diff --git a/15. Transfer Learning Build a Flower & Monkey Breed Classifier/3. Build a Monkey Breed Classifier with MobileNet using Transfer Learning.srt b/15. Transfer Learning Build a Flower & Monkey Breed Classifier/3. Build a Monkey Breed Classifier with MobileNet using Transfer Learning.srt
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+1
+00:00:00,490 --> 00:00:06,120
+Hi and welcome to Chapter 15 point to where we're going to build a monkey bridge justifier and basically
+
+2
+00:00:06,120 --> 00:00:11,780
+use the concept of transfer learning to get very high accuracy very quickly.
+
+3
+00:00:11,780 --> 00:00:13,550
+So let's take a look at this dataset.
+
+4
+00:00:13,560 --> 00:00:19,380
+This is where it was taken a stick and from a Kaggle project and basically it has about 80 images I
+
+5
+00:00:19,380 --> 00:00:22,240
+think of about 10 different types of monkeys each.
+
+6
+00:00:22,270 --> 00:00:24,290
+Is it a species of monkeys here.
+
+7
+00:00:25,140 --> 00:00:30,120
+And actually not 80 18:00 into the 152 images of each class.
+
+8
+00:00:30,120 --> 00:00:35,420
+And these are some sample images here and you'll notice that some are quite small and differently.
+
+9
+00:00:35,670 --> 00:00:40,740
+Different aspect ratios images of various sizes and quality as well.
+
+10
+00:00:40,770 --> 00:00:45,900
+So it's pretty much like what you might build as your own data sets effectively.
+
+11
+00:00:46,050 --> 00:00:53,310
+It's not well standardized not super neat not super high quality images just random images taken from
+
+12
+00:00:53,310 --> 00:00:54,080
+the Internet.
+
+13
+00:00:54,360 --> 00:01:01,280
+So now let's move onto Pitre notebook and begin creating this toxify OK.
+
+14
+00:01:01,290 --> 00:01:07,830
+So before we begin I hope you downloaded your resource file monkey Breede datasets and have placed it
+
+15
+00:01:07,950 --> 00:01:09,560
+inside of the territory here.
+
+16
+00:01:09,810 --> 00:01:15,660
+This is the translating directory so you have monkey abused monkey read our victory here with our training
+
+17
+00:01:15,690 --> 00:01:16,300
+images.
+
+18
+00:01:16,320 --> 00:01:17,960
+And each one is in full to here.
+
+19
+00:01:18,300 --> 00:01:22,980
+And let's go back and now hopefully that's set up correctly for you.
+
+20
+00:01:23,250 --> 00:01:28,070
+So now we can go back so we went back to full and open up here.
+
+21
+00:01:28,200 --> 00:01:33,630
+I already have it open right now so I'm going to go through this step by step so you understand exactly
+
+22
+00:01:33,630 --> 00:01:36,210
+how we can apply transfer linning.
+
+23
+00:01:36,210 --> 00:01:37,140
+All right.
+
+24
+00:01:37,140 --> 00:01:39,180
+So we're doing this with more on that.
+
+25
+00:01:39,210 --> 00:01:44,090
+And the reason I have to move on that for is because it actually trains quite quickly on C-p use.
+
+26
+00:01:44,430 --> 00:01:46,430
+So let's import ballots here.
+
+27
+00:01:47,010 --> 00:01:53,880
+And then let's define image rows and columns so we're going to use uniform square images of 2:24 by
+
+28
+00:01:53,880 --> 00:01:55,650
+
+29
+2:24 in size.
+
+30
+00:01:55,740 --> 00:01:58,620
+And this is how we basically define that.
+
+31
+00:01:58,650 --> 00:02:01,280
+When we loaded in we wanted to we had to be Image nets.
+
+32
+00:02:01,290 --> 00:02:03,970
+We've seen this before in our pretreated Model S..
+
+33
+00:02:04,020 --> 00:02:06,240
+However we haven't seen these parameters here.
+
+34
+00:02:06,240 --> 00:02:11,610
+I will quickly discuss this with you what we're going to do is that we're going to include the top and
+
+35
+00:02:11,610 --> 00:02:13,120
+said this at Falls.
+
+36
+00:02:13,380 --> 00:02:18,150
+What this means is that the fully connectedly as the last layers on the top of the model are basically
+
+37
+00:02:18,150 --> 00:02:19,820
+not included in the model.
+
+38
+00:02:20,130 --> 00:02:24,650
+So I'm going to show you what it looks pretty soon and in what shape is of center thing.
+
+39
+00:02:24,690 --> 00:02:30,420
+We just defined in what shape of this model to be this as why we define these parameters up here and
+
+40
+00:02:30,420 --> 00:02:33,050
+tree means color depth of tree RGV.
+
+41
+00:02:33,450 --> 00:02:36,990
+So this is a cool thing we can do with terrorist models upload.
+
+42
+00:02:37,320 --> 00:02:39,790
+So we have a model here called Mobile that.
+
+43
+00:02:39,890 --> 00:02:44,310
+So by addressing the layers within that Dudley as districting an array.
+
+44
+00:02:44,490 --> 00:02:50,450
+And we can basically loop through these areas here and actually turn it off manually.
+
+45
+00:02:50,690 --> 00:02:57,360
+The treatable parameter a flag that controls what it is should be trainable or not.
+
+46
+00:02:57,360 --> 00:03:02,980
+So what we do in this two lines of code here is that we basically setting all the layers in Mobile and
+
+47
+00:03:02,990 --> 00:03:06,450
+that's to be non-tradable basically fixed.
+
+48
+00:03:06,450 --> 00:03:09,160
+This is how we freeze DeWitt's right here.
+
+49
+00:03:09,690 --> 00:03:11,830
+So now we could actually print these layers here.
+
+50
+00:03:12,120 --> 00:03:16,250
+And basically what we are printing is the name of Leo number.
+
+51
+00:03:16,320 --> 00:03:22,740
+I go to the loop and we're going to print the flag Liautaud trainable what it's treatable.
+
+52
+00:03:22,770 --> 00:03:23,940
+True or false.
+
+53
+00:03:23,970 --> 00:03:29,970
+So you get to see all the layers now which is quite a bit a mobile that are set to false.
+
+54
+00:03:29,970 --> 00:03:31,840
+So this is pretty awesome already.
+
+55
+00:03:32,100 --> 00:03:35,290
+So I hope you're following simple code so far.
+
+56
+00:03:36,000 --> 00:03:42,090
+So now we're going to do is we're going to create a simple function here that basically adds to fully
+
+57
+00:03:42,090 --> 00:03:47,730
+connected head back onto the model we loaded here because remember we loaded it.
+
+58
+00:03:47,880 --> 00:03:49,180
+But we didn't get to the top.
+
+59
+00:03:49,200 --> 00:03:52,950
+So now we have a model without any top.
+
+60
+00:03:52,950 --> 00:03:55,070
+So no actually I want to show you something quickly.
+
+61
+00:03:55,290 --> 00:03:57,590
+What if we said this to true.
+
+62
+00:03:57,620 --> 00:03:58,090
+All right.
+
+63
+00:03:58,110 --> 00:03:59,220
+How would this model look.
+
+64
+00:03:59,250 --> 00:04:04,330
+So we saw we had 86 differently as the last one being removed.
+
+65
+00:04:04,650 --> 00:04:09,000
+So let's now print this and see what it looks
+
+66
+00:04:14,460 --> 00:04:16,170
+takes about five to 10 seconds to run.
+
+67
+00:04:16,170 --> 00:04:17,500
+There we go.
+
+68
+00:04:18,330 --> 00:04:18,990
+Oh good.
+
+69
+00:04:18,990 --> 00:04:24,770
+So before we head up to 86 now we see we have basically this is the top fully connected head.
+
+70
+00:04:25,200 --> 00:04:28,240
+This is what we left out before previously.
+
+71
+00:04:28,320 --> 00:04:29,510
+So now let's put it back in.
+
+72
+00:04:29,670 --> 00:04:30,690
+OK.
+
+73
+00:04:32,350 --> 00:04:35,090
+Because what we're going to do we're going to add a head here.
+
+74
+00:04:35,340 --> 00:04:38,660
+These are really as we we are going to add onto the model now.
+
+75
+00:04:38,710 --> 00:04:40,660
+So how do we use this function.
+
+76
+00:04:40,660 --> 00:04:43,720
+This function takes a number of classes.
+
+77
+00:04:43,790 --> 00:04:46,120
+I do our data sets.
+
+78
+00:04:46,420 --> 00:04:48,220
+We specify how many classes we want.
+
+79
+00:04:48,220 --> 00:04:54,370
+So for a monkey breed they is that it's going to be 10 and the bottom bottom model is basically this
+
+80
+00:04:54,420 --> 00:04:55,040
+model here.
+
+81
+00:04:55,080 --> 00:04:57,250
+Well not at all it's for us and it's.
+
+82
+00:04:57,580 --> 00:05:00,040
+So let's quickly see what this function does.
+
+83
+00:05:00,100 --> 00:05:02,200
+It takes a lot of muddled model here.
+
+84
+00:05:02,420 --> 00:05:07,310
+Guess's gets the output part of it here and we create basically the top model now.
+
+85
+00:05:07,660 --> 00:05:13,990
+So what we do now we have to find a top model like this here and now the top model we just simply basically
+
+86
+00:05:14,080 --> 00:05:15,450
+add these layers here.
+
+87
+00:05:15,670 --> 00:05:18,240
+It's a different way of Ardingly as no one cares.
+
+88
+00:05:18,580 --> 00:05:21,010
+So we added an adjusted to the top model here.
+
+89
+00:05:21,280 --> 00:05:28,600
+So for us we do global pooling Tuti we do a densely with a thousand and 28 nodes that again another
+
+90
+00:05:28,600 --> 00:05:33,590
+densely a here and then we do a final densely with soft Macksville attend classes we want.
+
+91
+00:05:33,790 --> 00:05:38,490
+And then what this does retune the model Top Model back.
+
+92
+00:05:38,600 --> 00:05:45,640
+OK so now what we do below is obviously we just load all Olias of need and defined number of classes
+
+93
+00:05:45,670 --> 00:05:51,640
+but now we can actually use our function here where we actually enter a number of classes.
+
+94
+00:05:51,730 --> 00:05:57,480
+We enter the mobile in that model that we created we loaded before and we add a top.
+
+95
+00:05:57,580 --> 00:06:02,000
+That's actually a we defined here to this model and that's why we call it the C.
+
+96
+00:06:02,360 --> 00:06:08,840
+And what we do know is that we use this cross model function so we use it now to get inputs here which
+
+97
+00:06:08,840 --> 00:06:13,680
+are defined as a mobile at model output speed the other possible are we going to train.
+
+98
+00:06:13,840 --> 00:06:18,970
+And basically this combines it into one model now one model where it looks like this when print printed
+
+99
+00:06:18,970 --> 00:06:19,920
+out.
+
+100
+00:06:20,520 --> 00:06:26,300
+So a lot of layers I just saw before 86 Lia's But now we have four sort six Malis is now these are three
+
+101
+00:06:26,320 --> 00:06:27,360
+to find here.
+
+102
+00:06:27,790 --> 00:06:29,250
+And that's going to show up right here.
+
+103
+00:06:29,320 --> 00:06:30,590
+So this is pretty cool.
+
+104
+00:06:30,820 --> 00:06:31,950
+And look at this here.
+
+105
+00:06:31,960 --> 00:06:37,710
+So we have five million parameters five point equally actually and trainable parameters.
+
+106
+00:06:37,750 --> 00:06:38,870
+Only 2.6.
+
+107
+00:06:38,890 --> 00:06:43,430
+And the non-tradable parameters which are the width of we froze our trillion.
+
+108
+00:06:43,720 --> 00:06:48,850
+So effectively we've taken a model of at MIT How was pretty complex not super complex like a Viji and
+
+109
+00:06:48,880 --> 00:06:54,850
+couple of others but complex enough and we've made it into a much simpler model to train.
+
+110
+00:06:55,030 --> 00:06:57,390
+So let's get to training of monkey breed.
+
+111
+00:06:57,400 --> 00:07:00,600
+They just had no training on Lockerby to classify.
+
+112
+00:07:00,910 --> 00:07:05,720
+So we loaded data sets using imaged digit data generators that you've seen before.
+
+113
+00:07:06,460 --> 00:07:12,410
+We do a standard thing here which you of which you should be pretty familiar with by now and then we
+
+114
+00:07:12,400 --> 00:07:14,530
+define some checkpoints and Colback sorry.
+
+115
+00:07:14,650 --> 00:07:20,470
+So we use stopping and checkpointing here and then we train for only five bucks for now.
+
+116
+00:07:20,680 --> 00:07:23,440
+That's because we don't want it to be like take too long.
+
+117
+00:07:23,830 --> 00:07:26,380
+And actually treating it separately in this window here.
+
+118
+00:07:26,830 --> 00:07:30,980
+So I've actually already trained almost five e-books and realize so much time.
+
+119
+00:07:31,390 --> 00:07:38,590
+So look at this here you can see after this epoch which took just under five minutes our validation
+
+120
+00:07:38,590 --> 00:07:41,180
+accuracy was 88 percent already.
+
+121
+00:07:41,470 --> 00:07:44,750
+That is actually pretty damn good for such a short space of time.
+
+122
+00:07:45,070 --> 00:07:49,580
+Now and a second night duration because it's such a early start to the trading hour.
+
+123
+00:07:49,630 --> 00:07:55,690
+Even though the trading loss is much lower the and accuracy is a little bit less 84 percent.
+
+124
+00:07:55,780 --> 00:07:56,320
+That's OK.
+
+125
+00:07:56,350 --> 00:07:58,060
+We can sort of live it out.
+
+126
+00:07:58,120 --> 00:08:03,280
+We'll let a train from what ebox and see how it evolves because training these pre-treat models when
+
+127
+00:08:03,280 --> 00:08:09,120
+it's something which is frozen is a little bit different than how we turn to CNN's they basically do
+
+128
+00:08:09,180 --> 00:08:12,890
+they do effectively converge and get a very high value.
+
+129
+00:08:12,940 --> 00:08:15,920
+However you do sometimes see some odd fluctuations like this.
+
+130
+00:08:16,270 --> 00:08:20,320
+And look we have it back up to 91 percent 90 percent.
+
+131
+00:08:20,320 --> 00:08:26,110
+If you wait a few minutes sorry about 20 seconds at least here we can actually see what evaluation accuracy
+
+132
+00:08:26,110 --> 00:08:28,540
+is at the end of the fifth book.
+
+133
+00:08:28,540 --> 00:08:39,640
+So let's wait and see what it looks.
+
+134
+00:08:39,650 --> 00:08:46,360
+One thing to note is that you can actually see our callback stopping callback actually telling us how
+
+135
+00:08:46,370 --> 00:08:52,340
+validation loss did not improve did not improve if we left this for 20 bucks and we had actually it
+
+136
+00:08:52,340 --> 00:08:57,060
+was here as well so basically no matter what this is going to be the last epoch because I'm pretty sure
+
+137
+00:08:57,060 --> 00:09:00,070
+I said my patience to Tree Hill.
+
+138
+00:09:00,320 --> 00:09:00,770
+Yep.
+
+139
+00:09:00,770 --> 00:09:02,360
+I usually always do.
+
+140
+00:09:02,840 --> 00:09:07,250
+So right now what it's doing had a reason why I stuck it two seconds even did two seconds would have
+
+141
+00:09:07,250 --> 00:09:13,220
+passed by the time I started the sentence is that it's predicting on treating all validation data set.
+
+142
+00:09:13,220 --> 00:09:16,540
+Now that's something that a lot of beginners don't know.
+
+143
+00:09:16,720 --> 00:09:19,620
+Take the seat pause at the end of it like a note stuck.
+
+144
+00:09:19,760 --> 00:09:20,770
+It isn't actually stuck.
+
+145
+00:09:20,780 --> 00:09:24,270
+It's just waiting to run on the validation data set now.
+
+146
+00:09:24,380 --> 00:09:29,480
+So it takes a little while to honestly because sometimes validation data sets are quite big.
+
+147
+00:09:29,800 --> 00:09:31,110
+Ah there we go.
+
+148
+00:09:31,140 --> 00:09:32,240
+So look at this.
+
+149
+00:09:32,410 --> 00:09:37,120
+We got 93 percent accuracy in such a short space of time.
+
+150
+00:09:37,190 --> 00:09:38,300
+So this is quite good.
+
+151
+00:09:38,360 --> 00:09:41,050
+So no it's actually go back to this main page here.
+
+152
+00:09:41,450 --> 00:09:44,290
+Let's look at our model which takes me about 10 seconds.
+
+153
+00:09:47,460 --> 00:09:52,570
+And what are we going to do once this model is loaded We're going to basically use open C-v because
+
+154
+00:09:52,810 --> 00:09:53,310
+messy.
+
+155
+00:09:53,340 --> 00:09:59,970
+But of course I wrote quickly that loads the images here and it runs into predictive that we just loaded
+
+156
+00:10:00,180 --> 00:10:07,980
+here and so on already and we're actually going to see the monkey class see how accurate a real ossify
+
+157
+00:10:07,980 --> 00:10:10,410
+really is 90 percent accurate.
+
+158
+00:10:10,410 --> 00:10:12,280
+So let's find out.
+
+159
+00:10:12,480 --> 00:10:13,390
+There we go.
+
+160
+00:10:13,800 --> 00:10:14,560
+So this is the truth.
+
+161
+00:10:14,560 --> 00:10:16,720
+US battled.
+
+162
+00:10:17,080 --> 00:10:18,710
+Yes that's like a Japanese monkey.
+
+163
+00:10:20,080 --> 00:10:22,850
+OK so fiercely it got this one wrong.
+
+164
+00:10:23,120 --> 00:10:28,310
+This is what Elmo predicted Whitehead had a cabbage in and no it was not a white hat.
+
+165
+00:10:28,810 --> 00:10:30,520
+Let's see if he gets it right.
+
+166
+00:10:30,520 --> 00:10:31,300
+Yeah it did.
+
+167
+00:10:31,330 --> 00:10:32,630
+Got this one right.
+
+168
+00:10:32,710 --> 00:10:33,110
+Pick me.
+
+169
+00:10:33,110 --> 00:10:34,020
+I'm almost at.
+
+170
+00:10:34,270 --> 00:10:37,010
+Let's see what the other is.
+
+171
+00:10:37,070 --> 00:10:39,000
+Gary langar definitely.
+
+172
+00:10:39,020 --> 00:10:40,020
+Right.
+
+173
+00:10:40,280 --> 00:10:41,590
+Pygmy marmosets again.
+
+174
+00:10:41,660 --> 00:10:42,710
+Got it right.
+
+175
+00:10:42,740 --> 00:10:44,090
+Got it right.
+
+176
+00:10:44,090 --> 00:10:44,990
+Got that right.
+
+177
+00:10:44,990 --> 00:10:46,210
+Got it right.
+
+178
+00:10:46,550 --> 00:10:48,010
+Got it right again.
+
+179
+00:10:48,560 --> 00:10:49,930
+Got it right.
+
+180
+00:10:50,000 --> 00:10:51,350
+So seems pretty good.
+
+181
+00:10:51,530 --> 00:10:55,230
+So aside from the first one model got basically nine out of 10 right.
+
+182
+00:10:55,250 --> 00:10:58,550
+Which kind of corresponds to 90 percent accuracy.
+
+183
+00:10:58,550 --> 00:10:59,560
+We got here.
+
+184
+00:10:59,930 --> 00:11:07,100
+So you've just learnt to create a model a basically a train model using transfer learning and you see
+
+185
+00:11:07,100 --> 00:11:07,850
+how simple it is.
+
+186
+00:11:07,850 --> 00:11:15,770
+You just basically linnets load it with the weight speed frozen and the top being not included.
+
+187
+00:11:15,770 --> 00:11:18,800
+Then you build the function to add the top whatever top you want to add.
+
+188
+00:11:18,860 --> 00:11:24,770
+I didn't hear all these make sure the Lasley is number of classes you have in your dataset.
+
+189
+00:11:24,860 --> 00:11:28,540
+Then you basically compare concatenates and compile the bottles here.
+
+190
+00:11:29,690 --> 00:11:32,890
+Well combinable I should say you do it your image under it.
+
+191
+00:11:32,900 --> 00:11:38,980
+It does define your check points and callbacks compile and we go and train.
+
+192
+00:11:39,400 --> 00:11:42,880
+So it's really very simple and I hope you find a disruptive quite useful.
+
+193
+00:11:43,060 --> 00:11:43,340
+Thank you.
diff --git a/15. Transfer Learning Build a Flower & Monkey Breed Classifier/3.1 Download the Monkey Breed Dataset.html b/15. Transfer Learning Build a Flower & Monkey Breed Classifier/3.1 Download the Monkey Breed Dataset.html
new file mode 100644
index 0000000000000000000000000000000000000000..7830b1f47a61090c8a62246626d83f289efe2a94
--- /dev/null
+++ b/15. Transfer Learning Build a Flower & Monkey Breed Classifier/3.1 Download the Monkey Breed Dataset.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1l-7wsAaDi89TpaFPjFW-oS8pdgSn_ekw/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/15. Transfer Learning Build a Flower & Monkey Breed Classifier/4. Build a Flower Classifier with VGG16 using Transfer Learning.srt b/15. Transfer Learning Build a Flower & Monkey Breed Classifier/4. Build a Flower Classifier with VGG16 using Transfer Learning.srt
new file mode 100644
index 0000000000000000000000000000000000000000..e89728d777035b28c3b94277ba11bcc9e0bc02ce
--- /dev/null
+++ b/15. Transfer Learning Build a Flower & Monkey Breed Classifier/4. Build a Flower Classifier with VGG16 using Transfer Learning.srt	
@@ -0,0 +1,475 @@
+1
+00:00:00,750 --> 00:00:06,480
+Hi and welcome to Chapter 15 point tree where we're about to build a flow of classify and we're going
+
+2
+00:00:06,480 --> 00:00:08,580
+to use transfer learning to do this.
+
+3
+00:00:08,580 --> 00:00:10,280
+So let's take a look at how we actually.
+
+4
+00:00:10,370 --> 00:00:13,230
+What is Flora ossify our flower data set.
+
+5
+00:00:13,230 --> 00:00:14,920
+I should say so.
+
+6
+00:00:15,060 --> 00:00:20,150
+It comes from the Oxford University's visual geometry group as called 17.
+
+7
+00:00:20,310 --> 00:00:26,270
+And that's because there are 17 categories of flowers and their images in each class say the sets and
+
+8
+00:00:26,270 --> 00:00:27,200
+not that much.
+
+9
+00:00:28,110 --> 00:00:33,900
+So this is some sample images from the flow Josephite the flowers 17 there is that this is the web page
+
+10
+00:00:33,900 --> 00:00:34,940
+from Oxford University.
+
+11
+00:00:34,950 --> 00:00:40,170
+And this is the link you can go to if you want to download it from day itself or you can use that link
+
+12
+00:00:40,230 --> 00:00:44,580
+I have on the left here on the demi site panel.
+
+13
+00:00:44,640 --> 00:00:49,440
+Please use that link to actually download it because I've already preprocess the data into a format
+
+14
+00:00:49,470 --> 00:00:54,330
+that is easily imported into Karris if you downloaded from Oxford University site you're going to have
+
+15
+00:00:54,330 --> 00:00:55,760
+to do a pre-processing itself.
+
+16
+00:00:55,770 --> 00:01:00,630
+And I don't think if you're a beginner you're not going to find that fun at all although it's a good
+
+17
+00:01:00,630 --> 00:01:02,740
+exercise to do sometimes.
+
+18
+00:01:03,600 --> 00:01:08,790
+So anyway our approach to this problem is that we're going to actually use a pre-trained Fiji A16 model
+
+19
+00:01:09,540 --> 00:01:14,490
+with all of its way it's frozen except the top layer and we're only going to train the top ahead of
+
+20
+00:01:14,490 --> 00:01:17,490
+the model with a final output of 17 classes.
+
+21
+00:01:17,490 --> 00:01:21,370
+So let's go back to our I and notebook and get this done.
+
+22
+00:01:21,710 --> 00:01:22,100
+OK.
+
+23
+00:01:22,140 --> 00:01:24,750
+So welcome back to our virtual machine.
+
+24
+00:01:24,780 --> 00:01:28,820
+I hope you downloaded the flowers dataset and extracted it to this folder here.
+
+25
+00:01:29,040 --> 00:01:34,170
+That's this folder called transfer linning and financing and Plaisted right here so we can quickly just
+
+26
+00:01:34,170 --> 00:01:37,910
+inspect it taking a look at some of those pictures.
+
+27
+00:01:38,330 --> 00:01:42,120
+Let's put it on toenail view and it looks quite nice.
+
+28
+00:01:42,120 --> 00:01:46,280
+So as you can see we don't have that many images in this data set.
+
+29
+00:01:46,380 --> 00:01:51,380
+So let's see what kind of accuracy we can get without transfer learning on the Viji model.
+
+30
+00:01:51,390 --> 00:01:53,380
+So let's go to it here.
+
+31
+00:01:53,790 --> 00:02:02,170
+So no let me just close some of these windows open and let's quickly go back to this one here so you
+
+32
+00:02:02,170 --> 00:02:03,350
+can actually see how I do it.
+
+33
+00:02:03,360 --> 00:02:05,080
+It's 15.
+
+34
+00:02:05,080 --> 00:02:07,090
+And we go to making a flower classifier.
+
+35
+00:02:07,210 --> 00:02:08,440
+That's this file here.
+
+36
+00:02:08,830 --> 00:02:10,260
+So now that we're in the file.
+
+37
+00:02:10,300 --> 00:02:11,800
+Let's take a look at what's going on.
+
+38
+00:02:11,800 --> 00:02:15,770
+So we import the BTG model that's easily done here.
+
+39
+00:02:16,120 --> 00:02:23,470
+Viji was designed to work open 24 or 224 by 224 pixel image input's Isiah's.
+
+40
+00:02:23,500 --> 00:02:26,450
+So let's keep the standard size and go forward.
+
+41
+00:02:26,530 --> 00:02:32,200
+So let's load the model with out his weights or with the weights of image's nuts without the top layer.
+
+42
+00:02:32,410 --> 00:02:34,360
+I should say so we do that.
+
+43
+00:02:34,420 --> 00:02:36,960
+And let's just print out the layers in this model.
+
+44
+00:02:37,060 --> 00:02:37,560
+OK.
+
+45
+00:02:37,930 --> 00:02:44,740
+So as you can see default actually is loaded here and by default all the layers are trainable.
+
+46
+00:02:44,740 --> 00:02:52,370
+True that means the default in of when you load EGD all the weights are trainable.
+
+47
+00:02:52,630 --> 00:02:55,090
+So we now have to set this true to false.
+
+48
+00:02:55,090 --> 00:02:56,490
+So that's what we do here.
+
+49
+00:02:56,860 --> 00:03:03,010
+So we loaded with our top head with Image net weights and we set all the treatable as we said this flag
+
+50
+00:03:03,090 --> 00:03:04,210
+to false.
+
+51
+00:03:04,270 --> 00:03:08,030
+So let's do this quickly and that's done there.
+
+52
+00:03:08,520 --> 00:03:13,450
+And now let's create the function where we add a fully connected head.
+
+53
+00:03:13,510 --> 00:03:17,960
+This is where we delay as we add now back to the top of our Viji that network.
+
+54
+00:03:18,190 --> 00:03:24,340
+Notice this is different to the layers we added in the mobile network and that's because PDG has a different
+
+55
+00:03:24,340 --> 00:03:26,000
+design to mobile and that.
+
+56
+00:03:26,020 --> 00:03:30,190
+So you're going to have to look at the final design BTG and replace easily as here.
+
+57
+00:03:30,340 --> 00:03:35,700
+And this here this densely a number of densely as dense units here.
+
+58
+00:03:36,190 --> 00:03:38,440
+By default we are going to use 256.
+
+59
+00:03:38,440 --> 00:03:47,550
+However this function allows us to specify it in here we can add 128 and it would be 128 units here.
+
+60
+00:03:47,890 --> 00:03:50,480
+So let's leave the default right.
+
+61
+00:03:50,500 --> 00:03:57,220
+And then you said drop out who said these things we input a number of classes which is 17 from the flow
+
+62
+00:03:57,220 --> 00:04:01,450
+was data set 17 17 Sivam should make sense you know.
+
+63
+00:04:01,780 --> 00:04:04,730
+And we just concatenated models here.
+
+64
+00:04:05,110 --> 00:04:08,800
+Well the parts of the model to get the full model and then printed out.
+
+65
+00:04:08,800 --> 00:04:13,690
+So let's take a look at and we see there 14 million parameters.
+
+66
+00:04:13,880 --> 00:04:18,150
+It's less than between 19 and 16 sorry BTD 19.
+
+67
+00:04:18,440 --> 00:04:23,180
+And with treatable parameters only 135 tells him that's quite good.
+
+68
+00:04:23,720 --> 00:04:25,060
+So let me just run this.
+
+69
+00:04:25,130 --> 00:04:33,150
+So we have fresh and no we just do it data generators here to deflower validation and Floetry unfold
+
+70
+00:04:33,250 --> 00:04:35,290
+as we said our size.
+
+71
+00:04:35,320 --> 00:04:38,210
+We can go actually just keep it at 16.
+
+72
+00:04:38,490 --> 00:04:38,910
+All right.
+
+73
+00:04:38,950 --> 00:04:43,140
+And keep going here.
+
+74
+00:04:43,260 --> 00:04:49,500
+So now we declare all callbacks right here and we just create we create a callback array which we pass
+
+75
+00:04:49,500 --> 00:04:51,740
+in here and let's run this now.
+
+76
+00:04:51,850 --> 00:04:55,430
+So I to leave you to run this over and run this already.
+
+77
+00:04:55,450 --> 00:04:56,800
+And it takes quite some time.
+
+78
+00:04:57,040 --> 00:05:01,540
+But what I want you to observe is look at the validation accuracy in 25 books.
+
+79
+00:05:01,540 --> 00:05:06,230
+The highest we get was actually 95 percent which is quite good.
+
+80
+00:05:06,820 --> 00:05:11,500
+So you keep going see did it ever pass 95 tree at one time.
+
+81
+00:05:11,560 --> 00:05:12,990
+So this is quite good.
+
+82
+00:05:13,240 --> 00:05:19,370
+So we've got 95 percent accuracy using transfer linning using Viji 16 in translating.
+
+83
+00:05:19,630 --> 00:05:22,710
+So let's keep going let's see what else we can do.
+
+84
+00:05:22,750 --> 00:05:24,080
+OK.
+
+85
+00:05:24,430 --> 00:05:26,020
+So this section here.
+
+86
+00:05:26,020 --> 00:05:27,620
+Can we speed this up.
+
+87
+00:05:27,730 --> 00:05:31,060
+So let's try resizing the images to 64 by 64.
+
+88
+00:05:31,200 --> 00:05:34,820
+You remember it was assigned to a can 224 224.
+
+89
+00:05:34,910 --> 00:05:37,660
+Now let's do this to 64.
+
+90
+00:05:37,930 --> 00:05:44,100
+So let's use this comment to setting the input size.
+
+91
+00:05:44,100 --> 00:05:49,660
+Now to.
+
+92
+00:05:49,780 --> 00:05:55,670
+All right and do the standard thing where we load with image that way it's we don't include the top
+
+93
+00:05:55,780 --> 00:06:01,810
+specified in U shape and we make the last train with three syllables.
+
+94
+00:06:02,190 --> 00:06:04,040
+So that's good.
+
+95
+00:06:04,050 --> 00:06:07,050
+And now let's move on to this.
+
+96
+00:06:07,460 --> 00:06:13,330
+Let us actually start treating the small so as we can see this model has a different input sites.
+
+97
+00:06:14,180 --> 00:06:16,010
+And let's see what we get.
+
+98
+00:06:16,010 --> 00:06:18,940
+So I've trained this before so you don't have to do it.
+
+99
+00:06:18,950 --> 00:06:26,180
+So what I want you to see though is that what what's happened here previously before actually did not
+
+100
+00:06:26,180 --> 00:06:30,130
+used the callbacks or that's it in view but I should have thought it and I.
+
+101
+00:06:30,410 --> 00:06:32,490
+But what I've done now is a discipline we do.
+
+102
+00:06:32,540 --> 00:06:41,660
+So we see some callbacks feedback from stopping so we see it's not increasing monitoring patients is
+
+103
+00:06:41,660 --> 00:06:42,310
+good.
+
+104
+00:06:42,320 --> 00:06:45,740
+So at the end Epopt 12 is what we use.
+
+105
+00:06:45,770 --> 00:06:49,530
+So let's go back to Iraq 12 pastorate ago.
+
+106
+00:06:49,920 --> 00:06:53,210
+That's this one 82 percent.
+
+107
+00:06:53,230 --> 00:06:58,340
+So 82 percent was our best loess validation loss and our best accuracy.
+
+108
+00:06:58,340 --> 00:07:06,500
+So you can see by resizing the images a 64 by 64 which is a substantial decrease in size 2 to 24 by
+
+109
+00:07:06,500 --> 00:07:09,580
+224 we got it into possessory.
+
+110
+00:07:09,860 --> 00:07:10,860
+How much was it again.
+
+111
+00:07:11,520 --> 00:07:11,850
+Sorry.
+
+112
+00:07:11,950 --> 00:07:13,930
+82 percent accuracy.
+
+113
+00:07:14,060 --> 00:07:20,570
+So that's not too bad to be fair actually sorry 86 percent accuracy we got that was fifteen point five
+
+114
+00:07:20,570 --> 00:07:22,150
+six five two.
+
+115
+00:07:22,370 --> 00:07:22,730
+Right.
+
+116
+00:07:22,730 --> 00:07:24,540
+So that is actually this one.
+
+117
+00:07:25,010 --> 00:07:26,140
+So yep.
+
+118
+00:07:26,150 --> 00:07:27,620
+So this is good.
+
+119
+00:07:27,710 --> 00:07:29,960
+It's not great but is pretty good.
diff --git a/15. Transfer Learning Build a Flower & Monkey Breed Classifier/4.1 Download the 17-Flowers Dataset.html b/15. Transfer Learning Build a Flower & Monkey Breed Classifier/4.1 Download the 17-Flowers Dataset.html
new file mode 100644
index 0000000000000000000000000000000000000000..b9565fce3336682388a21316a7b662d4b3ba2222
--- /dev/null
+++ b/15. Transfer Learning Build a Flower & Monkey Breed Classifier/4.1 Download the 17-Flowers Dataset.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/16KBCSvjMSCJSdcrvcws3g-bk9Ov9JrFS/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/1. Chapter Introduction.srt b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/1. Chapter Introduction.srt
new file mode 100644
index 0000000000000000000000000000000000000000..1cda743bccd8311ac2bdbd25784d48588e2637e2
--- /dev/null
+++ b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/1. Chapter Introduction.srt	
@@ -0,0 +1,27 @@
+1
+00:00:00,780 --> 00:00:08,490
+Hi and welcome to Chapter 16 where we get to design or customize CNN one week we're going to call little
+
+2
+00:00:08,490 --> 00:00:12,360
+Viji.
+
+3
+00:00:12,420 --> 00:00:17,270
+So in this section we've introduced the concepts of how we developed the tool VDB.
+
+4
+00:00:17,520 --> 00:00:21,630
+And then in sixteen point two we're actually going to use little Viji to do some Simpsons character
+
+5
+00:00:21,630 --> 00:00:22,720
+recognition.
+
+6
+00:00:22,740 --> 00:00:24,630
+So I hope you're looking forward to getting started.
+
+7
+00:00:24,750 --> 00:00:25,850
+Let's get into it.
diff --git a/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/2. Introducing LittleVGG.srt b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/2. Introducing LittleVGG.srt
new file mode 100644
index 0000000000000000000000000000000000000000..11d568316fb961aec0be05f931550b3687cb3299
--- /dev/null
+++ b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/2. Introducing LittleVGG.srt	
@@ -0,0 +1,87 @@
+1
+00:00:00,830 --> 00:00:08,220
+And welcome to chapter sixteen point one where I introduce little BTG and customize Fiji network.
+
+2
+00:00:08,460 --> 00:00:15,220
+So BTG little BTG is basically a dumb size version of EGD 16 or 19.
+
+3
+00:00:15,510 --> 00:00:21,320
+And remember Viji inspired networks all use a series of Treffry convolutional Lia's where a number of
+
+4
+00:00:21,320 --> 00:00:25,160
+filters just increase as you go for it and fit it into the network.
+
+5
+00:00:25,160 --> 00:00:29,770
+So let's take a look at a little BTG network dagga on network architecture.
+
+6
+00:00:30,080 --> 00:00:33,550
+So this was Viji 19 and 16 here.
+
+7
+00:00:33,860 --> 00:00:36,490
+So what I've done Viji BTD.
+
+8
+00:00:36,620 --> 00:00:38,640
+You can call them has 9 Wheatly.
+
+9
+00:00:38,690 --> 00:00:41,900
+And basically this is how it lines up compared to this one here.
+
+10
+00:00:41,900 --> 00:00:42,480
+All right.
+
+11
+00:00:42,650 --> 00:00:50,460
+So we have it first compositionally is here with 64 filter's Max spieling then our Ottilia here is convolutional
+
+12
+00:00:50,460 --> 00:00:52,700
+Lia's with 128 filters.
+
+13
+00:00:53,000 --> 00:00:54,070
+And then this one here.
+
+14
+00:00:54,080 --> 00:00:56,400
+However we don't have as much we just have to.
+
+15
+00:00:56,840 --> 00:01:00,070
+And then we have Max beling again and then our fully connectedly.
+
+16
+00:01:00,080 --> 00:01:01,180
+So we stop here.
+
+17
+00:01:01,430 --> 00:01:06,760
+We don't go on to do as deeply as here like even Viji 11 does.
+
+18
+00:01:06,830 --> 00:01:14,090
+So we just stop at it 56 count one count of this last convolutional filter and then we get straight
+
+19
+00:01:14,090 --> 00:01:16,200
+into the FC leads.
+
+20
+00:01:16,490 --> 00:01:18,570
+So this is a number of parameters here.
+
+21
+00:01:18,740 --> 00:01:25,810
+So let's build this in Chris and then we get to use this on the Simpsons joining on the Simpsons character
+
+22
+00:01:26,070 --> 00:01:26,400
+set.
diff --git a/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/3. Simpsons Character Recognition using LittleVGG.srt b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/3. Simpsons Character Recognition using LittleVGG.srt
new file mode 100644
index 0000000000000000000000000000000000000000..1b4209ba78be0bac1de518ef8fda5502ba2795d1
--- /dev/null
+++ b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/3. Simpsons Character Recognition using LittleVGG.srt	
@@ -0,0 +1,583 @@
+1
+00:00:00,630 --> 00:00:06,260
+So in Section sixteen point two we're going to use little Viji on our Simpsons character.
+
+2
+00:00:06,260 --> 00:00:09,180
+They are set to do some Simpsons character recognition.
+
+3
+00:00:09,180 --> 00:00:10,560
+This is going to be pretty cool.
+
+4
+00:00:10,560 --> 00:00:15,720
+So this latest ad was sourced from Kaggle Here's a link to it and it basically it actually came with
+
+5
+00:00:15,720 --> 00:00:21,060
+more classes and this however limited it to 20 classes of the most popular characters and there are
+
+6
+00:00:21,060 --> 00:00:24,990
+about 200 to 400 R.G. be pitches in it each.
+
+7
+00:00:24,990 --> 00:00:31,530
+All of which are different sizes and orientations and aspect ratios and images of yes as I just said
+
+8
+00:00:31,530 --> 00:00:31,770
+that.
+
+9
+00:00:31,780 --> 00:00:38,250
+So that's fine but it's good to know that even though some images have multiple characters some of the
+
+10
+00:00:38,250 --> 00:00:41,420
+images are basically the main characters focus of it.
+
+11
+00:00:41,610 --> 00:00:45,030
+So let's take a look at some of the images here.
+
+12
+00:00:45,360 --> 00:00:47,100
+So this is what I mean by the way.
+
+13
+00:00:47,100 --> 00:00:49,440
+Like Lisa is definitely the main character here.
+
+14
+00:00:49,440 --> 00:00:51,150
+However they are the characters here.
+
+15
+00:00:51,360 --> 00:00:53,600
+So that may confuse overclassify.
+
+16
+00:00:53,730 --> 00:00:55,140
+But let's see how it performs.
+
+17
+00:00:57,450 --> 00:00:57,860
+OK.
+
+18
+00:00:57,880 --> 00:01:03,090
+So we're in a virtual machine with Python Simpson's notebook open.
+
+19
+00:01:03,100 --> 00:01:08,420
+Let me just direct you to how we got into here in case you're confused says Chapter 16 here.
+
+20
+00:01:08,530 --> 00:01:15,280
+I knew it when CNN called it Viji and I hope you downloaded the data set in the resources section of
+
+21
+00:01:15,280 --> 00:01:18,650
+this file of the section of this chapter and place it right here.
+
+22
+00:01:18,700 --> 00:01:21,770
+And again just always check to make sure the images are here.
+
+23
+00:01:21,880 --> 00:01:27,610
+So we have characters or classes and basically let's go back to this file.
+
+24
+00:01:27,610 --> 00:01:28,400
+All right.
+
+25
+00:01:28,540 --> 00:01:30,370
+So let's have an open here.
+
+26
+00:01:30,530 --> 00:01:34,520
+So now basically you've seen the standard way we do things right.
+
+27
+00:01:34,600 --> 00:01:39,580
+What I'm going to tell you about here though is that we're resizing the images to the two by two the
+
+28
+00:01:39,580 --> 00:01:41,240
+two pixels are quite small.
+
+29
+00:01:41,320 --> 00:01:42,410
+So let's see how it performs.
+
+30
+00:01:42,430 --> 00:01:43,270
+OK.
+
+31
+00:01:43,270 --> 00:01:50,140
+So we're doing some data augmentation the same usual thing as we do rotations with shifting horizontal
+
+32
+00:01:50,140 --> 00:01:52,410
+flipping so we can get to characters.
+
+33
+00:01:52,420 --> 00:01:54,110
+Adds a variety to the characters.
+
+34
+00:01:54,520 --> 00:01:59,490
+And then we declare our generators here and then this is the part I want to show you.
+
+35
+00:01:59,740 --> 00:02:02,350
+This is where we build our little Viji model.
+
+36
+00:02:02,350 --> 00:02:03,040
+All right.
+
+37
+00:02:03,040 --> 00:02:09,020
+So I believe you remember from diagram should units lights that this is how it's defined.
+
+38
+00:02:09,040 --> 00:02:11,240
+So we have to convolutional is here.
+
+39
+00:02:11,550 --> 00:02:18,090
+Sixty four filters each and all the filters all the kernel sizes here are tree by tree.
+
+40
+00:02:18,130 --> 00:02:20,640
+This is typical of Viji family model.
+
+41
+00:02:20,790 --> 00:02:21,730
+All right.
+
+42
+00:02:21,970 --> 00:02:25,720
+So we have these convolutional is here then itude set.
+
+43
+00:02:25,720 --> 00:02:28,940
+Second second set here and then another set here.
+
+44
+00:02:28,960 --> 00:02:37,380
+These are the ones with 256 filters and then we have a final F.C. density is here with some drop out.
+
+45
+00:02:37,420 --> 00:02:41,270
+We have two sets of dense dense connections here.
+
+46
+00:02:41,890 --> 00:02:48,530
+And then we finally go to 0 number Asaf Max classify with number of classes which is 20 which we defined
+
+47
+00:02:48,650 --> 00:02:49,350
+up here.
+
+48
+00:02:50,890 --> 00:02:52,330
+So let's run this actually.
+
+49
+00:02:52,330 --> 00:02:55,790
+Let me run the first one.
+
+50
+00:02:55,950 --> 00:02:56,680
+There we go.
+
+51
+00:02:57,450 --> 00:02:59,410
+And then let's run this one here.
+
+52
+00:03:00,870 --> 00:03:01,250
+So good.
+
+53
+00:03:01,260 --> 00:03:04,940
+So we just display our models quite smoothly as here.
+
+54
+00:03:05,100 --> 00:03:09,620
+But luckily this is a substantially smaller model compared to Visagie 16.
+
+55
+00:03:09,640 --> 00:03:12,090
+Is only 2.2 million parameters.
+
+56
+00:03:12,390 --> 00:03:15,510
+And if you want to take a look at our model we can plot it.
+
+57
+00:03:15,570 --> 00:03:16,320
+Remember how we did it.
+
+58
+00:03:16,320 --> 00:03:22,830
+Ilya Ylia chapters so this is a visualization of one model shows you the inputs and outputs of every
+
+59
+00:03:22,850 --> 00:03:25,090
+layer.
+
+60
+00:03:25,110 --> 00:03:31,590
+It's quite long but imagine how long each doing 19 would be okay.
+
+61
+00:03:31,720 --> 00:03:35,620
+So this is where I should put some notes in for you guys.
+
+62
+00:03:36,910 --> 00:03:44,160
+Training our little G.G. model right.
+
+63
+00:03:44,200 --> 00:03:48,300
+So we have all callbacks here seem typical call that we've used before.
+
+64
+00:03:48,430 --> 00:03:55,330
+Checkpointing really stopping and leading rate adjustments on Pluto and a number of samples that we've
+
+65
+00:03:55,330 --> 00:03:58,510
+gotten from the generators before actually did.
+
+66
+00:03:58,810 --> 00:04:01,610
+And we're going to train transfer at least to any box you can dream from.
+
+67
+00:04:01,990 --> 00:04:07,840
+Always recommend trying for more but if time is of the essence and this is more of a practical educational
+
+68
+00:04:07,840 --> 00:04:11,840
+exercise as opposed to us trying to get the best performance out of these models.
+
+69
+00:04:12,070 --> 00:04:15,650
+So what we're doing here we're just testing a little VDU model.
+
+70
+00:04:15,930 --> 00:04:20,080
+It's just one here just to see how he performs on our data set.
+
+71
+00:04:20,080 --> 00:04:23,200
+So I've run this for 10 bucks as I said.
+
+72
+00:04:23,470 --> 00:04:25,260
+And let's see how it performs.
+
+73
+00:04:25,330 --> 00:04:28,190
+Fifteen percent on validation accuracy.
+
+74
+00:04:28,240 --> 00:04:29,100
+Not great.
+
+75
+00:04:29,260 --> 00:04:29,740
+OK.
+
+76
+00:04:29,950 --> 00:04:31,040
+And again not great.
+
+77
+00:04:31,040 --> 00:04:37,720
+Are there any accuracy to that as we train steadily improves the good to this column here on the training
+
+78
+00:04:37,750 --> 00:04:40,920
+accuracy keeps going up and up.
+
+79
+00:04:41,200 --> 00:04:43,680
+And also a good validation accuracy.
+
+80
+00:04:43,780 --> 00:04:45,300
+It also keeps going up and up.
+
+81
+00:04:45,340 --> 00:04:52,310
+So I'm pretty sure if I left the trim maybe 50 bucks it could have gone 90s into percent accuracy.
+
+82
+00:04:52,330 --> 00:04:53,620
+So this is good to know.
+
+83
+00:04:53,830 --> 00:05:00,460
+So we do see the flexibility and power of a simple Viji model that honestly doesn't take that long to
+
+84
+00:05:00,460 --> 00:05:06,460
+train 500 something seconds says no to that pre-book.
+
+85
+00:05:06,630 --> 00:05:13,610
+All right so this is let's look at the performance of this model so let me just title this section Vigurs
+
+86
+00:05:13,740 --> 00:05:15,660
+to make this a bit cleaner.
+
+87
+00:05:21,130 --> 00:05:21,970
+OK.
+
+88
+00:05:22,280 --> 00:05:25,680
+My psyche is giving some of this use sticking about.
+
+89
+00:05:25,770 --> 00:05:28,380
+But look at the confusion metrics here.
+
+90
+00:05:28,550 --> 00:05:33,890
+So we can see a high number in the middle rows in a row here which is also good.
+
+91
+00:05:33,900 --> 00:05:40,440
+Always good but we do see some issues here with some characters being basically basically misclassified.
+
+92
+00:05:40,760 --> 00:05:43,740
+So we know this is a 77 percent accurate model.
+
+93
+00:05:43,850 --> 00:05:47,380
+So we know it is going to have some issues but it's generally going to be quite good.
+
+94
+00:05:47,900 --> 00:05:56,500
+So we can see which characters is performing poorly on and the 1 score to find that may be interesting.
+
+95
+00:05:56,900 --> 00:05:57,860
+All right.
+
+96
+00:05:58,040 --> 00:06:00,140
+And point one score for him.
+
+97
+00:06:00,410 --> 00:06:04,390
+But a super high precision rates means a lot of false positives.
+
+98
+00:06:04,730 --> 00:06:05,080
+Right.
+
+99
+00:06:05,150 --> 00:06:08,510
+And no one else seems to be nearly that bad.
+
+100
+00:06:10,490 --> 00:06:15,130
+Devon to see may be here being misclassified as Mo.
+
+101
+00:06:16,030 --> 00:06:17,290
+That is interesting.
+
+102
+00:06:17,290 --> 00:06:17,990
+All right.
+
+103
+00:06:18,220 --> 00:06:21,290
+But generally we see a nice smooth diagonal trolled here.
+
+104
+00:06:21,310 --> 00:06:25,640
+This is a much easier way to visualize this data.
+
+105
+00:06:25,690 --> 00:06:26,300
+OK.
+
+106
+00:06:26,900 --> 00:06:30,670
+So I'm going to look at our model and see if I'm a.
+
+107
+00:06:33,500 --> 00:06:35,110
+Takes about 10 seconds.
+
+108
+00:06:35,180 --> 00:06:37,790
+I hate malls for this reason.
+
+109
+00:06:37,790 --> 00:06:38,430
+There we go.
+
+110
+00:06:38,690 --> 00:06:42,340
+So this is a messy open see if you could create it.
+
+111
+00:06:42,530 --> 00:06:44,740
+However you can spend some time going through it.
+
+112
+00:06:44,900 --> 00:06:48,440
+It's not super difficult to understand and use similar code before.
+
+113
+00:06:48,620 --> 00:06:53,930
+But basically we're just going to display right good display predicted over.
+
+114
+00:06:53,990 --> 00:06:54,550
+True.
+
+115
+00:06:54,560 --> 00:06:56,730
+So let's see how it performs.
+
+116
+00:06:56,840 --> 00:06:57,330
+Good.
+
+117
+00:06:57,350 --> 00:07:01,850
+This is Mulhouse is is not homo.
+
+118
+00:07:01,960 --> 00:07:04,760
+This text is too big it can resize it after actually.
+
+119
+00:07:04,760 --> 00:07:07,850
+Let me show you how to resize it as we're here.
+
+120
+00:07:07,890 --> 00:07:10,390
+So as you see this is this function here.
+
+121
+00:07:10,380 --> 00:07:13,680
+Draw test is where we actually drawing a text.
+
+122
+00:07:13,710 --> 00:07:21,750
+So this is the font size so we can reduce the font size here and we see how it becomes so good.
+
+123
+00:07:21,750 --> 00:07:27,270
+If you're wondering though what this did to here this is the Ticknor sort of boldness of the top of
+
+124
+00:07:27,270 --> 00:07:28,030
+the font.
+
+125
+00:07:28,530 --> 00:07:31,050
+And there are a number of forms we can use an open C.v.
+
+126
+00:07:31,110 --> 00:07:33,540
+This is one of the nice looking ones in my opinion.
+
+127
+00:07:33,660 --> 00:07:36,430
+So let's take a look at testify results.
+
+128
+00:07:36,450 --> 00:07:43,530
+This is Charles Montgomery Burns and it is in fact Charles I mean it's a poor very good dresser declawing
+
+129
+00:07:43,620 --> 00:07:46,710
+should be easy to spot given his green hair.
+
+130
+00:07:46,710 --> 00:07:49,140
+Very good side show Bob pretty good.
+
+131
+00:07:49,200 --> 00:07:51,500
+Kristi again Mo.
+
+132
+00:07:52,530 --> 00:07:54,140
+This is clearly not principles.
+
+133
+00:07:54,210 --> 00:07:59,440
+This is Lisa however because she's wearing a cap that's probably why it got confuse.
+
+134
+00:07:59,490 --> 00:08:03,900
+Although the captor's is not similar to what Principal is going to wear.
+
+135
+00:08:03,900 --> 00:08:10,290
+So I'm not sure why or classify as well that this would be a good time to actually visualize how classifiers
+
+136
+00:08:10,290 --> 00:08:13,360
+perceived the character's pool.
+
+137
+00:08:13,390 --> 00:08:18,400
+Again at Ibraham or grandpa.
+
+138
+00:08:18,950 --> 00:08:19,600
+That's pretty good.
+
+139
+00:08:19,690 --> 00:08:20,840
+So for classified.
+
+140
+00:08:20,860 --> 00:08:25,680
+Seventy seven percent accurate it actually performed fairly well in our test data.
+
+141
+00:08:26,170 --> 00:08:26,950
+OK.
+
+142
+00:08:27,050 --> 00:08:30,100
+So I hope you had some fun playing with little Ujiji.
+
+143
+00:08:30,140 --> 00:08:35,990
+It's a very good model in my opinion and you can use Adeptus do a number of your applications if you
+
+144
+00:08:35,990 --> 00:08:36,750
+want.
+
+145
+00:08:37,180 --> 00:08:37,550
+OK.
+
+146
+00:08:37,760 --> 00:08:38,060
+Thank you.
diff --git a/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/3.1 Download Simpsons Dataset.html b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/3.1 Download Simpsons Dataset.html
new file mode 100644
index 0000000000000000000000000000000000000000..e49e9a28b35c68edad355ac58819981ad202bb9e
--- /dev/null
+++ b/16. Design Your Own CNN - LittleVGG A Simpsons Classifier/3.1 Download Simpsons Dataset.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1GmS93M5h5CHmQWKzMtXdAZmNW2jmxo01/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/16. Design Your Own CNN - LittleVGG/16.2 LittleVGG - Simpsons.ipynb b/16. Design Your Own CNN - LittleVGG/16.2 LittleVGG - Simpsons.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8ea34b04663611388de1dc9a07274eadccf79182
--- /dev/null
+++ b/16. Design Your Own CNN - LittleVGG/16.2 LittleVGG - Simpsons.ipynb	
@@ -0,0 +1,600 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LittleVGG \n",
+    "- We're going to be training this on the simpsons character dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 19548 images belonging to 20 classes.\n",
+      "Found 990 images belonging to 20 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+    "from tensorflow.keras.layers import ELU\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "import os\n",
+    "\n",
+    "num_classes = 20\n",
+    "img_rows, img_cols = 32, 32\n",
+    "batch_size = 16\n",
+    "\n",
+    "train_data_dir = './simpsons/train'\n",
+    "validation_data_dir = './simpsons/validation'\n",
+    "\n",
+    "# Let's use some data augmentaiton \n",
+    "train_datagen = ImageDataGenerator(\n",
+    "      rescale=1./255,\n",
+    "      rotation_range=30,\n",
+    "      width_shift_range=0.3,\n",
+    "      height_shift_range=0.3,\n",
+    "      horizontal_flip=True,\n",
+    "      fill_mode='nearest')\n",
+    " \n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    " \n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        train_data_dir,\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical')\n",
+    " \n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "        validation_data_dir,\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's create our LittleVGG Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d (Conv2D)              (None, 32, 32, 64)        1792      \n",
+      "_________________________________________________________________\n",
+      "activation (Activation)      (None, 32, 32, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization (BatchNo (None, 32, 32, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "conv2d_1 (Conv2D)            (None, 32, 32, 64)        36928     \n",
+      "_________________________________________________________________\n",
+      "activation_1 (Activation)    (None, 32, 32, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_1 (Batch (None, 32, 32, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d (MaxPooling2D) (None, 16, 16, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout (Dropout)            (None, 16, 16, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 16, 16, 128)       73856     \n",
+      "_________________________________________________________________\n",
+      "activation_2 (Activation)    (None, 16, 16, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_2 (Batch (None, 16, 16, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv2d_3 (Conv2D)            (None, 16, 16, 128)       147584    \n",
+      "_________________________________________________________________\n",
+      "activation_3 (Activation)    (None, 16, 16, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_3 (Batch (None, 16, 16, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 8, 8, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_4 (Conv2D)            (None, 8, 8, 256)         295168    \n",
+      "_________________________________________________________________\n",
+      "activation_4 (Activation)    (None, 8, 8, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_4 (Batch (None, 8, 8, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "conv2d_5 (Conv2D)            (None, 8, 8, 256)         590080    \n",
+      "_________________________________________________________________\n",
+      "activation_5 (Activation)    (None, 8, 8, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_5 (Batch (None, 8, 8, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 4, 4, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 4096)              0         \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 256)               1048832   \n",
+      "_________________________________________________________________\n",
+      "activation_6 (Activation)    (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_6 (Batch (None, 256)               1024      \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 256)               65792     \n",
+      "_________________________________________________________________\n",
+      "activation_7 (Activation)    (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_7 (Batch (None, 256)               1024      \n",
+      "_________________________________________________________________\n",
+      "dropout_4 (Dropout)          (None, 256)               0         \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 20)                5140      \n",
+      "_________________________________________________________________\n",
+      "activation_8 (Activation)    (None, 20)                0         \n",
+      "=================================================================\n",
+      "Total params: 2,270,804\n",
+      "Trainable params: 2,267,988\n",
+      "Non-trainable params: 2,816\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "\n",
+    "# First CONV-ReLU Layer\n",
+    "model.add(Conv2D(64, (3, 3), padding = 'same', input_shape = (img_rows, img_cols, 3)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "# Second CONV-ReLU Layer\n",
+    "model.add(Conv2D(64, (3, 3), padding = \"same\", input_shape = (img_rows, img_cols, 3)))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "# Max Pooling with Dropout \n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# 3rd set of CONV-ReLU Layers\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "# 4th Set of CONV-ReLU Layers\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "# Max Pooling with Dropout \n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# 5th Set of CONV-ReLU Layers\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\"))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "# 6th Set of CONV-ReLU Layers\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\"))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "\n",
+    "# Max Pooling with Dropout \n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# First set of FC or Dense Layers\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(256))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Second set of FC or Dense Layers\n",
+    "model.add(Dense(256))\n",
+    "model.add(Activation('relu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Final Dense Layer\n",
+    "model.add(Dense(num_classes))\n",
+    "model.add(Activation(\"softmax\"))\n",
+    "\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's take a look at our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 7200x5040 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.utils import plot_model\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "import numpy as np\n",
+    "\n",
+    "plot_model(model, to_file='LittleVGG.png', show_shapes=True, show_layer_names=True)\n",
+    "img = mpimg.imread('LittleVGG.png')\n",
+    "plt.figure(figsize=(100,70))\n",
+    "imgplot = plt.imshow(img) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training our LittleVGG Model!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 1221 steps, validate for 61 steps\n",
+      "1220/1221 [============================>.] - ETA: 0s - loss: 2.8344 - accuracy: 0.1673\n",
+      "Epoch 00001: val_loss improved from inf to 2.76441, saving model to simpsons_little_vgg.h5\n",
+      "1221/1221 [==============================] - 458s 375ms/step - loss: 2.8343 - accuracy: 0.1674 - val_loss: 2.7644 - val_accuracy: 0.1619\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
+    "\n",
+    "                     \n",
+    "checkpoint = ModelCheckpoint(\"simpsons_little_vgg.h5\",\n",
+    "                             monitor=\"val_loss\",\n",
+    "                             mode=\"min\",\n",
+    "                             save_best_only = True,\n",
+    "                             verbose=1)\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
+    "                          min_delta = 0, \n",
+    "                          patience = 3,\n",
+    "                          verbose = 1,\n",
+    "                          restore_best_weights = True)\n",
+    "\n",
+    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss',\n",
+    "                              factor = 0.2,\n",
+    "                              patience = 3,\n",
+    "                              verbose = 1,\n",
+    "                              min_delta = 0.00001)\n",
+    "\n",
+    "# we put our call backs into a callback list\n",
+    "callbacks = [earlystop, checkpoint, reduce_lr]\n",
+    "\n",
+    "# We use a very small learning rate \n",
+    "model.compile(loss = 'categorical_crossentropy',\n",
+    "              optimizer = Adam(lr=0.01),\n",
+    "              metrics = ['accuracy'])\n",
+    "\n",
+    "nb_train_samples = 19548\n",
+    "nb_validation_samples = 990\n",
+    "epochs = 10\n",
+    "\n",
+    "history = model.fit_generator(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch = nb_train_samples // batch_size,\n",
+    "    epochs = epochs,\n",
+    "    callbacks = callbacks,\n",
+    "    validation_data = validation_generator,\n",
+    "    validation_steps = nb_validation_samples // batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Performance Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 990 images belonging to 20 classes.\n",
+      "WARNING:tensorflow:From <ipython-input-11-402806b8f986>:22: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use Model.predict, which supports generators.\n",
+      "Confusion Matrix\n",
+      "[[11  0  8  0  0  0  0 14  0  6  0  6  0  0  1  0  0  0  0  2]\n",
+      " [ 0  0  0  1  0  0  0  1  0 45  0  1  0  0  0  1  1  0  0  0]\n",
+      " [ 0  0 14  0  0  0  0  7  0  4  0 21  0  0  0  0  0  0  4  0]\n",
+      " [ 0  0 20  0  0  0  0  5  0  4  0  8  0  0  0  1  0  0 10  0]\n",
+      " [ 0  0  1  0  2  0  0  0  0 11  0  2  0  0  1  0  0  0 33  0]\n",
+      " [ 0  0 14  0  0  0  0  2  0  7  0 11  0  0  2  0  1  0 12  0]\n",
+      " [ 2  0 10  0  0  0  0  4  0  8  0 17  1  0  1  1  6  0  0  0]\n",
+      " [ 4  0  9  0  0  0  0 16  0  6  0 10  2  0  0  2  0  0  1  0]\n",
+      " [ 0  0 12  2  0  0  0 11  0  8  0 10  0  0  4  0  0  0  3  0]\n",
+      " [ 0  0  0  0  0  0  0  1  0 45  0  1  2  0  0  0  0  0  1  0]\n",
+      " [ 0  0  7  2  0  0  0 11  0 22  0  3  0  0  0  1  3  0  1  0]\n",
+      " [ 1  0 12  0  0  0  0  9  0  2  0 23  0  0  1  0  1  0  1  0]\n",
+      " [ 0  0  0  0  0  0  0  6  0 10  0 10 20  0  4  0  0  0  0  0]\n",
+      " [ 0  0 10  1  0  0  0  3  0  3  0  4  1  0  1  0  0  0 27  0]\n",
+      " [ 0  0  9  0  0  0  0  6  0 11  0 12  0  0  5  0  0  0  6  0]\n",
+      " [ 1  0  9  3  0  0  0  4  0 12  0  4  1  0  0  5  0  0 11  0]\n",
+      " [ 0  0  8  0  0  0  0 19  0 17  0  1  0  0  0  0  3  0  1  0]\n",
+      " [ 0  0 20  0  0  0  0  2  0  7  0  9  0  0  0  3  1  0  8  0]\n",
+      " [ 0  0 13  3  0  0  0  2  0  4  0  9  0  0  0  1  0  0 18  0]\n",
+      " [ 0  0  5  0  0  0  0  7  0 27  0  2  0  0  0  2  0  0  4  0]]\n",
+      "Classification Report\n",
+      "                          precision    recall  f1-score   support\n",
+      "\n",
+      "  abraham_grampa_simpson       0.58      0.23      0.33        48\n",
+      "  apu_nahasapeemapetilon       0.00      0.00      0.00        50\n",
+      "            bart_simpson       0.08      0.28      0.12        50\n",
+      "charles_montgomery_burns       0.00      0.00      0.00        48\n",
+      "            chief_wiggum       1.00      0.04      0.08        50\n",
+      "          comic_book_guy       0.00      0.00      0.00        49\n",
+      "          edna_krabappel       0.00      0.00      0.00        50\n",
+      "           homer_simpson       0.12      0.32      0.18        50\n",
+      "           kent_brockman       0.00      0.00      0.00        50\n",
+      "        krusty_the_clown       0.17      0.90      0.29        50\n",
+      "           lenny_leonard       0.00      0.00      0.00        50\n",
+      "            lisa_simpson       0.14      0.46      0.21        50\n",
+      "           marge_simpson       0.74      0.40      0.52        50\n",
+      "            mayor_quimby       0.00      0.00      0.00        50\n",
+      "     milhouse_van_houten       0.25      0.10      0.14        49\n",
+      "             moe_szyslak       0.29      0.10      0.15        50\n",
+      "            ned_flanders       0.19      0.06      0.09        49\n",
+      "            nelson_muntz       0.00      0.00      0.00        50\n",
+      "       principal_skinner       0.13      0.36      0.19        50\n",
+      "            sideshow_bob       0.00      0.00      0.00        47\n",
+      "\n",
+      "                accuracy                           0.16       990\n",
+      "               macro avg       0.18      0.16      0.12       990\n",
+      "            weighted avg       0.18      0.16      0.12       990\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import sklearn\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import numpy as np\n",
+    "\n",
+    "# We need to recreate our validation generator with shuffle = false\n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "        validation_data_dir,\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=False)\n",
+    "\n",
+    "class_labels = validation_generator.class_indices\n",
+    "class_labels = {v: k for k, v in class_labels.items()}\n",
+    "classes = list(class_labels.values())\n",
+    "\n",
+    "nb_train_samples = 19548\n",
+    "nb_validation_samples = 990\n",
+    "\n",
+    "#Confution Matrix and Classification Report\n",
+    "Y_pred = model.predict_generator(validation_generator, nb_validation_samples // batch_size+1)\n",
+    "y_pred = np.argmax(Y_pred, axis=1)\n",
+    "\n",
+    "print('Confusion Matrix')\n",
+    "print(confusion_matrix(validation_generator.classes, y_pred))\n",
+    "print('Classification Report')\n",
+    "target_names = list(class_labels.values())\n",
+    "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))\n",
+    "\n",
+    "plt.figure(figsize=(8,8))\n",
+    "cnf_matrix = confusion_matrix(validation_generator.classes, y_pred)\n",
+    "\n",
+    "plt.imshow(cnf_matrix, interpolation='nearest')\n",
+    "plt.colorbar()\n",
+    "tick_marks = np.arange(len(classes))\n",
+    "_ = plt.xticks(tick_marks, classes, rotation=90)\n",
+    "_ = plt.yticks(tick_marks, classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's reload our saved classifier\n",
+    "If we just trained our classifer, we an use model instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model\n",
+    "\n",
+    "# 77% Accuracy after just 10 Epochs\n",
+    "classifier = load_model('simpsons_little_vgg.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Some quick fire testing code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import re\n",
+    "\n",
+    "def draw_test(name, pred, im, true_label):\n",
+    "    BLACK = [0,0,0]\n",
+    "    expanded_image = cv2.copyMakeBorder(im, 160, 0, 0, 300 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
+    "    cv2.putText(expanded_image, \"predited - \"+ pred, (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)\n",
+    "    cv2.putText(expanded_image, \"true - \"+ true_label, (20, 120) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
+    "    cv2.imshow(name, expanded_image)\n",
+    "    \n",
+    "\n",
+    "def getRandomImage(path, img_width, img_height):\n",
+    "    \"\"\"function loads a random images from a random folder in our test path \"\"\"\n",
+    "    folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path)))\n",
+    "    random_directory = np.random.randint(0,len(folders))\n",
+    "    path_class = folders[random_directory]\n",
+    "    file_path = path + path_class\n",
+    "    file_names = [f for f in listdir(file_path) if isfile(join(file_path, f))]\n",
+    "    random_file_index = np.random.randint(0,len(file_names))\n",
+    "    image_name = file_names[random_file_index]\n",
+    "    final_path = file_path + \"/\" + image_name\n",
+    "    return image.load_img(final_path, target_size = (img_width, img_height)), final_path, path_class\n",
+    "\n",
+    "# dimensions of our images\n",
+    "img_width, img_height = 32, 32\n",
+    "\n",
+    "files = []\n",
+    "predictions = []\n",
+    "true_labels = []\n",
+    "\n",
+    "# predicting images\n",
+    "for i in range(0, 10):\n",
+    "    path = './simpsons/validation/' \n",
+    "    img, final_path, true_label = getRandomImage(path, img_width, img_height)\n",
+    "    files.append(final_path)\n",
+    "    true_labels.append(true_label)\n",
+    "    x = image.img_to_array(img)\n",
+    "    x = x * 1./255\n",
+    "    x = np.expand_dims(x, axis=0)\n",
+    "    images = np.vstack([x])\n",
+    "    classes = classifier.predict_classes(images, batch_size = 10)\n",
+    "    predictions.append(classes)\n",
+    "    \n",
+    "for i in range(0, len(files)):\n",
+    "    image = cv2.imread((files[i]))\n",
+    "    image = cv2.resize(image, None, fx=5, fy=5, interpolation = cv2.INTER_CUBIC)\n",
+    "    draw_test(\"Prediction\", class_labels[predictions[i][0]], image, true_labels[i])\n",
+    "    cv2.waitKey(0)\n",
+    "\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/17. Advanced Activation Functions & Initializations/1. Chapter Introduction.srt b/17. Advanced Activation Functions & Initializations/1. Chapter Introduction.srt
new file mode 100644
index 0000000000000000000000000000000000000000..0eb3c46bc469b64f57d9d6969494df35face7196
--- /dev/null
+++ b/17. Advanced Activation Functions & Initializations/1. Chapter Introduction.srt	
@@ -0,0 +1,27 @@
+1
+00:00:00,750 --> 00:00:07,020
+Hi and welcome to Chapter 17 where talk about advance activation functions and initializations those
+
+2
+00:00:07,020 --> 00:00:12,830
+are some features that Kurus allows us to configure when tuning or creating our CNN's.
+
+3
+00:00:12,840 --> 00:00:18,720
+So before we begin I'm going to talk about a dying real problem and introduce to you to why we need
+
+4
+00:00:18,730 --> 00:00:21,510
+liquorish real elu and previews as well.
+
+5
+00:00:21,840 --> 00:00:24,570
+And then I talk about advance initializations.
+
+6
+00:00:24,850 --> 00:00:25,150
+OK.
+
+7
+00:00:25,170 --> 00:00:26,400
+So let's get started.
diff --git a/17. Advanced Activation Functions & Initializations/2. Dying ReLU Problem and Introduction to Leaky ReLU, ELU and PReLUs.srt b/17. Advanced Activation Functions & Initializations/2. Dying ReLU Problem and Introduction to Leaky ReLU, ELU and PReLUs.srt
new file mode 100644
index 0000000000000000000000000000000000000000..ad716562aa0334a61d834ca94d27b09ea4308fab
--- /dev/null
+++ b/17. Advanced Activation Functions & Initializations/2. Dying ReLU Problem and Introduction to Leaky ReLU, ELU and PReLUs.srt	
@@ -0,0 +1,279 @@
+1
+00:00:00,560 --> 00:00:07,040
+Hi and welcome to chapter seventeen point one we are introduced to introduce to you some advance activation
+
+2
+00:00:07,040 --> 00:00:07,780
+functions.
+
+3
+00:00:09,870 --> 00:00:17,100
+So if you remember from earlier slides activation functions are what introduces the non linear linearity
+
+4
+00:00:17,490 --> 00:00:20,850
+that provide neural nets with incredible performance.
+
+5
+00:00:20,850 --> 00:00:26,940
+However what we've said before really is basically the activation unit of choice for all with CNN's.
+
+6
+00:00:27,090 --> 00:00:33,860
+And it basically left you at that and you probably will as usual if no one ever really isn't perfect.
+
+7
+00:00:34,020 --> 00:00:40,000
+I'll tell you why there's a problem and it's called the dying real problem.
+
+8
+00:00:40,090 --> 00:00:45,830
+So when training a number of real units can often die during training.
+
+9
+00:00:45,900 --> 00:00:47,250
+And what does that mean.
+
+10
+00:00:47,260 --> 00:00:49,240
+This happens when a large gritty inflows.
+
+11
+00:00:49,240 --> 00:00:55,210
+True that true that neuron which causes the weeds to update in such a way that the unit never activates
+
+12
+00:00:55,230 --> 00:00:55,790
+anymore.
+
+13
+00:00:55,990 --> 00:01:01,220
+So regardless of future input PERDIDA that really you basically is always going to be switched off.
+
+14
+00:01:01,240 --> 00:01:04,360
+So we're going to have a part of the network that's effectively dead.
+
+15
+00:01:04,750 --> 00:01:10,620
+So what happens is that exactly as it says here the output of this unit is always going to be zero.
+
+16
+00:01:10,810 --> 00:01:17,000
+So now we have basically wasted units and a wasted amount of connections and that work.
+
+17
+00:01:17,020 --> 00:01:22,760
+And apparently sometimes as much as 40 percent of them that can be dead because of these dying real
+
+18
+00:01:23,260 --> 00:01:24,020
+units.
+
+19
+00:01:25,850 --> 00:01:28,250
+So how do we fix a dying a real problem.
+
+20
+00:01:28,640 --> 00:01:35,240
+OK so fiercely Let's take a look at the different types of real functions are about to discuss you know
+
+21
+00:01:35,240 --> 00:01:40,130
+really it is a standard clamping basically at zero activation function.
+
+22
+00:01:40,190 --> 00:01:41,010
+Right.
+
+23
+00:01:41,060 --> 00:01:43,380
+Everything over zero is allowed to pass.
+
+24
+00:01:43,400 --> 00:01:46,250
+Everything negative is basically clamped at zero.
+
+25
+00:01:46,610 --> 00:01:53,120
+However what is leaky leaky real do has a small negative slope it's a green one here and basically it
+
+26
+00:01:53,120 --> 00:01:54,430
+is a linear function.
+
+27
+00:01:54,590 --> 00:02:00,830
+However it is a parameter in front of it that basically allows it to not grow that much.
+
+28
+00:02:00,830 --> 00:02:06,950
+So it's not going to have basically a factor that basically limits how much it can how big how negative
+
+29
+00:02:06,950 --> 00:02:08,910
+it can go.
+
+30
+00:02:08,960 --> 00:02:12,350
+What about you know previous tons of parametric.
+
+31
+00:02:12,950 --> 00:02:16,880
+And basically it is very similar to leaky Belu.
+
+32
+00:02:17,000 --> 00:02:23,810
+However parametric pre-New or parametric reel basically has a lot of function and that a parameter that
+
+33
+00:02:23,810 --> 00:02:31,820
+can control the steepness of the slope and Eliu which is exponential riu basically the negative portion
+
+34
+00:02:31,820 --> 00:02:35,830
+of it follows an exponential curve with these parameters here.
+
+35
+00:02:36,250 --> 00:02:37,050
+OK.
+
+36
+00:02:38,270 --> 00:02:42,660
+So it tends to be a good mix of the good parts of really and leaky really.
+
+37
+00:02:42,860 --> 00:02:46,660
+However it can saturate on large negative values as you can see here.
+
+38
+00:02:47,050 --> 00:02:47,460
+OK.
+
+39
+00:02:51,000 --> 00:02:56,120
+So there are some other exotic and exotic because they're not commonly used.
+
+40
+00:02:56,160 --> 00:03:03,600
+Activision functions there's Kerry-Lugar which combines concatenates Alpert's of tubular functions one
+
+41
+00:03:03,600 --> 00:03:05,240
+positive and one negative.
+
+42
+00:03:05,640 --> 00:03:07,050
+Doubling the output value.
+
+43
+00:03:07,050 --> 00:03:12,920
+Not entirely sure when you would use this but is a decent paper here you can read about it is also reduced
+
+44
+00:03:12,920 --> 00:03:13,600
+6.
+
+45
+00:03:13,680 --> 00:03:17,310
+So basically really six is just capped at negative six.
+
+46
+00:03:17,360 --> 00:03:22,350
+There is no special reason for selecting six other than it would be best for it is a four data set according
+
+47
+00:03:22,350 --> 00:03:23,490
+to this paper here.
+
+48
+00:03:23,880 --> 00:03:26,730
+And there are many others as well as Maxo at offset.
+
+49
+00:03:26,760 --> 00:03:27,820
+Do you get the idea.
+
+50
+00:03:28,050 --> 00:03:34,700
+However in practice I would suggest you use BQE real or relo.
+
+51
+00:03:34,880 --> 00:03:37,130
+So when you use something other than a redo.
+
+52
+00:03:37,370 --> 00:03:42,370
+So we've just seen some of the variations here but is no hard and fast rule and disciplining.
+
+53
+00:03:42,530 --> 00:03:46,340
+That's why some people called deepening Wolfer art than science.
+
+54
+00:03:46,430 --> 00:03:49,770
+That's kind of true because of the number of factors.
+
+55
+00:03:49,800 --> 00:03:54,210
+And basically there's so many interchangeable and dependencies.
+
+56
+00:03:54,380 --> 00:03:55,670
+How does your data look.
+
+57
+00:03:55,700 --> 00:03:57,670
+What is it going to be used on.
+
+58
+00:03:57,720 --> 00:03:59,840
+There's a lot of things to configure and network.
+
+59
+00:04:00,050 --> 00:04:01,880
+That's why a lot of people say it's an art.
+
+60
+00:04:01,880 --> 00:04:07,270
+However there are some general rules you can use that would generally get you good results.
+
+61
+00:04:07,390 --> 00:04:08,130
+OK.
+
+62
+00:04:08,270 --> 00:04:15,800
+So generally a good rule of thumb is to always use a real office and then you are just lending rates
+
+63
+00:04:15,800 --> 00:04:19,300
+to get the best accuracy you can with your CNN analyst.
+
+64
+00:04:19,400 --> 00:04:19,880
+OK.
+
+65
+00:04:20,300 --> 00:04:24,790
+Once that's done then you can start experimenting with different real functions.
+
+66
+00:04:24,920 --> 00:04:30,930
+You can go from leaky really to do as well as a nice progressive step you can take.
+
+67
+00:04:31,000 --> 00:04:35,200
+However in most cases it can skip leakage really and go straight to Eliu.
+
+68
+00:04:35,670 --> 00:04:40,340
+I always think others find I get better results with elu compared to real.
+
+69
+00:04:40,550 --> 00:04:46,800
+So if you want to just try to get the best network and best accuracy as possible use Elu.
+
+70
+00:04:46,910 --> 00:04:47,340
+OK.
diff --git a/17. Advanced Activation Functions & Initializations/3. Advanced Initializations.srt b/17. Advanced Activation Functions & Initializations/3. Advanced Initializations.srt
new file mode 100644
index 0000000000000000000000000000000000000000..d469e4a4a77a708dbecdb37c9c0a9a244c4595ff
--- /dev/null
+++ b/17. Advanced Activation Functions & Initializations/3. Advanced Initializations.srt	
@@ -0,0 +1,151 @@
+1
+00:00:00,510 --> 00:00:00,830
+OK.
+
+2
+00:00:00,840 --> 00:00:07,580
+So this brings us to Section seventeen point two where we talk about advance initializations.
+
+3
+00:00:07,700 --> 00:00:13,250
+So something we sort of glossed over in discourse is that we all the sales starting weights are random
+
+4
+00:00:13,400 --> 00:00:16,440
+or initials all the way it's but are it truly random.
+
+5
+00:00:16,490 --> 00:00:19,170
+And that means are they pulled from a uniform distribution.
+
+6
+00:00:19,460 --> 00:00:21,240
+Well kind of yes.
+
+7
+00:00:21,260 --> 00:00:23,040
+That is the default.
+
+8
+00:00:23,060 --> 00:00:26,970
+However there are a number of other initializations that Carrot's offers.
+
+9
+00:00:27,160 --> 00:00:28,420
+So let's take a look.
+
+10
+00:00:28,550 --> 00:00:33,000
+These are the many initialization functions Kurus can provide us with.
+
+11
+00:00:33,230 --> 00:00:34,780
+And let's take a look at how they look.
+
+12
+00:00:34,790 --> 00:00:35,490
+All right.
+
+13
+00:00:35,600 --> 00:00:43,720
+So this is what uniform distributions one would look like random normal random normal uniform some claret
+
+14
+00:00:43,940 --> 00:00:48,270
+garrote normal orthogonal identity glorify uniform.
+
+15
+00:00:48,410 --> 00:00:54,710
+So you can see that there are some definite differences in these functions you can do a normal distribution
+
+16
+00:00:54,710 --> 00:00:56,380
+style where it's more sent.
+
+17
+00:00:56,420 --> 00:01:01,160
+The bulk of the initializations would be centered around this area 0 you can do some which just randomly
+
+18
+00:01:01,160 --> 00:01:05,720
+between two point one to point zero one that sort of thing.
+
+19
+00:01:05,860 --> 00:01:06,650
+OK.
+
+20
+00:01:07,970 --> 00:01:10,820
+So we have tons of initializers.
+
+21
+00:01:10,820 --> 00:01:12,260
+Which one do we use.
+
+22
+00:01:12,260 --> 00:01:17,690
+So generally we always want to use a zero scented initialization within a small range example minus
+
+23
+00:01:17,690 --> 00:01:18,340
+1 to 1.
+
+24
+00:01:18,350 --> 00:01:19,490
+Typically best.
+
+25
+00:01:19,730 --> 00:01:26,330
+And this was recommended in Stanfords C-s to treat one costs can be division goes which is of course
+
+26
+00:01:26,330 --> 00:01:31,460
+I highly recommend you take it's pretty theoretical but it goes into a lot of detail especially in the
+
+27
+00:01:31,460 --> 00:01:35,170
+training part of CNN's and neural networks as well.
+
+28
+00:01:35,480 --> 00:01:41,810
+So some other good choices for initialises h e normal works pretty well when you're using real activations
+
+29
+00:01:42,470 --> 00:01:44,630
+Gourab normal works pretty well too.
+
+30
+00:01:44,840 --> 00:01:51,360
+And glue rotini form which is the Karris is a default random initialiser if you're going back.
+
+31
+00:01:51,380 --> 00:01:52,560
+That is what it is here.
+
+32
+00:01:54,610 --> 00:02:01,290
+So most times just so you know the initial dose of choice you choose it doesn't really impact your accuracy
+
+33
+00:02:01,290 --> 00:02:02,530
+as you get in the end.
+
+34
+00:02:02,670 --> 00:02:06,620
+However it can definitely impact a number of ebox we take to get there.
+
+35
+00:02:06,900 --> 00:02:14,070
+So it is something you can experiment with maybe after the fact if you if you think for some reason
+
+36
+00:02:14,070 --> 00:02:18,650
+that you read in the research paper that if you're using maybe an inception style model or resin that's
+
+37
+00:02:18,660 --> 00:02:23,940
+50 model it works better with this type of initialises initialization then you change it.
+
+38
+00:02:23,940 --> 00:02:26,360
+Otherwise I would just stick with the Cristi fault.
diff --git a/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.2 Building an Emotion Detector with LittleVGG.ipynb b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.2 Building an Emotion Detector with LittleVGG.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..538d57e665ce150ee5202a50cf8328fed5c2923b
--- /dev/null
+++ b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.2 Building an Emotion Detector with LittleVGG.ipynb	
@@ -0,0 +1,723 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using LittleVGG for Emotion Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training Emotion Detector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 28709 images belonging to 7 classes.\n",
+      "Found 3589 images belonging to 7 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "import os\n",
+    "\n",
+    "num_classes = 7\n",
+    "img_rows, img_cols = 48, 48\n",
+    "batch_size = 16\n",
+    "\n",
+    "train_data_dir = './fer2013/train'\n",
+    "validation_data_dir = './fer2013/validation'\n",
+    "\n",
+    "# Let's use some data augmentaiton \n",
+    "train_datagen = ImageDataGenerator(\n",
+    "      rescale=1./255,\n",
+    "      rotation_range=30,\n",
+    "      shear_range=0.3,\n",
+    "      zoom_range=0.3,\n",
+    "      width_shift_range=0.4,\n",
+    "      height_shift_range=0.4,\n",
+    "      horizontal_flip=True,\n",
+    "      fill_mode='nearest')\n",
+    " \n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    " \n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        train_data_dir,\n",
+    "        color_mode = 'grayscale',\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True)\n",
+    " \n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "        validation_data_dir,\n",
+    "        color_mode = 'grayscale',\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Our Keras Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import BatchNormalization\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+    "from tensorflow.keras.layers import ELU\n",
+    "from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keras LittleVGG Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_8 (Conv2D)            (None, 48, 48, 32)        320       \n",
+      "_________________________________________________________________\n",
+      "activation_11 (Activation)   (None, 48, 48, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_10 (Batc (None, 48, 48, 32)        128       \n",
+      "_________________________________________________________________\n",
+      "conv2d_9 (Conv2D)            (None, 48, 48, 32)        9248      \n",
+      "_________________________________________________________________\n",
+      "activation_12 (Activation)   (None, 48, 48, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_11 (Batc (None, 48, 48, 32)        128       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_4 (MaxPooling2 (None, 24, 24, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout_6 (Dropout)          (None, 24, 24, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_10 (Conv2D)           (None, 24, 24, 64)        18496     \n",
+      "_________________________________________________________________\n",
+      "activation_13 (Activation)   (None, 24, 24, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_12 (Batc (None, 24, 24, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "conv2d_11 (Conv2D)           (None, 24, 24, 64)        36928     \n",
+      "_________________________________________________________________\n",
+      "activation_14 (Activation)   (None, 24, 24, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_13 (Batc (None, 24, 24, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_5 (MaxPooling2 (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout_7 (Dropout)          (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_12 (Conv2D)           (None, 12, 12, 128)       73856     \n",
+      "_________________________________________________________________\n",
+      "activation_15 (Activation)   (None, 12, 12, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_14 (Batc (None, 12, 12, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv2d_13 (Conv2D)           (None, 12, 12, 128)       147584    \n",
+      "_________________________________________________________________\n",
+      "activation_16 (Activation)   (None, 12, 12, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_15 (Batc (None, 12, 12, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_6 (MaxPooling2 (None, 6, 6, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_8 (Dropout)          (None, 6, 6, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_14 (Conv2D)           (None, 6, 6, 256)         295168    \n",
+      "_________________________________________________________________\n",
+      "activation_17 (Activation)   (None, 6, 6, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_16 (Batc (None, 6, 6, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "conv2d_15 (Conv2D)           (None, 6, 6, 256)         590080    \n",
+      "_________________________________________________________________\n",
+      "activation_18 (Activation)   (None, 6, 6, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_17 (Batc (None, 6, 6, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_7 (MaxPooling2 (None, 3, 3, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_9 (Dropout)          (None, 3, 3, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 2304)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 64)                147520    \n",
+      "_________________________________________________________________\n",
+      "activation_19 (Activation)   (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_18 (Batc (None, 64)                256       \n",
+      "_________________________________________________________________\n",
+      "dropout_10 (Dropout)         (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_4 (Dense)              (None, 64)                4160      \n",
+      "_________________________________________________________________\n",
+      "activation_20 (Activation)   (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_19 (Batc (None, 64)                256       \n",
+      "_________________________________________________________________\n",
+      "dropout_11 (Dropout)         (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_5 (Dense)              (None, 7)                 455       \n",
+      "_________________________________________________________________\n",
+      "activation_21 (Activation)   (None, 7)                 0         \n",
+      "=================================================================\n",
+      "Total params: 1,328,167\n",
+      "Trainable params: 1,325,991\n",
+      "Non-trainable params: 2,176\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
+    "                 input_shape = (img_rows, img_cols, 1)))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
+    "                 input_shape = (img_rows, img_cols, 1)))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #5: first set of FC => RELU layers\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Block #6: second set of FC => RELU layers\n",
+    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Block #7: softmax classifier\n",
+    "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation(\"softmax\"))\n",
+    "\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 1795 steps\n",
+      "1795/1795 [==============================] - 607s 338ms/step - loss: 2.0255 - accuracy: 0.2012\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
+    "\n",
+    "                     \n",
+    "checkpoint = ModelCheckpoint(\"emotion_little_vgg.h5\",\n",
+    "                             monitor=\"val_loss\",\n",
+    "                             mode=\"min\",\n",
+    "                             save_best_only = True,\n",
+    "                             verbose=1)\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
+    "                          min_delta = 0, \n",
+    "                          patience = 3,\n",
+    "                          verbose = 1,\n",
+    "                          restore_best_weights = True)\n",
+    "\n",
+    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
+    "\n",
+    "# we put our call backs into a callback list\n",
+    "callbacks = [earlystop, checkpoint] #reduce_lr]\n",
+    "\n",
+    "# We use a very small learning rate \n",
+    "model.compile(loss = 'categorical_crossentropy',\n",
+    "              optimizer = Adam(lr=0.001),\n",
+    "              metrics = ['accuracy'])\n",
+    "\n",
+    "nb_train_samples = 28273\n",
+    "nb_validation_samples = 3534\n",
+    "epochs = 5\n",
+    "\n",
+    "history = model.fit(\n",
+    "    train_generator,\n",
+    "    epochs = epochs)\n",
+    "    callbacks = callbacks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 3589 images belonging to 7 classes.\n",
+      "Confusion Matrix\n",
+      "[[  0   0   0 439   0  52   0]\n",
+      " [  0   0   0  52   0   3   0]\n",
+      " [  0   0   0 486   0  42   0]\n",
+      " [  0   0   0 790   0  89   0]\n",
+      " [  0   0   0 565   0  61   0]\n",
+      " [  0   0   0 496   0  98   0]\n",
+      " [  0   0   0 401   0  15   0]]\n",
+      "Classification Report\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       Angry       0.00      0.00      0.00       491\n",
+      "     Disgust       0.00      0.00      0.00        55\n",
+      "        Fear       0.00      0.00      0.00       528\n",
+      "       Happy       0.24      0.90      0.38       879\n",
+      "     Neutral       0.00      0.00      0.00       626\n",
+      "         Sad       0.27      0.16      0.21       594\n",
+      "    Surprise       0.00      0.00      0.00       416\n",
+      "\n",
+      "    accuracy                           0.25      3589\n",
+      "   macro avg       0.07      0.15      0.08      3589\n",
+      "weighted avg       0.10      0.25      0.13      3589\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Dnp9kG4Ak92i7se8EXN0G6IcDd+6ykpKkKTfAh9LTkEm/B9gXOK/trf1DmjlOPwh8sp3pZTUw3wwukqRbOJu7l6iqdpilbAPwqnbb1CFz3Oq+46yXJEnTaBoyaUmSlmyZ5u7u1DQ8k5YkSbMwk5Yk9V7hM2lJkqZTAQMM0jZ3S5I0pcykJUmDYMcxSZI0MWbSkqRhGGAmbZCWJA1ABtm72+ZuSZKmlJm0JGkYBtjcbSYtSdKUMpOWJPVfDXPGMTNpSZKmlJm0JGkYBvhM2iAtSRoIm7slSdKEmElLkoZhgM3dZtKSJE0pM2lpib516Ae6rsLY/D77d12F8dr19l3XYHyuubbrGky/AWbSBmlJUv8V4DhpSZI0KWbSkqRBqAE2d5tJS5I0pcykJUnDMMBM2iAtSRoGO45JkqRJMZOWJA1CBtjcbSYtSdKUMpOWJPVfMciOY2bSkiRNKTNpSdIAZJC9uw3SkqRhsLlbkiRNipm0JGkYzKQlSdKkmElLkoZhgJm0QVqS1H/FIHt329wtSdKUMkhLkgYhNf5ts9432TrJ15J8qj2+S5Kzk3wnyUeSbNuWb9ceX9Ke33ehexukJUlampcCF40c/x3wtqraD/gRcFRbfhTwo6q6O/C29rp5GaQlScNQy7AtIMnewOOA97THAR4BfLy95Djgie3+4e0x7flHttfPaWxBOslPNzl+bpJ/GNf9JUmaQm8H/iewoT3eFbiuqta1x2uAvdr9vYDLAdrz17fXz8lMWpKkue2WZNXItmLmRJLHA1dX1bkj18+WGddmnJvVRIZgJfkD4DXAtsA1wLOr6qokxwB3o/l2sQ/w91X1r0kOA17fXntP4EvAC4AjgftW1Z+39/1T4N5V9fJJfA5J0vTa3I5eW2htVR00x7mHAk9I8lhge2BHmsx65yS3arPlvYEr2uvX0MS6NUluBewEXDvfm48zk751ktUzG02QnfEV4OCqeiDwYZqmgRn3p2nPPwR4bZI7tuUPBv4CuB9NIH9y+9onJNmmveZI4H2bViTJiplvPb/kpvF9QkmSWlX1yqrau6r2BZ4BfKGqng2cDjy1vewI4KR2/+T2mPb8F6pqYpn0jVW1/8xBkucCM98+9gY+kmRPmmz6eyOvO6mqbgRuTHI6TXC+Djinqi5t7/Uh4NCq+niSLwCPT3IRsE1VXbBpRapqJbASYMfsMsA5aCRJv2Z6JjP5K+DDSf4G+BpwbFt+LPCBJJfQZNDPWOhGk5px7F3AW6vq5LYp+5iRc5sG0Vqg/D3Aq4BvMUsWLUnSpFXVGcAZ7f6lNAnnptf8HHjaltx3Uh3HdgL+q90/YpNzhyfZPsmuwGHAV9vyB7cDwrcCnk7TZE5VnU3Tpv8s4EPLXXFJUg8sx/CrKWiHnVSQPgb4WJIvA2s3OXcO8GngLOANVTXzgP0/gTcC36BpHj9x5DUfBc6sqh8tZ6UlST0ywCA9tubuqtphk+P3A+9v909i44PzTV1cVStmKb+hqp4+x2sOpZmtRZKkwerVOOkkOye5mKaT2mld10eSND26mrt7OXW6VGVVHTNH+Rm0D+A3Kb8OuMeyVkqSpCnhetKSpGGYgsx33AzSkqRhGGCQ7tUzaUmSbknMpCVJvTctHb3GzUxakqQpZSYtSRqG6Zm7e2wM0pKkYbC5W5IkTYqZtCRpEOw4JkmSJsZMWpI0DGbSkiRpUsykJUn9N9DJTAzSkqRhGGCQtrlbkqQpZSYtSRoGM2lJkjQpZtLSEj3hO4/uugpj9N9dV2Cs8rMbu66CJmiIHcfMpCVJmlIGaUmSppTN3ZKkYbC5W5IkTYqZtCSp/5xxTJKkKTbAIG1ztyRJU8pMWpI0DGbSkiRpUsykJUm9F4bZccxMWpKkKWUmLUkahgFm0gZpSVL/DXSctM3dkiRNKTNpSdIwmElLkqRJMZOWJA3DADNpg7QkaRDsOCZJkibGTFqSNAxm0pIkaVLMpCVJ/VeYSc9IUkneMnL8iiTHLPJeOyd5wSJfe1mS3RbzWknSsKTGv3Vtsc3dNwFPHlOA3BmYNUgn2XoM95ckaeySbJ/knCRfT3Jhkr9uy++S5Owk30nykSTbtuXbtceXtOf3Xeg9Fhuk1wErgT+fpdJ3SHJCkq+220Pb8mOSvGLkum+0FXwjcLckq5O8KclhSU5P8m/ABe21n0hybvuHsGKRdZYkDVktwza/m4BHVNUDgP2BRyc5GPg74G1VtR/wI+Co9vqjgB9V1d2Bt7XXzWspHcf+EXh2kp02KX9HW7kHAU8B3rPAfY4GvltV+1fVX7ZlDwZeXVX3aY//uKoOBA4CXpJk1/lumGRFklVJVv2Sm7bkM0mStFmq8dP2cJt2K+ARwMfb8uOAJ7b7h7fHtOcfmSTzvceiO45V1Y+THA+8BLhx5NTvAvcZed8dk9xuC29/TlV9b+T4JUme1O7vA+wHXDNP3VbSZPrsmF2m4KmCJGm5dfEMuX0sey5wd5rk9bvAdVW1rr1kDbBXu78XcDlAVa1Lcj2wK7B2rvsvtXf324HzgPeNlG0FHFJVo4GbJOv41cx9+3nu+7OR1x1GE/gPqaobkpyxwGslSRqX3ZKsGjle2SaCAFTVemD/JDsDJwL3nuUeM18fZsua5/1qsaRx0lV1LfBRNra3A5wCvGjmIMn+7e5lwAFt2QHAXdrynwDzZdo70bTh35DkXsDBS6mzJGmglueZ9NqqOmhkW8ksquo64AyaGLVzkpkkeG/ginZ/DU1rMO35nYBr5/tI45jM5C3AaC/vlwAHJTk/yTeBP2vLTwB2SbIaeD5wMUBVXQOc2XYke9Ms9/8scKsk5wNvAM4aQ50lSUOyHAF6gebztqP0zu3+rWlafS8CTgee2l52BHBSu39ye0x7/gtVNe+7LKq5u6p2GNm/CrjNyPFa4OmzvOZG4PfmuN+zNik6Y+TcTcBj5njdvltQbUmSxmlP4Lj2ufRWwEer6lNtgvrhJH8DfA04tr3+WOADSS6hyaCfsdAbOOOYJKn3wuwPfJdTVZ0PPHCW8ktpRiltWv5z4Glb8h7O3S1J0pQyk5YkDcMAB9wapCVJgzANc22Pm83dkiRNKTNpSdIwmElLkqRJMZOWJA3DADNpg7Qkqf/KjmOSJGmCzKQlScNgJi1JkibFTFqSNAg+k5YkSRNjJi1JGoYBZtIGaUnSINjcLUmSJsZMWpLUf8Ugm7vNpCVJmlJm0tISfXftrl1XYWz25r+7rsJY1U47dF2F8fmvrivQAwPMpA3SkqTeC3YckyRJE2QmLUkaBjNpSZI0KWbSkqRBSA0vlTZIS5L6z3HSkiRpksykJUmD4BAsSZI0MWbSkqRhGGAmbZCWJA2Czd2SJGlizKQlScNgJi1JkibFTFqS1H/lM2lJkjRBZtKSpGEYYCZtkJYk9V6wuVuSJE2QmbQkaRgGuFSlmbQkSVPKTFqSNAg+kx6zJK9OcmGS85OsTvKQzXzdvkm+sdz1kyT1RC3T1rHOMukkhwCPBw6oqpuS7AZs21V9JEmaNl02d+8JrK2qmwCqai1AktcCfwDcGvgP4HlVVUkOBN4L3AB8pZsqS5KmVTZ0XYPx67K5+xRgnyQXJ/mnJL/Tlv9DVT2oqu5LE6gf35a/D3hJVR2y0I2TrEiyKsmqX3LT8tRekqRl1lmQrqqfAgcCK4AfAh9J8lzg4UnOTnIB8AjgN5PsBOxcVV9sX/6BBe69sqoOqqqDtmG75fsQkqTp4TPp8aqq9cAZwBltUH4ecH/goKq6PMkxwPY0k8lMwR+XJGladdG7O8k+wPHAbwAbgJVV9Y4kuwAfAfYFLgP+sKp+lCTAO4DH0jy+fW5VnTfX/TvLpJPcM8l+I0X7A99u99cm2QF4KkBVXQdcn+TQ9vyzJ1dTSZLmtA74i6q6N3Aw8MIk9wGOBk6rqv2A09pjgMcA+7XbCuDd8928y0x6B+BdSXam+ZCX0FT4OuACmm8eXx25/kjgvUluAD432apKkqZa0cmMY1V1JXBlu/+TJBcBewGHA4e1lx1H02r8V2358VVVwFlJdk6yZ3ufX9NZkK6qc4HfmuXUa9pttusfMFJ0zPLUTJKkLZdkX+CBwNnAHjOBt6quTLJ7e9lewOUjL1vTlk1XkJYkaZyW6Zn0bklWjRyvrKqVv/bezSPaE4CXVdWPm0fPs5rtxJw1N0hLkjS3tVV10HwXJNmGJkB/sKr+vS2+aqYZO8mewNVt+Rpgn5GX7w1cMde9XWBDkjQMHQzBantrHwtcVFVvHTl1MnBEu38EcNJI+XPSOBi4fq7n0WAmLUkagNDZAhsPBf4HcEGS1W3Zq4A3Ah9NchTwA+Bp7bnP0Ay/uoRmCNaR893cIC1J0iJV1VeY/TkzwCNnub6AF27u/Q3SkqT+q+pkCNZy85m0JElTykxakjQIHT2TXlYGaUnSMAwwSNvcLUnSlDKTliQNwhCbu82kJUmaUmbSkqT+K2DD8FJpg7QkaRiGF6Nt7pYkaVqZSUuSBsGOY5IkaWLMpCVJw+Dc3ZIkaVLMpKUl+vnlt+u6CprDhu9c1nUVNEFDfCZtkJYk9V/hECxJkjQ5ZtKSpN4LEDuOSZKkSTGTliQNw4auKzB+BmlJ0iDY3C1JkibGTFqS1H8OwZIkSZNkJi1JGoAa5NzdBmlJ0iAMcVpQm7slSZpSZtKSpGEYYHO3mbQkSVPKTFqS1H8FGeCMY2bSkiRNKTNpSdIwDPCZtEFakjQMw4vRNndLkjStzKQlSYPgKliSJGlizKQlScNwS82kk7w6yYVJzk+yOslDlqMyST6TZOfluLckacAK2LAMW8cWzKSTHAI8Hjigqm5Kshuw7ebcPMmtqmrdZlwXIFX12M25ryRJtwSbk0nvCaytqpsAqmptVV2R5LI2YJPkoCRntPvHJFmZ5BTg+CTPTXJSks8m+XaS17XX7ZvkoiT/BJwH7DNzzyS3TfLpJF9P8o0kT29fc2CSLyY5N8nnkuw5/j8SSVLfhCI1/q1rm/NM+hTgtUkuBj4PfKSqvrjAaw4EDq2qG5M8F3gwcF/gBuCrST4NrAXuCRxZVS8AaBJqAB4NXFFVj2vLd0qyDfAu4PCq+mEbuP838MebvnmSFcCK9vCnn6+Pf3szPudS7EbzeYbAz7KlXvrxZX8LJvRZLlvuN2hM7t/YL5b9Hfz/suXuPIH3GIwFg3RV/TTJgcBvAw8HPpLk6AVednJV3ThyfGpVXQOQ5N+BQ4FPAN+vqrNmef0FwJuT/B3wqar6cpL70gT6U9tgvjVw5Rx1XgmsXOizjUuSVVV10KTebzn5WaaTn2U6+VmmzBRkvuO2Wb27q2o9cAZwRpILgCOAdWxsLt9+k5f8bNNbzHG86XUz73dx+8XgscDftk3nJwIXVtUhm1NnSdItzACD9ILPpJPcM8l+I0X7A9+naRk7sC17ygK3eVSSXZLcGngicOYC73lH4Iaq+r/Am4EDgG8Dd2g7spFkmyS/uVD9JUnqq83JpHcA3tUOjVoHXELzvPfewLFJXgWcvcA9vgJ8ALg78G9VtSrJvvNcfz/gTUk2AL8Enl9Vv0jyVOCdSXZq6/524MLN+AzLbWJN6xPgZ5lOfpbp5GeZFjNDsAYmtczNA23HsYOq6kXL+kaSpFusnW5zxzr4nn869vuesvr153b5rN5pQSVJg9DFEKwk701ydZJvjJTtkuTUJN9pf96+LU+Sdya5pJ0c7ICF7r/sQbqq3m8WLUkaqPfTDBsedTRwWlXtB5zWHgM8Btiv3VYA717o5mbSkqRhqBr/tuBb1peAazcpPhw4rt0/jqbD9Ez58dU4C9h5oUm5DNKL0I7ZHowkL92csmnXNiXt03U9JHVhGQL04vts7VFVVwK0P3dvy/cCLh+5bk1bNieD9OL8c5JzkrxgIAuCHDFL2XMnXYmlqqYX5Ce6rse4JHlz34cZts/m5ty6rt+WSHJB+xxx1q3r+i1Gkj2SHJvk/7XH90lyVNf1mjK7JVk1sq1Y+CVzyixl834TcKnKRaiqQ9ux438MrEpyDvC+qjq146ptkSTPBJ4F3CXJySOndgSu6aZWS3ZWkgdV1Ve7rsgYfAtYmeRWwPuAD1XV9R3XaUudS/NLaK5fTnedbJJWAskAAA4rSURBVHWW5PHtzxe2Pz/Q/nw2zZTHffR+mn9br26PLwY+AhzbVYUWrViuyUzWLqJ391VJ9qyqK9vm7Kvb8jXAaGvf3sAV893IIL1IVfWdJK8BVgHvBB6YZr7SV1XVv3dbu832HzRTq+4GvGWk/CdALzMDmqlrn5fk+zQz2oUmyb5/t9XaclX1HuA9Se4JHAmcn+RM4F+r6vRua7d5quouXddhXKrq+wBJHlpVDx05dXT79/L6bmq2JLtV1UeTvBKgqtYlWd91pQbgZJoWyje2P08aKX9Rkg8DDwGun2kWn4tBehGS3J/ml+bjgFOBP6iq89qZ0v4T6EWQbn/pfD/J7wI3VtWGJPcA7kUzf3ofPabrCoxTkq1p/j7uRbP4wdeBlyd5XlU9o9PKbaF2GMp+jEwj3Ha66ZvbJjm0qr4CkOS3gNt2XKfF+lmSXWmbXJMcDPSttWajDiYzSfIh4DCaZvE1wOtogvNH20cHPwCe1l7+GZrpri+haX05cqH7G6QX5x+Af6XJmm9eSKRdwvM13VVr0b4E/Hb7S/Q0mtaBp9M04/XKSLazO78+p3yvJHkr8ASav5P/U1XntKf+Lslyr+w2Vkn+BHgpTfPeauBgmi+0j+iyXot0FPDeduZDgOuYZTW+nng5TXZ3t7Y14A7AU7ut0uJ1sbRkVT1zjlOPnOXaYuPjks1ikN5CbWZzeVV9YLbzc5VPuVTVDe23vndV1d8n+VrXlVqMJE+gabq/I81zoDsDFwF97ID1DeA1VTXb884HT7oyS/RS4EHAWVX18CT3Av664zotSlWdCzwgyY40/3d6m3m2LYC/Q7NscIBvV9UvO66WRhikt1BVrU+ya5Jtq2r5V6udjLQLlzybJkuA/v7beANNlvb5qnpgkocDc33TnXbvA56U5FCa5sivVNWJAD0MDD+vqp8nIcl2VfWt9ll7LyV5HM0Xv+2brihQVb17Jp3kacBnq+rCthXwgCR/U1XndV23RRngKlh9/UXcte8DZ7Y9om9ebrOq3tpdlZbkZcArgRPb/6x3BXrRMWkWv6yqa5JslWSrqjo9zbrkffSPNIvSfKg9fl6S362qLWoumxJr2uGKn6BZE/5HLNCrdVol+WfgNjSdFN9D0zx8zrwvml7/q6o+1n4R/H2aVQffTdOpSVPAIL04V7TbVsDtOq7LklXVF4EvjhxfCrykuxotyXVJdgC+DHwwydU0q7f10e8A922fY5HkOHraoa+qntTuHpPkdGAn4LMdVmkpfquq7p/k/Kr66yRvoSedRWcx05P7ccC7q+qkJMd0WJ/FK2CDmbSAqurls7S5tL80f+1fd1X1sVPP4cCNNK0Dz6YJBr1rhmx9G7gTTcsNNOMrezc0LslWwPlVdV+4+Uthn810Fr2hHdFxLdDXoWb/leRfgN+l6ZC4Hb2d5GpJM4RNLYP0IiT5JL8e1K6n6RX9L1X188nXakleMbK/PfAUepp9VtXPktwZ2K+qjktyG2Drruu1SLsCF7WT5UDT8eo/ZyaeqaondFazLdAO7ft6kjtV1Q+6rs8YfKptuv97mslaoGn27qM/pFkc4s1VdV078cZfdlwnjTBIL86lNEMVZp4VPh24CrgHzdCs/9FRvRal7a066swkvcx2kvwpzeoyuwB3o5kX95+ZZThED7y26wqM0Z7Ahe0XjtF+HL34ogGQ5EE0Izve0B7vQPP44VvA27qs25ZKsmNV/ZjmS/kZbdkuwE00yUY/mUmr9cCqetjI8SeTfKmqHpbkws5qtUibzKG8FXAg8BsdVWepXkgzPOlsuHlmuN3nf8l0qqovJvkNms9TwFer6r87rtZiDeER0UyzMEkeRjNhxYuB/YGV9Gt88b/RTHM627StfZuuddAM0otzh9GmuyR3oplaE6CPw7JG/6OuA77HxqFYfXNTVf1iZlhMO+91L79etxOAvBb4As3fzbuSvL6q3tttzRblsVX1V6MFba/7PrXYbF1VM0sSPh1YWVUnACckWd1hvbZYVT2+ncb4dwbyCKJhJq3WXwBfSfJdml+edwFekOS2bFxDtDeGNL8y8MUkrwJuneRRwAuAT3Zcp8X6S5pWm2sA2ukb/wPoY5B+FPBXm5Q9ZpayabZ1kltV1TqaxyejqyH17ndpVVWSE2lazjSlevcPaxpU1WfSrIJ1L5og/a2RzmJv765mi5PkybMUXw9cUFVXz3Jumh1N0wpwAfA8mrly+9qpZw3NYiczfsKvrkU79ZI8n+aL0t3yq8s53o7mC0effIjmS+Bamh7eXwZIcnf6O9/1cFaNG+gQrNQAmwcmoZ1Uf19GvuhU1fGdVWgJknwaOISNE5gcBpxF0xHu9X2Y6nRAPYdvluR44H40K+gUzfCyc2iWE+zF5Dnt/Na3B/6W5gvUjJ+MNB33RrsAxZ7AKVX1s7bsHsAOfZylK8k3af6f937VuJ2226N+647jX27gs5e97dxFLFU5NmbSi5DkAzQ9h1ezcTKAAnoZpGnWjrl3VV0FzULwbJx16EtsXDd3mn0COAAgyQlV9ZSO6zMO3223GTPL3fVmAp12+tLrk2zarL1Dkh369sWqqs6apeziLuoyJoNaNW6IDNKLcxBwnxpOM8S+MwG6dTVwj6q6NklfJtsf7Z06iJ6pA5s059Ns7Jy4PU0/jm/Tz4VPBqOqvp/kAGBmfvgz+9gicLPB/EreyCC9ON+gGaI072LdPfLlJJ8CPtYePxX4UtsR7rruqrVFao793kpyB+B/0i7kMFPex5ngqup+o8dtYHheR9VRK8lradY6npnW9H1JPlZVf9NhtTTCIL04uwHfbCdmuKktq6o6vMM6LcULgSfTfJsOTQ/1E9qWgod3WbEt8IAkP6ap/63bfdj4jG3H7qq2aB8EPkIznvXPgCOAH3ZaozFpl0h8UNf1EM+kGUHwc4AkbwTOA/oXpAfaccwgvTjHjOyHJrj1dTlEaFb0+URVndAuH3hPmn8bfWnqpqr6OvXnfHatqmOTvHRmEZQezwT38pHDrWj6DwziC0fPXUbTSjMzOmU7frUfRL/Y3C24eSao/YFn0cx9+z2aqSf76kvAbye5PfB5mmkBn06zQIW6M/Ml6cp2/eIrgL07rM9SjHZ2W0fzjPqEjuqijW6ima71VJpc9FE0c0C8E6Cq+roa3mAYpLdAO9TiGTRZ8zU0TZGpqr40Cc8lVXVDkqOAd1XV3yf5WteVEn/TDmH6C+BdwI7An3dbpcWZ6QSX5LYzQ5c0FU5stxlndFSP8TCTvsX7Fs0EBn9QVZcAJOnlL81NJMkhNJnzzHSg/tvoWFV9qt29nv70DZhV++/rWGAH4E5JHgA8r6pe0G3NbrmSbA08qqr+qOu6aG7+It4yT6HJpE9P8lngw/zq0J++ehnwSuDEqrowyV3ZOLGJJizJu5inh3pPmyDfDvw+MLPM5tfbRSrUkapan+QOSbatqj6uObAJ15O+xauqE4ET26FJT6RpetwjybtpAtwpnVZwkWY6JY0cXwr0MRAMxehSgX8NvK6rioxTVV0+s/BJa/1c12piLqNZmvZkfnUJ0amfze7XFLBhQ9e1GDuD9CK0z9Q+CHywXebxaTRTHvYqSCd5e1W9LMknmSVz69Nav0NSVTcv0pLkZaPHPXZ5O5VuJdmW5kvgRR3XSU1nxCtoetz3Zia7WxKD9BK18w//S7v1zcx0n2/utBaaz1Da7/4MeAewF83CIafQjM9XhwY2q53N3RqWqjq3/fnFdnYrqsqxqxq7qlqLQ/qmTpLTmb0VrXez2g2VQfoWrF30/XXAi2g6wG2VZB3NMKzXd1q5W7AkP2HjL87b9Hn2tHbayblUVb1hYpXRbF4xsr89TefYdR3VZenMpDUwLwMeCjyoqr4H0PbsfneSP6+qt3Vau1uoqhrSs8HZxkTflmao366AQbpDM61pI87s66x2Q2WQvmV7Ds04ybUzBVV1aZI/onlmaJDWklTVW2b2k9wOeClwJM3wxbfM9TpNRtvxdcZWNCv8/UZH1Vmicu5uDc42owF6RlX9MMk2XVRIw9MGgpfTPJM+Djigqn7Uba3UOpeNj1bW0QzJOmrOq6dZQZVDsDQs801gMIDJDdS1JG+iWWFtJXC/qvppx1US0K5AdnlV3aU9PoLmefRlwDc7rJo2sVXXFVCnHpDkx7NsPwHut+CrpYX9BXBH4DXAFaP/xkY6xGny/oX2i3g789vf0rRyXE/zhaqfNtT4t46ZSd+CDXR5R02RqjIRmE5bt3M8QLPi3cqqOgE4IcnqDuulTfgfSJJuebZOMpOkPRL4wsi5/iZvVePfOtbfvwxJ0mJ9CPhikrXAjTSr+5Hk7jRN3v1T5dzdkqT+q6r/neQ0YE/glKqbU8atgBd3VzNtyiAtSbdAVXXWLGUXd1GXsZmC5ulx85m0JElTykxakjQI5TNpSZKm0XT0xh43m7slSZpSZtKSpP4rpmKGsHEzk5YkaUqZSUuShmGAq2CZSUuSNKXMpCVJvVdADfCZtEFaktR/VTZ3S5KkjZI8Osm3k1yS5Ohx399MWpI0CJNu7k6yNfCPwKOANcBXk5xcVd8c13uYSUuStDgPBi6pqkur6hfAh4HDx/kGZtKSpGGY/DPpvYDLR47XAA8Z5xsYpCVJvfcTfvS5z9fHd1uGW2+fZNXI8cqqWtnuZ5brx9rmbpCWJPVeVT26g7ddA+wzcrw3cMU438Bn0pIkLc5Xgf2S3CXJtsAzgJPH+QZm0pIkLUJVrUvyIuBzwNbAe6vqwnG+R2qA629KkjQENndLkjSlDNKSJE0pg7QkSVPKIC1J0pQySEuSNKUM0pIkTSmDtCRJU8ogLUnSlPr/Z6bR2NdNyw4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 576x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import sklearn\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import numpy as np\n",
+    "\n",
+    "nb_train_samples = 28273\n",
+    "nb_validation_samples = 3534\n",
+    "\n",
+    "# We need to recreate our validation generator with shuffle = false\n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "        validation_data_dir,\n",
+    "        color_mode = 'grayscale',\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=False)\n",
+    "\n",
+    "class_labels = validation_generator.class_indices\n",
+    "class_labels = {v: k for k, v in class_labels.items()}\n",
+    "classes = list(class_labels.values())\n",
+    "\n",
+    "#Confution Matrix and Classification Report\n",
+    "Y_pred = model.predict(validation_generator)\n",
+    "y_pred = np.argmax(Y_pred, axis=1)\n",
+    "\n",
+    "print('Confusion Matrix')\n",
+    "print(confusion_matrix(validation_generator.classes, y_pred))\n",
+    "print('Classification Report')\n",
+    "target_names = list(class_labels.values())\n",
+    "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))\n",
+    "\n",
+    "plt.figure(figsize=(8,8))\n",
+    "cnf_matrix = confusion_matrix(validation_generator.classes, y_pred)\n",
+    "\n",
+    "plt.imshow(cnf_matrix, interpolation='nearest')\n",
+    "plt.colorbar()\n",
+    "tick_marks = np.arange(len(classes))\n",
+    "_ = plt.xticks(tick_marks, classes, rotation=90)\n",
+    "_ = plt.yticks(tick_marks, classes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading our saved model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model\n",
+    "\n",
+    "classifier = load_model('emotion_little_vgg.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get our class labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 3589 images belonging to 7 classes.\n",
+      "{0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Neutral', 5: 'Sad', 6: 'Surprise'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "        validation_data_dir,\n",
+    "        color_mode = 'grayscale',\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=False)\n",
+    "\n",
+    "class_labels = validation_generator.class_indices\n",
+    "class_labels = {v: k for k, v in class_labels.items()}\n",
+    "classes = list(class_labels.values())\n",
+    "print(class_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's test on some of validation images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import re\n",
+    "\n",
+    "def draw_test(name, pred, im, true_label):\n",
+    "    BLACK = [0,0,0]\n",
+    "    expanded_image = cv2.copyMakeBorder(im, 160, 0, 0, 300 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
+    "    cv2.putText(expanded_image, \"predited - \"+ pred, (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)\n",
+    "    cv2.putText(expanded_image, \"true - \"+ true_label, (20, 120) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
+    "    cv2.imshow(name, expanded_image)\n",
+    "\n",
+    "\n",
+    "def getRandomImage(path, img_width, img_height):\n",
+    "    \"\"\"function loads a random images from a random folder in our test path \"\"\"\n",
+    "    folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path)))\n",
+    "    random_directory = np.random.randint(0,len(folders))\n",
+    "    path_class = folders[random_directory]\n",
+    "    file_path = path + path_class\n",
+    "    file_names = [f for f in listdir(file_path) if isfile(join(file_path, f))]\n",
+    "    random_file_index = np.random.randint(0,len(file_names))\n",
+    "    image_name = file_names[random_file_index]\n",
+    "    final_path = file_path + \"/\" + image_name\n",
+    "    return image.load_img(final_path, target_size = (img_width, img_height),grayscale=True), final_path, path_class\n",
+    "\n",
+    "# dimensions of our images\n",
+    "img_width, img_height = 48, 48\n",
+    "\n",
+    "# We use a very small learning rate \n",
+    "model.compile(loss = 'categorical_crossentropy',\n",
+    "              optimizer = RMSprop(lr = 0.001),\n",
+    "              metrics = ['accuracy'])\n",
+    "\n",
+    "files = []\n",
+    "predictions = []\n",
+    "true_labels = []\n",
+    "\n",
+    "# predicting images\n",
+    "for i in range(0, 10):\n",
+    "    path = './fer2013/validation/' \n",
+    "    img, final_path, true_label = getRandomImage(path, img_width, img_height)\n",
+    "    files.append(final_path)\n",
+    "    true_labels.append(true_label)\n",
+    "    x = image.img_to_array(img)\n",
+    "    x = x * 1./255\n",
+    "    x = np.expand_dims(x, axis=0)\n",
+    "    images = np.vstack([x])\n",
+    "    classes = model.predict_classes(images, batch_size = 10)\n",
+    "    predictions.append(classes)\n",
+    "    \n",
+    "for i in range(0, len(files)):\n",
+    "    image = cv2.imread((files[i]))\n",
+    "    image = cv2.resize(image, None, fx=3, fy=3, interpolation = cv2.INTER_CUBIC)\n",
+    "    draw_test(\"Prediction\", class_labels[predictions[i][0]], image, true_labels[i])\n",
+    "    cv2.waitKey(0)\n",
+    "\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test on a single image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "from tensorflow.keras.preprocessing.image import img_to_array\n",
+    "\n",
+    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
+    "\n",
+    "def face_detector(img):\n",
+    "    # Convert image to grayscale\n",
+    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
+    "    if faces is ():\n",
+    "        return (0,0,0,0), np.zeros((48,48), np.uint8), img\n",
+    "    \n",
+    "    allfaces = []   \n",
+    "    rects = []\n",
+    "    for (x,y,w,h) in faces:\n",
+    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
+    "        roi_gray = gray[y:y+h, x:x+w]\n",
+    "        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "        allfaces.append(roi_gray)\n",
+    "        rects.append((x,w,y,h))\n",
+    "    return rects, allfaces, img\n",
+    "\n",
+    "img = cv2.imread(\"rajeev.jpg\")\n",
+    "rects, faces, image = face_detector(img)\n",
+    "\n",
+    "i = 0\n",
+    "for face in faces:\n",
+    "    roi = face.astype(\"float\") / 255.0\n",
+    "    roi = img_to_array(roi)\n",
+    "    roi = np.expand_dims(roi, axis=0)\n",
+    "\n",
+    "    # make a prediction on the ROI, then lookup the class\n",
+    "    preds = classifier.predict(roi)[0]\n",
+    "    label = class_labels[preds.argmax()]   \n",
+    "\n",
+    "    #Overlay our detected emotion on our pic\n",
+    "    label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
+    "    i =+ 1\n",
+    "    cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
+    "    \n",
+    "cv2.imshow(\"Emotion Detector\", image)\n",
+    "cv2.waitKey(0)\n",
+    "\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's try this on our webcam\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "from time import sleep\n",
+    "from tensorflow.keras.preprocessing.image import img_to_array\n",
+    "\n",
+    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
+    "\n",
+    "def face_detector(img):\n",
+    "    # Convert image to grayscale\n",
+    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
+    "    if faces is ():\n",
+    "        return (0,0,0,0), np.zeros((48,48), np.uint8), img\n",
+    "    \n",
+    "    for (x,y,w,h) in faces:\n",
+    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
+    "        roi_gray = gray[y:y+h, x:x+w]\n",
+    "\n",
+    "    try:\n",
+    "        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "    except:\n",
+    "        return (x,w,y,h), np.zeros((48,48), np.uint8), img\n",
+    "    return (x,w,y,h), roi_gray, img\n",
+    "\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "\n",
+    "    ret, frame = cap.read()\n",
+    "    rect, face, image = face_detector(frame)\n",
+    "    if np.sum([face]) != 0.0:\n",
+    "        roi = face.astype(\"float\") / 255.0\n",
+    "        roi = img_to_array(roi)\n",
+    "        roi = np.expand_dims(roi, axis=0)\n",
+    "\n",
+    "        # make a prediction on the ROI, then lookup the class\n",
+    "        preds = classifier.predict(roi)[0]\n",
+    "        label = class_labels[preds.argmax()]  \n",
+    "        label_position = (rect[0] + int((rect[1]/2)), rect[2] + 25)\n",
+    "        cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0), 3)\n",
+    "    else:\n",
+    "        cv2.putText(image, \"No Face Found\", (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,2, (0,255,0), 3)\n",
+    "        \n",
+    "    cv2.imshow('All', image)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "        \n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3A - Age, Gender Detection.ipynb b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3A - Age, Gender Detection.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1a0ee049e8efcfd8eb9d24034deedb62810159e3
--- /dev/null
+++ b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3A - Age, Gender Detection.ipynb	
@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's run our Age and Gender Detector\n",
+    "\n",
+    "- Please see https://github.com/yu4u/age-gender-estimation for source code project.\n",
+    "- In this notebook we re-use the model trained by yu4u"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5\n",
+      "195854336/195848088 [==============================] - 173s 1us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import cv2\n",
+    "import dlib\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import argparse\n",
+    "from contextlib import contextmanager\n",
+    "from wide_resnet import WideResNet\n",
+    "from tensorflow.keras.utils import get_file\n",
+    "\n",
+    "# Load our cassade classifier for faces\n",
+    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
+    "\n",
+    "# Load our pretrained model for Gender and Age Detection\n",
+    "pretrained_model = \"https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5\"\n",
+    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
+    "\n",
+    "# Face Detection function\n",
+    "def face_detector(img):\n",
+    "    # Convert image to grayscale for faster detection\n",
+    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
+    "    if faces is ():\n",
+    "        return False ,(0,0,0,0), np.zeros((1,48,48,3), np.uint8), img\n",
+    "    \n",
+    "    allfaces = []   \n",
+    "    rects = []\n",
+    "    for (x,y,w,h) in faces:\n",
+    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
+    "        roi = img[y:y+h, x:x+w]\n",
+    "        roi_groiray = cv2.resize(roi, (64, 64), interpolation = cv2.INTER_AREA)\n",
+    "        allfaces.append(roi)\n",
+    "        rects.append((x,w,y,h))\n",
+    "    return True, rects, allfaces, img\n",
+    "\n",
+    "# Define our model parameters\n",
+    "depth = 16\n",
+    "k = 8\n",
+    "weight_file = None\n",
+    "margin = 0.4\n",
+    "image_dir = None\n",
+    "\n",
+    "# Get our weight file \n",
+    "if not weight_file:\n",
+    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
+    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
+    "\n",
+    "# load model and weights\n",
+    "img_size = 64\n",
+    "model = WideResNet(img_size, depth=depth, k=k)()\n",
+    "model.load_weights(weight_file)\n",
+    "\n",
+    "# Initialize Webcam\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    ret, rects, faces, image = face_detector(frame)\n",
+    "    preprocessed_faces = []\n",
+    "    i = 0\n",
+    "    if ret:\n",
+    "        for (i,face) in enumerate(faces):\n",
+    "            face = cv2.resize(face, (64, 64), interpolation = cv2.INTER_AREA)\n",
+    "            preprocessed_faces.append(face)\n",
+    "\n",
+    "        # make a prediction on the faces detected\n",
+    "        results = model.predict(np.array(preprocessed_faces))\n",
+    "        predicted_genders = results[0]\n",
+    "        ages = np.arange(0, 101).reshape(101, 1)\n",
+    "        predicted_ages = results[1].dot(ages).flatten()\n",
+    "\n",
+    "        # draw results\n",
+    "        for (i, f) in enumerate(faces):\n",
+    "            label = \"{}, {}\".format(int(predicted_ages[i]),\n",
+    "                                        \"F\" if predicted_genders[i][0] > 0.5 else \"M\")\n",
+    "\n",
+    "        #Overlay our detected emotion on our pic\n",
+    "        label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
+    "        i =+ 1\n",
+    "        cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
+    "\n",
+    "    cv2.imshow(\"Emotion Detector\", image)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Note if you get the following error, you need to enable your webcam \n",
+    "<img src=\"error.jpg\">\n",
+    "### Enable your webcam by doing the following:\n",
+    "<img src=\"webcam.jpg\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run these lines if your webcam fails to be realesed due to error in code\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3B Age, Gender with Emotion.ipynb b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3B Age, Gender with Emotion.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..25a6862ab9fce1d37c2ea0d1c4a387cb7283027a
--- /dev/null
+++ b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/18.3B Age, Gender with Emotion.ipynb	
@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Age, Gender and Emotion Detection\n",
+    "\n",
+    "### Let's load our classfiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import cv2\n",
+    "import dlib\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import argparse\n",
+    "from contextlib import contextmanager\n",
+    "from wide_resnet import WideResNet\n",
+    "from tensorflow.keras.utils import get_file\n",
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.preprocessing.image import img_to_array\n",
+    "\n",
+    "classifier = load_model('emotion_little_vgg.h5')\n",
+    "face_classifier = cv2.CascadeClassifier('./Haarcascades/haarcascade_frontalface_default.xml')\n",
+    "pretrained_model = \"https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing our Emotion, Age and Gender Detector - Using Webcam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
+    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
+    "\n",
+    "def face_detector(img):\n",
+    "    # Convert image to grayscale for faster detection\n",
+    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
+    "    if faces is ():\n",
+    "        return False ,(0,0,0,0), np.zeros((1,48,48,3), np.uint8), img\n",
+    "    \n",
+    "    allfaces = []   \n",
+    "    rects = []\n",
+    "    for (x,y,w,h) in faces:\n",
+    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
+    "        roi = img[y:y+h, x:x+w]\n",
+    "        allfaces.append(roi)\n",
+    "        rects.append((x,w,y,h))\n",
+    "    return True, rects, allfaces, img\n",
+    "\n",
+    "# Define our model parameters\n",
+    "depth = 16\n",
+    "k = 8\n",
+    "weight_file = None\n",
+    "margin = 0.4\n",
+    "image_dir = None\n",
+    "\n",
+    "# Get our weight file \n",
+    "if not weight_file:\n",
+    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
+    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
+    "\n",
+    "# load model and weights\n",
+    "img_size = 64\n",
+    "model = WideResNet(img_size, depth=depth, k=k)()\n",
+    "model.load_weights(weight_file)\n",
+    "\n",
+    "# Initialize Webcam\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    ret, rects, faces, image = face_detector(frame)\n",
+    "    preprocessed_faces_ag = []\n",
+    "    preprocessed_faces_emo = []\n",
+    "    \n",
+    "    if ret:\n",
+    "        for (i,face) in enumerate(faces):\n",
+    "            face_ag = cv2.resize(face, (64, 64), interpolation = cv2.INTER_AREA)\n",
+    "            preprocessed_faces_ag.append(face_ag)\n",
+    "\n",
+    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
+    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
+    "            face_gray_emo = img_to_array(face_gray_emo)\n",
+    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
+    "            preprocessed_faces_emo.append(face_gray_emo)\n",
+    "            \n",
+    "        # make a prediction for Age and Gender\n",
+    "        results = model.predict(np.array(preprocessed_faces_ag))\n",
+    "        predicted_genders = results[0]\n",
+    "        ages = np.arange(0, 101).reshape(101, 1)\n",
+    "        predicted_ages = results[1].dot(ages).flatten()\n",
+    "\n",
+    "        # make a prediction for Emotion \n",
+    "        emo_labels = []\n",
+    "        for (i, face) in enumerate(faces):\n",
+    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
+    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
+    "        \n",
+    "        # draw results, for Age and Gender\n",
+    "        for (i, face) in enumerate(faces):\n",
+    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
+    "                                        \"F\" if predicted_genders[i][0] > 0.6 else \"M\",\n",
+    "                                        emo_labels[i])\n",
+    "            \n",
+    "        #Overlay our detected emotion on our pic\n",
+    "        for (i, face) in enumerate(faces):\n",
+    "            label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
+    "            cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_PLAIN,1, (0,255,0), 2)\n",
+    "\n",
+    "    cv2.imshow(\"Emotion Detector\", image)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing our Emotion, Age and Gender Detector - On Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[WinError 3] The system cannot find the path specified: './images/'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-d6f5a6d6ebc5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mimage_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mf\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mimage_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mimage_names\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: './images/'"
+     ]
+    }
+   ],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import os\n",
+    "import cv2\n",
+    "\n",
+    "# Define Image Path Here\n",
+    "image_path = \"./images/\"\n",
+    "\n",
+    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
+    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
+    "\n",
+    "def face_detector(img):\n",
+    "    # Convert image to grayscale for faster detection\n",
+    "    gray = cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
+    "    if faces is ():\n",
+    "        return False ,(0,0,0,0), np.zeros((1,48,48,3), np.uint8), img\n",
+    "    \n",
+    "    allfaces = []   \n",
+    "    rects = []\n",
+    "    for (x,y,w,h) in faces:\n",
+    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
+    "        roi = img[y:y+h, x:x+w]\n",
+    "        allfaces.append(roi)\n",
+    "        rects.append((x,w,y,h))\n",
+    "    return True, rects, allfaces, img\n",
+    "\n",
+    "# Define our model parameters\n",
+    "depth = 16\n",
+    "k = 8\n",
+    "weight_file = None\n",
+    "margin = 0.4\n",
+    "image_dir = None\n",
+    "\n",
+    "# Get our weight file \n",
+    "if not weight_file:\n",
+    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
+    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
+    "# load model and weights\n",
+    "img_size = 64\n",
+    "model = WideResNet(img_size, depth=depth, k=k)()\n",
+    "model.load_weights(weight_file)\n",
+    "\n",
+    "image_names = [f for f in listdir(image_path) if isfile(join(image_path, f))]\n",
+    "\n",
+    "for image_name in image_names:\n",
+    "    frame = cv2.imread(\"./images/\" + image_name)\n",
+    "    ret, rects, faces, image = face_detector(frame)\n",
+    "    preprocessed_faces_ag = []\n",
+    "    preprocessed_faces_emo = []\n",
+    "    \n",
+    "    if ret:\n",
+    "        for (i,face) in enumerate(faces):\n",
+    "            face_ag = cv2.resize(face, (64, 64), interpolation = cv2.INTER_AREA)\n",
+    "            preprocessed_faces_ag.append(face_ag)\n",
+    "\n",
+    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
+    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
+    "            face_gray_emo = img_to_array(face_gray_emo)\n",
+    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
+    "            preprocessed_faces_emo.append(face_gray_emo)\n",
+    "            \n",
+    "        # make a prediction for Age and Gender\n",
+    "        results = model.predict(np.array(preprocessed_faces_ag))\n",
+    "        predicted_genders = results[0]\n",
+    "        ages = np.arange(0, 101).reshape(101, 1)\n",
+    "        predicted_ages = results[1].dot(ages).flatten()\n",
+    "\n",
+    "        # make a prediction for Emotion \n",
+    "        emo_labels = []\n",
+    "        for (i, face) in enumerate(faces):\n",
+    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
+    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
+    "        \n",
+    "        # draw results, for Age and Gender\n",
+    "        for (i, face) in enumerate(faces):\n",
+    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
+    "                                        \"F\" if predicted_genders[i][0] > 0.4 else \"M\",\n",
+    "                                        emo_labels[i])\n",
+    "            \n",
+    "        #Overlay our detected emotion on our pic\n",
+    "        for (i, face) in enumerate(faces):\n",
+    "            label_position = (rects[i][0] + int((rects[i][1]/2)), abs(rects[i][2] - 10))\n",
+    "            cv2.putText(image, label, label_position , cv2.FONT_HERSHEY_PLAIN,1, (0,255,0), 2)\n",
+    "\n",
+    "    cv2.imshow(\"Emotion Detector\", image)\n",
+    "    cv2.waitKey(0)\n",
+    "\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using Dlib's Face Detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import os\n",
+    "import cv2\n",
+    "\n",
+    "# Define Image Path Here\n",
+    "image_path = \"./images/\"\n",
+    "\n",
+    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
+    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
+    "\n",
+    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "               font_scale=0.8, thickness=1):\n",
+    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
+    "    x, y = point\n",
+    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
+    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
+    "    \n",
+    "\n",
+    "# Define our model parameters\n",
+    "depth = 16\n",
+    "k = 8\n",
+    "weight_file = None\n",
+    "margin = 0.4\n",
+    "image_dir = None\n",
+    "\n",
+    "# Get our weight file \n",
+    "if not weight_file:\n",
+    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
+    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
+    "# load model and weights\n",
+    "img_size = 64\n",
+    "model = WideResNet(img_size, depth=depth, k=k)()\n",
+    "model.load_weights(weight_file)\n",
+    "\n",
+    "detector = dlib.get_frontal_face_detector()\n",
+    "\n",
+    "image_names = [f for f in listdir(image_path) if isfile(join(image_path, f))]\n",
+    "\n",
+    "for image_name in image_names:\n",
+    "    frame = cv2.imread(\"./images/\" + image_name)\n",
+    "    preprocessed_faces_emo = []           \n",
+    " \n",
+    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "    img_h, img_w, _ = np.shape(input_img)\n",
+    "    detected = detector(frame, 1)\n",
+    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
+    "    \n",
+    "    preprocessed_faces_emo = []\n",
+    "    if len(detected) > 0:\n",
+    "        for i, d in enumerate(detected):\n",
+    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
+    "            xw1 = max(int(x1 - margin * w), 0)\n",
+    "            yw1 = max(int(y1 - margin * h), 0)\n",
+    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
+    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
+    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
+    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
+    "            faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
+    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
+    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
+    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
+    "            face_gray_emo = img_to_array(face_gray_emo)\n",
+    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
+    "            preprocessed_faces_emo.append(face_gray_emo)\n",
+    "\n",
+    "        # make a prediction for Age and Gender\n",
+    "        results = model.predict(np.array(faces))\n",
+    "        predicted_genders = results[0]\n",
+    "        ages = np.arange(0, 101).reshape(101, 1)\n",
+    "        predicted_ages = results[1].dot(ages).flatten()\n",
+    "\n",
+    "        # make a prediction for Emotion \n",
+    "        emo_labels = []\n",
+    "        for i, d in enumerate(detected):\n",
+    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
+    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
+    "        \n",
+    "        # draw results\n",
+    "        for i, d in enumerate(detected):\n",
+    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
+    "                                        \"F\" if predicted_genders[i][0] > 0.4 else \"M\", emo_labels[i])\n",
+    "            draw_label(frame, (d.left(), d.top()), label)\n",
+    "\n",
+    "    cv2.imshow(\"Emotion Detector\", frame)\n",
+    "    cv2.waitKey(0)\n",
+    "\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### And now using dlib's detector with our webcam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import os\n",
+    "import cv2\n",
+    "\n",
+    "# Define Image Path Here\n",
+    "image_path = \"./images/\"\n",
+    "\n",
+    "modhash = 'fbe63257a054c1c5466cfd7bf14646d6'\n",
+    "emotion_classes = {0: 'Angry', 1: 'Fear', 2: 'Happy', 3: 'Neutral', 4: 'Sad', 5: 'Surprise'}\n",
+    "\n",
+    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "               font_scale=0.8, thickness=1):\n",
+    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
+    "    x, y = point\n",
+    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
+    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
+    "    \n",
+    "\n",
+    "# Define our model parameters\n",
+    "depth = 16\n",
+    "k = 8\n",
+    "weight_file = None\n",
+    "margin = 0.4\n",
+    "image_dir = None\n",
+    "\n",
+    "# Get our weight file \n",
+    "if not weight_file:\n",
+    "    weight_file = get_file(\"weights.28-3.73.hdf5\", pretrained_model, cache_subdir=\"pretrained_models\",\n",
+    "                           file_hash=modhash, cache_dir=Path(sys.argv[0]).resolve().parent)\n",
+    "# load model and weights\n",
+    "img_size = 64\n",
+    "model = WideResNet(img_size, depth=depth, k=k)()\n",
+    "model.load_weights(weight_file)\n",
+    "\n",
+    "detector = dlib.get_frontal_face_detector()\n",
+    "\n",
+    "# Initialize Webcam\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    preprocessed_faces_emo = []           \n",
+    " \n",
+    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "    img_h, img_w, _ = np.shape(input_img)\n",
+    "    detected = detector(frame, 1)\n",
+    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
+    "    \n",
+    "    preprocessed_faces_emo = []\n",
+    "    if len(detected) > 0:\n",
+    "        for i, d in enumerate(detected):\n",
+    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
+    "            xw1 = max(int(x1 - margin * w), 0)\n",
+    "            yw1 = max(int(y1 - margin * h), 0)\n",
+    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
+    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
+    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
+    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
+    "            faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
+    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
+    "            face_gray_emo = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
+    "            face_gray_emo = cv2.resize(face_gray_emo, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "            face_gray_emo = face_gray_emo.astype(\"float\") / 255.0\n",
+    "            face_gray_emo = img_to_array(face_gray_emo)\n",
+    "            face_gray_emo = np.expand_dims(face_gray_emo, axis=0)\n",
+    "            preprocessed_faces_emo.append(face_gray_emo)\n",
+    "\n",
+    "        # make a prediction for Age and Gender\n",
+    "        results = model.predict(np.array(faces))\n",
+    "        predicted_genders = results[0]\n",
+    "        ages = np.arange(0, 101).reshape(101, 1)\n",
+    "        predicted_ages = results[1].dot(ages).flatten()\n",
+    "\n",
+    "        # make a prediction for Emotion \n",
+    "        emo_labels = []\n",
+    "        for i, d in enumerate(detected):\n",
+    "            preds = classifier.predict(preprocessed_faces_emo[i])[0]\n",
+    "            emo_labels.append(emotion_classes[preds.argmax()])\n",
+    "        \n",
+    "        # draw results\n",
+    "        for i, d in enumerate(detected):\n",
+    "            label = \"{}, {}, {}\".format(int(predicted_ages[i]),\n",
+    "                                        \"F\" if predicted_genders[i][0] > 0.4 else \"M\", emo_labels[i])\n",
+    "            draw_label(frame, (d.left(), d.top()), label)\n",
+    "\n",
+    "    cv2.imshow(\"Emotion Detector\", frame)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Detection - Friends Characters.ipynb b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Detection - Friends Characters.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6df88be01f88928edd00b0d4ecba484eb3a0268d
--- /dev/null
+++ b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Detection - Friends Characters.ipynb	
@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Deep Learning Face Recogntion\n",
+    "## Building a Friends TV Show Character Identifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's train our model\n",
+    "I've created a dataset with the faces of 4 Friends characters taken from a handful of different scenes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 2663 images belonging to 4 classes.\n",
+      "Found 955 images belonging to 4 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "import keras\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
+    "from keras.layers import Conv2D, MaxPooling2D\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "import os\n",
+    "\n",
+    "num_classes = 4\n",
+    "img_rows, img_cols = 48, 48\n",
+    "batch_size = 16\n",
+    "\n",
+    "train_data_dir = './faces/train'\n",
+    "validation_data_dir = './faces/validation'\n",
+    "\n",
+    "# Let's use some data augmentaiton \n",
+    "train_datagen = ImageDataGenerator(\n",
+    "      rescale=1./255,\n",
+    "      rotation_range=30,\n",
+    "      shear_range=0.3,\n",
+    "      zoom_range=0.3,\n",
+    "      width_shift_range=0.4,\n",
+    "      height_shift_range=0.4,\n",
+    "      horizontal_flip=True,\n",
+    "      fill_mode='nearest')\n",
+    " \n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    " \n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        train_data_dir,\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True)\n",
+    " \n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "        validation_data_dir,\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Our Keras imports\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers.normalization import BatchNormalization\n",
+    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
+    "from keras.layers.advanced_activations import ELU\n",
+    "from keras.layers.core import Activation, Flatten, Dropout, Dense"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a simple VGG based model for Face Recognition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d_25 (Conv2D)           (None, 48, 48, 32)        896       \n",
+      "_________________________________________________________________\n",
+      "activation_34 (Activation)   (None, 48, 48, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_31 (Batc (None, 48, 48, 32)        128       \n",
+      "_________________________________________________________________\n",
+      "conv2d_26 (Conv2D)           (None, 48, 48, 32)        9248      \n",
+      "_________________________________________________________________\n",
+      "activation_35 (Activation)   (None, 48, 48, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_32 (Batc (None, 48, 48, 32)        128       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_13 (MaxPooling (None, 24, 24, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout_19 (Dropout)         (None, 24, 24, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_27 (Conv2D)           (None, 24, 24, 64)        18496     \n",
+      "_________________________________________________________________\n",
+      "activation_36 (Activation)   (None, 24, 24, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_33 (Batc (None, 24, 24, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "conv2d_28 (Conv2D)           (None, 24, 24, 64)        36928     \n",
+      "_________________________________________________________________\n",
+      "activation_37 (Activation)   (None, 24, 24, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_34 (Batc (None, 24, 24, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_14 (MaxPooling (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout_20 (Dropout)         (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_29 (Conv2D)           (None, 12, 12, 128)       73856     \n",
+      "_________________________________________________________________\n",
+      "activation_38 (Activation)   (None, 12, 12, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_35 (Batc (None, 12, 12, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv2d_30 (Conv2D)           (None, 12, 12, 128)       147584    \n",
+      "_________________________________________________________________\n",
+      "activation_39 (Activation)   (None, 12, 12, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_36 (Batc (None, 12, 12, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_15 (MaxPooling (None, 6, 6, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_21 (Dropout)         (None, 6, 6, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_31 (Conv2D)           (None, 6, 6, 256)         295168    \n",
+      "_________________________________________________________________\n",
+      "activation_40 (Activation)   (None, 6, 6, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_37 (Batc (None, 6, 6, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "conv2d_32 (Conv2D)           (None, 6, 6, 256)         590080    \n",
+      "_________________________________________________________________\n",
+      "activation_41 (Activation)   (None, 6, 6, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_38 (Batc (None, 6, 6, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_16 (MaxPooling (None, 3, 3, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_22 (Dropout)         (None, 3, 3, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten_4 (Flatten)          (None, 2304)              0         \n",
+      "_________________________________________________________________\n",
+      "dense_10 (Dense)             (None, 64)                147520    \n",
+      "_________________________________________________________________\n",
+      "activation_42 (Activation)   (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_39 (Batc (None, 64)                256       \n",
+      "_________________________________________________________________\n",
+      "dropout_23 (Dropout)         (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_11 (Dense)             (None, 64)                4160      \n",
+      "_________________________________________________________________\n",
+      "activation_43 (Activation)   (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_40 (Batc (None, 64)                256       \n",
+      "_________________________________________________________________\n",
+      "dropout_24 (Dropout)         (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_12 (Dense)             (None, 4)                 260       \n",
+      "_________________________________________________________________\n",
+      "activation_44 (Activation)   (None, 4)                 0         \n",
+      "=================================================================\n",
+      "Total params: 1,328,548\n",
+      "Trainable params: 1,326,372\n",
+      "Non-trainable params: 2,176\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
+    "                 input_shape = (img_rows, img_cols, 3)))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
+    "                 input_shape = (img_rows, img_cols, 3)))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #5: first set of FC => RELU layers\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Block #6: second set of FC => RELU layers\n",
+    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Block #7: softmax classifier\n",
+    "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation(\"softmax\"))\n",
+    "\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training our Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "166/166 [==============================] - 76s 457ms/step - loss: 1.1153 - acc: 0.5700 - val_loss: 1.4428 - val_acc: 0.4841\n",
+      "\n",
+      "Epoch 00001: val_loss improved from inf to 1.44279, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
+      "Epoch 2/10\n",
+      "166/166 [==============================] - 67s 403ms/step - loss: 0.7034 - acc: 0.7343 - val_loss: 3.7705 - val_acc: 0.2705\n",
+      "\n",
+      "Epoch 00002: val_loss did not improve from 1.44279\n",
+      "Epoch 3/10\n",
+      "166/166 [==============================] - 62s 373ms/step - loss: 0.6037 - acc: 0.7690 - val_loss: 0.9403 - val_acc: 0.6912\n",
+      "\n",
+      "Epoch 00003: val_loss improved from 1.44279 to 0.94025, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
+      "Epoch 4/10\n",
+      "166/166 [==============================] - 62s 373ms/step - loss: 0.5432 - acc: 0.7988 - val_loss: 1.3018 - val_acc: 0.5548\n",
+      "\n",
+      "Epoch 00004: val_loss did not improve from 0.94025\n",
+      "Epoch 5/10\n",
+      "166/166 [==============================] - 69s 414ms/step - loss: 0.4715 - acc: 0.8301 - val_loss: 3.8879 - val_acc: 0.1534\n",
+      "\n",
+      "Epoch 00005: val_loss did not improve from 0.94025\n",
+      "Epoch 6/10\n",
+      "166/166 [==============================] - 77s 467ms/step - loss: 0.4233 - acc: 0.8524 - val_loss: 0.6878 - val_acc: 0.7093\n",
+      "\n",
+      "Epoch 00006: val_loss improved from 0.94025 to 0.68784, saving model to /home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\n",
+      "Epoch 7/10\n",
+      "166/166 [==============================] - 71s 429ms/step - loss: 0.4130 - acc: 0.8636 - val_loss: 3.3402 - val_acc: 0.2971\n",
+      "\n",
+      "Epoch 00007: val_loss did not improve from 0.68784\n",
+      "Epoch 8/10\n",
+      "166/166 [==============================] - 79s 477ms/step - loss: 0.3821 - acc: 0.8748 - val_loss: 2.6729 - val_acc: 0.6283\n",
+      "\n",
+      "Epoch 00008: val_loss did not improve from 0.68784\n",
+      "Epoch 9/10\n",
+      "166/166 [==============================] - 86s 519ms/step - loss: 0.3622 - acc: 0.8709 - val_loss: 1.5067 - val_acc: 0.5197\n",
+      "Restoring model weights from the end of the best epoch\n",
+      "\n",
+      "Epoch 00009: val_loss did not improve from 0.68784\n",
+      "\n",
+      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
+      "Epoch 00009: early stopping\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.optimizers import RMSprop, SGD, Adam\n",
+    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
+    "\n",
+    "                     \n",
+    "checkpoint = ModelCheckpoint(\"/home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5\",\n",
+    "                             monitor=\"val_loss\",\n",
+    "                             mode=\"min\",\n",
+    "                             save_best_only = True,\n",
+    "                             verbose=1)\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
+    "                          min_delta = 0, \n",
+    "                          patience = 3,\n",
+    "                          verbose = 1,\n",
+    "                          restore_best_weights = True)\n",
+    "\n",
+    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
+    "\n",
+    "# we put our call backs into a callback list\n",
+    "callbacks = [earlystop, checkpoint, reduce_lr]\n",
+    "\n",
+    "# We use a very small learning rate \n",
+    "model.compile(loss = 'categorical_crossentropy',\n",
+    "              optimizer = Adam(lr=0.01),\n",
+    "              metrics = ['accuracy'])\n",
+    "\n",
+    "nb_train_samples = 2663\n",
+    "nb_validation_samples = 955\n",
+    "epochs = 10\n",
+    "\n",
+    "history = model.fit_generator(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch = nb_train_samples // batch_size,\n",
+    "    epochs = epochs,\n",
+    "    callbacks = callbacks,\n",
+    "    validation_data = validation_generator,\n",
+    "    validation_steps = nb_validation_samples // batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Getting our Class Labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_labels = validation_generator.class_indices\n",
+    "class_labels = {v: k for k, v in class_labels.items()}\n",
+    "classes = list(class_labels.values())\n",
+    "class_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load our model\n",
+    "from keras.models import load_model\n",
+    "\n",
+    "classifier = load_model('/home/deeplearningcv/DeepLearningCV/Trained Models/face_recognition_friends_vgg.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing our model on some real video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "face_classes = {0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}\n",
+    "\n",
+    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "               font_scale=0.8, thickness=1):\n",
+    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
+    "    x, y = point\n",
+    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
+    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
+    "    \n",
+    "margin = 0.2\n",
+    "# load model and weights\n",
+    "img_size = 64\n",
+    "\n",
+    "detector = dlib.get_frontal_face_detector()\n",
+    "\n",
+    "cap = cv2.VideoCapture('testfriends.mp4')\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
+    "    preprocessed_faces = []           \n",
+    " \n",
+    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "    img_h, img_w, _ = np.shape(input_img)\n",
+    "    detected = detector(frame, 1)\n",
+    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
+    "    \n",
+    "    preprocessed_faces_emo = []\n",
+    "    if len(detected) > 0:\n",
+    "        for i, d in enumerate(detected):\n",
+    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
+    "            xw1 = max(int(x1 - margin * w), 0)\n",
+    "            yw1 = max(int(y1 - margin * h), 0)\n",
+    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
+    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
+    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
+    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
+    "            #faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
+    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
+    "            face = cv2.resize(face, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "            face = face.astype(\"float\") / 255.0\n",
+    "            face = img_to_array(face)\n",
+    "            face = np.expand_dims(face, axis=0)\n",
+    "            preprocessed_faces.append(face)\n",
+    "\n",
+    "        # make a prediction for Emotion \n",
+    "        face_labels = []\n",
+    "        for i, d in enumerate(detected):\n",
+    "            preds = classifier.predict(preprocessed_faces[i])[0]\n",
+    "            face_labels.append(face_classes[preds.argmax()])\n",
+    "        \n",
+    "        # draw results\n",
+    "        for i, d in enumerate(detected):\n",
+    "            label = \"{}\".format(face_labels[i])\n",
+    "            draw_label(frame, (d.left(), d.top()), label)\n",
+    "\n",
+    "    cv2.imshow(\"Friend Character Identifier\", frame)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Extraction from Video.ipynb b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Extraction from Video.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4b98aa30e3d055176c9574e343903af6016a6e49
--- /dev/null
+++ b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/Face Extraction from Video.ipynb	
@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extracting the faces from a video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import os\n",
+    "import cv2\n",
+    "import dlib\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define Image Path Here\n",
+    "image_path = \"./images/\"\n",
+    "\n",
+    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "               font_scale=0.8, thickness=1):\n",
+    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
+    "    x, y = point\n",
+    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
+    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
+    "    \n",
+    "detector = dlib.get_frontal_face_detector()\n",
+    "\n",
+    "# Initialize Webcam\n",
+    "cap = cv2.VideoCapture('testfriends.mp4')\n",
+    "img_size = 64\n",
+    "margin = 0.2\n",
+    "frame_count = 0\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    frame_count += 1\n",
+    "    print(frame_count)   \n",
+    " \n",
+    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "    img_h, img_w, _ = np.shape(input_img)\n",
+    "    detected = detector(frame, 1)\n",
+    "    faces = []\n",
+    "    \n",
+    "    if len(detected) > 0:\n",
+    "        for i, d in enumerate(detected):\n",
+    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
+    "            xw1 = max(int(x1 - margin * w), 0)\n",
+    "            yw1 = max(int(y1 - margin * h), 0)\n",
+    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
+    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
+    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
+    "            file_name = \"./faces/\"+str(frame_count)+\"_\"+str(i)+\".jpg\"\n",
+    "            cv2.imwrite(file_name, face)\n",
+    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
+    "\n",
+    "    cv2.imshow(\"Face Detector\", frame)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/rajeev.jpg b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/rajeev.jpg
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index 0000000000000000000000000000000000000000..85a06abf6e7b76ca1864fcd24573a632b1370954
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diff --git a/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/wide_resnet.py b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/wide_resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..30aff0deeeba68c754da3e21480ceccc93c8dd9f
--- /dev/null
+++ b/18 . Deep Survaliance - Build a Face Detector with Emotion, Age and Gender Recognition/wide_resnet.py	
@@ -0,0 +1,152 @@
+# This code is imported from the following project: https://github.com/asmith26/wide_resnets_keras
+
+import logging
+import sys
+import numpy as np
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Activation, add, Dense, Flatten, Dropout
+from tensorflow.keras.layers import Conv2D, AveragePooling2D
+from tensorflow.keras.layers import BatchNormalization
+from tensorflow.keras.regularizers import l2
+from tensorflow.keras import backend as K
+
+sys.setrecursionlimit(2 ** 20)
+np.random.seed(2 ** 10)
+
+
+class WideResNet:
+    def __init__(self, image_size, depth=16, k=8):
+        self._depth = depth
+        self._k = k
+        self._dropout_probability = 0
+        self._weight_decay = 0.0005
+        self._use_bias = False
+        self._weight_init = "he_normal"
+
+        if K.image_data_format() == "channels_first":
+            logging.debug("image_dim_ordering = 'th'")
+            self._channel_axis = 1
+            self._input_shape = (3, image_size, image_size)
+        else:
+            logging.debug("image_dim_ordering = 'tf'")
+            self._channel_axis = -1
+            self._input_shape = (image_size, image_size, 3)
+
+    # Wide residual network http://arxiv.org/abs/1605.07146
+    def _wide_basic(self, n_input_plane, n_output_plane, stride):
+        def f(net):
+            # format of conv_params:
+            #               [ [kernel_size=("kernel width", "kernel height"),
+            #               strides="(stride_vertical,stride_horizontal)",
+            #               padding="same" or "valid"] ]
+            # B(3,3): orignal <<basic>> block
+            conv_params = [[3, 3, stride, "same"],
+                           [3, 3, (1, 1), "same"]]
+
+            n_bottleneck_plane = n_output_plane
+
+            # Residual block
+            for i, v in enumerate(conv_params):
+                if i == 0:
+                    if n_input_plane != n_output_plane:
+                        net = BatchNormalization(axis=self._channel_axis)(net)
+                        net = Activation("relu")(net)
+                        convs = net
+                    else:
+                        convs = BatchNormalization(axis=self._channel_axis)(net)
+                        convs = Activation("relu")(convs)
+
+                    convs = Conv2D(n_bottleneck_plane, kernel_size=(v[0], v[1]),
+                                          strides=v[2],
+                                          padding=v[3],
+                                          kernel_initializer=self._weight_init,
+                                          kernel_regularizer=l2(self._weight_decay),
+                                          use_bias=self._use_bias)(convs)
+                else:
+                    convs = BatchNormalization(axis=self._channel_axis)(convs)
+                    convs = Activation("relu")(convs)
+                    if self._dropout_probability > 0:
+                        convs = Dropout(self._dropout_probability)(convs)
+                    convs = Conv2D(n_bottleneck_plane, kernel_size=(v[0], v[1]),
+                                          strides=v[2],
+                                          padding=v[3],
+                                          kernel_initializer=self._weight_init,
+                                          kernel_regularizer=l2(self._weight_decay),
+                                          use_bias=self._use_bias)(convs)
+
+            # Shortcut Connection: identity function or 1x1 convolutional
+            #  (depends on difference between input & output shape - this
+            #   corresponds to whether we are using the first block in each
+            #   group; see _layer() ).
+            if n_input_plane != n_output_plane:
+                shortcut = Conv2D(n_output_plane, kernel_size=(1, 1),
+                                         strides=stride,
+                                         padding="same",
+                                         kernel_initializer=self._weight_init,
+                                         kernel_regularizer=l2(self._weight_decay),
+                                         use_bias=self._use_bias)(net)
+            else:
+                shortcut = net
+
+            return add([convs, shortcut])
+
+        return f
+
+
+    # "Stacking Residual Units on the same stage"
+    def _layer(self, block, n_input_plane, n_output_plane, count, stride):
+        def f(net):
+            net = block(n_input_plane, n_output_plane, stride)(net)
+            for i in range(2, int(count + 1)):
+                net = block(n_output_plane, n_output_plane, stride=(1, 1))(net)
+            return net
+
+        return f
+
+#    def create_model(self):
+    def __call__(self):
+        logging.debug("Creating model...")
+
+        assert ((self._depth - 4) % 6 == 0)
+        n = (self._depth - 4) / 6
+
+        inputs = Input(shape=self._input_shape)
+
+        n_stages = [16, 16 * self._k, 32 * self._k, 64 * self._k]
+
+        conv1 = Conv2D(filters=n_stages[0], kernel_size=(3, 3),
+                              strides=(1, 1),
+                              padding="same",
+                              kernel_initializer=self._weight_init,
+                              kernel_regularizer=l2(self._weight_decay),
+                              use_bias=self._use_bias)(inputs)  # "One conv at the beginning (spatial size: 32x32)"
+
+        # Add wide residual blocks
+        block_fn = self._wide_basic
+        conv2 = self._layer(block_fn, n_input_plane=n_stages[0], n_output_plane=n_stages[1], count=n, stride=(1, 1))(conv1)
+        conv3 = self._layer(block_fn, n_input_plane=n_stages[1], n_output_plane=n_stages[2], count=n, stride=(2, 2))(conv2)
+        conv4 = self._layer(block_fn, n_input_plane=n_stages[2], n_output_plane=n_stages[3], count=n, stride=(2, 2))(conv3)
+        batch_norm = BatchNormalization(axis=self._channel_axis)(conv4)
+        relu = Activation("relu")(batch_norm)
+
+        # Classifier block
+        pool = AveragePooling2D(pool_size=(8, 8), strides=(1, 1), padding="same")(relu)
+        flatten = Flatten()(pool)
+        predictions_g = Dense(units=2, kernel_initializer=self._weight_init, use_bias=self._use_bias,
+                              kernel_regularizer=l2(self._weight_decay), activation="softmax",
+                              name="pred_gender")(flatten)
+        predictions_a = Dense(units=101, kernel_initializer=self._weight_init, use_bias=self._use_bias,
+                              kernel_regularizer=l2(self._weight_decay), activation="softmax",
+                              name="pred_age")(flatten)
+        model = Model(inputs=inputs, outputs=[predictions_g, predictions_a])
+
+        return model
+
+
+def main():
+    model = WideResNet(64)()
+    model.summary()
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/18. Facial Applications - Emotion, Age & Gender Recognition/1. Chapter Introduction.srt b/18. Facial Applications - Emotion, Age & Gender Recognition/1. Chapter Introduction.srt
new file mode 100644
index 0000000000000000000000000000000000000000..c11ba1f75bbb63c0d75e0d633df672722520e6d1
--- /dev/null
+++ b/18. Facial Applications - Emotion, Age & Gender Recognition/1. Chapter Introduction.srt	
@@ -0,0 +1,47 @@
+1
+00:00:00,390 --> 00:00:07,520
+OK so welcome to Chapter 18 this episode of deep Haviland's because I'm no longer teaching you new Tieri
+
+2
+00:00:07,530 --> 00:00:13,710
+here Purcey what I'm going to do now is going to be like a history practice project where we build basically
+
+3
+00:00:13,710 --> 00:00:20,280
+an emotion age and gender recognition using face detection at first to find faces in an image or video
+
+4
+00:00:20,760 --> 00:00:26,850
+and then we pick out what emotion is on the face or being shown by the face the estimated age or predicted
+
+5
+00:00:26,880 --> 00:00:29,240
+age of the person and to predict the gender.
+
+6
+00:00:29,370 --> 00:00:33,890
+So let's take a look at a section and see how it split it up so fiercely.
+
+7
+00:00:34,020 --> 00:00:38,040
+We're going to build a simple emotion or facial expression to the actor.
+
+8
+00:00:38,130 --> 00:00:42,780
+And secondly we're going to bill and we're going to build and age and gender directed director.
+
+9
+00:00:43,140 --> 00:00:45,600
+And then the last chapter we're going to combine them both.
+
+10
+00:00:45,840 --> 00:00:48,780
+And it's going to be called Deep surveillance.
+
+11
+00:00:49,080 --> 00:00:50,130
+So stay tuned.
+
+12
+00:00:50,130 --> 00:00:51,510
+It's going to be a very cool projec.
diff --git a/18. Facial Applications - Emotion, Age & Gender Recognition/2. Build an Emotion, Facial Expression Detector.srt b/18. Facial Applications - Emotion, Age & Gender Recognition/2. Build an Emotion, Facial Expression Detector.srt
new file mode 100644
index 0000000000000000000000000000000000000000..74b3ae0360135536ae883fd0791d4906d33382b5
--- /dev/null
+++ b/18. Facial Applications - Emotion, Age & Gender Recognition/2. Build an Emotion, Facial Expression Detector.srt	
@@ -0,0 +1,1239 @@
+1
+00:00:00,570 --> 00:00:06,450
+Hi and welcome to Chapter 18 point to where we get to build our supercool a motion detector.
+
+2
+00:00:06,480 --> 00:00:11,600
+So let's move on into a virtual machine and go to a Python book and let's see how it's done.
+
+3
+00:00:12,050 --> 00:00:19,800
+OK so now we're here in our Python browser and let's go to Chapter 18 deep civilians and eighteen point
+
+4
+00:00:19,800 --> 00:00:23,340
+to is building an emotion that acts with little little Viji.
+
+5
+00:00:23,400 --> 00:00:24,920
+So let's open him up.
+
+6
+00:00:26,910 --> 00:00:27,960
+And there we go.
+
+7
+00:00:27,960 --> 00:00:28,590
+Should be loaded.
+
+8
+00:00:28,590 --> 00:00:29,340
+Here we go.
+
+9
+00:00:29,340 --> 00:00:30,050
+All right.
+
+10
+00:00:30,330 --> 00:00:36,040
+So now the first thing we're going to do before I even go into this not book is Let's go back to our
+
+11
+00:00:36,060 --> 00:00:39,100
+father explo here and you see these two directories.
+
+12
+00:00:39,140 --> 00:00:42,510
+Let me explain to you and picture of me for testing.
+
+13
+00:00:42,510 --> 00:00:45,810
+Let me explain to you what these three directories are first for this age and gender one.
+
+14
+00:00:45,870 --> 00:00:48,540
+This is our age and gender which is in the next chapter.
+
+15
+00:00:48,660 --> 00:00:50,820
+We're not going to touch just this just yet.
+
+16
+00:00:50,820 --> 00:00:52,670
+So let's leave this alone here.
+
+17
+00:00:52,910 --> 00:00:54,500
+Now this here is food.
+
+18
+00:00:54,510 --> 00:00:55,330
+2013.
+
+19
+00:00:55,340 --> 00:00:57,760
+That's a dataset that we're going to try it on.
+
+20
+00:00:58,080 --> 00:00:59,580
+So let's take a look at this dataset.
+
+21
+00:00:59,580 --> 00:01:01,500
+So we have two directories.
+
+22
+00:01:01,770 --> 00:01:04,190
+Some of the CSU files that we aren't going to use.
+
+23
+00:01:04,200 --> 00:01:05,630
+We're going to look at these here.
+
+24
+00:01:05,760 --> 00:01:08,990
+I would set up so there's a trade and validation as usual.
+
+25
+00:01:09,000 --> 00:01:12,480
+And now we see these are the emotions here.
+
+26
+00:01:13,310 --> 00:01:19,380
+There is something I'm going to do which I haven't told you guys yet is that we're going to basically
+
+27
+00:01:19,650 --> 00:01:26,400
+delete or discard the discussed directory because I'm going to show you the plots afterward but discussed
+
+28
+00:01:26,460 --> 00:01:29,320
+only has four hundred and fifty five images.
+
+29
+00:01:29,400 --> 00:01:36,390
+All right now let's take a look at fear there has a lot more images for television.
+
+30
+00:01:36,660 --> 00:01:44,290
+So you can already see an angry happy neutralising look at Happy How many faces on this artery.
+
+31
+00:01:44,330 --> 00:01:45,600
+All right.
+
+32
+00:01:45,610 --> 00:01:47,330
+It is 7000.
+
+33
+00:01:47,330 --> 00:01:48,590
+So it's quite a lot.
+
+34
+00:01:48,590 --> 00:01:53,200
+So these are this of go back to this.
+
+35
+00:01:53,380 --> 00:01:56,590
+This is a very imbalanced data set here.
+
+36
+00:01:56,590 --> 00:02:05,020
+So these two things we can do one we can move the disgustful the images here into fear because honestly
+
+37
+00:02:05,020 --> 00:02:06,140
+are a bit similar.
+
+38
+00:02:06,160 --> 00:02:10,090
+It's bit hard even for me to pick out what is fair and what is discussed.
+
+39
+00:02:10,180 --> 00:02:12,170
+But for now let's just delete it.
+
+40
+00:02:12,220 --> 00:02:17,070
+So we press Delete here and let's go back to validation and we press Delete here.
+
+41
+00:02:17,170 --> 00:02:17,690
+OK.
+
+42
+00:02:17,830 --> 00:02:24,520
+So now we just left at six classes instead of seven and six much more evenly balanced classes even surprise
+
+43
+00:02:24,580 --> 00:02:25,150
+surprise.
+
+44
+00:02:25,180 --> 00:02:31,690
+The second one yet has 400 something images but that's fine because surprise does look a lot different
+
+45
+00:02:32,050 --> 00:02:33,620
+to disgust.
+
+46
+00:02:33,700 --> 00:02:39,130
+So let's go back to here and cascades which I mentioned before.
+
+47
+00:02:39,140 --> 00:02:40,640
+No I haven't mentioned that yet.
+
+48
+00:02:40,660 --> 00:02:43,710
+That's coming up in our object detection chapter.
+
+49
+00:02:43,990 --> 00:02:46,500
+But how cascades are basically fiest detection.
+
+50
+00:02:46,510 --> 00:02:48,620
+Well it's a type of object detector.
+
+51
+00:02:48,970 --> 00:02:51,690
+And we're going to use We're actually not going to use detectors.
+
+52
+00:02:51,700 --> 00:02:52,690
+We can do that.
+
+53
+00:02:52,750 --> 00:02:54,170
+I should just leave it in Fanaa.
+
+54
+00:02:54,250 --> 00:02:58,170
+We're going to use these here for BCB or face the action unit.
+
+55
+00:02:58,330 --> 00:03:00,260
+So let's go back to this notebook here.
+
+56
+00:03:01,230 --> 00:03:01,770
+All right.
+
+57
+00:03:01,770 --> 00:03:05,210
+So this is how we load the data set.
+
+58
+00:03:05,220 --> 00:03:06,650
+This is basically a number of classes.
+
+59
+00:03:06,660 --> 00:03:07,380
+It was seven.
+
+60
+00:03:07,410 --> 00:03:08,690
+Now it's six.
+
+61
+00:03:08,730 --> 00:03:14,260
+Now we're actually going to have the number of rows and as 48th to him in size and they're all greyscale.
+
+62
+00:03:14,380 --> 00:03:20,730
+I mentioned that you may have mentioned it in the previous section I hope but it is all grayscale images.
+
+63
+00:03:20,740 --> 00:03:24,880
+Just take a look and see to verify all.
+
+64
+00:03:25,110 --> 00:03:30,810
+So now we also are going to do some data augmentation as we have become routine.
+
+65
+00:03:30,810 --> 00:03:33,840
+Right now these are the parameters of used here.
+
+66
+00:03:33,930 --> 00:03:36,800
+Feel free to play with us as well.
+
+67
+00:03:36,840 --> 00:03:41,720
+It doesn't make a huge difference sometimes but you never know it.
+
+68
+00:03:41,940 --> 00:03:44,180
+No normal rescaling normalization.
+
+69
+00:03:44,340 --> 00:03:46,420
+Now we just put our data here.
+
+70
+00:03:46,420 --> 00:03:51,270
+Notice we have a new pragmatical column mode in boats that I don't believe we used before because it
+
+71
+00:03:51,270 --> 00:03:52,910
+defaults to color.
+
+72
+00:03:53,400 --> 00:03:55,000
+No we're specifying it's greyscale.
+
+73
+00:03:55,020 --> 00:03:56,170
+So just take note of that.
+
+74
+00:03:56,170 --> 00:04:03,030
+So when you want to do a grayscale basically operation of training you have to specified here in your
+
+75
+00:04:03,280 --> 00:04:06,500
+day to Gen from factory function.
+
+76
+00:04:06,540 --> 00:04:11,800
+So now let's load this and it's going to tell us how many images and how many classes.
+
+77
+00:04:11,800 --> 00:04:13,330
+Six and six.
+
+78
+00:04:13,330 --> 00:04:14,550
+Excellent.
+
+79
+00:04:14,560 --> 00:04:14,850
+All right.
+
+80
+00:04:14,870 --> 00:04:20,750
+So now we move on to let me just make this a little more clear.
+
+81
+00:04:20,750 --> 00:04:32,110
+You can put it here this is basically imports Leesville Karris imports and put another thing here so
+
+82
+00:04:32,110 --> 00:04:36,060
+we can say this is Chris little.
+
+83
+00:04:36,550 --> 00:04:38,180
+He Jiechi model
+
+84
+00:04:41,390 --> 00:04:46,950
+so now as you've seen in the previous chapter of its Simpsons this is the what we're going to use.
+
+85
+00:04:47,240 --> 00:04:52,470
+So let's just run this and here we go.
+
+86
+00:04:52,650 --> 00:04:54,980
+Number of parameters.
+
+87
+00:04:55,020 --> 00:04:56,810
+This does look a little different much.
+
+88
+00:04:56,820 --> 00:04:57,580
+Oh no.
+
+89
+00:04:57,600 --> 00:04:58,720
+Because it's black and white.
+
+90
+00:04:58,760 --> 00:05:03,600
+Grayscale images that is wide enough parameters or less silly.
+
+91
+00:05:03,960 --> 00:05:04,360
+OK.
+
+92
+00:05:04,530 --> 00:05:05,560
+Happens a lot.
+
+93
+00:05:05,660 --> 00:05:06,840
+It's fine.
+
+94
+00:05:06,900 --> 00:05:12,940
+Now let us just label this as well treating our model.
+
+95
+00:05:12,960 --> 00:05:13,700
+There we go.
+
+96
+00:05:14,010 --> 00:05:18,540
+So now we have basically some callbacks sets here.
+
+97
+00:05:18,690 --> 00:05:22,000
+I'm going to pause here because I realized I made some changes to this code.
+
+98
+00:05:22,020 --> 00:05:23,570
+Actually no it's fine.
+
+99
+00:05:23,570 --> 00:05:24,130
+I'm stupid.
+
+100
+00:05:24,180 --> 00:05:25,390
+It is fine.
+
+101
+00:05:26,110 --> 00:05:27,270
+So let's continue.
+
+102
+00:05:27,270 --> 00:05:28,380
+Number of training samples.
+
+103
+00:05:28,380 --> 00:05:33,720
+Let's just double check this because I'm not entirely sure it just reflects the classes we deleted.
+
+104
+00:05:33,750 --> 00:05:40,060
+So 6:32 tree let's scroll up and let's see if it does 6:40 we did OK.
+
+105
+00:05:40,080 --> 00:05:40,580
+Good.
+
+106
+00:05:41,630 --> 00:05:43,920
+I'm doing things right for a change.
+
+107
+00:05:43,940 --> 00:05:44,410
+OK.
+
+108
+00:05:44,600 --> 00:05:44,940
+Good.
+
+109
+00:05:45,020 --> 00:05:52,940
+And now standard procedure how we fit using our data generators and all callbacks to find here and model
+
+110
+00:05:52,940 --> 00:05:53,750
+compile.
+
+111
+00:05:53,780 --> 00:05:59,210
+So I'm not actually going to run this is going to show you what I've run prior to the chapter and this
+
+112
+00:05:59,240 --> 00:06:01,160
+was with six classes.
+
+113
+00:06:01,160 --> 00:06:01,520
+All right.
+
+114
+00:06:01,580 --> 00:06:04,250
+And now all the way down.
+
+115
+00:06:04,290 --> 00:06:06,370
+You see we have 47 percent accuracy.
+
+116
+00:06:06,560 --> 00:06:08,110
+Now that is not that good.
+
+117
+00:06:08,220 --> 00:06:08,860
+All right.
+
+118
+00:06:09,080 --> 00:06:11,790
+So tele box is actually not bad.
+
+119
+00:06:11,960 --> 00:06:17,240
+I've seen people use similar models and actually sometimes much more complicated models someone who's
+
+120
+00:06:17,240 --> 00:06:19,380
+a full DTG 9000 on this.
+
+121
+00:06:19,580 --> 00:06:23,940
+And it is very difficult to get past 70 percent accuracy.
+
+122
+00:06:24,110 --> 00:06:30,680
+I am 100 percent sure if I try this for maybe 280 ebox I'll get probably about 60 percent accuracy.
+
+123
+00:06:30,770 --> 00:06:32,030
+So you can give it a try.
+
+124
+00:06:32,030 --> 00:06:36,260
+All right I'll probably do it and update it at a later date.
+
+125
+00:06:36,530 --> 00:06:39,020
+With this new model for you guys.
+
+126
+00:06:39,020 --> 00:06:40,870
+So that's fine.
+
+127
+00:06:40,880 --> 00:06:44,300
+So now let's look at the confusion matrix from these results.
+
+128
+00:06:44,310 --> 00:06:46,100
+It is 47 percent.
+
+129
+00:06:46,640 --> 00:06:47,020
+OK.
+
+130
+00:06:47,150 --> 00:06:50,720
+So normally what do you what do you think of this.
+
+131
+00:06:50,900 --> 00:06:52,370
+How would you analyze this.
+
+132
+00:06:52,610 --> 00:06:56,800
+Now I would say it's not that good especially not good at picking up fear.
+
+133
+00:06:57,080 --> 00:06:58,940
+You really got much fear correct.
+
+134
+00:06:58,940 --> 00:06:59,620
+All right.
+
+135
+00:06:59,780 --> 00:07:01,610
+Now what does it say but happy to.
+
+136
+00:07:01,970 --> 00:07:03,200
+Now this is these are comics by the way.
+
+137
+00:07:03,200 --> 00:07:06,920
+But even still it's definitely good at getting happy.
+
+138
+00:07:06,920 --> 00:07:07,840
+All right.
+
+139
+00:07:08,210 --> 00:07:13,080
+But the others aren't that great yet as you do see some mismatches here.
+
+140
+00:07:13,250 --> 00:07:15,640
+You do see fares not being picked up that well.
+
+141
+00:07:15,740 --> 00:07:16,860
+I mean it has been picked up.
+
+142
+00:07:16,950 --> 00:07:19,790
+Is this a different color from Stephanie from here to here.
+
+143
+00:07:20,170 --> 00:07:23,580
+If we need a good answer to probably tell but there's a difference.
+
+144
+00:07:23,660 --> 00:07:30,020
+But generally you can tell here fares actually being confused with neutral and angry a lot as well.
+
+145
+00:07:30,020 --> 00:07:38,330
+So this analysis says Our model is decent but not great which is obvious given its 47 percent accuracy
+
+146
+00:07:38,330 --> 00:07:38,850
+here.
+
+147
+00:07:39,320 --> 00:07:46,250
+And you can look at the one school is here definitely can see exactly as I said Happy is it's good at
+
+148
+00:07:46,250 --> 00:07:49,710
+finding happy hearted finding fair decent.
+
+149
+00:07:49,730 --> 00:07:56,360
+Everything else except neutral although in my experience at least for my face it picked up neutral fairly
+
+150
+00:07:56,360 --> 00:07:57,380
+well.
+
+151
+00:07:57,380 --> 00:07:57,710
+All right.
+
+152
+00:07:57,710 --> 00:07:58,770
+So let's just look.
+
+153
+00:07:58,790 --> 00:08:00,910
+I believe this was saved.
+
+154
+00:08:01,040 --> 00:08:02,190
+Let me just make sure.
+
+155
+00:08:03,350 --> 00:08:05,320
+It would be truly great.
+
+156
+00:08:05,360 --> 00:08:05,940
+OK.
+
+157
+00:08:06,290 --> 00:08:15,270
+So now that's look at our model as usual this takes about an annoyingly long seconds.
+
+158
+00:08:15,790 --> 00:08:16,210
+OK.
+
+159
+00:08:16,330 --> 00:08:18,550
+Quick quick and it's time Ali.
+
+160
+00:08:18,550 --> 00:08:23,950
+All right so let's get A-class labels again because I believe they ran it before when it was 7 classes
+
+161
+00:08:23,950 --> 00:08:24,560
+here.
+
+162
+00:08:25,150 --> 00:08:29,930
+And now let's look at some images not sure what's in this directory.
+
+163
+00:08:29,950 --> 00:08:30,330
+OK.
+
+164
+00:08:30,460 --> 00:08:35,500
+So let's see how it went we predicted angry and true class was fair.
+
+165
+00:08:35,850 --> 00:08:37,240
+So OK.
+
+166
+00:08:37,270 --> 00:08:39,380
+Reasonable fanfare.
+
+167
+00:08:39,430 --> 00:08:39,910
+Bingo.
+
+168
+00:08:39,920 --> 00:08:41,400
+Got it spot on.
+
+169
+00:08:41,410 --> 00:08:41,890
+Fair.
+
+170
+00:08:41,900 --> 00:08:42,580
+And it was neutral.
+
+171
+00:08:42,580 --> 00:08:45,190
+No I wouldn't say this is a neutral expression.
+
+172
+00:08:45,240 --> 00:08:51,580
+Don't know who labeled this datasets but I mean it's probably not Fader's probably some weird expression
+
+173
+00:08:51,730 --> 00:08:53,330
+she's making.
+
+174
+00:08:53,980 --> 00:08:55,630
+We predict an angry and it's neutral.
+
+175
+00:08:55,650 --> 00:09:00,280
+Now I would say you're probably neutral but it does look a bit angry doesn't he.
+
+176
+00:09:01,310 --> 00:09:01,570
+OK.
+
+177
+00:09:01,590 --> 00:09:05,870
+So this one picked up we detected fear but it was actually angry close.
+
+178
+00:09:06,360 --> 00:09:08,990
+This one we predicted angry but actually was fair.
+
+179
+00:09:09,120 --> 00:09:12,670
+We parted angry again pretty fair and neutral.
+
+180
+00:09:12,670 --> 00:09:13,570
+It's true.
+
+181
+00:09:13,890 --> 00:09:14,270
+OK.
+
+182
+00:09:14,310 --> 00:09:19,710
+So now we saw this and now let's actually try it on a picture of me.
+
+183
+00:09:19,800 --> 00:09:20,510
+OK.
+
+184
+00:09:20,850 --> 00:09:24,810
+So one thing I should have mentioned before actually it was an undisclosed isn't disco.
+
+185
+00:09:25,050 --> 00:09:27,930
+This is how we use OKOK cascade justifies.
+
+186
+00:09:27,960 --> 00:09:32,490
+Now we're using something different as an open Zeevi function.
+
+187
+00:09:32,820 --> 00:09:39,390
+So it be pointed if it classified we want to use that was to go back to this this one.
+
+188
+00:09:39,490 --> 00:09:40,050
+All right.
+
+189
+00:09:40,210 --> 00:09:48,370
+We have Eifel buddy and caucus's fires which I'm probably going to see in space are tiny it was going
+
+190
+00:09:48,370 --> 00:09:51,340
+to lead them if they were taking up all of space but they don't.
+
+191
+00:09:51,670 --> 00:09:52,250
+OK.
+
+192
+00:09:52,720 --> 00:09:54,700
+So this is a phase detector module.
+
+193
+00:09:54,760 --> 00:09:56,860
+Now what this module does.
+
+194
+00:09:56,860 --> 00:09:59,990
+You can see we've created as obstacles fiest classify.
+
+195
+00:10:00,150 --> 00:10:05,110
+Now when we get an image here this is an image from my webcam.
+
+196
+00:10:05,110 --> 00:10:06,940
+This is a function here that we're looking at by the way.
+
+197
+00:10:07,060 --> 00:10:07,550
+OK.
+
+198
+00:10:07,870 --> 00:10:16,030
+So we get this function we convert it into a greyscale image and then we pass this phunk face this gray
+
+199
+00:10:16,420 --> 00:10:19,700
+scale image of the webcam input into this.
+
+200
+00:10:19,700 --> 00:10:24,280
+This is what does the face detection does detect multi-skilled function.
+
+201
+00:10:24,280 --> 00:10:27,010
+These are some parameters to tweak to tweak the sensitivity as well.
+
+202
+00:10:27,040 --> 00:10:31,870
+And how many times like if you want to find small pieces a lot of cases you tweak some of the Skilling
+
+203
+00:10:31,870 --> 00:10:32,930
+parameters.
+
+204
+00:10:33,010 --> 00:10:38,130
+So what it returns do is basically an array of faces.
+
+205
+00:10:38,140 --> 00:10:42,310
+So basically if you have no faces the fact that it is written in some blank data because I'm using it
+
+206
+00:10:42,340 --> 00:10:44,770
+in this function for some other stuff.
+
+207
+00:10:45,040 --> 00:10:48,790
+But if it finds faces basically it returns this.
+
+208
+00:10:48,790 --> 00:10:55,820
+These are basically did the location of the face the X Y which is the top left basically x.
+
+209
+00:10:55,900 --> 00:10:58,920
+Let's assume let's assume this is a box.
+
+210
+00:10:58,930 --> 00:11:03,690
+All right so x y is going to be say let's say this.
+
+211
+00:11:03,750 --> 00:11:05,540
+It's in this box I'm doing right here.
+
+212
+00:11:05,560 --> 00:11:07,820
+Hope you can make it out is the face.
+
+213
+00:11:07,820 --> 00:11:13,610
+So the x y starts here on the top left corner of the face and the width and the height is which is this
+
+214
+00:11:13,610 --> 00:11:18,850
+way and hightest like the down measurement definitely it's still off the face.
+
+215
+00:11:18,850 --> 00:11:24,830
+So that's how we use no discipline Sivy fun function here to draw a rectangle around this.
+
+216
+00:11:25,030 --> 00:11:31,120
+And then we take description of the image here and basically we just crop it to get this fish out of
+
+217
+00:11:31,120 --> 00:11:31,690
+it.
+
+218
+00:11:31,860 --> 00:11:37,980
+And what I do is just said run this of all the faces in this file resize it correctly to what declassifies
+
+219
+00:11:38,380 --> 00:11:47,110
+is has been trained to detect 48 48 and X return an array of all the faces or the rectangle dimensions
+
+220
+00:11:47,470 --> 00:11:53,110
+and the original image muchall way but I was maybe doing it doing something with it afterward.
+
+221
+00:11:53,410 --> 00:11:56,560
+And yes I think I was just putting the label on it afterwards.
+
+222
+00:11:57,010 --> 00:11:58,150
+So there we go.
+
+223
+00:11:58,150 --> 00:12:01,590
+So we love my image to run this cool function and hope it works.
+
+224
+00:12:01,600 --> 00:12:03,400
+After that explanation.
+
+225
+00:12:03,880 --> 00:12:04,270
+Yes.
+
+226
+00:12:04,270 --> 00:12:04,580
+OK.
+
+227
+00:12:04,600 --> 00:12:07,460
+So thanks I'm happy I was happy actually.
+
+228
+00:12:07,600 --> 00:12:09,580
+This was on my birthday two months ago.
+
+229
+00:12:09,880 --> 00:12:16,480
+I was in Madeira Portugal which is a wonderful wonderful island that you really should visit and not
+
+230
+00:12:16,840 --> 00:12:19,520
+get paid to say that's just highly recommended.
+
+231
+00:12:19,760 --> 00:12:22,710
+When we met my wife and I went whale watching that day.
+
+232
+00:12:23,050 --> 00:12:24,740
+So yes I was happy.
+
+233
+00:12:24,790 --> 00:12:29,070
+So pretty pretty decent so you can load your images here.
+
+234
+00:12:29,080 --> 00:12:33,420
+One thing to note you can probably reduce the size of this Texas started maybe out of left corner here.
+
+235
+00:12:33,940 --> 00:12:37,240
+And kids faces more on the right hand side of image.
+
+236
+00:12:37,270 --> 00:12:40,390
+Texas not going to go outside of the image.
+
+237
+00:12:40,390 --> 00:12:43,550
+So that's one thing you can do for your homework listen.
+
+238
+00:12:43,870 --> 00:12:46,610
+So now let's try this on our web cam.
+
+239
+00:12:46,690 --> 00:12:47,250
+OK.
+
+240
+00:12:47,560 --> 00:12:49,480
+So let's try this now.
+
+241
+00:12:54,200 --> 00:12:54,440
+OK.
+
+242
+00:12:54,450 --> 00:12:58,830
+So you may have noticed a slight break in the code and that was because when I ran this I realized my
+
+243
+00:12:58,830 --> 00:13:02,310
+T-shirt had a stain which did not look good on camera.
+
+244
+00:13:02,310 --> 00:13:04,600
+So again I'm not dressed up.
+
+245
+00:13:04,650 --> 00:13:08,260
+I'm just here at home alone recording this.
+
+246
+00:13:08,550 --> 00:13:11,780
+So let me just run this.
+
+247
+00:13:11,950 --> 00:13:12,790
+And here we go.
+
+248
+00:13:12,790 --> 00:13:20,270
+So we see my face being detected for my emotion my facial emotion expression my microphone right here.
+
+249
+00:13:20,270 --> 00:13:23,590
+I hope they don't make any noise so they come.
+
+250
+00:13:23,590 --> 00:13:28,760
+So right now it wasn't that alternating between happy and neutral quite a bit.
+
+251
+00:13:30,480 --> 00:13:33,110
+Surprise they call it I worked.
+
+252
+00:13:33,120 --> 00:13:33,400
+All right.
+
+253
+00:13:33,420 --> 00:13:33,910
+Nice.
+
+254
+00:13:33,920 --> 00:13:36,230
+So it is working fairly well.
+
+255
+00:13:36,660 --> 00:13:38,880
+So you can experiment with this training.
+
+256
+00:13:39,030 --> 00:13:42,450
+One thing you should know you see this bounding box here.
+
+257
+00:13:42,600 --> 00:13:48,600
+This is actually bad because just take a quick look at a dataset here.
+
+258
+00:13:48,900 --> 00:13:56,550
+Something you can do for us in which I neglected to do for you guys but these faces tightly cropped
+
+259
+00:13:56,700 --> 00:13:57,600
+if you take a look at them.
+
+260
+00:13:57,690 --> 00:14:01,820
+Let's just open this quickly they mean tighter cropped isn't they.
+
+261
+00:14:01,880 --> 00:14:04,670
+They don't have that much space inside.
+
+262
+00:14:04,830 --> 00:14:13,180
+So generally because I've noticed this is this is not that tightly cropped hair on the webcam.
+
+263
+00:14:13,430 --> 00:14:16,250
+So let me just go back to this code.
+
+264
+00:14:16,370 --> 00:14:21,520
+So if you wanted to change that you actually CAN THIS IS ACTUALLY actually the opposite of cropping.
+
+265
+00:14:21,530 --> 00:14:29,860
+So I can actually change this to 20 20 which was because the default settings for the would Cascades
+
+266
+00:14:29,960 --> 00:14:31,030
+quite tight.
+
+267
+00:14:31,280 --> 00:14:37,690
+I actually previously I did some spacing some left right up down spacing so let's run this now.
+
+268
+00:14:37,730 --> 00:14:38,880
+See what happens.
+
+269
+00:14:40,320 --> 00:14:40,650
+OK.
+
+270
+00:14:40,670 --> 00:14:45,200
+So as you can see it is a bit it is a bit better.
+
+271
+00:14:45,380 --> 00:14:47,170
+Maybe we can reduce to bits even more.
+
+272
+00:14:47,240 --> 00:14:52,310
+And we can eliminate it all to get a much less evil.
+
+273
+00:14:52,390 --> 00:14:54,450
+This is stupid.
+
+274
+00:14:54,470 --> 00:14:58,850
+And so the w w just not do it at the height spacing at least
+
+275
+00:15:02,620 --> 00:15:07,170
+so I would say this is probably a small tightly cropped on my face.
+
+276
+00:15:07,270 --> 00:15:12,220
+And if you wanted to go even for that let's just not do any height adjustment here.
+
+277
+00:15:13,400 --> 00:15:14,840
+And see what that gives us.
+
+278
+00:15:14,850 --> 00:15:18,390
+No.
+
+279
+00:15:19,170 --> 00:15:19,530
+It is.
+
+280
+00:15:19,530 --> 00:15:22,050
+It is definitely more stable and better.
+
+281
+00:15:22,050 --> 00:15:25,830
+I would say OK cool.
+
+282
+00:15:25,900 --> 00:15:27,140
+So this works fairly well.
+
+283
+00:15:27,190 --> 00:15:27,670
+OK.
+
+284
+00:15:27,840 --> 00:15:28,500
+I'm happy with this.
+
+285
+00:15:28,510 --> 00:15:29,880
+I hope you're happy with us.
+
+286
+00:15:29,890 --> 00:15:36,100
+This is a 47 percent accurate model and it's doing quite well but decently well at least.
+
+287
+00:15:36,100 --> 00:15:41,630
+So what you can do as a lesson for you guys is trained us for more ebox.
+
+288
+00:15:41,650 --> 00:15:49,690
+Also try a different augmentations try different optimize if you want to use them with a fairly large
+
+289
+00:15:49,690 --> 00:15:50,360
+dating rate.
+
+290
+00:15:50,380 --> 00:15:53,040
+Just realize you can reduce that even more.
+
+291
+00:15:53,620 --> 00:15:58,510
+And you know adjust your template to options here and what you can do as well.
+
+292
+00:15:58,630 --> 00:16:06,150
+You can add more filters individually started 32 64 128 256 hub what you saw at 64.
+
+293
+00:16:06,370 --> 00:16:14,200
+Eliminating the tool to get us 64 128 256 and 512 and you can even add more densely as here may not
+
+294
+00:16:14,950 --> 00:16:16,850
+be necessary but try it.
+
+295
+00:16:17,350 --> 00:16:19,750
+Play with your dhrupad values as well.
+
+296
+00:16:19,810 --> 00:16:24,950
+He'd even add a whole new convolutional layer here instead of stopping at 256.
+
+297
+00:16:25,060 --> 00:16:34,620
+You can just go here and add one with 512 I'll do this for you but you can do it on your own.
+
+298
+00:16:34,920 --> 00:16:38,830
+Maybe change activation functions maybe change initializers as I say.
+
+299
+00:16:38,880 --> 00:16:44,390
+Although I wouldn't change to be fair I wouldn't change the Batumi position activation and iching normal.
+
+300
+00:16:44,610 --> 00:16:49,740
+But you can if you want but I wouldn't I think those are the best for Viji type model like we're using
+
+301
+00:16:49,740 --> 00:16:50,700
+here.
+
+302
+00:16:50,700 --> 00:16:53,980
+And last Leslee try for more ebox.
+
+303
+00:16:54,000 --> 00:16:55,010
+All right.
+
+304
+00:16:55,050 --> 00:16:59,200
+A good value for this a satisfactory value would be 70 percent.
+
+305
+00:16:59,520 --> 00:17:04,960
+So give it a go again and try different data augmentations as well.
+
+306
+00:17:05,220 --> 00:17:10,770
+Give it a go and see how much good if accurate model you can get.
+
+307
+00:17:10,770 --> 00:17:12,660
+OK so that's it for this lesson.
+
+308
+00:17:12,990 --> 00:17:20,510
+What we're going to do next is do an run an age and gender detector I guess you could call it classify
+
+309
+00:17:21,270 --> 00:17:29,200
+and combined them afterward into one super Fishell deep surveillance classify classify.
+
+310
+00:17:29,710 --> 00:17:30,450
+OK thank you.
diff --git a/18. Facial Applications - Emotion, Age & Gender Recognition/2.1 Download Dataset.html b/18. Facial Applications - Emotion, Age & Gender Recognition/2.1 Download Dataset.html
new file mode 100644
index 0000000000000000000000000000000000000000..37cee1b4aeb76ff3ae854c1335363a1fd5d62bde
--- /dev/null
+++ b/18. Facial Applications - Emotion, Age & Gender Recognition/2.1 Download Dataset.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1317edb3koW63Zzjxt5b-9xOK1ElQ07xk/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/18. Facial Applications - Emotion, Age & Gender Recognition/3. Build EmotionAgeGender Recognition in our Deep Surveillance Monitor.srt b/18. Facial Applications - Emotion, Age & Gender Recognition/3. Build EmotionAgeGender Recognition in our Deep Surveillance Monitor.srt
new file mode 100644
index 0000000000000000000000000000000000000000..bd78fbe226857dcb02147129ea9874750dd8ab33
--- /dev/null
+++ b/18. Facial Applications - Emotion, Age & Gender Recognition/3. Build EmotionAgeGender Recognition in our Deep Surveillance Monitor.srt	
@@ -0,0 +1,1547 @@
+1
+00:00:00,690 --> 00:00:06,150
+Hi and welcome to chapter eighteen point three where we're going to build a deep surveillance facial
+
+2
+00:00:06,150 --> 00:00:10,980
+monitoring system that combines emotion age and gender recognition.
+
+3
+00:00:11,010 --> 00:00:15,910
+So now let's go into an update on that book and our virtual machine and start building this.
+
+4
+00:00:16,200 --> 00:00:16,530
+OK.
+
+5
+00:00:16,560 --> 00:00:18,170
+So we're in chapter 18.
+
+6
+00:00:18,240 --> 00:00:20,380
+No footnote but book for this.
+
+7
+00:00:20,640 --> 00:00:28,650
+But is no 18.00 Reiffel notebook file whereas it is actually in here into to make it to quickly into
+
+8
+00:00:28,650 --> 00:00:31,110
+the age gender estimation for the hill.
+
+9
+00:00:31,110 --> 00:00:31,460
+All right.
+
+10
+00:00:31,470 --> 00:00:32,820
+So click it.
+
+11
+00:00:32,940 --> 00:00:39,870
+And what this is this is basically a guitar project I pulled it for one of the best one of the better
+
+12
+00:00:39,870 --> 00:00:42,740
+ones I should say for age and gender detection.
+
+13
+00:00:43,020 --> 00:00:47,820
+And the reason why I don't get it is for this code and what we're going to do we're going to use his
+
+14
+00:00:47,820 --> 00:00:52,500
+Pretorian model is mainly because it's this data set and it's.
+
+15
+00:00:52,760 --> 00:00:53,750
+It's not loaded here.
+
+16
+00:00:53,890 --> 00:00:58,320
+I hope it always will be taken up way too much space probably isn't.
+
+17
+00:00:58,320 --> 00:01:03,390
+But it's still said that he create He loaded he turned it on is quite large and I actually did have
+
+18
+00:01:03,390 --> 00:01:11,670
+a project prior to this where we were treating it on that dataset for age and stuff stop it's OK for
+
+19
+00:01:11,670 --> 00:01:14,280
+age and that's why.
+
+20
+00:01:14,620 --> 00:01:14,920
+OK.
+
+21
+00:01:14,970 --> 00:01:18,240
+In fact the dataset is here is not good at all.
+
+22
+00:01:18,240 --> 00:01:18,490
+All right.
+
+23
+00:01:18,510 --> 00:01:21,380
+So I'm going to leave that before I give it to you guys.
+
+24
+00:01:21,390 --> 00:01:23,070
+All right back to this.
+
+25
+00:01:23,130 --> 00:01:23,690
+OK.
+
+26
+00:01:24,030 --> 00:01:28,680
+So either way when you're trying this model you actually can train his on his model here.
+
+27
+00:01:28,820 --> 00:01:29,170
+All right.
+
+28
+00:01:29,190 --> 00:01:30,560
+He has the code to train.
+
+29
+00:01:30,630 --> 00:01:35,180
+However it is not quick and it is basically a standard training procedure.
+
+30
+00:01:35,190 --> 00:01:39,490
+What we have done before but it is not worth the effort to do in a CPE use system.
+
+31
+00:01:39,510 --> 00:01:45,780
+So I said you know what we will just actually just use our Pretorian models and we will just Pretorian
+
+32
+00:01:45,780 --> 00:01:52,860
+model and it's a good exercise to basically executing someone else's model is actually it may seem like
+
+33
+00:01:52,860 --> 00:01:55,620
+cheating sometimes but you can learn a lot.
+
+34
+00:01:55,620 --> 00:01:56,180
+All right.
+
+35
+00:01:56,220 --> 00:01:58,120
+So I'll quickly.
+
+36
+00:01:58,150 --> 00:01:59,970
+This is his project here.
+
+37
+00:02:00,180 --> 00:02:01,270
+Let's bring this up.
+
+38
+00:02:01,290 --> 00:02:02,780
+All right.
+
+39
+00:02:03,250 --> 00:02:07,890
+First what are we going to do is basically because he has some of the books he should actually label
+
+40
+00:02:08,100 --> 00:02:09,290
+what our minds are.
+
+41
+00:02:09,540 --> 00:02:11,350
+So student quickly.
+
+42
+00:02:11,400 --> 00:02:11,660
+All right.
+
+43
+00:02:11,670 --> 00:02:18,030
+So this is going to be eighteen point tree it's called us.
+
+44
+00:02:18,770 --> 00:02:20,660
+And second one we're going to run right after.
+
+45
+00:02:20,690 --> 00:02:26,360
+So we combine them both because firstly what we're going to run is his age and gender detector and Despard
+
+46
+00:02:26,400 --> 00:02:28,600
+be eighteen point tree.
+
+47
+00:02:28,710 --> 00:02:30,700
+B look at.
+
+48
+00:02:30,750 --> 00:02:32,080
+So now let's bring this up.
+
+49
+00:02:32,180 --> 00:02:33,420
+Stick a look at this.
+
+50
+00:02:35,020 --> 00:02:35,370
+Right.
+
+51
+00:02:35,380 --> 00:02:36,960
+So this is what I think I wanted.
+
+52
+00:02:36,990 --> 00:02:39,440
+This was his get help for his project.
+
+53
+00:02:39,460 --> 00:02:40,240
+OK.
+
+54
+00:02:40,690 --> 00:02:43,020
+Now I believe this was him.
+
+55
+00:02:43,060 --> 00:02:47,860
+If I'm not mistaken there was a slight issue with his project that didn't run off the bat and I had
+
+56
+00:02:47,860 --> 00:02:49,400
+to make some changes in the file.
+
+57
+00:02:49,720 --> 00:02:51,540
+Luckily I did make the changes in the file.
+
+58
+00:02:51,540 --> 00:02:56,620
+So if you you don't have to put this code from scratch his code is already in this field that we saw
+
+59
+00:02:56,620 --> 00:02:57,680
+here.
+
+60
+00:02:57,700 --> 00:03:03,070
+So basically he what's good about this still is that he does give some instructions some if you wanted
+
+61
+00:03:03,070 --> 00:03:08,690
+to actually train the model on you and he can trance on different data sets as well.
+
+62
+00:03:08,710 --> 00:03:11,460
+This is that is that he included in his file.
+
+63
+00:03:11,860 --> 00:03:17,000
+You can train Athill or you can use some of his demo file is the file we are actually going to.
+
+64
+00:03:17,280 --> 00:03:19,040
+That's the way it's open.
+
+65
+00:03:19,060 --> 00:03:21,690
+And you can see it here.
+
+66
+00:03:21,810 --> 00:03:27,160
+We don't want to doing it because I already have downloaded it and basically have been trapped under
+
+67
+00:03:27,200 --> 00:03:28,980
+I MVB basically this.
+
+68
+00:03:29,080 --> 00:03:31,910
+I am D-B data here talks about here.
+
+69
+00:03:31,910 --> 00:03:37,520
+Basically someone's script I am divvies Web site you know Internet Movie Database website extracted
+
+70
+00:03:37,520 --> 00:03:43,520
+defaces and actually labeled the ages and gender which might have been a quite tedious task but they
+
+71
+00:03:43,520 --> 00:03:44,150
+did it.
+
+72
+00:03:44,180 --> 00:03:44,730
+OK.
+
+73
+00:03:45,080 --> 00:03:47,350
+So these are his results here.
+
+74
+00:03:47,420 --> 00:03:52,730
+His Pretorian model is lost is quite low for age and gender.
+
+75
+00:03:52,730 --> 00:03:53,860
+Not that low in the end.
+
+76
+00:03:53,870 --> 00:03:55,490
+But it's fine.
+
+77
+00:03:55,670 --> 00:04:03,700
+And so he probably stopped at some pre-trained really something and got the Marlow's out before accuracy
+
+78
+00:04:04,100 --> 00:04:05,720
+and the stuff went to hell.
+
+79
+00:04:05,720 --> 00:04:07,370
+Actually I'm looking at around accuracy.
+
+80
+00:04:07,490 --> 00:04:10,600
+This is a one blue green and blue.
+
+81
+00:04:10,690 --> 00:04:13,040
+Like I said fine either way.
+
+82
+00:04:13,090 --> 00:04:15,080
+Definitely we're sitting here
+
+83
+00:04:18,070 --> 00:04:22,800
+actually is not fitting that is actually on his training data set.
+
+84
+00:04:22,840 --> 00:04:25,250
+So this kind of results to be fair.
+
+85
+00:04:25,260 --> 00:04:25,890
+All right.
+
+86
+00:04:26,110 --> 00:04:28,110
+Either way it worked fine.
+
+87
+00:04:28,120 --> 00:04:34,690
+And as an exercise for you guys if you're very if you're interested in seeing how this works his code
+
+88
+00:04:34,690 --> 00:04:35,510
+is here.
+
+89
+00:04:35,630 --> 00:04:37,360
+It's fairly well documented as well.
+
+90
+00:04:37,390 --> 00:04:42,460
+So you can take a look and try doing some cool stuff with this what we're doing now is using his preaching
+
+91
+00:04:42,460 --> 00:04:42,860
+model.
+
+92
+00:04:42,890 --> 00:04:51,010
+So basically extracted the code from his project code is code and doing it here and I it in the book.
+
+93
+00:04:51,010 --> 00:04:56,110
+So let's step through this code quickly it's not that different than the previous code just some little
+
+94
+00:04:56,110 --> 00:04:57,810
+things you should note.
+
+95
+00:04:57,880 --> 00:05:04,220
+Firstly this is how we load his model and we have to specify a hash when we look at his model.
+
+96
+00:05:04,360 --> 00:05:08,530
+So go back to our face detector results.
+
+97
+00:05:08,530 --> 00:05:09,950
+This is what gives us our faces.
+
+98
+00:05:09,970 --> 00:05:14,220
+And this is what displays our results actually.
+
+99
+00:05:14,470 --> 00:05:15,340
+Let me just check something.
+
+100
+00:05:15,340 --> 00:05:17,610
+I don't believe I'm using that function any more.
+
+101
+00:05:17,980 --> 00:05:20,420
+And I am right I am not using it anymore.
+
+102
+00:05:20,430 --> 00:05:23,700
+So that's to it on.
+
+103
+00:05:23,760 --> 00:05:25,050
+Totally unnecessary.
+
+104
+00:05:25,050 --> 00:05:26,400
+All right.
+
+105
+00:05:26,400 --> 00:05:29,020
+So now these are the moral premises here.
+
+106
+00:05:29,220 --> 00:05:29,900
+OK.
+
+107
+00:05:30,270 --> 00:05:31,360
+Just say no.
+
+108
+00:05:31,380 --> 00:05:34,040
+Can I leave this alone ok dept OK.
+
+109
+00:05:34,100 --> 00:05:39,230
+With none is all the specific specifications for loading his model.
+
+110
+00:05:39,270 --> 00:05:39,820
+OK.
+
+111
+00:05:39,990 --> 00:05:41,180
+His pre-trained weights.
+
+112
+00:05:41,190 --> 00:05:46,850
+So we do that so we have his model we loaded it we got the weights and everything from his model and
+
+113
+00:05:46,870 --> 00:05:50,770
+each the EF 5 format.
+
+114
+00:05:51,080 --> 00:05:54,450
+So no set up stuff.
+
+115
+00:05:54,630 --> 00:05:55,820
+We get his model.
+
+116
+00:05:55,920 --> 00:06:00,250
+Fitbit does or does not go with his model here and model that load.
+
+117
+00:06:00,270 --> 00:06:02,600
+So we get his model now officially.
+
+118
+00:06:02,600 --> 00:06:03,420
+All right.
+
+119
+00:06:03,810 --> 00:06:06,500
+So we initialize a webcam here.
+
+120
+00:06:06,510 --> 00:06:10,940
+Standard stuff we said is if you've seen before we get this.
+
+121
+00:06:11,070 --> 00:06:15,840
+Now what I've done I've made some changes to this code because the previous code didn't tell you.
+
+122
+00:06:15,900 --> 00:06:21,780
+But if a second person appeared in the diagram it would only show one if you guys are the time it doesn't
+
+123
+00:06:21,780 --> 00:06:23,210
+show more than one face.
+
+124
+00:06:23,360 --> 00:06:25,380
+Oh it may have him trash.
+
+125
+00:06:25,410 --> 00:06:27,380
+I'm not even sure you can do it.
+
+126
+00:06:27,390 --> 00:06:28,360
+Bring a friend.
+
+127
+00:06:28,730 --> 00:06:29,040
+OK.
+
+128
+00:06:29,130 --> 00:06:31,250
+I have no friends with me right right now the wife is out.
+
+129
+00:06:31,320 --> 00:06:32,370
+So I can't test that.
+
+130
+00:06:32,640 --> 00:06:33,970
+But that's OK.
+
+131
+00:06:33,990 --> 00:06:37,910
+So this is how actually got it to work with multiple faces.
+
+132
+00:06:37,920 --> 00:06:40,340
+So we do this here.
+
+133
+00:06:41,130 --> 00:06:47,430
+Basically we append the faces we extract here and then what we do we use his model that we loaded here
+
+134
+00:06:48,050 --> 00:06:51,200
+to make a prediction and this prediction gives us two things.
+
+135
+00:06:51,220 --> 00:06:54,450
+Gender first very male female.
+
+136
+00:06:54,540 --> 00:06:59,640
+I know there are many agendas right now but for now is history in the male and female because the genders
+
+137
+00:07:00,070 --> 00:07:03,270
+will still look like male or female either way.
+
+138
+00:07:03,270 --> 00:07:04,070
+So that's fine.
+
+139
+00:07:04,340 --> 00:07:04,930
+All right.
+
+140
+00:07:05,920 --> 00:07:08,600
+And so we have predicted ages here as well.
+
+141
+00:07:08,890 --> 00:07:12,580
+And this is all in a bit of a funny shape here.
+
+142
+00:07:12,820 --> 00:07:14,380
+So he does some reshipping.
+
+143
+00:07:14,400 --> 00:07:14,730
+All right.
+
+144
+00:07:14,770 --> 00:07:15,640
+And flattens it.
+
+145
+00:07:15,680 --> 00:07:17,380
+And then we get the results here.
+
+146
+00:07:17,380 --> 00:07:20,430
+So we get the predicted ages and this is good.
+
+147
+00:07:20,520 --> 00:07:20,900
+All right.
+
+148
+00:07:20,920 --> 00:07:21,470
+So we get it.
+
+149
+00:07:21,470 --> 00:07:28,270
+No no what we're going to do since we have multiple we possibly have multiple cases in this file is
+
+150
+00:07:28,270 --> 00:07:34,850
+that we go through defaces and go through the results and display the results later on here.
+
+151
+00:07:35,380 --> 00:07:36,170
+OK.
+
+152
+00:07:36,700 --> 00:07:38,250
+And that is it.
+
+153
+00:07:38,260 --> 00:07:40,110
+This is what runs forward.
+
+154
+00:07:40,120 --> 00:07:41,290
+Age and gender.
+
+155
+00:07:41,620 --> 00:07:42,930
+So let's give it a try.
+
+156
+00:07:47,660 --> 00:07:51,400
+Slutting probably a learning model.
+
+157
+00:07:51,410 --> 00:07:52,280
+There we go.
+
+158
+00:07:52,310 --> 00:07:53,950
+So that is pretty awesome.
+
+159
+00:07:54,260 --> 00:08:00,950
+All right what I'm going to do going to bring up a picture of my funny face and try to simulate this
+
+160
+00:08:01,010 --> 00:08:05,960
+with another person or a pretend person we're going to use
+
+161
+00:08:10,180 --> 00:08:12,540
+I guess is actually hard and I taught
+
+162
+00:08:17,740 --> 00:08:18,030
+it.
+
+163
+00:08:18,120 --> 00:08:24,160
+So my friend here she has some good face pictures.
+
+164
+00:08:24,270 --> 00:08:27,200
+I hope she's OK with me using this right.
+
+165
+00:08:27,600 --> 00:08:30,700
+OK so I'm going to bring this up here.
+
+166
+00:08:36,270 --> 00:08:36,560
+OK.
+
+167
+00:08:36,600 --> 00:08:41,530
+So this is me this is my friend is not detecting.
