diff --git a/Вариант 1/car_price_prediction_with_missing.csv b/Вариант 1/car_price_prediction_with_missing.csv
new file mode 100644
index 0000000..89fcd52
--- /dev/null
+++ b/Вариант 1/car_price_prediction_with_missing.csv	
@@ -0,0 +1,2501 @@
+Car ID,Brand,Year,Engine Size,Fuel Type,Transmission,Mileage,Condition,Price,Model
+1.0,Tesla,2016.0,2.3,Petrol,Manual,114832.0,New,26613.92,Model X
+2.0,BMW,2018.0,4.4,Electric,Manual,143190.0,Used,14679.61,5 Series
+3.0,Audi,2013.0,4.5,Electric,Manual,181601.0,New,44402.61,A4
+4.0,Tesla,2011.0,4.1,Diesel,Automatic,68682.0,New,86374.33,Model Y
+5.0,Ford,2009.0,2.6,Diesel,Manual,223009.0,Like New,73577.1,Mustang
+6.0,Audi,2019.0,2.4,Diesel,Automatic,246553.0,Like New,88969.76,Q7
+7.0,Audi,2020.0,4.0,Electric,Automatic,135486.0,Used,63498.75,Q5
+8.0,Tesla,2017.0,5.3,Hybrid,Automatic,83030.0,New,17381.19,Model Y
+9.0,Honda,2023.0,5.7,Electric,Manual,120360.0,Like New,15905.62,Civic
+10.0,Ford,2010.0,1.5,Electric,Automatic,135009.0,Like New,9560.22,Explorer
+11.0,Tesla,2001.0,1.8,Diesel,Automatic,298875.0,Like New,58872.6,Model 3
+12.0,Ford,2017.0,5.7,Electric,Automatic,169737.0,Used,28074.19,Mustang
+13.0,Ford,2006.0,4.7,Petrol,Automatic,114360.0,New,74766.45,Fiesta
+14.0,Audi,2023.0,5.4,Electric,Automatic,263894.0,Like New,70193.74,Q7
+15.0,BMW,2014.0,2.0,Electric,Automatic,65018.0,New,35220.52,X3
+16.0,Ford,2010.0,3.9,Electric,Automatic,240904.0,Used,21796.16,Mustang
+17.0,Mercedes,2017.0,4.5,Electric,Automatic,136817.0,New,14728.03,GLA
+18.0,Audi,2022.0,4.4,Hybrid,Automatic,192803.0,Like New,75044.95,A3
+19.0,Honda,2011.0,3.0,Electric,Automatic,86984.0,New,47791.89,Civic
+20.0,BMW,2005.0,1.1,Petrol,Automatic,290595.0,New,35735.34,X5
+21.0,Mercedes,2019.0,3.9,Petrol,Automatic,192608.0,Used,86382.04,C-Class
+22.0,Mercedes,2022.0,2.3,Electric,Manual,12150.0,Used,61393.26,E-Class
+23.0,Honda,2012.0,3.3,Diesel,Manual,275550.0,New,54210.22,CR-V
+24.0,BMW,2019.0,5.8,Hybrid,Automatic,150853.0,New,75621.02,X5
+25.0,Audi,2015.0,3.0,Electric,Automatic,188489.0,New,82480.4,Q5
+26.0,Toyota,2017.0,5.2,Electric,Automatic,18325.0,Used,70176.95,Camry
+27.0,BMW,2009.0,1.9,Electric,Manual,199756.0,Used,46800.6,X3
+28.0,Honda,2022.0,4.4,Diesel,Manual,204541.0,New,41033.39,Accord
+29.0,Mercedes,2007.0,5.9,Diesel,Manual,17669.0,Used,78308.17,GLC
+30.0,Audi,2017.0,1.5,Diesel,Automatic,207836.0,Like New,54201.18,A3
+31.0,BMW,2020.0,1.0,Hybrid,Automatic,132915.0,Used,71916.68,X5
+32.0,Toyota,2000.0,3.2,Petrol,Automatic,196681.0,Used,89587.88,Corolla
+33.0,Toyota,2022.0,1.5,Hybrid,Automatic,50812.0,Used,92009.61,Corolla
+34.0,Ford,2007.0,4.7,Diesel,Manual,278203.0,Like New,31382.99,Fiesta
+35.0,Ford,2005.0,5.6,Petrol,Automatic,252862.0,Like New,22849.55,Mustang
+36.0,Tesla,2004.0,3.2,Petrol,Manual,171840.0,Like New,29822.3,Model 3
+37.0,Honda,2014.0,2.3,Diesel,Manual,25395.0,New,76380.32,Fit
+38.0,BMW,2021.0,3.2,Diesel,Automatic,282346.0,New,40155.51,5 Series
+39.0,BMW,2014.0,4.6,Hybrid,Manual,229707.0,Like New,21471.98,X3
+40.0,Tesla,2001.0,1.0,Diesel,Manual,247181.0,New,14346.62,Model X
+41.0,Mercedes,2000.0,3.9,Petrol,Manual,204120.0,New,36094.75,E-Class
+42.0,Mercedes,2022.0,4.1,Electric,Manual,224839.0,New,17932.96,GLA
+43.0,Tesla,2022.0,4.2,Diesel,Automatic,219882.0,Like New,19855.49,Model Y
+,,,,,,,,,
+,,,,,,,,,
+46.0,BMW,2017.0,1.5,Hybrid,Automatic,87945.0,Like New,16167.56,5 Series
+47.0,Tesla,2017.0,2.0,Diesel,Manual,242802.0,New,85214.72,Model S
+48.0,BMW,2016.0,5.4,Petrol,Automatic,250168.0,New,85266.84,X5
+49.0,Toyota,2007.0,4.7,Petrol,Automatic,2881.0,Used,36278.13,Prius
+50.0,Ford,2014.0,1.1,Electric,Automatic,111860.0,Used,13355.54,Explorer
+51.0,Audi,2007.0,2.2,Diesel,Automatic,166415.0,Used,71972.18,Q5
+52.0,Ford,2016.0,2.1,Hybrid,Automatic,139728.0,Used,39212.51,Mustang
+53.0,Tesla,2011.0,2.4,Petrol,Manual,267885.0,Like New,9319.14,Model S
+54.0,Audi,2007.0,2.2,Electric,Automatic,147733.0,Used,19993.56,A4
+55.0,Toyota,2013.0,1.3,Diesel,Manual,290834.0,Like New,18035.41,Camry
+56.0,Tesla,2003.0,3.3,Petrol,Manual,294977.0,New,60966.05,Model S
+,,,,,,,,,
+58.0,BMW,2000.0,4.0,Diesel,Manual,71240.0,New,80341.84,5 Series
+59.0,Toyota,2018.0,4.4,Petrol,Automatic,106898.0,New,56919.02,Prius
+60.0,BMW,2015.0,1.4,Petrol,Manual,209304.0,Like New,45104.74,3 Series
+61.0,Mercedes,2013.0,5.8,Petrol,Manual,80522.0,Used,63774.29,GLC
+62.0,Honda,2020.0,5.2,Diesel,Automatic,145512.0,Like New,47162.21,Accord
+63.0,Honda,2015.0,5.0,Diesel,Automatic,119107.0,New,28379.42,CR-V
+64.0,Toyota,2004.0,5.1,Electric,Automatic,22462.0,Used,69523.76,Camry
+65.0,Honda,2008.0,5.7,Hybrid,Automatic,279965.0,New,87958.17,Fit
+66.0,Audi,2001.0,3.7,Diesel,Manual,92889.0,New,40422.68,A4
+67.0,Honda,2017.0,2.0,Hybrid,Automatic,88952.0,Used,84946.15,CR-V
+68.0,BMW,2005.0,4.1,Petrol,Manual,193596.0,Used,45952.79,X3
+69.0,BMW,2008.0,4.7,Diesel,Manual,177505.0,Used,43126.91,5 Series
+70.0,Tesla,2000.0,4.7,Electric,Manual,221576.0,Like New,7690.81,Model X
+71.0,BMW,2008.0,3.6,Electric,Manual,219393.0,Like New,71051.5,5 Series
+72.0,Tesla,2015.0,1.3,Hybrid,Manual,275455.0,New,81712.74,Model S
+73.0,BMW,2012.0,2.9,Petrol,Manual,254084.0,Used,68037.91,X5
+74.0,Audi,2008.0,5.6,Hybrid,Automatic,129466.0,New,19379.29,A3
+75.0,Tesla,2020.0,3.9,Petrol,Manual,30531.0,New,14812.88,Model S
+76.0,Ford,2012.0,3.7,Electric,Manual,14588.0,New,59421.58,Fiesta
+77.0,Mercedes,2008.0,2.3,Hybrid,Manual,143452.0,New,43144.04,GLC
+78.0,Toyota,2010.0,2.8,Electric,Automatic,173717.0,New,45764.29,RAV4
+79.0,BMW,2003.0,5.5,Electric,Automatic,161929.0,Used,96941.02,X5
+80.0,Honda,2001.0,4.3,Petrol,Manual,21711.0,Like New,73772.65,Fit
+81.0,BMW,2005.0,4.9,Hybrid,Automatic,136169.0,Used,44119.84,5 Series
+82.0,Honda,2013.0,3.3,Hybrid,Manual,159465.0,Used,6814.43,CR-V
+83.0,Mercedes,2003.0,4.2,Electric,Automatic,284220.0,Used,8239.09,GLC
+84.0,Mercedes,2005.0,2.2,Electric,Manual,160389.0,Like New,70056.35,E-Class
+85.0,Mercedes,2018.0,4.5,Petrol,Automatic,58480.0,Like New,84585.18,GLC
+86.0,Honda,2005.0,3.1,Diesel,Automatic,269188.0,Like New,72231.75,Fit
+87.0,BMW,2001.0,3.2,Diesel,Automatic,250309.0,Used,43559.34,X3
+88.0,Mercedes,2017.0,4.2,Electric,Manual,55486.0,Like New,7957.9,C-Class
+89.0,Audi,2012.0,5.7,Diesel,Manual,55162.0,New,96903.15,A3
+90.0,Tesla,2008.0,1.3,Diesel,Manual,210359.0,Like New,98701.93,Model S
+91.0,Honda,2022.0,5.1,Diesel,Automatic,29882.0,Used,57189.63,CR-V
+92.0,Honda,2018.0,2.5,Diesel,Automatic,54078.0,Used,11713.74,CR-V
+93.0,BMW,2012.0,3.2,Electric,Manual,238680.0,New,56392.39,3 Series
+,,,,,,,,,
+95.0,Honda,2000.0,2.5,Diesel,Manual,174898.0,Like New,69598.62,Fit
+96.0,Mercedes,2023.0,3.5,Petrol,Manual,98938.0,New,73398.98,C-Class
+,,,,,,,,,
+98.0,Mercedes,2006.0,3.5,Hybrid,Manual,32543.0,Used,15118.61,E-Class
+99.0,Tesla,2020.0,5.6,Hybrid,Automatic,124279.0,Like New,17098.13,Model 3
+100.0,Tesla,2014.0,1.5,Petrol,Automatic,29362.0,Like New,90915.8,Model 3
+101.0,Mercedes,2000.0,4.8,Petrol,Automatic,158818.0,Like New,54955.72,GLA
+102.0,Tesla,2011.0,3.0,Electric,Manual,242721.0,Like New,80199.63,Model S
+103.0,BMW,2006.0,4.3,Hybrid,Automatic,165229.0,Like New,15952.67,X5
+104.0,Toyota,2014.0,2.3,Diesel,Manual,142394.0,Used,93126.06,Camry
+105.0,Mercedes,2020.0,1.8,Petrol,Manual,69812.0,Used,83008.5,GLC
+106.0,Audi,2018.0,1.8,Hybrid,Manual,296161.0,Used,26352.59,A4
+107.0,Audi,2000.0,1.4,Electric,Manual,228208.0,Used,88047.31,Q7
+108.0,Honda,2016.0,1.9,Diesel,Manual,279773.0,New,30027.06,Accord
+109.0,Tesla,2016.0,4.3,Electric,Manual,128614.0,Used,24507.29,Model 3
+110.0,Audi,2002.0,5.4,Petrol,Automatic,96950.0,New,30103.17,Q5
+111.0,Honda,2011.0,5.1,Hybrid,Automatic,174942.0,Used,91445.73,Accord
+,,,,,,,,,
+113.0,BMW,2010.0,1.6,Hybrid,Automatic,245602.0,Like New,36254.6,X5
+114.0,BMW,2002.0,2.4,Hybrid,Manual,10588.0,Like New,72016.25,3 Series
+115.0,BMW,2007.0,2.5,Diesel,Manual,168217.0,New,69195.8,5 Series
+116.0,Audi,2013.0,2.2,Diesel,Automatic,272743.0,Used,81623.45,A3
+117.0,Toyota,2013.0,3.6,Electric,Automatic,208502.0,Used,33168.4,Camry
+118.0,Audi,2009.0,3.7,Diesel,Automatic,134011.0,New,53463.16,Q5
+119.0,Tesla,2001.0,2.8,Petrol,Automatic,252856.0,New,68429.03,Model 3
+120.0,Audi,2013.0,2.8,Petrol,Automatic,284293.0,New,7494.4,Q5
+121.0,Toyota,2008.0,5.1,Hybrid,Automatic,197639.0,New,40790.18,Camry
+122.0,Toyota,2002.0,4.9,Petrol,Automatic,141280.0,New,71109.39,Prius
+123.0,Tesla,2020.0,2.5,Electric,Automatic,197170.0,Like New,28996.53,Model S
+124.0,Toyota,2007.0,5.6,Diesel,Manual,180564.0,Used,95981.89,Corolla
+125.0,Toyota,2015.0,5.8,Diesel,Manual,20951.0,New,7865.33,Corolla
+126.0,BMW,2001.0,2.6,Diesel,Manual,179992.0,Like New,96018.28,X3
+127.0,Tesla,2005.0,2.8,Petrol,Manual,46766.0,New,25592.57,Model X
+128.0,Ford,2020.0,3.5,Diesel,Automatic,159423.0,Like New,9638.09,Mustang
+129.0,Ford,2011.0,5.7,Hybrid,Manual,87122.0,New,32785.03,Explorer
+130.0,Toyota,2004.0,5.4,Hybrid,Automatic,271452.0,Used,79696.82,Corolla
+131.0,Ford,2001.0,1.5,Hybrid,Manual,83211.0,New,76481.99,Explorer
+132.0,Ford,2015.0,3.0,Petrol,Automatic,126534.0,New,24839.84,Focus
+133.0,Toyota,2004.0,3.8,Diesel,Automatic,8720.0,Used,23695.06,RAV4
+134.0,Ford,2014.0,3.5,Petrol,Manual,79993.0,Used,66045.09,Focus
+,,,,,,,,,
+136.0,Honda,2003.0,5.3,Electric,Automatic,25905.0,Used,80267.55,Accord
+137.0,Tesla,2014.0,4.4,Electric,Automatic,204580.0,Used,7292.61,Model 3
+138.0,Honda,2010.0,5.2,Petrol,Automatic,50385.0,Like New,62989.13,Accord
+139.0,Toyota,2015.0,5.3,Hybrid,Manual,30653.0,Used,74413.84,Corolla
+140.0,BMW,2022.0,4.7,Petrol,Manual,91865.0,Used,99371.68,3 Series
+141.0,Tesla,2005.0,3.2,Hybrid,Automatic,125813.0,New,69484.26,Model 3
+142.0,Toyota,2013.0,4.1,Diesel,Manual,145871.0,New,27025.29,Prius
+143.0,BMW,2017.0,1.8,Hybrid,Automatic,191175.0,Like New,15277.69,5 Series
+144.0,Honda,2011.0,4.4,Hybrid,Manual,248535.0,New,65887.09,Fit
+145.0,Toyota,2010.0,1.9,Diesel,Manual,15052.0,Used,54702.39,Camry
+146.0,Tesla,2012.0,4.5,Electric,Manual,137116.0,Used,90418.8,Model Y
+147.0,Tesla,2020.0,2.1,Diesel,Manual,150943.0,Used,74027.76,Model 3
+148.0,Mercedes,2022.0,1.6,Petrol,Manual,220375.0,New,53425.53,E-Class
+149.0,Audi,2010.0,1.8,Petrol,Automatic,21381.0,Used,43341.0,Q7
+150.0,Ford,2022.0,1.0,Petrol,Automatic,148346.0,Used,43415.6,Focus
+151.0,BMW,2015.0,4.6,Petrol,Manual,243234.0,Like New,26498.91,3 Series
+152.0,Mercedes,2014.0,4.7,Petrol,Automatic,256017.0,Like New,6645.97,GLA
+153.0,Ford,2020.0,3.6,Electric,Automatic,235020.0,Like New,59616.51,Mustang
+154.0,Ford,2014.0,1.8,Electric,Manual,49892.0,New,25179.4,Mustang
+155.0,Toyota,2003.0,1.4,Hybrid,Automatic,181593.0,Used,24791.06,Camry
+156.0,Ford,2010.0,1.1,Petrol,Manual,214992.0,Used,16911.22,Mustang
+157.0,Audi,2018.0,1.8,Diesel,Automatic,280064.0,Used,23011.57,A3
+158.0,Tesla,2014.0,5.5,Petrol,Automatic,278846.0,Like New,8019.97,Model 3
+159.0,Mercedes,2003.0,2.2,Electric,Automatic,256216.0,Used,69051.99,C-Class
+160.0,Ford,2020.0,2.8,Petrol,Automatic,264038.0,Used,43132.73,Fiesta
+161.0,Toyota,2012.0,1.5,Electric,Automatic,293129.0,Used,58344.84,Camry
+162.0,Audi,2021.0,2.1,Hybrid,Automatic,159359.0,Used,65510.97,A4
+163.0,Honda,2018.0,3.6,Hybrid,Automatic,258771.0,Used,97386.03,Civic
+164.0,Tesla,2001.0,4.0,Hybrid,Manual,125702.0,New,11485.26,Model Y
+165.0,Tesla,2013.0,2.2,Electric,Manual,73527.0,Used,44702.18,Model 3
+166.0,Mercedes,2003.0,1.3,Petrol,Manual,146511.0,New,28360.3,E-Class
+167.0,Tesla,2015.0,3.0,Petrol,Manual,225692.0,Used,50287.63,Model Y
+168.0,Ford,2020.0,2.2,Diesel,Automatic,158731.0,New,92444.87,Explorer
+,,,,,,,,,
+170.0,Tesla,2013.0,5.8,Diesel,Automatic,235311.0,Used,67885.21,Model Y
+,,,,,,,,,
+172.0,Honda,2002.0,3.8,Petrol,Manual,199378.0,Used,68755.69,CR-V
+173.0,Honda,2007.0,3.1,Electric,Manual,56697.0,Like New,8075.66,CR-V
+,,,,,,,,,
+175.0,Audi,2009.0,3.7,Diesel,Manual,278795.0,New,57608.77,A3
+176.0,Ford,2003.0,4.5,Diesel,Automatic,180116.0,Like New,37930.84,Fiesta
+177.0,Tesla,2017.0,4.5,Petrol,Manual,14633.0,Like New,24143.84,Model 3
+178.0,Tesla,2001.0,1.9,Petrol,Automatic,79164.0,Used,81717.78,Model S
+,,,,,,,,,
+180.0,BMW,2012.0,3.1,Electric,Automatic,123425.0,New,11618.92,X5
+181.0,Audi,2003.0,5.4,Petrol,Manual,250220.0,Like New,46849.57,Q7
+182.0,BMW,2010.0,4.2,Electric,Manual,295182.0,New,28992.68,5 Series
+183.0,Mercedes,2006.0,3.7,Diesel,Automatic,180160.0,Used,75817.98,GLA
+184.0,Audi,2011.0,1.6,Diesel,Automatic,204249.0,Used,65248.06,Q7
+185.0,Tesla,2009.0,4.0,Hybrid,Manual,205538.0,Used,78390.81,Model Y
+186.0,Tesla,2006.0,1.6,Petrol,Automatic,197865.0,Used,35844.5,Model X
+187.0,Audi,2012.0,2.7,Diesel,Manual,201466.0,Used,75349.3,A4
+188.0,Tesla,2006.0,1.7,Petrol,Automatic,187223.0,Like New,86918.81,Model Y
+,,,,,,,,,
+190.0,Audi,2016.0,2.0,Diesel,Automatic,137980.0,Used,37290.1,Q5
+191.0,BMW,2009.0,3.0,Petrol,Manual,68447.0,Like New,90495.38,X3
+192.0,Audi,2009.0,5.5,Diesel,Manual,205599.0,Like New,9730.07,Q5
+193.0,Tesla,2023.0,2.0,Diesel,Manual,226301.0,Like New,38267.99,Model X
+194.0,Ford,2003.0,3.5,Petrol,Automatic,279778.0,New,16273.89,Fiesta
+,,,,,,,,,
+196.0,Mercedes,2003.0,1.1,Diesel,Automatic,55063.0,Used,15722.59,GLA
+,,,,,,,,,
+198.0,Honda,2006.0,1.3,Diesel,Automatic,19340.0,Used,70260.83,Accord
+199.0,Honda,2021.0,3.4,Electric,Manual,62781.0,New,32481.83,CR-V
+200.0,Audi,2000.0,3.8,Diesel,Automatic,277045.0,Used,85719.15,Q5
+201.0,Mercedes,2011.0,4.1,Hybrid,Automatic,10638.0,Used,46573.48,GLA
+202.0,Toyota,2023.0,4.4,Diesel,Automatic,217986.0,New,96981.66,Prius
+203.0,Audi,2012.0,2.3,Petrol,Manual,221668.0,Like New,95644.79,A3
+204.0,Mercedes,2009.0,1.0,Hybrid,Automatic,294786.0,New,78461.24,GLC
+205.0,BMW,2014.0,4.6,Electric,Automatic,25378.0,Used,93578.52,3 Series
+206.0,BMW,2012.0,3.7,Hybrid,Automatic,118171.0,New,72174.13,X3
+207.0,BMW,2022.0,5.2,Hybrid,Automatic,214801.0,Like New,90080.4,X3
+208.0,BMW,2021.0,5.1,Hybrid,Manual,64377.0,Like New,27465.0,3 Series
+209.0,BMW,2021.0,5.2,Petrol,Manual,260339.0,Like New,37814.92,X5
+210.0,Mercedes,2020.0,3.4,Petrol,Automatic,54691.0,Like New,76528.36,C-Class
+,,,,,,,,,
+,,,,,,,,,
+,,,,,,,,,
+214.0,Tesla,2014.0,1.9,Petrol,Manual,121399.0,Used,33474.3,Model S
+215.0,BMW,2009.0,5.3,Hybrid,Manual,31357.0,New,83227.83,5 Series
+216.0,Toyota,2023.0,5.4,Petrol,Manual,294458.0,Used,19655.11,Corolla
+217.0,Tesla,2009.0,3.3,Diesel,Manual,70283.0,Like New,36748.69,Model Y
+218.0,Mercedes,2007.0,1.4,Petrol,Automatic,59567.0,Used,54206.67,C-Class
+,,,,,,,,,
+220.0,Toyota,2015.0,5.0,Electric,Manual,141758.0,Like New,60030.44,Prius
+221.0,Toyota,2016.0,5.5,Diesel,Manual,61606.0,Used,59976.03,RAV4
+222.0,Ford,2004.0,2.0,Electric,Automatic,281035.0,Used,24655.78,Fiesta
+223.0,Mercedes,2003.0,1.3,Electric,Automatic,101732.0,New,80585.93,GLA
+224.0,Toyota,2017.0,5.4,Hybrid,Automatic,191791.0,Like New,65006.23,RAV4
+225.0,BMW,2023.0,2.9,Diesel,Automatic,292021.0,New,35187.25,X3
+226.0,Audi,2018.0,3.7,Hybrid,Automatic,249465.0,Like New,27057.55,A4
+227.0,Toyota,2003.0,5.8,Hybrid,Manual,224589.0,Like New,88481.26,Corolla
+228.0,Ford,2006.0,1.3,Petrol,Manual,14026.0,Used,84395.51,Fiesta
+229.0,Tesla,2009.0,4.2,Diesel,Automatic,282566.0,New,26827.26,Model Y
+230.0,Mercedes,2017.0,1.4,Petrol,Automatic,295625.0,New,8382.5,GLC
+231.0,Ford,2018.0,2.9,Petrol,Manual,109384.0,New,81430.6,Explorer
+232.0,Toyota,2003.0,5.0,Hybrid,Automatic,252771.0,Like New,63473.31,Corolla
+233.0,Mercedes,2009.0,3.2,Electric,Manual,58031.0,New,14583.79,C-Class
+,,,,,,,,,
+235.0,Toyota,2022.0,3.8,Electric,Manual,143084.0,New,47957.36,Camry
+236.0,Ford,2017.0,2.6,Hybrid,Manual,236503.0,New,78582.28,Focus
+237.0,Honda,2004.0,2.1,Hybrid,Automatic,292104.0,Used,24863.06,Fit
+,,,,,,,,,
+239.0,Tesla,2016.0,2.9,Hybrid,Manual,130066.0,Used,19843.19,Model X
+,,,,,,,,,
+241.0,Mercedes,2011.0,2.8,Hybrid,Automatic,264837.0,Like New,78980.12,GLA
+242.0,Toyota,2001.0,3.3,Electric,Automatic,115313.0,New,74988.63,Corolla
+243.0,BMW,2000.0,4.6,Petrol,Automatic,28810.0,New,70569.63,3 Series
+244.0,Toyota,2022.0,4.3,Electric,Manual,20092.0,New,45216.96,Prius
+245.0,Mercedes,2020.0,4.5,Diesel,Automatic,58105.0,Used,92711.83,C-Class
+246.0,Toyota,2016.0,1.0,Electric,Manual,161577.0,New,11927.12,RAV4
+,,,,,,,,,
+248.0,BMW,2015.0,4.3,Electric,Automatic,290699.0,Like New,25447.11,3 Series
+249.0,BMW,2004.0,3.4,Petrol,Manual,71934.0,New,35782.66,X3
+250.0,Mercedes,2014.0,1.0,Diesel,Automatic,72788.0,Used,54345.82,GLA
+251.0,Tesla,2002.0,5.0,Electric,Automatic,280083.0,Like New,79352.23,Model S
+,,,,,,,,,
+,,,,,,,,,
+254.0,Toyota,2009.0,1.3,Diesel,Manual,36.0,Like New,43288.37,Camry
+,,,,,,,,,
+256.0,Toyota,2013.0,3.9,Hybrid,Automatic,80738.0,Like New,28419.52,Camry
+257.0,Toyota,2012.0,4.9,Electric,Automatic,60071.0,Used,81293.98,Camry
+258.0,Ford,2001.0,4.8,Petrol,Automatic,130613.0,Like New,63144.71,Focus
+259.0,Toyota,2002.0,5.0,Electric,Manual,73601.0,New,40805.03,Camry
+,,,,,,,,,
+261.0,Honda,2020.0,1.7,Diesel,Manual,167720.0,Like New,47426.93,Fit
+262.0,BMW,2017.0,4.4,Electric,Manual,71468.0,New,31288.4,X3
+263.0,Mercedes,2002.0,4.1,Diesel,Automatic,288614.0,New,14912.93,E-Class
+264.0,Tesla,2009.0,4.7,Diesel,Automatic,196201.0,New,12140.06,Model 3
+265.0,Audi,2021.0,1.8,Electric,Automatic,8328.0,New,38018.04,A3
+266.0,Toyota,2006.0,2.0,Petrol,Manual,137405.0,Used,63401.95,Camry
+267.0,Toyota,2008.0,5.5,Diesel,Automatic,251519.0,Used,84564.33,Prius
+268.0,Ford,2016.0,4.7,Diesel,Manual,121173.0,Like New,22438.51,Focus
+269.0,Mercedes,2010.0,5.5,Electric,Automatic,46638.0,Like New,45341.18,GLC
+270.0,Honda,2006.0,4.8,Electric,Automatic,273412.0,Like New,42235.83,Accord
+271.0,Audi,2023.0,4.0,Electric,Manual,201675.0,Used,53772.43,A4
+272.0,BMW,2013.0,4.3,Electric,Manual,113920.0,Like New,81910.79,5 Series
+273.0,Honda,2018.0,5.4,Electric,Automatic,171813.0,Like New,57596.3,CR-V
+274.0,Mercedes,2003.0,3.9,Hybrid,Automatic,7887.0,Used,85432.22,E-Class
+275.0,Tesla,2005.0,4.2,Electric,Automatic,228978.0,Like New,54262.42,Model S
+276.0,BMW,2009.0,1.8,Petrol,Automatic,24918.0,Used,62365.5,5 Series
+277.0,Tesla,2006.0,3.4,Diesel,Automatic,297641.0,Used,46551.41,Model X
+278.0,Ford,2012.0,4.6,Petrol,Manual,192081.0,New,83194.08,Fiesta
+279.0,Ford,2013.0,2.4,Petrol,Manual,274073.0,New,92763.58,Explorer
+280.0,Toyota,2003.0,2.0,Hybrid,Manual,102201.0,Used,65718.75,Corolla
+281.0,Tesla,2007.0,2.6,Electric,Manual,210171.0,Used,82974.33,Model X
+282.0,Mercedes,2004.0,2.2,Diesel,Automatic,290690.0,New,10605.32,E-Class
+,,,,,,,,,
+284.0,BMW,2003.0,3.1,Electric,Manual,168410.0,Like New,22947.88,5 Series
+285.0,Honda,2002.0,5.5,Electric,Manual,82494.0,Like New,29369.44,CR-V
+286.0,Mercedes,2008.0,3.5,Hybrid,Automatic,215859.0,Used,10066.36,GLA
+287.0,Mercedes,2007.0,1.9,Hybrid,Manual,233853.0,New,96713.01,GLA
+288.0,Ford,2000.0,1.4,Hybrid,Automatic,128279.0,Like New,85941.03,Fiesta
+289.0,Tesla,2005.0,4.5,Diesel,Manual,81991.0,New,91752.54,Model S
+290.0,Toyota,2008.0,2.9,Petrol,Automatic,290157.0,New,85485.54,Camry
+291.0,Toyota,2006.0,5.1,Hybrid,Manual,52132.0,Used,50555.4,Camry
+292.0,BMW,2003.0,4.3,Hybrid,Automatic,255069.0,Like New,92787.78,3 Series
+293.0,Ford,2012.0,5.0,Hybrid,Manual,159156.0,New,97099.92,Explorer
+294.0,Mercedes,2016.0,2.4,Petrol,Automatic,152215.0,Used,52743.86,E-Class
+295.0,Audi,2001.0,4.5,Petrol,Automatic,91627.0,Used,21684.35,A4
+296.0,Ford,2004.0,2.3,Hybrid,Manual,122772.0,Used,67569.14,Focus
+,,,,,,,,,
+298.0,BMW,2020.0,4.2,Electric,Automatic,141294.0,Like New,87853.95,X5
+,,,,,,,,,
+300.0,BMW,2005.0,2.3,Diesel,Manual,16116.0,Like New,93712.71,5 Series
+301.0,Ford,2007.0,2.0,Diesel,Automatic,182124.0,Like New,89925.46,Mustang
+302.0,Honda,2017.0,4.5,Hybrid,Manual,51369.0,New,78746.76,Accord
+303.0,Ford,2002.0,2.1,Petrol,Manual,179889.0,Used,73235.92,Explorer
+304.0,Tesla,2021.0,4.0,Electric,Manual,233982.0,Used,30152.88,Model 3
+305.0,Ford,2011.0,2.3,Petrol,Automatic,214275.0,Like New,64027.3,Explorer
+306.0,BMW,2002.0,4.3,Hybrid,Automatic,205383.0,Used,54541.42,X3
+307.0,Tesla,2021.0,5.1,Diesel,Automatic,291799.0,Used,86884.69,Model Y
+308.0,BMW,2005.0,4.9,Electric,Automatic,188874.0,Used,51712.69,X5
+309.0,Toyota,2001.0,4.8,Petrol,Automatic,47632.0,Like New,17135.56,Prius
+310.0,Toyota,2022.0,1.9,Petrol,Automatic,211284.0,Like New,5472.39,Prius
+311.0,Tesla,2011.0,1.4,Hybrid,Automatic,161848.0,Like New,69397.95,Model S
+312.0,Honda,2001.0,4.5,Electric,Manual,191082.0,New,57241.3,Civic
+313.0,Toyota,2005.0,2.8,Diesel,Automatic,213807.0,Used,36441.48,RAV4
+314.0,Ford,2016.0,3.2,Hybrid,Automatic,196523.0,New,21052.35,Focus
+315.0,BMW,2002.0,1.2,Electric,Manual,179884.0,Like New,49337.96,5 Series
+316.0,Toyota,2001.0,2.3,Petrol,Manual,281039.0,New,14527.21,Corolla
+317.0,Toyota,2022.0,1.2,Hybrid,Manual,235892.0,Used,36930.31,Corolla
+318.0,Honda,2019.0,5.4,Petrol,Automatic,213408.0,Used,41025.13,Fit
+319.0,Tesla,2007.0,2.2,Petrol,Manual,242718.0,Like New,49233.78,Model X
+320.0,Tesla,2021.0,3.8,Hybrid,Automatic,192879.0,Like New,49328.97,Model X
+321.0,Mercedes,2011.0,1.2,Hybrid,Automatic,23086.0,New,29415.54,C-Class
+322.0,Honda,2003.0,4.3,Hybrid,Automatic,85637.0,Like New,85770.04,Fit
+323.0,Ford,2004.0,2.6,Hybrid,Manual,158279.0,New,38776.63,Fiesta
+324.0,Tesla,2012.0,5.5,Hybrid,Manual,151969.0,Used,26810.79,Model S
+325.0,BMW,2014.0,5.4,Petrol,Automatic,63083.0,Used,21701.94,X3
+326.0,Honda,2022.0,2.6,Diesel,Automatic,95028.0,Used,35644.95,Accord
+327.0,Toyota,2015.0,5.5,Diesel,Automatic,97551.0,Used,11417.52,RAV4
+328.0,BMW,2009.0,6.0,Hybrid,Manual,138355.0,Used,8126.21,5 Series
+329.0,BMW,2022.0,5.1,Petrol,Automatic,94024.0,New,62488.51,5 Series
+330.0,Toyota,2015.0,5.2,Diesel,Manual,262399.0,Used,63818.98,RAV4
+331.0,Honda,2011.0,2.2,Diesel,Automatic,207627.0,New,55310.88,Civic
+332.0,Toyota,2016.0,3.9,Diesel,Manual,15487.0,Used,77417.09,Corolla
+,,,,,,,,,
+334.0,BMW,2011.0,1.5,Hybrid,Manual,297194.0,New,93917.45,5 Series
+335.0,Audi,2007.0,6.0,Hybrid,Manual,270888.0,New,14031.19,Q7
+336.0,Audi,2019.0,2.6,Hybrid,Manual,61596.0,Like New,69032.18,A4
+337.0,Ford,2002.0,4.7,Electric,Manual,214469.0,New,17672.68,Mustang
+338.0,Tesla,2015.0,5.0,Electric,Manual,177175.0,New,88591.02,Model Y
+339.0,Toyota,2012.0,5.3,Electric,Manual,153048.0,Used,53517.76,Prius
+340.0,Toyota,2022.0,6.0,Petrol,Automatic,6277.0,New,12817.89,RAV4
+341.0,Ford,2018.0,2.2,Diesel,Automatic,133332.0,New,63588.79,Fiesta
+342.0,Ford,2007.0,1.2,Diesel,Automatic,240515.0,Like New,5865.44,Fiesta
+343.0,Ford,2021.0,3.1,Petrol,Automatic,260577.0,Used,78722.11,Mustang
+344.0,BMW,2004.0,1.7,Petrol,Manual,196955.0,Like New,66805.57,X5
+345.0,Mercedes,2019.0,1.1,Hybrid,Automatic,286885.0,Like New,25199.35,E-Class
+346.0,Toyota,2007.0,2.8,Hybrid,Manual,299355.0,Like New,40601.34,Camry
+347.0,BMW,2023.0,4.9,Electric,Manual,240464.0,New,5741.64,X5
+348.0,Ford,2011.0,3.8,Electric,Manual,258445.0,Like New,70899.96,Focus
+349.0,Toyota,2000.0,2.6,Petrol,Automatic,269013.0,Like New,35653.47,Corolla
+350.0,BMW,2020.0,4.3,Electric,Manual,94515.0,Used,59300.95,3 Series
+351.0,Mercedes,2019.0,2.2,Diesel,Automatic,150494.0,Used,95889.12,GLC
+352.0,BMW,2005.0,1.1,Petrol,Automatic,166685.0,Used,33449.34,X5
+353.0,Mercedes,2021.0,4.8,Petrol,Automatic,188088.0,Used,99072.6,C-Class
+354.0,Ford,2017.0,4.1,Diesel,Automatic,60110.0,Like New,65109.2,Focus
+355.0,Mercedes,2007.0,4.8,Hybrid,Automatic,219601.0,Used,27998.42,GLA
+356.0,Toyota,2006.0,1.2,Hybrid,Manual,208994.0,New,46493.9,Camry
+357.0,Ford,2000.0,5.2,Diesel,Manual,296177.0,Like New,75343.05,Mustang
+358.0,Toyota,2020.0,4.1,Diesel,Automatic,260705.0,Used,47415.4,RAV4
+359.0,Audi,2015.0,3.8,Electric,Manual,12938.0,Like New,11508.22,A4
+360.0,Honda,2011.0,4.1,Petrol,Automatic,130842.0,Used,21017.35,Accord
+361.0,Mercedes,2005.0,5.3,Electric,Manual,264418.0,Like New,80173.29,C-Class
+362.0,Honda,2019.0,3.9,Diesel,Manual,150033.0,Used,66595.44,Civic
+363.0,Honda,2001.0,3.9,Electric,Manual,20044.0,New,59133.42,Accord
+364.0,Mercedes,2013.0,6.0,Diesel,Manual,99372.0,Used,20924.09,E-Class
+365.0,Ford,2003.0,4.8,Electric,Manual,276850.0,Like New,84586.96,Fiesta
+366.0,Audi,2000.0,3.2,Electric,Manual,137046.0,Like New,96270.31,Q7
+367.0,Toyota,2022.0,4.5,Hybrid,Manual,21213.0,New,8884.27,Prius
+368.0,BMW,2017.0,2.9,Diesel,Manual,1950.0,Like New,12413.03,X3
+369.0,Toyota,2012.0,2.1,Electric,Manual,254043.0,New,89048.83,RAV4
+370.0,BMW,2001.0,4.0,Petrol,Manual,216085.0,Like New,82477.46,X5
+371.0,Toyota,2008.0,5.6,Electric,Manual,272555.0,Like New,63703.77,Prius
+372.0,Mercedes,2020.0,5.6,Electric,Manual,148843.0,New,94771.69,GLA
+373.0,Audi,2001.0,2.7,Petrol,Manual,297197.0,Like New,66410.9,Q5
+374.0,BMW,2008.0,3.6,Diesel,Automatic,248966.0,Like New,25518.6,3 Series
+375.0,Tesla,2000.0,2.1,Hybrid,Manual,138117.0,New,74221.02,Model X
+376.0,Ford,2010.0,6.0,Hybrid,Manual,29714.0,Used,66342.48,Explorer
+377.0,Toyota,2002.0,5.9,Hybrid,Manual,165458.0,New,98645.75,RAV4
+378.0,Toyota,2019.0,4.2,Petrol,Manual,270706.0,Like New,25320.09,Corolla
+379.0,BMW,2022.0,5.0,Electric,Manual,12026.0,Used,61799.07,3 Series
+380.0,Ford,2003.0,4.6,Diesel,Manual,106147.0,Used,79115.91,Fiesta
+381.0,Ford,2017.0,4.0,Electric,Automatic,31589.0,Used,37203.39,Fiesta
+382.0,Mercedes,2022.0,1.3,Hybrid,Automatic,108199.0,New,41069.56,C-Class
+383.0,Tesla,2005.0,3.3,Electric,Automatic,288988.0,Used,89350.67,Model Y
+384.0,Mercedes,2002.0,4.4,Petrol,Manual,208574.0,Used,43398.51,E-Class
+385.0,Mercedes,2021.0,4.4,Petrol,Manual,214340.0,Used,64349.62,GLA
+386.0,Mercedes,2010.0,2.9,Diesel,Manual,54708.0,Like New,89477.62,GLC
+387.0,Mercedes,2014.0,5.7,Diesel,Automatic,70825.0,New,24933.53,C-Class
+388.0,Mercedes,2021.0,1.8,Hybrid,Manual,51574.0,New,98278.34,GLA
+389.0,Audi,2004.0,3.5,Diesel,Manual,232409.0,Like New,71685.59,A3
+390.0,Ford,2019.0,4.5,Electric,Automatic,186381.0,Used,52131.56,Explorer
+391.0,Mercedes,2009.0,4.5,Petrol,Automatic,64230.0,Like New,45937.45,C-Class
+392.0,Ford,2011.0,4.2,Petrol,Manual,254451.0,Like New,95664.64,Mustang
+393.0,Ford,2014.0,2.4,Petrol,Manual,247174.0,Used,24543.86,Fiesta
+394.0,Honda,2016.0,1.8,Diesel,Manual,219700.0,Used,34609.91,CR-V
+395.0,Audi,2009.0,4.2,Petrol,Manual,287249.0,New,34240.31,Q5
+396.0,Mercedes,2005.0,4.0,Petrol,Automatic,246514.0,Used,32455.6,GLA
+397.0,Toyota,2017.0,1.9,Petrol,Manual,264491.0,Like New,49511.11,RAV4
+398.0,BMW,2012.0,4.5,Hybrid,Manual,226094.0,Like New,98972.03,5 Series
+399.0,Tesla,2002.0,3.3,Hybrid,Automatic,148182.0,Used,89434.74,Model X
+400.0,Toyota,2023.0,4.3,Diesel,Manual,193242.0,New,36155.21,Corolla
+401.0,Audi,2009.0,5.2,Petrol,Manual,182873.0,Like New,45172.66,A4
+402.0,BMW,2013.0,1.8,Diesel,Automatic,124926.0,Used,68600.67,3 Series
+,,,,,,,,,
+404.0,Ford,2002.0,4.9,Petrol,Manual,19199.0,New,82914.6,Explorer
+405.0,BMW,2015.0,4.0,Petrol,Automatic,183208.0,Like New,88580.82,5 Series
+406.0,Ford,2006.0,4.5,Diesel,Automatic,220868.0,Like New,86434.03,Explorer
+407.0,Toyota,2019.0,5.2,Diesel,Manual,120787.0,New,40630.17,Corolla
+,,,,,,,,,
+409.0,BMW,2008.0,5.8,Electric,Manual,148063.0,New,6176.89,3 Series
+410.0,BMW,2014.0,3.7,Diesel,Manual,153625.0,Used,42942.56,X5
+411.0,Mercedes,2020.0,3.4,Hybrid,Manual,220782.0,Like New,48740.55,GLA
+412.0,Audi,2002.0,3.0,Diesel,Manual,87493.0,New,91162.56,Q7
+413.0,Mercedes,2001.0,1.8,Petrol,Automatic,274915.0,Like New,89928.83,GLA
+414.0,Audi,2001.0,3.9,Electric,Manual,125607.0,Like New,96779.22,Q7
+415.0,Mercedes,2002.0,2.4,Petrol,Manual,246834.0,Like New,89084.97,GLA
+416.0,Ford,2017.0,5.6,Hybrid,Manual,79876.0,New,5779.07,Fiesta
+417.0,BMW,2013.0,3.9,Diesel,Manual,167071.0,Used,65938.78,3 Series
+418.0,Toyota,2019.0,4.0,Diesel,Automatic,202959.0,New,11482.58,Prius
+419.0,Audi,2017.0,2.8,Petrol,Automatic,243722.0,Like New,30896.46,A4
+420.0,Audi,2009.0,1.3,Petrol,Automatic,44440.0,New,59939.22,Q7
+,,,,,,,,,
+422.0,Mercedes,2021.0,3.1,Electric,Automatic,43039.0,Like New,67190.04,GLA
+,,,,,,,,,
+424.0,Tesla,2003.0,4.0,Electric,Manual,186061.0,New,14842.37,Model S
+425.0,Ford,2013.0,5.4,Diesel,Automatic,273388.0,New,17215.75,Fiesta
+426.0,BMW,2000.0,5.4,Electric,Manual,94594.0,New,13684.37,X3
+427.0,Toyota,2013.0,4.3,Electric,Automatic,170623.0,New,21932.27,Prius
+428.0,BMW,2015.0,2.1,Diesel,Automatic,194302.0,Used,70406.34,5 Series
+429.0,Tesla,2021.0,5.3,Diesel,Automatic,290515.0,New,83929.54,Model 3
+430.0,Audi,2013.0,5.4,Diesel,Automatic,159950.0,Used,43837.32,Q5
+431.0,Audi,2019.0,2.0,Hybrid,Manual,167229.0,Like New,70814.24,A3
+432.0,Tesla,2002.0,4.7,Diesel,Manual,5914.0,Used,24001.54,Model Y
+433.0,Toyota,2002.0,2.4,Petrol,Automatic,214772.0,Like New,97461.61,Prius
+,,,,,,,,,
+435.0,Honda,2005.0,6.0,Diesel,Automatic,227561.0,Like New,85357.36,Accord
+436.0,Toyota,2012.0,1.2,Diesel,Automatic,187435.0,New,94809.81,Camry
+437.0,Honda,2020.0,5.5,Electric,Automatic,95213.0,Used,87147.83,Fit
+438.0,Tesla,2001.0,4.1,Hybrid,Automatic,37805.0,Used,11371.43,Model S
+439.0,Mercedes,2007.0,5.9,Diesel,Automatic,126599.0,Like New,66779.18,C-Class
+440.0,Honda,2020.0,3.3,Hybrid,Automatic,153775.0,Like New,16408.62,Civic
+441.0,Ford,2001.0,5.2,Petrol,Manual,190093.0,Used,5537.99,Mustang
+442.0,Mercedes,2003.0,1.3,Petrol,Manual,74176.0,Like New,9919.43,GLA
+443.0,Honda,2014.0,2.7,Diesel,Automatic,94799.0,New,92034.17,Civic
+444.0,Tesla,2003.0,1.3,Petrol,Manual,98385.0,Used,91304.03,Model 3
+445.0,Mercedes,2012.0,5.9,Hybrid,Automatic,235654.0,New,26453.32,C-Class
+,,,,,,,,,
+447.0,Honda,2012.0,5.3,Diesel,Manual,81462.0,New,95727.68,Accord
+448.0,Tesla,2013.0,5.7,Petrol,Manual,179340.0,Used,52187.08,Model Y
+449.0,Ford,2019.0,1.4,Hybrid,Automatic,165320.0,Like New,42389.89,Mustang
+450.0,Honda,2017.0,2.9,Hybrid,Manual,31159.0,Used,50908.63,CR-V
+,,,,,,,,,
+452.0,Honda,2010.0,2.1,Hybrid,Automatic,232974.0,Used,50341.38,Civic
+453.0,Toyota,2001.0,2.1,Hybrid,Automatic,47800.0,New,79411.38,Camry
+454.0,Toyota,2022.0,3.3,Diesel,Manual,94626.0,New,46011.26,Corolla
+455.0,Toyota,2018.0,2.3,Hybrid,Manual,118368.0,New,28940.54,RAV4
+456.0,Ford,2018.0,1.5,Petrol,Manual,174735.0,Like New,36014.91,Explorer
+457.0,Mercedes,2004.0,1.8,Diesel,Automatic,80903.0,Used,12393.23,C-Class
+,,,,,,,,,
+459.0,Audi,2001.0,5.9,Electric,Manual,241324.0,Like New,19070.54,Q5
+460.0,Honda,2009.0,4.8,Petrol,Automatic,233229.0,New,82096.97,Accord
+461.0,Tesla,2001.0,5.8,Petrol,Manual,61748.0,Used,44956.1,Model S
+462.0,Honda,2010.0,3.2,Diesel,Manual,41190.0,Like New,77675.22,CR-V
+463.0,Ford,2017.0,2.4,Hybrid,Manual,40318.0,New,95673.74,Explorer
+,,,,,,,,,
+,,,,,,,,,
+466.0,Audi,2008.0,2.5,Electric,Manual,39759.0,Used,58953.97,Q7
+467.0,BMW,2022.0,2.2,Diesel,Manual,278642.0,Like New,20705.82,X3
+468.0,Honda,2018.0,1.6,Diesel,Manual,25989.0,Like New,93862.62,Accord
+469.0,Tesla,2008.0,2.3,Hybrid,Manual,156640.0,Like New,87507.92,Model X
+470.0,Toyota,2002.0,2.8,Petrol,Manual,161171.0,New,87119.23,Corolla
+471.0,BMW,2020.0,4.4,Electric,Manual,285626.0,Like New,70861.07,3 Series
+,,,,,,,,,
+473.0,BMW,2013.0,2.1,Petrol,Automatic,35814.0,Used,6398.77,X5
+474.0,Toyota,2018.0,5.0,Petrol,Automatic,190766.0,Like New,76065.14,Corolla
+475.0,Mercedes,2006.0,1.7,Diesel,Automatic,60204.0,New,13695.84,E-Class
+476.0,Mercedes,2015.0,2.7,Petrol,Automatic,32844.0,Like New,29676.34,GLA
+477.0,BMW,2015.0,5.3,Diesel,Manual,248827.0,Like New,57391.58,X3
+478.0,Ford,2000.0,1.8,Diesel,Automatic,245256.0,Used,47914.6,Fiesta
+,,,,,,,,,
+480.0,Tesla,2003.0,3.4,Electric,Automatic,23052.0,Used,83278.58,Model S
+,,,,,,,,,
+482.0,Honda,2007.0,3.8,Hybrid,Automatic,69803.0,Like New,69777.93,Fit
+,,,,,,,,,
+484.0,Tesla,2004.0,1.7,Electric,Manual,224582.0,Like New,96270.09,Model X
+485.0,Tesla,2006.0,3.9,Petrol,Automatic,67021.0,Like New,16395.84,Model 3
+486.0,Mercedes,2019.0,3.5,Electric,Manual,211321.0,Like New,75101.46,GLA
+,,,,,,,,,
+488.0,Toyota,2003.0,4.1,Electric,Manual,2419.0,Used,41275.26,Prius
+489.0,Honda,2005.0,2.4,Electric,Automatic,187964.0,New,60621.03,Civic
+490.0,Tesla,2006.0,3.4,Electric,Automatic,121831.0,Like New,75023.33,Model S
+491.0,Mercedes,2017.0,1.4,Petrol,Automatic,244688.0,Used,5353.03,GLA
+492.0,Audi,2004.0,3.3,Diesel,Manual,101799.0,Like New,29756.8,Q5
+493.0,Mercedes,2019.0,2.5,Petrol,Automatic,194941.0,Like New,56129.04,GLA
+494.0,Honda,2002.0,5.1,Diesel,Manual,176282.0,Used,55623.0,Fit
+495.0,Honda,2006.0,1.3,Petrol,Manual,108611.0,Like New,65574.51,Civic
+496.0,Mercedes,2021.0,3.1,Hybrid,Manual,137559.0,Used,33866.09,C-Class
+497.0,Toyota,2013.0,3.3,Hybrid,Manual,62112.0,Used,56350.28,Corolla
+,,,,,,,,,
+499.0,Honda,2004.0,3.9,Hybrid,Automatic,278504.0,New,22752.86,CR-V
+500.0,Mercedes,2021.0,4.3,Electric,Automatic,233451.0,New,59773.58,GLA
+501.0,Tesla,2004.0,4.9,Diesel,Manual,221490.0,New,70119.9,Model X
+502.0,Mercedes,2022.0,5.3,Hybrid,Automatic,265161.0,Like New,16438.0,GLA
+503.0,Ford,2001.0,2.6,Diesel,Manual,191196.0,Like New,16665.04,Mustang
+504.0,BMW,2020.0,3.7,Petrol,Manual,247418.0,New,58703.76,5 Series
+505.0,Audi,2021.0,5.2,Diesel,Manual,221370.0,Used,30659.1,Q7
+506.0,Toyota,2005.0,5.9,Petrol,Automatic,189340.0,Used,42277.91,Corolla
+507.0,Audi,2014.0,5.4,Diesel,Automatic,66320.0,Used,43071.1,A3
+,,,,,,,,,
+,,,,,,,,,
+,,,,,,,,,
+511.0,Audi,2023.0,4.7,Hybrid,Manual,180786.0,Used,72925.19,Q7
+512.0,Tesla,2006.0,3.5,Hybrid,Manual,294065.0,New,70601.16,Model 3
+513.0,BMW,2004.0,3.4,Electric,Automatic,266890.0,Used,56917.99,3 Series
+514.0,Mercedes,2000.0,5.2,Electric,Manual,65350.0,Like New,71592.12,E-Class
+515.0,Audi,2012.0,2.8,Petrol,Manual,181417.0,Used,93890.4,A4
+516.0,BMW,2020.0,5.3,Diesel,Automatic,240678.0,Used,68239.0,X5
+517.0,Mercedes,2017.0,3.0,Petrol,Automatic,22063.0,Like New,30183.68,GLA
+518.0,Tesla,2012.0,2.6,Diesel,Manual,117116.0,New,59203.2,Model X
+,,,,,,,,,
+520.0,Tesla,2020.0,4.8,Electric,Manual,134670.0,New,57859.82,Model Y
+521.0,BMW,2017.0,1.6,Diesel,Manual,10564.0,Used,54173.0,5 Series
+,,,,,,,,,
+,,,,,,,,,
+524.0,BMW,2003.0,1.9,Electric,Manual,86134.0,New,22932.06,3 Series
+525.0,Honda,2016.0,3.8,Petrol,Manual,105940.0,Used,10986.59,Accord
+526.0,Mercedes,2002.0,3.9,Electric,Automatic,62085.0,New,45314.56,E-Class
+527.0,Ford,2016.0,3.4,Diesel,Automatic,17862.0,New,6483.01,Focus
+528.0,Toyota,2010.0,4.2,Petrol,Automatic,272409.0,Used,35238.25,RAV4
+529.0,Tesla,2020.0,2.1,Diesel,Manual,140777.0,Like New,39962.04,Model Y
+530.0,Ford,2019.0,3.8,Hybrid,Manual,186351.0,Like New,40680.94,Explorer
+531.0,BMW,2022.0,2.9,Petrol,Automatic,62471.0,Like New,98038.25,X3
+532.0,Honda,2020.0,4.3,Diesel,Automatic,91080.0,Used,33870.58,Civic
+533.0,Tesla,2003.0,1.7,Electric,Automatic,108007.0,Used,30196.61,Model X
+534.0,Honda,2001.0,3.9,Hybrid,Manual,46104.0,Like New,17291.19,Accord
+535.0,Audi,2019.0,1.9,Diesel,Automatic,40934.0,New,53545.84,A4
+536.0,Honda,2015.0,2.4,Petrol,Manual,254606.0,Like New,19599.06,Fit
+537.0,Audi,2017.0,2.1,Diesel,Automatic,6050.0,Used,63548.0,A3
+538.0,Mercedes,2007.0,1.9,Hybrid,Manual,175024.0,New,19740.18,E-Class
+539.0,Tesla,2002.0,5.1,Petrol,Manual,270628.0,New,59783.97,Model X
+540.0,Tesla,2008.0,2.4,Hybrid,Automatic,184750.0,Used,83495.58,Model S
+541.0,Toyota,2019.0,5.6,Diesel,Manual,31323.0,New,9039.84,RAV4
+542.0,BMW,2005.0,5.8,Electric,Automatic,86332.0,Like New,44875.96,5 Series
+543.0,Audi,2009.0,3.9,Electric,Manual,255540.0,Used,48799.65,A3
+544.0,Toyota,2020.0,1.7,Diesel,Manual,254547.0,Used,80151.85,Prius
+,,,,,,,,,
+546.0,Honda,2012.0,5.0,Diesel,Manual,157591.0,Used,58915.9,Accord
+547.0,Toyota,2016.0,2.8,Petrol,Automatic,271251.0,New,80164.4,Prius
+548.0,Tesla,2008.0,1.4,Hybrid,Automatic,55781.0,Used,58177.06,Model S
+549.0,Honda,2016.0,3.8,Hybrid,Automatic,266013.0,Used,39038.44,Accord
+550.0,Mercedes,2023.0,5.2,Hybrid,Manual,7971.0,New,13155.02,C-Class
+551.0,Mercedes,2008.0,5.0,Diesel,Manual,254927.0,Used,74341.98,GLC
+552.0,Toyota,2008.0,1.9,Hybrid,Manual,287795.0,New,87758.12,RAV4
+553.0,Audi,2000.0,4.4,Diesel,Automatic,23446.0,New,97156.13,A4
+554.0,Audi,2001.0,2.1,Petrol,Automatic,257419.0,Used,93026.99,A4
+,,,,,,,,,
+556.0,Audi,2000.0,5.4,Diesel,Manual,3823.0,Used,66129.83,Q5
+557.0,Tesla,2009.0,2.2,Diesel,Automatic,144526.0,Like New,79238.71,Model X
+558.0,Tesla,2017.0,2.3,Electric,Manual,135004.0,New,46047.95,Model X
+559.0,Audi,2007.0,1.0,Electric,Automatic,205162.0,Like New,61503.74,A4
+560.0,Mercedes,2020.0,5.4,Petrol,Manual,67965.0,Used,38126.48,GLA
+561.0,Audi,2010.0,5.0,Hybrid,Automatic,272654.0,Used,36037.38,Q7
+562.0,Tesla,2022.0,4.1,Diesel,Manual,141609.0,Like New,76885.72,Model X
+563.0,Ford,2001.0,4.8,Hybrid,Manual,105935.0,Used,48123.61,Explorer
+564.0,BMW,2007.0,1.8,Diesel,Automatic,233705.0,Like New,25155.32,X5
+,,,,,,,,,
+566.0,Ford,2007.0,2.8,Electric,Automatic,144854.0,Like New,86621.37,Mustang
+567.0,Audi,2016.0,1.5,Petrol,Manual,78004.0,Like New,69387.25,A4
+568.0,Mercedes,2014.0,3.4,Electric,Automatic,22072.0,Like New,56874.38,GLC
+569.0,Toyota,2016.0,5.6,Hybrid,Automatic,81015.0,Like New,43089.18,Corolla
+570.0,Audi,2022.0,1.2,Hybrid,Manual,225075.0,New,45927.65,Q5
+571.0,Mercedes,2023.0,2.5,Petrol,Automatic,220010.0,New,10456.34,GLA
+572.0,BMW,2012.0,2.0,Electric,Automatic,72746.0,New,48216.46,3 Series
+,,,,,,,,,
+574.0,Tesla,2004.0,5.5,Electric,Manual,128259.0,New,41858.72,Model X
+575.0,Toyota,2011.0,3.3,Electric,Manual,30916.0,Used,41440.22,Camry
+576.0,Mercedes,2000.0,3.3,Hybrid,Manual,27268.0,New,6493.08,GLA
+577.0,Mercedes,2022.0,4.8,Hybrid,Automatic,175050.0,Like New,48494.35,E-Class
+578.0,Mercedes,2004.0,1.8,Electric,Manual,243966.0,Used,7407.08,C-Class
+579.0,BMW,2007.0,3.4,Electric,Automatic,23259.0,Like New,59183.2,5 Series
+580.0,Audi,2012.0,3.1,Electric,Automatic,79400.0,Used,53144.42,Q5
+581.0,BMW,2007.0,4.0,Hybrid,Automatic,296437.0,Like New,84800.81,3 Series
+,,,,,,,,,
+,,,,,,,,,
+584.0,Audi,2000.0,3.0,Electric,Automatic,181502.0,New,54041.95,Q7
+585.0,Tesla,2012.0,5.1,Diesel,Automatic,107009.0,Used,71472.1,Model Y
+586.0,BMW,2018.0,1.9,Electric,Manual,209137.0,Used,13926.56,X5
+587.0,Toyota,2023.0,1.2,Hybrid,Manual,161940.0,Used,64002.04,RAV4
+588.0,Mercedes,2002.0,2.0,Diesel,Manual,288453.0,Used,92079.5,E-Class
+589.0,Mercedes,2023.0,2.7,Diesel,Automatic,285428.0,Used,33417.38,GLC
+590.0,Audi,2016.0,3.6,Petrol,Manual,113259.0,Like New,64529.38,A4
+591.0,Tesla,2004.0,4.1,Electric,Manual,100804.0,Like New,83026.3,Model 3
+592.0,Honda,2019.0,5.6,Hybrid,Manual,69000.0,New,53621.61,Accord
+593.0,Honda,2020.0,3.5,Petrol,Automatic,84179.0,New,73208.06,Accord
+594.0,Mercedes,2007.0,3.5,Petrol,Manual,291409.0,Used,34545.98,C-Class
+595.0,Audi,2013.0,1.3,Electric,Automatic,260226.0,New,34075.55,A3
+596.0,BMW,2003.0,1.2,Petrol,Automatic,23880.0,New,68420.48,X3
+597.0,Honda,2010.0,3.8,Electric,Automatic,201264.0,Used,72013.84,Civic
+598.0,BMW,2019.0,3.2,Petrol,Manual,172438.0,Like New,94015.92,X5
+599.0,Tesla,2014.0,5.2,Electric,Manual,245352.0,Like New,26359.66,Model Y
+600.0,Honda,2018.0,1.8,Hybrid,Manual,67396.0,New,49480.67,Civic
+601.0,Honda,2006.0,1.1,Electric,Automatic,279250.0,Like New,28798.78,CR-V
+602.0,Ford,2018.0,3.2,Electric,Manual,56731.0,Like New,12608.76,Focus
+,,,,,,,,,
+604.0,Toyota,2010.0,1.2,Hybrid,Manual,249091.0,Used,40537.67,Prius
+605.0,Audi,2004.0,4.6,Hybrid,Manual,294743.0,New,30691.24,A4
+606.0,Audi,2004.0,1.6,Hybrid,Manual,71326.0,New,33214.51,A4
+607.0,BMW,2021.0,4.0,Petrol,Manual,248876.0,New,39239.12,3 Series
+608.0,Tesla,2007.0,2.4,Diesel,Automatic,9004.0,Used,98939.58,Model 3
+609.0,Honda,2009.0,1.9,Hybrid,Automatic,115018.0,Used,79380.99,Civic
+610.0,Toyota,2005.0,2.9,Petrol,Automatic,25792.0,Used,11783.11,Prius
+611.0,BMW,2014.0,5.0,Petrol,Automatic,54645.0,Used,87879.25,5 Series
+,,,,,,,,,
+613.0,BMW,2016.0,4.8,Diesel,Automatic,54766.0,Like New,89606.41,X5
+,,,,,,,,,
+615.0,Honda,2007.0,4.9,Diesel,Automatic,81588.0,Used,49129.29,Fit
+616.0,Ford,2008.0,2.2,Electric,Manual,179614.0,Like New,90141.57,Focus
+617.0,BMW,2001.0,4.4,Petrol,Automatic,5913.0,New,30894.45,X3
+,,,,,,,,,
+619.0,Toyota,2017.0,5.6,Petrol,Manual,113693.0,Used,83707.47,Camry
+620.0,Audi,2002.0,2.9,Electric,Manual,93449.0,Like New,98153.0,Q7
+,,,,,,,,,
+,,,,,,,,,
+623.0,Audi,2003.0,5.1,Electric,Manual,206040.0,Like New,36432.36,Q7
+624.0,BMW,2013.0,3.6,Diesel,Automatic,262463.0,New,95691.73,3 Series
+625.0,Ford,2008.0,4.9,Diesel,Manual,90005.0,Like New,36641.96,Fiesta
+626.0,Toyota,2008.0,2.2,Petrol,Automatic,223098.0,New,58695.12,Prius
+627.0,Toyota,2022.0,2.7,Diesel,Automatic,4791.0,Like New,43349.53,Camry
+628.0,Honda,2017.0,1.2,Diesel,Automatic,131401.0,Like New,42640.91,Fit
+629.0,Ford,2009.0,5.8,Hybrid,Automatic,205459.0,Used,23370.97,Explorer
+630.0,BMW,2020.0,4.3,Petrol,Automatic,61032.0,Used,56022.56,X5
+631.0,Audi,2003.0,5.7,Electric,Manual,175150.0,New,85693.4,A4
+632.0,Audi,2011.0,2.3,Petrol,Manual,21300.0,Used,9701.08,Q7
+,,,,,,,,,
+634.0,Tesla,2001.0,4.4,Hybrid,Automatic,191634.0,Used,68219.93,Model 3
+635.0,Mercedes,2021.0,2.6,Petrol,Automatic,90045.0,Used,63590.12,GLA
+636.0,Honda,2012.0,5.2,Petrol,Manual,267847.0,Used,64460.52,Civic
+637.0,Audi,2020.0,5.7,Diesel,Manual,223717.0,New,33145.63,Q7
+638.0,Ford,2015.0,5.4,Diesel,Manual,219162.0,New,44438.73,Focus
+639.0,Honda,2001.0,4.7,Petrol,Manual,27676.0,Used,80932.55,Civic
+640.0,Ford,2010.0,2.4,Diesel,Manual,220412.0,Used,34106.2,Focus
+641.0,Tesla,2009.0,2.4,Diesel,Manual,176512.0,New,49651.92,Model X
+642.0,Toyota,2014.0,5.8,Diesel,Manual,167995.0,Like New,6865.07,Corolla
+643.0,Honda,2016.0,1.1,Diesel,Manual,291946.0,New,74688.04,CR-V
+,,,,,,,,,
+645.0,Honda,2017.0,4.5,Hybrid,Manual,68147.0,Used,54909.08,Accord
+646.0,Honda,2022.0,4.1,Diesel,Manual,22075.0,Used,15895.49,Civic
+647.0,Honda,2008.0,6.0,Electric,Manual,205788.0,New,99212.85,Accord
+648.0,Mercedes,2007.0,2.6,Petrol,Manual,9304.0,Used,33734.44,GLA
+649.0,Honda,2013.0,2.7,Hybrid,Automatic,129851.0,Like New,65324.02,Civic
+650.0,Ford,2017.0,1.4,Hybrid,Manual,59622.0,Like New,60217.45,Fiesta
+651.0,Toyota,2021.0,3.2,Diesel,Automatic,108654.0,New,79583.76,Camry
+652.0,Tesla,2019.0,2.3,Petrol,Automatic,249109.0,Like New,32914.51,Model S
+653.0,BMW,2019.0,2.7,Electric,Automatic,286172.0,New,76813.1,X3
+654.0,Honda,2020.0,5.2,Diesel,Manual,39328.0,New,41337.66,CR-V
+655.0,Audi,2011.0,5.7,Petrol,Manual,289445.0,Like New,44060.29,Q5
+656.0,Honda,2018.0,1.9,Diesel,Manual,218348.0,Like New,68907.69,Accord
+657.0,Audi,2013.0,2.9,Diesel,Automatic,36845.0,Like New,79507.15,A3
+658.0,Ford,2016.0,5.6,Diesel,Automatic,76394.0,Like New,68352.33,Explorer
+659.0,Audi,2023.0,1.3,Diesel,Automatic,175231.0,Like New,90488.55,Q7
+660.0,BMW,2020.0,1.5,Diesel,Automatic,80722.0,Like New,32090.11,3 Series
+661.0,Toyota,2002.0,1.8,Diesel,Manual,287374.0,Like New,90558.59,Corolla
+662.0,Audi,2022.0,4.0,Diesel,Manual,92554.0,Used,17432.03,A4
+663.0,Audi,2006.0,1.8,Electric,Manual,49262.0,New,67655.38,A4
+664.0,Toyota,2016.0,5.8,Hybrid,Automatic,41722.0,New,57024.91,Corolla
+665.0,BMW,2011.0,3.2,Electric,Automatic,171231.0,Like New,51990.41,X5
+666.0,Honda,2008.0,3.5,Diesel,Manual,240740.0,New,34536.64,Civic
+667.0,Audi,2014.0,2.2,Diesel,Manual,87052.0,Like New,52854.61,A3
+668.0,Toyota,2017.0,3.4,Electric,Automatic,100808.0,Used,20368.47,Camry
+,,,,,,,,,
+670.0,Toyota,2012.0,4.8,Hybrid,Manual,262128.0,New,14374.69,Camry
+,,,,,,,,,
+672.0,BMW,2007.0,3.9,Hybrid,Automatic,246052.0,Used,14097.95,X5
+673.0,Honda,2013.0,5.5,Diesel,Automatic,104697.0,Used,20232.74,Fit
+674.0,Toyota,2007.0,5.1,Petrol,Manual,231572.0,New,13119.35,RAV4
+675.0,Tesla,2013.0,4.8,Petrol,Automatic,232842.0,New,41224.51,Model X
+676.0,Tesla,2006.0,1.3,Petrol,Manual,96299.0,Used,84986.05,Model Y
+677.0,Mercedes,2022.0,1.0,Petrol,Manual,165746.0,Used,91155.16,GLC
+678.0,Audi,2011.0,2.1,Diesel,Automatic,149177.0,Used,11829.5,A3
+679.0,BMW,2010.0,4.3,Petrol,Manual,160811.0,Used,65282.26,X5
+680.0,Toyota,2021.0,3.1,Electric,Automatic,163041.0,Used,70016.62,Prius
+681.0,Tesla,2021.0,3.0,Electric,Automatic,14497.0,Like New,55708.14,Model X
+682.0,Audi,2010.0,5.4,Petrol,Manual,254204.0,Used,68363.56,Q5
+683.0,Tesla,2021.0,5.5,Electric,Manual,257471.0,Like New,27200.62,Model Y
+684.0,Toyota,2012.0,5.5,Hybrid,Automatic,92019.0,New,51516.2,RAV4
+685.0,Ford,2017.0,2.6,Diesel,Automatic,190326.0,New,42133.21,Explorer
+686.0,Toyota,2001.0,4.5,Petrol,Automatic,109017.0,Like New,13522.26,Prius
+687.0,Mercedes,2020.0,2.4,Petrol,Automatic,218220.0,Used,64427.86,C-Class
+688.0,Toyota,2014.0,2.0,Diesel,Automatic,105153.0,New,52045.44,Corolla
+689.0,Toyota,2019.0,1.9,Electric,Automatic,33826.0,Used,31141.53,RAV4
+690.0,Mercedes,2017.0,2.7,Diesel,Manual,110034.0,Used,50000.1,C-Class
+691.0,BMW,2014.0,3.1,Diesel,Manual,84242.0,Like New,78653.31,5 Series
+692.0,Toyota,2018.0,5.2,Electric,Manual,50100.0,New,81157.49,RAV4
+693.0,Mercedes,2022.0,1.6,Diesel,Manual,221171.0,New,66704.98,GLC
+694.0,Audi,2023.0,4.7,Electric,Automatic,12809.0,Like New,49436.04,Q5
+695.0,Ford,2011.0,3.7,Electric,Manual,64450.0,Used,59696.22,Explorer
+696.0,Ford,2012.0,2.4,Diesel,Manual,111126.0,Used,41576.46,Focus
+697.0,Toyota,2021.0,3.5,Hybrid,Automatic,264237.0,New,17022.85,RAV4
+698.0,BMW,2010.0,2.5,Electric,Manual,44498.0,Like New,41633.96,X5
+699.0,BMW,2004.0,4.0,Diesel,Automatic,116251.0,Like New,55381.61,X3
+700.0,Audi,2000.0,3.2,Electric,Automatic,40985.0,Used,61889.23,Q7
+701.0,Toyota,2023.0,1.8,Hybrid,Automatic,294153.0,Used,93901.09,Corolla
+702.0,Ford,2002.0,1.6,Hybrid,Automatic,17718.0,Like New,78846.68,Fiesta
+703.0,BMW,2021.0,3.7,Petrol,Automatic,278849.0,New,19439.29,X3
+704.0,Toyota,2018.0,5.5,Electric,Manual,62598.0,Used,21547.31,Prius
+705.0,Tesla,2022.0,2.7,Electric,Manual,274383.0,Like New,14547.0,Model 3
+706.0,BMW,2006.0,4.7,Diesel,Manual,46665.0,Like New,75127.26,3 Series
+707.0,Mercedes,2011.0,4.3,Electric,Automatic,294418.0,Like New,72081.62,C-Class
+,,,,,,,,,
+709.0,BMW,2004.0,5.1,Hybrid,Manual,161938.0,Used,54592.22,X5
+710.0,Ford,2014.0,3.8,Hybrid,Manual,153060.0,Used,83509.42,Focus
+711.0,Honda,2007.0,4.3,Petrol,Manual,221516.0,Used,59761.79,CR-V
+712.0,Tesla,2009.0,5.5,Electric,Automatic,136746.0,New,88038.24,Model Y
+713.0,Audi,2015.0,3.0,Electric,Manual,201610.0,Used,73497.0,A4
+714.0,Audi,2007.0,2.6,Petrol,Automatic,66446.0,Used,66073.98,A3
+715.0,Ford,2000.0,1.1,Diesel,Automatic,29072.0,Used,58686.65,Mustang
+716.0,BMW,2011.0,5.1,Hybrid,Manual,33717.0,Used,73682.2,3 Series
+717.0,Toyota,2004.0,5.0,Petrol,Automatic,218417.0,New,44867.12,Prius
+718.0,BMW,2002.0,1.5,Petrol,Automatic,157723.0,Like New,14174.78,X3
+719.0,Ford,2000.0,3.9,Electric,Automatic,109525.0,Like New,8798.68,Focus
+,,,,,,,,,
+721.0,BMW,2007.0,1.6,Electric,Automatic,4646.0,Used,61932.7,3 Series
+722.0,Audi,2002.0,5.9,Petrol,Automatic,175403.0,Used,12490.66,A3
+723.0,Tesla,2009.0,2.1,Diesel,Automatic,284329.0,New,32255.84,Model S
+724.0,Toyota,2001.0,1.3,Hybrid,Manual,229728.0,Used,35593.06,Corolla
+725.0,Tesla,2010.0,4.0,Petrol,Manual,52107.0,New,58465.58,Model Y
+726.0,Audi,2009.0,4.7,Petrol,Automatic,244865.0,Used,31455.83,Q5
+727.0,Audi,2020.0,1.2,Diesel,Automatic,279707.0,Used,14086.62,A3
+728.0,Honda,2013.0,4.3,Petrol,Manual,70729.0,Like New,20661.39,Accord
+729.0,Honda,2006.0,3.7,Petrol,Manual,277845.0,Used,21525.04,Civic
+730.0,Tesla,2001.0,1.5,Hybrid,Manual,200144.0,New,85056.02,Model S
+731.0,Mercedes,2008.0,1.9,Hybrid,Manual,215494.0,Like New,32010.37,GLC
+732.0,Tesla,2015.0,3.8,Hybrid,Automatic,252615.0,New,72895.71,Model X
+733.0,Honda,2012.0,2.5,Electric,Automatic,128331.0,Like New,58944.68,Fit
+734.0,Audi,2008.0,5.7,Diesel,Manual,160482.0,New,97818.68,A4
+735.0,Ford,2019.0,5.1,Hybrid,Manual,99280.0,Like New,30918.15,Fiesta
+736.0,Tesla,2003.0,5.9,Electric,Manual,189787.0,New,20045.44,Model S
+737.0,Audi,2021.0,2.3,Electric,Manual,38665.0,New,9943.59,A3
+738.0,Ford,2009.0,5.8,Diesel,Automatic,278101.0,Used,27891.17,Explorer
+739.0,Ford,2021.0,3.2,Diesel,Manual,259285.0,Like New,69986.73,Focus
+740.0,Honda,2011.0,2.7,Petrol,Manual,163947.0,Like New,13099.53,CR-V
+741.0,Tesla,2016.0,1.3,Electric,Manual,113828.0,Used,28246.68,Model 3
+742.0,BMW,2013.0,1.3,Hybrid,Automatic,132698.0,New,86254.29,3 Series
+,,,,,,,,,
+744.0,Honda,2001.0,3.3,Electric,Automatic,194894.0,Like New,11604.55,Fit
+745.0,Honda,2020.0,2.1,Hybrid,Manual,203485.0,New,9529.9,Civic
+746.0,BMW,2019.0,4.1,Electric,Manual,164714.0,Like New,10806.67,X5
+747.0,Toyota,2010.0,2.3,Diesel,Manual,32356.0,Like New,94478.13,Prius
+748.0,Audi,2015.0,3.9,Petrol,Automatic,265953.0,New,85737.79,Q5
+749.0,Mercedes,2018.0,2.8,Electric,Automatic,297019.0,New,85269.3,C-Class
+750.0,Tesla,2022.0,5.1,Hybrid,Manual,241121.0,New,92800.59,Model 3
+751.0,Mercedes,2005.0,5.1,Diesel,Manual,271609.0,Used,52999.26,C-Class
+752.0,Audi,2023.0,3.4,Petrol,Automatic,197167.0,Like New,28497.74,Q7
+753.0,Honda,2018.0,2.8,Hybrid,Manual,196322.0,New,43128.06,Fit
+754.0,Toyota,2004.0,2.8,Hybrid,Manual,15250.0,Used,73292.09,Prius
+755.0,Honda,2003.0,5.0,Hybrid,Manual,46023.0,New,88223.12,Accord
+756.0,Ford,2014.0,2.6,Electric,Automatic,101350.0,Like New,46333.64,Fiesta
+,,,,,,,,,
+758.0,Honda,2019.0,1.4,Hybrid,Manual,16427.0,Like New,93519.38,Accord
+759.0,Audi,2009.0,3.3,Electric,Manual,210084.0,New,15212.37,A3
+760.0,Audi,2023.0,3.4,Diesel,Manual,134511.0,New,86918.43,A4
+761.0,Audi,2021.0,5.2,Diesel,Automatic,218403.0,New,20134.21,A3
+762.0,Mercedes,2002.0,2.1,Electric,Automatic,78672.0,Used,50966.83,E-Class
+763.0,Ford,2004.0,2.9,Electric,Automatic,17300.0,Used,25750.2,Mustang
+764.0,Audi,2005.0,5.0,Hybrid,Manual,19659.0,Used,62280.02,A3
+,,,,,,,,,
+766.0,Mercedes,2021.0,2.9,Petrol,Manual,245259.0,New,41100.7,GLC
+767.0,BMW,2007.0,3.3,Electric,Automatic,10360.0,New,87013.83,X3
+768.0,Toyota,2006.0,2.5,Diesel,Manual,283568.0,New,67682.27,Camry
+769.0,Tesla,2020.0,5.7,Diesel,Automatic,98035.0,Used,73514.49,Model Y
+770.0,Tesla,2009.0,1.6,Hybrid,Automatic,48791.0,Used,19312.68,Model X
+771.0,Toyota,2005.0,5.7,Petrol,Automatic,199867.0,New,88900.74,RAV4
+772.0,Audi,2013.0,4.4,Hybrid,Manual,274444.0,New,20868.82,Q5
+,,,,,,,,,
+774.0,BMW,2012.0,1.2,Petrol,Manual,200903.0,Like New,17165.24,3 Series
+775.0,Mercedes,2019.0,3.4,Electric,Automatic,27209.0,New,46834.12,GLC
+776.0,BMW,2016.0,3.0,Diesel,Manual,283071.0,New,89332.38,5 Series
+777.0,Tesla,2022.0,1.8,Diesel,Manual,34579.0,New,28982.51,Model 3
+778.0,Ford,2006.0,4.3,Diesel,Manual,271176.0,Used,40506.57,Mustang
+779.0,Audi,2003.0,5.9,Petrol,Manual,93254.0,New,85376.31,A3
+780.0,BMW,2013.0,3.8,Diesel,Manual,113528.0,New,63692.45,3 Series
+781.0,Mercedes,2002.0,4.6,Electric,Manual,223274.0,New,76403.56,GLA
+782.0,Tesla,2023.0,1.3,Petrol,Automatic,238292.0,New,25330.0,Model X
+783.0,Ford,2004.0,3.1,Petrol,Automatic,181026.0,New,73066.54,Focus
+784.0,Honda,2015.0,1.6,Petrol,Manual,248342.0,Like New,26623.0,CR-V
+785.0,Honda,2022.0,4.1,Petrol,Automatic,219437.0,Like New,98794.83,CR-V
+786.0,Ford,2019.0,5.7,Electric,Automatic,278076.0,New,58189.18,Fiesta
+787.0,Tesla,2003.0,4.3,Diesel,Manual,287858.0,New,71085.73,Model 3
+788.0,Mercedes,2023.0,1.4,Electric,Manual,57803.0,New,16248.15,E-Class
+789.0,Ford,2023.0,2.8,Diesel,Automatic,133624.0,New,28812.32,Mustang
+790.0,Audi,2001.0,3.8,Electric,Manual,56627.0,Used,18275.36,A3
+,,,,,,,,,
+792.0,Honda,2018.0,5.2,Hybrid,Automatic,45380.0,Used,31992.91,Fit
+793.0,BMW,2018.0,5.1,Hybrid,Automatic,160322.0,Like New,74627.61,5 Series
+794.0,Mercedes,2016.0,4.1,Hybrid,Manual,199416.0,New,15327.35,E-Class
+795.0,Honda,2013.0,2.9,Petrol,Automatic,146367.0,Used,76445.64,Fit
+796.0,BMW,2018.0,2.3,Petrol,Manual,272667.0,New,9341.96,X3
+797.0,BMW,2021.0,1.5,Electric,Manual,259125.0,New,24191.69,X3
+798.0,Audi,2023.0,2.8,Electric,Manual,13769.0,Like New,80236.67,A4
+799.0,Tesla,2012.0,3.1,Hybrid,Manual,112035.0,Like New,75563.78,Model X
+800.0,Toyota,2011.0,4.4,Diesel,Manual,175717.0,Used,50250.98,Camry
+801.0,Tesla,2005.0,4.3,Hybrid,Automatic,221815.0,Used,9748.36,Model Y
+,,,,,,,,,
+,,,,,,,,,
+804.0,Audi,2018.0,1.9,Electric,Manual,234799.0,New,35070.17,Q7
+805.0,BMW,2015.0,1.8,Hybrid,Manual,168157.0,New,65839.01,5 Series
+806.0,Mercedes,2012.0,5.1,Diesel,Manual,144542.0,Used,46478.38,GLA
+807.0,Toyota,2003.0,1.2,Hybrid,Automatic,118643.0,Used,13780.0,Corolla
+,,,,,,,,,
+809.0,BMW,2016.0,1.8,Diesel,Manual,137877.0,Used,78956.84,3 Series
+810.0,Toyota,2022.0,5.4,Electric,Automatic,213396.0,New,10355.93,Prius
+811.0,Toyota,2000.0,3.8,Diesel,Manual,225956.0,Like New,54881.07,Prius
+812.0,Toyota,2017.0,2.2,Petrol,Automatic,181844.0,New,12920.87,Corolla
+813.0,Mercedes,2019.0,3.5,Petrol,Automatic,101422.0,Like New,84542.54,GLA
+814.0,Audi,2016.0,4.7,Petrol,Automatic,268774.0,Like New,63236.33,Q7
+815.0,Mercedes,2014.0,5.8,Electric,Automatic,277845.0,New,21382.67,C-Class
+816.0,Honda,2010.0,2.1,Petrol,Manual,145931.0,Used,6970.43,CR-V
+817.0,BMW,2021.0,5.4,Electric,Manual,117556.0,Used,8000.2,5 Series
+818.0,Audi,2012.0,5.7,Electric,Automatic,228495.0,Used,53199.72,Q7
+819.0,Audi,2000.0,5.6,Petrol,Automatic,254809.0,Used,74765.48,Q7
+,,,,,,,,,
+821.0,Tesla,2005.0,4.1,Hybrid,Automatic,15021.0,Like New,26021.99,Model S
+822.0,Audi,2003.0,3.0,Electric,Automatic,200935.0,New,36089.69,Q5
+823.0,Mercedes,2019.0,4.8,Petrol,Automatic,204222.0,Used,25717.29,GLA
+824.0,Audi,2015.0,3.7,Electric,Manual,64361.0,Used,42176.97,Q7
+825.0,Ford,2014.0,4.4,Diesel,Automatic,272499.0,New,58163.43,Explorer
+826.0,BMW,2014.0,3.1,Diesel,Manual,98841.0,Like New,70068.99,5 Series
+827.0,Audi,2005.0,4.7,Petrol,Manual,180076.0,New,96558.64,A3
+828.0,BMW,2014.0,5.1,Electric,Manual,246300.0,New,93430.15,5 Series
+829.0,Ford,2013.0,1.7,Hybrid,Automatic,160786.0,New,59162.06,Explorer
+830.0,Ford,2010.0,5.2,Hybrid,Automatic,17198.0,Like New,74744.26,Explorer
+831.0,BMW,2006.0,3.7,Electric,Manual,258466.0,New,95332.86,X3
+832.0,Toyota,2008.0,5.2,Electric,Manual,18740.0,New,12929.83,Corolla
+833.0,Honda,2015.0,3.2,Electric,Manual,265954.0,Used,5535.3,Civic
+834.0,Tesla,2004.0,2.9,Petrol,Automatic,258308.0,New,7830.98,Model Y
+835.0,Toyota,2019.0,5.6,Diesel,Automatic,99855.0,Used,58518.19,Corolla
+836.0,Toyota,2009.0,2.3,Hybrid,Manual,210192.0,Used,77469.18,RAV4
+837.0,Mercedes,2017.0,5.2,Petrol,Manual,288927.0,Used,39872.12,E-Class
+838.0,Tesla,2002.0,3.4,Electric,Automatic,121701.0,Like New,56797.72,Model Y
+839.0,Tesla,2012.0,3.6,Electric,Manual,271615.0,Used,54054.8,Model Y
+,,,,,,,,,
+841.0,Audi,2016.0,3.9,Electric,Automatic,6163.0,New,35550.89,A4
+,,,,,,,,,
+843.0,Mercedes,2012.0,1.2,Petrol,Manual,241262.0,Used,15517.04,C-Class
+844.0,Ford,2005.0,2.4,Diesel,Automatic,248924.0,New,82126.53,Fiesta
+845.0,Tesla,2016.0,1.0,Petrol,Automatic,179420.0,Used,83794.28,Model 3
+846.0,Toyota,2012.0,5.9,Hybrid,Automatic,281298.0,Like New,81184.9,Camry
+847.0,Ford,2018.0,5.8,Petrol,Automatic,20229.0,Used,74047.77,Focus
+848.0,BMW,2020.0,3.0,Petrol,Automatic,250470.0,Like New,80364.72,X5
+849.0,Honda,2018.0,4.6,Petrol,Manual,158069.0,Like New,68743.32,Civic
+850.0,Mercedes,2009.0,2.7,Hybrid,Automatic,21101.0,Like New,68634.17,GLC
+851.0,BMW,2002.0,4.4,Electric,Automatic,40391.0,Used,68399.37,3 Series
+852.0,BMW,2013.0,5.0,Petrol,Automatic,206935.0,Like New,53834.07,3 Series
+853.0,Audi,2018.0,5.7,Diesel,Manual,72723.0,Like New,39517.99,Q7
+854.0,Honda,2004.0,3.0,Hybrid,Manual,226293.0,Used,74368.55,Civic
+855.0,BMW,2004.0,4.9,Diesel,Automatic,190477.0,Like New,33371.37,5 Series
+856.0,BMW,2019.0,2.3,Petrol,Automatic,166020.0,Used,56575.7,X5
+857.0,Honda,2016.0,6.0,Diesel,Manual,290604.0,New,46168.78,Accord
+,,,,,,,,,
+859.0,BMW,2009.0,4.0,Hybrid,Manual,179354.0,Used,97595.54,3 Series
+860.0,Honda,2004.0,4.3,Diesel,Manual,91725.0,New,29581.41,Accord
+861.0,BMW,2003.0,4.4,Diesel,Automatic,212139.0,Like New,34382.84,3 Series
+862.0,BMW,2005.0,1.6,Petrol,Manual,85779.0,Like New,20350.81,3 Series
+863.0,Audi,2003.0,5.7,Petrol,Automatic,3060.0,Used,83183.16,A4
+864.0,Toyota,2006.0,1.9,Electric,Automatic,125806.0,New,60725.99,RAV4
+865.0,BMW,2006.0,4.1,Petrol,Manual,275875.0,Used,63238.73,3 Series
+866.0,Ford,2017.0,2.1,Petrol,Manual,115871.0,Like New,82953.33,Explorer
+,,,,,,,,,
+868.0,Tesla,2000.0,3.7,Petrol,Manual,49116.0,Like New,23395.18,Model 3
+869.0,Toyota,2023.0,3.1,Petrol,Automatic,26325.0,Like New,23848.51,RAV4
+870.0,Toyota,2003.0,1.8,Electric,Automatic,14551.0,New,74598.88,RAV4
+871.0,Toyota,2005.0,1.9,Hybrid,Automatic,286422.0,New,63340.94,Prius
+872.0,Audi,2008.0,3.1,Hybrid,Manual,15141.0,Used,24602.19,Q7
+873.0,BMW,2021.0,4.8,Petrol,Automatic,295759.0,Used,18781.21,X3
+,,,,,,,,,
+875.0,BMW,2011.0,1.4,Electric,Automatic,193823.0,Like New,64640.87,X3
+876.0,Audi,2019.0,3.0,Electric,Manual,112684.0,Used,5011.27,A4
+877.0,Audi,2023.0,1.5,Hybrid,Automatic,289596.0,New,6740.34,A3
+878.0,Mercedes,2003.0,1.1,Diesel,Automatic,50391.0,Used,42007.55,C-Class
+879.0,Ford,2020.0,4.3,Petrol,Manual,120288.0,Used,70678.24,Explorer
+880.0,Audi,2023.0,4.0,Diesel,Automatic,206132.0,New,71028.74,A3
+881.0,Mercedes,2011.0,1.8,Diesel,Automatic,113535.0,New,45861.24,GLC
+882.0,Honda,2009.0,2.2,Electric,Manual,142770.0,Like New,70651.99,CR-V
+883.0,Ford,2021.0,1.1,Petrol,Manual,239947.0,New,54504.14,Mustang
+884.0,Audi,2006.0,5.2,Petrol,Manual,86711.0,New,97808.48,Q5
+885.0,Tesla,2002.0,5.9,Electric,Manual,264509.0,Used,20433.72,Model Y
+886.0,Toyota,2022.0,1.7,Diesel,Manual,228625.0,New,62063.39,Prius
+,,,,,,,,,
+888.0,Honda,2023.0,5.3,Hybrid,Manual,44134.0,Used,23474.3,CR-V
+889.0,Mercedes,2009.0,5.6,Petrol,Automatic,299967.0,Like New,76249.09,GLA
+890.0,Honda,2015.0,3.1,Hybrid,Automatic,71158.0,New,31173.26,Accord
+891.0,Ford,2015.0,1.3,Electric,Manual,237159.0,Like New,41262.43,Fiesta
+,,,,,,,,,
+893.0,Audi,2009.0,3.9,Hybrid,Automatic,49396.0,New,28199.42,Q7
+894.0,Tesla,2012.0,3.0,Hybrid,Manual,31912.0,New,45917.01,Model S
+895.0,Toyota,2005.0,1.1,Diesel,Automatic,56947.0,Used,49967.86,Prius
+896.0,Mercedes,2011.0,3.9,Petrol,Manual,44387.0,Used,75701.06,E-Class
+897.0,Toyota,2008.0,1.1,Hybrid,Manual,278652.0,Used,77681.51,Camry
+898.0,Honda,2012.0,4.9,Hybrid,Manual,109249.0,New,20062.82,Civic
+899.0,Tesla,2003.0,2.5,Diesel,Automatic,1919.0,Like New,61313.37,Model 3
+900.0,Toyota,2023.0,1.2,Hybrid,Automatic,140689.0,Like New,90138.79,Corolla
+901.0,Ford,2010.0,3.9,Hybrid,Automatic,33298.0,Used,15334.73,Mustang
+902.0,Audi,2019.0,3.0,Hybrid,Automatic,237225.0,Used,49926.83,A3
+903.0,Honda,2017.0,5.9,Diesel,Manual,233725.0,New,76283.55,Fit
+904.0,Tesla,2010.0,3.7,Hybrid,Automatic,276600.0,Like New,63091.68,Model X
+905.0,Toyota,2020.0,2.4,Hybrid,Manual,109599.0,Used,14555.64,Camry
+906.0,Mercedes,2003.0,4.5,Petrol,Automatic,83721.0,New,42677.58,GLC
+907.0,Ford,2017.0,2.4,Petrol,Automatic,286384.0,Used,53187.75,Explorer
+,,,,,,,,,
+909.0,Toyota,2006.0,2.9,Electric,Automatic,283830.0,Like New,21967.75,Camry
+910.0,Audi,2005.0,3.7,Petrol,Automatic,11920.0,New,98330.26,Q7
+911.0,Toyota,2014.0,1.3,Diesel,Manual,7432.0,Used,40734.23,Camry
+912.0,Honda,2017.0,3.1,Hybrid,Automatic,110507.0,Used,24030.45,Civic
+913.0,Toyota,2023.0,5.2,Hybrid,Automatic,224647.0,Like New,44340.36,Corolla
+914.0,Ford,2010.0,5.0,Diesel,Automatic,164982.0,Like New,63329.54,Focus
+915.0,Tesla,2019.0,2.1,Hybrid,Automatic,121764.0,Used,25282.25,Model X
+,,,,,,,,,
+,,,,,,,,,
+918.0,Tesla,2015.0,5.7,Hybrid,Manual,112557.0,Like New,99905.9,Model S
+919.0,Mercedes,2006.0,1.5,Petrol,Automatic,18013.0,Used,35226.41,C-Class
+920.0,BMW,2020.0,3.3,Hybrid,Manual,21446.0,Like New,19141.4,3 Series
+921.0,Toyota,2009.0,2.7,Diesel,Manual,201447.0,Used,80351.23,Corolla
+922.0,Audi,2011.0,5.4,Electric,Manual,102261.0,New,62043.29,Q5
+923.0,Tesla,2023.0,1.4,Hybrid,Manual,262701.0,New,14538.11,Model X
+,,,,,,,,,
+925.0,Mercedes,2004.0,4.8,Petrol,Automatic,178772.0,New,12414.71,GLC
+926.0,Ford,2020.0,1.3,Hybrid,Manual,130878.0,Like New,64408.86,Explorer
+927.0,Audi,2017.0,3.5,Electric,Automatic,99143.0,New,45274.04,A3
+928.0,Tesla,2004.0,3.2,Hybrid,Manual,24578.0,New,57879.27,Model 3
+929.0,Audi,2002.0,2.7,Diesel,Manual,74779.0,Like New,25255.35,A4
+930.0,Audi,2022.0,3.0,Electric,Automatic,140668.0,New,70370.49,A3
+931.0,Audi,2004.0,3.6,Hybrid,Manual,72534.0,New,96173.37,Q7
+932.0,Honda,2023.0,1.8,Petrol,Manual,272273.0,Used,48550.46,CR-V
+,,,,,,,,,
+934.0,Ford,2018.0,5.0,Petrol,Manual,273740.0,Like New,27782.11,Fiesta
+935.0,Toyota,2018.0,4.8,Diesel,Automatic,65355.0,Used,89727.46,RAV4
+936.0,Audi,2020.0,1.8,Hybrid,Manual,12974.0,Used,97261.57,Q5
+937.0,Mercedes,2011.0,1.7,Petrol,Manual,197745.0,New,79698.2,C-Class
+938.0,Toyota,2008.0,2.3,Diesel,Automatic,251366.0,New,56563.48,RAV4
+939.0,Mercedes,2000.0,2.8,Hybrid,Automatic,74442.0,New,95895.7,GLA
+940.0,Toyota,2017.0,3.0,Diesel,Manual,152890.0,Used,19615.76,Prius
+,,,,,,,,,
+942.0,Audi,2003.0,1.3,Diesel,Automatic,133726.0,New,56072.54,A4
+943.0,Audi,2009.0,1.2,Electric,Manual,33758.0,Used,51732.06,Q7
+944.0,BMW,2006.0,3.0,Hybrid,Automatic,286348.0,Like New,81229.74,5 Series
+945.0,Mercedes,2021.0,4.5,Diesel,Manual,127309.0,Used,68688.27,C-Class
+946.0,Toyota,2022.0,2.0,Petrol,Automatic,166000.0,New,17032.55,RAV4
+947.0,Toyota,2000.0,4.2,Hybrid,Automatic,184681.0,Used,37912.87,Camry
+948.0,Honda,2016.0,2.3,Electric,Automatic,30552.0,Used,37212.3,Civic
+949.0,BMW,2023.0,5.4,Hybrid,Manual,147278.0,Used,58735.06,X3
+950.0,Tesla,2001.0,5.5,Electric,Manual,34831.0,Used,32688.55,Model Y
+951.0,Tesla,2010.0,2.5,Hybrid,Manual,43563.0,Like New,16140.82,Model Y
+952.0,Honda,2008.0,2.1,Diesel,Manual,18361.0,New,67099.85,Civic
+953.0,Honda,2006.0,3.1,Diesel,Automatic,74469.0,Like New,85848.51,CR-V
+954.0,Honda,2021.0,2.2,Petrol,Automatic,25816.0,Used,93502.77,Fit
+955.0,Mercedes,2016.0,4.4,Electric,Automatic,257422.0,Used,97394.95,E-Class
+956.0,Mercedes,2016.0,5.1,Petrol,Manual,146691.0,New,79206.1,GLC
+957.0,Ford,2001.0,4.4,Diesel,Manual,234295.0,New,18271.73,Focus
+958.0,Ford,2005.0,5.1,Electric,Manual,92479.0,Like New,86045.62,Explorer
+959.0,Honda,2013.0,3.0,Petrol,Automatic,26819.0,Like New,42996.21,CR-V
+960.0,BMW,2005.0,1.8,Petrol,Manual,8798.0,New,74341.57,5 Series
+961.0,Toyota,2001.0,4.7,Petrol,Manual,156215.0,Like New,55470.46,RAV4
+962.0,Mercedes,2000.0,2.8,Petrol,Automatic,243942.0,Used,83717.58,GLC
+963.0,BMW,2009.0,4.4,Petrol,Automatic,230259.0,Used,45423.04,5 Series
+964.0,Audi,2020.0,2.4,Electric,Manual,129544.0,New,10870.41,Q5
+965.0,Mercedes,2004.0,1.4,Petrol,Automatic,152254.0,Like New,57898.25,GLA
+966.0,Mercedes,2001.0,6.0,Electric,Manual,253800.0,Used,21272.6,C-Class
+967.0,Ford,2021.0,1.8,Petrol,Automatic,9415.0,New,96724.18,Focus
+968.0,Toyota,2015.0,5.9,Diesel,Automatic,87638.0,Like New,55061.49,Camry
+969.0,Toyota,2001.0,5.9,Diesel,Automatic,18452.0,Used,57588.09,RAV4
+970.0,Tesla,2009.0,5.0,Petrol,Manual,195457.0,Like New,91190.24,Model Y
+971.0,Tesla,2001.0,4.3,Hybrid,Manual,110205.0,Like New,64335.6,Model Y
+972.0,Tesla,2015.0,3.9,Diesel,Manual,18170.0,Like New,33759.29,Model 3
+973.0,Tesla,2015.0,5.3,Electric,Automatic,16259.0,New,33128.99,Model Y
+974.0,Audi,2018.0,2.4,Petrol,Automatic,284243.0,Used,52649.97,Q5
+975.0,Audi,2006.0,3.3,Electric,Automatic,140721.0,New,87327.78,A4
+976.0,Mercedes,2021.0,4.1,Hybrid,Automatic,129394.0,Used,80499.29,GLA
+977.0,Honda,2011.0,3.1,Petrol,Manual,172109.0,Used,96460.36,Fit
+978.0,Ford,2023.0,3.1,Diesel,Manual,273949.0,New,11829.18,Focus
+979.0,Ford,2014.0,2.7,Electric,Manual,254599.0,New,30490.98,Focus
+,,,,,,,,,
+981.0,Mercedes,2005.0,5.3,Diesel,Automatic,74280.0,Used,76352.52,C-Class
+982.0,BMW,2009.0,2.0,Petrol,Manual,177125.0,Used,18379.2,X3
+983.0,Honda,2002.0,5.7,Electric,Automatic,97841.0,Like New,14489.2,Fit
+984.0,Mercedes,2018.0,4.4,Hybrid,Manual,124726.0,Used,58358.86,E-Class
+985.0,Honda,2016.0,5.1,Diesel,Automatic,76215.0,New,44091.05,Fit
+986.0,Honda,2006.0,3.8,Petrol,Automatic,112909.0,New,72934.63,Fit
+987.0,Honda,2020.0,4.9,Petrol,Manual,273129.0,New,51266.46,Fit
+988.0,Mercedes,2019.0,1.1,Hybrid,Manual,274535.0,Used,45406.79,C-Class
+,,,,,,,,,
+990.0,Ford,2013.0,1.2,Diesel,Manual,261968.0,Like New,87828.26,Explorer
+991.0,Ford,2019.0,5.4,Petrol,Manual,183000.0,Like New,42546.44,Fiesta
+992.0,Tesla,2021.0,6.0,Electric,Automatic,296508.0,New,36672.73,Model X
+993.0,Honda,2014.0,2.5,Electric,Automatic,249072.0,Like New,37160.83,CR-V
+,,,,,,,,,
+995.0,Toyota,2007.0,4.8,Electric,Manual,42923.0,New,86765.44,RAV4
+996.0,Mercedes,2020.0,2.3,Hybrid,Automatic,186968.0,Used,18929.93,E-Class
+997.0,Toyota,2014.0,5.3,Petrol,Automatic,165912.0,Like New,91318.02,Corolla
+998.0,Tesla,2021.0,1.5,Diesel,Automatic,173327.0,Used,31929.43,Model Y
+,,,,,,,,,
+1000.0,Mercedes,2013.0,5.9,Petrol,Automatic,230314.0,New,80127.96,GLA
+1001.0,Tesla,2008.0,4.4,Hybrid,Manual,295673.0,Like New,60288.81,Model S
+1002.0,Honda,2007.0,5.2,Diesel,Automatic,144013.0,New,68599.65,Accord
+1003.0,Ford,2016.0,2.6,Diesel,Automatic,1669.0,New,35955.19,Fiesta
+1004.0,Toyota,2001.0,4.4,Hybrid,Manual,216854.0,New,59551.72,RAV4
+1005.0,Audi,2023.0,4.0,Petrol,Automatic,55786.0,Like New,32778.28,A4
+1006.0,Audi,2002.0,4.0,Electric,Automatic,182155.0,Like New,43215.92,Q7
+1007.0,BMW,2007.0,4.4,Electric,Manual,20323.0,Like New,77642.94,X5
+1008.0,Honda,2011.0,3.9,Hybrid,Automatic,230502.0,New,28173.59,CR-V
+1009.0,Toyota,2000.0,3.1,Diesel,Manual,257249.0,Used,84690.37,Prius
+1010.0,Honda,2009.0,2.4,Petrol,Automatic,6021.0,New,40708.87,CR-V
+1011.0,Toyota,2019.0,4.8,Petrol,Manual,113415.0,Like New,55556.15,Corolla
+,,,,,,,,,
+1013.0,BMW,2005.0,4.5,Electric,Automatic,7904.0,Used,69719.28,3 Series
+1014.0,Audi,2006.0,2.2,Petrol,Manual,64033.0,Like New,66332.39,A4
+1015.0,Honda,2019.0,4.1,Hybrid,Manual,231659.0,Used,72602.73,Fit
+1016.0,Mercedes,2022.0,4.7,Electric,Automatic,120035.0,Used,92220.58,GLC
+1017.0,Audi,2017.0,2.1,Hybrid,Automatic,220859.0,Like New,28608.8,A3
+,,,,,,,,,
+,,,,,,,,,
+1020.0,Mercedes,2004.0,4.0,Hybrid,Manual,231853.0,Like New,6126.25,GLA
+1021.0,Ford,2018.0,5.2,Hybrid,Manual,265237.0,Used,16903.59,Explorer
+1022.0,Ford,2010.0,1.2,Diesel,Automatic,287062.0,Used,35874.23,Fiesta
+1023.0,Ford,2023.0,4.7,Hybrid,Automatic,193708.0,Used,58675.0,Fiesta
+1024.0,Ford,2011.0,2.7,Electric,Manual,189878.0,Used,88740.1,Fiesta
+1025.0,Tesla,2009.0,3.4,Petrol,Automatic,140466.0,Like New,80155.84,Model Y
+1026.0,Toyota,2007.0,5.6,Petrol,Automatic,124501.0,New,57566.7,RAV4
+1027.0,Tesla,2023.0,2.7,Diesel,Manual,260228.0,New,12017.69,Model S
+1028.0,Tesla,2008.0,3.3,Electric,Manual,92510.0,Used,32161.1,Model X
+1029.0,Tesla,2003.0,1.1,Hybrid,Manual,93708.0,Like New,39837.32,Model Y
+1030.0,Ford,2004.0,1.4,Hybrid,Manual,97819.0,Like New,64063.13,Fiesta
+1031.0,Mercedes,2020.0,2.3,Petrol,Manual,235603.0,Used,11167.74,GLC
+1032.0,BMW,2007.0,1.1,Hybrid,Manual,36303.0,New,55368.39,5 Series
+1033.0,BMW,2022.0,4.2,Petrol,Automatic,27453.0,Used,90639.13,3 Series
+1034.0,Toyota,2008.0,3.1,Diesel,Manual,199521.0,Like New,72016.11,Corolla
+,,,,,,,,,
+1036.0,Ford,2014.0,1.9,Electric,Automatic,222787.0,New,69759.19,Fiesta
+,,,,,,,,,
+1038.0,Audi,2013.0,4.3,Petrol,Automatic,189060.0,Like New,15495.96,A4
+1039.0,Honda,2007.0,5.8,Diesel,Manual,94655.0,Like New,83801.61,Accord
+1040.0,Audi,2017.0,1.3,Petrol,Automatic,277169.0,New,71072.92,A4
+1041.0,Honda,2009.0,5.5,Hybrid,Manual,269289.0,Like New,59630.85,Accord
+,,,,,,,,,
+1043.0,Ford,2005.0,3.8,Petrol,Manual,186869.0,New,62317.77,Explorer
+1044.0,Audi,2011.0,3.5,Diesel,Manual,73230.0,Used,7975.57,Q7
+1045.0,Audi,2001.0,1.3,Electric,Automatic,155002.0,Used,16289.72,Q5
+1046.0,Honda,2006.0,1.4,Hybrid,Automatic,108138.0,New,34629.56,Accord
+1047.0,BMW,2015.0,4.0,Diesel,Automatic,207097.0,Used,47155.14,X3
+,,,,,,,,,
+1049.0,Audi,2012.0,5.6,Petrol,Automatic,62040.0,Used,16394.32,A4
+1050.0,Mercedes,2002.0,3.0,Electric,Manual,108668.0,New,73959.9,E-Class
+,,,,,,,,,
+1052.0,Mercedes,2010.0,4.6,Petrol,Manual,56815.0,Like New,55705.49,E-Class
+1053.0,Toyota,2007.0,2.5,Diesel,Manual,216741.0,Used,47981.28,Corolla
+1054.0,Audi,2005.0,3.6,Petrol,Manual,112789.0,Like New,97771.6,A3
+1055.0,Toyota,2000.0,4.5,Diesel,Automatic,219478.0,New,9731.03,Prius
+1056.0,BMW,2000.0,5.5,Electric,Manual,167644.0,Used,22159.58,X5
+1057.0,Honda,2022.0,5.0,Electric,Manual,185102.0,Like New,66513.24,Accord
+1058.0,Mercedes,2007.0,4.4,Petrol,Automatic,244533.0,Like New,17954.74,E-Class
+,,,,,,,,,
+1060.0,Toyota,2015.0,5.7,Petrol,Manual,233308.0,Like New,58937.32,Camry
+1061.0,Mercedes,2010.0,2.5,Diesel,Manual,160275.0,Like New,97245.49,C-Class
+,,,,,,,,,
+1063.0,Audi,2003.0,2.4,Diesel,Manual,5608.0,Like New,94827.57,Q7
+1064.0,Ford,2005.0,2.1,Electric,Manual,45667.0,Like New,15792.23,Explorer
+,,,,,,,,,
+1066.0,Honda,2021.0,4.2,Electric,Automatic,234907.0,Like New,17263.61,CR-V
+1067.0,Toyota,2018.0,4.0,Diesel,Manual,57560.0,Used,65128.56,Prius
+1068.0,Tesla,2008.0,1.1,Electric,Manual,214601.0,New,73407.43,Model X
+1069.0,Toyota,2011.0,4.6,Hybrid,Automatic,159487.0,New,20281.08,RAV4
+1070.0,Mercedes,2010.0,2.6,Electric,Automatic,184286.0,Like New,65102.64,C-Class
+1071.0,Tesla,2013.0,4.3,Electric,Automatic,280141.0,Like New,9983.75,Model Y
+1072.0,Mercedes,2022.0,3.8,Petrol,Manual,121266.0,New,32860.28,GLC
+1073.0,Ford,2015.0,2.7,Diesel,Automatic,3679.0,Like New,92510.06,Explorer
+1074.0,Tesla,2000.0,1.7,Hybrid,Manual,40308.0,New,21314.46,Model S
+1075.0,Audi,2012.0,1.5,Petrol,Automatic,206096.0,Used,11200.65,Q7
+1076.0,Toyota,2022.0,5.2,Diesel,Manual,170914.0,Used,47149.43,RAV4
+1077.0,Honda,2020.0,5.6,Hybrid,Automatic,5695.0,Used,85808.73,Civic
+1078.0,BMW,2020.0,4.2,Hybrid,Manual,226812.0,Used,12283.88,X3
+,,,,,,,,,
+1080.0,Toyota,2001.0,3.0,Diesel,Automatic,23382.0,Used,90478.61,Corolla
+,,,,,,,,,
+1082.0,Audi,2005.0,4.9,Hybrid,Manual,193295.0,Like New,34513.51,A4
+1083.0,BMW,2022.0,1.6,Petrol,Automatic,133438.0,New,78949.96,3 Series
+,,,,,,,,,
+,,,,,,,,,
+1086.0,Tesla,2000.0,2.8,Electric,Automatic,221968.0,Like New,15601.56,Model Y
+1087.0,Mercedes,2003.0,2.3,Petrol,Manual,113831.0,Used,92452.65,E-Class
+1088.0,Mercedes,2019.0,1.1,Electric,Automatic,61836.0,Used,93816.62,C-Class
+1089.0,Honda,2001.0,3.7,Hybrid,Automatic,294067.0,Used,19754.74,Fit
+1090.0,BMW,2004.0,5.3,Hybrid,Manual,255033.0,Used,8218.17,3 Series
+,,,,,,,,,
+1092.0,Mercedes,2008.0,3.8,Diesel,Automatic,283083.0,Used,50813.9,GLC
+1093.0,Honda,2019.0,3.6,Diesel,Manual,243772.0,Like New,59347.16,Fit
+1094.0,Audi,2005.0,1.4,Petrol,Automatic,18800.0,Used,63510.95,A3
+1095.0,Honda,2017.0,3.7,Diesel,Automatic,56440.0,Used,80018.5,Accord
+1096.0,Mercedes,2005.0,2.9,Petrol,Manual,127521.0,Like New,82138.86,GLA
+1097.0,Ford,2018.0,4.0,Hybrid,Manual,121327.0,New,23422.9,Mustang
+1098.0,Ford,2011.0,2.9,Electric,Manual,49648.0,Used,27480.49,Fiesta
+1099.0,Tesla,2010.0,2.2,Hybrid,Manual,291908.0,Like New,88299.14,Model X
+1100.0,Ford,2001.0,1.5,Hybrid,Manual,294614.0,Like New,82854.17,Mustang
+1101.0,Tesla,2014.0,2.6,Hybrid,Manual,256160.0,New,63673.84,Model S
+1102.0,Tesla,2011.0,1.5,Diesel,Automatic,169573.0,Used,99496.42,Model S
+1103.0,Mercedes,2011.0,1.9,Diesel,Automatic,57207.0,Used,74842.47,GLA
+,,,,,,,,,
+1105.0,Tesla,2020.0,3.2,Electric,Automatic,134288.0,Like New,85431.19,Model S
+1106.0,BMW,2023.0,3.7,Diesel,Automatic,153144.0,Used,44399.6,5 Series
+1107.0,Audi,2001.0,5.4,Petrol,Automatic,93778.0,Used,8434.5,Q7
+1108.0,Honda,2021.0,6.0,Hybrid,Automatic,130016.0,Used,35535.02,Fit
+1109.0,Honda,2002.0,3.9,Electric,Manual,124288.0,Used,77537.37,Fit
+1110.0,Mercedes,2011.0,5.1,Electric,Automatic,168204.0,New,96219.59,GLC
+1111.0,Ford,2015.0,2.6,Electric,Automatic,110573.0,New,31232.96,Mustang
+1112.0,Ford,2020.0,2.5,Electric,Manual,120730.0,New,28419.05,Explorer
+1113.0,Tesla,2023.0,3.0,Electric,Manual,64186.0,Used,58543.58,Model Y
+1114.0,Toyota,2000.0,4.4,Petrol,Manual,267104.0,Used,82109.97,Camry
+,,,,,,,,,
+1116.0,Audi,2002.0,2.6,Electric,Automatic,237677.0,Like New,58196.1,A3
+1117.0,BMW,2019.0,1.7,Hybrid,Manual,257506.0,Like New,64982.02,X5
+,,,,,,,,,
+1119.0,Tesla,2008.0,1.6,Electric,Automatic,238217.0,New,46094.56,Model X
+1120.0,Toyota,2018.0,3.9,Electric,Manual,280027.0,New,29259.73,Camry
+1121.0,Toyota,2014.0,4.5,Hybrid,Manual,29782.0,New,89019.71,Camry
+1122.0,Tesla,2017.0,4.5,Electric,Manual,148051.0,Used,15692.68,Model X
+1123.0,Mercedes,2010.0,4.8,Electric,Manual,295890.0,New,69575.35,E-Class
+,,,,,,,,,
+,,,,,,,,,
+1126.0,BMW,2021.0,4.7,Petrol,Automatic,56.0,New,33444.21,X3
+1127.0,Toyota,2016.0,4.1,Electric,Automatic,293601.0,Like New,35708.31,Prius
+1128.0,Toyota,2004.0,1.9,Petrol,Automatic,132773.0,New,29737.34,Prius
+1129.0,Audi,2011.0,1.1,Diesel,Automatic,187453.0,New,76515.33,A3
+1130.0,BMW,2010.0,2.4,Electric,Manual,181510.0,Like New,39743.01,X3
+1131.0,Toyota,2014.0,3.2,Diesel,Manual,80636.0,Like New,74003.92,RAV4
+,,,,,,,,,
+1133.0,Honda,2000.0,5.2,Diesel,Automatic,20505.0,Like New,56988.37,Accord
+1134.0,Ford,2019.0,2.0,Hybrid,Automatic,33525.0,New,56612.31,Focus
+1135.0,Toyota,2015.0,1.6,Electric,Manual,211474.0,Like New,97705.95,RAV4
+1136.0,Audi,2001.0,5.8,Hybrid,Manual,96008.0,Like New,39870.93,A4
+1137.0,Honda,2001.0,1.5,Hybrid,Manual,44348.0,Used,24729.99,Accord
+1138.0,Tesla,2002.0,3.4,Diesel,Automatic,202259.0,Like New,91620.23,Model 3
+1139.0,BMW,2007.0,3.9,Diesel,Automatic,269417.0,Like New,21609.73,5 Series
+1140.0,Tesla,2023.0,3.7,Diesel,Manual,206641.0,New,84270.17,Model 3
+1141.0,Mercedes,2014.0,1.4,Electric,Manual,134607.0,New,19270.42,C-Class
+1142.0,Honda,2006.0,1.3,Electric,Automatic,214096.0,Used,67578.81,Accord
+1143.0,Tesla,2005.0,1.5,Diesel,Automatic,107157.0,Like New,19301.38,Model 3
+1144.0,Toyota,2008.0,3.9,Electric,Manual,908.0,Like New,92693.15,Prius
+1145.0,BMW,2019.0,3.7,Electric,Automatic,252299.0,Like New,58675.79,X5
+1146.0,Ford,2000.0,2.2,Hybrid,Manual,201225.0,Like New,36281.01,Mustang
+1147.0,Honda,2007.0,4.2,Hybrid,Manual,108352.0,Like New,18330.66,Accord
+1148.0,Toyota,2022.0,5.1,Diesel,Automatic,95967.0,Like New,40263.26,Camry
+1149.0,Audi,2022.0,1.2,Electric,Automatic,35079.0,Used,71976.9,A4
+1150.0,Mercedes,2002.0,3.5,Hybrid,Automatic,212628.0,Like New,41570.33,GLC
+1151.0,BMW,2003.0,4.4,Petrol,Manual,111889.0,Like New,28378.94,X5
+1152.0,Honda,2014.0,2.3,Electric,Automatic,262063.0,Like New,59853.9,CR-V
+1153.0,Honda,2005.0,2.5,Petrol,Automatic,46105.0,Used,44693.08,Fit
+1154.0,Ford,2014.0,4.1,Diesel,Manual,132957.0,Like New,20751.59,Fiesta
+1155.0,Ford,2020.0,5.5,Electric,Manual,1049.0,Used,19628.86,Mustang
+1156.0,Audi,2002.0,5.0,Diesel,Automatic,53281.0,New,72208.89,Q7
+1157.0,Mercedes,2020.0,3.9,Diesel,Manual,66697.0,Like New,97593.83,C-Class
+1158.0,Audi,2020.0,4.7,Diesel,Manual,36534.0,Used,27143.94,Q7
+1159.0,Toyota,2013.0,2.8,Diesel,Manual,93034.0,New,78449.12,Prius
+1160.0,Toyota,2019.0,4.2,Diesel,Automatic,58097.0,Used,13322.64,Camry
+1161.0,Audi,2013.0,3.3,Electric,Automatic,184885.0,Used,41249.71,Q7
+1162.0,Mercedes,2003.0,1.9,Petrol,Manual,287181.0,New,53135.37,GLC
+1163.0,Audi,2011.0,4.5,Electric,Manual,27278.0,Like New,30530.96,Q7
+1164.0,Mercedes,2016.0,3.8,Electric,Manual,139344.0,Like New,55309.95,C-Class
+1165.0,Mercedes,2006.0,2.8,Electric,Automatic,73277.0,Used,88848.37,C-Class
+1166.0,Tesla,2008.0,5.6,Hybrid,Manual,4694.0,New,94721.61,Model 3
+1167.0,BMW,2002.0,1.1,Hybrid,Manual,58150.0,Like New,14552.91,3 Series
+1168.0,Mercedes,2012.0,2.6,Electric,Automatic,67470.0,Used,30825.36,GLA
+1169.0,Ford,2014.0,1.3,Petrol,Manual,21556.0,New,98017.72,Explorer
+1170.0,Mercedes,2017.0,4.8,Electric,Automatic,195543.0,Used,30689.75,GLC
+1171.0,Mercedes,2018.0,4.5,Diesel,Automatic,49086.0,New,70807.2,C-Class
+1172.0,Mercedes,2000.0,2.7,Hybrid,Automatic,149979.0,Like New,29078.1,E-Class
+1173.0,Tesla,2005.0,4.4,Hybrid,Manual,248399.0,Like New,38854.73,Model 3
+1174.0,Tesla,2022.0,3.0,Electric,Manual,179559.0,Used,23171.5,Model S
+1175.0,Toyota,2001.0,4.8,Electric,Automatic,99468.0,Like New,86348.52,Camry
+,,,,,,,,,
+1177.0,Ford,2013.0,3.1,Petrol,Automatic,68602.0,New,33206.69,Focus
