+9957Izc01bWJkUmXVVU5OjujTp49wc3MTlpaWolGjRiI8PFwkJycbPQf7LCpJee+HQgixdu1aYWtrK9LT04v9PPssKkl54wxCVOx88MqVK6J///7C1tZWuLq6ildffVXk5+fXcDbqk4QQQsVxYSIiIiIiIiIiIiKqJK5xS0RERERERERERFTLcOCWiIiIiIiIiIiIqJbhwC0RERERERERERFRLcOBWyIiIiIiIiIiIqJahgO3RERERERERERERLUMB26JiIiIiIiIiIiIahkO3BIRERERERERERHVMhy4JSIiIiIiIiIiIqplOHBLRERE9BiaMGEChg4dauowqBrUln155coVSJKEEydOmDoUIiIiIrPAgVsiIiIiMyNJUplfixcvxscff4wNGzaYJL4vvvgCwcHB0Ol0cHZ2Rtu2bbFs2TLD46YYiDxw4AB8fHxKfby8mOu6Hj16YNasWaYOg4iIiIiKsDB1AERERERUvZKSkgz/37JlCxYuXIjY2FhDm06ng06nM0Vo+PLLLzFr1ix88skn6N69O+7fv49Tp07hzJkzJomn0Pbt2zFo0KASH6utMRMRERGReeOMWyIiIiIz4+npafhycnKCJElGbTqdrtis1h49emDGjBmYNWsW6tWrBw8PD3zxxRfIzs7GxIkT4eDgAH9/f+zevdvod505cwb9+/eHTqeDh4cHxo0bh7S0tFJj+/HHHxEaGopJkybB398fTzzxBEaNGoV3330XALB48WJs3LgR27dvN8wQ3r9/PwDg2rVrCA0NhbOzM1xcXDBkyBBcuXLF8NyFOS1ZsgRubm5wdHTE5MmTkZeXV+5r9uOPP2Lw4MFVihkAjh8/jmeffRaurq5wcnJC9+7dER0dbfQ8kiRh7dq1eO6552BnZ4fAwEBEREQgLi4OPXr0gL29Pbp27Yr4+HjDzyxevBht2rTB2rVr4ePjAzs7O4SGhiIjI6PUXGRZxrJly9C4cWPY2toiODgY33//fbmvQVF+fn5477338OKLL8LBwQG+vr74/PPPjbb5/fff0bZtW9jY2KBDhw6IiYkp9jxl1cf+/fthZWWFgwcPGrZfvnw53N3dkZKSUql4iYiIiMwRB26JiIiICACwceNGuLq64vfff8eMGTMwZcoUjBgxAl27dkV0dDT69OmDcePGIScnBwCQnp6Onj17om3btoiMjMSePXuQkpKC0NDQUn+Hp6cnjh49iqtXr5b4+Ny5cxEaGop+/fohKSkJSUlJ6Nq1K/Lz89G3b184ODjg4MGDOHz4MHQ6Hfr162c0MLtv3z6cO3cO+/fvx7fffoutW7diyZIlZeb9xx9/IDU1FT179qxSzABw9+5dhIWF4dChQzh69CgCAgIwYMAA3L1712i7pUuXYvz48Thx4gRatGiB0aNH4+WXX8a8efMQGRkJIQSmT59u9DNxcXH497//jR07dmDPnj2IiYnB1KlTS41l2bJl2LRpE9asWYM//vgDs2fPxtixY3HgwIEyX4eHrVixwjAgO3XqVEyZMsUwczsrKwvPPfccWrZsiaioKCxevBhz5841+vny6qNweYZx48YhIyMDMTExWLBgAf71r3/Bw8OjUrESERERmSVBRERERGZr/fr1wsnJqVh7WFiYGDJkiOH77t27i6eeesrwfUFBgbC3txfjxo0ztCUlJQkAIiIiQgghxNKlS0WfPn2MnvfatWsCgIiNjS0xnsTERNG5c2cBQDRr1kyEhYWJLVu2CL1eX2psQgixefNm0bx5cyHLsqHt/v37wtbWVvz000+Gn3NxcRHZ2dmGbVavXi10Op3R8z/s3XffFc8//3ypj1ck5ofp9Xrh4OAgduzYYWgDIObPn2/4PiIiQgAQ69atM7R9++23wsbGxvD9okWLhFarFdevXze07d69W2g0GpGUlGTIu/D1ys3NFXZ2duLIkSNG8UyaNEmMGjWq1Hi7d+8uZs6cafi+UaNGYuzYsYbvZVkW7u7uYvXq1UIIIdauXSvq168v7t27Z9hm9erVAoCIiYkRQlSsPu7fvy/atGkjQkNDRcuWLUV4eHipMRIRERE9bjjjloiIiIgAAEFBQYb/a7Va1K9fH61btza0Fc6CTE1NBQCcPHkSv/76q2HNXJ1OhxYtWgCA0eX+RXl5eSEiIgKnT5/GzJkzUVBQgLCwMPTr1w+yLJca28mTJxEXFwcHBwfD73JxcUFubq7R7woODoadnZ3h+y5duiArKwvXrl0r9bm3b99e6jIJFY05JSUF4eHhCAgIgJOTExwdHZGVlYWEhASj5yr6Ghe+ng+/xrm5ucjMzDS0+fr6okGDBkY5ybJstG5xobi4OOTk5ODZZ5812i+bNm0qdZ+UpmishcttFO77c+fOISgoCDY2NkZxFVWR+rCyssLXX3+N//znP8jNzcXKlSsrFSMRERGROePNyYiIiIgIAGBpaWn0vSRJRm2SJAGAYbAyKysLgwYNwt///vdiz+Xl5VXm72rVqhVatWqFqVOnYvLkyXj66adx4MABPPPMMyVun5WVhfbt2+Prr78u9pibm1vZiZUhKSkJMTExGDhwYLnblhVzWFgYbt26hY8//hiNGjWCtbU1unTpUmx93ZJez7Je48rKysoCAOzatctosBcArK2tK/VcJdVDZeKqaH0cOXIEAHD79m3cvn0b9vb2lYqTiIiIyFxx4JaIiIiIqqRdu3b4z3/+Az8/P1hYVP2wsmXLlgCA7OxsAMosTL1eX+x3bdmyBe7u7nB0dCz1uU6ePIl79+7B1tYWAHD06FHodDr4+PiUuP2OHTvQtWtXuLi4PFLMhw8fxqpVqzBgwAAAyo3UyrpJW2UkJCQgMTER3t7eAJScNBoNmjdvXmJc1tbWSEhIQPfu3avl95ckMDAQmzdvRm5urmHW7dGjR422qUh9xMfHY/bs2fjiiy+wZcsWhIWF4eeff4ZGwwsDiYiIiHhERERERERVMm3aNNy+fRujRo3C8ePHER8fj59++gkTJ04sNvBaaMqUKVi6dCkOHz6Mq1ev4ujRoxg/fjzc3NwMl9r7+fnh1KlTiI2NRVpaGvLz8zFmzBi4urpiyJAhOHjwIC5fvoz9+/fjlVdewfXr1w3Pn5eXh0mTJuHs2bP473//i0WLFmH69OmlDgT++OOPZS6TUNGYAwICsHnzZpw7dw7Hjh3DmDFjDIPHj8rGxgZhYWE4efIkDh48iFdeeQWhoaHw9PQstq2DgwPmzp2L2bNnY+PGjYiPj0d0dDQ+/fRTbNy4sVriAYDRo0dDkiSEh4cbXusPP/zQaJvy6kOv12Ps2LHo27cvJk6ciPXr1+PUqVNYsWJFtcVJREREVJdx4JaIiIiIqsTb2xuHDx+GXq9Hnz590Lp1a8yaNQvOzs6lDpT27t0bR48exYgRI9CsWTMMHz4cNjY22LdvH+rXrw8ACA8PR/PmzdGhQwe4ubnh8OHDsLOzw2+//QZfX18MGzYMgYGBmDRpEnJzc41m4Pbq1QsBAQHo1q0bRo4cicGDB2Px4sUlxpKdnY19+/aVO3BbkZjXrVuHO3fuoF27dhg3bhxeeeUVuLu7V+FVLc7f3x/Dhg3DgAED0KdPHwQFBWHVqlWlbr906VIsWLAAy5YtQ2BgIPr164ddu3ahcePG1RIPAOh0OuzYsQOnT59G27Zt8be//a3Ykgjl1ce7776Lq1evYu3atQCU5RM+//xzzJ8/HydPnqy2WImIiIjqKkkIIUwdBBERERHRo5owYQLS09Oxbdu2Cm2/detWzJ8/H2fPnlU3sEewePFibNu2DSdOnDB1KERERERUwzjjloiIiIgeSzqdrsQbZxERERER1Qa8ORkRERERPZb69Olj6hCIiIiIiErFpRKIiIiIiIiIiIiIahkulUBERERERERERERUy3DgloiIiIiIiIiIiKiW4cAtERERERERERERUS3DgVsiIiIiIiIiIiKiWoYDt0RERERERERERES1DAduiYiIiIiIiIiIiGoZDtwSERERERERERER1TIcuCUiIiIiIiIiIiKqZThwS0RERERERERERFTL/D+IRN4darRBMwAAAABJRU5ErkJggg==\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "- Observation: The orange dashed line (predicted values) very closely tracks the blue line (actual values). The model captures the peaks, troughs, and general trends remarkably well within this 200-sample window.\n", "- Interpretation: This is excellent performance for a time series model. It demonstrates that your model is highly effective at capturing the temporal dynamics of your total_emission data. The ability to follow sudden spikes and dips so accurately suggests that the LSTM architecture, combined with your data preprocessing, is working effectively to learn the sequence dependencies." ], "metadata": { "id": "L2uQ6u5ApOdF" } }, { "cell_type": "markdown", "source": [ "Result: The model is very accurate" ], "metadata": { "id": "8pq5HCo3oZxg" } }, { "cell_type": "code", "source": [ "save_path = '/content/drive/MyDrive/AuraClima/Agri_Slider_Model.keras'\n", "model.save(save_path)\n", "print(f\"Model successfully saved to: {save_path}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bhyvokhYoS5i", "outputId": "f3f0a8e3-d477-488c-9aa1-e39d4cef1929" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model successfully saved to: /content/drive/MyDrive/AuraClima/Agri_Slider_Model.keras\n" ] } ] }, { "cell_type": "markdown", "source": [ "____________________________________________________________________________________________________________________________________________" ], "metadata": { "id": "d9mjldgGjF7j" } }, { "cell_type": "markdown", "source": [ "### CO2 Model" ], "metadata": { "id": "bo8JKPk1E1B7" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "id": "0ibbVJCmpsRk", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "69d29998-2546-46a0-a90c-4d9691322f76" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "file_path = '/content/drive/My Drive/AuraClima/CO2_Emissions_MinMaxScaled.csv'\n", "\n", "# Load the CSV\n", "df = pd.read_csv(file_path)" ], "metadata": { "id": "w1-npB5rjXas" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 359 }, "id": "2-9ch-vXlqXB", "outputId": "bf77cf73-016c-432f-eede-d23879a7a42d" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Country Name 1960 1961 1962 1963 \\\n", "0 Afghanistan 0.010176 0.012215 0.016912 0.015899 \n", "1 Africa Eastern and Southern 0.240022 0.244291 0.238947 0.228401 \n", "2 Africa Western and Central 0.022155 0.023351 0.022843 0.025272 \n", "3 Albania 0.334134 0.364941 0.370842 0.287538 \n", "4 Algeria 0.146764 0.140804 0.124205 0.109448 \n", "\n", " 1964 1965 1966 1967 1968 ... 2009 2010 \\\n", "0 0.017065 0.018785 0.018251 0.019424 0.020448 ... 0.013644 0.020950 \n", "1 0.225235 0.217482 0.197850 0.181053 0.166294 ... 0.065989 0.073970 \n", "2 0.027837 0.036327 0.034975 0.030891 0.024810 ... 0.027557 0.033345 \n", "3 0.251709 0.242438 0.255838 0.235268 0.232796 ... 0.095285 0.110880 \n", "4 0.103134 0.107911 0.124088 0.108606 0.103309 ... 0.206084 0.221777 \n", "\n", " 2011 2012 2013 2014 2015 2016 2017 \\\n", "0 0.027829 0.020922 0.015206 0.014384 0.015044 0.013834 0.013368 \n", "1 0.068729 0.068761 0.068117 0.074097 0.073525 0.071541 0.071064 \n", "2 0.033979 0.031762 0.031803 0.034629 0.034292 0.033831 0.034193 \n", "3 0.118600 0.107618 0.111011 0.130743 0.133352 0.127806 0.146469 \n", "4 0.220302 0.233679 0.231670 0.260912 0.281584 0.265675 0.265183 \n", "\n", " 2018 \n", "0 0.013643 \n", "1 0.071154 \n", "2 0.038376 \n", "3 0.150057 \n", "4 0.279596 \n", "\n", "[5 rows x 60 columns]" ], "text/html": [ "\n", "
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Country Name196019611962196319641965196619671968...2009201020112012201320142015201620172018
0Afghanistan0.0101760.0122150.0169120.0158990.0170650.0187850.0182510.0194240.020448...0.0136440.0209500.0278290.0209220.0152060.0143840.0150440.0138340.0133680.013643
1Africa Eastern and Southern0.2400220.2442910.2389470.2284010.2252350.2174820.1978500.1810530.166294...0.0659890.0739700.0687290.0687610.0681170.0740970.0735250.0715410.0710640.071154
2Africa Western and Central0.0221550.0233510.0228430.0252720.0278370.0363270.0349750.0308910.024810...0.0275570.0333450.0339790.0317620.0318030.0346290.0342920.0338310.0341930.038376
3Albania0.3341340.3649410.3708420.2875380.2517090.2424380.2558380.2352680.232796...0.0952850.1108800.1186000.1076180.1110110.1307430.1333520.1278060.1464690.150057
4Algeria0.1467640.1408040.1242050.1094480.1031340.1079110.1240880.1086060.103309...0.2060840.2217770.2203020.2336790.2316700.2609120.2815840.2656750.2651830.279596
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df" } }, "metadata": {}, "execution_count": 63 } ] }, { "cell_type": "code", "source": [ "df.columns" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GlRp2USgmDpx", "outputId": "09a1cb66-fbd8-409a-de3f-2ddeb437dc84" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['Country Name', '1960', '1961', '1962', '1963', '1964', '1965', '1966',\n", " '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n", " '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984',\n", " '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993',\n", " '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002',\n", " '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011',\n", " '2012', '2013', '2014', '2015', '2016', '2017', '2018'],\n", " dtype='object')" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, Dense\n", "from sklearn.model_selection import train_test_split" ], "metadata": { "id": "sii2ffL0mKmd" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# One-hot encode country names\n", "country_dummies = pd.get_dummies(df['Country Name'], prefix='Country')\n", "country_features = country_dummies.columns.tolist()\n", "df_ohe = pd.concat([df, country_dummies], axis=1)" ], "metadata": { "id": "5aSSsXogrSMT" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "window_size = 45 # number of past years as input\n", "forecast_horizon = 10 # number of future years to predict\n", "year_cols = [str(year) for year in range(1960, 2019)]" ], "metadata": { "id": "k8HMD8CjmyZU" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Final sequence storage\n", "X_seq_list = []\n", "y_list = []" ], "metadata": { "id": "gAAEQMHarVR4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "for idx in range(len(df_ohe)):\n", " row = df_ohe.iloc[idx]\n", " series = row[year_cols].values.astype(float)\n", " country_vec = row[country_features].values.astype(float)\n", "\n", " n = len(series)\n", " for start in range(0, n - window_size - forecast_horizon + 1):\n", " end = start + window_size\n", " horizon_end = end + forecast_horizon\n", "\n", " seq_x_vals = series[start:end]\n", " seq_y = series[end:horizon_end]\n", "\n", " if np.isnan(seq_x_vals).any() or np.isnan(seq_y).any():\n", " continue\n", "\n", " # Create (window_size, 1 + n_country) matrix\n", " co2_col = seq_x_vals.reshape(window_size, 1)\n", " country_mat = np.tile(country_vec, (window_size, 1)) # Repeat for each timestep\n", " seq_x = np.concatenate([co2_col, country_mat], axis=1) # (window_size, features)\n", "\n", " X_seq_list.append(seq_x)\n", " y_list.append(seq_y)" ], "metadata": { "id": "ZSXZUc4CrVoA" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Convert to arrays\n", "X_seq = np.array(X_seq_list) # (samples, timesteps, features)\n", "y = np.array(y_list) # (samples, forecast_horizon)\n", "\n", "print(\"X_seq shape:\", X_seq.shape)\n", "print(\"y shape:\", y.shape)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "78adxSnXraI-", "outputId": "6a4aaa55-3013-473a-b136-39a7ad049075" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "X_seq shape: (1330, 45, 267)\n", "y shape: (1330, 10)\n" ] } ] }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X_seq, y, test_size=0.2, random_state=42\n", ")" ], "metadata": { "id": "zKNP7PlprcMm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import LSTM, Dense\n", "\n", "model = Sequential([\n", " LSTM(128, input_shape=(X_seq.shape[1], X_seq.shape[2])),\n", " Dense(y.shape[1])\n", "])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ACOq2KH3rfSU", "outputId": "2bb0afd2-3334-41ab-8f90-7f193b00fd9f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(**kwargs)\n" ] } ] }, { "cell_type": "code", "source": [ "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n", "model.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 193 }, "id": "gwvkuy6tr4Ou", "outputId": "b0dea532-c92d-4c95-a9ef-05d6fa49fc92" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"sequential\"\u001b[0m\n" ], "text/html": [ "