+
+168
+00:08:41,550 --> 00:08:42,200
+Oh there we go.
+
+169
+00:08:43,220 --> 00:08:44,630
+Thinks she's a guy.
+
+170
+00:08:44,680 --> 00:08:45,790
+I have no idea.
+
+171
+00:08:45,820 --> 00:08:47,470
+She doesn't need any of this.
+
+172
+00:08:47,660 --> 00:08:49,210
+And so I think she's a guy.
+
+173
+00:08:49,350 --> 00:08:52,360
+Now there's a problem here and I hope you can see it.
+
+174
+00:08:52,480 --> 00:08:55,630
+And I did actually see it in the code and didn't mention it to you guys because I was like wondering
+
+175
+00:08:56,260 --> 00:08:59,690
+how it doesn't work the way it does supposed to doesn't it.
+
+176
+00:08:59,920 --> 00:09:06,790
+And that's because exactly as I said it will detect the faces here but it only draws results for one
+
+177
+00:09:06,790 --> 00:09:09,760
+prediction here because it's not a pending results results.
+
+178
+00:09:09,760 --> 00:09:12,700
+In theory this is something I fix later on.
+
+179
+00:09:12,700 --> 00:09:15,240
+By the way so for now though just be aware.
+
+180
+00:09:15,250 --> 00:09:21,800
+At this age and gender detector only technically detects one face at a time not two.
+
+181
+00:09:22,240 --> 00:09:26,020
+And basically this is something I also noted here.
+
+182
+00:09:26,320 --> 00:09:27,850
+If you get this early.
+
+183
+00:09:27,970 --> 00:09:30,900
+This is the area you'll see here faces not being detected.
+
+184
+00:09:31,120 --> 00:09:33,430
+It's because your webcam has not been turned on.
+
+185
+00:09:33,430 --> 00:09:41,260
+So basically if this happened this happens just go to devices and webcams and tick tick tick off.
+
+186
+00:09:41,320 --> 00:09:41,890
+OK.
+
+187
+00:09:42,520 --> 00:09:43,250
+So that is that.
+
+188
+00:09:43,270 --> 00:09:48,790
+And if whatever reason this program crashes when you're messing with the code and the webcam isn't released
+
+189
+00:09:49,060 --> 00:09:55,120
+that will happen quite often I'm sure just run this line separately to reclaim your webcam so that you
+
+190
+00:09:55,120 --> 00:09:58,220
+can re-initialize it again later on.
+
+191
+00:09:58,240 --> 00:09:58,570
+OK.
+
+192
+00:09:58,660 --> 00:10:03,410
+So no that was age and gender and a Eating point tree.
+
+193
+00:10:03,640 --> 00:10:04,330
+Now let's do.
+
+194
+00:10:04,330 --> 00:10:06,630
+Age and gender with emotions.
+
+195
+00:10:06,640 --> 00:10:08,430
+That's the cool part.
+
+196
+00:10:08,680 --> 00:10:10,800
+That's deep surveillance partner.
+
+197
+00:10:10,810 --> 00:10:18,100
+So again let's load all of this 10 seconds wasted of my life again.
+
+198
+00:10:19,810 --> 00:10:24,610
+Yoga seemed faster this time maybe me complain.
+
+199
+00:10:25,130 --> 00:10:25,460
+OK.
+
+200
+00:10:25,550 --> 00:10:30,290
+So no this is testing emotion agent and using a webcam.
+
+201
+00:10:30,430 --> 00:10:37,350
+Now I'm not going to go through this code in super detail because it is a bit messy.
+
+202
+00:10:37,880 --> 00:10:43,640
+However what I'm going to tell you is that I have manipulated this code to support two faces.
+
+203
+00:10:43,640 --> 00:10:44,590
+All right.
+
+204
+00:10:44,720 --> 00:10:46,800
+So let's give this ago.
+
+205
+00:10:47,270 --> 00:10:49,880
+Bring up my friend's Instagram picture again
+
+206
+00:10:54,990 --> 00:10:55,330
+OK.
+
+207
+00:10:55,360 --> 00:10:57,490
+So we got an error.
+
+208
+00:10:57,610 --> 00:10:59,050
+Color is not defined.
+
+209
+00:10:59,210 --> 00:11:02,290
+Like I'm going to pause this and see what is happening here.
+
+210
+00:11:02,770 --> 00:11:04,430
+I know exactly what's happening.
+
+211
+00:11:04,480 --> 00:11:07,040
+I was trying to do some color manipulation before.
+
+212
+00:11:07,260 --> 00:11:07,540
+OK.
+
+213
+00:11:07,610 --> 00:11:12,140
+CBG are Spudis get back green OK.
+
+214
+00:11:12,240 --> 00:11:13,430
+It is going to.
+
+215
+00:11:13,700 --> 00:11:18,780
+Oh dammit what happened is that it claimed my webcam.
+
+216
+00:11:18,940 --> 00:11:21,900
+It's not going to get an image.
+
+217
+00:11:21,910 --> 00:11:27,850
+So what we can do as I mentioned before and I did not put it in this file accessible area just put a
+
+218
+00:11:27,850 --> 00:11:34,690
+cell below and reclean my webcam so I can run this finally again
+
+219
+00:11:37,860 --> 00:11:38,720
+I attempted it.
+
+220
+00:11:38,750 --> 00:11:39,300
+There we go.
+
+221
+00:11:40,460 --> 00:11:42,150
+So yeah I'm not actually sad.
+
+222
+00:11:42,260 --> 00:11:44,710
+I don't know why it's tinks I'm sad no neutral.
+
+223
+00:11:45,060 --> 00:11:50,230
+And bring up the multiple face Gladys's Merde is confusing.
+
+224
+00:11:52,290 --> 00:11:55,380
+At still in.
+
+225
+00:11:55,400 --> 00:11:58,780
+Why I doing a in front of me.
+
+226
+00:12:00,900 --> 00:12:01,850
+All right.
+
+227
+00:12:01,910 --> 00:12:03,040
+Come on detect
+
+228
+00:12:12,320 --> 00:12:13,630
+get a brief second.
+
+229
+00:12:13,700 --> 00:12:15,680
+But let me just bring this.
+
+230
+00:12:15,760 --> 00:12:18,800
+This is much easier when you actually have a real person next to you.
+
+231
+00:12:21,690 --> 00:12:22,220
+OK.
+
+232
+00:12:22,420 --> 00:12:24,430
+I'm going to pause this and get a better picture.
+
+233
+00:12:24,430 --> 00:12:25,270
+No offense.
+
+234
+00:12:25,430 --> 00:12:27,920
+This picture has not been affected.
+
+235
+00:12:28,480 --> 00:12:28,850
+OK.
+
+236
+00:12:28,990 --> 00:12:37,920
+So hold one second because the recording that really put it on as fast forward this bit while I just
+
+237
+00:12:37,920 --> 00:12:44,240
+waste time like.
+
+238
+00:12:44,410 --> 00:12:44,700
+All right.
+
+239
+00:12:44,710 --> 00:12:52,290
+So I found a picture of my wife I may use from a Facebook profile maybe I should use like stock images.
+
+240
+00:12:52,290 --> 00:12:55,610
+It seems like these images don't have the best luck but we'll never know.
+
+241
+00:12:55,610 --> 00:12:55,820
+All right.
+
+242
+00:12:55,820 --> 00:12:59,060
+So let's put it in front here.
+
+243
+00:12:59,240 --> 00:13:08,560
+I just saw a detective come back from back in says I actually thought it looks because now I can see
+
+244
+00:13:08,560 --> 00:13:09,320
+my screen.
+
+245
+00:13:10,210 --> 00:13:14,590
+And I can see my face and it's not detecting my face.
+
+246
+00:13:14,590 --> 00:13:16,370
+Maybe I should just bring this back slightly.
+
+247
+00:13:16,380 --> 00:13:17,320
+There we go.
+
+248
+00:13:17,830 --> 00:13:18,230
+OK.
+
+249
+00:13:18,370 --> 00:13:24,670
+So I know my wife is going to be happy with this but I think she's a guy who is 24 years old.
+
+250
+00:13:24,940 --> 00:13:31,160
+Probably because it's from a cell phone and the skill is definitely going to be off because of it.
+
+251
+00:13:31,450 --> 00:13:33,930
+I'm 28 when I'm 24.
+
+252
+00:13:34,330 --> 00:13:35,400
+Damn you.
+
+253
+00:13:35,500 --> 00:13:37,230
+Sorry to deduct it.
+
+254
+00:13:37,570 --> 00:13:37,830
+OK.
+
+255
+00:13:37,870 --> 00:13:40,620
+When it starts kind of to kind of going to hold.
+
+256
+00:13:40,660 --> 00:13:45,940
+All right so what this means here is that this code works.
+
+257
+00:13:45,940 --> 00:13:52,420
+It's not exactly 100 percent accurate with gender detection as we can see but that probably has to do
+
+258
+00:13:52,420 --> 00:13:53,590
+with my little fake friends.
+
+259
+00:13:53,590 --> 00:13:55,210
+I'm using my cell phone.
+
+260
+00:13:55,240 --> 00:13:57,010
+These people are REAL by the way.
+
+261
+00:13:57,380 --> 00:14:02,790
+But either way this should not happen that way but it works it works.
+
+262
+00:14:02,800 --> 00:14:06,810
+It's good that is directing multiple pictures multiple people.
+
+263
+00:14:06,820 --> 00:14:07,120
+All right.
+
+264
+00:14:07,120 --> 00:14:09,500
+So this is good.
+
+265
+00:14:09,550 --> 00:14:13,680
+Let's move on to the next one images so let's run this
+
+266
+00:14:19,570 --> 00:14:23,060
+OK so now let's run this test on images.
+
+267
+00:14:23,070 --> 00:14:23,560
+OK.
+
+268
+00:14:23,560 --> 00:14:27,300
+You did not load anything in my bed
+
+269
+00:14:30,260 --> 00:14:31,460
+not that or
+
+270
+00:14:38,140 --> 00:14:38,620
+OK.
+
+271
+00:14:38,630 --> 00:14:44,480
+So let's test this on some test images and you can place those images in the images folder.
+
+272
+00:14:44,930 --> 00:14:45,550
+Will show you here.
+
+273
+00:14:45,590 --> 00:14:45,910
+OK.
+
+274
+00:14:45,980 --> 00:14:49,840
+So let's look at Donald Trump oh this is quite funny.
+
+275
+00:14:49,950 --> 00:14:57,440
+I think it's a female 59 looks much older in my opinion but fifty nine and said It's me again.
+
+276
+00:14:57,460 --> 00:15:01,050
+I did some extra years to me because I'm actually actually 24.
+
+277
+00:15:01,140 --> 00:15:02,310
+But fair enough.
+
+278
+00:15:03,940 --> 00:15:04,330
+All right.
+
+279
+00:15:04,330 --> 00:15:06,560
+Queen Elizabeth and something funny is happening.
+
+280
+00:15:06,640 --> 00:15:10,930
+It actually thinks this bouquet of roses is a guy who took the ideas off.
+
+281
+00:15:11,170 --> 00:15:16,360
+Clearly this is a bad example of Mitt not working too well.
+
+282
+00:15:16,840 --> 00:15:17,710
+But there's a reason for that.
+
+283
+00:15:17,720 --> 00:15:18,870
+I'll tell you afterwards.
+
+284
+00:15:18,910 --> 00:15:19,800
+OK.
+
+285
+00:15:20,470 --> 00:15:21,270
+Barack Obama.
+
+286
+00:15:21,460 --> 00:15:24,260
+Male angry to the one not the best.
+
+287
+00:15:24,400 --> 00:15:25,480
+Is my wife.
+
+288
+00:15:25,480 --> 00:15:30,870
+Female tity she actually was tastiness Richardsons quite good and she was definitely happy here.
+
+289
+00:15:31,100 --> 00:15:31,590
+All right.
+
+290
+00:15:31,660 --> 00:15:35,350
+She was actually this was the two days of the pictures were two days apart.
+
+291
+00:15:35,380 --> 00:15:37,860
+So she aged a year in those two days.
+
+292
+00:15:38,260 --> 00:15:38,560
+OK.
+
+293
+00:15:38,590 --> 00:15:40,040
+So that's cool.
+
+294
+00:15:40,240 --> 00:15:41,750
+Now I said I'll tell you why.
+
+295
+00:15:42,010 --> 00:15:49,350
+That's because when we were doing this here we were basically Horncastle pacifies don't crop enough
+
+296
+00:15:49,350 --> 00:15:55,090
+of the face out even with the default settings here which are removed because you may have seen it in
+
+297
+00:15:55,100 --> 00:16:01,710
+a court because it just cut and paste some of these images discotheques or videos before we actually
+
+298
+00:16:01,710 --> 00:16:04,070
+had some cropping being done.
+
+299
+00:16:04,140 --> 00:16:07,080
+And I took it out because it was less accurate.
+
+300
+00:16:07,410 --> 00:16:10,350
+But now anyway so you libs facial recognition.
+
+301
+00:16:10,350 --> 00:16:12,380
+Let's give this a try.
+
+302
+00:16:12,390 --> 00:16:13,470
+Let's quickly run this
+
+303
+00:16:17,750 --> 00:16:19,830
+and I'll tell you about dealing with starting in the back row here.
+
+304
+00:16:19,850 --> 00:16:21,180
+I'll tell you about dealer dealer.
+
+305
+00:16:21,220 --> 00:16:27,460
+It is it's a machine learning basically package that was built in C++ and you can use it into Python
+
+306
+00:16:27,800 --> 00:16:29,210
+and it does a bunch of cool stuff.
+
+307
+00:16:29,210 --> 00:16:34,430
+And what I'm using it here for is for phase detection but it actually can do facial recognition.
+
+308
+00:16:34,460 --> 00:16:39,480
+In my open C-v course in the past I haven't included in discourse.
+
+309
+00:16:39,590 --> 00:16:47,170
+You actually do use Nonpoint recognition on faces for some cool projects like your detection and the
+
+310
+00:16:47,540 --> 00:16:50,400
+swaps as well more advanced with swaps.
+
+311
+00:16:50,390 --> 00:16:50,680
+All right.
+
+312
+00:16:50,740 --> 00:16:53,660
+So I'm sorry I'm retarded.
+
+313
+00:16:53,780 --> 00:16:55,690
+We are using the live on images here.
+
+314
+00:16:55,700 --> 00:17:00,110
+I really should label this that I think in the last section used to live on the webcam.
+
+315
+00:17:00,140 --> 00:17:01,120
+Yes.
+
+316
+00:17:01,250 --> 00:17:01,970
+OK.
+
+317
+00:17:02,120 --> 00:17:02,980
+So again.
+
+318
+00:17:03,100 --> 00:17:04,280
+So it actually is.
+
+319
+00:17:04,340 --> 00:17:08,820
+Now a trump he actually aged two years less fend off me.
+
+320
+00:17:08,980 --> 00:17:09,420
+Oh.
+
+321
+00:17:09,430 --> 00:17:10,790
+You know when you're younger.
+
+322
+00:17:10,790 --> 00:17:11,900
+That's good.
+
+323
+00:17:12,830 --> 00:17:14,130
+Fifty four female OK.
+
+324
+00:17:14,200 --> 00:17:14,980
+Fair enough.
+
+325
+00:17:14,980 --> 00:17:16,420
+She has a lot.
+
+326
+00:17:16,610 --> 00:17:18,030
+In that picture.
+
+327
+00:17:18,580 --> 00:17:20,200
+That's fine.
+
+328
+00:17:20,290 --> 00:17:21,500
+Tony correct.
+
+329
+00:17:21,970 --> 00:17:27,220
+Barack Obama 45 I think he was in his late 40s and this picture may be very very accurate actually is
+
+330
+00:17:27,220 --> 00:17:28,760
+quite close.
+
+331
+00:17:29,180 --> 00:17:35,590
+And my wife she would not be happy with each year but it's fair enough again because she was Tuti in
+
+332
+00:17:35,590 --> 00:17:36,670
+those pictures.
+
+333
+00:17:37,000 --> 00:17:37,950
+OK but fair enough.
+
+334
+00:17:37,950 --> 00:17:39,860
+So you can experiment with some different things.
+
+335
+00:17:39,860 --> 00:17:41,500
+You know it's not going to be perfect.
+
+336
+00:17:41,500 --> 00:17:45,440
+Age is actually a very hard thing to guess.
+
+337
+00:17:45,460 --> 00:17:52,870
+I myself sometimes have thought someone was to be in it today 25 or 26 and I learned later they were
+
+338
+00:17:52,870 --> 00:17:59,130
+like 50 which was embarrassing but they were very happy about my mis take.
+
+339
+00:17:59,140 --> 00:18:04,170
+So anyway we can run this with the lab using the webcam so I'm going on this.
+
+340
+00:18:04,300 --> 00:18:09,660
+And I've changed my T-shirt by the way back to the one with stain unfortunately.
+
+341
+00:18:09,910 --> 00:18:12,340
+Hope it isn't picked up in this camera.
+
+342
+00:18:12,340 --> 00:18:14,070
+No it does not.
+
+343
+00:18:14,380 --> 00:18:15,620
+And the bedroom doesn't open.
+
+344
+00:18:15,640 --> 00:18:16,660
+Yeah it's fine.
+
+345
+00:18:16,960 --> 00:18:18,280
+So again this is quite cool.
+
+346
+00:18:18,280 --> 00:18:22,570
+You can definitely see dealer has a slew of film right here OK.
+
+347
+00:18:22,940 --> 00:18:27,290
+And literally did my hair thing five seconds 10 seconds before.
+
+348
+00:18:27,610 --> 00:18:27,970
+OK.
+
+349
+00:18:28,120 --> 00:18:29,820
+So close.
+
+350
+00:18:30,340 --> 00:18:35,020
+So the advantage of the lid though is that it is better at picking up faces.
+
+351
+00:18:35,050 --> 00:18:40,600
+One thing you should have noted noted here is that it didn't mystic Queen Elizabeth's bouquet for a
+
+352
+00:18:40,600 --> 00:18:43,610
+face like what a hawk husky classified did.
+
+353
+00:18:43,870 --> 00:18:47,170
+So you can generally see that as better and more robust.
+
+354
+00:18:47,180 --> 00:18:53,650
+Remember for Cutie Caskie classifies it or this was a face that it was a guy which I would think it
+
+355
+00:18:53,640 --> 00:18:54,240
+was a female.
+
+356
+00:18:54,310 --> 00:18:57,660
+Either way if it was a face that is.
+
+357
+00:18:58,270 --> 00:19:00,340
+But that's fine.
+
+358
+00:19:00,340 --> 00:19:04,650
+So what I'm saying Bakassi cast philosophise a definitely faster.
+
+359
+00:19:04,920 --> 00:19:09,850
+So if speed is your concern or if your hardware if it's an embedded system you can use Hawkhurst justifies
+
+360
+00:19:10,300 --> 00:19:16,920
+and maybe tweak some of the parameters like this scaling parameters especially this definitely helps.
+
+361
+00:19:16,960 --> 00:19:23,770
+But these settings are generally well understood as the gold standard Woodhall Cascades like this is
+
+362
+00:19:23,780 --> 00:19:27,330
+the best depending on your application.
+
+363
+00:19:27,350 --> 00:19:29,020
+But generally this is the best.
+
+364
+00:19:29,500 --> 00:19:35,590
+So a dealer may be more suited for some applications if its accuracy especially is of importance.
+
+365
+00:19:35,590 --> 00:19:37,540
+OK so that's it for this chapter.
+
+366
+00:19:37,540 --> 00:19:42,160
+I hope you enjoyed it and I hope you build something cool out of this and I hope you expand upon it
+
+367
+00:19:42,160 --> 00:19:42,490
+too.
+
+368
+00:19:42,510 --> 00:19:47,980
+You can actually take this code now and train and train over these models or add in some new stuff as
+
+369
+00:19:47,980 --> 00:19:48,370
+well.
+
+370
+00:19:48,490 --> 00:19:50,010
+And ethnicity as well.
+
+371
+00:19:50,020 --> 00:19:57,380
+You can actually now take some of this data sets here like this one for one and maybe you can if you
+
+372
+00:19:57,380 --> 00:20:02,410
+can find like a label that you can manually label the agenda from this as well to get even more accurate
+
+373
+00:20:02,470 --> 00:20:04,060
+gender recognition.
+
+374
+00:20:04,060 --> 00:20:05,310
+So this is pretty cool.
+
+375
+00:20:05,350 --> 00:20:12,850
+And the challenge and in bringing both of these together which I probably failed to mention is that
+
+376
+00:20:13,530 --> 00:20:16,260
+they're both taking different sized faces to boot.
+
+377
+00:20:16,270 --> 00:20:18,940
+One is ticking and ticking in a color image.
+
+378
+00:20:19,120 --> 00:20:20,790
+That's the age and gender sticking.
+
+379
+00:20:20,820 --> 00:20:26,730
+Taking in a color image that's 64 by 64 whereas the other one which is all in motion detectors ticking
+
+380
+00:20:26,800 --> 00:20:30,410
+in a smaller and gray scale image.
+
+381
+00:20:30,460 --> 00:20:34,780
+So you have to sort of do some a little more processing here in our pipeline.
+
+382
+00:20:35,020 --> 00:20:41,180
+And basically then you have to get the results and make sure they're lined up like the same face is
+
+383
+00:20:41,200 --> 00:20:47,590
+tied together with his agent and the emotion which is fairly easy anyway and then plus and take it correctly
+
+384
+00:20:47,590 --> 00:20:51,480
+and then making sure the labels follow the face around the image.
+
+385
+00:20:51,520 --> 00:20:54,390
+So it was a fun piece of code to build.
+
+386
+00:20:54,400 --> 00:20:55,270
+To be fair.
+
+387
+00:20:55,470 --> 00:20:55,760
+But.
diff --git a/18. Facial Applications - Emotion, Age & Gender Recognition/3.1 Download weights file.html b/18. Facial Applications - Emotion, Age & Gender Recognition/3.1 Download weights file.html
new file mode 100644
index 0000000000000000000000000000000000000000..ac39eeb9fdf0fe76910e3a25d6939901c5f8bb50
--- /dev/null
+++ b/18. Facial Applications - Emotion, Age & Gender Recognition/3.1 Download weights file.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/17kyPQfUyk2un-d-XYFT54R0q9vvSxbuJ/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/18. Facial Applications - Emotion, Age & Gender Recognition/3.2 Code and files required for project.html b/18. Facial Applications - Emotion, Age & Gender Recognition/3.2 Code and files required for project.html
new file mode 100644
index 0000000000000000000000000000000000000000..4d21659a7b9895e4ccbd7ccf5fb7d12f3e3e4725
--- /dev/null
+++ b/18. Facial Applications - Emotion, Age & Gender Recognition/3.2 Code and files required for project.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1lI_gZM9QuxjyRIKGvOm63d1td5lgRz4i/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/19. Medical Imaging - Image Segmentation with U-Net/1. Chapter Overview on Image Segmentation & Medical Imaging in U-Net.srt b/19. Medical Imaging - Image Segmentation with U-Net/1. Chapter Overview on Image Segmentation & Medical Imaging in U-Net.srt
new file mode 100644
index 0000000000000000000000000000000000000000..199d97396ed6643b5163a7b9b2ca538288ca5f40
--- /dev/null
+++ b/19. Medical Imaging - Image Segmentation with U-Net/1. Chapter Overview on Image Segmentation & Medical Imaging in U-Net.srt	
@@ -0,0 +1,31 @@
+1
+00:00:00,400 --> 00:00:00,750
+OK.
+
+2
+00:00:00,810 --> 00:00:06,330
+So let's move on to image segmentation and I'll talk a bit about medical imaging and the unit which
+
+3
+00:00:06,330 --> 00:00:10,000
+is a very cool CNN that does image segmentation.
+
+4
+00:00:10,060 --> 00:00:15,560
+So this section is built up into four parts Firstly discuss what is segmentation Exactly.
+
+5
+00:00:15,820 --> 00:00:19,300
+And I provide some examples of applications in medical imaging.
+
+6
+00:00:19,480 --> 00:00:25,720
+Dennis are talking about units and how it applies to image segmentation and CNN's Then I did find some
+
+7
+00:00:25,720 --> 00:00:30,310
+units so we are going to need to know about which is the intersection of union metric and then we do
+
+8
+00:00:30,310 --> 00:00:35,580
+a final project in the shop too where we find a nuclear nuclei and I frequent images.
diff --git a/19. Medical Imaging - Image Segmentation with U-Net/2. What is Segmentation And Applications in Medical Imaging.srt b/19. Medical Imaging - Image Segmentation with U-Net/2. What is Segmentation And Applications in Medical Imaging.srt
new file mode 100644
index 0000000000000000000000000000000000000000..07b8da6d95d942d900b625a6c27134c4046b26f7
--- /dev/null
+++ b/19. Medical Imaging - Image Segmentation with U-Net/2. What is Segmentation And Applications in Medical Imaging.srt	
@@ -0,0 +1,215 @@
+1
+00:00:00,960 --> 00:00:07,350
+Hi and welcome to chapter or section nineteen point one where we talk about segmentation and its applications
+
+2
+00:00:07,350 --> 00:00:09,450
+in medical imaging.
+
+3
+00:00:09,520 --> 00:00:12,280
+So what exactly is image segmentation.
+
+4
+00:00:12,280 --> 00:00:18,040
+So the goal of segmentation is to separate different parts of an image into sensible coherent parts.
+
+5
+00:00:18,040 --> 00:00:19,740
+And what do I mean by that.
+
+6
+00:00:19,810 --> 00:00:22,500
+Basically you see this cat and this dog.
+
+7
+00:00:22,540 --> 00:00:26,490
+And then there's a background definitely in the back to a bed or couch.
+
+8
+00:00:26,680 --> 00:00:28,600
+And then there are some kittens in the back.
+
+9
+00:00:28,600 --> 00:00:34,400
+What we want in a window actually what we want is basically to do pixel level predictions.
+
+10
+00:00:34,480 --> 00:00:39,640
+So we want to actually know exactly what pixels here belong to the dog what belongs to the cat and what
+
+11
+00:00:39,640 --> 00:00:41,510
+belong to the objects in the background.
+
+12
+00:00:41,860 --> 00:00:45,620
+So as you can see it's a pretty challenging task because before.
+
+13
+00:00:45,850 --> 00:00:50,140
+Well what we were doing was basically a prediction of the entire image.
+
+14
+00:00:50,140 --> 00:00:55,360
+And basically if we fed this declassify it would probably give a high probability of what cat and the
+
+15
+00:00:55,360 --> 00:00:57,080
+dog being in the picture.
+
+16
+00:00:57,430 --> 00:01:00,540
+But now what we want to do is actually segment the image now.
+
+17
+00:01:00,610 --> 00:01:02,400
+So how do we go about doing this.
+
+18
+00:01:02,530 --> 00:01:06,800
+And before you even explain that let's talk about two types of segmentation.
+
+19
+00:01:08,510 --> 00:01:14,510
+So the first type is called semantic segmentation and basically this is just pixel level predictions
+
+20
+00:01:15,260 --> 00:01:16,870
+based on defined classes.
+
+21
+00:01:16,880 --> 00:01:19,740
+So we have example Rood's persons cause entry.
+
+22
+00:01:19,910 --> 00:01:26,720
+So it's simple enough to understand not simple to do but we can pretty much see how it's done here and
+
+23
+00:01:26,720 --> 00:01:32,900
+you can imagine this has a lot of application in self-driving cars because now you need to know what
+
+24
+00:01:32,900 --> 00:01:37,470
+is a road what is a building what are like ampoules people those sorts of things.
+
+25
+00:01:37,730 --> 00:01:40,200
+So it's going to be quite useful for that application.
+
+26
+00:01:41,730 --> 00:01:42,420
+Type 2.
+
+27
+00:01:42,460 --> 00:01:44,610
+So no we're doing two different things.
+
+28
+00:01:44,640 --> 00:01:49,890
+We're doing pixel level predictions which is exactly what we did before but now we're actually doing
+
+29
+00:01:50,010 --> 00:01:54,340
+object detection and actually object identification as well.
+
+30
+00:01:54,480 --> 00:01:57,290
+So we know Person 1 to call 1 and 2.
+
+31
+00:01:57,330 --> 00:02:03,050
+So this is a more advanced level of segmentation.
+
+32
+00:02:03,120 --> 00:02:05,870
+So let's talk a bit about applications and medical imaging.
+
+33
+00:02:06,090 --> 00:02:12,630
+So as you know a lot of medical imaging necessitates finding and accurately labeling basically things
+
+34
+00:02:12,630 --> 00:02:19,080
+we find in these scans because if you've seen a lot of these medical imaging scans are very very hard
+
+35
+00:02:19,080 --> 00:02:19,720
+to interpret.
+
+36
+00:02:19,740 --> 00:02:24,870
+And I think you need some very skilled professionals analyzing and accurately assessing what they see
+
+37
+00:02:24,990 --> 00:02:31,340
+in those pictures generated from the different scans and basically Often this task is actually there.
+
+38
+00:02:31,370 --> 00:02:34,810
+There is a lot of advance software being used in these tests.
+
+39
+00:02:35,010 --> 00:02:40,920
+However they still require a human to actually go through it and maybe label things properly so that
+
+40
+00:02:41,010 --> 00:02:42,770
+you know the machine isn't alone.
+
+41
+00:02:42,780 --> 00:02:43,360
+All right.
+
+42
+00:02:43,410 --> 00:02:48,420
+However this is definitely definitely a task where you can improve it because humans are definitely
+
+43
+00:02:48,420 --> 00:02:51,750
+prone to error and there are a number of applications mainly to him.
+
+44
+00:02:51,820 --> 00:02:58,380
+And I don't mean that are being done by convolutional your own that's an advanced neural nets.
+
+45
+00:02:58,620 --> 00:03:04,910
+So as I said is a huge initiative to use can be division and planning to automate many of these tests.
+
+46
+00:03:04,980 --> 00:03:11,040
+So it's a lot of tests that can be improved with computer vision not just her but surgery which is actually
+
+47
+00:03:11,040 --> 00:03:15,230
+going to be a main application in the future for the division.
+
+48
+00:03:15,240 --> 00:03:21,450
+However right now the trend seems to be in a lot of these medical scans things like CAT scans X-rays
+
+49
+00:03:21,600 --> 00:03:25,060
+ultrasounds PET scans and an MRI.
+
+50
+00:03:25,410 --> 00:03:29,590
+And so many different types of diseases to look for it.
+
+51
+00:03:29,610 --> 00:03:37,290
+This is an ideal area of startups to take advantage of and the use cases for this are endless from cancer
+
+52
+00:03:37,290 --> 00:03:42,510
+detection disease monitoring Alzheimer's and many many other ailments.
+
+53
+00:03:42,540 --> 00:03:48,270
+So computer vision can definitely revolutionize the medical industry and improve patient care and get
+
+54
+00:03:48,270 --> 00:03:52,460
+much faster diagnostics and even be used to find cures much faster.
diff --git a/19. Medical Imaging - Image Segmentation with U-Net/3. U-Net Image Segmentation with CNNs.srt b/19. Medical Imaging - Image Segmentation with U-Net/3. U-Net Image Segmentation with CNNs.srt
new file mode 100644
index 0000000000000000000000000000000000000000..6ad6d784b6032dbe31b1e676e57b4381db1bbed8
--- /dev/null
+++ b/19. Medical Imaging - Image Segmentation with U-Net/3. U-Net Image Segmentation with CNNs.srt	
@@ -0,0 +1,203 @@
+1
+00:00:00,370 --> 00:00:00,930
+OK.
+
+2
+00:00:00,960 --> 00:00:02,840
+So let's talk about usenet.
+
+3
+00:00:02,940 --> 00:00:06,060
+Usenet is a CNN that can actually do image segmentation.
+
+4
+00:00:06,060 --> 00:00:08,500
+So let's see how it works.
+
+5
+00:00:08,550 --> 00:00:15,570
+Usenet was created in 2015 and it basically was a CNN specifically developed for the biomedical image
+
+6
+00:00:15,630 --> 00:00:20,290
+segmentation task which is what we're going to use it for in our project at the end of this chapter.
+
+7
+00:00:20,580 --> 00:00:26,140
+You know it has now become very popular for entend included decoded type networks for semantics of mentation.
+
+8
+00:00:26,340 --> 00:00:28,150
+And it has a very unique architecture.
+
+9
+00:00:28,220 --> 00:00:33,570
+Called updown architecture which has a contracting part an expensive part and you'll see it's in the
+
+10
+00:00:33,570 --> 00:00:39,120
+diagram here where you can see this is how it starts the input image comes in and basically it goes
+
+11
+00:00:39,120 --> 00:00:41,030
+down to the contracting part here.
+
+12
+00:00:41,460 --> 00:00:47,490
+And then there's this middle area here that's called the lead story to the expensive part.
+
+13
+00:00:47,490 --> 00:00:47,990
+OK.
+
+14
+00:00:48,420 --> 00:00:50,110
+And basically the opposite here.
+
+15
+00:00:50,430 --> 00:00:53,140
+So first things first take a look at us.
+
+16
+00:00:53,280 --> 00:00:59,760
+We have an input image here and it's outputting an image or basically a segmentation map that's effectively
+
+17
+00:00:59,760 --> 00:01:03,270
+an image because we're going to use it to segment the image.
+
+18
+00:01:03,300 --> 00:01:09,430
+So this is a model label here the contracting part of an expensive part.
+
+19
+00:01:09,520 --> 00:01:14,880
+So yes I wanted it caught wanted to talk to you about was a bottle looking area here.
+
+20
+00:01:15,080 --> 00:01:15,710
+OK.
+
+21
+00:01:16,040 --> 00:01:22,090
+So this is how unit structure works is a down sample a bottleneck and then assemble the downsampling
+
+22
+00:01:22,130 --> 00:01:24,660
+part and unit consist of four blocks.
+
+23
+00:01:24,690 --> 00:01:30,120
+There's these tree by tree convolutional Lia's with all these really special motion and troppo to use.
+
+24
+00:01:30,170 --> 00:01:32,600
+And this is basically Fort Lee is here.
+
+25
+00:01:32,840 --> 00:01:39,440
+So it is these two kindly as here two by two max pooling and then the feature maps double as we go down
+
+26
+00:01:40,100 --> 00:01:41,620
+which is not unusual.
+
+27
+00:01:41,630 --> 00:01:48,370
+We've seen it happen in Viji as well as starting up 64 and then going to 128 56 and 512.
+
+28
+00:01:48,470 --> 00:01:55,450
+If you go back to diagram you can see it here 64 128 256 and 512 then it's bottlenecked here.
+
+29
+00:01:55,580 --> 00:01:57,740
+And then we basically do some dung sampling.
+
+30
+00:01:57,740 --> 00:02:03,540
+Again going back here to get output segmentation map sort of bottleneck.
+
+31
+00:02:03,540 --> 00:02:05,020
+Let's talk a bit about this.
+
+32
+00:02:05,030 --> 00:02:09,680
+This consists of two convolutional is what again Bagenal opposition and dropout.
+
+33
+00:02:09,740 --> 00:02:10,290
+OK.
+
+34
+00:02:10,580 --> 00:02:13,740
+Nothing majorly special here just how it works.
+
+35
+00:02:13,750 --> 00:02:16,120
+It doesn't follow a traditional CNN architecture.
+
+36
+00:02:16,240 --> 00:02:17,970
+What is a continuous growing.
+
+37
+00:02:18,110 --> 00:02:24,230
+There's this continuous growing here and then is this bottleneck and this is downsampling here.
+
+38
+00:02:24,490 --> 00:02:29,930
+So the upsampling part now the upsampling part basically consists of a similar pattern.
+
+39
+00:02:30,020 --> 00:02:34,840
+However instead of a convolutional Liya there's something called the deconvolution layer.
+
+40
+00:02:35,150 --> 00:02:39,530
+And then it's concatenated with the future map of the corresponding contracting part.
+
+41
+00:02:39,530 --> 00:02:44,210
+This is basically the major area here that allows you not to work.
+
+42
+00:02:44,210 --> 00:02:49,690
+And basically then it has to convery is here to create the output up here.
+
+43
+00:02:49,880 --> 00:02:55,490
+And if you're wondering what a deconvolution layer is because I mentioned it in this proceeding slide
+
+44
+00:02:56,660 --> 00:03:00,910
+basically it it basically reverses the effects of the convolutional.
+
+45
+00:03:01,190 --> 00:03:06,560
+So just imagine what a convolution of the convolution layer basically apply some transform that image
+
+46
+00:03:06,560 --> 00:03:11,340
+will a deconvolution layer Leo does the same however basically the opposite.
+
+47
+00:03:11,340 --> 00:03:15,840
+So it produces basically the output of a reverse convolutional.
+
+48
+00:03:16,370 --> 00:03:18,020
+Hopefully that makes sense to you.
+
+49
+00:03:18,230 --> 00:03:22,670
+What we're going to do now is going to define some more metrics you need to consider when making an
+
+50
+00:03:22,670 --> 00:03:23,760
+image segmentation.
+
+51
+00:03:23,900 --> 00:03:24,350
+CNN.
diff --git a/19. Medical Imaging - Image Segmentation with U-Net/4. The Intersection over Union (IoU) Metric.srt b/19. Medical Imaging - Image Segmentation with U-Net/4. The Intersection over Union (IoU) Metric.srt
new file mode 100644
index 0000000000000000000000000000000000000000..e052aad669ed6dd0814bafad606eb78476b2ae0b
--- /dev/null
+++ b/19. Medical Imaging - Image Segmentation with U-Net/4. The Intersection over Union (IoU) Metric.srt	
@@ -0,0 +1,267 @@
+1
+00:00:00,610 --> 00:00:01,180
+OK.
+
+2
+00:00:01,260 --> 00:00:03,490
+So that brings us to the next chapter.
+
+3
+00:00:05,900 --> 00:00:06,240
+OK.
+
+4
+00:00:06,240 --> 00:00:08,580
+So welcome to chapter nineteen point three.
+
+5
+00:00:08,700 --> 00:00:13,970
+Well we talk about the intersection of the Union now that means song.
+
+6
+00:00:15,480 --> 00:00:20,970
+Hi and welcome to Chapter 19 point tree where we now talk about the intersection of union metric which
+
+7
+00:00:20,970 --> 00:00:24,910
+is an important metric you need to know when we're actually training.
+
+8
+00:00:25,370 --> 00:00:26,550
+So image segmentation.
+
+9
+00:00:26,550 --> 00:00:29,160
+CNN So let's talk about this for us.
+
+10
+00:00:29,190 --> 00:00:29,600
+OK.
+
+11
+00:00:29,970 --> 00:00:32,980
+So let's assume we're doing some object detection here.
+
+12
+00:00:33,240 --> 00:00:37,960
+And basically the green boxes are true bowling box over this nice car.
+
+13
+00:00:38,340 --> 00:00:41,250
+So this is like a human labeled image of what it is.
+
+14
+00:00:41,460 --> 00:00:43,060
+And now our classifier.
+
+15
+00:00:43,140 --> 00:00:46,950
+Oh predicter gave us just bounding boxes red one.
+
+16
+00:00:46,990 --> 00:00:48,930
+So generally it's correct.
+
+17
+00:00:48,930 --> 00:00:50,510
+I wouldn't say it's wrong wrong at all.
+
+18
+00:00:50,520 --> 00:00:55,560
+However you do see that it could have been brought in a lot closer here and maybe brought in here and
+
+19
+00:00:55,560 --> 00:00:56,470
+a bit lower here.
+
+20
+00:00:56,850 --> 00:00:59,590
+So it's a good box but not the best box.
+
+21
+00:01:00,090 --> 00:01:06,020
+So how much of the correct area is covered by a protected bonded box that is this area here.
+
+22
+00:01:06,200 --> 00:01:13,040
+So honestly it seems like about 90 percent of all true blocks is covered by our predicted box.
+
+23
+00:01:13,050 --> 00:01:15,290
+So how do we measure this in a metric.
+
+24
+00:01:15,360 --> 00:01:16,070
+OK.
+
+25
+00:01:16,380 --> 00:01:19,570
+What is a good metric for this what if this was a bounding box.
+
+26
+00:01:19,570 --> 00:01:23,040
+Here it still covers 90 percent of our true books.
+
+27
+00:01:23,070 --> 00:01:27,210
+However this is a much poorer bounding box than this one.
+
+28
+00:01:28,720 --> 00:01:33,070
+This is where the union intersection of the union comes in.
+
+29
+00:01:33,070 --> 00:01:37,590
+So are you going to call it that for short is basically the size of a union.
+
+30
+00:01:37,750 --> 00:01:41,760
+That's the shaded area here over the size of our predicted box.
+
+31
+00:01:41,830 --> 00:01:48,390
+So you can see the size of our predicted box here is much bigger than the size of this predicted box.
+
+32
+00:01:48,400 --> 00:01:50,010
+So what's what does that mean.
+
+33
+00:01:50,050 --> 00:01:56,020
+It means that I you with this box is going to be maybe like point five competitive or you have this
+
+34
+00:01:56,020 --> 00:02:04,060
+box which is going to be probably two point nine so generally do it typically and you have a point five
+
+35
+00:02:04,060 --> 00:02:08,750
+is considered acceptable mainly because optic conduction is very hard to get right.
+
+36
+00:02:08,920 --> 00:02:15,180
+So we do have it's fairly lenient Trishul and obviously the higher the oil you the better the prediction.
+
+37
+00:02:15,490 --> 00:02:21,690
+And essentially I use a measure of overlap how good Basically the overlap is.
+
+38
+00:02:22,090 --> 00:02:27,640
+So before I begin to show you how we actually implemented you you increase and use it as a wonderful
+
+39
+00:02:27,670 --> 00:02:33,190
+metrics we want to so during training I'll tell you why we need this image segmentation.
+
+40
+00:02:33,190 --> 00:02:40,630
+Remember in image segmentation we're basically measuring overlap of like a masked image over the original
+
+41
+00:02:40,630 --> 00:02:41,400
+image.
+
+42
+00:02:41,410 --> 00:02:45,680
+So suppose we're developing a mask that covers this image with just a call here.
+
+43
+00:02:45,760 --> 00:02:47,240
+Forget about the bounding box video.
+
+44
+00:02:47,290 --> 00:02:48,890
+We just want to measure this mask.
+
+45
+00:02:49,300 --> 00:02:50,590
+And what if our.
+
+46
+00:02:50,650 --> 00:02:54,480
+So imagine we have a mask pure yellow mask covering the scar.
+
+47
+00:02:54,850 --> 00:03:00,370
+And imagine we have a predicted mass that is a segmentation algorithm that produced something that covers
+
+48
+00:03:00,430 --> 00:03:02,090
+a blob like this here.
+
+49
+00:03:02,440 --> 00:03:08,290
+How do we measure the effectiveness or basically the accuracy of this mass given that this was the mask
+
+50
+00:03:08,470 --> 00:03:15,370
+correct mask here for this and that is why we are so it is not just useful object reduction is used
+
+51
+00:03:15,370 --> 00:03:18,420
+for mass image masking which is segmentation.
+
+52
+00:03:18,430 --> 00:03:25,010
+So now let's see how we do this in Chris it's pretty easy to find these custom metric functions.
+
+53
+00:03:25,010 --> 00:03:28,810
+In Paris you just have to write a simple function.
+
+54
+00:03:28,830 --> 00:03:37,430
+This takes in a true way year predictive labels we compute and I use school and these labels say I'm
+
+55
+00:03:37,440 --> 00:03:39,720
+just going to be labels are actually going to be mass.
+
+56
+00:03:39,930 --> 00:03:44,550
+And what we do here in when we compile a model we define are one metrics.
+
+57
+00:03:44,550 --> 00:03:46,800
+Previously we used to use accuracy.
+
+58
+00:03:46,800 --> 00:03:50,440
+Now we just use it my own metric which is going to be this function here.
+
+59
+00:03:50,720 --> 00:03:51,270
+All right.
+
+60
+00:03:51,330 --> 00:03:55,100
+And then we train and when we're training we actually see the report here.
+
+61
+00:03:55,290 --> 00:04:00,210
+So we see that my own metric which is dysfunction here technically that should be the same name here
+
+62
+00:04:00,220 --> 00:04:02,420
+just remember that I should have done that for you guys.
+
+63
+00:04:02,430 --> 00:04:03,670
+You don't get confuse.
+
+64
+00:04:04,080 --> 00:04:05,870
+But it's actually going up at a school.
+
+65
+00:04:05,940 --> 00:04:11,380
+So instead of monitoring a loss and accuracy we're now monitoring a loss and a custom function.
+
+66
+00:04:12,000 --> 00:04:12,960
+So that's pretty cool.
+
+67
+00:04:12,960 --> 00:04:15,330
+So let's move on to the next chapter.
diff --git a/19. Medical Imaging - Image Segmentation with U-Net/5. Finding the Nuclei in Divergent Images.srt b/19. Medical Imaging - Image Segmentation with U-Net/5. Finding the Nuclei in Divergent Images.srt
new file mode 100644
index 0000000000000000000000000000000000000000..ea18e7fa02878c01e042aff40e44c42bfdf687dd
--- /dev/null
+++ b/19. Medical Imaging - Image Segmentation with U-Net/5. Finding the Nuclei in Divergent Images.srt	
@@ -0,0 +1,875 @@
+1
+00:00:00,660 --> 00:00:05,540
+It and welcome back to chapter nineteen point four where we're going to implement is project and project
+
+2
+00:00:05,540 --> 00:00:08,880
+is called Finding the nuclei in divergent images.
+
+3
+00:00:09,050 --> 00:00:10,980
+So let's go on to see a bit about it.
+
+4
+00:00:11,000 --> 00:00:14,820
+So this was part of toggles Science Bowl of 2018.
+
+5
+00:00:15,050 --> 00:00:20,390
+Basically the challenge was to spotting Nicholai to get speed up Achillas that was tackling used and
+
+6
+00:00:20,390 --> 00:00:25,670
+what we wanted to do or what they wanted to wanted to do was automate nucular detection.
+
+7
+00:00:25,670 --> 00:00:28,560
+So we basically perform you'll see the images soon.
+
+8
+00:00:28,840 --> 00:00:33,480
+We're basically performing performing Nicolay detection in these images.
+
+9
+00:00:33,800 --> 00:00:36,360
+And this is basically the writeup they used here.
+
+10
+00:00:36,410 --> 00:00:42,140
+They're designed to cells Nikolai's a starting point for most analysis because most of the human bodies
+
+11
+00:00:42,540 --> 00:00:48,920
+to tity trillion of ocelots cells can t and Nicholas full of DNA the genetic code that programs each
+
+12
+00:00:48,920 --> 00:00:49,780
+cell.
+
+13
+00:00:49,820 --> 00:00:55,880
+So identifying the nuclei allows rescissions to identify each individual cell in a sample and by measuring
+
+14
+00:00:55,910 --> 00:01:01,640
+how many cells are how cells were active various treatments the researchers can now understand the underlying
+
+15
+00:01:01,670 --> 00:01:03,690
+biological processes at work.
+
+16
+00:01:03,770 --> 00:01:09,200
+So you can see this project actually has tremendous application in the medical field.
+
+17
+00:01:09,560 --> 00:01:14,320
+And this was the flyer as well as the tagline I mentioned to you before that Kaggle used advertizes
+
+18
+00:01:14,340 --> 00:01:15,290
+contests.
+
+19
+00:01:15,470 --> 00:01:19,890
+So you can see it is definitely in need a practical need to get this done right.
+
+20
+00:01:21,870 --> 00:01:24,320
+So these were the images in our data set here.
+
+21
+00:01:24,660 --> 00:01:25,910
+Look at the first row here.
+
+22
+00:01:25,980 --> 00:01:29,830
+These four images we are we were given images like this.
+
+23
+00:01:29,850 --> 00:01:34,700
+These dots represent nuclei is and is basically different shades of gray.
+
+24
+00:01:34,730 --> 00:01:36,950
+Like these we're all in the set.
+
+25
+00:01:36,960 --> 00:01:41,900
+We actually have full color images here as well as some basic grayscale images here.
+
+26
+00:01:42,210 --> 00:01:47,760
+So these definitely made it could be you can use some open see the trembling functions and get these
+
+27
+00:01:47,760 --> 00:01:50,780
+mass with a human labeled mass here.
+
+28
+00:01:51,090 --> 00:01:56,550
+However when it comes to these here you definitely need some sort of intelligence to actually extract
+
+29
+00:01:56,550 --> 00:01:59,020
+this and label Nikolai's here.
+
+30
+00:01:59,250 --> 00:02:04,320
+As you can see doing this manually as a human is going to take some time definitely.
+
+31
+00:02:04,350 --> 00:02:08,780
+And then getting the counts of overcomplex images it's going to be an exhaustive tests.
+
+32
+00:02:08,970 --> 00:02:14,250
+So this is the role of two true original images.
+
+33
+00:02:14,260 --> 00:02:15,570
+The true mass.
+
+34
+00:02:15,570 --> 00:02:18,480
+And again more true images and more true mass.
+
+35
+00:02:18,480 --> 00:02:24,960
+So we're going to try and classify to take this image and put this image any image here and produce
+
+36
+00:02:24,960 --> 00:02:26,940
+a mask that looks like this.
+
+37
+00:02:26,940 --> 00:02:33,910
+So our approach is basically to use unit which is a special CNN designed exactly for image segmentation.
+
+38
+00:02:33,910 --> 00:02:35,280
+Tests like this.
+
+39
+00:02:35,280 --> 00:02:36,340
+So let's get started.
+
+40
+00:02:41,170 --> 00:02:41,560
+OK.
+
+41
+00:02:41,630 --> 00:02:47,630
+So we're back to a virtual machine and we're going to use you now in our medical imaging imaging segmentation
+
+42
+00:02:47,630 --> 00:02:51,800
+project which is finding the nuclei in divergent images.
+
+43
+00:02:51,800 --> 00:02:52,210
+OK.
+
+44
+00:02:52,290 --> 00:02:56,710
+So we want to make you want to make sure you downloaded data set correctly.
+
+45
+00:02:56,840 --> 00:02:59,180
+And I want to actually show you something in this data set.
+
+46
+00:02:59,210 --> 00:03:03,980
+It is different to the type of the assets we used before because now it has mass.
+
+47
+00:03:04,010 --> 00:03:07,580
+So let's open this dataset here.
+
+48
+00:03:07,580 --> 00:03:09,590
+So hopefully you've extracted it here.
+
+49
+00:03:10,280 --> 00:03:13,900
+And as you can see yes it has the same train and validation for this.
+
+50
+00:03:14,210 --> 00:03:17,780
+But look at this it's no longer images now it's actually footless.
+
+51
+00:03:17,960 --> 00:03:21,010
+So we have an image here.
+
+52
+00:03:21,050 --> 00:03:25,290
+This is a test image which we're supposed to produce a mask from.
+
+53
+00:03:25,370 --> 00:03:28,070
+And now what does this handful of mask.
+
+54
+00:03:28,370 --> 00:03:32,320
+And these are multiple files and if you look at this there are multiple images.
+
+55
+00:03:32,450 --> 00:03:34,780
+Each one is a Nicholai label.
+
+56
+00:03:35,000 --> 00:03:37,190
+So what we're looking at.
+
+57
+00:03:37,340 --> 00:03:44,550
+If you go back to our presentation here spring is actually can be like this what we're looking at right
+
+58
+00:03:44,550 --> 00:03:49,470
+now is basically this masking is basically all the images we just saw.
+
+59
+00:03:49,500 --> 00:03:52,520
+It's minimized us stacked upon each other.
+
+60
+00:03:52,680 --> 00:04:00,690
+So the data is not as easy to basically interpret don't interpret but actually use as we would for all
+
+61
+00:04:00,690 --> 00:04:02,010
+previous tests.
+
+62
+00:04:02,010 --> 00:04:04,150
+We do have to do some processing on this data.
+
+63
+00:04:05,700 --> 00:04:07,380
+So that's a data set here.
+
+64
+00:04:07,560 --> 00:04:12,180
+And now let's go to all part of the book really have it loaded up here.
+
+65
+00:04:12,840 --> 00:04:17,520
+So this code here is basically code that was provided by this guy here.
+
+66
+00:04:17,520 --> 00:04:22,800
+He actually made the most popular little on Kaggle targeted one for this project and there were a number
+
+67
+00:04:22,800 --> 00:04:28,110
+of contestants and this guy had probably the best example of how it works.
+
+68
+00:04:28,110 --> 00:04:30,340
+So those were under his code.
+
+69
+00:04:30,840 --> 00:04:34,500
+So we have images here 128 size he defined.
+
+70
+00:04:34,530 --> 00:04:42,150
+We have all folders wanting to run that and fine ports everything successfully and we set our training
+
+71
+00:04:42,150 --> 00:04:47,360
+parts of test paths and this is the part that is very important.
+
+72
+00:04:47,370 --> 00:04:52,620
+Remember we showed you how the nuclides are basically on one image at a time.
+
+73
+00:04:52,620 --> 00:05:00,690
+So basically one input image has a bunch of sub images that each one having one you can labeled on it.
+
+74
+00:05:00,690 --> 00:05:06,410
+So what he does here what we do here is we basically stack those images together.
+
+75
+00:05:06,810 --> 00:05:11,290
+So effectively you can read the comments and stuff I've left in here.
+
+76
+00:05:11,430 --> 00:05:19,540
+What we do is we basically take these images and combine them into one single image with all these Nikolai's
+
+77
+00:05:19,570 --> 00:05:23,040
+in that image because that's basically the final message we want to produce.
+
+78
+00:05:23,110 --> 00:05:24,770
+So we have to process.
+
+79
+00:05:24,780 --> 00:05:26,370
+I will try to get a bit.
+
+80
+00:05:26,770 --> 00:05:30,760
+So I'm not going to run it because it takes some time to really run it successfully here and you can
+
+81
+00:05:30,760 --> 00:05:34,070
+do so yourself.
+
+82
+00:05:34,170 --> 00:05:38,560
+In fact you will have to do these things yourself because it's run on my machines run on us.
+
+83
+00:05:38,700 --> 00:05:44,130
+These are like basically statements of safe but they don't actually store any of the data in this notebook
+
+84
+00:05:45,120 --> 00:05:46,470
+just the outputs.
+
+85
+00:05:46,470 --> 00:05:48,780
+So let's do some illustrations here.
+
+86
+00:05:48,900 --> 00:05:52,290
+He has some nice cold here that generates this plot and plot.
+
+87
+00:05:52,380 --> 00:05:58,950
+We can see this is image zero here and this is a concatenated are stacked on a mass produced from an
+
+88
+00:05:58,970 --> 00:06:04,320
+input data and he does it for quite a few images here so he can actually see there's a lot of variety
+
+89
+00:06:04,410 --> 00:06:06,590
+in the input images here.
+
+90
+00:06:06,660 --> 00:06:11,030
+There's these grayscale ones like this seem to be the most popular.
+
+91
+00:06:11,040 --> 00:06:12,850
+Then there's his color images here.
+
+92
+00:06:12,930 --> 00:06:17,400
+Then there's these here these look like something from a microscope slide and then these these here
+
+93
+00:06:17,400 --> 00:06:22,850
+are different than they are this type here you can see them putting it here.
+
+94
+00:06:23,220 --> 00:06:25,860
+So there's a lot of different types of images here.
+
+95
+00:06:25,860 --> 00:06:27,800
+It's not one simple answer of the desert.
+
+96
+00:06:27,890 --> 00:06:33,750
+It's a bunch of different types of data all looking at nuclearized and all having mask that are put
+
+97
+00:06:33,750 --> 00:06:39,940
+like this or a label like the shape like this I should say.
+
+98
+00:06:40,030 --> 00:06:42,190
+So this is a function we used before.
+
+99
+00:06:42,350 --> 00:06:46,440
+It's actually what we're going to use is with the example one I used in my slide.
+
+100
+00:06:46,600 --> 00:06:50,090
+So I'm taking it off and reading this MTSO.
+
+101
+00:06:50,400 --> 00:06:54,480
+So this is the actual metric here that he's going to use.
+
+102
+00:06:54,560 --> 00:06:59,040
+We're going to use in our project I'm not going to go through the detail of how it's carefully calculated
+
+103
+00:06:59,430 --> 00:07:03,370
+but it is very similar to the calculation we saw in our slides.
+
+104
+00:07:03,400 --> 00:07:05,620
+It's bit different from us.
+
+105
+00:07:06,240 --> 00:07:11,460
+Alternatively there were a lot of discussions on the Kaggle the message board about if this function
+
+106
+00:07:11,460 --> 00:07:13,200
+was the best metric to use.
+
+107
+00:07:13,200 --> 00:07:16,680
+So here's an alternative when you can use Feel free to use it.
+
+108
+00:07:16,700 --> 00:07:19,340
+And this one uses this one side of it here.
+
+109
+00:07:19,550 --> 00:07:20,030
+OK.
+
+110
+00:07:21,640 --> 00:07:24,730
+So I actually left another one here too.
+
+111
+00:07:25,070 --> 00:07:25,490
+This.
+
+112
+00:07:25,690 --> 00:07:31,870
+This one actually it was basically a consensus said this was the best function and you can see it's
+
+113
+00:07:31,870 --> 00:07:33,340
+quite exhaustive.
+
+114
+00:07:33,340 --> 00:07:34,430
+Pretty technical.
+
+115
+00:07:34,480 --> 00:07:38,380
+Someone did spend a lot of time making this function.
+
+116
+00:07:38,460 --> 00:07:40,630
+So this is the important part here.
+
+117
+00:07:40,680 --> 00:07:45,750
+This is what this was equally important this function but this is of course what I wanted to show you
+
+118
+00:07:46,260 --> 00:07:48,650
+this is how we build our unit model.
+
+119
+00:07:48,900 --> 00:07:50,370
+So two things to note.
+
+120
+00:07:50,490 --> 00:07:51,610
+OK.
+
+121
+00:07:51,630 --> 00:07:58,170
+You're seeing we're actually speak signing a more lovely and sort of thing model that we're assigning
+
+122
+00:07:58,260 --> 00:08:02,560
+is two variables here and then we have this s in brackets here.
+
+123
+00:08:02,880 --> 00:08:05,040
+So what exactly are we doing here now.
+
+124
+00:08:05,280 --> 00:08:10,420
+Well this is simply another way we can build models in Paris.
+
+125
+00:08:10,510 --> 00:08:12,060
+Paris is quite flexible.
+
+126
+00:08:12,060 --> 00:08:16,430
+So what we're doing here we're connecting models by having it here.
+
+127
+00:08:16,460 --> 00:08:21,130
+So instead of using Waddler add we're connecting them here and there's a reason we can't use model that
+
+128
+00:08:21,840 --> 00:08:29,040
+is because of the unit structure which is basically like a bottleneck at the bottom and deconvolution
+
+129
+00:08:29,040 --> 00:08:30,300
+is going up.
+
+130
+00:08:30,300 --> 00:08:32,960
+It's not easy or doesn't facilitate a model.
+
+131
+00:08:33,060 --> 00:08:35,460
+Add method in building this model.
+
+132
+00:08:35,460 --> 00:08:38,840
+We sort of have to do it like this now so we can see.
+
+133
+00:08:38,850 --> 00:08:43,640
+So you want us to find here connected to this lambda function which basically normalizes the inputs.
+
+134
+00:08:43,980 --> 00:08:46,810
+Then we have see one here and see one here.
+
+135
+00:08:46,920 --> 00:08:53,690
+So what this does it basically says this is this is our convolutional layer here would drop all that's
+
+136
+00:08:53,740 --> 00:08:54,730
+tight see one.
+
+137
+00:08:55,110 --> 00:08:56,870
+And this is another completion here.
+
+138
+00:08:56,940 --> 00:08:58,120
+Titus see one again.
+
+139
+00:08:58,140 --> 00:09:00,860
+So this is linking all of these layers together.
+
+140
+00:09:01,200 --> 00:09:03,170
+And then we have Max beling here.
+
+141
+00:09:03,390 --> 00:09:06,470
+Basically it's called the P1 connection.
+
+142
+00:09:06,510 --> 00:09:09,080
+So now this is linked back to these here.
+
+143
+00:09:09,480 --> 00:09:10,890
+So we keep going forward.
+
+144
+00:09:11,490 --> 00:09:17,660
+As you can see the kernel the sizes get larger and larger as we go down and then we do this here.
+
+145
+00:09:17,820 --> 00:09:22,740
+This is how we kind of connect and it will basically bottleneck or blocking the key point here.
+
+146
+00:09:23,680 --> 00:09:30,000
+And basically now we just go up up with the not yet it is called a mandate because it looks like a when
+
+147
+00:09:30,230 --> 00:09:37,460
+those diagrams and we're using a different type of competition here constitute the transpose.
+
+148
+00:09:37,740 --> 00:09:39,020
+That's basically how we do it.
+
+149
+00:09:39,030 --> 00:09:44,220
+Deconvolution Lisieux So it's going up and up and then we have an upper tier.
+
+150
+00:09:44,460 --> 00:09:46,720
+So it is something I want you to note as well.
+
+151
+00:09:46,740 --> 00:09:48,750
+Look at the output of the D-Conn..
+
+152
+00:09:49,020 --> 00:09:54,480
+It's basically a greyscale image 128 by 128 with one dimension.
+
+153
+00:09:54,940 --> 00:09:57,380
+Is a number of parameters not that much.
+
+154
+00:09:57,420 --> 00:10:00,280
+Definitely trainable OCP.
+
+155
+00:10:00,350 --> 00:10:02,280
+So no that's fits our model.
+
+156
+00:10:04,130 --> 00:10:10,680
+So we just do all the basic callbacks here and then we just fit our model.
+
+157
+00:10:10,980 --> 00:10:12,950
+This probably should not be here.
+
+158
+00:10:13,020 --> 00:10:14,180
+Let's show what it was.
+
+159
+00:10:15,460 --> 00:10:16,690
+And here we go.
+
+160
+00:10:16,930 --> 00:10:20,960
+So you can see I'm not going to run this now but I've done it before and doesn't take that long.
+
+161
+00:10:21,250 --> 00:10:22,750
+We're training it here.
+
+162
+00:10:23,070 --> 00:10:24,150
+It's quick.
+
+163
+00:10:24,250 --> 00:10:25,510
+Just over a minute.
+
+164
+00:10:25,670 --> 00:10:30,560
+Poch just quite quick and we can see C-L metric glossier my metric.
+
+165
+00:10:30,580 --> 00:10:36,940
+So let's see what function is that up there was this one here.
+
+166
+00:10:36,970 --> 00:10:38,910
+That was the big one we use.
+
+167
+00:10:38,920 --> 00:10:42,130
+This was the one actually remember didn't Kaggle discussions.
+
+168
+00:10:42,280 --> 00:10:45,470
+That was the best I knew it was appropriate.
+
+169
+00:10:45,510 --> 00:10:50,800
+You not saying that these were wrong well these are bad it's just that this one was actually too much
+
+170
+00:10:50,800 --> 00:10:52,740
+relevance to segmentation to us.
+
+171
+00:10:52,990 --> 00:10:58,900
+Segmentation is very different to OBD-II detection I use which use boxes.
+
+172
+00:10:58,990 --> 00:11:00,810
+So we needed to develop something custom.
+
+173
+00:11:00,850 --> 00:11:06,810
+This was part this was definitely a big part of the Kaggle challenge in this project wasn't just applying
+
+174
+00:11:07,030 --> 00:11:11,860
+to the data it was coming up with a way to actually assess the performance of this model.
+
+175
+00:11:13,120 --> 00:11:21,160
+So we can see a metric changing as it goes and our validation metric going up.
+
+176
+00:11:21,160 --> 00:11:23,020
+So we wanted this to go up actually.
+
+177
+00:11:23,080 --> 00:11:24,850
+So we wanted to get that over time.
+
+178
+00:11:24,850 --> 00:11:31,810
+So I said 10 ebox it was a great way for sex in terms of accuracy that is not a good metric.
+
+179
+00:11:31,810 --> 00:11:37,480
+However this is not this is an I.T. metric and you metrics don't really it exactly to what accuracy
+
+180
+00:11:37,480 --> 00:11:38,340
+means.
+
+181
+00:11:38,350 --> 00:11:45,340
+So 24:6 is actually a pretty good value as seen on Kaggle some guys got up to point seven seven 7 0.8
+
+182
+00:11:45,880 --> 00:11:51,570
+using this metric that training on some GPS use for multiple ebox and tweaking a lot of the training
+
+183
+00:11:51,580 --> 00:11:52,900
+parameters above here.
+
+184
+00:11:53,110 --> 00:11:56,540
+But point Final Point 4 6 is actually pretty good.
+
+185
+00:11:56,740 --> 00:12:01,420
+And you may have noticed actually the code just changed and that was because I actually had some confusion
+
+186
+00:12:01,750 --> 00:12:08,250
+here where I was experimenting with different metrics and I confused myself because you when you look
+
+187
+00:12:08,250 --> 00:12:13,200
+at a model of when you create a model it is specific to this metric here.
+
+188
+00:12:13,450 --> 00:12:18,490
+So when you load them all you actually have to specify what metric you used when treating the model.
+
+189
+00:12:18,520 --> 00:12:26,320
+So we just train using my metric which was a function scroll all the way up the defined right here.
+
+190
+00:12:26,320 --> 00:12:31,240
+This metric basically used all of these functions here so basically calculate it.
+
+191
+00:12:31,270 --> 00:12:36,550
+This was a best one according to this guys and Kaggle which was the most representative representative
+
+192
+00:12:37,060 --> 00:12:38,940
+of what a loss should be.
+
+193
+00:12:39,100 --> 00:12:46,420
+OK so now what I'm going to do we basically use the LoDo model that we just trained or you can use model
+
+194
+00:12:46,420 --> 00:12:49,590
+if you're treating it within the book you don't have to look at it.
+
+195
+00:12:49,630 --> 00:12:50,210
+OK.
+
+196
+00:12:50,560 --> 00:12:54,100
+And basically we just split up split this up.
+
+197
+00:12:54,150 --> 00:13:00,550
+The data are extreme data into 90 percent basically being treating data and the last 10 percent being
+
+198
+00:13:00,550 --> 00:13:01,640
+the validation data.
+
+199
+00:13:02,050 --> 00:13:04,860
+And we just create our mass from this now.
+
+200
+00:13:04,900 --> 00:13:07,730
+So now let's take a look and see how on mask look.
+
+201
+00:13:07,780 --> 00:13:08,360
+OK.
+
+202
+00:13:08,740 --> 00:13:14,610
+So if you just run this function you'll see this was a training image here.
+
+203
+00:13:14,730 --> 00:13:21,350
+This was the mass that we kind of calculated previously from these here and then this is the predictive
+
+204
+00:13:21,350 --> 00:13:21,720
+mask.
+
+205
+00:13:21,740 --> 00:13:25,770
+As you can see it is very very similar to this mass.
+
+206
+00:13:25,790 --> 00:13:28,550
+There are some slight differences at a pixel level.
+
+207
+00:13:28,580 --> 00:13:30,750
+However this is actually quite good.
+
+208
+00:13:30,980 --> 00:13:33,310
+And now we can do some validation data.
+
+209
+00:13:33,620 --> 00:13:35,940
+So this was the actual input image here.
+
+210
+00:13:36,230 --> 00:13:38,850
+And this was a predicted mask of it.
+
+211
+00:13:38,870 --> 00:13:44,790
+So as you can see this is actually doing a pretty good job at image segmentation and it only took maybe
+
+212
+00:13:45,010 --> 00:13:47,540
+15 minutes to train on a super system.
+
+213
+00:13:47,660 --> 00:13:50,240
+So feel free to experiment with this.
+
+214
+00:13:50,240 --> 00:13:52,640
+You could create your own mess.
+
+215
+00:13:52,790 --> 00:13:57,380
+Not sure if you know how well you can do is probably get some software out I'll probably put a link
+
+216
+00:13:57,770 --> 00:14:03,380
+to some software and resources here where you can start annotating and basically creating mass from
+
+217
+00:14:03,410 --> 00:14:04,360
+images like this.
+
+218
+00:14:04,490 --> 00:14:10,430
+So if you want to try some medical imaging class segmentation task or any other sort of segmentation
+
+219
+00:14:10,430 --> 00:14:13,170
+to us you will know exactly how to do it.
diff --git a/19. Medical Imaging - Image Segmentation with U-Net/5.1 Download U-Net.html b/19. Medical Imaging - Image Segmentation with U-Net/5.1 Download U-Net.html
new file mode 100644
index 0000000000000000000000000000000000000000..7f77fb97c0aaac745f8542b2fa190be310b8fe59
--- /dev/null
+++ b/19. Medical Imaging - Image Segmentation with U-Net/5.1 Download U-Net.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1X5vccywUQSv9VF8nTrldyBtIQqM_N5yS/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/19. Medical Imaging Segmentation using U-Net/U-Net (not compatible with TensorFlow 2.0, required to downgrade).ipynb b/19. Medical Imaging Segmentation using U-Net/U-Net (not compatible with TensorFlow 2.0, required to downgrade).ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dce73199417efbb7e15822292e8cbbc90b200386
--- /dev/null
+++ b/19. Medical Imaging Segmentation using U-Net/U-Net (not compatible with TensorFlow 2.0, required to downgrade).ipynb	
@@ -0,0 +1,808 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NOTE: DOWNGRADE TO TENSORFLOW 1.14 and KERAS Keras 2.3.0\n",
+    "\n",
+    "Create a new environment using Python 3.6 or 3.7\n",
+    "- pip install opencv-python\n",
+    "- pip install tensorflow==1.14\n",
+    "- pip install keras==2.3.0\n",
+    "\n",
+    "### U-Net Nuclei masks EDA\n",
+    "#### Part of Kaggle's 2018 Data Sciene Bowl\n",
+    "- https://www.kaggle.com/c/data-science-bowl-2018"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Spot Nuclei. Speed Cures.\n",
+    "\n",
+    "Imagine speeding up research for almost every disease, from lung cancer and heart disease to rare disorders. The 2018 Data Science Bowl offers our most ambitious mission yet: create an algorithm to automate nucleus detection.\n",
+    "\n",
+    "We’ve all seen people suffer from diseases like cancer, heart disease, chronic obstructive pulmonary disease, Alzheimer’s, and diabetes. Many have seen their loved ones pass away. Think how many lives would be transformed if cures came faster.\n",
+    "\n",
+    "By automating nucleus detection, you could help unlock cures faster—from rare disorders to the common cold. Want a snapshot about the 2018 Data Science Bowl? [View this video.](https://www.youtube.com/watch?v=eHwkfhmJexs&feature=youtu.be)\n",
+    "\n",
+    "- Alot of the work in this notework was provided by Kjetil Åmdal-SævikKeras who made the \"U-Net starter - LB 0.277\" the top rated Kernel on Kaggle.\n",
+    "- https://www.kaggle.com/keegil/keras-u-net-starter-lb-0-277"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import random\n",
+    "import warnings #\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm import tqdm\n",
+    "from itertools import chain\n",
+    "from skimage.io import imread, imshow, imread_collection, concatenate_images\n",
+    "from skimage.transform import resize\n",
+    "from skimage.morphology import label\n",
+    "from tensorflow.keras.models import Model, load_model\n",
+    "from tensorflow.keras.layers import Input\n",
+    "from tensorflow.keras.layers import Dropout, Lambda\n",
+    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose\n",
+    "from tensorflow.keras.layers import MaxPooling2D\n",
+    "from tensorflow.keras.layers import concatenate\n",
+    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
+    "from tensorflow.keras import backend as K\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "IMG_WIDTH = 128\n",
+    "IMG_HEIGHT = 128\n",
+    "IMG_CHANNELS = 3\n",
+    "TRAIN_PATH = './U_NET/train/'\n",
+    "TEST_PATH = './U_NET/validation/'\n",
+    "\n",
+    "warnings.filterwarnings('ignore', category=UserWarning, module='skimage')\n",
+    "seed = 42\n",
+    "random.seed = seed\n",
+    "np.random.seed = seed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Collect our file names for training and test date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_ids = next(os.walk(TRAIN_PATH))[1]\n",
+    "test_ids = next(os.walk(TEST_PATH))[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Creating our image masks of dimension 128 x 128 (black images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Getting and resizing training images ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████| 670/670 [05:53<00:00,  1.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Getting and resizing test images ... \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████| 65/65 [00:01<00:00, 37.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Getting and resizing training images ... ')\n",
+    "X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
+    "Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
+    "        \n",
+    "# Re-sizing our training images to 128 x 128\n",
+    "# Note sys.stdout prints info that can be cleared unlike print.\n",
+    "# Using TQDM allows us to create progress bars\n",
+    "sys.stdout.flush()\n",
+    "\n",
+    "for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):\n",
+    "    path = TRAIN_PATH + id_\n",
+    "    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
+    "    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
+    "    X_train[n] = img\n",
+    "    mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
+    "    \n",
+    "    # Now we take all masks associated with that image and combine them into one single mask\n",
+    "    for mask_file in next(os.walk(path + '/masks/'))[2]:\n",
+    "        mask_ = imread(path + '/masks/' + mask_file)\n",
+    "        mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant', \n",
+    "                                      preserve_range=True), axis=-1)\n",
+    "        mask = np.maximum(mask, mask_)\n",
+    "    # Y_train is now our single mask associated with our image\n",
+    "    Y_train[n] = mask\n",
+    "\n",
+    "# Get and resize test images\n",
+    "X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
+    "sizes_test = []\n",
+    "print('Getting and resizing test images ... ')\n",
+    "sys.stdout.flush()\n",
+    "\n",
+    "# Here we resize our test images\n",
+    "for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n",
+    "    path = TEST_PATH + id_\n",
+    "    img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
+    "    sizes_test.append([img.shape[0], img.shape[1]])\n",
+    "    img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
+    "    X_test[n] = img\n",
+    "\n",
+    "print('Done!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1152 with 96 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Illustrate the train images and masks\n",
+    "plt.figure(figsize=(20,16))\n",
+    "x, y = 12,4\n",
+    "for i in range(y):  \n",
+    "    for j in range(x):\n",
+    "        plt.subplot(y*2, x, i*2*x+j+1)\n",
+    "        pos = i*120 + j*10\n",
+    "        plt.imshow(X_train[pos])\n",
+    "        plt.title('Image #{}'.format(pos))\n",
+    "        plt.axis('off')\n",
+    "        plt.subplot(y*2, x, (i*2+1)*x+j+1)\n",
+    "        \n",
+    "        #We display the associated mask we just generated above with the training image\n",
+    "        plt.imshow(np.squeeze(Y_train[pos]))\n",
+    "        plt.title('Mask #{}'.format(pos))\n",
+    "        plt.axis('off')\n",
+    "        \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We define a custom Metric called Intersection over Union (IoU)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define IoU metric\n",
+    "def mean_iou(y_true, y_pred):\n",
+    "    prec = []\n",
+    "    for t in np.arange(0.5, 1.0, 0.05):\n",
+    "        y_pred_ = tf.to_int32(y_pred > t)\n",
+    "        score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 2, y_true)\n",
+    "        K.get_session().run(tf.local_variables_initializer())\n",
+    "        with tf.control_dependencies([up_opt]):\n",
+    "            score = tf.identity(score)\n",
+    "        prec.append(score)\n",
+    "    return K.mean(K.stack(prec), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Alternative IoU function\n",
+    "# https://www.kaggle.com/c/data-science-bowl-2018/discussion/51553\n",
+    "def iou_coef(y_true, y_pred, smooth=1):\n",
+    "    \"\"\"\n",
+    "    IoU = (|X & Y|)/ (|X or Y|)\n",
+    "    \"\"\"\n",
+    "    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)\n",
+    "    union = K.sum((y_true,-1) + K.sum(y_pred,-1) - intersection)\n",
+    "    return (intersection + smooth) / ( union + smooth)\n",
+    "\n",
+    "def iou_coef_loss(y_true, y_pred):\n",
+    "    return -iou_coef(y_true, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Another IoU Metric  \n",
+    "- https://www.kaggle.com/aglotero/another-iou-metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iou_metric(y_true_in, y_pred_in, print_table=False):\n",
+    "    labels = label(y_true_in > 0.5)\n",
+    "    y_pred = label(y_pred_in > 0.5)\n",
+    "    \n",
+    "    true_objects = len(np.unique(labels))\n",
+    "    pred_objects = len(np.unique(y_pred))\n",
+    "\n",
+    "    intersection = np.histogram2d(labels.flatten(), y_pred.flatten(), bins=(true_objects, pred_objects))[0]\n",
+    "\n",
+    "    # Compute areas (needed for finding the union between all objects)\n",
+    "    area_true = np.histogram(labels, bins = true_objects)[0]\n",
+    "    area_pred = np.histogram(y_pred, bins = pred_objects)[0]\n",
+    "    area_true = np.expand_dims(area_true, -1)\n",
+    "    area_pred = np.expand_dims(area_pred, 0)\n",
+    "\n",
+    "    # Compute union\n",
+    "    union = area_true + area_pred - intersection\n",
+    "\n",
+    "    # Exclude background from the analysis\n",
+    "    intersection = intersection[1:,1:]\n",
+    "    union = union[1:,1:]\n",
+    "    union[union == 0] = 1e-9\n",
+    "\n",
+    "    # Compute the intersection over union\n",
+    "    iou = intersection / union\n",
+    "\n",
+    "    # Precision helper function\n",
+    "    def precision_at(threshold, iou):\n",
+    "        matches = iou > threshold\n",
+    "        true_positives = np.sum(matches, axis=1) == 1   # Correct objects\n",
+    "        false_positives = np.sum(matches, axis=0) == 0  # Missed objects\n",
+    "        false_negatives = np.sum(matches, axis=1) == 0  # Extra objects\n",
+    "        tp, fp, fn = np.sum(true_positives), np.sum(false_positives), np.sum(false_negatives)\n",
+    "        return tp, fp, fn\n",
+    "\n",
+    "    # Loop over IoU thresholds\n",
+    "    prec = []\n",
+    "    if print_table:\n",
+    "        print(\"Thresh\\tTP\\tFP\\tFN\\tPrec.\")\n",
+    "    for t in np.arange(0.5, 1.0, 0.05):\n",
+    "        tp, fp, fn = precision_at(t, iou)\n",
+    "        if (tp + fp + fn) > 0:\n",
+    "            p = tp / (tp + fp + fn)\n",
+    "        else:\n",
+    "            p = 0\n",
+    "        if print_table:\n",
+    "            print(\"{:1.3f}\\t{}\\t{}\\t{}\\t{:1.3f}\".format(t, tp, fp, fn, p))\n",
+    "        prec.append(p)\n",
+    "    \n",
+    "    if print_table:\n",
+    "        print(\"AP\\t-\\t-\\t-\\t{:1.3f}\".format(np.mean(prec)))\n",
+    "    return np.mean(prec)\n",
+    "\n",
+    "def iou_metric_batch(y_true_in, y_pred_in):\n",
+    "    batch_size = y_true_in.shape[0]\n",
+    "    metric = []\n",
+    "    for batch in range(batch_size):\n",
+    "        value = iou_metric(y_true_in[batch], y_pred_in[batch])\n",
+    "        metric.append(value)\n",
+    "    return np.array(np.mean(metric), dtype=np.float32)\n",
+    "\n",
+    "def my_iou_metric(label, pred):\n",
+    "    metric_value = tf.py_function(iou_metric_batch, [label, pred], tf.float32)\n",
+    "    return metric_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building our U-Net Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda (Lambda)                 (None, 128, 128, 3)  0           input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d (Conv2D)                 (None, 128, 128, 16) 448         lambda[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout (Dropout)               (None, 128, 128, 16) 0           conv2d[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_1 (Conv2D)               (None, 128, 128, 16) 2320        dropout[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d (MaxPooling2D)    (None, 64, 64, 16)   0           conv2d_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_2 (Conv2D)               (None, 64, 64, 32)   4640        max_pooling2d[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_1 (Dropout)             (None, 64, 64, 32)   0           conv2d_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_3 (Conv2D)               (None, 64, 64, 32)   9248        dropout_1[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 32)   0           conv2d_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_4 (Conv2D)               (None, 32, 32, 64)   18496       max_pooling2d_1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_2 (Dropout)             (None, 32, 32, 64)   0           conv2d_4[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_5 (Conv2D)               (None, 32, 32, 64)   36928       dropout_2[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 64)   0           conv2d_5[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_6 (Conv2D)               (None, 16, 16, 128)  73856       max_pooling2d_2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_3 (Dropout)             (None, 16, 16, 128)  0           conv2d_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_7 (Conv2D)               (None, 16, 16, 128)  147584      dropout_3[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 128)    0           conv2d_7[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_8 (Conv2D)               (None, 8, 8, 256)    295168      max_pooling2d_3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_4 (Dropout)             (None, 8, 8, 256)    0           conv2d_8[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_9 (Conv2D)               (None, 8, 8, 256)    590080      dropout_4[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose (Conv2DTranspo (None, 16, 16, 128)  131200      conv2d_9[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate (Concatenate)       (None, 16, 16, 256)  0           conv2d_transpose[0][0]           \n",
+      "                                                                 conv2d_7[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_10 (Conv2D)              (None, 16, 16, 128)  295040      concatenate[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_5 (Dropout)             (None, 16, 16, 128)  0           conv2d_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_11 (Conv2D)              (None, 16, 16, 128)  147584      dropout_5[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose_1 (Conv2DTrans (None, 32, 32, 64)   32832       conv2d_11[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_1 (Concatenate)     (None, 32, 32, 128)  0           conv2d_transpose_1[0][0]         \n",
+      "                                                                 conv2d_5[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_12 (Conv2D)              (None, 32, 32, 64)   73792       concatenate_1[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_6 (Dropout)             (None, 32, 32, 64)   0           conv2d_12[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_13 (Conv2D)              (None, 32, 32, 64)   36928       dropout_6[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose_2 (Conv2DTrans (None, 64, 64, 32)   8224        conv2d_13[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_2 (Concatenate)     (None, 64, 64, 64)   0           conv2d_transpose_2[0][0]         \n",
+      "                                                                 conv2d_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_14 (Conv2D)              (None, 64, 64, 32)   18464       concatenate_2[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_7 (Dropout)             (None, 64, 64, 32)   0           conv2d_14[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_15 (Conv2D)              (None, 64, 64, 32)   9248        dropout_7[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_transpose_3 (Conv2DTrans (None, 128, 128, 16) 2064        conv2d_15[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_3 (Concatenate)     (None, 128, 128, 32) 0           conv2d_transpose_3[0][0]         \n",
+      "                                                                 conv2d_1[0][0]                   \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__________________________________________________________________________________________________\n",
+      "conv2d_16 (Conv2D)              (None, 128, 128, 16) 4624        concatenate_3[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_8 (Dropout)             (None, 128, 128, 16) 0           conv2d_16[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_17 (Conv2D)              (None, 128, 128, 16) 2320        dropout_8[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_18 (Conv2D)              (None, 128, 128, 1)  17          conv2d_17[0][0]                  \n",
+      "==================================================================================================\n",
+      "Total params: 1,941,105\n",
+      "Trainable params: 1,941,105\n",
+      "Non-trainable params: 0\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build U-Net model\n",
+    "# Note we make our layers varaibles so that we can concatenate or stack\n",
+    "# This is required so that we can re-create our U-Net Model\n",
+    "\n",
+    "inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
+    "s = Lambda(lambda x: x / 255) (inputs)\n",
+    "\n",
+    "c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (s)\n",
+    "c1 = Dropout(0.1) (c1)\n",
+    "c1 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c1)\n",
+    "p1 = MaxPooling2D((2, 2)) (c1)\n",
+    "\n",
+    "c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p1)\n",
+    "c2 = Dropout(0.1) (c2)\n",
+    "c2 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c2)\n",
+    "p2 = MaxPooling2D((2, 2)) (c2)\n",
+    "\n",
+    "c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p2)\n",
+    "c3 = Dropout(0.2) (c3)\n",
+    "c3 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c3)\n",
+    "p3 = MaxPooling2D((2, 2)) (c3)\n",
+    "\n",
+    "c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p3)\n",
+    "c4 = Dropout(0.2) (c4)\n",
+    "c4 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c4)\n",
+    "p4 = MaxPooling2D(pool_size=(2, 2)) (c4)\n",
+    "\n",
+    "c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (p4)\n",
+    "c5 = Dropout(0.3) (c5)\n",
+    "c5 = Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c5)\n",
+    "\n",
+    "u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c5)\n",
+    "u6 = concatenate([u6, c4])\n",
+    "c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u6)\n",
+    "c6 = Dropout(0.2) (c6)\n",
+    "c6 = Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c6)\n",
+    "\n",
+    "u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c6)\n",
+    "u7 = concatenate([u7, c3])\n",
+    "c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u7)\n",
+    "c7 = Dropout(0.2) (c7)\n",
+    "c7 = Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c7)\n",
+    "\n",
+    "u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c7)\n",
+    "u8 = concatenate([u8, c2])\n",
+    "c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u8)\n",
+    "c8 = Dropout(0.1) (c8)\n",
+    "c8 = Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c8)\n",
+    "\n",
+    "u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c8)\n",
+    "u9 = concatenate([u9, c1], axis=3)\n",
+    "c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (u9)\n",
+    "c9 = Dropout(0.1) (c9)\n",
+    "c9 = Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (c9)\n",
+    "\n",
+    "# Note our output is effectively a mask of 128 x 128 \n",
+    "outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)\n",
+    "\n",
+    "model = Model(inputs=[inputs], outputs=[outputs])\n",
+    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[my_iou_metric])\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Fit our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Attempting to capture an EagerTensor without building a function.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-10-b922af3007b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m results = model.fit(X_train, Y_train, validation_split=0.1,\n\u001b[0;32m     19\u001b[0m                     \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m                     callbacks=[earlystop, checkpoint])\n\u001b[0m",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m   def evaluate(self,\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)\u001b[0m\n\u001b[0;32m    678\u001b[0m         \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    679\u001b[0m         \u001b[0mvalidation_freq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 680\u001b[1;33m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m   def evaluate(self,\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[1;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[0;32m    187\u001b[0m   \u001b[1;31m# function we recompile the metrics based on the updated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m   \u001b[1;31m# sample_weight_mode value.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 189\u001b[1;33m   \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_make_execution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    190\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    191\u001b[0m   \u001b[1;31m# Prepare validation data. Hold references to the iterator and the input list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36m_make_execution_function\u001b[1;34m(model, mode)\u001b[0m\n\u001b[0;32m    569\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_distribution_strategy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    570\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdistributed_training_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_execution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 571\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_execution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    572\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_make_execution_function\u001b[1;34m(self, mode)\u001b[0m\n\u001b[0;32m   2123\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_make_execution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2124\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2125\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2126\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2127\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTEST\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_make_train_function\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   2055\u001b[0m           \u001b[1;31m# Training updates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2056\u001b[0m           updates = self.optimizer.get_updates(\n\u001b[1;32m-> 2057\u001b[1;33m               params=self._collected_trainable_weights, loss=self.total_loss)\n\u001b[0m\u001b[0;32m   2058\u001b[0m           \u001b[1;31m# Unconditional updates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2059\u001b[0m           \u001b[0mupdates\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_updates_for\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\optimizer_v2.py\u001b[0m in \u001b[0;36mget_updates\u001b[1;34m(self, loss, params)\u001b[0m\n\u001b[0;32m    507\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mg\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresource\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m     ])\n\u001b[1;32m--> 509\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_gradients\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrads_and_vars\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    511\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_set_hyper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\optimizer_v2.py\u001b[0m in \u001b[0;36mapply_gradients\u001b[1;34m(self, grads_and_vars, name)\u001b[0m\n\u001b[0;32m    438\u001b[0m         \u001b[1;31m# gradients\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcontrol_flow_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m       \u001b[0mapply_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m       return distribute_ctx.get_replica_context().merge_call(\n\u001b[0;32m    442\u001b[0m           \u001b[0mfunctools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartial\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_distributed_apply\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mapply_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mapply_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\optimizer_v2.py\u001b[0m in \u001b[0;36m_prepare\u001b[1;34m(self, var_list)\u001b[0m\n\u001b[0;32m    624\u001b[0m       \u001b[0mapply_state\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    625\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_device\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 626\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_local\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mapply_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    628\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mapply_state\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\adam.py\u001b[0m in \u001b[0;36m_prepare_local\u001b[1;34m(self, var_device, var_dtype, apply_state)\u001b[0m\n\u001b[0;32m    155\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    156\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_prepare_local\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mapply_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 157\u001b[1;33m     \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAdam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_local\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mapply_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    158\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    159\u001b[0m     \u001b[0mlocal_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterations\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\optimizer_v2.py\u001b[0m in \u001b[0;36m_prepare_local\u001b[1;34m(self, var_device, var_dtype, apply_state)\u001b[0m\n\u001b[0;32m    630\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_prepare_local\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mapply_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m\"learning_rate\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hyper\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 632\u001b[1;33m       \u001b[0mlr_t\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_decayed_lr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    633\u001b[0m       \u001b[0mapply_state\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_device\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"lr_t\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlr_t\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\optimizer_v2.py\u001b[0m in \u001b[0;36m_decayed_lr\u001b[1;34m(self, var_dtype)\u001b[0m\n\u001b[0;32m    679\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_decayed_lr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    680\u001b[0m     \u001b[1;34m\"\"\"Get decayed learning rate as a Tensor with dtype=var_dtype.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 681\u001b[1;33m     \u001b[0mlr_t\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_hyper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"learning_rate\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    682\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr_t\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlearning_rate_schedule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLearningRateSchedule\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    683\u001b[0m       \u001b[0mlocal_step\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterations\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\keras\\optimizer_v2\\optimizer_v2.py\u001b[0m in \u001b[0;36m_get_hyper\u001b[1;34m(self, name, dtype)\u001b[0m\n\u001b[0;32m    535\u001b[0m       \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    536\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 537\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    538\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    178\u001b[0m     \u001b[1;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 180\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    181\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    182\u001b[0m       \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\ops\\math_ops.py\u001b[0m in \u001b[0;36mcast\u001b[1;34m(x, dtype, name)\u001b[0m\n\u001b[0;32m    703\u001b[0m       \u001b[1;31m# allows some conversions that cast() can't do, e.g. casting numbers to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m       \u001b[1;31m# strings.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 705\u001b[1;33m       \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"x\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    706\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbase_dtype\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mbase_type\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    707\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgen_math_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbase_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[1;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[0;32m   1312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1314\u001b[1;33m       \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1316\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py\u001b[0m in \u001b[0;36m_dense_var_to_tensor\u001b[1;34m(var, dtype, name, as_ref)\u001b[0m\n\u001b[0;32m   1792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1793\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_dense_var_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1794\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dense_var_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py\u001b[0m in \u001b[0;36m_dense_var_to_tensor\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m   1217\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1218\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1219\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1220\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1221\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m__iadd__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munused_other\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py\u001b[0m in \u001b[0;36mvalue\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    527\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cached_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolocate_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_existing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 529\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_variable_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_as_graph_element\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py\u001b[0m in \u001b[0;36m_read_variable_op\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    611\u001b[0m     \u001b[0mvariable_accessed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m     result = gen_resource_variable_ops.read_variable_op(self._handle,\n\u001b[1;32m--> 613\u001b[1;33m                                                         self._dtype)\n\u001b[0m\u001b[0;32m    614\u001b[0m     \u001b[0m_maybe_set_handle_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    615\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\ops\\gen_resource_variable_ops.py\u001b[0m in \u001b[0;36mread_variable_op\u001b[1;34m(resource, dtype, name)\u001b[0m\n\u001b[0;32m    481\u001b[0m   \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"dtype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    482\u001b[0m   _, _, _op, _outputs = _op_def_library._apply_op_helper(\n\u001b[1;32m--> 483\u001b[1;33m         \"ReadVariableOp\", resource=resource, dtype=dtype, name=name)\n\u001b[0m\u001b[0;32m    484\u001b[0m   \u001b[0m_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_outputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    485\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0m_execute\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmust_record_gradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\framework\\op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[1;34m(op_type_name, name, **keywords)\u001b[0m\n\u001b[0;32m    466\u001b[0m               \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    467\u001b[0m               \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_arg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_ref\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 468\u001b[1;33m               preferred_dtype=default_dtype)\n\u001b[0m\u001b[0;32m    469\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    470\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\cv\\lib\\site-packages\\tensorflow_core\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[1;34m(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)\u001b[0m\n\u001b[0;32m   1278\u001b[0m       \u001b[0mgraph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_default_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1279\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuilding_function\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1280\u001b[1;33m         raise RuntimeError(\"Attempting to capture an EagerTensor without \"\n\u001b[0m\u001b[0;32m   1281\u001b[0m                            \"building a function.\")\n\u001b[0;32m   1282\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mgraph\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Attempting to capture an EagerTensor without building a function."
+     ]
+    }
+   ],
+   "source": [
+    "tf.compat.v1.disable_eager_execution()\n",
+    "\n",
+    "# Initialize our callbacks\n",
+    "model_path = \"nuclei_finder_unet_1.h5\"\n",
+    "checkpoint = ModelCheckpoint(model_path,\n",
+    "                             monitor=\"val_loss\",\n",
+    "                             mode=\"min\",\n",
+    "                             save_best_only = True,\n",
+    "                             verbose=1)\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
+    "                          min_delta = 0, \n",
+    "                          patience = 5,\n",
+    "                          verbose = 1,\n",
+    "                          restore_best_weights = True)\n",
+    "\n",
+    "# Fit our model \n",
+    "results = model.fit(X_train, Y_train, validation_split=0.1,\n",
+    "                    batch_size=16, epochs=10, \n",
+    "                    callbacks=[earlystop, checkpoint])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating our predictions for training and validation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "603/603 [==============================] - 21s 36ms/step\n",
+      "67/67 [==============================] - 2s 35ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict on training and validation data\n",
+    "# Note our use of mean_iou metri\n",
+    "model = load_model('nuclei_finder_unet_2.h5', \n",
+    "                   custom_objects={'my_iou_metric': my_iou_metric})\n",
+    "\n",
+    "# the first 90% was used for training\n",
+    "preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1)\n",
+    "\n",
+    "# the last 10% used as validation\n",
+    "preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)\n",
+    "\n",
+    "#preds_test = model.predict(X_test, verbose=1)\n",
+    "\n",
+    "# Threshold predictions\n",
+    "preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
+    "preds_val_t = (preds_val > 0.5).astype(np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Showing our predicted masks on our training data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x1440 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ploting our predicted masks\n",
+    "ix = random.randint(0, 602)\n",
+    "plt.figure(figsize=(20,20))\n",
+    "\n",
+    "# Our original training image\n",
+    "plt.subplot(131)\n",
+    "imshow(X_train[ix])\n",
+    "plt.title(\"Image\")\n",
+    "\n",
+    "# Our original combined mask  \n",
+    "plt.subplot(132)\n",
+    "imshow(np.squeeze(Y_train[ix]))\n",
+    "plt.title(\"Mask\")\n",
+    "\n",
+    "# The mask our U-Net model predicts\n",
+    "plt.subplot(133)\n",
+    "imshow(np.squeeze(preds_train_t[ix] > 0.5))\n",
+    "plt.title(\"Predictions\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Showing our predicted masks on our validation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Ploting our predicted masks\n",
+    "ix = random.randint(602, 668)\n",
+    "plt.figure(figsize=(20,20))\n",
+    "\n",
+    "# Our original training image\n",
+    "plt.subplot(121)\n",
+    "imshow(X_train[ix])\n",
+    "plt.title(\"Image\")\n",
+    "\n",
+    "# The mask our U-Net model predicts\n",
+    "plt.subplot(122)\n",
+    "ix = ix - 603\n",
+    "imshow(np.squeeze(preds_val_t[ix] > 0.5))\n",
+    "plt.title(\"Predictions\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating a Classification Report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thresh\tTP\tFP\tFN\tPrec.\n",
+      "0.500\t9\t0\t1\t0.900\n",
+      "0.550\t9\t0\t1\t0.900\n",
+      "0.600\t9\t0\t1\t0.900\n",
+      "0.650\t9\t0\t1\t0.900\n",
+      "0.700\t8\t1\t2\t0.727\n",
+      "0.750\t8\t1\t2\t0.727\n",
+      "0.800\t8\t1\t2\t0.727\n",
+      "0.850\t6\t3\t4\t0.462\n",
+      "0.900\t4\t5\t6\t0.267\n",
+      "0.950\t2\t7\t8\t0.118\n",
+      "AP\t-\t-\t-\t0.663\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.6627670368846839"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iou_metric(np.squeeze(Y_train[ix]), np.squeeze(preds_train_t[ix]), print_table=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/20. Principles of Object Detection/1. Chapter Introduction.srt b/20. Principles of Object Detection/1. Chapter Introduction.srt
new file mode 100644
index 0000000000000000000000000000000000000000..8b83bff974099743c8cd730898ea1db3edcfb115
--- /dev/null
+++ b/20. Principles of Object Detection/1. Chapter Introduction.srt	
@@ -0,0 +1,43 @@
+1
+00:00:00,390 --> 00:00:00,780
+OK.
+
+2
+00:00:00,810 --> 00:00:04,960
+So welcome to Chapter 20 where we finally get into Optik detection.
+
+3
+00:00:05,250 --> 00:00:07,900
+And basically this chapter is up into four sections.
+
+4
+00:00:07,920 --> 00:00:14,310
+This is where I introduce the concept basically how Optik it all started and what it evolved to.
+
+5
+00:00:14,310 --> 00:00:20,760
+And then in twenty point to start talking about more modern day CNN based object to directors.
+
+6
+00:00:20,970 --> 00:00:23,920
+So we go from our CNN's to mess here in.
+
+7
+00:00:24,220 --> 00:00:30,450
+And then take a look at single shot detectors SSTV which is one of the most modern abstract algorithms
+
+8
+00:00:30,540 --> 00:00:31,260
+out there.
+
+9
+00:00:31,590 --> 00:00:36,220
+And then it just goes yellow which is a competing object detection algorithm right now.
+
+10
+00:00:36,240 --> 00:00:41,210
+So these two are the latest and greatest state of the art abductor's and we're going to test them.
+
+11
+00:00:41,220 --> 00:00:46,040
+We're going to go through them in detail and and test them and proceed in the following chapters.
diff --git a/20. Principles of Object Detection/2. Object Detection Introduction - Sliding Windows with HOGs.srt b/20. Principles of Object Detection/2. Object Detection Introduction - Sliding Windows with HOGs.srt
new file mode 100644
index 0000000000000000000000000000000000000000..09804f408ac847c062469d2bb773b8c7e30edbdc
--- /dev/null
+++ b/20. Principles of Object Detection/2. Object Detection Introduction - Sliding Windows with HOGs.srt	
@@ -0,0 +1,303 @@
+1
+00:00:00,470 --> 00:00:01,000
+OK.
+
+2
+00:00:01,050 --> 00:00:02,430
+So let's start at the beginning.
+
+3
+00:00:02,460 --> 00:00:05,520
+Let's talk about object really object vectors.
+
+4
+00:00:05,670 --> 00:00:11,910
+So I'm going to introduce you to the history of it so fiercely detection is one of the holy grails of
+
+5
+00:00:11,910 --> 00:00:17,610
+computer vision because previously what we have been doing is just classifying like an entire image
+
+6
+00:00:17,610 --> 00:00:20,510
+and seeing what objects are what Hassid belong to.
+
+7
+00:00:20,730 --> 00:00:26,490
+But can we take an image like this and label each major component into being a dog car person horse
+
+8
+00:00:26,760 --> 00:00:28,340
+person in the back.
+
+9
+00:00:28,350 --> 00:00:32,230
+Not yet until we have come across up to detection.
+
+10
+00:00:32,640 --> 00:00:40,620
+So object detection is a mix of object classification and localization object action is it is the identification
+
+11
+00:00:40,650 --> 00:00:43,120
+of a bounding box outlining the object.
+
+12
+00:00:43,140 --> 00:00:49,590
+So like in my face here basically is extraction a bony box or on my face and this direction is perhaps
+
+13
+00:00:49,590 --> 00:00:53,760
+one of the most popular object detection algorithms that we all know.
+
+14
+00:00:53,830 --> 00:00:57,220
+We're all quite familiar with from using cameras in our cell phones.
+
+15
+00:00:57,270 --> 00:00:57,780
+OK.
+
+16
+00:00:58,290 --> 00:01:04,150
+So basically it DL tells you instead of telling you this object here is a cat.
+
+17
+00:01:04,170 --> 00:01:09,070
+It actually tells you where is the cat and that is the whole point of object detection.
+
+18
+00:01:10,620 --> 00:01:15,340
+So let's get into the history of it and start with horror Cassiar classifiers.
+
+19
+00:01:15,360 --> 00:01:19,140
+Now there were many public detectors before this.
+
+20
+00:01:19,140 --> 00:01:24,840
+However here is what made it hard to justify this is what made it mainstream and quite popular because
+
+21
+00:01:24,840 --> 00:01:26,340
+it was so fast.
+
+22
+00:01:26,370 --> 00:01:33,420