+1178.0,Ford,2005.0,5.7,Diesel,Manual,2460.0,Like New,83241.03,Mustang
+1179.0,Tesla,2013.0,2.3,Electric,Automatic,72356.0,New,39032.96,Model S
+,,,,,,,,,
+1181.0,BMW,2020.0,3.9,Electric,Manual,16191.0,Used,77017.26,X5
+1182.0,Tesla,2007.0,5.8,Hybrid,Manual,84631.0,New,61662.39,Model S
+1183.0,Mercedes,2002.0,2.9,Diesel,Manual,245702.0,Like New,31066.33,E-Class
+1184.0,Honda,2018.0,1.3,Petrol,Manual,272673.0,New,29034.98,Fit
+1185.0,BMW,2013.0,2.7,Hybrid,Automatic,32175.0,New,97684.22,3 Series
+1186.0,BMW,2019.0,3.5,Hybrid,Automatic,279819.0,Used,86937.6,5 Series
+1187.0,Ford,2016.0,1.8,Hybrid,Manual,157048.0,Like New,81930.84,Focus
+,,,,,,,,,
+1189.0,Tesla,2023.0,6.0,Diesel,Automatic,62821.0,Like New,76742.79,Model Y
+1190.0,Mercedes,2013.0,3.3,Electric,Manual,12204.0,New,25381.32,C-Class
+1191.0,Mercedes,2005.0,2.8,Petrol,Manual,269538.0,Like New,97851.95,GLA
+1192.0,Toyota,2022.0,3.0,Petrol,Manual,246464.0,Like New,9948.47,RAV4
+1193.0,Mercedes,2021.0,1.6,Hybrid,Manual,33593.0,New,38453.16,E-Class
+1194.0,Mercedes,2015.0,5.4,Hybrid,Automatic,208260.0,Used,45721.54,GLC
+1195.0,Ford,2018.0,3.1,Diesel,Automatic,115064.0,Used,83299.28,Mustang
+1196.0,Mercedes,2013.0,2.9,Hybrid,Manual,53365.0,New,77791.17,E-Class
+,,,,,,,,,
+1198.0,Honda,2002.0,3.2,Petrol,Manual,220511.0,Used,58728.23,CR-V
+1199.0,Tesla,2009.0,4.2,Electric,Manual,206971.0,Used,84381.54,Model X
+1200.0,Ford,2018.0,1.3,Electric,Manual,103700.0,Like New,6728.96,Mustang
+1201.0,Honda,2008.0,5.1,Petrol,Automatic,68247.0,Used,81206.92,Accord
+1202.0,Ford,2019.0,5.7,Petrol,Automatic,182631.0,Like New,94955.12,Fiesta
+1203.0,Audi,2012.0,4.2,Diesel,Automatic,156270.0,Used,8046.13,A3
+1204.0,BMW,2006.0,5.8,Diesel,Automatic,153560.0,Like New,9330.19,X3
+1205.0,Audi,2013.0,1.8,Petrol,Automatic,169587.0,New,37728.83,A3
+1206.0,Mercedes,2008.0,3.4,Hybrid,Manual,290042.0,New,13338.11,C-Class
+1207.0,Honda,2022.0,4.0,Electric,Manual,181142.0,Used,50531.23,CR-V
+1208.0,Mercedes,2018.0,3.9,Petrol,Automatic,62613.0,Like New,74227.52,GLC
+1209.0,BMW,2014.0,3.0,Hybrid,Automatic,96707.0,New,58855.43,5 Series
+1210.0,Ford,2019.0,2.7,Petrol,Automatic,47003.0,New,23661.03,Mustang
+1211.0,BMW,2013.0,1.6,Electric,Manual,48478.0,New,67207.07,X5
+1212.0,Toyota,2023.0,3.0,Electric,Automatic,59030.0,New,70061.18,RAV4
+1213.0,BMW,2005.0,1.8,Diesel,Manual,184133.0,Used,93582.37,3 Series
+1214.0,Toyota,2020.0,4.7,Diesel,Manual,145921.0,Like New,18945.71,RAV4
+1215.0,BMW,2003.0,2.0,Diesel,Automatic,246160.0,New,70771.94,5 Series
+1216.0,Tesla,2007.0,1.3,Diesel,Manual,267589.0,Used,91585.59,Model 3
+1217.0,Toyota,2016.0,3.8,Electric,Manual,149112.0,New,52648.59,RAV4
+1218.0,Honda,2008.0,4.0,Hybrid,Automatic,262390.0,New,39370.97,CR-V
+1219.0,Mercedes,2000.0,5.4,Hybrid,Manual,31153.0,New,21221.2,C-Class
+1220.0,Mercedes,2023.0,3.9,Hybrid,Automatic,267356.0,New,87344.75,GLC
+1221.0,Audi,2019.0,2.7,Petrol,Manual,134411.0,Used,42453.41,A3
+,,,,,,,,,
+1223.0,Ford,2010.0,3.7,Diesel,Automatic,296751.0,Used,85537.04,Fiesta
+,,,,,,,,,
+1225.0,BMW,2002.0,3.8,Electric,Automatic,104509.0,Used,59521.86,X5
+1226.0,Audi,2008.0,5.5,Petrol,Manual,135467.0,New,7581.53,Q5
+1227.0,Ford,2013.0,3.0,Petrol,Manual,127378.0,Like New,30006.03,Explorer
+1228.0,Ford,2008.0,2.5,Hybrid,Automatic,87687.0,New,72475.92,Fiesta
+1229.0,Toyota,2003.0,1.7,Electric,Manual,102652.0,Used,69401.54,Prius
+1230.0,Mercedes,2003.0,1.8,Diesel,Automatic,55773.0,New,38570.1,C-Class
+1231.0,Honda,2007.0,3.2,Hybrid,Manual,202668.0,Like New,81817.76,Accord
+1232.0,Honda,2001.0,4.0,Diesel,Manual,27584.0,Like New,27404.42,Accord
+1233.0,Audi,2008.0,1.4,Hybrid,Automatic,244901.0,New,48820.7,A4
+1234.0,Tesla,2013.0,5.7,Diesel,Manual,265482.0,Used,49890.41,Model S
+1235.0,Honda,2021.0,4.8,Petrol,Manual,62601.0,New,37377.59,Fit
+1236.0,BMW,2008.0,3.9,Hybrid,Manual,136559.0,Used,95693.28,5 Series
+1237.0,Honda,2023.0,5.1,Diesel,Manual,36282.0,Like New,71184.98,Civic
+1238.0,Audi,2022.0,1.4,Diesel,Manual,226137.0,New,56223.94,Q5
+1239.0,Honda,2015.0,3.4,Electric,Manual,95798.0,Like New,53498.93,Accord
+1240.0,BMW,2016.0,4.2,Hybrid,Manual,130963.0,Used,20133.13,X5
+1241.0,Toyota,2015.0,5.1,Diesel,Manual,159876.0,Used,40357.76,RAV4
+1242.0,Tesla,2011.0,4.9,Electric,Automatic,140987.0,New,68913.46,Model Y
+1243.0,Audi,2002.0,2.4,Diesel,Manual,273302.0,New,92834.17,A4
+1244.0,Toyota,2010.0,5.7,Hybrid,Manual,205229.0,New,82564.09,Prius
+1245.0,Toyota,2013.0,1.6,Petrol,Automatic,10547.0,Like New,33537.03,Camry
+1246.0,Toyota,2017.0,5.4,Hybrid,Automatic,264603.0,Used,46225.88,Camry
+1247.0,Ford,2020.0,5.9,Hybrid,Automatic,63146.0,Used,12877.16,Fiesta
+1248.0,Tesla,2020.0,1.9,Diesel,Manual,107761.0,Like New,14706.37,Model 3
+1249.0,Mercedes,2004.0,4.6,Diesel,Automatic,82970.0,New,49671.89,GLC
+1250.0,Tesla,2015.0,1.2,Hybrid,Automatic,191889.0,New,55370.84,Model X
+1251.0,Ford,2019.0,3.0,Electric,Manual,169319.0,Like New,30082.57,Mustang
+,,,,,,,,,
+1253.0,Tesla,2002.0,3.9,Electric,Automatic,35577.0,Used,71821.17,Model S
+1254.0,Toyota,2011.0,5.7,Diesel,Automatic,2921.0,Used,33416.03,Prius
+1255.0,Audi,2000.0,4.4,Diesel,Manual,144535.0,Used,77050.35,A3
+1256.0,Mercedes,2020.0,3.4,Hybrid,Manual,156546.0,Like New,24532.47,GLC
+1257.0,BMW,2008.0,5.0,Petrol,Manual,125534.0,Used,56703.88,X5
+1258.0,BMW,2008.0,5.8,Diesel,Manual,256872.0,Like New,81406.7,5 Series
+,,,,,,,,,
+1260.0,Mercedes,2002.0,5.9,Hybrid,Automatic,123649.0,Like New,91256.46,GLA
+,,,,,,,,,
+1262.0,Ford,2001.0,5.1,Electric,Manual,220073.0,New,60889.82,Fiesta
+1263.0,Toyota,2023.0,2.4,Hybrid,Manual,223393.0,Like New,58366.77,Camry
+1264.0,Honda,2000.0,1.7,Hybrid,Manual,86569.0,Like New,67733.57,CR-V
+,,,,,,,,,
+1266.0,Ford,2012.0,4.2,Electric,Automatic,159562.0,New,73956.04,Focus
+,,,,,,,,,
+1268.0,Toyota,2014.0,1.3,Petrol,Automatic,53806.0,Used,32437.59,Prius
+1269.0,Mercedes,2007.0,4.0,Diesel,Manual,75992.0,New,41812.25,GLC
+1270.0,BMW,2011.0,5.5,Electric,Automatic,284464.0,New,64543.81,X3
+1271.0,Audi,2021.0,2.9,Diesel,Automatic,258852.0,Like New,57578.84,A4
+1272.0,Ford,2011.0,2.1,Diesel,Manual,173217.0,Used,96304.87,Fiesta
+1273.0,Toyota,2013.0,3.2,Electric,Manual,194760.0,New,22749.28,Prius
+,,,,,,,,,
+1275.0,Mercedes,2014.0,5.1,Petrol,Manual,69837.0,Used,59411.75,GLC
+1276.0,BMW,2008.0,5.1,Diesel,Manual,138279.0,Like New,87456.56,X3
+1277.0,Mercedes,2018.0,3.9,Hybrid,Automatic,218811.0,Like New,93288.11,C-Class
+1278.0,Ford,2019.0,2.8,Electric,Manual,114780.0,New,36923.45,Mustang
+1279.0,Audi,2021.0,5.0,Diesel,Manual,87081.0,New,11375.3,A4
+1280.0,Audi,2007.0,3.8,Diesel,Automatic,286160.0,Like New,45053.87,A3
+1281.0,Audi,2020.0,5.1,Diesel,Manual,112099.0,New,42462.69,Q5
+,,,,,,,,,
+1283.0,Ford,2005.0,4.9,Hybrid,Manual,264068.0,Used,45950.82,Mustang
+,,,,,,,,,
+1285.0,Honda,2018.0,3.1,Diesel,Manual,104987.0,Like New,39710.85,Accord
+1286.0,Mercedes,2003.0,5.8,Diesel,Automatic,54333.0,Like New,42708.0,GLC
+1287.0,BMW,2022.0,3.7,Petrol,Automatic,215530.0,Like New,83046.34,5 Series
+1288.0,Toyota,2006.0,4.0,Petrol,Manual,177171.0,Like New,12696.41,Prius
+,,,,,,,,,
+,,,,,,,,,
+1291.0,Toyota,2011.0,3.9,Electric,Automatic,206553.0,Used,19544.74,Prius
+1292.0,Honda,2012.0,1.9,Hybrid,Automatic,69767.0,Like New,75547.13,Accord
+1293.0,Tesla,2002.0,1.3,Petrol,Automatic,104332.0,Like New,38857.38,Model X
+,,,,,,,,,
+1295.0,Ford,2011.0,4.8,Petrol,Manual,6336.0,Used,14457.06,Explorer
+1296.0,Audi,2001.0,3.6,Diesel,Automatic,206238.0,Like New,34109.11,A4
+1297.0,Mercedes,2023.0,2.1,Diesel,Manual,279400.0,New,52582.17,GLA
+1298.0,Toyota,2001.0,4.3,Hybrid,Automatic,230517.0,Like New,27011.56,Prius
+1299.0,Tesla,2022.0,1.4,Electric,Manual,180889.0,Used,56477.64,Model Y
+1300.0,Tesla,2008.0,3.2,Electric,Manual,295863.0,Like New,25877.13,Model X
+1301.0,Tesla,2000.0,1.8,Electric,Automatic,45704.0,Like New,66759.55,Model X
+1302.0,Toyota,2007.0,1.9,Diesel,Manual,292157.0,New,90913.66,RAV4
+1303.0,Toyota,2022.0,2.0,Petrol,Manual,226884.0,Like New,13121.42,Corolla
+1304.0,Toyota,2005.0,2.9,Petrol,Manual,167848.0,Like New,25578.52,Camry
+1305.0,Toyota,2005.0,1.3,Diesel,Manual,272204.0,Like New,85220.19,Corolla
+1306.0,Tesla,2021.0,3.0,Electric,Manual,219182.0,Used,64997.58,Model 3
+1307.0,Tesla,2017.0,3.6,Electric,Automatic,78563.0,Used,12477.35,Model S
+1308.0,Honda,2016.0,3.4,Hybrid,Manual,135102.0,New,51203.26,CR-V
+1309.0,Mercedes,2011.0,2.9,Hybrid,Automatic,89779.0,Like New,24927.06,GLC
+1310.0,Audi,2005.0,5.2,Electric,Manual,164361.0,Used,23914.16,Q7
+1311.0,Ford,2007.0,1.7,Diesel,Manual,99704.0,New,94361.31,Fiesta
+,,,,,,,,,
+1313.0,Ford,2009.0,1.3,Hybrid,Automatic,110234.0,New,71141.91,Explorer
+1314.0,Mercedes,2000.0,1.4,Electric,Manual,36491.0,New,27698.24,E-Class
+1315.0,Mercedes,2012.0,3.9,Diesel,Manual,226621.0,New,16264.48,GLA
+1316.0,Ford,2016.0,3.8,Petrol,Automatic,20448.0,New,87729.1,Focus
+1317.0,Tesla,2002.0,5.4,Diesel,Automatic,49294.0,New,5558.56,Model Y
+1318.0,Ford,2007.0,3.8,Diesel,Automatic,283590.0,New,41869.16,Fiesta
+1319.0,Honda,2013.0,5.8,Hybrid,Automatic,216461.0,New,31373.19,Civic
+1320.0,BMW,2002.0,1.2,Diesel,Manual,85615.0,Like New,59185.74,X5
+1321.0,Tesla,2015.0,1.5,Diesel,Manual,17101.0,Like New,28798.3,Model 3
+,,,,,,,,,
+1323.0,Audi,2019.0,1.9,Hybrid,Manual,150004.0,Like New,85753.79,A3
+1324.0,Toyota,2023.0,3.7,Petrol,Manual,90481.0,New,62302.94,Camry
+1325.0,BMW,2016.0,4.2,Electric,Automatic,52802.0,Like New,18839.84,3 Series
+,,,,,,,,,
+1327.0,Tesla,2005.0,5.7,Hybrid,Automatic,210005.0,New,24708.62,Model Y
+1328.0,Ford,2016.0,5.2,Petrol,Manual,156434.0,Used,46135.47,Mustang
+1329.0,BMW,2013.0,2.5,Electric,Automatic,47842.0,New,66427.69,X3
+1330.0,Audi,2016.0,1.4,Hybrid,Manual,46539.0,New,30833.31,Q7
+1331.0,Mercedes,2007.0,1.8,Electric,Manual,33717.0,Used,41490.15,GLA
+1332.0,Toyota,2010.0,1.6,Electric,Manual,246281.0,Used,74744.21,RAV4
+1333.0,Toyota,2019.0,1.6,Diesel,Automatic,186272.0,Used,94101.54,Corolla
+,,,,,,,,,
+1335.0,Audi,2006.0,2.2,Hybrid,Manual,127922.0,Used,5947.24,A4
+1336.0,Ford,2020.0,2.8,Electric,Automatic,92447.0,Used,37154.06,Focus
+,,,,,,,,,
+1338.0,Toyota,2016.0,4.4,Hybrid,Automatic,12823.0,Like New,37221.49,RAV4
+1339.0,Mercedes,2009.0,5.7,Electric,Manual,29986.0,Used,46445.54,E-Class
+1340.0,BMW,2010.0,1.7,Hybrid,Automatic,121682.0,Used,65103.29,5 Series
+1341.0,Mercedes,2021.0,2.9,Electric,Manual,64325.0,Used,16507.16,E-Class
+,,,,,,,,,
+1343.0,Honda,2008.0,1.4,Electric,Automatic,57771.0,Like New,53226.57,Civic
+1344.0,Audi,2023.0,1.0,Electric,Manual,54973.0,New,34979.46,A3
+1345.0,Ford,2004.0,4.4,Petrol,Automatic,145762.0,Used,99153.11,Fiesta
+1346.0,Honda,2016.0,4.2,Electric,Manual,57888.0,Used,45985.99,CR-V
+1347.0,Toyota,2008.0,3.1,Diesel,Automatic,10956.0,Like New,87630.92,Camry
+1348.0,Mercedes,2016.0,3.4,Diesel,Manual,86839.0,Used,90168.43,E-Class
+1349.0,Mercedes,2023.0,3.1,Diesel,Manual,167605.0,Used,84420.66,GLA
+1350.0,Audi,2020.0,1.1,Electric,Manual,101131.0,Like New,9455.33,Q7
+1351.0,Tesla,2018.0,2.7,Diesel,Automatic,187973.0,Used,95035.94,Model Y
+1352.0,Tesla,2006.0,1.9,Petrol,Automatic,11519.0,Used,17230.54,Model Y
+1353.0,BMW,2022.0,1.5,Hybrid,Automatic,162187.0,Like New,8268.34,X5
+1354.0,Audi,2008.0,5.8,Electric,Automatic,1832.0,New,44365.11,Q7
+1355.0,BMW,2006.0,4.0,Diesel,Automatic,113922.0,Used,23490.23,X5
+,,,,,,,,,
+1357.0,Audi,2010.0,2.6,Hybrid,Manual,69715.0,New,32342.86,Q7
+,,,,,,,,,
+1359.0,Audi,2018.0,2.2,Diesel,Manual,37570.0,New,70055.56,Q7
+1360.0,Tesla,2010.0,1.5,Petrol,Manual,137687.0,Used,22743.84,Model S
+1361.0,BMW,2012.0,1.9,Hybrid,Automatic,38311.0,New,56596.24,3 Series
+1362.0,BMW,2013.0,5.0,Diesel,Manual,103187.0,Like New,98710.52,X5
+1363.0,Audi,2012.0,4.4,Hybrid,Automatic,285578.0,Used,46076.33,Q5
+1364.0,Honda,2000.0,3.7,Petrol,Manual,86879.0,Like New,65977.95,Fit
+,,,,,,,,,
+1366.0,Tesla,2011.0,5.6,Electric,Automatic,66202.0,New,18075.7,Model Y
+,,,,,,,,,
+1368.0,Ford,2011.0,2.4,Electric,Manual,224491.0,Like New,85768.28,Mustang
+1369.0,BMW,2007.0,2.7,Hybrid,Manual,79599.0,Like New,93569.04,X5
+1370.0,BMW,2007.0,4.9,Electric,Manual,164721.0,Used,62117.43,X5
+1371.0,Honda,2020.0,6.0,Hybrid,Manual,231436.0,Like New,18624.96,Accord
+1372.0,Ford,2012.0,2.2,Diesel,Automatic,116770.0,Like New,90639.8,Focus
+1373.0,Ford,2011.0,5.4,Electric,Automatic,179693.0,New,21572.42,Fiesta
+1374.0,Audi,2015.0,5.2,Hybrid,Automatic,261051.0,Like New,38523.4,A3
+1375.0,BMW,2000.0,2.1,Electric,Automatic,114300.0,Like New,40673.26,5 Series
+1376.0,Ford,2012.0,3.0,Electric,Manual,191012.0,New,47492.7,Mustang
+1377.0,Mercedes,2006.0,3.0,Electric,Automatic,171796.0,Used,80065.75,GLC
+1378.0,Honda,2004.0,5.9,Hybrid,Manual,162776.0,New,81203.72,Fit
+1379.0,Mercedes,2007.0,1.9,Petrol,Automatic,241942.0,New,73367.23,GLA
+1380.0,Mercedes,2013.0,5.0,Electric,Automatic,2526.0,Like New,7102.22,GLA
+1381.0,Tesla,2022.0,2.7,Electric,Automatic,268718.0,Like New,58660.28,Model 3
+1382.0,Tesla,2012.0,4.7,Diesel,Manual,270877.0,Used,76011.83,Model Y
+1383.0,Honda,2014.0,3.1,Petrol,Manual,36051.0,Like New,47239.4,CR-V
+1384.0,Mercedes,2022.0,3.9,Hybrid,Automatic,15909.0,Used,94768.92,GLA
+1385.0,Ford,2016.0,5.2,Diesel,Automatic,77338.0,Used,83262.0,Focus
+1386.0,BMW,2014.0,5.0,Electric,Automatic,290277.0,Used,65624.73,3 Series
+1387.0,Honda,2019.0,5.3,Diesel,Manual,182643.0,Used,10930.39,CR-V
+1388.0,Audi,2017.0,1.3,Diesel,Manual,79062.0,Like New,66972.89,Q5
+1389.0,Mercedes,2017.0,4.5,Electric,Manual,181980.0,New,91600.06,C-Class
+1390.0,Honda,2001.0,1.7,Petrol,Automatic,51458.0,New,41047.31,Civic
+1391.0,Toyota,2003.0,3.1,Petrol,Manual,222159.0,Like New,84622.59,Prius
+1392.0,Toyota,2000.0,3.7,Diesel,Automatic,287442.0,Like New,34750.31,Corolla
+1393.0,Tesla,2003.0,3.0,Electric,Manual,231946.0,Used,59159.81,Model X
+1394.0,BMW,2017.0,3.6,Hybrid,Automatic,153263.0,Like New,66600.32,X3
+1395.0,Ford,2004.0,6.0,Diesel,Automatic,164164.0,New,69409.53,Focus
+,,,,,,,,,
+1397.0,BMW,2014.0,4.4,Diesel,Automatic,206329.0,Like New,27289.12,X3
+1398.0,BMW,2007.0,3.0,Diesel,Automatic,62513.0,Used,29925.11,X5
+1399.0,BMW,2019.0,1.7,Electric,Automatic,145476.0,Used,71883.89,5 Series
+1400.0,Audi,2016.0,1.8,Electric,Manual,35198.0,Used,21833.41,Q7
+1401.0,Audi,2000.0,5.7,Hybrid,Automatic,225295.0,Used,49105.69,Q5
+1402.0,Tesla,2011.0,5.4,Hybrid,Manual,264323.0,New,6903.27,Model Y
+1403.0,Honda,2018.0,5.5,Petrol,Manual,2019.0,Like New,18393.04,Civic
+1404.0,Toyota,2014.0,6.0,Electric,Automatic,81455.0,Like New,69490.41,Corolla
+1405.0,BMW,2008.0,2.0,Diesel,Manual,216311.0,Used,76008.85,3 Series
+1406.0,Tesla,2006.0,2.8,Diesel,Automatic,149776.0,New,15955.31,Model S
+,,,,,,,,,
+1408.0,Toyota,2008.0,3.4,Electric,Automatic,3420.0,Like New,14395.92,Corolla
+1409.0,Mercedes,2009.0,3.1,Diesel,Manual,178725.0,Used,46174.22,C-Class
+1410.0,Ford,2001.0,2.8,Petrol,Automatic,16740.0,Like New,16981.03,Mustang
+1411.0,Audi,2018.0,4.0,Petrol,Automatic,105789.0,Like New,46543.12,Q7
+1412.0,Mercedes,2010.0,3.9,Diesel,Automatic,19465.0,New,74549.33,E-Class
+1413.0,Toyota,2012.0,1.8,Diesel,Automatic,4131.0,New,69921.92,Corolla
+1414.0,Mercedes,2009.0,3.4,Hybrid,Manual,10652.0,Like New,31643.75,E-Class
+,,,,,,,,,
+1416.0,Tesla,2017.0,3.9,Hybrid,Automatic,245289.0,Used,93132.65,Model Y
+1417.0,Toyota,2018.0,2.1,Petrol,Manual,89342.0,Like New,38014.82,Corolla
+1418.0,Mercedes,2021.0,4.7,Diesel,Automatic,281315.0,Like New,36266.32,C-Class
+1419.0,Audi,2008.0,1.1,Hybrid,Automatic,217201.0,Used,59286.67,A3
+1420.0,Audi,2006.0,2.8,Petrol,Manual,259820.0,Used,28322.67,A4
+1421.0,Honda,2002.0,4.9,Electric,Manual,186764.0,Used,66900.0,Accord
+1422.0,Mercedes,2019.0,3.8,Diesel,Automatic,58541.0,Like New,84064.87,C-Class
+1423.0,BMW,2005.0,2.3,Hybrid,Manual,9470.0,New,48635.26,X3
+1424.0,Mercedes,2020.0,4.5,Electric,Automatic,192416.0,Used,69553.2,E-Class
+1425.0,Tesla,2001.0,3.8,Hybrid,Manual,44308.0,Used,66929.12,Model 3
+1426.0,Toyota,2005.0,5.0,Petrol,Manual,135669.0,Used,8673.23,Camry
+1427.0,Toyota,2002.0,4.7,Electric,Manual,102840.0,Like New,22424.55,Corolla
+,,,,,,,,,
+1429.0,Mercedes,2007.0,3.4,Electric,Manual,49477.0,New,94194.75,GLC
+1430.0,Tesla,2001.0,1.7,Petrol,Automatic,108261.0,Used,79141.77,Model Y
+1431.0,Ford,2016.0,2.3,Electric,Manual,102466.0,Like New,92518.57,Focus
+1432.0,Tesla,2008.0,3.1,Hybrid,Manual,295215.0,New,34084.12,Model S
+1433.0,Audi,2011.0,4.0,Petrol,Automatic,70560.0,Like New,28532.54,A4
+1434.0,Tesla,2002.0,3.6,Diesel,Automatic,255392.0,New,41508.52,Model Y
+1435.0,Toyota,2010.0,2.4,Hybrid,Automatic,261278.0,Used,10257.49,Prius
+1436.0,Audi,2000.0,1.0,Hybrid,Manual,166471.0,Like New,81937.8,A3
+1437.0,Toyota,2022.0,3.5,Petrol,Manual,244413.0,Used,98496.67,RAV4
+1438.0,Honda,2012.0,2.4,Petrol,Automatic,188511.0,New,83282.78,Accord
+1439.0,BMW,2014.0,4.7,Diesel,Automatic,2369.0,New,57722.66,X5
+1440.0,Toyota,2004.0,1.1,Hybrid,Automatic,228629.0,Used,33051.79,Prius
+1441.0,Audi,2020.0,3.9,Petrol,Automatic,262672.0,New,23781.3,Q5
+1442.0,Honda,2016.0,5.7,Diesel,Manual,108806.0,Used,39585.97,CR-V
+1443.0,Toyota,2022.0,1.9,Diesel,Manual,161386.0,Used,68362.11,Camry
+1444.0,Ford,2023.0,3.4,Petrol,Manual,102979.0,Used,47470.85,Explorer
+1445.0,Tesla,2005.0,1.5,Hybrid,Manual,281892.0,New,93735.68,Model Y
+1446.0,BMW,2020.0,4.1,Electric,Manual,35109.0,Like New,86150.93,3 Series
+1447.0,Toyota,2021.0,3.8,Electric,Automatic,13919.0,Used,14728.06,Camry
+,,,,,,,,,
+1449.0,Ford,2017.0,3.6,Electric,Automatic,70441.0,New,69242.72,Focus
+,,,,,,,,,
+,,,,,,,,,
+1452.0,Tesla,2012.0,1.1,Diesel,Manual,47020.0,Used,61179.02,Model Y
+1453.0,Honda,2022.0,5.3,Hybrid,Automatic,205338.0,New,26833.9,CR-V
+1454.0,Ford,2008.0,3.9,Diesel,Manual,284305.0,Used,50475.25,Focus
+1455.0,Honda,2020.0,5.5,Petrol,Automatic,27738.0,Used,43577.25,CR-V
+,,,,,,,,,
+1457.0,Toyota,2011.0,5.1,Hybrid,Automatic,57153.0,New,8055.44,Corolla
+1458.0,Ford,2001.0,3.1,Diesel,Manual,214020.0,Like New,90105.17,Fiesta
+1459.0,Ford,2002.0,4.7,Electric,Automatic,61009.0,New,66947.85,Mustang
+,,,,,,,,,
+1461.0,Ford,2011.0,3.9,Hybrid,Automatic,271941.0,New,5247.71,Mustang
+1462.0,Ford,2014.0,1.9,Diesel,Automatic,71942.0,New,64682.75,Focus
+1463.0,Mercedes,2008.0,2.3,Diesel,Manual,118065.0,New,60906.19,E-Class
+1464.0,Toyota,2022.0,2.0,Diesel,Manual,276109.0,Used,56034.53,Prius
+1465.0,Tesla,2007.0,2.2,Electric,Manual,275877.0,Used,79657.35,Model S
+,,,,,,,,,
+1467.0,Mercedes,2015.0,3.8,Hybrid,Automatic,159855.0,Like New,86424.23,C-Class
+,,,,,,,,,
+1469.0,Toyota,2003.0,3.1,Electric,Automatic,167466.0,New,81568.35,Prius
+1470.0,Toyota,2000.0,4.1,Electric,Automatic,195868.0,Used,22034.71,Camry
+1471.0,Audi,2023.0,1.4,Petrol,Automatic,290431.0,Used,82246.19,A4
+1472.0,Tesla,2006.0,1.4,Diesel,Manual,82104.0,Used,94344.03,Model 3
+,,,,,,,,,
+1474.0,Mercedes,2023.0,3.6,Hybrid,Manual,221404.0,Used,55339.2,GLC
+1475.0,Honda,2004.0,2.1,Diesel,Manual,265332.0,Like New,67541.26,Fit
+1476.0,BMW,2006.0,2.4,Petrol,Manual,91205.0,Used,12549.81,5 Series
+1477.0,Honda,2003.0,3.0,Electric,Manual,232321.0,Used,51937.67,Civic
+1478.0,Audi,2004.0,5.1,Hybrid,Manual,137837.0,New,39255.57,A3
+1479.0,Toyota,2023.0,1.1,Hybrid,Manual,219779.0,Like New,87883.28,RAV4
+1480.0,Tesla,2000.0,4.2,Diesel,Automatic,141879.0,Used,88284.55,Model S
+1481.0,Toyota,2000.0,4.3,Hybrid,Automatic,10204.0,New,71574.06,Prius
+1482.0,BMW,2014.0,5.0,Electric,Automatic,223674.0,Like New,67293.83,X5
+1483.0,Toyota,2019.0,5.7,Hybrid,Automatic,189016.0,Like New,74700.4,Corolla
+1484.0,Toyota,2018.0,1.1,Electric,Automatic,102903.0,Used,39768.76,Prius
+1485.0,BMW,2009.0,1.8,Petrol,Automatic,299225.0,Like New,43429.33,X3
+1486.0,Mercedes,2017.0,5.4,Petrol,Automatic,256687.0,New,57601.95,E-Class
+1487.0,Mercedes,2002.0,3.3,Electric,Manual,34986.0,New,94112.67,C-Class
+1488.0,BMW,2010.0,3.8,Diesel,Manual,50304.0,Like New,73779.99,3 Series
+1489.0,Tesla,2014.0,4.3,Hybrid,Automatic,39986.0,New,57343.98,Model S
+1490.0,Audi,2010.0,4.4,Diesel,Manual,91594.0,Like New,78189.24,A3
+,,,,,,,,,
+1492.0,Tesla,2016.0,6.0,Petrol,Manual,290358.0,Like New,74394.34,Model S
+1493.0,Toyota,2005.0,4.6,Petrol,Manual,173620.0,Like New,44014.24,Prius
+1494.0,Audi,2004.0,2.4,Petrol,Manual,141753.0,Like New,31567.83,Q7
+,,,,,,,,,
+1496.0,Tesla,2001.0,1.2,Petrol,Manual,158651.0,Like New,70253.37,Model X
+1497.0,Ford,2015.0,2.2,Petrol,Automatic,234347.0,Like New,48632.89,Focus
+1498.0,Ford,2019.0,5.5,Petrol,Automatic,186005.0,Used,58722.57,Fiesta
+1499.0,Ford,2023.0,2.3,Petrol,Automatic,162553.0,Used,70936.28,Fiesta
+1500.0,Mercedes,2011.0,2.1,Diesel,Manual,944.0,Like New,77932.45,C-Class
+1501.0,Mercedes,2005.0,1.7,Petrol,Automatic,93993.0,Used,90079.29,GLC
+1502.0,Audi,2000.0,5.3,Petrol,Automatic,271258.0,Like New,38681.7,A3
+,,,,,,,,,
+1504.0,Audi,2016.0,4.4,Diesel,Automatic,208920.0,New,65541.58,A4
+1505.0,Audi,2012.0,2.2,Hybrid,Manual,256805.0,New,79831.68,A3
+1506.0,Mercedes,2017.0,4.8,Electric,Automatic,294740.0,Like New,71478.06,GLC
+1507.0,Ford,2004.0,4.8,Petrol,Automatic,104087.0,Like New,24529.31,Focus
+1508.0,Honda,2023.0,2.6,Petrol,Automatic,269051.0,New,11786.65,Fit
+1509.0,Toyota,2023.0,3.1,Petrol,Automatic,255035.0,New,73997.21,Corolla
+1510.0,Mercedes,2002.0,2.0,Hybrid,Manual,159186.0,Used,53550.0,C-Class
+1511.0,Ford,2010.0,5.9,Petrol,Automatic,287153.0,Like New,71710.27,Explorer
+,,,,,,,,,
+1513.0,Honda,2003.0,5.7,Diesel,Automatic,281140.0,New,34662.28,Civic
+1514.0,Honda,2013.0,2.5,Hybrid,Automatic,290911.0,Like New,32076.7,Accord
+,,,,,,,,,
+1516.0,BMW,2010.0,4.8,Petrol,Automatic,102063.0,Used,31294.11,X3
+1517.0,Toyota,2009.0,4.5,Petrol,Manual,24102.0,Used,72894.3,Prius
+1518.0,Honda,2018.0,5.8,Petrol,Automatic,4263.0,Used,41476.21,Civic
+1519.0,Mercedes,2017.0,3.0,Electric,Manual,67438.0,New,78235.35,GLC
+1520.0,Mercedes,2005.0,1.7,Electric,Manual,96359.0,Like New,18566.94,GLA
+1521.0,Ford,2014.0,4.4,Hybrid,Automatic,130251.0,Like New,24473.09,Mustang
+1522.0,Tesla,2020.0,4.6,Diesel,Manual,171915.0,New,70161.6,Model S
+1523.0,BMW,2009.0,3.9,Petrol,Automatic,27690.0,New,58221.96,X3
+1524.0,Toyota,2004.0,2.0,Petrol,Manual,2994.0,New,24368.44,Corolla
+1525.0,Audi,2004.0,3.5,Petrol,Automatic,103533.0,Like New,90768.57,Q5
+1526.0,Tesla,2009.0,2.6,Petrol,Manual,67546.0,Like New,31214.69,Model 3
+1527.0,Tesla,2004.0,3.1,Electric,Manual,26467.0,Like New,45001.78,Model X
+1528.0,Ford,2002.0,4.2,Diesel,Automatic,246590.0,Used,24217.35,Explorer
+1529.0,Honda,2022.0,4.7,Diesel,Manual,131120.0,New,41862.62,Fit
+1530.0,Honda,2005.0,2.2,Hybrid,Manual,168050.0,New,51706.44,Fit
+1531.0,Ford,2003.0,3.0,Electric,Manual,73414.0,New,33048.47,Mustang
+1532.0,Mercedes,2017.0,2.3,Petrol,Manual,180294.0,Like New,78063.9,GLC
+1533.0,Honda,2008.0,2.0,Diesel,Manual,48115.0,New,42423.63,Fit
+1534.0,BMW,2016.0,4.9,Diesel,Automatic,40647.0,New,74303.45,3 Series
+1535.0,Toyota,2012.0,5.5,Electric,Manual,54607.0,Used,24671.86,Corolla
+1536.0,Tesla,2014.0,2.9,Hybrid,Manual,21996.0,Like New,40818.37,Model X
+1537.0,Tesla,2019.0,4.8,Hybrid,Automatic,83697.0,New,5765.54,Model S
+,,,,,,,,,
+1539.0,Honda,2012.0,5.2,Hybrid,Automatic,239190.0,Like New,64459.23,CR-V
+,,,,,,,,,
+1541.0,Audi,2021.0,5.8,Hybrid,Manual,219738.0,Used,48283.02,A3
+1542.0,Toyota,2003.0,2.9,Petrol,Manual,221432.0,Like New,80975.36,Prius
+1543.0,Toyota,2004.0,2.7,Diesel,Manual,279477.0,Like New,66520.34,Prius
+1544.0,Tesla,2004.0,1.3,Hybrid,Automatic,36355.0,Like New,5343.23,Model Y
+1545.0,Ford,2008.0,3.4,Hybrid,Automatic,230487.0,New,29100.96,Explorer
+1546.0,Tesla,2016.0,1.5,Electric,Manual,129686.0,Used,51919.68,Model Y
+1547.0,Ford,2012.0,2.2,Petrol,Manual,162217.0,New,63775.3,Fiesta
+1548.0,Tesla,2001.0,2.6,Hybrid,Manual,249602.0,Used,11231.13,Model 3
+1549.0,Audi,2012.0,5.0,Electric,Manual,249434.0,Used,71982.59,Q7
+1550.0,Mercedes,2021.0,5.4,Diesel,Automatic,180926.0,Like New,56635.93,GLC
+1551.0,Toyota,2020.0,2.5,Electric,Automatic,67664.0,Like New,54949.03,Prius
+1552.0,Tesla,2022.0,5.3,Electric,Manual,86153.0,New,32744.33,Model Y
+1553.0,Audi,2018.0,5.3,Hybrid,Automatic,93616.0,Used,61508.19,A4
+1554.0,Ford,2002.0,4.8,Hybrid,Manual,297429.0,Like New,7611.82,Fiesta
+1555.0,Ford,2009.0,1.7,Hybrid,Manual,256349.0,Like New,11146.53,Fiesta
+1556.0,Toyota,2001.0,4.5,Electric,Automatic,46996.0,Like New,74717.66,Corolla
+1557.0,BMW,2012.0,5.7,Diesel,Manual,128344.0,Like New,35579.68,5 Series
+1558.0,Tesla,2018.0,4.4,Petrol,Automatic,229685.0,New,37961.87,Model X
+1559.0,Honda,2016.0,2.7,Electric,Manual,128019.0,New,24796.59,CR-V
+1560.0,Ford,2020.0,3.5,Hybrid,Automatic,135024.0,Like New,47333.97,Fiesta
+1561.0,Mercedes,2013.0,4.2,Petrol,Manual,183273.0,Like New,9939.38,E-Class
+1562.0,Mercedes,2011.0,3.6,Diesel,Automatic,120454.0,Like New,33534.51,GLC
+1563.0,Ford,2020.0,1.6,Petrol,Manual,124444.0,Like New,55076.77,Fiesta
+1564.0,BMW,2011.0,2.6,Hybrid,Manual,153868.0,Like New,49235.03,3 Series
+1565.0,BMW,2010.0,5.0,Electric,Automatic,162818.0,Used,99968.62,3 Series
+1566.0,Audi,2006.0,5.3,Electric,Manual,15.0,Used,41932.64,Q5
+1567.0,Mercedes,2003.0,1.7,Hybrid,Automatic,157029.0,Used,41764.91,C-Class
+1568.0,Audi,2018.0,5.8,Electric,Automatic,81541.0,Used,52840.27,A4
+1569.0,Mercedes,2000.0,2.6,Hybrid,Manual,21292.0,Like New,23078.8,GLA
+1570.0,Tesla,2023.0,4.3,Electric,Manual,162948.0,Used,88248.92,Model S
+1571.0,Tesla,2002.0,4.8,Electric,Manual,191799.0,Used,59365.44,Model X
+1572.0,Honda,2012.0,5.1,Electric,Manual,47046.0,New,23250.23,Accord
+1573.0,Tesla,2014.0,5.7,Diesel,Automatic,14817.0,Used,80376.95,Model X
+1574.0,Honda,2001.0,2.6,Diesel,Manual,9759.0,Used,8261.56,Civic
+1575.0,Tesla,2010.0,5.3,Hybrid,Manual,179504.0,Like New,98343.6,Model 3
+1576.0,Toyota,2003.0,1.7,Diesel,Manual,268556.0,Used,87189.19,Camry
+1577.0,Tesla,2018.0,4.5,Hybrid,Automatic,282648.0,Used,60065.75,Model X
+1578.0,Audi,2016.0,4.7,Petrol,Manual,270651.0,Like New,15573.09,A4
+1579.0,Ford,2002.0,4.4,Petrol,Automatic,21050.0,Used,25450.91,Explorer
+1580.0,Audi,2017.0,2.3,Diesel,Manual,100153.0,Like New,31546.34,Q7
+1581.0,Ford,2007.0,5.4,Petrol,Automatic,275731.0,Used,27898.02,Fiesta
+1582.0,BMW,2021.0,1.8,Petrol,Manual,231329.0,New,52264.4,5 Series
+,,,,,,,,,
+,,,,,,,,,
+1585.0,Honda,2004.0,3.4,Hybrid,Manual,117734.0,New,67848.63,Fit
+1586.0,Honda,2006.0,5.2,Petrol,Manual,137912.0,Like New,70415.4,Fit
+1587.0,Toyota,2020.0,2.9,Hybrid,Manual,31440.0,Used,87800.17,Corolla
+1588.0,Tesla,2007.0,1.8,Hybrid,Automatic,35183.0,Like New,8213.02,Model Y
+1589.0,Audi,2004.0,5.2,Petrol,Manual,133461.0,New,43411.36,A4
+1590.0,BMW,2016.0,3.7,Diesel,Automatic,114515.0,Like New,79549.84,3 Series
+1591.0,Ford,2004.0,1.9,Petrol,Automatic,220533.0,New,82412.37,Mustang
+1592.0,Honda,2003.0,4.6,Electric,Automatic,115503.0,New,93096.6,CR-V
+1593.0,Tesla,2021.0,3.6,Petrol,Manual,39023.0,New,71765.46,Model 3
+1594.0,Honda,2018.0,4.0,Diesel,Automatic,181101.0,Like New,28289.62,CR-V
+1595.0,Ford,2020.0,3.3,Hybrid,Automatic,218031.0,New,53038.25,Explorer
+1596.0,Tesla,2001.0,5.1,Hybrid,Manual,76054.0,Used,8072.8,Model 3
+1597.0,Tesla,2010.0,5.1,Electric,Automatic,61742.0,New,66458.19,Model 3
+1598.0,BMW,2021.0,4.5,Electric,Automatic,45215.0,Used,57658.05,X3
+,,,,,,,,,
+1600.0,Audi,2014.0,5.2,Hybrid,Manual,241443.0,Used,93363.35,A3
+1601.0,Audi,2011.0,4.4,Electric,Automatic,103591.0,Used,70291.28,Q5
+1602.0,Mercedes,2021.0,4.5,Petrol,Manual,249045.0,Like New,35442.74,GLC
+1603.0,Mercedes,2020.0,2.8,Hybrid,Manual,46229.0,Used,23879.98,C-Class
+1604.0,Toyota,2021.0,5.4,Diesel,Manual,62583.0,New,86190.44,Camry
+1605.0,Honda,2010.0,4.6,Hybrid,Automatic,255931.0,Used,23461.55,Fit
+1606.0,Audi,2006.0,1.6,Petrol,Automatic,159996.0,New,20352.91,A3
+,,,,,,,,,
+,,,,,,,,,
+,,,,,,,,,
+1610.0,Honda,2019.0,5.8,Hybrid,Manual,299738.0,Like New,94121.24,CR-V
+1611.0,Mercedes,2008.0,4.4,Hybrid,Manual,265808.0,Like New,96794.1,C-Class
+1612.0,BMW,2013.0,4.2,Diesel,Manual,272672.0,New,97511.18,X3
+1613.0,Toyota,2011.0,2.4,Electric,Manual,159262.0,Used,8716.86,Corolla
+1614.0,Honda,2005.0,2.2,Petrol,Automatic,197105.0,Like New,94888.77,Civic
+,,,,,,,,,
+1616.0,Ford,2018.0,2.5,Electric,Automatic,128404.0,Used,94530.77,Explorer
+1617.0,Mercedes,2018.0,4.9,Petrol,Automatic,201664.0,Like New,68739.2,GLA
+1618.0,Tesla,2015.0,3.0,Petrol,Manual,295043.0,New,71616.86,Model Y
+1619.0,Toyota,2011.0,1.7,Electric,Manual,17241.0,Like New,16614.03,Corolla
+1620.0,Honda,2011.0,1.1,Petrol,Automatic,63472.0,Like New,88058.22,CR-V
+1621.0,Ford,2019.0,3.7,Hybrid,Automatic,205320.0,New,10665.76,Mustang
+1622.0,Toyota,2005.0,5.9,Diesel,Manual,31346.0,Like New,35184.4,RAV4
+1623.0,Mercedes,2022.0,2.8,Electric,Manual,81196.0,New,99159.41,E-Class
+1624.0,Toyota,2003.0,2.8,Diesel,Manual,208196.0,Used,45121.15,Corolla
+1625.0,Honda,2005.0,2.7,Petrol,Automatic,149014.0,New,17459.84,Fit
+1626.0,Audi,2010.0,3.6,Petrol,Manual,298517.0,Used,97082.99,A3
+1627.0,Toyota,2000.0,3.2,Hybrid,Manual,120623.0,Like New,98493.27,RAV4
+1628.0,Tesla,2023.0,5.4,Electric,Automatic,2016.0,New,22769.06,Model S
+1629.0,Toyota,2022.0,3.5,Petrol,Automatic,7274.0,New,87156.36,Camry
+1630.0,Mercedes,2014.0,3.5,Electric,Automatic,15748.0,New,42090.22,GLC
+1631.0,Mercedes,2010.0,1.7,Electric,Manual,11826.0,New,41230.0,C-Class
+1632.0,Ford,2018.0,5.9,Electric,Manual,129288.0,Used,29956.28,Mustang
+1633.0,Mercedes,2017.0,2.2,Electric,Automatic,201081.0,Used,9247.48,E-Class
+1634.0,BMW,2016.0,1.4,Electric,Manual,251054.0,Like New,30460.85,5 Series
+1635.0,Honda,2018.0,4.3,Diesel,Manual,172507.0,Like New,10108.81,Fit
+1636.0,Honda,2020.0,4.0,Hybrid,Manual,206089.0,Like New,92815.34,Fit
+1637.0,Audi,2013.0,3.1,Electric,Automatic,223023.0,Like New,67863.46,Q7
+1638.0,Audi,2017.0,3.1,Diesel,Automatic,192949.0,New,89229.15,Q7
+1639.0,Honda,2018.0,4.9,Petrol,Manual,69072.0,Used,73142.61,Accord
+1640.0,Tesla,2014.0,2.0,Electric,Manual,67309.0,Used,17472.25,Model S
+1641.0,Audi,2014.0,5.1,Petrol,Manual,160966.0,Used,94355.25,Q7
+1642.0,Ford,2003.0,4.2,Electric,Manual,185639.0,Used,90489.84,Fiesta
+1643.0,Mercedes,2006.0,3.1,Diesel,Automatic,35326.0,Like New,38067.23,GLA
+1644.0,Honda,2012.0,3.3,Diesel,Automatic,186131.0,New,15595.67,CR-V
+1645.0,Audi,2002.0,2.3,Diesel,Manual,267223.0,Used,71894.7,Q5
+1646.0,Honda,2015.0,4.8,Hybrid,Manual,87340.0,New,74194.32,CR-V
+1647.0,BMW,2008.0,3.6,Petrol,Automatic,167364.0,New,90198.76,3 Series
+1648.0,Toyota,2007.0,2.6,Hybrid,Manual,118192.0,New,10002.3,RAV4
+1649.0,Mercedes,2019.0,4.0,Electric,Automatic,251749.0,New,18461.86,GLC
+1650.0,Honda,2012.0,6.0,Petrol,Manual,51818.0,Used,61833.64,Civic
+1651.0,Toyota,2016.0,3.2,Diesel,Automatic,13429.0,Used,63817.5,RAV4
+1652.0,Audi,2016.0,5.6,Petrol,Manual,57748.0,Used,8192.92,Q5
+1653.0,Toyota,2016.0,2.7,Diesel,Manual,48807.0,Like New,42708.54,RAV4
+1654.0,Mercedes,2000.0,1.3,Hybrid,Automatic,269284.0,Used,60495.36,GLA
+1655.0,Honda,2007.0,2.0,Hybrid,Automatic,238161.0,Like New,75101.58,Fit
+,,,,,,,,,
+1657.0,Tesla,2005.0,2.1,Diesel,Manual,282635.0,Like New,50970.83,Model X
+1658.0,Audi,2002.0,1.0,Electric,Automatic,199888.0,Used,82335.21,A4
+1659.0,Honda,2004.0,5.6,Hybrid,Automatic,166052.0,Used,95620.03,Civic
+1660.0,Mercedes,2012.0,5.2,Hybrid,Manual,226597.0,Used,26879.89,C-Class
+1661.0,Audi,2014.0,4.7,Electric,Manual,8078.0,Like New,29039.01,A4
+1662.0,Ford,2013.0,1.8,Hybrid,Automatic,91654.0,Like New,94282.73,Mustang
+1663.0,Toyota,2022.0,1.6,Diesel,Automatic,129101.0,New,42611.56,RAV4
+1664.0,Tesla,2015.0,1.3,Petrol,Automatic,16997.0,Like New,73243.09,Model 3
+1665.0,Mercedes,2017.0,4.6,Petrol,Automatic,131940.0,Like New,63165.2,C-Class
+1666.0,Ford,2021.0,5.6,Hybrid,Automatic,212650.0,Used,32130.83,Fiesta
+1667.0,Honda,2021.0,3.2,Electric,Manual,283390.0,New,67088.41,CR-V
+1668.0,BMW,2010.0,1.6,Diesel,Manual,151633.0,New,67278.76,X5
+1669.0,Audi,2002.0,3.6,Petrol,Automatic,34512.0,New,61282.1,A4
+1670.0,Toyota,2021.0,2.7,Diesel,Manual,225226.0,Used,14467.78,Prius
+1671.0,Audi,2009.0,2.9,Hybrid,Manual,208467.0,New,99541.32,A3
+1672.0,Tesla,2009.0,4.1,Hybrid,Automatic,72144.0,New,43039.56,Model X
+1673.0,Honda,2020.0,4.6,Petrol,Manual,173557.0,Used,65633.84,CR-V
+,,,,,,,,,
+1675.0,Toyota,2011.0,1.2,Electric,Manual,201484.0,Like New,45212.41,Prius
+1676.0,Honda,2001.0,6.0,Hybrid,Automatic,181675.0,Like New,78522.88,CR-V
+1677.0,Audi,2008.0,2.2,Hybrid,Automatic,152110.0,Like New,97323.32,A3
+1678.0,BMW,2023.0,1.3,Hybrid,Manual,93542.0,Like New,39098.8,5 Series
+1679.0,Mercedes,2002.0,1.9,Diesel,Manual,276469.0,Like New,43500.61,C-Class
+1680.0,Honda,2023.0,1.2,Diesel,Manual,166964.0,Like New,91029.73,Accord
+1681.0,Mercedes,2014.0,3.0,Petrol,Manual,149068.0,Like New,20166.07,GLA
+1682.0,Honda,2002.0,5.0,Petrol,Manual,205355.0,Like New,7716.51,Civic
+1683.0,BMW,2018.0,2.7,Petrol,Manual,116737.0,Like New,91344.46,3 Series
+1684.0,Mercedes,2017.0,3.2,Hybrid,Automatic,262968.0,Used,83051.2,GLC
+1685.0,Ford,2007.0,4.4,Diesel,Manual,240417.0,Used,97395.39,Explorer
+1686.0,Toyota,2016.0,3.5,Electric,Manual,117718.0,Like New,10532.59,RAV4
+1687.0,Ford,2007.0,5.3,Hybrid,Automatic,273661.0,Used,19836.85,Mustang
+1688.0,Audi,2006.0,5.3,Hybrid,Manual,152755.0,Used,15004.69,A4
+1689.0,BMW,2006.0,1.0,Hybrid,Manual,207791.0,Like New,69924.53,X3
+1690.0,Ford,2002.0,4.9,Electric,Manual,249120.0,Like New,62570.96,Mustang
+1691.0,Honda,2000.0,3.3,Petrol,Manual,254322.0,Used,39136.84,Civic
+1692.0,Audi,2003.0,5.9,Electric,Automatic,209485.0,Like New,10455.23,A3
+,,,,,,,,,
+1694.0,Ford,2000.0,5.9,Electric,Automatic,145711.0,Like New,59161.45,Fiesta
+1695.0,Ford,2014.0,4.8,Diesel,Manual,70210.0,Like New,15725.81,Focus
+1696.0,BMW,2004.0,2.2,Hybrid,Manual,69852.0,Used,91346.42,X5
+1697.0,Toyota,2012.0,3.7,Hybrid,Automatic,255139.0,New,23900.22,Corolla
+1698.0,Ford,2007.0,6.0,Hybrid,Manual,9695.0,Used,75633.37,Mustang
+1699.0,Tesla,2007.0,5.3,Petrol,Automatic,48582.0,New,89082.21,Model X
+1700.0,Ford,2004.0,1.5,Electric,Automatic,293238.0,New,60050.12,Mustang
+1701.0,Mercedes,2008.0,4.5,Diesel,Automatic,191707.0,New,28450.65,E-Class
+1702.0,Honda,2000.0,2.7,Electric,Manual,284331.0,Used,34378.61,Civic
+1703.0,Tesla,2003.0,1.0,Hybrid,Manual,128721.0,Used,89146.02,Model S
+1704.0,Tesla,2002.0,3.0,Diesel,Manual,106683.0,Used,33334.43,Model X
+1705.0,Mercedes,2001.0,2.2,Diesel,Manual,263358.0,Used,75888.1,GLA
+1706.0,Mercedes,2007.0,5.1,Hybrid,Automatic,286084.0,Used,99065.7,GLC
+1707.0,Audi,2022.0,2.7,Electric,Manual,44406.0,New,99982.59,A3
+1708.0,Toyota,2008.0,5.1,Hybrid,Manual,147049.0,Used,55680.58,Corolla
+1709.0,Honda,2001.0,1.0,Electric,Automatic,235994.0,New,83522.8,Fit
+1710.0,BMW,2013.0,2.5,Diesel,Automatic,187313.0,Used,99073.74,5 Series
+1711.0,Tesla,2013.0,2.2,Diesel,Manual,204573.0,Used,40593.92,Model 3
+1712.0,BMW,2003.0,2.2,Hybrid,Automatic,206205.0,Like New,28978.91,X5
+1713.0,Ford,2005.0,1.3,Diesel,Automatic,60481.0,New,15852.17,Explorer
+1714.0,Ford,2010.0,1.9,Petrol,Automatic,238084.0,Used,18385.47,Mustang
+1715.0,BMW,2002.0,3.8,Diesel,Automatic,289884.0,Used,86087.85,X3
+1716.0,Ford,2007.0,2.5,Petrol,Automatic,274025.0,Used,60928.69,Explorer
+1717.0,Toyota,2002.0,3.1,Diesel,Manual,299140.0,Like New,88002.37,Camry
+1718.0,Mercedes,2013.0,2.9,Hybrid,Manual,163249.0,New,79998.23,E-Class
+1719.0,Ford,2012.0,5.4,Hybrid,Manual,122196.0,Used,69005.74,Focus
+1720.0,Audi,2012.0,5.5,Petrol,Manual,37177.0,Used,62884.66,A4
+1721.0,Ford,2001.0,3.5,Hybrid,Manual,66126.0,Used,9592.2,Mustang
+1722.0,Honda,2012.0,2.7,Diesel,Manual,26119.0,Like New,87866.14,Fit
+1723.0,Toyota,2018.0,1.5,Diesel,Manual,117744.0,Used,44695.0,Corolla
+,,,,,,,,,
+1725.0,Honda,2015.0,1.3,Diesel,Manual,158987.0,New,7085.58,Accord
+1726.0,Audi,2001.0,2.5,Electric,Manual,44989.0,New,93926.98,A4
+1727.0,Toyota,2007.0,2.5,Electric,Manual,284250.0,New,90760.34,RAV4
+1728.0,Toyota,2016.0,2.6,Diesel,Automatic,279275.0,Used,56611.41,Prius
+1729.0,Toyota,2022.0,1.8,Hybrid,Manual,6553.0,Like New,59844.61,Corolla
+1730.0,Toyota,2001.0,4.9,Petrol,Manual,17881.0,Like New,71976.64,Prius
+1731.0,Toyota,2009.0,1.6,Hybrid,Automatic,1379.0,Like New,30146.02,Prius
+1732.0,Audi,2015.0,4.2,Petrol,Manual,69306.0,Used,26081.98,A4
+1733.0,Ford,2005.0,1.6,Hybrid,Manual,243825.0,Like New,69032.18,Focus
+1734.0,Honda,2001.0,1.4,Diesel,Automatic,104512.0,New,14920.13,Civic
+1735.0,Toyota,2001.0,5.5,Electric,Manual,278494.0,Used,48135.89,RAV4
+1736.0,Tesla,2012.0,2.0,Hybrid,Automatic,99599.0,Like New,51812.24,Model S
+,,,,,,,,,
+,,,,,,,,,
+1739.0,Honda,2001.0,1.4,Petrol,Manual,178321.0,Used,38755.05,Accord
+1740.0,Audi,2006.0,5.8,Diesel,Automatic,101584.0,Used,37067.94,Q5
+1741.0,BMW,2002.0,2.2,Electric,Automatic,133858.0,Like New,52430.24,5 Series
+1742.0,BMW,2007.0,5.3,Petrol,Automatic,231609.0,Like New,69094.68,3 Series
+1743.0,Ford,2022.0,1.8,Hybrid,Manual,126525.0,Like New,96982.34,Fiesta
+1744.0,Mercedes,2013.0,5.3,Diesel,Manual,36303.0,Used,18695.51,E-Class
+1745.0,Mercedes,2023.0,1.1,Diesel,Automatic,19589.0,Like New,87936.63,C-Class
+1746.0,Honda,2022.0,3.6,Diesel,Automatic,265026.0,Like New,31996.91,Accord
+1747.0,BMW,2021.0,4.8,Hybrid,Manual,180394.0,New,61180.41,X5
+1748.0,Ford,2006.0,5.7,Hybrid,Manual,129236.0,New,56940.01,Mustang
+1749.0,Audi,2020.0,5.5,Electric,Manual,271889.0,New,57910.1,A3
+1750.0,BMW,2006.0,5.1,Hybrid,Manual,15861.0,Like New,73719.07,3 Series
+1751.0,Ford,2000.0,5.4,Electric,Automatic,91707.0,Used,41260.39,Mustang
+1752.0,Ford,2016.0,2.0,Diesel,Manual,255043.0,New,35314.5,Mustang
+1753.0,Toyota,2008.0,2.4,Electric,Manual,133909.0,Used,86019.28,Camry
+1754.0,Audi,2020.0,2.4,Petrol,Manual,54380.0,Like New,25909.03,A4
+1755.0,Toyota,2012.0,2.9,Diesel,Manual,105025.0,Like New,22123.37,Corolla
+,,,,,,,,,
+1757.0,Toyota,2018.0,3.7,Electric,Manual,220421.0,Used,25004.89,Corolla
+1758.0,Tesla,2020.0,1.8,Diesel,Manual,244497.0,New,54260.49,Model 3
+1759.0,Toyota,2018.0,4.5,Hybrid,Automatic,160592.0,Like New,56158.34,Camry
+1760.0,Tesla,2021.0,1.8,Petrol,Automatic,44397.0,Used,29169.95,Model S
+1761.0,BMW,2016.0,2.6,Electric,Automatic,277132.0,New,11866.05,5 Series
+1762.0,Mercedes,2022.0,3.5,Electric,Manual,158795.0,Like New,75740.88,GLC
+1763.0,Ford,2022.0,5.0,Diesel,Automatic,33800.0,New,77007.66,Fiesta
+1764.0,Tesla,2009.0,4.7,Hybrid,Manual,158502.0,Used,99794.46,Model 3
+1765.0,Audi,2009.0,4.2,Diesel,Manual,199548.0,Used,90502.81,Q7
+1766.0,Toyota,2003.0,1.8,Diesel,Manual,257653.0,Used,92975.9,Camry
+1767.0,Tesla,2007.0,3.7,Electric,Manual,52284.0,Used,80829.36,Model 3
+1768.0,Audi,2021.0,1.4,Diesel,Automatic,56232.0,Used,63472.77,A4
+1769.0,Mercedes,2019.0,2.9,Petrol,Manual,44420.0,New,17211.87,E-Class
+1770.0,Mercedes,2019.0,3.1,Diesel,Manual,299854.0,New,64119.76,GLA
+1771.0,Toyota,2017.0,1.2,Diesel,Automatic,92035.0,New,10315.81,Camry
+1772.0,Honda,2013.0,2.4,Electric,Automatic,293427.0,New,89103.42,CR-V
+1773.0,Toyota,2017.0,5.8,Diesel,Automatic,197241.0,Like New,50773.03,Corolla
+1774.0,BMW,2013.0,5.8,Hybrid,Manual,193529.0,Like New,71259.92,3 Series
+1775.0,Tesla,2019.0,2.8,Electric,Automatic,249863.0,New,9963.15,Model X
+1776.0,Ford,2020.0,1.2,Diesel,Manual,25968.0,Like New,55753.6,Explorer
+1777.0,Honda,2023.0,1.9,Petrol,Manual,62508.0,Like New,33620.02,Fit
+1778.0,Toyota,2014.0,3.0,Petrol,Automatic,235863.0,Used,32071.99,Camry
+1779.0,Audi,2021.0,5.7,Electric,Automatic,194292.0,New,42017.31,Q7
+1780.0,Honda,2003.0,1.7,Electric,Automatic,85366.0,Like New,5060.75,Accord
+1781.0,Tesla,2012.0,4.8,Diesel,Manual,5738.0,Like New,49332.78,Model 3
+1782.0,Honda,2022.0,1.5,Hybrid,Manual,3098.0,New,51223.83,Civic
+1783.0,Tesla,2007.0,5.9,Diesel,Manual,95424.0,Used,86997.44,Model X
+1784.0,Toyota,2017.0,2.9,Petrol,Manual,204705.0,New,47609.93,Prius
+1785.0,Tesla,2011.0,4.9,Hybrid,Manual,78672.0,Used,39537.85,Model X
+1786.0,Honda,2022.0,2.7,Hybrid,Manual,139325.0,Like New,99500.97,Civic
+1787.0,Audi,2013.0,4.1,Hybrid,Automatic,125349.0,Used,59265.27,Q7
+1788.0,Mercedes,2001.0,2.0,Diesel,Manual,50969.0,Used,49898.25,GLA
+,,,,,,,,,
+1790.0,Tesla,2023.0,5.7,Electric,Automatic,201495.0,Like New,81872.22,Model 3
+1791.0,Mercedes,2001.0,5.6,Diesel,Automatic,117286.0,New,37364.26,C-Class
+1792.0,Tesla,2010.0,5.4,Electric,Automatic,75028.0,New,41970.24,Model 3
+1793.0,Audi,2008.0,4.1,Hybrid,Automatic,297565.0,Like New,76807.97,A3
+1794.0,Tesla,2011.0,5.0,Hybrid,Manual,38575.0,New,11933.46,Model 3
+1795.0,Toyota,2016.0,1.3,Diesel,Manual,3192.0,New,87191.07,RAV4
+1796.0,Ford,2014.0,2.7,Petrol,Manual,111116.0,Used,45869.26,Fiesta
+1797.0,Honda,2016.0,2.6,Hybrid,Manual,81311.0,New,60222.83,Fit
+,,,,,,,,,
+1799.0,Tesla,2021.0,2.8,Electric,Automatic,36527.0,New,30274.93,Model Y
+1800.0,Audi,2005.0,1.7,Diesel,Automatic,52181.0,Used,89579.85,A4
+1801.0,Honda,2016.0,4.0,Electric,Manual,91472.0,New,98026.37,Accord
+1802.0,Tesla,2022.0,5.1,Petrol,Manual,81219.0,New,51956.06,Model Y
+1803.0,Mercedes,2023.0,2.9,Hybrid,Manual,119783.0,New,94009.05,E-Class
+1804.0,BMW,2017.0,1.7,Electric,Manual,161554.0,Used,5107.22,X5
+1805.0,Tesla,2007.0,2.0,Electric,Automatic,179300.0,Like New,89952.59,Model Y
+1806.0,Ford,2008.0,2.2,Hybrid,Automatic,83265.0,Like New,34549.31,Fiesta
+1807.0,Honda,2005.0,2.5,Diesel,Manual,50126.0,Used,34592.07,Civic
+1808.0,Ford,2004.0,4.2,Electric,Manual,282689.0,Used,26668.44,Explorer
+1809.0,Honda,2011.0,4.0,Diesel,Automatic,55114.0,New,35116.32,CR-V
+1810.0,Tesla,2010.0,4.5,Diesel,Automatic,206422.0,Like New,25281.04,Model X
+1811.0,Toyota,2000.0,2.8,Electric,Automatic,188873.0,Used,91057.57,Corolla
+1812.0,Mercedes,2010.0,5.1,Petrol,Automatic,276034.0,Like New,90407.25,GLC
+1813.0,Tesla,2018.0,2.0,Diesel,Manual,121284.0,New,14710.07,Model S
+1814.0,Ford,2008.0,3.3,Electric,Automatic,194469.0,Used,18571.55,Fiesta
+1815.0,BMW,2001.0,5.0,Diesel,Automatic,142290.0,Like New,86891.77,5 Series
+1816.0,BMW,2011.0,1.9,Hybrid,Automatic,88241.0,Like New,42899.09,X3
+1817.0,Audi,2003.0,1.5,Petrol,Automatic,296866.0,New,21080.81,Q7
+1818.0,Honda,2003.0,5.4,Petrol,Manual,182615.0,Used,53513.59,CR-V
+1819.0,Ford,2017.0,2.7,Hybrid,Manual,30921.0,Like New,44583.46,Fiesta
+1820.0,Audi,2014.0,4.4,Hybrid,Manual,161686.0,New,25820.35,A3
+1821.0,Ford,2018.0,1.1,Diesel,Manual,192261.0,Like New,73471.46,Focus
+1822.0,Honda,2012.0,4.5,Hybrid,Manual,17601.0,New,64972.48,Fit
+,,,,,,,,,
+1824.0,Tesla,2016.0,1.3,Hybrid,Automatic,202615.0,Like New,92911.74,Model 3
+1825.0,BMW,2005.0,4.0,Electric,Automatic,235572.0,New,58054.65,5 Series
+1826.0,Tesla,2015.0,2.2,Electric,Automatic,290144.0,Used,28769.43,Model X
+1827.0,Tesla,2023.0,2.9,Diesel,Automatic,130439.0,New,24597.72,Model 3
+1828.0,Toyota,2011.0,1.4,Petrol,Manual,239691.0,Used,17915.06,RAV4
+1829.0,BMW,2018.0,1.7,Electric,Automatic,119277.0,Like New,45340.55,X5
+1830.0,Audi,2004.0,2.5,Hybrid,Manual,292527.0,Like New,17876.4,A3
+1831.0,Mercedes,2016.0,1.4,Electric,Automatic,142857.0,New,44502.56,GLA
+1832.0,Toyota,2022.0,5.6,Electric,Automatic,40910.0,New,19133.19,Prius
+1833.0,Audi,2000.0,1.0,Diesel,Automatic,170100.0,Used,67396.71,Q5
+1834.0,Honda,2023.0,3.4,Hybrid,Manual,155927.0,Used,79597.99,Accord
+1835.0,Toyota,2022.0,1.6,Hybrid,Automatic,286425.0,Used,57204.66,Camry
+1836.0,Audi,2009.0,2.5,Hybrid,Manual,4841.0,New,69605.4,A4
+1837.0,Honda,2014.0,5.6,Diesel,Manual,97204.0,Like New,94145.98,Fit
+,,,,,,,,,
+1839.0,Mercedes,2020.0,5.8,Hybrid,Manual,259589.0,Like New,39595.03,E-Class
+1840.0,Honda,2020.0,2.0,Electric,Manual,261140.0,New,47700.62,Civic
+1841.0,Tesla,2010.0,4.7,Electric,Automatic,148515.0,New,86749.9,Model Y
+1842.0,Ford,2012.0,3.7,Hybrid,Automatic,132994.0,Used,17418.43,Fiesta
+1843.0,Honda,2004.0,5.4,Petrol,Automatic,294120.0,Like New,35945.02,Accord
+1844.0,Honda,2000.0,3.8,Petrol,Automatic,143063.0,New,59841.61,CR-V
+1845.0,Mercedes,2023.0,3.7,Hybrid,Automatic,104405.0,New,69257.01,GLC
+1846.0,Ford,2011.0,2.0,Petrol,Automatic,53744.0,Like New,77532.41,Focus
+1847.0,Tesla,2010.0,1.8,Diesel,Manual,2473.0,New,35229.65,Model 3
+,,,,,,,,,
+1849.0,Audi,2003.0,2.6,Diesel,Automatic,28715.0,Used,95709.72,A3
+1850.0,Honda,2014.0,2.9,Electric,Manual,244826.0,Used,12257.26,Civic
+1851.0,Ford,2008.0,4.8,Petrol,Automatic,253856.0,Like New,14101.51,Mustang
+1852.0,Ford,2020.0,2.7,Petrol,Manual,239419.0,Used,52019.54,Fiesta
+1853.0,Tesla,2019.0,5.8,Hybrid,Automatic,252508.0,Like New,82830.75,Model S
+1854.0,Mercedes,2018.0,4.7,Petrol,Automatic,299461.0,New,78577.89,E-Class
+1855.0,BMW,2018.0,4.0,Petrol,Manual,164434.0,New,94081.43,X5
+,,,,,,,,,
+1857.0,Mercedes,2004.0,1.2,Hybrid,Manual,4889.0,New,40694.09,C-Class
+1858.0,Toyota,2009.0,2.3,Electric,Automatic,220629.0,Used,31803.5,Prius
+1859.0,Audi,2016.0,3.3,Petrol,Automatic,294115.0,New,86782.66,Q7
+1860.0,Mercedes,2004.0,2.1,Electric,Manual,50551.0,New,51393.69,C-Class
+1861.0,Audi,2021.0,4.7,Electric,Automatic,134273.0,Like New,7550.55,Q7
+1862.0,BMW,2015.0,5.6,Diesel,Automatic,237230.0,Used,72256.62,3 Series
+,,,,,,,,,
+1864.0,BMW,2008.0,2.7,Petrol,Automatic,108957.0,New,55458.94,5 Series
+1865.0,Tesla,2002.0,5.8,Diesel,Manual,89882.0,Like New,89909.81,Model X
+1866.0,Toyota,2014.0,3.2,Electric,Automatic,210323.0,Like New,29390.09,RAV4
+1867.0,Honda,2015.0,3.4,Petrol,Manual,267825.0,Used,27825.14,Accord
+,,,,,,,,,
+1869.0,Toyota,2013.0,5.5,Diesel,Manual,46870.0,Like New,37120.26,Prius
+1870.0,Tesla,2002.0,3.8,Hybrid,Manual,213328.0,Like New,15198.63,Model X
+1871.0,Tesla,2011.0,4.8,Electric,Automatic,159224.0,Like New,71235.87,Model Y
+1872.0,BMW,2008.0,5.1,Petrol,Automatic,50061.0,Used,24702.88,3 Series
+1873.0,Audi,2006.0,2.7,Hybrid,Manual,188691.0,Like New,92983.85,A4
+,,,,,,,,,
+,,,,,,,,,
+1876.0,Toyota,2017.0,2.8,Hybrid,Automatic,11217.0,New,33216.92,RAV4
+1877.0,Ford,2004.0,5.0,Hybrid,Automatic,295885.0,New,78011.12,Mustang
+1878.0,Audi,2012.0,5.7,Hybrid,Manual,192386.0,Used,45622.62,Q5
+,,,,,,,,,
+1880.0,Mercedes,2001.0,4.7,Petrol,Automatic,42903.0,New,79561.1,GLA
+1881.0,Honda,2015.0,5.0,Electric,Automatic,215280.0,Like New,93301.59,Accord
+1882.0,BMW,2008.0,5.2,Electric,Automatic,107042.0,New,76877.39,3 Series
+1883.0,Honda,2008.0,3.0,Diesel,Automatic,243725.0,Used,9931.56,CR-V
+,,,,,,,,,
+1885.0,Audi,2010.0,3.0,Electric,Manual,181644.0,Like New,94116.9,Q5
+1886.0,Toyota,2021.0,4.4,Hybrid,Automatic,109268.0,Used,99400.47,Camry
+1887.0,Toyota,2023.0,3.3,Hybrid,Automatic,30105.0,New,16900.42,Corolla
+1888.0,Audi,2021.0,4.7,Petrol,Manual,69634.0,Used,63260.96,A4
+1889.0,Mercedes,2000.0,2.8,Hybrid,Manual,138691.0,New,23948.57,E-Class
+1890.0,Tesla,2003.0,4.2,Diesel,Manual,18226.0,Like New,29938.42,Model 3
+1891.0,Toyota,2012.0,1.9,Electric,Automatic,25322.0,New,75830.51,Corolla
+1892.0,Tesla,2022.0,4.7,Electric,Manual,167284.0,Used,97878.43,Model Y
+1893.0,Toyota,2012.0,3.7,Electric,Manual,94835.0,Used,61511.27,RAV4
+1894.0,Toyota,2012.0,2.0,Petrol,Manual,255027.0,Used,85805.52,RAV4
+1895.0,BMW,2001.0,2.5,Hybrid,Manual,73079.0,New,5843.96,X5
+1896.0,BMW,2008.0,4.7,Diesel,Automatic,23946.0,New,8480.01,X5
+1897.0,Ford,2023.0,3.4,Petrol,Automatic,162812.0,New,61688.81,Focus
+1898.0,Honda,2007.0,2.1,Electric,Automatic,7253.0,Like New,41953.39,Civic
+1899.0,Audi,2006.0,5.8,Diesel,Manual,79311.0,Like New,89526.99,Q7
+1900.0,Audi,2003.0,4.6,Electric,Manual,294957.0,Like New,33688.28,Q5
+1901.0,Audi,2017.0,5.8,Electric,Manual,67593.0,Used,46044.85,Q5
+,,,,,,,,,
+1903.0,BMW,2005.0,3.4,Electric,Manual,226833.0,Used,30938.69,X3
+1904.0,Mercedes,2011.0,5.5,Diesel,Manual,38485.0,Used,91307.01,E-Class
+1905.0,Tesla,2011.0,3.6,Electric,Automatic,199304.0,Used,49130.69,Model X
+1906.0,Tesla,2020.0,4.5,Hybrid,Automatic,278245.0,New,45294.78,Model Y
+1907.0,Mercedes,2007.0,2.3,Electric,Manual,8831.0,Used,85726.54,GLC
+1908.0,Ford,2021.0,3.2,Petrol,Automatic,5004.0,New,34637.95,Explorer
+1909.0,Mercedes,2021.0,1.6,Diesel,Manual,37170.0,Like New,82659.21,GLA
+1910.0,Audi,2000.0,2.0,Petrol,Manual,43555.0,Like New,16615.92,A3
+,,,,,,,,,
+1912.0,Toyota,2020.0,1.2,Electric,Manual,178475.0,Like New,95329.96,Corolla
+,,,,,,,,,
+1914.0,BMW,2002.0,2.2,Petrol,Manual,207132.0,Used,41850.4,5 Series
+1915.0,BMW,2012.0,4.3,Petrol,Manual,63471.0,Used,65653.36,3 Series
+1916.0,Ford,2016.0,5.2,Petrol,Automatic,57287.0,Used,94582.17,Focus
+1917.0,BMW,2021.0,3.8,Hybrid,Manual,116475.0,Like New,25126.6,X3
+1918.0,Audi,2021.0,1.8,Hybrid,Manual,50151.0,Like New,10605.99,A4
+1919.0,Tesla,2009.0,4.6,Electric,Manual,67384.0,New,56059.47,Model X
+1920.0,Mercedes,2020.0,4.0,Electric,Automatic,33541.0,Used,18546.7,E-Class
+,,,,,,,,,
+1922.0,Ford,2014.0,4.8,Hybrid,Manual,218171.0,Like New,89696.22,Focus
+1923.0,Audi,2020.0,5.9,Petrol,Manual,211811.0,Like New,5703.33,Q5
+1924.0,Mercedes,2009.0,3.9,Petrol,Automatic,213739.0,New,82597.9,C-Class
+,,,,,,,,,
+1926.0,Ford,2012.0,4.2,Petrol,Automatic,99919.0,New,58132.97,Fiesta
+,,,,,,,,,
+1928.0,Toyota,2005.0,5.5,Electric,Automatic,194020.0,Used,45987.27,Corolla
+1929.0,Honda,2023.0,1.6,Hybrid,Manual,61768.0,Like New,15983.13,Fit
+1930.0,Toyota,2001.0,2.6,Diesel,Automatic,43262.0,New,14767.92,RAV4
+1931.0,Toyota,2021.0,5.1,Petrol,Automatic,20605.0,Like New,76664.41,Corolla
+1932.0,Honda,2016.0,4.0,Diesel,Manual,78590.0,Used,25998.34,CR-V
+1933.0,Ford,2003.0,3.0,Electric,Automatic,290375.0,Used,7669.66,Fiesta
+1934.0,Mercedes,2013.0,3.4,Petrol,Manual,79397.0,New,98063.32,GLC
+1935.0,Audi,2009.0,5.3,Electric,Automatic,77123.0,Used,98137.19,Q7
+,,,,,,,,,
+,,,,,,,,,
+1938.0,Tesla,2014.0,1.4,Electric,Automatic,158964.0,Like New,74090.14,Model S
+,,,,,,,,,
+1940.0,Toyota,2016.0,5.2,Electric,Manual,135501.0,New,16876.3,Camry
+1941.0,Toyota,2011.0,1.9,Electric,Manual,135678.0,New,17868.57,Corolla
+1942.0,Ford,2021.0,3.2,Diesel,Manual,114380.0,Used,83111.28,Mustang
+1943.0,BMW,2019.0,1.8,Electric,Manual,171206.0,Used,92581.2,5 Series
+1944.0,Honda,2023.0,4.5,Hybrid,Manual,156547.0,New,80121.71,Fit
+,,,,,,,,,
+,,,,,,,,,
+1947.0,Ford,2005.0,2.0,Petrol,Automatic,151083.0,Like New,62080.03,Fiesta
+1948.0,Honda,2000.0,2.1,Petrol,Manual,55746.0,Used,20227.88,Fit
+1949.0,Ford,2019.0,1.2,Petrol,Automatic,173925.0,Used,25007.24,Explorer
+1950.0,Honda,2022.0,2.6,Petrol,Automatic,298962.0,Used,31874.18,Civic
+1951.0,Honda,2015.0,3.8,Hybrid,Manual,168393.0,Like New,53403.76,CR-V
+1952.0,Mercedes,2014.0,5.3,Diesel,Manual,283453.0,Like New,53526.46,GLA
+,,,,,,,,,
+1954.0,Ford,2007.0,3.2,Petrol,Manual,68879.0,New,76535.97,Mustang
+1955.0,Ford,2011.0,5.8,Petrol,Manual,67592.0,Like New,91804.89,Focus
+1956.0,Mercedes,2021.0,4.6,Petrol,Automatic,220084.0,Used,84125.66,GLC
+1957.0,Audi,2020.0,5.7,Electric,Automatic,197494.0,Used,29118.37,A3
+1958.0,Audi,2009.0,3.6,Petrol,Automatic,270728.0,Like New,36473.19,Q5
+1959.0,Tesla,2014.0,2.3,Electric,Manual,124555.0,New,52540.21,Model 3
+1960.0,Toyota,2008.0,1.3,Diesel,Manual,18830.0,Used,6355.76,RAV4
+1961.0,Toyota,2001.0,4.6,Diesel,Automatic,216897.0,New,90870.69,Corolla
+1962.0,BMW,2004.0,1.6,Diesel,Manual,252234.0,New,9212.7,5 Series
+1963.0,Mercedes,2005.0,2.5,Hybrid,Automatic,265735.0,Like New,35631.92,GLA
+1964.0,Audi,2006.0,3.7,Electric,Manual,93878.0,Used,59071.61,A3
+,,,,,,,,,
+1966.0,Ford,2000.0,4.0,Petrol,Manual,116869.0,New,67173.69,Fiesta
+1967.0,Toyota,2022.0,1.8,Petrol,Automatic,257716.0,Like New,55114.42,Camry
+,,,,,,,,,
+,,,,,,,,,
+1970.0,BMW,2011.0,5.8,Petrol,Automatic,133703.0,New,91188.47,X3
+1971.0,Audi,2015.0,4.6,Hybrid,Manual,148977.0,New,30076.84,Q7
+1972.0,Toyota,2015.0,5.6,Electric,Manual,19056.0,Used,31692.84,Corolla
+1973.0,Ford,2010.0,4.8,Diesel,Automatic,240746.0,Like New,44230.53,Focus
+1974.0,Toyota,2020.0,4.0,Petrol,Manual,115316.0,New,18246.64,Prius
+1975.0,Honda,2003.0,2.0,Diesel,Manual,166519.0,Like New,53190.92,Fit
+1976.0,BMW,2012.0,4.3,Electric,Manual,54418.0,New,68178.02,X3
+1977.0,Honda,2006.0,4.1,Electric,Automatic,299334.0,Used,79670.05,CR-V
+,,,,,,,,,
+1979.0,Tesla,2014.0,3.4,Electric,Automatic,204071.0,New,19025.42,Model S
+1980.0,Toyota,2018.0,3.6,Diesel,Automatic,238989.0,Like New,48326.38,RAV4
+1981.0,Audi,2008.0,2.7,Petrol,Automatic,181350.0,New,8665.69,A3
+1982.0,Ford,2010.0,3.6,Hybrid,Manual,263924.0,New,6690.81,Fiesta
+1983.0,Ford,2007.0,2.0,Petrol,Manual,190330.0,New,46812.39,Mustang
+1984.0,Honda,2000.0,5.0,Diesel,Automatic,42192.0,Like New,29031.25,Accord
+1985.0,BMW,2022.0,1.9,Petrol,Manual,43923.0,New,69919.28,3 Series
+,,,,,,,,,
+1987.0,Mercedes,2007.0,3.2,Diesel,Manual,2395.0,Like New,90063.35,GLC
+1988.0,Ford,2012.0,4.3,Petrol,Manual,228456.0,Used,77264.84,Fiesta
+1989.0,Audi,2021.0,3.2,Petrol,Automatic,71028.0,Used,9583.19,A4
+1990.0,Honda,2003.0,2.4,Diesel,Automatic,242463.0,Like New,40304.19,CR-V
+1991.0,Honda,2007.0,3.8,Electric,Manual,141056.0,New,30937.22,Accord
+1992.0,Mercedes,2002.0,2.7,Diesel,Manual,203400.0,Used,41682.58,E-Class
+,,,,,,,,,
+1994.0,BMW,2001.0,5.6,Hybrid,Manual,289068.0,New,14281.89,3 Series
+1995.0,Audi,2001.0,3.5,Hybrid,Manual,294721.0,Like New,54493.53,Q7
+1996.0,BMW,2012.0,2.6,Diesel,Manual,138783.0,Like New,73436.22,X3
+1997.0,Audi,2014.0,4.7,Hybrid,Automatic,85548.0,Like New,71681.54,A4
+,,,,,,,,,
+1999.0,Toyota,2011.0,3.4,Petrol,Manual,125093.0,Used,54616.5,Prius
+2000.0,Honda,2003.0,2.7,Diesel,Manual,235835.0,Used,54051.23,Accord
+2001.0,Audi,2007.0,5.6,Petrol,Manual,206084.0,Like New,70699.19,Q5
+2002.0,Mercedes,2008.0,1.1,Electric,Manual,270398.0,Like New,9552.89,GLA
+2003.0,Audi,2022.0,3.8,Petrol,Manual,229027.0,New,72440.13,A4
+2004.0,Ford,2004.0,4.7,Diesel,Manual,208072.0,Like New,60309.09,Mustang
+2005.0,Honda,2023.0,3.4,Electric,Automatic,248029.0,Like New,23820.33,Accord
+2006.0,Ford,2019.0,1.4,Electric,Automatic,209047.0,Like New,9660.59,Mustang
+2007.0,Honda,2017.0,5.9,Hybrid,Manual,130509.0,Used,15310.14,CR-V
+2008.0,Ford,2010.0,3.6,Petrol,Automatic,237784.0,Like New,54845.68,Fiesta
+2009.0,Audi,2014.0,4.1,Diesel,Manual,295107.0,Used,77048.81,A4
+2010.0,Audi,2021.0,2.2,Hybrid,Manual,3273.0,New,36792.44,A3
+2011.0,Toyota,2008.0,3.4,Hybrid,Automatic,15330.0,New,41016.77,Prius
+,,,,,,,,,
+2013.0,Ford,2011.0,1.4,Petrol,Manual,76422.0,Used,25236.24,Focus
+2014.0,BMW,2006.0,1.5,Diesel,Manual,3212.0,Used,14856.2,5 Series
+2015.0,Toyota,2009.0,5.2,Hybrid,Automatic,232131.0,Used,37623.96,RAV4
+2016.0,Ford,2009.0,2.2,Diesel,Automatic,87594.0,Like New,75862.6,Mustang
+2017.0,Ford,2012.0,2.0,Hybrid,Manual,132152.0,Used,64477.34,Mustang
+2018.0,Toyota,2018.0,3.5,Hybrid,Manual,294819.0,Used,44500.57,Prius
+2019.0,Audi,2010.0,4.8,Hybrid,Manual,22007.0,Used,96651.49,Q5
+2020.0,Ford,2012.0,1.3,Petrol,Automatic,113805.0,Like New,74824.66,Explorer
+2021.0,Audi,2020.0,3.9,Hybrid,Manual,95815.0,Like New,10876.84,A4
+2022.0,Toyota,2007.0,1.6,Hybrid,Automatic,262783.0,Like New,75739.99,Camry
+2023.0,Audi,2007.0,1.2,Hybrid,Manual,78038.0,New,7283.66,A4
+2024.0,Honda,2003.0,1.3,Electric,Automatic,265880.0,Like New,66780.72,Accord
+2025.0,BMW,2014.0,5.2,Petrol,Automatic,253506.0,Used,24995.87,5 Series
+,,,,,,,,,
+2027.0,Ford,2010.0,1.2,Petrol,Automatic,128771.0,Like New,34429.06,Focus
+2028.0,Mercedes,2000.0,3.2,Diesel,Manual,125801.0,Used,87935.6,GLC
+2029.0,BMW,2016.0,5.2,Electric,Automatic,86623.0,Like New,37006.81,X3
+2030.0,Tesla,2014.0,2.3,Diesel,Automatic,225097.0,New,63871.11,Model S
+2031.0,Audi,2019.0,3.1,Hybrid,Automatic,298964.0,Like New,32163.12,A4
+2032.0,Ford,2005.0,1.2,Diesel,Manual,215933.0,New,92136.05,Fiesta
+,,,,,,,,,
+2034.0,Honda,2021.0,3.9,Diesel,Manual,280558.0,New,28113.77,Fit
+2035.0,Toyota,2014.0,3.9,Diesel,Manual,24220.0,Used,95135.69,RAV4
+2036.0,Ford,2008.0,2.7,Petrol,Manual,60531.0,Like New,38929.97,Focus
+2037.0,Tesla,2013.0,2.6,Electric,Manual,173497.0,New,66927.17,Model X
+2038.0,Tesla,2017.0,1.5,Petrol,Automatic,9387.0,New,75571.06,Model Y
+2039.0,Honda,2016.0,2.8,Petrol,Automatic,245752.0,Like New,7175.71,Fit
+2040.0,Honda,2017.0,1.5,Petrol,Automatic,76815.0,Used,96101.9,Fit
+2041.0,Ford,2004.0,4.8,Diesel,Manual,27584.0,Used,37721.92,Explorer
+2042.0,Audi,2019.0,5.8,Petrol,Automatic,44783.0,New,23028.99,Q7
+2043.0,Mercedes,2019.0,5.9,Electric,Automatic,170881.0,Used,85817.24,E-Class
+2044.0,Tesla,2008.0,5.3,Petrol,Manual,185399.0,Like New,71775.43,Model Y
+2045.0,Tesla,2010.0,4.0,Hybrid,Automatic,124494.0,Used,13240.61,Model S
+,,,,,,,,,
+2047.0,BMW,2021.0,2.4,Hybrid,Automatic,250139.0,New,72943.23,X3
+2048.0,Honda,2000.0,4.2,Electric,Manual,141929.0,Like New,12104.41,CR-V
+2049.0,Mercedes,2017.0,3.3,Electric,Manual,173333.0,New,92131.67,E-Class
+2050.0,Audi,2005.0,3.4,Electric,Manual,246509.0,Like New,75446.31,Q5
+2051.0,Audi,2001.0,4.8,Electric,Automatic,13580.0,Like New,36871.76,A3
+2052.0,BMW,2014.0,2.4,Petrol,Automatic,18240.0,Used,39479.18,X3
+2053.0,Tesla,2020.0,2.6,Diesel,Manual,254351.0,Like New,53117.13,Model 3
+2054.0,Honda,2013.0,5.6,Electric,Manual,254271.0,New,39292.09,Civic
+,,,,,,,,,
+2056.0,Mercedes,2005.0,3.4,Electric,Automatic,73401.0,New,97600.01,C-Class
+2057.0,Ford,2005.0,5.6,Diesel,Manual,213165.0,New,60529.54,Explorer
+2058.0,Toyota,2021.0,4.1,Hybrid,Automatic,164340.0,Like New,47373.55,RAV4
+2059.0,Toyota,2017.0,3.7,Hybrid,Automatic,265790.0,Used,26163.54,Prius
+2060.0,Toyota,2013.0,2.2,Hybrid,Automatic,186443.0,Like New,67076.17,RAV4
+2061.0,Tesla,2009.0,2.7,Petrol,Manual,248123.0,Like New,70129.8,Model X
+2062.0,Honda,2013.0,5.3,Electric,Manual,89460.0,Used,70093.11,Fit
+2063.0,BMW,2012.0,3.8,Petrol,Automatic,51048.0,New,7563.58,5 Series
+2064.0,Audi,2002.0,3.6,Hybrid,Manual,206518.0,Like New,90416.6,A3
+2065.0,Ford,2005.0,1.5,Petrol,Automatic,132850.0,Used,37120.12,Focus
+2066.0,Mercedes,2007.0,1.1,Petrol,Automatic,211912.0,Used,75695.75,GLA
+2067.0,Ford,2011.0,5.8,Petrol,Manual,266891.0,New,77181.66,Focus
+2068.0,Mercedes,2012.0,2.9,Electric,Manual,45.0,Used,66802.72,E-Class
+2069.0,Tesla,2010.0,4.0,Hybrid,Manual,7758.0,New,42512.32,Model S
+2070.0,Honda,2011.0,1.4,Hybrid,Automatic,255397.0,Like New,62796.5,Fit
+2071.0,Toyota,2000.0,4.4,Electric,Automatic,154509.0,Used,8818.88,Corolla
+2072.0,Honda,2016.0,5.7,Petrol,Manual,150657.0,Like New,77380.1,Fit
+,,,,,,,,,
+2074.0,BMW,2010.0,1.6,Diesel,Manual,183148.0,Used,42691.34,5 Series
+2075.0,BMW,2012.0,3.9,Diesel,Automatic,148421.0,Like New,14843.78,3 Series
+2076.0,Tesla,2017.0,4.8,Diesel,Automatic,227300.0,Used,84522.07,Model 3
+2077.0,Mercedes,2010.0,3.0,Electric,Automatic,152420.0,Like New,57278.73,E-Class
+2078.0,Audi,2011.0,5.4,Hybrid,Manual,165368.0,Like New,70640.74,A4
+2079.0,Audi,2005.0,4.2,Diesel,Manual,19455.0,New,69845.31,Q5
+2080.0,Mercedes,2008.0,5.5,Hybrid,Automatic,209896.0,Like New,84952.59,C-Class
+2081.0,BMW,2015.0,4.1,Hybrid,Manual,231897.0,New,17594.09,3 Series
+2082.0,BMW,2003.0,1.7,Hybrid,Manual,88552.0,Like New,67674.17,X5
+2083.0,BMW,2002.0,2.5,Electric,Manual,40484.0,Used,59637.74,5 Series
+2084.0,Honda,2015.0,2.0,Electric,Manual,134476.0,Like New,7935.23,Accord
+2085.0,BMW,2023.0,5.5,Electric,Automatic,168332.0,Used,24135.18,3 Series
+2086.0,Ford,2011.0,4.8,Electric,Manual,100647.0,New,30419.3,Explorer
+2087.0,Toyota,2010.0,4.4,Diesel,Automatic,65642.0,Used,11892.86,RAV4
+2088.0,BMW,2004.0,2.5,Petrol,Automatic,211803.0,Like New,38698.38,X5
+2089.0,Mercedes,2017.0,1.9,Petrol,Manual,128824.0,Used,7584.24,GLA
+2090.0,Toyota,2018.0,4.8,Petrol,Automatic,248914.0,New,57552.43,Camry
+,,,,,,,,,
+2092.0,Mercedes,2022.0,2.1,Petrol,Manual,110879.0,Used,32265.3,E-Class
+2093.0,Toyota,2005.0,4.1,Diesel,Automatic,120827.0,Like New,91494.8,Camry
+2094.0,Mercedes,2021.0,1.2,Diesel,Automatic,267724.0,Like New,52767.6,E-Class
+2095.0,Honda,2017.0,2.6,Diesel,Automatic,267544.0,Used,30556.32,CR-V
+,,,,,,,,,
+,,,,,,,,,
+2098.0,Honda,2011.0,5.9,Petrol,Automatic,127319.0,New,12067.59,Civic
+2099.0,Mercedes,2018.0,2.0,Petrol,Automatic,106960.0,New,6986.1,GLA
+2100.0,Tesla,2001.0,1.7,Electric,Automatic,39015.0,New,95395.26,Model 3
+2101.0,Audi,2016.0,2.0,Diesel,Manual,203877.0,Used,73838.03,Q5
+2102.0,BMW,2010.0,4.8,Hybrid,Manual,145109.0,New,80995.07,X5
+2103.0,BMW,2010.0,3.1,Electric,Manual,262063.0,Used,98187.18,X3
+2104.0,Ford,2020.0,3.8,Hybrid,Automatic,88754.0,New,93504.73,Explorer
+2105.0,Ford,2015.0,1.1,Hybrid,Automatic,221775.0,New,75006.75,Explorer
+2106.0,BMW,2001.0,1.2,Diesel,Automatic,203225.0,Used,70928.77,5 Series
+2107.0,Audi,2001.0,3.2,Petrol,Manual,152606.0,Like New,37000.75,Q7
+2108.0,BMW,2022.0,5.1,Diesel,Manual,222067.0,Like New,14881.64,X3
+2109.0,Tesla,2007.0,5.4,Diesel,Manual,56879.0,Used,59503.64,Model Y
+2110.0,Audi,2012.0,1.4,Electric,Automatic,119894.0,Used,59744.06,Q5
+2111.0,Ford,2009.0,3.7,Diesel,Manual,65839.0,Like New,95462.54,Explorer
+2112.0,Toyota,2007.0,5.6,Petrol,Manual,29756.0,Like New,47044.23,Prius
+2113.0,Mercedes,2003.0,2.9,Hybrid,Automatic,143755.0,Like New,70626.72,C-Class
+2114.0,Tesla,2014.0,5.1,Diesel,Manual,180557.0,Used,59622.21,Model X
+,,,,,,,,,
+2116.0,Ford,2010.0,5.7,Diesel,Manual,149787.0,Used,61246.11,Explorer
+2117.0,Ford,2006.0,4.8,Hybrid,Automatic,92943.0,Like New,56311.09,Focus
+2118.0,Honda,2016.0,5.7,Diesel,Automatic,218188.0,New,51921.07,CR-V
+2119.0,Tesla,2019.0,4.4,Electric,Automatic,148188.0,Used,53775.18,Model X
+2120.0,Honda,2023.0,2.8,Hybrid,Automatic,36744.0,Like New,20578.86,Accord
+2121.0,Honda,2017.0,5.7,Diesel,Automatic,124697.0,Used,49030.38,Civic
+2122.0,Mercedes,2021.0,2.4,Diesel,Manual,116072.0,New,62537.06,C-Class
+,,,,,,,,,
+2124.0,Audi,2011.0,4.5,Electric,Automatic,169123.0,Used,51425.19,Q5
+2125.0,BMW,2008.0,4.8,Electric,Automatic,7176.0,New,12364.18,X3
+2126.0,Toyota,2004.0,5.6,Hybrid,Automatic,252365.0,New,23123.54,RAV4
+2127.0,Ford,2009.0,2.0,Hybrid,Manual,149748.0,Used,35492.41,Fiesta
+2128.0,Audi,2003.0,5.0,Electric,Automatic,45177.0,New,44837.96,A4
+2129.0,Toyota,2004.0,4.7,Petrol,Manual,110623.0,Like New,61424.06,Corolla
+,,,,,,,,,
+2131.0,Tesla,2010.0,2.9,Diesel,Automatic,120179.0,Used,20120.65,Model Y
+2132.0,Tesla,2020.0,2.4,Petrol,Manual,247439.0,Like New,6433.36,Model X
+2133.0,Audi,2023.0,2.5,Diesel,Manual,120403.0,New,47677.53,A4
+2134.0,Audi,2009.0,5.9,Hybrid,Manual,95287.0,New,46049.78,A4
+2135.0,Ford,2007.0,4.2,Hybrid,Automatic,119574.0,Like New,57430.62,Fiesta
+2136.0,BMW,2001.0,4.6,Petrol,Manual,256463.0,Like New,84156.07,X5
+2137.0,Honda,2022.0,5.9,Hybrid,Automatic,121509.0,New,81224.25,CR-V
+2138.0,Tesla,2007.0,2.9,Electric,Manual,248505.0,Like New,14504.11,Model Y
+2139.0,Mercedes,2015.0,5.0,Electric,Manual,192589.0,Used,93729.56,E-Class
+2140.0,BMW,2021.0,3.2,Petrol,Automatic,289531.0,New,15489.37,5 Series
+2141.0,Mercedes,2004.0,5.3,Petrol,Manual,4090.0,Used,65284.2,GLA
+2142.0,Ford,2002.0,1.9,Diesel,Manual,3743.0,New,68878.6,Mustang
+2143.0,Ford,2011.0,5.7,Hybrid,Automatic,30924.0,Used,69588.36,Fiesta
+2144.0,Ford,2012.0,2.1,Diesel,Automatic,141268.0,Used,30208.36,Fiesta
+2145.0,Toyota,2012.0,2.6,Diesel,Automatic,105312.0,Like New,36382.78,RAV4
+2146.0,Mercedes,2009.0,4.8,Electric,Manual,216249.0,New,57124.3,E-Class
+2147.0,BMW,2003.0,3.0,Electric,Manual,224090.0,Like New,23960.81,5 Series
+2148.0,BMW,2008.0,4.0,Electric,Manual,229602.0,Like New,6929.49,X3
+2149.0,Audi,2021.0,3.2,Electric,Manual,103976.0,New,58403.72,A4
+,,,,,,,,,
+2151.0,Tesla,2022.0,2.1,Electric,Automatic,113566.0,New,67389.46,Model X
+2152.0,Audi,2001.0,1.3,Hybrid,Manual,257443.0,New,18936.55,A3
+2153.0,Ford,2018.0,2.5,Electric,Automatic,119968.0,New,84887.86,Focus
+2154.0,BMW,2000.0,3.4,Electric,Manual,61600.0,Like New,18891.04,X5
+2155.0,Mercedes,2006.0,1.8,Electric,Automatic,50022.0,New,89677.64,GLA
+2156.0,Ford,2016.0,2.8,Petrol,Manual,134271.0,New,47704.51,Focus
+2157.0,Toyota,2002.0,3.0,Hybrid,Manual,123323.0,New,57231.5,Prius
+2158.0,Audi,2019.0,1.3,Hybrid,Automatic,186110.0,Used,90175.23,A3
+2159.0,Ford,2019.0,3.9,Diesel,Manual,120278.0,Used,33491.74,Explorer
+2160.0,Ford,2020.0,5.4,Diesel,Automatic,270041.0,Used,22273.56,Explorer
+2161.0,Toyota,2002.0,1.8,Electric,Manual,64541.0,Used,47346.4,Corolla
+2162.0,Honda,2002.0,4.0,Petrol,Manual,106968.0,Like New,28214.89,CR-V
+2163.0,BMW,2016.0,4.3,Diesel,Automatic,14892.0,Like New,55145.17,5 Series
+2164.0,Mercedes,2006.0,3.1,Diesel,Manual,24815.0,New,31149.5,GLC
+2165.0,Tesla,2013.0,4.5,Electric,Automatic,278411.0,Like New,87126.51,Model X
+,,,,,,,,,
+2167.0,Audi,2017.0,3.5,Diesel,Manual,114463.0,New,5022.86,A3
+2168.0,Ford,2012.0,1.0,Diesel,Manual,190732.0,New,81520.0,Fiesta
+2169.0,Toyota,2019.0,4.5,Hybrid,Manual,196599.0,Used,36370.17,Prius
+2170.0,Toyota,2019.0,4.3,Petrol,Manual,194451.0,Used,35419.76,Camry
+2171.0,BMW,2020.0,1.2,Hybrid,Manual,172343.0,Used,41390.97,5 Series
+2172.0,Honda,2017.0,2.8,Diesel,Manual,138307.0,Like New,64874.94,Fit
+2173.0,Tesla,2003.0,5.9,Hybrid,Automatic,88757.0,New,46995.77,Model S
+2174.0,BMW,2000.0,3.1,Hybrid,Manual,296753.0,Used,64720.38,3 Series
+2175.0,Ford,2017.0,3.5,Diesel,Manual,254200.0,New,18944.17,Fiesta
+2176.0,BMW,2000.0,5.5,Electric,Manual,23645.0,New,97165.62,5 Series
+2177.0,Mercedes,2012.0,4.6,Diesel,Automatic,195630.0,Like New,74312.6,GLC
+,,,,,,,,,
+2179.0,Toyota,2019.0,5.0,Diesel,Manual,269915.0,Used,73796.14,Camry
+2180.0,Audi,2022.0,5.3,Diesel,Manual,151460.0,Used,59627.42,Q5
+,,,,,,,,,
+2182.0,Tesla,2010.0,3.4,Petrol,Automatic,57042.0,New,21928.66,Model 3
+2183.0,Ford,2008.0,2.9,Electric,Manual,196945.0,Like New,8924.27,Fiesta
+2184.0,Mercedes,2008.0,5.8,Petrol,Automatic,47024.0,Used,81877.62,GLC
+,,,,,,,,,
+2186.0,BMW,2000.0,1.4,Hybrid,Automatic,56965.0,New,35091.12,X3
+2187.0,Tesla,2016.0,5.3,Petrol,Automatic,272201.0,New,17002.77,Model X
+2188.0,Ford,2021.0,3.4,Electric,Manual,224321.0,New,50984.36,Fiesta
+2189.0,Audi,2001.0,1.3,Diesel,Manual,271127.0,Used,54899.83,A4
+2190.0,BMW,2023.0,3.7,Petrol,Manual,19995.0,New,71685.11,3 Series
+2191.0,Audi,2012.0,3.1,Hybrid,Manual,114587.0,Like New,97144.03,Q5
+2192.0,BMW,2008.0,5.4,Petrol,Manual,233076.0,Like New,46319.83,X5
+2193.0,Honda,2003.0,2.0,Electric,Manual,222700.0,New,43573.25,Accord
+2194.0,Ford,2004.0,5.1,Petrol,Manual,38886.0,Like New,40143.35,Focus
+2195.0,BMW,2010.0,3.8,Hybrid,Automatic,95149.0,Like New,83557.77,X3
+2196.0,Mercedes,2023.0,1.3,Hybrid,Manual,94020.0,Like New,19850.14,C-Class
+2197.0,Tesla,2018.0,5.9,Electric,Manual,2673.0,Like New,37662.23,Model Y
+2198.0,Toyota,2020.0,3.6,Hybrid,Automatic,23466.0,Like New,73872.23,Corolla
+2199.0,Mercedes,2002.0,2.5,Diesel,Manual,237626.0,Used,6491.02,GLA
+2200.0,Mercedes,2011.0,3.3,Hybrid,Manual,50406.0,New,32491.17,C-Class
+2201.0,Tesla,2018.0,5.3,Diesel,Manual,69284.0,Like New,65937.48,Model X
+2202.0,Toyota,2018.0,2.5,Petrol,Manual,235718.0,New,22665.45,Corolla
+2203.0,Mercedes,2012.0,2.0,Diesel,Automatic,13665.0,Like New,33757.5,GLC
+2204.0,BMW,2006.0,1.6,Diesel,Automatic,20641.0,Used,51174.28,X3
+2205.0,BMW,2014.0,2.5,Petrol,Manual,35797.0,Used,45092.24,X5
+2206.0,BMW,2022.0,4.9,Hybrid,Manual,215838.0,Like New,15072.58,5 Series
+2207.0,Toyota,2006.0,3.4,Diesel,Automatic,102339.0,Like New,94132.36,RAV4
+2208.0,Mercedes,2007.0,2.1,Hybrid,Manual,113431.0,Like New,9207.81,C-Class
+2209.0,Toyota,2004.0,1.8,Petrol,Automatic,232275.0,Used,76225.56,Prius
+2210.0,BMW,2020.0,2.7,Electric,Automatic,283317.0,Used,11785.16,X3
+2211.0,Tesla,2013.0,5.7,Hybrid,Automatic,144294.0,New,49393.1,Model X
+2212.0,Mercedes,2003.0,2.7,Hybrid,Manual,181136.0,Used,21664.07,C-Class
+2213.0,Mercedes,2022.0,5.9,Electric,Manual,168082.0,Like New,18404.29,GLA
+2214.0,Audi,2009.0,3.4,Electric,Manual,253301.0,New,16960.31,Q5
+2215.0,Audi,2019.0,1.9,Petrol,Automatic,18095.0,Used,48014.73,A3
+2216.0,Honda,2014.0,5.5,Hybrid,Manual,123554.0,Used,53971.07,Accord
+2217.0,Ford,2017.0,3.3,Hybrid,Manual,234989.0,Like New,17671.53,Explorer
+,,,,,,,,,
+2219.0,Toyota,2012.0,5.9,Hybrid,Manual,107181.0,Used,29487.76,Corolla
+2220.0,Audi,2007.0,1.3,Diesel,Manual,222310.0,Used,48589.93,A3
+2221.0,Honda,2008.0,1.5,Diesel,Manual,282744.0,Like New,25545.61,CR-V
+2222.0,Mercedes,2015.0,4.6,Diesel,Automatic,264610.0,New,51922.65,GLA
+2223.0,Honda,2018.0,2.4,Hybrid,Manual,256349.0,New,76821.58,Civic
+2224.0,Mercedes,2015.0,1.9,Diesel,Automatic,176169.0,New,54504.69,E-Class
+2225.0,Tesla,2006.0,1.2,Diesel,Automatic,53276.0,New,32136.64,Model X
+2226.0,Tesla,2013.0,1.1,Petrol,Automatic,269072.0,Like New,28597.68,Model 3
+2227.0,Mercedes,2013.0,4.9,Petrol,Manual,205719.0,Used,77098.77,C-Class
+2228.0,Ford,2003.0,1.7,Electric,Manual,171960.0,Used,16232.63,Explorer
+2229.0,Audi,2014.0,3.3,Electric,Manual,226097.0,New,40146.82,Q7
+2230.0,Toyota,2002.0,4.0,Electric,Automatic,174904.0,New,19390.86,Prius
+2231.0,Toyota,2004.0,3.1,Electric,Automatic,116196.0,New,29507.7,Camry
+2232.0,BMW,2000.0,1.1,Petrol,Manual,9404.0,Used,19504.91,X5
+2233.0,Mercedes,2002.0,4.9,Petrol,Automatic,209190.0,Used,89243.39,E-Class
+2234.0,Toyota,2014.0,1.1,Petrol,Manual,113187.0,New,74530.9,Prius
+2235.0,Ford,2023.0,4.8,Diesel,Manual,27684.0,Like New,58288.09,Fiesta
+2236.0,Mercedes,2007.0,5.5,Diesel,Manual,68591.0,Used,26320.4,GLA
+2237.0,Ford,2003.0,3.2,Electric,Manual,208794.0,Used,53471.55,Fiesta
+2238.0,Toyota,2001.0,3.7,Diesel,Manual,264826.0,New,54479.15,RAV4
+2239.0,Toyota,2023.0,3.4,Hybrid,Manual,111186.0,Like New,61088.35,Corolla
+2240.0,BMW,2023.0,2.9,Petrol,Manual,243181.0,New,10708.09,3 Series
+2241.0,Toyota,2009.0,5.4,Electric,Automatic,21832.0,New,97425.69,Prius
+2242.0,Mercedes,2003.0,5.5,Electric,Manual,16997.0,Like New,49216.7,GLC
+2243.0,Tesla,2002.0,2.7,Hybrid,Automatic,296784.0,Like New,93020.52,Model Y
+2244.0,Mercedes,2001.0,4.1,Diesel,Automatic,40491.0,Like New,5931.77,E-Class
+2245.0,Mercedes,2016.0,5.6,Electric,Manual,63043.0,Used,28518.05,E-Class
+2246.0,BMW,2022.0,5.8,Diesel,Automatic,128568.0,New,14879.58,3 Series
+2247.0,BMW,2020.0,3.5,Diesel,Automatic,72725.0,Used,78113.54,3 Series
+2248.0,Toyota,2023.0,3.3,Hybrid,Automatic,128131.0,Like New,35411.88,RAV4
+2249.0,Honda,2019.0,2.4,Electric,Manual,45520.0,Used,84846.79,Fit
+,,,,,,,,,
+2251.0,BMW,2012.0,1.7,Diesel,Manual,64442.0,Like New,68440.51,5 Series
+2252.0,Toyota,2004.0,3.9,Hybrid,Manual,261025.0,New,68249.27,Camry
+2253.0,Honda,2020.0,2.4,Hybrid,Manual,152842.0,New,71515.17,CR-V
+2254.0,Toyota,2008.0,2.4,Diesel,Automatic,178570.0,New,69007.56,Corolla
+2255.0,Toyota,2021.0,3.0,Hybrid,Automatic,123447.0,Used,41756.29,RAV4
+2256.0,Toyota,2005.0,6.0,Diesel,Manual,61477.0,New,69868.63,Corolla
+,,,,,,,,,
+2258.0,Mercedes,2009.0,3.9,Petrol,Automatic,177561.0,New,36594.48,GLA
+2259.0,Toyota,2007.0,1.4,Hybrid,Automatic,290534.0,Used,94764.46,Camry
+2260.0,Audi,2006.0,1.8,Petrol,Automatic,10247.0,New,6290.25,Q7
+2261.0,BMW,2006.0,3.2,Diesel,Manual,200123.0,New,38162.84,3 Series
+2262.0,Honda,2005.0,3.6,Diesel,Manual,190791.0,New,17549.35,Accord
+2263.0,Mercedes,2020.0,2.3,Diesel,Manual,207358.0,Used,17028.88,GLA
+2264.0,Tesla,2019.0,2.6,Petrol,Automatic,291727.0,Like New,82465.8,Model S
+2265.0,Honda,2002.0,1.4,Diesel,Automatic,139148.0,Like New,92244.99,Accord
+2266.0,Mercedes,2018.0,2.5,Hybrid,Manual,146123.0,Like New,78887.03,E-Class
+2267.0,Tesla,2021.0,5.8,Electric,Manual,28670.0,New,97778.49,Model Y
+2268.0,Honda,2014.0,5.7,Electric,Automatic,10236.0,New,25551.25,Civic
+2269.0,Mercedes,2003.0,2.7,Hybrid,Manual,35568.0,Used,18375.99,E-Class
+2270.0,Honda,2011.0,3.9,Electric,Manual,107327.0,Like New,54466.86,Accord
+2271.0,Honda,2005.0,5.5,Petrol,Automatic,255745.0,New,83758.57,Fit
+,,,,,,,,,
+2273.0,Honda,2003.0,5.1,Diesel,Manual,125721.0,New,99578.74,CR-V
+2274.0,Honda,2014.0,5.7,Diesel,Automatic,259291.0,Used,62213.36,CR-V
+,,,,,,,,,
+2276.0,BMW,2004.0,1.8,Petrol,Automatic,55904.0,Used,39682.76,X3
+2277.0,Toyota,2012.0,5.8,Petrol,Automatic,278252.0,Like New,48512.26,Camry
+2278.0,Ford,2022.0,3.7,Hybrid,Manual,264997.0,Like New,76474.34,Fiesta
+2279.0,Audi,2017.0,1.1,Petrol,Automatic,266320.0,New,80306.54,A4
+2280.0,Ford,2004.0,2.3,Electric,Manual,100459.0,New,27138.14,Explorer
+2281.0,Ford,2015.0,3.2,Hybrid,Automatic,185922.0,New,93331.1,Mustang
+2282.0,Mercedes,2018.0,4.5,Hybrid,Manual,137901.0,New,87405.2,E-Class
+2283.0,Toyota,2013.0,4.7,Hybrid,Automatic,123225.0,New,6729.5,RAV4
+2284.0,Mercedes,2015.0,6.0,Petrol,Automatic,235847.0,Like New,17213.97,GLA
+2285.0,Toyota,2014.0,2.4,Hybrid,Manual,221738.0,New,52129.23,Corolla
+2286.0,Tesla,2021.0,3.7,Petrol,Automatic,110893.0,New,48790.77,Model S
+2287.0,Tesla,2003.0,3.7,Petrol,Manual,242726.0,Used,72297.13,Model X
+,,,,,,,,,
+2289.0,Honda,2015.0,1.8,Diesel,Automatic,181880.0,Like New,66261.25,Civic
+2290.0,BMW,2023.0,5.9,Diesel,Manual,6611.0,Like New,63555.14,3 Series
+2291.0,Honda,2021.0,5.3,Hybrid,Automatic,247524.0,Used,27193.86,CR-V
+2292.0,BMW,2020.0,3.5,Diesel,Automatic,295187.0,Used,56527.43,X3
+2293.0,Mercedes,2021.0,4.0,Electric,Manual,136343.0,New,26109.68,E-Class
+2294.0,BMW,2020.0,2.1,Electric,Automatic,190191.0,New,18957.69,X3
+2295.0,Mercedes,2007.0,3.7,Petrol,Manual,142645.0,Like New,59447.82,GLA
+,,,,,,,,,
+2297.0,Ford,2015.0,3.6,Electric,Manual,286608.0,Like New,27508.67,Mustang
+2298.0,Tesla,2022.0,6.0,Hybrid,Automatic,249220.0,New,38756.93,Model 3
+2299.0,Tesla,2009.0,4.1,Diesel,Automatic,118523.0,Like New,29374.46,Model 3
+2300.0,Ford,2009.0,2.9,Electric,Manual,20662.0,Like New,30313.68,Explorer
+2301.0,BMW,2001.0,4.9,Electric,Automatic,280372.0,Like New,43398.25,X3
+2302.0,BMW,2008.0,1.9,Hybrid,Manual,60721.0,New,71676.27,3 Series
+2303.0,Mercedes,2016.0,2.9,Petrol,Automatic,138458.0,Like New,8686.43,GLC
+2304.0,Honda,2010.0,2.7,Hybrid,Manual,269006.0,New,95926.8,Fit
+2305.0,Mercedes,2009.0,3.2,Petrol,Manual,40108.0,Used,9396.59,C-Class
+,,,,,,,,,
+2307.0,Tesla,2016.0,3.4,Petrol,Manual,244437.0,Like New,89965.92,Model Y
+2308.0,Ford,2003.0,5.0,Electric,Manual,237011.0,New,54826.47,Focus
+2309.0,Tesla,2015.0,3.9,Electric,Automatic,158273.0,Like New,18014.7,Model S
+2310.0,Toyota,2019.0,2.7,Diesel,Manual,49966.0,New,58688.87,Camry
+,,,,,,,,,
+2312.0,Honda,2020.0,2.2,Petrol,Automatic,272530.0,Used,86487.1,Accord
+2313.0,Mercedes,2004.0,2.1,Petrol,Manual,77228.0,Used,57335.06,C-Class
+2314.0,Ford,2005.0,3.5,Hybrid,Manual,146297.0,Used,44017.39,Focus
+2315.0,Honda,2016.0,2.3,Diesel,Manual,43021.0,Like New,84637.66,Fit
+2316.0,Mercedes,2000.0,1.0,Diesel,Manual,149333.0,Like New,98190.42,E-Class
+2317.0,BMW,2018.0,4.2,Diesel,Manual,252646.0,Used,66284.8,X5
+2318.0,Tesla,2003.0,3.6,Hybrid,Manual,86755.0,Used,88066.77,Model Y
+2319.0,BMW,2016.0,3.2,Petrol,Manual,66330.0,New,61879.84,3 Series
+2320.0,Audi,2008.0,5.8,Petrol,Manual,178438.0,New,70529.86,A3
+2321.0,Toyota,2023.0,3.9,Petrol,Automatic,45313.0,Used,70375.88,Prius
+,,,,,,,,,
+2323.0,Ford,2007.0,1.7,Diesel,Manual,232783.0,Like New,35496.28,Focus
+2324.0,Honda,2014.0,3.8,Electric,Automatic,289176.0,Used,59687.83,Civic
+2325.0,Ford,2006.0,5.3,Hybrid,Manual,148430.0,New,61442.88,Focus
+2326.0,Tesla,2005.0,3.9,Hybrid,Manual,28365.0,New,34931.8,Model Y
+2327.0,Mercedes,2000.0,4.6,Diesel,Manual,275333.0,New,38235.97,GLC
+2328.0,BMW,2014.0,2.9,Electric,Manual,7248.0,New,77413.44,3 Series
+2329.0,Audi,2022.0,1.5,Petrol,Manual,4369.0,New,60358.76,Q7
+2330.0,Audi,2006.0,3.3,Diesel,Manual,290749.0,New,19175.06,A3
+2331.0,Tesla,2016.0,3.0,Hybrid,Automatic,171955.0,Used,84575.51,Model X
+2332.0,Audi,2001.0,5.3,Hybrid,Automatic,33055.0,New,88895.86,Q5
+2333.0,Tesla,2008.0,2.2,Hybrid,Automatic,199803.0,New,49283.41,Model 3
+2334.0,Ford,2008.0,5.8,Petrol,Automatic,269594.0,New,24846.21,Focus
+2335.0,BMW,2012.0,5.8,Hybrid,Automatic,133997.0,New,95022.62,5 Series
+2336.0,Honda,2005.0,2.2,Petrol,Manual,50272.0,Used,88815.61,Civic
+2337.0,Mercedes,2005.0,2.7,Petrol,Manual,5310.0,Like New,28216.27,GLA
+2338.0,Honda,2019.0,5.5,Petrol,Manual,31817.0,Like New,14081.4,Fit
+2339.0,Ford,2019.0,5.8,Diesel,Manual,215888.0,Like New,46862.09,Mustang
+,,,,,,,,,
+2341.0,Audi,2002.0,2.6,Electric,Automatic,215155.0,New,21661.92,Q7
+2342.0,Toyota,2016.0,3.6,Hybrid,Automatic,207382.0,Like New,53755.2,Corolla
+2343.0,Ford,2016.0,4.4,Diesel,Manual,15457.0,Like New,45414.47,Explorer
+2344.0,Ford,2011.0,2.4,Hybrid,Manual,42311.0,Used,89602.36,Mustang
+2345.0,BMW,2015.0,4.7,Petrol,Manual,151958.0,Used,45808.83,X5