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val_loss: 0.0024 - val_mae: 0.0224\n", "Epoch 4/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 128ms/step - loss: 0.0022 - mae: 0.0210 - val_loss: 0.0024 - val_mae: 0.0217\n", "Epoch 5/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 79ms/step - loss: 0.0025 - mae: 0.0202 - val_loss: 0.0026 - val_mae: 0.0233\n", "Epoch 6/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 81ms/step - loss: 0.0019 - mae: 0.0197 - val_loss: 0.0024 - val_mae: 0.0216\n", "Epoch 7/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 98ms/step - loss: 0.0024 - mae: 0.0203 - val_loss: 0.0023 - val_mae: 0.0222\n", "Epoch 8/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 81ms/step - loss: 0.0022 - mae: 0.0196 - val_loss: 0.0024 - val_mae: 0.0220\n", "Epoch 9/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 81ms/step - loss: 0.0026 - mae: 0.0232 - val_loss: 0.0024 - val_mae: 0.0226\n", "Epoch 10/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 89ms/step - loss: 0.0019 - mae: 0.0198 - val_loss: 0.0023 - val_mae: 0.0209\n", "Epoch 11/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 83ms/step - loss: 0.0019 - mae: 0.0183 - val_loss: 0.0025 - val_mae: 0.0230\n", "Epoch 12/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 93ms/step - loss: 0.0027 - mae: 0.0214 - val_loss: 0.0023 - val_mae: 0.0211\n", "Epoch 13/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 83ms/step - loss: 0.0021 - mae: 0.0194 - val_loss: 0.0023 - val_mae: 0.0209\n", "Epoch 14/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 81ms/step - loss: 0.0021 - mae: 0.0194 - val_loss: 0.0024 - val_mae: 0.0210\n", "Epoch 15/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 134ms/step - loss: 0.0021 - mae: 0.0189 - val_loss: 0.0024 - val_mae: 0.0208\n", "Epoch 16/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 81ms/step - loss: 0.0025 - mae: 0.0199 - val_loss: 0.0024 - val_mae: 0.0216\n", "Epoch 17/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 80ms/step - loss: 0.0028 - mae: 0.0211 - val_loss: 0.0024 - val_mae: 0.0215\n", "Epoch 18/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 111ms/step - loss: 0.0026 - mae: 0.0198 - val_loss: 0.0024 - val_mae: 0.0215\n", "Epoch 19/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 105ms/step - loss: 0.0017 - mae: 0.0178 - val_loss: 0.0024 - val_mae: 0.0220\n", "Epoch 20/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 82ms/step - loss: 0.0026 - mae: 0.0214 - val_loss: 0.0025 - val_mae: 0.0230\n", "Epoch 21/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 81ms/step - loss: 0.0022 - mae: 0.0206 - val_loss: 0.0023 - val_mae: 0.0210\n", "Epoch 22/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 130ms/step - loss: 0.0022 - mae: 0.0190 - val_loss: 0.0024 - val_mae: 0.0210\n", "Epoch 23/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 87ms/step - loss: 0.0016 - mae: 0.0178 - 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val_loss: 0.0023 - val_mae: 0.0207\n", "Epoch 39/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 83ms/step - loss: 0.0020 - mae: 0.0182 - val_loss: 0.0024 - val_mae: 0.0210\n", "Epoch 40/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 82ms/step - loss: 0.0016 - mae: 0.0173 - val_loss: 0.0023 - val_mae: 0.0207\n", "Epoch 41/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 142ms/step - loss: 0.0022 - mae: 0.0187 - val_loss: 0.0024 - val_mae: 0.0206\n", "Epoch 42/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 81ms/step - loss: 0.0019 - mae: 0.0178 - val_loss: 0.0023 - val_mae: 0.0204\n", "Epoch 43/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 82ms/step - loss: 0.0021 - mae: 0.0180 - val_loss: 0.0023 - val_mae: 0.0204\n", "Epoch 44/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 121ms/step - loss: 0.0020 - mae: 0.0181 - val_loss: 0.0024 - val_mae: 0.0202\n", "Epoch 45/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 81ms/step - loss: 0.0018 - mae: 0.0178 - val_loss: 0.0023 - val_mae: 0.0210\n", "Epoch 46/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 82ms/step - loss: 0.0021 - mae: 0.0195 - val_loss: 0.0024 - val_mae: 0.0206\n", "Epoch 47/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 86ms/step - loss: 0.0019 - mae: 0.0186 - val_loss: 0.0025 - val_mae: 0.0230\n", "Epoch 48/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 131ms/step - loss: 0.0022 - mae: 0.0197 - val_loss: 0.0024 - val_mae: 0.0221\n", "Epoch 49/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 82ms/step - loss: 0.0016 - mae: 0.0176 - val_loss: 0.0024 - val_mae: 0.0219\n", "Epoch 50/50\n", "\u001b[1m34/34\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 80ms/step - loss: 0.0025 - mae: 0.0204 - val_loss: 0.0023 - val_mae: 0.0209\n" ] } ] }, { "cell_type": "code", "source": [ "final_val_loss = history.history['val_loss'][-1]\n", "final_val_mae = history.history['val_mae'][-1]\n", "print(f\"Final Validation Loss (MSE): {final_val_loss:.4f}\")\n", "print(f\"Final Validation MAE: {final_val_mae:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e7wt-q1rr8TY", "outputId": "800cb563-2801-4b22-d988-aa263ea96a54" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Final Validation Loss (MSE): 0.0023\n", "Final Validation MAE: 0.0209\n" ] } ] }, { "cell_type": "markdown", "source": [ "- MAE of 0.0209: That’s ~2.1% absolute error on the normalized scale.\n", "- Validation MSE = 0.0023: That's a very low mean squared error, especially for normalized values (0–1 range)." ], "metadata": { "id": "bIrffBUvv3ui" } }, { "cell_type": "code", "source": [ "model.save(\"co2_lstm_forecast_model\")\n", "# Plot Loss\n", "plt.figure(figsize=(12, 5))\n", "\n", "plt.subplot(1, 2, 1)\n", "plt.plot(history.history['loss'], label='Train Loss', color='blue')\n", "plt.plot(history.history['val_loss'], label='Val Loss', color='orange')\n", "plt.title('Loss over Epochs')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('MSE Loss')\n", "plt.legend()\n", "plt.grid(True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "s51M_gdttOEZ", "outputId": "66077a31-3181-46a0-9508-05251314b496" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Plot MAE\n", "plt.subplot(1, 2, 2)\n", "plt.plot(history.history['mae'], label='Train MAE', color='green')\n", "plt.plot(history.history['val_mae'], label='Val MAE', color='red')\n", "plt.title('MAE over Epochs')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Mean Absolute Error')\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "id": "df3ee76Avbmv", "outputId": "01a1d56e-a641-493f-cb8d-a541fcdd5c34" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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AAzZtghIvi3BFVQUd3+vIyGUja7UfgR3siXBJiZg1J7Ag4CL8+eefM336dGbOnMmuXbvIzMxk0KBB5DpYNWLLli2MGTOGSZMmsXv3boYPH87w4cPZv38/AKWlpezatYuXXnqJXbt2sXz5cg4fPsywYcMs9nPLLbfwxRdfcPjwYb766iuOHz/OiBEjfH68tcbLInwi/wSHLh1i1dFVNTcWuIdqHcXFKbf4eOW5sCQEZgRchOfMmcPkyZOZMGECHTt2ZP78+cTExPDRRx/ZbT937lwGDx7MM888Q4cOHXj11Vfp0aMH8+bNAyAxMZGsrCxGjRpFu3btuPbaa5k3bx47d+7k9OnTxv089dRTXHvttbRs2ZLrrruO559/nm3btqFTk+uDFWtPuJYirNMrx1tRVeG8oSwjbduGVnjQrmM9W05YEgI7hAXywysrK9m5cyczZswwbtNoNPTv35+tW7fafc/WrVuZPn26xbZBgwaxYsUKh59TUFCAJEkkJSXZfT0vL48lS5Zw3XXXER4ebrdNRUUFFRUmoSosLAQU+8MV4Vbb1FbkNUVFaM2eVxUWItdin6UViojrZT3lFeVoNVq77aTvvyfszjvpPGAAOqurilDBW9+Bq2gKCtAChpgY9Dod2vR0NEeOUHXmjMffmb+PwRcE4hikjRvRfPgh+tdfh8aNa70/Z8fg7nEFVIQvXbqEXq8nNTXVYntqaiqHDh2y+57s7Gy77bPVxS+tKC8v57nnnmPMmDEkJCRYvPbcc88xb948SktLufbaa/n2228d9nX27Nm88sorNtt/+OEHYmJiHL7PmqysLJfb2qPT779zldnzXzds4GL1D4InHCox/Z1XrlpJpCbSbruMNWvIBGJyc2t9DP5A0ulI3b2bS506URUba/Gav/rfce9e2gIncnPZv2oVPQ0GmgGH1q3juNW56C6h8B3UhD+Poffs2aT/8gt7k5M5NWiQ1/Zr7xhK3bw6DagI+xqdTseoUaOQZZn333/f5vVnnnmGSZMmcerUKV555RXGjRvHt99+iyRJNm1nzJhhEYEXFhbSvHlzBg4caCPujvqSlZXFgAEDHEbbrqBZZend9u7UCXnoUI/3F3cqDo4qj2++7WYaRDew/7nHjgGgrays9TH4A2nxYsL++U/0Tz6J4f/9P8B734GraL7/HoCMzp1pMXQomg0b4Oef6ZCcTDsPvzN/H4MvCMQxaN99F4Auqal0qsX/i4qzYyh0MygKqAinpKSg1WrJycmx2J6Tk0NaWprd96SlpbnUXhXgU6dOsW7dOrtCmZKSQkpKCldffTUdOnSgefPmbNu2jb59+9q0jYyMJDLSNkoMDw9360Ryt70N5eUWT8MqK6EW+zNIBovHDvtWVQUoIlzrY/AHp04BoM3ORmvVV7/1v9o/1yYmKn1o2tRhn9wlJL6DGvDrMVRWAqDNz6/1394ce8fg7jEFdGAuIiKCnj17snbtWuM2g8HA2rVr7QohQN++fS3ag3JJYN5eFeCjR4/y448/0rBhwxr7YqieeWbu+wYlXs6OqNRXGh9X6J0ce7X4ayorHbcJJvLzlftADiRaD8yJCRuBQ/2/vnw5sP2wQ8DtiOnTpzN+/HiuueYaevfuzVtvvUVJSQkTJkwAYNy4cTRt2pTZs2cDMG3aNG6++WbeeOMNbr/9dj777DN27NjBggULAEWAR4wYwa5du/j222/R6/VGv7hBgwZERETwyy+/sH37dm644QaSk5M5fvw4L730Em3atHEo/kGDt7MjDKZBBKcZEtUirA01Eba6cvArIjsieBAi7JjRo0dz8eJFXn75ZbKzs+nWrRtr1qwxDr6dPn0ajcYUsF933XV8+umn/PWvf+WFF16gbdu2rFixgs6dOwNw7tw5Vq5cCUC3bt0sPmv9+vX069ePmJgYli9fzsyZMykpKSE9PZ3Bgwfz17/+1a7lEFSoohsXp/yT+ysSrj6JhQi7gSMRFpGw/xEi7JypU6cydepUu6/99NNPNttGjhzJyJH2Z3hlZGTUOA23S5curFu3zu1+BgWq6KakeF2Ey6ucCJZqR4RKatSVK8p9MNgRanaGakcUFSmvqeIs8D1q8BCEIhzwyRoCN1HtiEaNlHtvRsLCjvAu1pFwfLxJkEU07F+COBIWIhxqmEfC5s89xCM7IhQK/gTDwJz5tGUVMTgXGFQRzs83LZQbJAgRDjV8KcIuRMJKwyDPIIHgjIRBDM4FCvWclWWTVRUkCBEONVTR9YEd4YonDAR/DePKStPfJVAiLMv2RVhEwoHBPHAIMktCiHAoIcumS1wvRcJqAR9wLU/Y5nEwokbBELgfjLIyk20jIuHAIstChAVeoqLC9I/tbzvC/CQO9kjYXIQD9YNhvtKyeW0REQn7H73echxDiLDAY8wF198Dc6EaCVdVGadc+xVVhGNiwCzPXUTCAcB6DEOIsMBjzJdQV2thBMITDnYRth54CUR/7WVGgJiwEQiECAu8hiq4MTGmS9wA2BFSKNkREBgRtjcoByY7QkTC/kOIsMBr+FqE66IdAYHxsB2JsBoJFxZ6ZZFWgQsIERZ4DfUSNzbWayLsbgEfILQG5iC4IuGEBNN3JywJ/2A9y1OIsMBjAhkJm0cTwR4JW3vCgYyErVb1QJLE4Jy/EZGwwGvYE+GqKqhFUZ06OVkjGCJhRwNzINLU/I0QYYHXsGdHQK2iYZcG5gwGC6GXgj0SDgYRdmRHgIiE/Y0QYYHXMI+EIyJM+afeEmFHdoT1SRxqIhxMA3Mg0tT8jXr+qtbQ5ctBVYRKiHAoYS7CkuQVX9glEbYW3WC3I4IhT9iZCIs0Nf+iirD6d6+oCKrMFCHCoYR64qi/6F4QYfPsCIeesLWIhUokHFa9ZkEwDcyB8IT9jZod0bChaVHcILIkhAiHEqonrIqvtyNhR55wqEXCqgirK3AHWyQsPGH/okbCkZGKEIMQYYGHmNsR5vf+9oSDuZ6wLJtEWBW7YM2OECLsH4QIC7yGr0XYxUg4qKctl5ebLj/VSDjYBuZUES4oCCpvss6iinBEhBBhQS0xT1EDMTBnD3VQTqs1VZoLNjsiIQGio5XHwhf2PSISFngNH0fCDgfmQilFTbUikpJMf59gi4QlSVgS/kS9MhIiLKg1PhBhi5U1XB2YCxURjopSHgcyEraXHQEiQ8Kf2IuE8/IC1x8rwgLdAYEb+MiOSC+E4gg37IhQEWH1kj/Y7AgQGRL+RNgRAq/hg0g4uqicI+/Az4ucRMLWdkQoeMLmkXAg+ussOwKEHeFPhAgLvIYPRLhNdgVxOmh3qebJGrIkAUGeHaFGwsnJgYuE9XqT8NckwsKO8D0iO0LgNXxgR6TnKZ5wlB4qdTXMmFOXVAoVOyJQkbD6PYGwI4IBEQkLvIaXI2FZlknP1xufayt0yPYKm6gncVKSch9qIuzv/qp+sFar/OPbQ9gR/kNkRwi8hpdFWGfQ0aLA9DxGZ5myZkQVsVAQYXNPWLUj/B0Jm2dGVFs4Ngg7wn/Yi4Tz8xXbKAgQIhwqyLL3RVivo3mh6Xm0zkGGhOoJqyIcCp5wICPhmgblwHLWnLl9IfA+5iKcnKw8lmXbansBQohwqGAufF7yhCv1lTaRsN3BOVXEEhMtnwcjwTAwV1N6GkB8vFhrzl+YD8yFh5vGNoLEkhAiHCqYC60qLl4Q4eZmIhxd5SBNTT2Jq6MIqaJCWW0jGAmGgTlXRFjMmvMf5pEwBJ0vLEQ4VFCFNjJSGfCB2tsRRQWkmOlTjKt2hNm2oMNennAwRsIgfGF/YT4wB0KEBR5inZ4GtRZhw+lTFs9jdA4iYWs7wnxbsGFvxlwwRsIg0tT8hYiEBV7BelDO/LGnecKnT1s8jXbkCasncVwcBjUKD8bBOfNawsnJgY+EHdWNUBF2hH8QIizwCj4QYensGYvnNdkRREZiUJeHCcZIuLjY5FUHsnaEK9kRIOwIfyFEWOAVfGBHaM9aRmAOB+ZUTzgqCr16IgdjJKz6wRERShRsHgn7c3VdYUcEF+bZESBEWOAhNUXCHoiMtQg7jITNIgm9GgkHowib+8GSZBJh8O+STO4OzAkR9i0iEhZ4BWciDB5dcoefywZAV30WOPSEze0INZoIRjvC3A8Gkx0B/v3RENkRwYXIjhC4TGUlLF0Kubm2r1mvtAyWIuOBJRF5PgeAs42Uk7PG7IioKPSqCAd7JAzKkvea6lPcnz8arg7MqXZEYaHpPQLvIyJhgct89hncdx+8+KLta6rImv9jh4WZfC53RViWibqgiP3pNEXMa7QjzEU4GCNh8xxhUCyJQAzOuRoJx8ebvk8RDfsOIcIClzl6VLk/dsz2NXt2hPlzd0X48mW05cpl2vlUZR81DcwRGRlakTAEZtacq9kR5rPmhAj7DmcDc/4csHWAEOFgIjvb8t4cb4twdY5wdiyUxJnZEc5S1KKiTJ5wMIuw6glDYHKFXY2EQWRI+BqDAXTV6yhaR8IVFbWqxe0thAgHE85E2F6KGnguwmeUHOHTiaCPUjIeahqYk80j4WC0I+xFwoGYNeeOCIsMCd+iMy1kaxThuDilkA8EhSUhRDiYUMU3P99W5HwUCZ9JhKqoGgbmzDw1QzCnqFl7whD8kbCwI3yLeWqiKsKSFFS+sBDhYMI8As7JsXzN2yJsEQkr0a0rdkTIRsLBmB0BIhL2NeYirJ67IERYYAeDwVJ4rS0Jeylq5s89jIRPJ4KhOhK2OzAny5bZEcE8Yy7UBuZAeMK+Rj13zdMVQYiwwA5Xrlj6V9YibC9FDWodCZ9JAEO0IlR2i7qrie6gDMwFc+2IYBiYq6w0fY/CEw481ulpKkKEBTZYi64jEfZBJCxXC5Xd5Y3MxSsUU9T8PTBnPunCHTtCeMK+QYiwwGVqEmFv2hFVVcbI60wi6KOdpKiZi3BERHCnqAXDwJwqwpGRphF4Z6h2RFGRchN4F+spyypChAU2uBoJe8OOOH8eDAaqwjTkxIIcYzZjztoTNvODkaTgHZjT65XpvxDYgTl3MiNAmTWnthXRsPcRkbDAZayzIXxpR1RbEQUp8cgakKLNZsw5ioSrT+KgraKmCjAEdmDOncwIFWFJ+A4hwgKXUUW3VSvL5yretCOqB+XyGikRmBxtioRtBubM0tMADOrJHGyRsOoHx8RYpiL5245wJzNCRWRI+A7rKcsqDRoo90KEBUZU0c3MtHyu4k07ojoSvpyivFeq3ke4AarKrSJGczsCgndgzp4fDIEbmHNHhEWGhO8QkbDAZVTR7dbN9FwtLmIwmCI5L0bClxoqAiXFmAm7tQiHih1hLzMCAjcw54kICzvC+9Q0MJeX59/+2EGIcLBgHQmXl5t8TnPBc0eEH34YunSBS5cst1dHwrkNFYHSREUjSxIAUplzO0If7HaEeY4wBP/AHAg7wpfUFAnn5yuDugFEiHCwoIpw69aQkGC5TfUZwbKQOzgWYVmG//4X9u+H996zfK06Es5poJyYEWGRGKqnLkulDiJh1RMO1UhY2BH1E0cirHrCsmyysgKEEOFgQKczRatpacoNTCKsCmx0tOXUS3Aswrm5phPwvfcs59CrZSwbKIIaoY0wzprTWEeMVidx0KaoOfKEA2VHiOyI4MDRwFx4uCnYCbAvLEQ4GLh4UflF1mqVyyRHImxtRZhvsxbhU6dMj3NylGWTQImqq32w80laoFqEq8VKU+YgRS3YB+YcRcL+HpjzJDtCRMK+w1EkDEEzOCdEOBhQxbZxY0WIrUXYUXqa+TZnIgzw5puK0FdbESQkkB+pDPxFaCOMaWph5ZWW77O2I4I1EnbkCYfCwJzqCRcXi1lz3saZCLdsqdzv3Om//thBiHAwoE7UUMXXUSRs7xK3JhEeNEhps3cvrF9vtCJo0QKdXik0E6GNMEaM2gorEQ6VFLWaIuFgFuG4OGXmHIho2Ns4yo4AuOMO5f7rr/3XHzsIEQ4GVLGtSYQ9iYS7dYMJE5THb75pioSbN6dSr5yg4dpw434cRsLVJ7ExEtbpAj6qbEFNnnAwD8yB8IV9hbNI+O67lfuNG20ziPyIEOFgwJEIqxGyJ3aEGvG2bAnTpimrCXz7Lfz4o7K9RQujCEdoI5BiqydsVOgs9+PIEzZ/LRgItjxhdwbmQKSp+QpnIty6tRKk6PXwzTd+7ZY5QoSDAVWEU1OVe0/siMpKpTqaihoJt2wJbduaLr0++0y5N4uEI7QRSNHKviMrZaoMZvuxtiPMK4MFkyURLANztY2EhQh7F0fZESp/+pNyv3y5f/pjh6AQ4XfffZeMjAyioqLo06cPv/76q9P2y5Yto3379kRFRdGlSxdWrVplfE2n0/Hcc8/RpUsXYmNjadKkCePGjeO82cl98uRJJk2aRKtWrYiOjqZNmzbMnDmTyspKex/ne7xhR4Cl0JiLMMBTT1m+zyoS1lQLvE0lNSs7Aq0WORgLuwfLwJwn2RFgioTtLfIq8BxnkTCYRPiHHwI2KBpwEf7888+ZPn06M2fOZNeuXWRmZjJo0CByc3Pttt+yZQtjxoxh0qRJ7N69m+HDhzN8+HD2798PQGlpKbt27eKll15i165dLF++nMOHDzNs2DDjPg4dOoTBYOA///kPv//+O2+++Sbz58/nhRde8Msx2+BIhHNzlUslZ3aEKjJgEuvCQpMotWih3PfrZ5qNBzaRsKZ66nJ0lVURHys7QmkUgBWMa6Km2hHBPDAHpskD6vcm8A7OBuYAOnZUrhQrK8EsmPMnARfhOXPmMHnyZCZMmEDHjh2ZP38+MTExfPTRR3bbz507l8GDB/PMM8/QoUMHXn31VXr06MG8efMASExMJCsri1GjRtGuXTuuvfZa5s2bx86dOzld7ZMOHjyYRYsWMXDgQFq3bs2wYcN4+umnWR6oSxJrEW7USPFw9Xolh9FZJCxJtr6wGgUnJ5tG3SXJMhpu0QKdwZQdoYlVRMOmsLuVHWHxOFhEuLLSdOyhOjCn9luIsHepKRKWJFM0HKAsiYCKcGVlJTt37qR///7GbRqNhv79+7N161a779m6datFe4BBgwY5bA9QUFCAJEkkWf+DWrVpoEYj/sZahMPCFCFWX3PmCYOtCJsPyplz771KNNypk4UdEa4xZUfUaEdAYFYwdkZBgelxYqLla6oI6/WWnrmvECIcXNQkwmAS4e++C8g5Heb3TzTj0qVL6PV6UtUBqWpSU1M5dOiQ3fdkZ2fbbZ/twEsrLy/nueeeY8yYMSSo0xStOHbsGO+88w6vv/66