+So basically this was a this was developed by Viola Jones in the face detection algorithm in 2001 not
+
+23
+00:01:33,420 --> 00:01:35,480
+that long long ago 17 years ago.
+
+24
+00:01:35,520 --> 00:01:40,960
+To be fair and it was superfast and it's actually still use to the number of applications.
+
+25
+00:01:41,280 --> 00:01:43,710
+Basically it's been optimized and tweaked to be even faster.
+
+26
+00:01:43,710 --> 00:01:49,890
+So it basically reduces the CPQ load and it's very very accurate.
+
+27
+00:01:49,890 --> 00:01:52,930
+Basically what it does it's a cascade of classifiers.
+
+28
+00:01:53,190 --> 00:01:56,640
+That's basically how it got it got its name and it uses a horror.
+
+29
+00:01:56,640 --> 00:01:58,590
+Basically let's go into the next slide.
+
+30
+00:01:58,660 --> 00:02:02,760
+Actually I don't have it in this section but it basically uses horror features and harsh features are
+
+31
+00:02:02,760 --> 00:02:06,210
+basically basically like you have rectangles.
+
+32
+00:02:06,250 --> 00:02:07,100
+Overling here.
+
+33
+00:02:07,240 --> 00:02:12,690
+You imagine a white rectangle here and one here and then there are different types of Arcacha pacifies.
+
+34
+00:02:12,810 --> 00:02:15,590
+So basically is just a feature extraction.
+
+35
+00:02:15,690 --> 00:02:22,350
+Basically what we learned before and it's led this box is that over the window over and over continuously
+
+36
+00:02:22,410 --> 00:02:31,950
+looking for a face they're very good but they are pretty hard to train and develop and optimize.
+
+37
+00:02:32,010 --> 00:02:38,010
+So let's move on to histogram with gradients and SVM sliding windows so sliding windows is a method
+
+38
+00:02:38,010 --> 00:02:43,580
+where we extract segments a full image piece by piece in the form of a rectangular extractor box.
+
+39
+00:02:43,590 --> 00:02:48,000
+So I mentioned it in previous slide when I was talking about this box being slid across this image.
+
+40
+00:02:48,330 --> 00:02:53,430
+What it does here in this image is a picture of my wife from the last bodybuilding bikini competition
+
+41
+00:02:53,430 --> 00:02:54,560
+two months ago.
+
+42
+00:02:54,870 --> 00:03:02,550
+And what it does is just imagine this window is being moved here then down here and then down here just
+
+43
+00:03:02,550 --> 00:03:05,670
+like remember how we moved across the image.
+
+44
+00:03:05,680 --> 00:03:07,960
+And CNN's it's exactly the same thing.
+
+45
+00:03:07,970 --> 00:03:14,430
+And we can actually set the same parameters like stride and the size of this box and what this box does
+
+46
+00:03:14,430 --> 00:03:17,640
+here in sliding windows with histogram of gradients.
+
+47
+00:03:17,700 --> 00:03:25,980
+SVM is that it basically extracts the entire hawgs all his brilliance in this box at different scales.
+
+48
+00:03:25,980 --> 00:03:31,620
+So basically it does it with image at one scale and then not a scale smaller scale and then this one
+
+49
+00:03:31,620 --> 00:03:35,480
+here and this one basically has no room to go right to just go straight down.
+
+50
+00:03:35,760 --> 00:03:39,480
+And it tries to match up to how gradients went what it knows.
+
+51
+00:03:39,480 --> 00:03:41,700
+It's supposed to look like to find the object.
+
+52
+00:03:42,000 --> 00:03:47,400
+Now as you can see this could be an effective way but it's not really that resilient.
+
+53
+00:03:47,400 --> 00:03:48,410
+Why.
+
+54
+00:03:48,420 --> 00:03:53,400
+Because imagine we have to do this for every segment of image continuously.
+
+55
+00:03:53,400 --> 00:03:55,680
+It gets exhaustive and computationally expensive
+
+56
+00:03:58,720 --> 00:04:05,370
+so previous action which is basically TISM feature extraction I just mentioned that and why would we
+
+57
+00:04:05,370 --> 00:04:10,740
+want to actually manually find co-features if CNN's actually eliminate that.
+
+58
+00:04:10,740 --> 00:04:16,350
+All right CNN's actually automatically find features by just running all these tests destroying data
+
+59
+00:04:16,680 --> 00:04:20,350
+Trulia algorithm and finding the last matching it with the correct last.
+
+60
+00:04:20,370 --> 00:04:22,770
+So that's what's brilliant about CNN's.
+
+61
+00:04:22,770 --> 00:04:24,760
+It takes that step away from us.
+
+62
+00:04:26,340 --> 00:04:31,970
+So as I said once of problems we're doing this is a sea of scale.
+
+63
+00:04:32,100 --> 00:04:34,920
+Imagine this is a simple image just 20 by 20.
+
+64
+00:04:34,920 --> 00:04:36,870
+So this box can be passed over here.
+
+65
+00:04:36,960 --> 00:04:39,630
+But imagine this was a much bigger continue TV image.
+
+66
+00:04:39,720 --> 00:04:44,130
+How many different times how many different boxes would we extract.
+
+67
+00:04:44,130 --> 00:04:46,460
+How do we know what size box should be.
+
+68
+00:04:46,470 --> 00:04:50,410
+I mean that's where we rescale image but how many different rescaling are we going to do.
+
+69
+00:04:50,440 --> 00:04:54,830
+So as you can see this is not a very effective way of doing object detection.
+
+70
+00:04:56,430 --> 00:05:02,600
+So talk a bit the bullet histogram gradients are not going to go in go into this in detail of taught
+
+71
+00:05:02,600 --> 00:05:05,480
+this in my other op and see the course you can.
+
+72
+00:05:05,480 --> 00:05:07,280
+The video is included free in that section.
+
+73
+00:05:07,290 --> 00:05:09,230
+So that's why I'm going to talk about it much here.
+
+74
+00:05:09,550 --> 00:05:15,290
+But basically the slides are here for you to go through on your own and you can pretty much infer from
+
+75
+00:05:15,290 --> 00:05:17,720
+these steps here what hawgs really are.
+
+76
+00:05:20,110 --> 00:05:22,090
+So now we move on to our CNN's.
diff --git a/20. Principles of Object Detection/3. R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN.srt b/20. Principles of Object Detection/3. R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN.srt
new file mode 100644
index 0000000000000000000000000000000000000000..a0f10d5430edc19b4cd349460fc30cb753e1239a
--- /dev/null
+++ b/20. Principles of Object Detection/3. R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN.srt	
@@ -0,0 +1,847 @@
+1
+00:00:00,460 --> 00:00:00,840
+OK.
+
+2
+00:00:00,850 --> 00:00:08,260
+So welcome to 20 point to where we talk about all the different types of CNN's going from typical original
+
+3
+00:00:08,520 --> 00:00:10,320
+on CNN all the way to fast.
+
+4
+00:00:10,330 --> 00:00:12,380
+Our CNN and mask are CNN.
+
+5
+00:00:12,640 --> 00:00:15,000
+So let's see what this is about.
+
+6
+00:00:15,010 --> 00:00:20,040
+So what does our our CNN sign for it sends for regions actually not recurrent.
+
+7
+00:00:20,110 --> 00:00:25,210
+That's a different type of neural net and this are seen CNN's were first introduced relatively recently
+
+8
+00:00:25,720 --> 00:00:30,300
+by in 2014 by researchers at the University of College of Berkeley in California.
+
+9
+00:00:30,580 --> 00:00:35,730
+And basically they had dramatically improved performance on D-Pa. VRC challenge.
+
+10
+00:00:35,770 --> 00:00:40,010
+This is the equivalent of image for object detection detection testing.
+
+11
+00:00:40,300 --> 00:00:40,610
+OK.
+
+12
+00:00:40,650 --> 00:00:48,880
+The example of some of the images in there that isn't so our CNN's attempt to solve the exhaustive search
+
+13
+00:00:49,360 --> 00:00:55,090
+previously performed by sliding windows by proposing bounding boxes and passing these extracted bounding
+
+14
+00:00:55,150 --> 00:00:57,380
+boxes to the image ossify.
+
+15
+00:00:57,700 --> 00:01:03,460
+So how do we how do we find these bones in boxes like holiday proposed proposed by using the selective
+
+16
+00:01:03,460 --> 00:01:04,760
+search algorithm.
+
+17
+00:01:04,900 --> 00:01:07,760
+And this is like a simple illustration of what happens here.
+
+18
+00:01:08,020 --> 00:01:09,360
+So we have an image here.
+
+19
+00:01:09,610 --> 00:01:15,820
+We have to propose on the boxes and then we have we basically use these boxes perceptual CNN and try
+
+20
+00:01:15,820 --> 00:01:19,140
+to identify what is in that.
+
+21
+00:01:19,140 --> 00:01:25,290
+So let's talk about the selective search algorithm selective search attempts to segment image into groups
+
+22
+00:01:25,590 --> 00:01:31,680
+by combining similar areas such as colors textures and propose these regions as interesting bounding
+
+23
+00:01:31,680 --> 00:01:32,700
+boxes.
+
+24
+00:01:32,700 --> 00:01:34,680
+So we have this image of some sheep here.
+
+25
+00:01:35,040 --> 00:01:39,810
+And what's left of it is going to do is going to try to like merge different similar pixels similar
+
+26
+00:01:39,810 --> 00:01:42,120
+lighting conditions that sort of stuff.
+
+27
+00:01:42,120 --> 00:01:48,250
+So we can kind of get eventually get something like this and then it's going to draw a box of each segmented
+
+28
+00:01:48,270 --> 00:01:50,630
+region and proposed that.
+
+29
+00:01:50,840 --> 00:01:53,760
+Now it is a lot more tweaking and we can do selective siche.
+
+30
+00:01:53,940 --> 00:01:55,040
+So it all depends.
+
+31
+00:01:55,050 --> 00:01:58,560
+Basically we can get little boxes or a lot of boxes
+
+32
+00:02:01,640 --> 00:02:08,240
+so when selective search has identified these regions of boxes it passes this extracted image to our
+
+33
+00:02:08,240 --> 00:02:11,420
+CNN example one tree and an image in it.
+
+34
+00:02:11,520 --> 00:02:13,890
+OK that would be a very good sign into use.
+
+35
+00:02:14,720 --> 00:02:20,740
+We don't use the CNN directly for classification though although we can we use an SVM to first classify
+
+36
+00:02:20,740 --> 00:02:22,830
+the scene and extracted features.
+
+37
+00:02:22,850 --> 00:02:24,710
+Now I didn't mention that before.
+
+38
+00:02:24,830 --> 00:02:31,990
+But what happens is that instead of actually passing to CNN here using CNN to get to classes what it
+
+39
+00:02:32,000 --> 00:02:36,890
+does it just gives you to CNN features which are basically a feature maps.
+
+40
+00:02:36,890 --> 00:02:37,690
+All right.
+
+41
+00:02:37,790 --> 00:02:42,590
+And then we use an SVM to classified a scene and extract features.
+
+42
+00:02:42,590 --> 00:02:49,550
+This was probably done to speed because if I if I'm not mistaken R-S.C. an end was the original proposed
+
+43
+00:02:49,580 --> 00:02:51,790
+object action and it was meant for video.
+
+44
+00:02:52,250 --> 00:02:53,680
+So speed was a concern.
+
+45
+00:02:54,800 --> 00:03:00,460
+So after this region proposal has been classified we didn't use a simple linear regression to generate
+
+46
+00:03:00,550 --> 00:03:07,060
+a tighter danglin box that's holding boxes we're actually basically size and fit around objects and
+
+47
+00:03:07,070 --> 00:03:07,740
+image.
+
+48
+00:03:07,940 --> 00:03:15,440
+But how do we know what the good boxes though and that's where we can come up with I an IOU metric that
+
+49
+00:03:15,440 --> 00:03:16,550
+we discussed before.
+
+50
+00:03:16,760 --> 00:03:23,060
+Remember you basically had measured how good the overlap of the predicted box was over the over the
+
+51
+00:03:23,060 --> 00:03:26,090
+labels box.
+
+52
+00:03:26,090 --> 00:03:31,580
+Now here's a problem that happens in our CNN's own basically or object detection algorithms.
+
+53
+00:03:31,780 --> 00:03:39,490
+You DO WE they don't often just propose one box they propose many boxes over to see Image image sometimes.
+
+54
+00:03:39,800 --> 00:03:44,100
+So remember in our view point five was considered a good result.
+
+55
+00:03:44,420 --> 00:03:51,650
+Well what if we have multiple boxes and you all want all given over one with zero point five.
+
+56
+00:03:51,650 --> 00:03:52,890
+This is a common problem.
+
+57
+00:03:53,180 --> 00:03:53,750
+OK.
+
+58
+00:03:54,050 --> 00:03:56,560
+So in the figures below let's go to this example.
+
+59
+00:03:56,570 --> 00:03:58,590
+We have four boxes in red.
+
+60
+00:03:58,680 --> 00:04:00,080
+These are them right here.
+
+61
+00:04:00,110 --> 00:04:08,930
+The green is a true box a true positive and as such we have one true positive and true false positives.
+
+62
+00:04:08,930 --> 00:04:09,470
+OK.
+
+63
+00:04:10,040 --> 00:04:15,460
+So the true false positives here would be these boxes here one two and three.
+
+64
+00:04:15,500 --> 00:04:16,010
+OK.
+
+65
+00:04:20,120 --> 00:04:26,520
+So the reason it's true false positives is because we have one box here the dotted line right now this
+
+66
+00:04:26,520 --> 00:04:31,440
+is basically our best box so that will count as a true positive in our predictions.
+
+67
+00:04:31,440 --> 00:04:32,000
+All right.
+
+68
+00:04:32,130 --> 00:04:34,040
+So for now it is ignore the green box.
+
+69
+00:04:34,050 --> 00:04:36,010
+He doesn't count in this coalition.
+
+70
+00:04:36,060 --> 00:04:38,180
+We just have one box that's really good.
+
+71
+00:04:38,220 --> 00:04:47,740
+He's a true positive and treat up on good or false positives so mean average position is a very tricky
+
+72
+00:04:47,830 --> 00:04:54,510
+metric in my opinion mainly because it's not that intuitive to understand how the formulas fit here.
+
+73
+00:04:54,730 --> 00:04:56,870
+What I'm going to say though is I'm going to try to explain it to you.
+
+74
+00:04:56,940 --> 00:05:02,470
+Outgo introduce into amount of left I'm not for you in a slide so you can go over on your own and try
+
+75
+00:05:02,470 --> 00:05:03,720
+to make sense of it.
+
+76
+00:05:03,760 --> 00:05:08,840
+There's also a couple of blogs that have some very good but pretty lengthy explanations for it.
+
+77
+00:05:09,070 --> 00:05:11,100
+But let's go back to the sorry.
+
+78
+00:05:11,320 --> 00:05:17,170
+So what I'm going to say is that actually in this blog here is a very good explanation for it.
+
+79
+00:05:17,290 --> 00:05:25,030
+But essentially what mean average position tries to do is that it takes it knows all the boxes the architect
+
+80
+00:05:25,060 --> 00:05:31,490
+proposes and basically what it tries to do is try to come up with a metric that defines basically what
+
+81
+00:05:31,490 --> 00:05:33,130
+it is like.
+
+82
+00:05:33,300 --> 00:05:41,680
+See object that to predict this but object to be predicted maybe for false positives and maybe like
+
+83
+00:05:41,680 --> 00:05:44,880
+some other boxes that were probably irrelevant and stuff.
+
+84
+00:05:44,890 --> 00:05:47,580
+So and then we remember this is a for one a class.
+
+85
+00:05:47,590 --> 00:05:49,280
+Remember there are different classes as well.
+
+86
+00:05:49,460 --> 00:05:56,300
+So I mean average precision is a way to measure how effective all of my object detectors were were actually
+
+87
+00:05:56,560 --> 00:05:57,490
+well actually.
+
+88
+00:05:57,660 --> 00:05:58,070
+OK.
+
+89
+00:05:58,920 --> 00:06:04,690
+So just remember this is a metric of how we baseline how we measure performance of objective
+
+90
+00:06:07,820 --> 00:06:14,420
+So now let's move on to not the maximum suppression and this is a technique that object is used to remove
+
+91
+00:06:14,420 --> 00:06:18,640
+overlapping boxes and thus improve them up scores significantly.
+
+92
+00:06:18,920 --> 00:06:24,400
+So what this does basically it looks at a probabilities associated with each box that's being generated
+
+93
+00:06:24,830 --> 00:06:30,590
+and the probabilities it looks at all the probabilities of the object being in the same class effectively.
+
+94
+00:06:30,590 --> 00:06:35,690
+So if we have like two or three boxes that say this is a call but they have high overlap.
+
+95
+00:06:35,870 --> 00:06:40,180
+What it does now it looks at the highest probability box.
+
+96
+00:06:40,230 --> 00:06:41,970
+That's this one here in red.
+
+97
+00:06:42,200 --> 00:06:46,730
+And what it does it checks the eye or you would do with the other boxes.
+
+98
+00:06:46,730 --> 00:06:52,970
+So in this image here we see that we have tree boxes here that object texture the doctor has basically
+
+99
+00:06:54,080 --> 00:07:00,340
+highlighted and these are the probabilities of it being a class so what it does it checks you over these
+
+100
+00:07:00,410 --> 00:07:07,940
+here and what it does it will drop the boxes that it deems basically not as relevant as main box.
+
+101
+00:07:07,950 --> 00:07:11,250
+So that's effectively how we clean up these boxes.
+
+102
+00:07:11,470 --> 00:07:15,420
+You do object that is produce.
+
+103
+00:07:15,450 --> 00:07:17,430
+So now let's move on to fast.
+
+104
+00:07:17,430 --> 00:07:24,870
+Our CNN's this was this was announced or released in 2015 a year after the original on CNN came out
+
+105
+00:07:25,380 --> 00:07:31,320
+and basically what happened was that the problem with our CNNs was that it was effective but pretty
+
+106
+00:07:31,320 --> 00:07:39,030
+slow as each but each proposed bounding box has to be classified by a CNI by CNN and as such doing is
+
+107
+00:07:39,120 --> 00:07:41,890
+doing it in realtime was often impossible.
+
+108
+00:07:42,060 --> 00:07:43,970
+So it required tree models.
+
+109
+00:07:44,370 --> 00:07:47,640
+And it also required tree models to be trained separately.
+
+110
+00:07:47,640 --> 00:07:53,760
+So we had to have a feature extraction CNN and SVM to predict a class and a linear regression model
+
+111
+00:07:53,760 --> 00:07:55,420
+to tighten the Bongi boxes.
+
+112
+00:07:55,640 --> 00:07:57,690
+You remember all these things we discussed earlier.
+
+113
+00:07:57,960 --> 00:08:03,220
+So this definitely is a bit of a lot of moving parts in our CNN's.
+
+114
+00:08:03,230 --> 00:08:06,600
+So what did fasta our CNN's do first.
+
+115
+00:08:06,630 --> 00:08:11,190
+Our CNN's fiercly reduced number for postboxes by removing the overlap generated.
+
+116
+00:08:11,190 --> 00:08:12,930
+So how did they do this.
+
+117
+00:08:12,930 --> 00:08:18,750
+We run the CNN across the image just once instead of many times using a technique called that region
+
+118
+00:08:19,350 --> 00:08:21,930
+of interest pooling our boy pool.
+
+119
+00:08:22,440 --> 00:08:22,880
+OK.
+
+120
+00:08:23,190 --> 00:08:29,280
+So our away pool allows us to share the forward pass of the CNN for images for image across that sort
+
+121
+00:08:29,280 --> 00:08:30,660
+of subregions.
+
+122
+00:08:30,660 --> 00:08:37,380
+This works because previously regions are simply extracted from the CNN feature map and then puled which
+
+123
+00:08:37,530 --> 00:08:44,360
+deadfall you only need to render CNN once image and basically use that output of the CNN going forward.
+
+124
+00:08:46,060 --> 00:08:52,510
+So combining the training of the CNN classifier and the bonding box regressed into a single model that's
+
+125
+00:08:52,510 --> 00:08:53,030
+what it did.
+
+126
+00:08:53,110 --> 00:08:59,280
+So instead of so we have the SVM feature classifier and that now became a sort of actually on top of
+
+127
+00:08:59,280 --> 00:09:05,870
+the CNN and old linear regression that was taking the boxes basically that became a bounding box.
+
+128
+00:09:05,910 --> 00:09:08,770
+Apulia parallel to our soft Leo.
+
+129
+00:09:09,040 --> 00:09:12,540
+So basically what it became is this.
+
+130
+00:09:12,610 --> 00:09:14,430
+So we have a feature extractor.
+
+131
+00:09:14,440 --> 00:09:22,360
+CNN we have a soft Max layers at the end of CNN being our classifier for the object type or class.
+
+132
+00:09:22,510 --> 00:09:26,120
+And then we have this in Peridot a bounding box regressive.
+
+133
+00:09:26,170 --> 00:09:33,400
+So that is how it our CNN's fast our CNN solve a lot of the delays and sluggishness of the original
+
+134
+00:09:33,400 --> 00:09:37,670
+RCN and so now a year later in 2016.
+
+135
+00:09:37,950 --> 00:09:42,560
+Our CNN's media release I should say so faster.
+
+136
+00:09:42,720 --> 00:09:44,650
+Our CNN's made significant speed increases.
+
+137
+00:09:44,650 --> 00:09:51,290
+However a region proposal still remained relatively slow as it still relied on selective search algorithm.
+
+138
+00:09:51,790 --> 00:09:57,220
+Fortunately a Microsoft Research Team figured out how to eliminate this bottleneck.
+
+139
+00:09:57,220 --> 00:10:02,770
+So how did it speed up rigid proposal select to sit your lies and features extracted from the image
+
+140
+00:10:03,280 --> 00:10:07,880
+What if we just reused as features to do region proposal instead.
+
+141
+00:10:08,100 --> 00:10:11,220
+Okay so that was the inside that made the faster.
+
+142
+00:10:11,320 --> 00:10:13,180
+And is extremely efficient.
+
+143
+00:10:13,180 --> 00:10:15,670
+So you get to take a look at a diagram from the people here.
+
+144
+00:10:15,970 --> 00:10:23,150
+So basically this line here is what is important what if we use those features to do rigid proposal.
+
+145
+00:10:23,440 --> 00:10:29,350
+That is exactly why we don't have to keep running this over and over for the equivalent of a bit using
+
+146
+00:10:29,770 --> 00:10:32,410
+selective search to generate our proposals.
+
+147
+00:10:33,880 --> 00:10:35,950
+So how do we do rigid proposals with faster.
+
+148
+00:10:35,950 --> 00:10:43,960
+Our CNN's so fast our CNN's ad a fully convolutional network on top of the features of CNN to create
+
+149
+00:10:43,990 --> 00:10:45,740
+a region proposal network.
+
+150
+00:10:45,940 --> 00:10:47,370
+That's what we're seeing here.
+
+151
+00:10:47,680 --> 00:10:48,780
+So it is now.
+
+152
+00:10:49,390 --> 00:10:56,720
+Let's go back to it is now a full fully convolutional network on top of the features of CNN.
+
+153
+00:10:56,740 --> 00:10:57,480
+So let's think about that.
+
+154
+00:10:57,480 --> 00:11:04,590
+So we have a CNN producers here and is a fully convolutional at work here that does this region proposal
+
+155
+00:11:05,980 --> 00:11:06,730
+OK.
+
+156
+00:11:06,930 --> 00:11:12,540
+So the authors of the paper state the region proposal network slides a window over the features of the
+
+157
+00:11:12,570 --> 00:11:14,930
+CNN at each window location.
+
+158
+00:11:14,940 --> 00:11:22,740
+The netbook was a school and a bounty box put anchor hence 4000 box coordinates where kids number of
+
+159
+00:11:22,740 --> 00:11:23,540
+anchors.
+
+160
+00:11:23,670 --> 00:11:25,980
+That is basically how it works.
+
+161
+00:11:26,010 --> 00:11:31,680
+I encourage you to read to people if you want to get into more detail but first our CNN's Anyway back
+
+162
+00:11:31,680 --> 00:11:34,540
+to this after each pass of the sliding window.
+
+163
+00:11:34,740 --> 00:11:41,400
+It outputs key potential ballot boxes and a confidence of how good this box is expected to be.
+
+164
+00:11:41,400 --> 00:11:44,860
+That's pretty cool and pretty complicated as well.
+
+165
+00:11:45,180 --> 00:11:47,140
+So producing the bombing boxes now.
+
+166
+00:11:47,360 --> 00:11:54,630
+So previously we mentioned we produced key potential bounding boxes these bomb box proposals are proposals
+
+167
+00:11:54,630 --> 00:12:01,740
+of common expected boxes of suits and ships aspect ratio and sizes this aspect ratio is called Anca
+
+168
+00:12:01,740 --> 00:12:02,710
+boxes.
+
+169
+00:12:02,730 --> 00:12:09,420
+So what's happening here is that all boxes the boxes we propose in this method are basically predefined
+
+170
+00:12:09,630 --> 00:12:13,150
+and there would be 60 of suits and shapes ratios and sizes.
+
+171
+00:12:13,530 --> 00:12:20,040
+So it's a region that the proposal outputs a bony box put onco and a scale of how likely the image in
+
+172
+00:12:20,040 --> 00:12:29,940
+the box will be an object so let's move on to mask our CNN's which is pixel level segmentation now Mass.
+
+173
+00:12:29,980 --> 00:12:32,430
+Our CNN's aim to combine it.
+
+174
+00:12:32,460 --> 00:12:38,170
+Obs detection action and classification with segmentation as we previously saw segmentation as the labeling
+
+175
+00:12:38,170 --> 00:12:41,880
+of objects per pixel level as we can see in the above image.
+
+176
+00:12:42,980 --> 00:12:45,220
+So how do Musse our CNN's work.
+
+177
+00:12:45,450 --> 00:12:47,590
+They are basically an extension of fast.
+
+178
+00:12:47,600 --> 00:12:53,480
+Our CNN's web binary mask is created for the objects detected by the box.
+
+179
+00:12:53,540 --> 00:13:00,100
+So you remember how you actually created a mask that was basically provided segmentation outputs.
+
+180
+00:13:00,140 --> 00:13:07,640
+That's what's happening here when in fact our CNN's so binary mask basically bit 0 or 1 is created for
+
+181
+00:13:07,640 --> 00:13:09,060
+the object in the box.
+
+182
+00:13:09,080 --> 00:13:11,190
+So we know we're looking at a box.
+
+183
+00:13:11,240 --> 00:13:15,390
+And we are creating a binary mask for each box that's found.
+
+184
+00:13:15,560 --> 00:13:21,590
+And this is the architecture of the network and the link to the actual research paper where this publication
+
+185
+00:13:21,710 --> 00:13:24,390
+was released or made.
+
+186
+00:13:24,770 --> 00:13:28,440
+So messy CNN's you something called R Y O line.
+
+187
+00:13:28,820 --> 00:13:33,580
+So the mass of put uses the CNN extracted features to create its binary mask.
+
+188
+00:13:33,710 --> 00:13:36,130
+So how do the authors of this paper achieve this.
+
+189
+00:13:36,130 --> 00:13:42,740
+They use our oil line instead of a pool as a Pooles feature map was misalign from the regions of the
+
+190
+00:13:42,740 --> 00:13:43,660
+original image.
+
+191
+00:13:43,880 --> 00:13:46,990
+That was something I read in the paper I was confused to why.
+
+192
+00:13:47,100 --> 00:13:48,350
+But they explained it pretty well.
+
+193
+00:13:48,470 --> 00:13:52,010
+So mapping a region of interest onto a future map.
+
+194
+00:13:52,010 --> 00:13:54,580
+So imagine this was the original image here.
+
+195
+00:13:54,770 --> 00:13:59,000
+And we had a feature map that we generated from CNN that is smaller.
+
+196
+00:13:59,210 --> 00:14:00,700
+OK because it usually is smaller.
+
+197
+00:14:00,710 --> 00:14:07,010
+And as we use your reporting and what happens here is I know a 100 by 100 image is now mapped onto the
+
+198
+00:14:07,040 --> 00:14:08,610
+two by to the to future map.
+
+199
+00:14:08,690 --> 00:14:14,930
+Therefore a window of 20 by 20 that's just good old size window here on your original image is mapped
+
+200
+00:14:14,930 --> 00:14:17,470
+to a 6.4 pixel here.
+
+201
+00:14:17,510 --> 00:14:21,320
+This is how a direct linear mapping would be in the future map.
+
+202
+00:14:21,460 --> 00:14:25,750
+Our pool however wrong's down pixel maps to six by six.
+
+203
+00:14:25,790 --> 00:14:30,730
+That is why our pools feature but when we use our pool instead of like a line.
+
+204
+00:14:30,740 --> 00:14:37,880
+You had a misaligned boxes or regions on the image may have been that important to any looking at a
+
+205
+00:14:37,880 --> 00:14:39,630
+big image with small objects.
+
+206
+00:14:39,950 --> 00:14:47,240
+But if you think about it if it's a big box and you're going to do a proposal on being misaligned can
+
+207
+00:14:47,240 --> 00:14:48,290
+actually be bad.
+
+208
+00:14:48,710 --> 00:14:55,910
+So I wrote a line of these bio linear interpolation to know exactly what would be the pixel at 6.4.
+
+209
+00:14:56,360 --> 00:15:02,260
+So it's a pretty cool nifty algorithm that gives you exact mapping without having to roundel pixels.
+
+210
+00:15:03,620 --> 00:15:10,640
+So these are some examples of mask RCN and segmenting and classifying images is pretty cool to see this
+
+211
+00:15:10,640 --> 00:15:11,030
+in action.
+
+212
+00:15:11,030 --> 00:15:14,570
+Actually it's very accurate in some in some videos.
diff --git a/20. Principles of Object Detection/4. Single Shot Detectors (SSDs).srt b/20. Principles of Object Detection/4. Single Shot Detectors (SSDs).srt
new file mode 100644
index 0000000000000000000000000000000000000000..91ce4e9f6c482e770ad9090e71ee079f25c0269a
--- /dev/null
+++ b/20. Principles of Object Detection/4. Single Shot Detectors (SSDs).srt	
@@ -0,0 +1,115 @@
+1
+00:00:00,750 --> 00:00:01,100
+OK.
+
+2
+00:00:01,140 --> 00:00:06,570
+So now let's move on to chapter 20 point tree where we talk about single shot detectors also called
+
+3
+00:00:06,630 --> 00:00:09,510
+SSD is and design not solid state drives.
+
+4
+00:00:09,510 --> 00:00:18,010
+By the way totally different SSD so single shot detectors so we previously just went through the entire
+
+5
+00:00:18,310 --> 00:00:21,880
+CNN family and we've seen how successfully that can be applied.
+
+6
+00:00:21,880 --> 00:00:22,590
+All right.
+
+7
+00:00:22,720 --> 00:00:28,690
+How ever the performance on video is still not optimal and it typically run even Anji pews at seven
+
+8
+00:00:28,690 --> 00:00:29,690
+frames per second.
+
+9
+00:00:29,860 --> 00:00:36,100
+Now SSD is to improve dispute speech by eliminating the need for rigid proposal so you can see the speed
+
+10
+00:00:36,100 --> 00:00:38,870
+of fasta our CNN's here and you'll.
+
+11
+00:00:39,160 --> 00:00:40,860
+And this is Ulo vision one.
+
+12
+00:00:41,020 --> 00:00:45,500
+But look at the speed of SSD is on relatively high resolution images.
+
+13
+00:00:45,520 --> 00:00:49,820
+They're pretty fast and this is running on a Titan SGP.
+
+14
+00:00:50,500 --> 00:00:52,060
+So this is pretty impressive.
+
+15
+00:00:52,060 --> 00:01:00,130
+So how did it achieve this improvement in speed as these use multi-skilled features and default boxes
+
+16
+00:01:00,160 --> 00:01:03,400
+as well as dropping the resolution of the images to improve speed.
+
+17
+00:01:03,400 --> 00:01:05,570
+That doesn't seem that difficult does it.
+
+18
+00:01:05,590 --> 00:01:10,740
+But anyway this allows a city of near real time speed with almost no drop in accuracy.
+
+19
+00:01:11,810 --> 00:01:15,240
+So these are comprised of two main parts.
+
+20
+00:01:15,380 --> 00:01:21,110
+We have the feature epic Scheckter and typically they use Viji 16 that was actually what was used in
+
+21
+00:01:21,110 --> 00:01:26,360
+the published people but residents a dense net actually could provide better results as they have actually
+
+22
+00:01:26,510 --> 00:01:31,970
+been better in the eyeless VRC result competition.
+
+23
+00:01:31,980 --> 00:01:37,490
+So anyway so we then we have the feature map here and we have the convolutional filter that we use for
+
+24
+00:01:37,490 --> 00:01:38,930
+object detection.
+
+25
+00:01:38,930 --> 00:01:40,620
+So this is a diagram of it here.
+
+26
+00:01:40,670 --> 00:01:43,060
+So basically this is the input image here.
+
+27
+00:01:43,130 --> 00:01:48,260
+This is a feature extractor for convolutional work here which was Viji 16.
+
+28
+00:01:48,590 --> 00:01:53,070
+And then we have the convolutional filter that we use for our detectors here.
+
+29
+00:01:53,240 --> 00:01:53,730
+OK.
diff --git a/20. Principles of Object Detection/5. YOLO to YOLOv3.srt b/20. Principles of Object Detection/5. YOLO to YOLOv3.srt
new file mode 100644
index 0000000000000000000000000000000000000000..da547fd532a4d595bc432b1d3a4f80312f230363
--- /dev/null
+++ b/20. Principles of Object Detection/5. YOLO to YOLOv3.srt	
@@ -0,0 +1,203 @@
+1
+00:00:00,600 --> 00:00:05,590
+Hi and welcome to Chapter 20 point four where we talk about you and we go all the way up from yellow
+
+2
+00:00:05,640 --> 00:00:07,470
+to fish and tree.
+
+3
+00:00:07,470 --> 00:00:09,700
+So let's see what eel really is about.
+
+4
+00:00:10,710 --> 00:00:12,430
+So you know you only live once.
+
+5
+00:00:12,480 --> 00:00:15,140
+No it's actually you only look once.
+
+6
+00:00:15,510 --> 00:00:21,110
+And the idea behind you is that a single neuron that work is applied to full to a full image.
+
+7
+00:00:21,450 --> 00:00:25,830
+And this allows us to reason globally about image when generating predictions.
+
+8
+00:00:25,910 --> 00:00:31,210
+So it's basically a neural net that actually looks at the entire image for us with CNN.
+
+9
+00:00:31,560 --> 00:00:38,220
+So it is a direct development from off multi bucks but it turns a multi box from a rigid proposal into
+
+10
+00:00:38,220 --> 00:00:44,400
+an object recognition method by adding a soft actually in parallel with a box or a press box classify
+
+11
+00:00:44,400 --> 00:00:45,070
+earlier.
+
+12
+00:00:45,450 --> 00:00:50,580
+So it divides the image into regions and then predicts bounding boxes and possibilities of each region
+
+13
+00:00:51,430 --> 00:00:58,840
+YOLO YOLO uses a fully convolutional neural network allowing for an input of various sizes.
+
+14
+00:00:58,890 --> 00:00:59,840
+So that's pretty cool.
+
+15
+00:01:01,680 --> 00:01:04,820
+So let me tell you how it works.
+
+16
+00:01:04,830 --> 00:01:08,760
+So the input image is divided into an S by a script.
+
+17
+00:01:09,100 --> 00:01:14,870
+If the center of an object falls into this grid that cell is responsible for detecting that object.
+
+18
+00:01:14,900 --> 00:01:20,580
+Now each grid predicts a number of bungling boxes and confidence scores for his boxes.
+
+19
+00:01:20,580 --> 00:01:25,270
+Confidence has defined as a probability of an object multiplied by the threshold.
+
+20
+00:01:25,290 --> 00:01:31,300
+I use school and I use scores of point five or less than 0.5 or given ZERO confidence.
+
+21
+00:01:31,630 --> 00:01:36,080
+So the bounding box is defined by these parameters.
+
+22
+00:01:36,120 --> 00:01:42,690
+X y width and height where x and y the center of the box and W H are height and weight by multiplying
+
+23
+00:01:42,690 --> 00:01:47,450
+the conditional class probability and individual box confidence predictions.
+
+24
+00:01:47,460 --> 00:01:50,740
+We get to class specific confidence school of each box.
+
+25
+00:01:50,970 --> 00:01:55,910
+That's how you effectively works so that your model is quite good.
+
+26
+00:01:55,920 --> 00:01:57,830
+So this is essay by a secret here.
+
+27
+00:01:58,080 --> 00:02:02,950
+These are the bounding boxes that we just generate from each cell plus the confidence.
+
+28
+00:02:03,000 --> 00:02:05,160
+This is the class probability map.
+
+29
+00:02:05,160 --> 00:02:10,350
+So the class probability map basically indicates if you go back to it here the probability of an object
+
+30
+00:02:10,350 --> 00:02:16,740
+be multiplied by Trishul school k and then these are the final actions here.
+
+31
+00:02:16,740 --> 00:02:20,220
+So you can see this blue object here which would belong to that class.
+
+32
+00:02:20,220 --> 00:02:22,270
+Probability of a dog.
+
+33
+00:02:22,380 --> 00:02:29,400
+Then there was a bicycle and then there was a background which ended up being a car.
+
+34
+00:02:29,440 --> 00:02:36,160
+So let's talk about the last function adjustments during training Ulo uses differential weight for confidence
+
+35
+00:02:36,160 --> 00:02:40,790
+corrections from boxes that contain objects and boxes that do not contain objects.
+
+36
+00:02:41,020 --> 00:02:46,990
+It penalizes errors in small and large objects differently by predicting the square root of the box
+
+37
+00:02:47,010 --> 00:02:49,780
+width and height.
+
+38
+00:02:49,780 --> 00:02:51,550
+So this is a little architecture.
+
+39
+00:02:51,800 --> 00:02:53,980
+It's a bit it looks a bit simple doesn't it.
+
+40
+00:02:53,980 --> 00:02:57,480
+However it's a lot more going on than meets the eye.
+
+41
+00:02:57,590 --> 00:03:04,510
+In this image so let's talk about the evolution of your little sister in 2016.
+
+42
+00:03:04,580 --> 00:03:06,510
+That was the seamier fast.
+
+43
+00:03:06,770 --> 00:03:14,150
+Our CNN's was released and it was voted open civies people's choice award at CVR conference.
+
+44
+00:03:14,210 --> 00:03:15,760
+Pattern recognition.
+
+45
+00:03:15,880 --> 00:03:22,670
+Unconference election two was later released when Basho musician was added to CNN.
+
+46
+00:03:22,670 --> 00:03:30,360
+So we do use Bachan musician and as easily as here which resulted in mapping improvements.
+
+47
+00:03:30,400 --> 00:03:35,230
+Map was mean average precision of 2 percent which was quite significant.
+
+48
+00:03:35,240 --> 00:03:41,810
+It was also a fine tuned a bit to get high resolution images giving it a 4 percent increase in map and
+
+49
+00:03:41,810 --> 00:03:48,260
+then yellow tree was fine too and even fitten introduced multi-skilled training better training to better
+
+50
+00:03:48,290 --> 00:03:51,920
+help detect small objects so that is it for you.
+
+51
+00:03:51,950 --> 00:03:56,590
+And now we move onto the next section which is the tensor flu outbreak detection API.
diff --git a/21. TensforFlow Object Detection/Go to the folder speciefid in this file b/21. TensforFlow Object Detection/Go to the folder speciefid in this file
new file mode 100644
index 0000000000000000000000000000000000000000..d4f14f2a2074f407ddda07999820db574a59a7ac
--- /dev/null
+++ b/21. TensforFlow Object Detection/Go to the folder speciefid in this file	
@@ -0,0 +1,12 @@
+INSTRUCTIONS
+
+
+GO TO THIS FOLDER FROM YOUR IPYTHON NOTEBOOK
+/home/deeplearningcv/models/models/research/object_detection
+
+OPEN THIS FILE
+/home/deeplearningcv/models/models/research/object_detection/object_detection_tutorial.ipynb
+
+OR 
+
+COPY THE IPYTHON NOTEBOOK FILE IN THIS FOLDER TO THE DIRECTORY - /home/deeplearningcv/models/models/research/object_detection
diff --git a/21. TensforFlow Object Detection/object_detection_tutorial.ipynb b/21. TensforFlow Object Detection/object_detection_tutorial.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3e4a4b6d62a1c57cf8f92cede11381ba116c356e
--- /dev/null
+++ b/21. TensforFlow Object Detection/object_detection_tutorial.ipynb	
@@ -0,0 +1,630 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "V8-yl-s-WKMG"
+   },
+   "source": [
+    "# Object Detection Demo\n",
+    "Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "kFSqkTCdWKMI"
+   },
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "hV4P5gyTWKMI"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "import six.moves.urllib as urllib\n",
+    "import sys\n",
+    "import tarfile\n",
+    "import tensorflow as tf\n",
+    "import zipfile\n",
+    "\n",
+    "from distutils.version import StrictVersion\n",
+    "from collections import defaultdict\n",
+    "from io import StringIO\n",
+    "from matplotlib import pyplot as plt\n",
+    "from PIL import Image\n",
+    "\n",
+    "# This is needed since the notebook is stored in the object_detection folder.\n",
+    "sys.path.append(\"..\")\n",
+    "from object_detection.utils import ops as utils_ops\n",
+    "\n",
+    "if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):\n",
+    "    raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Wy72mWwAWKMK"
+   },
+   "source": [
+    "## Env setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "v7m_NY_aWKMK"
+   },
+   "outputs": [],
+   "source": [
+    "# This is needed to display the images.\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "r5FNuiRPWKMN"
+   },
+   "source": [
+    "## Object detection imports\n",
+    "Here are the imports from the object detection module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "bm0_uNRnWKMN"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deeplearningcv/models/models/research/object_detection/utils/visualization_utils.py:27: UserWarning: matplotlib.pyplot as already been imported, this call will have no effect.\n",
+      "  import matplotlib; matplotlib.use('Agg')  # pylint: disable=multiple-statements\n"
+     ]
+    }
+   ],
+   "source": [
+    "from utils import label_map_util\n",
+    "\n",
+    "from utils import visualization_utils as vis_util"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "cfn_tRFOWKMO"
+   },
+   "source": [
+    "# Model preparation "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "X_sEBLpVWKMQ"
+   },
+   "source": [
+    "## Variables\n",
+    "\n",
+    "Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.  \n",
+    "\n",
+    "By default we use an \"SSD with Mobilenet\" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "VyPz_t8WWKMQ"
+   },
+   "outputs": [],
+   "source": [
+    "# What model to download.\n",
+    "MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'\n",
+    "MODEL_FILE = MODEL_NAME + '.tar.gz'\n",
+    "DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n",
+    "\n",
+    "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n",
+    "PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'\n",
+    "\n",
+    "# List of the strings that is used to add correct label for each box.\n",
+    "PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "7ai8pLZZWKMS"
+   },
+   "source": [
+    "## Download Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "KILYnwR5WKMS"
+   },
+   "outputs": [],
+   "source": [
+    "opener = urllib.request.URLopener()\n",
+    "opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\n",
+    "tar_file = tarfile.open(MODEL_FILE)\n",
+    "for file in tar_file.getmembers():\n",
+    "    file_name = os.path.basename(file.name)\n",
+    "    if 'frozen_inference_graph.pb' in file_name:\n",
+    "        tar_file.extract(file, os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "YBcB9QHLWKMU"
+   },
+   "source": [
+    "## Load a (frozen) Tensorflow model into memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "KezjCRVvWKMV"
+   },
+   "outputs": [],
+   "source": [
+    "detection_graph = tf.Graph()\n",
+    "with detection_graph.as_default():\n",
+    "    od_graph_def = tf.GraphDef()\n",
+    "    with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:\n",
+    "        serialized_graph = fid.read()\n",
+    "        od_graph_def.ParseFromString(serialized_graph)\n",
+    "        tf.import_graph_def(od_graph_def, name='')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "_1MVVTcLWKMW"
+   },
+   "source": [
+    "## Loading label map\n",
+    "Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "hDbpHkiWWKMX"
+   },
+   "outputs": [],
+   "source": [
+    "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "EFsoUHvbWKMZ"
+   },
+   "source": [
+    "## Helper code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "aSlYc3JkWKMa"
+   },
+   "outputs": [],
+   "source": [
+    "def load_image_into_numpy_array(image):\n",
+    "  (im_width, im_height) = image.size\n",
+    "  return np.array(image.getdata()).reshape(\n",
+    "      (im_height, im_width, 3)).astype(np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "H0_1AGhrWKMc"
+   },
+   "source": [
+    "# Detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "jG-zn5ykWKMd"
+   },
+   "outputs": [],
+   "source": [
+    "# For the sake of simplicity we will use only 2 images:\n",
+    "# image1.jpg\n",
+    "# image2.jpg\n",
+    "# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.\n",
+    "PATH_TO_TEST_IMAGES_DIR = 'test_images'\n",
+    "TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]\n",
+    "\n",
+    "# Size, in inches, of the output images.\n",
+    "IMAGE_SIZE = (12, 8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "92BHxzcNWKMf"
+   },
+   "outputs": [],
+   "source": [
+    "def run_inference_for_single_image(image, graph):\n",
+    "  with graph.as_default():\n",
+    "    with tf.Session() as sess:\n",
+    "      # Get handles to input and output tensors\n",
+    "      ops = tf.get_default_graph().get_operations()\n",
+    "      all_tensor_names = {output.name for op in ops for output in op.outputs}\n",
+    "      tensor_dict = {}\n",
+    "      for key in [\n",
+    "          'num_detections', 'detection_boxes', 'detection_scores',\n",
+    "          'detection_classes', 'detection_masks'\n",
+    "      ]:\n",
+    "        tensor_name = key + ':0'\n",
+    "        if tensor_name in all_tensor_names:\n",
+    "          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
+    "              tensor_name)\n",
+    "      if 'detection_masks' in tensor_dict:\n",
+    "        # The following processing is only for single image\n",
+    "        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])\n",
+    "        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])\n",
+    "        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
+    "        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)\n",
+    "        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])\n",
+    "        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])\n",
+    "        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
+    "            detection_masks, detection_boxes, image.shape[0], image.shape[1])\n",
+    "        detection_masks_reframed = tf.cast(\n",
+    "            tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
+    "        # Follow the convention by adding back the batch dimension\n",
+    "        tensor_dict['detection_masks'] = tf.expand_dims(\n",
+    "            detection_masks_reframed, 0)\n",
+    "      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
+    "\n",
+    "      # Run inference\n",
+    "      output_dict = sess.run(tensor_dict,\n",
+    "                             feed_dict={image_tensor: np.expand_dims(image, 0)})\n",
+    "\n",
+    "      # all outputs are float32 numpy arrays, so convert types as appropriate\n",
+    "      output_dict['num_detections'] = int(output_dict['num_detections'][0])\n",
+    "      output_dict['detection_classes'] = output_dict[\n",
+    "          'detection_classes'][0].astype(np.uint8)\n",
+    "      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
+    "      output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
+    "      if 'detection_masks' in output_dict:\n",
+    "        output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
+    "  return output_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "3a5wMHN8WKMh"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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7Yj73uGbhj5H24Q/XrH2Ma5D2d7u36GM41pJ2j9iI4xdO3CEoiT3CuY25+nq9ao13dryv+9/S/ip1ct39f3b3/8jd/wN3/+//8jcE0wW6Hb5XsYpDJgYE9/1HfMiIsGv+Ca+obzyNoUpvSPRMRtXwtukBiQzImLA7QqE+Ne7vJ5yhqpgyw8sdA7YWIhSjD0DoLhCPyeW+AQvGwjYXXk5oDEDPJlahUjTpQQK27McH47ndMPQ6E9zWA8jaPgarM3VkCQHU5sWFI+plxPsmfu91GBoyngl6GM6uzU/S+IhhWNv0cRgzvk9ni9l4fYKuYBO8GBNO6o+Pj9rsK1RyLzyaOfa9AqjJAXIdzIUDs0/oZ+9iuLwBa+CAczI9mvpuTfZ1qsLWKuAGHMNebJugnIhrzAcYIHsLPCUTDBG91v1gkxniGWPgGuHoKUO1eQ/KLRgV6aFRMgXuHuFQo2zjhP80DWQ3RBFODDYaQIS69i6ZSWfCR1tzQ47jdo2Blc4XMoQsElKUMQa+7jvH3YulAYB93zGP7Clz4fMSlFMOdJiIM58XjjFlv3B+vV6vMu58Vs/516VI0OYEcr2Y1324hsloAAE8CoSzT0WKNSLY3HsHwM0+F/fcqI4jFAzImR+cc5fo05CrvH3mrD8AxSIu4zwv+J2g5bCKfRNyOdEPbojl6OdaYB8xotGd9Z3svOBEaGhLV4beTX+U2UmCK64X2k9pc4QSDTLCfS3xu7aRDnhKVvQpH+IaKsYSeQ3JMd9BXooHG8w9pRhj1wiXr5BGcQ0r50iOJSyAhSZA43MfOxp7nInWXNl+7HG3ZSM/05l3On/LV40337PG1MnaOQh0uCch2b9+HySDqwWCAJWPBKkhJ6gxAR523iCQvQ6hhJRdEegn2B8iUA81DueWYEBdY0v1WcCWhELsBZIAP67j20LmYStAqWpJGVaCMc5zEiMyrrJ7fZ0ErmgkABRiO+QnJBhSOhD7O4Hqj04iHb2hlKgIRs7/QcBKggIHZ3CtUNZw9td0yigdaBICA4o8YLSQ405cRMBN8uGxfzVHtZzxKdi5n3IvcWxsu3HlmixWdoyQFLY5SdaXEZBrzLBNjRypPcMEn9dV3yPLO6ovfzATv9r+KiD3X785bCTQ1ACb4ZVHmEDU4bogH4rbNrZsbPuCLHpoZCli0k/V2sD7JNE5ymPfe6d3gdC03A6Xo1MUEeDeWG7wV4DEOScMG1/2pwd4I8iIEGo8z4YjTUbpfHcuwHtvrLWOEWyAtHsp8j6SFgvD/YSlgAOgtIGhru+McFeGGgf/nd6C37hGA9Jp5N/1od243vddC79/BwBsSIXtXyBj+Prhmv3Z+rt6M5Le3s/d8Vor9T8HMJMlKUmEG6COX+YV+k2RHxifYhvzncYYP7DhAPCVhrmDKmqJOgB4d0r6vWgqA5Df2PddTFBnklQ+SpdqFjpXM8Mfro9iNldKIiIEl5Zu7XL2toemcIhCP+ZxUBDGylTwSq0ndU4EqOPQMI93822pk81Qs3rdmwbtvvcJv2qEkUvHmnrYm/KGgWS3Vs3bOSf+9HphzllMBYDcqByfn58QNyy7H+vlXe/J8OGRIoQj+CdZFU4uUJ8Mi7vjD5+fBTS4OR9H8KxBMoXBTBwAx43A/fQNnSft/TkH9vAI+RWDe8LSHZAR3IQez867ASVnIWML/MiaAocpdYmNmA6HJHv1+TGhAxnmjbB7dz5dAB/NOUziIOZFzIflK9jzayaj7MmYT3SJUulId46TNLmPWfWt+NmUuu0xOY4ZQ8Se4IhzovJq/axJOoXLdo2le8h7jmsS7RpSIW8yg/fKuYSNMdNmOGAuwGsVUN75jtsNW62iBEGyjIpG0BkskiblXSLBNrs7TFBgv5xb80PaiAC2KioVfdbyQEQAPfrLmlcJ/kVGRD1S677cwrbQ0U/7iNThco8lSDL3E1EUlF6S87PyEThmdmPL02malJjkuluWEi8P8LUq0SzHF2TC0yGnY4Doc85LrBtDwmHA0JAs7g1AQ5JVfUbWN2RtexPENomdNS2tnH6A/gJpexkdDYJQ9p94RGxK5pbvvxOwGgDjXt32wbCjKEadexT3T0avMAc0HVXasgwZP6IFfb+lveea4z29+vIAYCDmkCfmUEdEFAiCk4X/SLB65b7GPfnKfiFxQrCtGbWp6F4+Q48SVe6LaLHSdNi7jfot7XcCcgEgNydzjHk0uiE9+AXLL5hluDsZUhMtnVdM/GCDuVmJHzaLm7onEFBl56WhTS/6vu8DbIbA165ks2BuDntBA0OPKCZPMNFdA8qNPr4jj1B/MRkrNn3KDMrw4+gbubl3dokbtxmX0mGrDiClhje85+GhtZp6YW2HmZ97yCzjw/t9XhdgXrrhvlDMMqnPDyNxDPHZtIFmsBsw5jVrFjT2loCB154NCJB/KmDZFk78LK6nQLGH7BNr9yIrxbC5y2HYP3QcZhPAa61ip2lg11qP8NNg0hA96pwz3JTfw6nlLMn6QXdc10yJwWgbejkcfiQOgwbNPTfgpzdNJvESDQDWnCuGFs+GwqhIjON2KY0d5/R93wGCJXSCnG+jMVCqWmvqa6/QEyLehc9rZqEFlifwn3NiygfurxcMgiTWilW/93psSpxzxTJ4SHrm1tIEH8c0NlrOxUd/j/FY497mL4AMqx/2cZRxj8+yDxhqrDHbhssHLpmh68PZBHeb10zuK5ayhTG5/j8+PpJZZUj9OLdHh5/j6jjsOM5ma3cmK2Eky/V02siK1TxG6CjjWSJgHUDIsV73WUvAY36RzfQdwCPmrkC2HUcfqP5kaPM86z42ZO3Io/DDUFbft808nj8YVAMKwIeMA2V73P1ImCIWABPDoLxnKjxtnJlB/dj9LZSRnMScSAaS1Hl6OeOMED2ARgK2IjMISpioBRTrPRCgomzlvODg/ETdi3bbd4KpdAQAQLbVfuk7GHVXx9CrJU89mWJ4Akb2pyco3yNAcn5PU0xR84YynhnstmLUPoG0LzWnkgllCHx76Hf57KsMduhOGTFlvw2Z0WcbcNGQi5hgm2JgxHhL7BiDNi+jJCUlS9BNADfy79CQ6nl/GBhB5ZqrvmpRvojmpHwpIztlX+asvbH2MM5dvpMfvX0ngNwz6Tr7DPOqa/d9druVXK1LI5w2XCyqAgkqmRQJRCWlYuGYJegdIdm0HDte67quiMrJ+fnUEUQe0pGYo35HeyASUqSFY7vpsIafM8+/EzsJJRmqoMj1t7TfEciNkFkwdoZxTWhu4I4vyH5h3wQpAsGVgxhaXWiGTzUNcy60DjroFV6agHcb7lQ9XirlAckINvmRcEJGRwXuOXkyke0aE/d9NKeeDBQ329gENRkMFCgCAiTd6+vhrTHjvUKcKTTnYrtvslI4bKQB2ti4HqIGgv2pSTY0QkCvG6qztMbhZd6R3IbDoO77hPwBgrDmUTUGfM6PHxgxmNSYsHUQAeCEVbJ1hq7/bKTXyax9/ny97nbtESHGbnSyTxhO4n9rM3p//OMfC9QSdPSw7cyQDo0H34NOBVSrnztrEaHLeI/VDDsb2RUic9r0YnkRQn9kePS9f2hgCSqrZeSi3p3v3T532N6nd0xnoBIL5MgReO/SjgK16cx0DGAJYGp8AZ2hR/8YH8Ve0nsnmK9waQIg8xfGFeCfoUUyF1PHY87Q0JdDl33pErpkPncxjGSc3NvcPmBJgXJGdhtPhuI/5vWYwxxTyge4jitJcWQyJ44uUcYom9D7nlEL2AEu0uaDmSEqU4Rzx+iA+6nuwDEyHCaqZEk6H5ui+Xrov6MvYt5WIqPTpkUfHEb9hDg7E9hDtn0eAbGJx/VQLFKfl7Q9QFZ32EemERG13OQJNOQATT47nY5q9rQp8bcBHlKa2+5yjt3zcwkGxTeWr2KZKAkodi5Jldis/dEfM+0MWbhg3OLe9kheRuv3Ub8L0BIJXow4cX+p+evHFnF9KEY5hMUucq21aNL+ic3iXBKEHjUcIYErq9sc4FcOfTrYdCDdA6GGLcnruhaw67aLZJTBa26YGUa30cYkukg+j3mUeJVASaSkGLCIeJAYA8JJGRIgr9smRzgiYagOwCXpQXDH/pwYlUfzvk+duXVsQuERUSDHX0YkfD2+rxIVEUZUDWLfMTHwRN5O5IJrtO7r8dleneeHfTAZeUXYpXLs5TCvfC4Z8XmdH49r6HX2Rs4t3u9KhpZEHedbgdmhDxtFx6DkWCPea85ZDjPXTjzDb2dzfx8gVw4bs9OA2tdXDmaGSBpg9RSJ33dUF5iiwJ1ZxpayhAyVDsOjg12AjR1Zhu6QFFebbmxHeh+RRKCQAM0epUx8W3nq8Vgxy+77DjZjRehuw3OiopjYkhQAFTrlM1Fv4ukpkv3actgKerkduHZm6oZh7aeBAlDZoiyZJCLFzrC/1jrJRp+fn7lokNosVLiTrMWdm3dnmLihMwQeP8/hzQQNbpZ87w5SRUKbSaH8K5ntYiFV8HXfWXXgsFUPNjR+8HBq2PcE0dycuAFzgwKAXz4+QMa3g1sy8QDK8+zG705WjkC4Erp2lC/SgSjBxAWNxsbn8/cs+nuvCiUBOBpaxAbfjR0NzC0hR+DmLmNEQqNEGGnfdyURbqfnfABwlLM5Gt7OcFboaBy5Dzcnfp4MDEuRlRwg15yIY9+vAgtbwuHY7j+dz2is6t47k1NCb3zLCZmXIyhSwLrWmh4GnCCvjy+/x7I+XJ8cAx8KTH2w+WTrevJFf26CG0YekGNNVrQDZd+xYZZWM7/fNwBuuJQauZ8SVwVYs9WcaBuueGSVYx5Qwz69+V4qMH93Yr1dA5mE0p2aKBE2RIAM6TMp8WTZG4bsE1J1rzEii74sGKOShnAc+LyN6XkkBKWMzcZGlBE7Wem0md0B4Xt1lpvjrTDALXSwtiIMm+touySTGRt9JfEwQuL4sd80MsR5b1aQ6M5DRRXfEhVDDkEm+g0s+ZEgKTwqFMjz/gHCTxk5rqX5Jhco8MV1IU/g2YEvzGD+CsYbO5OMI4HJUpu87Y7f5xp9r8yzPZjzKDsmVQmC7/4xZ0mtvDk49+tVjHvJ7szgnglNeOZ5uLOyRTrilNFogOt7b7xsY+BISAwp2clye0qJStqQrk8m00l5wIMcaFUiPJPbRQRq53fcS3/2bxlPadwDuLb/JkA+9qFdowHmwj0E0kCWTU1N7QzuPYBuPk9ev8+RmhcjrvEA5M0J6fe1nHf87xIH5fr0fZLMat7Xe57+POtUIq+CkVD89vb7ALnRs2AVgzEGxscnAMX2UYlmzjItOjOTP74eySZem9HZ+NpmkeG6vRxQh2UtP2pzbEfy2/CTrEQW1u5VLEMPx1vS8R1EARaVVWxlItuusPFaKwBYKz1FY8mEHg4os6Y7y8rGyfBg7bIVM5eyCWHoK0PCZJXuTOYBUFn7IvJDEk6x2Y6qJTlVQ9uZRox1/GAe8g4abD7P2mXUCe5ouDqTG8Z/F8NHFnmt9dAt3gnYgGPYCF4IEHo1h8iw9R/6kBsyAPi9CpjQsHXN6N47yrC05JHOaA7RYplnYzs6EOVoRTjRnhta9v963c/NrQHRbsxXSif435rgvuanO+ZIYOCKP/zhDzXmdS09bEDeBADK+PC+ZBaKNRI5Mog0yh9ZFoagus9Mvg/r7NJId3aV6yA+fzSglJFArEqMDZf6OVkxzmWGus3sVEZo/UgWhHOjbAP7ITfDa0RIcd+ZMJPX/cyqF3HhZK7IlrjCF/V+WYlDD4NSDqr20NxTM055SF/L7GfO4Z7QWbZBpbSqfZy2O8Z1Re1WP3IGVS17QMedv+92geMRf1OHa4fd8gPQOb9YCxkmMFPojDkxVWsD60ww54b4mYPI7GwmCocTFSF+M6vQvThSP3tIhOrbZhu41rjmOwsrItgW9VaZy3H671TsoWNMe8L63gQbnF8P55ybt50xJGgqBwNP5pd2k31Dm93XegBKlB431q1j77yvnBJ5MVWP1rITDJbMZdesd6aX/S4yKhFv4EggogpCft61yJBwHlAM3BBNRt7re1y31S8SkAudtctQNbJ+cdVj3cf2r7Vy7kuBKz5DrHeCNsloHOdaPPfHnCX9MPgpp9XWGHCkBQX4LljQAAAgAElEQVSk870EeKxDGafqSgedvYlIlCjEkWtwTTwAtZzoS9mO5tTw776G+/0C3CdWiSUZ3wGQOXucIEHk5bPwuR/gOT8vIlEQuNmpbu85R2ut4BCCAEqvH+NC+c1Z51Ue0wMMO4F5053/2KO/3n4fIDezGAMoSojAV4jFsQ23bfgKH7KKuHtUOgCA115Y0xFlcPRMjOH4Wndsphwoddz3xvz8OCGuOwpSm1ke9BCT6d4BGjC0GN0BwRSyQCd7XByY46MA+JBIkuPBA+zqOSeQISJO3gNMeOhCJHaJ35hV7xEFioFn9moHu4/J6Q5mAasqdmojOxPBa5DdYd8ViPdTgJzJI8AJmXKjGVdOzJZMAjuhcLLGBB6VIZ2G6dXKWcmOZD8+Q7z7Aahk9Pnsq7Gk63UXq9yBNJ+JBpP3LnCaq+ZmokwuNuDobqlNCwA7TjJCXo+gbQuKTaMDdMprpU5cpcKuPMDAAFyfHzEW9w3LTeB2C6YxQ8+saNDD3LEhHHZ9e+irX9lPX19fx2nbqfXzU4Kqt9BUHsNlr5V1YVcDJJ4RE6lqCnwOlwvLDoCuKEJqyWBRl5dzjk4PQ+s6Z+kMLR0yMkSmJ6rBP6HnO7Vpu2SEALZK0slhf6o8nQBqhxHiaq1NLO9NXSUTIaaG3KA0e+KQKTU3/nB9NB1kY1r9zD8madAeMBLC2rZdMsVDRwbtW/sdHeNezaI7kLv9u8aZjmV/nuZsc43JGCWBAKLiTM2BFvGgZGK1Ne/uuC3HsTlPBjS7x34/5Q3FvJyYkwEfpYnoKLtJZPbjbGQhizr2p0sqFg4AqPJqjbgImc0T6LtH1RrmVFT0BY6vvQpQcR09bC/7PkHq3lnbu/2+ImV+KjuwETBe11UyJdpcuGPihll/VuptVwLS41wRgI00oyU3SPvF6GfX5XIeqUZoG7biACY/CbDR93GAB8EqLNaaaeaatLVaAFS1ZHFV77vZaF67yJgEOdSqM5rG0Dcb1wVlHV3nyeuzCgg8ks2pqWb9Vkb85DpaU2psy7HsDkNjxc0sSy6eA4F68mr1eY4vnQV7Q2LvBFZ3TN6JoYoqN1ZX0OZV28NpK0yQsLfdeyg0y83R/gJNH6xaiZxkp4kJehIwbUJ37kcDqHRs9IoDV2S0urkpTRueUWi0aEiy/7Ev/3aY+zsBuZLGqzEQiMxomQOqEz6jbtuy+FxpP/SCu0AWwOSXqmt4H92dyDmg4PP6JdlBha07NIIWIFrmYaEIwKCSp4EofGqK2BNgZZb1uCYgEWYPJkdhEJiPZDpPCRheW7IwNWuHxmYcGY33NgAT4htmL1AryFB8GW9vEgBtxgQxIW1lMey9I1mlsTVkKYv5biwra9wZIiGE4A8W4VWWY6NR4fe68SPbw1ImBNPvLEeVDmOyoG3g4r3tAN8Wauzg7uPjo+5bQC43h2oNfFNTyY2Q/VAeqDeRvrfkEd/QDLeReeGYkCnnNUbON82Ehvcwadct903l6w6QjhmAnRszw23Fau39YJvJyLkcRqAY2dRMs4A/JJzEynz3Z4jb3WHz6OC6Q9ABANcTASQ3EwJYFl6v8c53oeRC5JSJQc4zGju1ZJA8IjwiwTapob77zkwCR5YAoGRADzkAfSV9suwjgRyv+8qoCzdQ6sO4Xkab09Sx1uaV7BcrhHQGLZ7xWY/ybFZeJwRNHfCh+NBZ4b+KMNQaP+xwVdvg/MoyfL2WMfu/M8fvjGYHux2wRSWTU4eY42RART84HtTisaILPMD7nPP8LEEDM6Z3OlxR4snABF3NsaIDQOBc6xM7ksH4nIJi49HmKnMCCAr4/QBQMzSvzammfZp6gPudrO3ar3AimzafCb59DDobx/GjDeUYDBFMOwxxRQWkMVcJoqt/NSKc2wfcT0SOEZeTSGdHwpF2u5/6JX1u5l5R69+ejqQZYNDUBk+Yn3c3Wwk8F1jWLfrvhOMZleGz7r1xubTTvNIx15B2kQxixj6BKJ1ZvfRhP+vgArNaf6xQwRM9K0E8x0NwZbShsf8jw+nNSe6RF83olww9kpPGsKrqYXJ/Mh/eWdbupP6sckC3c736Ccfw8Xw51tDIIeL86bKI6Mu21pPpPaQYqpaxbDom0pwCnDJ/vX50e/94t1mkUNieYx9ZZWEgokzsCxfBBEmTdF6zvvkQwZSJDz1l2n5r+52AXMB9RGhLedxmVDXoCQicdDxsQRzY7GNVeJ6KMhBh3zGfgmcmK917BeuV4cTlljR8MkuZwRuhDm/PmDqhfbRGtfHnd64EcXzutVaxl+UVAwcA7MZ45p9HhYJUsK1lWPv12Ehjc3xudo/NajxLeJS4n4ZG6FWhFsw1DgChfhWa8oF9QndzznOMci6QnmDBZ+yLu5+mxWe5kxmrzZfZ/AniOOHZJ6ONY/dsZ9sQqDXsYUPgsDFkiFitwN1PKL8tXAKZLudgQgrZRLJrNIoMc0Vi0qyatcWcr31YJDuMImv1xhyZZUAIJmi8yXhy4+SzcTPuGxMNOu/VwQudgQ56HozH2kdT6w4J9BB1o/mzHHNulPGzOCVq+wvXeCYj0Lh2UJoLo/4pInmCWzh+a+UctxNe7UcFG0GRoNYW+4HyIP53Xx9mp7RaAYqhpeebeSBHl4B0MN/7noCgR5E+5iwg3T/DOcpxOYxeylLmAFY6a22/q3A+bd0bKO0l8USiBiX0yGIA1By5fRdLuPepK3vYtqfEgiytiLSKAlkKK0sSMjxNp4EMeX/GfvgIx5pApVdOOZndEXbnqL7bE1WNwvbBzZdzyrHlnOnawWNX+awN1CV71e1GtzsFip32sQGVFsngM3DddsdQ23vTcYj591aTVB08apYAj+++YA8ipoBTalopK+DvzU75zJJCZe1eoyPuh/zoRATHpI4UB52cloyIcALj5LKoXhQ/j+dhRJR7CpPTdtqZLlup+b2PA8l5CEQUpzuGTMpmSS4AmFYcdvS4HUe238f3HQ7ExU+fKgn1ngyjS8wj0yMP0TkeDGwfi9537+3Xfl7X8Z9/5iElaBEAMwtAqUfrzc93J6PbDD6HtbnOKBvkMLTV99bWJt9xvBEIcoiM0D5oPUPvH8dGHELqUNgP40+7WRHlRhBsOTKS39p+FyDXG9hgjdxa0FxYnhtS0wbSUNNz0vz51qzvZgO69ykfQ7BnVkdnckLsvQP0AuXNRXkUMh30ck/mIQAYNm58VQiFodBacLkRkoEtDZIexq0MyRj41FYuI+c5k3OkTajTzgS5mxyBzCr76L1Ul2bo4WuvSFpqHjb7mxsJr1fVKa4rkjRa4X2CAbcwoAVA5Zn8xda9PhrLSjhLQ1Ylv+5T27RfgyAtDL+fjVJPCbXuCVef0AlhV2ZodOrAjReAA8BdpJLdgs04XvRaK6UEp27ty3aeTX7GA8ApxZS3XGbY4+i3CBDFvTTofWNT1Tgs4Zp4LavEjpJkCCqhiolWoU88BuzM2Zz3ufGPa1b4fUgAtJjjo8Aw/9syw1xzXpOx1NoAQ0P/IYb12hna8ioz5AkgHywMx1PitEAyfS5x+hOjCi5RuYD1ok0j1HZJ6L57Itg78KzNmABx6JF3WGwQkWiUiXXpdHxKJrgwKQ+IY8Eti8rTEcs1HvU+D9tajkSVsLJyfOKZngCM32GEiCF8nnfP3qokzwaoaj3hOBS1sfmR/Uw9x3kyKa4DwvfEWI4TwRGrFhQYkbOBEuhGVrQ9N9r8M1WLsbZ8ps5OQyJqw6OBTeywT/tEwkregCuSoLq+Nec3bVm3Y1z7BC4PEsVTjyl0DLSkRD1KxPlUkTUMiJ6KC7we12mUvWo/p9wsI0TuURZtCiKJKw+WKPCdz62wGgPKVgqsqmI1h4D3I/vIwzjYb96cm1h/DfykLKqPvUKws8qKN3Li3lnndgfbS1DO95tzFiNL4MpjX0lGEPjCItxfCeD5DCPHm1Ub6HhUdKuXo6txD3lLJV4RAOq5hg/F/nrV+/FwBs6vH9bSG/YccrSpbD1aW/MKeKxHtg5m+57Zv/sAzEkCVS6JnOsy0sE5/64J7mCSf0bDGZLlziijKHlD2t7+7CWFSOD/A9DlvWFQ8apf7i6lfx6QjKJklFe8oilRTaIl1Q1NGShlUX/eUejtdwFyRQIEXIjizWQXaZA50WcUDotTtSTDXbl52tpwzDLgnsL7lWGKMAIxeL/Mq3Qpww0yYtHdOFm8Y0QSwr5ZBmNAcbJhQ74ATGT28h2VHOTej5OJvr6+Hp4h/+Zmy7PCmX29PBY52qZdoU2MR2i/h5uBHxke/k5bKSn+3NaG3wsfOmrDeTAsbqUxVaAYvfu+8XW/8PJ2AERb1FGK6OhZyVQXGM16mqceYLA2w/OUtaom4VWixTyO9/MsZdYZXAKIiaPTmpAT4nR/fB6qlVywkvVdOMkLKh9wO6erKFCnmlE6YZYZ+55h7DS8996VmMb5+Vp3FFqXqMJQpz6lUZoWwC2ydrMwuSSrDJThv/eORKE7qosQUFSYuGX8v1dBKDaqAFeG5fJ9WNXC07Aw2YOOXTSD+/1I7OAz/jIv+EaC6zh+2TY3MqtQVo8EfEzWQWwMeFw6P3PDNYADN/Jfsh4j3/HTFWM7DB9Zpm0AOjDGdRIOQSbjAA73WLNkfNxDnjASgHmbe0xkLJCE7rCc8DjX3+f1UaHZKh9WY7geYcn+BxqVJm4e+5wVTqJGshdL3TfKK8N+75sYr18VTzjvk7UTR+mKkXPsZJIfdpL2RZFRjXSidI5kIe2wPzjafr7vyLX4LtPqoeDoxyMdGSKQBLcQg6x8o9zTlp1T42KPABZYAzqTbNp6J5glcFu26z2HA/o67KSZYXL/sTy8xY++s6rSyDMZKOymYS8vDXyPcMW/zxHbMQ7rJMIF2k4HKLT0SHZO0yEAeCx4d5hybdNOmp0QM57hcsp8KN+JhzzyLTqs9XkkmMrxr6jfTvA7R1XAkdHmTQPy3Af2vTIKssruS1YfOMBOqsJJ15KGNjPn1OoRThRDq8BD1vAIx+ceLb7Bmq81Num0db214zCgg8m0BIFl1xvoTJxS8+0NxL6PQwevD9DYxoy2haPxzsB2acBJxtJHNIFgte4znlCP48Pr5Q+zD98ApEdZN+06YyAOsMi9h/fs13MBAlzFaY1x2E9KM8YF6AeAMy9dRkTfFRjwsn+0mTpidOWxL/3l9rsAuQAgOh9hzCgwHwkqoUGKEi8dtMAMtu7U2qTxtwPqDI5xMbEgjAk3SZ0S4XbkAQ04YaQAZgEEOVnmR4Tasa0Yup0HT4gdLUwYogXY0Ye+U//dk2cju/Bad4ZeYqItO/IFht16WLKyXP2wpbZRWlxuOtQnlp4SDsyBP71exWARgMXJWrExVsF0ggM5Ye64bzwAN4It8cx8bnpw3Hx0MjPZoySY7QxdnbqfEWbdtQnM+VEOgbvH4QCtHwn8CQJ68hh1kdwQYNRvHUeABoOhw43QIN/JyPeMfSBDjkMrvM3Gow1nY42nBoDfGVFgX+ycn18etRwfYNwYxbDDzmYi3TlUxOsoShlaEpKJUx/UcwPku3NTBujpn6QESm6WWekL3/90trEDGpfYiHSOrCigWHQS8vheBfLAirj/V6v0wLnTWd1rRJ1sOiKxFnYBPkYpis2nM5wF3jtDAgBynX5TjeogKxnE1deaUPbg55TAfP73fijnVNDmULApnAOzrdc/XB/H8WobSd/wGLEii9GZpANmZ0WLuoPAfzPsqiOYNNMjZyK45fqm09LPke/gsMC1NylA2kXFkS5RK0mpUB2ggaOh5nP1cT5SigjR95Jy2yVKnzn/+9Qs7iFLghey33RKNCMT1PlfoviY13kPoEqrkaXj4SqsX9wdkV4JhXWx3T2PRrcKbRsOG98dBgLSuMZTi8g/rCNrzQ6Q+e+Rv6jwcEL4BBmca521vr1FOdY52YpSn1gzZKVRNrtAOmI/0RlM2hAF1i6tZABiqwTBTt68HuX60rGysBGXADrOu3G+EGxTi15R3LTtxVbmaYC9nCPnaTktmadjEodKnNwFlFNlOOtDcGRbBH3v7C3nQPzj5/Ihrttfa4+qNnjKUWiTBYeN9bf1SQfk3Y5wTvV7/Bpz3DXA/W/2Dxnjcjzw/Cyfz97XYhKLhU2aoynqxUbzWu6OOtLaNaIYTcMbz2TYiHHc/1oFxH5PIBcGG0c3M2XiGoI5T+H42zZeexVDQkMWgxET7U/7rkz9MEInKxhoBxzsqPanOrO6wvEoDMcTopblvm84dp0nTkMz9IJgxDnhrUyVZAkiuEbmKZ7lQHiNqaN0lmReqPsiQ0k2ht+N8FCyqGAywqywMiQ22ksH/HU0iQTLZOBghs/rwnYrgPbx8fEs55QgicxYhUmSyeX13ENqcnnWy1zUBA98fn7WYmR4/PUKdvZqgJmTkX3LDZG1EZlIpnM8krw0N8KaO6r4HPPBGEd29QkR8jSkq+l9p55kBoJYskZ774eurViuYnJQ94sNdFeWLtIBWmsFc53fu+R44j3Eznq1kv1QkgDV2MiNYeH9OIWOZXSqFnCy6f24xO6UkHlhWayPFPkz7Fnv3cJcNEqsicrNhQXdR7KEfK/a3OVpUFklgM4Tr8+/CcrJPDPEKSOShII9JQjdUKwCU3xeXktESvvHvub8odPxAK5CZ+swRj1Za8uTdQkd3PlszHOUlKAOs5CjKWf/zNa3rF5hSOCdZapYRuskJu16nj42bEMUQwmgotJLjAken61Qrx/GjhEMgpTHtTtrDIm6mS3szLBwhCsPEFMA0Dy8Q7w5VxHFuPLkSnUemWzJLB8AR7Yw5vJJ2gKOxIqONG1dzSEnCD467l6Ht1hvETCBad9x8MLRmge7/8oIW7d7grQhDIcvVuFgIjRBaOYe5Lr31vellUcAOq7Z2t/cq5+KoWOUAWeOv9vSmmuOh73i3D/AzAHEQR+MRnFdMrJpefonmf+a7zleff1wnXNuPCIh6YDf28tuF5C0U8aRz/had819lm+jE/per9ZF8iRBhs7JazT2sz1nRedYDQQn1M/W5TbvzjPbO5Ak+9ibv/2+2/1+n8cXmrSi20fwOeUpgXgHse9294dr/OTvxx8/ZfLenWA+s+IkbLpLgeQ+H/i3NXtvFmw+NNZQB/Klj9aI6IBlYx2Qn3kdf6b9bkDusn0SFtyx1wsvz5IlSAAxFJgDt21cY0bH5e9pVD7y5CjLwuLAWWRBlyfbluUpXhbghSWEOHjc+HhtVQ22Wc6i9G3YECxEqHlPFvsPvZw6IuQ0UmeF4zF2TSILdYcmMU/uGidxCnfLgvcTeqBOjAulLxgDsCTOGH/3Aq/rwhDFhdDUXXn/C1rhtD7Rp8ZJXsdB+LE4fq+ZJw4MjQ1s4sla18Yz8sxqP4uvn5bFdxn5ObNn+bEq6I8A4h7nKFfJmmJAJZLFhoRxA05otvofgrHIJGttGL6tWFwa1yFSbF8cuHD6woBKitRuwMwq0XBcV8gBel/QIZPDQlWt4sbSMaMXCINxjajxyKS7Yss7swegl0Uic1TMSv78ox0pzLHgOulM46OsmpzTi8rh0Ge1A1YkYD8x9H3pBZWPuk+/N/ubRrGSvSyK/sPjhLhUOoak4Qrwt/f9MKQsDyZDiwlno00ogEvnVQLAEbgR7PJzPWdAstbmlScSioxTpklaTdUx4Omcsh8IkD8yk1wxaoO/xjmRjPeJtXaYI66nd7172JQZR9Nq2/gIcOTIpbqukQmZZF/ppPW62fXs6ezJPhIQ7j1Tx9GuNyBXAKz1twytknuajstwK413aWctTlHyvTICh8fBDGf+DPB/xYI7pUWn72gfHsl8Ob/rd1kKaqZ0JA50eZM/AVUK63H8aAK5Hhoni04t8GPejR9BCqvJdGfquq6S8hEEDJE6IXDW+KDejzrmc/0s3ZSgvksW6JSzwk7NP5FYRzyBj+t+aEnAqq4rUPuryDmynvkp7iHLGeN5YiClhwHKvfYqRqpKx8znaXtfrzzAMpJnjs9ywt0j2YpjzsYIk+EAuHfgSD1zb93W9Gd7JxvNDKDt9h9tJvwwq4Xh+nPwO/2iKj+sr58B8W5fu52o53rMjWcjiIWfCFc8WntWFYx0IgQhucF66vE5XztYh0qC1nactCS506I/IlK5IKITkCs0rr+x/W5A7tTIKo6OTuH5GpB5OogGGSrApnzhGE6kJ/0hA7rIjO03rz4B0yuZONLmmYgiIhXKkWWYzbsyAwbD9yohS1DH0ANY1loQ2xgQ3PbUrXbmkwBtrYXVtJpxio1AXq/6Lmv0ktUswCTywwbHhUsQRVase/Gv1ws2FV9Z+gVIthot7JAMxM6SVrwXN+7X6xz1a1mI3gRV5s38ebpZ1w+HUTqL88gN0pjIAePdsPWs3znDyZmIzWSMEQ5LMQaxEUbyWEgwrvmZofzjuBiCGd6dNWqLkPVAbe3q/wLT2R+v16skGNBg4dxaso+FIzEkdH49rBcA+MZrBYDvR+fConZmVQjIGqqOAEuUOhBIkz1jSDRO4juMEEu5ccxvZ81ILUajEmv2W0H3xu5f1Luq1Pux/ue9dyWlzQacS+ua/XmvLwhW6LDtlOQCznG4nLME2rERGURTm5XOiLrC7nN6WYFLIFi1NNR0jh61QHutR/cKvcv2cmS7hpZgo9abpEwnaziOwROfTgi1HMNkyu+94ZqZxTNKLzGKxPF8PyzF15Hv9HVVTtE4zKTU8cGH5aXTzwS2AgR25nxfawE2cIiHXBOcU0gGsSIpesorVWWZYslwwpNyTl5zdSyPyggKRP1mGfAxYcoKKN12pHxM3xhGrndfEI8TucwXZurlSaDQrimeoKZvwrE+9CFV6SwhKxj0uUpH7b7vk3xY19m4U7tMvTX7nf2698ZwyaTFY+v5b7LhW/IkPmlMo3kc/DAAH6zQEKUOt0U+AAiua/4EWEdWrhiSpfhA+V6sA0knJ+zBzgNcdj1z9Ef0+dSwvwbWKD5azc7k8hmYa0NJTwAmf6xH7mUsuVdMYIuwFmhqjP11NUmKGe4ESB/JzDPJsUeYtlscX98AKNdIyZwgNW9q/bfoxqNJs+H53bKX+qM8wQkCEzQe2hf1+wC7x7npz/dwiNt1e3v/Hcemv8sDsHdmmft2RsY9ncd32YVDzwlvBO60oW9MtYgEWJWzj9AhjGttYMcJhIdI2nBbJSX5Le13AnJZaYAb3IU5Q5RMj7PqbjJZIbUZxtDNDs2Gs0ZcdjCatiOyZHNi5B54JvcpORQn82yYALdFQlltijzdC8cDqTCjBZhakjV3x7PUSQd5NNwBSk54ZlwzDEuGAi8dZfBoIoNNOqxX9NMB048Qhj4BJpBe9uuu4wkNz0L1P2NdyvDJ0eGV0cr/fmRgyvM5GG76IXQtzainwaskqn3Y/c5E8rMlLeAztRAwDbHOk0l+koC8ABlZY6DpkhoLcCfIdHlL1lFqW71kBCuN+3uiYd/8u1fNpjqj5FM+Q9QKjTqPy6xOi6InTaZX9gGBqvoA8FA9NWhZbYJ9nyCNMgWOh6ri9XoF018VFZ7GtAMsjuvndRWg1Y9Z8/Le+5GJ30ProUs2fHx8BAPKvmd/rpURhzPPY2OKg1L42clSXUKmtx36AbJDqGhAZ5PR/u5OVH/3DubrHbnptffpWsBLnu/a13/BHD9hdxM6djyjXR9rkHNu1H5zKomQvSsdbgPkFQnKOcnIT8mquMHl3Om2w3IOurd6o3s/+quDfpIHvYQR62HWGu3HII8RlVh8gjjMkes9gX4wN96ARauMkH1JB1whcJOWCHgAZYE1PMPQp5LMAa4EPJzrO9n3znb2SEPNS38e/833DIlGjum2h/6Tz0C2s8stKq+gaa9jDvQ5l+NgEg6Ak1l2LETOCdl7/k22zCwkcdTjRuTR4HL6jYnH1V9vc8bTkSrA6b0kHx7zvq+Z874xh6oKTZIOHNsTCfMCPlwfFT2QYJhdTiTiwRY+mlXyINcHQfPzY/54p37NcoSanT+Exemr2hsbIK/56Kg12OUdQNsPGYEl2m0Vi3p7B7LdXj+u+2v9Yr/+/d4X70llani8mykOIE9SoRhxPMGwwEKby/fthIMfp5ISTwAAg8H+K07Fn2m/E5DrmFOhIzdAzwzqK3V/mUTl7sHg5obJbHWyF8ARzQMhZejGXpqB5tGwConkA2vJNK6Ah9c3xogDIFQAy3PS7RTer0L1CUQBYPpTL/czowZEJno/ZpEssu6zGa5AaBWqvBK48L323hh6JdA6C98lGEZbGx/zqg3ogmL98StDzcEA7r1x318noSwndgEFS6bdTmjS7NSP5Xvde8cxqAk2V7IZJ7HtaBtp1HhKXQdC4l66atmsefk8iYitgGsylUMkD6rQB6CjIwMLJ2bBaswob+CmqckOMzO8DLdfuPQXTL3i9CbaH8RG+tFC7+hj0UAiW2fhlvFoSzuAJjfmmf3H5BlmlBPc9k0RU0t/uffGHF79xs9TB82EKCjr7B7JDJ/1HRTWpt6dKBxZxNSodhDM9zlIYaRRNz3fmR9XrY0ChwSUyfT3k+zIQPeQ5+d1VcQCiHAx0gGwtR7GrQ7wINjJ/16vOzJ/VetdO4NNRq6z0tyoYw6n05IOYzhET8fy3PesH86b7VKsf3cKjzRC8lAW1pyUYmpFpBI9g2WKjYVj6ONIjsqB4wbWJBjuji3HOaWGeCbzzFq7HfCH8/gsM0VmtTuuHAMmlsZmpqXzY53MRd3fjsN+LslkuNU34Rkj06s5OGo+MhkunNSsVOKnJivlJz2fgGNKZ7XrD/s4x1icUpRco0z26g44wVOPGAzPI8bz/bst07f/phMzxFP69jyYgw4Nx3L5FwwT8MxBmSPzSj5KVtBJENhJlitWn+ObmmS+j8gp7ShyGG/KymLOHTKnWMVyxJ/JlwVOr+cBQVX72q0cBgKjqXpqmmc4u2RlFjLG2G9OJOMOiFUAACAASURBVOKdqIkxWlCcQ1IYuaVE4r38W41HI9KqLqy+JWf1CkX+ZNq97fHbn/vXz4ilMycPIObvuv14B3v95z9d8+3a4HdbJLcD3od9z/dU0ZOoqHiAX3HAeIx5stbsp+7oOhSsuMBIWU/ejQhd4jSWKJWI+pRNkdlqT/zl9rsAuQKBLYffGa6O3LosSE+djAGy4thMEShWAamNjZuhGeTnVbDEC6C5R429udPDTBaL52HXYG80FvLGywRf2yorFfeGfn5GgWs3rPtoKgFgzkiQ4wlcnNzAASa1QeLoh3jE6yWKL1gBqL03bK1k5QAkO9AnNO9z6VULQ3awZJ09XbZxWxxpHElUh/kc46qJ3fWQZoat1EyHd8X79kUkDozPK8PWKA1vD7320j+wc6jDyR5WfK2vMpQiGWbNUGjfXMrL1wOGmLClH9dhrRBG8Wqs5b13lO7K5COYxXzQA6hoGK/rwnVduN1w44V7v7DsxhjX8TjJCvRM1zQQrwTPccyjlCPDeqw8DAM42t/P68oSO8Fs/OHzEy4xN1gdJDrM8DkylH/NmqMrN6xXalE5rtzwGUKKDW9V7VkAlVzXmT65ZlRcyHXCcFsHi2TtOttJLSydUDWvkm13O2uewIAbDjT04iFN6qzuqYMsQ2vjJfPsuXkxWbKM9DgZ6YcRfpZVIwPhIxxaSmZUtQ54IXCppCqV0q4Wa9Puxc8Wi+ItMiLHsLMvu5NXbCyZDSXQNVAbvmw/AN/j+NSYCO3e5zkWrNZjOWDIwvf7nKjIfmKSVj9Oed884jnmyAdPTswwJvuhjkTFEziUw5zAyUSBgax1GzpdSZt05T1uazVsJTbD3aQDBEVDNErYZd/dfmqpx2dPXoQkSI55eByEd7lIAVmgNKK9/ztI5TwrJ1G1KnhQ1vR6vcCTpLiXuAR72vvZPEDmV34GiGNWu/P0y/gM8LwVZieJegTyKHDK6NiyqEHs6hAZ2ecphfDYeyciT4U2lvfqUg2yrkUOZT9FdZ2g532c0ndkTaNKgzZi6pA6XSPNxC2W1lt4rqWytXyGFgFhNKWPw/Y46lgkJIYjQVs/avoxjimP4nPIQMw7NIlPZ1wJHNepNR9aRisnT/NFTwL32ce749Xf6x388hChiFy165j8cE3+TQ1tzcGf1NzvWmT+e2ngM4jAEUAXzV7y8AjmRj3AfQJnf8z9vLacZMUfyCC34HrMg2xEyhQyauPLs/bEb2u/C5BLPYq3UkIU55OJWssgGvUwbUWIhkfAuQmkDXBl0ObAsrTM115Y4nWa2dfXV7B164bC8amKgWAjBQtD4ljdifRuPY4ZvlOnWiU4UjspIti3pSf94wTuzxb/PkdDvl6vOCErB/Jm/dzcxCMELbWwHl5zhpgozo5woAKumHKK+Zfe0v1xCltMsOfZ6Nzg2fh58zhEo9cA9dxwbe3aOIcIfvnlF4hExYYnqwaI0FsfxfBM0ahWkZsy2TCCNy4EyhjkmmUYesJcbML3Q87AjOExrgJ1xZAk0GHVBG7wVSnBzvGYAUIUvu8H48Ns3/ewPIHOV576s5JhNLNMbMnxwlXvFhugHxYs+2HLybTnXO+JkhXtSC3xTL2oiNQxmcUm6DGAsSGd/2aosQy+tdJFolGLU45ms4ypnwhAHZOMqPIQ80ezusDJ8Gb/KU6dzgnBhj/Y01rTjdUhq4z2vDSun6nLG2NUndG+Hkvn20KcTKDk37wm5wRlFS9jKPnIZgwno7oAijVd5xtzyCLyXMtk0ntEqNaKA7ayusuK8f3KSBRbOWY4pfYeNS39VCP40AmFHF1ojputXRs1Necc92W7xrPbjTOnwilar/swP/oMK5czkddl1CI2fy1bqvFCgL0SnEQUZiAPeMHIgyJS1wzUsxkBFpDRNy+njpKKtQx/+tOf6rPUvPO4eIU8kpR58AFZWFYx4P34brTllFvRTvK4ZrL1tPFO8sSYxGd1UEbUjD1RL8od9t4PEOzueC2Lknyyag0s1vVOYF+2bQ5cY+ZcXidxOJ87rnfDJY8xHifRjfOM62anHGnnmB6wGbbobmUCixn2IHaY2EsnmLYvHuStFrp71cEVf8oOq8xdi/CJCJaiEvfKQRXKpAR7BbnxMa/H4SzujsmIbxJse+9c66E57eWtejlN5glhHPvfk81irzmREToQfX/pTG1hIJUTaTEv0oe2rsCrPvFGt3nmDUDnoSmUXNT6tyYnyjGfkATw2bcW/y7soxIyGTm272G3mmMS97WQOuCs/8AqKaWzcAdjbQezG88T5MWClyb/t7bfBcgFUCemMIzAQWLhd254NgQDUXDem4zB3R/AkqwCz3B+ZGZmfVNVBbmte31hW2iQ5szJtk6otzagNGpV2zMPP6DBhwr2vc5xp0DJBcoY/gQ8qmpJKAo49YWbDO3ZeA4o7V5gMLopv0Dog/kcOyclvfrnUaAHtAZz5A8D9fn5mWBLQM0kW2kc7QlcaPgqZJeAuwMLyc3YzCIBRbWVRzvjxWf+mXaYhrWD+A7WtgB/vF/g6WWLyTnJihJ88jkXnvpEXu/r6yvH+0hO3KPeL8vaFFPi53CIKrTfnpGhZSClHnhVf17zE7Y1T4+zo/XL5Dcy+V3fx4xwd6/PdUau5klbcx2M13eBM5fthFz70cX9mGNxr1qjEVY/GyDlFGYGzUiFzgnfuzTIZGQJzIO9zechA5J9V/KYfO53lqPGPRlwQ4Dw/Q56G3g+LOAzMaLWqLE8m9fnySy+sy9b/PEsrK9dDiZObdLOXJudWsB0Ys/aaokiPCTB/HFENp93rYWZGsl+bbMALFxPpdXFeV860CIRRSgWDIj7jVnP0u1J9UvO/Y+Pj+xPVN+yW868p61sp8DtG+atugmA18pEOWfY9Kw5gkWOr4iUdh6qUXEjKzJQfnJ/feG+v2KtJ4gnEOt9XzafMphmrztr2dcCx+o4X2c+uG+4rBznVWuQDu45qVHzGcKevdbC8uMUHw3xmXs9SljzJMFROCdHpmAI2+Cs4OOjpFoFlIBH1RhvsoHuQLCv+3yoSA/Z5BGVjl4tido95i5BKwG+4ThlnC+VOC04+Q7xsmdvkIhMkBWmM8GE8oq+JDAyeOXjcP71HAqRcLC5zhQjq9D8XPoTZIUXu2kVleQeVegw1sQ6c4pAlc5pAc50gDjvOmNb65VROpo/ayC4Jf3xOenIuJ2SosCTUSZzDQR7q/ajLhmdEcb5rimORKH9/B3vlFbXHDyKOP49AN8Y+lmR9DpdU6J8GPX5/Xq/pf1uQK6IANNq0hEolTFLPUycCoY4Ti4XxUNP18OBNKDcKJORYL3EMUYAW0FmVQ/YGNgObA9AHRM59aG44C27ssLg1gczxPv3/VWL4rXux4YKoMT2XVfJkJFmVjAXAYwJNtT2rAq/d/kDDdzWt1DF4GJ+ertowLgDIk6iYtny572G7vuC3+1n/TpVMFy1Nm83edTX5EK/9y7WhEyhgkbdH0a9mC+cI06BU1Jp15geLeCyc2ACmR1+h2CGR1b2/qFBptY1NGRaxiTK3AQIIKNHwMbTzfY+LF3XhrMPfGefi2DbDZOQ4dCg3/uU4AGZM5zNbWr0lbpGGNIPA1rvmGBrwfFeCmlZJAORjQaAK3VzkqfWcaNdljVNY8ElGPUKifO5CtS0eXTvFXKClgQ3RhTet/jiWb/jnGDVqy/EWByWi3Pu3fhxPpJt6Qb4Sh0g5yb1+Tz1jOCDh0XE/HmysQ8HzFFzsJJuRPH19dWK64+cT+mwt/X3cFT9RzkQ6xIfUJg2sWlCuTav6+lUE4xRTtF/XoxvY3IIyAmICSDJEvd1yLHa90oHdZcUYYxz4AErMTA5kuOMXN8nouR1GAk3fzqench4RJyAZEvP3uF2wADn/+v1wusVBx4YtPrQ98bC0UsWG07Q454OHArAsmnKDyKqcZwImGNCYb4AWETt6DTgyKb63Lzv+5FXQRu3kOx3I39gwWAPEQy3slWGox21lOKRMSRAjbV0SIqyxfl+FUFr49z3ONZ2pnShO3pqXlIfae9B5p61senUkQ0kqCNBwD2TjkgRMJaMKtdGZu5Hfwtc3pK0Sbpkhv52Rxy40TTKzVa4x3GzcfqlRJi8AT0gCNMz9wgen6B0QCLiN7ySasnCQiO8X1VamOjWGNIqF2nnPcU8UulJTjSsWXrglAsaPCUKz7C+pFadcivOGbLFaCx1ye805ArRVw7YqZxQjkcjuGg7TI+Wmbgu5kr2QwLb+j4mHDHujE47NkQ1Ki3kz72y0H5b+92AXJeQHQw5tdgYogTeWBuRrAGpcf4xmrbtbUPsHi6vw59zoro79hK88kSlfS+s+8mm9CzI/j16yGQCX8swfWRI+xl65HeBBGCNvYn7IIy/OXztMqgLnmVpwmCqKl6v9QMr07Wq/Z17IgQNVWe2e1+xv4qt2acsF7Wv/X4EEX2T4j36Bl7g2RwfeoAyjdtIMOmpdxvtuqHlDONJhpD9DQSrwCQCfgfmlZDD56aWiafiRRhsZiLWOJpealZXnsaWz8NNrpKKKhTomFkkHYhC8p296oxpMSp4aq5Ekpn1FVn0th7haJa+CVBxHIAaC1H4iuOnL02Q0saBc+DeUa7KEmjEQx7DxJB2bLqxCfe+jdO8qFE9a4HsWzG2uT6t6fl4wIAiktGuJqV5VAXIzaM2yTkLSHB+brc6xa/PL1aa4J8ObLudeDhzcsaC41uAMeUONU71vAhD7EfXyXdgjVuz0Ey/h3vvvR4OYAcE3fZ1J6XqDKs+dIswf6x74EifaurEjYPdsqcTy/Hvzmq3C9EPIfNhNIngs66zrY76Ze3kIQpbr6Mv3udQkJIk8Pma3fmZkw3EXCGg5hg+EpoQGt5g3Q/o4h5P/baqFoNHplLSwZl66rQCqDGhlIfrsUuXQm8+ivHm85JUEYwkrScWvI6z5nHBdO55/dJCrji+WwHolro+bf72KFsHGLZnXV8Z9dwc1+oHdyjXq8XcvvcJxS/bBSj5XACyvrOVLv3hjMlJNOQ+F2u/Madmx16xbyB1KmA5ddyXERplnkznEpFQ2gsRwUdKuWoMm7O2d0QwuRYq2QzRn+OaSRI8bYCZFbBadpdkiqeksplZVnSSYssl1z5tKFTgCODLkqC2c1blGrSy+8/rC47D2cuE1bG8TgesYSJjVPQkWufT1NhXkpw//5gdJ4HEjTgKfJb93CTiHDnNDnZgP/A5wKRSr9wD/sEYCPS7AY+zywq0pgY3otSj8n/Cgcj+9Hyrp1n/s+13A3LJRHW9aTEQTuZp5QTauDU+e1s7zpYTe51SN+9Fv1lSiEbDAMAjHPF5XSFRmMHw1nVzypDFcGthzn0A4loL1xjFMAeIOyWzfpaVTubkbDiR6GVD8Mc7QtgK1k+lIcsEHztGp8scxJGh+AiZE2jRuH4lUwI90oACItxU76Md7pKKzvi+Xq9zYpvIYQ3auwWTvR7e8Gu/6lpRT++wBvfXK5NVWrkqJvMkKHq9ji4YQPUrn38mgOiMcoEMeNTEbSCC4IK6RRdU8hFlGMzO7iCLBm1nX1CrCToRHidOfe12mlmG3XZursUQAbj3C4IBjjo9bFA+Iy0hRk7SVmiiHa4Lr/1HmOEx99hXpWsTiaOfycJcszaeXrKNYX0ejUqGaq1VIaWuSevF7xfOXPzIwy8YagqweLR43Rki01H1PYuZOE5COEVaMpPuWA15RhL4OwIJzs2SM/jRo/FzJyoSG3CvXUw23kWgHhrymIOotUonSDUSjqrUzk5Zhh8HpZLF+P72dFg5Xsix7gXpmYFOR5TPSKBeJbvyXQlECGbYuhPmfkLcR6ftWJnY64I6gSq9nNSdHlbX8pQiHrebKK/mSL8X+4xMzzuRwDqzzLiPfqSsCgW+QltqR0YVZ62XDSSLJyKRGKR+kqMSAHDcvr6+DoOW40Onmn8497aTgGB1nCMfq2OBsxQgHe+StwHHAcfRMQa5IXhlrfeVYVs+Q8nAXLAMWKZZ6vCFfa9aw8BJACbAMUT93J1VGKoChJ819vX1Vevu3fkBMrG12dz4vZUdDsJPS6ft7nWEtmcfMfpTa5J9tjc+xywZSWiRc5571PSNmuIH6HGusOyigwm6R1qyWDUmmeb3/U41khXjYcIRCrIDj3nAagcKj4NQ4I9nEA9JgFlURoqKshtDHDttuCnO9zL6UH/T0UuipTui5h6VJPxHvWvYkObcIA+h8RMdYuM7cB2ycY8JYN80v/vk6AiSXe7kAXEI54OTPGGRgJA+iANiBnerzyii0kKQMml3fMbhWWIhA8EGTVKAaKDleP/F9jsBuQKgFaFubBKAY3DWrrAYNbXF3OGpl6rJqMGodr1PZwymKiC52a/w5mxt/PLxWRtvD9+stcpL7ewxGScmoawsjM0NE0hGFXjowFRDizt11OTlBt69525U6H3305vKAPpJEqE2i8CiAAIzYRsb+ghN4YQPy+GQkzzHz41xTh4j+9Xfl5uDeCuGzYzi+y7my4DMyLUq5N2BSwGGVlKKz8yfVa1F8woDUvpQACrnRTcMsKfQn+Cb1/vKbGdq+HporurZWoxFncLnGcb2hTEEvo+Ol6wk8n16qSlWuFAIjFn0jizLFeNCprWDO/7NKgN9/dQzU1fqrTpDG0MDS/UdZ6zmFfyxMT/YoTZXog9OuS9KH5ioc2oK5/zJahF1EhH71rxqXvaQGOfvO5PU58m9z6lgHfwxkeg9wtMBXtmbxg737OtyzM4ieW5+SkaxscuQOnGKx8bG78henDJt3RntzjDvDeABonqCjvgTIG73qtDS2a7OvHOT/Vm0rJh4OydKcX5XzVdbxbiX80tZmQlcRklQCgyL1LsGi4Q60Y5rqtvUnlBZmnv2j5+a0sGInmgR+4GOU49CEXwyfMp9gvKAz8/PAsXc5LtdY1+V1MCbvrslaRaQwHjYmP75qkKRc4hSkwOan6xbOBJpi/czoqgAxjzsP8ea86avNdqe7pBxHV3XFaX1cJzPTgroPLImd6+9674ZbWiyDZyyjB2gA0+gxyjDGAOvPLDlvU+jPyTtpBwNNKV9OFFXyplc4nREz4NGGCXq79tZ4bjI2et0/oQy7AyrnOhLOajCfsnv+qkwQaa07sVLNmoyAOOqf9O29O9zvkQBjTzcI+UQ0t7D2/wpO9Xfwc685HO4HAKngLgKYMlQuwerKj9itKr2YOcAI0r2+n3iM60fHZU7JergiaGymsTSgi1+77u/1OZf/si/ifZMcioQmgP/MSbcXrh+ubL2q0YB4QyR9fAZWc2vTDT6kJnhOkCmYnmEeD58AlMhd1L77lgwQIApkZQ03jZU1nR9bBQZTrn3qrBdN4AdRKrEBKzN107BcN0BSlcaoDkG9tpnoiUAJFOhc8B2XFPnyaKvDFUoxDfUNswGMHJyuMd78Zxz3o/Z8G1DBFDhNJWTEPFwJuhZXhfW6/XYDKrfPCY3T0vi0bdmBh2nP8wiqee+7zAb10zJQTJkmjo2kTw61DHyhKoY8xvXx1XGu+bU3tAxsPZ6aIqnjqq8UQyknUgC++XKY2mxFm4VfJS2ctQG/XW/QquLEO2/8lCRleXfWFWBmeWrhZ/pKFyiWEJ5w4XxuuHXBd+o96VhoUNRxxsnY8Y+5xhwXPdaxY7PeZVBJDt4IWsP5nOUtCA3e4LzAm96NNHAAd8BiBy+9f+n7t2D9cuO6rDVvff57owQ6EVA4mFEDMICgoIxmFfAJiSAKjyEQyIilwETiRIQP0KFYIgfhVPBwYWN4yQEsF2xU2AwwbYojAGHR1wBm4RHBHYIQiAJJDFCCCQYzfzud87enT+6V+8+9zeSZiinanxqpu793fvd7ztnP3p3r169GlMMww5MKBAoOczQL1sxfoAWXqJCYN1b4x5W0vixr0cUtrFz2V1nsAdqzLln1iYD0TLmb/jej/hXZsHe2fX+n/P/eJox1rGJAAaITKg6R3o/RnLIa7qytyVfBgA9UCqDnfaZdwHiSdUgNhNZSYeyHA5ce2MMQM4BhMniW54ABBFfZzGeTWPvwVOOlCTk+mB2xFPBAuMcHusAp4ReBuWGLAydoTYQL461FVkCL0XxsVB1XWQR19uNjETabwMulwuux44RyjpNvT38zda8AY/I0ua+E2QzwMs1SIfP/LzgM7YumeVTIDsMpnNqhgZNbixT0KJlzAIUYPHTOAamAjdYwWMNRGaxV8PM5ZXqHg0u/jonF79/P7zDV+tOEdharL9Rii1j7m9CNjP3HB0nAW5vb4PjGdSurUNG4a8XChrvweZ0tg/cXk4V6Jipjdpaw9h3L7BUD163FhxO7ZklmnKXkqgYowBh4oEn+fVcyz5HzpOvDjtR/ZzzcUCoAMRshflOTspAAU4wJ9AE2ibMOhC8aIQznJaKWQsLSkTVZ8bMRh7i3rnLapFOIC7jSCk59B72BPAixwaYc3JBWwN4o6y59hl9EAuuL6J5Ff0u2gIHoyIjDd6T2xgLcFLysSIzYY5hw2IvS0GQYyxUIuMzvbh0cu/Bz2wzwVQtqK0BDa6M8QT4Ck8KJNeATL/WSJN8vn0aDu04pkLajQ9QkPCzq5jhJM3Re18yLeLau5lito6hwO2+w/rMzmKAG/dbzCxUoFM5p3OZWmunVN88Bm736yklTqPHQoKq75c92hmdWhRFmP9PpJUODT97wu+XvycabBLPYesznJLBnu6K1h1xWwjuyIr4u6oEGcFqbau4UGAil2kUxnRy/rhdOqZYqVhVzZavJo64QtzhGwLc2sgoV7a25K2CB8qWnFNLJylVXI89CwhXECI4opJ3v2PccSxHmgjy7VhamERFKiKm2jOFNswVApi+nnOGBq4lUpHFglqRhTOiAuB8v+GsqakrQJhTH2RMICropTlPrKLrDGgul4tLapVovX5GRZ+rc8pOcHQs53TenZh5gGgL8XLHYh2m9VBJRK3wmQ8IDvGDvMNpEswGkOPne/aMpDrKZcDhVI5GriMc4YKubmCVe1uRZeBcBGkiucbruLTW8LyXvPZkh+q/7/7unV3Pe8lr8/V3vwdWkSQPWr/PiQnP/Gjh1SYfNIruiDIRkRw2cVz3HOvk4MnS+6VmTO0iV7/yShS4aQY8RNW4r1wNwOf4SrH9QToY19VI2kZFaZk1IADBrNXdQJhzpHOBG5R5nABuR81SuYY479UD2lW42uHvM6PDH+kjqk6/2W4uuLm5Qe8dD940R20LYpmOPzxg2oNfX20ex7k6OKoKm2sOvJX0okWRk8iMh2yuQ6tzBa67jdSVJVruwW/LTFmPsXHqw8oacp/Tho8xcHt7i+sc2bnMm8msDA2DPilynM3gBZ9lX0nYbQ0UkeoiPL/YmVARrbOve8pyIda0qmbBbWZD7mQhrGnuVVJJqKG7BbWL9mLORQdJVFIFkCPtGItZNehmWYTGwE3uL9BMJJI2xJaqSF2zvg+CelYcVC8cjrWgS2ITQEoZOuhTijf1rKc/0V0jetwuilFktBKBnku3WodhDpd9m2O5dCw0tSbu4IYTz6zqPEaitKLqEmjwArQRwaKAXGX/qtMwdXO+MhZ6noG0wTWqWbAnnsmSQus0WzYmW3lXsIIKFMYCO1cEmTJdUUvCS3+c15PCyUUcpEzd8RDCXO1vbSA7mFVDQ6dYe8s0obey3Z1/ZKFdpwXlFKbgBDJ9A19HOLM1LcCv4UCLeieutm2LIyZeLFfT5T3aOLoDAV+cVFEY7L6mSW0AlgF0B22lopMzBGR07IUAPTvCsclC5SZ71OSLc87ldPOzZnRmqsj0yYAUDiNTeTRAKYPV2AFJYdaS41URo1qspQiDiPWaVoj95O0wQHF+bfMU3jHSIThCBoz0jzFGco+ltXSAKh9au3Ox2QI100e8t95O60pNU+B6mCVnjwaRCBR1a3vvOPaJcVhwuzTR4pqS8eW+nA5PIU6g9ZLmIn/xSFoDUXzOL+frer1mYRgP6WoweLhXAz32Iw7KxYurcx6mzX8fB4WM8+GeSBewOtuNpRdb0dX6NzVDoIEOk5bDw5BzR4UK6tNS+YCrqxam8j3PZsU/7xoo+unvdN1fdUz578f6+o6uV33bc9/hv5/3ktfmXI2yvqAdcx6LKzvPXHYr88JxtkHawCpIrBSM5JfPmQ1BalDE9yf9JDmXRcHFogkJ7UzXlnJM6bSGLd0jQB8m6DBPld9ZTzUNyYK7un7SjscaytqCvuQCaxc+R1DX/nH7a7lvUiqNzxt7E3Db4wVmipubG9zcPOg6qbSbdzKKdCx50YYmrST2cyLRNlYxsXnm6qItHUCOw5zzREEa+xFtt2em7Gt2i/8Pm5k5Isd9FQzF+UUK2Jzo/QKx5bAyaEjEEisgHBH08zyrTj2BFagm/5jnn7+HU0imHa68QoTQFvWJz5D8dln1MUMWEMD3JX/fbNl5xGvY7pVrncGAwnnyyZnHylTdXTf+vrsjyGWfLefWck7vrtVZbBgbwqRdA3wu5MAxzlSgpM8AoNaY2A6z4VlXZsdm0NhkW0EuVkY411D4GAPmXcCm8+DX+VKc87KHaWvZnUwMmKNIrM5RuLer/fY4Yp/OgWYzC/xoq/1ehju4lYpAcMNID5oL2BIJR3cF9IADa6KGbMxFRYpiGx/v9aRwcqtTSWknOpa+2IFLWwuekT5wp7XiMdAEmHYLkcXzmYLVsQw4LVi+B2U3bIxI8TD6PLdb9EU881BiJO3/8Am4PXYgnkOlAwMnLhKwdAb3QAlpOK7w7ls0IBWB6qJeWGCLH8zf0Sguo8CmGkuuihvMeYMNbKFYeXBmdjLsaYDp1NvIVBbT2AAA7bgiENS5uksdgagwWieSQO4ksAqSWHwF0ID4Jr599NGspOfBvG2bOzxz9ZrPTVN4wILNe7mHg3jMiUORvGUe2rstFITR5dCZhphdtqDRMapJOtpEbNDCar2HpwAAIABJREFUdsnEJqEjGgbcO9r5s9/uu2uRYib6se/7qkKloxColPaexXtETmtlLwOAPHxl8ci51jSCFKnzG44Eg0qmMtH9YB5lzpKXimU0uF66OucS6ZyPhVLFwZoBVPLXouVrcUxrUJc0CKI/WIWUiZL3UvQWY5drWBY6dek9ZcBE/LggAvq8l7wWr/q2554cU37/qm977hNCdHnd/ZtxX0oVaTNWmlTS0PtBwKBzouu5iMZyHy9nxd9spcqJbAKBxpdzgfaTqDFRPQZMdw/mahNO6GUJSthUITNJ/eyIAHcKD+VOG1a505Kd6GkZo1PAX9Y4W3Lz4ORaheppT6T2aKSBXdFkpOLBRdsJ+a21GOn0SgTlRE1RutLhPM/UXmbxYdI/4hlZjMU92LVlQRuLL02mI2sFgcuLjuEYmCDFaC5pPCynx8xO+sdsLEFe8ymAiLqJ2kyBdu6iLdP/iPHuFnON7cSPTXuvK/hmAXcTxXWObNjhjtAqtqVzaoLgAFMT2R2p6oAnIGRx3s0lr0Y5Ss5NggRhZ2sB3LQjMg8jKYCcB2YzxLyLajrB+zrfeFWlKH5mXY/VNptsLvsoiwazuhMuLXR28KuOY36PCQMBrLlqkMxpDzr92Sr44OIG58AugQsLHnk0nzGTpG/4/Y1QnlggEDNUY6pTFcypIwxcsoZkzJhzz+TZGNF5L/jn4l3WdgY28V5tnjOgT+R68nByjbyPlo7j1s5cU1M5GdtWooOd/B2bEAgs0mmXdnFtTgnOB3DaHD6ICznNOzLLgrCFxMxllKxUQebPwsjeXGDHQFPFfgyoGERWNE2jsfUbbG2lrRHOZqIBQmHtqPxMf0BxzEXO5/3S0enavPJTEPJbzi2dh/Ol6sYbNnETxQ7AchL4v/bmByLTzTGWLDa5kltqwJzO91XRTDdthcNWNygPFsqCjH33iFE7juMKwQZvDuF6rQdR6BjrsR+ZYt+0JRIDplmCV6USOpUxNzADjiDON8GYjoofgWxDEFqzZ+TV5kxekUSqBxx/EXSEIyLAboCyWGuMlEVLzLErdApa685PnAdUN8zgvHml7Tw59RIHZoejz4y8OS/RsDIclbWtOa8MOFrhETcj78vRiYqi7nPiZrvg9giuXkG4ToiHSHZWwhwp8zOnc8j9s4AjpIkQB11PlMUwm0LjPZtuXk2upcBRnGdXSr7ciDPborGvS0os6TUx/hreIA0pC1PuOrJ0euv3FdG9i9q+s5/zb3/17z//vt893uu5L/p57MOd9sE1CoVJILHHSq27Sxx7F3EQg+itL/1Md+qqX6ANVVUYcHJSgILyq6SGqFD14giKgLb8LABYscz5AKWjKHAuporvQZXmHD5Zkn0MSMDDXRev0abzfN/RgfdYCFotlF0ZgFpYUyhWZZ1r2KRFQXIOJb+HCjDWeHGMZ6gvqAiO/Uh5s6aKMXbPeYikNrqY22wBssBvnwfGHN4OtrsDcsQ5aTFHnp1y6TgVBSLDVxs6SGuYzcMazvsQV0IQBOocbeAdnFEcdpw4yImc++JI2lYXdp7EyS4AwJieqnbFLedJk4/ftfnvVaCtBUh15oLP6RxX/jzn0wAhZXGLgjJbjY4qNcHKGiRNy8y8TiMUkQxAg7n/YYaB6fMgkrSgEfvG5g60Dt0ZyPgZQO5tE8MxCwgQ9rii8101wSUzgWkBtE7rE27fSpA64TKFdNi7ArY6XPiKHtP9JfE6nBps+qt2tGgahbj/HO8oJLQpUAVMfGwhEzaBKX7Gu7M+T+c60XOoYI4rLn01rVE4FcaG84wt1pFNpwImUn+sbJDvp44mR653idqpx3vJE4V+//+4XvCCF9gPft8/is3BCkgaO29NCwBHiKB7V51wxkQLv4jFUAD2AbQOjImDPb8Dsa0TzoilOqD1OsKRmsdyVmhY8jWlCIYFPdnjWxog81TpvTafQNtCbsws0ELNCU+nfkqZ15nR7uVyOTmm/IxhltWx/N2ldY/ABMl51kC77uoj3nVMiYA0c5I4O1Vt2+ZGFF4UQOOV1fWBtPbeMfYjo3qmnyaAS7vApkfRBwydPcbNI1BoEP3DKXMnsBx8x9I1nbCkN7QwxCyO43PoXMiLhKEDHDGwMdHQMCCwNiDDoFOWNqCwycMqwuNF9MbTLgPStkQ4B7vZxb2Q12jT0zJzIHQvkXq0QumxMEBM6yMMNlPVbFKxte78PFsUAK7PmrI8oYLxWq45R58NF2jwax298MKdI3m0FflntTdpJgAyMDrmQIsg6sDE5hIWydGythwKP9w6JDhhm6x1wjFA/DtpNiIlMLBsX3xOzy8UhGt8jIHXv+LD8GS/nvuin0/JJqIdGlSHuxkpZjSggnk9MkhSLIk8BjvZhEZWMFffK+1jQUZZ8OXO51Ku6VE4dBdpAhZKy1S99raoYHG18toTpaPcEz8znahAetlBjXbHxNVIKiXpZNOVdK+Ge4fTCYCVOfCmC/2E5rrtGUuhpwUtrcgnWoj3ZxBOWkHsKy1ODXMaE6swkog0i/747Me4OioYAXU9RyrVDECi3jw/RajEgECAJ2Buq2vwwbWgqhhiuJGW+437kmsmG76UQAAyIdEqet93L1KjTCWQxVwJLMV9sgiXY890tcLHeN/30DDGsnvxXltrJ6m7PHuhaG3RqFrumZVlYvp8Hkem0Y/jQN8UsB49Csr4RIHjcd2BS0czBzza1PV8RPDHALqEmsOdlH1Z855ZGJnB4VjWPT3L/QNIx5iOOvdph+CQHb0pYA3TAg3m56tAxQE9/s3KalnSkUZZu3UPA16Ej3HgwHld8P0yiMBy7mVzCh5UvAtn7PXayKg2D6nrg7Q3roxm0c3Q84L4mI/7ePzkT/3U43J1nxR0BQQ9QQIBa2XStTfscO1YqGQ6zbVrnRtpZoX7qDimRzaeQl4E+lGiDsTmmkWDsBpDohdb8J4A3yBba6kHyte78HloLjYNOSxPcZDwrXLmRfpjj6i6l2wzSweAvKLkHIl5KiJa4VU0hleVjhFzPcHbcSSJ/GpnDuNmbgxuwwEDljNAZDo5g/H8VmSf/MCKVN7cvVgmnFA6X/2yrYrcttI3HIOtNW+pHIFIRxh23ALNu51gOipPDiqL9apBTCepFOPwkCParvkMcajEPW7BM87qWTUcx72sZJfNU2siq/sZHVxygvlZmR5t7tjt5mtBwpFUFoyoy4RdxXvMQxxdITcd7O2OFrQGryoVNC+0ARJNJ20kq7blLJnEA/IccSOfgzwpPv8Fjg7vLSgA6tqZIqvlMd+HB98lvqYsUaRpafzJIYsF6gautzTCdCDIgWuiubdN5BR0JQoZ/FQpwWXlpbN7FA9/GtHX/f3f96+FgwsAr/0Hz8frX/Fh55QrgiJTuo/RBiLSk0kLsPNhRc4k08HA2vN0erPOoexVys8lWomV/mezGsApNlxfzK7cBQ9oH7IewRbfnV9xx2l2B2ImCneiB8hy+MRID5Bo4LIKuxgIAcB1rjXGgJVUHq7f3BdiaWMU4vx0KzxOWTYHQHKP71uv4bDRwWiyzi8+S3VexhhoUbhZQQwqwgDROnguaUiR0HsvwaeEXmsThcrlZBNIoyLvXSdSNjF1aOFB7kTQJnpfNpydq+Ji7QDPE3JlK90p6U8MIuZMR3YCYTNnvgcvjXVHCTP/4aqhaW3DpSvMlnzhqbNama896h243lgrAnJRseiJ1GlGqL74XbBLY4wz/QfV5EEzEKxXBqSBrouZF/4VB++UyS3rhbQn7oHcT82g1jFMQf4SHUlfZwo7ls9SHVye8VkQf4xiiw1NXO1hzJkOLvcG93muHaz3EvoP4b9w/uib0G6RBoJixxRY8oeYgByAksP+xF3WJ4WT6+tfIyJcle7VswdwMhgUpV+V2+uQFKIHgVrUiz/LgjZbElr8jHRemzsUgB/I27YlLULCQHkkuzrNeLHUSrl41x8X4K4Xf59OIRbPjOnqdJ6aJkpzRFSdpP94DhZJVAekjlut8p2RHpiIopyI/lnJOqcrB1h0buIiqcgBozAa0zwkbaEt1+s176v+3gtdZjovydeK3zv/1ikERyACbE94s20pRp+Hoq3xzAizVIsTVajcUM4v+6qPqEAmJ+5yuWQmgQVwIktmiIVuANBuLvFZOB1INDAevHnhoI3p6NF0ig7nKtOKhoxgTeD7IZAVkYbjcImuXEN9fV4W2xQUiuMxyu+bnRscYBp2m86Rw0pt2T7QDJkRIBXmmDMPZzbmoFPJ5+H6c0efadOZ6b0rlqA++YHA4voxkzFsaSHTMbtrG9gZBwWhZ3qdyia1kPJfx4soZ9o+OM+2jvVdW8n9VG1dTftWFYN0SomqY/09bRNQqAtxcW7Woeg/V0g2rFkKEOGMEN0pgvTkWzOlX+02nyVtWtEaZcq82sO8txir5Lkei2OL4OTT7qemM/84M2tn3fWKdHOP79Hkg+uSgR/XqohkEwiLAPR6nO00g71U2eA+iHsiFWUYoNjcdl+vaCaZvWAdxBguEaYNmblY4MbZeeTPedb0vP9Vt0CEtrW22mkjioOsnFvc/3fOAiACXizn6Xbf09GdSspTCXLiXjn2VsdprsDVhqPT/llUkln2gbr79X5yPRWHL9d5Fkxprlc+U0Xd+Sz53rLUYWrQUjtz3i3mnhMwNQc7JjsbHvc5xgxgGMhzTeX+jK5qtOtVIYhZZDT3r+p+8vUctUfRXY/ZXcD36DCBN3VYkqg12OW5t363fC++hkXkbPJEv4dnMs+EMRf673xkwxAPKI6pGOLc5Sl4QteTgpNL0+zpfYH2DbYH+jMBU9/mvjA8xdBMYGwPy2r/RIoMBoN1l2bKyNfslEZxJ9mgujipFdGcc2LbGvbg+WIM3NzcwMbENTa9p3ImHtw23EbKXaGZzuFVHdCuDYWh6c7Tvmcqb5olTeI6D2CuRa+qLqEDOvn9nH6M+6bB28dAb901M6dg0iAwEmOaRYBL8Hf65vzQCXcUZOJkeDMdZ0j6QfLZQM6aoQWiQOUJLux9DvTeMMXQYt/X++flKfAHMGRCYXj09hbbzaWkqp0fy9bOjP7HGC5DpQqZEz24oaqK3Q6XyQktQWppbq3jXgQwXn0Pz+7Z0mWlk5zpr6BKiGlSHfagcKRWsznCNufE7ApFHEKb4tgnNIo8pgGydVxvr2htQ4eidSJ1YRTlgGyedmLwk8Y7eJsmoehQ1jJUoeZcRhXXrwUDMjgfzrDSirs6b3Dfd8wYQ6gG73WHRDXx4aS1UB5ZPGaJ4hg1pp4FY44cR2i0UFXBBqT2MezARE81FCCklJqcAjUBEA+TqBsDOJne9UjV13wT54sPPafY6vUBn/Uvg5cZgQG1n4nERfaIhawszDvME/iXOAwMA+NwAXlFS3moqYI+HRGCCtqwpGksVHqhNmaGX/kHZx4vETwe8m1zKlZNaaZdm14PgDiA5pyuCW7ng5YB5gkpNOfKpToMUd9wnOrF3wNIpzPR4DmBGUFLvobV2RPaGnY3LL62ggLgHOp437AVOlcLcc5fdTbJJa4gyJg7BKG0Ykvw/0RhKLJKGxGwEXxXifoGcfSVHfzSgdRVSNm1xRohD98gvUEpdzQNm27h9Drtraniujv33EzzfDoFY3Sc4WfkRQW7AZgDCkW7XE5UAGYFYYZdPQgaAWJQYaUXFDYzTxKfoJ7BOeIsIsWEGSaF4BgHJNhNIgYB9YxLYWJryQPNQHsMyEbEtUGLIzwZbEzSqBRmEzOUMppocNIN2ASqlsXctHFbWzSXGqipCCzGR8EMZMdN87UsZS158Zh5/AWDGZKXC3O7IrsXXUnYy8qVBbVtsbIYuT/oTGMAtsEsWsWTFhFnGGsV6lWzLlS5kXD0ZznDjwm3Of4LoMP1dduGeXghmxHEQDi48PNFTE5ZHkdQnYI4VaHRgjcL8LiHeoPs7kSncz4nZlAqgyTj5/J0akVKv5WxV+3QQ6BzLNuvgB0TU4JmCsCCioE67u/ielIgubDo0gXFcSw+qEJSFJsoAyd0n2uBKwA5VtcxDQ1YEt4RhpYLjc6WF2Gt4jYasYrkmix0rrdLHqotHBkA6Jvidl5PxqRGczwk+BmZ2purmriJ68BSpqv3DgvRud1W6plFefneBbmi0SBySv7OQQdjXDPSdQ3h6A4FgPqvgKc6nNPaveNWOMx1w/oNrC42kxW54koFnipd48oDyPmigZ6E3iclWO528JHWsMMLx2bMA9EY5386tYPtI9k9qKvTRih7Uw+2Jh0mpUteOAj3juXg9t4hg+goUjZnRZ+W96vaMfYZPGKgXy4APCLvEO/BbkS+l1E89kDTZhxGgTYxqjYM7HMEUYHIiaGh+fwZsp/71hYP1RtunKXhOA9ZAbsfmTbV3lY7YsC1OsfMFKjE67OjXqnuTgqAILmENGB3kZNMJ7fFSx9jZCaBAUSitwxYbIJdx1Q1JeDyPYpzZ/uRe4FBGJ2ZKgF392KWZMj5UOLBKGMmakrkfCL4/DJx79g9pTt8jXa7AaIFOSK9SES5VshX5RSz4NDbY1vvzIBw/81FH7grjcRszRGHY2vN1x6dUl1awRWJSvkuJa//fmS+fu/7Z6GBzKCISHbY45rbx5FBcI55cIUnYi/FmOiMQHk/0nnOMYgrUaJyz491j7RPFWlMBLcgs8OmI6Nbz3vzLEwoX8RY1zNnay01quf1WPMLV6Vx5NXnYAzPitj0uSNiN8se4fywcIqBG5Euk+Y1KhFsJDLeqg61I6NdWzqPaftK/YGVfTODejaCWlULpfg9gyWinACyPW6ii+U9T/OkkvJ0lQLjmS4/95hRAJCScHSc2KK+9+7czkAuGTzxc+gattYwdSG9Ljs4E3jhnqmgEM8ma44W5tqRlWb3gHvJRlZuuZnlWmCRc21JvI8jnDNd0pRC/X7LfV/PytM6P8mUraLM9IvitVbomXN6AwkxoJUul7QlbPnr+3id0/7vBrHdUfEyn+xSmVSdGDNeFQy7S6tgI5f6tzw/xtghDXm2zjm9mLM1dK1rybzG6gmguU8OJxdeqHMcizPDpgCE9f01JY2gZxK+dc1NamNiu7l4RxxbDsH6LEuZI2BpMwIF/cKZdK8AjnF1xzuczX1U7dSl6WjmxPjqmBLJvd33THm4o73FIl28LpdOcw3fB/oNLlpaPVKarMwyF2c6lOXgm8fSkuUzsTNRRU9pcABgv731cQ+noYkm369G5zSwrbXgRt5PIM/xLkaFC31r3TlYoVFsguShsjiCrRGzOEzVo/A5cek9tC8X98dTgUfSWXi/5NxVxx4oQvHq3YZuopBuHkc+k4Sjv48RgupYaDkmLl29oEPupO2Koz/KuKSBj7VGiguDnOSS2ToQPLvJ7IA79EzVAt51z8T5eCOQNDqSZpZreMKyIxQA7GMZakd2l0Mh5ilka8uxnFYKeY44uEWSRsPNWg9uvnfdiwzCVvCx9hCLeZhSa1IqkWVpLnI9zOkHCJ39izYPdErgR53nx3Ii6Ywh7A/mKs6sDhMl4fg83pFMcHngBtYU1+0B9MsFEkVFbFbCi1kGpp8xV7vLkwzVfXe4DpWU4TLLA7giWI44I5+Z1c51P7KSn1w62qMMRGPcmJHhaxPxkqWuQipOHm5sVBBjV+8diEYq8MCByLgrCazUJlFXNj9YYzRP78Vgpt5zPqeRQ7sCARbHxU2lY2xjqS9QhrJjdcdjapxcUs9mLD7tchQW31TzbyO7shU5s6bJ91e0nPc696RJLHkxR+YzONU7mtpGhA7JpW2yAJzjOE6NjYj8ehDIYrWo4p8zg+esfaBDGZX+TRSKReVgpzICOmlzlFm+eVpDNQAmUszGIZ4fAQyLzifiOrxZ3DoXZzjXLoJOMAaayZnSEnUG9bWc2zomHhzuSN1aOs9xPpDnfZfWA6A0n1jtkqnX26KIds4JC069hqY+SDeQ0gr9rh01y7OIP29lHZAOwoAVjQh0ZKe6j1kCYcG1BVFbrLPG5yzOZHXJNtpY1cUJZlBXaTlcw3Wek68M5HvwnFg2nnbWT+cEH7lGIoAQA7o8MQLCk4KuAAAtCnXGGJABiE4ch6eCarVqEraLw8VNuA/XPARcTqWbp1e2bYNnQNfioYGrjkedqB6tXCVSK5zQiZDlOdzJGkNOFAgAiSh66nYVW2l2YQt+ihmMfEOxbKtnAjQrKEOkZVRD/F81wClFM+eyME1CKSx269nu8HSpJnEcB65zx4M3TwnUyyId4QZWCnpBiZsZyDDAtJTeZ6xQDtyleBDZhXhuBXAdBy49aCIxj2je2cXsSI5ZGkrzFB/7co99wlpoZB4D7eayDqpyEJ6i0HiuI6R96ORjTiDmuSLuXGscfwAuT6eAhq7h1hxdGRrdYESSJiHmWSMD7ltfTGc9UFo1c44mgEvvGezZNGwRLZMDReTWVIExcMTzGSJIYrFfOnkNMKQe8DIqEYBgeLtfILQLvfUwx8wLMAY00C1VL2bY23L4cz9iOUXHnLDmuSZv27lSWom8DVfVaOVgP6INcQ2qJpN65l8l3m85oUulAZFVmXMm11PljELy6uo0HTpYALzVpXgRorecVBy248He8fC9W/zo//t/4qEHXom/8KavwPgV4N/5WGB7VNCvL8D8pQ/Fiz/sZfi4534Y3vPmWbg3H0VrAjsOQDcc84BuoawhDTb2LGikk333qogR5lxpSiDnkuj3bsMpHwYISqFJCTws5oBjVRHNE+8v6gWAZTMZNHLf1HoCR/rvl2OcwcfOZ4h7MDPgGDgEWYgFwGW34nOGwCkzxdZkUA8Pkt2JsNN54DZlFcd6W+LCF7SQqgw1hXq/V1uFYlcWUhll9nqOyQSw6SqEPBL0kLWGxCk5YoZenGOnY3WnRKjiuF6dfsbCXWbkxoBuGxABUgtbTVoI7ZWW/QfeQ7HFdD4qmsq1RQdRRLDRLg5gu2mY+3QJxVRCCXR1FgWewzDknHHdmsveoa8i5lOqG+7cXUNVgtQ7BHotTTGPuc4erlPVzJCA7xnnsjvH573CcTQz9O7rIvSA0uE6bEJNXI7LHFQQmUlJ0LARaz82mA3YPEuZAqsomXumBl+VNuMBCzBlQkywx98z80ofIsJIR7iBXMsxornWssDyONzOFprGCA64Aq5kpB2Ay51Nwh/cWxhILSeyB0L1atrynzi+AkekjWhkzAn3lEXGootLkikEe9gABzMOQFwFpIJk9SyfCPob5mOZx3d6PWmc3IwiosLRsNLxVc4jq1/hhhDmfcn3ILInZyra+crm+rBdFAOxaMX18Pi5vKp8Bjl429Zdi5IoTCAH1CJtYeSXzmdEbGF4K9qMOXGIv1b9jz1VFJt27Hse7Kz8BwpSE7JMPhYDrCI1EVwioqI26fV6dVRvjNMhJrLe74GbG+fzqhsGRQPEe18fxwFtjfsIu000XaoXJpJGtfKYeRFNbiJo07CHY8KxuAC4mOAK5FwPc5RUYO7M6uok15na3G8B7SlTo2hom+Y98eIzHoHspTzZIA2FqIOgq8DmgcOKkDyf404AlIcDfz5DPkiCMiHAscecRpe1Y1wxTaDiaO0+Qq84eMwmzZ/OVkqRvDYcA9vNjaMIQWOZCGckqBSK4pTH+HHdMVhp8APeSnqzBnw1TaSBgtL5g1kWJNDgu4a0ZmWscY11TxEesWdFBSD1Y2vAmIB2uOZifH5bQQF5YWaGFmsjD5HhXGFV9WdpfkBJ76kbDCJvxfHTHmjZMZ3LeueqDhk5e+lQTQXmjv5Ax0//i5/GV/7C5+Leh/wWGja0197iTT+h0N8BnvfB/yHuPbrhp+79DP6PR78dP3J8Nz7gbVe81yOfgu/4N78PY96DygadXsQ4BzDGgX6RTJVeej8hoHfXYT3kW5m/oxyoLoXUgaicFiC1WkkXmWZ5AHLvZkMA+KE+grNX06RVio6pXcSh2FsUX4b9nXZ2ooCV/qaNpS075lzrElRHGHm4zcN50lreJ7N5cz2bK4ys8SP6ClkUNY7sda6mNYmaCUAZOtJzpIWcn1na5mFuLRUGm4ahZ7pBooLwveMao6t2QOKcQBmLSqWRmC8zW1KD4iL5F+q2hzPDOTyOid4lbROA0/7m2XQNNBIWOuJUw7BFPbkeh7fwFd+vEwqV6fk1bWkrFAj1G++MOIKDrKFCYNEJDUFDQ3FaSFHyGGsFHDXLNY+ZyHFQpt1ZjjGhgx8b39PvTTHhEntmroVrY6Y6xnG4Q6ktNKHNQZ1tri5uEx0Kd453m1BxJP1ejJ2oZgATA+3zAQAqOJiRjfNvSpEL04XoSmuwouTAQjw/MwKYgjvbIuICk+au/EDo4FuRR+MZHxkEH7tQUIh7yOxF2pVVL8GMSZeOXQ/olILAT0wzTPUupUdo76stWhP3Op9Z+ZmIzzeDkJoQig+4uQCT2ZYzR5p1NDy3TJ27bMEPfrzXk8bJXdHvSMTQjNH1QmtqyowHOSs/M1W8FwkYFkYNQ7thev6si0vDm7wbc2foEMG47l7Qtg8MG6nFeMyJ3jr2cay2pHFY8T4HVjqW96xzorWFiOQzzVX1So4Nny8Pg2Px2sYRPCf4hphwUW1SGi69O7IDSQS6956dU1QVcnugaUOT7qLjuKJhcXjFAEMgCb1jNyeOuzPTcLvveOBySSSy0aBMg4YixB5FccftgQsF6w/Xy9xtoTvDzJGBiDBtTsx999bF01Fut4jeQe6QKyADx7DH5AmRm3rTehplUXdGZzhuAKDaXOi9d8gcMDjPlr3HR0WUe3Mk1yyJ8CIN18PCsBwAnDJA9QUFsOmG6/CoWcTlaq7Hjq116GgYOgBxRFbMuX6rYGgJyOe8TZ8HQRgrDf3ICHQc4Qvu9mRBWsjK6Bqfc7GfWxhPv7njbOWQdIk8CSM+XIt4+n2RPuNOdsynQ66hLOLOJauk9+vVAxMgdCWdN3jA8n15+BJxbKodKt/bAAAgAElEQVRo27ZSboH0SIyTRDpxtVhdHfly/5Xuhe/QDiWffSRN5a3HI3jOn302Putlz8WPQyFfNfHt3/63IB+seLdPexrectziWf0ZOPZH8Mn2Evw5fQre/LbX4hU//J34rtf8AJ76H38QfvQZ34Xf/9QPxzZccquJYts2wJAGm1QGFFSRF/d/DcRrSlDj3nvv2d899ZDnXM084meGmTqibmGRzhME2BAcRfJyo6HGsAmZcPt8uKi7loxOIobFvprEM+lC5OpendOD88kMl7jzASkSYHPRvioazcN5iuB67EmxyDMixiYDOHjczv1v4mnjWK6YEkoC29obTXw/2ZwerKsjhyjpcN7XsJmOtMRBn3OlC12riGemfmOftpC/nGZokf27TqcSJYWmRabiODAD1SMNTe3MSebZykwnkeRVOH3AIjvKM6e1hjniXELDlMVjpYMaizbsw2rZzM9tzW3UMb3AlZmWFntSOSZxTqqIZyhpdwAoDNfQNYZ6Y4emy1lu4TMYFKIbjmOPAGm9TyuZiZwnBmy9w44jgzxg1Z54NskwTTHiHoYAbRrm3NG3htvdsrDKzGDHAglmrLWs3YHrSR/XWF9YfFcqMmGM5RCaYaqii8DGAWkCDtlxDDRsAM4UngnDBs/aMMAjwtuic2bXRcWcZZ0AyIyug40HNAJyr0l3zXgI8hmrpeKZSLviBYw9mwX11l0LPl4oW0/akBfibRhzBQj8n8V5ERE5FeM+C/mOrycFJ9cigtr3W1ALNlHd0IuYoRvq7U+DmyeLb5aEehEcsgboJhpC4CaaEQAg/4SbmZHl1jqo2Tmjwld6tA2W6H3eHQW7hARXRQ142Cic7tB7P6VgmUYmLWMC2T7QHbVzmiNJ/3Pi3vXqNITLFrqqB8bcYXpg69GGeDjqzaKKB7WvNG841WPuueEHQp93jODBbrgeMwvAhnmEnrI2WAeXTk9Je4OHQorvDdhaVpZeohCLNBIzy2fI6JWz0rZod8sf+KGw5MBCbq1Fj3NzfuYB75hGY04ZHHbfokFvkTKr+o9Tj1WcIQLEmEEWH3NOb7pRkVxH3/11ve3RLYfpl+jE1hqOHYBpis7zoMhmIW1gi/vRaD9bxbIpQZbIdKTxyffzQ3Zkipuv8eKxJV+UNAULJKk4S8dxYFoEjfAKYsAdeNUljL+JYl5XhkJEcBvBxDE9cBljLD44VjoV0x1rR/6XmD9TpEzfjeHPQhpCdVBsDM8oSKTg9iM5si2CB9oG8kwnzNeBWd7P3YuSaOSuk693uVzwPT/xj/El/+jZeOHn/CHg+4FPfPW/hW/9rlfga37kW/Dib/06/PhvvA5veM3r8Fn//R/Gi77r6/E3/q/vx6sffg0eePf3wYs+4+X4h1/xvwJf86v4lJ/7BHzxG16K1/7262PO9uQMc245btDHNsuJ2MhCtiq6u48j6QeTXPYY4zwwprc/p92ktByRRxZAekC0uoGxXiAbFdgCJo6oTSDVgXJZaRuj6jyDtPh6PfYoDI7gLILgizbY4Yf0Jpr1B1zrSTeYy87zfXBaRwXkiHWW7ZOLc0lnhIVUK/O26A8n+gV8rM1Cyk0WbY6FYSKyMpDD9dbzvoHklkoURkEluO9Oj4Bq2pJ9jKUbGu+RCHtpbU3H2bM3q5jpeizbSHQsMz+y0GfXwG3puE+ZGCa4Rqv1mh0yWUGj00UWPaKD9xNnIdxO88yY7O5pADXAmeJPacPMqC4H0O1EILZiEBvR7CeADBANn7nWapaRfsYRe2SYS5mRD8ssQqLAqpAZz9Ui6JsHDvEGIfu4P5hgdou0LF+nS09ahq/JebgCEAMOFk/TdnG+FBNjRMv3uc6jLepBsr0wz4wZ54utZ2ZWgrUfjnpbctx5kR97hK0FNAAWtyHcF3xWSo4inNqOZa+ziC/GsSN6ERyWtov35EyKnnZsFptVg2Iz8zbKUMaoj+t6ciC5Ir5oRTGGofctD1wA2Y7WzFxitUSN2dmJ+kWxs0TOqEZqNRbkBDhP3E5nqLUkVIu4hJZu3dGw0t2sosBMcTG14o6VZDEdo28RQdMNe2jC8VmOQDE2cSmmqt0rYnggOk7N6+6olmy5SFWbdx9rDWJLH/ZqA9MmBgC1VbnLgEC2nn8Hdednsw1j7rjZNty7d8/HVjwyZB9w6uPmRQM0l3HxnxtmK4Lk2uHFVMONx+aROTuQNVXnmCI2KOASO1MjxSaANUTnbP9cVTzQN0CD22WGAwPaNnRZhT6eau1ZyDZhkOx2FKnziPgnD0N4hSmdqiMOJE97uqPG6vVpA2NadoDj4dlv1JEGKTxhbuTjgAQ6CTq/DCh0UTVYkLAF5UanLYdyhhOHlb5TRKYhkFqqRhwjKCh56DdkgEyiUwQht+PA1gQogc3AQgC4/6Qp7l2vft/7jiHABqwDvqgpnBC8cCi2tlCWQSm8qG6vWZEMRC0Q6u4ygpTtqcYQWIGZYHXnIt/zfvOznLytNdzajp/6xVfiK3/sD+H4Pe+Bj3zOy/DIq34dn/t5fxm3csHEM/C1n/6V2HCDp/YLnvKeiu98r+/A9//NF+NHXvlP8Zu/+n7A7W/h5x58Ab75j/yXeM4nC7725mvw0k/5Wvz4t3wvPvq5L8a3vf9fx6G3kKMH8j6gsp0yP6drTqd4mEIsij9KwMUgNJ9pzOxIWJ06wAv/hgimAnNMYAYiOpGcP7bGpj4s/37CsOnintKmCgKJpMZ5pKubKqZ4C+k5gdvbW7fLgc6zsFBrl6/irFVqA4M0FNUHPxMMMkm1WHScbOFr875Oa7SvY4TUoEhmBQBAR02FR3Cuq+5jzAPWG2we0G3D2PfIMCDTr0Q43T44EtzDcWIw4nqgISsZnGYpDpLSkY4W4NVRJl/UwL0FtHAsZgAIW7GnBDxM4ImbpoAtbeoaDCVabueOaBVlnaHEMI8Dra924XfDyGHmLcS7p+e9+ZZk/QBrO/yNHa0c4cjRATWz7FZ2jXlU8/qSWnzo5+u6z0WrcgoOYo2w06XwPMtAWzJIMJDK5U53bw0HyIWfnlUq553XDKxAS3TR+bh22Roec0K2zdu+S9lzWHSwS4x7a6VA3BYlMibWOYjc97H2x3VftUFcL/H9TkrnNCiLMeM5xgSke6ttA+dqg1j4DL50Thx28mXNnAYFOrfx/n52+5yf1lHMq4p5okcVsJG+HM8Mzi/RaaoyPd7rSYHkLq251ZVI0DI9OgQpMVIdW8qriCweUtVybFywIpmCh4qjcvGZp+hXJCsYzQwXCX1TidTFvtKERAdPCBtwQm5z0xWnuPKkoJK9ndOZwao6X58lwT/FSiXVw9wt1RKLDymvA0s/M3ld01UNGNVy0bNC0swWx7m14MLGWAHO8ymSIYzm63PloSpIR43o+TSPKrV5sCLikmPVAeJGUFXYFIyxB53hNtFfojIZnR9Hkt23fgOdhusRRVpqKS13zInb4BMx9U20QCGnyP9qex6YjIIT3Y3OMIo4lPtCjamTq0D+zSr0Kk5bd7rLRDVAR3KcuCbm4Z99RRRuBVo2jCglvPIYkeI6FkKb6LOdUXPfPyVlKuLFUS20lbXhOFbL6t0Grjz4i3Rc1bYkWjNk4lBSH+ZJcYIX9/O+74FeIziVMzjH7lBx/DAtOjHZqoJvbEyxGprk4TzOXcGy+OwxUNLcl1Nw2BX/+yv/Ob7+lz4ZD33Me+FjL5+AT33Bx+MPftInYZ83uGcGvfdG3BwHjuMWjxxXPHTvdzC3G3zKf/YD+Kt/9rtxeeuGR3rHB77+/8Yf/a7PxJ/4ov8RDzz/38YrfuF78NSvfhg//3Pfh8957R/G2x+ZsH7ADs8YjRHjZXfdBLdTNiURywyIymvYNGbG2qgFMbRXRJSO6S2riapcqV6CsKGluUFF0+mE1n3PgIV2lleqVsT3XRUPXC7nQJjPFmuBaDpUE5Gmc0a7lk53II9eX7HUEojKg/denW9EcEVaTGgemwAHFhpHGhUDJFXPWjkSObKVNqIbIO1iFcrnc0xd8n23oSQDdf1nrr+sBTlGouW5x0NyMbOFenZeADrVaywrIs25IdKsoRyBI/iSc6YSCAPQzNhFFMzz6r7PjK+p3ADL9ctAX5qmozpi/Vb+7QqWLOlw2ltIhwXfN84u2isRwY4j6yvonPrnuhqA10pwLVJPei6VHFkZuUyHB+jiTnUpxhJJHrftIacYdp0ScQ56rMB8H8dpD9IeWpNsCa3woDMzU2Pk2SuyaFccb9a6pDRd2d9jDOzjiDPFzja3oK9cuyaunGC6pO9EnPYzMVbnUKKo42zLaxaFc922UOQoGeDrcbiNL4ExqXO+RkuhtBiGriA2FZL2PSVhqwLW47nepZMrIn9LRH5dRP5F+dkzReSfiMgvxtdnxM9FRP47EXm1iPysiPz+x3UXmXJcUeeYe8LsN60YXSCre6ljiGnosioYyU2ixi4271YW9xiFPUtepzqMwJrUQxZvic5zdTBnMQx1868Ji1SSre4rzocbuWiOOXC5XPIAEmW05vfrB46mAHmiD4XXO+eEjNWgYAyXwJJjBn1iLRYRSQpCC4POVD6moXV25DkijXcuqKgbuS5COt+cA6YcgCX/VblDq8MNO5oxtbYcM17KhlZYhxjvXVWxh+ECfJPtIzhWYPQbEkPHHkFSx/Wey6R1WR1x6MyaCbTdoMnFi/Bg5XeWtBEguIr+QH6vMU535Xp4eDItRqeTa57rq/eeQQjTdK7n7M9KyS5ebL+pcZhCdbUgLfdCZ6Hqyi76yUgnj1qhHiwZgJkZDfIL5xGpYCwUyKTshdjLySvGMoi8rxyLfgmqRVAXiNiYgdrNTKNuZX1x7d+07kg+FkeVTvsx50nn+TERUqzAbM6Jh970ZnzDD386PvSXPgbf8vDX4Wtf8DfxQv0MvPw9vwifefkk/JH2cfjEhz8Uz3oIePurX41Xveon8Gtv+mXY9iAwJh7dLvjqL/wb+IrP/fN4lb4dH6HPxv/0zS/HK9/8ZvzOI4Jv/LF/jKd81Rvx8698DV720B/HU8Z7AHIL2y2dFHmM28wGBMeAhBORhTLhrFEKi06nO/5L+WJGswAGvVwDQGm2gEiYsE1tcWwq5YqoqNvkFXBmzQLulxJk0x5Sd3IdMjAO5Rbt5+5KeZ9E8NjWNdK+7g/3zNrw2enAIdCkLQqJbLgOte9DrPSwrcCMKPAWKBqDCo39BXUeIQsxSSsiB5wZQdp5Fo2mM2EGHCsjUg/u6vxxjKvzupzCeG1bAAHntp5HtB20JzXokXAQGZCTLrTWx3LCaydSOtAMjpIGgqW7zPNjjOFa3IKkVSU4AD+/E+GL5/cmO0jKCOeezzWn05YGC86jcYRNifO/nWwP6R1sBc7CLNIXrkWVaGVi2wI14JJsFcDh2JBKkv5J/H3WrwCxL2IfmeTf8xplXDm2CZzJQrITlLNlvxMpJkAzZmbr7tNFZsAS17FPCLblyML50x0t+OsrACM1jO9zt/6hfp6MmbY77cRdgCEzwkThHdDT8JcyqGYxcLZwnjjn49/59XiQ3P8ZwKff+dlXAfghM/tgAD8U/waAzwDwwfH/ywB80+O+E0SkaDO6IZUOWWM5Upe+nQxCpmFEc2ITTcVCWZkO4Abk5lE2B0BLJ4bvy69MDefGr4jevL9N4n1Rtp7lzgDnqD7QN3ST5E/O6U0FmL7kAsJ07dimG663RbuT1cFA6iiit8UH7k6N4MGHaRiydOuouUm0Ywhy06f+LIp2JRe5LaevjgkXMg8IjblggMHx4TwRpeYmFhGXzrKl3SkiQWdxAzRwRuS4iXrveeiTq4oYF0Z/bM0MANvlAQxr2MN4Nr2BSENrN1EQtEPVzTSdpjRAc7Wa9fFAfhaNCHl5AE5rpQYBRBxpLDinRKs5roh5YNe1E12gBAgWPMQMZgqHTgORZ6/6qjVJtApw5/T29jYLD1WrVM9Mp5pIG+V/eBC2Fl0Gg3eXmqGxfvZ9xzxWcwk6yzJm8qobi0y49mUhcVBJJRU+e847OcjHsRBF1WyZ+Vgobt3rbQM+6K88D1/6yX8VX/8nfwyf9NGfj6c+7ano7UGY3qBfGt7tgffAs9/7A/EJ7/up+OMf/p/ijz7nxfgIexZ+5Wd/DA/92i8AAH6xvQZf8P1/Cv/wy74bn/nMF+L9nvlR+Jmf+kb89Bt/Gm9861vwX//c9+P1n/8Qvve3fwh/8e1/CX3eZHakHnL1GnGY1nXF4EZV0ym9qEvaEWV7cLvkflVmL4IbSMfOv1o2vUhN8WiiQQeW+5pITXU6TujzdKkfShPWAFLDicgDUs6fr/AGJ3ftsAIrrV3WKt9/zsNF+ltpld49u8BsFB0JaS0bCtBhWbZwBeyntSEL4WQra85F2v+CsLOSPccRK7MyirNSpQz5XjzgW2upHWtmuUYqMGPmmZt0voiuWeFgJ+K+Au06jtk8BOfivuqknexMeX+uzYn1OgYxeSZE8XNjUWr5W+hqWT/K+1b7RmeO7Z7rs7H50G1wpLWkub39bwtU1eeMzT24Tvk8mUmNOeLYc7wdKPI29WM4ha1mKqo9anbWfRY5a8nu+55jVcczMyaRkWstOoYSAWUQAhRKHDMglvfJDo3k0dI2ZA0G7X4ExzM6tWZgWfa1QiC6nrGeP7xqrQ6DT+5Z7mUGoNUh5nnoS3me1pWDU5bBF+o6fgdgxTu63qWTa2b/FMBv3vnxZwP42/H93wbwOeXnf8f8+ucAni4iz3lcNzIMuN7zQ+r26h3MDm/rm6jetOw+RY/+hAaYndImhzgnlEZoHwbM4c4AoXKZwDy8wn7Y6kMe+8Cmy0SpBj82igLiQ3G5XAJt25yPeaclaCJRxXBmNAqDXDqGHZlSp0PGKJKOw7QDNiduLpfUbpXN07s324YBVrmORd6fgtDrdsNzDPS5kOZhK5VQ09mKFYm1dgOqUfgBuigC1bFdC3ZtKKbJdxYjEWG7XtERzq14xWnlg/G4JKLT9SbpCI5+Sr6OFfAzHPNN1jhXpEJEMu3XxIXFzbzLivaGgR2KAyJuxNjMY6V5zoUr6eRNb5ZwKQdevi7mkc/Tewd6Cy6e5M/cOMVmVKbAzilHzIkH+pbrI8ddBVu7xCHYwolYThA1nMkbB5DpOA10cGs90TdH+DWcoJjPrsAxvbDDiKotR5+oivaGR8e+qs1FUmWCho2FiNJaFnOQR9m2KEgsXLI00hHFd8jaf3BjvUWzCHdWJMcoEXpIIGfjMQ3ktIY+Br7iG78C3/BRX47P/sgvCc78QIv2vP4e0SIcLWgdgvd+2vvgk37Pi/CFH/JyPP/mffCdP/PX8YM/8F/g733O38MjY8Mf/IT/CPiVX8dr3vwa/JXv/fPYbjp+6/YW/8sbvwnj5b+NP/eGP4MfePhH4rmWFNz9N3msYi4eMiGyn8ghxxvkX54Lz2gnNcbw7KRGs4S2us+lwgWdgIJsYZ4DNxb3saFJl8haBb0mD/hSHJcapNPTlNvme6UGJRL7fY8ghmoRXRVtamqP82BNGbpwqohEVQfEzNsi14O8Bsd0KDPrIpIUMhFv4AOOqa4OiPXvK3WmGYN7y8BfxGlz1WZW1DbvN1DzenZUG2tmiN5q2I9baCtFVrLG0GTRw5j+9x3k+x66+NEJCIVdZmMKPk+2Z8bZFjEoRRSTjSgS5dqk3CDR9byKjWOWyz9jnD5Xo1A674N2bh6Z8p/wgqd7xx7p/EAWpeW80JkMC5KfScc/C3V5/gaH9tI19WmlzC/HkfuIlALqHRNMY1GaiASyvfjnpGXt40igYy/F1HRomdEh4s19QhUdrov0mWzRTBhIEJVPUGWse+H64t8zqMjsBs3RXAEA5kyUucV6T5sSc3qJANv3G7LYMAEZE+zDWxN7+3PNwDR9CalOMB739bvl5L63mf1afP8QgPeO798XwK+W170+fnbfJSIvE5GfFJGffMtbftMbITSJbkcLzdjnSKjfyqbVMKK10EtEcHNzkxPKAWQquyvQuqyDGufWhUNLq02L4pk50GxC+zI624x+2oenDbZtg1x9cRJtYbTjaaJWopBxWkgsiGJ6pfJ3HQVd6g9MCWeUBkddjusONSJBPTfM0JkRNg1nTc2mmgOrrWWlHI/Du3/JOIDg07J7Stu2iLSQkd2cE+M4tyNMMn9pIMG2ka6FuRDmioomzziecU7Kjy0Em2gSN1tXzdarVWIpjdQxcpOSe+uBEYviFroMXTJF5O52bemg1QwDm5XkYcJDKgIuaS7JNjUO6nAQ09mTZezbtmV1rFJqiL8L55MFKp1pZ4NLomEdgAsV3/Dgzc0J3fWq6NVpjRkEoil8Dh5CJs4Va9sF0AuMMlLFoCocnTATNFxg1jAnTshfL4hCRa3pjA2BG0ElL94djescSYmoczsBTGXrWGrhxn6evncrVYTPRFTidNmOH/6X/wxv+73fij/1+X8Nb8e9k6MhTVOVYgu5OiKlFkjF0x98Bl74vi/CF7/xhfjsD3g5Hn7o1wEIHj0OvPRPfzM+er/Fuz36dPyxb/583PSBp2zPwl/+i38B/Tuehhe+7t/Pz0wU6s7V+2U5/H2tTzqXdMYq8p50hXKY5DMVe6plTQNIGk1FVe4iaDxw+D483G/HAgF4L/W9eu+49C25+iLk5HdYIAsyQs9YVyOHS2Rq/P48/ct9IZF+XrSgliAFszB8LjajqUE67jy/RTBV6R9Mhx+xt3Ov2KINcI0SlaMzl3SIO3PArEhFi7fWErnqZS6P48i1z/dQ7Z68gwKm6O0SNQxB3bKzugnBEf8sPXFJuS95Zfo6nC3ONaX1MpMW1LEomTrt7zYsutqFE29rvclYoAjPwYok1v3H13C8axZIzNDCSU+A6xhZ6+JI4kgaBWIOQTtpZ2Q6z2zIyTly5552qJ3uL2Uw40zK5lDs6Ee7Vf7NTn7btp2Q7Yue6waIaPLsZIbsho0zpq3sBVHOeUbxeQ5mY6UYf52WTjcLEnkfNZjiv3k/BC0YUKc6C32Isc4gBn/VdtRzwMxwmAOLteZl3xddlXuTNQv+3njc1+/Wya2TYHgiBIn1d99iZn/AzP7AM5/5jDRK23aDQyzFjoEQyh7l0BHv8kUjQmeJgxPvf0pZA774Z1TxkZReHVJO8AhiOgd0j8U1YDhiUyg3rDnafLlcoAZ0Wfwo9oOfcyaF4HJ5ABXxA1ZaJyPNu4u8RIvSVq9qdzh7cjb9WaICF274j+sekeKZl9ij6MnEnSsPGFak2LaO2313bdli/ERWkcExV3ehk9OjGjI4YdALv49pc74P74lpbX9NHNjT0QjyZhEc5yHhqNoq7FLVlMmqBohFNHS0ifglr7h0hvEuN6H0UVBgRzaPCACqM7CkeNL5mi7yzWdkg45KoVic7XXoecZmBpovjvjO5bzMQBf8ENdE/SgVtW2boxJ9OdwV4aYcEYvSoIuqAvXX7iNafxbkLelB0wBjKs2Ff3ggeCajx2uWtjWd/yx0CCTH2kKLYtA80AJW4YkqdJobfaw02dY6rPlhSyksP7Qi9Sh32m4WVMJfcL+gzBgDX/dbn47/4dMewtuv97DNdnIcqUhCSkyPIMha98yQCaAN9/ZH8Gmf+XJ87of8Cdy8+Rave/0v4Wi3eNqYeMmX/ig+6qlvxfu913vh8/7O1+B68+748E/8YBz/7dvwHH0qvumN356OwmM5uft05YrRBPNYziUzC9eU/VoHiu8rzZafzH70kOqSsn8q/WCGpBdtE39e0cTTPhtFVg8LYWT7WxbLiUgG1JXrW6USFRPb5uh5s0X9IW3KbVE0WhBXKKlZJNqSxferBTJho8K5n7jfqeNzOM8YCTzcbBewEJqNgLiXmepm0R8Pfq4brr9s8GCFRymaKCSdLg/IzoWU5K7WM9Bt/RrvU6p5zpNNopPj8zLvo3XV81KB9VljYMhyUFyZ41yIyMBdUtIt5KJkUWrcMTry+bgjM1iLQsD1fmznvbIaeyhYELGc+WyWBbLVqWvi59AY49TSuVIWtJPO4OcW5+uI/4nI0tnPq4I5PB9VTnNEabw5HWkfAm+qw8BLVyt51rpwfXDvEdGcADZpqTjBQnpVTWlUOu8KeIYayHbSPJOSM1tatSeCeywZRTqy3De1gJSBooM5SNCNZzrXPzMHR7Q55t5i+2POf5vRQGiudVl9JDrJ+fcmT8jh/N06uW+SoCHE11+Pn78BwPuX171f/OydX+GcGnrSBY7pxVWXywXbzQWpv1hgdEZIfIiKOvB3NFIAwgHomfKyMVOOIvmmUtBO2InjwoPgdrrDu1dHOKK3GQa9Nde4Y993YHE32SdcVZMjmg4Z1n34xK60K+D+AAuu+HcsbBtjpOwIoiWh9pbGZsKy6tjMNYRZ+FS5LsNmoCNIGkTvHdqQaXQu9oquWZGGpoGvxrDr4lMz/UldX276MYYLVlMwPCJwlQu8vUYUZwU9pPeekS6Nx0o5+sauSCu7ypBPp0V3kzQRjgfnnOOYa6w45Hw2fgZpMwspXlkDwOeOxjOzEqrJRR5m2WHpcIjNU8ttdSnLoChQ8CMUK4hYp7NSMgOqrnPLwkWuH3cevHDm0rcsjmFHOBp25zwKmiBQvJGHTX4WJITGB4A1hyKyes5jjQe5hNJaOutcU6eAligvoqAJi3pSU3GcU9JEOB81SGvtfgjgY77x4/AX3vd/w6U9Ba2tgGuYpYweq4Y1UDZrAk3nMXh6egNTw4PyVHzeR3wZ3udhwz/5yR8Erh3X69vwVV/yHXi9PoTf+6wDv/TQq/C6h38Dz3+p4Ne+4e34M9cv9fmWK47gD9ar6ZaoE5+BgSvUqSLMbNUA2sIp51rOtR3vW4uuiAhxzdY5oAMhZX9xr9SmIvy9v6/bENI9uCfZDCP3V6x7ry3YHNAPW5A0JvG9IOE8sKkN1x/XwoQkRSuR1na2lxzHu3QMBi6YwiMAACAASURBVDae9nZlBOnuxM+QU+Jz8P0987iUC9JJi+uibdmdcUaMKds3bQW6THGnrBQDal2KPRwb3y/IltAAUmmEetH1XKPzzD2zU+khLj5XBuhjBKd51TXw7KAdqsEqa0+AuFczp8OFw6hYGTrSzhTOL70WJ3XYzOJXjmnS9sJhq/cr0jKIZ10JMwA8Q3GsznK07Qz680wbS20CWM4R94tFZumYSGDD127UFgQymrQEK5lnRdbZcI35vdlpbxIIqHSAimYyS1hVFRjUcNznnClTV+lqAPLe7lKZ6CjT9l1noT3NmWvV1XuOExjnhapLf5lZ5iw+LCADwRxXgvB7ugZYlgHNUfjsAA4pcoUiYBbp8V6/Wyf3ewB8QXz/BQBeUX7+x8SvjwXwNlu0hnd8WemWJRQ79kPs3u2OsV9zoJroangALANuC6FUVWAf96GXY4wkr3tTCeAmir+6NrRZ+LJz9czmYcwFR4pEv2yYovE/cAuPVn0DGHq/ADjzhrsKHugbSGPg71LPEsjItk4sDy53FjQdPRHxSuFwGjMNx4PLgN6WwWK0VVN45IndTcff3NwshBWlGG+MLEwDjd2x2gdz4xKoYwpODZAZzRrif94zkaNt24ImMRfS1BfPtabRRciXXkuJY5eObomMtyiC4zx2VUjbchOQm8VU1eVO0CEiKVjvz7QK2ej0g452vIbcI/5Paae8P6EkjC00XZ2fysOYY8/ghnPNPfFA8MITdbMzx3qYI+J7GGP+LR3DMQbGfqTBJhq1lQK/qpzB8eAYeqABTzsFT5SVyyiIKu+dY825B5YzwM8gj5v8TBpMivAT9abwPB2O+hkMetOxCAf17vVFH/SZ+Pjf93HLGMbfc475XpyfYQbbRzhvSKUWsQFgpgbliz/yy/FZ7/+pUNzD9RD89nwQf/p5LwceGfivvu9P4gOf/jw8/P6Gp38n8OzfeU/83bd+JwzdtYwf46IzWlE3NkfYZDk+mZKMNdtEMmNREVc6R5WDW9db5bbXnzM7UH/2WOgzkaDMQPSeGTn/4OVoImw46Q65XsydeKZ3+TvvOuZdMbnWFQ1NohaB+pwoQWZZg0n3KXawdmlTVW+c46hFQVjPsld1bCotxNcsl9OSd1wySo7Cihm0IceIDtVFz4GaYNHYajDBM5Nc45vWvRVwASBoJ/j+VHvgnqPT1VUTLT2lse1cwCZmuXao2FCRvlw/stB/H9+QvKJ84FiZQP67IoJ1vWXGqDXc7tdsFpGOH4MLtmWerKVg46XiGBW01Wzxr/lZnLOspZCehV5EwemsznBu2bTIRFJxgs4q0VEXq3H/wxv2DCRXmM/Tlo550vioAnGUvRFzlig5M9nBr/W1vFDjGSgvg0nev41lx7oqbvc9KDlYe27OlDatASEbRvQARUbQWgDgdhy51ut5x8CV1ByOQ8qQzZm0jJyPsUC4Srl4vNe7fLWI/F0A/wzAh4jI60XkiwH8JQD/noj8IoBPjX8DwPcB+GUArwbwrQC+9PHdxuJYcuGNsXsUEmoBqp7WSUkcc94d+2LTGCxnYaVp7qYASFMQERzmOrjYWbi1OypD3qLmOEQxzZKJuR4H2qYw8UXtRU8h/aUCu14jhVvI/yU6B5yjm6ha+R0jsEQLWssChiHn6AioBzqy+pwOcz53LDreP1MnvI6IkoetlF89MJnepnQLD6hh3kCAG+qwVX05sUjthx3YLi0340RUiQYydWDmnPlNS6akIZ7+6hqOhiBbMd4NFjBdpuimLcke1Ei4oBru0LrOq0ZaNFOks0j/cJPaGiOOOSkRB2Vycrzv0GBoVNMBna5RGMjMEYGVBUog26pyllDg0Lk4VxdtmZ6utBsi0nzfdHaiEMiO1fGMf+NjhSzu2ILzlfrBZexyzclCC2p2gQhai7V21zA1kZQ0472axDOChUEXz7wUiSsAjjYHXcB1PldQurGLHFa1f2Z3iIA8RlHXV/0n/w1gPVHk1ppzwuaEbD2LI4kCurM5VltqohyyuM5DFLe74T947gvxprf9Blrbcdze4hM/4jPw4Ft/GV/+vC/DW27fim940Tfguf/50/DQt7wFL33NF3p1+LjfLEuxXwxkJhZK61QmXUGsWaJv1+NwWaDiGAOIA3Ehv/yM3Ec486C5tlQ1qUqpQlAoCbR1GkWWTP3znu4iV46c+t9XdZXdJqSzec1Ex/DmMOpyQ1NmBmmOojl1hI5Hol2C5JznM2V2KwKkWTiW5hXfXRDvaRlEsLBt2ExFCDp/VxbmKLs/+VhQk9XHzrMidR9BzxSVzNiwQctcck0spKMd8fl0XWrOaZ03clOvIZ9IW3TveuuvnTMLfR45rtDu93VLpZPgfFMxAVhZuirtRQqYtHPhLwMKTCLhoZKR62Slwp2Pv+gcTHNznfD5WQtCnnTKV8U4U/3idr/ma3PO4z1oJzxgXgoGPCM8o7hn0J97sFB5Zrkvnsm18yPRX9+f4ZbOs69Si7Ize2iWlATONx1bItrMfjCToJDlHJallUFLFPnSwT6OIz97xNzdUtNexYFpnO0pfZ5Ets3R132w5bvma4liX6/XULtZ6h2kWc0588zFXPUONRNSgRUzA7TjGNfyind9vcuOZ2b2+e/gV//uY7zWAHzZE/j8vBLqZrTQeiymiesxT45GpjRaw5jetGGMSG3M6b27t46rnR1cLmCAhjZQDgDWSfJfKc6m1I5c0TMjK3JCx+5FE9qYtuDrGvTi92MyMYcXmLnTOgJRFFQ6s6dC7XTw7Pu+Op9FRxQaY27GXPRjoPcNiC4wvfeIPCdaKVZh9TvH5CINO2byb7mZiNCd5iUOWY37ZOciIsUKREWn4jgmWi9OWBDlx/Ce93NO9G2DBD3Bndnl0JgZtCtabOiLeH/12RoQnzfHAUGkBOM+eyCVTV0H9zqHv76gknlQhBrGPpYWpolgjpEORJkg7+h9HHkYK4IjbJFVMEBJEehFBWHOlGBRPnu/LH1f0hGYlgNynYzjwHZzyXEDuwXN0h4YC7mrTiVRDY7nHAOtt+LsKZqY3/MYvhfmhDWcuIeUlRv7furyYyoh0i2euZgHNhGYCnAYJmZmHCpSW4ujuC7b1n18J7DfXtH74jwTGRJzbnDy72AA6PStYsMa+OTnmkIfo/Ds7Y/cQxMDtHkHsBi333n4Ybz7097D0bIJ7OFsuK3anBsWB18WqnJsQzHATPABx9PxG297O97t3Z+J43rBl33uX8MD/d/Atj0VX/eGn8Xbnn8PeKkAX214w6Nvw7NvnnbfPZLjxoOE2QK3RZqHv9Q2tlEQ2FpLR1PmxCV0heecmMOpF54RWmuT48h5qw6grxpJSUYWip54rKqYY6KRsmLrOQIbhWXgaLj0nqntZQuB47hGOlywG+CqDs4/98N4rDa6ZoA5VYsULjPzz1NNUzvnXN3ZyvpQVQyEXqxZ6Oq6FOURGTLPXkSr9zHRmqIRiAj73ZIXDUBkcbgBsMNU9GwEILBQIRAzoAQb17kyiIrIrkU3UO5nE/jnz2XfEEFdE1nARhTx8fk3InyxF4/jcDWK6QANlR9iMnC77/kzOqGdDuGcGDEPmB58VEBhwlJbGHG+sDai8i1teAcx3qcXEJaKeoEragRgAxFMWfJ0wKIxqioeyABo4t716trmKgmx10wEC2MZjMw5M1AxdU/ZwvZwLEkHaRZLazrSaxLIahOM/YjOclH4VRoxqSq0OPl256vE3CgE8Z+//k6gfiSyK0DUT3DseZYQLPK6gmh4wazIHdvsY+2+xUUbDp47iGYPcwJx/45Pycp2FzvEfUyRJ+/SaHyQvG9+9tV8zPfI5nT1jqikPPj5eHVlnuL8vqvrd0tX+Fd+0WlrreHm5iZQCl0tfIcT29UAHYtDk4Ve0wvVssCoFC0AcZhOV2UY+zx9LrmVg4YTZ0coU7ay0sRiwKYbWjds6hxFMcRBqiF1sia6tUU/SO5t4RPyc/plu09hYaV8NBsFqFwgKCi3bsCURI1r04j6/gC7eyGRMDbNyOeNIrlTMVU5CPh8wHJIWczCzzLz1BnGObgYNk9zwvQ5x6FG0tXBSVQ65gpxD5l+LQh9vS8T4GZbY5qp+hgHJ9uby8OUzeOR9tl55Fhs2xYoCHmxsZ50OWW1YrZuyOymY2dU8YQAm+UaYxCzl4ONqSdfE7KK5Ipju55hOZUKT4OmAdy688YD/fDDUE9V3SaOrrVwsrgeh6dKsEnJBBzXZTDHALSkBUv3HKKO8zEaBgDApSsul55IAbU9AawiRhRUU6q0m9yHAvHq2/9H3duHfNtuaUHHOs/r+t3P3s7sPbObaUYxZoi0RjOi2JVJOpVSw6iUJWEhZeUHBkURjGlJSGWJ019FBFNgBZn9JaUQ4wf0BaJikxSF4deYk2PNx9a93+f+Xee5Vn+sday1rvt5x9mboJ4ueHnf93nu+/e7rvM6z/VxrGMd69ObunzSoGRCxw7pz33uc7Hnj9wvLNWxbEvJHJNqyOG9jQEIJr7z234xXp4HZAMThp/6hZ+Nz339F/Cb/9Nfg/Fnfh/+4usT3/RzBN/4J78Zf+ArvyfX/naPrYzZ33Xe866SLvnPZt69LVYIKvd/L8sC1bzEwDY/J5IvUm86ZYb3QjSso4tM6oZ6sNgTsXxvcyS6fIUKRPKh437OqGB1PdncL3Y/E9ncIkeWgXkW+r7hPTNpIvWpN3ANuLKPUJg+fpd/33WyGZgDSFSM3eKFDHPYwcAYB0yn+yNEZazZDAb+/D5Hmq88gx2FpHLFgEDHh4ES3wOpR2ljuRbxzrzBsxo/iR52n8XvTd7w1pz86NW9qCAEH3TbfXon1zfL+ShUnxSpTsPiPXCPeANq2M0xbjaZfRJZsVCvMFmsK/2ZNeT9bTUUQ7LMzvflv7OTPkGKHnhHVlWrrtgxIMAKMGlt2DL/efHA1+E75PP1te4VMcR+VxRy26W++vAcvrduT5cqtlbD3N6W+7l/ZyZ5e2M9LzzGjLXTHIgyxrgpbVAOkDaYdqiDiv6CYhpbU5ZCnDGi7LQhOlyir1dS+5k1I03iq8dyP5og142MQ+vreeWLVqonmPm8azPsURvhgmKJN4Fx0ZdYlnF7RnlMc/TrDE4rETNVnMNRQqACERynw//PhcdxJr+HZSezC4ITG4bzdEF3GdaMODzQNTY9HQCq7MTn9pfp3NK9fXYzOW8ignUp2BVOtHjrJymHpGsDoni8O6FWKF6Orhwuo9KnXTEg4GQiibJ8z/57s8laT8x5D8y4Afl5GZyJc+ySPxuHcECSk0VOpo9/LWqJE97dWTzXXY6oO1YMd6AyJ96rG6AHRjqfbuypsUsDRKdLdQqWjQ45grt4DwC88aT+H1zTLH8hA5+3uosWhn7BsruWndcSTh2qqcXYkU0GFaQNZDkNJWxPh8xnLWOwo8O4ysdrIPlnfK6cDDZnNiJ0h7739jLW4ZP3Ur8XXi1Za6Wm8HEcuIxB7LwbvyxnnukEcshES4KAGt95zpncybf/0IkONpsFIjINeD6fOOdRCQdRHtVbQNL37ikj19vLZjGiVg0GHzd7iWXToKLE4Em1kGF+/met34yH/Nu+8HfgCYXgwu/6/f82/rX/8rfgTzx/GF9+/0MY/8sX8NN+EWD/9Su+//V34dPMcqKS7ZyhnRsm93NOR9tRFKV5nhAx6JjFx2vnitw9Eeezp/JBUjTqOfn+ydnkGeiOuieel9xLwRzYwWD1Mi2+sCF1lZ3nrLmXZXtZvduciYmFprHd1DpkRLd8SF5RGeNWeoY3UTrPuaH+cSY4CYqd6uSaE61iSZpVBJ57qGKjmpL5uQuWQcuIxE3VFSIYiGICZlXdYRWR+tD8PQYITAygCrt80iVQaHQ/x9wv3EMcC7utmn56oCXiAxGWlsICJ4xSuk9J04qEyrAxh/szjaEzHGLTUXpWLU/24oSeMhNp7j8AMX2zhh2U3a1BT3zGS9pwDHSUNgCd2aTzBpsbW+m/AR2kbHAk+GWakzrXWti2YKLRjFagw8TMpj7GDBy2tFQxHtUz4n0GO/mxPVC9pJq7EwSMPfN61X3LPG+TxYh6UzLTVHJkteq6oedJj2h7ZIwRFAQ/0++XD5u4rp2gRgd++Hs7aAtmhmPfNfPnnBirpCpzX8YEQgA50VKDckGNXPo6+iFfkw9M5E94fTRBLrOgPr2DmTmzqL03loVUid4XuU8M6mhFatbBsxjyoXRtvH7yPjNYJ0BzRrdhqpepjuOAvZm17S/H79uzz7OBeSM/35HfCricn+pxW89w+Xtm/n3Kbvlb40dNGToepxugK6afcewrnHrA4CfRg+Z8VuuYZLaUcmlxGPnn/H0A0UR34DwL4e4lkbdOGEAhSdK0G+cEjuBUXwtD73zDM7pp+W+WhbY1vcagGNgOlCK6uF93oxHEPsCokci8PzbuMfifEpI+uiBbb01/x3E0mZmVzsXMMGK0b5aKAt3gmuQ6j5ENkwyEBbVHqS7BdWR2m2WzeCYASf7n73JUap4jBqFj3NYda0Ni0x6uDJ/OuyNNfA90METyruvK4J/BCn+WjRW6VnL3iLYXutjWf05sgyNaNLZoAWRDUfj5iU6wDBYIHfmn3HcM1LrBzuDYNs5PUVfIZ47LzBz9F1YsNAKNQtkZGHI9lqo3KdmA7gomVIHDTvy0z38bvvCjE3/pWvhHf+H34Md/7L/H17/f+Nd/xX+C3/AP/Er8haH41u/7En7nl/6rlE7s19Kdz97tHp01ERRPzlszVFSkdlM04J8l4o4qUV7b7SCdM/cc9zkDEf4OaQt01ORac+/5eOdKyHRI7aNoLoVayuI9hr+nIY6g+16+U9V4rljtyObYpvs5UShprxYUUlu2nPxSBpDZuGVB3QoUbjTt3NH2dk80ua4HKxy7Jsh5MBfUsjb1inv8NSWqqkmH/ERy9d+vK1Db6NdQ5wJv8SDh8XhAAbx/VlWFvoZnBe1ec11iCAj9g++dQLyjxyXPmDU1GzM84v2IGc4xcW3F666Jbgxm+Ptv3wcranYV0sdKGTv82Qh1XVdWpRgkzznvCkZDElVG7MlE1J++fmvdlSXYjE37nJMdYxDHqxWybQL3YaGjz+TmDBt/Rb8KgPKz8OCbfNiedLAykAEq73XdEyVPiLyydcgodY74896sxT3JJnnZ9yoL46u+H/q+6A3bPC+AB+TkpfMqm1iKEpfurKaxx2EzQQrgQdXpCTsSQ45ylvPI5sQ6k0WXw5DUhv9qro8myHXDa1kiA8qYmyCnIJ0vD+yBlOPoQQCNTjo4NCoA2B0/k5x9vjyg13JVhWNinuSPhUMeIbTctPQSZRslF/N8Lly2buW9MQCIOk1hlOaoo1oj0cQGDsa/a1OPMTI7HrMcFoOAx+MRwV4j6Ecw0bufPYiaiWowEFrqQwkYTBL54cVn9cOwU3OQ3ZtvkYre8MR32gMPOsgMVEWyO77LHvka9RJIyZjpMdy4HD7CU5cPe5hz3sqiXEu0ZIJB41vpnCNGH3tHdpUY8zPiGqgy3xgDdq1KwJquKJHp3rjHpjSW2wSF2rrhLtQ3s38ruSNvhFi3oI3NVB15reah4knzWc7YP5hOnzHc0WEaW5b1ehA357xzoUah5lOCT3ueKQczZ8n6YLgGsZeOiaQ5ZszMPalAERCR+pKIJZCKIB114P9XxaLKzfy7cqqfUqJEd94VNOd5uhaGjVuzij9b7BEicfG8XUtTAxnfeGI9L/zMd9+O7/uPfyv+sd/6C/CLf8Y/jJ/z2b8TP/xZww/8F38En/mOL+D//POfAz4reOpXPrjHbNwZVXLu4u1MgOnwRnx/R/PEaoAK0fz48BjnO1J7mtJN3a7ymUTkg3tLJYNwvN2J80yd0wMDREDU0X6u1WUDJjMTBX8/VskNKxtHBVbcGxpDAXrzK4NWPgfP+TMaZtKOjxppTqPMpmB/Xk271UvepLcBuPU6XIFkzYNSlEEfui5sxLlsaJgH5Dv7C/rFpJMazazmJYIZAeT7dUVg3JRLRnW/c73Zz0IVGpHijrK6QtAjlQPUbvdDKsaUahh9Pp8VOLUpbdkobhXgEDQiysvPAsI/mWWQ1mklnc7CPQ7V5peKgug7288wZccIavE89+pQjtuNgH5AUpFmxEtmtZiUg221f9kodpwjh/oQ9eZ55fnl3o3FTi1oItD8/r0reO2+VUYkH0GVJAUxQaY24pyykuRpy75XDJnoZeIokr6mVwMlKGpA0X/4O7S7fJ5tFRgz8evxQPdhuQdNvVEtGjt7NeKKkfIEEL+W6ydtPPt/61rPK3h63hwBhHM1QMwd6RKfvgRRqH6KtFUYvMNc6WDP4uvQIAFI6gHUgHN6nwK8GWFsgcmADIFFFpQIlBrGJMLo4xTnnDET3sXpJ0IrFwOmExgbA46SLPWMVONlufFnoFvIQpfcIPJyQyAMN+1QjmXdphgyYOP+e2MMyHYSufZ1imawt8hQ/+9C2gRjOEo0Iht7BGL2wX2OEotetnCMO5cuGyxm