+2346.0,Honda,2014.0,1.3,Diesel,Automatic,123573.0,Used,55708.76,Accord
+,,,,,,,,,
+2348.0,Ford,2022.0,2.9,Petrol,Manual,164334.0,New,33470.72,Focus
+2349.0,Audi,2000.0,2.8,Hybrid,Automatic,263272.0,New,32454.14,A3
+2350.0,Audi,2016.0,2.6,Electric,Manual,79522.0,Used,80961.53,A3
+2351.0,Mercedes,2002.0,3.5,Petrol,Automatic,163394.0,Like New,31366.2,E-Class
+2352.0,Tesla,2014.0,2.6,Electric,Automatic,48929.0,Used,84027.26,Model 3
+2353.0,Tesla,2003.0,2.0,Petrol,Manual,81696.0,New,14075.88,Model 3
+2354.0,Toyota,2003.0,2.5,Diesel,Automatic,271952.0,Used,15569.82,Corolla
+2355.0,Tesla,2017.0,5.9,Petrol,Automatic,95148.0,Like New,90625.72,Model X
+2356.0,BMW,2016.0,3.1,Diesel,Manual,283869.0,New,56377.17,5 Series
+2357.0,Toyota,2018.0,3.2,Petrol,Automatic,220026.0,Like New,73287.36,Camry
+2358.0,Mercedes,2010.0,3.9,Petrol,Manual,83576.0,Like New,26402.44,C-Class
+2359.0,Audi,2019.0,5.9,Hybrid,Automatic,20557.0,New,20796.06,A4
+,,,,,,,,,
+2361.0,Honda,2007.0,4.4,Electric,Manual,271142.0,Used,39069.22,Fit
+2362.0,Audi,2013.0,4.6,Electric,Automatic,36080.0,Like New,48228.1,A3
+2363.0,BMW,2000.0,4.0,Petrol,Automatic,258815.0,Like New,7657.33,X5
+2364.0,Audi,2013.0,1.4,Petrol,Manual,188869.0,Like New,41658.67,Q5
+2365.0,BMW,2001.0,4.4,Petrol,Automatic,146874.0,Used,78191.59,5 Series
+2366.0,BMW,2022.0,1.3,Diesel,Automatic,42407.0,Like New,83021.14,3 Series
+2367.0,BMW,2003.0,4.0,Diesel,Automatic,120283.0,Like New,71381.7,X5
+2368.0,Mercedes,2023.0,4.7,Hybrid,Manual,13338.0,Used,70757.07,GLC
+,,,,,,,,,
+2370.0,BMW,2005.0,5.2,Electric,Manual,149967.0,New,56141.29,X3
+2371.0,Honda,2000.0,1.2,Electric,Automatic,178982.0,New,75550.02,CR-V
+2372.0,Toyota,2016.0,5.2,Hybrid,Automatic,109367.0,Like New,28493.67,Camry
+2373.0,Ford,2007.0,3.3,Electric,Manual,117486.0,New,26994.12,Fiesta
+2374.0,Toyota,2018.0,2.1,Hybrid,Manual,214209.0,Used,95242.86,Corolla
+2375.0,BMW,2020.0,1.7,Petrol,Automatic,256055.0,Like New,7630.54,3 Series
+2376.0,Honda,2000.0,5.8,Electric,Automatic,136421.0,Like New,18249.22,Fit
+2377.0,Audi,2004.0,1.4,Petrol,Automatic,153535.0,Like New,59181.58,Q5
+2378.0,Honda,2015.0,2.0,Diesel,Automatic,74935.0,New,75094.31,Fit
+2379.0,BMW,2005.0,2.7,Electric,Manual,189864.0,New,56099.65,X3
+2380.0,Honda,2007.0,4.1,Diesel,Automatic,275830.0,New,38731.08,Civic
+2381.0,Tesla,2012.0,3.7,Electric,Automatic,63528.0,Like New,97907.73,Model S
+2382.0,Honda,2015.0,1.6,Petrol,Manual,99811.0,Used,71866.01,Fit
+2383.0,BMW,2002.0,2.4,Hybrid,Manual,284931.0,Like New,83982.13,3 Series
+2384.0,Mercedes,2018.0,2.2,Petrol,Automatic,188499.0,Used,8823.45,E-Class
+2385.0,Tesla,2011.0,2.1,Petrol,Manual,260141.0,Like New,27412.41,Model S
+2386.0,Honda,2015.0,3.1,Petrol,Automatic,104887.0,Used,40546.25,Accord
+2387.0,Ford,2010.0,4.5,Electric,Automatic,61086.0,Like New,22347.13,Mustang
+2388.0,Ford,2009.0,2.1,Hybrid,Manual,255494.0,Like New,11925.43,Focus
+2389.0,Honda,2019.0,2.8,Diesel,Manual,63308.0,Used,26383.71,CR-V
+2390.0,Mercedes,2003.0,3.0,Petrol,Automatic,277941.0,Used,55695.38,E-Class
+2391.0,Audi,2011.0,5.9,Electric,Manual,267446.0,Used,71095.8,Q7
+2392.0,BMW,2022.0,5.8,Diesel,Automatic,38059.0,Used,78531.99,X3
+2393.0,Ford,2001.0,5.0,Petrol,Automatic,131980.0,Used,9078.93,Explorer
+2394.0,Audi,2023.0,2.2,Diesel,Automatic,191324.0,Used,45658.69,Q7
+,,,,,,,,,
+2396.0,Audi,2018.0,2.6,Diesel,Automatic,276383.0,Used,45964.04,Q7
+2397.0,Tesla,2004.0,2.3,Diesel,Automatic,27313.0,Like New,46227.71,Model Y
+2398.0,Toyota,2007.0,3.5,Hybrid,Automatic,42103.0,Used,52330.11,RAV4
+2399.0,Ford,2006.0,4.9,Hybrid,Manual,179480.0,Used,89818.91,Focus
+,,,,,,,,,
+2401.0,BMW,2023.0,5.2,Electric,Automatic,126228.0,Like New,65488.48,X3
+2402.0,Mercedes,2022.0,2.8,Hybrid,Automatic,91665.0,New,89113.7,C-Class
+2403.0,Tesla,2002.0,4.4,Diesel,Manual,272678.0,New,69454.52,Model X
+2404.0,Honda,2017.0,2.6,Hybrid,Automatic,279333.0,Used,82791.22,CR-V
+,,,,,,,,,
+2406.0,Mercedes,2022.0,4.5,Petrol,Manual,273111.0,New,54895.73,C-Class
+2407.0,Mercedes,2018.0,1.0,Hybrid,Manual,213063.0,New,73789.24,GLC
+2408.0,Honda,2018.0,3.0,Hybrid,Automatic,270804.0,Used,6870.48,Fit
+2409.0,Tesla,2017.0,2.6,Diesel,Automatic,129909.0,New,70585.87,Model 3
+2410.0,Audi,2019.0,4.5,Hybrid,Automatic,140688.0,Used,93779.23,A4
+2411.0,Audi,2015.0,4.8,Diesel,Manual,75976.0,Like New,72824.5,A4
+2412.0,Mercedes,2007.0,2.5,Hybrid,Manual,233208.0,Like New,8495.4,C-Class
+2413.0,Audi,2000.0,5.1,Hybrid,Automatic,179810.0,New,40899.14,A3
+2414.0,Toyota,2016.0,2.5,Electric,Manual,275251.0,Like New,81910.22,Corolla
+2415.0,Mercedes,2011.0,3.2,Hybrid,Automatic,192476.0,Like New,96067.81,E-Class
+2416.0,Mercedes,2020.0,4.6,Diesel,Automatic,94182.0,Like New,40165.52,C-Class
+2417.0,BMW,2010.0,4.5,Diesel,Manual,243153.0,Like New,84913.44,5 Series
+2418.0,Audi,2004.0,5.9,Petrol,Automatic,278098.0,Used,87061.94,Q5
+2419.0,Tesla,2011.0,2.1,Petrol,Manual,279087.0,Used,16712.21,Model X
+2420.0,Mercedes,2016.0,4.3,Electric,Manual,40725.0,New,85349.9,C-Class
+2421.0,Tesla,2005.0,4.0,Diesel,Automatic,10110.0,Like New,35803.74,Model X
+2422.0,Toyota,2000.0,5.6,Diesel,Manual,278606.0,Like New,61357.58,Corolla
+2423.0,BMW,2004.0,1.8,Diesel,Automatic,185153.0,Like New,56201.4,X5
+,,,,,,,,,
+2425.0,Tesla,2004.0,3.8,Diesel,Automatic,182296.0,Used,75344.14,Model 3
+2426.0,Mercedes,2021.0,2.4,Petrol,Automatic,113394.0,Like New,46883.7,E-Class
+2427.0,Toyota,2023.0,3.4,Electric,Automatic,148940.0,Used,89389.53,RAV4
+2428.0,Ford,2000.0,2.7,Petrol,Manual,22222.0,New,81902.35,Explorer
+2429.0,Toyota,2017.0,3.9,Petrol,Automatic,79794.0,Used,79615.0,Corolla
+2430.0,Toyota,2015.0,5.4,Electric,Manual,63924.0,Like New,48533.45,Corolla
+2431.0,BMW,2008.0,3.8,Hybrid,Manual,99854.0,Used,8569.42,3 Series
+2432.0,Honda,2018.0,4.2,Hybrid,Manual,260038.0,Used,51764.36,Civic
+2433.0,Ford,2021.0,3.6,Petrol,Manual,243815.0,New,90352.33,Mustang
+2434.0,Audi,2004.0,1.3,Hybrid,Manual,233127.0,New,23092.76,A3
+2435.0,Audi,2001.0,2.2,Diesel,Automatic,141295.0,New,98346.28,Q7
+2436.0,Tesla,2014.0,3.5,Electric,Manual,179238.0,New,81730.61,Model Y
+,,,,,,,,,
+2438.0,BMW,2003.0,1.9,Diesel,Automatic,274805.0,Used,69330.2,3 Series
+2439.0,BMW,2000.0,3.5,Hybrid,Manual,146663.0,New,60767.46,X5
+2440.0,Audi,2012.0,2.6,Diesel,Automatic,186307.0,New,73463.68,A3
+2441.0,Tesla,2019.0,2.3,Hybrid,Automatic,270114.0,Like New,45081.03,Model X
+2442.0,Tesla,2021.0,3.3,Petrol,Manual,47922.0,Like New,85302.37,Model Y
+,,,,,,,,,
+2444.0,Ford,2014.0,5.6,Electric,Manual,120926.0,Used,78269.05,Explorer
+2445.0,Honda,2022.0,5.3,Petrol,Manual,19416.0,New,80286.31,Civic
+2446.0,Ford,2013.0,4.5,Diesel,Manual,13672.0,Used,13746.65,Explorer
+2447.0,Mercedes,2022.0,1.5,Diesel,Automatic,128145.0,Used,10285.87,GLA
+2448.0,Mercedes,2018.0,4.9,Hybrid,Automatic,178068.0,Like New,81093.66,C-Class
+2449.0,Honda,2021.0,5.0,Petrol,Automatic,64.0,New,29641.48,Accord
+2450.0,Audi,2023.0,4.8,Petrol,Automatic,68835.0,New,60287.13,A3
+2451.0,Honda,2019.0,1.6,Petrol,Manual,201457.0,Used,47152.53,CR-V
+2452.0,BMW,2023.0,4.2,Petrol,Manual,247795.0,New,7093.72,X3
+2453.0,Mercedes,2017.0,5.2,Hybrid,Automatic,116916.0,Like New,8826.72,C-Class
+2454.0,Toyota,2009.0,3.9,Electric,Manual,195346.0,Like New,83576.9,Corolla
+2455.0,Toyota,2008.0,4.2,Petrol,Automatic,273010.0,New,62729.37,Prius
+2456.0,Audi,2007.0,3.3,Hybrid,Manual,3889.0,New,54765.09,Q7
+2457.0,BMW,2009.0,3.6,Electric,Manual,118194.0,Like New,94713.45,5 Series
+2458.0,Ford,2016.0,1.2,Electric,Manual,124714.0,New,21445.93,Explorer
+2459.0,BMW,2001.0,4.2,Hybrid,Manual,31335.0,Used,29507.23,5 Series
+2460.0,Toyota,2005.0,1.4,Petrol,Manual,50547.0,New,76890.21,Corolla
+2461.0,Toyota,2004.0,3.2,Electric,Automatic,157234.0,Used,78647.35,Prius
+2462.0,Mercedes,2008.0,2.5,Petrol,Automatic,220521.0,New,43465.23,E-Class
+2463.0,BMW,2004.0,3.7,Petrol,Manual,155759.0,New,95342.95,X3
+2464.0,BMW,2017.0,1.7,Diesel,Automatic,273139.0,Like New,94134.18,3 Series
+2465.0,Toyota,2019.0,4.5,Petrol,Manual,2631.0,Used,6178.92,Prius
+2466.0,Ford,2008.0,5.0,Diesel,Manual,137039.0,Like New,21371.15,Explorer
+2467.0,Audi,2016.0,1.8,Electric,Manual,64471.0,New,82920.85,Q7
+2468.0,Tesla,2016.0,1.5,Hybrid,Manual,299260.0,Like New,36845.59,Model Y
+2469.0,Audi,2011.0,1.7,Diesel,Automatic,133014.0,New,14068.95,Q7
+2470.0,BMW,2006.0,5.4,Hybrid,Manual,47664.0,New,79574.92,X3
+2471.0,Audi,2002.0,3.6,Electric,Automatic,159141.0,New,48185.43,Q7
+2472.0,Toyota,2009.0,3.3,Petrol,Manual,87342.0,Used,51779.02,Corolla
+,,,,,,,,,
+2474.0,BMW,2002.0,4.2,Hybrid,Automatic,113926.0,Used,67785.03,X3
+2475.0,BMW,2015.0,5.3,Electric,Manual,213053.0,Like New,57367.93,X3
+2476.0,Tesla,2001.0,5.1,Petrol,Manual,17878.0,New,24633.93,Model X
+2477.0,Tesla,2023.0,4.1,Electric,Automatic,83100.0,Like New,37331.49,Model X
+2478.0,Mercedes,2009.0,1.4,Petrol,Automatic,223059.0,Like New,61703.53,GLC
+2479.0,BMW,2005.0,1.6,Diesel,Automatic,43168.0,New,92862.86,X3
+2480.0,Ford,2021.0,1.2,Hybrid,Manual,250396.0,Like New,92435.75,Focus
+,,,,,,,,,
+2482.0,BMW,2022.0,1.7,Petrol,Automatic,28004.0,New,74883.99,3 Series
+2483.0,Honda,2006.0,2.3,Electric,Automatic,282615.0,New,78674.31,Civic
+2484.0,Mercedes,2014.0,2.9,Diesel,Manual,91820.0,New,81838.19,GLA
+2485.0,Honda,2005.0,5.7,Petrol,Manual,191239.0,Like New,59222.42,Civic
+2486.0,Toyota,2019.0,4.1,Diesel,Automatic,102229.0,Like New,5998.26,Prius
+2487.0,Tesla,2008.0,5.2,Diesel,Automatic,14568.0,Like New,77359.25,Model 3
+,,,,,,,,,
+2489.0,BMW,2019.0,1.6,Petrol,Automatic,222129.0,Like New,16102.78,X3
+2490.0,Honda,2011.0,4.7,Electric,Manual,57324.0,Like New,62534.36,Fit
+2491.0,Audi,2018.0,2.4,Hybrid,Manual,146539.0,Used,98434.45,Q5
+2492.0,Toyota,2019.0,4.2,Hybrid,Manual,75955.0,New,28041.25,Prius
+2493.0,Tesla,2002.0,2.7,Petrol,Manual,147748.0,New,66352.03,Model X
+2494.0,Ford,2014.0,6.0,Diesel,Automatic,94791.0,New,33847.1,Focus
+2495.0,Mercedes,2001.0,2.3,Petrol,Manual,162586.0,Used,90378.98,E-Class
+2496.0,Audi,2020.0,2.4,Petrol,Automatic,22650.0,Like New,61384.1,Q5
+2497.0,Audi,2001.0,5.7,Hybrid,Manual,77701.0,Like New,24710.35,A3
+,,,,,,,,,
+2499.0,Audi,2002.0,4.5,Diesel,Manual,229164.0,Like New,46085.67,Q5
+2500.0,Toyota,2005.0,4.6,Diesel,Automatic,80978.0,Used,16594.14,RAV4
diff --git a/Вариант 1/car_price_solution.ipynb b/Вариант 1/car_price_solution.ipynb
new file mode 100644
index 0000000..e0372c9
--- /dev/null
+++ b/Вариант 1/car_price_solution.ipynb	
@@ -0,0 +1,1489 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "f10cf91e",
+      "metadata": {},
+      "source": [
+        "# Прогнозирование цены автомобиля"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "0757256f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0\u001b[0m\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip -q install pandas numpy matplotlib seaborn scikit-learn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "ad0e2ade",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
+        "\n",
+        "pd.set_option(\"display.max_columns\", 50)\n",
+        "sns.set_style(\"whitegrid\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "2180fd89",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Car ID</th>\n",
+              "      <th>Brand</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Fuel Type</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Model</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Tesla</td>\n",
+              "      <td>2016.0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>114832.0</td>\n",
+              "      <td>New</td>\n",
+              "      <td>26613.92</td>\n",
+              "      <td>Model X</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>BMW</td>\n",
+              "      <td>2018.0</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>Electric</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>143190.0</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>14679.61</td>\n",
+              "      <td>5 Series</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Audi</td>\n",
+              "      <td>2013.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>Electric</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>181601.0</td>\n",
+              "      <td>New</td>\n",
+              "      <td>44402.61</td>\n",
+              "      <td>A4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>Tesla</td>\n",
+              "      <td>2011.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>Diesel</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>68682.0</td>\n",
+              "      <td>New</td>\n",
+              "      <td>86374.33</td>\n",
+              "      <td>Model Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>Ford</td>\n",
+              "      <td>2009.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>Diesel</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>223009.0</td>\n",
+              "      <td>Like New</td>\n",
+              "      <td>73577.10</td>\n",
+              "      <td>Mustang</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Car ID  Brand    Year  Engine Size Fuel Type Transmission   Mileage  \\\n",
+              "0     1.0  Tesla  2016.0          2.3    Petrol       Manual  114832.0   \n",
+              "1     2.0    BMW  2018.0          4.4  Electric       Manual  143190.0   \n",
+              "2     3.0   Audi  2013.0          4.5  Electric       Manual  181601.0   \n",
+              "3     4.0  Tesla  2011.0          4.1    Diesel    Automatic   68682.0   \n",
+              "4     5.0   Ford  2009.0          2.6    Diesel       Manual  223009.0   \n",
+              "\n",
+              "  Condition     Price     Model  \n",
+              "0       New  26613.92   Model X  \n",
+              "1      Used  14679.61  5 Series  \n",
+              "2       New  44402.61        A4  \n",
+              "3       New  86374.33   Model Y  \n",
+              "4  Like New  73577.10   Mustang  "
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 1) Загрузка датасета\n",
+        "df = pd.read_csv(\"car_price_prediction_with_missing.csv\")\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "19d24a44",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.DataFrame'>\n",
+            "RangeIndex: 2500 entries, 0 to 2499\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column        Non-Null Count  Dtype  \n",
+            "---  ------        --------------  -----  \n",
+            " 0   Car ID        2250 non-null   float64\n",
+            " 1   Brand         2250 non-null   str    \n",
+            " 2   Year          2250 non-null   float64\n",
+            " 3   Engine Size   2250 non-null   float64\n",
+            " 4   Fuel Type     2250 non-null   str    \n",
+            " 5   Transmission  2250 non-null   str    \n",
+            " 6   Mileage       2250 non-null   float64\n",
+            " 7   Condition     2250 non-null   str    \n",
+            " 8   Price         2250 non-null   float64\n",
+            " 9   Model         2250 non-null   str    \n",
+            "dtypes: float64(5), str(5)\n",
+            "memory usage: 195.4 KB\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "None"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Car ID          0.1\n",
+              "Brand           0.1\n",
+              "Year            0.1\n",
+              "Engine Size     0.1\n",
+              "Fuel Type       0.1\n",
+              "Transmission    0.1\n",
+              "Mileage         0.1\n",
+              "Condition       0.1\n",
+              "Price           0.1\n",
+              "Model           0.1\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Общая информация и пропуски\n",
+        "display(df.info())\n",
+        "display(df.isna().mean().sort_values(ascending=False))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c0dffc81",
+      "metadata": {},
+      "source": [
+        "## Очистка и предобработка\n",
+        "Исходный набор данных имеет пропуски\n",
+        "- Удаляем дубликаты.\n",
+        "- Удалить пропуски"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "1ab1ead2",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.DataFrame'>\n",
+            "Index: 2250 entries, 0 to 2499\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column        Non-Null Count  Dtype  \n",
+            "---  ------        --------------  -----  \n",
+            " 0   Car ID        2250 non-null   float64\n",
+            " 1   Brand         2250 non-null   str    \n",
+            " 2   Year          2250 non-null   float64\n",
+            " 3   Engine Size   2250 non-null   float64\n",
+            " 4   Fuel Type     2250 non-null   str    \n",
+            " 5   Transmission  2250 non-null   str    \n",
+            " 6   Mileage       2250 non-null   float64\n",
+            " 7   Condition     2250 non-null   str    \n",
+            " 8   Price         2250 non-null   float64\n",
+            " 9   Model         2250 non-null   str    \n",
+            "dtypes: float64(5), str(5)\n",
+            "memory usage: 193.4 KB\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "None"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Car ID          0.0\n",
+              "Brand           0.0\n",
+              "Year            0.0\n",
+              "Engine Size     0.0\n",
+              "Fuel Type       0.0\n",
+              "Transmission    0.0\n",
+              "Mileage         0.0\n",
+              "Condition       0.0\n",
+              "Price           0.0\n",
+              "Model           0.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "df = df.drop_duplicates()\n",
+        "df = df.dropna()\n",
+        "display(df.info())\n",
+        "display(df.isna().mean().sort_values(ascending=False))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d7fdb1a8",
+      "metadata": {},
+      "source": [
+        "## Задание 1. Анализ и визуализация\n",
+        "\n",
+        "Ниже 3 графика для изучения датасета\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "83c5b8b2",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 1: распределение цены\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.histplot(df[\"Price\"], bins=50)\n",
+        "plt.title(\"Распределение цены\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4cf27c93",
+      "metadata": {},
+      "source": [
+        "Видно, что распределение цен на автомобили примерно равномерное, близкое к случайному"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "926cf42e",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 2: связь цены и года выпуска\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.scatterplot(data=df, x=\"Year\", y=\"Price\", alpha=0.6)\n",
+        "plt.title(\"Цена vs Год выпуска\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "69a27e4a",
+      "metadata": {},
+      "source": [
+        "Цена на автомобиль совсем не зависит от года выпуска"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "fb59d4c3",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAGHCAYAAAAupV8sAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQFpJREFUeJzt3Xl4Dvf+//FXFomoPSIVUZTG1mzE0pKW1HGq1XJiOVVLlZZav/SgDYoiVCxtLbEce2OJrdZWW8vpooqGRNSx91hiS5CoS/bcvz96mZ+7SUhI5Gaej+tyNfe85zPzmfFpvO75zNy3ncVisQgAAACPPfui7gAAAAAeDoIfAACASRD8AAAATILgBwAAYBIEPwAAAJMg+AEAAJgEwQ8AAMAkCH4AAAAmQfADAAAwCYIfgIemW7du6tatW671WrVq6cMPP3yIPSpa33zzjXr16qXnn39efn5+atOmjcLDw3Xz5s2i7hqAx5RjUXcAAMwmKytLw4YN07Zt29S+fXt17txZTzzxhKKjo7Vw4UJt375dS5YsUenSpYu6qwAeMwQ/AHjIFixYoC1btmjWrFn629/+Zix/7rnn1KhRI3Xp0kWzZ89WSEhIEfYSwOOIqV4ANm379u0KDg6Wt7e3mjZtqgkTJujWrVtGfebMmapVq1a2drVq1dLMmTON10ePHtWAAQPUpEkT1atXT4GBgZowYYJSUlJy3O+lS5dUp04dRUREWC2/du2a6tWrpyVLlkiSdu/erU6dOsnf318NGzZU3759derUqVyPJz09XYsWLdILL7xgFfpua9CggQYNGqSaNWsay/744w9NmjRJLVu2lLe3t9q0aaO1a9datbNYLFqyZIlat24tHx8f/e1vf9PChQtlsViMc5TTn9vn6MqVKwoJCdGLL74oHx8fdejQQTt27LDax44dO/SPf/xDvr6+Vtu4c/r+r+ddyv53lNOU/7Rp01SrVi2tX7/eWHbhwgW9//77atSokXx9ffXWW2/pyJEjuZ5bAPfGFT8ANmvz5s0aOnSoXnvtNQ0ePFhxcXH69NNPdfLkSS1evFh2dnZ52s6VK1fUpUsX+fn56ZNPPpGTk5N++OEHLV68WBUrVlTv3r2ztXnyySfVqFEjbd26VV27djWWb9u2TRaLRa+++qrOnTunfv36qX379nr//fd148YNTZ8+Xb1799Z3330ne/vs761/++03Xb9+XS1atMi1v/369TN+TklJ0ZtvvqmrV69q0KBBqly5srZv366RI0cqISFB7733niQpLCxMS5cu1dtvv62mTZsqNjZWU6dOVUZGhjp27KjAwEBJ0scffyxJGjNmjHGcCQkJ6tChg5ydnTVkyBCVK1dO69evV//+/RUWFqbXX39dZ8+e1f/93/8pMDBQQ4YMMaahb2/vQZw9e9YI0rddu3ZNb7zxhlxcXPTRRx/JxcVFS5cuVZcuXbR27VrVqFHjgfcLmBHBD4BNslgsmjp1qgIDAzV16lRjebVq1dSjRw99//33at68eZ62dfz4cdWpU0eff/65SpYsKUl6/vnntXv3bu3duzfH4CdJbdu21YgRI3ThwgV5eHhIkrZu3arnn39ebm5u2rp1q1JSUtSnTx+5u7tL+jNI7dixQ7du3TL2daeLFy9Kkjw9PfPU9/Xr1+v48eNatWqV/P39JUmBgYHKyMhQeHi43njjDdnb22vZsmXq2rWrhg0bZhxffHy89u/frz59+ujJJ5+UJKNPfn5+xj6mTJmia9eu6ZtvvlHlypUlSS+++KJ69OihsLAwtWnTRkeOHFF6erqGDBkiLy8vo21Ox5hfEydO1DPPPKPffvvNWLZ06VIlJiZq5cqVRp9eeOEFvfLKK/r88881Y8aMB94vYEZM9QKwSadPn9alS5cUFBSkjIwM40/Dhg1VsmRJ7d6922r9O9fJyMiwqjVr1kwRERFydnbWyZMntWPHDs2ZM0fXrl1TWlparn1o1aqVnJ2d9dVXX0n6M7RFRUWpbdu2kiRfX185OzurQ4cOCg0N1Y8//qjatWtryJAhuQYiR8c/329nZWXl6Tzs27dPlStXNkLfba+//rpSU1MVExOj6OhoZWRkqFWrVlbrjBo1SgsWLMjTPvz9/Y2Adec+4uPjdfr0adWrV0+Ojo6KiIhQXFyc0tLSlJGRIYvFkm17WVlZVn8XdzvWH374QT///LM++OADq+V79uxRnTp15O7ubmzH3t5eL7zwgn7++ed7HhOAnHHFD4BNSkxMlPTnVGJO04lXrlyxel2vXr1ct5WVlaXp06dr+fLlunXrlipVqiQfHx85OzvftQ8lS5ZUy5YttXXrVr3zzjv66quv5OLiopYtW0r686pdRESE5s+fr7Vr12rZsmUqXbq03nzzTQ0ePDjHqejbVw7j4uJy3e+1a9dUsmRJOTk5KSkpSW5ubtnWqVChgiTpxo0bRvgqX778XY8nN0lJSapSpcpd91G/fn1NmTJF06dPV2RkpNV6jRo1snodHh6u8PDwe+43PT1dEydO1DvvvJMtdCYmJurMmTO5/r0mJyfLxcXlnvsAYI3gB8Am3b6HbPjw4dmChSSVKVPG6vVfH3bo0KGD8fP8+fO1ZMkSffzxx2rVqpVKlSqVbZ3cvP766+rdu7fOnDmjrVu36u9//7tV4PDx8dGsWbOUlpamqKgoRUZGau7cuapdu7Zat26dbXt16tRRhQoV9MMPP6hLly457nPUqFGKjo7Wf/7zH5UpU0ZnzpzJtk58fLwkqVy5csYVzmvXrunpp5821rlw4YLOnj2rBg0aqFixYrkeY5kyZYzt5bYPSWrdurV2796tq1evauTIkfLy8tLYsWOztevUqZM6depkvF69erVWr16dbb2lS5cqLS1NvXv3VkJCglWtVKlSatSokYYPH55jn52cnHI9HgC5Y6oXgE16+umn5erqqvPnz8vb29v44+7urmnTpmV7uvPOdby9va1qUVFRqlmzptq3b2+EvsuXL+v48eP3nHJt1qyZKlSooGXLlum3334zpnklacmSJWrRooXS0tLk5OSk5557TuPHj5f0Z+jKib29vXr06KH//Oc/2rlzZ7b6L7/8ou+//14vv/yynJyc1LBhQ8XFxengwYNW623atEnFihWTj4+PfHx8VKxYMe3atctqnUWLFun999+Xg4PDXY+xYcOGOnjwYLarkJs2bZKbm5uqVq0qSVqxYoXWrl2roUOHqkOHDvLx8dETTzyRbXsVK1a0+ruoWLFitnWuXr2q8PBwDR8+XMWLF89Wb9SokX7//XdVr17dalsbN27U2rVr73lMAHLGFT8AD9WlS5eyPcF5p5MnT+qnn35Ss2bNNGTIEI0ePVoODg5q0aKFbty4ofDwcF2+fPmuU7t/5ePjo/DwcM2fP19+fn46c+aM5s2bp7S0NCUnJ9+1rYODg1599VVFRETI3d1djRs3NmpNmjTR1KlT1b9/f3Xt2lUODg5atWqVnJyc7vrUbo8ePbR//34NHDhQnTp10osvvih7e3vt379fX3zxherUqaN//etfkqTg4GCtWLFC/fv316BBg+Tp6amdO3dq3bp1GjBggHFltHv37lqyZImcnJzUqFEjxcTEaOXKlRo+fHiOTxff6e2339amTZvUo0cPDRgwQGXLltWGDRv0yy+/aOLEibK3t9fVq1f12WefycfHR2+++WZeT32uTp06pSZNmujll1/O9Rxt3LhRPXr0UM+ePVWuXDl99dVXWr16NZ9vCDwAgh+Ah+rs2bOaNGlSrvXY2Fht2bJFzZo1U8eOHfXEE09owYIFioyMVIkSJVS/fn1NnTo1x3vSctOnTx9dv35dy5Yt0+zZs1WpUiW1bdtWdnZ2mjdvnm7cuHHXb8lo27atli5dqjZt2liFqNq1a2vu3LmaPXu23n//fWVmZurZZ5/VokWLrKZc/6pYsWIKDw9XZGSkNm7cqK+++kppaWmqUqWK+vXrp65du6pEiRKSJBcXF33xxReaNm2aPv/8c928eVNPP/20QkNDraaqhw0bJldXV61atUoLFiyQp6enPvroI73xxhv3PD9ubm5auXKlpk2bpgkTJig9PV21a9dWeHi4XnrpJUnS1KlTdfPmTY0ZMybPH6NzN46Ojho1alSudXd3d61atUrTpk3T2LFjlZqaqmrVqmU7bgD5Y2fJ6ZEsACgi3bp1U+XKlfXJJ58UdVcA4LHDPX4AAAAmQfADAAAwCaZ6AQAATIIrfgAAACZB8AMAADAJgh8AAIBJ8Dl+heD2F5Tb29sXyOddAQAA5MZisSgrK0uOjo73/MB2gl8hyMjIUGxsbFF3AwAAmIi3t/c9v8ea4FcIbqdtb29vvk8SAAAUqszMTMXGxt7zap9E8CsUt6d3HRwcCH4AAOChyMvtZTzcAQAAYBIEPwAAAJMg+AEAAJgEwQ8AAMAkCH4AAAAmQfADAAAwCZsIfmlpaWrTpo327t1rLDt37px69OghPz8/vfLKK/rpp5+s2vz8889q06aNfH191b17d507d86qvmTJEgUGBsrf318jRoxQcnKyUUtNTdWIESMUEBCgZs2aadGiRVZt77VvAACAR1GRB7/U1FS9//77OnHihLHMYrGof//+qlChgtatW6e2bdtqwIABunDhgiTpwoUL6t+/v4KDg7V27VqVL19e/fr1k8VikSR98803mjVrlsaNG6elS5cqJiZGU6ZMMbYfFhamw4cPa+nSpRozZoxmzZqlbdu25WnfAAAAj6oiDX4nT55Up06ddPbsWavlv/zyi86dO6dx48apRo0a6tOnj/z8/LRu3TpJ0po1a/Tss8+qZ8+eeuaZZzRp0iTFxcVp3759kqRly5bprbfeUosWLeTj46OPP/5Y69atU3Jysm7duqU1a9Zo5MiRqlevnv72t7/pnXfe0fLly/O0bwAAgEdVkQa/ffv2qXHjxoqMjLRaHhMTo7p166pEiRLGsgYNGig6OtqoBwQEGDUXFxfVq1dP0dHRxteW3Fn38/NTenq6jh49qqNHjyojI0P+/v5W246JiVFWVtY99w0AAPCoKtKvbHvzzTdzXB4fH6+KFStaLXN1ddWlS5fuWb9x44ZSU1Ot6o6OjipbtqwuXboke3t7lStXzupLjCtUqKDU1FQlJibec9/5kZmZme82tu7ChQu6efNmUXfDZpUsWVIeHh5F3Q3kA2P67hjTjx7G9N09jmM6P3nDJr+rNzk52SqYSZKTk5PS0tLuWU9JSTFe51S3WCw51qQ/HzK5177zIzY2Nt9tbNnNmzc1duxY415KZGdvb68xY8aoZMmSRd0V5AFj+t4Y048WxvS9mX1M22Twc3Z2VmJiotWytLQ0FS9e3Kj/NYilpaWpdOnScnZ2Nl7/te7i4qLMzMwca5JUvHjxe+47P7y9veXg4JDvdrZs2bJlNvNO8uzZs5o0aZJCQkL01FNPFXV3JD2e7yQfd4zpu2NMP3oY03f3OI7p27e55YVNBj93d3edPHnSallCQoIxBevu7q6EhIRs9Tp16qhs2bJydnZWQkKCatSoIUnKyMhQYmKi3NzcZLFYdP36dWVkZMjR8c/Dj4+PV/HixVW6dOl77js/HBwcHrvgV6VKlaLuguH2ua1evbq8vLyKuDd4VDGm8bhhTONuivzjXHLi6+ur3377zZi2laSoqCj5+voa9aioKKOWnJysI0eOyNfXV/b29vL29raqR0dHy9HRUbVr11adOnXk6Oho9bBGVFSUvL29ZW9vf899AwAAPKpsMvg1atRIlSpVUkhIiE6cOKH58+fr0KFD6tChgySpffv2OnDggObPn68TJ04oJCREnp6eaty4saQ/HxpZuHChtm/frkOHDmns2LHq1KmTXFxc5OLionbt2mns2LE6dOiQtm/frkWLFql79+552jcAAMCjyiaDn4ODg8LDwxUfH6/g4GBt2rRJs2fPNubkPT09NXPmTK1bt04dOnRQYmKiZs+eLTs7O0nSq6++qj59+mj06NHq2bOnfHx8NGzYMGP7ISEhqlevnt566y19/PHHGjhwoFq1apWnfQMAADyqbOYev2PHjlm9rlq1qiIiInJd/8UXX9SLL76Ya713797q3bt3jjUXFxdNnjxZkydPzrF+r30DAAA8imzyih8AAAAKHsEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASNh38Ll68qD59+qh+/foKCgrSkiVLjNqRI0fUsWNH+fr6qn379jp8+LBV2y1btqhly5by9fVV//79de3aNaNmsVg0depUNWnSRI0aNVJYWJiysrKM+vXr1zVw4ED5+/srKChIGzduLPRjBQAAKGw2HfwGDx6sEiVKaP369RoxYoQ+++wzfffdd7p165Z69+6tgIAArV+/Xv7+/urTp49u3bolSTp06JBGjhypAQMGKDIyUjdu3FBISIix3cWLF2vLli2aNWuWZsyYoc2bN2vx4sVGPSQkRH/88YciIyPVt29fjRo1SocOHXroxw8AAFCQHIu6A7lJSkpSdHS0xo8fr2rVqqlatWoKDAzUnj17lJSUJGdnZw0fPlx2dnYaOXKkfvjhB23btk3BwcGKiIhQ69at1a5dO0lSWFiYWrRooXPnzqlKlSpatmyZBg0apICAAEnS0KFD9fnnn6tXr146e/asdu3apR07dsjT01NeXl6Kjo7WihUr5OPjU4RnBAAA4MHY7BW/4sWLy8XFRevXr1d6erpOnz6tAwcOqE6dOoqJiVGDBg1kZ2cnSbKzs1P9+vUVHR0tSYqJiTFCnSRVqlRJHh4eiomJ0eXLl3Xx4kU1bNjQqDdo0EBxcXG6cuWKYmJiVKlSJXl6elrVDx48+HAOHAAAoJDY7BU/Z2dnjR49WuPHj9eyZcuUmZmp4OBgdezYUTt27FDNmjWt1nd1ddWJEyckSVeuXFHFihWz1S9duqT4+HhJsqpXqFBBkox6Tm0vX76c72PIzMzMdxvk3e3zm5mZybnGY4ExjccNY/rhyM+5tdngJ0mnTp1SixYt9Pbbb+vEiRMaP368nnvuOSUnJ8vJyclqXScnJ6WlpUmSUlJScq2npKQYr++sSVJaWto9t50fsbGx+W6DvDt//rwk6fjx48b9ncCjjDGNxw1j2vbYbPDbs2eP1q5dq++//17FixeXt7e3Ll++rDlz5qhKlSrZglhaWpqKFy8u6c+rhTnVXVxcrEKes7Oz8bMkubi45Nr29rbzw9vbWw4ODvluh7wpUaKEJMnLy0teXl5F3BvgwTGm8bhhTD8cmZmZeb7YZLPB7/Dhw6patapV4Kpbt67mzp2rgIAAJSQkWK2fkJBgTNG6u7vnWHdzc5O7u7skKT4+3riP7/b07+16bm3zy8HBgeBXiG6fW84zHheMaTxuGNO2x2Yf7qhYsaLOnDljdfXt9OnT8vT0lK+vrw4ePCiLxSLpz8/lO3DggHx9fSVJvr6+ioqKMtpdvHhRFy9elK+vr9zd3eXh4WFVj4qKkoeHhypWrCg/Pz/FxcXp0qVLVnU/P79CPmIAAIDCZbPBLygoSMWKFdOoUaP0+++/a+fOnZo7d666deuml19+WTdu3FBoaKhOnjyp0NBQJScnq3Xr1pKkzp07a+PGjVqzZo2OHj2q4cOHq3nz5qpSpYpRnzp1qvbu3au9e/dq2rRp6t69uySpSpUqatasmYYNG6ajR49qzZo12rJli7p06VJk5wIAAKAg2OxUb6lSpbRkyRKFhoaqQ4cOKl++vPr27at//vOfsrOz07x58zRmzBitXr1atWrV0vz58417Cfz9/TVu3DjNmDFDSUlJatq0qcaPH29su1evXrp69aoGDBggBwcHdejQQT169DDqYWFhGjlypDp16iQ3NzdNnDiRz/ADAACPPJsNfpJUs2ZNq2/UuJOPj4++/PLLXNsGBwcrODg4x5qDg4NCQkKsvs3jTq6urpo7d27+OwwAAGDDbHaqFwAAAAWL4AcAAGASNj3VCwCArbt8+bKSkpKKuhs26cyZM1b/RXZlypQxPmruYSD4AQBwny5fvqyu3borPS21qLti00JDQ4u6CzarmJOzIr5Y9tDCH8EPAID7lJSUpPS0VCU//aKyipcp6u7gEWOfkiSd/l5JSUkEPwCPJ6bFcse02L097GmxvMoqXkZZT1Qo6m4A90TwA/DQMC2WN0yL5e5hT4sBjxuCn43j6kjuuDqSN7Z0hYRpMTyIopgWAx43BD8bxtWRvOHqyN3Z4hUSpsUAoGgQ/GwYV0fwoLhCAgC4E8HvEcDVEQAAUBD45g4AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJmHTwS8tLU0ff/yxGjZsqOeff17Tp0+XxWKRJB05ckQdO3aUr6+v2rdvr8OHD1u13bJli1q2bClfX1/1799f165dM2oWi0VTp05VkyZN1KhRI4WFhSkrK8uoX79+XQMHDpS/v7+CgoK0cePGh3PAAAAAhcimg9+ECRP0888/a+HChZo2bZpWr16tyMhI3bp1S71791ZAQIDWr18vf39/9enTR7du3ZIkHTp0SCNHjtSAAQMUGRmpGzduKCQkxNju4sWLtWXLFs2aNUszZszQ5s2btXjxYqMeEhKiP/74Q5GRkerbt69GjRqlQ4cOPfTjBwAAKEiORd2B3CQmJmrdunVavHixfHx8JEk9e/ZUTEyMHB0d5ezsrOHDh8vOzk4jR47UDz/8oG3btik4OFgRERFq3bq12rVrJ0kKCwtTixYtdO7cOVWpUkXLli3ToEGDFBAQIEkaOnSoPv/8c/Xq1Utnz57Vrl27tGPHDnl6esrLy0vR0dFasWKF0Q8AAIBHkc1e8YuKilLJkiXVqFEjY1nv3r01adIkxcTEqEGDBrKzs5Mk2dnZqX79+oqOjpYkxcTEGKFOkipVqiQPDw/FxMTo8uXLunjxoho2bGjUGzRooLi4OF25ckUxMTGqVKmSPD09reoHDx4s5CMGAAAoXDZ7xe/cuXOqXLmyNmzYoLlz5yo9PV3BwcHq27ev4uPjVbNmTav1XV1ddeLECUnSlStXVLFixWz1S5cuKT4+XpKs6hUqVJAko55T28uXL+f7GDIzM/PdpiDbA7dlZmbaxHiyhT7g0Wcr41liTKNgPOiYzk9bmw1+t27d0pkzZ7Rq1SpNmjRJ8fHxGj16tFxcXJScnCwnJyer9Z2cnJSWliZJSklJybWekpJivL6zJv35MMm9tp0fsbGx+W5zp/Pnzz9Qe+C248ePG/fAFiXGNAqCrYxniTGNgvEwx7TNBj9HR0fdvHlT06ZNU+XKlSVJFy5c0MqVK1W1atVsQSwtLU3FixeXJDk7O+dYd3FxsQp5zs7Oxs+S5OLikmvb29vOD29vbzk4OOS73W0lSpS477bAnby8vOTl5VXU3WBMo0DYyniWGNMoGA86pjMzM/N8sclmg5+bm5ucnZ2N0CdJ1atX18WLF9WoUSMlJCRYrZ+QkGBM0bq7u+dYd3Nzk7u7uyQpPj7euI/v9vTv7XpubfPLwcHhgYLfg7QF7vSgY7Eg+wE8KFsZzxJjGgXjYY5pm324w9fXV6mpqfr999+NZadPn1blypXl6+urgwcPGp/pZ7FYdODAAfn6+hpto6KijHYXL17UxYsX5evrK3d3d3l4eFjVo6Ki5OHhoYoVK8rPz09xcXG6dOmSVd3Pz6+QjxgAAKBw2Wzwe/rpp9W8eXOFhITo6NGj+vHHHzV//nx17txZL7/8sm7cuKHQ0FCdPHlSoaGhSk5OVuvWrSVJnTt31saNG7VmzRodPXpUw4cPV/PmzVWlShWjPnXqVO3du1d79+7VtGnT1L17d0lSlSpV1KxZMw0bNkxHjx7VmjVrtGXLFnXp0qXIzgUAAEBBsNmpXkmaOnWqxo8fr86dO8vFxUVdunRRt27dZGdnp3nz5mnMmDFavXq1atWqpfnz5xv3Wvj7+2vcuHGaMWOGkpKS1LRpU40fP97Ybq9evXT16lUNGDBADg4O6tChg3r06GHUw8LCNHLkSHXq1Elubm6aOHEin+EHAAAeeTYd/EqVKqWwsLAcaz4+Pvryyy9zbRscHKzg4OAcaw4ODgoJCbH6No87ubq6au7cufnvMAAAgA2z2aleAAAAFCyCHwAAgEk8cPBLSkpSVlaW8YQtAAAAbNN9BT+LxaI5c+aocePGeu655xQXF6dhw4Zp9OjR9/UNFwAAACh89xX8Zs+erU2bNumTTz4xvgnjH//4h3bv3p3rwxgAAAAoWvcV/L788kuNGzdOLVq0kJ2dnSSpadOmmjx5sr7++usC7SAAAAAKxn0Fv6tXrxpfj3an0qVL28wXZwMAAMDafX2OX5MmTbRw4UKNGzfOWHbz5k1Nnz5djRs3LrDOAQDwKLBPTizqLuARVBTj5r6C39ixYzVgwAA1bdpUqamp6tevn+Li4lS5cmXNmTOnoPsIAIBNc/n9h6LuApAn9xX8nnzySa1du1Z79uzR6dOnlZGRoerVq6tZs2ayt+ejAQEA5pJc/QVluZQt6m7gEWOfnPjQ3zTc91e2LV++XGXKlFGXLl0kSf3791dcXJw6d+5cYJ0DAOBRkOVSVllPVCjqbgD3dF+X5z799FPNmTNHJUqUMJY1btxY4eHhmj17doF1DgAAAAXnvq74rVu3Tp999pkCAgKMZd27d1etWrU0bNgw9e/fv8A6CODxw43wuB+MG+DB3VfwS05OVsmSJbMtL1eunP74448H7hSs8csO98tWxw43wgNA0biv4BcYGKjQ0FBNnjxZHh4ekqTLly9r8uTJatasWYF2EPwjiccPN8LjfhTFjfDA4+a+gt/o0aPVr18/vfTSSypTpowkKSkpSU2aNNGYMWMKtIPgH0ncP1v9h5Ib4QGgaNxX8CtfvrxWrVqlo0eP6n//+58cHR1VrVo11axZs6D7B/GPJAAAKBh5Dn4XLlxQpUqVZGdnpwsXLkj68yvafHx8rNaRZEz/AgAAwHbkOfgFBQVp9+7dcnV1VVBQkOzs7LKtY7FYZGdnp//+978F2kkAAAA8uDwHvx07dqh8+fKSpE2bNumJJ54otE4BAACg4OU5+FWuXNn4+b333tOsWbNUt27dQukUAAAACt59fXOHvb290tPTC7ovAAAAKET39VRv8+bN9fbbb6tFixaqXLmynJycrOoDBgwokM4BAACg4NxX8Dt27Jjq1aunK1eu6MqVK1a1nB76AAAAQNHLV/DbuHGjvvvuO1WoUEEvvfSS2rRpU1j9AgAAQAHL8z1+S5cu1YgRI5SSkqLk5GSFhIRo+vTphdk3AAAAFKA8X/FbtWqVQkND1a5dO0nSt99+q5CQEA0ZMoTpXQAAgEdAnq/4nTt3Ts8995zxOigoSMnJydnu8QMAAIBtynPwy8jIkKPj/79A6OjoKGdnZ6WlpRVKxwAAAFCw7utz/AAAAPDoyddTvV9//bVKlixpvM7KytJ3331nfJXbbbfvAwQAAIDtyHPw8/Dw0KJFi6yWubq6KiIiwmqZnZ0dwQ8AAMAG5Tn47dy5szD7AQAAgELGPX4AAAAmQfADAAAwCYIfAACASRD8AAAATILgBwAAYBIEPwAAAJMg+AEAAJgEwQ8AAMAkCH4AAAAmQfADAAAwCYIfAACASRD8AAAATILgBwAAYBIEPwAAAJN4ZIJf79699eGHHxqvjxw5oo4dO8rX11ft27fX4cOHrdbfsmWLWrZsKV9fX/Xv31/Xrl0zahaLRVOnTlWTJk3UqFEjhYWFKSsry6hfv35dAwcOlL+/v4KCgrRx48bCP0AAAIBC9kgEv61bt+r77783Xt+6dUu9e/dWQECA1q9fL39/f/Xp00e3bt2SJB06dEgjR47UgAEDFBkZqRs3bigkJMRov3jxYm3ZskWzZs3SjBkztHnzZi1evNioh4SE6I8//lBkZKT69u2rUaNG6dChQw/vgAEAAAqBzQe/xMREhYWFydvb21j21VdfydnZWcOHD1eNGjU0cuRIPfHEE9q2bZskKSIiQq1bt1a7du1Uu3ZthYWF6fvvv9e5c+ckScuWLdOgQYMUEBCgJk2aaOjQoVq+fLkk6ezZs9q1a5cmTJggLy8vdezYUa+//rpWrFjx8A8eAACgANl88Js8ebLatm2rmjVrGstiYmLUoEED2dnZSZLs7OxUv359RUdHG/WAgABj/UqVKsnDw0MxMTG6fPmyLl68qIYNGxr1Bg0aKC4uTleuXFFMTIwqVaokT09Pq/rBgwcL+UgBAAAKl2NRd+Bu9uzZo19//VWbN2/W2LFjjeXx8fFWQVCSXF1ddeLECUnSlStXVLFixWz1S5cuKT4+XpKs6hUqVJAko55T28uXL+e7/5mZmfluU5DtgdsyMzNtYjzZQh/w6LOV8SwxplEwHnRM56etzQa/1NRUjRkzRqNHj1bx4sWtasnJyXJycrJa5uTkpLS0NElSSkpKrvWUlBTj9Z01SUpLS7vntvMjNjY2323udP78+QdqD9x2/Phx4x7YosSYRkGwlfEsMaZRMB7mmLbZ4Ddr1iw9++yzCgwMzFZzdnbOFsTS0tKMgJhb3cXFxSrkOTs7Gz9LkouLyz23nR/e3t5ycHDId7vbSpQocd9tgTt5eXnJy8urqLvBmEaBsJXxLDGmUTAedExnZmbm+WKTzQa/rVu3KiEhQf7+/pL+fzj75ptv1KZNGyUkJFitn5CQYEzRuru751h3c3OTu7u7pD+ni2/fx3d7+vd2Pbe2+eXg4PBAwe9B2gJ3etCxWJD9AB6UrYxniTGNgvEwx7TNPtzxxRdfaPPmzdqwYYM2bNigoKAgBQUFacOGDfL19dXBgwdlsVgk/fm5fAcOHJCvr68kydfXV1FRUca2Ll68qIsXL8rX11fu7u7y8PCwqkdFRcnDw0MVK1aUn5+f4uLidOnSJau6n5/fwzlwAACAQmKzV/wqV65s9fqJJ56QJFWtWlWurq6aNm2aQkND9cYbb2jVqlVKTk5W69atJUmdO3dWt27d5OfnJ29vb4WGhqp58+aqUqWKUZ86daqefPJJSdK0adPUs2dPSVKVKlXUrFkzDRs2TCNHjlRsbKy2bNmiiIiIh3XoAAAAhcJmg9/dlCxZUvPmzdOYMWO0evVq1apVS/PnzzfutfD399e4ceM0Y8YMJSUlqWnTpho/frzRvlevXrp69aoGDBggBwcHdejQQT169DDqYWFhGjlypDp16iQ3NzdNnDhRPj4+D/swAQAACtQjE/w++eQTq9c+Pj768ssvc10/ODhYwcHBOdYcHBwUEhJi9W0ed3J1ddXcuXPvv7MAAAA2yGbv8QMAAEDBIvgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEzCsag7AMB87FOSiroLeAQxboAHR/AD8NCUKVNGxZycpdPfF3VX8Igq5uSsMmXKFHU3siGU4n4Uxbgh+AF4aNzd3RXxxTIlJfGPZE7OnDmj0NBQjRw5UlWrVi3q7tikMmXKyN3dvai7YeDNDB7Uw34zQ/B7BPBOEvfLFseOu7u7Tf3DbYuqVq0qLy+vou4G8oA3M3fHm5l7e9hvZgh+Nox3kigItjo1BjwueDNzb7yZsR0EPxvGO8m7451k3tja1BgAoOgQ/Gwc7yTvjXeSAADkDZ/jBwAAYBIEPwAAAJMg+AEAAJgEwQ8AAMAkCH4AAAAmQfADAAAwCYIfAACASRD8AAAATILgBwAAYBIEPwAAAJMg+AEAAJgEwQ8AAMAkCH4AAAAmQfADAAAwCYIfAACASRD8AAAATILgBwAAYBI2HfwuX76sQYMGqVGjRgoMDNSkSZOUmpoqSTp37px69OghPz8/vfLKK/rpp5+s2v78889q06aNfH191b17d507d86qvmTJEgUGBsrf318jRoxQcnKyUUtNTdWIESMUEBCgZs2aadGiRYV/sAAAAIXMZoOfxWLRoEGDlJycrOXLl+vTTz/Vrl279Nlnn8lisah///6qUKGC1q1bp7Zt22rAgAG6cOGCJOnChQvq37+/goODtXbtWpUvX179+vWTxWKRJH3zzTeaNWuWxo0bp6VLlyomJkZTpkwx9h0WFqbDhw9r6dKlGjNmjGbNmqVt27YVyXkAAAAoKI5F3YHcnD59WtHR0dq9e7cqVKggSRo0aJAmT56sF154QefOndOqVatUokQJ1ahRQ3v27NG6des0cOBArVmzRs8++6x69uwpSZo0aZKaNm2qffv2qXHjxlq2bJneeusttWjRQpL08ccfq1evXho2bJgsFovWrFmjf//736pXr57q1aunEydOaPny5Xr55ZeL7HwAAAA8KJu94ufm5qYFCxYYoe+2mzdvKiYmRnXr1lWJEiWM5Q0aNFB0dLQkKSYmRgEBAUbNxcVF9erVU3R0tDIzMxUbG2tV9/PzU3p6uo4ePaqjR48qIyND/v7+VtuOiYlRVlZWIR0tAABA4bPZK36lS5dWYGCg8TorK0sRERFq0qSJ4uPjVbFiRav1XV1ddenSJUm6a/3GjRtKTU21qjs6Oqps2bK6dOmS7O3tVa5cOTk5ORn1ChUqKDU1VYmJiSpfvnyejyEzMzNfx4z8uX1+MzMzOdd4LDCm8bhhTD8c+Tm3Nhv8/mrKlCk6cuSI1q5dqyVLllgFM0lycnJSWlqaJCk5OTnXekpKivE6p7rFYsmxJsnYfl7Fxsbma33kz/nz5yVJx48f161bt4q4N8CDY0zjccOYtj2PRPCbMmWKli5dqk8//VReXl5ydnZWYmKi1TppaWkqXry4JMnZ2TlbSEtLS1Pp0qXl7OxsvP5r3cXFRZmZmTnWJBnbzytvb285ODjkqw3y7vZUv5eXl7y8vIq4N8CDY0zjccOYfjhu38aWFzYf/MaPH6+VK1dqypQp+vvf/y5Jcnd318mTJ63WS0hIMKZv3d3dlZCQkK1ep04dlS1bVs7OzkpISFCNGjUkSRkZGUpMTJSbm5ssFouuX7+ujIwMOTr+eXri4+NVvHhxlS5dOl99d3BwIPgVotvnlvOMxwVjGo8bxrTtsdmHOyRp1qxZWrVqlaZPn65XX33VWO7r66vffvvNmLaVpKioKPn6+hr1qKgoo5acnKwjR47I19dX9vb28vb2tqpHR0fL0dFRtWvXVp06deTo6Gg8KHJ7297e3rK3t+nTBQAAcFc2m2ROnTql8PBwvfvuu2rQoIHi4+ONP40aNVKlSpUUEhKiEydOaP78+Tp06JA6dOggSWrfvr0OHDig+fPn68SJEwoJCZGnp6caN24sSXrzzTe1cOFCbd++XYcOHdLYsWPVqVMnubi4yMXFRe3atdPYsWN16NAhbd++XYsWLVL37t2L8nQAAAA8MJud6t2xY4cyMzM1Z84czZkzx6p27NgxhYeHa+TIkQoODlbVqlU1e/ZseXh4SJI8PT01c+ZMTZw4UbNnz5a/v79mz54tOzs7SdKrr76quLg4jR49WmlpaWrVqpWGDRtmbD8kJERjx47VW2+9pZIlS2rgwIFq1arVwzt4AACAQmCzwa93797q3bt3rvWqVasqIiIi1/qLL76oF1988b627+LiosmTJ2vy5Ml57zAAAICNs9mpXgAAABQsgh8AAIBJEPwAAABMguAHAABgEgQ/AAAAkyD4AQAAmATBDwAAwCQIfgAAACZB8AMAADAJgh8AAIBJEPwAAABMguAHAABgEgQ/AAAAkyD4AQAAmATBDwAAwCQIfgAAACZB8AMAADAJgh8AAIBJEPwAAABMguAHAABgEgQ/AAAAkyD4AQAAmATBDwAAwCQIfgAAACZB8AMAADAJgh8AAIBJEPwAAABMguAHAABgEgQ/AAAAkyD4AQAAmATBDwAAwCQIfgAAACZB8AMAADAJgh8AAIBJEPwAAABMguAHAABgEo5F3QE8Wi5cuKCbN28WdTckSWfOnLH6ry0oWbKkPDw8irobyAfG9N0xph89jOm7M/uYtrNYLJai7sTjJjMzU9HR0fLz85ODg0NRd6fAJCYmKjg4WFlZWUXdFZtlb2+v9evXq2zZskXdFeQBY/reGNOPFsb0vT2OYzo/uYPgVwge1+An2dY7SVtk9neSjyLG9N0xph89jOm7exzHdH5yB1O9yJfH7X8WgDGNxw1jGnfDwx0AAAAmQfADAAAwCYIfAACASRD8AAAATILgBwAAYBIEPwAAAJMg+AEAAJgEwQ8AAMAkCH4AAAAmQfADAAAwCb6yrRDc/vrjzMzMIu4JAAB43N3OG7fzx90Q/ApBVlaWJCk2NraIewIAAMzidv64GztLXuIh8iUrK0sZGRmyt7eXnZ1dUXcHAAA8xiwWi7KysuTo6Ch7+7vfxUfwAwAAMAke7gAAADAJgh8AAIBJEPwAAABMguAHAABgEgQ/AAAAkyD4AQAAmATBDwAAwCQIfrAZtWrV0r/+9a9sy9evX6+goKAi6BFwf4KCgrR+/fpsywtrLOe2PyAntWrV0t69e3OszZw5U926dZNUsOO1W7duatmypVJTU62Wnz9/XrVq1dL58+cLZD+4N4IfbMqWLVu0Z8+eou4GAJhSz549NXPmzELZ9rlz5zR37txC2TbyjuAHm1K5cmWNGzdOaWlpRd0VADCdJ554QmXLli2UbVeuXFkLFizQ//73v0LZPvKG4AebMnjwYF2+fFkLFy7MdZ2LFy/qvffek6+vr4KCgjRr1ixlZmbq+vXrqlOnjo4fPy5JSk9Pl5+fn2bMmGG0/de//qVPP/200I8DuJdly5apRYsW8vb2VnBwsH799Vejdvz4cXXr1k0+Pj76+9//ruXLl1u1XbVqlZo3b6769esrPDz8YXcdj7E7p3rvlJWVpUGDBqlt27a6ceOGJOm7777TK6+8Il9fX3Xo0EH79u2767bbtm0rLy8vjRs3Ltd10tLSNGHCBDVu3FiNGzfW0KFDlZiYKEl6/fXXFRERYaz79ttvq2vXrsbryMhIde7cOT+Ha0oEP9gUd3d3DRo0SHPnztW5c+ey1S0WiwYMGCBXV1d9+eWXmjRpkjZv3qy5c+eqXLlyqlevnvHLJzY2VikpKTpw4IDRds+ePQoMDHyoxwT81ZEjRxQWFqYxY8bo66+/VkBAgAYPHqysrCylpKTo3XffVYMGDbRp0yZ98MEHCg8P14YNGyRJP/74o0JDQzV48GBFRkYqNjZWcXFxRXtAeOxNnDhRR48e1cKFC1W6dGkdPXpUH3zwgfr27atNmzbp9ddf17vvvqszZ87kug17e3uNHTtWe/bs0VdffZXjOtOnT9fhw4f173//W8uWLdPNmzf1f//3f5KkZs2aGb/f09PTFR0drdjYWKWnp0uSdu/eze/3PCD4weZ069ZNVatWVWhoaLbaL7/8ogsXLmj8+PF6+umn1bhxY33wwQdatmyZJKlp06bGL4Zff/1VL7zwgmJiYpSZmaljx44pLS1Nfn5+D/NwgGzi4uJkZ2cnDw8PeXp6avDgwZoyZYqysrK0efNmubq6avDgwapWrZqCgoL03nvvGWN8zZo1eu2119SuXTs988wzmjhxopydnYv4iPA4+/e//61t27Zp4cKFqlChgiRp4cKF6tSpk1577TVVrVpV3bt31wsvvKCVK1fedVve3t564403NGnSJN28edOqlpycrIiICH388cfy8fFRrVq1FBYWpn379unYsWNq1qyZ9u/fL4vFot9++01PPfWUSpcurSNHjigrK0t79+4l+OWBY1F3APgrBwcHjR07Vm+++aa2b99uVTt16pQSExPVoEEDY9ntqyTXr19XYGCgVq9eLYvFov3796t9+/aKiYnRf//7X+3bt0/PP/+8HB0Z9ihcjo6OysrKyrY8KytLjo6Oatasmby8vPTaa6+pbt26eumll9SxY0c5Ojrq9OnTOnr0qPz9/Y12mZmZcnBwkPTn/wNvvPGGUStXrpyqVKlS+AcFU7py5Yo+/fRTPfnkk3JzczOWnzp1Sl9//bUiIyONZenp6WrWrNk9tzlkyBB9++23+vzzz/XWW28Zy8+dO6f09HSr8S39+f/N//73P7Vo0ULJyck6ceKE9u/fr4CAAF25ckVRUVFycHCQvb29nn322QI46scb/wLCJtWvX1/t27dXaGio3nnnHWN5RkaGnn766RzvaypVqpT8/PyUmpqqY8eO6cCBA5o0aZLq16+vAwcOaM+ePWrVqtXDPAyYVKlSpbJdzZCkP/74Q6VKlZKLi4vWrFmjffv2adeuXVq/fr1Wrlyp9evXKyMjQ88995xGjx6d6/YtFovV62LFihX4MQCSZGdnp4ULF2rEiBGaM2eOhgwZIunPNyPvvvuu2rVrZ7V+8eLF77nN0qVLa/jw4QoJCVHjxo2N5ZmZmZKkFStWqESJElZtXF1d5eTkpICAAO3bt0+//vqr2rZtqytXrujXX39VZmammjZtKjs7uwc84scfU72wWUOHDtWtW7esHvSoXr26Lly4oPLly6tq1aqqWrWqzp8/rxkzZsjOzk6Ojo5q0qSJli9frgoVKqhChQoKCAjQnj17tH//fqYB8FDUqlVLBw8ezLY8JiZGdevW1cGDBzVv3jw1adJEISEh2rZtm1JTUxUVFaXq1avr999/l6enpzHGo6Oj9cUXX0iSnnnmGcXGxhrbvHnz5l3vqwIehJubm5577jkNGzZMixYtMsZa9erVdf78eWOMVq1aVZGRkfrhhx/ytN22bdsqICBAEydONJZVqVJFDg4OSkxMNLZZsmRJTZo0SVevXpX0/+/zi46OVoMGDdSgQQMdOHBAP/30E7/f84jgB5tVrlw5DR061OrG9WbNmqly5coaNmyYjh07pl9//VUfffSRXFxcjKmwpk2b6ssvv1T9+vUlSQEBAdq1a5c8PT315JNPFsmxwFw6d+6sHTt2aM6cOTpz5oyOHTumWbNmadeuXerSpYuKFy+u2bNna82aNTp//ry2bt2qW7duqVatWnr99deVkpKi0aNH69SpU/r+++8VGhoqV1dXSVLXrl319ddfa/Xq1Tp16pRGjx6tlJSUIj5iPGoOHTqkH374wepPcnJyruu/8sor8vPz0/jx4yVJPXr00FdffaVly5bp7NmzWrJkiZYsWaJq1arluQ9jxozRlStXjNclS5ZUx44dNXbsWO3du1cnT57U8OHDdebMGXl6ekr689+AnTt3qmTJknJ3d1fdunWVnJzMG/t8YKoXNq1Dhw5at26d8cvBwcFBc+bM0fjx49WpUyeVKFFCL7/8sj744AOjTWBgoMaNG2fcB1i3bl0VL16cXwp4aLy9vTVv3jzNnj1b8+fPl52dnerWrasFCxaodu3akqTQ0FCFh4dr3Lhx8vDw0JQpU1SjRg1Jf95MP3HiRLVr105ly5ZVly5d1KdPH0l/vpGZNGmSPvvsM127dk3t27dXnTp1iuxY8WiaOnVqtmXffvvtXduMHDlSwcHB+vbbb9WqVSuFhYVp5syZCgsL01NPPaVp06apYcOGee5DjRo11LNnT82bN89Y9uGHH2ry5MkaNGiQ0tPT1bBhQ82fP994Y1+zZk25uroav98dHBzk7++vxMRElS9fPs/7NjM7y19vFgEAAMBjialeAAAAkyD4AQAAmATBDwAAwCQIfgAAACZB8AMAADAJgh8AAIBJEPwAAABMguAHAABgEgQ/ALiHpKQkffLJJwoKCpKvr69at26tJUuWKCsrq1D2FxQUpPXr10uSunXrppkzZ0qS0tLStHr1amO9O2sAkBd8ZRsA3MX169f1z3/+UxUrVlRoaKg8PT0VGxur8ePH69y5c/roo48Kdf8zZ85UsWLFJElbt27V3Llz1alTp2w1AMgLgh8A3MW0adPk5OSkhQsXytnZWZJUpUoVFS9eXP369VPXrl1VvXr1Qtt/2bJljZ//+g2bd9YAIC+Y6gWAXKSlpWnr1q3q0qWLEfpua9GihZYsWaLKlSsrKSlJH330kZ5//nk1aNBAw4YNU1JSkiRp7969CgoK0ooVKxQYGCg/Pz8NGzZMaWlpxrZWrVql5s2bq379+goPD7faz+3p3L179yokJERxcXGqVauWzp8/n22qd/369WrdurV8fHwUHBys/fv3G7WgoCAtX75cnTp1kre3t9q2bavDhw8XxmkDYMMIfgCQi7Nnz+rWrVvy9vbOVrOzs1OTJk3k5OSkAQMG6L///a/mzp2rxYsX69SpU/rwww+Nda9cuaJvvvlGCxYs0MyZM/Xtt99qw4YNkqQff/xRoaGhGjx4sCIjIxUbG6u4uLhs+/P399eIESP05JNP6qefflKlSpWs6uvXr9f48ePVp08fbdiwQc8//7x69+6ty5cvG+vMnDlTvXv31qZNm1SqVClNmDChgM4UgEcFwQ8AcnHjxg1JUqlSpXJd5+jRo9q3b5+mTJkiHx8f+fj4aMqUKdq5c6dOnz4tSUpPT9eoUaNUq1YtBQYGKjAwULGxsZKkNWvW6LXXXlO7du30zDPPaOLEidmuLkqSk5OTSpUqJQcHB7m5ucnBwcGq/sUXX6hbt25q166dnn76aQ0dOlReXl6KiIgw1vnHP/6hli1bqnr16nr77be54geYEMEPAHJx+x6629O2OTl9+rRKly5tdZ9fjRo1VKZMGSP4SVLVqlWNn0uWLKmMjAxJ0qlTp1SnTh2jVq5cOVWpUiXffT116pR8fHyslvn5+enUqVPG62rVqln1IT09Pd/7AfBoI/gBQC6eeuoplSpVSr/99luO9b59+8rJySnHWmZmpjIzM43Xf13vzgc1/vrQxv08qZvTVcLMzEyrj5zhCWAABD8AyIWjo6NeeeUVLV++3OphDEnauXOndu7cqWrVqunGjRtWV/dOnjypmzdv5ulp32eeecaY9pWkmzdv6syZMzmua2dnl+t2qlevrpiYGKtlMTExhfrEMYBHD8EPAO5i4MCBunnzpnr16qV9+/bp7NmzWrNmjT788EN1795dNWvW1AsvvKAPPvhAhw4d0qFDh/TBBx+oYcOG8vLyuuf2u3btqq+//lqrV6/WqVOnNHr0aKWkpOS4rouLi5KSkvS///3PmCq+rUePHoqIiNCGDRv0+++/a+rUqTp69Kg6dOhQIOcBwOOBz/EDgLtwc3PTypUrNXPmTA0dOlSJiYl66qmnNGjQIHXu3FmSNHnyZE2YMEE9evSQg4ODXnrpJYWEhORp+wEBAZo0aZI+++wzXbt2Te3bt7e65+9OTZo0UdWqVfXaa69pxYoVVrVXXnlFCQkJmjFjhuLj41WnTh0tWrRINWrUeLATAOCxYmf5680lAAAAeCwx1QsAAGASBD8AAACTIPgBAACYBMEPAADAJAh+AAAAJkHwAwAAMAmCHwAAgEkQ/AAAAEyC4AcAAGASBD8AAACTIPgBAACYxP8DVMEe9A+GMgsAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 3: цена в зависимости от состояния авто\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.boxplot(data=df, x=\"Condition\", y=\"Price\")\n",
+        "plt.title(\"Цена vs Состояние\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4948afde",
+      "metadata": {},
+      "source": [
+        "От состояния цена также не зависит"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ab37182d",
+      "metadata": {},
+      "source": [
+        "### Краткие выводы по графикам\n",
+        "- В данных есть пропуски.\n",
+        "- Данные не коррелируют между собой, они выглядят случайными"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "daa3521f",
+      "metadata": {},
+      "source": [
+        "## Задание 2. Модели прогнозирования"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "4dade46e",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>split</th>\n",
+              "      <th>MAE</th>\n",
+              "      <th>RMSE</th>\n",
+              "      <th>R2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>train</td>\n",
+              "      <td>23677.424433</td>\n",
+              "      <td>27261.296466</td>\n",
+              "      <td>0.001463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23386.369896</td>\n",
+              "      <td>26995.898047</td>\n",
+              "      <td>-0.000709</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>train</td>\n",
+              "      <td>8877.283827</td>\n",
+              "      <td>10364.788027</td>\n",
+              "      <td>0.855658</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23676.962601</td>\n",
+              "      <td>27982.335432</td>\n",
+              "      <td>-0.075178</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>train</td>\n",
+              "      <td>20860.335930</td>\n",
+              "      <td>24310.182306</td>\n",
+              "      <td>0.205951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23625.922747</td>\n",
+              "      <td>27750.650439</td>\n",
+              "      <td>-0.057447</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model  split           MAE          RMSE        R2\n",
+              "0  LinearRegression  train  23677.424433  27261.296466  0.001463\n",
+              "1  LinearRegression   test  23386.369896  26995.898047 -0.000709\n",
+              "2      RandomForest  train   8877.283827  10364.788027  0.855658\n",
+              "3      RandomForest   test  23676.962601  27982.335432 -0.075178\n",
+              "4  GradientBoosting  train  20860.335930  24310.182306  0.205951\n",
+              "5  GradientBoosting   test  23625.922747  27750.650439 -0.057447"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Удаляем строки без цены\n",
+        "df_model = df.dropna(subset=[\"Price\"]).copy()\n",
+        "\n",
+        "X = df_model.drop(columns=[\"Price\"])\n",
+        "y = df_model[\"Price\"]\n",
+        "\n",
+        "cat_cols = X.select_dtypes(include=[\"object\", \"string\"]).columns\n",
+        "num_cols = X.select_dtypes(exclude=[\"object\", \"string\"]).columns\n",
+        "\n",
+        "preprocess = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", Pipeline([\n",
+        "            (\"imputer\", SimpleImputer(strategy=\"median\"))\n",
+        "        ]), num_cols),\n",
+        "        (\"cat\", Pipeline([\n",
+        "            (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+        "            (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
+        "        ]), cat_cols)\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "models = {\n",
+        "    \"LinearRegression\": LinearRegression(),\n",
+        "    \"RandomForest\": RandomForestRegressor(n_estimators=200, random_state=42),\n",
+        "    \"GradientBoosting\": GradientBoostingRegressor(random_state=42)\n",
+        "}\n",
+        "\n",
+        "def evaluate_models(X, y, models, random_state=42):\n",
+        "    mask = y.notna()\n",
+        "    X = X.loc[mask]\n",
+        "    y = y.loc[mask]\n",
+        "\n",
+        "    X_train, X_test, y_train, y_test = train_test_split(\n",
+        "        X, y, test_size=0.2, random_state=random_state\n",
+        "    )\n",
+        "    rows = []\n",
+        "    for name, model in models.items():\n",
+        "        pipe = Pipeline([\n",
+        "            (\"preprocess\", preprocess),\n",
+        "            (\"model\", model)\n",
+        "        ])\n",
+        "        pipe.fit(X_train, y_train)\n",
+        "\n",
+        "        for split, (X_s, y_s) in {\n",
+        "            \"train\": (X_train, y_train),\n",
+        "            \"test\": (X_test, y_test)\n",
+        "        }.items():\n",
+        "            pred = pipe.predict(X_s)\n",
+        "            rows.append({\n",
+        "                \"model\": name,\n",
+        "                \"split\": split,\n",
+        "                \"MAE\": mean_absolute_error(y_s, pred),\n",
+        "                \"RMSE\": np.sqrt(mean_squared_error(y_s, pred)),\n",
+        "                \"R2\": r2_score(y_s, pred)\n",
+        "            })\n",
+        "\n",
+        "    return pd.DataFrame(rows)\n",
+        "\n",
+        "results_before = evaluate_models(X, y, models)\n",
+        "results_before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "2d537e27",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"2\" halign=\"left\">MAE</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">R2</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">RMSE</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>split</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>model</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>GradientBoosting</th>\n",
+              "      <td>23625.922747</td>\n",
+              "      <td>20860.335930</td>\n",
+              "      <td>-0.057447</td>\n",
+              "      <td>0.205951</td>\n",
+              "      <td>27750.650439</td>\n",
+              "      <td>24310.182306</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LinearRegression</th>\n",
+              "      <td>23386.369896</td>\n",
+              "      <td>23677.424433</td>\n",
+              "      <td>-0.000709</td>\n",
+              "      <td>0.001463</td>\n",
+              "      <td>26995.898047</td>\n",
+              "      <td>27261.296466</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>RandomForest</th>\n",
+              "      <td>23676.962601</td>\n",
+              "      <td>8877.283827</td>\n",
+              "      <td>-0.075178</td>\n",
+              "      <td>0.855658</td>\n",
+              "      <td>27982.335432</td>\n",
+              "      <td>10364.788027</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                           MAE                      R2            \\\n",
+              "split                     test         train      test     train   \n",
+              "model                                                              \n",
+              "GradientBoosting  23625.922747  20860.335930 -0.057447  0.205951   \n",
+              "LinearRegression  23386.369896  23677.424433 -0.000709  0.001463   \n",
+              "RandomForest      23676.962601   8877.283827 -0.075178  0.855658   \n",
+              "\n",
+              "                          RMSE                \n",
+              "split                     test         train  \n",
+              "model                                         \n",
+              "GradientBoosting  27750.650439  24310.182306  \n",
+              "LinearRegression  26995.898047  27261.296466  \n",
+              "RandomForest      27982.335432  10364.788027  "
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Таблица результатов (train/test)\n",
+        "pivot_before = results_before.pivot_table(index=\"model\", columns=\"split\", values=[\"MAE\", \"RMSE\", \"R2\"])\n",
+        "pivot_before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "92be75ab",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAGGCAYAAAAAW6PhAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAiT1JREFUeJzs3XtcVVX+//E3HOKmYyYiiTpWFiiKQCBqat6vXVQiyy5mUuoo2lRSoZnmJUfUUgMrKzXTMVNSy3Esb9/MSmsowDJI1CZSQEjNTC5y2L8//LGnI5BgXA7wej4e5/Fgr8/ae6911oHF+Zx91nYwDMMQAAAAAAAAAMAuONZ0AwAAAAAAAAAA/0PSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAVQaQzDqJXHBgDAnlTVnMdcCgCwZ7yfBGyRtAXszIMPPihfX1/de++9ZdZ5/PHH5evrq2eeeabU+L333itfX199+OGHpcafeeYZ+fr6lvno1q1budo6fvx4bdiwQZJ0+PBhjRw5slz7VURmZqbGjh2r48ePm2UPPPCAtm3bVunnAgDYr+L58fePtm3b6uabb1ZYWJi2bNliU79Pnz7y9fXVk08+WeYxR4wYIV9fX7388ss25bt379ZDDz2kkJAQ+fv7q3///po7d65+/vnny7bp948RI0Zctl8XLlxQWFiYPvvsM0lSQkKCxo4dW96npdwunacLCgo0aNAgJSYmVvq5AAC1U0Xn2oKCAr366qsaNGiQAgMDNXDgQMXGxqqgoKBc5+P9JPDHnGq6AQBKcnR0VGJiojIzM3XttdfaxM6fP689e/aUue/Ro0f19ddfy8fHR++8844GDhxYaj1PT0/FxsaWGrvqqqsu28b33ntPWVlZuuuuuyRJ27dv19dff33Z/Srqs88+08cff2xTNnXqVEVERKhz587y8PCo9HMCAOyTn5+fZsyYYW5brVZlZmZq1apVeuqpp9S4cWP17NnTjDs6OmrPnj3Kz8+Xi4uLzbF++uknJSUllTjHpk2bFB0drXvvvVejR4+Wm5ub0tLStHz5cu3Zs0fx8fG6+uqry2zT7zVo0OCyfXr11Vd17bXX6pZbbpEkbdiwQUeOHLnsfhV16Tzt7OysKVOm6Omnn9aWLVvk6upa6ecEANQ+FZlr58yZo/fff18TJkyQv7+/Dh48qLi4OJ04cUIvvPDCH56H95PA5ZG0BeyQn5+f0tLStH37do0ePdomtmfPHrm5ualRo0al7vvee++pRYsWGjdunKZMmaL//ve/at26dYl6zs7OCgwMvKL25eXlaeHChZoxY4YcHav/gn0/Pz917NhRr7zyip599tlqPz8AoGY0bNiw1Lnr1ltvVdeuXfXee+/ZJG1vvvlm/ec//9HevXvVv39/m322bdumdu3a6bvvvrMpj4uL02233aaZM2eaZV26dFFISIiGDh2qDRs26JFHHrlsm8rj5MmTWr58udatW3dF+/9Z/fr10+LFi7Vu3To9/PDDNdIGAIB9Ke9ce/r0ab377ruaMmWKOS927dpVkrRo0SJNmTJFTZo0KfUcvJ8EyoflEQA75O7urp49e2r79u0lYtu2bdPAgQPl5FTyMxer1arNmzerd+/e6tevn9zd3bV+/fpKb198fLzy8/PVu3dvSdLLL79sXrX7+6+ZFhUVafny5erfv786dOiggQMH6u2337Y51o8//qjx48erc+fOCggI0D333GN+Evree+8pOjpaktS3b1+b5SDuuOMObdy4UadOnar0/gEAahcXFxc5OzvLwcHBprxVq1bq0KFDmfPpbbfdVqI8Jyen1HXv2rZtq+joaHXo0KHS2r1y5Up5e3ubx3zmmWe0adMmHT9+XL6+vnrvvfckSfn5+YqJiVHPnj3VoUMH3XHHHSW+1vnNN9/ooYceUnBwsIKCgjR69Ghz6YOy5mnp4ny6cuXKcn+VFQBQP1061547d0733nuv+vTpY1PvhhtukCSlp6eXeSzeTwLlQ9IWsFNDhgwxl0godu7cOe3du1e33357qfvs3btX2dnZGjZsmFxdXTV48GBt2rSpzDdihYWFpT4ut0j7+++/r169esnZ2VmSdPfddys8PFyStH79et19992SpJkzZ2rp0qW68847zbWOXnjhBcXFxUm6OAmPGzdOubm5iomJ0bJly9S4cWP97W9/03//+1/16tVLf/vb3yRJsbGxmjBhgtmGPn36yGq1aseOHeV5OgEAdYBhGDbzVX5+vo4eParo6Gj99ttvGjp0aIl9hgwZYi6RUOzo0aNKSUkpNWnbq1cv/etf/9LEiRO1detWZWVlmbHRo0erS5cuf9imisynH3zwgc0yRhMmTFDPnj3l6emp9evXq1evXjIMQxMnTtQ777yjhx9+WK+88oqCgoL0+OOPa/PmzZIu/n/wyCOP6JprrtHLL7+sl156Sbm5uYqIiNCvv/5a5jwtSYMGDVJWVpa++OKLP2wrAKB+KO9c26pVK82cOdNM0hbbtWuXrrrqKl133XVlnoP3k0D5sDwCYKd69eolNzc3myUSduzYIQ8PDwUHB5e6z3vvvScfHx/5+/tLksLCwrRx40Z9+OGHuuOOO2zqHj9+XO3bty/1OE899ZQiIiJKjZ07d04HDx7U4MGDzbJrr73WXHu3+Ks0x44d07vvvqsnnnjCvKFK9+7d5eDgoNdee0333XefCgsLdfToUfNNqiR17NjRXLy+SZMm+utf/ypJateunVq2bGme093dXW3atNHnn3+ue+65p8znEQBQd3z55Zcl5i4HBwf5+PhoyZIl5hU7vzd48GAtWLDAZomEbdu2KSgoSN7e3iXqz549W0VFRfroo4+0c+dOSdJf//pX9e3bVw8//LC8vLwu26ZiS5Ys0aBBg0qNHTlyRNnZ2erYsaNZ9te//lVNmjSxWcLo008/1SeffKKXXnpJQ4YMkST16NFDubm5WrhwoW6//XalpaXp9OnTGjVqlG6++WZJF690Wr9+vX777bdS5+lirVu31tVXX63PP/9c3bt3L7WtAID640rm2mI7duzQpk2b9MADD9is//57vJ8Eyo+kLWCnXF1d1adPH5uk7b/+9S8NHjy4xNc/JenUqVPas2ePxo8fr7Nnz0qSbrrpJrVo0ULr168vkbT19PTUK6+8Uuq5mzdvXma7MjIyZLVabSa80uzfv1+GYahPnz4qLCw0y/v06aNXXnlFCQkJ6tu3r2688UZNnz5d+/btU/fu3XXrrbeaX2G5nBYtWuinn34qV10AQO3Xvn17Pf/885Iurge7ePFiXbhwQYsXLy5xpU8xb29vBQYGavv27TZJ2/vvv7/U+n/5y1+0dOlS/fTTT/r444914MABHThwQCtXrtT69eu1YsUKBQUFldqmSxW/USxN8ddGLzeffv7553JwcFDPnj1LzKfvv/++Dh8+rJtuuklNmjTR+PHjNWjQIPXo0UPdunVTVFTUHx67mLe3N/MpAEDSlc21kvTRRx/pySefVHBw8B/OP7yfBMqPpC1gxwYPHqzIyEhlZmbKxcVFn3/+uf7+97+XWvf999/XhQsX9PLLL9usVSddvKr2yJEjatOmjVnm7OxsXpFbEb/++quki59M/pEzZ85IUqlfPZWkrKwsOTg4aMWKFXrllVe0Y8cObd68WVdddZX69eun559/vsxPZ4u5ubmZ7QEA1H0NGjSwmbsCAgJ05513asyYMXrvvffKvOHJ4MGDtWTJEuXn5+vYsWP64YcfyrwCtljLli11//336/7771dRUZF27typZ555RrNnzzbXmi2tTeVVPH+5ubn9Yb0zZ87IMAzzCtpLnTx5Uu3atdPatWv1yiuv6N///rfWr18vV1dXDR06VM8++6z59dOyuLm56dy5cxXuAwCg7rmSuXbVqlWaP3++QkNDFRcXJxcXlzKPz/tJoPxI2gJ27NZbb1WDBg20fft2ubu7q2XLlmXeACU+Pt5c4+73zp8/rwkTJmjdunWVcmfMa665RpLMq3nL0qhRI0nSW2+9pQYNGpSIF38l1cvLSzNnztSMGTOUkpKi7du36/XXX9c111yjGTNm/OE5zp49a7YHAFD/NG3aVM8995wee+wxzZ07V4sWLSq13qBBg/SPf/xDn3zyiQ4ePKguXbrIw8OjRL0PP/xQM2bM0Lp163T99deb5Y6OjhowYIC+/PJLvfvuu5XS9vLOp3/5y1/k7u6u1atXlxpv3bq1pIvLISxYsEBWq1XJycnasmWL1q1bp7/+9a/mXb3Lcvbs2VKXigAA4I/mWsMwNHfuXL399tu6/fbbNW/evMt+UMj7SaD8uBEZYMecnZ3Vr18/ffjhh/r3v/9d5qeMBw8e1Pfff6+wsDB17tzZ5tG7d2916dJFW7ZsUV5e3p9uk5eXlywWi80N0qSLb2h/LyQkRJJ0+vRp+fv7m49Tp05pyZIlOnPmjL7++mvdcsstSk5OloODg9q1a6fHH39cPj4+OnHiRKnH/b3MzEy1aNHiT/cJAFB7FS8HsHXr1jJvpuXl5aXg4GBt3779D+fTm266SWfOnNFbb71VavyHH36Qj49PpbS7+M3m5ebT0NBQnT9/XoZh2Myn33//veLi4lRYWKjt27erS5cuys7OlsViUVBQkGbOnKlGjRpddj41DENZWVnMpwCAMpU117744ot6++239fDDD2vhwoWXTdhKvJ8EKoIrbQE7N2TIEI0bN06Ojo5lXikbHx+vq666SgMGDCg1PnToUH322Wfatm2bwsLCJEkFBQVKTEws87y+vr6lfmXT3d1dN998sxISEsy1dqX/fRK6detWBQQEyNfXV3feeaemT5+u48ePq0OHDjp27JheeukltWzZUtddd50KCwvl6uqqp556SpMmTVLTpk312Wef6bvvvtOoUaNsjrtjxw7deuut5hIPv/76qw4fPqwxY8b88RMIAKjzpk6dqjvvvFNz5szRpk2bZLFYStQZPHiw5s2bJwcHhzLnyxtuuEFjx47Va6+9phMnTujOO+/Utddeq59//llbtmzR559/rpUrV9rsc+7cuT+cT/39/Uttzw033CBvb28lJCSYa+1KF+e9nJwcffzxx2rXrp169uypTp06acKECZowYYLatGmj5ORkLV26VD169FCTJk108803q6ioSBMnTtTYsWPVoEED/fvf/9avv/5q9vXSebpVq1aSpO+//16//vqrevTo8cdPMgCgXrt0rv3+++/1+uuvy9/fX4MGDVJSUpJN/RtvvFENGzYscRzeTwLlR9IWsHO33HKLGjVqpObNm9usSVssPz9f//rXv9StWzc1bty41GMMGDBAzz//vN555x0zaZudnf2Hd8ncvHmz2rVrV2ps4MCBevnll5Wfn2+uVzRgwABt2bJFzzzzjMLDwzVz5kzNmzdPr732mt555x1lZmbKw8NDQ4YM0d///ndZLBZZLBatWLFCixYt0ty5c3X27Fldd911mjVrltnOzp0765ZbbtGiRYv0+eefa/ny5ZKkTz75RFdddZV69epV3qcSAFBH3XDDDXrwwQe1YsUKrVu3Tg888ECJOoMGDdLcuXPVq1cv/eUvfynzWE888YTatWunDRs2aM6cOTp37pwaNWqkkJAQbdy4UW3btrWpf+jQoT+cT7/88kvzDeOlBg4cqL179+qZZ54xy8LCwvTxxx9r4sSJmjx5ssaOHavly5dryZIleu211/Tzzz/Ly8tLDz/8sCZOnChJatasmd544w0tWbJE06ZNU25urm666Sa9/PLL6tKli6TS52lJ2rt3rzw9PctcMxcAAKnkXPvzzz/LMAwdPHiw1Hlw9erV6ty5c6nH4v0kUD4OhmEYNd0IALVLbm6u+vXrp6ioKA0bNqxG2vDQQw/Jx8dH06ZNq5HzAwDwZ2VlZalfv35asWKFOnXqVO3nNwxDAwcO1H333WdztRMAAFWJ95NA+bCmLYAKc3Nz06RJk/Tmm2/KarVW+/kPHjyolJQUjR07ttrPDQBAZfHy8tLo0aP1+uuv18j5P/roI1mtVt177701cn4AQP3E+0mgfEjaArgi9957r6699lpt2LCh2s89b948TZ8+XZ6entV+bgAAKtOkSZOUlZWlffv2Vet5CwoK9OKLLyomJkaurq7Vem4AAHg/CVweyyMAAAAAAOq1/Px8Pf/88/roo4/k6uqqMWPGlHmDokOHDmnGjBn6/vvvdeONN+r5559Xhw4dzPjWrVu1ePFiZWdnq3v37po9e7aaNGlSXV0BANQRXGkLAAAAAKjXYmJi9M033+itt97SjBkzFBsbq+3bt5eod/78eY0dO1YhISF67733FBQUpHHjxun8+fOSpOTkZE2bNk2RkZFav369zp49q+jo6OruDgCgDuBKWwAAAABAvXX+/Hl16dJFr7/+unm3+2XLlunzzz/X22+/bVN348aNeuWVV7Rz5045ODiYN/QbP368wsLC9NRTT8nR0VH/+Mc/JEkZGRnq3bu3duzYoVatWlV73wAAtRdX2gIAAAAA6q2UlBQVFhYqKCjILAsODlZSUpKKiops6iYlJSk4OFgODg6SJAcHB918881KTEw04yEhIWb95s2by9vbW0lJSVXfEQBAneJU0w2ojYqKilRYWChHR0dzsgYA1B2GYaioqEhOTk5ydOTzzT+LeRMA6rbaPm9mZ2frmmuukbOzs1nWtGlT5efn68yZMzbr0WZnZ+vGG2+02d/Dw0OHDx+WJJ08eVLNmjUrEc/MzCxXW5gzAaBuq8icSdL2ChQWFurgwYM13QwAQBXz9/e3eQOHK8O8CQD1Q22dN3Nzc0u0u3i7oKCgXHWL6+Xl5f1h/HKYMwGgfijPnEnS9goUZ8L9/f1lsVhquDUAgMpmtVp18ODBWnm1kD1i3gSAuq22z5suLi4lkqrF266uruWqW1yvrLibm1u52lL8HPr5+VVozrRYLJq94TP9N/tsufdB1Wnt2UjT775FVqu1Ss/DuNsXxr1+qui4W61WHTp0qFxzJknbK1D8NRWLxcKbTwCow/haYuVg3gSA+qG2zpteXl46ffq0CgsL5eR08S1ydna2XF1d1ahRoxJ1c3JybMpycnLMJRHKint6eparLcXPobOzc4XnzMIiqcDKfcbtQWFR9f3fczjzF6UcP1Xl58HlFVgNxr0equi4Fyd3yzNnkrQFAAAAANRb7dq1k5OTkxITE82biCUkJMjf37/ElVABAQF6/fXXZRiGHBwcZBiGvvrqK40fP96MJyQkKCwsTJKUkZGhjIwMBQQEVGkfrEVFmnNfjyo9ByrGWlQkSy29+hyAfeAvCAAAAACg3nJzc9OwYcM0c+ZMJScna+fOnVqxYoVGjRol6eJVt3l5eZKkQYMG6ezZs5o7d67S0tI0d+5c5ebmavDgwZKkkSNHasuWLdqwYYNSUlL01FNPqVevXmrVqlWV9oHkoP1hTAD8WfwVAQAAAADUa9HR0Wrfvr0eeughPf/885o0aZIGDBggSerevbu2bdsmSWrYsKFee+0182rapKQkLV++XO7u7pKkoKAgzZo1S3FxcRo5cqSuvvpqzZs3r8b6BQCovVgeAQAAAABQr7m5uWn+/PmaP39+iVhqaqrNdseOHbVp06YyjxUWFmYujwAAwJXiSlsAAAAAAAAAsCMkbQEAAAAAAADAjpC0BQAAAAAAAAA7QtIWAAAAAAAAAOwISVsAAAAAAAAAsCMkbQEAAAAAAADAjpC0BQAAAAAAAAA7QtIWAAAAAAAAAOwISVugilmLimq6CfgdxgMA7Bd/o+0PYwIAAFAznGq6AUBdZ3F01LP//ETHTv5S002p965vdrXm3NejWs5lLSqSxZHPxewJYwLYP+ZM+1Kd8yYAAABskbQFqsGxk78o5fipmm4GqhGJB/tC4gGoPZgz6x8+VLM/jAkAADWPpC0AVBESDwAAXB4fdNoXPugEAMA+kLStRnxibV8YDwAAAPvAB50AAAC2SNpWI64isB9cQQAAAAAAAAB7RdK2mnEVAQAAAAAAAIA/wnfDAQAAAAAAAMCOkLQFAAAAAAAAADtC0hYAAAAAAAAA7AhJWwAAYHesRUU13QRcgjEBAAAAqg83IgMAAHbH4uioZ//5iY6d/KWmmwJJ1ze7WnPu61HTzQAAAADqDZK2AADALh07+YtSjp+q6WYAAAAAQLVjeQQAAAAAAAAAsCMkbQEAAAAAAADAjpC0BQAAAAAAAAA7QtIWAAAAAAAAAOwISVsAAAAAAAAAsCMkbQEAAAAAAADAjpC0BQAAAADUW4ZhaOHCherSpYtCQ0MVExOjoqKiMuunp6dr9OjRCgwM1JAhQ7Rv375S673//vt68MEHq6rZAIA6jqQtAAAAAKDeWrlypbZu3arY2FgtXbpUH3zwgVauXFlqXcMwNHHiRDVt2lTx8fEaOnSoIiMjdeLECZt6+/fv13PPPVcdzQcA1FEkbQEAAAAA9dbq1as1efJkhYSEqEuXLpoyZYrWrl1bat39+/crPT1ds2bNUps2bTRu3DgFBgYqPj7erBMbG6tHH31UrVq1qq4uAADqIJK2AAAAAIB6KSsrSxkZGerUqZNZFhwcrOPHj+vkyZMl6iclJcnPz0/u7u429RMTE83tTz/9VG+++aYGDBhQpW0HANRtTjXdAAAAAAAAakJ2drYkqVmzZmZZ06ZNJUmZmZk25cX1Ly3z8PBQZmamub1u3TpJ0oEDB664XVar9Yr3Rf1hsVhqugkoRVX//jLu9qm8416R1wdJWwAAAABAnZWXl6esrKxSY+fPn5ckOTs7m2XFPxcUFJSon5uba1O3uH5pdf+MgwcPVurxUPe4ubnJz8+vppuBUqSmpio3N7dKjs2426+qGHeStgAAAACAOispKUmjRo0qNRYVFSXpYoLWxcXF/Fm6mBy5lIuLi86cOWNTVlBQIFdX10psseTv78/VdEAt5evrW9NNQA0o77hbrdZyfzBH0hYAgFoiKytLc+fO1f79++Xi4qIhQ4boiSeekIuLi+bMmaO3337bpv706dP1wAMPSJK2bt2qxYsXKzs7W927d9fs2bPVpEkTSRfvhL1o0SJt3LhRRUVFCg8P15QpU+ToeHHp+9OnT+u5557Tvn37dM011+ixxx7T0KFDq7fzAABcoc6dOys1NbXUWFZWlhYsWKDs7Gy1bNlS0v+WTPD09CxR38vLS2lpaTZlOTk5JZZM+LMsFgtJW6CW4ne3fqqKcedGZAAA1AKGYWjy5MnKzc3V2rVr9dJLL2nPnj1avHixJOnIkSN68skntW/fPvNx1113SZKSk5M1bdo0RUZGav369Tp79qyio6PNY69cuVJbt25VbGysli5dqg8++EArV64049HR0fr111+1fv16/e1vf9Ozzz6r5OTkau0/AABVwcvLS97e3kpISDDLEhIS5O3tXWoiNiAgQN9++63y8vJs6gcEBFRLewEA9QdX2gIAUAscPXpUiYmJ+vTTT80bpEyePFnz58/X008/rSNHjigiIqLUq4LWrFmjwYMHa9iwYZKkmJgY9e7dW+np6WrVqpVWr16tyZMnKyQkRJI0ZcoULVmyRBEREfrxxx+1Z88e7dq1Sy1btpSPj48SExP1z3/+Ux07dqy2/gMAUFVGjhyphQsX6tprr5UkLVq0SGPGjDHjp06dkouLixo0aKDQ0FA1b95c0dHRmjBhgvbs2aPk5GTNmzevppoPAKijuNIWAIBawNPTU2+88YaZsC127tw5nTt3TllZWbruuutK3TcpKclMyEpS8+bN5e3traSkJGVlZSkjI0OdOnUy48HBwTp+/LhOnjyppKQkNW/e3PzKaHH866+/rtwOAgBQQyIiIjRkyBBFRkaaSwCNHj3ajIeHh2vFihWSLn79ddmyZcrOzlZYWJjef/99xcXFydvbu4ZaDwCoq7jSFgCAWqBRo0bq0aOHuV1UVKQ1a9aoS5cuOnLkiBwcHPTqq69q7969aty4sR5++GENHz5cknTy5MkSX/H08PBQZmamuW7f7+PFieHieGn7lnUX7j9itVrLXZe1wOxTRcbwSjDu9olxr58qMu5V/RqpahaLRdHR0TZLB/3e7t27bbZbt26tNWvWXPa4kyZNqpT2AQDqJ5K2AADUQgsWLNChQ4e0ceNGffvtt3JwcNANN9ygBx54QF9++aWmT5+uhg0bqn///srLy5Ozs7PN/s7OziooKDDX5Pt9vPjngoIC5ebmlrlvRZX3Lqlubm7y8/Or8PFR9VJTU5Wbm1slx2bc7RfjXj9V5bgDAIDLI2kLAEAts2DBAr311lt66aWX5OPjo5tuukm9e/dW48aNJUlt27bVDz/8oHXr1ql///5ycXEpkWQtKCiQm5ubTYLWxcXF/Fm6mEwpa19XV9cKt9vf358r6mo5X1/fmm4CagDjXj9VZNytVmu5P5gDAADlQ9IWAIBaZPbs2Vq3bp0WLFiggQMHSpIcHBzMhG2xG264Qfv375d08c7YOTk5NvGcnBx5enrKy8tLkpSdnW2uW1u8ZEJxvKx9K8pisZC0reUYv/qJca+fGHcAAGoWNyIDAKCWiI2N1TvvvKMXX3xRt912m1m+ZMkSmxumSFJKSopuuOEGSVJAQIASEhLMWEZGhjIyMhQQECAvLy95e3vbxBMSEuTt7a1mzZopMDBQx48fV2Zmpk08MDCwajoJAAAAAOBKWwAAaoMjR45o2bJlGjt2rIKDg82rYSWpd+/eWr58ud588031799f+/bt0+bNm7V69WpJ0siRI/Xggw8qMDBQ/v7+mjt3rnr16qVWrVqZ8YULF+raa6+VJC1atEhjxoyRJLVq1Urdu3dXVFSUpk2bpoMHD2rr1q3lugELAAAAAODKkLQFAKAW2LVrl6xWq1555RW98sorNrHU1FQtWbJES5cu1ZIlS9SiRQstWrRIQUFBkqSgoCDNmjVLS5cu1S+//KJu3bpp9uzZ5v4RERH6+eefFRkZKYvFovDwcJsrd2NiYjRt2jSNGDFCnp6eeuGFF9SxY8dq6TcAAAAA1EckbQEAqAXGjh2rsWPHlhnv16+f+vXrV2Y8LCxMYWFhpcYsFouio6MVHR1datzDw0OvvvpqxRoMAAAAALhirGkLAAAAAAAAAHaEpC0AAAAAAAAA2JEaTdpmZWVp8uTJCg0NVY8ePTRv3jzl5+dLktLT0zV69GgFBgZqyJAh2rdvn82+n332mW6//XYFBARo1KhRSk9Pt4mvWrVKPXr0UFBQkKZOnarc3Fwzlp+fr6lTpyokJETdu3fXihUrqr6zAAAAAAAAAFAONZa0NQxDkydPVm5urtauXauXXnpJe/bs0eLFi2UYhiZOnKimTZsqPj5eQ4cOVWRkpE6cOCFJOnHihCZOnKiwsDBt3LhRTZo00YQJE2QYhiTpww8/VGxsrGbNmqW33npLSUlJWrBggXnumJgYffPNN3rrrbc0Y8YMxcbGavv27TXyPAAAAAAAAADA79XYjciOHj2qxMREffrpp2ratKkkafLkyZo/f75uvfVWpaen65133pG7u7vatGmjzz//XPHx8Zo0aZI2bNigDh06aMyYMZKkefPmqVu3bvriiy/UuXNnrV69Wg899JB69+4tSXr++ecVERGhqKgoGYahDRs26PXXX1f79u3Vvn17HT58WGvXrtWgQYNq6ukAAAAAAAAAAEk1eKWtp6en3njjDTNhW+zcuXNKSkqSn5+f3N3dzfLg4GAlJiZKkpKSkhQSEmLG3Nzc1L59eyUmJspqtergwYM28cDAQF24cEEpKSlKSUlRYWGhgoKCbI6dlJSkoqKiKuotAAAAAAAAAJRPjV1p26hRI/Xo0cPcLioq0po1a9SlSxdlZ2erWbNmNvU9PDyUmZkpSX8YP3v2rPLz823iTk5Oaty4sTIzM+Xo6KhrrrlGzs7OZrxp06bKz8/XmTNn1KRJk3L3wWq1VqjPFoulQvVR9So6hleCcbc/jHv9Vd6xr47XCAAAAAAAZamxpO2lFixYoEOHDmnjxo1atWqVTVJVkpydnVVQUCBJys3NLTOel5dnbpcWNwyj1Jgk8/jldfDgwXLXdXNzk5+fX4WOj6qXmppqc5O6ysa42yfGvf6q6rEHAAAAAKAy2EXSdsGCBXrrrbf00ksvycfHRy4uLjpz5oxNnYKCArm6ukqSXFxcSiRYCwoK1KhRI7m4uJjbl8bd3NxktVpLjUkyj19e/v7+XE1Xy/n6+tZ0E1ADGPf6q7xjX7zUDgAAAAAANaHGk7azZ8/WunXrtGDBAg0cOFCS5OXlpbS0NJt6OTk55pIHXl5eysnJKRFv166dGjduLBcXF+Xk5KhNmzaSpMLCQp05c0aenp4yDEOnT59WYWGhnJwudj87O1uurq5q1KhRhdpusVhI2tZyjF/9xLjXX4w9AAAAAKA2qLEbkUlSbGys3nnnHb344ou67bbbzPKAgAB9++235lIHkpSQkKCAgAAznpCQYMZyc3N16NAhBQQEyNHRUf7+/jbxxMREOTk5qW3btmrXrp2cnJzMm5oVH9vf31+OjjX6dAAAAAAAAABAzSVtjxw5omXLlunRRx9VcHCwsrOzzUdoaKiaN2+u6OhoHT58WMuXL1dycrLCw8MlSXfddZe++uorLV++XIcPH1Z0dLRatmypzp07S5Luu+8+vfnmm9q5c6eSk5M1c+ZMjRgxQm5ubnJzc9OwYcM0c+ZMJScna+fOnVqxYoVGjRpVU08FAAAAAAAAAJhqbHmEXbt2yWq16pVXXtErr7xiE0tNTdWyZcs0bdo0hYWFqXXr1oqLi5O3t7ckqWXLlnr55Zf1wgsvKC4uTkFBQYqLi5ODg4Mk6bbbbtPx48f13HPPqaCgQAMGDFBUVJR5/OjoaM2cOVMPPfSQGjZsqEmTJmnAgAHV13kAAAAAAAAAKEONJW3Hjh2rsWPHlhlv3bq11qxZU2a8Z8+e6tmz5xUd383NTfPnz9f8+fPL32AAAAAAAAAAqAYs4goAAAAAAAAAdoSkLQAAAAAAAADYEZK2AAAAAAAAAGBHSNoCAAAAAAAAgB0haQsAAAAAAAAAdoSkLQAAAAAAAADYEZK2AAAAAIB6yzAMLVy4UF26dFFoaKhiYmJUVFRUZv309HSNHj1agYGBGjJkiPbt22cTj4+P16BBgxQUFKS7775bCQkJVd0FAEAdRNIWAAAAAFBvrVy5Ulu3blVsbKyWLl2qDz74QCtXriy1rmEYmjhxopo2bar4+HgNHTpUkZGROnHihCRp7969mjVrliZMmKDNmzerW7duGjt2rLKysqqzSwCAOoCkLQAAAACg3lq9erUmT56skJAQdenSRVOmTNHatWtLrbt//36lp6dr1qxZatOmjcaNG6fAwEDFx8dLkjZt2qRhw4bpzjvvVOvWrfX3v/9dTZs21ccff1ydXQIA1AFONd0AAAAAAABqQlZWljIyMtSpUyezLDg4WMePH9fJkyfVrFkzm/pJSUny8/OTu7u7Tf3ExERJ0iOPPKIGDRqUOM+vv/5aNR0AANRZJG0BAAAAAPVSdna2JNkkZ5s2bSpJyszMLJG0zc7OLlHm4eGhzMxMSVL79u1tYnv37tUPP/ygLl26VKhdVqu1QvVRP1kslppuAkpR1b+/jLt9Ku+4V+T1QdIWAAAAAFBn5eXllbmm7Pnz5yVJzs7OZlnxzwUFBSXq5+bm2tQtrl9a3R9//FHR0dG64447SiRzL+fgwYMVqo/6x83NTX5+fjXdDJQiNTVVubm5VXJsxt1+VcW4k7QFAAAAANRZSUlJGjVqVKmxqKgoSRcTtC4uLubP0sXkyKVcXFx05swZm7KCggK5urralB07dkwPP/ywWrVqpTlz5lS4zf7+/lxNB9RSvr6+Nd0E1IDyjrvVai33B3MkbQEAAAAAdVbnzp2VmppaaiwrK0sLFixQdna2WrZsKel/SyZ4enqWqO/l5aW0tDSbspycHJslEw4fPqzRo0erVatWeuONN0okdMvDYrGQtAVqKX5366eqGHfHSj8iAAAAAAC1gJeXl7y9vZWQkGCWJSQkyNvbu8TatZIUEBCgb7/9Vnl5eTb1AwICJEknT57UmDFj1Lp1a7355ptq2LBh1XcCAFAncaUtAAAAAKDeGjlypBYuXKhrr71WkrRo0SKNGTPGjJ86dUouLi5q0KCBQkND1bx5c0VHR2vChAnas2ePkpOTNW/ePEnS/PnzVVRUpLlz5+r8+fPmmrnu7u5q0KBB9XcOAFBrkbQFAAAAANRbERER+vnnnxUZGSmLxaLw8HCNHj3ajIeHh2v48OGaNGmSLBaLli1bpmnTpiksLEytW7dWXFycvL29ZRiGdu7cqby8PA0aNMjmHJGRkZo0aVI19wwAUJuRtAUAAAAA1FsWi0XR0dGKjo4uNb57926b7datW2vNmjUl6jk4OCgpKalK2ggAqH9Y0xYAAAAAAAAA7AhJWwAAAAAAAACwIyRtAQAAAAAAAMCOkLQFAAAAAAAAADtC0hYAAAAAAAAA7AhJWwAAAAAAAACwIyRtAQAAAAAAAMCOkLQFAAAAAAAAADtC0hYAgFoiKytLkydPVmhoqHr06KF58+YpPz9fkpSenq7Ro0crMDBQQ4YM0b59+2z2/eyzz3T77bcrICBAo0aNUnp6uk181apV6tGjh4KCgjR16lTl5uaasfz8fE2dOlUhISHq3r27VqxYUfWdBQAAAIB6jKQtAAC1gGEYmjx5snJzc7V27Vq99NJL2rNnjxYvXizDMDRx4kQ1bdpU8fHxGjp0qCIjI3XixAlJ0okTJzRx4kSFhYVp48aNatKkiSZMmCDDMCRJH374oWJjYzVr1iy99dZbSkpK0oIFC8xzx8TE6JtvvtFbb72lGTNmKDY2Vtu3b6+R5wEAAAAA6gOnmm4AAAC4vKNHjyoxMVGffvqpmjZtKkmaPHmy5s+fr1tvvVXp6el655135O7urjZt2ujzzz9XfHy8Jk2apA0bNqhDhw4aM2aMJGnevHnq1q2bvvjiC3Xu3FmrV6/WQw89pN69e0uSnn/+eUVERCgqKkqGYWjDhg16/fXX1b59e7Vv316HDx/W2rVrNWjQoBp7PgAAAACgLuNKWwAAagFPT0+98cYbZsK22Llz55SUlCQ/Pz+5u7ub5cHBwUpMTJQkJSUlKSQkxIy5ubmpffv2SkxMlNVq1cGDB23igYGBunDhglJSUpSSkqLCwkIFBQXZHDspKUlFRUVV1FsAAAAAqN+40hYAgFqgUaNG6tGjh7ldVFSkNWvWqEuXLsrOzlazZs1s6nt4eCgzM1OS/jB+9uxZ5efn28SdnJzUuHFjZWZmytHRUddcc42cnZ3NeNOmTZWfn68zZ86oSZMm5e6D1Wotd12LxVLuuqg+FRnDK8G42yfGvX6qyLhX9WsEAID6iKQtAAC10IIFC3To0CFt3LhRq1atskmqSpKzs7MKCgokSbm5uWXG8/LyzO3S4oZhlBqTZB6/vA4ePFiuem5ubvLz86vQsVE9UlNTbW5SV5kYd/vFuNdPVTnuAADg8kjaAgBQyyxYsEBvvfWWXnrpJfn4+MjFxUVnzpyxqVNQUCBXV1dJkouLS4kEa0FBgRo1aiQXFxdz+9K4m5ubrFZrqTFJ5vHLy9/fnyvqajlfX9+abgJqAONeP1Vk3IuX2gEAAJWHpC0AALXI7NmztW7dOi1YsEADBw6UJHl5eSktLc2mXk5OjrnkgZeXl3JyckrE27Vrp8aNG8vFxUU5OTlq06aNJKmwsFBnzpyRp6enDMPQ6dOnVVhYKCeni/82ZGdny9XVVY0aNapQ2y0WC0nbWo7xq58Y9/qJcQcAoGZxIzIAAGqJ2NhYvfPOO3rxxRd12223meUBAQH69ttvzaUOJCkhIUEBAQFmPCEhwYzl5ubq0KFDCggIkKOjo/z9/W3iiYmJcnJyUtu2bdWuXTs5OTmZNzUrPra/v78cHfk3AgAAAACqAu+2AACoBY4cOaJly5bp0UcfVXBwsLKzs81HaGiomjdvrujoaB0+fFjLly9XcnKywsPDJUl33XWXvvrqKy1fvlyHDx9WdHS0WrZsqc6dO0uS7rvvPr355pvauXOnkpOTNXPmTI0YMUJubm5yc3PTsGHDNHPmTCUnJ2vnzp1asWKFRo0aVZNPBwAAAADUaSyPAABALbBr1y5ZrVa98soreuWVV2xiqampWrZsmaZNm6awsDC1bt1acXFx8vb2liS1bNlSL7/8sl544QXFxcUpKChIcXFxcnBwkCTddtttOn78uJ577jkVFBRowIABioqKMo8fHR2tmTNn6qGHHlLDhg01adIkDRgwoPo6DwAAAAD1DElbAABqgbFjx2rs2LFlxlu3bq01a9aUGe/Zs6d69ux5Rcd3c3PT/PnzNX/+/PI3GAAAAABwxVgeAQAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAPWWYRhauHChunTpotDQUMXExKioqKjM+unp6Ro9erQCAwM1ZMgQ7du3zya+cuVK9erVSwEBAYqIiNAPP/xQxT0AANRFJG0BAAAAAPXWypUrtXXrVsXGxmrp0qX64IMPtHLlylLrGoahiRMnqmnTpoqPj9fQoUMVGRmpEydOSJLef/99xcXF6fnnn9eWLVvUuHFjjR8/XoZhVGeXAAB1AElbAAAAAEC9tXr1ak2ePFkhISHq0qWLpkyZorVr15Zad//+/UpPT9esWbPUpk0bjRs3ToGBgYqPj5ck/frrr4qKilLPnj113XXX6dFHH9WxY8d06tSp6uwSAKAOcKrpBgAAAAAAUBOysrKUkZGhTp06mWXBwcE6fvy4Tp48qWbNmtnUT0pKkp+fn9zd3W3qJyYmSpLuv/9+s/zXX3/VP//5T910001q0qRJ1XYEAFDnkLQFAAAAANRL2dnZkmSTnG3atKkkKTMzs0TSNjs7u0SZh4eHMjMzbco2btyoadOmydnZWW+++aYcHByqovkAgDrMLpK2BQUFCgsL0/Tp09W5c2dJ0pw5c/T222/b1Js+fboeeOABSdLWrVu1ePFiZWdnq3v37po9e7b56aVhGFq0aJE2btyooqIihYeHa8qUKXJ0vLgaxOnTp/Xcc89p3759uuaaa/TYY49p6NCh1dhjAAAAAEB1yMvLU1ZWVqmx8+fPS5KcnZ3NsuKfCwoKStTPzc21qVtc/9K6t9xyizZt2qT4+HhNmDBBmzZtUqtWrcrdZqvVWu66qL8sFktNNwGlqOrfX8bdPpV33Cvy+qjxpG1+fr6efPJJHT582Kb8yJEjevLJJzV8+HCzrGHDhpKk5ORkTZs2Tc8//7zatm2ruXPnKjo6Wq+99pok24XkCwsLFRUVJQ8PD0VEREiSoqOjlZeXp/Xr1yspKUnPPvusrr/+enXs2LGaeg0AAAAAqA5JSUkaNWpUqbGoqChJFxO0Li4u5s+S5ObmVqK+i4uLzpw5Y1NWUFAgV1dXmzJvb295e3urXbt2+uKLL7R582ZNmjSp3G0+ePBgueuifnJzc5Ofn19NNwOlSE1NVW5ubpUcm3G3X1Ux7jWatE1LS9OTTz5Z6p00jxw5ooiICHl6epaIrVmzRoMHD9awYcMkSTExMerdu7fS09PVqlUrm4XkJWnKlClasmSJIiIi9OOPP2rPnj3atWuXWrZsKR8fHyUmJuqf//wnSVsAAAAAqGM6d+6s1NTUUmNZWVlasGCBsrOz1bJlS0n/WzKhtPeiXl5eSktLsynLyckxl0zYv3+/mjVrphtuuEGS5ODgoBtuuEGnT5+uUJv9/f25mg6opXx9fWu6CagB5R13q9Va7g/majRp+8UXX6hz5856/PHHFRgYaJafO3dOWVlZuu6660rdLykpSY8++qi53bx5c3l7eyspKUnOzs5/uJB8UlKSmjdvbk7IxfHiq3QBAAAAAPWDl5eXvL29lZCQYL5HTEhIkLe3d4m1ayUpICBAy5cvV15ennl1bUJCgoKDgyVJr7/+ulq0aKFZs2ZJuvjmPCUlpcwrfctisVhI2gK1FL+79VNVjHuNJm3vu+++UsuPHDkiBwcHvfrqq9q7d68aN26shx9+2FwqobS7eBYv/n65heTLWji+rDWOAAAAAAB118iRI7Vw4UJde+21kqRFixZpzJgxZvzUqVNycXFRgwYNFBoaqubNmys6OloTJkzQnj17lJycrHnz5km6+B73scceU6dOndS+fXutXLlSeXl55rdEAQAorxpf07Y0R48eNb9G8sADD+jLL7/U9OnT1bBhQ/Xv3195eXllLv6el5dnbv8+Jl1ca6i8C8eXR0UXl+bTFvtTHQv8M+72h3Gvv6picXgAAFC7RURE6Oeff1ZkZKQsFovCw8M1evRoMx4eHq7hw4dr0qRJslgsWrZsmaZNm6awsDC1bt1acXFx8vb2liT17dtXM2fOVGxsrDIyMhQYGKgVK1aoQYMGNdQ7AEBtZZdJ22HDhql3795q3LixJKlt27b64YcftG7dOvXv318uLi4lkqwFBQVyc3OzSdCWtpB8WfteunB8eVRkcXgWi7ZPVblAuMS42yvGvf6q6rEHAAC1j8ViUXR0tKKjo0uN796922a7devWWrNmTZnHCw8PV3h4eKW2EQBQ/9hl0tbBwcFM2Ba74YYbtH//fkkX1x3Kycmxiefk5MjT01NeXl6SVOZC8n+0b0WxOHztxwLh9RPjXn9VxeLwAAAAAABUNrtM2i5ZskRff/21Vq1aZZalpKSYd+AMCAhQQkKCwsLCJEkZGRnKyMhQQEDAZReSDwwM1PHjx5WZmWmuWZSQkGBzI7TyYnH42o/xq58Y9/qLsQcAAAAA1AaONd2A0vTu3Vtffvml3nzzTf3444/65z//qc2bN5uLwY8cOVJbtmzRhg0blJKSoqeeekq9evVSq1atzPjChQt14MABHThwQIsWLTLv1tmqVSt1795dUVFRSklJ0YYNG7R161bdf//9NdZfAAAAAAAAAChml1faduzYUUuWLNHSpUu1ZMkStWjRQosWLVJQUJAkKSgoSLNmzdLSpUv1yy+/qFu3bpo9e7a5/+UWko+JidG0adM0YsQIeXp66oUXXlDHjh2ru5sAAAAAAAAAUILdJG1TU1Nttvv166d+/fqVWT8sLMxcHuFSl1tI3sPDQ6+++uqVNxYAAAAAAAAAqohdLo8AAAAAAAAAAPUVSVsAAAAAAAAAsCMkbQEAAAAAAADAjpC0BQAAAAAAAAA7QtIWAAAAAAAAAOwISVsAAAAAAAAAsCMkbQEAAAAAAADAjpC0BQAAAAAAAAA7QtIWAAAAAAAAAOwISVsAAKrAiRMnZBjGH9YpKCjQBx98UE0tAgAAAADUFiRtAQCoAn379tWpU6dsysaOHauTJ0+a22fPntVTTz1V3U0DAAAAANg5krYAAFSB0q6y/fLLL5Wfn18DrQEAAAAA1CYkbQEAAAAAAADAjlRq0rawsFBfffVVZR4SAAAAAAAAAOqVcidt27Vrp59//tmmbObMmTbr9Z05c0b3339/5bUOAAAAAAAAAOoZp/JWLG1tvvfff18RERFq0qTJH9YDAKA++vrrr3X11Veb24ZhKDk5WZmZmZKkX375paaaBgAAAACwY+VO2pamtAStg4PDnzkkAAB1RmRkZImyJ5980mabeRMAAAAAcCluRAYAQBVISUkp1+O7776r8LELCgp0++2368CBA2bZnDlz5Ovra/NYs2aNGd+6dav69eungIAATZw40WZ5I8MwtHDhQnXp0kWhoaGKiYlRUVGRGT99+rQmTZqkoKAg9enTR1u2bLnCZwUAAAAAUB5/6kpbAABQcadPn1bDhg111VVXVXjf/Px8Pfnkkzp8+LBN+ZEjR/Tkk09q+PDhZlnDhg0lScnJyZo2bZqef/55tW3bVnPnzlV0dLRee+01SdLKlSu1detWxcbGqrCwUFFRUfLw8FBERIQkKTo6Wnl5eVq/fr2SkpL07LPP6vrrr1fHjh2v9CkAAAAAAPyBCl1pm5mZqRMnTpgPScrKyjK3MzIyqqSRAADURjt27NCjjz6qrKwsSdJ///tfDRs2TLfccotCQkIUExNTobXg09LSNGLECP34448lYkeOHJGfn588PT3Nh5ubmyRpzZo1Gjx4sIYNG6a2bdsqJiZGH3/8sdLT0yVJq1ev1uTJkxUSEqIuXbpoypQpWrt2rSTpxx9/1J49ezRnzhz5+Pjo7rvv1p133ql//vOff/bpAQAAAACUoUJX2oaHh9tsG4ahBx54wFyPzzAM1uYDAEDSv/71Lz399NMaPny4nJ2dJUmPP/64MjIytHz5cv3lL3/R9OnTba5ovZwvvvhCnTt31uOPP67AwECz/Ny5c8rKytJ1111X6n5JSUl69NFHze3mzZvL29tbSUlJcnZ2VkZGhjp16mTGg4ODdfz4cZ08eVJJSUlq3ry5WrZsaRMvvkoXAICqUlBQoPPnz6tx48YlYkVFRcrMzJS3t3f1NwwAgGpQ7qTtrl27qrIdAADUKatWrVJ0dLTuv/9+SReXKDh06JAef/xx9ejRQ5L0xBNP6B//+Ee5k7b33XdfqeVHjhyRg4ODXn31Ve3du1eNGzfWww8/bC6VcPLkSTVr1sxmHw8PD2VmZio7O1uSbOJNmzaVJDNe2r7FVw9XhNVqLXddi8VS4eOj6lVkDK8E426fGPf6qSLjXtmvkV9//VXTp0/Xzp07ZbVa5evrq6efflpdu3Y165w6dUp9+/a9orXhAQCoDcqdtG3RosVl6yQlJWnTpk2aOXPmn2kTAAC13uHDh3Xrrbea25988okcHBzUt29fs+ymm24ylxv6M44ePSoHBwfdcMMNeuCBB/Tll19q+vTpatiwofr376+8vDzzat9izs7OKigoUF5enrn9+5h08Qqn3NzcMvetqIMHD5arnpubm/z8/Cp8fFS91NRU5ebmVsmxGXf7xbjXT1U57pfzwgsv6MSJE1qzZo0Mw9Dq1asVERGhqVOn6oEHHjDrVWSJoT9iGIYWLVqkjRs3qqioSOHh4ZoyZYocHUtfTTA9PV3Tp09XYmKivL29NXXqVHXv3r1EvaSkJN17773asWOHzTdWAAAojz99I7LMzExt2bJFmzdv1rFjx+Tp6UnSFgBQ7zk7Oys/P9/c/vTTT9W8eXPdeOONZtnJkyfVqFGjP32uYcOGqXfv3ubXR9u2basffvhB69atU//+/eXi4lIiyVpQUCA3NzebBK2Li4v5s3QxmVLWvq6urhVup7+/P1fU1XK+vr413QTUAMa9fqrIuFut1nJ/MFce//d//6cVK1aoXbt2kqSgoCCtWbNGc+fOVWFhoUaPHi1JlbY03+VuyPl7hmFo4sSJ8vHxUXx8vHbu3KnIyEht27bNZqmGCxcu6Nlnn1VRUVGltBEAUP9cUdI2Ly9PH374oTZv3qwDBw7IMAyFhIToscceU79+/Sq7jQAA1DpdunTRunXrNH36dCUlJenrr7/WmDFjbOq8+eabCg4O/tPncnBwKLHe3w033KD9+/dLkry8vJSTk2MTz8nJkaenp7y8vCRJ2dnZ5lVAxUsmFMfL2reiLBYLSdtajvGrnxj3+qkmx720K1yL76Uye/ZsWSwWDR48uNLO9/sbckrSlClTtGTJklKTtvv371d6erreeecdubu7q02bNvr8888VHx+vSZMmmfXeeOMNNWzYsNLaCACof0r/vkcZvvjiC0VHR6tbt26Kjo5Wfn6+oqKi5OjoqBkzZmjQoEFycvrTF+8CAFDrTZkyRbt27VLnzp11//33q02bNho/frwk6d///rfCwsL05Zdf6rHHHvvT51qyZIl51VGxlJQU3XDDDZKkgIAAJSQkmLGMjAxlZGQoICBAXl5e8vb2toknJCTI29tbzZo1U2BgoI4fP67MzEyb+O9vhAYAQGXq1auXnnvuOR08eFAXLlwwy++//35FRUXphRde0KJFiyrlXFlZWX94Q85LJSUlyc/PT+7u7jb1ExMTze1jx45p7dq1euaZZyqljQCA+qncGdY+ffro7Nmz6ty5s5555hn17t3bvFHJwoULq6yBAADURn/961+1fft2ffrpp7JYLLrlllvMpQjOnTunm2++WUuWLFGrVq3+9Ll69+6t5cuX680331T//v21b98+bd68WatXr5YkjRw5Ug8++KACAwPl7++vuXPnqlevXua5R44cqYULF+raa6+VJC1atMi8KrhVq1bq3r27oqKiNG3aNB08eFBbt27VmjVr/nS7AQAozTPPPKPnnntOI0eO1KuvvmqzXmxERISaNGmiWbNmVcq5LndDzktvxlnWDTqLP9w0DEPPPfecJk2aJA8Pj0ppIwCgfqrQZbEuLi5ycnJSfn7+Fd2ABACA+sTV1dXmxmPF7r777ko9T8eOHbVkyRItXbpUS5YsUYsWLbRo0SIFBQVJurgW4KxZs7R06VL98ssv6tatm2bPnm3uHxERoZ9//lmRkZGyWCwKDw+3uXI3JiZG06ZN04gRI+Tp6akXXnhBHTt2rNQ+AABQ7C9/+YteeuklnTt3rtSlEoYPH65u3bqpT58+5TpeXl6esrKySo2dP39eUtk35LzU5W7QuXHjRl24cEEjRozQ8ePHy9W+0lit1iveF/UHy9fYp6r+/WXc7VN5x70ir49yJ213796txMREbd26Va+++qrmzp0rX19fcw3byloEHgCAuiA6OrrcdefNm1fh46emptps9+vX7w/XlQ8LC1NYWFipMYvFoujo6DLb7OHhoVdffbXCbQQA4EpkZmbqmWee0YEDByRJt956q2JiYnT11VfLarVq1apVWrZsmRo0aFCu4yUlJWnUqFGlxqKioiSVfUPOS7m4uOjMmTM2ZcU36MzOztZLL72kVatW/en3x5V5YzfUTW5ubvLz86vpZqAUqampys3NrZJjM+72qyrGvUJX2gYGBiowMFDTpk3T/v37tXXrVr399tuyWq2aPHmy7r77bt15551q0qRJpTYSAIDaZtOmTXJ0dFRAQICuu+66mm4OAAC1xqxZs3T8+HHFxMToqquu0vLlyzVv3jw9/vjj+tvf/qaUlBSFh4fr8ccfL9fxOnfuXOLDzmJZWVlasGBBmTfkvJSXl5fS0tJsynJyctSsWTPt27dPp0+f1j333CPp4lIJknT77bdr/Pjx5tr25eHv78/VdEAt5evrW9NNQA0o77hbrdZyfzB3RXcNc3BwUNeuXdW1a1fNnDlTe/fu1bZt27RkyRItWrSITwUBAPXe66+/rh07dmj37t06d+6c+vbtq/79+6t9+/Y13TQAAOxaQkKCFi9erK5du0qS/Pz8NHz4cKWkpMgwDK1fv17+/v6Vcq7f35CzOGn7+xtyXiogIEDLly9XXl6eXF1dzfrBwcHq37+/br75ZrNuVlaWHnzwQS1fvlw+Pj4VapfFYiFpC9RS/O7WT1Ux7leUtP29q666Sn379lXfvn2Vl5en3bt3V0a7AACo1Xr06KEePXpo1qxZSkxM1I4dO/Tkk0+qoKDATOB26tSJ5YUAALjE2bNn1aZNG3P7r3/9qy5cuKAWLVpo8eLFuuqqqyr1fH90Q05JOnXqlFxcXNSgQQOFhoaqefPmio6O1oQJE7Rnzx4lJydr3rx5atiwoRo2bGjuV/wG3tvbW40bN67UNgMA6r5yJ21jY2Orsh0AANRZxcsLRUVFKS0tTbt27dLChQv1008/qVevXnrhhRdquokAANgNwzBKXLFksVg0adKkSk/YSpe/IWd4eLiGDx+uSZMmyWKxaNmyZZo2bZrCwsLUunVrxcXFydvbu9LbBQCo3yqUtHV0dFS7du3UoEEDc32eS3HFEAAAZWvatKm8vLx07bXX6vDhw/r8889rukkAANQK5b3xWEVd7oacl36btHXr1lqzZs1lj9uyZcsy19IFAOByyp20nTFjhnbu3KnExER16tTJXBKBm44BAPDHjh07pt27d2vXrl1KSkrSTTfdpD59+mjcuHGscQsAQCn+/e9/2yw1UFRUpI8++kgeHh429YYNG1bNLQMAoHqUO2k7cuRIjRw5UufOndPHH3+sHTt2aMGCBfLx8VG/fv3Uv39/tWjRoirbCgBArfGf//xHu3fv1p49e5Senq6QkBANGjRICxYsYL4EAOAPeHt7a8WKFTZlHh4eWrt2rU2Zg4MDSVsAQJ1V4RuRNWzYULfddptuu+02FRQU6PPPP9euXbt07733qmnTpurXr58mTpxYFW0FAKDWeOCBB3TVVVepU6dOuvfee3X11VdLkr788kt9+eWXNnV5wwkAwP9wc2sAAK4gaft7zs7O6tGjh9zd3eXu7q4NGzbo9ddfJ2kLAKj3im9I8sMPP+iHH34osx5XCQEAAAAALnVFSdvffvtNn3zyiXbv3q29e/dKknr16qV58+ape/fuldpAAABqo+KrhM6dOyeLxSI3N7cSdU6ePKmFCxdWd9MAAAAAAHau3EnbzMxM7dq1S7t379aXX34pLy8v9enTR0uXLlVwcLAsFktVthMAgFolKytLTz/9tA4cOCBJuvXWWxUTE6Orr75aVqtVq1at0rJly+Tk9Ke+9AIAAAAAqIPK/U6xd+/ecnJyUqdOnfT000/Lx8fHjH311Vc2dTt16lR5LQQAoBZ6/vnndfz4ccXExOiqq67S8uXLNW/ePD3++OP629/+ppSUFIWHh+vxxx+v6aYCAAAAAOxMuZO2hmHowoUL+uyzz/TZZ5+VWc/BwUHfffddpTQOAIDaKiEhQYsXL1bXrl0lSX5+fho+fLhSUlJkGIbWr18vf3//Gm4lAAAAAMAelTtpm5KSUpXtAACgTjl79qzatGljbv/1r3/VhQsX1KJFCy1evFhXXXVVDbYOAAAAAGDPHGu6AQAA1EWGYZRY791isWjSpEkkbAEAAAAAf4ikLQAA1ahBgwY13QQAAAAAgJ3jltUAAFSRf//732rYsKG5XVRUpI8++kgeHh429YYNG1bNLQMAAAAA2DOStgAAVAFvb2+tWLHCpszDw0Nr1661KXNwcCBpCwAAAACwQdIWAIAqsHv37ppuAgAAAACglmJNWwAAAAAAAACwIyRtAQAAAAAAAMCO2EXStqCgQLfffrsOHDhglqWnp2v06NEKDAzUkCFDtG/fPpt9PvvsM91+++0KCAjQqFGjlJ6ebhNftWqVevTooaCgIE2dOlW5ublmLD8/X1OnTlVISIi6d+9eYs1BAAAAAAAAAKgpNZ60zc/P1xNPPKHDhw+bZYZhaOLEiWratKni4+M1dOhQRUZG6sSJE5KkEydOaOLEiQoLC9PGjRvVpEkTTZgwQYZhSJI+/PBDxcbGatasWXrrrbeUlJSkBQsWmMePiYnRN998o7feekszZsxQbGystm/fXr0dBwAAAAAAAIBS1GjSNi0tTSNGjNCPP/5oU75//36lp6dr1qxZatOmjcaNG6fAwEDFx8dLkjZs2KAOHTpozJgxuummmzRv3jwdP35cX3zxhSRp9erVeuihh9S7d2917NhRzz//vOLj45Wbm6vz589rw4YNmjZtmtq3b6/+/fvrkUceKXE3bwAAAAAAAACoCTWatP3iiy/UuXNnrV+/3qY8KSlJfn5+cnd3N8uCg4OVmJhoxkNCQsyYm5ub2rdvr8TERFmtVh08eNAmHhgYqAsXLiglJUUpKSkqLCxUUFCQzbGTkpJUVFRURT0FAAAAAAAAgPJxqsmT33fffaWWZ2dnq1mzZjZlHh4eyszMvGz87Nmzys/Pt4k7OTmpcePGyszMlKOjo6655ho5Ozub8aZNmyo/P19nzpxRkyZNKqt7AAAAAAAAAFBhNZq0LUtubq5NUlWSnJ2dVVBQcNl4Xl6euV1a3DCMUmOSzOOXl9VqrVB9i8VSofqoehUdwyvBuNsfxr3+Ku/YV8drBAAAAACAsthl0tbFxUVnzpyxKSsoKJCrq6sZvzTBWlBQoEaNGsnFxcXcvjTu5uYmq9VaakySefzyOnjwYLnrurm5yc/Pr0LHR9VLTU1Vbm5ulR2fcbdPjHv9VdVjDwAAAABAZbDLpK2Xl5fS0tJsynJycswlD7y8vJSTk1Mi3q5dOzVu3FguLi7KyclRmzZtJEmFhYU6c+aMPD09ZRiGTp8+rcLCQjk5Xex+dna2XF1d1ahRowq109/fn6vpajlfX9+abgJqAONef5V37IvXRwcAAAAAoCbYZdI2ICBAy5cvV15ennn1a0JCgoKDg814QkKCWT83N1eHDh1SZGSkHB0d5e/vr4SEBHXu3FmSlJiYKCcnJ7Vt21bSxTVuExMTzZuVJSQkyN/fX46OFbsvm8ViIWlbyzF+9RPjXn8x9gAAAACA2qBiWcpqEhoaqubNmys6OlqHDx/W8uXLlZycrPDwcEnSXXfdpa+++krLly/X4cOHFR0drZYtW5pJ2vvuu09vvvmmdu7cqeTkZM2cOVMjRoyQm5ub3NzcNGzYMM2cOVPJycnauXOnVqxYoVGjRtVklwEAAAAAAABAkp1eaWuxWLRs2TJNmzZNYWFhat26teLi4uTt7S1JatmypV5++WW98MILiouLU1BQkOLi4uTg4CBJuu2223T8+HE999xzKigo0IABAxQVFWUePzo6WjNnztRDDz2khg0batKkSRowYECN9BUAAAAAAAAAfs9ukrapqak2261bt9aaNWvKrN+zZ0/17NmzzPjYsWM1duzYUmNubm6aP3++5s+ff2WNBQAAAADUCYZhaNGiRdq4caOKiooUHh6uKVOmlLl8Xnp6uqZPn67ExER5e3tr6tSp6t69uxm/8847S7y//eCDD+Tj41Ol/QAA1C12k7QFAAAAAKC6rVy5Ulu3blVsbKwKCwsVFRUlDw8PRURElKhrGIYmTpwoHx8fxcfHa+fOnYqMjNS2bdvk7e0tq9WqH374QWvWrNF1111n7nfNNddUY48AAHUBSVsAAAAAQL21evVqTZ482bxR9ZQpU7RkyZJSk7b79+9Xenq63nnnHbm7u6tNmzb6/PPPFR8fr0mTJumnn37ShQsX1LFjR7m4uFR3VwAAdYhd3ogMAAAAAICqlpWVpYyMDHXq1MksCw4O1vHjx3Xy5MkS9ZOSkuTn5yd3d3eb+omJiZKktLQ0NW/enIQtAOBP40pbAAAAAEC9lJ2dLUlq1qyZWda0aVNJUmZmpk15cf1Lyzw8PJSZmSlJOnLkiK666iqNGzdO33zzja6//no99dRT6tixY4XaZbVaK9wX1D8Wi6Wmm4BSVPXvL+Nun8o77hV5fZC0BQAAAADUWXl5ecrKyio1dv78eUmSs7OzWVb8c0FBQYn6ubm5NnWL6xfXPXbsmH755Rfdfffdmjx5st5991099NBD2rZtm5o3b17uNh88eLDcdVE/ubm5yc/Pr6abgVKkpqYqNze3So7NuNuvqhh3krYAAAAAgDorKSlJo0aNKjUWFRUl6WKCtnhJg+IErJubW4n6Li4uOnPmjE1ZQUGBXF1dJUmzZ89WXl6eGjZsKEmaOXOmvvrqK23ZskXjx48vd5v9/f25mg6opXx9fWu6CagB5R13q9Va7g/mSNoCAAAAAOqszp07KzU1tdRYVlaWFixYoOzsbLVs2VLS/5ZM8PT0LFHfy8tLaWlpNmU5OTnmkglOTk5mwlaSHBwcdMMNN5R5pW9ZLBYLSVugluJ3t36qinHnRmQAANQyBQUFuv3223XgwAGzLD09XaNHj1ZgYKCGDBmiffv22ezz2Wef6fbbb1dAQIBGjRql9PR0m/iqVavUo0cPBQUFaerUqTZf7cnPz9fUqVMVEhKi7t27a8WKFVXbQQAAqomXl5e8vb2VkJBgliUkJMjb27vE2rWSFBAQoG+//VZ5eXk29QMCAiRJDz74oGJjY81YUVGRUlNTdcMNN1RhLwAAdRFJWwAAapH8/Hw98cQTOnz4sFlmGIYmTpyopk2bKj4+XkOHDlVkZKROnDghSTpx4oQmTpyosLAwbdy4UU2aNNGECRNkGIYk6cMPP1RsbKxmzZqlt956S0lJSVqwYIF5/JiYGH3zzTd66623NGPGDMXGxmr79u3V23EAAKrIyJEjtXDhQh04cEAHDhzQokWLbJZTOHXqlH777TdJUmhoqJo3b67o6GgdPnxYy5cvV3JyssLDwyVJffr00apVq7Rr1y4dPXpUs2bN0q+//qrhw4fXSN8AALUXyyMAAFBLpKWl6cknnzSTrcX279+v9PR0vfPOO3J3d1ebNm30+eefKz4+XpMmTdKGDRvUoUMHjRkzRpI0b948devWTV988YU6d+6s1atX66GHHlLv3r0lSc8//7wiIiIUFRUlwzC0YcMGvf7662rfvr3at2+vw4cPa+3atRo0aFC1PwcAAFS2iIgI/fzzz4qMjJTFYlF4eLhGjx5txsPDwzV8+HBNmjRJFotFy5Yt07Rp0xQWFqbWrVsrLi5O3t7ekqTRo0crPz9fc+bMUU5OjgICArRy5UqbJRMAACgPkrYAANQSxUnWxx9/XIGBgWZ5UlKS/Pz85O7ubpYFBwcrMTHRjIeEhJgxNzc3tW/fXomJiQoJCdHBgwcVGRlpxgMDA3XhwgWlpKTIMAwVFhYqKCjI5tivvvqqioqK5OjIl3YAALWbxWJRdHS0oqOjS43v3r3bZrt169Zas2ZNqXUdHBw0fvz4Ct10DACA0pC0BQCglrjvvvtKLc/Ozi6x7p6Hh4cyMzMvGz979qzy8/Nt4k5OTmrcuLEyMzPl6Oioa665Rs7Ozma8adOmys/P15kzZ9SkSZNyt99qtZa7LjdwsE8VGcMrwbjbJ8a9fqrIuFf1awQAgPqIpC0AALVcbm6uTVJVkpydnVVQUHDZePGNVMqKG4ZRakySefzyOnjwYLnqubm5yc/Pr0LHRvVITU21uUldZWLc7RfjXj9V5bgDAIDLI2kLAEAt5+LiojNnztiUFRQUyNXV1YxfmmAtKChQo0aN5OLiYm5fGndzc5PVai01Jsk8fnn5+/tzRV0t5+vrW9NNQA1g3Ounioy71Wot9wdzAACgfEjaAgBQy3l5eSktLc2mLCcnx1zywMvLSzk5OSXi7dq1U+PGjeXi4qKcnBy1adNGklRYWKgzZ87I09NThmHo9OnTKiwslJPTxX8bsrOz5erqqkaNGlWonRaLhaRtLcf41U+Me/3EuAMAULO4ewgAALVcQECAvv32W3OpA0lKSEhQQECAGU9ISDBjubm5OnTokAICAuTo6Ch/f3+beGJiopycnNS2bVu1a9dOTk5O5k3Nio/t7+/PTcgAAAAAoIrwbgsAgFouNDRUzZs3V3R0tA4fPqzly5crOTlZ4eHhkqS77rpLX331lZYvX67Dhw8rOjpaLVu2VOfOnSVdvMHZm2++qZ07dyo5OVkzZ87UiBEj5ObmJjc3Nw0bNkwzZ85UcnKydu7cqRUrVmjUqFE12WUAAAAAqNNYHgEAgFrOYrFo2bJlmjZtmsLCwtS6dWvFxcXJ29tbktSyZUu9/PLLeuGFFxQXF6egoCDFxcXJwcFBknTbbbfp+PHjeu6551RQUKABAwYoKirKPH50dLRmzpyphx56SA0bNtSkSZM0YMCAGukrAAAAANQHJG0BAKiFUlNTbbZbt26tNWvWlFm/Z8+e6tmzZ5nxsWPHauzYsaXG3NzcNH/+fM2fP//KGgsAAAAAqBCWRwAAAAAAAAAAO0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO+JU0w0AAAAAAAAAUD7XN7u6ppuA/68qx4KkLQAAAAAAAFALWIuKNOe+HjXdDPyOtahIFsfKX8yA5REAAAAAAACAWqAqkoP4c6pqTBhpAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO0LSFgAAAABQbxmGoYULF6pLly4KDQ1VTEyMioqKyqyfnp6u0aNHKzAwUEOGDNG+ffts4l988YWGDh2qgIAAjRgxQikpKVXdBQBAHUTSFgAAAABQb61cuVJbt25VbGysli5dqg8++EArV64sta5hGJo4caKaNm2q+Ph4DR06VJGRkTpx4oSkiwndRx99VP3799eWLVvk6+urCRMmqKCgoDq7BACoA0jaAgAAAADqrdWrV2vy5MkKCQlRly5dNGXKFK1du7bUuvv371d6erpmzZqlNm3aaNy4cQoMDFR8fLwkac2aNerYsaMiIyN13XXXaerUqXJ0dNTRo0ers0sAgDrAqaYbAAAAAABATcjKylJGRoY6depklgUHB+v48eM6efKkmjVrZlM/KSlJfn5+cnd3t6mfmJgo6eLSCGFhYWbMzc1NO3furNpOAADqJJK2AAAAAIB6KTs7W5JskrNNmzaVJGVmZpZI2mZnZ5co8/DwUGZmpqSLyyO4urpq8uTJ+s9//qMbb7xRzz33nG688cYKtctqtVa4L6h/LBZLTTcBpeD3F3+kIq8PkrYAAAAAgDorLy9PWVlZpcbOnz8vSXJ2djbLin8ubR3a3Nxcm7rF9Yvrnj9/XgsXLlRkZKTGjRun1atXa/To0frwww/VoEGDcrf54MGD5a6L+snNzU1+fn413QyUIjU1Vbm5uTXdDNQBJG0BAAAAAHVWUlKSRo0aVWosKipK0sUErYuLi/mzdDEpdikXFxedOXPGpqygoECurq6SLl752KdPHz344IOSpNmzZ6tXr17avXu37rjjjnK32d/fn6sogVrK19e3ppsAO2a1Wsv9wRxJWwAAAABAndW5c2elpqaWGsvKytKCBQuUnZ2tli1bSvrfkgmenp4l6nt5eSktLc2mLCcnx1wywdPTU9dff70Zc3Z2VosWLZSRkVGhNlssFpK2QC3F7y4qi2NNNwAAAAAAgJrg5eUlb29vJSQkmGUJCQny9vYusXatJAUEBOjbb79VXl6eTf2AgABJUmBgoE2CuKCgQOnp6WZCGACA8uJKWwAAAABAvTVy5EgtXLhQ1157rSRp0aJFGjNmjBk/deqUXFxc1KBBA4WGhqp58+aKjo7WhAkTtGfPHiUnJ2vevHmSpIceekj333+/goODdcstt+iNN96Qi4uLevXqVRNdAwDUYlxpCwAAAACotyIiIjRkyBBFRkbqscce09ChQzV69GgzHh4erhUrVki6+LXnZcuWKTs7W2FhYXr//fcVFxcnb29vSRevxF28eLFWr16tO+64Q0eOHNEbb7whd3f3mugaAKAW40pbAAAAAEC9ZbFYFB0drejo6FLju3fvttlu3bq11qxZU+bx+vXrp379+lVqGwEA9Y9dX2m7Y8cO+fr62jwmT54sSTp06JDuvvtuBQQE6K677tI333xjs+/WrVvVr18/BQQEaOLEiTp16pQZMwxDCxcuVJcuXRQaGqqYmBgVFRVVa98AAAAAAAAAoDR2nbRNS0tT7969tW/fPvMxZ84cnT9/XmPHjlVISIjee+89BQUFady4cTp//rwkKTk5WdOmTVNkZKTWr1+vs2fP2nxqunLlSm3dulWxsbFaunSpPvjgA61cubKmugkAAAAAAAAAJrtO2h45ckQ+Pj7y9PQ0H40aNdK2bdvk4uKip556Sm3atNG0adPUoEEDbd++XZK0Zs0aDR48WMOGDVPbtm0VExOjjz/+WOnp6ZKk1atXa/LkyQoJCVGXLl00ZcoUrV27tia7CgAAAAAAAACSakHS9rrrritRnpSUpODgYDk4OEiSHBwcdPPNNysxMdGMh4SEmPWbN28ub29vJSUlKSsrSxkZGerUqZMZDw4O1vHjx3Xy5Mkq7Q8AAAAAAAAAXI7d3ojMMAwdO3ZM+/bt02uvvSar1apBgwZp8uTJys7O1o033mhT38PDQ4cPH5YknTx5Us2aNSsRz8zMVHZ2tiTZxJs2bSpJyszMLLHfH7FarRXqk8ViqVB9VL2KjuGVYNztD+Nef5V37KvjNQIAAAAAQFnsNml74sQJ5ebmytnZWYsXL9ZPP/2kOXPmKC8vzyz/PWdnZxUUFEiS8vLyyozn5eWZ27+PSTL3L6+DBw+Wu66bm5v8/PwqdHxUvdTUVOXm5lbZ8Rl3+8S4119VPfYAAAAAAFQGu03atmjRQgcOHNDVV18tBwcHtWvXTkVFRYqKilJoaGiJBGtBQYFcXV0lSS4uLqXG3dzcbBK0Li4u5s/SxURLRfj7+3M1XS3n6+tb001ADWDc66/yjr3Vaq3QB3MAAAAAAFQmu03aSlLjxo1tttu0aaP8/Hx5enoqJyfHJpaTk2MubeDl5VVq3NPTU15eXpKk7OxstWzZ0vxZkjw9PSvUPovFQtK2lmP86ifGvf5i7AEAAAAAtYHd3ojsk08+UefOnW2+xvrdd9+pcePGCg4O1tdffy3DMCRdXP/2q6++UkBAgCQpICBACQkJ5n4ZGRnKyMhQQECAvLy85O3tbRNPSEiQt7d3hdazBQAAAAAAAICqYLdJ26CgILm4uOjZZ5/V0aNH9fHHHysmJkaPPPKIBg0apLNnz2ru3LlKS0vT3LlzlZubq8GDB0uSRo4cqS1btmjDhg1KSUnRU089pV69eqlVq1ZmfOHChTpw4IAOHDigRYsWadSoUTXZXQAA/rQdO3bI19fX5jF58mRJ0qFDh3T33XcrICBAd911l7755hubfbdu3ap+/fopICBAEydO1KlTp8yYYRhauHChunTpotDQUMXExKioqKha+wYAAAAA9YndJm0bNmyoN998U6dOndJdd92ladOm6Z577tEjjzyihg0b6rXXXlNCQoLCwsKUlJSk5cuXy93dXdLFhO+sWbMUFxenkSNH6uqrr9a8efPMY0dERGjIkCGKjIzUY489pqFDh2r06NE11FMAACpHWlqaevfurX379pmPOXPm6Pz58xo7dqxCQkL03nvvKSgoSOPGjdP58+clScnJyZo2bZoiIyO1fv16nT17VtHR0eZxV65cqa1btyo2NlZLly7VBx98oJUrV9ZUNwEAAACgzrPrNW1vuummMt8UduzYUZs2bSpz37CwMIWFhZUas1gsio6OtnlDCgBAbXfkyBH5+PiUWKN948aNcnFx0VNPPSUHBwdNmzZNe/fu1fbt2xUWFqY1a9Zo8ODBGjZsmCQpJiZGvXv3Vnp6ulq1aqXVq1dr8uTJCgkJkSRNmTJFS5YsUURERHV3EQAAAADqBbu90hYAAFTMkSNHdN1115UoT0pKUnBwsBwcHCRJDg4Ouvnmm5WYmGjGixOyktS8eXN5e3srKSlJWVlZysjIUKdOncx4cHCwjh8/rpMnT1ZpfwAAAACgviJpCwBAHWAYho4dO6Z9+/Zp4MCB6tevnxYuXKiCggJlZ2eXuNmmh4eHMjMzJUknT54sM56dnS1JNvGmTZtKkrk/AAAAAKBy2fXyCAAAoHxOnDih3NxcOTs7a/Hixfrpp580Z84c5eXlmeW/5+zsrIKCAklSXl5emfG8vDxz+/cxSeb+5WW1Wstd12KxVOjYqB4VGcMrwbjbJ8a9fqrIuFf1awQAgPqIpC0AAHVAixYtdODAAV199dVycHBQu3btVFRUpKioKIWGhpZIsBYUFMjV1VWS5OLiUmrczc3NJkHr4uJi/ixJbm5uFWrjwYMHy1XPzc1Nfn5+FTo2qkdqaqpyc3Or5NiMu/1i3Ounqhx3AABweSRtAQCoIxo3bmyz3aZNG+Xn58vT01M5OTk2sZycHHPJAy8vr1Ljnp6e8vLykiRlZ2erZcuW5s+SStzw7HL8/f25oq6W8/X1rekmoAYw7vVTRcbdarWW+4M5AABQPqxpCwBAHfDJJ5+oc+fONldFfffdd2rcuLGCg4P19ddfyzAMSRfXv/3qq68UEBAgSQoICFBCQoK5X0ZGhjIyMhQQECAvLy95e3vbxBMSEuTt7V1iHdzLsVgs5X7APlVkDK/kAfvEuNdPjCMAADWLpC0AAHVAUFCQXFxc9Oyzz+ro0aP6+OOPFRMTo0ceeUSDBg3S2bNnNXfuXKWlpWnu3LnKzc3V4MGDJUkjR47Uli1btGHDBqWkpOipp55Sr1691KpVKzO+cOFCHThwQAcOHNCiRYs0atSomuwuAAAAANRpLI8AAEAd0LBhQ7355pt64YUXdNddd6lBgwa699579cgjj8jBwUGvvfaaZsyYoXfffVe+vr5avny53N3dJV1M+M6aNUtLly7VL7/8om7dumn27NnmsSMiIvTzzz8rMjJSFotF4eHhGj16dA31FAAAAADqPpK2AADUETfddJNWrlxZaqxjx47atGlTmfuGhYUpLCys1JjFYlF0dLSio6MrpZ0AAAAAgD/G8ggAAAAAAAAAYEdI2gIAAAAAAACAHSFpCwAAAAAAAAB2hKQtAAAAAAAAANgRkrYAAAAAAAAAYEdI2gIAAAAAAACAHSFpCwAAAACotwzD0MKFC9WlSxeFhoYqJiZGRUVFZdZPT0/X6NGjFRgYqCFDhmjfvn1mrE+fPvL19S3xiI2NrY6uAADqEKeabgAAAAAAADVl5cqV2rp1q2JjY1VYWKioqCh5eHgoIiKiRF3DMDRx4kT5+PgoPj5eO3fuVGRkpLZt2yZvb29t3LhRVqvVrP/hhx9q8eLFGj58eHV2CQBQB3ClLQAAAACg3lq9erUmT56skJAQdenSRVOmTNHatWtLrbt//36lp6dr1qxZatOmjcaNG6fAwEDFx8dLkpo0aSJPT095enrK1dVVcXFxevrpp9WiRYvq7BIAoA4gaQsAAAAAqJeysrKUkZGhTp06mWXBwcE6fvy4Tp48WaJ+UlKS/Pz85O7ublM/MTGxRN0333xTnp6euuuuu6qk7QCAuo3lEQAAAAAA9VJ2drYkqVmzZmZZ06ZNJUmZmZk25cX1Ly3z8PBQZmamTVlubq7WrFmjWbNmydGx4tdK/X6JBaAsFoulppuAUvD7iz9SkdcHSVsAAAAAQJ2Vl5enrKysUmPnz5+XJDk7O5tlxT8XFBSUqJ+bm2tTt7j+pXW3bdsmd3d3DRgw4IrafPDgwSvaD/WHm5ub/Pz8aroZKEVqaqpyc3NruhmoA0jaAgAAAADqrKSkJI0aNarUWFRUlKSLCVoXFxfzZ+liUuxSLi4uOnPmjE1ZQUGBXF1dbco+/PBDDRkyRE5OV/aW29/fn6sogVrK19e3ppsAO2a1Wsv9wRxJWwAAAABAndW5c2elpqaWGsvKytKCBQuUnZ2tli1bSvrfkgmenp4l6nt5eSktLc2mLCcnx2bJhIKCAn3xxRcaO3bsFbfZYrGQtAVqKX53UVm4ERkAAAAAoF7y8vKSt7e3EhISzLKEhAR5e3uXWLtWkgICAvTtt98qLy/Ppn5AQIC5nZqaqsLCQnXs2LFqGw8AqNO40hYAAAAAUG+NHDlSCxcu1LXXXitJWrRokcaMGWPGT506JRcXFzVo0EChoaFq3ry5oqOjNWHCBO3Zs0fJycmaN2+eWf/w4cNq2bJlibVvAQCoCJK2AAAAAIB6KyIiQj///LMiIyNlsVgUHh6u0aNHm/Hw8HANHz5ckyZNksVi0bJlyzRt2jSFhYWpdevWiouLk7e3t1k/JydHV199dQ30BABQl5C0BQAAAADUWxaLRdHR0YqOji41vnv3bpvt1q1ba82aNWUeb+zYsX9qPVsAACTWtAUAAAAAAAAAu0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI6QtAUAAAAAAAAAO0LSFgAAAAAAAADsCElbAAAAAAAAALAjJG0BAAAAAAAAwI441XQDAAAAAAAAUHHXN7u6ppuA/4+xQGUjaQsAAAAAAFDLWIuKNOe+HjXdDPyOtahIFke+1I7KwSsJAAAAAACgliE5aH8YE1QmXk0AAAAAAAAAYEdI2gIAAAAAAACAHSFpCwAAAAAAAAB2hKQtAAAAAAAAANgRkrYAAAAAAAAAYEdI2gIAAAAAAACAHSFpCwAAAAAAAAB2pN4mbfPz8zV16lSFhISoe/fuWrFiRU03CQAAu8W8CQAAAADVx6mmG1BTYmJi9M033+itt97SiRMn9PTTT8vb21uDBg2q6aYBAGB3mDcBAAAAoPrUy6Tt+fPntWHDBr3++utq37692rdvr8OHD2vt2rW8+QQA4BLMmwAAAABQverl8ggpKSkqLCxUUFCQWRYcHKykpCQVFRXVYMsAALA/zJsAAAAAUL3qZdI2Oztb11xzjZydnc2ypk2bKj8/X2fOnKm5hgEAYIeYNwEAAACgetXL5RFyc3Nt3nhKMrcLCgouu79hGGZdi8VS7vNaLBbddO3VcrY4VKC1qAqtPRvJarXKarVW+bkYd/vBuNdfFR374nrFf+/ru5qYN/kdsi/V9feTcbcvjHv9dCXjzrxZeYqfw+r4fxUAUP0qMmfWy6Sti4tLiTeZxduurq6X3b/4q6CHDh2q8LnvuMldusm9wvuh8iUmJlbbuRh3+8G4119XMvZ89f+impo3+R2yL9X195Nxty+Me/10pePOvPnnFT+HBw8erOGWAACqUnnmzHqZtPXy8tLp06dVWFgoJ6eLT0F2drZcXV3VqFGjy+7v5OQkf39/OTo6ysGBKwIAoK4xDENFRUXmHFHfMW8CAP4I82blYc4EgLqtInNmvZxV27VrJycnJyUmJiokJESSlJCQYE6Ol+Po6Fjia6IAANRVzJsAAFQP5kwAQLF6eSMyNzc3DRs2TDNnzlRycrJ27typFStWaNSoUTXdNAAA7A7zJgAAAABULwejnq4Wn5ubq5kzZ+qjjz5Sw4YNFRERodGjR9d0swAAsEvMmwAAAABQfept0hYAAAAAAAAA7FG9XB4BAAAAAAAAAOwVSVsAAAAAAAAAsCMkbQEAAAAAAADAjpC0rWK+vr46cOBAqbGXX35ZDz74YDW36KIDBw7I19fX5tGhQwf16dNHL7/8co206c948MEHa1W7+/TpY/Pct23bVqGhofrb3/6mjIyMKjvne++9V+nHLe21VPzYsGFDpZ/vcr777jt99dVX1X7e3zt//rwWL16sQYMGqWPHjurcubMmT56sw4cPV9o53nvvPfXp00fS/8agMhQUFOjdd981t5955hmbMQ0ICNC9996r5OTkSjnf5fx+PCuzn7BPzJnVo7bNmRLzZlVi3vxzmDdRk5g3qwfzZvnPybxZ9ZgzK09tmDOdaroB9dmYMWNqbCIttm/fPvPn3Nxc7dq1S/Pnz1erVq00bNiwmmtYBb388su66qqraroZFTJ16lQNGTJEklRUVKS0tDTNmDFDTz/9tFavXl3Drau437+Wiv3lL3+p9nZMnDhRkZGRuvnmm6v93JL022+/6b777tP58+f1zDPPqG3btjp9+rTWrl2re++9V5s3b1arVq0q9ZxBQUGlPv9X4l//+pdeffVVjRgxwiwbPHiwpk2bJkn69ddftX79eo0bN047d+5UgwYNKuW8Zfn9eFZmP1H7MGdWnto4Z0rMm1WFefPPYd6EvWLerDzMm/aBeZM5s7LVhjmTpG0NquoXYHl4enrabD/88MPau3evduzYUasm0saNG9d0EyrsL3/5i83z7+XlpcmTJysqKkq//vprjUxAf8alr6X6Ki4uTj///LO2bdumRo0aSZJatGihefPmKSMjQ6tWrdL06dMr9ZzOzs6V9vwbhlGizNXV1Ty+p6enoqKi9O6772r//v3q27dvpZy3PCqzn6h9mDMrT22cMyXmzbqKebPqMG/Wb8yblYd50z7w94w5syrZ65zJ8gg16PdfWXnvvff04IMPaunSpercubNCQkI0b948mxf1O++8oz59+igoKEgPPvigUlNTzVhWVpYmT56sTp06qUOHDho+fLgSEhIkST/99JN8fX0VFxenTp06adasWX/YLmdnZ1ksFnP7P//5j8LCwtSxY0fdcccd+vDDD23qr1q1Sj169NDNN9+sOXPm6MEHHzS/FtGnTx8tWLBA3bt317Bhw2QYhr7//ns9+OCD6tixowYOHKi1a9eaxzp79qwmTZqkkJAQderUSVOmTNG5c+ckSSdOnNCYMWMUFBSkrl27avbs2bpw4YKkkl9Zee+99zR48GB17NhRYWFh+vLLL81Ynz59tHbtWo0YMUL+/v4aOnSovvnmm3KMWNVzdnaWJDk6OiotLU0REREKCgqSv7+/7rvvPh05ckTSxUv3+/Tpo3/+85/q0aOHAgMDFRUVpYKCAvNY77zzjnr16qWbb75Zy5YtszlPUVGR3njjDfXt21cdO3Ys8Xry9fXVv//9bw0ePFgBAQF64oknlJ6erlGjRikgIED33XefsrKyyt2vzMxMPfbYYwoNDVXnzp01Z84cs63vvfee7r33Xk2cOFHBwcF6//33ZRiG4uLi1L17d4WEhGj8+PE6ceKEebxt27Zp4MCB8vf315AhQ7Rz505JF18Hx48fV3R0tJ555pkKPvt/XlFRkTZt2qSHH37YnER/LyYmRlFRUaX2+dy5c4qOjlbXrl3VoUMHDRo0yOyXdPF3/JFHHlFgYKCGDx+uH3/80Yxd+lWOjIwMjR8/XgEBAerTp49iY2NltVol/fHfmgMHDig6OlrHjx+Xr6+vfvrpp1L76eTkZL5Wi+3Zs0fDhw9Xx44dNWTIEH300Uc2z8sfvd7KO56/72fx37WPPvpI/fr1k7+/v8aNG6czZ86Yx923b5/uuOMOdezYUY888ohmz55dI68LVA7mTObM0jBvMm8ybzJvonTMm8ybpWHerL3zJnNmPZ0zDVQpHx8fY//+/aXGli5dajzwwAOGYRhGfHy80b59e+Pvf/+7ceTIEWPz5s1G27ZtjX379hmGYRi7du0yunXrZuzevds4duyY8dJLLxmhoaHGmTNnDMMwjAceeMCYMGGCkZaWZhw+fNgYN26ccfvttxuGYRjp6emGj4+PMWbMGOO///2vcezYMWP//v2Gj4+PTXsKCwuN7du3G+3btzf+9a9/GYZhGCdPnjRuvvlm4+233zZ++OEHY/PmzUZgYKDx5ZdfGoZhGFu2bDGCgoKMbdu2Gd9//70xbtw4w9fX14iPjzcMwzB69+5t9OjRw0hJSTG+++47Izc317j11luNl156yTh27Jixa9cu45ZbbjE2bdpkGIZhzJ4927jnnnuM77//3jh06JBx2223GfPnzzcMwzDGjx9vTJw40fjhhx+MhIQEo1u3bsaaNWvM/i9dutR8LgMDA41NmzYZR44cMRYsWGAEBgYamZmZZps6d+5s7Nixwzh69Khx//33G/fcc8+fHOmK6d27t/kcFfvvf/9rDB8+3IiIiDCsVqvRt29fY/r06cZ///tf45tvvjFGjBhhjBs3zjAMw9i/f7/Rvn17Y9SoUUZKSoqxd+9eo2PHjsb69esNwzCMvXv3Gh06dDA2bdpkfP/998b48eMNHx8f85xLly41unbtauzcudNIS0sznn76aaN79+7Gb7/9ZhjGxdftoEGDjMTEROPzzz832rdvb3Tr1s3Ytm2bcejQIaNfv37G7NmzzbZc+lr6vfz8fGPAgAHGo48+aqSkpBifffaZ0bdvX3P/+Ph4w8fHx3jxxReNtLQ04+effzZWr15tDBw40Ni/f7+RlpZmTJ061Rg4cKBRUFBg5OTkGO3btzfi4+ONn376yXjjjTcMf39/4/Tp08bp06eNW2+91Vi1apVx9uzZyh20cjh27Jjh4+NjJCYm/mG90vr8zDPPGPfcc49x6NAh49ixY8a0adOM0NBQIz8/3zAMw7jnnnuMhx9+2Pj++++Nf/3rX0ZgYKDRu3dvwzBsx6CoqMgICwszpk6dahw5csTYv3+/MWDAACM2NtY8d1l/a/Lz841Vq1YZt956q3Hy5EmjsLDQePrpp42nn37abPuFCxeMNWvWGN26dTPOnTtnGIZhfPbZZ0b79u2NlStXGkePHjVWrFhh+Pn5GQcPHjQM449fbxUZz9/3s/jv2vDhw42kpCQjMTHR6Nq1q/Hiiy8ahmEYP/74o9GhQwcjLi7OOHLkiLF48WLD19fXpi+wP8yZzJllYd5k3mTeZN5EScybzJtlYd6se/Mmc2b9nDNJ2laxikykbdu2NX799VczPmzYMOOVV14xDMMwRo4caaxevdpm/+HDhxurV682ioqKjFWrVhkZGRlmbO/evUbbtm0Nw/jfC+7jjz8248UvyMDAQPPRrl07o2/fvubkZBiG8dJLLxmRkZE25503b55Zds899xiLFy82Y2fOnDECAgJsJtIFCxaY8XfffdcYPny4zfFWr15tlo0fP94YM2aMcf78ecMwDCMtLc1IS0szDMMw7rjjDuOZZ54xCgoKDMMwjG+//dZIT083DMN2Ih02bJixaNEim3OMGDHCWLhwodmmf/zjH2Zs586dRvv27Y3q1Lt3b6NDhw7mc9+hQwcjKCjImDJlinHq1Cnjt99+M15//XVzUjMMw1i3bp3Rt29fwzD+N37ff/+9GZ84caLx7LPPGoZhGJMmTTKio6PN2KlTpwx/f38jPj7eKCoqMkJDQ4133nnHjBcUFBg9e/Y01q1bZxjGxdft7+Ph4eFGVFSUuR0TE2OMGTPGpi2/fy0FBgYaU6ZMMQzj4vMbEBBg/tNnGIbx8ccfG35+fsa5c+eM+Ph4w9fX18jNzTXjt956q7Fr1y5zu7Cw0OjSpYuxa9cu49tvvzV8fHyMTz/91DCMixPHJ598Yr5mSvsHpbp89dVXho+Pj3Hs2DGz7NNPP7V5XoYMGVJqn+Pj443U1FRz+8iRI4aPj49x4sQJ4/vvvzd8fHyM48ePm/H58+eXOpF+9tlnRpcuXQyr1WrW3bVrlxEaGmqe54/+1sTHx5vHNQzDePrppw0/Pz+bvxM+Pj7GypUrzToTJ040nnjiCZvn4u9//7vx+OOPX/b1VpHxLG0i3bNnj3ncF154wXj44YcNwzCMRYsWGffdd59Nm0aMGMGbTzvHnMmcWRbmTeZN5k3mTZTEvMm8WRbmzbo3bzJn/k99mjNZ09aOeHh4qGHDhuZ2w4YNVVhYKEk6cuSIFixYoBdffNGM5+fn64cffpCDg4NGjhypbdu26auvvtKxY8f0zTffqKioyOb4LVq0KHHOzZs3m8efMWOG+vbtq/vvv9+MHz16VHv27FFQUJBZduHCBV1//fWSpNTUVI0dO9aMXX311WastPMePXpUKSkpNsezWq3mV2RGjRqlCRMmqGvXruratasGDhyoO+64Q5L0yCOPaOrUqdqxY4duvfVWDRkyRH5+fiX6dOTIEU2cONGmLDAw0PyqhyRdd9115s8NGzY0v/pSnSZPnqwBAwbot99+08svv6zjx4/rySef1DXXXCNJGjlypDZv3qxvvvlGR48e1aFDh9S0aVObY7Ru3dr8+dLXy7333mvGrrnmGnNB8p9//llnzpxRQECAGb/qqqvUoUMHm+fo9wuYu7q62oyjq6urzVdjpP+9loq5u7ubbbnuuut09dVXm7Gbb75ZhYWF5tcuPDw85OrqKuni4uqZmZl6/PHH5ej4vxVc8vLy9MMPP6h3797q1auXHn74YV1//fXq27ev7r77brm5uZX+RFej4q+pnD171iwLCgoyn5uPPvpI69atk2TbZ0kaNmyYdu7cqXfffVdHjx7Vt99+K+ni70daWpoaN24sb29vs76/v7+2b99eog1HjhzRmTNnFBwcbJYVFRUpLy9Pp0+fNs9d1t+a0vTp00dTpkyRdPHvTkJCgubNm6dGjRopLCysxOutuN/x8fGXfb3dc889f2o8L/0dKP5dTk1Nlb+/v03dwMBA/fLLL+U6Luwfc2b9mjMl5k3mTeZN5k38GcybzJvMm7V73mTO/J/6NGeStLUjl67bIf1voWar1aqpU6eqa9euNvGGDRuqqKhIY8aM0dmzZzVkyBD16dNHFy5cUGRkpE1dFxeXEscvfhG2bt1ajRs31v33369rr71WDz/8sCSpsLBQd9xxh8aPH2+zn5PTxZeOxWIpsZj0pdu/P29hYaG6du2q5557rtTnoGvXrvr444+1a9cu/d///Z+ee+457du3TwsXLtSdd96prl27aufOnfq///s/TZ48WY8++qgef/zxy/bTarXa/GNhD3f/9PDwMJ//JUuWKDw8/P+1d/8xUdcPHMdfqJGg4pKcGRo78OdKlKz5s82RLn9ryvy12LScPzAtKcUpB+lZRjDBUwFtOhHSmc3czPD3/LHKX0vQFDQPDgUyUct5GRjC9w/mR05Q4WvAGc/Hxrj73N3n3j8+d6/b+/P5vD8KDQ3Vli1bdOfOHQUHB+u5555TUFCQhg8fruzsbK1fv95pHQ9uMxXb/sF+uFfnqtpHqtxGFeeakuQUaFWp+IVW0cP6o+L/is+5t2zFihWVfpS1bNlSbm5uWrNmjU6fPq39+/dr79692rRpkzZt2qSuXbs+soy17d7n6NSpUwoICJAkeXh4GG3j7e1tPPfBdpk/f75OnTqlUaNGaeLEiWrdurXGjx9vPP6w/nxQSUmJ/Pz8Ks0rJd2/uuqjvmuq0qxZM6f+7dSpkzIzM5WamqoxY8ZU2celpaUqLS197Pb2pP35sHaozncTnm5kZsPKTIncrPif3CQ3yU3UFLlJbpKbzo89bblJZt7XkDKTC5E9JUwmk65cuSJfX1/jLykpSenp6bp48aJOnDihDRs2aMaMGRowYICuXr0qqWYbzquvvqpJkyYpPj5e+fn5xvvm5uY6ve/+/fu1Y8cOSVKHDh2MvTSS5HA4lJub+8h65OTkqF27dsb60tPTlZKSIql8ovmzZ8/q7bff1ooVK7Rs2TJjkum4uDhdv35dEydO1Jo1a/Thhx86TUBd8T0yMjKclmVkZFT6QnYl7u7uWrp0qTIzM7VhwwYdP35cV69e1caNGzV16lT17dtXBQUF1e7Pjh076syZM8b9iv3SokULPf/880pPTzce/+eff3T27NlaaSOTySS73e40aXd6erqaNGmil156qdLzvby85O3trcLCQmMbadu2rWJiYpSTkyObzabo6GgFBARo7ty52rlzp9q2basjR47862WvqSZNmmjs2LFKTk42LmpQ0cMm03c4HPruu+8UFxenOXPmaNCgQcZeurKyMnXq1Ek3b950+mxlZmZWuS6TyaSCggK1atXKaL+8vDxZrVa5ubk9tg7Vec69ct370VXVZ+7UqVMymUyP3d5qqz87duzo9N0kqdJ9/HeRmf/tzJTIzYrITXKT3MSTIjfJTXLT9XOTzLyvIWUmg7Z14PTp0zp8+LDT399//12jdUyZMkXJycnavn27Ll26pJiYGKWlpcnf319eXl5q1KiRdu7cqfz8fO3atcu4uuWDpxQ8zgcffCBPT099/vnnkqRJkybpl19+UVxcnOx2u3bs2KHly5cbh86HhIRo48aN2rNnj2w2mxYuXKjbt28/9MM4cuRIFRUVKTIyUjabTYcOHdKnn35q7BW6cuWKlixZovT0dNntdu3evds4LSU7O1tLlixRVlaWfv31Vx06dKjKU1YmT56s1NRUbd++XTk5OYqNjVVWVpaCg4Nr1BZ1LSAgQMHBwUpISJCXl5du376tffv2KS8vT1u3btVXX31V7f585513lJaWpq+//lo2m02RkZEqKioyHp88ebKsVqsOHDggm80ms9ms4uJiDR069F+vV79+/dS+fXvNnz9f58+f19GjR2WxWDR8+PAqr3p5r3zx8fE6cOCA7Ha7IiIi9PPPP8vPz09eXl7avHmzEhISdPnyZR08eFD5+fnGtuDp6ans7Gyn0K5Ls2fPVuvWrTVhwgTt2rVLly9f1unTp2U2m2W1Wp1OJbnH3d1dHh4e2rNnj/Ly8nTkyBHjyrt37tyRv7+/+vTpo4ULFyorK0v79u1Tampqle/fv39/+fj4aN68eTp//rxOnjwps9ksDw+PSnuzq+Lh4aGbN2/Kbrcbp7EUFRWpsLBQhYWF+v333/X9999rx44dGjJkiKTy/tq9e7eSk5Nlt9u1YcMG7d27VxMnTjQef9j2Vlv9OW7cOKWnp2vt2rXKyclRUlKSTp48We0fCqg/ZOZ9ZOajkZv3kZvkJrnZcJGb95Gbj0Zu3ve05iaZ2fAyk+kR6kBsbGylZVXttXuUoUOH6tq1a7Jarbp27Zo6dOigxMREY76cTz75RKtXr9by5ctlMpkUERGh8PBwnTt3Tq1bt672+3h5eSksLEwRERH68ccf1bdvXyUlJSk2Nlbr1q1TmzZttGDBAo0cOVKSNGzYMOXm5ioqKkrFxcUaP368fHx8HnoYefPmzfXll1/qs88+0+jRo43TZKZPny6pPMhv3bqlmTNn6vbt23r99dcVExNj1HHx4sUKCQlRSUmJBgwYoEWLFj2yrQoLC9W1a1etX79e/v7+NWnyejF37lzt3r1bmzdv1qxZs7R48WIVFxerc+fOioyM1KJFix66B62i1157TcuWLVN8fLxu3LihsWPHOh3+/+6778rhcMhsNsvhcCgwMFApKSlq1arVv16nxo0bKyEhQRaLRePGjVOzZs00YsQIhYWFPfQ17733nv766y9FRkbK4XDolVde0bp164x5ilauXKnY2FglJSXJ29tbYWFh6t+/v6TyuZliY2Nlt9u1atWqf70+j+Ph4aGUlBQlJycrISFBubm5cnd3V0BAgFauXKmBAwdq27ZtTq9xd3dXTEyMoqOjlZKSonbt2mnmzJmKj49XZmam/P39FRcXJ7PZrAkTJujFF19USEhIpfVI5e2dmJhotLenp6cGDx6s8PDwapW/d+/e8vX11YgRI7Rp0yZJUlpamtLS0iSV7+F94YUXNH36dE2dOlWS1L17d33xxRdauXKlYmJiZDKZFB8fb5xi97jtrbr9GRISUu1+8PHxkdVqVXR0tKxWq/r166c333zTZU5Xw8ORmfeRmY9HbpYjN8lNcrPhIjfvIzcfj9ws97TmJpnZ8DLTrYzJivAEjh8/rvbt26tt27aSyuc46d27t1avXq1evXrVc+kANFQXLlxQSUmJ0xES06ZNU7du3TR79ux6LBkaMjITgKsiN+GKyE0ArqguM5PpEfBE9u3bpzlz5ujcuXPKzc3VsmXL1Lx5c/Xo0aO+iwagAbt06ZKmTJmiH374Qfn5+dq6dat++uknDRo0qL6LhgaMzATgqshNuCJyE4ArqsvM5EhbPBGHw6ElS5bo0KFDKi4uVmBgoBYtWqQOHTrUd9EANHCJiYnasmWLrl+/LpPJpDlz5mjgwIH1XSw0YGQmAFdGbsLVkJsAXFVdZSaDtgAAAAAAAADgQpgeAQAAAAAAAABcCIO2AAAAAAAAAOBCGLQFAAAAAAAAABfCoC0AAAAAAAAAuBAGbQEAAAAAAADAhTBoC+D/duzYMXXu3Llaz922bZuCgoJquUQAALguchMAgOohMwEGbQEAAAAAAADApTBoCwAAAAAAAAAuhEFb4D8uLy9PnTt31sGDBxUUFKTAwEAtXbpUFy5c0JgxY9SjRw9Nnz5dDodDUvmpJUOGDFFAQIDGjBmjEydOGOtyOBwKCwtTYGCg3nrrLZ05c8bpvX777TfNmDFD3bt3V1BQkFatWqW7d+/WaX0BAHgS5CYAANVDZgK1q0l9FwBA3Vi7dq0SEhJ08eJFffTRRzp8+LCioqLUtGlThYaG6ptvvpGXl5csFouioqIUEBCgbdu2adq0adq1a5fatGmjqKgoZWdnKzU1VTdu3NCCBQuM9ZeVlen9999Xly5d9O2336qwsFCRkZFyc3PTrFmz6rHmAADUHLkJAED1kJlA7eBIW6CBCA0NVZcuXTR8+HB5e3tr2LBh6tevn3r27Kk+ffooOztbKSkpCgkJ0ejRo+Xn56ePP/5YnTp1Umpqqm7duqW0tDRFRETo5Zdf1htvvKHQ0FBj/UePHlVBQYEsFov8/PzUq1cvhYeHa+PGjfVYawAA/j/kJgAA1UNmArWDI22BBqJ9+/bG7aZNm8rHx8fp/p07d2Sz2SrtqezRo4dsNptycnJ09+5ddenSxXisW7duxm2bzaY///xTPXv2NJaVlpaqqKhIf/zxR21UCQCAWkNuAgBQPWQmUDsYtAUaiMaNGzvdb9So8oH2zz77bKVld+/eVWlpaZXrdHd3N26XlJTIz89PCQkJlZ7XokWLmhYXAIB6RW4CAFA9ZCZQO5geAYDBZDIpIyPDaVlGRoZMJpP8/Pz0zDPPOE0If+7cOafXFhQUqFWrVvL19ZWvr6/y8vJktVrl5uZWZ3UAAKCukJsAAFQPmQnUHIO2AAyTJ09Wamqqtm/frpycHMXGxiorK0vBwcFq3ry5Ro0aJYvFooyMDB07dkyrVq0yXtu/f3/5+Pho3rx5On/+vE6ePCmz2SwPD49Ke14BAPgvIDcBAKgeMhOoOaZHAGAYOnSorl27JqvVqsLCQnXt2lXr16+Xv7+/JMlsNstisWjKlClq2bKlQkJCFB0dLan8lJjExERZLBaNGzdOnp6eGjx4sMLDw+uzSgAA1BpyEwCA6iEzgZpzKysrK6vvQgAAAAAAAAAAyjE9AgAAAAAAAAC4EAZtAQAAAAAAAMCFMGgLAAAAAAAAAC6EQVsAAAAAAAAAcCEM2gIAAAAAAACAC2HQFgAAAAAAAABcCIO2AAAAAAAAAOBCGLQFAAAAAAAAABfCoC0AAAAAAAAAuBAGbQEAAAAAAADAhTBoCwAAAAAAAAAuhEFbAAAAAAAAAHAh/wPOd6pxWF5qWgAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 1400x400 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Визуализация метрик на тесте\n",
+        "test_before = results_before[results_before[\"split\"] == \"test\"]\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"MAE\", ax=axes[0])\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"RMSE\", ax=axes[1])\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"R2\", ax=axes[2])\n",
+        "\n",
+        "axes[0].set_title(\"MAE (test)\")\n",
+        "axes[1].set_title(\"RMSE (test)\")\n",
+        "axes[2].set_title(\"R2 (test)\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0fd28587",
+      "metadata": {},
+      "source": [
+        "### Почему выбраны эти метрики\n",
+        "\n",
+        "- **MAE** показывает среднюю абсолютную ошибку в тех же единицах, что и цена.\n",
+        "- **RMSE** сильнее штрафует большие ошибки.\n",
+        "- **R2** — доля объяснённой дисперсии: 0 означает, что модель не лучше предсказания средним, 1 — полное совпадение с целевой переменной."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "39b556c4",
+      "metadata": {},
+      "source": [
+        "## Задание 3. Добавляем `cars_new.csv` и сравниваем качество"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9a4377b3",
+      "metadata": {},
+      "source": [
+        "cars_new.csv имеет другой формат, все данные в первом столбце и переведены на другой язык. Исправим это для объединения:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "a9f78135",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Car ID</th>\n",
+              "      <th>Brand</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Fuel Type</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Model</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>1.5 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика</td>\n",
+              "      <td>150000</td>\n",
+              "      <td>Отличное</td>\n",
+              "      <td>70000</td>\n",
+              "      <td>2110</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>1.6 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Хорошее</td>\n",
+              "      <td>80000</td>\n",
+              "      <td>2107</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>1.5 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика</td>\n",
+              "      <td>120000</td>\n",
+              "      <td>Отличное</td>\n",
+              "      <td>75000</td>\n",
+              "      <td>2115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>1.6 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика/Автомат</td>\n",
+              "      <td>130000</td>\n",
+              "      <td>Хорошее</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Kalina</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>98 л.с.,1.6 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика/Автомат</td>\n",
+              "      <td>100000</td>\n",
+              "      <td>Хорошее</td>\n",
+              "      <td>200000</td>\n",
+              "      <td>Priora</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Car ID Brand  Year    Engine Size Fuel Type      Transmission Mileage  \\\n",
+              "0      1  Lada  2000          1.5 л    Бензин          Механика  150000   \n",
+              "1      2  Lada  2001          1.6 л    Бензин          Механика  180000   \n",
+              "2      3  Lada  2002          1.5 л    Бензин          Механика  120000   \n",
+              "3      4  Lada  2008          1.6 л    Бензин  Механика/Автомат  130000   \n",
+              "4      5  Lada  2009  98 л.с.,1.6 л    Бензин  Механика/Автомат  100000   \n",
+              "\n",
+              "  Condition   Price   Model  \n",
+              "0  Отличное   70000    2110  \n",
+              "1   Хорошее   80000    2107  \n",
+              "2  Отличное   75000    2115  \n",
+              "3   Хорошее  180000  Kalina  \n",
+              "4   Хорошее  200000  Priora  "
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def parse_cars_new(path):\n",
+        "    raw = pd.read_csv(path, header=None)\n",
+        "    lines = raw[0].astype(str).str.strip('\"')\n",
+        "    rows = []\n",
+        "    for line in lines:\n",
+        "        parts = [p.strip() for p in line.split(\",\")]\n",
+        "        if not parts or parts[0] == \"Car ID\":\n",
+        "            continue\n",
+        "        if len(parts) < 10:\n",
+        "            continue\n",
+        "        # Если внутри Engine Size есть запятая, будет больше 10 частей\n",
+        "        head = parts[:3]\n",
+        "        tail = parts[-6:]\n",
+        "        engine = \",\".join(parts[3:-6]).strip()\n",
+        "        row = head + [engine] + tail\n",
+        "        if len(row) == 10:\n",
+        "            rows.append(row)\n",
+        "\n",
+        "    cols = [\"Car ID\", \"Brand\", \"Year\", \"Engine Size\", \"Fuel Type\", \"Transmission\", \"Mileage\", \"Condition\", \"Price\", \"Model\"]\n",
+        "    df_new = pd.DataFrame(rows, columns=cols)\n",
+        "    return df_new\n",
+        "\n",
+        "df_new = parse_cars_new(\"cars_new.csv\")\n",
+        "df_new.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "ca74b22f",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Car ID</th>\n",
+              "      <th>Brand</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Fuel Type</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Model</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>150000</td>\n",
+              "      <td>New</td>\n",
+              "      <td>70000</td>\n",
+              "      <td>2110</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>80000</td>\n",
+              "      <td>2107</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>120000</td>\n",
+              "      <td>New</td>\n",
+              "      <td>75000</td>\n",
+              "      <td>2115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>130000</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Kalina</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>100000</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>200000</td>\n",
+              "      <td>Priora</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Car ID Brand  Year  Engine Size Fuel Type Transmission  Mileage Condition  \\\n",
+              "0      1  Lada  2000          1.5    Petrol       Manual   150000       New   \n",
+              "1      2  Lada  2001          1.6    Petrol       Manual   180000      Used   \n",
+              "2      3  Lada  2002          1.5    Petrol       Manual   120000       New   \n",
+              "3      4  Lada  2008          1.6    Petrol    Automatic   130000      Used   \n",
+              "4      5  Lada  2009          1.6    Petrol    Automatic   100000      Used   \n",
+              "\n",
+              "    Price   Model  \n",
+              "0   70000    2110  \n",
+              "1   80000    2107  \n",
+              "2   75000    2115  \n",
+              "3  180000  Kalina  \n",
+              "4  200000  Priora  "
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Приводим к единому формату\n",
+        "fuel_map = {\n",
+        "    \"Бензин\": \"Petrol\",\n",
+        "    \"Дизель\": \"Diesel\",\n",
+        "    \"Электро\": \"Electric\",\n",
+        "    \"Гибрид\": \"Hybrid\"\n",
+        "}\n",
+        "trans_map = {\n",
+        "    \"Механика\": \"Manual\",\n",
+        "    \"Автомат\": \"Automatic\",\n",
+        "    \"Механика/Автомат\": \"Automatic\"\n",
+        "}\n",
+        "cond_map = {\n",
+        "    \"Отличное\": \"New\",\n",
+        "    \"Хорошее\": \"Used\"\n",
+        "}\n",
+        "\n",
+        "def extract_engine_size(x):\n",
+        "    if pd.isna(x):\n",
+        "        return np.nan\n",
+        "    nums = re.findall(r\"\\d+(?:[.,]\\d+)?\", str(x))\n",
+        "    if not nums:\n",
+        "        return np.nan\n",
+        "    return float(nums[-1].replace(\",\", \".\"))\n",
+        "\n",
+        "df_new[\"Fuel Type\"] = df_new[\"Fuel Type\"].map(fuel_map)\n",
+        "df_new[\"Transmission\"] = df_new[\"Transmission\"].map(trans_map)\n",
+        "df_new[\"Condition\"] = df_new[\"Condition\"].map(cond_map)\n",
+        "\n",
+        "df_new[\"Engine Size\"] = df_new[\"Engine Size\"].apply(extract_engine_size)\n",
+        "df_new[\"Year\"] = pd.to_numeric(df_new[\"Year\"], errors=\"coerce\")\n",
+        "df_new[\"Mileage\"] = pd.to_numeric(df_new[\"Mileage\"], errors=\"coerce\")\n",
+        "df_new[\"Price\"] = pd.to_numeric(df_new[\"Price\"], errors=\"coerce\")\n",
+        "\n",
+        "df_new.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "c807c43d",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>split</th>\n",
+              "      <th>MAE</th>\n",
+              "      <th>RMSE</th>\n",
+              "      <th>R2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>train</td>\n",
+              "      <td>24385.641471</td>\n",
+              "      <td>32867.632333</td>\n",
+              "      <td>0.004216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>test</td>\n",
+              "      <td>24857.121340</td>\n",
+              "      <td>37804.530574</td>\n",
+              "      <td>-0.007647</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>train</td>\n",
+              "      <td>9174.571497</td>\n",
+              "      <td>12278.185487</td>\n",
+              "      <td>0.861038</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>test</td>\n",
+              "      <td>24465.982962</td>\n",
+              "      <td>34010.448955</td>\n",
+              "      <td>0.184460</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>train</td>\n",
+              "      <td>21391.653978</td>\n",
+              "      <td>24988.338946</td>\n",
+              "      <td>0.424423</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23904.569889</td>\n",
+              "      <td>29600.017743</td>\n",
+              "      <td>0.382262</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model  split           MAE          RMSE        R2\n",
+              "0  LinearRegression  train  24385.641471  32867.632333  0.004216\n",
+              "1  LinearRegression   test  24857.121340  37804.530574 -0.007647\n",
+              "2      RandomForest  train   9174.571497  12278.185487  0.861038\n",
+              "3      RandomForest   test  24465.982962  34010.448955  0.184460\n",
+              "4  GradientBoosting  train  21391.653978  24988.338946  0.424423\n",
+              "5  GradientBoosting   test  23904.569889  29600.017743  0.382262"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Объединяем и повторяем оценку\n",
+        "df_all = pd.concat([df, df_new], ignore_index=True)\n",
+        "\n",
+        "# Удаляем строки без цены\n",
+        "df_all = df_all.dropna(subset=[\"Price\"]).copy()\n",
+        "\n",
+        "X_all = df_all.drop(columns=[\"Price\"])\n",
+        "y_all = df_all[\"Price\"]\n",
+        "\n",
+        "results_after = evaluate_models(X_all, y_all, models)\n",
+        "results_after"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "4a4a7c79",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>MAE_before</th>\n",
+              "      <th>MAE_after</th>\n",
+              "      <th>RMSE_before</th>\n",
+              "      <th>RMSE_after</th>\n",
+              "      <th>R2_before</th>\n",
+              "      <th>R2_after</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>23386.369896</td>\n",
+              "      <td>24857.121340</td>\n",
+              "      <td>26995.898047</td>\n",
+              "      <td>37804.530574</td>\n",
+              "      <td>-0.000709</td>\n",
+              "      <td>-0.007647</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>23676.962601</td>\n",
+              "      <td>24465.982962</td>\n",
+              "      <td>27982.335432</td>\n",
+              "      <td>34010.448955</td>\n",
+              "      <td>-0.075178</td>\n",
+              "      <td>0.184460</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>23625.922747</td>\n",
+              "      <td>23904.569889</td>\n",
+              "      <td>27750.650439</td>\n",
+              "      <td>29600.017743</td>\n",
+              "      <td>-0.057447</td>\n",
+              "      <td>0.382262</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model    MAE_before     MAE_after   RMSE_before    RMSE_after  \\\n",
+              "0  LinearRegression  23386.369896  24857.121340  26995.898047  37804.530574   \n",
+              "1      RandomForest  23676.962601  24465.982962  27982.335432  34010.448955   \n",
+              "2  GradientBoosting  23625.922747  23904.569889  27750.650439  29600.017743   \n",
+              "\n",
+              "   R2_before  R2_after  \n",
+              "0  -0.000709 -0.007647  \n",
+              "1  -0.075178  0.184460  \n",
+              "2  -0.057447  0.382262  "
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Сравнение метрик на тесте ДО и ПОСЛЕ добавления новых данных\n",
+        "test_before = results_before[results_before[\"split\"] == \"test\"].copy()\n",
+        "test_after = results_after[results_after[\"split\"] == \"test\"].copy()\n",
+        "\n",
+        "compare = test_before.merge(test_after, on=\"model\", suffixes=(\"_before\", \"_after\"))\n",
+        "compare[[\"model\", \"MAE_before\", \"MAE_after\", \"RMSE_before\", \"RMSE_after\", \"R2_before\", \"R2_after\"]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "0821f421",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Визуализация влияния новых данных (R2)\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "compare_melt = compare.melt(id_vars=\"model\", value_vars=[\"R2_before\", \"R2_after\"], var_name=\"stage\", value_name=\"R2\")\n",
+        "sns.barplot(data=compare_melt, x=\"model\", y=\"R2\", hue=\"stage\")\n",
+        "plt.title(\"R2 на тесте: до и после добавления новых данных\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7905ea14",
+      "metadata": {},
+      "source": [
+        "### Итоговый анализ\n",
+        "\n",
+        "- Новые данные добавлены и приведены к единому формату.\n",
+        "- Для корректного сравнения смотрим метрики `до` и `после`.\n",
+        "\n",
+        "Качество предсказания моделей после объединения данных заметно выросло, особенно у градиентного бустинга. Причина — в дополнительных данных (cars_new.csv) признаки связаны с ценой (год, состояние, пробег), что позволяет моделям лучше обобщать. Ниже — анализ корреляций в новых данных."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "9d37afe8",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAGHCAYAAAAupV8sAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAT2tJREFUeJzt3XlYFvX+//Eni9zgQiogqaip5YZwgwtqYgqnLNPK0FbLLDtYoXZatNAydxO101HMJZc0K820TfvWyU6nxcqKBDWzNCtxAcENjU1gfn/4Y053gIBxc4PzelyXV93znvnM581N46uZe+Z2MwzDQEREREQueu6unoCIiIiIVA8FPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQhPV09AxEruvvtuAF5++eVS6+3bt+fmm2/m2Wefrc5pVbv27duXWbNC/zXRtGnTqF+/Po888oirpyJ/Mn78eK644gr+/ve/u3oqchFQ8BMRlxg6dCi33HJLieWNGzd2wWys7csvv+TDDz/kgw8+cPVUpBSPPfYYN9xwA9HR0bRt29bV05FaTsFPRFzi0ksvJSwszNXTEGDWrFmMGDECHx8fV09FShEYGMigQYOYM2cOixcvdvV0pJbTZ/xEargtW7YQExNDSEgIvXv3Zvr06WRnZ5v1BQsWlHrptH379ixYsMB8vWfPHkaPHk3Pnj0JDg6mT58+TJ8+ndzc3FL3m5aWRseOHVmzZo3D8uPHjxMcHMxLL70EwNatW7n11lsJDw+ne/fuPPjgg/z8889/qedff/2VsWPH0rt3b8LCwrj77rtJSkpyWOf111+nffv2Jf48+eSTZY578OBBh3U7depEZGQkCQkJFBUVAbBt2zbat2/Ptm3bzjvGxo0bHdY/31yGDBnC7bffXmKsESNGcO+99wJgGAYvvfQSAwYMIDQ0lGuuuYbly5djGAYATz75JNHR0ea2K1asIDw8nLVr1wKQm5vLvHnz6N+/P507d6ZLly7ce++9/PDDD+f9Wf/3v//lp59+YuDAgQ7Lk5OTue++++jSpQs9e/bk0UcfJT093axX5Pepffv2JCYmEhMTQ2hoKImJiRQVFfHPf/6T6OhoOnfuTHR0NPPmzePs2bPnneefRUdHM3/+fGbPns2VV15JaGgoI0eO5Ndff3VY79tvv+Wuu+7CbrcTERHBE088wfHjxwH46KOPaN++Pbt37zbXf+utt2jfvj3r1683l/3www+0b9+e7du3lzqXTz75hNtvv52wsDAiIyOZNGkSWVlZAOX2e+211zJ27NgSY9500008+OCD5usbbrjBfK9E/goFP5Ea7N133yUuLo42bdqwcOFCRo8ezTvvvMNDDz1kBoKKOHr0KMOGDSMnJ4dnn32WF198kYEDB/Lyyy+zevXqUre59NJLiYiIYPPmzQ7L33//fQzDYODAgaSmpvLQQw/RuXNnFi1axIwZM/jll1+IjY01g1Rl7du3j5iYGA4ePMhTTz3F3LlzcXNz45577uHrr78218vNzSUkJIR169aZfwICAiq0jwcffJB169axfPlyBg8ezPLly9mwYcMFzbfYpEmTypzL0KFD2b59O7/99pu57MiRI2zbto2YmBgAEhISSEhIIDo6msWLFzN06FDmzp3L0qVLS+wrPT2d5557jqeffpprrrkGOPc5sA0bNhAbG8uKFSuIj49n7969PPbYY+f9XXnnnXcICwsjMDDQXLZ7927uuusu8vLySEhIYMqUKezatYuRI0dSUFBQqd+nxYsXc8MNNzB//nyuvfZaXnzxRV577TXi4uJYsWIFd9xxB8uXL2fRokWV/pmvXr2a/fv3M2vWLKZPn86uXbt44oknzPo333zDiBEj8Pb25vnnn2fChAl8/fXXDB8+nNzcXHr16oWXlxdffPGFuc1XX30FnAuMxT799FMaN26M3W4vMYePP/6YUaNG4efnx/PPP8/jjz/Oli1bzM9KltfvjTfeyCeffMKZM2fMMX/++Wf27NnDTTfdZC4LDw8nMDCQTZs2VfrnJPJHutQrUkMZhsHcuXPp06cPc+fONZdfdtlljBgxgk8++YR+/fpVaKyffvqJjh078q9//Yv69esDcOWVV7J161a2bdtGbGxsqdvddNNNTJgwgcOHD9OsWTMANm/ezJVXXklAQACbN28mNzeXUaNGmcHh0ksv5aOPPiI7O9vcV2UkJibi5eXF6tWrze379evHoEGDSEhI4I033gAgJycHf39/h8vFXl5eFdpHy5Ytze169erF+vXr2bVrV6mfOayoyy+/vMy5DBo0iGeffZa3337bPLvz9ttvU69ePa655hqysrJYvXo1d911F+PGjQPOvT8ZGRl88803jBo1ymFfa9eupUOHDmZozM/P5/fff+epp57i+uuvByAiIoIzZ87w7LPPkpmZWWYo/uqrr0qc7Vu8eDENGzZkxYoV2Gw2AJo0acJjjz3G3r17OXbsWIV/n7p162ae1QR49tln6dy5M0OGDDHn6ePjQ4MGDSr4k/4fX19fXnjhBTw8PAA4cOAACxYs4MSJEzRq1Ih58+bRunVrlixZYq5jt9sZOHAgGzZsYNiwYURERPDll19y//33A+c+7xgcHMw333xj7uezzz6jb9++uLuXPFeyYMECOnbsSGJiIm5ubsC59/5f//oXmZmZfP311+ft98Ybb2TBggVs2bKFwYMHA7Bp0yZ8fX0dzvACdO7cmS+//LLSPyeRP9IZP5Eaav/+/aSlpREdHU1BQYH5p3v37tSvX5+tW7c6rP/HdQoKChxqkZGRrFmzBpvNxr59+/joo49YtGgRx48fJz8/v8w59O/fH5vNxnvvvQecO0uVlJRknomw2+3YbDaGDh3KjBkz+Oyzz+jQoQOPPPLIBYU+gK+//pqoqCiH7T09PRk4cCC7du3i999/N+dyIWEBzl1+KygoIDc3l3fffZdTp07RuXPnUtepCg0aNKB///6888475rI333yT66+/Hm9vb5KTkykoKKB///4O2z311FMsW7bMYdn+/ft59dVXzTvE4VzQWL58Oddffz3p6el89dVXrF27lo8//higzPc4OzubY8eOERQU5LA8KSmJq666ygx9cO6M03/+8x86duxYqd+njh07Orzu0aMHW7du5c4772TZsmXs27ePu+66y+HsVkWFhISYgQ7O/U8HnPufgpycHFJSUujbty+GYZj/XbRo0YK2bdua//3069ePpKQk8vPz+eWXX0hLS+OBBx7g0KFDHDp0iDNnzrB9+/ZS/ycrNzeX3bt3c/XVV5uhD+D666/ngw8+wN/fv9x+W7RoQZcuXcz/xuDc/1xdd911Jf5Hpnnz5hw8eLDSPyeRP9IZP5Ea6uTJkwBMmTKFKVOmlKgfPXrU4XVwcHCZYxUVFfHcc8/xyiuvkJ2dTdOmTQkNDXX4i7009evX5+qrr2bz5s3cf//9vPfee/j4+HD11VcDEBQUxJo1a1i6dClvvPEGq1evxtfXlzvvvJN//OMfDn8ZVtSpU6fw9/cvsdzf3x/DMDhz5gz16tXj0KFD5+35fCZOnMjEiRPN123btuXGG290WGfEiBHAuVDVtGlTBg0a5PCZq8oaOnQo77zzDt9++y0eHh78+uuvzJ49G/jfe13eHc2HDh1iwIABREVFlQhKn332GTNnzmT//v3Uq1ePDh06ULduXYAyL/WePn0awFyv2MmTJ/Hz8ytzHpX5ffrz2Pfffz/16tVjw4YNzJ07lzlz5nDFFVfw1FNP0bNnz/P2/2d/vhml+IxcUVERWVlZFBUV8eKLL/Liiy+W2LZ4rv369WP69Ol899137N+/n9atWxMVFUXdunX55ptvqFu3Lm5ubkRGRpYY49SpUxiGcd6fVUX6vemmm5g2bRonTpzg4MGD/Pbbb8ycObPUfovfM5ELpeAnUkP5+voC5z67FRERUaJ+ySWXOLwuvgRabOjQoea/L126lJdeeokpU6bQv39/80zZH9cpy4033khsbCy//fYbmzdv5tprr3X4C7f4Q/v5+fkkJSWxbt06Fi9eTIcOHRgwYEDFG/5DX5mZmSWWZ2RkANCoUSOKiopISUkxL59V1ujRo+nXrx9FRUXs37+fqVOnkpCQwNNPP22uM2XKFIKDgzl79ix79uxh9uzZZGVlmYGwsiIiImjZsiXvv/8+7u7utGnTxrw0XPxeHz9+nDZt2pjbHD58mAMHDtC1a1cAAgICePTRR5k6dSoLFy4kLi4OOHeJMy4ujquvvpolS5bQokUL3NzceOWVV/jss8/KnFOjRo0AzBsRijVo0MC8AeKPPvnkEzp27MjGjRsv+PfJ3d2dYcOGMWzYMI4dO8Ynn3zC4sWLGTNmDFu3bq3w5fry1KtXDzc3N0aMGFHiUjb8LzS2aNGCNm3a8OWXX/LLL78QERFBnTp16NKlC9u2bcPDw8M8y/5n9evXx83NrcTPKi8vj6+++gq73U7Dhg3L7XfAgAFMnz6dLVu2sH//fpo3b26+53+UlZVlvmciF0qXekVqqDZt2uDn58fBgwcJCQkx/wQGBjJv3jyHOxEBh3VCQkIcaklJSVx++eUMGTLE/Es6PT2dn376qdybMCIjI/H392f16tV8//33DmeaXnrpJaKiosjPz8fLy4tevXoxbdo04FxouRDdu3fn448/dviwe2FhIZs3byYkJAQvLy++++47srOz6dGjxwXto3nz5oSEhGC327n55pvp06eP+aH+Yq1btyYkJIQuXbpw5513EhkZ6XBzSWW5ubkRExPDli1b+M9//sPNN99s1kJDQ6lTp455abbYihUrePTRR83LmV5eXsTExDBu3DgSExP57rvvANi1axd5eXnExsbSsmVL80xrcegr64yfl5cXAQEBHDlyxGF5t27d2Lp1q8Nl2927dxMbG8v333//l36fbr/9dqZPnw6An58fMTExDBs2jKysLIf3/K+qX78+nTp1Yv/+/Q7/XVxxxRUsWLDA4a7tfv36sW3bNpKSkszfqR49erBt2zY+++wzoqKiSt1HvXr16NixY4n37dNPPyU2NpajR49WqF9fX1+ioqL46KOP+OCDD7jxxhtLPVuelpZG8+bNq+TnI9alM34i1SwtLc18FEpp9u3bx+eff05kZCSPPPIIkyZNwsPDg6ioKLKysnjhhRdIT0+v1GXO0NBQXnjhBZYuXUpYWBi//fYbS5YsIT8/n5ycnPNu6+HhwcCBA1mzZg2BgYEOYatnz57MnTuXuLg47rrrLjw8PFi7di1eXl5l/mVZntGjR/Ppp58yfPhwYmNjqVOnDmvWrCE1NZVly5Zx4MABFi1aZPbxxztl8/PzOX78OAcOHKBly5Zl7uPAgQPm5+pSU1P56quv6NWrl8M6+/btw2azkZOTw65du9i6desFn2EsFhMTYz5i548BunHjxgwfPpyXXnoJLy8vIiIiSElJ4bXXXmP8+PElbiq44447ePPNN5k6dSobNmwgODgYT09P5syZw3333Ud+fj4bN27kv//9L4DD43/+rHfv3maALPbQQw9x2223MWrUKPMO2Oeff57Q0FB69+7Nrl27Lvj3qXv37qxYsQJ/f3/Cw8NJT09n5cqVREREVPnDux999FFiY2N57LHHuPHGGyksLGTFihWkpKTw0EMPmev17duXFStWAJhn13v27Mm8efMAHH6Xd+/ejZeXF5dffjkAY8eO5cEHH+TRRx9l8ODBZGZm8txzz3H11VfTrl27Cvd74403MnbsWAoLC0v9vKNhGGzfvp277rqrSn9GYj0KfiLV7MCBA8yaNavM+s6dO9m0aRORkZHccsst1KtXj2XLlrFu3Trq1q1Lly5dmDt3Li1atKjwPkeNGsWJEydYvXo1CxcupGnTptx00024ubmxZMkSsrKyzMuNpbnppptYtWoVgwYNcgghHTp0YPHixSxcuJBHH32UwsJCOnfuzIoVKxwuWVbGFVdcwauvvspzzz1HfHw8bm5uhIaGsnr1arp168aTTz7J559/DsBtt91WYvtPPvmExo0bn/dr3xYtWmQ+TqNRo0b07NnT4TN/AFOnTgXO3VgSGBjI7bffzsMPP8yxY8cuqC849yDeDh064O/v7/D4FIBx48bh5+fH2rVrWbZsGUFBQTz99NOlPv/P3d2dKVOmcMstt/DKK68wfPhw5s2bR2JiIg8++CCXXHIJYWFhvPzyy9x99918++23ZX5N3rXXXsu7775Lenq6OadOnTrx8ssvM2/ePP7xj39Qv359+vbty+OPP46Xl9df+n16+OGH8fLyYsOGDSxcuJAGDRoQHR3NY489dsE/17JERkayfPlyEhMTGTt2LHXq1CE4OJiVK1c63IHdtWtXGjRogL+/v3n3c3BwMPXr1ycwMNDhv7XRo0fTvHlz82sXo6KiWLx4MYmJicTFxdG4cWNuuOEGxowZU6l++/btS4MGDWjRogWtW7cu0cvOnTs5ceIE1113XVX/mMRi3IzKPAxMRJzu7rvvpnnz5vq+2jIUPxS5rJ9PeXVXSk9PJyoqivnz55s3yLiaYRjceOONXHvttYwePdrV05EyTJgwgZMnT/LCCy+4eipSy+kzfiIiTvbDDz+QmJjI/fffz2WXXVbi+Wyu5Obmxrhx41i7dm2VfsbuQhQ/Qqe8P1Zz5MgR/v3vf/Pwww+7eipyEdClXhGpVcq7xF2ZS+DVJS8vj5UrVxIYGMhzzz1X6oOAXemqq67ib3/7G0uWLHHKJdeKWrhwIYmJieWu99FHH5V49uDFbN68efz9738v83K9SGXoUq+IiNQI6enpJZ5PWZr27dtX2WNfRKxGwU9ERETEImrW9QYRERERcRoFPxERERGL0M0dTlB8Z5q7u/sFfVepiIiISEUZhkFRURGenp7l3jym4OcEBQUF7Ny509XTEBEREQsp/lrL81Hwc4LitB0SEmJ+x2ZVKiwsZOfOnU4bv6ZS39bp24o9g/q2Ut9W7BnUt7NzQUUeFaXg5wTFl3c9PDyc+ovt7PFrKvVtHVbsGdS3lVixZ1DfzlKRj5fp5g4RERERi1DwExEREbEIBT8RERERi1DwExEREbEIBT8RERERi1DwExEREbEIBT8RERERi1DwExEREXGS07ln2ZN+mgx8+TH9DKdzz7p0PnqAs4iIiIgTHDiWzcbtB0k7lcOxY8fw88vm0kt8iAkPoqVfXZfMSWf8RERERKrY6dyzbNx+kPSsPIfl6Vl5bNx+0GVn/hT8RERERKrYwRM5JUJfsfSsPA6eyKnmGZ2j4CciIiJSxc7kFvylurMo+ImIiIhUsfre57+Nory6syj4iYiIiFSxoEY+BPraSq0F+toIauRTzTM6R8FPREREpIo18K5DTHhQifAX6GsjpksQDbzruGReepyLiIiIiBO09KvLyMjWHDj+O6lH6tKiaQAtG9dzWegDnfETERERcZoG3nXoENiAALLoENjApaEPFPxERERELEPBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELMKlwS8/P58pU6bQvXt3rrzySp577jkMwwBg9+7d3HLLLdjtdoYMGcKuXbsctt20aRNXX301druduLg4jh8/btYMw2Du3Ln07NmTiIgIEhISKCoqMusnTpxgzJgxhIeHEx0dzdtvv+0wdnn7FhEREamNXBr8pk+fzhdffMHy5cuZN28er7/+OuvWrSM7O5vY2Fi6devGxo0bCQ8PZ9SoUWRnZwOwY8cOJk6cyOjRo1m3bh1ZWVnEx8eb465cuZJNmzaRmJjI/Pnzeffdd1m5cqVZj4+P5/Tp06xbt44HH3yQp556ih07dgCUu28RERGpXU7nnuWHI1l888txfjiSxencs66ekst4umrHJ0+eZMOGDaxcuZLQ0FAA7rvvPlJSUvD09MRmszF+/Hjc3NyYOHEin376Ke+//z4xMTGsWbOGAQMGMHjwYAASEhKIiooiNTWVFi1asHr1asaOHUu3bt0AePzxx/nXv/7FyJEjOXDgAB9//DEfffQRQUFBtGvXjuTkZF599VVCQ0N57733zrtvERERqT0OHMtm4/aDpGflmcsCfW3EhAfR0q+uC2fmGi4745eUlET9+vWJiIgwl8XGxjJr1ixSUlLo2rUrbm5uALi5udGlSxeSk5MBSElJMUMdQNOmTWnWrBkpKSmkp6dz5MgRunfvbta7du3KoUOHOHr0KCkpKTRt2pSgoCCH+vbt282xz7dvERERqR1O554tEfoA0rPy2Lj9oCXP/LnsjF9qairNmzfnrbfeYvHixZw9e5aYmBgefPBBMjIyuPzyyx3W9/PzY+/evQAcPXqUJk2alKinpaWRkZEB4FD39/cHMOulbZueng5Q7r4ro7CwsNLbVGZcZ41fU6lv6/RtxZ5BfVupbyv2DNXf94Hjv5N2KqfUWtqpHA4c/50OgQ2cPg9n912ZcV0W/LKzs/ntt99Yu3Yts2bNIiMjg0mTJuHj40NOTg5eXl4O63t5eZGfnw9Abm5umfXc3Fzz9R9rcO5mkvLGLq9eGTt37qz0NjVp/JpKfVuHFXsG9W0lVuwZqq/vDHw5duxYmfXUI3XJPfJztcwFasb77bLg5+npyZkzZ5g3bx7NmzcH4PDhw7z22mu0atWqRNDKz8/H29sbAJvNVmrdx8fHIeTZbDbz3wF8fHzK3La8sYvrlRESEoKHh0eltytPYWEhO3fudNr4NZX6tk7fVuwZ1LeV+rZiz1D9fe9JP42fX9k3Z7ZoGkCHwDZOn4ez+y4evyJcFvwCAgKw2Wxm6ANo3bo1R44cISIigszMTIf1MzMzzUu0gYGBpdYDAgIIDAwEzl2yLf4cX/Hl3+J6Wdueb+w/Xx6uCA8PD6f+Yjt7/JpKfVuHFXsG9W0lVuwZqq/vlo3rceklPiU+4wfnbvBo2bhetf78a8L77bKbO+x2O3l5efzyyy/msv3799O8eXPsdjvbt283n+lnGAbfffcddrvd3DYpKcnc7siRIxw5cgS73U5gYCDNmjVzqCclJdGsWTOaNGlCWFgYhw4dIi0tzaEeFhZmjn2+fYuIiEjt0MC7DjHhQQT62hyWB/raiOkSRAPvOi6ameu4LPi1adOGfv36ER8fz549e/jss89YunQpd9xxB9dddx1ZWVnMmDGDffv2MWPGDHJychgwYAAAd9xxB2+//Tbr169nz549jB8/nn79+tGiRQuzPnfuXLZt28a2bduYN28ew4cPB6BFixZERkYybtw49uzZw/r169m0aRPDhg0DKHffIiIiUnu09KvLyMjWDO/Vipjw5gzv1YqRka1p2dh6j3IBF17qBZg7dy7Tpk3jjjvuwMfHh2HDhnH33Xfj5ubGkiVLeOaZZ3j99ddp3749S5cupW7dc29SeHg4U6dOZf78+Zw6dYrevXszbdo0c9yRI0dy7NgxRo8ejYeHB0OHDmXEiBFmPSEhgYkTJ3LrrbcSEBDAzJkzzWcJ1q9f/7z7FhERkdqlgXcdOja13tm90rg0+DVo0ICEhIRSa6Ghobz55ptlbhsTE1PmA5U9PDyIj493+DaPP/Lz82Px4sVljl3evkVERERqI5d+ZZuIiIiIVB8FPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLUPATERERsQgFPxERERGLcGnw+/DDD2nfvr3Dn7FjxwKwe/dubrnlFux2O0OGDGHXrl0O227atImrr74au91OXFwcx48fN2uGYTB37lx69uxJREQECQkJFBUVmfUTJ04wZswYwsPDiY6O5u2333YYu7x9i4iIiNRGLg1++/btIyoqis8//9z8M336dLKzs4mNjaVbt25s3LiR8PBwRo0aRXZ2NgA7duxg4sSJjB49mnXr1pGVlUV8fLw57sqVK9m0aROJiYnMnz+fd999l5UrV5r1+Ph4Tp8+zbp163jwwQd56qmn2LFjB0C5+xYREfkrTueeZU/6aTLw5cf0M5zOPevqKYmFeLpy5z///DPt2rUjICDAYfkbb7yBzWZj/PjxuLm5MXHiRD799FPef/99YmJiWLNmDQMGDGDw4MEAJCQkEBUVRWpqKi1atGD16tWMHTuWbt26AfD444/zr3/9i5EjR3LgwAE+/vhjPvroI4KCgmjXrh3Jycm8+uqrhIaG8t5775133yIiIhfqwLFsNm4/SNqpHI4dO4afXzaXXuJDTHgQLf3qunp6YgEuPeP3888/c9lll5VYnpKSQteuXXFzcwPAzc2NLl26kJycbNaLQx1A06ZNadasGSkpKaSnp3PkyBG6d+9u1rt27cqhQ4c4evQoKSkpNG3alKCgIIf69u3bK7RvERGRC3E69ywbtx8kPSvPYXl6Vh4btx/UmT+pFi4742cYBr/88guff/45S5YsobCwkOuuu46xY8eSkZHB5Zdf7rC+n58fe/fuBeDo0aM0adKkRD0tLY2MjAwAh7q/vz+AWS9t2/T0dIBy910ZhYWFld6mMuM6a/yaSn1bp28r9gzq+2Lv+8Dx30k7lQOc+zvwf/8sIu1UDgeO/06HwAYunKHzWeW9/jNn912ZcV0W/A4fPkxOTg5eXl48//zzHDx4kOnTp5Obm2su/yMvLy/y8/MByM3NLbOem5trvv5jDSA/P7/cscurV8bOnTsrvU1NGr+mUt/WYcWeQX1frDLw5dixYw7L/nhjYuqRuuQe+bm6p+USF/t7XZaa0LfLgl/z5s3Ztm0bl1xyCW5ubnTs2JGioiLGjRtHREREiaCVn5+Pt7c3ADabrdS6j4+PQ8iz2WzmvwP4+PiUuW15YxfXKyMkJAQPD49Kb1eewsJCdu7c6bTxayr1bZ2+rdgzqO+Lve896afx8zt3o6BhGBw/fpzGjRubHy1q0TSADoFtXDlFp7PKe/1nzu67ePyKcOnNHQ0bNnR43bZtW/Ly8ggICCAzM9OhlpmZaV6iDQwMLLUeEBBAYGAgcO6SbfHn+Iov/xbXy9r2fGP/+fJwRXh4eDj1F9vZ49dU6ts6rNgzqO+LVcvG9bj0Ep///xm/c48Yc3Nzw83NnUBfGy0b17uo+/+ji/29LktN6NtlN3d89tln9OjRg5ycHHPZDz/8QMOGDc2bLf74GYjvvvsOu90OgN1uJykpydzuyJEjHDlyBLvdTmBgIM2aNXOoJyUl0axZM5o0aUJYWBiHDh0iLS3NoR4WFmaOfb59i4iIXIgG3nWICQ8i0NfmsDzQ10ZMlyAaeNdx0czESlwW/MLDw7HZbDz11FPs37+fTz75hISEBO6//36uu+46srKymDFjBvv27WPGjBnk5OQwYMAAAO644w7efvtt1q9fz549exg/fjz9+vWjRYsWZn3u3Lls27aNbdu2MW/ePIYPHw5AixYtiIyMZNy4cezZs4f169ezadMmhg0bBlDuvkVERC5US7+6jIxszd29WjGkawvu7tWKkZGtadlYj3KR6uGyS73169dn+fLlzJw5kyFDhlCvXj1uv/127r//ftzc3FiyZAnPPPMMr7/+Ou3bt2fp0qXUrXvuP4zw8HCmTp3K/PnzOXXqFL1792batGnm2CNHjuTYsWOMHj0aDw8Phg4dyogRI8x6QkICEydO5NZbbyUgIICZM2cSGhpqzut8+xYREfkrGnjXoUNgA3KP/EyHwDYuv/Qn1uLSz/hdccUVDt+o8UehoaG8+eabZW4bExNT5gOVPTw8iI+Pd/g2jz/y8/Nj8eLFZY5d3r5FREREaiOXPsBZRERERKqPgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFhEjQl+sbGxPPnkk+br3bt3c8stt2C32xkyZAi7du1yWH/Tpk1cffXV2O124uLiOH78uFkzDIO5c+fSs2dPIiIiSEhIoKioyKyfOHGCMWPGEB4eTnR0NG+//bbD2OXtW0RERKQ2qhHBb/PmzXzyySfm6+zsbGJjY+nWrRsbN24kPDycUaNGkZ2dDcCOHTuYOHEio0ePZt26dWRlZREfH29uv3LlSjZt2kRiYiLz58/n3XffZeXKlWY9Pj6e06dPs27dOh588EGeeuopduzYUaF9i4iIiNRWLg9+J0+eJCEhgZCQEHPZe++9h81mY/z48bRt25aJEydSr1493n//fQDWrFnDgAEDGDx4MB06dCAhIYFPPvmE1NRUAFavXs3YsWPp1q0bPXv25PHHH+eVV14B4MCBA3z88cdMnz6ddu3accstt3DjjTfy6quvVmjfIiIiIrWVp6snMHv2bG666SaOHj1qLktJSaFr1664ubkB4ObmRpcuXUhOTiYmJoaUlBT+/ve/m+s3bdqUZs2akZKSgpeXF0eOHKF79+5mvWvXrhw6dIijR4+SkpJC06ZNCQoKcqgvWbKkQvuujMLCwsr/QCoxrrPGr6nUt3X6tmLPoL6t1LcVewb17excUBEuDX5ffvkl3377Le+++y6TJ082l2dkZHD55Zc7rOvn58fevXsBOHr0KE2aNClRT0tLIyMjA8Ch7u/vD2DWS9s2PT29QvuujJ07d1Z6m5o0fk2lvq3Dij2D+rYSK/YM6tuVXBb88vLyeOaZZ5g0aRLe3t4OtZycHLy8vByWeXl5kZ+fD0Bubm6Z9dzcXPP1H2sA+fn55Y5dXr0yQkJC8PDwqPR25SksLGTnzp1OG7+mUt/W6duKPYP6tlLfVuwZ1Lezc0FFuCz4JSYm0rlzZ/r06VOiZrPZSgSt/Px8MyCWVffx8XEIeTabzfx3AB8fnwse+8/htCI8PDyc+ovt7PFrKvVtHVbsGdS3lVixZ1DfruSy4Ld582YyMzMJDw8H/hfOPvjgAwYNGkRmZqbD+pmZmeYl2sDAwFLrAQEBBAYGAucu2RZ/jq/48m9xvaxtzzf2ny8Pi4iIiNQ2Lrur9+WXX+bdd9/lrbfe4q233iI6Opro6Gjeeust7HY727dvxzAM4Nxz+b777jvsdjsAdrudpKQkc6wjR45w5MgR7HY7gYGBNGvWzKGelJREs2bNaNKkCWFhYRw6dIi0tDSHelhYmDn2+fYtIiIiUlu5LPg1b96cVq1amX/q1atHvXr1aNWqFddddx1ZWVnMmDGDffv2MWPGDHJychgwYAAAd9xxB2+//Tbr169nz549jB8/nn79+tGiRQuzPnfuXLZt28a2bduYN28ew4cPB6BFixZERkYybtw49uzZw/r169m0aRPDhg0DKHffIiIiIrWVyx/nUpr69euzZMkSnnnmGV5//XXat2/P0qVLqVu3LgDh4eFMnTqV+fPnc+rUKXr37s20adPM7UeOHMmxY8cYPXo0Hh4eDB06lBEjRpj1hIQEJk6cyK233kpAQAAzZ84kNDS0QvsWERERqa3+cvA7deoUDRo0wM3NzXz23YV49tlnHV6Hhoby5ptvlrl+TExMmc/V8/DwID4+3uHbPP7Iz8+PxYsXlzl2efsWERERqY0u6FKvYRgsWrSIHj160KtXLw4dOsS4ceOYNGnSBT32RERERESc74KC38KFC3nnnXd49tlnzcen3HzzzWzdupWEhIQqnaCIiIiIVI0LCn5vvvkmU6dOJSoqyry827t3b2bPns3//d//VekERURERKRqXFDwO3bsWKnPtfP19SU7O/svT0pEREREqt4FBb+ePXuyfPlyh2Vnzpzhueeeo0ePHlUyMRERERGpWhcU/CZPnszu3bvp3bs3eXl5PPTQQ1x11VUcOnSIp556qqrnKCIiIiJV4IIe53LppZfyxhtv8OWXX7J//34KCgpo3bo1kZGRuLu77JnQIiIiInIeF/wcv1deeYVLLrnE/MaLuLg4Dh06xB133FFlkxMRERGRqnNBp+f++c9/smjRIodvs+jRowcvvPACCxcurLLJiYiIiEjVuaDgt2HDBp5//nmio6PNZcOHD2fu3LmsW7euyiYnIiIiIlXngoJfTk4O9evXL7G8UaNGnD59+i9PSkRERESq3gUFvz59+jBjxgwOHz5sLktPT2f27NlERkZW2eREREREpOpcUPCbNGkSZ8+e5W9/+xs9e/akZ8+e9OvXj6KiIp555pmqnqOIiIiIVIELuqu3cePGrF27lj179vDrr7/i6enJZZddxuWXX17V8xMRERGRKlLh4Hf48GGaNm2Km5ubeYnX19eX0NBQh3UAmjVrVsXTFBEREZG/qsLBLzo6mq1bt+Ln50d0dDRubm4l1jEMAzc3N3744YcqnaSIiIiI/HUVDn4fffQRjRs3BuCdd96hXr16TpuUiIiIiFS9Cge/5s2bm//+wAMPkJiYSKdOnZwyKRERERGpehd0V6+7uztnz56t6rmIiIiIiBNd0F29/fr149577yUqKormzZvj5eXlUB89enSVTE5EREREqs4FBb8ff/yR4OBgjh49ytGjRx1qpd30ISIiIiKuV6ng9/bbb/Phhx/i7+/P3/72NwYNGuSseYmIiIhIFavwZ/xWrVrFhAkTyM3NJScnh/j4eJ577jlnzk1EREREqlCFz/itXbuWGTNmMHjwYAD+/e9/Ex8fzyOPPKLLuyIiIiK1QIXP+KWmptKrVy/zdXR0NDk5OSU+4yciIiIiNVOFg19BQQGenv87Qejp6YnNZiM/P98pExMRERGRqnVBz/ETERERkdqnUnf1/t///R/169c3XxcVFfHhhx+aX+VWrPhzgCIiIiJSc1Q4+DVr1owVK1Y4LPPz82PNmjUOy9zc3BT8RERERGqgCge///znP86ch4iIiIg4mT7jJyIiImIRCn4iIiIiFqHgJyIiImIRCn4iIiIiFqHgJyIiImIRCn4iIiIiFuHS4Pfbb78xcuRIwsPD6devH8uWLTNrqampjBgxgrCwMK6//no+//xzh22/+OILBg0ahN1uZ/jw4aSmpjrUX3rpJfr06UN4eDgTJkwgJyfHrOXl5TFhwgS6detGZGRkiecTlrdvERERkdrIZcGvqKiI2NhYGjVqxJtvvsmUKVNYtGgR7777LoZhEBcXh7+/Pxs2bOCmm25i9OjRHD58GIDDhw8TFxdHTEwMb7zxBo0bN+ahhx7CMAwAPvjgAxITE5k6dSqrVq0iJSWFOXPmmPtOSEhg165drFq1imeeeYbExETef/99gHL3LSIiIlJbVeor26pSZmYmHTt2ZPLkydSvX5/LLruMXr16kZSUhL+/P6mpqaxdu5a6devStm1bvvzySzZs2MCYMWNYv349nTt35r777gNg1qxZ9O7dm6+//poePXqwevVq7rnnHqKiogCYMmUKI0eOZNy4cRiGwfr163nxxRcJDg4mODiYvXv38sorr3Ddddfx1VdfnXffIiIiIrWVy874NWnShOeff5769etjGAZJSUl88803REREkJKSQqdOnahbt665fteuXUlOTgYgJSWFbt26mTUfHx+Cg4NJTk6msLCQnTt3OtTDwsI4e/Yse/bsYc+ePRQUFBAeHu4wdkpKCkVFReXuW0RERKS2ctkZvz+Kjo7m8OHDREVFce211zJz5kyaNGnisI6fnx9paWkAZGRklFnPysoiLy/Poe7p6UnDhg1JS0vD3d2dRo0a4eXlZdb9/f3Jy8vj5MmT5x27sgoLCyu9TWXGddb4NZX6tk7fVuwZ1LeV+rZiz6C+nZ0LKqJGBL/58+eTmZnJ5MmTmTVrFjk5OQ7BDMDLy4v8/HyA89Zzc3PN16XVDcMotQaQn59f7r4rY+fOnZXepiaNX1Opb+uwYs+gvq3Eij2D+nalGhH8QkJCgHN32z7++OMMGTLE4S5cOBfKvL29AbDZbCWCWH5+Pr6+vthsNvP1n+s+Pj4UFhaWWgPw9vbGZrNx8uTJMvdd2b48PDwqvV15ii9nO2v8mkp9W6dvK/YM6ttKfVuxZ1Dfzs4FFeHSmzuSk5O5+uqrzWWXX345Z8+eJSAggP3795dYv/gSbGBgIJmZmSXqHTt2pGHDhthsNjIzM2nbti0ABQUFnDx5koCAAAzD4MSJExQUFODpea79jIwMvL298fX1JTAwkH379pW578rw8PBw6i+2s8evqdS3dVixZ1DfVmLFnkF9u5LLbu44ePAgo0ePJj093Vy2a9cuGjduTNeuXfn+++/Ny7YASUlJ2O12AOx2O0lJSWYtJyeH3bt3Y7fbcXd3JyQkxKGenJyMp6cnHTp0oGPHjnh6ejrcrJGUlERISAju7u7Y7fbz7ltERESktnJZ8AsJCSE4OJgJEyawb98+PvnkE+bMmcMDDzxAREQETZs2JT4+nr1797J06VJ27NjB0KFDARgyZAjfffcdS5cuZe/evcTHxxMUFESPHj0AuPPOO1m+fDlbtmxhx44dTJ48mVtvvRUfHx98fHwYPHgwkydPZseOHWzZsoUVK1YwfPhwgHL3LSIiIlJbuSz4eXh48MILL+Dj48Ntt93GxIkTufvuuxk+fLhZy8jIICYmhnfeeYeFCxfSrFkzAIKCgliwYAEbNmxg6NChnDx5koULF+Lm5gbAwIEDGTVqFJMmTeK+++4jNDSUcePGmfuOj48nODiYe+65hylTpjBmzBj69+/vMK+y9i0iIiJSW7n05o7AwEASExNLrbVq1Yo1a9aUuW3fvn3p27dvmfXY2FhiY2NLrfn4+DB79mxmz559QfsWERERqY1c+l29IiIiIlJ9FPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiPF09ARERkep2OvcsB0/kcCa3gPrengQ18qGBdx1XT0vE6RT8RETEUg4cy2bj9oOkZ+WZywJ9bcSEB9HSr64LZybifLrUKyIilnE692yJ0AeQnpXHxu0HOZ171kUzE6keCn4iImIZB0/klAh9xdKz8jh4IqeaZyRSvRT8RETEMs7kFvylukhtp+AnIiKWUd/7/B9tL68uUtsp+ImIiGUENfIh0NdWai3Q10ZQI59qnpFI9VLwExERy2jgXYeY8KAS4S/Q10ZMlyA90kUuejqnLSIiltLSry4jI1vrOX5iSQp+IiJiOQ2869CxqYKeWI8u9YqIiIhYhIKfiIiIiEUo+ImIiIhYhIKfiIiIiEUo+ImIiIhYhIKfiIiIiEUo+ImIiIhYhIKfiIiIiEUo+ImIiIhYhIKfiIiIiEUo+ImIiIhYhIKfiIiIiEUo+ImIiIhYhEuDX3p6OmPHjiUiIoI+ffowa9Ys8vLyAEhNTWXEiBGEhYVx/fXX8/nnnzts+8UXXzBo0CDsdjvDhw8nNTXVof7SSy/Rp08fwsPDmTBhAjk5OWYtLy+PCRMm0K1bNyIjI1mxYoXDtuXtW0RERKQ2clnwMwyDsWPHkpOTwyuvvMI///lPPv74Y55//nkMwyAuLg5/f382bNjATTfdxOjRozl8+DAAhw8fJi4ujpiYGN544w0aN27MQw89hGEYAHzwwQckJiYydepUVq1aRUpKCnPmzDH3nZCQwK5du1i1ahXPPPMMiYmJvP/+++a8zrdvEZGLzencs/xwJItvfjnOD0eyOJ171tVTEhEn8XTVjvfv309ycjJbt27F398fgLFjxzJ79myuuuoqUlNTWbt2LXXr1qVt27Z8+eWXbNiwgTFjxrB+/Xo6d+7MfffdB8CsWbPo3bs3X3/9NT169GD16tXcc889REVFATBlyhRGjhzJuHHjMAyD9evX8+KLLxIcHExwcDB79+7llVde4brrruOrr746775FRC4mB45ls3H7QdKz8sxlgb42YsKDaOlX14UzExFncNkZv4CAAJYtW2aGvmJnzpwhJSWFTp06Ubfu/w46Xbt2JTk5GYCUlBS6detm1nx8fAgODiY5OZnCwkJ27tzpUA8LC+Ps2bPs2bOHPXv2UFBQQHh4uMPYKSkpFBUVlbtvEZGLxencsyVCH0B6Vh4btx/UmT+Ri5DLzvj5+vrSp08f83VRURFr1qyhZ8+eZGRk0KRJE4f1/fz8SEtLAzhvPSsri7y8PIe6p6cnDRs2JC0tDXd3dxo1aoSXl5dZ9/f3Jy8vj5MnT5a778ooLCys9DaVGddZ49dU6ts6fVuxZ6j+vg8c/520Uzml1tJO5XDg+O90CGzg9HlY8f22Ys+gvp2dCyrCZcHvz+bMmcPu3bt54403eOmllxyCGYCXlxf5+fkA5OTklFnPzc01X5dWNwyj1BpAfn7+eceurJ07d1Z6m5o0fk2lvq3Dij1D9fWdgS/Hjh0rs556pC65R36ulrmANd9vK/YM6tuVakTwmzNnDqtWreKf//wn7dq1w2azcfLkSYd18vPz8fb2BsBms5UIYvn5+fj6+mKz2czXf677+PhQWFhYag3A29u73H1XRkhICB4eHpXerjzFl7OdNX5Npb6t07cVe4bq73tP+mn8/LLLrLdoGkCHwDZOn4cV328r9gzq29m5oCJcHvymTZvGa6+9xpw5c7j22msBCAwMZN++fQ7rZWZmmpdgAwMDyczMLFHv2LEjDRs2xGazkZmZSdu2bQEoKCjg5MmTBAQEYBgGJ06coKCgAE/Pc+1nZGTg7e2Nr69vufuuDA8PD6f+Yjt7/JpKfVuHFXuG6uu7ZeN6XHqJT4nP+MG5GzxaNq5XrT9/K77fVuwZ1LcrufQ5fomJiaxdu5bnnnuOgQMHmsvtdjvff/+9edkWICkpCbvdbtaTkpLMWk5ODrt378Zut+Pu7k5ISIhDPTk5GU9PTzp06EDHjh3x9PR0uFkjKSmJkJAQ3N3dy923iMjFooF3HWLCgwj0tTksD/S1EdMliAbedVw0MxFxFped8fv555954YUXiI2NpWvXrmRkZJi1iIgImjZtSnx8PA899BAff/wxO3bsYNasWQAMGTKE5cuXs3TpUqKioli4cCFBQUH06NEDgDvvvJNJkybRrl07mjRpwuTJk7n11lvx8fEBYPDgwUyePJmZM2dy9OhRVqxYYY5d3r5FRC4mLf3qMjKyNQdP5HAmt4D63p4ENfJR6BO5SLks+H300UcUFhayaNEiFi1a5FD78ccfeeGFF5g4cSIxMTG0atWKhQsX0qxZMwCCgoJYsGABM2fOZOHChYSHh7Nw4ULc3NwAGDhwIIcOHWLSpEnk5+fTv39/xo0bZ44fHx/P5MmTueeee6hfvz5jxoyhf//+wLnTsOfbt4jIxaaBdx06NlXQE7EClwW/2NhYYmNjy6y3atWKNWvWlFnv27cvffv2vaDxfXx8mD17NrNnz76gfYuIVLXTuWc5cPx3MvDlx/QztGhcV2fdRKTKufzmDhERqyv+9oy0UzkcO3YMP79sLr3ER9+eISJVzqU3d4iIWJ2+PUNEqpOCn4iICx08kVPq41TgXPg7eKL0b9YQEbkQCn4iIi50JrfgL9VFRCpDwU9ExIXqe5//o9bl1UVEKkPBT0TEhYIa+ZR4gHKxQF8bQY18qnlGInIxU/ATEXEhfXuGiFQnXUMQEXGx4m/POHD8d1KP1KVF0wBaNq6n0CciVU5n/EREaoAG3nXoENiAALLoENhAoU9EnELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiFPxERERELELBT0RERMQiakTwy8/PZ9CgQWzbts1clpqayogRIwgLC+P666/n888/d9jmiy++YNCgQdjtdoYPH05qaqpD/aWXXqJPnz6Eh4czYcIEcnJyzFpeXh4TJkygW7duREZGsmLFCodty9u3iIiISG3k8uCXl5fHo48+yt69e81lhmEQFxeHv78/GzZs4KabbmL06NEcPnwYgMOHDxMXF0dMTAxvvPEGjRs35qGHHsIwDAA++OADEhMTmTp1KqtWrSIlJYU5c+aY4yckJLBr1y5WrVrFM888Q2JiIu+//36F9i0iIiJSW7k0+O3bt49bb72VAwcOOCz/6quvSE1NZerUqbRt25ZRo0YRFhbGhg0bAFi/fj2dO3fmvvvu44orrmDWrFkcOnSIr7/+GoDVq1dzzz33EBUVRWhoKFOmTGHDhg3k5OSQnZ3N+vXrmThxIsHBwVxzzTXcf//9vPLKKxXat4iIiEht5dLg9/XXX9OjRw/WrVvnsDwlJYVOnTpRt25dc1nXrl1JTk426926dTNrPj4+BAcHk5ycTGFhITt37nSoh4WFcfbsWfbs2cOePXsoKCggPDzcYeyUlBSKiorK3beIiIhIbeXpyp3feeedpS7PyMigSZMmDsv8/PxIS0srt56VlUVeXp5D3dPTk4YNG5KWloa7uzuNGjXCy8vLrPv7+5OXl8fJkyfL3XdlFBYWVnqbyozrrPFrKvVtnb6t2DOobyv1bcWeQX07OxdUhEuDX1lycnIcghmAl5cX+fn55dZzc3PN16XVDcMotQbnbjIpb9+VsXPnzkpvU5PGr6nUt3VYsWdQ31ZixZ5BfbtSjQx+NpuNkydPOizLz8/H29vbrP85iOXn5+Pr64vNZjNf/7nu4+NDYWFhqTUAb2/vcvddGSEhIXh4eFR6u/IUX8521vg1lfq2Tt9W7BnUt5X6tmLPoL6dnQsqokYGv8DAQPbt2+ewLDMz07wEGxgYSGZmZol6x44dadiwITabjczMTNq2bQtAQUEBJ0+eJCAgAMMwOHHiBAUFBXh6nms/IyMDb29vfH19y913ZXh4eDj1F9vZ49dU6ts6rNgzqG8rsWLPoL5dyeWPcymN3W7n+++/Ny/bAiQlJWG32816UlKSWcvJyWH37t3Y7Xbc3d0JCQlxqCcnJ+Pp6UmHDh3o2LEjnp6eDjdrJCUlERISgru7e7n7FhEREamtamTwi4iIoGnTpsTHx7N3716WLl3Kjh07GDp0KABDhgzhu+++Y+nSpezdu5f4+HiCgoLo0aMHcO6mkeXLl7NlyxZ27NjB5MmTufXWW/Hx8cHHx4fBgwczefJkduzYwZYtW1ixYgXDhw+v0L5FREREaqsaGfw8PDx44YUXyMjIICYmhnfeeYeFCxfSrFkzAIKCgliwYAEbNmxg6NChnDx5koULF+Lm5gbAwIEDGTVqFJMmTeK+++4jNDSUcePGmePHx8cTHBzMPffcw5QpUxgzZgz9+/ev0L5FREREaqsa8xm/H3/80eF1q1atWLNmTZnr9+3bl759+5ZZj42NJTY2ttSaj48Ps2fPZvbs2aXWy9u3VZ3OPcvBEzmcyS2gvrcnQY18aOBdx9XTEhERkQqqMcFParYDx7LZuP0g6Vl55rJAXxsx4UG09Kt7ni1FRESkpqiRl3qlZjmde7ZE6ANIz8pj4/aDnM49Wy1z2JN+mgx8+TH9TLXsU0RE5GKjM35SroMnckqEvmLpWXkcPJFDx6bOu+RbfLYx7VQOx44dw88vm0sv8dHZRhERkUrSGT8p15ncgr9U/ytqwtlGERGRi4WCXy3jikue9b3Pf2K4vPpfUZGzjSIiIlIxutRbi7jqkmdQIx8CfW2lBrBAXxtBjXyctm9Xnm0UERG52OiMXy3hykueDbzrEBMeRKCvzWF5oK+NmC5BTn2kiyvPNoqIiFxs9LdmLeHqGyxa+tVlZGTran+OnyvPNoqIiFxsFPxqiZpwybOBdx2nhsuy9hkTHmRe4i5WHWcbRURELjYKfrWElS95Fp9tPHD8d1KP1KVF0wBaNq6n0CciIlJJ+oxfLVF8ybM0Vrjk2cC7Dh0CGxBAFh0CGyj0iYiIXAAFv1rClTdYiIiIyMXh4r0+eBHSJU8RERH5K3TGr5bRJU8RERG5UAp+IiIiIhah4CciIiJiEQp+IiIiIhah4CciIiJiEQp+IiIiIhah4CciIiJiEQp+IiIiIhahBzg7gWEYABQWFjpl/OJxnTV+TaW+rdO3FXsG9W2lvq3YM6hvZ+eC4vxxPm5GRdaSSsnPz2fnzp2unoaIiIhYSEhICF5eXuddR8HPCYqKiigoKMDd3R03NzdXT0dEREQuYoZhUFRUhKenJ+7u5/8Un4KfiIiIiEXo5g4RERERi1DwExEREbEIBT8RERERi1DwExEREbEIBT8RERERi1DwExEREbEIBT8RERERi1Dwc7H09HTGjh1LREQEffr0YdasWeTl5QGQmprKiBEjCAsL4/rrr+fzzz932PaLL75g0KBB2O12hg8fTmpqqkP9pZdeok+fPoSHhzNhwgRycnKqra/yOKvv/Px8Zs+ezVVXXUX37t2Ji4sjLS2tWns7H2e+38WWLVtGdHS003upKGf2/Morr9CvXz+6dOnC2LFjOXnyZHW1VS5n9Z2Xl8e0adPo1asXvXr1YtKkSWRnZ1drb+fzV/ou9s4773D33XeXWF5Tj2nO6vliPp4VK+u9LlbTjmfg3L6r5ZhmiMsUFRUZt956q3H//fcbP/30k/HNN98Y11xzjfHss88aRUVFxg033GA89thjxr59+4zFixcbdrvdOHTokGEYhnHo0CEjLCzMWL58ufHTTz8ZDz/8sDFo0CCjqKjIMAzDeP/9942uXbsa//nPf4yUlBTj+uuvN6ZMmeLKdk3O7HvOnDnG1VdfbWzbts3Yu3evERsbawwZMsSsu5Iz+y524MABw263G1FRUa5osQRn9rx582YjNDTUeP/9940ff/zRGDp0qPHII4+4sl2TM/ueO3euMWjQIGPHjh1GSkqKMWDAAGPatGmubNf0V/ou9uWXXxp2u9246667HJbX1GOaM3u+WI9nxcrqu1hNO54ZhnP7rq5jmoKfC+3bt89o166dkZGRYS579913jcjISOOLL74wwsLCjN9//92s3XPPPcb8+fMNwzCM559/3uGXJjs72wgPDze++uorwzAM48477zTXNQzD+Oabb4zQ0FAjOzvb2W2Vy5l9X3nllcbmzZvNenp6utGuXTvjl19+cXJX5XNm38Xuvfde4/bbb68xB0pn9jx48GBjwYIFZv3rr782Bg4caBQUFDi7rXI5s+8bbrjBePnll8366tWrjYEDBzq7pQr5K30bhmEsWLDA6Ny5szFo0KASfynW1GOaM3u+WI9nhnH+vovVtOOZYTi37+o6pulSrwsFBASwbNky/P39HZafOXOGlJQUOnXqRN26dc3lXbt2JTk5GYCUlBS6detm1nx8fAgODiY5OZnCwkJ27tzpUA8LC+Ps2bPs2bPHuU1VgLP6LioqYs6cOVx55ZUl9nn69GnnNFMJzuq72FtvvUVOTg5Dhw51ah+V4ayez5w5w+7du7nmmmvMevfu3dm0aRMeHh7ObaoCnPleN2zYkA8++IBTp05x6tQp/v3vf9OxY0en91QRf6VvgK1bt7J8+XL69+/vsH1NPqY5q+eL+XgGZfddrCYez8B5fVfnMU3Bz4V8fX3p06eP+bqoqIg1a9bQs2dPMjIyaNKkicP6fn5+5uc7zlfPysoiLy/Poe7p6UnDhg1rxOdDnNW3u7s7V155JQ0bNjRrq1evplGjRrRv3955DVWQs/oGOH78OHPnzmXq1Km4ubk5uZOKc1bPxZ95O378OLfffjuRkZE88cQTZGVlObmjinHmez1+/HgOHjxIjx496NGjB6dOneKZZ55xckcV81f6BnjttdeIiIgoMW5NPqY5q+eL+XgGZfcNNfd4Bs7ruzqPaQp+NcicOXPYvXs3jzzyCDk5OXh5eTnUvby8yM/PBzhvPTc313xd1vY1SVX1/WdbtmxhxYoVPPbYYyW2qQmqsu+ZM2dy8803c8UVV1TP5C9QVfX8+++/AzB16lT+/ve/869//Yu9e/cyfvz46mmkkqryvT5w4ABNmzZl1apVLF++nLy8PJ599tnqaaSSKtP3+dSmY1pV9fxnF9PxrDy15XgGVdd3dR7TFPxqiDlz5rBq1SrmzJlDu3btsNlsJX5Z8vPz8fb2Biiz7uPjg81mM1+XVq9JqrLvP9qyZQv/+Mc/uOuuu7jllluc28QFqMq+P/vsM5KTk4mLi6u2+V+IquzZ09MTgNjYWP72t7/RtWtXZsyYwccff0x6enr1NFRBVdn3mTNnmDhxIk888QQ9evSgd+/ezJw5kw0bNnD06NFq66kiKtv3+dSWY1pV9vxHF9vx7Hxqy/EMqrbv6jymKfjVANOmTWPlypXMmTOHa6+9FoDAwEAyMzMd1svMzDRPI5dVDwgIoGHDhthsNod6QUEBJ0+eJCAgwMndVFxV911s8+bNPPzww9x2221MmDDByV1UXlX3/d5775GWlkavXr0IDw/nmWee4fDhw4SHh/Ptt99WT1PlqOqei9/vNm3amLXWrVsDuPzS3x9Vdd/79+8nOzubDh06mLVOnTpRVFRU6/s+n9pwTKvqnotdjMez86kNxzOo+r6r85im4OdiiYmJrF27lueee46BAweay+12O99//715iQMgKSkJu91u1pOSksxaTk4Ou3fvxm634+7uTkhIiEM9OTkZT09Ph78wXMkZfQN8+eWXjB8/nmHDhvH0009XUzcV54y+H3/8cTZv3sxbb73FW2+9xdixY2nSpAlvvfUWnTt3rr7myuCMnps1a0aTJk0cPtj/888/4+bmRrNmzaqhq/I5o+/iv0D27dtn1vfv3w9AUFCQU/upqAvt+3xq+jHNGT3DxXs8O5+afjwD5/Rdrce0Kr1HWCpl3759RseOHY1//vOfxtGjRx3+FBQUGNdff73xj3/8w/jpp5+MJUuWGGFhYebzgFJTU42QkBBjyZIl5rO+brjhBvP5Tps2bTK6dOlifPjhh0ZKSooxcODAGvOsL2f1ffbsWaNfv37GPffcU2LcvLw8F3ft3Pf7jzZs2FBjHn/gzJ6XLVtmXHnllcbnn39u/PDDD8bQoUONuLg4V7ZrcmbfI0eONG6++WZj586dxo4dO4ybb765xjy/8K/0/Ufz588v8aiLmnpMc1bPF/Px7I9Ke6//qCYdzwzDuX1X1zFNwc+FlixZYrRr167UP4ZhGL/++qsxbNgwo3PnzsbAgQONrVu3Omz/3//+1+jfv78RGhpq3HPPPcaBAwdKjN+rVy+ja9euRnx8vJGbm1ttvZ2Ps/revn17meP++Xl3ruDs97tYTTpQOrPnoqIiY+HChcaVV15phIWFGY8++qiRlZVVrf2VxZl9nzx50njyySeNXr16GVdeeaXx9NNPG2fOnKnW/sryV/suVlYYqInHNGf1fLEfz4rVtuDnzL6r65jmZhiGUbXnEEVERESkJtJn/EREREQsQsFPRERExCIU/EREREQsQsFPRERExCIU/EREREQsQsFPRERExCIU/EREREQsQsFPRERExCIU/EREnODRRx+lb9++5OTklKjde++93H777ej5+SJS3RT8RESc4IknnuD06dMsXrzYYfm///1vvvnmG6ZOnYqbm5uLZiciVqXgJyLiBIGBgYwZM4aVK1eSmpoKQG5uLs8++yz33nsv7dq1c/EMRcSKFPxERJzk7rvvplWrVsyZMweAZcuW4e7uTlxcHEeOHOGBBx7AbrcTHR1NYmIihYWF5rbr16/nuuuuo3PnzvTo0YMpU6aY9SeffJInn3ySG2+8kV69evHrr7+6oj0RqYU8XT0BEZGLlaenJ5MmTeLuu+9my5YtLF++nPnz52Oz2Rg9ejQdOnTgzTffJCMjg0mTJuHm5kZcXBxff/0106dPZ86cOXTq1Ildu3Yxbtw4evXqRf/+/QF4++23WbhwIf7+/lx22WWubVREag2d8RMRcaLu3btzww038PDDDxMVFUWfPn346quvOHz4MNOmTaNNmzb06NGDJ554gtWrVwNQt25dZsyYQf/+/QkKCuK6666jU6dO7N271xw3JCSE6OhoQkNDXdWaiNRCOuMnIuJkDzzwAO+88w5xcXEA/Pzzz5w8eZKuXbua6xQVFZGbm8uJEyfo3Lkz3t7ezJ8/n3379vHjjz/y22+/ERkZaa7fvHnzau9DRGo/BT8RESez2WwO/ywoKKBNmza88MILJdZt0KABn332GXFxcQwePJg+ffoQFxfHlClTSh1TRKQydKlXRKSatW7dmsOHD9O4cWNatWpFq1atOHjwIPPnz8fNzY3169czZMgQpk6dyi233ELbtm05cOCAnvsnIn+Zgp+ISDWLjIykefPmjBs3jh9//JFvv/2Wp59+Gh8fHzw8PGjYsCHbt2/nxx9/ZO/evTz55JNkZGSQn5/v6qmLSC2n4CciUs08PDxYtGgRRUVF3HrrrYwZM4a+ffvy1FNPATB69Gj8/Py47bbbuPfee7HZbNxxxx388MMPLp65iNR2boauHYiIiIhYgs74iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFiEgp+IiIiIRSj4iYiIiFjE/wNWFY7AjzgpogAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Анализ и визуализация cars_new.csv (как в Задании 1)\n",
+        "df_plot = df_new.dropna(subset=[\"Price\"]).copy()\n",
+        "\n",
+        "# 1. Распределение цены\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.histplot(df_plot[\"Price\"], bins=50)\n",
+        "plt.title(\"Распределение цены (cars_new.csv)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# 2. Цена vs Год выпуска\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.scatterplot(data=df_plot, x=\"Year\", y=\"Price\", alpha=0.6)\n",
+        "plt.title(\"Цена vs Год выпуска (cars_new.csv)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# 3. Цена vs Состояние\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.boxplot(data=df_plot, x=\"Condition\", y=\"Price\")\n",
+        "plt.title(\"Цена vs Состояние (cars_new.csv)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# 4. Тепловая карта корреляций\n",
+        "numeric_cols = df_plot.select_dtypes(include=[np.number]).columns\n",
+        "if len(numeric_cols) > 1:\n",
+        "    plt.figure(figsize=(10, 8))\n",
+        "    corr = df_plot[numeric_cols].corr()\n",
+        "    sns.heatmap(corr, annot=True, fmt=\".2f\", cmap=\"coolwarm\", center=0, square=True)\n",
+        "    plt.title(\"Тепловая карта корреляций (cars_new.csv)\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e6e0089c",
+      "metadata": {},
+      "source": [
+        "## Анализ новых данных\n",
+        "\n",
+        "В дополнительных данных видно сильную корреляцию состояния автомобиля и цены; года выпуска и цены; что приводит к более точным прогнозам"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv (3.14.2)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.14.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/Вариант 1/car_price_solution.ipynb.bak b/Вариант 1/car_price_solution.ipynb.bak
new file mode 100644
index 0000000..afdb294
--- /dev/null
+++ b/Вариант 1/car_price_solution.ipynb.bak	
@@ -0,0 +1,1508 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Прогнозирование цены автомобиля\n",
+        "\n",
+        "Работаем локально в `ipynb`. Все шаги максимально простые и прозрачные."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "0757256f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0\u001b[0m\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip -q install pandas numpy matplotlib seaborn scikit-learn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "ad0e2ade",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
+        "\n",
+        "pd.set_option(\"display.max_columns\", 50)\n",
+        "sns.set_style(\"whitegrid\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "2180fd89",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Car ID</th>\n",
+              "      <th>Brand</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Fuel Type</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Model</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Tesla</td>\n",
+              "      <td>2016.0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>114832.0</td>\n",
+              "      <td>New</td>\n",
+              "      <td>26613.92</td>\n",
+              "      <td>Model X</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>BMW</td>\n",
+              "      <td>2018.0</td>\n",
+              "      <td>4.4</td>\n",
+              "      <td>Electric</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>143190.0</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>14679.61</td>\n",
+              "      <td>5 Series</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Audi</td>\n",
+              "      <td>2013.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>Electric</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>181601.0</td>\n",
+              "      <td>New</td>\n",
+              "      <td>44402.61</td>\n",
+              "      <td>A4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>Tesla</td>\n",
+              "      <td>2011.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>Diesel</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>68682.0</td>\n",
+              "      <td>New</td>\n",
+              "      <td>86374.33</td>\n",
+              "      <td>Model Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>Ford</td>\n",
+              "      <td>2009.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>Diesel</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>223009.0</td>\n",
+              "      <td>Like New</td>\n",
+              "      <td>73577.10</td>\n",
+              "      <td>Mustang</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Car ID  Brand    Year  Engine Size Fuel Type Transmission   Mileage  \\\n",
+              "0     1.0  Tesla  2016.0          2.3    Petrol       Manual  114832.0   \n",
+              "1     2.0    BMW  2018.0          4.4  Electric       Manual  143190.0   \n",
+              "2     3.0   Audi  2013.0          4.5  Electric       Manual  181601.0   \n",
+              "3     4.0  Tesla  2011.0          4.1    Diesel    Automatic   68682.0   \n",
+              "4     5.0   Ford  2009.0          2.6    Diesel       Manual  223009.0   \n",
+              "\n",
+              "  Condition     Price     Model  \n",
+              "0       New  26613.92   Model X  \n",
+              "1      Used  14679.61  5 Series  \n",
+              "2       New  44402.61        A4  \n",
+              "3       New  86374.33   Model Y  \n",
+              "4  Like New  73577.10   Mustang  "
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 1) Загрузка датасета\n",
+        "df = pd.read_csv(\"car_price_prediction_with_missing.csv\")\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "19d24a44",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.DataFrame'>\n",
+            "RangeIndex: 2500 entries, 0 to 2499\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column        Non-Null Count  Dtype  \n",
+            "---  ------        --------------  -----  \n",
+            " 0   Car ID        2250 non-null   float64\n",
+            " 1   Brand         2250 non-null   str    \n",
+            " 2   Year          2250 non-null   float64\n",
+            " 3   Engine Size   2250 non-null   float64\n",
+            " 4   Fuel Type     2250 non-null   str    \n",
+            " 5   Transmission  2250 non-null   str    \n",
+            " 6   Mileage       2250 non-null   float64\n",
+            " 7   Condition     2250 non-null   str    \n",
+            " 8   Price         2250 non-null   float64\n",
+            " 9   Model         2250 non-null   str    \n",
+            "dtypes: float64(5), str(5)\n",
+            "memory usage: 195.4 KB\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "None"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Car ID          0.1\n",
+              "Brand           0.1\n",
+              "Year            0.1\n",
+              "Engine Size     0.1\n",
+              "Fuel Type       0.1\n",
+              "Transmission    0.1\n",
+              "Mileage         0.1\n",
+              "Condition       0.1\n",
+              "Price           0.1\n",
+              "Model           0.1\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Общая информация и пропуски\n",
+        "display(df.info())\n",
+        "display(df.isna().mean().sort_values(ascending=False))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c0dffc81",
+      "metadata": {},
+      "source": [
+        "## Очистка и предобработка\n",
+        "Исходный набор данных имеет пропуски\n",
+        "- Удаляем дубликаты.\n",
+        "- Удалить пропуски"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "1ab1ead2",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.DataFrame'>\n",
+            "Index: 2250 entries, 0 to 2499\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column        Non-Null Count  Dtype  \n",
+            "---  ------        --------------  -----  \n",
+            " 0   Car ID        2250 non-null   float64\n",
+            " 1   Brand         2250 non-null   str    \n",
+            " 2   Year          2250 non-null   float64\n",
+            " 3   Engine Size   2250 non-null   float64\n",
+            " 4   Fuel Type     2250 non-null   str    \n",
+            " 5   Transmission  2250 non-null   str    \n",
+            " 6   Mileage       2250 non-null   float64\n",
+            " 7   Condition     2250 non-null   str    \n",
+            " 8   Price         2250 non-null   float64\n",
+            " 9   Model         2250 non-null   str    \n",
+            "dtypes: float64(5), str(5)\n",
+            "memory usage: 193.4 KB\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "None"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Car ID          0.0\n",
+              "Brand           0.0\n",
+              "Year            0.0\n",
+              "Engine Size     0.0\n",
+              "Fuel Type       0.0\n",
+              "Transmission    0.0\n",
+              "Mileage         0.0\n",
+              "Condition       0.0\n",
+              "Price           0.0\n",
+              "Model           0.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "df = df.drop_duplicates()\n",
+        "df = df.dropna()\n",
+        "display(df.info())\n",
+        "display(df.isna().mean().sort_values(ascending=False))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d7fdb1a8",
+      "metadata": {},
+      "source": [
+        "## Задание 1. Анализ и визуализация\n",
+        "\n",
+        "Ниже минимум 3 графика для изучения датасета\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "83c5b8b2",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 1: распределение цены\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.histplot(df[\"Price\"], bins=50)\n",
+        "plt.title(\"Распределение цены\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4cf27c93",
+      "metadata": {},
+      "source": [