w75WVFRQYTb1t7C6uI5Op0On0zl6mxG1jU3b0lLC1X01bGhcJDIsNRUpN5eqs2eRiovRohTPMdj5rLCYGCSgqrAQWadD88cfaAFD8+bozdtrNLB1qzIhRJaNIizJyooZWpR15orLi4391JaWokEZkFO3yZGRyucVFSG7cOw+5+JFwgE5IYEqg0GpOqcSFoY6lKgz+858giwTVlKCBOgiIiwXbq0BKS6OMMBw5Yrld2aFw/MohPDnMWjLytAAVVqt43M1M5OwZs2Qzp6las0a5Ntvr3G/zo7B3eMKqAj7Gp1Ox6hRo5Blmffff99um3PnzjF48GBGjhzJ5MmTHe5r9uzZvPLKKzbbf/jhB2Ls2QQOyMrKsngek5PDAJTUr1WbNimXR0BfrZbGwN4ffqDhoUO0BI6cPcsRO77VrQYD8cC2deu4nJtLx/XraQuckGX2O/G5yiqVS/QtG7egOXOGjiiR8I8//cgfMX8A0Pv0adKBfUePcqq674U6HUnArz//zEXzhS0DRNKRI9wMlEVGkmV9vAYDd1U/3PD991BtV/kCbUUFd1SXH/1+82b01sWWnJB86BA3AaXnz7PWBW/SV8fgT/xxDH3PnVP+jw4d4oyTv2uXzExanz3LuXnz2FP9P+gK9o6h1M1FdwMqwikpKWi1WnKslvbJyckhTb00tyItLc2l9qoAnzp1inXr1tmNgs+fP88tt9zCddddx4IFC5z2dcaMGUyfPt34vLCwkObNmzNw4ECHEbZ1f7KyshgwYADhZmle0i+/AKBp0oShZr/A2i+/hD17yExNRaqO8q/u3p2rhg612XdYo0Zw9izXdu2KPHgw2iVLAMi46SZa2Gmvot+rTLYY2H8gzc8Ww//9H9FV0K5vb3o37a30o7oCW+eePbl6wACysrKIb9wYTpygd+fOyE727y+k6r9ndFoaQ+30R46MRKqooN+11/LDwYM234HXMBtMHnT33bbFlpyRkQHPP0+sTmf3GFQcnUehhD+PQTtnDgBde/Wii5O/qxQTA999R4s9e2gycKBiCTrB2TEUmi+15QJuibBer2fz5s107drVqb/qKhEREfTs2ZO1a9cyfPhwAAwGA2vXrmXq1Kl239O3b1/Wrl3Lk08+adyWlZVF3759jc9VAT569Cjr16+noVqow4xz585xyy230LNnTxYtWoSmhn+YyMhIIu34SuHh4W6dSDbtq2fqSGlpltur80a1Fy8afSptfDxae59V7RWHVVYq1aGqZ8VpW7Wy3x4wyAZjPnBsZCza6gG8GB3oJb2pL9UWTFhcnDE1TVLrTFRVubaisK+p9oSlBg3sfxdRUVBRQVj1ZaK735nLqHZVbCzhzjxIe1SPAUj5+YSHhRmviBzhs2PwI345BtXei411fq7ecgs0bIh0+TLh27Ypz13A3jG4e0xuDcxptVoGDhzIFS/W35w+fToffPABH3/8MQcPHmTKlCmUlJQwoXqq7bhx45gxY4ax/bRp01izZg1vvPEGhw4dYtasWezYscMo2jqdjhEjRrBjxw6WLFmCXq8nOzub7OxsYx7wuXPn6NevHy1atOD111/n4sWLxjZ+x3pQTsU8V9hZipr5duvsCOuBOTPUuhFQXTuieh/RjgbmgjlF7Q/FOiEjw/7r/hpIVAfV1IwUd1CDGvOUREHtcWVgDpTI965q48rPWVJu2xGdO3fmjz/+oJW6IGUtGT16NBcvXuTll18mOzubbt26sWbNGuPg2+nTpy2i1Ouuu45PP/2Uv/71r7zwwgu0bduWFStW0LlzZ0AR2JUrVwLQTV0qqJr169fTr18/srKyOHbsGMeOHaNZs2YWbWR1SSF/YT1bTsVchM0iLLuYi3BFhan+gDMRNliJsNlin/n2UtTMT2L1cbCI8LFjyv1VV9l/vfoHRPK1CJ89q9w3ber+e6OjlUhNp1PE3N3sCoF9XBVhULIkPvpISVWbO9c9O6kWuC3Cf//733n66ad59dVX6dmzJ7FWwuCKP2rN1KlTHdoPP/30k822kSNHMnLkSLvtMzIyahTSBx98kAcffNDdbvoGVyJh9QRyJRJWhSA62pTmZgc1MwIsC/jE6CDb1ckawZKi5qII+7y/anEkdYKMO0iSEg1fvKiIsFVwIPCQmqYtm9O/v3JunzunnFNXX+3bvlXjtgirgwbDhg1DMvOtZFlGkiT0wVRZKxRwRYRTUpTHroiwakW0aOHUVzSmpyGhlbRGYY2ucsGOCLbJGjWJsL9+NMzKhHpEYqJJhAXewZ1IODJSuSI9edKvC4C6LcLr16/3RT/qL45EWLUnrlxR8nrBPRF2YkUAlsV7zGbduTJjTg6mSLi42PQ3bNPGfhvzHw1fDgSpIty8uWfvFxM2vE9N05atCcB34LYI33zzzb7oR/3FkQgnJ5s8QrXWqSuesHkk7ARzEQZMkbArM+aCKRI+fly5b9jQtniPivmPhi9FuDZ2BAgR9gXuRMJg+g7MZ2H6GI/yhPPz81m4cCEHDx4EoFOnTkycOJFE6ymjAufIsmMRliRlm/qPDa5FwurJ40YkbL6PGJ0LBXyCSYRVK6JtW8dtzD1hTzIXXEVEwsGHpyLsx+/A7eG/HTt20KZNG958803y8vLIy8tjzpw5tGnThl27dvmij3WXggLTSWKdHQG2wuxFO8JiaSOzfSi1I6qFV5btF/AJJjuiJj8Y/JMdUVVlqgUsIuHgwZ2BOQgNO+Kpp55i2LBhfPDBB4RVzyqpqqrioYce4sknn2Tjxo1e72SdQJYJs57OqM78S0w0CZs51iLsjh3hYiQcrq2+PK/+fA1QVVbdz6oqUx2GYLUjjh5V7p2JsD9+NC5cUHJ8w8NtvzdXESLsXRydv85Qr+aDWYR37NhhIcAAYWFhPPvss1xzzTVe7Vyd4ddfCXv0UXqEhcGIEabtjqwIFevt5tGoOaoIFxeb7AsP7QgAQ2l1PQhz0aoDkbBPfzRUK6JpU8/zS4UIexezglvB7Am7fbYkJCQY6/Kac+bMGeJ96beFMomJsGcP6du3Q7WPDrgnwjExjlPOVAH94w9lNFirrXHCgI0Ih4ej1yj7l9WI3VxkzU5iOZgiYVdE2B8/GrUdlAMhwt7GfKWcuuQJjx49mkmTJvH5559z5swZzpw5w2effcZDDz3EmDFjfNHH0KddO+TqNd60b71l2u6uCDvCXIRBEeAaCpDYiDCgi1KsCVmdNqtGEuHhltGdv5cMckRpqZJYD65Fwr7sb21zhEGIsLdRz19JqvH/wUgoeMKvv/46kiQxbtw4qqqLZIeHhzNlyhRee+01r3ewrmD4y1/QfPMN0pIl8I9/KGuKOZqyrGIuws5W71VFWPW/XBACeyJcFRUBpZVQZhUJW9sgwVI7Qv3RSUoyLQ9kD3+KsKeZESBE2NuYZ0a4Wp4yAJ6wW5GwXq9n27ZtzJo1iytXrrBnzx727NlDXl4eb775pt0qYwIF+brryGvXDqmyEt55R9no7UhYpQY/GLBY2khFH1k9SKeKqyMRDhY7wjw9zdk/WfWPhk+zI4QdEXy4mxkBwe8Jq1XU8vPziYmJoUuXLnTp0sWtoub1maN33608eP99ZWXXAIqwxdJG1VRFKT+iUmm1uDrKsQyWgTlX/GDw78CciISDB3dzhCE0PGG1iprAfbJ790Zu21b5ghcurFmEzW0KV+wIFTdE2DwSNkRZVUcLdjvClfQ0CM2BOX9X86uLuDtlGUJDhNUqat9++y0XLlygsLDQ4iZwgkaDXl3x+M03TYNKjkQ4NtY0w8tHkbCFCEcrJ6tGFSsHIiyrJ3WoRcK+6m9JibIqNnhHhEVNYe/gSSSsesKlpW6tEVgbRBU1PyPffz/MmmW6fAXnyf1paYp14RcRViJGbVn1yVuTHRHoSDhYRFiNguPjbVd7doeYGGUUv6pK1BT2Bp6IsHkp3oICUwVDHyKqqPmbqCh4/HF46SXluSQ5rftLWppy2e2OHeGCL2lPhFWx0lZUX8YF88BceblJ/AJtR3jDigBTTeFLl0RNYW/gycBcWJjyY1pUpHwHwSbCOp2Ov/3tb8yfP5+2zgqmCJwzZQrMnq1c8jRq5DyHUY2SnUXC4eGmCColxblgV2NPhOWY6rXjymoQYVXU9HrlM13NwfQmJ04ovml8vPMfMTDVjvDVj4Y3coRVzEVYUDs8iYRB+Q5UEfYDbnnC4eHh7N2711d9qT80bAiTJimPHeUIq6SnK/c1Casq0i5YEWCngA9AtLIPYyRsr3iP9fNARcOupqeB7yNhb2RGqIgMCe/hqQj7OVfY7YG5+++/n4ULF/qiL/WLGTOUFV0ff9x5uwkTYMgQeOAB5+3cFGF7KWpS9T7CK6oHJOzVEgZLEQ7U4JyrfjD4zxP2ViQMQoS9gSfZEeD3XGG3ryOrqqr46KOP+PHHH+2uMTdnzhyvda5Ok54O69bV3K5bN1i1quZ2HoqweSQsVX+XYRXKTEiHdoRGo5zYFRWBj4SDQYR9EQn7cbJAnaU2dgT47YfQbRHev38/PXr0AODIkSMWr0muTg0UeB8viLAmRhHhiJpEWN1WURG4SNjVHGHwnx0hIuHgoq6KsMiOCFIaNlTuXRwwtSvCsUpKVKQqws5O4uhoJVoLpUjYF32VZWFHBCueZEeA3z1hrw5r5+bm0rhxY2/uUuAqb76p2BuDBrnU3J4Ia2MUEY6orM71rikShsCIcGWlqXi9G5GwVFHh/Zloly6Z/k41lA91CSHC3iNEImGXB+ZiYmK4ePGi8fntt9/OhQsXjM9zcnJIV0fyBf6ne3f4y19MKzPXgL0CPmFxSqJ6VKWMQTY4F+FA1o84eVKpGBcb69oqFmb915jXmPUGahSclub+P7s9hAh7jxAZmHNZhMvLy5HNooiNGzdSZhUFyWK+e8hgs7wRoI1VpkhHV1W/XpMdAYGJhM2tCFfGIcxEWOvtqaje9INBiLA3qWuRsCuIgbnQwZ4dEV4dCRtXXHbFjvBmJFxYCLm5Nbdzxw8GZTJL9RWC1yNhb2ZGgBBhb1IfRVgQOtgT4TAzEa6oqnDNjvBmJNyrF7RqBZs3O2/nrgiDaUq2r+yIYIuE//c/WLq0tr0JbUJkYM5lEZYkySLStX4uCC3s5glXp7lF66BCX+E8kvD2wFxJCRw5okzlvvNOy7X4rHEnPU1FLU7kKzsimCLhkhIYORLuv1+5uqiv1DYSDrbJGrIsc/XVVxuFt7i4mO7du6OpXntM+MGhhd0CPtUiHKODMlcjYW/ZEeY2xJUrSpbH1q32Mw5qEQlrzFfg9Qa+9IRl2fVlecw5csRUhjEvz7IyWH0iROwIl0V40aJFvuyHwM/Yy45QhTW6CvL1NYiwtyNhNfOmUSNlvbjDh2HwYPj5Z9M/BSjicvKk8tgTO8LbkbCv7IiqKuWqwIViTDaYX0UUFXmlWyFJbbMjCguVIlUuZhx5issiPH78eF/2Q+Bn7NWOMI+Ey6vKXcuO8HYk3LIlfPkl9O0L+/fDXXcp07b/+EOJjDdsUAQqKgqaNHF9/9X99erAnE4H588rj71lR1jXFBYi7Dm1LeADihAnJ3uvT3YIQA1CQTDgzI6IroKKyjL/DsypIty4sSLEq1fDTTfBxo1KZFK9sreR665Tali4ii8G5s6fVyyDiAil397AuqawJxNAzEVYeMLuD8xFRCjnd1mZ4gsLERb4ArsirAoroCst8m+KmrkIA2RmwooViiVRWamsMtG7txIhX3st3Hqre/tXI2Fv2hHmg3Lu/CDURG1rCh86ZHosImHPJtEkJSki7AdfWIhwPaUmEa4qKgxMJGxeoP2WW5SorqQEOnasnTdnXDXEiwNz3s6MUKnNwFBVlTIwpyJE2HMRvnBBiLDAd9gVYa2WyjCJiCqZqpIi/6aoWUfCKq1be2f/vhiY8/agnEptRPiPPywXqKzPIuzpwBz4NVfY42uoyspKDh8+TJW1VycICeyurAFURCjRpr6kBjvCVwNzvioA5YuBuWCMhM2tCKjfIlzbSBj8kivstgiXlpYyadIkYmJi6NSpE6erT8THH3+c1157zesdFPgGu5EwUBFZLcKlxf61I9QUNV+JsC8G5rydI6xSmyjMepKLEOHaiXAwRsIzZszgt99+46effiLK7J+zf//+fP75517tnMB32CvgA6CLUBwquaTENTsi1CLhum5HqCKsfmciO8L97AgIbhFesWIF8+bN44YbbrCYttypUyeOHz/u1c4JfIejSLgysnqYoKjIlBbm60hYln0vwr6MhIPJjlBFuHt35V5EwnXPE7548aLdwu0lJSWilkQI4UiEq6pFWJNv5oX5esZcfr5J8Gtavt5TvC3Cy5Yp/Zak4ImEZdnkCffqpdwLEa57nvA111zDd999Z3yuCu+HH35I3759vdczgc/QG/ToZWX1DGsR1kUqz7UFZv+8vp4xp0bBCQneKYxuD2/aEWvWwNixyuOpUyE+vvb7NMdTEb5wQbEfNBqoXgeyXotwbbIj/GhHuJ2i9s9//pMhQ4Zw4MABqqqqmDt3LgcOHGDLli1s2LDBF30UeBm1bgTYirA+SnkeroqwVqtMo7XGm5Gwr60I8F6e8KZN8Kc/KWlg996rLCvlbTwVANWKaN0aUlKUx/VZhOvqwNwNN9zAnj17qKqqokuXLvzwww80btyYrVu30rNnT1/0UeBl1PQ0cCzCEYXFygZ7VgT4JhL2pQh7IxLevRtuv1354Rk6FP77X98Ud/FUAFQrokMHU+W0+irCsmyKhD0ZmPOjJ+zRZI02bdrwwQcfeLsvAj+h+sFgVcAH0EcpUUNEYamywVEU4c2BOX9Gwp56wocPK+U1CwuVmhbLlikrdviC2kbCHTqYLJL6KsLm33Nd84S1Wi25dpaguXz5Mlofl3wTeAdVhDWSBq3G8jszRCsnbJQqwo4iYW/aEb7OEQZTUXdPRXjMGKWfPXrAN98Yix35BOuawq6iinD79iYRrq8paua2U12zIxwVb6+oqCDCk7Bf4HccZUYAyGpN4aJqcXXFjqhtQX8/RsIe2REGA/z2m/J42TLfF0m3rinsKvYi4eJipf/1DW9Fwu7+EHqAy3bE22+/DSjZEB9++CFxcXHG1/R6PRs3bqR9+/be76HA6zgV4Wqxii6ujiRqioQNBmWQqjY/wPaK93ib2tgReXkmIfN2TrA9YmMVr1mvd72mcEGBkh0BSiRs/n2UlHg/gyPYUSPhsDDPKtypImwwKD9kPvz7uSzCb1aPAsuyzPz58y2sh4iICDIyMpg/f773eyjwOk5FOEaJcGOKaxhZNqu4Rnm5d0TYHwNznoiw2r8GDXznA5uj1hS+fNn1msLqoFxamvJeWVbEx2BQfOH6KsKepjxGRSnftU6nfAfBIMInTpwA4JZbbmH58uUk+7jQscB32F3aSKXa64wrrhYrR5Gw+cldVla7S/RgH5gzX3rJX6gi7OrAkLkVAYqQx8cr76+Pg3O1mbIMph/CixeVv6EPr4DcjtPXr18vBDjEsbu0kUp09eoaFcpkDociLEneqx/hx0jYIxH2R/+scXdgyDw9TaU+p6nVNhIGvw3OuZ2iNnHiRKevf/TRRx53RuAfnNkRGmv/0dlJHB2tCHBtMiSqqpSID4J3YC4URNg8M0KlPqep1WURvnLlisVznU7H/v37yc/P51Z3l5wReI1Dlw6x+fRmJnSfgEZyfoHjVISjrUTYUSRs/lptRFgVYEmChg09309NmNkRbo91B8qOAPdF2DwSrs9parWZsqzipwkbbovw119/bbPNYDAwZcoU2rRp45VOCdznsVWPse7EOtLj0xnadqjTtk5F2CzrBXAuwt6YNadGmSkpvl1a3GxgTu/ue4MtErZOOauoALWCoT0RFpGwZ/hpwoZXVifUaDRMnz7dmEEh8D8XipT0pB3nd9TY1rkdYSXCzk5ib0TC/khPA5MdoabUuUMQRcKa555jyLhxSMuXmzYeO6YIc3w8NGli2i5EuHZZO36yI7y2ROzx48fFUkcBpKBC+bX+Lee3Gts6WtoIICzWKhXHX5Gwr6NM65Q6dwiWSLiwEM377xNRXEzYvffC//t/SiqauR9sXk5WiHDd9ISnT59u8VyWZS5cuMB3333H+PHjvdYxgXsUVii+396cvTW2dRYJh8VZpZq5IsLeiIR9LXDm/4yeinCgI+Gvv0YqL0cfEaFkeTz3nLKyshr9mlsRILIjoG56wrt377Z4rtFoaNSoEW+88UaNmRMC36A36CmuVKqeHc87TnFlMXERcQ7bO1raCOxEwv6yI3wtwpKEHBmJVFHhfn/9UdvCGnsivGQJAEdGjODqXr3Q/uUvsHChyUu3FuH6HAl7Y2DOT56w2yK8fv16X/RDUAtUAQaQkdmfu59rm13rsL2zSDg8LtFyQ12xI0Dpb0WFe/31VwqdNdYinJ0Na9cCcPamm7hq4kS0bdsqNY2Lq79/67IBrmRHyLKlhWGPFStgzhz429+gXz83DiKAhJAd4TVPWBA4VD9Y5bds576wUxGOdcOO8EYk7M8o05P+mqfQNWjg/T45wloAPvsMDAYM115LaVqasu3225Ui882aKcfWp4/lPmqKhHNzlff26wf79tm+XlUFzz8Pd98NP/8M//lPLQ/Kj4SQCLsUCXfv3t3l9eN27dpVqw4J3Ef1g1Vq8oWdiXBkgtVsyJoma0BoRcKgWBKu4q8UOmusBaDaipDvvdeyXWam4gsXFdn+DWsS4V9/hfPnlVv37vDkkzBzpvK+3FylfOe6dab2hw/X5oj8izeyI4LJEx4+fLhPOyGoHdYiXFOGhLPaEZGe2BGhkKIGph8Ud340AjEoB5YifPgw7NgBWi2GESOUx+ZER1tmf6jUJMJqlB8VpfxN3nhDibifflp5fPasUsFtxgz4618VsXfFvggGQihP2CURnjlzpk87IagdBeXKSRITHkOprpS9OXuRZdnh1YsxEtbYEeGwKErDIEbNNnTFjgiVSNgTOyIQg3JgEgCdDj78UHk8cKB7/ahJhC9dUu7/9Ce4/35l0dI//oCnnlK2t2sHy5dD27Ywa5ZSEvPcOcXCCHa8bUf48MfHY094586dfPLJJ3zyySc2GRMC/6JGwt3TuhOuCaeosoiT+ScdtneWHaHVaCk112ZfRsJlZSaB8IPIyZ7YJ4GKhNWawgBqPRZ1hWdXqSlFTRXhlBQYMgT274eXXlIq6Y0apdgVHTsqJR1bt1bahool4c3siMpK76yl6AC3RTg3N5dbb72VXr168cQTT/DEE0/Qs2dPbrvtNi6qUYPAr6gi3DCmIR0bdQSc+8LOPGGA8nCzX3xnJ7EqnJ7+Y6rnS3i4yX/zJaEUCaulFEEpKh8TA3fd5d4+XLUj1JWZo6OVDIiiIvj8c8vypO3aKfdqtbZgxxuRcFycqSC8D31ht0X48ccfp6ioiN9//528vDzy8vLYv38/hYWFPPHEE77oo6AG1OyIxMhEMtMyAee+sFsi7CwSHjBAuV+/3r1leFTMrQh/+IxqJOzJwJy/I2EwiTDA8OGKKLhDTSlqaiRsXTjJ3koUqgiHSiTsjYE5STIFBz70hd0W4TVr1vDee+/RwSwxvGPHjrz77rusXr3ao068++67ZGRkEBUVRZ8+ffj111+dtl+2bBnt27cnKiqKLl26sGrVKuNrOp2O5557ji5duhAbG0uTJk0YN24c58+ft9jHP/7xD6677jpiYmJIMj/ZQxA1Ek6ITKBr465ALSPhCLPTwpkId+oELVool2qe5I/7O8qsjookdy4tAxUJg6UIu2tFgEmEdTr7PzzmdkRNhKoI1yYSBr+kqbktwgaDgXA7S7yEh4dj8GBBwc8//5zp06czc+ZMdu3aRWZmJoMGDbK7ojPAli1bGDNmDJMmTWL37t0MHz6c4cOHs3//fgBKS0vZtWsXL730Ert27WL58uUcPnyYYcOGWeynsrKSkSNHMmXKFLf7HGyYi7A3IuGKCLNULGcnsSQpuaoA333nRo+r8XdNBvUHpbRUEZOPP4YpUxSBsyrRGrA+mqMKQEqK6arDHcwjZ3uWhLUd4QwhwrXbjxPcFuFbb72VadOmWUSW586d46mnnuK2225zuwNz5sxh8uTJTJgwgY4dOzJ//nxiYmIcFoefO3cugwcP5plnnqFDhw68+uqr9OjRg3nz5gGQmJhIVlYWo0aNol27dlx77bXMmzePnTt3cvr0aeN+XnnlFZ566im6dOnidp+DDVWEEyMT6ZqqRMLq9GV7OF3eCCsRdhYJAwytLpu5apX7q9L6+1JfLWc5Y4Yyu+zBB2H+fPj0U2MebsD7aI46OWT0aM/WtgsLM1kw9kTYkR1hD1WET5+uXUqiv/DGwBz4RYTdnrY8b948hg0bRkZGBs2r1106c+YMnTt35pNPPnFrX5WVlezcuZMZM2YYt2k0Gvr378/WrVvtvmfr1q02RYQGDRrEihUrHH5OQUEBkiTVynaoqKigwuySrrDaZ9PpdOhcKI2otnGlrbtcKVOiuNiwWJIjkkmLTSO7JJs95/fQp2kfm/blOuVyXIPGbn90ZiKs02qNpR/tHsONNxIWFYV06hS6335TLAoX0WRnowX0KSkYfPB3sUZu2pQIQDIYkKOikHv0QCotRdqzB/2RI3b7EHbxIhKgS052vwRmLZGmTkUD6J95xvl34ISwhASksjJ0eXmWqWUGA2GXLyvHlphY87ElJRGWnIx05Qq6Awega1cPjgiLvnvlf2H/fsImTUL/4ovIZle72rIyNEBVWBhyLT5HGx+vfAeXL1ucH86Owd3jcluEmzdvzq5du/jxxx85VD1S2qFDB/r37+/urrh06RJ6vZ7U1FSL7ampqcZ9W5OdnW23fXZ2tt325eXlPPfcc4wZM4aEWixGOXv2bF555RWb7T/88AMx1YtjukJWVpbHfXDEiXPKIqx/HPyDVTmrSNOkkU02n679lMspl23anz6rXBEcPXiUVZdW2bwerjFFtBu2baPE7AoCbI/h2o4dSd21iyNvvsmxP/3JZn9Xf/EFLX/4ga2vvEKx2crB3XfvpgVwKC+PY6ts++FttB070uj55ylLSaEwIwM5LIyMNWvI3LOHi1u38otVHySdjmHVEVDWnj3o/vjD5320YexY2LNHuZnh6nl0m0ZDHLAtK4u8s2eN28OLixmqV8rbr9m+HcNvNZdAvbFxYxpcucKezz/nvNm+VJps2kTLrCx2Tp9OpQvZLt74X7j6iy/osHs3l157jV/CTHJ27blzpAJ7Dx3iTC3Ore7Fxco5+uuvHLOTH23vGErdHKR2W4QBJEliwIABDKj2qfJ9PK3PU3Q6HaNGjUKWZd5///1a7WvGjBkWEXhhYSHNmzdn4MCBLom7TqcjKyuLAQMG2PXUa8M/Fv8DiuGmPjcxtO1QNq7byJ5teyAVhg6yXWXjw2UfQgF079qdod1tX8/6dwygWBk3DxpkXGnW0TFoTp2CXbvocOIEVw+12t+ZM4SNHImk09Hv4EEMkycbX9JWfyftbrzR9n0+QKfTkRUdbdF/KTIS5s8ntaiIodZ9qLbcZK2WAaNG2c8a8DPunkdhqalw4QJ9O3dGHjTI9MKxYwDIsbEMdnFGrParr+DwYXrExNDNzvcV9uyzSEeOMPDSJQxjxnjtGJyh+fFHAFLz8y2+P231AhNdr7mGLrU4tzTr1sG6dbRPTbU4R50dQ6Gby0m5LcL/+te/yMjIYPTo0QCMGjWKr776irS0NFatWkVmZqbL+0pJSUGr1ZKTk2OxPScnhzS1SIkVaWlpLrVXBfjUqVOsW7euVlEwQGRkJJF2/KXw8HC3TiR327uC6gk3iG1AeHg43dO7A7Dv4j67n1UlK9PhoiOi7b8eafKKw+PjbfxIm2O4806YNg3Nli1oiovBfDXuN94wXupqly1D+9ZbpkkI1ZkHYenpnnmeHmLR/2qvUzp5knCt1lJoqwfrpJQUwmvrLXoZl8+j6vM+rLTU8m9cnXIlpaS4fj5WZ0Rpjx1Da/2enBxlWjOg/e0329ft4JX/herBRenECcJl2ZSSVu0Jh8XG1u7cqvbltUVFdo/J3jG4e0xu/7TPnz/f6AVnZWWRlZXF6tWrGTJkCM8884xb+4qIiKBnz56srS7RB0r2xdq1a+nbt6/d9/Tt29eivdoP8/aqAB89epQff/yRhr5cQDIIMM+OAMhMVX4I1enL1tSUHaGPNtvuivi0aqX8g+r18MMPpu3nzpmm3EZGKv+o5qlsgUz/UmneXBnAqqhQ+mtOIDMjvIWjCRvupKepOMuQ2LjR9NifRbzUc0ivhxMnTNu9lR3hhyI+botwdna2UYS//fZbRo0axcCBA3n22WfZvn272x2YPn06H3zwAR9//DEHDx5kypQplJSUMGHCBADGjRtnMXA3bdo01qxZwxtvvMGhQ4eYNWsWO3bsYOrUqYAiwCNGjGDHjh0sWbIEvV5PdnY22dnZVKojpsDp06fZs2cPp0+fRq/Xs2fPHvbs2UNxsf2MgmDGWoTbpbQjXBNOYUUhpwpO2bR3trwRgN4sEq4xO0JFTVUz99/+9S8lIrnxRiUTAZRMBFAyKQKZeaASFgYZGcpjdbFMlWD4kagtjkTYnfQ0FXMRtv5x37DB9PjAAc8m73iC+mOi9kvF29kRwTRZIzk5mTNnzgDKxA11QE6WZfR6t9exZfTo0bz++uu8/PLLdOvWjT179rBmzRrj4Nvp06e5cOGCsf11113Hp59+yoIFC8jMzOTLL79kxYoVdO7cGVDS5VauXMnZs2fp1q0b6enpxtuWLVuM+3n55Zfp3r07M2fOpLi4mO7du9O9e3d2WFeoCnKqDFWU6EoAJUUNFHF1Nn3ZWe0IAH20cuLKkuT6pZwqwqtXK4tOnj8PCxYo22bOhPvuUx4vX65M7igqMkUrgRRhgKuuUu6rfVIjwfAjUVtqioTduUq86irFrikqUorMm2MeCRsM9usT+wLzUgnVdggQUnnCbnvCf/rTn7jvvvto27Ytly9fZsiQIYCy7NFV6snsJlOnTjVGstb89NNPNttGjhzJyJEj7bbPyMiwewluzeLFi1m8eLE73QxKiipM/1zxkaalibqmduW3nN/4Lfs3hrWzmqhSgx1hqD5xq8K1hLs6nfj66xX/8eJF2L5dKYlYUQHXXQe33qpETs2aKeURV68GNT87Nla5BZI2bZT7uhgJOyri44kdERmpWE/HjytRZ3q6sv3yZZPo9uwJO3cqloR1kXlvI8uWImweCXtj2jIE52SNN998k6lTp9KxY0eysrKIq56Vc+HCBf785z97vYP1nUp9JTvO70BvsH+VoVoRUWFRFqJq9IVzHUfCDkU4WrEgqiLc+I0OD1dKLYJS9Wv+fOXxzJnKzDqNRlmKB2Dp0uDyWx2JcF2OhD2xI8C+L/zzz8p9hw6gZmD4wxcuKjLZDuCbSDgYPeHw8HCefvpp5s6dS/fu3Y3bn3rqKR566CGvdq6+s+bYGrq834VeH/Ti7V/ettvGfLacOerMOXtLHdUkwnKMMsvKfNKGS6iWxIIFiuXQp4/ldFvVkvjmG9OlfyiIcDD00VMcFfHxxI4A+yKsWhE33ww9eiiP/SHC1lUb7UXCIeAJe5QnfPjwYd555x0OHjwIKJM1Hn/8cdqpX5CgVhzLO8b076fzzZFvjNsOXjpot61aQU0dlFNRRfhY3jFKKkuIjTBd8tckwupUV124myJcbU0ZUaNglW7dlH/iw4fhgw+UbcEgcKqNdvy4ZfFu9Z+8LkbCntgRYL+kpTooZy7C+/YpUWpt7QBnqN9PgwZKuc+cHEUsExO9PzBXWuqz43E7Ev7qq6/o3LkzO3fuJDMzk8zMTHbt2kXnzp356quvvN7B+oTeoOeFtS/Q6b1OfHPkG8I0YfRM7wnApdJLdt9jnRmhkhqXSqOYRsjIHL5smVJUU+0I2SjCbp4eqanQq5fy+JprYPBgy9clyRQNb9qk3AeDCLdqpdwXFJgu06FuRcK+siMKCkyz+W66Sck0SUpScsN//92DDruB+kPSqhWo8wRUS8JbkXBCglLYvm9fn9XMcFuEn332WWbMmMHWrVuZM2cOc+bMYcuWLbzwwgs8++yzvuhjveGTvZ8we9NsKvWVDGoziH1T9vH8Dc8D7oswQPNEJZXwfJFlGc+aIuGCNk2p0MLpjGS7rzvl6aeVVRjmzrVfI9h6JlUwRJnR0aBOpza3JOrCwJw3syPAJMInTypCt3mzkg1x1VXQpInynfvLkjC/Urn6auXx4cNKzrCaqVVbEdZqlR+TLVt8tvCA2yJ84cIFxo0bZ7P9/vvvt0glE7jP1rNK0aLHej3G6rGraZ/SnpQYJVJxJMLq+nKJUbYnSHqcMnp9ocjyezGmqGnsp59VNksn7Wl454lr3T+IUaMUIbvuOvuvt22rRMkqfhS4/PJ8NlzZQEllie2L1r6wmkYHwfFD4Sn2RFiWPY+E09KU6NBgUHx9cytCJRAirP44HDliWTvZl3aIl3BbhPv168fP6mioGZs2beLGG2/0SqfqK/tzlZrI1ze/3rhIpyrCF0vtLx3lLBI2inCxfRF2FAlHaiPJj4ZyfFQ1TLUkwK8iPHvzbN489SYLdi+wfdFahP299JKvsJeiVlBgihTdjYQlydKSUEX4pptMbQIdCZuLcJBNN7eHSwNzK1euND4eNmwYzz33HDt37uTaa5VIadu2bSxbtsxulTGBa8iybBThzo07G7erIpxXlofeoEersRwsM4pwhB0RjnceCTsU4TDlxC2v8tHihqNHw1/+okRkfhThLWeVyTrWfw/AdsKGeXpaKCzx7gh7kbBqRcTGuj4j0px27ZRc8F27lJxgsB8J//YbVFUpsxJ9gSuRsB9rkniKS3+d4XaqLL333nu89957Ftsee+wxHn30Ua90rL5xtvAsBRUFhGnCaJdiyjJpGK1EKgbZQH55Pg1jLCMX4/pyduyIJvFNADhfbPKE9QY9BllZAcVZJAxQUeXGWmzu0KQJPPoorFsHvXv75jOsqDJUGWcP2i12bx0J14VBOTCJcHGxYiFoNJ5nRqiogvff/yoi27KlclNp21ZZ1aO4WIlM3agx7RaORFhdvioyMiR+QF2yIwwGg0s3T6YtCxTUKLhdw3YW4hiuDTfmANvzhV2yI8wiPzUKhpoj4Qq9j0QY4L33lDQnP13qH7x4kLIqZXS7qNLOKhOO7IhQ9oPBJMKgiCJ47gerqIJXXb7AwooARei7dVMe+9KSMP8xadVKibhLS02FfELAigAPPGFH5OfnG5cYEriPPStCxdngnFMRjrf1hNX0NHAswlFhyiWqzyLhALDzwk7j42Kdk0g4OxtKSupOJBwVZSodqloSnmZGqFjPBzC3IlT84Qub/1CGhytZOWCaQh0Cg3LgBRFeu3Yt9913H+np6cycOdMbfaqX7MtVThx3Rdh8uXtr1Eg4uzjbaEGYR8KOCviodoTPPOEAsPO8SYTtZkckJ5vWdPvjj7oTCUuSrS9cWzuibVvLy/xgEGEwDc7trZ6qX5cj4TNnzvC3v/2NVq1aMXDgQCRJ4uuvv3a4xJCgZtRIuEtj24VHPY2E0+KUBPYqQxWXS5VLUFWEtZIWjWT/61f3pQp8XcAiEnawAKoxGj52rO5EwmCbIVFbOyI62uQBp6eb/m7mqCK8e7fiRXub8nKTvaKKsBqhq5FwXRNhnU7HsmXLGDRoEO3atWPPnj38+9//RqPR8OKLLzJ48GCvrxhRX9Ab9By4eACwHwk3ilVOMndFOFwbTqMY5b3qhI0apywDjWMV4ckpznGpIl2wU2WoYk/2HuPzGkX4+PG6JcLejoTBJHg332x/8KtDB8UKKSqyrcnhDdQoOCzMNK6gRsL7lYCmzolw06ZNeeedd7jnnns4d+4cy5cvZ8SIEb7sW73h+JXjVOgriA6LplVyK5vXU6Id5wo7E2Gw9YVdEeHUOKWWc1lVmWPBCiEOXzpsHJQDjPWXbTAX4bpiR4BjEa7NijN33KHc33+//dfDwkwrMvvCkjD/IVF/BNQfBrWgfF0T4aqqKiRJQpIktFptzW8QuMy+HOXyqVPjTnYtAqeesJMZc2CbIeGKCMdFxBEbrhT8ySnJcdguVFCtiMYxSlRrNzsCLAv51MVIWK2kVls7AuCxxxRRVyvn2UOtsrh7t+ef4wh7P5JqJKxS1wbmzp8/z8MPP8zSpUtJS0vjnnvu4euvvzbO7BJ4jjM/GByLsE6vM0Z4jiJhNVdYjYRrWtpIRY2Gc4rrgAhXD8rd0OIGQLEj7Nos5p5wfYiEayPCkqTkAjvDl4Nz9r6ftDTLlLy6FglHRUUxduxY1q1bx759++jQoQNPPPEEVVVV/OMf/yArK0vkCXvI/ouO09PAsQibR3TxEfHYQ42ErT1hR5kRKqmx1SJchyLhm1oo+axVhiqLLBEjqgifPGm6pPVCJLz93HaulF2p9X48xhd2hCuYi7C3xxbsibAkWUbDdU2EzWnTpg1///vfOXXqFN999x0VFRXccccdxnXhBO7hLEcYHIuwakXEhMc4FFVPPGGoO5Gw3qBnd7ZyOXxjC1NtE7ted3q6MvKvCkZkZM3RXg1sP7ed3h/25v6vHXin/sBchGtTvMddOndWvGHz5Y+8haMrFfMc5roswsY3azQMGTKEL7/8krNnz/LCCy94q1/1hvKqco5ePgq4L8I1DcqBZ54w1J1I+PDlw5TqSokNj6VjSkciJOW47YqwJFmmWzVuXOtpr2pWxi9nf6nVfmqFeYpabYr3uEtUlLLSNigrrHjTlhAibEujRo2YPn26t3ZXbzh06RB6WU+D6AZGwbRGFeGCigKjpwuuibC1J+y2CId4JKz6wd3SuqHVaInSKLMBHQ7OWYuwE3KKc8gtyXXa5nTBaQAul1025mr7HfNIuLbFe9xlyRLIzFQGOm++Gdau9c5+Hfna9cWOEHgPcyvC0SBncnSyMWvicpnpH9nZbDkV80pqsiy7b0eEeCSs+sHqCiVRWkV4aswVBqeDcpX6Srq834Vu87tRZahy2O504WnjY+sVTvyGeXaEv6wIlfR0pdxlv37K5IohQ5C++KL2+3UlEq5r2REC36Cmp3VuZN+KANBIGmM1NXNLwpVIWJ01V6Gv4Er5lRqXNlJRI+Hs4tCeBWkU4SbVIqxxQ4SdRMK5JblcLL3IheILnC0867DdqfxTxseHLh1y2M6n2IuE/SXCoEymWLMGRowAnQ7tAw/Q6rvvardPRyLctq3psYiEBa5QU2aEirG4e4lpwoYrIhwVFkVylLJM0YWiC/UqEjbIBnZfUAbl1Eg4WqOsn+dQhNVcYXAaCZtnO5y4csJhO9WOAGXSSECwJ8K+9oOtiYyEzz6Dxx5DkmW6fPhh7WbSORLhuDjTUlVChAWuYMwRTrWfI6xib3DOFREGS1+4pqWNVOqCJ3zk8hFKdCVEh0XTPqU94L1IOK8sz/j4RL59EdYb9BZRcsDtiEBFwipaLbzzDoabb0aSZTQrVni2n6oqZXVlsP9DqfrCISLCbpe81+v1LF68mLVr15Kbm4vBqjjHunXrvNa5uk5hRaExUurUyHnha3sibJwt58QTBsUX/v3i7x5FwiW6EkoqS4iNiHXaPhixHpQz6A1Ea5VIuKjCwcBcixaKWOj1ziPhclMkfDL/pN02OSU5FqVDA2ZHmGdH+NsTtkaSkP/0J9iwAel//4Pnn3d/H+oxSJKp8p05nTvD+vWm5eqDHLdFeNq0aSxevJjbb7+dzp0dDyYJakaNgpvGNyU52vnKxrWJhM0nbKhiWpMIx0fEExUWRXlVOTklObSOaO20fTBiPSgHLkTC4eHKsu3Hj4OTvHdXImH1BzY6LJqyqjKOXzmOTq+rcaKM1wkGO8IMw7BhaKdNQ7NtG1y4oAzeuYN6DMnJ9pdOeu455bubPLn2nfUDbovwZ599xhdffMHQoUN90Z96RU2TNMyxK8KV7onwheILtEhsAdQswpIkkRqbyqmCU+QU59A6OYRFuIkbIgwwcyasXKmM6DvA3BN2FAmrItwjvQe7s3dTqivlRP4Jrm54td32PiNY7AiVpk3Ju/pqGhw5Av/7n7LUlTvUNKW8aVN48cXa9dGPuO0JR0REcJX54IXAY2qqGWGOUYTL7NgRDor3qJjPmnO1dgSE9uCcvUE5cCFFDeCBB2DZMoiJcdjEIhJ2MDCnZkZkJGUYhTcgg3OqCOt0cO6c8jiQIgxcqF4kmOXL3X9zXarrgQci/Je//IW5c+fWiTqzgabWkbC7A3NueMIQ2oNzRy8fpaiyiOiwaDo06mDcXmN2hIuYi/D5ovN2l4JSI+EWiS1o11DJXw2IL2w+9Vpdfy3QItynj/Jg/Xq44mZdjTomwm7bEZs2bWL9+vWsXr2aTp062RRyX+7JL1s9RJZlp0saWeMtT9jV7Ajw3tTlbw5/w5J9S3jv9vdoEG1nIMUHqFZEZlomYRrTaW4UYXvrzLmB+cCcjMzpgtO0bdjWoo06UaNFYgvjj15AMiS0WiWqLy01CVgAPWGAkqZNkTt2RDpwAL77znFdYnvUMRF2OxJOSkri7rvv5uabbyYlJYXExESLm6Bm9AY9By8d5FLpJSQki0jNEeoKGRbZEdUz5moUYTM7Ql1B2S07opaR8PNrn+fz3z9n1dFVtdqPO/xx5Q8AOqRY/m1VO8JhdoSLmEfCYH9wzl4kHPA0NZUAR8IAhrvuUh58/bV7b1RFOAiOwRu4HQkvWrTIF/2o0xy4eIDpa6Zz5MIRHj36KLmlucaFN69qcBUx4Y69RxVnkzVqTFGrjoRLdaXG+gVu2RG1iISzi7ONSzf509bIL88HMM40VHFpYM4F1Eg4QhtBpb7Sri+sinDLxJZUxCk/fgFNU8sx+/sHOBIGMAwfjnb2bFi9WonSnXjwFqiDi/U1Eha4j0E28P0f33Oi7ATZJcrKxxISaXFpPHntky7tQxXhsqoySnVKrVtX7YjYiFhjm1MFymCRvwbm1p0w5Y3bW57JV6jZC9apf94SYTUSVq0k6wyJ4spiY5vmic2NA3OXSi/ZRNF+wTwS9lfxnpro1k1ZMLSsDH74wfX31TE7wu1IGODLL7/kiy++4PTp01RWWhbH3uXLJa5DlIykDBbcvoAzB89wR787aJ7UnEaxjSy8ypqIi4gzRl2XSi+RFpdmXJK+JhEGJRourCh0T4S9MDBnIcIl/hPh/Ip8AJKikiy2e3tgrkdaD3Zd2GVjR6hRcFJUkvH7aZbQjLOFZzl86TB9m/et1ee7jbkIB8tlvCTB3XfDW28plsTw4a69r46JsNuR8Ntvv82ECRNITU1l9+7d9O7dm4YNG/LHH38wZMgQX/Qx5ImLiOPBzAfpmdCT7mndSY9Pd0uAQcnbNR+cU6NgcFGEq31hVRz8FQmvPWEqXZhb6rzsozcxRsJRlpGwOmOuNiKsN+iN6YFqDrJ1JGzuB6sE1BcORhEGRYQBvvlGSaFzhfouwu+99x4LFizgnXfeISIigmeffZasrCyeeOIJCgoKfNFHQTX2RDg2PBatpuaFV1Vf2NXljcAUCRdWFBqjbnc4ceWEhTj5MxJWPVtHdoTDesIuUFBRgIySotkjXVnCx1EkbC7Cav2KgPjC5iIcBH6wkeuvV8T0yhWl5GVNyLLwhE+fPs11110HQHR0NEXV61Y98MADLF261Lu9E1hgT4RdiYLBlCus4koknBSVZGzniSWhRsHqys3+9ITVgTlrO8LcE/Y0112NsuMi4mjbQElLyy3JpaSyxNjGKMIJIhJ2ilYL7mRJFBQoBXwguI6jFrgtwmlpaeRVVzBq0aIF27ZtA+DEiRNiAoePMRdhV2fLqViv2uGKCEuSRONYpZKYPUuiTFfGf3/7r8N0L9UPvqu98k9W0yoU3sSRHaGKsMPFPl1A9YOTo5JJjk42Zqeofrv545ZJLY3b2qVUi3AgZs0lmP1YB5t4/elPyv3ChXDHHfDOO3DkiP3FQVUrIi4uOAYXvYDbInzrrbeycuVKACZMmMBTTz3FgAEDGD16NHer/o7AJ6REex4Jq56wiisiDM4H597+5W3GrxjPw98+bPOaLMtGER7daTSgRJ+e2BruojfojTnU1naE6gmr/fEEVYTViScZSRmA5fRlZ57wsbxjTlfj8AnBakcA3HYbdO0KFRXKxI0nnlBWyGjVCv7zH8u2dcwPBg9EeMGCBbxYXRzjscce46OPPqJDhw787W9/4/333/d6BwUmzHOF3RZhDyJhcD44t/XsVgC++P0LjudZFug+cPEAOSU5RIdFM6jNIOMMPX/4wuaDltZ2hFbSEhVWuzQ11W9WRbhVcivAcnDOngg3T2xOdFg0OoPOaSF4nxCsdgQoyxDt3q3c/vUvuPVWZdupU/Dkk4oFoSJEWFlhOcysfNy9997L22+/zeOPP05EiKzpFKo0iq2eNVd2yeXZciqeeMLgPBL+Lec3QMmDfmPrGxavqX7wjS1vJDIs0th3f/jCqkjGhMfYPc64cKWWQm0jYTXKzkjMAEyDc+bF3M1FWCNpTIV8/O0LB7MIA2g0St7ws88qi4Hm5UHHjlBerqzIoSJEWOHnn3/m/vvvp2/fvpyrrsr0f//3f2zatMmrnRNYYm9grqbZciq1tiOsIuGC8gKLyG/RnkUWnq9qRdyacStgmnbtD1/YkR+sEhehiLCnGRLq/htEWUbCqghnF2dTZahCK2ltrkAC5gsHuwhbExsLEycqj81n6QoRhq+++opBgwYRHR3N7t27qahQpmMWFBTwz3/+0+sdFJioTXZEfES8xfRoVwr4gGM7Qi0+1CyhGb2b9qa8qpy3f3kbUAa9fjr5EwC3tb4NwDjA5w87wlFmhIpa2N5bkXCrJEs7QrUimiU0s0kfbN8wQGlqwewJO+L++5XsiV9+gYMHlW3BUA/Zy7gtwn//+9+ZP38+H3zwgUUFteuvv17MlvMx9rIjXBVhSZIsorLa2hG/ZStWRGZqJs9d/xwA725/l6KKInZf2E1BRQFJUUl0T+sOEBA7wtFqJWok7LEIlzsfmLOXGaFijISFHVEzqalw++3KYzUaFpEwHD58mJtuuslme2JiIvn5+d7ok8ABFiJc4dr6cuaY+8K1HZhT/eDM1EzuancXVze8mvzyfD7Y9YHRD+6X0c8YCfrTjqgpEq6tJ2y0I6xE+Er5FQrKC+wOyqkELFfYPEUtVCJhMFkS//2vMqNOiLCSJ3zs2DGb7Zs2baJ169BbAieUUCuCVRmqjAM/rkbCYOkL1zoSVkU4LROtRssz1z0DwJytc1hzbA1g8oPBv3aEq55wre2I6v3HR8Ybv5uT+SftTtRQUSPh3JJciyWSfE5qqlKroVGj0MqvHTpUWfU6JwfWrBEiDDB58mSmTZvGL7/8giRJnD9/niVLlvD0008zZcoUX/RRUE10eLRx9plaL9ctEfbAjkiLSwOUKE+d3KA36I2rgnRN7QrAA10fID0unXNF59hwSpl+qvrBYIqE/WpH1DQw52FNYesUNbAcnHMWCcdFxNE0ving52g4LQ2++MKz5YQCSXi4qeD7okVChAGef/557rvvPm677TaKi4u56aabeOihh3jkkUd4/PHHfdFHgRmqJeFRJOyBCCdHJxuLDalWwvErxynVlRIdFm2cthsZFmlRljMtLs2ioLrqCQeFHeGtSNjMczYfnHMmwhDADIkRI+CGG/z7md5gwgTl/ptvlNWZIXR8bRdwW4QlSeLFF18kLy+P/fv3s23bNi5evMirr77qi/4JrFBFWC0g4+q0ZbD0hF1ddl0jaYxWgiqge3P3AkotXfPR/0d6PmL8Ubi11a1IkmR8LSCRsIOBudpmR1h7wmA5OOdsYA4CXEMiFOncGa65RqkZoVZaq8+RsEpERAQdO3akd+/exJkvJCjwKWpEqeJrTxhsc4X35iginJmaadEuMSqRl256CYBxXcdZvBZUnnAtBubKdGWUVZXZ7F+NhPfm7jVG4s0Tmtvdh1pNTbV0BC6gDtCBMpvOermmEMblorYTzf8ITvjoo4887oygZtRIWMXXdgSYMiRyS3JpRCNjJJyZlmnT9unrnubx3o8TGRZpsV398SiqLKK8qtw4ddgX1GRHxEcq/8CeLPapRtlaSWvxt1cj4W1nlYJWyVHJxs+xpleTXsa2sixbXDEIHHDvvfDUU0p9iUaNlEHGOoLLkfDixYtZv349+fn5XLlyxeFN4FvUIj4q7qSomUfCrk7WANtIWI3grCNhFWsBVvvpr/oRNeYJh3s+MGe+bJK5eKoDc2qBIkd+MCg1iCO0EVwsvcjxK8cdthOYkZxsKgBfh6wIcCMSnjJlCkuXLuXEiRNMmDCB+++/nwYN/LN8ucCEdSSsDjK5QnJUMqM7jaZEV+LW0vOqCOeW5FJcVWxcyr1LaheX9yFJEo1iG3G+6DwXSy/SPNH+pbo3qMmOqI0nbJ2eptIy0dL/dSbCkWGR9EzvydazW9l6ZitXNbjK7X7US554QsnuqK5nXldwORJ+9913uXDhAs8++yzffPMNzZs3Z9SoUXz//feijrAfMRfhuIg4l1bVUJEkic9GfMY3Y75x6xJYtSOyS7I5WX4SUETH0eW+I4yDcz6MhGVZ9ml2hL30NFDSB9V0PnAuwgDXNVeERK1EJ3CBvn3h/Hl4++1A98SruDUwFxkZyZgxY8jKyuLAgQN06tSJP//5z2RkZFBcXLuFEwWuYS7C7lgRtcE8Ej5ZdhKw7wfXhD/S1Ep1pegMygh6TXZErSJhO/tWB+fANjK2pm8zZaHPLWe2uN2Hek3Dhko9iTqEx9kRGo0GSZKQZRm9Xu/NPgmcYC7C7gzK1QbzqcsnypT6CI78YGf4I01NjYK1ktY4scUa48BcLUTYnp2jDs5BzZGwutryvtx9Hk8aEdQN3BLhiooKli5dyoABA7j66qvZt28f8+bN4/Tp0yJNzU8ERITNIuFTZUoOrCci7I80NfNBOUeWiyrOHtkRVmUszTGPhGsS4SbxTWiZ2BKDbODXc7+63Q9B3cHlgbk///nPfPbZZzRv3pyJEyeydOlSUurQrJVQIZCR8OWyyxRJStSmTld2B38U8alpUA4s6wm7myLmzI5wJxIGxRc+VXCKrWe3WkzxFtQvXBbh+fPn06JFC1q3bs2GDRvY4GB56uWhNjc9xGgYY6qA5c5suVp9ZnRDNJIGg2ygUq4kNjyWNg3auL0ff5SzrGlQDkwirC72aS+lzhGOBubAlKYWpgmzGKRzRN9mfVm6f6nwhes5LovwuHHjRFJ5EBCmCSM5Kpkr5VdIiPBPJKzVaGkU08iYJ9y5cWc0kvvDCUY7wociXFOOMFim9RVXFrslwo5S1AC6p3UnMTKRHuk9XMpaUTMktp3dhkE2ePQ3FYQ+Lovw4sWLfdgNgTukxKQoIuwnOwIUS0IV4a6N3bciIHjsiDBNGFFhUZRXlVNcWWxxdVETzgbmGsY05MxTZ4gOj7Z5zR5dU7sSHRbNlfIrHL50mA6NOtT8JkGdQ/z0hiCqL+wvOwJMg3NQCxGO9X2esCt2BHieK+zMjgAl80KtOlcT4dpwejVVpjCLfOH6ixDhEEQV4fgI/xUxUQfnALo0dn2mnDlqJFxUWURFVYVX+mVNTbWEVTxd7NPZwJwnXNdMsSSEL1x/CQoRfvfdd8nIyCAqKoo+ffrw66/OU3aWLVtG+/btiYqKokuXLqxatcr4mk6n47nnnqNLly7ExsbSpEkTxo0bx/nz5y32kZeXx9ixY0lISCApKYlJkyaFzISTsV3G0qlRJ4a0HeK3zzSPhD0V4aSoJGOU6Ctf2BVPGDyLhA2ywRhpuzPt2xlqvrCIhOsvARfhzz//nOnTpzNz5kx27dpFZmYmgwYNIjfXvm+4ZcsWxowZw6RJk9i9ezfDhw9n+PDh7N+vFJUpLS1l165dvPTSS+zatYvly5dz+PBhhg0bZrGfsWPH8vvvv5OVlcW3337Lxo0befjhh31+vN5gdOfR7P/zfjo26ui3z1RFODUi1WF1sJqQJMnnvrAv7YjCikIMsgGoOdJ2FXXm3IGLB/y73JEgaAi4CM+ZM4fJkyczYcIEOnbsyPz584mJiXFYEnPu3LkMHjyYZ555hg4dOvDqq6/So0cP5s2bBygLjmZlZTFq1CjatWvHtddey7x589i5cyenTyuFZw4ePMiaNWv48MMP6dOnDzfccAPvvPMOn332mU3ELFDo1LiTch/XqVb78bUv7MrAHHgmwqoVERMe41ZGhTMaxTYyFvD55dwvXtmnILQIqAhXVlayc+dO+vfvb9ym0Wjo378/W7favzzbunWrRXuAQYMGOWwPUFBQgCRJJCUlGfeRlJTENddcY2zTv39/NBoNv/wi/hHsMeSqIWx+cDOTm06u1X58nabmaiSs+unuiLCrAu8uaqqa8IXrJy6nqPmCS5cuodfrSU1NtdiemprKoUOH7L4nOzvbbvvs7Gy77cvLy3nuuecYM2YMCdXLfmdnZ9O4cWOLdmFhYTRo0MDhfioqKqioMA0mFRYWAooHrVOXXHGC2saVtsFKt0bduKi9WKtjaBilpINdKLzgk7+FKpTx4fE2+zf/DmLCYgAoKCtwuR+5xYqFkhyV7NW+907vzX9/+y9bTm+pcb914Tyq68fg7nEFVIR9jU6nY9SoUciyzPvvv1+rfc2ePZtXXnnFZvsPP/xATEyMy/vJysqqVT+CgdocQ0luCQC/7PuFVZdX1dDafS6VXAJgz7Y95ETm2G2TlZXF5QuXAdj9+25W5bnWj01XNgEgl8oWg8G1paqsCoAtp7fwzXffoJVqnuhR38+jYMHeMZSWlrq1j4CKcEpKClqtlpwcy3+WnJwc0tLsT/tMS0tzqb0qwKdOnWLdunXGKFjdh/XAX1VVFXl5eQ4/d8aMGUyfPt34vLCwkObNmzNw4ECLfTtCp9ORlZXFgAEDCA93fVWLYMIbx/Db5t/4dsO3JKQnMHToUO/2T6+jfI+yssXwQcNtJmGY93/jzxtZc3kN6S3TGdrftX6c3XUWTkGbpm282ne9Qc9Lc16iqLKIlr1aOs3DFudRcODsGNSrZFcJqAhHRETQs2dP1q5dy/DhwwEwGAysXbuWqVOn2n1P3759Wbt2LU8++aRxW1ZWFn379jU+VwX46NGjrF+/noYNG9rsIz8/n507d9KzZ08A1q1bh8FgoE+fPnY/NzIykshI28GY8PBwt04kd9sHI7U5hrR45UfuUtklr/8d8ivzjY8bxTdyOHU4PDzcONGltKrU5X4UVBYASp62N/seTjh9mvXhxz9+ZPuF7fRs2rPm99Tz8yhYsHcM7h5TwLMjpk+fzgcffMDHH3/MwYMHmTJlCiUlJUyYMAFQalbMmDHD2H7atGmsWbOGN954g0OHDjFr1ix27NhhFG2dTseIESPYsWMHS5YsQa/Xk52dTXZ2NpWVlQB06NCBwYMHM3nyZH799Vc2b97M1KlTuffee2nSpIltJwVew5c1hdUc4YTIhBprNxgH5txY7NN8fTlvc0PzGwD4/vj3Xt+3ILgJuAiPHj2a119/nZdffplu3bqxZ88e1qxZYxx8O336NBcuXDC2v+666/j0009ZsGABmZmZfPnll6xYsYLOnTsDcO7cOVauXMnZs2fp1q0b6enpxtuWLabR5yVLltC+fXtuu+02hg4dyg033MCCBQv8e/D1EF+mqLmaGQG1S1Hz1kQNc+7uoCxiufroalHkvZ4RFANzU6dOdWg//PTTTzbbRo4cyciRI+22z8jIcGnNuwYNGvDpp5+61U9B7fFlipo7KWTGactuCF5NdSNqQ5fGXWjboC1H847y3dHvuLfzvV7/DEFwEvBIWFC/UO2IwopCr9ePUEXS15Gwt/OEQZlNOLKjElgsO7DM6/sXeM7UVVN5fcvrPpvRKERY4Fd8WT9CtSNc8Ww9EWFfRsIAIzqOAGDV0VUeLb0k8D6XSi/x7vZ3eSbrGZ99hhBhgV8xrx/hii+87sQ6l9dgc8eO8GSxT29XULOmW1o32iS3obyqnFVHvZ9DLXCfned3AnBVg6t89r0LERb4HXVwrqYiPocvHWbA/w3g1o9vdcm7DeWBOVB+oNRo+MsDX/rkMwTusf38dgB6Nenls88QIizwO66mqX20+yMMsoESXYlLqVuu1hIG90W4oqqCUp0yE8pXIgwYfeHvjn5n/DxB4BAiLKiTuJKmptPr+Pi3j43Pvz70dY37dbWWMJhEWGfQuTRAqO5bQvLpslI90nuQkZRBqa6U1UdX++xzBK6x/Vy1CDcVIiyoQzSOUdLUnNkRq4+tJqckh3CNMvvouyPfUamvdLpfT+wIcC0aNp+o4csFOUWWRPBwrvAcF4ovoJE0dE/r7rPPESIs8DvGSNiJHbFw90IAHu/9OKmxqRRUFPDTyZ+c7tedgTl1sU9wTYR9mZ5mjeoLf3vkW8p0ZT7/PIF9VCuiU6NOxEbE+uxzhAgL/E5NnvCFogt8d+Q7AB7q8RDD2imroqw4tMLpft2xI8A9X9jXg3Lm9GrSixaJLSjRlbDm2Bqff57APjvO7wB86weDEGFBADDOmnPgCf/f3v9DL+vp26wvHRp14O72ypTeFYdWGJcXsoc7dgS4J8K+zhE2R5IkRnSozpI4KLIkAoUaCV/T5JoaWtYOIcICv+MsRU2WZaMVMan7JABubXUr8RHxXCi+YBwoscZ8EU5XLQN3Vlz2dY6wNSM7Kb7wN4e/obyq3C+fKTAhy7IpEvbhoBwIERYEAGd2xJYzWzhy+Qix4bGM6jQKgMiwSIa2Ver3OsqSKKooMkbJPomEq/3mBlG+j4QBejftTbOEZhRVFglLIgD8ceUP8sryiNBG0DXVcX1nbyBEWOB31EjYXv0INQoe1WmUxarO5paEPdQoOFIbSXR4tEv98MQT9lckrJE03NtJKeKzYKf/qvtlF2czdMlQVh5e6bfPDEZUKyIzNZMIbYRPP0uIsMDvOKofUVRRxBe/fwGYrAiVIW2HEKGN4PDlwxy8eNBmn+4OyoHjxT6XH1zOQysf4nTBaeO2vHL/DcypPHLNIwCsObaG43nH/fKZ3xz+htXHVvPmtjf98nnBijE/2MeDciBEWBAANJKGlJgUAO749A6e//F5so5n8fFvH1OiK6Fdw3bGFYhVEiITuK3VbYD9aNjdQTmwHwnLsszjqx9n4e6FZM7PNE4f9tVKy864qsFVDL5qMDIy7++o3RqJrqL+KJr/ANVHdlzwjx8MQoQFAWJUR8Xv/S3nN/61+V8M/GQgj69+HICJ3SciSZLNe1RLwp4v7IlI2qspfPzKcc4XnQcUYR+5bCSTV07mXNE5wL+RMMDUXkqd7YW7F/plGvOlUmWh1DMFZ5xmotRl9Aa9sXCPiIQFdZa5Q+Zybvo5/u/u/2N85niaxjcFlIh3XOY4u+8Z1m4YEhLbz2/nbOFZi9c8sSPsRcIbTm4A4Npm1/LCDS8gIfHh7g/Zm7MX8L8ID75qMK2SWpFfns/SfUt9/nlqJKwz6Mgptr9adV3n0KVDlOhKiA2PpX1Ke59/nhBhQcBoEt+E+7vez+Lhiznz1BkOTz3M/in7SYuzv+J1alyq0ab436H/WbzmLTtiwylFhPu36s8/bvsH68avM/5AgP8G5lS0Gi1TrpkCwLvb33Vp1ZjaoEbCUH8tCXVQrkd6jxrXKvQGQoQFQYEkSVzd8GqaJzZ32m54++EALD+03GK7J3aE9WKfsiwbp0bfnHEzAP0y+rF3yl4e7PYgd159Jx1SOri8f28xsftEosKi2J29m1/O/eLTzzIX4TOFZ3z6WcGKPwflQIiwIMS4p8M9AKw/sZ6T+SeN290pY6liHQmfzD/JmcIzhGnC6Nusr7Fdg+gGLLprESvHrPRLZGRNw5iGxjXn3t/p2wE681mM9T0S9segHAgRFoQYrZJb0b91f2RkPtz1oXG7N+wI1Yro1aSXTwu2eMJjvR4D4KtDX5Gvy/fZ59R3O6JSX8lvOb8BIhIWCBzySE8lf3bh7oXo9DqgdgNzanaEKsI3t7zZa331Ftc0uYY+TftQqa8kKy/LJ59RpiujRFdifF4fRXhfzj4q9ZU0iG5A6+TWfvlMIcKCkGNYu2E0jm1MdnE23xz5BjB5wrWKhKszI/pl9PNeZ72IGg2vubSGKkOV1/d/ueyyxfP6KMLmRXvspUn6AiHCgpAjQhvBxG4TAdOUXneL94DlYp9nCs5wIv8EWklrM1EkWBjZaSQp0Slc1l1mzXHn9SQMssHtTArrqnb1UoT9PCgHQoQFIcpDPR4C4IfjP3Diyola5wmrVkTPJj0talYEE1FhUYztMhaAz37/zGG7KkMVvT7oRcf3Orq0dJOK6gc3S2gGKDnD9amofHlVOT+f/hkQIiwQ1EibBm0Y0HoAMjIf7Pqg1gNzxtS0IPSDzRndcTQA3x791mHhodVHV7Prwi4OXTrEljNbXN63KsJtG7QlNlwZmLSeFFNXKaooYuiSoRzNO0pseCzXt7jeb58tRFgQsqgDdB/s+sBYc9eTFDWdQUfWH8pgV7CLcM/0nqRFpFGqK3VY6eyjPR8ZH687sc7lfauz5RrFNqJFYgugflgSeWV5DPi/Aaw/uZ64iDi+ve9bY20TfyBEWBCyDGs3jNTYVGMEp5E0blkJ5ot9ni44jUbScEOLG7zeT28iSRI3Jd8EwNL9ttOYc4pz+PbIt8bna0+sdXnf6t8xJTql3ohwdnE2/Rb345dzv9AgugFrx631+8CsEGFByBKuDWdi94nG54mRiW6thGy+2CdA97TuJEYlerWPvuDG5BsBpcTl5VLLjIZP9n5ClaHKmF7167lfKawodGm/RhGOqR8ifDL/JDd8dAP7cveRFpfGhgc30Ltpb7/3Q4iwIKSZ3GOy8bEndR3Mo+FgtyJUmkc1p2vjrlQZqvjq4FfG7bIsG62I569/ntbJrdHLejae2ujSfuuTCBtkA8M/G87xK8fJSMpg04RNdG7cOSB9ESIsCGlaJbdiYJuBgGe1fi1EOCM0RBgwrrphbkn8cu4XDlw8QHRYNKM7jzbWX177h2uWhLkn3DxBqeHhTv2IgxcP8ubWN6nUV7r8nkCxdN9Sfsv5jYTIBDY+uJE2DdoErC9ChAUhz5N9ngSgS2oXt9+rirCExI0tbvRmt3yKWo95w8kNnCtUah1/tFuJgkd2GmlRBN9VX7i2kfC0NdOY/sN0vj5ofx3AYKFSX8lL618C4Lnrn6uxaJSvESIsCHmGtB3CwccOMm/IPLffq4pw19Sufi9TWRtaJLbg+ubXIyPz+e+fU1JZwmf7ldxhdSLLra1uBWBf7j67K1tb40iEXZ30odZcPnjJdvmpYOKDnR9wIv8EqbGpTOszLdDdESIsqBu0T2nvUdEdVYSDdaqyM+7rch+gWBJfHfyKosoi2iS34aaWSvZEo9hGxpWC159Y73RfsixbiLA6YaOsqsxmOrM9rpRdIadEKQJ/LO+YZwfkB4ori/nbxr8B8PLNLwdFoSYhwoJ6TZ+mfZCQGNFxRKC74jYjO45EK2nZcX4Hf9/4d8B2aahbM5RouCZLoqCiwFiPIiUmhciwSGNxfVcsCfPo9/gV/yxK6glvbXuL3JJcWie3Ns66DDRChAX1mldveZX85/ODPj/YHo1iG9G/dX8AjuYdRSNpbJaGuq21a76wGgXHRcQZ0/bc8YXNV8AO1kj4Uukl/r3l3wD8/Za/+3wpe1cRIiyo10iSREJkQqC74TGqJQEwqM0go42gclPLm9BKWv648odFEXxrzK0IFWOGREHNGRKHLh2y2FdBeYFL/fcnr216jcKKQjJTMxndeXSgu2NEiLBAEMIMbz/cGLmaT1xRSYhMME5AcJaqplZQMxdhtyJhq8G4YLMkzhScYd6vysDt7NtmuzWpx9cET08EAoHbJEQmsHDYQp6//nnj+nvWuJKqpkbCjWIaGbcZRbjQdREO14QDcDwveES4qKKICf+bQIW+gpta3sTgqwYHuksWCBEWCEKc+7rcx+z+swnThNl9XfWF151Y5zDdzJ4d4WokXF5VzokrJwBTlkmw+MJnC89y46IbWXtiLdFh0bwx8A2/FWt3FSHCAkEdp2+zvkSFRZFTksOBiwfstlFny3kiwkcuH0FGJikqyVgQPxjsiD3Ze+jzYR9+y/mNxrGN2fDgBq5pck2gu2WDEGGBoI4TGRZpzP5wZEk4syMuFF1wOhVZzYzokNKBqxpcBQRehFcfXc2Ni27kfNF5OjbqyC8P/eK31ZPdRYiwQFAPqMkXtmdHNIppRKQ2EhmZ80XnHe5b9YM7pHSgTbJSgyGQdsSKQyu4c+mdFFcWc2urW9k8cTMZSRkB609NCBEWCOoBqghvPLXRri9sT4QlSTLWVXBmSajpae1T2hsj4bOFZwOyNFJ5VTlPrH4Cvazn/q73s3rsardWWwkEQoQFgnpA19SuaCQN+eX5ZBdn27xuzxMG13xhYyTcqAMpMSnERyiF9U/kn/BK393h3V/f5UzhGZolNGPBHQuCZkKGM4QICwT1gMiwSGOhd/OJFSpGTzi2kcX2mkRYb9Bz+NJhQLEjJEky+cJ+TlPLL8/nHz//A4BX+r1CdHi0Xz/fU4QICwT1hPYp7QFbEdbpdcaFUm0i4QTnInyq4BQV+goitZFG31WtzetvX/hfm/7FlfIrdGzU0Wb6djAjRFggqCe0b2hfhNUqaRKSTWF8NRJ2VNxdzYy4uuHVaDVaAK5K9n+GxLnCc7z1y1uAMiPOUc50MCJEWCCoJxgj4cuWIqxaEQ2iGxiFVKWmgTlzP1glEJHwrJ9mUV5VzvXNr+fOq+/02+d6AyHCAkE9wZEd4cgPhpo9YWNmRHWUDfg9V/jgxYPGtfX+1f9fQTcjriaECAsE9QRVhE8XnKakssS43V56mopaSa2wotBuZTS7kXB1rvDJ/JPGGsW+5IV1L2CQDdzV7i6ub3G9zz/P2wgRFgjqCQ1jGhqF9sjlI8bt9iqoqcRGxNIwuiFgGw3LsmwxW06laUJTIrWRVBmqfL5a8y9nf2HFoRVoJA3/vO2fPv0sXyFEWCCoR9izJOxNWTbHkSWRW5LLlfIrSEhc3fBq43aNpDGmw/k6Te27o98ByiojHRt19Oln+QohwgJBPcJehoQzOwJMg3PWGRLqPjKSMmxyclVf2NeDc0fzjgIEZWEeVwmdPI4QRa/Xo9Pp0Ol0hIWFUV5ejl6vD3S3PCLUj6E2/Q8PD0er1dbcMMixlyFxqcy5CKu5wtaCas8PVlF9YV8Pzqm2StsGbX36Ob5EiLCPkGWZ7Oxs8vPzjc/T0tI4c+ZMyI3eqoT6MdS2/0lJSaSlpYXksavYsyOcecKAUn1sO/xn5394rNdjtEpuBZhyhM0zI1T8kaYmyzJHLyuRsLkdEmoIEfYRqgA3btyYmJgYZFmmuLiYuLg4NJrQdIEMBkNIH4On/ZdlmdLSUnJzcwFIT0/3VRd9jirCRy4fQW/Qo9Voa/SEx3YZy4e7PuTn0z8zfsV41o9fj1ajNUbT9iJhf6Sp5ZTkUFRZZOFBhyJChH2AXq83CnDDhsrIssFgoLKykqioqJAUMAj9Y6hN/6OjFc8zNzeXxo0bh6w1kZGUQYQ2gvKqck4XnKZVcqsaPWGtRsvHwz+m6/yu/Hz6Z+ZsncMz1z9jNzNCxWhH5B1HlmWfXD2oUXDLxJZEhkV6ff/+IvT+k0IAnU4HQExMTIB7IvAm6vepfr+hiFajNV66H7p0CFmWHVZQM6dVcivmDp4LwF/X/5WtZ7YaB+rU6Nqclkkt0UpayqrKuFB8wduHAZj5wQ1D1w8GIcI+JZS9Q4EtdeX7NPeFS3WllFeVA85FGGBCtwncefWdVOorueuzuwDFwmgY09CmbYQ2wpja5qs0NTUz4uoGoesHgxBhgY/JyMjgrbfeCnQ3BGaYp6mpVkSkNpK4iDin75MkiQ/u/IBGMY2M0bM9P1jF12lqIhIW1CkkSXJ6mzVrlkf73b59Ow8//HCt+tavXz8kSeK1116zee3222932L+lS5ei1Wp57LHHbF776aefHB5rdrZt0fO6hHmamrkf7EqknxqXyoI7Fxif2/ODVXydpqZGwqGcngZChAXVXLhwwXh76623SEhIsNj29NNPG9vKskxVlWs1ARo1auQVb7x58+YsXrzYYtu5c+dYu3atw2yFhQsX8uyzz7J06VLKy8vttjl8+LDFcV64cIHGjRvXur/BjLkd4YofbM3w9sN5uIfyw3pLxi0O2/kyEjbIBuN+Qzk9DYQIC6pJS0sz3hITE5Ekyfj80KFDxMfHs3r1avr160d0dDSbNm3i+PHj3HXXXaSmphIXF0evXr348ccfLfZrbUdIksSHH37I3XffTUxMDG3btmXlypU19u+OO+7g0qVLbN682bjt448/ZuDAgXZF88SJE2zZsoXnn3+eq6++muXLl9vdb+PGjS2OPS0tLSQzP9yhXUo7QJl2rF7SuyPCAPPvmM/xJ44zqtMoh23UXGFfRMJnC89SXlVOmCaMlkktvb5/fxLws+3dd98lIyODqKgo+vTpw6+//uq0/bJly2jfvj1RUVF06dKFVatWWby+fPlyBg4cSMOGDZEkiT179tjs4/jx49x99900atSIhIQERo0aRU5OjjcPywJZlimpLKFEV6Lc+/Fmb1FHT3nhhReYOXMmv//+O127dqW4uJihQ4eydu1adu/ezeDBg7nzzjs5fdp50ZZXXnmFUaNGsXfvXoYOHcrYsWPJy8tz+p6IiAjGjh3LokWLjNsWL17MxIkT7bZftGgRt99+O4mJidx///0sXLjQ/QOuo8RFxNEsoRkAm88oP2r2ylg6Q5IkWie3dmph+DISVtPT2iS3CakC7vYIaO8///xzpk+fzvz58+nTpw9vvfUWgwYN4vDhw3ajmy1btjBmzBhmz57NHXfcwaeffsrw4cPZtWsXnTt3BqCkpIQbbriBUaNGMXnyZJt9lJSUMHDgQDIzM1m3bh0AL730EnfeeSfbtm3zSRRUqisl4V8JXt+vKxTPKCY2ItYr+5o1axa33HILCQkJaDQaGjRoQGZmpvH1V199la+//pqVK1cydepUh/t58MEHGTNmDAD//Oc/efvtt/n1118ZPHiw08+fOHEiN954I3PnzmXnzp0UFBRwxx132PjBBoOBxYsX88477wBw77338pe//IUTJ04Y87ZVmjVrZvG8ZcuW/P777zX+LUKd9intOVt4lp9P/QxASrR7kbArqBMo8svzySvLo0F0A6/tu64MykGAI+E5c+YwefJkJkyYQMeOHZk/fz4xMTF89NFHdtvPnTuXwYMH88wzz9ChQwdeffVVevTowbx584xtHnjgAV5++WX69+9vdx+bN2/m5MmTLF68mC5dutClSxc+/vhjduzYYRRlgX2uucaySEpxcTFPP/00HTp0ICkpibi4OA4ePFhjJNy1a1fj49jYWBISEoyz0ZyRmZlJ27Zt+fLLL/noo4944IEHCAuzjSOysrIoKSlh6NChAKSkpDBgwACLKFrl559/Zs+ePcab9ZVVXUXNkFBzeN21I1whJjyGJvFNAPjm8Dde3XddSU+DAEbClZWV7Ny5kxkzZhi3aTQa+vfvz9atW+2+Z+vWrUyfPt1i26BBg1ixYoXLn1tRUYEkSURGmmbYqDOoNm3a5FC8a0NMeAyFzxVSWFRIQnyCXz3HmHDvTRiJjbWMqJ9++mmysrJ4/fXXueqqq4iOjmbEiBFUVlY63U94eLjFc0mSMBgMLvVh4sSJvPvuuxw4cMChdbVw4ULy8vKMs9xAiY737t1rc/60atWKpKQklz67LmE9wcJdO8JVJnWfxKsbX2XKd1PomtqV7undvbLfuhQJB0yEL126hF6vJzU11WJ7amoqhw7ZLskNSj0Ge+3dSSm69tpriY2N5bnnnuOf//wnsizz/PPPo9fruXDB8cyeiooKKioqjM8LCwsBjBXSzNHpdMiyjMFgMIpLTHgM+nA9MeExfk36l2XZbV9Y7bP1vbof9dg2b97M+PHjuesuJXG/uLiYkydPGl8374P5c/O/i7Nt1sdhMBi49957efrpp8nMzKR9+/YWfTMYDFy+fJn//e9/fPrpp3Tq1Mn4fr1ez0033cS6deu4++67LY7N1R8Ag8GALMvodLqATVtWz7Xaztprm2wpXkkRST6ZCfjCdS/w69lf+f6P7xn+2XC2TNhCcoSymGhtPk8V4daJrQMyg9HZ9+Buf0Lb0faARo0asWzZMqZMmcLbb7+NRqNhzJgx9OjRw2mEOnv2bF555RWb7T/88INNClZYWBhpaWkUFxfbRIVFRUXeORAfUl5ejizLxh+a0tJSQBHZxMRE4zFkZGTw5ZdfcsstSprSP//5T2N9BvW9BoOB8vJy43OAsrIyi+eyLNu0Maeqqsq4T61Wy6FDhwgLCzO21+v1VFRUUFhYyIcffkiDBg0YPHiwzY/dgAED+OSTT+jfv7/xmP744w/i4+Mt2jVo0MAmWgfl6q2srIyNGze6nKLnK7Kysmr1/jyd5UDo8X3HWXXSN1bMAzEPsC9yH6cLTzPow0G8ctUrhElhxmM4UXaClbkraRbVjHtS76lxf3pZzx95fwBwes9pVh0InIVk73tQzy1XCZgIp6SkoNVqbbIScnJySEtLs/uetLQ0t9o7YuDAgRw/fpxLly4RFhZmLFHYurXjSkwzZsywuJQtLCykefPmDBw4kIQEy0G38vJyzpw5Q1xcHFFRUYAiNEVFRcTHxwf99NeoqCgkSTIel/ojExenzKhSj2Hu3Lk89NBDDBo0iJSUFJ599lnKysqIiIgwvlej0RAVFWXxN4qOjrZ4LkmSTRtzwsLCLPZp3U6r1RIZGUlCQgJLly7l7rvvJjEx0WY/o0aNYvz48VRUVBiPqVevXjbtNm/ezLXXXmuzvby8nOjoaG666Sbj9+pvdDodWVlZDBgwwO4PhavIssy0o9MoqlR+UIfeMpSujbvW8C7P6XKpCzcsvoHfS37nB+kHhjKUZt2b8c+t/2T5YVP64D9H/pPUuFQne1KyLfS/6YkOi+aBux5AI/l/aMvZ9+AomHCIHEB69+4tT5061fhcr9fLTZs2lWfPnm23/ahRo+Q77rjDYlvfvn3lRx55xKbtiRMnZEDevXt3jf1Yu3atLEmSfOjQIZf7XlBQIANyQUGBzWtlZWXygQMH5LKyMuM2vV4vX7lyRdbr9S5/RrAR6sdQ2/7b+179TWVlpbxixQq5srKy1vvqtaCXzCxkZiGfLzzvhd45Z+WhlbI0S5KZhdz59c7Gx9IsSY75R4zMLORlvy+rcT+rjqySmYXc5b0uPu+zI5x9D860wR4BzY6YPn06H3zwAR9//DEHDx5kypQplJSUMGHCBADGjRtnMXA3bdo01qxZwxtvvMGhQ4eYNWsWO3bssEiHysvLY8+ePRw4cABQZkTt2bPHwjdetGgR27Zt4/jx43zyySeMHDmSp556inbt2vnpyAWCwGM+OGevCI+3ubPdnbx6y6sA7C/ej4zM3e3v5rdHf2NiNyXfe8PJDTXuR/WDQ32mnEpAPeHRo0dz8eJFXn75ZbKzs+nWrRtr1qwxDr6dPn3awqe97rrr+PTTT/nrX//KCy+8QNu2bVmxYoUxRxhg5cqVRhEHJUcUYObMmcZ80sOHDzNjxgzy8vLIyMjgxRdf5KmnnvLDEQsEwYMqwgmRCURoI/zymS/c+AIF5QVsO7iN1//0Or2b9wbg5oybmbd9HhtPb6xxH3WlZoRKwAfmpk6d6jCx/6effrLZNnLkSEaOHOlwfw8++CAPPvig08987bXX7BaDEQjqE6oI+yJH2BGSJPGPW/7BqrJVdE8zpavd2OJGAPbl7KtxYkddSk+DIJi2LBAIAkP/1v25vvn1PNrz0UB3hdS4VNqntEdGZtPpTU7bGidq1BE7QoiwQFBPSYhMYNPETTxz/TOB7goAN7W4CYCNpxxbEuVV5ZzKPwXUHTtCiLBAIAgKbmqpiPCGU44H5/648gcyMgmRCTSOrRslR4UICwSCoEAV4V0XdlFUYX9Sk9EPbtA26PPtXUWIsEAgCAqaJzanVVIrDLLBWGLTGrWEZV3xg0GIsEAgCCJuzrgZcOwL17X0NBAiLPAy/fr148knnwx0NwQhijo458gXrmsTNUCIsKCaO++802FR9Z9//hlJkti7d2+tP2fx4sVIkkSHDrYLRC5btgxJksjIyLB5raysjAYNGpCSkmJRzU4lIyPD7qKdIh88tFAj4e3ntlOqsy2EY4yE60iOMAgRFlQzadIksrKyOHv2rM1rixYt4pprrrEoxl4bYmNjyc3NtakbvXDhQlq0aGH3PV999RWdOnWiffv2DutH/+1vf7NZtPPxxx/3Sp8F/qFVUiuaxjdFZ9Cx7ew2i9eKK4s5X3QeEHaEoA5yxx130KhRI5sVjYuLi1m2bBmTJk3i8uXLTJo0iebNmxMTE0OXLl1YunSp258VFhbGfffdZ7GCytmzZ/npp5+477777L5n4cKF3H///U7Xi4uPj7dZtNO6EL0guJEkyaEvrK5VlxKTQnJ0st/75iuECPsDWYaSksDcXCzoHhYWxrhx41i8eLFFEfhly5ah1+sZM2YM5eXldOvWjW+++Yb9+/fz8MMP88ADD9S4OKs9Jk6cyBdffGGsvbp48WIGDx5sU7QflIVZt27dyqhRoxg1ahQ///wzp06dcvszBaGBvUkb5VXlvLjuRQA6pNhaWaGMEGF/UFqKJiGBpGbN0CQkQFyc/25uFJieOHEix48fZ8MG06DIokWLuOeee0hMTKRp06Y8/vjjdOvWjdatW/P4448zePBgvvjiC7f/JN27d6d169Z8+eWXyLLsdOXkjz76iCFDhpCcnEyDBg0YNGiQ3fXinnvuOeLi4ixuP//8s9t9EwQWNRLeenYrFVUVlFSWcMend7Dq6Cqiw6KNldjqCkKEBUbat2/PddddZ7QJjh07xs8//8ykSZMAZQWLf//732RmZtKgQQPi4uL4/vvva1zY0xETJ05k0aJFbNiwwWJhTnP0ej0ff/wx999/v3Hb/fffz+LFi22WJXrmmWcsFu3cs2ePzeKkguCnXcN2NI5tTHlVOetOrGPQJ4NYe2ItcRFxrB672ijSdYWAV1GrF8TEYCgspLCw0LhcvD8/2x0mTZrE448/zrvvvsuiRYto06YNN9+snPSvv/468+fP58033yQzM5PY2FiefPLJGhf2dMTYsWN59tlnmTVrlsOVk7///nvOnTvH6NGjLbbr9XrWrl3LgAEDjNtSUlK46qqrPOqLIHiQJImbWt7Elwe+5E9f/InyqnISIxNZc/8arm1mu+JJqCMiYX8gSRAbG5ibm1M7R40ahUaj4dNPP+W///0vEydONE4P3bx5M0OHDuX+++8nMzOT1q1bc+TIEY//LA0aNGDYsGFs2LDBoRWxcOFC7r33XpsI995773U4QCcIfVRfuLyqnIbRDVk3fl2dFGAQkbDAiri4OEaPHs2MGTMoLCy0qM3ctm1bli1bxpYtW2jYsCFz5swhJyeHjh07evx5ixcv5r333qNhQ9uVHS5evMg333zDypUrLQr3g7Lqyt13301eXh4NGii1Z4uKimxW3o6JiXG4dp0geBl81WC0kpaUmBR+HPcjnRt3rvlNIYqIhAU2TJo0iStXrjBo0CCaNGli3P7iiy+SmZnJkCFD6NevH2lpaQwfPrxWnxUdHW1XgAH++9//Ehsby2233Wbz2m233UZ0dDSffPKJcdvLL79Menq6xe3ZZ5+tVf8EgaFtw7bsm7KPA48dqNMCDCDJsos5TAILCgsLSUxMpKCgwO5qyydOnKBVq1bGVXkNBkNgPGEvEurHUNv+2/te/Y1Op2PVqlUMHTq0VqstB5K6fgzOtMEeofefJBAIBHUIIcICgUAQQIQICwQCQQARIiwQCAQBRIiwQCAQBBAhwj5EJJ7ULcT3KfAFQoR9gJqyUupG8RxB8KN+n6GaViUITsSMOR+g1WpJSkoiNzcXUGZtybJMZWUl5eXlIZljC0qebSgfg6f9l2WZ0tJScnNzSUpKQqvV+rCXgvqGEGEfkZaWBmAUYlmWKSsrIzo6OmSX6g71Y6ht/5OSkozfq0DgLYQI+whJkkhPT6dx48bodDp0Oh0bN27kpptuCtnL2VA/htr0Pzw8XETAAp8gRNjHaLVa462qqoqoqKiQFDAg5I8h1PsvqJuEnrEnEAgEdQghwgKBQBBAhAgLBAJBABGesIeoifuFhYUutdfpdJSWllJYWBiyfmSoH0Oo9x/EMQQLzo5B1QRXJ/cIEfaQoqIiAJo3bx7gnggEgmCkqKiIxMTEGtuJou4eYjAYOH/+PPHx8S7lnBYWFtK8eXPOnDkTssvthPoxhHr/QRxDsODsGGRZpqioiCZNmrg0KUhEwh6i0Who1qyZ2+9LSEgI2RNPJdSPIdT7D+IYggVHx+BKBKwiBuYEAoEggAgRFggEggAiRNhPREZGMnPmTCIjIwPdFY8J9WMI9f6DOIZgwZvHIAbmBAKBIICISFggEAgCiBBhgUAgCCBChAUCgSCACBH2A++++y4ZGRlERUXRp08ffv3110B3ySEbN27kzjvvpEmTJkiSxIoVKyxel2WZl19+mfT0dKKjo+nfvz9Hjx4NTGcdMHv2bHr16kV8fDyNGzdm+PDhHD582KJNeXk5jz32GA0bNiQuLo577rmHnJycAPXYlvfff5+uXbsa81D79u3L6tWrja8He/+tee2115AkiSeffNK4LdiPYdasWUiSZHFr37698XVv9V+IsI/5/PPPmT59OjNnzmTXrl1kZmYyaNAg44obwUZJSQmZmZm8++67dl//f//v//H2228zf/58fvnlF2JjYxk0aBDl5eV+7qljNmzYwGOPPca2bdvIyspCp9MxcOBASkpKjG2eeuopvvnmG5YtW8aGDRs4f/48f/rTnwLYa0uaNWvGa6+9xs6dO9mxYwe33nord911F7///jsQ/P03Z/v27fznP/+ha9euFttD4Rg6derEhQsXjLdNmzYZX/Na/2WBT+ndu7f82GOPGZ/r9Xq5SZMm8uzZswPYK9cA5K+//tr43GAwyGlpafK///1v47b8/Hw5MjJSXrp0aQB66Bq5ubkyIG/YsEGWZaXP4eHh8rJly4xtDh48KAPy1q1bA9XNGklOTpY//PDDkOp/UVGR3LZtWzkrK0u++eab5WnTpsmyHBrfwcyZM+XMzEy7r3mz/yIS9iGVlZXs3LmT/v37G7dpNBr69+/P1q1bA9gzzzhx4gTZ2dkWx5OYmEifPn2C+ngKCgoAaNCgAQA7d+5Ep9NZHEf79u1p0aJFUB6HXq/ns88+o6SkhL59+4ZU/x977DFuv/12i75C6HwHR48epUmTJrRu3ZqxY8dy+vRpwLv9F7UjfMilS5fQ6/WkpqZabE9NTeXQoUMB6pXnZGdnA9g9HvW1YMNgMPDkk09y/fXX07lzZ0A5joiICJKSkizaBttx7Nu3j759+1JeXk5cXBxff/01HTt2ZM+ePSHR/88++4xdu3axfft2m9dC4Tvo06cPixcvpl27dly4cIFXXnmFG2+8kf3793u1/0KEBXWaxx57jP3791t4eaFCu3bt2LNnDwUFBXz55ZeMHz+eDRs2BLpbLnHmzBmmTZtGVlYWUVFRge6ORwwZMsT4uGvXrvTp04eWLVvyxRdfEB0d7bXPEXaED0lJSUGr1dqMmObk5ITk0ulqn0PleKZOncq3337L+vXrLSrepaWlUVlZSX5+vkX7YDuOiIgIrrrqKnr27Mns2bPJzMxk7ty5IdH/nTt3kpubS48ePQgLCyMsLIwNGzbw9ttvExYWRmpqatAfgzVJSUlcffXVHDt2zKvfgRBhHxIREUHPnj1Zu3atcZvBYGDt2rX07ds3gD3zjFatWpGWlmZxPIWFhfzyyy9BdTyyLDN16lS+/vpr1q1bR6tWrSxe79mzJ+Hh4RbHcfjwYU6fPh1Ux2GNwWCgoqIiJPp/2223sW/fPvbs2WO8XXPNNYwdO9b4ONiPwZri4mKOHz9Oenq6d7+DWgweClzgs88+kyMjI+XFixfLBw4ckB9++GE5KSlJzs7ODnTX7FJUVCTv3r1b3r17twzIc+bMkXfv3i2fOnVKlmVZfu211+SkpCT5f//7n7x37175rrvuklu1aiWXlZUFuOcmpkyZIicmJso//fSTfOHCBeOttLTU2ObRRx+VW7RoIa9bt07esWOH3LdvX7lv374B7LUlzz//vLxhwwb5xIkT8t69e+Xnn39eliRJ/uGHH2RZDv7+28M8O0KWg/8Y/vKXv8g//fSTfOLECXnz5s1y//795ZSUFDk3N1eWZe/1X4iwH3jnnXfkFi1ayBEREXLv3r3lbdu2BbpLDlm/fr0M2NzGjx8vy7KSpvbSSy/JqampcmRkpHzbbbfJhw8fDmynrbDXf0BetGiRsU1ZWZn85z//WU5OTpZjYmLku+++W75w4ULgOm3FxIkT5ZYtW8oRERFyo0aN5Ntuu80owLIc/P23h7UIB/sxjB49Wk5PT5cjIiLkpk2byqNHj5aPHTtmfN1b/RdV1AQCgSCACE9YIBAIAogQYYFAIAggQoQFAoEggAgRFggEggAiRFggEAgCiBBhgUAgCCBChAUCgSCACBEWCASCACJEWCAIIPaWkBLUL4QIC+otDz74oM0aYpIkMXjw4EB3TVCPEPWEBfWawYMHs2jRIottkZGRAeqNoD4iImFBvSYyMpK0tDSLW3JyMqBYBe+//z5DhgwhOjqa1q1b8+WXX1q8f9++fdx6661ER0fTsGFDHn74YYqLiy3afPTRR3Tq1InIyEjS09OZOnWqxeuXLl3i7rvvJiYmhrZt27Jy5UrfHrQgqBAiLBA44aWXXuKee+7ht99+Y+zYsdx7770cPHgQUFamHjRoEMnJyWzfvp1ly5bx448/Wojs+++/z2OPPcbDDz/Mvn37WLlyJVdddZXFZ7zyyiuMGjWKvXv3MnToUMaOHUteXp5fj1MQQLxX+E0gCC3Gjx8va7VaOTY21uL2j3/8Q5ZlpSTmo48+avGePn36yFOmTJFlWZYXLFggJycny8XFxcbXv/vuO1mj0RjrRTdp0kR+8cUXHfYBkP/6178anxcXF8uAvHr1aq8dpyC4EZ6woF5zyy238P7771tsU1dlBmxWSejbty979uwB4ODBg2RmZhIbG2t8/frrr8dgMHD48GEkSeL8+fPcdtttTvvQtWtX4+PY2FgSEhLIzc319JAEIYYQYUG9JjY21sYe8BauLgYZHh5u8VySJAwGgy+6JAhChCcsEDhh27ZtNs87dOgAQIcOHfjtt98oKSkxvr5582Y0Gg3t2rUjPj6ejIwMi3XIBAJrRCQsqNdUVFSQnZ1tsS0sLIyUlBQAli1bxjXXXMMNN9zAkiVL+PXXX1m4cCEAY8eOZebMmYwfP55Zs2Zx8eJFHn/8cR544AFSU1MBmDVrFo8++iiNGzdmyJAhFBUVsXnzZh5//HH/HqggaBEiLKjXrFmzhvT0dItt7dq149ChQ4CSufDZZ5/x5z//mfT0dJYuXUrHjh0BiImJ4fvvv2fatGn06tWLmJgY7rnnHubMmWPc1/jx4ykvL+fNN9/k6aefJiUlhREjRvjvAAVBj1hjTiBwgCRJfP311wwfPjzQXRHUYYQnLBAIBAFEiLBAIBAEEOEJCwQOEE6dwB+ISFggEAgCiBBhgUAgCCBChAUCgSCACBEWCASCACJEWCAQCAKIEGGBQCAIIEKEBQKBIIAIERYIBIIAIkRYIBAIAsj/BxFAOshWBCoJAAAAAElFTkSuQmCC\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "model.save(\"/content/drive/My Drive/AuraClima/co2_lstm_forecast_model.keras\")" ], "metadata": { "id": "j2K701jLvsZL" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "# Update path if needed\n", "csv_path = \"/content/drive/MyDrive/AuraClima/CO2_Emissions_MinMaxScaled.csv\"\n", "df = pd.read_csv(csv_path)" ], "metadata": { "id": "GpwPmkaLy9PL" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from tensorflow.keras.models import load_model\n", "\n", "# Update path if needed\n", "model_path = \"/content/drive/MyDrive/AuraClima/co2_lstm_forecast_model.keras\"\n", "model = load_model(model_path)" ], "metadata": { "id": "yA4nw3_IzG2c" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "country_features = [col for col in df.columns if col not in [\"Country Name\"] + year_cols]" ], "metadata": { "id": "A9iT65yy7EFQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "year_cols = [str(year) for year in range(1960, 2019)]\n", "country_features = [col for col in df.columns if col not in [\"Country Name\"] + year_cols]" ], "metadata": { "id": "mhhI8VFi7HFU" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def forecast_to_2030(model, series, country_vec, start_year=2018, end_year=2030, window_size=45):\n", " predictions = []\n", " current_series = list(series[-window_size:])\n", "\n", " for _ in range(end_year - start_year):\n", " if len(current_series) < window_size:\n", " raise ValueError(\"Insufficient history to predict\")\n", "\n", " co2_col = np.array(current_series[-window_size:]).reshape(window_size, 1) # (45, 1)\n", " country_mat = np.tile(country_vec, (window_size, 1)) # (45, 266)\n", " input_seq = np.concatenate([co2_col, country_mat], axis=1) # (45, 267)\n", " input_seq = input_seq.reshape(1, window_size, -1) # (1, 45, 267)\n", "\n", " pred = model.predict(input_seq, verbose=0)[0]\n", " predictions.append(pred[0])\n", " current_series.append(pred[0])\n", "\n", " return predictions" ], "metadata": { "id": "7HlcYH7vwRyC" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Assuming these were added during one-hot encoding\n", "country_feature_names = [col for col in df.columns if col.startswith(\"Country_\")]" ], "metadata": { "id": "bLvvwYICzr2d" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "year_cols = [str(year) for year in range(1960, 2019)]\n", "country_features = [col for col in df.columns if col not in [\"Country Name\"] + year_cols]\n", "\n", "all_forecasts = []\n", "\n", "for idx, row in df.iterrows():\n", " try:\n", " country_name = row[\"Country Name\"]\n", " series = row[year_cols].values.astype(float)\n", " country_vec = row[country_features].values.astype(float) # ✅ ADD THIS BACK\n", "\n", " forecast = forecast_to_2030(model, series, country_vec) # make sure this uses country_vec\n", " years = list(range(2019, 2031))\n", "\n", " df_future = pd.DataFrame({\n", " \"Country Name\": [country_name] * len(years),\n", " \"Year\": years,\n", " \"Forecasted CO2\": forecast\n", " })\n", "\n", " all_forecasts.append(df_future)\n", "\n", " except Exception as e:\n", " print(f\"[{country_name}] Error: {e}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8-a-JR0JyDH0", "outputId": "c9f87118-6168-404c-e50e-845729313619" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Afghanistan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Africa Eastern and Southern] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Africa Western and Central] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Albania] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Algeria] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[American Samoa] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Andorra] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Angola] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Antigua and Barbuda] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Arab World] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Argentina] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Armenia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Aruba] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Australia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Austria] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Azerbaijan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bahamas, The] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bahrain] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bangladesh] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Barbados] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Belarus] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Belgium] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Belize] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Benin] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bermuda] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bhutan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bolivia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bosnia and Herzegovina] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Botswana] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Brazil] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[British Virgin Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Brunei Darussalam] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Bulgaria] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Burkina Faso] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Burundi] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Cabo Verde] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Cambodia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Cameroon] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Canada] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Caribbean small states] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Cayman Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Central African Republic] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Central Europe and the Baltics] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Chad] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Channel Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Chile] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[China] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Colombia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Comoros] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Congo, Dem. Rep.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Congo, Rep.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Costa Rica] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Cote d'Ivoire] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Croatia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Cuba] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Curacao] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Cyprus] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Czech Republic] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Denmark] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Djibouti] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Dominica] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Dominican Republic] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Early-demographic dividend] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[East Asia & Pacific] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[East Asia & Pacific (IDA & IBRD countries)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[East Asia & Pacific (excluding high income)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Ecuador] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Egypt, Arab Rep.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[El Salvador] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Equatorial Guinea] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Eritrea] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Estonia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Eswatini] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Ethiopia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Euro area] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Europe & Central Asia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Europe & Central Asia (IDA & IBRD countries)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Europe & Central Asia (excluding high income)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[European Union] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Faroe Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Fiji] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Finland] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Fragile and conflict affected situations] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[France] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[French Polynesia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Gabon] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Gambia, The] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Georgia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Germany] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Ghana] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Gibraltar] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Greece] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Greenland] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Grenada] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Guam] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Guatemala] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Guinea] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Guinea-Bissau] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Guyana] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Haiti] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Heavily indebted poor countries (HIPC)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[High income] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Honduras] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Hong Kong SAR, China] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Hungary] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[IBRD only] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[IDA & IBRD total] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[IDA blend] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[IDA only] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[IDA total] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Iceland] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[India] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Indonesia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Iran, Islamic Rep.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Iraq] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Ireland] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Isle of Man] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Israel] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Italy] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Jamaica] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Japan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Jordan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Kazakhstan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Kenya] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Kiribati] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Korea, Dem. People's Rep.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Korea, Rep.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Kosovo] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Kuwait] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Kyrgyz Republic] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Lao PDR] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Late-demographic dividend] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Latin America & Caribbean] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Latin America & Caribbean (excluding high income)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Latin America & the Caribbean (IDA & IBRD countries)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Latvia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Least developed countries: UN classification] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Lebanon] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Lesotho] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Liberia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Libya] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Liechtenstein] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Lithuania] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Low & middle income] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Low income] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Lower middle income] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Luxembourg] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Macao SAR, China] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Madagascar] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Malawi] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Malaysia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Maldives] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Mali] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Malta] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Marshall Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Mauritania] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Mauritius] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Mexico] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Micronesia, Fed. Sts.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Middle East & North Africa] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Middle East & North Africa (IDA & IBRD countries)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Middle East & North Africa (excluding high income)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Middle income] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Moldova] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Monaco] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Mongolia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Montenegro] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Morocco] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Mozambique] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Myanmar] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Namibia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Nauru] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Nepal] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Netherlands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[New Caledonia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[New Zealand] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Nicaragua] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Niger] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Nigeria] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[North America] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[North Macedonia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Northern Mariana Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Norway] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Not classified] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[OECD members] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Oman] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Other small states] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Pacific island small states] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Pakistan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Palau] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Panama] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Papua New Guinea] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Paraguay] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Peru] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Philippines] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Poland] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Portugal] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Post-demographic dividend] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Pre-demographic dividend] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Puerto Rico] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Qatar] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Romania] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Russian Federation] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Rwanda] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Samoa] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[San Marino] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sao Tome and Principe] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Saudi Arabia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Senegal] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Serbia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Seychelles] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sierra Leone] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Singapore] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sint Maarten (Dutch part)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Slovak Republic] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Slovenia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Small states] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Solomon Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Somalia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[South Africa] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[South Asia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[South Asia (IDA & IBRD)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[South Sudan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Spain] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sri Lanka] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[St. Kitts and Nevis] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[St. Lucia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[St. Martin (French part)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[St. Vincent and the Grenadines] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sub-Saharan Africa] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sub-Saharan Africa (IDA & IBRD countries)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sub-Saharan Africa (excluding high income)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sudan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Suriname] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Sweden] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Switzerland] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Syrian Arab Republic] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Tajikistan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Tanzania] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Thailand] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Timor-Leste] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Togo] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Tonga] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Trinidad and Tobago] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Tunisia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Turkey] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Turkmenistan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Turks and Caicos Islands] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Tuvalu] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Uganda] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Ukraine] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[United Arab Emirates] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[United Kingdom] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[United States] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Upper middle income] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Uruguay] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Uzbekistan] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Vanuatu] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Venezuela, RB] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Vietnam] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Virgin Islands (U.S.)] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[West Bank and Gaza] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[World] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Yemen, Rep.] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Zambia] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n", "[Zimbabwe] Error: Exception encountered when calling LSTMCell.call().\n", "\n", "\u001b[1mDimensions must be equal, but are 1 and 267 for '{{node sequential_1/lstm_1/lstm_cell_1/MatMul}} = MatMul[T=DT_FLOAT, grad_a=false, grad_b=false, transpose_a=false, transpose_b=false](sequential_1/lstm_1/strided_slice_1, sequential_1/lstm_1/lstm_cell_1/Cast/ReadVariableOp)' with input shapes: [1,1], [267,512].\u001b[0m\n", "\n", "Arguments received by LSTMCell.call():\n", " • inputs=tf.Tensor(shape=(1, 1), dtype=float32)\n", " • states=('tf.Tensor(shape=(1, 128), dtype=float32)', 'tf.Tensor(shape=(1, 128), dtype=float32)')\n", " • training=False\n" ] } ] }, { "cell_type": "code", "source": [ "df.columns" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RmEvNX3eyGSr", "outputId": "86d65078-44a7-4335-84a7-70540a1b8bd8" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['Country Name', '1960', '1961', '1962', '1963', '1964', '1965', '1966',\n", " '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n", " '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984',\n", " '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993',\n", " '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002',\n", " '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011',\n", " '2012', '2013', '2014', '2015', '2016', '2017', '2018'],\n", " dtype='object')" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "markdown", "source": [ "____________________________________________________________________________________________________________________________________________" ], "metadata": { "id": "bpbxYTBUG9O5" } }, { "cell_type": "code", "source": [ "import os\n", "import pandas as pd\n", "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", "import joblib\n", "\n", "# Paths: adjust as needed. If running in Colab, mount Drive and set paths to Drive locations.\n", "agri_path = \"/content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv\"\n", "co2_path = \"/content/drive/MyDrive/AuraClima/CO2_Emissions_1960-2018.csv\"" ], "metadata": { "id": "KRufo-Hu3ZtW" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# === Model 1 scaler: total_emission ===\n", "if os.path.exists(agri_path):\n", " df_agri = pd.read_csv(agri_path)\n", " print(f\"Loaded '{agri_path}'. Columns:\")\n", " print(df_agri.columns.tolist())\n", "\n", " if 'total_emission' in df_agri.columns:\n", " # Fit MinMaxScaler on total_emission\n", " scaler1 = MinMaxScaler()\n", " df_te = df_agri[['total_emission']].dropna()\n", " scaler1.fit(df_te)\n", " joblib.dump(scaler1, '/content/drive/MyDrive/AuraClima/scaler1.save')\n", " print(\"Model 1 scaler saved to '/content/drive/MyDrive/AuraClima/scaler1.save'.\")\n", " else:\n", " print(\"❗ Column 'total_emission' not found in agri_full.csv. Please verify column name.\")\n", "else:\n", " print(f\"❗ File '{agri_path}' not found. Place your agricultural dataset CSV in this path.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k47v1gMEHuGq", "outputId": "6f994cb0-0f0d-4ed7-fd4e-7ba272b2a67e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loaded '/content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv'. Columns:\n", "['Area', 'Year', 'Savanna fires', 'Forest fires', 'Crop Residues', 'Rice Cultivation', 'Drained organic soils (CO2)', 'Pesticides Manufacturing', 'Food Transport', 'Forestland', 'Net Forest conversion', 'Food Household Consumption', 'Food Retail', 'On-farm Electricity Use', 'Food Packaging', 'Agrifood Systems Waste Disposal', 'Food Processing', 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils', 'Manure left on Pasture', 'Manure Management', 'Fires in organic soils', 'Fires in humid tropical forests', 'On-farm energy use', 'Rural population', 'Urban population', 'Total Population - Male', 'Total Population - Female', 'total_emission', 'Average Temperature °C']\n", "Model 1 scaler saved to '/content/drive/MyDrive/AuraClima/scaler1.save'.