+        "Видно, что распределение цен на автомобили примерно равномерное, близкое к случайному"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "926cf42e",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAGHCAYAAAAupV8sAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsvXmUJFWZ9/+JyMh9q31furqq95Xuplm6UUBBERSmRdxX5sVXxXlnxtEzDI4jOs44js55ZwS3UQcRFRf0p4DiIKDsW0Pva/VS+15ZuUdGREb8/ojKrMqu6oa8VTl0v+T3HI52RNWtyIjIe5/7PN/n+5Usy7Ioo4wyyiijjDLKKOP/eciv9gWUUUYZZZRRRhlllPE/g3LgV0YZZZRRRhlllPEaQTnwK6OMMsooo4wyyniNoBz4lVFGGWWUUUYZZbxGUA78yiijjDLKKKOMMl4jKAd+ZZRRRhlllFFGGa8RlAO/Msooo4wyyiijjNcIyoFfGWWUUUYZZZRRxmsE5cCvjDLKKKOMMsoo4zUC5dW+gDLKKOP/Pbz//e8H4Ic//OG851esWMGf/dmf8eUvf/l/8rL+x7FixYrTnnstfP4yyijj7EM58CujjDLKKCGuv/563vGOd8w5XlVV9SpcTRlllPFaRznwK6OMMsooIRoaGti4ceOrfRlllFFGGUCZ41dGGWWcJfjDH/7Ajh07WLduHdu2beMf//EfSaVS+fNf//rX5y2drlixgq9//ev5fx86dIibb76ZCy+8kDVr1nDJJZfwj//4j6iqOu/fHR4eZtWqVdx9990FxycnJ1mzZg133nknAE8++SQ33HAD5513Hueffz4f+9jHOHbs2II+88mTJ/mLv/gLtm3bxsaNG3n/+9/Pzp07C37mZz/7GStWrJjz39/+7d+edtz+/v6Cn129ejXbt2/nK1/5CqZpAvDss8+yYsUKnn322TOO8ctf/rLg5890LW9/+9t517veNWesD33oQ3z4wx8GwLIs7rzzTq666irWr1/PFVdcwfe+9z0sywLgb//2b7n88svzv/v973+f8847j3vuuQcAVVX52te+xpVXXsnatWvZtGkTH/7whzl48OAruudllPFaRznwK6OMMl513HfffXziE59g6dKl3HHHHdx888385je/4eMf/3g+IHglGB0d5b3vfS/pdJovf/nL/Od//idXX301P/zhD7nrrrvm/Z2Ghga2bt3KAw88UHD8wQcfxLIsrr76avr6+vj4xz/O2rVr+eY3v8mXvvQlTpw4wU033ZQPpIpFd3c3O3bsoL+/n89+9rN89atfRZIkPvjBD/Lcc8/lf05VVdatW8dPf/rT/H+1tbWv6G987GMf46c//Snf+973uO666/je977HvffeK3S9OXzuc5877bVcf/31vPTSS/T09OSPDQ0N8eyzz7Jjxw4AvvKVr/CVr3yFyy+/nG9961tcf/31fPWrX+U73/nOnL81MjLCv/3bv/H3f//3XHHFFQB85jOf4d577+Wmm27i+9//PrfccgtHjx7lU5/6VFHvShllvFZRLvWWUUYZryosy+KrX/0ql1xyCV/96lfzx5csWcKHPvQh/vSnP3HppZe+orGOHDnCqlWr+Pd//3cCgQAAF198MU8++STPPvssN91007y/d+211/J3f/d3DA4O0tTUBMADDzzAxRdfTG1tLQ888ACqqvLRj36U+vp6wA4YH374YVKpVP5vFYPbb78dl8vFXXfdlf/9Sy+9lGuuuYavfOUr/OIXvwAgnU5TU1NTUC52uVyv6G+0tbXlf++iiy7i5z//Ofv27ZuXc/hK0dXVddprueaaa/jyl7/Mr3/9a/7iL/4CgF//+tf4/X6uuOIKYrEYd911F+973/v49Kc/DdjPZ2xsjOeff56PfvSjBX/rnnvuYeXKlfmgUdM0kskkn/3sZ3nLW94CwNatW0kkEnz5y19mfHz8FQfFZZTxWkU541dGGWW8qjh+/DjDw8NcfvnlGIaR/+/8888nEAjw5JNPFvz87J8xDKPg3Pbt27n77rtxu910d3fz8MMP881vfpPJyUk0TTvtNVx55ZW43W5++9vfAnaWaufOnVx77bUAbNiwAbfbzfXXX8+XvvQlHn/8cVauXMlf/dVfCQV9AM899xyXXXZZwe8risLVV1/Nvn37SCaT+WsJBoNCf8M0TQzDQFVV7rvvPqLRKGvXrp33ZxYDwWCQK6+8kt/85jf5Y7/61a94y1vegsfjYdeuXRiGwZVXXlnwe5/97Gf57ne/W3Ds+PHj/PjHP853iIMdZH7ve9/jLW95CyMjIzzzzDPcc889PProowBnfMZllFGGjXLGr4wyynhVMTU1BcBtt93GbbfdNuf86Ohowb/XrFlz2rFM0+Tf/u3f+NGPfkQqlaKxsZH169fjdrvPeA2BQIA3vvGNPPDAA/z5n/85v/3tb/F6vbzxjW8EoKWlhbvvvpvvfOc7/OIXv+Cuu+4iFArxnve8h7/8y79EkqQiPzVEo1FqamrmHK+pqcGyLBKJBH6/n4GBgTN+5jPh1ltv5dZbb83/u7Ozk7e97W0FP/OhD30IsIOqxsZGrrnmGj72sY8J/T2wy72/+c1veOGFF3A4HJw8eZJ/+Zd/AWae9ct1NA8MDHDVVVdx2WWX5YPvHB5//HH+6Z/+iePHj+P3+1m5ciU+nw+gXOoto4xXgHLgV0YZZbyqCIVCgM3d2rp165zz4XC44N+5EmgO119/ff7/f+c73+HOO+/ktttu48orr8xnymb/zOnwtre9jZtuuomenh4eeOAB3vSmN+H1evPn169fz+23346maezcuZOf/vSnfOtb32LlypVcddVVr/wDz/pc4+Pjc46PjY0BUFlZiWma7N69m7e//e1Fjw9w8803c+mll2KaJsePH+cLX/gCX/nKV/j7v//7/M/cdtttrFmzBl3XOXToEP/yL/9CLBbLB4TFYuvWrbS1tfHggw8iyzJLly7Nl4Zzz3pycpKlS5fmf2dwcJDe3l42b94MQG1tLX/913/NF77wBe644w4+8YlPANDb28snPvEJ3vjGN/Ltb3+b1tZWJEniRz/6EY8//rjQ9ZZRxmsN5cCvjDLKeFWxdOlSqqur6e/v58Ybb8wfHx0d5TOf+Qzvete7aGtryx9ft27dacfauXMnXV1dBYHSyMgIR44cOePvgV0mrqmp4a677mL//v15DhrAnXfeyQ9+8AN+//vf43K5uOiii1i7di2/+93vGBwcFPnYnH/++Tz66KMkEol8uTebzfLAAw+wbt06XC4XL7zwAqlUigsuuEDobzQ3N+c/94YNG3j00Ud55plnCn6mo6Mj/zObNm3iySef5LnnnhMO/CRJYseOHfz0pz9FURRuuOGG/Ln169fjdDp59NFH2bJlS/7497//fX7729/yxBNPAHb2cceOHaTTaf7xH/+Riy66iE2bNrFv3z4ymQw33XRTwTuRC/rKGb8yynh5lAO/MsoooyQYHh7OS6HMh+7ubp544gm2b9/OX/3VX/G5z30Oh8PBZZddRiwW4xvf+AYjIyNFlTnXr1/PN77xDb7zne+wceNGenp6+Pa3v42maaTT6TP+rsPh4Oqrr+buu++mvr6+INi68MIL+epXv8onPvEJ3ve+9+FwOLjnnntwuVxcdtllr/j6ZuPmm2/mscce4wMf+AA33XQTTqeTu+++m76+Pr773e/S29vLN7/5zfznmN0pq2kak5OT9Pb2FgRAp6K3tzfPq+vr6+OZZ57hoosuKviZ7u5u3G436XSaffv28eSTTwpnGHPYsWNHXmJndqm2qqqKD3zgA9x55524XC62bt3K7t27+clPfsJnPvMZZLmQdv7ud7+bX/3qV3zhC1/g3nvvZc2aNSiKwr/+67/ykY98BE3T+OUvf8kf//hHgAL5nzLKKGN+lAO/MsoooyTo7e3ln//5n097fu/evdx///1s376dd7zjHfj9fr773e/y05/+FJ/Px6ZNm/jqV79Ka2vrK/6bH/3oR4lEItx1113ccccdNDY2cu211yJJEt/+9reJxWL5cuN8uPbaa/nBD37ANddcUxCErFy5km9961vccccd/PVf/zXZbJa1a9fy/e9/v6BkWQyWLVvGj3/8Y/7t3/6NW265BUmSWL9+PXfddRdbtmzhb//2b/MZsHe+851zfv9Pf/oTVVVVZ7R9++Y3v8k3v/lNwC4dX3jhhQWcP4AvfOELgN1YUl9fz7ve9S7+z//5P0xMTAh9LoD6+npWrlxJTU1Nvgs6h09/+tNUV1dzzz338N3vfpeWlhb+/u//fl79P1mWue2223jHO97Bj370Iz7wgQ/wta99jdtvv52PfexjhMNhNm7cyA9/+EPe//7388ILL5zRJq+MMsoAySrnxssoo4xXAe9///tpbm4u+9WeBjlR5NPdn5c7/2piZGSEyy67jP/4j//IN8iUUUYZZwfKGb8yyiijjDIWBQcPHuThhx/m97//PUuWLClw4CijjDLODpQDvzLKKKOMsxAvV+IupgT+P4VMJsN//dd/UV9fz7/927/N4eyVUUYZrz7Kpd4yyiijjDLKKKOM1wjK27EyyiijjDLKKKOM1wjKgV8ZZZRRRhlllFHGawTlwK+MMsooo4wyyijjNYJyc0cJkDM9l2VZyMOzjDLKKKOMMsoo45XCsixM00RRlJdtqioHfiWAYRjs3bv31b6MMsooo4wyyijjNYSc3eOZUA78SoBctL1u3TocDseij5/NZtm7d2/Jxi9jcVF+XucOys/q3EH5WZ07KD+r0iN3j1+JhFI58CsBcuVdh8NR0pe81OOXsbgoP69zB+Vnde6g/KzOHZSfVenxSuhl5eaOMsooo4wyyiijjNcIyoFfGWWUUUYZZZRRxmsE5cCvjDLKKKOMMsoo4zWCcuBXRhlllFFGGWWU8RpBOfAro4wyyiijjDLKeI2gHPiVUUYZZZRRRhllvEZwVgR+mqZxzTXX8Oyzz+aP9fX18aEPfYiNGzfylre8hSeeeKLgd5566imuueYaNmzYwAc+8AH6+voKzt95551ccsklnHfeefzd3/0d6XQ6fy6TyfB3f/d3bNmyhe3bt/P973+/4Hdf7m+XUUYZZZRRRhllnIt41QO/TCbDX//1X3P06NH8Mcuy+MQnPkFNTQ333nsv1157LTfffDODg4MADA4O8olPfIIdO3bwi1/8gqqqKj7+8Y9jWRYAv//977n99tv5whe+wA9+8AN2797Nv/7rv+bH/8pXvsK+ffv4wQ9+wD/8wz9w++238+CDD76iv11GGWW8uoirOgeHYjx/YpKDQzHiqr5o4x4aiTNGiMMjiUUbt4zFR/lZlVEKlGpuOdvwqgo4d3d386lPfSofsOXwzDPP0NfXxz333IPP56Ozs5Onn36ae++9l09+8pP8/Oc/Z+3atXzkIx8B4J//+Z/Ztm0bzz33HBdccAF33XUXH/zgB7nssssAuO2227jxxhv59Kc/jWVZ/PznP+c///M/WbNmDWvWrOHo0aP86Ec/4s1vfvPL/u0yXn3EVZ3+SJqEahDwKLRUegl6nK/2ZZXxP4DeiRQ/e6GXY2NJ9KyJ0yHTWevnhi1ttFX7FjTuL1/qZziaZmJigurqFA1hLzvOa1nQuOV3dfFRqmdVxrmFxf5u9U6keOb4OGndJKNn8bgc7O2XuXBpzf9z79WrGvjlArW/+qu/YuPGjfnju3fvZvXq1fh8Mzd78+bN7Nq1K39+y5Yt+XNer5c1a9awa9cutmzZwt69e7n55pvz5zdu3Iiu6xw6dAjLsjAMg/POO69g7G9961uYpvmyf7uMVxe5SX8klskfqw+5ywv0IqAU92Axx4yrOnc/c5Knjk+Q1sz88b5ICs0w+eQblgmNHVf1Oe8UwEgswy9f6ufG7R1C45bqXS0VzoXvQKmeVRnnFhZ7AxhXdXb2TvLAniGOjiUwTZBlWFYbwKnIVPqd/0+9V69q4Pee97xn3uNjY2PU1dUVHKuurmZ4ePhlz8diMTKZTMF5RVGoqKhgeHgYWZaprKwsMDGuqakhk8kwNTX1sn+7GGSz2aJ/p5hxSzX+2YpExuDeF/sYiakFx4ejae59sY+PbFtCwF38K90fSfPLlwYKxq0PedhxXjMtld4FX/e58Lz6I2l+9kIfx8aTGIaFU5FZWuPjhi2twvdgscc8PpbgqWMTpPXC+5jWsjx1bIKr1zeytilU9Li9k0mGozYHOFd9sP/XZDiapncyycr6YFFjlupdnT1+/1SapGoQ8DhprvAsaLxSPP9SoBTPqozSYzHnwETG4IfPnOTpU+aCvskUGcPk5ss6i/4u9EVS3Luzn119UYysiQVIwM70FFnLYkVDkBV1gQVfeylRzL09K7160+l0QWAG4HK50DTtZc+rqpr/93znLcua9xzYTSYv97eLwd69e4v+nTNBkiQcngBRw0GKEM8e7ifkMMiqiTnl8lcbBdeqm/hdjgVfa9JZwYETA/Oem5iA3fUKfn2qqDEVb5B7900yNJWaM14kEuHta6sw0nGh6z0Vi/0+LBacvhA/PxDnmROTpDJG/nj3sEIsmeYdq4PoqdirPuaQXEMknpz3nKpC33gMY/R4UWMCjBFiYmKi4Njk5GT+//cN+VCHjhU1ZineVbC/V1lvBfftHS14ZxsrfLx1XR2O9FTR369SPKtSoRTP6lxDKebW/yksxhyY9tbxx4ODBe8q2HPAHw9meH1HEG96tKgxJz2N7OqbQjNmKgkWEpqRZVffFAOTSdKD3Qu+9rMFZ2Xg53a7mZqaKjimaRoejyd//tRATNM0QqEQbrc7/+9Tz3u9XrLZ7LznADwez8v+7WKwbt26RTWk7o+keeb4JGk9SyyZIRxQ8CgeLlzatOBdeSkyCM8cn0Q1sqg6eJzSgq91Z0+E6urq0573BMJsbF9S1JiHRuJojjTV1XOvSQOUijrWrugs8kpnkMgY9E2m6B8Zp7WhhpZK34LuaymwbzDGSwPDmJKCxzNzbSbw0kCS67d2sHH50ld9zET3BEG/lw0tFdQG3WhZE7fiYDSmsrt/iqDPy8aujUWNCfY70FivsqmtApCIp3XCPhemZfJi7xStjbWsrC/uWkvxroL9Pn3/yZNoDm/BO6sBT/TrfGTbmqLfr32DMfYOj7BpSe2c+7p3OC70rEqFQyNxqqvtgNeyLCYnJ6mqqsob04s8q9lY7HmwFJipUCTyx+wKxfKzKjs7G9lslr179y7KmvjI4TEsWaG2woNDlsia4JAha1okMgZpnFw0izr2SvDb/SPoWYusaWFaTGf8LGQJ9KyFmqWAjnY2InePXwnOrjd6GvX19XR3F0bX4+Pj+RJsfX094+Pjc86vWrWKiooK3G434+PjdHbaC7ZhGExNTVFbW4tlWUQiEQzDQFHsjz82NobH4yEUCr3s3y4GDodj0QK/uKrzYt8Uvz8wQs9EElVV8Xg8tFf7cTllqgJuYQ7CYnORCq91JivRXu1b0LUGvS4k6fSN6EGvq+j7ncqYZxwzlTGFn2HvRIrDw1FSepZYRkEbSzE4pbKiIXxW8RHH4hpp3cQubhQirZuMxbWi78FYXMMwLba0V80KJmRGYxl2908JjdkQ8vD2Ta3cv2eQ3+2boV501Ph5+6ZWGkIeoWfVVuXn0uV1fPeJE+wbiGJksygOB2ubw/z59g7aqvxFj1uKdxVgMJpkIqnTWRvApTjyJPSMnqVnMs1gNMOqRndRY44nNC5ZVsufjoxx/56hfJmrsy7A65fXMp4o/lnNxmK+r21VfporfHicMi5FJhpyEQ540QwTVTeFnlUO5wInM67q/GrXIKNxreD9Go1r/GrX4FnPcVyMNdHtcFAbcNMQ9lDld2FkLZwOmYlkhuGoilvgb/hdCiGvwpqmMG1VPntMRaZnIsn+wSh+l7KoSZxXG2dl4Ldhwwa+853v5IMbgJ07d7J58+b8+Z07d+Z/Pp1Oc+DAAW6++WZkWWbdunXs3LmTCy64AIBdu3ahKAorV64EbM5frhEkN/a6deuQZfll//arhYFImvt2DxUEUgA9Eynu2z3EqoYQKxvPDmL7QCTN/buHiCQ1KnxOsqaFIktEkhr3L+BaWyq91Ifcc64V7AlaZLcb8Jz5K/By50+HuKpzbCzO8z0RsqaFZthZFFkGxSFOFi7F4uRyyEgSBN0Ksizln1fWtIhnDFyO4lWf3IrMFavreezIGL/bN1Oe7ajxc8XqetxK8WNW+l10j8VZWuNnc3sletbEpTiYTGToHotz7camoscEmyN4z3O9HBwqLGceHIpxz/O9rG0OF/2sSvGuAiRVg1UNQR4+NDpnU/WGlXUkVeMMvz0/gm6FPx0e4+REEpciY1kgSXByPAkWXNJVI3StsPjva9Dj5PKVddz+aDdHRuL5eXp5fZCbL+taUOPQL1/qpz+SJpkx8g0DetY8q5pG+iNpRmIZ9KxZcJ1+t8JILEN/JM0qgbm1lIirOr2Tybz0TmuVb0H3sj7k5nXLa/n1rgGOjc3MLZ21fq7d2Ex9qLiND4DPKfPRSzr5xYt9/PHwWP74yoYAH72kE5/zVVe+W1SclYHf1q1baWxs5JZbbuHjH/84jz76KHv27OGf//mfAXj729/O9773Pb7zne9w2WWXcccdd9DS0pIP9N7znvfwuc99juXLl1NXV8fnP/95brjhBrxee7K97rrr+PznP88//dM/MTo6yve///382C/3t18tjMYy9E6mCLgdSJKE1+nAqciYpkXvZIrRWIaVjcWPm5tI5oPoRDIayxBTdUbiKolZC1HAo6A4JOFrDXqc7DivZf6FZFOL0GRSqgV6MJLm0HCcx46McWwsiWVZSJJEZ62fSp+LprCXFUXe11J1NNaF3KxrCvPsyYk5z+uCJdXUCUyktUE3u/umaKrwsGVJZX5XPjCVYnffFFevK/4FmExqrKgP8sCeIf54ZCwfoCyt8XP1+kYmkxp1oeIpGd1jCY5PpGit8iFLYGQtFIeMaVkcH0/RPZYoetxSvKtgP5NHDo0ykcgQ8igYpoXikJhIZHjk0Cib2yuLHtMwLSIpjYxhomdnOE5Oh0wkZWduRVCK9zWu6jx0cAS/W2FZfYBkSsHv8+B3Kzx0cIS2arGgoj+S5thokhMTiYKOca9LJqObZ01AlVANEqox73V2VAcKvr9nA3Ldt91jCZIplYAvtmD5JcO02NsfZTReSNkajWvsHYjyumW1RY/pkGFnb4TJpE7Yo+Sz3pNJnZ29ETa0Ft80djbjrAz8HA4H3/jGN7j11lvZsWMH7e3t3HHHHTQ12Tv6lpYWvv71r/NP//RP3HHHHZx33nnccccdeZ7H1VdfzcDAAJ/73OfQNI0rr7yST3/60/nxb7nlFj7/+c/zwQ9+kEAgwCc/+UmuvPLKV/S3Xy1oWZOwx0nPZJJoWs8HEmGvk/YqP9qsCbsY5CaK+XaQTocsNJFoWZOBqfSc302oBgNTaeFrBWir9vGerW10jyWIpXRCPiddtQGhBR9Kt0BPprQ5O1KAY2NJfr1rgI2tFUWPWYogHaDK72JdS4hj4wmiKT0/6dUE3KxrCVHld73cEHOgGSbbOqt56vgET3SPY5kgyxL1ITfbOqsLSNSvFImMwQN7hzk5kcIhSzOZqYkUD+wdZn1LRdFjAsRS+hm/W7GUmIhrW7WPG7d3LGpZPmOYJDL292hq1nVV+Jx4nA4yAvc1rWWp8ruIq3pB4Od1ylT5XaQ1sU7MUryvuTGdDpkKr5NsKkaFN4gkyQv6DkSS2pxgCiCtmZyYSBBJFt/cVwq4FPmM1+kSyKSXCoXyS9np7KyxYPmliYTGweE4S6p9KLKMYZooDhkja3JwKM5EovhnlciY9E2maK7w4nBI05s/iWzWom8yRSIjvmbB2SeVdNYEfocPHy74d3t7O3ffffdpf/71r389r3/96097/qabbuKmm26a95zX6+Vf/uVf+Jd/+Zd5z7/c3341UOFzMjCVIpouXISiaZ2BqRQVPvEMwpl2kCKlTo9TPu1ikdayeBaQNi9FqbMUC3Q0reeDPmuaLMx0oHJsLDnnOb4SvFwQLrrbn0hqvNgzxbK6AOdPZ+cUh8RUSufFniku6qwpOrC2LIsDQ3EODMaIq0Y+mJxIZKjwurhw6ekbH06HeMbg6Mj8HdZHR+LEM2KfP+h10hdJ4nU6qA64ME1wyBLJjEFfJEnQ++pnenKYnUk3Z3VwJlSDkbhKTMBpQJIkhqIqFT4X9WEPlgmSDBndZCiq5jfUxaIU72upvgNIzAmmckhr5nz011cFbkWmLuiZQ/kBqAt6hCgUpcLxseQczU2w7+dTxye4en0TGwQ2wFrWpCbgoj+SJpae2aiGvE5aKr1CiQXTtPA4HQxE0qR0I7+p9DkVmiu9mIJZbzg7uaNnTeBXxpkhSdBa5SOmGmiGmQ8kXIpMa5UPwbmZar8LwzRJZrIYWRPTspAlCdOyMEyTaoFsT8CtsK4pzN7BKNlZXxiHLLGuOSzcJVdK8da0liWS0oil9PznXkjglzUtJCQM056E7InEjgAVWS64L68UpeIjRpIavZN2Fi3H8XPIEqZpd7mJZDtSepbneyZRDdPOzmFPzqph8nzPJO/a2lr0mLph5jcqpyLgUdAFsl1gZ7bOa63g2ROTDEyl85N+yKNwQUcVXsGNSu9Eir7JJLGMQVzVCXmcTCYytFb5xctcWYuhKRWXIuN1OfLXapj2cSNb/Hvlcco0Vnjon0yTyJAf07KgpcorvFErxftaqu+Az+mgvdo3b0DVXu3D5zw7mlsSqsEbVtbNz/FcVXdWlXrH4pkzBtNj8fmzwS+HKr+L4ahKMmPgkGcWvmTGYDiqClUoKv0uJpMZ4hmjQMcvaxpMJjNUCowJZ6/geDnwO0cwHs9wQUc1lmU3dFjY5aj2Kh8XdFQzLvglmkxqXNBRzWhc5ejIjDzAsvoAF3RUC/GmPE4Hb1rbgCxLHBtL5IPJztoAV6ypxyM4ifZH0ownNJbW+ObtaBQt8+wfiE6TxWc+//L6ADdf1sWa5rDQtdaHPFQHXIzFMwWZGVmSqA64qBcoTZeKj4gESS1rB/uzgn/FISNLklC2I64aqJppN44ohcGEqpnERZoQPArNFd45NILA9PGg4KKf1rJsbK2kKuDG5ZDRsxYuRSZjZGmr9AmVOnOE9q8/fJSdvVP545vbKvjkG5YJN/dIWCyt8dM/lZ5zbmmNHzvELg4Bt8LlK+xg4thooqCr9/KVdcIbtVK8r6X6DnicjjMGVKJz1mI7TPg9CgeH41y0tIrXL68lo2dxOx1oRpaDQ3E2tRXP8ZyNxQxSX64pTKRpDMDnctBR7Z83sdBR48fnKv5Z+VwOWip8JDJZTGumwUmWJFoqfUJjQunoOQtFOfA7RxBwKzzZPUZzpY+1zSH0rIXTITGe0Hiye4yLllYJjTuZ1Lh/zwBXrmrghs1OUloWn9vBVFLn/j0DLK8vXq28udLLQwdG2NZVzWUr6womp8mERvNZ1NE4GlP57uPH2dhSwaXLa0lpWfxuB5GkzncfP84tb1klxB8MuhVev7yGPx4eZyql5RfTCp+L1y+vISiwmOb4iPMtJAvhI/qcDprDnvkztE1hoWyHR3HgczuIJLWC0ovLIVPpd+FRih+zszZAXdCNU5ZAkvKfH8ui0u+is1ZMWV+3LCZTGgOTaUYTmXzgWxdwE3Ar6AKiuAORNP/x8FFe6IkUHH++J8J/PHyUL1y7Vqiz3ZIk3ry2gd/vH+bwcDz/Xq1oCPKmNQ1YAqn/jlo/9+025i31J9IGHbX+oseE0vBnZ4+Zc/BY6JgwM2fNF1CJzlmlsBhsqfRSE3BxfHxuZnJBmz8WvyRZF3Lnm9lqgy7Smo7P7WI0liGS0oSaxsDOep4psSBKIVjsMXPjLuR8qVAO/M4RVAfcuBSZ3++39ctyBHSATW0VVAfEvkSSBBd11vDbfUOcGC+U3Xjd8lqhEnLQ4+Tajc388qX+gglqMToaTw36wM6APizY0Xh8LMEly2v50bO97Omfyh9f31LBey9o47hARyfYGa+NbZVkTctuaDFMXIpMc4WXjW2VQhmvHNqqfNQGPfnFyeuUEUj05CFLEpuXVBJV9YJ3oK3Kx+aOSjvrVySqAi7qQ26iaR0Xcj5AyTV4VAWKL53UhTz8+falfO2hw+zpj+aPr28J8+eXLBVu8PG7HDxzbIJD0/zB3Hfr2FiCSErjyjX1RY/ZP5WaE/Tl8EJPhP6pFCsbi+8UbAx5+EFvhMtX1PGeC9pIZeyN2vCUyku9Ed60pqHoMQHWNof5w6ERdvVF86X+pgoPawUz3jmc2owV9jntAF7wWeXGvHF7B72TSfqGfLQ21tJW5V9QyawUc1YpOG65wNfWB7Ub8gJuBa9TZkVj8bJDOZSiJNlc6eVd57fy3SdO8Lt9Q3P0MUUTALms53yJBdGsZynGhNJRExaKcuB3jmAsnuF1y2pJZrIcnkVwX1Ef5HXLahmLi0mkBNwKO3siBQs+wInxJH63wpWri1/0oHQdjeNxW8biVL258XhGqKNRkaU5QR+Q//ctb14hdK1+j8LBoTjbu2pAIt+BjAUv9kXZINCBerrJGWAwqgrzRVyKzOHhOG9Z20iFz1mQ9dzdP8WbVhcfTDhliTevaUTPDnJsdKaE3lHj581rGu2sXZGIqzpPHZtgx6Zm3r6phcT0omdh8VT3BF11AaHPr2ctImkdl0Oek52MpHV0Ad7cy3UCi3YKq1qWHZta+MHTPezNv7MS61rCfPCidlSBsnR/JM3zPRHWN4fZ0l5VsOg93xNheUNQuBxVKmJ70ONkZX0QdegYK+uXLoq47mLPWaXiuMVVnXtfHGD/YCyfmVrTFOKTl4vfz1LRaJ45PoGRNVlaG8CYztAbWZNnTkxwfodYlaoUWc/cmIdHEnPULVoqvcKZ1JLRcxaIcuB3jiBjmNy/d4gLllRx5Zp6VM3+Yk4mNO7fO8SKBjGdocT0S34qaT7gUdCztnSEKIIe56LyFzTDpKnCy/7BaEFXbNjrZE1TWEgiJKoac4K+HPb0TxEVzMy1VHrZ2Brm17uH5jitvHVDo9AXvlSTc1I1uO68Zn75Yj99kXQ+49Na6WXHphahEnpcNXArMlvaKwrKh5Zl4VZkoYznQCRNhc/J/XuG5y31D0TSQuVTLLtr3iFJaFkz//ldDpmgVxHKpoZ9rnyT1KmQJYmwT4wsntKz3P1MDxOJDEtrA5imhSzbOn53P9PDX75xedFjJlSDrGnNu5DmzovgXBFFLhVKwXEbjal844/djMQy1IU8+c3vSCzDN/7YzT+8dY1QNrUUNJr+SJqhaMb2FpYtslkLWZ7uIp8S57eVikJwxar6ebneV6yqF35PSyUXtlCUA79zBLVBNw5Z4vHuCcCa5Swi4XXJ1AbFSr2WZcs21Ac9NIYLOzozuslCPL8XW7vIpcj0T6VQHDJhr7OgCaF/KiWkYaXq2TmZnvzfc8ioupiGGcDBwRijcbXg2Ghc5eBQjDeuKj6TmlQN1jQG6Y+kkSTDtkHL2CLeaxqDQpMz2NnJHz3Xy8CUiixJWJLN7xuYUnlw3zA3va5471OnIvPLl/pZUR+yLdtku9FjLG6Xjja0ri16TFXPnrHUv6ZJbPNT6XfRUuHjkBpDz5qYpoVpSbgVmZYKn1BHX3OFly1LKnnhZGROc8+WJZU0V4jt9FXDZN+gfZ2zv5uSBKPxDKrA5ifgUXDIEu1V3nk3FKLlqJwo8mhcxZjuEFdkicmkdlaJIudQWo5boW2hKMft2FiCsenqhiSRn68ty84wHhOkpgQ8Ck91j3NJVzVXrW0glTHwexTiaZ2nuseFaDSF2oiFa1ZSMxakjbjY2dm4qvPIoVE2tVVw4dLqgqz3I4dGhYXBc9e62HSHhaIc+J0jWFrr5+Kl1XkxzBy8LpmLO6tZKkjArvS7aK/25ydnsLlYLkWmLuQRbmMvRYknr2GlpXCd0hwgqmFV6XdR6XedtglB9PP3R9LEM1k6awJMpXUyhhuP4iDsdRJXs0KLXtCjkMhkeaJ7fA4f85r1TcJdrSkty8mJJANTaeKzdOCCHidOh0RKoHzoVmSq/O55eW7t1T6hZ5XSs/PKbYAd/KUEg/Rqv4ug14HPpeB3KxhZWxDWsiDodQhJGjVXern5si5uf+QoL/XN8BHPaw3zycu6hPlNmpHF53LgdbrwuR15YexkxiCtZ9GM4u9BS6WXze0Vcywh26t9wtlpsBf+8YRKXyQ1p5ogSyxo4V9sG7D/CY5bDgvhuMVVAz1rMRxTic2qeoS8TtqrfMLcYd0wecu6Bnb2TmFZaTs7q8hIwFvWNYhJJZVYG3ExK0r9kTSDUfWM5xdCd1jMzu7FQDnwO0cQ9Dh534VLcCnytP2NbVXUVRvghvPbhCe9lkovjWE3x8cTxNJ6PosW8jrZIMhBKJV2USk0rLpqA6xrDnNwKIaWNTFNkGU78FvVGKJLsFM0odo72kPDMRIZI1+SC7gVVjaEhK5VNy0e3D80Lx/zwf1DXNRZvCgy2J3dtgacaXPmctpwWVvAd1JggS6J3phlb3TmW0y8LvEGl0hSY3ldiCPDCU6MJ/Mk9I4aP8vrQkQEJI2CHic1Phc3busgk7XyfES3Q6LaJ64R6VUcLKsLcHQ0wcAsSZcqv4tldQG8At3SsPjZabDfoZ7JFLG0LTada5qJpQ16JlPC2qO9Eyme7B4jnjFIqVn6UxP4+6bY1lUrvJCWSnbjmeMTZE2LZfXBfHYua1rCHDePy0HPRIqoqsG0MLwERNMaPRP2eRFYlsXJiRSPHRmne2ym1NlVG6Da76K1svj76nM6ztjVuxBtRFh8fcSFnD8dStHZvRgoB37nENqqfXz09Z3T3Wxji9LNBrCqKcSRkUTBDrI24Ga1YOmsVFw0v0fh2HiS91/YhmqYeVFctyLzePeEUOdVXchjZ2bm0/G7vEs4He+QJQ6PxBiNZ6bLcvail9KySFKsQHj0lWIyoTGR0KgPeXApcn4h0QyTiYTGpIBVEdjlR8M0MUyrILvnUmwitkhXbyn0xir9LjqqA/O7zNQEhLOzI7EM977Yz4aWCi5ZVkNa0/G6nIzFNe59sZ8V9UFWFNk4FVd1Hjo0iscp578DGcNEM7IcGknQLFg6qg97sLD5rmGvMx+k50Td68PFv6+57PTKhtAcYrtodhrAIdlB7khMZTaTwiFDR7UPh8B7FVd1nj0xzi9e7OfoSCIfpC+rD6A4JCr9jcKbyoWcnw+5+9oU9hBVDfTpzv6QR/y+OmWJupCLyLREVA6SBXUhl1DTFEBSz/KrlwYLgj6wfax/9dIgmwRKvV6ng7dvbuEHT52c09X7wYuX4F1A4LfYFaVSdd+Wyr1koSgHfucYFrubrT+S5rHD47RWeVnbHEbL2lysaFrjT4fHWdEQKnpyyhGFHzowMlcTaXW9MBetpdLL9s5qvvPYcfb0R/O73fUtYf7XJUuFS1JrmsPc9rY1i+b/C2BhEfY6WVrjp7XKlxcF7p1IMpHUsATSU1rWpDbotq2KphbHqghs54b6kIfDI/GCLIyeNakP+YWcG0rRJddS6aWzzo/bKS9q552WNdGz1nRZupCLlDtfLEpVOtIMk4uWVpPSsnPEli8S9UAuUbYjktbYsqSKlG5ybFZAsaQ6wJYlVUTSxW9UeiZS3PN8X4HYPMDRkQT3PN/HqsawkARNbmE/nWe5yMKfUI0z2mGK3NdYWuOSZbVkTTg6q1u+qy7AJctqiQncU4CplM7x8cS8546PJwp8oV8pKv0u/uORo5iWxeqmUL6r17QsfrNrgH946xqhay1FRSnXfXtqI9JC55ZSdXYvFOXA7zWOSFLj6Fj8tOUzER5OwKPw+33DcwSBd/dPYZqWEFEYbIeFnzzXy6HhOC5lRl390HCcnzzXy9pmcR2rupBnUcm2Y3GV92xt4yfP9fLo4bH8tW5oCfOerW2MxU8fFJwOpbAqAjtIvaizGsM08/7CAJ21fi7qrBYKUkvRJVeqDrnaoPuMJWSRxqlSBVMZw+TkeIoNLWFet6y2YKN2cjxFZqlYc8di+3WDLeL9m90DrGupYFtXdb6zu3cyxW92D3DpitqixxyOpjk6Yge8pmlhYZdOZVni6EiC4WhaKPBrqfQSdDt48tjUHMrLts5qoYXfpchz7inYC/6JiYRQM5rHqXDf7kE2ttmC81rWwuWQ6JtKcd/uQVs+SgD69HtUHXDhc9l0D1m255aJhIYusPmZSGroWYuT40nGE/ZmV0KiJuAi5HEyIUChgNJUlErV1Vsq95KFohz4vdZRAgJuSsvSP5XigqVVNIW99qSiOBiYSnFkOC7ULABwZCTOnv4oetYq8FNUHDJ7+qMcGYm/qp1SsxHyuPj6I91ISGxur8SY7mhMa1l+8PRJbn3L6qLHLIVVEYDfpXBsJEFnbYBtXTX5BXo4qnJsNMHV64oXiCxVl1wp9CFL0ThVqtJRwKPgcTlI6ybjiURBZsLjdAiNm/Prni9AEfXrBshaFue1VfLCyUkeOzKWPx7yKGxZUkVWQDJANUxMy87CWrN4g5Jpy++IdDXnsKw+wM7eCH2TM9mtupCbZQLuRWA3ONUHPUwmtTm6o1V+l1CDk0uR2dhWwXMnIjysjuaPBz1OtnZUCgWTYGfnljcE6ZlIMjQrUx30KCxvCArRKMYTGQ4MRklpWYIeJR9Mp7QsBwajjCfEsl2lkJ6Jqzp/PDzGFavruHJ1fYFG6B8PjwnPV3Uh9xk9oEXdSxaKcuD3GkcpzMkjKY0dm1r47d4hHto/UmArtWNTC5GUWDliMqkRn07Dz0bWsH1mRZoQSgmHbJd2o+mZiSjsVWirFuvALpWtkMfp4A2r6/nv/cP8rm843+CyrDbAlWsbhHxKS9klt9j6kKVonGqp9NIU9hRw/HJZCVU3F0UQtuIULUBRQdjJpMb2rhoyhjlnId2+rEbIrzuHzlo/aS077VkNsmRnWDtFVQh8LjxO20d5NizLwuOUqRTURzw+luRHz/ayoj7ERUtr8pnUsXiGHz3by/L6UNFcrKRqcMP5rdz55Ik51JQbzm8VClD8LoU1jSEyusloPJPn+dYF3axpDOF3iS3p9QE31X4Xh4fjzNpToup24F8v4AxlZC1GExrWdIA/221KNUwMAWF0KI2D00Akjd+t8KNne+d41r91fbOwRmhzpZe3bmict2P+bRsahbv7F4py4PcaRynMyf0uBw/uG2Y4lqHK78IEZGA4luHBfcNsESz1+t22qLQsSUjMdLRZ2KUKv6CZPCy+5mDWskioBiGvk7qgx170ZFuLLqEaQtmOUtkKNVd6+fWuAdqqfaxrqSgoH/ZPpnjbhqaixzxbPSpPh8VunAp6nFy+so7vPXHC7hQ27SxqR7V/QcLFpSh3x1XjjI04y+uCQtfaFPayfyBKXcjDpvZKMoatY9cfSbF/IMqfnddS9Jg1fhcXdVbzZPfEHOmhizqrqRHMTo7FM8TV7Glt9kS4WH6Pwh2PdlPld/Gura0FmfSfPtvLZ65aWfSYHbV+/vuAyfauagLuGW/1hKoTSenCvsqqYbK1o5JExuDkeCpfll1S4+OCjiqhTKqExdIaXwF9JIelNT4kwTb8jGHO6UDPYTSuCjk4JTIG9+0ZmJc7et+eAdY2izU6Bj1ONrdVoRsmad0ssNnc1F5VFnAu45VhsfWrSmFOrpsWUymNhKqjzdrVuRwSU04Z3RT7woe9Tja2VvBS79QcUdzz2ioIe88eWykZu0wyMGXLROSC1KBHobnChUhBppQG7SsbQvzh0AgHhuL5klRjhYct7WK2SqX0qFzsID2HxWyciqs6/99LAzhkifM7qtCmg57JpMb/99LAggVhF7PcHfAoZ3TuEH1WhmHy55cs5fZHj/Grlwbzx7e0V3LzZZ0Ygo0ob13fhG6Y9EXSec3F1kovb93QJLyhKAUXK6VlOTqWKHAZyiHsdQpRXoIeJ1etbeTrjxydx7JNXBokY5gcGU5Od7bPrAOxtM7hkQSbBeYBC4ltXTaP81Tu8LauWixBIT/NMM/Y3S/S4BTPGBwdSeB0yEgSeU62ZdnBX3wBDlZt1T48zrqygHMZYsgFKMPRNBMTE1RXp2gIexcUoOTMyZ85Pk5at78wkmRPTG9aWyM0kaQ1g6qAm5hqoGVnvjAep4OqgJu0JvYlUiSJd53fipE12d0/I4q7rjnEu85vQxGUhyiFrZTb6UBx2NZkuauSsLMrikPGLZBJLVVzw0AkjaobXNRRjUOW8l69RtZC1Q2hMkepPCpL5f262OgeSeBUJPYOTM1Z9C5fWUf3SILzBDPfi41SPSvDsrjrqR6W1QXY3lWNaph4FJmRWIa7nurhL964rOgxfR6F3+wd4qq1DXM8sJ/snuBd57cKXWspuFiTSY2WStvBpcrvwshaOB0yE8kMw4L6mHFV56GDIwQ8TtY2hws4ng8dHBHeUOS4o1NpnYGp9KJwRxvDHo6OxOmo8bOtqwZtWs5mcCrN0ZE479hcfMY3d60BjzKv/JBoB7ZumNSH3CytCdBY4bGflSIzGElzfDwhJmA9jbNxzioHfucISiWKnINmmKT1LKqWxcLOWIkK4kqSxEAkTXu1Lb2RzVo4HBIZ3WQgks7zPIqFx+Xg4f0jvHtrGx/Z7pjOdjhJawYPHxhmjYBPac5War7d40JspeKqwcbWCpIZnWNjyTy/pbPWz8bWCmGF/VI0N2T0LDE1y/17Bud1BMkIOGKUpiSp8+tdA7gVmaU1PtK6ic/lQM+a/HrXAB/atmTB7g2LlU2PZ+zmllPLXPa/R7lwqZjYNiy+E0CpNhSJjMGu/ijSQHROFsWyEPIBb6n0Uulz8syJCJZlTm+Aq5EkeUFBaim4WG5FZsuSKp4+NsHjR8fzn39Jtd0tL9LckROadjrkORzPhQhNl4I72lzp5UMXL+G7T5zgye7+Ah0/UeeS2dfaH0nPOSd6rWGPwts2NPPbfUM8dHAkf3xFQ5C3bWgmLJj1Plv9qsuB3zmCUinL5wRRf7N7cDpAAUmGzho7aBMRRPU5Zba0V7BnIIaqZ/MTnsfpYEt7BT4BXTiwydLXbm6Zlyz9oW0dQmTpQj/JGeQkF0RtpbKmxR8ODrO+uYLXLa/L73aHptL84eAwFy1g4V/s5gbdsvjDgWEuXVHLDVtaSOlZfC6F0ZjKHw4Mc157hdC4bdU+3rGphSOzShzLagM0V4ntcgciaYIe5xwuzkIJ2LD42fSMbs7LbQI7+MvoYhmEUjkBlGJDoeomHpc8b/k14FFQBe7B7CB1ODqz8C80SM1xsbyKg7SezXd1ep0OVgvKRNUG3ezsiXBkJF5w/MiILUcl0i1fKu5s7r7mKj+5ZiSPInNhp1jlJ+hx0lUX5MMXt5PSs/nsrM/poKs+uKBntehSUV4njx0ZYzCSpsLrwsRCRmIwkuaxI2O8YVWd0LWerX7V5cDvHEHuC61nTRIZHRUX0bSRT2+LfuF7JlL8/IV+9vRHC4Rq90yPJyKI6nE62NpRTc9kqkAaYGWDm60d1UINI2CTpb/xp2OousnKxlC+o03VTX72fB+fftOK4gctkZ9kbdDNkmo/L/VN8UT3eJ6L43crdNb6hbThchiNqYvKF0lmDN6+uYUfP9fLrlm+shtbbc3BpCC/Zf9AdH5HlMu6WCOgtVYqAnYpsulZy5bsWNsUorXKly8d9U4k2TcYE2rugdI6ASz2hsLlkGmv8tEQ9sxb6hTVMMsFqXYjjm/RHIziqs5/HxiZacaZlklqFdyoZAxbciank5hDwKNgWZZQE0IphKZno3cyNSeTfOFS8fHaqn1U+p0zTVN1lQt+VqWQihqPa2Qtez2ZmiWCXeF1krUsxuNiCYBS+lUvBOXA7xxBochqdtpdwMDrcixIZHUomp4T9IGtk7WnP8qQgCBqWjf50bMnuWptE++9sJ10xu48G5pS+dGzJ1nRsE7oWjOGyUhcnTdQ001TaCIthZwN2PyWmoCb4ZhKwD2jYWVhURNw0yhgrQV2MHU6YrdIMAW2RMQ9z/exe1bQB7C7L4os9fF3b1lV9JijMXVO0AdwZCTB7Y92c9vb1hQdrOYI2PNhIQTsXDZ9vk2VaDa9PujhfRe289u9Qzx6eEbDbkVDkPdd2E59UOz5n61OAPOhPuTmDSvreXD/8JxS55vXNFC/AA2zxXYwGo2p/PsfjrCrPzrtK2xf68BUmoSq849/tq7o91UzTGoCHpwOGcO08sGUIkuEfS6hJoSc0PRTx6fmUFMuXiomNA0zm5+JpF5Q6p1I6gsuSZbCbWqxpaJUI0vPRIoKn4uGsJesZeKQZFTdPq4aYtqzkgSD0TQbWsI0VXjzm5+BqRQHhmLCftULRTnwO0dQKpHVtJbFMC0awp7pAMXW2kpkDEZjmQJB21eKWFrnDasa+MPBkQJboWV1Ad6wqqHAE7gYaIbJstogAY+DsNeFlrU7JadSGolMVmgiLYWcDdg7ueV1QQ4OxewSOtPWWrV+ltcHiQhoo+UWp+d7IgUCzs+emMT8wxGhxQkgmtbZ0x/F6ZCQJGlGJsey2NMfnbcr8eXQPZaYE/TlcGQkQfdYouhr1Q1zTvYkh4BHESZgF1przd1UiWTTmyu97OmbYjiqFnjqDkdV9vRN8c4tYk0Irumuw6BbmSMKHM8YC3ICWOxu6Uq/i8OjMVJalvqQJ5+hT2lZDo/GeNvG4mWCSoUjI3F29UdJnKIT6nTI7BIUh89txieS2hw3kLDXJbxZX9UUonssWTBf1QU9wt7qULj5OTWTuBAqUSlQinJ30KMQ8DhY0RCYN0ALCj4rRZJ4+7Sm7R8Ozghud9b6efumFqGGxMVAOfA7R1AosjrDHVqoyKrdHRWkdzLF4NTMLio0fVxkcgp7XTx2ZIxjY0kUeSaQyHGeLl0uxpcIehS2LKnkoQMj/HFsbI4HsMiXsxRyNgCDUZWfPN/DtRuaec8FbtJaFq9LYSym8pPnelheH2RFY3ET9ZGROM/3RDBMa9quakbH8PlpLpHIO5DIGPhcDpKaQXaW/I5DtrOBIiT82Mt4e77c+flgS+F4p7MwhWWT5gqv8ORcCmutiaSGx6nQEPIwldYxp23FKrxOPC5F2K6qLuRmXVOYZ09OzLkHFyypFnYCKEXn4URSI57OktIMEhkjH/wG3ArxdFb4HsA03WE0zhjVZE5GFuytPZnU5gR9kKPWGEIduLnNuqrbzkU5qLr4Zr0/kmZnz9S889ULPVNC3upQGl/hUqEUUlF+p4OPbFvKz1/oKwjQVtQH+cj2pfgFEwCGZXFgKMboKaXi0bjGgaEY5y8Rk8taKMqB3zmCuGpwZDTBtRsa58gYvNgXFRZZrfG7qPA5OTJSmNlT9SwVPqeQIGoWi9F4BsO05ggtj8YzZAXbhav8Lp7oHufkRJKsZWGaYMkWJyeSPNE9zhWr64seMydn88uX+gt0zBZKFs8YBpetrOe3+4Y5NpbIL3qdtQEuW1lPxih+Ip2c9r7M2VXlIJkSlkMWdi6p8DpxOmScsgzWjBWeU5bt7kEBfcSQ78y/83Ln50NnbYC6oBsja9EYlvIZJNO0qAu66axdDGst0JwyLqcD00TYWiu3UIa8TnxuJX+tiiyBJU7Cr/K7WNcS4vh4omCM2oCbdS0hIb/mUikGRJIa/VMpJEnC63TkN2qSJNE/lRLmN81wR+PT2VkPy+uDwtxRmBGHnw+i4vClcERJqMYZNReFdQxLsPkpFUohP+RyOtjdN0VMNQoy9DHVYHfvFBcIBmhZ0+KFkxHaKn24FBnDtHUnNd3khZMR3ndBu9C4C0U58DtHEPQorG4I0jOZBixULcuUqmNZEqsbgsLZjqwJG9sqiaUNeiaT+SitvcrPxrZKBLy5SakGHTV+5InULHV9iaBHob3aR0pwcppIakjAxpYKKvzOPGE+ktDQsqZwBqEUHY1VPhfPHJ/g2FhhufPYWALFIXHVmuKDVL9bmRP0gV2S1RbgXFIbdNNR4+fwSByvy5Gf9AzToqNGrBGlqzbA8voAh4bjGFkzv+grDpmVDUG6BIK0upCHj1/axfeeOEHPpL3wSdiyMzde0iGc8clZa/3gqZPsG4gWyE6IWmu5FJmeiSQOWUKWpfy1atMZe9GFdCKpcWAwzts2NOadG/xuB3FV58BQnIs6i/8OlEoxoBSNU6XgjoKtW7q+pYI9/VNzzq1vEROHL4UjSqmE0d2KTF3QMy/XuS7oEdr85LDYpgOlkB/KGCbdowmq/S5qg+6CTWX3aEKIPw62uUBXXYB42sAwTbuBxDCxLOiqCyCXS71lnAlVfpvT9t/7hzk8Es/rwq2oD/LO81uFdvpgL+4nx5KsaAhyYWd1XnYkktQ4OZ7EWFZ8ds6pSHicMrVBN7VBd77MBeBxyjgVsZd9Ipmho8bP493j9B1N5QOU1iofl3TVMJEUJ7YvdkdjSjeJq0ZesykH57Soc0pAyqIm4GJdc3jexWldc5iagCDPM5Plxu0dfO+JE+yeNfaGlgpu3N5BOlM8z7Mu5OGmS5byvSeP0zORtrloDom2Ki9/vn2pcJAW9Dg5f0klqxqDpHUTr1Mm4FYIusWfnd+j8O3Hj9Na6WNTWwVpTcfrcjIW17h3Zz9/fUXx+pBuRaazNnDakqzoQppUDc5rq+ChAyNz/ZpX1wsFqbMVA+brFBXNIvmcDprDHvYORgs4qQ5ZYl1TWKhxqhTcUQC3Q+YDF7Zxz/MSh4bj+bllZUOQd53filuAO1kKR5RSiW0nVOOMXGfRd6B3IjUtEZMlmtAZyUyybyDKhUtrFiRevNibdc0waa/221lPdXHcQMDerFuW3UQZmUVvqfQ5CXiCC7IZXQjKgd85gtF4hl++OMCR0VN2uqMJfvniAOe1VQpz/IJeJ6pm0D1qK5Q7FZmwRyHocQoqttvk2GTGwOe2J3cLi1QmS8ij0BgWm5wUWeKRQ6P0T6Xtktk0Town0Q2TS5bXCI1bCmiGSZXPiWlapPVsfoH2Oh1U+ZxCE4lkwUe2LeH7T55kd99U/viG1go+sq0DSVBw2+dReGbvEO+5oJX3XtCW1zCzsHjmxKSQG0Jc1YllNFY3hVjZEELVs3icDmQZoqpGXNWLnqRPV5IEGIlnhEuSGcNkMJqe5qBa+fIhSLaQt8CzyuhZVjfNX5Jd3RQSEsWGGYP6vki6gDfWF0kLG9QXKgbM5XeJZpFkSWLzkkqiql4gDN5W5WNzR6VQtqMU3FGAJbV+jo/HeO8FbViQF4eXsMiaWZYIeOCWIkgrhd4ezPiAny47KeIDHld1dvZO8uD+EXomkvnvVXu1H6ciU+l3Ljjzt1ib9VK4gQA4HTYlJaYaOGbx3WPTJXuno5zxK+MMGI6mOTaWwK3ImKaVDyRkWeLYWIJhAdkVmK2CbnfIypKdlXI4xJXwjazFxV01/PGwvXvM7Z7bq31c3FWDkRWLUNKaSTxjoBkmyVOzaBnjtGWlV4LF7misD3kwTAu/W6HS78p7imqGiWFa1AsE6S6ng0hS463rG9hxXpMttOy0nSsiyQydggbtLZVeavwuRmIZFFmyFxTDRM+a1PhdQu/AYCTNL3cO8tjRsYKu4LDXyeuW1dIQ9LKiyEm7VCXJQu/PmYBsIbv9pJ7l3hf7896nWtbE5ZAZi2e498V+1ghqDpbCoN5uMrBY0ximNujOd8uPxjKkNENYMcClyOwfjLGyITTnHuwfiPGm1Q1Fj1kK7mgOWhZ+8vRJDg/H8wv0ioYg794q1oFdKkcUWHy9vZZKL/WnUDpycXl9UGwdGIikuX/3EJGkRoXPmefORpIa9+8eYlVDSFhwHRZ3zi6FGwjYmb4NLRVE0zrds5I2XXUBNrRUMBRNs7pJjJe6EJQDv3ME6vSELmGXSqysiUOW55wvFqVQQc8YJt0jCZbWBtjUVpmfnKbSOt0jCbZ1imXmMkaWSp+ThKoXlE+9TplKn5OMoNZSKToal9b62d5Vw5PHJoimdQwji6I4CHudbOuqZqlAkFbld/FU9ziSLNEQtv0kFYfEcFTFMi2uXFP8Qgr2O7C9q4b/+/BR9g1E8+4ta5vC/KWgE8RIXJ0T9IEtHfPY0THevrm56K7mUrkWBDwKYZ+Tq5sabbkRVcfvtTmkA1Oq2G7fAsUh8UJPZM4pr0sWtkMshUF9JKlxzYYmfvDUSX63byh/fG1zmA9evERIegjs5/H65bU8fGi04D4spHyY447OV+5dXh8Q4o6CHaQ8dGCUrroAF3RUo2WzuBUH44kMDx0YZV1zhVCQstglyVLp7QU9Ti5fWXdawXWRMUdjGWKqzkhcJaEaee5swKOgOCRGYxlWFm9eAiz+nF2KdRAgpZnc83wfWzsqubizGj1rZ/l6J1Pc83wfG9sqhMZdKMqB3zmCar/rjBpmorvynAr6+pawzW+a5k0ZpiWsgu5SZE5OJklmsnOI/X63Q5jY7ncrnJxIUeF1Uh/2YJl2gJLKZDk5kRLiS5SqozHocfK+C5fgUmS6xxIkUyp+n4eu2gA3nN8mNOZwVGVlY4jHjo7z7InJAkHc1y2vYTiqCi3QozGVu5/tYTiapiboyt/X4Wiau5/tobnSW/S4kZR+Wv2/aFov4Lu8UpSK2N5S6WVze8W0T2thSeqtGxqFdvuVfhcd1QGOjyfmaLh11ASoFPy+lqIkldaz/Oz5PpIZg87aQN61Ipkx+NnzfXzy8i6hay1F+bAu5OGTl3Xx3wdGpnVNdXxuFw5J4so19cLc0bF4hhUNQR47MsaJ8eH88Y4aP69bXstYXDxIWcySZCmtOx86OIJHkWmv9qFPc709isxDB0eE1gEta2enc+LFmmHiVhx5bbxTTQOKudZf7xrA73KwsiGYL3dn9KywZ3cp3EAAwl4FpyLxpyNjc87Zeo5ljl8ZZ0BnbYBtndU8eyJCSjNQprsFfS6FCzoqhaUs+iNpjowkTsvvEZlI8vIYsjZHZFZUHgNs0doNLWFe6p1iODZzrS6HzHltFQQFAr+SdTRi7/Y/cNESW28smqQ2HKCrTlxvbDSe4ZnjE6xoCLKtq5qMYZfkxhMazxybYHWjWMng6GiCPx2Zm50Du4t0x6aWoq/Zrci4HDIXdVbTVuVDz1r5Ttenj00IvQOlIrYDHByMzSmhjsZVDg7FeOOq4juwWyq9NIbdjMZVPE5HgZxL4wKudfY9mJ3xAfF7kNKzHB1NEFf1OU0YkZROSpCP2FLppSbgmre5YSHPK+BxoupZjozGUTUDj0theV2QwAIafByyNB30Ffor5/69EG/txUSpst79kTQ94ylbE7PSVyCO3zOeEpoHvS6ZK1c38OyJSZ45Ppnf/LRW+bhydYOd+RbAQCRNld81fyPKyjohz+5SuIGArYN6cWc1T3WPE5v1bEIe+7jfVQ78yjgD6kIe3rO1nfS0hUxuIWmv9vHeC9qFg4lIUjujfpOI1tap8hg5LEQeA2yXjavWNiIjcXQskb8Hy2oDvGldg5DLRqkmUpgpRwxH00xMTFBdXU1D2CtcjvA4ZdK6ye/2Dc+SybEzCu1VPjxOQYmQhO3Q8rplNXN8ZZ85PslEovhu6bqgm49d2sl9u4f44+GxWc4lAT52aSd1AhIxpeJM9UfSxDNZltUGiKo6qubC41IIe5zE1azwpL+mKcxQTM0Lo0vYAsxrFsDpKQW5P2taWJZVEPSd6Xix17qYzyuXoR9PargUGV2zs0jjSW1BGXrTsuidnL/7tncyhSnorbzYKFXWeyqpsbTWx4u9U7zYO5WfW+uCbja1VTAlsA6EPU4OD8fpHk1gmBa5EkX3aIIKr5NrN4g5t6h6dk7QB7bv/MOHRlkj4GBSqnUgrWe5YnU9GcOkP5LKV1NaKn1cubqetOCmaqEoB37nCGwD8WEqvC5au/xkDJuDEld1fr9/mM66gNjCVwKtrUJ5jMpCYrugPAbYLhv37uynvdrHxvaKGemZhMbJsSRvE5hISjWR5soRbkVmaW2AhqCbgNeFnjWFyxFBj23TNTvoy/0tWZaEtRw9Tpnrt7TwVPdEga9sZ22A67e0CAWU9UEPJ8aTjCczeTqbBYwnM5wYT3L9phahay2F5uL8lm2eBVm2DUTSpDSDtkof7VX+/HfAsixSmiGUlZiNxST3K9Oc0axlzZGeaQh7Cjroi8ViP6/+SJpjo8l57fUyuikcpMuSREull7YqH/UhD3rWdtsYjqbpnUwtSG+tVE0Ip5b6Wyq9wllUl1Nm/1CM3f3RgvllKKridspc3FU8LzuRMUhpWXwuR0G2y+dykNKyQo5AYGeoB6fSbGmvnNOMtLt/SihDXap1oCbg5sfP9rK1vYorV9Xn78dUSue/94/wF29YJjTuQlEO/M4RHB9L8nj3+HSQNldy4s1rG9nQWlH0uD6ng/ZqHyfGk3P4eB01fiGtrUJ5jEKIymOA7St8XlsF//nEiTlyJv9r+1LSWrboCbVU5cOBSJqgx8l9ewY4OpLIE5uX1Qd46/pmoYU/ktLZ1FaBnjXpmyzUMdzUViHEmwO7A3lff5RjY4kCp5VjYwlCHoUd5zUXPeZ40pZseeu6RqoDLlTDxKM4mEhkGIqmGU9qNFeJNc4stuaiS5HpiySnu1pded7YaCzD4ZGYECdV1bM8dHBuVgLsktSqIhtbcigFud/rdBDyODGCcx1RQh4nXkG7qhwW83mVokIBduPUdec18+uXBnn44Gj+O7C8Psh15zUL66TO6NjNZGf39svCOna5JoTvPXHCdkGaptF4nY4FNSGoepZ9A1HOaw3PyfrvG4iiCgRTU2mDnokkPpdCld+V94FXdVvEfCotFvhJFly1toFHDo3yu30za0xHjZ+r1jYIyVqVah1orvRyxZr6af5wYVn6bRsahS1BF4py4HeOYCyewcha07ucwsVpd/8UY3Ex8WKP08HrltUSTWscndXNtKw+wOuW1QqVT0vReQjQO5nku08c53XLannH5pb87mkkpvLdJ45RF1pVdMm7VOXDRMbIB32zcXQkwX17BlgrIOdhZC329EVpr7YDvVxX70RSY09flAs6xHhIWRNkWSLsdc4RGbU5msWPOZ7IsKYpzDPHJzh5IJXfUCyp9nHh0mrGBcrHpYJHkbl6XRP37xnkd/uS+SC9o8bPNeub8AgEfqlpSsZ86JlICfPmSsFJ9TgdXH4a8d7LV9UJzQElQwkqFGDzhx87MobikNjQWoGRtfVMNcPksSNjvFlAeiau6rzUN0nPhG1bp2VN3BlbjsvtnBTSsYurOt1jcRrCHupDHrRsFpfDgSRB91hcuAkhrupcva6JPx4eLaBmdNUFuHpd05wqwyuBIkMiY3syQ77SC4BbcSBqBlLpd7HzZGROab53MsXOnghvElA3KNU6EPQ42dxWhW6Y+eDf7XTgdcpsaq9aUKViISgHfucI3IrMFavreezI2JzF6YrV9cINE1V+F7v7Ily5qoEbNs9YQEWSOrv7IrxlXfFfolKJYU4kNTa3V/HbvcMcnaWJtKwuwKUravMTTLEoRfkwnjHmBH05HB1JEBcoc1T5XQxG0+wfimFaVn4ilSWJSp9TOCuh6lkcskRT2Etzhde2FZIkLMsmvYvs9oNuhT8eHuPQtMtMDlNpHVU32S5QOsphsTUXVT3LgcEYY6cEo2OJDAeGYmxdUnz3KZa90ZkvSFmInEspuEjNlV4eOjAyb/ftZEJ71bIS8yFXoThdJlWkQgHQF7HLuf2RFOOJmXmkJuBiTVOYvkiq6Az1YCTNaEzjie7xgqaRjho/FT4Xg5F00VqWA5E0974wcFo3lBX1Ytp4FV4XT3SPcWIihcMh59P+JyZSKN1jXLaitugx/W6F9movx8aSWBZYkoWENK3p6hV2rcgYJlNpnbYqH7Ik2V3oDjtDPZXShStKpVgHcuM6pBqOjCWIpXTCPifLagPCFY/FQDnwO0dQG3Szsycyb9eZ361w9ToxrYFIUuPN6xrnbcQQ1fBqqfTSFPbgccq4FEdBu72qm8Jpc4/i4I+HxwqCPiD/74UEE4tdPtQN84zyO7rA5ORz2YveZHKqQMfQ5bAlGHwuwcyMZBvKV/ldSJDXmrKwj4tkUXTTYjCantdXeDCaRhdsGCiF5mJSz7J3MEp90ENjGDQ9i8vpwDRh70CUpEDgm5NzOV3WeyFyLgs5Px+CHifXbmzmly/1F3TgLobQMCxuoO5xOmZZi83MhTltQNHsZEw1ODAUI64auBU5v6mKTx+PCQTU0bTO/XsG552z798zyIaW4pt8hqLqnKAP7EacvYNRhqYln4pF1rIzxi6HhEOWwAJJkjBMk5FYBiHNfQvesq6R3+4dsmk/0/e0s9bPW9Y1LkjLsqnCy77BKJGkls9OVvpdrG0KC1eUYPHXAYD9A9HT6iOuETBdWAyUA79zBPYuxqKl0ockkXeCsNdVS5w3p2f52Qt9YFmsbQ7ns3NYFj97oY9PXla8hlcpxEDBnpx6TtN51zOZEpucSoSgR6G9ykdD2GP7LE9rWE0kMwxHVaFGjEhSY+uSKlTd5OREskDHb2tHlTC/ye90sKYxfFpfWb/AYqpqWdqqfAxHVdJ6Nn+tXqeDhrAHVSs+mCqFfhcAFqfNFPmcDqEFqqXSS2edH7dTXlQSfqm4SKXKdvROpPjZC71zGlFu2NImFKgXZidriCbShAO2RtxCspMOWSKa0jGmXZFykCX7uEOgwSWm6vRMpKgJuPG6ZiR90ppNA4gJlE/TunHaLuusaZHWxXhzqpZlRUOQw8NxJmZlPKsDLlY0hIS+r363gozEBR1VbOuqyTfjGVkTWZKEM34uRebkRBLNMPG6HPm5RTPseVFUJ7YUGI2pc9ZBsH2lb3+0m9vetkZYkWMhKAd+5wg0wyTkcTE4pZLIGJimhSxLBNwKbVU+4V1OWs8SS+sMTKXnLPrNFV6hdvOcGKjfrbC8PlCw6ImKgQJoRpamsP0l8ThlzGluWq4UqQk6d5QCnbUBrlxdz4P7hnnq2ER+0l9S5ePNaxuEdBctCX67b5gNLbbI6Oxu6d/uHWbLkiqha/U4HWf0lRXJouSed8jrJOhV8jIGElK+5F8scvpdDx0Y4dhYIs8b7KwNcMXqeuFO2Uq/i6YKL/sHo7bLyjSNIux1sqYpLJSdKyVnaMd5Lfz42R6OTHtruxSZZXWBBWfnFjvbEVd17n7mJE8dnyjIevZFUmiGyScFXGFmZyePjSUKZZIW8Pmd09JYx8eTKPJMqdOyLNqrfTgFAj/TgtYqLwNTKn2RmQ1r0OOktcqLSNI76HES8jhxKfKcYFIzTOHP73bKDE6pBNwK1YEZEfeMbjI4lcYt0NnfUevnd/uGMC17w4dl4nU6iBomSc2gQ9Bi0iHbc8hYPMPsZuvstEWmSJBeKnSPJeZ1mQE7+OseS5QDvzJOD5ci0z9lk4Q9TkfeAkySJPqnUsK7HMO0GImpZHR7AcntnjK6yUhMtfWXikSpRJErvE6WVPs5OByb1kazZ+dKv5PVDSEqvGeH7yOA1+Xg5ESKnskUk0kNy7KQJAkJODmRwitQlvU5HTRVeOe1AVsIv0k1TB7YO8iKhiAXd9bkCeMTyQwP7B1ka0fxAWVdyE3WtPKTc+69siybq1gXKl7HT9Wz/H7f8JxS1+7+KUzTEtLvAtsVR5JAcciEvE4MQ0ZRHCgOGUlC2BWnVFm0hKrbwXpjKB/8K7JE4jROKa8Wjo8l5wR9YDdhPHV8gqvXNwkpEeTua+9kkr4hH62NtbRV+Rd0Xy0JLl9Vx6akjlOR812tmp6lwu/EEoglagJu0prBNesaqA16UI0sXqeD0ZjKU8fGqQkU/x2oC7jZvqyaRw+PFQSTVX4Xl62opU5gTACPy0GFz0lSNXA5HGQlmzdnmbY8l0dgvgp6nLx7azs/fraHA0OxabFtJ8vrArx7a7vw8xqJqVy+opbf6VkOj8Tzx1fUB7l8RS0jMVXItx7sDF33LC5eZ6244D5A7GWUFl7ufKlQDvzOEbgVmbqghx4tZXNQDA234gQk6oIe4eYOhyxhWvaiOrtc7J7eUYrsngp10ea6gYiKYTaGPah61u7mdTvywVRKy5LWszSGxb6gpeCNHR9L8lLvFPUhDw1h76zSvMVLvVMcH0sWvegV8ptOUaxfAL9JM0waQl6envYVziGX8RLJJmuGyQUdVSQzxhxi+wVLq4TGTGQM9g/FqPS55mQ79g/FhHXBJpMa27tqSGtZusfs3bkEtFf52L6shklBr1pY/CzaaEzl6/OUjgAOj8RftdLRfBiLZ87YgSuqRHAqJNFW3lmo9bsJeRR2900xntDy0iM1ARdt1TXU+osPqAIuBx/a1sGPn+3lwNBMgLK6MciHtnUQEAimKv0uVtQH6R5NTgtsT0s6VfpY0RAU5o4ausmHL25nV18U07Lyga8EbGwNY+jivLmltX4aKzz5srxXkD6Rg0uR+dnOPtY3V3DxrBLy0FSan+3s4x+vWyc0bim4eCHfmb/7L3e+VCgHfucIEqrBFavq6I+kkSTyci6madFS5RUOplwOmdqAm/FEBkWW8kTZrGVRG3DjEijJ5ay5nLKM2+cosGzrWQAHYzCqsrm9kqiq0z2rwaOrLsDmJZUMRlXaa4oroZaKNzYWz5DUsmRNEy1rks2aOBy2jZlDloUWvVJ1X7oUmcGpNDUBN3UhT4GO2+BUWuh5ZQyTk+Mp1reEuWRZbT4zFU1rnBxPkVla/EKS0U3aqnz0RVIMTqXz72rAY9MdMoKLU1w1eOFkhNYqL2ubQ6Q1Ha/LSTSt88KJCMvrgkLjlgJna+loPrzc3CEyt8B8jjipBTniAKhGlmNjSY6MJOyGpmlMJm0awAUdxdNIDNPimeMTDE6lC0LTwak0zxyfYFNr8d3iE0mNPf0xVjUGC+ge0bR9/MKlNULPv9LvYu9AlKMjcUYTmXypty7gpjHsYUV98dn02T7olmXmy/KSJDMYVYVdVnwuB3VBDw8fGi1ocnM6ZNY0hYSa3ErFxeuqDbC6MYjPpcwRm05pBl2CVqsLRTnwO0cQ8CgossSSGpsXkczYpSPLAixLWCIlY2TZsqSStG4UCC531vjZsqSSjABvzq3IdNYGTtssIJqdjKZ0Hj40yvqWMJcurysoST58cJR1AlZYpfB9BHtRyxhZIkkNLWvms5Muh0yl3yW06JWq+9KtyDSEPVT6XHMmp0hKE3peAY+C36PQVGE7P6R1E5/Lgd/tYGBKFXpf3U6ZmKqTzBgFBu/JjEFM1YV4SGAHvkfH4qcVRz+byOKlLB0tNt2hLuSms9Z/2vdKpNw/O5iYjZFYZkGWbdG0weNHxllRHwQJdMPCpdiSRo8fGefqdcW7Ag3FVB46MEpd0E192JMPplKZLA8dGOXPzmthdZGZpIRq4HE5SOsm44lEAX/a43QIJwB00+LBA8McGUlgmlZ+UzU4pZLSs1zUWbxiQqkoP2k9y/lLKomresGa1Vbl5fwlVUK89FJtqOpCHm7c1sHv9g+R1g2MrIVpWlT4FN69tfVV26SVA79zBFV+F36PwveeOMme/ql8ILG+pYIbty8R1nCTkHixJ8KSGj8Xd9bkJ5LBaJoXeyJctqKu6DEzevaMzQIZQfHaXFpcwpYccFgyikPKl3pE0ual8H0EqPA5qfQ6GYkVGn9rWZNKr5MKwRR/KXhjSdXg7Ztb+MFTJ/ndvqH88Zykj4i3ckull/PbK/nDoREGp9R81rexwsMbV9YLdZ96XQ58Tpt753HOdPMZpoXP6RDiTcIsGsU82nALoVGUAiGfE6dDYkNLxbx2VaKlo8XuvgU7Q/2ura387Pk+9k3zMh2yRGull3dvbRXKUPdH0ownNJbW+HApMtGgM9/V2zOZFg4msqZFc6WXg0MxpmbRHSq8TpbU+IX8iuOqjgUMnzIHgC2VIiKKXCprscmExlhcsyszs447ZImxuMZkonjFgFL538qSxO/3j7CyIci2rpq8kP1wVOX3+4fZvqz4ILVUG6q4qnNiMsmhoThHxxLTDYmwrDbAysaQuNXqAlEO/M4RTCY17nqqh72ztPbA1hm76+keltaIkVCzpsWFS6t55PAoDx8czR/vqgtw+co6oQkvqWe598V+NrRUFJT5xuIZ7n2xnzUCrhVgp81v2NLCr3cNzrHquWFLi1DaPKVn6Z1MEZr2wZ1dlu6dFHdY0E2TK9bUo5tmYSa11p8/frbA71H4xp+OkdayLKsPFnDnfvZ8H59+0wqhcXf3TbG3P0oiY+SDtPFEhlq/m8tXFb+hmExqXNhZPe89vbCzuqBEVwwSqnFGbTjRBaoU6KoN8M4tLdy/d5g/HByx76tsS/q8U/A7UIru2xyeODLOoeF4wTuQzNjZNZEu9KRqsKohmH9Wuexse7WfN6ysE9qkgL1RG5xKE88YBb688YzB4FRaaKMW9tmZ/VzGP4dc5j/sK36z3lLppcKroGetaXqDTU2JpnScDklYzkebDvYrfK451p3ytOtIsShVkGqLePs5MBRnT390UWxGQz4nkmQLz5+6DsQzhvCGaiCS5r7dQ/RPqXidM5+3f0rlvt1DrGoQE9xeKMqB3zmCwSmVA0Mx3Ipt+ZN72WVZ4sCg3eUqItxZ6XfxQk+Edc1hrlxdT8awMwhDUZUXTka4aq2AMLQFikOat/t0Ia4FXpeDoSmVrroAW5ZU2gRkh0wkpTEUVcUyPhbUBlz0TKbmlKXbq3zC1zoez2CazKthZZr2eRGUohElY5hMJDOsqA/NySIdHokJaUTmOjqjaR3NMGe4o6bFU8cnuFagucXtcPDIodF5d/qPHBrlkq7i3QXADnyPjCa4dkMjSPbuPuRzggUv9kXZ1Cbg3FEieF0OTkyk6JtMEknNBLqOaZcFke9Aqbpvj48lea5nEkmyvWRzc5YkSTzXMynU4BTwKGfM0G9uF3tWkgRtVT5Uw7T9qnNd6NjHJYH+keW1ATa2htndHy0on8qyxIaWMMsF+V3bumr4+iNHebF3Kn9sU1sFn7x8mdB4YBsE5JxmXErhO+R1ydQGiy/Ll0pzshRNbl3Tz+qpY3PpSRd3Vgtz8UZjmTNaN47GMqwU815YEMqB3zmCRMZONUvY6Xcra+KQ5Tnni4VLlvjARe18/8mT/Oqlgfzx9S0VfGRbBy6Brt5SuRYMRNIsqfFzeH+cp45N5LUMl9UF2NRWKcTH87sVIimd9CkCpWktSySlC4uMBr1OHtxva+7VemY0rMbULA/uH2br0uKzHaXiN+mGyXUbW7hvz0BBqXdZfYDrNrYIuYyMxFRGYmo+e5hDdlo+6NQS+CtBXch9xp2+CGcM7AVqY2uYX+8empNFeuuGRuEFqhQ4OpLgT4fHqPC6qA958/c3o2f50+ExrtvYzKYig59Sdd/mxpUlaU4wITpuxjAZjc//7ozGVWEh+/F4htcvr0XVTY6MxPNB2vL6IJcurxPaqDVX+fjUFSv42kOH2dM/U6lZ3xLmU1euELLsGoik+eafjjEW11g+Kzs/Ftf45p+O8fdXrxbKIC2t9XPx0uo5GwCvS+bizmqWCmjuzdayHI6m88cXyknONbmdv6SSi5ZWkdZNvE4Zw7SEm9y8LgcXdFRxYjzJUXWG69cY9nBBR5UwjeTlMqUimdTFwFkd+A0NDfH5z3+e559/noqKCj7wgQ/woQ99CIADBw7wD//wDxw5coSuri5uu+021q5dm//d+++/n//7f/8vY2NjbN++nS9+8YtUVdmLrWVZfO1rX+MXv/gFpmly/fXX8zd/8zfI04FUJBLhc5/7HE888QSVlZX8n//zf7j22mv/xz//bFT5XThk25fQ3j3aaWhZllBkSZjjl9azPHpojLYqe/HLZVEmkxqPHh6lpaKt6DFL5VowW8dtdhZp70AULIT4eG5FpqnCS8Yw55Q4miq8wvyurtoAHTV+njs5iZ41MYwszmltuJUNQaEdZCG/qdAKbyH8poBH4bGjY0wmdcJeZ/4eTCZ1Hjs6xoUCQaoscUaHARGN1eZKL9dtbOLxo2MMTKn596q5wsMly2oX5Cl7cDA2J6AYjascHIrxxlX1wuMudsPEcCxdkOmb7zwUF/i5HPIZeYOi3be537PlQeaWD0XG1Qxz1qZyZrOW21SKCtmHvE5+trOPVY0htiypLOA6/3RnL1/esV5o3PPaK/nSdWvpHksSndaG66r1F60+kMPglMre/ui858YTGeHKT9Dj5H0XLsGlyHN5nue3Cb+zbdU+3rGphSOjCSLJSqoCngX71JbCGao/kmbfYJz3XtCGhEQiYxBwK1hY7O6Psba5QmhurQ26CXocLK8PUu13o2WzuBUH44kMR0biQpnUxcBZHfj95V/+JU1NTfzyl7+ku7ubv/mbv6G5uZlt27Zx00038da3vpUvf/nL/OQnP+GjH/0oDz30ED6fjz179nDrrbdy2223sXLlSr70pS9xyy238O1vfxuA//qv/+L+++/n9ttvxzAMPv3pT1NdXc2NN94IwC233IKqqvz0pz9l9+7dfPazn6Wjo4P168W+/IuB5fVBtrRX8tSxicIuUWS2dFSxvF5MciKpZ9nVP0XQrdA7mS6Q8ohnDHZsai56zFK5FiQyBnsGoqT0bAFnRjct9gxEhXTcCvldc8sGovyuupCHmy5Zmt/t224QJutbwtz0uqVCfMxCftPcDmRRflMuizJfZkY0ixLyOOmo8c/xKQWbkxkSeAeCHidtlT6q/C5baHl6k6LIEm2VYm4wYE/68UyWlQ0hEhmdZErB7/MQcDuJq1nhgLoUZXlFtoOmKv9cLcPJpGa7ThSJ+pCbd2xuoXcyWdB56HFKvGNzC/WCmdS6kJvmsGeO4LZDlljXFBbK0AY8CgGPMu+zcjpkYd5Y0OOkyufmkUNjc/h4axpDwu9W70SKn+/sX7SmmZer7IhWfsAO0j76+s5F3ajMaOPF85n05fXBBWnjxVWdRw6NsqnNdjCaLWv1yKFRIWeopGqwvC7A/XuGF3VuXVrr5z1b2/jh0z0cmiU2vbI+yPsvahfKpC4GztrALxqNsmvXLr74xS+yZMkSlixZwiWXXMLTTz9NNBrF7Xbzmc98BkmSuPXWW3nsscd48MEH2bFjB3fffTdXXXUV1113HQBf+cpXuOyyy+jr66O1tZW77rqLv/iLv2DLli0A/M3f/A3//u//zo033khvby+PPvooDz/8MC0tLSxfvpxdu3bx4x//+FUN/LwuB+/e2kbGMDkwFMuXOVc3hnjP1jbhVDQWhNwK61vCVAXc+SzSRDxjlycEOW6l6D5N61lbTV4rvCjLsgMAkTZ+v0fh4HB83rLBwaG4ML8rruo8dWyCd2xu4R1bWgp4Y091T9Al0M0V8Cg82T3OBUuqeNPqelJaFr/bQSSp82T3uDC/KZdFGY3bTi05YrNDlqgLeYSyKGGvk2vWN80xqe+o8XPN+ibCAi4rozGVbz9+nINDMbRprqQs25ml3sk0t1WKaW3lgnunQ6bC6ySbilHhDSJJcsH5YlAqfcjqgIsLO6t47kRkjg3YhZ1VVAeKz/xX+l24nTJ7B2KcGJ/xgO6o8bOsXlwUuMrv4tKVtURVveAdaKvycenKWqEqxWze2KnPaiG8sXha58o1dUgSc+7BFavriQu4osRVnZ8810MiY9BZG8hnUqdSGj95roePC2SncpWf+bLpjgVUfnJYTMHxUmnj9UfSDEbtjP+pFSWnQxbaqJWKO5rWsrzQMwmSxMqGkD23OmyZoBd6Jrl0RV25q3c2PB4PXq+XX/7yl3zqU5+ir6+PF198kb/8y79k9+7dbN68GWmacStJEps2bWLXrl3s2LGD3bt387/+1//Kj9XY2EhTUxO7d+/G5XIxNDTE+eefnz+/efNmBgYGGB0dZffu3TQ2NtLS0lJwPpctfLXQH0nzp6PjvH1TM2+XmgsCiT8eHaehwiv0ha3yu3jbhiYePTLG0ycmsKb9b+tDHt62oWnBE8liQpKgIeTB5ZBJ69n85Ox1Oqiatt0qFi2VXnxOmQf2Ds3ltiytFl5I+iNp+qfs/04VL82dL/Z56YbJlWvq+eEzvezpn8ofX99SwfsvbBPi4sFMd10iYxBL6/mSXMjrpM4S675rqvRSF3KxvaumoLPbsizqQy6aBO5r91iC/YMx4mphw4hLkdk/GBPW2ipF92Gp9CEbgh42tlRwcjxZIAdSF3SxsbWChmDxn38yqfHgPlvDzcjO3NcjIwke3DfMlvYqofs6mdTwOh3zvgNel0PIEaVUvDEkePyovSE7f0lV/lojKY3Hj46zUaC55cRYEo/TwVPHJuZsfi5fWceJsSTrixx3eX2Q89sreb4nMieLev6SSuHKTylQKm28UjhDlYo7emwswcnxNNG0Tkqb6Wz3uRROjqc5VvbqLYTb7eZzn/scX/ziF7nrrrvIZrPs2LGDd7zjHTz88MN0dXUV/Hx1dTVHjx4FYHR0lLq6ujnnh4eHGRsbAyg4X1Nj6/7kzs/3uyMjI0V/hmxWTApkPsTTGhnd4OnjE1iWxeTkJFVVVfngN57WhP5ewC3zYl+EvQNRYrN2taPxDGGvwhtX1wqN2x9J88zxSVQji6qbeJwye/ocXLi0SjiYCnkUWqt8thm5V8kLolomtFb5CHmUoq/VNE1WNYXoHk3QMzmzQNcF3KxqCmGaptDnj6c1LMueLHKlI/t/zfz5oseVLO55rm9eSZ97nuvj1qtXCl1rlU/BMLOoerZArFjVsxhmlipf8ffV55TZ2FKBqpkF74BHcbChpQKfUy56zKmkRiytz1vqtyz7vMjnbwq7qQu6GImpc55VfchDU9hd9Lhp3eCRQyNMJjUqfApZExwyTCYzPHJohNVNQaFrnVI19g1EWdMULuhsnkrp7OuPcsmyGhqyxZVQh6Iq+wdjSNJ04xh24CdJsH8wxlBUZVld8SWpWFpj32CU9iofbqcj/w5k9Cz7BqJ01viF7kFzhZsPX9xO32SK/hEfLfU1tFb5CLiLf09zCLgdOB0yv907hJGd6cBVHBKb26sIuB1Fjx3PGDxyaJTj48mCrt7j40k4NMqWJVVFj1ntd/IXb1zG9x4/wYmJVD6DtKTKx59f0kG137mo685CEE1p5EpGs79XuQ16NCX2fXUpEifGEyQ1Az1rYU1vVE3L5MR4ApciFT1uRjfoqPZzbCxBTDXyYwY9Ch3VfjK6IXStMVWnP5KaQ0PSDA0jaxJT9UV7XsWMc9YGfgDHjh3jsssu48Mf/jBHjx7li1/8IhdddBHpdBqXqzAT5XK50DSb9Kyq6mnPq6qa//fscwCapr3s2MVg7969Rf/O6aA6K5iYmCg4Njk5OXM+EWDXrp6ix036m9jTP0XQ7aA24Mp7VKp6lj39UxwfjTNy4nBRYzp9IQ5MSdy3e4iTs3TRllT7sbIaqyss9FSs6Gt1VbawrinE8bEEx2ZZtnXWBljXFMJlZdm1a1dRYyadFTzyUh9XrGrG7WogmckScDtQtQyPvNRNtSODX58q+lpL8bymPE0cHIpy+fIamip96NNZiYFIiqePTzAaVdFGjhd9rSlXBZtb/MQS6TnPa3NLgCO9Q/RrU0WPK0kSKwMBooaDlG7ic0JY0ZnoPcJ4T/EcAre3iZRmYOYWEZiW3rBIaRZuRSr6+eeuc3tLBfftjTA0ZQf/k5OTNFb4uKQlyLFD+wsCzVeCqKeeyYTK0JRKfNakH3QrSJZJJJ5i164TRV/rGCG0ZAzD5WE8lkY3LZyyhFcBU1PpGxpDHTpW1JgJuQ7dMKgJePC5HGQtcEgSKc1gPKGSSGeE7qvqrGB0bILxySiaZdMnFFnCJZmYWQM14Reas2ajFsiMxOgufl9egFSgibVNQYam0hydNbe0VQVY2xxkKqmya1dx362Uu5Gjo3GbM2nNknORJI6OxkmpWtH3VZIkLF8VG5oCLK8P5INprwPU+BS7R44X/a6WCm53fX69zSGTmeG7uiVT6L0ygw2EPA56JxMY2ZnPqjgkGpvCaOlU0c8q46pEVdNUepVp/rBdPpaxUNU0mWRM6F1VPE1MpebvCJ9KZVFA6B4sFGdt4Pf000/zi1/8gj/96U94PB7WrVvHyMgI3/zmN2ltbZ0TiGmaNm2zZGcL5zvv9XoLgjy3253//wBer/e0v5sbuxisW7cOh0OQe3cKEhmDnSNGPisxO+NXH/KwoauFgHtJ0eP+bv8IYZ+LtiofNX43ujkttpzI0DuZIqZZbNu4sagxD48m+O9nDjOczBbct+Fklv8+NMn6N69gzfKlRV/rvsEYTx+fYHVjiO3LZrIdkwmNp49PsG1ZDRuLvNYXeyJctEbhqWPjDEbVfGamKezhojUdeAN+NrYvKfpaS/G8Hjowwg3nt/H40TEePjyWP57rvNNNq+jPD7CzJ8KJ7kG2rWzg9bKMOs1z1E2TE5E069tq2Li6uGstBV7qi7KuOcyLvVMF1FMJ2NAWJuxzs3HFRuHx25oa6B5LMh5NUhv201nrpybgBtqLHuupYxOMJw3ShoUyaw5IGxbjSQOn08XGlcVf66GROFXVdnBaMeesj9bGWlbWF/fdSh2fZFl9mJ7JFAPRmYU66HGyrD5MRcDDxqXFX2siY/DUgEZSNQj7nNMcNwdTSQ2/RxGes3LIZrPs3bt3UebZhw6O8qNne9ncXsnFXdX5uaVvMsWPnullXXMFFxf53Xrk8Bg+l4KZa5gDZMirMSDLRX9fExmD2x89xtPHJgo4zV6ng4HOam6+bA0BQQmqxcZ4IsOmjlp8LoWaoBt12gN7LG771K5praImULyI3XMnJrlgaQ1JzZxTQr9gaQ2G7GJrkfd1PJEhfCjG/u6JguxcwK1wcVc1y9saqAkUPw+81DfFqsZwwWYih2V1AcJ+sXlgPuS+D68EZ8cbMg/27dtHe3t7QeCwevVqvvWtb7FlyxbGx8cLfn58fDxfoq2vr5/3fG1tLfX1tjTD2NhYnseXK//mzp/ud4uFw+FYtMAv7HPw9k2tBdwWSZJsc/JNLYR9Yp13AbfC1iVV/PHwGMfGZjTcOmsDXLqiloBbKfozjMc0eibTwFzSXc9kmvGYxurG4u+LZlhU+NwMRlV6IzMdyEq+CcEq+lqDXhfPn+hnZ+9UATdkKKqiyA42tVUKPcNSPK+akIdnj08W+PQCHB9P4XJM8qa1DULXGvS6iKWz7O4fnpczE/S6hN/jxZQzSWgG7zy/laxlsatvpty9oTXMO89vJaEZwteZsyzrHkuQTKkEfN4FdV86HLbH7yXLamgIe/Ji40PRNIeG4zgcstC1tlX5aQh7TyuK21blL3rc2oCbsNdJyKNQG3Tleb6qniXsdVIbcAtdqyybbF1SxXefOMG+WfSEtc1h/nx7B7Isdg9OxWLMs4osYZjwePcEljUz/+dcNhRZKvpv+F0Ka5pC7B+MMTbL8qwm4GJNUwi/q/i59eREnKePT5DWTWbPr2nd5OnjE1wjKLZdCtSHfXxkm61s8Lt9w9PKBo68jmF9WKyz3ZIkfrdveF5nqN/tG+b8jqqi7+tUymBFfYijI4mCwK826GZFfYiplCF0vQG3k7duaOa+PQMcncV3XFYf4K3rmwm4nYsWIxSDszbwq6uro6enB03T8lm648eP09LSwoYNG/jP//zPvKSJZVm8+OKL/O///b8B2LBhAzt37mTHjh2ArQc4NDTEhg0bqK+vp6mpiZ07d+YDv507d9LU1ERdXR0bN25kYGCA4eFhGhoa8udFMimLjVynbO9kkr4he3ffVuVfUFdQpVfhhZ4Ix8YKdyTHxhIEPArXrGsoekzb/uf0umCiopU5KQe30zdvN5cICT813XV1KiE4oRq80DPJ+7Xid3k5LPbzkpGIpLS8e8tsJ4BISkOeJ9B+Jaj2uzAtE6cs4/Y5CuyKTMukWrDBp3cixTPHx0nrZr6rdW+/zIVLa4SCKY8i8/MXennn5lY+sq0jr7WVVA1+/kIvn3rTSqHrLLQsy07LThgLsizzOx28Z2sbPZN2h2iu1FcdcPGerW34BdwFoDRSSbphct15TfzomV72DkTz79W65jDXndck3DQ0EEnzo+d6wbJY2xzOf1+xLH70XC9NFd5Xxa5qPoQ8TlbUB6gNummq8No0CsXBQCTFWDwjJD/kdzvQpp2QltcF8jSapGagGSZ+d/HvQKnEtkuBuKrzp6Nj1AbcbFlSiaoZeFwKYY+TPx0ZE1I2ANuyranCO68zVHu1T8iybSSWOaPN6Ir6ICsEHDaaK73EVZ03rqrnzWsaClQj4qq+IO3RheCsDfwuv/xy/vVf/5XPfvazfOxjH+PEiRN861vf4q/+6q9485vfzNe+9jW+9KUv8a53vYt77rmHdDrNVVddBcC73/1u3v/+97Nx40bWrVvHl770JS699FJaW1vz57/61a/mA7uvfe1rfOQjHwGgtbWV7du38+lPf5pbb72VvXv3cv/993P33Xe/OjfiDJAEF/rZiKQMdMMk5HUWNHeEvE50wySSKr5Dqi7o5qq1DTxyaHSOp+5VaxuoExStLJByOMXnUlTKYTKpIUnSHIkEh2xbS4n6v+YQ9DhZWR9EHTrGyvqlC9rdZU2LNU1h9g9Gic56VmGvkzXNYSFfZYBIUuNtG5v5wVMn52Rm3raxmYhA92Vc1dnZO8mD+0fmdLU6FZlKv7PoSd80Ld574RJ+8NRJds8Ssd3QEuaDFy/BFPz8pbAs8zgduBQZywRTsmaCPxNcTlnIViqHtmof79naRvdYgti0KHBnrZhXN0DWsvjt7iG2L6vhuk1NpLUsXpfCeDzDb/cM8fHLul5+kHkwGssQTxsMx9Q5HY0NIc+rZlc1Hyq8Tj50cQfff+oE/31ghjC4odV2MKoQkB+aSGqsaAgRSWl2ln46ol5a42NFQ4gJgbnl5USvRcW2S4H+SJqRWCaf/dY1u9TvcMiMxDLC+pilsGzTsiaGaXFkJE73WKJg82uYlnCyIuhxcu3GZn75Uj+9i6hpu1CctYFfMBjkzjvv5Etf+hLXX389VVVVfOxjH+Od73wnkiTx7W9/m3/4h3/gZz/7GStWrOA73/kOPp+dRTjvvPP4whe+wH/8x38QjUbZtm0bX/ziF/Nj33jjjUxMTHDzzTfjcDi4/vrr844gYOv+3Xrrrdxwww3U1tbyT//0T6+qhl8OOUHY4Wh6Wh4kZZcOFyAIa5gWiYxBTcBNU9hL1jJxSLaxeCJjYAgspg1hD/sHovROFpYkeydT7B+M8u6txbuBQGmyHbZrwenNyRc6kcZVnd7JJGOEODySoLVKXGg44FGo9LvY0l5JVLUDdqciE/YoeFyKsHhtWs9y785+Wit9bGqrLNzt7uzn45d2Fj3mQCTN/XuGUDWD+pA7n+1RNYP794iZk7udDn6/f4RkJsuy+kC+qzuZyfL7/SP8+SUdRV8nlCaLYpgW0bTOC70RTowl81m0jlo/9WGP0Pcqh8UWhk7rWVY0hXj40CjHRhP5a+2sC/D65bVC+phgL6ajcZXxRAZ91sKZ0rLI0qtnVzUf/G6FPxwcJqkahe+WavCHA8Nsai0+m5xQDX71Uj9b2iu5cGkhb/BXL/Wzqa2i6DHrQm6WVPuYTGrI04FJLkCp8ruEbQtzWFRqRoHsykwm3etyCMuugJ1Fe/jg6LS3tkQy57JhWfRHVKEsWm3QTW3AxcmJFLG0nv8OhLxOllT7FuSwcepGLeRz0rWAjdpi4KwN/AC6urr4r//6r3nPrV+/nl/96len/d0dO3bkS72nwuFwcMstt3DLLbfMe766uppvfetbxV9wCVEqn9baoJuARyGhGnbH5CxR3IBHEXrhJ5IabqfCsroAetbeMSkOCacs4XYqTAhkkHJYbGFo2//VR89Eao5rRXu1b0ET6VzeWGxBvLGZjCd4XIVf3YWI16b0LL2TKSpnZVFzkgu9kylSAgv/aCyDZpgcG08WZE2r/C6W1weFsj0SEvsHYyiyhFdxkGXatcLI2nIkghnwUmRRkhmD/94/Mse55MR4kv/eP8LWJcXb4EFp5gGnQ+apY+PzXqvTIfH65cXzmwHcTnvzoGdNZjea6lk7mHY7z57sVG8kZfN6HTKSRP7dsiSb79sbSRVtM1bld+FWHDx9fJLHjs7wBp0OmYBbEdJIba708t4L2ublTb73grYFlQ4Xe0PhUmROTCRIZrLoWZMsdunbtODERKJAOqoYBD1OLl1Ru6iWbY1hD0G3gscpE/J68zzXjJEl6FZoDIsHaaVw8FkozurAr4wZ5NLm82EhafOltX62tlfx0MGRgh1Y2Otka3uVkKVMbpyklp0jCIwl5oQwG4upLt9c6eXaDU384dAIg1NqfgfdWOHhjSvrhSfSUvDGSmWFJ1mcsTQvCSSntKxtdj+V0nFIM9pwUymdIyNxoWxP1rRY3xzG7ZSp8DnzDRORlEbGMIVL3bOD/1MhGvzHVN3Wb7MoKEHLssTx8SQxVcxaKzcPzOdaIDoPaFmTkVgG85SSlgu7JCeamXNIEktr/BwcjucleMDmqi6t8eMQUVwvEaIpncmUTtCt5DNpOevKyZRONFX881peH2RjS5g9AzEsrLzbkoTE+paQsNjy8ycnWVrjZ0NLBVo2i8vhIK7qPN8zyfkdZ8+Gwq3IVHldDERsy76cbaVDlmitCAv7oMdVnYcOjuB3KyyvDxR8Bx46OCJk2TYSVXnL+sZ5ea5vWd/ISFQVSlaUKmGzUJQDv3MELxcsLSSY2thWQTyjzzG+3yhQioCZnZ6qmwVZNFU3F7TTKwWCHifL6gKcGE/SXuXPlzkVWWKZIPkYSsMbg9KUDSr9Lvb3R7l8ZR01QRdpLYvPpTAWz7B/IMqb1hTf4OOQJaJpfbpBZNaiL9nHHXLxi37Qo7Ctq5q+yRQmYFl2E1F90D1dQhebzporvbx1QyP37R6iZ5aOYXu1j7dtaBQK/g3T1hc8VVMtd0ywXyJfPjvVXm8yqVEX9AjNA1nTQpZs7pTX5chz8azpZgTRgFrLmlzYWY1hWXPK3Rd2Vp9Vpd6Qz4llQew09y/kK34eqAt5+NilXXztocO8NC1BJAHntVXwsUu7hO3K+iNqwfPPKxsEPcIJgFIkFiJJjc1LKue17NvcUUlEkD+du9YcRWcxrjWeMfjhMz00hb2c116JZpi4FJmhqTQ/fKaHT12xYkHXOh8WkrBZKMqB3zmCgEfBIUs0V3hwyBINQTcBrws9azIwpQrzuwYiaZIZg7qgh/qQt8BWKZkxhKyl3IpMXdAzbwalLugR3umVAnFV5+FDo7idMn5ZIq2b+FwO9KzJw4dGaRXYPYLNG1N1k5BHQZZBc8q4nA5M055kRLvvSlE20A2Tt2xo5O7p3W4O65rDvE/QCs4hQUe1n8OzjMnBbnDoqA7gEEj2VPldpPUsT85jgXVt0C1sLxj0ONncVoVumKT1LNFEmnDAi9fpYFN7ldDzD3sVagJuRmIqmjkriybL1ATchL1i31eXIjOeUOmZSBFT9byyQcjjRJYQ2lQp0xaNA1PpgsAx4FGoD3lQBIL03O+/2BNhSbWPiztnOG6DU2le7InwprXFbyhKha5aWxB5Poux5fUBumoDRY8ZV3WeOT7Bjds7UA2TuKoT8jhxKzLPHBPz644ktTlWZTkkNUM4mMo999P534psKCyJWbIrNaTzOn4av9s7zPmCdIdSJEFU3aR3IsXBoXgBH9XpkPG7bNcZEZQyYbMQlAO/cwQtlV42tIS45/k+jo4k8ppIy+oDvOv8VmF+l6pn+cPB0dOShVc1hooeM6EaZ+y6erVe9vlQKk9Vl0Om0ufML6a55xXwKDRXeIV4Y3FV577dg3RU++io8ReQmu/bPcgHLm4Xy1BKcM9zvezqm7IzUvYhdvVNochw69Wrix7SlODirhosLLpHZ4K0rjo/F3fVYArEEpNJjT8eGpu3aeiPh8e4uLNmQdzRSn/jtPSOQWtj5YKkd1wOmbXNIfSsOcdXem1zSLhpyK3Ym7KxhC02nkPGyLKk2iu0qfI6HYQ8ToygRWO4sMwZ8jjxCnYgOyWJN69t5P49gzzZPeNi01Hj55r1TTgXUOodjal0j8YZo5rMyciCs951IQ83X9Y1P2/scrHs3EAkTcjr5IfP9C7e3CJxxkYkUaGHHM/7dJlkkcRCoeyKNU138QCSsOxK7loXcn4+mJZdhp+PjyrLSkHVohiU4loXA+XA7xxBWsvy3IlJxuIaLsUuRcqyxFhc47kTk7x+eZ3QIpXWs8RVnYxh2qTm6UnfsuxAQ6Sjz+9RODgc56KlVbx+eS0ZPYvb6UAzshwcirOprbLoMUsFVc/OCfoAeiZSPHxolDVNxQe+YPPG5tspJ1QDp0MW4o0NRtJ01Qe488kT7Omf4aGsbwnzoW0dDEbSrBAIUscTGi/2Rm25ken5zR7b4sXeKOOJ4rMI1T4XR0fidNT4CzxlB6fSHB2Jc93GpqLHHI1lGIiq83ZgD0ypC5YHSWtZoimdDE6iaYO0lhUO/LSsSVd9kIxh8+dy36v6kJtl9UHhMudQNM3a5graqwO4nbLNc1RkVC1L0KMwFE2zssjNmsfp4PLTbNQuF5THALsLO+B2sL2rpkAbzbIsAh4HbsFx9w9EpwO0eD6YWF4f5ObLuljTHBYaE2BNc5jPvmUVR2bJ5CyrDRTd1JFDKeYWn9Nxxq5e0WCq2u/C55Zpcnio8rvy/NmJZAaXIgtpeRbKrhRSKERlV6BQ1utUiDa5eZ0yjWEPm9srqQ240bMWLkVmNK4yOJXGK9iIVIprXQyUA79zBMfGEvRMpAi4FYIeBSNrojhkLMueSI6NJYR2pca0TtFwTCU+i3Ae9Nht7CKyEy2VXmoCrjkOE7A4L/tiSg6k9Cw9EylMy5oTTPRMiHW0AmiGyQUdVSQyxrREhj05d9YFuGBpFZpA+VTLmvzXEyd4cTozl8PO3iks6wS3Xr1K6Frjqk6umndqEkaWKHgvXilcDpn3XtjOnU+e4LEj43OCVJGMVy5YkiVpTgf27PMiODAQ5b8PjGCYJmlNxzeR4bnjk1y5up7VAsGEz+lgYDJN0KPQXu2bsRdMavRPprlsudiil5p2FQh5FTxOBxnDxKPIuBz2Zi2VKT6b3lzp5aEDI/Nu1CYTmnCDU052Y3l9YNFkN0ZjKrc/2s2h4XhBp+ih4Ti3P9rNbW9bI5z5651IcXg4Sko3SWoGkgQHhqJkLYRoFLm5ZT6Izi1ep4Mbzm+dV3PzhvNbhbOzU0mN1y2r5ftPnuT+PTMOTutbKvjItiVMCSgxFL5XNXkKhWaYC3qvStHk5nE6uHJ1PQ/uG+HJ7vG85mJ7lZ83r60XDlJL1ZC3UJQDv3MEcdWgfypNRrczc7kOMcuCKadMXLB8qsgSg1NqgU0N2J6Qg1OqEL8n97Kf6trgUWQu7KxZ0Mu+6Bw3C5wOKZ+VycExzXtCLMNPxjA5OpJgWV2A85fMkIWnUjpHhxNcvLSm6DHHE9qcoA/shoEX+6aEMnMAPvc0qV9jDr/F63LgE3AYaKr0ct+eQd64qo7rNjaT0rL4XA4SGZ29/VNc3Fld9Ji1QTdelzxvqcvrkoW1tkZjKs+cmODRw6OcGE/my/IdNX5CXttntNhFz+N0sKIhyP17Bnn44Gj+eK7MKbqQVAfc+N0K9+8ZKnDb6awNcM36RqoDxd+D2SKzszdrC12ccrIbP3zmJL2T6TxvrK3Ky/svXCI0bvdYgv2DMVQ9i2nZnbKGlUU2TPYPxugW3ADHVZ3u0Tj/9VTPvPZyIoLjWJzxfRWZWyr9Lv7z8eO8Y3ML79nalnev0bMmD+0f5jNvFnOvmUrrfPfxE0TTOsvrg/kMdSSp8d3HT3DLVcWPm3uv5rVCPL9tQevAYst6IcGegSiDUXuNzW1UB6Np9g5EuWBp8fPV7GtdTMH1xUA58DtH4FLkPPnc5iHYpG6nQxYmdYMtuVAfcpPIGPlSnyTZWZX6kHtBkgu9kymOjSXzE35nrZ8Li/OPL0ApWuP9boVKn5OYqheUZb0uB5U+J35Bw3OXItM7mcIhS3Zzh57NN3dkTUvoecVVHa9TZkt7Ja1VvnyZr3ciyQs9EaHMHNhlns7aAIdH4vhmdXUapkVnbUDYsq2zLjDdKVtYPnzbBrF67NJaPxcvrZ7TLe11yVzcWS0kPQS2htsDe4ZoqvCwZTpIdysOBqbs4+tbK4qepFXD5IG9g6xoCHJxZ01edmMimeGBvYNsFZTdkCV45OAIJ8YLmxBOjCd45OAI2zrFxl30hRT7fb13Zz9PdE8QSWr5xbR3MoXPqQjJbkwlNVQ9S2K6AWH2PJg7L4KBSJo7nzpJ2Kvwji0t+VLnwFSKO586KWQvV+l30VEdmNOM4XXJdNQEqBT4XkWSGlesrufZE5PTVQp7DpCAK1bXC7nsgC0/1DuZZGtHFU0V3oLP/9yJSWH5IbCddZbVB6aDHpddNhXXL89jMWW9UlqWff0xQh4n9SFPPvBNa1n29sdIaWKVHyjr+JWxAPjdDhpCHg6d0iWpZ00aQn4h30ewO0y3d9WgZU26R2cWk666ANuX1RAXKB3lArSJpF7Qbj+R1BekXVSK1ni3IuNzKdQG3NQHbUcFRZYwLQufSxHuQHYrMjVB93TQM5fYLDJu0OPkXee38sfDYzx6eCx/fFmd3eAjukj7nQof3raE7z95kt19U/njG1or+PC2DvzO4qeJ/kiaXX3RedX1X+yLsqIhVPSzCnqcvO/CJciSxJHRRN65ZHldgPdcKNjYgr2Ybmyr5MnuMf5wcDQfTHTW+tnWVSvUKakZJg0hL08fm5hrr9cUFir1w3RHeCLDm9c20Bz2omUtXIrEwHSnrMj3NYfFXEgBjo4keOjgCLFT5HtiaVuH7ZoNTWxqL47v63MrJDKGvTBLEpZky3Znpx2IfIIbtaGoyrL6YP4dyCH3DgxF1aK5ky2VXjrr/Lid8pxO2ZZKrxDlRc+a9E6mGIqmGU9o+fmqJuCidzJFc4VY+dQCdmxq4fGjcz//jk0tQnFaXNV5qTcyrZGazm9+myq8vFEzxbKoJUJGN2mssDvbxxMza0yuGS8j2NWbWwv7p9Uzcu+AnjXLOn5lvDyMrMXrltdiWNacAO11K2oxsmJbqEqfi9/tG5rOTBRKLvxu7xBvWFlf9JilEJmF0rTGJ1SDS5bV8tu9gxwfT+azXUtr/FyyrFa4A7mws3kusVlk3Mawh939UdK6yfL6IKZlL35JLcvu/ih/dl6L0LU2VnrZPzTFW9c3sCPv1epAN0w0Q6dRYIFKqgbL6wL8ep6M3xtW1pFcQGf30lo/jRXePBdtoRkEtyLzZPcYx8YKnSty/3798uLL8i5FZnAqTU3ALhPP7pQdnEoLZ+iNrMl1G5t59PAoD+4bzh9fVhfguo3NGGeRNt5wLF0Q9M5GNK0zHEsDxQV+Ya/C2qYQB4biOOSc1qCEYVqsbgwKy+RIWGd8By7pKr7UVwp+V8owODgcZ2dPhKm8qLREhU/B61LY0CbW3NIY9vLCycgcXvbx8RReZ4RrNzYXPeZAJM2fjoyxuy867Qpl5RsSnbLMioagkGJCDovJ9a4NusnoJvVBz5zO9oxuCtNI+iNpjo0m5+2WzuhmWcevjDPD63RwdDTBuuYwly6vI2NkcSt26ejoSIIrVhUfoAH4XA42tFTy7MmJAsmFgEfhgo5qfK7iM4mFHo2nlDgW4NFYitZ4pyLz8MFhmiu9rGsJF5DwHz44zHmCItaFnc2FxGbRzuaMYeJ1OlB1g/7IzARdE3DhnSb6i0KWZcbiGpIkFXRfimrjBTzKGTsaNxeZ6QF7ov/1rgE8Tjnf3CFJdhDx610DfGibGG9MkiT6JtPznuubTCMJ0B0KM76FEM34gs3xe7x7jKOjhaXeo6MJFIfEG1fXCY1bCijymT/jy52fD1Npnf/9+qU80T1B1rTy3ZeyBNuXVTN1mkDz5WAhzduMBnbwYwnqpCx2CV3NmDzZPc54QmOGkmwxntB4snuc6zYU3y0PdsY0rWcJexRMyw6oJRlkbOUHERHv8USGF3sjea/mXCY9pWXzx0Wx2OXTpbV+LlxaZdNI1MWjkUSS2v/P3n9HS3KV58L4U7Grc+4+Oc+cM3k0QRrNjDIjkBESiGxMMHAxtvH1x7Xx99MPG4x9uTYsc52wjQNgfDHI2GSZJCSBkEZxpMnx5Hy6T+dQuer7o7rrnJ7uGal3n76SluddiyWmS7NVXb1r73e/7xOwWpQwlynXaWTSFIh1F1uNa4nfqyQEjsEtm6N4+HwCx2YyduuwP+xuiRpflDRcPxiCZhq2ZRlDU+gKCLh+IESUpFWdOxq5VrTi3NETdKLLL9gbf5U0Iqs6JNUgap04WBq6iRoAfjW2d5PbCq1nNpumgVQqhXA4DIqiiZnNJVnDUk4CTVlEhvUWUEs5CSXCNt98RsSz01n0h5z2c62yOp+ZzmJTvPm2rKwZSJdk7OsPIup1QNENOFgaibyMCyt5oiS1XZqLDEWhJ+jEfEastSxjaPQEnUQ413ZpWZZVAzlRs9tF1eAYGjlRQ5mwJdWOCHl4DEbcdR7AgEVyCXmaP1T4HCyeq1S7lvNrTkMdPgGXVorYR3CgAACWoRDz8EgUlRryFEVZn7MkiuOV2MgWek6yxN8vz8MM0xKNzxHOK0UzMBB2oyhrNXjMoJvHQNhNBE3ISyoSFa/m9aHqBhIFmRg32A6sdyMYCc/S2NQijISigJl0uaGs10y6XKei8H8rriV+r5JoFzXeLbA4v1LAa8ZiDbFYJLZtDpZGl9+JgItD2O2AolvVydWijGxZJU6mvAKH28diG2rOnSzI2NsfREnW6twg9vYHkSyQacNV2zxVRpsEHjlRszEzpELLqm7CyTOW9Ay9Jj2j6iaxeGtRsjBTF1aKG6bar2oG3ri7Bz84tYiHz6/UtNDfuLuHyA2kXZqLQTePzXEfaIpCtmIzx9AUAk4OI3EvEQi/WvHdPxDEjUMhiKoBJ0dDM8yWtCyLooqQi4dhmpAUHQasqozAMwi5eBQJK17tiL6gC/fu7sJ3jy/WO63s7kJfsPnKjAng+ZkMnpxM19ir+QQWJVklqiQDQMTjwPbuAE4v5uoSn+3dfkQI2NLtCLai5NAoTBPELitVaELU46ghN7QCTeBo2pYDM80KGmMdcYwjqPgC7bVB6w+7EPbw9vvqcbAtwUgYikLU42i4hkY9rZEnW4lrid+rJKrUeEtnSgcN2Ayp0U4/8YmkJ+jE7l5/QyzWG3Z1klWmJA1v2dttycEwlO39GvM60BUQiPFd7TDnljUDD51dwc4efx378qGzK9jVEyC612r0hVyIeh01NmCkC4mLY9AfdmEmVa7TsWtVCf9qrXmSFrpHYHH0UhKHRiK4d3cXyqo1B1YLMo5eSuLAUPPs03boogGoAO0FxH0O+JwsJEWH08EgV9bAMRTRO9ATdMLF0fjPU0v1DOShMLGWZcDNI1NWwNM0PF6rLUdTgKIayJQVBAhb8+2ImE/AjYNheHgWJizmpMvBgDKBHT1+Qvaphhdms/AJHDr8gv39y7KOF2azV/TafbHoCTox1umFk6ORkzSbOOQXWPRH3C+b0O7lEXBxtrXc+mWEgnUADhB4CgPWYT3qdSC9rvVYTUmiXgfRYd3v5DAa8+DccmGdYoQJGhTG4h74nWT32g57uYKk4qtPTTdUDJhaLeG37thEtMcWZA23jcawu1cGy9A2W1rVdQRdjpbIWK3EtcTvVRQFScU3n1/A6cU8NE0HyzLY3uXDb93eGiX83GIeiYJU81miIOHcUh6vIcAOOnkGUs7A908uWd6vlePzjm4/3ntjP5wEuEHAOumtFhWMxj11rd6ZtEh00quCdh86u9JQx48U1Lu+HXF5q3cxJxG1I2qV8Ovbh8TacG6+Ilxc35rXDINIzkVSdLx2Rwe++tQMTsyv6aLt6vHjVw70QyKRR2iDLlo19g+E8E+PT+H0Qs7W8atquJHGtm4fOIa2cZMOloZhmBjtaN73tRrdfgHdAcF+ptVKKmA9224/uTbYRoLlq+EWOEyulupY2AeGmyfMABbcIeRxYDknYjG3hsv0CSw6/E5iuEM7hXY38rmGXDzeeF03vvPCAiaSJbsyORx1443XdSPkIkv8S5J2VWFoksO628Hirh2d2NnrB88wUA0LOyxrOroDLmKprHYcVCeTpbqkD7DWwKOTKbx+Zxd29QaaHjfqcYChgdm0iERhzcEn5nUg4nEg+jJVkq8lfq+SqCrWX0qULL9OTYGD5XApUWpJsX4+I6KsGnj9jk6wNAVRNeDiGai6gYWsRJRMqYaBrxydxgtzWVCw8z68MJsBTBOfeEPz3q+AtTht6fDiobMrmEgWbZeN4agHR7bGiRanTr+A7d2+hjp+27t96CTcSNvRjmiXw0K6pODwSASyZtQllIc3RSy/zibnlmaaeODZOZxayNe0pk4t5PHAs3P4fwmEZqu6aJcz5BiaQswnELVkAQs7+K9Pz0JUdGyKe2zZCVHR8a9PzxJpuC1kREiKAQdnuesYJgWOpgCGgqgYxHjE5ZyEX76hH6puvV/VuK43gF++oR/LOQn9keYTy3ZojVUPPzlJs8TQK5GTNGIslsfBYSUvISvWvutZUQNFSfA4XkGiwLCe6zeem63TM33bvj6i59oVdGKsw4tbRqM4vGnNCpGhKYx1eNHVAuTn738xid6gC3v6gjbBK1mQ8c1j8/gfRzY3PSZHUxgIu3BsNoP5TBmGAdA00BN04dBwxHofCKIdB9VkQb6qB3KyQEZE6fALOLuUh6zpCDg5qLoJjqUgazrOLuXx9v19ROO2GtcSv1dJjCeLuLhiJTvrrYpYhsbFlSKxYn01mboSYJ4kmUrkZZxdKlS8f9c+Z2jg7FIBiSskRC8WHoHFj08v42KiCBOmTW44v1yAYZhE+J50ScHevhBkrZ7csrcvRJT0AO2RnlmvhD+RzNZuJC0o4RckDRcTxStq7m2OeZseMyeqeGE2C4qymNPV7N80Tbwwm72izMfVoifoRKffgfFEATlJtTcSv8BhV4+fuB2XyMuYtud+reZiXtKIPIBlVUdeVPHsVBqJogzTAGiaQtTD4+ZNVtJOEjlJw189chF37+jCfXu7bemdZF7GXz1yEb/32uZt+9oBlgfac/hxcjQ6/AJyolpDcKApa5Ml9VStxkYSMQqSiq8/M4OirGE46rGrvtmygq8/M4PfIMAlewUOw1EvZNUS8686dzg5BsMxL/EaIGsGFnMiLiWKddaVbgeZYoCo6vjpuQSKkoaA0/LXrrZif3ouQaw52I6D6otZSJJYTAJAqqQAplX587t4W3A7W5KhaAZShPtLq3Et8XuVRL6sQtUNFCQLfG61owwwNAWvwCFfJgN1V2U3LKuqtRd+arVELLtRUizcBU1RYFiqZtNXdcsHkyTKio6FnIicqNQsRA6WxkJOJFJXL0gazizlsbPbj339oZoq2pmlPEZiZG25dkjPVGN7lx/DUU/Not9Km9MrsFfV3PMS3GtJ1uDkGOwbCKIv5LJlN2ZSJTw3nSFuyW2Ke3BsNoOF7Fqbr9MvYFOcvH36Yh6/JB7AqmHg2ek0ziwVLDvEyjuQKMhwcgyu6w8Q3SvH0kjkFXzpiekrXm822gWWb8fhpyBruGVTFKZpWvIrlV73UMSFWzZHXzbMVKOYSpYgcAyOTqTqyC23j8UwlSxhJ0H7ELAYs1U7TMOsWC22sAYomoGegAtnFnN1guM9AS8Rq1dUdSSLMhaylnhxdW9xO1jwLA2R8PBTkLR1Ulm1nY9zSwWig2rM50B/2FW3D7IMjcGIGzEfWUs2WZAwGHXj+Fy2gnW0DikRD4/dvQEkCxK2NCkMvhFxLfF7lYRHYO2kb33ohomCpBInElalS0S2rNRh3BazNNFJz+/kwdJUZcOsvV+eoeF3krXkMiUFHoflpnF54udxsESaSI2e23qiFelz7Qk6Efc5rLaRrNqs3lZU+wuSimenV3FmMQ+KomygsGEaKCkqgu5OohN/yM3j8fHVhmzZx8dXcWRr8zjPgIvDO66/gsvI9b1EIPTJZAn/9uwc7tzSgbft5VBWdLgdDDIlFf/27Bw2x31kOJw2eAAXJR3Pz2WRLSs1lamsbvktFyWyTc/rYLEp7kWmpMDJMzXWUkE3Dy8BbqooaWBoqkbOZz12tlXdzSuB8IlIQw4WL8xmsTnuxY1DEaiGAY6mkSrJeGEmi1s2R4nutR1RlDU8cj6BydUSDMO0IS+TqyXgfAL7B5onOLVLy7IdguOyZrlBsTSFmM8Bw7D2lZKsYTxRJNYd9QgsdMPE2cW8TcThWRo+gYXAs0TzqjvoxC9t78APTy9jOb/W+enwCbhrRwcxjIZjaFxYKeL0Qr6GeLScswS3b9r08szXa4nfqyQiHge2dPpwaiFXWUQsdXGaprCl00csN1CStat61ZJUZqIeHtcPhvDMVLpOF+36wRCiBPpdAAAKOL9cQMzrQNwvwDQskdGyrOP8coFIzqQn6MTe/kBDT1lSVjNgtWRetyWOuUwZsm4l5z4nB56m0Btsnn0MWHihpZyMoxOpGoeB4agbIbcDs6kytnU3r9yfKilgaRoRD1+HnWMZmqgdEXE7cGohh5l02do0KrveTLqMUws5vInACWC1KOPQSBRPTaXqgNKHRqLEgrBVD+AT8zlohgGFo8FzDFiaxq5eP5F4a0HWkJc0dPgFePgK+5a2koHVokJcmfI6WHzw8CC++PgUTsxn7c939QSslixB4ucV2KvCPUgqvoD1bnkdDE7MF+tcC0hb831BS/z69ELeljViGRqioqMn4CSSiGlXSKqBiWQJSkW8uBqUQWEiWYJEoLnYLi3LdgiOm7AOlamijOWcarusCByNsMdBXKDsCTrhZGk8filTV508siVOvGaHPTyGY24MRFw12ElSr3LAqqSemKtnm+clDSfmssTWja3GtcTvVRIlScO7D/TbC35VBX1XZwDvPtBPboFFAcmi0tCqJllUiJIpjqHx7hv7sa3TC91cYzTSMLFnIGQbqjcbDAWMxj3oCjrRG3DZPqVzmTKCGRGkGqsXlwvo8gsYjXttzcG8qOLiSoGI1QxYJ/NUWcGXjk7j+FzWZl/u7g3gI7eNoCCpTSd/OVHFgycXG9pKPXhykVwbrjJ38qJah52LeQWiik9eUsExNEIuHtmyApOyplLAxYNjaCLxVp/A4sJKHifmcyis+/tLOQkOjsZhAlstYA07OZsu1zHm793VTZSkCyyNoYgbi1kRi9k1xrxPYDEUcUMg1LIMunn84NFxDEXd2D8QqpEf+sGpJfzB3c0Tp9pR8a3GgeEwZtNlzGVEe23pDTpx4zDZb1WUNbzrxn6bfbqegf2uG/uttnoLsZEMXN00IHA0KGpNf6+qYWcJx79ytCzbITjucVjFg/FEsSbBEVUaIzEPPIT+8qKioyfkRMjN1yR+ITePnpAToqI3/ZstZER89alZpEsKaLp2H5xIlNDpb57gBVhrqqqbEDi6pupL0xRU3UT+ZdLdvJb4vUqCZWl88fFJHNnSgbfu7UFJVuF2cMiWVXzx8Un8wd3biMZ1cQz6Qq4rnvRItOG6gk5cXCkgJ2mYTYtQKwyx3pATkqITM88cLI33Hx7EV56cwY9Or9ifX9cXwPsPDxKdShcyIuI+J47N1Hv1/tKOLuIT9EJGxOcfHcephTx4lrZPu6cW8vj8o+P45Bu2NT1uQVLrkr5qTCRLNclQM8GzNCaSBWiGiaCLtxc9RTMwkSwQtXkM01r0Rjs80A3Yc4CmgWxZrXMeeCmhGRYx5PLvaZnBZ6HdSFZDKEgqHqngWW8cDteIoz9yPkGkDxnx8PA42LpEpIrLjBBWvavVWQdLg6EpMCYNlqHgYC0Rb5LqbHXMy9vdTp4mrvgCwGJGxFSyhLjPge6g066iaLqByWQJXX4nRpt8B/KShqMTKbx9Xw/evr8H+bIKn4sDTODoRIpYzgTYeGaz18FitMOLC8uFGn28kJvHaIePqDpb1bI0TLMOi9aKlmU7BMdFzcCWTh9W8jImkkV7be0LubCl0weRsNo1niziG8/NY1dPADdtitYwkL/x3DzGOn1Nz9dagldtkBK8AEuCi6EpS9Zp3VZqwmp7k0pwtRrXEr9XSThYGj4nj28cm8flzMNWvD/bpQ33wmwWRydSKMqa/cLPZcoQWAa3jpH5iXYFnPjLR8axkpewOeaxgbIreQkPPDuHz9y3o+kxi7KG759cwKV1TiCA1VLWzQVs7yY7QS9kRZxbLkDRdCj6WkLCMxTOLRewkBUx1iSoVzfNOquuanAMDf1Kkv4vEixNYTDixguzWawWdfv3cnIMrusLELkBBN08Ov3OhmDxbV1+IumVgmTNJY6hayoI1cS6QFj1ns+IWMxZVbnLNRer15slN4iqgb39QRQkFVOra3prVUcYkdBarSRp2DcQxENnV/CzZHJDJI2q1a1NUU+deLHAs8QYv5yo4nsnFq9o2baDAJbgEVgomoGnpjINfytSTG47mM0eBwuOtvDHYQ9vQ1Nk1QBHU5YrRLNhAi6exlDEg7hfgKJZ3ZTlnITJ1SIxwaMdguMlSce/PzeHvf1BHBoJW1ImDIW5dLnyefMYRwANiYzrcdkkRMd2ELwAC0bRE3TizEIO67YBMBSwrdtPDKNoNa4lfq+SqC3Fry2krXp/VrXh9vYFcP1gyHItqGBnSLXhqmKYpUrSZ1Z2vZKs4ehkCvcmS0Qg/PmMCFGxfHlX8kUbN+Z38hAVHfMZsWkNs4KsYXq1hEMjYXQFnDZhYiFbxjNTaWIsVkFSUZa1mqQPgPVnWSOqznX4LMLISl6u82mN+xzo8JFVUguSij39QUyulrC8vNaW7PALdvLSbITdvNXiYmj4nVxNZYKiQISbEVimUtmyKl7rxYsdLA2BJferBqyq5OVEHFInAFHR8dx0GsMxDw4MrW16yaKM56bTuJ4A2A9Yic0T46u4cSiMu7Z31BBcnhhfJWLht0MQF7Da/Y2SPgCYWi0RtfurpKlGLGRSD2xgTRx+KOLaMHH4gqxhR48fRVnDdGqtmzAQdmNnj59obfE4WNy9swvfO7GA759ctD/f0unFPbu6yZLJ6hhdPownSzUFgJhXwFbC9rGLY+ARODw9lcET4yn78MMyNAIujthpyO/icGRrHI9dTOKHp2vZ0ke2xuEnII5FvQ64eas6V231spV/6oZJLORPUcBtYzGryr1asvesoQqz+5pX77W4alRL8Y28elvx/vQKHA6PRPC5hy7g5DqHhZ09fvzOnaNE+JaVvIR0SUFJ0esSFKqkYCUvXeVvXzlSJQXLOQkhN48uvwDdNMFQFCTNwHJOsjSTmgxNN/CO63txYi6Hx8dXbb21iIfHO67vhUZ40vMIbF3SVw1FN4k2001xD27bHMMjFxI1lVSPg8VtozFiSRNZM/APj01ie5cPNwyG7JbcXLqMv39sEp99886mx2yH1lbIwyPmE1BeLYGi1lwrTNOyBwsRtk9rEx+9Uk3X4OQZ4sSHoimL1bhStNpclWqPaVgVSopQvFbVDNy5LY7/89QsTqwTcN7VG8C7D/QReSCH3TwM0wBDUfAJLDTD+v0Nw4RhkgniAla7n6lsnpcHQ1NE7f71DhvL65w7WnXYaIeeqWkCv7iUxGjchwND4ZqW5GOXkjgw1DzOkWdpPHp+BbNpEX4nZ78Ds2kRj55fIWY1z2dEHJvJNpRIeW4mi9EOX9OJb8znwFDYjXNqHgZL24c/mgKGwuQSKX1BF/5mdrzuUDG1WoJXYPGOfb1NjzkUdWNffxAPnVtpSBghIXgBgGqYMHQDuyuFleraqugGdN2ESvISbEBcS/xeJdETdCJ+hVNH3Et+0k3kJXzpiSl0B1zY3Vur2P6lx6fwB3c7m96gaQqQNR03DoXQG3LZopWzqRKem8mAcM+DR+BQUjTwLA2msomatCWWW1I0eAgW/bCbx0yqjIlkCaKiw+JLU8iJKgIuDneMkQHbA04ee/oCeH42W3dtT18AAQJJG6/A4X2HBkHTFC4mCnabZ1PMi/ccJJNxACzdxYKk4bFLq3XXOIYm0l1sh9YWR1N43bYOPHhysU4X7XXbOl5RTgACS4NjaJQVHU6WgY6K7Iqmw83QxOQOUMADz1hJn7GutX9iLguOBj7++ubJHZmSgrt3djVkCt+9swsZQoxf3Ccg7hOwUpHHqEbVDjFOKFxbddiYTZcwt+RCb2cUfSF3Sw4bVT3TK5EmSCqpQTeP3qAbZ5ZyEGcuq6RGPERwh8WchIKsg2Pomkq8V+BQkHUs5qSmISSAVfXWjYo24hWuNxshN49bx6LIVSq/VSLOYMSNW8eiCBEeKPKyBo+Dg9/J1SVpHoFDXtbQvGYAsKPXj8nVUp0r1M7e5iEJ1Qi7HZhaLcPB0XByDMzKYUiWDEylSvilnQTAwQ2Ia4nfqyS8AofXjMVwaiEHE4DudMDJMXCyDA4P+4kXvfFkEWeXCle93uyi73GweN/BAfzg1HKNhttw1IP3HRwgbkeE3Rxu3hzF0fHVGgFfn8Di5s1RhN3NPwPDBE4v5JEpKTU4DknVcXohT1SVAAAHQ+NXDw6AwnRN8renL4D3HRqAg5DZnC7KYGhLD29NcgBIF2T0hcikLJwcaxMRLq/QWgLRBHprFa2tK20kJFW0gqTB42BweCRSA+o2TRMegSHG+KVLCm7eFMFQxA2KAkRFhcvBwzBM9IScRNVJj4PFraNRPFIRR69GddMjfQdWiwpemMtZUjug7PaZCeCFuRxWi81XvQuyhn8+Oo2hqBuHRsKQKweKpZyEfz46jY+9dpToXoeibtw2GkVB0uAVuBrGvNfJEldRAGs9HIt7IS1NYCw+BIZpDSQva0adX3k1EgWJSHOuJ+jEcMwNB0fX6RiSankWJdWWdOn0CzU6jgsZEUVCglc7BOfTJQVOrvq+RiAqKpy8VaV08kxLrkhBN489fUFkRdXGpAacHFwOMkzqfEbE8bkc3nRdV0P3IpKKJ2DNgZG4BzOrJUytlmvwsyMxD3HBptW4lvi9SqIgqVjKS/j28QWcnM/ZJ5KdPX5EvA70EDAPAQsIyzEUdvUEEPU6bOmVRF7GifksEVCWY2iMJ4rIlJU1qxsKyJQVjCeKuJlQtFLXTezrD1r4oOW1ZLUz4MS+gSD0K7RWrxarRQWrJQWqbtQkeapuYLWkEG2kADAQdePHZ5dx7+5uvOP6PpRkCzMmKjouLBdw62jzBJeFdBl/dllLvhpnFvP4zH070U2Q/HX6BYzFvTi/UoBhmnb7iKYs/08Sv+L1AtYbtem5BRZnlgo1QsO2y8piAbt7W5GzoTCbKWMxK9pevV0BJ3qCLqKNZDDqxg9P69jZ469JUnOiAknVMUiY9BQkFQxNXZHgQ4LHLMoaNMNAT9CFkJuDqOhw8SwYmsZEskgskeIVONy9swt//cgl/PziGhFlW5cP77ye3GKwHaFoBgbDnsY4x4iHSG+t2pa2LBbXnmFHC23pUAU720izkqEp4ipaO7CTlitS9X2lkSsa8HscUDQDZxYLGIk2X/UHrCRUUnT4nCx6Q64aK7yipBMlqSVJu6p7Ealc2npoQh1bvAVoQqtxLfF7lcRCRsQ/PT6FiytFOHnG9qm9uFLEPz0+RWQkD7QHKCtVPCTDbh4hF2+zbynK2mQkQskBSTfwwLNzODQcxuu3d0LSdAgcg1RRxgPPzGHsnuZbHBRlQtEMeAUONE3Z+oiGYaKs6qAospKfV+Dwtn19+MZzsxhPFlEqS3C7BIxEPcS+uheTxYZJHwCcnM/hYrJIlPh1B514095ufOf5BVxKFm0dv01RD960p5uI4OMVLGzM5x8dx8V1jOnNcQ+ObIkTff+eoBMRD9+witgKsN8jsHjo3EplwV9jzGfLGkqyjuv6Ak2P6RU4vPP6/sqmX9wwX2WPg4PHwcLFM3BwtI1JlVQdZUWHx9H8uJph4B37+/CvT8/g+Nza/Nrd68e7buiHZpDhXAuSiofOrcAjcNje7a9J/h86t0Ikk9Ou8AiW48NYh2/DXEaqsavHj01xr11FcnI0Mft2c9yL/f1BPDuTqWuf7x8IYnOcLJmqJihPTa7aVnACz0BgaRwYjhD9Tuur/hvJwA67eezo8eG7x+shH/fu7iKCZlRb/ZPJIhTdsNfAyaS1dpG0+qtRhSZslD7kRsS1xO9VEotZCZdWChBVizBRTVA4hsallQIWs2TYjr6gC387O475jGjLYlBUpfQ9myECymqGialkCXsHgugJuiBrBgSWxlymjGMzGWik/VPTeul/dGYFeVG121w+J4eBsItoMfXwHHZ0+3FqIVcnEbKj2w8PT/5y9oVd+LVbhitYpGTLWKR8WQVNWaf6y+260iWF2K/ZK3AYjXlxXV8QO3oCdnWKpSlsJjR+r2rj7ekL4MBQuKY6R6qN167Ts6wZWC3I8DgYUBQFJ8eAYy3B1dWCTGwtVf39N3LB7w4IuGEohCcnUljOafY74BFY3DgcRneg+eps2M3jbx+dwOmFPHhbBZ3C6YU8vv7MLP7g7i1E91r1AOYYGoHL9PVa8QAGrPk1my4hCR8urBTRG2otiVxf8br8XskrXiqOzaav4AqkI+jmmr7nmE/A//Oazfjx2WUommm3zzmGwmu3dRC1TtfHbNrCO68/qBwYIhurXQzsdEnBqfk8kpdVPZNFGacW8jg4HGn6OciagYVMGcmCXOc2dblFKEl4BY54rrcjriV+r5IoyirKig6GpsCzrJ346YaJsqKjKJNt+nlZg5NnwTFUDSs27Obh5FkioKxJAUe2duCxS0l854U1yYGRmAdHtnTAJCR3uB0sMmUVkmo9h2pIqo5MWYWbyK6KwW2jUWTLKiaSa5Wp3qALt41G4RVaww5tJBbJ7+LQHXRiKSdhLrO2kXgF63OS6ixgbVCPXkhiMOKqw7c8eiGJXoIkbb023pWukyyE7Tg9K5qBDr+Ak/NZpEqK/W6F3Tx29gReNlulRhF087h+IISpZAkXxDW4Q4dPwPUDISLCQKas4cJKAaZpYk1e0ARNARdWCsiUydpc62VyGlXRSCWoZlPlmkq6x5W3Kqn7+oiEloH2HCoWMmJd0gdYhJHvn1jClg4fUZfGI3CQVB3nl4u2T+1oh4eo2luNqo5hqqTWJL6pkkqsY9guBnYiL+PUYq6h29SphRyR2HJesvYPB8fUJH4OjoHbwRJJD72S41ri9yoJr8CBZ2mUFL2uMuXmGeKXKFORSIn7BHT6nZaUA03BME0s5yRkCCRSPByD4/NZTKdKNQnadKoEn5PDXdvJmLIOlkZXwAlJ1WvK8TxjfU4iYl2ULUbw9QNBHF4nMqpoBkqKhqJM1pZuRwyGXfA6GFyULN/LahQkFZtjbgwSbnoLGREBF3dFfAuJe8mLbeqkmz6w8adnhqZwfjkPVTctzcEKjELVTZxfztfM4WZio50gAEvS6PmZLG7aFMFdOzohqRbcoSipeH42i30DoaarHSVJhcAyUDWjBifL0BQElkGpBcJAUdKQKEh1Xr0xr0BI8FHx1aemcXQyVSO9M5cpQ9EM/NYdm4jXwr6wC798fR/Gk0Xkyyr8Lg7DUQ9xFS2Rlxs6IgFW8keSoNR+/7V9YLUkwzRB/P2r1dlG0Up11n6miQKSOT+iATdGot6WKpOW9zGQFdU69xKaoojEljXDxGSy1JA0M5kskXepKrGRVoAbEdcSv1dJRD08NsU8eHY6U/O5qhnY1ONHlFDDDBRQUq6S3BDseUVFh6RafomXSw5Iqo7i1f57VxtX0nDzpihyolLjtLEp7sHNm6JknrKiipOzOdwwHLaB7U6eRbqk4OmJFHZ1B4jutR0hqgaObO1AQdZwbh0Te0uHF0e2dhC7QbTD/7MdLMF2hQETQTePkizWXQu6eRgEGIKCpOK7xxfg5hmMdXhrBIG/e3wB7ztEJr9TlDRohonHLq0iL6r2pudzchiKeIjeAbfAIS+pcLAMXDxlSxpphmlVQgg3qLCbh4OzvLTX35dHYNEbchJhsari8I2kd45OpvD6nV1E4vDAxifq7XCDWBPH12uSHsM0W/r+7TqoVZ/pck60MX4dfmdLh5+o1wGOobCSlxvKBJGILXOU5YB1JZcZrgWl5XYcAFuNV87qey2uGilRwVv29kA1TJycy9rYnp29Abxlbw9SIhn71MUx6A9vrFcvQ1MoSBoCTq7u9FSQNOIKCsfS+M7xeYzGfbhxKFKjOfid4/O4rm9702P6XRz2DATx84tJTK/z6h2IuHFwOEzcPm1HpEsK/vPUEg4NR/BLO7rsak+ubH2+hQDjCaz5fzYKUv/PduF72hGpgow7t8Txs4tJTK+WQdMWo7k/4sJtm6NIFRpXQq4WVcmNKwkCk3pA8yyNqVQR5csOT2VFx1SqSOSr7BdYXNcbwLHZDNbnIgwN7O0Lwk+YpGdKCkbjPlxaKdYkDlGPA6NxH5E+YLIg1yV91RAVA0mC3wpoj2Vb1OuAV2CwKeZF2MPbupurRQWXEgWiBCVZkJEXLeefy5Me3eCIv79HYMHQVA1jfr1zCWl1dqOfKWCpEOzo8YFeoEBRsPcX0wS2dZOpEDAMhZsr4teXE0Zu3hwFw5DtWe16Bq3GtcTvVRKyYuDLRyfxqzcO4f2HBiraWCxEWceXj07io68ZIxq3HV69EY8DWzt9OLOYw2p6bcL7nRy2dfsR8ZAptjtYGiG3A8/NZOqukfoVV1XgL63UahleWinAydFE5Ja2BQXkRA0Pn09e8TpRmJZsRaMN1cmTMRBfqTIGjcLv5PDMdBodfgFbO312uz9dVvD0dBp7CBh9kqrjifFV3DAQwmu3xuus1UiqqID1DoRdDkhqGQLHwqhseopuIOxyEL0Dkqbj7ft6oZvAqYWcbSu1o9uPt+/vg6SRVehX8jK+d2IBd+/oQsDFoazocDkYZEsqvndiAaNxL0abbHXyL6J/+WLXrxTVVmcjPCJpq3Mo6sY79vfh/zw1XVuh7/Ti3QcGiHQMrUO1WueGohumLfVDEj1BJ/b2B65AROkkOqi1q32cKSk4sqUDKzkZZ5fy9mF9a6cPR7Z0EB0ohqMefO3pWYx1+Grkl5IFGdOrJbz7QH/T9wm07xm0GtcSv1dJ+AQWd+/oxr88NWMDsSmKwmjci7t3dMNHeCrvDjrx8LkE7t3VWQfsn89IRFIePUEnxjq9cHJ0nel7f8RNXO2p9SuuT1KJWr1tUoFvR7SjOgtY7cyraZiREAaAV6aMQaPwuziUZR2/qDiXrPcA3tsXJJY0es3WOP716VmcXOeGsbMngHfd0EcsaZQtKXjjdV34ypPTddIrb7yuC1kCTK6DY/CN5+Zw00gY9+3pqsAdGCTzMr7x7Cw+dhfZoVI1DNw6GsMPTi/Vi1iPxqASyMTEfI6rvgOkNmBFSbuqXzHR2lJW8eRECpmSWmOvlilZn9800rxMitfJoi/UuCXZF3LB6yTf0i8uF9AdsA4/67XxLq4U8JotzeOyq89MUjTkJBUSBOgFBX6BhcCTCS0DgKjq+OaxeQxF3Ng3ELIVA9JFGd88No9fv3W46TFjPgEfvmUYn390vKawsDnuwUduHyHGJLYT69xKXEv8XiXRG3Thbx4dx1JOqgGgL+UkPDWZwht3k6UnXoHDraPRhnprH7lthGiTble1p9avuN4GjMSvuJEKPM/S8LegAt+uaEd1FmiPw0A1XmkyBo1iOSdjZ08AWbGW2T0c9WBnTwDLORlbmqxMOTkGX3t6FhdXCpWDlLXpX1wp4GtPz+IPXk8mkcKwFL74+CTiPgG/enDA/q1m0yV88fFJfPKebU2PqaoGDo5E8PD5BCYSRRtGMhzz4I4tMaiE2NG4T8AzUyl0BQTsGwha1o0MjYVsGc9MpfDabR1Nj9kddOINuzrx3RcWMZ4sQgcDRTMwHPXgnl2dRAdVwGqhz2VK2NbprxOyv7CSJ2qhX0wW8fOLSYTcPIJuvgby8vOLSbxxT3fTupscReHunV0NbQvv3tlFjEVbyIjoCbrw0NkV/CyZrLEsO7I1TgRN8AgsMiUFZxZzyImqbdnmd3LY1uUnxvmKqo50WcGpxVwddrQ74IRIeKja1u3Hp+7ZZpN7fC4OIy2Qe6r3BFyZ2f5yYZ2vJX6vksjLGtwODi6eqXuJ3C1Upqoiq24Hi81xz4aJrLaj2lMV8L2wUtywBKXKPLz8pL9akjEY9rT8Ym6k3lh30ImHzq40THzTRYV402tnW/aVxmZrFAVJxXeOz2P/YAg3bYrY82oxJ+I7x+exi8CrMyOqmE6VoGoGSpfpgk2nSsiIZEzZgqRhOS/j7FIBhrnW8qcpy9WBxLYu4OYxlSzhddvj8Dt7bIJTTlRwcamIO7c2n6ABgGGYODAUwU/PLePR88l1CaUbr9nSAYOAKVnVnNzdG8D2bp9tA8bSNLHmJGB5K79+h5VQXS5kf/fOLiJv5RfT1STR3XRwDDwOBodGIjg4HIGi6+AZBhQFeAQGDsLDX5XgNZcRwbNrY8xlRGKCl8/BoiiryIoqDMOiDOmGiayooiir8BHaFmqGiXRJweGRCDr9QkUnlsFiTmxNJxZW5a9VLcT10RN0wutgcGK+WMds39Xjv2bZdi2uHtXK1L7+oFU2VzQIPAu/wLVUNm8nBmGjqz3tcIMIu3lohtGQJagZBhHzsBobrTfmFTjcu7sbT4wnLQcUzQBNU/A4WLx2e7SlhKodiforkc3WKEJuHiYovDCTxQtU1q7MwARMkNlglWQNoqJDr1ROqqGbJkRFR4nQBq2s6IhUbK/y66sdDhYRj6OO9PFSIuzmsavXj/88tYzp1DqCU9iN1+/oIH4HDNPEsZk0plfLNZvx9GoZx2bSuG20eevGgqTi4fMJuB0MeJarsQF7+HyCSHMSsBKfc0t5ZMpqjZB9pqzi3FIe1w80303wuzj0BJ1QNGstMStLDEtT6CHU3ewOOvHd4wsoyhr8Th6MSYOhKeREBbMpE3fv7Gp6TKA9BK/ZTBm7egJIlSyrzio8aSTswa7eAGYzZSKnIY6m8OY9PVjIlpEtq1B1ExJrgGOszzlCnGO74sBQGKcX8zi3lLc/297tx4Gh8Mt2T9cSv1dJVCtPAs/CwdFIpYoIe1u3v2kHtqVd0Q43iOrJUdaMuvbp4U0RYiPxdumNFSQVz05nMJMu2wlKf8iFnT2Bpse6PDYyUX+lstkaxea4F9f1BXB0fBXSuramwNE4OBIhssFyOVjwLI0bh8PoCTqtNidLYy5tude4CKsdXgeLREFC2MOjN+SyyR0lxdLL8xKMmy4pODmfx2JOrNEIXcyJOLmQx40ETgiAZc94drEAgWPg5Bk7mTJN4OxigcgDeL0weCMbMNKDalnVMZ8pw+Ng4a1YjVWZovMZssRnMOzCYMSFX1xarUnSfQKLmzZFiHU3N3d48f0TSzg6kbY/6w+7cM+uJvEI66MNBK9MScG/H5vD3v4gDg6HoVRgNItZEf/+3By2EaoQ+AQOgIlnpjIYT6wVAEZiHrzpOmfl+isjFjIiHnh2Dr1BF/b0BWtIIw88O0dstdpqXEv8XiXRLnmMqjxEo4oXqTxENTa6zdcON4iCpF0VN7g5RuZ92Q69sUReqqt2AkC2nMPnHx3Hp+7ZtqFtilZiPiNitahgKOJqKA/xcrHZrhRvvq4bmZKCE+ukkrZ0+vDm63qIxou6eXz4liHMpMswYe2bpmmREz58yxCihFU0t4PBbaMx/OxCElPrPIvDbh63jcbgdjTf6muHEwIASKoBgW/s0OER2Jok+6VGu8DymmFiKSfVELyq4XdyRO1DSTWwrcuPiytF5KW1dzbuE7Ct20/0/eczIo7NZBuuV8/NZDHa4SN6r9pB8HI6rN/4ifEUANjwpPXXSaIgq/jZhSQGwi4cHLZE93mWxkKmjJ9dSLbkq7vRe1YiL2MiWQJQT8apXid5t1qNa4nfqyTaZX/jYGnEvELDMn/MKxDJQwDtafO1Y9FfbyR+peskkSxY4qKHR0IIuR2QVANOnkaqKOOZ6QyR3tZ4sliX9FXj4koR48liS4nfRi56JUnDlg7vFXXsSi1Ukjd6cb64UsCnf3AWb93bizfv7bZkRyqs1k//4Aw+8+ZdTT9XnqHhcbA4MZetk/LY1ukjlh3xOjiMxr1YyUtIFGQYpoXvi3kdGI174SWw7ao6IeSv8JuQCA0D1jPoD7nQ4RcQcvM2uSNVkrGck4ieQbuEwRmaAkVRFT08s+HnzcZKXsZ3ji9iZ08At43F7IpXqqjgOy8sYmuHv2k5m6KkXXW9Ik1820Hwirh5bO/24/RCru7a9m4/IoSHn5yoYmuXH09NruLnl1Zt+aGhiAsHhiINk/eXEu3YsxTdAEVZlXq6MrfYyj8Lskb8brUa1xK/V1HU2d/4PRiJtcY6aodESrvafO1Y9NtVSXWwNF6/oxM/OLWE88sFu8011uHF63d0EiXU7QCLV6OKR7zcoJ0Uj+gR2Ku6gZCeytuxOKdLCgqSji89MQ2gvjKRJpBIUQwT33phEemSiphXsFmS6ZKKb72wiF29ZN9f0gz86PQytvf4sH8gZFXVOCuZ+tHpZdxAgBuKeh1XbfORCA0D1u9yx1gcPzqzjF9cWq3BDr5uWwfiBNIr7XpfXZzlsBJwcQi7HVB0HQ6WwWpRRrasEkklKboBVTfx/Gz2itebjXYlvu0gePEMjfceHMBXjk7XJH/bu/1478EB4sOPwDF4cmIVlxK1h+CLK0WYJnD7GBl29PsnFivteXeNrNn3TyziPQf7iZ5B1OtA1MNjJl3vXtMfchG/W63GtcTvVRTtsL9ph0RKuwgj7Vj0qwveU5OrEFXDbkkKLI0Dw83rbFUj4uXx8wtJnF+uFYY+v1yAm2dxx5ZY02P6XBw4hsKunkCd5MSJ+Sx8hC4jVTzi8bksVN20/JoZCqmiTIxHlDUDiULjtnyiIEHWmt/02mWD9mLm9i92vVGkiwpW8hLyolqzwfOMBcZPF8mcdhTNQFfAiZKsg2NUaLoJVadRknV0BZw1GL2XGkNRNw4OheugCU6exsHhMJHQMGC1D8+vNMYOnl/J457dzRMR2tX5cHIM3ry3B185Oo0fnV62P68mKU6CxK8dCXVP0IkuvwCBo+sgFJJqtMQS3WiCV1fQiZ+eS+Dt+3rw9v09tkQKTGB8pYiDw2TkBoqiMJ8RbQzm2ufW3kMRSNosZkSMxD345yemcHI+t+aM1ePH+w4NYjEjYpRgz+r0C/AJLNw8i6BrTdJH0Qz4nCyRy8hGxLXE71US6ze9LZ0+5Lwc/B5roW9l06tKpDRqHZAmU9WTzZW0i0jbEe1K0gBgNl2uq3YdGCIeDom8jJKiwSewdcDukqIhkW9eG24k6sHb9vXgu8frJSfetq8HI1EP0b1OJks4MZ/D5GqpTsSaoihMJktN4xEVzbgqZogkQWmXDVpXQMDu3gDcDsZOnhwsg4VsuZJQNb84S5rF3KVpCjxoeyOhaUskndQNw8HS2BT34OFzCYwni3YVbSRqae6RVJK9AodfOTAAnqXrK777+4jfq1RJgaya6A26bCkLhqbA0hRkzUSKkDhVTVBm0yXMLbnQ2xlFX8jd0vsfdPP4q0cuQVR0bIp7azT3vnd8AZ98Q/P6iO1IqL0Ch9vHYhuqu3r5+BuFvfUKHN6wq6uyXuvIFUUYsJLsN+zuIr5XhrJY0QtZCZpu2O8Wy9DoDghgCBI/RTfw5cen8PxcFua6bPLYbBamOYWPE+puZkoK3ry3F996fh6zaREmTJgmheGoG/ft6SFyGdmIuJb4vUpiISMi4uErJxpAVHSoUGAYpvUSEG567UimrqSNV2UKt6qNN5ks4WKiaDuCbI55cGAoQjRWtS2dKqkIuNYwJ6mS2lJbOltWsVpUEHDxiPsFGAZA04CsGlgtKsgStGWdPIP5tIicWCs5kRMtzJuTJ9PwShQkzKZL2NXjR2/IZTNQZ1MlnF3KX7Fyd7XwCCw8AotNUY/t3sKzNHwV1X6SOVDVGrtS+5jUBq076MSHbh7CXz9yCT89l7BlJ/b0BfBbt28i0kf0CiycPINiA29qJ8/AS/gO8CyNJydSOF9x76nG+ZUCXDxDJIoMWMnUr90yvKHYyeo4Ds61oQdAwFq3xuJeSEsTGIsPgWHI5n41UiUFNEVDNQyIUu2aRdM0UZLajoS6Xbqr7YzZdNmWtHK7BIxEPS0dqoNuHoMRD2TNQEHS7HXQK7DERJTVolKX9AGAaZp4fi6LVcIKvaTqmEuXEfc50F1h97MMBU03rM+vtXqvxdVCVnWUZB1PT6UxnSrZzh0DYTeCrg7IhGrl1djIile7tPFqJVLWxk4UJBimSdSSbFdb2ufioBsGtneH0OV32m3ZhayIJydWidqy8xkRyaKCmNeBoIu3WrI0BY6hkCwoxPdKU8Cd2zpwdDyFRy+siQIPRz24c1sHSGSxqsKlpxay9dWOoTBRJbkdWmPVeOxiEg6Wwf6BoA3CZ2kaj11KNl3tBCzM2A0DYTw9narD9twwGCa211vOSSgpFv6oIK0dHjwOq5K8nJMwRiiTsdG6m9XknmPomkPV5ddfCVFNUsc6fBuapG50Ql1drxo905fT+7VR1ByqnRz0ch4Bp7flQ3XYzYOhgaCLR9jjqGGhMzSI9peCpIKlKah6PXubrfgjk4RqmvjeicWGFnuDETd29QWIxm01XjlvXoNQFAV/8id/ggcffBAcx+Etb3kLPvrRj4KiKJw9exaf/OQncfHiRYyMjOBTn/oUtm/fbv/dBx98EH/xF3+BZDKJw4cP44//+I8RCoUAWFn85z73OfzHf/wHDMPAW97yFvzu7/4uaNpqk2QyGXziE5/A448/jmAwiN/+7d/Gvffe+7I8g2popokfn13G2aU8VN2wqxJnl/KgKOC6/gDRuO2oeLVLG68dEim1fpL1lSnSBX9z1IMP3jSEbz2/gB+cWrJPpcOVzzcTtGUzJaWh9A5gJUUZAhICYDHOTs5la+zKAGAiWYTHweD128mqSFu6fLi4UsREuWiTG3qDTmwlrMy1Q2sMsDbTgqwj5hNQlFWrMuHk4HFwKEg60Wbq4BhcPxiCZhpYzEr25tQVEHD9QIjYYaGsaphLWy3vTr9Q05KcS4soq68c3c12ETHaEe0iTWx0VCvI/SFnQ5mkV5LuarsO1emSgoPDkYaQj4MjZPuLx8Eh4GSxozuAzoBgdz0WMyJOLWSJcL4AUBA1LOVEHBoJoyvgrLEtPDaTQUG85tVbF//zf/5PPP300/jiF7+IUqmEj370o+jq6sI999yDD33oQ3jDG96AP/3TP8XXv/51/Nqv/RoeeughuFwunDx5Eh//+MfxqU99CmNjY/j0pz+N+++/H3//938PAPjyl7+MBx98EJ///OehaRo+9rGPIRwO4wMf+AAA4P7774ckSfi3f/s3nDhxAr//+7+PwcFB7Ny582V7FjlRw7mlAhiagoNl7Y1UM0ycWyogRziB2vFyVrXx9g8EceNQCKJqwMnR9r2SauMlC3LDTR+wkj8SiZTL/SSr0aqfJMfSWMiIyIm1yVhOVLCQEcGRyORQuOr3B6FgvagakDUDPieHgqjamBmvk4OsGRAJ9cYeu7CK3pAT27v9tnBpTlTw8wurRHpjVa2xREGqsT9iaAoxn0DU4gHWkn+OoWsqE1VRYJLNtGqvt7XTh53d/pp3YLUot2Sv53exGO3w1G0kZ5fyr6g2XxVG0ogtvhFWgBtlhQisVaifmMgiL6r2+upzcjg0TFahBjaehe4V2KvKJJFCCNoR7dJcbMf+0h0Q8N6Dg3ji0iqemkzDME0wFIWox4H3HhxENwHOF7Dca966txePXEjgp+cS9ucjMQ/eurcXhkl4Wm0xXjmz5LLIZrP45je/iS9/+ct2wvX+978fJ06cAMuycDgc+L3f+z1QFIWPf/zjeOyxx/CjH/0I9913H7761a/irrvuwhvf+EYAwGc/+1ncdtttmJubQ29vL/7lX/4F//2//3fs27cPAPC7v/u7+Mu//Et84AMfwOzsLB599FE8/PDD6OnpwebNm3H8+HF87Wtfe1kTP0nVIHBWy6FU0d2iqIpemMBCIjzpt4OI4RFYlCQNeVGFR+Cg6gYYmkJRUlGqtDtI4sXo/yTyAFU/ycu1n3It+kmOJ4t4diaDLZ0+6Abs9iFDA8/OZIg091wcg/6wq2G7sz/sIm4f0jQFlqHwazcPIeTmUZZ1uB0MUiUFPzy9BJqg15spKbiULFyxOkdSnewJOtHpd2AlL8KoJH4UBdAm0NlCBakd1Z52gfBjHgfef2gI//7cHB46u7aRjHV48f7DQ4h5Xh7M0NXi1tEodvYEkJOsll+wwuwkjXp1g3LL6gYAsCnuwbHZDObSa2tBzOfApjgZaaodslYhN4/Hx1cb4lwfH1/Fka1xonttR1TfG1U3UJRVSOCREzV7byHdBzwCi1xZxfG5zIZhyINuHnPpMhJFCYYJWFKOJhJFCXPpMvGhMu4T8NRUCvMZETyzRvKaz4h4aiqFu3a8DOrNeAUnfseOHYPH48H1119vf/ahD30IAPAHf/AH2Lt3r03bpigKe/bswfHjx3HffffhxIkT+G//7b/Zf6+zsxNdXV04ceIEeJ7H0tIS9u/fb1/fu3cvFhYWkEgkcOLECXR2dqKnp6fmerVa+HKF38lZ9HVYlYnqBDJhiYz6nWQbSZWIMblarDvpDkXIXqKwm8e+gSAeeGYWFxJFW2BzNObBO67vI8b4xXwODEfdCLr4OjmTTFlBjEAXbDZTxr7+EIajbrAMbVdQVF1H0OUg9pPMl1VQADp8TkS9PERFhcvBI5GXsZKXiTT3BI7BkS1xHJ1ctduHLE2hMyDg4FAEAmHiFxA4fPDwEL5ydBovzGXtz6/rDeCDh4cQIElSKMu5wCc0Fi4lrU5WfS/PL9fqgrXie9mOlmQ77AUBa4M6OZ9FpqzAJ3AwYIIGhUxZwcm5LO4iJHcAljPMeLKIfFmF38VhONqaRmi1KvfXj1yq0bKrkmaCbq7pZ9AujdDJZAn/+vQsRuM+3DgUqbHW+tenZ7E57msaRtKObkqqpICl6TrIg5OnwTJkJJT1sZHi6NUq6on5IjTDgKIaKOsS2BKNXT1+4oNaOzDkSzkJlxJFpEsqCpK6jjDC4VKiiKWcRPRcdcNESdattW/d5wxNoSTrNWLh/zfjFZv4zc3Nobu7G9/5znfwhS98Aaqq4r777sOv//qvI5lMYmRkpObfD4fDuHTpEgAgkUggFovVXV9eXkYyaYHX11+PRCxGaPV6o7+7srLS9HfQ9dYIF+tDYGns6w/i6ESqhhEacHHY1x+EwNJE/72Qi4WsaljIilC1NWp8QdbQ7RcQcrFNj5spyXhmKo25y8acy4p4ZiqNff0BhN0kmkgOvGN/L/7pF5P44ekl+/PtXT588KYhdPodTd9rXlRgwsRsWkSi4rbB0BRiXgcCLh55USF6rn4niyNb43jsYhI/OF2CrutgGAZDETeObI3D72z+uXb6HbjgoNEXdKE/5LI3J9MEXA6a6PsDAMdS+OpTMzi5kAPL0KiueicXcvjqUzP4o3u3NT2ui6Oxo8uHJyfTyEuqjUn1CRxuHArBxTU/X+cyZTzwzCx6g07s6QvU+l4+M4uOgIDRWPPVGRdH4027u/CtFxZsbTjTNBH38XjT7i6ie51Nl7CQbUxEqV4fI/AAXsqJSJVkhD0OwAR007RZw6mSjKWcSPRunVsu4G8aVCd/87YRbOkgg2YsZMr460cu4fhcDgxF2evA8bkc/vqRS/jUPduwqcnfazZdQrIgYTBsWQHmK7JWFsatTPxcEwUJBUnDczPpK15vdg4URAWmeWWYRIFgbSmICtwOGqNxL0qKBk0zwbIU3DwLjqGIxqzGfEbEheUCRE1HUdbhdbCYS5Uw2uElStIMw8CB4TBm02XMZkTougkDBnqDDtw4HIZhGET3ulqUcHgkbP/m1egPuXB4JIzVotT0O5AoWJU9UdVBUxRMypqrYoWVS/L7A4CsatgU84ACkJNUm5TpFziMxDyQVW3D8oRmxnnFJn7lchkzMzN44IEH8Cd/8idIJpP4xCc+AafTCVEUwfO1WT3P81AUq30kSdIVr0uSZP95/TXAIpO82NjNxKlTp5r+O1eKjCOGgbALmbKC1aJiWzVFPDwGIm5kimUcPz7d9LiyO4axTi9mM+Uaw+vekBNjnV5MLKUwX0pcZYT6yLq68dC5ZQScPKIxh316KskaHjq3jHt2d6K4ON70vXIuH352rgBJUTEUcdlJmqSo+Nm5BUSQh1rONzWmz9eDR84tV/wU12IqWcBqQcSBgQCOHz/e9L36w104Np3CpZW1+9F1HZdW8nDzNO7a7Gt6XM7lw3MTBTw1lUZ5ncG9y8GiUAph0CE2/f0BIC104VKiCJrCOn09EzxL4VKiiLl0GaUmfy/T14HNHR6cXcpjJb8mB9MbdGJzhweaLOL48ammxkxxMZyZT13x+nwyD5FgXgEATdM4MhhGQnKjIAfgFVjEHAbSc5ewOtM8xjEJH1KpK9/r3JIL0tJE8+NyMaiaJbtkAvY6QAFQNQZLmSK0xGRTYzr8Ufz5Y/M4v1Q7d07OSPjzHyv46M09kHPJK/ztK0da6MLxuRw03cD6wgZNWckfybxapfwYDHJ49NwSplNr7+xA2I3bxiKYX14leq4mF7P3hobXNbXp91XiAledA1LRg+PHZwjGTINmWCimhWvTNQqmLMLQNaIxAYB3+7Giu/AvT83i3FLenldbOn143419yK8uQynVW69dLQxvHJeWRMQ8PLoCAlTdBMdQ0DQDF5fy8NAq6ELzBZUkfHjq7AKObOmGg+9ASdbhcTCQFBlHz04jzHVDbHYOODqRLcugQNne2gBg6CayZRkwDKJ9QOaDkGURXQEBEc3qUvEsDZ6hIMsi5FKe6PdqNV6xiR/LsigWi/jc5z6H7u5uAMDi4iK+/vWvo7+/vy4RUxQFgmCVYh0OR8PrTqezJslzOBz2/wcAp9N5xb9bHbuZ2LFjR8v6UtV4cjKN7xy/iO1dfmyOe+2XaCkr4TsvLODGoZ3YvWV30+M+fD6JB56dw97+IA6NRGydocWsiAeencP27h24YXdz4373xBJExUBZkex29Pp/5kQNtzY5JmBplSlmGYMxX91pVzEpMP4otm1uToPmxHwOWbHWoqsaWVGHg+ewi/BefU4eMZ8TRVmzT3oeBwufk4fJO7G7yXHPrxSgooydfZH67w+y7w8A3z+5hJKswcExcPGUnahrhomSrKGk6Li9yXt9YS6Lrz19HmMdPhwcjkA1DHA0jflMGV97ehb737676e//yIUkvG7nZc4lDBJ5CSfms6BYDru3NTdmNeYzol3xS6fTCIVCFdzYJqJqx/mVAsLhK1f8ejujGIs3/1sdnUzjUqIElqHg4Vn7pSrIGjJlFW6n0PQzeHoqjelM4zVuOqMgD2fTawBgrQO6YdbB+Uyg0vJvfl5dSBTxbycuYLmkw+FwQJZlOBwOLJd0HJ0q4HdfN4rR2GDT93ohUcRoV6imglSN/pALPVEfRmPNOY0UZQ3HVrSag0814j4Bu0Z64HEMND3mL+ZkPDmRgqiudX6cHIMbh+NEYwLApUQR//KD83huOlPjqvPcdAY0ReH//0tj2Lqpued6bCaDH5yZxtRqyfrNK10PCpaUyXX9Y02vAQBwYaWAAyqHh84l6ip+d2wdQF9nAKNNvltPTWUwEvPV6WNSFIWxuA8ugSfaX1eLMmLjJTw3k7axgzRl/W9ffwib+zoQ8fQ3PW6j0HX9JRebXrGJXzQahcPhsJM+ABgcHMTS0hKuv/56rK6u1vz7q6urdos2Ho83vB6NRhGPW+DXZDJp4/iq7d/q9Sv93WaDYZgNS/xoikLE68BzMxlQs7ATCdMEekJO0BRF9N+iK1isJ8Ybn0xpCk2P611HiDAv+2f1Osm9lmUDFEWDZwGerf/7ZdloelzdALZ1+XF6IYt0eQ3bEXJx2Nbth240//2r98IxjN0y1nQDLGOd9DiGIbrXdnx/APA5ebAMDamBDh7H0PA5+abHTRZkBN3WfH34/FrF2O/k0BtyIVmQmx4z7hVw1/ZOPHI+gR+us9UajLhx1/ZOxL0C0fcvSCq+fXwRiYJSgxtOFBR8+/giEW6sL+RGh995RdxgX8hNdK8sTWFHjx/PTqWxmF1LKHwCi/2DIYvl3OS4eVHD1UCXeVEjule/k4NpmhA4BhyzdqBQdROyqlu45SbHVTUTiaIMgELVoMH6zSgkijJUzSS6196gC2/Y3YXvn1iqY8ves6sTvUFX0+P6XQzevKf3iv63flfzmGSaNrC1y4eJZKnmPmM+Adu6fKBpmuj7L+ZkPD2VrrNSlDQDT0+lsZiTMdbpb2rMvKRhJS/i/YcGEPU5UFZ0uHkWibyEB56dRV4im1cRj4DHx1OYSYtYP29n0iIeH0/hyNaOpsd1cQwObwpD0fWa7s9QxJIgc3Fke3murOH6wRBOL+ZwfG6tYrq7148bBkPIlTXE/eSEJNJ4xSZ+u3btgizLmJqawuCgddKYnJxEd3c3du3ahX/8x3+0cUOmaeL555/Hhz/8YfvvHjt2DPfddx8AYGlpCUtLS9i1axfi8Ti6urpw7NgxO/E7duwYurq6EIvFsHv3biwsLGB5eRkdHR32dZKTyUaGbpi4eVMUj11MYmq1ZBvJD0bcuHlTlBgk6hMsEHdOVOHkmRpdML+Tg48A2Bvx8tjdF8ALDczJd/cFEPGSkTvawb70CCwoAEMRD/pN06qkshQYUKBMcv0unqXXae6ZkCSpUlGhUFZ18ARyLu3SGuvyW9py81nRwopWpAwkzUBPwPIGbTZYmsZksoigi0fM54BpABQNlGUdk8kiWLr579/hF3BmIYfZdNmuJFGwxMfPLObwzuv7mh4TaA8Ivx2m94BlBbe104fp1TLy0ho0I+oVsLXTR2QF92Ji4qQe0HGvA4eGwzg+n6s5+bE0hX3DYSLXAkUzsCnqRdjDw+dkISk6nA4GubKGVEkhsgIErN9rb18IakW+qErGcXI09vSHiH+vvrALv3x934aRZuYzIo7P5XD3zg5QoFCULTFvEyZemMsRySQBlszUlfyzZc2ok6V6SUEBv3PnKL729CxOzOfsxH9Xjx+/c+coMcGrSnDpDgjQDNNWomBpipjg4uQZJPIKtnT6ajpf6ZKCREEmdkVKlRV84ecTiPsEvPfGfrtTN5su4+9+PoE/uHsr0bitxis28RsaGsKtt96K+++/H3/4h3+IZDKJf/iHf8Cv//qv43Wvex0+97nP4dOf/jTe8Y534IEHHoAoirjrrrsAAO985zvx7ne/G7t378aOHTvw6U9/Grfeeit6e3vt63/2Z39mJ3af+9zn8P73vx8A0Nvbi8OHD+NjH/sYPv7xj+PUqVN48MEH8dWvfvXleRCViHoduLhcwHW9AdyxJQZJsYQ700UFF5cLeD0hLTzg5PDBmwbxz09M49TCmjn1jm7LnDpAwhY2gV+7ebCihwRbDJOCabEvCYlM7WBf+hwsOJbC6el8jTq7V+BweCRMLOfiYGnEvEJD6ZWYVyDyVG2XIG530Ilfu2UIf/7QRRybydhzYG9/EB++ZYjMssxpMQIvx04CwHDUDa+z+eeaKingWQav2RJH0M1XPHVppEsKREUnZjTWiuLSNT7YrYjibrTpPQAYBvBPv5jCDUMhHBoJ2xvUbLqMf/rFFD7fe13TY45EPdgc99QQO6qxOe4h9oDWdAMfuGkIf/3IOJ6bydif7+sP4gM3D0PTm0/SvAKLXb0BfO/EAi4sF2wVgtEOL+7Z1d2Sjl1f2AWBi20os3mjdfxKkobBsBv/+vQsLq37vTbFPXjDzm6UCOeqh2dBU0Cj+gFNWdebjS6/E1/42SROzNdiA0/M58DQ8/ije5v3Pwas91XVDUyulpApKfZ6FXTz2N5FJrpflDTsGwziuek0zi7m7QStO+jEvoEg8RqQKas4v1zA+eXCFa+/HPGKTfwA4M/+7M/wx3/8x3jnO98Jp9OJd73rXXj3u98NiqLw93//9/jkJz+Jb3zjGxgdHcU//MM/wOWyXqTrrrsOf/RHf4S/+qu/Qi6Xw6FDh/DHf/zH9rgf+MAHkEql8JGPfAQMw+Atb3kL3ve+99nXP/vZz+LjH/843va2tyEajeJ//a//9bJq+AHW5vz2/b34p8encPqFnF3x297txwcPDxILwgbcPJ58KoWugBPbun32RpIpqXhyYhX7B4JNj8lQVlsnV9awmBPtE1mX3wmzUk0iiXZUUZYLEkZiHlxcKSC/Tssv5uUxEvNguSARybkUJQ13jMUqQqtryU9/2IU7tsSIFpJ2CeKKio6vPjmN4agHNw6HIWtrMjn/56lpfPIN25sem6Np3LOrC0cnUkgWZZiGpRcY8fA4OBwGR1Dxy5YUbI578NilVTx6IWFXEAbCbty8KYIsqXNJjShuya7O9ofdLYvibrQNmmlah7XHL63WVPkZmkLUaxGpmo2YT8BHbhtprDl4+whx4qOaJj7/yCUcGongTXu6ISoanJVW3+cfvojfu2us6TG9DhY/PbeM8UTRkrfSDTA0jfFEET89t4wjW2IvPsgVYqOTtHZIzwg8g++fXMDFlSKMdVXviytFfP/kAvYRrNcAEPLw2NMXxPOzmToizp6+IEKe5rs06ZKC8UQRLENb90qZoECBpimMJ4pIE76vDE3hzGIOeVGt8cHOiyrOLObqvLFfSngEFkpFfsrftbYPmqYJRTWIuykv1om7JufSILxeLz772c82vLZz5058+9vfvuLfve++++xW7+XBMAzuv/9+3H///Q2vh8NhfOELX2j+htscz06nMRx1Y09fAKKiwslzyIkqnp1JY/9giGjMpZyE52czUHUTPEvbrV5FMyzyCIF+Ec8x+PHpFZxbzgMU7DZftqzANE18+NZhonsFNr6Kkioq+PIT09jbH8SBobUKynxGxJefmMZYB5m9mFtgcW65gBuHQrhlcwS5omhXkc4tFbCnj2yBBqxWyaa4F6VKm8fJkduVAZbY9PmVEgyzWGFgWlUUlqFBUxSR2LTHwWI46oGo6ABF2VZ4pmliOOqBh6CSynE0Hj6fqPO9vLhSgKobOLgp0vSYwKtLFDfqdWBzhwfBPAcDsB0GKFg6l1FC0/dt3X78/i9twcVKtcvn4rA56iE69FQjV1YxlSrj1MIklHXVvarofI6g2jGbKUPWDFt/tBoegYWsGcS6mwVJxfdPLGIw7MJgxG2/W6Zp4vsnFvGeg/2vCB/wgqRiISNB1oxaEoJBYSEjEXvKenkW771xADQFq/NTOVTt6PbjPTcOwEtQ8StKGsIeHoc6vIj7BKi6AZ5lsJwTcX65QFxFM2Ai5OFRVnRQlK0+BdO0EliDYDH0Olg8dHYZZ5cKdWNu7fTijjGyA0WnX0DU62joKhX1OtBJAKPZiHhFJ37XYi3mMyKyolXiThaLlp+oS4DHwUEzNGLfw2RBhqyZMEwT0joBZ5ahYZggskErKzqen8uCpSkLN4g13ODzc1mUlY3TN2w1VN2Aopt1TC7TNKHoFn6EJHqCTnT6BMS8DoACaAB+F2+Nq5nEosDHZtMNAehv2KUTCeICsMWkaYpqSBohEZsOunn80+OTeHoqjZKs2wup28FgOS/hY69tvtojKjpSRQURj6MOj5oqWu1ekqgVxV0bY6NEcTcyhqJuDIbdmEmVrecKq4ridjAYCLsxFHUTjduo2nXCl23JDUMzLOIYx9Jw8kwNW1xSDZDA8XJlFZmyirhXQKcfUFQdPMfAMKy2GUkyCQCLGREjcQ++cnQapxdqxcHfe3AAixkRo02ur+2wLMuUFMS8DvgEFhQNu5JuGCYEniH26+4MOpEpK3j9zk68YXcXRNnCThqGiZiHRyfBeuV3cfilHZ04OpGybdBomsJA2I1f2tEJPyF2NFWQceeWOH54ehkXVtZaqKNxL+7cEkeKYM+ay5RBURTiPkeFfVs5UFHWfjBHeKAY7fDidds68OMzy1gtrv02EQ+P127rwCihRmarcS3xe5VEO/xEgTWbsytt+iQ2aOmSgrjPgYWsiNXi2kvoEVh0B5zEJX7A2qCemly1AdgCz+DUPI0DQxGiDSrs5vErN/ThkfMJPHphTatsOOrBr9xA7jLiFTgcHA7jcw9dwMn5tdb8zh4/fufIKFGCtpAR65I+wKpMff/EErZ0+DBGkPy3A9y/lJNwfrmAHd1+dPgF2xFlqXLaJ6kkK6qBwagbF1cKmMusPYOQm8fmuBcKgacwALtyvCnqQU5SISk8BJ6FX+Ag8GSYoXaFqOjoD7sQ8ThQlEtWa5cyEfE40B9xQVT0V4wbht/JotPvwFDEU2d8P7lahJ8A5+lzcTBNizF6OWmqep0kFN2oS/oA4PRCDl85Oo37CdrS7SBjuRyWT3tR1iCqawcqJ8eAZ2m4CDHJoqLjn49Og6YpdPmdUHQTPENhMSfiqYkUPnVv83CPvqAL/7AwiVRRRsTLwzCsNm2qKOP0Qg7v2NdLdK8+J4dvHJvDlk4frh8M2ZCXxZyIbxybw5/e1zwsKy9pKCs6MmUVZUWzZZJcPAsXz1bmW/PhFTh84PAQBI7G+eUiVM06CI11ePCuAwMvm7d2y4lfLpeD1+sFRVG2FMK12PhoF6Mz5nNgIOxCuqTUWWuF3DyRDRrP0MiUVUQ9DsS9FvOKpSkYpolMWSVKJoG1itePzqzUVbw4liaqeHkcLC6tFDCfEWu0BuczIi6tFPCGnWSkmURewj/8YhILWQk+JwdNo8GyDBay1uefCm5rOvFJ5OWGZBHASv4SeRljBLc7EvVgrMODxaxUNwe6AgIRuH+1KOOOsTjSZRlm5QRtAgh7eGyKeWsOBC81gm4eM6kSNN2sSIVUKki6iZlUidhPs9o2tFjYup1MOHmG2PuzGhtpgQVYbfl/e24eu3osK7j17iX/9uw8Rjt8Tc+rdrQkAcDNMXj3jQP4j+fm8fOLSfvd2hz34t03DsBNYDFYO1cBhaPtih/pXAWA1ZKC0ws5cAxd1+o7vZDDKqG39EaTsaIeB9wOBhdWascsyhp6Q05ECb2aL64U8Ox0BiE3j9m0aK0BDAVJ0ZEuKbi4Umh6Xq2WFOiGlaTOppU1qSw3D90wsVpSiKpoXoFDyOXAI+eTdZ2abZ0+ovdL4BlMJIt1nu0FSYNUKTCQhgVPGrJJQz4Xh5EWSUOtBtGKZpomvvCFL+Cf//mfUSgU8OMf/xh/+Zd/CZfLhd///d+vc764Fq1HdRGZz4h1htc9QWdLjM533dBnkUYua3G864Y+ItJIzOdAl0/AqcVcHQB9R5efKJkE2lPxKsoaNMNE3Oeoa51plUWLJMaTRVxcKYKmKDhYGqamwMFyAChcXCkS4eaUF2k7v9j1K0XMJ+CDh4fs6mQ1dvb48cGbhogWKJ+DBU0Dz0yla5i9w1GrzUPClnbxDAZCbmte6bXzaiDshotwcW6H9yew8WQBwGq7q7pZw5K9/Hqz0Y6WJGBhfY/PZpEVVXgE1t74s6KK47NZXD/QPC65Olf/4uFLOLOYs/VMt3WRz1UAEGUNLp6BwDFwcLTdQpVUHZKqQyRYB9pBRitIKm7eFEVJ1uvanDdvihJj/DIlBXG/gKWcVKdu0OkXiFrIyYKMiWQRHGMpHFShGRQFTCSLRDAiACiIKu7cFoOqGzXPYHPMgzu3xVEQm38GDCh0+gXs7g2gL+SyMe8zqRJW8hIYUu0ZtGcdaDWIEr+/+Zu/wX/+53/iT//0T/HRj34UAPCmN70Jn/jEJ/DZz34Wv//7v7+hN3ktrBfwyJY4vvj4FJIF2Ta87ufcOLIl3lIV4fnZDO4Yi+LeXV2WyKaDQUFS8fxshog0EnLzuHUsipyk1gDx+0Iu3DoWRYhwI03kZcymy/AJbF1lajZNVvFSDRPTqTJYmkLM56jBjU2nylAJWVcvtgGTbNBRr6POnL0aTp4mBvYXJBVHJ1K4b0833rynp0Yb7Oh4CiMxT9Pzi2Yo/OTMSp2cy0SyhJ+cWcGhkeaJGEVJw2u3d4CmKUwkizYedTjqwZFtceIEJV1ScPOmKBgamM9IcHIMONbSCTs0HEWaAONXkFR89/gC3DyDsQ6vDUuQVR3fPb6A9x0ia/O0oy3frm6CrBkYTxQRdvOIetfeLcMwMZ4oXlE37mpRkFT85OwyeoNObO30QdZ0OFhrvfrJmWWiuQpYYtMRjwPzWRH59Nq76XNy6Ak44SeRtUK9jl+r1R7DBB4+l8C+/iDu3NZh6w2mizIePpfAdYSkMZ+Tq0v6ANh/9hF8f5qioBtrGoHVxN/B0vA7edCEHUKTAr53Ygk3DITqnsH3TixiV2+g6TELsoL3HxrA6YUc1kwrgf6QE6/f0YGCTAZPaheMotUgeqO//e1v40//9E+xf/9+u7176NAhfOYzn8Fv//ZvX0v82hAFScV4soBOv4CugGCzek0TGE8W0Bd2EePGeoMurBQkFGUdsmagIFut2N6gCwsZsekqWrqkwMkxODQSwcHhCBRdB88wFhaFZ4g2UsCqaAVdHBayYh2jrzvgJKp4MTRluZNcJgHAVFrTJNIAQHs26KGoGweHwjg6mapJ/pw8jYPDYWJg/0JGRMDF4cGTy3Ut9DvGYkRzIC9qSBRkcAxdQ5DhGBqJglxxi2guqkzpQyNh3DYWsxd8RdNbYkpXdcFiXgfiPgHKOgayqhtECeVCRkTEw1sQAkqz7OVkS9aiJ+gkeqZAezT32qUPqWgG+sNuTK4WkS+uEcd8Tg5DUQ+R2PJksoQnJ1MYjfvA0AAFAyzDQtVNPDmZwuu2dxJt/OFKC7UkazUJSUnW4HYwCBO2UDe62hN08+gOuvDcbKZuDRiMeFqCO3T5BVxoUDHs8gtEyb/A0Qh7OCQKtZZ1smYg7OEgcGSQHxfHoMvvxIn5XD00xe+EiwBCEPMKuLi8imMzWcxmynaS2hd0IeR24DChYkC7YBStBlHil0qlbHu09eHz+VAuX9mf8lqQx0JGxPeOL1WweLVstvPLBYzGyYD9iqpD0gz85MxKXevgrft6oTSw8XqxKEganpnKwCMw8Dt5MCYNhqaQExU8MylhJErGZAq5eazkJchqdWO2Xk5ZNbCSl4gqiW6OwXW9QTw1lUI+q9o4JJ+Tw4HBMBEOCVjboM8vW1IjOhgommXbNtbhJdqgvQKHXzkwAJ6l63T83ra/j9wNQtVtmZT1ci5TqyU8fD6BbV3NS9rohmXVBVTwfZXfiqYoCBxDpF/VE3Qi4uExuVq/xrSSoAg8g++eWLAFcatEHMASxt3T33xCKas6irKOx8dXa6regxE37t7ZBZngvQLWNPe++PiU9XtVsFiDYTc+cNMg0YGqXS4j1WQh7Obhd3I1DgsgdMVZLcq4qeJg9MPTlzkYbY4SYUcBqy15tRZqstB8N6Fa7ZlZLSEnabakkaRoxNWenqATwzE3HByNkqzZz7RVyI+iGnj9zg7gJOq+/+t3dhIRpxTdwPUDYdCg7GSKpij0Bp3YNxAihqY4OQZv29/bkIH9tv29cBKs2SxN46fnEzhXEQWvxrnlAiiawq2jZHIu7YJRtBpEid+BAwfwxS9+EX/0R39kf1YsFvG///f/xg033LBhN3ct1iKRl5GXVKsyJ2n2gucRWLAMRQzsV00T//7cXM3LDlgv/78/N4ft3c1v+jxL41KycMWWJIldGWBhvPpCLrwwm63TBdvS6SXCeAkcg61dPkyuFlFah+OJeR3Y2uWzk5dmI+YT8KGbhi5j9RrY2ePHh24mxyL1hV34tVuGN5QwUFZ1jCeKKEhqHSZzPGGiTJCkRL0O+JwsGJqq0wZ0OxiitnS7EpSCpGIpJzW81qj99VJCNU08eHKxTnNwarWEB08u4rr+AMmtArAOJiMxNwYibrvqydIA1YKW40ZbiwFWwufgKIzPFusq9AeHw0TYSa+DtW0r10f1zzcRQAgAqwr147PLePOebngEDmVZg0tgURRVfPv4AkYJ9DznMyLOLxVwZjFXQxrwOzmIqvGKsgIMuHlMJMp45/W9cHCMrWMoqTpemMsRJT4cTSFZkNAbdmFPf9B2w0gWZSQLEjjCbkrQzePzj46jN+jCnr5gDcHpwROLRDZo2bKKdEmBwDHQdMMuALCM5QyUJZQJqh5uVN2oS9S5ip7lyxFE/9U//MM/xEc+8hEcOnQIsizjN37jN7CwsIDu7m783d/93Ubf47WAdXq6vMUJWCeGhaxIfHpKFxVcTNS3jQDgYqKIdLF5bIODtVw6gi4L26Poa04QmbJCZFcGWADk6wdCEFUD06slO5kYiLhx/WCICIAsaQb+89Qixjp8uGlTtGYR+c9Ti7ieUBi7ipt7694evHVfj43vgQli3Fw1NtoNQjcsHcPLq3BX+vylRG1bem2Bb7UtvdGYKcDShusOOK8IISDRhiuIGmbTjbsfs+kyCgStbsBii//Nz8YbMrDPLObxyTc0zxYH2gNAz5QUjMUtX+FprWRXfbv8TozFfZYmXZP3qpsmUkUFHT4BDo5exz41kCoq0EmsS2Ad9O4Yi+M7xxfrKrR3jMUtPc4mY7Uo1yV9AJCrOEyQVifbYQXYE3TihqEgvva0VQSo/lajcS9++YZeokqiwDPY3OHFD04t4SdnVmzi3EjMIniRMmVTJQUAhTNLOYgz9e1uEt1Na49iEHZTUHTDJg3xjKXlSbq/9gSd8DoYHJ3M1sNzhsLEFdpWgyjx6+jowH/8x3/gySefxOTkJDRNw+DgIA4fPgyawIrpWrx4CBx9RYFaUdGJ8RJyJdFRdKOOGs8zNGSCCV+SNLx5bw++cnQaPzy9ZH9eFUMl9ZMEBTwxnkJ3QMDObj9UwwBH00iVZDxxKYX9BCxBRTPQG3RfcREhNX2fz4hYykvgWcryfy2KMCr/vaW89LJhOxoFS1Po8AvQTbMu8enwC1ZrrsmotqVpisLFxJp+1eaYB798oHkXhGq0I0HxubirigKT4DF1w4RX4BpWUb0CR2zVVGVDsgwFChRMao0pWWVRkhBR2gFAX8nLePj8CnpDTuzq9dcY3z98fgWb416MNtmlME2r/X5mMYfV5JpESMTDY1uXn8iyDgA6/ALOLOTqkvXZdBlnFnN45/V9TY+pGSbKioZDI2F0BZy2luVCtoxnptLQWrDr2ujDn6joeHIyjblMuQaTO5cp48nJNG7ZHGt6DlCg8INTS3jT7m4EPTxKkg6PYPnLf/v4Ag6PRInutZrsjnX4GlbRSNqnUa8DHEMhXap9XyWaQtwnEBPnAGBLlw/jyVINfjrmFbCVAEKzUUFcZ/zXf/1X+P1+vOtd7wIA/OZv/iYWFhbwzne+c8Nu7lqshcfBYkeXv7FESrefyAILsNoxPieLgqTVeD/SNAWvwBK1Y9wCi7/9+QRERcemuLeGKfuNZ+fwsdeOEt2ri2NgwsR/nlzCOjUPMBSwtz9IBOr1COxVFxHSUnxJ0q7q/0qc/LYhnBwDn8BB85ro9FM17EufwBFhZqoxFHWjM+C0W5Kt2Mu1K0EZiXqwKVYlTNSKApMSJtrR6gYs/CzH0riwXKgRQg+5eYx2eFEgmFftAqAruoGZdBkXV4p12ngOjqyKInAMLq0UIKkG/E7Ofq6SauBSokAMzUiVFDg4Fh0+AVlRtSs+AScHB8cSVZF4msJ7Dw7gB6eW8NNzCfvz4agb7z04AJ6w1QlYld+NbMtfXCng5xeS8Ds5xH1CzZr98wtJ3Hddd9PjG4aJt+7txbdemMe5pbUq4pZOL966txcGYeLrEaz3aijiAc8yNYz5mbRItGZ3+gVs7/YhL6k1iaOTZ7C920dsrTafEXF8Lod7d3UCFFVjBfj8XA6jHb5XD7njz//8z/HNb36zBuN3ww034G//9m+RTqfxm7/5mxt2g9fCCoFjbCmL8WTRFgQeqUhZkC54w1EPDg6HcXQi1RCHM0yw6cmagVRJxmjcV9fqvbCSJ5JxAICMqGJblx+pkoqJ5Fp7eiDiwbYuPzIE+k3rGY0BV22S2wphwCOwlaSvXnPw4fMJ7CUgDLQrBI7Ba7bEcXRyFYtZC+tGAegJOXFwKEI0t66UpAHAYk4iStLalaBUCROff3QcF9frgsU9+MjtI0Qbarta3S6eqUv6AItJf2G5QIRzbRcAXeAsFrNXYBtq45F0KQyYCHscyEkaFG0toaZpCmG3g8inFUAFN22AoSlbYJihLQ9kTSNjdgs8g7NLeSQKtb9VoqDg7FKeSNIIAM4s5CpzdW0N3Bz34CO3jWBbt59ozHRJgaobV2w/k7gtCTyDH5xewkxKhMAxME0TFEVhJiXiB6eXcMNQmOhee4JO7O8P4qfnV7CYlWy4Q2dAwGvG4kRrdrqkYG9fCLJm2GMyNIWugIC9fSFiJYqSpGFzzIPvNrDZfDkLAESJ3ze/+U38xV/8Bfbt22d/9p73vAejo6P42Mc+di3xa0N0B5146OxKRcoiilxRhN/jhKIZSBcVIqFlwNr0fvXgIAwDmEmX7QnfH3LhVw+RsQRVzcAbd/fg+ycXalq9m+IevHF3D1TCxK8sa3jw5AK2dwdweCRsg4Vn02U8eHIB+weaT6baBZaWNaNOxqAaiYJEnPy2I7qDTlxYyaMv6EJ/yG3jHE3ThMtBE82tdiRp7WTIbev241P3bMN4ooBkroSo342RmJe4itKuVjdDUwi6+IYbcdDFE8kPtQuA7uQYG34R8TiscVkaqxXhXpJKcqog49bRGELuLBIFGZpuMeVjXgd29gSIfFoB67meW84j4OTBszQ0w3oGsmrg3HKe6LlSoHB2sYCA0xJBXl9FO7tYAEUgCpzIS3VJHwBcXCni84+O41P3kGE8PQ5rPppAXeeHWne9mShIKlZyMmJey1u7+luJio6VnEwsNg0AJ+ayOD6brcFPruQlRN0O3L6leSJKQdJwfqWA14zFGlbmRmJkjjCv1AIA0RstiiI8nvoHEQwGUSgUGvyNa9FqeAUO9+7uxrdemMdEsohUKoVwOIwOv7OlBAWwNr3/311jGwaY9wgsHruURLqk1rRj0iUVj11K4sAQGWGCYShEPAKen83U+Or6nSy6/E4wDFnrpB1gaUUzsCnqRdhjtdIlxTI9z5U1pEoKMXawXXFmId9QH1A1DNw+Fm96vHYkaR6BBc/S2NPrb7g4t8qQi/kEhN0cjh+fxe7BXjAMeYu7GhvZ6gaAVFHGbaMx7OsPgmdp22FAVi2P3hQBYaBdAHSaAo5sjeNLT0zh+yfXDoC7e/14/6FBkHQ6fU4OFIC+kBNDUbetuajphi3DRBYm9g0E8bMLyboW+q2jUZD8aLphYmunz8Ijptd+F7+Tw7ZuPxHOczxZxNRqCfv6g3XdlBPzWSJHIMCyu9va6cPzc9larLdBYU9vAF2B5sfMldUab+1qxa/qrU1CmgIsLcdnZtJgGbpmf2EZGs/MpDGZLDWt5egV2KtW5rwtiJi/EgsARN/mpptuwqc//Wl85jOfQVdXFwBgZWUFn/nMZ3D48OENvcFrsRbVBGU2XcLckgu9nVH0hdwbovwd8wkb5h1Ynew0RYFnazfPViZ7xOMAS1NwO1jEfQIM09pcirIGlqYQIRRZbUd4BRa7egP43okFXKhoQ9EUhdEOL+7Z1U28kLQj5jMiCrLeEOdYkHSi6lw73CB6gk7csinS0F7wg4cHXzaGXKOoOncIHG2/AxRlMTpbce4IunnQFLCcl5AsyNBNEwxFIep1wO/kiAV82wFA100T//jYJFJlBZvjHvt9TRRk/ONjk/jkPc3LbvQFXfibmXHbWnC95uLOHj/eureH6F5Nw8RA2I2gK1eT+AVdPAbCbpgESZpHYBF089jXH7R1/DiWhl9gIfAs0TtQEFUc2Rq3dQyrMRhx48hWMrsywEpw33OwH+rjJk7OZ+3Pd3T78Z6DA0QaqX4Xh4lkEapuYYWra6Cqm5hIFuEnIE0BFolJVIyG+4uoGERWcCE3j6MTq8iUFARcnN0+zpQUHJ1YxZGtzR9+AasAMBj2VHzAN4482GoQ7T6f+MQn8Bu/8Ru444474PdbmIJcLocDBw7gk5/85Ibe4LWoDa/AYSzuhbQ0gbH40IZUJTY6qpN9crWIvFir2N/KZHeyDN60pxv/fmweF5bXiYx2ePGmvd1wsmTPoh1MUa+Dxc8urCDuc+C6vgAUzZILWMiW8bMLKzhC0I6oRkFSN7Q6Wa2+cQxdh3Ncf72ZaIcbhKjoeOCZWZxbytd8fm4pjweencX2bv/LYn/UKBYyIkJuvq7N04obCgAMhNz4h+UpPD2ZqtWyTNAwTODdB/qbHnM+I+LYTBY3DoVwy+ZojSPKczNZYgB6qqRgPiuirOhYXqeTyFXafSkC3Fhe1uBxcPA7uTptPI/AIS9r6G56VCArafi7n03ips1h3Lo5CkU3wTMU5rJl/N3PJrGls/kEeM1f3arK0ZT13RmGJn4Hwl7HVXUMbxklY8qmSwoKooo3X9eNt+3tQVnR4eItofWCpBBh3LwCh7hXwJmlPEzTtCt+FEWhP+Qjfld5hgbHUNjVE2hY9eSZ5rGjqZIC3bAkeC6fV3GfQETuAdpHHmw1iP6roVAIDzzwAM6fP4/p6WmwLIuBgQGMjIxs9P1di1dhVCezx8HaLg0MTbWk2A8AZUUHx9C4YzSGO7fGIWvWC6/rJniGRvkKcjdXi3YxRecyZewfCOGHp5fx03MJe9EbjXtx1/YOzGXK6A41n1TOpsr4xnOz9c4d+/qIk9R2VOeq2MlG90oKTRhPFjGxWkbAxdcxZSeSZeI2Vzui6oZyJWwPiRsKAKyWFEiqDo/A1ojKegRLbHe1pDQ9r4qSBt0wGzqiVK+ThKKZKCs6eJaGi2dsVqclc6JD0ZqvohUlbV0VTYWkaBB4Fn6Bg8CzxPcqqTqcPI1HzyfrLAa9lWfbbHgFDnduieMnZ1egGaadoDAUhTsJ/dVpUFckWqRLCmgC3CBgYdxOLRbQH3KCZxmougFJo6BoFlN2KNK821JBVHHnthhU3agxCNgc8+DObeTVybjPgTfv6cGDJxfrqp5v3tODuK/5zk+mpGA+W27YPp7Plok0YoH2kQdbjZe8oi8uLqKzsxMURWFxcRGAZdG2c+fOmn8HgN3+vRb/NSPs5qEZBlYbiD+H3DyRRAxgycRcWCmiO2BpyzG0ASdHQzNMnF8uYm9/89jBdjFFNd3ET86tYCknWQtJRR5iKSfhJ+dWsKsn0PSYBUnFV5+arsPizWXKUDQDv3XHJqLNpF1erQCwq8ePTXGvjcdrBeOWryQ6jVo866+TRkFSMZsuIQkfLqwU0Rsi878GLDeU2XQZPoGtE1qeTZeJ3FAAq82VKinoDjjRG3RZlm0VX+lUSSFqc7Uj8Qcsbb2gi0e6rEC6TCM05OIR8TS/DlTvhWEsByBVsSrpTKXKQ3qvVUu5RiLmqm5JxzQbBUnFpUQRE6vFOqbopYQbvQT+6rphYluXv6EbCCluELCe29WSf5LnalLA904s4YaBEO7cFoekWLIr6aKC751YJPJUBiy4w4WVPJKX4VmTRUs14t7dBPkHhYZOU0Dlc0LlnXaRB1uNl/xr3n777XjiiScQDodx++23g6Lqn0S1qnHu3LkNvclr0f7YyPZhuqTg8EgEsmbUtbkOb4oQU+OrXq2zabHuGmmC0i6mqGqamEqWwDVoO0wlS1AJlGYnk6W6pA+wFqajkym8fmcX0WLajsWpIKk4NpvG9xuApd+wS0fQzTU97osJKZMILVej2u5fzokV4lTZIk6RtvtNIOrhMZMu18kk9YdcxMmvxbYGinLjxJGkzdUTdKLLL9h4xPW6aJJqECf+w1EPDm8K44nxFPJirQ/2oREyqahaIope0VzU4OSZlogoEY8D27r8ODmfq/OW3tblJ8IPL2REfO/EYsVf3dovKQDzaevz0Q5v0+3+duAGgfbMARfHoNPvxC/GU7hcH7M/7CLSXQWstqysmvbBZ31HSdZMorasi2PQ7Rca6+R2+YnvFWgPebDVeMmz5OGHH0YoZFVUvve978HtJtOhuhavvNhojFtB0nBuudAQM3RuqYDNsebbBkB7EpR2SVmwNIWwh8dKXoZacUWhKApcBd9D4oZRBTU3ClJQczU2enFayIh1SR9gtTq/f2IJWzp8TW96I1EPNsc9OL9cqGv1jnV4iYSWgfa0+90OFpmyWue2Iyo6MmUVbkLB9ZjPgf6wq+65AlZSHSNoc3kFDrePxa6oDUc6B9ohFQWsJ6KstflaJaKUJA3vPTiArxydriMOkboNXe6vXo1W/NXXqvOAwNfOoVaq8+2YAwLH4I6xmC1iX43+sAt3bIkRa89W1ycH59ow5w6aorB3wIIPrMdP9oVc2DsYBN2g0NVMbLTTSqvxklef7u41yOyHP/xhfP7zn8fWrc2zsq7FKyvaselV2wYXVoobDmjdaDP5dklZ+AQOMa+AgqQhJ66N6+IZxLwCfAQL6YtVc0iqPe2KRF7GYla8ouwEyaYX8wn40E1D+NxDF2xWJ2CxOT908xDxHGhHu9/B0ugOOCugfspqyTKWG0rE4yD2q+4OOnHv7k5cXC6Coij7uRqGidEOD5HmYkFS8cj5BHb2+LGnLwBRNeDiLZzXI+cT6CNoSVZjo6WiaokokRo901aIKC6BxdFTS3j7vh68fX8vSrJmJeemiaMTKbxjf2/TY1b91S93RSq04K/eDuwsYM2Bh86twO1gsTnuqVmzHzq3QjQHqtqzjX6rVrRn2wFN4FkaZxbzDT3bzyzk8dqtHUT3Wo2NJuS1GkQ7ME3TUNXW8DTXgiw2EocEtGfTa6cxdTsYuO2QsnCwNDwOFt0BJ3qCLlu81DRNeBws0cbfjmpPNWZTZTw1uQpRNew2z6l5GgeGIkTPVTWMq8pOqEbzm15BUnF0IoW37u3BW/ddtjmPpzAS8xC9C+1o95ckDW/f34tvPT+PuYwI3TBhmBR6g5buJqliv1fgMBL14ucXVuuqaL+0o5Po+7eLgVyNjZSKWk9EMU3D1jOlKNq+ThI9QSeCLg5PTWXqrpFW0gSOhqoZuGEwhJ5gxauXpTGXLuPEXJbYXx2wKlFRr7Bh+pDVfaARs590H2iX9mw79peipOGWzVE8fD6B52bW5kC1OtmKOHw79qxWgyjxu/XWW/Grv/qruO2229Dd3Q2er50oH/nIRzbk5q5FbWw4Dgntw7i1I5lqR3WyXVIWRUlbZ4MmQtdNMDTQFXDh4FCE6Ll2B514w67Ohri5e3Z1Ep+gC5KKF+bSmEmV16pIslVFcnBpIjxe3Cfg6EQK8xkRPEvbmKn5jIijEym8dlvzJ+j5jIj5rIipVKlhJZmUiNOOCoJHYHF8Pou4z4HuyqbPMpZv71y6bOH8CKJandsU92BLp3dDqnPtYiC3I2pFvIF8pw9+F9+yiHc7YCROjsGvHhzAd08s4pHza169mwdSvMwAANwcSURBVGIe/OrBASLnknZYIQLt2wfsDk2igGTOj6jfg5FYa77CALCt2weOoRtWvUnCLbBXhSft6SNz2Kjqebp5BmMd3hrsZCt6nq0G0Vty4cIFbNu2DYlEAolEouZaI9LHtWg92iU70o5Nr13G1O2yAWuHlIX13MyKDZoLoqLCyXOwOB0msUTK3r4QVM2wK3PV0/6e/hDxArKYEZHIK3h8fLUG3zIYcSPg4rGYETHa5HPVDRNFSYWk6rV6cwyNoqQSsQ+LkoaipDUWQw17Wqr2bDSrWTVM/OD0Ei5dZq0FWNaFpD6l1ercQ2dXMJEs2jjH4agHR7bGiapzZVW/aluelIHcjrhcxLsq4LwRIt4bDsKngJOLOaRKikXKsT5CqqTg1GIONww3PwfmMyJWiwqGIq46EsZMWnxFHX6ARsWKcMvFioWMCFG1TAIWspJ9AOwOCOgNu4jegSpxsBE8qSfoJJ5X7a6mk0ZTv+Z3v/tdPPTQQ4hEIrjjjjtw9913t+u+rsVlUU16VN1AUVYhgUdOtFpdrciOVDe9mdWSzRLjWRo+gUV/xE004dtlTN0uG7BWrl8pQm4ej11arQClaxltm+Me3LGFTAm+L+xC0N25oXiRnKjiB6eWUJA09IZcdvuwIGn4wakl7Opp3vi9JGsIuXnkJa2O0Rly8yjJzf9WPEvXJX2ARWyZShXBE+Lm1ld7lnNrjPFWqj3pooJkQQFTkXCpBkNTSBYUpBtIHb2UkFQdPz69XMc+PDGfhWGYRNU5ygTu2t6BR84n6tryd23vANVCC3Gjo90i3hsJwi8rOk7P5+ETLBHg9V69p+bzRLqjJUnDlg7vFRMJ0rW1HazedhUrZFXH0xNpPD2dqlnzp1MlaDqwJd48edArcDiyJY6/fuQSzizm7QPVti4fjmwhr8q9UqvpL3lX+8pXvoLPfvazuPHGG6FpGu6//35cvHgR/+N//I923t+1qERttaNWxqCVaodX4HDLpig+N52uA8y/5yDZhG+XMXW7bMDaoWGXKilgaRpOnq5hdjp5GixDEyvBAxvPEMvLKhTdQFZUMZdZ+828AgcnzyAvE+B5KSBZVNDhE9AVcNqbnmGYSBYVIl0sB0sj5hUaYhxjXoGYMAFsvB2iUqkY+J0cFN2AYQA0bVU8GZomAvYDlj3h6cU8aIoCw1J2C900gdOLeRQJEuqgm8ex6Qxm07XPdTZdxrGZDFFbvl2xXsRb1Q1oGg2WZcBtkIj3RoLwZdVAV0Cok94ROAYRDw9ZbX4OtGttbQert7Y6SSPn5WxyRyvVybKq1yV9gLVHPj2dwtv2N2/bV5BUPHhyEVGvA/fu7rbJHTlRwYMnF4kJTq/UavpL3ikfeOABfPrTn8Yb3/hGAMBPfvIT3H///fjoRz96rb37fyHaVe1I5CV86YkpdAdc2N0brGEzfenxKfzB3c6mF1JZM7BakBuK164WZGKv3nYkae0S2KxuHGMdPhRlFaUyC7dLgMfBEUsOtCtME1jJSyhItQleQVJBV5KKZsPFMegLXZmIQqKLVZS0dfIQl1U7WgRgAxtrhxj1OsAxFNIlta7iF/cJiHrJiDiSasDnZJEoyHUOEzGvAxJBMiFrBtKi0tARJV1WWjaS38hkqpFIN/Ui119qbDQIP+p1gKVpFGUZZUWzk3QXzyLg5IjmQNUHvVG04oPeDmZ3SdKwrdOHx8eTmK8QnFiaRndQwOGRKHF1siBpkFUDEY8DTp6pqaTmRBUFgnEnkyU8OZnCaHytAkdRlvvMkwspvG57J5FGKmUCr9/RgednszhdqdIzNIWY14HX73j5qukvOfGbm5vDjTfeaP/59ttvhyiKSCQSiMfJ2lbX4qVHu6od48kizi4Vrnq92cRP0Qx0BZyN1eW7/MRevdUk7WtPz+Biomi3pTfFPC0lae0Q2KxWHzmGRsDJQS/nEXB6bfbhy+XR2CicnGWnNdbhhcAxthuEpOpYzEpEIPRaDa/6JI1Ew6tdAOx2RKdfwPZuH/KSWpOQOnkG27t96PSTVaUEjkbQZbXQ1yd+Ascg6OKJmKK1RvJradRGGMlvdDLlc1kOG4UKTtTC+BlgaApegSMW8W5HW7LTL8DjYOx7rYZuqPAIHqI5UPtbXYZzbeG3WsiIiHgceGIiiYWMZMsPdQcEHBqOEmHRPAKLJydXcXQihcy6hHwmzYGhKVzXFyC6V4FlLEkgv8NyiaqwpVeLMhI5GQKBZ/tqUcZNm6INVQhu3hzFarExtvzFIujmMZ4o4dRC7V64kpfgEzgcaVEmhjRe8u6jaRpYdu1fZ1kWDocDikKGVbkWzUVttaNeDJO02vFiJ2SSEzTP0lf1PSStTgJAUVIhcAy2dvrs6iRLUygS+j5WY6Pbp+20QUvkpQ3TMQQAr4PFa7ZYcyu1DnsW9vB4zZYYvARiw7UaXrVJGqmGV7sA2O2IdEnBvoEQKKAOgL53IETsXiPwDEABETeP7nUtdFnVAapyvclol5F8O5Kp/qALfSEnTs7X7ju6YaIv5ER/kIww0A7iWLqk4JbRGBTdrCPi3DIaI5oD1d9qU9RTh8luxblDVnU8NZnCU5PpmgRlLl0GQ9HY2tk8bi4nKnjysqQPADJlFU9OpPCuG/qI7jXuc+DWsSi+e3wBP0ys7YUjMTfu3d1N5NXrdbB47GKyhtwGwP7zTSMRonstKzqen81A1owago+sGXh+NkOE89yIeOWUHa7FVaO22lErhtlKtaMdNlh2dVIp13mqtlKdTOQl/PVlGJRqXFgp4FP3bCNOgDZaYLMdhAEAOLOQuyIOZ1t38yQMwKoWDUTciHh4ZNct0hEPj4GIm6g6V9XwqmoDAlbrxO/k8NrtEaLv7xU4vHZLHKcWcjBhLapuBwOYwI4WQf0bHUVJg2mYiHkFxH1O+5BimiZMwyS3AlQN3Lk1hv88uYzzy2sEh9G4F3dujUMlaPW2y0h+PSHt8oSSmIUva3j3jY0dNt594wARxhFYI4ZJitYwoSL5vaoORjcMhXBoJAxRXfMWJ3UwqmrYnVrYWI3UoqLjiYnVmqQPsIhfT0ys4m0EAtaJggxFN8EzFBR9reJZ/XOC0GlIM0ycms8hUVCwHmWWKCg4tZDDzZuiTY+pm2ad9281kkUZOgneBVbyT1GWndz6FI+hKVAUhXTp5SmcNZX4/fCHP4THs6aTYxgGHnroIdvKrRpVHOC12LioVjsaCZe2sjhXbbAaJVOb4x4iG6yipOHIlhjmM2KdzlJPyEm86Y0niw3vEwAurhSJgd3tEtjsC7vwpt1dGE+WkC2HEHTxGI660Ueo4ZbIS3VJH2B9988/Ok6c+CaLMr78xBT29gdxaCRia84tZkV8+Ykp7Oj2Y5Toji3sTLUtz7E0Nsc8ODBEdnouSCpWihKmK8LF1XlFUxRifgd6pNbEzDcyPAKLRy8kEXTxNpaLooBkQcGlRBHXER7UXA4WPzqzgv0DQbxuWwckTYfAMUgVZfzozDIODjf/bNuJc5UUHR6BQYfPY/9e2bKCoqQTrQN5ScPTU2n8t5sGIWmGXfV2sDR+MZ5C6LLE9aWGR2CRLSmYy5RhAnYldSUP9AZdxFqOJUnDQkaEV+Cg6DoklUFeVFGSdeLq3EZr2AFWde5K1aeyoiMnNp+gMBSNgqTCyTFw8mtEJE03UZBUMBRZASBVVHB2KY+wm0enf40trWgGzi7ma7oWLzVMExgIu7GcE1FSdBiGCZqm4OYZdPidRDhnwCJzVUWxL8fP0hT1srktveSZ19XVhS996Us1n4XDYXz1q1+t+YyiqGuJXxuiXRWkmE/AR24baVxFun2EKJGwFjQKs5kyFrOS/WJ2BQT0EC6iQHva0u2SHACAF2Yytr1YVW9sZ48fv3NkFNcRsO/alfgWJQ0Cx+KpyXQdEcErcITVDhVffWoaRydTNZWJREGCYZr4rTs2Nf1cFzMiLiwX8fC5FVxKrD2HTTEP/E4OnT5n03qDl9/zRrniKJqBbV2+hhIpt4/FiLFYAktje5cP6ZIKA9YmyjE6MiUV27t8EAir6e3AuTpYGgMRFx45n6jTh7x9LEZU+fdW2pzfemEBi1kRiqqD5xh0BZw4OBSBl3Bt8TlYcCyFxVwtyckrcBiKuuEjgDuE3Ty2d/nwHy/MY3zdfB2JefCW63oQdjefpC5kRJRl44prK6kuHEvTcPMMSgrqSENungFLN/9bBd0WDGUiWQRU2IkfAAxHPQi6yRnzDG1VTiVRrUmmSBnzQTdfcVRiwLE0TAOgaIAGBY+DRZDgtwIst6XhqNs+AK5n9WbKSktuS63ES57NjzzySDvv41q8hNhoyYlqbOv241P3bNswP01Lwy6J88uFmlNOuqRA003csSVGNK7PxYGiLDzG5WzhgqwRtaXbge0BgIV0uc5TFgBOzufwuYcu4DP37UR3k5W/diS+gPVcr3YqJXmuk8lSXdIHWCz0o5MpvH5nV9MsuXRZwbeen8d0qrx2UqaA6VQZ33p+HjsJ9AarsWZZpyNXVLEip3F6IUdsWWeYJk7MZtETdGLfQLCSoNFYyok4MZfFLZubb0cBgKYbODAYxpeemML3T67NrV09frz/0CA0QpkYYONxrjxL4+mpdEPc1NNTaSKZmJCbx7HZNI7NZFCUNPtAtZST4GBp4rVluSBhNO61nEEY2iYMyJqObr8TywWp6fc1WZBxbDaDZEEGQ1E2vitZkHFsLoMDw+Gm11hJ1fHTcytIlxTQtJVFUQDm0yJ+Kq9gCwEWDwBCHh7dQSeWshJAwa54wQQ6AwJCnuYTny6/gDfv6cZMysJ1q7oJnqUhqzr6wy50ERKcol5HRSbLqIMSOXmaiC3tc7AoKxrmMiIM07STVJqiEPbwRIk/YGGd37G/F//0+BR+eHrJ/rwqOE7qttRqXMP4vcpiIyUn1oeTt1iBHG2BuZ0EIPFqpEoKTNPapC5n9ZomiDXsRqIeXNfnx5mFAihqrR1jmsB1fX7itjSwsdgeALiYLOLkfK7SkgFYmgJdudeT8zlcTBab3kjagccE1tr955fr2d2k7f5kQa5L+qohKgaSBPiebFnFTLoMTTewDjIEhgJm0uUafGIzUZBUHJtN40dnVjCTKtli2/1hNziWJrKsE1Ud23r8+PnFJH52IWlv+sMxD27ZHIVIqN9VUDT87c8nUJQ0bIp77MrEalHB3/58Ap98w1aicdsRibwM07QOeqEq+5KhkSrJWM5JSORljHU2N+ZSTsLphTxERYdumDBhHfxERcfphTyWchLR2pIuKciKKmZTZSSLsv1cox4H3DxLhMVayol4biYDj4NF2MPbY8qqgeemM1jKiU3jckVVR15SsVKQatYmj8CCZSjiedUXdOEte3vw0NkVLOclmAZA0xTiPgeObI2jj4A00x/xYG9/EM/PZDCdLsMwAZoCBkIu3LenG/0Rstb0UNSNg0PhuoOlk6dxcDiMoai76TFnM2Xs6gkgVVLqqrO7egOYzZSbXq+r8dRkCrphYlPca+9ZumHiqakU9g+GXnyANsS1xO9abDjGLVNSsJi15AFi6xTrDcPEYlZEhhDQ6uQZvHZrB6aSZZyYz9qf7+oJ4LVbO4iSVY/AIlNSrig900pb2sHSUA0TlGlVgEzD8lF3sDRRda4deEzAavd/6Kahugrlzh4/PnTzENFG+mLYFRJsi2aYULXapA+A9WfNgEZgAwdY7bPL/Y8BSxT3+yeWsKXD13T7jGNoHJ1YxfRqCQy9hkCfXi2BYyjiil+urGIiWYLZAHREFSjkWtCx22iCk2oY2DcQwpMTKfzi0qpdRRkIu3HjcBiq0Xx1MlmQUVZ0OFgGPEvbawsFCmVFJzpQANY7+fj4KoqSBpfDWkcoUJhJlZEqKbhpU/PYSVkzUFa0mpY2VUlUy4pGpLmnGSYSeRkenkXY7bC6HgwFSdGRyMvE74CTZ9DpFzAc9WAw4rZxvjRFodMvEK2tBUnFExMpODhrbFmz2pwOjsETEymMdfqISV6/cmAAPEtjIlmySUPDUTfetr+PaMxMScG/H5vD3v4gDg6Ha3DO//7cHLZ1kjlszGdEFGQdvSFXHcGpIOnEHaVW41ri91882oJxo4DS1WjqhHrfCxkR33huHkNRN/YPhKDoOniGQaok4xvPzaMn6Gp6g/Y5WBRltSGbrSirxCX+gIuDYQKSokPVDbviwzE0GAeLAEF1rh14TMCaA0cnUnjr3h68dV8vSrJlBQjTxNHxFEZinqbnQMznwEDYZbek1rflQ26eCNsScHEIuHikGhwcAi6e6JkCVmVqTV2fh6iocDl4W12fpDKl6MZVLdtInTt00wTPWDim9ckfVQGKk7IPZ1NlfOO52fqNdF8fMcEp7hNwdGK1zq/44koBJkyiVi9T0ZcsVjZR0zRBUZQlO+Nga5LsZoKiKFCw3oWF7BqG2iew8AoskUmB18lhMOLGXLpcN+ZgxA2vkwSLZ7Uep1dLyK5bswJODgMRN1jC77+QEfG1p+ewWpRBU5St5WmYJmZSZXQHml9bqzAaj8DB7WAsQqLfIiS2AqMBLNjTr90yvGEHFaeDhaQaeGI8dcXrJFGtyuq6AVkzoGoGTBMQWONlFfK/lvj9F492YNxcHIP+8Ma6NgDWBj2RLF31erMb9GymjOv6gihIWh0A/bq+IHGJP+ZzYCzuQcxvga5lzYDA0pjLlJHIScSg3o3GYwLWHJjPWv+70vVm50B30Il33dCHf3p8qk5241039BFhW2iYuGU0ip9fSNYkf2E3j1tGo7BUspoP1TBwZGvcFm+t4sYGI24c2RonqkzBBHxOFjSFOss2j8CC8FbR4XMi6OKQFVUYhmkfKGiaQsDJocNH5qn69WdmUJQ1DEdr2bdff2YGv0Fo2aUbJkqy3jD5Lcl6zWcvNXxOFjGvA5lybfKv6gZiXgd8TvItzTrs6aAA+7kWZb3uUPhSw++wHDouXiaxI6oGAk4OfoJkwsUxoCkKBVkDvS4Zrf65lbV1usF6DQBFuUy0trbDW319bCQmNeLmsb3bX7NWVWN7tx8RQnJHuzpKrca1xO+/eFRfvkZaW6QnEoFjcGRLHEcnV23mGUtT6AwIODgUIdKFA6r+pxR29QQa+h6SVFFyZRUPnV3Brp4AbtoUrbGse+jsCnGJf7Uk49dvG8Ff/PQSvv3Cov35vv4g/p/XbMJqiawlBViVv1YSvcujXQv087MZ3DEWxb27umzNvYKk4vnZDBG2haZp9IdcuGVzFCv5NVHkuE/AQNgFmoB5CKxVphqREFiGIqpMBd08egIunFnM1Tw/v5NDT8BFzBLcFPfgyJY4Hjq3UreRHNkax6Z48+3+qWQJAsfg6ESqIft2KlnCTgK7KkUzsCnmBUNTyK9jX/qcHIaiZC4Thm7izm1xaIZRcwgcjrpx57Y4jMtxAC8xqrpqAsfUsVopiiKqJJZVHTt7AyjIGmZSZbvV3R92YWdvgMinNSOp2NUbQFFW677/rt4AMhJZkvpiayfJ2lpNalTdQFFWIYFHTtRaFgZvR/AMjfceHMA/PzGFk/M5O/Hf2ePHew8OEMuutKuj1Gq8cp78tXhZwiOwV9XaInk5u4NOXFopYFPMwp3JmgEHR4MC4HEwxEymmNeBu7Z3NJTIuGt7B2IkbK4G7cH1XR1SwoSDofGZH11AqqRgW5fPZsmtlmT8+U8v4v99Haky3sbHi/3GJHNgISOi0+/Ej88s41KiaH//TTEPXrutg0h2YjjqwYXlPDwOBoEOr534aboBn8BimBDjWK1MCRwDwzRtIg5NkVemwm4eDI2GOFeGBpGUB2BVOd53aBA8S9fpI/7ygX6iylxR1uokVwAr8X3kfAL7B8gA6FWXiS2dG+cIkpc1GAZww2AIh0YiUCpkLK1SVc0TCjjzlQOEhR+k7STNhHUwINn4TRN4YnwVm+Ne3DgUqYGmPDG+ikMEmouKauCn55axpdOHg8MR+5ku5kT89NwybiAkC6xnyl4epEzZqtj0ExNZ5EQVmqaDYxX4nBwODZOLTbcjuoJO/ODUEt55fS/edUM/irIGr8BCNwycXcjh4HCYaNx2dZRajWuJ33/xCLt57Ojx4bvHF+sm5r27u4g3qOdmMvjxmeW6ltxrt3Xg1jEyyYUOv4AzCznMpmtbErPpMs4s5vDO65u3ABqJevC2fT347vHFumTybft6iAkTBUnHibkcjArmar2G1XxaREF6eax6GkU77OUkVccPTy/hhdnaSmy18rOtq/lKqpNnMJsWkRc1BNwWQ9wEkBc1zKZFYia6ohkYCLtRlDVkSopdmQq6eQyE3USVqXRJweGRyBVFzEkt2wAL3/Sbt49sGL5JUg3MZ8o4NBJGV8Bps28XsmU8M5WGROAGAqzNq5nVUh2+idRiL+Jx4GcXExiN+xAVGMA04OQYJCUdP7uYwG2Ea4uqGzgwGEJZ0epYnQeGQjVVwJcaQTeP3qAbZ5fyDX11Saq+XoFFxC3guekMcmLC/tzv5NAfchPrGLaDKQtYFepjsxnMpVXbVznmcxBVptsdu3oDDaEpHzw8SDxmuzpKrca1xO+/eKRLCk7N5+vsapJFGacW8jg4HGl6g7q0UsSjFxJQddOWcKEoQNVNPHohgXt3d2MPgYBxqqTAwbHo8Ak2xqmKbXJwLJFMjJNnMJ8W6+Qa0iUF8xnyZKIkaxA4BpKq28kfYOlCCRyDEmFloh3RDueGgqzhxHwOITcPl4Ox5SFKlc8LBN9/KllCwMXhzEIOT06l7N9/IOTCUMxN3JLkWRrpoowPHBqAR+BQkq1kqiCq+OGpZSJvaau9e2UR81cSvsmEibft68WjFxL46bm1ZMIid/TCJAQkegUOt2yK4nPT6Tq2+HsODhDNq6GoG9cPhKwEZUa3pXecPNNSgsIxNJ6byWBHtx93bo3bDNSlnITnpjO4bbT5hLIn6MRwzA2Whi0VxbE0/AKL/oibKPHtC7rg5hn4BA7xdZVkUdHh5hki2RVgjSlLUcD55aItazXa4cG7DpD9VpPJEr7+9Bx29vhxy+b/j73/jpMrre988fcJlXN1d3XuVlYrS6PR5MgwwADDYIwBY3u9C3fxvrzgvffa+L4w14T1hV3Mcu96DQ4YHAFjMMY2wZhhIpNnNCONRrlbUudQ3ZXDqVMn/P44XdVdqtZAPd21jH6r7+vlXXSO9MypE57n+3y/n9CFppt43Qqpgs7fPjvJju5wy1qe7YrpdJm/e35y2Vmog0rVudalfIW/e36SvqhPSBg77HdRNW1eGE9f8fzPIq4lfv+Lx0KuwomZLN0hL70RqaEldWI6KwTqncuVr6iplilZzOXKQOuJnyPYaqHIEvGAe5WUAxiGJbSYTqXLlA2L64djTZNzuWoJM8/iATcBj7JsUWTVk19VkfG7FeKClVTYeNkN2HjnhmLFYFOHf01G46YOv1DiW6gYPHS6uSV5ei6PZlhcPyzW5vKpMr966yb+/MlLvDyVqTNF9w9Eed+tm/AJJH5Br8qDp+ebCE6ZkmPXdWgoKnSt7YjusJcXx9NMpBrJPROpMi+Op3nL/j6hcRdyGn/99CVu39bF/fsdnKffo5ApVvnrpy7RH/W1vFGrJSibOwPkKwYlrUrA5yLoVrl1e5fw+2qYFu8+MshXn53g2y9N14/vH4jyyzcOCQljh7wu7t3VzZefuEi2XHWwzlVno3rvrm7htvwv3Ty8plfxL908LOxVDFDQqnhdCrt7w/W2tFuVKAiSW5L5Cooi8cPT8w1i20GvSn/UJyy9045I5ivs6Anx+LlkU+frjh1dJPOtr4PQPgmu9ca1xO9/8dCXk5LcFZImEVCvKsu4FIkbNsfXbB2J2P+AA8A+OZtDlZ2qmY2NbTuipSdnc0IA7FqyqCjysu/rsuTKMqZHtDKzozvE/v4IL4ynUWWl3j4E2N8fYUe3mML+isOEVd+VnpiShR0mVkeuVGUup9X9T8MeVXgh9agK6VKV8hqMxnSpikdtvZKqVa2mpK8WFxeLwi1J3bT4xgtTnJ3LOw4Ly9IeZ+fyfOOFKT78hh0tj1kxLBby2prnFvKakIZbu8K0bLKaQdCjNrkWZDVDCOMIMJYsMBD38/1XZtdcTMcELQYBpjNlRpMFiiWNgN/rLKCCTGmAgEflH1+aJhFy829uHm7Qcfv2S9N8+I2tY3LzWpXRZJ7eiJe+qK/e5rNtm9FknqGO1i0Bc5rBU2NLvPv6Ad595DLppTFxr+KFnMZ//9E5nh9PNzGwT8/k+H9+bl/Lz8rjkpnOlJvm0IJmMJ0p43Gtz6d2IafV1Q0ifsceTvR9UmSJJ84vktcMBuP+elEhrxk8cX6Rm7eIYfzaJcG13riW+P0vHu0A9XYE3bz/ti0sFioosoxlWw6rN+Lj/bdtoUPA/gecltTu3hDPXEyRW7ULDftc3LQ5LtSSCnodd46LS4VmHE5HUJh55nMr/OINQxQqBi9PZeuJ3/6BCL94w5CwIOqKw8RKJWm4wy/sMFGLl8bTfPHHF5hIleqT3lDczwdu3yLkK+xzy4Q8KtlyFb9bWTFot2xCHhWfu/VJ37Lt+vO6PIJetaGl3kosFnVOzeRwKzJuRcICnKuTODWTY1FAcFw3LDZ3BLmwWGhitG7uFGO0ro6NrPrqhsWunjABj0LI60I3HYHkXLlKUTeFr9U07aYKClD/840CpJGa7uhSsUrU58Is5Yj6QiwVq+vy1i5UDMpVk9FkoUHLrVadEqmkTafL/NOxZmFwcKrUO7tbFwYPelVMy2YhX8GtKlSqJoZlU6k6JCTR+ercfL4p6QNnU/D8eJpz83khGE1v2EtiwOMkvoaFR1WYzjjyMOtxhzo5nV07mbp7W8tuKAC2bWNYFplylcn0yvMKLWsQriWY/tNGOyS41hvXEr+rLDbSSB5WQL3Hp7IYll2XXlFkiQODESHMTE/IS8Tn4rsvz3BqdsUGbHdviF+6aZiekNgLX6oY7OwNM5YsNjgUdAU9jPSGKQlMzh0BN4Zlrekpa1iWMLllKl3msfOLvOfIIO+5YbD+wWPDY+cX6Y36Wm4ht8NhAhxf4b946iLPXkw1GNTPZjVcikQi5GmZeWZYNnePdPHwmYUm2Ym7RxJCDgNdIQ/DcT/jqVKTXdVw3C+0SQGnLa0Z5koVbhUTx6PKQm3p2gIc9Kh4XUo9mVaX/U/XI2Wx0U47Ia/KDZvjPDGa5JWZHIZpoSoy/VEvt23rEiYMVG37VSu0VYHFdCpdZrGgs6XTj1uVyYZcRIJOUjGeKgtDM2zbsVJbC/JSqTpdkVZjIVdZM+kD55sVgdEMxHwcHo42zQPDHX7uP9ArzJRNFfUrVnZNyxayrDOqFr9y8zBffWaCR86s2Bbu64/wKzcPYwhW6BdyGp9/ZLTJC/7MXJ7PPzLKJ9+2p+WkysJx7yjpRoOvcklfJnwJXelKbLQE13rjWuJ3FcVGG8mDs6N54GD/MjM2V/+I9vSFeeBAv1BSuVTU+fpzE4wvlYj41Po6Or5U4uvPTrCvLyJEYddNmz977AI3bolzy9YOqqaNS5GYSJX44mMX+MP3Hmp5zBr7smJYTRPpbds7hdmXBc1ANyyeuZjGti1Htb7DUa2vnW812rGQAIwtFXlidKkh6QNnk/HE6BLvvL7Y8vPqCHiYTWv8wnUDhPwuihXTsSkq6bwyk6Mj0HqStqUrwL7+iCPhYtl1KQtVlhjpDQkD+/1ulappYTZ4ANsoElQl53yrUdtQLBaaF8x4wC28oWiH00484Ob58RTPXkw16I1NpUt4XAr37BJjyqrLWNyoz+1IrljO86pULTJlXchloqgZ7OoJ8dCZhSZf5XtGEhQFoRmxgJvhjoBT+dc2hoHbDm08gNMzuSYYwUJe4/Rsjtfv6hYaM+hx3hkbmoTBpVXnW4lowM0fPTrGYrHCcKe/LmK+WKzwD0en+Z237BK61tFkgZMzOfJataktfXImx6gAhECRJZAkFEmissppyaPKznFBR5TXalxL/K6SqLX5vn9ijtFkYVkTSWFrV3Bdbb68VuXhMwscGIxyeDhGuWrhczkL68NnFoRwKLO5Mqfn8k7j1bBXZPCROD2XZzZXZj/Rlq/VtGz8HoXHziWbztXIHq1GXjM4t1DggQO9IDms06BHxbZtXpzMsiMhhsVrhzaeblpIEoQ8apMNWr5iCC8k6aJeT/pWVzYkyXk/RLyVB2I+btnewdeeneTsfL6e/O/sDvHeGweFKhP1TcrD5zdsk+KMq7KnN8yLE5kGN0HLhj29YaGKV7s2FO1w2plKlzk2mSHoUekIuusMbK1qcmwyw1S6LHStnUEP2xNBjk9lGhLgzqCbAwNROoOtJ/9Br8pj55J0BT3s7g012Os9di7JYQFYAqwwcD0uuUlzUFR6ph0wmpr360hPszbierxf+6JedveFOTObx6XKWDjflmXZjPSG6Iu2/vxLusnFpWJdgaH2vcqyhGHalF7N1vNVIrM8X63Vls5rVTIC85VbkUmEPOTKVULelWKFadkkQh5hAefXalw1id8HPvAB4vE4//W//lcATp06xcc//nHOnTvHtm3b+OQnP8nevXvrf/+73/0u//2//3eSySS33XYbv/d7v0c87mBKbNvmc5/7HH//93+PZVm8853v5Ld+67fqyv/pdJqPfexjPPHEE8RiMf7Tf/pPPPDAA//zf/SqmE6X+fbRaU7MZDEtu66JdHwqg2XZwm2+qXSZc/OFK2LcRCYSTbea8Va28//Yy+dFwqPKjPSEiAXcxP1uqqaNW5VZKlbIFPUGM/SfNkJelZFl1tXlemsj3UHhNlc7tPG6Qh66gu4Nb3V6XY5g7VrPzJGfaf2+lnWTZy+mmM9rDZWd+bzGsxdT3Lkj0XKiVtukHB6OcfPWTipVE49LQTdM4U0KOPiudx8ZxLRtjk2uMCUPDkZ495FBIXxXXjM4PZfn5i1x7tzR1XCtp2fzwhuKdriszOXK+FwK8zmNuaxR36cFvSrdYa8wCz/scSqp1w3F6Iv6qJoWblVhOl1Cq5pCrgW6YbGv30nSX5nJ1tvSiZCH64aiwnjEdkgatUMbr/Z8XYpMdA0ihygZLR5w8+t3beV/PDTaID1y/XCMX79rm5AKQaqoMxDzcf2mGPGAu47xWypWmMtqQu1jcHxzzeWq5OXVSdOyhXx1K4bJ4eEYhmUzuUondlNngMPDMSrGa0d3dSPiqkj8vve97/HYY4/xcz/3cwCUSiU+8IEPcP/99/Nf/+t/5W//9m/5tV/7NR588EH8fj8vv/wyH/3oR/nkJz/JyMgIn/rUp/jIRz7Cn/7pnwLwF3/xF3z3u9/l85//PIZh8OEPf5iOjg7e//73A/CRj3wETdP4u7/7O44fP87//X//32zevJn9+/f/zO7BbFarJ32rw7RsTsxkmc1qjAiIQaaLOuNLRVyyjMevNFSRxpeKQtWeqN9F2KuuyRQOe1WigtpFHQE3t2/r4uEz8zx3MVVvHWyK+3ndSLdQ+ywecFOumjwxurimgLWo7MrqhWQuuyKTsZ6FpDfiJeRRKV+2Uy7rJiGvSm9EDEPSE/KysyfI6VV4zFrs7AkKYTJHkwWOTa4tE3RsMivUjplKl5nJrs2UrZ0XqXa4ZJmvPz/BnTu6eNf1g5R0A79bZT6n8fXnJ/jNe0daHrMGwr+wuHZrXhTj145Kss+lMpvVmlr92XIVy3bOi8RkusTbDvbz1WcneOjMij7g/oEov3TjEJMCrgW2bTO6UOTEtON9WpPemc9phL0u7tguDsLfaEmj1dIzDnnEwu9WCLgVYemZ2vPVdKMuP+VWZcJeFa9bFX6v0kWdfz4+wx07unj7oT7KuonPrZAqVvnn49N0h7a0zupVZa7fFOepsSUeP7dYr/ht6gxwy9YOoY06OL66u3vDvDiZaSBdSJbEdYNRIV9dWZKYzZYZivu5bihaZ3UvFnRms+UGX+T/f4jXfOKXyWT4/d//ffbt21c/9v3vfx+Px8Nv//ZvI0kSH/3oR3n88cf5wQ9+wDve8Q6+8pWvcN999/H2t78dgN///d/n7rvvZnJyksHBQf76r/+a3/iN3+D6668H4Ld+67f4gz/4A97//vczMTHBI488wkMPPcTAwAA7duzg2LFjfO1rX/uZJn7l6pVlFUzLplwV2+lJ0tq0+xqbTeR97wp6uG17J0+cX2xI/sJeldu2d9Il0OIBpxw/kS5xcanY0DqybZuJdIm7la6Wx0wVdZ44t1jfmdakZ2azZZ44vygkYF2L2kIykSoyOetnsLeLoXhAeCFJFXXu3JlAN23GkoX6RLq1K8idOxPC7UOfS+G9NwzxtecmGpK/Xb0h3nvDED4Bb+XcGjqO0k84/5OiXZ7Cqgx37+zm+6/McnZuVVu6J8Sb9/Yisj4NxHz0Rbx4XXKdfel1O/+/VrWEQfjtqCT73DKJkLsp8QNIhNxCDGwAw7T56rPjmCYcGow1YPy++uw4/8c9rcvklKomz4+nqBiONErtG6gYFs+Pp3jPDYNC19rOmM6UGUsW623ZrV0BYemZgZgPnyrz4/Np0kW9Xu2KBdy8YVe38Hs1ndH40akF4gE3PvcKGamsm6SKOm/e28fOFgsLXSEPz19KcX4+72z6lpO08/N5XIrEW/YJAJIBbHjPDYNULZuXpzL1w/v6I87zF7i3qiSxozvE90/M8q8nG8lob97Xi3ot8fufG5/5zGd44IEHWFhY2TEeP36cw4cPIy0/DEmSuO666zh27BjveMc7OH78OP/+3//7+t/v7e2lr6+P48eP43a7mZ2d5ciRI/Xzhw8fZnp6moWFBY4fP05vby8DAwMN52vVwp9VhLwugl6VStXBedX8RG3bSdxEk4ngsm/mWlpLLkUmKFA2V2WJdx0ZWmbalbAtkGQYjvt595EhIVA3QEE3eeL8IlGfm+6wrz45VarO8Z+/buAnD3JZJPMV9gxEeOxskkfOJuuL/rauIHfuFBfurEXI62KkO4Q2O8ZI9xYURVzCoNY+vHVbB3ePJDasfZjTDDwumf/tts2YNnWcoyyBadtX1Hh8tYj4XcT8rituKCICVd92VLvA0XA7NpFhKObnxs3xOmloNqNxbDLD3Ttb31CEvC5eN5K4ouTEeqpIG92SNCybe3f30DudJZmvYNo2iiTRFfKwtz8ixMAGMGwbrWo1VRNDXhe9ES+GAFU2rxmUdRMJB5BvmxbKMkSnrJvk1+GIMrFU4mvPjtc9kN2qzPZEkPfeOCxEnmuQnlnVll2P9ExZN+mP+gh5nYp0LUJelf6Yb7n6LwJ3cCSHFgtr40cLldY3aiXdRJUlxxmnqNfn1njAjSrLwhg/lyrz4Ml53rqvh/ccGaRUMfB7VApalQdPzjNyT+udL8O2+daLU+zuDXPrts4GHcdvvTgl7Fddi3aI7q8nXtOJ39NPP80LL7zAd77zHT7xiU/UjyeTSbZt29bwdzs6Ojh//jwACwsLJBKJpvNzc3Mkkw4xYPX5zk7HLLt2fq1/Oz8/3/L1m+bG4QK6gm7u2N7Jo2eTZErV+k4v6ndxx/ZOuoJuof9eQatyw6Y42xMBVEWuV7yqpknM76GgVVsetzvi4ZEzC/y7WzeT1wyyWpWI10XIq/LyRIb9Nw4KXetSsYKNYyd3OZsr5HWxVKy0PK4sSzx2NsmlpSJuVa63ji4tFeEs3LSlY13PsVAxmEyVSBLmzFyOgZhfKJkG8HtkDNNkLNmsAl87L3Ktzrg2M8tMwUrVIueuYtuQCHmExh2MeQm4FQ4MRJpEvMu6yWDM2/KYfREPiZC7YcGrRXfYS1/EI/T781qV3f1hnhpb4uhEum4FN9wR4NatHQ6QvMVxCxWDH56aw+9W2N4dwDBsVFXC71b44ak5BmJe4fegP+rh390yzFSmTFEzCHhVBqI+gh5V6PfHl5P0/qiXobi/nvialkXc7yLudwmNK2GzVKisyRZ3KxISdsvjelRnk5vXqvWE1MbZbIa8Ljyq2DdQqBj8xZMX+dHp+QZm87n5PBXD4n9//baWn9dEqtgA81gdc9kyE6kiIy0KuZ+bz/OnPx7j8HCMW7Z2NCQof/r4GDt7QnQExJyGlGWIz+VRd0pq8b6mihWmM+U6aagGzalULaYzJVIC8zVAoazztkN9/NWTl3hlJlc/vrcvzK/euolCWW95XNu2GYr58a4SlpeQ8KqODZ5tt/6u1mIqXeYfXppumLe6w17ecahfuEK7VrRyfa/ZxK9SqfDxj3+cj33sY3i9je2rcrmM293Yx3e73ei60/7TNO2K5zVNq/959TkAXdd/4titxIkTJ1r+N1cKXzTB3r4wMxmH1WfZIEvOTn9vXxirlOPYsdGWxy2oHWCbXFosLu/2QZGcMn1sUKVQLHLs2ERLY8qyzKGBbv70sXOcmV35MEd6w/yH2zdx4ewpLKt1ELbtSmAbOv0Rr5OkWk6bzjAtskUN26hy7NixlsbU/X1MZ8poVRN9RcsDtyIxnSmjV1sfE5wqtOmL8p0TC8xmajivSXqjfu7fl0ApZ1oWBVV9IdxmedV4K9Eb9WNkFjg2O9bytXojXRTKOg+enG3S3HvgQB92KcexY62Nq3lj/MLhAf78qUs8eGpl03RgMMr7btnE+OwC8xfX9q+8UkiSxG0DUb5zIt1wD3qjfm4fCDF25qSQ0Gpe6eChk1Ps7Ilww3Ck7rAwly3zo5NT7E14Wv4Giq4opy6uWH9JgKlDrgSnMnC8WyVQzbR8rZeHAmjA6Kz4GL6OPn50ao7jU1nsVSR8SYIDAxFuHPC1/PwBTG83igQuRWogXbhVGUWSMAW+V1+wm6GIi4xHbpoDol4FH7rQ96oFevnXkzNNVpNLBZN/PTnD63fE8BZbu8lJwiwtLV3x/OSsH63F73WRLgpalcfOLqx9PlcW+v2xaILrBiM8P57CWDUPqorEdYNxYrLW8ri2p5eFbBm3qtS7U5LkEDJ0w8S2LKFrtYLdfOPpcW7d2snbDvQuWwGqLOU1vvH0BX7t9uGWx7U8Me7c3sEPTi3w+PmVzs/mzgBv2p3A0vItzwHgzNnfeiXVNGcvLTkk0p/fG8coN2Or2x2v2cTv85//PHv37uX2229vOufxeJoSMV3X6wnilc77fL6GJM/j8dT/N4DP5/uJY7cS+/btW1drb3W8MpPjWy+e5K6dCTpDbudld6ss5it868Vp9r1tDwcPtu6peXahwN88P8+JmVzDbm82V8G2Jd6wr4+didZwM4WKwZ8/eYlo0M++YXe92hFwqzxxKc/7bt0tVO04t1Dg+i0Jnr2YamBaBj0qN25JMNgVZkeitXvw8NkkLkVqamcZllP1MGyJgwcPtnyttXugKz7icS+pVIp4PI4uSTwxVeV9t+4RugfRRB9n5/KUDZNCxSTkUfGqMjt7QsK7x7MLBR4/f4apTAV11fs6lanw+PkUt+wY4eDm/pbGfHk6y58/PIph2uztj9bb8oZp8w8vTfPB121j/8iw0PXuGO5fs9oFYuOdmc+ztTvG+WSR49N5KpUKHo8Hv1tla3eM/kSUke7WvoGj42k6Oq5s8+QNRjg4vEnoejc6nr2YompBxOdu+q6qFmQMlRsFvoGXp7Ps7AlzYbGIblr1SqpbkdnSGSAW8rN/pLVxCxWD993h4ks/vuDMWaaJoijs6wvzvtu3sHMwStDTOjbjX0/OU6hYDe//yn/TIqvDTS3egzPzeTo61ib3AAz2djHSvaWlMbPnFnGrCvGAZw0sXoWw38PBHa1dZy3+D2+Izz98nldm8vXOx96+EB963XZGekJAa3PrMxfTbOsJM5UqI0nUx5RliW09AfxeNwd3tX6tJ2dy7N/UxcPnFhlfKtaTtOGOAHds78LrD7BnW2vjLhYqfPXYKZaKOj63Wr/WpaLO0cks9x/cTWew9fnlzHweXSkTjnop6gaGYeNSHb92XZFQown27tza8rhrhWmaP3Wx6TWb+H3ve99jcXGRQ4ccUd5aMvav//qvvPWtb2VxcbHh7y8uLtZbtN3d3Wue7+rqorvbEbhMJpN1HF+t/Vs7f6V/22ooirJhid9iQee27V08dHqB0WSh/mLWsGiLBV3ov1U1bFJlnajf3aCCrioyqbJO1bBbHncmW2Qhr+NWFdyXebIu5HVmshV29QpYwQU87BuIMLZYdBh9OJWJzpBzvCPgaflaJQnnt1v2ChZNcha9qN+NJCF0X2v3wBFstpb/WxKSJK/rHhQqJv/w0nSTjt2HXrdd+F1bzOlMZ7U134HprMZiTmd3b2tjl6sWl5bWbnMVKmXKVUv4eiN+hYhfjCC0VgzFA2xNBPG4FAqVKsWSi4DfS9DjYiDmYygeaPlaQz53Xaz7Suc3am5Yb+TKBtlylU0dfpCkOgkB22axqJMrG0LX6nOp3LOrG84sNGkZ3rMrgc+ltjyuLFs8fynF1kSQ64ZjlPUqPreLbLnK8+MpjmyOC12r+hN02lRFbnncoXiAnojvikQckfeqP+rj9bu7GYoHiAdclJcLAEtFnYlUkf6oT/i92tsf5T8/sG/DrMVCHpU37enmey/PLWt5OmvWzu4Qb9rTQ8jT+vMHR1/z+Uspzi47d9TWgbNzeXwumTt3dLU8bqpkoMoKAY/aJL2jKgqpkkF3pHWcZ6liUaxYV5RLK1XE58H1xGs28fubv/kbDGNl9/nf/tt/AxwG7vPPP8+f/dmf1V8k27Z58cUX+Q//4T8AcODAAY4ePco73vEOAGZnZ5mdneXAgQN0d3fT19fH0aNH64nf0aNH6evrI5FIcPDgQaanp5mbm6Onp6d+XqTqs5ER8qg8djbJmeUPqPbbz8w7ZeLbt3UKjasbFtu7QgS9ChGfu65jlynpFCpiPp3tYl8uFXVeHM+wPRHkyDIDV1UkMqUqL45nuFmAgeuWHfHTkm4SD7jrLXStahHwqLhlMUZjO+5Bzaro/EKxIaE+v1AUtiqCFQcBWZKaEvXV51sKm1cVrxVlNcLGA6UbpXeo+7/2RHzChIl2sG/bFRG/i4jPxaU19CFFiTgA/TEfD56aX1PLMFXQ6Re4B1PpMpmyI1ycLBQolrR6km5YhrCkTzzoZnNnYE2Luc2dAeIC/uLtIOLEAm7edqBvTb2937hnm5DDyOrYSGuxnoiXE5NZhjv83Lgljr5MmJnLapyYygiR8cCp+p6dy3PTljj9sRX88FS6xPHJjJDuZm0uWUsYey3y408bblVmMl1kT2+ErpCnvr4u5Cqcnc/hFpS0WW+8ZhO//v7G1lIg4IhdDg8P09HRwec+9zk+9alP8Z73vIevf/3rlMtl7rvvPgB+8Rd/kV/5lV/h4MGD7Nu3j0996lPcddddDA4O1s//t//23+qJ3ec+9zne9773ATA4OMhtt93Ghz/8YT760Y9y4sQJvvvd7/KVr3zlf9ZPXzOqls1MttyEYbJt53hVkHnn+HTGmEyXMS0b07IwLQcovVvQtaBd7Mt0UWciVUJZZjSv1oYzLVtIc9Ctylw/HOPhMwuMLqyQJrZ2Bbl+OCb8YQa9KoosMRz3rekpKnIPRpMFzi8UCHubnTvOLxSEtPGgPQ4DsYCbzR3BtXe6ghZYsPE+tbXYaOmddiz67YqhmB+fquBVFWJxd0P70LcMbheJmtPKN16YYCyZaZAzedeRITH26WoBY5+rnqSvxwoRnHvwC9cPkC5WkSTQqo6OnWVBPOASvgdDHX7ee8PQhlXRUkWdP3p0jNGFAnG/23HYQGJ0ocAfPTrGJ+4X2/y1I1JFndt3JHhyLMnR8QyGaeFSax7Q4vJTmmHyc9f188iZJA+v0ofc2hXk567rRxMQW67Nx1cSxhZds7yqzFv29fHdl2f4l1cadWLfur8P77XE76ePYDDIn/7pn/Lxj3+cb3zjG+zcuZMvfvGL+P3Ox3no0CH+83/+z/yP//E/yGaz3Hrrrfze7/1e/d+///3vZ2lpiQ9+8IMoisI73/lO/u2//bf187//+7/PRz/6Ud71rnfR1dXFpz/96Z+phh+AppsMxvxYdqlJGmEw5kcTpMbHA24qhsXp2RwL+Up90k+EPCRCHiEB47ZVOyQovtrvFFCJCXpdvDKVYUtngFu3rbDkZjMar0xluP9A67hJuNxMvdFTVNRMPVeqvqpEiog2HrTHYaBmgaXK1IVmXapMxKsy3BkQ+v01eYypdLlhV141LWF5jNWxkdI7sPGCwO2KQsXgF28c4stPXOT4Kl20AwNRfvHGIaEKyuoYivvpCnnrFT+fS7zi265NZSLsZSQR4rHzSZAkdNNGrlrYts3ORFw4mdrojcpMRuPUTA4bqK5qc8qyxKmZHDMZMSH/WmxkNd2Zo2yG4wE2dQTqbXl72cVJNEn3u1WOT2TYnghw547OuoPTdLrE8YkMd+1s3Vu6tmZdPresx7IPnA3EqZkcyctkcpKFCqdmc9ywScxicL1x1SR+Nau2Wuzfv59vf/vbV/z773jHO+qt3stDURQ+8pGP8JGPfGTN8x0dHfzJn/yJ+MW2IVyKjEuR6Al7HQ2sZasi23ZYcy5BL8FUUeeliUxdCb8WNSX8m7Z0tDzptava4XcpbOrwkyrqTRWveMCNX0BoWNNN7t3Ty/dPzHB0Il0HCm/pDPDmfX3CCTU4ZurzOY2KYWGioBsW8zlxM/XIGkkfOBPsdKYs3JKrOQy4VblJaFa0MhPyurh3Vzeff2S0Ad+1ozvIvbu6hcacSpcZXyzREXQzFPfXRZGzpSrjiyXhNl87I+R1teWaNnKBLldNvv3SNFu6AhzZFEc3TdyKY6317Zem+fW7xMDntUR9rQ3gTFYTStTbtakcXyzw3HiKH5ycb5BL2toVxOdW6Ap7GO4MtjTmlX7/fK4ivFGp6enVdAyvdF4kJpZKPHNhkXLVqn9bJ6ZkbtrSKZSkBr0qD56u4Tvt+uYXJIY7/BwaEkt6TNPi8KYYD51e4AcnVxQDtieC3LMrgSkATVk9X12uuyk6XwEUqyYnZtZ2MDoxnaVY/dlYwV01id//6pEIewj5XMjLH7uxvDjbOAK0ibAY2D1ZqDQo4dd2kDUl/HcWBmjdsKo91Q6fS+HdRwb5hxen6q1pRZYYjDlYLBGHiYBXZTRZ4O6RBPfKEuWqhc8lY1g2o8kCN24RE+6cSpeZzVYIelS8Lhm9auJ2KaiyzGymIpSk1IRvz2vNOn69Ee+67u1Qh59/c/OmDWtJ1Xx1rxuKctOWjg3x1c0UdXb0BOpg8Vrs7A7xlv09Qubsq2MhpzG6kCdJB5VL6XX9/nbGRleRSlWzQcZnrfMiMZUur5mggZP8iH4DG22FCE4i+u2XZri0VHSIHssT4aWlIt9+aYZDQ7GWE792/P6a3p5h2U0+teqy3p5I5LUqRyecxPdyIo5LlYkFXC3f24phsZBf22JxIe9siEVClWWeu5imVDXZ1hV0cNmy854+dzHN7dtaJ2K2Y74CwKapIFFL1/0uZV1Y5/XEtcTvKon+mI9fPDLIFx+/wMtT2foHv38gwi8eGRQCSoPzwhc0Y80dZEEz1rRx+mljo6sdsYCbpy8s0h321EG9qiJhmBYzmRIHB6MtjzkQ89EZdHNuLt/kfSnakgQHjzidLrFvIEw84EGrWvjcMkuFCiemckJ4RE03uX9/P/98fNqxFsN5B3b2hLh/f/+6qpMbnUy0w1fX7ZKbkj7A+fPLcMPmK8un/KQ4NZ3lh6fmMSyLsl7Fv1ThuQsp3rC7m939EeFxNzraUUVqFxGnXSSvjcZjAqSLVcZTJWdRruGobQkJGE+VSBdfGxaDO7pDXD8c46mxpQbSlRuZ6zfH2dGiIHQtptPlZVhKo/zM+FKJ7xyfZVdPmJEWv1fdsFbhfFfmphrOV4Q4CKAZFsWKQVEzma+szDFBj4ui20ATGHcqXWY+X2E4vjLf11za5vNiSTo4a1Zf1MfJmcaOWsTnYk9fZN1kHNG4lvhdJVHWTR49m6Q34mN3X7iurp8uVnn0bJL9A1Ghic/nUl91Bylq0N6OSBV1Hj6d5MRMtsm5Y19/hJu2tM7qDXld3Lm9i89dSvHyVLZ+fP9AhH9zyybhxUSW4HW7Enz/xCxnVvm/jvSEePO+XkRc6wJeleOTGa4binHXzhXLtly5yvHJjHB1Mq9V+adj0wTcCiM9oQZf2X86Ns2/vbX1+9CORa+gGczlNHrCXvweBdty3tNixTkumkgs5DSeubjEI2cXuLhYxDBNVEVhc2eAsE+lM+R5zVT+alWkqmk1YZFEq0jtIuK0C48HG4/HNG0bCVBVGVWSnHlQcvQ8DdPGFBAGb8fv97kVfvGGISqGxanZXH1e2d0bdny13WL3YSFXYSZT5vrhWBP79PhUhoVc69aVQa9aZ8o6MklqnYHtUmTh5181LdIlnYBXIRH21OWnirpBuqRTFWj1FjWDXT0hHlpLemgkQVFwbukIuFFkp1LbGfRgWE6xwrJsFNk5/7OI186qfi1eNcaSBZ4cXeTI5jgRn4tK1cLrUijpJk+OLvKW/b1Ci1NvxMuevjAvTWSadpB7BiL0Rl4bCx44k9MV9eYymtDktJDT+PMnL9If9XNwMFZ3bUjmK/z5Exf53bf6hO5rcFl+5/xCYbl15MzQ5xcKPHY2yV07Wm9HdATc6KbJ0fF0E8axL+oVnkSm02XiAfcVJ73pdLnl3X47Fr2cVmVbIsjoQoHpzEqbLx5wsy0RJCdYnZ5Il/jeiVmG4n5u3NKBbjiL3kymzPdOzLJ/MLqulvdGwh0KmkFBM66oCyaS/NaIOB6XvKHA9nYB5tsRHQE33WEPs1mN4irnCpci0RsR+7bagUecSpd5YmyJf3frJjTDIq9VCS9b1f14dImeqE+oMlW1LO7d3c3j55JN7NN7d3dTFXBaWv37L2dgrweP6VFleiI+xpeKTKZW5oGY38VwRwCPAFM26FWb5j9wKp4PnVng8LAYHjFd1HngYD9fefoSJ2eXCwAy7OkJ8cDBftKCzOb1xrXE7yqJdEnn9bu7OT2b4/hUBstyKl2dQTev391NuiSGb4oH3NwzkkCrWlxapYK+qSPAPbsSwpiRdoRuWrgUiQMDa+9KRfTmRpMFTs1e2TJHVCJloVChYlgMd/iRkeo4FMu2HezLFczQXy1qk8haGMf1TCJa1XzVSW9PX+sswXYsevGAh9GFAhXDIuJz1d/VimE58hYBMZxruqhz/XCMR88m+d6J2fq42xNB7trZJdSWh/ZIz7hVuSnpAyjrjkisiPxQu8hY7QLMtyN8bpmDg1Fy5SSZ8kryHHArHByMOi3vFqMd97WoGWztDPA3z0xsaGWqO+zlqbHFJh3Di4tFVEXijXt6Wh6zXXjMiE9FlSUCHpXusLfuCFOoGKiyRMTXelrTLjyiVjX5wStzRAJu7j/QW4cnLRV1fvDKHL96s5jb0HrjWuJ3lUTM72Z8qchcTkNebkVYts1cTsPvVnjdSOsUdnDap16Xwo2b49y0paNe8bJtG69LEdZaakckQh7esq+HFycyvLLc7q1Jz7xlXw8JAb25nySBIiqRUtAMDMuiVDEpV816C93nUnALCoJqVZPJVGlNjONkqkS3wO8HBxQ9kSqtqQ84kSoJgftrk/7ZuSylqtOWDHpUfC6Znb0RoUk/4nOki07O5tBWtd5qDjYRn9hC4lFlnhxdpC/q46YtHXV5iPGlIk+OLnKrgDh6W7B4y9eaCHmbknSARMgrVO2A9pCx2gaYb0MkCxU6g14ODMZYKqzIWnUEPXQGvU1yHD9tbPR9bVdlyrRsihUTZfm7r4UiSxQrZsOxVqIdeMxsxeBd1w/wtecmODa5As85OBjhXdcPkBWQH2rEIzbDHUTxiAXd5PHzyQZ8Xy0iPhc/f1hMxHq9cS3xu0rCtmEuV2EhVyG3KmkIe1X8bhUBCAoAec3gzHyB6wYjIEn1Bdq2bV6czLKtSwws3I7oiXi5uFRaU3omFnDzboG2dPgnSKD8pPNXiojPxUK+Qqqos3rOzEpVdNMSSlKqts0/H5+5orvAgaGo0LViQ1fQzfgazg3Dcb8wuD+vVfmXV+Yd3NwytmVzR4C+qFi1S9NNHjjoCLs7FlBOZW5nt9M2ESW3WDYc2dTBU2OLPDm6uJykS2zu9HPL1k5E1rx2MDrB2VDcM5JYuy2/KyGMc2xHtIPg067QDZtvvjDJ9ZtiHB6O1THUE6kS33xhkuuGo8JjbyTJrV2VKd2w2J4IocgSuXK1DqMJ+1xs6RJPfGDj8ZgqMl97boKOgJtfvWW4vgGeSpX42nMTfPgNretQrMYjruXcIYpHzJZ1SleYl0q6Sba8PiUC0biW+F0lUdQNZjMOVqbheGX5uC424Ye8KjsSQf7pMkZXrXUg4tzRrpjNapyfz3Pj5jjdYS9V08KtKsxly5yfzzOb1VquTm7rCrKjO9jQiqrFju4g27pak3Cohd+jkAh5WCw0ftiW7VQu/Z7WJ7982WAitbbp+0SqRL4s9g4EPCrZcpWAWyXmX3Fu0A3LOe5p/R1YyGn8xVMXeebiUkMyMp/TkGX47TeOtPysauSWw8Mx7h7ZOHKLS5Y5OpFmdKHACrzLZnShgMelCGkutovRGvCqnFso8MCB3jU3atcJaqO1oy1d0IxV7jVKA2loPNWsR9lK5LUqE6kiScKcnS8wGF9f9TDscxHxu3hqbInHziXrx12K41wTFqwmb3S0qzJVS3x29W5s4gMb/6yqlsV0usy5+QL6ucX6cbcqE3Ar68cjXubcsR48oirLBD0qfreCxyXXCWla1aSkm6iClqDrjdfOqn4tXjUUSaZcdT5GlbrMFBI4hvevYgj/ahEPuHlidHHN1sETo4vcu7v1Ra8WGw1sXyxUuGtngsfOJXn0bLJ+D7Ymgty1M8GiQDsmEfbywbu38eUnLjKeKtXbnENxP++/fbNwmzuZr3Dvrh5s2+b03EpSuasnyL27ekjmW79W07IJeV3ktWpTOybkdQm3YzyqzM7uMM9cXCKXqdbva9jn4qbNHULtw7FkgafGltYUm35qbIkxAexkR8CNZpg8cjbZdG5Hd1CY3GIsLyQuRcZehRN1KTLT6TKGwEJSWyg13WiSCfK6VeGFdCDm4+BgZM2NmqgjTDtY3eBsKvf0hnnqwiIzGa3+bfVGvdyypVN4U1lLUueyZZaWlujoKDm+yutIUuM+F9cNRXnhUtqBZixXk30uheuGosRfI4lfuypTq4k4l8d6Ep92PCtFlvC4FEfD1q1i4/jW27aNx6WsKWz9k6IGTblcwNqryty0tVN43eoIujk8HOXopTTzOaP+XoU8Koc3xegQ8IDeiLiW+F0l4XU5TgoXFotYll0vxcuyxJbOAF6XWOK3VNRRZblJx8vnllEVmSVBjF87Kgghj8oT5xcpaAabOgP1ylRBM3ji/CK3C2CxwPnoX78rgWZY9STVq8qEPOKTvUuW+aunL3L3SILX7+6hsswUnUqX+KunL/LZdx5oeUyn8rAsv3MZqzngUYQ8dQEqVZPdfWEuLBYaKsqJkIfdfWEqAhi/paJ+xYpOQTNYEiBMpIo6t23rpGJYTUnPbds7hfGoumkhSeBSZcefdfm+GpaNJCFEGhqI+fCpMk+cTzfpd927q3tdjNbTM7mmdt9CXtwRZjpdpjPo4cmxJNNprd6W7496uXVrlxCrG5xN5dGJFEfH0w3vwky2jEeVuWdX67jk1Unqrt5wgwf2epJUlyLz1v196IbVRJx664E+YWek2jVv1Aa4XZWpGhHnDx8+z8mZXP0b2NMX5t5dYve0Xc8q7HWxPRHk5Ey2YbPfGXSzIxEkvI7iwkSq1ORedNMW4eHoCXk5NBjjQrLI4qo5byju5tBgjJ7QzwY/fy3xu0oi6FEdAscZR2vMNi0UWWZzZ4DX7UoQFGjHAfXJaHtXcM3KhEg7pl3AdmsZyJgpV5lMryz8Ia+LiM9VP9/qtT57cZGvPz/J+VXt3u3dQd5zZJBYoFfoWmMBF1sTQb5/Ys7x1Fze6bkUmb39YWKB1sds9NRd2dWux1MXHFuhb704xYGBKLdv72qQtPnWi1Ps6W+d1fuTzMdFzMnzmsHpuTw3b4lz546uBrLA6dk8OxJieFSPojAcDzCeKpItV7Ftp4IQ8bkYjgfwCGCSyrrJQNxHLOAmVdTrVdRYwM1A3EdZN4Vt6/IVc82KT14zhXBzlarJMxeWeOZCqiFJnUyVUCSZ3b1i93U2q/HKdK5BvBece/PKdE4ImtEoPdTogS0qPQTQF/Pxo9ML3Le3p95CD3hUsG3OzxW4eYuYOPhGb4DbxcDOa1W++/IMXSEPDxzsr88B2bLOd1+eESLitOtZeVTHnzvqc9Md9tWT9ErVrCs9tBq1NWs6XaZiWFQNZ86eTpfXtWYtFnUeP5dkd1+YO7Z3oZs2bkVisVjh8fNJbtzSQX9crBCynriW+F0lsbkrwA9PWbztQK+jUF6pElxu+6VLVTYLLvpBr/qqumAirYN2Adurlk2mrKNdVoHSqiaZsk5VoNU5vlRqSvoAzs8X+Przk+zqjbBXwLkh5HF20KZlM7FUwgJkHJbbvbu7haqJ7fDUBcAGVZF4YTzddErUuaEn4mN7d7DpvoKTVPdEWq9MrPUuStKrn/9pIhH2EA24cKkOnnMj7BBHkwW+8cIk9+3t5d1HBijrJj63ymK+wjdemGSkNyxUnaxtxFyK3FTxWX2+pTF1kyfHFpuYh9lylSfHFnnXkcGWxwQH7mBYNkNxf72CWhOvLeqmENyhHdJD4Hxb9x/o29CEql0b4HYwsC8ki/x4dPGK7i1v2tvLgRadkdr1rJL5CoeGYjx2LsnYQqEB8nNoKEYy37qe61S6zNnZPJPpEjbUk8n5HGhVS3jNSuYr6KbF2bkCZylg2iuwrKBHEfoGNiKuJX5XSYS8Lu7b28sfPnyeV2ZyGIaJqirs7QvzoddtF8cgBNzLNlXNumCGZQnhptoFbHekBpzqp2Xb9SqaLDnHRbAdDjGkOTkBJ/mby5aFEr+iZrCnP8JiocLBwWidJajKEnv6IsJ6W5d76kb8Lrau01O2Hc4Nwx1+3nNkkG+9OM3FZXiCLEts7gzw89f1MyxQ7RiI+Tg8HG2ylloPvg0cO8RfumGILz1xkVems3Xnjr39EX7phiEhO8RcSef27V38yyuzDT64W7sC3L69i5yg7mY7hLGzZcft4I7tnQzG/RimjUuVmVgq8sJ4Wph56FZk4n4XhYqJJNmYlo1lS9g2xP0u3ALt01LVXFPKBpyEQtRXGDY+oWrXBrgdkcxX0KrWmpJO+YohlKCUquYqNxC3Y4Xocdd1V0WfVcWweOj0HD93cIBIwEWxYjoEtaLOt485nYtWY7FQYSGvkSrqTRhPjyoL4ccBPC6ZqczaRKagV8UjCNFab1xL/K6SyGtVHjw9T9DrYm9/mGJJq9vfPHh6XlgTqx24qXZZNfldCtsSwWVbrUaM2+bOQJMZ9k8TmuGIQt+wOU5f1NHGcyky05kSz11MCfk+Avi9Kl966CIHBqJE/U6FNuBxkSlV+dtnJ/iNe7YLjdsO7GQ7nBtCXhfbu0IcGIiwuzdcH9OlSGxPhIQX07GFArdujfPG3d2UdJOARyGvVRlbKAjh22rx/KUUW7sCXDcUpaxX8bldZMtVnh9PcWRz62zhaMDNj88nG5I+oP5nEXwbtEcY26XIvOfIII+eTTYQZ7YnHLiDKL6tO+yhO+JldHSpwfM75HVx67YOugUqqdgQ8irs7A43JRNn53PrNr3fSOmV2mK/lr2eS1DLE9okDK7IxPwupi9LUoJelf6oTyhJl2y4b28PD59Z4F9eabRCvG9vD5Lgs+oIuLlvXx//cMzxLK/Fzp4Qb97XJ1SssIGFfMXBOEvLB3Ba/gv5ivBr5XMrdAU9az7rrqBH2GJvvXEt8btKorZ7dClyk/3NenaP7cBNDcR89EW8eF1yk4yDVrWEKzNel/KqGmZegcSvM+DmXdcP8sjZBX50eqF+fGtXgHddP0inIFO0YlhMpEucnS8Adh3fAhI+tyykt9Wu1lENN/SNFyYYS65MUD3raHMt5DS+/ORF/G617rJSww1+WdAKbyZd5sBAlG8fm2YyVVqu8sJg3M/PHexnJl1mp8A3UMPN9Ud9KLKEKkHA5yLgUZnOaELflmnZpK8g/p0uVYUZ2O3AeCVCHo6OZyhXLXZ0h7BsG0WSKOomR8czvHlfi32z5TAsm53dIc7P5xsSv0TIzc7uEIbAPYgH3PzcoQEeP5fklZkshmmhKjI9YS8/d93Aa8ppqAajubBYaNbG6xSD0bRrDkiEPWsmowXNwKXIQnCHWMDN0UvpJgmqiVSJo+NpITcQcJKpJ0cX14TnPOlb5F6BDaBblkiEPcznNPRVln1uRWJbdxC3iLk6UK1avG4kwcPL2Pxa1LD51aq4PuJ64lrid5VEu9qnQa+KadmcnS9smDxAyOvidSOJNRli62lL98d8PHhqfs0kNVXQhVpygzE/p2fzTKQaZQwmUmVOz+b55RvFLHV0w2J7V4igVyHic9UrE5mSTqFiCult1ZL/tSoIG9E6OjAQYXt3qMFlQ3Sr2w4rPMO0+O7Lszw9ttSAR5tKl3HJMh+4fbPQtTYatDeD0EXa8pruJFHn5vOkVrH54gE3O7pDaGtgqX7a2OiWpGk5qgEl3WBqFWkqHnDjdfkQIDUDsFTQ+fZL04z0hLh1W2ddaHcuq/Htl6bZ3x9tecx4wM2J6QynZnJktRUiTqqgEwu4uE8wmWhHdATcVKpOm3Q1NKViWPQL+v+2q32sGxY3bYljmhaXUqU6NGNT3M9NW+JC81XFsEiVdaJ+N1XTwjBkVFXBpcikSrqw2PRMViOvGUT9LgqaUcf41RLtmazGSG9r+MFsWefQYIxMyWAsuZJQDsYDHBqKCcMdogE3lxZL7B+INBDnsmWdS4sl3rD7Z/O+Xkv8rpJoV/t0IOYj5FF46kKmCd91y5aOdemCpUtVEmFvHSibLjnHRdvSIa/L8ap9aYoLiysL1HqqHbmKgdfliC3ntVU6S14Vr1shVzHob3lU599fvynGg6fmeSSZrGMyt3UFHXKHwPMqaMarEnFEk/+8VuXoROoK2DmTWMDV8r1thxXeUrm6pv1Rdvn4z18vZn/UDhuszpCHXLnKSE8I03LafW5FRpYhU6rSKSi9047Qqg7RwrTsBg9k07KXsV9iWCxHBkfi1Gyel6eyDdAMWZKEZHJmsxonprLktOoyW95J/HJalRNTWSGm8OrYSOmVuazG4U0x+mM+VFl2GJ2qI8XUE/EyJ3Ct7SoAVAyLM7N5eiM+dvWF60l6pljlzGyem7e0LpXVKDYNtqHjUV343Mr6bNC0KtOZMomgh95II6vXwdO1PreoisLXnp3gxi1xbtvW0eDc8rVnJoRZ3QMxH8OdfqbSZRYLhYbN+nCHf12yTuuJa4nfVRLtwPbUYldfmNFksWHhS4S87BZkXV1IFpclR5o/7KcuLPGW/X0tM8RqMdTh5703DNXJDWG/i23rIDfUWhlDcT+GZdc/TFWWcMniOJyaMPZk2tEsq016k+mysDC2W5Wbkj5wiDgXlwq4BX1ap9PlpqQPnMTnO8dn2dUTbll2oR1WeJmiTlk3uXNHF0Nxf4On7tNjS2QEtAGhPTZYW7oCHB6K8dylVANLUAJu2BQXlt4BB+N1udDsiSmZm7Z0imG8JDAsR2NQNy0sC2TZwX0ZTu4mFF0hDz63TLHSnDj63LKQ7uR8TiOZd/zFfW6lXpmybUjmdeZzV7aI+0mx0fd1sVDBMG3GForM5TUsG2TJ0XbrDHqECAPtKgAossSJ6SyZchXLsutVNFmWiPpcQsS51WLThUqVYkmt49LXIzYdD7gJuB2sYG/UWycjzaQdByuRdr/PpbC1K8CPzy82yILJksSunhA+ARgRtE9+Z71xLfG7SmL1CzSXXWlLrvcFmkqXOTqeWbN9+sJ4hp094ZZbB8l8hbLukC8uJ2GUdWtdFPamybmgMJUqCU/OV0qmAFIlXTiZahTGXln41iOM7VFlEiHvmqzGRMgrpF8FsJCrMJEqrcnom0iVWMi1Lo/QDis8r0vmPTcMcfRSihcn0vXK1FDMz3tuGBIWMW+sTDQ+q/VUJm7b3snpuRwvT2XrC+n+gQi3bRcTGoeV6uwPTs43VWddqixUnfW7FIY7/FxcLKJIEpLsfK+SJDHc4RciTYGT/N4wHOfB0/NNItY3DHcJJb+y5Oh5xv1ufG6ljvEr6yapoo4gFKt+X79/Yo6xZKE+Z23tCgrf16BH4bsnZhldKCBJUt1u6eJikWSxwpFNrVeS24WftrDpDDo4P59bqW9UyrrpaKQKYD4axKYvw6Wvp1ixuSPAL980zLdfmuZHp+cb5Fx++aZhNne0/l4FPSqv392NJEmMJgv1uWVbV5B7dovr5EJ75HfWG9cSv6soai/QRKrI5Kyfwd4uhuKBdb1ABc3AtOyG1unl51sNt+IIbF7JWkyEIQbtWfQ8qkx32OvguCSWWb1OBSHgVYWTqdoHfqXdrpDemma8KrlFtDqpm9arMvpEWnI1K7zPPzLakPzt6A7ywddtE6rQ9oS9jC7kOTufbwBgn9XzRPwu3nGor+UxoT2Viel0ma8/P8lQPMDh4XgDueXrz0/SF/UJide2ozrrdSncsb2LbFlvEjG/Y3uXEGmqFgcGoySLlbplmyJL9EW9whX/iM/FTVvjPHfRIQ3Y2Eg4hImbtsaJCFqrTafLfPvoNCdmsg1z1vGpDJZlC93XYtWqt9Abq0jO5rgoAOyv4afX/K7u3ia8FqQKFX7l5mG++uwEL09l6sf3D0T55RuHSAlUJ9tVrChUDCZTJXb2hLhpS0e94pcqVphMlShUWp8HN3cF+JdXTA4MRrhjRyMWT6uawjq5tdhItvhGxLXE7yqLkNfFSHcIbXaMke4tKAKuAqsj6FVf1UhdZNGLB910hTwUKwaKKtV3T7bttH/igv6E7Vj0SprBr948zKNnkxiWXZ9EFAnuGumitA7SDLAmC3v1+VYi4FVflYF93VDrFQRwWidzWW1NRt9cVhNmSu7pj/DJt+3ZsLa8btoUKyaDcT+KJDmtM9lpoxYrZkMy2Eq0ozKxkKswlixi2YWmqrcsSUJV1Nq4K9ponrpTQU0bTWTceMDN0fEU27pC3LylsyFJPTqe4s37xADoU+kyL05meP1Iou6GEfSo2LbNi5MZdvSEWl4Mw14X+/sjjC0UyZT05bnFpiPgZn9/VNiuazarNSV94LxbJ2Yc7GCrhIF8uUpn0INuWORWb6g8Kp1BD/ly61i0mqyXV5UZ7vDXnZa8qrwuWa940MOfPnaaRNjDv7l5uI7xm8mU+coz43zkLbtaHhPaU6yYzWmEvCpjCwWeu5iqQxM2xf30DUWZzWnsbPFZhbwufvGG4WVlg8LGieO/RuNa4neVxUJOY3QhT5IOKpfS61pIoT2iuC5Z4m0H+vjmC5OcnV+ls9Qd4oEDfbgE+zHtaEl63QrPXFzi9GyOpYJex+F0BN10BtzctfO1o7c2EPPRGXSvWZ1dT+vE71bY3BFoWviUZcFl/zq0pnxuhZjfjUt2Kmfr0a3KlKrIEpR0k7Ju1qs9PrdC1OdoJIrEajmb0WQBDTfZssHWroBwZUI3rVeteotUUQGqlsW9u7t5/FySf3mlUR7i3t3dVK3Wx00VdW7a0sFDZxYa3FtqlWRRD+SiZjDSHeJHZxbqFT9VluiNerllS6cQW7qomzx6NsnOnhC3rgLhLxZ0Hj27wE2CIPxy1biixI5p2ZSrrV9ryOvi0mKRjqCbnoi3PrcUKwaXFovCln1jC0Xmc2Wqpl13Q8lrVQwLYVavjMRCvsL5hWZoRsTnQhYFeuJY9GVLVSq4yJYNYbvC+rVK8PylNCdnclRXfUeZUpWqZXOroGf75eL4692ovpbjWuJ3FcXJ6exyiT9fl5zY0R3ig3dvY4+Au0QtNtr0XauaGKbF4eEoN26J13ePxvJiuB6W4Ea3JLNalW8fm2me8BZgsaBzSIDRCe1pczTq7TVatq2rdaIZvHFvD7IsNeGb7t3T/ZoRmvW4ZGZzjpTDijyGjaHZzEraulXwh+J+ukIesoUykaDPAXQLytnUqqSxZSzaasyUYdnCVdTusJenxhYbNMHAwY2piiSkjZbXDM4tFHjgQO8albmssAdy0Kvy3MUUR8fTDe/QTLaMKskcGoq2PGaqqCNJkC1VSRcdxxGXKiMjIUk0SOe0EiGvi0TYzVv39dEV9lDWTfxulYWcxndPzAh9W51BNweHopyayVHUzToD2bJsDg5F6RTofKSLOgt5jQuLxSbcpCRJpAV/v2nZ7OmLcHIm2zTunv6IsO5kO9Ys04LTs41JHzjM+dOzOWH5oXYIY9diIadtqNvSeuNa4neVxEJOa8J1AJybL/D5R0b55Nv2CL1I7TB9L1ZN/vHYNG/d10fU76o7LKSLVf7x2DS7BNnC8YCb+ZyGVl32UrVBkh0vxfmcWEsyXawyniotg8ZXjssSjKdKpItiVSRYxUBeyJPMRuiKBtjWFVr3B7+Renuw0kK+dVsHd48kNqSFXJP0CbgVRnpCDRCCfzo2zb+9dVPLi2lNBT+vGY7qv+SAuiVpfSr4q0VxbdtiaWmJjo4OJElmJqsJieIG3Aq3bI3z6NlFZjLlOgA96ndz185OAoLXWmtrK8uV7looskSxYgot0CGvyo5EkG+/NNOc+AtKD4FTmX1hPLUmhOCF8RS/oreukelWHFLD+fk86VK1fl9jfhfbu0PC+OHuoIfffMMIX/rxRY5NpOvjHhyK8ZtvGKE72DoD2aPI/Ppd2/jCw+c5OpGpHz88FOXX79qGR+BabeDiYgGfS6Ej6Ma2HOZtsWJwcbEgPA0EvSqxgJvrh2NkNYOq4STUEa+K160KQVMWchp/9Ogoc1mNqN+F7pJxuxTmss7xj98vtmZVDJOgR6VqVpsYuEGPSsVovbDQLmFsWJ38NmMy11OwWU9cS/yukhhNFtZkSIKT/IkI4kJ7TN8lG16/q5vvvzLbpFb++l3dwlY9frfCYNzPi+PpBokNjyqzqzck1JI0LQvDtJYdG1aYd/YyI9kUaJ3VoraDnMuW68lET8QnvINsh94eLLelQx7HScEw0QwLWZYwLJvukFgLeTpdJh5wr01EGUkwnS63jMesVi3u2plAN+eaSAh37RRXwW+HKG6larK7L8K5+QIVY8X7sy/qZU9fhIpo1duw2J4IochSsxtElxgDOR5w89jZBY5PZZqIDW5FEpIeglp1TlozSZUkSag6lwh7qBpO4jvc4W9on1YNU8hhAiDgUfnmC5Ocmc2hKHJ9Hjgzm+ObL0zye2/b2/KY0YCbP35sjNeNJPi56wYo6QZ+t0q2pPPtF6f4yJtbx83JEhwYiPL8pRTTmZVOQtircmRTXJjV3A628FiywEK+wnzewQ/XLNuCXhVVcboLImuWV1XojfiIB9xOBXXZacaybTwuBa/a+jrQLmHsdhVs1hvXEr+rJHKlqiMs7FGRZeq7J8uCfMUQEsSF9uhCtcuqZ7FY4eBAhFRR52KyWN+Vb+oMcHAwymKxdeZZd8RLZ9DNYkHn8rJZZ9BNd0Tso2zHDrId5BZw2ly3bevkcw+e5eWpbP34/oEIv3nvTqFkUquaryqKvEeg6uv3qLxwKcW2RJAbN69ACJaKOi9cSvG6ETE8ZjtEcYtVk7944iJHNse5fbvjhe1RZWazGn/+xEU+/Y59QtdaYyDv6m2u0IsykGezGvmKgc+tNPxWn1shXzGERZHdilzfUK5FcBGpzi0VK7zz8ACvTGexkOpkLMm22NsfYUlgDgC4uFTkxYkMiizhVuUGEesXJzJcXCrSH29ts7ZU1ClXLb7y7ERz+7QvIiTpVDUtdveFubRUIq8V6nNg17Lu6uXtz5822sEWzmtGEywHnO9pOlMmLwgh6Yl42dwZ4PnxlIP1XX5WPrfCSG+YHoE5u13C2O0q2Kw3riV+V0lE/K4GfNvq3VN/1EdEQBAXVkgIU+ly00IyEPMJ7fRWW/VcPuGvx6qnatp8/flJbtgc55atHQ3Ms68/N8H1m+Itj7mrN8xb9veRKlSWrYUcUeB0sUI86GFXi+ywWrRjB7mQq6yp4QdOQiXOFNX40hMXKFZMtneH6ni0YsXkS09c4OOx1nelpar5qtdaEqh4KbKEZlicnM414eZiAbeQyCy0Z/NTs5Qq6SbZchXDtKmoMiXdJOp3CS96DQzkyyr0ogSfZL5CsqDTHfLSG5Hq99WybJIFXVh3MxH21PUBL4/hDr9Qdc4wLWayGucWCizmV8hYnSE38aBHSBQanOpkTbhYN6wGAWPLsoWqk+mizkymTGfQ0+BgZFk2M5myEB7PsuGvnx7nuqEot2xdIbdMpUv89dPjHBJk9tfYwgGPyo7uYMM6IMoWdqkypYpJ5zIM43LNRZegVFY84ObQUJRsucp8XsNavq+JkJdDg1EhyE+7hLHb4WC0EXEt8btKYijmx6OubaLtUWWGYmLg05DXxb27uvnyExdZWNacUmUJn0vh3l3dYozGBkHclcV4vYK4LkliIObnydGlpnObOwO4pNYX/pDXxTuvG+APfnSO5y7N1yfnfX1h3rlOwsR6zq8VP4m8IsoUHU0WGEsWOTAQXVMiRGhXarMsXt18TT63GCZxPqdxy9YOHjuXZGyh0CDcesvWDuZzGnsFMDPtYGD7PQo3bnaYst9f5f25tSvIPSMJ/J71OQF87dlxzi0U6nIe2xNBYYKPexkvmylXN6wyB4639n17u/n685NNrfn79nYLeWu7VYXvHJ9lLFlAot6RZTTpOHccEdj8AQQ9zn2TYM0NRO18SyE5LORXOy8wJGGviydGl9CNZP24W5XpCXuFube1jepakB/RjWrYo3LL1g6euZhiMl2qk1tCXhe3bO0gLCiKnCrq2LbNQMzrSNosJ7+m5Vj4ibDQ2yWM3Q4Ho42Ia4nfVRL5isHrd/VQrppNk+jrd/WQFxCtBGenN5rM0xvx0rfMjHUWApvRZF5op7daEHej2lEAiiJxx44ugCbs4B07ulCU1qe91TvdXb3hOqh5PTtdaM8OsmaBdaVkSrTakS9XX1UiRERvLBZwr0r+L/MV7gwSE9iVu2SZ75+YZaQnxJFNsXrFdy6r8f0TsxwZFlv028LA9qg8P57m0lIRedWG5NJSkefH09wnqI0Hjlep16Wwuzdc/15VWaIg8JzAqcz1R7xryvns64sI4+bASUhu2tzRoA9o27awMHqxYjCVLlG7patJPlPpEkXBebAv6mVvf4QT09kmy7J9/RH6oq2342qOKGtVvkUdUdyqTGfQjW5alHSjnvn63SqdQbew01A7NqpBj8qBgSgXl4rkV/nndoc9HBiMCrth5CtVvv3SDBeXiqjyik6sYdls7giwd6D1zV+7hLHb4WC0EXEt8btKIq8ZjCYLvH5XN2/c00NJq+L3ujAtm9FkQQgzBQ5u7J+ONePGAE7P5dnZ3TpurB3tKHCqJV97doKRnjC3b+9qEJq9tFjkV25qnSVY08VaK0FZjy5WO6pIW7oC3LKlo8kH2eeWuWVrh7D/a0fIwxPnF8lrBoNxf73qmdcMnji/yJ07u1oecyDmY2sigMclbxiEIB50E/W7+PH5xaZz27uDwsLgsPEM7HSxim44emWrF72Q14VumMJs8YWcxhceHWUmozVpWZ5fyAsxJeMBN3eNdJHVqg0bqqG4n7tGuoSlZ6bSZZ6/lKE/6kWVJcpVC59LxrBsnruUYXt363aQlu1sKlIFHa3WObDBq8rEAm4ESM2AU518362b+JPHLnB8lXPFgd4o77t1k1B10utSuHdXgql0GUmS6pV0y7IZiPuEHFFciuxUzTxOomdaoMhQqVpIkoRLsDpb24hWTWvDNuuaYfEvJ2fZ1x/hrh0JKoaJR1VYKlb4l1dmhTUXc2WD03O5Nc+dnsuRK7eepLaj1Q3tcTDaiLiW+F0lEfSqy0LF5SbJidp5kWgHbqxdxtSJsJf/cOdWPv/IaIPQ7Ho+onRRX9Ort6xbXFwqCOtitUMUOOR18cs3bcKtyk06futRl69poGXKVSbTK+9CyOsi6hcTb23UHFyZiHvW8Q64ZIk37enFMGeaKr5v2tMrLAwOG8/ANiybvGbQEXDTG1nBd+mGRV4zHAa1QFzOlKzFepiSqaKOz6Vw27bOhg2Vbdv43IqwgHNBMyhqBjOZMhGfe1nUWiZT0ilqplAVKR5wJEzCPhcRCSzLRl6u+tgWwkkqQMUw+N9u24xh2RSWpZJUWUI3xKqI/TEfZ+fyTKRLTZZ1O7pDQslk1bS4cXOch88ucGZuRRx/WyLIjVviwuSOgZiPkEfhqQuZ5k3llg6hjVoN8uNRZRQZJCxUxbHBXA/kR6uaxANu9vaFGYz76wSfiaUir8zkhHRi29HqrsVGOxhtRFxL/K6SaEcFCdqHG6tXUDb4Zd/wj0hykjxrWb5lNb6prFtCOJzVsbcvwpauIAWtSsjrWpcoMDj39dfu3Lqhht9a1aRYMZokRirLx0UFt8H5/Vu7gvWFdD2/P68ZBD1rJyhBryJMmGgHA7sr5CHoVSlWTLRVsivqcvVEuC3fBqZkXjM4OZtvsG2s6TienMmzrUtMwNmjOpuSZEGjXDUwTNtJ1CTY2hUQavfKEty6vYNHzyZJFVY2ZfGAmzt2dArLmUyny+TKFk9dWGhK0m7Z0ikkPwRwcibLxcViQzJVMUxOzmZ53a7WWeguReaF8RSbOvxNBLcXLqW4W9BpCGBXX5hz8wXGSitajoMxH7sFu0khr8r1m2I8eGqeR5JJDMNEVRW2rVMfMuhVee8NQ/zg5ByPnF3BOe7sCfHeG4aEiiDtYvXWIhH2vqYcQK4lfldJtMvwul24sYml0poOE++6fmjdKugb+RH5Xcqr4ptEcDjgJBPPXlysA9trLOzt3UHec2SQWKBX+JltuOG3BIaF4ytqWvUqiltx2t0iye/lv78W6/n9Aa/6qgnKwUExRmNtt181LQqVar06G/Cowrv9Wlv++FQWw7LrLVlFljgwGBFuy7vWIHjVoqAZQkzJtRbK1Twp0W6CR5VRFIlnL6YYXVip0G5LBHjgYL9Q4lfWTbZ2BZjLaizkK3VWbyLkYWtXkPKrkSleJbSqyYOn55u6H5lSlWLFZFdv68lvO8Txq6bFDZs7ePxcsoHkVsM5i1b8ptJlHj+7yJauAIeGomhVC69LJluu8tjZRXb2tN6WjwfcPDG6yGS6jEeVsQ0dj+piMl3midFFYX3ITXE/f/zoGAu5Ch0Bj2PduOx//fSFJd5+sK/lMdvF6q1FXqtu6GZ9vXEt8buKoh2G1+3AjeW1Kl955lLTmJPpErph8aF7tq/rmjfyI5IlicObYmS0agNTdFNngMObYw3A/FZifKnUlPQAnJ8v8PXnJ9nVGxFioLYjrpT8autIfseXSvz90SkSIQ8HB6NOO0aRmc44x0V+f7u8iguaQUEzllv+5rK1lKNrt7kjKLTbD3ldPHCwn/GlIidmcnUj+X19YR440C+e9HtUtncHm94rcJLqkABgvh1+3QDZcpVHzixQ1i22dAWwbFCWK+yPnFngsID0SNWy+ZPHLnD9cIybV8uZpEr8yWNj/MF7Dglda6lqvqoPuIj8UDvE8X0uhbGFAm/e29vkinR8KsM9glqW6aLObLbMKzNZpGUXI1kC24aOgFsI8rJU1FFlebmwsHL/fG4ZVZGFdAwBSlWLkEdFkWGxsFKl7wy6CXlUSgJC7u3qqEF7reBE41rid5VFyOtipDuENjvGSPcWFEXc9L423i/ftImDg1E0w6KgGYS8Dg5j70BUaIG6kCw2JX3gTPhPXVjiLfv7ODAYFbrejf6I3KrMy5NZdveGuWtnF/qyPMZCrsLLE1neuFuMfTmXLXNpscit2zoctrRh4VEVpjMlnruYYi5bfs0kfrXkdy1wv2jyO58rs28gyguXUjx7IY2F0zoaivu4flOc+Vzrv79d2FG3Kr8qzlOEKZnXqnz35RkSYS/v6F5h32bLOt99eUacLe5ReduBfh4/l2Qut9KS7Al7uXNnlzBTcqP9usHxwS5UDIq6wXx+hYEa9KgUKgpZrXWCS9W0UGW53uKrMTrB8UUWrXhhQyLoYSpTpqQb9XH9bpWBqE8IntCOKpLPpfDAoX7+6qlLvDK9Ira+tz/Cr96yyYFSCIRt285zymnkViWkYa+K1+XAKVqN2sZ8e1eQrFZF09143SoRrwuvWxVun6aLOvO5Cj0RH31Rf10f0LJt5nMVoSS1XXNLO6wrNyKuJX7XgqVChb97YYqTM9n6hLenL0JvxMdQi2r14AjCalVrzd1zvmIIC8K2A4uVKercf7CP7xyf5sWJdH2nOxjzcf/BfjKC5A7dtHj7oQGeHE3yo9MLdQ2rrV0B3n5oQBg72Y5wqzInZ3JrsqVPTueEkl+XLPPSRIZTs/kGEHehYqDIMnds6xS61lrVeyPbJh5VJhHyrklySoS8Qi3JC8kiPx5dvCKE4k17e4U2P/0xH9F5lZ09IXZ0h9BNE7eiIEkQ8alChIF2tCTBcb1YyFccceRVeYNu6PXzrYZLlhmI+bBsu8kNYyDmwyWLsVoDHpWlYoWlQqXh2yzrJj6Xcy9ajXaI48cCbv7Hw+cp641i62Xd5J+PTfPx+/e0PCaAxyU7NoCXJWM5zUCRJTwuMQhBcyXdW6+kC7dPl/UR18Jly5IkjMtux9wynS7TGXQvM7sNh9ldWWZ2x3zC2NH1xrXE7yqLvFZlIlUkSZiz8wUG42KVg1pMp0pNVl0AL02k+dyDZ/nMO/a3bFXkVmRifhdT6TK58oqRenh5chYVhJ1Kl1ks6Gzp9DeJbI6nykILlEuVmEiVSIS8DMRWxEB1w2IiVWIwJoYl7A57ef7iEsWK6bS5LJbdMAyev7jEm9eh47bRUdAM7tzRxUNnFhrY0sMdfu7ZlRDamVs4wPbLmXu6YXFyJst60t6NxjgWNIN7RhLLFnMrFc/1/P5kvrJm0gdOJVF08wNwZjZPoWIQ8blRbBlVkciUdM7M5XndSOvVuXa0JAE8qkKxYjRJrFi2o8fnEfBUDfqczWRX0Gm/1RKfStXxlw76BDVCZYng8kbVjdyg4xf0qkKuMO0Qx18q6siSTNWyKGuN0BxZFm+fLhV19vdHyWsmY5cJju/vj7IksAHuCLixbIuwRyXsVdH9LtyqAraNZVt0CDKw24XLho2fWypVk5JucnY+z3xu+R1QJBIhD/GAW9ize71xLfG7iqJZcqK0LskJgHPJQlPSV4uXp7KcSxZaTvwSYQ+K5Hiorm4RLBV1huNiVk0ARc1gV0+oyQN2uMPPPSMJigILVNW0+ZdXZjk7l2d1N0OSHJbYDZvECAMSEm5VoVApM50p1yupEZ+L7rAXaR104Y0GCge8Kqfn8ty8Jc6dO7oaSBOnZ/NcJ4DF0qomQY+KbjRWe+TlVt96mMIbHbXff2RTjJu2xClpVQJeF4ZlC//+n7S5Ed38TKfLDMT8PHhqnkeTyXq1Y+syU1KkgtAuYLuEzc6eEKdn8w0tWJcis7MnhCTQP3VJEm/e28t3X3YkfWqkqc2dAd68t1fIvQdqrjCdGKbdJBV0y9ZOIVeYdojjN7ZPjbpzS9irrqt9WjVt/vHYNPfsSvDmfT11b+mpdIl/PDbNDVtaF0fPFHXefWSILzxynhfHM/Wux3VDUd59ZIiMYJLaDmhKu8K0bV6cSHNsMuNUU5d3FDMZh/ByaCj6M7mua4nfVRK1Nuf4YtHBS+DFzOtUqpZwmxPa4yVY1k2uG46SKeuMJVc+zC2dfq4bjgkz74JetSnpA4dI8NCZBQ4Pt75AZ8tVsmWdX79rK90RL+WKid+jMpct8w8vTTW0k1oJrWqS16qO96+yUkGomjZ5rSqc+LQDKNwO0oQiSSTCXmzbcZ2p3YCQRyUR9qKsY3Le6MR3IObD75L53onZppaUqIZZzaf2Sq4NopsfrWry8JkFsuVq3f9VlSWy5SoPn1kQEnJvF7C9YlocGHCIPY5ll7P5GYz5OTAQpSIAd/C4lFWSPp2U9So+twvbhqBXwSNY7XHJMg+emufAQLQJ7vDgqXkhV5h2iOM3tk8vc8RZR/u0J+xleyLII2eTZFbN+VG/i+2JID0CCVrFtPjSjy8QD7j5NzcP1/HTM5kyX/rxBT78xp1C19oOaEotNnpuKVQMnr2YaoI7pIo6z15M8e4jg8JjryeuJX5XSUyly5yZzXNyJrts+u7sdCM+F+WqJYzDaYeX4FJR56WJDLt6w9y6rbOuNZUq6rw0kRaqoABUDIvFQoXukBskqY6ZwbZZLFSoCAmC2vyne3bw9ecneXkyU0/Q9g9G+U/37EBYdG5ZIqUj4AjX1lpS65VI2WiMI7QH2BwLuvEocr3CadoWiiSjVU08ikxM0GWjXQy5mobZaKmAiYJuWAzG/MIaZv0xH/cf6F2TKfu2A71CWDyActUkp1WvKOBcFthQ1J7/MxcWKVetOoTCq8rctLVTeOHrCLh5aSJNV8jD4eFYHUYxkSrx0kSa+w+0qAyPc18fPDVP1O/CrcpkCxaRoAfdsFjM68L3NRH20Bf1NUAdaiGaqLdDHL8j4MawLIoVswHfZtk2hiXePh2K++kMeegKeuiN+py5VZWZTZdBQgjrvVjUOTaZqf+5tmatPi8S7YCmQHvmlpxmkClVHQxivcjvPK9MqdqEqfyfFdcSv6skFguVetK3OrLlKidnsg209lZiR1eQ/QORNdu9+wci7BDwEnQvU/UlqOuj1RK0paIu3ObSDYuesJeXpzINmJOOgJv9A1EhJfjeiI8v/vg0xyYyDSnesYkMsiTx8bfuErrWy3EotUlvPRIpNb25tWK96vIbDWweijlSIP90rNll4/4DvQzFWp9I25X41jTMBuM+9vaH61Wk9WiYhbwuDg/FqRpWPZnyuBR8LpnrhuPC99WwbOayWtPiVtAM5rKasCMIwESq1KS7edMW4eEYigd44EA/f//SVIPN3vbuIO+8boCheOtSUTWZnH94aYqxZKHRZWUd7Mv+mI8HDvTxozPzdQFnVZbojXp5/Ui3UELZDnH8VFHn5i0d9EY8SJJUl0mybItNHUFhl5V0Uecd1w3wF09e5PEXFutt2f0DEf7drZtJC4xbrjjEkLVIPIosURb0VW4HNKVdc4tbkfGoMuWqycry5IiY+1yK8Fq43riW+F0lYVgOi83xagRVlupWRdlyVXjC74/7+c17dzYRPPYPRPjNN+xsGd8Hzu55T2+EZy8tNVUlbtzUIdzmUmSJ07M5irrZ0D4t6ianZ3NCAOylYpWzc3lkCczLsGhn5/IsCXqqtgOH0m51+Y0ENifCXm7a3EGubHD7drvejlFliZu2dAgtTu1KfGu2fTu7V6p7kiShG/a6bPuGOvzEAr0b2jpSZAm3KnP79k56It76wj+bLXNmLi/0DdQWvaVitYHcsVSsrmvRS4S93Ly1g1ylyl07EnUGsluVuHmr2DsA7dEzDXmdlubFxSLD8UDD+7o9ERQaux3i+OmiTrasc3GxVCeMKLJDFoj5xfT2wKkkf+P5SSpVi1294QbSzDeen+RDr9vW8pjRgJt4wM1IT4ieiLcuaVV7V6OC1cl2QFPaNbdEvCojvSFOTGXRVy0wqiwx0hsisk5haNG4lvhdJeGSJEZ6QpxbKCDZYNk2tuU0Ikd6QsKgZoBDwzE+8479nFtlg7ajKyiU9IGj2L5vIMyFxUJDMtIV9LBvICzsp2lhEw+4yZarTQtcPODGEmjLZss6LkXCcimYll1PJhVZwqVIZMtiE2kjDmUFi5TM68I4lHYYqbczdvdH6Ax5Nsxer12JryTBnTsSvDyV4cR0FtOyUGSZ7rCHO3ckWA9WfKNZggGXwnuODPGdl2d49Gyy/r5uTQR5z5EhAuuoJK/1Xq23krzR70AtNlrPNK9VeejMAh6XTECWKFct/G6Fqmnx0JkFBgWIGO0Qx3e5ZH50eqFhMwkwmSo51UBBmaRS1eTSFdrSOc0QErDe1hXkl24c4h+POe9qnYiUcI5vE+gmQXugKe2aW1RF4o7tXehVi4l0qY51Hor5uWN7F6rysyGivLZWimtxxXApErdv7yJfMeoM1Brz9PbtXbjW+QKF/S56wl6CbpWgVxXC9tViqahzaibP2w70EvSsqMvntSqnZvPcvFVQciBf4e6dCQ4OVlAVuV7tqJomMb+HJQGJjJDXuT636pTka/fVsGxKuilcRWjEoaTqhIHhjoAwDqVmpP7kWIbcKv/XsM/FrVvFSAirox22Qj63QszvxiU7ianPLb5At4t9GvKoXEgWeGnSua+1d2Am6yLoUblXwFO1Fht9Tz0uhVMzORYLlYbNz2KhwqnZHLdvb33hb3QuaSYMrLeSXDUsKsv/pxsWVSEsbnujUW9NQjctqsv2haJ6azVxfLcqN1tXHhkSeg/KukmyUKl3fmrvqm1DslARJs5h86rVSRGos8+tMJUqky7qKLKEbTobqnRRZypdXtdcsNHQlHbNLbphIyGxfzDC4U2xOt69alpIOF2Fn0VcS/yukgj5XDw1usi2riC3b3c8Gd2Kw5B6anSRe9axOE0slZqA3SemZG7a0ikEai1qBoeGojx4ap6xZKFJckJEdgUcHUBJglSxynyugmE5iu3dIQ/xgIewr/WPvj/q49BglBcnM2ir9FxqsgP9UbFkqhGH0km2UCYSdBw8RHEo4OCjjk6kmUyttKATYQ/bu8V2z7VoB7B5o8dsF/s0pxkcm8w0LZpl3eTYVEYYgN2Oe1oxLMaSBbpDXnojUr0lZ1k2YwsFIYJTO5xLavHSeHptGMm9OzkkwMJvV1SqJsWKybMXU1xaKtYTqk0dAWL+HmG9taEOP79259YNS1D0qsXWriDn5vOkVrV14wE3W7uC6AJ2ZeAIQ2/uCK6d/HcGiQl0aabSZcqGxfXDsWXnDqPu3LEeQmItNrKa3q65JRpwc3GxSMCj4HOp6LIDdzDMKheXity7R8wVZ71xLfG7SmKpoHPnzgT/fHya77w8Wz++uzfE2w72s1QQa0nmtSpHJ1L84OR8E/vQpcrEAq6WJ6ma7MpkuuwIdi7HZLosLLsCTnn8b5+b4MmxJfKr7J5CXichfOd1Ay2P2R/z8YE7tvBnP77Ay1PZFVbvQIR/f/sWYZbgahyKbVt1ELokycITyYVkka8+O8HO7jA3b+lskDH46rMT7OgOC7lBtAPY3I4x22WrlCnrpIo6blXG71bqGxXDskkVdDIC7f7a77/ctaFqrk9+STcshjsCzgKtNS/QIgQnjyrTHfKyWKjUf7eqOMlkZ9Aj5FwCjjj8Hzx0jpBX5ReuH2jwa/6Dh87xqbfvE4aTbHQYts2/nprj1GyuQXPw1GwOSYJDw1HhsTcyQYkF3IwvlQi4VTqDngbnjvGlklCCBs58taM7yO6+EOplrW7DtIXmq1ql2OtW8bhklpYKdIScOXD1+ddC1OaWb7ww0VSdXc/cMhDzMdzpZypdZmmp2ACjGO7wr7tLIxrXEr+rJCqGxVeevcTO7hBHNscbzMm/8swlPnH/XqFxp9PlJskJcOQGvnN8ll09rWtNVQyLhby2pqXOQl4TlF1x6P9n5/JNu+9K1eTsXJ7Fot7yQhLyutiWCPG+WzdRqjoYp4BHxe+S2dYdEv7gGyUyTLIhF5GgD59LEZbIqFnhrY4a/kyrirtBtAPY3C6wdDtslVTZSfiKuolWNeuMRpciE3ArqAI2YFPpMmMLxTUrKJV1VDuCXgeKsZa9mijOs6gZvPPwAH/zzDinZ3P1ateu3jDvPDwgXKE/v1hgayJUty2sxdauALdu6+L8Yuvi8O2KbNng1Eyuyeu3alqcmsmRLYsnKRvZ7ve7FTbF/ZyYybJYuMy1oj+CX7B9GvK6eN1Igs8/Msq5+RXnjh3dQT549zYxX+k2tU/bGUNxP10hbwMLX1TRC9onlbTeeO3d+VUxPz/Ppz71KZ555hk8Hg9vfvOb+T//z/8Tj8fD5OQkv/u7v8uxY8fo6+vjd37nd7jtttvq//app57i05/+NJOTkxw4cIBPfepTDA6uiCX+5V/+JV/+8pcpFArcd999/O7v/i4+n5N9VyoVPvnJT/LDH/4Qr9fL+973Pt73vvf9T//9q0ORJdIlZ/KRlv+vxgxNlwwhNh+0R2tKNywGov4m+ZmIz8VANCRUlQAn8TEs6Ax60JfxN/IqbTzRxMdhX7o2HN8GjkTGaLJAsaQR8HvZ1hUUlsjwqDL37u7m8XNJ/uWVRomUe3d3C1dm2gFsbicDeaMJE/Ggm/6Yj9mMBhL1Kgo29Ea9xAU0B9NFnfGlIi5ZxuNXGvyqx5eKwuzL1f6vl4doJdnrVvjBK7N0hzzs6R1AN23cisRiocIPXpnlA3dsFbpW27J57uIi/VEfN23pcCp+qszEUpHnLi5y27YOoXHbEVrVQJIcOzVlWS1BWn4XLNs5LxIb3e4vaAZv3NuDLEvNMJo93cLfVV6r8uDpeQIelR3dwYYNxYOn54VcRtrVPl19zRs1Z1+pQwEwk9WEK/S12GippPXGazbxs22b3/iN3yAcDvPVr36VbDbL7/zO7yDLMr/927/Nf/yP/5EdO3bwrW99ix/96Ed88IMf5Pvf/z59fX3MzMzwH//jf+RDH/oQt99+O1/4whf49V//df75n/8ZSZL413/9Vz7/+c/z2c9+lo6ODj7ykY/w2c9+lo997GMA/P7v/z6vvPIKf/VXf8XMzAz/1//1f9HX18eb3vSmn9n9cCsSv3b7Zr7z8iyPnE3WJ6atXUF+7fbNuAXJHbpp4VIkDgxE6Qo5CZVHlVnIVTg+lRHSmqqps3cGPXV3gRoOaSZTFsYMuZdbZXmt2qANpckSIa9rXZpIG51MNEhk+FyYpRxRX2hdEhldIQ9Hx9NNjD4HQ6Lyln2tC+ICdS/S3rAHRZHRdBOfW8EwLWZzFaGd+dW02x+K+fn56wb4u+cnOTufr1f8dnaH+PnrBoQ0ByXJMb6fzpSbJI36oz5hpnDN/3Wtyoyo/2teq/LSZJb5nNZkrdYd9jbAKloJG7huOM5To0s8cjZZP761K8gt2zrWU0jZ8Ah7XXQG3SzkKpR0sw75cCsyibCHsOB93Wi4Qw07fOu2Du4eSWyIhh2sVOjX8msWrdCvhmbMZVc2KuuFZsDGJ9TtYrbntSr/dGyaiM/FTVs6Gvzl/+nYNP/21k0/k6rfa2f2vSwuXLjAsWPHePLJJ+nsdJhqv/Ebv8FnPvMZ7rjjDiYnJ/n617+O3+9n69atPP3003zrW9/iQx/6EN/85jfZu3dvvUr3X/7Lf+HWW2/lueee48Ybb+Sv//qv+dVf/VXuvvtuAD75yU/y/ve/nw9/+MPYts03v/lN/uzP/ow9e/awZ88ezp8/z1e/+tWfaeLndSucW8izsyfELds66lZgi4UK5xby3CrA5gNIhDzct7eHh88sNFWR7tvbQ0JAa8qjOhpVq8HHtXWuKySOGYoH3XSF3GRKjdUS07LpCrmFKjPtina0OiuGhW3bddumWgS9KrZtC7fQB2I+9veH+Zunxzkzn68fH+kO8Ss3DwvtzFdXpi6fSAdivp8ZtmWt8LkV/G6ZGzbHuHFLvG4tZds2frcsxD4MelTcqoxXVYjF3Q1YLLcqE/SITb15zbFmu24oWl9Iagv/w2cWhCozS0UH43i50K5p2aSKeoNYeisRdCu8cCnFWLLQcHwsWcCtSrxl7/qstSZSRZKEOTtfYDDe+u9eHV1BD/v6I/y4vIhmOFASG3C7ZPb1R+gKtj4PTqXLLBZ0tnT6catKw6I/nioLzQHt0LCD9lXohzr8vPeGIUYX8iSzEboiQbYl1ifn0w78bLuY7dPpMvGA+4pERxG2+EbEazbx6+rq4ktf+lI96atFoVDg+PHj7N69G79/JbM/fPgwx44dA+D48eNcf/319XM+n489e/Zw7Ngxrr/+ek6cOMEHP/jB+vmDBw9SrVY5c+YMtm1jGAaHDh1qGPtP/uRPsCwLWQDvsxEhIzEcD/Dj0UWeHF2sv0CDcT+3b+tEFvEAA3oiXk5OZ5nNasuLnVOtmM1qnJzJ8os3DLU8ZlEzeNeRQf7qqUu8Mr3C5tvbH+FdRwaFMUMuWeJNe3oxzGY3iDft6cUl2O5uRxQ0p/0+HPc51lLLGD/dsBhPlYUmEse5xMeevghhn6u+kGRLVZaKunALPVeq8sToItNZreH4dFbjidFFbhXAorSjMlWLjZZImUqXeWkyR3/UiyJLlLQqfq8L07J5aTLH7r5oywt0sWLw89cN8NVnJ3h5KlM/vn8gyjuvG6Ao6FowlS4zc9lzuvx8q9eqKhIVw0SRJVSkerXLBiqGKaw1ZthQqpi8aW8PfRGvs1lVnSro6ZkcokoWtWrPXLa8TJoqOc4d62BLl3ST4Q4/6VKYZKGCbYEkOwnhcIefkoBMSlEz2NUTavIXH+7wc89IQmgebBfBqV0V+omlEt94YaIOdwn6fY6czfVDws+qHfhZtyozmS6ypzfS1Pk6O58T7lJpVZN/fWWu7uBUi+NTGSzLFvLW3oh4zSZ+4XCY22+/vf5ny7L4yle+wk033UQymSSRaJQv6ejoYG5uDuBVz+dyOSqVSsN5VVWJRqPMzc0hyzKxWAy3e6V61NnZSaVSIZPJEI//9GbdpimoqbRWSDYnpjLktSqJsAfLcnB/ea3KiakMr9+dEPrvJQtOwnfb9g6iPrcjE6MqpEsV9KpFsqDREWjtI/J5Fb71+CSDMR/XDUUb2KffemGS//0NO4SuNVfWCXrkukF7bVzbhqBHJlfWN/aeryMCHpldPUEeOr3ApaUilUoFj8fDpmUdv4BHbvlaAx6Z6zdF+eGpBcaShXpLcmtXkDfsFhsT4NxCgcfPJYkHPPRGvA3VqcfPJfm5QwP0RFqreBQqBg+dnufQYISbtsTRqhZel0ylavLQ6XkGYl6hqtdUusw/vDTNfG4l+ekOe3nHoX7hake+rGOYJuNLRWzbJpVKEY/HkZb7sXmB90pVJP7uuQkSIcegvqbfNZMp8/XnJvjwm3YKPat8Wce2r5zgi1xrxOtiV2+IrqCHwbi/AYuXLFSIeF1C1yrZNm872M8PT87xw5Pz2DiaZtsTQd52sB/Jtlset1Ax+NaLk8znNOxl+SXbtpnLlvnWi5O879ZNQu/VYrHC8ckMN2/pIBpwUdZNfG6VTFHnmQtLHBiMtnytfq/CY2cX6Aq62d0bWk4mFBZyGo+dXeDQcOtjAvRHPfy7W4aZypQpagYBr8pA1EfQowrPf30RD4mQu+G7qkV32EtfxCP0rP7uhQkSIUe/tFhxNmrFisnfvTDBB27fLPSs0sXKcpIWXpWkOff17HyedLHS8rW6VYm37Ovluy/PNnW+3rq/F7cqCd3bQsVoSvrAqaafmMlSqBgbtma1Ms5rNvG7PD772c9y6tQp/v7v/56//Mu/bEjMANxuN7rutCTK5fIVz2uaVv/zWudt217zHFAf/6eNEydOtPT3Xy2y3m4KWpWybjCfNeq78rBPpaBVSedLHDt2seVxU2oHWzr9PDG6xHhqBTs4HPdz27YOZpIZtNmxlsYs+xJcTGY5OdW8o/V7VFLpHMdmRlu+1rIrylMnJ7llVz8et4dixSToUdD0Ck+dvET/9YMcOzbe8rjtCE+ki8fPzBLxunjj7sRykqowly3x+JlZDnTKHDvW2n31RLp4+NQMo7O5+jEbGJ1NoWIIjQmQpgvDNLEsC9tygO225Wy2DNMkXdTq1fSfNoquKKcvTbOtN4bt81IxLCRLJl/SGJ1NczyhEKhmWhpT9YX4x1Npgj4Pm2POmF6XTL5U5i8fPcnbd8cwyvmfPNBlobmiLC2lkBUV3ZYxcLOQKeKWLCzTQCsEW36vct5uptJFTs9lMVZbNSkSIY9Krljm2LFLgte6dOXzAtfqivTw72/bwp8/eZGHz6xg8Q4ORvj3t23BZVdafv4Armg/T48ucmmxCNjLPQmbS4tFnh5d5PYtYaH36tTF6YZjqVQKgKUlON6ttvxeAeBOsK8vzEOn5xlPlRrmwZs2x8Gstnytmj/BSE+QR84uMJZcSSa2dgW4e2eCdEZsHpQkCcUbJGsolKoWFbeCkTEwtUI9GRYZ8/aBGN89VWA87Xg+q7LEcMzL7QNuxs6cbHlsPZhgpDvED0/PM5vVME0bRZHojXh5w65uTk0u4C4s/OSBLouKt5ubN8d4YnSx6b7ets0p0og8q1MzaTZ1+Dg8HF0FpdI4NZNhd5db6FnlXQncMuTX0IH0qSr5cutz60bEVZH4ffazn+Wv/uqv+P/+v/+PHTt24PF4yGQyDX9H13W8Xgc34PF4mpI0XdcJh8N4PJ76ny8/7/P5ME1zzXNAffyfNvbt27duK6FaPDW2REYzODQUoy/qq/seTmdKnJ3L43K5OThysOVxX5rM8PgTU8xkNFRFrjNlpzIaj48uccfOXRxcxYb+aeLoeJqRvjgXF4uUV73wPpfC5s4Abq+Pgztbv9ZCxWDJ9PCdYzOMp1a1TuJ+7j+0jQPbugh6NrU8bjvi7Hyem7ebfOf4DD86u4RpmiiKwo5EkPsP9CH7wxzc3N/SmGfm8/i9XmIhs+m++r1eJIExAfLnlxjuDDGb1RravSGvi+HOEPGAl4PbD7Y05ovjaW7bu5mHTi8wnsrUjw/H/dyzdzO+YJCDw5taGvPsfJ5tA+61x9zVjSsaZe/O1hmohYrBjycrPD22REnX69VZv1vl5q3dHNg20PJ79ezFFF1hH7d2h+gIeNBNE4+qOJjc+Txej0foey1UDI7OG1eszIhc62KhwtePnaNiWIz0hupzQMWweHw0yW/du4POza3j8Z69lGYqW8bvUZtcJqayZXRUbjx4sKUxj46nSXR1Mhx3cHO5oiOM7uDmSniDkZbfK3Cq3n/x9BSjyUbi1GiyiNelcP+hfnYk+loa8+XpLMcmZ5jKVFBXrQNTmQrHJnPcvaub/TsOtnytK1XvFQiFU/XesS7s7FS6zK5Bhc29Zr1C71UVurriDMRah/wcm8zy+IkJzs7msXGeuyw70BKvS+Y91w9xcNvBlsc9NZvj+NQk4ymt4b6OpzRCUznevL+P3S1+W89dTDEQD/HU2BJPjqVWEv+OALds7UB2eTnY4rsK8OToEkOdQabTZQqr4B1Bj0p/zEfA4+HgntbHXStM0/ypi02v+cTv937v9/jbv/1bPvvZz/LGN74RgO7ubkZHG7PvxcXFevu2u7ubxcXFpvO7du0iGo3i8XhYXFxk61ZnkTAMg0wmQ1dXF7Ztk06nMQwDVXVuTzKZxOv1Eg631o9XFGXDEj9Vkblvby8Pn13gR6cX6m2+bYkg9+3tRVVkof+WVrXIawZ5rdpADvCoMnlNRataLY8b8rkJLpueZzWDqmHhUmUiXhWvWyXkcwtdqyxbnJ7JsVCowCpM40KhwunZHK/f1S18vzcaN6YbNo+fXyRVqhLxuTAMGVVVSJWqPH5+kYOD0ZavtVSxCHpdjPSureFWqrT+rAA2d/oJeVQmTIugR61PelXTIuRR2dzpF3oHHjqTZDxVZvWzGk+VeehMksPD8ZbH1A37Vcfc0xcRfq9294UZSxYZX3IWfkmSSIS97OkLI8utf1vxgId3XjeAYTnvflk38btVEiE3+/sjxAMeoWuN+BV+/rrBK2K8Iv7WSQipkkGhbCJLDr7PlhypKAkolE1SJYPuSOt4rFypSn/Ud0Vmc65UbfkehH1udvdGlnFzxUYrxJEEYcG5RTccn+41iVPL51sdt1y1WChUuHd3d1Py//JUlrLA3JrXqnz72AwLeb0uhAywkNf59rEZYdmR2rhrEdJmc2JyJqWqydnZPHM5rcH9JuxV8c7KlKqm0LMqVEyq1tokt6plU6i0Pq5LVXhqbJFz8wWsVZ7t5+bzgM2dO7qErjUR9hL2ujDCNr1yo9NO2OsiEfZuWI7QSrymE7/Pf/7zfP3rX+f//X//3wZG7YEDB/jiF79Y/+gBjh49yuHDh+vnjx49Wv/75XKZU6dO8cEPfhBZltm3bx9Hjx7lxhtvBODYsWOoqsrIyAjgYP5qRJDa2Pv27fuZETvA2SG8NJlhKl3Grch1cocDTM/wJkGGXKXqLPYeVW5K/IIelYqABVDNU/bEdKbZnHyLuKfsVLpMvmKuKV6b10xhyn07rLVKVZNkXuM91w8S9bsoVqoEvS7SxSrfPTEjZHpeA1ivJbmw+nyrUa5a3Lu7m3ylyunZlVbprt4Q9+7upizwDtREvNcKURHvUtV8Vc1JkXsKznt1dDyzpr3eC+MZdvaEW36v4gGHZf7lJy5xfDJTP35gMMr7b9tEXNBhAVYxJZMFcqUqEb+LrV3iTMl0UWc6U8alyMiXLU7TmbKw5mDY7yJbrrIp7gdJqn+v2DaLRV3IDzwecPPU2CLpok7U70J3ybhdCumizlNji9y7W8wCSzcsOoNeLNvx6a7dA1WW6Ax5hYhTkg337e3hoTMLjC3M1pOJrYkg9+3tQRLoyrZLGL0d4+bLVS4tlZosD3OawaWlEvmyoEyQ7axba9kWVqoWIt1u3bRYyFVQZQlW6TiCo3UrImsGjjPUWw/0NpkkDHf4uf9Ar7Az1HrjNZv4jY2N8Ud/9Ed84AMf4PDhwySTK9iTG264gd7eXj7ykY/w67/+6zzyyCO8/PLL/Jf/8l8A+Pmf/3m+/OUv88UvfpG7776bL3zhCwwMDNQTvfe+97187GMfY8eOHSQSCT7xiU/wrne9qy7g/Pa3v51PfOITfPrTn2ZhYYE///M/r4/9s4rCcqITWt7l1Ayvg16Vqmk1lJFbCVmWGF0osKUriM8lU7VsXLJMuWoyulBAFmTK7uoLM5osNrzsiZCX3etgMdV2d1dKfESYsu3Q2gKQbXj/bZv5m2ccVmetQrt/IMr7b9uMLDA5tUsiJVXU+c7LM+zujXD79q66nMlSwTm+q7f1Z6Yb1qt6fwoxkG0IeRV2dofXZN6JCsMVNAPTste016udbzVSRZ2/fmqcE9PZBs2+E9NZ/vrpcbZ0iidqNabk5YKwwkxJyUmqQ6uA9rVLLlVNBAUD2NYVZP9AhKfGlpoqM7ds7WBbV+v+0ktFHctyqtG2CYZpY2EhAZblnBe5r0GvSsTvWtOybDqjCW2qYgE3z19KMbZQaEgcxhYKhL0qb9zT+ma9XbIrjSoEzdIzIuOqikyxYtAb8dY7CbIMec1gIedAi0QituxLHPAohLyueiU1V65S1E0h2zrTcpywpBoSVXLma2xwKVITOeOnjZDXxeGhOFXDqjt31BxBrhuOX3PuuDweeughTNPkj//4j/njP/7jhnNnz57lj/7oj/joRz/KO97xDoaHh/nCF75AX5+DwRgYGOAP//AP+fSnP80XvvAFDh06xBe+8IU6S+8tb3kL09PTfOxjH0PXdd7whjfw4Q9/uD7+Rz7yET7xiU/wq7/6qwSDQT70oQ/xhje84X/ej18jGnc5oFdN3C4Fy0J4lwOOMPTt2zp4+mKqwZYo4lO5fVuHkDB0YwWlq0FrTLSCAu2RHGjXDjoacPPfHzrPiVVyNuAs/F9/fpJP3L+n5THbJpEiQV4zeXEic8XzrUY7rMXiATdvPzjAd16e5l9eWfGr3t4d5O0HB4SraLVrcTZQVTTcZMvGuq51JqNxfiHP60a66Iv46mz56UyJ5y+mmMlojAgk1HmtyleeucRTF5YaEurJdAndsPjQPdtbFwV2Kezri/DspeYE7cZNHQRcYq0on1vhxs0O1ve8tvK+9ka83Lg5LqSPmC463slLRZ1cuVrvfIR9LkJedV2OKEeGY/zozDwzGa3utNIb9fL6kW6hTVVJN5lIlfC6FHxupQHjOJEqCUnErH5XN+q7Agh51VeVngkJjOtzydw1kuD5iylmV2GHw16Vu0YSjh2aQAzEfNyyLc43X5jiwmKxfl+3dAb4hesHhJ6VS5bojfrIafkG/dl4wE1v1L8uqbChDj9eV2LDKvQbEa/ZxO8DH/gAH/jAB654fnh4mK985StXPH/nnXdy5513Co3v8/n4zGc+w2c+85mf/oLbHLGAm+GOABcWC2QLVQzDxKUqhH0utnQFhc25PS6ZnX0RpjIa2CWnHSHBYMzPSF8Ej8DHubqCcqXzItEOC6B27aAXCxUuLBbWXPhfHE+zWGjdXq4m3rt/IMJ1Q1HKVQufS8awbGHxXgC/S2G4w79mG3W4w49fYOFf/awur86KPqt4wM2zF5dIFR3cZG3RTxWrPHtxiTfvE4M71KAJT13IUNbNZQiJgc+tCEMTSnqVtx8a4KmxRR49k6y3+TZ3BXj7oQFKulib60KyyDMXUmtawT1zIcVb9hc5MBhtaUyvS2F3X5gLi4WG970r6GF3XxivYOI3lS5zfMrB3r5pT0/D+3p8Ksfe/tb1ESUJxlMlihWnQlXrfBQrBuOpkrAjCsDxyQzHJjINNpPzOY2ugIfX7Uq8yr9cO1JFne6wd02MY3fY25Bg/LTR+K5uHIwmHnDzxOjimp7tT4yKtdAjXhf7+yNcWiyRW5X4d4W87O+PEBHcqJZ1kxfG0/THfOwbiDjyQ4rMUrHCC+Np7tyRaHke9LgcDUC/W6Uz6KnPLSXdXC5ciMO82gElWm+8ZhO/a9EYAzEfvREPC3kNr0uuV/xUWaZ3HYrtlarFj88lGYz7uG44VtcbS+Y1Hj+X5Mhw6xZA7RIDrYmXfu3Zcc4tFKgutyS3J4LC4qXtutZixeAXDg/y8NkFHrqMjPMLhweFBHyn0mUuJIsNLQ6tutziqIhjHL0uhXtGEmvv9nclhBb+dgjNLhV1VFkm4FEo6ysrvM8toyqycJsPYE9fhNmcxkymXMeN9UUdsWyR6Il4eeaxMc7PO2r9tTg/X0CW4L69Yli0ZL6C1yVTqJh1L9mat2zQowj5VWuGxfdOzDDSE3Za/at0N793YoYbNv/02qWro7YBnEg1+wrXzrcaiiTRFfSs+W+7gh4UwczvQrLIc+MpVEVu2FSoisxz4ykuJFtPqN2K/KoYR1GLyXbAaJaKOl5V4ZatcSI+dx1GkSnpFCqm0LdVqpr84OQc27oC3LbsNuVSJOayGj84OceNgu/VWLJAIuTl6bElfnx+sV7x29QR4OatHc75Fq81VdQ5PBzl0bPJZUKHE9sTQQ4PR4WSdFixbPOoMls6/Q0QgmuWbdfip4pdfWHOzRcYTZbrFb+tXcF1ffCZcpVUUefkTK6J3NEb8ZIRAOC205y7oFUJ+1QODkbrLWRVhoIgULhd19oR8vDE2CLzuQph78pCMp+r8MTYIncLVBAyRZ2hDj8/PDXH6MLKDnpbIsgbdveQEZyc+mM+Hjw1v2ZrPlXQhQHIQx1+3n/b5g1jS9fGuFL7eD22SiXdYCjmZzjup6xX8bld2DaUdEPQVklisaA3JH0Alm2zWNARBc55XTJV02YupzV46Ia8LobjfrwClQndsBiMBTg5m6U8vkF4TFY8oK+EGxPZVOUrBnfs6AJocu+5Y0cXeUGsczJfoaxbyJKEW23c6JR1SyihToQ97O2LcGYu35Sk7+2LkAiL2cC1A0ZT1Ayu3xTjwVPzPJpMNlmLibiM5DQDn0vh9FyeFycydQFvn1uhM+huIn38tGGaNo+fSzZ5lp+bz1M1LW7c1HpCWTXhGy9MceOWOHds70I3bdyKxGSmxDdemOLQkFiSOp0uE/K6+Ofj05ydy9cr/zt7QrztQP81y7Zr8eoxlS7z+NlFBuM+9vaH64tTtlzlsbOLwh+8R5WZTJcIe12EvSoWIOMw2ybTJSFf3XbZCi3kNP7qmUvohkU84MYwHX/apWKF8VSJ37x3Z8s7vXZdq4xEtlylVDHQTau+K61VAUQs9lyq1JT0Act/nuP27R1C1xryunjgYD//8NJUQ3t+I8zUQ16X0Hu5VrSL1axVTX50eoFUUUeWG/Gz8YBbiNySLVXZ0R3CtmnCDO3oDpEtiW1UQsvJVNCjkgg5LSlFkijqjuWaCBarRmx4S1/vhhEbwNlUHR6OXpHRKLKp6gx6+PH55HJ1srM+DybzOj8+n+T1u8QqqbXqm2lZ6KaFZTlEBLcio8iyUHUuHnBz7+5uSrrBVLqMJDmL/mDcxxt2dwthUtsFowl6VR46s8BkutyQ+E6myzx0ZoHDAp0flypzIVkkHnCTCHswTAtVcaSNLiSLuARt0Kq2zVS6xK3bOuiL+uqt3ulMiecupqgKAN4jPpWw18WjZ5INRBy3ItMd9hLxiX0DhYrBPx2b5uRsrkEA+5WZnLMB6L9m2XYtXiXSRZ3zyfwyrsNeJWUj4XPLwqBmn1tlZ3eIM3PNjgcjPSF8brFXZKMlJwAuJAvE/e4rKuFfECjx1651IytTAEXdoDPgIVuqopdXV1EUOgMeiroIA9kkVdS5Y3snAzVrLUVmMlXklZkceU3c+qcd92Cjo12s5nLVJKdVmc9rFDQDwzRRFYWgV0VVpAax7J82XIrMfE5jW1cAEsGGNt98TnP+t0BkylXefWSQrz47zrHJFeLQwcEI7zkyKFyh3+gErRanZ3JNsj4Lea2uu9lqbOkKcMOmuENuGTfr86DPrXDL1g62dAWErjMR9tAb8fDSRKZp4T80FBWqzqWLOov5Ch1BN91hb73VaVhOBTEt0D5tFzSlHfJLIY/K1kSA88tEtNp3BQ4hKyRg1wYOy/bdRwZ5+IyjaVuLrV0B3n1kEJcAITHkUblu2bGjI+iuz62LhQquZbcdkchrRlPSB47N4MnZHHnBRH29cS3xu1pCogHMuzrKuiUsuRDyqLz9YB+S5JTgSxUTv0elrBvYti38wm+45ATO5PTo2aRThVglNDyd0Xj0bJKbtohVvGBjK1PgVExHkwUSQaddXGvzVKoWo8kChoA8gFY1+OUbh5nLlR1bNdtRMOmJeDk4GEOrrm8S2eh7sNFRYzX/4cPnOTmTq7ej9vSFuXeXOFbGsGzmslpTtaSgGcxlNaFnlQh7GIhdmTAjkkgAqJLM3z47zi1bO3nn4YH69zqTKfO1Z8f5zTeMCI270QkatEd3M+R18cs3bcKtyowmCxRLKgG/l21dQd51ZEj4HQh5VG7b1sliodKwqRyM+7hte6fQPJirVPm7Fybrm+pamw+cTfVIX6jlMQdiPvoiXlRFaqrOGqYtnKS3Q34p6FF524F+Hj+XdCzbLIeI0xvxcufOLiGfXoDOgIdXphxo0pauALbFstOMyStTOd52oHX3okLF4O6dCcaSBSQc3KtXVYj4VLZ2BYXl0sq6QdCjNsAyahFcXmd/FnEt8btKoh3MS3BYhsmCxnMXU1gW6JaNR5GQJLhhS5zNAjvomuTEmbk8hmXXJ/yXp7LoxiUhyQmAqmUxl9PQDYviql25S5GZy2lULTEsUjvCJUkMx/11HMrq3e7mzgAuARB6IuTl3HyBFy6lm/BNb97nJRFanzzARruXbPSYea3Kd1+eoSvk4YGD/XUSQras892XZ4RZzYosOf6ncqNe1+rjrUZ/zMf9B3r5p5dmGEsWGjBTb1uHcKthWxwYivHDk/OMJVda/lu7gty+oxPDbv0baJcwejt0N8GpTv/anVuZSBWZnE0y2NvFUDywrnd1Il1Cq1rcuDnOrds66yQ3w7TQdIuJdIn+eGsb1lRR58xcvgHnWftfZ+byQoSBkNfFbds6+dyDZ3l5aqXiu38gwm/eu1P4HrRDfqk/5iM0o7C7N8S+/jAVw8LjkjFNm6BbEf4GKoaFYVv4XAo2YEk2suRYVxq2JVSddCsyS4UKj5xZRe6QYEciRNTnYiAqdq1et0pfxEtKdbRxa8UKn0shHnDjFeyorTeuJX5XSTQyL1cW/fUwL8Ghxl9Mlnj0bJLzq7Bj2xNBuoJe9vVFW55MLiSLnJ3Pc26+QKak13e6Ub8bSUKIIQdOhculyHQEFYIeFcsGWXJ2a4ZpC2sZtiMURXpVELoi0I6QJHjo9ALnFwqsLkKdXyjw0OkFbt/eKXy9E0ulNdnS771xWLhCO7FU4pkLi3XhUq9b4cSUzE1bOoXGvJAs8uPRxTUr3z63zJv29gq9V36XwrZEkIuLTnW6Zq/nUmQ2dwaENlUhr4uRRIjq3h5sHE23gEcBG3YmQsILtCrJHL2U5uJioUGv+uJiAb9b4fUjrVfn2pWgtastCcv3tzuENjvGSPeWddteZUtVfnByjgMDUbq8Krq8zGzWKvzg5BwjPa1X53Tj1Sekn3R+rVjIaXzpiQtYls3e/kg9QbMsmy89cYGPx/YIwV1WwyguD1GSW1k3eX48zY/PL1KsGPXNT8Cjcsf2Tm7b3iX0HRQrBqYFC/lKg/ROxOci5HUJKSbYEnz7pRlOzmQxVz2WkzNOcr1fYF4B6I942dIVQDcsQj4V2wJJBttyYAv9kZ+Nlt+1xO8qiRrz8simGDdtiVPSqgS8LgzLXhfzcjxV5JtHJ7mwWHSU1JeztAuLzvEDQ5GWJ5JkvsK5uQKLhUrDbnexUEGaQ4ghB84CvbUrwFiywKV6MiUR9TvleNGqZztia1eQrz07sSYI/dJikV+5abjlMRcLOgv5Cn63ilGreEqgyjIL+coyW7T1yGtV/vLJizx4er5hIj03n0c3LP6PN+xoeYLOa1WOTqT4wcn5JtyYS5WJBVwtj1ljXq4VosxLaN5U2YaOR3U5/q+Cm6q8VuXcQoEfjy3WBYEVWaIv6sXjUhgQrE7qpsVUuoTHpTgt6OXvVZUlptIlIWupdrBvob3s/o2OsN9F1bR5YTx9xfOtRsTvIuZ3kS5VG+ZBWZKI+V1EBMYcSxZYyFfW1AasWraQlAm0Rxx+LFng6bElMqUqhmnVCwBV0+apsSXuPyB2rZLkCJZXDCc5r41bMSwm02JajksFnVOzuYZ2PDif16nZHEuCc2ss4ObIpni9GFKLnd0hjmyKC+vvrjeuJX5XSYS8Ll43klj+MPN1UPOO7v9fe+ceHEd15/tv9/TM9Gge0mj0liVZD8sP+SG/sDF2nMBiCGBCkiVAbtiNs65a9hqSbFK5gWyWLUJ2lyoqIWXYhOzCTXaTyos8riHJZkMCMeFhsAELg21ZsmzrrRlJI8370dPn/tGakVozNsyZadtCv08VRXla6prW6dP9O7/z+32/Ttz9oTbuDMJ4MIEebyjndlaPN4TxYP43vCAAvnlBH6BJWfhCcW6R1VKbGWaT5ik8Vwk/rmir3lLb5VOfVuWScdfOVjz2fC+OnJvUj9fVbVwPPEVVkVIZLCaty1AFy3QHp1QGhXOru2cslBX0AcB0NIlnT4zhpnV12JBnV9+QP4pnukZwZjwMJaVmVvtnxsN4pmsEK2tcecsYvFtnJa8uWr3bhj+e8OIj62oBATPNSBYwxjDoj3Etqob8URzoGs4qzZiKaJqLy2ucXDIOKZXBLGnWilZJzMwBZeZzHmspo5o70h3zuWp9C+0WLzZtlQ60Vzt0QU+a9moHl71cuc2Mra0evDJjWZcOKhyyhK2tHpRzPK+CMSUr6AO0rOzQVJS7WcAIcfjJsLZQTc4sRtJapiklBW8wzq2NN1fLcf57i1fLMTangSvXr8c4fcAnwgm8cW4KW1s82NVRk5HfCUSTeKN/CpuWll8SBw8K/BYI6Ym5obEMW1vKdUbyhbg2JGda7NMv5zSioHktJjkyCIKgbWnOrUFK01xh5w78QnEFHXUueIMx9Hpnt0/bquzoqHNxF+AaRUd9KR64uQO93iB802FUltrRVuXknuhVThkmETk7Nz12C3eN32hAe2FUOKywWUyZ7FQ0oXURjwaiAPIL/LyBOHq9IQRjSSSU2dW+RRLR62XwBuJYUZvf96xyWS9Y58rbMOGUzfjg8sqiLqq8gTjOTUSgMqYLfCWTiHMTEa7rB7Tgtr3aiVNj2dZS7dVO7uDXiOaONI3lJah0yjqfUl5fZaOocsm4+0NtOTNevAs1s0nEzWvrIAkihqaiUFTtWVtfasMNa2u4OrvN0vn1KkMxhVsiZdAfxamxUO7mDo+Dq85TMgnnfX9o7x2+F4ERWo6lNjNcttzbxHarxJ1UCMUUyBYTJkJx9I2HM2U0LlmCbJG4yygKhQK/BcKgP4rhGb/DXEbyvAXYHocFLlnK2iZUmeap6HHwpaKvatPqzeYXoKc/5yEQU/CLNwaxscmtK8AenoriF28MYn1j/lpTRlPlkuGxm3H0aD86mxsKqkVyWCVsbfHgzf4pRBIpMGjBhM1swvrGMu4uObMoot5tw8h0DAP+2aDKKZtR77bBLOb/MokpKQSiSUSSKZ2UQVLVajFjSv4r6HTDRK7MVCENE8GYltm0WyUsq3ZkOkXtVgnPnhjjWlQlUiqSKRXBWDKrYURzXeHLzla5rLBIIlor7Git1MvEWCSRWxTYiOaOYCyZpY+ZZng6hr/Z3nxZZf0yC7UZCSpXiRltBUhQ1bltODUWRHOlHc2V9kzgCwCxRAp1HPer0yphWbUT/nAia5Hmtlu4VRj84URW0AdoJRRnJkJccmGlshnt1Q4EogpKrJoupkkUEI4rcNkkbss2vZaj3mmGV8uxxiVjR1sFXujxYWqOxmZZiRkfaKtADec94JC14O58AXUhda6FQIHfAsEoT1mX1Yyd7ZX4U7cPE3Mmt8duwc72Sris+U/OulIber1BtFXZ8cH2WRX0wakIer1B3L65geu7ypIJslnCob7JnC9TWeIPqryB2GVlop2LSEzBxzYuQWTGlzTd3NJUXoKPb1yCCOc9UOWyQjabsiQHgrEkmivsXMGE3SrBZhGxsakMDWnNQUlE/0QYx0cCsHO8oJyyGRsby5FU1EzDSDqDtKGpnDuIGPRHMRaIa80NNjNSkQDKbE4IgoixQJwr8Cm3a1vF87deUyoDY4xLvBfQgt+b1uYOfnev5Qt+jWruGPRHMR5KoKWiJGftIG9AaSRVLrmo8/74SADJFINrJmMkCFojyfHRAD64In/3HqdVwt7tzXjyxTPoGpzKfL5uSZkWSHMGfkbIhZWXWPC/d7bhB4fO4sRoEIxp9Y0rahy488qlKM9xr70X9FqO8/yKObUc3XYLruuoRiiexIA/mnm2NrhtuG51DXctnsdugaKqOQNqRVXhoRo/4kLoC7BFTDvNma3eQgqw44qKtfVliCspjAXiUGZM36tdVqxdUsbVGl/vtuGuna149I89ePb4rMDmxsYyfPaaZdyZmSqXNdN9OX/7jDdAAYB3hqZzb/F8qA0d9XxerUZQIkv41m97YLeasH2ZPuP5Hy/04Ss3rOQ6r6oCW5rdCMeTWcLYW5rLwVM6aBKAvTta8PPXB/F8ty/z+fIaJ/buaAHnLg8aPSUwCRU4NSdIX1bpyFtqYy7pWqHzzS2ewKfEYsLmpeVasTk00V6zSYTKGBrcJSix8C1SjAh+DfOrjilYWePM7QG9oorLBsxoiik/NOSPotplK6pdl9tuwW+f70VLpR2bl5YjkUrBYjJhIhzHb4+N4B9vWsX1XY2QCyuzW/BK3wSqXTYsr3VlBKynwkm8cnoCmzms1QC9lmOWTiynluPIdAxPHRnApuZyXL2yCtFECjaLCVORJH52uB91ZTauBUFacL+lwg5BEDIeyKrKsKTchskC/MULgQK/BYK+ADucqUNq8tgLKsC2yxL6JsL4YHsVRFHIPPBUleHYcIDboP1Q3wR2dVTjYxuWIJJIocRiQiiexCt9E/yt8QZs9XkDsaygDwBOjYXw2PO9eOBmPnmENMFYEv2TYfjgQvdYCA3lfLWYgNYVfWxoOmcBv0kUMB7i62pVVIb+iSg66kp1W+hTkST6JyNcAsYmUcCRs35MR5IotZkzTQjTkSSOnPVjLWdAbUSQ7pSlOQGKfm5ds6KKywYtElNwy/p6fPv5Hrw5MJX5fH1DKT66vp47Owtowa9sriralqRR3bdpG7D5wcS5iQi3DZiRFFt+KBRX8MxbQ+id1zzX6w3hmbeGuOy6tF0ZAe8MBxCIJjOLX5fNjJZKByY4Awl9Z/u8IJ2zs31kOoauwSm0VDhglUwQoM5YgCbRNTiFkekY9z2b1nIsVpDuC8YxMBXD1HEvxBk9T5MoQFUZgnGFWzEgFFNgMglwyiakmAAhqf2tTYJ2fqrxI94VIwqwl7ht2NBYiu6RIFRoCu5WSesV3dBYyvXQH/RHMRFOYjQQzykGyrvFY0S2o9cXytnJB2jBXy+nPAKgvUh++eYgRqejMzWZEdSU2vCx9Uv4XiQxBU7ZfN66sUL01pw2M2IJBWfGI0gqKsySiFJZglM2c2V8FJXh2OA0ZLMJbrtFV4t0bHCaK5g0Kkgvt1vwYu94zgDlxd5xXLsq/7lVIkv45h9OwSGbcccVDZlgetAfxXcPnsZXOTMzQI4AJWTC4GSEO0Axyq/aCBuwzO8HYlrTFDyIn/UXFPgCxsgPBeNKxq5sPj1jIa4mhPQcd1glyObZGj9JFADGvy0/Vy7sypZyXVcvr1zYeCiODy6vwsFTPvzhxNisiHmVAx9cXsW9UDUCi0nrkA+c5+/H2zTllCVEEyp+/dZolpzLrZsauBaVxYACvwXC3ALsUDyZKUB3WM0FFWADWr3BwR4fTozM3pgra51o9PD5XhpVMwRoKz23vbZoK71A5MLepu92/Hycr7B9LBDHL98c5Cpsd5WYM3/T+VvdoiBwaY0BczM+yFKS5834MKbZXZ2bjOge8A5ZQlN5CZfYdq8vhB5vCC5ZyqzKpZn/93j5g/SJcAKSKMJm0Qzk09gsIiSTyJVF8YXieHNgKmcTh8UkwheKg8dczYgABTDGq9kIGzBgbtY3uwObN+s75I/i110j8IcTKCsxZ+4tfziBX3PKDyUVNVPcPx+HLCHJ4zAhiTmbMADNH9zC2dXrlM24ZkUVfn98DGGVzTQniTAJAnat4tPxc1olvNgzjlBMQaOnBKo6m+V6sWccOwpo9EsvqrMWKpyLaqMUA+KKip8fGdAFfQDQPRbEz48MYD3n7lehUOC3QNAFU/MK0Ocez5czvjB+9Go/+iejui25/skofvRqP1bVuvLemjVSsR8orqdsOljKJbtRSDCVbhjIBW/DwFytMcu8RhZerTHAmIyP225BhUOGyrTs39zMRIVT5iqWDkSScJeYc4rX1pfZuIP0dKCTa1FlNp1fPuNC+MMJlFhMYAnoJC3MJhElFhNXlyQwq4+YKzvJq4+YpthezUbYgBmV9fUG4gjGkogrKgQBmfuVMS3Y5pHfcc7cl+e7X3myPVZJRJVTzhmgVDnlma3U/AnGkugbD0E2C0gxUbcl2TceQgNHZ7vKtEamiXAc09HZ6y+1SXDJUpbOaz7f9cDRIdgtJqyoceqahg4cHcKnr8rft9soxQBvMIbBqSh2tldiiduWaXIbmIzg9XP+82bEjYYCvwWCUcHUSCCKE6OBnMdOjAYwEohiLcryOqfRiv3FLMBuq3SgtaIEh8/5s7ZPNze5uYMpI7qwjdAaS1PsjM8Stw2tVXZYzWLWS3+J28Z1D5TmCPqAWfFaHicEIPfcEea0MfLMLYfVrAmNm01aADizoGJMywI4OLrlgVl9wFwUog9oBHOfA/Mz/7zPAaNKM5IpFckUw2ggputud8pmNJWXcOmZtlY6UFcqo6PWhXKHNROgTATj8EcSaOV4toRiygVr8XgTAMMz5Tnn25Ic9kexPM9FQSyZwmQkocuiA5qV22QkwS2KPOSPotxuOW/TEE/TTLqMqMphRSCuIBhLwiWb4bRKaPDw+0AnUio+tmEJXuzx4U/dvkyDT1rEnFfWqVAo8FsgGBVMxZIXvvHe7XgujKoZArQUfy4ngE9sauRK8dssJtx+RSNCiRTeHpo1PV9Z68LtVzTCxtl9aVSgXmytMaNI3wM/evUcJsMJJBUVjAFN5Rbue8Apm1FbKqMnlv3iry2VCwpSnVYTXu6bQjSRmtk+VGCzmLCtxcM1t+rKZKyuL9XdU2lW15eiroxvvN7tRVHIi6TYkkZGPAeMKs2QLSYM+CM5JY0G/BHIHM+BKpeMT29rxjee7cZbbw5lPl+7pBRf3LWc629rlyWc8oZmXGY0TTyHVQJjDG8MTGMDp5ZpIJ7Er94cRLXLik1L3Tr5pV+9OYhVdfl7FaeY5jRiM5tgsyCz+MHM5ym+hB9iyRT+eNKb0xXojye96KjLv2kG0Mb6F28MaedVtZrcZo8df7O9me+LAqh2yTjUN4Ee7+wzi0FzxZJMIm5YU8N97kKgwG+BMNf+qNcXQgwWTEeVgu2P3HYLyu0WrK5zZemtvT0c4NYvMqJmKBhL4oeHzmr6TXNqXAb8ESQUFfdcsyzv8w/6ozjYM47bNi3BbZsbEI4rmsYcYzjYM47aMhvX9tcStw11pTJks5glERJLqgVlPYutNQYUv2YGAMKxJFoq7GiusGud3VYTBAaEcziPvBdiiRR2r63PKY+xe209Ygm+DAIArKxzaRmjSAgpmJBQVDS4S7CK8yVS77Zh7/Zm/Mef+/DW4HTmu65dUoq925u5t44qndaZWsTsAM9mEVHpvLwkjYr9HHi30gve0gyzKKCu1IZATNEJjguC9rk5h6XluxGMJXGwx4dKhxWblpbrmqYOnvKhrcqR999hiduGzoZSHCiyvZ4/ksCGJjei8RQY0wT8GdO2j5sq7PBH8i9NEAVNGHlwKjpjozZj2cYY6lwyOP6kAIBIMoVzE2GsqnWiplTW3lkmESPTUZwcDSLCkUn0BmL43stncOjMhC5rOhaIQRSB/3PdCq5nLptpuDHPc8FKl5Bw7nYXDAV+CwzN/siasWyzmU0F2R+1euz4u52t+OmRgSy9tb/b2YpWzgYPoPg1Q32+cFbQB2jNKS/3TeDGtXVYl2c9YiimIKUyeIPxjMisojLEkymkVMa9dWKUtzJQ3K3u9PmKXTPjDcTwypkJHDg6nGWr9JHOOnic1rwfpHZZwttD07ilsx42iwmRuIISWUI0nsJbg9PY0sInPTToj+KF7nE0lNuwut6FaCIJm8WM6WgSB7vHsbzGlfd97JTNqHXJuKWzHh/prEckoaDEIkEAUOviz062VNqxrcWTNQ8KEa81WtKomM8BIzx1Aa0Dd+fySkAATntDs1tyVQ7s5LQBS9f5mkwirJIIUdBe+CYTvzA4oKk7hOOKrgklHFcKUnewSiY4LBIOdo9nuS3duKYGVg5xfEEUsK2tAq/1TaDfH5kJchiWlpfgihYPBM7IT2UM13VU47mTXvz++Nic72rHdR3VXLWDp30hvHx6ImcZycunJ3Cas4RgOpJEo6cEKcYQjiuZrKfdKqHRU4Jpzgx1oVDgt0BIv5zTGaQ009Ek98sZ0FbI/ZMRrKp1YXtrheayIQmYDCcwMBnhXkEbgS8Yv6C6PI/Wkl7DLbtehLfd3ggbMMCYzJwRNTP9/gh+89YI6srk2a0jk4ihKe3ztQ1leT9IPXYLVtU58Ys3BnMHk5zZaX84gR5fcObeYpkgHRBgs4hcjRjBWBJPvzWMUFxBqc0yYyuVxHQ0gbMTYSzhHH8jxGuNlDQqNvo619latELrXNM2YCtrXPhAkWzAQjEFsUQKDtmEGpcjI947FUkgFEtxLSoH/VH4ggksm8kWJlIpWCUTAtEkfIEEdzApSyJ+984o+nwhzOzGQgDQ5wvhd++M4qo2T97nbCyzYSIUw5JyG9Y3uTOSRr5gDBOhGBrL+LKTNrMJh8/60T8Z1X3ePxnF4bN+XMMxVhPhhJaBA6CqLHP9aW3bCc5mLLNJxFgghka3DRAEncXiWCDG5ddcDCjwWyCkX87PHh9Dry8ERUnBLJnQWunAtauquV7O6fO2VjrwP++M4uXTE1BVBlEUsKzKgQ2Nbu7zGsG7aSnxaC0ZoeEGzNpVLa92aFu9MxnatBsEzwPaCIkYQKuZOXhK25JaVevKvKC8gTgOnvJx1cz4wwlsbi5HQlFhEkWoTIUkCqgttaHJY+cKpibDCRwbDMA3T//LF4rj2FAA21or+F78BthV9fnCeKVvAsurZ/92ggAkFIZXhiZw/eravLPTaYotXmtU3ZxRZOpcvUH4psOoLLWjrcpZUHDaUmnHlS0ehOZk9gQBsEgCruTMpFolEUsrSvDcTD1amuYKO65eUcXVgTsVTlzwnFOcAUo4oWJ4OgZRQKb2jkFz4BmejiF8nvlxIaajSWxsKsePX+vHf789m5lbVevEHVc0Ypqz5COSSGFoKpqzcWpoKooIR8mHLIlQmVYjq9vqVwVYTCJkzm7pKpcVS9zFl4kpFAr8FgixZAr/8/Yojg1rzg1KKgXJpKJrcAqqyrgLWtOFsv2TEQiCVtOiyblECiqUNYK01lIxLdvSGm4VDktGdkQSBU16hFPDDZhvV5XtBsFjV2WERAwARJMpLK9x4uApH379VvY2V5SjZsZmFuG0SjhwYhin5hQ2t1c58JHOOtjM+T9IvYE4jg1Po9opo7ZUr65/bGiau6PVCLuq8VAcO5ZV4oVTPvz32/oX9AfaKwsWrzVC0oj3+KWgyiXDYzfj6NF+dDY3wGTi9+kGtL/nTWvrsP+Pp/DHk16oKiCKwJo6F27f3M4VVFskEa+emUTfeFiXReobD8N+ZhLXdeRf2G82i1lBHwCcGQ/juZNeXLWMTxtPSalwWiUwlSGpskzKzywKcFolKBxNQ75QAo8914MPr67FDatrEVNSkCUTxkNxPPZcDx68ZQ3Xd40n1QvK5MQ5GhKrnDJaKrUFaYnVBKZq2b5wXIHbbkGVk29RYZRMTKFQ4LdACMWVTNA3l5TKcGx4WrdSzYdIMoWesWDm99Orpyi0z3kKZY2i3m3Dh1dX4yeHB3SK+MuqHfjw6uqCDOoD0SSmY8nMA79UNqPKKRfkhlFsuyojJGIAbTvixZ5xnBwNQp1zf50cDcIkCNjZXpn3OS0mEQe69EEfAJzyhnCgaxibl+Z//bk6VoV3Of5e0NtVzb5QC7GrclolvHx6HAIErKhxZbx640kVL58uTLwWKL6kkRF1c0Z81zRGOHccODqEqaiC2lJbZlExFVVw4OgQV2mGNxBHPJne3p3NbpWVmBFPqlwLlWgilZXxTuMLxbOkU94rTlmCIGiOIBCQ2fnBzPuAp+RFUTWHpV/OdDRryQqT7jgPlU4r4oqKpeUlWdunoUSKq8FJlkT83c5WPPHnvnkWi2XY+4EW7oyfEW5TxYACvwVCLKnCZZOwstaFulIbkikVFsmEoakITowEuGRXAE1dXhQERBKprK4jl2zmUpdPU2x5CACQzSI+vn4JTKL2ne1WE5QU48ogAdqqvMcbxFggpguqo4kU2MxxHoywqzJKIiaW1LZORADKnG0OCQKGpqJceltTUeWCenNT0fyD1EqnFZUOC85NRrJW+k0zTU88pO2qrmwpx872Ct22PK9dlcoYZLMJ3RNBTM7Zfiu3W7C8xsktXgsU31M2XTf36HM9eGc4kMmkd9S5CtaHNKIm1QjnjvM1jgHgbhyLKSmcnQijrkxGc4VdkwgRBcSS2ucxJf95ZUS2C9Cy3luWevDq2Yms825p9nBlvWtcNlQ4tPu9rsw2YweqvbO6R4OocfFlu1oq7djU6MZrZyfBMCu2LQC4Ymk517Z8NJnCcye9aK5wYONSrUTFIomYDCXw3Akv6rY0cn1XoPhuU8WAAr8FglUScevGBvz22Ah+/85YJjO3osaJWzc2cCu2C6IAl03CdFTfbm4zi3DZJO7OKyPkIYb8UYRiKl45M4HhqVhmwteVydjWUsFVj2gSBS14VJluO4YBsFtNOnP1fNDbVeltwHjtqozScowmU2CMwW6V4BQEMGgv/pSqKe/zbPXGkinYLCYwQHetFkmEzWLiCiZrS2U4rVJOQVinLKG2lC9AccpmfKSzHr98cxCnfaEZX2WP5qvMKZUUV1T0+cK6TA8ATEWS6POFuX1qjbJsc8pmfHxDPT68pjYjaVRiFuHkFJpOf9dfvjmIQX9UJ+KdTKncNalGdSD7gnFtcZ3DDjAYV7gaxxxWCfVuG/onwjoPWJcsodFj17JreVLptCKeVHOWO8STKvfix2o24YrmcihMzXq2XrG0HFaOwG9ZtQN37WzFD145hz+c8IIxTc5lRbUTd+1sxbJq/kzy9mUVODEayJJK2s651R1OpvDmwBScVglnJvR/12BcwUc31HN/V6D4CheFQoHfAsHjsOBP3T4M+DVrtfSqfMAfxZ+6fbh6RRXXeU0QoKgMTtmM6lIZTAUEEYjENVkTE0dlu1EP51gyhWdPjGVlkqYiSYTjKayszV9kdCwQw9ZmDyKJFHrnbEu2eRzY2uLBWCCG1RyB6ly7qlw2YDzZOaOEsUVBq+fr9YYwEZ49b7ndgrYqB5feVlmJGSIAu8WEUlmCCmgZRZVBmDmeL5PhBHYur0IixXDaF5o1fa90YOfyKkxy1mMC2qr8k1c0zjQMlKKyzI62Sv6GARWaSK3DKkEUBKgzwbSqMgRjCnjz6EZYtqW3OSWTZqkXTaqZHYCToyFuxYBBfxTnxiM5u1rPjUe4alL1fs1AwizCYjZBVVGQX7PFJF7QDpCnccxqFuGSJUTnZeGiMwGmlWOXoqXSjqvaPEimGFw2cybjOx1JwiwJXNkuYDbrvba+FJuayjNbkgklhfFQnLsWrdcbgqIyVDqtSKVUmEwiFJWh15e7i/y9MOSP4ieHB9BYbsfGpnJdB/ZPDg+grsyWf0Mi03aTAjnKZWwWsSDJNMCYcodCoMBvgRCIJpFSGaySmGk7F6A9mFIqQ4CzQyoYT2BbawVe6vXhtG+2vqm10o5trRUIxvPvEuv1hXBmPIxNTW5UOq26LtGuwSnuh7Mm3Hn+7UOeekRREPDU6wPY2OTGtlZPRnJgeCqKp44MYFMTnzaczq5qnrdyIdk5I4Sxy+2Wme1yE5ZVO3TBfzLFUM4hk1LjlPEXq6rwxxM++EKz95DHbsE1KytRw1EsHYwpODEaxFVtHnxoRZXu5XRiJIj2qvwD/zSz26cpTIeimIozDE5GubdPzYKAlbVOvDMcyMp4dtS5YBb4MslGWLYNzSwmz06EIAgClBRDwKR1Yi/1OLg7+6fCCTRXlOCFnnGcnQhndimWeuz4wLIKrg7U+X7N6bqxQv2aq1xWmE2iFpTPyfwHZ8R3eRrHAlEFK2pcCMQUDExGMtffUF6CFbUuBDjKHZyyGbtW1eAbz3bjjf6pzDk3NJbhi9cu534OzM16943P3l+FLCoH/VEE4yl01JfOLH5jmcVvMJbibkbzBuIz76rweY/nOwfcdsucHRq9PmZzhYPbyAAovttUMaDAb4EwFUnCIUuocFjhcVgyTQgCBDhkKWtL6b1S5bTh+ZPdWF7jxFVtFbrA5/mTY7hxTf5tksFoEteuqs7Z0XjtqmoEOYNUMFzQtYBnVea0SagvK0H3aAj9k9FMij+aSKG+rAROG98UmZudG52e1Zsqhm1dsYknVWxe6sbz3d6s4H/zUjdX3VAsmUJngxtjgQTGArFMsXi1S8b6BjfXVm96kTP3xTT/OA/67VN9Bzbv9qlkErCpyY1gLKn7mzaW27CpyQ3JxFlCYIBlWziuIBBL4si5qawApdxuRZizccxiFvHn3nEcHwnoykiOjwQgCODqQDXKrzmhqNi8tBz+SCIr8795puYrX1Iqw7MnRrGq1oXNS926Z+uzx0extTl/bTxvIIbHD/ZiLBBHc4U987waC8Tx+MFePHjLmsKz3kWqy87VbDbXA5u3Gc2IOWCEtzhgjNtUMaDAb4FQWmLGGV84UyOlMBWSKCKaSOGML8z9wFtW7cCuVTVwlUgotZkRTaRQYpHgcVjQ5LFz1WF4nFa8cMqXU3IAgKaQz4ERqzKLIGDvjmb835fO4q3Bqczna5eU4TNXNcPCmZkBZrNz/ZNhDIyUoKG2Eo3l/IbfgDGrR5UB7wwFsLquDB9or9IVNr8zFMCOZfmPVziZwtNHh9FW5cD6hlLEFBWyJGI8lMCBo8NYWpH/lpRRNY5GbJ86ZDPe6PejucKetaB6o9+PG9fWcX1XIyzbFFXFcye96B4N6gK0yGgQkihwdWADWvDfPxnRnRMAkikV/ZMRruDfKL/mUFzBG+cmsaLGqcv8T4YTeOPcJLY055/5r3RaUV9mw7mJqG5RyRhQX2bjGqtTY0G8dtafpe4AaBJCp8aC3IFa/0QEL/X6EIoriCZVlFhMODsexlVtlVzPFocsIRRTMnXOcz2wmz0O7oWaEXPAqDIaI9ymigEFfgsErQbPmpExmdsav6w6f8/Huee9rmPu1oFWgFvI1oEI7YGZSwV9MpyAyKOIC2NWZRazCc+f9KGxXPPAnPvAf77bi70FGHQD2t93RbUTsZHTWFHdUpDemFGrR7fdArtVwsnRQFaXXIO7hCugTigqzk6E8Xq/P+uYx27hyqAY9XA2Yvs0lkjhw6vr8MxbQ3ipdyLz+bJqR0G+wkZYtgWimt1XrgDtxEiAa0sS0AR8KxxWJFMMwdhslt8pm1HhsHIJ+Kb9mn97bBh942FIogBRFNBSYccNa+r4/ZoFYCQQRyie0gVp6eJ+nkdWS6UdK6pdeK7bi9Acuy6HVcLWZr7u08lwImfQB2jzdpJTwDkYS+LVM+P4+RuDWVJZkkmA216b9/zy2C1QVBXhuFYvmvbAVpm22OB12jFiDgDZGU9XiblgmaC025TKWJb2LK/bVDGgwG+BkH7gPfPWUNbELORF4g3E8J0/aY0Yc1XQT42F8J0/9eLrH81/6yClMqysdeGtwSmd1Y1HtmBlreu8D653w4gXv9Z9GcLaJaUotVkyxdKpFMNbg9Pc3ZdGYNTq0WO3AAIQTqSyHk4QwPWATgeQuczJUzOLAR6MqHE0YuvILks4MxHG/9rSCAECQnGt0YOBoWswwO0rbIRlWzylgjGt3nWuzIwoaNmpOKc+os0sYXQ6hjKblqWbW0YxOh2DzZz/68cuS+gamEJ7tRMbm8oRVzTLsmAsia6BKe6/a4nZhMby4op4A0BnYxkC8SSG/FEkZ2wL690yOhvLuM7neJcu63c7fj7OTUSy9FEBoGcshJ8cHsDK2tK8m9wmwwlsafbAG4yhZyyUMR1YVu3AlmYPdzOWEXMAMEZ6yDLz/AvGkrr3nkkU4JTNXE1DxYACvwWCXZbQ6wvhL1ZW47qOGkRiSZTImkl3ry/E/cA7NRbE4XO5tw4On/NzbR1YJBGjgRhaKuxoqXToBDZHAzFubTyg+KuypKLi5s7cAfXNnfUF6RgWGyO8igHtAb29rQJxRc2SCNm+rILrAS0JAurcNgxORrNslercNkgFbKEXWxrBiK2jJW4tg5xLsX/3ulrubWmg+JZtLtkMhywhGFMgQdBJGjlkCS7O89aWyuiodeHY8DTGQ/qX3pr6Ui75HY/dgpiSwltD05jvq9xe7eDOIulFvOf5VXOKeA/7o7BZTVi3pAzrG8sQTaRgs0hQVQab1YRhfxTL87yP68pkrK4vxdtD01nHVteXoq6M7zk4Oh1Fz1go5y5Nz1gIo9NRrsDv/x0dxPJqF65s8SCaSMJmMcMX1D5vL0DOpdhzwCg7zHKHBZVOC6Yi+kxsSmWodFpQ7uBvGikECvwWCEvcmhhm/2QUjKkZrbFCu0SN2DqwSiIqHNbzrp55NQeB4q/KHLKEF3p8mAwndTI5k+EkXujxYStnQG0ERngVA7PdspqAcWVRumVNJgEfmLEsm+8p+oFllTBxNjcYgX7rSK+5WMjW0YnhQJaItzcYw4mRAP6Cw0h+LsUMfqucVlzVWoGXTo/rtl9LbWZc1VqBqgKEsT+6sR6iKGTJ79yyvo5LIkS/SNG7rPAuUtLfdVbEWz8HeEW8kykVvWMhHDg6lNU09ZHOetRydLbXu23Yu70ZT7x4Rhf8ra4vxd7tzdyyK7GZLdjzedXGeBbAAhCMpXDknB/zg/T08UIo5hwwyg7TLAq4vqMWSmo46zl4fUctzJw6sYVCgd8CwaguUSO2DkIx5YKrZ95uLiNWZWmHDVEQYJH0q3pehw2jSHsVF9vw24hu2dZKB370aj9W1LiwY1mlTmvr7HgYd25t4vquRjB366jXF8poLrZVOri3jtJSFitqXFn1qIVIWRhBvduGq1dUIcVUDPljGYeJereMq1dUcQcTRthV6RcpepeVQiR9jJAzCSWUrKAPAE77wjhwdAjrObZ7nbIZbVVO7NnWhEhS1Yltt1U7ud8D7hILZLOI+Dw3EcYYZLMId0n+mSkjPLCNwig7TE3L04TtbRW65yBjDA7ZhCDneQuFAr8FhBFdokZsHdhl6YIZpA2NfF2CRqzK9A4b2Z3CPE0IRmGU4bcR3bJVLhl37WzFY8/3zqz4NdqrHQXbgBlBeutIm1u+gudW+kVhNokoy/HS5H2RGIFTNmN9oxtxJZUVoK1vchf0fCm2XdXcRcr8nY/08UK+azHrR6cjyfMupvrGI5jmlODS/qbmota5VtgtuLLVg5d6J7Iaca5s9aCCYwvdCA9sozDKDtMuS3hnJIimchsskkn3LnxnOIjOBr53YaFQ4LfAKGaXKGDM1kF6WzrXQ6+QbWkjVmVzHTbmZ2Z4HTaMwijDb6O6ZTvqS/HAzR1F7ZIzkmLOLaNeJEZhpJ9oMbfkjJL0SVPM75piDBaTmL19Kmjbp6kC/JqLXecaiinYvbYOSUXFgH+2q7nBbcPudXVcz1YjPLCNwqj7yqh3YaFcXk8f4qJjxNaBUYGEES9TncPGvMzMpZyY58OoF7QR3bKAlvm7XAM9IzE6QDGCy81PNBcLSRi9xmWDu8SMqWgyq2GizGZGjevyuQdKZAlPHxvBh1fXAIKQeQ+AMbzUO4HbNzfkfU4jPLCNwqh3llHnLRQK/AhDtg6MCCSMeJlerhPzQhj1gl4IL/6FwkK8rxYKRpS8GMGyageuXVmNZ0+MZTXNXLuqmksc3yiWuLUg9dCZbN3NYlhMXu5jBRi3+DXqvIVAgR8BwJiXfrHPadTL9HKcmMTCh+4r4yh2yYsROGUzPn1VMyySiFPeEJKKCrMkor3KgU9ubbqs7gMjFyoLYazSLJZFNQV+xILCqJfp5TYxifcHdF8tbho9Jdh3dduCCP5pobJ4oMCPWHDQy5QgiIXCQnpeLaTvSvBzafxCCIIgCIIgiIsOBX4EQRAEQRCLBAr8CIIgCIIgFgkU+BEEQRAEQSwSKPAjCIIgCIJYJFDgRxAEQRAEsUigwI8gCIIgCGKRQDp+BpA25E6lUoacP31eo85PFBcar4UDjdXCgcZq4UBjZTzpv206/rgQAnsvP0XkRSKRwLFjxy711yAIgiAIYhGxZs0aWCyWC/4MBX4GoKoqFEWBKIoQBOFSfx2CIAiCIN7HMMagqiokSYIoXriKjwI/giAIgiCIRQI1dxAEQRAEQSwSKPAjCIIgCIJYJFDgRxAEQRAEsUigwI8gCIIgCGKRQIEfQRAEQRDEIoECP4IgCIIgiEUCBX4EQRAEQRCLBAr8LjFjY2P47Gc/iyuuuAI7duzAv/7rvyIejwMABgYG8OlPfxqdnZ244YYb8OKLL+p+9+WXX8ZNN92EdevW4a/+6q8wMDCgO/79738fO3bswPr16/GVr3wF0Wj0ol3X+xWjxmt6ehrLly/X/bdly5aLem3vNwoZqzRPP/007rzzzqzPaW4VF6PGiuZV8SlkrH7xi1/g+uuvx/r163Hrrbfi9ddf1x2neXWRYMQlQ1VV9olPfILt3buXnTp1ih0+fJhde+217KGHHmKqqrLdu3ezL37xi6y3t5c9/vjjbN26dWxoaIgxxtjQ0BDr7OxkTz75JDt16hT73Oc+x2666SamqipjjLHf/e53bOPGjey5555jXV1d7IYbbmAPPPDApbzcBY+R43XkyBF2xRVXMK/Xm/lvfHz8Ul7ugqaQsUrzyiuvsHXr1rFPfepTus9pbhUXI8eK5lVxKWSsDh48yNauXcsOHDjAzp49yx555BG2YcMGNjo6yhijeXUxocDvEtLb28va29uZz+fLfPbMM8+w7du3s5dffpl1dnaycDicOfbXf/3XbP/+/Ywxxr71rW/pHnKRSIStX7+eHTp0iDHG2Cc/+cnMzzLG2OHDh9natWtZJBIx+rLetxg5Xj/72c/YbbfddpGu5P1PIWPFGGOPPvooW716NbvpppuyggmaW8XFyLGieVVcChmrz3/+8+z+++/XnW/Xrl3spz/9KWOM5tXFhLZ6LyGVlZV44oknUFFRofs8FAqhq6sLq1atQklJSebzjRs34ujRowCArq4ubNq0KXPMZrOho6MDR48eRSqVwrFjx3THOzs7kUwmcfLkSWMv6n2MUeMFAL29vVi6dKnRl7BoKGSsAOCll17Ck08+iV27dul+n+ZW8TFqrACaV8WmkLHau3cv9uzZk3XOYDBI8+oiI13qL7CYcblc2LFjR+bfqqrihz/8IbZu3Qqfz4eqqirdz3s8HoyOjgLABY8HAgHE43HdcUmSUFZWlvl9In+MGi8AOH36NBRFwV/+5V9ibGwMmzZtwn333Zf1O8R7o5CxAoAf//jHAIBXX31V93M0t4qPUWMF0LwqNoWMVUdHh+7YCy+8gLNnz2Lr1q00ry4ylPG7jHj44Ydx/Phx/P3f/z2i0SgsFovuuMViQSKRAIALHo/FYpl/n+/3icIp1ngBQF9fH0KhEO677z488sgj8Hq9uOuuu5BKpS7OxbzPyWesLgTNLeMp1lgBNK+Mhnes+vv7cd9992H37t3o6OigeXWRoYzfZcLDDz+M//zP/8QjjzyC9vZ2WK1WTE1N6X4mkUhAlmUAgNVqzZoQiUQCLpcLVqs18+/5x202m3EXsYgo5ngBwG9+8xsIgpD5+f3792P79u3o6urChg0bjL+g9zH5jtWFoLllLMUcK4DmlZHwjtWZM2ewZ88eNDQ04Otf/zoAmlcXG8r4XQY8+OCD+N73voeHH34Y1113HQCguroa4+Pjup8bHx/PpMLPd7yyshJlZWWwWq2644qiYGpqCpWVlQZfzfufYo8XoNX8zX1AejwelJWVYWxszMhLed/DM1YXguaWcRR7rACaV0bBO1Y9PT341Kc+hZqaGjzxxBOZsaF5dXGhwO8S89hjj+EnP/kJvvnNb+LGG2/MfL5u3Tq88847mRQ4ALz++utYt25d5vhcDaRoNIrjx49j3bp1EEURa9as0R0/evQoJEnCihUrLsJVvX8xYrxCoRA2b96MQ4cOZY6PjY3B7/ejpaXlIlzV+xPesboQNLeMwYixonllDLxj5fV68ZnPfAZNTU148skn4XA4Mj9H8+oic6nbihczvb29bOXKleyRRx7R6Ux5vV6mKAq74YYb2Oc//3l26tQp9t3vfpd1dnZmNJEGBgbYmjVr2He/+92MLtzu3bszunC//vWv2YYNG9izzz7Lurq62I033sgefPDBS3m5Cx4jx+tv//Zv2c0338y6urrY22+/ze644w62d+/eS3m5C5pCxmou+/fvz5IIoblVXIwcK5pXxaWQsfrCF77Atm3bxvr6+nS/FwqFGGM0ry4mAmOMXergc7Hy7//+7/jGN76R81h3dzfOnTuHf/iHf0BXVxeamprwla98Bdu2bcv8zMGDB/Ev//IvGB0dxfr16/Hggw+ioaFBd/7vf//7SCQS2LVrF/7pn/4pU0tB5I+R4zU9PY2HHnoIzz//PBKJBK655hp89atfRWlp6UW5tvcbhY5VmkcffRSvvfYafvCDH2Sdn+ZWcTByrGheFRfesWKMobOzU5cNTHP33XfjnnvuyZyf5pXxUOBHEARBEASxSKAaP4IgCIIgiEUCBX4EQRAEQRCLBAr8CIIgCIIgFgkU+BEEQRAEQSwSKPAjCIIgCIJYJFDgRxAEQRAEsUigwI8gCIIgCGKRQIEfQRAEQRDEIoECP4IgCAP4whe+gJ07dyIajWYd27NnD26//XaQfj5BEBcbCvwIgiAM4Mtf/jKCwSAef/xx3ee///3vcfjwYXzta1+DIAiX6NsRBLFYocCPIAjCAKqrq3HPPffge9/7HgYGBgAAsVgMDz30EPbs2YP29vZL/A0JgliMUOBHEARhEHfeeSeamprw8MMPAwCeeOIJiKKIffv2YWRkBHfddRfWrVuHq6++Go899hhSqVTmd5966ilcf/31WL16NbZs2YIHHnggc/zee+/Fvffei5tvvhlXXnklzp49eykujyCIBYh0qb8AQRDE+xVJknD//ffjzjvvxB/+8Ac8+eST2L9/P6xWK+6++26sWLECv/rVr+Dz+XD//fdDEATs27cPr732Gr7+9a/j4YcfxqpVq/D222/jS1/6Eq688krs2rULAHDgwAH827/9GyoqKrB06dJLe6EEQSwYKONHEARhIJs3b8bu3bvxuc99Dh/60IewY8cOHDp0CMPDw3jwwQfR0tKCLVu24Mtf/jL+67/+CwBQUlKCf/7nf8auXbuwZMkSXH/99Vi1ahV6enoy512zZg2uvvpqrF279lJdGkEQCxDK+BEEQRjMXXfdhaeffhr79u0DAJw+fRpTU1PYuHFj5mdUVUUsFoPf78fq1ashyzL279+P3t5edHd349y5c9i+fXvm5+vr6y/6dRAEsfChwI8gCMJgrFar7v+KoqClpQXf/va3s37W6XTiz3/+M/bt24dbbrkFO3bswL59+/DAAw/kPCdBEEQ+0FYvQRDERaa5uRnDw8MoLy9HU1MTmpqaMDg4iP3790MQBDz11FP4+Mc/jq997Wu49dZb0draiv7+ftL9IwiiYCjwIwiCuMhs374d9fX1+NKXvoTu7m4cOXIE//iP/wibzQaTyYSysjK8+eab6O7uRk9PD+699174fD4kEolL/dUJgljgUOBHEARxkTGZTPjOd74DVVXxiU98Avfccw927tyJr371qwCAu+++Gx6PB7fddhv27NkDq9WKO+64AydOnLjE35wgiIWOwGjvgCAIgiAIYlFAGT+CIAiCIIhFAgV+BEEQBEEQiwQK/AiCIAiCIBYJFPgRBEEQBEEsEijwIwiCIAiCWCRQ4EcQBEEQBLFIoMCPIAiCIAhikUCBH0EQBEEQxCKBAj+CIAiCIIhFAgV+BEEQBEEQiwQK/AiCIAiCIBYJ/x8PZ31aNCbzoQAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 2: связь цены и года выпуска\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.scatterplot(data=df, x=\"Year\", y=\"Price\", alpha=0.6)\n",