\n" ] } ] }, { "cell_type": "code", "source": [ "# === Model 2 scaler & feature list: regression NN ===\n", "if os.path.exists(agri_path):\n", " # Define numeric columns used in training.\n", " # Replace the placeholder list below with the exact numeric columns from your training script.\n", " numeric_cols = [\n", " 'Year', 'Crop Residues', 'Savanna fires', 'Forest fires', 'Rice Cultivation',\n", " 'Drained organic soils (CO2)', 'Pesticides Manufacturing', 'Food Transport', 'Forestland',\n", " 'Net Forest conversion', 'Food Household Consumption', 'Food Retail',\n", " 'On-farm Electricity Use', 'Food Packaging', 'Agrifood Systems Waste Disposal',\n", " 'Food Processing', 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils',\n", " 'Manure left on Pasture', 'Manure Management', 'On-farm energy use',\n", " 'Rural population', 'Urban population', 'Fires in organic soils',\n", " 'Fires in humid tropical forests', 'Total Population - Male',\n", " 'Total Population - Female', 'Average Temperature °C'\n", " ]\n", "\n", " df = df_agri.copy()\n", " # Convert numeric columns and fill NaNs\n", " for col in numeric_cols:\n", " if col in df.columns:\n", " df[col] = pd.to_numeric(df[col], errors='coerce')\n", " if df[col].isnull().any():\n", " df[col].fillna(df[col].mean(), inplace=True)\n", " else:\n", " print(f\"⚠️ Warning: Numeric column '{col}' not found in DataFrame.\")\n", "\n", " # Ensure 'Area' column exists for one-hot encoding\n", " if 'Area' in df.columns:\n", " df_encoded = pd.get_dummies(df, columns=['Area'], drop_first=True)\n", " if 'total_emission' not in df_encoded.columns:\n", " print(\"❗ Column 'total_emission' not found after encoding; verify dataset.\")\n", " else:\n", " feature_cols2 = [c for c in df_encoded.columns if c != 'total_emission']\n", " X = df_encoded[feature_cols2].values.astype(float)\n", " y = df_encoded['total_emission'].values.reshape(-1, 1)\n", " scaler_X2 = StandardScaler()\n", " scaler_y2 = StandardScaler()\n", " scaler_X2.fit(X)\n", " scaler_y2.fit(y)\n", " joblib.dump(scaler_X2, '/content/drive/MyDrive/AuraClima/scalerX2.save')\n", " joblib.dump(scaler_y2, '/content/drive/MyDrive/AuraClima/scalerY2.save')\n", " joblib.dump(feature_cols2, 'feature_cols2.list')\n", " print(\"Model 2 scalers and feature list saved:\")\n", " print(f\" - scalerX2.save\\n - scalerY2.save\\n - feature_cols2.list (length {len(feature_cols2)})\")\n", " else:\n", " print(\"❗ Column 'Area' not found in agri_full.csv; required for one-hot encoding in Model 2.\")\n", "\n", "else:\n", " print(f\"❗ File '{agri_path}' not found; cannot compute Model 2 scalers.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "haYu4K4oHwkB", "outputId": "740a20c3-d382-4fda-b10d-f40e65d0d93c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model 2 scalers and feature list saved:\n", " - scalerX2.save\n", " - scalerY2.save\n", " - feature_cols2.list (length 264)\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ ":23: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].mean(), inplace=True)\n" ] } ] }, { "cell_type": "code", "source": [ "# === Model 3 scaler: CO2 time-series ===\n", "if os.path.exists(co2_path):\n", " df_co2 = pd.read_csv(co2_path)\n", " print(f\"\\nLoaded '{co2_path}'. Columns:\")\n", " print(df_co2.columns.tolist())\n", "\n", " # Identify year columns (e.g., '1960' to '2018')\n", " year_cols = [c for c in df_co2.columns if c.isdigit()]\n", " if year_cols:\n", " # Stack all values to fit scaler\n", " co2_vals = df_co2[year_cols].astype(float).values.flatten().reshape(-1, 1)\n", " scaler3 = MinMaxScaler()\n", " scaler3.fit(co2_vals)\n", " joblib.dump(scaler3, '/content/drive/MyDrive/AuraClima/scaler3.save')\n", " print(\"Model 3 scaler saved to '/content/drive/MyDrive/AuraClima/scaler3.save'.\")\n", " else:\n", " print(\"❗ No year columns detected in co2_timeseries.csv. Ensure columns named '1960', '1961', ..., '2018'.\")\n", "else:\n", " print(f\"❗ File '{co2_path}' not found. Place your CO2 timeseries CSV in this path.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EneHHTRgIAjs", "outputId": "5ade72d9-b43a-4556-e4d7-c3133229b0e9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Loaded '/content/drive/MyDrive/AuraClima/CO2_Emissions_1960-2018.csv'. Columns:\n", "['Country Name', '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']\n", "Model 3 scaler saved to '/content/drive/MyDrive/AuraClima/scaler3.save'.\n" ] } ] }, { "cell_type": "code", "source": [ "# === Next Steps ===\n", "print(\"\\n✅ Completed scaler preparation script.\")\n", "print(\"You can now upload the generated scaler files ('scaler1.save', 'scalerX2.save', 'scalerY2.save', 'feature_cols2.list', 'scaler3.save') to Google Drive or use in your Streamlit app.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yDvWDk1cIGLB", "outputId": "9f3d8d5c-b304-4a27-9b3c-2b0ce38d165f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "✅ Completed scaler preparation script.\n", "You can now upload the generated scaler files ('scaler1.save', 'scalerX2.save', 'scalerY2.save', 'feature_cols2.list', 'scaler3.save') to Google Drive or use in your Streamlit app.\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import joblib\n", "import os\n", "\n", "# Paths (adjust if needed)\n", "agri_path = \"/content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv\"\n", "scalerX2_path = '/content/drive/MyDrive/AuraClima/scalerX2.save'\n", "scalerY2_path = '/content/drive/MyDrive/AuraClima/scalerY2.save'\n", "\n", "# Check files exist\n", "print(\"Checking required files:\")\n", "for p in [agri_path, scalerX2_path, scalerY2_path]:\n", " print(f\" {p}: {'FOUND' if os.path.exists(p) else 'MISSING'}\")\n", "\n", "# Load agricultural dataset\n", "if os.path.exists(agri_path):\n", " df = pd.read_csv(agri_path)\n", " print(\"\\nLoaded agri_full.csv. Columns:\")\n", " print(df.columns.tolist())\n", "\n", " # Define numeric columns used in training; adjust this list if needed\n", " numeric_cols = [\n", " 'Year', 'Crop Residues', 'Savanna fires', 'Forest fires', 'Rice Cultivation',\n", " 'Drained organic soils (CO2)', 'Pesticides Manufacturing', 'Food Transport', 'Forestland',\n", " 'Net Forest conversion', 'Food Household Consumption', 'Food Retail',\n", " 'On-farm Electricity Use', 'Food Packaging', 'Agrifood Systems Waste Disposal',\n", " 'Food Processing', 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils',\n", " 'Manure left on Pasture', 'Manure Management', 'On-farm energy use',\n", " 'Rural population', 'Urban population', 'Fires in organic soils',\n", " 'Fires in humid tropical forests', 'Total Population - Male',\n", " 'Total Population - Female', 'Average Temperature °C'\n", " ]\n", "\n", " # Convert numeric columns and fill NaNs as during training\n", " for col in numeric_cols:\n", " if col in df.columns:\n", " df[col] = pd.to_numeric(df[col], errors='coerce')\n", " if df[col].isnull().any():\n", " df[col].fillna(df[col].mean(), inplace=True)\n", " else:\n", " print(f\"Warning: Numeric column '{col}' not found in dataset. \"\n", " \"Confirm if the name differs or if it was excluded in training.\")\n", "\n", " # One-hot encode 'Area'\n", " if 'Area' in df.columns:\n", " df_encoded = pd.get_dummies(df, columns=['Area'], drop_first=True)\n", " # Build feature_cols2 list\n", " feature_cols2 = [c for c in df_encoded.columns if c != 'total_emission']\n", " print(f\"\\nDerived feature_cols2 list with length {len(feature_cols2)}.\")\n", " # Save feature_cols2\n", " joblib.dump(feature_cols2, 'feature_cols2.list')\n", " print(\"Saved feature_cols2 to 'feature_cols2.list'.\")\n", " else:\n", " print(\"Error: 'Area' column not found in dataset; cannot one-hot encode for Model 2.\")\n", "else:\n", " print(\"Error: agri_full.csv not found; place your dataset at the specified path.\")\n", "\n", "# Verify scaler shapes if scalers exist\n", "if os.path.exists(scalerX2_path):\n", " scalerX2 = joblib.load(scalerX2_path)\n", " print(\"\\nLoaded scalerX2. Scaler mean_ length:\", len(scalerX2.mean_))\n", " if os.path.exists('/content/drive/MyDrive/AuraClima/feature_cols2.list'):\n", " feature_cols2 = joblib.load('/content/drive/MyDrive/AuraClima/feature_cols2.list')\n", " print(\"feature_cols2 length:\", len(feature_cols2))\n", " if len(feature_cols2) != len(scalerX2.mean_):\n", " print(\"Warning: Length mismatch between feature_cols2 and scalerX2. \"\n", " \"Ensure preprocessing matches how scalerX2 was originally fitted.\")\n", " else:\n", " print(\"feature_cols2.list not found; cannot verify length against scalerX2.\")\n", "else:\n", " print(\"scalerX2.save not found; ensure scalerX2 exists.\")\n", "\n", "if os.path.exists(scalerY2_path):\n", " scalerY2 = joblib.load(scalerY2_path)\n", " print(\"\\nLoaded scalerY2. Scaler mean_ length:\", len(scalerY2.mean_))\n", "else:\n", " print(\"scalerY2.save not found; ensure scalerY2 exists.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NePKrPYWIjM7", "outputId": "35b60668-e9e8-4a0e-c52c-352e90de332d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Checking required files:\n", " /content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv: FOUND\n", " /content/drive/MyDrive/AuraClima/scalerX2.save: FOUND\n", " /content/drive/MyDrive/AuraClima/scalerY2.save: FOUND\n", "\n", "Loaded agri_full.csv. Columns:\n", "['Area', 'Year', 'Savanna fires', 'Forest fires', 'Crop Residues', 'Rice Cultivation', 'Drained organic soils (CO2)', 'Pesticides Manufacturing', 'Food Transport', 'Forestland', 'Net Forest conversion', 'Food Household Consumption', 'Food Retail', 'On-farm Electricity Use', 'Food Packaging', 'Agrifood Systems Waste Disposal', 'Food Processing', 'Fertilizers Manufacturing', 'IPPU', 'Manure applied to Soils', 'Manure left on Pasture', 'Manure Management', 'Fires in organic soils', 'Fires in humid tropical forests', 'On-farm energy use', 'Rural population', 'Urban population', 'Total Population - Male', 'Total Population - Female', 'total_emission', 'Average Temperature °C']\n", "\n", "Derived feature_cols2 list with length 264.\n", "Saved feature_cols2 to 'feature_cols2.list'.\n", "\n", "Loaded scalerX2. Scaler mean_ length: 264\n", "feature_cols2.list not found; cannot verify length against scalerX2.\n", "\n", "Loaded scalerY2. Scaler mean_ length: 1\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ ":39: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", "\n", "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", "\n", "\n", " df[col].fillna(df[col].mean(), inplace=True)\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.preprocessing import MinMaxScaler\n", "import joblib\n", "\n", "# === Model 1 Scaler: total_emission ===\n", "agri_path = \"/content/drive/MyDrive/AuraClima/Agrofood_co2_emission.csv\"\n", "\n", "agri_df = pd.read_csv(agri_path)\n", "\n", "if 'total_emission' not in agri_df.columns:\n", " raise ValueError(\"❌ 'total_emission' not found in agri_full.csv!\")\n", "\n", "scaler1 = MinMaxScaler()\n", "scaler1.fit(agri_df[['total_emission']].dropna())\n", "joblib.dump(scaler1, \"/content/drive/MyDrive/AuraClima/scaler1.save\")\n", "print(\"✅ Saved scaler1.save for Model 1 (MinMaxScaler on total_emission)\")\n", "\n", "# === Model 3 Scaler: CO2 year-wise ===\n", "co2_path = \"/content/drive/MyDrive/AuraClima/CO2_Emissions_1960-2018.csv\"\n", "co2_df = pd.read_csv(co2_path)\n", "\n", "# Extract year-only columns (e.g., 1960–2018)\n", "year_cols = [col for col in co2_df.columns if col.isdigit()]\n", "co2_values = co2_df[year_cols].values.astype(float).flatten()\n", "co2_values = co2_values[~np.isnan(co2_values)].reshape(-1, 1)\n", "\n", "scaler3 = MinMaxScaler()\n", "scaler3.fit(co2_values)\n", "joblib.dump(scaler3, \"/content/drive/MyDrive/AuraClima/scaler3.save\")\n", "print(\"✅ Saved scaler3.save for Model 3 (MinMaxScaler on CO2 values)\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VBqYa-7tJczh", "outputId": "d0f9fab9-5ee0-4d02-dfa3-9b080a631b09" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Saved scaler1.save for Model 1 (MinMaxScaler on total_emission)\n", "✅ Saved scaler3.save for Model 3 (MinMaxScaler on CO2 values)\n" ] } ] }, { "cell_type": "code", "source": [ "import pickle\n", "\n", "feature_cols2 = [col for col in df_encoded.columns if col != 'total_emission']\n", "\n", "# Save to Google Drive\n", "with open('/content/drive/MyDrive/AuraClima/feature_cols2.list', 'wb') as f:\n", " pickle.dump(feature_cols2, f)\n", "\n", "print(\"✅ Saved feature_cols2.list to Google Drive.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zE8wa6m6KJY3", "outputId": "d7ab8696-9f1a-4e14-ea2c-021bc04cf406" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Saved feature_cols2.list to Google Drive.\n" ] } ] }, { "cell_type": "code", "source": [ "import tensorflow as tf\n", "print(\"TensorFlow version:\", tf.__version__)" ], "metadata": { "id": "KDDZKYK7KjDU", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "bc51b878-0fb8-481f-96e3-2870f3cdb379" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TensorFlow version: 2.18.0\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "qiVRv2D61pcb" }, "execution_count": null, "outputs": [] } ] }