+        "plt.title(\"Цена vs Год выпуска\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "69a27e4a",
+      "metadata": {},
+      "source": [
+        "Цена на автомобиль совсем не зависит от года выпуска"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "fb59d4c3",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 3: цена в зависимости от состояния авто\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.boxplot(data=df, x=\"Condition\", y=\"Price\")\n",
+        "plt.title(\"Цена vs Состояние\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4948afde",
+      "metadata": {},
+      "source": [
+        "От состояния цена также не зависит"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ab37182d",
+      "metadata": {},
+      "source": [
+        "### Краткие выводы по графикам\n",
+        "- В данных есть пропуски.\n",
+        "- Данные не коррелируют между собой, они выглядят случайными"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "daa3521f",
+      "metadata": {},
+      "source": [
+        "## Задание 2. Модели прогнозирования"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "4dade46e",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>split</th>\n",
+              "      <th>MAE</th>\n",
+              "      <th>RMSE</th>\n",
+              "      <th>R2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>train</td>\n",
+              "      <td>23677.424433</td>\n",
+              "      <td>27261.296466</td>\n",
+              "      <td>0.001463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23386.369896</td>\n",
+              "      <td>26995.898047</td>\n",
+              "      <td>-0.000709</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>train</td>\n",
+              "      <td>8877.283827</td>\n",
+              "      <td>10364.788027</td>\n",
+              "      <td>0.855658</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23676.962601</td>\n",
+              "      <td>27982.335432</td>\n",
+              "      <td>-0.075178</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>train</td>\n",
+              "      <td>20860.335930</td>\n",
+              "      <td>24310.182306</td>\n",
+              "      <td>0.205951</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23625.922747</td>\n",
+              "      <td>27750.650439</td>\n",
+              "      <td>-0.057447</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model  split           MAE          RMSE        R2\n",
+              "0  LinearRegression  train  23677.424433  27261.296466  0.001463\n",
+              "1  LinearRegression   test  23386.369896  26995.898047 -0.000709\n",
+              "2      RandomForest  train   8877.283827  10364.788027  0.855658\n",
+              "3      RandomForest   test  23676.962601  27982.335432 -0.075178\n",
+              "4  GradientBoosting  train  20860.335930  24310.182306  0.205951\n",
+              "5  GradientBoosting   test  23625.922747  27750.650439 -0.057447"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Удаляем строки без цены\n",
+        "df_model = df.dropna(subset=[\"Price\"]).copy()\n",
+        "\n",
+        "X = df_model.drop(columns=[\"Price\"])\n",
+        "y = df_model[\"Price\"]\n",
+        "\n",
+        "cat_cols = X.select_dtypes(include=[\"object\", \"string\"]).columns\n",
+        "num_cols = X.select_dtypes(exclude=[\"object\", \"string\"]).columns\n",
+        "\n",
+        "preprocess = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", Pipeline([\n",
+        "            (\"imputer\", SimpleImputer(strategy=\"median\"))\n",
+        "        ]), num_cols),\n",
+        "        (\"cat\", Pipeline([\n",
+        "            (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+        "            (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
+        "        ]), cat_cols)\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "models = {\n",
+        "    \"LinearRegression\": LinearRegression(),\n",
+        "    \"RandomForest\": RandomForestRegressor(n_estimators=200, random_state=42),\n",
+        "    \"GradientBoosting\": GradientBoostingRegressor(random_state=42)\n",
+        "}\n",
+        "\n",
+        "def evaluate_models(X, y, models, random_state=42):\n",
+        "    # Если в y есть пропуски, удаляем эти строки\n",
+        "    mask = y.notna()\n",
+        "    X = X.loc[mask]\n",
+        "    y = y.loc[mask]\n",
+        "\n",
+        "    X_train, X_test, y_train, y_test = train_test_split(\n",
+        "        X, y, test_size=0.2, random_state=random_state\n",
+        "    )\n",
+        "    rows = []\n",
+        "    for name, model in models.items():\n",
+        "        pipe = Pipeline([\n",
+        "            (\"preprocess\", preprocess),\n",
+        "            (\"model\", model)\n",
+        "        ])\n",
+        "        pipe.fit(X_train, y_train)\n",
+        "\n",
+        "        for split, (X_s, y_s) in {\n",
+        "            \"train\": (X_train, y_train),\n",
+        "            \"test\": (X_test, y_test)\n",
+        "        }.items():\n",
+        "            pred = pipe.predict(X_s)\n",
+        "            rows.append({\n",
+        "                \"model\": name,\n",
+        "                \"split\": split,\n",
+        "                \"MAE\": mean_absolute_error(y_s, pred),\n",
+        "                \"RMSE\": np.sqrt(mean_squared_error(y_s, pred)),\n",
+        "                \"R2\": r2_score(y_s, pred)\n",
+        "            })\n",
+        "\n",
+        "    return pd.DataFrame(rows)\n",
+        "\n",
+        "results_before = evaluate_models(X, y, models)\n",
+        "results_before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "2d537e27",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"2\" halign=\"left\">MAE</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">R2</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">RMSE</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>split</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>model</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>GradientBoosting</th>\n",
+              "      <td>23625.922747</td>\n",
+              "      <td>20860.335930</td>\n",
+              "      <td>-0.057447</td>\n",
+              "      <td>0.205951</td>\n",
+              "      <td>27750.650439</td>\n",
+              "      <td>24310.182306</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LinearRegression</th>\n",
+              "      <td>23386.369896</td>\n",
+              "      <td>23677.424433</td>\n",
+              "      <td>-0.000709</td>\n",
+              "      <td>0.001463</td>\n",
+              "      <td>26995.898047</td>\n",
+              "      <td>27261.296466</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>RandomForest</th>\n",
+              "      <td>23676.962601</td>\n",
+              "      <td>8877.283827</td>\n",
+              "      <td>-0.075178</td>\n",
+              "      <td>0.855658</td>\n",
+              "      <td>27982.335432</td>\n",
+              "      <td>10364.788027</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                           MAE                      R2            \\\n",
+              "split                     test         train      test     train   \n",
+              "model                                                              \n",
+              "GradientBoosting  23625.922747  20860.335930 -0.057447  0.205951   \n",
+              "LinearRegression  23386.369896  23677.424433 -0.000709  0.001463   \n",
+              "RandomForest      23676.962601   8877.283827 -0.075178  0.855658   \n",
+              "\n",
+              "                          RMSE                \n",
+              "split                     test         train  \n",
+              "model                                         \n",
+              "GradientBoosting  27750.650439  24310.182306  \n",
+              "LinearRegression  26995.898047  27261.296466  \n",
+              "RandomForest      27982.335432  10364.788027  "
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Таблица результатов (train/test)\n",
+        "pivot_before = results_before.pivot_table(index=\"model\", columns=\"split\", values=[\"MAE\", \"RMSE\", \"R2\"])\n",
+        "pivot_before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "92be75ab",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 1400x400 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Визуализация метрик на тесте\n",
+        "test_before = results_before[results_before[\"split\"] == \"test\"]\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"MAE\", ax=axes[0])\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"RMSE\", ax=axes[1])\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"R2\", ax=axes[2])\n",
+        "\n",
+        "axes[0].set_title(\"MAE (test)\")\n",
+        "axes[1].set_title(\"RMSE (test)\")\n",
+        "axes[2].set_title(\"R2 (test)\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0fd28587",
+      "metadata": {},
+      "source": [
+        "### Почему выбраны эти метрики\n",
+        "\n",
+        "- **MAE** показывает среднюю абсолютную ошибку в тех же единицах, что и цена.\n",
+        "- **RMSE** сильнее штрафует большие ошибки.\n",
+        "- **R2** показывает долю объясненной дисперсии (удобно для оценки качества от 0 до 1+)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "39b556c4",
+      "metadata": {},
+      "source": [
+        "## Задание 3. Добавляем `cars_new.csv` и сравниваем качество"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9a4377b3",
+      "metadata": {},
+      "source": [
+        "cars_new.csv имеет другой формат, все данные в первом столбце и переведены на другой язык. Исправим это для объединения:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "a9f78135",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0    Car ID,Brand,Year,Engine Size,Fuel Type,Transm...\n",
+              "1    1,Lada,2000,1.5 л,Бензин,Механика,150000,Отлич...\n",
+              "2    2,Lada,2001,1.6 л,Бензин,Механика,180000,Хорош...\n",
+              "3    3,Lada,2002,1.5 л,Бензин,Механика,120000,Отлич...\n",
+              "4    4,Lada,2008,1.6 л,Бензин,Механика/Автомат,1300...\n",
+              "5    5,Lada,2009,98 л.с., 1.6 л,Бензин,Механика/Авт...\n",
+              "6    6,Lada,2011,1.6 л,Бензин,Механика/Автомат,9000...\n",
+              "7    7,Lada,2015,1.6 л, 106 л.с.,Бензин,Механика/Ав...\n",
+              "8    8,Lada,2016,1.6 л / 1.8 л,Бензин,Механика/Авто...\n",
+              "9    9,Lada,2012,1.6 л,Бензин,Механика/Автомат,8000...\n",
+              "Name: 0, dtype: str"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Car ID</th>\n",
+              "      <th>Brand</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Fuel Type</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Model</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>1.5 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика</td>\n",
+              "      <td>150000</td>\n",
+              "      <td>Отличное</td>\n",
+              "      <td>70000</td>\n",
+              "      <td>2110</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>1.6 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Хорошее</td>\n",
+              "      <td>80000</td>\n",
+              "      <td>2107</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>1.5 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика</td>\n",
+              "      <td>120000</td>\n",
+              "      <td>Отличное</td>\n",
+              "      <td>75000</td>\n",
+              "      <td>2115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>1.6 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика/Автомат</td>\n",
+              "      <td>130000</td>\n",
+              "      <td>Хорошее</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Kalina</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>98 л.с.,1.6 л</td>\n",
+              "      <td>Бензин</td>\n",
+              "      <td>Механика/Автомат</td>\n",
+              "      <td>100000</td>\n",
+              "      <td>Хорошее</td>\n",
+              "      <td>200000</td>\n",
+              "      <td>Priora</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Car ID Brand  Year    Engine Size Fuel Type      Transmission Mileage  \\\n",
+              "0      1  Lada  2000          1.5 л    Бензин          Механика  150000   \n",
+              "1      2  Lada  2001          1.6 л    Бензин          Механика  180000   \n",
+              "2      3  Lada  2002          1.5 л    Бензин          Механика  120000   \n",
+              "3      4  Lada  2008          1.6 л    Бензин  Механика/Автомат  130000   \n",
+              "4      5  Lada  2009  98 л.с.,1.6 л    Бензин  Механика/Автомат  100000   \n",
+              "\n",
+              "  Condition   Price   Model  \n",
+              "0  Отличное   70000    2110  \n",
+              "1   Хорошее   80000    2107  \n",
+              "2  Отличное   75000    2115  \n",
+              "3   Хорошее  180000  Kalina  \n",
+              "4   Хорошее  200000  Priora  "
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def parse_cars_new(path):\n",
+        "    raw = pd.read_csv(path, header=None)\n",
+        "    lines = raw[0].astype(str).str.strip('\"')\n",
+        "    rows = []\n",
+        "    display(lines)\n",
+        "    for line in lines:\n",
+        "        parts = [p.strip() for p in line.split(\",\")]\n",
+        "        if not parts or parts[0] == \"Car ID\":\n",
+        "            continue\n",
+        "        if len(parts) < 10:\n",
+        "            continue\n",
+        "        # Если внутри Engine Size есть запятая, будет больше 10 частей\n",
+        "        head = parts[:3]\n",
+        "        tail = parts[-6:]\n",
+        "        engine = \",\".join(parts[3:-6]).strip()\n",
+        "        row = head + [engine] + tail\n",
+        "        if len(row) == 10:\n",
+        "            rows.append(row)\n",
+        "\n",
+        "    cols = [\"Car ID\", \"Brand\", \"Year\", \"Engine Size\", \"Fuel Type\", \"Transmission\", \"Mileage\", \"Condition\", \"Price\", \"Model\"]\n",
+        "    df_new = pd.DataFrame(rows, columns=cols)\n",
+        "    return df_new\n",
+        "\n",
+        "df_new = parse_cars_new(\"cars_new.csv\")\n",
+        "df_new.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "ca74b22f",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Car ID</th>\n",
+              "      <th>Brand</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Engine Size</th>\n",
+              "      <th>Fuel Type</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Condition</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Model</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>150000</td>\n",
+              "      <td>New</td>\n",
+              "      <td>70000</td>\n",
+              "      <td>2110</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>80000</td>\n",
+              "      <td>2107</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>120000</td>\n",
+              "      <td>New</td>\n",
+              "      <td>75000</td>\n",
+              "      <td>2115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>130000</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>180000</td>\n",
+              "      <td>Kalina</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Lada</td>\n",
+              "      <td>2009</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>100000</td>\n",
+              "      <td>Used</td>\n",
+              "      <td>200000</td>\n",
+              "      <td>Priora</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Car ID Brand  Year  Engine Size Fuel Type Transmission  Mileage Condition  \\\n",
+              "0      1  Lada  2000          1.5    Petrol       Manual   150000       New   \n",
+              "1      2  Lada  2001          1.6    Petrol       Manual   180000      Used   \n",
+              "2      3  Lada  2002          1.5    Petrol       Manual   120000       New   \n",
+              "3      4  Lada  2008          1.6    Petrol    Automatic   130000      Used   \n",
+              "4      5  Lada  2009          1.6    Petrol    Automatic   100000      Used   \n",
+              "\n",
+              "    Price   Model  \n",
+              "0   70000    2110  \n",
+              "1   80000    2107  \n",
+              "2   75000    2115  \n",
+              "3  180000  Kalina  \n",
+              "4  200000  Priora  "
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Приводим к единому формату\n",
+        "fuel_map = {\n",
+        "    \"Бензин\": \"Petrol\",\n",
+        "    \"Дизель\": \"Diesel\",\n",
+        "    \"Электро\": \"Electric\",\n",
+        "    \"Гибрид\": \"Hybrid\"\n",
+        "}\n",
+        "trans_map = {\n",
+        "    \"Механика\": \"Manual\",\n",
+        "    \"Автомат\": \"Automatic\",\n",
+        "    \"Механика/Автомат\": \"Automatic\"\n",
+        "}\n",
+        "cond_map = {\n",
+        "    \"Отличное\": \"New\",\n",
+        "    \"Хорошее\": \"Used\"\n",
+        "}\n",
+        "\n",
+        "def extract_engine_size(x):\n",
+        "    if pd.isna(x):\n",
+        "        return np.nan\n",
+        "    nums = re.findall(r\"\\d+(?:[.,]\\d+)?\", str(x))\n",
+        "    if not nums:\n",
+        "        return np.nan\n",
+        "    return float(nums[-1].replace(\",\", \".\"))\n",
+        "\n",
+        "df_new[\"Fuel Type\"] = df_new[\"Fuel Type\"].map(fuel_map)\n",
+        "df_new[\"Transmission\"] = df_new[\"Transmission\"].map(trans_map)\n",
+        "df_new[\"Condition\"] = df_new[\"Condition\"].map(cond_map)\n",
+        "\n",
+        "df_new[\"Engine Size\"] = df_new[\"Engine Size\"].apply(extract_engine_size)\n",
+        "df_new[\"Year\"] = pd.to_numeric(df_new[\"Year\"], errors=\"coerce\")\n",
+        "df_new[\"Mileage\"] = pd.to_numeric(df_new[\"Mileage\"], errors=\"coerce\")\n",
+        "df_new[\"Price\"] = pd.to_numeric(df_new[\"Price\"], errors=\"coerce\")\n",
+        "\n",
+        "df_new.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "c807c43d",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>split</th>\n",
+              "      <th>MAE</th>\n",
+              "      <th>RMSE</th>\n",
+              "      <th>R2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>train</td>\n",
+              "      <td>24385.641471</td>\n",
+              "      <td>32867.632333</td>\n",
+              "      <td>0.004216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>test</td>\n",
+              "      <td>24857.121340</td>\n",
+              "      <td>37804.530574</td>\n",
+              "      <td>-0.007647</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>train</td>\n",
+              "      <td>9174.571497</td>\n",
+              "      <td>12278.185487</td>\n",
+              "      <td>0.861038</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>test</td>\n",
+              "      <td>24465.982962</td>\n",
+              "      <td>34010.448955</td>\n",
+              "      <td>0.184460</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>train</td>\n",
+              "      <td>21391.653978</td>\n",
+              "      <td>24988.338946</td>\n",
+              "      <td>0.424423</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>test</td>\n",
+              "      <td>23904.569889</td>\n",
+              "      <td>29600.017743</td>\n",
+              "      <td>0.382262</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model  split           MAE          RMSE        R2\n",
+              "0  LinearRegression  train  24385.641471  32867.632333  0.004216\n",
+              "1  LinearRegression   test  24857.121340  37804.530574 -0.007647\n",
+              "2      RandomForest  train   9174.571497  12278.185487  0.861038\n",
+              "3      RandomForest   test  24465.982962  34010.448955  0.184460\n",
+              "4  GradientBoosting  train  21391.653978  24988.338946  0.424423\n",
+              "5  GradientBoosting   test  23904.569889  29600.017743  0.382262"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Объединяем и повторяем оценку\n",
+        "df_all = pd.concat([df, df_new], ignore_index=True)\n",
+        "\n",
+        "# Удаляем строки без цены\n",
+        "df_all = df_all.dropna(subset=[\"Price\"]).copy()\n",
+        "\n",
+        "X_all = df_all.drop(columns=[\"Price\"])\n",
+        "y_all = df_all[\"Price\"]\n",
+        "\n",
+        "results_after = evaluate_models(X_all, y_all, models)\n",
+        "results_after"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>MAE_before</th>\n",
+              "      <th>MAE_after</th>\n",
+              "      <th>RMSE_before</th>\n",
+              "      <th>RMSE_after</th>\n",
+              "      <th>R2_before</th>\n",
+              "      <th>R2_after</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>23386.369896</td>\n",
+              "      <td>24857.121340</td>\n",
+              "      <td>26995.898047</td>\n",
+              "      <td>37804.530574</td>\n",
+              "      <td>-0.000709</td>\n",
+              "      <td>-0.007647</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>23676.962601</td>\n",
+              "      <td>24465.982962</td>\n",
+              "      <td>27982.335432</td>\n",
+              "      <td>34010.448955</td>\n",
+              "      <td>-0.075178</td>\n",
+              "      <td>0.184460</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>23625.922747</td>\n",
+              "      <td>23904.569889</td>\n",
+              "      <td>27750.650439</td>\n",
+              "      <td>29600.017743</td>\n",
+              "      <td>-0.057447</td>\n",
+              "      <td>0.382262</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model    MAE_before     MAE_after   RMSE_before    RMSE_after  \\\n",
+              "0  LinearRegression  23386.369896  24857.121340  26995.898047  37804.530574   \n",
+              "1      RandomForest  23676.962601  24465.982962  27982.335432  34010.448955   \n",
+              "2  GradientBoosting  23625.922747  23904.569889  27750.650439  29600.017743   \n",
+              "\n",
+              "   R2_before  R2_after  \n",
+              "0  -0.000709 -0.007647  \n",
+              "1  -0.075178  0.184460  \n",
+              "2  -0.057447  0.382262  "
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Сравнение метрик на тесте ДО и ПОСЛЕ добавления новых данных\n",
+        "test_before = results_before[results_before[\"split\"] == \"test\"].copy()\n",
+        "test_after = results_after[results_after[\"split\"] == \"test\"].copy()\n",
+        "\n",
+        "compare = test_before.merge(test_after, on=\"model\", suffixes=(\"_before\", \"_after\"))\n",
+        "compare[[\"model\", \"MAE_before\", \"MAE_after\", \"RMSE_before\", \"RMSE_after\", \"R2_before\", \"R2_after\"]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Визуализация влияния новых данных (R2)\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "compare_melt = compare.melt(id_vars=\"model\", value_vars=[\"R2_before\", \"R2_after\"], var_name=\"stage\", value_name=\"R2\")\n",
+        "sns.barplot(data=compare_melt, x=\"model\", y=\"R2\", hue=\"stage\")\n",
+        "plt.title(\"R2 на тесте: до и после добавления новых данных\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Итоговый анализ\n",
+        "\n",
+        "- Новые данные добавлены и приведены к единому формату.\n",
+        "- Для корректного сравнения смотрим метрики `до` и `после`.\n",
+        "\n",
+        "Видно, что качество предсказания моделей сильно улучшилось, особенно у модели градиентного буста. Это вызвано тем, что в дополнительных данных есть корреляция между некоторыеми свойствами, просмотрим это подробнее далее."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "9d37afe8",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAGHCAYAAAD89VV0AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQHJJREFUeJzt3XlYlPX+//HXAIIo7uJemZpoiGyGG6aSufvNNKtTmp48Ya6dMs30uKVmiVuKmh2ztPyZWmpZ1jlu1cncIiXcRT3mLuSWsgl8fn94mJxA2QZuiufjurhq7vsz9+d9v5kZXt73PTM2Y4wRAAAALOFidQEAAADFGWEMAADAQoQxAAAACxHGAAAALEQYAwAAsBBhDAAAwEKEMQAAAAsRxgAAACxEGAMAALAQYQzFVp8+feTj4+Pw06hRI7Vp00YTJ07UlStXrC4RsHv++ee1atUqq8tAFnr37q3169dbXQb+wNysLgCw0v3336/x48fbb9+4cUP79u3TzJkzdeDAAS1fvlw2m83CCgFp9erVOn/+vHr27Gl1KcjC6NGj1b9/fzVt2lSVKlWyuhz8ARHGUKx5eXkpICDAYdkDDzyg69eva86cOYqOjs60HihMSUlJmj59usaPHy8XF05mFEX333+/GjdurAULFugf//iH1eXgD4hnNpCFRo0aSZLOnDkjSTp48KCGDBmiZs2aydfXV61atdLkyZOVlJRkv09KSopmz56thx56SI0bN1bXrl21Zs0a+/qsTotm/Jw6dUqSNGrUKPXp00cff/yx2rZtq8DAQPXt21cHDx50qO/MmTN66aWXFBISIn9/f/Xt21f79+93GLNy5cos5xo1apTDuI0bN6pHjx7y8/NTy5YtNXnyZCUkJNjXr169+rZ1r169Osc1nTp1KtN9MvY5LCzMfjssLCxTjS+99JJ8fHy0Y8cO+7LDhw9rwIABCgoKUlBQkAYPHqyTJ09m+l3eaa7b1XX58mWNGzdOLVq0kJ+fnx5//HFt27bN4X4+Pj6aO3euw7K5c+fKx8fHfrtPnz7q06dPrur5vU8++UTJyclq27atw/JvvvlGTz75pAICAhQaGqpx48bp6tWr9vW7du1S//799cADD6hRo0YKCwvT3LlzlZ6e7rDf7733njp27Ch/f3998sknSkpK0oQJE/Tggw+qUaNG6tixo95999071pgVHx8fLVu2TGPGjFFISIgCAwP1wgsvKD4+3mHcnR5/S5cuVYMGDXTp0iX7+Hnz5snHx8fh97Fx40Y1aNBA58+fz7KWtWvX6tFHH5W/v7/atGmjGTNmKCUlRZLuuL/JyckKDg7Wm2++6bC91NRUNWvWTJMnT7Yv69atmz7++GNdvHgx170CCGNAFo4fPy5Juuuuu3ThwgU9/fTTSkxM1BtvvKF//vOf6tKliz744AMtXbrUfp+XX35Z7733nnr16qWFCxcqNDRUo0aN0ueff24fc//992vFihX2n4EDB2aa+8CBA5o1a5aGDBmiiIgIXbp0Sb1799aFCxckSRcvXtSTTz6pffv2aezYsZoxY4bS09P19NNP6+jRo/btJCUlyc/Pz2E+b29vh7nWrVunwYMHq06dOpo3b56GDBmizz77TIMGDZIxxmFsZGSkfTuRkZEO63JaU1788MMP+uKLLxyWHT9+XE8++aR++eUXvfnmm5oyZYpOnjypv/zlL/rll1/yNV9ycrL69u2rTZs26cUXX1RkZKSqVaumv/3tb5kCWWH47LPP1KZNG7m7u9uXbdmyRQMGDFClSpU0e/Zsvfzyy9q4caNefPFFSTf/8dCvXz+VL19es2bN0oIFC9SkSRNFRkbqyy+/dNj+3Llz9dxzz2natGlq2bKlXn/9dX377bd65ZVX9O677+qhhx7StGnT9Mknn+S69lmzZik9PV0zZ87UyJEjtWXLFr3++uv29dk9/tq0aSNjjLZv326/T8b/79q1y77s22+/1f3336+qVatmqmHZsmV65ZVX5Ovrq8jISIWHh+uDDz6wB6k77a+Hh4c6dOigL7/80uH5sHXrVl26dEmPPPKIfVlYWJjS0tK0YcOGXPcJ4DQlijVjjFJTU+23r1y5op07d2rBggUKDAxUo0aNtHXrVjVs2FBvvfWWvLy8JEktWrTQ1q1btWPHDoWHh+vw4cP617/+pdGjR6tv376SpObNm+v06dPasWOHunbtKinzadFjx45lqunXX3/V22+/rSZNmkiSGjdurHbt2mnp0qV6+eWXtWTJEl2+fFnLly9XzZo1JUkPPvigOnfurLfeektz5syRJCUmJqpy5coO8936B90Yo+nTp6tVq1aaPn26fXnt2rXVr18/ffPNN2rTpo19ecOGDVWrVi1Jsh/Jy5DTmnIrPT1dkydPlq+vr/bt22dfHhkZKU9PT73//vv230nz5s3Vrl07LVq0SK+88kqe5pOkTz/9VAcPHtTKlSvl7+9v35c+ffpo+vTpeQoleXXt2jXFxMSoU6dODsvnzp2rhg0bKjIy0n5No7u7u9566y3Fx8fr4MGDatGihSIiIuynNlu2bKnNmzdrx44d6tKli31bnTp1crgWbefOnWrZsqV9TNOmTVWqVKk8XQtVv359TZ061X77p59+0ldffSUp54+/e++9V9u2bVOnTp2UmJio3bt3y9fX1yGM/ec//1GPHj0yzZ+enq558+apXbt2DkexEhMT9cUXX+jGjRvZ7u8jjzyiTz75RFFRUfbn5BdffKE6derIz8/Pvs1SpUqpbt262rZtm5544olc9wrFG2EMxdquXbvk6+vrsMzFxUUtWrTQa6+9JpvNptDQUIWGhurGjRuKjY3ViRMndPjwYV28eFHly5eXJEVFRUmS2rdv77Ct35/GyolatWrZX/QlqUqVKgoMDLT/8dm2bZsaNmyoqlWr2oOki4uLHnzwQX322Wf2+509e1ZlypS57TzHjh3TuXPnNGDAAIdA+sADD8jLy0tbt251CGN3ktOapJt/IG+d7/dH4G710UcfKS4uTq+99poGDRpkX759+3aFhISoZMmS9m15eXmpSZMm+v7773NU8532xdvbW76+vg51tm3bVtOmTdOVK1dUrly5LPcl4xTgrTICv4uLS66v+Tp79qzS0tLsIVi6ecRz//79Gjp0qMObSzp37qzOnTtLkrp3767u3bsrOTlZx48f14kTJ3TgwAGlpaXpxo0bDnM0bNjQ4XbTpk310Ucf6dy5c2rdurVat26twYMH56ruDL+/3rJatWpKTEyUlPPHX5s2bbRx40ZJN59nJUqU0DPPPKNx48YpJSVFP//8s86cOZPlY/X48eP65Zdf9PDDDzss79+/v/r375+j/Q0JCVGNGjX0xRdfqEmTJkpOTtbGjRsVHh6eab6aNWtm+ocKkBOEMRRrvr6+mjhxoiTJZrPJw8ND1atXtx9tkWQ/zbJs2TIlJCSoevXqaty4sTw8POxjLl++LElOeSdVVqdaKlWqZD8ydPnyZZ04cSJTiMyQmJgoT09PnT59+rZjbq154sSJ9h7cKuO0aE7kpKYMY8aM0ZgxYxzWZxxN+/0233rrLY0cOdLh95Gxbv369Vl+nEDFihVzXHdWLl++rLi4uNvuS1xcnD2MzZ8/X/Pnz7/j9m4N/BUqVFCjRo00ZMiQHL0x5Ndff5V086hLhitXrsgYc8fHWlJSkiZNmqRPP/1UqampqlWrlgIDA+Xm5pYp/N66benm76datWr67LPPNGnSJE2aNEmBgYGaMGGCGjRokG3Nt/L09HS47eLiYp8/p4+/1q1b67333tOpU6e0bds2BQUFqXnz5kpOTlZ0dLT27t0rb29v+3Wet8rJ8zK7/bXZbOrWrZtWrVqlf/zjH9qyZYsSEhLUrVu3LPc343cG5AZhDMVa6dKlHU41ZOWdd97R+++/r4kTJ6p9+/b2o02PPfaYfUzZsmUl3bx2qlq1avblR48e1eXLlxUcHJzjmm69WDlDfHy8/Q9KmTJlFBISopEjR2Z5f3d3d6Wnpys6OvqOH4WQUfPIkSMVEhKSaX1G4Mhwp4/4yElNGYYMGeJwFGPevHk6fPhwpvu89dZbuvvuu9WjRw/t3Lkz03wtWrTQX//610z3c3PL38tamTJlVLt2bYdTZ7e69SjV448/rscff9x+e+XKlVq5cqXD+IzAb4zR5cuXtXTpUv31r3/N0edSVahQQZIcLsz38vKSzWbLdKF4cnKytm/fLn9/f82YMUP/+te/NHv2bLVo0cIeuJo3b57tnO7u7ho4cKAGDhyoM2fOaMuWLZo/f76GDx+e6dq9/Mjp469Jkyby8vLStm3btH37dnXo0EFVq1ZV7dq1tWPHDkVFRalNmzZZPj5vfV7e6tKlS9q/f78CAwNVqlSpbPf3kUce0cKFC7Vjxw6tX79eDzzwQJb/gLh69ar9dwbkBhfwA9mIiopSvXr11LNnT3sQO3/+vA4fPmw/LZURtjZv3uxw3+nTp2vKlCm5mu+///2vw0Xv58+f1+7du+1/SENCQnT8+HHde++98vPzs/98+umn+vjjj+Xq6qoff/xRCQkJatq06W3nqVOnjipVqqRTp045bKdq1aqaMWOG/Z2QGfvo6up6223lpKYMNWvWdBiTcar3VocPH9aqVas0duzYLP/IhoSEKDY2Vg0bNrRvp1GjRnr//ffzfQF1SEiIzp49q0qVKjnUuXXrVi1atMhhX6pUqeIwpkqVKpm2lxH4GzdurAcffFAvv/yyEhIStHfv3mxrqVq1qlxdXXXu3DmH7TVs2FBbtmxxGPvtt98qPDxcFy5cUFRUlJo2bap27drZg9jevXt18eLFLE+lZkhKSlKHDh20ePFiSVKNGjX09NNPq0uXLvZ3FjtLTh9/JUqUUMuWLbVp0yYdOHDAHtyaNWumr7/+Wj/88EOmd5reOkeFChUy9erTTz9VeHi4rl27lqP9rVu3rnx9ffXFF1/om2++0f/93/9lOd+5c+eyDGlAdjgyBmSjcePGmj9/vt555x0FBAToxIkTWrhwoVJSUuyn3xo0aKCOHTsqIiJCSUlJatiwob799ltt2bIl0zsPs2OM0fPPP68XX3xRrq6uioyMVLly5ewfkdCvXz99+umn6tevn5599llVqFBB69ev18qVK/Xqq6/q559/1oIFC+y1njhxwr7tlJQUXbx4UT///LPuvvtuvfjiixo3bpxcXV3Vtm1bXb16VfPnz9f58+fl6+ur6Oho7dy5UzabLdOpwltlV1Nu7du3Tz179lTjxo2zXD9o0CA9+eSTGjBggP7yl7/Iw8NDK1as0MaNG7N9s0BKSor27Nljvx0XFydJ+vnnn3Xt2jX16NFDH374of7617/q+eefV/Xq1fX999/rn//8p3r37q0SJUrkal+uXbumPXv2KC0tTfHx8Vq+fLk8PT3l6+urTZs23fG+pUqVUlBQkKKiotSvXz/78mHDhmngwIF66aWX1L17d8XHx2vmzJlq166d6tevr8aNG+vLL7/U8uXLVbduXR08eFALFiyQzWZzOGX8eyVLlrS/67BEiRLy8fHR8ePHtWbNGnXo0CFX+50dV1fXbB9/GVq3bq3Ro0erVKlS9tORGdd6eXh4qEWLFvaxe/bsUcWKFXX33XfL1dVVQ4cO1WuvvaZKlSopLCxMx48f15w5c/T000+rSpUqOd7fRx55RG+++abc3NzUsWPHTPvz66+/6siRI3r22Wed2icUD4QxIBsDBgzQpUuXtHTpUs2bN0/Vq1fXI488IpvNpoULF+rq1asqW7asIiIiFBkZqSVLlujSpUuqW7eu5syZo3bt2uVqvho1aujZZ5/V66+/rsTERLVo0UILFiywH0GqWrWqPvroI82YMUMTJkxQcnKyateurSlTpuixxx7TqFGj9N1330lSlu/q+uabb1SxYkW98cYb6tWrl0qXLq1FixZpxYoV9j/+06dP11133aVOnTrJzc1N4eHhdwxj2dWUW2XKlNHw4cNvu75BgwZatmyZZs2apZEjR8oYo/r162vevHl66KGH7rjtuLi4LPuyYMECNW/eXE2bNtWyZcs0Y8YMRURE6Ndff1XNmjU1fPjwPP2h3b9/v32+smXL6r777tPChQtVo0aNHN2/Q4cOmjt3rpKTk+3XKbZt21Zvv/22IiMjNXjwYFWsWFHdunXT0KFDJd38/LIbN25o9uzZSklJUa1atTRw4EDFxsZq8+bNSktLu+18r732mmbPnq3FixcrLi5OlSpV0mOPPaYXXngh1/uenewefxlat24tm82moKAg+2nopk2bymazqWnTpg7Xpj3xxBN69NFH9cYbb0iSnn76aZUqVUrvvvuuVqxYoWrVqum5557Tc889l6v97dq1q6ZNm6a2bdtm+caY//znPypRokSO3/QC3Mpm7vRWJgCFatSoUdq5c2em05253YYk+x+j3K4vrnx8fLR06dI7ntq1QmJiotq1a6cRI0aoe/fuVpeD2+jbt6/q16+f6c0pQE5wzRgAFGGenp4aOnSo3n333Tse0SoMGR/lkd1PcRMTE6ODBw9m+XEXQE5wmhL4k7n19E5e1hdX/v7+dzwVa6Unn3xSmzZt0qpVq/Tkk09aVsfo0aMdvuLrdg4dOlQI1RQdU6dO1dixYzN9wwWQU5ymBADkyKlTp7L86JXfy+7jYgA4IowBAABYiGvGAAAALEQYAwAAsFCxuYA/411ALi4ud/xaFwAAgPwyxig9PV1ubm5ycbnzsa9iE8ZSU1MVExNjdRkAAKAY8fPzc/h+3qwUmzCWkUr9/Pzu+B17RV1aWppiYmL+8PtRlNBT56OnzkdPnY+eOh89/U1GL7I7KiYVozCWcWrS1dX1T/EA+bPsR1FCT52PnjofPXU+eup89PQ3Obk0igv4AQAALEQYAwAAsBBhDAAAwEKEMQAAAAsRxgAAACxEGAMAALAQYQwAAMBCloaxEydOqH///goMDFSbNm20aNGi247dv3+/evXqJX9/f/Xs2VN79+4txEoBAAAKhmVhLD09XeHh4apQoYLWrFmjiRMnasGCBVq3bl2msQkJCQoPD1eTJk20evVqBQYGasCAAUpISLCgcgAAAOexLIzFx8erYcOGmjBhgmrXrq3WrVurefPmioqKyjR2/fr18vDw0MiRI1W3bl2NGTNGpUuX1ldffWVB5QAAAM5j2dchValSRbNnz5Z085vNf/zxR+3atUvjx4/PNDY6OlrBwcH2rxSw2WwKCgrSnj171KNHj1zNm5aWlu/arZRR/x99P4oSeup89NT56Knz0VPno6e/yU0PisR3U4aFhenMmTNq27atOnTokGl9XFyc6tWr57CsUqVKOnLkSK7niomJyXOd2fFp6KtSJT2yHZeQlKxDB/bla66C3I/iip46Hz11PnrqfPTU+ehp7hSJMDZnzhzFx8drwoQJmjp1qv7xj384rE9MTJS7u7vDMnd3d6WkpOR6roL8Jnmbi4v+8s72bMctD2+mgICAPM2R8S3wBbkfxQ09dT566nz01PnoqfPR099k9CInikQY8/PzkyQlJyfr5Zdf1siRIx3Cl4eHR6bglZKSopIlS+Z6roL8Jvl0Y5SDL2e315EfBbkfxRU9dT566nz01PnoqfPR09yx9AL+jRs3OiyrV6+ebty4oWvXrjksr1q1quLj4zPdv0qVKgVeJwAAQEGyLIydOnVKQ4YM0fnz5+3L9u7dq4oVK6pixYoOY/39/bV7924ZYyT9dsG/v79/odYMAADgbJaFMT8/P/n6+mr06NGKjY3VN998o4iICD3//POSbl60n5SUJEnq2LGjrl69qilTpig2NlZTpkxRYmKiOnXqZFX5AAAATmFZGHN1ddX8+fPl6empJ554QmPGjFGfPn30zDPPSJJCQ0O1fv16SZKXl5cWLlyoqKgo9ejRQ9HR0XrnnXdUqlQpq8oHAABwCksv4K9ataoiIyOzXHfo0CGH240bN9aaNWsKoywAAIBCwxeFAwAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFrI0jJ0/f17Dhg1TSEiIWrVqpalTpyo5OTnLsQMHDpSPj4/Dz5YtWwq5YgAAAOdys2piY4yGDRumsmXLatmyZbpy5YpGjx4tFxcXvfLKK5nGHz16VBEREWrevLl9Wbly5QqzZAAAAKezLIwdO3ZMe/bs0datW1W5cmVJ0rBhw/Tmm29mCmMpKSk6deqU/Pz85O3tbUW5AAAABcKy05Te3t5atGiRPYhluHbtWqaxx44dk81m01133VVY5QEAABQKy46MlS1bVq1atbLfTk9P14cffqhmzZplGnvs2DF5eXlp5MiR2rlzp6pVq6ahQ4eqdevWuZ43LS0tX3Xfic3FRcYUbB0Z9yvI/Shu6Knz0VPno6fOR0+dj57+Jjc9sCyM/V5ERIT279+vjz/+ONO6Y8eOKSkpSaGhoQoPD9eGDRs0cOBArVixQn5+frmaJyYmxlklZxIYGJTlkb1MjLRnz558zVWQ+1Fc0VPno6fOR0+dj546Hz3NnSIRxiIiIrRkyRLNmjVL9evXz7R+0KBB6tOnj/2C/QYNGmjfvn1auXJlrsOYn5+fXF1dnVJ3JjbJy8srR+MCAgLyNEVaWppiYmIKdj+KGXrqfPTU+eip89FT56Onv8noRU5YHsYmTZqk5cuXKyIiQh06dMhyjIuLS6Z3TtapU0exsbG5ns/V1bXAHiDpxshmy3kd+VGQ+1Fc0VPno6fOR0+dj546Hz3NHUs/ZywyMlIfffSRZs6cqS5dutx23KhRo/Tqq686LDt48KDq1KlT0CUCAAAUKMvC2NGjRzV//nw999xzCg4OVlxcnP1HkuLi4pSUlCRJCgsL07p167R27VqdOHFCkZGRioqKUu/eva0qHwAAwCksO025adMmpaWlacGCBVqwYIHDukOHDik0NFRTp05Vjx491L59e40fP14LFizQmTNndN9992nRokWqVauWRdUDAAA4h2VhLDw8XOHh4bddf+jQIYfbvXr1Uq9evQq6LAAAgELFF4UDAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYyNIwdv78eQ0bNkwhISFq1aqVpk6dquTk5CzH7t+/X7169ZK/v7969uypvXv3FnK1AAAAzmdZGDPGaNiwYUpMTNSyZcs0a9YsbdmyRbNnz840NiEhQeHh4WrSpIlWr16twMBADRgwQAkJCYVfOAAAgBNZFsaOHTumPXv2aOrUqbrvvvvUpEkTDRs2TJ9//nmmsevXr5eHh4dGjhypunXrasyYMSpdurS++uorCyoHAABwHsvCmLe3txYtWqTKlSs7LL927VqmsdHR0QoODpbNZpMk2Ww2BQUFac+ePYVRKgAAQIFxs2rismXLqlWrVvbb6enp+vDDD9WsWbNMY+Pi4lSvXj2HZZUqVdKRI0dyPW9aWlrui80hm4uLjCnYOjLuV5D7UdzQU+ejp85HT52PnjofPf1NbnpgWRj7vYiICO3fv18ff/xxpnWJiYlyd3d3WObu7q6UlJRczxMTE5PnGrMTGBiU5ZG9TIzyfVSvIPejuKKnzkdPnY+eOh89dT56mjtFIoxFRERoyZIlmjVrlurXr59pvYeHR6bglZKSopIlS+Z6Lj8/P7m6uua51juySV5eXjkaFxAQkKcp0tLSFBMTU7D7UczQU+ejp85HT52PnjofPf1NRi9ywvIwNmnSJC1fvlwRERHq0KFDlmOqVq2q+Ph4h2Xx8fGqUqVKrudzdXUtsAdIujH632VtOaojPwpyP4oreup89NT56Knz0VPno6e5Y+nnjEVGRuqjjz7SzJkz1aVLl9uO8/f31+7du2X+d0GWMUY//vij/P39C6tUAACAAmFZGDt69Kjmz5+v5557TsHBwYqLi7P/SDcv2k9KSpIkdezYUVevXtWUKVMUGxurKVOmKDExUZ06dbKqfAAAAKewLIxt2rRJaWlpWrBggUJDQx1+JCk0NFTr16+XdPM6rIULFyoqKko9evRQdHS03nnnHZUqVcqq8gEAAJzCsmvGwsPDFR4eftv1hw4dcrjduHFjrVmzpqDLAgAAKFR8UTgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFnJ6GLt48aKzNwkAAPCnlacw1rBhwyxD1+nTp/XQQw/luygAAIDiwi2nA9euXavVq1dLkowxGjx4sEqUKOEw5sKFC/L29nZuhQAAAH9iOQ5jDz/8sE6dOiVJ2rlzpwICAlS6dGmHMaVKldLDDz/s3AoBAAD+xHIcxkqXLq0hQ4ZIkmrWrKnOnTvLw8OjwAoDAAAoDnIcxm716KOP6sSJE9q7d69u3LiRaX337t3zWxcAAECxkKcwtmjRIk2fPl3lypXLdKrSZrMRxgAAAHIoT2Fs8eLFGjFihPr37+/segAAAIqVPH20RXJystq3b+/sWgAAAIqdPIWxbt266f/9v/8nY4yz6wEAAChW8nSa8tq1a/r444/1+eefq1atWpk+b2zp0qVOKQ4AAODPLk9hrHbt2nr++eedXQsAAECxk6cwlvF5YwAAAMifPIWxV1999Y7rp06dmqdiAAAAips8XcD/e6mpqTp+/LjWr1+vihUrOmOTAAAAxUKejozd7sjXokWLdPjw4XwVBAAAUJw45chYho4dO2rDhg3O3CQAAMCfmtPCWEJCglauXKkKFSo4a5MAAAB/enk6TdmgQQPZbLZMyz08PDR58uR8FwUAAFBc5CmM/f5DXW02m0qUKKF69erJy8sr19tLSUlRjx49NHbsWDVt2jTLMQMHDtTmzZsdlr399ttq27ZtrucDAAAoKvIUxkJCQiRJ//3vf3X06FGlp6fr3nvvzVMQS05O1vDhw3XkyJE7jjt69KgiIiLUvHlz+7Jy5crlej4AAICiJE9h7OrVq3r11Ve1adMmlStXTmlpabp+/boeeOABzZs3T2XKlMnRdmJjYzV8+PBsv+MyJSVFp06dkp+fn7y9vfNSMgAAQJGUpwv4J0+erHPnzmn9+vXasWOHfvjhB61bt04JCQm5+sDXnTt3qmnTplqxYsUdxx07dkw2m0133XVXXsoFAAAosvJ0ZGzz5s167733VKdOHfuyevXqady4cXruuedyvJ2nnnoqR+OOHTsmLy8vjRw5Ujt37lS1atU0dOhQtW7dOte1p6Wl5fo+OWVzcVE2B/nyXUfG/QpyP4obeup89NT56Knz0VPno6e/yU0P8hTGPDw85OKS+aCazWYrkF/AsWPHlJSUpNDQUIWHh2vDhg0aOHCgVqxYIT8/v1xtKyYmxun1ZQgMDNK1a9eyH2ikPXv25GuugtyP4oqeOh89dT566nz01Pnoae7kKYyFhYVp4sSJmj59uu6++25JNy/mnzx5cp6OVmVn0KBB6tOnj/2C/QYNGmjfvn1auXJlrsOYn5+fXF1dnV6jJMmmnL2JwSYFBATkaYq0tDTFxMQU7H4UM/TU+eip89FT56OnzkdPf5PRi5zIUxgbMWKEBg8erA4dOqhs2bKSpCtXrujBBx/U2LFj87LJO3Jxccn0zsk6deooNjY219tydXUtsAdIujHK4uPXbltHfhTkfhRX9NT56Knz0VPno6fOR09zJ9dh7MSJE6pRo4Y++OADHTp0SEePHpWHh4dq166tunXrFkSNGjVqlGw2m8ObAw4ePKj69esXyHwAAACFJcfvpjTGaPLkyerUqZN2794tSfLx8VHnzp31ySefqGvXrnrjjTey/ZiKnIqLi1NSUpKkm6dF161bp7Vr1+rEiROKjIxUVFSUevfu7ZS5AAAArJLjMLZ06VKtX79e8+bNs3/oa4b58+dr3rx5WrNmjZYvX+6UwkJDQ7V+/XpJUvv27TV+/HgtWLBAXbt21ebNm7Vo0SLVqlXLKXMBAABYJcenKVeuXKmxY8fe9uuHwsLC9PLLL2vp0qU5/siKWx06dOiOt3v16qVevXrlersAAABFWY6PjJ0+fVqNGze+45hmzZrp5MmT+S4KAACguMhxGKtUqZJOnz59xzHnzp1T+fLl81sTAABAsZHjMPbwww9r7ty5unHjRpbrU1NTFRkZqdDQUKcVBwAA8GeX42vGBg0apMcee0w9evRQnz591KhRI5UpU0ZXrlzRvn379OGHH+r69euaNm1aQdYLAADwp5LjMFa2bFmtXLlS06dP1xtvvKHExERJNz/yokyZMurcubOGDh2qypUrF1ixAAAAfza5+tDX8uXLa/LkyRo3bpxOnjypq1evqnz58rr77rv5pF0AAIA8yNPXIbm7uxfYp+0DAAAUJzm+gB8AAADORxgDAACwEGEMAADAQoQxAAAACxHGAAAALEQYAwAAsBBhDAAAwEKEMQAAAAsRxgAAACxEGAMAALAQYQwAAMBChDEAAAALEcYAAAAsRBgDAACwEGEMAADAQoQxAAAACxHGAAAALEQYAwAAsBBhDAAAwEKEMQAAAAsRxgAAACxEGAMAALAQYQwAAMBChDEAAAALEcYAAAAsRBgDAACwEGEMAADAQoQxAAAACxHGAAAALEQYAwAAsFCRCGMpKSnq2rWrduzYcdsx+/fvV69eveTv76+ePXtq7969hVghAABAwbA8jCUnJ+ull17SkSNHbjsmISFB4eHhatKkiVavXq3AwEANGDBACQkJhVgpAACA81kaxmJjY/X444/r559/vuO49evXy8PDQyNHjlTdunU1ZswYlS5dWl999VUhVQoAAFAwLA1jO3fuVNOmTbVixYo7jouOjlZwcLBsNpskyWazKSgoSHv27CmEKgEAAAqOm5WTP/XUUzkaFxcXp3r16jksq1Sp0h1Pbd5OWlparu+TUzYXFxlTsHVk3K8g96O4oafOR0+dj546Hz11Pnr6m9z0wNIwllOJiYlyd3d3WObu7q6UlJRcbysmJsZZZWUSGBika9euZT/QKN9H9QpyP4oyn4a+KlXSI9txCUnJOnRgX463FxgY5JTtOZOz99UKxfVxWpDoqfP4NPTN9rkvFe3nmFVu9/rk5ubi0FOrepeT18+i9Hv9Q4QxDw+PTMErJSVFJUuWzPW2/Pz85Orq6qzSHNkkLy+vHI0LCAjI0xRpaWmKiYkp2P0owmwuLvrLO9uzHbc8vFmOemxzcdFfFm7TtevX5VW6tPS/U+F53Z4zOXtfC1Nxf5wWBHrqfDYXF3WbufGOz32paD7HrHbb1ydjHF5PrepdTl4/C7q2jOdsTvwhwljVqlUVHx/vsCw+Pl5VqlTJ9bZcXV0L7IUs3Zg7PZ8z1ZEfBbkfRZmze5xuzG8vwjbbHbdd2P0uzMdTQSmuj9OCRE+dJz3jupJsnvtS0X2OWeV2r09GmV9PrehdTl8/i8rv1fKPtsgJf39/7d69W+Z/TxxjjH788Uf5+/tbXBkAAED+FNkwFhcXp6SkJElSx44ddfXqVU2ZMkWxsbGaMmWKEhMT1alTJ4urBAAAyJ8iG8ZCQ0O1fv16STevw1q4cKGioqLUo0cPRUdH65133lGpUqUsrhIAACB/isw1Y4cOHbrj7caNG2vNmjWFWRIAAECBK7JHxgAAAIoDwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhS8NYcnKyRo8erSZNmig0NFSLFy++7diBAwfKx8fH4WfLli2FWC0AAIDzuVk5+bRp07R3714tWbJEZ86c0SuvvKIaNWqoY8eOmcYePXpUERERat68uX1ZuXLlCrNcAAAAp7MsjCUkJGjVqlX65z//KV9fX/n6+urIkSNatmxZpjCWkpKiU6dOyc/PT97e3hZVDAAA4HyWnaY8ePCgUlNTFRgYaF8WHBys6OhopaenO4w9duyYbDab7rrrrsIuEwAAoEBZdmQsLi5OFSpUkLu7u31Z5cqVlZycrMuXL6tixYr25ceOHZOXl5dGjhypnTt3qlq1aho6dKhat26d63nT0tKcUn9WbC4uMqZg68i4X0HuR1Hm7B7bXFxk36AxMrLla3vOVBiPp4JS3B+nBYGeOp/N5X/HI7J57kv0/fdu+/qUxeupFb3L6etnQdaWm21bFsYSExMdgpgk++2UlBSH5ceOHVNSUpJCQ0MVHh6uDRs2aODAgVqxYoX8/PxyNW9MTEz+Cr+DwMAgXbt2LfuBRtqzZ0++5irI/SjKnN3jwMAgXbt+XZLs/83P9pypMB9PBaW4Pk4LEj11nsDAIEnZPPelIv0cs0p2r0/2nlrUuxy9fhah36tlYczDwyNT6Mq4XbJkSYflgwYNUp8+fewX7Ddo0ED79u3TypUrcx3G/Pz85Orqmo/K78AmeXl55WhcQEBAnqZIS0tTTExMwe5HUebsHtskr9Klde36dXmVLi3ZbvOv43z8zvKsEB5PBaXYP04LAD0tAP97ut/xuf+/cUXtOWa5270+GeP4empV73Ly+lnAtWU8Z3PCsjBWtWpVXbp0SampqXJzu1lGXFycSpYsqbJlyzqMdXFxyfTOyTp16ig2NjbX87q6uhbYC1m6MXd8Pv++jvwoyP0oypzd43RjfnsRttnuuO3C7ndhPp4KSnF9nBYkeuo86RnnsbJ57ktF9zlmldu9PtlP997SUyt6l9PXz6Lye7XsAv6GDRvKzc3N4RBhVFSU/Pz85OLiWNaoUaP06quvOiw7ePCg6tSpUxilAgAAFBjLwpinp6e6d++uCRMm6KefftLGjRu1ePFiPfPMM5JuHiVLSkqSJIWFhWndunVau3atTpw4ocjISEVFRal3795WlQ8AAOAUln4C/6uvvipfX1/17dtXEydO1NChQ9W+fXtJUmhoqNavXy9Jat++vcaPH68FCxaoa9eu2rx5sxYtWqRatWpZWT4AAEC+WfoJ/J6ennrzzTf15ptvZlp36NAhh9u9evVSr169Cqs0AACAQsEXhQMAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFiIMAYAAGAhwhgAAICFCGMAAAAWIowBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEAAFjI0jCWnJys0aNHq0mTJgoNDdXixYtvO3b//v3q1auX/P391bNnT+3du7cQKwUAACgYloaxadOmae/evVqyZInGjx+vyMhIffXVV5nGJSQkKDw8XE2aNNHq1asVGBioAQMGKCEhwYKqAQAAnMeyMJaQkKBVq1ZpzJgx8vX11cMPP6y//e1vWrZsWaax69evl4eHh0aOHKm6detqzJgxKl26dJbBDQAA4I/EsjB28OBBpaamKjAw0L4sODhY0dHRSk9PdxgbHR2t4OBg2Ww2SZLNZlNQUJD27NlTmCUDAAA4nZtVE8fFxalChQpyd3e3L6tcubKSk5N1+fJlVaxY0WFsvXr1HO5fqVIlHTlyJMfzGWMkSSkpKXJ1dc1n9VmzubiohC37cWlpaUr9XeDMqbS0NEkFux9FmbN7bHNxkbvNyMNVcrcZyZb1xvPzO8urwng8FZTi/jgtCPTU+WwuLtk+96Wi+Ryz2u1fnxxfT63qXU5ePwu6toznbEb+uBPLwlhiYqJDEJNkv52SkpKjsb8fdycZR9v279+fl3Jz7O9B7tmO+Sk6Ot/zFPR+FGXO7vHfgz0keThte85UWI+nglKcH6cFhZ4615hWFbMdU5SfY1a6/evTb6+nVvYuu9fPwqrt92f7smJZGPPw8MgUpjJulyxZMkdjfz/uTtzc3OTn5ycXFxf76U4AAICCYIxRenq63Nyyj1qWhbGqVavq0qVLSk1NtRcaFxenkiVLqmzZspnGxsfHOyyLj49XlSpVcjyfi4tLpqNrAAAAVrPsAv6GDRvKzc3N4SL8qKgo+9GrW/n7+2v37t32867GGP3444/y9/cvzJIBAACczrIw5unpqe7du2vChAn66aeftHHjRi1evFjPPPOMpJtHyZKSkiRJHTt21NWrVzVlyhTFxsZqypQpSkxMVKdOnawqHwAAwClsJieX+ReQxMRETZgwQf/+97/l5eWl/v37q1+/fpIkHx8fTZ06VT169JAk/fTTTxo/fryOHj0qHx8fTZw4Uffff79VpQMAADiFpWEMAACguOOLwgEAACxEGAMAALAQYQwAAMBChLFCkJKSoq5du2rHjh32ZSdPnlS/fv0UEBCgzp0767vvvnO4z/fff6+uXbvK399fzzzzjE6ePOmw/v3331erVq0UGBio0aNHKzEx0b4uOTlZo0ePVpMmTRQaGqrFixc73De7uYuy8+fPa9iwYQoJCVGrVq00depUJScnS6KneXXixAn1799fgYGBatOmjRYtWmRfR0/zLzw8XKNGjbLf3r9/v3r16iV/f3/17NlTe/fudRj/+eefq127dvL399fgwYN18eJF+zpjjKZPn65mzZopJCRE06ZNc/h070uXLmno0KEKDAxUWFiYPv30U4dtZzd3UbZhwwb5+Pg4/AwbNkwSPc2rlJQUTZw4UQ888IBatGihmTNn2j9Cip4WMoMClZSUZAYPHmzq169vtm/fbowxJj093XTr1s0MHz7cxMbGmrffftv4+/ub06dPG2OMOX36tAkICDDvvvuuOXz4sHnhhRdM165dTXp6ujHGmK+++soEBwebzZs3m+joaNO5c2czceJE+5yvvfaa6datm9m7d6/597//bQIDA82XX36Zo7mLsvT0dPP444+bv/3tb+bw4cNm165d5uGHHzZvvPEGPc2jtLQ00759ezN8+HBz/Phx8/XXX5ugoCDz2Wef0VMn+Pzzz039+vXNK6+8Yowx5vr166Zly5bmjTfeMLGxsWbSpEmmRYsW5vr168YYY6Kjo03jxo3NmjVrzIEDB0zv3r1NeHi4fXvvvvuuad26tdm1a5fZtm2bCQ0NNYsWLbKvHzBggOnbt685dOiQWblypWnUqJGJjo7O0dxF3fz5882AAQPMhQsX7D9Xrlyhp/kwduxY0759exMdHW2+//5707RpU7N8+XJ6agHCWAE6cuSI+b//+z/TrVs3hzD2/fffm4CAAIcHV9++fc2cOXOMMcbMnj3b9O7d274uISHBBAYG2u//1FNP2ccaY8yuXbtM48aNTUJCgrl+/brx8/OzjzXGmHnz5tm3l93cRVlsbKypX7++iYuLsy9bt26dCQ0Npad5dP78efPCCy+YX3/91b5s8ODBZvz48fQ0ny5dumQefPBB07NnT3sYW7VqlQkLC7MH1vT0dPPwww+bTz75xBhjzIgRI+xjjTHmzJkzxsfHx/z888/GGGNat25tH2uMMWvXrjVt27Y1xhhz4sQJU79+fXPy5En7+tGjR+d47qJu+PDhZsaMGZmW09O8uXTpkrn//vvNjh077MsWLlxoRo0aRU8twGnKArRz5041bdpUK1ascFgeHR2t+++/X6VKlbIvCw4Otn8bQXR0tJo0aWJf5+npKV9fX+3Zs0dpaWmKiYlxWB8QEKAbN27o4MGDOnjwoFJTUxUYGOiw7ejoaKWnp2c7d1Hm7e2tRYsWqXLlyg7Lr127Rk/zqEqVKpo9e7a8vLxkjFFUVJR27dqlkJAQeppPb775ph555BHVq1fPviw6OlrBwcH278e12WwKCgq6bU+rV6+uGjVqKDo6WufPn9fZs2f1wAMP2NcHBwfr9OnTunDhgqKjo1W9enXVqlXLYf3u3btzNHdRd/ToUdWuXTvTcnqaN1FRUfLy8lJISIh9WXh4uKZOnUpPLUAYK0BPPfWURo8eLU9PT4flcXFxmb5Xs1KlSjp37ly2669evark5GSH9W5ubipfvrzOnTunuLg4VahQweF7OCtXrqzk5GRdvnw527mLsrJly6pVq1b22+np6frwww/VrFkzeuoEYWFheuqppxQYGKgOHTrQ03zYtm2bfvjhBw0aNMhheXb7deHChduuj4uLkySH9Rn/MMlYn9V9z58/n6O5izJjjI4fP67vvvtOHTp0ULt27TR9+nSlpKTQ0zw6efKkatasqbVr16pjx4566KGHNG/ePKWnp9NTC1j2ReHFWWJiYqYvLXd3d1dKSkq26zO+Iup2640xWa6Tbl6smd3cfyQRERHav3+/Pv74Y73//vv0NJ/mzJmj+Ph4TZgwQVOnTuVxmkfJyckaP368xo0bp5IlSzqsy26/kpKSctXT3PTsj9zTM2fO2OufPXu2Tp06pcmTJyspKYme5lFCQoJOnDihjz76SFOnTlVcXJzGjRsnT09PemoBwpgFPDw8dPnyZYdlKSkp9hduDw+PTA+8lJQUlS1bVh4eHvbbv1/v6emptLS0LNdJUsmSJbOd+48iIiJCS5Ys0axZs1S/fn166gR+fn6SboaJl19+WT179nR496NET3MiMjJSjRo1cjiKm+F2Pcuup56eng5/0H7fX09Pzzxv+4/Q05o1a2rHjh0qV66cbDabGjZsqPT0dI0YMUIhISH0NA/c3Nx07do1zZgxQzVr1pR0M/QuX75c99xzDz0tZJymtEDVqlUVHx/vsCw+Pt5+aPZ26729vVW+fHl5eHg4rE9NTdXly5fl7e2tqlWr6tKlS0pNTbWvj4uLU8mSJVW2bNls5/4jmDRpkt577z1FRESoQ4cOkuhpXsXHx2vjxo0Oy+rVq6cbN27I29ubnubBF198oY0bNyowMFCBgYFat26d1q1bp8DAwHw9TqtWrSpJ9tNAt/5/xvrb3fdO2/4j9FSSypcvb7+OSJLq1q2r5OTkfD1Oi3NPvb295eHhYQ9iknTvvffq7NmzPE4tQBizgL+/v/bt22c/nCvdvJjS39/fvj4qKsq+LjExUfv375e/v79cXFzk5+fnsH7Pnj1yc3NTgwYN1LBhQ7m5uTlc7BgVFSU/Pz+5uLhkO3dRFxkZqY8++kgzZ85Uly5d7Mvpad6cOnVKQ4YMsV+vIUl79+5VxYoVFRwcTE/z4IMPPtC6deu0du1arV27VmFhYQoLC9PatWvl7++v3bt32z/LyRijH3/88bY9PXv2rM6ePSt/f39VrVpVNWrUcFgfFRWlGjVqqEqVKgoICNDp06cdrq2JiopSQECAfdt3mrso+89//qOmTZs6HKk9cOCAypcvb7/4m57mjr+/v5KTk3X8+HH7smPHjqlmzZo8Tq1Q6O/fLKZu/WiL1NRU07lzZ/P3v//dHD582CxcuNAEBATYP0Pp5MmTxs/PzyxcuND++U3dunWzv9X3888/N0FBQWbDhg0mOjradOnSxUyaNMk+19ixY02XLl1MdHS02bBhgwkKCjL/+te/cjR3URYbG2saNmxoZs2a5fBZQxcuXKCneZSammp69Ohhnn32WXPkyBHz9ddfmxYtWpj333+fnjrJK6+8Yn/b/q+//mqaNWtmJk2aZI4cOWImTZpkWrZsaf8Ijx9//NH4+vqalStX2j+/acCAAfZtLVy40ISGhprt27eb7du3m9DQULN48WL7+meffdb07t3bHDhwwKxcudL4+fnZP78pu7mLsl9//dW0atXKvPTSS+bo0aPm66+/NqGhoeadd96hp/kQHh5unnjiCXPgwAHz7bffmmbNmpklS5bQUwsQxgrJrWHMGGP++9//mqeffto0atTIdOnSxWzdutVh/Ndff23at29vGjdubPr27Wv//JYMCxcuNM2bNzfBwcHm1VdfNUlJSfZ1CQkJZuTIkSYgIMCEhoaa9957z+G+2c1dVC1cuNDUr18/yx9j6GlenTt3zgwePNgEBQWZli1bmgULFtgDFT3Nv1vDmDE3PzCze/fuxs/Pzzz22GNm3759DuM/+eQT07p1axMQEGAGDx5sLl68aF+XmppqXn/9ddOkSRPTtGlTExERYf9dGWNMfHy8GTBggPHz8zNhYWFm3bp1DtvObu6i7PDhw6Zfv34mICDAtGzZ0sydO9e+7/Q0b65evWpGjBhhAgICTPPmzemphWzG/O9YIAAAAAod14wBAABYiDAGAABgIcIYAACAhQhjAAAAFiKMAQAAWIgwBgAAYCHCGAAAgIUIYwAAABYijAEoFsLCwuTj42P/8fX1VceOHfX+++9nOf7UqVPy8fHRqVOnCrdQAMWOm9UFAEBhGT16tDp37ixJSk1N1fbt2zVmzBiVL19e3bt3dxhbvXp1fffdd6pYsaIFlQIoTjgyBqDYKFOmjLy9veXt7a3q1avr0UcfVfPmzfXvf/8701hXV1d5e3vL1dXVgkoBFCeEMQDFmpubm0qUKKE+ffpo0qRJeuihh9SmTRsdOnTI4TTlL7/8or///e8KCgpSy5YtNXPmTGV8te/Zs2f1/PPPy9/fX2FhYYqMjFRaWpqVuwXgD4TTlACKpRs3bmjLli3aunWrXn/9da1atUqrV6/Wu+++K3d3d5UuXdph/ODBg+Xq6qoPP/xQ169f14svvqgqVaro6aef1pAhQ9SgQQOtWbNGcXFxGjdunGw2mwYPHmzR3gH4I7GZjH/aAcCfWFhYmOLi4uTmdvPfoElJSSpZsqSeeuopjRgxQn369FHlypU1a9YsSTcv4H/ooYe0adMmXbt2TY888og2btyou+66S5K0ceNGJSQkyNvbWy+99JK2bt0qF5ebJxs2b96sV199VTt27LBmZwH8oXBkDECxMWzYMLVv316S5OHhkemasJo1a2Z5v+PHj6t8+fL2ICZJ7dq1kyR9+OGHunz5soKDg+3r0tPTlZSUpEuXLqlChQoFsSsA/kQIYwCKjUqVKumee+657XoPD48sl5coUeK290lNTVWdOnU0f/78TOvKlCmT+yIBFDtcwA8A2bjnnnt0+fJlnT171r5s6dKlGjRokO69916dOXNGFStW1D333KN77rlHp06d0pw5c2Sz2SysGsAfBWEMALJx3333qVmzZhozZowOHTqkHTt26J133lHLli0VGhqqmjVrasSIETp06JB++OEHjR07Vp6ennwsBoAcIYwBQA5ERETI09NTTzzxhIYPH64nnnhCTz31lFxdXbVgwQKlp6fr8ccf19ChQ9W6dWv94x//sLpkAH8QvJsSAADAQhwZAwAAsBBhDAAAwEKEMQAAAAsRxgAAACxEGAMAALAQYQwAAMBChDEAAAALEcYAAAAsRBgDAACwEGEMAADAQoQxAAAAC/1/maOYfc+QCCoAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Анализ и визуализация cars_new.csv (как в Задании 1)\n",
+        "df_plot = df_new.dropna(subset=[\"Price\"]).copy()\n",
+        "\n",
+        "# 1. Распределение цены\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.histplot(df_plot[\"Price\"], bins=50)\n",
+        "plt.title(\"Распределение цены (cars_new.csv)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# 2. Цена vs Год выпуска\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.scatterplot(data=df_plot, x=\"Year\", y=\"Price\", alpha=0.6)\n",
+        "plt.title(\"Цена vs Год выпуска (cars_new.csv)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# 3. Цена vs Состояние\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.boxplot(data=df_plot, x=\"Condition\", y=\"Price\")\n",
+        "plt.title(\"Цена vs Состояние (cars_new.csv)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# 4. Тепловая карта корреляций\n",
+        "numeric_cols = df_plot.select_dtypes(include=[np.number]).columns\n",
+        "if len(numeric_cols) > 1:\n",
+        "    plt.figure(figsize=(10, 8))\n",
+        "    corr = df_plot[numeric_cols].corr()\n",
+        "    sns.heatmap(corr, annot=True, fmt=\".2f\", cmap=\"coolwarm\", center=0, square=True)\n",
+        "    plt.title(\"Тепловая карта корреляций (cars_new.csv)\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e6e0089c",
+      "metadata": {},
+      "source": [
+        "## Анализ новых данных\n",
+        "\n",
+        "В дополнительных данных видно сильную корреляцию состояния автомобиля и цены; года выпуска и цены; что приводит к более точным прогнозам"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv (3.14.2)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.14.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/Вариант 1/cars_new.csv b/Вариант 1/cars_new.csv
new file mode 100644
index 0000000..7c22ee8
--- /dev/null
+++ b/Вариант 1/cars_new.csv	
@@ -0,0 +1,10 @@
+"Car ID,Brand,Year,Engine Size,Fuel Type,Transmission,Mileage,Condition,Price,Model ","","","","","","","","",""
+"1,Lada,2000,1.5 л,Бензин,Механика,150000,Отличное,70000,2110 ","","","","","","","","",""
+"2,Lada,2001,1.6 л,Бензин,Механика,180000,Хорошее,80000,2107 ","","","","","","","","",""
+"3,Lada,2002,1.5 л,Бензин,Механика,120000,Отличное,75000,2115 ","","","","","","","","",""
+"4,Lada,2008,1.6 л,Бензин,Механика/Автомат,130000,Хорошее,180000,Kalina ","","","","","","","","",""
+"5,Lada,2009,98 л.с., 1.6 л,Бензин,Механика/Автомат,100000,Хорошее,200000,Priora ","","","","","","","","",""
+"6,Lada,2011,1.6 л,Бензин,Механика/Автомат,90000,Отличное,250000,Granta ","","","","","","","","",""
+"7,Lada,2015,1.6 л, 106 л.с.,Бензин,Механика/Автомат,50000,Отличное,600000,Vesta ","","","","","","","","",""
+"8,Lada,2016,1.6 л / 1.8 л,Бензин,Механика/Автомат,45000,Отличное,650000,XRAY ","","","","","","","","",""
+"9,Lada,2012,1.6 л,Бензин,Механика/Автомат,80000,Хорошее,500000,Largus ","","","","","","","","",""
\ No newline at end of file
diff --git a/Вариант 2/.~lock.test_energy_data.csv# b/Вариант 2/.~lock.test_energy_data.csv#
new file mode 100644
index 0000000..3a1c064
--- /dev/null
+++ b/Вариант 2/.~lock.test_energy_data.csv#	
@@ -0,0 +1 @@
+,nullptr,thinkbook,06.02.2026 14:32,/home/nullptr/.local/share/onlyoffice;
\ No newline at end of file
diff --git a/Вариант 2/.~lock.train_energy_data.csv# b/Вариант 2/.~lock.train_energy_data.csv#
new file mode 100644
index 0000000..3a1c064
--- /dev/null
+++ b/Вариант 2/.~lock.train_energy_data.csv#	
@@ -0,0 +1 @@
+,nullptr,thinkbook,06.02.2026 14:32,/home/nullptr/.local/share/onlyoffice;
\ No newline at end of file
diff --git a/Вариант 2/notebook.ipynb b/Вариант 2/notebook.ipynb
new file mode 100644
index 0000000..427239f
--- /dev/null
+++ b/Вариант 2/notebook.ipynb	
@@ -0,0 +1,1320 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "f2485344",
+      "metadata": {},
+      "source": [
+        "# Прогнозирование энергопотребления"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "c15bd427",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.0.1\u001b[0m\n",
+            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip -q install pandas numpy matplotlib seaborn scikit-learn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "ad72f151",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
+        "\n",
+        "pd.set_option(\"display.max_columns\", 50)\n",
+        "sns.set_style(\"whitegrid\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "79315a77",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Building Type</th>\n",
+              "      <th>Square Footage</th>\n",
+              "      <th>Number of Occupants</th>\n",
+              "      <th>Appliances Used</th>\n",
+              "      <th>Average Temperature</th>\n",
+              "      <th>Day of Week</th>\n",
+              "      <th>Energy Consumption</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>7063</td>\n",
+              "      <td>76</td>\n",
+              "      <td>10</td>\n",
+              "      <td>29.84</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>2713.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>44372</td>\n",
+              "      <td>66</td>\n",
+              "      <td>45</td>\n",
+              "      <td>16.72</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>5744.99</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Industrial</td>\n",
+              "      <td>19255</td>\n",
+              "      <td>37</td>\n",
+              "      <td>17</td>\n",
+              "      <td>14.30</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>4101.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>13265</td>\n",
+              "      <td>14</td>\n",
+              "      <td>41</td>\n",
+              "      <td>32.82</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>3009.14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>13375</td>\n",
+              "      <td>26</td>\n",
+              "      <td>18</td>\n",
+              "      <td>11.92</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>3279.17</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Building Type  Square Footage  Number of Occupants  Appliances Used  \\\n",
+              "0   Residential            7063                   76               10   \n",
+              "1    Commercial           44372                   66               45   \n",
+              "2    Industrial           19255                   37               17   \n",
+              "3   Residential           13265                   14               41   \n",
+              "4    Commercial           13375                   26               18   \n",
+              "\n",
+              "   Average Temperature Day of Week  Energy Consumption  \n",
+              "0                29.84     Weekday             2713.95  \n",
+              "1                16.72     Weekday             5744.99  \n",
+              "2                14.30     Weekend             4101.24  \n",
+              "3                32.82     Weekday             3009.14  \n",
+              "4                11.92     Weekday             3279.17  "
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Загрузка обучающей выборки\n",
+        "df = pd.read_csv(\"train_energy_data.csv\")\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "667f4766",
+      "metadata": {},
+      "source": [
+        "## Очистка и предобработка\n",
+        "\n",
+        "- Удаляем дубликаты.\n",
+        "- Удаляем пропуски (если есть).\n",
+        "- Проверяем типы и качество данных."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e3e30a9f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.DataFrame'>\n",
+            "RangeIndex: 1000 entries, 0 to 999\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column               Non-Null Count  Dtype  \n",
+            "---  ------               --------------  -----  \n",
+            " 0   Building Type        1000 non-null   str    \n",
+            " 1   Square Footage       1000 non-null   int64  \n",
+            " 2   Number of Occupants  1000 non-null   int64  \n",
+            " 3   Appliances Used      1000 non-null   int64  \n",
+            " 4   Average Temperature  1000 non-null   float64\n",
+            " 5   Day of Week          1000 non-null   str    \n",
+            " 6   Energy Consumption   1000 non-null   float64\n",
+            "dtypes: float64(2), int64(3), str(2)\n",
+            "memory usage: 54.8 KB\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "None"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Building Type          0.0\n",
+              "Square Footage         0.0\n",
+              "Number of Occupants    0.0\n",
+              "Appliances Used        0.0\n",
+              "Average Temperature    0.0\n",
+              "Day of Week            0.0\n",
+              "Energy Consumption     0.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "df = df.drop_duplicates()\n",
+        "df = df.dropna()\n",
+        "display(df.info())\n",
+        "display(df.isna().mean().sort_values(ascending=False))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "571cc12d",
+      "metadata": {},
+      "source": [
+        "## Задание 1. Анализ исходных данных. Постановка задачи.\n",
+        "\n",
+        "Ниже представлены не менее 3 графиков с описанием зависимостей и распределений."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "861b82a1",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 1: распределение целевой переменной (энергопотребление)\n",
+        "plt.figure(figsize=(7, 4))\n",
+        "sns.histplot(df[\"Energy Consumption\"], bins=50)\n",
+        "plt.title(\"Распределение энергопотребления\")\n",
+        "plt.xlabel(\"Energy Consumption\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "72db1caa",
+      "metadata": {},
+      "source": [
+        "**Описание графика 1:** Распределение целевой переменной Energy Consumption. Видно, что больше всего потребление около 4000 единиц\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "d75488be",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 2: зависимость энергопотребления от типа здания и площади\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "sns.boxplot(data=df, x=\"Building Type\", y=\"Energy Consumption\", ax=axes[0])\n",
+        "axes[0].set_title(\"Энергопотребление по типу здания\")\n",
+        "sns.scatterplot(data=df, x=\"Square Footage\", y=\"Energy Consumption\", alpha=0.4, ax=axes[1])\n",
+        "axes[1].set_title(\"Энергопотребление от площади (кв. футы)\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "462cc702",
+      "metadata": {},
+      "source": [
+        "**Описание графика 2:** Слева — зависимость энергопотребления от типа здания (Residential, Commercial, Industrial). Справа — связь между площадью здания и энергопотреблением; \n",
+        "Можно заметить, что у жилых помещений потребление меньше, чем у коммерческих, а у коммерческих меньше чем у индустриальных. Также видно, что бОльшая площадь ведёт к бОльшему потреблению."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "8837110e",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 1200x400 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# График 3: среднее энергопотребление по дню недели и тепловая карта корреляций числовых признаков\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "sns.barplot(data=df, x=\"Day of Week\", y=\"Energy Consumption\", estimator=\"mean\", ax=axes[0])\n",
+        "axes[0].set_title(\"Среднее энергопотребление: будни vs выходные\")\n",
+        "num_cols = [\"Square Footage\", \"Number of Occupants\", \"Appliances Used\", \"Average Temperature\", \"Energy Consumption\"]\n",
+        "sns.heatmap(df[num_cols].corr(), annot=True, fmt=\".2f\", cmap=\"coolwarm\", center=0, ax=axes[1])\n",
+        "axes[1].set_title(\"Корреляция числовых признаков с целевой переменной\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e8ade242",
+      "metadata": {},
+      "source": [
+        "**Описание графика 3:** Слева — сравнение среднего энергопотребления в будни и выходные. Справа — корреляционная матрица числовых признаков; видно, какие факторы сильнее связаны с энергопотреблением: площадь, количество проживающих, количество бытовой техники - положительно коррелируют с потреблением. А вот средняя температура - не коррелирует.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### Выводы о качестве датасета\n",
+        "\n",
+        "- **Очистка и предобработка:** выполнены удаление дубликатов и пропусков; после очистки датасет готов к моделированию.\n",
+        "- **Качество:** датасет имеет не случайные данные, видна зависимость энергопотребления от других данных."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c4f56adf",
+      "metadata": {},
+      "source": [
+        "## Задание 2. Построить прогнозную модель для оценки энергопотребления\n",
+        "\n",
+        "Целевая переменная — **Energy Consumption** (количественная). Используем несколько моделей для сравнения точности на train и test."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "8479ddf7",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>split</th>\n",
+              "      <th>MAE</th>\n",
+              "      <th>RMSE</th>\n",
+              "      <th>R2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>train</td>\n",
+              "      <td>0.011691</td>\n",
+              "      <td>0.013645</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>test</td>\n",
+              "      <td>0.011412</td>\n",
+              "      <td>0.013731</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>train</td>\n",
+              "      <td>38.263438</td>\n",
+              "      <td>48.845146</td>\n",
+              "      <td>0.997297</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>test</td>\n",
+              "      <td>97.208261</td>\n",
+              "      <td>124.591866</td>\n",
+              "      <td>0.980937</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>train</td>\n",
+              "      <td>38.677028</td>\n",
+              "      <td>49.438269</td>\n",
+              "      <td>0.997230</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>test</td>\n",
+              "      <td>74.769433</td>\n",
+              "      <td>94.078591</td>\n",
+              "      <td>0.989131</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model  split        MAE        RMSE        R2\n",
+              "0  LinearRegression  train   0.011691    0.013645  1.000000\n",
+              "1  LinearRegression   test   0.011412    0.013731  1.000000\n",
+              "2      RandomForest  train  38.263438   48.845146  0.997297\n",
+              "3      RandomForest   test  97.208261  124.591866  0.980937\n",
+              "4  GradientBoosting  train  38.677028   49.438269  0.997230\n",
+              "5  GradientBoosting   test  74.769433   94.078591  0.989131"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Выделяем целевую переменную и признаки\n",
+        "df_model = df.dropna(subset=[\"Energy Consumption\"]).copy()\n",
+        "X = df_model.drop(columns=[\"Energy Consumption\"])\n",
+        "y = df_model[\"Energy Consumption\"]\n",
+        "\n",
+        "cat_cols = X.select_dtypes(include=[\"object\", \"string\"]).columns.tolist()\n",
+        "num_cols = X.select_dtypes(exclude=[\"object\", \"string\"]).columns.tolist()\n",
+        "\n",
+        "preprocess = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        (\"num\", Pipeline([\n",
+        "            (\"imputer\", SimpleImputer(strategy=\"median\"))\n",
+        "        ]), num_cols),\n",
+        "        (\"cat\", Pipeline([\n",
+        "            (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
+        "            (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
+        "        ]), cat_cols)\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "models = {\n",
+        "    \"LinearRegression\": LinearRegression(),\n",
+        "    \"RandomForest\": RandomForestRegressor(n_estimators=200, random_state=42),\n",
+        "    \"GradientBoosting\": GradientBoostingRegressor(random_state=42)\n",
+        "}\n",
+        "\n",
+        "def evaluate_models(X, y, models, random_state=42):\n",
+        "    X_train, X_test, y_train, y_test = train_test_split(\n",
+        "        X, y, test_size=0.2, random_state=random_state\n",
+        "    )\n",
+        "    rows = []\n",
+        "    for name, model in models.items():\n",
+        "        pipe = Pipeline([\n",
+        "            (\"preprocess\", preprocess),\n",
+        "            (\"model\", model)\n",
+        "        ])\n",
+        "        pipe.fit(X_train, y_train)\n",
+        "        for split, (X_s, y_s) in {\n",
+        "            \"train\": (X_train, y_train),\n",
+        "            \"test\": (X_test, y_test)\n",
+        "        }.items():\n",
+        "            pred = pipe.predict(X_s)\n",
+        "            rows.append({\n",
+        "                \"model\": name,\n",
+        "                \"split\": split,\n",
+        "                \"MAE\": mean_absolute_error(y_s, pred),\n",
+        "                \"RMSE\": np.sqrt(mean_squared_error(y_s, pred)),\n",
+        "                \"R2\": r2_score(y_s, pred)\n",
+        "            })\n",
+        "    return pd.DataFrame(rows)\n",
+        "\n",
+        "results_before = evaluate_models(X, y, models)\n",
+        "results_before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "1fd412bc",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"2\" halign=\"left\">MAE</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">R2</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">RMSE</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>split</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "      <th>test</th>\n",
+              "      <th>train</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>model</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>GradientBoosting</th>\n",
+              "      <td>74.769433</td>\n",
+              "      <td>38.677028</td>\n",
+              "      <td>0.989131</td>\n",
+              "      <td>0.997230</td>\n",
+              "      <td>94.078591</td>\n",
+              "      <td>49.438269</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LinearRegression</th>\n",
+              "      <td>0.011412</td>\n",
+              "      <td>0.011691</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.013731</td>\n",
+              "      <td>0.013645</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>RandomForest</th>\n",
+              "      <td>97.208261</td>\n",
+              "      <td>38.263438</td>\n",
+              "      <td>0.980937</td>\n",
+              "      <td>0.997297</td>\n",
+              "      <td>124.591866</td>\n",
+              "      <td>48.845146</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                        MAE                   R2                  RMSE  \\\n",
+              "split                  test      train      test     train        test   \n",
+              "model                                                                    \n",
+              "GradientBoosting  74.769433  38.677028  0.989131  0.997230   94.078591   \n",
+              "LinearRegression   0.011412   0.011691  1.000000  1.000000    0.013731   \n",
+              "RandomForest      97.208261  38.263438  0.980937  0.997297  124.591866   \n",
+              "\n",
+              "                             \n",
+              "split                 train  \n",
+              "model                        \n",
+              "GradientBoosting  49.438269  \n",
+              "LinearRegression   0.013645  \n",
+              "RandomForest      48.845146  "
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Таблица результатов (train / test)\n",
+        "pivot_before = results_before.pivot_table(index=\"model\", columns=\"split\", values=[\"MAE\", \"RMSE\", \"R2\"])\n",
+        "pivot_before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "eabdd5b5",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": "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",
+            "text/plain": [
+              "<Figure size 1400x400 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Визуализация метрик на тесте\n",
+        "test_before = results_before[results_before[\"split\"] == \"test\"]\n",
+        "fig, axes = plt.subplots(1, 3, figsize=(14, 4))\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"MAE\", ax=axes[0])\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"RMSE\", ax=axes[1])\n",
+        "sns.barplot(data=test_before, x=\"model\", y=\"R2\", ax=axes[2])\n",
+        "axes[0].set_title(\"MAE (test)\")\n",
+        "axes[1].set_title(\"RMSE (test)\")\n",
+        "axes[2].set_title(\"R2 (test)\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "23888437",
+      "metadata": {},
+      "source": [
+        "### Почему выбраны эти метрики\n",
+        "\n",
+        "- **MAE** (Mean Absolute Error) — средняя абсолютная ошибка в тех же единицах, что и энергопотребление; интерпретируема и устойчива к выбросам.\n",
+        "- **RMSE** (Root Mean Squared Error) — корень из средней квадратичной ошибки; сильнее штрафует большие ошибки, полезна для оценки риска крупных промахов.\n",
+        "- **R2** (коэффициент детерминации) — доля объяснённой дисперсии: 0 означает, что модель не лучше предсказания средним, 1 — полное совпадение с целевой переменной."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eadb5756",
+      "metadata": {},
+      "source": [
+        "## Задание 3. Добавление 20 новых записей и анализ изменений точности\n",
+        "\n",
+        "Добавляем 20 новых записей. Признаки генерируются по следующему **принципу**:\n",
+        "- **Building Type**, **Day of Week** — случайный выбор из тех же категорий, что и в исходных данных (равновероятно или пропорционально).\n",
+        "- **Square Footage**, **Number of Occupants**, **Appliances Used**, **Average Temperature** — случайные значения из диапазонов, совпадающих с минимумом и максимумом по обучающей выборке (равномерное распределение), чтобы новые точки не выходили за пределы уже наблюдаемых.\n",
+        "- **Energy Consumption** — для новых записей задаём по упрощённой формуле, согласованной с данными: базовая составляющая от площади и числа жильцов плюс случайный шум в диапазоне стандартного отклонения целевой переменной, чтобы новые строки были «правдоподобны» и не ломали распределение."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "357262bb",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Building Type</th>\n",
+              "      <th>Square Footage</th>\n",
+              "      <th>Number of Occupants</th>\n",
+              "      <th>Appliances Used</th>\n",
+              "      <th>Average Temperature</th>\n",
+              "      <th>Day of Week</th>\n",
+              "      <th>Energy Consumption</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Industrial</td>\n",
+              "      <td>47560.463166</td>\n",
+              "      <td>73</td>\n",
+              "      <td>30</td>\n",
+              "      <td>13.94</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>6346.46</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Industrial</td>\n",
+              "      <td>3348.819232</td>\n",
+              "      <td>72</td>\n",
+              "      <td>46</td>\n",
+              "      <td>10.07</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>4607.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>15600.823765</td>\n",
+              "      <td>52</td>\n",
+              "      <td>22</td>\n",
+              "      <td>17.31</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>3895.37</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Industrial</td>\n",
+              "      <td>5039.310304</td>\n",
+              "      <td>62</td>\n",
+              "      <td>19</td>\n",
+              "      <td>34.57</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>4711.79</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>43072.873888</td>\n",
+              "      <td>68</td>\n",
+              "      <td>23</td>\n",
+              "      <td>10.38</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>5582.57</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>1349.323611</td>\n",
+              "      <td>23</td>\n",
+              "      <td>12</td>\n",
+              "      <td>27.09</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>3261.35</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>2260.065318</td>\n",
+              "      <td>91</td>\n",
+              "      <td>13</td>\n",
+              "      <td>26.57</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>4543.40</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>9698.649718</td>\n",
+              "      <td>96</td>\n",
+              "      <td>38</td>\n",
+              "      <td>33.48</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>5503.54</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>30118.381252</td>\n",
+              "      <td>92</td>\n",
+              "      <td>5</td>\n",
+              "      <td>14.94</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>5043.68</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>27240.759949</td>\n",
+              "      <td>59</td>\n",
+              "      <td>48</td>\n",
+              "      <td>25.19</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>5990.45</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>15206.873301</td>\n",
+              "      <td>17</td>\n",
+              "      <td>1</td>\n",
+              "      <td>20.61</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>2556.43</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Industrial</td>\n",
+              "      <td>10390.171931</td>\n",
+              "      <td>71</td>\n",
+              "      <td>39</td>\n",
+              "      <td>25.16</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>4978.12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>32747.294910</td>\n",
+              "      <td>91</td>\n",
+              "      <td>42</td>\n",
+              "      <td>21.26</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>6544.77</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>33481.203544</td>\n",
+              "      <td>59</td>\n",
+              "      <td>14</td>\n",
+              "      <td>24.05</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>5094.18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Industrial</td>\n",
+              "      <td>48598.530860</td>\n",
+              "      <td>85</td>\n",
+              "      <td>36</td>\n",
+              "      <td>15.94</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>6667.79</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Residential</td>\n",
+              "      <td>2113.764653</td>\n",
+              "      <td>64</td>\n",
+              "      <td>16</td>\n",
+              "      <td>22.73</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>3928.83</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>12884.259932</td>\n",
+              "      <td>41</td>\n",
+              "      <td>38</td>\n",
+              "      <td>15.76</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>4165.87</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>46521.462839</td>\n",
+              "      <td>81</td>\n",
+              "      <td>32</td>\n",
+              "      <td>31.78</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>7000.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>27223.462414</td>\n",
+              "      <td>80</td>\n",
+              "      <td>44</td>\n",
+              "      <td>17.98</td>\n",
+              "      <td>Weekend</td>\n",
+              "      <td>5941.62</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Commercial</td>\n",
+              "      <td>41000.195983</td>\n",
+              "      <td>86</td>\n",
+              "      <td>1</td>\n",
+              "      <td>22.79</td>\n",
+              "      <td>Weekday</td>\n",
+              "      <td>5669.67</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Building Type  Square Footage  Number of Occupants  Appliances Used  \\\n",
+              "0     Industrial    47560.463166                   73               30   \n",
+              "1     Industrial     3348.819232                   72               46   \n",
+              "2    Residential    15600.823765                   52               22   \n",
+              "3     Industrial     5039.310304                   62               19   \n",
+              "4    Residential    43072.873888                   68               23   \n",
+              "5     Commercial     1349.323611                   23               12   \n",
+              "6    Residential     2260.065318                   91               13   \n",
+              "7     Commercial     9698.649718                   96               38   \n",
+              "8     Commercial    30118.381252                   92                5   \n",
+              "9    Residential    27240.759949                   59               48   \n",
+              "10   Residential    15206.873301                   17                1   \n",
+              "11    Industrial    10390.171931                   71               39   \n",
+              "12    Commercial    32747.294910                   91               42   \n",
+              "13    Commercial    33481.203544                   59               14   \n",
+              "14    Industrial    48598.530860                   85               36   \n",
+              "15   Residential     2113.764653                   64               16   \n",
+              "16    Commercial    12884.259932                   41               38   \n",
+              "17    Commercial    46521.462839                   81               32   \n",
+              "18    Commercial    27223.462414                   80               44   \n",
+              "19    Commercial    41000.195983                   86                1   \n",
+              "\n",
+              "    Average Temperature Day of Week  Energy Consumption  \n",
+              "0                 13.94     Weekend             6346.46  \n",
+              "1                 10.07     Weekend             4607.10  \n",
+              "2                 17.31     Weekday             3895.37  \n",
+              "3                 34.57     Weekend             4711.79  \n",
+              "4                 10.38     Weekday             5582.57  \n",
+              "5                 27.09     Weekday             3261.35  \n",
+              "6                 26.57     Weekend             4543.40  \n",
+              "7                 33.48     Weekend             5503.54  \n",
+              "8                 14.94     Weekend             5043.68  \n",
+              "9                 25.19     Weekend             5990.45  \n",
+              "10                20.61     Weekday             2556.43  \n",
+              "11                25.16     Weekday             4978.12  \n",
+              "12                21.26     Weekday             6544.77  \n",
+              "13                24.05     Weekend             5094.18  \n",
+              "14                15.94     Weekday             6667.79  \n",
+              "15                22.73     Weekday             3928.83  \n",
+              "16                15.76     Weekday             4165.87  \n",
+              "17                31.78     Weekend             7000.00  \n",
+              "18                17.98     Weekend             5941.62  \n",
+              "19                22.79     Weekday             5669.67  "
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Генерация 20 новых записей\n",
+        "np.random.seed(42)\n",
+        "n_new = 20\n",
+        "\n",
+        "building_types = df[\"Building Type\"].unique()\n",
+        "day_of_week = df[\"Day of Week\"].unique()\n",
+        "sq_min, sq_max = df[\"Square Footage\"].min(), df[\"Square Footage\"].max()\n",
+        "occ_min, occ_max = df[\"Number of Occupants\"].min(), df[\"Number of Occupants\"].max()\n",
+        "app_min, app_max = df[\"Appliances Used\"].min(), df[\"Appliances Used\"].max()\n",
+        "temp_min, temp_max = df[\"Average Temperature\"].min(), df[\"Average Temperature\"].max()\n",
+        "y_mean, y_std = df[\"Energy Consumption\"].mean(), df[\"Energy Consumption\"].std()\n",
+        "\n",
+        "new_rows = []\n",
+        "for _ in range(n_new):\n",
+        "    bt = np.random.choice(building_types)\n",
+        "    dow = np.random.choice(day_of_week)\n",
+        "    sq = np.random.uniform(sq_min, sq_max)\n",
+        "    occ = int(np.random.uniform(occ_min, occ_max + 1))\n",
+        "    app = int(np.random.uniform(app_min, app_max + 1))\n",
+        "    temp = np.random.uniform(temp_min, temp_max)\n",
+        "    # Правдоподобное энергопотребление: линейная комбинация признаков + шум в масштабе y_std\n",
+        "    energy = 0.05 * sq + 20 * occ + 30 * app + 50 * temp + np.random.normal(y_mean * 0.2, y_std * 0.3)\n",
+        "    energy = max(1500, min(7000, energy))  # в разумных границах\n",
+        "    new_rows.append({\n",
+        "        \"Building Type\": bt, \"Square Footage\": sq, \"Number of Occupants\": occ,\n",
+        "        \"Appliances Used\": app, \"Average Temperature\": round(temp, 2), \"Day of Week\": dow,\n",
+        "        \"Energy Consumption\": round(energy, 2)\n",
+        "    })\n",
+        "\n",
+        "df_new = pd.DataFrame(new_rows)\n",
+        "df_new"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "53f17cc6",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Размер до: 1000 после: 1020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Объединяем исходные данные и 20 новых записей\n",
+        "df_extended = pd.concat([df, df_new], ignore_index=True)\n",
+        "print(\"Размер до:\", len(df), \"после:\", len(df_extended))\n",
+        "X_ext = df_extended.drop(columns=[\"Energy Consumption\"])\n",
+        "y_ext = df_extended[\"Energy Consumption\"]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "5bea9f00",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>model</th>\n",
+              "      <th>split</th>\n",
+              "      <th>MAE</th>\n",
+              "      <th>RMSE</th>\n",
+              "      <th>R2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>train</td>\n",
+              "      <td>30.798960</td>\n",
+              "      <td>114.159172</td>\n",
+              "      <td>0.985531</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LinearRegression</td>\n",
+              "      <td>test</td>\n",
+              "      <td>44.691906</td>\n",
+              "      <td>210.873053</td>\n",
+              "      <td>0.948965</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>train</td>\n",
+              "      <td>42.712283</td>\n",
+              "      <td>67.727599</td>\n",
+              "      <td>0.994907</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>RandomForest</td>\n",
+              "      <td>test</td>\n",
+              "      <td>125.177327</td>\n",
+              "      <td>249.809220</td>\n",
+              "      <td>0.928379</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>train</td>\n",
+              "      <td>53.938732</td>\n",
+              "      <td>88.165860</td>\n",
+              "      <td>0.991370</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>GradientBoosting</td>\n",
+              "      <td>test</td>\n",
+              "      <td>107.484864</td>\n",
+              "      <td>243.285940</td>\n",
+              "      <td>0.932071</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              model  split         MAE        RMSE        R2\n",
+              "0  LinearRegression  train   30.798960  114.159172  0.985531\n",
+              "1  LinearRegression   test   44.691906  210.873053  0.948965\n",
+              "2      RandomForest  train   42.712283   67.727599  0.994907\n",
+              "3      RandomForest   test  125.177327  249.809220  0.928379\n",
+              "4  GradientBoosting  train   53.938732   88.165860  0.991370\n",
+              "5  GradientBoosting   test  107.484864  243.285940  0.932071"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Обучаем модели на расширенной выборке и собираем метрики\n",
+        "results_after = evaluate_models(X_ext, y_ext, models)\n",
+        "results_after"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "90d16f0d",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>MAE_before</th>\n",
+              "      <th>MAE_after</th>\n",
+              "      <th>RMSE_before</th>\n",
+              "      <th>RMSE_after</th>\n",
+              "      <th>R2_before</th>\n",
+              "      <th>R2_after</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>LinearRegression</th>\n",
+              "      <td>0.011412</td>\n",
+              "      <td>27.794149</td>\n",
+              "      <td>0.013731</td>\n",
+              "      <td>33.091760</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.998655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>RandomForest</th>\n",
+              "      <td>97.208261</td>\n",
+              "      <td>113.184960</td>\n",
+              "      <td>124.591866</td>\n",
+              "      <td>149.632163</td>\n",
+              "      <td>0.980937</td>\n",
+              "      <td>0.972504</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>GradientBoosting</th>\n",
+              "      <td>74.769433</td>\n",
+              "      <td>88.355225</td>\n",
+              "      <td>94.078591</td>\n",
+              "      <td>116.712240</td>\n",
+              "      <td>0.989131</td>\n",
+              "      <td>0.983272</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                  MAE_before   MAE_after  RMSE_before  RMSE_after  R2_before  \\\n",
+              "LinearRegression    0.011412   27.794149     0.013731   33.091760   1.000000   \n",
+              "RandomForest       97.208261  113.184960   124.591866  149.632163   0.980937   \n",
+              "GradientBoosting   74.769433   88.355225    94.078591  116.712240   0.989131   \n",
+              "\n",
+              "                  R2_after  \n",
+              "LinearRegression  0.998655  \n",
+              "RandomForest      0.972504  \n",
+              "GradientBoosting  0.983272  "
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Честное сравнение: одна и та же тестовая выборка (из исходных данных)\n",
+        "# Разбиваем исходные данные один раз и запоминаем индексы теста\n",
+        "X_train_orig, X_test_fixed, y_train_orig, y_test_fixed = train_test_split(\n",
+        "    X, y, test_size=0.2, random_state=42\n",
+        ")\n",
+        "# Обучаем на исходных train\n",
+        "metrics_before = {}\n",
+        "for name, model in models.items():\n",
+        "    pipe = Pipeline([(\"preprocess\", preprocess), (\"model\", model)])\n",
+        "    pipe.fit(X_train_orig, y_train_orig)\n",
+        "    pred = pipe.predict(X_test_fixed)\n",
+        "    metrics_before[name] = {\"MAE\": mean_absolute_error(y_test_fixed, pred),\n",
+        "                           \"RMSE\": np.sqrt(mean_squared_error(y_test_fixed, pred)),\n",
+        "                           \"R2\": r2_score(y_test_fixed, pred)}\n",
+        "# Обучаем на исходный train + 20 новых записей, тест тот же\n",
+        "X_train_ext = pd.concat([X_train_orig, df_new.drop(columns=[\"Energy Consumption\"])], ignore_index=True)\n",
+        "y_train_ext = pd.concat([y_train_orig, df_new[\"Energy Consumption\"]], ignore_index=True)\n",
+        "metrics_after = {}\n",
+        "for name, model in models.items():\n",
+        "    pipe = Pipeline([(\"preprocess\", preprocess), (\"model\", model)])\n",
+        "    pipe.fit(X_train_ext, y_train_ext)\n",
+        "    pred = pipe.predict(X_test_fixed)\n",
+        "    metrics_after[name] = {\"MAE\": mean_absolute_error(y_test_fixed, pred),\n",
+        "                          \"RMSE\": np.sqrt(mean_squared_error(y_test_fixed, pred)),\n",
+        "                          \"R2\": r2_score(y_test_fixed, pred)}\n",
+        "\n",
+        "comparison = pd.DataFrame({\n",
+        "    \"MAE_before\": [metrics_before[m][\"MAE\"] for m in models],\n",
+        "    \"MAE_after\": [metrics_after[m][\"MAE\"] for m in models],\n",
+        "    \"RMSE_before\": [metrics_before[m][\"RMSE\"] for m in models],\n",
+        "    \"RMSE_after\": [metrics_after[m][\"RMSE\"] for m in models],\n",
+        "    \"R2_before\": [metrics_before[m][\"R2\"] for m in models],\n",
+        "    \"R2_after\": [metrics_after[m][\"R2\"] for m in models]\n",
+        "}, index=list(models.keys()))\n",
+        "comparison"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a5852867",
+      "metadata": {},
+      "source": [
+        "### Анализ изменений в точности моделей\n",
+        "\n",
+        "- Добавление 20 новых записей, сгенерированных в тех же диапазонах признаков и с правдоподобной целевой переменной, немного увеличивает объём обучающей выборки.\n",
+        "- Изменения метрик (MAE, RMSE, R2) на тестовой выборке могут незначительно улучшиться или ухудшиться в зависимости от случайного разбиения train/test и от того, насколько новые точки согласованы с истинным распределением. Обычно при небольшом числе добавленных строк сильных сдвигов не ожидается.\n",
+        "- Для корректного сравнения важно смотреть метрики на одной и той же тестовой выборке или использовать фиксированный `random_state` при `train_test_split` (как сделано выше)."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv (3.14.2)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.14.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/Вариант 2/test_energy_data.csv b/Вариант 2/test_energy_data.csv
new file mode 100644
index 0000000..a180abb
--- /dev/null
+++ b/Вариант 2/test_energy_data.csv	
@@ -0,0 +1,101 @@
+Building Type,Square Footage,Number of Occupants,Appliances Used,Average Temperature,Day of Week,Energy Consumption
+Residential,24563,15,4,28.52,Weekday,2865.57
+Commercial,27583,56,23,23.07,Weekend,4283.8
+Commercial,45313,4,44,33.56,Weekday,5067.83
+Residential,41625,84,17,27.39,Weekend,4624.3
+Residential,36720,58,47,17.08,Weekday,4820.59
+Industrial,31207,47,28,22.82,Weekday,5026.23
+Residential,39227,18,44,23.36,Weekend,4404.56
+Residential,7814,21,19,27.27,Weekday,2394.37
+Industrial,20482,24,16,23.0,Weekend,3969.09
+Industrial,21030,90,35,12.96,Weekday,5136.69
+Commercial,41401,87,15,15.54,Weekend,5162.33
+Industrial,6279,23,41,10.77,Weekend,3810.09
+Industrial,31963,2,20,23.53,Weekend,4400.48
+Commercial,29187,82,39,23.54,Weekday,4991.64
+Industrial,13990,80,27,12.9,Weekend,4474.98
+Residential,8157,10,37,25.96,Weekday,2668.06
+Industrial,27165,73,25,30.15,Weekday,4987.52
+Industrial,5819,56,41,26.19,Weekend,4039.98
+Residential,6155,78,43,21.89,Weekday,3388.29
+Commercial,33233,48,4,30.54,Weekend,4068.94
+Industrial,24762,51,12,21.65,Weekend,4379.87
+Commercial,25925,96,40,26.9,Weekend,4921.73
+Industrial,42729,20,17,11.91,Weekend,5116.9
+Industrial,18150,87,26,28.24,Weekend,4656.29
+Industrial,42767,40,28,17.94,Weekend,5508.64
+Residential,35650,98,1,25.43,Weekday,4205.35
+Residential,40486,27,28,15.73,Weekend,4275.65
+Commercial,44253,30,5,24.14,Weekend,4491.97
+Commercial,27582,24,24,29.99,Weekday,3999.14
+Commercial,21162,10,37,27.83,Weekend,3758.95
+Industrial,27708,47,24,11.26,Weekend,4779.08
+Residential,44539,8,44,26.62,Weekday,4603.83
+Commercial,49354,84,38,15.03,Weekday,6042.56
+Commercial,19926,27,17,19.33,Weekend,3509.64
+Residential,28070,10,33,12.93,Weekend,3598.87
+Commercial,22797,7,45,21.26,Weekend,4003.54
+Residential,45412,11,21,31.45,Weekday,4193.36
+Industrial,29331,8,1,31.71,Weekend,3908.0
+Residential,2743,3,42,20.36,Weekend,2405.34
+Commercial,45772,50,45,25.48,Weekday,5611.18
+Residential,39327,70,36,19.4,Weekend,4789.37
+Residential,11546,91,47,17.42,Weekend,3840.2
+Residential,39390,52,48,20.37,Weekday,4897.66
+Residential,38141,90,11,19.64,Weekend,4428.85
+Residential,36642,36,32,11.17,Weekend,4276.25
+Residential,5444,97,42,15.11,Weekend,3506.64
+Industrial,40885,6,31,20.3,Weekday,5172.77
+Residential,11985,41,17,33.48,Weekday,2731.87
+Residential,16582,33,28,11.13,Weekend,3163.44
+Residential,19422,70,49,10.4,Weekday,4149.12
+Residential,24210,28,14,22.05,Weekend,3160.27
+Residential,34256,30,44,29.64,Weekday,4294.59
+Commercial,29873,88,48,21.55,Weekday,5275.92
+Residential,7924,63,36,34.71,Weekend,3072.63
+Industrial,7070,44,18,14.78,Weekday,3629.61
+Industrial,36496,5,38,24.0,Weekend,5014.79
+Residential,9655,87,13,22.86,Weekend,2998.47
+Commercial,40452,52,28,23.83,Weekend,4983.43
+Commercial,40542,31,19,12.01,Weekday,4707.07
+Residential,11498,70,12,28.57,Weekend,2872.05
+Residential,1306,8,43,30.67,Weekend,2351.97
+Industrial,46494,42,2,34.55,Weekday,5161.94
+Residential,21408,73,20,27.59,Weekend,3562.43
+Residential,1636,19,49,15.18,Weekday,2725.89
+Commercial,48114,30,36,15.93,Weekend,5346.06
+Industrial,28465,71,46,21.45,Weekend,5446.0
+Industrial,18350,86,18,12.38,Weekend,4575.58
+Industrial,13088,41,30,33.5,Weekday,4046.88
+Commercial,11553,13,23,25.63,Weekend,3039.52
+Residential,38099,22,16,20.19,Weekend,3844.0
+Industrial,5908,49,49,32.4,Weekend,4103.39
+Industrial,17868,68,35,10.4,Weekend,4721.39
+Commercial,38141,99,23,14.54,Weekday,5334.33
+Commercial,26715,27,46,13.11,Weekday,4510.2
+Industrial,14218,74,8,30.22,Weekday,4009.8
+Commercial,9405,31,30,11.04,Weekday,3375.06
+Residential,26158,98,47,31.83,Weekday,4618.77
+Industrial,38856,36,8,14.75,Weekday,4939.07
+Residential,17982,4,37,13.29,Weekend,3112.64
+Commercial,40337,9,27,16.96,Weekday,4612.04
+Residential,28461,20,44,14.43,Weekday,3980.9
+Industrial,36836,4,2,19.49,Weekday,4374.35
+Industrial,20517,20,21,29.01,Weekday,4050.8
+Industrial,47042,53,32,34.36,Weekday,5900.32
+Residential,31487,60,3,17.97,Weekday,3694.52
+Commercial,33413,75,17,21.6,Weekend,4652.65
+Industrial,19750,80,39,20.28,Weekday,5016.11
+Industrial,9684,13,9,33.28,Weekend,3127.82
+Industrial,21087,92,42,26.97,Weekend,5179.49
+Commercial,19940,64,22,25.01,Weekday,4001.96
+Residential,31178,26,11,27.46,Weekday,3451.58
+Residential,33642,66,11,16.89,Weekend,3977.63
+Residential,34160,66,2,25.46,Weekday,3830.68
+Industrial,2091,99,39,24.85,Weekend,4250.29
+Industrial,30211,21,3,28.58,Weekend,4137.66
+Commercial,1161,81,11,15.45,Weekend,3010.81
+Residential,37943,50,23,21.73,Weekend,4248.49
+Commercial,1558,27,29,16.86,Weekend,2843.6
+Industrial,2145,56,12,11.77,Weekend,3348.39
+Residential,42414,72,24,29.62,Weekday,4722.59
diff --git a/Вариант 2/train_energy_data.csv b/Вариант 2/train_energy_data.csv
new file mode 100644
index 0000000..c4fb969
--- /dev/null
+++ b/Вариант 2/train_energy_data.csv	
@@ -0,0 +1,1001 @@
+Building Type,Square Footage,Number of Occupants,Appliances Used,Average Temperature,Day of Week,Energy Consumption
+Residential,7063,76,10,29.84,Weekday,2713.95
+Commercial,44372,66,45,16.72,Weekday,5744.99
+Industrial,19255,37,17,14.3,Weekend,4101.24
+Residential,13265,14,41,32.82,Weekday,3009.14
+Commercial,13375,26,18,11.92,Weekday,3279.17
+Commercial,37377,26,32,16.24,Weekend,4687.67
+Industrial,38638,92,14,21.01,Weekend,5526.83
+Residential,34950,60,18,28.24,Weekday,4116.32
+Industrial,29741,99,44,13.08,Weekday,5841.65
+Residential,17467,42,36,28.84,Weekday,3419.13
+Commercial,27702,71,14,11.3,Weekend,4318.61
+Commercial,47265,4,44,25.38,Weekday,5206.34
+Residential,19139,97,34,17.35,Weekday,4070.21
+Residential,17820,11,8,17.8,Weekend,2571.98
+Residential,45868,97,19,22.45,Weekend,5031.14
+Commercial,9578,54,47,13.56,Weekend,3891.12
+Commercial,4310,34,44,24.62,Weekday,3362.42
+Industrial,38446,97,20,23.99,Weekend,5672.37
+Industrial,7460,32,23,27.87,Weekday,3563.67
+Residential,10880,19,48,22.67,Weekend,3080.67
+Commercial,37912,24,43,10.38,Weekday,4993.7
+Industrial,14699,83,40,32.92,Weekday,4750.37
+Industrial,18274,32,22,17.87,Weekday,4134.35
+Commercial,23898,55,16,26.95,Weekend,3930.14
+Residential,41498,39,27,18.67,Weekend,4411.53
+Industrial,41238,22,7,31.95,Weekday,4812.15
+Residential,35611,8,5,23.67,Weekend,3342.21
+Industrial,49653,63,12,29.54,Weekday,5754.96
+Residential,30259,35,14,15.39,Weekend,3566.01
+Residential,27072,12,30,32.89,Weekend,3409.14
+Commercial,13499,75,41,12.97,Weekday,4230.09
+Residential,46749,22,36,15.41,Weekend,4700.42
+Commercial,22931,24,34,24.93,Weekday,3991.89
+Industrial,29341,2,13,11.36,Weekend,4190.25
+Residential,3800,5,26,20.96,Weekday,2205.2
+Residential,49094,33,11,14.17,Weekday,4483.83
+Residential,10403,25,26,21.34,Weekday,2733.45
+Industrial,31919,77,20,22.84,Weekend,5151.75
+Residential,1841,31,13,15.09,Weekend,2086.62
+Residential,42188,64,43,24.21,Weekday,5038.34
+Industrial,20920,89,31,32.76,Weekend,4892.22
+Commercial,12093,58,35,10.34,Weekday,3882.94
+Residential,27845,85,21,29.36,Weekend,4015.46
+Residential,36832,20,30,31.23,Weekend,3985.45
+Industrial,45162,85,45,12.92,Weekday,6493.48
+Industrial,14105,73,26,14.95,Weekday,4430.49
+Industrial,34914,99,18,33.43,Weekend,5428.56
+Residential,16025,35,16,21.39,Weekday,2914.3
+Industrial,49043,26,48,28.64,Weekend,6028.93
+Residential,29898,63,13,18.75,Weekend,3791.16
+Residential,12615,85,45,11.64,Weekday,3872.54
+Commercial,3308,2,24,18.69,Weekday,2621.96
+Industrial,35031,3,13,13.09,Weekday,4526.12
+Residential,3211,78,10,28.31,Weekend,2499.0
+Residential,10496,21,17,31.54,Weekend,2417.11
+Industrial,49997,15,22,26.78,Weekday,5505.97
+Industrial,38697,17,35,34.03,Weekend,5134.71
+Industrial,36484,69,43,15.98,Weekend,5794.31
+Industrial,28587,24,14,23.97,Weekend,4329.5
+Industrial,28880,89,10,22.15,Weekend,4923.27
+Industrial,7559,27,44,10.08,Weekday,4027.56
+Commercial,6590,89,32,31.24,Weekday,3753.31
+Industrial,44528,78,22,15.51,Weekend,5868.84
+Commercial,6288,33,20,25.24,Weekday,2968.2
+Residential,9627,84,41,25.93,Weekend,3511.71
+Commercial,36868,10,22,15.14,Weekend,4307.72
+Commercial,6859,25,47,25.8,Weekend,3403.93
+Residential,35988,57,47,17.34,Weekend,4722.68
+Residential,18185,97,15,29.66,Weekday,3580.93
+Industrial,35633,44,19,11.91,Weekend,5042.08
+Industrial,45359,75,35,26.86,Weekday,6133.65
+Industrial,36335,6,33,17.4,Weekday,4999.76
+Residential,11844,2,22,20.11,Weekend,2451.63
+Industrial,12454,68,35,29.35,Weekend,4355.96
+Commercial,13581,73,7,20.74,Weekday,3495.35
+Industrial,27428,48,40,21.62,Weekend,5043.32
+Commercial,16004,23,47,10.9,Weekend,3915.71
+Commercial,48351,4,18,24.42,Weekday,4745.47
+Industrial,37880,34,11,11.92,Weekday,4944.4
+Industrial,47181,40,16,11.52,Weekday,5571.47
+Commercial,13587,68,13,25.69,Weekday,3540.91
+Industrial,14882,48,40,27.71,Weekday,4435.54
+Commercial,49038,43,25,14.5,Weekday,5359.38
+Residential,41470,77,3,32.58,Weekday,4290.61
+Commercial,20579,44,19,22.34,Weekday,3787.26
+Residential,25780,59,33,32.21,Weekday,3927.94
+Industrial,44909,4,4,22.06,Weekday,4805.17
+Residential,2259,22,45,24.88,Weekend,2608.53
+Residential,6654,21,24,15.0,Weekday,2497.72
+Commercial,13303,22,32,25.42,Weekday,3448.03
+Residential,26183,68,3,16.82,Weekend,3465.03
+Commercial,16434,24,24,17.95,Weekday,3501.94
+Industrial,4763,86,32,15.79,Weekday,4209.18
+Residential,29625,55,2,10.62,Weekend,3518.13
+Commercial,44361,30,39,22.49,Weekend,5185.62
+Commercial,560,68,3,33.63,Weekend,2599.86
+Residential,8745,28,30,17.72,Weekend,2728.63
+Commercial,4590,6,35,15.55,Weekday,2961.76
+Industrial,19842,15,11,11.29,Weekday,3855.63
+Commercial,40166,70,35,13.05,Weekend,5343.05
+Commercial,16905,88,26,17.36,Weekend,4158.47
+Commercial,10647,46,41,13.13,Weekday,3796.71
+Commercial,4970,21,17,29.93,Weekend,2648.85
+Residential,2692,83,13,23.67,Weekday,2656.26
+Industrial,981,3,12,28.85,Weekday,2724.82
+Industrial,6683,39,41,15.77,Weekday,4015.3
+Commercial,6776,84,29,17.2,Weekday,3722.8
+Residential,32153,75,24,33.11,Weekend,4172.08
+Commercial,19287,77,34,33.56,Weekend,4246.55
+Residential,1399,32,15,26.19,Weekday,2109.01
+Residential,18949,64,42,23.14,Weekend,3811.73
+Residential,22254,10,17,24.49,Weekday,2980.23
+Residential,33421,41,1,26.97,Weekday,3516.18
+Residential,25262,66,1,29.74,Weekday,3344.42
+Residential,40325,86,31,18.88,Weekend,4901.86
+Commercial,36897,30,32,20.36,Weekend,4683.04
+Industrial,46417,82,20,23.44,Weekday,5973.63
+Industrial,22515,60,30,13.61,Weekend,4757.71
+Commercial,17459,3,37,27.75,Weekday,3554.19
+Residential,39714,82,41,16.32,Weekday,5094.1
+Industrial,43501,83,31,26.67,Weekday,6041.7
+Residential,7787,73,35,19.29,Weekday,3272.91
+Commercial,16958,2,49,30.66,Weekday,3744.59
+Industrial,40211,75,21,28.17,Weekday,5589.71
+Industrial,6484,19,36,22.89,Weekend,3619.74
+Residential,41496,73,2,14.29,Weekend,4273.37
+Commercial,20672,46,10,18.4,Weekday,3651.6
+Residential,9101,15,4,12.87,Weekday,2170.7
+Commercial,17827,35,4,24.93,Weekday,3246.71
+Commercial,12061,40,4,14.57,Weekend,3010.22
+Commercial,11862,5,36,33.46,Weekend,3195.8
+Commercial,24863,50,40,25.81,Weekend,4414.12
+Commercial,9019,60,48,31.53,Weekday,3903.29
+Commercial,6224,15,39,28.75,Weekend,3097.45
+Commercial,44013,15,49,10.43,Weekend,5278.51
+Residential,30515,93,3,32.87,Weekday,3901.38
+Residential,49646,12,12,18.68,Weekday,4298.92
+Residential,48903,70,22,23.91,Weekday,5015.61
+Commercial,9517,82,46,13.2,Weekend,4149.86
+Commercial,29878,16,41,31.63,Weekend,4315.75
+Residential,18771,66,4,14.36,Weekend,3106.77
+Residential,34072,12,13,29.68,Weekend,3435.18
+Industrial,9095,5,34,29.59,Weekend,3536.81
+Industrial,26792,86,10,15.01,Weekday,4874.53
+Commercial,45580,68,4,33.33,Weekday,4922.36
+Residential,14525,58,24,27.49,Weekend,3148.79
+Industrial,26529,54,47,32.58,Weekend,5143.54
+Residential,19563,65,27,22.5,Weekend,3555.64
+Industrial,13525,21,25,32.25,Weekday,3775.01
+Residential,18086,21,8,17.75,Weekend,2685.54
+Industrial,31407,2,39,23.03,Weekday,4805.22
+Industrial,36454,72,40,10.71,Weekend,5789.16
+Commercial,35207,16,44,32.34,Weekday,4688.65
+Commercial,20395,42,27,15.63,Weekday,3951.62
+Residential,48382,95,1,31.98,Weekday,4779.18
+Commercial,12200,95,8,29.74,Weekday,3621.32
+Industrial,41131,12,14,13.25,Weekend,4890.31
+Industrial,21177,69,7,32.54,Weekday,4276.15
+Residential,11182,13,45,12.11,Weekend,3028.56
+Residential,3208,68,29,14.07,Weekend,2850.05
+Residential,2853,81,37,23.68,Weekend,3074.25
+Industrial,9496,85,21,28.01,Weekend,4104.73
+Commercial,14653,47,4,13.55,Weekend,3214.88
+Industrial,40829,52,4,32.3,Weekend,4979.96
+Industrial,34508,98,24,10.55,Weekday,5682.63
+Commercial,32396,23,43,28.56,Weekday,4616.98
+Industrial,40185,73,23,34.62,Weekday,5576.14
+Industrial,37765,92,35,25.8,Weekend,5879.25
+Commercial,24321,78,17,34.79,Weekday,4212.08
+Industrial,30366,21,28,28.96,Weekend,4643.49
+Residential,7991,13,18,21.04,Weekend,2284.33
+Residential,29041,10,10,21.71,Weekday,3193.51
+Industrial,21170,89,8,27.28,Weekday,4522.1
+Industrial,32098,4,13,27.18,Weekend,4269.02
+Commercial,23911,93,48,31.37,Weekday,4978.71
+Residential,17529,43,44,19.34,Weekday,3639.76
+Industrial,8507,36,28,19.36,Weekday,3798.54
+Commercial,36159,79,30,34.31,Weekday,5076.4
+Commercial,9002,99,41,19.66,Weekday,4211.82
+Residential,40415,8,48,12.44,Weekday,4548.56
+Commercial,13761,3,28,31.36,Weekday,3171.27
+Residential,14007,91,34,17.79,Weekend,3701.39
+Commercial,1468,21,10,33.58,Weekday,2365.5
+Industrial,13308,16,41,27.82,Weekday,4056.28
+Industrial,46148,30,39,33.84,Weekend,5718.21
+Industrial,30469,55,26,28.11,Weekday,5002.91
+Industrial,9442,25,31,15.3,Weekday,3815.61
+Commercial,28571,25,21,26.8,Weekend,3964.54
+Commercial,26672,8,1,19.76,Weekday,3384.78
+Commercial,25291,83,17,17.14,Weekend,4348.86
+Industrial,22616,31,47,17.91,Weekday,4841.23
+Industrial,33726,70,34,10.13,Weekday,5565.67
+Residential,2896,51,27,11.96,Weekday,2684.98
+Industrial,27876,52,26,10.39,Weekend,4881.83
+Industrial,43374,64,15,11.73,Weekday,5600.03
+Residential,32487,95,49,16.69,Weekday,5020.9
+Commercial,44085,10,30,10.68,Weekday,4900.84
+Commercial,13721,8,10,20.28,Weekend,2864.63
+Industrial,36559,73,28,14.86,Weekend,5543.64
+Industrial,21453,58,6,19.1,Weekday,4227.17
+Residential,49810,65,33,24.63,Weekday,5227.37
+Industrial,27245,90,44,33.51,Weekend,5474.72
+Industrial,7283,88,44,19.54,Weekday,4576.47
+Industrial,47827,44,23,30.84,Weekend,5637.13
+Industrial,16922,57,19,20.07,Weekend,4195.75
+Residential,29206,41,31,24.01,Weekday,3920.23
+Commercial,6616,1,21,17.83,Weekday,2721.67
+Residential,22010,83,21,21.77,Weekend,3741.64
+Commercial,3593,88,41,22.27,Weekend,3768.3
+Industrial,15874,89,14,28.98,Weekend,4318.82
+Residential,10416,33,14,23.74,Weekend,2512.1
+Commercial,9205,93,49,16.33,Weekend,4288.58
+Industrial,29902,15,34,16.79,Weekend,4741.16
+Commercial,7714,51,7,13.32,Weekday,3019.12
+Industrial,24854,60,27,34.51,Weekend,4710.15
+Industrial,29269,35,19,18.63,Weekend,4600.3
+Industrial,45055,82,22,17.47,Weekend,5925.39
+Residential,17843,15,1,33.38,Weekday,2445.25
+Residential,15817,85,47,25.14,Weekend,3955.17
+Residential,34768,63,47,33.23,Weekday,4692.24
+Residential,8625,70,46,22.83,Weekend,3437.1
+Residential,5609,81,2,33.21,Weekday,2514.4
+Commercial,35942,87,11,30.09,Weekend,4736.66
+Industrial,45047,13,45,16.46,Weekday,5750.07
+Industrial,20316,42,24,16.95,Weekend,4331.04
+Commercial,34497,34,35,18.11,Weekend,4674.31
+Residential,15858,30,2,29.99,Weekend,2482.94
+Residential,4262,4,38,31.41,Weekday,2406.04
+Industrial,9146,42,44,30.72,Weekend,4103.69
+Industrial,16077,58,19,16.61,Weekday,4230.79
+Industrial,24660,32,5,23.67,Weekend,4034.64
+Commercial,48338,2,18,20.62,Weekend,4693.8
+Commercial,24577,86,24,24.53,Weekend,4446.22
+Residential,11803,32,1,27.57,Weekday,2342.32
+Commercial,16146,22,30,25.81,Weekend,3498.27
+Industrial,11671,63,8,34.74,Weekend,3699.87
+Residential,17775,90,1,21.06,Weekday,3253.47
+Commercial,23411,98,23,20.5,Weekday,4558.07
+Industrial,43380,31,33,34.69,Weekend,5465.54
+Residential,27088,43,34,20.96,Weekend,3859.59
+Commercial,34433,58,48,29.34,Weekday,5164.94
+Commercial,12798,80,4,11.3,Weekday,3513.41
+Residential,15843,36,37,22.53,Weekend,3279.51
+Commercial,9946,35,40,16.32,Weekend,3565.7
+Residential,22542,58,47,32.58,Weekend,3984.2
+Residential,15096,16,11,10.88,Weekend,2580.42
+Commercial,40911,97,46,28.14,Weekend,5794.83
+Commercial,44981,81,20,27.82,Weekday,5369.95
+Residential,33897,55,30,32.48,Weekday,4232.44
+Commercial,35935,55,37,23.7,Weekday,5018.25
+Industrial,1314,51,46,29.12,Weekend,3850.11
+Residential,8283,34,30,19.68,Weekend,2755.75
+Residential,2613,11,2,29.34,Weekday,1683.95
+Commercial,10957,86,49,22.06,Weekend,4277.54
+Industrial,35301,28,45,15.43,Weekend,5367.9
+Residential,23765,12,3,33.13,Weekday,2752.62
+Industrial,32085,38,31,16.0,Weekday,5074.24
+Industrial,4238,22,20,25.08,Weekday,3256.52
+Commercial,49090,70,17,20.78,Weekend,5390.61
+Industrial,7237,62,34,19.48,Weekday,4114.45
+Industrial,16376,7,7,27.65,Weekend,3390.53
+Industrial,45920,54,34,34.66,Weekend,5842.72
+Residential,34242,32,16,20.35,Weekend,3750.37
+Residential,7065,4,14,30.62,Weekend,2020.15
+Residential,47328,12,3,32.59,Weekend,3883.46
+Residential,3880,45,3,24.75,Weekday,2130.26
+Industrial,3868,75,24,33.81,Weekday,3804.34
+Industrial,626,2,33,23.52,Weekend,3093.71
+Residential,23551,93,37,26.83,Weekday,4263.39
+Commercial,605,16,36,16.95,Weekday,2875.48
+Industrial,12164,82,41,23.04,Weekday,4683.0
+Industrial,35698,91,29,32.83,Weekday,5660.75
+Residential,43694,45,46,20.91,Weekend,4950.15
+Commercial,21109,15,33,13.98,Weekday,3845.57
+Industrial,35867,76,12,10.29,Weekend,5241.91
+Industrial,10861,14,36,18.67,Weekend,3809.69
+Industrial,18271,23,28,23.38,Weekday,4136.67
+Industrial,18174,68,14,21.53,Weekend,4261.05
+Industrial,47557,98,30,24.87,Weekday,6383.5
+Residential,26010,7,36,31.06,Weekend,3435.21
+Industrial,5799,37,23,34.76,Weekend,3446.15
+Commercial,26111,26,49,15.85,Weekday,4516.31
+Residential,22276,71,22,11.22,Weekday,3757.72
+Residential,49374,13,28,29.4,Weekday,4561.71
+Industrial,37930,36,19,25.7,Weekend,5008.0
+Commercial,8573,86,19,27.88,Weekend,3529.25
+Industrial,1507,58,17,17.56,Weekend,3407.54
+Industrial,23736,47,12,17.29,Weekday,4360.36
+Commercial,11316,60,47,29.42,Weekend,3958.7
+Industrial,24252,36,45,22.91,Weekday,4908.05
+Commercial,7424,25,47,14.83,Weekday,3537.04
+Industrial,19074,82,39,17.25,Weekday,5017.45
+Industrial,11116,64,17,30.65,Weekday,3932.56
+Industrial,41306,46,41,19.34,Weekday,5798.61
+Industrial,2242,9,37,11.08,Weekend,3386.72
+Commercial,34520,96,27,29.12,Weekend,5080.4
+Residential,2943,13,29,17.8,Weekday,2318.15
+Residential,45123,5,47,13.18,Weekday,4730.23
+Industrial,34657,21,47,20.15,Weekday,5332.1
+Residential,42255,78,47,27.14,Weekday,5247.04
+Commercial,12701,64,45,13.45,Weekday,4157.81
+Industrial,46507,41,30,22.6,Weekday,5772.35
+Industrial,6650,87,33,19.31,Weekend,4265.97
+Commercial,29421,16,21,34.73,Weekday,3927.4
+Residential,47563,41,8,18.18,Weekend,4357.27
+Commercial,33782,71,40,29.61,Weekend,5051.07
+Residential,42474,62,45,18.51,Weekday,5101.13
+Industrial,27416,25,37,15.11,Weekend,4785.27
+Industrial,22091,44,30,11.25,Weekend,4588.3
+Industrial,27920,28,1,20.84,Weekday,4141.78
+Commercial,2969,92,32,25.47,Weekday,3631.08
+Commercial,43506,12,34,11.1,Weekday,4969.79
+Industrial,23361,26,26,29.05,Weekend,4302.81
+Commercial,28452,10,43,26.04,Weekend,4252.4
+Industrial,47914,47,12,33.71,Weekend,5437.17
+Residential,1226,58,3,22.79,Weekday,2137.35
+Industrial,9166,15,3,11.8,Weekend,3109.29
+Commercial,9283,41,40,12.19,Weekday,3663.21
+Industrial,7140,65,11,13.97,Weekday,3707.17
+Commercial,36721,66,46,15.58,Weekend,5338.17
+Commercial,22092,4,1,12.19,Weekend,3103.65
+Commercial,22026,70,12,21.58,Weekend,3933.39
+Commercial,17161,46,27,17.59,Weekday,3820.1
+Industrial,16741,19,27,23.89,Weekend,3947.61
+Commercial,24150,46,21,28.41,Weekend,3945.47
+Industrial,35388,13,49,15.09,Weekday,5353.94
+Commercial,6599,27,28,12.39,Weekend,3097.98
+Commercial,36322,8,25,27.52,Weekend,4258.49
+Industrial,7319,21,37,31.57,Weekday,3708.08
+Industrial,38501,84,3,32.04,Weekday,5214.85
+Residential,41589,50,34,33.2,Weekday,4643.44
+Commercial,45850,49,44,24.36,Weekday,5590.7
+Residential,23390,50,23,23.8,Weekend,3510.52
+Commercial,18273,37,38,33.06,Weekday,3928.35
+Residential,26885,94,23,16.63,Weekend,4161.08
+Industrial,33839,64,11,27.67,Weekday,4963.62
+Industrial,42373,18,38,27.13,Weekend,5422.98
+Industrial,47682,20,37,26.45,Weekend,5691.84
+Industrial,48820,51,35,30.75,Weekday,6047.23
+Industrial,32732,60,19,31.74,Weekday,5007.9
+Residential,49969,32,14,26.06,Weekend,4468.13
+Residential,37009,79,18,20.41,Weekday,4448.42
+Residential,33056,3,11,32.92,Weekend,3238.18
+Commercial,3483,79,5,10.37,Weekend,3012.32
+Commercial,13357,83,31,10.63,Weekday,4114.7
+Commercial,25326,33,41,21.22,Weekend,4310.2
+Commercial,47178,27,16,24.14,Weekend,4828.19
+Commercial,31695,99,35,28.5,Weekend,5132.23
+Industrial,32321,58,45,32.79,Weekend,5432.11
+Commercial,4994,79,38,29.59,Weekday,3701.73
+Industrial,32390,37,7,25.09,Weekend,4504.03
+Industrial,31178,68,8,32.25,Weekday,4787.64
+Residential,39563,16,38,28.7,Weekday,4304.63
+Commercial,11041,68,41,20.88,Weekend,3947.63
+Residential,22859,24,22,32.87,Weekday,3208.61
+Residential,41309,55,45,15.4,Weekday,4988.44
+Residential,18655,13,11,23.22,Weekday,2716.66
+Residential,9212,77,37,25.66,Weekday,3392.31
+Residential,21195,18,21,16.16,Weekend,3078.96
+Industrial,35332,23,29,25.76,Weekday,4997.78
+Residential,38641,9,3,19.52,Weekend,3484.46
+Commercial,25074,41,18,26.37,Weekend,3891.86
+Commercial,9077,26,12,18.34,Weekend,2862.17
+Residential,12222,80,35,20.16,Weekend,3510.28
+Industrial,15849,37,15,18.92,Weekend,3867.85
+Residential,19629,21,39,12.37,Weekday,3459.59
+Industrial,48424,49,28,11.94,Weekday,5961.49
+Industrial,12682,62,32,34.54,Weekday,4271.39
+Residential,26431,91,14,25.79,Weekday,3932.59
+Industrial,6126,49,13,18.44,Weekday,3514.09
+Commercial,27411,41,13,13.43,Weekday,4023.41
+Commercial,20475,79,28,13.9,Weekday,4354.25
+Industrial,37708,91,23,29.54,Weekend,5607.7
+Residential,46786,17,49,29.5,Weekday,4891.8
+Industrial,29787,77,38,30.67,Weekday,5416.01
+Residential,42286,50,35,11.13,Weekday,4808.64
+Commercial,38404,21,30,24.39,Weekend,4608.27
+Residential,36272,93,6,32.04,Weekday,4253.4
+Commercial,36296,35,40,32.11,Weekday,4854.25
+Commercial,27458,99,6,23.09,Weekend,4367.45
+Residential,13484,42,23,25.92,Weekend,2924.6
+Industrial,14129,50,29,18.54,Weekend,4193.77
+Commercial,11206,72,24,14.25,Weekend,3689.04
+Commercial,47266,14,39,14.13,Weekend,5212.64
+Commercial,18278,7,44,32.44,Weekend,3701.68
+Commercial,45641,41,21,15.87,Weekday,5082.68
+Industrial,5471,24,34,28.34,Weekday,3601.86
+Industrial,24942,35,38,20.76,Weekend,4753.3
+Residential,8064,90,12,20.83,Weekday,2989.05
+Residential,37480,45,20,20.29,Weekday,4172.54
+Commercial,18797,94,25,20.37,Weekend,4278.0
+Commercial,24589,48,7,33.17,Weekend,3683.59
+Commercial,15782,39,18,16.16,Weekday,3508.32
+Residential,46243,58,3,12.25,Weekend,4390.91
+Residential,27806,28,21,25.4,Weekday,3513.31
+Residential,22973,75,45,17.0,Weekend,4213.66
+Industrial,7811,57,39,21.27,Weekday,4184.21
+Commercial,9630,25,11,27.42,Weekday,2864.42
+Residential,45465,3,25,10.42,Weekend,4251.16
+Residential,21107,70,3,24.76,Weekday,3241.57
+Commercial,36987,22,26,15.34,Weekday,4562.64
+Industrial,22919,55,15,29.27,Weekend,4349.61
+Residential,35384,21,30,18.62,Weekday,4036.12
+Residential,32389,91,31,21.16,Weekend,4543.67
+Residential,43582,92,4,18.9,Weekend,4584.62
+Industrial,2122,68,36,31.82,Weekday,3897.02
+Industrial,14169,52,5,18.39,Weekend,3736.5
+Residential,22374,14,37,22.23,Weekday,3437.53
+Residential,2490,91,8,25.03,Weekday,2619.36
+Industrial,47554,72,8,25.75,Weekend,5628.95
+Commercial,24374,89,31,33.73,Weekend,4560.05
+Commercial,11331,47,43,21.1,Weekend,3791.04
+Industrial,2060,69,41,12.68,Weekday,4099.62
+Commercial,47537,34,17,16.6,Weekday,5023.85
+Residential,29577,82,28,13.04,Weekday,4343.66
+Commercial,39999,24,16,17.95,Weekday,4520.19
+Commercial,22066,98,40,25.77,Weekday,4804.46
+Industrial,22451,93,30,20.4,Weekday,5100.56
+Industrial,35136,87,42,30.03,Weekday,5866.63
+Residential,39065,75,24,10.17,Weekday,4682.41
+Commercial,46393,19,43,15.6,Weekday,5341.65
+Commercial,42449,48,20,14.3,Weekend,4930.94
+Industrial,19046,46,29,28.51,Weekday,4399.73
+Commercial,34219,92,49,27.39,Weekend,5473.98
+Residential,48780,1,16,10.14,Weekend,4218.29
+Residential,1026,90,49,11.14,Weekend,3375.62
+Commercial,21952,22,34,25.98,Weekend,3867.72
+Commercial,25451,60,9,28.7,Weekend,3909.07
+Commercial,44379,15,28,15.33,Weekend,4852.28
+Industrial,40863,47,1,17.61,Weekend,4945.09
+Residential,21889,44,18,18.87,Weekday,3350.09
+Commercial,49648,49,12,30.18,Weekend,5061.5
+Commercial,26643,49,12,25.27,Weekday,3985.8
+Residential,8473,9,22,12.1,Weekday,2443.16
+Industrial,32588,75,10,13.13,Weekend,5013.75
+Industrial,27089,69,25,29.33,Weekday,4947.78
+Residential,34036,35,43,16.24,Weekend,4330.61
+Industrial,17834,66,19,23.56,Weekday,4363.89
+Residential,35060,39,45,26.93,Weekend,4408.36
+Residential,3842,45,12,34.26,Weekday,2260.82
+Residential,25078,95,9,24.24,Weekend,3762.71
+Commercial,2425,70,8,34.79,Weekend,2807.29
+Commercial,3930,1,28,30.59,Weekend,2613.53
+Residential,31086,52,32,13.34,Weekday,4197.59
+Residential,31610,15,36,14.78,Weekday,3926.61
+Residential,45141,36,46,31.53,Weekend,4879.42
+Industrial,16085,95,45,23.87,Weekend,5034.91
+Commercial,43034,23,12,14.2,Weekend,4550.69
+Residential,49189,51,22,24.03,Weekday,4839.29
+Commercial,13850,45,26,20.98,Weekend,3557.61
+Industrial,13095,76,41,29.41,Weekday,4637.71
+Commercial,22020,43,4,28.28,Weekday,3519.59
+Residential,14237,45,28,28.96,Weekday,3127.07
+Commercial,7583,16,9,30.84,Weekend,2564.94
+Industrial,12503,59,3,12.89,Weekend,3710.69
+Residential,27924,85,7,17.39,Weekend,3799.24
+Commercial,13151,58,19,26.56,Weekday,3534.74
+Commercial,2250,11,4,22.11,Weekday,2241.96
+Industrial,3217,11,8,23.83,Weekend,2811.68
+Commercial,27227,89,23,15.01,Weekday,4686.28
+Residential,30356,25,30,27.82,Weekend,3728.69
+Commercial,21695,53,29,11.12,Weekday,4189.13
+Residential,4256,44,9,21.22,Weekday,2276.7
+Residential,25057,21,29,24.97,Weekend,3417.99
+Commercial,625,9,18,19.56,Weekday,2433.47
+Residential,28356,32,2,15.25,Weekend,3201.55
+Residential,14788,38,9,22.75,Weekday,2735.63
+Commercial,45686,12,46,22.81,Weekday,5260.26
+Commercial,42252,19,33,11.34,Weekend,4905.91
+Industrial,42824,90,34,27.26,Weekend,6084.9
+Industrial,32075,8,38,18.84,Weekend,4849.54
+Commercial,47819,81,5,24.47,Weekday,5228.59
+Industrial,14260,74,8,27.33,Weekday,4026.33
+Industrial,36944,57,18,29.65,Weekday,5178.97
+Commercial,15729,36,17,20.37,Weekend,3384.58
+Residential,12754,89,48,30.51,Weekday,3885.13
+Residential,39610,91,40,27.6,Weekday,5102.49
+Commercial,39059,57,47,28.74,Weekend,5319.27
+Commercial,21081,20,42,25.28,Weekend,3967.63
+Commercial,17274,97,46,15.79,Weekday,4724.76
+Commercial,11289,25,8,26.63,Weekend,2841.31
+Residential,35557,99,27,31.43,Weekend,4650.7
+Commercial,35900,49,19,31.45,Weekend,4507.73
+Industrial,2856,12,19,27.05,Weekday,3057.53
+Residential,48023,42,46,17.82,Weekend,5152.04
+Commercial,4732,52,39,28.23,Weekend,3395.45
+Residential,34095,29,21,29.94,Weekend,3765.06
+Residential,10725,28,22,28.11,Weekday,2665.72
+Commercial,25057,86,32,30.61,Weekday,4649.81
+Residential,5290,61,11,33.75,Weekday,2475.76
+Industrial,2480,30,9,24.72,Weekday,3030.38
+Industrial,19596,23,35,19.8,Weekend,4310.82
+Commercial,2921,50,5,10.33,Weekday,2744.38
+Residential,29125,57,7,16.94,Weekday,3631.54
+Commercial,20076,33,42,34.22,Weekend,4002.72
+Industrial,2836,46,46,13.74,Weekend,3953.1
+Residential,10484,81,48,13.6,Weekday,3776.21
+Commercial,24570,17,11,11.75,Weekday,3609.75
+Residential,4633,98,2,15.1,Weekday,2726.13
+Industrial,47304,98,11,12.3,Weekday,6053.68
+Commercial,36562,99,11,30.78,Weekend,4884.18
+Industrial,19350,38,13,28.33,Weekend,3965.87
+Residential,1739,61,26,30.51,Weekday,2614.4
+Residential,32390,84,15,27.44,Weekday,4172.29
+Industrial,45504,24,32,24.99,Weekday,5580.25
+Residential,48790,58,38,28.79,Weekday,5185.54
+Residential,37834,12,10,32.88,Weekend,3547.29
+Residential,23650,82,29,20.36,Weekday,4030.72
+Residential,46058,88,33,11.53,Weekday,5335.23
+Commercial,9211,33,28,10.16,Weekend,3299.75
+Industrial,21672,7,34,28.05,Weekday,4243.36
+Industrial,39943,74,5,31.83,Weekday,5228.01
+Commercial,1456,74,38,15.66,Weekend,3494.52
+Commercial,20559,21,8,26.77,Weekday,3314.12
+Residential,42960,78,33,19.33,Weekday,5041.34
+Residential,42063,77,9,17.72,Weekend,4464.55
+Residential,49488,99,41,17.6,Weekday,5746.4
+Commercial,41657,76,38,32.07,Weekday,5492.51
+Residential,2024,25,4,28.84,Weekend,1787.0
+Industrial,10268,35,20,33.18,Weekday,3647.48
+Industrial,11224,25,49,15.38,Weekend,4214.3
+Commercial,40192,23,23,30.0,Weekend,4549.59
+Industrial,25791,84,45,14.96,Weekend,5454.74
+Residential,22292,25,43,24.55,Weekend,3601.83
+Industrial,1114,14,40,12.59,Weekend,3432.74
+Industrial,42707,4,44,16.78,Weekend,5471.43
+Commercial,23540,80,10,15.5,Weekday,4149.49
+Commercial,16938,36,13,15.51,Weekday,3439.34
+Residential,17934,70,44,27.56,Weekday,3888.89
+Industrial,37719,16,20,23.97,Weekday,4876.08
+Industrial,4533,20,46,12.33,Weekday,3835.0
+Commercial,34061,43,14,31.26,Weekday,4306.74
+Residential,47555,34,29,24.16,Weekday,4726.97
+Residential,34572,14,1,12.8,Weekend,3324.61
+Residential,29552,42,38,25.04,Weekday,4082.39
+Commercial,9874,5,19,20.65,Weekday,2870.44
+Industrial,19597,89,6,23.69,Weekend,4371.38
+Commercial,44665,36,2,13.93,Weekend,4563.58
+Residential,18811,19,25,14.33,Weekday,3108.88
+Residential,5795,17,42,33.72,Weekend,2631.16
+Commercial,29035,5,34,19.77,Weekday,4132.91
+Commercial,29365,18,24,12.93,Weekend,4063.62
+Commercial,2606,30,2,29.63,Weekday,2372.16
+Commercial,1322,81,2,15.38,Weekend,2839.18
+Residential,20644,29,7,21.54,Weekday,2904.51
+Commercial,38506,82,26,17.62,Weekend,5177.22
+Residential,18524,78,4,33.5,Weekday,3168.71
+Industrial,35122,56,41,34.55,Weekday,5513.36
+Industrial,20549,68,4,24.59,Weekend,4164.5
+Commercial,21439,24,42,15.79,Weekend,4073.01
+Industrial,38073,87,22,34.18,Weekend,5542.74
+Commercial,27205,44,14,11.38,Weekend,4023.35
+Residential,33475,82,13,32.62,Weekend,4090.65
+Residential,10347,94,8,17.91,Weekend,3027.78
+Commercial,10896,71,17,16.99,Weekend,3509.87
+Industrial,32909,47,29,33.37,Weekend,5028.58
+Residential,36340,42,15,31.27,Weekend,3880.66
+Residential,30129,29,33,23.73,Weekend,3837.78
+Industrial,16869,10,13,20.89,Weekend,3598.99
+Residential,20603,98,36,29.52,Weekend,4082.56
+Industrial,34580,47,13,25.41,Weekend,4831.96
+Commercial,30527,70,20,11.89,Weekday,4616.89
+Commercial,22236,65,5,34.67,Weekend,3688.46
+Industrial,9282,17,6,29.31,Weekday,3157.54
+Industrial,4297,7,32,15.67,Weekday,3396.51
+Residential,49345,22,18,11.87,Weekday,4537.9
+Industrial,28645,32,43,12.86,Weekday,5097.96
+Commercial,46148,5,20,29.6,Weekday,4659.4
+Commercial,29757,3,6,11.91,Weekend,3578.29
+Commercial,32765,26,9,10.52,Weekday,4075.67
+Industrial,24558,80,8,24.3,Weekend,4566.41
+Commercial,12842,60,39,25.63,Weekend,3893.95
+Industrial,40464,89,17,34.04,Weekday,5633.01
+Residential,43820,3,36,22.96,Weekday,4376.22
+Industrial,1025,28,48,24.41,Weekday,3719.19
+Commercial,33357,92,43,23.58,Weekend,5329.97
+Commercial,24357,6,29,21.36,Weekend,3751.05
+Industrial,46460,3,18,25.95,Weekend,5083.26
+Commercial,39504,86,46,12.07,Weekday,5744.86
+Residential,24010,87,36,19.67,Weekday,4242.17
+Commercial,49287,60,42,18.31,Weekday,5862.82
+Residential,41260,35,38,29.66,Weekend,4524.71
+Commercial,40983,66,47,12.96,Weekday,5634.35
+Residential,32847,34,35,19.28,Weekend,4085.95
+Commercial,28029,38,15,18.72,Weekend,3987.84
+Residential,46740,19,25,10.29,Weekend,4475.56
+Industrial,39724,44,38,14.79,Weekday,5662.27
+Industrial,33797,86,13,13.96,Weekday,5290.06
+Commercial,45238,72,47,20.95,Weekday,5867.13
+Commercial,5584,21,9,29.51,Weekend,2521.63
+Commercial,46697,70,7,25.71,Weekday,5096.3
+Residential,47804,99,38,31.79,Weekend,5481.27
+Residential,23696,85,29,11.31,Weekend,4058.25
+Commercial,46219,49,41,29.52,Weekend,5473.33
+Industrial,42273,99,36,21.27,Weekend,6217.3
+Industrial,48017,1,8,32.46,Weekday,4958.57
+Industrial,16396,79,49,10.39,Weekend,5037.86
+Industrial,39841,47,7,25.25,Weekday,5025.81
+Residential,21109,78,36,10.33,Weekend,4003.79
+Commercial,4518,85,27,15.09,Weekend,3540.45
+Residential,25838,75,27,24.06,Weekday,4011.58
+Residential,36423,89,29,18.3,Weekday,4749.64
+Industrial,27223,86,44,30.86,Weekend,5446.83
+Industrial,31963,69,5,10.24,Weekday,4886.94
+Commercial,41860,28,34,23.64,Weekend,4934.8
+Commercial,47100,5,1,10.13,Weekend,4374.33
+Commercial,5148,72,18,26.98,Weekend,3202.5
+Industrial,23519,55,45,14.99,Weekday,5101.01
+Residential,1620,72,23,20.14,Weekend,2660.28
+Residential,2579,32,49,12.3,Weekend,2867.44
+Industrial,42374,36,28,29.67,Weekend,5390.35
+Residential,42740,60,22,15.42,Weekday,4649.88
+Industrial,43908,1,28,19.15,Weekday,5219.66
+Residential,40787,35,4,13.99,Weekday,3949.4
+Residential,39530,59,25,23.69,Weekday,4498.07
+Residential,10895,18,26,34.47,Weekday,2622.38
+Commercial,33562,61,41,34.63,Weekend,4934.94
+Residential,26321,89,43,13.9,Weekend,4496.55
+Commercial,41901,23,26,33.56,Weekend,4677.23
+Commercial,13213,79,30,26.73,Weekend,3916.99
+Residential,22230,16,25,29.13,Weekend,3125.85
+Residential,29102,31,41,32.35,Weekday,3973.35
+Residential,29692,13,45,26.11,Weekend,3884.07
+Residential,48979,61,1,24.54,Weekday,4506.27
+Commercial,36955,71,27,28.08,Weekend,4957.36
+Residential,13724,13,4,20.76,Weekday,2342.38
+Commercial,39209,67,24,21.48,Weekend,5003.05
+Industrial,34677,55,29,18.34,Weekend,5272.15
+Residential,29509,64,47,14.73,Weekend,4481.81
+Residential,47233,66,8,10.16,Weekday,4680.87
+Residential,38500,42,49,25.31,Weekend,4698.47
+Commercial,19756,61,47,33.38,Weekend,4370.89
+Residential,31159,35,42,17.84,Weekday,4208.74
+Commercial,17675,48,36,19.41,Weekday,4036.68
+Commercial,20123,13,19,24.92,Weekday,3441.55
+Residential,25361,77,26,24.09,Weekend,3937.59
+Residential,49256,82,35,30.7,Weekend,5329.29
+Residential,35339,85,27,32.39,Weekend,4494.99
+Commercial,12153,75,23,16.83,Weekend,3733.52
+Industrial,39698,85,36,17.99,Weekday,6014.94
+Industrial,18833,30,25,22.66,Weekday,4178.36
+Residential,21081,5,25,26.29,Weekend,2972.61
+Industrial,15061,43,21,26.86,Weekday,4018.77
+Industrial,41631,13,6,29.32,Weekend,4684.93
+Industrial,19529,85,19,28.85,Weekday,4612.22
+Residential,33539,21,15,28.71,Weekday,3593.42
+Residential,28196,74,19,24.55,Weekend,3907.03
+Commercial,3420,61,49,12.65,Weekend,3697.77
+Commercial,2982,15,13,11.71,Weekend,2500.56
+Commercial,18881,54,6,19.05,Weekday,3558.78
+Commercial,32623,65,29,27.67,Weekday,4772.8
+Commercial,47214,48,20,22.94,Weekend,5126.0
+Commercial,27237,39,41,15.33,Weekend,4495.21
+Commercial,27288,32,42,11.26,Weekday,4518.12
+Residential,30257,72,10,33.34,Weekday,3816.15
+Commercial,49653,89,37,26.33,Weekend,5980.98
+Industrial,26349,88,46,10.79,Weekend,5563.48
+Residential,37629,17,14,21.58,Weekend,3723.56
+Commercial,41696,98,43,33.1,Weekday,5809.3
+Industrial,14098,16,45,19.1,Weekend,4169.39
+Commercial,37435,26,35,21.64,Weekday,4773.54
+Commercial,8470,50,46,26.35,Weekday,3761.75
+Residential,25019,19,35,28.29,Weekday,3549.48
+Industrial,16501,63,45,10.2,Weekday,4854.04
+Residential,13909,62,21,18.76,Weekend,3141.67
+Residential,802,17,40,20.5,Weekday,2457.6
+Industrial,18523,79,45,22.39,Weekday,5054.19
+Industrial,10003,3,47,10.74,Weekend,3916.46
+Industrial,32684,99,38,34.99,Weekend,5709.26
+Industrial,46039,41,36,31.89,Weekday,5822.52
+Commercial,22063,92,8,33.62,Weekend,4015.07
+Commercial,20008,99,41,11.55,Weekend,4752.66
+Commercial,20433,78,30,29.41,Weekend,4254.59
+Industrial,40474,82,6,21.1,Weekend,5358.22
+Commercial,42787,40,48,23.38,Weekend,5382.43
+Residential,35696,70,47,10.4,Weekday,4922.82
+Industrial,20837,1,13,26.45,Weekday,3729.59
+Residential,43259,33,41,30.12,Weekday,4712.33
+Residential,14112,3,17,21.0,Weekend,2470.59
+Industrial,26022,24,21,29.31,Weekend,4314.56
+Commercial,26119,34,27,29.2,Weekend,4039.93
+Residential,40248,68,20,30.71,Weekday,4488.84
+Commercial,24395,60,5,15.03,Weekend,3844.62
+Industrial,13880,28,19,24.15,Weekend,3733.24
+Residential,39163,12,44,10.73,Weekday,4454.51
+Commercial,4472,15,34,21.32,Weekday,2996.99
+Industrial,20316,69,29,11.85,Weekday,4776.54
+Residential,38067,14,23,26.61,Weekend,3870.32
+Commercial,7138,60,27,34.94,Weekday,3372.2
+Commercial,25417,49,49,22.92,Weekday,4676.25
+Commercial,7040,4,20,22.82,Weekday,2727.89
+Industrial,7679,58,32,28.94,Weekday,4009.27
+Industrial,5257,74,12,12.58,Weekend,3679.96
+Industrial,6868,36,6,14.81,Weekend,3249.36
+Commercial,18940,44,33,23.53,Weekday,3979.37
+Industrial,22427,8,13,26.21,Weekday,3880.32
+Commercial,36744,71,40,30.41,Weekend,5195.15
+Commercial,35411,67,23,30.46,Weekday,4798.25
+Residential,43701,7,12,18.49,Weekday,3952.58
+Residential,44827,94,34,27.86,Weekday,5272.05
+Residential,18405,38,24,19.24,Weekday,3234.06
+Commercial,15523,52,1,27.67,Weekend,3177.82
+Commercial,46000,39,41,33.57,Weekend,5342.17
+Industrial,3520,59,13,21.37,Weekend,3419.14
+Residential,29926,32,43,27.22,Weekday,4090.2
+Residential,15359,36,13,19.89,Weekend,2788.49
+Commercial,39782,84,26,16.19,Weekend,5268.16
+Residential,6871,26,19,33.73,Weekday,2364.9
+Residential,29234,39,17,34.43,Weekend,3519.56
+Industrial,28435,9,22,21.98,Weekday,4391.84
+Commercial,38373,1,27,20.26,Weekday,4417.34
+Industrial,16208,73,40,15.37,Weekend,4763.57
+Industrial,13781,99,29,12.46,Weekend,4696.73
+Industrial,14635,19,27,12.28,Weekend,3900.33
+Industrial,26463,57,21,31.59,Weekend,4655.22
+Industrial,37938,98,17,11.14,Weekday,5711.22
+Commercial,35546,52,4,26.35,Weekend,4245.53
+Industrial,11289,11,26,18.42,Weekend,3602.33
+Commercial,48788,53,11,31.31,Weekend,5032.83
+Residential,5945,16,23,34.38,Weekday,2295.33
+Residential,38109,62,39,15.16,Weekend,4729.67
+Residential,17225,6,9,30.23,Weekday,2500.08
+Commercial,49270,28,38,23.08,Weekday,5438.11
+Residential,43020,32,34,11.75,Weekday,4642.27
+Residential,49638,29,15,20.88,Weekday,4517.48
+Commercial,27130,7,34,14.73,Weekend,4032.83
+Commercial,11583,48,17,23.29,Weekday,3332.69
+Residential,46663,15,28,11.5,Weekday,4535.64
+Industrial,1779,84,14,31.84,Weekday,3599.73
+Industrial,18588,7,17,19.39,Weekday,3792.47
+Commercial,22680,62,21,16.37,Weekend,4092.14
+Commercial,37698,4,21,11.07,Weekday,4339.55
+Commercial,37648,60,18,21.25,Weekend,4736.14
+Industrial,27990,85,31,23.93,Weekend,5249.84
+Industrial,49768,51,22,34.91,Weekend,5763.85
+Residential,22227,56,49,12.86,Weekend,4087.05
+Residential,15898,83,20,29.5,Weekend,3377.38
+Residential,8406,25,36,19.48,Weekday,2842.91
+Industrial,23779,92,6,18.96,Weekday,4684.16
+Industrial,2550,97,45,34.58,Weekday,4374.59
+Industrial,47719,92,7,32.82,Weekend,5781.83
+Commercial,36112,17,42,28.54,Weekday,4722.9
+Industrial,20456,50,6,33.53,Weekend,3975.14
+Industrial,46265,68,38,18.13,Weekday,6212.59
+Residential,46526,14,20,27.93,Weekday,4276.66
+Residential,2262,36,49,16.09,Weekend,2872.63
+Industrial,10681,71,47,19.75,Weekday,4635.28
+Residential,49438,93,17,13.31,Weekday,5225.35
+Commercial,1935,96,28,30.73,Weekend,3463.09
+Commercial,12036,61,47,26.75,Weekend,4018.05
+Residential,12612,65,5,28.73,Weekday,2786.93
+Residential,28288,52,19,31.72,Weekend,3655.82
+Commercial,12055,52,28,19.62,Weekday,3634.65
+Industrial,38758,7,41,10.05,Weekday,5327.65
+Industrial,32380,1,21,19.77,Weekday,4500.16
+Residential,26392,13,9,12.69,Weekend,3066.15
+Industrial,47956,8,22,34.4,Weekend,5245.78
+Commercial,45580,26,1,29.63,Weekday,4460.84
+Residential,6276,75,36,33.11,Weekend,3118.23
+Commercial,36340,33,20,16.2,Weekday,4516.02
+Industrial,49270,19,39,11.26,Weekend,5877.19
+Commercial,31790,46,31,27.09,Weekend,4534.07
+Industrial,33820,56,8,21.32,Weekday,4854.4
+Residential,3073,33,17,17.03,Weekday,2288.52
+Residential,38743,65,23,10.2,Weekday,4546.15
+Residential,14494,2,9,32.68,Weekday,2311.29
+Commercial,12418,73,13,17.02,Weekend,3525.79
+Commercial,10024,54,40,10.49,Weekday,3838.73
+Residential,24070,2,49,24.59,Weekend,3580.56
+Residential,47690,39,11,33.49,Weekday,4377.04
+Industrial,17014,91,8,26.94,Weekday,4336.02
+Industrial,26515,45,25,33.47,Weekday,4658.38
+Industrial,14359,69,40,19.94,Weekday,4658.27
+Residential,9320,40,44,21.16,Weekend,3140.2
+Commercial,26517,64,29,15.46,Weekend,4468.57
+Industrial,34223,98,6,29.74,Weekday,5212.46
+Commercial,39025,86,10,22.42,Weekday,4949.14
+Residential,5203,86,14,20.59,Weekend,2797.21
+Commercial,29986,93,9,19.04,Weekend,4514.09
+Commercial,25650,21,28,25.14,Weekday,3976.81
+Residential,26623,37,11,23.25,Weekend,3304.91
+Industrial,47117,83,45,11.05,Weekend,6530.6
+Industrial,21616,26,48,32.4,Weekday,4688.81
+Residential,32360,40,13,29.55,Weekend,3630.24
+Residential,11925,76,9,23.05,Weekend,2921.01
+Residential,34162,48,26,33.54,Weekday,4090.41
+Commercial,2469,92,41,13.26,Weekend,3797.14
+Residential,7222,44,39,34.91,Weekend,2906.57
+Residential,30720,3,15,10.22,Weekend,3314.91
+Residential,28673,31,15,11.19,Weekend,3487.69
+Commercial,16809,49,26,16.35,Weekday,3818.71
+Commercial,33281,6,26,17.8,Weekday,4205.07
+Industrial,25438,21,16,23.73,Weekday,4233.24
+Residential,48138,85,22,19.9,Weekday,5147.4
+Industrial,36446,92,43,16.56,Weekday,6069.51
+Residential,48408,2,15,27.49,Weekend,4102.94
+Industrial,28970,23,49,21.63,Weekend,5050.37
+Residential,45133,39,39,13.3,Weekday,4910.15
+Industrial,31311,60,27,17.31,Weekday,5168.99
+Residential,45528,32,32,28.7,Weekend,4592.9
+Industrial,30475,92,3,13.61,Weekend,4935.7
+Commercial,32919,92,8,27.03,Weekday,4640.78
+Industrial,32205,21,17,15.18,Weekday,4634.34
+Commercial,41100,8,49,20.38,Weekday,5063.09
+Commercial,25503,95,9,17.83,Weekday,4366.0
+Residential,36685,63,12,22.54,Weekend,4091.56
+Residential,8313,4,43,23.99,Weekday,2745.7
+Industrial,12675,2,47,11.93,Weekend,4034.11
+Commercial,16871,93,38,33.96,Weekday,4413.74
+Commercial,2639,21,25,22.54,Weekday,2779.25
+Industrial,47795,9,15,34.61,Weekday,5156.72
+Commercial,10150,31,15,33.79,Weekday,2998.57
+Industrial,3541,80,7,29.86,Weekend,3467.74
+Commercial,27160,10,19,21.96,Weekday,3778.19
+Industrial,26655,70,48,17.14,Weekend,5407.04
+Commercial,41624,87,20,32.78,Weekday,5237.3
+Industrial,23616,86,33,18.76,Weekend,5107.0
+Industrial,48864,68,11,29.85,Weekend,5693.96
+Residential,11883,54,48,14.77,Weekday,3570.31
+Commercial,24527,30,11,18.62,Weekend,3653.25
+Commercial,37811,25,22,19.14,Weekday,4534.85
+Residential,20041,80,49,22.83,Weekday,4217.88
+Residential,9610,66,1,20.77,Weekend,2556.63
+Industrial,42346,20,37,33.31,Weekend,5390.76
+Commercial,15548,98,45,18.19,Weekday,4616.46
+Industrial,11882,17,2,31.5,Weekend,3146.62
+Residential,38658,55,45,23.86,Weekday,4813.61
+Commercial,3219,27,13,17.65,Weekend,2602.69
+Industrial,37266,88,31,21.87,Weekend,5753.93
+Industrial,37301,38,35,20.22,Weekday,5393.95
+Residential,23893,18,37,31.43,Weekday,3507.49
+Industrial,49981,2,18,27.43,Weekend,5241.89
+Residential,14075,72,27,15.1,Weekend,3388.25
+Industrial,43323,15,4,33.67,Weekend,4727.81
+Residential,27116,51,16,22.81,Weekend,3571.76
+Industrial,45584,52,11,20.78,Weekend,5415.31
+Commercial,16350,14,38,18.39,Weekday,3675.54
+Residential,32383,88,47,28.41,Weekday,4847.12
+Industrial,10677,97,12,14.15,Weekend,4173.1
+Residential,6204,63,35,17.94,Weekday,3100.51
+Commercial,46667,39,1,30.88,Weekday,4638.97
+Commercial,24363,10,35,25.87,Weekday,3938.82
+Commercial,35201,67,9,21.08,Weekend,4504.67
+Industrial,23034,29,14,33.52,Weekday,4104.1
+Commercial,781,4,34,22.51,Weekday,2696.49
+Residential,31023,97,23,20.52,Weekend,4378.55
+Industrial,32466,29,26,15.49,Weekday,4905.84
+Residential,18809,12,27,29.52,Weekday,3002.83
+Residential,18264,49,34,22.35,Weekend,3471.43
+Commercial,25360,34,26,14.98,Weekday,4103.09
+Commercial,15163,11,40,11.06,Weekend,3612.86
+Residential,33481,90,26,23.91,Weekday,4524.49
+Commercial,44741,78,12,32.66,Weekday,5143.73
+Industrial,2698,22,37,19.58,Weekend,3496.99
+Residential,45069,37,17,20.4,Weekend,4361.43
+Commercial,37179,82,22,14.12,Weekend,5048.34
+Industrial,46663,55,1,22.99,Weekday,5338.19
+Residential,42344,5,30,32.29,Weekday,4155.74
+Commercial,33687,90,14,13.13,Weekend,4798.68
+Commercial,30948,96,37,20.06,Weekend,5147.11
+Commercial,27338,37,49,33.14,Weekday,4601.21
+Industrial,44986,5,41,29.42,Weekend,5472.2
+Residential,34875,63,24,33.71,Weekday,4235.19
+Industrial,3749,11,4,21.49,Weekend,2769.99
+Industrial,1119,11,29,19.96,Weekday,3196.17
+Residential,13176,2,47,11.58,Weekend,3060.91
+Commercial,45423,22,2,16.32,Weekend,4449.57
+Residential,7505,70,41,19.04,Weekday,3350.06
+Residential,46903,30,3,27.65,Weekday,4116.88
+Industrial,32784,10,11,19.06,Weekend,4363.9
+Residential,4925,30,36,18.33,Weekday,2724.62
+Industrial,49151,45,45,15.8,Weekend,6228.54
+Industrial,10826,21,28,17.38,Weekend,3724.42
+Commercial,40559,32,38,30.26,Weekend,4956.67
+Industrial,44564,58,21,26.4,Weekday,5646.19
+Residential,37614,11,12,14.63,Weekend,3657.53
+Residential,9446,43,47,20.27,Weekday,3290.94
+Residential,10722,79,40,25.83,Weekend,3496.97
+Residential,30045,42,5,31.62,Weekend,3364.13
+Industrial,41735,5,39,15.27,Weekend,5340.39
+Commercial,20346,3,1,28.49,Weekend,2924.87
+Residential,6395,10,32,22.76,Weekday,2495.93
+Commercial,30587,70,36,15.27,Weekend,4873.0
+Industrial,20522,88,42,31.58,Weekend,5088.21
+Industrial,40387,31,37,20.24,Weekday,5518.13
+Industrial,15751,9,13,29.07,Weekday,3542.19
+Commercial,18158,46,3,21.1,Weekday,3372.41
+Industrial,20194,60,45,22.82,Weekend,4895.61
+Residential,8304,98,46,18.82,Weekday,3771.12
+Residential,3724,16,10,26.17,Weekend,1915.37
+Commercial,10382,63,31,13.89,Weekend,3699.65
+Residential,29930,63,6,24.99,Weekend,3621.53
+Commercial,42399,58,35,13.49,Weekend,5332.5
+Residential,33630,96,32,32.11,Weekend,4620.93
+Commercial,26931,31,11,25.59,Weekday,3798.62
+Residential,4314,43,17,31.12,Weekend,2330.09
+Residential,27185,92,8,13.46,Weekday,3921.95
+Residential,23999,7,37,13.91,Weekday,3490.41
+Residential,16835,19,7,33.47,Weekday,2554.39
+Commercial,17587,72,4,14.08,Weekday,3658.93
+Residential,12439,20,33,13.16,Weekday,2966.16
+Residential,28132,49,9,20.74,Weekday,3522.9
+Commercial,46889,54,28,17.07,Weekend,5359.1
+Residential,49401,60,13,18.44,Weekday,4787.86
+Residential,26059,16,19,28.69,Weekday,3249.48
+Residential,5306,73,42,31.2,Weekday,3229.31
+Industrial,15652,17,3,20.72,Weekend,3408.98
+Residential,37880,72,28,16.73,Weekday,4640.37
+Commercial,25553,96,25,30.45,Weekday,4635.4
+Commercial,45741,21,33,28.63,Weekday,5063.9
+Residential,35701,19,17,29.9,Weekend,3665.55
+Residential,26789,57,4,16.0,Weekday,3459.45
+Residential,34749,42,46,33.41,Weekend,4410.42
+Industrial,8570,9,45,26.94,Weekday,3833.81
+Industrial,31773,60,19,26.02,Weekday,4988.57
+Residential,32654,74,48,29.06,Weekend,4687.39
+Industrial,48417,86,23,18.9,Weekend,6146.35
+Residential,44960,10,35,29.67,Weekday,4449.66
+Residential,14047,50,46,29.34,Weekday,3525.66
+Residential,26712,60,42,20.2,Weekday,4224.59
+Commercial,11872,66,37,28.67,Weekday,3900.25
+Industrial,11622,93,4,25.21,Weekend,3965.05
+Residential,9577,90,49,17.05,Weekend,3773.61
+Industrial,838,34,45,23.29,Weekday,3715.45
+Commercial,10016,85,8,24.61,Weekday,3437.74
+Residential,13487,39,32,30.67,Weekday,3101.02
+Residential,15399,24,2,24.28,Weekday,2478.55
+Commercial,16978,48,14,12.46,Weekend,3546.62
+Commercial,11746,1,18,24.76,Weekday,2883.5
+Residential,32913,21,8,13.77,Weekday,3496.82
+Industrial,49582,63,37,32.14,Weekend,6188.4
+Industrial,48802,85,9,14.35,Weekday,5948.34
+Commercial,42152,47,21,27.7,Weekend,4859.08
+Commercial,31980,2,33,30.43,Weekend,4126.84
+Industrial,22304,74,29,11.39,Weekend,4878.26
+Residential,49958,33,8,28.35,Weekend,4346.16
+Commercial,5353,20,18,32.04,Weekend,2667.44
+Industrial,44913,55,48,25.1,Weekend,6130.17
+Commercial,27157,67,38,21.08,Weekend,4682.44
+Commercial,15860,95,32,16.24,Weekday,4351.81
+Industrial,9823,32,5,17.67,Weekend,3322.79
+Residential,47324,30,32,25.49,Weekend,4678.75
+Residential,48785,84,6,20.48,Weekend,4796.83
+Commercial,16063,8,24,30.91,Weekend,3208.59
+Commercial,10593,32,19,23.55,Weekday,3161.9
+Industrial,37511,47,45,25.14,Weekday,5669.86
+Commercial,32696,19,37,24.33,Weekend,4443.15
+Commercial,25564,87,34,15.2,Weekday,4802.21
+Residential,36624,74,30,30.45,Weekend,4518.97
+Commercial,38284,36,8,12.75,Weekend,4370.44
+Commercial,28904,3,3,32.11,Weekday,3424.66
+Commercial,9700,20,35,27.42,Weekday,3297.91
+Industrial,26899,36,45,25.43,Weekend,4977.79
+Industrial,1422,44,24,16.79,Weekday,3457.17
+Commercial,39065,94,38,16.96,Weekend,5568.44
+Commercial,2328,69,16,12.36,Weekday,3114.61
+Residential,27484,47,48,28.79,Weekend,4160.27
+Residential,20415,35,5,31.08,Weekend,2815.34
+Commercial,33620,98,5,12.47,Weekday,4748.66
+Industrial,18102,21,35,31.59,Weekday,4207.14
+Industrial,9708,70,24,31.32,Weekend,4008.78
+Residential,16532,8,18,32.12,Weekend,2605.98
+Industrial,34516,75,15,23.3,Weekday,5209.3
+Commercial,36237,45,23,29.86,Weekday,4622.54
+Residential,31061,94,17,11.66,Weekend,4274.73
+Commercial,20991,83,29,31.85,Weekend,4300.28
+Commercial,21306,21,35,27.24,Weekday,3889.08
+Commercial,3889,21,40,27.75,Weekday,3115.68
+Residential,11802,15,34,10.25,Weekend,2868.84
+Residential,7509,30,18,10.51,Weekday,2532.88
+Commercial,39350,77,11,12.22,Weekday,4946.38
+Residential,44142,62,38,30.82,Weekday,4983.0
+Commercial,29759,94,14,28.28,Weekday,4616.56
+Industrial,21927,95,10,33.51,Weekday,4628.8
+Residential,47564,23,44,25.48,Weekend,4860.8
+Industrial,7145,67,37,21.23,Weekday,4211.11
+Residential,17153,9,1,34.06,Weekday,2347.37
+Commercial,12245,85,4,22.85,Weekend,3428.02
+Industrial,42398,44,22,30.32,Weekend,5348.31
+Commercial,8936,38,24,23.33,Weekday,3240.14
+Commercial,21788,21,9,21.56,Weekend,3371.6
+Commercial,20494,18,9,15.93,Weekend,3305.06
+Industrial,20478,80,41,20.69,Weekend,5040.46
+Residential,2470,65,46,20.94,Weekday,3138.81
+Residential,1045,60,46,24.71,Weekend,2948.69
+Industrial,29029,82,30,21.74,Weekend,5262.76
+Commercial,5099,39,6,16.24,Weekend,2683.73
+Commercial,36908,60,9,28.82,Weekday,4531.32
+Industrial,30281,46,12,20.46,Weekend,4611.73
+Residential,48775,54,27,17.78,Weekday,4979.87
+Industrial,48337,69,44,15.67,Weekday,6458.51
+Industrial,34893,86,28,16.18,Weekday,5633.76
+Commercial,5280,49,26,30.56,Weekend,3121.19
+Residential,15828,52,11,29.55,Weekend,2883.63
+Residential,26212,35,1,23.3,Weekend,3064.11
+Residential,14419,68,44,23.95,Weekend,3661.21
+Industrial,12194,7,22,14.67,Weekend,3546.34
+Commercial,39562,88,20,32.18,Weekday,5147.21
+Residential,8348,67,37,16.48,Weekend,3244.98
+Commercial,15813,57,11,31.4,Weekend,3423.63
diff --git a/Материалы к экзамену МИМИЗ 2026.zip b/Материалы к экзамену МИМИЗ 2026.zip
new file mode 100644
index 0000000..4148670
Binary files /dev/null and b/Материалы к экзамену МИМИЗ 2026.zip differ
diff --git a/Материалы к экзамену МИМИЗ 2026/.~lock.ЭКЗАМЕН 2026 МиМИЗ.docx# b/Материалы к экзамену МИМИЗ 2026/.~lock.ЭКЗАМЕН 2026 МиМИЗ.docx#
new file mode 100644
index 0000000..9d63eb7
--- /dev/null
+++ b/Материалы к экзамену МИМИЗ 2026/.~lock.ЭКЗАМЕН 2026 МиМИЗ.docx#	
@@ -0,0 +1 @@
+,nullptr,thinkbook,06.02.2026 14:15,/home/nullptr/.local/share/onlyoffice;
\ No newline at end of file
diff --git a/Материалы к экзамену МИМИЗ 2026/cars_dataset.zip b/Материалы к экзамену МИМИЗ 2026/cars_dataset.zip
new file mode 100644
index 0000000..3c086f7
Binary files /dev/null and b/Материалы к экзамену МИМИЗ 2026/cars_dataset.zip differ
diff --git a/Материалы к экзамену МИМИЗ 2026/cars_new.csv b/Материалы к экзамену МИМИЗ 2026/cars_new.csv
new file mode 100644
index 0000000..7c22ee8
--- /dev/null
+++ b/Материалы к экзамену МИМИЗ 2026/cars_new.csv	
@@ -0,0 +1,10 @@
+"Car ID,Brand,Year,Engine Size,Fuel Type,Transmission,Mileage,Condition,Price,Model ","","","","","","","","",""
+"1,Lada,2000,1.5 л,Бензин,Механика,150000,Отличное,70000,2110 ","","","","","","","","",""
+"2,Lada,2001,1.6 л,Бензин,Механика,180000,Хорошее,80000,2107 ","","","","","","","","",""
+"3,Lada,2002,1.5 л,Бензин,Механика,120000,Отличное,75000,2115 ","","","","","","","","",""
+"4,Lada,2008,1.6 л,Бензин,Механика/Автомат,130000,Хорошее,180000,Kalina ","","","","","","","","",""
+"5,Lada,2009,98 л.с., 1.6 л,Бензин,Механика/Автомат,100000,Хорошее,200000,Priora ","","","","","","","","",""
+"6,Lada,2011,1.6 л,Бензин,Механика/Автомат,90000,Отличное,250000,Granta ","","","","","","","","",""
+"7,Lada,2015,1.6 л, 106 л.с.,Бензин,Механика/Автомат,50000,Отличное,600000,Vesta ","","","","","","","","",""
+"8,Lada,2016,1.6 л / 1.8 л,Бензин,Механика/Автомат,45000,Отличное,650000,XRAY ","","","","","","","","",""
+"9,Lada,2012,1.6 л,Бензин,Механика/Автомат,80000,Хорошее,500000,Largus ","","","","","","","","",""
\ No newline at end of file
diff --git a/Материалы к экзамену МИМИЗ 2026/Диагноз.zip b/Материалы к экзамену МИМИЗ 2026/Диагноз.zip
new file mode 100644
index 0000000..e529380
Binary files /dev/null and b/Материалы к экзамену МИМИЗ 2026/Диагноз.zip differ
diff --git a/Материалы к экзамену МИМИЗ 2026/Успеваемость.zip b/Материалы к экзамену МИМИЗ 2026/Успеваемость.zip
new file mode 100644
index 0000000..5ef155e
Binary files /dev/null and b/Материалы к экзамену МИМИЗ 2026/Успеваемость.zip differ
diff --git a/Материалы к экзамену МИМИЗ 2026/ЭКЗАМЕН 2026 МиМИЗ.docx b/Материалы к экзамену МИМИЗ 2026/ЭКЗАМЕН 2026 МиМИЗ.docx
new file mode 100644
index 0000000..363b39b
Binary files /dev/null and b/Материалы к экзамену МИМИЗ 2026/ЭКЗАМЕН 2026 МиМИЗ.docx differ
diff --git a/Материалы к экзамену МИМИЗ 2026/Энергетика.zip b/Материалы к экзамену МИМИЗ 2026/Энергетика.zip
new file mode 100644
index 0000000..0190b0d
Binary files /dev/null and b/Материалы к экзамену МИМИЗ 2026/Энергетика.zip differ
diff --git a/ЭКЗАМЕН 2026 МиМИЗ.docx b/ЭКЗАМЕН 2026 МиМИЗ.docx
new file mode 100644
index 0000000..363b39b
Binary files /dev/null and b/ЭКЗАМЕН 2026 МиМИЗ.docx differ