695s3w1VQEreR9gUNQYeGNji5buN6NT1l39DkOiYRAyvWzH0gkjxm+lgdC3IjDKibzrsVrZiE1fqMMKgS/Hy8uLPHp3CKfTOdRNvKIEFEsbERZgRB8drlG4ug1QP2uP9myavOR32rOCTKJvSiTXDNSDepCUCjT3LSUc8W5Sg6qW9OExpdGnEeyXDwuHy3M3oan+R0rxVOOKa4ynN/D0xsAgOFubM8hoA37/tfVqsH5uWEsHOqXgV/AqdB/eO+KScAVdRGa1LnBdRSIyB3SY0LRdjhajhIQMLxSHMBtN4r3OMfEcwA1rAIGNgmOGn/9V/HX7rL/u38KNf92X85eNL+Nl/03v8ph/8zfjlv+Bb8Lf/75/HH8Wfw//144a/+I1/6YN7vAeDHvCyuY7vuiecnmwZdgSZglkNiAB2dLILPMBasGgmlQz+dFVTq3FN1dWw5xhYeyWQQGdl8fy9EsWmNHJVM/HUSjqykdA8UNW1cZwDq01PIu0l7YyZ6zYPbxwlkAFVbNphCdQz1ucMfvs5OS1PwgbfeZHuXCWpQnT6Y9utWQibUlURwKECmUSbIiHmvherBj3aAAcZnFJhoKTmxjEtYsyqlJEawcTuGBPDjxYMlfSmvvssCtpbJD/P9Rze7Pk4w4/4Mw04+9HiPIgBLzLcrg1AGTBt75Q3rqF4ILTJs93FB/exzxzFLcmutDjrR+uHMfOu/2OMatKlfVeBjEqG3ZZXIMVrm2EwIRBAlj8f6UjTZiKO2bRr/szcZ9rOOQM8cv8ZJ4yg24zhAw/ok8wMuvVWIeWZYELeJc/YwIpYfyq4kDJC+uPr+/d493CZUgIk6QPD96jWGVYYXiMg3m1QAxWLYOZ+JP55ruDmmgFBGyB1lDFQx1N7oKvi7wkNtaftZELL2MSPke+Fl/OELk2bAsAbVAVZNZqq6Ue/muujQHINpW9LQ9fRg2EArg0JOY8hj/r7yDLdeVuW1DoaR2mK7gRYhlZV2Jhe3jpKSeEO+5dxcEQljC3a+FID2OxglL2CYunIpiJVzWk7NLqjlMtBnVWiKCwXcU3454kkc5wfOxppRFBIeOfVzuAdPg7nntpVToqfySuJ31KI68myw/ahE9RbJDWg0BtgXZHBoZQf+B1+H74oZwykSLQ1yovkeQK4vcu1VqLUvZmskBoriTRlpr+xrUqcuhHUguu2PtIPMPDBmohIyMTFBKzovu2T2/iepe3hnr0nWiFEdlYGa+nkWYbaLDdJNHgQ2fZDcURVgGu0t6NTGeS3ARY4vCO8ZJ583XwgSJsuE0i0GJJ6sJSjOwtx55SmjigRrWAzi7MiVgU7sf6XFQrDwPqUcR9yItX5nXs0Ap7JYH7X2exlNyIaqYE6ZwZAPxEnlwgKh4Ug0LNxTOj2dS75G2QzHiYiMLyXl8W82sR7cyd04Wd908/C/uQT/Mj14/i1f+RX4be/+1X4p7/t38U/8st/O34MF77rK8CP/OgPfnCPOsj9tCrJmt6e3ZHMqghwrdhhzvcCINYOGRA5IliBVEfaEOvCykGew0m5r/hOFL+un6cR6hdQTU56luHbOwfYFBuUqUjuWPVxIfrg/xmRs1Z6HlKi9CiNVPZYsFrCqVHcKxTI7wE99xDgKFI1C9eYaqKZHfHu1Kh8J6ppq082HwV/cYBcX1ejoQ9hBYTnutMIxqimWCJt5GBvqTPQh1jwHrO7/pg3fVIfBhMa83GmybN2G6Bpm7P5q02V4/shYtvteffnJqGBfa2WkAfggX1bSwsbkhVasdAab8n9dgS5A11UOegJlIhUQ22g1PRbqhr+7MIBVgQ6wu7+yPVqK7kln5fnnbJ0vWGd74+9FQxAWTWiMstcd9t/vT7zs7Mp9FrpW/qEN9LFeFb8Z7zCOyOZU1XANobiZkOJFvMdkheLlhiyykeg577nD+zdqDCBqp8EB81Sw9nPwl2WFKjqEJUgMAa2FNBJu0owhnbnq70+iiAXXADgtohpKIc7ZRkGnQa19zenejAoDNTsMoXO4jLtIIzzxVx7pRNgs8wFYEFwjQGbBzTEuOec0GulogMP41oLV/Dk6NDMtje9TR98oPCA1zlWJbydL3wIDN5tGxGZlwulOHa9jN/RaD+oekNxfIymQcbz5jh6xpTZezhKi4w7DyQ3j7ZEI1QEXAeS3ev6QTLBTTpgGOcAJiDmzWTjOLJktM3f0QpjScF/Gv7O2aHucW84yWBVi6pSCGMLZvUJZrl0pmut5DyrsBN6ZANA/6xChJFJDo2Mr1Xtoey2HqQnaJL3+Q568Jy0ESJ+q757z3Dcc4QItzcW0cgMONrhjqc++4AkWk9HRaWMyvDvxpcOa7T7psbp6144HifOGA2b43jjvnn/Oor3R8fHBkOnKoRDHxTijyz9qAbAbmidExfi9KbYlLiKrc9mBB99fO9KSFR9mQAAIABJREFUJreXnDHveg6ev7SO33Yx4Kax3XsDx0hniZPrXoLzqRfZhMqB6K6P4B6oBkQqOHzDZ74Ov+Y//4fwP/zw9+M/+Lm/C//h9/9B/DU//Zvxif5l/FU/3fCNnx2Y7z77wT3KDiTbqvzL9bo5jQjiGHBwXcysSuQtieeeYOMPn4PJQz9vbDrid+X5CFR7miStLMvFEUVTdYDBRx/BzT0AIBwq8FxXokm8T1YWehndA6EITrfCYl/SoZpVcyJtFWlXQCVTRJaYHLHZxvdrOPFZtIq3VJDcf/EsLKszWGVguqS+Y0oT6gegQ3BdrxXkrg1EZYSyZalPLCOT0x0Jn+83lL2ZRZVSVKNWV3MAGiIZ75LvN3XTBRiPI/f0ZgKrpVOsgSB7oFfBUE/yANxGsZOfqUPzeUkjyd8lkj9n9hT4GXak3Ibbb9o1T1K9ySv7IsLGsaE1bdmijym1jC1OueE+Xap4jXghwYd4p3tvYDUKlFIJ494g2cv+/Lm1nxhsMhwe41DFYQR18np9ZjDffZE3CFudXSsUt/OuaYsYQGIHvUb1xkHmO+WEOsYWO3TAOQZZUfrZ3Dd7XxgD6aOZZBn96vKRyrpWxhzcw6Ql3IBF2O3/ee/dfy7dNwT5J7s+iiA3Ua7gtXV0rTL9AciJielNUFQsYPYtAIZnInLdpZmYvXeUmA/uf+aKBqrAFIPuCzasuJHH8OazII3zcM85sZ4XzHgoVzpaG1EeHv17PpSO8vnrO8uPALDEy7g8O4VuITriS86ML/6QgYGJY5zAfiQ6y03MTUJHTUOT1IhAZTnJpou1M5CcInj3eCQKBNy5d4m2BHrpWpU+glVD4YBotczK1NOghuOccZCS+xVGr6MkHR2kQWRgzPsyixHCKN5y8bm8C7YT7Nmo9mG2Ws2E53mWLFc8LyeZdcI+Za+IDtI5K9CGLARCch4uHSdNSgXAdb36O4sggqMXAWC9hvC81pCDBZ+zTgqKiEuDqTrBX9dOrphqBXxsDMiRuuZNMNzfnICznldOqeP7ftJ4ITrjidQ1hO4YMxMxd25ubLtweecnOtpS78sT013oW5R3OT2rI+gMuM5AkHJqEABs3DjnvPoeyvNy3VHZG1rc+IlEo0Y75zkwAhVUejlUsJbg+//BP4hf8g2/Gs/1CX7+//pF/KUf/RHsOTH+3Lfj9yvwLd/wLR/cIzl8RMrZLU5uocZ6DBvtrCORT3aEZ2UKyGCNKFGiXIHIcI/3ZstUMGi/2ysuXBuqVQxyXZvzZemc/3AvMXG89hOPMQMBm7eAu++Vl/MMtL/xbRO1LTR5qctA0Y7dUKA3zyAieL0uvJyPDLCeFNzXN5Setid60N/Rs2nF9WW1kQkz92Ln2YpI6f7OgecqKUFWLN0WUVrTGz6TY8o1bgjYY8wcrZz2T0ueLSlLDZnmniM/n5WkTA6sQBBO/OuByVvNWSapipC1YjKoznd1oOXIhmOu0Vv08Aou/5gKG5YBE3/nqWUnns1fdxthZomiAsgqj0ibaEg/huZ/zWlP9DPcs4lsA3h9fQWAW3MZ4xi+exGXuTMV2Ha76QGfZXVvX+umjpSc7jgjRGp707ZLprWGdYmGvu3/jOORSkO+r698Lmv7ks9PahtPnqr6tDmzm68FvAJB2+dxSVSQAsjwapq/50y+4cnbMTiS3LLBkH0yHY1nYslk96u9PoogtzuYmrg1MtMjUZ7GTtWJ19wAOmt84RgDD5mZ6fPae2fXPJFIHtSX05vD2N06AiHqB/4RkkS54SPTO8/Tm3i0MncRwRmZN8KnsozDYK0efkQJObJ3WaBkDg9RD/Y9GAp6gSFVIGzEd6JGJPcywK1MJcgmFo4pZENFR4XWWhmMOnIwStwaxaPluu802v73DM74zLq8bEry+ynjhqBIIMGJkuRgjRJUB4hMFfrbm4W4D4o+8WaLq2eYlIOhk7zpe84q8/O5JBACN3RFD6BxyXVvxr8bAwYKSfWgo41g71b2j88gIscglIgpxpHOAnCaCr+TU8Y4/paGgmgLhxX08Y5cU440Jj+Nv9edH9EQjjd+ECWOcizL3AwG+joQDQCKR4vggRI1uQUgDOx2VQ1o6BkIJLrXnpVBpSMdHzahfdqVRvMWnI9MJDp9KoOz/vt2l/UCEIEokv4w1AcafOvnvx1/8k/9b/j13/KP4zd8z2/EH/qf/hBev7LxU//ab8T/8XXA5/B1H9wf99yAlLwP7jbOA4mmPCByQxRpB4gCAUguP58B4x7ocDqRgwjjVsLmvujJAffMa6uW7b2TFsOGM+p9MmHNYNEq6Ohrzc/uFT+f7GiYwjK75rt6+3tZhn/zmbv5St47qVo92ALc56T2bdirDERy/Qv5JvWH60g7oeqNS6640vRoI+jq9oZXBpBmiaj2hiT+HcGITJa7rm4EU1nV0l69uKOuZpb324EF+gXuE76zTkfrNkO1dFazKS2uDjrcRoib28pnNKWxIVVVCwi6ioLXgyGo3UfCxveQ8y/TKSUETHJ9w7bRRnCdKjmMAJmARdDEsgoUtBT6Kcp7QhX7unBYUQ85ka2/Y3Jd+ZnU+uV74/vg3uq2iO+xgLenN5tZ+c+uukNAkfKMtAfp80U+sJNznnD1p8fNjnKf275K4UJKvcnEqVb9LHDPdY4yfU4maVc1l57Rs9EnFxaT+ye/Po4gF6W52rlRDKZ0CF41FBHiwHABKCv1EA+Cn+9ffXpZM/7H4wGMkSLUDMZYtmHZ4iW4ngxMzAzLfKTkFSUfBkmZxSmwtmEtxd4W6JvhsldMWxGMuZM+TFJKakAwVRvpPpxL3PcMGQ2frlLZOVCbxMxgjwmEsP5zr9uGnb17lk6fpaAoxWFIrjMNMQ9jjoq1SkRYomfwzM0KVMbLrNhmdZHyuzJDHh72HcNRHzGX1aECAe8HsTf4vXW4AFNHN7OchAo+t1kmIB01ukxdNF0YNLT7tqahG86Yz5NXOMqT/GwpPqeq3gJHEUknmAEoneSuDBlquAy5Jm8bSwB4U4e5ceXgBjqqTMg4QlILFUL7DEGMqwVpNlVuvUJb9ZPreRN+f4u03QMOVHNKBKKP0JbugT7QSpRNFocGl2v4FkW6rAaBDDgdiY6XQXqOWA10i84yzweRoH0fAdqvRPeovEJ0xgy6Sg7obdmM5TyuxxiuTtCrRqpRCtXoWIbh6z/7zVi/44/hx//8j+D8zMTv/tN/AH/8v/3DOB5fxhffG+bL4ye+R9RZ7A13DAyyWz2D8FJ/6BSgTKYCTWHykIiqkBpQ5z3PBYNeVJXibUJOLibX7blWrL3vbn5etytA8alZsu7BCp+V+2OK90HsacFnrSA/74d9Cy2B7AlJHxLRh47keR5A8th3U4BByeYxAWHixX3dbWXSS6K5ms2ZtB2kxuR5Y5A07oF2vaNQfiBdgTzjMfC6VzTqRMIQ9vv8zDtHXPdOXyjTUd7eRMqz2d9r7mMJPV8GkOyTif/m+OBM7FtAFgflhhrynaznlaPerwB8ZI5CHqNxy6mGMTrXqnpI++KNWpXQpp1Bycwpx4GHX+2DHgaHRvC+kvtafFr/nacrrESgnSpBEpNQ171X4cI9aaKf5ffwWRC2m3uYyTSfpQNUz+fTNXytAnnadJ7fvu5QB5YyAYm9xf06jhk+6yWTYVZUnFYRsQzfK/tjBLn/mLTwnqcMvMwzn5vVWYIRgFMgXGAAnryPsi/p44IWygrN13J9FOoKgiqrcQqOmY8KnNNH9/Lh1Kp8TuN1jImtiodM6MsRxkUxx/QOSSIqhiTib91YiBLA8qEAVxDELYzGVh8RzDGvGh2Rx4zGszFhy0f1iQx/kuPwAGw7B/CYD+ilgADLNmYbKbis5lHLnJgyPUOMDkuirwIkcktB8rVWaOXFdLc5MhvkQTQzoDmFbmx61pY/i0LFYMNnw4/6XBNfwzTc4QS3VckICIdvCo3SznmeZUh6IGz+fsZRep2cc35zTGIYA2nYQWkSvfLQYzRuoAzYGNmJ3/lMJtFdG0nSUsU0uwUBY4yYBofbPa9w1BlAzYZsB2/Z9oYER7fv055kuJRPoHMQjCFw+2aQeUL2vnGb9m5dqmYA7sZpx3udlMkbA0J95lYuEhHoBZzHgWHercxy4OPhI4bJscvyEb8j7t/KN9Q6xFpI07U0885b/velClHFGffDbuUeCNGRJj3GShIo9U/HzE5+jSRRAGh0hL/dh5PrIxWIvr0YqKJN6yHKJucDewM6nV/P++S9qxggwGjOZbZ7ANzxPHFhTA98tpz4lb/pX8bzNPx7/9Fvwz/5634N/r4v/t34G37uN+HnPb4DP+U68WkXu8+59hrG/xiO3jChY3Bn5koA5Ll3lE1VXQnA3ozvZUBrBlhwmlXzzCVK7C/GR4OaQkah6P3ZmaSIxM/DsPXK5k3alQHxdziqiZXPMgAIkevbs08cpsAKZYLLObmzNyNqiMzHPiCq5YojuH0H9+sQAZi4mmEjSqyyocELpROHVIe8rgWIjxffALTbHSvKUe51Vj22ArOSheOM8wa42sAYWNeVSDoAyDFh29Uzxhi3c+PnJabEcf8PV0+wONO9cuUJ4jPvq1ckudYEl/ges6IJwYMgxO66vsEFVfMx4RG0SlvntRZeQqFHRFLNYJhhk+7HfTLjPMZ52tspJEpU+un6qXNOHO28e+Bo/q0iNzvDfcagEwCOTGJaM1+cGTFz9ZU5MGXi2lZUHtqbOX2k7S4ZU4JHHTQpYCWSVDit5jxPrAAcDrjqQPKuRUAyqpk5eKeKJchKadKnGBzDA9A5Xb6P6hu0nWbmo95lYCowAry59MKUkUokfkYmYP4zrBZIPENW6EYF0fRdo9kuCV/BpPxI1FzyfI+IozoQoubvrzjBH4IVP9H1USC5AHCOmYggM/sbujOqnMAuQwbGRHNM4KN/4QMTEhHcQRrfBY3vyGavEBd+Pp83/g9pEpzWM4ycTcHr66uX5F5fXVJKXXt1Dx84QUM2oHhdr8AEFJ59sgnAEReBWnHhll7ZcMGxp7x4GKqZYmIP5+8yEOBaEYnq2ndAlekykI2LP8e/AwAZOw3q8/lMg8PDWSXvO8KU/40qXyngXD4618bn0VUUA3eShVRVEOX85CsQc//Zhb0vHGN6+T/Ev8cYObTBExhLg0oJGpHguVrx67pT5fqa1YAC7oNEJH2jlkKHFMHfWtDMf0RINRixB8iz0zuKliiCI69DHom85aE/7qobqpodrBboA+VeWHnY5nQcmRMcRNFHtPbhJG/5T6/XlaU7/7xA9eSe7CTqNipp4fkkEkVEj2hM7tXQEe6lSZaoeF+XNJSpJ1oMhIOG1Ce3sSzek5NPu3LfSzU+YgwfiTkLGbqumsZ4hkrIgTLsUxoKyQAqkCQiEQ9Z+MEf+kHs17+MH3j9U/if/9gfxXd/+xcxvnvg7z++B1+21w/urwfWbysL5LGTn6iwrE5d2xI964hsRxt7hSXpQzyHXBstdM+fp42PjmcjnYwKLAx6PRGO/QgvP3LUKFHO1D+N/XuzU0zwtPa3CbD0wkoObmjPBv2KjY55DuP7yXtcsOxv8IcY+aza1mvK8IanqLIQWctmKy1awo06Y6X7yr3N500amBRlIt8J4KoSoZDjPQkeZA42PMbPpN55VE5Yzu5qNY7qFpf22ivlCzNZYPLX0NVOrRARPHUlNYzr8wwU+wnNvUL7yCYmcjoVqH3Q9uJr9AJsKcqPieQESEfTA43eWr0k8R35Dg7vp2BFqlQ8Cj3n3uFZ6u/QwYejKAJmCY5RX9wrJEhlBIWPATbzyX1rBN94b+wtuc9I1SJ661Js5s3GjR7D5IKDPrKxN4JyxPezAbJXDQrEkfS3XEvGJws1qa5TNs55xDAWr3Zv3BNii/We87zFJc/uZ0ZRL7i36AevOI/ZnGjFiyZXXYdXIcaj+m44xZH+rY8u/lqujyTIldCwvL+w4zgyMyR3kmXv1FxEy4zmwIZlCQVAOl3/uTpcLzHTHagSfQZ7FhmQ4XbgZSlwbW9KiA3oxnz59ya/yhUcFI5usYSz1vJuUAplR+lkNhkcvmD+/5UIMstL4+as+6jeIXc9WjoOGvobAtsCU2ZHvbyiWkETm/rY/NADjR5s8fPS8IKb3YOgFTqGGbTslQET3ztpFD3ooNGkrBiNuojg2s9bIxgzc4ThPc7gVz4eeDwe2XSXxrfde0cAc5JWO1TJm2roMNefP0PDEC8zPzfnjUc1wN/lTOOnrVHByznAxsZz15hLR2xqxjuNYsq+ZLfyPZjLBILfpXr7OwZnPcnpTY3neVbHcEMFpxUlgAGPSCgQNCSetCAaVTaXruflHHNDBukMehDnT1etJwMeBm38czZh0njyuTqq2BHOt9dbecGORghmrndOJFSF7So3ZnLHUmg4qDo/LEmH0zbBd/zMvxG/4l/5dfilv/afwj//z/yL+OTH3uPzP+OH8fd803cB14dGvO9N/pOILBOpKGMm37m9Y6J9XRHFAxxLukI62ZYU58/SWfNZ2/vNn499RO4vA0JFs5UIBQ9tzYltfHCXICNXk02jU6r07sCFQIU0hUou+e44pW90hLOdwRml+hxFyjWKvdY59rknYtLmCA12UhU6wNApMTcU1CqxduWRxsM8Gh2hBckSAeAVFIQEMVpgmgnWrJ4MPscx7pWHvrfzGqWsw59l8u7NQj4tkUlR9yFUUUn7pkVrGOPA0nsFxW3mHXThfaXaA/3ZKLoBg7QEncKvcd8QAOtAQQasu6T2+Lu0haRpPB4PV0qJ7z3aWaBPMStt28dx4hjeVE0bRLu1zfCYRzZ2ZYXxLIk2AncAkn7G9etgFOCxCO2xbYVsS0WIbhPoP/p30NrRRk4RvEQwz/4QPz9RRQg7znXXFqSLlZpUDpJi0Nt6oDpIBSCHbJEywgl5Mye1+v1xrbiXD7CKMO8Aksj//9QVBG4IWW7VDZzjHfblgHvX6GS2fEYj2ggkUTADwfLs5dlL8SoxqQQZ2ODasOedO0jX3zk5LpI8sEyxh2KZS4pc14URpauJGuNoIi4jBgAYsCMI1yOapThzOpoAzjGxGdi1oPrmrGNNulHIjRkGvxs+ZjtpiBiUzNKs7UaOhzj5NKrV4Rsbjp39biTiXkBObvBGxyit3lkyR7zfeR4fGDzAs84UsiY3p41tFqssn99jOgE7IDHSl52k/D4At+C3oy4WQyEY6GbAGhUC7h/+vu813LUJVaNT1G7NKnQwe3vzoO174DVlQFTy83j4j+PIkpHfL5s3NrxwFSX8GWhqIDC5LsGtJkeMgQkRrXwHrWSW1QGU8+lJClDKCwySkk8ee082paU0eWn5ztUdHbUuc981+tHs1koLxeM6U12D+qkdhcwpcuGMmTz2IB6IkruVaP2nXUyge7LGf5PzNsWyIeQYZz4D1yz3WktoJPZOBjPxHb/j9/w7+NX/6q/H9/6bvxG/8Is/Dz/2t/wwvvNL/yw++5lvwPHuU2/RUazGn+e+458lMgdgjANs9eDa+3M2SlKgK9deEbxQs7uNgw5+XA8eAWTAmvsq9h3PzApFgimuEkAHR1tOnV/eX9qK+J2kAGg10FGn9zgOH2F7HDhsArYrqaaDNQcELtNEfhPFhuT3X1Y2c2nRmPgMXHcLXuKYaPa3kqlMnFuSYG1PUWOWgf+cE3Y9MZkg7JXnY6nLXDkC15oX0SaZwRExcv7nnNlkdm2XoSK625Oaxyi7zOmTfaQs7zHpgapFzQCysUpXVWK6Pfdk+Azakic7Yzp3uqoAvu8YxEO1jZeW7MAnb9jVaNxep3oIy/xMoIcr+XCfyVXJsWyNjv2RtnLv7YEtgygzPOLP1VaqSWSw14I2oALLW3LcGiYvfcWUw3tt3lDPkl+9N6a0YN7cNxxjtr2wb8AQAanTSkGGcQEDVK4rB57keQ+/zn3OwJnnFGuHelW8s9OVD5iAyZyYS/CCgXlEf89WHNN//zEf+Vk5FptnW4Mfj+LiJqhpKNuQwIV//svx4lUNG2W75J7E/2TXRxHkQnwK2RUcJYjCxsJ5CuZZRoWGb0Cg68LZJv6oeQloBin+3eORB3ft9y7fIzs3xoZBHkdmUSwd78uJ7RSYVlWs/UzdzR1j/mROPGNM5qsu11xd0QDyfM0yvG11oed9pePbe+OKRolPnq/uzE2wn1caleu6clLU3hsqwFP3LTCiGP5q68aLB5P/DXUaRWpLEpGJi/Jo3GR+/xd0l0wM1FzzNg4lRyu/risdHsvcV9A98rCulSgAm2MUob0bGeAWn7zkBnJnQkKx9G6EIStHG1IXlSj3nJ44pGRSQ7fMDPt6dQQLpQH4fFJbWKHYOB9hWFpGL3O4AscYLnM2vDTdu8P7tSLIvExhM0pQ1yvkkBvywax8m2IPf88+hE0xp4B6v3kGjuIsdmQnHRMsjW5H/bpDuvYOJL0QvMymUQaIgRS1c1mWeq7lvOHm6LZpGieFYYhh24rEqQKteR6pB+kVGmQZmUEXZaYwRiWtZrfAB2qp4EHtyLcoJPUg+1q8vZjg3isZ/nOcdsVO5AwwpAJNd5wjg8Z8B1FypWoJEfahhi/+/L8Xv+dP/xE8/5sv4/d933+Hn/LL/gL+hb/1e/DEE/P69FaJfeM8dt5pOQ0++4GNZQrDvnVWd6oQzwDlpTbRaU7xCnQYMTpURpNWGj2xHal7LSKwGdWSvVN+rjfEisRQFUMGitQ2PcKhDRupufoa+zSDDK1ys1k0bQLZeJVJXSTMHUDg+yb9YoS0Xg8EU0KJ1QHxe1uGm6b3gehX2KWZyxPEPdWR7k7VMWzYELyK4p2I+yMmSwy24rMUwGVEwZv93erTwIJHTz1gR9qqv6AnJxwW04OzniT1cr6ZNyB2WhVUs/xMG8R9mOVt84Tg9foKzHZoSa/aU0LQKQKeYxZQA8su+kSydynmLPPPet0r71cY+FP2MyqPRH63+HS267rwcp5BaSgfSE3zZQGMmaOMx3F4c1a8B95LBmLnkXtB4lxTCeCcE4YL0Ht/hffZlG1fUk2eKpojgZNKIHd+Otf5eJw5bZOfnQi1Fd3mbVXUzPA4DuyQiHy9rtJ0py2HV4UpndgraTbEZxBYxFrHgBJU4b6JRK7rJZ8Beh3HI20Rz+AWFL0taIQ5OU4vXHoBsnJk/Kf52r/S9VEEubzlRGUcloHoHaWk4yaPbHuKm9IZLJV0dJaabfz/nI6D5ixRQeG1V6JqKbOVjq/9fxjRKlt7MPxcF2w8YDIqQz68/Ox80kAIxA3UguH5fOIrr++hMQSAGbkjZ8FXhbRst5fQcUMPuQk6issASiOABMqgcW2JNt/XpqbvbHMNVgB4fb0KGRJHsR+RIWLtLC8Qded0LTbdsRGtIx0MWgB4l7vUofEALBqYgu/HEb2CmROMciKYmWuihgHTQD1Z1jWpaUi5BwIZFBuQy2DL5XfW81l75k05ZlsFRPycW0kXNSloX2yyCX3MloB07T8GfXTqe9+RRZEZTRZF7ufeZnDIMs91XTntKfdpGGSf+FfSPuSS8Txw3+Re2JpJFdQpLtnp2u6FwSonADnfckCkqgnPtfDu8cg16ZzQx3DtTpbarkC40uFxXVfxrnNt0nm0CXhxNqTvjbeX3oPj7ry74H9Pzvy+/R0RISLnGkQL48/caVcw+qWvfBn/3L//vfgff/5vwTf/iR/FP/F7vxn/0o//Z3hZn8c723jKh/dY9g+5b/pYbDPLRM25bgYZocXJpFaKjsFndITRP7OXiY8xcrKgbLd9rCaxapET15qNVhiGIpsYs1oTCPpqa5pl1djvUIMOT563Xmlfz3lk70QmaVFduMzJB0xGuVYAEkF7i1Rm4twS/o7SvUWpr8sVNta29AmPWc7akzVyfNs+Uy01FashHUMNa3uFEDLx3Or8e1aDLEbP7+L0kxpEkKYnrRkcWjV+yr43YHY6Ec8SqSS9nN99Z0+gKKVFHdQrEq6U6IvpbZTHsu2d9T3BRvvcAyOUbryClqPKObjpPBJJzalpEIhsbJuuvKD79t7W84oG7qpocQ9OGXj37l0mPxyJnWgmqR7xfq+wf56ANw331qR2vT4L0QdSDeIYE8+lMRFVMUBVgUBogxahGBlMbil/xYoak7+ekKgqzpcH3j+fGSd0VJnPQr+SvSLt3DMxgDZ5PPox0eJdU8Z0V5wk1oZ1wN+j7BADAJvtkFSEY7hEa+rk2r1CTR/T0easpCiVhnw8NH/G/f5Xf30UQa6A3CXPAkUsOv3C4DZplpSSEI6fa1qko7hwzLjcGXrDAzAyKPQxwfdGExoDd/yOXvBz35ahzCzlad7yXVkGYlbaEYTO78nxp81IpZh5Q+l60PSMoKuT5m/f3bJqZqVdBohXL7F3NJDBjok3HC2SxKMxj++hf98Y3t06bOQoWaKkGCM3dz+Q7AQGyinl2saEOf59lsozW1RHmVWTJP94PG7NYsndap97jJKq40HqzRQe6FcjEwOmjALCaZGnxo5qGs5n+2/uQ6dSOHLV19r5b0WB6MGlWWlhOrJs8cwu4XLOUoYAKnDh5/C5jzGgqIao9XxWOXdrSm4xcGGQw8Dz9jkRrA+E5Nu+ayP3kibXuDtO7j8+HzvcOV2I5a1rb9iwRL3ojFgd6IFnfv/2EdsiPoaXATD3jM93L87op9qgCGaIpGWwEEZ1vfl/nldHn+tS1UD3dpbg+gSn57rw9T/F8CO/8w/jr//uvws/9bd9Cd/99b8Z3/mLfinm3rhwOgL0KfdHOzDnjEbZuwQbk+3k1YWsWzYYpmsYKcFkbTnYmDnid3tCwFIskykihsfhDVG92Yn0kreIYbdTq3keES+VHpEYsJyftheWI1lzSlOsO2lRaYdiuh4rG1wfUmZ8r1r+Dr+f/53VhnjXR1Bz/OxPvw82t6mfIaLVNeWtznnKVrJPJN5FNoqvIp4mAAAgAElEQVTG6N21VnEz58SmRi1Kum7pzkl7OaFre/PSvqJSZvUMHexgMmtmGVBxj/dx33sbrhbUsMEwz6/UWlsE7PQ3+T62S0PSLnUwaRryd46QBXyMslsyAYV4Ih9Xl3bz+2WVpBp8lypejpc70BWTScfwAOzaG3sV3YY+n370wUTA2jCfUTQpruP58shgku/hhn7vjTEOD2KHJ5H7usL+GB6zSvKUoMu92lSSSBMgUs+zv3fo2rNyDa+ysSlPI7FmrMR9Z+aKDNnwGDGMiFdfPPHpFYzmJ1mV5Zo0hLhT9aaM8Pm+PvRJDFjJ/e12i++Va9qR63mIq1lA0o9hfW2NZx+FhBgal2VO11gd0xLBuYwjVVdsHsMEA9TaBCRSA8CJgef7J+ScWFdxlIAo+UyXKXNZDM0sZJsBSwBbWFo81c6j0SGY6o4g+SpEtXDnTd0oACjkqSPLc04c/B0BjDD+3ommpAOO536uC0NH8nVmZJwpiyL+fDJCmkeOeLadpZg+MELFHZmZuTSaUTPWn+Pl5cWfPwKjO3p5wUKeWRVpOE0VwwxqBh3AjJG72185bAosaBvrujDPIzfzpYbjKFSkG+wRCP61rfGsaEwHhhSncwibFGNq3fOCnDOnGGkYd8T6YbXATGpG+urcqYZ2YLixuXRhDp/spjRWZjAaSPH3gAjoaPw4ZQfmMluP48BTdxonEWlJ4ExeYu6zQH7UN6cjCuF0vZnT30mOvWxNiGOEdvQQN8JzYEaSwP24zSV9aAC3eclqq8ILKuHI6FgSIRRo/M44JvRJVGBjDg+3VrzLbLrZO/eWjCi164agKgrzPAr9i8uw8WQZMVAFqGKGPFEOsBg16KFff+H3/s1fu8n6f3j97n9jAPgzAD4P/NnvxZ/7s9/7V/4FdY6j4YLq+EDLOANMMyBoPzI8iWBXM/fTnL7fYYARgRySwftWl/qSliheYngg+gDUZbUSmRKg6nFxu0OApZgtkWZHvMwBCc5jht1sDoZ5rKil3mDiewW6g5vp5+VooAYDjhwz2xLpKaWIYGYheVXVwR60HY8zG6m8XOrVBVWFwG0ZRql7dASZ59MDSEDGSMm3GVUFa0HREIEEGkr+JICkyiGSZN1OFRm7GuTkPLJPYgPJL4VUVVIV7gNEYnz4gTmH65GPgSkHtDW20qZ/5uUdtr4C5tJNgwExUM1Vuzrvtyoe54lPPvkkS/xbDEPdB6mq07+WI5ZjuszXDr+GaODyQHzhmA9suM9aa0GOI9YdmNvXJSuSLXHisNdMfoU+Y8O2xZQJw7YLckwfnmQCNWAeI/p4WuNUaNWLOBVwjqOAKhkwcXu9IyDdawNmOI8DCsHe1cNB+bAxjpq+F/7HBFlNShBuF82Jvu+cE884A75/POE654TO4TSIsOteFSl+7t7bn0fv4ISY79P1fGZl6rKivl3hFwaicQ7A+3VBzP27zVgb9kuh+OfcJ/1sALhV4yE+jh7hQ0j/Y3KrO+hsEJiYN5Fe6421+StfH0mQW7w6kQNmwAXDOF7SeXUkiFkAUT067L23KyDMgVf1wNbg0Dl/zwPhE++f7/3FmFMHjnlks4TJhg1kxtPLnHtvyAju03BiP/VsgTuHhdkNjS9U8Rp8pmkeJB8PD2Kfz2fyS+noj+PI0kiuVKAD5B7bdmL6FUHpel4eVERw6XGV4QHPpBYU9nRO6qNl6Ie6o9mq2KLRVQ7IlJqa0yR8gGoEHDjg9l4hNpMnTGN7nifW+/eQx6PQYCk5piuC+c7jo4O6gjvE8goTG5hgPiZs1VhhiM+8P+fRUF+/FEcS77EDGx2CuZ2fPeYEroWJgdeBLMHprgyd6BHimcw82dKtgIlzFkUwYt1hwS/TGhSwh2BtYM5yumNOb5qLBrwD5HRGMybceD3XwpjAwsAp7oAZQB9m7gx01+Qea+VsASS4tGdQBogSLQYNRMS4aG8mLnUesZmnNQZ/RxwHzaCIwQ3HHWfSwWl/IngJTlg2oKBKxV7sG9jbm+0kErjpTBRH2xlAnCfQqBYWe38yWYlkmTbgW7/rj0FE8EP/HwS3X+v1037JH3d+oRpgK0v3ibQdfuYTQTsPrL1xjhjpGa9QGqIJVedLQ4L7NqtaFGVnGSPF4gEfaet6MawQFFLoCNWAQaHiygMGwZZqPgEiyAaS0uQUjkhyExk/4tzU5K4J133mYIAHJyWh0MJeLescQgB4VZ+4xkTvavuRgTQBksUxqHPkvT816EVMkML+cs34/exDYBDSA+hOkSDlYMGy8VLVbVLqQUdgXpU819OuilsF+oqqCPnf0095ogOTUP2QPKsESt4GuCKGtZ7+zscJs2e9t71Dr9j3EFE5DRvweDySXmeq3nuwdtrc91rjjGmXvBFqJ9I8ZcKW28Rs9GWPwaVZgSCd494TwQDOMrj37SYYckJ0QabhsAdglJdUBx+i0ZkgiwKQVbQlkRnnRjwJHAbYxNKFOV9g+8qqjokA5uoDvQorx8QcE5e8YgirXgLbHvgDrtM7xsTSUhPyBNcADEzbkFFKAxK8YLnYhHoEx9xBJe5Zt4NawWHTlSc4hjhPplV9IQ3RopdIjqMqGhGvqBmOeXgCd0xYq5JkJcUK8bc5S7pTPD7B9h6C4zxK3hOhOb03Bjyo301v+Ku9Pg66gpQzZbkBsuHuvFBPP+g1J9sXMBDQy5u8LlTHbC8f9e95ritLkqrrdkj23lHOkSztcpMmty82wWsEYF5e3vdmgWbk3KDUVJUDofEX2r+vuyax3abxNNSwUMzq1pTgbrHsvCOA3tdKSgNRr9fripLJvfTIe3RN4CYxBG88e+qq6UVDqrzd5k2T5vB+kZdZQtsDkolAbnywC9epAKSjPJ/P5Fc/lzcHsMyCHrAOd6TXdeGpJS801PAyj1Q8MCttTGBhOjyavEpdoZ8ZWbMCeEUJxg+tSTQMEBiokbC/2n3x/RLdyX2T+9ed2pyN9wnBtQ02fQ06LYMGiIjRnBN7hYxNfN+cMwNuUiWS5tHOFznmPjVopwPvZV1SBLZEtSNwtqXqTQ5W9BKW4rkveM90us9VXNoDcjtD3OuvEaxRTFyjSc/POJ2Wwqw4wwxsUgtXvenwLReawe1tgqLUfdzf2sd7cdw2g8m9a968I+PtSUL2Kff4oJaol+jJlcYY2Fa2Kb9LoueBf6Q+iaprvAII2gASTcM4oLpSESATwUiC6dwQyF/asviaTmPosm/5jkVrAAiQyRPtCQOkDG6F0+hqDOhbHWj2XvSS64oEz/qepO1lJSNABdcbLZkm9jycAZYw8E35PxQNiI1BPBd8Rwza+Jyp1hAJAJUSPOGPfd38ApRSUrFHNOhqDJS329xnS4QdVNg3O5dBL67UXu6+AojEEs6ZnzIyMJE5gyITIM8Rn7WHJzBajYuDYh7k9h4OZtjoTWwVcK3hFYpj1CCRjkqmDQrFEdIJh2ywkZnxQvelpER1qUw2uF7bG9pFJWxj8V/nJD/9mcF4xgOpjuG+cJ5HajgLKsg7YyQ1133Fut3oM5HkelWM2tQlEUobt3foaw+nfcw5IaO4vUDZXw4vYozQ+xuYVOQwo6Mofu4X6tyaAFOOBLWI9PeqGW0Cqaf0G+w/YaLap7v6tcCGRZGSORwAqgb0k18fBZKbAcCc2JfiPP0BrssbOIZEeU0moBsrSmVENFQVK7KnYS4rtkJGhTxGa5PGMmhRgQxvqFBzHo13fd8b2ZIS0EpTLB+noxgC2IZqoCPHAW2GdxpuE6YApFF/jNI1fHl5yU5yGstEsqM8zSx2zgP7eWEe86bLOU/PqrbeG0JkTKwIqF9CLohI6Y0rHEGeG0ZLPuINMQnObsqVyAEVdRTGYrhGBF9qTnMAkOX4OScu3RgmWXbsiMfhcFBghSUHlvc5HM1UQWrMUlKNh5rcr0T4RLAa4kdn2zlFZj5FiIb0DD3lrQvQgfPxwOvr5WV2AFd7l6/b+d/SAt9e1lumXiIj2mEGOR2NnjKwOg9yeGPGCA3lKYKN4j95hq/5/ZCiNfA98R1CFBoSLFkZaM4xx7xKNSec51nIuTnKa4HCpLOLvbnWwhbJxhkGNizzlmPWm0HPgHM05xm83yvOck8wMqsPwz4AaAsmyEHzfVu8ro7obzOf8DMnvvW7f6BUHIAIchQSYyg7PaU7W2NJLSoWlL2ySLR0lLoAP5t34PvkwsuMszEQE7sKb2DyKCJeAhW3XyOSojE6929kU8frdeWEQlZ5BqtV26dw8f32IRt0bDDDOQ4MqwrW4/HISoRuD1rWNpDh66yHC4bhSCP/cIx6D42b1+3vrdLFUr+oT4sEMtn1KVQnFBTElyidRyIa9+qat8COBsmOosKQ+pp7R8kXBk7PXepnkDaTvEQIsPYKeTzBMWOKZqw/JZ1e5pGJHSXHuOcBr8SIOGXplIFlbZDEKm1eP0/efD3Fp05CcBv5+xb84J7czaZxXWf3UUAOoiBF4tobtqp5mnSPtInigc0jtJRXoJkMqIc4iPHu8UjQBwBsHq4lvQeibRqqzkM55wFbAjmquuL3MHDMgU+uy9H69jwbsW5bcxhCPpsZ9vZ3KcMgth2ZHZXowyw6c9iY5e/TFoAJD8wl6CXDO/7nmI36pYC4zuz5Ujq76DbZAJh69S1AtmkEEpoc5y7FE0hrBNR4xrX83O0NOdwPMulxzquAbmKvheNxOg1xvkSQWJQER4Apy+YgkJoj5dde7mPNfGoaEJUcSSCRCR8rdUj5vVID2deFeZ7ZB+HqRkjQo9PiDoh/lwgQPorvMaUDJ3m5E7ZRzxM2ygPpr56w8FEguQA8w319OnxvlpH7tpVIFeWC+MK7oaeznnPCnstHO3JUcEMruCFT7F9mcUqaUWTj0uPxwDFGIlT9Ws+7BiG/D8eZjQo06q971XxtZkxajQIvLy8YY+D19TXlytjMQSTBrmo2OOf06W69FAbceKSqiuvaUOV0psiuzTdj1yFEGLCU/9pt8lZbPy9nS477o9F1bcH1gRNzTb7S2uOUmosBobUyhhUVgoEMgwsiM4/juHHxEjUR8n0ZQIYeZSJgJQsHVGJ1o8IEcsIGmGN4A8jeO7s7/fv8c9kowsYRCwS0NzXQEBBJeT6fuc8SMYlTOMYoKkELesjf4vpTjzZRC2nyVm24BlDfwU5nnrXSqPRs3H8aqWNKIXMmc3vvdMb8Ga4laStUHygkyLIygtg3vM9enchgNAwXZ8v3akh2P5/+vMfjkcklzxkNOceLlq1oJWwBzpdHovIm7vTI+ydSwPfDPdF50CNQP0pcUUqIz2Zmb5DmQn/W85l0GneC1Q19K+uLYK0n2IQ7JZQrmv3hPfa9kOcUjlARifN1RO5X/g7ve5prb0okWhfVRxBd7ZE8iFapn6isV3Zcp7S68eNz26AZrsEVTp70LCZHA5pBJx1e6QL7hL0MLicbWnaimOQ2epLsSbAYSpHgjcKAS39VcnVDg60GRjwGNdlrb4v4iFGbjnw/10oaRZbxGZBuDxaOMVJzORWCIqBkNcQRVQNiRKuu5YojLSHh+em9HzbLV9AnHDFevN8zx78+OAQhPrt4vJoIHyuLA94Mt1DoH4MWM6ePXORYAolirkD8z3kAx8QZaiBspOXgEH6GWNAeMmjkWUZWT3nGMnkB4J01AYahVRxEvMIrzRYI8JiPagSMvcaeFp6rcx5pBwDgkCOTS+7nfv7oS6oq7HvxOA48xsxGVKLxXOts3NWq3jJpeZtc++CWkbaN36cBkniFpg0HkWo2rWQ7NI7j3au69N8pMehjjvQhXkmNSoerAOTgoUdUch/U241qKIbzgbN5LPbeEX/uVcSQCw0aZ9+j2XgGavOOrLp7U+nIiX1f7fVRBLlE6h7vXmC20+D4QX045H8ePj9eIrhtSMTeO3Xf2Hko4p3W7L610HhkSfw4DsgoKa4e1E1DyjDta2FGbMCXwJfHZjbexzhidjxHKcaGez6fePd4xGSrHYL6SKNG40sDec4jJVto1Pbe1eEdG+KCD6cA4DIsDHTCuJ5z4vE43EHuEG5m40YLOnuXfjpEFDLMg17B8M7A0Z+/yusikmMpM9tezoNmqYLalET9GIzqkDbC8a4Dy0AmJ9tYK0u3AG+vcGbqmeDrVeNDbwZo3HVDJTJWlgr5MwzgILPQ5mjAmiHEXl3mALX/HhGESaByLi0zkl6QWas6unBZzbIfcZDFShSbFzVrAWA8SmYoDz5Rme26kV6B8KQrVRzC2GgI4pOqASYPIng5a5zwvmrCHLuaAeSEKDpzyiXt7ULxT63pa85Vq3PUk4sVzYf7WlEu9p9jMw3LYGZNoi3u/2bEh0S5NKhKDe2icsA5OeMdvJn8DgaE3BOA04HErEZtxt8xaQNwE44nraMqHvcOYsQ68hywq70jznTIRIu943zUedEPp5Lxz9HskSeFp79/AO/ePfI8J1eulfRzelFfV64Th4pITIr8FLT6OEY+jweb1posR94v9yfpKqzOjOGVmWximT4diV3bM+hqlGpK/iOYPHtV4HGcJT8U5/mcnrD2JJcl0p7Epw1rCReb5XogyADl+f4V1+szzuy9Gsb3YYcnTrwncgoTALmeHqbJTAQtqSDHjICylCP4zikZJ+IVhKcWyMBz1kfG8jsfj0eizmtU4Fg2q86Nr73k1E7uixmUiZ6Mcz9vf0i32YKwZbHv5+nB0HTNYU9ugnISSWMHk0YguvyeEe+SgZGjjQoVpMSlV75CuegYqZXNd3PpBdhOzVbaV99bM5//GLPULYaVokwLhhNRnzw/yJG6fS9Qw9htyvHBniOdqNsOaeudI+i1FJsIAh2RZGTFDJ4oKXYBNw0IPB6nKwHFmgwIcEQip8jEm/5whD0dcaaP40gOPfdE0kPC7uS+0P42kVQF25WEjTECiW5Aoy3fK00C8ZSRfoxu/Ku5PoogVxBjPdU19rB9kcgFta0uKRIo2TGQQRI3BBu7qJ/7tF1Zrrjmrkghn0CVkrO8epwYBoxtkDambhHNEUlu4FDDScfTxMR5YJmJPd+/+mEPDUUGcgBuGoprLedQAXg+vTxNBDsDcKFYd5OJkTJmDHg4N5ySS3TARIiBQujecjp71yWDou70KY5OXpoHqSMdM0tYwMA5ztQ5ff/+fXLOyF0CcDMEZvaB4ZQIBq7rct3DOOg9GOVBd47ykf/vahz3Jhk+E/fWu8fDB4cwqOY6N0fG4M4CaeiSMt2h9BKatuCvl92gmtn8GQ6EU4gehwcjnB7VqxMAkhqA9nlE7x7HmUa6BxSFUrex1Q3RYdmMz0GjQ+TWIiFiMsQhCC447waJ93C1cZ1MqDjukaUvluw4etP3bjiVs5zryPNZXG6g/o6VHe5bGuviOpdwPvcZn4/XjHVgUOYB3bjdH9f4CGoCny9tz5DbHujB+wcBJQOsXVUoKpy8DUT8mQeWGsxm29N6+8xEC+M93CoUDLTinpk0E+HJILpVa0QkO8pvcmv5DpmUjuTVi0rKptmb/eS2pPjaXIMeZDORMzOYliC938vyihUUEjBv1z/lfqAN63YDuA/r6D/fK1p8dx1V4nknotXXkQGvP1vth47EM/i+PZtVVeqcR36HV1miZyC+I3VtraGXrTrJagrf646EhuOTuQfY3HuzBUQJY8APz0dqre8a4c37cWSz9lMPyq69C8HfikNcZcXXWBK02ntn0DJGffeK9SJCSYR44V6pIwAAAO9YyUH1mPg/ZedpC4gMJ2o/JyAzP/vxeGSQJ+0++Iy8CEZxehoiQOe46c5tfXl5yTjgiapaHceRKHgmKECqS7ydLshnStQ2UFeuSWy48qNmAA6osjZXfRhZZWvvm3xerEKOe9BPQA7wpDOD1Phq2kD+PMY9pExwqdkDrikrU5zkWDFGk0obrRJqXV4VX/X1UQS5vAwuy7EHBepLusv14/jiYkGUItulErBiahhRKOfNcBLah6Xq0jMN9PIY2I/pKKnunPLkRsZ7vjFGyNrUfQJIh9MHFxwPD1z6hBaLDccmGwZT8whd0lAeuPZKgfkqXYw0ojzELNedc+IQl7E6jndQBRRR9gIcDWiUAl/D4DQFkmgSEjZSDuJqRtPX8D45ifdBdGVhY+E9tl4YoDC/jzhUWJaeAOR8ecqW0KH0wMHEjVqK7g/nH/KAMXjM9y2uc5wZKJ0Pitd77Rhp+bySulF7yGkNLg0TQZYubxoTgwzDOT2Q31J7ShgEDGSWfECyeWGeJzi04rKVTSadO0fdX6CMHVAIU5fMIcr2ARJoVdbqieB5eqWBAzVoULkXMotmwH6et0Si04Io3k4E9JDQsp73e0r0GKVJ7O+tgrTXy5NbKnos1eDBzzTwjrrFvhsCIadVK2Dkf3eEQdX3tV56430TBXk22o4HXRO6gWM+MpgmWv8ukhBWEeg4qwSL25kAPIhyak6dE4k90JGbQqVc89epL22yWHzvGKUvzfXPZlGz5kC0KAPhsDEkJRZ1xQj0lhQw4WFTjBkbJcsGE1liIOprtAFdyd/rV6+k7Df3OCO5qBRm4IzyPxq6A+6JWCMGTF5W9nNDfmTfE/3s+D7QnFxHxI7PyjXsiQLvke+LzzDbRKkKru6jsIlO6yreLis505AVBGt78rJCNUkd4x7kREdSY1gB21aoKkJzmkEygRI/d60cPgvt5/qResXPSoCDSbpVAD7GcRv0kIgmezegOIYkf3SbQocCdobk4JG8dgex6GdWgkZZ0RPuNc1G3+daYf8ruPL4ynckq6CIII1njc+/945EbzlgBiRQBW2T1ogst0TngNu+9Kfhi4lIUzHkdV8RO4ykJNL2EmDg++8TO3nOWFGDui9zhZlCurn3sjnMHEXlaHqe52PM7F/wfeNUQdLrejLDz3wcR+o4c9BS2iIbBcIcNbCD9zSAlOXUkC7MBmORpFTwnjLYbraDdvG5FqB3zWfEc30NQO7HFeSq3gPQc5YWoKp3aJKr8TgOF0A2qfncW0vcODbVnBOwI4KeIw0enRCNHDcxM3yMkZO59qV5YDqnxgcHSCoC9KYg7/CcuZmIjgH35iGOlM2ySQSRfWRfRwr2vm6Ov/PI1lreZAJgrfeYL4EiRHYsZhnoEPHtTUIDE4edWR7MMvlRyYAjlMuD41hnGuzK7Jdrm86BKW6gr/2ErkBBAWAjjSIDHpZJeX+kPHSExJ3wvE1NU3iZhsiGaTkeKP++xMtZJVBYdqCPo4y6l8gsObaD72wIFAOv7y9sYdl5QJeXDUcYr2mlfelI1bjtawB4jDNR5xEO6XEcOMeZDS0Mfv1+pb4jzostb/LrASWDAqLJfd1I6aDTHdGKkYGcVNf7MUZ2BveSNJ0/DT+NkwzDEMupWACSv0WHKeL8VZmlM3qeJ86DSFPtd6cOFDLIEdLvArHWy7v5+2fz6oiPquIxvGmUgzwOkBKyy8Dnu1ZANEfYMkHc4uXVI7RiuxbktOYkcefKAkjEjoFArh9tSFv/AwK1VUF9C4LGGMBuiC9c7uowanVbJq2IUb10FkSjM6Ge07mqEQT39zoglQDFuzraM5HeACBKz3V+e5BI1JO0Be4Fot9s6O0OOFErqfIyK0W0514abRUgqWCHKCaTxv4+KPXIvoB8X7EnMkAW3GgB3PepmW33AJVX/1k+F5MV2nBOyux0AI64Pilzhs5rRyYousI/aPUnEHUnUs69n9zw/Qy7XJUTgkNTRlIhsqIxBC/niX1dt+pUPt8QiNagHCYao0mj9UE/zusdOIeEz3CFEEFNwDoej0iYar2p8zoaoETAov8zo0mUOvcMxhh8cf/uHg/E957nCdjI5uuhRYsD3G5PuVOLjIhrC/p5jnOvzel664GCiohXIVrvj6s6REXG7PbsBHsSoOH7lGooI+CTdEsmGEcpbuxtUKvGQwJZ7kdmKkKxWsDg3s9UNZv1GIM+daA4yUyOqczTKxS+jlHVOI88MwR7ut8hDTD3jVQVmYkLE9yv5fooglxDZWxZBm6GwNaGLgNkhbRVNfbsOAjcGFccTgaSZoZlC2Ybz/V6W6Asy2rpsjJQ48J4EBKGeEyMWQjb+/fvb5SD51pZol1rwSjWvWss6ZwznasYAnWNMsh8uDE7kIMmaFhocDxAcS3AFY0kvZlpBKJmAuzLZ0UTgWX5pKNi3Ui/nBMbxZPLstyqjHNAfMLUODEDffVO5aBOwBK9hRpeL9etJTqQ2bpolmq5zt05HMfhSHBDFvMAbucaubbu9I7RpwfQ9d4L7RnqiFRmikBMMqp3/Rb1fJlHZMFBU2gT2B6kn5gbiPPlM5nJdwNMLhhG+65AGyQy5FQWCLL+kANHTOhKjpyQq1xcWAA4Xg5IK3EmohmNCHR4txL6ru5xEWr4ROPJqDOnsSYflFo7qh/XEfuOZ4rBNqXxOjIvFmhD/K6Pw5ZEgs/pxvYUIsVH3EmV8FQVI4ZCdHT0rYMnKmMm4PRCVU26kImfM8qlDZTBpmE18e7hAQBrh2SSZGJ3ykwEhlffq/n+aEvYNBhBlnf1u3NCC8pGBiL3Ujh7C0QEsAo80xGj8Urj987zvE89HFV1YSnxbRA5QNmsOpt0WrRXWSlhRcHu+4NI7ThKCJ52odB2JPfvFAB2fCDflcF1c3CqimGhK932eHJ7tZ6XQQXXg46a90CZQSLTU5q9bN9PRwy7I8SZ0LxBxFK2LJ49g7/xJqloaN/j4Ej5uw9qmyuTa39eybI76Wn8nRyx3ZI+xDvv6Dy5lLQh76NvhT/bKVL+h5U45JmC5bvoE/4GBEfQDuSIyqIIVDS1uvndGUjNcVtbVy2oUnpya69XUGIKVuOpZVQJnVUYckwplfZynk5RGnZ7L5fVREUGkZX812jjruzE97GxIcamwBOqSK7sxEyqBs/OUr3pGyMmB3oQO3MPogFIvl8rCMz1v0IylP4SHrcwYeb9v0wHOPa1ah+ETWSfE0FUWNsAACAASURBVM8R/31EU6CZr5ef7eNmXzKWCMpGTqizoqcxIXsLmPT/Z7NlnrXT1WS4e7My2M/ET3J9FEEuUGU+4ICZqxGstbCfF1QAjChFhx4uNxrLkcxIXs4HKHLMrMlfEJGVO4+QmVTylMy5QLeGlhndgFri9n5QHreSg8Pz1TRggWKwrMHF3lZKDJONSapY5uiUrctRZwui9sXuQ2BvnwamNm4lzSzZ2c7gAuobKxsUoimHxnueR+g79jJOjTnO0kdDItYA5gDWfsJkZOOK7e3lRRk4z5nGPYObCMZHaCImiouSRmGnL50OOYMdJeaaPaIpgCNGTRo/uZWmmDClcRAkOtKd0fkGMVXUWFgJLlR2rs6RaArL9USXzkCUKfJumyMYg96wN8bRqCaBKm5xOoHaynXpQd1J5HiWXFwaYKmAwN9zaCmPohSQ29RLno6awekjqpBr52Qansd0hgymGk/brNQwLr1wSBv/Sb3b484vy7JcyE05F9kbOwCFYuQ7yrUVR3XoxL0TemUFRbXUUniOM9CNfXCZ84m3SZ7XRFipuTjuqhEcu32+vCT/ND+bCE5LjMjT61WhjqInkglPlIZunBKNjWppX65271x3BVLfuP+diDjyHMEr4NzBfl54D/x8MWQjTAWwVXa8WiBJpLQHWrY1qVTk5F/7Q5oRA9sbEhTfRUdoUhxXxcDa7zHFKQHaAxWRGA+iOAMFM5SeaQazsQd69UxEbhXBDH5xD6a5nzk8hAGY9mbON9rZ3OsdpCG/8C2I8FzLtaD3Tu58Jp8RZD3bWGL6qHMeODBqShZqaEqvzjGh5X+bGTgtsF8EO2y7bjrWLvpQTMRUdalKVnV4jmiPRQpZYzLIwI02h7ZiHBPnOBGzLrNxkAnWtXcqyoyjceutklBFTdJKdHkUAphKFcMrbZ7oB6gUPN0z6GMYHl9cptWwJZLBbd+nPHsZZEYgOB5HUg9yj44Dz6CFrPWEWU0nS03lWGdKso3DlV3eJqYMbHnOfVldbo58eI0kXoHUw82GvO0+NbV5pTjclATL9zOGV7eo3tASmEpwKiFcEeOwoXppKC2thQHaAKeCpH2JOIeqJmmb2169JZzHAzAf2d73b1YVmXB9FddHE+SSBuCZgPOWjqaTyQ72JRw+UHwtoOgGDHoY9NFwczHP+QiuVJVj3jZsnNM3HtT5voITUDewngGuNGBTKlDOTE/uzVpZtg0Zrynjlr2ZSKF2Vmjg++drogH+Zx63nMMAe0L2qmaPOcOJe0DE8mkiBbucbz+wLqPjB5klQBqRns2T10zZHXKRXq8rHQUzT10rnf/SnZyrbeoqGFLd8gCyrMOmPm5qGiERuU156mi8IxrFe8MIrhADYpHsID4geJlODZA5k6tI9EGJvKIQFqCC8JHSYv4istNYRuOf1WSwg1qPxhJOZbtH7BkAKSHE90zUnwgNy5DcM3Q6yeEy3IwYjplqADSYL+ORXKpsZomyK/Utu1H9NKPbG3D8PkcG3TJjMhTY3TwSEVtaFZopLu1koWXKklivMKScE9wGrOcTusoA973GxIz37etBPnaVLXnWlrLDfmSQzPftSE8ZWjZr7NCfpbTeZSvXhUMBmHACQV9owZT/e2Lbgj6v4MMaxnHmz5lUyZMl/mz2C0duAgRbK89pd9DsmO5o6ltUuQe+GlSqjnLy98QqWU8b21DClG1rNob2tCexTGQow5jazbh3iLMJZowDr9eGoobuiO0M5Le53jRmldFJSeK9M7GlogkDtaR/xB4llYz3nc8uknx52sJevcjScgAXrOxkshQgC9VBsuEn9m0lAncUi+gdA8crzkAqycTP8ayOsHc9oaI/YJmdSSADYUd4iSw/kqNNf0WUm6owvJigMsjrayVBhdimGLP2fu4PIIGU3vTYKQRs+iMlEPDgnijpEc3H7KXoiT+nd1Hm8zzPog/CG/J4HyveQTa54f48I14Mzx+HjvAfVnOGFp9ZdlActVBh7k32bPDs8HxwvbhveAaYXDGB3lc9bw7waKokz0Q9S8mFvvK6Lg9I6ZdC0vFlHvk+GMCramrzdgSb+4Dvsk9gNeMIdcv4SmTCFZs/lOTjoBSv9kaSGza1vmtjzhP/N3Xv77Lbs3wJraruvZ/zGQ10kkHGAZNJJlMMBgyNNBkjMx1EmGQCBRPxLzAyMBEEgxFMBAUNTGQwMVAYRBSdwElEZVRQUeF7z7u7u8qgalX3fu917ueCA9cHDuec98fz7N27u36sWrUKsJxI17AW5RNjyqmZ/f8Qyc1stJwRQg1hyqFBexgxbdcrCwYAmVbyMUv2A2gttdyEKJjVPGZKiTHrVN3ThVoeHslSd3cJiH4BeqUEmQXPpmvD3a/SjmOAwz9EFoDksCXp/KV9+PWURqWvhWeswJOFWe126ldruDNYr02WKAPRwbv3Cno4TaYfurR0+vHzrSgAUMXnusJwrQUxgZjAbMI9g5IMNhYz2ZMrlY+0ScqxOP8fz4bBvFmIZp9Z4xmsmFldLxOREyU4nQ73AZ3q8zzV5EGUrAI+bJUEZMAJog7tKN9jl/JujbW783m21vC5Lnxar3uu7Llt3clpBrlaBba8xq6Jdmc2PecELMf24j262rGgupF2JkKfpGoAQa24pAMmxcfkuegM+mXrxbJrFeYQE/z48eMlufQdRWRSQGN8BievvZSVh5ZoNsvEV6LHouF8o5lSap04KamQMNnKItrCaemxV8SDbkKUgc+fwQwbHYiuIa+FiUTYk/Vy7KwAMPmLct9WtaiBGa0B2mDJZ67P96Q/HAn5bt4BIA33dRV1hddUXfLYXLZ10JzO5+Duof4CVDn7DOJPxQEGGvy9U1WigqzkwBdl4XDo6/jdClRoh2W/xy7/74YdJjKREMZ5mM8Ih65vnnEFpNmkwgoOEyYRQVOgH9qqDsWYB1J+qAQAqMCuOYOzlHIk2p/+BtjgiIikxvasPovBYPNw7Jrvz0STzWWyck/n3iy7ns/p1GrediCfh+4SdC5qoZNI+8XEqWe/QL3Hof17Irhn4sHgBXXtbZf8ifL1Vuj3OTHybBw9q6QV9CX6TLnOHfy2OuM8Z1fr2bwrxYtH7mVfq9ZNVStBcHe0pMv1+yoVJfFNx2g5vY//5zVUs9eaL/vJ6q5q2PbznMU9kWtqpVYB3ZKkbOair56KmhJJlH+DA4eqD8/SXLUnT1UEJmFce1IGq7LCxDGvic/+BKbMN/2h0Frf9o9cXq59nfW7l1Rk+feVI64PqlP566PKwthsZFV4WXCEW/otKj0xuD+bV8UBRcPyoFagaSS1uW/plyBJiUm0/kR3f9/rjyLIrdKRbb1KEQfW1uf85EjAZ06M+VUH0H2FLu0nyldjDFyydUor++5Bg9gBbfAZK7NM1KmkLCgp1DSg8TbSSTZgJfdMBZaC63yArTXc942r9SqBiwj6kbW3q78a3uICs5SM5MBg4roz6EpOG+9njBF0gTw0pBywXLwMWE8gDphrC5BnmawyU0qfjacQoBrjKA3QHhxAZWkhNmSVCFsYRlIvTgPrsqefNEl9Pt2cnas1mGziPDlmJzca2JzBIV5B65lVRqB0aEFOlsP1HQykUPWJcEZJPzJFd68yITPTUzeXv0cKBZ9dO5wLUVhm8eEwNhd4l+8oDxTGrV0d15WNaEeZVVVjnOTKoDO5TUsAs7kbGeCAOq6rVZDdkU2BqhVE08ABgK0MHr+1w5+0ndNZnoFvIQfu7/vK9yA3GbnuRU3xzYFmGe8MxGqteY2JQESgEKjGGKMCLZ7xcTiFtRbkIk0JVdKsZPGwCec98X2qu1w2z5/yWACyMmEIOcOtu2pmaGsrbFCubYwBtQXBjEZhDboLObFEjvc6b14x0S73aJhsOeFv4vidfI+Tg8jgWb49s+8Uk0XHkyVNIod89gysThRKfNNb+B5vVC+rMAeXkQ6WDV2xjlbXNm29PweB+vAzAyQ7pI0ggEkhpNxTJ2JNnnPsVf+txOfkVLI871DAdlDG4Jc2SUQqmOH3IllOzfScdneOaefvUeOW58UsOKm7OnOMLVUtFJMJKoeeELkkmIFjHVnZYYLPFwOgqoR5jJLn+Obee0n+RaPW7pHhWrSkpp10CNq32CeBwqr0XK9VagDhF68KiIhm0l4YNa27vq4TyOanpL9Vg6dIBWLAVsDg+/Ns89kTfKigvSmG+Ebz127kOpu9+T5MrBV70FJUdKwqenEWtxwf/TTSFlYCnPsyKlU7GSXqTF4t0kYCSHpWnkfEGvD9d1XA8MzxqvbxPHJgQ8ceF839Ug2luXejgv1uOOQAnjh7qL6POp8MyH3bbrhirqxqJljEBvuKHzx7MnLQg4hgDmAaio5R+61dAbQYlU5+fej6RxHkAignwo1oFiMklww0CJ41AbybewIhbWjXDVvAj/YJx53lmJ11zj2KU7cgv4jECD4LhIRljshmiHAE3xDIQyV5HblxxdiwEWXiF39trOR3saxr1eUvskuRbJZ6ngeQ3f3MkptMpJOTLb2SE0YKsXYEmd+yVOBWn6eqhUogEavrCExONJGDAsJIvCkX4gZ2mVDSiwZkJFpNDUvyo65UMej3VaWKch5K3VurrmdmrdwLlQWvnYkDYUhOVJiIDYBCkTwDCJHkH2dmWgGZBfevmrecRjw4v2xWK6epWiXOyPS3NBvVAqqZhwFgNVCd/CrDtPjaLvdwy6xy2nSSNICnESaqx3tc7iVTFwF7SJYtjl5uG0Er9DmdwLmuZ6c6EZVKJvB2KBXEOMvCOygthCgdIQ0zgxAcxvFVzi30ancndyW9Ja6fZd1TCo2o2bTQ5mRwxSEpItHMxr1BBJPv/8mzH88mOZg2Mvnd553ow6mSUM150FqTE82RYz98R49P9DpQmN/+fYABv0WgnPuFWtWKsA9skKuuaGez4arg4UT8rmyuPJtriOaf10rggV9ba1VwUGVHBrOg1nKcNybYlbja8XMnekunRQQ2JyJGQLrga9ObTkpT/EokDF2juhRVqR1s870DeXujUmpbdkkcW6LLdic7E78K8o9O+EINaWt8czlJGbC0AwKrJIPVijknVPprLxBpbI6X1NYpVUkg4bSTPuZrr31/z32GNRH2UFD5GiMQVgfa8kO+K/nDV8dv1nhNiOPnRtB4oetV6wXbUxnZ3MSEjPuQE7oYrDO55KRFrvlXnvG9r/be5DPks+ZEuTNJZxWPIAaDsUu2TjupEmdDqbuXvShbSUoNkHQ3LXtV6hTYgV6MhM/nsLbuulpKc5FqZZv7eiZUlufD27t5+Px3DWbJhsW5vAJq2vEaRETby38fVetqSE7FlbPCejYfV5Cf8qmkipz2jTazEPPUd6bkGPd/TdWcDyRtm1nEV+cei6px9mgRRPsDRMT+aILccCKyFw/AWI6mF5Za6eTe910cGz7ksTYvD+poLFn0liWeO5p6mDXphekTtzRYE3hXWJMKwJpEtzhldD7XVZmju+Pqv0QwzSxIFeJboqic9dWqU/5CCETH4YxGOGaPNmd0yh9IpWWW+ePHDzQN7UwGfHNOyNXx0BknIiPuIRMkgPeYQKQS4tPaA/F7LBCbsXZQNln6EIPZRrgKbc7gqh0HjKU/3sOVmqrsTo3mF1SzAYOclnQHAFW+BHJb69EseDhkHmQa0RPN5f9Lg5Ni20DN0w4HHntkuNVoQQ4D4FCGXW5NZCzLO4EASQW0Jw/T3d/IsWwtxHYYGhr7jQbHi9cazTwr0Vwp5FtEShPYzDB8jy1moLHWRrmeNATRFOL1+efoROR6sSlyBzjv4Jo0GWb0XPOeE27MLMaaTsfX11cFzOeZjt+Lc03UqQKEXNNnjt0IWKW1lKPzSNyWbK4quYF8nYElsBFKNr8xWHtNwUrjTRmrs6nr0yJAo4Rd2KKckkbxfNnPm47b/NBaph4wjq507GEluxJ1aFZjO1gOSRA5Rwsj1w2vJIN7g2frJfzONZ8/0XwjpQySaDf4imA9A7tMvkNRYidp0A5pVzTx5dpxbTnG27ClC6Fbu5Sfy4rJ8zwFJnDPrDWCdsTx5u3CtIEm2a3OJHNxuExU8Uw2V5vPoInj6/kNIFZTAc8AteTMaFvNKnh3f0sucrzoq7qR1QeeMf67kLR8RkVNk43sVbB67N3aK/n+1IUl2jnWrMlv3MO0waHDvZtrT9UMlqUZJJw9IwAgV8fPOV4NdLH2GwCh6kadHXkH+9dh10ylxtl+robranAJMIRyjEwwaWuABBqA6OtQqn20SgqY9HPAAz/7VC/aPuPdhHfShM6E4dyfLrsiRfUD+ngARyUUmNijr80s/u/2sk08n3yuDCj5PQ7rILBVQeVhkwo9BV7nh0ALwTzaT0Bfz/Dch7HG+3NtrgIkuC6WZ5lfB4J+QGUXNmB6Bsqa+3qmWgalKyXvlYOReP4JDrH3QlrD13heFRR3L9WoAgbUXwDSr3313/8jf/dfjjwk07EUEaVfF24IXAwSjLUKEiQ1SCmBVNqCHqWkIfEbJ2+lAicHgIm7dTxuaFDc/cIzRyGQgTLGtCVmguIoFJbDBoDMilsP3s+xqSqbHhPoDV9rQcbE59MQ0oSxea48xGsamji8NZg7em54JKXgwcK1LkyP5rdpVk0DDLbZLCFrQEWwPI2/GdQUcIemAx0ycVk2+WBB8AG8oXfFtK86/OSuEX3jgemtASJQP3Q84wfjIMwHJtuJfxK1CPQ8D16S0hj82zKg76DLgXqun+TIlXbg6SD4+b3BjZPEFOvI/IFARXrtIa+ST1FaMsM9DbznmolIXM9hkIqXJ9kMowrMiXZd1dgWQxbjxSsRAFMkaDBpNNoVE4WeOXFl09Y8jAz3cAewfAEikByC4hhQbS+kwwVwrH0/RKKkYc6nZF+axwXRwHKIBZ1D8aBzvWK6UvC0VTWqFUCK67dX1swAxjBxa4yelET+yT3THtOHxhjVzMTu69pX+bym7yqPqMBmlJovUUzdQcuV9JjvwS9tQHODaJSXHQtLBPBA/xzk3zaIAHMutCavQQjAG00sJI0JRAYeQJTZqW4ChDA6K0XkwMb1KWwYpIUjuSXGub6CH9lyhCciMviMuM8PAMBFYO5o+MA6qkHGxCDhLWNPH8hlTz3xnz9/FjfyNUbVw3Hx+QDZHOUetivPJwNNnhfkGnVRmO+9xgDH3CEKSG+YY2Yg7Ul1+ATnLxtr3a1sDZ97M4GljTYPtEdFcfUPAKBT60wFRv78weW1ObHM0HRXPdi8HKivw46BQjzXdMzaG1QUzxg18WomALDMcKliHiVdF8N1N/g0yMGvfKFvTMLGwHXfUNsUn6JdHTa4SdAaavx5JvI1OYyJag91gLvtSWesGPEzS6fbBdJ73H9+7smD1h6Vxed5CqgREawmEA+6lcHRWtBxSqLs2KdMpO8fH8AcV2/Ro4KID5i8Fa2gCZpvWtcyC1ubdLX7+oExvzAzYTkrIycdzAE4k5bcTxMeE1Ld4arl54S2VRVNDDc6ZqLzy6L5eWlSHb89G2rxOsLs8/PLPzFpwk4yVBWe+wcJnL2kALGpEWWD3PFpgNkeV0wqxTwolYW6IyoTi0lzXvt5DUx4fVr6ui1tWGoIw9AOylQlQenjeH21zzKh4CwBNhGeSftpX9wdz5dD5CvW7A8IdP84kFxmGE3RIBtd2pSjXV7S7OBj1D9XNV2xcYoPvTRT7dkLTukiCVkToggsNzBzlwzQLm3FmeR1ADiuZc9e59fP7E01J8G0BkPo2xbXpbUc7+i4nTIbB7fMUShWFzYYNPx8nly2o2li7E5iHphnjkC92kYTdrmIWouCLjcEC605zJ6jbL8RKSIvgTcdzRO9Q02y8x1wifuU9klnHoFyoZvtzT1j40Yg7r3oBOdaM7m4cr1O430f4x3J9+XeOYMkwIpbdKIYbP5qpGPgt50HpXuI9p4i2XUggVcD1UjVibi/uQNc2Z285EATFS2eYE6lOdEzIgRTUbJcDHgatrZhJVjJ4Qwu91t2quhAyeelTA85wux4ZSLBP3POKp1V2dCjI161Y4zN5+VnxV7rVTY+g5pwjoHU3fePOnMso/GM8Oc9r4/PmkjBV2ov+8rO4eOaudbxrDk6MweBpPh40TU0ztjMgSdmQR8gispggHvkvMfT4Nf3kyt+fe46cwx+1bauKBC0JzbVtExYyatlgnkibyx1no6bqFhpWraNRJvEmgX3khq2VpUJBlLc18Au/5OSQFtw7t0qiafd4bWea/G5rhoaQw6xHHuVZ4z7EmtzSCtRaeGsgmue6FtWPmgri8cHxB5KfinpFDXW+uCczkNKkMk4ee0MRJjssrpyHdfGF7n6HAIzxoDn+xUyzBHObBxDiw7ydPREyYggc88zMBhjFJ+yKBJZDSMVAeYxlv7YN98R7DPYEAne76krzz1w2mGOcz/fhz5CU5bLEqCoJvLFa93TDmm3WVKn/+L98hnNZ9S0zxPtZildbZ/B80Ue9phfgOpuBs9nyvNwAh3nvUwcSiEI0OyxiQeGB3sKHRaKosd7qrOGA/HE1tRlZeyUzWLwzsSGwxpIk+GayLLS3QdQVRg91of7inJ+DNorBjk+l2gqqy98rvx+Sbwl6szhIERrWYHkc4UGXfCxXWmlnnR83rZfc86iKo0xspIHzEx+eEboW/l7vdPnbDv4a15/HEEuciEgABLWPnRkW7uqQQyIB9z1KnT1ah1+zPTuvkuGyCKsc3MC0c23VgR0EqiGf/sdadHJKTflqKIodsoOqQLQTYs4SfPc1JTp4gZq7aru6bEWkB3cct+FDnM9vO159K1LaJF27EYJdaBl0MmGAd9lbsqklENOFCFI/yxBr2iuQxq2tjWET6NAtITcJRGpKUHk6YaDXYA6ls+S/1oC9Pt+ZWk0Amcg4mkAgI26TbNqSotnsJtBgG0Ut6B3IAw0kBHwZ8ONbfFqHvCWzphThyQ5uiZW60OEJPjCFsH8gULEXthc1wj6tJz2OQEISGrF2pxlNqedWf11RYl2Ysu2kIsK82x6SF6wbV1eGm4FgJn6hYYqv64V7zVt1fhiZNPMa68cDoXIK3lzpzOO/W6ISXyb00onyaye11YVlQxQVRViA0k2PRBkJo5ZpUjDRuoGJ72d543odK3DsYfjd1ZqQ3uuQSDb2jum7YCC91QIHZ2e7TI1nyvvcQcegMuFacBaYzeyfaN81GSf3Hto4Tgm6IB803rcsQ6uLoPEGpeZge1kRz6TtDUOYf3gw3NUaVRFNgJUDtpYHpz1WROJbmVX99fXV/L7k/ea+/PUlabtG+sY5XzcbwSbG4Gk5jk1d5cfOqwjnXw+66koDqPNLbN1BvwuUkNsir50PIMavpJDL84AbsIBzAp0Tx3iSQ3k006fztiO9T/2k/vCzGpkDM/x6pcofil2M+UzR+0pdub33sumF/dXd/lbPPibj/hOsOME1TkpwAK7r4DP7kTS+OLP/nLdtVfO4KdG65rjEo6jpkoEOfoZ+Ep8zZtsWklrBYKwSRkquH98XhKWFcQdGsTfJRZZzj/pa7wG3tMZyONYezahXS6li1w21xW2jj4g3zKR3/dAK852AC18f/EYCBH7/s1vZ4AbvntLOfL5xbkJj8WEiRrCRf3Bns4ZPmj3ErBX4bx/+oNqNOcaHfZ6rVXNiUwCzgTnifnnMDh+zgFWvJn0Dv+qs36qcrj7nraWcQp9/xij9PQBYNkOrk+FmD+EsPBHEuQKLmTGnBOs2JADc9gCGocIHAFS8YU0ebVEb7CwsHLD9EB+07ARQWgGSI+pJD4310NToaArN0h2VvqemqKyA12ZBj9GmZ7Of3kYBMqmBNoU5QeOcmVZ72sM3Pe9JZh4cC3oCUSCIpBMble7ILjKydIIn+VScYXMDqCHaHP/4NMUtwqgDUBwZ1uWAU8UjcEkkQ05/sTo4zOA2Jk2nQc7pKl9GAH22Ym9+VH83hlAsCy2ja5WIkBj1onGY6OI5mn80zFMj/drObluzK9ERlY6gcgSeejXcsgiNziCayIfDHrJuzrXyt0h09A85ahyLzDJscx0GZC+GnLyRRSYBlx8KzpQY5gNfkT9AZQDeee4qQNNKsO3671a3xwn1eRPvRvE6FBUNc7M4STjkRxls4NPz/V3d9z3j/oZBjJmhqtFMMn7JgpJxPd5Hvj0GAE9HUCMPXYPbc4m5PrGnq/SMqQaa4qiQN5sBkXUtGTQT4cYMj47mC3O4DfBdj7POg+FmhpcJuPTshOclERkkc8hmli286ST2Gv15hyfDqn4+XTwc734fsvlJcv0fSIZA4Wvr68XWsbzTU6eLIsk6Lg23tP3RLWQnbwe4BhYMwPJuvK6Hwax+bt0uHT6YU92Ew2rI7xHQzblLK9pTT0l+EphRPbeOisQmoismdUers+YCxPRRPzpH6y5u8d55qj9Xc9Rk4IiYUO+67ir9qKOrbVKK1pky05yTTkVch3Pmo6eaDgb4KgR/HOOfYbkrZdKv3g+K6J15+tsZKPN9Qymz2EaZ7IHUBcZ9X1N/WvLJJtfJ1UGc/PJF3sk8izz/J7cft4z1YLEvRD381ppV9ngzES6mmWxaVkcZHDaOjYdnonsWgt367hUoDNVGmRLo52KArT1EUDGMCUmL+XDMqb5+vrKZ3VwTFXK9r/iCZHyBaf9YRWUQbrkOagBEIxpEvTwRJL5vuSj1/N3L4UVNiJSqo22CQB+zrHv/Qn7WQChCjwn9jXs0dZUvqIdBfCK54pOsxZ+ZLPz7zqztVYH7/n3vf5IglzHsyaWx5+SGUk0wHxikCiOd5mCDWVnADYNUA+S/pOlfQaIhPw9NXA9uYWVvUk0qsGOZhAwy5lQB9Z88iEIXFugHn5MFkl04pdPjA5sV2i89t5DXkbDSGoPVMlzAhRFqiuYZdZMI3cYlyvHEiLLKyPLSXe/sFhilSyrNYE0wFvHsx48NQIOigAAIABJREFUE1gzeDhBUZgVJEVHqNdal/NOY1D6s4dj+M4JArI5Zu5DbpajAzMYIaK8kSvgsVmbfqyQixJHTL+zrSTRE6lRBI8PwKtD+XPv0ZhjDHz6BbcJrAwyevAMuXcio9+OUMRrdKVZoBVXKhWQB050OaRyAskca5WkXOgK7wafQu8sDBGNK7DLw4Fs6OswE1FTWKHhRAYYgNHBcJQv7ynK8sDXeI4gLDWLKReUyRGNfMuGRj7DFzVBUQY3rg2F7JoZhhFZxKsZ4ufPnwCwOeMSvPBn2m+d3ZXPnujn9Anvin43iA8sDWO4ZgaZB72HJXMOOKDTLCcoWp8BvPmPVeI/7g9A7TvFnoB1Gs0z6BSR4vDjCIQrqEhh9tL3PdBUvle979od13RgmovO92PywQaNPAh5diNhoGzPiag/z1Poa607A42kFdABfW8GYSB6NsowoDodlVnwJiPJ2jJ8LI9yb7yQ8dyTZxDFwHrO+Vuj13kdA7spj4gYr5VB/pncrXTkLB8bPIPLCCiWeAa0jvH8LBvHNRprYWLTg0SizF3KESolfH823PF1nkWOdeZeY0WQCRo/25QNVVqao9SonSm5NrGRdQZBvOdKjI5/nwjiGdQBAHoke9QLJqJHlPZcf/dVNAHKy9V+8jdaWBP7bCuM8P02R3RXPytYOxJDJncxKnYjfJYl8+tzB6/fFh5b5XNXrpu0mKp4+lazPYWUa8syP+8xUPyIR+gbTyQYiCrDcyR/p4zYJOUsEf34/bGfM962h3thjaBumO/E5UwoidyyqS2GQuXQk0yOLPc7qwIAiibIPVP+2yhLesgOmkVzmHtJj64c1rTW1tLluPuQI4sJk/MZgLSjqkTaw04ANgUq6Gfns6Ft5T7CXH8QlPtHEuSSI7VFmeecVVqkU2QGZjMXcW3+DrBLvp6at5ql67VWIVTA5rKIK/ql+/ecvxObOjR4BwDD1xzBNU1Yl8bSfWX2fkD+QJKrB7qEssIv2mOgwH2XwQaDDkc2y2S5jcL3vnm0vXf8SFFvlotoyEQEv+SIYSDLU62hR7UegthQd6dCg0AvQI8ggCW+mtZ1lCfO+zpLNvx/oGBRZlREiYeyMDRqzDqBnTHywESZG+XImYETMXkZIjoL/s2hR/n/67qwfJbYeASCMVijkoEUbhdJAe66H4VqBxCl3vjceP/qRjXkH2qRKjgYQFWxsszsvvU1mRCUDBmsplPtgHFPG+Je5vAN8QX4bqBgifUskxe6hGNIBlGmlJfpOSDkdGpAlBpZ1nrmxJXcVTraKmXnWT0za55XllWpVlFBhIToPSsZgZznvk9K0BkIRrLFYDSSB4VvPV8TrDXgGsE9DTadBmVm2O3PAR90OO67MarWyY+ucxr5RDpeiH2ukeR9VqDWW5XM7x7juC334pkAci05RchUYMNeiDMd+fl/IjEMms6gNJLYVojZnnfvdf8MZE5O8dnJXaja2kACy5vA7uymwz4DnDOxBVDBIh0ugySu/UsV40CwvwcatUexVWB2ULU/T7ETLybSJa12TJ9iUn0G3XVG5r5XVcUlO3iR9t7vfC70R3y2N/ZZaC6v+6pmLOxeEgZ6io2UsYI1vp5XeTieH33jpt0VEmv+2mO/K3jlM+d0Q+6XlvxhYOuyMgj5rgABSot9e0/YlpAjcMPplWOMsmlsbGNiTnvHayZwsNaqKaHF0Tyugz/PpLbek2d6Lui0+twmGsNI8mwzOT7R4riWM6FlpSX8hPnMdV4BjGEnpVGVahXkcTIj13mrshikB5WhRmwfdqHWGW9FhAjeFxxRTXxs7WeW/z8ryAyOCSrQZ2vyumkHwja3eh8i4KTk4FhXT7sn7vjl8ylknXJ0pw3nGruHra5zvo5x8Ymaz2+fc33upJik/QMbovECi/4AIPf3B7ki8udE5D8Rkf9WRP4bEfkX8ut/WkT+YxH57/Lvvz+/LiLyr4vI3xKR/0pE/pFfcyFh3CM4MDPoR/Dz558AFvzcIjpPh+eAAy7sjx8/8iHuDJkLS8N0TnTRLGMZHN05fjVKzfG7W3TCEJSFfn8g0jDMa9kiGO31fzp5ciel9RBvFw8JDnfcXV/o863MUt+cFVUtJwmk0Zet7Vm8HZEqfVGeiAdUWsOVwQ3nSDMY4BqpKn75fHI3ZLdzNqmQTkGJLXYMnw7e3SFom1+X18SNdZYj6FQpvwSwxEQEYU98oaFmifN0cLxH6qQi14OczGDuHghxZpc0ZMhJT2eZjcY5Aqdo8KOBKKSQB/IM/gVlZFs+R15P8TuPsYcwC4eoUoEWG5JO5xFyWZKI0QVJ3VEqgPAzGNSeOo0nysfrGmuPLK7SfT6bzXneqA/5cPx5vgpp0N3sxv16ItMiUte6BC+lAEDTEM5KFoiQsvxXnGKi7U5+9UZbT0k7Bg8nn+3cf8BbyxKq0ZiZXex37xWosDHrdHaFhLX2KlVzvThI4kRU6VBO51zOLN8fDa915GcxUWBwuDL45/uc/NITpeSefD1PlvKZ5B/VFHcvPuQZHBXak4ELzz0VMM4mRyIsJ/L4JD2jFFV4f4c6AfMWjoVl1Yd2x9MOnYE/15hn80SFz3PKYPqk77AMHsnc2Oig7qa7SIZWJX07oN0BX9jtXqVY7qc6H4fNZIJCtPhEsNdk6Tj3Vt9NzOReIj9Tk5bAROy3kn6x1/7fIIy/zjsTwedAV1u7NrDUogFMDejYHfgMUlgdBA6lBlV4Nj6ftCjaBNUYy0sdbwYwp6wh7RVfZkE7PG0Z1/e0c3yd54sACvfxBgeicTECYCu6Ctf6nATHcyQS0oCCQCL7pTXkKQbzOPqlwVnP/TRY/cDhIzw+f82duHLPEgzZlLg92IaJHxtqV56bz+eTyZ9vBDjXn+sHYKOfeiQikFeSe/4h1eGsuNSkNguUeCSyfJ7LfmdM4ycNxgrhh2Uz/wJmxlBz7ooNfY4vAKu9bDoVNZhgUaXi175+DZI7AfxL7v4XAPxFAH9VRP4CgH8ZwF939z8P4K/n/wHgnwDw5/PPXwHwb/y+D3Akt5XO0gEsRe93ZfNAbuQc/XaiCl8jynXPgVicyAEPPHBMe0ku3uObwG4tD8/aBo3alwqB+QNVlENjl3sguw52Z0sL48eJHtG4kw1NaSzhnLKzEUMGmGd35nm4z3nkzBQ3+rKzYzqAQBGy4xW7A1skStOd3Ckz9LbHKAKAFIq+jeU5YaUCThGIJsqQE+pmoZl5H1jQBrhs8ju+JtYYGJ4GXA3TBkw3ku/uVQ4BUML1K79O3i4nzpXeqpDLlKLVEDT9JZz5SxJtGyHugeiK3hNtSKcYvhEZGpATgYm/J2RNXBJPhIEE75mi99N2mb1QtjQahbRaUBSIekxIahDPcpyDe2tRDSEn+OmhMJL7oLWGZ45Cz2FeU5XKQLedOBGJPYPdckI5yvoMKM/Mn9fEz1ULtJMG8ztCSaSOQw4sJ3qde4i8UZ47OjOOCGYgxSCiHROVeO18TgwSyElmIDQlz1ICPKwiMDgE9hQ/vuKeUEGRYfNv6ZC5NmeQrQCUiJAAgwFAltBP6lOV9PNsyLVLeRXkp508+ca0hUStzilC54v78HmeV/Nerdthj+qcrNBh9in4pX12sIWNQhey8+058L7MDMsGwGYip2PmWYjkbKwtf8XrrWe5juEU7lhrI3XhjL9Nw6s9fvCHbY+Vj/dnE3Kggawqkc4F7A7vGvGatrrlfljk3/ru0g+Ul1WNJ5qGzcKvABgP111TyzxsEPeWsoxshq/kJ3Md54wJiOEX914nalf3z+AzExAqiYz5FfSxRHBfKih5VfH7nhTA7St4JsitZV8I94GZ4efzFcn02kj9OYa6Sv/5fIh8nwgh72WaVSMqY4bvZ5zXTfut8QORADatIM4sqF0MSrneAHnPQNOr/o8FzJHVwN5imMhRrifgwJ+nysjCws85YDk6mutWEp/6VjPiPRRqnXsaCQqstTIZfyf5tK+hYJCBId7XV+jxOdDGd+K40fNt39w9Jr0OYOXZsZG0g2ZY86lg+DpoHM8kkh+j1BWGdlbaEWpZpgQkMmG3q/bFifby/P9/iuS6+9929/8i//1/A/ibAP4sgL8E4K/lj/01AP9U/vsvAfi3PV7/GYC/T0T+gb/zh9Rn1ZjEWNgF2C47x4NB3SiwSdaehubM8s/glhunoPcJAFvfMORhorTQWmQdMYMd6Mjgc3WsdaowJPctUT9hF7UJPtcNh6J7BpEaZWc2MkEeNAHssRpKEONC7SW1tdEvVGAbmdHetDyoJ1pjc+Jm85Z7Nv0GJaHjmDID1KEMmsFGPAKV2Nkm15YHbDjpI3l4Ut6rpkppDMBQ6RH5gxJngqeFAaiuaEuea3IRK5s+EB7yjs/nD3DCmePWmFR2ZeNSBH09GqLsq0pZ4biOyWoc+5j3pck9ZXMXP4PI8YkozzQQTFRcFM9cmDNLpyIvQ0W+K/fk+RLZfFHRnbTEA+WoWS2ZNXbYVqlMUKX4E03nvmdQVzPPE4WugGRZBX0n/5t7pJzWokHaKM/5TDjFr5yjHwhe40CPROgYjPcOiu1vkfCNojOhNLMK4nhGyBFjECwiiRZsXnGhv21rAJ9l0eUxQU5V0fqWx3Pf09tODibP4XcUlbYHOHSWddNJuJ5fY8BlN900UQzPCUIHKsNkgw07RATPakcFpRbJ5dlUczanMRj5HjjwWvmMWwvJJQ5EoUPiegenNaShmqbzOqpowEG/wS69nsHpmcTzWZ88TPoClkKLD3s0pJwa17WnfXPB2ShMRI9I6As4aMfo5G/BkipgpUyQWqNcr0xWV17PzKDhmaEqwzNC+8GkcIJUrVYADIOoojfl+aVtqnsSed0b13mtBZWOOYIMNGdIMTkWrrtlFSeQ0Wf8rDXkM/laA6I9eaixj84q2Xc71XvH85xjjbUqrYGMHkN+bPOMC+ln5/x6N8JxD5M/Gr//PkdnY3Ohi0fF9kygXaQS+fNzKgg+r/GwU/xZfr/GviPpLQ1V7Vwr6AuFah6+i5UiSoN9rjvt4Q7E2dTIV1x7LzCM1VyCLxXDrIXx9URzrm/t25fNxq788FwBKUua9n4tr8p1Ucpy/9H2WaLACw6BRmN3VyylNn0oUPixR2nDP+2Tz+SKfdE7ol8lqJ4r6aD7lVUqeQNsbL4mqPX/NZJ7PoB/CMA/DOA/B/Bn3P1v57f+ZwB/Jv/9ZwH8D8ev/Y/5tf/398U+SOx4DqmqDKyWF++WJZFwGpYNTvswFgI7o7uRzSbALneJCOQOmgEPQATRkdVizRj3Ky03sAOpmRn8u98ey9lE8bn76yD11uC6pbzgAdG3ltPSNBCEGv3aQ05FOQPbAdVAgCPQ346VxpMITs+DMHyX1M7gJoKHDJjyHhgojLVimogER+fufSMb3yYCVUCCoBoEwpQO/lt+xYO0hybE+xIJp+Ee4iWDpLZLpeda6jFp55kjmmpsd6Sf2rUsyXQI1Ba6KKbF9DfDngF+Cbv8W3btpgG2neE2kRgacAQ9pyOOtW+vgHQT/3c59Luj+B4Q8G8eYmqoEnGhAVtj4Pp8Eg3e/LxzP56SZeXsM6EAM/hDWocleyaSJ3J5vvd5ja+EC++kg8+eiRdAZZJYK+RejueP0keGao3kZfBaSalv7psea9Zaq2SOAQsdHJEurgMDTVIjSO9haRnai6/5Qpv7Rk7joL55ti+qgbyfRaGwtpUSKnhI+cFzpCavNQK/IyCVHXzVCNzj/rgfyds9zwL/zcEOZ0PS7wIDiPCcZ+Hkwp5IcMjuoRQx6rweCQWRRrMtL8egNejxewhPJV1Hwq6JwJ0oejW5MaBIZy/ir3vhvv98PkU/Oakab2Q7gx6fuzHoDMCwqT6savFaga280KVjOSDXHehgBoxnpck9wAEG4BW8RZQZFCjZtLsYv/uUXT/PZDwfh+qda9/xPBNNL3z9HNmEFbSyfn1eyVro+kZQ1XVPBf0ObHAP8O/r2tQKooJ8HgWeZMLNSYWsZjHhqqQVqOfH/0tTjGWVmFRiswK8CRv+7Xx9S6BO6Trudw4X4Z50jzPIYPQdcG/qkUjDNI+BITxXlmX6ow+CVRMRwY/7UxWslj03wB71LrJ1+U80N+s8RfcaY+xpqAcQQqCA+5HrW6o+XE/ZwNg87JXBA/OZDkXbKijG6kPEXOdeh0alY41ZVY6Qc2zZEL4bjMMePSC1hRSF776wqi0ZT8znnYyLyAvI+B5n/L7Xrw5yReTvBfDvAfgX3f3/Or/ncdV/SHANEfkrIvI3RORv/G//x/8OwOBrwmaUW7hRP/0KFLLvIKYkX3TPuz8NjlkI/weVILr7zk7IUxIFAMxyLrIkGqkCdIM2Ky4rPGcqmxd3J9A/jv1rcF/RJNMblk+4D/TrKNM3lmW+AAS/xdo+BCRyqwK6QpXBjeLk+pLfKImsPBhDMvi0oCAU97Y3DHHcPa6vJ9JDlQBVxd3uOkDsQu3Y1IjvTjCc1NjlVwbVslUsgJQfEWD6RMvD8Ryo0ivLzADI0+DXs2HHcI6FjRJ3BjDIBrm2jSUbgzgIQ5oGBtOAZ4XKAHKkpCHQXwDwq+2OUsdLKium++w5359jRDSz+cjWN2eK6zLNaga9mcF0N7FNOJbsBkLYLHrLd9SKe1/vq9bBdHNGJRMUJhbfg07LP8N3134laGlA2UFNGsSrkxobzTydCvlphZ4c10tjTYMlrRXyxaCpEx07Akfez65m7JHIi/sjnadZ8lv75moCGYwkOsiAK55lq/sEwiCTgwYEd5jvWaOsj6rHiRi9kJPks6vFmYxEI6TOtG/0fM4ZiW1rsIVCJ2rN7HgmeR2S18COZD7fsyR8BqCFfPpvK0ac1aF3gHcGhd+4vk1f5Xy+DxFMDlRYa71sxjksotaICiAZ7HH/Xi0acZmAIB1tBIOJZBNRWlsJ57vDLIQs0bIYhJDlfSKJeqqQHDx2WRjZpLqOc3Ku19lIW0ACdjI+bWFKIo3RL1o2i7YVJhjmaI0c4ZAHhEqoBXDdlsHX7va35jXKfc6wgDvwXZjzgWFh2oBqqKoAwCUNzQ992wzQmgOfkhFcsDkglsHcEWDwTDDo+p7ckqLx8sOetp5n//g69/e5vifFydOGwVZUOw+UuOU0wxhvjGry4nPkPhUR9PuqBOhs/pVjyzDZoH2h/WLCtfWgQ0LRoElFTJs9V51NM8NEK7s3RtAEWg90lpVi7s9zDSLoDj74GF9oTWKgRdqyM1lnEn+it5VsWIx3jgQl9zqpU/nznO4acqgTrTmasCq8/QKBiKCN5vOzmOza2h7FjQQHXRqahb1+noyvUhMaGtVkpJJUoe+KQ+XHMSQVtWwrkpyJx2nrfu3rVwW5InIhAtx/x93//fzy/yJJQ8i//9f8+v8E4M8dv/4P5tdeL3f/N939H3X3f/RP/+k/HV/DwtV2Ng8Af/L1E5qopiRHaoxRTo2GsLT1qBv35EzoESoN8TtxSG1yIQ02HbdcxXcBEAZkBjdvpaizSnJEIBDdWVJkakTEDnUESGXhVyfiEPfU+w8AvYZU0AF0UfR41pVdOiKg5CQUmzt7pibklSNHQ0Dck3KxtYFDEmSLTwtyqlt2+4pGV/jZMLBkO4wIDLUOvQpLgyzzvZG8OpSUl1rA87U7Ok8nrqqF3j5jVcY2xm7awtgli6IGlKzbMb3sCMIkr3laNtiJQ1NwnwhfBSmSgUPbzTwt0T2uATP34LYSTdAs+a8ScechJKokabBVg9vIUvtuQpHicYL86Wwy0KMEW/xY8xxKEQ6K/OGziYO/Q0m62OcUTm/V9BToYQZAYolY7wCGnbciAk0kVYFCxlbyBs9ym5lhZjLhRc+Jbloa6N57dNEieWtMUtIRnQiP8ll5UF2ohdmuK84CEQbbqBifG1UjiPySr9ZEw3nmZ55C6WfwwuSLCc59UDbiTzqB66AV5TkhIgeNZopnBipy6W46jaDdioNLNPR7svA9QD1/n2vE4ItyYiKSvQd7ahKR3kDRMrhB6rn+jkCEAQRL7fd947qu31KNkKbF4+RwG/ct/2VlV5/ac6WWcwTrXDvub66NXDkiW7a9nHMCHsN1zsBWjv1gFtrfLG7y9/i5sSc2+j6fUbxNVmFOBxtVlo2M8+z6Mlx64XkedG34+vo6StyKPEaQ7CNYHvzZzSMeMXzGUnHDDtnKbMpdMyUvXWAZSMw5cfdrP68eNshcIvjNRilPYGgcGsrk+z4ZVEf1MFRM7gRAiDgXLcg2RaHWfK0YpCJU/0ANmSkd4kP9gQAHmx3NtswWUXf6w3rpph/SFiJ94c/c7/QbFTg7lXsy+c6pjkh0+ZKweYjOh+JM1yQ8W3ieJ5/zwvj5hATYjMylbParWnWhZxOfIYbbAB1zbNqGuwALpadbTbcHiht0hVk0iAqg5wbmpq1C/7lHVfd00Tj3G+mPPcJqWNjiO6u2ZlZT0sxmVZmjzyf3XK7Ry29WsBnrONbEs54KiJtEIC3Wqu9jzQnIhdZ/RHXBFTO56L4sn8luSuZEQ9p2BV7779e8fm+QK7Hy/xaAv+nu/9rxrf8QwF/Of/9lAP/B8fV/VuL1FwH8n75pDb/z5W5YqrCVai62yxRydQxbsDFDpSDLm19fkeXQOFcQWFy/+Fq7Or7GwMwHTI7JNMuAZ2FVuSHkyMwMXzYBbfFAWqvpXsMW5JgW1rERSL0vzJn0grYbGNykshXXLSxOVQQaqdkEoX3vmD63M7Hgh8EW7rYdnvbgzj3Jb7n7lQjl2qiKx7QakWNamwcP5jVAYQHQDkGrwOAlk4R3iZ1o1O/iDtNpnVIkLH2RB1lOMgMJ7T2aANMhSdvPdinKMJKDWEYN7/IdO7M5frcnoV0Q1JNz09Mor5XjQQ+HXEgtgsPEJjd+hrtDVCPoml5crHONiEov34hunqkqwTKo5GfCPFAdHCMubZXT4wAEomgvlEQ4JSeCfCPHtbWSU9qNbquSFz7/symNDS58ZpyKx0Bw5fCSyPSzI5hDN/Qqp8FXlZi+ldfZQPIdeS4jllSBZ4VDsf4u1bMM+nYAb3STCduSvYfZ3KbY0/POZhuihnHOekmb3b2X4YVMNHGYy6sRj9fDQFLEiw8typLbXhfyYU+UcjvO9nJkIrv5Ke51bcQJm5savPmDx4x3eTRy0F3y7/f9Wn/VtzrNa09/Q1WKumOG8fXU1yLo2wjUlTw9Di54PWfsQLyuIfmZTDaIcHJtIuDb6hVq+/zxnCyPJk5ya8/y9qYAbaRSMjivpit9NwQVHaoBy7dawrBRcoTXdQE6ILIgsuAzkEfPaVpVIZG30gkDK6LcTH7CHivGklAXcsdYP6H9PRhgzp8Y4wu97eDoazjGErg1zLUDRIIB13UVRWgJ4FBM2Qn1WTb+zkPn/gRQkxt9BZIYPtkKOAkbuGoPqmqNeheRai6kD38NOZCtfsH9ZxZyU6/KjWx7t+RQScn7VfNqvg2wh6gryoczID/tgqpCOiAWaKTPU0kh92xWFcb8E0Qvkdc5C5pC3G8Tx9JIIJ8ZcoelBCEOSExhrbUwq8D1tAu1R3VTP2jfTzrKmRDzazyvT9LiTAFP7f5oAANUbgCKZcC0L8wVqeK0tSXICJx5wwpkEuYN0NRgXo6Z1VGxQKZbaxFjgWfA0K4caTwfiNG/hV2MBksHevLZ3Up+7Ne+fs1P/2MA/hkA/7WI/Jf5tX8FwL8K4N8VkX8ewH8P4J/O7/1HAP5JAH8LwJ8A+Od+3wcIQnZDIHgSqSSh/QrvAmuAWxSvOZWjDAAzbCK7B98numcNaFJliWnxgO7e0yGuQtvciSjtst8VESvgnps2A9zekYUMNDFgkmsKrIkoMflb7sJN0LKZI7R7L1jAZGjukN4A06IDzDlxf34J5K2heIqS6EuUIz6BmsGTsL3LxEBspEBbUwNWHqDK3Fs2JZAuQKCFxLZrc+Du+44BD+px3WZQlcoQge0ciST3fDYnSvakwWLTT2uBfrDc5e5wsxpl6hohkrSGjp0Vs9wTpHSDH3woAPA0nB0CN2p2vssgNTfcLA5pfANA7I/lXtxjBj2GRM5waBPm7xFZnGYw0XgvVajkNCY3WCL/ayxc7d6Ir/ScuOUlySYiaAyW1wJaK9H86JyNa8ER8J3UHENICdNZlXydHvxb3WT+H3fPkhUAOSZ5uaA1ZJa+uaVNNvd12oJ8Q4Hd38h1OchDASACRtC61u8AeX8iuO/dQALsJKSSMKILx74ImklysEWgB5KkeoVTyedOXW4OGvEjGOY5W27oshFwV0XTQOMhE8vbK5Ck0+HadG1Aa5lQBRIovjAyrg70Q15NfyelgPd87l/y+87fi5JlLyoN0fPvQvJBCwMgO4Dm/gGSvrL2BDxWE7i3+Fx7201FlGOs6z9s33KBdq2KEhtjTmccFI0UsbekpiVboYkCXP/WsFaQcAr1zmBKLMaBQhrQM1nw3y01NsaIbnAG7onsstJBW/Z9EIVNgYth2fZX5F/Gou7kIBLuXvd29+BxgsFaa/BU2+n3lZMHB0yvXG+Hu+GX/sumMOU4VTmuNfar4DfjwZXXYitkOR0LkAXRTTXovUfpOkvKzRzDHdoVc2y1jxNFPfcI7bAIIL7pANxnay0Yfy/t1UyhfyYDzxOawKXJnZJYjk2nYeAaoEHYjiZRqbC1B6LQZ9E+PYlQlw1TxZ1UthjIFCxLBuIEc3hGTkqdNIWJ53VmteMIGuOexva5aSfdg3KxJPw5aO+aQmbq3ua1uwdvOhp6Ixbhs2KsUgndEQRT4i7uIRVssOmH50AKo06w7KQPDvycX7i14yOKYRJUCxUAAsNE6xkbCeDDIYq09QLAMNdPiEeiPJeXHV4GQMOhNL2wzNCbwtZEax9Mm/CkgK7lUA96aqzvBARQd2DFPQVo6NBvoMjf6fV7g1x3/0/BiOi3X//47/h5B/BXf/VHRrdDAAAgAElEQVQVIDP1Y3Y1x+SKR5ZN7geRsdhAW9qHU33yh8Lhj5klmixpusPy92UZLKkE4sDMzI1aol3bIR8UPJopDqwJ7ReWPWjtA01JK0UE6iuh/bUMDYKZG33f50LTo4MZiu6CrzSU09gVHxzDr/Wggevh8BoYEQ6/IaaoQACTGHqAplDvL8eoOd54PgPaDaI3xA1THGLBvbFvByf2/kLTMMjX1eBN0OSKUsoyeHMoDKqO5S3kVdYo8vvZuCDuMZN+xbNqrcFzqMeaEyEtrDDbgSOAVyPQ7ri1VwNiPIN2HNosv7crUOJ8BmuFvjKD7Z/Pg6sBWEfjQO8hU+fRiChQaBrlaYaLZZo09iqydXMlsn2kg+ka872bJFUhP6N7Bk4SwQ58T1Cr9wQAEehyPOLxuYlIPHOgL6vSG1qLAM4dU8Mo0GgWbUUMM9GRO4eK1CCGtdAaIKaxd4/yNRIduY9pai4dtiIJhUfZK/NIONcxn19xbzNopYGlHJ7luTydzJ2NkoXaIJo1yWvm+zLQQq4ZEGh0W1s1gUnx1Rp+jmfL0InjQjZzpKMcY0D6FY5YYn91RCIrV49ny2BqrYiGWNaWC1hRBvUG+DQ01Zg2KIKeHfQ7UArPpK0XLzMGyMytDYxNwfmOyJyIaqAmKWsIwFr7LW7v998/Ubk6o4dz773DD0S8Gvq+/U6UVnNEKJNBs5AFE2S4svcDqVRugM3gK7aj6cVdIIh90aTDUt/asZPMQJKC02pGPrDHHnaFYUDlhrSw7V0VzxhQ7GE8m5KT9+0BDqwMjMyiNyKClp3wmA30FvZTJezvWlExtLkiMUwb/nD/q+IylESlNIWOBeFUM9tJrUfchbvdwT2tITQCzIGrNyxzaFYExpq42g2IQ/uFG/E1n/msumD4V4ylXUlTwlblif8vqNxolwLrC2aZECYNjs88moa2DeZ+JqIJVThQPS+qUn5FbD8/UhV4b+qKaRM/sjGSdBVNJHaZYa5ojnVEgEP75ek3yYmHpTwjgCvL7InCRMJglNUKeuCzHph13HeoH7kI7KDVKRA+KoExY7IkqHUEGuZcALz2ZWtX0hniDHQcoELk1ZVwn+toiOdkNuFtZdC7/Q1tOo5kP85o6MM3aTB7grvtitYO5QcDrqawTBwggp/zQceNjo5BpJpAgVtQJz2Cc3jqJGsEtrQr4acvQBbMA/SaMzm96lC34Lqv5FSDyfKAkU4Hx4/+C4aF3CgTCYVgJqAoy5PCpPhDxkH8QeoKf7decTAOrcQDBYEKnuAwHCR0xaouGXa57+Bl5UbonmWSlHbxZfBhSIIhWGJdIxt+qsyyDzLHs8aoupadiFGGkBkPbdiCNcmyaHBomwbvb5d2DP3aPLrY0MEXIzoyn6ek08JZRPPa8gdTBKGtCwhmDAoQx92CtnC1XggSMlhteuHHjx+ZOQK9K7pe6HqBfMLWUqItS88hi5yZ3nXDNQ53b5JoSPDEtBk+2hF0bWzlhkTo7utHIkhhqGwB930Xr09VIVl2iAw3iBZFgejH0Ar5Jt+UGr7IAI0Ogj8PsQruzjGhpByMDLwu0eJ7l1SJCC5taBKcJsEEEOT86xPBIfmEZ6DQWkiXnYHAGCOGfaxwnBFsNkwDlgZfDghj/tgqPl7slmyiy6YlCoxXV3oGOZLcYTrLrt+aQhy1br33cvKvRiIVaAvqS2/3bjJMPeqr9eL29pSAkWw8YGVEs4Gggi+VF3rLZ3M2S1jcfAVSVya507ZsDX93ZeMQeWy990ISl1iN3y196uJ1OrzFevXkHUYw+dvyRZw62JX3FE0SlgFcTBBUcPLQlRUTyyYjNpyKreSmzoDRtVcQCY2yW9BVWg6gCdqLPalDbVtWkA0eTAy4v7iGPBf9vnYgmUNe7vuu88S1598vNJgJQf5hQkI6ykktqCpKIqT8DEo1semVfPC6Vt/T8zTl0LS36h7fQTZif5WduXYw5o6xHiwJzt7P+exEB0CMsV4hiaQOzIVuwPoaRRfhnuS5oxSU4R240ca5H2ONc4Kbu5cWOGlHMEe/wxayGe+8ttl2srC+RnHHe+8hxSjyui76s3q+XQHE3mq6IMshi0oGhmVBnzA4+ooehM8vN7AsuPsqNWY56BTUbg/b3NqETAuwIG1G11ZAUqzbmy6z1ipuMTSSTQapVfE6UR7ddI5Yao0ytkVDNLWwuY/GGG80Vrcay1mtorYuqTumCWTl2SWKS0pIa1EJmx77WLpgrOd49iifstyxZt7DJWXjiO5GwrRlR1X3OdHkNpsm+tqALw/q2TnaPpamF4qN3tDahUt+gTcpe6+nHek7uCe1s6vAMDC97QZ2DeqhJ0fYsBMrX4ZbewBoM64BptC+sOw3kRipQqVhTEOXG2tFlSJ6fARdbpgolngMXSJq3aICAw7eIKXLO6R1/BwzKxGKpoaPSFDSwvGgN337CrMYna2C1Rr8D9A5+KMIcsVRJQkzw5hfiSJlOUnDOI35FcZuGp7nT8LYyOboxKZ3yIrBAM+auzMXADyNn2XziaEWEUAdUv6OiGCZ4msZzLKnXyYEHf4c2nN0yM5GDGSXq1ejgqpizSyFlHRRGvWkORA1FY+DfVtqyyYRvd3AL/1Gb3fgS+YwSqRlqb/LHt0KZFNECwUFHsLWMkiQjvv6UwAMyI5L4JjyNQFbudFMoSC94EJjx6UnzcIBiKHfqbUrO0ARieackGFjIhKk8k92Vbcu1XhTqNBRKuMa8veJ0tNJk5oiiaJUScaBrnGddLwlVt12Z/wZJM+ZVANoNtl1jOHwMVET5rKkwq5wcovFg0t4peNn0F9cwmyGulpHJBOxf3/5fMKw9l732CSMNLlrNOynLB6wAzpmvrEGmXiQAmJvzd6zWUwh8GEl3Xb3qwI7BkAzOaxjLUBbrW01bcFLHkhVKyCg8y4uNg7Uwt4l5JJrUjaKJGo73mjHGfC31iC+R1fCgrfl7kci9E1Wb85CHStpWKs6qVnhMbOSyomfSS1kkwNxsvoTTa6/QKQlt48IitVPtfyMGlwjm0/NZ3X+rHjYs0JZbb3W7gzAgAzqBZU4nwHqSR05aQ8nz5U/a2Yl63Sewf1DTE42wgtEY2dxiFecuTEGRsoRIe+tqCRIutQhar/3REveF6oJRtFwtRt63bj7VVzPJg4j13ciByfEe1ANhXuRz7uJAHOVmsi5HoDVPXIKWGu74kUdUK4P/UahfMce5xk1DTCHDVnTtrTa/txtu4t6lfq1LcGKs4rRXHDLhbsHOHK1jna34OvOhZ70kE71nkwYxXfTnWrPASiBpHME+JyznueJ9PPeruvKADmSu3XsFW+Z2BxI/ptKFYnEsFEJd42Vb7onCM6jsdiBlhUQrtHMpEkRdpkBzRIUuEA1FlUtGlUkNqEvLInSau9vf4OoJF5ds4Eqh7X0GJgSaPXJed9nLRq645qFgJxrTJLrG9yh2sKczw56Wb3I88PGrzMR3RNJ49/Upo8E5Uk73+EeGrbRbN4qSD1VHdZyuMb+sTVhs+HSKxtFB8wHemsFyNmKHg6Cf+7B+b76B3M+UE997vWzKi9MMtBnyXrCFnwsqHYsUahS89lg9lQc1hG2oguyYusvhYzf9/qjCHIdWzuPZTaiLFDFXKG1ZosMmoXr+uBPfv4s8jZ5lV9jheB+ojCw3cVqMiqgdt+jdhlUkUN6f3plaWhhMJtoENVdgWXwFs1eXPAyTB7SJgwqLtFCwigTtkf/rkRcApX63IquIQ7uMx4++WBX67D1BelSkjL3fUMdieBt41QoT5cqV7iv7HAPLlnvWcoIIaukFBBp64VY0jgteKGeLN9JAy5tsCUAy9wIQ9Klo2V5Lso3BxdQY5xxlI2jU17lhrZoaOMzQh4QsUh4OMGM2sLs5ubY2JGGhxxkdp2zDKoKQCMwieEDR7ZbjtrQmgPLoKGxUujHmRDFYIYwlNGEJ4H0dcF1bY6elbi/JJc5M2tBXlN/Odigdexpcmcwc3Ykv/4gAyk2IeT/z/GWL/k5bHSXTYA07sNGloRwBJE7eDU4fL0bhKhJe9JMiOwXb9rZ6CclV9NEsOSUHIsJRb/VAd929YNBHO1DGO08f3I2HQnm89SziglAFO1HPVcGPPybjUWcAOSCmHq4BlwXmizEeMoIrJpb/JFIbKgLSdS3+5bTqhJ5OrmxnkKsqX/JZymyObZEd2N/4MVdPxuX4ky+tYbpFNvV6zpo9qsR9Xid6xGJi5Wc1hnUfQ+guXeJfjNoZ1B+BgNEksJe5kjkDEp4DZGwWb3P+bfZBHxl4tRLp5r3ooryCcsnvjJJ3In/puq01rJvYNUatda2FFUL2hKvC/aWMGqtZRlW6tydTVP8PVWFjyOxzM/ke5ATzPGnJ7JL/6J9X98rafGJph/c/VM2Ic7vgHsqv4yDAkaKTwbrbEp2XWhtq2vQ5lHBIh/fb+kMV9DV2nEuk9NJzfFDK9xnJJ9EVEMyK+lwbctsMcBnoGzgiPJAbDlwhrakbMic6SOzadBSNSOT3uj38RcYwsox6YesDseabYCCuuWOXBfBex3Yz4KJOX/mHqekXertDqvPo2120Xj2vifxLQWQvHV4DzofHMhKE6sFrGBVdQ9tqyxYADZrhfpTVWIOn3PubdLHflLTmXZEZkwktQkXT18WtnGtmNq34EGdMQFsRWXcAtQripBJyiJmpeNKHfoVyDArOVTPOicUwmO/dtP/dwLt73j9UQS5wIEACGqkIUv4mp2e0IbfPL/BwsJYT5VpzQw/f8YkF9hb5oSE/3KaSV0o2Zp8xQFI3bCxir+liDJkHDYJvoggFlxj84gImhH1iiCVE8aQ3KngnsWB0UTwXt2bLCubo2nq/Wm8931H4PrjunG10K0DwojpDTQEYhEOrxVtYa2xO/iTYhH0gZCgkd5gvjmAK31pSDN1KKjIkGMN61o333epQnqD9Dhsnxxt7BoHno6092jo+WQQe/dWqIaI4HO1Ko/+8vngR78gTfHjxw+oeATNvqeikX5gM7g+d+/xXo0B7Vn+PMvqWW73nGOuVJDYXe3BtwZEO6hjWvd70BrOxqkTgeLniTjuTi5WQ0OrUjedFVUHaHhOg011DA66oHPjv/k6UaponPD6N79/NkNVyRuooRIM/uoa2lbZYICyZGtEq2oEVHmNXbWuk02hTXbn74rNE1z31kv/VZZXef28D7OtRkGHPtbK4FKP+06Zm7WgycVeK7qYP9ediM3Wf6Xe9sk1ZCC+k4+3yPyajpHXsuAQDYQskJJNdzCgqgRfiSpS4FwhOX7aayDFrS277TdtgoGLwUuJocTf0+jLMsjaKh/cD2utjeiuHUCJSAVeDLjo5E6U7gycz/vi9/pxDhjonjYU3C9I6lYiyuypaH2v+YlyVwCse02Z0IQKTZTkWW6Os6GB5K49PrqCfvey2WE/7/f9tN2wygSNa1T3NmKIgs0F9D0am/QjBmik0AAodB5ILWxsQIVBkIjUz517jK/gvLIylE2HsnnsJ4p6npllX1m1amjXBkciEBTopUUBm2kPkNf3HaVl0rv5okxUdgMybSKTAe4H1aA7Bdcy+2LGgM1VclV6KTSlzTjy+0mAi8oxRa2RXaVhdYWJFJUiqE8+Uk2jaHu0fcfI+iaKz3XVOeG1c/+wEfdJWmSpthDoatHcegswny/YjETbxYK2hJDvika/sEUX9DiXA/CjZyCfp6DBoVhmgPSgRiTCHEjpxJXUNZsDZtESFuBG2HEqcsxpWCuTIbEC4/LWai/H9RAIW3CbMdBKiHqf6G8ma64wCR1mkaw85vVLNsyxn9exMH3CvEF79FSIhkoK1Sgi5rsQ4JJsRae1G8VPECRQvVnN4b/m9YdpMfxdegWAZtDrwnpGEJu7wh7DjYhSlgA+Hvy4PwiD59Am1c2+lKMWF/wZQKJoSlg/A9/rurKZyXEjCXzZ6NUkp3g1TYMtaC2CIpYsIQ39l4718wkpEOloEKzuwBi41DEnU9ELQEiDuHsExQD8a0CujSwAyE3T4L1BzWIgAQBpXnD/kxQ/wxOUARP4iIa3rsCcC9r3iEgJXY+gp4lDsTPN6AQFRBQ6kM1IsRZAIJkiApkT0i9w86qkZuoyoPVA8NoN2IOmDZJNg9yg8XsLTRVXCwdoErn5dV34Gg/+VItN/pGOwMEEqwO36J6W1na3PjNw1Qa5w5iFdE3P4M6xRHF1C8J/63jmVxnk68cvkKnoosFVkwkzOkKiXD/CIHyNope8qS1e6wzsQOAMRN0a2r1wm2CqQiDox5Fzd2iLMFd7zzIigLlw9QZngIYdiL0cQEuH2yOiEPfseA2lCZHMzOHAWlHSalt5IJD5+J3gKs/N6fVUlRCBzwXR6Grljo1mqQzej/KiiGCuBU1nvtEoD669RGMHEMb21gZ/ZgWXp+E3OGYiHn/PFSiVZCsTn0MgF6hnEsGHw5bjadH5PTwaBkUEay58MvGVXD9LBy+twZ+npOHcHT/6Vci6eUMzOs8FaV7XjUiBc70BQSIRuEqFQ0Wx8ru8dq7DKVHHCoSvCe9XBAww3Dky2iyl8lQxczY8ExKcCK97BchELJd7dbhz336XLauAoG2x+iuDifNnzqBZEeusM6o+l0SAKMnPXR6yW1oo5Qjbo/oKUsMOpo53NgL5SvuRihFVArczGIoSOYBSWNDj+liVqD1zVAece/1I/lrr0eDVW1WP3B2OCKJiDPS7U51p56mQYraAg5tsM6dpAqWOwrWuMcULWBLAhZmljdBUoIkK3Rg7KY4z01lmy89ZVYGxiIiyoTAC1K810foeDaxH5QYa5+SkoZBLuytZeZYt/JYqkl74hdauAGBaiz/VXJR7XhsEwbvvw+EQWFPcolXJ4L2NbBhs1zEVL21WjQG/OlaqQUhX2AotWFZoK5GKpx/d+fpu4OS+dhGIWfhDd3x++SVpUAakvn7vCh8R9LoIzAG3DlEDvEMk7FQk3fn5AqzxFf4TksmvANLhEv1C1gRiYVu6BFe5Nc1Gu6xg5N6PyjLgsKBw5H0Dgn594MsRzDKHeYA7gMXXeqvg0ZbkuN4eTekWjWKWg62kNbiH7fm5Bh4X9E+cQ8+ks103zJ9YDzju1FAHGqQtqASYJu2CrQB+gFClNFM073ChQoUD0iEwuBiufgEHxSP6QLaE4q95/VEEuZIG++fPn3UzX7+Jf0841LIE23LDZOmfyMglGgGOKm4H5qXVmTqfgT/140c4ktYxvp4SoveumMsq+GpiWAgE7LGFW4Ebks7JAVGIIErzV4euNGIi0AWIAuYKtAgEXT2CD20QN3jLoOKOMocgkFsaDhoZK/kRh60Ia5sI0B0+HG7A1UL/US7BTUK4MviLxEHbBVGHLDlQ75XBXTpk6ZgiuC0cdZQmgnbgvrBUoEZReYGrYKaxRwYY4gbRHl2P14CiY6SKQkxnihndqgJ3w+UaiYEKPp8LY6ZRsS3R1FdoEgdX+4IhysKUwOGwjN4kUHNsgy2toc0Jk7zWHkLpMScbgairwWBwEyx/kjYQEiso/pNAP58wjmtP+hERmIRwuqcGYmshSUWnRxR/TaD1Bp0WikYNeMYEJ87xtdaCN43AVoE1nn2w3WFjRPAlW15neY54NUtOXQwICTmJDHxEAAPMHV/JidYMuGOwR5ThLFHak5+pvb0mA4oD2iIY5hk0s9JoPbVqWS6mE4lmkHASbsGRFgmZH3hSYSQC9Wetl2TO1Tp+swYQlMSSKGJwUEGaRie2QQC34Gaq4kZ2R/M6SXpaqwIS7h1vscefFZJfX0kRQSbVhblpQzMGKYZpAtFRWsxh1xzwlOuT1G1dYSiKCpLXcupbfmUyTiktOv0vrHBsnqoRY1QnNEB+dDZ8rRXBQL6nYvOnT6WLN6qzud38PhFvdpUzsSECKRKd9SwxuwA9kwjoRtGrGoJVaiZBJwiVAgYeMZ6WdiwrGtcFuBUtgJxe90NK8ED45Aq7U9Se4xqI1vEe+e8KcjIxaIlcL6cNT6QbmzJiKyhVlklhlepr7VBBDteLyX/wWZOe0XLyE6sb4mhsDEN8jvuEUmt8bprIRsYXNPmmdgSou9wczn5TS1Bc9TlnNSiLCObYMl+qoQ094WGf76smiJob4PHZcSuC3iV8DKtcjvCPGVTSDhvClvi90WhKR8U9RHMrrwPYlSwFMMcslJpVUDgga6uKIJNyswhM+fWRdCyYBdc01+m6Lnx9fcWoXouGqd98zRqg400Aj9K6SouKLgTTH5hNfPSO0j2DasHLH7gZzB1jfSUgAyx/IFMBNawnaXRiWMtwXQ1zBRDXxLHmc9CTgNY9ZdAAXZExaVMM/8LyEYnaIkq9yuasZVVZav2K/TtDmenLFkSAJh0CYFg0zI1UVugXYGPrPMMckBE0Tvl/mHu7UG277TzoGmPO+3nW3pvs7rq1mAY3TVvSCIkFTZQ0LUhtjKB4pJ4U8UAq/oFn4g8e9KBQULEHLTkVJUeetBRRrB5UKCitRVKhxf/gX0hsqjF7f+u55xxjeHCNMedc77d3/DaY8j3w8n7fu9Z61nPf95xjjnGN67oG6G7ik+DCBK7rDRa0ltTpaH3HIWmaDigGCBN2yXsqzueM4Hle3cbap5AdZ/+/Xl8OukJyLR8pljiDa73CHDYOQRXPbjweD04KS6PtQvpatm7e3r66D1zBmkNfQbfENuSn7vGbl+wWjjsrHOAg0Mue/KG5GcnTaYmsbXSkpoiUyXf9/Pn+ZsUrtjX5iu+fCK9w00TPz9AarG/0ha2J7ee4Aq6DAjxs5NFQBu4ZVOEYvg3d+YeIQw2raK3BjkAcXUlnUAZl3ht64YXN1SKU5O88Wl/X3HsHWiHZbDsuxO84bDiZSpfpOg82Q8TIoJ7fhxQO+U626noNGx1s7Vr0BGh61mIPeqjtwDnc5+SmXfmf97v+1uRfEtHoH6/h+D4R+gae9IgK4DUZ7fQ1xU6psn2kW5yF4urVfcvrPA5VCazpN5GBjVSPvug8xXEt0Ubx0eo+Fg8a2JPVFEWL4H2sttNJ66i/i0JUB2qYQ2Jbz5R4owzaK1kp9LR+r8QCqpaauBJdchgboMfBmvfsTMZqfVXS3WQbowNYvNlq+661WjEoSpRG8ZqDFIo7uW42twB2fX/sBGfeYyFLtSbnfXpYbi0CvS31Q4Lk05ZdIvnFW4C3XAOc+00Sadv82uIZxl4T53M+77V8tBU7k8VLdgt38fr8QLRXp6NU8ofIRvYY6kr46/9rb12t0Nda+CUWrBHd/N7iHV/XtQ7vEiqVwCqCKF/RW3aHAB+0C7so1ZVY1TqpV41DJ5ef11FdwYAt+tJek5tuUb+n7ld1Tdydlh0esNfYnSLl6FWFbDrcUWDXe9VzWDzy4kxje8gvOkSKJYubX6hs7x1vb2/reQ7bAy6KRmSxnTKQ6HY9s+JPruL5KCaqG3PSAczG+v11b652nFvJuz35vbvY32tlcdXP+Jj3/wQHuBbnKjoi4sPo7SWSzL39yA5oraHznC9NTdHBGgTjvqESuKQtNLniILUFSPpA2SFSyBgj+b+SnV6buHIthAERRDXpmGFrGqdnjG5NMEeep4oPsZfi6se6v+vsf2xqSaRlYcsOr2Re1LRcmlg4iuY53Djp0O7dsbxap7d4f4Bu9NeH58CuQHXqCR5EcHJkaw0tElxUFh1FDVRV6LVFgPWZRYTT4uKLJ7jAlwTJBTb8vsZ0Nio2QxVmEyIXnv0CuiBuTohuV18VNJCcUVEaUAvIaTOjaCTH7VUAr5v3fD4XamdwdK+EmMKs12GgXqR8IpQDM7mfSJGatExCbOL5fCTHldk41ZaJOgof8LSAPhp8DFa4QYTP3RGg5xz5MoqrXfhsMEHQfq2DGHDEtNV6quQJqPZsJohZUZ+tsaqaiPpV0rm5Y5aOF/QHFehaXEoesAMhgUd/sq2UwwLaxcP50a9VIAw4ul6IoPlza0SMgzsakYfW//nnfvJv2pr7oX/kv14BLqIGZpDucEcOAclhAgHA9ToSmD0NSmQntLx35Q/M5wDswrNaey6ARIm5AICIgUV5ld5AU4QzAV50BVA4U0FvzjsDQyG4O2EaNlPcBZTrApGbeyXYqorhvmghyxQfB6oSgZaHp3uOxFSirsiEr6gT5ddae7IU6eK+EqDi69ZBXknWTt4PCkXuP4AUG69EadEbuH45L/1eexT5+SPRBYscE3k1IA1oPABRpU9ztj+0CdAEl+5i8aRciCpeYejAh5HG61D3zXcUaZgxILLRt5ObufiFn/x7vV8VNHUvV8s7k+GaongWFa01zGE09J8fD122erc2oQRNmyqQQru5k/1php70rTDH0I8J10okys7Kt5MEk+6kpLjTEUbb9ukE43ULpMgGq5szBUAYGi5YIoPS6HBz2nNFcCQ5BY0CFEcUSAN7gYvi7dHgPuFF13KH5Hpvmci12IWOOy0lm9O6MjcWqTiqgBmQrh5PeWaCL0CupRrQ8gFRRVIizGhn1yhG69W1iImwPYlsvO4EPvjrw3KfyjEBzAz9utZaigj064KuQglrPbYjDrBzJMDciHcVJQX8yMWEr5DqsonjQCBqUtrjmIhXnyE/BzUSyX8W+h8/LtIhWIydye9Gf6vreCbM+/qqEGtrr8w58eDh8wGIWOuvX0tEHea4el/DWSr+Vk0V5igvXmklStwi1CYpxPUOC8Pb1eGyKSvLEQNAa0+E036TfvYNLUhrkWawQLqAULPjJuxMenY9RfCQ3QHqfWsZzANNnwjcSQHz7JIR8OiqGADEDdEixbcBaR02gaunF6+y2zdnChFTaNk6IMKCLszREli4HgKRHALzuHAZ8wLpHGAi12M9n9Y4VKRfHT4TpXYKgLuw6xbYPvEVvxxRrIyjKDS0UAxxiH1flNwvB5IroM9ju65FlqcDwQUNR2tPcmyfulBEXDXGD9t4vLcVLGTww5AAACAASURBVBcFIPlU19UOxG4v+HXQi+Qo3kI3G9QN1/ORCKAifEJAFDaa4q1fm7D+uAAVPFpH7xduD8gYcMtJXpoj9yBQocF8NAN8ZoLrHL8YbE1cqpAuKMWLje8AQhROk6Y+JcfcInk1wTF66dfNa2ubd+eDycQ9czpPEOk0BQczhNBVQBWhBgf/3MaRurCatpIJhLIFfs9XBpGGgYkxb0QYZkZnvTpVl0mH8CaLL9Wk49K2XCf+Zr5YxW+1ubtnu8XRg4VEAz1kDQHzF1QMoukLK0lpUFqNnckGAzcFSzWK0522RNPZVOazmImuUNG4kpamMNdcF4bXIJuTB/FGC8vRQj0gtue4kyfJ/n5HsGjyrVYtpW8hdL6Sk1jo7fo79uQud8ftTO7G8RkKETkr74VMZtelDrOI2LPMbfvVVvsa+XddI6v4nYwVurfXomHOd7Qex/fLehbuc7lxjJsCNQolYomIVpIC4KH9w/XU+zmYBGH6okPVq5CiZ+t0dXDHHDzYpn3kGn83xPtEbOuzD5tbuJdo7ULs6j5+YiZf96wmJu7OTN7PtLQ7P8OJ1Jc4TyKAau/aHshTn/l0wAAYYwrlb62x5Qu6RpRdmLficXoiUCzUhhzKf1UmDQGY78S9UGCTPBCT5kE0KkWo+Swejd69hhwZHkzcEQb3gz6guxPRGrlEkmI0AgOx7kehjNJ0+UNz3Q80SXpOcoRbJnen+Ku1thwGrucTnkJcBzhYPgKIQxB6b0qOgkkhOahYk+CsCabubmQ9B3ZO7MPaRXrHLpvLRP2La7yAItk85rCdvEEVz0aqRRVXHGyQQ0GwRY4dFBjXOlmIq8qB+DOJpGDqEPEmULRct+Icdb27K3UGAQS7qoNVo9lXgSuynt/63uy4mhyOBJmM19S6kwa1+OtJU7EIDAxID/rs5po+ed+MJZwUNjARGjnkJ4WNCqA3Hu+hJBlddOupol7FYDl6G10xJJaY0cRh+CzjOQWbon11PddAjYvjbFSpwbAZ6Nfeg1XU9a7oHYjI4im7otWxQO6105XEwjHF8IoXYAp9JDAZFOIFDNoFBgMuYAjPsJpdQMoKPqwfjwn3yfexLdJ2BCBCsfzlJWv4Qq8vRZKLDFwAPiQ6vKlva+OgfGd9H3hXa+SYCj5sVLIOG77y/GrC5ls89Hw+SXOoG+gOST9GQ7Y+xXOeM1V/ANDTRowbhorxtQnSXsTd0SLb3dI4MSmY7DyuTCznzWkizsNrjFceTuRW3miYQwDTnGSWxPDAWkAzLUkCBm1lG+OQyUMdXgMl9qSUSIspG0l0HzQAl8npZwHDrMA6uXncDlFKmW7bzQ0NDk6ol5mRGxqNLZrYfpy0s8m2dh46XZQWZITgVzD8kT/8P3/XZfK9/v17fW99//f+uUoyPlpW5SJCCPDyiXcbGSgpGCAaS5Toumqy1keleV2LIjhiNK3xho/Vui7KgE8mje/3Z+SRzwmbsbwsAaArXTmqfRdHMAeYNKsm1zyHODgCdmc73XgYnjQgydYj8TGs9rEUzJmK4kr+PqjC8yCsZHRNfcvP9cGHcWw3E67zVODO3Waun22iQLaczxb6nPNz96tQHlVlETk2x7ASwwd2q3rPk0/7sogc2rBdJSRyguKRcFayMSd9tx2B9/d30l2O3+c+Fyo9zDAwUwC0Y9N93+tn6l6dcetMXEnXEdz3vZCcShpc28E75Uqbc7+vIYjk52F90igYK7iuqm1c31OUEk0EKzLRK9pSUWAqqV0FXXrNfjZu+uKmYIgdBUsO60aji74C4LuIg0hHeySI0FU3XUB3wiGFKJ8G/cKYaBn71j5UxTSBZ0Jp6ZtdqvdaI/W8W3E120ZSe3Z2SM+gkp7Jtaw9Uev5SsBmrb16nvk8rqRrtcYiiM8Fy+puJZtHglbPgJEri5CQxXEEDnuv47pfrxdsHKJS97XW67mfVIv6zDUNrvbGzOmN/Yj55WRQlIT1PlIt7U1jOJ9FXTDddg5YLvdo0QTq91die1IUWktPdi8K4/4cqkqtjm+dhojAxr1cS8rlpNZyy2SuCvTrujB9e4rXOd+EjkenBuGDMDoBhhDA7IUrOxBmLwATM+6cdsmCTHJE8+lmEcEpdGKyzg7kGqPjiAKS+YS9IGJ0SmoctkCXpeowJGXo2VNHwqlsda8Yv3fsqOdz0i7osrDpUtUd1uxIeAuEdVxyQeSCRDpGgcCMpPXe7ZNTfi2HaYztac5zrwHRofIA5AkzwZzcO7dNxBx5P7/460uR5GpWhWW39Hg8Fj+3Fvha0MhqLA5z5Kyu2tVXdd6uTu4HBNKuXJiPVY1IJjGRVaqqwoO2TytY+j7gIuIDkbw2Yx1KxdGpAyYSVXUBp5FB2d6KVGfm+03fm4ouEJ5K38McP2wNtiikLmBAHmJNrw8Vb8WgavfWa9pNNLgBAYNcshLnM0EbRoW0OzATXS4eX3kEqgQX3YGEFtqzuL5p71R/yr+2qmyD4Y7x4fCt12+UoH76te/2Pf/tz/+O7/oz56s2Lg9qwxicVEUOv31AL2nHJisgkmP1MbE718r6U50Cmx9mj1ciVGLBOR3tei7U5LwfCh5eIw/DZS20riMwfGBmQuHpRzvdeT15jfeRKDYhEm9jUwXOltsacCIbRWLxCMCweH81LeqkPxSic7bkV2BsxCJKre3uH3w3pdaGEXWLkMUTPFGm67GRloiASl+JLfJ+9UzCz+KljPgrwaj/ns5rKSrFOXDi5K9WcC8ksr5W93X6QGuy7s3pL3uuJ2CjoVWgv8adU5tiJfU1PYoiNFv3qHjN0zlhcSOSOT7YqVGo/V/PphLsuucnEnxy4OxYw+uQze9rV/+w/iphfL1eax18msAXDeb0Iq11vLiftgvFSshEOH7bEm2rdfhBbY9KZNLlweyIRQGIc0CIbE5wdWzWz8emdhRFoCfCVG1iV2x0EPgcnxjAQjlXYptJbyF+J0f/etLaqjQJIrtLsTopn3Q6yGFll7M0DmU5GBHLFWBRDiLw9nisvXA+Wz0KvHMNOLIbEh+R+kJR63030vzRc3lq8b/x4Z7Wn1VIyF6XyCLmtMVbtBzZQE39+xqwE9tnuAqC+jPdgYv6hxb0hpfWETkm9ryu+uy7AxzJh64Cw5brSa3joplUsX3Sftwp0DwHAfX+yGQ9tUcsx4ikaltFFEE5TverKXolMK61QWv6QQeCjKO90jmn3/Ftc53vM7t1TWIBTlXQ8gzCyomqu3fGvAUIrX/XBfpU3NQAYh7/HRVTGWtgwKM/1/t8WuiLtOU2YsF5sFyfRb/k/hivXVx8kdeXIskVpL+fKL72lTc8RBAGfCWtKGoRXc8nnk/epHNhmgBvb2+ICLz1mqBTAqn08svqEviI+MLTc9LJWeLDcKjZmtjCakiA2KTqWshc1J1IngDtsavB3rMVX+1OJ2Jam7QWEQA8RKBCU/RlFZWLS0y4OWsqUACokYFBftb0NG+Gsc2HTY+gC4VDGrXcbnkI2g6ilbxVUHPIOgTvabmgJ4ZNDDeMSZPr14iFTi2rFTCBLF7wSRT3yQ3Cw32jRgA+3I/v9TqT1UpkP01o6/t+o6/XK4xUl5mJC6dhkf8J7OBlZuhSyMwxYjc0k3qOGkXovhfYhzqAD4f4SjrSv3SEw+e9ENUzuIsIEPQjHJlQ0ITbPhQ3hfZVkhwwYNoScqy2DyqIb9svPrM9lejk8AIlcsuD9mKiKpVwieQBxd/7er2YrB6BP2CrHb6KnCOBrOErNdxlHfogpcUCywfSna4YTQ5uYv6+1XqPzQ31ObfHa/4p9KtVp+S4PsaOLUKt57lQ9k+KsrrG1whMB15jP+9TCHgmcpVYV/LLoE5B2Vl0OPZAhhoGcqenbyHp5ENzHPcYr7W2qV7WD+ujDpWKqee+P4u1iQlr+2e4B/KAzy7EsBsTRUcJmBivSYjUVFJfh3MhwIX0Ec2f6JAP9+jcH6frRASLMndfIpUPBUiGjypE6lqqYGmtLa/WQnR578GYmANnIjiNstYVCtE3SdpVCXqYHHjS0SQYD+oaH105MEQEDQ1vjw5xtvI1P++H5ErZGn5ejxS1UdxzUvg2XzUHEdW9rAI1i62RSbKI7P3UGrQT/GEFaes9VfsS1XG4TVut+9rfnLCWntjXtWwDT442kXci2fQq1iXcBc6kuehBnPxWUx0BrGRSgeWjvp4FkMnlHrtdiHeh+yddid9DdPOe1ZlpH+LwWeBZ7AErqkBLP3s5eMtVIBQAB2ABS1U406qtQzR/fnXkcgy0XOlDe8F9piAXKymu/y8hpbKXuwEp98VjpfVkfLhmGwM9gSQAcBurS1BId8UxeKzxxxXD29EdZ+wzOj1FX/dP5cohLsDVlLQbTSpluitB5haVoWHellNaKdIuOlTFbP49MfOPCIv4om20Y8980Zd8kcTiN/v14z/24/Ef/uk/i4YAoXpWUS5syRePcJgtyxdHHngo5Su4QcGExURx5axuEsxBQ+I6JGvTukPkgiq9ZJvQPxWR1RBSAQi2600BmdvXcR0APsjjVYX4RphhO6G+jebYizOpmjmho2FzMYH05oXiYkmEaYZIW5ne6EtXG04CMHU0kIdWCfgzOV6muhZ7lw4JB0xhGIsuMH1vknqVb+N5+LjTozeErSiaeO/ECMBqb0s40fEcpezKpKjptVqKl7CyVmVi/Sv/yU/8Jq60j6+/9Wf+IsIFEwLgRV7tnJwcFO+0YJONHqg+8rAkUZ+FSiHZ2bZ3ojZ1OFdgPFv6531eQoV6X98tuUp8+O4KaHC0sF5woWKXAXkLmer9a3212MEDwPoMAJYoDcBCJx99Oy+gt8XLq2SlUB5P/mUl6B2yTN1rvZRoA8Bqda7xx3VY6Bbm1OeqvxmIIy3vGlwnxE7bpH3ojWOaFFHiQ0yVh/FKbAsRDEBDERprrTOxYipcBS2wkZtVdOSa+BTxrv18JpGrFX/Enfr5eg8Aq71PlHPfG2lU2z/6tYrCVYD0LV4qGsb7fS/bp1qHy0s3E8f67722dSWWEUK9ADLJjEIqSclY16vcOxGGDsbYeRveHj2f+e5EWJwq+Z1UlABrJRBy8q03KnomOrVftNfkuIm3fuF22+IsYKHgtX7nnLhEcbtlx4DI1YWe2gXDCF0DhErwWTZyLVg42GAcV60pnFxvNmil6OIwH3mescgvOkshZtUdqft0nsN1rUWpOa/d0zN9JSldcZXrQOziruydHofHNzdLwxW1dxyRE88cWYApoJ4ItBRiFzmyuq/zlB2HbcG24kvE8hWvIqLsACUGWnvCfULUId4w7Ab6c1ExFFiOGScaXO99xogVl3glK5HcMeBawi6ELtcRpNSs1tyd+2EY2/2XKCkjwRxEj8LldB85E99dLDIen+BckxpXnP6yAo6HDt57hlbP5DGnfSU4MVGcbF9xtkTHZad3FsQl5H1oyw5qPRdJ4IngEgBSFzxHBi/SGvb6FxZxZty7Y05ICB694TUmWt/nTT2bGlTlGAij+H9mceixEfZH4wwDXp8jpMHEIM5hFKIBXXpGX/FGgoXPH/qHfha/8Au/8IVYC18KdwXGz0gPToe/uFhau9YhMhP9vERh/kKTC/Rn25wgEQGG4dlbtvsd1/O5UKXiHC7OCYj0ug14NDy0Y7hhtskFGArMCesKiCJKOXuMHu694x7veFxviGmwOVjNXJ3CtuDidr+BoiV0gdmE4gFRg7mzPdbTj9fY9lNIcm950Lmlr58PABuRsZgQb7AY2QohujVzM9/3nQf0jZcYWh5IPoEpVDSfrYlCP8w3Wsdg0+CicDeoOUZ3xIEK3cEDuq1Kmc4DAk0iukOFn7/cCe75bVzXM9uZ/vnF8Zv4iqA/LA88ugOECO4xAAl0ObmSkgltgOrXvpA11QAS1VENDuVQx5wM+K/kMeEICFUhV0DnZKAbPZEiVsrnIcjn1lpLb1l+3Z0o5SwU1ymKmm6srjMxXc8QHw+IZZvF0xevnNQUIginAr1EBieqTQ6wU/kOgYlgxJa9Vtu9rrXJRg7OBOtELavtzcMkco10ADRB95uiF6qfW6JnxRnD+r3tSJqW+0ElXHnNHY3OCmHwudfdp4hrJUdnEbEQSgBRPyvyAXW8nRzI2wZaHO1WFXpxZgItQtEosL1yzattnP60I591mtFbGCcYeyDgaD2n+bljRA0w4M/WaN1KbDhYJoso24XBB7oNGuYcCMLbPEjnpobwRgpiGnpvcAuKZIx7wwptMiA0xU2J1nHojK1Da7gvPcOFgMVGJWu93TmeeaHCqlD4QogVjFcC0H4R+1lQcb5FgoYJzzUmIJ9TmmDeE9EU2nd3gCjTptW4CmJOXI8jCZOAiGKGo6li4IZEI3jgDpdIdwbPBCjvs24KR/2u4odyvDMd21ui9Yurml1K0vt6Wu0l5xRAd6HfdiZoDgrVV0dmcM1QcMaulAjg80ZTTpwURd5nQISDl3BlV7E1ngsiaKofYtpC2/P5VmwZFrga4F5T9gJIwErkgs3tSBAZg+C+EOpC4evv8g/mPs74DLo1dMgqEKcbBw6Mwzte9bjuQ0AHcsAjKHoLpwd8A/2AuXdJq/BI328csasKqnA6H7Ry4knfZAQ0+EzZFUiNhRDQqzMU0GWrOILURQfjxuokrr3LJLhJx5zjA4XndkNr9GIu4LB3TkPTzs4P7/mmyKyiLuPTGEVxihzSFUATDBjkwXOpKfOSXQQMmA9ofVYjx8eM8wSGTTz0IhAIAfzF6J57BHFnN4V779Gf0F42m4BL4N3n9yU8+1IkuQAQkhWzKK4reRuCfIiGt5zUcs+Bpk8+GOMhfz1IVRBV+AVAlJZamagpAEFDC6fPbARm2nT4AFqOGHw53QsonspDUoFIVLlrA8zhShXhdbFCDW+4ZymXG9ws+bNsYzCQEylQFZgrmnbck6pviGZSRLQokD6RiXpBapoJbcdKoZl5EyJoiySSbSPTREdiVW00aL4gOXuaiEyitQeXiZ9DVtLRtME9cEXALe1KImpcyUajkDyu1pY4px3tTqa8aWqu5Ly1YLJYScSv/mc/9f/LWvrmP/iXUO3gQsdqE67XKlIUZgxqM3mUUBp1z3lD2nPZs0jyNRWBEI6UHGbwQfTIJ3nOTOxoVl8H9OKeYR84V9tTzCQ4DnpOh0S2AVWZdOopABCE0z+5NnoJLoj47hafp7Cm9ZZr0KGNyV1xdAHSdG73xe1zbOQSIB9NIhjc3VfCPyxbrhentTVQVX7PrQwvCzYVItWvwWs7BzqEADAOwSj+VKEbEQMQSdVxw/TJAOoB849+rUXXsDSE50E+V7IK7Fb2sAm1nUzVc2Hy3z93z+s+OIgcv8a9OxfHz5KCwZadNYpbkOi4NGCOTY2ICBi41gh7NMjk3nqN95Xodm3QaBhuOdGOqm4PilznPVjQJ1L0nUkkV0LRoxHRBeDTIb1jzhceONEpxoMSCo35GbweROyhC9QtTLgoKV3hsJcxfrQOv19QPegPElDRNflucRUzwbf0vmWyTEsmFQqGxxgLFCjktp7pnDOT/kFLpKN48kyCA0B8IqSJCE6nEuQUqVzX/s7kbd6khomsxHZ1LTK5UdmCI65dB4LOKCZIufNGonGg+BT79Ew6Dh0GHJK0maIj9YtDwO/Jgo7m/o2whQjQGLNaD4QX/QCLZ2t+cKhb+7AHCvsUUVoWAonAMTHcNIqN8Lec4Ael/Z5ELHtKVV1FJI7ODDzdEmziRqBLIoqN92vYDfq1PlZRf4641hqOc8Tv+j7PoSQVBltwPd3J3YwI9JZxSVjsh9C+ayGTRwxSVUgWtPX7PZPniqtVVIlwiE6kCC18j9hmkSCgGUAgskMWTsuwDiBMIGrwUIgbJnNHPpkIDHNcObY4lP68bg6LDhWuxUvyDNWWHb0nPJy2iMIE3Sz3xyhxXVqljsnPVnErC34kYFBWoAAnogHUb9Cn2jDmnphoZuj6BEV1RG4FTxZGAFrvcH+HxhOvyaloLyPoJqA9XcXkaAEahyhMFNoMZo5LwDkBGaMQXOtf9PWlSXIlRk5kGdDyIxWBukMblZVjvICC/Gux9WsHeC3F99n6aTAM9GhsB8cETIjahtO71hTAgEAx7xcejzf4mAA4uhPBqWfiTJpbU3SXNeZPmuQITsX0QLogERGdgIOJQQNQE7VcsjIMBdwQxa9yfia7J9rjgoA+uSNRaFEOeJCcR10+oRzl2slf9HsFFwVNmD0r04gJD+VC1kqE+N7mFJwpciRrUivKNNsEsEzMqzVy5TzvVwp9/JgQFcIpPO7kXfXHtagcK6FyogXfrTT7rX/wv8C/9Cf+afwB+6fwQ3/o6/hf/6dfxM/+vj+IH/qd38JtE+KGVwC/+h//XR9+bt45GSYANEEE6SBjfn4IyH0njzZmJuKCjkIkZSeSxgSRYlc+pyiBizOJYGCxFeBdP7ZYTzoB3BGNLh5V3Ve7q6yF/Gi3LsoD7MO1nijCdE+xDCd2OQSPHKABgAdQU8x720tVOxbYE8uKT75a9ACGn44HG714Xhds7CA/5ySilQfI1SgmsDHTSuqwPxN+zuLjVjuUvNhMXufEQPLY/IY+Ou77HVd7oAeL2nGXly6T1x4Ncm1Lt3OgRcxI8RmLXGTB5l4OL9WBIHpfyc5936ut/ho3oII7ghboUmIyHgJwil3hNHK/ZwCC9MHs8GmrAGsQzGDxC+c46vl6QTuFsGaGIY6nKsfx9gcA8uk9BHOUTzLb8Iq0mZv0hA2Qa+oGaHNOFovAzAlLz4udg416ylobdEdJTr04hMEOQND9Q2MVkfP+zh4VnQVy5B66JD13C+1FLGQyXkaU0Nk0BY6hHIlqAkDL9f3ZZ59xfWDmOFJHV7bA934gp/Y+hIm1Fmp9F1pb6GnFA3ciXYU68z0zaRPG31K1EVjJ84gD23HptVFkn1DJQUPBJMITVWtCo/w6uN0NpV3Q3uCY0Gho4hAJPK4H71tyTxsC3mgJBWFL/q1fC8mT1tCBFXOqA9Mahwr7jBLd0z1CuDaaslNCRDPgr4n+2P7dwwwPbav7wFjHA28mV/zsFEXkhLnsOgFM7irdVmwhZiWzDQ2uvG7H7iSR2wmCPL3luXw8X+Gzqhhy3zdRWs3Swx0237PA2ehpFQEVb8N4dZKf2X1fS30CiViJc3m9ChrPx9YwpuW5VnG6YUYkRSB9nrVGApMyyPyEzhtmNyKYkEsn1aBBEWCnVhs1L9MHWgJFU5ETQ32BYp6FozcFbiLajPk3Qil09gBjAkh5nMMgzYAQdsUi48gATCQLvTznceF+DejFjnILhb1u6NuDcW7czME00LUDClxliQeBSdB20QP3PfHoDRY3whRNH4gYEO8Yxi71PQeHZnwfry+F8KxeRRKf4QhtCCTkHnPP9QaWZczp3wbbXpDVDgBIHG8KoMbaRZLpsYU9wzkyNax4gANritfcqMtMRCOm4fabCFNM2EyIPZWFwBbVNAEud3y1XflgASCrVavRm8oDKQQeDXMQ2Z4WqEkpqpoqwwoIlSBWBdmyvXYnym3J8bQVeDwmtF2A+PJaLZS1FOxdNo+o7HUqQai2M5HHvFfCtltxvwp9CQECiuE7oYkxP1TKEaX0FZh9PsmV6fg93/wd+MZv/zr+gZ/4+/CNH/gGfu3/+jaaKH7xr/x3+LVvfwc/kCOaz1epbQGKZNQ5hOPkBIbUEAKDOcdMQgXaixcoCLnWRKqZ3rCnfZVnq3UJYCSTXNves6fNTK1xPst0B0Bb67A+X/+E0zbnTJGSMCHH5vGuRC62Y0MJmjTHXp+8RJ8fRVPLqP1AuuvQuNrhlypbEHQOnKGJ/U566z3q/V4poJDWMV2XTc+E47Oxi5HrupBecrz3JYoEUiRksOK6XQrk4WLjGKhQSVJgr8HcXwAFqkN4j4qSM93Td5TOCPcci2ZRB+Z93/veqSy07Grkvd1eyRS/Lk1xj8H3hcJDSM0PIiMu/CyugJF3AHcWkGXnExFwG0SCnHZgROC5nu6bVki1luacbL0fCLq6YQb9ekO4Js0H7pFUjC68Zqerw7Abd9juLuSTApDcPTBuBpuw5LPH5gnnfb99phANeJ+DI2GFRXU08mAX37gXl5n34M44RMrNLojK3UWy5TkCgDS4K+6blJ3ikkfEB254JXor1h0COAfj1/scux0+Nj1l2o0STQFI67N0SsHBFwxFA9F2yw1S0w3v9BmeQp2GC9baJL2M3SPotufqcaWN1uaFwy2nRAH3zIIZmfTK9t39lLd6CsNYSNMhwxDLkaISw3O6Yzj969ldihVLXjYXlWDFgRR3LseM6tLkPV/Ffb4kEexP/ZeZQqaLkJ1FCFJAm2g4KwSukYjlu3pSnyouF32GCZ2g/Ig9n6HF5v7SBYhUGnJA9ZM19PmJrCodJTjewvTtrMHvpxXePce63ipEOQgiC9MImAugLalZjB/8Ywudve8bAUCt0b9dFc2zi2DbElHBPEJs5wrj9YI26mLWM3IK3DkUojMB14n38Z5dv4Hby6klloaD8VUWLQ5D1nCsNXbeHIgJD058tDmZS8FwtQfzHndc/QkrNpQSuOM92DkVXJbl3hd9fTmSXJFEXJnwtEZ0VtszkxCKxjQ5N2U4fnL+tOXEjQDaZCutqwCRKC52MiLZcmlOXq5E4J4AQNXf2YIvtTMAwKoFyuByG6kKhr6S0RM18LgBT3PmVD1LgPY15QWaqnNu5kx8mgKHK8Oc9L1dmz02IngGj0Lk6vdX+/Y9fTbp8ZvBJT75vHno1BjJCmJXa5m0YRUPl2j6FvvySa17NlMY4SBCMKavA+EMvqXu13QnWMKI4zVx4+//6Z/Cz/+pP4PhF37t1/83fPVrX8dnv/5t/PCP/Qgev/Vr+Bf+zX/lcz9351AEFLKpys8xt2hm2DySv2xhZmsdSDzPt+q8fC0rWJ4HarVnWD9utwAAIABJREFUS8UdmfiXAr6Kr/reOrhFWIycKFPNGK+vF6KqKfAh8nCtBLASuMW5k20EXolnqYrPw76KsDrosYJucWiTl12HwacJNQ6ULIsm5Bp2PwoKm+sA8lzr9xwo4U0l8fuaG2IG4t52a8ACzzCHQzyLS1HE5HuMFHUOmyn88cVNl0QPJZHX6fkZdLsxjBdpJaeg9EzYC/HBZJL30L4/X/5eEyZ8Y05I4/V6FJ9bABBlYbE0UdY6YxgwgzzObOHNFLKWtzHF6KV0l5xSNBF+ju7l7y+xIM3VWfSezgrXgyK8siQsoYmCHZjy23VDFh47npSt7NU67920I/YJAh0+BIIL4g3PQsLnWMkfE5+KX0TVLD6637j7Kiy5LrG+xmsVDPMsLPZYYzq9zEUTqn1aic+IPeSjfk/kPa7fV1+rmDknE3DRil+2bRqXjVjDdOSUysY91xsmhOuVnX4i4Gir0DqpMtQzyNKNOHZ890xIi+rSrg4YILb54bsLkp/puM5aD4/HYz1nDcY8B9DbYxX/CqzPUueOmUF8d424r3wh4BwMEUvxX2vtPGPck64QwWgQbZ07J/Jbz/oq/n/GUgBJhWNnyLIAP/dqrUWJvrQFdZ/52XsmUnvwxsm1lwOg4Lm3p4HVs2LxPtf6ZROnrfdkbB1rDTFPMehQPK83uDqAzsIGHXZzP4zpqPHLmICiLVDJUwyreZ9DHyyClRSaOhsqrs0ElxQNV3vAZt0bScvRclei+Awi6cYRsMlpmee+qbOIBWlA9AGxBnHqNtwZl6Mj6Sx7NLqqJn3GEdm5iAg0fcAHO1wtPfNbXOj4KjQAxJbDEf/M+KffHwHhy5HkxnZViJDk9NFfzQJU8xtvYm348qAEmIxEIigOwwSHLfiYUKfCSiUwYgAdmM6Rd6wmDpsjyUEK5vAUE1hMlGcqxDHHANfgtvcJS2p5pB8cdlIT0jDDKWjzgVkcowY4Bj97GNxvcNrIRk7GsGUxBSjGYLtaoy/0JsJo/P7KVlGi3NMdt++EpSvbZ3WPJc5JMhnUj8CkwCcBjCT4IrcvlCy2opek/+0tWpZRZb80DqeCStLYwrR1KJ2vgQvf+jt+HP/l//hz+Nf/2L+LGd/AV3/wm/iP/vJfwj/3x/9l/G0/+wP4/T/6ez/3c02vFewLEapAXclvmKf63Cl2SRTq5ITV958o0hm0TzuoE7Wotl0lvScfrtZaeaTy91WANiARt3O6VSW69bOnYAiHaKMSgvq+cRzY5+fllouV/JRiu5TSkj+7kvJcB4VKwNmurtx8X3t5HwrueyYydmWS4PAZK2Ff/2+FPJIGcNtEqCG6r+AUxsMN1tDLPmby38xv3D5XwiwiFDa6A9HhAxhhWAM/gomJph9yHWiVIJ7ofD3bslZCjrRVT1PzpEa9xkALh1hDmEJcMMeATYEZUVcm9dmKlyyanIf4o19wpaDithuqFxqegKfpe4pk7vSjtDAaCsmDqFfeJwmHaXUE9j6EzBxxDrg1zOEL9b2EQpeHprF8TMxgMmKueN3OFmlU4jLTUzjvRe8oNbk7YyVFlK/175HIpUTAXgZ7Ddi8YcKf0caCZM6JaTyg3fng25HorELRGU/EdlI/zPB6vQ4UXtZ+qPvg0yBZxN/3/SG5smXHVUn27mgsBPgm2q1oiElgwmPmObQnyq3E1fJPulKUc0p1HGqFV1JaPtdADl4RgV6kG4kEXjbTKnInecVJrs9d19NiF7l0SpAVh1mE8+yQFAtXsniim8MMr7x2S6pfxYdF1QrFGC8KmRpIa8kO5zkoQpNbKsIzY6SItl41ZOl0+SjE2N0RLWl9+bulMdHh6bcnANKtgGLV9xT81hqoZ1k2ha5bZLg6sMHcorxkm7GLNGfuoZi0mawCzcEzZGRcxeaH198+mSd4C9xjEKEOB8xx3++rg9pE03Ks6BOHODT3CDs5lViSDjXCUujLTq2l4JTr/30NBgpxhCtewykkqyIrAj6ccdbT8QcXxmR3ohXyPTMPqliZfrUju4wRinQDgyZIFxl3GRaegHTcRsrjdEM0drPMAp+ZYSrXpvQGIPMbCQwJWEy4cvz19/P6ciS5AABW7mn/usY1UpVNqsLIShvJ2QP2WFOPwLCsJJOKcGc14tAFcfNwPvzuQEVg+W9ON8hjjyRt0hHOoQnT2e58+VyODQtRVSISQB7609KFgYcceXE9g/RIM/YieAOxFkqiERlQrFrgUshQY0FgWRlFYM4bkX6Y9rq35+fRUgEyGc1WSyW3nE700W+1bHwqWQJq0+bP97aC8TnZyrG9UusliQxABepY6GBtxEKJv5v3XbPAV5/fwK8E8O/96Z/D2y/97/gn/p6/G3/jv/mv8Ed++mfxy//+L+Fn/sA//LmfK8S21seyN9Od6JJ75ce9YoBr8vF7zip/J6bbWxFIhKtstxIVr+LivBubH7gPNn5ebnJ3YI5UkWL7qDLQnb6GG10xM8z7Xi2+89/PZJqTatpK2j1mKnyxPBJrmmBZ1LCdTmX6fd8LHdiUE/tweHS91vpv7VqThVq7UrlLJfN+Pr7W+YgUxXkhd5GHKkVn5RJwD4ONG8PptwiVnB7kmUgq1HUNajB1NAHUq/hL+6jirLlDncgkgFWU1TWtTkXSEqANvb1R/OqCFoouD9zROScebLlDBHAityLH+6Qo855jIYPDOIVw+W66w0H0c7jlczPUWG0JwbDBGIMGDSbM0wRxO142qKo2KsbXQT5vUC3vQHaNRnaRComhaxRbpdo4pfH99R0+D0ZW3jfsMcPR6KzhENwvHsIujnu8f7iXRZGAyqKUTLc1Ca1eI5M5c+BlI6kUTFQmmNS22Pv07JQ00ZXwFP3gU1SxCrHaKy9jwlyiNhEBsn18Jkhc2wJorPhZwEvRfiqxrn8b84V7DoTn55g3J9jFHje9ErsopirFNgqH36+8JkfLr1dcOOPK8jXNJH/5/WaYqUJ1CTP1gmrn3sp1/yGp922VV//fNbUcEUC28SHc316+wiU6dVnPbIECOaVzIqBRUzpjnzdZcPXixWZHwiIHmARSgxOwcW/wIHR1GjNAYoyxnGvqXCtOcXmzVodgdW2Pc0P1sQr3etYzE9lCnz1uODi5s65nmgByocSvcxLBhwdGCiZVFe/2wgDtwkyy+5OCq5e/rymgI0iTVAvQU+KBMQLTX7QPlMxpFlAS8DlWPPEgOAcb7Hg4Rw0DPGxUi8oYy2VJO0XrWmebV+Yq8Eler7uTH350EUSAdrUs4iqfyOlz/YEpDhwddZoE0VFKW0ODQJcXt63rk0BS5rCQ7uLvf5HXl0Z4Vpyu8795bY7ednsZgUUoX+2361ped3RNyOTsapQDBHBdJC1r9pnCk3OTPKfouy1hxsNJW6xgOBGAC9QHIkgi38bPHTZoBI7kQQYMDRfm67M1oc0QecMdEQ2yWjtCFXSQjK16eEWKpJVXBip1aAjEDP35xtZ8MDmo1zg4UzQaAV0p1ue9YHZwMUErpeL3AQCOBBfYHCqfWIrTNWVu0l9RnJ6mPg1tzXsncuZmiMbfd88X2zaTNjKOj3ZGe1EEZAr+sX/8m/irf+WX8c/80X8D//mf+6v4nb/767DxwgsTen++Tjtb8+UeUb8rMgGtkaCVPAEfje/roNIjKbPkCGmuzfI8jqCzh2gqsetAz2vTvP97HKZnosLkDfBUwfLAuP2moMIcvlq480PL7E5xnQl5gt07ACbKn35v/V3t+9pHs6aBsa+37t3tc3GuWggVvo0IYGu1riTV0rtVOAbFFuGKkLnuKRU2yQXO+2vB59vSqUEA+Aw4HNq4Xp6Ne1aa4jbD1S6uP+tg84QIZ0hgWoPGhAxW/G32tFgDZtO0O3oldWXiQoeZr8laGpKiUnro1jrwFIm21iDToGgwNdgrD07pUChuTKjwcLjahTveccUjrd2uFLwiBTOOphfM6M3cROHqe1yu8mB8fuUJuwfFKRkLPBzTJyQaRkx0v9bzhnNG0PXItTwn238q0HCINkxxPPQN4ZZtTiac2mk5puKIOQFkAuSlfSDCK4nyTk3RT66/hgcRYqXFYuuCGTtBqHXng37fsLR41Gqn5/S5RCRrEpOqLtu7QKQdlKJLw8jOWSGwbCMPhJGCUUV87eNKalmc1tQtxn1kh2oVrrAPBeraSwDex0RP2tcIX+LK2zKeJdrI5CkJDeooFWtXir/YbbmXqMvC0FshXZ0dTlEE6IQww9CUhVUc4s8aL69gzBCl37c0JqEnqMPYx5a3iYBsnezE9Z7tbgES0QvP1vPjQgx2LD1q+E3FjIBYgytV/rWPFrUk/67neMZnryTXA5952iBOW37vcMcjXSUcuzNgjrV2WuoaqpijfeKeZlgxvLQONXxAIegXO02VfC2tRWTRn96sABZNrLUMl67k46piTtKLbDjQJ2wC+tbgg2ikXh3ipCJFrnd2PJi/IIAIByygwYlnpDSy+JkSEOm0Bcz1bnPi6s8N2LV0vGh0VjEzqLwxFj8b1A0uXEsagFwUWL69vWFOT7FdFklIMAzFl2Wc7L0jpqNfbzDJoVnrvlGk15X2qe1qRIkjIB3QscEtnnsGlYsFOjZ9cKYDz33feF6P7H512OqS2OavfYHXlwPJFbYktQHhbDEMc9pGIGCelQf9JdZM7YVG3rb8XBUByxn2LQjBbw7lrv4LVQFIXSje4hI7KKujJrZamhZU9LarWrQ8BO77ztZ0Tu7wAW3PbOMz8bDYVT4tv4ozmBXgEYRqVv1GHbIdmJyfEMcIuk2Yb9TWkRyktifVRATe73u1fXiNmXAARB1lK3AVW6BQLW93RziFWKtlnsGqPP00/QlH+HIVIF2B/2YCWI4S/cr1QAtfZtbl4frpa7rjO6/AT//UP4mv+lfxY9/6On7/z/wofuVvPPHttwv/x/1t/Plf/l8+93MLsfG0N8oD864CRLZ/qgUtmm438twywYFS+VlVfs92G7L1PuEfRuJO9xz7KYDSSm0i1ojdokMUukSdnWbLGOAoRqKb6viAiC/kOVLxrImyHtZOixJQz/hIzOtV/ESu78a2dJOFHIdkgWQ8vB0byeD7xkKcGwSvsLW36usArV9IPyrRy8i12bBM52cgpuJlL0wlt7jVxJtMxl+DB65NWv3ZCAz/NkwG2iV5NDNmXBHQ643IijaMYGu59w5Li62HbmN88twF8JZ0o91NKPS/6DYL9W/kiXPEc7bPYiJw4xKKu7QDaBNdAVeKP905eZAiO6rHJQDxC4Ahkvz0fD6JeqSPtflnQHdYjM2zTGQESEcUm3AfCNyYzhZ8xQ+9cljCzUR2hOESxbAXLPnRVvSuAUwjYtpFMV93toeN9z4Hk7he5HSa4+U34/PM7g+Jw0yObyZqJzrIiUtE77V/9Bt3ZxJdwpymz+WtXO4YFs7W9Bx4zdfqdlQ8i0gDea2ia480r3VPIMQwQR73XX7OYwLTMO+Re2ontq90J6G/d6DFxTNWN82r4kB1RdgxI4VLguNb2Wa/dodIWDD0vgaysuMSjM42Zir2GV/CnP9dCHqwc0Gdxu4UzXnDtRBsxo33ea/1XUJKzJGTuXQJ97bvtOS/tw/uOhGCdl1LrOVA0ugqtoyD53q6EjA2DjuobHneKoDQLCQThWZ05M+scejV9RNFTx98DU1hrq+9y3i2bRvr7+q+dm0r5nF8sgNdYcm/dQhClODISeNYdAfGExcK7V7+65DGBFE7J2i2nh25IH8cE4iZLi7tgiQifl0t6UgBjla50S+QlqUU2SG410mjHEBjB+fR6YMrIng0JCf6gltL/VKDCHMU9YDHlcK7Br0e5MI+G1724ucRzy5LaSEC2uibPAdF8gAgXWDxAnKd0KHZierDIBg8A8oJSBweN7Q39H4tyhiHitzQRreROq+uJ51EOvpeQ0lLYQ7FouSLvr4cSW7k4jQiI5E0g6hxu43CtPKBXe1YL/Pyj6MyvcmyzSH3z2Byei7G4nWUZVZ5izbZoivAkwdH94LeH3DjFCY+EFmKencnpWFOBHpyGfkg+RkmD67FkwQA+hS6O16v1+KDLguuCpzV1vNs2blnhY5sOe9xoHOm6KLarWYpHtsjXIsbVaIsBdb4z0quK0legjBJ7rEKHv3C2+Px8eu+baSK11UtJIn0Ss1gPoattmcIll/rpy+2JRQ//K0fxl/83b+IX/p7/xZ87e/8Yfzen/gmfvRbP4I///N/Br/6n/6Fzy+nrNarGmz5zM/WIJCHoHu2Gtkq88nDyKfhfn/tIJcTaKrAOn9XiRGrpV/JpEJypONBa6lDf0w8ukI9VkI5bC6S/4f2Zd7jYTP5bw4JT9SWCcD0AbPksZut+14jnavjUKjZQrjKI823wKn4k/BYbhK7ZcvfRTSaAV8bsjhjC/D1euVYx+DnzYTAZJAaJOTqlbpX8nCkywn3G/laTBBLKU3ngis7AwOAYKQ/Y2Dg9f7rQHTMIHL7vB6Y48Upf7WfkqKCniO7AUAfGDWZsNGKyxPFqSKnitXioIvQhJ0840R84ECMLFI6dQBj8FCTogKAqD/Y2qs1xOEaisejQztFTpHm+RBHrINn27E5Am4vqrMLLRNBeOP0viN5t0lV+Ui3GAF5luY5TlUDEoqYHfdrAL2BjiedRAXr8GjAnc/DJi488Hov5TNj9uv1Wp2vil/hsgRyi1LgNbFRsuhP9FQraRkYM7maxsKveJ3skMTycz2LvZNHX0jqfd8fXE7ck1eexXd1XO65h3mcXriYfL9S5c/gNMpCP4HslImsMdr187dzwIzNWLzhO7mixQGfyQ8++aErIT8S2rOzVkXYWax/pFnNtb/dHZdsQWXXlp7P+yyt6XAOxuoVQ5qmTzZWMT8m91wV0kTgeN961NQ+7pGT71sxkMr/vrtmRyJZIESNL370rT8BNiWBcQOrk1sDHUL2vfm0m2UJXtRnKF/pep+mFDil4nKPnI8L1fTunWuwRQ4TQYpKnZO7WgA6WcCMQZG7JqrcVPHsF2IaHvqGhgfCKZCvqZW9P1jMjJvieWGhEekzrhDyykFqAl0d5lpPCD5reOSZUAN10pGqKzzyjFDATdmFMXpwT+MwrfJpL/RWH7QYbM8OiK+1gpYDrMDP6Yq1bvh3di4sIG5wcpMWaEgv/QFoghtNVwwxJ/iJqUB0wNqHNfBFX1+KJJfcpsjRirxBy7exH+Mms5XVK8ES+VxSRnXHwJzOudMC9jYSNduWQVVNk2NKRDIDXrZwzLbbAAD4HPS1LGoO8JHD6Ew8SnHAhHQsPlQFoApmxUla/qS+LW6IUGzeoBzXigzSHwL7nB8S2dcYVJr75hExoI4MnBQAaSY3rG55kI/xQgRRz/f7lSgyEgXxZU9SAaWCBu9aLLR4JWvZGgyhW8X6LDjGjn6Xl/YGlcC3fudP49t/8q/hH42/HT+J7+Anf9dvw+/7Xd/En/q5fw3/4h/7tz73c2MMCnvMUVOLioN1zkjn58W6x/UqoVolgMMm0DkJphAwhOKhbQ10aJnwX9e1CoYSM81EbYCD9yZHcqk5SCKw2ufuzt+VXM5hjh4NHR3uwKN/BVfy22q6GLSKokw651ydBmCvuxCHKiMIFdLZovW06TsLrHQl8CwGTuFYY/aKOzndj879+8j9uTiR5pxZ7qlCnxNu5KjVqFqASay9xmpb1UAVga8iRCLgM9DAQQ+AQrTD4kKgY9i9RE8+KMwQN5QdWETaJhUyXu2/o3i+hL6NAs6Wr8NuqYVBM3smkY6r8YBRNPScwNiVB8V1cWR47w+0qy/vVc3EVK+OaYHHg6O6AwO9ZVGWB3eXZ44mV7ayW+cAiOI7ThYjPdXRrRXHeuTBUgf9ToZf5pD8dz6PAdGcAlUT46CY5jBJJ5gU+Lo7onVgcO0SfSWtrGvH6zUWmlpAgxlH316SnZX0Ci7U3ozJIfeBp8DWWUDdI+8/eaTlcVrc5gI7xhjs+kXnfukdMQ0NDwBM0l/jpn0bgkKw5NR2VTzeiKTXIbtjdl9oZiV1i06W8aaoSHWtqG7BEVtOOpglYFCEhhM9re+p/Vp7iWtpF+pHyFqvKqTJZWVs+MBLlrK+O7+X3UGIo+u1zp0T9KhElT67u1O4rg25X5tA1eECaOyzu+5LIemnz27t94qRlbhrCm7Ls7iuu66vzqMS6dXPVmJW51DdU1UOUahnUgmwCPUi8MiOXXbChKix9Pq+tq6XQMB2hmitpTYgE+VQtEzQVRXSLzgGQmZagU40dVxtAxnaL2hvkMZnpFoAnyCgKSDkoJqrcU02RdKyNv98rRmcWhcCFObb5qz4tNKAS+m8dB0e2UDxerPAcuBOj/WzI8auHa0PFS35++x0zdKapDgTEYAI+qXsblUuMCdj4LjR+wVA0JJyxE4nEX7RLNI+v/S/5+tLkeQCPGCralUIMGzB2mcSpMV7VI6KPV+r0pUGRcMwis9wNfj9ygqewiAzg7XaqJP+qMlLvZSHWGsXetc81CedALAX0QoCubEY6gOBjturbcU2fc1Jr01xBsASnDAp2o4HFZiAPd+8ZoaXK4TPRGqDybYl99VB0ZpkeVqBuV09kbUHQjxRQMoqIxEYVtm5cRI5oRBg5hxuIwk/E+0xBhNAM34/iDYpsJXp4LMse5xC1CronChFveivqvgt12/BX/ev44//if8A//af/Hdw/w+/jOtr/w9++2/7QfyeH7o+93OrvWZ7es+5+etr9e/F7yZ9IpaAQ1UpdJFSxT+oeA2H29hq+MZ2mzrw2WefkUuV1ziLW6abH1jXe79vNXghDGOMbKElehiS1lQTBj6nnur12wcUpJIwZ2WwuP2msXdrW7zpr3UP+DlaDiXwde20S5rrHlb78rPB9zsLyeXfO+91n2hVx6RacCW3PWCBbOdtE3bRLcjQRFM4Hjiy07CLOrPBpCd5YYbAPW96WELoT+2OyNGXl14s1jAxIbAeMDEM2by7ZoF2KUQH5vgM0jZiXk4M025SeUpUYyXGcajQaaAU5A8hmj8hEH0s793nG/0oSQUi7Uq6o7VAv9iJaZ0t5npeEsDzK2952DFxLz7jo3Xc806U6wFtTx6oLfeBOEyIiDe9MCHQi78DzsPfELha4DVHouIgnWcOzOwWeUwMG6RKeMar41C/Xwa5eAg93xqaAvd8UTOQyHE4fTrDuD/GsOWfCe1oUsAAB22MFyciXe0BThAzai0q2SkVuwpssFgTkZ14Z2dtzonHG0VkobLWbgRHHpPapogxOXkqRYU2eO3P5xOaZ00VPyvBk+0gU4lkfV/5LgNYz5zridHvjHEisiy4Jk5e+1it8VPYWgnlya8tWk19rZLFejl4JqwEOtfw+31nIpP7U0CuubHomEmhq3u5fl/tSWy6QSVThYrf0ykqTM/5SnDX9dl+v3L4KVpUywKLnx2rIFnDGnzTttbzzAS2Es3e+7oPqopHv9b3FhJd906xQRoAeCT44U5Od4lRmzq0TYhYFq5Pxp4uaCopuKUbk16Bfikel0ClimvgSgRYArjaRpNJj+OfsigEeibtfE8OirhgkBVHQ0jfdJ1w5Rl2tYbHQ6FN6FBQ8Vr6AqRaIs8inObaO7nfnt3daYZQwfPR0ftj6TZEORjrutKGNXStOxGK/202jKR9IvULTNCB9vgKc4rg3njdFO2VIFpEMO539CYJqhg8k3Jz4fClSCoqysP7i72+FEku22dZ8TidCWrxRfJ1e2wCeE3LqYqvODYlznIHoim5odpWe6g2nYIL+tlqBChRjjHS4qh5krfLkD4T7iACcx3I7XRb7c3FY3WqFgHQ8SFVu2HbjqqC5xm0gI2WVqVZVk5l0n06I5BrC3z2ehFJyFZ7DM5brzaYHxu7fra4Laokz1elXZQHYPsshu1hAEAOACiVdn5maS25p5GoXv8gJOT37KBHrmwm7EcQ/PSlCDy/1jAx8HoZvvmtn8Kf/e//Av7yX/+/8ZIBeXw3Ni/W8wY20nIGx3p+dggM6u/V1hYiam/9wUQRpDdU4RXBiUdUifIQeV6PVcTUtfqcaFb3JNa9raECZ0u8TPtr6EDkQVQoh7RrCScA6lnqQFrCOCV3uLiKABDePuyZOWcKitI1A8eBZo7x2Wu1LQvBrIOC1n7+wdS/PiOtd2pULAWWXsXZTCpO0ipak0x4FK/XwD0nntJg7xN+T1yiaK5oQv6Y2Q3ODaTKuydtgYIEQLLFIh57ZKcNRIoTa/pRExZ+HSn2ux7rHjZLC6JEOjjZ7jC4t0JCAl95slU+XIHWyTl7NACC5/O51N1Xo5WONnarVBUPvYj26oW3xxO91brMaYIl+GmCx3Wt4vrROq7rwuPtSZGeKnq/0pKH39OlZxuyLbeaOSeuZz+Gm1CwY0761RwOjSufmS8Xmkjl7pzGpKNEcOlUMnzg/f0dElij1yOEQEE475+TniMii2c407u4H4mFymMjoSAq1HCRJqENM25AlF6zIsDsEBP0opIkSixBl5I5yVzW3nDHHgpS7enHlQmGbfHaXstFKWOL+mx7k9KzvbLX3khazgTbyJXwAmw1f0hIgWVxWC37tcZiuxuciV0V54WIqm7Us6hIFWsjSDuofzu9hit2aV5PDF9rAFAUQzjSE31TAfOz+Ee6FrDdG0RIi4H7Ya+2u5bFrQ3f6GtRpTSfTe9MyupcP2M6gA9J7Ho53R9qWMZ6jrHF6WdRwOf9sVgwAQGhQjrzHiF05Sf8VURjJRwNNTRmonfGlZDd1QSAt6sjjANvCGZt/3v1PejJxr07iCqJCBfoVDalHxN8gOi0RXG1dSHWe/gRBbYNgbcH36s9rgQZgHAm03DSJVQiEfQXvyfBKVEOTlp0TweezyfCyse3ACyF4EKgnKFyTYdwAqAoHu0Bn4KndnJ6baYXvS0buuUXLYwTrTVcj6+QuvB9pLlfjiQXAdaPvNnRO95tpIKxBBIQ/3G6AAAgAElEQVSxEhBpmly3tmgNd1oPLRFTobbOqWPa20KKCzG77xsXdnv2aj1bIvmQLblXDtSQjUoEw7dit9pN7WKrhy3Vj7+rquJ+tjCyCq7pPPX+/UgcimJxbtgxBl4ZYGsu/O0UBlQBQISXzgc0EY8lFlj8K/edsOZSmNMxLIc1VJsC5JytlqDZCl4L0bBP6Amh+9CoinLudl+1y7hNv/dCjAhEe0DuG+3BKvqKH8S/+kf+MJ6Pb+DZP+/KsBJZ3e3ZUl9v+oZsagWISgMbrRPZX68DRjMAAlhcLgBAjRk8WuDrXmQBUL6T5UVsx32ICMybqnA6FF2wKXiNTCqn5DqkSTcRNyL2qopniuvIFQMe2dqqxB6aHPU8FAtBeNnAnQIedybII/ZEs+JKO4Iol5A3vIzC47QzSn9f53ve8zMAjjE+gwapDWzvGaayVWt5CN6vmtj3xDDhtMNo678nbrROtGmmhc1be+JlE00fkCYAAlci+C7kvQaUdl9Xh8a1BjlUYuDOFpkd7dmZbVeOOZ0Ussq2WCoh31R6ZTcBemchrMp2mrRIqtAWEBIMzu5F46hkCUD6UdRkTLjS+/TRLzS9+DvE02/e9vTHpnS4KEFUz/0eirfrQS/uHFctyQ+GCmBA8z1Jiwe84o4ByL1oL7VHzAF9Km57h8cLNt+BNsipQ8vkVDEtkXoIHC94BLQHVLfaniJiEGH0nAqnOdFJXkv8CWn8N3coyBWEO8zf2YEyIy2lAT4FgnRHEFkTMVvv/Lz+gspY3QxO+YrlbDFs5s+kuh+adlc7pp3xuAqiE2m9jq6JH2cVw4IuK7Kytorj3te5ULFpdfaOBJdxLe0ysd0J2pGMlZ85lEDMdV146w1dgeux0U4RWaLfkUmIw44kbiPGrrI8Zc9CdwElRZXCRlhZzCjjh+7hFCHp156JTlE2zverz99kUwzqdSa3FccWEpvg0Dq3ZCfgJ6e3zod630J0I8/OKt7Xex6uC3XGLYBDOL2uaGqLcuNzUeb2cIxNy6mpaZVo0ytflqal1h1BFEVT8oVLw0AbxH2+smsbwPKWHUtc7Q60Qws0g37GM4dTmQai06dXugNCH+yZiDIAhI6Me4reHuj9ga4XmgptCbtBuwIgbSoAuIy1nj3SM3uJOQlmao9lJdjalf7/zxUruWYc6JOWkR7gJLTv4sT0G7y+FEkuAtDGZLBdRBvqMCrv2Q2NV/Kwf9yMNmC0ptgVTP1cFyI21X6o99lCHFkH2Im0nshxec4Wv0Qb1vjHCig2Bkf3pdigxhxW0ll83PLo3Kr0VJCCCPQKDhXcrrYQjg/JkxRvrDiYFIzUJq2EunyGI5Nv6J4tX2g0BUGpysxWNRKd5mhgwQ1WqZvUnz6wXqglFmVi5PufTgYlKKhXq+lxx/ecr2r39a54ffsFyAMKegn+8//sH8Uv/cJfwy9/4/N83jqglzG7ymqBr0CcVBiRtMmKTRlYSXuKjBb3MQ+W1YJruiyUzq8XP28FfGChPLVeASSqpvQxBNgqyEEC5FhWgrRFFSaO62o0IXc6cbgi2+Z0L4AquiZanGt38VEPsVP9fXqBVlEGMHgPuzPwpstB7RcoXjbQEDTwn746JrVvLFLBnn6pJVgDAIdRwax93c9xz92ajYD0Bjd2eSwLst47ruv5/1L3NqHadVt20JhzrrWf8373alIEwaIUSij8aYjVSMNEG1Ii2NOGfYVAWrZiQ22maSstO4E0tBcQ/MFeICUBAyo2okhASmxoxIZaVaRyv/PstdacNsaca+3z1a3UdxXheuDle7/3Pe9znmfvvebPmGOMydEgKKLQx5IRFeNe9wymGiy/V4746z5tfuB4Qw3okkIUmZBsFC81fFwXuihkESn6uF7bXg0gnSbUQBN6iqd+yMWU4Gek4GruBS+tNVhINgDnnojIHqtXMm+NgtzpC9d1obVsblTQu6E13Qm4KTl/rB8b1hhQuR4TnJkLd2Qja3NQaU+OM5GTpo33wB3jc+DVk7ePwLj5TLgfjt/SE1vps5vNgySfjtgxn7fBXfQr5hZrKozG+evhwRu50hi8/7eziRlOkUpNOtaaQHAt94wJyKQSPa9nuGwBG8++72ZxLzlRWtvd871jZRUR+366b9Hbs3AqfUE1xitmTvwOj7Rix9MR4hlb6pkqp5Q6gxvdFq7kjTiOP1F5TE8h6XWd/Hh7m5/4p3zhL/GyphXPAqwVrSzP9PSHsC0myn+cz+7a6OQzv1asK9BAVXduF6GIWXGcEDzjYsWk48n91VWiRMU8XwlC5M+prXElJq9z8UNEvApG5qWcMKJydfJOcfQlVSw/RW0SZwGIJdfZzCDd9vVyrD1FeJ7xuq+Hksiz4lksihDhrDpBYRv51UR1dw6TthuOmvyaGdrVwalIbonc0wny1VvQHYG0pZqyNjS7oHooQS3544iFGHdqjXg/KGr8GX9OTv0O7/dcK5oIGC04VRBLk5/bYRAMH6ilYMW533HWAyJZaHctyfCP+vql8MkVEcSkBc2mBwi98wAeCnrc6n4gKAjLwigU29NyLawscDTHjXekIjbsjMvrtVIk1CK4UzwOb7b+feR4fS162AIgF7FV4Tth1nGvhXDHpbbNlenskXZZQY7UCIo+SpTD6TJRuhKsPIs/Dhq4ZnG5b24UC9gsGtXxAsesOkEuYY3co7G7XaQxrER/HUATwZqJpvjEnL7HcxUQxYwUDMdGHu97UnADcnKmT5iUtRgtPwQKzdHczMLdF6+lZMHPYfnP5+RWIBxj4G/8938N//j9ZxBj4fd/93/H//x//V38W//hX8S/+Z/8lwD+xy//rsQwtYp3oyt48C4ReF1XCio8fQgVnvfm9Xph+oChQVSOjQseI3o5nPFVe8DloKV7vP9AdvehFYGrY65PCr80V3+qwDLA+kiueNrZNTPMcSNCgViwKug4cMBYnzB7ccyqja4jAkyn7ylEYDndqO1ddwoQa2NYoQPTAyKkOXy+B0yJ8n5YhwdgKohleK8AQOsaBNc4c1LNlaZIGxjsaccFX5xA9P4Bn5NCK+Evy01Iu1DU9KoG0HoAmFhDIQj0V0cIxRhLartXLgFodBegKHRCux41fp4Dd/rVqiiQ96n1F+aceAl5cTUpof+kAc6FBF1ovScBrBoJL7oqmCb0qzwPPWNM10aebCZuS06rCdGyqGZMFc0FK76a/l8fL94fNyB5af2iknoNwAzwJoh0e1ARRCi6fiCcfpVoRK40SC0pZ5CftIt8OUWuQ+c56aGI1mmfOATaG0Jzc5zE3jwFEVxG+yFtjHUqVN1rGFSACKXHKhzWFtwN00uAIpgDgIzk8vFlq/AIcJW4zwXpA6KOOQXvubYF14RDF+MfLj7m4gvohogbkZzv8ogFSP8olwFSknifPbhVS0VSyU6Lwc/MQ+ELkh7bkeLMKvJaaxi3YorDdcK0M86AE0UkODJlbXHhs9FXPTzfJzfXI8WI9XwXUsa/BJQTOwGfyYEAIPyMlTeQVBURUq+y6LaM0U+EsyZ+FbcaBF4Uozh5tJ7RaniqcHs6VdTrziyMiktLrjffs8+jk1m5ia3ycNGPnkBDvXb9nFr0IKrbqxw4Yt96fwWePJHrDcTsvHrE4c/P+IzrPMO6xZUhQEsQyAUQcJHDSxXLBdDTtDwR6Z4T4AgB4c61G/cI2QJRFwdDdeZ9BXxNCOj/3uwiDQSAKJjXt3D+cO/nol2ZmWLVFEHYBAYcWAq1GwGFqCCm8r0HBeuwBoHDF5ui+3ZAGiYmetZeGCUk5HurRgQQiICOMDdJB57T5IAiwoEA/ZmRtDcRqMZeDCZC/94f+/VLUeRGAGUpVQdqc08WLTgq+ao03PPzofArbuUZZ1gwILBAa9AIXGh77C+qeI+BV++kLPSO+36z8wnFHVTHrkX1OQvcQ/7n+wNcFrlnANQP57XGiXUbRJhwyFPKAAbeYDEq63tvuKTxoAaOwbd+HQOFUw2uQgumlmKcMMWaDqyFyzruCoAVJP2ss8QjkK05E3FOu7F+0O3POdAgiDlRSzSeIgyA3aekpUioALO89WQjkZ48vIjY4qFx081iq3x/zjO7kTAx/Pqv/cOwv/sH+Cf+6d+EzN/AP9cF/9vv/y38G3/7wg+L3IjAyxpc0jYrJiQUzWzbBM157Hfw4AZuoYYXRymvL4iuweMREB2Sz2EIuAxgOiSfnda4DtWU3M12dfg9YYVwhOBlVyKma9tCqbEIb6Eb6XpZh6ji1b7jalFL0VUAZgpfglf/KVGG9NsNofOArpWekg53UgPg3GTD+5JotZOLPtbC6wLGuCHW8vkw9MhFLK1tq3wGHjpBAMgCtxBC+h/OWAgxNO2Jyhle/RumLzTjqNTUWMwPig2ubxfebzYq3ohe7aZQwM9hecb8UCxETvgrZbCYoMkRH+57/UDaiU7nKNB4HhCxxSjvTi7xmMe0XCGQpkTfazFFBFqJ7ATQiO3oUQgQlGIMeKKEStt1D0docqPjiGI8R7j0sDXABB5sfmd6c0tSV5oIZkV1dyBoBaQtMCcFgHNy89LVO9+35l76oOG6OjLmeo4IBboUV7dMfIkuUh2J19VSBEOVNkfhVx4tR+ts0lWBJQsI4F5OygAcEY1nRh0vXPA4vFOfa4+MeT8d6/uAXETru/FMQpg85+Q0YaUuAZEoXYAJe+UEJmPce9xfGt8IAGNuvnwhiDXdMlWEMw+FR/qLYhfJXRuBiJbPaIpvZnCZx7xHspvS8qmdYvE5navi7Qu6n/2OKmlJxS1/Tigqd1bhH5Frp4Wj8e3c8LDk6sYYqVm49tfFaWohv3l96pnFo/HiFHJtJLS+nqhpNZZnMtf2dSXap3uk7DklvdqF9yBdqN5Dfa5nUfosVOu8FP2vJgqOOKDSI5fu64ZASzQ/Hq/J+mNtvUI1xsCZxo3bdzxBxKZRStoefuT7NUQu9znoZF2LtbgMRzTpATA4uD7XrMPTbk8QCDGMe+HqHcgFWZ7oe3FZI4vZCM/xPqDaTjwEwTTHgnRSFcebE95mhtxJAUVAZgAhuBfFmoILEYtxxXQvK7JggR/iiFCIKNa4YV0Rk7gr0ewAAlyWkTVDJLPUDOTyyoIvhzXWOeqG0JlLsXRTC3/s1y9FkQuQM7ZWen+ugF2GwtYFASlxga3kc/nxH43iQp4DYaIcBydygeLVSKDhbLKqArDG7WiCWKcorc5wr3sEb847E9dMkdwoIY6UibZuC6vdzT6EVzVScQSQG9FiOdRY8LBgyv3lSt/PzeOskSAMywfRkSS2053h4VGZv4Zz3EofwyBKVAmeDHGIcj2n5fW59MIY7zPOB4NJoT+FwG5fQsguoNmdVaAI3MVn6g0IUhDGGAg0SHMcO/Tz9f6b/8L+/a/nf3/vvzh//w3Av/tP/eFnKSKwVLKwVUxv3OT0KBw+2nFlsNY3LaGek4P2z33PLUC0URSfzqAwnesnr3y93fWrYPog+roGn9F1RqP1LK1sxvY1FCkFZSIShusy+GIQIb/JoCGA53NaooC1uPO7ArkDMHL1mgdcOv0GwiEZEC1Hnij6BQCRBrhArDwqb/RoKbzk99SY21VgzSBwrLHgI1FGDXpKa/Icjduu3APa6V1tvW0PSHfHnAvX9YHwAR+B6yIK/BJA9MVRVm/AFFxi8MVGCSZ4QTBEoeuo7SFCsYcdHmE9m5sTn5+j5TlHcB1udIXpC/e4ocpx2cxRuQKYK0C/BMFa9LN0EahQ9DFws8aULLDX4dVbJvX3JD3LQ+AzoKZA2i71angAuBKxf8mF6QOR1CIRoqUbbYvcZgRyWtUA10TqHbguNl2tKXq6QYTEjoGmCk9uqUghZrJHzCHYfFcKTjg+BLhli3ExXVXUsokxuNOPvKYqS4GmtD4DABM6UGAsvNVzoyDzQe7J4gi5t12ghnOD28hiucbXr1fHGtxo6KDFkqQopnWec3FFxJ1FgeUo1zDm5xYPFZo4cysek75u2plHjq1hTPCtbavAyhW2YzjjTDU77mujjlWc5Rjny6g/BEz2wrxleIzegVwRfVbfIkWRLacmbnxO+f6D1zR56U8ahKpu5P66Lnx+/5l0lbONUkHdS3Fli1s51tyalCrYtqakgJksTp/NHnBQ0eKiFgDh7njPhd7zeijV9suP3uKJ5pbFWOWoQqfDnc83zvvgYo7ME2vBXhfMSTtgHDzr7TkRPPH/6TQB5D1/0Cnq+1QVLZB+t2N7xgKCnpv15uOzPj/TbiriQvidQNoLwwd6N04o+5VFNxsfk4aZz7y4ITCyARcsX1C7MAbnar13ghBULOeULdBaJ/e2tB24ENOxUP68/N6i/vB5ybilndz/WJx0S3DCW587e58SR9Nl4qwRJtorKSBeaAb0Bkg4Vk7zK25zevmLsWx/aYrc52jhul4Y8705tO/B7pdjC85mW6uL7BAVilCsoVlDrDeaXYB2ogA1Qsm98XqxuwqttXUcC7hmso8gv3aPhE4i8Hg8lHn/JN+3QbDgG13QpFBYojekHvBLAphllp3dTAOFAACpCfZ64fv3+1yfChiqm3slICrXU7m/VEiHkKPM3YFnOcd0IjDo5ohGcCGBrhQ3uG/awUaQw5OH1b4EgS9IWASWCTQTgehBn7cNTIrPHPzzV6ewZf2CZPK/39cYA/1FJKlpjo+UnaklWnMWQ4zcjHVtVPlqLUVinlb4LF6qf+Q9zoDeDCPRHIp+AjDFC2lyHcqOd56tTPXFZqyQupM8rAksbavG4CgvNMhj9TzkPqDGZCtywZUqX61O2Qe6dqxcfeohWP4Jk1eKKAUIx/d/7w9wffcTNGkAnKtJm+H9nmhXx7oDoh3Scpy1KKwyVbg20E0hbZvaB6ZPcHGO4XqRl7vcAZ+4rBGZEIG98vqgEBeu6Xw1wRgCVUsuZ4PpAhJdfHsgF3mhIzfmkW2AhgX0K8U9PHvaA/BDQdpi1bwPLNaY4Hqz3AMPyArM9X0+I3ThcBVoBAwXlk+4Lm5F7A06He814SIYqghJIVMWJKUtIN+OCa5bowuGOmDAEEXz935/pCYp4Mk9RfK0O+kDDoXGGQdfArzrbBYChhQjJVdfQ6EJCoRy82K7KJpZ4nhZpx1dHB/MbZ0lnBCoMr6ZkWI23NFzcUXLONeU9mG1hVKUqOaIw6EMcDsjYmKmvZg23tOudJSYa0GEG6Dmm4gWBZUUoxCEMKyMOWsQMV0iwJjoF5XxPQbuwTFwYAJCPT8Re93nETjFTBWgw8uKkLQvC9p+uQLNA5Mz113oASnINIIec4ytCYA7FhhPN0oPxvsqGAHk4h26i4TGzjEApyk9N3LNdFppOQmhSJuADT1fWdjN4BSxQTe9Zk9LoxyGSAfaHOu1sDdbxrH2q+ctwvfCgNoOVjGO/M3jbPAs5vyRDwqcqhXxa8x9RmvJgThS6J1iYJwCucRrhaACQK2OpbczJznhx6YMNSU1YExJGznQIrMEYCh+efF6YxfUW0T50JEUSOFZP0AkKWFtT1EjHCMdLH4IdvBeRNIhiba6Noz5hur52S1BEOmOtZDUTkZCqamLG95vuteIaLLTBNABj8Z1vA5YaL7ewpKjwzBc8DEB072VUNQgyS28MjZ7AO0yotmdE0N0gc8bLi+CtsEJ6H2/2WSDDeqaOYlTzvBjndgigvyeG9EtmzLDMrryuP98X/0/6uuXosiNKKK9QmSiPAZdqRxt6vtBY2FlnJiEAkou63Vd6du5cHUlCpzjrvUgutdYZWbgkt4x5p2j9Sh64/7eBXY3tDx6AWCQsd7p62iGmAz4UEV7jJdqhP0c8XPsFHs0Jpq7zp0oTOtXbs7ybacCMMEDxf0RAGUVU2O5JN8bE4uJQNO6jAIXYKhv+7N5D6RsbI9syse2CN9cM0tRwbGcwlaS7pFi0UBEoLUeuca6Tt6zr6+Fd3H09uIFVXz7s38d3//N3/p//Bx9++f/xu6sa9w7feT7zW1aoMUJ17I6UTKwO7VEkIq/VWr9Z9/4HHeVswd5T9ijt+ULdAvouNcN074/4xfqScRGjZ4o46zXCxo3qLVcysEQbyBS0cVSIHC8o0VI6peVNkF6obdESXJsa9BUywa+/QP/4C60KdwKzPFGvzh2bcbtRDPeidBwskL0Dnj1Dp+B1i/AgB4XDA+LM/tAc0GANm71fK1gISQh6K2nA0maf/eOtRLx1EY0IgLhHP+dpBEQJ0Lf1OCtXDEonnyPO0dbR3jyHA0/RSwjnKuGkXY+a6EZnxtYA+ZN9CwCy99wjiky2c0sALiuk9ZRKSa5a7JCh4tIV451U0DmUjaFiibUBXAZQo7XNa3hRkBtYc6V9Oonh5OLKsa8UU4E0hoE5UZDgRqWZ+PZAGFjWQhbtwYzNsIegX4ZwoUiv3nntQ7Y69o2XSIGUYGu2KjfLnREOC0wigfJYQW6Kd7vhW4f5CyPiRCnyK0aH3HciYC1ZrmG9uglzAw+SAVRBcbnm/F/vAHr0Oi85kJfVqVzP5tWo20YR7wliGUjOqdgLqD3b0C6K9R0SpHFkwfu4IKMSxsFMFWg1j1xovKyIsfD1GUgn4pusnmKnnG7xr4zqTekzgkcFEKICFfKJzhxh0PvtQ39N4Vqx5eiA5DScpxk2Pg8C7Qq5sI13b+y0FTGicph9cwV3YAN7vn55VGLOEKx3vsP/MFP7FNtEHH44spYWUfcpUZf5iqwe9lw+qGDVdFaFKVqIkQFc02IGmY42soC3h3o5xkXF7i9YZMFaFPSYWCGcH/EB7qhLF9sCMdZYFTFbV37oimKEuUub+pnzCm6Y/3ZcyESP0L+bEvHJymNDKcYmGu7OqzliJiMVeFZBHJSynrA2WxAENIwxyda7wifpPhopD1jbu4UUkZggGPAXXBdLWlaBlen6FMDKqT/qV0QccxFlxEkpSeck6JC8McY6E0x1wAkRcEL4HKipAUqebutNQx2dtv5okGS0yD4BSi5vxxF7kn6RQmYgOvunsxevHkzAMlOBCy+yH05W6cCQIzAVGdwjUPe3xYdOVqvgoYKaUAWYMC+qD646UpccF19c23NbNvxSBCliAA8uzxVYC7fB1M1vfeifEazMC3UxR39unLDUyZhIMVSX0ctr4cjwbzHhu5XooEAR+CtUVUtyjH5ygBQwVReHfY5sRoDX7fDxfUcbb7f9Ps1M3z79m0Xtc//siX4yo+KlQm8CqfHaGmjBzWqSIFMHfz/N1+b4yVn8YLJSQArGwttghaVkAPIQkKFG1i2x2okfxXP93rGfIUyXK3nuJg+tyV0C3CsU4n5iZxMJ/faQWQLTbYtHXBGZjXeu64PuhV8Bi1lWkdrX5c31Lirt46ZCKr4obywcAfElcKFXBpioKjMeoMu31Z67qcJanbBcixMxXBQBOEN7UpRnZFqJNLoDRuBEQvtMqzFS+1O5FBW8hkTwTuJM9E5o3ipGVd9l3AMABeaxBGW1Fddq24cIzbl6L3ubSWTEtjtLVXgaBHC4mQtFhruk4b96puK0DY/n5MO7F33LBLfKQhcC1kgMZkSnwnc6QcpjZu1kCbxhbxK0Ji9UNg1JwQdjpniNcMaC9ALHp+74fx+DBZdcfy0KxFUY7wnTkWZyiatZUEskVzciO2FWk10xbsqcDcHNM5CgOfzXU0Fn2eiTRQ4GV6d2gC9WvLUHTeSaqGKz0HUP5ZzrC90XSBwIBhvFt3WOX1o2oBJcWY3w/IJQWkzJrp1rKilAuUBnkVCUPg7BpV75sCcK5vMtpcXiDR4Lj5R64hVXHSHRkP5BosGoIYYLFzrmlC1bmgIxCIPdq6RFAXm7rLHE6HAbupDAB1BFGw9VqomTPE8C8yfR7DInGrAGlgaEFeQx3TG4/x3TkRvHRCjzn4t0wBOUVeFW/3767rgc3JldT5rNQmtfGnWOR6PpKd4evdaigpzfK7akqM+d1yd950CX2ackBRkST3TOZ2paw7GKUVSOVyg8tqotXuwqYvg7x80u01tyoJ6TlrMmRy3iS80vUdOFDnNL/n32KBPLY6p61jXrn6v2fhY7xhKXYtPima3Vil/hiGnftbhS+A+8MpFL1zmMSHSENEQGDBT3POGFu83aTUWfJ3r+siGXACUo4bCrNbvGtxjT2cLICP9QaHisJ4TkdYQM+DpLWwGSAi1OsH8wmtYIjRywsON58dz06xRlFqWc3D+fq71/z/hGVDdpCJiYuae9WYcMa+gd6s2g+ZmKeAQlzGI9Ejj2kNSTCmiskgld/J4V3Yb1AfWIfVtYYNEuXbQzg1N8x5M6lUEqUJFMcLxcgY2u3KcgZ6cKoEpD3ShQOTKMBm1FCcASKSR/L21A9TKVZbkBgWAWGesQHeHUt8anil/LVIYxlo8+CghG8fq4RPRqJTGSGRZqiNX3DP5nf61mHAnGryiHkJ63V0tu93kko6V4hmhI0RTxXgUGaqKMB6WGnfFmOh/9rfJkdQUY4kk4uP4yes7uE8MTKx7IOR4P3Ls5RA/vNu1FuQyJuJQcjSF/K53eG62i9xehSw0BwsYrdckD41IFqcOMN1Bhj6kRMbEOnxR5DHICOSqajyQ0pZrEoVcYLgjlNdx5J9PX6fYGo5X7/jZJDe6X2wGwjg+rsaiGqq690+O2DMAF+9WlRy9C1RRvz461gyEkv8nwa1YjtP0VdE954SpQNC5qXAErPFzNhNADZ/jE99dH7BMNpYLC4r73PKcejc0HDePmhYowICHuT9DNRM1helmWMpxfPHL9fHMFkpS27xUiSKYGSxOIqr/vteAifFeRsdabzTj67v7RmXg5Nd5FqSqipCJCfKm7xTtjDlxvTTRjo5xD04Hxszg37F8ksNmAnHLgjkFVTLZGMhEbwZfihmcDKx1QypRCSDaMVWgc1LomIV6FZpL2VgyLvRERmPbodV9reeJ6voJ5PN4NToLoBnHxZDk8RowBbUrZs7JZ1AFmToAACAASURBVDOywFgLrV3paUvkHmBRNGKlGMfw6p2Ioyt6fxEVDqB7x4yfodm3LLwcboHWX8DnxI0zJfto3ziVa0peNRTWvuFzfmacAL5fpMVwIwSt1d6TiKgu0nXIkU1kzg2wXILRG9xvmAQ8O66mhvDFMbTVAhzJJ/gUuCKCNSZghrk+cWVxhlXARgftkzTjTHGYz3ZM7URl+0r0OBZ0u+vwfigAye2ZVeR53IAYOc5yI9bXxRErOGKve3/fN0wEP4Oj5RQkHqu+d1HWvlLQJBHQbWvmRxDXOxuNWEk5sQbVjnl/4tK2hc8ENLksJITxhEp+Uvlaa6SNhSCMkw8t4WdQYBW+ktqQzziAsKB+ZZ78EMYG1KwRTczC6vainei2+4yVCG8kTQXHLScEmMqcAFAcesR6BwDaDUWi3Pt7oiapbLjXnWLIWl/tx+v3NE2AxGQDGAvWGkKItCoOajwkIN4gwhzhi0+nL7qehJA2ue6fEfUfE9I4CVEhFSvmgChI63CgvXoi48whak7XpPQrl0lE/PVqnAz6ymVJnhPWBIWE9wsSaNIQ6YzjAuir6E8pAB1zW73pqwGbPPjHf/1iDN7/D78O8drQ209o0j4D70F/S6pFJffXFxlejs+btt0xuTs6FJpIbhf9snBhZGChF2x2DdJhlqMVM6qq9RQnZR1TCFH9ueU4dwSLRZW2P8+8b24Iya8qMoi0HNRDcyzLDrMKyiK5r71ZZ8SCWM9uTNlpPUZJFbSeopryy+P4gx6z88FDJpnfEELO8UrqiITn2OnhCZgFJa8f4JNF0FPEYNlh7W4tkdVaXhGrdtRzq1X5EBY6s8Yp1BQMCBZEtr8fn7jnKCbKl69tkP94ntzp/wkcAYWlAXyTLICC26GI1i5ECNr1wTswibo1VbTkF461sN73vrYvbVwRG8AaN+Ac/VCsd67d6dSPJyRAri3yGjLRNHq2ipEfme4OLQOXRNIYwHHoE+19Bk1V3QJBjrPTXmh5fla6X6xqMIIj56LpWD9WO+RBnk1KvbNgpUjOiIyr7RXNgG8kv87cbpacI8amihgTl7bNAawpybOpElh+/xkPNrVN4+HYEjupc0nHQUiupDe8rO+kL+s4ZBSPDqBX7noPorNx71HpyIItgiuL9+eKQVZwDMQgfedzfG60RnskeszNbHQw0K3cLyeATRdZkwl75spkZ0OqoCc3gibwSwY5zFlg33NRmBgNIQelrQRcS0Dcv94XNXyJG/VvKo6Sg83E5EjEd9U1xr5+9XlnFjDlRVuTlefGSV6YI97hNKw4+Y6rG5oQ2cNyiAVE6QF63zdGBNa9MD/ftAxrfY9ux7r5XidHpJ/rZsNhL6hcmEPQ5ZFvlm6utCxnbgE/XwibmBWLW+Fiwu9BazwnyuzueC8QuQ2+3yoiCjDwmCceIZ+1aHi/3+lPfviWlVMqdjuCWy0zfsybgqJljJlnmnbocdMdkuu9y7+Vuo2ALIOs1xd0uV638uF931hr4b3mz6FAfHUjQZ67WkrzQ0FnLTjZz4mdRRfdjMV3+a9mDPR5Y603VCaskQayZoD0vDOtcrDIKoeSWicfwSZ45sZFW/wedZDnCmotIpH3yHjoDuZwP/cjVk2MT2Ff3sDP+zTnJN1R5tbU1FTqeUb2/0fsP69ruf14gw3uk/O784edphVK0WNobkQDfZHdfS+xYkxr+7U5rXLSpnDDamvpcgheCDSoXvs+Kwzv95sElzzzXcqykHVQUaU2lVEMboHeDbTdNMCFFmF7ynSeA4LdsgvRDcYkBS0Wn2dkfNpuKr/A1y9FkUvkNTmhSkX78rFvaquCE7T0mf7gBQXYQQvRXynunRRHhw9tf4wHiLxgH6LqsuuQRgQ+59hCJJqIHzPq97j3e5+f733g7kzkTLgp2ECR4c9iBKIsvpNA3djeO1S4M5vG7NgFflNlop5zj1dViXb1/sqAl56iGYBqTB2JnNXDyfeIbcmxxzqrvGAVV2tQxA4sz2DFX2OjQcjiIgftJ0nGMd6ugrUSeN2/MVby8rA9SSWTaxXb0+lnGlJbbRwjdN/HkUs4yvbmfqwzRihMmBCaGe7gUoKNZBjvV2vkCJkJfH4COGO7OW+EpLdpcIkFgL34gshRrpU0jntq9PYUFyCfp947rCTXyWkbvtCuC7E4XtwIiB3ru93AZKPUrv4leNazvfm+OFSgorBQqMXmZSX9oOzv6nuK51YrR+lBDdIL8p6GCUQWDAstRTS1AKQK8VLiHzrSCXIigm8/+Ql8zr184onyAyAHVAKxTgNT18Xd9/NSP7OKdEnBJ1AbnbCRxfIErtd63hsWh7y3tX2OHDVy2nbxqrwWZVsIZ6HKxQ6A5nXR5FLSpJ2uDRBPJPhcH6jSUF3JwWzSmIzWgloi7FE8fp5PsVz/nTvi1RyQgaaPjV9mWUByKYhlYzcnR78qbSfv/f0ZM3rvaFeHXrmBTAXws0Slnq+6F+4UN5bh/47twXtYicuRTVLP5iZqdXHZIrJxf/ULH9dHFggG9wW9OguaPHexFsa8cVX8V+xFONd14cM6pyqf71PQDadIJ9LAzB0GeoRbb6hNUnuleQQpR0L+oIRCRpruuwFjYc04Cx5UMKbT5k8VMZ5j1QZI6gM6bQVr69/T+H6MjKH3gOf4+U5ebTVzPtcXbvr7/cadjdY7uaA1NVnrrJA/8TvPx0NgvSkOAoqmhPnxvudu8Oo9kkbAZ6GhGnTdBVydoYp/7epccJMN7VgsROtZ2hSo1jdCrBC8+rWpQ1XAMi/kc7hOA1qrmKsBDxdMx15AJOnBLQHMBLNELGNDUkNaOT6cGLif58dEp2LYjq9rYQ4koDY4kVEu7SluOM9AbJqQI7LRWVsTM8Hao84cz2SetQzza9FyrLa4igRMTxNPulzaKvqiFZcDPalRlSeiKaw1FuhajTkLZnXF7ZwakaqUQtIscr+9PhI00p3vPnKZzNW49IbXjTnsUtvr4as4ZvxN96mM145g3IFsF6yq6eq5+Xl2o3+/r1+KIhfAfviJeJ8OB9F46NfpUKuD3URrcG2ux9yoiAZwr1p6UGre0xmNRfXqvvA5krbeAOdCh7L6gDJoXhe7nNfrhZ6q6XZduGcpUtMHENzkIuEw9exWjrk1VY/sfurmbZQOE2OxiObf5fg137eYEQ3ttAPicvoJwPH64IFgINK9urGKXSCL5vyZMKJJ5OAEruvaTUYFpzpg8LMVq5Ig/5gG0SN8d7ErOa3lagBkka/Hy/HOkUyhncWRY7ACkN6dtUb1jsGNWS07v1zjXBw1ZDP0FEYQnaSYCn4j5vjiNQmwuWKit43CRpQlmqMp6LzggPQGST5og0BDIf3CWoF22UaqX72zqQDdL54o5fOz7mYkDoesOMAANqpZhfHm6Rb/zMkTrntRr7W7YTmCqzpfAIM9rbWuXWSF01WC/y9fuGdlhTfnKbZjnDGc2DFX12Z7u48+BF9PNESSGxlr7QD2/B6AgjY6XihpIIVybT7XKfrrZwBZnMThmKtqWtZgoz41LXgWaDUSFm45SF7bEX/GOt/DRLcyuScClZ//av0LtUPB+3W1Dk0kK8Q3+tVa2mLZEdZqLrlpjcWuGgDJZyEbDU1xBgtCFouRLhDnyB7P8Sr0SZM628/iUZSo6rZi4rXjNes5hjfjzyw+5vKjQTDr+94V9Wk/b1VIp43aPcdOyE25dpaCRHKd+2WwJni9Lnx898ISIEw51jRqNQ7vHQk8TCDdImZ8zyYuLdX47AfUBq4X/YDDBaaJFmHAWiAwEJbTF7/Ps6w00l8xk1sqdDnRdFxYuToehs/Pz40Ej0Unhcpl7imcyrX07mmAb9UIxX4WmefaRr2bJl1lOBHdnCoW6FITODRgxMIIbg2958AYVeQvQFkoVnG9bRCTlrLC9wa6Z6O8R/MbKOIkpfJnZAFXeXHHg8pvmROf3OHntKEmSN3afrarSIaySK4Nk4w5OHlIHHDmV6aCtgtanrcHyBDIf8vi57raQSQz/nWxTeOhAB57bW492+XYxOZKcirCqYPUFGBOzMVpV/07x7nOdS1NhMtlHmDGnBMKWobtcwvZ1xUp2r76RzYBJ16LGFrY1lbck1Sh5ac5qRgC54IXawHRAQgRWigLZ6nJk1rSvt57b0Dvnd/rJyeZMXd8+/aNU7uMDxUnVRUrn7WN+OdnWlXMTzKva2mJu+8mssCLX+Trl4OTmwFQXwpZggmO7SRh+Geyr4v2fpc4CFClaE21kX8rirfnaly/qVINh7Ukx4MoypMrxYM8qaFKonSvZBwBROBO38dCjiACFELmDihAy540r9fg9ip3oHXM6UAeHgZ9JHGe438ATOJOXlCXRkudx9iCXEXdSXQLoALQlbvME9UoMR4AvN9vWGeiDdDhoXh7vbUvCvuelITiN0p2yILYvKhClHngag0yr5EAkEJy50PoAXaPz8TqWA/uUdqv5DUu54WF5O8ERTfaOL6Sx3uuYNm070KXY2se7iogiYQ1SLppmBy+pmnjEg6AXCc4PUmNwsHIUXigOIsjkYDkWptRKey0m2qRauGYiAVIUB/dWsPyAcGFIvlHRJqxj50Yyupn8yTlbMeqRGKBgwSEbF7X7pQjNk/OFzcsrQcd4Iyt078yA240wfv93gX0nBMDjp6TgsPlqwSVdj0APCcHheg/C3pOPRzqwr3pEVvY9LQaotvCYypRqKsvors1MlSOkjU56QHQ0WM/X/z8QAbW5OR/SQoaiERc6CWa4rcVWE6eIB0X8v0Iud+cJMiXxB0R+Pj42AWkiOBbCRMv204wHM2e9eGXNsQLwFxYwgTPbc+JFDlFZ+6PJPxAt6vQLx/KKpJWnqmaCgDIJrboUcc1pT5HFbFF11lrcVyZE4Tiyje7znX2uZMat7LFFtpUkmYBL/neMwbmWbrvm8CAaVqYCSY+oaZ4qWBGKrZNMAfdLHq7AASGL1z9BTitkMr2CFDc64YqzR21tfQZdvTrO8ah5MaGKJB83CaOKXQEAQRrpS3hxzdgLNyDgElSlrE00Nwx/XvY1eHrRpeGCIMq6VIRdKpYg3ZMlzQMXwgH5qT1k6ebQMiN6ZxSitAP3cOZb5Q6hhFE2yIWXOqZFczxwvI3FILv/RPQgOBFNNonHQVWwK6GuW7EKIeNXPLi6WPaqGFRFP2C3GrSbzpExpezicxLNXn0hyCrtn1upPiBkndNhx093ODy2R0IxFz04Q7sZyVWJC+Xzyw9yrn+OfW01L9YbSgliLWBlXwdZOPLszhTpO2wPJ91BiQUyOK/ps3PBSGFgvamCRpputF0qApmglbkOje636jQE/99Aw8kvQRVNVHSfE9sKhdCWSzvyU660DzjrGQcExzdRoFF1cx0u6irici4Hbj0NL8/zKn3fGf91TEX7RaPO8kOA7t5raUnezHWA/GupofPAUCbM/r0S+aQSK79CMfChKaYWEQg/otBuX8skisiHyLyX4vI3xKR/0FE/mL++T8mIv+ViPyOiPxVEbnyz1/5/7+Tf//rP+JnEJl0KuCvTsFOKXvXuHexs9baDytUE6bPTgALFuSc1FdAU1nb95jehByTZ+fE90A0QuPBf0wSufUGi7kffr06LjGUMfd1Xbi0Fk/QkH5l1xGWCUMNlxH2JyeSSYeqUya72xesBV5GD9dCjwpdiXhwCYPrLUWCHFM5XKt5n9Wya3EUzuuBLax7ImBUkBo71PiqvGUXRe9ctRfHXhGAZqeZ5s7F1SukfBdDwxFuGKGYjs2DfSJS5Z3p+V5KiDBdMafjTn6ZVgEgZxsUk5VDomGt9JNdRIWq2FCjUMqdKwQneICqowwRjACTYMRubsQ6kMb21e1TxLgOuimgD2UWpSuvhQv5XmWFE+nh7OP75C4/dtQbEH6jixPJy4K63p/UCC0Ag+2NeCtH6scy7av/ZsTxiebI6eHWkWPtewx8+uQa3vSc/jIxyXvZCz2rZ7FQFZHkxzEpFmr8TGY1BqwAjWZfAl81isUnjUzo0FNUb76fYCcnuJNnmYgPgH1fC5UiEpHTlW48j3AK7ETSxibjxXI0B+2fEBvpqklQnQmu4jy8OgAb/XryVK/rYpxKex5VLouo17muK3nGikZcb3s4V1NfSxgY8xIBi6+G+KrkjRd3megyaUVmFPddaeG2k3fRCOSMoOvzKLD9dE0EenXSrR73tPUHLQEUp230u2JDFcQ1qYuDoAOJ8rrg4+ODNC1Jf1czXP0jkV4hV/dSaDhe/YKYojeBXR2GgCjgTgpB1+/YVBYoYiufuwXTTpR/Logy1nbhQofrutCaUvDYkhLUFb0BrSt05JkRTuLCFIFJsZ8ZXq8Xp3iJhmv37U/sIG96gWPnuQocK02JYvrA2+9Ej2nRJfkMk7O94IMc1RDDvSZ+9r6BZUmBUdzjZ1kYc/W0hEJ84I6Be77hWNSevKlH4Mp3CqTnOPS8WL4BErWz6p3Ttclm6jFGpi8tvzjib5tvXDn+ed7NbNt59kRcic4fap8+ruUKp8VjMM60og+pYKzIc95wXQ1iXE2t2fAVFe/1evHZCkA9NrcWjNJfXGh4jomKdrtSy9EQFwvHyEKw4kxdNzEuXCpNxnu+McZ7n0mAHFifC2ORIjOivOMPshtOO70qNt9gni2ud11T5mLfuckVCCENZgSnTYVoPzUa4XQ3uPE9PawtmwwjOk49g0GU1ErGcvpWr/t7jHUfio3KjusEV5LXn8/ID7nJVVt8AT4AWCS9Mie593KsuLEi3ZICWPPonn7s14+hK7wB/FZE/DMAfhPAvywi/yyAfw/AX4qI3wDwuwD+XH7/nwPwu/nnfym/70e8EcGah6e4MmnxQHC3s0qNXFngRqRiMrlG4idxAweafx4wgCtze+8nKJvm9qUSbvkWs1DgMtEb8Hr9JAMht8pob6kajSxGz55xqG2vUiB5OU4e5HTyWWdyRdnt4PBy5oQ7i4VS8QPYB5CvJ/Al0CzspizEXHDJ4NNoQ1ME7z1KkiMaAXLcRDg86Rln4051WyZ6qBXww+NbtFYZi3ypEb5Hka01Gka7o7cX0dIQdDQMTEQ7opdIBNgRm59Z/Kc9rnIWIAL6mXrSJ/ZhETusnqUp6NI9JhU0IHm14bw2HJ8uqCaCDAfcYHK28Lg7TcJT4LcFjDmirqUNHidASjY/RYHZz19QUU8E7vgjMsAtRANFgUH/TxFJ54cqCA7ftvhY8LPR58l1rmcuxL50+c9xT42PTXIFaDaHxdsSkY3GVSHdcmwlIoiRzcTj+VxR/LrTmD07+IOg/mG+8qZKVBDMETyAL9fJHp+3XrviAe+XH9GCFK/wPor//K8EkPMIxppECLdgPb+nis6KP74o8DETtH6K0Y3AJ4q17db8cP1775t/dl0XJy/5eVcI0PoR51RSqs+bSNJaTOj1xQIhAH0IWZQTpWoeruvaqvd6b8+i44cJ6D0GXPx8T5yxYv37sryr/5/ZoFQM02ZfRG9FVanCvpJiv2xzH8OJXFYYZYNueL06rlfDdV3o/cJHZ0H66oKf/uQbPlTx7brw02/foV2Gj+9e3Oxm5/MCiSR3Q7tsJ0tHpE8wRcSr7Mr8NIMfr+9IKYmcSoXDUsPQe+cvu/DqF5omWNLymTCOfMMdmmfHVKBOj3TRs+zj42oJsCik0yXB1Pd4vp6j98/eiBAsUQw4xqMAYENGQOG9HMMFPgKBdho24XkvvcmOk4E/5KZi+iiOgtSJl+TWtYz1z5yyFu3BNO99nYM6H08nj6I6VZzcAtdqFP1w6Wl5xetdQkiTI+TMF0IsXqtmV9YGnFbxc8sfAnjqs9VnqetMG67AGN8DcNJpJmkpBRxceV4drAEiJ8zWZBe/VyMwVBah5eEMENSqc5e29LinA8G6xyd53s+C+omIF6Wv+NdVZCI5sCMO+nxPJ2iTHPNuHR/22s1G0QDLc738muGBj+vFIt9JtULe+yo+6xwRlGETUOvdNxCVxXD9f/0qnRBdtLh7/Pv3J+NyXEfQrELP/YNh/qivP7bIDX79Qf5vz18B4LcA/Ef55/8BgH81f/+v5P8j//5flGcm+yO+VpB7Rgsd3bukJbvmSgSP9wUkl4Ykcdvd4xZ2FMKhRnFTHqSyZambX4iVCBW+0jpMDhet2wfCjd3/nLgAipQgyVkjEtOzayLXZeHqstdZhgrESoyUwFPyd5j8j6MAR/EMyqU9bYlaVdKfcyZqyYKBvwJI7iATPzugIojHwr5O0LoG2K8XWVzVV43Hq1BgUSm4b4dIh3saay9gTcvuj2OWMQbt11LZebUrt/gI34eTxM4COXYyKXFUUTMca987bu9ZX3wcOWql+AfJOYOyYaqlGSuwi/cqHqu7ZbKte8DCZUXxI9tGM+p7ibLwXgBnZ30VIh/XxfcoNXIi0shrt6AKqAnWeu/mow46g4rsorIKq+0+kEiri+c4P7/HDweyuuuDznHzWV3jHwaY1toueCxkI/FIAdMeET0CWTWOvXdo3a/km9XP3hOE4qpnIi1ebMR6nO/z63C7jj0ccJKpqWKmn3bx94B2JgnyvJdEMF/W8e1VqKDhQiYxkHdWPLLXtw82VFYcu8OlfqIh1/VxRnDJuS4U5FkM1L+tRuFqHRbpPKLn3lv+fWvXl0LgiWCXw0TxnAH6Be+NTEZ3GFU94plEx2pldgEEVXhUQn+ibb3TFm6jLNmwPL1xI46tFf+hb6rUbgiyyMY8NIkqqHdz80Den8+8p+p7heP1em0+4HVd6PZiQfnxwuvbB16d16xdBjFu4Nr3qjV8vAw/vXjvX68XrJM33VV2w1+86JWr2a+P/BmXbVRr+cBH3iczhSrw+uh4dSP6K7KRcdXjLlNTDuXhgDyvDyi6K27wdV1Q8Jo1NTRnBrCenrRFaQPQO9dtX+0D4oEOwxiLXvKrYbzpGGAJVLAgDaK3mkJbEfSm8DlAVxkj9aedRraeA821zfx98So9QZrUx1ShWoKhoFD8FF8PH+x+Np45Dqe0bD6Lx10/Q4STVlUlFSxzNp8hzyaDjdd1XZwMxtGVlAC8nsP5aD6/TInct4anBMD12WuSUrG+6FWW+cNeHXpxU58vRZMXkl6/UelqzgFstHw3pfo1fpR4vURrqnTVqYasp2BsU9OEArMSf5brROX+FoZLLpjR4nThzWucDjCM4enpn3Glfl4J/D6u1wFaijst6UyROaNAh6IHWi6qqHjybC7mJMggaePmCNzjE60BxJ0EvvgeJFdoa/xiVe6Pwn2FFdd/C+A3APz7AP4nAL8XERXp/lcAv5a//zUA/0t+iCkivw/gTwH4P/6o1w/gy0pEonlExLhVJQs44Xi2K33cVjjuHNWz0HHuKb8aAHaBEeT2yEeH3bz4+2cJMzOTChBhe1OIiCJTGMeNpegV8oH6BYzPBVwGmUBL1HchAG1QdQx3WnsEuz/+S4VIrm+MgTkcXTtG0g7YSR/ka0kGpzrsveHzvokINk3FKy3QDhLjiHAsLEQinL5m8kopgGitQfpBv55K+Dk5RlmCFLxMIqlCD89QAUSx2vdoi6KO1age36IvQQoCFKaCMEdXsNA1IGxiesDkApQdchPD0AU4fTeVjzWWHFWvu2PJG4MLWyBC39sabetqcKH5f917S4XtnA5oI6dH2C1HikdGjdZkYk2KYSToxYscXbnPLc6iG4GgKRKZz61IY6KlQEFiQKRjIAtzvRFqiNXQtCFiQZQNUmoAEElkiPR+LFSVMwChgl0ZsKYsXGJYBvQF+j4GnzFPM2l7NYx7HPTdOK4rBAXIZisWoF83FxVPrRC6tRa00Ng06i4V+jNp7CJTgry/iC1y60ae/QrbzWad+0IdT2I95w484RhLEalCjqKMeAmqDkd9bwrUcmoQiC68xwI0oC64+utwxJQcWTVaFSHXgRb/HMDmCPP9HSpQ7x1dHD64ZUk6A/+2vhGeOdGLjY4IXhEwvRJBUUwsQCYEF1y5zICJvaXYCfDQ0ywAwBK0dqY7dZ9+uCCkEOGM5QD6RnxqDF2uKJEFiQY2Km8pbHNQmyB6nFPququRh6iSY3BQQPJ6veiUo+1M3B7F3wK5noHYvN56fpoa7pHrq4vK0oisR8bFfnEDW+8dl3JL5hLgwsNAXwBr146hMUhh+Uj+frloXNp24SQjk3EAqhTCRTheV/Lj7YNTqkvoT4pTMN+5uADusNZwA0TDXsc/PIKbpE7DSc6M+LlfW4uSmUMaY5gHYOZo7cJKrmRk/rnXhC2ijl1fWDPQO5/nJgRBXAQ+Bq7eEc7FPSu4gry1i570cMwZ+PZ6Ya6Fhk5k3+jiY8JVwU2NBVI2vp7Aykd7cV6RsWEX9VcHxoIgdlNcqP/09WX6UNOL5g0uEyotwQpKyLsoZvJVBcBc5SVf8UJyeYHh9nsvS6lf1shPfjZsLN5KpD13/HGnqFvF9lmpWEktA4E6ctjpkOONeUgcMDW8x53PYkfk1DdAatSM2hAq8GaAaG7/AtQb36s73IMA4OTkU0B7Sb8UKxY00qllkW9eHu0mgtWcnrrSMOaANYWk53Jb3FjJU54FvscXmRdrhUixWSBynT0mFz4FgFCD9YY5BW5Bn/TpaLn1FAlmOeg1rU0A0MoUy2i5JwGzF6vcrAeWAyoOabK3B/7Yrx9V5AaJoL8pIn8SwH8M4J/8hX7Kz/kSkT8P4M8DwK/+6q8yCI8Ft2Nl5TKIqpnsjSZEMoqfqWjTMdP+ZdyDFyZFK6oUcFmwjxtr4lv7eHBuFprFDvwVBKfzZqgZPDliWL4Ps8sCZke7FAuBZhzB+31DO7ffYDmsC8Yt6K+PPa6AvSFexSWL6SkLnrYkty/oWJhKlFIi8AKXTgCeXRcLS79rDK14f/+J/krebRpp+9QsArOjHROL9RTm53ur4MMUshz3fePj4wMfF0np4YGVNiITayvWmzg8vkePzhWKFz0oTAAAIABJREFUvnBBYd3wed/4uC7MRN7OnnOD+AKMgqzLLjjeyYs0rM4x/aUNQwbgB7H60Auf93tbyVkoXlcW8qsBSsX6JR2uAuQWo40gIHCvhatpir+AZhTeIB4Iv9Go3ZpCUjQ2fBB9iQEtJXEWDF2I+quwNF3J4TxrMLMwihciBi75Blnkyy19077NFB0Cn44L5HJ7Y/B7SS0sWeQip1b7gqD1i7xhOEwMYkfFvtaAKV1CVAzrEiDYRJgk505IidmvDyaFZoqowjCvSRU7NXEo9MAzwUQK1s74m4hVofRMBLUGNPlXXRBmiMGAVQKFQ/lgoftegxxZERY8rcHzGnLCcFwe6lmrAncX5s2AGMAq5P98cYpDAcweGQbQuiUCEcDVt+/r83WLS1jxynon5QBgsdYb1E9xueKG5ghSRDDGJ67GVcuq4EKHOWBTgZZJJakXRKaTsoIz/q3X/tKg2nG0qP9WYnZ/cLKVhQyLioBjQiWpB4Ooipim2jk/lgCykFaO6XwyF0xYpK0AanMl70ltpHTanIGN6n3feL1yjBs3EPw5rpt1sYt3C4GCy3cW0pnCEy31wPXx2oDAlcKdkYhhff75+ZnxhA46ugU+53tY+KTVVb43Bc9Vbb0qcV1NCrqRE9rkIOLVXFgnAvbqjMsl5Ksz4Kkl29OKOWGpnXiOzwHSr8i9nOkq8QHRv4dLO+Y9cFnDz+akoK8JZAkbueviFDE4qpbUelxW6CTP+CtH6lCOrC3Rd3UKlWOrpAFVgQv9pus67Lye9J66d1Xw76J1cv0xUmJV3P56Vtea5CCn3Vgsxxt3Fr9E93vGnM9cDT7dIZ2ccQFooaYCy59pQiR9pq1VnZ1xLxpyoBwggKXCYgxnaURRXdwByECz64tm4bm6+GgKeIPHWsx1HnuZDQJnIuOOkMA0SQAu9oTHg/eiNYW/J5cQiR4xFwSYRNAp1zo/34RAQAk8ZzguaXCwiCx7VhR9zFfSRR1mmq93zmFN98yUAIMCGoF7BJoIVAlIGgLzdk6qJraJwAiBCld8Wxes6bk9FGiJ2LfrQhsTnrHcEPDpMH1hxKA9rh/g4sd+/ULfHRG/B+C3AfwZAH9SZDOA/xEAfyd//3cA/KMAkH//JwD8nz/ntf5yRPzpiPjTv/Irv4IVgT8Y7y/CGQmOPLRU7WsQ84i5qQIzCwtJmyMx3UlBIvDxGAGKsZviCID8nADgk4hqcV9KGe4+IWlTtkCvyykBD6XQSEgir1Hy6/WCJpwuXbBmwPWBRJH4yLE6HKIvUGCwEHOk56Xj+7Q6K7/Xn70/+V6DClr43EKte9KAWvp1RFvCIL/W2D+7EhYiOVlW227IxapxYI193dPk2i6S9q3RMivHNKpt8zsVQnQ2gokLILL6GOmLZHeYQd0a7xuDFNXbFqn8lCoqnBvFklNIxDE5fpqoghTqT1eOhRuQw1vK5xDaDGNREDcksnhdef/5XsZ8Q0AhCyRRKmm8p9pyY1dgFU0gr0ErA34IihlM2orBZKHL4cq63rBG7pzlEoUVVPivnglv8vrTnPxONwFapgnYeM1cMSr5XFAcQD6UaoPC0bvBBLii5YrbtIRTNhaq6Yec7gFiwFKKiCSpFbWwYI/yNEdK943pC9+P+4sYiXZEkwI+wfa8XGsgwILz+nixk093gWMnd7yo9xgtiATt8xqpvpZjIv9sPApd3HwxEcYPN0iO6TL+nMUnErs4jBQWjRW5ZZDrlwsdgjh83ju5ugNNwV3wwOZyM3HQ4eWekxMVN6yRyKgU39lokyaPJRh2FOvbdk0DFg6NQzfBAxl90jqePOD9mkHbIVUlNx2LyF3X/VwhFCsM93t+uZ6bS1fUKyzA+YyUjeMelz7eAzLylMitfD4rYX5O8qTLZ7b4u0/eXvmVlk+69YZmsc+4A6ilAEexLV/us0Pw8fEdAGwK2o4LepZn1PUGsP1MkWP3V++QrXIfmxKx1qLNoOpOpsWdL4qHBV/v2XypHoHPpqjkOLv+7FBW+LovA67rA9f1AVNHtyvPO7CUqLJIJJ+7AU0hnrkFVLHX53ahG8J7jcd4nte5XQ/efXy1JNzPxUj3jnC813tzRatofb/fFFbVBCnvKZcvHdFW3eOI2DZTc0640got7AcNnJwFMDVBUlXaqK11aIhJ/6PH8KE/VR6s1/R14kflwrIqK1oObTQnVD23DZ4YI0I7TBc2nzWWL4rKlf61Syg0qwUIIcDtc9vjsaEFea+xEKDdZXlje9Otz6gm4gnOcAFLagvSzpPOO2OLpN1982Qj1nZfWuEYvsAFVJEUR8brPZ1L3VLd55qQiKYdnY9DvxNHYDBmKeByQ3MJheQ2RJNsnkMxAll78X5F0GPcQ9B6xwS1Eode+MSX//ivH+Ou8A8lggsR+QbgXwLwt8Fi91/Lb/vXAfyn+fv/LP8f+fd/PX7Eu7rvG+3qWwSxwjGVXQo3sXxuCxOARsU1Inpu/apkvNXcWegV/9TM4Kn6bTmyq8Re31f8VIh/CTbu5OJq8nvGGGgdR6ABIsAR5ABFCF49zZxTIduQ1mhZqLgD2Du0nUkwlx4AvjlM73FjgT6ugG6DZoAHoYRY5UpQAbzGXrHoKIDFdahNWgo8Do+4VO23E6X6zl67eYBzSLx5WXJG6a01vp6cwjbXEKG1trcsqYJISI56en9xU5OmaCV9Eh1cl6va4Cki4OGqVaUNLUUUe/tcFkx7pJwodQXSal5UOc4hTyGtW1q5EyT/WzpUDuK3kyF4gBVpZo7I4oTvqY5TFZvQEwx2UMqlIiIGD8EabxbMZpCkKOzlD+Amv/uevE6JIJBXR//osaqoPYUO6jlPnq4aYOGcWuSYTBq5UhKOSwxInqGBrh+xDYWPU0JTxZU/qxmN++u6vN88UyWucBzj+tvnXu0Yy/d2u7LfqSQ7/atwqhIegFy/WYnqq6tBIfZVOHjMtL7R3CLEKcycE5+fnxgrfaCLZ1yTjmdjlOd4Dt9UjELqntzBCI5YmfzGPn/uwBy+hZR0neBkpTjmPo/3NIuxYzzflGPDCNnobegF12MtVOd/LSbQetYrfj0XstBTmv/1MGBlMVoUowKgfe7PWcLVHQ+BXUiymTnFWBWLpaxXbY9lF7KL//qcM/mCdT2rsY4sREI4eVvhZxtY8lVZgHKiVzzJeh1px3GlpgxdSCf7uL4Wmc9C4Xx/Ngd6xHZSLhPJyVVpp6jXRpeNSNvIaqrkIYyMwzG+rgQNes+17GcRx36+3b+8N4Ac3BCDxMKVTiH0a7/w7Sc/RbcLH9cLvRu+XS9craMZyEHubHALxOGEkJ/p1a99j4pXX/x+4Ajd6izX+6vnjpzPo/Gg6OwsIql/s318634/Yms1BNsxpNlB2uM0OIX4Povmej70gTw+G5d6bp882Cd3H3is261CftXEIdfQ+3FL2YX8o8CsfBU+mVuQntSSPwuneaoCmXZp2djf6xTb4ryeHlCh88caE5gsUE9BfprY2qoqkSLavIe0gCP3frzv/RkjAuEdTTs8ef6tXfv5IAglW1jqTi0Qhcb+9Vo7tpf0vRzfL24knZkr4dWEJzdaF+ZIRL6/AMiJPXFv8Ei09Dx5/2ppBpMtfpEy98cgub8K4LdF5L8D8N8A+GsR8Z8D+LcB/AUR+R2Qc/tX8vv/CoA/lX/+FwD8Oz/mjfDwB9YY++bgBmL4VgWPMWDS8J5vyJIvq3q5fOGMPVoqMvnAsMDZ9jqh+OnHt0QFYzsYTBDpI79SYGH8u7ngwzeX6p6D/DtJawvp+e8FyARtOCrSShaqiqFPKzCuiPCF9E8szi73PE/IVsBT9csil4UFcsSMzc85gfeI6czSoBz0VK1D7DFZSCIDUXavtXpRskiCUCBmvW3kdW8Qk4aXUWhlJrAcK4vSbqTuTUSctX9l0g/hJrFHAep5yHwudsXTaVP0DI56DliZeF+tp0sENrpVStRK8BVkmrJBmJNWPGYvvkesLFTTV+ORYAAG0unI56oQtuSiLhAFWwOCKnjpkxnruBqYGaJVEMoGKjv92wU+ScSv7UFjOd7zTRNyxyNwr21hI1UMN/K0ZvJvd4FTwtRXx5iOqcBYQcg2+Z0Dngh0rs4m2wYjg2dEbNur+r1I8pqVzR+R6mN5Vff1834nL1W/OJ/IClxqiSboTpiVKHdAA5NWFd0SsScKlWRer9f+vhX0KCHtQPEyFnA+z9rj2gZliTzSpcI3ikyT/IP2bH58FStroJaIuHteSvpvb4qMnNW6AHI72cwRtW0uc8UsEVq0yaJVki5LrYGnCbzCYGjacxhEJ5SN3N4DsbCbc3fnWvEHL/X23MyVHteGuXULtSCjLIrWWhA9/roNghh8/2U9xee/FpRwVfPmQgeAyTMlQvvHZ3GiyEQYkbQWQdO+n+cvRUo7Nmm8nkbhb6KsVUxVQS+ws345x/IhSeNox2Ltid5WY1xOCNwyaQlK6NcGPov2aorEdHP0AUdtMWShd8Q/9blKiHjM8osD6l/id5293bAk/SmW42UNl1A0qQr85OMb/m/q3mbnlmTJElpm7h57n0w1DHiBHtWkmcCgEYNGgjkzGNXzNeonYAISSIgxLSZINUD1EH3z2xHuZsZgmXnElzfrVl5R3SRbSuXJk9/P3hHh7mbL1k/Xhv/49QOQhXbQL1Y77QbLzaYcJFpO4wrAKRS1+OcMtHDAdNNxNPfOKn57P/JzZzHX7qbLhBOAsffdtu3AqqjhGUuaxhZhiqRPcnKcVW/HhxLDPYrhume1HxSgUq4DhaTXNe29c6/ELdatqdbzFUbOcyGZz2aoxLrVqBcSrUr/4igxslLApRmrXAUzgBT53tftaAfsYkx4+O1PvdaiSA+x12pdu2pqa0JjwULYgntG7cVFk6k1Ek3ReuDyCc33c83b7u/5LNbarnO39Tc/T4ojXXMyG0wsPMbPkEZxG4DNlTeJDXwECIaEM+FRwXO3pvgl7Ism3/b5Z4Ml+P2vf5STGxH/J4D/7Df+/v8G8C9/4+8/AP77v+I9AHHnnGvvOZq96DE4+AFPI2oXKzCOAbGbPL6uC5II4HLnhSfrmubr442WNhu9BcwXzmlwo3vBwiLKuXyjjM2BeHVc5lg+0fQFWxOq9LKMnsrVJnC/0JAiGgWAdvumliLeWZb6ycg/d4OAZuW021Jo0IDcPS1lsmtZZohcsEN0F+L1wM850aMDenfeVSTUgq/D+zgOfK4Lr6Z7vCF5DyQ3FN8Icno4QrAiMPrBWMxBvbZGWtsARGIaxxOAwsbtT/jksFXYgyBoeTNIkvcIxLo5nGsttE4kTXuDnSdiF+/ciMcYsERwC4UJLf5j7LhaFZpujzEQCMYGN92FDhZjoM0mVDrOtfDqFUwQ5DBpg7qTdyv0ql2RTYYGZpyQCFzTIZ1obpeO1oFwMKENzkagK5A8t46GEMuMWF47d0eshaYBtQYEBTfXum1ivNAnEHFw4bMfEojrQoQlzzRpDx8jrSJdJ6bfo3yOqQMOilpiLog6mhyYTncGBA9Y5usxYEFdyCPUO7mO1wy78Falub+FA3Y3JtMWFsAoSKWop9T1m670GJHuAieImuReQ+rRI5r0pbw/RdK5PIC4MPTA9flQTJmJbl8l3gjA20OQigf/VhjKYcj31shLpAdzHhoLgHSgCSKR2/CAy20jlCoQ2Jq7AVRVeHg28CedEAbFNC4BtwsiB1YEjlSRm/Nw+TjTiQptIYd+5mTDOOpUAYw0K7OAKB/GJjSWd2eIjtmEOvfK1zhwLQagwEh5CZsw0O5JQbTWc1rFw5sNaoklRZJrmuKiNjr8WrBGu6odlx2BaFRMt0bUtYQ8tYeNQY5sKCBOnUR4QLThdOoPeldAnNxYpX9sNdWKwAXHqw1cVgLWh4WV3JZvHA/zVvni1Kj3Bks6k45UjkfA0HYwCwKwYFOwgjxIDZC3msWHRaD1jnXNzeHUpDDR8isLDKHceaZtYVcAmZAWlohgTYeEhZhy5TIpzhkt7qYorx54wJpD3Lbt1vKZSPoduet+e6urJGdWVuoXbnFnCZdJZWIYQbkMXZGOACkMhWJb+VX6JTx2OIT0Dhj3FH0gv1twGzXdyEmnOa0/c40eeZ8tY3r3WZCg1dEZEEVeN8+GaRlJnAjjcupjquDf+4AKxBlyo0lNeSLDd1ONfdZFBMbxwrUWEI4FIrbDgWiyOdl1HpoEugFTDNIbwm6ecmttZwPUBMXSr72KTt1nvmT6XEDawVS8uKclhsh9muACi/gBr7P0VyFN1FxkeSYCCGk/6gET20h98aYDPCMDpFxWs2o+eb47EAnLOQRt0P5ViyMsAzKDIGEH5kyNQK7R7fKTzeZ3Sdxffv0hEs8C9FVrXTFXoLVBr1AkAoPsND1whUNn4FwT1hrsTwuvH2+c10XjdbBWoPAqY339hBwH7bWSHrA8gBRIeG44cy3ocZA60dMwXIgyWG4KWiT6cExx4Jxor8EHvb2w7ESDI5S+h+XFWPZbIUqFYTpHaGT3HAGXBmLQBs+FsDwFA8lALJ5TvUQEP70Pfi7c44iuCiu0ITk1mvzgH68XAFqQKYfOGK1z/A7G+3JkU6IAFvRNFPoiYgk4dPSNTABK9S6AtW5u4hOlGK1huoPOw3mPsjMrjmN9LQsmJN/qg94H1ANzrm/ioSriq+BBODwC3WKP0EJ0I7iyEinJhgEgCb/ueeTYcQUPdwZ1UKCBJjC7mDwVNFtfbgjQaqw2JjfAzWGHQWbZOf2A+clDPxqWBwILMwQt7VO4yXCk3YRKVhHFZd8dBwDsFCGOoem6gVAKAZdBddBJAIVWDKykShzHgLljZtMDAMupqIYvIGgO72YYOTpbRbmQTF1jLOBGCQuNjQiMPnA5VeRD6AIh+J5ZXnGg/CyK1jpsGbQ4kY+xLf0o+90oCUWnfNbWRq6rQDVk4e6G1js8uD60Z7ITGq6iNzVlo2TgGiy0o0Zoje4a+bDluhU2JsD+/rw5e/1xDMom14O84/KHfT67NQ0aTeEhmE6hZIPCXNA7m93Po3CzHOGulQ2oXRzB5PXgfeJaCiHVg3vsBUWDCe+N5qgwkHzNCMyTsZ1u5A6bGcwjfXgFAUFvbTfRdWiXd7G0vukEZkTePSc8R6YIKvhzDuW1c2UCowMciYbvkfWccwvFakzKZ47BEIwmX0BvqDh4wKFpmyciGC64wtH12EE3HBff9k0igmUsCznAohuOu2c9IVh5Nk0jaj+DlmUlQnQjaPEakqEPthX3MNvx2IXIFV0FvVF/IvksxtzAgEP32aH9Fk1xvs/0NITiUNA2saVIXm9EbsfpKiB8ILb9XkTAp2H0ThpPExatga3VKPcJldvejns7nxtVuoN47U8qUG/YvuQC+qvG98Lk2WAgud9Pmh1pOth8a04XfRe+hQyLkM/86+nPO/kXHQpJgSeCz3GPp3Uhn5eZep3alyqEqq7jE9XdtL2c3pKWwGS1WoOA7GIQylQvqOwpb1mDraBwfk3Q/SDtvLb1ZzChzt3R/d5zNZsLfgZOme97zlCneo9Vz/TWt7CufO75nikQ3Yg1L1aupkh9FP/r6GN724roLvIjDJ7vQWsKzRud+A0LZyY3XhQ3ekdI0O1BFGtNuA2IdITe9qKbNrOR3N+P5f51MrV/j68xBk2Qs9qvlLAafZYoqRBJApdMHtsj2rwAyzOUANgLrw6v5UbUVGmObEGKgq+1rVsKFYqg/dRzfPRJMdhyYyyjNKzr5Dhwflj05AYTfh8GdVhf15WJTJkaJiwcytJpZgQggC18CPE9fntaI9UYDLh5wwAf8vISbUrBRI2b+f3k/bQme7OiQT6TYkZTaLt5zcfR71CAMPo65u87BukMfBERbS3HcskfbCIYjfSSig8u5O+56X87cNaC+9pK0uUMm9go0Eb+s4iU2B3v0Qc/Sx6M+xmSeBwefDYuW3vTJMeL42TzEqM4kdJFIV99VpFM2UkrMN7jKtAzxW5mEEMfG6lpKa7xNQFtcNw58SzsSCFZzunozZW8G4Cy3uJIh56VRdFY1ySqNc/HKDj2aGuMgXN+dmLRnDNtje7NRBX3IfO0b6rxcW44O9++zOQF36J5y2Req0FSUk82vzm/p/i1tQbq+zcaV8VjPjtPjmjdW8mmdtotcAKw/YSBfAaUz+PRXxypeQAyNvWj3svmci+jsjcL/Ov6fOPoFjc+5KYz8P3E456ma8taOwXsSYWIoPp5muGc/JqZ92fxodrr1lZsSkcE/6nYanOgPGXroAzjvmNz3ebsdkdBF5J9XbdveF3b58FevLx6z10Z4qC9eIy2RWLS5XHfb/T+yd1trUEs3TCKV683H7vSE7vmKFT7vpb1HjYHFKDTjJM2Us9gnR+e9oJ131iUpqWZz63DKEScXt2JRNkElCLEvCg57bK9L4kkRxssdp/3F7iTnuo91fNb4imbN0XHzBiwk/sev65s+mzfF7OJHhQ3ijLamAUggyEkC4tqvCv8qD2ayDpjag313vea9RR0w2MDTfV9m3Iksu8F/C6I9gTMb7/Z53O11wVScJifD74w2n2eSGAnmz2LnTp7KhlvN/4POkpRo75NgfRRgAlho3LhAHxPnvbelM9dnTFVwN+0D1ISWiKaYwwW0EmJKBoI9ybbZ3btx/VZWuO0oEKXImgL1rVt+owkF3afw8kt5s+ULZSs31G10NH7RlufdUVds10X6E1ngZKKV0Vz8cX5z4O/LjdHX1oKyB/P+d5f6v7ZgoBo/F4PCbzwulMQLy0A3LSUAlCqDhORf3JO7r/3lwD4spl8rcUc+QAiN82weyHUYgl0zK8PR1yPixDZjQ/RtLkxfK6LG0qKGzw7rfLEfb1ee2xEEUelrQRMHRFELqPpLtIkFJrj/pBUGIukkfmt3jZjoXHNz95YikNWm2o9BOW7N3Oj2w9gDGhvO5igTKX74KaCXJzHcVAc1A6o8M+t02tztOOBgqUYRAPHjzeQZte9BcbR0BE4xhtompGGitGUCUKjQbSjNab8aEvooDf6NhpFV6+miPgQAYJDctGj5UgQN3+r935vknkQVae6uaWRCH/+vzqI6uBtwvSoO09c8fFKxGLDY9Jxzu9uAaWW3Vw4pO9yBFYABtpqbUW80quy+Kdmk0lyCiKpUjZjlg2MIozF+gzAROHRcBxvALo51tockSr1nYIjNx90rxWhNUyFZohkzGTEjqp+KoZ310+2FxNlkOEV7d407hFxHVZIjvDdTdfPK1ugcrzYvp7FqRX+ecp90NemD03HlN7gD49Xx61Kr9e3QiFi863beO2uPsppJG5OV0TsJre+biXvj2peKrvnpMeoTwoevjJec3kA1rAmEe6V4orruohDVrS2TwrwImDpAlIb+zdLoYelUhUVLBoNpdB3qynCzKbB7ijr3KXNU+WcJvCtkRNXfHpDYC56zdp0fK4LCzUOV0w3TEm+cwiWsVlaTu9gM1JebK5bcR94mOrf0wTLQh6eBSgyfjgortzBCH3czWuQg/4aA4F5h2rk+nrSmtqgkHOnhuWUqPV7rxRpWbjc/NXSItS+UocwkEI2efCsIxDO4qcAiI676HR39BCck79rP0cim/9cn41xsYxsZ/w2f2dvN/K5kcNGRw021bKbx3IDcPddrF1uW5TVPSOPRRDKgIqj0UFBq7B9xNVKaxnEUZO8LHhFH1Ore6QNl+0+8Sww6748/Wsr1Kjet6OmSikQTGN/7um3pVwBGw0sml7jSNHv2OP2nmc3hd90L9HeoO2VupFKOePkSVrfTZCI7EajK38OqdKxebH1DD/riWpqeDJVAU1tDDnGsWlOFOwmfSIWC7P83XUtNSdXpRXpvUPSDlB727zvcpY5OoAwNMGO+r3gd4gCAHT9VgfVMz/N9l5X+18VpdNs3yfgnvRsusQ+I+T+f7nn7H1nck95TrnofKH7HkeUiNHw46CosqYA+9o0uippq32W+440habQvEvP5kKgaNufu8TvSBDmr3n9QegK2EkdRxPAJ45OMYk5uTa9NZxz7up+5kFboxJNJLW1QWRrztsnTwXn9YWWbgKXTwzp++BZayESyYEqk1MiEAjMmZvVIbDr2lYwkg88D4DYKJ9A6GloCx4cN2mQZ7WFYEWHeIw/ACRZ/uaXonf81Gi31FfbnCKiDbQxy7oBDflUhkOPgM7YB0whGz+UvpJAOj70A2IcKbfW4MFRPPWggR+j70Oh3iMJ5Td3ay3SHSIC1/zC6C/YvFgoakPExfSXSIqBOQ4lL2flKK+u6ZUKSgqCstjpTGt5ZTe6uXtFg8gi7sJimEIuVIPxs0RgtCMPP9DZIhHOQpVqwcMdIzjaJZrKrPsViogFjQYzBx0HFO6RSC4HNtQ10iN2rc8WeVwpPHQjPUFCYes2vI8I2GdxYzDGOdZBveD4MbgW6nBurWdxwpGyNsV1XhQIPcZons8whVSyxS8qnHY8E4mkkac1nYbiETNDIzhVQRZvox8QmY+i96aiXMVz1HIIaEDczUqhuQ5AUzS1bZNEMNdd0FfT+kSLIt/v3myDPGaLDFHBd8TbIbh8MXjDDKHJj8yozCqI+iGYF2kWCCqCVei7ab7gunDOVDqbwTVw5IZLs30FvOMKoqHk0n0/dJ4oJN9gbOrHq3VEOCk87ogAdAIeVPQD5Aaq5DhUgZa0BpUbZQ3Lkb/caUSyNCdGfKbDubbXZegHtrE6OXhr/9keDcOajNrcYRuNiZTcewQrvvBjvIEHEixBrrqIJD1LAGEi1woA0WHrA2kschClcWCR6OB1DQ2IG0b7GWecQCh8O6ZMCEpQnHG3n7nRrqforoqFacY1nih38T1VhHZTi8+29p4BLHyuyrLPIyBl40Z76Hs9gVSAKgigCm1ju2nUtdnWdkp9gVns8bU+EFtIjrqbcN/JwpX7Rk/GcyJyo2ds6+1JezfIbEbcFF7NQvqEGPwbAAAgAElEQVSyD1EsSSvOml7qzeMMr+nO7XTC/Y5Tp1xuvB41OnejN32mX9ZIvahFIo0cUVIweS2awETQGuluvSk6DtL2wtEAiCz00SAenJaG4DjevPYKiNx7w2sc2/avJde6RNZVIBaXX0TQ5Z4iFS2HLHgW/C2FvZqfQxOxdXc2dT31CY2ibeTEregx1XQUatmCHP338cqwl0ZuuzNAouhcISAPXxhiUrzaJzixbdp+hbDfCPh3fcO36UzcLhHVANTPZ/BGUg9rXzB6+hsCI7iHIa+RmD/s4NhUv8ZI6gMe+1/sa1B2YpdPSDasdwKio5WgnJUVpi+E9mJS/K7XHwLJBcAqXullihG47MSKhdfrxQ0g2EGFc4TPLqryoEHu0gqK0AoOR45SlSNMC2ayt1AYjPnSRjXkaJlyVMVcpeqAY9TzPAHcqUz1YkffMI3drQgjcE9fG/WJmORcChfWawxUZG49cJvDCt6UV8tc7iCF4JWdOKmQOSY2dteHDKgYjgy/LlT3GAIEPe8knMhiCxaDSsNwJr8tpor1tm1K+JAF+tD0wUsEyCY8Efc5J1GPubDmiRDFuoiOVfvU2wshjSEXhcKX9Ri+86+eB0FE4PP57LEN7ZV4WNX45bqYIDOXYxkX24x7bPocG0YkNcSJUh09OXtBPjjH/UzQI2/adiSmRWykRDKzXXF3u5FuEi2v/3V9aI8GZWMmgnnRTaPS+0QTdUq0H0qup0NvyyR3dBBBBHCP+tftXwgnml3OITU2La9E9eSg5chYwaasnreizZjZRpBL+Y/eUE96cTjnPFP4pagzDE76xx7h+U0dqYO27gkt0mwLwAoxuzZ//lEIIg9Uvf0tZ411w1PAya+rA7h+3jOPPcxhElh51G2Xlbx+K5XkYQw24LM4M6TAgNw7yranFMzFeav79bQkq8aixpKISZ5qoiKqd/zuaYvIP4CmDDYoQ3uO3GXfo20vl99fjQv9PYGZpu7LBM37HqsDjF/tCgQM461p05eCzUfE6D78ArsZfKJ5TVLotRZCHF0ZglH2hi7AFcY9OZh6Z/ME5r3n8qEaRAuT/1vIG9xxKMGCahTn+tqoWjWlfJ456Xse1my+Ocb3pAeUxaACMMkpmwTORfu1aWuPgB3ASnqY5/3kz8xn06mpiDCc57mBifUtWCb3o/PalIbvtCTf+1Nxyp9oGddyFjApcITqtorjRKvhyqnS8ntqUROFGbYt7Nj4c0JQ6KumAwFXBdPv6vpJAg39GLn+46YrgfuettvHuagutT4cSKpD7M96U+ocbRApJeJPPYC2BkRH78VH5b29UWDPaSwoohv8WXVmmk96FneFped+5Oc4kirTsi4oYGXTN6ppr4Y5r4GFQ8ZDZCUANPakg3sT9vX03NOgTH8TERZ6xaHNPc1TkPcc/Uu7v6YivOGxbclEY9cjdZ9rz6t7XGen2X3vywd871OPxrsKwNo/WWRH8oVjTw6Ll120j2oY6F+eVFLczyA89lTkOfGos6QmskBOirQjtOFcXxAwIEade9NNfwCy+MDebH/H6w+B5ALgQ568Fi5ufFM6H73jtMVCTxUDRLDCHcsMUqiW3yEJzGF3NEuxRC5Qcn8dhUiOVL3WSDpE8KfPB+/j2KpKzTFrPTxdyXNyBPwxdlyrUAsKX8h14TjbpuE1ONIPQXqf1mH8nY9mkopjD3gS1Icyd1yVCVLHaOhoyYE7YDZppyS0EhnS0HqDaS6eabQFQ24uvQGd728Xgu0mknskr6bGHQHMFVB1nHkNw6sTHxzjg+jeXLcaV5puf9jnmEypLMzxMsfJdajaXN/J8w6IStoqkWenwrFOU2GYR/IDRelIoarsx/NACNzoYI2zPDeZmV04eUJEhYc2iFIAJ+L74DJbRKDAxdqFCTH8XCxmZx7GitywOzv8mJOI48UOmcgmXSFSjLy5p0+0v4r/lV0/Hh29P/hWALZbwu7wrY6x5IsvoLcGZONSI9Sy1KI2U78duHVtiodW9/A4XixghBHHp+XniptaU0Xwc7RFziroOFBUo0WHh+LF2bQtmBiimI/kLf748px8NJ8q+5570AUitNJ2yqqKworLDdqIkFLcSjFl5FqEOUVIKlCLbBCzAEHwhkE5cst1r40IhqaPMd8lv25zz7IIUQV8ciRLEVOOYPNFhLxvtKa+l40I7nubjgUzDO9DIVGFij32lwHktX3yMovuo6o4c32qsBhA10Skbj5/PZdR0xz3jdwrsihXahkWWKjw93EUfvotnB0Pft/Mw7CJkcPeAAnj5ElYgEnk3uT67ZD2RIvrEN2cX2HzCDOI8sAdOcJtrdF1RJJ7mZ/xk/ZN0tLhAG0Xtqo3EkawLq2q5oS3lrSZwGXYTYtl9G0V5/pYt9zrqtAkglrcxro/ZwoBS1sg0rb3+76H8t3judV7b207Lm5ajN/7wm4o4ztXtn6uCNfJc9RdPHl3ht+oKjB0+y3XWuZeT1vQ3g54nN8oO7QPLKcYuqF0CEK/P2ujv9i0r2vHIIso0BQ+lUmP4XBt6PpG+MVrpEzJI+rNz8MIbGAk4AERIBRNHFciiu1xPlXjLtwQ+N5z8uZ5b+VXFIjNv06Oet2Vaqw3pUIUOGRzZUU4eTOnPVwhwMUdr3299stnobr3xMd/Fziw75/ef3+vD0H0RMZrPWW8svgjwAMV/pF85fx9Q0iXWu44Rs/1kuis3uEolnvb94k1J2r7cygAD7wP0iPr2urjutXnqLrw977+EEiuCsVC15rb060emEo0qX+LBmQpDhlYxsPJFbyhe6OkP6aIcJMKhX0udgB5KNVG83QBKARgTkPv711gEdHN8aTZTqqxtDGD3H6gACBpReaLhyqLxDykojar2IlXIwVkRMsStXRLdSsXUR0IPRoMgvf7QB9EfWLxoXy1F10QdNACS28urIgwFnAcGGNgHG9YOK51IcLpOxkN8zN3caNQ+IrdUQbIVfvlvLDSgxbKHcpXcXtu0roIE558GZZdeI276CJXjKVPIaQOpOqXfE9XSURbviEhlUhXnEYRwbsruiuwFOZMrnpavmijoryuc/k3FuLXRn90pPScqEKVqULVhTPEoq7pk2dGu7FEjNJSB8Au2mfmzHPj1URugFiBeVlOJ27u3nMsFWaw5K7VFAP4HorwHPXHo3j8MY7kc/OzjDFw2trXvZNUjdbfCBkPpM33s78FlLEeHOYOj5Wc7QZoR88u/xsq+GtkFgDANb5M4NMRixOXWvdEKO8NvZAdk/trGEecTUy+v52ohKQsNfp4WhaWAaKT8ECzwAHw2XY6A1TqURUkEgFJ3jRy41dVQBrMgRBn/nt+Zk6GHOFpweMlSFsc4c0UbbWOOCd6T0S2kV9a97y4fE9u782bZjrYtLWFRUCNVpPigfY9UStYRBWiWc9urR8W5Lr5dtwbS9h6/46IDGppA00V73Hc4lEomg6qqZV7MEIZExtEY4ZKTu2KF8n3crSAdAfQIemYUL6rFCJxfx49/U5HTQb8fl+PhpAFWgk/2xZaqupOZlLIjliuSdUM7uXXmjiNE6IqQqnX+B5QUb7uNicuu2iZVFO7DAq5rmuvtf38G6lApCQon5PLUghq/O+5tg5jrQVM7hMh2Gdlrfvas+s5Ga1vm83ijwI3N75G4XVv6/7WpIyNPS3jek0G081AAbjSdqoHfebb6I+1DYyjGq2ABxHF3jttzqRG/skXdtJFQh9TLuRkJAyOW7ArWZhKOIbek1CCRIFwrpuGwEh+awjga8KXZAiOoCMwoBg6sBA7xROgVkcVGNLxap3hSaq08VOKw7oq3seRQrkswB5UiJHn1h414W6Kfoxjc6xfuUZbazeynBM+ETqnFA/3SCS8ADHes/t5r7W8m2I8qF/9fvaqiK8mtwR79f+PPr41wlX8RrunJWOMja5GkO5S08LaW6gF4RlbAmggaUJ+o8L0pB55DTlVGzkxree6q2KMF3463oCt//+5K0QQuezaUoTEh2XkGGePZ4w8J5cJDMFQjvTE4lYeBlERKLvg0z8MeeiCGRSIufvuSGfchuohFRMIuF88ZOfc48U6/Ik8Tixw1GhOekGkUKMeHlXFuuY9KiDL95vSsaD4z6LwbqaXo/aGJTTelkZ+lIiQJWaO8/Ph6LgRfVWhQntV8QN20HzgAIBhEwPcADANAtro2AK+5sJlHHWjRgTTdxFdXbssFsQUNzWoHBS8YOG0iyb1TpHeDKasOQLaBj5r7hHQ5USV55yYc+JzfcF85lh54Von4MF7JjzQf/75572gapRYIpwVC2gOEQNiAfPC+3hRMORlwdP2wUBHDY69pbU9Ri7UaiNc8K04bh3o/UAp5+uePIWEANAbc7dnFiXnnDtgoTaXay2Os2JCD9+HTsgd5/wsWhdoy7TUt+gI4Air6B43zSA3mDQ9Z9HbIO2FcBa43BApEjRdABZsfRAxeRA1pmINUYx+FzpNx35fTKMDYnBSUcI5ivywEalY93V6NgeAArKABlwx9+euAt3kFrZEIlYvaRsRFiHqMM1whm0BXm306I2er5EHZCguWwjcCV/TUkkej0TCagrzUKuNe4yRyCnjdYc2HLmFdukMb8mN3ONEH2CqleVIGTxQLg985oJpmbwHIz7jdrD4VnjY93AMd/+meK89aUhy+UXQDjYnQxqGNPSjQRsFjy15r6P1Xfi9Rk/6Df/++0jzbliIUgFDgKP1jIftmaZHioDAsSQw7YKBz64J1dNmgdHkcRgmKiMDbzkY+hG2i1hkyxkRiIdf65q+r9NuSHbxdI+FW67tkQp/aQ1TAtZY2I7Xnfi19J6uMNmJ++mcX3u0XGcRGyqOa2cYfllMpDyX4etz4ZwZdINCqics1n42jVYQEK8RcoMXjcZvpL5GtK01eBOKhqG7MNln16NwKUCAu37QXi1ftQZVKWyuMb2n2GqL1oTi2XI6uNKpgj8TOKAMjMFCtHtKU40Tud/f6X1zGmxFUpV4NtazDctzhQNWWNwpZy3H2dXga3MI+qbdTXg2hcH9BEAUAohFh47WoEqfcoAi5BAAcuGdSHmT1FgY6ZOOCXdSiTgxWmidXOQSbRXwtRuofA4LWR+DRbT2hnfaNf4pnW8IMq0dWlGCP/rts4AdCYLx2XvGZd+gxvPeA2xAGI+baP1oDwrF7RyDfG6eNJr6/cvvSGWbK//+bvYiqZKKjLRefJ7rvdfPLEHx0cdelzWRqkK8nrWjlZsK9/VVjXBTekqvDHbSgb+ixv1j0BVEyE/VB+fH5oQ5R98aQrRRFbbI06UvHakNR+/kw3ginyFoY2BEZIRn8v0QNCiPDp8ODPYDlagmEOjrhYoArpsCgLC/WQoPkEryK7tEg7d7fNhFN+80guIQi6Q1SEBbx3XNb+PorjcpnQ+r0sw/+U05BYeH47oW3u80lV4BwDHdIe1WwR+tw2xyPL03fYrhRBpsXRthW+KMlA2DgnY0XfvD7w/0EJ4ky3OBANNOmKby2AAIrZQQgfPrQ8U00rf1oPPAHa9oGTH8gvvEoYIZDWKRApRXve09tv36+kLZXtXo7PPLF/To2fVlEaWAyLERFNJaatO+R9lIRKdGnYVSVSFHIsEBCEVabt9XlhrH10VTWAG4d6yLCXJ+XbQvAs3iNRpSHnUXYu7whf18euT7lbJ1u6MV4RRrBIASc0CIhoU5zedFcDyKp4hAPwYuvwAbyRuWRGg5xLYAZAhiJQfSDSs5dBGxg1Ugt3I3gv7CEQK3wAIPJgUV4QDRjHJioJfsXSx/Ph8aymc0M+fQku4P2FOU1phuRXtPbvSVsmaPuM3k4cDU4XY7dBTtgJQUul1MZ9RrHTRDOkwcCEEEknpiaGhAZ5DIS3lAld1VIUfFNZ8g97QhKI5qkoEClhtyiv6yMIFzRL6ckaTvwchmV9+qa+AegT65pnsUuQuUpJMg0I43qTg1sj4GyuYOFmjq6Eoh45wz/YZ5CDfjvjY6+bH2QEfr32a2JzLunqKelRMqFrj05c2Uumy6QwUaNydzUy5QjU+OrAMITXuvBRxd4cGAiaIg8fvL3u+mWwDYFoM1Oh+DdoJF33Fn0yrttqKqac6hLIir4eSaPwG8YMKWoiVPWnvDmc82lmEcDTYjrQfHFhB6GLTdx2y5M9S1i6KlBacCqo0sETACmI2KkJLTyPsllSgwlM/yng49XBy21SRYOIUKYt0FkplBBvdkjbIMqxEzGxKRgKTCfYxBWoYKusWmvTnyLAXP0WexxSjtCwApIa3l73VHqKJpz8FqesgHBdsoVLL39CoXuCgQdwqZl7+yCAChy0E87D+hmBHoi0JMaHBK6rk3+y06q2AEswk3hbZsrjY9iCJLkQNw0AvWOZFZCLzGIGiRdUxNMsrtRYI1whTZZz3pHnl/Snxc1DF57PnZBFYS3VOzUkh51UwbxVVN/WPyvyuqGpHaiNi0tAJ+IjnhvI98RmIRsOracC0jBTPX1pwTP15vAnu413G976KdmgTUb71Cvf/6/5unK5JHTE6s0z4tjFQHM4MkHdHC/yrh2R+iyAWQ6SkcWXfpsFj40RSnX8lvmwgdcBDper/fCANN1BdTaH68XjAj72Y5Fd/jSDGVCwKWRv0X2nEglIXqqzO6V47X7WuXnVON+lY4NG5uEjq9VHtr6F3xNaluDwRRxWDX2dvNE3OwKGriTNIJ8s0WAs0SFuuNyMdieloE09bORYQN7ggNzJljXEw0YbdkIZhzwUZgBvPaa4yoRh7ulAkb3MAuOxHBeF4VgfkJ6R3TPMVmsSMWzQzRAuZ0FiA3+J1jYkMkslobHcDNdeaDvK6ZhX8mLhk5iWfGCVJgsfa4ypcBSjsRqGD6LajQHF8NUSwgEVke/CKCVwhMyF9uncV6bZiqCm8KsRSfTF73KoiaZNqaCEI65jrJBw0FlDRFinw6oE6SvBuW60bsRejEseBokUpxPShssl+4kNHJEcuNjWbc93gNUab1sQ/iiIzCzc2TzRj//n0ccF8wabAKDEkLF4pwAOhEk4GVo1pFINoAnNzT1hr0Im9XWoCPJNXsrXW4pHUegNVkx2GrBpNM5215VCPV2sQqba9QgCZMmBPlhAVmUDkw1BPhrmdMs5BmAEuN3jia5IEjKPpRx1zZxAWTmmwJ4DxI1vrCaC9c14XxftG9wBsMLEjQ6Dk7tGEVCpIIoIXDJ2ktbfRv4hRa2bFYhWSYQ+4j/UFZoR8xud2iyLHc2JSQok/VKLtG6U/EpvYSSWVyvbcauXfQeUVE8OP15ntQAOj48XptVGpikVqiih8xWIT8nPczgObAR7IgDlpUUSj02qCBgLQZK7RyOcQVTTJAI11JgCAn1gEEoO44pGEipyhuEGVDPcDkOPUD3hfMPBPCTkAca3GkfIMBpN2iKXAtFm4RkEcCH1SxnbxV8VKit2wKG1O9Og/S93HQE1f5XA2w8fE10fqLjdIAwgvJ4v1ZnwUcna4FYliSVky4G0OikPU8sBm1iK3Cb8FngRzemtzQOccWp3qHNjbxbSCC7zeCIqydSJd5f2VPKKB2pXUGEVhSbjxuDj6LYdIEQgRHgGfZupKmwMK0qArV6DXVbSV1zYkmLLRfKrjm5Fr12HSYa10AlMKzWFDXFIsRKVQ4ItJxyE6odoIvDk5xk44hOc0iMJRZEkq6Hwsz8j6nEwwRZHBPGMQapDc2COD1nCksdxBR1nAW8CHQx7h/T+KcvGzJ61SgiEhnSIkKmhD46O24a4Cc4KkqmjoinNaAQs9p5HkWoEDDwVRM7W078TwpbMhrUOeXmeFoB/cg4blZCNmTnjPD0QVoGakbFnt6tZKKQ7952VML7uWChkAf/Ey7KM2mqvbDck34tfNDUQ9CA0cMNiBQQFdOBrPo1bTFG4wEPyRBO7Rva//3vP4wRS43ARYu1zq5eauiyYB9jIdSrsZj0JaljOpFBC3j+4h8Cd6VfjQvLBFIbxklaLQmQaZ4jQNrKtSd9jZwiAw0oV1ZFTxmBgw6MPRjbLiddAPDq4zOkfGwSCP6g9GzcKIlXZiWZRUkUT6kB7v/7re1lIGjAHq7Ei2VEKKSvcGuidaSjO4OS8RxXoY2WBBLTMjosP7GnMaNJd/jcqIvETPRi4Z/9zXx6i+Y16gMWOYIHUBQeb7N/4Mj+bKtqgVYKM/z4ea4JJWeOTKei6OnMpB2lIekcTQkaZ0TtIEDGpp0XMlt/ToZwtEbxxtUvS9ej3C03IxFBD55qAuIyltQ/am943NdW9zhD7uV5Q5xx0qYmJuA0uePZ2ciSql8L5P3TX+hhZd44MyQDw2lBQoU0yayv+ZIJot/IlA3sk9KRY4Tk8ZTKNWNsBHxiAho/r2D16ShQxNRLuuwEtx5+hO6AZ///V/9VWv2/B1fU4zh9vizAZj/+f+I8hle3onYuWEKEGsSXU8farhkOg8P46gJDRTNBVOIkrud6O2FuSYAyfUCWEyOQKcnAtNyfOzoaHANNDhgCuSzGN4S0YjNdQOwk7J2TOYKiioaeehmE6rAqw1AGYE6Dpq9D+HYrTh1M5F8tDI4j/To5sTmyYPda6jfJIX6004d05F7z42m7FHgYPMzRsdpDjXkQZWcSMvgmGDhqk3xUjD+1ZETA9K4pDPmeVmg94G1Tjo6LOCV1WQ1xq0zzGZdlmieEJlRFmF2spCRFNXMAIaRfoVwaD9Q8ckeHCnPiO2oUAp8SEClQxR4CQAXLEgCFN8P4eKq1/79nDBU8brSSk8RgAD9oM1bNEB1QJMP2pEF6qCtEe0uD0DnzS0XijVFsvCGseRoLDFqukTzfRZII5G3d0alHn0kP7csJ9k8OLIpW07RXgCzJhjBNRAZkHGtEyqAQNG0aFxIUZIAZugquIzJg8tvwalFg8D2ftqT0rEuQzSgj47oBCckWFC20XGeJ17jIG0nOfStceLnhhTzpvivD/j6cO06pxCXCd6Z2BZhCOe1f+6z3K87UeC4eG3j5me7AC2nB2FCetSs/ZP7TUvxdUPDtAlPmmBNTZ50nSp0mzAlTxo5yXQSEHQZAGg5R1qTY7rm++HES+IWXQ0B0Mueq8b4tHMUcoiQK5VzQKM3MEQQdqL3F0ovUUBCaw1oROlVB+OCEaTnmEMbkV6sia4Dsy26nGhHO0qwd6PRfWg2rYGhHQ4FfO1rXDQzWKK012RQTDZ5x3HAI2PIs5HRJljogHy4tnugJp1sUDnFH41JaKMTQAihreLvff1hilxuArE7iJlCBQAYPwbKakRwOyFsHosS9Rjj8XHcNsdxPNCvGmepczFSHRyQfqSStcN93mM5M3hukMWz9SRcdyW3s6cCeid6CbZn3jkvNFG8x+AYV4AeLHTdbY+Wmcd+eyQuN4wAJn7l6Zmbc40llk8spQsDR18D7gtqKxG7BjGB25lcMAE+cytyLzf8/OqJlLb0Kz0favXsnpex3fYFRAdwixmKq1zcsycnrg7onohqKAtJbUTYiGCycKZlUqA1dp3XOtGkA6JYy779PlWqcT15merpehABQ2fksAZkASPHeC5GNW2+PzPLJRXfUsS2q0O+d3UiTnNOyHHALm4m7gYRTxufB8qW4zYV4PP5UDE9gferIay8RnPcq4CvC+P1hqcX5+iMP3Yk4T4Vvgp2+eXdtQn+eYhfWID5vgcMv+ImGXEfCFBF00ygkWMLmv7mb/8ef/ev//leQs///vX/+0uvv/nbvwcA/N2//ud/9uf6GVG8u9XQXx2OhR1lDLpVuAAxV9KSWPQM4UjZwoiCZ7rXsgu9H/jl8ws3eFkMxzBFRMeKhZ//r//2d71/4M83xud0LLOv4L/6bwB7dOf/5f8CIAWAK5HmVOCjNay85mzQHDPWFri5LwTnTnuyNG1hHOMW4GUReUjHsjv8oNCeojyMMdAkEW/tnD6FAO3YUbwhCd2ifMXzect41p6TDnJBAaSwUSE4F0e8tT8bDFjY9l+APxAa4oafNbne8kCXYOxz0w6DwIWNuGxHCEDHD2iNc41eq6KGWETwlwNtCJB0rFAiYFvtnntH8SbXLip59iA5jESWaTMJST2EBbpQv9FDoJrWbY37v7hDNFOtBu/f0IFjdKLMUUU0i5EuHaOR4lP0jtEUMRrmdXE0jk6k0BYuB4vdLCRYHHGETrQm0A9S+i4JSDC6taZeNfXpCVSMVglbnT70xSUXwZIc+Vu50/Dpl5gI/3MvVWl0roGlx3imTUaAxXLxL4XhDgxoAiKtZKhtEMw1cfSi/M3cM9O9wsqXW6DquB0iSnhMx4XlipZFuwdpAlnNkRsqnMCtrDHcnOd/+l/33rejEAC8ImOoW8vpxZ0Upume8RoHLDhRQzXD4aQIYaGb8Fq3StATHIMTPPcKNsiJXqKc9UwOkA5RNmpQusVUaI6qctagnGjC2ESXHaeIYGSao3eCOg2CyEY7VCA64REY6JsO8KSkHQncFfDTlYJs7QJNRUJkI1m1WJg/itrY1CFPh5LWuSZ8GUZTXPaYeIVjOkWjnC4C4QtHf21LvWl/jeysWtg/wEulQ3Fn2jcR4OiEgMJS5d/wakzd2kih3GpyquHpieiyNrrFxLMy6M5FnwtbgujWZ030g7y4mZnOK4s8ODciqm8zLng6PlPSOL9SR5hSUl9rZhDjiNnd0Zw/04OcNahuX73IRalO02eFJPleoZJFdlIozAwtsL06t09kELkuArgkwvl1nhtlvD7nPe52+lGaGTxqEyUHuEjuJQaqzsoNPIQfBSzazasuzoy748vmHqNuL7/s9H1+cF5fAICvzwcmluIvQfjaIqJpH5znBfMAhFZfEI6ilgLn/GV39fkgAWoIX7e/qRnMTsafolDAdNKwQuACUKqwP2tuMQA9We+fP9eJUMFnfmA28afP175GxQP9XCdmWDpukGrw6oLPabh88mcE4C64ZsCTIhGtQ4WhGO4LRx84+mvzk2aJBxMRv2gAACAASURBVIKo8FMIudYi8im+FdThkkKZtfnK9X3ujkiU6Nejn7/527/fxWn992/9+/e8fl0Y7+9Vg3liwbYQ0fC1wIIgUwTFO9zILfVcT2s5pi30eEGk8V7Vs319ICFY14Rg4HNOmDpOP/GLzT9737/1GX/P63l9fv3nemmOMu9s+duXuDiddPPIBmn5VsGXdU/tIbW2w8hFLEGjiGBFIyUj+a7luhHB4Iy1GPF8flXiIlHFHomsKVJNThHa07uSY1sCBXwPRNPqEIXGth8y455FgS121DCApEJNOCjSIt+8DlJOUwBGkQeIusOJ5oU6Zrq6FOe0eLnuvm2lugIuDvSBaQGLtq34js51T8T02vZMqoB4coSVHHPyOTteo+F9KN694edXxxgN/9FPL/z8Y+Cf/TjwPngOvQ9+7atca7rix6vhNQQ//6DH+/vo+PEiqv4aDUdnwd8CGDqIMqsg5kWqCRog9Hv3mBgJRlg+Lw7ck0QVUqVcsRYgftNjKka5CiKO1AXuF0w4cWht7IkAR+hjgxuApj90Jlb1u9VrSdPjZKnSvoCuzoJOKWDsPek95bnqt1tNcWLr/fHZUeA4II3Xiz7H6fbTaF8pIjD/7DNo5V59lJ82JSMEjwqoqM+D1MVgPf4e2Rzent5dG4s0KQEjqZKctNhujNZaWDXSV0l3jRT+BXVG0hQG+nFLA6l70ASS+L5C6Ktb529N6IaUrzd5/NAO1+L1s9g0m+hK33KVE4KFIaQrNiH97+gjaT1IWoVhNK4TOhgF3r2hD4Iq74OuElU0V7LgnlpU4AmrhT1lvJtZTm1bdyy7ct+hO1bU3qCS1xIoyMDRt2Aemumf2r+FN2lOMX7v649R5Iog4oMlBs0uqPgjPCdoCM5cd8Zd1kZlxrGIp4fmnHkYzgDQcV03UiiNIpLTeLjS5kJxRgAu+Hy4cEQ1R6SGthxqFFSEKOZ182GbsbusQymsY3483x87xCML5+u60o8TuOzC6Rd8Ldhl+VAvji6NG66Z4TzvUADgLmZoJ3UbSV+Lh8B15bg8D/1SONbBVRGCEUEiOQBox3LdG0OJzZ6j9/p3/W4iimsrSMuFYc6TD3GOFzt4TSWAc177s5zzAwMf4mkXdFsGAZh/wvrKkRcazEf+bH4/F0wk0qrwxZjZvRFqh9tIz9lOkUMq55cbrhytRvKY3a/sYxzXOnfx8CwwWECxkJ2XMe51Oq6LfsTuvg3l63ksexWOklngNlGEMYHN52T/puT8XdMYarFSDmYHR65OLt5OZyq7q8fhIE1hQtszc3zjcjJaUrnZCW1aXNmsIGhzxWKZW0Ghrc/itP78RGZ/b1H4DyHAo1FAgsYGb31dEPA6z4/Dpu/76jOxUg+c89ppcZ95QRT4fE3MaSyI50XV8tcHIg3XaUzv8vj2nv7fFvH/EKpd37vStaF8Tx10ZKln6/qct9dtHhznpE3ceS2YJxdZFOc5ETA6uijSPswRGJjwjVaeV4Y75MH8uS7U6K8fYx/4yyeefpNseDK+OJ+p4oCrkjpQfL36egVH5FsdD6Tgjfd1pNgqXHIUyca0qE4jx+AeF5ZdTIxyB2bgnIbL2eSvycKslcNCPFwEEqkF6CajAfhpDCnIAld7p56iDmA9oMXp1QAao4IBHsrHq+M1WJy93z+hdxYjvFaK1gYYaf7KmPOBn376CX2AxezxA71lnG7YdqBQZRBL6J0uVUX7/VIsC7rMrEzLsgMWyEbcNvocmRq2RV5Sntt82Zz7TKzrBacobgVBpT3ZyVedL/Vnz3F0TQ8lAEvEb8WtP6h/to1YYyE28joUN/O5ZxVdpNwbSmBb1oohoF0hSOsDOm353BExsWzQ0zoCUg2RPOgEYShf8zrDAKCLMg3Nb79fitH4LiLsDobIqdc0o4gS1Aux4b5T61qkJaUHDmnb7ioeSXzl7LGCQRi8xran13AnnQnJj37aVgr9u9EbQQzwfUU7YNE2ms212hJxvq97g5CP3QRHOqqUU8VrtD3hTuLcfU9z8lQaGO332Vie6hEBRwVtGN+fKq5rJT/tjuVuwjXK+HDFtCAq6w0whnk9aws3JszxmcrGulD839x9f/v1xyhys7BsAizhofS5zp3fjUhLpKGwLCg9VnZVspOvruvieKalcXgm+dTiPb8uIAa7/GBXaYs+f3PF9u6LZTg/F2IpvL9xQjGFYwcmnDGSlp32hZU813igZYVEXNfaXpPn/CBccJlgWUDauFOHIhXysbCM6Trv8cZad4561AMd9yF2lbl6cskAFqsmtCW6kAVYeoZWhyhR2dW+ieKbS5rosIKc4CpwAYeL45fPBwC+8VDPVN7ailvla+TZ1vj8PM+7MK97Jbe11HKDa6NCPpRjVQDLTqg2uiWA/15T8JmLm7Uh4zgbzstgduFzXbB1Qnv56wrCgIZ3ulxgG6bfIq5bsf78p1AHXru+XRE+6yRKnd6ahUwUp7pVITCpmibNQjCgeI8XjjZoXzPuiEhTxzonsy2hCG/p68oJwccXViWgAdtpohopgNfyWifCaCNUqTMWVx52uUVk80T7G/5VFbK/VfD9Fp3h97yelIW95IWKbXjFnFLNa7g9Yd0Ma5245sTnPLGMI1URxS+eaLYA2qlOX5bc6nBoO+AeCPjj+f2nK+L/sf9vFrgmhWxCSfZueFvSqiwcC5m8NS9y/UPQdaC1DgN9pqHcL8wCsTKeNXK9WABCoWg76F35y+cL16JlIQWj+VwKcDvNMC47rA76su0qn+FH+lbcASBsdIFrnVgOigSV5Irax4A8qOReT9tTHEm/KDpacCy9jEUrJx+30C4iGBLibHZEJF09+Ln586i1cBVgBBsM91S6O45MswzY/h74hQhjwW8LY5Qln2aSFxFeFmMsiMrHtJpmlmZpZQaBjM7EOQHGqwPF83Si8GM0dCF/vq5t0dA86RMRkY0Bpy+WvFuirrelXAiFfvUqdL/ETU+uJJBOEfnMlZgTSNEXyEX1B/pevE4Jg80L7qClU0uU3gDXgcsD5yToUOYz5F0SSW8iOOL2ZC6rrfv+Glpyw5/vWdPZoUsnnWADLgIDC7bz/IUIbMZH3yh/BUnd1LH9HLZGMXtLG1EpMGfhWYQDvOi196+1IEFBmMvgFCDRRgmHLNqLaQDT6Z3sqQPYISlBn+6nt3lk4yih8CxgqzgvdJMFd2wq4/bVzv8vGNByVlL5lhZYyDSAfPYsnzmm9j0pbzUtQuM+QS9rQJHPk1C8P1rfFpsiScHKfbaLwuNK0bwwfpybMxB5hsoB4uwGXwvXWjvJcqWvPoJ85ghaja5whoZo+SX/xe332+sPw8ldiylTaoKQxRHWogDC5gmXgQjsTbiSlIp/xvx6oiUt2rbkYvVPp4UQQyDQMRDgRVVRXFiQJnh1qo+nXdDeMcVg87yFCqopXguYMWu+L8UZHPt7OKCBZbSFIU2cD6eUBVCk/ZjTCkwEiMsgR8c5L7xahzpFUPO88P7p2OML+KJNk1Dd+36/Kayw+0BZi9Yo51pMTrkW0O5DA/mzNNI1IovfM2zH58aiahce26Jm0xVyzFCob8tu190RCwgY5qKtyPL0uBRgrgnppXAlp2YuboKhtC8Jc4zjjXk5cJIyUAQvzz9HLsTRO+ZcaI3pS4r0MRYnMTJHaL7o0asiTLwC7+150v/4gzMREIddvjnb7g5NX9TaIDki5PP6SeVwNQ9dC9kKREvFfXatTUlv6UPpWgCD7ESkiWkDhxIhMbvQWk/hxyTPiv7giGDTdPSGr6+v7fXcDxL9n6M57W9gOb1rGyC+0JK72Nudb49I/8fQb8Xer1+/VRT+Q6+/9LX13zY5Cvz4ArxjiOBjE6/kzIWvPbZiutaBsIDJhTVB9bQ4ZC5Mn6Q5LMcKQ3OBJhUikmdWBcKzkAV+m3f8lwr7f+xz7+8NBeJKfjG5fhK6xbJbUJj9BlEy0jIEg2N6nzjarXAOEKFayzYS3loKHCMAL075uN1C0mjdxTNMQyn8ccPphpH0LXPg6+sLRwbxAMWPdIQC0z77UJzhWNM2V09rfJqjUREq+JFTDTw4w7W2NnXGJ3o7tiVgrLo2hUJyrxkZpCAKnOeZxRL5zBqcqEmqyJsCYZHc2AbPRDhzpm7R87vD8jo2SvcxBq3VOjjhc2dT+hQZ15+Rn30ZuZ4zXSMAwE3xmQtHoyvQbvqz2HP5rjivnxWiRKwlcFGByZ8X5IN6TnUAh7qjdeoCaqrnCX5s5CqbkvJ0LboM7dEszyM6ykiOtstfdnNpI+CNKDDKiUIZDABPEElpTeXXBIFch1va8LWWz+fN66zzVIL2W+FENOs6HL1v+9DLk16gCvGGpgb3tp87+ugmHTA5vt5IG6i0ulpv7guaIFpPG82jHXChF7tC0KGwwA08Pe6Ru0N63sMq8kBqxwo6IkE7tTJlBzrIKa7QCQWbJFXJuHGKDKWRdkBebyHPDmTRe1lZx5GjChGKn+F020AHMBFLgN7RhaK4SD0PZa2K3stujqLpjVgjKSjSCTTitg8MHft6tNbgwXhuF6ZNjkSSRzvgYBIpn4mMB1aKewW2gbgDTITsx4HzM+mSpQ1dHQCdQ+rAV+lpM8dERvHAX8PKle/jkv9vXv/pv/gX8W/+zf+AOU+89Ejid3UXnhww+bbJlEVYoYQOokPlJbrW2kbfHMWnx2b+3C4KBK1B7LEJX2mEbMKAB9EOeHZAcqtzyW/n2KbroF9kI3rzOthFNskRmZPnefQXtI10S0gSeHXz7uiNFAtxvuejdSyduyMjT7fQC8ncaPp8Xon8iQTJ9euCR0OTht4bmnL82/uxRzL6HJfpIylFFS2IQJbdSRV5ZwheypHN0IaPk4ZRYwSHAd62Sh1IoVhaoSIClp3+SjSOJtiCDxZ6FadzgfGkmUZmThsrN0RrkCSyiwhEHU0PuK0UCQSkpe2Wr80h4nN0C1G0t0dHenfxazmajp2u1HokF/k7bcRAAcq1Jl4HxQI72UUVPh1DB4n9vfNebdEAlfcWjmsysY5o7gJjJjgKdVNAKiIzk5DMULnv7p7xq7GRkI0o20SLAXRnCl9r9MQVSUNtfvZ/97/9da4K/xQv+Zf/E/50XkS7pm2BpnmOG4FE5xo6brQXSs9jpiKlUDNH9EQJQC6cKhX3wdG6qeM/+bv/7j/Y59P/4n/efzZXjI5EaR7JYckLvwWBt/gRoTf6maKqoiVEMA3r/f4Bi5nFc/6yoIUYXw7tA10F79drH1pqFHn6Mqa8pWq+9w612HZJVdhRLErRCwr5Bb4X6XIr3r/ZBj1SqYiG5fVRxeWL1lBI5E24XsLIt6+J1dMy6InWhWTsspPXWw0ngJympLjGg1xP8U1xkDBEI5r9PkhPYwOyYKIY0vFrD9KyQ9R9ZOpGdQ2kJrAYZdHU0zVgI/jtFsLVtaix/kwrRQdolWax+dz1eZ7F9aZmwfb5RlF00snAvyvayHqgu3uil1/HaVrcTUv+fQSFUBXGsj/1o0Gr57ZiX+EOaQObY1mTw4crz2j3vmtlU+O+edoSRHE9wzOW+/5+IKN2lShwhOxiViTSerC0B/Px3GSghq+9V4rm/hmKpQSOqgFyAOu6uCeDTYHkGc/i6w4fqXOj7u1+j1UrPBB7BspMAl0SUDkoL4tbL6HqeT7UFSeq31orO3D+HiMnfS3/9nvrHH82Z/W6n8H8HPWzgG39WIVvIe1FKXHP9V7uFkKHqr2fuacOirx4yYXS0HC54UiKVP3s+pk8m6nJKotIeozzOqxs8kRkxyj/V//1f4P/49/+299V6f4hkNwAGEvbmcfVhOIRqMCaQMFKXofCzwVfdB9YTyNsZbb9aELO0fEDZtcDNYm90F/9B6ZPhAXa+gBjkOO0uCkhOn70QSJ8cOx3ZMqGg7YethYjcpVWXqaCOA2vVN0rMqbQTjg6fuoHviaFaJJd3BmO1gNh5SAQML9oxg7BKbTXIqJz8CBID8d5BQSMhT1jJvLYMC/HgMBWJp0JsObESnJ4cXRFngluAgRHJpHCHVeFykAsAxowYzENyoxkcGenCHCkxoUcsFRarxzR0m+WHNbWOmZ6H4bYPkDVq6hxohzLYDD8NF50vChP20VbN3Oiuj0TXHo0rMXR/B7BC9OuxhikNAiRd4nMcxcW2iYNEROug1HKdYhKqbFZ0AMUaVzXJw/dhoZMwur0QP1ppAF/CuQk+HtbU0ADGrGVsbYefDYeKTtcRHDAcEGXwoOIpNmJsAXtI5GzsiOKVIJnFG3a7/TWuHlDwJwDoXWaMQnMfWI0QU+nhr/29eNf/a9McMvDqhTJa1EwV9n1hdBEBGRw87quC+u8IE6x2BgtTcppVbPWYqRpa2jhON0oCrSJDnr0HgZcOSqGULTS0rLGw3CFoZng/Toga/0HLXDrOryPFxHBTp68/oqXynkgbfLUOs44cWij0vw4AKPPtolDxalgBlXrowuuxcagj36n5OFGofog+r184QNN1wkBYjJGGQrYhCu9xP00hAS+5sI/+/lnmH8gUNDGryPUM2Qi9t4h+bvqMKdBvqe9YQOjwTkdKxEmnEmIWImgZXPYhIUvwMnHHl0DOBPp7TlN240qaA15XSliTDBkNBYnvlKN3jSjx3OsC0100rGCEazTfHNVv/zD5kENkIYh/JprUsysoXBZGTTS0YLetgCdWFqu6wiCC2hEhkmn5DhZQiG2MH3hymhbcc/QEtCxwR5WWDlFQ42WwyAamLnur4te7XV9mCDmu/EFsKlAYwwsu0NGItfoaC253MoCyjgujvQ9VQDLKGLsyuAkj9vLWaqQxIO6ByDmYjMaFBN1IYpsZkCQ3jUUQANiLYh0ItNe1l+3OwaLSwrntB3c17KpIQenbdQVVpMTQwStq/gIalqpOS770KMYssE0ANCDU5QW9KlwCWgCjJbTyT8r/JZtekNRUnrvXP9JJxJRtOb7HjKdjGcq9IDLldPO4iMP9LEQrgAWwol46v9D3du83NZt+UG/Mcacc+3nnPdyLZUEg1oaidEkgpYVIhX8iAh+gA1Jw2DPlg1B7PkfCLYEG7EpSBoSsFEQJRiMxvhBICgGScAEk0A0JlWVpLj1nmevNecYw8ZvzLWfc29h3TcgvG64VN33nvc5z957rbnG+H32jjmDGpKcCNH67BJZz3IO+vbKTl4CLWaMbzNuv4e7ow1DxGvoVeu3nKNXYc2+9yQV1rmwUB9OxiSLwVWrrPoQDNhXgQHUDfP927A7p5d6W4U2Irpmgpwdbccsqr+e9T/l63sx5CJBGnY0UhRrYZRRItdCa4oVJ8SBx2MgTscKFO3MB0MsZxtNlOYlE6bcos/zBFTvaKi1LqDRADZaQwZpZliHKdt32AZkgGW5nh3uPKC++IU3pRlqD40GY/PTWnwwyIRqxyRvBwjbWs41Ge6sSro9AM8nMseNNhpeJo+1Ft7e3gBQd4ps0HCoJObMckwaWnvgefFi9DXRjDq+TEcXAcq5/z6f0GGQSf3x1hatGXwABssliDI5ZpzU1RijnaTaRhg0rbBqtWpvA2vt7b0QiOQC00S5BDgAKHJySDUTIpMCRHWRT2duX3ptoFUAEOHoR+N3YI6JBdWO0d4gc0KMOZb94OfYytgx58TEhbSOWI7HYLD8MQae71flW1J/R1S4nObK97krHF/6scb0Bw9AFzNSg+5VBJETIvOAmmFeE3rsaLzjZdYwBRYRdROl3jwXoIYos01PwSVbe5WQqnoeySgWEWa0WlUfpwf68SDyHIxp83vo10L5Wj3UGLuT6fjx15/71f8Av+df/Xn8J3/k/8B//H/9Jvyff+Mv48/9wr/z1Z8RGHwyPYLxN2CGq1BPuFGTHdgOFZgLsGo4F0Ef1HmpMp9xXo6dc20P0uzugef5RLMD3bh4yjJIUyCJUDczaAimlk7e6X4eo8MjoMfxt300ffs7fhGtP3A9n0Rat9knN0qhaAdb4jaa1lqDOE2VAIfQXk2Km1nijULEf5emtJIyNBuYV8mEwuv82dISoqqeBg3qeUWUS40wauy5KoS/BlHKmxaWB9CyWviqehPO9BYl6zMXa1p/7csXjMac2FSBlzGU7VFyhx7f6Q+FAE6vDOm2W+44PDFasR6ewvcQxXRd4WjBmCNNXvsLwerYDCZPCLX1S14aS765eNXN7iFwLhpbUeT+Rb9AF/0qfQJg4UI4XfeijRIyQzVJOtQBx4U0SrEyDJqCy59AJ47lnjX0kj4moxY1OBZVnglxIlJbOpDOXGCJBIQ/PwodDUmWZAC3EWonCW2GMGYgzCGb1tXKp5WXFpf3OksPojwjNB29UPYsBJllStRm+9pDe33MeH13WoUP6VxoWdHOM/e6rkr1IKu2ksyqb4DFhOh9CkwT0hUhil7nXAavR//oVfmApu/3pNKYF1+lHQQMKFVh3DXPnwaB2oDHO4GJOw5M4OvCssbyEjDSkKjwTl0CpgSsFlOU/nyB9xNLjSgXSH9FNL4YZyLaXMTlzgyWTEx/MdIIVPScoJvAk5GpyylNNFGEV2RcxXcmFCsDC/w8GxqZi5JhEkkGpQViZDtUkagK6S1Nj8XrweO+HvZ1woV6S37ifnbt+z0qYWH5/q74HagZuhJsgdDEauPrGD8t0IKItaAL7lIZ/h0ckCFkpm59PiZa6/iwP/2Gr+/FkJuofEKviJAKixYwh05iIc2Yo5l5H3R+UQLg8yQSpAYvykrrap9zAvXgeT6fvLByAZ4Y4xPmoj7GZJsBPrQMSXJTF6lQ+YAniybO68KQhmwKTMbfjLfKnowE7AFPR9cDc73j6YlHbxjllE8z0u/paNahbogG+FqYkrDlyG54HAeHx9I9UUMuOKTBitj2DLaVSaIPhUlj1JYS2XLQger3kwnc/jRxXSVqP5+wcRCPE0OeDjnIX+x/f8W6Kf5Vv8ecF47jDec60WHQCrXOxU1V+4s6dXFIGNpQ+HlVbAqwluPNOua1cLQDQKJ1HsTSpFyiNBd1awi58Na+KW1TIBtpkLmqZaVrGXKA4+3tRhvxoCbwGAOSwPHomOdC6wMqfMDYfRC8agaZJWk34h2u1DTZbhtrdx/4Nq/1xkP48eCCsuICPiAcrHhtdODfCwcHQmtA04Nmy9UhWuRjJg6xmyYk6stFwkQwHpQg+FImhICxOl60b2Si1bU9OvOU9zDw8fVP/mu/G+sd+J2/ZUH/6rfIP/8XgV/4+s9sVuSWCSgzp+meZX7t/iwAPgSyMOvtpE4A2g0phlyJcdB8tCVLdtdI/hARZDlgHddkLrIByNbYRmUc9AKJH44fwHU/5CeG2le/+49++x9GbweiNbQvv4x/79/6Z/Ff/C/An/8Lfwn5p/65r/7s6G9wZy7yRGBIKe2DhqZYDnWB6oBKVRJP5jpGFOXb+M8gKLoybrlCPCnZSdPS8XL4CVFgOqBRwfpaTYsHsr5PN4VK0IcwGnItVha3AVsCaUT6E4FzUdvonrAl8OTgp/W55mJLYMSCySCDFiXnT4XnhLnC5RXCv+OaUkDGB/vhKF898PdDPj5Q0pnJLFdRGAzncnR4yQoKpbUaRjLpzVjOIbikDzsLmG2PvPd3ooBEaZABUryZOGNV/m9Ry0VXW2tACNa8alAksMGf2eBZC5UoAMc5EyxUJK0uIrhi0g6gilwX2hi1bLGEQ8Qwg3mp6bzfoQKtNJ1Zyx+EEUtEiIX1wJr3wq7yAj9MBjXBWiULBfYsXxhFs6eg5D0oQEMLXcvST4LyL61yEnDAV2UpSFNDS36GKah2rWKA0muoFvjyYq3IyoTyL9z10FTiNSaMNK3vwZBrwRoTDKyKeFKcAFG+ioJuVL+kKpTXDHgNmyJWzYEBq1WDUCGvRzUOwyGLiw0FzMh0ODvp+exT6mDXWmSegnIK5QDAYd9I0zNXgNnekFfz123kEsFub9uI75wTozVolnEwDGKCNSvyzy8ycPnKl53pNawD12Js2HQ+i5qWobs12KRSsNe/R7is3fIEPtd29JnD9ACKaVFV1snXZ3Kf5yDhodU+KnXeb4aFcipGn70MjVmsbKWcZL7YK7zkNi8pEncI8YSURHBViyrARVVKU71189/l9b0YcoHaVo35iTFJx6kyd++KxOPxKO3shUyipM/rAjb1MifUhc0hIAIcSUpfm+F5nRhj4N0pcqaej0hLHzQxiQQkqyziA+rg7mgpONSYzVroBWucAoFgD/01ccFxtANR9Nrzer4OxzKFSQLP63k7ruc58dbLL9WAwESU8SuIC8MTpLqaAGR+Sj8rQAbCF3p1gy8FbEf4YMEEuK6JBFtlfDIVoY2OZg3P5ztsDLa3AKTZuiEmm9pQN3vXQZMbeMP7ShDAfIcyvhYSjmGKy4N06XLoYaWPVMgizaPdMHrH+/s7fnC88VA4hO1PBSCYKpa3+4EmBqgkFA+kCQ2GrSFk4fSJY3QOCsJMx113mrYgJeAPD0Sy8KNpxzgMyy+YdPQKcgcAhN3D8goe+reOd7GU49rUfMlm7lIMO1AhqXXAOYPuhRnAm/Ix0ZfWLqotp2JnGIkDoPRIABetJnqbAj4iWmQGJg4lGiPgthwJRsrVYrjAhp2dQdrF8P5j92JOmt1+7h/9B/GX/sRfx+ef/wUA/+HXf2Zv85XScVNYpljTITmh/SD7AEBhd/60rqi4Gi6f57xgTeCpkKCDuZnV9UVKvyVwrYA2hVoAl0N9L0OveLVmB9qhVX7geObAr759wduH3/3x+CGsCeZaWMffjf/oD/0Z/P4//l9jzXd8PQ4DYcyvRZUDZL4aFllmoFx8kgO4gSjnasLfbzuzxW6qfB/aCoE8mKSCtTA9IKWHFQAYii8r8aYHI6GS6KjPhfammNf1SgepiEIiKxORwutuEGkknRqw5HItIpDpTIkxLdnTRSTp+Y7W3+CD9K3sB25RzuJxp7GwOGDCyxgkyEEJrAAAIABJREFU4oUM5m0W29fFRnL2kIw05OLDsHW60RcShw4IFihLLe315a8aY3eoKU6ffDDXNHcC0CA7E75w7ntkpyAU28GlhJmw21y3fy/f1dGqgBiuyWzqlHkvZxEBXYolgGoi4rrlaTuXez2fN/2KknltNLqXQWEGzxERwaOzYSwyoLWgfdRWXvVd83NfYFHxouM9iJBOryXHDFd5Qjaq+1EDSVpc0SA3Ci758k48SlajYFuyO2tyUwR2aH1/i0uqknZ+s4PGKUFFXikigwUUAA2w7jiOjrgc2jYiO+AXpYiMQAxksA56L/L7O7/1pMm69ogFzSzjYDB/1ZQIuAiaNJoNq9URIGrOIqRdNJFwsNo8MuuMjmJnAyoODcoYrQmbSlWghf5DHWbHHReo8kqI2Ive/uz3vXqtV5JDIzBLjW5JGfYS9zGBg+wgTclZ2mQWJr3Kl4S41a3RjUo8WRkY1WrJ4bMkA0nj+9bg74r515lFM11vSSNoGrxKfBQCSKW+GNBu3W/JlhrRebIWX2vpN5jI56PeYIgYS4Eitra5NOjgoHzri/dF+1O+vhdD7kbMuu06UlLZvZCtoXT8ZxnSRjuQKfj89oZrLXSzCoIWzOIZMh1NBwPGk3RymmC44jovvB0Hdt7cqq1HBUivsPXaZLY7l0aIihs7n+giHzIFHbkEx0EU0uvi/Pb919DT4FCIdATK/CVa6RAAMvA2DhofpAKh84LngiQfxMdoGFl6p1WtHxFAN0rHLwrTmwpWbgoqMB69MkUdUP5e4WVgU8CW4xkLmWxWQQa6dSQSPhfF+ACGM8NvXZPogvChxa7tvCPMRARaCQo2OhGMxjUtBWiZcBOMpmxxUcWnzwMKalof44HAvA9cKGmMz59/iOUXXACrG5GDQmX1mbEbKQK9U2Zxx5OhlnNQuylCDcUxBs5qhGoYEDVItd10a7AA1nnB7s2xqPhCikI6rIZ5Q0W2CKUXVxkW9nC+H4yyFFDcN7snpS0RwfgodxiAUG40Aof2dh/QIQteVFNq1kOfEgDUJv18PinBOC9k6fBar0PcnQaio2HGLPr29mLfL7qQG9wP/Lu/teEP/tJPFvhu04hKOdvBbd49ITrA+LwLkYJuAykL4QsIxdzDR3wsEQhk6zDtuNaT3xcAVLWqWOBhB67Jyt7VFRVvire3N+b+xoSDyFXrDetKjG747775k/iq60ziNj+KJt7R8HP//L+8FQg//mkQbQiFx8LRD4jstjy6gF1Z+LLSkdbh6RiuRWX7rT3LrCHLBCsdrXUcMnCdP8IYB1S52MRiYcwViZHlkhYi/+IXULQwhGHzW0rg1SxGdJJIIi4GvEckunGIODFfRkXRkhVw6JFkrzxNcAHJbdCkq97n5FkR/M88qR+EOiaAFolVGr+tmdZCN0VI37onWOjiXJYV6NilDhOXv3OIC0p0zuuqRBDwe8hE9EBMnkUONpHxmuT5dvpVA1YihWyeOeVZ2Nr0en2MVsySamXQDU50i2dxZNzFNrugJfZnAWH6R+MilPHKFH1FUhFBVCGyygd53oa2j8bXjQp+NDNR9tHgdTpkgTjUFxPsOIwIMYr92+jqXix23m9iIbTfecIC3C1VW3azStrB1J2owoT6Z21AleauVOaR79bSbWRj0cTWDpchr2Q4nopYF1ISY7Ba/QaUypx0v2d71WrHWlgC9HrGmiRS7P6s+LUSlZ06IWm3SddLxsT7g9d3imIla32zPqPtuZA8IWWaGxh3a1xDlOSHNdIGItK9lQEdye9IjK2o9V2WOeIecL/SLH9gOPb/vwd7AHeEJ8AGNhRTsfDS4K/lZSwPZOKOu3uZ4hjTY5kQ4Rmt+oGVq4XvCg64AYWi4XKBR6JJVIlW3CbApoqurNvdwIsZv3MENemiTLPac4n1UcthgSVlZExxNBGkU+9OkQ0Z6F3YJKDx/Nc/q3/91/diyEU9IDOZ/3ccHWueMOloXSBCt56pIYXi6yEdDsFxJEsI0vDQjhNBUXonxd7H4z6I5mQdsJY2znqDgikLgGLmRLMGdcAGqcj0wGMc+LXrCSvbaTfF4+BNEk5pxWidzU1aOp7aiBh3BXgJy1sfL6ru1lk6jv7A07+FJDdfRUCl81CbC60dOOd1bz1jDCJjhS48VuBCAs3QrQNwUiPd4JOIQ2YxOE4t4XI+wJY7jqDeNTWoYzTAamOySqLojati1EDem93ZwqqOMMG6EtoPxEocj45rPqEZsCyt5XZsVyuXJABpGEGjRAIYTbH2w7obMi7ekEa6TRurXa1R86VJw8TOikQ4HdJgpWo3LT3fdpMW3VcD55aeRQSO1pESmALck0MNohkKkQaxVeitYqogVsBCEJpwF4gKzuDyRZPHoHRkkUxzOCCl6Sp6MicHjwuAWIOgQ5SJGa9sQ4eBJkXLoOs4ad5rZvBYN2JG0xZwpt+9s1Yoha9qx5OG3fzz8WWgJOVvfvsr+Bf/nsRf+bO/CvzDv85dm2XsKUqKGkS27IWArTulyRV1XsulIQX28JA3ehAJLJ9FZxD5jCAiYcGos94aVAVDgNWeLPYoBEqblV7fIRMIE/zV9X/jr+GXv/q9tW0NeqDbGxKLDnyXn3yPQV3z1gp7ACYLzfj7XhdjcNxPNFHQMhqw2yDD8lYrmpAaej5oUhPiis+fPyNB1M0zsBLIFWilWb/yiVIeEqW7Y6Ecz3lBIwFnc9k5n2hpuCTQnNf7NUkJTtBY1YUu8TuazAPaKrWBsnDAGVN0RcAkYUlj1r5nP8aM8f8SRXINZAED2/0eEVjNmCUKoyFYDV3sXgRCi7qcinYkrjMQlvA50VxuujPA9z6/ZcRgOwZ158HoJWu8t5v1qlTmyGM9sfwJC60B40M+9sfvqqj0MKK/NEGzQEXUKEGwToOUL0QwO/c8nziOAxnJ9sw9hAqD9nc+snhiqcOX4wdjAJpVu54Qn7DW0R4Dayne39+/GvjcORTv58o2XBP5BI3CXqZCAU1/sYfSRqlMFqNWEp6o891aDfqiXEqdzWU0JpFBYmUrYL2OReAGXKye4ZFsiO6DbFQkta+wA9oN3XcLI+8pUb3rnfdQt41J2y+wUVGAQM8opLOX7tQacK5JmaC2W0JlCUZpTcCa3jpOSqW4fEpW9NXy1zNVOMjFPjMdCKmmuRKImXCJ1TQ4LrwdHdd1YqZ8GJKpb+2Dxs4bmZQPWbx4sWLbwLn/TCZnlt57ZQ43micLsNo66X0PNrW7jl5EKm85Snr00qJrDQHJ1aVi/iblASpA8nqSbmSJc0HtwBVE401ekqIA0AO45JUt7ZAClAwZhmcKRg3657qg2e9lKVM413RWjgeAJoEzFmWXGUy4WgkIpaW+BN9hxv2+DLm7s5lo2FoU7btoFS0U8uXUZrX2qSgmorZ9UHuznI0j9saDePpCr3rAq+QDqoocA49ylc/kl2LD4OeF0EB7GK75ji6donJdePv84IkZgU+NN9Lb2xvWNeHOLE9EoMmBMwIiIGV8cHgbbWdBtjs66OijTDodyyd6P5i9el4YvWOVqQZIrGTubSgP/Fgc8uazcl67QZFMGnCHwqm7XKx1NVVMZch2qGOowjXptOxgZuQbc0m1DtAdyi6ldBIhbiDokBL9365dAZAGaQn3CyKBqwxUKfLSfJU5a2udHcHPbfTKk104/UP82ge3NCS4DJU+7KYahYg8nM59bULkkooiXAHSy2qIMzEGJQo0EPmtOVKtet0acDleVRpFoliDVmglG81UGqaT9rVNSQvLQWAC3fIBeVW7CgxkSOu6gWPV4UhU80SCBRaPo98oRGYidLHTvStgjiaGNL21iZsiW04dO8AkEspzdl5kYLTSjrefPAK+/A+/DwDwCcA/BODf/70/eceSBnbkUjiIcBENIM0HOGYCOF95vjOpqUQmHNQ4Ehnweiig6nwp73F/afygibiIhGQtrRkGOxp0cXhb84nWyCCcBiAW1i/9KtbP/MrXv/uf/pd+Qpbw4/99v7R8AJFJcVgufLkuPB4ftGyeCPQaeq+KJWRM0KENKxbNgBDEvBDRoK1SL+CkGMHsyXU5DmvInEQ5hXKsWIl5TUTveLSGMEHXhisCzXBrAkcDejEWodT9Uv2cSBdgMCpql4+0MSoTdtFZfXHR3DWajqQ5lnJRRvg0wVyVFFKoaA6BTkCyQ8wxkw5vmEBSqMlU+hEkmOr5jPeSXVHmkdeCtY5v358YxwF5Xni0jivYcsj+go55nbfW8/zyTilbTggE4jSgthpaXUE2bs7yNNSC3l8xaXuBHGNgppNDKdbOw+9EFC05wjZWJdjS5HlCM/Ht+Y7ROpdEidLAV9kDEnFSSrHen2ij40fnO2MKhXFRKxM94jaajTFuZJGsEO+RqNQaq2iyFEo/3n3i0QwSRKA/RkJ+lCxYK8ReiDJCBH5drIYdzCjeCKBmsQ5WZrN6rlHaJ1iRSFE816LMIiglWHNrthOtv+F5LViykMTr5/RexRie0DbuQRcAIubNSmt+WKjKkN0aG+UyAV8BOBFtyhwqijECqcdLctFeWa8bQeRzjsP+Wlyi90DdK5Yyw7Fk//1cKlae0KoR7qI4FwE0Qb+LUESrXGVe6J2scxbYc0cF1utOFsgXA7CfGRFg4Y0HB09hCkcgibIHc+89yfBha121snEjYb3DqySlW/1ZKfR7BpdMIXW7i13mdb5+x3aiocEVyHih66IJl0k2tHSG2/sT6fBgTj3uzNzKjJY93Jek41rIzve4pEGC8hiIc7FSysXcKXX8LoKF78mQW447cFqPUIgOBterVmZa1QumVDMYqR9GXjAI+zgYMExHYnvlq6qx4zm5/Tw+GSQSMxKfG00u7o7x9qmE3sCjf4Ms6mRfBIKG/mAl3loXnrHweGtAUW3UvCQ+W2eObz3cJfPOm22jVQPJUTdTgwlpTpSu8ptvvsGaZ+X8UpszHge0jFXvcSHACz1l94EXNQbWDIoC4tTjonIF31qvCCm6V9/GG67rCbtppuS/3zsjsAbr+bp1LASlE8qIGgBYorApQOcmD6MIXlrAIgDpSExWc2orFBfc7kEnPM1xE8jE81qQBUijllQMsGbIWSYQsLRhPc+7One0zppjZye9amMQfCHXRaSzPcoZ4RNZB2htzpLGyDqAspGuNIdIg+Oq2BVFSuL5PGEP1v5QG1uHkRreF3WRe1P/8nQM+YLR3xDhyHAOyM2gIpR0p2FFYowDGYGppB7NFIEL6yrKtxl8CdA44HahL2kJKSFkRyaH8OtaEO14ngutc7u+y0gi0YDSWnUGi/9tvNIdvqgp4+ezaU8OVtOdw1YhuaEGrImjHNyqgKDzUM2FwKpKYytGZ6NXuw0qodpxzQuxK2AzIddCBpkSS0WWRi2uEzM6/saXv4Zf/vItnr/rv8Tjf/tXfur3d/5jfxRrXpBrIVRgkviyFg6zOzFia6N7bF0yY7YkBRHKKttN/e7lAyCKuzqmBH0HUGhQmzo6Y/ve+sCyYDujABmGz6qUEQXNtWtOfKpBSEQwxABZtXEqVkwiHhmlndS7Nn0PE6RgBWLMYWYeL5EslYHmE1DcMh0z6lRTAHjwwWYN6sEEAl+FKsqdbRoROKxjOctb2sEmxwbFeNCIJ6l4NgMC+DzeiCo9Di7FfRcB8ffIppiqaNEgGoikhKWLYsWiW72ygqnBdGi3qneOF7Jqht3kdkd0+ZanLMAMsYSmIxB8+Whuy0xYo55Z7EBLHloGgSh12LdUSY1SpRTMzMooVpzPRGvzXuakGBHGrb1kBreeEXxWivFaYI5tNdZVfjIQ99K20cAxxs2aZCZmTmqSVwBWPgAo4mK5iEKRS1gk014rYGbi2gvQWsUMRrE61BjvJtKNvkvloWdFHSoAJPC8+HmmMJM28EIwaVMlWLU1pDRD0Yy8Tb5s5gO2nCzdma0vhnQaAndLaFzbwMbPSzVZ+VytakdXrHVBsSUelGKp0NAbi+U8NInSWNxE7qUjVzKz3QQ+HdnItqgduNa6B9fNhO7l4y4fatRcw/POs+e9nUAwxlP9VZMrlbSSWSlLAFL0PjtpmmNqgftCk5LOgUOm15IAABr8Ge4MAYhiugHwOe1J47RWzjIqChUCpOHydTOI5erhOVBAE78fGnDteCVcnCtoNpfKiq94TBFlKJUKzIiIqyS9ufH/SyR3x00k1qp4JOdFfU4+GOFMNjAxajMV1anOKV9SYQGgUA9gQRBoe/M0KapMQOPaNiXwYUmo3XCtwKM1IBzyeECSOp7URJxb5M1B47EUAsOCAAI0yVtDY5Xjx4OQfdJn0rxgIlh5QZUDmoqRCsNri2utAQ7qewWY51XUyYIo8xpbGwB2xiwR0m2GefqJjkGNUARMqw2uN1zXE5/aA2s5Pzcj1ecfqfkEUAHfKxy6EtmNjtdG8TkqZo2d4g5ftfl5QxoP31wCaQbPhTwr7me2GvhL7mBFG5oi1LGSLUh+XTQpaUczwfSrNMZGt7DQbHEbucwwfdKtX07Yo3VcOZmLnLwx10poK4rfaUQI54PbysBEpIYtOjMcKXSXa28cHptBVwIxQbvtpLnMHY8+SNcnGMfjr7B9a9SfNgtM0RqKAQ86u+mkLn1zFoJjbwAWkewKIZ/O+KiS7VPHG2B8lAJzPaFZ6EsuxHL0gzqsFB5ia13Ibnj7p/84Wmv40X/zz/yGd6r+7j9WwxqpzPf5aufRQvuQig5St6KChUogEMbMyTaBeNLYVTKKMUaZ5l4h6gLBzEnzB4AIYxQWeNiFO6YBbeI2AKoLgA6fX/Czv/m3Y/zNX8S3/Ud4fIcT6cuXb3GMgSuBXIIFonUhiVVmCXR2xYcI1nI8OgcyU8WaBseCLcVSlhCIlpHIATSvpZ6tb6ZFL8IZ+6PUgpNtoHN+Lb6/x3hD5IU0QdMXuiXJZkW1kiEw141JK1UZGnjd15mJNvptavpY3EBNXkIrMm+/7kIKf2k9zYC1I89EkCaQ+++jp0CELJxtXTxQ51/We1v41A5ci1FPjNYCWgekjGIqDe/5ZGScA0sEbwdjuHqnAbSDAwl84ng8EFkm1CVoBgbLh5fBszSlW/51njiOo5z+e5FyyjRQNDfDTbG8pFcFYHSjDOxo2wzEzxPC/HTtDX5O2BgvyVYS1MksSRKI5lolcKgZuiVy16AKypdCudF+1rTk/T+dTEm3fg/Fc0udkssXz47ShyeRxS0n8mBJjOJVDMSzXZB5QloHsiLAlMOvgkbxgMCUnhKmi5R2WBufse5s/NqaU8jNLu3PCR/Q5j3saiHhVuxc4hVbB6m2vSoyylocMhNrcpFGJq6c6EJ+CQGIMFlDk4wrJLgsQSFNINHAGEymBEACPi9meLvDU/kMD77v0ehxIL5YchgD1jwhYnBxntW1+LGM6dWKBuBl7BS+9+t6/joofDHaMSHoSJxA9tfnFjsDn59nANS3IgFZOLO8R6BZ1LRjtIpE1Aa/vpR8pDTinkjpvF5XwKkALnaIhseIwLW/EyGqvs8GKJH7XA7tnXGP46AsTBp8Oqwb4pxYjXLOC0yuaEZAa7cp7tgz2RK77yDK/V4MuRsRz0LiIgIddQirMd+5NDCZWbmJyk3bL+pd3OGdA6iqAK6g7YgDRhg1kSmkcpbvnFBu6Z50PR9HJSO0AYYIFKK0Eu14EHGcjCWy9iDKqopwwHoSSVaF9cFoo/bAOU+09qo0ZKMLQ9qHDWRO0m3CYdh9YZ4vfZJHMGNSGTLu58VDeV5ltur1UKPGJX2R8lVSTbz4iFpc7wvH43FH1lh/0TOUHgiiorK2XGCIwntC3MtVPuGx0AVY5SBPDQ4wG0OoCBGtViERHnaqAMSY47to/GIt5cKhb4hmsHRk8AE8zwvNGofXpJkhk8hvVGyQCFEaGuyYse/TgaPjy3lCDRTHK13Bow2cyxFgA9y6FlLYvx41SCATYfw5poYmB7K/I3xBnEuAGBcoIGFdMb8kPh3sYfeNDOeBLYrNDOqe05mB2Bgpk03pap+zxI3cht2dJkt5Ob4zBNdJQ5tfATuog/O5SK37hI6DBgdrWNc7tGVlgD5h0tB7I7uQCcAhQirrp3ndmq7IW5es4O+cHne/uhhzWmmKMhoQVYHa+CUVC87Qf0lECrTi8/I+wHa5g0ECbIazhCzgPRxNmDV9SMdcE90a3t8XHoch5ULXjqEP/Mz//Fvxv//sL+Ef+C1/GP0z5R2WYP5lKkQ7riQKv++3gYYvc0KkA8q63UNb6eCop+0wLF/o9c8jWb/ZvHRti0LVDOpdBMDchqgIaoG903iZVMiJAMzCRNHUDt6MATFhbJU6NHnvaiFhGznbOj1VhSTbDqX09TSGlqFLiLbs7Mv7+qrP3qOxajO+bnR6UeDURs95ErFtfHzSAJNoO/KvdKA8518awtF2SyPt5TQjXTU4SrnUmZva2xsiGYk4RjU2imIYZUZWtG1DGXrSYMeo90TH/Dj48G7J50IgkTsNRfWOl3yueVPGSIdpx8z3Oz816zPuVSfeTTngJZBlGlPl2YvKv5VKjOm9/8RnTSSZJluGa78KMCQdDZS3bcbOSrtsqChKAazT0KdgmUoUOPLRzOYRcNFXLFNJIHbCBKJMT7L1r4VQc2sF0LDOiusyreKkxEVbNUEOUFrQWmN27nKIUCbgmXcpjSQTirZZDPnSL98NlPWZRWk356Kmn8+4uI2T0533WV1XMQPXTmaoYiLNF4WulTIiuSUBBXCl4nk9McYD4Ty7rZpWVzjMGs5VOmIErkqVWSevl6PkLxzAPwyuyUXcyvvSJKFiFVuG+75QUMahorAa2vfCaTWwUolA4IBYZvkW6uesQr8Zg4m7xEgAGv6alW8iWORT0ayjETQTKJaThQpnQlPmwlp636P0GDHOLoB7dmAaSTFvyaQLSXqVrLwwnEPqPLEGMaLwMqz8KsqYQzEag+PV5AYEc4EXGcLv8vpeDLncVNgss4KD6kRAq2rRS2MVCbjxwwsBrLRHEEU20iZ+rYqVok5FpNNA4IkrSRtRJ3J8pYvJTGb9aVEeZtBgrIY2phjc1FEzTA+0VcJ8ERpmhFIEByD1BYWuDxQBD9fzPKFaZjA4Vhni3BlPEhXRI2AcytYqTQ9MNawEzi9XNTxRBwiA8WkrYYXurpiUBzhgLYEa7Pcg7h9cwHtbbG0gqBPh57wmltXFbEoTge4IHkPKwnQOFK0xBmbrZAUTnokmg0j4CubcCSX8j8HolWEGL0RhU69sZxP0SqyQkmCLBVLildmoBmSjntd5yGY6Ue25KjhaXi7obJiLLIA7TVhRn3FA4OrovSEvAeYiSoiAOHN9LYG30SmXWYyPGxt5Ef5ukoFj1LBfmk5JmsBMQZlAHbTjUMwMREiZ+nhYWWmWvMo3WmOpSW8dkEVNsQmu+YoZmlpxceu9DiyaApcvkHnidXhdTzwGHa6Q7RAPfPN7/9vXkKNA+gUVQzPDc2YtYPNF7y5HGzxCfC6kMLYM4ALCw4yFKu5R90SiF4WrFWXEz05v97qU6TIlcZ0nINQGiilOX1jJuCkHF8ZrLnRR5uea4wpKhBYEdl74A//4v46f/6d+H/7kf/8/QlRxPS94b3iI8SE6T5oPrZboBJ6oSk+/0LPh08EQsule1zTlMhY0XHjp3X0BMKKuZq/yARpzgJasmNbGeLoIosMAUTovPbp7gm1F/L6pMQRCEpKNNrQEljMKcA8H9CHwO9kDpmXFAoWzdEcFMyohRTdz83XMlCnrqpsRnaJRhJpWolxxL51cNJX6xeSACyh2YQkAbMPa1lV6vopJdpUngHsA9Ol3bFCCujwWBZBxQ2NlhIIs3h3XVPr5+78rIGEccYLLe6piE54fE1Cmv4pfbjc/HIdVkkAGxU9BnTOHQGrbHSwfKpcBNa94mYw0OIzsny2ZUDNmYDuLMqjxLx1mEvmmByIhBXC0SuoAgstrUq+erngMJreGeg0oebNBuo1lALAlFGIVN0XUvu0c2+TvzYQYQuoa/IyWO+UhVZ+rgVu283ER+sgIXLU4dKHcwFUAZZxaaw3v5xPdGq+tuj6iroP9cygJ4b25E1k885YBzXVSu1uDPaQQT5F72Od1Tq8DQNOetGIy9LhliPslwb9DPkgCI5VxgUkWeOtmfTKVYqOtkoVQFxDAiCx8teB81OSuD59fiMC6FmDFpKctEUStsREBOgEUUKLU1EuXL2C/5436V7wmk3oCXXBLQK9K86jk2/q9mIBkxiQQhyATdxrHfu2/Z98bt4kYLHYh0yNkwZuV8z3xvE4uivUdahuUwG1mc10siPjxdBHljPNdXt+LIVekOpWjqmelEMtVN7EsqB6QRboH4I2eEGgQPVGmanxFBa1kUsBohlBG9pAS2Dl0dagJI0DaMaDM4UH4RIgxfSBXbet7wyY2V502iCqcyGTFIgDqYhubRiSD4njwAnw8PiHqZroWh4NcgXOdUDuQpR3eQ/h22WYSFcpEUdV0T+6DHYtB8q41iJ98f6KJ5qXtU275Am51WuYxnwsrwYDqTKhSK8qHUt6bZniyYS0mfIvtU+Czmt90wRq35d6YJJEORCoIhE+MPu5DRxLw+doGt97RKvbqnLw59gN7hePTMfC82B6zqfIdxyNC5DUzadCZClVu84zH4Iv1w0T6+V69HLNlgDHjgHY51EgrdhmMDAgviYDA2gGBo1tHa1wKHo8HB5OkflEV3Ghz1la7NWY8FG0FAoKVu1yBw4SXRg9QzPcLrXWiOquGzDKIeHQ8g3psCQLBrbEqlYcP7xdVRdOXke3tqMi2qOKAwD3wr0XZgLVORLBfzCMtbXMK2C6HpOGu1SJU1L0a6MoV1gunTr7XagY0OeCyEBL83LwGk2rjwlpVY6twX3iC5pEUQ1PBJQshRHrn5chWjThB2tedWY0igice+K/+6H8OQSCewShBVcCZUNFQ5TI+uVQTPqI4AAAgAElEQVQLc0c9Ezbo+qVetUGFxSGHGJYDanyIso1toXfq08ZoQNJUI7Xk8oHEGDoOAjTYbXRwI7JEw3k9BqirlXwxXr7494gQUQx/RU4hspiRkrLUQ367xndcz2ZwtkTpY1pCRNxav1X10x7gwFuom1b0kPVqnRyDg1ljaQqtZZPEhOrrYSWoHFKhCVFKJ+lBOhw0kPUPLUfuk8iPAKplFC10kybRaibLLEq/kFoBpJgTr1SFhAGeWJKIXDjqnor7GtiZ1zTM3Kbdqh2+H1jCQZ9+jMrAzSplgMKXQDRu7e8ecAMJmDBRJRjS5OGQfH3/Cl6jFBcD1PYKMB0XQF/EogSCOnitM5B58fy+iHxGJf14EDjpUixCLar7WRPJRisAaNrr+42KzMiqMN4PbCKRFixd2HGPKcAsc+5tIqt/nsHncdcO92qaFMEVC308MPPEENxeC6CWrDJ5U6tKVHy0jnNWdB3q3ADp8fs5uZeoIpCez4sJSCD4Y0IUGBkYrUMReF7nPbSZUCr5MQVhP7NMD+RaBNXq9/rYG5leOuDLgVreNAq4KPTa6sy7fy49X/crVnJe1irTEJ5HUXnqfCmuTPg8X8/OirhT4yxk2kuityAWUJSeupJCLq+griSTCeB+BkMYyUffBGA22MxZM5LitbS1ysRWLTY6ed9o/f5sJMU9r+z0kfu8qaV7Z/Uywo7MScOCtsftQXL5yZbO/7fX92LIBQStDbSgrgpK5LS/0RCUO2wZKAnAPqwcUzetztxRdeqmqM+k6ey6rtqqTtZdftigMh1eFNOsi3pTfREXVBuaNEy8IlugDREnRBgphVjQ1m83YiZ1aXf8RybgjDR6PB6Fygii4Y4Sg/B49AhkpTWkLPhqMCie54R0ao260NV/+WK+HxYUCY/z1vLN84JZp15NiA6jc+A98IBXg0vmhCR7rN+s4+lBrexcREiMN5aiU0MpgfSJ1mnAme8n+vGAz8AxBuCsUFYnfSugxGHOQmXqIJtz8qB3LjabuoqiMuec9z8n8lPbYyTEBI9x3Fv0RoMYW8OBsWmFmRj1zuknhjZcK9BNELLjkBQhgWEdYYkepLAkjDow9dIDVaNNp+FlZkCuhZ6og5QSgHY0HO3AEsX17RNmin4cEE8YBg8oZQqGx4QLEbLnZPoHqaCTffSRWLIQTupuBXVLFokVT1gNLe/XO47HZ1zrAp11wtg4PiHZ6HQvAnghaBlAKLwBuLKQGbIkvXcgRmnlgNQO5CqDg92B/xo0hkCFQ2gSlVvzHWaGBsWVvkENMiySWFEPVAg8aEwjSKeIqwLz9dVsFZlIT1KGjQPOqEzQzItISySOMeCLi61YQ4PCIfjh3/X31uFs+MGmRVXxzTiwPHH6s7KKF44qRzjsAEAtszsxlGYUHqc4RuMws9aqoYq5sdaJrLWjMboIi0MTeB+aWdGxpaedTge7GuY6ISXZWj4xHgdpxxpoVxSFPieZk6jiiI1UFQMA6L0k8z+JdRUIIExnAWgW4sCb99BrZpiLWbX7GR8XcBnjs9Ip6ZBkYYdAcZ4s2UGZZfpohRwRkTLJ+x7eQ22A71+E5+CcT7bTBduZIoWJEBIIOLCM8USqrBttXBJE7KaYd8LITmABqhY1HEiyelyOgC6GKxZrtNXKsMOHMQeaBILnWIZiqUDFtyKR/5tegDDRQJTDZRglcJvNI+PPZWfNdX/ec060QQ0sGQSebWzgVKQ4TDoqwL2WVLaLmSqu6wkal3n+eMmmMgW4HAkOW1dQJpa+EP2gGW8FolVyw5xcfmrBvdZ5PwcjEr0SUVa9c9byEuG9UTZnlOENMgkTS7YJTeocWbnApk2CPGZSqo4Hwi+Ileyqhm9eC3lrqEVYj6ujIa51y/U25Y+Ie6lkjixd/a2W+CwGIkqW0YKZ8dfiYhvlDdjMCM2GCa2CjJ0YcXtVmiHKeJdJv8+WpHTbQJKW6V1LQkADqAuBMgvFBNko2797AS+rltONvm8mhF1DbOvbz8Cd279Na4J+I8dQmqdjAdDEzBOAwrJXAyxVUibKtCPdpSuCwv9vDTFnICK9+DGkfL/3u3q6QD+edWQ0LiWjrFnNoPX8jtzpHXs2SxymAF7xYiqUQHwXLPd7MeTyRglA8t5Idtgwg8idgfbHppd5IdMm7gBIuzCSKqHWcG4noztbvnyibUlA+t0KAhABkE0HRDCTVxIiHXNe1NMqqzDXAqRNKOyOZoLITePuC+66Jt76YJd3f8DXYvi/18NNEvPJjcjMcM4LCMGFyXxcgIdoEJk0AwKLmbOR0EHNWwRNBMMEj8dnvofGQSczqaeNgLaGRG2zSl0RKbDAioXHg3RBQ7INRhn9ZaU3VBeiddMwjP3UCkF/PHjxVjSa2KhvlQa4BKOSxoNFFqMf0CDFyPuJB1HUDbTmROgg/QnUEJP3Z2tHB4K0CR3mpEPGcbBtKIGugMQ7pn4CpmLYCYdAzNBNIR4V08Tt9fFGBEyU2uVWTWeMC6o+90bdMyoyZ16Ot2PcGyh70QM8kh1YjscY5czXooYLzal8WFiCxuPAcXQ8nxe0jDhXOE44xf+aUCeTsDzQs/SZ1kiPd9biCnig7ngobfuQ48G30w/uQzEK5XG6b3vvCCiGADsngdWrzPcEqnEGu2b01cIEMAqKLEE1YaVgYlNh1JIieH9GEGmdi/FwKcDVGph1SrT5cRyYUxGTSQbjmwNvhS52laoBNWQPHNro9rZW3wM1dUMNM06oNoyDDvJDBKkd65poQ9EAHPmp7okH3p/fwtoDXYy9tsIh48HgX5ixOvucV6HxVfmMhB29DnutQHzBnJVh6a+g+5hezmzAVbj0VCbkfjCYGbO9K67nuV6xd6bKGLpCmFGISBcuFR/pUD54+L2t0lnuZRxaZ6e+qNQ5mT4SuXAGC0xVAEvFt893SlhGr7Yt6tzfxqe6P0D06TqrAarKQgyktp269/37+XoF3m9J2H7x3xNkdoRz6LnkRC5Wd/tK0vJSZTNt4LqeHHriJRNaPm9TYwojxnhuxt2KSNMdS3VUOjzeITqQXtFlRur4rTfMWLCsz18UOTlwa3zUhtLMpYoaMhXm/jL6id4DYWC3V5JiVzPmwdqozGTKE9b0um/oI1igiSorN9sUXFZ8M1HAvBytMWKw9cFoO0Vp5AWnT3SzrwYU6Nf5wRd4/ep2IBgHnK8yWQnT3Wc10dddalCsVZIiX0Hp04yJ9MY8WZCCkiQjxZnoRVNvDW76C/n/WJTAcyhraASQifdKgQinLj3BZWX7KDITLh1+MnYyndf6nPOrLGhP5jXT6EmzIbL07146bWMxTFaiRhZLtBFlkWBxhAJIgYdUkyLjQZsIpHKUZflWPfC+2BKMeh5uz0h+kA68NN6U640GrHVCrN8LVCrLFDwVGfXZyVXMyJaGCK6SD6WyBAKlu97Xxukcpk0pndjfw8fXLcvZIFS1jUJKcFGRonvZ9oxbWx0FHEkCbrWQ1XvcS8x3eX0vhtxi2sG4x23+mJCdHVoP0gyUqDoQtzaQWXY0VFDTsQ8bSiD2MKtFixPNHINDzs7PjUXHXppVtSihehelAxgo6J8bk4AtanfrmX/MM2zoRkMVjRSr0ht4sD2fT+rASie3nMjVgoOPXNIoqg1vo7ZjOEZ7gxyvgS/3xtg7utl9M4gkPAXpNONMD1wJRoiUAxNoQGvIbOjgxr0gGI8DWM7frVzZXamBy0x8UgrhdyySmt2xNzuzrxu/C0qfGpYSOTsOIvKRzGSc64SYAZkYx3EjGxmsbmWG4SvbcB/CCYE05tOaMfMS2FWKXhmXn2C5EKPjz/7Kt2hfHL/t7/9NML+AekB21Yrx4tCIEKQ6UDnBXTlo98G4tzZYUytCRP66njj6AzQvEL2hJrWSFMxwTqe+qHeodlL4pSHU7GhKeinnha6CZol3ZzsZQ9qL9i7acmhRPAtwW4gg7eu2YA5GJe3tPSro3WrQFmZkApQ0ZDClF7nD4wWmCaAhYxYy8YoM4rBKJGLFNv3x8CLyQ8lQU0HAgEUErdkgCq00UfHgZz5uHxWNlIIDgg5FVD2lZuBzN0Sn2ZGMjQKjvVoKDfikjEdyb4CSWTHtd9nHtUhx7gew1YImj17IDn+XoYbntaBvn+AZGE2BoFkrlqL1Xu/hlTObwocKP/PB916acQWR1tFe1cObStzI2/6utsFmz3gKgVcJA7BpbMozIBNZqSgprwfM1g7y7BMQaKkBBJSXrNhDBJ3jm4Z3V0AMz+fJ5eMZ0KMz1m4tmvxE7xplXxMo57xGYl0Xh6xkegyk8nq3cadkTazHBo03RW96FB0qBp9MjQgowgMNYDC8Gc51QsDlWBZeCPpyNKMsbKM8ZkaTnsidjy7iZBBil3UY5XAoQb8wFcJ9IWDIaxZKqLiC0WgrnBK5ihFzX3j0Qb10TIRKSX06GbRIBBxdBC4C1PPBM+DzgyekJHsKK1NwRWPVw54Mp2BYZ42vCpo2Dq2wl7a4PAnQkkTUZ7CvPS63vH5XLTPnul6Lse7lgYhwlNN+J5fw0VoDXs1dH8GdFOazbk3nRvVWBlkVr4KQYnekFiWRgG/WBkGAp5bpnSQhNdjxc3+ZBoFXFnhu7XR99is3FsbrvWnDihMSbH7zWJUpHABokt3L20ZgeV8GdElpwcEF6mZJBKIOASBayCq2BriMfJWewvMHNzO9B1MRwTwvOEAJUrJwAlb3dc0ZKGBrI9j73//YrEddeUXYAbcMIZM+BeRLf78mF++5W/1K1hIr72F0189v02oIGbzUl679o69HkjFk+zu61oTGZsgBU7uNkeuWB5URXmhg2xIGKiMGl4z6zOecX2s7foPX92LIFQBDOgJsGLOm0KLmZrDqtIVwqzWO+U5BHNLpurUVyILSP4q4VQHsBw1IJ1I/R/2dVBuWCZ2753neSQtQheaA6O6D5oGq9orP2JD8ptvb6DAncnXrVkQAMYy6IT5/JuK6Fi/UHRY+muGIjpUXzAQNUtKIwKF2/9mdv3tvbs6mrpB9g28JObWvYgILaizFtpu1DhtUnl3r0PA7uiYi8NaJfpBPTDRrgLJJBnjpn60+A5qkkjV8wQdPGFFpM2N+q9SglArTAwsLSBAVCyDrktymhLuCMyqW6EZdXhFGSEfUwNXAh8mBib/1/Mv4M3/lP4P/0p/C7/+dP8D/9L/+DvzF9W/gX/i53wbo4tBgColqb0miHrNufK3v7+P3+9qqObQ3fZkI7gdFmW1OZzxaZGmpAEbX7IMxAKCzuhUXaDzjz3r0QXQZzrB0JcqQWYdwc+Y0FjIooqQPQYMDsJGDDgarb91lbcv1tSpAKrwddUhtVEbhO6O5P15mkmpbgtor9LzoXX4GzEQI0UJiatBohR7U4Knit7Y3l7HkDAuWDSZbm1uIaMRtpork//ZonT9PAl05qG6WItbrQIVyyDGwtGLr8jYqsJKRhVbX2jjY8U66U2GthuJC7VtrWBmY66qkD2cQvHWMmsE1SE2nCtascwIoaZUhV1A3v5fSki7wIc0/vzYqtwRd86vrvbUHVqG6+5q/qh0K9TtOX5tU/+pebdgVmnzwdu0cLGFcLPoD7hd6H7jCkdMZv8QplDq+OSkLmUQkszTwAmpYkU6p1Kaqgbs4g0tqg4ANhVb3t/VOli4b3p8nmnEhTGyNMgcLc+cDsnNQTBf0MsJei6h3MybbbMr9o66yW+OCJ2xaaiWNYqJFLW5NoEsxi5VZwizjBABnBmoYUAJgPOcFiURvinnRRINcgB/wWDDNl7baGpf4BBf1krQJpLJzOfCfz4mwxVpveX3ne/Hn4JIELXK3caEkDy//wqaRt0RpFfhw3892IOVl+JWZCKFMZJvw9rNmN60hgcTr5yO27IQLL535ANLKUBgYemBeu80rSudchsK6B8dx3HQ+GrX+vK4qOzd5dr6kFBWvOVlnu9yRztpfdaLFiNLsGup5F4A0CMBlxABF53NHiGDOxXtnejVKVqHJHa+15SVoiJigmYsMMQI1mNUZVAVBa3FO2UDYzuw34TPGI6qxrMy3+UJn93nFRBAyh936PUTvJYafbf1uFdX344bSj/8d0A/LOtBKM7tTi2Y6i01uqSCqHS2of/dX/fBmqVQVqHPxvm4qHnUnG3vGfb3t73FHAG4El/XAdjNOe6DfIMWtafgpXt8N9/3/8MUvqaQKsmqqb6QlTe6NN5B3647WBwug8s8/dF/f0H3C+FzjBWE092Qnfd4gRdPSBLYKiQwVog8a1GWqIgRo7YFIUtpSm2vgdXjw72tAPcB0vELY84N+JQU46uEm1vB2fIMxBlpP/ODtgbc+0I8DTYFh7W6/YfVgQs2h4tRjHYIluyqxTElCTWpiwmNWlEeVMojC6/fe2h6Grhd6hU1x+v1ntFFP24t6p1Y3blNFgGY7E4FKQ1rDSg4hnlvrV6gUdqXmdaOBNCdRDC+mOI7j3iRbG0gZEOmvZq3SM0EqDiU5AKYRUdK//gfxT/yFv4U/8TN/AH+k/Zv4lfjT+F2/+T/F7/k7/238oT/2i5RIxCJFUpIDrZKBpps+1Jd8Bq9/tm9Q62VsaIZQIO67iXqrUQO7tQFVpeZKqFduotCuGE3x6THw+e3AW/uEt7c3/PDoOAZwHMDn44HPnz/j0RsejwcH/YOmuLCNICzsyuTWmWt4HJ2SE12vUG+pLNZqdVvgYeUfHnrTHSuYC8lDaFPIeg8pLjQDWW+3zCQru3ZrsQwCa4ExEmoBSqsTzah1FhEuhGbog3p00wfva6VBbh9yW7KiqjcqB4B0cBOIdDS1upbzljvtAfywo5YxLhmiieUXZq4a8PXlyDdAjUUIaQI0Sjg6FL0bVIChjNDiMrnwZh2tA5O/AJK3fz1sylABakD3YA+UZrTOLIDfhzZBPwa6KLQWqf3nJQEt498Y45UkUOYenkG8hg9r92C7H3wfH5wKfkb8vhqGAVnXqKTC1NGb4vE48HjreBsKHQIdHZ8/fUJDYnRBfyhEn3i0hb/vB8A/8rN/B8zzHny8Fg1+Bjx/+d0lTB1kPzrOOcnyWCA75Vvvc+J9XmSWEq9EiDKWNaUhK5C4ZgIyEF410qb3cIxChdroX51rlPC8vnc+7Hnta+sQY4tmOOuT1zVxrYkv+UQk8P7+jlgLaxIoeL8cnsA1T8QiggUAzwisijqEE63tx0BTuwfJiICHYa6AL6k0HQ5HKwNzvmPlRMZCyEIWo2PaEcmf8T7P+zl3659bfxnqCh0kYc9ngWRlhYchXDFDbqbDIGhaVbdV7OE19KHO/tzRdOgoaOg+J+j6I3o858mzqBHBNAEebcBa4ngYZXJKQ2M3ve/9R+v3Is/zuL1+fp033QrtBSn/XlKQzIRmSXyCldj9/2HuvYPtOq4z39/q7r3PuQkXkQgECDCKOYsSFUyJClQcBY9lBcqhnMfyyPGN7ZnnkV2WJY/esyWnkdOzLNuybEtWztESlUUxUyQIAkQicrzx7N3d6/2xeu9zNVVjy6/8qnyrWARwLw7O2Xt391rf+kJwVMH2TV85gkBwGfGK06IFwRW6pD03Ro2wSWzWLuXN+L2OzsWoTIQkQ+H9Gno8ntbYzQll2lVCZSh7boy98K/bC/omAsxPvzQenUvJShrDd4i4CuUiJS1WX1o8fcff11IfJO2mTsb7talwIITAZG1hLAb4FYpBs9zbu3XN0vhZsPfQaltE+ZR7ZOeI85Z66Ve8f00GPHSOGd2+3U8NJSOu6T+juHH08Xf79e+iyBXpunHrFGyU7wnB9WNfvOuVu21WU1MWj9Wu6LGFTJ/sYpsb/cMGneo8UYeKMByUCz1W9IXaOoZhqCAWHz3tCsaE+Iw3rL6oHrE0GTF7qcrbCWddTaADWHztqbtOB3uoXBAGgwFVsIXmi6WHFG5y9zl8OTDtQUpUXsktqCtekFrUtT733aJHUCcoZqLePRgJ461aV2AblnaHYOE2584ax5eHvnRRXccnYhtgd2A1sS2LwPUUitRG2iwsLq+w4+mpI+MRV2/LJjaeG4SKStUEhwV5SFHNngv60VmX3e4k0HberKJUyYNv2Ld0ltV71vOJP7+b9565nAse/Vv2+aeg8Qg/dM0v8Lk7Hycl40OBoeoxFuFKWRYrO+VOGb2S6+i6BjcX4UEnKhJPyEDw1OIZ+GD/OduA67rGS4B2jN6FYJxRL86elbqmqoxD7IKnHk4QxDEYCgMR6kFgZjBgMBhQTw7xQQlVJng1cUoppgKu50A2sVjKlXXnS6OTuyZhJcJTEM/uHtlBVSx5sA2tjblv5sS7fnzcNR+VuIIYWzfuK1Oim0K4+I2qYE4fnUDF1lTSpi8CuzHWyveScxn5N+Y9223idkPG6IYdMoXXigmlbPIbcFSgltyGhnK4Q+1r45Km0r6JIa+oUVKabHZhOBsbG5+vvJ/umRCjb4gIkoqoRJS6C8JQQzc7b1vj01rqVcfX9N6EUj231mPPRGUFanB+jKSVgpW+4M39uBfo0ZOu2Ou42+ICiO8nKC44qkHNYGLIZF0xqANTg5qJiQmmq4qhUyqJ9lw6h8MTqJk6e4zRiZPo7gfxAxsF50x/+HXv14tjEOw+2/0eBzI0TcPC4oicS+xzEUFGNY/MiapCFEIdzD+9FO6ow+S5xgvXLFa0lc9LKYpiSZ1S3xX6WoSuVZlC2HuOORHjyOgEJYREs3FRm2Sx2k0zwnkplnKR5aYxnUUpVNq2QYrmflgsKJ1zBo641K/JngZktip2r3Lb7zXFaIOkHqEmS0Uuol4lGI9VxLzAxZKkHLafmkbSzsdhXVvnRSfIM3/rNpmdpY3DPVJZkhrOxIXO232LMY73vqz9NewQu5RGoHZe4hO2fZhjQ4fUdc9nVdlZWFUmuhM1r1+fLNAouFKIO2dJYV0h5iyRrBvd9/S17pz0Qs24ZvDeF2uyxCBg5za5IK5qFBcPSQotqXBukwIy9p73vkJTdyYYINNF5Dbl7IltJiYltbmnJaiaVielcUyvTTyjOd+0xaGhmxCucP+xOsZQao+36UDxS++mh20Z4a/8r0d/XYnFdcVPu0ONy5rtEdEVPPKVZ3TOmaZwnbuzwmgZte1bZV/uGviVdIWuWDVgy9JfBTsfKx/IXXiNlGlBJ1RjjNKv5PN6ZzE+HdVO3L+Cq8C/I7qCYmbr2Sne1/3mZA+wcWUd1tHEYi8EBcZfoeDsvRiL6tg4qZarnTFfSSlFQNu21F2XWC5oKEb8omb7lNTQrkoVFwbEpqWqTCU/GAS8KKM29Zw/+xqTwIF+XNkdvlDI2ppstCMeUcvQpnNsUPPM7V7Tl0MzSzLivBNEElKVjctJz4ntvP4sXtI6WEvwAsnGpVIBJxWk1Gd42zjHdXthsVfCxmFqKPJiMU3vxlddWpKqHUzdgZ0RyJHgoSnexSlnPCY66vxUnTgc5rvoEVq1jagbzwQcqSx+JfVjNwl2j2KMVHUgJUWkpQmO9Sf/io/s386bpu7m0PZF9n3pc/zF7FZuDr/FNy74Yw4d/yLHH3ic5avXMRDPUqsMB9O0sSAh8p0CmG4j7YqGXEZLQGmA7Ms2fI+XQD1RRI5iEdJ+UBcf3+KcoVBVA7NaQXqua+08SUyAoc6iJck24stlbElWaje2TEKNYtJbHAHCGHGuvXXKg6q2hs87SObDmcUCJvCG7mWfyzUojU8Z1aVOxBETzhkSpNlcRMyAaVxUdiPVUQle6f7MeWeFBGYN2GqhjGDofkqRKpjTaFecdV+K9vfEmpua0XLb8xjBaCFei/9oWWchBBTjF3s3LtxtlYLqkM7eyagcNqZzzheHFawQRsyetZTkTjxtaowmkgVPNksxF2ydJjvwq8pcHkStGHesKNDy+FDq0IzKGZo0MRj063lYTVhjp4XvZhWThQeU+9QJYFc2AhTRVCy6g/55Ls4pnV8o2ZEKKpXyiNoZVQaxolrF47SFdswTTCg+F8s250h+igGOpSNnkOE2qCi0MosL9eIY+PLZSUgr5KIgz9mIFbFJDEOwmOuqRmqz85OJwolXMQGYGCXGBcFLIMbWRDXO0bbLuGCOOq6gx0004oHEVBqR3E/8zKWgwXubOLU54eqKUbQo4DZnJCmhMjqHHwx6uzQBPI7F5cYcZJqm8GKFamKI8ybwraq6R+AyhSomtj/HPEbkslrRH73x57vntE32bK4c5WITaePsulRcTiwZD2za0BWBSWFUGlR77q2ga6J5+yYx5w5LayiTKRFzocmxDxvqztuVNnUdhaFLuNJkYlV19M9Kh9gNQ4WFQRhKGlyFr+uCPCvSUSrK9DAXp48UUz/J7N5Ht6fYZ7JrKuW1c0FaK1PiWRPfAS3FmSIreAk0SQs/28Jhmmwotoohsd4Xi8LiExZ8XdLjio2a6wJAMKqeGlXDaBcdeGbUo3bUIGFIx69XtTTURHGkKY2Ec2MLthAqo1T117pQA1ZQFldSSjoapqr2tqompBsDNFK48rlMn4M48+p20nNdTYdQ9pyuEbNVRxBoi+VcpycQCtCoBQsoU92OVuXKFAtfpu3Ol2aqaA1WFLXdZxExYXEq+pDUR3G7fw1b4d9JkSuCK5ZUJtwzCNuibyHHIaoNSmsbrw9mwVE26coHUqEKaDb1nVCQOIwErQDiCFKhJONKVTXemdilzQkK+ipq3nFOLH6vkrGKsR4OaVMnnFE7AAoP0YEJqbKpCFs1z0ARAfE9KhPs26akJlrB5Io6vogSyOVhIhdkMRaOsCGz4rrEqkI2x9AyR4UL3Qbg0Swm2OiKFhGSwKA0ErnrlkohLKX7Q0zQJGJoCup6pXe3sEZtg2ZrAgaVKZsdlblIuBpPIpUNsDMZTzkRfOHoqNnlpBSMw9Y9Dyj4AS4XNDqaWExK3K70CVM2copJUI3UIgxGB5DlNzFMf8ozn/UUEp7aT/BreYFPvPdTPHHhNZ6QfgIAACAASURBVPDuBT7w0xcjGlgeNQQ/yWg0woexN3FHsQjB+Fuj1DKsB8UiKowpC6GGXNT/YgdFEgFngQFZI64ocn099n5uY0Nn/K8CGopiuIQlpBJNa+Mv7YUOKmroF/RiP1wgRjtY+rFYskCMDll1oSLmiARnXOBOQOkdztWk1PRc4ozggoNYlSmIhWS4rPhQjTeuIOYlmlpDcMozHpOOg0aCQzEaheaxJzQuGN9dHUkSkkHEoZJ7n2OwqQYYwk7XaLgB6oTaO1IWa4CL4M27sWAkFLV2FWraGK0Zkdw7HbTZxrmimc570rjSqdiiFe5XMusjlx2tmE91kARFIEIR8RWmT6FGVIgm4xW6DgX3JqpqzPbK3CVGoMU2KJjAtPLW2AcHhCFIQ24thrM7iOzQccSCUhdnOjtoS9Nv6PU4aQvoAxY6b9sYI1VxUrHn3lT+KmJBO5R6OjmqykE0gapzGRyFjw8zez7DYOk4KdbkHVcjJTLW3qXRepbaojWoHF7NZq0OHu+L6GRYMyrjbecr00EUVxAfCgUrJpIr8bdk41FOOENanUIlxYjfgJGYlCYvM/QD4/6nURnEm+CNwp9uY5fkZtMvL4rL2WKTxQrIYW28Xed8v58656grRzOKUBcnlkr6BKzgQ/nMFtihMVpKXYEEVBU0FJoKBZXPvQhoZbNXe2+0jhXj4SYlgrP3ZONyhyP2780mRYa4afFTzqqFPmOCsMoLePOQ6j2W+wZvvJ6yghg6UlpTK2S1stdOHTodun1gvB91RZuvKlBDu3tEtogEUzljhTGy2KGcTWzHtlRo7/3qQ21CKrrqqrN3M22Ic66nBK2c7kjZ66vKk5MHF4lqgSadO5FpJGwdSDZP4d7JoYBwsbV/2wlk9UhJ8HOuFHnZKEii1vykbA0JoQi6VgjWO5G1psgox4IqF2GZKqktDdGKs/p/LQ7LIi/UNOx81bFozM7uQgfFiltLdKvIOTEMxqXv7tnKiaumaGEQVUCb1FsauuBXXF8dB3k46ZMWO/1SGyOTRcgdHVQdlaY0e0laxJW46BVAk0hl+0gJC9MVdIh/6evfRZFrtK3KvPGcAx9521/9Fldtu5Kvfe49/OgP/R7TGycYDGdIadE8NZO3iM+kBdoXki8bcM6mrEFKgpmNHG0kbouh8sGQiy4VC0E6CxNnZHDJlIQhIagiBW0Zhqr4m9pXB+WH0uVqz2kyCkB2ecXoW3Eu470JfYYD8+kEjAvnPWgkue7wzobeJCPiO98hdOCLAXabDQGyTtYZYlQoDs4rPvqitjbxmTqI7dgiphfPuU7QUP6uQhTIUamqgVk5SVu2kYSqLzy4ljZbyIYVKRWxieDAlKCx/KwdIjFlXM4onqXlZUwlb3wmr4L4mrZpINimIVl730KjhpkNCmoqaNFM7TxHcWxo/5KNYZrpCfOp8BqIaRkvFbe+4jnsbmv+n3su5YrLtzCKc7hsvrQmLBubpHf/adl8K/GWlObNI3dQ0Dk79AKhpA85tdhDMz4XfOtQB4gSWxOGRSCUxithIglD4TxRlbqM47Woq1eOkEIIkIx7WTlPq9m4Wq6ibVp86BAXCysQMcTcGr/KXAhyayhY6b6dt6YKcYScSU4tJlhrmpCJi4tMDKdx2pJzRKisEC1iiySOoGY4jzc0ihxwZFIslIgi2GybgiBnsfz0KpCxxkc6pwMMHXZkur27LyoQVKLZ/XhDIVJUUlDzyC5K9q7XF7UmyLxUHV2qXLK5BppiocHY6F7wqKjZe4nd3yxaJhEehyPrCDWlHJI7xDugpfhwWIRn5U10kyiR2d7s/CbqutBkHMNBx9PtXEQCmpv+cFEx0/u6rnvqB5SRtpbQloIAETy+iDT6sbKMxR8pJUJpfCYnJ8cIb0xQbNGcpmJv54v36Thhqktf8mpiJ6nsZ0KoOOPW0978YmYf+AxTA0d0w54ekB3Gq3WOHJVYqAtJOr6/QFTaInhJmKraEG8brCctd2xQ4VKim+3EnPDR0+ZYhKqRKL4IihPD4BEq1Ne0o9YCf2KmLc1QzOaVOgwTJE0W1VxEa1J7vK9pc8ukt3mjL40KQChNHQGL304JqgDJ96Nnc/igR8WCK4IrxYAN87bqD/SOrtIJqM2dwIIAUhgX1yKeHEcFybdJSyc4c0Ww1bYje85zKPc/k9x4bYTO5k60CH8wbnOyPdu5CqQ0/lVNbiMajWtPiTQ3dwvjSvvOSi9FkmQ0h5672gEGbdvisGc7Zhu9d2FKVih500a0Ccmmz0EdCTV9DLYndvt0ipG2KU2S931iongLtUilaTJo3LyXyQrBmupYRGaxyVSVw/tAzMvWCCWBrgiTApyh0CbE5ULxsSlajFDVZrllHrnl8/huEmhAjKWnWsGZcio6EJswaOG/igOVcWnW0bPMxSiZ04D43tmldtbMxmQFp6hRKLtUMcmCJBMnqtCj/eZuYGFLki0ApvfJTrGfVlpZSU8D6UXhTvBFpNs13ymZzsmVv5uy7YNRlexgorL006aJlhRaPmO2RYCvjVIWXGW1U8omFNXxT64sfr+br38XRW7qIiNFiLS41vG0p17BVz7xeVyuuev+jzOzsIGz9w14xvc9BSkG8Eg28/jUoMGT24z4yjrIWmiXXW8jg8c6AR8ZVBW5zah4fHBQQYgZ54VYUly0qGyVEi9Z2ajARBQRR+jHM71ApoyPg1frep2g7bL53JZRMS7h1LS6ycNim1maO8yZ00tsOu8iJl3xpEUhmVUZKsjQ8ZEvv5+ze4VbnvREtl14Lk0CLdGFxqnNJc3F02rbMR3toJIuNtJ6XV/XxZtu3OGKSPFNLbYxTWPcZ6ApMbJoRRMbRMx7WF0uKJ6NmpbalrocxE1rCENqcrEri6TYMnBCJhClJauzxZ+t2HODYt3jzPs1hIRWgktm1h2zo/auNC2TtKIMZUBVjfji3buoLnwGP1b9Nenkl5iYfBZLI6HWSHZQu4pcJX74Z36UlBeoXY14xUvsudg92l1cLDrUBMYhCpW3+MuuEK7dGO3u43hThA7FHkV8NUB8MgWwmrWNzw68EiXhtEYxW6ys1tikcjiMEbXS6ReRmflUFh5xbqnqgtYlT4rlfYtx0HJH9i/RjlKgvywYIuYEp5Cd49TcHHv27+Tj73s7xxePU0/MMjnMPDSa5Oef9zque/L15GbZGg8qvJp1m0GeGcVhKvsKX+4dTmmjoZ+Cs1LfZfNyrGpaSTiM/+7UNsmOf71yU+snDs6mBCIZXwcb13rzEe0ER6K5OD84u9bS4J3QptamAd6iv1U8SkuOAsQSN5nwCNk7gjpsVpJREnVtSnctkuSuuYzJ+OMm5LCC34zdHZIM2a6dp8V4k2B8VB8CnWWU1xGtNxFlk5oyKTD01UtE1Uz3S4tizYuhBIbYF2EomuxAl+KsUKg2SjJEtjQkiAfvkWRNqTgT1/R8RNcJDlvzIBfp90MRQUryWrzi6aSjpzh21W0sFdcEVe3XUIc42bg7MkrSU5u8CpFoI+tuk9KmjPaVjBWWoxRplpYZFvpNCBXgiGLP3HJMlrxUHAeccyw0keAqRssNKoqLRVip1vgEExmQaG2CIcWxJOSeLjIo07hupCy5oHCazR2g8EHFlShdZ64WVlzVaLb1RRrH0DoXbE2kEV7KeZLHUdVJxsEKEWsMY4xUndC6TDmTmJNJRtBecGdroItNFU1kX5C1jrNJabDFXCNaxcIrSAyGgbY1mprDg1e0bY2aIxg/VcYUgW6EHHMkk8qUziZHicyE98axVitm7BwsEwIxZ4ExzTKT2rKmasg52GSsFFMdItg1/SLjlLPUJpK30rNNNt52CLE0rUYIs1E56nHFlQVJhtx7iKMiOtVMTJmko96rv3JGPbP605IKKQlfIok4AgkWOhJzU0AzX84qbwgxmLuKZoTaImpdwDyac081MAClgG3OnIUQK6Kz2vquKgt3aAv6LoXrmjT351PH7VWRQq8Zr8OUzDnFiQnJvQpRwGfTU3QpeZTra/tV6uuNoK740tuZ5bzZi7bFfxrK2RScecQWdN3UvUqWltyW51zs+9pK31h1BXeKgvf0zZ7RFb57wsJ3LTwTES8id4nIh8vvzxeRr4nILhH5OxGpy58Pyu93le/v+Jdee//hPezcdZgvfuV+aj9gTpVLLn4O1z7vBXzxsU+CC9z70EdYbB/hDb/yKvbcedg4hGUTcHmSisD9h/bwvk99iaqeQPIEdT0AX9mGnFeDVAzCDIGJIoIw5XdKhsR0OdWmCo6F91oZ0li4UFIEIUB/+FSdkrMcCE1jRPGcKx46doqjS8rATVD7gNcBIQTml86yvDjH/l338/ieb/LQ1z7I2b3HaVJTiPYO9QHvYOfBR3nfJ97H7LkzfOvgO/nWzjv4uf/xq6hO2SEonjY3NIrxOL0grhrzvMoGaaObwpEs46g+Ac6NR/SdGM9XFRZyYI9UO0pojpCNp+nw0Hbj95Edaq6iyWoG6INAFDVf2tyAmFrW3BWgcoK4WLxZQWpTd09UgeDFBHnBMXTmLDAxGDI1nKCqKqZC4MTJB3n7n/0OH//A37Hn8ZPMnj7Gz8/dwnG5lCu37yenGu9NRR0oXCc3QPAEV1HXJnKs67pHNzqCftepVj5Q+cCgMjV0J/YxSytbPq1iLg1iCzNmEEy5HaM5LcQYiS2mYG5bQ3d8EZao0RxyGi/HXuBXhJgiwqCq+8hMQ/k6Zok5BHSEfx/AF4HLWOBTxlqMrWWgU2KDV+PYHs1n+K23voSPfeQPWX35BhabAYdORU60DdXC43zo797O7f/lvzNRrbeI22BuCS6Y93RdDxkMBgzqKaanaqriSR18zbCeIKj0iujgKipfm2ATR6VlxO4LulecVbpDtGs2uiKh45mG4nYAxrNzSC9y6nj6TTKeumSje3TUIXVCLlZprlJ8KKl+4os2c4VLizfULFtsEMEb3aJTzDsJvXhPxdM2SspCG0tYrQixm1aVpr67lzmZSE99ZXaAuaEKE/09MlHfWJTR/T3nOkGuUknhuEaz6OtcKbrXyNhUoo0Qk1FNuuuZC4dbcxlDF45lzpmmab4jPEfphCG+KLgTyXvmNp/DEvaanVdpzxl0htw1TUNbfKI7UVITY4kTDygVKTrMmMDRWdq1qcG+a+hdJY683BiFJVrxrdFSpazhN15kVVkB0ZKsQE+e1AioIVJRTGFufPnCx/dSBEHmOpCS2jVrTLBkoleK9sCasJQSTdkDY1ZiMqJG9xk7Xr9xGDuxq42og697Gzlb177nUjaxCHCLILiJbbFqdCZGKshh11BoQeNWisUiYw5t915yHIt3VW3SImJCaJua2UDY9CTSP0tJs9ncJStWcs5jZDsEQkncFHJJz1rp02vCV3wo64jedcdQT/vz7rmJMZvILiVgLHxTVfPJx9HGMQouXnCpRNMnAwNa0TIh63Z3c+BootKMQLMv18ioZK02Pfobxc4MLbQLoz+WM1ULpa/oFNQJ2SkpQxfX3iGjOdmzn1sxYVrHt5ZEI9GmAdg0CXVFFI7dg/9lkmcUvy5IpFsj49H92CPd1nuMccUUuWucxlqhTqCXo4n2ibk8w+PJwsrGwgSrtYnXCh3E9Bb04FBwHlcEtQ5D9rtzdex7bwJRcVrAnfF9735dF76299KL7f6/fP1rkNzXA98GVpXf/zbwu6r6bhF5O/AjwP8s/z+lqheJyCvLz33/P/fCnpbfeMev8KKZl7H4jGku+95buGkVvOppt3P7S3+Ab973ICEc4NDZPTz9mc8gTCaEmsf2PMa6zRs5Oncv/+fbX8cVm2/gxL6GW694Ip+8709Z7S7hJbe9nJbIuz/6OtaufjKP5wN88zO7+ONf+2P2Pb6TczZvp41LrJqcGo8jsAfs03d8hOk1m9m6/jw2zE4yNTVBXXxr9x09ysbZtUzVZsflPfhhzeFTx1iqW971t3/C1vMu4/73/AVrB9exbsvlvO4//zBfP/gA9+/cyZUXrufuz36cJ99wHTsf/gpHj9/P9dxumziQJBNTYqE6yR//9c8xcfgYz1z7q+z8zD00Rw4xf/IYf/KLO/iZN72OBVlgbm6eMFjN1ERF0K6j7sRkAe+GnJg7zdqZWRKtedcOAnEU0RypfF1qNgfqTFCUYj+qEFcRqtY4R9429iCJalBswqIVIiO18YT3gmRHXQ0MWQ728Fcu9d1YJlElCLW5XKRCoeg21doVtbBzeId5I9eex3bv4+JLL2DfsTPgFjnTPka79BS2PWEDF993lHuufD7nD3+XpeU5ZoZDWsq4l0AsY83e1QGoQ7BFbWcHKD3fKIhRXkQsbSw4G+UAxXbKnuFsID2eDsk1gZBk6+L7jbggD1HBZdvochEsqUYsVlQIwRMLspmEfnQUQjA6jmR8DoWzWyE5UYdOvauIqwmVY7QcUW0JPpFLASjFzijnSC02tUACVC1vfcdbGI3OYffSaZa/Oc/U1obBsWUO7gedXMWufJjZHad5yi++mM+/6V2WIlhG0VZwVWUc2OKpioK3U7grvqptnakVLNAVYLYTeGcmm06kVBxV34qrKmRLNVMBzYHBoCt8tYSwBLI2JuBK2tOIUr2iQNYST9uWIJfKONPSRZMiJNeJeDzEFi8l6cpXNHGZQZgqtCDzyGyT8cRytHjXXFDP1JZEPBWaglYgnfd08bSN0dw2nAUBZAXvByRGkMfvG+xSaonFlMqT224cS/9+lHLpUocWJ7J6S8pz5UCTjrtrxvG9j2cyy76UIp1yPudsBQ2pIE8VTQa0LYKSbI9PtAKw80YVtYj1zu3DIUgdSG3si6dcSDvOVcTW8FTnPTFl2qYT1lFEPYWyVJqXqhqQswlx7cC26RNqQrpQWwpeZzcYpcWNih1lAieepoW66lDCRFBHRKmLU4J0NoBeWG4TrhTLIpaiZ6KibI1FXbPcLFHJgOggJOPQdw2HE7NCcxL6wqPjmJKKi0NBT+2zOoRk9zwZglh1dDU/3lM6vmuOEe8sDW7UeRgby6L3Ke5R9fL3uuKpqqpe1G0/p0i2Z6It0bKCodQOj0oqa7QqavtOnLTCMYLM9MQkmk0MLXQWbmrNCOWcSh5Nrvw7igtilCoZ2v11iRAGhqKrcTk7+kzOVkitCo7VmzcRxLH7S3cycf5GlqIWSpcCxQlClCx2TS04xrQrFi9ePHpXhkkle75ioSDgpF8TihBjSxTFa3HQoOP/lmsg4whzMBellDIqFSlaCFFUi4YeDkrzV/5+d686RNZXgVgS7FIqk+JiHdr5iEe1ZsgAD/s4IubB3BXMzmcgWCx5Hqf+2ev43i1BZJx8h4z/LKW2BNoYyprLvmh87IC2kexMb5PFKGM5twRx4CBQlSnkmKIlTmmb8Zro3GB6lD5ZQBVpbKv23X59V0WuiGwFXgi8Efh5sX/lVuDV5Uf+EngDVuS+pPwa4D3AH4iI6D9ThrcpcsXqI3xr/me49w3KSzdtZNOWZzC86sn8z7e+juc/7/Xs/dYuXnzbf+bj3/w8H7zjMb7nBTfxwMMHmTj9KPPH72Uj23jo/vcTRjv46N1/z92P/QP7Fw+zf/QQV2y5kf1LH+fgwW/y+OOPM1ufz6//xe/RHHuQn/3p3+DQ7jNce9MEOZuAZTSyXPjV58xz/4c+wcwNt7L5hkvZfd9jXHLl1Sw1I+741G9x1Y6f4PInX8ZSPMF7v/ZBPvjpu3nZLdu586ufIciAR+76DEuc4Yxu5lUv+UXe+E+/x+grv822K/8rH3n3W/nSp3eyavJ5nFqc5/Ci8O2de2mmYe/xXXzmkY/ytQ+9m4u3buDxky3XbrqQz374Ley4YQNXXPwMnnDuNTx8x14+defnmB7UfPzBD6D1FEtH5nnF03+Qa6+51DzpsvLRB7/Go4fmePSLH+a//uqv8Za/eAvXbL6W+SX4iVfcTuvMrkmyMQhT6iySLEgiNRFPNPcHp8XnNOCyIUAiQuVMTDTQVJTaBWmCUgAVFEwcIVpRI6pM1pUp/FXxlcdrhTrti6O6DuSU0DDk4OljPDY6yp1ffSsXHHgSd3z2XVxz4a0wfAKbzt/EyE9w/ad34q7bxAUbZvnEt77NTU+/moGzjnAlV73b8BETB3R2VZozoRwOIkKbM76oz6ELzyhk/LLZ5mwGc0FNSU9WvGZOnpljzexaJDd4Amno0NEICPjCCkWELl1o7Gtq42UvDtTCN3wokwbv8E5RdYhXE9Zp5EQWFk4usnXjOk6dPsnr//A3WFPNsGpqE7e/8AXMrF/LtsGIRmrIA1RTQYFrwBqqMwtH2HXwIQ7lSbb5aRKR/Xsadi6s4qmTI4aDKZr5lrkH57lq7SoOHjvGN+66g1e/7D/i8Dx27Ahb125G3WkCEzx84gAez9zZJQ4depQnXXsL+w4e4LytWzi0dy8XX3YpZ+bOMjucZTktsrqeINGCeLw6RCIpSJEuJVRtw9Piwarm9WPXxCwHSGlEVVD5CW9+k94VYSaYtZcGGycqaBnRDcQZR9RZ0e+xZyJHJVSB2DqLe02Cl4pRu0zE49PIirkcEGcoTke/ymLcPzTTJmuhrQA0hK8Sz6jY78VsE5xKPKPcmqAv22EHRcTnzT+4Tcs4iuVbVpxmYmcFRDQ3gyi9y4A1BNmuX4y02aZgRGs8W2IZXTp8XTNaatDKE6PZF5mNl+JiNjFLiORsYpsmLeN8hYtiLgAuo9GRnKnVJSvLOZa0w4REg0uTRoQhmUzbKokRQY3X3GYt4tREbG1kqRRLtti5KgiZFlSKg4ddpxwc3YgjiUWixwQum4DXZEFq1wk7hBdGI4bdYQrEHIvAMiLa4kKJ+W6zcZIrEwxFEnV2pGwBLKPFhqoeEBXaqJh/taPVBo9HpEQQ42niMr1VpAh4owslUWJrXM6mMX5/KnGnSTO+a6pLYQnm8CIIMUVcaRh7e8BeUDdOxfKFb70SyVtpy9XtcS54UmwIlaG7HRqKWiPnXCa1lHj2XPYTK8icZKqJQc9v9r5iqV2mLhNGDR6iAQfq7d4FlOBqBEfwVZmsmqiwbUp4kfc4yTSLkYng4c1/wtl3/A9+RzNnwxre+rZfZ+0FF3JifkAczlAlk/ip2nUQ4xHikjXZjcv4rMQ6IdH24ZTtvaQotudrhlbAR1JpbBBQhOADbWpIGqmKCsd46GICdrFiNKeOh1x8xLWM3LVMMCSyMOqEYYmBcZDsXmfTJmS1/UXEXGpSG5FQ3Jl8IMd2XBRmCzjpQLsgJbUuFcSfktBWQk1cEVOXm1kmYGNHhy5KN6VOiNYWNN5sUZXW9CqxIYQK70thnZUoUAUp+7NZqHUTjSoMrOHI0iPNMUaLR28bwF7boqbHaPG/5ku+GwhYRN4DvAmYAX4R+CHgq6p6Ufn+NuBjqnqliNwPPE9VD5TvPQo8SVWP/+9ef9Va0dd8f81oOXPwSGZqcpb5E0tMr9nIbErkOMehNnD7j/933vOPP8f01Hbm2gNMTl1MdeoafuWXb+VXfudt/NxP/jR/9vbf5dTCbi695ka+vOcu/GiZDethYug4u6uM/phg4+pF7prbwHUz1/H4ty7lbz/yy5w8e4rTx4+xarCKU2ePczLs58A3HuKi7U/k0ssu4asPfJOt5+7gwK67OPeCKzh1ai+jEyf44sFvcteXvsW66QhyCEaTzOkM3q/i0JJnzVxm3RO38Mi+o2w6vsi1lz+ZQ49N8MNv/B7e8dY3c8EVOzi49xtUp5/Ora+8nn/63J/x2ImzXLLxMh7dfZBXvvhH+MJ9dzJ/8G4W3D4mJj2b1l7FkQP3cMk1P0vb/g0nTqzhvr0Ps9xMcPHap/BLP/u7HD65j7/92sdZe/IgVbONS6/fwJ2P7CamERetOx+3OM0LXvQSGAhhohobiGfzkhx7UaqhDxp7FXZneC0lJlnEzLSdpPGIUjxebfTbPWfizfMxacZ3HscpQ1UI+OrJGLdHRYipofY1S/kwr3/jKzl+9DCXrzuXs6cOc87WVcydnGbn/CxPetoz2EjNrsOruPr7buKnpm/lvZ96Oc969R/RLi8ZjaIbWXdj1/KeOoPsHoFIXehGxEzMbKyCFoN5HS96oNjPpd4lwztHaloe2fsoF51/JaFK7D9yiJ/4P36Xj7/z98l50cZ0rqITkaTUruiU05jnjYkLXLHiWSlAyzlSUfOOD3yID97/aQ7sOcYrnncbb/7Yf+H64TO56/Ej3LhjLZurCU4tHeUHbn8LN1xzPt++fw87tm5jw7rVTIXMco7UledP3v/7PPDJL/KPy8rAn+IaneXYociedoobr9rK6vQITavMpUnaMMezzr+U+cEMT7npqVyyZRP3PvptDpw5xs1PeD6BE3z0w29BpqbZuvlGBoPEN76+k01bt7O+9jy+PMelF13JV+/7Es+55Xv5+y//NX/wY2+nooUsLMdlZiZm8L6i1RaNI0NZW/ogk2GoeOzoEc5Zs858bQNU2Zqj7vYI1sjknMGbUMQAMGsexNUkzJZHnOutBD0KYYIYGwYiZIQmRqTytEk5fOo0q6dXMyGepriX5C58wMTaII6sXfw4dHY5sQXnrZmBaD6aGsEPSsRpGduVYjlhfEDvHGSzliOL+R6LUTA6WzsbSS/1VITxmNWQdICYpSCKxa/WZYIbU1qQIaO8RDEmI5VReeWxQj+3RDLVimQt70wIKSq0Mg56qepgXFS1z5C6AsrRUxG69ynR0MuGUnCLJ2NG/E66BCvpVfiqynJqcd73MaxgHM/uumgpuMAS/AbU9nn9ABGxVLcSUhJHDTnmPnAmqtmJpabF4fDBCq5QCpm6NsTbBW886d52aoyQ+gw+WMHS+T3nIrzSFPtmurPiSppxviK1Y+9wBUYp4YOjkk7cOEazuv1sPOaP/b/fJZF1yWrdV+f60+3V3bXraC1tifztYnWdc+NkOGcNW0qKx4SFntoEVHDZpgAAIABJREFUQmClnrMicbzvO5YWR7aP0VGtTDQqIuTgqZ23eOjKIWpJW85jnvACOIubHTnlnEbYd8UP8Jot97I4P8OWwQIvvfg0r/zrBzkba4Yx2XTKWdKgj0Usl01AbjZOjiSCi4mqqskdhckUu/3Y3nDPzpnBFAXq6akxOZtYtxZzOfHqenQ05mQOM6or9hezIzMrN0oiJsbvLcirNkUYXs5WsDAaKc+E+QurCetVTTSbx1HHTWz7AI+Vtm+Vt6K0qipy035nkExnBdcF6gArI3o72kLXHGWNPdLcbX2WONlRT7r3bpM2YYXA0lVG+aG4AK2YbIhICY0o17z4TecVbh3Pve253H3PPd8VpPsvFrki8iLgBar6n0TkGfwbFbki8uPAjwNMTnHDs2+BzbOrORNGzMzWjM4ucOLxiG88bT0gLbS4epZzNnpOzM8xsbFiqjnD8eEzueXWm/nWF36LB/avZcsGz+hExXU3XszXDzzIxMIC88s16DRT8yeZmGypK5jenvEhE5oJzvMv4Yo1r+XaV13A7l27GE4Fjh09y/68h7s+8DHOv/RSTrcLXL/+cs7dvpWGhqm1m1habHjgzi9w7ffcwG0/fTvnyyzzZxybzlHccMRi8sxUW5lcHrJuaoJ1L7iAQ3d/meVjyuVbn8VLfuB7eeSxT/L+L/w95841XHDZUzl26FH2T6/ioTse4kmXXsVcdYzqkKe+YS1b5hq+9tjXmDh3GwtzIIcWqOqTrKlnWGaRjedczzkbr+R0M+Khh/fyqtc+m09//Itsqs5l48btXHbtlXzrvm8wf/Qo2QU2z1zCpgvPZ/3MDNsvvIjhREDFRo1INtubNuOS0mS1wzsLGjIU0Zh4wbWKBA8JurjgThyjaUTla+PUhQAxMSoLUSixj7E1QroTQyqcxcjibHzUEhkGx/33fYS3veMXmHt4knWXrmPu0CLbd5zDobOnierZss4zf+YSJn7wzbxt7RM5emxEc8E+ZmeEmJaofE1Kbe+sIYUkr5KRVKHOMReXmZ6qcKPIvtNn2fXwo0xN1zz16ieS8hKumubwyZOcMz1NCsqeY8e4cstWYlo0flf2DFzi9NlT/Pwv3cKzbvgdnv/aa3nTn/8+++97gOc99af43td+DwMcXgdkiSZMUFP8d7xZb5AZrqAQ2VkqUJRMRcWjex/ixNIiO4/s5p5/eicPHnccnD/A2moDDxybY930Bhbm5lk/2XJoYS1rZyqu2jjJZ91RNrbTLJw5zvxk4A3P+WX+00tfxnv+6W3su+9DzM8vsFQvsXbtMmcWYOFIxfrVq4nNKvadOYlrlti/aiML+0c887zjTM7ULA63k8+cZDIJC23NydUNx5cDQRomzgbOlc08/YW38IUv3EGqlpmaUfacUrZMT7DnzH6G6zew3gk3bHkFmy66AOdHvPNv/4qbr30SD58+ybU7LuV5N93EzGCA5khLxVfvuZ+du+7imw89yMue/UrqNZPsefAebn/Ri3tzf4c9j5nQb7Yxt/jKUK2heAt2cMafa8kYrq08fvIkf/+5T7NmzWb2HNnNMNVs3nouO++8n6fdeD13fOWjLC00bNp8GT/xwz8CYkJDcZmYwWc7DPvCq4ydu0KyK4DsgLTN3mdQbz7SnfdxLgLIlM1QvZJMbEuoDXZQO7XP6Tr+NZlB8KhUfSHTRCvkUTs4g5Pi32u80kEdaKMVERkrJLvRJ07RKNTOBHiWlGRK8FSKJF/+HQmOEivAwI+t9tqUzDHDl0JXYJCFiEIGHxw+Q+rcCLKhT8Fr7zHboYwAsR2R1dMqNE0DKgyqyiiNOVs0q+s42QJSwnSK3s4OWsyYvkTLVpWF4SwsL0FSkowb32QvXMbCdti7gr4axaVLR4RRTGgBxUTUki2lc/goky2fIZujTIzRmurWeK4dZ74KgdhmlmMkVCVq3ZutoHT6iVD1P+9ljNZ2IqbOyxQnPUe2s5OyxMJxmIIKPd/YBfmOKGYXStqjFl4pNgnqLLWyBnMhwQARnCHfWc3Bw3srWJZHrfmdFkTXUrAMZJDsCVBog0YpcmKca0VoSJY6isJv/w3P/8ffpDo1JMwOuPXUMX7uzg/RTl9hVLyC1belQbSmx2gxKUX7vUi/dkRNh5GKWLVN9rmaFI3KpNKfGZIUKYEsLnhim3GVETC8OEY52uctOp5UBJRdc+a9Jy+3hMKlN19ic6Kxs9cK5pV0FA0OcjN2PHDjJiW1NkmsS2GZaY0TXSwHu2c4x7KXJEsS0yx9wxWLUNl1VBDVFZPF4m3siqd0Z6FZOOxdYdx/lrgi4c2NBYIiQpeE2AVKwLgRd0jfHIh3aJvK5Ks0BF3DrsJtz3vOv2mR+ybgtUAEhhgn933AbcAmVY0icjPwBlW9TUQ+UX79FREJwGFgwz9HV1i33unV1wthIJxdTAyrIbUm5hcGzJ4T2bknsSV4ptavYbjKsXYxs2a4wJKL7NKWhTzD5KChrjLDNMOx3ZMcno3cOLmZAztGLH51iePhAE+/cBk2Z7aPAnfvzJy/OjOX17Bh1dXMPHwVr3rn63njn/w2upR4wjrl0T33s7q9iA1XOnZ99RA3PfU5uGqSjVsGNAstn7nv/VSnMuvWDvnA5z7L0ZNTTNYzPLowYr1Os2XVaZbOiVyxejPzsp+v37NAGDqefPF2rr/pZWzYoBze+yEOHVFO73+U6Y0DqvXn8tD9jyFZuWz7avbuPsV8gHQI6vMr9i14Ll0dyO0isrCNx2eXWXtSONWc4prtNV//9jxXX3ERU+0s9ZptzG7cwqtuey2f/dYX2fnw/fzAM5/N33z201y2bQur1l/J1Vefy7F9J5ndegEP73qEA3v3semSrTy+5072nTzAxduu59XPfg3il03MIImqrTial5j2NXWCUcgQSySqWNoQZPPyzS2SzePSa0Zd3S/ebhyjMha1mHAhkorXagiOxTazWI248UmX8axrYNXUOuppWD1xLgtnzrAQ90KYIU6vYuHMCeaHb+R9v/YuwtydvORN/40//fnbYdOQaQXIxUO1IjjIzhKkGjL37P48b/jjX+OqLa/maU++nNf942/yhENDrtT1XHDLf2Dj1iELc0f54Hs/yNv+8K85PLeHM3sf5Q/e+QF+/Cd+iQ0za7j2iik+8KWvc2jvPXz8Gx/j+nAza564mY/9w5v5Dy/4EcLoal7wwqezZnaKmaEjSsLnMuILxbapbFadsCwlxQUFVb55792czEd46BsfYfbcWfbu+jTLcYGjx2dY2pc5fHzI4WVh7bpVxCahUy3V8ioONZmlZp5LNtecPaBsmsxUazPthmlGJx7naS+/gDP33ccTVm9ibr5lYepxBM9w1XqO7400xz2sXcv6OrFq5gEGs+ex67FtXDC1yOGzZ1heWkXy03x6fpFbtk2ydHiBVS5z/9ETbF63DTdaZP2WLQw8xHllyin+3CmWaTkVE+nUPFsnVzOaqTgxd4blZg3D9hj1qsyO2ctYPHqA2auezvc/9aX8w2ffw7fu/iLnrluNjCaY0BkO5jle84wXcuOTn2TXUCgUkDFaFVDwJrzLJZTDaVUSoGDPYweYXXMOqZnjyw9/goe+cScP7j/OxNQGjqcTrAmTiGauuORq9u//NqFpmJFzeOrLXskTduxgtLDEupnpIhKzwlrUuKjRLaPFQUPU0YoyoTXkkVmLkawoJCEaQIS68tTFIlALahT8+GAw3mtGwqAUfSVBLigpmvMMmL1boxbzGYKjclWPppkPslmIOTURZdu2NrGINqLOAim6gmYVEVmMSFXRJBME+WyuCeJ9L3LqeILOK07MlQVvftdVGBveO+3QzArVaPGuKDkXioLSH9bkcaqYionoWhV8ALfCsSE5Q2+dD+RSsNjhbpz0ShQkE7M9D5LFfHYpIl0F9Y4YlUZbE/Lk3KN1dsDbNCEL+CLiNSGjOd3kTO8kUhUfW+9DcStoyn0MPaKZKaJHFQbB23Qie5ba1rx/S8pchSNrQ3BW8EZKQVUEZn1jpRkXM9mbeMoH7YsFnDUrkpTszCUiuEKTKsEybduivvMd9uTYpW5awaa5jJMF0ECUREid841RT1woNJ1CK0pJaZtkVnlqVKKkjaGYGJ9fnJbm6zuFkwmPd5HZOeHN113EXw3XszzKXLNuPT969U6e8qe7kWgaikocuTD9Y7SitiscnXOlmXJobJFQWYNVGgQn5i2fGFPItDSomiLBheJe4RmlMXoaXCniSkNSUSzWxB5i+yyGgNfeHG5iNAuvVNxPYjTNSNDx9DAnc42oKU2GKnjFZ5t0mFMOOAJKgzhH1XkDl2em3zNK0mnHeV1JZemK5oIH9F7jFt5TrpdJCegEzV2R3K27DtnVwq8Qb24vqc19M9ZTdLLSauobWO3Mvoul2jjsg/7fsP0EnvOc2/7titzv+OGC5Krqi0TkH4D3rhCe3auqfyQiPw1cpao/WYRnL1fVV/xzr7t2vddbn6/sPQJ53pGWhAsv3USbW44cP8HCnLJm9TpGaZ7paom9h5T1a4ZcumWGve4MRw7MsnZmwNrhKSq/yMBdyekmcvepOa5bc5onbF3k7IIwHxMzZwbMOWX1mjUMFzN7TsyTq0l2bLmJ/bNHiI+eYf3GJbZNruJsVg4fOoDUm5ie3oGunuX4kX187zNfyOcf/hQPffkYTzr3Yk4e28n9J/fz7aOBDaNJjmlmsKamTWdZGqxi7eqGK7adZNIpB4/McP3WISwucNn123ngsSMcPDxicmlAPXWKRe95bDEyGEF7pOb86xsGDcQ45MFmmS1xyNr1I47sHbJ/NMHsWc+W6wY8vucU56xd4sDpVWzdPs3xnQe44sqtHG83MVw1z7WXvprjj32WMHMVT9y4gbvu3c+VT72ZI/4U//SBD6Cx4ie/7zW8+/3/N7p+BtcE1g8nSCwxO7qVn/1vP8m7P/Q3XH/Fk4mTc/z+h3+fgwvb+L9e/pPMTFYcPXmKS3bsII4aQ5FUQSIpj8cbneJT1IFLiKsLV9BI8eodPmQ++K6P8LLv+49kH7nn4Ttozyp/8Ec/yJXnPYF69V6iE3bvVo43LXWY4JKL1/G4i9SnhO0M2L1vE8/7w19g4zdeytvf+4M844mbefH3vIYN21aDJrOIkQyaCLNDbnv5yxltPJ/JtJ/1ayZpjwvPeO5NvPfL72ZCI197sOK6dUOGEyMmJ6eIZ8+iwxnr/NnDuavOY//BxJmj0/zyr/8qH7zvN1l18h727L6ADZsq3nX8KFefXc2aGSEfXcXyORu48PyreOVzn8+1l2zi+MGTnHvOJpwHsnJq4QwTU9OoB9qGEDzHjh9iatU63v/pH+XQ43t5bO40YXk1pzWwY+0eoq5n48SA+bnT7D6+irWzwic/lBlMbqB1wqqpmu2zC5x0S6ybbHnijlm+etyx+M2G8580xe79sLjNccnZAVsrZWn7ScJczRKB0NS4qQHN0hm2zLbsPDbNOlVOTCqr5yvWbnXc/1DCN56mcSwEYTSVuPGG7cjpRU6PMsv7zlLPnMOq9ZnFUw3HwzQ7Fs+yb+oMgzQLccT0cEDOy6w7dxMuLHH22DLVWqE+e4RRXmJmcoZGpjkvr+HeMydRBzPDSe55cIlzt2znta/5KU6dOcEFazdwwXlbaKIgagr2xXYJ5xwPP7KTredvY/XENA/veYgtq7ewactGPvzJzxJzxZ4jh3nlzZfzzg/+JW1OPHBsRJ5PLA2X2bZqxLWTG7n34BGGqwKeios3XMKyH3Dy9HGqah0/9WM/ZkVga9Zw3WaPJNqyUUsGDcKkGh+2LuE2zjlCBcOqLnxwZeBLDLdKYXEbfaWzArJkqZI0pd1BXZDr4kOrWMKVqJCJaLb0sSbboUsWkiuOAclQLLKp25NGozqEQN1ikweAqqZtW+PYRTFUEhuRtrlYABYqTt2Z0aDUUpv3eEHRYlaqcrjWzhfbqILmlrFnTEpdda9ndKLOYzdlWI6G0AUPGhNRi29wMn51qKSnaoAJzhItoegERDyCOcCEUJdxf7GDdJ7UtLQJQl0Rl0ckUSpfkTURxKJ+xXm8L8dw8IZSRwvwiVrS1XJXeEhPNxIJxJKUpupwobuHoDmSfY0rjg4azO4rYGi9loAC8Y42ZXxSnJdCzzGLSq/WqHgEH0wglcu0Acr4Xl3h/hYLK2KP0omY60r2Fk+tqpa2Vcb0mYRmoRGhGHNTO7GoeTFeeE4j6lAVFL6gyyXVsxJH8toXYG3MCJk6OBrTU5qThCTaCEk8+VNf4NEvfIrl0Rl0h+eW5/wQ9zz4Mba98PUgofhT23sfFZszVHFJGVQW2JGKn7hoAhfMHSerpZlmQ4yN+pFQByGD8wHBONMZsf3ZjRHv3NkOCsRWGVa1WflhoVGIWdxRRvOGYHgkmKVdqGyC4kRwOZnPby40HRKSsonoHKUpFeNgi00TFKjFHGaCy7S5k3aWyQEmAnPOtApdodkV/kDvp90JIvsI3+DRNpVEtzLtZYUgloTLhYrjjfcdcD3NwVd1mfKW2PcS7FTXdZ8TUPnaRHqdIDO2hLr6Dg6uE6NIPvd5t3Hvvff+/17kXgC8G1gL3AXcrqojERkCfwVcB5wEXqmqu/+51123TvR7nlUxu3qSR78xYt2mZaRew9LJzJonVBw6vcTk2fXsO3uYOMq87CmR3YeU+ZPwSB3QuVkmR1MsDOeZmhxS1Rlp5glnKtZfMs/a9ZHzVfn23hnUN2zZOKDVSFxcy8FjS7TpLG44hKktbF+3zJFjLVu2HuNUypxdVJZOTLBt3eUc1ePMcYJzmiu5+ebtfPijj7BwcjcvvuxSvnZyF1OV48D8BKeOKA/dN2Jig9JI5Mk3zzFaI0wemeaOIy2rk+dFl3uW1yTWTNccO3CctWuUh76dmd+/il2h5dI64I8rCxctM/CR6fkNyOqznDxTcXppnnPXbGYNkcbNcHTPUer10xzdd5i169ewXM0xP9ly2fQEzm3m6U+7lfd/4vNsWH8Rk+sqLnvm5Rz46Fe56Kpt7H1gN9XEFEcWh0xsWmK1m+LQ3AnaEwPaRlizbZkLhuvYfuOzuf/rf46mGdaft5kTux7jTPasYxZ34Q6q+YNs3/hcFrNwy423MjkzzVTtWFheIgwnSLFh4BJJKxpxnJ07wckTZ7nwnC00QRjUNbFZZBSO8LILX8GbPvg3/y917xokaXbWd/7OOe8175mVde2uvk1Pz32YkUYaIQkZMCAEkrku4DDYu14Hu8KYDa/DjnWsdxeMYddre2GJMNiLsY0xxhfAgECyjcAgJI1GMxrNTdMz3T19qa6ua2blPfO9nXP2w3mzWvgTX4gwFTGfproy6803K5/zPP/n9+OdD7b4lV//R3zu+CpRMuTa8ITq7IAbDXhH0qaoZCySGfX6GaZGEB2mNLYq5Is2v7b9PfDmH/Ct5NjaDf7Xj/4Km2fb5YhQInAn5p/6tz/F3u0Dbr2+w0GY0ZOaVbXAW0zobjUI5AmffKvONz3QotIQfPKLQ77yiubWNUFUk7Q8S6daYZ75+DNYe9eQkUmwuaFRqTA7gfFBnaNsRiVPmKwF1G4rbF5hND7iO/7776QTXOY7v+WDICKUD6N0wn/6xD+lUdui1V3lYHIHYQumeY9XPvebtIzherKLSWrEKy3a8i4qVMwPEqLNFrPGkGqmGOiL/No/mnOx7tPuSB55hxNaeNt1/N6UsN3AeB2SO7vkusFsEJCbGY16zkgdMzdVWosaq7JDY1WxMz5hdaWgPyo429xkvJhQr8RU1IyBFVx7w2NULBimNUbtgAfPxcxNTqWf0qg2eW56zHubNZSy/OZA8nVtRTwf4JkqKp4SyjbhisbLUrxKyNv3xpxrNKjXMm6OpviBx5pfpekHpPUxsYXjpILPGvujOReqOclQc7v1ONsixNM+K2vbfPe3fT03d1/k1tWrJIshk+yIsdeimhjWvA1mUZsPfuDdfPi//ZuoRouP/ZO/z5maYOfWG/wfP/HTVAMfXZnz5Hqd+WSKCCpM5xPCRgUzLvBqdVo2pP3kB4hVyEe++oNuXKvv46OWGXZtJEKLUpdrXAGj7rOWPXkfD5aXYHU8twQGrltZLEfvRY70PfchqAu37Q6wXI4suzhFkZGfEjxc51R5TvxiiqLcdF8WXW5iYI2gkG5hTRhLrkUpOCjjMwYyozE5JJnbMPfLrLApCSYu32sRQuP5cYltWy7XuELXFxKJG5PmxqJK4L8pLHiSwjjBzHKUGeA6r8q6MXdmXFdeLAUmy4iCcos5nvDISjzWssA01i3Ruu9dGhxF+dzKBZhyaE4p/TFIZ1Hmvh3PfXi74szZtyV4oExGIEJHZyjHreCKA1kuSxYic+SaJSdU3df9StwyGNpgpDNkuhffaYCFBIr8lNJQAFZqAqFQEgQ+xhYUuUVLi5IehS7lIUhC33P6YruUoriRvSu8Hc5v+fqkWiPKxU0HBZaI0jzm0GS6JDWUUgppCKzrxCJcMbzEALqOqMtPn6L0rEOIuesJCEVhC5YM48JoiiVzWUhSlSCff5HWkx/gd37p1/naP/dNxF7Ey7/105z95v8OYRTCc9MJgTssGpGRG4kv1CnrHs8tmWMLrCm7j1ogfBdr0Ei3ELy0xBmXFz+NdKj7GWjBctwAlIuSxji5ie87hsyppU675THlCVfcGbcoWCj3/pcC/OJ+brkwBk84lKjOC8eLxtw/5Fpz+r4XyhW47uDiOvdaONqK0EuChnEaaNy1XcZYlq/38tcQQpQ7OqY8BLqdHVvKGZbYt/t0ELeb818Wv+7esmA9hNT8l/WmLZkULizmpkxLeVduy78jS+JCSWiRUvKN3/iNvPLH0cn94/pqNYV956MKL6hRC102604/ZGvjgL3ZCjW/SsMHEYypVRdMjzKibkSnnvDWzlneuj0klQmVuEWeakSe85XvW2Vwc8B2O8UUIV40YZQLEqOo1lJW6oLBvsSPVxhbn/ddvMQre68QKo+JFcwLjyBscnK3R62TMFcxSnbZNitMWgnZruV4mrIZh2yfhT94+4RgphhMEiyK5lbIzRtTFk1NKgUNK2mPEg5rio6qIj3D+79yl/09uPOWYL1bITMJRT9mXPFpqAXH1YB31TrM9BGLXoWjZIK3GXHSn3J+tUt/f4KsRqhM88hWnX4yAG9Op4hp1iX9bMLdPjx6NmQcXmHxUoXGMwIbTah5glme0++/xVpjjWiS4F3scpBFRMOEbNpm62wDr5PSpE7SK4grEhkMKSY+2g/QEvTCkhYL4lpKoRWZN2crfiefufU8s2SNH/uxf8ZP/D9/j49++w9w+cENjvo3+Ke/8s/xZxEPP/gu3v3MU6STKZcePI+VlhuHr/EX/+p3ctae50d+6P9i5bEqf+avfwO1UFHNPdbqaywOPQRz5GbMCy9rrDdkdb1CkAVcXp1Tq7dRDUN+p2AxOEcjho+89y/w/u/5kMsTA0jFq/uv8mN/60epeQvSHhz7Ce98ts0XT3aR/Tpx5FGcDKkGFY6CkA1P8diDMB6dsNersJjP2dwQqFkfIzpUA0Maj5DiEuGWZn9nwqAnGU0W1PtVKtUZ3pmYWb/CdJFRnGvz0OqIh+2jfN9Hf4RbB3f5+X/7qzz0YJOt6IRbieShSo1kMObMu97LWzc/xTTZ5+jtqxwNDtg/WsMX97hwOWCxsDRinxuHXTYjV/gUFnSekS0qVBpNKr5geGRY5BNq3TaZP2WoxjxdXaPXS8iyiHEiOb/hcZD1wGqCLCSZVql6VVqNCjeGewxFyjlV4UwkWQSCqG1o1hWTZEKuW1x/SzK/tEJ/MWdzfoCabtOrJojjlOlawKONnMv+Gq+c7HOuUuH2/JhmGqE9jyuXG0zMjIbIeaM/o51ViGWN3HhUvIBGIcnCBL+aUyElyzWmvYWIDBELVGYZJQ2Kwxy1OmLhVUhTycWHL5GNq6yx4PffPmK9MkcVc1JjeerKe3jhhRf57UPD3kLzZ9/71WxuNvmOpx7h3o1bfOxjv0z1rCs8vUATxxqRAZ6PlSHZXLEVVrD+CnqY8L5v/06efPhh/MBDCUdrcQuv0okEpNuoLooM67nsqZSeG0/b5civ3DJWktwahPDKhRWHOXNjv/vjXE8oCmucBZH7Y74lKm+5xLjs7QjhmL22LEK0tm4ZxGaumLMSrzQ1Lj998+WiqHHPgbIzmSwKirKbJcT9hU7KZRvPd8Wx+8AXp6YmIazDUVmLFQZrnJFKWZf1XVIqtCktejhuNrpgKQYQwql7TwkBypQHi+VIWN6nBZRoQseAdVIIay2+DCns/dygtc5qucSsKaVIswJbCkqstfetT7okWywXvDD41hUmTkTg9gqs1QQqQKcFQrnYgsWZ7SzO6FWU8S2nZzb4zmB0eihQQqJ8z3FxSwNkIe6PucMyirOM6wBuvM39RVuXV3aLggo3HRBlRMMPlvSSL1tyXWqWNXieYycXLFm8TmpkyjynLRGGnhUoz/FknR0zx/diV/Q7qMIpPlDbAmE0XuBY10VO2blzo3x8BVaQZRmRY71hn3+ejfd8Lb/wcz/Pd//Qn8fTkrt//0ep/M9/DV84wYq7cD6UEQ5KA5lSosx7O+SXU/Tmp3zoHLfzgMjJjHWxAeXqe1F2yB1Zx1kZ/9CXcHEhg8Vqxyf2xZLvapGh79T0WYEsl9FQjgbi0I/O/OgJ6agr5WKmX2bStS1RjMsFcSkpsvI+FC4S5CuHEZMlj9wK9xjKUBrmlItqaFseHpVbfjPmdG9Blrn7kjHjoifWHX4RbkIRhB4YjTbSkZiEKhX2Ja7OK7XZOLun0WBEXhbUruuspHQcZLfqWB42PBcVghIfb7HS4gl335dvCb75Q9/0Ry5y1Q//8A//Ub7vj/Xrx3/87/zw0497vD3QjHPJQ2stbh9pzmwFTBdTblyVoCtsbc+52UvIRhUWiUIhSAmzDVPUAAAgAElEQVTIsgivVsfmE6ytEdQajEXOI50jboxytJ8yTH2EFzHKIs5uwvCwyuXmZWxtjhZzmt4TfO2H/zRvXv1dxpOYVkfjJQEn+1Xmk4jUal7ai2hUcrpnDKszj/ReRtCsYljw6PkAFjGv3ptyNJ2j04wzl0KUHAEZw31NWPNZi3waxnJnMWHSFyymhm5rBd+fsLPnoU2VTjXieD7mrpCYMYwXBb4XsbsbU7MZvh+SH43xoi5iL8RrLrg+tKSiQ10kDI8SdoWmdwTbFzxOZhnZyQnPD2Me7FaRIuOVnSnZ/B7CbzM5anIym/H6TViRAY1qzj3TpxbCwXhCMQkRwZyxLxjPLTvHC2IhOBnNWVDQJKfQc4K4YDiOGacDtjptFuMjPvmxX+KBc2OipMmbd17h5//FXyPf32e+eIn9t7/Eq8fX2Tne47c/+3FkKPkXP/VdPLL5bg4ODnnh9RHPvOcKw6N/y8kQ5uMz3NvzmNQSqgufSZxz5uFjkgOftmjQkJqgErM/WTC4O6MatfFXCia5YnD7Db7h278XKzOEDAjDkJ/79z/By2/uUa9UkZNjHnvS0KnPWKjbXAnqPH55gD8POR746CihmwiyapvF9IgdmfHqlyw71PBT0B2PSqXKTi/hZN/SuzEkXl3DHmXsF5JJS9EOJZ0ipHeoCTyP8z1BQwfk8YTnB/cYLF5nvvt7tFoh2VqXei9hPp1Q31zhs5//RRSSz7z028RVQdSATnvI5e0G6ciymMSo0RqDvRCzmLG62UUkOYeLu3jRWTZFQDKz5Isc44VMrOZzpkpnHDO6axipiImYMBQ5Vofk2Ql1qYiFx/GhT19oZicLROpznI24stZhVmSu0LOau5OcsZGkgyndS12uPz9hLjNiT3F0Z8aZRsZXPNbBCzXDeci1neucUTVGhWUx8QnajjAhZz4Gj3rdY0FILAU6E0SBIQLQkkIKiszHR7EoLO16ncHkiCRxODaMxAQF3VWPL041NSSH4x57vV2SAvJijlIhPZMh8hhb0fitgi99aZ+zeYvb+7u8fueL3Lhzi4+9+ArTSoMoKLi0UqVnM3KTUkdwNJ0yKBbkOmCsxxyqE6qTGXmlwsWLj1Gp111nylhyJIHvI6MKv/2Zz/Dc6y+zN57w6utf4olHHycUMCsEgRJ4KiDLUzfSFW40CgJhjDNWlYtrwhq0lVjhcFmAW1oTzqemrS1xWU4tbS1o4cqaovzANtY4i5IxbjnOOna0sQYrPYpyTIuQKDi1KglAlEWe8j3iQBCGAVEgqUSKSuATBIIoVERBiB8G+CpwxarQSM8hlKwDcDKezPn3zz3PA9tbVJSPkQYlFIHvOVsUBk/JshB2xYpDKZUVCLi4Qlk4xJ4b6ru2k3AiHqUcgglQUhD7oePKKkWgnPnJE6C+7LABy47dkvZQdqzLhZrQd48nUXieO0ZEKnYZTANCuW6b53luS99z+ngtcoLACUSAMl+b46sQJSSRcmQAJSRGLqkF4rRrZ8rOnyg7jlK6YgJrykLJZTddN1K7aygFlDl1U3avl4ZAUXKxtMAhtXTZpRXusX3PKVZF+XoJY5GnGVrH3PaVs9t5ysU/rHG5ZyWEM0f7PgoIlE+RLxxOUjieqld2h5FuAmHLbKtXEi2C0EcahRdawkYNe+8lHnjmPYSVGnmWs/jMC7S/+j0ldsxZyJTyy0UyUerGrXsc4TqlRglM7kQhqhyt4wtnANMWL/Qx2uFEjVLLdyEiEGVBWBZrQmHLwr+kvJdFoXRdcPfGOUVGBtKB7GSpnvYD131VwhIGYUlKWU5OXLbdGovwwVE63HO0X0ZBUJ5jixvhDi3GWgrhmOIODydO6StFeY9obZwhUcgSf1hivqw5zR1bXOwE6wRPSpS2TVNgEajlNMFFsVGqnDSIwA1CJGjcXoGUnrP3GYk1jn+tShOfMzfa8jDr6E25NthyEdtiHCvbWAwF/+oXf4kf+OhHf+SPUl/+V9HJrdWEfcezFTaaETs7AVFN0E/7XFlt0htMObjV4uLlFYra66wEkE3r7OYzYhkgrIdRMbv9hE6wwk11QPfoLOOjA776/VPqGxGzfsKj6z5vDASxMNi8gt80rNhtFtGM4XTKlbWv47c+/+94+uEm43HA0aTHPAto6lWGRkDFkIwDvHzBhY1DOrEkbHeIwm2OxgPONFKuT4eoEbzzqQb/5vdhnB9h0phKM6c3yzi5GzKdtmA95EE9IzxnWF874WRa4+q9lIfbkAY1BtdyulGADFN0KgjXQoK5ZTCJ2OoafA+mY8vBwCJMyJnLCXvNmHAIm1pz9eSEJjm0Cir5WdaaCe0rlnhRZ3BnyLh5ERuHDG5dJfDrFJmi3sgZxx5ibjnTyAntlObmRZJqzGSkaeQB433N2rrkJBnQjbrsziwXWwHz4ZC4C7ahGQ4gDiN0YNi9OeTdD68zTzKGtk8xvsTxzj2evLzNyckNjN/EervMx5KVdpWd/i0uXbxCbwCLpEk/SekNXmdz3bK7s8LxNQ8vyOk+FeEbRZpq2pHlwXMPc+P1tzGDOQc2phUHbF8ZYRcZx0NDUF9jtUh551P/E//DD30f14bX+cTH/gNvvP0HPPdan295NnYjM++YbArtsEVqX8HzYzx1kdduHOOFHrN+wNp2jV99aY/GLOXQ3+ZhLyUPoAiriOOMix8cM38zZbN5nlE74KXrt6kXW5jphJUwpuL5qIpg1huRmZSHNytMo5iN83OOdjTGVlmp9mg0MzqtbYy/wThMkCd9ssUeMzPjeJrwjovnKOZTJrMZi2HK5rk2Unqsr0SMgimLPZ9sVuEkj/jEL8x49smYaKXJeOizN8sYiZRap8EFmVP32+h0wbwm6e8fMtrscs73aeVDQumxe23B3A/odqoslE8gp2g7Znxsyb2Yx7sJfa/GEEEtSplNIPRXeG3nHqthQXf9YepqQG1zzsmgzU7Won6ww2a1xYvhiHeKBm9nc1YbinMViTQBVRNQ2DlHZkHU9dgIXcTj7g2Fqk9oRU18ofGwbGyvczy5zSjJqJdZad8LiSYFu4ucoZezH3psK49iponCGmuxT/94Qa0dYbTmdj6kyzZv/O5tXvIKvv5SndhUISk4lpIrD0+Ijdu66Bc+W36FN8cjvPmCardNs2mRgaI5yombAcd5xstv5zx54RJdP+QoT7kxucex7fFM9zxf9fi38hu//HFWNuusdlZREnZ2dhFnVzhSmh/9ru/j5Rde5pUi4Qff/yHOdla4eXLMdrNOkedYYDwd8ZN/96f42d0+H//x/4XHt9Zc1KDs+kksWko3BrbaNbeMRBMS2Yws9hBJaToql888T6JFwI07b3F+8zIY8AOP/d4J2uQEEmQU0I0qeF5BnjqlrOd5FPkCFfgYmyOMo0UoFThepylQXoSWBQhNtjAOM2ScmOCLn3+RT3zuU2ydbfP93/29pNp9iEohMHm5JFUuoBlyDB6y5NOawsUXll2uZVwBozFm2TUWp91svqwDZ8pFQFleJyGWFAyXfnUfr/eXn4RwWeWizB56BpQMnYFLmNO4gSxRW05OrF0cYLmEhMEW3h+yRMkS7ytKwYskoNALp3BdLhsaFxdQvkemC1dslr+HZwpkeeBECmzulo5SXZTsbvedWZqjPFwHPHf4NCmd1lUUkCQJSko85cxlVhjAlhlRZ4bUy5m2ceIZz/PIjCaQLgKwzN4KYZDSLRMaKxGyIPDczzU6PxUFAEhl8ChVwb5HoR31QtuCMHSUBWV9UOBPZ4j/+wq1v71HVgRgNNlv/yfM+59lVvHRwnOkknJBUJcRBSvLBcNC4/n+qTJc+u6wdh93JU5JKJnOCIVf5pzLUbySoDPHzPY8rBAgcgoDnnXFZqE1eIFbUBMlSktJslzjKddFL6zb5veEhEI7GkyZr3WZ+gKhfbdEmXOa2z1F1ZUaaScaMYjCxTC0tuWhwymePd9NIHJrT3cUKAthAfjCvRNOEZYyQBQZQnru32AJ3F1Q3g8KY9zh2xPG/RzPL5M97houc8qFyMuJg8BPBVoprMyJlMtGG4E7QFvrbKrl4xRCg5Zopd2+Av7p3oIQgg9/8zfx6qt/guIKjZpnH3rMQ5qMdqtGsojIWFAcgfFSnnniDC9fX5DXLatC0uoMeGvfsHXBUAsFybTg5S+12Bn6PHOhwmblHv0jn4uXfT6/v2Cl3cEnJUyHtGs1ZouIuJoTtXKyzGc17tCpPM3zb/8qG3GX1fWI2zsCOVdM/IS3BxVWG4ZqEHNmY0GjmXJhfcyX3uhQq9UY96ZsnPXoF/d4civnjbct3Q146Vqb114XkOZMK5bxgaUIO8jZgs0oYFYYth8Kee1kH2KP89WEwBp2+3UeqmlUPSOdBbx1M6VZr7L+wCbZnQO8esgizanSxrTmxIGhrzXNWBFPfQ6ygvNbdV67vs9WNaARF1y6MuGhDUGmLJ9/6RKTE49sdoLfrjNPCo7nhlp7leHhLhfOrxPVxhzeNXzgSo3FygoPbx+y93oDq8/y3d/9Dbx857Ps3LxGrRkzTzJGvRF7swArBO1aQJEltDo5nbmHUJJZ0+PWlwbUu3VuHI5459oaC3OLnZ6moutkWcbZDctJplGySyeGY9MkM/fo7VSwhWQyExyeDFm7EHNse7SyFn4gCM0RrWqL1bWE5MSnp1apkNCu7GH9deTC59ln/ht6/pRvf/8PUFld8A//3p9jfaXL1dtvs7qimMw1sa5QER7j+QGd7Rm57DIZeHh1w8L0aAcV8umQtWaL33y+zRdujnliM2AhPPJjwzseanN9tMulyw/yyRdu8MTli9RMzuD6lDSOSWdTqtUY7YeMejN2E8M3vreBUJaJd8SZeJV+P6UVhEynU8L1Gt1KjPICKrZHEl1lXoFqUzC9A1HWRQaacdZkRfn4Nclnr0644MWINUVDJkTtCkYKup0eySTkuRc99u5UyUzEVEoeb2V4KsYSIrOUwSJBtX0YzSFuUDcBDRE5gHdkeGM44H2NLkWUoQKFVTO2YsFn7o4wtSbHkwGNluDp9RUKCaNBQR7HeGmPwPdZWT3Pjdd9xPoM/xjmkwzRhCOdcnE1JAgM+cQQy4CoMmE0njPNqwT1gtV4QkEVnTSpKjDWZ61eY5gpVpuzcglqQREFmEpEdTDlREZcN5LmSFBID5sZRLSgrjwqlTbPvblDLYg4Xo1pm4R+oXm83uDcCG6PNTrJOLaSKxcmdLpn2PIbZKnlzlGPiZ1jBk6wsf1UgzgZEVc6HPZPqDVXiKMqJ4OcZJYS+wtyMaPpNTk+sXzmrXucOXOGJ7cvUI9SRgc9dBpSKTw+7w0ZB5JVNJebAceHU66dqdIZW/7sN3wvf+FrvgaD4Qf/+l9kmq5zOBN8Kp7wi9/xPfzp930VHpo0L9jtH3FxfRuTzssOpMB6IbtHR/RPRnzfT/wkP/a9f54PvvNJgjDm4//pN3jr4IgvvvwFwpUmeaKYJjO+6ps/wM6NmyySjCsXz3Lz6IT/7bu+j5WogrYFHgEBmkw40Pzrd+6xMD5JPuZzr7xBd2OTT730Ml/cu83Ofp+PPP1OPvpt38LltSYVr8IimfL9f/tv0JaKH/6bf4f5JOHM5hpBWZwaHHcXY52cwvP457/0r/jWD38jzVoTo/Vp1neJZ3Mf2I4pqpT7Gcuohtb61JoGhqJwnVatHXd0WWydFr7WqWudrtnFKQqhEbglIkE5qv0yzNFyubUoqQeFEYSePeWBCgJsueGOFHhKnI7Zl0ZGIb3TzKwsHO/5fj7U5ZDzcjNdGo1UjsigrcETLqpgsGirkJpTjvOyUEjz7JR6IIQ9ZS9rLKJwXWAox8bF0gDmhCaFMW7krp0p8JSUIUskDLBU+mZFTuD5SOkEtqoszpUv0QUURY7vSYQCIT0nLiiXkZQnXHfQuk6fMhITC4rv3CSSjqwhv/cfYI/78OFvYhytYKSPj3efDlCqvLMsc+puApxkxWKNAZkjSuqPsO55FJkbuTthXQHaw8ETynppqTa2bmriWK/u+rr7UJKXu2Us0X7CTS4EFmE0uZQo4dTuxrjFS1vm2F1kw+BLH43FaHt6PDNWo+R9tqxQrjsttLyPxXMnW6Dkd0tHYpBSlvf38vdQ7rp6TisvhABVRimkw7o5B6s8PTAYJfClKCVOmiUNTEoJuSYIQjKjHflFuHtfuF3zslssTqMtlM9SGY0fBBQlOUUIgXZIJoTVLu/OMhtu+chHPvwnrMit+/bdz1Y5OVxQzA2PPvwAc3HIcX+KEjWms5jRdMS5c01GoyFPnUt58KGYfZ2QY9hs11npzGilki/eEXTasHYh5PqtCf/htwIuvXuF3ZePubzuU+1WObg3ZzKOCaozFsSsBU0eP/cEL+7+Jg80HqFe3WcvS9j0BGEMv/ZCnYfO1Ll9t8+VCzO6LZ9xv87jZw7YGZyHVDBRt4nqMb2bC7Y2OuwOJ3z2PxhqF9bYG2YkyhKIKkViEa06W0cLNi8FHKiCfXa41GgQVwfuJOZpqqnPm6OM6ahNR0OrUSdNDMPBgtD38JowP5pTf7pKlvWp3w3dH6xVaPhriPGCwbmc/vUJ59ZXiVbv8kQs8TzBJ7+wzZZfY86MYTaiGoe8tfCpGsk7NnP2jsF6IdM9ycVn+7zzbEajGZPEC6YLQ0gbxYK19jnquk8nGuH5E3r9KndPtrg9MBRScZTM6eZt0vqcZNYjqNSZDkMODw+oxqvUgxnJSNFqBeigzkeefYr/83d+i24RsN2tcvXuBFFpEo0NQs25MVSMBFzo1BmOEupVj8MsYSuIGeuU6miX3rmY91Q32N+ZUosDvFgQ13Iq+ZxHHn8Xw0aDRksx3P0kcfEQbXnMeJQwEguCvIbIK+yND+h2PaqyjlaW/lwTrk+YDxaciZtc2zdUK1uEuebGLCOZ+CR3E0aZRr6nw9PZlOO5hXuS1Xc1MLZN7zPXMGGDwBcMRc7EatIMvEgRC59uNGKlW6drA7zzCeMjjW896jUBto+IatC5wVRDbQw68WhUz4It6I8rTH1NtpbT6UeYdM6B9Xl2tcqdZI9Jv82VJ+qM9l7jxTfew91XjlBBldqG5PK2oJpVeEunnLMBx9MRfs2ngWYqFbv3fLrSyTHuFQWLyZgsrvCEH0KYcOWCT63mMy08Xts7oZc1idZznogHPHy2xetvTZn4bS7WLBknhEELrSs8t5jTSbvI3SmTlQpppLhQnROojLZfJYj3kK2UveOALWoMtCbWIwgq6HGdWk0ySz3O1Vf4zZ0dnjqb4p0IVitdZosMvRLRjKbsJD6jA0WQ+8xUjc1mgq1aJnPNi3f3WPhbtMZTqpHP6+MxQRCwJgPquWAejFhpBKR+kzNa0zNT5n6NjbN1LkUdxOiYk2JAd22NzarrNL7Yn1GfR7QjSKsRGMube8dUA8lK3KKjU4p2lZ07Y966LlFnNdurId7UMjNNFtJwJ03ZCqt4+gA1snhhwFhbglqFc6pGnlm+/wf/Mv/gZ38cPQgYF5qeV/D0Rg09FKRNn2kKW3GFwWDCX/7ev4S1khu7V3nz3nXU7iGfPphz/lwFr+rxt77rr/DJT32c3r2rXJ2MaYYBx8MGo2pCnCr8MKNasfhZymFS5ZlLj9IVFb72m76e1VqVdlzjM1/8Am/fuMvVq7eZmdsEwiOxBcN5yiTQNBsrRKOCaiGYBwqRjzhuZDB3y0+yXePCPGIoLHMsQeBxOepyqdvlzvFd/voP/i3S0YhCGH7m1/41B70DztgRf+Wv/ihV4zmNrvQxpiArnE1OLykOKFKT4Es3TveVRyY9AmspdEZUdtySYmkE80uSgOvyDnRCM6xhsoXr4JaxBWtcQWykRFlNkafEYURmCtClGEAtP8zdsqtDJAmM9J2Fr+y6G6zbxJeCoBwZ6zLf7Iuy61Z2PY1xGU9XNKV4gU+eLpFi93FLtvz3VtzPlC45yRhddpXhfmbb0SqcMte6rKS9jyW7z4/1nEUM4brg5j5rF0ozWwn7R7mcuMtrloWeLqUCuBH2sqMXKO+U96qE6xYiTBlNcZEV6QmEFjQP+vzdD30FX/eekGcbKWkP7N/4V/QvPksuhMvSls9HCoEUXlmgu6jNUpghUGBzTtFXAori/vWgvObLw8WXd98RZZa9jCXo5XUsoyynooayoBZlUFopVV5byNEI6+IaSjhCg1uyyhHSQy3xgVI4OZotrYzg+NIlsg9rXFxASArh0JtGq3KC4qISLm9tThcrXSG7ROuVQqeSvwsGNOAprCnKrH95FytDkRuHw7OmlFw4WoLvh6Xcwi3ELR/HqCVmsBSFiJK+YZbYtpIcU8pVME4qI62LaJllzKLUHX/kw9/8Ry5y/6vI5P7Mz/y/P+wFATLNqagKi1xxY7KgrjZoRpbBNCfsLnj2wSltk7P1dAV9orlzT7G6atl7O+HGy1Vee7tFL/N5/sYE5hnTPjx2tkk4njKdFfhFRHLSYpKDNEMqgQ8LKOaw3dlG2wN2BymLRUGnHROlEc8nIx6pGa59qeCrHovY3rI0oxyPBrN5ymA2p7u6iqEPIickQMdTmi3DY19T4RO/55PHM9a7dR5YLYhFQk1Ibu/PefSpDd66OaBVDaicy6mNGtzei7h9K8AUdRaDgLPtlGyouDUcUFURlWpIjkfv4Jjt7XUOx1O8pCCdLzh7ZpNH61164ym51iSDhEbV52hvTjVps7aiIVTcutvlTGfO9mrKWFiKgUczUtRTzWIumVYUk8MFHzxX5/LDMXksuZse0b+bkQ4Kjt6csr6+oFE9IshmfOFewo19wUv/uYbMQrpa8OnfKXjizBm6D9cZ9y3VzONwLyVNZ8hA8WZvwjsfeAAZTrg7mCL1Ht3WI9z40nWuBG1mM8PB9Rlh6JNlJzSqAYdTTXqcE6U+jZnHeCKZD2bUhIdIK6g45MRKOt6E2YHC15IAn6E45onty9y8cUAQHbI4eh3b8inyJvduDvFlnYHZQ8+q9KeKPDQwN6h8QrvSRBSG8UywMDl5XkdMO6R2h1xIvvDphEkvobq1RYAiPZyxuKuo1HNsNUSkcybjYy5sNnnxKIVFylYzJx1JCkKyXEBYcLEdEXlVjtKMwYFhmqbIYI4vcjwijg6nzIMzHN1ocibukhUJMuuSez51z2PoazfEzQWbK1XarRVEuiAMBIusxud23uKNyRrXPnuLrXoHryl4wF/n3EbCc8OCd+Nx0CnY8jSPPdXi8jsCbughB7f3OUwkK5FkPoNLV7ZIjnqsbVrWu5tcPR5x0hO8/taUxDZJDyc8XIVDkfHqbo/Ht1fwo4TJKECHLbJUMBOCVxcSeWdCK2pzt50QTvpUexLUCXvDCRmG+S7kcYP9+RhvkTPrF5wEFZp5jfG0oD+tkRQJzWbMwY6mmAYcZAVh4DPSGYiY9VYGMmRnX0NUIETA/nzAx/f3mM/XmRtBU1oGvTFb7ZgzjYjQ81AyZaYUFzsNqtrnZn/Gg602UR4zSPpM+inba10eOHuea9d3OBhrrhPCVJJPxsyKEJmFVOSI82tNOnnKYCfBjC1FvqC9krB3Y8ZDrYA0HRK3O/Smc5pYAiOpmR4b7QZbcUyj6XNvUdCQEb15QVTx+P2bv8uDeYUbi0MWeZMHulUSkbBWl6gGsBjxaHuVB0LF6ze/wM//+mcI/SnnFhNkOOOB7RValYRgOqd3+AL2+B6hH3LTxEx1ineywGtr5sWQdX+F8OaCgafY9QJG0x7J8A6f/Pzv8rEX/jP/+N/8Gw4m9xjt3ubYDDiYpfgiI/QDVhpVZmZOMjMMioTVqkXpgqOWpGMrGBvR9Dzu6pSVIGatEnEnWlA/KZBqwWJxQmJSfuW5z/LxL3ya1z73POcvnOPNmzd56sMf5Kf/6S/xzR/8EBUtHabJSKQXYJQiWgaRFchCYpQh8D0+86mX+dv/+p8wHGl+9ld/lf3bQz7+8sv4tSqvX7vJP/sX/5Kz5y+DrLN75zYf/cmf5OPPP8fFs5cYDOasdDtUwga3d+/SbDbKkavFD2LSInWjWKmwQqOERVjl5uaeh0KRGQhkiLRZWUiDEAWpUPjKoyiMW8BBYrWraCwC42mXkRau4PGUAKEQuAyqFYpCeY5HjqMBOFSb634pr7QnCoMvJYUxZbe2NN+pssMq3Bge66xnbvEow1Mh2mhybRz5QbliyBOqLGBlqQm26EI7xFY54hZqGSFxvUz35bB3VjiE3lJuovPi9DtcxlWgjaZsLrpOcKPGY7LC//eK4Odu1vn4jRrPPl5FXnkvwmqUkoRhQByGeJ7L+7tH1u4/KfF9x6VWSqHKvCrWZWWVEFjc2F8oiae804Jb66LMxfoI3yuNei6rirRYpVyUxncLpNZoZwD0PDwX1cXmjmerhILCFcRqmXcvc/XWgLDCiSdKFq6g3DvwSh2zcLg+lyLQBL5XXi+LJ8opgXCUCF95jl1dFuIuh23xlIcnBL5yOnUX67CgBKES5bUAKd19rIQs5RflvWUsxuYEQViaz1wExrPlK+25+88TTv7ksrxOpuIrAdhTGYUoDwi5KFzUyN1BLkOOW2bzPcm//MV/+Scrk/vAlRV75XKVg1mP9KiKXzO8cW/OumfZ3BLcGYZcUiMefNZnXhSMb/sEIiKorHDz1RPOXcg5cwnWH1mQGZjPLe26RGSS2YnkqJ9RvQyXtlzWZv8NuHE34vmrBe+oNck6AU+1/hRfHP5HqtoyrgzwBGzWPKZFiJ7MCYpHeOZJy73JDaQfEcxS9nsex4mhs56iE0snhu12wMmiYKYNoqIohpqfe17x0w99kH947+MEIdQrZ9h/QfP+D53l86/3+VI24mu2A16oBcQ3JiyymLOFYBhqjqaarRVLMZthCfAzy1ALGqrK3mhBowFRxTBdCNSFFRpHQ5QSRPWQYjykgmJUGLoPeDyyOaQVg6WFt5Uw+1zM7Tua3VmVKGSJw6YAACAASURBVLYk9YKuqnOhvkJSpFz2J9zwLbdlThgJnlq7x+OXQ75wJ+P4xCJz+Or3BXzyd+ocH7W5+6U+JuzQ1Q4lk5sFzz69SdKaUrT2aYkW1/sB2axPtyV4IHwHn+3fxY5zwsqEdz/2VXzid3+HC60642bEwb2Mu5OUzVxSiJjRaIBsVzjXatLvzdGjlPZmA5NavFhx1N7lyspZ1tKCtxOYL8Zc2ojp7+VsdNqsbKQYU5DkknSnT+Ohi4hsTGESVJoxFob+rEM3qBOqBVWRUK8IcmEZjSsI0+cgk8RoVtc2uTPZZ34nYndi8KzPUVqwJmCzWiWJwd+QKM9yLpoQZSt84uZd2qrFg03JoF8QBW329ISFB5eDABkHzGsaG0o8IVlvHhAvfA6SGfU4YquuuZn5rKRN9P6MfDVA1MAbxti2oKIVuQioKIEXSWrhHm/8/jHpU2dphx6XVxacZJfoeAH/7pP3uLt7wKMPb7GvIr7ucY+uXqCbCzyqREXBuy5cZC4LXnu5z8uv3OPGInKZ1qLGzmiK3U55j7fJqDPl6XOC0cGEZnWFmS1YDDO09olWJLbS4/AgoONtUy8WRGGN564f0dpYZ6QHXLywoJ7BRGraNibwa9zbTxC1GrNKysORYdLL6dTg+r0Ga7FlXg9Rcp0sGRPXNHcHI0SW01QhppoQNTwuxQ0Cq/i9N3cRtQ5+4aN9y0mqKAYzBmHIJBvC2MPYFc6KKSvbGVrWGO4nPFjzKSqagdVsV6ESjbm1aKI2fMy1KQ89dYaqSKm3AhZzS/8gIYgE8kyX+Ss9sk6Fuk6IO0221IJbvSk6mLPRrlDVA67NJlgRkeQesxtnCamQ1VPm6ZQLa66jsduXrGvJc0cBnariocttbu/dpNJts2Lq9G6PeXtd82gOMvDozwdUqj6BX2cxL9hse+zfm2AnVcRWwldtbTCZFmgse0qwGfpgczJbcJQIHgyqvDUCNTugl895xRd82/k12kbw6s0FX8g8HvB9jOrje12qc82LwvDMQpOsd0hnE67mBc9Um2gzJw4zokbAYgg6EuSzhN1UUtcZlzodVuWEng2YS0N/MCLw64g8ZM1LGDcqBGNLPYyRpke13mVqcoLI53CYEVuoixGFX+M4E9RXOohxj6Dqcy+V5FrwlZUOxAJ/fY3PfPrznNlYYSQTtqTHJMiYatgfBIhQ0UymNJstqlZCPGXm53TTiKMsRc81NVEB7eFXLbrlYfp9Hn/o3Ty2cZEX9t7mPe95Lx9YP+v+XkymFDKgHTvKQqo1tw57HB0O+OWX36AY3KG+uoH2CsaLlGe2H+KZB7Y5u7HOeLpgfaWNsIY3rl3D+gFBxeNca5W93gkPXNhCF5aT6ZiDeyeMhgkzkfP0I5e40O1g9Kxk3zrdqv2yfpe19nQBCeBU7FHio5aZYbPklxu/7DjmCHGfLmGFochcnMQX97OS4PK8RVGQ2qLExrl87XJRzkU0QJXEBis41T27ATulwKTs8klQxo3ihefQeb70sWFB+/YRn/4fP8Sr3Tl/5tv+EvVv+xuOpWUclUCFEYLsdGs/1YZkvnC/32kmeJmlLmMt0imgPanIc40S7ros1eunsgOg0BahApR1C3wsiXFCnRbqaIdMU8pp7pcxBqldESikwpRmNbc8Vv5/PAwFpuy4CwPCk6edd1UWwRZHO5Ell1viY3F5ZmvFH1I4FybHCOkU2yV/VlicgEMZt+NkLZolb1u5LKxwaEDc1UK50w+ZEe7wlhUsRTfK807xYssvh9tzWfJTzJ+wyMKQYVyWubSlFYAVTqgipee6wVKQZhmeUlir+daPfITX/iTFFR77ikv26mu3eeYbLnC412clhiCLmI8TaquKlCEPbMDZhyx3dz0O3jhLvRIytAnveEQyPlpw9iHN2/0avZM+7XrCmrrAG7sjRKPD/hfvUjtn2Whk3Dux/ND3gJ4U/PQvrRIKiwpzut0nqG19GmlqKOYYI/BWNOlRheOe4V0PfwXXrl7n7IOacU9Q9SZcP2kS4G7udnVCb2pZ7wp0D+otS8/A97/3fyc7uUd6c0SyuMuz3/It/Md//BukM4+Npzd57t5NXui/StRocH2cEaeKG3nBQys1JkPF/GiK8QraDR8V1Bkc9xB+gLARuR9jxZRYpizyDG81JE4F9YblznDCulej5kcgDN/xjvdhqy20DdCyYHoy57F4hYUnmNsEpQS1mmSRCWQi6W6uoF97ngtf9xQ/8hu/gKeGPLGZUrQuMNvf4fbEcGZDsF1/F7/wT65zT4dc3liB3py43kDrlEYcEp9Y5FbC5vvaTMf73D20dGoNyC3z8Yyg3ubtvbu0alX+7Ae+jX/9qV+h0Sg4yeu88ekecqWK9TT+XNOsRgwjgzYe9TCm6M3wPYGoVjnJ5kibUQ81D5xpMZ71WYlD4pUKo7uarJnQmElqq4a1TpU2gsPKOcT0NjrtYbMaI215/Wab5jyjed5HFHPONAXjeYpXq+F5Cw4nR9SDGkaD15Dcfa3JjcEUvxKhRxnUIxoiwC9S6p0KqmWI8gmbi4wdr47QEuNJWoVG+AF7vSl5R3IlhKi7yu54gidCPL/CZmsfYxWVSsRkMsGmmgWCFa9K4MVMiwVjBBtRhZnQ1EPNLC/wZZdJaPj6i1e5Uyi8rEk9qDKYHaLCB4nFBX7t1z5PTfjsvZHzdX/1STr+PrmVjIykKRTb2yucjHYR2rCuJCKe8tLxATff3mZ6nDC+FXC7lfKMaPKa0HzNhuHMZoU74xOgoBavMd/NCDYMtc0Fd/YljzRbDBYZ9ajDsDfluX5GrTrlysacTb3Gzt0ZZ85UyUeWe+MUgoiVqqGtBEOtuM0CuW+orcasTGKuVkLizPJgAP+xX/BgbUpgcsL2Cte9Q94fd6iEoIMVrt3aR5oK7ShlrjxeuT5njBsBp2OJCGMaVjIpjhFezPlGjUZ3iC8MG50uA2/AamYYpTHXBpL5fMKVhzqcrUec7ypsbrluDoj7qxymKV9zucGLz1nidp/V9TUaoeDarSHeekBlbKCYwGbK3mhBGFU5vFbhgVbEyjb0iblza8xeUkG3C2qTBldvjTm/EvPEYwWeqrL72oTFSs4TnQpH/oxGpUFy+4iLG5u81j9BSR9vUSX3M57cXuPG2/v4dUN/mrCZh7S3V0gXM66lFjWJeXB9wt1xQa4UlojBwYJLFzqEFZ+T8YS8GtIaaqbSI1oEDOd7/P/UvWmwbeldn/e875rXnvc++8znnnOHvt339jxJajTQjQYESIpwCRCGYIhR2SFQSkzsEDAJDikXdkg5dgXiGOwiHgAXFoYggSQUzUO3Wj3e7r7zvWce97zX2mt+33xYp2V9C6nkA15V59Ous0/V2btW/df/fX7Pz2y3MZRgEMxoNwWRcsgHFkasUDqi47vM5BQsTS9uc9WGxwqLyCiw0hjTUdgnObZvIjZcJqEiGQnCIuWBhkdryeTkbkZkhSS+xFEeadPEOgkwcgPHreDlg9JR3emwOeqxUDFo2hZ5bZ7BoMfq1ODlTLJxoUtHDkhGGYdJRqsi2JmGHFku7tTHFdD2c8ZRwrpXJcompE2XBReiVGBIn6Af4ldqHA9D/IqBtB3WK1MMFMdTi26rwU9934/wW5/+OkdHW3w2OOBf/eTf5H0PPcSP/52/DUsdxsMJZggD4WCbLpKMecdkqmZ0ag51S2E5Fscqo+gpbMuisHN0XsWVOQcVj2QYMxtGTG2Tx1s1gsEIbbpooTh3ocFfe98H0UHC/EKLjl8OM4UoZfsSyIqC3BS4RU6urbKT4DSwZ8jyKB9RTmtFfhpYyxNyrXClWSIDpyHA72SE3xxeypmi9DML49THrErWVWtNkZfMqS1LlETlCsMxKXOdCkOaCJWRnSrkzNMpXRqUhUGiwBBmyUYjaAhNVGRox/t27S2Unti8KEORbzZw6lQxDaPTwV8iRHGKQnCq5SuHPFWUTKlplpaBN18vL1kGuyyLpMhLG4IodXVvBse+/T851fqlsmy2BIXUEiVU2ZRH2fz2bZ5clAP+m2x3IRWWLHWQ5cPHaVDtNHSp9Sn+ccoPI/LTVjKFFhbWKbLypsUjK3Iwym1xrhWmWZY0GKoMH5raQIkEdFmQYZgmWZGWGkD1H1y6SkjyIgVpl3XJlNiEcWpr+LadRLzpQ5alrUGKcnt+Gn57k6H/drX0d8yjb5ZUWEhy+eZ3QyJ0xgd+4ANc+QuWQfylwBV+/dd/7VdW1gxe3hxSVzknR5o0VbTsOnt3AzbO19jtueweOox3NRceqqD9jKk+RsoadRHxpW/V6M6fwXNrJEHGa8dDFhcM8vSYhUfrVEc2wajCilfnn+6COig4206othsUOuTchQ0ODu8Shy0yZ8bukcLWHrvTnDhqMdeVhHGCyCMsK2UUGWjbJU8gH085O6+YDqsYMsVoSMKx4P4H7sUOLIxNzX2P38tw5xVu9K7QjJxSV3O+zeNLCzwlW6T2lOHRCQ80fYquz7W7GZ4M6K91sIM+2axgHEbUmnVQBoPhkDlhYM4SpFVF2mBZGbbIubsXUjdqxHkFq7xPcH7lArOkQpyOSVJBkQji4QH7UULTdnFlzMpCByudIMQUp+6RXN3k3AfezXj0Zzz1eEas3spD7/spxNaLBElEGEHdXefVr/fYWG2TTBL2yMlyCETEtJjRdDxqnR5ZJDClzUK1wXQ4RQiXIAzIRUSWStRUM7++wK3NbV4wl1icDeiszBFUj/DrFcTMxLFj2u0q09GYJAZlOpiWQMVD0iyjZtt4eUZIyFqjQ7CfEOc5Iw3TY1hd8pAtyajXpzcNqc2fIQ02cSxBHglGKezeHOJ1uhwfDTnTdQAXw5YsLrb45k5KPm0w3Zc0V2O8ySIv7afktkMRRxhdzSNGhduzE0xLUpVVLiyl9EaSYuYyzCSdQrA/CWjYDnGRYgmXLDOZ8ySFIxEzxVFf4XgCrTIwm1iJjacthkGZK0/CGeQSA0ENRSBcKDSxMPEjaESaO3GAY7TZfEGydTQliPZYWljl5MYJ3flVznTHPPq2HHPVIEgcDtSY6SzGLcCJ4XgUICyPNFLYRsjRWBCobeozg6eevJfcvs7CdIHlR2asy5jVMw5GxWXYn1IIh0JJxmpEPvOpBDbP9m/S6C9QLOaMDzLUUPLqYUJjPmGSthDjjHEM+0VB06zQ9etE45TEAKFtxknOGdvDNCy0YxLbJkUmmZtpwjjnWpixXjfwPItgonBtg05ljuVWhcksZPXMCscHEzyhUEZMJXJpFxUmRUbimXhVG0cqulpQWFVqcYS/XIOOYtrvk/QL+qrByTAjSCT3PrJMmt3irF3HTGPwCs76LX7z+i0e7rbo3U5pLqV0nTpHJwrHtultT9mbzmjFLtL3mOQTltoLBJMco2jSN03ubCpO9iXz3SYvBDFn7CbjSYxA0shDLt5n48qQe1YtNuY9hmrM+ETy8qHHPQ0HlfZIGzZCamoNTdW3ORmHzLRDdGhSiBpLZsFL8QxpKswFH7ef4JoKIStMcpOor/CqJugpWRQTp7A3yEmKnLlOzvxcROHNaFYkRTHmLRuSsdYgI1rLPlJGuAsua/eazLkmwTWNLatspwlZCJ4hyNKc4YmiqsH2BJuBxPF8No8HjFyHyjjFcODweMbzSiOqPu1ggjUtyHWNm8GAhJzlKihVYRTHDIWml2hEFFN44IqUG22HR1s+ST5iEoWIpqJWmChX4DiCwvCojAR+DnVH4y41mQQBVdvGazbwVIqZKIaDKVRbDIqQil1nkiiS0AIzRVs1jkchsyThuWsvk01H3D4YsbjY5lPXXuTuzSsYzhBjOiWdama5y9h1yLVGppAEOX7FQImYNIlJXYOkZlITFXQlxlMGwZZNJAp67oiNTBCHChkn5GPFWAu8IuWi7zBTAS9d+yYvvvwKn/raS+zolPvW7uGzX3yOX/n3n+PrN2/xtW9c4V/8+df54fc8g9IZUpSKJ3HK4yrNqU0AQCNMjcootW9ClCEnyhDWm/XShmWhFSgUFCVOIQ1RHq+fhgeLAopCnaqjSsWXYZRta1qU6AKUCjMtRPlzesytdIZh2mSCUpMmTZQSQEKhOGWEy9pspRVKnA7WQqKzDMswkQqkY+O6NlIaZFmKpKzKlaeoQFHkmGZZbFFupEtDRFGUTVv6VEGGNk5LQQwM41Qj9iYDfBrmK9EQ41QJZpY4hzaQVrnxzrUuTbRSlsObKM27hjzFU7TGMK3ye6JKpvfNRrJSFweSUrMlzLJko9B5yQKL0oJhFPJ0wC0tIVLKEoU49SsblNt1VFkUUm7jNYYs0YESJ5bldpkSSUGUNeFIExOgoORoUQhLAjlCmmgUhrAAUSIrp4q+8n1K/7aixFC+83pTzyZOdWXoN80TbzquBb/3u7/7F8YV/lIMub/xm//oV9LAoF4VvP3+hOd7BdXCxJQ500KgD5vEoeTu2GOxmxJXx0y2hjxxb4uKXuEHfugXUDXFO87dx2B3wG5vj1p1A4lNf09RjQtCpbg4l3EyM9gQI9bOeBwM53jltSNeDyu0B2s4Z27z/KsRm3s2ddFkGE4xRcFgf4F6vUYQ36Lm5VRdg+2hZsWPyMYRrqv5+q1FplIS5Bm3DzR/470/hnVriptJ3EJyd2vA+fc+w429P8LbqhJFNufveYB+fMTwYIe1vEHNtfj0/jG3xx5dJyPzWtS3B7DkYEwFM1exVDcJk4LWksXOviRxfaIkZqoEDeFwz+MmUZpwdC2h7XmkomC53abqzXGYK9JgjB0VGJYJWcFTT1zA9ipUG0083+Y9P/BO7nv0LB/+yC/w0b/yQb515S6D2bNcv9Pg9f5TDHZeYnKnjzAnDEaSuc45sp7NXhgyKgqKIGZdm3iWiyEUryWHpJicHEiu9hOKUNNoN3jl5h6uXUHainCQUbEkKxsLfOvwFfwwYc7RHEZjnNhj4Od0lUlueCS7fZqqjqh5zCYRwnVJKg7tMMQMNEiQhkYpQRTE9O9q+oXCFgmL8x4T1yWf5riFSbPTJJjeQhUuZuZgVyqw2CF/4wBt+FQqLhMjo1OziEY5g7Dg/pbCsw5RUvHGnRBRqTPvSda6Xc7acK0/JbdyIlvjWBbDXkYcZTS9KqY2GRszjEqNamETk2NEmkrHIvVswsEMTUaj5pJONasbDzO+O2ZmTGkVIc25Ns6sit90GBYpk7ggwaEhy5tbZjgEgxmZ0jTnXbKkoGcHSDPn3PIqg/EeOKv0sgOKYoDjGpjeGW4P91lr+ngNGzMRRHHEfLNOMp4yixNujRNqFYcnF89yGL9Ku7rAAxc7pMaMabjHWqdOJF0Gu8d47TZXtiNe701YM+B6L2SpJVhaXKBoHSBEBTOoMI4Dui2P6GSGJ3MafpW0khA5A1Yaq9w8foNGdQG7Lagrj9sHY6JQU20LsqTAtuoc3x5wNFOE0ynzTY84CalagjhOSLQgDqYoZVDYNQZbJySujSsVP//xv83f/9QnWZQma06Fo2IKmaCZTvjAhTWuDCMcW3G7JtjdHrO43mYeh0GSkiNp2y0aMgQv5TgrqDVc/ESRETKvXKKeAX5CVEimYkIycZGW5uh6jCU8dGaQEjFJbKrOiLPzy7TDJp+4E1GfFcg85mAy4p6qQTqMyOrHvGutypn76kzFgI52SSchV+MZ4yhjxWnTv5OycyCoO5oksxgdFLitLuODPv1EcOdEM1/1cRL4I2OC7VdwdmDZdYlqBsNZShylGGmKljbdjqQwwMyq5LbDRsMkDDM2OmWRgvBqHA5CPNvn6mHOaOSyyAJpMMFf8DB0wfgkomiZ6FBwshtxJBSpzliRFr0s5aFVnwfP1bg5HtHIBdfyFNOzeVratBs2qRlTW66jdcq76l3yepPp4S79PjRrPtUarHkOhQl4iiK3yasOtmXTErAwZ9GMc6YptB0bPZwyjSU7QUIgwHclxAbFBHyVMW/bSBRu08ErMnYOD2k5HnO+wSRKOQ7Ba1gwCZFpgaclybzF/qTHnDfP0TTmephj2IpKblDVcL5dQfdH7KUJfq3JVJoUQwcZ50RqRpDn5K5mNu2x1PBxhOYkM2CaI0YJhrI5yCbUz8XMN0wmcYqRmiSZQYBBrmMMBZVOlVGcsisUyUww1RBJSdGf8H9+8TN8a3tEpiKyfMJwlmJEBr/77EtcOe7zlRt3WejU6daaYIMQJuMsYZZkuIaN4QiELtum0CVbijTppTm3d08wvDq+aYG0CSYBqVbEeU6mDSzTJVYwGUUkUmGYEsM0SpTAkKe1wFnpcM7LFH85Jmmk5tQ6W5RH6FogzHLYkXkZAMu1waltDIRBUmj2jkcEhWazF7AfpvTDjNEsZqaNb3tXG56H63rUfBtEWb1s5GWIqlD5aUjvtIVOGqUGzTDKTXFeohdKljoxqSWlFlejLE1e5GVznNSnhRtvPjyUjKwq8nKDLUs2WOtyUFUITMMi1hmFKDCEdToAnrqD8wIhxbdDgJaAnLIdDnVa6nJasFJ6rctQJDKnEKf4QFFgUuIJFqW/GDgdVAFNiQmIU1e2kqV9Q5eBScMwyVWObZlQiPLTsk3yUkOCUiXCIQ2jzOwJXeqZdWni0CgMwyLXqjRFlL0fQGlnkIaJVOLUr1zaLhClVUGfvj8U/Nvf+z3+87/gkGv+/zSn/n+7hGCybzKpDLn8nxT8dFfw8rYivAuVtuDuoM/Dl2rcuLWDn0lOjg3m5xbZHvX50E/8LDe2d3j84Qf48p9/kS+/8SKHjHjIvMBq12ewe5PX7ppsW4qwWGetcUScKvaiKdevZ6jCou6ZbB4e8ti9K8zPD/HqKdMjmPcWuHDGZf+sQXzTJZzV+RfjiEZS5ye7FziaHrIXH6OEQ8uosjss07U/9dATmFu71KRD820f4FLlazz7+ZjXn/0CuZmSLiqqOw3GxTFuY4FrkyvMGRXOeSt8fGOBE3OfL/V9jt2c61c0Zx9ymR1IHljv4GBye3KLtY02h5szTEeRiAqVuSH3NiQdV/Pg2Qb/7cf/e37ur/2XNM/Nc7Db42wtJ4lnWL7N3jjAaaScX2nyjqee4KO//POs+2/hf/h77+MbX9zhmWdW+Nmf/giTMODFrxwwqL0F0xrhbl9l1Jgh1BKmfZHv+677mYRXWU1tal2br2zPqHdaqEwylzu8cnLCpTNN8kAy7Q343p/9IZ5Sh3xq70vUaz6jIsCLcjbm5hj0T1hwPEwH8qlEyBTbqwMK7hb0Dgq8hoFKDIbWDGdSYFWqJLFCDGfk59rMHcakhofnhazVNHszwfJT8yTOjDTNOYoi5GTCht/i+pFkuRMxzgpEeoGWGHJ8F/YvQuWBDsn+jEGoCLwa1dmARzuX+as/9Db6z2X8yM9/mH/yz36VijghEjV+8e9+nA/9Z++nxgarlzsYUzjJp8hZSKViM40dbkSCilJEAioSAiMHH6JKxCya4DtnmQiLtTzi7mjK5WqFSRKSug5r7hy5jDELSb1T4Zu9FG8m8SpAJujMVbiVKYrDPYbKZFg32RhE9MwMb+Qwv7bMrYM+Oq0yX5khUs1gVOOV3ZT1+hssdpfo7ccYFYe6Y5EJm73ZDt7SMnOpTbR9zKq1wEkwZbXzGJbtMjg64pF76jx7Z4UXXjmktT6mVbF48bWXqOfnybyU5fUO55+AJHa5+0rE3Fwb07dor0vy2OT4cMZqd56xOeTQ7OFHFguzLicnfaKiRm8a4sgme4OYc2urHO8NScIqhqnIxxFUwc5SDAzMDHpGzkpsUDUNClGQJ5L9LGfv6IRloTGOZozmTf6n3/pTPvbkBximAT/x4fuxU81vfep1ppU6rZ3XqQxzioddViVUljpMb8fkdsjbzy4SpjPieMKhytB5g3NY9A7BqEp2JimDbMg7zlZ47TgiSObYqLnILnhWSN4UtOp1gnGfzmwJOXdMENVZGTkcpj0edRN2ApNVUac2C8iVwXK3zbsevoftowPC3SmmaXOnookGCkM6aFHFiFIadYOmVSEKBYNIkEmP/n6fyLXpCxPtWIyTBMe08Ssd3Dwhbrq8FAZs7w8533GZc2ok45SoiLixb3PO90gslyEBTVOztJZya7+GSiW6mxBXKviRRlhNKsmMwcmE2oJFSxkMwpidvoMvCpbPNbl7coyDTVsLsqzgifU2S1aAMASVtsFkYtCu1zk8OOEL6YwLfoWJ0Ky4Bo4ouJWOEIXNuUfu4fj5XRIK2jQ46CVsJSGrlqQjDKpSoKXBncGErl3DKDwIc3YmYy52z/LKjbtMTJuqhEVhMcvBUjm5ITmQGjFKWIltRsKg5rbp9QsCrVn02lQ9g+NIkQ8jaFbohTPObHoIv8F4EmHlDmdsh8lkyrzUtISNOJjg1GDBs9kPAjbtCo1syJ50OVuRDHNFmKdcaDcIiwm+cqlVJC3bYDiLCWcjXGkzOPCwmgUXHcHtQUaiYU+luJnikbk6h1lIteqgZzHtVpuDwwHKUZyIBOlWGA0Tcq/MkrQdj+NZn0JWGO0dITOL//nqp2nIGl6rxTA/wikUVcNnkqb0fcUTG+eYsy0eP3+eF27d4NrWAcNMYVkOM24xTXMcFDWhIUwwKxWGeYGlBYa0yM0Uy9SksSAQOZ5jYwnB02cWmOvUiIKIxy9ukMcJg2BGmKaEaRlI2hsO0Z7Llb0jmsJmvlUlHsyYZopu00daOedWuty+s89oHOM5PqM0oVGtEWTlZtbIc2KtmVhlVa1XCIxccGG5xuOXl/ie+y6RZgEUkixLcB2L0TRBCZssy0rjRFZgOpJQFBgWWNoGpclFhqE0qdaQalRRemulMil0WeYAp3aDonTTpjJDFhoDiSENsjeVbCjS3MAzDKR5WkX95kbbNijyGCUtBCYFpaM5K0p+WBUFQsmywQxOfxf06WtaaiwpKbRAFwa5FAiZUWgDYQkoUWnAJNenamVRog2mECijzTf8DgAAIABJREFURFmkKP+AKcumvDzLsGyPIkkQhvgPLK4q28wEAlNaZTOglqgiw5KSRCkMy+A7V7m6KE8IDKN0ABeUqr789CHHQEMB/28w278UQ67Wmu/94UNGmcX1HZvbfU0+TllfqVG5t8H+bJ9GXPDLP/oxHni0w7/57G9z+PoJ2ruPdtpiYh6TmSbHgyGXFi9QJeBCp0I4vcljDzXJxYCvbsE9K33clkHdtGk2cqTt8eynQhjPEAoK2hTENOwOy/fO8cwT30u9lXLtxhHP3/4UBydNnrJW2GwV/NmdKZ2uwV5cx1dVhJFSyWz+6vt+ADc6IM5z2s0q3cFvMNg6xnK+i0RbjALwVhKsIMOptJimE9KKxcGVl1l54O2QKZr2BTrBF/nWixa8tc3BCz72suClvX0uNed45GyH4WDGW7pNvrHZB9ckKEycNWgsbuDq63zqN/8B/+gXPsB/9Wuf5J4HHmUzCWkYTbaDMY/Q4nD7Gj/013+cazfv8Is//GE8o8udl3vYiwn/9t+/yuYoYmv3GtISXGrBJK2iz+SYhxnmeoe700Ne+uoJl99xkenFbUQ8oGY2GcxOiKXHG0HMueV5dvo9FmqSuXOL7OzscfbiItlRhf5IcebSmNnEoOs5nGwlFFYN5xiW/Bq+b/JMd55Xrl5lWjOZWJCYIWcby8z6I4y0YF5nHMaKWJp8eHGBP92/yaUln8sbMcobs3G5yzde3qXXU4zqdVbchDPtBp/ZDmmEHlXpMzo28MWA7UTwusg4N7DQkwDDb3BtFLNRt/jiZ+7w8X/9azz4zsdZ+eg5Pvm5L8Dqvfzo8iO8cn2XT//OP+fS/BluH9o0TUlzzufWtTFRrnjL2Qe4vDbPn778LMnEpGXVWfMg7UqS2MERZ/nYxz7Kv/zj32evGNKfFsyETRJKzM0tqlXNwe2I1cuXeOsTjzB33xm2f+fvE0sfVSSkGByPEhIG1C2H5WaC0amxe7KPVZkxCNpsuBWk7iMSA1PB7UlOFoMVa8xag87Y56RqUoiENIB0ZiLpkF85pD3XxHc7vHonoFIzkFYFy4wZBVWcPOb29gCn0SANbPZWCx64/AjBC1PMRpWTbYETmmgXunUfoQqC42PCQ4evHJssK5O9nQHpmiY98ZFZQZamVArFmcUW43FEsdnntdxilISsNByKMOFw3qJtC2pRQVjRXD0waLvw2FyVRtVAmdAwNMdHGWpzxKrRoD3nUFuRHEYBZz0DQw75lb/xIVQIg2TE//Jz7+W5r7zM5rZE5Ql5z2fb8GAy4GK3oLLmsG1NMU2Bs+oz20ywAsnA0nQM2N8dYVUkzZbL9V0IZYvaXI3rJwPapoO56HGUz9jfH3KmXafR6mE0c9JpwXEecfahc7z0zRv0x5KqE5O7Dn6YkVQj9vcKevt9VCHxahrh5MSBRuWau2qMYfrUMpNk2sNseLQyyaSTE6oCOQqxql2mSpNmKQd5yErcpJoljEXBbmvKxWoTrSzGkcPJVBG6mrfN1zgZZ2RFhBtnDHyXS02Dza0cIyiT2M2kwJyzacsUvymZxDaFSEhmEa5yuLdtY7V8rh7t8c63d7HIyScZ6qjPcJBjd2ysWcycm+AVHcQ4x3QdFmuCKJE0srJdrtLx6U1SemnE3kyxvtFC2Tn1EA5nBZ1KnUjHtKqCeJZgzyzO5gZ3OyGTsWbNtnGzgv1wQrvaxAojvNRjsBMibZNGBKOOxNUKLUx2JhlHymCu5jCaRvhVwXgyYzwtKLoNGpaPq208U9NHcV41uTvpYWORDPr43Q5pOGWmMhxZHgXXrJxqolkJDLxuh2v9Ab7hUeiCVAnS0RivYYBlUnccHBWT+4Ki8KgEE/yawaLpESuN1xTIWJHvRlzyOyR5RFVIRBLieC53ekfIVCJ8nyTI0I2C5bpLmGXY0qGYTqDSQsUxs/GAeqVOtVEwTPaRVgZhQVLkZIYi9SC1NDe3j3lhlvCtVzdRNRs7E/hKULELZKbJdYxlGWjDxC2aUEgcJ0PnKZaV4noeFSFJmVER5RG/ncHBaEzQH3EUJHzuyjY1R+M5BjM0jjAgzVGGQ6In2NJCUbB/MsC0LfJCMwlnKKEYjbfQqsD2LJI8xHZMjsMxyrTxMrClIM5iZFHgOA6O0FQdk63dKZvHKb/+2Vd5z/oiD610WV/tYicCV1q0Gj6aDJkY/NlXX6AwDNIko950ODwcoqyc4czEbtgsVhqcW5pjEgdoYdHrzZgFEUGakJkCB4HnO1QqFWylyYqc3d4QrQziNMG2baK8IBEmllS0LJtmxSHNys1wmsYstausLS9iigjLMqlZBillCHwapIzShKrrIExBlqUUyiDNCobTkCLLcSyDZsVhsdvEKBtUsM3SECFUuXUu+djvKEwRGiVK/lehMIQmT09VYLJsTcvSBGmUNgRxymbnKsMQJlmhUVmCYZrkRYZp2CXaolRZhgH/QdkmCqQhSZOs3Fiftt+VvPRpcNJ2vsPQ8f98/aXAFX7xv/uvf+X8RTgZS7ZupsjCZKtn0D8uGImA+KhgOJziVXr8/h+/wGR2TJjDI2/JqLoR4egaN29cZXg3YVr0eHanx0O1B2lVYp66eIYt/wxPntlic5TQ25dc3YpZanUZb3rUhUWoYbxVsPaAZLQbYKsJ73z3xxEzxcajz3Dlleu8/5kPMwhu0Ls1oh8YXNzIKdSU5prHpaULuCLjQ8+8k1qwiTMrEIsRk1qPL9+6TSUXHA5X6a7ez1wInw4CRGcZe3WeWX2B7qV3cOGv/CA/9rk/IcvgI08/RfbIPcz5ivXGCq16xq0XbrBYa3GQJGwf9XjPW7tsXgn50Mc6vPjVHoSa2zsp96+9jcOdNwjv+PzBZ17kn/9vv8ZAWjjFNoc7X0Qd3MHrmvzkT/8I//gzX+cj3/99JI7LJz7/AtdnCerE5vs+/DTvvH8N6Xq4nTXuuf+7CMcJq8vL/Ojf/Ai7gzu8513v4N0feC+Fivnud34Xjy2s8tt/9A0WFhr09/dYb83x0R9/Lz/y9NN8/fqXeP8z5ymOv0wejfjS1V26610aG+vcby+gggRvuc1ae53hSUymZ8S6zhnp4EeKBzuLXFcheZQwnaR4ecTl5gIPrtboJSHT6ZiV5Rb4CQ9eDGm7Fe4cCeJkiudbkETs7VToWjkEmiy1aCaSaqdP/5ZmZSmharfIipBHLkUsrtepGq+x3JjSbG+w9foOv/R3/hafv/onvHbta3z1X36Ct51/gEOxi+dJlJoRO1PySYO19hlcYfH4xSf5mQ+8m/ZSm74YwWCO+bNdiuMRJ3HE0TAlT6Dl23z62St8/4ee5uDOXWprDdYtQTxz6CwkjIsAVTd4/KnH+Ny3nuXTX/xzJr6Fb9s4qcZxBfFsSrU9Y7hoMe8n7O3uobVibe0eKllMIMccD3s0qPPCIeyOU1pugbQSVlyf0QxMQ3Jz6wijucxBNGHeyRG4OJYFUrDfizDyAtupMt05gHqDZDbj+HaGyASm8PnK7pggmtG93OWwkJzkMyI9JE8FveMeOunTXujg+ya+49LSHnGWkQUGca6RyqKeZ0jPYnOSUXMaDG+GDOoVHgoSjNjk2jBl1algqZwij9mXJrMZ1GTAgunSzxW9aYQrBFXfwFeKY2kydV1ENsbp57znex7k6YfXmVt+kEZbsTq3iF2ts3SmzbN//FU+OT7k0rriHWs2s9GYJy4XNC2b5QaE8ZS6n/DwvFvenEODNBxQ6bYhjYnxkIXDQWbygnETJ66RpxFG5rPiWaxYJru9Q/IYkiLH9xqMh/D69ZArJxNcq019GuCoks3L9YT1UFC5dC/7IqLmm7h2QdyqMprkxElBpVDU3IKNNR+VCQb9IQ9dtHhpG1YbPnpi4xgJ867BSAqiLOaB81XujqcsiwaZLWCqEWHItiNYbAjmA43hNBiMB4Rxzjg0WGwGTIIGsyRHWpqRjsmzGTUk0TjGlh6qLrDSGUtLAcv1AQtuzCd2FDevHzM/LHCMhKo1YlR3aEeawPQ4DCzmpU0/CHCUxZ4pUWT4LZONCzWKnQnzk5zXKjYtwyEcj7FyTcWTDIYJcU8x8mHmmAx0wXGaczvNaAlJFri0qgXtts9goCj6AUXdJg1TxlpRNT2OtSKzy8ICT7qM0qxku4uEcBThOS6dpk+YZpxMJriWQS8cYyuXm1lAsxBYs5hEZtT8KqNkSlpoIpEwVQ4UBYGUHJzkyFTgFLCdBlQoj42lkFQakjm/TjLLsTKN4YJQFifMOF9rkpoGURjTcWyiIsURggt2jbCSYZkudcfCKlJ6swTTsXEti0FR0EwzRF2S5CFnfM1eZFHNDY6jHFmA6RZEWUQeRdhVk+lgjEgjYkPhOxVkBv5xims7BNMESYbtuRi+R5yXi6JZnOIiiZOciZYkliYhwilybKHxHAuVzCDP8LSJIzQiSTGExMkSzjQNunWDhZZN0zCQuSLMMuJQYwibXOWEqSLLC9Iko+qZTIqMijYQOkeYgiRNMH2PaRqB45FLQaYNwizHlCaxytBOiZBJgCzHyEv1l7KgYUgms4Rre8d89fomX3v9Lp9744A/fvEat7YO2Oz3GaeaLIuxpYnwBR94+8Pcu7bMar3O9t0Dnt+f8cbWIW/sj7h+MmR/OGEUZ4yilCTShGlOb5Ky1ZtyEkf0pgFaC+Iio9CCrNA4to1UCkNJUlUQpDlprpgVBaHSTIKUzd6EGwdDru8NeW2rx+s7fa4e9NgaBJyMIjb7Y24d99kd5uyNAw4mM4I05zhKGaQFJ2HGazsDru8P2exNeG2nx5XtAXePp1zbHXBzFLLVnzANUiZxUgbgFMySnEmYcDgIGGcFmTaYxBl5WqIWvTD6dk33NIpI8pwky1BFQaQFmVLkeUGQarb7I4K8IC0UQZwzTFKSTFEUgiTLyVRBkkOUpMx0zjgp2d1ZVjCYJfzRH/w+P/szP/Mfj0LMrQj9Az8Ir12tIgYpyURjmVVEx2I4TOgR8UinxkvhgLc92uDgjRHf/XSd4+GUSk2jRnDhnjVe+6M5Ljzlc/vgBk++dYNbh9/i5k3BKDDoegVGoVk5t8Joa8D8fU1Gb2jOXRR8/cV9nnniPJ37tpkMMl57WfDaLc0//PXfZn/7f6fe/R4Or0fM1ed59fo/5ZVdn3zuFmNMrOOcReZ5z9s3GG/fZHn9cUSnTbz7GvNtxTe3bmLq+2gkLebNt7F3ssXnd76MXzvD+59+N4k4w/ve8RjPP/8SsbvJ4bHBc1++wj/+e/8Nscr5d//rLyHdp7j3PXV+4if+CUvnFUJa1O/LcF5vc+atQ+rdHlnW4Q9+w+SjH3XwoiPq1YJLlyv86c0PIrYGvPsjH+KDTz3Kjf2rXHv2czx+/xluqAcRDZvju4dcuqfFP/zlf8CT7ffzsV+9n6d/5lf50PvO86n/4xZJ3cGJK3z//L08/VGf61snPPnAvczXNhgXJ7g23HnjOr/zyavMrbo8/I5n+OW/9T/yW7/0i/zBc3/CDz75w/j3XeCN+CU2v/AlGvM5X3zuDRoL97BkVOkIDTrkvgvv4htXvkGuEmIVMKclVbeFV5Ec5ilDe0ZxI+bJeYsH7nmKk+oBf/CJTc6ebXCoMsZyQO/WlIfus9FVSIoZc50K1E+Yr5bNLbeVQN6x2b2d854P5kx6EqcNy22PYS9k3zaoOHPIiWa11WVynDDVa1QWLtG/9RxOY4FZtU6cj/GKhJW8y0nWZ3ku4yiq8txncmp2lcsPPsajF+q8fuMq0d4R3/h3d3nsv3gL+1e+ye1eBg7kWcaj588hMsHiex7kxU9/Fis1OBlu4rUX+O53NHj5aJ94JrAmFjIzGEdDxmmLkRHT8kxWXQ+SMdpxcKopu1No2zEpOU2nysplwRv9MU86OcMpLK8vYFUCXnhpnwcuSK58Y4XqaovxMOfgpIYcb1H3lvALi0oiEWsWjpSM/THnu3Ps7+/TNtdZfyTnD790zJlezqhdp6ZCRhWb/hHcGAyxLIf2GZeOPcWauORmDvEAah3WKh5tv4ll1Xn+zg6v38xYbdRZXs0IRgXhZIrbWqQhZoiqombbLBiSxMooEoszl9fZvLuN7E3RVo3NQ9B1i1l2wnCWoW2b+Y5LJk2eXPRRBxN0M6bWrWBKOJg0kM0Wh4NdPCzqDRe3vsJD3ftwnvsaze9+isPJAZ//3BeorzawdUStITFlSrNm8eLeLR568En0UYCOE6YK9vYEfqb4hG5xYRLheZpNZ5+22cAd29SXV6gdDNnZGzPXqFKhjuVnNGqCw0wgbZ9vbh4RxzaeBcsVn/FxSrcpke0G3/euy3zh2te44DSIjIgbQcaq62G4OXd6xzzS8DEQtFY6jMdDEqNKPzRxdiU74wm7/YDL60sEbsS8t8bBzhGp4cIgRtYMRkWGrXJ2qi5vnXnkhk2/d4SzVCWPMmqGYHAP9F/RVIuUuY5DaCYs+x5mBqHKmJkaq+rw5PKE2VHKuaVSVfWvbtgsNuZId0IGNZvFWkYiNc5AoKsF++Mqi7jcNnI6YcGo6RAMT/iRc+e5Z63J1nDC8Y19/kRPMRyb+0yTqtKEeYDLPFlQ4DRglMyQRoHnN/BtF7taxbUiRBTh2xZ1q8uL1/ZxlcGBAW2vysl0Qs0yyewYYVq4WCgp8bOC3LUZ96a0HJu6S4kP2BYbloWBzwvDMetLS/jDCdU4YShNYjGj6ptspTGuJ5kPHcYalmoV7qQTutLhSjpFFIrVxQo1MWEsbZZUgYGgotoUuUY1CgYnM/L1GquxYhjl1EwPqymQ/dJZexCmqLrH3f6Icw2XNeVydyqJzJialXM4jbh3vU01z/A8A0uEbM88cmkSHheIcYVGJyfUBcKxMJnhOQYqlgSVClXpl+xxZuDXW/SzDMe3ka4ktcrz7CTJcKSJl1tkAvpJgmmauAgcSyK1wjIEWqV4pk0S5Qih8V2XeBZhVRxyM2GOOtKKWHUdHFEwjhISWeEkCJlaFnGqmQkDV6cIoTBtG6ICS5h4lsC2LHp5TJBDxfYp0gxlaBbqgjQsDRCx0MxVbGbTDKnLIoci12AZSPtUq2ZDoHJq0gcg1hGGtHEMkzxK2Gg16dYllmFy71yDSRwzHgXMdVu8cO2Y/TQjFpDojIrlIHJFJjUOFsCpxkvjWWDZEtOwSYucOM+xbRelFI60MR2QRWmhqLkOcaZAlFXNeaawzLK5LIlzpO2ghcKQ5SxnC4tC5mWhiLLRIkVqsCyLHEGe5jjSBiPHsUyyTGOd4hBKKbKi1K9JKTFNyqpwZeBYpQnBshzCZIZl2GhTn4bf7LKVEA3CxFblhlbLrPQp69KGIQwgB8t2kEaBEOWSRJQAdllCUoCBwczU2IYkU7ps8BOlJ9k0BH/3Yz/G7Wtv/MejEGsvCH35omR/IljKXBztcHycEVo1qMZoXVBYMwzfptqcUdMmO8cKpKZdSDpnCh58UPD8y03qFixcGOBVIDmAvb5gEkDVBzUzOH9mmfkLfb71asgH3zZHEPVY9M+yV9zl9qHkS19XPPg2kBFYHlzuClTmI5erPPvnR3T9RcbHh8SmyaIn2AnA89qsbvRZXsoxrPs4ia8xHUhcR9EsbFYaNdzmgOPZ42xUNdfDOzTnzyLiOW6PR/iVddY6+xzdPUYXI+bblwiHY7KiQt6cwvCYLBuRRh2SYMCokpCnLnKiac4l9A4FlzdsrEoHu7LESy+9hGNLuisLnDctDqVmZv51FsZtzjyxyEpVEERjcJbYCo/47J/8IcE0w8lH+EaFd3+oyp998v/CrXY53+wSHZiE9WNuK5eN5h2KQYix8H7etXGZr139BjXD5tWDlPfcN8fNwZSl9lMshAOSrTt8Pt9kzr0XUW3SqBrsHrxKYR+R96vc7A+pWjUSNWUpPs9bHr/A7/3hn7L+8DofvdwiNVyeu9bj+cEhnllDqpSpbfDr/+njfP/P/WvWV+7h/QtN5pZc/vill8nvC7hXaWpum8+/nOJVTMZZDsshVuJwIZGYZsoj71OYUnN4qOm0BVdvajAl1czE8i2292KeeMd5smgfoZdhEmD5a2hRoHObmWsipaRueIwGE2JTMtfw0MUI3zrL85/v8fBjb2eulfOFF7/GeWlw80sD3O/qMIiP+MbNMW9ZWMSIFZVmnTv9CdbSlDPtOuksYhYVhEKjjw22VEjN9RjpglXX4i2XmrwcxkQHAZs64V1mk6QIERjU3RxNQr8Wo7dhsym5P2kyvGdK3emzYSqSuEqYjahVPcgTHFqEWYusZ/DWx+f5Z//mLt1WjZVzLk4Ko6hAhBWqbZdRb4v7z65TdTU3DwfsHGRceEiwfddha5RScSWzoY/lCXbiAaQ5jXaVh5cdru/FNPyC5aagnjvstyQLnklzAdY6EccHOcNEYEY+8bbDrVshyXuXeMg1KLIxZ60Wv3PtCh9wVlFehrIyro/3WZlbQFZcRkc2nYqmXtXYlsfXX9rnwrrB5knBW1czrGqTMBhQrS9z5ySFlkHUC7j//Bzb13tMZIv1i2c5t3lEb7nNbv81GgsSUbhItc/OqIY9tnBtk52dDOOsx0NLPtOj6f9N3Zv82nam93nP16x+rd3v059z7z23IVlkschqWI1UiiwXZCGKEiRuYhgG0iCAg0BKBvbAQAZ2ZsnImWYYJAgSB2kQWxakSJYsVakkqsQiWSTvJW9/T3/O7pvVr+/LYDP6H2q+gY21Bnv/vu/9vc/DVj+kPdzi0fsf8eN6QH1h8ZKavMrYawm+NTzmopmxZWJ+++Nr3un0eXx6w7uvdTjYifmLRzlRLfmzdIzvBZxWFW8ELt7CQ7Sn0GiiyLDVjZA4FF4K+z71WcHB3haPRp+RiD668Fj6ln5RMQs8/Lhm9Lsu3/+3Y1a64PEXM+KtbR59MiXptjg5zUlqh4Vf0Zaa80VK2Y24X3lc5JZ9VSH3fLpTw2k550m1oO10GVpDmZWopMNWIZimBdthl8vDJe66oNKGt7YM9bIkHWjaFhbLiPm5ZiwLuoFgpwi4aipcBxZWomVCmLvkYsSHYUz/esHf/sY7JLdrfvbHX9CKe/zwcoQQDr2gZC0sSaL44GzNvbDPvqgh9MjbFRU1QZ1gEod95ZNTgCz47tF9Pv39cx6ZBb7V1DpgQU65nvHV+3uM1jWLqsTNSmq3ZlVKDC5RUwI+V3WB9A17VrId+wShor3tcT1qqMsCW2SkAxevrrkT98nSa5ZpQHXL4yBdcr1yaYeQW0Pdjwina76Yp3S1TxAXtHxo1hG1jFlWa0azGm9P05qn+LLL/MIQ7VQkngtrzXWR4gQ+T2xDr8zp64CrGfiDkDC4QYYeda6JlxWjpCZNNX4jcL0NZi2qHcY3htQTxFsBbpqhjYbaMgpbqMoQaYk1LitjEY3Gd1zCbsRJNqIdBXiuplmWBEZTNoZaOEhr8bSDKzcdyrIxaBqskgRBxHqe47tiQ1docpxE0iwqMsfwRjdg6GkcK3AExJHL0+kULTucTNecCoHvSWRRk5UWTwf4CpRsWJiGutHUdY1VFuM4vJ5o7g8iinUBpuYqrWnFCU5lSKvN6L2V+FwXNa8WCyrh0CA28hApkKpmbTdM25BNWJvbnNe2W3x10KMsS3LRcNRr8+jzMU/Xa4SjWVcNsav/ynbnwiasKbVRHFNR1QVGKlztUmtBUYOvFUZAXpVEWuEi0RKEUlQGMmo8oaEsCJWHwIBWZLbe2MmqGqQm0ALZWKTnIK2hLOuNrldaVKOQEiqzUQe7UuE1CumpjblNgLAN66pBIwidYGNpMzWN3dAP2kHIrMjwtKYyFUZ5eEJR2gZbNwRqE5iV3qDEGgsKh8bUBAjKpsZxBKZZbz6vXaovl+Y0AlPnKNdhmeXEjkNVzb5kyAm0bfjH/+gf8uzJ05+fkDvcc+wv/buGP/kfwWlrZmmNkQ75vOKb3/ZZuZvRUnGl2C6GnK2u2T+0rNaSOoTMq3nNU0zygB88WDLzu7y8rEjWa15lIGqLXwhezqF13/L1e336+yHZ9Jy8bthLBNVSktcNv/17LniG7TuGu60NPiX2BL1dSRHAj/65h3QMrl+QeHDxJKEYFBwNAr79VkiuMoSd0iSKw94dJqennF3lvPe2ZJoLfnTSsLwQ7O5p7t5+g5P0Jaunc/aPEpaTJTMrUNeS9UrQdSRf//WNuvGzR5IQD7efUTSgQsHsxGLnUExgFUPsg/YhaAQ3c0s4FDgNJIFD0H+XfFXRjWvqdZ/KnXN5/lMc1xL14FavQ2YyxLTNOG3o3M4Y+AOyMw+2H+MYQT2x2FjSShKeXjXMVmv0wCcGgiyjl9xCqBw1dFi88qhVwnTykKJ0cJ2IUJW4Xo7xC/La4MgeTt1m2KpZWkseC2Qxp9VJqRY1fUdR15qziYeoYWlKQpuwZEQ73EV7K9YLhWOn2CYk6W1xs6h5Lq5oiz32LgOW1rLuT6Gw+CpgXaaU8xE3Z2Bb4EhFz/eZj1NiL+bk+ZK3v/c2KzNmUWu6Yp/51Q1+N8Cxc3rxLmPHI7CGbqBRrsesLgldhTxdU0vDcDchz3PiGLJM8bM/O6csB2xtxfSTJS+qnMsR9KoIx4t4dHLGpJ1TlT6TYMXbJVhHIbOGDwXs3hjy20O8swmqrbh1JDHjJU3niNuNw7KYMDUFVge88fqEF2fX+EeQasEd4VOdrRj2YLKA5ytFNw5JiwX/5hP461/bptdykLbNnV7IJydr9vYE/+8fj/ju131uLmOaa5fDoc+omTK3PkFREB/7fMX1eeLUlNU1Tz90GN00nLxw6NxWLOOMN7dbHLmSzy5zpO2xF9U05UuquEfcaD4wC+5FR9w/WjFeTkmiNv3uHml1w8tPC15kPt86Dvnl73f4/MmYdKU4PzGcnU+4fS+mal5RTvrodpeX13P+xp2EH04e8dTs8uu3cmYXhuejmu+8E5IuDIu5QCqYK0kn8pAEl7C0AAAgAElEQVS5oDE5rbaPJ32erhbcW7rcRCGNu8J3Klw0DiVhkFAWAdfTkp2h4OH1CJsq3r1zi3lqkNWS28cll2d9KteSns+5XlhKLejdUgy3d+gbh3adsJQen12N+ObthDpxmVeW/+2f/SVb+y2uVMqxG7PTWP4iqBhGksNDhzAzmLXD2ZklblfgLznePuCni4x3tiqenBmkUARRzJZfcX1VkLcEv9LXrLTis2VGs/C41SkxwTbr2pAqj1sio/QrllNYjhpsJkmBOhAs6hXH7Q7TZcZ2O+bFMiNoWZrLBVjN8HCLF1WHj15dsnsjuPdaTFqXmMBh6GZMn18zGq1o2h3i7T4vvjhDCpednR4ftxq+B3hhQnpmWaZr3v3ekPnTBbrV5uXVOX/rB99nmS159eoVy8kaMQz45NGcu7sewrH4FhbCxS9c6jzFH3YYhi69qMXJk+ecVh12WgpHFjQry8g67EoH7TR4XsDYWp7/7CWxhHe/csyj9RJjLTdlTiAEiacItCIMJGm9JIl8lq7EsQ2ekjTBBlplaSh0TZ6ucLMhiQ++A9VshQpiPr+AA1Gy8jbK3m7H5elqym23zyfMuaV8jlqCLxzDfi6o8ox+P+KLKbw8n/H1vR2en4y495UtLp/e4PRjDoYJq8uUXtJlURmWa8l1PmYQaf7kVcrdLc1uEDBdZ8S7La7H17ze2ePDkxuO/A5GGNAlW3tdzucVJq2oU9h3I0ZXKeJWi3plMJEL1iEZJqxHGU6jSLMlVTsitD5CNYSuwyotiGqBcDXzWuBJB1c7OLLBsxLtKmbrOSoIqEqBagpc6eDZhrglicOA0WLBvUHClhTEWhEEAb5NscojLTNAk5eG07zkZFrihwmzrMIV0I18tjqK+TJnkm7kDbOiphGSSbXiVx8M0bYhNZZqXiOsZLsdox1IVxnjqmCqQ7J1zirdLGMZ1SCUoFGSxAsoixxtXHIrmOmGXxm2WOdT7h4c0HElz57P2NntEvia08sxQoGvBa52eH6TM0s3HF7XExRFsQmOyrI2FVa7KEexbCo6SlI2hrK2m0mTdnClJLOGeWXYant4VkJZU+QVfuxTYEAYrDA09Uay0A59jKnx1Ea9bLDURtIISeBIKmpsXhI5HhqDFBXGGEq1YfQGnsYF6qyg6yuyfAkKfB0jTQPNRuTgaUWlGmgKKrMJ5doBUUPTVChpsUZicgOeR2MKHC/Y3BpnKUHgbQQcckOE0FqTVxkWB6UtjrDkZYOr3A1hw1b81j/8r/n8iyc/P5zc//a/+yf/tHMo+OZ3Q/70jzPKanMy6yUxdhmTXsTUN9Dr18zFHHetyWuPomiYLRruFgoTCdzQZbauaMYZA9/h83HJVwaKKLEEA0HmWhJX0Wlrtm/XvP/DnLy2fPEUPr6B/R5c3lg6wuHxI83BVk3iedycGR7+VHC816Bsg6h9bs5cripDjgItEYHGag9HZzw4/hovPz+lWfUR9Q339yWfnFgOtzUv5w1r4PLc0HGvKaucsOzwwccLhm3IK4HIDX7XUGvDp580ZAUMduHOXoO0lqKGjz4Q3NuDwR24PJPsbUGcSKYzgcYyiEBqwSzVXF/UzC8uefvBGePpiEwknN/02e4f0U+8DTd2XbPQLfaSIWJVcNx9j49+WtDppaxHBdlVw3AwpCxzTJ7hOQ3SMTz6cc3vPa1pzQa0926YnuaMLhRJL+UvfviScEdg6ppBsoujBdKZ8OIziykFjsh4/HxGFS0ZOQuccoFja7KZJZtbCg/+1x81HL5RwDrmdhLxRt2QdwsubnIupynd0McTPWw8wTQ5n3/q0GrFLL/I+NrRgLye8bXBPRQOVreoPIeWEizOao6/sk/b9ckuSt755tu8951/i3d+4Rfpb7U5GPR5/MUTRs2cnhPiNoLJqeX59IL3b07YmbrMJ2teLFak+QqRgqcFi2LCNJ0S+j3cYIGXCAZbCi9Zsi9CrhdLToKGtohoqRA3qvjpRye0KBFrhx3PQ+czVqnH55cT7NSysoJ8XuF4ksPDmma9wGuHmFXARVNQ1jNa2kHsrHFFzKw2xIXEXRWYZYNoGaJ2l5NVyfoFyJHD9dzQ9we8sdtmPIuYVZCvfMbXM4r0FYPtDi9PClrDPn9+uuL1W2u2jnxOrxqikM0UZRAhr1O2en2++qBHV/h8/9+vSVdnRI1Hr+WykDds7eT0Ow5uMqelHC7LAc/GMwZ2Gzlb4y5TlHZ4dbFkfrPi9sEhgSv40eyat9o9bkbnaBnjYPjg/Smm2+fReIbntLkdBzz76Io3vyopIp+9wQ5edkW6sAx6FQfbhpOZBMdidZcDN6Af+fRjj5Zr8UxNPi4gXbDX2uN0Zvjd0ymDMuILU7IYKyLPw/E0xpE01Yxnoxq/idFKYlYZSChkThm0mFyuKWSJijqU2mH5YoXpSIJMfGknFKTK5eLyEaFskRrLlu/wq29vY8wZckdAZPHbNXd2Qt7t9/CjkuPtA2pR09u3XGUZfhhznS1psoZPzmoSx0PbGrk2tBxNWedYU6FcSVNIhBL879OGW6sW1+NTkj3FZFEwn62Ry5K0K+lsj3jzay7tgeL1bYescEknE3xdQm15Gjs4WUG7kxBHkGUlH94sWY4ybrVczKrix9crmspgG5ftbY/hzhaHMRy1LLrtcrqWiAryRHOymqGtpF473P+FNUpaHCF5Mwo43u3wajXl1fkLVLEg2nLYbfWZ2RG3Oj6eMOD6dFqayG/odBW1XlNmE9qu5Buv3aWw8N2v3OPl+Stuv7XDi9NTckfhNhWF13CajzGRptNtwWqEkHNWwQJvGDDY12wNSoaDAr9d0fYsUagRZYatSpSSNPMFpmrwraJelGRLGBmJwvLkZoXbapMul4QsUZHEWpeizqmaNWHWo7quUY4DpuBymtHKNJmt8GKJk67IpWU7HDDUikBlOBga5VHUFWVR0FSCxXhF42uqNKetNI2wVFOByj2arMDVMctiDWFMcTWnUgK5srgIXMDOK9zKQF0hpQ+5oaolVTnB8yyvlnM67YR6uiRxFX6k0EiyosQXHp6RNBJcC/txyO1uyEUzxwhFiMVVCl8ZrtM5judSpSWhtoTS4niWW7sDTJMRSUs/FnhUBCJgla65sxfS32lRZymdVkJZZmhHEjk+3VgxCDVVmTGvKnJTEHgKx7gUyworatLakjbgoThZFISmYVXU9KMQaywXowWzaUYvTtCuS21LyjzjqN/HbSrKxtL2fb6WaO6Gkh3PsOtL3tjxedsraLvQCI1Kc/qRg68Uy9kEZQ1puiZ0DL3ARZmK7bBhL5a0VElb1RxvJ4SqpEXNnUSzrS23fcORMtwO4MBpeLMbMXRSDmLYCSxHXsqDpGFXrzlKDD2VsZc07HgZB1HFvlxyHFbcjkqOY9iRGTuBZejXxGJJT2UctQU7ek6PFdtOzSBoCFkyDCyJKvHtkmGg2QpLQrPCNzmhBKEska/xlUCICkcasCWBp4AMayviJEKUOb6n8aTGc12aqsaRgiiOkcLQjkJczwHbUDcFUlk810Frg20qlNjUCj0pqZdrWq5PVVS0Ap9ASnSTEYcB/9dv/y6/+Vv/1c8PJ/e//2f/zT/9e3/7Dq/GV7yzC2+9cZuSGdL1WWYZk9V8c5JaCi76Fa1CYKRhd8fn7/7iFh991PDVX2pRZjN0DXXL8sllRafjsLcrGFtJXRoC5RA9H/C977tk1xlXN4Lv3oY79zrc26q4nlrWtcNv/I079LdD7t4Z0GtfczOxxF24uIDugSXs13z8mSUMAxbLhtr2eLGYk9VzZFqS+ue8/dq3WeYfs2wUeWZ4mcLTU8NrxwInhesGtAsYQRIV9APobknee2A5fAA7XbhzRzAtwa0EcSC4fmV5/EjTcQXbPYsVcHYKsS9ptQ1PbiwiDFkuDb5n+fSZRDmGQEBZWrCCVW6o1ZgD7xkTfY9nY0kW/yKJM+b//Jc3jJ7MUXGMzkvCUHBy0YBckVUhVZ0TtCSLicH1YHRp2R1q3hz6BLmPbgSFhFW1YJaVtGI4mzSUK4OjhmQXU9JFzleO3oHwkp881uztdykmGWoGZgo3M0sxs9ycKW4uYbtlaQowjcBxfbL+itEYDocH7AZ7ZOsFYeLi+Hssr0f093Iuzz32bw+5sguWxmdRphSF5aOFQruSwdBwdLiDMA5NYPnl734HrzdgMr/hp385Yng4YLEueffBV7HFGbe2e8z0mJ39LpdnCwbziA9OL5lpj7ACdQ11XtPrGmyrgyRAm4iLsUPSWeF2JFW3wd3yCLZrJtdXtFsDurFDHBTE+ynFJOfEuuyHEWnlUOYpX//qAYcPusTJcw5395COoEmXuDJinkmqas5klNNPuiyKET+tXcQYAr8mTRWuX5HNKxrh8vnDkn7PZdGUfO+tLiubkhyteDmZcXu/RdBPmF9PkWLNTd5mvp6zXGu8rOJruz74axA5u/sCyoCt4y7XL5ckgcOymdIPe5QmI7tR7N/pkpUzTq8KtnoxTiqJQo/xyykHg4LdbZdlJniVZnzn3T6vrhaIoMP505zVNGBUVLz5rRa9ucBmEwKvzXgyo9ONef8Pczr9lFu3Wnz2TLBc1xzcTjg9W9IPa9KVwems2fF6THNFOPR5fmOIVIcDU9ENW5xmGbPLDEc7HO5WqMBj3VgWqWLQjvk4LDjKG2op2VWa/AouxjBfzhludxjlsC09ni0rZqOSZCdg7VTkszXjst6gdijZ68FbhwPOR4KmzshmLl+sG14VNXt7LR6fjWkv1owrj5FpeO+dI8z0C7aTlMGwzWXh4ccxH19JDj3NskkZy4xFBXW6pApcjkOPwz2XZ09mvHbviHZiMU5Nu+/RzCumwqFMHQg03wg8VDNm56hLO5HMzksmS2h2EurpgiZLWM198kXK6XrBg1tz6qyHDHycyONnowu2U4nT1OSLDKMkX91SuJOCc0ewvM6plKSWLifpmraf4Bcls2VJXWUcbRvu3wvY0RZLweCxZnvqECpF16bERhMGPUQOkbS8HI+plxYVK56scy6WpwStmIEMkZXAuB7j82u+ce8eV+mctQutuoRshhFLblYrnGyEchvOz1M+ySyv5xnnaoGtFXvbHkcdjRVr/t7f/w/ZHuxw//YhLyanTC/n9DtdilSTrRZ4oUc+n7Fc5twddJmsBaZuWGQlsYoJVMh0OedyltOSLo6rqIsaFTm0tebJMuVB5DPUcNBqo1s3ON2ck0XE5HTGVl9TC4eTq4rVKqKX5AS1wycXhi/ckgNXoKqKWWbxfQ9HgBsL6sZgrMNhIijmJcu8xHcgzRwaJWiFCldYsknKXm8LVTSkyuKIGm0zQkciMktpGypP09QFvusifEmYaPLK8t3dHnKVsTQ12jT0XY1jUpKBpjQNhTSssgJFTWYq3gxDDiJNVlU0eUZsat683WPoW3ZiyY5rORz2uFwVrGYr1mXFG1sBbpNj8jWu51FpWK4bJvMVUluy0tJISdVUpEWJckp8R9KLJb1Ys9f22Gk7+G5DL9Hc2nZ5qyP55r7PN3Z8vtK1HPQS+rriVsclcgt29iP2hy7dwDII4ShR3N0NOYwqXttyeXNLcy/R7LQVvUiw3fHptT0Sr6EVOiSeoC8LOu0A3WQ4qiEMBJ6T0U0atmIPn5LE0ShtCLySdiQYthRBs6bfEmx3BKFb0o8b+jG0Q4UvU/Z6Eb7MaUWWfqjxKEEUaJsTxT5a1fjSgE2JHJe6ylHS4LgejW0QdU1lK6LIR5gcKNFK0lAQegFNUxNIi9YOke9Rr1dUdcrBrX3Kco1oKvKiQdqcxPeQzkYr7GAJHYuQhiBQmDrFVxJhGwSSlu8iTUPgSup8RRx4+FrRlDVaWorFinw+wtYF/XYLa2sakxFEbaQRCFOjBHh6Q+AwbKxzebpA4aK+/K7/+3f+Nb/5W//lz8/i2f6+tv/Ff5YQeH1S9xZOFeEpw198+CmnPzNc2pTZxYIbC7e7ITd1yq2hyy8e7vHGr+U8PavpimuyTBC3t/idPz3j/b+AXuBSqoY37nj0d3IOjg2OhDtxn7ouMXqNzAwXFhwLspFE7W+wnm4Weazn4udwcDDhD//oOUmoOJsaoq7l6UddTl405NbSBC5FarjVKnlwa03/KxurT7crWCxAl5aVBacBFW06LVZAXkk8Zbk0krN/01CXDnZV8R/8A5ApfDYDJUAJQXsNTWzxA0lfQqng8WeGaSpYGsHD1HDoadzcsHts+H/+RPODbxpqbdnr+dwKOzx8fMmqsHT6cHxX8dMfNxy/Dk/Wb5I3Dmb6IdljQZHA1945Ikw8jvcHPPvsIdIUuFIh/BTfNzg9MJXFLgUycaHcYjZdE3VmLFYGryvJJ5ZJYbEN5EpxX4aEUYGsHIb31nz+ArZagvHIsig2rfKajWnHDSxlDfd2PKY3JWn1Gud5TtR3kEXN7Z6PEgs6oaR2e3jNkqvVM/bD11mZBZ8+O8cuNHpvn5OrBbh9mmUKVnH3sOHOICJXDa3OLUYvT/DahwRSUgrB+793wevf2uX0LOWXf+MdFssbXj77CX1t+fiPlzx7OuU/+fvf45//zimVzfnVXxxgXIW3NeAqE0Rpw+zqIelc8ODtXaT6FMeNUcWAUQXbyRZPnkxAJPihQKsVV2PL5Rdrqiji/VHFFmvu3PG4bNasLi1f7WWk7HJ+miFxUVpj8pK7nSF5XCDljMytyNIWvc6UTkuwvL5B1AF5kBDrDF9ZjreOMPoV51XKfFXxzvB18uUTFt1dHv2JRxNWoCZ0E8nRVsH5ZRfV7SGqEYN2iDCCVZWQUXCzsBwXAUlfMxlnNHRZVzNkkhJGCzyZ8OFnUyLPZ+c4oRg1ZBPYvh8Ru9f47pDxVYlTt/no6V/ihW+QpxlvfvU263zMG3diXo0vuKl9Tp4KDhMP5Q/44nqBWwle34eFcbCLOfdfv6GWA+bXOdtHK+JwyPWk5mycc/Rej9//gxtuFTm2s0VLBQw8QS+pSFuSI9/hi/NrCDqYy5on14oPVg3fCCJatmRpCpK9Nlm+QusaJwwpxJp6FiO9CMfPeDUr+LU3FbNFxOc/m3Jw13KTRxwMJRcPa3pHA06vR/SjHs/HM7yg5q2jfUQ2g6iF0T7l4pKtWyG+lpTzCT+9ienNGkSrz6xcsu9JjF/ys9MpP3h7SFpccn5tafIQ21XooM1tHWDKlExU2Ospq0DieB0GIqTyRminxdBx+fxyyq3BFkngM9ESWzQ8fLEmETXfudvh0l0S6JLPnxTErZisLHk4q2kVbJSfZkVv0MXXip4T86enMz6bremMfOZDRU/5OOsl39wfcjK5InB9tnaWvMgMQeKxHVr8JuE6g+snlruBJDkqoQ7ZFi2UNry/OCU0Pu2eYLW2jEeGu68bnELxwcjlax2fnTb8+PkVv/H6u3w8e8KtUBC5OYuly7gICHyHk2tJ0m5zfrPiuOPzyeVD/KhDvNVmJ3apZIlfFYyqBZqEm0mBCjyUUTRZSuDHmNwQFIrWoaFOU7y6zbe37vGTs1dc3FzTdCLe6Qf8sJzRLSpOK4XKfQZd6HQ9tquam2aJ47RxyxluFDFJl1SOZmvdIVMZ166HyV1G5zliu+BYKCZORLM0HHYL6nUNKqYdRDTunKKoWJc+07XgcMdjoATNKmMsK55e+5zPM+qyYr8f4K0l7Ysap1ux7MxI4v3NSDt2Nr97JVxVlkBHiEpyPT5n0FEUHvza/S7fePBtHp1c42iPLKvwE4dMFVQIlsuU13Zus0rXBDoCUWFtQ9RKSAIPgBKDRFHlBUEQk6YpThBim5rGVLhKob60g5VVgdABla5phSFpmpLlm/6s52jMBqaKoxpQmizLNqpbJQm9kLqu/0qrK4TANJs6Z13X1KIBXALXIy9z6romjtrk2XrDlLUNpi6RjsYKCLRPts7Rvk9dV0i9IQlICSarwVqUs7GbIV340uaV5WsUCqk1rhUIx6WoC9brNd1Wl6qocCOfqim/1CkbTCM3YgdRYypDmGyehaLa3DQ3JUW+kToUpsRzA6paEDiassrQWBrHwWZrbANKu+R1inYC4jAiMxmr1YpW0MZRLovpDCXB833mizFOEOKFASYrMPVmyU06EkcHrNdLoighzWo8V1HlGcvlGOV5UJf4SRfpaExlaLICzw1QLl9a6xRKaoytyNZLXOljVYPjRTS2xotdqqLGVgJHbwx6oGmqNeumIPA3S4Cyspi6oahK/sE/+id8/uTZz08nt7cl7Xu/AW9s9bh8OGb3sIMabhPFXczCRSzG/MEPP+O17TbvX67xVcOtzpDjdypm0wmT600wU1YhYsnR/dvEfsnLP58yOfW4yjLe/s6Szz4T2H7AQZCSraE3FFBbdFdQN5Z7rT7TG0ljt7lcGIJ2wWvbA6RMKesIred89nJMpa6Zl8CVz8efJGSLCXeOHPbigrsHMAstdS548MDy8Skc+oKLM6gMxF1oUoh2JXrYsLqBqwsoLgSOhB/8wPKvPhXc6cDjC8ugBfv78PipwM7BCoiFw95WRVVKkp2GvgvOQPAnv7/Z3lyULn/2tOLv/pKlu+MidY9edcnj57B91OXj51N2h3BwV/D0fXBDQeVoHp+UfPO1b2CHKdPLh3zl+G3G588YXa3wPId8UXF8e4tKucTJFR//Wc39r1genkAvkezftTx+bLEWZguII4EfCKZzw8tX8Ku/sEVZXjPYgesSivNNd41c4SRwlkKkDE9eWNoDaGzA6aLh+7dKbk4PScIWXyxf0W6FHHct17OcTryDLHN2+wmpnxLrl0wWBn9P06n3ucpq/offO+PN1oBEWFRl2N4riLYFwito0gG9Vo4t9zFeTOVIBjJGuCkm7/Do0Sm3tvZJDn3cbIQoL5muCwo751/+TxbthLz7dZeirugO+hQohp0eT5/9Ed2tN3A8zb3bJ7x8NWFn5w7jZcj42Zz9t+9RzAtcVbMIU1rXLuvVhEdLyctPG2qrEV6D9lKiYMBWu+YPihHfTNsIp8/zh6dIC/3dhE5XEduU2c45d/wuF9ea7cMGWxSML0tS22K1VNy5JXHdS/wk4JyU2Go6i4juQcxVuiJb1rR1Tne/x4dPUy6qJW/2WoxvDH53l+X8nLDu44QS34cw2+aiqnjgxLyaztCV4tVqgQzXdHVJ4HpUumC5aNM0GYNuwPl5D+lOGew0HO05TGcrhu0uQcun+FzycGboa497d29x6/U1bSMp04aXn434aFqxM2zx+OGMG7NgsRri3pe0vRXH3oqqmWHZY/d2zqvzp7TjW9y8CvF0QxMl1KVipD3W6wVau7SqDN8o+h1F0qpZLBaEuseqdvij0xXv3U04Oy2Q6wBt17T2Ypp6QhIK6qDP77qG1y5vuD3sk4htfvzwjHeSgFmZIZVPGWi2u3DzIoOgRei6FGXGyXyGLxVv3d5DqhUy6cOoYfvA5dHoGulDq5lzng/ovSqZxw6Jijm6X7HIljxfK7zpiLe+cZu6bPj0J2Py3QxTRRxUmnRZQuTS34k5n47x65CVF7CTBISzGUZaOlGXH6YFrUqy25bcjX3+/PMT6igGp6RKcw62IkaNQ7EqeaMjeLxsKDJFVVpEXbC3NWQxnZGPStpxj4+blKuJ5M6uxCsj6ibHNQ1biWaZCo72Syazhqmt2d3b5v3rFTuhJDgr2HE9+scettKk0zWtqMf/kr3ift3hrRAGh3OywiOXUKcZy6rDZJTz7x3f5nQ650VT8dePX+PJ+UMm1RU6aFOkFhO6xG6PZpkSpg7tYcBVOqauffb2u9zVJVZnTIXii2XO6aOGb93zWMqAcZYjXZ9yMWV6WXJ8u01jR2Qzwd9579tcrSZsh0dE3YR/8cd/wLsP3mZ2ecnnixE/niwxtUfbFTxoCtiz+FpRFpZ2JHGqjDzoISaGcplh45Aag2oWLBfX0E1YlD5MVnzvzfd4tbqi7wZorZHCcu92n6PdQ7R1OLu5pr+3jas1gXQplwYpfbw6x4kCVvWctK5oaZeiVoQ0ZMpnPJ7QG+zR3dkiHV/heC1qY7Ai39QMPIdVukY5vQ3TtC5oTIUUDdZsFseMFSihqM2GYOAoTUUBcmMHy9MF0hVg1cYMpv7/ZSyD6wQ0dYWtchxfYayDUJvDe41FSrURJNiNM0wJibGgEVhrWOcZjqcR9ktbltTYpqCqCpAupiwQRqFc9VeBV2vJzdUVnf42Qm+ktlWa4/kuRbWxxwkhUK5ivlzhKI3rahw33Kh7MRR5iXIUVZaifJc6z6jKhk6nR2MB0eBIh6wskFr8lea3LGoc30N+aRlbpWusBFdLtFQIocgrgxtoXKHIi9Vmsays0c7msGCMQWpFUVXkZQWmwVFQp3OqNEcnPtZxkCIAqwkCDyUso5tzcmPodrbw/RBTNKxXM6IkxnMD1nkGGKTnoK3YMHNNRVmWAGitWa8yWkkf4UrybLXp5Wq1OSjEHYoq/9IIVwGSvCjwfR9Xe+RlSW0L0tWcPJ9TFimO71MuMoJuTL9/TF5m1HmGH7aoqgJha9xWRJFmyMbiu+GXamL4j37zH/Po8c9RyE06wr73PZctr6Sze0g+n5PuGOpVgRf22d86phuWvPzhM+68Y/nh4ymdRHI19ajHGfVa8Na7gtnccPPU5asPFMt9l0PnFuPFOT/5YIqr4dVlQ6vvcPpZxff/juDHv2epF7AYw+Ebil/+QYd0LPn611zOZ5brsY+vBbW2XE9f8a3hO8goJ/ElqXvKv/7JlLCBAXAt4OTK0pSQbIMuILAQd2A6glYfymlE2085jy3pS3jzAdzcSLzI8uzjFrAiHhqaFhQlrOeWi1ryN+8avvhEkFYKt21ZTxvub+1i80sWXUGJoTt0ePG44p0H0AoFH76ABzuK3taAk9WcnX6IrFbYpiSKEuq84s+e5cSB4GhP8P6PGuJAcO9IYdagRIPVMC43y1xAfwMAACAASURBVG1xx0FXFSKQOL7gYMdijcD3DVdTwcW5welAlAhMZrleQtISfPTnkJabAv27X2sohoauhdNnimePQUnLa9sSf6uhMJZHc59ep2J13tALXca5YTmP+Zt/7ZgPHn2A7yZcjFLiKGJ3P0LYiqifIswOyDX19Iam6fPi1Q3HdwWLxkKrw08fCWYfFfzn/7FibdpkdcnWTsTJR89p+3fZuttmMXG4uTzhzpu3mc1yBttbvHr5kNXSZ3uwh3EC5qczvv7Xjnn/R/8z27ttfvwvFuwfHCBtSbljsSufShrISraESzEQDNsBrraMmytcu0OEw7yoaCnDjkzw7+3TTiCSDt7UYy7nsLTk6Yyra58Pp0/58OmCHT/gci2Z1D4HQc31ecWbw30mLEhNQSkbzuaaem/JmwXMdly+d9Dl5ckXYDxCT+IOKprG53BoePqB5s6dkIdjiVu+QjctnF5BXsXUtoO6nPJ8VNOJA2q/5tBTjFLDUnq8cVhj2GNlKu57BVUg+HAuWNwY9KeWakezpVOmrksQNbSDmslJgt52qBlxv7Y8Ltd47jF3jwy0MvJryV7Q5nf+9JThN9/kP32vIg4Fw7CLUJJqGbK6EvzhHz7nvXdv8cmLEf/H568I/Zo39hoqLyJYKfLDM1yt8JoaUY/44nEfJ2g4nx/Qcl0cci5Lw5vbAfl8RCsO0YdrLh41+LJFsp1ST1u4Gnb3e5yfzrm5drHBArSiJV0mLixVhHRq9E3NwPdYrjd6TRsURK2I5Srn7o7mdGKYjWGmJTui5Los2Jo7bO8FTHsBXcdDzM9p73VA1azGmr22z49GF4RjD7+9yzdDl4vyE5wHBxjh01s/JzUV3f09ps+mHL71Gq9eXlJlK1YXDg92hpy2DdVlxXJVo2KHRLqc1FMO45DlTY66qbg0NWUMXpTw9Tsxq+uC172Yz2djWkkfGYEt1xwEkrijscWcnBirHBw61FryPJ3TsoK73UOeTR4zLQ2VtaxyxWxSsZhM0XGyqfM0m6nWxdOcX7yzx8XNNb/wK99iEA95/PxDtKe45ba50pZ0WuOUEbWwbHkh5+UlHW1YBhXCz9lTt5BLgypKBoMtKhSfPnnI/cMtgkjjt1o8vX5EXnuMRwv2ujvkeQ5Vyt/69X+HMJas5yscP8HVDq5wUbKiMZKqTMlMwXJhSZcp29t9xHaMWOU0Ftphh+VyiQoCatOQRANWywuMMVgpEMKh+tJAtdGY1jiuu/nDqwTCd6lMSVVVeHhoV1Ebg7DVBqTfVDSlxWiDp1xAYoWiaSxaS0yzCSVCuSAsSinK3CKt2qCimjVCCIo8o5Eurusj6xqh5OamLdCkZYG1giAIsHYD/DdFhVCaoigwQOj7NJXBdSVl3VCaTeAI3JCyqBGupqoqtNUYCY40COXRNAXa9bBNTbpaoRwPkDiuy2o9A93g64gqXSEciXZ8HC9iuZpvQqaBej3HaXVpTInnhl+GWIN2HYpVipSS6XJE0u3SNA2+cDZB1nXJ0xTH8yjLGiklWZESKoW1LpVsCKKEpmmQFpo6J45brBZjlBMD0NQGYwuqqsHzHECi9eYdUNZgKxSCtMjxQh/hONTl5vYzdD3WiwkGgRP4SKVxXZ88rWjKgqLMsI1BOJqk1SNbjTc3u2FC01TYpsL3QxwvwFGWsja4erN0J01FY0qkUCxWS+J+H20VRVljyiV8+WxaBZSVRVBjrcIKgxQbPGMUtJgtpkgJTVUTd7pYa8mKEtfVUFkEhnS9xAt9KqvwohCtXUxTkM+XlNUaKTyUG+JoSFohxTLd0CbSOe3+1oaXmy/w/RDZaLJ0jhtGm2xRK6wV4IDjuP8fde8Vs+uan3f97vbUt379W3232XX6jJ2ZuGBbacQBJEBCIkIgSixFwZggJI5impIjyAEoEnDiSERCIEhwguXITuxxieXxeDz2jPfMbmuvtfYqX3/b0+7KwbOV4xzGp9/Z+z3tuq//9f9dCCFomoZh6JkvlnRtQwiBLMtGN1t4jDRY20Nu+Jm/9vO89+GjPzmLZ//df/9f//zx5wRPLyLfPx94dD6QXShQJatVR70xQM3pXuA61Lx055pf/E3B6TzgveT1zyXefxdOX4WX31R8+8OB105fpvKaWTWlv71j9/HA5UXB+488n3tpxt17kt/+/zz1UjHvE0UheULLdBb53g+ukeWW8w93RNkym+S0H+/Y5udYEj/Y3nBaVQx14vXpgjfemjNfzhDDhm6j+e67ib5T+F3ivXcFBycQI5x/PLJ6n12P45O5gXKa+OShwHWaTgauvGL3YeD6QuAvM05qz7P3Z1RvCYYu0G4C6+sZG5mRv90QzyNuY7i8iHQyI8bAH08Fnz+qefpQce/oFvfuLdmut+Abzp4lJv4Qs3/A2eWG9Xlg04A6gUIJtm2inkne+0QxP0gkEoulwKhA22eolMirSGPHDdYhRI5uC1SZePN4htIDRSkZfCJXMC0Ub9+tqYqed14TvP848d1vCO7MBdUs0G7g/t3IxU5QzSVVCrhVpKoKlocDh0Re/YznavOUyf6CEod3JSezCTutia7BflgxiAuE3BAa+IN3W77yTk24sVghSG1PNVjeek3S+5bYe0z+KmnXEOIecap49swT7TX7iyOGDnItaZvnLI+PcS5QZAM3zz5G1obvr1Z89vQ23/z+x3z1qw8w2Q1Grrj/4DV++Vsf8/k79/idRx9xlE1Zr8bQvjcvqGcnZItL/o/ffMxLiyN+8J33iLOCyeqSvqwQrUMLQRklwUC5f8RrLx/yzoOX+Itf/Cw/8sYJP/7FYx689B5fVDt+8fevOcim9OWWcjJhfb0B65E7ibUzpjdgTiT7dwauHg8sTpf849/I2N4kvvCSI1xbzttrUqxYBTjcq0i7xJuHJanJseKCp9uBOJtzvNeTnGIyvcXtfMpcbPhH3+m4nyIfNGcMxTE3O8GL25Kv3er57jPN4TJxdBzY7M5JSwXPJKfZwCLVPLm84Cw/wDU9zwbB6SBQjUJkN1Tlkt/6xR/wH/xrPwbyBqcSyRmcCqh5z8svJ7ousF/0fOkn7/Pbj7/Nk0cVhS0IWeIwC7TaMfgVQs65vdhRl/c4OnnEn75V8uHViu2gmCSFUxWxbFjfXPHm7bsIYRj6wLs+46mNzLOeQbYcOsm2i1y7G156UHK30Lx/kdhEQ+8Nr+aOF6GhDBneGjZpwMcelXUEEtOZ4P3zRPUc0sWO2XLBNougF+w+fkE136fZSOqnpyzswFkNf74+5psvdrybNhS7c9JewScrwTRYrmzGN88jRRfJZcXN449pa8NUS2Z6wkeXDQcp8O33t+zrJVNvUU3D0dERm80lb1b3KKqag7rm1ZMjGnrevHXEV04nzI8KdNYw1TtOqprCGerllAP9CrcXr5EHje+2uCBxveRgVrC+2fDx9Y6XZof8+m99j6WK3F0mXp/e4p6ecefVU4Tb8IXFgn/13pe4f+c+X/3i2xwfLnj+5JKH3/pdXjy7ptgu6NWAe3FFLSrW58+4//YJf+prX2KSefb39/nRH/5R3lqcMsHyxbe/wv7RAZPDJVmR86e+9nlu379LUWoWsyPuH77DO3c/w6v33uBo/5Qvfe1zvPn26ySZYykxxRxR1AhTskqC3pR0QuNEhSxnoDTzk9vo2QI6iKbAR0GKAlmVDC6gVE7btAhZEE1NDIaQBNoU4BNKKxCSwuT0vRsrWKMnyYRUIz90sAOF0SQ3Ck1TZKQwIEUGEVIUpBDIdU4IDhEtxhQQPba3eOtQyiCKURQI4YmDRWcKLSWaEe0VokdqSV7U5CqH6JBIvAx4bwlOocuMXBeIJIBETIFkHQgoiwKZJEVRMAw9Ji/IigJd5GAtIdjRbZUCGwX7e8cM/Q4tNcn34+/WiuV0H5LHCEVRlAgxLpEJoJ7PsENHOZ1hmxUhRkQQ5LqgWb1gaEeBWxQGvEMQ6TY7FAZrHbvNGu97TFYQFaS+I1cK61u83+Laa7r+Bp0CEotznma3RhUZ7XZLXlTkRc4wDJiyxoWIzhVXl5fkJkcJgcjV2ODlI1LCYBPeB8rJnEQg+IQppoTo2DVbBJLkAihJpjOWx0d45xmajqyakkQiREumDEPXYXTGdr0mBYXRkuaq4ezighg6RJ5BSvTtQAzQNFuC7VFKYiYTCOCHsYq4GfpP28NG911oTWY0dVlRlBXTxZIUHCEmqmpGTBYfAlVRUlY1aIGIgejFp9EHBWFk9JbZlKIqCW4gDJF+tyXPFfnsEJEKbOzp+zXNzYb9/duYT6doLkKVTzCfNpeFYcA5hykzklQMfYdAUJkCYkSjkFlG9JbFbImWif/3l77BX/2TlMmtZiLdeU1zaz7hd1/s2J8LbruCyaHD4jG5Z8Z9yleu2dM12eKC1U3ipYmg9RlPNh1DI7FOYGNgYgSyOmFRHbFfB/ptx8PHZ/zOP+5ojzPKTvH1H8/Zlp7f/993/MQXj9kc3vDO21P+yS+d89o7gp2H20eCroHzmeELJw53UZBcyRlX7PVfoJ0Gwiff5XCamLz9Om5m2fz6Q+59/itchBv+wd/9kNkEwlwQX2hmBay2kdZE3n4l0V5LinmkNPDiySHvPblgtifRA7zYCe4ew0UfubqpeHO/YxCRfqeQbUaeBxaLSF5l3GxaZqcFKu+5uZDs7yfqKczUlDyfIvwFZt9yeQFtC7Xe53AheO/jS9ooCBFslagQ3FsKbhrY2sSkBO0Fpk5IC52DQ3LWVc+do5LG9fzWrybYwcHdJW//8IoHewkzkQShOJGeWCWEPuD9968o60TcQhMNwiuOloknF5Z7B5qXbjuiGLm29/ffYpU87DI2yw7RljA8Y7W6xmSv0mw/4I/+IOPqJvDtm8C/9ZZiNU+oTpFs4HkjuLuYsBc9n9QOLyVF4ThQiSgFl5cg/C3MkNg/mnHtIu9+3FOuHe+8tsfETLmKPZ2HW4cGUwuefLKirgqu/I5SZZzMCzb+KbrRbC0sM8kzOaNYrbi5GXi8WvPym/d5/9sXTKSmOnLsHylePm35/jf3uAwlP/JDmodPzklZhywe8NNvvMVb01Ou3A6hBtZ9h0w5qixodoFpWTC4wGvzW1zsHqFkwPuc77/3+3zn4cf8xtMA2QGPnnqOdUGt15zXjtmDnrdv73P9/jWiKKnCnPqVNSdiyy/8suDHPgNfeFOxOrNMyoq66FDTOR98xzHZl/zgMcwOLN/9duRwfkolIncPNb/7R3D3CwNlJfn7vxcRMeOOidy9E/j9j27xxuljlkXPeZNzsD9l/dBSLzQHs4wX1Yazp5HPMEXIyGqX88pSMcwSw+yGb/3BPn/up97gKyc1Ny++x4O7R9SHx3jTcbN7waQsuNmNh6wwyTl0kfOzK1TUKDfwcL1lUuT07YAxOY8vPyDGLb/38GU++/oVS3HEH/zRlurlgr1lztff/An6h9+DzLNNWy43MDSWwyryg12H0ht+aHmbLi7Y7gTkJS82A5ublveeNuQmZ3Oa8dae4aNnAaFr8vWGo3pgMl2yXyk+euRodorwbIVdSB6fGh7Ygux5z/npkoVU1GQIHfjS/SmvfeEdfvsb/xsxu8sqefwausWE2S7RxI5OdZxWJaI1vHx7j3d3LVfnDa+fTtg8v+HNl+/w5FlHGwzFvT3szZaUJcq+wnhDoQIDkvtU7FLLJ+cX/Pt/6ess9wPzxZRhm1ju7XO9vsIxsPrkOfPljLI+Ybdbc3y0x4Aj+QbjG158YnnWrHj9c2+zmO/hup7JYg/swNn5U+7cfsBuF/E6kIwhJE/mPbmuyesJnfX45KjrIwKJJBIi9ORVSbPaoaVE6gwfAy4a8GuUzMkKg20b8jwf2Z8BpCrxbHE2kmUZLiaEgeQiQihSsDjbkukp0XukkWitx/G7UgxDN8LuU0ImT3ABgQKZKKd7uGBph5asyEkDSOkYuh2yqNAh4p1AaIURkSgVKTi8H907IRQpOWzvKKscXCCkiJAR/WnD1xA9dVaOYllrfAxoqRiGgbKcElxHEoLAgNYZfdsh0GR5TfI9w3bDZDnHWsu23yAwpCiZz5e4oUMKTZZlaKUIMbLp1ghtyLMa1+1IIRCdxUwmKAnOOWIM6EyBgxQDShtWzQYfE0YI9ub77NoWkIQUKeualBIhOlLbkUTL0FmK+Zxh25AZQyYUPgbQhs5ZynKCjYmqquh2DXWeEQQka9neXAKS5cExfbej73ukVuiipMprTF5ifYcSGZFEcpZgckxwxOjoncWUJTE5RJDMyjnnzx4yOTjAe48qMuLg2TXNKATrfUIIYMb/u1CQyYwsCbz3DNEyrDdkpUapEuca6tmSrhuwtkcJgZUek+VURY0fLEmMsYRCK2xM498Y0EoRY8B7T6ENu80WnecMLlHnGjOU/K2/97/wc3/t30OKnH43RoG0KRhpxIEIKC3QquLZk/eo6xqTleRljfcR6wem0zld05JSQMixvjc6gVAGmxwhOKp8gt2uqesK6hzsGIPsw0B0kdwUROtwrUXWmr7ZkEKk7a8QtBTZnJuLa/YPblHOJug8ww1jdnq9eUxR5f88O11P9yizKdtmS1YWCCHY7XYAzCYVTdch1BhV6Xdbhm3DZDnnP/zP/5t/YSf3XwqRm+Ui3X5lxvl1w71KUi08/aIgSwOhjTQ7wede2mP2suNb39zxzh3BdC8ypBlmtmO9Sezlkot1wijBzcUBs9wyuA3Llzxyd0QrL/CN4Xf/0HH2Ys5bn88J3QVv3E60/SnLe88okyTPBDfXkT7AxCcut4b7tyNnF4GD13K+9YHjJ08z/vAy436pEVnLIAcOD48RIrEsT9j6gZX9EF14mpXmoz9OnJ8lHtTw0WXinc8qhAtMDiX/7J8Ebt+R7JqM7ZDThQGz7XksCoxw7M7gdZvQL8NMRor5KD6Tltw5jNx5IJnuC+pswfFhybOrgNHPefZEENtEMvDwCixQ9/DJc7h/KuhtIp9CXgh2Q2K5B6/sHfM7D89wCKoKzq8gNIrZLFJIwfpyRCBV+Tj2mp4U/NDpZwnuEoCmUiAyirpEOMPaSqaLRD+smao3qLSil5dcXbzHTBUM/RUfn++Y1yUxrUnAcpZhraVUb7MbzljUV1wOJSbrMMUrTKpjVGw5mh4SkqBLHhm36H7g/WcfcXWd0TSOPgysm4H7C0NbOkxe8+vvdkz7xEsvQ9fc4abZktVzKj9jWfd89K2Pye6d8Lxf8uotQbZKDGpGceyp0xoVJQ+vrjnYWzDJAo82jylbCNZxsH8LIQQXq8fspYqLpaRQFfs68WIzcHUmmZoJy8Mr6moK5SUfPas5vdfw0fcj0sJLL7/Dq9Oczz94ncQNmXD0puC5O0NEwaI4gJRxICfEOMGma8o450DPIA2cxYdcrm/4zsPn/OCs4//++y/gVsGb9yJtJ/js/YqD+YZqL2Cl5LCA07svE90G6S8xGnoRsYCRAiGh3UpyIvMTg2luY7IdkhN6N0GrE3bdFT6t6C4C01hxfnVN0pL3vqcpl8+xdkU+ndLcbNmvX+J6K/jm9z8mv3/McSeZLiX1UcFisaN/OuUb3+5YnjxjZ17hlaMlR8nyUz/yE7TbS958cJtnT96jOCq5aFckk1PkgtpoEBEZOrb9jlLNCVvLtF7QpRViDs4m6k3OjgtEW1BOOhoLpjAYNeHCJ1SQ6NzipeXiYsps2RL750ymJ1grKaTGxD06uWW7vSHlBbM643oTsNZy7VoeNj17H8751ecJ61pOD9f8+Oc/h0wryiEjHhhWVwW+9axvLvnGtwbu3rrPs/UFVZOR6sSgehYnCvScv/6TX+MXf+mX0cuS95+uedoGbpuCq+cDt+5k3C40yUmGYY0NktvHoOsOEzPOnu/4V77+Z/mH3/gmr78+JZmaIk84MeOtxR2G2DE7OOBrX/4qv/ON3+Tum3cpiwmT2TFX52d0Q0PbBHza8bnPf426NLRDQ1YuiN4zdBYRA/X+jKvVGbmZYbISk8D2npBGYVFNalw3Lhr1g4Xksa5nVtYoodmtNwC0biA3Gm3K8R1QZAzdBmHMKEBtICLHZiopUCmClGPvvbOoTI9xBGkQQqEKjSbRtg2EhMrMCM7vBsoyx8icJARDsyPTmhACnW0/bWMSMPYNIWQkMzVu6BFK4FMiBI3JFNvhhtrURJ8w2ejoGcZWyiQEIVqMySAEhJBEBdFBij1KGkDhrUOXBmEy+nYNMaF1Tpbn9F2H1hqAYEehLbRCqogPAWMMtm3JdYYXGuc7ZK7HbKmSGAyd6wkkjDQooenbNUVRMgwDOisQUeBFwidPGBK5MqQUx4WgriFKSYojQ1UmidGjQDGFITI6kCIJoouQJNJkCBUJIRAGS0gCYwwxDajKoIQktD3SBkRdj61f3YYoFSSFkBlD6KjyguA8Osvp2g2TyYR2u0UpRYgDmSywLo6L3CmhTUk7tGxvrplMMrTJsYNA6oTWiqKaM9iGq80Vt47ugks425KExBiFtZYXjz5C2HNuvfFlTHFE1zeMdQ4eY3L8AEbJMRIiAslGgu1wzoFM4/KWzhF6LEBwrsMOPUqN17Cq52zacUlMIiBEsqoG77hYn7FYLGBIGKNobEOVlXz4zYf88i/8A95/45q//df/Ju3Qk0lDtBaUwuia9dULpBhzvsWkZre5xDvHcjnH9ZDlFUIoXPCc3nnAenWNZHShbQzshg7XddRZjdaS8yePKUyBd4GkIke37kCmP3WvYVZO6ZqWIfaUeaBtw3iYiR15tkBLwdC0DMNAXk/I8orORYpash16JvUMEdNICZEV/bAjz2r6YRS4QUS6tmW52CeSsMGTSz0eSLOcv/wf/yzv/gtycv+lELmLqUg/8jnN+Y1nJQSnReLOG/DoIbz6Ety0UOw0T688X/hhePuBoVOB1SB4tdpjeetVpiZjYz9h3TWsrnsqM6NEkZkeLzo2rkWYCVdPr7m8hI2Cr7/zBnf34Ne++31OpnNuL4542u8IzTkhKnpvOZgtcann2QcD9VJx/35B3y+p5RO0uU/fFJi5IbjxJbRpVngtWOxLWt9jUiKfQXQTDvNTwvCcTSfYyw/R+5Fcf0C3VdjG0G4L6knOID9EZxMysUDlJwz+hjxPzHMNKuDSFKUyxOCJQiPY4IIBn5C5o4uSxILoOkJ4wSybsbOJSZHjwh5TnWjlDhc8W6sphUdnNSCpXIExOdv2EVFUrFeXZHrLWefoWovrelaNxwWoF5p+XfL68UtcXz1lekuz9ZpMOKazWzgneLz7mD11j9V2RdOvOZlXqExwNP0MiC3Xw5bz6xuKbsXuzDI9VjT2HnvFmuJoxml1jN0OtPqGXbjF5SeO114XXN1smGYFpk4EBF1zzXL/VRrXUddTJhLaq4dsU8ViMse5jsfPnnMzBP7w/Rf8ha98me8++hjZak7qA26yyD/9R++TlKAypxweDlgVWcocebzHYX5NrmesusTV6obPfvk+Q/KcPf2Ar719j9/8tWds+itmx/D8uaDVOfPSMFGW2gQ++Xif+mTBZv0h917Z48njHWG3xC83HN1pef9X4J2XIdSa+WLgtTc+w56Q3KQd2VThOedE73OzDmzWz5lNIErBTZ+4d/jTnOZTnHMEnRN6y2yh2TXXpLjmQnQgDLurLUWytLKl6c6YHMzxcUouYJKV9O0F+5NTUozcDGfj+EgYkstJaUeV32a3tZSVBLMmxXv4LnBYLplScpU2eF2SCUkXP2QRbkPK6MOAmU6RMSFEg49rNlvHQX5KrUse7c7QfYY0LevrQNeDjRKbC0x7Q65fwmpFn58xEZrF/BCVJG6w3FvWGDNAO6PxHfOypvQBZ+GTZxcc7hWcnV9RFjOKQjLPP0M9lXzw/vc5eLDH5umH5DGnqioau8KaHVQHhOoanwoKXTM1r9J3nsVsxtOLgVJnHN1OlGpKkxJt78l0oncG3V3T2ocsJ2+zXSne+/AD8lnBD3/uSwze8p33/hmhu82LR8/J5pJv/t7/RT77MqWZszcTfP7Ntzl/ccX+/SUXHz7lx//t/4hf+4e/yHxvj1uHhyQdWZ6ekgSoLGLwaFWwmB8TbIJpjtaSduspy5I8q9itH6OzKYO3FLomxYiYTPB+h1EZs+UBrtliPdi2Iww7Jnt7ZFnB+vIamUHfNyidk1WKYWfJzJR2t0KrDIwYm9hcS0iC0G6o8tnY2jSbMgwDzkeEkJSZ4H/8W/8tP/Nz/yXG5AipcYMDGFFXsaNdrakmNUFmlHlB36woZwuQGW63w2QKiKQk0NLQuoYqL0hJMAwtIYBQOTJPJOdQISGkQRhNjOCCJ0aP1hlaZQS3JvpA7wNGKpIUeG8x0hCdBS1BGpJI5HmOlpoQYbNZMV+e4O2W5Dxe+DHbm8/wNtDbDuc69pZLhl1L0+4o64KuaUfxVlXU9YQUBTF5kJGQJMRAcGMesRs66rLGIBmGcRGoyEqcXaEmc2LwSB9HIRAjdVHjkwMERpfIvKTvWmQChCd4S3QeIwxRQkijQCyKihDHBa7BbpHOYtsOmRliFATh0Vk2ClfXUeYztJ6QlESKhB3W1OUBnR1GMSyhnk6w1hJ7iyxGV3yIPaJ3EBX96or57buYuiYNAzF6pMzwgFACGcf7Ygg9PkQMksxohJBY1zA0O0wxQ0qQQdP3lsmsRsmcwW4ZbIvRFRFNkgOlmhIIJAFow8nxKU8/eo+i3mPoG2AsL4h2DTLDh7H61qiCelrRu5YwCExZIgj4IUEM2NCTrEcXJd12g/k0RqHyAqVBxESKEescUudIPWarlQy0mzUCT6En6HlN3/eETcu6u2axPKQTgmkh2T4a+Du//n/yV//Nf50sFeRFRbAeU5QgM6xvaHcdxhjqxWTMEmuFazryIiOmhHN+fD6DoO/bcUJge6aLydgzJsYYTru7oS4mNP0OIRJCJpxN5HmByjSqtqLpNwAAIABJREFUnNNtd+ACJtfsVs8pl/vIYo5EjPxw58myHNs7jMlJoWcII92iLEuC7/HRMzFz4rAdiyaSxAc53od1gTaS6EZX3/Y7JpMFm6s15aTmL/+V/5Tvff+9Pzki9/XX6vS//g9fxbAHrIh5j1H7KF+zc9sRc+EuYHKHgEQ6kNHTpBtyfUhCYlTOZvshnR3Iy4x+e8F0cgctSpJv2O7WRJGR50sG01FnGuEceRm53l5i1BLvGBu7ZEuhc6TIWK92TPdv4+wTore07R6uzTg9ecBAz9C3SD1QFjUhtdTZAZv2Au0i1/6GOj8goZGxw+spsR9QSuNTTxA9GRGRxm3T1t1QZXcxImdrO0wW2djAQb1Aq5ZES1Uc4OINzsMQe2q9DyISUORSkuRAcj3eCXRWEYVks74iz8ZSBqEPsW1PVkIynu1uh8n2YDtgxQ2Tcg8V7pKXz0i2ZFASKTw+WFTUmNjRlgsyKrT6NASfZVxvV6gYmc4P8H3H0G14sbqgC5ZuuyMTjj/zpZ/ho+d/zBP3mPP2DBcsn59/hXN3RrsTvLL/gI+f/xGbZo0ZBqZK4fopX/rc1/ngxTd4+P2Gv/Dn/wwfP3lBXgbyrOLZzQuWs4CaHSHTgk1zTa4ESkq0tKjFCdfPnzFlgUsB9jsqk7O7yZgtD9msz6nMjIxAEBrZRlbn15y3L9htjvnei4+4tVQ8OF1ys7EI0fHkUUe/lqjTnrfeuEXlJIvFEUWq8LpFl4ar51u+8/Tb+AjWSr7/dEo9XzOtJCe14M7LcH0pePQE3nkgmS0s7SBwuua42CHVPZQ1nCxfojE9fnfFfLZH2EaKLEdXBVILQlJksSZJiwiOZ8mSGYkM4/OfjGYxmeDXK6rpEtdb+iyidcauu0HFnq2N4C4pi0NSgOQsB5Oa8+YpQwpMsgLtJgzGUGWKod+idcbNboO3O+q4ZKoEsSgIk5q7/QkrvcHJLTJlkGm8daQk8G6LbXcotU8WJ4hhx4Pbp2y3axQdItM0gyUmi/MVi/wI5R1Ez4V+xiw74NkmgPAsZWBplkznE9aPLuh8T3Ke49NXWNx5i2ldI0QiolmdfcjBvc+TshpSRgyCrJhgsohwFu8iWVmCECQpiCLDk8BFhn5L8J52c40bLrh8dMG9N75Enyw3Zy9Yb1v8YHG64+rsjJ/8s/8Ogw9YP+BCoMxqhqFDmIrZdD6OWXOJKWvKLOeTj95jvjyhcx6RIklLkIk6q/A2IVQkqUhyEhsDdujIlIaYWM6WFJlk01l6t6KQBVEp8IHD2y/RbF4AClNM8F2kGxpMURDx9Nsb8mnN80dPeOnBZzBlDUlz9uiP0VmNx7I4vEWeFVxdP6bZNFSZITlotpfIrEZFwen9d7jcPkYnwxA8Knhs2zNfztjZHh8GtDQc3XqVp+//EWVdEM0MGS2+tZBl4DuyvCZER0wDwUVkXqOkwQaLluAGT0ieUkv66Kl1hTLj4pOzHX3bkeUSXZySRI8LgdoIms5DHEWalhBCGl3bZoeIDiUdLnicSEymxzSrC2bzfTwCSaR3PdqUyKTpbUedF4RsdDR1jCACbduiZEVWCNzgcc5R1hNSGJvQFILt5RnL+7dwFkQU6KqmXbcUtWToG4wS6Dwb85xyXFaT2qBFRrdZo03CBYkoayZVzW57RSY0yuR426MyjR8s0hhSluH6AVQGQY5FPDEhRGLXdxSqQChJShFle4bYIbM51vWgA0ZoZFaTcIh+IGUZipEU4GPA2R47eKazQ6LyaGmI1rG5ecFkvke7bcmrsQ42hMTF5WMODk9GIkOMRBGRaYxM9N6B64FEiAKJIi8zRIKr1RllWVMVJW27Iww9/dBSVhWrp08xWUmxPKQoCupyirWewY2CVWUGlwRG57h+R2EykspwCaqy5uLRD5hVJZtNy2x5NOK8spGCMJtPaBtLVpXYbkvXDpR5jslLXPAoaVBZhhQa1zegFQwOxIBPkbyYEsLAdrMmKwwyjlG5TCrczqKLnLye0Ldbgh2I3qOXU+b1AUoptjcXlNU+zjm6bcvP/vS/wX/1d/8Gp4dvoPIMGSD4gd466skch8PoDJ9gc/YJJ6evYJNj6BtkErTbHZPJBDKNSyBDIpIwZmym80NL30dMJti0F+S6RIoSN2xIzjKtD0iZBFXQ+chEmzEbrgViUqApSUmQ4oAfIipXKDUuQTo7jLSI1I+LmQEkiigc/mZHTJ6Ip5zOUGh80uy21+T7NWqIRCORQ2K3viQvC7xL/JX/4uf/ZNEVXv/MLP2dv/01fFdRKc9mOGNRHaMZN9VjUKThCX3UmHxGlVcQE5+cP2bvYI73nnlxC6nG7cJtZ1nWNWc3z4nkTKVG5xlDEuAzPC1a7nDOgu/QkyXJSrIokGVF53ZY26JVRaUMnW/J9gXBrolWUMsclX2RTDfc2HNCH8dAekq4wdCzIgzXNH5HSg2GjLn+PHq6h5M7YtiSm4y+c5A80TXUkz2SmNEMLYPvmFQ1zeaCRgfKqKnzDNt7UJqtH4iNZXowo+/WFJM53iVIdoQyDx4hSowpsZ1nOp9STHOSiGxsQicBfkM1UYggcVYS24G6mDL4MXsUaeitZb1pqasCHTOsikyNZh0cUoyYkWabKLIc67Y4GTBKkecVtm043LuD8wmtNZvhCuUNmS643GxI2uCDRMvAxeY7ZFmGCDllNedwfovHN9foIWAbz/HBKVXT8bW3f5Rf+IW/iQtLXv3iy/zg6XMGVojuZZazjq4I7B/MyUrJ9eoRtgOdrmkGyY/92F/iD//4XYbtE2R5QC4XJJnQEowaH6x11zEvYFJkFNOcKSWqrMiSpm1btsOaDTe8WjxgXy3ohGC1uuKqu+HR+pq3jr7Ew2cfoYqamXqVxcuaq+4TKpMYumHc5vUrlNCgeq4u1vzoW3+Os90ld09vkbod/SC42A30fc+66WlSYBIFp8dzPn76mL2TI4QMNFuPEgVCgRYwmxRo2/K8b+myQOwHZFbh8Ng4kMeOOin8bIF0DpnlKDHF2S0u00zzDNdHkvXEuGPbPseo0QFZVAfYMGH7vCWKwP7BjD768SWVNGpvjvGBYAPTFJE2xyARhUCFQFEVbLeXJOEYhjVf/sxP8qu/8hGPn17wF3/qNbAeETy75orTg1fRpqDrWg6O73B2fkPUGSfHtykPamRxi9iF8QNZTuh7T7fdMJkf0OpImTTb7dU4HnWWpHtcX0C9RDtPDA7allQocmNoti0hJvS8Js9K2m6HSAnnRmSSEZKkM+YHt0YXxDbjsokI5PUeWkSaTcfs4AglAirXPH3xnL65ZlruoVWFiw5lNNb2yCBGN64er01RLOj7FqUlUhi0HPN1RsrxfhGBpCX9bkMIAVOW4yKkKXB2S5VXNNsOXVUUKmCtJ6XEdLak6zqid3RtS1GWDLYHP+CCYDKdgVG44PB9gxIZtu/YNFfcOrmNHSCrl7hhjBLYEMiL0XWUEYxSIz0AB0LhscQhYMqCm8sz5tM5MQmk1ESZSEIiVMbh3iFPn75HMZvjGwvR0/cNk2pCCOOYVujA0EeULDFZommv+f3f+BZf/pGvUVYV1rUsDo4pzIxud4U2o3Dabbc8e/wu33v/CXdeus3e4W32Z/v4fkAZ88/FrVAgiBRFju1bhC4RMhKcRQkBcsRcGf0pxzTJcSzvBSZTpGiJUqGTorm5Ye/wiOXJEaurZzx/+oj57HAUxqpEpITJc5RSiOi4vPoEQaKcnNK3O6bzfYauQTIC9qUy9G5c6NJSMww9RbGg3V6RIZDGEFNCTCpyUQDQdx1D26AKTVlkyDROebyUEEf+rGt7lNFIKXH9FmMMSSiUMqQUUAygJnRDi1CKIstwcaQBKDGO2vu+J6vqsYJ1sGRZgZCKQCR6CzEhkkXlNUM3Lr3leT664irDD5YQEjZYuqEfr/XgR8cuJUyejQSJzo2VuJMpXbPGFJrtdktdz4hBIvWIBbNuHPnLmAhhIKum2HZLVuQEPxBJNJuOopgQ4+jeN01DlWuETyQ8TTegtaHIJ3TNmi5tyMua6WRJUdQIFDozKGVYr9ekFEhJkJc1ShustQx9Q1GMOdW+adFFhS5KYnAYY8bnOFowCkIkVxofx0ZSa3t0UaN1RpGXGJPTeUutRzHdNgMf/9Hvc/vOAWa6R7ttSMriB0/yDuccWZ7jvSeEhPMtpcmQeoYNHd5b7NAyqXPKeom1jqQkWhqccwiZkAlmiwOs7ZlMl2y7gaFdI7NEldUoZUCNC41D36P1p1GEtqOqNF1w6JghUQgdCbEfqcifTodTGIjSoAkYk48IuRhIKY3mmVK4vicvDM5CFAZhAsIoZJQjjSRYdt2KJBN5VvOf/Nzf4N0f/AmKK7z66iT9z//Tj6PNCTOzYJokJgyjC5MKuuRRqQWhRwaedmM4325JckvvLEWcI2Rk8B0im9BuB8rZjmFr0CrgRaTzmiyWVDkMbkU7rKgnB5iiopQJaz1BRoQIGLOkKqYM20tC0zEogU/XTOcLhq6ll4JSKLQoaJueTAsEUKgTXOGx6ZxgxtMqvkYOkeuNpy46Jlrz0bNvMZvuj5uQucV6B11PMf8KFQWb7SXVXBJnEdu1GDSzdA+vRgQKraMfNqBWdD5gin16t2Wi50gp0aZGiwxDhjWBzcVTlIYo0/hx2O7IFg3bzTW1OaXIjiBZTFZQ1j1t6ynzCqmgt9fU+oiuX0OyRFNwvb5hUszZ7AYqU4KyqBy6bksmCupyRmtHNmKmx4OCc9dMylOiT2xDgw5zssIAW6YZuFTgnWO9XaGlQNY9nWtJbo9a3eK14z/N4ekJetehfcPu5or5rOLd9/4ffvNbf0g2SWTTBeerNT/09o/x3ct3ubd8mWfrDq3B5DuCvMJ3JcFnJLtlPj8hxAFMQSoydrtn5GpJ7wsm8hmpu2K+9zoyLfHG0qcNE6Wp0j5Wjfmrxj0FKmTIGKRls3HcmtxmtdkymQmum8BiUrFarSimNSHuqOoCieLq4iNeP/3TmOkp87zG+pYPP/oDrp59wM6XbKueU2foU0BPBN5YNpsXVOKI/eoOQkVOD/b55qNvsFcdM907GcewInLWBJIfONyryWKA0KGHKav0EBvOGfpb3D+4hVIThBXc2bvPqr0kyyva/pyQ1qhUcPvoR8myGeV0wfX5RzgiVm4Jdsssn5BSOy762I5UzFnme3StI8tKpDIYVbB3cJ9us0JlA+efnPNrv/E7fPUrP8L+1OD6getnTzm6fZtBGGRWobVkGC5IasrFVcfx0RFDcKOL5Dum2SgklBBk5QTvI9XREUZJympJRJJnE8YZx4Bt1qx6h5CSop4gRA5SsDo7JysUMTjybEqMkSE5NpcrlEoURYG3lmwyJbY75osDBj+gVI5QYPKcoeuww250LYJH+ISPEJPH6ByZ5TTtlv3ZAqKn3TXoYkrEsXrxEeX8FiIl9o9ukaRASsluvSGrczKj2F2/4OrFJ+isxlQ1QoFUc6bzCe32GpLin/7Gr3D5/hn/7s/+Zywyw3p3w9BvSQgW+wc0bU8xmdLc3GA7i5SKpCxVXmAibLYdUUtkXtKtL0h2h1DVKGiS4PmLx1SmYnFwiowD+IANnn5zCUYwmR9DTPR2+DQjOX7EptM5q5sLnO/J8oJ+21HN8k9dz8mnAPk0igLbIaMkCkvXWnADvm9IbsAqQXF0wsHeXe6/+haXVy+4fvwBfW+xtmdv7wBiYrtbUZQTum6HKccPdBCQBgeBEekUPNdXLyinJSlElge3SUZh2y0pOvJsdNhyk2NTIHiBdYGqqnBdT9KgzHj9X3r1s/z/1L3Jr65bft/1Wf3Tvc3e+3S3v3VvlatJUjgmlgJRgAnKBEUCAhGKMEhgBgZkFCKBB2BQ/gUGTGGIBAwsAQIEIRk4IJTEpOxy+VZftznt3m/3dKtlsN66BitThOpMzrn3bJ2zz/M+a63v+v6+zevPn/PZz36fXrf4fCIFj2l6RNKopkdqQdtskEYjSiD4QsyC7FekEpQSWJYFJUr9/FXNHm1dRwqhRnL5lRgniq96ZnNzx354zLpWzW5YPUiQIjEvF/xlpmSB3m6Q0qCFoGs6TpdXSKEpqRrZpHJkkclxwbgNQkGRjrieqkErFsQVIsimoQiFFpoUK2hMmcq+Z49EYboGEa9jfxIpFfq+JRZQxUDMaGtrxW8IyLQCmZQlaEOI1fAXY72snS8PGKPpuy0IQwwrtnGs84LtBnIIiBRrnqtR2KZhPh4oJdbvRTbIYsgi1QKHGEnBI65xbJBR2hLWQGsV43TPZT2yv3kLVTIZyXS80G72SAnGNQhlCPOEkJrNZsub+5dsnz4iLZ714UAmsSwzSgSUGdhsNhxff87lco9p99w+fa+C/WlEFJj9mWgkImbKGlCszNMFIUSVWbkt0nQIXU1rRRm0FuS04mdP2znWELBdT0kJ4op2G7LT6CKrXCBHSqqXM4GEmBBC4FPVEpckUArO5wljNOt8Rst6CdKNI8ZCzJHgp6u2W6JFrbC+lEoG5CSZXr+i3Q+0mw3zEiEXbGMosrLJ7dAjYkaUxOHhns3NM/wSEdKzjAcKkm67J4rE+MXn9Ps7NrtHnB/uiXNlzFVr+PXf/G2+9/0f/eKA3I+/0pb/6G88oR9u8MLR5YF28w7zPLFtGlRecY1mPJwoOmOUor0diDGyaZ/g1xfIUnVOBYsxe5JYIB8Z5wsxZ47nl2zdE7qbd5kejszhgHINN5tnfP+zv8fQ13GHDJ4SG3J0uMHVW0iZkUZiheOczui8cAmOwoJePE2XeD3/BCsfE88X9nf/ONGccW2DND1ryYg4YVXASEWeTvT9Bp8Lp8sD2TbIdUSokdObZ+y3G2LxnKfvEWPDHPfc3UheHb5Po2/p26coJTDNDpAo6Tgej+weO8ZjRlO4TJ6breHsIawj29aRSbT2huO6YnSgbXp8OeOjJPuCXz5BxxPCfMiwfYtlOlYWUJ9R8hYrBXl9wG7fQ4uWuVwwBBCCyzqhTa55fKtiWSEaaGgIOSKkxJgGuawoZzmtR2IyaN2wayzBgzWGaVwQAI3HyUiUgnVJRDpSXHHOsRFvIfQdt+0jmv6rPHXQiBUR4cWLn3BOkaePnvJH3/2v+N4f/gG//LV/lhQmPnnxd7kQefasQYT3MJ3kxYvPaO2GN/fPubv7gG9+9A3+j5/8Lk+Gb/Dx+1/hpw/fYzyNtMNjosrMJfFo8wHLyxd8/YOP+L9+9gOCFjSm4VEnKrDXAiltdYbSIHMglAu0tXVHyJZUFCEWSryAjxwukaFzFBGQ0pDSmaFfOI+fUuLK4AY0E0YkNvovsGl2fPDen+d3f/9/5M9+6y9yGCNbN+CajmnylJTZDBZh9hznBx7tv8bQ3yJLQNgOSYsskSAXcmmRuppVCDNrXikoijBoESgJ1sniYw2CH6cDtjFst1uUMMQsSWlmmhdSGQlxpLEOswZOb46EJeFPC22neX54zm7YIErgPJ156623CFlg3YYiM9v3v8X96+cgW/bPPiRJkCWTKDUsfTrTuJ7x8oCQhnbYYIVhmY68OR1QGXwsSKlxStPttyhnSeMrvvjsU7r+jngFO/M4sek2BDJIwzKd6VpDQVKCJ6we1zou9y9odjtImbM/0DV3PHr8jJwhi/qZ7ne3HO9fsOkGfMyEEIg5IKUkY9k9fsKbF1/gxwNW173ZaMcaMtvbuzrazhBTYJoPPH30Lj/4o+/QtC2Pnj4j+IKRios/s65rZdwENAh8ANMOfP/v/y79O9/g0a5FGc0yn9Fa12D6cCELhbicOT+M7G5vCWkmzAv73WP2b79DIrFczsRpJpSAcC1IRWN61uBJweOMqVOXIqEIxrggkWixssyhsmwSiqih7UUqjLAkCVpZ8jSRc2SNCxhDvMw0/S25eFQqSGvQIhMpTMtIyQsiJYpypKQQqv7btNbEWE0qYk0IVUf2TbdFKoXQmpwTpUBjGiDj14k1TBVELTNJjFi1YfUJO+ywTqIQUCpTtgSPtZbTy8/ZPn6P0+lC01i0aXEOLnOVJIQQSEsNzVcSLJIkAto25BI5zxOt7shxQdoWlQo+F4zIFBlIa8YMW3KOYAR5rXIApRSuGUgh11YtoUgpY1rLOi+0qiZUlAQFQYwB7080rodEZQ61oiQIPte4sLLQ9D3ee3z0qCxQrqs6WpHIiZo52zSApOlq1qwvK9Y2nC9Ljf2yGiGqISj5FWE6uqYlR09OlSm3tqngHVEvBkLjQ82tLemPZXqX8YFuuAGhOJ/uabSjiGowir6yodPkcc5glcXLQJgWBAvZZ1rbEogYpZnmC02z5YsXz9nd3SKEoG8H2rZn8StaWUoqjPOIsxKEJeQqLxmGDWEJSFHlMSl71nlinSPj8R5lJMOjZ4zzRE6Km01LjAktTdWHqwwa1ulI47aEdUbZnpQXxunM0N+i9wPLcWQ+Haq3wAiW4BnHkb7vyT6QY0BKTT/sWH3EbQeUc4yX+y+zgkk1aq7ftdw/f0F/e4MVDp8LKEm4TIQ4soTaVGqGPYXwZe6syFXPrt3AcT1ghUEU6FxfZTIqEXyib1uKUPhYjYeiUKcayxmZNUZLgkhY2xBRrMcHbG8RWZF8QTtNplbKiiJJYWKdCkPbEdOMaLeIFFly5nZzxzyeUUoyL0eW0z3Rj6A3DMOu/llxRQrDr/07/yF/8Ief/OKA3K+8b8t/9jff57z+lJw0pjzlnH+MVjC92SPRdJ0gLzs++PBbzGWhyCPndeR8+i43t1/npvkI7+sH4TY7jD1R4krKGjfcEsOZvExo1XAaJ9bpga5ryFojRUIkzWm55/Dqj3i6+zqnS0K3CjNETClM0w/ZNN8m5wXFjOqfMc1HWiFJ+QtSuXCeMn3XMcVH7Hvw08L5co/a7JAu0FpDChOtvOU4erp9RyMtxzixdVtm/xmUG4ggTaFp9oTLmU9+9EO+8tE3OfvvIkrH7ANFZBrXcThf2JqPaE01A704vaYRd2z7D8nhxHl+zW64QSVQrWNaC1IbTBk5TJ9j9++QU0DnAW2O9XYdG6zriEFihWYpR+IckVaTU42OcVbg4wPO7hGqmhcu4z3t5hblW4RLxBzJSyDTYUQ1TjgD2mwI+Z6kq3u60Q4ht1AUOhWU1Zz9TI4/gzIyuI95NX+CzhbdfZUyD+wby9hkwnrG2S2ezHh+jeu+Rq9ueXv4Co+Gt3h695jeuloZKQ1mekMxPWF8Q5QeKYGYUEXw8PyHiOGW241jvaxM5VKzFefEfvuM3jX4cs/z1z/F6Ufsn3wdZMSvI2FJSKkxQnG4fI5SCkxD2w4oveXm9gN8MJBXlApEBClnlNKkkBEyIqVGSIPODcI0SClBCNDXMHg8okBRGpEyYylYFZHFIkqulzxd9X4KxbomitJIFfmv/+5/x1/5J/8y5AthFhityWXlfBopMiL9iswQ56pPNH13DfZ+gySB6asbGI9TDUpYzusbmn4gFY3KHcrPHC4XVmG4e/cDHn38LYQoaFUIy5nL8cQ816KENctqlNCO7eaOw+FAkYngL6R1hVTbiX5eL4k2pLxQokIVCMljtWNeLhgkRUtEyTRDh1UNEsPpcE8WoW6yMWG6HikVaxCQPSIVUon4MDONR0y3Y9vd0Axb2k7Tu4aXL59zd3fHyxefMy9nrLUU1yMTNK5DW8X5eGGZzsSwshvukMZiWkWKBakc3ifQglwSKQWctJQy1z9PNqSU0EphbIs1DafzAT+PVyZ0Q04CYRQ5JugUIlqM6ypYFpHjYeJ0vOd//Z//e/6lf/mvEqXDNB2KSI4RoyRjCKx+ptUWg65MovUIoWH2XM4TbmiQUmORrCWgmxa/1MIAqOBnN2zIsVamprCQUyJnjzC2lh6kgjbgU6SIqv1O00qYJ0oIGNlSrCKrhGoMMtra4iUlwkjwM8s1wD9JsMoSRcLpCsSK0RUcJbCuAjjIiCw4nQ8oYxClMsk5QyoSkTNGa4wRhJQRRVJEBF1oZMO8enIsKO3QTuPnGWOqJjukCaNdLSEoGtWYGk5Ppmk6xtMZjCLHQGs082WmGTasy4LSBmkE1mmWOeKUZomRco2JynkiS0lKkRALXdNyPB/qPkCmlILRPcgKpKQyTOOIMQKEJaaZlD0Ug7YtUFBK0JiWlCKn+YxG4K5a0hShJF/XPgmfPGGZ6ztdFCXlq6zmQCm1YGI63NNZS9JtrcdtO1rTI8jE6Fn9jBAQQ41ec201pykh0V1XtagJQojM4VITEXxg07as61wnML1lLaDSSiqSHCPFCWSB6TLSuR6pG2SC2R9Z84wuhsyEoqNzAyGvdW11OxAZK5t645CiArNc2WVZJEUKlHPM80Sapi+Nk+N45uZmhxA1Qu58/5q7x7fMwePaDpcdD4dXLOWMCblKerqem5u3CSkwn5/T7fekIhDSEccz43hCKHjy9tew2nKcjmihaZwhrCOHw3O6fsc6TxTj6I3DL5719EBKhc3d02pIezjR7Xoa07CUQFygbSyn4xekoqvJUFmUkIyH11wePsdogegUXfcEkQ3CCGLJSBJhKbRuYFk9pm8AQdPUCC/dOPK6ErIgpxmpLbIIQqjZzEsYEdmzXjzGWmIIqNbRtw2TDzgNwSckIJTh/vCGzWaHUAo/jaxv3jA9nBnevmXz1tuEqU4+wjzjp5HtsAO58vr1c9p+YNg8JaQFZRzZV9nfr/3G3+B7v0ia3A/eF+Wv/9uCfSvQqsAEYym0PXT2luNhRuSZzj6mcTtG/YpwumFcPZu7A43dIxkoy8DqD+g+8+r4Obd76LbvEGaLlht2zR7dWpSBkA6keSEKR1MUAFMKSBM5Hl8wDD3jNEMpjNNEnDzbncOUwiAdX0yVNs9ZIuRzWj2SM1gjOayZ4gtDA615m+MaiHhKjkjpKSKxsW+TxILMjhenFzh3x3Zv8GtGlnuKXLFyR4lIe9C0AAAgAElEQVQjm/4dfBKc4pl1fsOrQ+GX3mt5cxAseeKu+xq2WGbxBafygBHQiI9RUZHthEwSozTjfCHkgm0HCCM5aJLbIEJBqIJqPONypGGLywK/KF69esXde08YRGDMgsZ0eAoxHDDiHmHfJacJKS0FRRAD/qGwe6SAM6cxMriB5DW923NZR9YocM1IyTNdbzieHjDujiW13La1VcUvAiECKdxjbIs0jxDihilGvv70Kxzuf0zbz/zDP3zJ3TuZ45sjF/GE4WbAx5n9tXd8kBvi9H2Wa/PQW3e/yrPNOzSqpe0eI9WOnAzbR2+jpCOHC0Z3JFFv+ciqlQMQSCSCgiXJiFIJkQUlRZDiaqaQRAo6A1KRskUw1ZijLCgZihakEEBR+82LpKgVKTQpRLLqUVmS41SBrXYsY6w973Hicv85QmqUrrdsIxZiXq9NRyveS2IGY1u0bsh+5c39p9zd3pLax6i84MdEVh6ZG6S2bN55m7QG5tWzLie2N3tWP5O9RVqHLJJut0EaSTifmC8rj959C9fXkfnl4cR0OGLaHl0U07qgHXgp6LrhqvsT7Pd7Xj3/CdEnsjTEUKN/nLUYp68gpfazN93A/fPPGC8PhBBYlhOquUVJye3dU2TMnM6vePTuBzRdy3Q8Mp/u+eLFDyuT3WxZcqLttvjljMyOIg2m7RCmhotLbdn0PWldSFrSdgPOdqBgeX1PJlFkodluISReffE5IRVEqo1IpVR9azXTFEgZJSWhZNq2haI4Huvl0VqL1rZqI6n5p6lUnbAyijCtFWw5y+ov9XlHTSmCnKG1jlUW2r4jAjfbRzWD9fzA3/qf/hu+/ef+KbTT2LYj+sR0OFFKIOQVWwyn03NyMWzvHldDoIpkJCkESgkUMoSM1S2qdcRcXfvWNlV+kCXEWOt6bUOO9TMNuY4+pVZIoRFWo6VgWSdiKte1o0kENLWVSkpByRKpQIkK6Cpw9mSqLtbYlnk644xg8RGlDMoZwhLQWtdoJl2QwqK0QQBFpEqvS1ELCEohpVg1o4D3vj53arOXyBJV+VuylCitkSXWaLF1RUqNlpnJBzSC9Vr1a4xGKcFlPLEbdnXdlpmcBav3SCXQyqF1bXMKIdT1m2sEWsmRvjUsy0wohb7vIRaSgFRELTkQqgLGRlJiIc0zbT/QWMdyfU9KgXWtbKlpXHXIFzDOsPr6mcYY0UITvKdvGnIWTKd7fPKk7FFKoJqG7Gec7ZGmIywXpCxooRnHe6zVTPO55gS7R/TdnlQKS7jgpCNFgTSaFFZC9izjRA4JhGK73VNcQwoTyrQobdAlEREYJVjjyhgjfd9T1oCVikS6NpJlnGwoUlFiIuWJJAIya6QxkBIxJk4PB2xjaTcDSdQ9VRQgBh4e3tBvBpRpySGilKUoiV8XtJAILSrD6RekkVAkIJHWEUKdHjrd1SlHXLFOgqxFECEEVEoUafDLCk7S9z1xXhEWKJmIRApNawfIBalFrf5VlmWcWNYLiYRxhvlwj5CatC5oVZjjCkWxe+tdxBoZT+dr7XDVbitjKfEaoVcSMklUv6uXuKJRJmKaHqUFS5LYxpBjqlPZnFnmCEoic+H88BrjdG02y5nFZxQSqReMbjC6ZQmReR7ZDk01PCKQoTBPB5AC1+6qYTd7lsuIUophv0PbhnWe69/dGErKBJmQxdRzcl1QguulRJPWiZRqEoYSYLd75umAtRYjW/6VX/93f7E0uR99JMpv/FtgvOT2SUHjaJXizWGkHyRSdrx3921++vnvM54v9C4zrxJaiWhuWU8vud1KDpPgZv8x5+WEIsFwTxoVyXc8fvSnabTi81efQpmR3ee0FIzcEVZL171HMi0+vWK6/AhTHN3u6+xbOC0v8cuCFwmZFLtmyxoLs/+M07QyF9jdtiyvF0zTotWE3YBYHtfbsF8Zx5G27SlM2L0nXt7Cz4FGnVFGINsbpjCjjUSV+2u0C6xHaASMCwx3msbumfIBWwqrzxhnmQ63rObIbfs2WhYa1xMnz2QyLs8kWXj1+gv2mw9p1Y62Dzx/810a902O6YhRmr7fMJ0vCJkwUmDXLW89+gqHYyA0J779Z/4SP/rR3+d7P/5dhIzg73gyfBPZf58Qb0gYFu05++ds5B2qBA6vX9HfPUMEDWHEpCMvXv2IYp5w1wy87Qa+9ZV/mnl9w3b3FqPPrMtrxnxmDhfeefSnsa2mzGeU3RJXSWMsORmePH6Hxb9G6Cral0hsd8OyJmxrMWqPMA6EA1N1byIlKIqiXDWTFIEUCbRk8RFH3eBKqbWGOUMBikpIKSCJGqKNqRsttYRcK00ukBEIUYOzyVdgfK2NjMtcdWdAST+XLRhiuBAInD7/jG64odsMxOme8+WAFhIUnO9PLGPm3Y+/RggndHdbdWFkRGm4efSYQsJjCOuMlYX5eOb1w8jH3/4VlrDUVMYUOZ3va3yRqr3w2g2sYUHIzHIc2d3sasLBeESEym4VCf5yoRSJcw4/z3T7XY2CSYVhc4dQ9fdevXiJMhqKZthUqYrUquYmBk8RGaUM95/9mPPpHpkK0uzJCTwZwoSQkWHTVfe7NpAi65oQ0qKEws8e0bZYKeq4rHgy9bnrUis0izGIIgnJU3RCJo8VBophnE4VaCC+BCFCZDCKVCAVKCniZHWgdwpOOWC1QwtYkycuI+s01pGfdfTdhtFPKFkNGMpYlvmED5lN21FEbYBy2tRInrCiVUMgY3WNV0qxoLQgxhWgru3TEWMcCgMqI7RA6paUq/ff6tpp7/2CdVUTXFKuoH2dMI1DykIoCm0EpTjiMiK1rSCPCEUS4ozTDmNq4xui6hFzAWurwUmQSd7XCDIFSAM51Hd8nlnWiabdgK3GLeQV0F2PImMMZQ3ka5i+lBLbNiQfMFpSBBRZIErWZcK1DdPlXBlh15ATuLYDatRVjokSE9Jo1nUGJK5pySlRSkTIUtnC9UIugq7dYJsd2S8kVeO4tDLIotHKoqy9jmQjMfqqR1QWRCaWSEGSokcLWcsBtELmRImRct0LSq7M6bKslQmPGasl02UElWmbjgA4DCnMJEGNBEuedVlouy3zGirIzaK+J07TWEsqpSbppER3raUtSUL2pFhwXcsaPMo41nXGOo2UDbkklFLEtJAuI1JEPnv+Pd579xsI3XN+eEAqsE2PNi1a130qhYiSkTdvXrC9eQZZ4ZdLZfQ32wrAEFjTVDZeK+Z55tWrnzFYS9vs0a5hPJ8Yydxtb5GmqQa7XCg+svoLy3ymKE1Rhk0zoNx1iiUF0QfmcaR1TZ2O5cg6T0glWGJgXs70bkPXDFwuJ/pNz3geafsBIwzTfKLfbpjnGdsMFShzTZ7xNa1DWI0zlmk+0bmu7iNNS0jUvaFE/Bo5nw7stwMxALmAqu+ZVi2JVEsxlAYkOUdsYwirrwBzHAm5XnRTgXiN+xKqZtgCvDle2G4actFYKShRYp0mZ8+6nhHakH2VVtiuToBihqHd0LgBlOT88LrKiYzmfHiD0Q393R2X+1esfuLm7glrWEkhYawlC4lWDW0zsEwTRWaMbUGEmv2MqqUsWqBFneQuMRKXpZZL7G7qc2/6amQTsjLoOSCSpyiJEhqpHDHUyuplPdG1Q5WfXbFR46o5sutbpLWkkjAoQkjM44RQEqlqLrR2W/7ar/8mf/CLFCH21Y9U+Zv/8RYfD6gET5uvc+YNfbdjWn7A83vNq+eJ21byeHeDiODtwDz9mFlA00BvBI29ZVwHZDMxn17TtoVwGTBacJjPvP/4myALi7/HizfEsS7WwqbmxbpHzP4HtGaiU3+KH77+DgJL70ZsK5h9odGPWcIDS4lsOkWSd5jysor8KZwmcBsDRZLnZ0zzp1iXcHQgHcIZXj68pHMdt/ZDjHngsnyBajouXqIGwTRL9OlIN7zF1h1R9ikXH0n+C+K0J7av2boGpW7Iy8JpFbz/+Bl/+Mlrdk+e8MHuGc/cju89/w7Ppxf86OWRf+ZX/wLHw0vu37wg5hNvv/M+54tHNpKuDZj0VXw8MzQ3hHji0fBn+PEXf4f33/2Qv/fjv00vH9M3b/OBfZ8PPvyYdTyTCoyHe7QwPFxWigw8fudrbLu36Y1jGJ4StUVIj1MbZHGItgPhyOOE7Le1qUUBISNLIpsGFQukQFTVlS2lJJeAzAqkpcSEUHVkmEJG23rzV8VSiqiB6UJc3emCJEN1TRdBkQolEmE1aFWq/jIvlQEhVrYqV1d4jPWwyyUS/UyKBaSjhIXkR8bjAa0Vl/OR7X4HZJacicXjskLawuVyoG/qyNk0LYRCLoJ+/4gkIzRPkDKQYgtGIa0jx6WCbakgxWpCUYkoFKQFYRrObx6+NGd0g8OHiaa/IS0j82nCX1lGZTuK1FgzIExi8ROPhhsux1fEJEA1mL5nPZ/ZbHb1Jt9qvvj8p4jkebR7jOu39Ps9h4cH9ps90QcO4z3rJdS+dpnqxhkvvP/xt4hrqKxPURwfTtw9fYKfY+2yliC0olWZ02VB5FI70l1LEZmSA5f717z90QeQBUIUnr/6FKu6a+ZlJq8L9HskGeUaRKy1kL5E0jphbd1AhZVXIADCr+TFY9oNfbfBtRXQpCLxoZqlogiEVDXR6/lEWQPDbodWksPljCwKLQo+nWtCQtvX4HtUlapIxRIWGtMjdEOioBEgK4CFyuAgNcYYlmXBT3Nlf5VCtg1FFHpn64W31DglqQUlq8pApsQaIzHWAzNHjyyabCoTqpSpwKsUFIYoPXFdUBqMcUze14lCTBjXIgGkJqVQQQQKBYiUWUutkg2+HsjzeqFrWlIBbRtiTjhpKmiJmYBgvZwpRuGMJiwB1WhCSDVh4LouffIoFMo0NUA+VPkVUpKIhNOZ7DPDfiBRqsa21ElMKAIhK6P8c8CXUqqAel0oKRJDqOBUZVJZSMVgjEVpjRAGKSHlykZTBOWaArGuK0qaaqpSCatsTYxQhilcUJ1DZMlyOpJyNfI53dZ0BKdRyZDTShGlygdcZeyTr+ykdHB/PDE0G2LMONsxzWe0rMyzMe46maklFCEXcpH1koaAHBmXEWd7Wtvi17Hud0KgW8P5/gBSkUWm1XUKNZ4X+r5lWSdCWtn2t4gsECZeCzf2TMsI5CtIqTFXTbfFhwqO62UaYllJ5/o1ztWLvp+vOmhZEyiEqNr5sq4YNEuZkUKgmgFRautWotDgCH5C6lpuQazgVVlHLJklQCchzYFiFMUYGmWQxmJ1JsbMQk0raVzNYkYKRAjYxhCDZDp9gVAG3e4oSIa+r+9K9qQ1cLicuH30jAK1La+s6GLwIWCUAaN4eLhHF4lrLNrUKcTldMFAXd+tQmIoCi7HC+VKbjjnKkFSFkiJ1rUU3aKsJaSAWGfiNDH6kaYdEEKQkbUNre0pi0cqzWG8R6RI4zTFnxn9hd3Ne1WHLSvG06qWP4j1gqaQux1aGGJckdIwhUi739P0fY3u8gvK1oivLGWdIMRS33+RUUIRc6kCuTQxTid0kXSbO7TWLCHgtPkyA3u+jOQSaqya0oTgSTmgSibmzLC5qYkxygIwLytayVooYhR+Xjgf3xCCx2ZJv9+yffqIy3livsx0m4Ekql5eSUlShV/7N3/BQO5HH9ryW/9BQGmIAWYPRcPjBqKU9CazztXAMPkdOqW6iagDL9OIEwJ8JAeNtj3BjzilucwLnTHsW8Wnc0JOsG0/xDSfgK+L98VYaIVgomClQuTEeYUk4VlfR/a3XeZM5vYGTuOOFC64dsP9fEEFx6Qyp3vPL/+S5ujhJ5eIpWG7Sp6953h49ZpPf1/w1kcG2Uoe7RzPX8887d/h7Ufv8oNX3+G2aQlvLD9ZPqXfbfnGB9/gxcPPeHu752/9+B9yV+B2D3ful/FqJXrPu5tvoVUCIlOeUXrgg/3HILYM+w+RpcaokCa0eIQ0EtNtiCIxn4907Q1KO5ZlQoqm9nRnjxYGYRoIgUJCC4eQkhQzynWsS0TJOu5UEhCGtC6gMlK1ZEo1F8RayReLrZq7XABZF1euNZaiZAoGckJoSY7LNbJHkOICQiBEPTy0asgFhIQiYtVHyy2UGv9T2aMV4ZcvnfdkgUyCHAM+zIx+QiM4Hh/otOKz1y/ojam5siEzzgvD7WMu0WOU4nf+2n/y/+/iAP613/s72KZq4iiJbr/Fj0digs70hHRAuz126EhrQjlVgY/ITOd75sOJrB2NNrTNwDzdU1Lm/v6AkJHf+Z3v8i/+83+WJ1/9x0iXl7z49KcUlehvHhPGM5dpQVNARGLIhLzQNT3L6YLtB4Qs9PvH3Oxva5yR99w8uuP+9Ssau+P+/h4pBMt0YjkdsW3DO1/58BqO3mHFQn/7Nq12PJxfggRnt7R9x3I6oEzLw4vPGS9H8hrx/lQPon5TDTXOoWWpkoEY2Ny+zTTdM54e2OweYVrHcrxgkUjbsrt7zM++/3tkEpvhjiKqJCCWpTJgpkWkjJJ19Nw2W4qTEKpm2Fm+1KratiPrhuQTqtnSNoqYfAWcaNbLCqKCTh9XUjoj8sr54Z6b23dRSqFvbhGpAuCCYJknhs2e4BNGacI615/jitIaLVtCqeYRWQLrklC2guGYJUImtNCsKdYIshCRTpPWeNX/Spq2I4RCKFVbKpBQSmWRYyKtEwhHLBGlBIKJLB3LNNe62VSIEpyto9CcI8p1lCgwrSMnf5XslKshWCFEBQYlXp9HqmxpXe0aaRRLXK7/1hmVQUnJ6XSsumy/oPontG1bJ3Vk1rUak2KMKGNQReL9hDIWraGIhpIFRUD2CzGOGDMwzvc0jeU8jcio6YYWrTUlG7IQdVoVBFIorNb4stS0nywpWVwvxelaJRwoPuJ0BWgleKLMtVhhmbHWVekDAbe5A1G1xEpp1vlCRiIlhOxrNvK04JoaPZelJk0jKS6Mq0c2YIWicRu0rjKSZZlQtn6vOdZYppwDTdMgiyKKzLIecHYDWaKkIeQZ27ccX7/GmQalLLIURBGE6OmHHVmqykzKmgSw+IgZbtC5ViWnNCKSIeSVJUw0XU/MhTKtzNORtrEo63DNlhIFs59rQoxPKJ8QSHwWtNs9xmref/stfvLJ97jMJ9I8orRA4BC2A1m1vlZVk6sQiaw1JXgUDuUGhHYIPEZJ4prBCJSQ9XkX6qV2reyzlKKy8o3BF4U2tV64VYqcBGsMaCFIQFgnYlyQuqu6Y6MQSiGLqpIlWaqELV4Nd1qwzhPWWg7HNzRug20c8xpIJWONo+uGWsyRc33u14uwSJF1jeSwgoisuRCXlUZLpF4pSmHkpl7SdreICNPpzPTwGdtNg9o+QZseJTKXcz0jmv0tIEkhVs+CNKSwMK0Ty7Ig0spw8xSdYV5O9P0doRSU7ki+nuGkhTVkuq67ZlcvHO7f0A83tK0jhUwWV1NhFEgrWacZY23NjY5TzbBWfc0vXhaW6VJjKq8YtCiDVYqSI7rX2KgpsnA4HWu8YKNw2jGHyL/+6//eL1bj2XvvivJbv6XRdsu8PND0gjkUuii4hIyyChEHylp4WARv7VusCViz4UQmSvDqJYc3sEmReUo86r/JXfuA14KhL7y8fME/+N9hM2uam5bNk4mvP0v87d9r+aX3E5fQcLgkPnq8cLoU3nt/IJxPSFPz7JIu+BW+9wm8/wyKhz/37C+ht47D+Yek9Ak3mz+P6h6hWLn4DiPOdP03aDdbnm4/5HD5gh98+l+ya36FG1fI7R3LciLFhfnieO/jP8V0uMe2Gpl29Dc3LGOk2z+jcY+I8YHkBaY1SLkD6aBk0Bqu1YYUVdlJqq4x5XrgFiFQQlz1YHWMXmRBClOjYtDkWLVuKdcDL5MQWlDRVWVNjNLEXLVaRdRO6VwElIRUgpQLJYfryF+CKOTsKUVBCZBiNX5wNVfkiYDHCkcRGplqTWQm1WpO0eGvoCGlRImVsarOaokf7yE3hLzU1qLxc3JSbPePEKZUs0GOTA9V2xREoRk2tSueTCEiVUCr/o8rJUNB2IDqn/Jf/BN/5f/Td/8/vS7T3y5//N+/Xf74Z4B/4w/+T7K8MtopobVGlcA8B6bo6wh/rRuMEKq2ORlBKAmrqwZLiwApkVHoLHk43tMPNzycX2KwDNsWnyIi1BFcM3RMKdFoh18XnLakFNHaVGPg4YjICWUsSVQWqDMDkVKrPGWgaToKpr6Hqeo2KQEjJVkVPvv0x9jS8OTZB7RDz/nhNbLUoPQlVEnH5fQ5AsPd7TMQDkUBnVjXSNv3la2Wllyq6TQJWOcRSn0eUlX2UynFdvOIkmON7InxqntrMf0AOVfHt1BIoSilkMk1S1k7io/QVtPT6kc6cw0/xxKTRxvDstTKWteIWt163VvzNTas6eozbvoBmwuYOgIMQiFEQWZFTDM5xwqSdcd8urBOJyCh+jpWDL5cR8qyjjWvOk/bN7x++SlDd0MRmWU+4ZxDqgaomtYsoMRATletcoyEFCEs5MRVu1pYp4X5+ALTbhl2t6Adzrn6vaVILgqMqkH7ORGjR2SBaRoOx1ds2vr3CmVQSlxZohqPts4LMWaMkzTWkXN9t4uSdZ+hRg+JLOolWFxlD4l6+FJjqzQF4+qYVJlqjDOuRYjqwo5egc4YKfFhqcaqqzwlXfcoNzQUgJTJ80pIhaQLJYK8MpnH0wOb/aaWIageYiRJUEoQ0kLJ+cv0C6Xqu5NkpHGONaxoVaPgUqpGUBJXTeiMkjWtYg0eYmCZTwxNS0zguobi0zXbV2DaDUuaq5FqXFjXQNs6Ypp58eqnaGF59tZ7zKtHo1mmC1Jm3HCDdIbTmweM1ggjEVZzOL9iMANdu8PZvjZzUZMq5kstYcI0VdKVA5fXP62JE/2OYfce0/kN++0d6zpzPL6gaTdILWjaHapkxsuREguRmv6RUmCdZ5quw7m2VjNLg9aGwyd/xLPOUG6fcSoHSAbTDSgUD6cHun5HLrFOPHLgcjxCXHFDB1ja4TGZRC6h1ggLyeVyoLGa03ipkhjX0vc7coiVLb4c6Yctr159Rt9tafZ3tXiGhRQKS1iRxiBKpGnrWF5xNbdJRfQrIa5Y09F2A9lHUorMl2MtD1H5mtlbWMJCzAmVEmG6cPv0rXp+xdrq1nc7jpcj1rW1QW5esG1znSwGCqGmKZVMnNdKPjUORU0aGacDYT3RbG9ZxhNhjUhhoNU0buD06oHGCcRVSnS8/4Lt7R1aWeJ6xnV7Siw19tG0CNUgSl0jS/L0bV1n87LAVUOec6ryKAElpMrKiwKpTqrqJKXK4CgRqTKN3dXJgCpXrXpCZZCiRucdL2dsjnTdgKeSFuvFo1TEbnaVHIuZf/U3/jp/+P1fIOPZu2/r8pv//pZ1yWy2kZBnZpERa8sjl/mD76w8/eAW9D1SPMKGI4qP6Z4pxrTgGsnlZ18g1VdRt4L55U/45371X+B/+e5/C21DXCPr4QW9fZe/+LW/yre/9iss4wW7u0VhmMYzfeMoKpNEWxvKYhVkZ12zHEtYUXbzxyxl8GSh0SUhroJ7aWuUiERDGonLA9M803U90t6RikA1BZmb62iniqxLSRQiukhyThW0ZoEksaaCFJkiQKoaH2KuQFRqAbmQWKvDXmpK9pUhFYKcBIhCTJVV+HkGJfDlYYV2dTaGpISClIqrsJSkI7ooMuU6ypT1wImpHkJUow2Ia1xPIYUj0dcxdvQTUEPEc67gIC0rD/c/5W6353yZEFGwf+erTJcTWksaNyBaWw9/FnQuZB8Q0uBjXVDaDuRmT7N7egW8mUIgeU8CrGspMiBLxnuP0g1pnnFaEYvEe1CNRshq+jHdhvl8qVrmXEGkblqMEfznX/3V/xcQ/Uf9+uc//p/g9E9+3Z8Erj//8Sf/3z/qa/7y//Y/kEUt1ci+OtWVUqzBs7l7SliO+OmC1QbTOpSQBL8gZcfuZouxklevXjEtC9uhRhKt44Gm2/P9H/yY6f5n9L3jK9/6VZblHqVapLT4uCJFomn2gCSEGiPkfXVkJ7/iY6qObtdXln+t0gHXd/j5XDWKzXA1BdUEiSJrp31cJhq3IaQVKRwxzBWoK4k0ipQFPsxoZao+TEiMs5ALc1xqBrOS+OWIdprL5VIvRrZFmoZWdwgqIIkxcDNe2H7ll/jZ/Qu01F/G7QhtMFlRYgXW1mq8X6tLWyiykIgSEKKQrnrSJdeqVCU0VhkiCmstIcwgSmXSc9VBB/yXl7OSq1Yzpozrq/YwxUz2Hm0NiUJMtdZVFepIS16ZwxLqiFIqbvZ3nB8O5LSg1P9N3ZvF7Jam51nXO67hm/7/30PtGrq6unp0tyeMkS3AFkZKiJLYibBihIREIkWIA3OAEBghThgOnAghWUgozhFSkEJQJIyCkJKIOCg22MTuQAYP3e0eaurawz99w1rrnTl4viq7HQ8dycZ4HZX2t/f371p7rfd93ue57+tWxGXisL9jXO0wyrK5ukJXQVUVJEGrNcH4hLSci8hGbhIvm+cj3ju0NSQFgx6kM14r03QUeHw4MWzWkm7VDCHL6NpaJ51D1xPizDLvGXzPHIWNewoTXlmGfiNSHDTGwP72ngbozlGUZrO9IM2LuP1jxvUdqIw2htYUJSsw+qzD1eS4CEosLZQsxI2SA9ZL+MLp/i3KnLh89FmsE33laTnRdR1Gy3fnpdL3PXMMGFUoTRGmQHHSMR1XPcr2pBzQtRBiZRw25BLoDBxu3hfaSddh6EjHO4b1JTkmYqk4b8hpoV9tyapRoqwv1IZx4IeLMxpxYDmK/lpryCWQqmFJRwYrnbLj3depTg58qlRqFr1zKo1xlDSreZ5RRUyqne3AmbN8S8t4vVQOxxf0/Qrre9EZl4pRmlIzJVdcb2SPU4bpeEc47ulGON7tybVwsXmJfhw4hUXkM8eZfnCM6xU5Jrp+xTiO5Hxef4eROEfSco/rewmFaAZrOlJJOKXxqw5rGhLZ8p0AACAASURBVA+/8Etcf/pzxGapWbqcXdeRSySUzMoNxDMb94NDltZO1p0apTt/ONAZS4h7CXZZCiUEVpc7rO1wtmNZFlw3ShrjIqFMXdeRU8EPlpwa2lkZ+RtPzQ1vIKZEDieM60hFAjBMN8pho8rUwqDJSyCWs6SoZkpdQGk6N4oXowQqUFrFlMJ8OtGM5eLhA/Iyg+2wyot/oAiOLtzfst0+JuTA82dv4TSstztyKqRQ8ev+XFQrma6YBqFSSmI+7NkMA6kWTD9KWE6toCr97gF1mYitCMNWj9RUWaanFGXou7XowNc7AGI4yIEzBbYXG2LJ5ASbzZZaoNlKmAJVVfKyQC6sNztUjcQstUc4LvKzR0euYFEYp+lWW2yF/eFAobE/3LC+2DH4jkzBKqlB/q0//+/zS7/8zSHE7Dfzm36/L6UL1+/fsnppzTvvVC7Hx7y2eZWr3Tvc/9rMn/iXv5OnX/8C6/4KFT7CJ7/le/nHX/yHfPHvv8WnXgEmxScegn7wHtp9K93r38mFveJPfvJHuNnL4vvp7/8XUGvPfNLgHjE+dpizsNmu1qTOYZtCaSOuzZwxGKoW3aPpR9R50W9KSWqIgloNqla09cRYcNaTkphkqntA7ytKOwoKYw2qiblFawO10VREAVpbaA1tekqJtGpoxmCdwMixv655KiVTNdSoaa2gW6bqSssaKKANGkVtgGoY5aip0LyY2WrMWO9AebTxpDzR8onp9vqsOz2n5BSD0pGSMyksxGUiHW9prQkfskS6sZN7iOV+/y7jsOW/+0v/FX/uz/8H2H5DydCtttJJ9hanEq+9/mlyq1xsFHNUxGYoePrhgtI57g43hCViysDl4yv8ox3KDKiMYFliQoXK8dnXqTGhu4bttjI+9p6uF4i90g2l94TTRFMQg8IPjtc+8wnm5ZaQA/u7O/puhXcDz58+pfeOJWemF8/RvoNP/NPP628sQn9zgfo7Xb+5eP1mL61kYdC1UVphNe6IOTB2Pbdvf4X1qsdajV150pxZ8pHnX32Plz72SW6ez7SSqFZjtWZZFgnaUJUYFz75iTdBvwnpSDMeYwZqy6QoMau1KmqsmO5MAygJ34mzXCsvkbNALYVwf00mY40nHq6FYqA0rWaUNPVRrXK8v8djMMqR0oFWLbEkuu2GvBzBW5oa0LoxKBmX0lmoVSJLY6UzRjp/tTGsLbsHj/n45QU3z75OvxkxvWa+PvH15+9yMT5mn+44DZ7bZzekVsmUs4bVoEqjtoy3K1rLxBjOrFfZ6E0DtKYC2jlc1+FajypJDIdF/m1SnEix0I8rVNNoKzpclTVudMTUoEmIgHIyPlVF00rDuxWtNrw1KBLeNnQVE1Y1Sox1RaYpqjVePH9KrwytFciNbtiw3l6RUkBbR1gWtLJQk0D/0ZhOYP7KWayGkBJOdxgLrRMSQMmVrkFq4HpDnGbGfsD2K1rbUUoWw2Ut9OMgzyVASUxRor+344pcCtvtjkhh221RuTEvR/J8QivH0G0ZH19JR9c5rHKELAWutx1ZI1KnOVB9T4wLYDneHtmud4QlspQZqxurrgclmuVZFTrfk1LipSffynwMYDTHec8wDPR9J1p9k4nThLe9rM3GoOjQreCGxNZ3YqzLBW0c49BTi8aPBdUsnfLc3d/gto9orTA4x/H2BXU5kjoN1gtjtCn6lQOzwqlGsw2tGto4tCqEeMJrxdO3vsTV5oKUE67rCXOkuZ5Vf0XOCeVg+/jN8zi4kOOBru8pQGctfhwJS8IhcbOlRpTrCWGhhCPerbBDR2kZb3tqUpzmBe/kYbS7FTUuUoCEWf59B086zdw9e0ZabnnlzU+zHCOVxjTPhP2J1cUGf3lFqQk3XvLGpz7O86fv8/aXf4mcJ3ReiDHy5COfwnVrmgHrLYNzlKwxXuGcEa9C0vg/9iOYX/sKabmR7m+Y2N8vlBLo7MBcj3g3Uq2SYpCKIktjZTkQ6gZvNSlNjG4UE+hasd/fYa2jNMGsed+zxIWaLONK7kfKhXBaWKaK6Tw1BwnDsAlVNdNy1lu3TMhgrBySdWuc9hPFItpp58Cep6GtkeaJw/EON45EE9hst3g3MsfAahipMdH1a0I8sb+/haZxKnM8PeU0RV5+7VXicWJ79QCtNBbLS6+8wfH+OfP9AbylXw+sdxID3HwUiV8KtFIYhpUQLjJ0WhHazHQ702xmtdrRWmM6RbrtSlIAQ0XrwtY/wvdOaB/FYFwntANn8dqQc6C2TNf3DL3m5vkLTGvYlZhhc0poA71fU4sYzNabgbJU5nTLervFeEejYZGAkhfvvYOaMlmJ+W9oitP1M/yjJ4T9kTR2tJL5Z2nO/v+ik/vxj/j21/7CP08KW1brhtMfZTDfzevf/p1gj+giY5RaN5hOU4qSbo/KtAY0L92OirhcVUXhqU1RWkYraDpgK+SmgRGlAlQt4GpzdlHSZKRgBU3SVJXOU2t8aBEGlPZoVcRxW86g7tRkUa6S+91UE/evktFUa0I6pSAdUKVpZxaiplEr51OK/RCn0xBnOs1Qz6MDAWm3c3ejQavUGDAYpnDE6kbTGq9HFJFcAnNspHmhGxzzfIN1A8ebPf24gabRbSGFmbvrp/QrS7w/UnFUrRjXg4DDY2Sz2WF6z3QKGNujqqO2gEFxOh4xo6G6xGheY396B9e9gt1e4ow8Y53fCC6kVaa8F+5i7ql+oO97yaXOgVJEvK9rZYmFWvaklPG2wzhLzYWYEy0LrL2VmSXC0K8pVfPgyWNyPKC1wxlPrVKory9ewo8rjne3HO6uCWli6DeEUonzgYurKyyNlCMhzKzWl/zlz37vb9ld/f/y+td/5n+V+2FkBF4LxJjJuYJumFbRShbmlitOj6ixI+SDjGTxUBNoJ9AHLYYmqKT5SLe+pLWFmipNe2op9M7SkDG01U463GimOGEsQGa3u5TR5uFIdwbMi95SkVsW+PxZoxXOAPOmFkyTtKc430mMZcn89A//6B/cDT5f/+Yv/L0P9eJGaaqplKVgzy7vogFrqA20adLFwZDIUkg2LZIMoKRCIWC1IdckXYjao9vZqX92yldt6Mwgh4laaTrjvYeaMd5xvL0Xl783zKGgwowdHLkUdK4oL7pOazx+fSGGD2NocyblmVYWjO4w/ZrUKp1zFIpA8VtDa0cqQTSxRlFzpbbGsn8fi8OO0smUwkFSo4yy1BZZcsL67kOUWCwis3AKus6T8BzvrlHK4JUhxCP94FHGg+mkO9tk/Nh0wxhHTDL9cWc9aClnSUBTFEk1B32mfKSJHAOWRpgK3sGwviAWhTWKhibnhnZiYMxlIYWF3jhiCNAUF1cPhOOaA6U1+n6kUpjme1q1jP0GmmKaT6y2l1TK+UAToJypClrTUmbYXDClk0hb0kxBUVIjxxuuHr4J1jGfbjkeX6BUxQ8XdM5hFMTQ8N2KpkXfrEtijoHNg1dQRtFiIdUCSuGdY767F+OssdRmub57xs3xXT7+xmdwbSDFiWHcEpYJdKMft6SS8VYmfiFO2GZRqkozwDhKCkzLgmmalhZOcWI7PsCvPLoosnWiry4RS+NwOIiJdFiRc2QJSWKPl4lpvpc96XTLauhJAS4fviSFXm04v5HkMS9RyWXJxP0tXmX0dotRa5rX7G/3YDROacbV+f6khtZNONBasHXkCkYRU+VwvMErQzcOOG1o2qA60Sur2gg5440n5AmlLF4ZfC/RuHFJQGUOM8NuK3xkODeHEtoZlNZUrVFNk2MUbrOSglZCNCCrRpgXjLH0w4BSDqpI7qb5Fp1nwrxAFQ370K9w3ZrSEvd3NxKqUqHvRxg8y/0J1Rq1Jg6HE/1mIC9HVE4U5xnWF+hmmE5HlMlY22OqoRQxaBstBJZus8H0HhbH8fRcTFCDZ1jv6JunhIDrOrBaJixZEHRKKebTwmo9oEs7I+pmtBIDrR86DJppWiiIjl9raRbuXzxDW8MwrolKUcMdfe/PGn5zlrGcsH6k71ZYFLE2WZeUZfSa2jLLcRJiSgr8O//hf8avfvEPUeLZd3/7Z9rP/U9/CcxIQ6GtE12hlg2HVs+6Us5FoaIpWWBqE5MNraJtE6h1a2g0Tf16caq1FoF4blQlulD1geP+XFTSqkDX0wdcVLnaBz8fAbprZT7UupbG+SWQTUsbRy0Jrc5JH+jfMG5slOnXdT7GG1JaaLGJu9YK/kYrc97wCirJS5drFmNInVG1EOKRlirxeI/ve37x81/hwcMdrz0eSflEaYq0BObrG25vbynDBmstw9Bzur9DaYdyMHRXjKutuKenIyUWSj5RtcH4jmW5Z7PtadVi2grjG86vWGLGG0so0Grk7bffltHYxUfYXj7m6uo1qqrgNOvRozPnLnSiW12Qwol+tyHGhTRlSoikshDrDM1ilWFcddy+9RS7GbGD6C+d9pJLXiONitXqvFlbXN9z9eR1aspMYeZwOFCbpsaFuxfvst5sePzoEb/6+Z9nvbnEDmuq1rQWYDmh7IrV5cs4W0k5orTmf/wX/8QfxCvxDde//au/QAmBGEX3CHw4fvTeMx0OGCOECXLCmA5SoZrGKR55/dU3SPMk2CNEY+j6NbpJEVaqdGZzyzhjqAWJrlWimRPcVqRpfdZGZw7HW/r1it46cqrMOdKh6d2aOZxww0Cjsl5tiDFzPNwLgcRpnHao0ugGz2G6Z+0M/8P3//5qn7+Z6wf/3t88J4QZqlaS0FOTYOsUqFKoDbLK4mpv9fxuy+RENwCBpn/k9ZfFDGYUh7tr3vnalxjWD+QAECqrfkVRiZQiIRYqmsF5apy5P14zrDYYO6DO2LaUksgZciPlk/zdogTW6DOqS2svJist74O1DjS0FGnn5yaXSM6RtixoPWC9Q696OI+rNVJk2VrJ2goD2hSsGXGtEUqWznFnCCHQrJbDS2soLzzQvARymrHektEo7emUokbBOOWyQIGFSD+sqc1IupzzpCLaPZGONJzTwh/vNBQxmvW+Ey2gbpS0QAVvDNPpROeFca01aOtQxpwbHoFmC4pCOgZaytjzyPaD0W06ew+s61EOtHJ401OTlntWsshF6kJaAuNKYqCNFapLbAmjwBlFLjKSLmcDWsuJpjoshXnZ47zh7vZdnL/g6vIhxnZoHDVHpmWhpgnXeQ6nE7ZZrBkZdhcUCr2TUIi+7yXAwkpjxFpNyAnXOeISWZYTtUVB7zkxbnVGihJqZomR7biilUDRTZ4x1cszURrLaaK1RqoF3blzt1u8FTEtYjrsBmIMWI3EBbdzvdkqq3XP/f2BRiDOCyYH3n/nXR6/8VFq03izQvcepSJea8bVmqo6EhXTKqkI71erc7MiizyiWEjHmUJB54Tv19TSUL1FZZEU1QrL6Y6Lqx3LspBjouWC32wIKbId1mCEKZznIIEeTeE6j1GQl4U5R7w1LGGSIr9onFVULTKRob8gTXeMmyumwx2qFpR1dIMnlUZLGXTDD1vCUtCmUnLEDZ6WKqUVQhAOb44Z7x2nwz0KMZV2fgBjubu9Z3OxwjVFrRLig4GSFlbjBVprbm5u2N89ZffwNebTPc4Zcop0rmN18Yjj3TXWNcwwYJzn8PyEMeC8YkkzZMf24gE1CL7QDB1YS4mF4/Ee7SxWaZzzxMMdbnD4YXc2e8szVWm4Jmi3rneQI1iPMTKVaUpjEcrLMosWueuFVX2YDrRWuXnrKzz/8i9yPd3y+kc/yTh03Nw8w2rN9upVXv3Yd3B7d8O/+2P/Jb/yhV/7w1Tkfrb93N/479HqjHQ6d1clQE4KTJQWJyIOdEHRqG2hFIdRntYS5rzYVypauTNcHCoJa2URTynROY9ukJsUsx9oUmvNWJRknRegZpSzqPwBrkPYqeps8tIShi1YEsAaRW2KWhIKg3GWEBLaeqzVtLxQIygtiTyliPzAuU46yucuEPlAaQatDaoEYrglG0WNhXDa02LmdDyy3V2imiWmdyjJ8sv/+Ct87rs+g/Yd0xRYr65IKTH0I7U3hPnAcpjxukM7y/4oJoJ5uaPplcgQ1pcM60uqE9yWdaCKwhbp0MW6YPR5c+h3NCxoLXD3nFhCQhkxgRSlsTRyq3TWscwnjsc9tt/QOcUcJ7yxYC26KkJeROxeKq0ZfAc5FnYXD8E5UkrULB0aozWD7wT4bSKr1Zplnsgxsb95DtYx3T1niRU3rBk3G1IqeKcxuhIn0Vy3qtHWYT3UXDG2ZwkHrB/4qe//k9/0M/xv/NzfpehGrU1O+q2I47xVmvJyz8osG8UZep9CwHsLRlFro1UnEbbqnMqjGtqNxDRjdEU3qGic9qhSKa1RasZ1VhZqg5gzdCcpbsZIjKkdCGl/Po1PWO3xQwclU6YJs7pAaUstkn8OAsWnFcJ8wvc9eZk53e+xnQMvhaBgb8RQ48cNqkSm5SSOedXYrHc0Y4nLgreGEiLVeMgJ7UX/vVpdsb285L9+8ubv2XryW12/na76N15/5u//74IDM5BLodWC6zx5jsSWUblhraGcE9S0VcScsU7Tacvl5QOunryBH0dqmkCJg7wsR5Q1UuRvHkNL3Lz1JUKcWO1ehhR4752vAjId0trRqiJlhe0MLVWMtcRpplutBMMV5ZCNUeQcWXUjKWfqmWDSWiHkRGqZ7SiaOq3B2o5KpdSGNaMczCmkHM9SiEqjCIuz74RJ2gA0XTeI0SxlXG+Z9veMq905clmfpQyVXGHoHDFXmhFzXZkDvnNnr0BDGUVWUizUXKQpkcUsi9GEmMkpoK3omiua3ouxMqWEUVbwYEU6ioPzKGPwvheYfikUlcS7gEZ/IMxrBVdBn3FsTQHWMdgeSqBmMdMEMut+kITI2hhdh+8dU5IiIIUovgYD3o+0AvPpnnF9QWpNTJ4lgTV0XXeOpD5ipK8i98EMlHIQxmmWGFXTwHSe3EQr3PJZItdkmpBSorSE0ZVxXIte9Px5a4VCxnU9JUXiMnF3uKMbRpztZCKgRcKScz5PLCyrvuP+dENnHbEVatIY1ei7kRQk6EYPjpYFHSdkHDlgNOewGLRxkBOtFk53B3LNNBLr3WNOhz11vmM5nrCd5xQmrh4+ohSDG0asafTesEwSe6y1Js4Ly/4Z5fYe9crrrDcvseo898++yv2zp5iLLVcPX2YYd6Sw4PtODqK1AY0U5P9RGTE1OtdhDZi+5zQdBRGmjEySTkfR0+cgUcedp6WMUYaWG9N8T+8H4XUvR/x6TbWWw+0RpzXed3hnKbUx+u5DLXsrBeMs2Trm/Q1dP0KT/aHQhHASMoUzFxmD7bywe7Ujh8jhxTWbzYYQZqlvYiWmCQeclpn97fvEcOTiakctIi8yLz1it7mg5shq3HGapa6ZDs/oOkmaW++eMJ3uOU7PUDFSpiOrR68xXr5MOE0cD/dc7a6YwonBd5iqaEX8NVO8IdTIuLpi9/hVwnEiHfcMD66IMfP4wUvcvngf21sJ/QknYipot6HFzPH+q3Trh+i+52rzMsfrtzjdHCl5j9+s0BSOy556PmStr0bGzSUhKrTx2HHDj/6nf5Evfvlrf3g0uQJCFySPSAsULQg7sZUq2hilMH7DXGdp5SsNNLRSpOUW329IZaaESNxL+tj48KF8v1aCK0JOuy1FUuWc6y2pNiJ8bCw5ySatNa3KRpcbmCTOY9ttaGlCW0taxF2uUYT5RLWgqpPOgGnkmLAUYjgQg6BldJCOby4TjUxeMqoKSHqeD6zGjvm4iI6xJIq2DJst0+EOq0aKqgyjo2rDnDzWnVD2VVqzfPq7HqGcoeXEdn1ByhBb5XBzy9Ovv8uDhxd4t+KUI82IVnif4DoPPH7whGHckanMfiOgfZOFwtAyU7G8+tGPcbh7yny4J5M4Hm7RDZTWdN5TqxPXctE42zP0lrubrwtvtpeu0NB3NBolJdb9gNKOZZkoVWEN+P6CcDqxTCcSCt91HE8TrRZCS6gqmsY0HQXv4z2Xjx7zpS/8E8J04uLhS4QYuHz8GtX1vLJ7zM2Lp9AKq37FsPacpht07+n7nlwVpShaLdjeMcWA63sU3T/TM5xrFl2gksUuxcR67EhROK3FZpQWDWPJFdUsfrR0riflDCpi1itSiLgMSwwkKnZ5hlYDxRnmKFi0rIpkjeuGbpVlOWJ7Ty5JpDJJ0pJyLFgni/dxf2KzWeF9j+tXYmKrYLsea1bkFGhGk/IkC3QoGN2jrGE+HbDWgBd9rG6K+TTjOkdRwnit88xcTuL6b0U20zYTpsx295hEZLVZs1kLyq62Rk6J3loOd7fw5LcvRH8vDH6/23eCQEQqEVs+mBpBTgVjNL3taUWMlq0UlLXnlCJP53o+8dl/TpBOStFUQpue5vzZCLfm7tn7HO+vOfw/v0RMC3G+Y4qBxi/Q0h63esDorrCd5zbs2Y4XOGdJIdOtB4pS9F4ON845+fvWSizSCYo1ibbSyxSsNFhZS7WS/ORUh+sVKSwY08uhpyGSqVLxymMVzMvEEgOFQssJReN03LO9esBpnnF1pio4nRYhEsQC1hCmmbzci0a/aA5mYLPdksvCYb/njTfeQGvFs2fvo91A0h4HHNORcDqwHdac5oDSmZwMxg5sLjaAEBVKy4RFOrvWWjko5obv14zVM3pHyoukojmNdwPTNDGuerSVABHnHLUViJkQBV/WFEy3t8wGTDasdxc0A6tue+YDG8oUmaYbbp9n+sutRGVXjdYW7Q1hyQxDz/rRE8IsZrhqHHqBJURCSHjfoWrDdh25NMFSlcxutyUFcc9b7fHeM+coxsdztHXICVMRs6eueGWpCorSpLww+EHQZVWjkqKGjNYV70YePVxhrDs3jjStVE7TxDB2WO2Y5pnb21sGa4XjrsQ531pDe4PTwtMNk6QDujO9xZx/Xk1wOu3x2kkgSV/x6zVeO9K8EJYTx/tn+FpYXT7A+o4uzRhloARevPc+bT7hfM/m6oJ4OuKdxVuP8yuG19aEuXC//wrvvHjBxoH1kbZUXnx5prVGLjNutaLzI914webqAiXwEfb3N6w2I0s4UVJG+55hM8pk40ylUc7JvVky090dm8sHlALWNEqJbMYdsTamF9eo3hCzoht6rh4MaBwlncSorBqlVkm1CwHtDNmIl6YowzwdGVdb/LiikTnc71mtVpg2EsI9WRVcbagCqQbqHFhtLqhloi6ZpCuH956T6ontS5LuWbPDX72G6gY5gMREdIX98Z7HFw9RStOvRQKwWn2U6f4WvXHEPNE7T3Jbrp5s6VrPQkPZhhpWrMcVWNC9wfoVpIXT/Uwo99w/v0avPBcbQz4dKGnGazi9/w7XL97lbtgyjGv8owt6O7DZvMLpeEuulvFyZNh8ivHighc3z/naV/8vLi4uGFeOcOq4ef4O1nd47xkuLwURpx0kqDnhnGIwoJX+pvfmb6rIVUp9FTgABcitte9WSl0Bfw14A/gq8COttVsl7cifAP44MAF/trX2+d/p+xuVko/kaumMFtZflLZ+pWC8Zz7ccvu1n8cWxWGOdLsnXD18RE0Kqytf/Ds/RTjteeMzn8U4z/10zdf+0YmPf88fwxgFpkKz0mlQ0JJFlwndOVqJ5GmP7lYy2iOjivDalvuZbhwJSbBBMR0AhT13cGqaySlgO8d0uGXdrYjzzJz29MOalCt5f0fqBxyRtN+TYkGdIf+7h08EzaQV3hlyrRhbQQ94v0Epw2E6sbt6iVQyKc0cjgvjuGWZbolTRXsHdab2He8+3TOfntGh0TguH7/C/ZJ5+JFX+MoXv8Rrrz7BuRW2G7C7RyxzZIXjy1/5ZdLUWF9sWG0fcXFxwWuvPqJkxfF4zzCsuH7vy+A9ptek0FHSkZLBakUkkucDWEdRjfk04RdLiAXjzzq+mshxIqUiXdWuo7Ys2J/OYbDMJ9Hqbi6uhAjRWZZpoWDocfhVT6iZsduKM9f1TNPEavOQblyRUahBdLdZNZ7fPAUUxoqu9DhFSrM0Ywg5A6BtJ9nmFYwSY4np/umX6PLJ65Q//SP80T/7r/HXv/ePfMNnS0q0GIVCce4QpXhOQWqFFsE5RSKjncWUhNaWFDO5zKAV4XCHU4Idapw13tmg64zrLN40ctbo3tJaZEqV3jp8P0pUqlGCAVLuzBqulLSQYmR3cYE1BqxlOR5xas087RlWV5QaUA0hDsSIsg1tteTcN0VzknU/bFeUlFG14TcDKtczxN6hc2alevx6y3rzgNc+8S3k+cTxcM3nf/ZvYTC4bgD9BVbjlmFzwXa75vK1N+gePvkt14XfS4Pf7/ZnAIyXVLtKJS7n6U/IZKtRAWpt1JQoNLrO0TvPkhOlZb7ypV9lvdqhtDBEQ55IhwPT/YEXb3+J5grGjTjVkeYj1Sis16x2r+LMmzSzpsUjMQe80aCko6pbZDpWQor0w8h6GIhplo5MjXRJ6DBGd0CmmfShq7t8gP9roCxMU4KmqHkiL1k2MS2jeG3BK4hTwHpDZ3oO4R6VM7vdBoyncw1n+3OGvSeHczcZTb/ecpwPsrZZR+8ltey0v+YHfvCHZbqlGk8+/W28+Mrn+Yef/3k+/R3fx4M2ktsV0/6Acx0FSz945pAJKZzTmxx962keynICZanTTH+x43Q60TlJSNLnUIuUwI7qzCZOkDKNynQ84pvD2cZ6M5IrtGqxDyDlijOd0Al0oS33aG2JsbK+2EFa41OiaTn4qN5BSaSkwFaO99eULI52O45oJVKVzne0fJ7SOU0OkWwb4/ZSDsO10rTGKjlElbPDvaRAAJq2KAPNGkrVWGWxvSPERdIXW2KKiYbF2R6spRrQeIwqLLGINM1CigmjNON6h3Ga3lo5uG7WqCqUhpXvWJajoCMXgzEdvleMdqAZjdYGrTtBjblGDIsUmEbR2y2tLmA8MSwom/DG8+TNj7NMAXtOkdO1MJ8DNB699BijPSFlthdXoLKEUChNv31IzZiKOwAAIABJREFUCAurHrowsXvwUN6JWtDWYprHDh2HeZJI2FZowO3zZ6xGT6iJYbeDArUeqdaSY6DuG84GaqqE04F+5TnEWZK+rKOmhDOWw/GOskQJq6gZv+kpqRIPe2oUyU1ulWEcz+t8RVlLqwptG6cXL1jmCMbihx6cJcXAYX//ITf+FCLdasRaSwpHirGs1+uzztUxHU8s2VBN4slLLzFse8ZhTYzC2N298a1CXkJRQ8EOit4axj7QWqYkMF7juh6lYDtssNYQa6WWzDBtSdMdN3fv0YzQS+ywRrmOmiqXmysalgXLuO0YNh2rl15hs31AihUVKjX8GuODgTcefoIlf5S8zNRamQ8RZU504wXDasMyw2H/gnRc+Mo7/zfbh4/Jy8R7v3Ig28jFds34YMT3G/QExiqRw5TIgmLYDLhmuL95ITKqb/L6puQK5yL3u1trL37Dr/1F4Ka19uNKqf8YuGyt/ZhS6o8D/x5S5H4P8BOtte/5nb7/uz73qfZ//vX/FnU+WS/zCa2E/6Za4YPxWyNCWlB2jXJOEjKMI5eZphXKWXRqIE01jPYy6i2F6iWFrHxQ2BhDDAHbd9Q4C3KjWKwRHImAtCNNCWjcrUYxM2FIpdIPO+bjNfl4T0wL4RRwqjKf9rQSzqlLYhCp3XnTqgbvFDEGNuOa3KpE9eXC8f4GrQEDCkfKjafP3uOlRx8V7alxGJdJIWPMitoUxiRy0SStMQ8/zntv/wOu3/4aWY1cPHqd3eYxD19+LKlIrodWGVdr4iLon9QSxq3IITFqC0ZYo/mcTGScJsUZVWUk3Y8Dy7LQDT3W90ynwLgePzR5qKZoJQtKzWrKPOPPnNJaKykHun5LNwj9YJqOzNOJhiKFSFONRiKGKjqzfKJbjwy2437/gnHYgbac5olwvCEvM8YbmC21ZErKFMCsB1RvoWicG3De06rQMprSIpPuDBrRb1tlxCRIlY1DKXKI/M//6g9+w3O6+8//HJuLf4nv/rYn/NQPfKOU4Yd//u+Qa4FUAHOOezQYd2Z/Jnkmm1KkPBOun1NSpdtu8cPwIaHAKgW6k9M/hY4PdLDxvICKFnK17jBaRk8xRi6fXDI4Sd5qaeJ0mglzJqdCyZlu2GCd5vLykquXP8KLd7+MVVpCMTZb5tM9yjqmaeL+tEcVWPUb5uVI148YYylFOJW2XzOfjjgvo+MWM533YjxxHTEluvXI2K+4v34hJs0EftAoVTB+w2e+9Xuw44o47zkd9vzEK2/+gRv8fuhn/qaMcI0R8+mHnNuKAVISE2tOBeMMGINzlpYrpSm0VdRS2F094OGjS0ZTWU4T7733lHhGXLWqwSi8seRzOpOmMaWGQt4TrS2UKnEHtYBWWOVRraGMwZ2lO601bCdr4wcpZq1WcIUUxeTaQqLURFEV5TxoQ5gDY9fJJKNEOj/StCLMe3rfQYFkPLoEUlnQ3qCKdC5L1pyrtnMcskhtcplRGWiF0ipYx9XFjo9/7lvFkGc08XCH3TykpUWkXMc77p7+MrtHnySFiaYdtiWqNqyvXuFL/+QfsVpf0vWanCa26zXT7ZFYoeQgXVZn6cYdzva88/bbXOyuoCac8zx99h4AuZ4juhu0JFPBmirGKJpWlDQxri+JqWKtYV4OQMVaL+Pe0wkqdM6yLJPIplrG+BV22MqanQOj6ygaGTv3/kPDTgqZWjNUhbeQdIWiz2NtweoNQ0cpYr7LWTCP51grtHWkNKOUwygJ+6i10vedSBQakvjVPli3PbpWaRR1A5XCHBZSChil8X5EKZH85bjg+xUtF6wfUUazhINI3LIhxSwFpBKTV46J3p4nOO7ML25CCqo1k6NEuVon8gqlNA3oOy/YzQrKOXIRJJ4+Y/zmMKNLYxxHMWlrCWKw1kAQX0rJmVQVuSSskYlpbJlWEhpDXgpdb5njibhMjN2OrOTwU/NEP24pypCWgHJWUvNyISx7ait410G/Rjew1uGclXAMhLVca6ORRSaWkmD4zqxXeReaYDu1osY9xhicWxHP66aqjSVJUqL1w4cG09LOEXdNglJSEkO1NpBbJhdFONzjfY8x7owiFBNmqxrVndF/KWGxGGelyVHF7JvIEtcdM946UpzES9Bkih7TjFNaGopKM794G4Xj7uaeOe5Z7a5Yr3aiKXeV5f6OEE/c3NzQpht0d8l3/tE/wvL8xHvv/hrDaNhtXkKbLfcvfoU8TSwlsN0+xFZoxnMKB1JRDGuHaTDf3zNstty/uKe1zGtvfoy7r7/F9f2Ri8dXNOXZ7h6xLBNxf+BH/4uf4Etvvfd7p8n9bYrcXwX+ldba15VSLwN/t7X2aaXUT57/+6/+5t/3233/d33uk+2n/8pfEFg5klzkzZpa5WVPKtBSxFqNc1vO7UNxfndreXFKoiiFKlW4tq1hqkWZQi0FYy3trNldTifcYPB+Tc0NTSXGPfMxYq3Hby6gJYzuzuzXGVTCadFQzffXNN9zOrzFZnjIFCMtJGKYsEqT5oX5tND3lel64uK1Nzie7vHdKFo3p9HW4FcrmupRGPxgOd7c8f777+Ftx/rqEgPM8y1UzRIM3cYxrjdMIfP0+oYXN4FXXvkU2nc82G2luMsarRKqG6jKSmegVIyu1BJJ4QTannPrLcPmgn69xhqBpGsgVc3lwwtub55SwsTN02tiOKGUoetHqoJpWnDWnxmG6ayFAr9yHG9PVBT92FOCuPYxfLhwK23pxoElJLquoyTpfBqjz5pSGce+/vpH+dKv/BLH4wusjRizoTbFZrUjpkJtGbSFdqItjaoU3bhizidct6aGhG6aqg21ZdnAlZHDRRVDRUoJ2zS2X2GRQAtUJIbM3/iBP/UNz+lPfux7+E/+zJ/m+37wc/zU9/3QN3z2p372b1O1hGTIBpPRpp3ZpEW0yNZLnGsO+HN3NJ7Hz50RqgEgY+BaZQQZs7jetRb9svZohH2YKxjT8ZE3P8X+dEM43HPcnzgdbvjM5z7H/vaWZT7xyc9+O7F1XL7yBF0yKS6k5SDdimfP6dYjOWSaMljXk1vheHfN9fPn7NZXuO2afrslzHv2L26IMbIeNwybDaTCs6dvE8JM57e8/LGPQmvcX1/z4v2vs9ptyTly9fAB97d3lBTw4wrvRh4+fMjNi7d48Orr/MSTb/ld16Hf7+uHfvZvY85RmVoQEqJTLYWcMxjENEelVKT7UkUS0sjChg6FNz/1aXIMPH/7i9wcjmxWAwpHsRZQKAdOOYyz4lZvRcgCUUbqxmmJfe26s05WsQSJdq3nkBfRVxpinqmq4qwkVgHUos6BKg1SRVmhvmglZBPrjGjZaxP9fANCAaf5wKbwgYyBGqUATgu2OZztaLViOmHRoiEn8RiASAmaqrz2yqs8fO2Ns37TwQeHtLMZRaUo4TBuFC2n6aAcJbLb9RIYkxaJ83YjVVs5wOUDmAEVRec8Hw9cv/suv/h//Cyb7ZYpHhk6g7IjwyDyn74fqEqjrefhk5fZbi5Zb3fMYaGVyPH2ltIyh5s9VSvWmxHXDaRcsNpx+egRqigOh3uW6YjvR+72t6AKYb9Q9Fk/W8Wl36qS7pkzhFSkja6axDSrRjlL5EopKGTCpE09e07MWaeczlpSQ8xC6jmdDqhSUcZIl8sY0Y4reRbJMIeFzlviaWazGsUoaKTgiTHSjwPGDizLxNB5OmOZZ9lfjeuIcSGrzNhbalakKRHiEVrEDi9hhgFvQHuP8QZ7DulQyrAsi+yXKuGsyBpaQYpKa9AaOtszHU9nQou4boxzciA7G7r0ecCc50C/GdE5EJeAMz25VTKNrhOKgXjHrRSUBuIpU3QSPTeZNCeMqiL32VzRdSPKGGJZ0KkRU6CURElB8Iqml5AW06GsUDNSq3hjUUqLZrYu5yhvTZojlIztPFCxvVBmUBprPVSDVZHSRAPtx5VotpOYhgEpgBsk3cghYtHSdGsyJTHW47zidJ6c+GbPe1+RQlWJOVPCbBRGCX9XAplkytG0pKm1JrJMpS1znIhhj22G6/untJQgwTh4jO24engpqYlac7y9psVKmAO7i4cU26iqonNhXg48f/ou8TAR81NafI/r+z2Pn3wbvXmZcT0wasWpJuJ0oPOK3g0U05ND4fnT99nsPIMd6cYNfacovSbXNc52qM5zvLllcJbT8Yawf85/9N/8Vb789vPf0yL3K8At0ICfbK39ZaXUXWvt4vy5Am5baxdKqf8F+PHW2s+cP/vfgB9rrf3Cb/f93/ktn2g//Vd+HKW96AyVpcREN46SxqUUcZlJ80S/uaLvV5yWIyorlPPksmCVYr59G+Ue49aeMN+TT41HH/moaHBLoGVxhfarNbYfuHn3y2wuXxF9kENA5cWjuk4Wo3AihxN+2LEskZBuyMd7+m5Dzgsh3EOOGN0T7ieGlea9995jvbugVw7TnTslu5fRvpcTYj+Qpj1oKXBqkRPe/lZQO/1qha1QsyL0G2CiaIddv8R8nPnCP/gFnr7zZWauCfqazfiI3fAd7LYP2L36Co9efoX1aisnw1KpOZKmif7BjtU48OzZV9nfXPPklU+xe/QazvRc37xPqZU0L/8vc+8aq1u73nX97uM4PYd5WHOd3sPufveBTcGWVoi0gEproEIiihQQq6gBQqxGox9M/KCG2IRoNFEMiRBQklpb0lBaooJKW2BbaIU20LLdh+7Du/d+D+s853yeZxzuox+u8a7d3dZ2b9MGxpe11pzPXOtZ4xnjHtd9Xf//70/bD2jruLm5wa9O0edP3qZrWo6nSTBerUehKUm6tpmC04aUpNtuqyMrieRMOWMRt7dSmiVJMMR7GeelKknhKZViKnFJqMZRckVnQ9s0pHxCqUooCl3yly4cnVFrepB0Yh3eaVJZcU9Z0TnhFuumvux+UT2d70nzkVwk9SorMa1UJf9GriN/6bd8ebf2X/2//gs++lPn/Lbf/AG++zd+y5d973f/1e8jGwVVY1Esi4y7W6uZphnfbwklY2vCtTKC0VajjCcvM223paRMjhOqSvcXZ9YkODE3LouYJrN2aCVGyJSFfbzZSpe9pChaW40stjUwhoXzq/djrcZUePTuF+j6hu3mnOl4AiMJfoW1I+grTsmDy7ue2ziLUQNonSPHTMoRbSXJavA9kSCykWFgmQumZuzQ0dqGhHR5ynpuUkr8+m/8Rt59+9Mstwe++zd+ufTjKzl+34//r1ANc57RUTZnOSzrdMTgrSShURTTcsK4HqpBO3moWG1AGRSFUhXVQklQV8B7KEiqn16xfapiiRQatFYyrieiqyXkGVMNWC0b6xjIZUabiLYtaQpgO7ISKgbAEgLOS7FaC3jnOJ1OWGtZ1o5b42RnOIdERTBive/lni6ROgcCilITSmu8c6ja4poi0Zt1QRcoVRNzYBjEfOmMvAcxcGnRGDcZY1uJSUaIAFlZ6c6tWfUpJSmmcqLEiHJOYoJti9arOSoVzu9d8fDV90kCEhLJrXJEIR1NZTvZFNcK1lGVgpJYxhts22KNB+WoiG6SJBOm8XRLCoGu3fDpT36ctz/7U3hjsdrQdjtU46jNIJ9fmFFxxuqGvu0kZKPC6fAUdMNwdoG1LfuhZwlHMeV1W8I08/jxI7quQzuHqcJS994Lmk1BKmLQ0xqc6whhRmMkXGaNJC8pUy0QI8b5FSclXXiKmI2dNkwhgq6UPKOdI0yzBBwYWEJiHEe0tkJtaCx1jYFNKa8mSFhqZuNbCcZISaKfi5YUPWs4nU7r5rqSwoJOAe0sOS+UZeb2eE0l0vQ7tu2emhEJi/PUEjg+e4dnzz/H9fU/YL+7y/Hgef/7v4laI7QDoSjubC9xqhI9mPYMAIWg8pxzWNOTayKnmRiDbNJPE03fYZqWPB1RVXM6XrPMkau7D4krTck7C5LV+JI/G6aRsEw8e/aCs/Nz2nYALU2EqiLjiie02tHZjhgmcpWCtRQAoTmEHMkhiglRK/qmpWotkonWYnLBoJink6TlVYUyihISzllCiDhjGOcTuSxorSgKrPMU22KVp/UNqc4rYcCA8qtRsFKXBWKm210QSaiYqCzkCF5XafrlJGEX0xHbNrimJSyJpnXSyIpCSbCuka7/WgzrXLl9+i5xOaAbjW17rLKoYnGNJ+RANpIOZ1cWtfcepRtSiHgrU6cYCr5vieFEXTK6GG4P15xOt7RNg3GgvKJVHcsUCIcnUCPJOHTylBxY0pG+2+B3DaSKpaUaRxiv6TZ7TtMt3g3M80Rabri4/xohQtayPjfKMd6+wLiJsoz82//Jn+ZTn/vKOrlfqfHst9Za31JK3QX+D6XUx3/uN2utVSn1VQ0alVJ/DPhjAK89uItGXKi5yoPatc06HrUUpbDtgG92EqVYAt5q5rDQaovVLVCEveYcymrQBd81zNfPUI2DGsklyY1ZKnF8wX5/Rokjc4zYTjAYeVl4881P4G3h+VufAR7i7MC9B6su0Xq++OyztLqj6Vr0RpHyzP7h66QSee3D9+TB3+0pyO485wy5EGohTScwlqoa6jxScmQeZxovDuhlzgR/gb26wu0uCGGkNV5wZm7iG7/td6Kzoy2VJYzwntvYNmSrSaUyTyecNvT7LdvdfWrNjNOR6+sTijPe/6EPEUvmcDtxmN6l9xYV5zVO8JZpWrCuZVmKOILxlGqopUi8bK3E5YR/T+geo8hBrMP5njxFcl2oRR5Ucy5oLcB8O/RiAly76q0ypBzJpYJKWOdR1pCdQkfpwqbi6Jxg4kqMklKlFKYmlFZkKrrZsCzTSrzQWC24sRITBune6wxhvGE+nHj9m347T94dCYeE3yDJP8uMMg6tM67b/oJr9tn2Dcq7H2P72i/EitmmJc9RXMel4L1MEzKVthdMkimVqhtxnSsNa7yytZY5yPmhGLquWWNQNaGsRatvaE33Mh5XkHYWTMZVTZoSVNHH5lzXsbsiRovWhTheE6rCWE/f9/hmYD4tqLaFkhi2G8mWd4aqEilUnHFCxuhaapQuQQaqVXjboJXCNZ3gmJRnO2i09tjBUpQk4MUky0Iqcn+SC7vtHXJVvPa+j3Dz5PFXs2y8POZTIIUgXc1hx9nlBdPNDTVllrRgncP7HqcNfthTkhQ+bdcRlplpWnDWM2x6jqcTCsH25bDQty1atVxeXuJ9y82zx7z7zmdQyrI9u0fnG07HA3FeiCWz2++xjefm5pY8BzaDYZ7EsX18+oS28RifUbanUphCwDiDSoq0zKAsISSsspSY2PQDy+0RYxzzcqRpBtCOZQWqW11YlhlAkD5ZMY1CwdAmEoIDFpzzTKMA27uhY7o5SOxxnqUIVGrVyhdM09INWmK+tSfXSo0Vq6HRjrAWBiUKzUFrRZwm2raVjp0CckFrxfXjp9i6bnprxW8HUlioubDf7CjqgHOO5y/e4XBzzYNXXuf5s4MUkuVIKoXD4Za6RJ49f8QbH/q1XD97mwyEccQgKWvnlw9pVsb2uAjCqLWeJYmW1/oObyQpMZdEAvz+CuMdJco98vTpY5Es5Iw+jBhlGIYNKSVqTDjv6AdDRdImY8mUlEFXnLLEcSbkScxmsRBixGoxraoincaQE75tcMavcihNjgtKCzvY+0ZSKcsi3cFF3od3FrPpv6R9TlkA+wqc9SzLKIlzRjMdTy/lRN450qq1hsLp5pZwc8vt9RPOz/cMV/fRpiPPk2DFmg5oUMCL68d4bfBeE9JC1w60Zxe87/KcyxcfpOkvWaYnEEfG46fJtwWOlk+Mn+Vi92G63QVFO6aoQVc253vhZ4+VpVa6QfSrm7MttWrmOeBClM3mfMI6x9AOjPOJ/vJMsFS0TOOBXCYOT6+x2uDagWo89+/fZ5mPzLc3NJszDqenDMOW1jZMxyPFO0KZCIsUqf2wZ4kTMS6MOZGRKUrOBYsRznRxLKcD6kYwV6fpyGazYXfhCGFhPk1sm44lgmsM18+ey/vuz0VL6z0xzAyN59Hjtxj6Pc44lE7EZSGEmeOzx0zTY+JxgiDXR3v5Kn44Z3f+ENNqxqTonaFUIyl7wx7vJJTHKs0cF25un1FTJSfp2mKdRO7mCKpydu8uedlLHHvfkI1+GTluc6apcm+UqnF+wFmHUgbXiX8phoVC4emTd7m43OF8T4iFi/0D7upXSUFyCWqqFKDpQV1uyTkRjKJWQRLeoSNVS0gLDpGJ9N2GdHlBHiMX2ztUoxmMpsZIWmaGTQsOxutrgQQ4hzYW2+6o+Suqb+U+/GoRYkqp/ww4An+UX0G5wke/97/mNN7Qtj3OCKLDWc083jC/eJuiwPf30CqjbAMlY6wk2JzGG+FUeikCrAZTV1d42zFPL2j7gWUKaNvRdoZpvMU5R5pHbp+8y3h8l5/52BO++Z/6DQSf+B//uf/oqzovvxrHN/9PfwrnHP1mRz/shF+oJeWJFKlxIYeIdh7tW4m9zRGtRM+zRJEGsC7ivtGiXVpNTe+lOaV5AqUoqhCmICOZEElpRKWCGzZUI3nUWlnpIpZATloMLqpSjZG/M1YosmhJzLDFGtmRoq2ko7UC747zCWcqIYF+L4oYBdrKWFXJOKfmiDEaYwxzXrAoWMMxVFFYbYTHWCNn51fM84zRhc29K1qtubk+ie7n+AjbWJZxoeqObrvDGMfh3c9iuwtSNdy9e0U3bDicnvLd3/Atv8wn9KXj9/6Nv0aoIqkpBUzRsqNvHaYYlhLlc1ikuNBag5bO6jSd1i6yyPDycpSEMFXJWiQKjW2IRR6QlAglU40nlUijG9SKvpGxtJVNURaQ9xxHnNcygtMeVWYKa5y00jhjSVlic6dpku6eKnTDjrCOTbXWnI43aI10IfJM1QrrWgkDMAaN4KB80zGFhabtCUFMUEsYJRgBhVcWt/Hcffg6V/s9f2Jz98vO5X/49BOcHj+nuob9xRWHFy84HL788/iOn/phfDPQDTtSrmRdWK4PpHkBUzkeR87Oz9HacTxc0/UDxjWomjmdTviupSyRmoVEkFSh7RtcrYzjyPbsnnSNElw/f4xSYHJlzgulanSRSOi2GzhOo0RJ1xltHK+9/w0MC+G0sN1c8Hf/7x/h7v33EYsV5m1dqLURXncxlHhEo7i+vuX88kKiu41iWha0NbhmIwmKJa0UmAzOSNfPWFIM5FKppeC9fA65KpYkWjyjNEUV4hLw1sumap5RRmGNmBSnZcR2wleuVUbhtUqRaqxeeeJKzGE5SvTsimiqkocgh1YSjLNORFTjRKMLFAUpFQm9saCWiDGWZZll/JyLoPPsOsZ1njBPeOuY5uN6HRq01WAFm6eVkkSwKpIS4zuJKq5iKK6lYDvBUinlAEn6a7xHvcd9jrLRDPMR0wqdohY5Z8JnFxmcilGCFJLQEsZxRJciWuw1Jj3nKqziVMkOlK34xjHfSshCXIJolGvBeYXqWozr0MaTSTJOYOWpOmkceGOpGZZFuo7v6XhjiRgUxrXYRkvaVqycrp9QDBQLeVro+y1dvwHvGecZp8DYFm2E8jJFoTc0XUPJC1WJX6LpOxmZa0NcZpxrUNORUhfCzZFKhNaCUuQMrZcoaCikqKkJ9leXONtQlCaGxGbbMM0n5vnENE04Y5iPI9N8g6qy5mhtsaaj1AqmMN3coBJ0u17G7rEBFZmOB4gTXduSlshcDhz1iU3p0JsdrT1jc34F7YbeG5xuKUVkH0bDW5//BMo67t17jWK90Be0SG4ONy/o+xaVAqfnB0wjEcSn5YZNtxHcqN/gmx7rGkpdEOW+hSD4tt39e8QssrpaK02zxRnPdLrFti3T8yfk48Q0PeP66ecp0zXTfI27uCCMB1Tt6TZXbM5f5/zhGwxDRyrQOENKq9Y+VYoW7beu8kzXWqOrmEpLjpSsCKHQbCRt1TQNOYh/oibhZne+e4kBTDGS40SYRqxSnMKIB65vX2BcR3NxxmZzIfI7I0zlVBLT4ciSFjSFzdk5pjcy2dQOhZXwImCejsR5wWiNcRrrO2kCzgtSKkPOkRITbduigNN4/TKYosSZf+8//a/45Gc//yvTyVVKDYCutR7W3/8O4E8APwT8YeBPrr/+4PojPwT8O0qp70WMZze/VIH73jFPE3maKbZhHK9pvWOaT1xfX3P+8J6MshzkJMo0rf268EmRc/3oszTDDqU3KGsxbU/TddQycv3sbXz4AIcXM9bdEOeGUzhyt9PASNMpurOO3/pNX8vzt76IH746fNSv1nH36j7t5gzj5IFisZgirNrWtaRaMa7l/uuv0rY943jkU5/6NH0rY6u2l5zvWkE5SwgzNUWO8wHCjErXlDxw9eqHiVrjHC/HKNZpGnchSVe+ASWufWMLKRaM26BUIOaM9Y5pWTBOko1SKSjNy5CNJQj/01jRJaXbEW3btfsxcnpxy2Z/QTWa+fkTqo5M0wmKwRtPNZqQM863ogO0ouEqecI3hrBk+s0WVz0vPvfTzIdr3HBJyCOnw3OG4YqLi4doI90D17bEqiV5h0pz51VKkgfGzfEF1zdPqcp9VZ/V43c/x/7yFaKqeG0kCCNDOixEo4mq4lonnc+1wxVjZoyRbhjwylEBnEK3PUuQTp1VlRAl7cg4i85g+y0ljDJODgWNImvFMs6S3lcjTku4h3WOTbchl7jGyE4iHSiLxGNSuLl9hvaO0ynhG+kqNa1jGq9xzUAugTTLxqiso3jvWumoV8QQtZr3TNMSS8VYTyxiyJrniVwSyik635GLYnvxkJ/+B5/l1bs7+Ce//Fz+5I/8Lezg0Qxkfpah18TTBN/wpdc8evvT4DTxUFBNg8Kz6bZAJpbInasHHE/PZYSpG956+/Nc3XsgOkdruHn2jM1mg/KarukwxhDmI4/ffZu+2/Pm5z9L3zrmw4lYIo0VtnTNi1wbWQqRhKJphGzQtHte/+CvoaTIkhTPbx7z1ltvcXZ2n8984h+S1YxuOq4evMpyI53pVzJ7AAAgAElEQVT6cX5BjAu/5bf/C7wfCCnRtFvyMnG4fc4XPvX3eXzzNssYUBZcd8b+wdfivSVVRZgmnHPcv3OX29sbipaNyrDpiSfhguumwfoOt44htVZsN3tikfeQUqYfGoz2KKfIVa/hNhGNGFRyEXnMskzr/xe8NaAVqZaXm1TQ2N5CiuRUCEmoEMfphLUO3zqICm8libCqQtt3oCxKr0lsSuQVJcbVbGNwpZGCM0NcFuraxU+5rI5/h7OA0TLWzoXqLM530pH2hpyS8MtXLWsJC9PtxOnmHUloApxtSEWt656j73vG6xfgHU03oGpGWUPS0LYtno6ljmybVu5RFDkKqzaWKOaoGNl1kgCVayWlQlUJpQsxzDjbYYphzdYipgVlHIVMiYW0zFjt8E1LWWVDxlm0VszzIgls8whVY7xhuHdPIqi1Xc9rYppP+Bzp25ZpmoBKXgKu0fTeEmNkCRMua3I5UWtlnBac1WjTrLzogo6aHAvVe2LKODytkXM2niZiXjjfXzJ0HUp7UliYwhFdMq5rGUcx+LZuoNEdIY+0d+9wls45Hp4xvnhEKYXLux/Gu4a5wP2H97l+8g7EA+P1u2Rd2G0+wOXFBWOdaZqOJmXsONAsiVxGpvicbX/Jo7c/wXT7LsrscO05nfUMZxuUNex3G3LIHN75NC+OI1evfA3N5gJMy+X99xHTjI6R4V4nMg890KgrajpxOkqNsiyi5a0lkeNMybDf7tncvWK+PRKOt8zhBtVZzOUDst2y5ApRqArZO0gN7vKK03zNq223aoO3bDeaOJ1wzSWma8lpIYeZ8Tag25a22+M6R0b8GUoJr15IO5aqpLxTVdYqhWjyw3ikBDEietexaXZI0Hmh3WyY54TvNjS9UJ/2RqG04SJnaYw5WWPECBjRpeBsy7A948IZQknEUjB4kqrkNaFVW4Om0jYDvRO0X1EQloRWhuF8LxvBnCU7IE0vpywX52eiOa6ZWgKu8V/xs/mX7eQqpd4AfmD9owW+p9b6XUqpS+AvAq8DbyIIseerPve/A74NQYj9m7+UHhfgG772g/VHv+e/XHVy0hlSOnF6+gXOH7zOdLrFuJ55iXS9kzjAorDtlml8LoYo6wjjCWU3AsXPIyXMpOM7KF8oybAcZ3QeGYNAwouKtKolZk236XDGo33DfLrhL3z7d33FJ/H/z/GVwOm/+b//Lm6eH9jcucvm4oxX3v8GWlm2uwFVNdoYrO+YjhIVaYwhLp7jIt1ObTU5F0oR4xNZFtZCxrlMDYkyB7K7g+k7SslYYyhhwVpNiogpaZ5INyeUa+gvBD4elgPpMOOMyEY25/fEhKMr0yJ4i2HbE4PCa4f2hlgyaYnUWui6jidvfowyfVJ2jeUBF698kM1mR1oNWKZpVp1fQlGIKdF4x5REhzs0PcZa4nTNOE10xmOaFmyDrjNLFhyP973on4wm5yzdLduwlIjV7wVwRLRpUF6TJoGtT4eRv/q7vv2X/Bx/z9/4KxhkrF9rxqjKMgVs32KzZlkSVRUwDdqDqWvXGyddrxopWQD1sWaM8eQcsdajdcVjiKoQakaFIrpDCras0ZQlUKaKbuU9GGMk0KQoVE5op6Qr0wyr+3nE24bj8UjTOEmHahy6rpi3IIXNtIw0bU/Vhljl5xrXv4ybHKcjm03PmDO6WNmVU0nGvgwbcKbFeYjLjC7iPFYEStb88I/+L/zA9/0tfvPXfT3nf/n7vvyc/s2/jM6WWAtOO2KaMLrhB7/lSzKR3/W/fx+195hcccVirBcXvIpgLTUWqpbFlxIpK1mjpIxVVoxQjUOTUcpKt5PMMq+SF9OLhtkYclGivxtPaFWZl4jxYkoN07jeMxNuaJnniDEKZzzWr8EfVREOL5jVzBxnctVs9AavvKxlrSYslWHoGJ9/gReP36Yazfn5HdrdHdqh5+b5C0I4stnfIZZMnRJLCWyGC5TRNK2wUsM0sduccRwP9Jszci3kOVDGhXa3g0aLY18LaSblAlaS8pT2eDcQcgArzQNWz2hUGm0qLR2gJQWrCmrNGIP1QvbQTq8u78ocJsEbGihaYYyM/K31EMWUmasY3JQBZ+T6S0hBrRsDsUK1KIsYY7TCK0fKAe3FrNcPDfM4i3YyysM8lgmrPSWAs5ZSAqpEivJoC/PpBh0iqVRiGsnjDdPxObbb0Ww3aL3H6or1He2wIdYFVapcJwUWArvNFTmuZlVrJICoCGC55pl5OlCrotls0bah5kxWvGSyZ53xgK5aEidjxnlNHGeK1qtLHkyFWBK6KsgObeC4HDCqYLWDvMbAW4k3zjWyYmDp+42Yz0JA1YyznaACUySNE+PtDVMa6do9VmVsY+n6O2QqxhhO4zW+H9DW0DYDjkJNmYRIMYpSTONCWkZ831FiRVmFUmJIdClye/2Y86s7LDW/lFMZGtpeNgI5LhxePKfxA32/5dmzL9JqKxi2tmWcJ5xtWK6/IF6O4Q5903I4HMjLzHKcaXYdbTOwnA6MxyccDl9ke3af7uI+l5evUbDCyBdxG23bU0OlGkWMBUuiaCtJnxqqysw3E8UUwbZ1jhoTKhqyq1hrJV1vCdBpUhJddslKNGghiEE4Jsbbdyh1IaqOSkuM4jPanF2u4VOKZmhlQqs0dSroRtCTxnmcbSko8riQcmA6PsOrjnmZ6PZbCYjRHk3FN4aUEss4saRIjUlIDEqRKTRtSwqJ7fkFRa3MduVQJAkZsQ0lSQKrxaJC4LhMoOUaNb6KCdQ0mLQGwJQsE5WcMd5QUsA1W+aSUDri18S6EGZqNfJ+tOijtX2PejUCEtqxxDUquSjMatBWymC0k+tSVb79D/3r/MzHPv4r08mttX4G+Ppf5OvPgG/9Rb5ege/8Sv7x9w6tLW13TiUx394S60Ljd+wfviHpOrYlFs327ArlPHo6Mh9eMD17xvbeHa6fvEu1mapm5sfPaM4aPv/3fob9gw9j6wRNxfdbcoIxVVI8odVAv7uk73uePfocqhqSk+zk/e4c+KXh9O8VpD+Xv/lLve7nH/9fcPqf+7Wv/6d/J7kUGW9lzfObzzOPJ558voBT+H7gMC50jUVVA8xoO8iOzXhikk5CCond5fla8LVcv3gGVVNUoj1bdX9GMS4Ba1bguPfE8Tmd25BUQ7aGy8srbo+PSKXimy0GkU+0oWVaMto4zLBl8IF5vOV0e6CiYehxRXBdxkDIMI23nL3yOlW/Rq2KOAdqyUyp0HWbtbgoQoHQCa0NnUoY5/HGE1LAGnELKd+zb/cYLS7maToIEsZYQoJpFr5lUw1KG1mka8AZSy5JbqQlMy0jejEYrQlzxbpfvqNfUmCZD5gVYK2qjItYJhbtxDSnW1Azrdm9HGmmsqCdEnOOceSUsdpgikJVQ9aZUAvVgckKn2ExopwrRZEwEnN6CmjrMLqukb+FsOpnq6mUmrHtgHMNKSpq06ON5eJqR0mJqEVTRZV5s/eOskR6vSNQROfoPbptyaFSU6JaRdeKwayUiFKJYi2qGTBak3Mgq0RjMiEVFOAaj87yfp5cP+c//pN/lu/8sObbfscf4sd/3jm1ypE09Ga7Sj5+4efQbiRXvmorOK+qcN2Acw6nKkvKq8Exo0wro23rMY0kQ8lUOItW2CGRyEZhW+lohvFEKQ5lPTEXpniLzhLhW9dI8JgDjXWAxp1t0VoxGIN6b6NXHN6CMQrT79irC/Y1M4eFkiKVyubigjne0qKgJoY7r+D3d3C+Z4mFXArLomg351jOaPuedHOL3/f4WiEnKa5wWBT9nS1FZ7bNBTmmtduUMHfOhBm7FGzbUHUFpUh5wdmOqsSoGJck6YTjTLKKoeuJSa1yIdlwlFJQOJa40HYDYTxxGqc1Qb0A0r2dDxMxzTR9R8iZbi8JiTEtayfSMudFMIMJdArgQIdE0QodHZVIZUFFSQHsbM+Yj5xf7nnw4AFxCYTxwNN4wLctH3jtazk+vybFkf3ZJVlZnr35BX7yo3+BeX6OCRl4yPbyA9jtGZuLC9rmDHd+n00tq7u/EmfpbJIKKY3UUqjW8oEPf4Rhdy4BKDnw6DOf4Obdn2ZaZor/MCYfyCGifOHxFz5De/Yam/SAlJ/jmytsvxHWqzEQIdRECRMxFyloncX6Fq8r2ogEYVkCVmmUbyg5oI1l4zaiE65VplBGCDLNVslkp0RKrYRZzIcOxeEYWQgs4cRmfwfTes629zgzgjvzvscaTwwjZY4YXTk/u8OSJkqKxHJiURJffZqPtG2/RkcrhrMLWQ91JuVAmGaJqM8Fbx2nwwxolhxo2halJ64PL9DeopwG7YlVEWthd/UqxlqyMThd0aGgU2J/9UB08yWic2XY3yPXQkmVZbplPB3YXt1nc++SbXiFwZ9x/eKwdsg1h+sn3D56ilKKZn9G0Q7nDASZOh6ub7CmklRmv7vE2Ibb8UTrHV0cyDXh2gGnPCFGwvQCZTXlphBSZLfb4azgSrJp0b7BdIbm7J4QHIigRY9tlMUqT0EmpylO4guJCWughorfbKjFgJYAq37wlNTgnEwjzNxKhHFmTYtVpAyH25HWJLrGgZeYZICN33I43OCHLUsUicOYKr3PlJRIqZKPI23by5TFSIx50++ka5sy2jeokljmka4dKFTsWohq6yi5ECuE5UTfNqQgspnj8SnjQWRZZthRlZBEmsES44z3LSkl4hKwVlHWjXSJC9YobsdrcgygCr2TBsNXevxjkXjGig3LFfxwji2JkhZxeteK7y/I84xVlsPNY4zN5OUENXHz9gvC7ZFYE8d5ojvX/P3/4T/n133NJf/zf2P5A3/qPyDHRLjOkCxDd4fu6n3kBGa7w3Rw1W0xrie9eEEdD4yT+eXfMr94gfuLpSn9YsXuzy+U3/v9z/35R5/6OMpWSI6Hv/YjfPDeR1imE29+6k2y06S40GhF4zrCnDFmg9Eaql3TXQxpiZg1UjRmGX01Kw8xpQg1y8VaK9tNL7IIJ8zGfnshvFWj8c7y7vUzCAVtDblWnB9AOTARk5XAtlGYpqW1TvRwzlNroWYl76VReKVk/KssxspC3PgEGbzRxCJRwFVJfK9tGsgFpbx0jpywaDORksRsEZaAI5FtJZeItZZcFU1v0fVLl3lRiLYxzuRYWJYDuljU2rWxriWnAkpMj//yR/9PUslrGl9iWaKgeJwnLTNFC9+35MA8rpIA5ajeyAKwOadpPBUnOCQrCCq3MhYLonmsFQyKqismy1jHrh2BUNdAiaxJSLRnJVMKNK4hlcx4ff0lQ1oxKKUppuDbhnm+RTUy9jVKC5UgLMQlkJFxZ44FVQVZlAiiUcwF5VpiTKi1sFNU0pKxbYf3GodH64K1LeEUUE5hMXgr4PNahd+agxTem2HDX/9rf4ZvffABLn7vv8K9r/tNv+C+WuZMToFiRpquYdjeYX82fNlrtLacDs+wzU4McL7BIln04yKgdOE6T9QYhXuL6FILUMKC11KQnqaTxBhHGc2HfESvTnrvPcvxhn7YUMKEVi3FLILDWk4U67GxkNcs9vfII73VmFZMliYnYQqHQtMOnO/3EjpQheAwWM9ST6AruYL2suHo/dr1sh1QoUZCLGx392TzmiNFK+HFZnnILTlRg0LpgMazLBnb96KxxQhLfE1zVF7TaNkYpCQFU80Fayo2VuI0M1ZNTgjlpkZc61mmE9PxwOXFPV4cHsuGNFWMF+1lLYHlWlikTd9hvKen4frFC1QINE2HchbfbqCKiS7HhXEWiY5Siq7fkbQkWjnrmePEMt7wLFZeeeMj3Hv4PigZ7Rq6XcMb5w8pupBQDGeWw80t/88//CSf+9jf5sXjn+Du+z6E7R+ilio6P5vxbeZ0esbm/FVs25FThBypBbpNx6455/rRcw43B+7dfcDDD36IYXf2MnpWlcArH/51lCd/h9d//T/B25/4KHd/zbfxmU/9BD/1I3+Ji92GfPoE+fnrRNvz8IPnnK4fMU3PaP0e47fMywmjMs2ww67oS0piDgZXMl0vSLIxLZgsk7bT6RZrWtrWynWrFTkuhHleE/YsRjtSmOg3g2h1gfPtToxW7iFpySi9GoZqxriWlILIW7TGtopaBQuWa8E7Q1UFVTRZwTDsMM5QUqGGwnE8rCloBW0yjWvQXb/SViaWEFBKM0+3GG1WbFpDjCMlCs/ctq3If6wnzQtqnPj0Wz9DjM/geGSMt7T9Pc72VzTbc1ANTrV0m4HTOKGV4unbb9MMG/x24HQ6cPXwtZUkUej3Vwz7B8Inb5xscmtCV02shc3dS8p7G9jTxHK8xZsiSainkW7TQYxUAYix1IhPipwifdOwTAvHGHA14fuB2yfP8U3L9eMndG3LHGds19D4nmU+oLxlM5xT1zjuttnQtEI0CSGIoddY4iJT6dtcsbZfI9cNjR8k7TBIg6hkmI4LMS5QC6at5JBpzrY0CD94f34mrOcQiLOYzW+uZ7rdHVSFzaZHrRpwkLRTRZKp1HvR0Rla33Dz/Anb/Yab45G26SSiOAoWtGuFxuLbDoWB7oxdu4daRSpUZ6zTLNOIMyJrEepFISVNzRJFjNYobembDaoVBvF71/NXenzVxrNfjePrPvy++re//89Rlcd1ibBEwviMEmbaZsthfId4eoLzPY+/8CkatyPMjmHTMIaIjo5hv+HJfM2Zfc4P/Lk/y6e+4Pn9v+Gf5zf94d/HW09vOd8LxsVvBtLxEU/eeZP7r+0pIVJzS9GG/s4dWq8o2vDf/rbv/EcOp//jP/MTZDTaO6wpPH33TVJUQJWxQljIVZFzpe0sccpUuwZnVCUGJKNJIZJtFf5szSgsqLwC0rNIPeIoQHD3pfQvXQvGeooVM4a1XlLLcsDqNSe+CC+vlhU+7Yy4gXURcPhmS81RHJ1poVgNaWE6XUtBGwL9TrSSIcxrpDKEtJBSwesWZQAlneCcBfdCzWgrHSal9GqGEgObcYLWadxOXNXzacX2OOZYoGaW+BRTLM4rSlS01nFMlaZtKdVglTAcnWtEH5g1tpFEMIecV6uk0xxWN3fIgXmeMEVE865tAI9uZFEyukXVSnGGmpJ0UEFYv1k0co2TzUHTtiwxEBgFfaVlTPpeQtXpeEOOC3cvX6UqYe06rViioNJKXihFDGRaV5q+IyWR+ZTaStc5Z2KtFAM5JCxgbKaoCigBtiuDMRanhRkJBd8JbSOXuDIjNWmOKGOk04kU07rRoGV8RszkMjGXkb/yPX+ev/hDn+St68d8/5/5Hn76j/7BL7vuv/3Hfpi4zHS94Y0PfiM/+Xf/OoebJ3z09/+7L1/ze374+0ipoLD4tmOpmsa2uNaC0kKqkABpMXCVNZKXTCyB1nVUXWltz3w6kolUVqh9jWQFjbEsq/az5iqbblOx7cA8BYyR9Lc4L6icKSFjNkpMYsrTaAW6kBWif2y3KAPa9hgrhi+VTnTbDdfXt3jXESZJ6vPWCekgZKqxKGdx2lCMIs+BSsa7lnFZcE6zhInNsJMiGYXWEJdJSCVzYJoz/XaDMSueLovrX2swxhPSjFGaXCLT9TWNa3HbPXGZVilHJamIigmrDFoB1qCKxrVblmWRjWUSx34uAV0N1mpCTizLjK4KVRJmxRCmrLBGomypkdPNEW1l3apaUWqgb3rCeq+UmDAlEJMlzDOH04nGSeGejeLy/C7j7Qvm07Xo/xvPrtmSpxNGW6b5iGs6ApVNv8fYlQyhm5dc0VorVgv0vmr/MvL99TfeYGgH+s0OZfTagMnUeEs8vUOOAe0tzz7/Nj/w5/9F7j34Z0Fd0tx7wKb7INb0bK82FCqqWIq1NJszMQIbQb4pCRAkx0ypooHXKOmi5Ujb9dRYCVMgpZE4Hcm2COfb93L9hCAJoqu0YtOLVGsMEoLhh1b00BRJWFMFUyzzMq5M70SaXuCHc5Yk+DZvG1I8YFpDDQnrO2rT4pqGWqHT8syIqaJLYppuCOMNTg8opXj+7C1SzfRnGzbtJa5rJeLedoJZIwurfo6kWLl+8oi4TDhVqSZSnUJVTestjd9iuiuazhOnifJeIMOKCHOto5RMCDN9v8GYAdN0snlz+qVMJuawSk/EzGS9o6hKSAunR89ouhZFgr4R/0tUssEbJxSQVr11yVBqoGtagkpsuj01BrIxUCQoxVpLngO20cRp5DRNnF/cFaN3XjBFNpLLJGmrMcr3dSrEnFnSDGmhaTfEGNcAlw6nDc63lJpYcmHY70E5yntGzJqpCVzTSFR7rUzThG+bddO6+m+alnlc0FScN9IZNpVaNCkHlLaE0wtqUrjdgCqOtvVynyMm1LbbSJR8KlhVqCmhvWdOla62jOFE1/dUXSixEmpk0zp5bjipA3IpGNdStCIHBSpiozzjUw0oA97Lc/EP/mt/hI99/BO/ogixX9WjMvK9f/o7eHD+b/AN/8wbxHpLZxtKGXn69qexXjGdnlCbLePzFxzTI9z563Aq2NByOL3g+/+tn6Oh/QPwKoEf4wf5sZd+uK/++EdZ4AIcbl7QbnekUHh+PBFTh+8aShJt02nObLcDuIbpeAMOuqYn5BO6eHIR8HupYQVDS3iAMpZxnmmdJoTEON2gtZEbPRdBiDiLM7LAzyFitEaRUUnRGEvBvORmaqXl5zRQ3oNcV6x25DlDybIYASpUtHPs9w+oaHoUMS0rc1KSnEqpOOOhLOS8YLXgbFTVqGKwVgEiPFcrLsvmSgqSRHTSlX5zRoijFG2lkOIiYy7n8dYzDK+glOHuvUuYb5kOE6/euSLEwqNHXyBOma4ZZBFSDtcqYkgM7SBu55rF1V8TzZodPzRbOttKiMg6/q1lpml7UilUpTDKUVWhoF/uek9TpHM9uVHknIDMHATyr6rCKjHYGOtwRtO4HdtGRsyqigYyHEdKrSSd0MajdYP1wpdUBpaUsK4HNHqFhlunSbnivSUgO2SlEq0TQH0lE0pFF0Uk0zRenLdZNhvVGBxONHidgVCgaGKWDVGKlcpM2w1gKksQTef7HrzC26cfY6DwyU/+Qrl+SQt//Ft/Nz9eKz/5o/8bruvY2vMve01OiVoS0+ma003i7OwBN289omkch/GZoJT6LWgxzbjGiqkoJxpjcV1LnhaOp1u8NajaEFWFkFC2kTFxiti2Bwq2adHFchwXcnF0nYU8s6SFfrujJo3WkXG+la5qnTkl2YhZa5mPBw4vnrK/85CsMo2T63c63ZBrJebM+OJNtHZs9mfUFQXo+wGVM1ZbTqcT7dCDMTTaUWJms90zjUeGZktcJkoBbx0BhaoNFk3WmW5rSOOt3M+l0LsBpw3aWY7jCd82oj2kwZ15al4kqeh0oqoim5bOoNBcv3jK0PUY36C1w+SE962kFmaN1jOuGUghULB4LfGxBsOST7TDgLItTbGkFBhPJ3KCdrdfWbKy6au1op0kVOUkD8MwLSgga8PDD71fCoQCXafRvmG73TMdD1gFS63oErF+zzgd2N+7IpQZ5zwqZox1WLPDNH7VTie5P9DEkKVzqRQXd+7RtWd0nYciWkGspmqL9vdp/Dl1WXj7i5/j7/3Nv8PXvPZHaIY7BHegmisxP/ae0/WIaywpLfS7K05Pnst9dLvgrwas36B8j1IVnMLkyBySTI1iJDNjjGXoW7TZUi4vWEJE01BTQTtFu62M44neeUrJ5CDIwa5rhVMexE0fx0BcIsOw5fb6HXIN+GaL7zuq3or+NETKcuRTn/04/e6cy3v3cX0vHds5MT67lqCAGOk2A0lLKl+727I9uxRjUY1sH16gsxIjapECseiOmpJMkEqlliTkHefY3bmDMRWnW9IccFvNMkeado9tLDUaQhxZclr/DwO66fBGowzM44mmHQBFGE/U00SORcy9HagiKZBWO6Z44vpmIuWFbtez2Vywv3cBpdIYz+144JSidHLzBu/XAKTVcGbbnlY1EpFdNCFljBbiABjG+YjThtPxQOcd1Vi2mwvRrVtLY6XgL7nQNQ7jO26fnXjnzZ+lSZVqKpvLc0FQas0cR6bTU65jpO22bIZzhu2OcLrh+aMvonVH3w/Mtzds9zu6/RnptDBNE7uLc7quw1JJSvwu2ipwimGzE9NkTViTCClQlGKzPZeid9Xk1qwlKbBWIONwYC03z26EoJSiBGrEa1movWXMGVMsh6PGuoau3aKM4TQFWBK2WmIQUo9VjjlFrAZbLO2mJSdBJdayaoCze8kb/0qOfyyKXJLhX/qOf5+f/X+pe9OYW9ezvu93j8+wpnfY05mPbWzs2CTFTAUEIU5bhYgmakvSSigiSYsIpOmgVtD0Q0kHqVIbkURKFCiR0kZNq6ilStOkSkprCpRKUMxsjO3jY/sMe36nNTzDPfbDtc4OhgiM1CqwvxzpnLXf/e73rOdZ93Nd///v97M33Dz5OE437KaBrFvuvXCH/eGKdnUbh+TCXn71Xez20hBvTtacvPL+/9+/xd8smvBrD8Pf+Lf+S5RZ4l2Hv72ma3riGGjaHmUNz7/wsmDBTk+pQI2TPKnvn/Dgwae5efCYzeYVpjTzdLyAm8d0zTm1FsCwGy/Q2pKcp+kbbrYXMp3RigJM8QBAmIShKLKATLdcYZVlvx9IRRA6cZrxzqO9xdAQUhAoujfiDMyJmBXGanJIkAQsn4+YGNd6YkjPVKgoOaDUqjDWkWJEZU01UI5gcm/lANW4TtblOZOLBPSt1cdDc6XUgrYa03tslQmCMe5oVrJopK1pjTpibyqmNqhq8FU+vMM4oBU0jWRW38mvDcMARnH33gvcfvld1DBSi+LJg9doXcOr7/kSVqsV26dP2V3uudptCYeJ0zt3mcNIjBMnp3fIBaYYaHzLNI+MhytpqyP6yn6xYgz1iNdSxFQI4UAFOXBTiSiszMnYbNYcDjvCnIhpRBmHUeaoYAafC3PKqCKom5IyqY7YIjDuUhzOe5S2gjqaJmzboE4/nwkAACAASURBVHxLq72gqrSW3KEyonFEbhbOiqvcW80cZrBODvBNQw5iBVRwtK5prNVM48wwDzS+A6PRxqGdpvNrSo34rCk0QmDQDtUsqDGRbeK7vvXr+ev/w8/w4NGOs193rQVt+Je/7VUWP/ZZ/sS/+Vd4/XM3/OgPfccXvCZOkVxGtCrkmJnmPYtVS7WKs5OXZB2qLUY3xDmQpsBMFPQciTlEStaCPcqBoio5F4xvhbPsLKrpgUqcR2KQCW8l0S0WzIcDxjQsVj05zBQmybS7DmVari8e0llP2ywIKbA6uc0cZ2ItWJ3Z3zx9hmq7ePwmnd9IfCUEdhcXNN0CjcEoyUzvLp6IFCfLZHlUMOxH1M0T+tUpKQZimnFNJ3D7Kge0eX+APKGilFB2XKN1T1nfJicFc0fOM3keyN5TsiZMl7QOYjCs15L9tAq2854pT7R9w3KzJhdLnOIznavRFozDaJn0KwzTPItFsumgQN9qQoy0pqJ1lRzzYkHKhZwEkZemmWnY0jSOcTezWJ9BVeiqWJzdpaA4tS1TnDGNQ5XMcLOjugGKIU47Gm0llmIs1Uh0JLsWmxRON6ilWJ1KDlKQapwoT1OACtZpcq6EINpshWE4SL7Yeg1kDhdPWZ6cE+eZT/w/P87jz/4Kp8+vSGnBfAg0/cvgTtBWqAohzVjvMK5hv31C3B4wVaP6hsP2mn6hqcOOOSQWJ2spd1VIGXKcpfAbBdcHUHIUS6O2TOOeUjXL09uMw4FkZ4hSOjZO5DwxRmrJgo9DY6xm3F9Q80zXbQg5cfX4bWqJ1DRhmhNOFg3v+70fZrcdGJ7eYNuBoVE409F2C1TStBt5D9WUSdOBmnbsIiyXa4Zpxi86vDEUJW04rS2da1DtkeftZBvWgpS3UEeVecZ0hWoUy+7Isc4z3jpMu6Zd95SixfAVEyXMMmjpDNRCSDNU2a6lNHDYbnHJcLm/ZG4Kt28/h3aWu3dOsc05xXQoo+m71RGFptm0a7TXxMNBzHG1PtM1LxZL2n7JOEa00+gURO1bQSuPbeCsP2ccBvq8oIRINpBjZDwMeO85DBNajfL5Xg3XN5esVkvOb92TPLWVa2i5XsjGcLWm6yQ7nXJgtx+gTsSp0GmFNo4wbrG+EtKO8mRmmg8opXi6fyzFulpYndyV//8HyaE32rPdXaOMpttsUFbTWs847p5x/lNOVONRNOSSIcvDZjrMGNNga2QsB9kSv3MmipndxQXrzW0hnEwDN9srtFJcXHyW1aInlkiYO1Zn9zDGcO/eHa6ePOHiyRsszs5ZbW5Trj05H7BOYXxHzvGLPrv9jogrfOClk/pX/vgH+fA3fRWPnkw8vQy88uq70c6ibaXt1zy6/wZGt1il6E5OyWHm6vqSF9/9IUrI/LWP/MnftCj263/9kzK0v9nk9rf62u/83u/+5M+Tpgt2hwe88anHXF4/wekVy80dbj/3PIvTU5rGsT5ZST7SFHaPHoD1LNcrri+e4mxHjLP4z4Os7be7SfzwWZizTdeyu76k9XITt9aKvxyx1agMNUk70blGoufaCLtWV1QuOO/RShGKRBiMMdSc5L8rQzh+cBmrn2l7Aary1BrJWmxmuWhimvFWXOrWetnEaw2qUsjHA6aUXbw76oNzxXr5AKipyrS41GN0IVCqHIK9bUQvWLXkheTj63hIFyPRYrmk5BnQxFQoNZCSWGniFGSyZwypVlzb8eILr3L+8vMMVw/pF2dcPn7E6nSNswsyHFXER5FBmITBWeSDIqYR23bsbvZY64kpUS3oWfLBxjfkMLPbXmK9Y789oFHYXrTOzjnSuCeVQE6BZnGGyhCz3CSV80eXuMaoKjcmBdq0VC0xAnNkLyoFqVbQRVBCUbi5IcxkMjEnqFEiGNaSa6U7ThHfQbxxPEgbY8k5UcnkWtHWEaZRtJPaUYJwR30r5TS0eNRDnI+H6+P0PojMRcosHucWzGE8rq4jDz7zM/yr3/Y93PInvBauf8N19y0f/bto9vy5v/mQdz34YX7wr/6zLPo7/IWX//yz1/zJX/4Rpt2IyoV+veL+Zz+JUx636jns9pgkU5ztNAh+qe3Y3LpLpKDRKCUsRt+ssdqwjzuZPKXjlJtWVsVGoXSlZFDWUoLkFq2T9d/h+inL1YYpZXnPK7mWlJJcNYjIQyE/41orMUyQMu1yIZOO7TWmaWjcsUluLMP1gDKJOOxpVx21ZnyzgKJQ2rHf77HeoYzDIgG6ZrEmB5k+7x4/wLUWoyo5JjYnHX3TcuelV2hWp8yHA6UmHr75aRYna64ePSLVls3pHbS2PH78kEXvaPuO1dlLfO5Tn8AYQBVSVOSkMFYOKVVXQqzYlBmmKxarU0KBRdNiXEOuSbBZOaENwvdtOgwGasU0nhACOcgDunWOZrFB1cJh2KGS5CRTmYWgcJjw6yW+X4CS9ruqhTBNsiKulf70TA4qOVME6os2EMMOrRqUk6mjbTuosg4X+kaUaATlWaFre31NjgXjNM1mg3UtTcxsrz5Hu7yNqgmtMiFCnPdoElMIVFqM0lhvGAdZRbe+O2LOA23bksosBs0iil/nHBgthbxmQastaTpwefUIlMFqT9P2RFNoTSO61+vHhJDQumV1dpeTkxNu9nucdnIvP4pOjHGkMrPfX2KtxRWYpyhT7zAwjImua9hsNkzz4ShOGUkh41RPJnLYXdN1HbEUKpowD7SLHt+dUOOeEkZKUti2pVAwZsFh/5D17bvcf/iIVdez3pwzbvd0ywWmadHGo2wlJ9lMpelAHA9Uq3FNQw4ZXYT9bK1FNwZ1pFRY44QwIVkuVK3MMWLbFlWT9ALUkVRgFblovPHEOeGOJKFqNMZ1THEvLHGlmHYjOQwUp2m7Nc4J5SeXRNv1hCnijMTXlLGySrdOptFaUUohzjPDYSuRsQima1BKC25xmtheXpLSnna5IOfC+s4ZJRVKhr5ZEqaZnBL74QpnDaZrICuWi7XEpPIkUcX1GlsVKVVKjMQixJ54ONCsW7p+RVWKYQh0znJ5+QY5Kc4258xhYB4n2m5NrnJe8Islxiogsd+OrDZreSBPlYvtUzSC9hwPA1Zl+tPbNEljTlqsbcTQqCCM10zDCFoRr/cUU3FeIjjm+CBjmoZSDb5fYFyPy4Gry8dk67E5U3Ok0Z7DfE2/PqGmzBgTf+Z7v49f/fRnf/fEFVzX81Xf8q/wePtp+lsN7733EjlD0y/QqiHlyAuvfgnXFzsMHqMtuvE8/67bxHjFNN38ln/G/xc0hF//+/9Jv37+Jz6GwRHUjF+fcHe14eWXX2YeD5Q0s+o7Tu+cYrzHaMc87vGrlv3D+7z5+ieo3lC1JiaPbnvUJBmg/vQOzfqUx2+9RY4JnORgmq5Fa9jtnmBtT5jEviOT3UqeEjUkdJOJOKxpaJcbae0rwzjuSXFGaYtRVjI5c0WpgmstNclfuGr5gC4xYRqH1QalLFYpdBlRNEcVrKeUSswFUwpJJYxRpFBptGMumRAySudjOSLLet1UydXqQo3S1FSmw6SIBjySNeaYTQVN38vE1swT43R4VpwyxtG2Z8xhZL1eYlzDeHOF1oZ2uaJdnbBPkeGtxzx365zdfqYox//1Yx/lQ1/+9bS2F6VlFr5mKJXGNoTdzbOVUby6T55nZt9ju577r79FaxYsl2umeU8Osu7RREJSdO2KNM2E+UCpmdZ5tOtIxsv0u1iZvM0TeppRfQdTFlLCMR7husQYI8pmYhhxSn4ysci0XmtN55YMeS9vRi0H2qK8FExyQRvFfiemKm8t8yiFJ+M6KZ2oIz7GGFSuLPyCOR2IYxIO6PHfe++IU0JZwVFptNiYXDreEIVuMU0DTdsdvz9DazQnz72bP/2d38y2jXzjJz8PfPoLrqFTN/Mf/rW/zf/9d/4Rz33wD6De+mp++kd/AH5NIml3dU3JMj1e21O+9Pf+AZQzvP3Ga5x15xitubq+5qW2xZ/dYvv4Eae373IYt4zbLWChjEyHS5RV6GZBNYoSK73taH1HOtq+9uONqDJDoPNOWMZG07Ye6++Adki9x5FDomm8UDRKxDT2WV5ZVy2KWOvIxhHChK1L+u6UOc3Eo0EupUS78eja0i06ShSebAwTbbOkWkvfL5nCiDWWKUycn92VzF0KqCnRNpbtxQM++FVfR9N2rG/fxrqFcG3nQHeyJFfNe26/m7y/YXP6MulwwVufe43br7zK+9/3EoerK7ROvPnxH+Fd7/1aZusZLneoec80RfbDxHve917BpbnCk92Bey8+z8X9p0y7G95443NoUykl0DSiqq3Rc3Z2RsEy7Q/06w1VDMuY1rPc3GGMgWo1xMzGdMxhwlnJkbveE4OiWzVMuytCDhz2I4ZEt9iQU0H7Rh5KjMW1HdPhRljhFdpmRUGywEUpVFWiUbYOVbQYC3MG1HFzVDk5u02ag1ACwkTvDFEdOLv1AsZCzILg06bgjtEqjyW/YzQ0DefnKzERWg3ZU8sgGfaouL65YLVaMYZCJqNypes6tk/f5tH1Fa3vaPuGUCdKCcyHiZwG/OaMZDac3n71KOiwzCFx8fQpKEsikHWlaRy1aOZxhJJZuBUpBLEltg2EROPXtEuP1hJp0F4a+KUoXLOW+3m7hGlmToJzSnnCVLh89BZFvcF4NdC4ljvPv4z1YjbTi55OP8fh+obnz2+hj4Xjszv3xDKnNUUpyiybojnscc6xbEQ2ULRCLSspZEx9R+EeqBpyLbLBCDPTNNC3LViHtpZpEHyboqCcRqHRxlOLlIetUYRyxA1WxXB9Q7taUHNGO8v69IR5btAOoYakSusXjPNeuh/WEI7iFK0tbWsZ9iKjKc5RQ0FZdVQHJ7rOkrTBWs8wDDR9xwvn7yaUgkUxbgeMqaQiuLhxliysX/Xcu3ubOIaj7TUxhcLCevQURN5wCCRrSUOQTUFr0dUJjSMGHj19xMnt51FHIt/JrVdwOqOVw6pbnKhKrPXIw05sr5+yuX0H45aQ9szjJMMXo1mtNlA1++sbzl98nsMUKRmeTNe0F1doDPN8RTGt8KqTlJ/LcoF3mvPbdwlRIIG6avbXlwz7A4frC1zXE6cdqU60/RlZNYL8bCt2dqR5Rjt97A2V3/wg9mt+/Y445Co0c67cuf0Bxmlm2W14evEY0sDh+iHrdsmbb7yNa6A7PaewJmfwWA5DZZqvf8s/4zcjHHyxNAT4jYfdX//7n158Et03bG41mLrGccLdu+9GeYVWDtMa5sOAVpX9/inzYYdftZy/+B7uvlB5ePE2425PcVBtJRVFoy3jcGAM19Q0Yl3DMG5lZZESVE3r1qAUdtHhrWMYr8CC7ZdAZhxH2vaoq0z5OKWboWaWy56aZeK0vb6RSZY3pN1If3ZKmTO5zMdpVD2uCrS43Q2kYcaYlnk6YJSwVK0zoDRWa2oUWHzWVTJz2z26aoyRaV8IE6pK0UtRUamCNTRWDvw5RmKcabwn5XQEvgdinpm3I0o3dIuWkmTFVUoUvBSZ66uBGGDZd4zTjv3ukvTWp8B4+vUp1w/eQJcI2nCyvsvDz32WQCFUw6svvYc7z90mjAds05CmgtGWE3+LenKGdQ1Prx+ijOa9H3g/m1svcvH4gnsnK5zS5OEpDz73GOM6bOOPOLcdymhcuyCXyP23Ps807mibWxhvsNrQNUK/CHVAdzLlWq9P2e+20BqMEjRNjUJBIASUNRjdiS3IiT990XbsdjvJ+BlFUVKC6DctBqgYfN8BsNvthJ2aK401oAVGD6CdxdmKiUXc5CkfD96VOIygK/M8ytQwCM7Oe8dc9vTLjhQHloue5fkttLbsrme+6V/4swzjFeuP3OKn+OYvuNb+67/9w7z3rOczP/IX+exbN/zAJ/5zPvZLB977a15zcfV5vG2xtPzqk89g7RlGLdB5RtkZ2224+8q7yCQuHz6kaToeP7l4Nlne7i5x3gouLM90qmCqIzeKWiL7oNCNwcSMLorFopH1XBSQ/jhGht0BqxQp7Wn6VoQnxnPYXQmXdrUhlUKYDigjWT6tDdvDnsWiZ94PDNf3aZcLhnnADUsMhqqPhSDb0rYe3fRcX17Q+o4QJnStDIeIsZqUR4yB7XBBY8TalYvhqz/yz4EGtzynpiP7t0oe0nrN4eoxVhkuL684v/0cWnnCfsed5+6xPnuOkjLdaaVkzQvvv8P1kys+9o/+Bn694kNf84chX/HlX/11FK3waMbcsdg+5PWf/O+5+4FvodoT3r/4oIgNLGglquqSDdOwkzKph8N0ha0KtCMpx83NpWyQGjE4oR0pBeZxh1OGcl2gFrZPNNk4lBcDpNJO3oPGEccB5y0pO8arJzgDWmUpKipHKZEpCb+6cR7vLTlE5pzpfENjRfOqrExAa0r0i4Z5iCzajimOlFSZxh2JSpgm5kEmuEt/ItefNhgjFrCUNSkbapnw7Qk5ZKb9JFi0znP28oukWFnljKISxplhu2N5epemXaCL5O57s+D6+gLfK07P7hCyJs6JsrSkw8B4uKLtG+yRlBBColktsNWK4KXr0DWRopQWabWUkFthlqpqyfOAajrCNGGUwfgNMcyEOHLS97Rnd2SzuL8hJlgsNrx897Y8JJiGYZjwbkHe73j46G2qr5ydnuCxpCljbWA/XTGFCdctgMJyfYthGGi9lcJkBOUMdZ6oVj9TmU9DoGiDt0L70Ejp2ruG87vPC8WlzGhvJbdpikQ8piibpmJx1oOREmNjF6SSjiZRua8UFKYoDtMOmxX7yxvwBt91QiNwjhhnlPEyKPCeeR6JMeMaESKEcU+y8hlaUqYDYUEXkS1572m8oLO0shSt6TcLlILRBFZ2RZoCyjZcPfgM3WLDquk4TDMoJVIaEt1qQ+d6xvKO4Kcw54COhcNuwtgGv1xysljCnMlDZE6T4ACZiSGwuXWH3SGxHy4YhgObkwUnt19CATEkNmtBHI7zROd7huFAs1jSdpbrJ59FN+eEsEMjpUFVLYv+TB4yFCiqPFCkCZLm8f23xdKHYZoG+dyyFWsb0BXlOgwO3y4ATdd1OA9tu2GYdyitscZizBcva/odEVf4si95vv7Hf+IbuH1rRbvyfPR//Qm+4SN/CG1P2F9vee7Flu3FlvXak3UkHq65//B1Ulry+z709Vw9+iz/y5/9oX/qNASAb/2Jv4t1PSoLJuvWc6/QnW6Es1izfEjWxM3ljXilx52oVH0vr4kTV08eMc0D+6PBqiRQ1qFiJpOoFbmgFVAUXdNgtKOSpamvq1y0SZrklIIyK5KSYLhz8gZxvn224leqCjakJIzrQGdZxTSGkszxv0u+TsQKFm09JYhvvTk6r1OIKKOJccZ4+WBDaRRVTEm5CrosB6Y00vmWYuzRXiTtf+MaUpKcbwkjSntBSpVCSkHiF3VGK8mmKWXwbcNud0PbiN1LR9AeUoTqZFWsjk1XaqRoQ0qSMdWmkHMkxoxyHu9ajPGcntzGdA3Pv/ACtU5kimwWpgPWaMFI5cL28gHj4ZKLB2+i8Phmwc144IMf/ois7K1j++Q+biXFmnF3LXrCHLh48hlSrlh9Sn96LN5oQ9uvmHdX3FxfsttegZHMoOt6kUsUWZ8ZlaWQpwu1CrMVCso6MasRydVQivjR4xwoxmEq8oReE76xElOpwu3NMYiEQ2W8MgJsMRZntBQgmo4UA6pkQpzItbLqV5QccY3F+p4XX303VlemYca0S/b7rURGbMPD+5/nR3/u51i2K/rk+Plv/7YvuIY+/oe+hv/sP/oLHPRAHTr+2C/9AA+fv8P3/LG/8ew1f/TH/jucbXj44G1IAa0dtj0VioYW3NaYI23rCCmi6jEuEKVVbboT8vE96ZRmmrdHpF2LqoqsEZoBmjwHlqe3GLdXx7jKJMgy7f5xS9tI7nhzdorNlZvrC9GSFohlxDQ9JYuDPtYi18Nx/V9iIaREmvfkeRLtNkVQcGVi2O8oyspa2FisWWCaHmUyuzhhi8VbwSDde/EV7j33MkVlMcDZhnk+SBwmjsQwcnp6F1xDzenZz7NWhcoB5RvJ1s8HHrx9n/XmhE//yi/y5JP/gNXmRb72D38HpV1KscZJ7KmUwtM3f5HXfuWjPPfie6mzIY0z6xdf4fa7v5IHn3udEgLL1Yr12S0ev/kZVud3eP3Tv0jne2qYcLZndese99/8RQ5TZEoLdMms1ycklWn7VjYrR/Vo1RVKoZSAPoLwQxwJB+k42L7FHaeJVLm3FAQJp5SRLLrR1ByEuBDikVySgIrTllQL4zRhjcYZhTIeZQ2qcpRSZJTtqMcSUSpKsIe1kIoQLIytFAW1KEqdEbUGpHlmHPZEAinMmFpo/BLtHV23QFtLLvIgG8LM7uqaWke0Vfh2gTeegqbtJLbhFEzDSNWZYjLEisQWCzmBX4g6dRwH+qPiuub47CBvnaOkyng4kMKRFlKksZ+KPOzGOdF6i++aZ4SCPCXCNJOmK/I71jbnWJ5usF7W+d606CSGr1pFPz1PgbZrqCh2N1viPLFYdGgPrl0Ln3ov8TutLcrJ95BV4XB1hcqSi3VNg3EetJN7p20kapdm6a6UjI6FrCVepPUxllelIJ1jQrctaRZiUC2JnCPKcPzMrjL1jwV8wWpLxTLHCaeEEZxzlqiZcVASXeOpWcugYhLqQ86Vvl1ASeyGHV0n8aHdYTxGviZ0KewOW5abWyJi0QZvzTHqE0lxIqZE61vB8CnDPB/Estl5sYXFKFGOKr8v58ww7DndbHBOM80D1rfM+5GaM0/efJPze7eoStMuO0rRpAzTvKNp16TdNb51PH30Fuf3XsRqxzCJdCenCZ1auq5Ct0Jrw/7yWq6FnNFKBimbkzMOx3iUylVstYcbjDdo06A1PH3yiL5v6LsN++HAarUhZfn8aqxjfz1hGy9iGVX4c//Bf8KvfPK1Lyqu8DvikPvBV2/Xf/AXvxNjlhyeviVaWu+otWHZr0imoJoV05hp2lMUTzHqFN86hvnAD/3BP/NP+6/w7Nc///f+Ft3inPVyydnZmTwlOk2NE+MwMO23bM5v4Zsj2uXyEaOKnJy/C62NiAXIPLr/GWJMXFweWCw3UsLKFeUarLXcXF9wdnqbYb+FuVCNJdVIv1xztbvCaQUlypQUJwBmo1n4BavNCbuLp5JBzNJOHYY9KWSUkmC9a/wxRO/QztI0DlUl0TgMAzFmcq6cn2yYpsA0iX++XXTMwygoJvI/doFf32A1gmZq++OHuxx0rXakXPCup5Zj1tRIcqfmGesaEgIhp2bmecS2K8gF5zwlT8dGv6DEUoHWOlQtKKMZY4JcaBqZ5i66pTRjrSZEoQtkskyO0CgrooW+71mc3+Jsc4diMtXA7ukjTm+fE3YzIczc/8ynsX7m8uHbnJ29CFhStczhMXE7YG69QI4zrTPMqTDOkTtn52jjsDoz7K+Zw4HOn5CzYbVYEpyh86KRDUUygu+oPZ0+PsFa+edwvWV1eso4bQHIOUu566hGdF3PEGbiPMLRRlTRaC1A8TDs0f740BQL2ipqLUd6gaZzhng4YFYrUkpHRqil8S1xHOXnG0Z01TR9x2q14OTWOavVht3Dz7Pd7/ncpz7OvZdeYEoN5Mh09YiP/dzH+dKv+TqurwOf+FP/xhdcQ//+Gx/nMASurx/y/q/9Zviu74PzHd/3n37/s9d8xyc+xuXD+xKviZHDNLK59wK7i0foCk1zwtmd2zx59JDr64csT88xriOFicYgE5bVGfvdgZAnFo1FGcf+Zi8PUWWgFsmGn53f5XDYkeKI8z2ZiqpipZvihLXys1RYdoc9Tmm0ydSkZP2bRmIVqoPWUpoUMcoxC60lwxuVopSEth5SJc4HusWCXOX7NYgG0x6z/O/cu99Zq2+317z8ng/gbM8hjFw+fkAYDpydneK6jgdvP+T9H/4KvFa0fXc89E90qxPqswdGTR4P/OxP/Ci/+qmfpm0XvOtdr/Ce9301oWh2h2tOzs/45C/8DF/9tR9BqUocR2IYCPMN6+feg62GFCfc+g7z9hE3T99iffq8HC5SZh6u8f1KiCqrE8p8oMTAYXvFm5/9BdZnd/iVn/wpzl/+EtrzF/HGcXbrDk3X0i4l9rPYnDDtLpn2e25217RNT86Fq8sn5Dmgm5a+7xnHiaqEWY2k3DHOE+eAcVYKU1pWte9Es0KRaIJ+B/9EFVKNbyQX6rRwuytYK+a4mvIzmUMMlaorJYupK1dLPfK5rTbkMmGwxymkQPBxRg7BBZQRkkvN0gGwGkBjvMYYSyqJFBLetfhWsGEahTYFbaGGcrwXHKU+Rcq9Rh3ff1Y9YyLPYcQ3DUbJgZQS0UfsX+M8IVa5D00DUn02YMCoinENJYWjfbMBI7a2KdyQpiwdkiNmzjaOrl8wT4ejIluoGIfDgPet5J+dBW1RtmJ0y7IRzN88RyH5tKKb9tZRgpAnUAXr5PvXyGdHVUbQjmHCmWNUSyU4XltpTkc60eFIDIhQjEQMepnqFkSjrkolznturq7RnWfdbphjoesWzPNI2y8wTpNSQWvNsL+h7RpUMagqHYkQhG6UghyGu9bRLzvCkXpwcn5G0ZocIlUZ3FF2UFNGWTm8lxjovOYwT0IRSokcE12/ZJgHqq0wS2SlWy44TJEadmz3e1abE2w93qOUloiZcpQ4kI4P9xhNiqIo311e41YW1y5IWRTcxiIHzmbNbjuxXPUydQ9iDJx211QFbdMzTluW/RqtLTc3N0xpFjZ1ivTdklhm0JWSMk1zyjTv6duO4bAl5+N7N060TY9fb1BKCtU3NzcopTE186f/7e/hU69//nfPIff3vffl+g//0nehXGE6TJA1/fnzxOEa7Xvm8T4f+/hPY+fK6a3fz/s+dMI8KKyX0f0PftN3/7b/zH/v5/6no1VLuKMmV8KcsI3hL3/NF6pcP/qV8PA1xXiojCgUljMKpNNgigAAIABJREFUbwDfG/MXvPZf/5mfYEwT0/7AOGf6psUtHY2z1DTz6vs+TEizQLGtou87Pv/LP8t4c8Pq1m2m+cD15Q15CvhVT8wQMbJmVZB1S5kCzhlKCHKj7RpyqcI3zZVqG4wu5BIE96I7IolUAr6IVUtbQ1ZQ60jNvXy9lChIUWKOEyVVUbuazDxI1tdauVBijBi3kCmA0s94nPrYdrZGvn4pBVPTkTdZiXMhxpFcC841og60hmqF56tixHc9kUKZMxrFMG5F3dxIKUBrCKXijRX7V5SmaihKTC2pYryjMZZaZkJVoDKN9eTqyPMkZQBVoEDSMtFVFblZeUfXai4fvk4+bHG6g7bBkBl3EYyh5kAcd/Qnd1id3WEOBzbr29xsRzlALXpqTWBkurFYrKg1M097Qgh03YqcJnIOzGlAZ8kA5gTtyS1pEscDxjV41ZBtRefKsL9mtT7h8vKpGIdMy/XuCZvFhpSDcIO1ECysVRz2W3zb0DYrgXUryS2P48h6scT7lqvtxbPphvNHxmw1jPOAoqKVIyk5ZBl31FsajVUNSVWcNqxPzlFNw0vvejdhGnjz059iePwaabpmDllyWvPIzdPPctadM3lHni2b932Q//1bvvO3ff1+5Id/EK08q9PbLM7u0Cwa9pdXOFcIh8hwsyOpilIVv9hQNdy9dYcQZh4/eFNWhrYnpEizbJnnEVU1JSZCSHinqMZCktKktZqIIYYBbcWU5LURM1c5FitLZgwzVINTFtO05DJS1VFTrT0pz6hksL4FCrbt0EgWkWwxTh7UnG0Y99diezNgaTFWJCmqKHJMKGdRIR0P0hFKoBxjAYv10Uc/jtSYcFaRcmGYRl563wcl168q69M7hDEQrvbczDuGm4cs2gbbeA77G2LMpCQlUO/X+H7FrRdf5N3v/ZBM6ZBJUXt6D1WylMtKBieWqTRcM+6uWZ6co2zH8OQt+rsvwDyRShbCgW0oh0suH3wc2/TsLp/wy//H3+F6f+AD3/CvQXuLxilCqayaU5ply+5yy+O3XmN1ck7Wib7bSBvfO2pKghw6PkiEmjAipJWpurIY25CCbMO8VqQk0R+lFNmCw0ACpyCpQsqFetyIFFNplSdX+UCuNYs6mEJJBW1kKlprplSJk1WylH5LhJKYY0ZpB0nyyjlncs4yBatFisRxwh4b/yQIYRIusDVYpbHWc9iPUONx5a45DNe01mGtaLHnfWR9dkpKiZQiplkQ5wnqSIqCbXRNj/VODv5WUShYu8TpSiqVw7jH2watNaZA1qBMgVBQGOaSnpWWlVLP9MHF1GdymqLeKR9nKSDVSswRZ1tyTPi+JxUgzRLrKpUa5fod9tfUXBjTTH+yZnkssjrfytfOx5/pPKNqwfUNOUn8LoQBZ3qG7QV9Z0B7WuOZ5oy1mjGNVDJ9tySlTLEepxRGKaIthKuBZtngm5YUI8NuoLOeajW6baUg3nhKFqKRVnLQ7le3YDhgnGVIQbjKYaKxHu0d482OMQldI0wjZt0zz4rNeslwdcl6tWJ3dUnbL3n85D63Tk/ZDk9JY2Vxdpu+W7K7eco0TaT9RH+2pO97lFlKZEN5Hn3+l2jPzinOcdZuGIeBxepUtNo1sx9uGA9POTt/jsM40Hcb4jjjvOYwDkBhHEWP7XuHypUUJoY4cnZ+l/XJS1IBj4UYBrGNHm44vX2Li7cf0jjN0ycPcN7QdQ1+eecZR7td9+SoGIeMKpnGa0oIZC34SYXH5cAwD8yhMt08xWBoVgsOYeDP/xc/xK++9rnfPYfcL3vvi/Wj/9X3kIqAp68ub/Amod2Kxi+wtiGrjhAnPvVLP8X7fs9XYVtQNVG+/3/kr//P/9sXfL0/9bGPk3SLMY7XPv0Z6rTlJ7/tj37Ba773F/8+RQs4vaSMUZWEtKu//yv+yBe89ke/Ap6+brjYZ/ZVXPRf2m74hema78tf+PP7qr/5l2m8pV/d4t3v/UrufcnLVKtR8wxNQx4O1FwwVhHmA4cnFzR9R1GFXAtxnighojGEPOB8w36/Z/v0gnkaGBPcvSs6QxUDMSbBU1FkTYFCaSvr/SqTIWMa/LLFFsgpgDJkJU9zuQ7UIGaSPM4UY4lhwDUtHCkHtSo63wi+yBqMlkOQ045hHrAYSq1SrCmZUhKu7TBV1iXeS9bTOE1OcoNWpjAHuTmij/ammNC1kCoY73DaSOHBapz1zGHCKkSLWd2RHCBrtUJE2xZUQtGgK1ijJGnvrZSqjJSiFA3aKmKJ+LZhPAzoogTwrQxjHCjTDqYdbukhew5hT1WZmhXO9vSmxzjNEGdShJIm3GINKdO0K0pJRCJN01GykoZ4VRwtjVQi2mrKGCle4PpGW6qSwz7KYX0k18phe6BxHaUUuq4jxRnrOsIUn3FYlZJplJQyFMooxjjJ5PL4PVjfYnVF4dFOQ4rEKpPAXMAQSFScsnJdHP31RlnGlKmlUEoS6YKxxCjvh/FwicoFrXtc0/Lk/ut0rcXpSJj3ONvy9PHrNGbk8f0Lnn/3i0zDQL94BTZn/J/f+m/9tu8Zf+RH/j5KgfWecdpSi8IqSHXCak9OlW61AC3/7+cY0Fkmc8p5WSkn4b8OcQYKnfMY60ElUjW0TcM4CifW6gZtLMRKqolcApgEBVrjSOl4rjOKHDOHuRzLLmIfU0bjjEMrERlkMt57iu7QRYxSoYBDxAs5RYxR6FLFmuYdczjQNj1JW5RoyITuESO1SgypGoupWg7fWpNSRsckIpOSSVWhrGFlO4arxzSrBWkulHnPGw/vs1z0eFeJQdN0DUUrWucJpTBt9zTeMU6REBKbO8/jnOP5V9/DatWzWHZoddwAaQspENOIV4aLz/0C7eqU/sXfQ9ldY1a3qHFPPT5wz7sdb731GqZmfvEnfwTGLb//X/p2Xvv0J4ljxvdL3GqDsy0pz8xBKCzOQokRbRviPFCLlWurVKZ5h0oTNcP+cIBa0VWytm7RsehPpN3vGzkUH7dYcxSqTAqZtnHPJqvOtEzzjtY10qJvLNp5UjzmV51M89+hNGiURGWQCWuOsiJPKVEoxJQln2g0FSNyCYWszWulbRrmIDhCaz2H3ZbWW/bjQN935GEQ9XMMomouhcZo5vlA2y/EnlgbqlZo7+naBSFOUixT4K3GOGnsK+fJOcpWrU6UKoVSYxRTynTOkw6BGgOxVFDy/gz7iao11cj7zbUtpVYqRTooxlCyptREPBxks9dLZIQUcc3iKOKI5FhAG7SyWG1RBMZB4nCZLFxvMq5o0JYQJ6zrmPY7ERkYQ3EabSyd80wpUqojzYGadhLFU1Bmhes60ckTQUesbrje3mBqwRmPqjwrH+caqcqijtvAm4tHbC8esLr3Iou16OStlcjXw4f3abzFIHGJqhu067Gdx3UOMxQSBlVG/MIy3YyUaogkvGmIcWLZ9WxvHlCmgUyWB1oSu/0lTbNic3KXoSaWizNsVcRx4GbYsj7fsFq+wBhGqI6l96QcmdNMrbPov5VivV5yfTMSdMQEOewXbUhDQKeJOSdct8Aoy3b/gIqVKIlS1GjRywW9aYmpgq3Emuh8xzTvsUaxu7ogjyNKHx/ujaH3S6xfMU0DWcsDnbNLfC+M6pQC4eoJ7XoNtiHlIrrxFElhRLmGNAzUqlifL/m27/5ePvmZL26S+zuieEaplHy0OiXH6a17XF48Il7/Am/+3Jt89Md/GjUdyB7+xT/+77Jc9sxpFmbo3/vYb/hyi7NzxpR5+uACrx13nv+y3/Cam+0FJhVSFdxFyUH4jvk3QoY//M/AP3yr4puOaRw5qbDzmX74jX+VfuFxzRKjPQ/vv0VNI6vbd2jXS/Q4klJiPAwsT0/QuiMCas70t8/RNRF8IsUDN0+voWqePnpA2y7xzQnPfcmH2F49Zj7sj+DshsYtmacBa0RVOoWMcZJ7LTXT9BtSnlC7QrbHldgReJ70BNriGs+cZpSCOgWckxtq369pbANFMkOlFNIwUcIFunrS5s5RigDaGDH0PBNQRGpWeNdy2F/QNgumkHDakGuiTBmlZXqOq1jrpLwQM00rZZMQE431TKWS44AplXDEUglnVhNTxdgWaxEeZNB4pzm9c8ZmsSDELeM448wZuQSurh9xsrnF5cUVYdyze/qQkge6rifNgaB72n6FXjXM7YKEZJFN0xLnkcb3aCN4nGFM+OUCs3SUFCAkiq5UpUiqYKyV4ooyVAslziz6NTnPOLdkGAb69YI5JRFvKEfrO6IXi5xWM5kqgOwYWHRLdLFo79AgUz4KOWmoMzmD0hbn3VHyAH2/pMRMf3JCmgNzHfFKMe0OdI1DFYV1iLmuVhSaUotMfciYRcuw39P2GzloDYmbB59ks16BshymwObsNofrR1gXoDpONg05jLhugdqsyduJe698OfurR3zphz/E0zdfYxsi+4vXac8y3/jf/GV+/Nv/nS/6dvEH/9u/hHuhcrPbsqDFaoW1ju1+R9to0OAbK3a8JGtKk8uzlR0kKolx3OGsF1a08kzTgLEJ23h0rUxXW3Tf4KwnTBNaOfr1mpwmpqigeKrO5CR2tVAc2oBdLTg7tehSefLoIYvFihIL1lu6/pS795Y8uXzCol8xh4Fxt6WEIqvraUI3DmtlIp9rFO3lnDDKEKcoRcJjtEHhMNqK6z1mVImMU8RYizo662uOzMUwhpGaFG3bclW2aDUz7mZUMTgFd59/DoVDW8OqZsKRoUwtNLbQnN8hxMztczkEVBxaK24e3ad3r7KvE20r7G/rDaBJc6I5u8OtD3wDNQfICbu+Sxp3PH54H6Msr3/2k4T9nvWy4cGDz2Fci12ecf+tt1l1joeXb+L8C2zfepsRx6Jf0/SngCMmQ+taYsjkaqEmMArTNngHTXMLNRXWdzVxuiFlSDXiXUeMGUImlYFxL413lKK1DXOSuE8uEGOilkCJE0Y5QpgxyhDGiTJNRz505bAdcY1MJiUSIIUpqzQ5CRMYDVq1UCecUaSiJEJhFKo6ITtoQ5lnhhRIaqYGiCrQdC2Q6Zc9efh/qXuzWGvT9DzresdvWMOe/rGm7qqeYnAaJ7EtkCUbIgWJKIAwIAtFWCAbIZBjJTjkgATFEDFEWMKRMCAlAiGBTywyEEdxIgIOjoQTW7YTdbfdg3uoruGf9rCGb3pHDp5Vf1RdHDhn7X1YWtr/rj2s732f576vKzBnsVTVlDHKUKsmZ1kNGwU5QdKBOGdW1nK3uwEyVjlCmChtJ4xt7SAGSspyaMmFYhIlSinInMQ/GNBYbE2UqliORxrfk3ImhoWiFMfhSNUG3/dopalOYYx8D/RmJQMeI5NPaxWJglYyOHJdg0qWnEam8UAlUWvm7npH2zSExtG1a3LNp/KzHKw3F+eYWogl01rLsCxc72/pmk4Kgc7ilAxgjPeYzoveOhcpYekty7JwtjnHOs8yH9icnZNiJaEgjy8P5do77jWPePCxT7KcLGrxcCeWMaV48PgRy3SgdWuUNXI5jAuqZNRRDKKt9+iTjRITGfc3LNNeLJ6NZjy2rDeX+M2lHPqtJVe4qokQ9hxu7iTLWi0hB+xmw4OLK7QKLEnI67o1TLOUiW9vntG3K5b5BusKw7jG+zWdNkQF07KIsMQ4jtMt9x9dUqaC9YrKBf1myxQzuiSyKehUeP6NLzBNO0qFzf3H6ItXKGSGeabbnlNXl7Id8dIXarw8CxsjAxarPRUxzu3urnG+opsNcwQ1zoxpZnPegdZs75+zvzmwvjgj5UrEgPqdH12/LQ65Wit0Lcy3R9aXl6SQuPfGZyjldT7+scr3/as/BHrg//zbP8eTr/99frW8wqvf/SZfXw585rs3wPWHPt983PP//PIv89b9N/lL/9Ufp/vCMx4fv+UfzTNae+oias6wLDROVHbf+vGj//of5S/8Tz/H426hcZqSFP/W932a/+JvfNTWdHVvi+tf49XX38C3a5q2A6dJc6boCqXSdx1pmikq4/sVaZ54/7e/yHDzguPxyOp8Sy6FME4o55l2L6BUxi+PxDJTSxRmq5WLjNMtYRY4cimyUmhbjwKO189YnV3Jai1H0eKFQDWZWg25DISpEhcxqGgD07TIg/nk1MZ7tPaomunbFfPYygGOCtqjSiDJCQttFNhGcmQKpjTSrLfUktDlhCKLp8OY1SinKEbKDyVGqAV1Asw7ZQl5wSoHWjNOI33TQ1V4JxM3fyJGKLum5om27fk9v++7hM03DPTdI86BMo/cvniXOu/ZpZmrh58hvfM2l5/+JMf336cSSIcB7StpGcmmonRD61f4XiIOnBVKroQaKEHRNqCto6goDFODTGk0rNqtcFi9TNrQhqw1y3Akp4nrw3OMV+xeTJA1Vp9xfv+Cw+F9xjDg3ArjN4S54lslpYF5oBRYr87QFFIeiKFg3QbjNzgNOQzMsWJQONfLKtVZasqgF2xSGGt4+Mqr5LLQpom+ucR6w+17X2OuO8ZpJsfC2vVEBzZNbLav8PzpV1FFce+V1xgPN6iSMa1jCQfoNnRr+Vmw3XDv3mMOh+eUYU9z1TMPM/df/RTjtOeN3/fPM82DFKuq5uzBw3+i94vXP/vPMR7veNjfZ44jusASAxdXl7imA5VwtiMUaPuWMEykOLEMIyiFcoqwVN78jn+KOVTW/YYX771HmGbW6zXDuCPlAlVhl0JVGowCCvvdtVziysLxxXN8uzqVaRSpDCcs4IH27OGJlVuIy5GsFHFyTMf3eP7eTCVwNNCZhlw1WIfNmWHa0bKWQ1HjxE5UZVWeliAXo80FxkixpJQJ4x3GaZSysnpdd5LXzRqrFSkZFgqNbVAGbNMTp0kmZs4Tc2K3O3L/4pIhFGrOhDlQdBKZSAaKwzmLbxQhJJZYUGUhhchm1fON3/ocOC3yB4dMSw1gerT5bZwRlFrjO47LAfLMarsBoFl7avQM+4ixLW7d0xvHcX/H7fOvkeKA9VesH32SJk6Mw5G0f45BUYviaL0cqotC50rMiXQqmMbDTi7fywl1ZxuMalHe0JpC5xvGZaQ0+RTBMpQ5gKnoWvGup2kaYtQvS1NGSzRAsrlgcqUYRdMCOaK0AlXIShTFS8k43aJNIS0VbTM1W7mo6gxpoWRFKpZCpFG9CGS0xhVPbk+87mUi5HQq9kRW7RmxLPjWEEOiX/egWubhiNIO7Q1ai6o1FOGi29PFu9uuTpILmdbu7+RnMpWJHCvT3XvY8yv6s/vkmOj7NVSJtKkPNOKbc5QxOIDQ0XTtSa+e0BSyWGfkMqYquSR841FKEG3WepabOxEpmMrucKRzK5Yl0bcNc1yoJXH14CE5LKi2FWKPMZgTI9wgVrclTCIPmRM5Ct9YlULbaIZhIMQZ1xpub3eoNHMcrmmcYtmNMs23im7zCO9kHb4/3IlgKIHzPco6eUalG0oaUO0arbdoFSlhouk2tE2PO5WwdaPIp4upMZHV5oxlfySMM8enL7BnK5xztKuWdvUaMSka7VnCgLKaaX9k3A/Y1gGWfnvG4XhAKSebWdMyzQM5ZHpnKXXB+xVhkG5KngfyNFFM4eHjRxjl0eYBOQWOw455/4KlVNb37uP7c+oJAXlv+yolKrALh7sBbR3H3YFUEvvdNQ8fv4FThsvXPo62lhASh901qk5oHLXCUhLERNdu0FaRamKaInEMUI50fsWIhuoYr59R2kzBs9leoEzDXArNeMft9dfxWObDOedXG1JayCVzvAkofucJhG+LuMJnP/lK/fk//+9zdn6Pz3/+V/DujMdvfYqYZ1KMnF09IJdRDlvhjJ/5xb/Pf/5nfxoHvN6d8W+PH+bkfu9f+mG+8ov/N3/v736T9vIeD77xgvW3UMZ+5G/8tNAIXraYNeMcaJzhf/nBn/jQa3/45/8zfuTP/ST/8DcqF9uGNy42/Cubj/HuW3vWP/dhxuef+ObX6M+umIY70hJo+w2qFnJZyNXIGt1aiipMu+enKaNFu8rbX/6C4GXCDmUbvFtTlSXXclr9V6iapm9QNVO1gjRTkzTtXetACfdvmXcYp6FK87SkIGWl06En5wxmIS6FtlkTY6KxDqoVPqE5gd5BFJ8pYqsmYXCNrEvrSVFrrSedDF7GOIoWM5m2QkeoqkF9QEuAE48vUPJMqfbl1KlmUKpiNaQkBYhqLNY1Umxz7UvXeMoBayooQymSLXvwsbd4461PEKcjtUoMJS0j83jHzbtfpd9saTrPO9/8Otv1Y548fcrZ/QdcnF0y5oGyHxmnQI0T2WqKBpMjw7Dg3Yrm/D79dkOtE6Y6GmXYHW+wrnJ3vSeNgc3lVh6W1pJTJIZCLBOqwvn5K+RwZH/zDtur++znkRaHsYqzy8dMc6DtO2KcyTHTtS3T8Yi2DcPh7kQv0Gi/Yn93izMKUwu6OWe1vWCaTiiZVKm24IzCdy0pZ7xvOX/wiHvbrbCTjSYvd3zuV/42K90JZP38MZePHvHi7a+xTDPhpGjMKVE3KxyZ3jiy8ShjKRXOH7xKmEfapiccJ7IB54XGEZdEmI+Mw5HLy1eoJdH6hqVmSiyM4xHf9UyHGeU1aR55+OrHyARChHXbcNy94Pb6Bq1hdXaJMpp2c8Uw7CR7mTMhjGJbJUuUBlB4qpO84MPtGU9vngpKCUW7akFVds+eYFVPLgHnO0qqwtRMIrMoBVmlxRFtWrp2Q60iV2hax7i7ppQkF0hTpPSxOuODmL5rRNlpTmU1shT6tFWUFLHeEGOlbVsKkml33qKzFC9TCqASRnfEWqhaUVLA+o6qKqqc1Nr6ZI7Sp4OYAVNbSg3o0xTaWoVS+sRBHTHaM+yvaVY9RQFZJrBLrBjrWbmGMc+yNchyaPuAJqG1FlmIdpSY8E7oKDEuGGcxXp1yzFKoI4ooQax/QbL6KVBzwRtL1kXylEkuwcYYLJnD7RPa3pMrnK0eUJsV1QieLqVMDpHGKCleNe5EdgHnZPuUcyalmTCll6t3XTXLfCM5SGfpz+5T9jvC9IS7/TVZa974jn8WaoM2jgx441nKgleyWq26EoKUZ1gqqlZqLSid0cqfokqJKQVscaR5oXGerpfsbXUKf+ozKKUQO0RGe7mEpFTkoIJcVgFiDMzzgrKanAutOx1YrccVA6VKZjtX5mkiDLfYVnLhCov3jikEQJCAJWW0UdRcUTiqSoJ7ypVSgzBLMyglWx6lFEULNtBZhXKGeZjZrNdCEdGOmqSDwak0ZVpLVYm4JLQqKGcgVZZQaJse/cGBPQWysyiVWaKi0Y75eKTpHTUG6okKJAsThe0a0GI5c84L2rJEQlpEbmEtOUOKM43RQoHIma6T/Hff9KR5oiSorRWWSc74inQPXENcJqw2lCjsYmdbsjFkJiwNJUamKeCcxjaNYDm1Eua80fh2hVaWphU5zvNn7+Jcx/bskjAsVJMoMUgHxMGLp29DMbT9Bd4Z5rRwcXGFfhmfgX6zJZxED+RC0Y40Twz7O6pWeFuwy4rubMVwvEN5IyKSYcabBmWENLO5XLOMI9r3xHFP0664ffqEzcX9l89x7yzzvGDxFAVnF1tCnHDWU4vDGiFYxCJltXyKZ8zzSCmJ1m2p1bAsE76T2KPDskx3LPsjdr1BNz35uKfGidt3vkl/vkatenbjkYePXsNrS9jPHKYjxhU2l49o+y0hFn7kx/8kv/Wl30V0he/6zOv17/7MT1D7cw67J6z6R+jOEMNM1R3KRtpq+etP/xafffYqY77gU//0a/zYn/9hfvbnLX+2fljx9of/+g/yV3/6F2ifjNRXH/Mv/sCP8bf+9J/+0Gv+o1//y5Jrs83JThLIZYKU+R9+4Ec+9Nof/j9+Cr3+Df7dP/G/4uwFrq4pqfJdbz2k+asfjkv8B1/6R8RTiSHNA7kElHY0fkUsGd9A05yhTebm/fc4vv8+w5LAWpxzhClgG0sxipwqjXMEikyrpoGYK0YpFFGc5nmk5kIqwq/t2xW1SIO4FigGmqZhHAb+3i/9Qz7xyps8/tRK+FoUnGsxGDKJHCJUoSKoJLnYUCLuNFUIRYoARomJC+uIp/ayNR14S15mwTCpk+1JyeEjx0JWYkNLcZapL4rmtLqKdUGpglIWqKQia1ljGrF4+Z6YF0BBzqe10kDUmc5tJUeFwVTYPnrEx958k1wiIR4IxxuMcXz1S7+GZ0NxK1ZnlycmY2a1adndHgnTTLe+Yn21pZRC6xt2188pIaGM4Xj3hNfe/BTPnz9lGWbOH9xj3O8kG+k1BceqP0eVypJnbm6e0XqL0R2pyoO87RuW40wpSUgSKlOSXBRk4qGY44ivDpCWtjOGOUU6f0bRYlyjKFH1VkBl0GtKTTinWErEK0sMgdZ5Xjx/D2eklZxSIttCCtD7nn5tONy+zeF4h/f36NyWWRWWEunaHl8q/vwVahGtqO9XWGWwbSM/0/mI8r2sA0mUAMs4UJS8MQtLck1WmrbtuX32PmEeMF2DM56SCq1rSaiX2bB5fIaKFutXuLahW3ccjjtKjHjrmYc91a8FFTSNoCJkwew0nQDbczFszi6oqeJMJUwTwQiZoNT0suRjtaFkB1VjOyMXxDhK6dJoOZQljXIWp6Wlbq2lak2DrJeVAeMMMVso4YQgkmmf4PkU3ngylWE40LYtYT6iqNQsGWd52GlyDliVuX3+vpRXmp5aDNZpDuMdftPTsBWcTtXUMpMW8KpS1UmxbQGliTFQjSEuiZVrGJaBrusk46kdWDkgGityBGs9IUVizjRNh9NiJKvOQMmM0xGD2JyUVRx3Yq1qrBRCrRYVNMjlACOZQKqRy3XjThgnTYqnQp8zUA21RObjgXF4ijW9FCXVQgkLNhuSbeWC5yymWLBKGNzKgogVKaUI9q0UkqrSR6gSY5rngFWasAws40KjDMtxR84HLh8/xq864ijrXtutWYYR64RRjdE0/QWNblHOkstCIVGJpHlGkURXWz1LxI44AAAgAElEQVQ5LthGJtSxSMbWWyNT/apIp8u/tRaD4JiapkORGQ63UOX3RmsFxqCNvKfFGCimI4ZZylXOkYusyEmJlDON91RVUFWwdNo3pFoARQgLdRlpGkeZA7FkrBMElnItxjm6xjEeB4pRpGmhaRqs6wTtqFuqgmVZyCkIii0V4cyuWhrnqUlRc2YcX7BMzzBqLVg0FNV3mAq29RhjWMLENB0wJWHbHudbtG1xToQUGMFOCuWusMwTTnuck7/RouV3d1kWWi+sb28hpplaNEYrco7srl/I35+34AzGtrLdqtI5cLYjpMqUZnzfoIsSoccy0LZrrq+f0jc9ynim8YauWYM1LDWz3W6l82El47+kSO86iinSAxiOorPW7h9f+HSW9/Qiq/oaBG82Dnc0m4bh7sDu7XfoVj1qc3YS/mgKkZwT1nfCMm8adFYMw40MiTS4fi1Uj2mhYNmeXTDPu1MHpkPViMIQYxQde9fjiiHGRMwRpWSYU23m5sUTIVvliqmWBTGP5RRYt1umONC2LbnAYdhxd3PHx9/6xEkZbVjmI04pcolC47Ae161OtJFCXTIx3qKKIYQRnQ2pKFonFJKoE2UppDmhGsf28j622zDPcsn+0R//U3zpq7+LjGclZ778m7/GK5/4/bTdPYo25FhQtkMXQ05QfOSv/eL/xi//0mP+y5/6r4kcuBsq9+xj4O0Pfb4X7z5H58rnDdxbnvDKZz7+kX+zGk9VmhA18bCX9V6cyeqjcYWlVszwz/CT/8nX+TN/7stctonZaP7Or/46f/hbXqut5mJ9KSv0biWWm7SIjWiYKDM8P3yFMO0hRlIq4AzWC6Nxdb6Vpm1VKJ/JgEETy0LXtXiglIxVjTR21RrbWIyRlm9OlbY4AlCtQdtM+dwX+Iv/3U/yCzcX/Ev7O/7Yz/4s88pgrYZTlslosVlppVBKzELjPNE2G3KcUcbhTBW8Vy2klGmtPMhKreiSiVPEWAmRO2+ouZCVTFs/WPXlsmC0PvEuM8dwAC2r4NY6Us5YKzQJTm8EzjcypTMGVYUfiMpUMrYaYlwEtl0tm3tXhKr55jffZ3t2zvEwcf3uN2nXPcfdNVYdMc2K+XbPYiTf++J54Xx9wXLcYTC8/eRtDAqtslAeqmK9OePy3qv82v/7N/HNQ5qm4fiVnfBF9cncUwpP61Ocr/Jg1C1xKSQ1SqmyXROGRaZ/ZCBTYkYpQy7gT1zPtW/ISQoX+oQfWrWaqgw5Rhq/RSlFXALedaSaUDpTYyYljUlZWIbWkmuUfFmBVCqrsxVxnrErUVDOcWF18SbnD1sO+zvyNItRJhvatpeHtlasL18nDHfsb55hthcQeymSxJHpxROud3dChCjQXVyyeXCfe1ePefqNr3BcnoE23GK52F6gtGUJC1O4ZdOeo/otjfGMww2pHDDG0W16jseRJc7yhtm3BAKd9eSqWG5f0K7P6bsOpTekFOi6Ffu7Z9Sq6DcrwRspA7pjc3nGfriTDF2cMa5lGSeadU+qGRCGJsrgqChbZC144muWUggpS1wGqFEKnNZWkQyYBqczJTqaxhGXxDwOsup0nuMkCui+7ylFpr7y97eiZLngCVDeQyo8evXT5CIXyYpM9S7axyir5FIYgzBTvcfoii6VaZxOD1slPvpVD2ic1eRU6NYt2lRUDJRc8KoTO5fvmJaFXMFoh2898zQLWL6c1s3ag5FiUazpFIs4x/ctNResgpgElSTrfCuHIwxFFWotgj6KSQQvpeC8ZVkC+RQxsNrQr+7LGntchCShPKaVw08qkRwyMUk+NeaMbRw5ZbT2OO8kGqQUtSSUMlQgnlr3OSfGELHWUbWhubzCuVeIeSLtC/vnt5irNS4lmr5DKU1rPTlX6rwQTaJOQaajpsUqg/GWVAqpZJKqdOdXkAFV6VRLrlIkizGSncNpuQAoZSgkum4DJVONo9+IithZQ4mJWAolJ1zT0rqGkDKm9cQoB2VVNdYokjPoWlnmIDg7rdDeURQ46wiTDAf8+hyjNM4rbJXNmrZeEFjzwjwluv6MSKbxK1LKLNMiF5iuknKk71u8XgEwTROrds2SAmEOL8tXfXeOd/1LtF2JibZt0F6KaDkkrFtx3gvxxTeGZdhT0kKYFqZxIC9HsvI8ePBAIllWLjP7/ZESZrqmRbcdfSu82FQyIcjWI5VEroYlzFw+fAWl5NkaUkGf/q6xWihDVeNUolTDfDegTSUZg/ct0+EGXxdq8SJTWq0oVMFk7Z5y81QOsb1tGaaBrl8TlSLHQApyiZ7GI9b1wjue9ti24/rpMzbbc/rNGpRm3N3QNmuWQ8J5z4M3X8doTzaGtnHEaWIcF3zXc9ztmIeFuSrW98/p1hvmccGmxOHwgovHD6m9xzUduihau6HkQFWKZYpMwwvOL65Yd2u5SChN7+RvWtPLJWZW3H/wFs62lBqJ84LXCmsU43wgNoBuyNpQlMG2a1576x4FQ+tWLGXm3uOPkcYJVCKmQt81LCERpkQmolKAMDINBeVafNeT5pndfuDB40eC3JyPJL+wuroUXF3MhPmItl4iQb/Dj2+LQ64yjje+8/vBWWo2pGUBa1Bpwfs1qiSmQfFT//IfZ/8DWwZrMHHLD/1r38M/+MW3P/L57NpwmzWbS4PaF5r2/COvuX3va9LOB2xWpKzQtpVYxLd8mO13UhrNK29tWG//Q54tV7xx0fKPvv5RtVy/PSdNO2q1dJtLqhIQtEoFv1nz7Le/yuXlJfNoOdzeUlUixYlsGtELjiOm7bDWkGPFGkNMCWsMKQSiipAyWZmTk7yyhAHfrkV24KzIJWyDwxHSwq8s/xeXr8Jf+J6RT57/IXKdKdERUjoVxAQJpbWFItzOHCK+WaMqopgFLE7MZ6XQdp1wIZuelGeKlhWjqmLaynlBNZ4SkoTsrULVlpwXtJF1mVKapmmE/bgETLtC5RNTMGVUrVA9Mcu6T5A95UTEkMJZ4wXtUpSUte6eP4XyHk47vpEjbWsh7ZnmHX3ziGoETxLaBVOkHGOCYtgfTtOBEacjuSjW2y3DeGCJmXEYmKZbLh98Bm0yyxQxGPr1Rhze3rAsMiHSWqFUIacB4zwpFrLS1OFI1e7E5ywobSSfrDQ5QykyUTNWWv+likVGG0epiloqTbuGCFCxjSfngDHmJSnCYlmUYbs+l5Z5hXEc5BJUKrV6rM3CvNWwDDOL0gKtD5pmc44yLW2txDzh2nMO+1sOxz3uBFMfbm7AHqgmSwylaTl79DrWCY3ArFrKUrl+712Jm/RroGC8oziFw2H9hrW6RKvKk3e+wL1HnxQ9ZnNGXGaOwx1dfyH/rW+YY6CxDdMwo23l7MEFMXmcb2SikzJhGLCuY9U4ilJ434BqcM5xnEe0NdQi6+ycM/1mjVYabyTyUk6r9FpFOqCUehknCPMRKEzjHmMaUYtrhbIai9A12u0Z092egkb7jnXfk2JBq0q/XuGMJ6QFVSvWe3TVaJ1JytH0K0IqGO2pJskDhEAKAdV1GONxqqUoSGrEGYsymgISHwoJt5LDR10WutWG6XBH1zjmuBCmG0JKnG0eygpeiVbY9yuSCmIQqsJPnpaAUprqHK5aQZWdCo6hVrS3lAjOIN8PpYnKYKlonKDFLKSq5HfQCNJOVu+KGIMgmYJkJ63X2NrijBYhjSqsrRwQKZlcM0pH+mZNzrCUmZINthHOcFhm+pWsvbU2aJmtSpnLVgotUFFR1LTzPOJsg6uZcRxR1mOdxd+/xFtHNZDyKUqAqJ5zXrj36Iw6H3n3y7/K2Su/l9W915gXhXUGfepzaO1OF6lF1vhUljDKBTANZCxWaW6un7O+vBTVecz0vvsABEOYZtJp4t1Yh9KWkjW1JoxxEsdAtLa1RLTrRSHdW1pnCdOMa93JIKlptKPR6iWHPMYFZzTOeIlPZEE1emcZ5pG2W+O8o+00sZUtqVIKWxoKlZALjWtxbXmZEdZaWMKu7XDKMquBOE0yrSyBMhXa2hKWIBSJYhiXQS4EmxXVZHnuzIntdsu8NPTWM+wPJ8KL58WTr6Gdo1+tuH7yTdaX56SEyHTaExf58IyCiI38asO4zHirsW5F5z6I1MgAZjwcKWEhhQLU04BHVMBxGShFs7n3ScHnbYV565zjcP0C03jUUtn4DTEmjPLMc6SMM5w01Sp72k62vul4ELIRcPXgMY3TjMsgDOysmYZbxiFgUmKe71Be03SemyCbU90oiNC1ay6uHpGtxlhF4zuquqW3noveMx5mVBjIceHu5g7tO6abpxSvaNcbmq5jt7vFuQ6jLONux+r+fbS2IndQBY2orUM8SN4cuUSqojg7uyJPA3p9RjlppC9XW2qW2FbWmU33EGWrEIWUw1nIytKfn6O1h6TIJaAbQ9rvCGlGWccaiXtgHbbv0KutbNpOAwhLZLsx2MafCsS/s49vi0MuwJxmWBJaeYwyeO3ljTYuZCRvZut3Et2IN5ppnlmd/37Wb3wn8D9/6HM1RL76TuL4IvPw44ovfvEd+CMf/vfM/c+ilcLpStUOlz9Y8U8f+dqe3v4mTmne/fJXmedzpv2BqVuxzBZIH3ptUxR6dYFWlWU64P1JtRkLOUS6TkGduH7/a3TrDSEX7HotWb+QqUWxjAvRWIqaqMXKpDMVYk2QM53fYKwWLqWxFLXGALFE8hJo21YEuTbRmZ7xfcvln/lVhi/9Fb78W1/js5/6DN3+HcZg0WpNVeXlL00us6zCuzVhWahVE5aCIlGV2J2UUYQcIWhcqyhoYCaHijZeDjrGkJaBpuspJaKrQPBlzTZTrTrZisQgg82kZSCExGq1QuUTW9eCzp6sNCUJ0DwlMcEprYlpJMSCdw21BCqJMs3Ek6FIjEWGsMzY1fqUOQqgMmHO5DxjE/i+kc79cZCHo6vcPLullhllO+a0kJKmXR1x7gzXtWhriKVi1x1QcEUOpykHjNWAIy9HClbyizkzl4XGgW1bIS63Cq09YV6otRBrQCVDTAvOr3Hd6ZBbCtRMimJ5MlWTqqaeCkW6IAD0k7lnOg5ok5lmKd54v8U3HoxiGCYKp/LIeg2qyEVKC6Ui54hyHoVlnK4pcyArxZIzulSq7qhLwHctNUmTXvmWnAoLmnO3ovhKPkoeu9aBssh0dHc4snIN0901lYjRDVePPk2OJzh/ymzWl9w8f4fiAzFM3D5/G93ak6FIoh3TMKKmZ8y6I097SrMGY1CuASOTzGnc4aphPw+4foV1mma15e7uGSobDnc7Ls426KY9wfFlMjrHRFFa5AtpZnfc0bgWlRON98Q4k5JsPXI+mZV0YXlxxPgVtigilRKkoTyPszTU1YBpeqwVXFMlcnd3wNsGgxeknEpoK2vqlCqqW502HRCTfD9LlZ+RVYbd7o72bE3nGrRu0GRKFrnH6uKKNI60rrLevCWrd+sYb57je08uGecghIJuG+EEO0VrJKesqkhRUpwwpiFr0QGUXLCmAVVotFj2tEqUotBK03mB2XfWE3NBqUxj1qQYcdbhWYn0QktUSaVCTCPVWRQaVRaWlKBajFWSZy6eGAIWYRRXCmk5sWW9OSGrNJCYlxltpPDpVy0qVUqOTNOIUZnWtJACxlua04S4nuQeOc7CI1dSjL1++wnHMPK93/8HaXzB2Td5/TPfyzTs0LZld/ucb3z5NxkOL9i0G1S3ojm7R8W+PMh3zskaWXnmcc9cI9uzhmUaqdVgG8txGjC6hWRQGkoVk5zSFlczIQa0hlwTrlqKcigj/99GVVGsZ1A603YiEFE5MYdI23oolRyF25uV4Ax1qixhwSkAi/WWlT8ZLWMinkpjKSW6pkc7BSc83TAd0TpgbEuYFblGvHWEeSBoR6kZ47wg2JTiOB1ZpgFVZYuhykzNkXbVQQ3y93HcsYwzYVYY37OkhLEZ28jX26x7jjc38nPtLGEcCeFAOO5wTcugemIO0g+YA+s0s4wTSSXWmzN6f0apM7txYdNfsV51RO/BGsJuwnXyPqB0wLqOtjGMw0KpmZgyOJjnGWMtNSk2ZxtSjBijCUmRw4xpDCVD1g6tNEmD0QX6jvPVPckC58ww7FivL0nGUvosBje/MI57Hr/5e9Boqm7RJ+RfmkeGuxeUati/eA9jE2lWHJPi/N4Vd+PEWp/hu5bce1SNXDx+ROO2LI8fYQigHMfjnqvLB9xev2B1tqG6ivUWaiUgGECrNOXEuJf3uSyb2XmhloXkHMN+QHuHolB0oWQhy1ASJR6pQZ0utZXj7oYpZTwa6zqWkqgxMNw9Y92scaqCUyjtqV2HW/eM+5m+XTPFwLpfk2tEOYfOwuX/J/n4tjjk1lLpm5YlLRjtUQWyOK6ogI6KoiTcv2pbxsMNJS8U7vGJT330UHq+Nnz3ZeFLHXz/pnL3a//gI69JZHJOgm2ZB7zxpDIT/3/oCq+/8d3YamlGQ54rD++9yXw8kuaPTnL/2A/+Rf77v/knSWqg15UaCsPNC9arS3Aes7ni+ulXefD6myjbcOY8y3DEVC2TQL9iGG+ZhpmE4vLqIfvDHfO4p297YskoeypAAPWUvcxWEVOkpoLW8nWVpVJcRF38If7Trzi22z/KL/3QNzm8eBt9um5W12BqkkmL0hjbsYwju/0Nqmq8afDGkItCG1kHLssihyvdkOeE8ooS5I1WVYP3+h9n7+YApiDMGE+a48tVbdWO4pDXUDHO4I144wsGZTwpFMG7NY6UKtYrnF8R04jTDWG6w2LIS6AoaNoV9bwhLzO5Cq+yFI1zDcsiWbKcpYGrtaCatNfElPGrLTkfGY/P8Y0c+rv2nFwTK7diqBNpnrG1YUmJUJQYyGrGGY22DlMUFUWaJcOqjMd6hzMOXI+JUQpCeZG2tUKmC8agCOQIY864fiN5ugwpTcR5wbmWkjJLmnDGY7seimE6jtjuxAK2Qk/wpynNanNFUpUUEoRA5x1du5EHO4kQ5OvcXp6jWUjzQJoWCD14xfn2kqmZ5fJZIYaM61akEHFeYxspvIVF8sBUx3g44qxhTrOggVSlasWwn/DeMs6JptucMFhIZs0rck3EECirSy5f/w7CYU9h4P7jT1KNZNJX25Y0LZicMXZL27a028+wH/ZiDzMKUma/v2O7uWIeJ9qVTOzjMrFMT/DesDCxvegY5iOtNhgl9It0HOm6DWM84p0Rnq3rwBrm/RHvWzESFVmJe9cSVUWritUdlELCQs2EFKnV0zSd/L7HQCgFNMxhoe9l+qQwuEbsabJNSRwOe8GgLTO1KmzbEmOk1IUcjhjdMy0Dl9tzFlVOOlpwpmK0lplpkguSVpo4HyQ3qjSb7RZbG0xnyaXStecvH2p5imAyGMn82gY8MMxHfNtQspAkUOUkZtHUUgTebiWrn1ISK1yQVn0xClVAIQWplBdKVWiUrNa1FeNUThLHOOG7rLUS2ypJJu1KU5SSfJ82Jw6qHE6hyGW5Ztq2I84L1MoyjKjSoHWlb1aM044cZxrfMgwT3nWMhxtqSYQp0PctJTlCSQzzC8J84P5rr/H+O+8yvP8uXdtwc/M2KcycP34D0/Rs1mf4ds2SIo2x+FjRveVwe0OJlTFGvM0ssYCubC7PyLSsey1/h1noBUafDllKIe8iDlRhf3fE9y3GSClvmSO5LhgtF41cKyDc6xQM2sok0xhL38shEyWGM28dThWqAkNB11PGusLhuBMZRogv/2ZBrJFLKYI7O9n9nLEUBaoq2t5RS0tO4USvUMwxEOaJZSyi2m47UlIYq5mmifWqozGejFjATEwYt6a9OJdoV+dOEpOOEiLT8QZrDOf3H8rl31dS1JTyAPNICwLTyXtUBXLMGKvRdsB4h86asB+prcXqnmGcUUrjnMUmi2+d5LpVpaBQVYYBMRScF/ycSobD3QFHYBgOPH8vopYZrGJ19QjfrTnrH0I17KdA23mccxQSjTVQHVkFXNcS80xWTi6LTlHyQncSRVjXYRtDnBQxZWyNmBI4u/8GORxQWgq4Rlm285GcF7JDpDvxSNu2jIcBFQNhK39D2zOJyq26LSUlrq7uk0Oh0mBcz2F3SxqOTDsNZLZXZ+RxJEwTvu8prmEZRqbdjmo0aEO37ihTYTYGVg23b79DPLyPsoqL+x9ne3aPuURs13PPNqRxTy5BJvTTnlXn6BrpATjTMoaB7mxNLYbt5gynLHZRzLsDS5rp+zWrzZZaT43I3+HHt8UhVynFMAzSsA4HvF5RpkAtoLVGaS9v7nBqEgeWEnndH/mx79nzG9/y+R52f4R/5z/+F7h/+dt88ec/x8Wl49e/5TXNzRdRdoPrrlDKsD9cg06iFvzWry8EQp6wyzWdMsThwFc+9w4v+Og3+t73/QI/8+MdP/rf/Bvs94IcO7+4IiaFz4nnT97n3iuf5Pb2feo8ML7zNhjN4XAAYyWArxSYjNEdN9fPUcrg255YC9b1aHPKmpVMXhYKGWqLdyug0rY9MQ3YNlOy52Kv+Gt/oHJz2FNtpbWObAzTEvG5kLTcueYUaKqn7YTl+sEbLkVjTuF63TQ0naekijMtIGuKYrJMgowCBfqUZ4sx4rxjWZbTd8iKCahocs3Y6lBeU3KmlHqyQgX5epYdORbJnc6JtrHEeRa4ubUMxzuMkgmOW3UUBfM04ayRKcec5MGqFY1v0dVQlSEVaYqnslC0TEQ0DcP+gLeVdrOh8aKANdZjVUclsvEreUMqlloyvfIY5aj2ZNYyLbUkbNWY6glxovFbwavphnwq7MRc8CjKkCjWCC8wzSxhpOlWNF1HnDPKga6FrMVqZ43DdS1tbgnLQBiv0aXKCnFo6VctMSzkKA+ieRywTSGfxB0gWb+cs0x1aXG+4403P02MB56/+3lWZ5d4F0lFLjPTfiQow7oz6FiprhKWEV2hzoqsFH3jWVSm5MrV4/t0XQcpUrHs7m7wTqQETc341mGNJ4TMnDJt35OXmRAy3dpycfUZXjz5BiUs5BwIOZGSFoakd4TbHZvtJcvhQLvq2U9HXuyfoIymJoUzrUDIU+X2+QtqUbhetiFNuwIHJURM1HRtT98qcs2YpoOisMoyx5HONri2RUVQVZNqYnVxD6UsXks8pFax5CmS4LxqlhVwEQyX956aJWsbpixZSGXIVaQAMQtT2TpDCAGrHaEEHJpm3Yta1pqX+l5VMinPrLdXZBTt+nTZKJq278g5MYwHuQApyds7a4WcUFswhvGwoHQm5QpRy6XPaOI4ngo6rcSZCpQsv6vOObyxxOWEZjodoj/YMFhlhQWdI7Fm0gd5z5O2uBQxhmmtUWThGCuRuRhrASXWqFJRqmC6Vg7P4SSxSBXnDMRC1ZUUI3iJoWjdEZaJXIVnnZGJolLglT7t2RIWKc+2/YacE05/UAyEzcU5lELYiBSn6sqKBlMfMF0cUBhymrl88w3G/YGH9/8A03RAmYaV9YAmTXt6ZRjniRQP6GlPyaLQTinQr3raTc80jty99xTdNKjtmRijSmH/5H3OLu4LESJm+rMNh8MeXSqrzfoUnYmEZZHfj81aMvVjwDmxTFLFspaj5GBt27PEBbT8XhpgOFwLAUKblyxmkR1nIR5YR2ozuibQYE6UCucsBpH6hHmmaS1GCW1hnseXKvEP6CPGGHy/lp95BUWg6bYoDb7NUkgOQfKe1qNaj2qE/Wp1y7IseCs/M902OIuUHrHMJVFnUEBMCYoV05yS51BchOWaaWnP5LDmNw2pM6SwoArUnFimwG6caboG76QPonQkWwh40QUbw3F/S0qJuGS0Bu1bLvrNKVrk6VxPVkmiUzHhjcE7xzRNks89qerX/Vq2bMNIs75PjgtaIzKWFFmGGbP2TMNAPlZCyGxXK3JKuHbFs698jmG54+LBKzi/omnPaXxPrq3o2othGl9Qkd+J491Tqq7YpmP/4g5FZQmBFBWr1ZYcpWh+8/Q5d+O7xGmHbVes77/OME/Y4vBdw+3tc1aNlBOzMThVWeKRd7/6Dnn4BjiNMY85f+UNVhcfY1kit89uCMeZ1cUFuu2YZxkw1VIwtuei6TEmELNm1fXkENk0Htu3lFyhKFSS95tuuyUWT6nmpWhG699lcYVSpEVptKZGmIo0kLXXUIpoC63DKIepnqaxrBTc3zzg9evEb3xLXMFsX+PsLFP1Gd3289w+/9ZjMPTbS6qu5HxHiorzXoN2xDh/5LXzuGNcZt7+0v/On/qBK3ZfeMzv/Tv/Jn/wB//bj7z2J/69/5HN+Yqv/NbnefD4DeZZn3Avhuotjz/x/1H3bqGaptl93+85vofvtA+169A90z2tGc1IciTZCnaQkQwyJDIBEwyJk5icFBAhjsHkcJOLhFwkBHITSDCCkJBALhI7AQWiCxObBHQhIUEi2xmNpTn0zHRPVXdV7ar9Hd/Dc8zFertDkANjX0n7rqG6dn17f9/zrmet//r9viKbtv5LXB4OsMmMaeIrf+JnxEJWC29efMLp/JqaNUo1eGsIIcrBrwq1OOKSkzGdw5VK0QaD4INGIka3mNbxf/4Xf44/+ef/J9Zv/hbTs19ENYGYIrpWWicHdM4ZFKz8mmmaiFHIBk3XUuJM1Vk2XamUMmN0i9agamQKAdHea5QuWDQhV5z2hDKjdaF8xosNCddqalZYVSi5MAwnrFHUDFXBEA70XUtNBes8XeMW/aTCaimyrDXEqlmtVthqSCrI9uksB/08ztRFkynKTDgPF3rXYa3QJnIE5+S1G9MQa2Vze0uZBsazvH5dNWEeuQzSpSx5RmkIw0m4v/aGru8x6zXaOIY0SczCOObpgjKWOgtAPMZIXEaQpkKxhWzkd6atopiWTd+hli3lfiVb4jlHSg5iyXI9SVnSeKDkSMhn+n4Fs2B7zq/2DMOZfttQlaIoh3WrZVt4xmqLto6qWOgCQqoQo9KWx0//BOSZh/ETHj17gnENb198iGt33N5+mf3xJR/RO68AACAASURBVON8jzoduZwG+ut3ufnCe6ThzPPXH6M1nN+8pG5vqViGy2tqkYKq73Y07Q2lnKmp0qzW5NM90zyw6VrGHKhZM4wH1uuekh1pTqil09z0PUZlcrGEEOi3G877B5rOgxbguG40eZ5RKIrW9Os1cxmwWhSzJRTGy0DXtpi2pxQIYcS3MpEIYY9SDq2cZFMvJ1zbkOdKmE9YVwilEKznYf+azXpH6z0ohbOK1himYU+ZCtqsoEqnE+3RnSxtoi0GhLGqCsp7cgyM84iumq7rGMYzXbuSIpAqUxA0IQ74tmcOI7EixawXwsM8ThhnudrdkstESoV5HHC+w1iFVg1lwXp51wktZRygLp0731KyXESt6QmlYL1GKU/VBWMcaileQk6kkNBlJmTZ8C8x0zSeogquE7pKDMPCdBW0IBSwCj1VcDJtMkYhFC2DLkCWzl+t0kGMWb5nSWW5PMs5exmOUKRIlw6+oaqKLg1lsZylMLCU4+iul+VUaxhOA1MGYzXrzZZaNSrPWFVQxjBPExlo/ArjOiqIqKMU2vWKeZ4x/pq2MeRxIsaBnDPTfMIpQ1bCmTamofWO4i2H/QtCVqyuHtFu16Rh4tPvfkMuEF2PxnB88wmpRFEth5HdtmWYB8a3Lyhh5u3pDTd3X+AcI9PzCzd3X5CdhCnIcrN3zMNAjpntbsPD/cdYU5kzcsYUWK06sk5QwTU9KgnB5TRMrFdbcs0yP1WyH5DLQu6ZZkzTU1KgW7VMw0zbOlJObFZbKIkpZbyV3G6NAbPQG+YxgGkWE+eEdS3KyIV7u7smppGSQSfIeQatqCVSrWcKMzpLlK+UJGp3Z5aFTIVr5EK01rIQqrVCtYopzNKJ1gW32QIQc6RtOiqaxjm2N5pYJaInKnONVlUoQ0WRjQID6/WdOEaMEb53yjSuXc75CYymM2vCPBHmwOFyjzLQ7TboErAYUIpxks+bypm3L7+LdhalNCo7+n7Fo7s7Qk4cLme6boVvhDZkVARTuX3nR9D3nzLPmZhPTNOImjNGNyhdSSoRp5m2s2jV0G4fMYWBqg01V9oqmvLNZo3vHaU0oALKt7i8pcSEW7dMlz0mJUqZCdnRtJbzcKCehY2Yq6Tvm2ZFc/VjaA3O31B1QyayevSY6yc9vWuw1jMMZ0pTMMaiCyKDUIpqeyGOpITrWowyHN88SCGMRjmDRTHHmToFjHWc3jzQruSy8sN+/aEocpVWbFZX0l3wLahILhVTDRUlm/RaoQukPNN4R00noqvM/vIH/r6/9rN/6f/9jz/1D/6eZRix6xWlZOoUuMSTFCDuD94QGlPY3T3j+YeWH3z6fdTwY7juF/iZn/yDRe73vvcRhki3eoRpt6ybHrvuZEksJuZxLyOqEvCdoeobrnyHVoaX3//e58B2peXWY4340l0jW+VhzrReHgSFSuMaqs447RZPeGSFo+4ML//u/8Gf/Gf+e4bH7xC+9bucykQ7jpSYKKZgXEsOkZJHnOs4HY847/FaHjzj4QRWcsvWShZQG0eOAbRhTgWrYbhMYIRva1yDLY6qzuQwoX3HFIIoip2hxEwqDu9acq64pqcSaHpPTeDXChUzyjpimDlPF0yFoMGoinGOcRjI8SLgfwyqFc2sbgs1Vmg63NL5KEWC9E5L93WaJmqt+K7FaIsxlXGSDHFOgfm4J16O5MngrQbT46zHGImT1Dqz3lyTq8X4jm63QinFcJkZT2+p84QrE1rDMQT69S0pjvRtR6JwOL7F0NA3G7bX11zCiGt6jLMUFBK7DhDUcsHLWAdtv2acAtN4QKmA1ZlOa9Jloul7jmNgvV3TXW1RThGD5DIpmhwSnW9IBfJ0olpLyplUJ1yG7/z+76AMxKGgpxljNMfjEZ0S6fKcxhpeffs3ob2j766p4ye46cLw+sLp8DGuWfPu+z+FbTtsZzm8/IQ5VtrSoKlM8cKcCxtjQd9yOXxEuX9D1gWF4TLN4FpSHLF15nyR5QPvPa3vOA0XmDUpj9SQKemMcsjvdM5QDWMZaE1LrvIAaldr4jSz7u+I8YQ1lpRgZUV5XJaHt287unZFLYmU/JINNigtXVdTLNpmnL+Gzy53yuBcQ8xBohZOi4QgVWy7wW86iJWikMx5KhKHUKKgjnHE2I40zWiTqcoIxk9HYrzglwLcaUWaI6UGMB2qWYvO23mc0agqClTBmCkwmnE6o7Ms0XSLurR+tjagoG97YZ02jqIXnq2RwhunhY2aC+hMqRmydOhymemcZb6IJVJrKxpuvcSWGkE6VS2XNl09miTFgZVpS1FaAPlWoUvBGWFJpxTEnlZkIx2KvH5jqaLZwhhQRQsnWDmUE+6vUuAWUssUA97YpUNbwffSYNCWOSeUVkQNq01PnDNWad7cv8Q5Q9dtaJotmYrzHd52qFJJEbQ1hDBKpGEaaIyD/IbXn9xjml5y2K5FTzMxZq4eyXRwuEyMMeOc5/rJVylWy/vLGNzK0j76gli0rOwbxFxoncJ1njpHzvcP5P0R7TRq3fKk/QJjjLh1y+72MXGSeNE8BjrfcXh4i3eKtrG8efUxqgKnE0UbXN9i+p77Tx9Yr3ds1jvG0xGypVu1rLuOeTxDhFQl5/82vGa9Xi8TCYnHtUtsZr3eAhqtE7loaopo6xjHmdZZME6U7Fqy9NosnXsvZ3Yuhd3u7nM8ZJFAJw7hwdua0Ti061BWCqGyMHtLTPiuRZuGGDK1BKYYBHtpHAW5eM3zTDjcEyfBoY1h4k2defLkCePJULXC+RarPUkXKbyKoWkWq51WJCI5aIwyKGVpmzWq1ZALc5pI4UzOmdPlnuHhAWcrIZ6Zz4lzo9lc7QgZtJei3FrPNAX6bo2zLfM8ksJhoe1UHt5+iqmV44tRNOVDIEWZcq9ubnC+ERqCqsRQUBpe33+CU5VmVTHumu7qKVOKeO1ot7dcLm/56Pl36Iziyu148+Y5SllisfRXW66evcfGrxmOR/I8UaZIjonqImZ9y6q9xrmV7GDERLfeyTlUNXkcCblgXIKiqNrR+BajLKVkcpxxeQHuhQnjLa9evMKgaFdrUZ8DyYocRTkn3p1c2fY9YwyUKaEbh3GO29tnTGRRrP+QX38oitxaC9P5ACqis6E2cD58kxgynX4Ha8+8fnjBdnvN/v5Iv23p11+i6z32+g9mcn+Yr7fPP8T3i20IvRiuPJE/mLOtqjKc9zy6vuH7p+/Rm8qrTz/h53/+Zznzm/+fP5unj+Q2XI989PUfYEtL//gLbN95ircNfncN4YJrGsL5xOb6inDac75/xRfef2cZBzq0lQWAw5tXaKV49fo53vdkq2ESmoGmMF0EgG60IpWAM5Z2U9HTmdvNHdO1om/XTI+eoUrGm0rQGhaOoG01VEsokW69kuWtFMm6YjtLjomS4RJOeNdRUiTnmYTC2x7rPP12WXRQEr5vLKRkROWbA+2up6ta/q6SULGAVjgrmSXwjNOFNEiBZa2lVGEEuwVAXgyyEFKXTdrrK8bLRLGGEkcMVlTH2lByJMaEdVpwQTFRayHESDGZUmby4cSchaGprRQTegZlO26/+CWq6YUZnIQTXJWMh13XoVWBrHjyRfl9FaV4/eIVjQkY5Qljj1ltaMKZrBt2dospmpATzbrFKMVwPJHzSWZudabEiTgbySc6hVFu6ZxHNB3ny0jrG2gbTNPSlJFxOKFMhuLYNYZa5f9PC2S88w1FTLtkBahI0hqlI5DoOs/ldGGzvcEqYLNDpQeGubK73TENF5Tecnzx+6zXX2X77I7hNLN5/NPs7z9EqRV1wfO8+f7vkqJGK8EGufWaVC7E+Yy1jvnyCWU8MR8vbJ884jgcyTVSSsWWFSWfqClSTEsxnpID6TwK6aFpyEU29qtuoHpsC+QsnFdvqZcsDNEWLuOACrIkF+OM1olplK3qxnuSUpimQYVKzIHT+QFUxrYd6/WaGBIhSlxHK0UsCYyGbGitIcQZ4x1WW1HtEtHGorQoWuMsyCNFocRAMWoBzkdKAd92ElFvlPCilSGpClrGxwYpfJ2TM0CbnqwLVjekNKO9dGGNboRHbWSJM5xHKhEJAHgIgpzKSz5eHtTLMt3bB9CWUpYKWBusKSjVYY1ZpjeGkEecbajFUKzFdgqdLMZZ7MoQxwllG8HodZ2wlLURYxyiA1dJLhZKKVG5JsgqE6eR1WpF0cLGzqqQakQDxrfEEFDeUqOiIoQD4yy6JIyuFJIs9ySZRDmjoGbptCohxJziKJ9fLajDPBaapqXtemoRS561sjhUtZbRcONJuVBrwrYdqiYqYmmsuy0pJGrW9HfvYRqDWuQdjb8jzRMhBJIxdLsdTa2oGrEaLlky3MpYFi0OJVRikmVDKOQ4c5kuxHlks9mhH18J5qtoaoXORS7HPWE6UPsEuaFWmYJpI/G+cRzw1uCtJXYVQiLNM3VIlJRIF8VxnLB9yxwgxBZjHZRFbHE8cWDGNo7L2wOmaXl7PPL45l3Ow0gtkeeffCx0iNWGaizD4Q3rzQ1KNVyahjwPuK5HmR6Mg4Rs0auMdp6cMq2RTl8IE1oXZpPxxor62RnSXJhzoimakhPjdKTp7pBot2I6HpimidYvBrrGUTA0RsvnLCeKaeluekKeaLuWK3NNmCWCk2qh5so0nSlacQ4zfd+TnZApigKKWiYDiqiVPCvCTAlZqC/DjFJ7Lq9fU6omhoLvG7pHveTtlWLnWw6HA7YavPV0V2IgLWlis72CumOazhwPD6xWO0K8cNV5UjawFoJGdYZVvyVFhWk7lKk0G0uNgXe3N1hbZEkwyoVA5SLa6RpRKfPV979KiAltNT5eY5Sn6Vou5z2d80wx4/ueMMPNZoWqMIcB77dQQJkWVCb5zMPrj4mlsFk9xliF0jDHiE5AnhgODzStoaaMdxaqovoV1Yh4490vvEeNGdMaYoYaEtP5TPGW8e0bhuGMq4qD86yur0VwNQbiODCdDyKZyvmHrvX+UMggfuKLN/V//Pd+kWDg0eMfxW82lPlEThM59jQ+kKtFm4YxBvqmJ4MoOq3GGM9/9Yt/9Yf6Xv/sf/Mf8jd+5Te43m5YHZ7x5OmO16eZZt3wc//OP0bQjv/hz/7Ff+TX8i/+xv9OrgO2akrK+H7D3bsf4Dp549hOoUxDqRPjm9fkdMbMgTBMNNsbVo+e8fE3/y/W61vuP/kB10/f43jac3V9B1bRtj0f/97vcH54yWU/4rdXKLMhac1mt2UOF1Zdj7OaFA+09ilf/q0Lv/LNX6e7+Ro/++e+SLUJaz3nQUgCtSpCnhmPI8SZtuvEcrW4wXPOizFIeI9aa6qWDoezHoCKJedKjhdKhq5piFnh2xWo08ILFmxYrTIaQldhxM5BmKFGU6oi5RlTk2R8C1AVSSXSfME3Hdp6hkkOJK0MxsrPOsYZpcQDD9J9ccaQirCGtalEpICsVT6AMQrSqSrRXTpnlw6dQtNhMMT5gnOG83ngp//0L7C9vmXYf8K4/x4vvvnbgGYaMzGP5Axdc41eX8niW0k0foU2MMVKmk+M48DdzWM5LKkULKt2QwLmFEgZeZ1a8su1VlTJNM5gugbf7ajjG6bTG0rVcliHgm82XKYR7Q0Ry9r3TMMZ13nmMOCM5/0PvoruOh7dPZWxIhP71y95/f0P2T16xsvvfoP58ooYZ/rrJ9y+8xM0m1sqs3S1Yl04h5rz5S2lRnSJsoG9ecru9o4Xzz/iersjOyhhZtgfyeczbrViOA+YVU8OE7WKSKHrd4RxBGXxfYc3Le2qJ04zXacYp8j59FbG18pgioaoUFqwPturDcf9gxSEVnO5DDSupXEtaFmESilIV78t+GjI1mKMvGeNUcQwf26f8m1DiiNKNVi3QtWJKU3oGpgD1KrwfS+yhFJxjZd8fKrS4Y0ZSsB3suQYq8J+huUJM6v+amHlItbFKtnQagW2r0vFVslCgqYaKwIB44lhoriCqRJ/0Fkvo3G1IK8KvhUcISWjreNyucgC5iLAqEXhGi//BuOoVnKT3orFLBVQNkN1zAs2SQMpFlBlIX1o2sagrSNMEec70mLwyjXJFMo5rJMo0mdjSWW1dOMUwgFWMo1KcVmWrRFvtWiDlVr0xDLiZNkJLsvvqgBWQ5jmz5msStWle56XzXcjXVwj51jb9LCca0VVTBVcmF6kNbUoTFUoJwSMVLMsw6YZ6xpSDoRJspSCQ4rkIZLqTHd9i1GZFGZODw/Y9Qbve6wrksufAyllDIbZFLxtUMotNBwZh3/+8wU5a1XGaEeNIhgAGdlXGsFsWkiTyFCs1VyGA+azPHbjOT3ck+LMeDzTNo6aEqdhYL3ZYY0U3L7t5LKeNTVVqppwbUNViTmNqDhzPh+xzRbtPM4p6jDLYpexVOOZ9gcup08xRuFcS9Ud3faOzeYWqw0vvvf3wDuun32R6XwijZE0TzTtBshsnj7G9NdkzLL8J+isME7S+FGKUCUTa00LSvS1JScRz82CwVPGLJ8bpIvYN8SQ0KUuLPOM9q0sR+YqC3SL4EVbQwqRqhYCxLKElueZGgNzmCi1st1siHHEacfx8EC/2lHyTLULpaVWKJVpvuAtWKUYx4nGbyhVppHrvudyPrO/fyAhr8t1DqsVXbeSCUxRpDzReINuPdauqVmRUhHNcaki//CK6bTHKcUlReYpc3t7K02VthdyA0VQa42n8w1jGLG2pYYk7zPfMoQlsmMbGqexyM+rFDlfUkrMKYO1aKtpm46kKlWBrQZFpuaKzoqcZaKjjCGVSphGrPWYVCg1EGeJpRgv+MbP6ozGOkH8tQ7lWkKaWXUbYp7J55mm70Ap/tIv/xW+8Xvf/KNjPPvpH323/up/+q8Inir3VF2pemYeJ6zpsG1H32047w9yszaSESMFEhNv71/zq7/0X/5Q3+uXf+3fp8wXen+DWa25vPo2ym0wN09QxaOK5a/9k3/5H/m1/HN/+9ewXmDoxhspAIzDzBNFN6ANj+7eRTVG3sw2Mdy/YDofuNw/R7Pj6stfxbcNvu057l9w+OjbnB/OpJJRVh5mNRRUglQnWF/Rb5+AkVEtKRJLoF+tOb38Lv/1vea//V/3HK6+xPd++ZY5JXzXMI0zIc5Yq5mGI8qIRpEqnUplms9HPzWzLIwVMU2FQCJxvbsipYTRLSlXalq29qczDuHjfiZK8N4Dhmma8G2LcUIEMEaRx5mQArbbYJ1mPu/RZWYaj1xfv0tyVjJ1ulKqGINSSDg0cyhoIz+bxZ7BZTkcTQHXNkzDGd+vyEVJdKQGjF4sW9kwTwPOe+l0lSKGHOzC1NRo5fnH/8yf5XR6IIaRVlc++e7XUUScbRmmmcP9h+zf3NO3j3n07gd89NE38W6NdytSU3nnC19jHgeevPsex/09lcTh7UuMXbN9fEc+XQjjAeOuSSWDFeZoSRO7dsd52hOXzlgY92xunjCNJ2YiNmuxC7lC0+9AG477A963lJLod7fc3L3L9fU1eUEmKaPZHz7GzFpUndqRpzP7/Vsw8ObbH3I6vZFi1K9Z7x6RjcG2K9pmhanQbFacTns2qw3VOXIstOsNVmmhbdgGCxxf/oAcTgwhE6pitVqx3lwzHO4pKnG+f6DbPRF6R7hgrcYYx+X4lna1JqeJigPtKWHEaikOpnkQC5m3y+VLOlm2avpW8FshLFKIOhHPE6tHT+jWHU3Xcz7LOHi1viKeDsQ5EIaRYR7xxoGRbmtVlUqWDqEqrNdbLocjaZzFCGYMVI2qFa28FFtOE2pciuFFAFCDqLYp6ApFz1jbkJPBWLtkWAON70kpipVRO4o4b6FGxpzxupLihCqKqjxOOyGv6EhOCo0saWmt8a6lpECMchlxrsEaL2iiKH8mpoChktKANl74q7qXrlkp1DBQyKSSaXyHFIpWFjyrwXlPSksRrWTKlGpGLereWOsibKliDbNyzHhnKVkuAHbBGJYkcRWVCqoqGWWXRFFVKBV1OQOrILFqregqOXOjLDEVSpT3iNJySagloFUji3a1ENOEVhaF5IJDCMsl1xHGiLOVac4YZagIpN/7jlqraLenM23bofuekpJ0jxtB3BkUtQTmONP2GzBFuMKuExFMzngvF6w5F7EFViEe6JRw2hDmkRpnala0ux0hJFTJuFXDcf8Gpy2mXQlr2RTCNHM6HVhtVjRNyziO1FrIakZXzeXhgcb1cmHvHakYjG5o+jXKGLzvpZDJspNSo5IMuJUC27gGlMHYyjSeaZqW+XLgMg74fsPm6o4SC2EQZNn+7XPG+SzUj3mkt5lmtSNUI5a87RpblkRWrZRUmYvFd2theOfMfDnSba/Z3T2mqgUxRURVC7ou+woRrSxxHj8vcjUFjJbPga2yHl4tuoraOaksF7uiqFEuRrXK5cw4yfxTKo3z0gioUnA7vcgtShKmrraEHESxqzQpZ0qesRrpimuYQ5AOu7JY7YlZEI9aLzitcCElQU9aaxcmcqboSkgz5MB4OhJLwPuGOmvyFGlWEiucQwJv0UrhWyeMWWupoRBLRGnBg+Vl6lVrJVxGlJdl8sZ7VKrENEg9cZZJkbJKimgjbPpCpQY5I5p+QzYNmEJNhpRh9+wpjdEc397TrVeMg6BMhTAkeulSCjorSh6xWoyMkYTRjqbvySFQlcWoivKKGAohRaGwkLHaMYdMqolf+jf/Kt/4vW/90Slyf/JHntW//h//y2w6yakpt8E0ENOZ1vQYv+F0nFlvG1IMOGcIlzNKW1KeUFozPgy06xWlJtptTwojWjnseisZrWhxZmAeDhjdE8io3JBNi/GymWmsxSjHzdv/gP/oL9z/Q72Gn/ulH+fZz/9Ftp3hkAA0qt2ibj+gOoVTctg2taBNJ0WMsaxWK4z3aD3j9Eoe2Juezgm/NFGZpj2vvvN15jf3i0Uti0IxFnTXEeqMouXx46eyTaks1VZiODMdE4cc+V9+9TW/sbf8yr/+BXZ3dwzzQJoTMQ3S7agjTnekOaBJaCXc01wzVc+QlHTS4oxFRrihBLxtcNZL11U5kRjkgNWFrCRDbbWTzdgkqKOSZkLYY3SP7RwWL91kVWXzVRmsBl0CGYWucBpGrHG0uxVhGqEU5iHSesks5zRhWkF4Kd1jWogUPGvRvpYqB4jVy/KXxTkp4mNaGJQFmqan5EUrWTKamawFPm0wvP8jX+ZwfC3q4jIyvPgBxShKnfC24eWL7xDOZ0o1ZO/Y2hVm+4jqWrLKhNOeOif67TNu33vCFGbODwe8bdk+umF9/YSX3/sWl4e37F+85OmPfYWua4lT4NOPf5/V9TUG6TR616MMhDoz718zzZlutSUrTeNXVBxWG+YcyXPk+Po17/zoB3z5qz9Ju91S0okyDSRtuTx8n+P+QMmOzfaK+08/wZCZNDx59ozd7ovEYeL5R19ne/MO1TQQJiqJl88/4sk7X6Zb9QK2dx2v759TTGE+XSjK4UoEJYVE0245DHuu1zfUolld73j18Udsbp8RhgeyammvrxjffkK77SlBHmy5VFzXEuYJlYOod22D3+4IcSJWaJue4bzHWcN8Gei7DZmZKSTUorMtRTKfYdyjVuBUiwmGqMTqk5NCGXC+p2kcl/Me23Y4Iy72UBOmFlnMSQlcI3vfxgMWTZLuMAK4L1k6hAlD6yGFSud7KVKdZGpzznjjGcNlWRIqy8PZ4xoxSaWs8M4x1ywxh1qhCFzfuBUk6YynIAB/zYLyKgslpXpSDRilxNJVJ1RxopSuMzVIF9S4SlKZEiJzzOgSJZ6gHUoZGtcQUyLXTN9vGKZA5zxZFVCOZApOGVStYq4sebGeKaZU0EWWSp2zZCq5Fqw2OO2ZwxHvrZggS8UgHedEoZiKU6LiLoDXilQD3il0s0JnQ+M8U5CCp5SEzgq7cJBLrdQKSZVl16Piq6bkKF3RoshpEgkNmbxgilSSP1NrXS7FmZRFVR6nA0pXoKEsBYbx8nfFKgVsouLRVDRVy/NWW8U8TXTNFcUKkzeVRDqemC5vSXFkc/UIXQ3KeULJEu3SCoXD+YqqchHU2lLmyJACTWsYp7PEtoxCh0xdDJNzCuRkIReUF9lJ0YbpfKDMIxqN9VamAlh8K9KJGBQ5jGjr0CqTxhnlDf1mR06KamE4nDDhgl7dMB7fEIYjxhgav8Z5zVwH6pSJQ0A1meo8HS1Key7zTJkSVUU2j3aczwPbqxvq8UxtEik73KrjtJ+xZN6ejzy5vQVbEEJMi/ENNQ6S3dSOy3Bkff2Ez9KHXhlBzDlDVVCVRA9MlqXFhJKiWydKioDGNR6TFEUVChJT0Et8zqgqn66YFhZ4ljgLGUteivcMRjOMI13biEXTwDQNWNMRQqBpPNo5SozEKgIFUwulaEIdQcVF/pHQSFd6tZJ4YE4zBi0LZ3FGVcvp9IB1Hc40WC/LnsZo6nK5ikmeG1o54ixLaapUbA2EammaRp6PSmON4Xzek3LANx3GZ1IQMkyohs6IqGR/PtF0VxgVJfJUqxjfutXSXEjMIaKopDCQYuGyf+D67rHEEltDGALZKKzrSbXQNmsu04G2bbFaYTBcpgljoOvW/Av/2r/xQ3dy/1BkcnOMTG/vUe1T9pfv0DR33D6+QynPNIwwVdbbK9AW6yvHN89p2hXGVrq2I81w886GNCWKUpQwEk4PaG2JaSBVuF4/ZcqRTOGyf41deYgTtTznPBzZtFuGbGltpB7f/EO/hs17T+ldQbmOVo3Umgnxgjq9ANuC24BuOPcN5TzInE2LflX0zpGm39H2K3wsPITX7G625DgT4oXx/BZtKtPpNavdLdU6Yo40mzu+/MEHDMdXpGkmpUwsMyQBBrbrDdPDS35u89f5hf0Orf8K3dWW/HDi/Oq7uGbDeBHP+ZRPpDhhqdSwJyormVWt6XovJrqUAIUylc6YRfsYERlqIlqwjSUvSzetbQmLbz2mQBz3qHmic4ZsRIPGdgAAIABJREFURnKAGBNt32GcoWSFNhqVK/L2rGQ1Y0ylqMTlJDxUZ4z4vXVBF0vjLSFMpJwo40tsJ8KKYnuJMCxdWlU82splKFcxxjndUJTG2kIKGW00OUQUAW0t7z59l48//n263WN+8L1vcbVbczr9AO+37L7yx6mhUNKI04YYGnimsO2aR08/gHrhw2//febzwPruHZ6+8zXisMcoy8NHL/BXG2q4oKyj7a75zu/+Nuv1mmF6S//shuly4XJ+wGLY3LwLulLjxJQLqJk0T/SrR2R3Ztc3pJRJc2EKF5rVmjCONE0rYqHrnh//6Z/DtgVnO7KqhGrpqQR9i3ee/nbHHEau7x6jE6jGcXj7kv0PXqDdGkPL8299naurp0zjS4xu2KxX5GngzThTyQx65O6dH+G0/4T98w8litLe8uydH6Max/Z6S/fyE9q25Xz/iv3DPcpowuEe369R3YYn73wRnnwJZaWrHuaJHDP7l9/jzScfoXTBuxVj9bh5plpRxV6OB7EDToVK5uH8ICY473AYLqeBrmmJZaZfb4l5QmkwXSOb+3pBJc0TIQdKKLTdjjAdmZ2naoWJUD/rxrQtpSZiyMSwdILmgLYwjVEoElkiDdogXSq/IqVERaMyMtqrmnG8iNI2nD+PAqUUmGcpWmuGSJI9Al1R2lFLgeplgbTI4mWaL3RNK8toRWGNY4wXjPJo7alqppSMMw3KKrSueNsT9IwulrygwKgix9BYjHZI91YxzaLm9r4jpYT1ijlP6KqJecb6RsQ1JYNyuIXbnKisOrFNNUaTUsE5Q8yCNZynRQ6jDUaBaT21aCp10dAq+XnFiVoTcZ4oORCjRk0FYzzzOGCcJ4SRxndEDWEMaF3Fg6YcsRTpri5LL5RKHAPWeOpivkpVdN4xRjq7hlLQWgkiLWWRGrSefv1FjK3kalBY3CLK0SXTWHmtDg0pg5OHf60ZZ1qa7Yb5cmYaDwxTWnBbmdX6Rs4jI9GrXBONdyjbS5FeEi57hvNzDJJD1aYRwo5p2fQ7Yk4Se9CaVM2SVW2oXjOdHoiXDDoTpxmtlVAglKbprtBIZzpMkRAzJo/kIRKTwq062s0adOW0fyBiWHcbqBWsx9hMt9qx294yjxOpVOYsXeW+X2OazDGM+G4FUWJrq9WG7DIxHTm8eUXVijI2pBKxpkMby3w5s12tsM7R7zYYhMaUi0I1LapGsikM+yO1FjrbML/8lEyD33Rklj2Uh5PEnDDcPX5GKKLhNbbjdDmjjBTEWhViHbhMgyxYFalBdLWy9FiESlE05Ci0ipQSrgVwkJREKnxDqyAHWSIuRQmnPEa63jPHgk2VlAGtJXvvWnSt2GRkD6FmUnWCewuREBLjKGx/3TpqyWjb4hrPxkv8IsdMqhHnvGAII3jfYmsn0aFa8H5LIckOkAIXDNooVK20SpFD5vbuSzwMR9AZ4zS2UxjbkMYR6y1hDGyudlyGgG0N0zzSWoPrWkouhCQTVa0K4/nCzfWG82VG3VyJXTIrxktgHE/sbp6RSsYZRwoDfdujayVNM9loVs0G7StaNfAPwLf+/339oejk/tRX3qn/83/yz9Ovr9g/HNmtbyh1plk/kq33YUJ52Uw8HY40jSOFgDEVcsGkQogn9pcDz975KufjA9YVHt6uMM0Nl/2Fv/WbK87HFju85ud+oTCE7/OnfmYlzNLOY80N46evaTcP3L797zCTZf+NTAmV/Iv/Fpf0RkbffsPL1w88vW3J0eLWPbFGXL1loONyfsAYy6v717z7pR9FJ0sME1XPhLlQ9ZqyuqUWRa6VeZ6oFcnV2parm2vaTcfV5pF0dGrkcnyNsw3t5hqbM29evWTz6BHD/Rvc6praaXQeqTHhm45w2UOsXE4vcXpF3Wz49t/4z2nKLbz/p9n+sXeZjp9indzaYoK29VTt8E1DypPkamIBa0EbYpxk1DqK5o/OLx8G+VB1/YYZ8NYyR8nHWu9Q2ksUwELNgWb50MUA1WjKZ4ikWhjjBa08vrGQIA8TMQw0653wErWRW2mVwgBtKUS0kodvqQFfJMcXlKLoBLXibCcHtLHYtpNt7hwxylNKohaD0pmS9eeYmJ/4mT/OfHrg8Pw7HA8z3fYR27snNGvLfHrDdBo5H870m0cc376m1lEWl65uCSmyvX7Ew8ODKGDjwDSf2R/u6Td3XF99iWdf/ALn+YSOJz76zt9nc/0+xgNZM8UBUzXGNlw/veNb3/gtNIbt5pYwT8TzGbPacjzcc3X7iBxHVMlczuJ/X/VbYrXUMlFDwrQCGX/3K/8E777/3qLxrMzzzP/963+Tlx/+Lptnd3Sbp4zDUXpOZsUHP/oVzsNrDg/f59Hd17gcDuQiy0tKZTFe2Z5ZQZ2kWzeFmTJpHJaSLlQdpEO+uQOVGM5vSETee//Hefv2AeM6+r7Hac/l/IZaIsN5vxxsgsiy1mJsXUgAvcQSlslCSFFG/CrhrGccZ3IJrHxL61vmMlOVLF2F0/Q5c1QwapGaRUXpjSdMRQQptZLiTNNKds+USLu5lWW4NHEZzmx3O968eknbC46rdx3YljhPNLs1l4cH0FBiQllDpuJXnRRtUdG4hhwjVUscwCAPxZIyyks8oqSycKZbWWLLoK1DYSTLWyopFRrfSv7PW4lLaGGz1lrJudJ3W1zTkmshhFnyb2QRTSiwKLISdnQYA76xTKqgQpYLpXOoWhe0oKJmyfJmuZ2jlyyr1gZVoRq/dJ/K53nHnPOis5aRbkwJAlQDbb8SecGCDZL4xIy2fhHDFOyS6dVaOpA5Z0oNtI3j7t2nHF+9YhxmtO2IVck5kcrn8hdg2QcoZKVIqqJyRlUFKZBzXfKUGU0Eo3F+0ecmQ9s0clbUvPxZyTtnY4izIKiqkgJBKSWxliJjc2stqkCq0g3ufEeKM3FKsprgl4VTFUDDPCdMMnjXoGqimCo3nGo5nQ40TcPD279DZx/TdGtqbdB+RQ4JHPTdmloKqUSmw4BSgcZapnkg5szu6gmNtujWcxpGmcQpRagJb+WzVRGzYk0KFtNlrBbTNNhcZRqQ5N+mlMIbS04JowspGI5v7zkO91Ajj+6eUEwn5IyaKBEhKMWI9R14K6PyEIX44hwpZPpuR9WWVGdUiqARYUNMAoZzPdrJv30OF+bhzPb6lhxlkc9oRwxhQdoZQpSFxRxOuHaHUkbeh6oQTpLz9n0nSMEI2Sby5UDb9hzOE9vtFqUkp1pLErZ2tVwuJ/q+J0wDcRa7mvEG4yzKWGKuuKoEgZgjcQ4oo3Hus4iRpVYlExitlkU8jXc9VMnEy4VFUViy6OUzI5lMZqrRpDhjbCsTDyDVRI4JpRTNZ3EmpUA5cl52a4xF10CZC2rB/EmmuBCHiUyVXRwSXkk2WimF9h3OKEwtjDEwzpGSZowueGtJIeKbFdo6DvevsK6hW3vmNFOLwvpGRExmhaoyyXILvQFtsRrGy0CpGV0LpvUY3TJezlhd+Zf+8r/L733r23+U4gqP69/8z/5V5jAwh8Tm5jHECW175nHP1jbc/L0Pqc9/B00lbHbMH7zPYXjF09MOPjzzyZ//abSzpKhoO8fhYc/tOyvmEcbLS+bTGe1byvnM5tHX8B2Yqx9BV8s8fRutblBqIp0P5Jy5zJ+i4okyV/affpubd76GWz2mlJ6bZx8QrMa3HSpUEmdSbui3T8hTFFSKWxHCZ/EIeZ01BuYpUtP4+QKXRpE0Asb3FqrDVJjsF3n/p/4M7mZL0ZXT/g2tlgcM3uPQjMOZ1e6KcdoLiuZ84u0nz1lfbcllotMdH/7Wr/Hp3/11vvoX/m0++Tu/zRf/qX+aSss4BMkMzTPersh1wCHZOfESJpIW45z3zbKQpSnTmTGMNO2KWDIG6PsVc5EHoNONHP6LD70qGc1d9vfUWmUcktXSlW8EKJ+FEBHGgUyR0eYc8b5dLgAKhcY7y3Q+UaollkCNAe0butUVMQ1S7KIly1i0dI+JGKzwZ+uykWnlAd1ov+Q1K+M4sttd8+yd9xnimf39G24ePWW6nEnDW/b338dWhyGiyomqV7htQzhGUjRcPXuPYTwSq2yi3j59xsff/T1809E3W1ISz/u2aYiD5EBtbziOE0215OlC23X41R1hGjkcnkOpdE1LdR633spyYtMxDxPrq1uG44GisiwaGUE/hTBRY8K4G6zLFOTQXK9uuLq94cUPvoNvLGmJq0yHB/m99bC5eYZzDXk8cJkueO958uwn6DaPGM4HvvX1/400nQnDBKWyvfsa/ZVEDgTt16G7G/pdT8xnjGvZrXa8efWWZrXmen3D+XxkfdVzvn/J97/zbbzTtHbF9p332F8ObFaeUEbevnjNur+STlq/xtnCfv+CzeYJzfUN5TIRxoHD/lOUaRYqiSHGSlWJd7/yx8hVUfMsvvU5E8cjpWZev/gQ23q0soR5JIYz1jfU4tDG0/qGy3TAaoOxHuUa+naD95pTOAvRIcNw3OMsYoMznnnKNF1LmBOmaXDeUpkpY8F3Lf31Dc7+P9S9W6tuaZqmdb3bsfk2c7O2scuMzMhNVWV2lpQ2pVgIgv9AUMsWsaHpttUGQTwQu8+UPhXEg/4FHgmCWqKIDQ0KYiFYUlW5icyKjIhcsdaaa64557cZY7x7D56xVv2GPMzI2LDm/L4x3vd57vu6OpRSvPzqc6zV7B495+brrzEa3OhF3KAc2hjJ1puKxVNtoUxpjdpUKllekkukVYUfe0KY6Dc9VnXkljFKDsY0Jwcz0vtDWiyBzgqWUDXhSS/TjDYdGNaV7komyWnlTa8vyfX/rxWasWycgyoHxVIKaE2MGacgxnerVPDDiLIGq7Ssl0uAZte4USSXhh/lIKzdQK1KsF45r5scUQ1rYzC6EXPhu9//MfPxnl/+v/8L7nBLU+C6Lfentwwf/YDN+Cldb6lFsZR1qqS1WDUtstavlVYq1jpUXi/fOUrUQ60FoCXgOrMetGUypf1KNmgVZQU/p5SSGIFVUCqpVYwVsYW8CKqUEEnM04KjJ2n5azEtkmFVojLeWE82RrCSqoEapJNQy3oYF6lBzpXTwxvU8YGQF6qGcdjQrMb4Pc4ZrBlEmNIyzsjwQXdu7VM4IUs0KQ45RBc+h4ntuKPUim6JWBM5FuI0y6rdCaPWd1qwlKqJrcx7yfcroBpKPBOmhO00+/2elBRaFZTfkOLEMGx4eHtHPjygnAbXMW4uKN7QYpHtotWSwZ8mdDSoTiI9YV6Yl4TvDJcXG0qUDKvCox89whj5vPWd53B3z7DbUnKkNovrOjmEriZM5QzOGdkI18z8cIvSTayGNaGrYn6YWU63xLywvdqAHejshpojOSa6wZHnE0n3aOuoJbDZbnF+y3Q8MGx36M6Q5hNuGGXQUhrnhwPnuwe22w10muwEK6mKgjKxnI70bhTk2qZfdc09MTXR1qczeVqwOpNTxA4bNvtHpJIw3uO7jjBXXCfKao8mlIC1Vr4DofDer7BmiEtpskVLC53taCWz1Ir1HSVFOt0LoclpInWNbICqBlssqEwriel0RnmDQiI5LS+CQXSOMjfG0RKXgOs8c5hoxqKQ4VnfeVCBWCwYg7WevET+3b/3H/52Fc9+8tnz9j//43+L27d/ge+2aJ7IC91LY/Th4Yg3O1SX6XuLjpowfsox9rRXX9F1kfGZQZVMSwq/26NI3N/fc3H9Ea7bg7ak6YzZX+AoxCnD0LHd7Amng3iRhz06RmYifQnknPFuQ0gzXX+N9TM1ylpImUoKEWOhlErzW7mh1oIzihQVqSZSCnRGih3UIm3cBrUsklMtSSYkrqPURdA/WW6ApTTU8JTdZz/BXT5iP0h4++HNS5ZlwrcCzpHSAyU+YE6BcX/F+f7EuHe8/tUvUBnYXPPiy/+Lsf0hf/Pv/BuS/+sd0+EOoxUvv/oVDy9f03U9V9/5lBbFBrO9vObmi5/SXTxH6czp7T39VtArzo6icGyNw/Etw8UFXjfu794ynyewSJ4pJox3jF3PdDqju42sfJ0Yc+xmZDkdqC2jrSXEmZoSqiKZ5aGnJFk955xkYlAqtnO0eMsSnMCsO0dLcqCmZaxynE9HXAe2Oc61MHQDxq636xQwSoxUzRQps7Qq618A5Zjvj7RypPcdqiVUDSgL1x98yP7Jt3k4vuT89gVOGY5HmaCVKi+oJRT8oGHOUtjwAzVFLq4ek84TUziSyyw2IKVQveF4d8PGXYB3DL28fGsxEvrXBrJMmjGahBRuqtJ0neRj/SDIqTDPXFx9n/PptZik5oV4Er2y6jtaWfXGWfPsw6c8/egThosrTve3DMNAvP+G29tfULHcvvgVeU4Us8GS0GZgOt/h/B6MpzSNyU0g75s9Ue959PQZJZwYd09prXB9teGvPv8ptvZ89Hv/HKoUqtYSyzi+5bDc8PD517jLK4bdFd3oOR3uiTHy9MOPUCUT5glU4Xw4CskinNl0HaFOKLNFYzjdHek6xzK9xozXNDQlNXTvRDgynxh2O8yg0E0TFiFfPBxfk89H3OCJBWqd2AyP0VnKQHMIdN0AReG2lhQWlLGE6bzGkgpXlx/KWFIljO7k8+wdJQVUtbRaaWUmt0TVlbEfOJ0mjPaYXqZRWllpudeK8yPWD3Rdx/H+lpwz8+FWmtFTwo09yhRyPKHo6IaeZgytVYzZEOIRZ2Xl6IxbebkzcWXRCn0Bcpwlq1dE/pJypnMDoFGjozey3qxKNMXK+VVOAd6I0bDkSE5HKBblNcQsBIE644c96CbTqFIwKDFB0eT5V6uA711PSmdyKbi+lxV7znTOv49xONdhx55TOHN9cc31xWNuvvqCm9efY25veHP357RcUK6n7za0/oLhyQdsNk/o/BPc6CkZlHGUeBbTYMsYLbnfthritDIkZLpUa4ZVe9uMlJWMEUzVOU64bkOMC8b35DBjlehurV11usZT04KqFbMSaWKRbLpXjlo91WYpxtWylmo1WRW86t4XhtBrUSpPUIUBa2lgO/xmC62hCyszuVFqQClBySnX0EaEKbXO9HrDEias6dbi2EYu+05jjUaHRowzId1TliJK584yXF4zupFUCnmeSLFQ58A4WLI2zCWCM4x9h24GbT1Kd+v0PdCylPas2VJVQrkeVRs6V6ZlQRmDMQ5nFE1X7LAhxUA4HwjzTG89umWWkOl2A7Vout5RC5we7vGdxXV7rNY0K1GiPAd672VjqjSJykUv4oiiJCNt8aR05vRwxA5WPt4JlvNLlihEDrcZ8Noxmp5zXKg64uxAyIXp9JYSEy3JVqkfBmgdjQQIzeHVyxfs93t2+2tyqjjfqFS8HygR0fQ6L0Y2q8jNEm5vuPniT+n7DR987w8ZH18je5NCM7BaPuhdT2wFlYpMdq0w0qsCrHlPSdLrUMd6wcW9OweEWYYWRRXGcUsICdVYhwBnOie5cHLC+pFYJmxTMjTMBeLCOSeW8xlFXVXfmo3roFR0bSzk9xc4OzhSOKHxWOVly72IqEZZw5RkwFKmgtKFsBxxdhSBRcs43fG3/+N/yF/+/Ldokvs7H120/+kf/zGud1gaX33xCzbDNdoXrO/Yjt+m9Z6mJEM0jFtyOlPTQgyO/uoS1RZUf40OZ6bzW1w/0LJDGzBWHpS5JHZPPkVZw9BvgUZtkbxMpCmgfYfrepb5AEDve463N7h+w9uvf87+6kN6B9WOuOsPGPoLajrCKgrQSji7+Xym6UacZ4zrxb6VIi1laptFotgytRWMafICRILkvd0Rl0hqFmcatWiU8RizIz/9HsprOsTSooxn++gJtCS6zDnSKJzPNzizx19fk+YTX/3q5/zob/yYORiU3jA+eU7Tq1AiTqQYhRtphHZwvrnh9OolYT7gXMdSCqVG+t2GeTqwLBOPrx4TUqE1Q9cPzCljlMcoAXO/g7q32DDOkosT7amWQ2Br0tTebrcs4UwrjabkZaiUYplmlJWyi7FV1pmtsizLih5T5DSTa6FV8Y73o/A8rXWEknFrgUdrLViodSVZS8H5gRhOaOVwZiTVMzkGltOZskSG/YhqBWUNqiTCHPjxv/Cv8OhbnzIdXvPFn/2P5NMNJWtSbGh/Sb97ztXzb/NwPHJ59ZSbm6/kzzdH+v4a5w2tFVKc2V0/5/TwGlNnbm9vsUqjiqFpR6kz2g988PG3UW4khJk3rz8n3r5kDpGrZ58BcP3kQ159/SuW+YFu3BHnE6omcgIzXoDxWGOwSL6wtQYdLH1FZc1uuOAP/vk/Is5n6nIm5IWu3/Dw5ktUbfQXz1hO33A8PXD48ufMy5Hx6gOWMxzOJ8Z+wNqerh+Y6oLRPY+ffsIcEmq6wwx7No+f8errX/L29JohnimTXOj660+geJb7VzB4nn/wLbqu47As6BIoU8RfPaY0Tb/Zc3mxkwzv8Y43r78hHo5oI9NJ7y8JMbK/fsRyvqOqjusnjwkpMux2ECpLXjCmoHLl7dsXxNRwrRFOD1QyvR/X6WGlJBEU6NgQBpCRbPx8QLsRTI81UkypuYAFq/r3GuxmYXAbUoyC+GlrNKLJNNLoSgzQD3uUkuZ2Vg1nO6EgBJEtaFOIOaCaJoWFvCQ630PfIT7QgjJaTE2mkih0fpSSpVLkOJNiJC8zvXec5xPdOKBVLxNP79ENaaYPjqYcputIU5amOpphM5BbI04nkQI0YQ+jDbpZmgKVJ3w/yIG3NWrLWCOTmpQSpWrQM2WRy2TOGeUsnbGoVik1EkKQMqUa1oMaYJCcOxFnN6As2hmRaxjDfF5E5Xp+w/2Ln1M5UGgM3bWsnjdXKOfR3YD3j2X62e0oSrPdXFDVan1sidIqZp2UCQYtUfOqU24SxZmXzMZ15LxgjJPtgfMCptcywc1podOOmCPNGJSx6FYkduIMbbXTKaXIy5m8/vd0ERa4ppIFkEnvt1RjUVWhnUUbJyrbcKbb9JhiyBSm04lhe4nXlhACtUygmpRpkxwso8pQKq4pYqjEtAh6TRXwmaR7tt0Fxg903QatK7oz6JXc0VqjIChJOTPI2E+ZJgQQZHOWa0FbL/ENhA0Ogn3zVj6rOCPTwuowRksvwllSjIRS0E1IJtZ02F7K5gDaW4gLMdf328I2zYQcAI23EgEID5k431McaKvB9PhuYNhuqO/ee6FSw/weN6a0BgypLsTThOsVnR4I88J5PvJw/4bBjkLlWQUs/b7Q7Z6zubrCoojnRo4LFWjN4Hsjha9YwBp0LZQlMp1uMU7jhi3W9pzuzyyLYNsuHl9IlM71bHaPKTmgdKbbbWjFo70n54oqma7znGvGRY0bRpqq8jzSslGtTeIR2ErOcjxWpdKqgU5jrKi7nXPoCkVnWpazSWsKtxoHlTLvt7LLMqGbIARNyRTVUCviM84iOjENtBd6i183UuJMNRIxbAHbNMZqUjhDs8zTQdToBZrO5KohJ7bdhqXMEsEqs0yWc+Lv/6f/JT/7/Ne/PYfc3//+x+1P/qu/j2qVMB1ROLxxRBbGi2vuvvqGyycfkMhotwWlJJsWZ1qDpBqm69FN1txKKch5ZbhpMJZxuBCou7VQFlq18ku3CnKSVcNuR8rQaUsz8hJcjmd5AfWOFj3L4WvGRx+DG8QHrgRpoa0WB/XawlymEzUlGomyREKc6F0ntxlraBFyrtjOUqV3SzYeNV4RlcLFClUyNc73qHEg909RuaJXyLzdbhgvHqG1YrcfyElUjJsnT2WqmPKK/FJM84l8ekApjXYD+6trUgvEJVBj4uHlF0yHB1q6J705YpQlNkV3eUExCuM0SzhjjRNcjjGkJYh2tQTxfceIbZamPRkYui16vTUrbQRM3TTGCUFBN8P8cKAh8HZlIJcm2Cnn0VYQLsaCypVyllVc8xbXeRSSp7VmAJXJOVFKklWVNSgjGKecq6xdkczguwd1WM501jOdDwzjjkQSLWVc/3wp4IzCba74nT/4l+n7nvvbr/nyp/+U08u/ojzcrbETiPlI667ZXX0onb+8o98/Z/PoKTUXnj39FouF8+mecj5QKzx99gwz7Bg6x9e/+HPu3rwQ5qaW23ZOBdcPKNUYOk9NFb/agfJqbxt6S4wzSjdiyDjnsabHDBtKlpfLMGxk9ZyP2H7gd37yh9h+kJt4PKGVgpoo8Yh2IyUHlvkttVhCSlx/8Cnf/OxPOR9ecfNGymbjuOHu5pZyyPhHl8LMpBFSZLN/xun1K1o+o/tHjI+uqW3ifLij73Y4KyzZ08MLfN/xye/+S6QQOd3fcvnsM3RXmF7esr3e8sX/98+Y6oHt7nd59tHv8PTjj6hF/t7eStnw9sXXLHHBeMd0usf6kbBkfD/Q7y7wQ086nRmHQimGxx99H9tb7l+/wmvFN7/5Qh70qQi/OiS0qQxmTzZywUolvrewDf2WEGZCntGqwzjpCFitmOdANzjmGGXN3DTGyc+mlILzFqWNoKasp5YgaEAaRRRpkMvaTLfCpQRKk+lzLQHfC1pOo8B2KOz7IpVSGreu4O26HtdasGdmzdOuHm4UlVQy1kj2FKMpBHRR0Ay1/PX0RdeC7hw0TakJq+WQoZwTZm8uaKcJIa0iDE0sCylEnBFeptZCTcghUrNkapWueOuwoxBWjPbvWaPKQA0BrTpiKmwu9jzcvsGahimN6fTAw90L0uktXjdU19FWLODQX6B0x7h9TmDGdULZUX6DNmYVrYi+trMN70axvekmkzxVVhaupbQKtZBDRq9sXWWqlM2qpr3j+foKTTLkNckkP6SIagh9Ys3yttbkoFYmjIFU5JkkU61Vc+qMXGBAkIdZcEq0CmSm0wNWrxjJ0jiHI6VWHl09fo9ZZJXDNLVa6hp460R9XhZqAmUb3g0oa0A5OsnfUEKUDVorUCVS1w2rBj5VwW5VeY5aZ6Qfw4pCo6CbxHisteRWJZOtPTVn2V61zPl0z+ntW/q+F1qNLpiul22GVrQpaBafAAAgAElEQVRmKApcVdQ50uJC842qHX4coKyOlpbXyWQC63B+g1IrezVHwSg2KYotS8L3HlLBtEo8P/DmRkqwD6cbenbYcbMSFKxwlZXF2kxeFrRyTMtMq0F0xBQUjhjO5AydHak0+sePGIwhliR5+1aoIXF+8xozwmazI8wT5yly8exjqrbsd5csMbDdXZBTkC1jU9TzxOF4x9WzJzTj8Lbj7vYG4xyq69j1WxqGtMwcjneMl3u0NYzjSCueGE8cXr9kv91wjgvdcIEbBnZX1ysXWGKL1gm2q+977Jr9TTlQm6LrOnJM2N5Tc0Lj6Iwl5pmKJqezRCm6jhKFNV5yQivRbZe8kILEI6iF8xShykHbey3F0yLTdb9+BiT7L9/nGIrEfrKUKf/47/5H/MVPf/bbdMj9pP23/+hfx7sNYNlfXBKnhdIqpUGOD6B7+t3A9vIDprCgayKd3pCP96hug794hLI98bjg+w2NiLGKigPECKZsh/GKVCoWkQ70dqCkCeMNh4e3OL9Da6BU3n71C64/+hauGyi6x3dwug/Mr77g6Wc/AjxNN/kF1yQg7dZoWVHWA1drCqULeTmhjCWHimqF1j2ie/YhU1Kc3rwmpURMFt0Um4uRUis/+aN/ldPphMKyhCNWaZZ70Q/3fU8sM3p9MBqn8drQu4FiFWVudI8v5QWa0uoTz7SaIAdZqeGJ2mC1ot94lodbbl/+lHwfBdA8iLEF7bGdJywnlPYYrUVHmDMtVpkyeNB2BPSqAm1gLSkJn1IC7xltCnbYMZ3ODM6Rs+B68jLLC8objO4xxrFkySD6zgCZFOHy4inH6Z6cM503NIx86axBY1C6UrLG9o6SpJGvtcUYRymBkoUPm3PG67ayPh1JyYDBe09cgigKm6x2t7vH2O1IeHjFrz//33Ex006RUgMxNbTxDENHUIlcO1zfYfQ1qo24yy3bbS9rxxJRsa4Z2Uvc5op+s2eKkd3FFdNyRz28oFbLPAf2l094uL0BrbDW4m1HXBqpzqKS1ZVliQKtDwesH7FDx/HuDdthi9Ge+XSCXBgvLnB+y4/+6F+jJNjsd2A0rcy0nEmnN7h+R7WeeL5jOfyC09d/xv6DH3I4NpoeKCXRDZe8ffVLlLHsr5/xmy9/jbc9ZFE9b3ePyMaR0wPz/YH8cKa73pPzQq6GjX9M1JkcF3b7p+jeMx9fklKhG/Y0o2mhyLp+M5DjhCqJaX6Js1tU8Pj9hn674+WXX8iLr8wMF48xVta7ITyw3T3C+Q7rt9y9+Yqhu+bh/lc4YzHDFVoVrBmYQ2Q77ghhpsWFFN9gNKSs2e2fUpSRaW4pzPOCswatZEI3h8DYe1LNkuEsAY0hTwtu2KGqwnvLlBYptKwlTSWcLyl91orvurVUspY4OymahOWI0h1oy3a75+7hDS1lttstTSlyXPBuK5sRoylKVn4skvevFDlkuk7EE00J97pZsWytw4FWEr1x5Jbx3YZShU1Z6rr+1o1WMmjRnmrtKCWS4gTKoxXyM9GA0hhlySqim8FoRc0TGkdqUp4hFelGV0XSgUpEV4XxG9palAkp0fTK5a6GvtuBjAII5wPn+7eE8wu8dzLZ1Y3u6gkly+bOmZFGQHUXeBrdsKWiwDrqOnEX8UbBd1pEC35DZ3uU06R5xjoIIQnNZAoMQ4cqhVIqJQfJLRfhiDrnwHakZOh6B0qRqgDxXb/BaP3+4Jd1xBqHbY2cIygLWtivrilSkUNbbQmUCHhSrVAK3SovaFoxOAtF0wwsJQirPC3SFbaOVirBaIyx1CWijTwfQcvhdc7Cco75r/nDJdMZTbOOlhtmcKRUaEomoKrKuSLUhd46SpYsMVrjlEU5C07Uu1Y7UhLMHc1J0fJd4bFljHdoNdBQlOXAtNyhS2OwPWjP3XKPMYrT3S0X2ytyMSi/ZbO1NBJGaXy3RQ0bliXKdBRNVdJxcdoyDAPLfCI1JCbkR8L9kTQdycuJqhe6jQxiWmuUqKg5siwLS6w417Hp4O3xnl2/Y9NfUmoAV1iCvK+chb4fmXNBY1jOC/G8YJvEB5b5zHY30A9b2qaRK7RzZtg6xt1jUrPk1OQ9Z3rynLBdE4lOXXDV8HD7JXbcMlzsKCuidNjsqE1TzwulTSxlYuj3aLuhdw5lLTnOoDpG45hjQFuY0q1sVEphs3+OdgPXj56iTaNk+14QRS3yGW8FZaxslIowhI2y1BgoulJ1w+HxvmdJJzQidrCrDbOqhrEOXRuqQW5RpvClougxdhX1mG7lXwupQjCAkvEuqaC0p6iEToV/++/9A376+S9/ew65f+OzD9v/9t/8J6gqZpC43JOTYthtJEekpDTgXY9piiUnYcFZjVVgOyldlPNMN17QjMWgUK6TNS0J00lTUVs4HieUcew2A+RFECElEcNMf/FY0Ba1MGpHMEUa6tWIIjPPdP2W6RixpicXCcWHvJDTIjfO1iSi0BTaVKbpJIiWusLNk7SOYy1U36P3n2BdR7KGxo5PP/2UHAIhJ86HB1k75QnfLNYo5tMRta59GpBLFE6sgRwyqr0DzwtxoJaMxdGKRm1g8COpWtxmgBwI04zrB+aHGwzh/Qcq1IbzSh7MgHcOaOTlTKyNkjWb/ZU46VVF2YpbuZbejIQi6yujLGptRhcCRWdiaIxeMp1aa2gBciCVhldiAApKGtliW1O07GQ9qrW0Zo1MlZTpaDXKeiaLOUiRuHjyBK8su+tL3n79JcZZxs0jfvXz/5uN8YxPnmG3V9y/+jXT/MAnz7/FdH7L6e5Ems+kHNhebFFqQyiRznhcb1imB8GrNcNmf8Xp7RuW05nts+9SamMYKvNyxOsN5yqTLaM04f5ITEVegiXRqoY1D2fLTIuZ/slT9mPH8RSJIVPCQu8R/mhS8rnyQC2CsVn5h9Vm3CCw96pl0kWx9KpjGK+4+PZ3uH7yMf1mxI8jjfq+KVvmI8ZZzsc3bC+ec775nJoTvYbb88JoLTEa+ounVG1I5xsOh7dkU7n/za8Z+h1Df815mhivnzG4kd988znb0XG8eYv2cDg80A+XMq2yGpsb83xHsZVqCkM3QnPU5tgOUtRDy+TLkJjO94DC0uP3AykpmYpmxWG+p7MyfclVaBtWN87nM8p4Np0D7ynxQCkZ211Sm2QzlbEoMjlEhm5LqYsUoVqE6ujshsSE7UbqkrE0KRN1W6xxtBLRSigGqIR6N/VokJHfj1JAqmIRzI0YI06tB9NSqEoRlsJ2v6HWjO87tO9Wbqch54hSDt1kIqdWGogxSiQS44BeV9y5aVSOa6HIozRULYgyBWKAS1XoBShKDbRW8U4Me6oqqla4lT+t8NSyiPmpyIRZJoRiFZQDnoWqyTWhSiGWzBwmnCpQ5VmqV1JGSAWnDappgdPrTDUZXSxozeAHqhHjGNbQQoAYSLOUrebTHeTAMGrhw2qJh9RUadUS60xvB5QbaQa06VExgjL0w4amZeKlrMKbHoBudOQliTjCalEzKynOohU1i2yilkTTSabNBSFYuLZyRle2Ng5VV1nFuopXWhjKqaxiDa+wzeFaI9dIVQpjrcTZFDjnaFWBqiitMVqTYhEZiTG03FjWadrQqbU1D6VVtFWUqHBWk3KWIhASHXO9E1tbLcS4yHdOSwlYyqPyczLvLIulUZXwhecY1g5JoeVCW3PW70geQ+fIuZJSoJpKzQ2r5BAsFADp19SKmNeMaJcpEVUyOQZSmqmpkpeZqhWbq0dYaxmGjkQkZ4vVnSC8jMMqTWqBFAtKOeHn14JRf/1ZVdVgjMF5SwgzBsNyvMebjikc0G1mmWbSPLOUgPLgrSHFBdqOftxjaDTTZHszBVxvsdZh7IhzhvPtvfwcAV0U3djx9vY3jN2ezdUFutvRjMYrRT86TlOkLRMFgzKZZizODYQS6YctDhkokRKm1yi1wXcjvqtMpwnfOarW5JDfiyqsFSmSN5baIoebN4wXGzKKcbMhTI3N5Vby3rWt+GCNb42itFxUmmbc7WjNr4SZ+b1kptsMpHmmKY23kp3OIdOsmPtCimhlqbbI5H2ttrQmsRWUkElUkQhgTeW9sKO1glpL4co4jLNSai1ZaCtNtj/v/n3WWv7Nv/3v85c//22SQXz2Yfvv/uEfY7qR03TLOPZYu+V8PguAut/S9z3Tw5HBK1KDYXtFUyPWRlo+M92/ZHfxHLSmopiOC6brcX7Ajb2A20kUEtbvaDHQSiGeD/jBU1NC50AoMOyvaW6A1HCdBWNFO1kmjG0o3DqxrWs7WODaKZ2J8yIv8irlGMmgLThvqQlqljVjpa4PJ00rizg93RX68hPU/inGFrTp0MbTDYMUJQrY0khE0nyW/ItRLOFEnDK7y0eksNBUJSyL6PxUZQoLfe0ETzQKGlkZ4SIq1VA50HRPraC8gMNLBm8HAotk/5rk4OJpEqg/DVxPUWJbyUoBkZRhdFIWK62inNwAKQuqyuqr1ibNW60wrqN3PZmI89CqlNVqMTRVWEpg6Eb5MttBMkelolwjJgFlSx668YMf/QTlPKoFXAXGjpuvvpASR864/oLzw4nN9SXd/kLUiEWmK7e//kuuvvc3Od9/zuHmF4Rzw/WPePLxJ4zXTzm9+QbXjcyzTMnScsfrX3/O1dPnaJ355vNfYftHDPs9NUXcoLi5uUUVhdUdfhxJ5cCw27PfP+V085JmYbd7RlrumE5H8rKAE1Xi6B1VgVY9cT5hbS+5xxyFo1gKRVXcuCWGiaIK6XyWKbL1GOUIMfPkyROs6TlPgel44snT54yPLnH9QL/pmQ53XD77CNJCiCeW00QNB9Cau5dfoNlx9fwRL/78n/Hm5Ru228e0ZogtkOdA1prr508pYWC82EkrGIWyje3VM1oK3L7+mt2TD8jTiRJn7l7/huun36Eqj1GN3eWWh7tbagFrpMk+xZkWI/1+S5pPtBQ5Hw+Y3tP1I5vdY7H9qIK3jpoa8/GWoqXxb/sRqxRKaWkoW00rEw0r88Aqm4acKv2wUg1aI8VZDsoOliViEaRdTWDMqmA1GlWNxFRKFPlKzczzqnu1okitCryX3Cq1YRyiK3X2PdoH6+CdaWsJWGWJLVFrRGuPaiJYME1RVktaKYVchBErpIFemtFNDs3Gy4HjnXGoViExNDR1Xa2bdUrr+45UC6zqbqXk+2m1o6oovxPr3qtmtZapp/xv/V5PjPY402it0lazVDOakiRnqt6trL2wew3ycstKEEVxCUJ9SCeRY9SK7xxW21UrakApmurQNVFbkkLvO4ubsaQkRRWFwXZSnssVtsNI0+uLlXeymSaoJmdpJZFbJoeMdyPGWEINOCQOY/2AtoZCXlFh8rPWWhPikaEbhXMcw2q7rGjVi1bYuveIsloCrTWWOBNKZb+9ElNczO/FCwWJVXk0RcvPW5t1S6XUOo0XFXVJ8vkHBWLylb9fCcc1LoGsqmy5tKYkUQRTkzCIVxWwJGJWjW3l/WXNKkXI+f3v3SC61rBOZJVS1CTUCFfBeInFrDAhLGL/aw1iDCL+KHJZyaVRlYK8ctmbRGmME8FFaZm2Xgo658lUvOvfEy8qMk1sgNWWVLLkeleVs1pFHoeHB4kL1CYH/5JloBQnWk3YUghtwvkdpRryXHC9ASMc6/PphEuRGhJZFY7Lic1mg8rg/JZ5PtL3clly3nC8v6MBj548xXU7aoNWDef7W5b5xGbn6YYR70ZiVqQSURZCawz9Fu97tDV429HKTAmKGCfi8QY3XmL7DaVl/PaC6eaey8dX5FxoWt63JSz03UhOM8PFFaU18pIw1qINPJwe2G/2EutTisPDxND3VN2wWlML+HHE6gG1ZPrOcn880GqUgZUxHB8eMMawu3r03rD2LkJljEOpgsqCzktVoitF17VQq6lZOiJUET1hNLrK8KBq2WKIVEd06LlKOd8oveb1FX/r7/4D/vJnv0WH3B9960n77/+Lf49mm6yf1aqBHR/RjwMtS4Y2hYAxiuHiglQaih5nimRFrZWgdFwwzlBm0Vi6QfSxIUykUOhdL7BpqyEG+nFEaS3wbQVNO4zfrNibJtmx94174VPWXKgp4+wAaQGjSKsOEaBFhI2ZAs1Aq4k4nRm6jrgElJEP07sHD06jnIPuI9g/I2UIraB0pqZC14+0puRw0w88uX7C1eWW8/Elb198LQ8H5ZlLEOCz0izhRK4aw7rqT+I3p2bJ0NQi3vqa0SqTi6wzne8lZN6arGprkpiB8mgMunMScFcVSiWHdYKgAKWxfSerjBypaOKyoFSlkYQzqXr6bqSiUEoexNYbcknkMlGykqyzlslPSRntoGRZYfSdk6mNMlhdcd2OnBe+/YPf5fLxM47nA0PXEw93qI2jZU043XD74tds9s+wDh7uJzCaGs4oXdhcf4duHBh3V5T5RNWa45sXTKcz/e4R/X7LZhw4H0+cj3fM54Xt7pKcFpZQuX5yxXL/huPhAd/1xDARTrfCHbaDrI1rQWUYNzuyKpyPM08+/ARlNMN4STi85sWXvxBtb9fj3SixFOvoDETrUE0ycOl8Zrvdcp4eaNWQNbQV4m+MaEg1Cl0bMSeRByiFsVKQzHPks9/7Az7+7PtY00gl0nUdMUgZ5XT7GzYXl1gNb29eELLCakM83jJePiakxtMPP2Y6R4wyvL35mrwsPP/OD8kxcfObn5HLzONnP+BwvOd0+5Lv/PgPKCly++IL7m6+QPkNVhvOh4nh+oKnz7+L1p4aEm9vvuY7v/f73L36mofjLZvtJXl+w8X1d5hSFZpIgaHfyQpQGa6ffMA47FG6cD49SMGFhaoS05sDh+Nbjoc7RjeQS8SNljDD7nJDrorpvOA6S5wmTndv6PYdTQkTmJhpuWCNYjktjLtLSq0y2fONVhWqWYZ+Ky9nryGB9b1M9axMRV3nSWHBe8/5POOtRjlRcFalhYudyxqTkVKYbIf1e/yd0UJiyC2+L63Vuub0i9ACcmVl08rngVrR1hPX+E8tQvSw1somBIM2csHEaGwB7SVrG0um1CafJ2tkDel7uUA7T1trUkYr8dZrLdKElNBtXQEnORAZZ0ScERaxrNUsnQlt6a1Mqqc0MwwbYp3l8FUbRjtyXNYDeKXVTIwSH3DNYL0j5b+evKYU5XCsRUKRapMJV22kuqyXBJkMOS2WNCnOVsnpVokxYeWwZKwcWLu+l+e/gArJcxKkXxX7nkLTSqYqUSejDLSGVVooDUYmz0ppTAmcw4JXneRhQSbkvSUsZ4kKaC0TQGOxnSeHhHbroV87wVa1Sg5BSlWp0HWeeZpgPeRpJ4YudCMtMn1NYcZ2BmO8RDes8Fdbbjgjh91SJAOp1+9AKXIZKaWsl7YkGVQnbGbVNMpAqJK7LClSY5KY1rwwvT1iO4UbPP1+L9vWFeeWSsZiV2Y6mCYs9hpnSlbE9fmktcZamTL6viMn2Yy21qS45Tp0Z1BGSZFQaRSaWrLo6ksl1cY8n3GdxWiwSsszNFfyaWGZ34rmfpkoJjHYgRwDsSp24042Np3De4tg5L1MH2tEaYOxI7FkfG9I8/JefBJjILPKYNbvE/7ddiMxjiO1GVKQqWYuM+PgKSnju5HldMZ6L+9NIxlaQ0fvO4bNwGnlZLfcmNJZOOtdL3numHBeLpWpWrwXDJddqS6970lF2NCVQkyVen/m//mnf8LVkz2Xz55y8eRDciiUJeN2PeeHB7q+Z7x4RDaWrt9inMifQjiTUlktokky8HHmrBSD7jGqYs1ILQHTddSYOZzuMK1hnUZ1Do3Ct4HUKtoUtDWyFQJojX/n7/wH/Plf/hYhxH7/B99q/+s/+c9YSkC3fp1QanJc0C2RFHgtujdVMrkKrWAcH8tDsy1Y0+EMhOUk6wkjHMzSMu8AcFUbWeMCUUVUBoNDDRWnNyhb0AykVCi6ivzBas6HAzkk0BWauNrRGY1Mb1WVQ4z3smbPKUjbNldZ99SK0xZyxhpFzIGSBUVTmkXtPkJff0huPbVmaoWiCy1NlIQ0rJXhez/8Cf3ugrxMXH74MXk+Eg93TOc7ltMJZzpiOrOcJ2xn14OPYHtyCegmLNoUF6w3lKaoOeO6Tm6BSosCFAFzd9YRS2XY7oihrJD2E67vyKpBksak1ZL10sqQsmBZci3Sxi4VahC2plG0xKrXFJh/zQ3vhfKdy4RtDrSjVUNIC6PvyC0RS8Eqxfl8ZrPZYpyB2nH97BOG3VYMPdMZ0ykokvH94MMn/MWf/gnxcKIsmavv/R6Hu1cM7oLh8kNyCTz79DNabLz+5gsG53j6yfcYLq548eVfkZeJ25tvqMazv3oqN1atML3Hu55X3/ycoXvEdJwY9wNxnnjy7GP63ZXY6ZaFuMxoCtPhgZAqeT5yd/8a4/YUCs4rmQoYS66Fi+tnlLRw9/Y1/dDRho5xvyN+c0s1kMKCsyMFhe01utoVLxbkACMJtzXTx5pBNKQ8o1WV7Jc2xCXx0affZXNxzZPnH4CSG/QynyjzCSiEEAjTmcvrD3i4uyFOJ45vX8lkqdvT7zacT29JufLo2XOs25KTEnrE1RVm6DjfH+i15f7hlnmeGTW4bsBcXHC6e4UKM2/vXqH0CCqzufiYi8dPObx6wWA9MT9wOj6ga+b6wx/y7DufklNjWSZe/+Y3hPlII7LdXKEaTOcDl0+e8fSjbzPsrtFaMrU1JJwu3L/4mvn4krA0QnzFEntiLvIgLoXOe0KM0vjuBshFkDlobLOC6DsfKWFBdxbjNCCHgVZl1ZbbOt0UZCwFWbHNKTM6eaEopaUs04rwTNU71uj6zzTolJGXPVKAosi/p2lIJdNKxuielmdak2x4K5W6SgdUk01HaSK4kIuOrNIVhlwiKYlAoaaEKlIowshaudYqXOvWKDlSqqLvewoVqwy1Foz3xCQ5d6MVcYmCy7INmmx5Sq2E5STSh1W8UVJGPAxJeLNJ8vopVrw2NFWJOMmT5kAFSlgwGCkSlgXVPFZJua4hh9ylBHptUaZbD2cF0kRa0Wmqc1IKc5acEzVlBjeSGxgvHFyrLE03tHXE2uicl8x1EuuabobYIoPtZSulZULaSkMDVSXKSl/ISYYkgzfEAn3nqWmNF1TRCbel4vuOmhupBHBKJulJ7JKtVQqCCDNONnGFhtMy4W0WcggIw1/kG7FkjNGgNM5aUlpA+fXS02h1IRUIYcHYToq8DRG9kGlNJsBWW3IpvDsnaL1WkpRCGU9KM7o1nOrFxtUSVRUaQptQZdUv14jxUohclkgOi0wuVJELWutwg0OZTri+SqOso7XCcl4AKeTFtjAMHVY7tPai67WaECNgZDOpFLkVUsnosBbuBk/ThpKlbEkuQgLJCZMXIf8oTzKVnGAYOooDp6ygxqJsUWrN2K6SqsGbHqU0gUKvpY+jlZXnfJmhgEMKzugC/SilLuXIMUlpzfVC+PGamCukSjwfiWmSGBAK6yV77UaJcWkj2uDzvaC23j27GhlrChqHH3ohGjShX5S8EGrj8uIJ83LAomgFYk4YozDdyPHrbzjGI4/2PW685P7Nl5zv7tgN16i4MF5sObUZY6+JZaEzMM8zj599l373iNSyxOlUY7PZgtPUvKL45kj65iXHceLxkx8yPxz44s/+T+lb7Td89jv/Iv3+EeN+y3Ka8KajFsP59Vfc/PL/4M3NX9D1Dus9eviY//y//h/4+Rdf//Yccn/82Uftn/yjv8Wn3/2B2EzWAleOR/lSDZ7RDcK17DqWfMb5DWkB5+TPWVWhhrhOsgrdZsd8fMAgDxvtO/Iyk1OUdWKDfnBoDIeH15wfDux2O3S3wW93WN/TcqHkiLHSjLU0ljkKMaHG1QwiYf2iM4qOXAPEStMNUzWKhbqu9FsRVm5pmVo6mnKrJ7qgWyEUh3Z7qunh4ppmtwBoJ4SItmp6d37HeHlBWI5srq5waN7evabFKtYTZ6m5cLy/wRjDZuhpShPiCSLUlrCmcby/wRZDUorcskDGl4QfHCXIAfvd5MB2l/LgXx+y7ybVNItzq7u7GZZcUEoOuZ3S1JiY4h3W9WRnyQk22wtZJ7VIro0UM/8/dW8Sc9ua3nf93nattZuvPc2t21WHqwrssh1L2DSKQIxQGMWDjAlCQQhHHkCwMU2w5IERkwQBMiFCMhkgMmCCgwmCBCkgsJUgOwq2q+xqbtXtzrnna/feq3m7h8Gz7qlSkC2DigFn9J1z9vnO/vZee71P8////kPoEWZ1C6M6XOs9zhuF6/tGzQuuiyxJVx9NeobtjpIWXBVqnvnqP/knGfYDzTR+63/4bwh1JrdbxscZ/BllnNleXFOWxGazZzaO7uySzgfCpuOT73yAsQvOihrraiNst3TxDLEdsR8YT/f4EEnTS6R5WvXErkdMR3AFW2EUsLYhdsDZoiv1qhKS2vTAOS6PdPGMejpAOMM4PWRsKFAAbynTA+mQiWcX+ABpUa2qRkQWzBzotz3ZyOuDqBUhdp7aABNAKrb3yuV0AsuC6wf6fkfnej7zhc8SN3u8bZzuX/Lw8gX9+YXyRFumVctuf0GtmZff/jqWhu0Gnr/zRU6He5ZcsdFy98H7zMeZlNW0Qy304YLT9IrYDeRp5OHjbxPP3iLteq6vr2nHA7Kc6PbPqXXCuQHpA6b2HF58xJO3L7EN7l99wpJ0HR72Zzx/94dowBAsx3tNJcJaXn7w+yzHB3LLJHqurq7ID0em1FjIXF8+oSC8/dZnX+e0U+EwPRC94fblC52ymMj2bMPt/SdE61jmib6/YOh3PD5+jAEODy/ZDmcaYHPxBiAUVyAbDEWnPDiKaxwOD4iLOGkM/RbnIqUsOG+QNGKNo6yJcTZ4bNGl77A5Uy71up4WMSRZcBIQmbUYCD3WqLbY2EhtixappRJCR84ZH3pwUOqELR4fva6vo6EW0UmuVIz4NQABhEZBNDihZIKPLG3RSVMpbLoIT9EAACAASURBVMKgsq0YSeNEiFGnWGvIQqkGaUVT8KYjfYhoeqml5UzzSmuwwTIMA94KrUa81U0T3lFyptRKq/l1SpMzBufAMGCDrjxbgSyNGITWCs52WBypFFzvEOPUOFcLZMvSFjadJskZY1QkYIwWjSIUwHlPM42aC96pm7yUphHBxuta1RpSXbSZAnwxVFtppYEP5LrKPExdMUiCVaYczRi8sYTQqb43NWysVAtOAsb1YLLGsDbwwdJENCJ4XdsboGR9Hp8ahVxQA0+uSc1ftWFdpmWra39Qg1+nMpeqibMqV3GWuoxrs6UJcRhLk6rnsl1xZ6kySiKGHmkzwVkcgSUXmmgBF61TGc40gnE0cSzpQL/Z4do6SfWeOU0a2Rw9xkbVnBsHrVBo+E7RkGqA0pqhloIXlWmUnBFnsetU1xkLrdFaVSJAQKV/1imCDuUcHx4esCv6LOeM+KKPM98LRKkILgZMLiCKwBOMTomNwfueaTrgg/6sUjLNWMqiZ1sxDWcjuIY1ldgNkBqdD+TUMFEbEOOd+ntaXM9XpecY172WHEmBzvaKC2wFm4XDwy3TNOm9zICPhiAO2ysTd9icq0wRoUghLydyTmziHucCcbeltpnj7S1x6AiD07S3hwXnlEt+enzFvDwg1SKL4IdGuIiEbsP57pLxMfH4+Aix48mzt9mcPSX2gZob02mhmsrV9Z77lOmlJ/jE6WFi2PaM48eaumg76uPIcT6CszjTCOWBmhbuHr9N55XsUUphd/mUn/mlv84ffPeT//8UuT/6pc/K3/iVv0Bwlvl4xLtBAelVL9pPtatnW51+URvjOGLcQAiryappB15IGKM1Qkta9NZlZrtTiYPrIzIuyr6sD3pBW68HdzijgYZKfBrbuK78WtNpQU364S9pJudZRfilMpcjwVkQhymsGt0KriqLsOgHBBpL1ckdDDhbWdJIsFFH8ziKyatgO2KGt/FPPkMTh4ket+rMrLWvXffBedI8UvJIZwxzzbz59hcJQ8/t+x9yvL/j4mLHND1wdn6tdAqrK+y2rpMGZ5gPd/hNr+kqfc/Nq1d01lPyiHhHC4H5+AIryhd03TXW95pO4gacM2QgjzObiwudyJsIJpFyhVw5TAdqHulCT7ffYsTh8cxzwXjBGSHYnmmasLGnj4GFgjUJ4y3WblhqITrVEkrRuEuwfOErP8rZ5QUvP/g2n7z8AHl8yf2r77DfBcZx5Pz8bWpLlFI11G1JmM6xtEzor4hxrwVjzlAXJCWsD0xpog+R8VTougFxDhu1yNxsr8njsq5j1SUvIrhgyTlRq0o5mrVE25DcXmsxtfNXeYG01UhDBWeoKRO6DbYZSm5Ym3AxsIya+75MM8MwQDZUqzSMygKiruoQnCa7VcMyJ7rglf3q1BFujNDvt3zlh/8EiOMw3ZAOL1jubmjzPdZfE598jhCF8TRjrPDe3/9NLvcX7N94m+31W9TlEWmWzdUVH3znG1xdXfN4c8fTNz/Hy9sbxptvMt5+wvbJW2w2O7rNNb7vOR1uefOzXyanSkkP3Hz4bcbjpJ8xUxjOrqgZPveVL3O6u+HF+99ht7lmd33Gq/c/gNiTyyNjdtTTA5fnFxQM3niNCy26Pn745ETsA5KVQUnvaQS8bUSvjNSHxxvmcSRJZbvbUWfDfnvGcnrkcZzwkiguEPsdtS34qBOQT4uTPB+oSUjWQyv03Q5jlU9pXMfQdRQM1ji6s54iEJslFdXD1/mozYpx6oYOGvhhW1NmZdXVrbXqHLfBspSJmoS5jQSjzGNvNoS+o5lPo3R7fIiUuugUqAuUnNcGyb6+p7kYKC3j1yz4Uspr+YI1gTEtal4qAqZqkQhg3YoDEqQuNHT6KwaMtNUsqjIDFwJirOpmq7ryRUQlZ6sEAcBbLexbNsTtoPeAPuqUKiedloogrqqMqmrD3fmAWINUhe+rRrXHWPt6iq5GJA8I1jrFO1qdvHpvyXgNkrBWKTRW9aapFmqe8a2pZCMqCUGcx9RGt92qSXaeFZd1OmKdNrdSG4R1mlybyohMAGe14C+ZJsI8nXDB65QKneRrdLG85nwjFnEBtxbAtoeS1EQmzWOMo1ilAZS8UGrFqYZMqQ7WIEZfLwssZcGgMa7BuZXtasBqQZRzxqDxqiYoRi1Yp3p7Y9RcOM/kcYRcycsdUJRl6jd414PxVAMx9oSoMkHQoJNsK56AsRZjlZshoq+5yKdFN2AqJva0WnBYnU7jaKbhvCenBRMcZQG7Dp6KNGRO+ADj/b0aKV2EGHVTaTzGQCpC7y3SVpZvFPrujNYEMco0biYrAQCDtPQ69W88PiiWdGlUJ8RgWI5HpvmBupwY/IaxHAjDBuKOzXCGD7owA+j7HhsiGA1HMUbTTwHEVoLTIUVFkFTxwM3tR8zHI7UeyPR01tNKox8CXb/HOWixe03W6LpzahpZpgmsIQYdhFyeP+G0yhentNDFnjyfeDjc43JBQmXLjsbEcjqSThOEhSAwzwtxtyEFOJ1O5LuXnPUDp2nW7II6I3WhdA3vnzC4S9LoaB3Qb5UqdHHB3gX6LuCGDhM32FJY0kh0O3KdKeMrxpvvrGEtgrGW07xgnWdzccHP/uKv8rVv/AAnucaYC+CvAj+CyiL+JeBrwH8NfA74NvBnROTOqOL7LwN/ChiBf1FE/o8/6vv/6JfelV//Sz/Dd77xW7zxxueJZ8+JmzP9gAvgB0xp5JZ1DQZr+xlJywHjVKtjvSfNJzCN0J9rbFytmJrWpI+N/pnAPJ7wsQPXVKdbVbid85pbHpxOlWVF7zSNqHMurFGZmZaSmjbQFXkT5T8KFYp+GEVGRHUR5Dkr09V6ZlTP1USZof1mS0mLgpJzw7hIlULtrrD75+AHxmUmRgXBq2N/BasLYFUjqJt/NT3V7JGceP6Z5zx98w1KSYT9GfvzC8ZxpNWEQwjRUaeR8TiyubjgeHxFGk/Y+cjjxx9jXWN68TEtZpI4thdvI6bHe7MaCVQQnqyFYOh8AB+wRrV5UhOxHyjrGaQ6w4Zx+sFOxyNx2FHyQi2qGxMDy1wQVN8m0RJjT0qa5V1T1qnulPA+UlMmbrbkJRN6p7nrLWv0b00gI0s98PDqluvLz9HE01rGdk75k7aj685xXUTWSE4x+ho7Y2licE7XZ82Ak4LQwOjkKy+FsCKs2gquXk63OOmVEeoNGMFHNRUgBus60nICU5UmMnSKOVrTjUpRbI91a1xv+7R4cGrCSBXnKj4EUmuvo1iDDdiwJnKtE6ZaM9DWaZVhuLzmH/sTP0k+PLLM95Q6cXp8xdn+iiwzj/evKEdB5MSrb/8DTHGc0kEnLLKjiuX5u5/XiX4xxHPPODmeP/8c969u2J5f0G11E+G6SJqPSCngHKY1Hu5ueLh5xS7s8D1gAhdPn/Fw98B2f8arl59w/fwZh/sbpGTm00gxgbPNoPGP08jl0ye0mjkcDkTn6fuekicOd/fYbaA2w92L99gBY664boOzHd32HCkHpscj3bbTRtF3dLsL7l59RD4cla1pG95Uwq5jOhR2F5c0G5jnI8s8sxl2nF++gQFsH8lV2Gx63vvd30SMJzpPmjPiG+f7Pak05lzWz9yOisN3FpMdm35HaUUblqL6x5JmDWhwTotHYxSXtxaD1juNrTYOL0Gxf163S1L0MM5Z1/TNoGvNlmmo4xx0zV3mCRsiXd+/1uZqfGx7zZSuuYCWst/bYAVPsIFSE2V9bEP0GgFt2FtWM3CqBB9pNJqUNSBDi9wYo8qncsM6v5qVdPIkpq1g+ghWtEgwhVoLBl3Pz8tCSgeGONDFQWVtuhbRdXIaaSVrDnGr6/Oz9Ls9NWVKqdocZT0nttvt6iLvVHvd2vqZzZTSqM5oalQWpvER+p5+2IE0bFZuesuFiv5sJVfidlCu9TCsSW/1dTBNLU2xSm1WPm7TaPcyj8hqBrQmUr1XdFjsscETjWPJaUUtqdwsBCVeGGOQrN4J5TNHTRbLi9I/sp51fp1g55yxQfm0xtl1Smxx8qm+W82Kc54xIqR8Uo55bjposIL1gh+i0hxspwXsesY3GtJY6RyiqMZFz1QN9pmxot+fip6NpWpQx7o9dFbPFOd1jZ+zpsHVmhm6Xp9jr0OGYCxWnOKrgl1/3kLvVW6x1ILzkbQslDKTxmm9LhrD/gLnPcvDCSswHm4ppbw2+sXOEs8utTZyhtP9I3Wa1FRtJp5cX7CcGi4UpvGOJJXmN1w+eQeKSoiESLfZa0FvNAHUGEdeZjBCTZnx9EB6eEluamgtzbEdVHLiuzPC5ozgh7VJbPpzHR+pMjPPBR8vCd3aOMUILdOFgTRX8vGRw+MNp9MdmIU23eC6DZ0xHE4jaU7UPNN1gdhZvBjGrPSjrht4fDjSmYJ1QjFVC1EUQ7fpNjSfaKZhm8eXHtMG7NaTilA6GKfEZdxhzzsNuFgMNhuwDmMju23HcNbjgYe7W1xpDLuBFATbb/hX/vX/8AcbBmGM+VXg74jIXzXGRGAD/AJwKyK/bIz5eeBSRH7OGPOngD+PFrk/BfxlEfmpP+r7/9iX35W/8Zd+lnR8IG6fMOcT3m/xXcSLV7afmLUrLnRxQ0uVw+GBYdvjgnYvphawelNbDie80Wi/rtsqxiI4TPC0IlRJqkshv04IYu2yjbW69jcC1WgMb8k4I+S03pjaSCua91xaxrZGyVXTy5rBGYMRyEUPzJxOOD9QGbCba+TyHbKAzDMZdRlv40AWXZ+IsbQ00g07xFgEizWBkmeNEZZGaY0YLF3oEWtpdSZEyzSdSMdZixTTSOOJq7ffoYs93WZgmkeevfMuvvNsN+dMpwNmvZk1azS0IVXm+aQTgqRygeANr37nf+XVq3u6YJiXB1xUsb1zl7BbDWUr71OqYsPGPFOPutoqNbHZXyrXMVjA45pVIf+SoGSmpAYLvzlbi9xAEz2jKAljLKkuqgf2EWMcsjIrTHNUMsYvpGXCLglvHTmf6Lu9pp65ntBZ3QAAKc/4MCBFNwKlGqoU+mE9cEtmThPBb+m3q9PdVAxVmbwxsIwV1zusa1hZJ1JoWpIxhmZ0Sha6gBED2eB8pOQTJhpsdlQjKyJKDRXWqhQklUW/X8mkObPZDYyz3lR762lOCJ2mvNkWwCjKJ0tjG3dUzHqjn8hjInYDP/FP/3PMy5FtCKRyT22R4Bzz4cTmfM+Lj9/D5Hu8i7z4+v/Jw/GW0A0spyNh94Tx8ZFaM0/e+gL9/gku9hw/fsHx4YZghKkKw1nP8eGW8e6W3cUTkrVYF3n6/F12Z1eMNx8zn47gG2I6qvXYlBFj6S927M+fsT275Bt/8HcJ8x3itxi/o/eGshxp3pOzsN2dcTzc4S0sc6Xve7rQc/nWF5gfb7CihpjWeqblli5umE8ju74nS2MYBt5454t88I3fZ2kTm80Vu4srLp+8paEi62aIdOTV+9/lcHy10gAqLcC+uySGDUJmkYosC9Y7lpIIcSCniZYW/Paa3nhandkOZ8Rdz+H0yC72fPDetxQJ6Bz7fsfpdNCmqNto8Ro78nIgesX+1Sp02zMsid3Vcx5uXjHsLnEehm1PnQsff/AtNYyGtUgJ2hyL8VSp2KrrZ7fGxDqnK9dcZo2fTWqccU7Nj2VFVbGGAIgYQnBUJ3gcWQrGdWAdrdQ1gltxU85FnQIK5PV+XktRnJjRBCznLMs406wB0WvYWg+iRXlZCs6rVCGXieAH/fOaXkct11zUVLQOI+qnTPTWqDLSWkbEqjlpTpisU2S3NtFLTohkLaDFEcNAk0waD3TDGa1CnkZi35Fqwa6u8m7oNRhkjWa2xuGCJ4+J3DKm96p9tetz8p4lJWyt9JuOaVloUpQiNOsk0DnH6ai4M4dTg5o1DL5jyZXry0vuHh7IZSFYh9/sMNJISRFyqp/ViayxDSkr09xanfS6QMmLSiCwrwOEsksqgzGq37UYbLUanoC+nir/aEoHEo2NbW0Nemgaab+0jPcasoK1yjA3ajZbTgdl7lYtdFMpWHSz0Kpi1IbdOfN4T+j2LCV/LyBD7Hp/VPOiFYuQFWvXqdufUqlNn9dUJr0Z16oxvYAdHI/HkT5EwprCZ42hUqllVtmHBKY5k5aRvo/YaNgMA60KdUm6AY6eYgPRecWDloyw4L3TAl8M4nTVbo1KYZQ9b7Bx3eY5o8SBqlP208MNOVe2K8av1qIT221U9JcddFtQFc1Wm6OkAznPPNs9p4UGCHeHE5vthXpppDKNRx4+/oDL/RX54YHh/JpiJk7LA8PuCopgjkeWdCKVpjHgZkHyrDp2b0hNKIeRJJnaRrwb6GykyoGz/VPEqNGztcRmiEjz0BwYlX128YyxNTbbM8ymW6/nHtc8/WbPVDPH8UQeH/EIsd9AcDy+/y2oie5qQ54b/8Z/8Nf4xnc/+sEUucaYc+C3gC/I9z3YGPM14J8VkY+MMZ8B/mcR+bIx5j9bv/6v/uHH/eFF7mfl1/+Tv7BidiJLLWyGc2pZKEvCBENY+ZpzKkSvjkhrNPUK0AvdZawIWQy1JFhXVVIqvj+j5kS162QgBKbTA31UDYx1ERed5oOLUAqquaqNtJxoMlGlYFld7nWhedSwYVQ6UMuEyZoT70xV7nkaQTwu9rSzz2D8U0wXSM1o2pq3q0nEaia81aQPoqayxE75dyIodsgZlmVaV4UGs+alAXgnYBolnTh9vPD0i2/S7zbg4PmbX2GcjkjSFCM3bHAeXM3cfPgBMQxcPr3mww++Q11GHj54QTCVTENsZLONjDcfYmzDdU4h83ZZD69Itp5qHBICMfaUZvAr7sZ7C81R8og1+vqIdTxOBwVaV0eaT8R1BepsZEToO6faZyyizDFNyyoL1qjrW1oldjuWlAh9h62JXBrO5dfTd2/VEOcIWCckykqAKJTWCF2PtIRZoyp93INkQhdwolQPrFcO6Kp9FQq+WULsVD7RbXVqbQ01nTBYjAkgmThoXGhBMDS8mNcwet+pzqg2j7FqJul9T2kKEod1LV50hRoMNCq16k05zxVnGtiAHTy16Vq4SYLiSVmbLEfEuoZxMHRXHMYT+/Mdb7z1Jmk+8K2v/Tb5Ufjhf+qncM7wcPuCkhY+/PpvsRw+4vzNz3F/8yEhXFPqQjec8ezdr5KmykcvvsmPfPWfocjCXA5sNhd0XcfH3/wa73/rf8M3y+bsKY8Pd/g2U+cJLt5h2wUOdx/S5IS1Oy0O3HO2V+fqzm6N6XSg3/YgE5vhglQKeU4YBGLQyXTfIQnSMmFMI69GmYZl0+3UxNMK82nExQ7bGYpAF3qMNOY0UeaFzdDhguV0OLDb9EwlkKQjBMNyOmLEs+0iJZ0Q54nbYQ1X0AbUOacmV9E1eeji6iJb9P0Sh6mGVmY2mw2ZijGdBl4Yw5geiF4T7lIquOCRrDp4cY5g7OpwF0paECP0nd6P0jySqzAMAzVYjo8HdpuoASKpIA2qFWLsWBbAKHEhGKvr0DBQqWuo7Pc0noCmnTXDlGc1ColyME3zxL5HpHE8PLDbniHOEXcessZ2NyzbYWCZG9GveteqOlsjRouyuiZ8Ge1kXRh087HKyGpZ+bJWNxLeaEHReYMAzRaVc4kaZ8VWJDemrPxjQ0NMw0VLLqLcZOeoUnG5UCs0VDLS77aAQYxgmk7T+7DnNN0itTDPMxadKu6ePH0t25jnWe8fdSEbi/dritxqBKwrEcCizWs6HNjvL/jk5Udszs4xDubjATGO/dUZphnmeaTvN2oQThOtKWWjVcPubEdKdZWdZCQXnDMstXE8Pa5Ypw2dD1ivmEVnlGhjXMD5dfvIp4VW0AQ6o+/PbrdbtbKOsjR6pxPDpRXVmxu9xsuiEe7er0MKazC1QdSIapVm9ORlZh5P2Kix7XWZMcZrzL1peLfVCGznsGIpRcitkFJSlnt6xJqOznnqaSKayFwzYbfDe9W6N1OwG0VlNoTYnxF8JHudHM8pEXCIsTgipWpEvJeqjV/KVFuJRglD83IidhvKGuDSZMEY0ZATAcSyVDXuVQr5OOGKcMon6CP77UDJC7k0bBO9rqVhfUcrWSPjHcQwaE3lHa0F3Joap/6dRimZXT+QlgPWB+aUGA8jttPnYaQp95zCcipqsFyTL3NWbnheThTJTHcvSI8P1DIzlYlhd63VQzO0lug3A4ZAFgOmsow3bLdb5tHQ6qLJrZuAeDW/xrZlnBPn+y1tfsSFHcc8YaNnCB2+OGy/g85iQiRUjzWVJc+AVZ1wLQQXCc0h1vEwzwydo7Oe21cfcLntuT/eMKcJ7xoswr/9V36N73z08AMrcn8c+CvA7wA/Bvw94GeBD0TkYn2MAe5E5MIY82vAL4vI/7L+3f8E/JyI/N0/7P/40S9/Vn7tV35BO5xa8L7DrIaxmgu1LTjbUUQnWtZaqGtyWRc1rCFEaJVWJkpNuG7Ae6fu3NZ0IlcL4j1pPLAcZ7b7ARcDWENANWrNOuXvrRqZRiVTyGWkHEbVha1rr0ZlWQ7aZa/Ils4MzMuoq/ySaGJW1iOI38DuTVq/J1uPcVq0tSbK5Haade0wLGvHJ7UhOFJKGDQpKJUJ53tyXuh8B8bgVxwLtTC3kWdPv8j1258lLSM2GKb7AyZXimtstwOHwyMeS1tROq1kxscD83gHpPVgq0ynI67bKPVBisLim04JvAMfek6HR8W/2MhSG8ErN9AOqoEqzTLEjpKn1cG+J+72LGWmicWZqIVbLkrdQUH2wVvKsuiHvxpKk3Wqa2g1Y41GUCJ5Xc1mnFVEjH+dsb5SI0S1cdF5XBe1e14yoJijlGdF1XhHNZGAPt5m1ZRlCmG/WeMjK3ka8eIwva5W++hZphm/G5Cm2r6asqb4WeHm1Qt2Z9c004hWDzoReW32yFX11TkvSKkMfaQV1WMa4zCrq7nWCraujVjBWS2KLI5cE81YaIm6ZNVmiaZV6deZaR6pjy85f/oGNzc3bOIZwVnOn+74wo/849x8+/eYTcU1x/L4yPkb72KHDQ8f/C5+c8GzNz7PB995j876dXpcGacHPvrGx1g7MtfE87d+iC7u6S8uKemeYDrm5cDZ9SXL8Q5H5DRqqpbz8HD3XQWLh0FX5b5hpsru/IK47TBxII03PL54xdWTa+5v7pEQtFg4PUCrpDwSuy1m2GGbAuyHYU9eZlpuGjYC4PU+4oed6v5wNNuQlujjQF2EfvAad03VFKtpBhdxXtmwIagZxLuAsyplWaaZEJW/KhVcv1u1miOlJLo+MvQb7j/5SFFI4ijG4PqeGAKNov8fyq/1XteSOVX1IRiNjO18p3xX9HpJp4MaZEQwqVG9pduvASPG0oVO3eSAC4PimkzExFVf6fU6dKVRDWuMrVBqUlc3aGFmAkUSrDHpbalrUSqYEDSwQsA6wdS0MkwtxmtaXEULVbs29dErLs56q3pbF8gt411HyvpepTSrYbHp5xagloSthpI1z34uuhY1zbDpt2tcsgZLlFLot1pYiKzUiGowUkh5Xjmx2hgaGmRNJRSj2MeyaNqS6To675hPGl261IlWAvFsTxdUVtaFSNcNmCY06zBVcWSyTlbB4LugRZX3uKYDkDzN0BkKhuh2dD5wejzgN5FuUL57ETUZHU/3GCp9t+dw/wH97jlTWohWo4adc7Rei1Ef4+oDEfCRzndM44LxBm/VUGxW8sU8a6DJp0gvKxpYotpuDQBy1lLEq/fFysqGNap/bVrY+hjIqxn5U46u8aq59qtRyxjUSLne/ypN5SLO0wWvjPTWsL5H6oJdI4ZxlmYtjQbMSI3YrFPX+aShF6FTEkTnA3WlMfSxA+NX6YvgbE+rWTWqMVJzInQ9JS08HI5sgkGabuds7NZpqeL/KoVPA3Qcluk06vcQJQgE45Q37RzFVObxqFxiqdSmgU2lFEzR8Kn9fo8Jnjll2oyaDIMnFYdpleDUvG5jpx4OFM/mw6DvHULNE1IbMUbyGnpSqhqPyyIsrdDmhEwHfBBSGvH9HgmKWfOmIbVCSeTxEVc8c1GMq9PFNq2cNASkGfzQ0W8uAU3da1VxpaBhF6fDqP6mIazPdUuIW6b5nvn0SMszw+6cJesGrd/tORyP+OZhKfTRU52e5Wk64mzRIKBS8SGTF6Vm/OJ/8Tf55h/TeOb/mI/5CeDPi8hvGGP+MvDz3/8AERFjzP8jB5sx5s8Bfw7grWdXmGpxrmK6SE0FcXoxET0mCVhHiJvV0SpIc+zEkG3FuI0mZhiHqQ7XBS1NV8atcRFHXvVNgu87ukGjSI1VQ9iUHjF2S+cdhYqtjWILLWsqjV0B86xA8WKURTrEAWfW9B6jazTv9WaQldqjEz5jcbsLZDNQvWpOgzdYq8YAZ+NrXVUxTSOCraG2uhoqCs5qyouwQaSpbsmgjk4qoanmbbt5xpO33yGliePdS+bTI7kmhrgj10Kdj5S0YLaXOOOZpxMtTXQBjnlhu+uZHg60athfvUHKJ6Ypsd89o+QjZSna1ZeGdI3+/Dkln5A80w0b1TfPBb94cvCEuGEc79dC3zKXkX5Wk0SrDWOUqWiNGiVqKYgXjAxM8wkf1cVtrKdMC+01Eq7S8vK6wdACOBOjHjZzTXRdj02NEKNORFqDZsglK6KmZJ0e0LPfnVHLgpPM0J2RUiJeDrqGq5ZlPmDjgFSVQJRWsE1vluIsZtcRvGrhkjS6rifXRhDH2fmFkgFCwNrCeDoiVZujUhL7oSctjdhtKUYnRtE68qo9NE5oprGkQoxCnUZciIzzkX5QQxVSMWYGKm4TadWug8SKd5pn3232ZITDWLh6+jbb82d8+as/Rk4jab5nc3mJPY2q/U0T99/9GuH8kiI99Tjzzd/5DZZ0ojTFx7zx5j9KZw3X1x0ffPyK5++8Q7CVeb7l7hvvYeuJlOHtL36Vs/M3mDdXXF8+w3U9rz7+iHQ88slHH/LZr/w4E9UhuwAAIABJREFUxm8xttJvd/RxwJBJufFwd0+/f4t3vrhnevwI291SqnAaH7GlUOYT9B2TVMqLl3SdURNKmjHWUebCZrOBvmO7OUdaYZke+Mxnv8TXv/b32PQ7sii72nhDaoaFzGZ7zvXVU1JTLN5H3/oD+t2W0G3pho02wfPMcTzoVoSF+8ORTdcjp4lxHHFdBCrTY2Hwb7M/u0JkpJwK6fGGM/uE03SLtwb6jtosNmyotZDnio+Bvh9oKUFDTWGxwxlN5rIXXo1XtWGt1wbPW53Ytrzmzl+QkhpovV+1rTiqqFnM2gDR4koll1kd5Q2WuxGkgGjyHFZWk5zBrUiDhtdQHB8U2WQtTSZA8YDGql4/oqtvN2wRqUzY9bXR4jfGiHOBKY8MsSOVyjAMq/Na5QUhBJbFKMarWhDPloq3KlcSKsFFNQSLpdv0zOOE51MaTCKnupq1ErktuAIh7gF16Pe9ovy82cFGwKlMbpkTftjT7QLnxlJLQpxwf3tD6B13Ny/x3uJjT+w7NXE1oaYjramPYJoyWQpubqQp4bdPwAjnm0vaslDSI814TD6RHipeLpnzwjiO+JLIxpAo9N09VgLkCXM6UAZPsR3BRcrxnuC8TuesTtzbdMRsHPuzgVwN83LAzTMhDJQ5YWplbpk2H9ifX2vsbMn0q6Z+qVWTh0XWZmJYtd2WYB21qvt/OUxkCpILtQjONqroAMp0G30P7BoEUIUqqv22VpMrl1knxN5rLDTSaMYhKy3Cx4ipFSk9JggtNrzsiGcqe2mi/6+QiQQkQ7KFzkfasurLrTZEy3iizEe8bbQ8M9VM54yGIAWrZ4k3UJT3WvJM7JRdXmkYZxjOtljrkGkCpxNVH5TY5CWQ3YRpGTHCEDyzUdYvwRLFsbSZNiujeLg8w8Wn2lQYfd8GF1YvBRr6YfdYUynZYqwjOIf0G/KSVdsfhXk6UNJEkC3GK7XIbrcYcwXAufcs04Nu96pQmspPxpLwz1QOundA0zAOEyKSLWnO2tCbGZrSk4LrcBbkOGBXg31/8UQxfBaKFE6HB5bjjBuEzeU5vXtCToDLZIB5pJZCN2x0q7soVtP6yn5/TlpOXFy9TfUaqiVriqtzf/uPX2v+MSa5bwD/u4h8bv39n0SL3H+EH5hc4fPyN//zf5dS1PHq8EjTTkX1HkURXoPyc1tpynQkrCEDDStCyQtDt2UcT9jYkGo1Uch4ghHmZUSsobebNSnsRNxscd1AdBFozOPEUiYMns555jxrqITvqHlGloz4FfotSVczyGsaw3/5p/+dP/aL///Vrz/9P/73xLDl/M1r+tCzv9hz8+Jj6jJT2/diFMN2j486uZ7uXnE6HBk2W8bHT0AKxgYFk4toWtkqi3DOUalsug25nMhGES/GVrANvzIqTbSMqeKN1wM1F0W9YXQ1JdDHgTIL0arOK89HiJrXXpph2wcWqXgTKUmTZ2wc8ENAjK5mWmqKwpkTfuhYlnmNLs6UVuiMozZdf36a9BK6nnHUtR5WjToh9uQ04b3FuUg+Fezgtfs2BozDhogzWTmHVhmifadGuBCVkWyr5bQ8Mvie2sA5T2nQR8eUJnzoEfR1WrKaUUyuasQRSBRaTRxfvsBb6K+eahynhdN4oOVE30Wc21KC5fzqM3gLqRRwjel0VN1cixhT8aZhUDlOtY48Jby3HB9P/PBP/hOUeYFWeePNz7CkR9IygzgyDbscOd6+BO/Ybs44CXz7H/wGm/0lF+dPOb/+PJ98+DtcPHvGJx++VG1ov6XfnlG45+r6y+yePKUf9ty+/Ba0zO7isxxPiSdPrpBcuDve4FohzUdaKtTgSVnd8svpBQCf+/JP8Pu/+w2ePNnx4hu/TXf9DpITu4trnjz5LGILDsfu8pxxesBbx3Iayanw4qOPVurCxLN3foi33/kCpQp3L77Ft37/71PzTOwu8b1eHyIGiqG7uOLs4hJy5b3f+w02F5cUCSoQEs8+WpVVSMEOW3a7HcuUKBW2mz3WCEkycdiz2ey4u3lBmSrL9MD4cGK767HOU6aDZtabwuZsh4urxrwFuu0ZGEu/GYgx8vjqFh97SpqZxhP3d++R7j5h2JyTxULY4l18XSxUo4XfXCqmgXG6Xn+8/Yjo9VCvJRP3O5x0+F2nyGTfYy16WGW9XpY84zywEkIUNyar2XaV8wDON6RpoZBKxvqI9SoD+9RUF71lyUooca7DNOH24Zbd7gzvAsGhzu8ugAmkmqA1nWqLGo9iDBgjRNdRV91832006EEssprr9Clndctj6dDCvrWKRK8rX2NoZKSyBkfoz5jaREoZWTJlnjE2Y11Hb3uOZUW4RYvMj+Syo5qkwQ+tUetCtwPbIssyEUJHcB1xt2G6PyHLopxS0Wlhmj9hKQvBWIz0xO0F3XZPNZGuC0hd8PsN4/EAkxJUYoxILjyMD0rJsIbx4Yaz3U4lVlis7QnDjkrChWs2XtGWxzwR3AVnZ2fM1ilwPze8s0jRlKmaG6aL+E6HML3TAVIDHdwoluH7Nmke21mW8VG3ZsYznhK56qYk24XgHd4EjKDXlLHqQbFOsZvO06QiQAgRF7rXm6u+32lQkxF9XMlYB2ma2W4GbLUsRtO7VNNtSXmCamlOJRrOuNd6YYxhCGFFlhaw4IyjIaRW8c5hVl1ypik+LRik6BDOWosP6kNxRnRIYjdkV/Rz00CaVX9PM+SkjWvLM6YLVOc0WMU75cevnOOcRA3EZt3mURGnRCAXPK2qSQ6E1gwWWeVSK5GhZqxtq7SprtRJxRAGX5jGDCVjg77um91eN6Sim90ieU1udJSUiUa159Zp6Ext07oh10TEPM8Mm477+3vqVDFSaC1xfnnFaZyVLd/3r82f0jzGGUxUmVltHrdGMDs8Eh19t9EAnvEBay2PpyPdEKnN8K/93C/x9W9++wdqPPs7wL8sIl8zxvz7wHb9q5vvM55dici/aYz5F4Cf4XvGs/9IRH7yj/r+P/alz8l/+yu/QGiCeM37toCPkZoalcKn+tqSJ4LplNe3wq/TshCGXqMMp5FhvwUa8+ORUgplOtDtzujOn6zIL8WUdEOHGGUqllIQa5U36Szj6YAtakposjqNP11xOyilISyYqjxH0gI0/tqf+aU/zuv+//rXL65v61+U//vXf3F9K3/6b/8608MDFtjtn7N/eoXvLOPDHdt9z+nhHheVPJHnTFhz0vdXz8npSJoeKccHNd/FLbtnz5mOB/Aw3X7Ebn/O7e09nbOY3rOMmeB7lqwfYKzRFV2r1E9lA+JxXa+JUGJ0slqK3jSMoXedopJMZGnq5I4mICTEag47n3I/q06VjLOqzawWKwXTFDc3lkTvAjboiqzMGQGMUQpHrRnrN8Q+KCrHNWRNfJOqmu+l8jo2Oa0oOnGRUivBNsqc1ujXEW8dwUaaLPTecH9zy7AVymzA9bryDOr+FWc1FW2cMAJhv2Gaj2z6Lcu0sIk75nYklcx20AStmgvOBWjgwwCSdUVgOorJWCNIUXZpyZoOlPOCNYNKeSzKxzUG8RZqw61JUQ93R66vr3n3K19StM90pJXKZrdlajMsI8vjS5bDR3z0O7/H2btf4vkPfZVSCsfbA3e3Dzx9doXZnLPd7nj/679JFc9bX/4xemt5//d+k7H0BNfz5he/xNWzKz547yN+97d/Hdsq++2bDJs98/GePvQkpwX2p2ipuWYMAUNkGzcMG4+Jlv7sTZ49f4t+v+Xm4+/iveP+8YH7D7+rrGEjdHFgmh8px0R/fUm/veL997/DG2++zbDtOb66ZToeeeMzn+F4PJLTSHWO58+fMy+F5597h7tXN5TSePvNt7BxQ8oZvLKGH25vMHnClJHJW9JYuX91w367oSE83N/Qd1sSjbiL5OOEqb1qxyncvfw6HqEtmYu3f4Tt+Rt0XYc4z9PPvEXKEy/f+wMeDvccbm50EusaeS6EPoLxtFCJwdGs0mBqFso8QhNC0ylqzhk37BWVVXWFK21U8kLsMcaw3W6ZpomlNJztccMZVmb9HFpDXmO9c00Er4bMtlSdntWM8/E17cC4RiPi0MIEANM4TRObzQ5odL7jMCof1XuPb5ZkVDa0CcOaNthRk0rC+r7XCVcuuNjj+wFsoJpGh6OJ4qNq1YFD8APFVFKeFZRVDNZqYdbHjlIay3TQzUst3N++IueFIvDs+XNOD4+0eWazDcylcrbbaQPaILe1oK+J0DT4wPY9tTiG3Q7jOmqd1DBqVH4kqWDo+fwP/zCP73/Cw6sPqVSsyczHAwWltuyurmjVYaSoHC080SjV4yfEsAHTaKkQDBzTPd4kptsDTjLH0yPs4MnVu9rgFKEQuHs4sjvbEMNW70UF5Y876M0Z03zAdtogtZTxdovzolKB2gjdQL9V2tGyLGpWDBrdnMYTwSnmzZmoRIZOP6PWe5UUTZnD7UuW5Y5aF4INDP0VpgtEH/BRPR6u22gELGvwhNNo89YEK+Ddhnl6oN+oVEIRiRXrBGmW3rvV5LQafdOMtdp0tazkj1IKuRWi0whgJGDcvPKR9eey1ireMDhc0yaprAg1EWWMiGlI04kuaGEszeFtRdZUu1qE6Fd9uoBUxbYZo6xrbwPFVDwRH4S8MrudM1QxgFf5ZrDg9B4vrYCPr5s4KQvWBZZlIZqAHxzzclID5NpEGJyGrRgBccqEDh5MpZWMDxtqLjRZpRZS8Chn2VoPTU2KbakrQs0qn7gcWXNBiF3PPB3xPjKnhb4bENFQExHDOJ9WelShD2rEm6cjx3zPfn/Otr/A2A4fggZ85YxZA1xEGsfjoxJfjFIQ/9Wf/2X+4Fvf/YEWuT+OIsQi8E3gz6Jjvb8OvAu8hyLEbld97n8M/PMoQuzP/lF6XFBO7n/3n/5btCaalmMbzUY1jFlHRjuRT1cbRhS2IMWQS6Lre4wV1bykxOlwh0kLsdvRXz7DtAUftqSmK3+Lx5oGplBqJZ2O6wduv6JnHGKFmpW8oI57LWLbamqRIhijTFNb8ppeJvzqT/97f2Qh+od9/Q//2R/26/uL2e///ff/25/+W7+GiBrUWhJisGz2qnd0UnAxsB0iw/4c43VqWueiB8p0D8vE7skZL7/zTfrhHH9xpdKIJCRmglPQtIuNx9tbTNX4WCPaeZeSyfOCDYFCBhsA+39R9yaxmq37fdbztqv5mt1Vd/pzr2/ja8c3KMaE5CISSJQICYEEA4gJSGEQEVCQjSULARMkBkwsJBAD5oklhJBQQApkYEV2nGAj4tjX9m3OPfecqnOq3VW7+ZrVvC2D/6oy17KwBzbgb1pVe9f+9rfWet//+/s9D1V7KIFaHNo5KT4YJTvnWtFFwfL/sVaYyc5qYci+3vws2lC0W4QcUHOgbXpqrqCXCZD1b/JfVRV0VhgjObA5ZhrtSaWiGrdkeusyoauAZNUELG7JWS0WKjmOFXj7RMlhURKPnK0vmF59l/n2CWlsaM+2DFmxWr3FEAb6zd03sYrDONIBTdczzBPKQyozDss4zmQFq75FmQ6FZPeaBYheyGgsSmvmaULXKizexr5RdOqsSTHifI9TkNNMVFK+QClymAEp6cRY+OSj32Jzco97999B6cw7X/iA3/rm/87KerrNKa0r5DCyPbtHmhP74ZrjcabJgbP3foTjHPnKT/xZri6fwDQSx4nV2lNxorHdnvL4+9/G1frG6rfqPImJt977CiFldlcvONzesL7zBWlQTwP726f4TjGOsN2ecff8lOvdC6ZXz+naDUd7zt0HHzBOO2I4stmccLh8grIeTCYcJqY5oXIkKcfF/XcYb2/ZDSMP3nsLkwovXzyi6bZ475nnWxk0VIjHGbc95fT8LdanYuH5/JNvsj15wHxIrE5OWJ1sySWxajseP/mEO2+9z2q7IhwOPPr4m/iaef7pZ7jz8zeTrBAHvIW2bQjDnnZ7R47rm7ucXmx5/Ohj+m7D1dUNpmbMumP//CWNKdi+pdkI9mqzPgdlaDvHs0+/R0iF9uT8zWRJY6hTItWA9Z5KQ9rv6M9PCDGLlTEcmIaZadxTSmF9siWhYB7AZOYMzUYmw1UVtGok7lBFTJPyjDJmackLgrH1DcraZVOX0XkpO8HCho5o5E0OgDN+yQQP3Nx8jmvu4hvhrCYlmUPv4DAcaZueMM8YLV//TdsesL4njPMih82EEmk3GwDSGIVuYTXKarFXIrSTO3fuEMaRaT4QYsSrlhfXz9h0vZRxyIRUSHPi7p07TNNAsbBaX6BVIS+M26w0FUeaJXfpmw5rFFklalI0F3c5PP2cNI2omKGMqAVxuVptCMXgW8M0HmWSrIROc7y6pfcNWSfhxeO4efmcvrE425LySKmBkGY2JxeUakhxT4mFnBXdakMyItWhakiGxjYoL3zWcZhYbzoZGJiWkjVhvqbGTOfX2MaCUrTdCdU4phjQzOQ5sLv6FBUmGQZsTun6+zTtuSzAfGWxMJBzplUNp+s1c05LmSpTlRHT1ziJPEZLjhvzWkSxUAiQ+6/Ci8xESy8jV0GkGWPk/l7laxTyG9OX0gXjWtK4ZPI1sjEj41RPWqakZHl2hSCLeK2NZFCtFILJhZITWEspwsF2rpFhjlaLLe41ZaJK2UwJk9pbi3NuwZFKcfl1mVi0zIGsfkevbYqQKpKqS7xNU0p+IxzRWlOLIi6bldcUo5gT6Ip3MhVVGJSuxCrXW9WRNBaUkey0aw01J3LlTQclLZ87U/VCR4mEHDBKUeLSG8kJpTTeZsY80eiO42FH0y5dnNfM/BixxrzplkzTREwydOlsi/ZQfcHTUrL9nbWVU3htGIaDdA+0JlEhRUINlBr4Gz/7X/Ldjx/9oWVyqbX+E+Cf/j3+6C/8Hn+3Av/BH+Trvn69zmKiK13TLYYPKUKgxKGunBFyQpqxpkErjW4sbbch1klA377F9wbf35HS1uJF1xRKDGjTUEtgmgdQEW+lubo+vS+52VpJJcl0UMlx1ZymJWy+ANpLxSww6pql/Y7WxJwpr6cW/w+v372A/b0Wqb97Ifu7X7/fn9ckHyjtNNpU7rzzgBQiicRmfY+z+++gSwbvicc9MU30Fw0pzJycvMv4/Bk3Tx5z8d6HXH/+EHNYM94E2jt38bUlpkDfr7k9XnP+4IuE8UB49hmmaubjUYoV3jEe99j1Bu96MRhpDxj0kreuTpNCpFKoWpOD5G6sd1QtKCivWo55EFi2VkBm2F9hvMZgmeYjTbtdnOoWq6Bp5GIz1soiMFWqURTEGtSqJJKLsixcvSeGSax02kjGuUiZp9bFgqMLfX/COI7kmNDKcHZ2Qg572s054fY5Tu9IOuN9JdxeYvs1x+Njmv6CNN9i3IqYFF3XoWNastqCnlM1E0rB+pZV10qsYsGItd1GQOTLFHwKB/k8xoruW/zak2LALSU4Vxtql8lzJJNJuVDKRMwTIQsXWjpDlTTf8OGXPiQWyzTfYlvNJ9/9DdpFH3vz5Huo/VP85pzD5Q2pZEx7wj/15/48L/aXbNSG+uySm8efMF/f4voVpgSePHzI6t4p4fNPyNVRasOdr3ydVzev6NfvsD1ZEUvhZghYDL65w3rbcPPJ/8Ht8SEWS999QJgKH/7oPyc2QlNoNh17U7i9vEaHT3j26CFN+0XO79xFG8+9D3+Edr3l4Xd/jeNxjzNKilG7Gy4f3iwXiOXmpoOYMbZBLXY7rRyHwxXnd+5S2obqDZePP+bl08pm1XN+/4d49vE3UTpzeXWgyRbvNDkX1ufnfO83PmHdbGibDhUCV9OBe194h+eX17Sdp7GOfn2X3dVzooNcLePtNXmecfe37HYH7ty7i8Yw3zxnniPxco82he7sAb7dUhtPv6kMV7egMrt9ZnN2h9vLp0yvnsrvB+hXp/h1j1aaOSZquAUin370ffr1Fuc7EpHt2QW2bzHKE8JE1xpqv8XYFRuqGKesxWsvRZcShSYw7oQx7jus8ei6MLNVRWlHLovxUctGqus6apXojpR7K27Z9M1zxDZbzu98EXIhaSVKB2tZ+Y4xTGw3Zyhr6PuVaNdjwRsvnFoVyfMopRTKm41wvrohxkQxkgMtyTLuD+R5ZnW+RVvH08dPBNs1DvT9GkVhu9mw6s4Zx5HWG9qsiX0SGFe3IdfCOEShcTQ9IQSc1lzf3uA8OFMJ4y03w4FCptEwDk/QrKg64DuL0j1df848FVRj0DFSi6FpjDzjXEfVkdX9NXGesdpgl2xquzlDz4nb/Yh3W0xb0fMNrl0zjiO+O6e2FaLCKjmS99ZhbMNwDIRaaQrYqnjw1l2G4w06Ky6vHmFMw2p9Qtt3xLlSgTBHSFekWhiPO6bdDkPENJZ2fR+32tC2p2jb4LqOqgo5JUoSMhJhYs5XPLkdqMZR6kilp/UX3M4HnPdY7eTSzBlvHYd5wnUSK0xxou22HMenaNVRcpAp9XQLVdHfuSsnCDFTQ8Gve4kEKDgcbmi8ZNlTgGbVkpdj8WpkwaV1g/ZakHoVaoV5PMgpS5ZoQKZgEO15KQlvGzGnoUmqkmrGFgO1YLWhGCMRk6pIIUOoUio3asHeZZGzWINvBGsapok5Z4wCRUG7llgzlRnvG1ItstiOCWWWxW92Mp2dJpRTwkDOBXIk1RlFI3KFaaQG+b34RY877wdQ0hnIVeKIyjmcczS2paRK4xscRSIpSbClxQoUoMR50R4rVhvZrIdpwptCrVbKgrXivCfGSNdv2DpHTsJqnqdI04kFFW/wSjLBzjhCHNmeXLxZC9aYMN0WphHFcrL5B3z9gRa5f9SvlDM1Roz3DONhIQ4UtHekmGhcg21XxHRk7ddMZVwyQYUcB5wVLmOmkOcFteIXTaOyDCHQmgZnG9JSwBjnPRRF026Y53nJZNWlha4W8LqM63WBlBPEhDNisqJqaWku4PJaM5E/0MbizYIWfu9J7v/97/1+i93f/XUATs5PODEX5BTotyfkXFl3a4zXvLh8xcl5YRxuGV9dcvbWuxhduXr2gjTN3LHv4M4uOG076niNbz3D1SNqnDk8/64cQbmOfZkIeeboe3HUe/fGLJSLojpRUaqiOc4Jbxwsul+nNMMwyHRIg1Eeb+xi7DHENAGOxrSkmnHaibY2yW53dXF/wRsZqmreRAqsreRZogwxRnJjFjJBK8xapUg5EPZH3HrFMI/4pmcaD2jTUhdEmDcWmkbymaqgq6OS2WxPCcOeqgL373aEfGB7cY7rN4RTz0e/+kuU0NJpI5zIYcKYyP4gRa7m9C5+8zYpV9btilQnfFHiDQkO23lSquQgHI057GlsK5D2UlC1kpfMlsoFaiBnJzerGnB5JuVKqEe0kxKcVpoGz5wzOsgNJyRNoyXf5ds1oQgWyrQtRWlBS5keoyJ1e4e0PuWQJeesUmUKO/7hL/wvtKbhONxw7/4dHn76D3AqkyZRLB/HPe7hfZz3fPDDfwrVbpjnATveEKeRX/nlX+TtH/pTXD36HBVuUNYRwkC70qz6tzH9FuUsthhevnzJyZ0H6Dnx8vnnghTKO0opXNz5OrUcmGeLHkZ2z2Ze3r6i7I/swwGrAlkZmlgEvUdHLJV8eMk0jlzcv4/q3+flJx+h9UQsme9fPeJic5/d7kYA5CUyDRts+YSm2aB9y1nzLofhuUwpc2Y3DShTGPJB8rarjtPNliEcuHN+zhhGTKu5++A9XNdx2L2CpmPTnnP16gkpz/RmhdGWVy8uicrRbU/wjfBqlW5QJaJrIc5S2MF72gJzSpzceYdhnqg5ik1JOaZ5j+vWeAWuOweVWJ+/T4pQdWIuM9OYabpztHGYLuF7SyoJQ4PKBVMVmkAuM0prQPi17XqDDxGtDBpNTBnv1oQ6EZN8RlOaqYSFpHBgCLNElrIiTjM5jPje02zX7KcjTlvmMaCUwfYKXSrzwsmOKZCjwxQwVVNt5nj7gjKM4DLd+V3adoXRiCAgFlIc8as1WIPShpqhW/WkOGFdQ44ZnJUpsXOk6cgwHyhhpo4BvVqjlCXEI9M00PWSrZZpv0JpKeTIq3Ln7gnHccI7TwkNp90DrPWLxbCiSmXY70glsTtcc45Hm555nukaC8qjjMYEL+SZMVPGGyYtk+EaMtQk8ZHpSNsocC1JRVabe5RSWK3PoZXnl42idg7HA+M8kA4jq/4E7SGNIyoXrl5cLsg9xd2zL4BxzPM1YRbNc1UNXjmG8YoYRnzTcvHBezLR9x6vVuQ54VrH4eULXj26wXYO36/QVpGyQ1uDU4V0jOwvn+O3hn5VSdXgjMVrQ12moOPuGuVFTDLFPTqDd4Znn3wfbMX5lhwOrLoWTESVhnLY43qJu01pJI+BXm/AW87O7zHGiFeKVS9H8SYBRXM87DBe6DMpRiHYaE+zblk1XljjOYsdrxSK1qzaHm0daIi1YrVDlYj1Gr9QlFSqOGeEtuM6eR6VjHMyga0pE5lEW52UoD9rpdusWbFMqEvBW0dUIjZSi2ktIVl7EVAl5rQXk13M1DFz/fQJpoByFe9lw6SUoncdsRRqiOxuj3SnJzgrmy5rLW7VUzKkHKivedapiJbaZJyypCrUGu+dDKeqTJetg5gLKleUMVRlUSWjVaHUAnhc01BzJtW8kDmE562W+1c1ioBM5lMJGO+Xgrkh5kjFY/tGytON4rVw5g/y+v+F1vfHvvx+/Z9+7qdpvJeMrPLSyi2RahyqaKyuSxYlk4vgKJUCSyUnwBm0spSUhWtaMsob2eXUiFeGNCua1ZqqZQorphgI8YBxBpOEhKCUoLwk57iwT+MoIfKaF/STJ+bpDfcwZ7kB/e2/+l/8vovTP+rXzzz+HK0Vt7tXOGcIwwGDIdXK5uQEY1vSNFJyZli816vTLcNxYnV2JuF041BNAyEwDZc8+vV/hB4PlBqpC5jcaCmWVNtI7s9I7labhlAzJjuyE/bveBzxi6mpIuxN5TWvW9h2cYLGkeNXAAAgAElEQVRnE0k1kaqmsRtULZLFoWC0FdlEFUrGPCbaVm4sMSc8cnSlSiW5IDeFqAV7hMYZgYd7LTGXrBcA/ByFm2s1qmTJEpYqEYVcSSFy9uABH3zwISHPhLLj8Oj7xNtnoBU3z450F+fQaKZxR9pLFmwcj3LcpFa41ZpsDLe3t2zW5xgM47TH6hVFCxasbdcMw0EyhaYVmHlJEpmhYjSMw46m8WhjMVVjC+yAfr1CO0FAhSGRUpVpt1EYREDgjUdZw5wLXlnmNOKcmINKNpKpco2ciqSEaxuqTdQseUdB3XmM66AawTCloyw8dCUiOJuSMsO45/75PZ5eXpJVZZU1U5IMptYT2+4DIl64w6tOUENV4RpPGY/oUuRoeEp88PVv8Ojb3+HkQceXvvrPcvn5b7Jadbza3/L+F76Gsy2VwPjyMw7jgPYNh5tr2tJwSHBycYfqMzeffiYT294zDze0Tc8YZubDDTrASAbT8eCDL3Hz/LHER+aBajW9kRszyi0c2YpyHmPzYsQT6oFFMU3CgOy6BtCkMGO9HG12bc88HbGtJxVYNad0XcPN7csFQ2ZFu+wEH2i1LBJyzCKr0ZV5nmlcS1ZlwRcKuSBkEQeoVMQOmCZUlYJKyRalMyVH0lQwvSYsqlSthKGsrRctdBmkWa0UvlmhtRS3NJJTH3YiQ3htAlR9Sxz3GNwbnFfMBacjJWesa5jnW1KFzm/khE4gNWgDcZTpsIgKEjFVsLKxk+tHDFB5wTrmOWEaT5qOwlhtFMM40nUbYSLnyjQF1tvNm25ArQqlPdN8wDYWVarkkWsVkUceSd4QE6hqpAuQMtYovBJyhNGCrSslyftcFHOaJWaVZnDyb7WRGILY30SYISgxKbCFONJuzlBGzGvzPENJaCpxTugUBNdWR2zUDClJlt4att0JynmEVlVJ+wO2M8QQ6LcbhqPwlefhihbN4fiKWnuqV5R4oMZMmg8c5sTJySmN7en6DcVsoAY5xdIFpzpwijDNmBo4zte0TY/2vQgJVMX7NWmaKdPAyclb0FoOaZaFqXVYA+NhZDgMqHDAr3t825NrAiyqwuF2R+M6tHccp1vhpS586TBGQo6cn/ZoGuJ0ZLzdg5VNeaoR3Xb0qzN007LqLwgpUQo0TbN8zgIKDXVEVc08j8QQKCXiTUMpmW69FsJDzijTUYyUKY31b+ICyltydGiUSC2KyGB0RSxxOTOMO/p2g/dr5gWJKdZAES1pJIpnUGQNVpsl9uDQZGKUDd3t7a0UzaqijBE09L5hmg+YpgVliEHwZSlEslqmsEUwX95aoSZ44X6rxUjadisykm+eUmSaD7R+RZonsA6rLZmIUTIAycVQihSXx+nAqu3IQM4BlcHZloyj9XLvA4gUjHWY13p7KtVYsbUaK4voDNtVLz+feF9IelGCa+SUPIJC1gWhZlEzp0rnZCr8V/763+RbH33vDy+T+0f9+rEvv1f/7n/zHwsg2Whc45cSQSYVBMkyz2K80sLUU9qKGnASkwZGciwapG0bZSrzuk0MUEMCbTFLySnHQikB7RQxCHfRa8+4n1A1LcraKtDoFGHJZIq61eL9KeXkAdp7ajH8rX/hX/v/9H18/fprv/aLXL94gsLT9z37y2ds7r8l0PGcmMZAGMSodHJ+H922HC9f0q07ds+eEffXjPsdQRdR9NYZnTL9ZsNcZ1ANuQQwhhANunFiOrKiBK1VURWsfMtUK0obwjDSGkcoszysFq7j699z0zpKWspRtYJ2stnIE9625BqJJaONp2SDRYgKRWVqBaed+NhLoeDkgU6ChY9rkf8XIAtxKnMSHmldim4pRHIJGGNRNFj1GtBd+JGf+Amsdrx69gnj/gVpf8n5e19lc/9tTEr89i/9PbLz9P2W6XDN+sH7rNbnPHnyCFsd+90VpuskW5bBIW3frAqqauzCaVYGwvx6ejXjjJfmcpKNh/WOGEZQAkr39JiuIetKqQuQvgjGrKok+UktmDKbFb7vmGIkxwnjGjrfSL45i446l0LjPaaKKhQVpdSjjBxFuZaQA9a1i15WLxvPSUQjdSYOsrDwjaFoS5wHVMx0/bnwbZVjZsLZnpolMlLKxHxzDenA7uohbfMW3dkZelEU++oZx1fo+Ar0AXPyFb78E/8ym80KFQ4M80jjDIerl2wvHqDqzIvPfp0wgDt/S45Tu44X3/k1mtMTdDFMMZEijHFEpyKaXZVIxZDrnnBImN7BLI17ZeU9sa08cHzXy8TMSJHJGEXNA6AXHqeovFUL+/1L+tVKOLxtT1ULbqtofOuEUBAKqZZF+yqN+6bviQn80lNIcSSlGe2lQKhfFzZRb7Lyp2d3OOyvaLZnqBTQGblOHTKF7TuOx51sZJdCZL/d4K1mOBwpMUkTvkxY01JKZU6TcHWVwfuGw/4KorjkaxVT1Xh4SYyZ9dk9VIVhGHB+BSpirCWHmTgGEuDahq7dEEOgzjOVhHKamiZO773L8TALCsr1i1ELYpiYc6J1K4oGowXSr6sWlJHRnJyc8fzpQ0qNlNSw6j1xHMhS6hCLplKEEElRFh/et3IaVCaU9nKtWE0aRbagnV9Yr/LQb1ctNVWmFClKEEwlRHKeRQBTFPN8ZE57nO/xviFTscbjlKVpGkIaSTVTMegk+MMQI42XgUm3ahnGPeNhlqPetqWxjnE6EEqkhEzX9ExhJMxHtDFgpDRVA3hvMVXyr0qviWRQcl9xWiaatbGUrCHI0TKZRTI0UeaIcYa65D+ttZRpAGvJOmN04fjiiqwqlCNhhtZ1S6TD0TYN+5trXHMCqUr2VVWsieTZENJMRrFer2ldK6ZNJVETtSC0co7M40jNkOaBzbrHdCuZcq7WYC3aefk3GGy1mFopWhNCIo1H3EpiSfNwTQwDukTabkOogtFztbI/DqxWncSD0hGNIcSI8Y6UEnffeQ9jVmjXE3ISeoaSzobTomlPRYxrtVa0FQReVUKgE8tZlYJejAzDQGc0thdTaYxR0KBZS8zQGKypwr+ORT6LRWx/olu2QsColXk4oqwRvfYSnUxZ8ueJhE5AUZJDN4rGerRWJISFbJQQOsIYmKKY3pwSLrLpZOFaZlmIFyWEJNXJvb+EKPf/onCqMBWReShj8daQl25NQjjOJQiuM1eJ0qlaieOEbzuJM1qxBCZVcMbJkDFkIhFvNToLuUg3LX/13/+P+NZ3/xgtcr/+5ffq3/m5n34DWdda41ad6BqXD0COYWHlyW664ikqLbKETDUWXRXKOKzyNM6LNME4YpmxWlGK8B9zLLiuJ9ey7Gy0cF4X6kKKEyXPS9jc4hSEMspuXRVClEWYOXmPsnkb5YTI8PP/zF/6f/29+xvf+w2ck0UbWEKMWKNQNVNUXcLfjjTuGacDeR7I80AaR7RV5OmGGguJThbwaaCSaFzHNM9oFk1ujNSiUU2DtoLCMkZMT9VYbOsZ50RWltYajFnhlGI4vpJAvNGcrCQ7Va0s7LQ1S+61QgaNMIKt84zjCCktNzDeIH2qMVi8ZP1KRJksk/Qon5tUMjEUmkbMN1ZXWYSrSokySc61oE1dppFZsp7Ok+ckMYsY8G4l7NGqufPWGdM44gk8fv4p6zZhlWN9/gGm3/D88fdojMbqDXEa2T37Dqp/nx/6k3+akmeOwxXzvGe9bnn28eMF09Kiug3aaxrfQRGGp7UNlQAlSB4MUEmhrJQY9HJTVF7jteBdXm+6tJVSglZyNCrWLCdH266lhiyFJDIYjTUaIozHA7pxGCv+dLVQS1IO6MqbljXIoh+lSHn5vrninSPEI7YawcUpRWstRSVQUUpPIWJ0i3KWFI9LCSkTDgM5HikLLqdbiW3IKIghUbQhHXcMu88xvmfbnTPVTNPe4Ye/8RcxOrPa9JQo9nbjhFxglOc43KDSwPOHD/FK7gXXZaYpStA8puHq888xXY+1CtucsrvZ0xiLZQZTeHX5gtXZBSF1kpPzFZMrrjWMOZPHmbkY9LxnniO9VTT9FtO0HI8jxvY0NoMvhFnwV3GeOb/7AGtbUgpM44E03KJaWUzVqlBZ8HixWJTSGKdouxXDfod1hTwVss14rWnac2KMjLunkDWnd+/y/pd/nOcvPienAa0aXLPCWMHhOdtTdGK13qJspYTEq+cvaNan7K9ekOarRYWuKKkS50B/fkbXn7K7fCr3XJXwTjMe9wLurxnfNijXMN7eoJ0gz5ztcU1DqoHd7Ssas6KUxDje4owjloo3HdYJjiiVuBSLNeMg+XxnvEggnKFgCMOMbzrCfMQ7TcoTFUtOEOZCvxWTUhoG+qYnHHYMRC5OHnB7+ZDtnbe52t+iWdF7mThOeV6Me8IStq5BVUWthtvbVyhdaPwWZQqxJM62p0xBNqXt6QX5eKBMMl1r7IaL997lo2//FlrDarVhvTmllELf93z7n/wyIdzIqUaUYu1xumV1ds5qfW/BdWVSSLTdBpUm5pQgC3ZxTIEPv/g1Hj36DkZpcpnlRCbBlEc6ayFoqI6gFdv1hmkKOO9lwxorqRwxncO6jpQK3m7IZSbGjNEZUiTrwt3tPcZpol2fUYjsLl8wj3t0ycxpZrPZ8PL6CZv+LuMU6NYrwlzp+06QblqLAKM5Rb3BSBZhLJeCXsqDeZ4IRQZPrwt1IR7k59eCEGsbj7U9JWfwVgxv84z1PePtJSaVN5uurGZRqhtHyY7Oe6Y8U8ZbSdcaK2zdMckmiIxpDCXJ6YVSioiw3bt+TYkWo1twYNwaZSvWN8tgRmOMxlTFPA/sbq/p2g3GOemBdA2uIOKIJdubyyIiUop5zlSj0dXI/VkJKs8vRemiKk57YjqIvGbRKLM8F1OM9OuGGBARiS7kmKhEnGoAORVCK2otIgRRlrkkQf0pvQiDNDlmqtU4rSlKFNRKL8rnpXOkjREWLgprWvJyzRYyRsupSp0jOSWmOOG9p+sbfHdKUYUcgsAFaiRNlXA8oJVlTjMx7N5EEWzb4PwK6ypONzjnyCWRo+Pf+Zs/zXc+/uSPzyL3x770bv2f/6ufkvyK0sIeXBYsRUmj3lRkcbQsaJSReAI1o7S0UXOcMMajtCwarPWkMEMjNg2r9RsDSajCO6y1UpXBMFNCJGXJ+VZVpBiUoQZwRiw52mRUccwxgF5j3BrdX8DZff72N/7yD/xcP/krf18YgMt7XFTEVMccArK+0FzaPX/h575JPf8A1Ixqe6pqwVh49AhejHz3P/+zFDegtOXn//S/+APf4z/83rdksRcz+9srLh58SAg37K9f0p3dZX1yznwc6LqG66snDDcv6Fcn+H4FJTPud2jtmfZ70nRLmvaA8GizWmxx1mCsBL3DNOOcwzjLNI0YLf5s1W6lLTzM1DpJQ3SurM5XZJ2ZUyWGgDOnxBLpTzZCLggLgg0lu2SSZKMKlDCJ2jQEGr/lGCZiGnBaNkPOa0qTMWolCBfFG/SUMg0lRLoFW5bnEaXkIa+9wRj3psiVo2Sva0kLCrgQQ6HOkVUvKtV7737A5uSUKYx0XUOdBorrOGbwylBy4Ml3fp3by2u6TY/PE6U74U/+8/864fiMzx9/ws3LT+nsCrc6Y/fkU47Xj3HNOb5tlyPMt7j48Gvcf++DZdNS2B92PP/Wr3OcRrRpOLtzwfPHT7B+OaGIC7dZCTNam4aS58V+V8DoRWdryMmwbs8IYcc071AJUrU0J1sMCbQhzhH92nxUlOhITSsPJjTGaVKaBKSvLCULn1FpKGNEm44hzLQGrK6YxhOrorWOadyxu77ieDiwPb9Aec26WTFnmeCnGNAlknJApURUjn69xTgocQSjGPfS5iZFGneHt3/kR3n52aec3XvA+vQMtCKmG26HgbPVfcbrJ1w8eJfpcMtw/X368w/AFI7X14zXTziOsDk55eLtdzG5cr0/UMOBuN8z5sjp6SmH3Z5gPXkcQCXeeeseQ0hcPruiHGe2b38V13tcqwhXNxx2V8wp0qxPOD+7z2cf/zqqzPQXb0skIymmmHBdR4yBzekJ0zgSVcYrJ9rcDKZFyCQpvNncaCt2u1I06+2K3csrUk2QZhrruPvFP8HZvXcZdgPxsOPpw+8QyYyTtPTneSY8v6Z6j3YiPvCrBte1PHjrhxmGIx988ascd5d8/O3/U6IEumF12vPq+TNi3mFdR9Nt0b7BYeUo0Uk2HuOoKaFy4jCNdO2WmAtGabZnpzR+y268pe81umrCMHF98xLvG5RRTMOI842wQEuh8Y552tF1Zzz97DN817Hueqou9Jsz8oJqclaQUM4ZTk8uaFrLcbfHeINtV7TdmhePP6M/XTPtdjjn6P0JKQfakx5VK598+l1O+zs8f/JbgKJb3UWbSp4yxjUUKyeMMSWsyhzHkbZt6fuekoJcZ6rgbM8Yg0SNjKFpe5TvMUpzcXYG1jDMI+PuKUOY8akyDTNlztiTM/xmw3YlXZEwXDHv96KdbzzWaCm/TYl53qERDBbWsT25w37/gpvLz8jK0Henwka2BuM6sjIoBWGe2bQ9rvdo44jxwHTY0bQrrNugrWa7ueA47Ll8/gKjkkzlV4ppd2DVn2KNohBIU8R2m8WZ4Yk1M09iiqtV4ayIQsS+JiKPmCDEAWykeR1BC5Xd7S1pHFmfnqPbVoYgS4SoMZaqCnbBgM0xYp1wnl3jMXUx8xlhlk9xh0ojNSrGUYYCq80G23TEIBlYiSZWvJXOBSmQE8SYqTEwDJdgAqo0wjY3Fte0oC0OR3bgjKXpL3BGEV//LkoWbq32zGnGWI21Ml3WWnjTryM0zrzmw0eMllO7EALYSspHrBOtsDcNqso9NVsramGjFtqPW4hEllg1Si9ozlJgiVNqDGMaBEPoHDlFFJYEkBKtXZ6HyoBW2GokimPym+u3VqhTIDu538eQaP0KUwsoQemVGtHVUmMipknKbmTieBAs5RxpO4/1dtlEejlBjAmrC8N4LTFSLMq0qBrk5zRekHEho4vhr/3Mz/7xmuT+2Fc+qP/rf/efUpPwWm1VBLVMjoqGJAB/0zpCkumidS2lJgyiJ0xKAMiNlwmcMgv3tApbzaGJZcKbTnh1ShGKsHdDTpgUpdlbKyWNgo6ZJgB0dcQwyIdp3i0t/BYzA85hNl9kvrjHf/+NH5zk/pu/+gt0tl2OHvPvoEaMIHdUKvy3z77P3/of97y4e4bO4rLOKcD+CLvP4PAOv/af/RnW61tKdPz8n/nzP/A9/u1/9L9BLVLeC3u293+Yt7/8VaiJME543zKF8Y0oQc0j1niyBmUawnDL/vqabrORh0rXE8IN04unvHr8aMm1jhzrhFNy3KamwqSiaA7DDqUzqjmnLPYXoyEqR+Na4hSZS2B78RbjtKNZrVEBppSpMdFsNmIGKmpRTUJIM43rxMoUJ4yG4+5I9Q7ftVij0WimeZbjoUZjlbBhc5WpZx5HbPXoaqiNfzOtLSkLOswAZJSuDMcIJb/hIVor0zOtLOuV4+TinNXJKU1/Qk0Tw/4ll08/Jh5v0a6lbR+w3pzz8Du/yvr0jOOwRw0t+tRxfnGf/e4Vzbbj5PwDNA3z7hn74Rbtz5hurglxkMxWruIz15nzd/4E99/9kMNhz3zzkMsnn3G43eFdi2l6ainLEaCi6TzzVLi4/7bka8NM31lu9zek6Yg1PePtC/Jie1PKoJuK0msaL1a2qgupRIxx1AKuaSlRsmslzEx7OebOMYkXfttQq6OoRJgT3rY4JfIVrURBPM23xHEgzTKFX905g9dZxsMtMY3k0tB1PbkaFEnKVVog88a1zDWjykikMI8BT0LlyjgUbFOoSZi6r66esV6dcPnsIWdvX7Ax54z7F7Srt/jRb3yDj/7xL3P98jE//q/8JN/5xb9HDTt+9C/9FVZNi3GOOB8Zry7ZvvUh0/4lzz7+Ld750o/y2fe+Tdd1ZKV4+/2voa3n8tWn9N7x9NH3cXEmtmfEcuTexZfkyDEdwfT4vud4+wLnLL3reXX1gpOTu4zTXj5nbUe+OaD7HhBcl7GK3q64ubphGG9Zn/ek40g1ltO797l98QLfX8hkZUqkskergqoiNgiHROPXzMeBEEfWq4abl884eftd5tuBmAPrB28Tbl8wj4F3P/wqc9hTKYTJYk3LetMCmf3NI148e45tWtJuj7Md7XpLjSNDkaP6MAZU9qxWnQwklniN2t8wlYRdn2HXW0xKlKJYn255/vi7hOEVeSoo7XAnW0wyTPsJ3YJt1jir0b7KVFUb9oeBptvilKakSKkRkLKKtYYYNNZ5chol8rZomJ3zrDdnrNb36DdrYf3GiFVQER767cvP0N0GQ8dHv/kPMW7F+d07NP2Gbael0HV2B0zDqxdP2b36TD6LTQ+mQeeJ/W7Hpl+RbcLqhjQGjIVYCxnH/Qfvsnt5JRraMLPZnpJsxqBgjowh0iixi4WiluPrmZPzU0qSPK4yEI63zLXSm4bj4YCOiblMdOsVtjqUjsTxSO06ctF07Yp5ELlRmg+MQ6DftDLscYpSNSUnjLZY70gpLhO7JDnf9oxUBcvYrxw1Blq3Zp5n2u2W/dOH+H7DPEb8qhGteNVUq5cC6i3OeZzt6Ps1sWRhzNaIUX6J3EAqRXBYqVKso8SCd4797Su6laDgwnCUTXOa6VcnqKWglbOidVIUs9pjnGGednjfooxmDJUY96gmY2YF1WDMovtNM65tIGWKyRiEd5vTJLzakihVsuAlLwMrrXBeYZQl5UqqQcrUccS7tSh2cxDznPEYrwnDjPYLEUBpGd7EKoKSJHGAmBPaepmSWk8qGa2EWMJcMU5RTEUVh0YKln23ZYoDVmuslqxvVVXYvFjhZRuFNR3zPICTNZGlSEHMNqQcaDBCb0CT8oA3gjMbZ6FOoTQWmPdHTNMwlySnQmjQRn4XS19JlYpzXhB7MaGMXEOlzhStUaWgqsK3DV17iggtCiFlbCc5aYpopY2FeUo43ZFLks3CcOTf/an/hG999PEfn0Xu17/yfv07//XPovUCga/jwqt1GBzaVLS1VOVQqWBspaIpNVFm2cEUJfDolMXYUXOmFGljlppIo3yYa8qUkjFegMpzPMhFmSM5gVYQ50guw6LcjRjdCLduPmKMpmJIMzRLDiq299Db9/j5v/iv/sDP9W/9yi8sx8O/kz+timX0H/DW8Nd/4bf51et3ucahw0xlopYCxx08/wz9LPL3f+Yvc35Xsn7/w5/7l37ge/wbv/R3MUoKDrkqXH/KvfsXGNugqTSrLfM8YkzDtD9iEK6fcVIWcwbG4UDVInHwNfHk4bc5PH8sGeVmhVFabrA6k4eJmiKu9YzTLd54agikMJEwYFqULsxZJhrWNSQlHF3f9+Rh5uL+++z2L6UoWBNNK7m7KUUoSjKJqWC8oipFiQVtRdihraKELDdFZ5nCTCbROU/JFeUN1nl0qZQFHiIG0oqzkIhSZEtZjs7nhKpGImlFwOG5wNsfvMfZ2XtUY2i9o5T0RsV8e/MSYwxttdzcPGMebxj21+Sc2azuEOKMzaC6U5rNHZSzNI0nxchnH/0aKRzpmlNZ6BXB7GzW58SY2d5/h277gOdPP2feyY06zpeoOpGTxpoW4xtKgbZZYYzixfVLulXPyrfMRMowk6b9knUCozLFgFYN43CgbzwhJCGSKDEMWavJVdF2a2mzVo3RmRLkJme1oIi01oRUcQZEjVWpVbHf36J0xtgWra2YkFavC5ySM7TWUtNEmieca8TmBdK6tw01TAzzgCqZkkXraqynoMnpyP76MU1zggtq2ZwqqJGoC6VOtNpwuHmF3jpWWFTV1NpQbcdqY5lKIhlNqwxvv/fj3Ly64u0v/RDf/+43+fALX8Svz3Dac3n5KTUm0nxF2t2iNme8896XJbeI4uXlp6y6npfPHrLqzskl0m1PefbRb9KefAm3PkV5xbR/ydX1M4bdge1qS9tvGaaZHANf/NrXUbbD6czzR58Qs6VdrwjzkdubJ5xt38L5ltV2xdXzZ/SrDVUVdtevMMUwhcjpuSzcVhfnyz0lcnZ+Qa2VT771bcarxxxvXkHn2JzfI8wj8zAyBVj3wit2pqU5vWB7511MSqQ8Mh6OfPbJb2Dygcaf0K03pKVxnZOQQjbn70hmcZ7wviMMO8ZpT7gdOMYrmq6iuYvvN6L8HQfWfceYM+Nwg/GK87c+4Hx9j1c3l9y+esFXf+THpeCmLGnO3Hv7PYbjDc8ef4LRDYfDQM4zmsJqc4H1lXE6cLK+jzaWbt0TpgN2fR9tFPvrK043Z1w++1zy4nEiLRrwcb+jS4ViG47hipJnnO+Z0oDOM2kaJIKxvsv9d7/GHANXL56wObvL+Xvv4ZoNq5MTjs8e8uLpI+ZxYI6B7ck5qlpqmRmjFE/9+pRpP8t1YRSWTFWa1fac66sXtOtT+tUJKEvftrSdlemZMUzTxIsnT0jTLPKaNLE6X2Gr5/ziAdv1GVOU49+PfvMfE8MRS+YwTmBkoamsIh32KDxKW1zvwbSY1lOWTPw87NG+odGOqqFdooPOdkyTnCTYVcM87HAGwixiHrXosNtOE1MQ65rp0IsuXim5P6Slc+CbRkpXpaApGN2QK6Qs+c3jcEuYjrSuR1tD125k6FMqVWeOw0CzuSCFQUqMVaapJUpmVZvMdEzUEnG+ZYpHcppJZZbstbJ03Qk1yzAFjAxrlORdwzBSk8I6wbkZZymvy5sKIgmDEqTVUkrLS78EpJOCKkh2TKEzy31DTrxEXmEBoTNpZXFGAZW4DOXkC1lR+JYsz8TGUdMMVe7XKRasUaRYURq0LlgtYqWSxSkQc8QqWejnLHZB5yT/T0mC8cpFZEFViexByZrgdQ4754yyEk/IcUZVKTcb5HmtrGDUig4MxwlVFM61hDBTVMIpKyfuTiKU2sm1V6i03QlhkMy5bxp8L8W4HCJ5nqRgbgvOaenJKDm911rzk1seoHcAACAASURBVP/eT/Hb3/noD4+T+0f/UnRdT5pniso4s6Io+QBIPrCiZtAqYb1hHka01UuxTFOQFUpOAUWRiYJVmGqY0sj/Rd27xVqa5uddv/f4HdZh71171+6q6q6e7nHP0TOM7dgJgfgKEmJAiDuDDQZjLghYESQhiawAAQkQSBEiSJG4AUUJMfIVBwcTQ5CFZDueMT4knhnPoWe6u7q6qnbt4zp8h/fIxX91W63c+NK+6ptufb32Wt/7/g/P83ucttjGUWJCOwMhE8fxcEGLYUQZTw6zaFcQo1IKEWsPgmmVJY0jJrnsrRb35jxg6jV1Hv+xTyX6GbCHdbzE/yK0eWUouvD3nt/gvv4t1O98BV6O8P3/lKBkzhbUMVGI2HtGOsUU/rFn5HmS7gfL0fqEo1fO6FbHop89xLNa38gacNlg9JKcJvI4U0Pk/affwFqPX51ibOblt/8RaEVnW/xS8DYpRpxqMcphO0XaB5zzKHVC3/fEOTAM1xg8tlnjvWa/n0Fp4qETXrQrQpxQyrC5uUU7i3WaGBXDsBMXudKSmFOUBDl8KJUwlpLmQ3qKHDyuyoFztFwQkxSmTSsvZY3Cnq0qyX9jLKVGmarkLElDpaJiwrlW0tSywmmF0i3f87kvUTHs9hum61s++M7Xubu95PXP/hFevv9NwhR5/ZNvEfSO81depz15xDwGhusnqFxZ3Dtjc3lJkyqqbdhcXDGHkRJm7p29wfLohGG75dEnP8OUJ559922GecQZw/N3n2DMB6jGMIeZ1RJS7ShB4xoDJTEME3ku1B5sa+ibDkJhqNNH0arHD17j9vIlJcKsFLox2KbDdZbqLbYVsxKHYA2DpA45Z1E5EzEYpfGLTpy2BlIoOAxaF7AW41pqyeQpcnb/ISklprShpsycA3Wjcd2SWiec+fBgNLjFMfN+gFgkVEUbaskoqzjqzqgqUWLCKiuXsDZY67j38Jiy2wpqTQsxpeqKS5FF+wqb4Y6TrpPLRR3S9YxFhUK2HaQBR2YaA9/52q9hrebq4htMKWPKzPMnT+maHqUDKuw4e+1TfOGHf5T9cIdvHTVM5HBHHe7wixUPH36SF8+fSGqg71g9+AInxyuefO03aO6dM8fEa69/nrPzR7z3zrcJ2w1t21KPF6gAlxcfsGhXzNHgnKUgoP6Hr36Cl09f0PmW4fIp4Lm4u5OEwTpTlcIrze3zbzLvVty9OOLu+o7u3jFvh28Qw4Z7pysefe8f4e75FTHNVLXlqH/ERl/C7TXTsKexjnm6Zb+54fLrv0itDW7xKikllg/OePX1P860HUlzYD/tSdtr+sYSk2F79ZzhZsfZ2QPGuqM0ltXxOQNXWHWKNQ0oxzgPHC9O8WcNWsM901CcwXdLlu2S28sX1DFw/+Q+l8+e0C7POHn4EGUC0xhx7ZJ2cUSNiaOzNfu7QA2XbG4vDgVWJA6ZR29+jsvtFsKG69/8Cvuba5rlGfvHj7i9u+MTb30Sq46Yx8DFxQcor7ga9zg9kvJAd3SK69eHuNmKw5JmwaEN0x6tNd3JGVpZ7p48Z4rvixnNKGLOnCyPqUZW0jUk0jyiUUxhYBwSx2f3iePAcLdhHm4YNyPnr75BNYabD54zLnfkaZCisAxM04Bvj3BuhXEGVSaGTaBZLNjdbrDKc/nkuUBd1UxKAZTE6GIa5mlPuwDtHMfH5+x1j208aY5U59jvN6hhFkymMyz6jlkhSKyqyUmhbEsIibZtRfccA9Z4YkrEeSbtB7q2x7SecRSpSJwTmJFSoWk9Yc6S+pcSxsudKezkRGsVz56/y70T0SE7bViv75P6E9mohsA8TbJJinv6fklP5fLt3yQGUCXifCO0CaVYLNdsxpHV8SvUkIjTTDWZ3e6OUhOqzeQSuXn/A9qjJc4vqdrJfaCFhtKtlvJebPfEKRKHwBR3JJVpfSeEmXYpmCvXUWISCcNhAFBVQmkxbFGQWqRA0ZacLUY7VFbMjEJXQKEjMmmvGe8sVXGYUGcUWsIQQmLOAbBokBCHJA1Ki5iHh1n8BLUIfspoI8OZKoZCBWLgz5UQJvbbHV3jmXPBescUClZLMV+mQDqg13TjSLmKt0MplMuMc8L1i8OmN3O3v2bZrQQbqmB9csww7KQuUwX2e0GY1YRvO3zTQBIKRtt1gCaXQsxyL7X9ChWliZ3CwKLtGcatxCAXSzl4RH5f1eUfhEnulz79Rv1f/9qfo5KoJQknl0wxBm9XaARvk6aZnGVy6xoLTqMQLdbvJVEVlMrkvEUVQywZopiblDYUU9AYdKmkaSSnemCzST6195Y07yXKNwSUlzVyTkkud6AUwVIpMqUcEkiy52d/4j/+2Of60V/9ezjdUqjSbR4YpylGkk6UzvH5/+L/gNLB9RW091HFQbem1i0qV/j6Be/8rZ9gyi+pCn72j/3Jjz3jL778QMxguaIozHESmUYQdEnbtuynUVZiMeB7i5v3xJK5vrigW1h0guFuy+76A3QdmPbXKLVgebTGVnHIT6lS9Ugse0xUYFd03YKryxesF6sDCkSBlymhbXqUciQTBBeUDaiAcj25IOa4LGux6hpxt6dKRsyGTd9RsqXWzBwDrVVy8Lseo1s0Dqwh1IhtPTmAbgwlVqw7aHyMTNFrFaOBLeUQtRmJ40Rlpk4Va3sylWJgvV6SUkHbSls11WTGeZIu96DbsohuanvzElOy8LTHEdtltmFL3x1L6piydP09dJ0FxxYVvW/ZDzP98T2mcUvoj3jt8WcY7m4Y5wFlE3WqGOOwRKJpxFFqKhTBR6EFE6UruKanNoY471HFUYyilEjYyWR52Z2yOj1jGK/YT5Wu61BUHjx6k2EYuLx+F1007WIpkwWtmTY7tG9laqAMKQWUQ0xxKWJdR6yJEjJl3qO0pj1A7LFiPkspCWuxcpiwGFR1qFJkWlcU1rUYb5hiOLCpC4pCKpnWtoJwajypSphLmCasEfPMh+aEYiyNa5hyoHOWMOzxzrDd7fD9SqYheaaUlqZxlLgH7QlzwahKZz37krFVodJIDBPGaJrT1/j8D/wJ0jBAjRSjuH7vbfy9c7rGsD4+Yx63fPfrX6ZtHPu7G9Gs6QXzbsAfLfFNx/HxPbZDFD/ASUedDS8++BbbzYaH63vcvLzEdB2u64FEmSeGQwqWiyKXcv6YrCvTfpApYK0isbKiKzw+e8w0BXIKpP3Aol+yHXeQKk2/4OjBEfs5EIfI+fk5VxcfkHOlMZZh2OH7JSFH/MIxjXuc7WAaUHZBroWT9RF3t1s+89lP8btf/Q2MtZAyumjuf+INsqmQFd3qmDAnXn38Fl/9h18RBuYs71sYt/R9z9HxK9hmSTGV508/4OTkGOUq890tdx+8S2kbHn/u+7n6zrfoT+5z9fwZvmlktd30WG2YNu9xdHrG9uaaMIxQE+N+ZHHvDNsZKh7TWB698haXz59CnlDOs7/do50ihQFnLHMuwk6NopmMGGopNO2CFCfIsuEoNYkEYMqM2x1FDbQLSYIbdcG3Rxx1PVhLNQdWeIyUkKhezDlWaWqRpns73OCNTNhqmZknjW+aj6JiYxpo2hYT5UwCUFqmaVEnuk7Me2pOaHPwHCjIGtoiRtWqE9p5chYcXCGTp4BWLapv0FX9Xhxv4xn3G7Q2eNuJD0VXMWdSmYY9ShdSFU2qK4Z7Jyfs91t2dxtMq1kc3RMfSNdJYILSWKPQSpIaawyC3HOWfdiQqXit6b3IBqgaY5ykaxlDxZBTxNTC/uaK5rijaEPeXFKA1p+icyXWIAlzXeXmxXP61QOKMVjbgq6S7mgUvrGEOVFixnqNIVCiYj9OnJ4/ZIgz5IyqirZthUphHDVpDpkl1DJhcmVIksqqtEzCU8m0ix6jMrka9vs9rUdqA4zUM9ZQxglVRJ/quiWhzihjyTEBGn9ImNPak0oEipigjWWz3zFuJahmmgaUMvSrE1Q1qBrYbre0iyVpnshhJsQJ6y1dewaNwzeWuNlRiZimRxUYx5H+qCWMBaUL1TgsCZVBOUvYTbRtS9M3xCKpZdZ48dZ4j3ZOCA0fYj2NJiUZlEyzvGM1JiIBhRHcodekqDC6EsYAVUgdSXlKhX7RoDDEANRK0gVTi6TSKUMoE7pYfuqn/wJf++bvb5L7B6bI/d/++l/GHhyDCgN6phZNDgFlWqwxEoOXJzIFr4W+UNJeUCDGUNDENGIbj8qGXCo1Rnwnq7lCpX6o9a2gSyUjhrJaK55WxNvzICQHDLEmtG2E+1QCKQ6UqkG3kEeMbT9CXv3P/+p/9rHP9WO/8ovoA3bIe0HTUMzBNZ35Jnv+pb/xFWpU1KNXUDc7qq6oF89QzQnNZx/T/9yX+fWf/wnmYebBo9f5bz7xxsee8eefPyHXwrTZ0HYLlM5s7zaQA7vtDcZonr3/bX7gh/40uxRkd7/fszxe8uK93yS8f8Pi4SPu7m4xume/uUQ7RQl32DCJrMMdY3yPW62Y4y3aCCe1hCSRnt5TcqTM8SNGKNpKSl0jcOgw7bFVYa0nTDPD/u6AZVKo1lGCxrY9XicSQdZPyjNudvRHK5TKlJpF7N6dMO135OEam0Z825PQVG0Yq2Xhl2QdsMtjUi6CwilJ1izKQC4EZHo3xYRzC3IstN4Qo3BqNZWqDpPOA1QdKwER3niZgqKJYRCdUBypRR9WckZ4yjpRUmIadrLu8x2+b6m02GxpgElJMluKoxTueoe2lrY7gqwZ5wnnDLv9lq5Zk1PCOC1BFRpKORS8ukc7y8krD7l8/jZht6N1LZvtFf1yjW4WaNOCyjjbMI9iTkt5pm0ltamxhjEFMRkoqEUMnbYqqpPbVvLeRdMlrOWZpm3xzQqMNCXaOMYQIGcwkMPBsXuItlSpMscZ4w3GefQkiB3beGKZKVpRs6Icvtt6SNNqmpYYJOxDFzGGGmXksGwNThsxCiXIFGrKeOcoRS7CqBTuw7WXRqgqIZERp7O3BjKkmgjDHt041us1+9sd4/V3efSpL3H+6meZpsDdzQvuPTin6z1VO7RtKeOW97/2ZQIt3bJjzAGVFMvFKccPXsN1LdcvrzAW+m5JyIFutaDRPdM0MW2eM4yBo9Mzbq4uydPAHO7IFaxzxNtbjPbC6NVgF51oI7sly+4EZ1veffcJ1iumcaCnSlxo29J1J4SQWK2O2NxdkUuh6y0hFPIU2U13GCrrfsGUJ6oxYtoMFZMMVSWqqcRppO8lYKFW+e3thztS8TTLNVrDNL1HtzgXE+Juj2s8R+sTNpdPiQm6/t7hnLCScrlaSjM1T4R5T7UdpmRsFYpCniLPL19ADriFRUfN+uwM7VqUN6icSNOIazpJW8uy5k0pArMEoIwyhXRNS8oyuSNKQ130jHcN426m6Ra0fUepVlbpahbyi66cnJ3x4sn7lBRoFkv2+y2ljiz8AtesuLm7xlSZhI3bDafnr5HngVAz/eJImoOcyATKYd1f8YeCTFinmImCo86ROA/EMIB1dP2apAxf+P4fYr8fYS588PRd7l58h1omSgnYxQKmPfcffJK7uxu603t0ThoPo2G7uSLHEdesSRXiHOT/d5oZ00DXN8IfzkYmtE62aHfXN+iq8QvP/fuPefbBO6Aqebelca0EKfiedr2UqWrNpJqwpiXGSGsaihXU4LC7xYya7bMn+JXmbtjRnd5Dl0SeIr4/ou9WYjpe38N5OXfmcaJME9m2WAdOi34XrYlplqTSqiQVUFdJOVViVE/TLOjGauXcLoE4jsRpz1wrthUSRKMc5EBJI2PYcHL+GsqsqAhXWdeA1jCMgQZHIOD6/kAikc8nCXEzNSS6dS8s45TYPHuBP3EY7Sg10bVrKgd8mBaerT6khM5j4N75KfOQGHe3UqCHQOMlfnkYdjjXEYcN8+0Nq3tLktaUnGmXK1IwqFrR3mGcZk5R8IQpybCNjPce23nyFHGuY7t9gdnf0py9Lu+m0hjT4FQhknEHDu4UEv3imBwTbd/JnTAHmqajUKVJqU42syaJZ8YoYi3UHFG1kOdEqYp20R/kbyPOeDG8Wcs8pYOBPBPTLCbcKn6S/W6DN56f+ks/wzfffucPT5H7xbderz//3/7MoapHfpgpiXksJUq1B8TFobBIk1x0hw5CW/lnCZFqJLVDqUKJ8gOzpsU3mqzEHR5noRvoqilGpkfzPKOLEci1MgcshsTS1QrFFCpRprcZbJVgAbQiZoWxDT/3Y//pxz7Xv/blv/973D+tDg5Qh8qQS+KXt0/56f/hGbfDDMZATajxUsw0caZ96/sZLzd86698kTgHvD/mb/3QP/mxZ/yZr/0qJWt82xCHid32hv5oxTRN+MPLZ60WlAeeu7srbEpMYUcet6xWKzZXt7iuJRVJSdG+xTcLFsev0S8Vb//Wr5FSoBIhVmy3Yhz2+G4JHFzQjRNIdZbIvRwTWSnBqBqHOXy3qchLncOMQdH0C4Z0x3S7FeNfHnGukaKmP8LQorSY9kTPZMSl6iz9wYSAtuQyY70mDE4myZ0YFms1OF1JcZY4XiuHprKRHCQ+M9RGIOVxLylM3lMLKNdS63hAknWUavB4chbyQUwTttWQQNVAPuSJd600NFXPOLs8iPG9fGEqkVCUaULFEdO3aOfQRglPsmh0l2ibFap2oAIxZrRvyPMkiToITm8eB3JQaO9wqrC5fkYl0K/OBDgeEzVH0TK7Ba6RuMtpmmibNbptSNNE6xuSEnj9PO9xVWQb08EQhyropmfcD7gCIQeWnRTVAPtxR2dbajaSSqOU8CtNJk0JVaqwi53CUARlZjVRy2pKxYp3DaEmqkrECjpLUTyNI8ZbSo5YKsOU6JcLco6YojHKkDRY69ntBtq+oXGS2pWzrPW81hQbycnIs7UmhCD4nxKZU6RddmJiNY7We1KamdIOnQJKLzHWsb15ilUBpwqZhtc/+8fZ7He8+uan+OZv/zpHJ8dsvvV/Mdsln/6hP8Xbb/82C7+kW57y+lvfx/tvf53tfseibTBugV8f4RYLbt5/D9LI5u6ONz/9veznmXkYsEZx/ugttNV0izVazQy7Lda2TJtbirWMw5bbzTNOj87p3YKQ5WxUpqFZLAjTyDxGwnZHtzoiDHteXnyD8wdv8p33vsHpvUcsjs4BfTBmzpw8uM/ubsew2WBz5OrZu5w/fgPbSHO32WwwzjOXmfvHp+w2W66v30f5NV1vGbcB2y4Ybp/T9z0XLy7pF1JoeutIUZFDBFVFSuIdDz/3Wcpc8J0UxLlo0rzhxbO3MTiWp69ijGF79z7jzQ1ziqAU9oAv01qjTSHuR2pxVO04Oz/l6vo5ShmWvcTx5lRQRnP+4DV2L58zh0AogzSnpiXWQkLT2EZMltqJp0EHjIK0v6NfHYspJxRU2LHb3LE+f4h3a0KQ6ZdyhjkW+qYnzhOFQjlEtqY5iQwoVHLdUbFCbMlw78FDYircvvgAlQsxJ9rlitX6hHE/cLuPvPGJT3L55B2a8xNa45n3W4b9nm7hKXHG+2OoladPvorKTlbhYY9bW7abK3q/wjRLnO25urpg0Xuarqcys91uWJ88QGvLPAXWx0eE3Z5pkMmgth25ip4TNLFCaxyYiFZe3rmaqGTCnFgeHbPb7WidsHiPTs8oY+D22bvUEtjcvE/VYPSCGAOVmcav8O0RIQTaRS9RwBkqmVLrASOVsNZhnTugR8VIqLJwaSsFmkKtBVcsru0+Ck3JaRIGecnc7W/YTSOmMbS1oWk6dAXlLYlMHBLONOhayHVge3vJ0foM1y6I+z2XwzXLxT2865jCTLtc4G1HTCMqG0Lc4xsLqsHYQowfFoxazmcl9UHjO7KGNEfGYUvNiVJnOt+jD6EWRR0CK3SmaY4YNxPH53IuUxK2bbi7HumWhn55inaW7bCjW/SErcgpjdaklMQn1Dpqgpo03XFHGAs2jyQUzrdQK2HYiLa7a3HeU/NB4ZorIUcq0PoWZSxF4kywEaYwYrVgQlMNgktEmk/nGkyBesDJtd2CaZhEIxwm8W4YaQDmeaRBjIzW2gNbvPBv/Dt/7vetyf0DUuQ+rv/7f/1nqcYSpoGmWwJVHMM6oUtPLoIOSlXyn1OuAkrWMtVyxlOLZi5RDnmShAmkSMiFqiSJyaAwvkGTqbliWgFPG2spCUyV536YElK0rCtUDmCKFMhKU5XC6gMOrFlQquHnfuw/+djn+vFf+7+lYK+F3ssUUFWNdZViOr728qv88//l16k31zBvgQp313D/CPQSreEv/wv/HP/mn76PVi2pBn72h/6Zjz3jr447bj94lzCPDLcXzCmyOr5PygFXKpvNLc405DRSlKU9WnD34qkAzA/UCWMa9tsbQsmgPTUGMYMZgb4bo1A1yYrGWIwWMxCqoWQl8X26HCgSklIEoJVkhBevqUlSsUqtmFoI854UIaUdOtyCseRiqaah95aaRl5cf4AthW7poHToZgW2wTcCsFcfBkdYCUBwjUelQixVUr0s6GzQh8zvjKxcjFFS/OtCRVOdI8WZOI20vgEDnTtijgHjHbrCPA3EPImMQAm82jQVbxVTSjCDUpkpzLhmgbdSeGVrUSFQk6VUyEoCSLxWpDwzjBNt16CsEhlG0cQ6oyukEFG6FYD/aiWcXAOKGedakdrMmdoZ5ts7wjwe1maalIUYMQ07VsevUKoTp66e0dViTc9cCtY0OK2IRf6OlSymsqIIJdMteqZpINNglUWrSooDbd9Rq8C6lVJobagpg9PEODNNgWa1IA+JtltQYhFYfk24vkWnxFTFTGlVh7aGcjiLNB7lLLoCBkpN6FqJYcDYTgJaaj4gppbUPFOtoub6kelCCaaDojPTeIdzDTZrqvGopLBtR1XloGWsH3Enc8zMIeE1JCZab9jvEtY0tG2D1pE87kAbQhoZhkAYnsF8QYlbpt3E2WufI9tTmlaCL4Je84N/4kd4/9u/zXDzhN3tUx4+/jxP3/5tmvtv8sanf4Bpt2d1fJ/dPNI4jTKGm2ff5eb95/j1iuHuJSWMOLNGtac8/tyn6E/vsb+6oJTAsNlSi+P8tTfIuaLNTHt0j75fYnJmN0Wev/ce3sLJ+pyrm/e5vnqXebvnldc/z7Nv/gP88as8fPwpmm5BVpmLt7/D+vgIS2KaZtpVT9uuudttOTl/QAyBu6fvsN3dYN0RqUSSmYn7G1TNHB9/gkLGLe/x6PGbErySk4QbLJa4pmeOI3e3l/TJMdaRxfqYMM2klChlx/LkXHSGKC6fPSXHQMkjNxdPuff403i/ZHN9w8U7X6Oqwuuf+iKbm0u22y0PX3mAO1ox7QON77l6cYF3Eu07jxO77S33Hzzi0Zufg5J5+vbv0HQtMRqyaWicYiqJOs3M+w2vPH7M1YvnlJBYrXsur6+Y5xteffwWc8yEcUJrzW67ZR4Hjk/PGO6uKVisdfQnp7RNTxhG9rtbkU5VKzHzuy1t5whhwtgOQ6SGJAEkUTYRxijmOpGDJuw29Acij2+bAwdeBgo5DmCWMA407cG7UjPTcEnUTpBXtqWWQucbQo5YZUhJUitTiqQ8kYrm+PQ+u+traqtZNMdY64lzZByv0VUKnNubZ3SrNWF/R4w3RLOj716F6pl3o2xN2yW+X7Ds1oQQSPMO3yxxTgv+s0TIMMUdOsp0dZwjR4s11jl244AxinaxZJom+nZJijObzR3eNGhrQUmADjWyv93QWIezhtK24o9BWOPCW42UeUsKO/rj+4QSsF6SQJ1fQFGHoVr9SCs/5RnnGnRWTJsLds+e4dYd202k61fYVQsxSlpr1TQekXoYMJ189pi0NIjjwDhtD2EYC5SB3TQfpBr5o2TOHAtN11KLIYYdbdMwhYlpO0BK2OUCrcA6j3Ge4e5KqEQlcnM7YRae1mqW7QpfK9FUrOvZ70aMk+JxtVgTTUIXjaniiwFISiRx2ooJsSqZqOacMbRkJlQujDGRCtQ4MY8jq3Ur5KZpIyziqjBOBgilIIFDTUuZ9xIMZTyUyrDb0a8PQ6GDxhmjP5L6TClTa8Zq+Ml/7y/ytd/9w1bk/rV/F6rEHH7o5HVOdH6lVlkOV7C+peokOp/DJdu2vbAl0aJPKjI+rwW0Utzd3TIPt1gvSI2qDrGDylF1IpJkJVWDuPp18xHTT+lE0fqgK5WVt1WacRzFpRoryTZo5fk7/8pf+djn+vFf+3+wriXnjDOyMjfOoqmMNHzNdfzLv/AedTfAbQSCOMLnSFsf8MVPvsp/+COPOLbXPIgburDhb//gxzm5fzWMsuouGkogTNKxFWtJu+0hLcez310RtntSHujXR5RS+NZXfoXWrIhhxi064dzmikeTayRQRb9TCyonchiFjWk9WFlfDMNEt2gJYaLiJc522tL5pWii4ohxHTHIJGR39YJaxXU8xYAtAjhfn58y7jXbuwu8BWc15tDNr9ZH2GZFrpaYJ6z1jDmLXlhpnJMpb2udoNTITHmmFsdqsURbxTwmCplWNcKwrJBVQtWZrDwqB3GeHlZTKRWsNhhfKNNESjuwYnpctCdMecIoRdUVbzzzlCghSONgEsY7hs0N7SufwBVF5RDBqEXc37iWWhWhzofEJtHcpjGAt4cLyBNLYZwmGq3JOohhsWgM8l31q2OKEXJBHvfEXPC2kYlRSpQUyEY2IBjkMsxyNrRNT5grKQaiFoNhu1pDksjTWivWSiOni/CHjROMX+MdhUzjOkljc81HiYDDuKFt1xTtsNJfyNqybT+axhgltAptKzW5g3SiEoOk7hijoBiU1uDk0DNVjBzGaVKsUAxaW1LY4Zxj3MsUIJJoXStGUl+wSpLHBK6uyEkTSqVpDCkL03MeB7R2pJJouw5TW7JRNEaCaGKaGXY3InfIgVQqrpWoXp0rcdzTNRFrWma/ghSJpWKqQ5lAmF6iq2blevbzS177wp/ktbe+xHT1kqubC3abp3JOtKcs1vcJEBD9RAAAIABJREFUMcrZN264uH6P09NXIVuW3Rnt+RGdsYxj4NnbXyWOt0KbGG6IxdEdPaQ7PsMuGt566wtoFC+v3me4fMnd5n2cW9Ctzzg5OeXb//BXaPoTtBpZ3nuD+w/eJITE7uaOkwenACyOTrh+8Q6LxYLNNmBq4oPnF3zyk19gc/s+jTdcPnuf/TAQy4hvV5zcO2d3s4WCrIP317JF2Ee6RU8tE1ntCaEhTyOucYz7HfPdiFuuZX2rCtUtmKeJBssU7vDFCFzfF4xdo5uO5fqImvZkC73u8O2CYhSNWfDy6j3KNOGbJdvtlu3tLcfrnrvLa5xzDDnSLpaS8seI71f4pifXStevWZ3eI6dI1y65vr7mlVde4erpezz/1jfRq4ZPft8PY70h7ve8fPIO2nkWR2vSGLC68s63vo41hqZdsN1dcHL6kLubK5bHD2msIqSZpBJEzX6/x1lFqRN5L7HaAdHxKl1JSWNaTx1msBlcS9jOgrjLmRxGTh+8xjhdU7Jh3g985o/+MLVmri+uuXzybbr1Ga1RbG5foL1nvbrPfneHMqBNZrU+RduW7e0NSomx6+7ygqEGVu2aEAuL9T2UVdw7XnJ7cYlvDNq0hGlm3lwQEixOjhhzptOe3YtnkirYGEySKFusJFrmmrDaoJsFjRVGbyWSC+AMak7kMjCOIwYDBpzv5OzrLDlXDIph3JEP7NvGyATRKNEcu36NtZpqG3IKlCLGrr7psNqI1EFpQkq01pJSRiuY44BxihQVJedDkEolF4l9p1qcc9SwI8VIKAWdK916gSpw8fS7LI9atOspusG7hpg0vvMffZYcZ8KYyWVmeXwi5niTca5lniLa9ygy1nmYA9oYnNXkXLHesN3scY09eBo0ad4f4nEbdKMZhoG+7YSJPm+lofEOhxOTV9sKwWEu4t/Z72maBtd4YhoxMTHNh38v5Y8QfF3Tk3JmqpmuW3w0GLRazGXKGtmaFoUzVTabxRC1pJ2SZ0GyFdlUqqrJ08SQ9rhmwRQnOq/o3VKm+M6gnSbHiHGOn/zpv8Q3vv2dPzxF7j/xqdfr3/3rP41WlpwLxnekIJcPWmDNKSXGcZZIxDpjs0T6lSxQY5KsrIqO8hIhqItpTOSwI4QRYxX724F+sWCm0K9W5LlSrMWUCZ1G6duUkctLK7SRCSXV0tgFzPKcZLNoW6onVkB5/qcf/7jx7Mf+31+gmIYP8SHGNrz0HdfNGX9na/jbf/O3YGHRdZDJaJxgu0NVhV7dp1ktGJ4F/quf+gGarvJ5t+eXP//Gx57x09/8Bvc+8RqUyLTZUEphu7niZLHi8vlTzh48JpbC7eU7zGFP151LQUTEUwjbK8btLbvrW4EtxyTweWsZw4SmsN8M9MuldFWuA6UYY6JZrAQmPu9p2iVhTqAr5VA4lJSpaU8KI+MsmJNSo9Ab2iU5BaZhC4jQvekWuNwQy0wlo7MB52laxW430fZLSkVkGKYe4hUdWiXylElZobR0e85JZGnJUZokdZgu5yo80RAwxonMAg56IdD6kDqlEmVMmOWSEiaUs8T5BjIHnnM+SFFkoqgArQVgng5IN1Ur3lqmYcT3S/kd5UKYZna31yyOjuTgbB3zdEfNlRg0R8fHGCNItzxHQtzTes1+mlF+yapfMIeBUPao2soUphacqvL8kClh/CiudI7y/2i6npyUTBizEq5wrjjvSaqCKUIliGIg09oewjSEZ2q1o2gFpVBLxBhFSYcAjhgPU2DPHCdyOoREWIsuFtc2pDqhtKTP5QjWIzIT1VJjohrPNG6pteBbh8aw2WywSgu4XltKCVQLJEnAQWuMqpScyDGTDg1uiRrrDRXhOaYSWTQdcYpoLRIHRRRySpXLxvWCFRxSwVmIoWKKhgJ+0VEIgMYpGGPC+1Yc1GEgTSNatcS0xXQdFEXOlbbviBm8NwdDq+HNz34f17sND8/Oubu7ADJqfMmT73yVk/PX0V1Lv3gFquXm6hnWGxZHrx3oF4bl4kR+cxqG3TUxVzYvn4ju3MuqPkVDrw3jbqbawr3XHhPnwH57w/HZKyxWR1zevOCV41NevnjB2eNHxHHP/sVLmsUZ2+uXdCdrthdPuH050t9f0oTEzfYSu2yw7SmdP6Y7a3j6u79O71YcHz+gOMN2u0ebhB4z4yS6xZR3xDzTrc9IIWN0YrpJODVR24Zm0RKGmdPzt9C+5+LFP6LOkUYtaddLuQxzwTQtMe1RxdJpT3u04vryOdO4YXFyzPnDz2HR3N48p86Zu+1TTFG8fPqSV7/ndV5eX/DmZ76PNIxcvHhB37Qs74tGOGNwdsmjtz7NuNlyd3uJOxhlnCpsN5dM+x0RxYM33sRXRw1gVy39+gg9Rl48e5sXF99kpRv2MfLqJz7N7dUFfd8zhDt0iZQouu8hZoyWLULXPsK3oife7zf0TYeplWa14PbyJc4X4r6SdaKzS2gcxljmEA5cb4UyljhP3D8/4+jsAbvbK771W78hesdpIJuKWy84Ozvl2fMnnBzfZ3expdaR/uQewzDhlyeiVy+VaXfHsl+w31zRHt1j2G7wy1OUc+AMnVtQcpYCEtGEmloYhxtymTFNj/dLSg7EaQe6kkKU+7BZUFOmGk+aRmzboIqjNQ3KFqpVzONMGCem6ZpYC2cnD/CuIysoOdMvW9Fpbu/QVnCQvrcoDolxsUp0bqrMKR4ioCUi2GoxSd3ur6l1Zt2e0TeeXbiDGqQpXpzTLTyxGtI4451smq33ko5nF2Ka+mjzIPdbv1wzzwHbtFh7YBxXjVGKmOthqyx1V84ZTYEwo52Wyb2CGipDEKydVurQSE8oj2yZasU2LXEObLd3nJycopWnKrBWo7Ujp4puDDkmDIZ5vpEhm+0x2uIaz263pW0caQ6oGqnaUFOQyHjvmcZMiYlm4aCK/lnlzH5zgzYrtpsLrAPlPEfrU5TS+L7Dess8Ch4sxYBtPN44QZP1LTHNNLZB10yYBnwnje08Cxc/zBnfOMIUIBdc2zCNgXmeWayW/Fv//s/84Uo8+9KnX6//59/480zTgPOdgJrhsKIxgPyArF9Si5VVI1LMlhAOCUGJHMVYlXKg1FnE+1Gxvbmh7YRNp6vHuRPK+adRbYuuA1E1NE6D7kSWAGgyWRcgHZirmjwPWKNRNcvI3ihq3KCKRVfN3/yRf/tjn+tHf+kXRLeoxV1edeWX3Kd5rxr+8//+f0EtX4M4Q+epuwmevI1qjqjHx+A6VNjR6Vu+93P/Io+/YHnk4fSHH3/sGT/5m1/m/OEbNOtWggQqUGGetnjg6vlzbGOJMROnUViuxyd0qyPe+fIvsn/5Dt57lDumaS0xTKKXK4qqG4ZhwjWJikY3jnEc8d0xVXcUBQp/SEXRGBWoGeYwHuQfe7SKGOcpUXiG0xhRRlG1ok630mn77pDiovFtzzTcQi103ckhKUimf8potLbEUA+Z5YU47oghUKcZaxqCTlgNaCfJRF2D8UuMcYe0nZFa5d3QVX5jShlCGHCmR2fB0oU4ELNEqTrnPmLCShSxYZp3aOOotWKUJcSBVb84HHbQtUvCeIsyTuKhD2YuYwwqAaUyhRlbJ+YkxYtzLShDNqI1zFVhjAUy5PHwPjiUauVvbER7qZTCKdFBKV1RSKpfSgFThbNYUUQlE1NblfwmykRNspoyTlN0g1bChFZkShWnM1VjlWPOCdd4SqpUAo3zsmI8GDVzzhJWYcyhMRR5yjQPEi9sPjSWFlQWGY9SBZSQNZquPaT0lI9ScI76e6QSGeYdJQ4QYH3++NBQHb5HDdSKbzqstUy7DZnD31prQpylccFIulGtGGaG/S3ed5BFX910S4lWbjtilhQ8bxtSEhOTYHhEk5ZLoJDlb1eLJDeVwjQP+PU9choo1VBCJMz50FBZKjPFwdn6McevPKIxhmwNnffsbt4nDwNz2JGVofiWl+9/Ax0XqAYIhtc+9UW0t+zvbrm7e0bfnx22GQ5VKk/f/V2apaexS07OzgghMc8Ddy+foPWSoi1YxyuvPGS4veb00SdYndzjd778a6xOjumXK0yvadue7c0Nw+1T+qMzhps94eZt/GJFyIXd3YbF8ZKz136QV974DN4kXn73O2yvrlGN5/79e1xcvEe/WHF7c8XVxRN0K/HCJ4v7KNfSdY7Lp29z9uqniFOisZaLq/cZww05ROK+4vUBEB8Lqm3l+1aWpmtxy3v4MDPd3ZKbNX7R0fglMSVy2LK7ucY1gRQCZAU2o/0RR2ePuL54wcPzx0xxx3j3gv7ojEefeJPL58+4uX7JuB9Ytidsr/dM+Zq2szSt/D6bxZoU9zTNAqICb0kxsz5/hJor0zhycv+Mu5sLLp+/iyWC7Qj7l3StZw6F7Hq2ty9QccI3sDh6jXmYMCjiMJHjhs3wAUfdI9puzZRmuntL0J5SLE13JNK7xVIadKWIMdP3Pdu7DdVYjo6OePDKJwSZVgsqzVxePaFfHzFOW6x2ovcvkXFKLI6OKdkIuagm5ptb2tYzhZHF0TGvf+GP8eTrv8U4Bln7N57d9laS9G6fka1D+w5jHKv1GftxoBZDyTPeFMawYXl0nzgnSoV7j16jsS3j5lbMVMay2+149fHrPPnut4klYHVBq8o+jFhl2E4zR8sF3loqDQZDu+gJ4yS+Fi3NsFIK7z0hROI00PQdoYB2irzfk2dpco7vryCDN0tyCeR5wDWCvsw01BxF4lQVvbFYrZimCV1n5ljpfEcoA0dHD4RsozVN2wszniIJbDnLhvhDTa42TDWztB7VOOIg539JE7kUjPOEKcr3UzLOWMZhwGjLFHc4Z1BGinmQs0VrQ9GSVEmph2GTxPDWmmlsQ9Uy6QYxA2M0uvXUOdI3nhRHSlLMUUg1Kc3kqlG1Ms1btC7kMbIdnhyGRAvWR4/x3qLKllSqDFW2EmeeSmSxPCYl0I1sxscUMJMlay3R352iaS1prIeQENnaO9vIdkEpYg2M+4mmXUhKaoGf/LN/4Q9XGMSXPv16/bv/3X8gKBDXUnGEnFj0R5SSsFoiYcftc6ptJHVnEoF5u1iSa8VqyGGi6MK0vSMe3OohRHStlFTQKGpx2IefQ3VLqlWYZk2OilAmDNLxaK2pVVFcgSopGzUrGcMXRc6y9nXaMBbJ5m6053/8oz/8sc/1r3/ll8g5yurEOb5xWfkHy4f8/NNn/M7ff0KwClBS9L39HaoxsDhBFUvtl2hfIW4p34I/8x/9Kb5V7vin/9kvfuwZP/H//TKN6XjwPd+D1lZAy0o66hTGg4HP4hee1nu0XUJJ7G5esjx/nZJ33D17znT9hEXn+c7XfpXWd6TqOXnwGONanNZcPHmX5XrFxcUlJ6+9zuUHz0Fbyu4ZIQzU/pw0b/B+TaYSwohtRHfqakMtkXlfWB4fUQ5xhK1vaGwrHD2tiHNiDgNWW1xnaRdnbK9egushTofiKRETmH7ByVnL1bMX1JhYn99nc32D3ovWremPiDGLIQDpWr23WCQAASwpTfTrE6bbS7K15Ox49PBNbm9vUE2m0Y5xs6FpRAf8/1P3Zk+373l91+s7/qY1POMez977DH369ETTIJLYJBFLBY1FpUJJFA0V4lABI0a4SNQLhyrLUsuyTG5EuYmaKiomUkioRBMKIQSIQBpsmh7PfPb4zGv6Dd/Ri+/qQ50/oa/OxT6117OevdbvO3ze79dL4Nntc3djP5GVKo3pnPHDwPHdRyghubou/EqtBUpkgosF8eN9kVLIQpmIISGsJsuyqatkKb5s+w1S1Qhl0GS0EeQYqK3FhcQw7AqeJoOLAVtXZO+QRpZbGGGLuSyU4oUSiphCsfNJAUIjJeQ8MfU7gvdU1QJtG6IbCGEsm00VUaZBZ0MURdLhYihayOQwWWCMYeondGVJUpJ90QKXm9lSFkAp/Dh+WDSzVpOjIvlEt2iJaSL4Ar/fDT11XTNNnrHfoRHYqqXSM6Y0UlUdU5hI00jdtqVUB6TgkRm21+fISjBJQVU1zOenDMMOISGMO3wfaOdHOBFouo4YyziNvQUvZc/Ub3AulE1zVqDKDZBza6SoSyZdZZIYYX9LrmVFjgMSRaxaZPKMccKaGSiLwpLjDh926FrTdXc4vvMSi8UR88UCN3p225sidome5+98gd5FZscfp7KJWduQlMDUM168/3XOP3iPo5M77LynX1+jaoUVFqNnCJk4vXuPIUnaZkl/vaKdt2ireftLv8247sFU1FrjUmI3FOyaqixOOF599bPcf/hp3n//64TdJZfXO5bHB6w+uETWsDg+IVPiJ6rpuHzrzSLMyaVUmf1AMlCZMi5uuznX5++XbHgUYAoPdXZwgtaSqmqom44n73yN1dVbaNuglWJKill9zPzogLY95PziHeKQWZzcompqpmHL9c0V2/OnvPLa5/jKl36NNIIWmiz3PYLWls+gVLhpg6gM45A4aFt2ZzccPbq3l5EYju49QlMjjUa5xNXTb3Dz/Bt0Byfsxm2ZMiZwrkTckrJUpsNIyXq9pq4UXd2UAyqRyY+YukK3NVo1HBzc4/0/+EdoYRk2a9Z5oLWGtm0JXmHnc6ZpwEhDThM5RoRSIMqmbXSJpjvCaM3xyS0uzp8V3Jco5CBV1cRQRtoqlyxpygohMzmWeFCrLdvtGilAVQqrCgsXJFmBEIaqrmlagzu/YTP2+ORYn5/TNA3NfIGyim52SHCZ7dVTQhzRdrYXkiRyGMpBUQjaZonzHhUcoy/WUCUN1XzB7mJLzpnIQN11+KjQUuGmoZTo+g0+j4W/qkvnRsREThG/X4uN1mw2G7SxDH6LFBqNoD08Yeq3hH6FUA3tYsE4jjTdEu9GjJCFgGOK4l2iQAvcOBLdCMLRNMdEmTB7EVFi/5yQec/7BTntiQ3BgyoTwe3QM/lMbQU5lUNxonwPUir54xwTaefQdUMyAUVitxuoZgtWVytmzYyuq5DS4mKgUpaUIy4MNO2CMWzRWeHdgJAGhIVYxD1SShKZJItRrbINUoiCYEuJprZE50nGECbPuN7spT0Z35dYkdivW+3xcSl8pcR6XFO3FVIU8+W026FqjdrH32w3x2fQoRQDez9QNx0hONI0IVRAtzOiT2U673oUFf040GpDEBGtGoxuSCmwmVZomcDKcqBULZXUaF3xZ/+9/5Avf+3r30Kb3Ncf5l/4az9JXZdRcaJCK0uOHmsNYf8zipAIKiKA4WaDrfRe8KDJ2SByX7JUswXjeo1Mgj4UZqHAI1LGTxlpali+TO91eY1ZS1YWO6shSZRRkD1ZabJ35eYn75vaVYd3CXTGIImEcmIMib/xRz+al/3h3/jlokrMgdrAf/PbnuNP3+O//LlfQZwvybPys8jNFem9D8j3XiFXFnFxA6eH4DfkIFheP+dHf+Rf5cwEPv6D3/GR1/ihf/j3WbQdylhmRyflw+sDm+0VL736MuiOaX1Jd3qLPA2FGBB2PP/K75Oc4vYbH+f87DHHx8cFTzVbEDdbnj55izu37uOjY3v1mHi9JaYVUTaErLn18NPsLt/HLo/Q1YJ6eUJlFZvVGZfvfIXFw08yPz4m7K75+hd/jRQVucBW8QFqHQnbTC0MUQa6g1NOH77Cbtzx+Mv/BDENjHJCq0N0JQk34x43pjGmY+gTpjvk9qsf4/zpl2BY0x4uMaLm9P4bfPD1/xfpJlYrz8PPfAezWcvXfu9XSZuBrAQOQd125TR6XG6MkxQsj+7jh3PW/UjsL7BmwXJxwObmktuvfortxTMuL86xWTB/9SWq3KJVwg8j15c3rDeXNPMKkiK5gcq0XK8u8MOW4Dx1ZcmhZ358lyg0VduU0p9z5Eli6oZhXNGaipi+yVaWJFU2h1LyYaZMiIzEMCZHbRtIhSed3EDemwK/KUOYpolx2tBWbXmApYGxH+hm83I6jp6UI/iEsoYpeIwqZY6IwEiDNBIfAy4PCAGVqdDC4sa4Z1WX5rqyBp0VQmqEqchpIoTM4mjO+vqqFMxyptYdLk74HElxQPhIVR2ATqhcRo5CCERMRAHB781oUqBlJqSECAqpy6EpBF+KMKtLuoOT8jtNHmkTOZiizc2QKGQUazWTGxDfzNupkj+utCi+9XGi0S1uCKy2X+HO3U9weXPN8ck9ju/fZ9j2rK6fImjw0w7ilmxr0uQRVb2PNbhim0qKpplzc3NFNTvk2/7o9zI7OECg2d1cUFUV68tndMsFV8/ewaUBd/VFBBo7e0g3v82zp28itMK7xPL0NmwTOzdSLQ6QItJvd3SzA45v3Uc3c6xtee+tr7GYHXFz+RRTCdqq4eTRq+w2W8b1NaMrN51GC7y0DLsdV88eI03Fax//NqZxw7jdkeLEOHl0o0FGTo4fcHznIZVt8G5kWF8w9A437ghKsL55it+skNnQntzi1q1b3FydE6ZEzhMvffyPcHV1VW7hbi5xfkJJTRIBgSYlGG4uObh9j7qakZSnqjq0VeyuNpy//Xv0YURUhk4vGMYt3WGH246EwWOXC2azo1IinR+QfOTk9i1ePHkPYVsWiwPGfmDqr+n9RDtfQIDnb/8eVbso2l4VCf2OjGF5fMJuXFG3HYvlMWcX52w3V2hpqaqGYbspKKmcCTkgRYUQsNk9p+977t57A2tg7CdaaxjGHbZtyMmSgZBdwejZtuR3+w1y6qnbBePmimmzI5nM4uRV1lfnBaemE6RIdIKkNVIm6mqBqQ0xUsyedYsLU5l8xcL3ruu6XL5ICSFitWEMjugdB3fucf3sBdmPJHHFtBaYKlE1Hb73OEZUkAzX76NmB6h2SWtKkQ/n0G1bpEzakE1dDqzrHlTCNC3jdkc767h+9nWstgwusb15n25+l8MHn6RZLhFZM04r3O6Go8NbDJvtHgFnGAlYZdCA6hZIETFmzjQOSFNUyKN3uGHAVJZxc0EYL8jSk4YGn6GaHTFNE3du3UW2ppTMZKEuCVl09FkE+l3Bi5rGIqxEqgqLLOQa5UFJRMiIJEh7k16MEZkVIZffb/KRSJFOVbNF0QZLgcxyb20cy6bXBTCSqR8wpi4Fe2lK0SuV1/QpUlUVIQe8j0z9iqpS+CljZU2UoXQ3BBhb4aPH54ykQpNp25YhRIwodMesJDCSx0zYTkWKYQTKe3IaiDqh9AyZDB6PrESRjyhJrTui82hbE9EkP5AQhBgRwlPVHSl5gvfkmHHjqgitRochIKWirg1ORZTtIBpCyjR1wQkiy7TQ6orgCz50nCYkkhgFP/5X/jO++q2Uyf32jz/Kv/TT//mH6lSfAjpLsg9E0v4mtSx40TtUrXAxU5sSPNemIcWMlIW/Fn0AN5CiBKULoy31pUiTJcGD8yOyPiabY7JSbMcBKTXS1CStqbsWNCXPlDIyZqQ2hFCaup60HwcEpDYoIflfv/t7P/K+fvg3/t7+dJtRXc1/+lbD//SvfJLl9/8U3P0eaCk82Xe/Rp4dII6PyBdPoD6GJiFUiwgjrU9sf3fNn/mPvo9P/uh3feQ1/vV/+HfROaNk8WpPw8BstgRA6Zrlx16nqQuSrb88I/iBnASnr75RNIFKI6qOsLkiTgNh2OA2O5RVPPnGV7l+93c5OTlBJgc6sbv6GtNgmC3usd4+wx6/jq4WxDHTnjwiSsvLn/0M2SlkJRiunkI7Q0XPk7e/jKyXnJy+Skgrttfv4XwmjZL25IRhdY6ZLaibY1bnb9LNjjl79i6bF2fcflhuSbfbK/rBE8cbcpb4IbI4uI2cLzG5QVU1SkN9oFg9fo9PfOfnefz4MbPDJcbWVPMlu/MzkndcPn2GthW7my1YC3VDvrlAHd1icesYf/2cp28/59bDR5w+epmDxSHj+oKmWzBOK56/+IDTw3tcXj1n8+I5zveIukZbQ60a+s2INkVVbdumoFtyxqTAzo001QwhJZfXT1A+FdSVFFjZ4aYBVWmwNbEgAmkqQ86xeMmjKydkJ6nmXckHpxLjSPtRf6Ur+nFCVqbcRkw9YSqbuCwSjAPJ7HnSKZFIGF0jpN0/gB1SG3KSSClK83g2I6SplM5EhRKyZHb33nOX8l6O4gvPuF+jlCy3H2FEG0M/9dSqIeSpoNF8ydEqZQhToGo6+nXJapMFYyx4mUW3YLfbEWJhniql2I0jddPgv2kD9FDbfZ6ZjHMObSrIJc8l91k0kQvneuhXBOGZL26xWB7hJ8d2t6KzDSkVQ5KpGoTfIqoFd176LJfrJ1w8fY9WG27Ov4E2M7QyNIsZY/A0tmFcb0l5QJqE9wkhZ2VRqCtO7j7i7O33qA9OGdZrdpt3OFo+olkuaFrLenMBpuXwWHPn/mdZr9dUlcEqeP/rXyCljm3/glYV+HyMSw5Pb3Fy9xFvf+338WHi6PZ9wgTIxOWL95Fa0y0O2K3XtHZeSrAI7Lxj6G9YHByjTcX1+Rm6mZESPHj1U+UGPCemfsN2uy1oKDdwcHybFDLXlxdoSWniHx2ScyTGos22ViNzYV7udhvC5FhfPaE2ic3lNblpqGxHXc0Jw4qjlz5RLhaUZHX1jLzbEYmImFDygPmdQzbXV2Q3lM/jtGIaM7OjexzdeURUggbP21/6x5impe8d0ip0jKymLTEITuYVD9/4bt766hepZzNefv3TxBh4/u6XOXv2LvN2SdAdUvkiDwkeFzWVOSDmTNfOixY8RcaxJ8WJulmyODrhyZOv8srHvo2zD75BniKEjLRl+jQMA/WsK4VWbRCq4vTeHa7PzwghcLA8ot/esNqsuPvgYzx571109LjdNXXblQNx17LbOrSEzIgyJV6Xs9rjHGVR3ouMkpacBcOwQdUla55SiQZ9s1gdQuCgm7O9PkNbU3L+046sJFYqYvasP3iX7e4Djh58kv5qi9WC2eIuzl2TmorOLBEKhJNlHB4CIibG6Gjnt/B9z3hzhTxqOL71AKMqvJSIGBjciNUS3IiMCp88KTj60dH1Fq+GAAAgAElEQVQujjB1gxSBaTCQA6aShJA4u7qkMrDsDpi8Q6LIoRSuMwLbFN49UDKtZKYwQNQYqUAk+n5LzglhTLmIGotN9frZc/IYqGYVzfKAGHdMfihdiMUthJII70kp4UXeW9DKFMo59+HkzkeHnXel8BUCIjrqZl4eUXt51XZ9iZWCcTtRtR2y1iQjSF7uo1YapQVKWpTpILki1VCKmCNZFkRqefYLjJDFEEs5tGdVpozb7RaZSw64qgqTN7FX1UdXeM0h7kUVY0E/hoCwEoHC1l3hplOkK/OuJo6BSYAfNwiXUcaipGbWzhmnHqxGVOWGPKUSCVSqINwmHxAxFLFE8sTkCFkULnWaMEoS8SQUtaoKckzrQuCgxC/+3F/8K3zlG99Cm9zPvv4w/19/9acQaWJ0DqUrpG4RKSO0+sPbrHKFBUCkLGpaRHzYm8gwqGxI0UNyJUfrPVJmpDGMmx2mLTBmITI51kTvCVkhTAOqJuoGvbhHEAKpIzlKVNqXa75ZMhICbe2+0aiK6k5JfvaP/nMfeV8/+lu/REISUuYiOH7m/AE/8UcG/qk//bOwPIT5MWJ8Qb68gdfegMqi3vsGSVhyfYioMzlNICf4uy/4F//HH+Lzf+6jCLEf+dVfJE9TKd+ESBCe4qkRpHHCNMckaXj9c9/FlEY0pfQwXp8xP75L8P5Di1xAUMnA1fvvkdKK4fyMxb0HbB6/Q3/zPrvtTeGuToKD2zNsNaePFpNh+fB7kIe3OTo5IITMMPTUbce0vaFuO66uLspYLkW62S0+eOdLxM2K5+/+AUd3P8Prn/8eXrz55VIwvHrGuO5JzazcBAhHHnu6ZkY2hjxNVHOD9wPT2iOUpLMNV5sLDk9eYdxOOBnptEDWR1idSCnicoRUsX76e8gM/W6DrBTWHpBcT84jQi1p25opQgiOTs2JtjjRr6636Dxw+tKnmN29j1/fIKo5y+OT/a1og8ySul1w9vhNVhfPGXfXJGlKJlWU0paWhuQ2DDEihcBlR93dQXmPyhmZNeM4kmwpNMRssLaiNiVrK1UpYipZphupsnvCQIX3npwUMfZIUbLYOUmqpi5WQJEZ3IQx5WGfpCKjMboQMgQly2qVop8K4svYppSa9iPQmDyJkp0dL644XByyujmjPTyFqi4/U0r4uEWjUabFKJjcjhAcujLsVutyeJSSqmswuibkgvaRQhe8n0hkXcaoJAUhkmLG1hZJQfEEigWuwMMDUkAee2ROBfxPoUJEP6BnB9S2RgpbSimmPCNTdkx+IqaMrgyt0NBUzOpDtKpZrV7g+xtSFAhjywg4rxBpot++QNoTlF0gosMT0Flh64bsR4KICC2p20Nuzi7QwhE2I2H3BMi4lHBRotQObRRXT56jc8Vr//Q/z9Xugs999w+yWz3l2ZtfJIXI4fER5JFJDKyvLlAq8+iVz7Pabrg4e0E3PyC7QKo0s8VtUpT020tScNhmgXcDfuiROKr5CQ8evlJGy0j6y+f0V9cE51lPWya3oaoqpGrRpiUmhw+Zyuiicybz8mufoq0O+ODdL7J6+lWkSMjZHUzVEbxAm4itFsQ4EVNANwqXDacnt9mdv8vm/IzZ8g4xB4LLNAdLshTsrj7ACgttx+HBLYbtGlEtqU1m9eRtLi6fIIKgtRWrYSBlw/zgEKEVbjynO7iFD5qDwwWTh+Pbd3j89vu8/OoDnl9f45+/R1CaoU/UdVuwfE0sFyN7pXvbzfEpIk2N6z1dNcdPjkSmm1U4aXG7a1aXb1HlTJYdyVjG7TmL2RzfO7ZjQJuK5fEtTLtAioxzE1ZogsooUUbLViiUsmQhkKYhDj1ZOcK+oNS0LX3fl+hbVkhKpjLbCu9jychahab8/1qaovQOm/Lnsqxf2UXSNGFUSxYwrM7puoZNfw1mn2MdelbP3qWrDO391wgpsuxus7m+QqY1zx9fcjBTSHvIsOlpTwo5QKiKGKE2Br8bWJ2/jaka8uKY2dERzjna6ghVFTNhP6RCEkCidUCI8l7qbkYUJfuax2ukmSGSQBmLblu0qRBhJOaCvxRSMk1FJ97MF3g/FdSZn/CDw6cRqQ1d0+03+nvcmndlgyok/c2KWEtu3b1HDA6lLUPfE4Jjt77ANvPSRckZKw3TuAENRImsNEKUm96cM1KXW3yhwE+x9H1SiTd675EIdv2Kui0xCGMqkguMbgIraeslw9RDElS6mO6msWfrLjhd3MNvHPZgjhMZFVKZInQVle1QpmYaBpLIZKWwSqNtkQBF51G5rGsJtX9mUpjjOpFjxChJwKC+6SoII0YptoMnqVy4z8MVAk3dzshZMW5Xpf+hDOOuR9TlAqLYMovfoJTh9L7LtFfIK1XkGCIVL4yokbKY+1wcadoFIoCVuoiPfCDLDFLwI3/hJ/nKtxIn97OvP8i/8N//+8i8lzoQy42UD2VBklPRxk2JYhSYioEpJxIRIUp7PriA2aOPUnRQbM0okYspSYHIvhSmlCBNCUENwhe2nJsQ2jCGRJYNujtGzY7B1tRVxxAyQodSvIlFP2oo9rK66fjZz380rvBnf+sfFFuRgp/5yg1vh4/zqPp7/PW/seNKVKRpA9pB6hAvvw5f/QL56A54kMNT0rJGXEFWCXHhefzLf4n/+eS1j77G//NzkAKkiJaCfuoR+/ELuajwyBHnS/xCiYqPfefny624ECQ/YtuKfnWJtQdkkajahun8OUl7nn3lN9l88AWa3BH1iro55vL8Mc/e/gr37n+CnUvcuf9pRL1EJY0+esDs4au0p7dRFJizVYrHb38NoSpe/tR3sbp8gZaJ9978DcKLM6LVmHAH8/I9ju+8TmNrHr/9+yilWF2dc/+llxj9yHpdSn7dfMnzd5/y6OOf4OrsMco4kup4+ROfw+1ueP74LQya5a27nD1+i361oZ53NAvN0/ff5/T0Vln8hzUHJ6fsrs9Jm2uyAOcs0iTIEaFbuoNbbK9f0K+2nNx7qZTnnMLpqZRcdju65RGSxOryBUFOaFFBCmQNpqrwYaS2h2Wz6APNcs768gV+CqWkkDOHJ6dMmx1aa7rmAC8mpt2WwW8BSxjHYpMRpfRY6Tmb4Zz+ZqKZVxipsFZjF0d4lxCmBj8iicWXTo2SiehHqAwu+A95iNoacoSYE0oJYixcVL/dYK3em2gsIqk93NsT00TTLsD3hKF8vozWDOse23YoI3F+y9ivaO0MZS1GSsYcsLojBkeWEUgoWaMri3OCFKeSCcsaVVl89lA3yJSQKSKTKqg2KRFS7hf4SEbS1Jab6/cLE1fUSFWhm6rQE2LC1A3TbqDSZeQXY0QlSZYJUcN22lCLmtrMQCV0Lg/lvt+UjHvsqWpLiBWKjJ5VkDVZFkmHtXPcGAhpU1r3WjKNPbaqcH5EGYNf75iGKzqdcOuJUYzU7by8j7rDGA0IhoszBjfhdUPjN+TxPVb9lm4+Q2vNw0//SQ7vv8H8YM75W79HY2bEoJlUyUyTJB+89yXqZk41u4NUc26efICQ5RZpcdgSk8VIw+X5Ba985jvZrM5ZnT1DyIxa1Lz86uf4nV/7eQ4OTti8eIKLW9pmyfLoNYKytLOWruv4wq/8A05vHXJy/5TNzTUhZtr5Kao5ZnF0zNX5YypjuT4/w1rN0d1XOHv6hFr4ErkRNT7CFG9IIeE3Vxzf+zif/uPfz/nz97h5/pTl/VdYLk5QOfL0619gcDsUhmkYqeuSzQ5Ept2IMYLtOFEZQUiSbr7g6sU5RgYmAodHd9DScnL3IU3bcf70Mevnj5F1kfYIbWhkyxh26KYti/Pg6KeRrm5Q2uK9R1CYoc4V5qjElFLQviFfJi0TaQxIWyFTJGWBqWtmTcuTF++i7YwUErVqEHvZURHvFLOhVBZywSxOOZXNsBTk6EmybFSgJvnAvO243l5xeusOz568R5UlPrsSpSGRY8J0c2rZFuTV1FPVAheKXEY3hiQsUljQiawsrdbs1pcYPaOys/2t6AaCJ6Fx2VM3SyxlLU1ENLDebbFCoK0i7E2D41R07ZAKNxWLVAY37YDCC477XKnAgBGoHMpNa9KoPYowCU+KDqMMu37cl613jNsd9WxJspZhuyP6gbZZoI0lR6gby9B7bF0Mkl3XlVhJDAgtCjbTR0gSqytELcghYI1gDLHkXlOGEGnreel+NCVaM/lYJkapCKb80KOqBikFMQcsshyS98bKnCPbfo0QRetb1S1JSISRMLpCEfGekDzHiwNu+nKLO+9qQBFEIDhP9g6mVOgvCaZcJklZCkLyWFN+BlSJSeA8IRdkqk+e7ATGSoZhg9aW5DwxJWRTYUIxfraVxYvMtt8V+VAeUULTmoqgNVNwJVu/F6hEHNra8m/vYqFaREECYs6E7FDSEsaJtm2IRLSqyLIoy5VSxJxLL8oHfErIlMjZ0zQdIWZ+5Mf+El/9+pvfWpvcn//vfgId856hp8oCpIvRKMZYWoe5XJ8LmfF+RArxYZkmxtLQNkLjQk/eN59lTiA8Of1hCFybjAsenWqSDHg/FYamMJAnkIbJZyq7QIQtUdYgK0R3QjQW7AKtFFEajFXEXKQEf/OPf/9H3tef+ZVfREqJkhUf/1u/wx+rvp2f+FPv8e/+2G+yfvkh8oOvkppj+PR3IcKIuD4jPb1C3m3Jqw3oRZFDKE2+8Vz/8n/M/3D/1Y+8xo/+yi+S8rjXAwqiH8lZEsNEebBRbEBSEp0nZAtJcfLoNY4+9jFCHEjbDd1iSX/+LhdP3+HhK5/m+uoFenkKccvVO18ErWiaY6Znv8/1xRPGyXE0OyWZnssrx52XPs6zN3+D6Ayvf/6HcId3OL79Crau6DcXhBzx/cS069k8/y36K097dIjwEaErzJ2HzNo7vDh7ip52rFYraiuZVmt260uqbkE178i5jIK6xSn3XvsE18/fJftMVR3y7MnX0HVFcJl6fohtlywPLIPz2O4utqpYLg3vvfMNxrMXLI5OOb+5wF++T9VotOr2ebAJoQU5S5R32FmLrDrCdsc0XXJweJerzTViX1Cs6g7nB9pmzjCuQUcqWxbWxcEhOQykYUI2DVYfIhcz3JQ5PT5BGk2IJSe3O7sAJdnsNoQQaJoZ7WJeFojthtX1FVIbjk5OuVrt0DqSCGSXqGpB9oHRpSJWQKJyKvkx4ckUlJm2BlVplLCIJEELfEzIXPiG2tTEFKjrBTn2CF0W8tDnIsaQktX6kqqW7M7XHCznuL4wPTcXNyirqJfLkkvUhlv3H3DrpVe4PHvM5eN3cNvHIGdodYxQrmTEhWDa9SyOyy3eNBSuaQyCxfEhZr5g3FzSb3oO5zOGYWB3dVlMa7NjhAxFujDtDXRJoGxX0D7TtvxbeoGfrumnLQfzA4xd0LtIJStU2yJkeZgKFCpLlEyMNxui8kxhQgtLawyq1kzxDxXFAEKVmMk4RBazmpwiYUrFGKcyKYz0O0eloKoqcoqIXGIMWu5vopVCYAkEksho1aHShBCJ6D3D9kUhdOSIYAep5bXPfh/kkaPbD1hffcDq+ox7n/oOvEvoEMmTZ3P5gnfe+V2yFczrlzg8ecAw9UzDGW13zPr6jKxB5BkyxhJPkRnVdhitODw6LSiz51+lPTli3CSyH9leXSFNIuQJKxWmXhD3Y9Yx7PCO8mzsIz75wratGsJuwivLg9dfw29v2O02jNMaXKBpjtBNxfUHT5CzY4QsmKh6cUocAypDP25JuwuqWhFCKpvcxQFWV8Ts8MFiVGYSW4gKJSqyVMyamrqZMz+6xc3NFeurp7TW4DNsNiuWbcVmsyFpSFGidE1dFXJJcBPJZ0xbkYJnsz4jIVge3GLy7sMpY3KCxlYMaWQ2v02UAekjq6sLZsbgUk/f9yQtsS4xv30CsqPfbAkeFouDEjXKASUzwhiSMBihSW5C6kx0juQ8427N8qAj9pExpUIGUXD68AFPvvJlrDbl0GwqPA5byVK6NjVGK8Zhwoe+fIClIGVHVVVUzSmbzYa6LVEBgSZ4R6Xagn2qKnzo0XWJXyEkuITLDitjoXkERV3XyBQZhzWz2QKkwJoGFz27XU/TziA4srA431PVMySiHI5HT9WUydK022DrDqQqBSgyfk9AquoZk9tCzPjoaa2iHyO26qjnC8abx0hp6RaHZCHZbXp0FiSV9zl+jU8KdCR7gYiZKD1piqg9wSPF8sycfKQ7PMZtt7TNnM3qCjdNRfNeVyUykQpPNwNSlXU3J0lzUHN1dk7ygoN5w2bYok1D0zRFijR6BAXPmJSgq0pnYHO1BpMJ0TNb3Kaua3xM4BwYWdbHMZFDKAVkpYkUBXUSlKywFFht8bFsbhtbVLyFyW3IvpQOlVKM04TWBucmhJBUQuHCyDf3iLIyGFmRdSa7cutf9hSUDG0sJWCRMy4ORWaUKpQR5VkQMhjFFMtBKIQAY49o52i1/ztSmdy3TcN2HKiNRVcVxIQQRcpkbc2/+Rf+A7781W+h4tlnXruXf/6//vECch9HhNakMJSTRSgnu6QEImoQkRwDRmsympA8PkasLU1oP41k4RAKyBJEWbhicMWckctYUxsFDlKeCErt77T2ql+tSSEXLqdQKFk231oqcnOK7u7TJ0FUReNazYqD+W//iX/5I+/rR377VwjDxAvt+Wd/+p/wJ1b3+Os/8zle/Y6/hvjUS2AT2TSIBx8jf/mrSBVJNiEMcLUlix1wBHGC9/+Aq1//X/irLz366Gv80i8wyQ2GorcM0aGSJE2b/c2cYfhme1pbooekJcvFCd3xA04+8QbJTRDHgk9KE/0woLPHuYTfXJPbQ57+/t9hePZ1skykEKjsXZIo8YXdJnH08FPsVjfce+PbEItXOLr/iBfvv8utBy+X8f2Ld5hWPbP7L3Pn1h3G9QW7/orh4owxerbPH5dMkyljdSUC1B0pSmpjSWFkHAeEVIxuwmqDWdwmjD0Hs4Z+twFhsG3HNExshzVXV4+RbsdSd6jDY+rFS4jKwHiODBYXIrKzCO+xdcP6/Fnh184W2FbTDwMhJESqsLroF4WNoCtUKuISmhl37tzDuRFT1ZAD19fnTLse4SdcdJB2LE4esFnvyNNEyp7Ktgw+0RwekCcPscffrAminGR1Fvg8YWWhlylpCX5k8BkO5lTtYckTXz3m5OGnGW4ucOsNy5M7NAcn+BRJ/YrN1TPIAR8DURhSztRdS+xHcjK0iwW7caDrWqIf2E4bRNZoAioZlM00zSnZKIZ+g5CF66hMhZEV2+trTNvixoGkMloU1WelysFqdAnnE1pbjOox0uOGAEmDtCyOTrm5eUGtKlz2hDGUQofVHB7f4+Tuy7z15S9idCSmkuuatjdoYsnZqZammxOCKz+XUsQ04n0owPLk2G125N2GdtYRjETKlrquGUNEZ0uOCRdHTC5lB2MbhCp6aKkzqmlQwiKTB10RnEeqVMoqvhTfhn5H8DuUUrTNgpwl1hp2wxYrBbZuS7xJKoQSJVbiJkiRTX+D1QalCxVjigGFL4UiBogJKys225EoA4tlR96t2O5G8u6KrE9oly2Xl+c8ev0Nrq/e4ubyGSfNnHZxn/rgPvXhLciJafuMWy99hn67w3ZLdps1zjkePrpDPwam7Q3trOLFkzeRqSKLHtUsmKaJzXaHFg0iZ7ar59RdjRAK4RVCZ8YpF1mBFEhtqZJGp8yYR5AFnF8ZxeDLRnmaJryfQMFieUplZ2yuLpidLFFUZAHL27dpmznvvfkmYXJcPH2bu/cesNmsMTIgcmJ0E013jNKRpGpyTDBdk3VNUy3I2YNRxGwRsWAAhUz7LPJtbj14nacfvE0jNLvxmqaqWY8jIkFdVzSHC549fq9Md6aJ6D317IAQAqf3PsFmfU1tJWM/EOKE857atigjGTYDi1nDZn0FKhD2Fqtld5ur9WO6xQkyCdzo0MYUK1zOCErMCF2QUCmXRr5IBecn4pahv8LHSF13BA/tfIauO1zaklwkTZ7eeZpFy7gbmXWH+BRRqhRXrbWQ3B4/5glTjxI1EUEcA+vVOd3REmk64ugxdSYlqKs5g3c8euOTPH/2HmnsSVNmnNbUs0Os+eYYfi8X0obt+pqUA6aqaNp5UbfvBQFSJcbJg3NkUTCSMZcYQUzTh5cJ47ClHwcqqVkeHZU+iRZ4P2JVU5CLuiL2GxSlrX9wcMDq7JIoE0qXjdnoCw6yrusSWdMSrVsSiiwj4zhis8ZUhXGvDWRpCdEhKIIqkctEWWT2QouG7bhmNpvtBROOadOTomN79gzZVhzdfY2cSx8iy4QfS5GqaWuC8yjVgQhsNyMxOIwqJWBSYDsOZAEHh/eRSSGIRU/si948C1kueJJns77h5OQE50ak7Qgx0bYtKQZSzJSobi4HsxQQSZNSuRQMZCpVft85ZhCeqm6JyZesdNXS9wO2rtn1W3SWtN2cyQ0kWdTLIroSZYiRnERxBChDDBShQxrRe7X69vIMTClqq+CIsnznpzHt5Uz7/+5jojFGspT8+R//Sb7yrURX+Mxrd/P/8V/9W2ihy2IlLCmVL7rUouh4RdnQSlka5iVwrck6QYQUBCIpRHZIm/FhQoqKmBLaSESM5e8x+6wJGpX3twy22vNSBTFAFmr/INRINLI7IrcLZD0jZgOyhLcBYi7Qd6Ekf/N7vu8j7+uHf+OXoYL/9v97xj/6ref82PEBL/2xF/w7f/nrbEwm33uIePlV8vYGnjwuath5S3Yj+AyihxRh3ED/jJ/+4Z/i2X/yAx95jR/81b9NGy1JBoL3ZZxCIvoJJQojVIiMCSVMrpuGEBxTznz6n/nTmFYzrM+pq5bsJtwUmN9/ia/9+s8xUwGR5jhtUYxUixmzasZufc7FzQp8z+7yXQ5P7rLbOprl65y+/im+8Zv/N8f3XqV79AbWNmzPzqgWFS5KdjdbXrz9JnXrEWmiPXzAzDSsNk/KRixItE+I+oDlw7usnz5lu37B0J/hxw1t22HsASlUoBqObx8TtSaPI9PgiIzk5MlKYlAoKUHW5QSoK6Y4cPf+Ay6fPyYGcNOKYddz92Of4eTOXV689w3cOBG1QKsaEcCNEzmMnL7+OeaHR5x9/WvsXE+MnqbVXD1/yuL4fjmkTa4oS5XkzqP73Fy8YHN9RT+sELNbGNEgxMSwuaHuagia3eoxeo+pEsYybJ4js+bBG2/w5K138Gi+6/N/ki//7q/j3Q7b1kw+UdUzenfNK48+w+X5ObUyTGNPyoKsNGHaEKaB2/fvcvXsHF0pzq8/IK8v6O5/gu7gAXavu45J4idP3WnW12coKxj7FTacsktPmPyWhVyi1YxcmT0M3ZCDZowTkoCubDlcSlkUwFKD1GRjmNxuf1jJIALj5TUhJJSRGBXYXg/MlreYfM/i6JiQJ8bBIaXl+PQOz5+9SdMtMAn8NFKZmt6N++JaACOoVcewu6ZtZkThSyRDJKw0xGFgYMLWDUSNHyd0VUPWdLMl9bxlc3lO9ANZKlRqyazx2wsUFUPImG6GsgZTz4gxlghHyKACKU6EOCBVTb/acXj7HpPbFBMclM2gaJDC4uIOpb/5DU6orEq+2A/E6PcNbVCqKhanMEAK+9/53vIkHf31BXkcmR+ecP7ijKoWKLskTk/KojA8JaklUVa0y2/jO773B5jihBh2rG8uOHv+Aad3HjHseuaLI+JwxtXzdzj51Hdz6/gEd/WUZ+dv4zYvsLOXaGanzJenfPkPfhsVSwSn6hYMUyDLiSoq1GyOUDO65QFSQfIeITO7qwu896zX19hKo7PARU0eeu59+6e5fO9d/Lanajt2ux0Hp7eRUtNvN0xhQz9ec9Qd410Z/wohSNnQtLZ0MaRkvX7MbHaH0PfYSrDabBFCMJ8d4HMq2cdUNKnrmxc0TcU4RWKQNFVLnHqSKpuWqiqFopwzURWEn5ZQtzOmySNDom4blJqRk2Cz2YAcmM0WTENP8A5iKT42s5ZpWBHwPHrl47z79jt08wVK1ozjDi1UuamqCmpRRphiQqKoaglCU7eWhIJQlMi7/qZwwccJoxVSKrarFTE4rNVFkNDVZKWpq5ZKNoXBncqlkbIKN5UCUL8+R4iEnwaIkradfcjdzUYUTbmwRYqeMtM0AhCmFcd3X8FNoJuuKGCF4PzmBVZDHLdobRm318SsMFWNlgZyyaZmEWlnS3QlSZPfF7cUsjL0u5HKKFKKVIuKFARWWEL2ZKmZ+gAiICV0BwuST7TNkg/e/gp1pahkTdKGcb1CKlcY4ASG3UgU0M0OgL2mVniSd1y8eIHtlhye3mW3nXCxpzGSsT8jU1F1c0QSzLsDtkORJVTtnOQDVkmSDKRYil9NvWScemSciKJM18J2jbYzVFXhRcJI+yFnPWRJTA5JKCWzWLLbPkVkcPhpwLSG6DOVKp4A129p5zPGwe9ZuRD2kgu1f/7eXH/ArCvfKdOUPHNlZxhlEapIPHyKxGFg3F1R1xYhK8I4YpuaajbDhQhSYm2LFApSLNMfHz+0ZzrnsLUtCLy6ZJP9NGDrdi9NUrhhP7HSGREDdTWjn7Z47zGmKRcUU09gDxNImaqyH34PpZQopXEu8Of/4rcYQuwzr97JP/tf/Bs0tt4HwjPKaFKI5fQnMj6LffkLBAV7MnlPmjxSCciKpjogTFuyKuatkERBIIWBShqUVezGHVZpXAyoRCmnpITMoqh8dUsKAUyN6m6h6zmxqkoZAFdUdEoXPEgqnFMRBMlnfvZ7/6WPvK9/7df/PrOq4eHf+h34x29z/r//Zf7OH/yf/Nv/wv8GP/CnoJmRZ0eI7ZfhMpGlRNiGPJzD/0/dm/1at6X3Wc9oZ7e63Xzd+ZrTVHOqtx13iRwcoVggEWEUQAgpQiYIogQkBJdICHMRiVvEP5ALkJCsCKEASmKRxCLYCdgul6vsKrtcdfqv391aa7aj5WLsnFApS/aNpXhdbi1p7rXmmkuoimEAACAASURBVHOO8b6/93kcoObC3+uPSDmQfuucX0z/4w8c4xd+4x8wDVdY3d1aS8ouXUqF946QljJR6sZPbSTJbPncj/8E1xfXVG1VbCpjT5z2iJSpTs+5fvox3fkZ0/OPOLz4FvvXL3nw6DPMMXHy6Au0my2v99fcu/+Qyxef4C+uWX3uKwgq8AN69QY5eaJbGOdrPv7O1zE+0ccD53e/yP3PfoHoHVplQlxQSvDR7/0Gy6FH9xNBG+586Uc47g9Mh5m2kyxuJLoj1qxRtkMpweXhmvsP7nK8vCQ5SEKw+IW6aT/dHOl2hcwJa1s8ATdPRDfwU//av8u8v6E/XPLRB9/D5xrlr6nbDdMyk3JVdrUk8rIg2hOaVmLmG26GhegDwR/ZPniD4/6ADJZKVixuRClFUA4qw6Y7Zb/fk0WBuKf+ORmNqgSkiKlqciwbr5vDM4zaYiTEJDg5eYDPgX7MrHcNC47U79mudwyHI3a74vrySI4zKXpWTUttNKNPZJ2Qes1wc4F2npQHVqsNh8OrQjmY9vi+J+SZsztPCN6wWe+43l/h84yVHdPNt2nvfJ71+gFeN8xxQnmPW3pq0aB1B7Lwct28sN5uuD5cF+yNUggh0W1NEIk4DWy7DbNztLVlWjwxDKAlISUau2LZ75FkRAOazBIUKWqa9QpyqU4pBMqWCXFHYnQTkoSOHpVAJYegZsme7Hvm42tqYcjdChFrpuSouzW2XnFy7wFXLy84uXfOxXvfpr+5IKnMIg407GGc2N37Mbyo0HZdWNvKkl1PdCOTS1TrTbEIxnzL6czMU18msY1GWlv015VGZLBVi7RNWdTkzHw4fDooE1wZAjVVjUqi2JJ06STFJRJCwlaSw3hDa2u4JXYkIlop3DTSrRqG8YaUFxSZ4BNVY9lfBvIceNm/5u5GknN5wFRWcnn1e7jkON9+kcef/3e4e/ctVKtYbVdcvfqAHBKrk7uM/ZHD9Sti/xrb3WE4HmjP7nPv/pu8evEhq1WRQJi6QSnBPHqC8Bhp8FMsA48ylyqeS9RthxOGxlj88VAINtETYsT7gFGWMRyoWwPRYKQlicwSyxR2mT0w5LAgZaLavMGyvya5GdtoZreUqnJdyAbDcEnbrEC4W2tcTQoUE5PMhJwxRlLVHd57fAaZyuBWTgqj4Xhzyen5PV6+fk6rNareQI7oqiaEogcnJ5ToUEYxzTfs9y9Yb3bMw0y7PaPb7phvJpSQ+HmPMA161SLmkeAmok8sIWOs4ub6wNm9U3ROBStVaw6XL4hzoO1OEHUxXZFASEnMEatKFWxJPbXcFTWr5FP9qzESKM8Ioxw+TLglYVTJ3x+P+9JVWIayYPEeKypUrYnLTLc+Qak12TuWNBGWPe16gx9nkBktTVmoS40QmRQcKUKzWjPPjqQiVpV5luQEUhSpSyKSY1H3igzj0tPuNpAEIiT68SWoFd3qpAwBpkTVGHSlqaUlyUQcfcnuaoO5FUn5IZPyQtUaopDMs2e73bBMR+YI5ExXabIrlBgvoT++RBOxlaYfHbVusLoi+0DIgcUdUHWZYwnDXAg1YcA2NVFWhYBz2DMtE7tVRZgucVFydv+zpEmQvSfaDrXeklxPs94BArf0iKTIQiOJhYxgBShJ026IU6aqMgqFCxEtSpfRC49LGXMrIJJSF+LScE13+oiU3a0MSDG4GSMgCYNuCqbL2rrkeHWh0yzBQ/JEV0Q9IhZ2eg6SQMLKsrkqds+y7sCq26qrvI1MZHKKt8N+BWWJUtSmPPNSKnY7LRRAeaaKhLW6UGlC+lQlrG1VuNHJ8wt/47/gO3/MTK7+o9/yJ/8SQrBqW0QqTNyoygR5TiWwHYVFJscyTujGYJUiuaU80OqG7BxCJ8J8RdSZFBUphFvOZwHwi0oQfMbK0oIwgEAQUkYkSZIZqTUpC0K1w65OUeu7ZCXJ2RVriQO0KAYbLEYo8hzxApRVP/S5Kiv5Wx+8x2dmy0/ff0QnRv6jv/g/wJf/DMICd1dw8X04LoiqA0rMIjc7ZJrJzpPnjJCW/MW3we5/6Bj/8O98xF/4Sw/LwlZ1pDyDlCzBkXVGUS5qJdakFEpmarggZ8tqc4oQmeP+I9x4Td2dUlcrXnzyPd5456vI7JjywulbP8H9H9kyh4i/fMHqwdsMvaNiYj5ONLuHCL3l+v2POXnzEbDw/Hv/gOnFJQ+++rOsNjsevftVwnFkkxbWd95hnAqC7fLVa/Ts0I3F1m/SNJHQvmA/jDz9ne+SVIa0kKeyYWlWK9CWKCQxwUl3BzcLTLViCjN3334XoiLEmf7mKdJL3NiXNncqeldpLNls+MavfYP7Tx5j7ClJDYjjFS4Echboas29R++glOLy9VPaew8hCaabV+zHmZQlD97+UZqzc4LrWV7+IyY/4pRi3bYk4WE5oLLByQ1ndx9zuNkT5yOmu0O3O2ez2ZV85OJ58NZdDpcv0a9LhYGY6K+e0fcXNNs1Vkw4D1IFTNUwO0/UFV1bY4Rm3B+Z5j3j8ZpDyrRNx3yYsXKkFo5oBbU6Qek1J/fuoWpFMx5Yjo6sJWhDIxMxRdanb0BlSkWr2VB3LUJ1LIcbbKWp6oZOKYJUqGhJcWEaD1jTMo+3A3lhJrmE2Zzw5mc+R4ozx5vXGGs5/P7X8XuHaE8Y9gdOzs5xU8CnntX2Di6BkpHFDZi2QmTJ4XBD163JqsItI52xzPPC0l9SG0l0R5SWCN1xcvcx7337N1ltz1FY1udfQDUVmaKobCqNpeH68Irr54HheEPsL1ndfQO1PaEykmE4omtZWvY0VFIiVdH6Zh/IeUO2DZ2IHPcHbLuiqhqMbpFEutoisuMwXtPJNUp5hKqxquDZjjevEaKl2bS3Fj2HlKCrjDWF1TnNM0pADAGtFckEunpNzoLTbYNBMbojCIGfRoZxZNWd4DJ02ycgIsKbMlCZMmePEsmN3LNfIqUSgcEFcjhi6s8ghEcqzfNn3+Rw+ICXH/8B6/UDTs/fxYSF7x5+l+nmPd758Z/jcz/5lxlcpL38gPVmQ1aG3dmOcLzh/HTH+d3P4lLmW//X3+f+O/cYhj2n549Z7R6hrKRt1hxurpjmG8bXHxPnht39e1iz4vrmEjkdqXcnGKsQ1wa39IS5R7QdqERTddgYCCkxu5HWNBzHgd2JYf3WO5i6IYnI8eYDwpiZjj1dVSNXpzx487M8+/h7WJPIWXCyucf1y5d46Yg+8MYbn6VrNaPzvHz2HkafYo1i9j337z+i2Z7QdGvq9Y5Vd8L15Qu8m5ndwn5/5M133uX58/cJxw8hFivh2dl9Yo60jSYvjsPHz1hcj7YKkkL4mZv9h5xUG2JOXLz4HutasT9eIHLiMHcEoVHVmpWG4folmA6ZBup0Sj8eWFLBSW02G64vLyAJtB557/Aamyz94TVV3dLtPoettzipsN2O7aqmv3oGWRFNg/ZTQXBlRddsSrY0ZMx6V3KWW09yhTGs2hojij49zrdyA6lAG1ppGceRylj0SU0KRdduTcBPC7pqMVJjugolDYNfULUluiNtVSrmq0rjxwOqXTGFkeXFC0b3EZx/DtueUbUbwmEk1IakSiQmplwiWFtJTgqrFWIVWBa46Sfq2oLwDMc9YZpoVh1ZK4LLEGaG8UCSmqY7pcYSreJ0bcpQfC4xSoFmLRTezQS/kOxCbQ3TMiLlrfZXJvLpY7J0qATH45GNVMTkEKZn8iNKJTrV0p6dgarBahq2zEPPer3Gxwkfy4anNWtabUlVIIpSjVeTu7WkGazU3Hz4Hru750ht0bIq7GVd7o8+RaTSYNsiViIRwkwYF2SCyU0lC9z3NOsNNzc965OOFB3JKbyLdOtNEWhIg6wrfAyE4KlkRRYCvCCISG00bizFK6RAZoU0CmNkiRyIhE+ZyppP8WtaKlJOaCGISyAtM4JEigIlJGG5xcQpSc7pj7++/Jelkvu3/9u/Qs5gdENYHNpIQg7kGAqhwBTjUig1MEgRKTWLn4uVCJDaEPGfwsRlKqgwYypcDOTbcLhQJcuVcy4XJKrkOdWOfHIH2rOyK8u5DLDJiMwVJEHKASk0IkeyLAy7LDIIyf/0F/7SD3yuv/obf4+/8Xe+ye8/U/z3//pP8GM/0fJT/8ov8OKLP09+8zFymsjLAvseTFcq0KaBeUKGQGYm9zdIN5G/8pj82y/5xd/6Wz9wjJ//lf+TlRkQWGIQpLzALZsupYjWpvjCU4QUObn3Bm1jCWPRF27f/ApqcwoxwOLAGub9FePlBZiF4eVrHn71z5Xhi7CgjeTD//dXcJfvs7r/eYK7QMs1uWrATxwvn5G7E1at4sG7P8Pv/+r/BjrTX31AXW+YR1cmWHXFaleQLPvXF0zLQrc6Q5sVdWuIqkZ5hepKXujkzpsl6O5HDn0ZQjo7Oefq4iW6rmm7LSnBMBwxzYr50KPjsVQVheb07D7CWGzb4I9Hlv01LiSabcdwfMWqW3Pn8dcYl4EwOY7DBcFT8pS6QkjPMA2k5UhddWCKSCLNDtE2SBUZbl6wPb3L8eI1Uim6zYbFT0jZIq1E2KL9tVkhTI0WmmXoC/g7ZkxtmHLA3Ga/EI55DpiuZEKXZaC2miU5hJc8+OyP8vzpN1BzLAMpRiKSwhjDPA0lEzocC5JP6pIpv1ULSwTTMqNlBULgRCrAdiFJYcKqGuFGglsKu7Q+uRUuOGQqSuuYQZkGRChMR9OQlkDMEaVzaVHahhgWwuTouo4gEuuzU9CGPPX4aeTy+VPqymKkojt/xDiUaecQZ0IKeDdibF0GFeJI9jNRday6HUJGxNLjckRIg5ZrdLMhiYCmRJ3C2BNU0a9WSbLebRGqhZRuGZIzSdoS58kLId2KYKJAxDKIFf1YuJS3RBUXHbYplr6m3pYBJQLJZYRb8HkpA67jEZkSKVcoW5fFf56pzA5dbVC3BA4Xbo8rM5VsiTFjdF2ymK60ZoUo1R2NRWjBMg04iiFOZEgesiiWvbjMSDIZjexabNUyTwf83CNQGNMQhSSPUzmXVcb5oaiMsy40Cr0wTAk7OaL01I3leLMnxYXx9W+xOX2Xuc9svvBnee83/zY/83N/hX6a0VrzvW/9PebhimX5Zc5O/31WJ3fYnrzF9HpingeybOn9x7jQ8ODJZ7n35pcZXr8ia8l8LIY63Z2wOtuUe6RzvHj5MVVjMVhGd+CtL/0kr56+RMmEO+xZ4lBMWFTYusLamqbaMU9HQnS0zQYpNS9ff8LjJ+9wefkcHwZc1Nw5vVMGsZJAVy22bjBiw4sX30VLxbLsESKiRY2LGmstRkl8TMS8cHJySlwiwiiuLl7T1pGlX0pch4ipNyxDT8yB+w/fIovMxYvvl2x6gGazYemvkOmI94Km1UzXz5mmnuPhhsYKhDRs7j1i2u9RVY0xFbW5y2HJtKsGHwO2qVmWhXW75rg/IKpM3VYIb9CNQQQYxgP+GOgvX5PjgaYpXYWmbvExoE7OSAFsVaGExU1zicPNic1mc4ucS6ik6OeB7cmWqU+ItGBVAzaSssLYks+OacEPI7Y2+JyQ6bbz40MZGp8Wqrqhn8aiz5aJjEdkidEVSlWgS7dUGkWlPbM3LJfPWZYFVbVU62JCtLd0JZk0SToMFm2KQMbWFd4lgl8QSZSoolbM/XBbwazQWqIqWzoEKGKCnAKLC4gs6daFbR5jkSIUJfiE1hIXfWE4J02Q0HY1x+t9sbxpQbodAk+3shAjJUsIVE1VrGVZImzZRAc/o5Tg0B9pmzU5GYzSiNhzdfEM0+2Aooif5xHTGqQ2yDkiq6p8L6a61RvroilXZUA+hUR0EWMkPmeMKrEJWzdFB5wSKgsiJavdrBqCmwsOLiuiKPQmpS1SZGKMpRAXEwpFTEUWJOU/L/xJKZm9u+3uCfy8oIQmBUekZINVLkD4xD/LCzsEYGtdZlJSQIgSr/jr/+Uv/unS+n717Qf5l/7rfw9pbbm5S4FMCWQpfWdTsk1GlxaAFmXHtiwDiIRVHXEpYWptPQlYckYFWcDCt4BhLwxIScqufKFCkZQg5wYhW6r2AWl1j6gDMoC4ze9GmVCpZKNSDFgpPi3Rx1ywZjEJ/ue/+G/9wOf6j3/9f+ev/R+/zt/9Xsfrv/lX+dVX/5R/+9/466Sf/c/hZIeIDvHRh6S6hroFPAwLmApRNWS/h9jD1Yw41fDbr/lvPvmlHzjGf/Jb/5hxORKmou/NOYLUuHlACHGbZTHEef4UkP3wna9Q7bYo4ZnHCZkSdtWhK0t/8RqpIv11T3d2xny8Zrh8zYPPfw3nPfvXT1l3DfubC1arc4wWODdznGaa7Y5wvCgKSdvCuPDig1+njgemGcyuQaqWcBypV2uurl8Ro0OKgnzKQpfhAVuzufOQmBPz1OPGAWFWGC3xWdJWNe3mFBEXVg/eZL9/xbQ/UqM4LkdEdKhYFIxBuCIMUQ1C6oKny2Uivkg6CrauTLdqutWKae5Zrbe48ZJ5f82SJNFF2soSo6bd7Qg5UaXMOJTqepaRe5/9Ei8+/A5mlmSjyDHR7e5iq45XL59iraXtGo7714Rcc7JaceyvODs74/WrF6RcQvoAOSuUKgu9TEBJi5sGpHOkLEiiYIkqXRFvJ2B13eBzxlqNUgYXZ4SpsUkihWWajxATU9+jTE3VtNwCR5hHj20bSIGwHFjXO5K25X9ImSQC5KJHzTGQY6S2FYGMkJHkE1JYhNaQFmY/48aezekT1nfPaOsTbFvjk+fV0w8ZLl+w0pKQJMHNBfXUFaOV84qzuw9IItHfFJLDzeVLuvWaHBTTNJGVRGCLvUkmzh68DSkWM1ScWLynP9xwsj1nmXucWwCQKRNmj9QeUkWMt9+5jkWAcbhgdmVjvWrP0K0EbXHzgDGmcEwBoQz+dhhIRUFYRpY4Feh5Vvg8I6VCq5btiS2mM224vnhFvT3H6prsFVRlgFFXxQw3RV8W50isSFSqI9wOBZm6KVxu7wlSssRyjbtxoNvuqHSFC0URndyCTOWBYDdrhDJFzpAK/jAL8G5EV8XoppQip1juH3kugzW58JddduBu6G9KhU/IhMGgrGHqe9qmwuc9/WGPlYa12ZDEDYmENFuCkCzpwHT9EYwfo0TC5TXbzZcJ0rO+f4by9/EqYaSg70dyhN3dc/bDRFWdYFVLt2qZ5mvS7FBNmdtI+RaXmAy6Ebh5xNYrjoeJRlSk7FEqs8QJqQzGNNSqUBUAaCxz7xEUxvK89Dx86zO8/71vcbK7yxuPP8t3v/GrUBls1SIyiGgQpiElx9mdc56+9weYZo0SgcPlU2y7pq06fJgxxuBjQqsWYSzdqqKfRkxWCJEZhgljFEpIFBPaCMapoPzGfqCWukSRUmZOe4YlUq07OnOCHwbCvCDrBmU0i3es1ndYhgNBBkSuEcyoqkGLBm0Ap4o1q8qleKR1uQ/GDLfkmnHYY21dFnCxtMWVNcXwGSPZJGKC4DJ3Hjzg5uUrUlwQVqLsumx4lUIlybz0+GmishKZZ/r5Gq1btDQIW2QGbX3C4CaYA1VlUUaD1fj5QEIWRvMwE2Sm3u5QdEUcExytkBwP16S65EullEgEWlTYThOi5HDxGttVGCtYxkRdbclxIfuFy1cv2d69hzS6EJZyJshyfeelMNhDXFitdyyzZ9U1hTZDscdJrbDCMGePUYKYSjegMRtSnlmmGanLIG2OGWVXdHZdjG7Bs4SMYmEaR+q2LkPx0RBjZFkm6k0hGJhqBU4jNWgD/bxgbbk/p1iu3+BGciwijCQkyQeEjETviCEgjfoULRp8Zuj3WFNUw5vNBmU0UpfhQiEEEkFAlZgLt/xy9c+eAZnkC2ddUmI1YU63FezAnMKnEYYcIkJrYgxlAx5KoSXHYuwTIiNI5OQhRLJSQEaaCufKQHMYPJUpcYqUEv/Zf/U3+e57H/7piStkMrauiCpj5O2JSJHsZxAGItTGEhCFEBAiKSwkAyqWVb8oVzAxl5MkpSJrQRTgfUZJBTkTRUBEsLpmAWIKWKPJscCKsyy7EWk0SYIyhalb0EIQsiTMnsqUyc7sCotW2D/kc2XBuu3gm5+gRM9f/ul/k8wWWa1IHz9FPL5LErLwRioNokY4X5h2WiJlR5Ya9h+Rv/QmfPL0h44xzYCR2NqSM+QoSRGUNYiYiso4RLSi4KrmmZvXzzm+/23e+uKPQraYtug8t0haa3j+8orz+3dLy3Z9yr3zt0lzz9XTj6iE4sXlC+48fkBlN7z84HcZby44vfcO0/7AdNnTGPDjBUIr3nz3z0Ll+fA7v4zvBSn1DMeJu7YgmWbKosAlQbveIu0J0sDQ70kZUo5sz+8yuohGIpOkWZ2BqqjPH/Pi/d9lubpks9ng3UjVrVjmBZ9GTGPBJ0LwmLouN3Vr2RpLanZsVydc3LxgutoXFa7M3Fxd4pgZLl/iZo9q7iGFR+keZTrOHj/k4vnHxOsJtW6wogwW9fPA8+98mzkOxOYU21RUQbK/vCRwTSUTCs/162v8NFLJiavjAds1HPZ7TNOCSGSXkKq0mXK+tfyJGlLF+ZOHYBTT8SWHp88RvYPV7SUsFTmULPvU7zFNRVgy1WqFz5G4zAW8LTObs3u3kQxN3/dUTctmuyZMy61LXZU8e0ol560Vum4KqUQaTF0xz455cqBu20bakACVEjFL2vWKTHHNP//9bxccXwx4VaQOSiuGMKPIiFwytzHMyGiQOfP0o/fp2h0JydwfUFlyuLqmbrYIF3HJF52yd8Rp4uVFj4szcxzQCpQWCKm5et0zDY7VeouSIJVAaYk0DZoGT8B7T/SeeX5OCoHV2QNmL8BDmgJRLCWTH4txJyqN8hMawTiOGFthGoNNGpdimYyeMt4diGrg+bVDd5Y0O87uvAmyJmWojCVbTfQLYfGgNGYJjPvn5DARuhUTpfWZHIRhYJguiClx9vhtuqYiocumLSb2+wvWu7ukJGm3W5bgqbQGJEkU9TTOlbmHOBFJt0STVdFQZ0FCkJ2hEgsxgneZdnNCMB2nTWk9KhluO2lgqhOqWuPyjvpkYT56hFwhzCOMhLEfUEajveHs3hYlfgxZNfjcoqOmripc8ththc7g8kJXbzFCo4Wm7Sw5SURVihq2OUHqhawyNy+e0jSGpb/BVOdMLuFCRivLSWe5ev2S8wePGIfLYrrTK5RK9PM1y+jo2i1xWbjz5C3meebm2SecnJ/jhpnNyUO6s7tcXh15+ws/xXF/gXMzw/GIkZDigpSZ4eYVd964z+FwIM+Bkzv3UW3HPA2c33/M1YsXGCWpNhvCOOLnCaYDw/VrBtdzdvaQ46vn+P5IsgKH4mT3FnWzAmHRuiVXDS4mOr3BLgGlBErWLDiq07sYsy5mOCZq29Lt7nO8esYSRtruDSQOaSwGTRaZ3s10WRN0KhlWH3AukiJsNlus3CF0YFk8G2XxJIKb8CHg5xmdFNktqEZzuHkNBJIKRL+wWa3JYSH0C9eHmdV2gxKKHCPHY09VtTg/M0zXWKGo2zXX+0s8nkZkRq9vpS6ZRXjazSm5n9A509QdbphodmuG/YBUictlprKW7CORhKg0wYPIE1m3xTS37jDasrgeU9fMy1CGvGJme+eMpikb5ZACISbIgugWZIa2aslZM40LgsT11QUyg647NIJlLItbZRQ+hKJfl5rr62vadUvTbshSsfiRHAV5mFhGSrSk0jS1QghL156QTSoVZVGXvK2fyzNgVZ4HKlX4HLAKRCvJMaFJ5Nuim2k34MEozRQ9qhPkfEvDyaWa6pMvxT2p6M7uYLUmoXA+IpUqZjnKoD4IYopIIUGAj4msIIcMSZTKb8rkuCBCKiKSmAgxo8h4vxT7oY8kuScsntoYJndDzoVMZXNFEhFjLFl4tFJFlSwU3i1oJRAIjNWk5BFQuNHij7W+LY+mP/Y7/yRfObNEByEWOPOSyDgocgtijoTbilKlzW1JXSCSQIiSj0k4EpEUHXWzxbtA5DYPomqgLFZzzmhjCalA1xu7ISaJlIpl6Ek+EKXFbHdY3UIMjG4q8QlAIFFtQ84QUwJTdjVayR/+WBEetRJe7gnmSP7J/wCWSHYB8fAN8s01PHmMHOaCFckB0ZbMHSaRZg+6RZzuoHoC8us/dIxaS8a5iCnIhXkqQhFIQNG1aluRksItCW0th+ECW1kuP/wDpn6g6XacPX7CYRyotmsenN0j7/cYHRAIxuunzM++SaNWVG99njviHs9/7/vUX3vMkx/98wRfcETPf+c38cLRbncsteLek89wvR9QSD73Z/4ay/H7XDz7hOZ8QkwD7e4NGmBxE7jEMDqsv8CFkRQ97fqc5D2LGwlhIc3lYj4sPW6Y0F2HO75kvbnDEAJnj96i2exoTI3drZmmPXFJjK8uEGmg31/QnT7k5O5Drp99yIuPvluqX8dXNE3L8fiKcbrhrS/9GNeXL0hp4vxkx3QYUapEDa6v96QsEDvF7tFjqtUJTdsSxivG6z3d2V3GeeDb3/hl5NFjpGV17x2Erhj7HqmKQ9zUGiENOTcsKUDMZMCIUqX1y4QyFRJb2Isq8uKT77MxNVmWmAW6pmLFwkRVNeV3kwTbO4/LQInUVKcPOe6f4scDtd0S5onT+3d5/tF7hKFHYwiTR+iEkoE49AhiMeEJiTEWmQq4O4eMaovFJkeB7ZpyUwseqxUxFEZiTJHj84/Iy8hkz+hWDfOwx642aKERAnyaiu1tnqibFcGN7PcvkVmjZUNbd0yHZ8TkUXaNkZZKqcKwbA3KDYzjxK7bknXCVgnnM1XVoQXUVcUyeLLVtJuKHBPJCIgJTIYIx/kZzg8AVFXHPE6cnT3EJU1lEjH0RYSBJfiBdNtu05XBJ4fAInIs3ZC6LtEpldEpIuqSAa6ahsquUbUslA4pGC73tKuOngWZDIjAEkfC9QFrLU2rkWpNKC5NTGVhVUOE3aaiqiqGoWDRHaeTOQAAIABJREFUvMssoQz8tXVDGPblfxkGUlViD5M7YnImDBO2koXkcYsW1DHihpG9y9Sdoq7vEZHoZgfLTFspCCBjRlcNS5qZHFS6bKoWPzMc90QZaZoKrWpQFW1lGdwBXQFEms15MRplSao0ykV0W5TSCsV+GNGmwegKrS3ERNYanSPZVmQKhi76hawtIjrOHj7CAdmeUnXFpthK8NOMsYLNdotfJrRtcJNnGa9BZUIqUoKkMmKJXD77GKESvj+wrySVsdQyM14+wy8Zd6zw8xHVNLSrFYKa4+EVQmfSELi5uGR7/4zV/RMePvoakch4vGZJnpMcCeOE0Rm9qXHuQD+8xG436LzG64CSFfa8xVrN1dUVcz8gCuaHcZ5J1tBPN1R6S/aOZtOUQTe9Zhwmmk4RBTDd8PH176HygIyu8G9rSRRbdKppN1uG/Q378cCDz7zD5z7/s7jscccjJydnfPjJe+ismeYrvv+7v86d84ccA5AWHr/7RV5e3nB+7z43V09JcsJNga1QRLGgsiJGyc3+JWKZaHTN3dMThqknOsfiIuqW963WKx598V2OL1+S4oTS6fb3OGObhpgSm1XLpu2Y57lYTbMjikjXrBj7A1kapjBRNxXJJ6q6RBXL/ExinK9x+4RpOlww5GZFIqNiojIWpStc1qWQpjVCJURK1FbgY6bWa0Rw5LggZYU2NTkF/DSQjSG4BRETlW6Iy4JUcOwH2k2HxHJ2usVJVwoWPoBek4wrQogcUJ5Co4i3KEJmYlCFRnEsWe1CwSgWsapdMywzptK4nAl+wWhNQjBPc7kvGYOVkmlxWKvwOZN9QhtDiglJQaTmKBESiOCWCYkmTCPCWjILS0gE55nHMkC9WjU454q2eRqpm12p6KsSmxqXHoko2DdpidkTpwVdQ3BzKd7FgKlqxmOPbVpCyHS3nF6BgJhJOhEi1LoiC8XGWHRdNirCSJZpRsiMxFJEX3+8178Ui9wsMooCJRbelVZBkiSZSLIYnqRsMFKRkoM8YWUHaFIIJFXMOVpJMBXjPKOURRBBa+TtRG7O6dNqrLUNwqwx2qJ1DU3NggWdMbKE7nNMxBQwwkIu7QmjwCeJFIpELppWo/95++v/9xLJ8KXHGe5tuTlO8OvfQn3xSyS5kOsNgi24kWTE7Y9OkTYtYrxBNBUiNyQidI8RJ2egjz90jGV6iTY7QlpIolRsExl567GOt7uz6EPZlWWJypJlGElVImsY5yserb+KWjUgA7jAMQWaOw/471Zn5UCf/xcO/OgPOZE/9Yf87V9834/8Ub+GH3z9h7/2v5CkZHuy5Zv/5P9mvTphHm+QVcuqrpBnX2P31j2uvv89Xn7vV4ljJClLs3nE9u5d5r7AxOvVjtOHO/rDnm//xt+lMWuiUFjTcufJZzgeLjjbfJadjEQfOH/r81QvX5CCI4uZ3fkDosxoW1Pfu8dx/4pPPviAGL9PrQXBRWSeye//PqqquHP+LuF0gNRgMDgVMeu2cJ/TwLQElIqEODMuM6vdCdkLjm4ky0jTrEkREAVPl4g0q1OazZZ5f4UWNUFE5nwkoXj16j0qu6FpTvH7PVJBcJHri6ckaRAhEKqEUpYXL5+TlUUbTY7FlO3CQsBhtCA4hd2t8TmgpERLg3cRUSnc2JMWT71ek0P5HTVNQ54jIftiTqqB9Y60PUNFENSstoopBayMBBXIl0dc8jSbHVkosl/Ynd9DLo55nDhcfRur7qDqEzAJUXXICD66slg0GulGLg/vY2zHPDtM15FjgBS4vrmhbnfI5ECUrNuyDNSmJkWIfmGeDqzvPURQxAJnqweIHEhjT0gLKmdiSFwfr7h//w0WnUgu4OahZMzCxMnufhELypLlUxSMkCegdKlSuLnH5o7kIoGEqRRLOJJlIgwR1/c0uwbVKqJLqGZFBHRlSSmibIWMgtkX7uZ46DEChLYs/kjVWphnQuoJSSLdgTBFokyIpmE4vGbTdlTNaanaec8wT6zrFUsq3vlVVapXfulJKXHsPUIrDBFbtxgqhmXEx0RTVaQU8NHT7VZYveXF/hXK1mjdYrQuLVa7IaFouopl6Km0xUtBCJ7aVogUEcLimMkClvmI1wotFy73e+7feYRMEMKEqTa4ybHMBzCqbKSHnmp1gq0My+KL5lYkpDVc39wQ8DT1puC5ZKBpW4zOHJeJKEpuPEWIy8hmd0I631F3K2YXuDl+Qre6g1SytM/1ihQ9WRr6/jWn53dLFtNkzusWXdUcLj7hw/7/od3cZxmfopsHTP010QdMZen7PXa14+zhl/DznmZzgpArnvzsYy4/vOb5iw/YVnfYrO5x8+p9pptXTP0lslqzpIpqB6oW4BxX159gc8FMtfoNlsM1UmtWuwoXLKuqY7i+RlcRfCCmmcO+2PVO6nM+99U/B7Jm3WyY1YokI0/e+ipVU7O/ueQrf/5fJSMQPnLsr9g/ex8tHPvrK+yq5XRzD9N0XL7/Hiye8/uP+OTVh+ToqW+pIeN0TW07DjFgqxp0Ynf/bZwQDDcjh8sXVHWNUS1mXWN0S1U1HMaBq5untPMJUgsu9pecnD9gmWf8MLDa3Cek+VaLm7j76E3215dUtiYuifUbK6x6wtT3ZeNTFYRVt3qMP0zc3NyUqBagut2nKmShwJgGqRpWu6LAXnVb3HCkWq04Xh5Jb7xBdD0hLKzX92jbDrRhPBzZHQ/knHn9+hMWNTP5AB6W5YZV21HXNYlMPy1USpNGgVpZhnEqneMsCAKMbgnRlYrs8UDVrQjDNYoSIxiGAS0tkzZYoZBIyIFlWYjKlELJspD9TIoCHyumaUGrMseUoyRpyeT2qAQ6B7TtiPORY39F0zSk5HF+ZNedMvUXIEs2u65rcK74DKrANE7U64qcJH4Jt9EDQLsSuZMVWhuiBGMMplaMg6OuO2JKNJttIX7kxJwXlNI0zQ6RJvBFOSytoa1WGN0gRC68YvmnbJErEEzTxOl6yxQWfAJ1q+qNKRWvfRJk5fHBY6VGwm1MQSJEJuZYQtEpoW2NVi2QSCqTqNDVHag6spKoqiJohTEdS4wFqyGAFLC3LEWRIPkyuJWTIESPqSwyBYwyhFSiFXCLyfjDvnQpuF9F+J1/xOXznwfxDfKP/6fkCuTiSNOIaOsyuJYFopHkBLK9TzQBoRZEWgoeZrWDh8sPHULJBj/3BeVBxvtQsoXOgSgt55QzKWVqa0gh4oJH5ltw8y3D7vvf/DqnDx5j6vKdW13h5hlWf5Jn/o9+vfWFLzOMM9eHV3zpJ3+KD7/zdSormYYPmDinW215/5/+Qyo1s7vzNskn1mcnvP72bzGJC8z6HvcffQWpFCEcGMcDmUBwn3Bzc2DWsOoekOyOt7/8U1xdPufyw9/l5W//YzrTUO3OaKuOw/P3mb3E2rITDm5ANBsabRj2e2SKbE7P6IeB7WrD7CbCEjFVLtmq9ZYgNT5eEbyi3ZyUqgYKTSIs5byRclFn9iNaWKRO+BRYb3YMw8TVqxcI6RBConLGpURVNex2TyikFokQ3LIMNbXZ4UNEWkHIGWkk3styXUlDlOU3bxpbpCAABjwCqQzq1qteNy3TXFrPQghimjBJk7OnXt0hpYjNmanfE13ptOQYkVkT44I0CpbIYgJGe2qT0fWW2S345QBWo2eDkAK72lKtd6igQBWcT3AlH8dtRbddrxhFi5D3iFlQyxWT91gc83hNXbVlIbZEhEhQNTTdGTJT8IPJc7JZsyxFndnaDmNVsah1LcsEQiaqastZ1RFrDYtCVRFyCyayTQa3BCSSbAvvtlp3TMOMzAZbWXJ0CDkT/FgscLeV6qwkSxwREZrNDmTZ2Dcn21vn+6FwS1PE5UylLWTJyu4QVcTl0h5erVakeSHjmOaRNE5EprLJiQPhoLn74EsEGYCJYX9Amo52vWbxE5Vo0AKkqlikYzg8pV1tqeo1CQnClGE5JMpWWLEq/GMC2paOQ/CCTu1ApBIpy5msJDJntDX4JZKS5GZ/hRQlnkHX0pxscEEQomK1eQOCLy3n7Ln/YEdVNfhhQOeiV0Vm1nfukSiVLJCl+6drrLUlnzwdEbloha1tiAKk1NQpkULGBcdme8oy7VFCo7oC0J+8Y73ZsPhAFrA5e5dlmjBGIogshx4xzvRx4DNf+0lCdGzUlgaFuLvm1W/+E7ZP3mV89YqPvv0rjPGS9cnnOF2taVb3CUaSR8fUT7z1ma/w4tUzFIpp6lnmNa9vvoduVzx88x2efvgBT774Y1S2xS0TF88+YLy+wDmHTxPBTZxtK2ZuEOIux7EHJOvtCVEpqqgIKdLdfUTykqYRnN97wstXVwjhGS8/5B//r79EnQxv/vTPcO/tL3N2ds7Xf+3vc+fxF3n5rd+kf/KElBJawegzbopkLMZI5ilx8/IZza7hZHtKjD2Tv+Ttdz7L9cVzznYPeP/Va1b1luH6QFYVUtborubi5sCTtz7DcBg4ffQQ21XIlBiueozV1E1DdecObz75Oeb9a549+z3a9dus77wFQvH/UfdmsbZt6X3Xb7RzzjVXs9fe++zT3q7uvUX5usplyw0u21iOFFmJkkhBMlJIFHBiIhMIQfAShFBZDkoekRBvSEhIIEBWSOxYBIGjmBJ2jOWmytX41q3bnXtPv9vVzma0PIxly1W2wTwk2PPlbG2dc5ZWN+YY3/f/fr/9+VN2ux2qaQ4kBcHqaoXWFWPvyVkgR9j1OyaTCSLviN4yjh7XrVHJ0Uwq9vueQCT0ewieupkTXcmzh/GacdwSU42dzFG+Jm4Gqlu2iG6OZty9fYtnjx+yeXFDdfs2rz14ldrW9Ls9R6ZhDJ7lyZLrzRWERBw7Vv01Vb3g+OQOKSV2ly/or67xQmDjBFu3JASd80yaiphiuQ/vNyQCWglyqmnb8h2cqDKfgFAY3SBFRFP0xCJJjLSMOeD8gLWGcKjmCpVAZhrbMmx2eKHBeZQSzBenRBKNmmHrBaFbsWhPkMqQ5mXQ0iXPZDLBKkvcZTYXa2RVLHdC5N+N3GUgxYgbepCZfjOQhaSdtSQf0FVN5yIqQV3XqJCRGdaX11hbvqsFy1toC0LKw+xA4v9LJfePxeDZp187y//o7/5lCLFMlStNdKW1J6QhoUteUXhIgiobUnRgVGk5YUkikVOZYkRNEFmiqzOibaGZl99nWQaOoi/t/KzI0RX4OyW/klTBVcQhgjaF0Xt4PUUGqTI5ycOQm8EoW0r5OfLf/eA3c3J//Jf/N1Tb8cp3/Q2yuI9qBuKP/R1EnWDsyH4HSSMmC7JUCKNLO1UbROpIcoGMe7Lr+Ve///v51Z/863w+f/mbHuOvfuEfIxWQKkLsUVIS/OHLmkplLXa+gJmtxcVAYytCosQYYibKXBSlIqEFuBxp7C1OHrzEf/Wpt/6Fve8/LeCn8jf//NO/57P7Uxn+2i//A+586nuo2tOiBFw/5hu/8r8Qx4H5rQcgp7z06e8heIW2gssP32VyNCsV0vUFNy+ukEKhmwZrLctbZ1y89z4b/5yj01fYDpfsnu+4dfIqu77j9muvcfX+h9z95BuMmxX7EHhw9wHvf/kLuAjjsMdai2xvlZB9jMQQyDmRRGFp5pQYdmvMZElWHiMq3OAxWjL6QgqAEmcRWaGURNSqIO6aoq5MnpILqyIhJfwQULJBV8VGI1UixIilGMakAq1bRg+QUCIfaBQJhCGJRKFQejhoaUN0xFyGlKqqeMZj8ihdg5R4H9ESKmmJxiJI+LFDa42VuVSlVbmBAHQ3O47v3EVWZdHVBvr9BjeMBNcxuNKy0nVEC0O325HCgK1nVCdLopOlzRpGtGnJQRRvfQi/C4ufHC/Y3azRlTxQTsRhiNBjrMWHseTYlClrR0pI6YqaMoKPgkkzY3AFR1PVh9Zl53Chx1iJTJl9f05Onkl1p4DcU+FORhdoZwt8Gsm5DMSKXFqOMYNIXRE2OFEGenRGikyIe7ScYcSkGK20YIiOphLFvFa3qM6xO3+BtZouO2RjCjIol6Ha8TBMKqIjy8LZFNKjqxkaWeQZKePHK4TRBUg/DOSY6NPIbHZCymVzMMaRKEF5j82CrAJatfi0J4QBryYYPUHolqaS1LY6sFYVwTuMKF0g32eEjxgjkI0k5chqdU3btkyqozIALDRGaKII+KHEVGRdDkseg7UVfiwAex9Gxu4GncvQcV1bclKgynuapGHcd+i6WAirpmXXbUshJHq0MlRKHXKKDaMrr1kKkRgFOV9jciKNsSCVkiE3ZdGR1RRjKnxO5OgLq9R7Una0zQSF4OS1ewSvirjk6iM2ly9oJjMcHcPugmGr0HXk6MGbhMGRHdx9/dOsry7ZrV6wXT2BmFjee4WnHz/k9u03eHH+mAevvFEG0fprfBdYnN7h4sXHBfC/7xl2HYt7L3N8smTsB+Z3XuXyow+5ufqQSE1MPRXFsuv9iNQN3WZg0hi0Elgz4eL6GZPFEpkyhMTy9Dbz+69xfO8VdusNOTluLj7i9PYDRNuyfvgx0Sf2uw2p3+NkQoXE/W//NMe37pNxaNmCNFTaMLgtSkiuLp+x367Jbsf22QXJBERzi5gd2SdyElhZ4XxPPhBEqsqAS0gXGd0F5x99wOnL9zGtIiVLyg1V3VLlIoDQ0yVmapm0J1y+/5D1zRPWm3Omy7tIWeQr4+B59fXXefurvwa5Iw87oouMw47l0SukekLd1kU+5cYyWJpGPCOh66kmC8K4Ie4fIkl0fsb1JnL3lVfIeMyqo59PmNenTE7uoMlMp1MefuNduoP0pLKZftiQjcE0DTnVNPoYrTXjeIHQAqtbojD0PmGUZLu6QImIthOa2RSjVIkBpAFEQzHJFPV479Mh/7ovDO8ksVoSKASLFH0pogGC0nE2lQSdUblGBFHoCiKSUyAjSdGTsoSQUKbcz3Lw9M5jquIIkFLiQ+HZOh3KaziWgmElNN5YUhxQIhMOHQElHEPssXaCjgqEwqUMLqKUxMdionPOY6xk9HvqtiX2Y6l0h8P8FYmf/Dt/l3fe+/BPzuCZEPJg8dEMKZSyvBQIY8lZkv2IrCQ5GpCpZD+0YUwORVMYqW7A1hqZDQmFmt3GiRmyrpCVQBkDuVRkFQbdaMbRoaqjAvsmgSjDFOPoaSYTkiztSmMVaUwgSs4VCSkWzms3HnTB6fdz24TwuH3Lf/b3P8d/+z+ueDScIN9ckD98XhbRbMjWQvZIn0kHYoPwkZwlIjvIGbrAxVzCZ+4B37zJTTmAN0X/SYGaJzEWdIiscN4X1aaWSCFLvjAGhC65MWstIZUNsR8dRlZoqRhaw4vnnp/+tvI4v3cD+q2b0d/5HfzBf+d3fv6pP+Q89Xs3uN/6/1RtxXtf/HkWt9+kmdxifX3DfHmbEDLJnnB6cpsPv/wlJu0UVR2jqmPE9ISZVrRHDzh7VRC7ga9+9VeYa8EHX/oak8mEmX4dqU55+dW3mH7nMQBx7Fmvrjl7/TV23b4YqLqO9774FJcSWWTqSQtRE9yeACRXwNfNZE4s8BO0apifNcXohSZLQWtMUWI2LcEFfHaHQa4C8jepRumBbn9TxBBKE12EmNHaoOuWHAFkyYOPmSwTjgJvjzETRUSrRIpgTEXwjnwQJ4R+ICaHUZHgBNGP6MqSRcI2tpjxtKAW8wLuFhR9s6yIWqLJROGRMrHdXNO2DZqaFCOmKovu8s4tRPTEIdJFh7IZozSmmZCk5tZiRhKJQKI2LaQrtFRs95fsz69LW15YNAbvyybSuwgxoSuJVTVj12GmdcmABYnQBS8ICZcgoagmk8K1Dqk46JWhCwPKGqrs2W0vihEoU+JQPiK0oDENzu2I2dNUR0hboaWhSNoCOftSPfIjyTv8uMWlQmXIxgIHLGGSGKXQMpORZNGj9ZwcyjCG1jUpRYwSeBIplsHAKDLz+7eL3WwcSEocsv6JbrthUhcOdh493keE7/Epsrvq0KamaifYaU1z9BLr7bYISpRF6SnT+SnGVPSjK9W+6oSsNNpE3H5DpU0BrptTKl0QSDE4ZC4T9fvtDS7tSb1gUk0ICnAWH27AWcbdyLQ6xTnP5OgYicG5AZRGGsvg+5IzJDKEjHWmfO4nNTEIun5LLUZyBOUHohupmwUpBbJOhSqRBcZmrJ2XgZYUcGHESoVSsF1tMCKxTwMhDqhU0cxaSIa9WyHMnthlRACjppzcvs2YCk7ShYjf3xTup8/U9YSkanSjsOqIxektQuyhmkLcI02iu/iI2rT4uMMHx8mDz/Do4dtsdtcs5IT5nVeoqxkPv/5FKlMOKra6xfG9WyRh+dR3v8n68QecHb9MVTU8f/g+fdgxO75D3++LyCiP3H39VQgaZIOYGB689EkuPn6Xq+unvPzm9/Dh+19CSkV1wOwd33oJ7zrO7s4Zxh1KViQyS2vw+w0pe549/gq7zSss1ld87Tf+T6B0OISVWNNwq1ry4M1Pc/P8OdcvnoLwuH5L3Z7w+IN3ee/r77BoWq7PXzCbHqOXGcuUpjqm61foCu6/9CrzxZLdi2uuL58RdQZZk6VkPwyMfsX66iO0tsymdzha3MY0lmwnnLx0h+3NNdO8JASHzh39bktPopkuODkyHJ28ym6z5vr8EbYWNFXN9uoRzke2m2vu3H2FL/3SzxMrz2yyxMyO0GqJqmqcH2gmDSpJ0mEN7fdrxrEnpg1GWryPYCzNne9FJcNUVdxKhd06pIDWJW44+o548ZzaVtxcPKOaaar5WVHdKsVsviD6niFAdjsGJ9j11yhViBYiZbRRCFnTuR3WtCAUOexxm8DlcMl8cYZRMzxbpu2y7GVSRMuMthZlj9Fk+n3HGEDkPTdbj5aRZrpESAr1AlGKKghyymXtUhmVDMmqAwmhxuSEqAU+ZUSUiLqiBbIEEKQokToglMLmUNbIWqKTIEsFPpaBfSVLJKIy+KRplcUhUJUpPUznUG0Fh8E6IROtlAyjp57NySlg2mPG4FGZgqXz8dBN+qNdfyw2uSlnkAahNTrI4oKuDFKqAgJWpsQSEAhxwHzkTN2cMEZByAbV1MT6DI9ieusBw+rmIHdQ+DHTdT1SSowqN6Nuv0cGhVADKsZi5wI4tNp82kICKQXZS0LvsFWDoExhipygKpiVJDLpD4grhJhB7/jrf+4n+c//0z/P/U//KE90X47bJXABKZGFBqMQ/a5Mqc+OoEsIEUlCIXLm/SMDp7Pf9xhSaBAChTzEKhyZosDLqViQRhcLlSKDlKFEOpRB1nNcSpjKYLKnmpQccgqBmDrow+97vN+7Uf29P3/rpvf/6d/9QdcfVMkF8GHk3us/RLs8o7u+5NbdU55+dIO2Are74iom7r/+FvtuB1nQb8/ZPn7C6uIZOkiO5nfJpuFo/gC3ecbs+Ay7mHP60j2GmxVCWZ6//y7D9imLs1fJKbDvborJZ33D1c2GWk/IGGQFPge888iosZUAAjIo1jcr6qZFyURkhxsFQkr6sEcrQ45Q2WJdcqEcPDCpTKZiyqAlksq2SF1iMMkKRMokAaiaLCMig8oBoS2RMgS23+yxsynJJ5SCGCNuLC1MITXdfo2UBZUVswQNzaGqD57RJ1QCicSloWSkpCQBQmkMijD2eAoeZ1pPIUoGtyaJhNQafCpQewWQyDJDrtl2RdVa6Yrt9oqYElVVsdlsSZSKXlPPMFVpN6eUGHpHyAmZSy5WZEn0CWEKu1bnQEgSpYrowIdIzhqpDNpWxBgQulTEyQkpNLWqCTkSIzRNUUhmoXAHAkF0kSwCRhqEKOjAvu/RNqGlwKCLsMVHwjCUtaRqMIC2ijE5jGoQKNwwoFQNsswB+GxLdVwpos7ouiYlhxs7kivu95xCsZoFSYgZaysyh/yxH7BZ0O+3iNRztLhF340E21JbqJaF171fn7NbXZPMjKppENaCbahsg/cBNzhqWyGFIQ4eL1doMcXYGiE0RlcMfSCETBDlBlgZQcgapRusnCFnuRieXDlA1foEkcoajVZIXXjDCchS0ZgpEYm0EP0AUmOVQiVIEva7HUprainL61ruogXyT2Z9/YTJ0QKFOax3NTEXyP9+v6exmex63LBHVTUpK5SKoBK1joyrZ2RWeFcxXbyMqAZoyn3g+uIxXb9n2szLZ9HUSGGoj2Z4Ai+//Bne/fUvYHXm13/z/+B7f/Qvsn/xkPn8jNX7X8W0U2S7ZN6W7sI7X/0Siwqq2V0aZegeP+R83NJWlv32gunpG2jdg9SIJIluT1IJ4TvOn1xip3OOjz+B6zumJ0v0ZMFyXoZ8mqrm+eP3CF2gnZ6wvPMJqumSfrvh277jc5jpgnHdsXryDcZxoK4rnp8/4+T4CDuZ0W07Ts9eIeeeYODktVdp6zsM3TkLKTha3uf45AxZT9hdbbh+8Ziby/c5f7EqaL+qpp7MsFpCdhwtFyxPXua1T38HVVOjZMV+/ZT1zUMqG7h1901ghmpq6tMatd/gxh3R71F1SzM1TPQZpyd3CF4giEQiIQ24MZL1hMnCFmGJGkmDww+XxDTQbVdcXz5l84VfpJpUHJ8dk1PC2Jaj5YLQjyxObqPrCqMtSlUFj0hZM2IOhSKTA0JZKlPjXebo9jFSJobUIaNCJIVIY4nH9J7NdkVtS+dMKcO2vymRJlmhhKTrBnRTIyvNvh9omhl+6ElCo/ScuVYMWZOT4uTkLhlP1cwIIVFqr4qPH77DYjmnaU+IORAZOW7voW0N2bDbXlNVRe+rpEEZixsGpK6RRtO0E0IIGKGpJ4ksDMJqFGXIqzKarDM5FSJESongAkEl8AklIjFo1EEGZLUmIwkhgpKkJFEC0LnE9sjYqiKMEXnoUAmhUSkhlUDIVHjcEWrT4NyAkZoYIiGORDI5UbCGOZNyxFqLlhqJJklFShmRIB+GE5WQwB89gfDHJK5wJ//c3/8JYnRkkUB5yBaSgGxQORW0l7JIWSPaOWNOBHESIAK6AAAgAElEQVRMkjNSvyVFhVgs0CqXCkIIWG1IWYO1CK3IwZNiyfbJGElCIlLJdymtUbGU1qPOiBipjC04E20gQU4KrQ6lf61IMQKgTRnE+B/+1F/4puf1V7/wj5EmQ4o0leNnf/Nn+PzuL3H5xScIBlLMCDMhDwH8CPMpNLOi4J3PyfsdXF5BnKJ+5E3SP/0FPv9P/stveYyfgVyVYZfQF7SGCGVzlMDHiJS2ZGXwvwukztEjVGkFS6nJOHKISGSxISEISvHdf/HH/183qP8ir7/9wT/n+tGHGHuEai2YCaHfcHX+hNaeoitNHFZkIRl2N+jK0A9rwm5HUy/IeoKpF7TTOdv9OfX0JUI/sru8xFUVn/2B72fc9qzOrzm5f4a2NU8fPaa/eQLDCzbrAaEVVXPEnVde4vzFR+QoyjR3cAjfM+49KRpsVeH8jhQDVd0ghMHlASNqYsyIlJGqTM16rUAEjBSEkIq+mYOzQxSer1GWaVUx5kySulQvh7IQlQFDX6wxVUXINT5SUGVWMXbrosZOgYA4qBYVVpccmxQJqQVZBEgCKSIiKULvMaZiiH0BMvZDufmrYpFLIoFX1FVDEg5IjCmUfGOSROeRRqKrclqJWeGjQJAOBiZdBiNjRpiKzfVlYTRKqGdLtDWcP37M8tYdqnbC9YtHhKEvHMjkaeyCfiy81iQ1qpogKc8hplSGEnKCEAqsPXiSD0gU2eaCQ8sCH0akKvg3a2pIsmhFs0fbogyXWh10qeW75AaH0hoZIwKJsWWRzwI4HExkEGhVKuRD32OVJRtwQ6CqJ0QypEBOHmtLCy6nPV2/Zj69i1BlOlnngBvW+DEhRSS4Ht20CGFQGKTUDMIVWHtlSa50qLwbGHHUpkYayRjLAFwlJGH0RF+q24hA79aM48ikmhFICFMhVFUQipLCSBURKWp0lqWSKQ43Iz8SKoVImVrZEjETskx4W8t+vaMyNa4vet2UHcNmTThwXqWoMUaxizsEGoLDpIGIKh2LFEoVPXmUrIrswxhkzEWQMXrGsadtKnweaOo5MSViCKAcSjbkBPV0hhs74vCM1c0LTpdvEAhs9luqqkILCaLBTuakKGlsXd7LbNmMl8ggCWFLGlfotmFYP+PTf/rfYYrH58TRyV2GXZFoPH/yVSZNRtsl69UVwzjjeBpwSVI3TblXjQNu8AzdNRePv8K9N76Hre9ZTu+y63Y0lWXz9GPW+z2vffaHOLt3lxQyH7/9JTI1s4Xh8cU1p/UxUUbGzTm4HWPaE9WUT//gn2Y+O2G7uaLbrHn//XdYntzlzul9vN/jQ0Y0DZpIcJF+d0POgkqXopKZLairslG6uTinW6+4vvmQZjFDhhqZh/Ie2Mj85E2MslSzivnR8rC+KPRkQkiwvbhASsnTj94hDDdsthdY01I3S/r9Dik1J6d3aaYLcgysNxfsdjuMMVTGQgjszi9Qtabv9+UzGQa8L6II3Vpkht1mz/HRkjH6w/1OYqoKVdXkXIgdShdvsEyleoqElMt3W2tbcvAxE9yOfb9GqgmNqZjOJviQqGYtbrdjd72imjbs1hdFTpMls0mLH0NhJSvBzf4K29S09QIpBMOwPxwEFZHD8FeSCKURRrI4mnL+9Blndx/QrYvoSGvD0Z0HpLBnHAa2m5JnT0SQGaPKmuW6UGyrKSCzKdVo19FODVkYrLJsuhUyA7bBSIsxBltPGH0oh23nsHVTcIS6EG9mbUP0gX4/YCqLsTV9GKj0jOgHmqoMPhOLile109KZLDqIsnHNubD6RS73zKYcgEfXIbKkNpbOjQhjEDEigCxSmUWRkuRcMZ4JSe/2CGEOTOnMX/sP/pM/sgzij0UlFyEgSLRuCHmAnEk5l1OXj2QJ1swY3R6pBBaNiJL9+hznvkElFiCXTE5PiWEgeodPIwEgRmyIxFyG0ERIxOQRQSIq9bs5lBg7ri7PCcMepSUhzjiaz5FV4W1OJy3kEY9EaokJ+TC4ZZAiF6fyt16ybNLRiT5W/Otv/QT/3v/8LlQdKRmgRmpFqhQsThC2tFuFauDjh6AbxO1T8s1A+rVvkOvfX8kt1a9YEC5So4Us3EljDi29hFGacdghZEKKYlQpA2kCIoz9DqUERsmSZ8wQc3FO//+5wQVwq6cslwt2XSK6THdzxXj9Dmk/cu3ep6rbcuNulhw/eMCLR++RGWnbU6p2xhgD1+//ClfKMqk6Lsa3eelT34e5LeifPOPL/+x/PRx6BJcfvIueRlx3zuLumxy/9jnMiyfIFLl48oRn736DSCQlw65PGN3Szo+YTiu0rRjWF2gUu5sbutWmtNUjbH3Pye27rC+fMlENkYT0AWksIoBVNSHuUdYgsyYpT6JG6wpZS/LFGuc6lC4hfDuZsN9vMLUEKRiCJqceERM3w5pGNcymBUbvwwBZk8aOrCWimZRqmdbEMZJEIGNLW0grhDalWmwNIglMM8OniE+es7sPuHrxlObomM12hZEKnyJoRQiOJDVOOtQq0q169DKRqxpZHZGFLmatWKJBffDMmorb9+6ibM32+WN2MRO7DffuPOD54/cYNzdMzo7xfs9+4xERcjuSsyDstnQhM5ktCikiC7zUBcWXwmHj6kki0BzPULLIPxKHDX0sleOIQwpAlucwJo/18iBWgZSKEUiSsPM5OReIfmMV464jikBTNwwM4CsSiUjJWTeTCTklRj9iNHi/JY++4LHCyF55kAZCYOwcY9oj2CKrTBSgsmHMxZo1XZwxjiNSGlJSDPsVutYIH/BCo7IiZM8YC1tz0xf8T8gBoxQOQVXPicIwDj1kTzNvsNMpVlT4mPDeF/RXN6CUxihBFhW1LKYqYw05B1S2xKZGC8k4jiRdIb1H2AzBsx92hJAQ3uP7LTFEkILZYoEP5UavBWx2VyhjENpQWQi7EVxPFoEYR8Zs0NWcjERJgRp3dG5TNqbZ0lSFa26rBnzE6NL2tPUxKVHEJ/3IcDj0qiS5ePI+1fwIpWskDUrXmKYGoZCiwPz3lz1eZ9rjI1ROhFWPyOJQObb86s/8bRbVD/HJH/oRKj3n4y//MkwqTt94g1afMm7PaadLGhPxbkDYKYIJyzt3UdnzwTtfp7p1m9dv3UKoCU2es756TH/zjJe+70/xqe/9AV48f8x8fkbVTtldbZg0c8SkYVi9g/EXPH7+AXp+xO1XP8Pd+/cwdYWxNTnC1dMLuu0V11crPvs9P4zVhuePH5FzQCN58Y3/C7ftqI/OmLRHdG4ktRPmRycIkXn7N3+Vu6+8iiAg0h6VYNj2GClpjSIqxb17L5NzxcX1C9589Ttx25GL5x+itGS/62hmc6yWbC7O8btzdFPRTo7R1QRERd1AFrDb37Dd3jDGRPJdWUuGPavrPVNTMblzzOnLbzKfznDdHl1PiTFiFFw+fcxqtYLqKS5mallTNS1KC7pug4kZTJl7yDGVaFklGIcRqxuGoSPmhNQDKZSIipCWT332c2yu96wun/DoyUfUtkF05XOnpzXee6RdotVIVUsyBiMPcgihqeoJQmhcDggvylBbcADIVOaMssykIBj2N1w9f0qlLY8/fB+NwIuIHDriecS5gBAR129BTGjbFikkcfAooZi1U4axQ0jB2F8wuJ75fIr3W6Rq6Hcjlda4XYdPe1R9RN97eq8xpkGICqUUlaqICDISa2MpyoiyMVc6IVJkost9QmtDTB7viiFQKBjWe4TOWGnJ2qJFhdKKLEpnJlWC7bCnMbIIZbIkK0ldNwxdj7YSZSTJefywR2mNFiBiIMpApQUpRiQZcUDC/lGvPxaV3M984m7+2b/375YJap1BRrKowDmEKt6NGHOBoqffOVV0YFq0uY0xDS5F0myKH3qcC1ggho6cBWNSaKno9tfEnJE5kEPCWkNMFq0tSa7odj1ht8MgubxcUQvLdDKlPjtD1zXa1gijEQpkDAXArCckndHS8DN/5se+6Xn9W7/0T8ip5P6UMsz1ltv/xQcHw1qpNCWpQdYIU4xHDB1iu4N5SyYhfYbYkciInPn8P/j8Nz3GX/5n/xNQIWJCI8hKMviu5IylIudYioQ6YaKkdz3altZFFoWrKymV63DQ+WWR+fm/9Lf+pb3/f9j1H773G1BrXnzwLsPqBdlJJssFq8t3Obl1hxeP3mb0nqWdYU5fJ6iWaTNBCEG/XxNij2lbZtNjhJRszj9kt5Nsn3wVY5dEVSbrKzujXd7n9mtvEJHErHjx0fvIsGazfoa1NSoElGnIpim0DpUxumWIEalLa1dnUSgJxjCZNmxWlwxOcvfNz/L88bukcYuWCSlqco7oBM4l6mlTqpvGorSh31xibM3Q7+kvN9x743Wurx8j/EDnisZSCU3MDlUfEYWmthVvvPVdNLM52+6G9eaKedtyffkUmwTDfsDFgo+qpcaHnkRG2wlCVfjkUTlRy6J4XN65y+MP38XIwt8VKuO9x9QVIhWVI0oVY51K5FwWyFpVyJDItghUxuSIGCplcW6gamr6bc903hL8WKIDpkUyovUM3+8JGcbhkmF7RRaQpGaiJmx2N+jKok1pPWaZcaHH0qJywjQTMhHve4zMDP2IMg3ReapJS0AWO6FzVFVdujs5oalAFgB5xqGkLkN3uliklKAs/1EShSwtN1MA7WiJ70ey9OQ0lDy1L9hDawRGTfEyAwmVM5VuiYes/b7f0u8uCrZIQhwi2mSYtLSTY9Ko6FzR8Krk8KFnGAbM5Ii6gu31OUFRBtpsQ22KESnmwvP2wRGjL8INWVS0CYFUDd12RwhlU1FPGlKITNqyic+5SC5SEMwXR+AjPjiEHFmtL1ie3CYnTcyFyd3vOyZzDbYmjj2NbvDe4/1ISgl0iUn4LuAGgcwObSArCCGx31+Rxj2nR3fZdluqZoaZtgihiEHgVmuyESjpUO2cSbvE947Yrw/KaYX2HXuXmTQzus2+dCxiJKVS2Z0YzTj2kBTZKBITlNEoIWnaGkHhnqe+DBP6cThUELfMT89IeWS3XjGuXoDcs5CK2Cxo25f58P2v8sN/5t/gt7/4Ff6V7/wcw7jGVIlu94T797+b6HtuVtecvPxJ6roi+8DF5ROW0zl+v+f82TdQ5MJuX5xwtLjD8dnLoBr67QZjDN3YoUTm6OwOu65j1lg+evvL1BNLqual2GIbTBK4saiap7MlwY8Mh3iN1Irt5pqZgYtHH/Pwa7/F4ugWfT8ibMeug4ltEPGG+299jqEbCAdJTjNZsNrsODpesLreEsdzxn0Zvh6FRMmG5dGcLBJKTGnbGc53LE5OuXn2HquPHlO1E5wp4qTJ4hhTz5BQrKIxEtOASKZk1ccBWsvETDg7e4NmuqCqKi4uP6BqZ0xnC0iS2HecP3qf9foG0Wq2z6+YVBVKBZQ07L2C5HDbHbqSCFsGPwHCUOYSysFego/oyhIRVMqCCoXQIlShMmXQ0hza6hIpCglEoPBRIrUo3azclaFYmYneYUxVzGemWCe1rLCmpnMOpSVCRKIfCtM+a8b9lhh9sZURMM0JwQ80doqgFObqaQvJ0ztHVVXEkMk+IIlo2xDIBxAvhNEVLW7uUFIWGQSRnCwiJ4SSjHtPTCMChbSCHBIiKpRMmLp0v8YQsbIp3zmZMUaBKPQfraaMwaFiRKhEygoXE5VWxODwSmGSZAi7g5ZcYVVFlBGTNL8jRfBpRCZROp6RYkJTAakbGtsw9J6kMj/+7/9HvP3Ou3+CtL6v38v/6O/9zYOCFlJ0hbdpalKUQNnkQSI5QGmE1qgEzjm0rouYAYVQCudqlFXoOhOJbHdrlJ6QPIxuR6CoObM05HBDypLedVw+fcjFs99i7BW1uMWbr3w7YrbA2jlqNme6bAlSM+53LGcNSENMuoD4deIf/tm/8k3P68f+6c+hZTkRSQlKZ779v/6QYTPAxMA4QlWBug3yHG7WiHZWLCJ+gHQo93uPHLfkl76Nz/83P/lNj/FXfvFnIWdkLtPfQhQ4fD6gcLIQZH/IxQRPVVX4UHJhPoykKMmhR4tSfYoHa8nP/djf/Jf07v/h10/82j8H43HRM5/NePzFXyBFiZ0fQUwoCbvra04+8VnsckHyA9cXK3JM1JMGWTWcP/x17rz07WyePSo5pdM59fIOw+45j776K3zyB/9NQr/l43e+gVSGeXtKNZ3QzitcOCCSek+WmdzvWO2fEH2psirTkKUpmCIc1oKKhhQ1Lm0ggrQNIUDQe2SUyBDJAqbzBUbPUI2nqk6YH9+l315y8+wRu5uLcgCRGZsNptGIKFDWMoaIsXUZioiSk5fvsnlxyfHydiEgJEW3X7HfrTi+cw+RtqSsEMpSz+dUVcWjt3+Vyk6p6wk3zx9BMAS3YnAOUy+Y3Tpl13ecnNxlv1rRzk/YbVZEAlqCEGUjYIUiukiSgRQySmvGfks7PWM6PeLF1VOyH8i6bCibSuPcgJJAcIXOgCcjObn9Bt16R8CjRNmcxBgRVgGJ4MBWDUGX1ncjmwNoPKBVXfSoyqKIBD+Wm62t0MoWkUssmcsYxaGdXTbzZF1y+LnEioJKyCjKhjA6QgiIPJJTQkiNEIp0eA+DG1EyYKoJSjaE2BWNZXQFa+hGyi60tN2D25ByKDnVmNG2KYKamEqrWCpcDiQpMKbGSkvIiZQKY3LYD2hZfPJCDWVjLCo0NSE7UhgY9ltUVdG0c5Q0CJkPvvqxGIhkoLZLRP6dQblIyoGsKLnW5EFNsWZaSm2Sg0e+BxLOjaSUsarQG7wb6LsbhuyYTU/JCvx2Vw59pgbhSQmCK8KSfj+ihUALR6BUvvphi9UCHypUZamb2+z3VwiVyD7TNFOUCEjbFGUtpWihdCHrCF307ISB4GKJxgSHoCa4DgyEQxZcmuaARrNF87q7RrmAc1tC0JiqoTS4MkaBrFrwe7bnT6jshCChaQRDHJiPiaDKhPsYMuPu6yzmksXdf403v/NHef7iEa+89d0EZ7h+8dsspvcZ95fsN+foesrs+GVMO2f7/GtoOoZ9R724w831U7rthohiNllitcHrhunRfV48+Qbz+RHZB5Ko6VdrTj7xGZanp/zSL/4CLz94iePTJaaa8eLJQ5SA64vnLG/f4vjBq7R1xeWzd0r0p15g5reYHS3ori/JQeJ2a4Lv8HZKt7pmNj3m0TtfIYYVyrZ4J2jnczIDQkna6ZTt9RUvvfQWspkym5+x26z56Ou/hV1MsLYur/NqT7CCyaSY30xbsbu5wucWHQf6cWBxeoemqjn/6D1mt44JsSeMMDs5QQrL+eNnQGbcjbTzFltXGC1AlsihqCSzyRxray4ef4iRFXIyw0yPOL1zhyQFm2eP6DY3TKdHYBKVneL9yO7ymhzg6epj5rMTgotYKQiJIoRRNVlDHEIhvuSEQmCUJuaA0KoMg4qMMaYYypRBZF/iCbl0Vb3QaGHIyRezK5QhQylQORQNtBMIl0B6lJ2QcqCMGJfvohIS3424fijoRx/QtgFScQkICCKjTAPJlYEzbRm8QxJIIiJkhffxMKMEye2JaSgRq6oiZ0UaSyFPCgih/FthJSlU1LYqlWsDXdchmgnzdk4WCoknRYX3njEFJrrELISqCsEiZ3L0RelbNaggSmzSZPR0SgoZEUosSWZASWTmUNUur+u//bf+4z9hcQUoJ27A+4TSgiQEIZZKgDVtCYkLUDqhpCAKjcOBKZtHkfKhKpN4fPEVjtoTZkenqBRZGklihCqVgZ3Yo40hJIHglIRAcMQn754g4udI7HBuwKeKenLCat3TjVesXlxRi5YkB57ue3yWDJvEA3tC9ebt3/ecuvUVbduQoio4DyuYy8Q4K7gUhCp/8qRgbCrI1y8Q9eHDaSbk1Q5MJpmqnDa/5copIRTkIOhdwYSlVHKdSilSDgRR2iRaaFL0KGEZfEQJgaosCY81VbG4HBR+33r9ja+9TWJFpSfM79xFuGKektng+jWXjz4g50StwEzPyCoydCPb83dY3H0L36/YPH+fPF5gZrc4efBJAoJuG2jbCZuLK/77H/7z3/SY737pH3JydkYOc65dB5NbGFmx3w3sVhfMZi3rUdK9/TV0fUw9n1IfHWHqKe3U4tc3vPVdf4GgDc3iJaTwvHj4nO2T51xePaU6eYNHX/8SWrZouSSpgSGP7G72XF1BPV2w3z2nMRafPMvpMcez7+P80dt4F1FGHuxamt1uIIqa2cmdcirvHLGLxN3I2Zt3Ob79I/TDwHu/+UuE8Rz8lspCvlZM6hWXH39EVU+RWJan91k/e4KLHmUyWQt8KhP4k9kxWkvG/Q4pLFePnmKEZjvsuLt8jXe/9hsoFE09YX9+TVaGMKzRrSaMiUFA7AK9H9hvO4RtUZVm0lboMKKUYnR7GmPo18+QSXL59AOktKUrkD3WWpAQx4GmqQlZ0sURkQqmL3TXXHdPkG7L0G8gKJb332JIIy994k1Wzz5iHzy2XqBlLLrU1QVRKXI+5IczJStnJngfUVYglECJRB4VmHLjMQLW2+fIZJBG0w07jFSlJaZU8cynSG0bYugReY+WR8jsuTn/uFSSc2Jw42E4zxJGiaszeE9jdFHeSoFMJRsthKK7fEIIgfb4HmoiuLn4EJ8Ny+UCnyWMieglk9mCYdyRRIYKhNNEoWmmU0IU1JVhvbrEqoSbGDI1tbQQywCHUA6hwYeAqhoyCVtVoOaQ96QhA4HNZkfV1NTHZ4V7i6LrBtrZlBTBTo5Jbo9RmRAC1lpyihg1LUNkQjA9OUH4WAZrkSRf8GPb9RWzxQn9doOoLFpEpB8Y+huoErluac0JbjviXYfRI67vodJYNSNHQwyJUQaykZiqRQqLFZIuOo6XL5OyY+x68hiJ4wodBkbnUaZwu2VzhHMjtl2gsydmgRaWbbfGbSO10QgUMQwIIQhJYABTzUGU79HgN1gfGYc9WkoiomCQ6jJRrqwlUWYsgnKsznfM909wjUDNa5St8H3PuAlsb56hFydshxX1tMX3DmMbmrMf4Par381+d8NscZ/Qa4wVNPoMM1syWdxieu+TdM8eMt48w2+f0kwVYTsyMY7n7/zvZaAxGY6nD1BiJHqH6M6RR0tOlkf43DM/PWW32SH0htXFrxK6W3zn934HKRp01SCiY/Abbr3yOkf37uH3e2J0rC+26OqMRduy63cQLZvna/rtnsEFhptLpvOWh1/5AmO3YnF0m251gaxt0VpXDZIWF2OxUe4Gqqri44e/jccXbF9Xhm23T0ZmszO0NkXROrFs1jeFTnLhEbFCqZ7erVnePWPon9DYu2gjCJcruhxAWbrrPSnvaCaFFV0JwTBsEfSs95HJfMa2WzNtLZvrK3zvaFTmer8jjx3jesc7bcPk7IzNzYbGLjha3ub0wX1iVzCK7eltgvPMnaNpLFmXA69wEUPCuwGdJFiJDz1GlEFIF0ZMY0AUhmvyDh8DzvU0tS0Splg6X1KVe76QCSPKhtXHSG013XpbsHf/N2tvFqvplt53/db4Tt+0511Vp6rO3N2nu92NHQ/pmMjYCiQkEJCwFRQiEyC+CZHgygGJSFwgmQuEAgJEbpAdBDYgSAIZZDCx5antbrrbPR2fPlPNVXv+5ndYExfrs+n2iR1b4pNKVfut2nvtb9fe3/us9fyf389YJDD43GGKqmMyGWUzokxYI+gDlNMRuhlyUUsg6IgYQCkYuhxrcl2PMuSBMJmzx6CxQhMFFBL6HTN/iImiHlM0E4Y+YXSJqCNCJKJUCK0I2/yzhSJHN1wu9KuRofMtq9WCWivQBX1ocUNAG8kQBSI6hE957ikGZHSkFBm2qx2FSGLLCclt8tBzkiAinRsg9Wil8lCpNEiZcsT1D/n4pxa5QoiPAT/3bZdeBf4G8DO76y8DD4AfSyndiCwV/pvAvwhsgX8zpfRRH+13roLQ+QkkAiGFPEEXBFYXOUOmwJYVvt8ihEQjQCtcyJPAQiZkSgSheenuG5S6wfkelWKeTh9W+CgpqjLPiEhPGlqiNBi9O83pEkHcgNAgKqqiQAwrjhoL4wakRaiIkbfATnA4+pDoksHqj34pj+7cwfdbdNlklEu/JDoPbktyEVZrlNDE8Qh50ZH2G5JeI9qBNKlIzTHitM5heT3DvvOFj6yhTKYqRPIO0vv8g6R0VrwKqVFyQERBEirXr0JRGkVKCje4nRY5KwQ9YKP9yDp7RQvVAfXskL53pNKg6xm0LWY647h5C991VLYhJoWsLA/e/iKnr32G1cUjfBScfuIHsPWEfvWU0EWWz5+wWZ6z8CuK6f5H1rT1Hq4Dv32BKfdQ0vH80SMOju9Q7U0ZQotpEvQOLVr6bUcIK2SM6Jfvc3j/EyzXHcOyJXYrFmfv4rYdZWU4uGUQwXC56LCNgCA4unWbsix59P5XkAj61XOMkgxaIYzl/OoJRVExavYzf1AbPI715Q0gic6ziM/xSGYHR5zfvJdbbB885eq9R8TkKJVBjPbpw8CgbW5PeUlKjs3yiuR7pIzIAhoxQhV5U6aMZDSb0c43pKDyplBowrDEzCYsLl/QzW9Izu3A/zVNU3Fz8YiYeobe09s5qd0gbUEQgUJJVr3DqIG2bxmGjno0zjlvKYhDJAhHSonp3j5Xl09QStC2GwSJ0f4RNy8ecXD7lCQr0tAxLjLaz4xPOT7+biaTMa4PzOfP2KuOEHaCVmdYG3C9y9Yv75HCEWRC+gEf8gat9y0aj1Y1kUQ/bEmdx21a0mxKVRV0/UBVlISocoZ01KBEpN9ssWWd6QAhIZIjdi1SSZbrR1hV7DLODtcLymJKFCG39FRBHMB7QSxhcD1lUwMQvMDIgqP7b9F3a4bB0y3XlGZCLTVxAGMrpBSUM4tHUBqFlBE3VEQV0aLA+ayWDRrGpweofMZCCorBQWktlDXu5gVoiElgqxKhDISAUgLnJabItramaaj2RrTbBSFIElCY/H8l1Ri/XaNs1jPrGigAACAASURBVJxraQi7Qhciyu4YlV4AAiMi2/UKkRymaNjfO8bFgDYVapSn0UPbI1KPVmBcD1qjZyWBKsekfEAm8N7hNj3SWuwon2AnpRB4fMr5PEdEixopWjAFxozQxYhRZTLXtN3+rkkp9QOx29APPdexxxpJ7CN9l0hWU+kGrS2+7bCNwYc2W81ij7VF3tjjSdJgyoJuyCbB9XZL04zYbja4fiAmwWQ6wk9qUn+di8TrbBdUs0NG4xIdDSNTsN7coNSGlBruvPopNttrbr3xPVTNIc/f/jLr9VM+/t1/mvmzM55efsDR4Zi0bZlfvk1ynsPv+ueYnLyFSD1PPvgK2pZMZlOcX7C6eZti9Arz1SOuvvo1Du/9WWaHr9AcnTLd3yJffYttt0XKyDd/+f9kf/+QZ4+/Dm3H0Ruf4vrdDXsnb/Dw4TeYHRzTTA/Z2zvm6aP3iWJAxjkheBbPPqQZT7l8/Ixw+xSk4vjuq2gz5fj2m8yvHyJE4uil11leXhITuG6gXTnauKQ5HFGYCSIK4qiEGNmbHIBUrDcdjbGEFKnLfeJIom0gqprTW4e4FGhGx1w+e8b5w3eYTWuWiwWlbNj2S2Ia5dkZLfP3XFXQVAIhFcXIkqTgcHKCkonaDaRpQYoDVB1GBsRdlaNDUvGxj3+Wq/MbUtfx4sE71M0EFSXrTUEIgX5YEZY6b/ZUbslvuh6ZTO6USXbDVB4rDcKWWdW9We34sQIZMzXHb7PFNcUOPwzEpInRMRrX6ELhokemHlIejLu8OmO6f0SInhgSSecstLUSYwrStseprPLth9zbdrEHmXFdwUu0jAx+IPgtEoGLBdYq/GZLlIIkJELln6XO9zTllGEYGDUTBrdFoRmVlhAT3g2EJBAi0LU9Vmu8i0hRsOk2lGVJt+0Jqs9ZYZXrCyvyIOl4VjFsN5keUZXZYObaLJUpS6QpGNuG3g+5UPdgtEEiMg9cDSDKnO8XCSUsg0yZX/0HF5Tf8finFrkppXeAzwIIIRTwFPjfgL8O/EJK6aeEEH999/ZPAn8GeGP36/uB/2b3+x+0BkjyjS4JtFR54lYDMWJGNRrFMAwUZZ1zXjIhpUZJka03ZO9z7xxSGDo3YFVBTBBSj64qgs8nxlIWxORQhUUg8rXdxLHWBd57KqMzNgOP0hmML4NDIvFe49oWWzRUpsBai9shbr79sd1uSSmxWW2JoSeWiku9RXoB/QDTfcLqBmRLuvMmQiqoXiYmiejPSS+ekDbXqOvHfNxdU3337Y+s4YXBh4DZwZSl0kTfEX02mqlkMuBZgFIqP9/ggLgDRmcF4/hgyuZmwWtvvsFqtfjIOmK8B8NAN1+ix1P68wu2g0PH3G6JfiDGwMI9z2F12/DKW9/L+bd+C580x7c+hi4rQux58cEHbC6fU1jJeHbC7P730XYf/ba99+nvwZoJT9/7Lfb3D/nW538eYQoW14Lj2/e4vrgm+R6RNJ1/Rs+GuKpJSRDchkdf+H9QOHrnaY5epZ7V9C7hCchyhlteUBcDfiNpxvusrlcsZMetu58ldnOuL19gE6AUHgHKEqJC6nxqFGNAak1VzyjLEu8SUUQqJTl//pDRZIxQRd5FC0nvAgiPH3q0KUgJfIqURQVBZ/SRLInRI0j4AJu2pZCSveNjbm6uiMFR2CNUP1DqRDANceg5vH2b+YsnVFXOGq7XN5nsMB5Rje5S2cDFh09Rs0OKccPm8grVWExUhGFBWULYdLhOYqsyAxE1+BgwZcn14hxTVGgJti5wYaDrtpjRPquVwxooTt7kpTffZHV+waO3v4aSBZeXVxSm5PDwZc4evsf85uuEbompmoz6KkpA5uJRW7xUO3xNRAlDpUpOX3mT+fwMt7rGmQH2pmyWG+LgqBrou4RKWdSBb/GFxhvFtCpob7Y41zM6uc3e6Snnj99hVNZIqTHljKvzM5pmjFQWvODOnXs8f/whRlrKpqTvN0CJ7xS2rpB6J0TwHqEk5WQCkIs15zBFhe8D5WyKtoauXRG2Le22pbAV/XpJlIJiklmd5w/eZzSeUugxcpIZrMoLYimI3ZpyNMt4QpEYfI/0gn47B6lpmia38mSiqatdIZelJ3HoIViasiKSJ8ed75BJIwx5ViAl2ralqipwOXe82awpmxqpTWZ3Kp2pNgKKMQzesVnPM04x5IE0bWoCkcH1yMIwBIOgxOBo6gZjHSF2tP0WZIEIjm7oiSqTQcIWvLLIJEBvWSwdKEXhG4wGQcC118gY8L1DAbpMjKUBrVGjmsqMcDEQQxbzFLqhdT3aKKTOfHCVsn652qvIgmV2LemCsinYtnOklAxuQdoMrP0WgSQZhZSG6ugo53b7DjY9G7dB1IGqKmg3A0ms+Oav/z08FZP9j/GtL32R0m3Yu/UG733+F+m3j1luLjl7OFAWJzz/7X/AtIo09z5H9NegPHe++yeYX3+DcnqfmR5xKd/EhIHp3mvMbr/J5aN3mHzsJTaLBYqO4FaYeooPgoPTY5Jfcf/Vz2An+4RwzTe+9nf4ylcTH/v4n+P8nbfxq2ukCjT3Ps1w/YDjV99ifnPNGx/7FNfPP+TOvWPa9Zz92ZgkM2VjvV5STU52xBCHLAz7sxNO7n+S1WLO5dULjme3CQmC7/Ftz/X5GSF5+nVEBYcfeto2kEYDwViOjk8ZHR6xfLzg8QdfJMWOot7DNA1nlxc0hc0K4SLiuyu6VYsLnpc//UnO3/mQ9eKMsi4RMhClAtNglEXXNSRPDANGRnQskSrrjwep2Dx7DiEhhaCwJUqJjABNLcEJynGdowNCUcQcMyztHpBVOkE4jDQ5ey8lPkDoeqw2v1vL2Eojy73dHMLA4A2TsaXtlhTFiLZbETcKpRRG1yhtaMYjimYCyjK0S4Lv8NseHT3L8+cUdYWpxmihKZsmz0DsmLYkQUwBXUiMkFnTKySOjE9M0TMMHhVDvv/vhsGUKUhKYm1JHwaQFuc6UsgdOakKlAw45xhVI1zvGTUlm6GjrvN9Vo0qbMxDd1VRZMGKNigh8UPupESzQQmLtorockQqxBxZ8H4ABFrUJDmAkYSUMAiiN/n7ZtjQjGY4ocD1hDgg/gjGsz9qXOFHgPdTSg+FEH8e+KHd9Z8GfpFc5P554GdSDvt+XggxE0LcSik9//0+qFQZ9E50Wa+rFKHPL95JJkI/oIoaW5T0bbcr1gASKXlQCikKfBAYa3LLHYlIMbdKUoWQgXIHQHAum9OUikgp8IPLSCIhcqZGK5KImMLi+gxrVzqhYiI5QUhrCmERPhCGgaSLzMH8PY9SGKSRKF3gZYHXht/4Kx3f/2/8bTi5nxkm2zniKpAWv4K4forotqTtGaPyJQrZ0p7c4QePbmP2p4xvPnrCmpXEgiQhBpX1vtLmG4HVONdn3q/VeOczPkwqpMjazZgkEU9zcp9br09pO89sfPSRdUQM9N2K5cUTprfv0bkbqi5x8eSC8cGY1WrB6a27FAevcPHBN9EIzt//Gs57hJnx7MlDJiPD8+dfQW40Wg8ECgYfePHlr9FFCZ/6PYvGiuvnHxIjPPnwPUajGfWdlxFGsby44vhgn1hURD9Q1Iab52csNktSmHP14QWj+ohNTChauudfpbvZZ3bvFdZXT/CDR+kGXY6wlUEFR+zWCBF5sToDGbC2ROuSfvBoYeiHNUO7ImzPsZWmGxbUdY2KkquznnpSgtR4IanLiqHrAYctCxabNUU5IqYsY/ByYG92wtB7ht6BERhVUlQjXHD0wwpjBOX4mHq8R3ALppMDNjc39L1jenKX+dlDFFmOcnP+GCMlUhvcsCLKTE0IruTmxRXPr54xO5iwefGC1TwhRY6/VMrgpUF4z97BKX1IBJmZt0pllFwk4/SEysirwSWStJSTCvo8fKJwDKsLnrxjuHX3Hkf373Hx4gJrLYvNU96df5WiKChGI6q9PfrVNcaUIAR2POPWyx/n4vkzdN9mNFq3RirHMNS02y3rsxtk2lBOZlwvLmmqhnuvv8njB19G+C2+za3DZlSz3Swpiob5h9/AC4fVe1w/7VheNIRd9suHgSoJxgcHdMslOoJINS+eXxKUpiprhIyUytK6gRQ2yJj97Z3vsTLndoNvScqy7bekFJDDADESQh4ukzohUdSFReqCg1u3iNETBZhguP+x78OFnC+NYcjeep+ZmW07MDUFnduSFBRFwdD1TMb7ZOpcIiSPMQUx5sEpmcC3jq7LnM7eBZQCoQS1GQN5cE6onIMrigoB2SymBYUqST5vOEMSuG1PWSr6kI1QMkqaep8h9iib8Y5SRZKLyKLI0+oIUurZtmv6PmVknXIUtszDaoAxJd3Q0g1tVpG6DcIIVKoYH4yywnmQKFLmF5eWzeqGohlj6wat+jy55lJOfmmD7BLaNFkSZCwmSVL0+G5LWdV0bSQlRyFLfMwmM6Nr+r6jXV4TfY5w1eMxGIUpNUOfiKGFCL5dM19ccFCP8S6wNzvmanXGICz13n3USLG4vKQc36a7vmJSTVkkwYtf+x/ori5oXvnj3Dq6y1f+8f+MD1+kGN2ipeR7j0948N47HL30KT781heoy5p3v/YrPHv4qyhalstzxiPLzfJDitMf4NYnvguCR5cGPyRW83Pq/XtU5ZhuvWY1P6PqrqnqKZ/7k38VU9Wst1vEy/exo5qYKvZP7vL5n/2b9Bff5K03P8Py2RdhNWdIEpkksb/E+y0bt0FSoavbRHVMwlOOJviu5u3Hv4apsx3xw4e/iQsD9bTBjvYpRiValZzerZgeHkIP3WbN1cVzHr33TaQcuH78EF0kxsczklAoXdJvlhipmN+smR6d0C6vGBVjhDA0QjF//Bw7aZhNThEx5+r7zZx+6HBsiYtn6EJSjiY4b4hS4LYtRZmZ+zH1CK0xO+vb+nd43SRc7DFtJChLqQvC4HBdS2Em+Z5ZCpJM9PRIVZKSQ0hN3UyzvIawK+Ai6+2aTlk0CmvyYFhRTDO+q9rP8cqY78cxRgbniKaElNB7J4gQKPcFhck4Qu89RmmiI5/GCoE0GUeZos8ZWR9RhSZ2kYRAhIinpxt6Km1JKIqyzMQVs9NZrxfEdsC7DcJKfIyYEBh8IIVIMymwtkSELJ0YUsiHEXHAlgU65bysTXYnkykI0VEYDcHgZcTqiognBYG0OhfiesfoJTsPNILUC/rtFiEEi/UCP6yodIEe11w8e5uUFMJLhvWSsDNW/mEef9Qi9y8A/+PuzyffVri+AH4nlHoHePxt7/Nkd+33LXJBICuNGHIVGlyHKndTjM5TVGWeNkRgygxpTiKnCowoSDGjpKUqEAna4JChRwlJCoEoDEIrZIKEQxtJ6jKvLhIyXcHHnSp1yGZdWZDCbnq03VJWloRjiG6HwAgEHxFWI10guf4jz0oOjm69IuoKKoUSPaf2Fn/hz77Pgy8+Y329YehrNmcdJ4dj2qOC5WaGnlVcsmEeJEftCxbrMbecQt46/MgaWQm7ywxpTfQOtEaQ6FyeqvXSZbTaICiKAiEicadlzdCyyPyDb3GRBIe37qGPjz+yTjSG7bZHTw9wLlAUe4TO0ew1lMcndMOER+8+RH39l6Hcp9kf8EPHzeqSUk3RleHm8fvcuvsJrs8eMBm9Re8C+6cnvPsb/xcifXRnVu7fZXr3VVSfGMLA8uwJ3i0I6wWmXFHd/iS2qrDTCbIfMM1DDih2fvZnhDQwDhPqusaMBZvWMz26Qxp5Rk1B7BNVWbN4cYMHjk/vUZqaD7/5W0xP73Lx7BG3X7uHTAUpahaXjxF7I6KUjPcOMcUErSXVpGF79YRHX/sSrr1hPBvTHN7mYv6AcfMy29UlhWjRRhOihsry2t1PMqy3zBeXVHWZB6OkxNQF09Fthv4KXR4w9I7V5TO2V88wo5LZK3c4f/cBq7CmaxcU5QhrKrrg8KFl6G6wesb+yRGTyQxpsjQidrfZHze8/aVfBWEJpkRbw3Z+jtJZRjGkzI7dP3qJshmxfPaMvluAqkELrGnocdjGImJivV0hYsd2uWKz6NBWU1ZXXF9/hbGakdIW4Sq6uGUya/LmVGnc1pFMjVQp6xvbLR+8+03u3HuDqycfIlOHkYrpwSHDtuX84W9T1xO6tUBUFe7DBWvT8+5Xv4ApJEYUzDdPMjJrC7WRRNGi9w/YdAlRaKyXeWrYaVIUNOOSdtjgr1vYXHJ+dUE9OmV2+3VsOaGPWyxZ+z0uJ3TB0Masy25GFSIo3NARI6zmDykmFaaZoQgEqUhSIglEHyB60IHN9ho/DFhlMFphVUm3XO8oKxYhAk8fPmR6eIjQDVaVrLseYw2ISN92iN0AhusHjBWIGAkiYaxFZLcMAkk9qWi7NdqXxLCLLSTw0UGS1HsjtB2jElkaYxTrtkMbQ6LHlg1SQagskoRNuZ0rVI5HWFPghi3rts0DXSlD4oUwhPYGlyJWBLq2w5ZFLhZ9T+x9zh+rhNYjiqZASE+gQCeFMQVD2OTXHBmxQlPWM0KE2cksP/ewQJCJFFKT7UwRivGYvlsiBLjtkHmrRUlpNCEkRpMKF9ZAgEExGp2yXi9BF4ynL2Gkwrs2t4BFz2YzYITEb3pWyyWmDBm9jAQlWC2vKZsxoijYLm9IbaQa3eLw4JTf/IX/FSU/4Kp9yic/+y9xdfOE5Tf+Nu/H97h18Cfp/ffghiW+6/m1v/PTdPMNX+9/jm37ZUaDpWhOqOsJQQr29BHBweHJ96H39vBOsrx5znR2iFINo3oPHR3BGopRzTYsqKqCpCIuOuJ2iZYWPR7RlAWr1Q0PvvwLvPq5HyQuL7l8/Hncao7UB2jrUMLgllt6LlCqgigy8zR4tN0x5ukpZw2QcM4zms4YQke/WbOdv099eMid1/4ZLi/OePbbv87o4Db99obN5XNeev3jrBZznNniQ0QNGmUqXLvBmoKmrrGVJ8bIeHyU1dxKESSEKDDKEXpFkp44BMrmEB16FIL68F5WOOvEfPEcXIdQBbJPbNc36P09ypjYXM/RZbag1s2Idu0orWTdLqmme5nwpGF0uJflFDLuqAw7bGO3RimFxrFqt6hC71i5u8MDkzusPuS4F95hTYlQkrZvqXWJJyFSJEmQRUY2ppAyv18JYj8wX17jhy2FSnTC7njcMmNPvaQoDVpBt1plkc425dccBMZokAW1NQwbj48b3DAQes/APHOibUO1N8GnmsElapXvQ6WxIAUxJBSSIAJSCPCeKIGYNeKFtQxhm3PsIiH6AXSiTxERNgSfyBOc2aIpY9Zp976DQD7ETAN9n6OlUtZZ0HIwQqlx5qknTXXyBgiFTwOFkWhb/P7l5O+tLv+wdAUhhAWeAZ9MKZ0JIeYppdm3/f1NSmlPCPF/AD+VUvqV3fVfAH4ypfTF3/PxfgL4CYDbh9Pv+aX/+t9D7phtKQmCAE3Kp0fRk5JA67zriRjYTSf6lCfuREw5y+IDQYDvM+9VS9C2zqSBEBlST2VsHpzSEqEiwzCgZElMPSl6hC4gKVzMkQcREgmPCgNJp0w/QBGIGKWQNkOOf/pH/5Pv+Jr9lX/0M3hTE0WJiwFblbg0cHHzDf6d/+JXmWmwQ+RGJ1qmeBL3woTP8y4Hbz/l/PKKT9+5hX1pD5lKfvizn0P/l//xd6zxF3/l7+cWWgi7SUiVEVrdmqIocgaYmO1QKb9YOeeIqaOennL/5Teyz9oolB1nnvDQ8Z+e3PmOdX78V3+a4fkFxeE9jK5Zhsj9T36Od59e8q/92E/zShozenfFzzz+SYbtB5h6xuLFY0b7E+rpEeiC5fUFF4++wclLnyCmAh8G+mFJGtaIKPnvvvdHvmPNv/Qbv4BUsL48I2629N01RS2YHn+c0eEh5eQY7wPLzYr3vvwPKd0esYhUFdQv3WVcHTNuDmlbwfzygrIsqfemSKtYnC/Z2x+xWC+gu2I0nWLHJ6yXj0ndlsvHHzBcn3NxsySFyHhSkbShKmfEJNFBM19suP3669x542MEGZjsHbNdtJR1Q9e3LBcrxoczfL/EJsWm3RC8J6bEZtVipaBUgQ/f+wa6HlFKcB1s10uM0gzJUSgIKVI0I6p6Qreas1otGB/sMds/5tH7b9OUDbLaJ4oO2iUhJO5++gc5e/AuYWgxxYSDvQmyMtTTI77+pX9EWjnMpGS/PkIVJavFFp/W9PMLotYoUyKcoj48oixLLp4/IwiZJ4p1LozaziFLizAgAyAUhampJrd59P7X+Owf+0GeP3mP8e1b7B+c0HdrmsJy9uApPrRYI5jPr6mnx2yXC7QU9EOL3Q1YdkMPQqGVyBSA4PFxwfjklPZmA0iqqiGttwQTcZsV3g/U432CKjI9xWVSS+oHdFkS+gGiwosBNJiyoKpHCC1w7Zq+LzDAdthSVRadJEMXUNYQpCSEvMk1psAPAYRE4DINRoLfqSyhRMREoRI+5uEwj8INW7SW+DZgRMTHhM8yaIwVuRBsHUrLfKobNiCz0lYIRZIlMQ2UytCHnhA7TDL0my2kyLaf0+iSAcl0dkp0kUFJCi0YFtekOJC7kQ5ZjJG2RFBQiAKnABIGRVJ6Z5TLxYYMhsGtdxPbnr5bobVEVznuFIesGNfCEhVElw1JKiWMLfBhJ/AQEPoNXddTmhJlCvp2RZIK126QSeNjIKqItQUiGIQMaGVRyuwmxx1GCYQbaLsVgVzgW1MjG4kUBuE1znticDs6RCaDKL1jiztIKbPBXd8z31xTmAqbIpvtghh8bk8HRyAxq/eRUtL1m9/FrFmh0ZMp0/0jNlfPWC2WaGNze7l0rNZPSGJD1y9p9HHuOlaAnGJFnbtwMaLsIWm7RBUl3mhif4OUOS+96ubI5KmEZTU85LXP/TVOj15lcuuEm8vn3L33cc6ePWR2cEq3XTOazIhhg0gZjlFNjujWK0YHt2jXC3y/oqwaLp78OqE3bJ7/Flv3mFE9Q+pT7LjErW/YXD1i210go+D6akFwHZP6ZQZnaOoDLm+eIrSANGZy8jKmakjTU0pr8E7g/EC/7HIUJm3YXj4iao0xAmFHlPWUEEHZArWz5CUkbhiQTtCuV3jjKXVDUVcMfaYFaStRwhGCQXpI6B0bv8vCozagjSHIBDZjALXeDWEHmbsKcaCxhq7bsA0dPhSURUMYNgTfEkXuqsmipigNAYOqs8DBhPx1HciDt7oPpELSbTogUlUlSUtskVFlEgVCZD62iLiUo5Zalb97CKWU2rF3K4RPOcMqIbgBJTMrVspdll1Jtus2M30Tu5PgTAEaXMt0f5opMGiCzAVqjCCihp2IJ6Q8w+OHgBYDUuaT4ZA8hW5yt1FkNNrvxBlVVEgi3kWSCUihdye34IaUX0dCpkzkxL1ChszZjSLmCGUE7zqkynzrQln62JEEFBSopOn7PjOElSD6FiMk/WqFEAplJSKC0JK//O//R7z9rf//6Qp/BvhSSuls9/bZ78QQhBC3gPPd9afA3W97v5d2177jkVL6W8DfAviu119KUhpSzG1EKQUpmdzmjy6fICQYfD5ZzWmTSAoRQcgEBn6nDQ+t74nJY6QmSYEPHVIpQKB3bfu6qAj09MMCktxhjSIiJpSQDGHACE2IHUru2l60qDQmSpDR5wndaHNI/J+wWYibJyihSNUtSrtPiBEhNC/ff5Nx8fPU9jbBSG7XM1QvuRzL3EYVd3n2pa9y//g2Xnm2wnFvPeNf/dwP8r//njUEEi1lzhXFiNKSYdtTSEsYAkrlb/Ku2yKSxhiTMUQYwvaaD377He5/+rsAxfzqRW4l2I+KLXR9hLk1JYoK1VScHL7Gv/Uf/ixffPfr3KscYXVJ90P3+Fc+85/zc7/6o6zWc2Z3X2F0cMqLh+9x/eQp/fYx3o1I6X1G4xpbHuDaNalbYtToI2v6fsvy+kNqKQjG8vJn/jR93yLMhPPzc+z6kunhEbPZLT77z/4oD774f9N1A8YcEi5aHp/9EkN7zeHdT/H07Ksc7b3Mi/cHpicfJ7Rb5u8/o90uqCaHLK5uI82K8d6M1WJgcvoK4t5rHOuG0+P7qNoQgmd1dUV3c069d0gzOUGQ20nLyys+/OKvYicHbNYd/fqSYX6FmpzkYYA04qU3brO4uAA14HrPtp0TtomRVFw8+zqXw5KmOsSaMagaOdxQjPdZLTf063zDvnP/49w9OaLdzFk+fsadV15neXbO/bc+zeX5A4YLR6LkxcN36K4XzA6P2C6vuYxrUrCo4oKDg5fo1Tnr6zlXyxZVjHApUlQTqtEBxd4e1egQrQ1X19fMn59z+uobdNuWYbtie/McjWB2dEIvslhEeIUfQs5aLt/l9skx54+/RXSO7mpFWxxSNDOWqyumewc8ee9tXGWo6hHdcon0Hic97eaazg+47YKkK4wY4aSmjz1laZGy5vKDJ7nF7QWhcQirGVc1fjVQFgXDdose7XF8+2WuL55QVCOG1ZKyksSJpB88dbEbBo2wWW2QKrOOrc0v3mXV5FZ5dNTjEUnLjEkbTej6nMcVxmf5QwgUUeCGniEptC5BD9nGFDxSKKIReOcoihEpRmxlGPpFdrEHT1U2KB3pnUDYCWEIBC0IyeYsqs+nSc7nNp2oKpzr0BL63qHLApA0ZYEUkUpUIANBBkDSuUQ5PUTIQN+3LDbXNErivSMlh9I+T+YLGPyK9foMFbJq2tSHRN1QmCLfeDBUVUXvOtwQECGLdggSXWmC97l1y84U128JQ08nsk3Oe4eQlt4FtM9ms9516AhJJcbjES4I4k72qsmbcykSvo8MXQciMrAiyi2j6jY+SCgV0flsoooVUguMrTLz2LqMXur6HNlKnuBc1jQPWxpTsB16fCIPLE4tySsKO2LbbfFDT+gDptzDpy22bLBFg2vXPH/0CAsghiwq0HlaXdR7WDmiHN+i6zom5TFeSYrK5LkSJzPyzGjM4S2Ejyi/JehJptMMm53EY8po72Wq8vtZr5XRhwAAIABJREFUnr3HiweP+WP/wo9xcvs1Xjx6xnq7Ze9WTaUrRCExqaGoalIK9MMWPRoTERhboLWhWzxDeU1RCLayZ39vDyGPaab3CcOKZLfMDk6wi4HFzQuO92ou1luu1w8oy4rl6ltoo1FFxeHeHjfLrzFsFP76NhfdhtnhfYYIo+k+yRZoOeXgpU8w+A09A+3lhnZ+hhyXjNVxxoNqC9FQ1A0yCcrpFGsM/ZBf92qdD2982uKHLUZbOp8Z0GUxYt5vmNYjks6tfasFQlZ02yV6pnYbu2xeLEZ7DCJ3TyamJESJsYIuNMQgUTJHs7zwSCnAO1IIWFvj0pBPcOOAVBbRJKSwqGmJLSqcb5Ha5pPWXSteSokocjRQkO/TUYnfvXeIGLBoou8zo7mLON8zrgwiKEQMDFLmIbU+02fadoXwnnoyyfp1U1IaRd8FFBFhPUFI0CVGWGKRxS1SiB15psakHmUsSgoSu720zyQfS4lPieCB6FFG4mMi6fz5C5nQRuL6ASEL+m5DYWtilKSQIHkGAm7bY2U2xLohYiuFTArnIm27YX1zRlmWBFGwXVwh44a265CFQhnLcu6YzSYor4k6zxVJATH4j1aov8/jj1Lk/uv8f1EFgL8H/DjwU7vf/+63Xf93hRA/Sx44W/xBeVyAlCBFkf31RJxIyJgzdjH1aFUQkYgY86lMyt7joiiQoSCJQBQRrQSdSCg5QsuW5ByCAggoKYgyZXyGTAxugy4LpCowu0lykXLW0MeASJEowrd9jglVjPDOYShBRRIWmXanxyl+9HlFh8Rju2t8/wJkja0PCWHEX/2XP85/9nc3xOAYiZbDac1psFxJhSkNe0cTurMWIQteE8fYvYpbr33UeAZ5wjJGn6eK/YBI2cqGABeyocQoyTDkoQwhBKlNmMM9br/xXRR1g+tayrJEiMTy6ipvTb7tIbst5dF9tCl4+t5v8jd+8h/yWy4w6m44W7b012tetxb5+oyv/Maa7//nT5lfnfPBF3+T5JZotSBtVoymH+Pm8Qe0xrJaLrAFLNYvsq7wT3znmo/e+SU0mgerK/amt1l94R9jpOTw5F5uacXI+7/2y+jJGIZr1ssrrIwIc5u0XKL1hOLgNmZ8h/1ujZOSqDKR49YnP8X8+hZHSpL6gA+JZjbi7OEHHL70MfwQ2b99h9C3XM+vWD+eUxjDannFydGU9fyaR+98FYuk7wX7J4cMw5z2uqOZzrh59g4OSxUM1+dvU5bHvP35bzCe7BHChsJEQDM6OGK73vDyZ36E9eJd0soT9JSisvRDyfWDbxH6DmvGlPuSZ48ecGfc8OIrXySSSLNDZDHi/NkLbr/5GfzsiMXZE66unzHZH/Hi0Reg9xzf/yzz7kO0HVNtS6an95idvsL5ww+wVqGlzZinqPHLDRc3S/bGhwgZiaXk5tkZ/bAiDVtKlakB2+2aw9MT2u2c9fqMZnpK6Fb0i3NiuMZGybIfuDuqeP+X/1uGPiLVjGK0R1GC3X+Jm6s5OsDh7Xs8ff/LKGFASvS4RJua2AFCUZkSkRRSCibTW5mmQiAkQZKa0EM1KaiqmhAlQ99z+eAr2NEEt7lBmor5+QOEiKBL+vkZsU9s+gWFLjBFQ1SKob/Ah56yrjDFhPl6hZFrytEI0ETnGZxntaMTaBMwMdJ2LT4GNDB0Hbq0xCiwRUUSgthn8yBOEeMAsUPL3MbUgIwDftOiTUkfA7ZqEMFTjiyu7xDKI0LEWIWtG0JISFMhieg6oYXGbTsq0yCMZvAOH0GWAj8MqCLhZED6SBCW/b2XUUXCqhLhofeZX5tSPjGaFbfot46qqenbDq07ehHQIauDEV0e+HE99J6oLcpotq3DDV0eEPZ+x6yNRD8glcCtFwgpGWJPWde4oUdESVmMiJVHCeh9ygXDkCU6pi4IwzrfJ4RnvL9PVAElDkhxoCwLRA+DDMSo8cnj+i2uB20Lyqqg267wMmFkLiysLuhDzEpmVaAKQ2012iT6fgCXuLpaUJouc467jul0yrC+xgdHSC3K1Kw2PQWBbrPA2MQQB3yR4yXHs3vM1ysqU1LXke1qy3jSYMsp0QnWwlPU+WvTrjcomQgpEkjIEKmaKZODE/ousNxuYHlDrSrqceRLP//3efT0q/ypP/fjLJfPcO4NRk2D9x5loWuX2KKkmh4Q27wxynr6xOrsBcQVrt1QTQ6Y7r9G7yLOL3nw3q/xxie+j/nza2Q54fjuKVI5Jv4evs+n9baoEDFwc3FOny6QYkXbPsW7X6Qq32KxOWdSvsTqyTcIQhL6AaNfJYqEOZ4xPr1L8gatLcpEAg7ntvnjx4iV+WQzSpV/fmI+xbXW0rcrKmtpuw6FJgVPUB1VWdMFhxYRlKfvO5rxEeOjE5IUGFXsOPw6M193RsJ8aipxPmFlQdIAib7bouoSFzwpJogCR7frKGvatsdWdY5ApsybbTcblAEfWpS0xOBJyuZDu6DQWhGcx9qC6AOF1vgwUNiKqHpSMkhtGVUqv76pzLyNJMyQCQMSz6jQOLGzE0dNoRRDzDxtObRsNwuCi0BikBtsMUKmYkeGUEgk7eYaoy3txu1iRgIh+nxgh0aVlpSbMcQI63aLVQpS7pr71KJ1xkqm6NDCMHQdQilUykx+ay3KmiykCB1IRbvuCcMcXKBdPsLUim7lkVqz7nrGTc1svAcy4x+b1w6xokDaIuvVnUcrhVJ/+NL1D/UvhRAN8KeAbzcR/BTwPwkh/m3gIfBju+v/gIwPe4+MEPvLf4gFENJm/BURJz2FrnKUTTZIGUkhoE0u4hLDjvOoAUkSKZuMAKECsvf0XYu1GiUicTfJp2xBdDs5gxb4oUM6gdQFhB6vJULqvK41ubWXcrvAaoULAq0a8BEXHFVpGIiQBPqfwLBVSpGiRPoN2iqEv0FuAfWMH7h/h6ftF6irPZQqSaHmupqiuoHXRM1vv36X/voRI3OL5/PIW8cNQg4fWcPIgkjKyVqXkNKii5R1mzL3rGL0pN2EvyNbzYRJnN55C1PW9DfXdM5TFyVD6JidfDSTq4oaaadY0fPf/y8PmK8DH3YPubtJ1IvIrJzRti3rg4a/9dc+zw+995c4X3yd0UTR96BajQ8VYf2Q2gqag0/wxp94YzfgNwYd+QL/1Xes+cN/8T9gs1pSVCNefPAVhvUZ3bYF3XH5/DlWRfYmE8zRIbq6zyeODrm6fIoqavr5Je2zDyinFc1Rw/nznv5qRTU94OLRu9w8f5Rvxkpji4YQAi6tEemGxcUTtJpxc/WC7cUZ3XqBUgXFOJMyLj+saMYTYr+hDwMxDbx477eRokLZhrP5c4ypMMIQo6FoTrn7xg/jujOcP2dx7hm2PX4IbOYXCFFx+NKrzF+sUAnc+gW6aRCmQk+PGNmChKFXEQvMHz7hpVc/xZOH72VywP4tivGM9bMXuGFDPT1i/6WPY6i5+9YfZ3G9IsYtm3d7pmbM7PYnKfcO2S7PsFfniJBtX8YqpFJoldCyydnLuqKeGML1ivuvf5rzy4e5PZUSpwev5puIWHMpvsXgNsyv3mdyfBtjxjx///9l7k1iLM3S87znjP90hxhzrsqsqavH6sktShQpuUlKKxk2IA9ceOuVF14a8M4re+mFN9ZGC+8EAzIEmIDcNEWYQ5MU2d3s6q6uqq6qzKqcIjJjutM/nNGLEyTALgrgwgR4l4ks3IyIun+c833v+zx/zL2bX2Bz+iHd/C7Krospy7/g4JVvc/LZI8g7crXHw5+/h0wBRWSxvMkQHMZYggBja4LfYUwEUSxlwWdUZbHC41eXjGmDkYJpB8PU01qNrfbYnZ4TtpeFI1kZpGjodM+UBEJI9uYKESJuvEAagwoerQyVDqwvn9EtZmw2p+TVCjYbJqNAK5T3OGHw0jJJcHkixRltCzpr8iYwuDU7By6OdHaGQpF0jbJNmd6lhA8B087Y9TtSKmpLjGLcXCCsLGvLGDGmIboRVVeIIFHXdJnJOSqlCNebnAwQrkH0CiSgpMFoGGKmqjRCB6SoSCHQj2NBKAKQ0MJgusOyFlQOIRJtV+HzgJE1k/dUM0tIFi0UlSkTtmn8i/hYolaKaRqwtiYFQdXMoc7sxgv2D24y+oDVFVJqZJOL1ENbVKrxYUBbS8yZurH0Vz1KS2SSIDSmNvTjjqrpCCkSYiLsNmXaZgVZGJp6jswWWxnc5EluQEnDNPYIKzC6YgqeJDRaCWRbk+KEMhrXr4n9yDoEOlt4uSKucAxcbHoWVcNe3bLuJ66ef4YPWxbzmwhdIazGGlk2DqLC+5Fl1zCNPSkNyKMGKSTbkw2iUxjZkCZPVVu6w4YsFX3vWNYd235DFAX8X2x8jmgP8VYzrjcM2+/xnV/9T6mbOV/94m+gVUV0Ae8mUooYcw35n0ZEVZGniTBtiVPP2Yvfw0bH7Og1Du58k2EzIIVAqsD9B3+PzXrLjVe/xtmLx0iTrs2eEsIFYfeSy7PHVHYBqmNvr6W99xrP32uRi5GX/SO+/ff+ax5+eMbh8hYpeE4e/wFh/T5Z7tO23+TF44+RumJ29AYpN+wd3iHIlnZREFh+iCgT8GlHjDviWKahvfMoqRjGHWQLWiNFJoVcyr3bNbN2TggBLQrKLsREioLgHVkqGitKPC8l3LQjZ0nwkizK2UNJIIK2NW7ssaZGCUVUCZ0yPkQGt0WajB+vLw9SgvcoXZGlRORM8pmq2mMIPdE7QJG8x5hi4pumAXNtJB2cIyXIykOITNljddnE5iRwfixRF19ih9O4w/sJF3dYa7G2LvKSbEnOU81qrLZkpYu0QilM1rgwsd1dXRN6GqIZERSeOBnGYQAEMQTi7jpa57cI1WPUHpiOqAPEgKAIbqStMWaGBdw0YpRmHD1ay3LpaDRxGki7yOrsJWO/otpLVGaP5eGSkBRZ5YKnuzVjyhlhl7TNITG7YtsUgiQiUlmqumUaPfn/b7pCznkHHP7Cn51TaAu/+Hcz8N/+jf8F5T8Cfc2UyxmrLTI7sq1IEUQuEGFVCSIKY2dleuoSWmn8dZZWKkuYPMZquuqIjCP5hGk6ku+LulQpcoq47LHiGu7sr9mWcSTlCChy8AglkFGShcHFCNkRRURkBQp63yOgNJL/mkmuCxNKymLE8gZpBT5sgZq9ecedWzXrlzt0XvIegbc3gU/2M7M0cvTqMY+/91OaZsOBOOJxv8Xz+RF9CA4pCiC5kIYELgWEKfQEc+3QlqJkcSSpqPxMzWcPf8zy6pgpSRoNtTnCVHWZOP3Cq25vcvXwz3nhRv7lv/gxx68vWDBgp4pp5lFJE6bAwXOHuPGU//f//ilfeG1gdXpC6l8ScIjZPpIO3AV+9wkf/O6fMw2glIF6Dn9VGMcP/+APaW2N6Tpu3P8ms9bi3MSThz9mD8vp4/chbqn7NVLusVtvqLuW3WqNSol7r38L3dbYpmZ+cIvl4ghRNcxS4OrknMViwbDZsl09I6QVRlnavQckv0ZOPXfu3EO+eZ9aJp5/9iHPHn7EjVs3GLdbLp7+iGa2T2PqokPMhqwqhmksxhtd4/qMrgMy73H65D2OX/0Kr7/5awVZpz1xs+L05EN2ly/5+U//kJltcUnSHGuyEIQIyraMbqRSFdLUhBjY+R3j+YaqqckiMZyt2J3vwI0kMyCFIVWfIonce/0b6DojVMdr3/mPOHn/PTaXT9htn6N8LopVaxlXl2zHLa1dEoGsRxKJ3caUKWcTeXnymLG/oGk6CBuefPxTpncr3vzad7n3xre5uPyU3fqC5d6b9Jst/+i//J8YLp6SUs+qD+gE/faSg8NX+NlP/y3j9JKqbVhvH2HUjRIRkDVDKBNQoSR37twqfMk8cHX+URGiBEdGMI4RowSDf4F3E7WeU1mNEhptFmx3E3uHC6Za4sLE6LfsL5Y8e/oB+7MD+u2OrDJR5JIRHxLt3gFRZDaXG2KCtDrHGMMwrlGzhqox2FyUwVK40gNIumQ33RNGIZCpQwsL4ayoOlXN6FbkJJDCELaOSXqMkMybmqvTBNmgEuhmTjYVyhh0tqi6LeB1rcFYwlQybELBlCeMFFyenlIf7eH8QAg9IWtMVVB0pMII3kwTylq8qWmqGaiICIqqKs8uqU3J9rlIHHZIB0LClB0xCnRdo6aMrmqSKL/Qw+RxIZO8R1eGGD1SGcI0Ml8e4qcJ084ouEJPu9xjFwK27kr2P+VyyDea6Ip9URtDihORzNX5M/AT3nU0TY22c4zUyGZWolkpUmtD1hkh22KvUxQWr4wMgyN7UCGQdKSddeScCWEEIdFKABk3OfphhVQRQdGt5wBTLMW2qlkiK8XkHWcXnzD2gYODe8y7PWCGEJb53QVjgk43uGks4H4bCclRNw1CzFhvTznfXLDfzRn6LY4FXdMyBIe1mpAr6r09Bhep9o4JbiLJBG2ilfsIIdj220KIOLiF2yZOL05oDw7Znn/GjXsPaBdLslFoWZU1uTHEaSBrjTZzhG746j/+7zj58f+FV4F+40iuZ7P+mOAn7r/5LcZhzccf/D63Xvkqh7fe4eXLh2Q/4C9f8smnP+Lo+D6yO+T4xi12aUe0x3ztn//nuJ3n7XbJ2WePePWNjjAMjMOOe1/4LqSRxI4xV9i5QoqO4eQR07Dmg58/Rdojjl75JnZ5g7reQ1c1xIhuKkIS6FwuU0rkoqY3DTJed0xyRik4mi8JEYxWhSiUA31KhQcdYsnGSllKmiFhm5acJaKGRhlinJh8INeaGDyV0shrGkicJkIsU19ra3zoIZVIzegnSJkQr7C6rPD7zZp2uSReX9Kz0EhblVV7zrS1RmRQqmIaHKY2RK5RZjExuh4REwJF1VYYOWMaekKKVAczDBVWtqSdI0lFrSRGztAogkiIkJE0CBx+t8OnnimGcjBVojzzwogWNT5EskhU1ypsH0amOGKUpKoFzslSYBzWaK2RErRRdPMZUZZYgbrOxMcElbFMYSAMA5uLFZunz8isisQmbwhDhRQ12s0QtUHrQonQRITtyjZ5WOHHibqukUYXh4IUuBBQtvprhVX/odffDeOZACIIEZES8A5Zz4gB6tYACZd3SG2prcEITbKKVJfDm5oCWWp0VTNbtIT1juhLMQZK6YQkqGqFD1MpqoWShYo+oYzE5RFUuc0Zaxh2PUoLMgXwLES65mN6tJWQRDmci8LvVfqvsZEJiKmUusieHMBqS8qCOEkOfeTpJrNbel6r5zy8esFR2kdaQ728xafBMIXM48M5N54lXPj8QTqEACohUmlAagmkjDKGadsjNZAjnogVBh8AZRAxMY5XyODRxrL8wlcxzQznRlCfx6Hp/ZvsVS0fPvpj8pcaPh42NEJj04p+Urwfzqm158+bgX8o7vGv/sc/5r//X17HNC26fYNdGEAY6tmS2e1XOP/oJ9TdHGMCY5TkdP6595zPGgSK06cf8OIn3yfV+7z9D36V27cf8Om7P0W196kYubo6JyRHWj+lbRbE5Dk8vMunz09Z3nrAfK9i1s558rN30fWSJBXT5oq4U8zmS8Z6n7t3v8zJJw/ZXl1SNZbBX/LzH/0Jppszrc/xqac5nPPy7AThtrTCIQL0ccTaBqEFbnBII5mGkTF6clK0omZ//y7zO3dx/Yaf/Ps/5MaNW5yefErTKu6+/ipXq0vuP3iTq5NzZkrSHt7j6ScfYbsGKcDO9pi8IAwTWmui32L1km6hCCGz2UTe+OItzq+2KAf7t99AmRriyOnpKdHt0FkwhJ4cN7z49CVCCOaLQ1Yn7+LShkrtoc0eIWyxFDzOpt9i1II0BKR0DDEwnx3ghh1Hd77Cg6//Uy6HK9YnL3j+k5fcfO1VvvEf/ybRe9x2Q3+5xTvL7uqK55/+DD/uMNayOn2BFjU3b36JyQWWixZkDT7y5re+jQ8TRkS2ly+Zrs7wObJdX2FsizKeqr3JuLui2VsQ04QfL7BSMYQeFTtIE+PuAqsP2FxuriNJM5Ky9GNk/+BNYKSZ67Le1RXZF272ZrVGtx2220eJFqxFpB1aXzBNgbrZZ3NxhtZLXADd1BAknQxolkzD+hrGHgmio24bVNOBVCgqjDREH5BakGQkMGLnmlZ1KGnw0SF14fAaW3Fx8ilZClRlyhQoC2rVMO0GECUqVeuG0I8IEanbjikohDLUdYuR5ZnXNB5dd+QkGNyEX/fXzyKJUhKLRmrLELdoFElRGOTSoLKgrmomFcgx4mNChjU6OHzMJDLr8x2qLurokCKzLBGmIeLL80NeszdFIoaeEMDYFmkUQmQqO0MpiQ9TKdnFiSgVVDNm3ZLoE9YIUgarNFlLVE6QM9MokDojREAKiRASW9dk73AiEFVDoyRRRZKfqGzHOAwoK1htL8k+s1gsCKHEF7KFWZcZfIl2JSZ8DDT1nPqgo7l3VPjrPmErXRBkYUJGcFkCCZEFMWaQNTk4RBbszW+QqiXRwd7BIS5rNBpRGVyY0LrYtJazfdzgUKYum8QwIqThxclDFos9zKLl6mXk+Btf4OpqzW/9y/+dX/3nv4GoSvxuu7pktgB0W35/2RbSxO7qnHZ+RJp6Fve+zugucFcr/OoJymTmh3fZDFdsn55w8/Yv0+wdcnb+DOkdwU1s1p9w+/gm9fwYrxKL29/k/t0vM+56wuaCHEZU2md5eEhtFSfPPmI5O2Rwlro/4/mLR3hhqOs7CGVxB69xIAM362+hTYN3Oy6e/ZyrYFguDpGHHdqAkS25BBbxEaSqSi5UGFAKQ+HzaiGRwRfoYaJYGYcVo9eIdF0Yj7JosaW+jl4IYkj0MZZ1vBEIr9DKkoMjhIEIJTojBFlkoijlYSGAkEqWXzd4d0WcejQBqzNu2pCmsjGY3IBUCt8sSQLa/X2CC1RVhZ8CwpeCXI4FVRpFRspESGX1L+tS8qy0LkSnKOjdRFoUXq9KmpRGQhZoMyulNS0RyQIeGaGqyiUvEXGh4CGVEuRQNOLee7JR1LJhZg9QlcD3I/M9gc8CqzQxRhLlUJuSwFBIHGMeECnjXUZLRUgDwW2xRnJ0/ybY+0hrsLJs0oOuEVFjrrXtWlbE6DGiXJa1kmjTYChmQiMNnvL8maZNiZD8DV9/Jw65Whu65QFKG2LwIBx+t0UGiUsDUhva+YLoPApDEonh2rghpSr2DC3wzrF7+QH95YbgFd3ha1TtPtEPxd4xBIyxpJQx2jI5h5IFTlyKDWU0nhNlBZCv1xcpIZMipb7w3SaPNJqUIyoLpJmR+Dy3TeSSdYHS5MV7hAKRA2EUfOfGbVJb8XunH7I3JCapMAiaCFbM8bXkGUtes3N+8xuvI9zn4wqV1cQcyUqVSUYq/uroEk3TFOam1iifiPHabR0TRte89o1fZXZ8AH5idXnFcPacFOF8/RC+81ffZ/vxz+j9jn/3u9+jcefYPlBVe/TjwCQNwZaow9c/kkx3X/LsTsXpi9c4vn+InR1w485Nzk8fcfnwEWrZMb//Fm6KMAZembfEHD/3tS0P94gx8+byy2z7NZuriff+8Pcx2iHCSIqey9ULbLfPbCYY+4kQdoTJ8fSzH7LfNpz/9EOehB6yp2tvYltF0x1Q33/A2ZMPcNOGabvh0cVTqmYP09QIXYQkMW/RbiCEC4ReElYD8+Ues8Vr7B0ds+0ju8vnrM/OCm1DSJAGqRSzZg6ymMlW/RmrR1d0dQMp8/zZgFIBPyg+/uQ5N+9/HelHbHeDYXvBsLnkla9+hd15WS1prVkulsV0M/TIdk5VzQt4Paxo6pr18wohI48/+VMevfsnZZrZ1miVqZoWsiHkgNYCI2BKPdurHXX3Kgez2zSLhmp5wLBbcfb4Y6qm4ebsBlovUEJTVx2L4yXr1WnxmseOKARp6Gms5eLJI55cnfCY79NUJY8+oZAhICsNSlAvWqLPKFVhrCGS6GYdbtghdUaZ0rJWSiLJ7O0f8eTkY/qrh0hpiL4r+eP+ObdeeQ0vNcLvuP/VX2e3esLLx0/oDg5ROdFvXmBzQxIV292I0hOz2SH9bo3SFlMfE22GMDHEAWEztamw3R5IwW51gRIQ+575YkbMFbbpuLzoWbY3kNJSyfKATiYwTQoha0wrUcozWUFj5+SkcFNBEPpxIueyuRCmApWRsWFWN7ihGJFiFICiamZMzrH/6pfJyVFVFcSEFJbRRVQbmNV7oD2jG8rFNieiTMyqlt5tccOWCUslNVFrpmFAy3KorfeWiJBBG0SOpfBCAAkuRtq6JV0PA2KG3ThgbcU0TcybjpyK5lVLC0bRJYBy4K3kxLDdIa0h4/9yBSyVIriMkAKVJKEfiTkUtmd0hOxJOSKlJHqHlnWJQvQFj+SniUxPihGjK9AVWINGkwOgMuNui65qxs2I0RJjamKMuBQI3qNyZrddE8PE5B3aqLI1SR4hJFJBkAmlZ9RNojYVORl6t4/PgRh6EoYYy4p5GiPKGAQa09lSkk4V3m1RekbODmkMMmVCcjTLQ9ywJYaE1kWxLJ2imu0jRCaGCZEyRmkEFp/1NUM+cHD8oBwyYs+rb9znJz/5E1699zZ37h7z/ns/49adt3h58YK2m5WtXc6QPARPchPdbL/whHNNc/gqM/MFLtwfYeQtgglsL1fMljPaW3epZzfxWaJspl3e5sXDP2P/rb/P2ekj7rzzz1DdHZruFpmOyoJqB+ysIyTJ/t5bJJl4/fg1Vp/9FC1Gfv7eT7h58zaTrDj/7AnTMOIZkaZlVt8G1RKrPWbHd9jrlihTgdvhtlsShVkfBUitIckSEZBFQiByIRHoShCTR4uKlDzkyGJemMZCaXwUZQquFwUPKiX5GqmXmcrPTiZyFPiY0MYiTMJKSdMW2cNfkDoUAUkmZIV3jhQ90Tts1eKnkWpm0Y1h6gesVHRpRgzXdBVlGM/PMPUcTymwhxiIBBQKpSq8m8iiIP+srul3K6xpSTlDLmeS2rRlkuxGspTwX+UjAAAgAElEQVRUZo4QinHsUddmNGWra2auwnuHlJpxCmShC5IreiQKkTRK16VkrzKmakhuxFaltCiiZ+gdiICSgn6aIHhW2wus6IvMIVmi92Q8OUeavTl106GrBf2wIfiejEFYyCLT2ZosJIhMSrHYX0nX02JNDhGjDCoF/LRBKsU4OqrKlAHj3/R8+Tf+m3+Lr5QSwQ+k7YoQBzIOKedIYjn45lQKVUqhVMZFTzO7tn6IiJCKKffs1s+odEuzXxP7Hj+ek2JBj0mprxF3FW5cI4xBoBCqI4YJn8S15ay0HaWSQL4WRZRCnEAXW5mA5AMShTQa77blQ/cLLyEMMYxIYVAIhG4hJ3IOCGZUKrA6l7zDF3nx4qw83G/P+DN3zpd2CRTcpEZennD8S78OVff5713OiBQZt1tiBlN3ZfoQSmC+NIkp+02VcCmhpGKaPCcfvYd+XHP39TepWosYFcEm6vT5/y3E8oBXD+7w7v/6nGAb0vYxTZT4Zo9pOaBO1ziZWBvNnUlycvmSP/5X5/yz/+EBm6fv058+IVnFzdff5urkjMvNJcP6JYvZnI9efoLfPoPv/tX3fP7xI7wXvPHNL1GLyHTxKYfHFE3sk3OyH2iafaTS9EPkC+/8E16cfExtNNLUbJ59iG4TjdkjjpmsDGcXL+HinKaeUYuJYbelmnfM0NR7hzjnWJ3/nOC2kBom09DsHyCFwEUYtwO+h9PPTgsmhhFRS3IWSNuirCD6RIqB4/uvIFKFtoZnj57gtjuQgjCcEgW0puHG3QekfuK93/seuhZ84Tu/jJ3tc3V+ie2OsTpxefaYfn3FGDaoZJgfLFifbNiOI1rVpLhjLd4t0Rkp0fuCfrtC68KJnjalYR9TYiLStXN6J0nljMbV+RN24z7jkydkJhpl2Fw8Q4XMsHlU1nI+Ias73Lr7OqvTj7C2Qj7zuHBCGA2LWc2462nrjsvhOba+SVt3ROcY3Q7dNFRmRm4KDzKmok/drbYcHM7ZxMj9V9+kXraM6y1jGDh/8jOMHpjdfoDbTUi1ZNpt6ZoZq7NnZBXR9T6NkHQHd3j58FNWF8/RZEIv2cRLuqZi1tW44LnYnGGRmKphmC5JwVAraK3Bp8wUE027j3cDZv8VFs2SYbpiGkey7Jh3FhfW5KxYXW3QRiKUoapLbGgUkUbM6fZnVAmU1kQXEYuGSCl2pighK2IKCAldNSdnSa7L6lVRmvfx+rmjUxlJuaFHWUOOsRBSsGUi6gJK5OsJjSKEEZ9KKW9KA43WCJPLBdeYUq69LphFUTTKMqWyEZMKYSUiCXyMiAS6sihhSs5RKRZdhxCSKU5ko3DThEzlcChEhUqeIDPV0jLuRqQsKK+qWRDcBlNXhLH8Ahe1QPhM8L602CVkaRDZ0CyXQCL6gMqBIMT1AcMgyOQgSH6g8mUyp5sWJySmmRGCo5sviw1ritS1IeSJlC05BczCIiaDrVT5nrqIUJIsFFI1+GkELVApME4OP+6I0oNO6M5AdHSdQogKETRB+GL3msqmxTYaREsOmWxrZIaUJ4ILTK7Hq4CuLDKDlOVyEsIaqaq/hPFLqYnOYwSEHFBGI7Mrhw5vsQdLDkVF8CsGO/D6nTf43r/51/zyr/0GMWaEjATnUUqgcmD0A029LAMdIVHNMaSJzbilqee0syP2bpRLePSR7W7F0a0HnJ2doeoFr3zp1zh58SHf+u5/RUCjtC0T4nGFH1e4foeyphRYtyvkcg9CoO97Vifvcjzb5/TlU/yL52xjj5rdZ7H3CilUKD1DmRmYOcZUTGMpTzuXkFogZSSGAWUsbnTUtiFnj3PFRCeiR1eaIDJYzW4sSL8YRvphwpo5ClG2KaImhCJjEUKgUKAkwrQlIikSORhEusZuilSikSQkqmwxsiyK25jBCiqdyUlim5YgHM2sJUwZKWG5vyT5gE9F8NLNDoljLMpBGZFYTN0AEJwny2verBhpjKGVhdhhqwohimkTpQmuZ4pb/G4gTiPr7cAUBvaPb5TcbghIqTBGU3dzxhDQusF7R9V2pAxCJoJSVKImjgGtNc55IhNh5wnZYWSFn/xf4lUTCe+2GFlhTc2Ng2N8yCThQRmqqilnoXTdfRKW0Q1U7Zww6mIgRBKyhOiI6fpnldI1taFgYuPksPWMkAMxDZA8IZRN/+QTiL8949nfyiunSOx3qM5gcg2iIUuBFA3Cj6gIKaaSEQseO2tBKQgZYWzR23lN073J7uw5ewe3iM0V/dUVgR0wLwdIUWYNdTMrSrpcGssAla5IsSA+0AV87aY1la1JIhByguzRsgHhCoA3JcbYY6xG+M9HCUQICFW0ezlDFAl8AmuohedLb72Ome/4+puvMOVvYEzFz87Pwe/zwxc/4adbyG/MWAwtv/1bf8K3/vF3Pv8eFC5wu7fAx2K/iiGh64JCkkKRKOtR50EpgcyFNdevLpG2JruIkpZ6eQM/DNT7B597H79ecbl+zMMPNXdvvM7lJx/xye4xXfUW/mefEuQalZc8aR3L85bTBv7Fv/1tfvN//m94+ugxozsni0x/MaOxRyyloT1YcuPNr3PbzBlWE7/P//ZX33P1kKj2+egHPyKP58yOj7n94Mu8ePgTZvUFziqSiGgsPmQ++NGf0FmP975MH48WbMeMv3yB1vuEkFm2++TkObh1xLC6pEbhdhNm1nJ1foFuLFIfsj97QBSajGNwE9YGSALbNExDoKoMKZWcY/lgwu1X3uTRpz9nuawZ155HP/4+bneKMXdoWknSc6K07B0dcnFywiZm+h9+n2FzRleX9cyHP/gBPu1AWEgKKzPT5Pni179Ds3fE5Yufsz67YLZ3k+HlY6RKHN98jacfP0LajNKROGra9oiEIfgVLuxw0xmz2ato1RDJzJsy4crGICqFEJra1ozTxDT12GqPi5Oik9S2pp4b3OaE7ctL9pcHxOiZguPVN/8Rzz5+j5wizXwPn2Uprby8xMiKxeErvHbvNo8//HOCH6gMXF1cUlX6LxvOPZl3fumfcvninGk98fH7v8/x0dssj9+gP39K9J5hWBP9Q0Ic6Pbf5mD/LT78+HdYCBi2Fcev3uTr3/0v+Mkf/Wum1XOWB6+xXQ/o2YzkHLt+hbSG4699m852PHv0iNfefodxHDl/8h5q7JF6H4ekXRyShMcNI1a1tIf7+H5NQjHfq9BS0cyPSGFEKUlInr1KcPONr3D59Dn95mWRyeSMMgk3TYSUSdIjr8uzUmuidwQRmIYB5zcQJZVdYCpLCJIcAkKWkqwPoRze44DUNZt+ou5mCJ/KoVUJUtihtSBMAzF72rohuYjzpcTlQyj8VCRu2JVGdRZIPCSHUxVpLUg+o2UpLO22F0VPHYCqgVCwQ/EvIPF+IAlFu1gCEhkrKltye7OZLc/dWAQZldkjxIRpGkDi046qrcjxWg0sHCYbcAHnJfxFflJoRAjEvCJHVbKNtsWqYqicZUUWEZVU4SQ3HW63IUlBbS1pjEhj6VRFEBGkoO5mhTSTJaK6ntIJR/SJbr7AXe2Qdc1USZquI447tFYMYUQlTVQZmQQyRxAJJTSqLRu7KZZptGlmJVISJ3KKLKpjJj8xs7oU7ExD6tf4caBbHCCukZd58qigUDaRUoQA221P1bXgI5PbER9d4PwpB1/9Bzx44x0efvIBf//XvosxJd63vVyBguXskN2woV0eIkSGEMqBLY9A4P5XfwNIpKzIvhymUwo0R5Fsa47tAhkcu3Xk8NbXmZLFtBYxjeyenWBVxTidoZs51fwIWRc1bbi6IMrMjftf5Pzxv+PpB7+DmVnGXHH75rfwInN2cc5sdg9r5iQhSZRCViZC1FRG4KKm7lpijIV/rMFP5TBkKo0UpZA4BQ9pIBmFtg2jH7DVHCmXiBiIuTC+c85YW5EJBBeATPRlna9EKX1CycCXiahBa1OYyaFsQLOIaKnRjWAYHUIrLBqfMopMCBFTzVAJnB8L5lMbRMoMqx1KWjKOLCCFLdvdDil1oRWIieiL+KXfrTEBoswM3hH9OSFJrGlY7B9gOk3bHiDaBYtDAVIUdi5lIFNZzeQCPkiUtqSUaJpS+ByGHQJHmhJ9iOiUcK7HUSKQiUAY1gy6yC1qUyGlIktN290i5IQICaFadF1+dkroa9oCVFWLSz1SKCrRMgWHtgbvHUpKUswk3SCUxlqLUIbkAylGYhgRlWGzfoYQBhUEWUtCcDS2woUdMfq/8fny78Qhl2v9bppG+r4H1ZQPq05U6npKoFVpP19jJFQS5CwIk0PJiuQ13k1IW3iHSDD7xzRCM407ApEYSzs7JodGkHIgB4Uyhpz89bQYamtxk2Mxm9MPDiELJkTripBjmYzmCKYAwePoSnnqF14+TGgiCdCmJkwDmAYZNBfTyNW25ov332KjLCopXjx/yuh7Lp48xfmnIHoe7p7yaG+Pf9hptPz8QToRyVkyOl9UgQJMVeHcWAxHwWG0ZvIZo1vAIa+/diUFOYx8+sGPOLh1j+QT7c1D/PT5WER3MCOkyEe7x7yp7nDva7+Gnp7y/N0/4OobvwQXD0mPn2DcMSv/hPv6m/xR/pAwwq23vkLsr/CxfIDq5ZLNZkDW+zz77GNqK2i7z+PRdi9f0OdnNFXLbH7EeH7J+5/8n9RzS1IRHzJaZCbXlzVRHOj7kq0KSpFdYpgkr73z6+wuXzJFjVKZaXXBxZNPCwy+nqNbjQsj0laE5JjvH0ACq+ccvP46bnOFG1ZM2y19f8Fmc0ml58zmC/zkCqJGSk4++wiTHP3VyCuvv8XOHdCvBvqrR+RkUKGgcDZX5zRVS86KmHrmM9ht1tTKQnQcvnqf4xt3WD85Zb27YP/GXT796H1C3qJz5Ozk59hun2W7QKsFQ6q5+9ZXyRqObt/nwz/7PshEGEa0qTHtDL8xtPv38OOKlAVp3JWtQhQFvM1AHD3Lo32G3RUHd77Aa9+6x4uTj7g6PyFEibBLbNsxTo44jUzC8v4P/pimKmB7U9XE5NicrVkuFtjFLZQwDLsLunnL44/eQwhB2+wxRU9y0HQzajHn6uKKyiq26yteffMbnD37kLp5jeQj/e6Culqij18hISFr3LTim7/yn/HJn/4OYjzl7KdnnMR3qQ/vI9Uew3CKbQ+IE5jZAS2anDMv3n+GtgLjPR/87r9hTAmrS941hC3dYo/zs08Yp0TXdaAj04lneXQLGXsWd16lXR6xW42EzRXr86dIaXDJ8/BH/579mzexuiKE9JcyAWUMYowoWVFVM7ap2JLCEEiqp65aqqrCmqZMMXzAWnU9uYSQSxJp12+pq46+76lJTOsnWDXDhdLeVpXFj2OJcC3m5BhpmgoRQEhNv1rRtS2j98UznxNKC4QApVqQEYVBC1s4xApmsxkxS3JalWeJbRjcgFE1w/YKrQxZZMJmi65njEPGCsc0esgKIwWjd0WeIQCVryc3AiFKOQpVEE1IDTERlaaSgphL6zpEh9SGTi7IFnwqZaDkRpTSjOOA6gxKSExV8EyzbokLIykmbNciiPgsysE8eTIZEPgQSWnC6poYMlJo0hDJlSaGiEwKLTQxKiSaudljdNO1YRO8d1gMQcUydTYWkSJCS0Ia0HAtBSgK2r+wdyppEDnRtnOStWx3O+pqRt9vqaqGi4vTomB158ybW5AEw/oC7z1f/5V/wg+/93/Q3b1N2814/uyUEHuurq5YzveZL0rM6+XpU7qmpaobcvSAIQWHbpbk6AomU1fgBoSRCFWTSQjTQgbZXxBCwI8ObSw+J4xMyHFg9+IRRmZOP/kzqoN7LBf75GhJ04QbzsoUz2dyllx9+pwbb3ybOGZ03LALZwgsRwvFOD3j5ORJOSBXe1RqUS7ncQ3A5B2r1QlNO7ueeFqEUIhctgDjdSZWKQWm5OOlLocynwJKSKI21wfchJSFZ43M5JzISiGlRlFMfgXDOSFkpG3nhMkRgiPGXKIisnRspmkiDL7YRZQuOC+pkBik0AgkPgsqW6J4gYw20PiGJCRaXbOS81AugmRS1Iy7HiUk8+UMe7Qk5uttdgwkf58ccinPGwXakFwpo2dEYfmqQkCRUpKyRMoirbJSF4EFmZQL2k9mTVtJsjFIEa/LXwqrFEYo0uImwqjryFyJJ4XkC9ZPCCxFxpEosRFELAjAacK7EakMWZXPbl3XEDUCg1SgpSHETIwj6/WaHK43MipTmxqRBfPFLbTyhFSEGSmVyOVcHqDk51n+/6HX34lDrlAGqjlKHdDVgew9iABaEVMieIcWmZAmrDLFPy81XAO9J3eFoMaFkabpUMoQrjOeiURWkkoqplSsI1IqlJQQwONJKYMIiEpBEriUy+0wRoQWVNISMggMmPKLR4iCJSkPaU0Snz+AGqPIRBKqID6aFpEjyXkO9Q51+gNOhl+nPZgzW87Q3T4P9B2O9u7ypaVg9/+csXdnn9n9NzGX10WGX3jlWC4BMmWEVUXtGWKJUkhKYzoHhCrrGyklQkqkLIB5pGZwO04f/xyURF5+hlH2r+o8gN5LnpxL2tkb/OTxDxjTFus0x7e/zhfzHb7/2W8B3+FX336H3fyA7eqKV48lw8vb3Pv2A3YvXyJzT8iWkDy3bx4gbcVyfoALAy+efvq5r609WKLTyKI5ZhivWJ1+zM1X7vL85DkpgW0OGLyn6g6QusJtNtSzmuXhDabRszy4C+uBl6drlE4sDjsuzp6T6pamrenX59g6s1ptiM5Tz5bl+ykMIXqGsOHq9Bk3bt5inTKmOeL+0Tu8/9M/ZTx9gk6WaZyouhqERVcBFRUHRw+4Or9gmja43Ui7uIOZ1fj1JcKDS4p7D77Ai2dPMFbid4I7b3wRESxhClw8f4Z/ObE8ukHXCXarJ6RxR1It8wdvIut93BTwckUK56heskqalBWbzYCpNVM/IG0mX6Nk5os7RWE9myGEhvm8+NI9aFFg6L6JeKFRsyOuVmdcjhtEjhwdv1JKjXVFHAPnj5+gSeQ0UFmDbTTSWpKoEJMi+A1jv8HtdmiTeXL2EmMMi+X9EtfwDktE1BptTeHWXq64/eW3GCcI4wvSxWdcjWuuXp5w77Vv4LZbLj97F7k44PLynLm5wdnJp2jdUC9nhN2E1R2Df4E1DXb2NRaHxyRjWJ2cItkiJ012I7at8KOi2/8KjRuKZCBGVCrPgps377AdPFXXUVcdF7xANQ03736Tl88e8/TjH6D8iv3D2+Rdj6wbpr6nPaoZVqeMV1MpqY6BbBR2bljuL1hvrrh55232UsVsNgNp2O0G2vkNXj5/xvrlh0i3ZQpTIbOPPaMH3VjGYUNdzUmVxdZlIlLbJRlP07boZsngdmivSMmzOT8n9FvqxpRL7cVAtIpL1WGzZVivy0W/tqQUWU89AktVGfJsj9X5CXIKBDTNoibVFdbMiUTsbAYpM69uo67LKCCJPtBVpWTjGdFS401BQ2o0xMjgepqmuWbpNmQSyZcIV0qFRpCFImaJtg3eB2xVldxjDgVVZjS2llS2RSHYJodW8vrAqZDa4LInCoU0Cp+5nqRBig6jbDFBKolQGWEtIqkihhh3ZClIIZIpHYrgNAnIRhFxSBEBQxIaUSkigjR5MIIci4ygqWqQAh8DdVMzjQNSFZ26zBKiAZ/wOLSZsVwumTY7pBDsdqc4erq2RTa3mbxELTPpbMM7v/KfgGy4+41fZ+9giVKS/VuHpDPH6ZNH2AcVnz36mBhGXn/7y6SYEcKQY6Z/+RxEhM2auuvQQiBSid0RA1kYhFSkYSAnh1AWKSxZSQie+WyP6Dyy7uhuCqbVKXsPvkldLwgRfFwz+StUFgzrl3z0w99muay4+9WvsfnkXaRK7NkETY20hv58TRYTqZ4I4wa3K0XGPm6pzTEi38PoGcPunLUING1FvTjA1LeIfkCkBokgNB2malG6QUuPHwaM1tgkCckRRcJWHSkpUs4Yo3DBl4iKhuwSxa2ryCGhVYULjsmPGCkRWZNzwFQdMTlE8iirr/GcCREzMSSiiEVdfs2nJyeGCFpqyIkYTIk6hojMHhcDRhZqBCKCcGgV6XdbqqgIoy5nBlEmnBldjGJWEVW5BJatTyAlqJVFKoEyHWEK/x917xJrWZbfaX3ruR9nn3Pvua+4ceOdEfmoysyqsl3tamw3NqJp5DaNhOhpM+gJEyTGDJkwQSAYI16N1MjCIGgk07KRQX40blelKyurKiszKzIj430j7uvc89iv9WKwrgvaWUgMQGpvKRSzODpxztl7rf/6/b6PiMSJRGktMkaMTHnhmRLR5lPdbDqLKFXiXMRaBSGilMzvK+RTXqk0MSWUyLENISRdDIBDjIkowNq8cJ9oyRg83mV8onA56lAUmaWb4lXUIEaMFojRE0Siqot86iUCSkqk0viUqSaahB89Vhv6vv//HiH2//uVIEaJ9wNGpByKVjk6kAApK5LSpOgZgkTrCVEEBIqURkJwWFPTLtbYvSLr/3RNbu0ZoAQ3UBaWfmjzm04uZ3StAhEppM2LW0WeJghAWgSe88slzbTMudwoSD4ilcGaCh8SRmdN3l++lJBIaRmGgc24otIlUkZ0kXC94Vfe+xq/9/HI8cXn7L/zLjoU9GFk5/Amf/jBP2Bys2R3e59mnktwKXx1IQ0iF0q0xvWeQEJJnXNgIQe8ZYoMDqwWhORwEQg57G4rnckW5EjGMDqC+eouKS4v+W/+s/+Obz14i5fcYVlsIYqSg9AiNpf8y7/y77Ez3+JFEYmnS+L1kjff+lU+/vwT7v7SN2hmFW1vUXhm22+gipLQr3n46UfcePB1dq+/8ZXXbOb7PH34XZqpputXvP3ed2jTEn12zEReY+V75jvXWFyckGxJYTIcfHHylLE/w5iCg+tzXjx8Sr17IxMJSo9WBUlJ9m+/T1UV6M8/wg9rhhSgDSyXS+b7b1BNZnTDGZ9//pj5fI5bLti8fMa82aZ4d5/zs5fUtiSFmCkSzZTz51/y6tlDtIkkRoKO9JcjureEmBDJcHD9iBdffsxkMiFeLhBjzyY8ox089fYsb05Sz9nJM4rCoIqGutoCbegXl1hTUjeWfrCECAEB2nJ47YhoS84WL7Em0F4+wkiFURVBZeaiDBkIP8iEFIq6LjBRMQqPFJFh6DObFcF8d4/Uj5w/f4FIJW3/DFtPsTZj5qQK9N2G5elrJJGCglUb2Nqe5kVHSgzeM5vuE4aOvu8BTVFWFHVF1VScnz2nXz9m9fIpu4eHFKWFGFHFLmevv2SiR15/8cdsNgLdTLBDS2U0w7jBmpLdg+s8ffgjDm59jVWf0XHdpkV3F6ih5+z8mGmzzfxol8WzBbbZYf/WTfxyxfJyjdclxiZcu6ae7dA0NafPHyFlxTDCOCypqopuM/Di4fcpSs12o4jsEQyIWU25PUNuZqzOn+JFiS5rUlFhmg7pIqFdctGvCcnz6Iff5ejWe3z+wR8z29ujX29I4ZxmUhGHnBtVtkFEMHZKM1OE4CjNBCEtrj/Du4zbWoUxL0aiYEyP2Ns9wDuBnjbUTYOZzgie/DlNppmfi0YVCu0CUhp6H7D1lJmxrNslL15+iZWCvcN7SGMpbZmZt87jRZ5oSUZilISwZggRLfJGX0WF1DUQKEyJUBAAqyVCJKL0zCYNQ9dRFLlUJpAk5TFRoUzJGCRXN99sN5KRYeyznhWDMgXOeXSSuDHhCBS6RnqBJOXjzpjv09IoXOgzWjFUhDAgRoGTWXUuYkIpyTgOCCymbkgmT4ykzLSf6EXmpCdBxDO2LaawyBBo2w5bFkQiti5JoUcogylszlaPA8LaKxV9ZqcKFISEosgM9xA5OX6K8z2TyYQkJNOtI2YxECmYmAnRRpLq4M4+58sN88Zy7eYthJA0WzNefPkZxiims22USBzsHzKpS4oqt+m7fpUXgz4PO7YPbrK+eInQGhEjXTcgpKeodxEpM+mFFfgw5BNIJ7E7RyTvUFYShxZZbFFd32OzeM5yccH88Cij5LSCZk5z7U323/w2qycf8uInv0O9X5CURtsj2teP2Jy0NNtzRNeBA2G2ERPN6BUm+Xw3EVvIeovy8Ha2EgBFKem6jkiFlAKjS0gK129AOxAGkmCMI2EUCNOjgmGzaZGAsALMFKlM/uz9mEtaQqAwiBhIgNUlSkPwMZe6tM2Nfm3wwUMICKNwcaQQGqEERmpEFPgYc9ZaKFwcGF2Or8S+z/KHqqLvR6SKjG2gFxGjI33bUdYFSiekzgV57waCFygXiWFJcCMb36OsRSlFnyK2mOIjDBhG2rykcgJ1JapYmwTRYMwE7wRmVuXFeEw43zF6jzYJQUHqHG5oGYaOohAIXWGkxY9jjgZqfXUKXCGFziUzlcjuKcVVA5RCFaTkqHRDdC0+StyQs/elzX2qwXeE2GMrTYXBBUdd1Ix9R5ckhR8JQiKUxPXZ5ipSwBYix0r/X17/XCxyU0qkOCJFBJ1vfH+xq1fCIguBj56iLJCiIPhESA4tA7o0IGeMfY+dFXRuQykqVIpIbRmDJ4mUW6UiYKkYh/UVZSCQBMigcqzc9QSlEEliRMKNDqRgOquyFliKXDizFRFHcD1KCcKoEOar2K1AQMaEUoLCFEghSGSAOcJR1oaqmTK/echq2SKkYnbnkM3lkt3du9idwMCaJHco53NU/Gq5bRyzplLbgjTkaIVSeZE6tg5ZgPAaoRwoRXCBiEOlnAHqvaNA5mlz8CiV8GP7ldeJdsrLYYfaz5lsv+KdtM/HM8HlaEh1wSwYXBs5FBVtHfkVdcDl5JD/9s83/M9/+if88q/t82/+1tdwm5c8++yfYJoKEQa2qm1OPv2AZmcH/pKDwk5m3HzrVykmFfWsZ7l+DVLyxjt/HdtcZ+/Gbfq2I4qRYXlOURV893/5z9HNIdV0i63tCd2zzxHtExaXZ+g37hLHgc2yw4eATW+zPr5k7BZM926z0+xxeJ8+764AACAASURBVPMWl6cv+PTj32X5uqeopiitWLx+RGwNk9k1VpdLuo3GTBqi7/EO1pfH6IslUXp29vYxKk/b5pWhKHdYnP8Yf3KOdwJedCgZWbx6RVFM8O0aT8JM5vSuRShNkgm3WSNkiYg9ofcM0nDz3jvMru3w5WffB+eYTrcZ2x4lBc8ffUyKGqEksiyZH9zm7MUruvGE2e6MGAPjeoFCMMo8dXAx7/SjsEhVUFcFEc/gPOvzY0RUbE/3cLFDT7ZwYUQE8MKTXCDogq3DXeZ7dxAiUJ0+xbuWvutRsiQJUHZGtXOdur8Apbg4P8O1S9YXgPAYG1GTHara0l1e8sXDj6i3j9iutxFjYFw+QscVUkrOjz9BJMndr/0GW3fe4qd/+r+zvXOdobvESsticwwxsrO3xWLxkno6oa4qTp6dMplMcOtTHv/ojKAFaVxTxIoxgJ5P6dcbhsUlRlhG7yhNwbBeM0SPtDXTwzfo16/YOriDnWxRzWqm9XW67oKz08fMbx5yefKcJPORvlQFQo0U1T44GGRCGcn56WO25tv4FJGlJ/YFIRTM9g9ZXJ4ifEfyiZgiYzJILfImY3VBPWswokAYTVHs4aUgyRIpHKMfqbRByYrkeqQQmErgug6lBKU1FPMd+uWCEAckJUl1V8eXmmLScLd5B4RGxJJYSBIehowfazuHLTQyWJJIxFqjQ8DKPB0KXcdmtQILdT1DUCDGjHryyhOjYxw6ktHEYSRGn5WmRjC4POGUKuPTEJGuBVtPsi0tGjocwmuSdyzGDWoYGF2L0QIvMhs2uA4pNIGAiBq7NScWFSkNSK0ya3r0FFWJiyMJRyELEtksZ6xCCUk3OEKMpCtbphK5/FwYQxgDXimMMZnLjMhsXRFRAsa2o49jxmINDlsaZJJEGbMy2lRom3P8OhYc3blPIJJMfu9pcChZs15cMC6eIqqGN97/Fo+ff87edIt6OqU5uEYKjtXZBdt710BKyrLk+MUz0uBYXygO3rqPSRa3XmGbmnJrgjaW2K8Jw0AENv0FWljc5QVjGBmXK7av3UIEibEVSVm0KcBnWkHEg1GEFAhti5U1k9v7hG6D0JIUI2JcMYwefM/04F3ulFPa1Zpy3rB6+CEL9znFRDKMa8pijmfBZnyF3FRsZEbGFcUE34/4sKFUTT6RwhOZUdZTCpUlR9GnnEM12ZDoxg3jpscMGmUt6DyplEqSBsewuWT0a4pJTRcCtqgYxoRXI5XOKuS/2J0lKcAItCgJCkIcYYwoa3B9R0iBPkIMPaUu2AzjlRwlIILDyUTQme2bMOjCIkhImV/Xe49UKYutkkOqClsadJOtYhGJcwmrNKPcICgplKIxhsFtUEJRqwLnBspC58jPmAtvTo0kJHWRkX4WSLqgVAUhROKVZERSMiskw+BIYsSWFmOmmcgTWlRV410iJEWhcj5YSoUbBoJMFEIQQmQYBwpd4P1IFHkJmlJitTnNCvRoUF6RNPSuR8jE4AaMSYzDBpUUQihC8gid0N4TUGhEXi+FERckQQw5u/VXbZIrJAjpEQqc8yidwEhIBoJicAMywTCOJJGxM8hEFz0pjJTVhKIqkcrgxhEpBD5F4tCipMm7KARSaaSAuthh7FqkkKTekfcFgqiyrjCkgCAgpEZdGYciI3iNvMqCpNFTmBLIfNoYhq+8LyUkzkeiiFdHevmVlMoolJk2fP8Pf8w3/rV7bDaa7f0D3KojrAKzg0N+0P4J3yjf4NUgeUOOpPTVCasgIpLOeA8FSijimPM1psyUOa9izqL5iL5aAMuMmMRKRfQBYqLQihjIJbu/dH3v8z9icwKqHJiJCafuEs2chQ9MbIFMcDb0VE4zlTO+Nw6snz3iaPcap6Jl+qHgP/izD/g3frPknW/dJ6kR4VYM519i7Q74n8MZXi+Rboky2+zc+iZFs40yGakzdgueP/wUkqPeu8HWwTsMzvOv/L1/n4c/+QBEw97hHcTRm8zPTujjmu78hP2DIxbhC3buvc/6xSfYepsUCtYvviCUP+Wnr55z7d67/OJv/H26i1M+/9H/Srh8xbjxlDs3uFyfcP3mPfau3+L42UsObt1HJElUgTG0IAoKY0jB01+eEXzi/PIxzfw99m/tUE6282KrX9GtHcJDqj3Lk6f56KjrWJwdoynYObpFOduhjx5rKo5u3+Pi9IynDz9ne7rHyr2ksDU33vhFnv30e+zdOODavbd49fwF7fEjVl++5Oabv8x6+QoVPOv1BWN7Rr09w/cDSlcECjAVQksGHxBuAOHy/XK1oZlsI0uBaDXJe5TKWsyyKui6lugDUXqef36CVQXaltgy8z2rsmFoN7h2hVue0LbnFNNdpFCo0mYQer8GHblx5ybnLz5jffkR/uyU9pkgmki5c4uyOmB+6wEXTz+jlIqiOuD0syccf/mScqLpLo4x1YxOtGxvH+Gdo/WBm/e/xsnxc5arcwpjWF9cUtc1+uqmKasapQqkV6hkIOUWtDQmm8eSp6obokoQNO2rZ0iVOHn0CIemtIah/4DDu3epZtsslxesN4Ek1mz6DU2dFwfSCGRRslVN8MNIrAtC1xG9Q8QKURRM5gfEomRn6xCBYlo1FJXi4Q++RzEpGbqWer6fsUMpgsoGqMpOcW5AIClkgSomTHYPmFVTVucLmq1tqsoQh46XT59kdeqo8eOaoXvFZGePKCLCJGwyOb7lPVVhsdoyuBFbSWJMFFYzDj1aSFL0uL5ns9mgVYGV+RjVTjTJR8ZVi3ABWeaJE97h3YLQDaiqoCi3YYxIHMPGo7XC+4AfY56ABUdQLcvliEolbRsxPpFMQIiU5RgCrIk4IkJrjJEgJrmgJUxuw+OvREG5OzHGS4RVjOsWLRXSQxIdSpeZRDJKhBC4OIDMx/Q+jcR+oKhKTFFSaMMoNUSfEe/BYYzGOZ+neFpgRUEIgbquMxqstLkI5Dw6KQKBJBPx6vnnR58JOX2gkBZleqZlw3m7oCkSP/jg93j73b+GLWp0MeP5o6fs7+8jpaTUDaMPDO3IpNxisj9j2KxZHJ+gD/ZRRUGIiaKZIoJnvbzECMnJy6d5MqcD5yfPuX/0q6R666qjIolopLIo7yGODO2aoq5zBpW8qBc2D37cGCgnBucdutkhiR5ZGMZxQE0O2dnbxnUd1R3Duze/xeMP/0P8qH5GKTq68R6m2ifpkieffUCUOTcdeuh1C4xZlavOMTLzq6umppMhTxT9leRDCqqtLaypcc6xaS+IKVBoi640yc0I7qocphTt5QXe9yhV0BuX892mpDD51CB6B2qk9Q5TFBAkbrmBK822ChnRuQnnCFuyWnZXJxQWqa+wZBi0qVFiREmNS54xZWurkJq2XednsipwXjIMI7bK6C+pBGiNEToTY4UgOE9pZllJjcAWOZ4pxcik3slZe5tQCIZhgKhxMoEfkUkQhcSNI0opVEh0fY5dphgZ44YowBiDMhNigKKwOD+QUCSfv7daK2TydN1AoS0yjGzaPi/e8aQoUEphdZmNtkIijcy/yWEkBoEREhHySX1hS1abVX5da9G2QBBREapSM1whw2QYCa7/qzjJjYxuhRXlz5zE0Y2kKBEqI73KcpqzsNKQ8EidIOUPve97rCnwoaUoa4TU+G6JsTXaZpuGpcL1G5LKGSoBqKgQV37oJAQoR4wdBkXM6pw8VRMRKQTBBaRWJBkQV5pfhUQAVnx1AeoDWccpMtc1I8VEZkuKxDhKjt78FC2/waTo2fQr0sLTho6t3V1uXbvB2jmacSTubv2Muft/v6RSuMFhrMILcMOQJ2hJIoPIXD0lESI3Z7XOaJr8fykQWiFIRCHx0WOVRcSvFs/++//qjyjDAz5++hn3dt7ii3FBPFlSFSUFFiErRJI83ZxyWM5ZlAKdBIvLM6qq4EfPVrw3tfzX//CMf2t6m1/9l96F0BNvfCPvaH9OEuPgnTfx7QatKi4XF/gkqJo5x8efcP32fW49eI8xQJTQ9yPbB9eIvmP/5nu0ixP++B/9J8wmJWV0VG/cpZA7+BCZHr2JEDPs4dssXjyjnOywdePrNLtzLi+W9OMFF49esz5+QVhc4scNs2v3mV1/j+l0i+XilE8++gN2dnZ4/MkjVKrYf+MNlovHhM8+ZkBQHtxjCC2SAVtWnHz6ASumJBRD0qRwQTm9Sd9tKIqCvj1GqAKxuUQ195DCc77pmEuDVJEv//zPePnJDynKhsGfE1dg9IST42ecvXxBjGvWZ4HF2TPiOBDbDrPdcH7yCYWckEKiaiZMdmpClDQFGFHiU8z2GxLIQFnk6bQg4OuEFIF+s6Sab7Ozs8ticYZKc0KKbE5e4cPAy5MFpqqZ790guZFVO6DLmm69ohu6HIuYbGf/eKGzQ0UKCAZtKm6//YB2s2Q4f0q/foGuptRb+/ThGYvFp2wuVzTHR1SlZmv3bTohmd19g8WLl7kQM1PsvP01Tj95zOgG3ODQIvDwkx+wNd/CJwfBogh06zPsbE4KibFz2OksZ9lRIASFkUQ/UNXbdMvXKFORekgqURYVarrN9YMjUtT4/jxbF61ms9mwuHxNNalRpqae5Va3EpKiqhH1hO39G+zuzNmcL1mevYK4xCXAa9rVGa8fPaOcVlhlWUqBd5KiqVB6xkzPGYMnk+8hCE/VWHw/QvT0w5pmtkWhJnSvLwjVQDOZ5+PJ3UPkXHFd1gyrU744fpLFOpOcnzVlgY+B2Hm87tj0a2TUOKkJkiyPQSKlp1AyT86UoLKGwmyRyI10ZGIcB0Jw1NESo8O1uUjmZLav2coSQ2Jcr6+mpIYkI2NICKUyJ52I0Iqm2mcI+WFtpyUxkheYfgB8RkCpAh+HfBo4OGwtr/TGHb1zQAlG4Lo1EoHQidA6Nu1AOSkpdIGQGbKvymzLS1FikmEMI0pJUrJXpabIet2SosKUGatmbab7uNAjlEWnjH6UxhAdBJURaXFwtKser8BLgy1yqzwIie87yrpGJoE3GiMV1facSTPD09O7Jfv7d3j40Y+4+06Bdy1f/vgjXkx2uH33Louup5kfsLrYcPrqBWgwQlFOJfO33qUbWopqhutW+L5ltTinaRpG11PUc9r1JdfufB3ns100xhGhJFo34D0pDCQzoZgZksgFTiEEstbEsUcaS6E0rrtE79wAXSOFQrgRXaTMCxceszVHNTVCCO7P/2OSX+NdFl2oos7yIimZ7m/x5Q9+j9RqZP+K88WfUuo9bP2Acn6IX6+QxnL2sgVbU2xtI02NtTVRQ3DgQiCESD2ZI1XMp6ohkbRAK4PWGj8OSJtLp8kNiOCY1RMG7xAELhfH+UEUE1ZcZW39QFHP6KPEEBEpEMYO3IDybT5Z1ZZxeEX0nkJXRC0YxGtSUPk01pS4saVuKpRVSKGQumToV1RFjfGezdmCuq5IMdEhsKYmyUCM+Zk/uh6hC2SMCCVIKaBUjk/m+KFAFmDKLFlAamSqkCRkAkxmCo8pYLRCqoTWOWKQwVGJ5LNKN/ieEHy+8SSFViqXxLRANbksq+QWs1pcWWUrYshq8RRyXCefrEh6PxBEDqKW1TSfDviEF4Z6Z5/kU5Z0hZB/Z0bm+4iPWKWJSSDV5K8eQkxIxWx2iHctCskQfW4tXmWZpCkRMhE9EEeScHAlHNPS5BJYGLC6ysDh5DFlhRs2CFnigif5AaUsSgiia5E6Q48t+QY/tstcykopFx/CmI9+yRQHpQTJBELqM4YnJJQIxAAqSbz6qnI3pYDrV/kHbipCys74fvQYUyBwhGDouwu+2CyZ7RxxNGko9YR4uWIqA9qCGEuC2UHprwoThNLoMi9UAgJpiqwmdjHn20TMWTAJLg4EOQDZCpSURIQRIeRVPs2QZMKq4iuv8/E/Lnj7b0juVQc8ffIJ17/5S6Sf/IReVRS25kUheVArFvUU33tuiIqX3SWqKdEusRIlX1685vp2xX/xH32X6802d7/TEJYDwkQQP+erKHapJlNOn31KSoHLy+d0pqTtEw8v1+zt7XH65GNCitz9xt9ic77CqMjTjz9h69ohv/Z3/x36xZqtwzdRShJix9BvWDx7wsXlMe35CsaezhusHHn87HN8t2SyU9MuB5rGMrv2Lm3n2QjF6ZeP6Lcm9DFw/91/kbEfWJ99F6cmnD855ea773OSSg72DmgvV6jNU+LgaYc1qmwQ3tGNHfObv8Bk/gtMt7Zo3YrkAnb6txmWp8QE050tuuMTYoTpzTeZzubc/eav8N3f/QecLL6g0VtQlCThs6o0DsQh0a89s2bO2ckTpEm0YUNTzxjWZ/T9yGS/Yew9m+Wasj6kLC0xjqyGNZXqUTGxWp0ihMKFgDQFIg0oIfFMWKyX9JdrSitgXFNPCpCW2c4ebb/BjwNYgzUN3eoyT5mq2RWQXFLXE1SCrlsTRse1Ww84PLrJ8xeP2ZrvsvAbmr17tMslsutIzlLWiclkxvLFAqG22USHrGomxS7F3QZhSobXT3n26KcYDPhIoQQIwWSyg4s9o+sJjMx2Zqi0Txh60A1KtrTrC0RQNNs7ueFeN1y7cx839EzmB7w+eUFMHVaCDwObV09pT18RxoSLIZuUyKWbyf4+aejpuzVKOdp+Ta01J6966tkOx48+Y2f3DvsH1zl9+QWHt+/TnZ5jqqzUne9mNFvvOiZlg6kkKmjazSnSFoQU6VYOqyFEh9k6QOmAlFOMloz9hq7rcn9hqFgtnlDaimeffIgtt9hcvqSeTCjqCiVrkojMK0s7tAihYKIoxZym5MogljI3XBlU8rggs6JUAy5PmSJ9Jl7EESMU6ICSgUDe9Kv4f51ioTWisFiZo1QiFcjCou2UNHqiFQhn0FJlKUWfKIQhxIGuX1Hqmm49IiWM6WoxLHIMIYRAaS0+CGIMWKupqi0AokoosYPrB5wbmO6WVNMOIzUtCgZPSoHYJaJJJCWJziG1xo09trKEPmGsoa5KuGrL16VBaMU4ekpTMvRrVFURfWJ0jhAdw8WG6XxOEFkSY1IgSkHoR3wQJKWxRZGb/johvSRJwfnJE14+7hBjR9HUjGFkZ/eA89fH9H2PqQTD6jX/5B9/ytbWNBv0hg1Hd+4zO7jNYn1CUzdsVi26yPi4xeUZmkBZTUAKlstz6rJiMtujaXavytSeIfbU010kgRgDspwhMsQYEd0VvnPM0oT+Ei23CCLS+w6zTmg9QvLo5EhCIPWEaCak5EBPIXaocoYbNaaUyBSzlruZIusas/ubzK/9An7ziFfPHmI+k9BGxvGCi5/8kCGtuXb0HqJXRKHpjxuijOhqDzPZw1Q1CVBVRXKBMYxIoTNT2miUMSgsCIFRAmUFOk6RIRHlSL8aSN6xtbNFDJLkWwgRLQViFGzckqJs6LuRiSkQUkNTZia2giQ92jRgMitbiHS10JSkIEHkLoyLjmEzYquacX2OFuBWS6RwBJVYr0uMqIhK0McW6TWmsAybERkEsupwAgaXmNQ1g1uihCYk0KogOA/KonSZB3neIVRBafKJQggmK9+FYBx95np7aPuO0liCFmgB45DQSLSR+JhwXZv12wSilz+b0g7eoW2BRdKKQPQeoxVCGmSMxCSpmpphGDDGZDa2y54EiEgSrgCERMSILQ2jc9SmZqLzQNOnnNX/K7fITSkx+o7gPElrlMoMWluWRHJgu/dZd2l0SUbWDiSRG3hCCCQyTy3JSr4w9FhVIlKOKCRG8BHv8kMpyYBIguQ7huBRf2EmCyFv/pMimfyfrGTOCIuUc3JCKBKOeGXISbpAjj+HfEBEKAkY+tChhSZJh0yGMOa24eql4uC9Lb4Tppx88hm/XK3ZuZUw25G/+3/8iG/deps37uxy715B9F99DZHIpIjkc4t6dGipstXNFoyuh5QLD0ZbEAHvAkFElI8kqUAlQgq4zmELk5uhf+mytxOxbbmQjmY+5Q9efMK7t3eYPF+yDC3fPJM894pbeo7zkco5tkpFGEb+sDH8uhesjed1WPP74pLHv/E7/N7pv00QF4jBYKqvfhXLaooblpRNQxgTdWkZU+DWresMZyecPv4R82t7XL78mMff/22EnjGZ3WZWd7jTn/LyFXgfWPUOqzT1bIeHn/4+cjOwf3APH14QqznXv/6ARk3pP/h93vz1f5UoStQYOH39lLKecL2qMOWUbvEalEWWJT/9+CMMgaLZoyprooLHH/45VT1luQk0s11iWOJkg1meEWPgzi/8FkGWDO4cqyoul2vc6hSKKWdP/ik7ezfYunWf/vSYxesnuAR9N3A5nTIsX7M92UFrQ3KK7YObtN0lYVhz9OAdzi9e8uD9r3HZtqhin9BfMplcp5jsMd3dzUg+qZAGIBI2x/z5H/xP7BzeZndyi+A2nDz/gkIIpAadNGUaCLai2N7Ht2vu3Po6T9IafI80Ba7v6DcddV0RXG7+Li9eI5Sm1A2lLWnXLVp7xDDi0oax72gOD/j2r/9tRDUleLh7f5fHj/8pRzff5PmnH7DZvEY5jaquU29t48YlN/d+kYTHBkGi58WXPySJ3Eyuptu0mxErNcXBDN+u2ZnucnJ6htQTZk1DO6zoh8j2zhZVM6Nd9lT1NbrNCeuxJ+kcmfJuw+p8RXd6zKZbM5lv4YY8mfYxouOI7/NxpVQ10ta88d77fPHZBzTNhMvlOeNyjZ1OqaWh2wzMmm3GvqcpQHTPePLxlyih+Pz736UqNN63SKVRusGLnOPv2w0iOsbQ58mZkwy9o5nNOTi8ja0sm7XDltdpFy9YXb7G6BKjK4YxsmJNGgaGeEZZz3BDR90U9P0avCOpbNI7OXnJ/GAPMKTkCXFA24JhyGVWJSwxRELUV5gh6LusSxfJ5IhXihnJFDUhDhhbZUuSbtBJoGPCxwSFQmmNdw67LUkuMfoBtxkzcSE6jK0JPmFDoncrYgHCWCqpSUIjbQkiYvWM4MarUpdCF4rReYSGorJ4N2byiVbIqPOgQWdSQN9HtCnpxhFtClRh89BEiBwj8Dk6p0UiKIVCkWyRAf7JE0RmkQsh0ASsUMSY/y3fOTAq522VRTcFg4tUtgQDUuR7rWkMw+CQIhFDBKFIMfOFg9swrSqSc8hqG2sKhFBcrFZszQ2b5Stwnqk26BsNUmYTVGrg4aMPST/5kP3rdzk8uM//9jv/Aze+/iZlNWHncJvLyxX3HrxL8B33HryP0IrZ9j7takVZVujSUBQNboi0iyVlqXPhuWqunjcRlpdEvyTqGVaXDP0GiaRu5rjBE11PiD16ax+RDMkPCNfmxaBfE0MPgK6mJDcQXY9WBT7kDo5IoJpriPI6R9Nvc/edDX/0X/676GKKkkt0fMr5uWY2uUnZvIeUewhtKKstumFEeUXUBnzAuR6pBBJFlCLnVceEF+PPJvWh9whrQSd8skyvTQn9iMEQSaQ4w40tSltSGNgio0tTofBeIJVk7Dq0KtCTHAlROl5ltHMMLyNRK2IQCCUJos9c5CRIo2drvgUhEhFMqhLnJUFKvO+RKpHGFmMz2k0bRVQ93juGkABJ1y5JaIg5V+5dRBmJcBskBiElY8rihc2qz9z+FAlXKFZCgpClMkaXeJ8nrEE4lFAIKenHkcqWJHs1WHQCKQVjDKSQpSuu6+lNQaEUUWRMm5Eq3zuv0GsGmafJISG1xvusAoaEDAGtFEnnU2iRcpHPuwFbVBQqYweF+Gq88f/p+udikSvIKAtTFkTnEMIijSDFnI0tqjq3HBkJuLwgFYJC6qsfhobgCYkMPfYhg5VT1iZqK4hCc3l2ihaGSb2Fj54kBEIKbBL0XY+pirzzTOTcm8u4mYT/2e5DRLLmN+WTw03fY6sS/XN2FiHk9xLxiKtChUgRQSAmw9hL3vkFzz/87X/Ef/qg5/A7NyiLA0SIXBTHbNtAefaMvd2Kb731rxPTz9u9RM5OL9jZ30K5keADqJGuWxJDw6TeYojjlf0MQkxokycf2W4zQkyYymLsVYP050xVP7lYcf32GzTR4iYB8f0PePCdd/gns3PeOIdXYs2F2aZYntEngzAlhS25aJf8tYuWeGTYtRPOK/ibnz1l/cYXvPzJM/bf2waRM29s/bOvqQtJvx4o7Ay7c4BPnvNH38M/X2Gaimo2xzQ32X/7iKG/oJjsoVWNdxvW7QksnmLlNtuTbdabC7Qt+fav/T2E9Gw259zQfwOfBKtXL0lTSf3W11gcn3H41jc5e/JTNufnaK256FuabUssdsCtWTz/kqPbdxkX56zPVyjRMq1vUF6z9K1j1sxAaUL5BkK2FLKk3hVcnD6lX/WU23vo3YL1xTl+cwFFYv/OfZ785Iekz37M1t5t8IrN2SmxhXllWZy9QIRIuXcXv1lwevoakdaUZcmXH/453eaYpx9+l+35TZIUCBlZnzyjcz3N9AjpDWPokGqfe9/4ZXaOHvDX/87f5/lPP2J9+jlF2fDGN79NtXtAGC/RSTPdv08cHE8//YxVeM3zx59T1hWrVct2PeXBO7/Ej//4f+T15x/TNA29M0grM8OxmJG0ZDKbEGOk7daU1YSt6TY799/HV7v45TlGGXo/cnjj6xz/9HvI6Tb7d2/SVEeoMfDJn/w2YtCs7JKdB++gdGD1+hjb7ECSKN/iL15y/fAQLRva1TlySLw4fU4SkvF8wcCS/f0bpHbD5eYhyS3Q9W02mxU+riBplsN51kUS2Zy/xGpFbSrwESPgcnGODh2rdpWh+rJiultz9vjHnL76OPODz09pbEk5nYEU+cEWI74fkUJyuVgzmyqMqhmip5nlI+MkDagqnyglR1UVgCT1UE8K+m6D0pbJfI+u95w9f03fr7G2ZpTPmE5mKDXLXQVhGccVttAIU1BPpgQXMVaxGUaqSU00uUXuvWf/6IgYyPgkkUsz/ThihCX4EZcyHsyaihQghEhhMu0mhIAQkTg6bDVB+ISQ20SVkCIr2rUxhBAodH5YRd+TTceeqQAAIABJREFUsp+VmAKTehtRZFVqvp8P6JAIOGyV0UVSFaQ45myf1PirkrJPA1XRgM+L5b8YPowuIHyk1BVBkp8lSWYLlMqDiigLIE9wQ0wEctFIKUkYW6QyjP2Q1b1X+LIxRJTJpVA5RpLNCmQtBeMQKHWN1IJ+GNEyXuUfBSRYbpZZ2Sqz1n09ZHPd4EesrQgxCyWEVphiQhSCotn+2UllCI5mWjMC0lhkUiB9jtYJCTrrYJuZzgvo5SWf/tnvMg6XXLyCveu3UOxz4+4D1uslUkR0OaGqtxjHnqKcIGwBUuRnYFyhZAAsaEVqLxBKEr0nEhC2zqeu6xbne1JsESjKre0sk5jtkZIhtKcgErpo8vNZKKRW4GOOCAiFLGqiKrApElP+7ggCYX2ONgXLjUc2CTudExiY6wf0qWO9voD+++ALVCFphxaEYVpriuIudu8OZdGAzPnWlMC5gapuGHzA6gpERNsCpdQVn7qi6zuMNHiXZSlRJVRpM0JLVVhpEAzUWxVj8MjoMGGCljlGoK3OPY2YN0NKXX2GyRGlQKmEElVW16ZEKi2j85RCE2NidbEkjj2Db8Hqq2iFY1RniKQJMTF0HnQkEBFCU9kCqS2FEAxdj5CWlARBSHRZM7pNzgD7ChlgTAEfAtqVGKPwYWAcUo769HkzEK+Y1oW0pEKREowpCzEwiiRSjjf6gSGMhCFdsbrHPACU+U9GnEXcMF4tZvNgEyXxV7xppCAFf6X0zn/HmBnIpERZVTjncN5jpf6rp/WFREqKGLNwISaFcD3WZvatDzk2EKOEcSAmkFowpJg5tSLhE0gRcpO4yABmIsgruLNSgt3d3YwJXK9pu0S4WGC2LGVpKcs6I2ZknmIIAdIUBJd3FqXN6BohAKExClzsKcsaYiBXCP7ZS5JyqUMaiJ6kQISANhXDKJGq4O+8901+8/3nXP7ZU6RskFIz6IRu1/xN9UMebE24c+tfIAlL/DmWDxkT0yI/gITS6CtsiCnqXORrL3GrDSw7nAJ5sIsuK4IfUMJS6szwja7PuWSZMg/4L12r1XOefLnN+voOUlTce+dbXPzwU771tVs82XyBdD0X1mOHbZrRYZNArwO6nBDCgB9GilBwNG04kSPP9BnrE89eFIQA1nx1Aa90hVCWcu8ai5Nn6MLyxi/9Fma6R7x4xsXz55ksUVlEmnH69Cnzgx384hlxdcKyvUAVinvXrlOsay5efsH5i0h7+ZLNsy9IXtBMFdI28OCX2N19G20bzp69YjK9jrlXcfzwIWYyYdmdsjp7waQKpMJy/uVLIqs80YwFL55+hEmC9uIZ3XHN5fop5dY1Nh3c+8bfYuvogDQIvEvMducsX7/irfd/FTOrsOUeYVxjjaJbtjx+9APu33+f3h0z+qesjztWpydsX79De/kKEQckMQPgTy9omm1K9jBFnycXpkKpCT60NMWU7vKEyWwfLQSje8LDP33B/u13mR9dZ//edzh6+9dRShAZWF+eE5wkCsWjjz5jtjWnnJQM3YztZpIzuuvXtG3Pjz/8s8z43L9F1Iqm3keErLqOwTBsFjnDVU4oypoYOsTWLi9PztH1IwoMm/6SoBOHt97m4K1vM5ns5kaxgYuzVxzdf0BK28z3DoiiZjMsEaFh9fILhJpQVkeMpWaMltFJQjWlKqcU00jXXqKFovGK4D1DCDR1iRMzhn4JtkbpknG9zvimGJjtHbI8P+Pi8hgjNcmNaC3BK5QtOTzaIUmRAe9uzc5hQ5SJKOZom2j7xPz6bXRVEzeXsF6guo7VaoOUcH5ywnS+jyo0F8sLrNbEOCJtlafRVcZt9W2PlvnBUtc7eeoiJOW0QiuBbfaQCqoo8D4ibX4IBO9RVZFLtcHTbXog4lzKJ1zCkxz40CMLhfMCRk3nLlFaIK0hkXsOWkLZTLOkwQek1bg+Z26FVBgZcD5QFgX/J3XvEnNbmp93/d77Wmtfvtu51+mqU1VdXdXXpN2+pnNxbJRYiQLBEyKiSAiEBBIMCGTGFDFAQiIMYYIQESZIVhBKZIzSig2ObdxO3O2u7urq6lNV59Spc/nue++11ntn8G63ZFcRmQHI2bMzOev79rf3Wv/3+T/P70mxAeaVlhTvQbawa4qeiiTOrWI2+NiCQiUjZYEUWmmGMAga9sg6SWbfXd91FF/JOHLyzPM52khqtIAg+gmtOjo3tKExb1pVa2nKqJKJIgXTNO0HmYZzEjHv17QBJZodrSTIPlFTpsQR5Qy5JqxZtACh7ZqPWCjUSjGNEescTmoUnpCmthlLBbdcE8qMT54wjtjlgl659rCPlX61aA93X9FKYYWmFkVJHvb+cCVby1OujU2KFnSmhb6QAvANxW8cxq3a2ldmLj/+kJsnLzEJWJXIdnzBm/d/lsOTW1QpWA4aYdoWJMwjIGGwjc1MIk4TcZ6IyaO0QRWJsA2PVckIoSg1k6drpnFLnE5ZrG9hugVWdmQHYXOB6lbNTjReUKXD9N1ebVQtQFgmxBxa8cb2AlyHLJmcrxHBU8UO70dWJ3e5+bmf5vDVH+N3f+nf4caX/ybv/+7/znr9OebLJ2irODp6wKJITChod4dN+B7XH75N199G2RXoNcoNJDkQq0FqxRh887OS6bVFWoh+bn9f2SE7ICcSElSr1IYWjteuI0XfPmtC4XoDVdKZDinbEJdTauSRklpDoLAoCjmFhvcqlRIyqRYUgik00kmtpVVOK0s/rIlpIovWDaCVa1zzhUCWjNCGVA3a0H4WrViKY5TeH5RrpiIpckHOCSM0JQSsco3/nwO6N8jJUJRHCEF1zXsrtGvBxFKxVZJFIabMtBtJUkJJ7OqW9WJoITbkXtgTKCFbyYx0hLhr22YEOecfDa4lJYooaNuqmUutDb2mNCUVUK2hrrYOvFZcU0SzXvzLFjwDQY2VKiSpQKk7dBX4aUsssdEOcsWoxox0pqP4grCFGFtfuJEGkUMLlqWAyAmUQ5aAKq3/O9f2gdIDHDtDPT6khnZiiDkghKYW3zwfudkShBKEMGNla0wpBQbdhpXGimyJySo/6ZfNwqBzhxCZLDNW9AhryAi0WbRh3iyovEH3+t9HhRao64yhiJ6DUqg6Mbx6F5kKSn9y+CzKwQJEkQhZ0H3Dphhf8HUiiog5WGMOblC0JqYdOXqscZRYiLHZQBAgSVBorMM/8lri6BcdQRXWVeF95l3/HvfjW/ReM/sNy2eS1J2R9QkfXl7xp/VL7HJllyJ5J6ndzGJaMSrPLu74b/+j3+G/+M4vEmuipk9aJPzlc5SS7C6esrrxGYo0hMsNV0+eMm6es1ofUuPE5vwJB3cfcPezDxAxMs2Gwd7HDktOn+/49v/299rKZ9oQ05YwPaW/+Tphd4U9fourZy+QH7/L8/ffQeuBHEYuDk8I509J2+dMe6KAcktCWXNy+x7aa7qDt7iedoxn59y895OcvPoy1q158cE79C8es7x5j/Xtl4F2AJMqtYet7OmO7qOsYT73pP6Mi2ePoTqkHLl1eESm4o5usjIDPnneeumrnD97gTGOyxfPsF2HFB120Ix+RCqHEosWTiyVEgNGG+I8o2RmN49QEs4K1q+8glSnfPDOu6RvO4igasZjqUqwPnBcvXjB3Ts3+PCjd6gqI/0V108bkNt0jmzaoBPVArFakf2GEgpW7hPyeUtFcXCw4HqcMRRCCpydfshX//y/zumH7/Pe09/gxt0vc+flt9hcbugWS559/yHnLx5juyXLIzi4/RYfvftNHn//d9EHr1HCGSdHB6TdI6RwXO/eI3DUEIFpwfLkDmFqVdeiZnSJ9Dc+j7ED2+vH7MYN6/UxTIG5Gqow2IXaQ/9HNs8fkZJnWCyRSlD3hSm4JblIfGzVnXMMVGGoytEZGEdFLa33/fnD75Brh5NQakAhMM5Rc0R0htlf0YmBzrbbr7UdWgn6xZKMZ7cZUUow+i0LeYNMQMj2MyolKLlZtErOICUyQ3+wYrO5JlbfBpGqEUWTxEzNTaGKPlBixNqCrJk4Fnbec+PwLku3BpGoSKS2YBMpJXyaMHqBQjfrltHUrBE1IJVCq0j2vmUmpKH4gEieKhLatOCMkVCsJFVF37VaUSiInInzDmMM8+6SLDMxWorRDW9kLPFqh6gS1VlEqSxWA1UYZJYURfv7KIgxMo+BImak0VA1MicEM8LofW5jRqc22Dq7QJtMSrtmM9il9ihNkiTboC2oe3RYwtqBqiUxt+KfeQwoaxAochIYs7doGN0wkSFSlMBKgx4WlD1lRwpNZwSb3Qa3v5+LClJWhJQI1ZNKJKcKtl27ClCiIIqheIE2miIqoRRsP5DmvTpeK+dnH1HJnI9XLE5OiNeFxXDA0x8+5MXTJzx4/XUOj2+1lfmcMG6ghEYKKKpQxh0xJLrlmsH21BgI06bhuXxog5Vq7VQhRqSEzbTj9uv38Ltrxt0VWULXL1pltOgZ1h21FkiALggKpAQYqkmUeUYUT54DJRcEiatnH1BKwRlDGJZ89mf+PZCOX/gPvkUsHhE0YZl4/Y2v4/OaTq+Qq9sYIfit/+U/w/geYSPaSWLYsV7fIyWPlIkYr1C1R4uMUAOy6lZcEDJaJUAiasBoS4qFIgshJHrryCVibU/NCWEsqkAVigYkEmhTCeOM6RakUvA1Y4REO8U4jmin0Uoiwp7Jb3UjVKSMW+4RZoCumYWxzUrlLP0CUtVNsdeavipUziQR6XUL16fYDshS5FZSISohS1xnKUky2AWeirIdMgJS4tyAL56qBWahMMlQEq1OmhZyazF9QZVQdaKaitUOUsaU3A7W0rQw8d7CkHNum5lp27Yj+6prgNFvkUIzWMecdszTJdocUFOEziJKatfMmaxk2yhkSUoRKTVBtLbCP+7rT8aQWws5bAhl73PSilHMdKnjaguDm/cQ4gYjn2sGNKYYas2M04Qsrdu6iEypiWW/xOd9yEoKyIGq9qdI0XxGOUmqqMQU6d3AOG9aA5WPLZjmdwghMcZBDrj9zfR6s0MUg3OuRTlTQuhPedOFQapMVo5qV2S3IOgDjF02KV8bcq0oLThY/jxP/tGvcP+exthb7ea17tDmBub+bfIftDv8kVei9W6jJWnfCy2LRDqJjh3SLKAU5uSxWqOKafw/2QJ2tjPt5itFQ6LI7lMpDlsg1oB67hEmcfjyA67mD8jvnTK9fBN5LrlVIV5O2JVnZxQfMnJChz7ocbuC1Y7L6HnrpS/x/Rff4P84/iHv/t7Igy9qiJ/83Ux3QA0butURc5gYbOH0/B1MbYGoEjfobsHq8HOUkFitb3N19ZST1/8s1Mj7b3+DpXX4dErcXVDDTO0XvP61v0FKI9un32M8f8zhrbuYXhGnEaLk8M4dfIDu9n3SzSPC1FB0wi4Ri9scrE5Y3niVThv8o8esVydMu0u++xu/xtHtlxC95cZrr+Psgqv3f8B2nJl3M5ur55R5h9SW1Y27bKctX/ypv0i8uiSlDdenp5Q0sjl7ryXUb73E8a0HxDHw0du/Thk9U46tajU2JnIGrOyIUtMd3eHBgy9x9PI9nj97yPzsMdN4jVACd3yDq49/SNi2zvHJb5F1JOQrnErYYaBcVm698Xkev/ce3dpxfv0u1StydsSS6YYerEO7JZUW/jRmRa2NOqIwJL9F1MYoNUawuTrj6uoMpxXd+hgpLf/s1/4Bpk7c/9zPsL0+5e3f/h1WR/cYz9/h9MU3EESq/wyroxNifAHyADm+R3jxDnSK7zz+PjV8jwP3c8jumO7khOot1sF8+YhpmkAqusUaURVlu2OUGwoFZ1qrnBCyqY4Uqu6RymK0afxZUZBVojDksmDaXrC8c8zQnVC2G85fPCaFGaVh9DOYBTXsqB6EnihToXNzC3a4AS0N4zxhrcUtVqQ8IZQjldqGFuuYc0E5B74VDIScWAwH5BwIMbJYrBA5k1KkYJs3lUJNAqUK47hrJIRaGVSPKAa7VETZkWpCFU3VDiULpUik0YgQOFxoUi4kP2JN41LGOFOqQLiMFR1SCEKcGscyR4SshByh+uarE4YYEkJkyCMGjewMtYRWvV4zNUv04PDRQ60k71ksFijhqDLTHa3JKJQPSGXp0cw5UGSBNCNCJFuDToJYA7ZoKi2MXGh1wMooZGk4w1ybLUtVQ9ISpQpsM5NvNIlSZ8L1SJGJQMFKh+pMO+xkQ8oTUmmcgJoLoQRkdmQfkLqirCGUGVUkUg6UBNO4ocaA0AvcsqPGhHUKnCFXTQ6Z7DcIqeiMxSiN0K01C+0QMVKlQSuHUolEbf7ivIMAzkAVmlQUPu0oZNImM/sJYxIazdHREUImrse55VJWA/3ikO9/7x/zlZ/4BUTVbHfXDG5gs71gOayIIaHJrYiptAKOFD2qtgFGiBbAy2luCloqTH5kffwS588ecXzrAZsXz8h79nZvDpG6lTXUWqkpkWLAdLSWt3kipf0WVDuU6Zk3jVeuDeQquHj6NmHzESLDsnyNG3d/HGMP29BfLW/+/N9u/umaMdPM9vSHzGfPUP2az//U38L754Qx8NFH30VMT7h8/i3CtEOpylgqi/UR/cGrbN5/RBAD1q6JKNywoFuvUdrgdyNFCyiFtz7/RS4uz7l8cUYqGXKhSkApQogYK/eFGxk9DPgpNOVVV0ptM0nf9ySRyUiGRRPUrOgZmRv7uhaU7FFGtXDj/j0qABnMHlVZiyDWjLYSkRRhzhhr0b1C5kZakELt203bc9WourdW5L0PNxPiiBaSFCI1B/xFYNrNzNtLMDuKnBGhB7HgcHkb2bUDaAWUG9p7UBJCKHqTuZ4ii9UARWJ0QVSaatvSSdSa0UY2VvUUSSmhqsV2jhQrxnYgZLtfITCybS+gtIpmJalSIz5lo/0vev2JGHJrBWdb5S1khDSI0nN6/k2cvYWRL2G1JtWA7QXJz4haCH5LiFuMNGQp8KlgZYWS2G4uqMhm/BaZUAqmXyJS8135MGGtZJ7KHkM2YpQlxxkjDSU1a4JUkpIa+676hBCNF6lpHelZKUQu5E9RIq1ZEFevgnOt+lcrbFEgGmg8J5C0QdsvX2Gnfp04vka1ATXMlOBh16EOGgfx07zWWkiyqEgh9p60va+mNIVAV9GS8lJS8ozUHSFPlFjaQF8Skr21o7bVTSmfXAXcv/9NyvkruBuWi/k56luPeP78HW4cHePMfa6V4jQHHixWTHHGCtOQJ7oSdhGfE1M6ZZhuUO4OuLufYRwe8V/+2/+Ev/ubfwkfn33ims8evs3h7fsop0gXT3j30Qd85sFd3OFdUgqkVEhZcXTwCpdPf0CthcXygN35Kco6Xnn1Kzz2b3PxwUPuf/XPoOIVXt7CpwPYvqDXPdoGwukHBNFTEaT6jIurd8lZNApGNxBlR5GOG+vA5XtPKC+/jDk45Gp8zri54Hzj6a0gXp1xuj2HbDhVsDxYA4XFcs12+xRrNkgnsVYQp4/pZeZ3/9F/T8ozShcODu+AGji5/zUutxccLI751m/+RltvdzfQGqzVzNMlhzcfEEtoXnEh2W52ZD/zve/9c9z738MJherWVBZYOZBmx83XPsvZO79FmiNX28TRnTew/YYqK9Nmx8HNEza7yJ1X3+Tj938fWyWpekrNSDcw+pESIPgJhWAer+mEZBxPEc6g7RF6fYComqIrConqjrhxcAdfZqw2lDlgTEcdHU++/wPcSrD7+LtcPUlYc0XMl2gxIM17jNv3EUrip4+5s3qNYLbUQXIsHrBwfwXhT9lsE4xbhu6YWiVKQVDnzQ/nI1MKhLxFsoR4xsX1x9y891VOn5/SH9xksVxjD1bML86o2rSht1astsQCznSsDu7hLzbEct22RBYGtySTOZIL5imipUcvbyOtoD/ukdrg00QVUJPgYH2zeRILyNwhpWzquahEJHaxphQwQ0u5mxTxYUI5iyXj47x/YChK2FHSTMkBaQZyLkgFWWSkUYTpkraXceRCG2gbkZWqLCEWOmfoO9sUQiuYZ4HSkpzb1jqVka44ioB5HltbXslIo9E1YWphd7Vl6JeEHBjn5zg9QPJUYVF+3hMQZONah0I+f0ZV4Ud5iWkcGjasxFZURYcQiiQzGwpGSFSRmK4jhkSNia3fIWrbiHSqNvh8lYjuEKkcfb8gjlf4aUswpuHPSkUazdAtUEKiXIefdpi+2bYqljkX2HqEqyQUg16S/ExWBiVyE0qkZFj2+HmLlAYnant860ROFad7/LhjuTRobclWU6SiBI8sEWcWCNNW5XFfmZpjQqhC8tsf/btWR/IzWhSMFEgjSB7iNjKlF2gJVQtEkGSTsVIic2JKW+RcqEXRDwO7q4+58dnXuH/3i3zlJ38WxJJu2bX3JiWW62NyjCwODve1yonJjwzDsllDtluk0TTKfMPsRb9rB/D1EX68Yn14QJZQ5rmxj6UglEJXKilFhNJ7/JzcZ2wkytl95XxBpkiRDnd4G787ZbM5Y93fZfvim+T0ArW6ye3uDiJDHC9w3ZrN5QtimDFdT01tzS2kRstrwuPf5jzMdOu7KHXEmz/x1zFIdN5xcf5BC4p7wXe/+39ydOMWKhU8BSk7VL9E4Ei7mcvnTyjHN9BTh1saPnz4gxZyrBmjHEUrlGw8fudcO3yWikQw+QnnHEIUvPd7MQxiiggrIVWqBaoipBm9t8vU0rIUKRRiTmhtiXPEaoNSTa2FjJTNWlNSJZdGcKhSIVJk9gWZUkNwqYxudzRiyCAndJWo0hOhZYm6BdLuC6KEZnEIntgOijFhjUQCFUveh+5TnjBaUpSkVtNaY4tgaSwSgfcR1bXfrdaKqBLtLD7sKMXsh/9mpcgpNWSf7RD7w6lSzfxZpggp77MCldFHnFPUVPfvxR/v9SdiyKUWpt2GqiTWNr5irpX18ZdbF5coxDRSpKGG1qVcS0AgGucQQ913RlfKfsVY974QhfeBfrUmp0yJhRwjooL3Hq06SiptTZf+gHFXqFSUbtxZSTNFN+i/JsdIFr7VWWLINSL1J7FbeXELaSxVK2pu3DmsbA0qPpKVbpXGtSCi4uStX2T8wTkH62PqdAttM3V1jzKAHSUhf5JfW0pCW0MeA1IJhKqtzre01WOmUKTAaNfahmRjSgoFafIoq9otrBZ8yQzKNZXuj7z+yi9+ne//auUyBmz0GAMvH674zvU/5ccu/g0O0gG/znNSCXxmtWTyicgVKWu2YWboFlyfnXN08y63bc/P/Nhf5Bu//D5v336H5+/+LDdeX3zimrfe+BK5BubrU4b1AV/4mZ9DLZZUPSDKxHx2hR16puunCCE4+/ghlZmFtJy+/R1y13P82p/m4DNfbO/V/BRCZvfO30OnHbpz+MsnIHuUvY2WXfNbxokUdjjhqGlHrR22Cq7mJ3gkVx88JSeBLJLYLbAicXX6nOpWiKpBTOSoeX52jcySJ48f0ncWH2dcB9en55RqsesDupVB1AXdcklMiThtiNNMqYof/t4jjl86Zlje5tbJLb7/z76BiolcApdPX1BKwhyvyBjuvPwazx++Q5y3CLWg9j3T2XsUmZFFYWvHs3CK0Ibp/IKjVz7P7nLD0fEa+jUvvXHC4299i3D+lOePf8gULzG6YI96UAPKODpjkb1F7D1TJye3CH7k7uGb5JwI80iJbTWlVGvUi2V/Ys+SGrf4UNoNXTS29eX5M9ZHn2MaH1OqYd0t0bLgMyip6fqbrFaOKh0ij6iSMN2CafsQGSfUXi3y+YK4GwnlHGc6apIgJUu32LeHebS7xclRR8mKo9srlLak8Qm7zWOW3W2kURQt9zaApkptpy1W6bai1BIpYksjV9UQhwKs0xAKRUxoYcjZk3UhU9EZhJ+Zr84ooqX7pdVUKTCiMgwHKGXwObVGrtIA8loZXG4d8eN01cKzpRW65BwpKZLizFKqNkiXgtYFoqSi6O2CXCSQG44rBKxbULOg6zpEDuQ5IKwhpNxKY/6gAEcJtOqBCjXh+p44JxAVpR0peWr0GKNJYUJRWPYrRHEgFSlEfEgc3r5JDoVxu0F3jr7rEHkmxkpBtfuZ35L8poWHR6hDwxnGkCl1ZugPuQoTpSTWhyvqHLDDIUIKsirYYd3A+6YnRk9NvrUUdgsQ+8/ankqTREY41xLkyyUlFqReUlVrjBQ5IYpH1FYl6rQjFFAyk0pEFEmIiRwiMu4oecJpx7i7prOO6eI5Q2fZbX5IvDIo3SFdj5IdRhhi3jCHLUoZqBlne6pIWLMEt8KHCa1b6UE0TYnz846yvaYkQa4JpRse6mraIFEYXxFCkgQIVencmrlcc/vuZxnjHT73xp8hpMqjj6/Ynn2P4XDNa5/7PJ3rqLUyThuMdqAgeI8A/Dzio8ehydEzx8jq6Jh4teWD7/4Wb/7Ez2FXN5g3l5iuR4pCtgd430grJbdAs0KAFkhjIVVKykhTqT6Rc0TJvn3WSVATwg70tqdIuPPWX2B16/Ok7OiGA+IUyGUihcjVxft0wwHj5UULlolCLRGZ5naonR4hQiXN73PrtS8Rrp5ydfYEbSR2dRex7PjSV444euUr/P6j/4bNi/eQtmPMhuPjB8i8bKirecYu1xghuHl4zOPHjxFda1GVVZBCa0zNxOYjbZwPXOfIMWB0q4cOKaJyRSnbckdCtiFdSHSmNQDm0OaMWslV4KxmHK8xxpFTICcQcQdKMoXUhI3tiFQWRCHFGWQlCxhch+o6Yk3IbolC0i0MSu25q8WBbAOlFAJpDNU3lVQKwUK2YHpWihwbC7iUgo8BaZpCO8ex3QO1RHtFyQprJClHlJJEHxCyIosgVyghIzGUHBsxK2WKrBijoEp222uMduQwNquZEBjVIUUDCUQqi27ZxItPoT/9i15/MoZcIVC2qSh+ntBWtRWsjlRfW5EBhThtqdKCSZQUKUWgpKPkiZLayaAEj0FTVSLIhKoO1y3JPhBSxAjIf5jdAAAgAElEQVSDD9ctZKEsMSRK3KFkJZdIDRZlDKPfoYpCk5HKUIpAqNwSixJUmMnOtHYPXenkJ2XW9lO3kxV137wW2xc8VjBSNp+Qa14f+dKfQn7vV1msWiJRLQQHh19CV0UQEfMpnlxZ2gnZGEMikots3jKpoOTGsKTgS0CbjhgjQrS++awKsmpkzq3HXMiGWvsU50UfO4ysrKVhHhNPN5c8fv11bo2PuD77mJsnt/l5dZOraSSkiiGxzJaYPV317YupYXf+Qz7gDrc/8zKh/Hc8On/ML/3nn+dv/89/7RPX1MYgsmR5eBs59JAl5ALE5skWAkxt3MNugTQKJQ/YnX1Irdecvvddrp68w623fpzV8R38dIp/9l389Jzh5hdYr07w04zuD7F6zbg7pYSwZ2RWrq7P0PoQzK6duGWHjIUsDSk7OLiFMZlUgOUREk1nO2Zf0FahhGGeZw66NSFvObpxQhh3pDDh1JoaKtt8Qb844OLsmjyPLIZjkIUUR3rnCR9XZLfjO+98k9lvUCKipQHRQhPlutAvFjx/9weImjFmhe0Kfr4EckvC0jy32h4Q4sjieMX28mOUMpyn+7z85Vfxm8rJg5d58fCSw5cWuOhA9qC6fdo7ooWj5kyeA5gesz7h3u3P8+TJU27evsnDb/9f3Ln3Gi+efYAykGKCWqhxg1KK3W5ktTpgqonrj36HUgdWN15mChuKVBBACQt9x0BFBkHabShqwzRHhJFtYFAZZV9C9hU5enwckT4hTaazS2JRuPUddFUYGcmm9d0zJab5XYx7sVfAHXJ9E9dZhMvksKNMHumWCG3JKdINlhpbWY3Ijjn6PcJGNwC77qilhU6V6YmlUDCI0OpgrZHQH6DSmlJSG+Zo3++aPdfnL1gsVuziFSKD8AFobNmbN+8y+x2r1Yp59kjrkEqzWEhS8MxRMU9nlBroljeRRQISLR3jvCPWQGcss59xRjHvAsIHFB6pMglFjh1FKpJpLV3NzqAwVkKSCNP8hkPfNU9gLuhOUl1Hlg3xk72ntx1+ukYqA7rSHXXMOeLc0JrTytxwWaV5SlPOaGsgDSzWC3LSiEPDHC7oVIesklxmhF6gc+N7Ku3obCAbiV3IlvKvraEsk7HdGmhDgzaKGjM1hraNKZEqCkiByppYMsppcmhIpxJaAUBrgazNPtVZdKYFY7Um+h0yK6wcQG8pJTKPGyiVUDLrm7eIacbVDuckebuhZE/xiSB7lDE406H6HlErftriBosPW2qWVN2hdAvYCSvJVaDtAn3jhLq5Znt9TSgbDo/uwU5TSlMzlVD4UFiYnpIl66Mjrq63qGz44Ltvc7E7Y7G03L59H7q+oTkLlBwZpytsZxBJEMaZjEdEw8WLDzHOIp2mH47JMZGK5K0//9dQRXF9eY4oGWUNUncIEZFKcPn8Keu7ryHZtaG2JioKqdrzr5JIvqnofrclTxPbkn7U/mV1BWU4eeXnmecR1yuUXaBMYbzYoU3h6MZ9SIk5QUw7SpxRNTGPDRt6ePdPUQ7vEovDKMn5s3epOmCHm5QcWJ68hF3d5PLZGV/4hX+L7//m/8rTH/59SoKr7cSiu41Z3+TH3/w6m/mK549e8O53vsnLb36Fi6tzgt9So+Toxi02uwmnNBMBLVqZBjkRc0QriZSNQiKto8TUqtRTRKrK5FuL3jhusdrgy9hUchxTTfRSshk3ONeR5plFZwi5YPqWGRpurZDCIWsFdUCVASUkGUMKES2b8FZjIvhGdlICkowYqZhjQBKpQlNqotRKyWEvEDqoEi0lUUqklnSmx6DJslCIaFrRQ5UK00tqKFRXySFTtCTNEaUUKUZy3KE60yp/5x3ZB4pTZLfEiIq0ihgmqB6ne3a7LUFu6VRHlAWRKjlWdvMlw7CET2ll/X96/YkYciuAaPWySnbUkIDY2mlqamidMCNVhxaBaZzaqVsoqIlaazsh5gYujwVKLA3XojzjdiKFCduv8XGDlIKYEmJMjWNbC2mekdYikYSYcXYgp4gsmpRacKymliw1tVD27WVCtsCc99Mnfq/oA1qVBk4uzXCv0ESazyTHiNIGEQsUSXb36V+9y26asOsbPHjwBbrwlHWOXBlLCJ+8RsqhPRCUbJWSRrc2MyGQVVFzQYuW+E219dkLmsVCa40oqnkgU/sgy6IQn7IJ+No9x3e8YtkbHtaJeiKwm4kXeK7EBY9rx6tC44YOERNWr/d2icxzJqoyrFMPfsPTJ1vCOPFXf/Jv8Su/8g/4h7/82/xr//hN+Ff+8DWnzTXGKkqKXD97Sn94RJWOOO0QWrBcHDE9f0EV8PiDd7lzfJ+pjAhzm+M3X6V/6WOePX9M3I6cnv8a8/YFgzLce/nrjNcXjJsRt36FnEZ82EHOjFdn+KQ5WLSHTfSnKHdMjApZWw2rOzhgLhXhI34KDd1UDWjweUJnMKWFzZadYfIBcsd4VZDC0S8HpGhry0G/wna7RSjB+ugW/eBAVbhOoAohFVINJJVQ3cCy66Ea+tWS66sr7t1/mc35lpffeIPrfMV08Zz58pxKxpoDammtNym16kahF8Q4YU2Pk5oYPA9/7zeRpoVJDp1jXt5qJSlTRWuJDyNSw7RX3HIt1DJz/fiK68fvcnh0wqNvv0+nDB+9/3vgC2NVdMMabRVGQ807fNgggkPlyFt/7t/k6Xu/31SyJIizoTu6jTY9et/HLl3GpB0vTp9weHBEDE09vE4TN+69wtn7j9G1HUpDGdF1Qa2FFEbgOUVJfFXo2qOUJOSIXXyBlVHMJjOnS8bNBen5EzrlGE4+hy8GWyJynsjbC/ycUPqExZ1XKRSWbkmtlUgiV9lCfmXC73zLDFBY9A5ZBTkVpqoQwvwocV1kRCgQVeL9SE4RP41A5fz0GZ1bobVguTzg8uoKZQ3Zb4kpkmZPjQkfrvAx7RPvGVE70nYikHFuATJjlaImCPOM0IU5pMaZNoqql02xTTukVNQiyGMiGUUIO6ZUCULgjMJ2AzYKphyotaJdu78oa0gpoLseZSyZCWF1QztqQ/YJpbp9cKdS0QjdNb6tCnTCAiBlY8MSd8iqORwOiKUVUYgcKaJg9mKG0AZqacEUoRh0R4kVVEbhSaWVKxSgzBWVK6lmSBMhBIQyDMqw3Zy2LMIMMcxNxS2ZnCrCQmhZNmqxzDlibYefGtZo3G0aak47tLNIu6ZKcNIgpEWwwLm2kp3ExNI64i4SY2yFFrIgs6QueuzqHrMf6br23ZPCUIVCma5RPUTF10zdRYIacIeOlXkFKSWD7JnSFqho6Sgx42NGG0uZCllWqor4eM2dz9zlxp3XWy1uKVyevmj3giJYH9xo5IZc6daHxGkkMXL31dcJqfnwlWxraHXcIbMg58BisYCQWuDNGlRn6fsF3eqEGrZItwCbW44kJbZnT+hWC8TeP3365IeMl4/Znj3i6O5bnI1XnNz6DGlxQre4gxCCriqKlQjbSh2EqAS/pVSNtQcsFyfMl08YwyU5RAQjan3IcPMNdtML0uaUR0++i1Eec/gSmp4Y4fLhP2ecPG7oefStK4xRfO6n/kNqaaHQMl/y4It/Drs8pi8v89F73+DBa6+TY+LW4THz9ZajVw4IqZEEzs9OeeX1z/L+u++gdSs5kBWoC4pv6LucA8poSimtCEFU6C25FIR1SCS9OWwbGWOQuR24liIjJfR9T40C52jqt7Vtg1MrCEGpHpEGMJoYRpzuWruqlohOEOYZZQ3EppwLI+idoSSFkoZEQda22VBSE0JBSYMPE1LoVnJRCklkyh7bOsUtSneYJCi6CWqyOKoZsVKStEJKRac1Qg5QG4Na2VbTK6jk8ZJUWvhOkbDO4ePIen1ATRLhQM0jSQYCY+Nhj9dQ/iUbcgWAkvidx6k941Ypavat1i0V7LCipBGEaI0/wkBsnFutNdGPSNuwHVUrrNatYrIUjNK41eHel6LJ7BEeMmHUQK4B1Wt88mhdCVPA6QEjxb6CVGJs8+s2X45Cp0qUGbSCLJFu+MTvpXcv8CGiD+6RTERGQxaRlCpQWyuJjwglSClgXM88aY7vv4RbD9y7+XmG/iXk9Y6wuSDfPvzENRSCDJSckaL9P1UUCB6tG6M3l9ZWImqr5EwpURFQBKV4NI4sIqYoco2IT6ErHB0rLuUWez1RjedArngcEy8tNPXFJcPxCmmOieNIkI5dmrltLaEKNJbrapmvrrm1HCg680xfcVVPSK8ecF1e8E9+6wefGHLlYChx4vzpD1gc3cVazbPHD3HDAQfrW8y+BQXH3Qu66UlTIV9+wOXFOc8uP2SlBOniO7x/tuHNr34NKTy9PSDmwGZzhq6Krd+xWHV7ILYhVkdBcb5z4D6Lsmu284TsLa5f0VnBdDVhV01Vl8ZQ0aSQiTnQWUA6EKqtqkrEDB1SODSWLAtWCmIJRFVQ2SC05fD4mHl7xRQKSkWUXVKoaBMpNaGHAZUzMWRUqWwvLlBS8fzDhxQ9EM8/YvYbKBlRwQ0HCDswjpekcYM1C5RQhHlCa4OpghhGsjIY65BS/Qhjg2p/NyMtyV/ROU3JHiUVoRRQBSHbejPM7QEOUE2k749QB5ISmwc07Dak6QyzWHFycossmlr77IOHSATOLil5ou/bqquURGTeh5wCw+o2X/3Cz/H+27/F0iZe+cLXOf34A85Of8Dq9pIyN465UQftRoxHpoHeDoQsUGGmFo/WDi3WIPYUCZnozAK7EMjjzzTFrdZmydASIdaIwxXCj5A27KaPKUkglaYIyeLgDjVPFB+QaWR92CPMGiFE29Ls7U9KKZg1c53pFytqzgjZHo66rDC1BwpJZQ5v3EBgyLlSc4KueW3n6Rrbmf2hWVKWtxAhsRgGZC34aaZzjhgqpmvKScwZay160ETvybTPZ6mRkg01F0oRyBypsjYLSklUZVmtLJvtjFQZSYFeI6JrwSMnESlTiSwHy/X1BRJFbxTT3O6PWlncsKBET8qxfeaqJugNehgQWJKMbQslms8xFdn85j5hdKOZaNsjASEjCttA9tNE7zpKaqrYtDltIV4ryXEHQoJaIkVrsvLTSE0aYxzjxSlzSaxWh0TvqaIJD5KKUAa3UEzbCSEl1lliTiz6DqEdRVni7FmtD0kl4vOE1RYBxJIAgaoJIQVX19v2gO72/kPbqtOX/XFTvpxqeeXtGW65ZDe3OltlUxvai0IoiUThTCQLhSwZMwwNLacs1nWshza0EjMLZzHJY7Xh+vqSruuoObK5vsIOiocXv0OVfTv0LnoWg+Ps2VMGN3B86zZHN2+jSgRt6dUS5RSm1JaIl5owT02JVbKFjgC16Fv4bbehkhpWznUItyLNG7TWVGEoRmP6HoGkKst2+hClJcI5br/+NbRbMI6nhOTp7KJdp8IcJwa3Im13xDSj3AJyxHVH6OEQiUJIR3/8CvPlR/hhgRsO0XLNfP4DjKzIpUNpC7TgZXz2Ns8+/j4PvvLXuTx9xLx5xhtf/8UWcpYGa3pKGSlZQvUo3fHTP/+X+dVf+q+4+fIbbC8muv6E68sN9uY91kVTDjuuL55y8/gGignRC6Lv2U475jKy7Nb4cUfKAms7SkxUnckYco7Nv02GkCg6kWMlpolSCp11baurO0qNkA0KzTiOKGUoYm6CllBIUYkh0XcaIXqqDPh5izZds2HGNmhrtW8ZKxmpodTGni1C7odmgzKRkmlIwZrpzRpfdsQYUa6HEjHS7OcpSY6ZmgNSsWdZV5w27WfMHiEzfo4Y07ZFy2FFtY2O0gof2mZLichqWLAbE7Zz1ApysUbGHYrGTxamQ6o//ugq/t8YeP+/en35sy/Vf/h3/5P2BseMUYJp3jb7QfUYuSAhWjBNQYot/Wy6jnC12Q9lBUGgCkksmSIqWkqUUOTUwlhC6cauzRGtFTW23vcq24pBWIWstimhNXN+fs7J4a3GVxSCmGaM60ixIlMhxojrZOuLBv7Hf/e//v/9vfubv/rLCKWZY8BoRSlN+UZJUhix2pFiU3ZLbcUPVjU1JqXQ+MB7lU8lgXaCLOB/+Et/4w9d5xf+p/+Yf//vfJuj9RHnmwt4vuURgV05Yx3XrPs7HB4eo49W1DmRYiamkbQ6oFMrrlxuD80pggKne+4MA95e8+1HT/lX3/1pHpS/84eu+Z9ePEEuBmSI5Hlkc3XG0Bmk6kE5dpfPyGHL4ugWcb4m1L0XKASePPynOEZu3nqLoiohj7hFR7o+I/sJd/BZ0njJ5flDtttr8pyaYjMXqlpSksQtD9heb4llonYL8jyyWiwbP7kKwhSgW+BkYyGn7LFDT6FilCUkj8oNFSOKwKglWWf8uMUoxXa7ZegWDcWDxCiF6QdSbW1U3vuGLjOaQkYJTbhqKXilxL5wIOJpJ3EjHSrmVsObPVIolK1U2dppEK35R6nGgsaINlSFhEyZXBqNJNH8qGhHpw0VxY2797k6/ZjtfAW5sR775XE7VGWPGxZ47+m6juvzs6a81YwzlphGjl//civWePSQTCSEhCgVowWwoFs0j+A8T7hOQU5MIXJ4eIt5vkbEDX6GcRM5fPl1vvSTf5ar0yd8+K3fp+qClrXVd5q26jd2AdWQg6TTET9dNRVQQa4Sg2XyG5xzVGGhBDCyhcgUpPEZ/zd1bxpr2ZrfZz3vuKa99xlrvnWr7ti3p9vtHtx2O4ljg4mFIpIwGRIIIIEIQojAF4KE4AtIkRAfUCQkBAQpE7YICYpIjGOTOPE8dLfdt4c79J1qrlNn2GcPa613vRMf3t0dte1I5kvonG9XpapdderWWu/7//9+zxOzAiFp9w5J2mKjxouMEIrVco1CsjjYJ4cNYxyoqiN8iNh2j5wjgt0zQnVAYBi3ZJkJbqLuWvARPw0cHF5j8ploYPIZFSPaKiSiOOJlgpxIzpNSYhu2VFVDJCOiRMuy6pPaMI4bmrouCtZdYUPlYoNMKpNRGFGV3LSRSMnOyij/kTPeB2y9YHSrXZSsQqDIPhTMWBIEl6gqtdOFxlLazalQZkLcXfIKKizkhAsbjLKUERdFFYwpB0whGMKIFDXKaERIZHxhfw8b3DTSzhpCLLD9ShumANZaYi6KdKsV23GNNhb6kWHaksJEtZiB86xGz/xgD2stIEm7Z3bWgjREYk5FYToVJGUvIo1pEVDiXBKMbXBDj6nqwlNVhuAdevfczVKj6waRdVnTkkEkTCyHh6RKDiylQI7le28EuFiKQsJKZOE0kbNE+LFgy0LJdxvbkYhIcsmi79bTOii244CtGnxwaKkwptjtpmmibjRpgoBm//gao1/yyutfYD7bR8mKbX+Jcx6rFSlk6m6GbirSNICSu5KkKVrmne1NfnsQkjLRDyXDrRWIb29UKZM7acsKX0oQmjxuGJf38f0lSndsNyeMrufWS58iqQajO5Isv67UBn/+CGUqZNvh3YgxDRGFyB4pDdGVi9Vw+RjbGdyzE86efZVu7zZCRIbz95hdvYsfes7P3uL285/h2bMzfOjJMVFfeY3jqy+C0Ehdk8eJcVxirUabxDRGFldf4OLBm3QHB5iqY1hvaPeOQMHl03Pe/vX/naPrz7F9dkq/DSwvB6r9G9SLBS+9/mna/X2MNKzOnvHGz//f2Ebj3EDV7jGJRFsfMuVI13SoJBlSQZV+W1BRtxUjHjE6jKrp+7FEpURE6DLNbds5ZEkWxaqXxhXBj9RmRkotiIneJ7pmhogT66AY+1OqnInDitnxHrJqsbNjttuLnVlW0eiObEpxPBEha4SMZeuuyqAn5kSKCpnCbjiZSCRqbYihTLJTyAgly0ZFZKTIZJF2Cm/FOE0IpjJYpDy7YirvUeccRjdMfkMOA03V8qf/7H/Bm+9++PviiH1PTHJBME4DOUgaU9ZYxs6K9UZ1JUcaRWl5Z01VabIXhfFq61LAEZppKnrMytYEORFjZHKFYyuQRUlIKjenWMLiKCBClnZXcCtudCU1V64fESIkP4ERZByjm6j0jBCG4moPHi01o1////Kdi35i2m7Qs45pGjG6lFoKDqzo/ISIhdMIKGHwu9u/qiq8mwqQPjhktpDKA/R3fi2uXGN971c4/uQBc7NPvwhUmw2Xbs06nXCpG5JvGNeaWZgIKqGzoEoQbMT3I7MrDRpPZWfFNR9qXnvhNc4e/jV+Pr/Bv/07PlPWLbl3JF2TcOzfvMN4ccq0XWFnh9hKE9SMnCQ0HduvfgnVzjh67bNIC2lY4tdLkoO9my+Q1MTm/IRKWab1Em9n0CwwMWFbQX/ZU5D2GV1VLC/OMLZm1i44vnKHVb/k8vIhs4MGLepi3kuetFqR646P/8E/zLOn3+L8/gkXqwuavT0aYxj6c8b+DKsqvCqYKi1qZp0mxpHaaFLWECN+WJOkRmaoTcOUDJkSySHAtbsvsTl7hpCw3p4j1z3NtWvIDG7akl2EENF4slFYFCGWFajIpRzRLAo2KPgJ1YBKqbATo2AcJ7TSTH7nL/cDqJr33nqjGP/qcngRqUDRZRYINSd6iTbgp4iuK+pmToqe2eEdFldf4MH7X6POIz7uipaioT04IjmH0eBDIKfA8y+/xPtvf41KCXCe7WaNGzbksEHajtmNY4Ib+dov/wKDW1JJhZuKdUlIX8QIusJdRrZbR9vWqLol58TQb9CNIYaiEW+6thwUBahKM4WAaWao6MmzA6qQiUEQ+5E+XTKqQ5TRSKtouxkiwug8bbtHIxq8cyjRIr0gyqLKNMIglMbvyq85OYI09KNjXjWQM5eXl5B3F9OQELpi2kldqqrCe4HUFlFbKlOhmRGyQUaPSsV2FCZPwmNsU5rYkSINAKLw2KZMkbMTVLZgqyaRCKFsgJQyhBDxqTz/+nFNDCOVXYDPBZNoMs57dFVjpC0SnCxIIqNUyYIPwxZZGUQoa1WfHEYqKtsgkisURK3J0TNOPcn5gpISkZy39GfLHd2iTBBVzKTxnKcXJ1i1oJrtc7Fe0RzcJo4CkRPj2jMaSxCBcVOmRMJq6q5BYMmzY447T9Ia5y7QuoasGIaBJAWNbgsTWRcWahJwRIXPhfcZhUOpDFJjZzNEKOvjYXClNCY92QpIsuDWJl+GMkoybieaqrTjQxKI+G2GacFNSVU47RhBDoLRj1SyZCRJGREsla3wocgzKmt2qKqAtg3RlQa7bTuU0kQhMEmQfaLSFdJWaAPt4YwHT+6R0z7Xrtzl4f0HXNnzHN66Sbd3SB2+bZcKpCToVxvaeYeMuRS1jSCzi0LGRJwCwTuEEcTgiMkREuUCksYiRpi1pTcQS9RA7NS6Zr5PNg2m7qCq6YBc7WOSLHrlNCEC+MGhqrrY8YYRU1dgZpA8wkOYRsbLC0zTsbh6mygspr5GVJK9o1usz5+y+MhtnB9Rledjr/0IiBnzmxMZT3/xCNUeYcyMyfdYXRN0QLNmXF9g62Mqu8/l0/do9+bEyUPYUtUNfnvGFBOzK1f4/L/4nyKiImye8Nv/8C/xkY+8jKrnXNx/g/FyRjP/DP1mTRCG68/d5uzJe3SVQVDsp9uz9zl4/nlWZ0+59fKrDF/9GpiGKXqcW5PbluboCtnMETLTHcwhgu4qRr9BhlBEE0ogYs/6/m+j7JorV17nwZu/ysn5r7BoFkTl6Ns7zOd38Gen6PE+U4wo07I+UUxVRlUHVO1dqr19nB/5lS/9NS5Ex9GdH+Uzn/oCZAihnA+kMOVii0DtrGWIhDQGu9P2os2uRFbe6Uopxn7Y/ckD2SiEy7tOkAAlIUe0Euhcg8q01bxIWPIRKWVCmhD/Hya53xuHXAFKyMJ+zKHwbEUg9j0+etCGPAWMbQk+kqeISx4rdPlxAspIsJoYE37skVoSncfqliQSOaXyIsxgJKUQkxJSJdLO0hFGQ20bfOxJurwAcp52kzhD1rIckMYV2mrAEyJkX15GP/E//Wl+6t/7S/9EvmX/0k//JFoJgoiYqugwQRCSI0+y/FhOTHFAywppJVJoQnIYLC4JYi76ySQjxYuSiSmVIPvv+KrEnOc+WVSE32ousKtTxrAiVok4bHlphFYvWV+uGZuG5KGdH1JnTR9GvNR87GnNh/OIHgJnOtKZwI0nS174yEd4/OTt3/WZ2XsgQSxZ5GG1IrmRzWbDYbNAdUdYVXiDYbPm/jt/ketX/jDL5gaz4wWnH3wdPW/JSFYnHxREU4qEcWQanpAPX8VNW0x1SHtwleHyXbrrB+XhuVzRGIlqJ3JKnJ99iDIV+90BcdsjTcbjkari4O5rbC4ec+/rT7n1yvdRmxOqVnPx6B6+H5nP9umamouTRzTdjH6zBBsJRmJlyzCe0y5uEF25TKVhhU+5sFWVxcwO6VdrGlOzenSPkDyjX1F3+9jZDJJBaE1d10Q1YrTEu2Kwi9RUdUeOrpSYrlzn4tlDZAjUzZzTh++gbINo97DSFt+5EDQ7vjK2ImXH/rX9MpGLgQgsjl7m9OQDGm0JfsQKC4HdxbGmmV+nPjxk+eQZV1Rp4T959gCpNVXVkvPIsD6lqUtJCp8hTbz/1kAtGuxijrF9wceEBaZbkKTA4Wm6GWFaI3Ok21sgnWTaFuJA9FtGLjGiwk8OUXeM/ZIYFFW7RxYJO2tR05beF9IIcSwXCSFQZsAlTwqZpCvG8Zy2OWLR7pErjRshj5JmPkNQQP0uBgiBLCRaZvrhkrq2ZcWYAmkakUYWmkGjkFayV1doAcoKpJR4JYk+MdMzCA6hVdESp4hSEZEiWarin0+gdtN8n8HlRFYBiS6HNUCogDWmHEhEiTAIIbBdTYqprAdTxlYNbhro3ZpKKyprSBQkkQ1zhChlFN2UtWnbzvA+Fh63scgpkYRm8FvylFFKQhhxUyDlgaw0YUqkEdq2I6TAtOmJOVG3TXn2pA3b9RoxrhBCUdU1vR/wbmJ/dpV6do3m8AbRV8Q0cXBwE+EjuquJQpSS2RRoKo3M5dIjhUKw5DsAACAASURBVMZnTZ4SSnhCjmilUfVVksjk7Ok6S/CF5ZkjRZsuMlZrSBU2JKTqmMRIkg4hJTFpkpqoZEM1LyisOAoqY0hZFTOcDqUIpxRNU6GEYIwDORkao9FBo4TGRckUS/RDYfF+w8HsiCkGRDLE5EEX4VAmQhZ4H4vMQknW2103JQrctGHrLhBSoqWlrTuEaBBVg2oOkPWCF1/7AtdvPMfZs3Ou1h3HN67jxxE3ToQp0C5mbLcbtNbM9xb0q/UOtWlLTErC4DbEmPH9hMweqTX9+pR2b47wkY33aFOR3Jb1mQbdYWRgdngNIxU+BNx2TT2bI2KmWVwlRIdInpgyKSTCFAjTgNaarRto5iUGpLGQA8SRMI2EwSHqCts2FEJeYJpGmu4KIVtmN19B2xZx+QglLQGNTAmkwvuAnV3HJ0eaHG4cWV88QsWIqEqMxVaay/MPyaEnxKs03SHKSE7vv0FlNUkoJrdF6xnt/lVijLz62X+B6LacXz7hzif/BDlnpuUFb/za32d2qMiuZxgf028cdTXHr9dk3XL+gafdm3N6dkJ9/Rp+dUGzZ3FPHC997gdZXY588P5vMetu408fEjYr7NE1nDAs9q7Sr+8Ttifs3XqOF7/v+zHNnDf+4d9A2YipVgx5oGJO7h9zuXxAZypS5ej9hpwi89kR+21FVjW2Epjo6J3ncz/wJ5nCkv7sPt/4B7/OR7/4HyBlRSVbNm7AKomPAVMV9wCU/85ZY+E75Bi1i1Km6BE6YIxhuw2oAFlXEDwxjmzGNXVdg+0QEqSsEYSdJRFQhV/O78Hy/8d9fU8ccgWU8bTfFqarlwS/KmsPEoyloRq1Q1UtWQQMZd2ulNqpEmNBqYRQDqzeU2tNzAMiZCQao+d474pN6tsEuRSLiUhFjGqKkcqoUtRRave7y+TkMEKXJmRTI0LRQoqcUFoQY0DIf3LfzizKmkAZTfAJEju0kUCqRMgCkFihSRGSiPjoMUbtFIORjMITEAmMqgjTSG1Lzup3fml3hT/xb3b8Lz854N2a87Ck9iM2jbTqCu+GRzzPbY6bY8Zpw0E1x7mJrZyISXJo9nimItfONKeHkm7KnIYlU3NA8Aq39+R3/xlD0QGnPFHVC4w05KbmsCkvN4VjuDghbC9JWfH9/+pf4Mlbv8j7v/RXOLz7WY5euMvq9H1m1Ry3PSe6E9Lk2G4G2uaAtHrKvLtG3Gy5ePABYnK0+9eLqehgoMmZqrvG5uwhT9//DZobLyGlZBoGpnXPvLtKyhV+u2HsSwHuw6++xWK2z9nqDO/XbLeXNPU+StfM92/igufacy9x/95bXNt/gagEB4f7tHvXcOsly4fvIa2kanfZNOfZri6odIOuq7LqTILD7ohAwdhE4dCpvOx11ZGCx1Q1MUHOiZhGwKDNHLdeoUyLFCPerdjbu0UUGmqBrizR7V4eQqNNxZQiQmiW55cY2xSCRU645X1MjEx+oO0WTKPDGEOcPFY3XJw8hCcPUEbyra9dUClFnu/TWMvke5ANUu8KmylRVTVKWISq8HGiX60YhmEXMRJs1ivqWYdSiime4aaAypLN6oJAJkaYdUeEPC8yB93RHQjyuCwbDrVmdfqI+cEr2Gri6WZNW5WShbYljzoFR0oRoxviLpbQLa7R2JYhDLDZolVLVVcwBTbuEluXrCY7w1dkAhFLhCkHJhexzYIwbknbFdZXeClYrgYODg6YNj1KQJQTQUG0vqz1YyilVhdJaaJu5kzh21rRCXZaWpQkJIGSO2SZ1oQcdiizkvcnabLI5fCRYlnJq6ossUJAK1vwPTESfCm6KG2QVuInR2WqAnLXkiTL6jmLgB+3RD8RU3HTEyW6qgiyiDi9j8goioQDzWbZl++TLna4YbUmykJRqZoGXR9glWGMEwtrUVKTyEwxIXEFfyhEkWWloiMXVpB8RGFxY8KogLINdVciRQqFdx5tS8QhhZEpOka/wojyfAzClBKuEKBrwlT0upJMzgNTLkriECMilM271wM5iCL0STt+aJh2enWQ0ZN8T3Qj7vSU7bQhTxtWlO9vRCHblsousFEyGF9YwusVzXzB0JdIi08RnRWNqXHeg9XMuwV+6JnX+yQVmbJjXy/oxisoZYiu3wkIDE3d4VYbRiTH8yv0Y2D/4Ij1es3l+TkJhUyZfn1Kv14SZUDrBe+/+R6H146QKE4fPkZryexwn+Q3zBYHPLj3ZR6/9xtcuXINJbfkq6+zWveE/ow4PCapiuwidnGDj37xjzJ5yNoQxsDkEzZrpuUFwljqeUfYOtwwYqxmGNbE5KmrGX0/Mt+/QhY71q535d+GEOi2LvnzaUQpWSaI1pL1AW7b8+T+mzz3wqdIdOS4hX4i+wHTtLj1BZWdoW3D5ckzwnTOdnVCN5+BnxiWZ2g60uoxUhvy5imP77/DYtawXT1iEBX7Ry/QT6cgHpKmJeP2gm5+QPQD+3vXiMMSoxtESrzy6vNUreXZBw9Yy/foWaLXIzFEontCZ28xbgVztWEQkIcNU6q4/cof4MGbD7H0GBTjk9/g2cWbfOrjf7ygKIczUv/bVHvHHL78HFf2bnLv4de4evxRpHvM6dMvo+2HVOKA7eYR1258lnF7Sn38Ak/Olgi5IqeJ1XZNHWbMjhvcuKSnlBZV2MPsP4+5c8BnPvlHmTYT5+MpKs2YzWaIqsPGiZzCd+JJJEGYHFkpSAGRIuthQKuMcyOSiTF5bF3D6BEmYaoW28yo6pYYQhF7xQT9gE+gLKASWlQlNpN//4fc75FM7u38t/78nyWkFc18j/7iaTGlxMJEk7ubtowCtCIzYfFEIYmivASFkKSJsqZOZWUrcyKqopzzKUNWaKGJYkKOCWFBq44Ut5Sr4O7XE54Qc8FyhVTC99mTskcrQ0wF80MswoUoQWZJFrrcOCbIWiJShOwJvrjcZW5JoS8gbyFIKZNSRKhEHQ0PHv00j5ZLLlea166+zt7tO1Sz51GTY/KG0F2lu36DSZtCUSi6C1SikBukwU1rjKzwMSNk3mVxBVKFkr9MDolBZhjjllobUiwHXXQxHmll+ct/5LszuX/qZ/8qIr7Hf/xv/SLvc497Jw8xZLasqW//Ibb3v8nRtY9ithndzdmPHePYow9qLimZva6aEURmljVbo1nKxBfnz+Gvet756l/nJ771je/6zP/s8VtlDZLA7l0ljEPB68SJcRwJy3u41T2myTE9OUdceY39K3OS7YgnH+LQ2PGU9fI+y7MHVB0MfaRtDgo72WZCtgxbULMDaj1n8IHjlz+PSxtsp7DVAXNd8f63vsSVO1/k/odvYPyAYGScAlpopGhQ1mCtRNX7rC4e0VQ1l2enXH3+JYahxxhDTpZ6pnHB07Say7OeFCJikAiryKnH1Jm+70ljQLqE3SuIp4JtyRiRUbIl+wG72MMPI64w9xmngUoZvE+kFLGtxfklJpnSyK1muG0PBAiR2naopipWmSRQaJwbC9HCF450FhlhMkoVpiU5UFlwmwGtapCGIHJhzk6ekAvns6p2Jj2tC/0kZbTKWGXxbsQ7hxu3BYYvTeGSNg1a1yizY0ILUQ7OyWMjhJyIY0H2xZSRApCCAOSpxJBm3QIpMs5PJKFx/ZpZO2cMHh+2tI1gWmU248D+/uF3MFSJjLQV0Y1MySNDIjkwixaRMsL8I1uQoGb0jnnTkrMvL2BbFX6kW5HIrE7PWT85x23OufnibUJOHOxfYdMP2K4rK/1U0VZ12aLUlrTDDIYQIEUUAu9WhGkiThmqBts1hdQgVWGKR4UkklO5lEcPMZfDliSU9R+SKWVkBlPNECmhEeWwljI+TkUXLCgZx1gYnbW0CFEIG0GWQ0aUCZUkcVqXQgsCqTJ+Z5Fzmx7ZWSpbU3BmiilPpcC7U9tqrdGqwsWeStVEBCoX5mjwEaHLZUkphahacizPUS2KulxmSdI1MQtyLEi3sXc0XV0EMWECJakxZKHRVbE6xWjwJKzVJL9CyYwWiiwMSEF0Y7GWpYlaVQRRpunCSgpZqdgvRXIImSAFQkpMwaOzJkmHkQYXCqJpWk8kFdmfdfTjGWJaEyng/bq+WYD/0iC6Q5S0oEBJSwyZujKMIhCioDMVzjkICi881lqMMXjvqGdztv1lic3Zdod12hbUom7wsUz9nR+JW9iOjk4qNpcnJD3S7V/l6OCYy9MH3HntM7z1S7/J6vweRilClTi6epcP33mbabhkfvMTfPqLX2T/6g3GTTGUKWOZz1u2z85BSTbn30KFE2av/Rj9gxPe+um/zjcfP+IH/pX/iK7rWJ8/op7BpBJ3n/84e90BzjmGy55mr8MazbPHD2jrhtPTU6gVL33kE+gsito5i2K3a5oiY/KBHD2mahBK4/oLwuAIoaedNWw3E3uH11hfPEYrU1Chu4jwZrOhmy1wyxPW6w/w60vqK7e5+NY3eP7zX2T77Jw03Ofy5C1kcxMlW6R/xN7VGzx78gEzexU720N219DNHsvLExaLO2itsV1DWJ0T3JoooJ7NEN1VgtvQNFdIU+bNn/8bYAPbaU0IE33f87kf+Zf55q9+he32Q567e4vJbVl0BwyTp2tr5nc+jo2KD974Kv32CVdu1Zj5NQ6vvIYfHedPPqDbP2Z5/122936By+GXysRVC1IvSCaWWHwUhARWg1QZp7vynNavMJ+9yP7RpxHUbDfvkrs9VFIY+QJmUaHqOaaaMV4see/NX+Tmx36Aut0vB12lCubMVmShiNOWHF1BDwpRhpLR49JEDpFx2CJ2hJVhO1JXc2xV/t/eXi4RMUC2YCns3yDJ0vCf/Ff/NW+/+8E/PZnc87NTtk97uqsRv73E2jn4niBFuYGPnqqekf1IFBIrDSIrjMi4XBh8KRZrTrErBJSCkBJamKKhU4I0FYSP1ApZFS99VB6JKjDtWDIiZDBKk4PCQGn/xYDVDT76skIlFX2erFGy3PwjpUBQmwbvxjI5QVA1pQ3sQ0RoVdzju8NtyuWm02fH/pXPo4zjtes9F+vA6bOnHIU5QgbIFdruwTRhmxpEmT4ZbYnZo7Jg8iNGKWKOSG0wSpdindKkmEnfuW3lAq3WDcEXjJpWFTmXtV7y29/1dxSjp61uMb/zgPTuilH0DKIE5D9ZVXyZSOUk9V7LuZpgb0FcKVK4YKYOEcHDdEo9r3k6rZGjZra4xZfMmn+uucv/1X/zd32mG8sD3VrL9tkzctgitMJtN/SX32JvfhXZ3UCZFbOXjhDdFXq/oWoPcPlN0tiiDm5yePACx5+uuXjrK+jZCTpmEOfEIHGDQ8uayXuyyiye/zim2efWK59h8qXpff7ht8ghc3L/K7S6QogaN3iQmeAjMW+xtERh8f1TDq++QggTN4/u8vTht7h19yVWy1NSDoyp5bnbr3L/3pepaoOsr3J4dJPN8hHK1Jhmn1duXWV1eso0Tcz2Drl4+JiqVpydfUiVBFF5Wn3Agw8/AGnJPhTCgcykStBEz2Z7xnJ1zigsV+Y3ETkjCMwPDmnajovTU5Qy5UUrFFYFQvRkn0B2XHvhZebX9ug3Z1w8eYzRC3LO1EZy0fcc31nw5O1vopUiUPKDXdOwnYpeEjnRVjMuHr+LtZZ+NbF/eMTSXRYZQa04nt0sDzu7oL8857lXPoEHHn/tV2iUx5NZO4+lIXcNJFUUkMFhtMJPPVJm3Dgi5T4xaeziADesCWFDrcq6NLipYJLI9BdbrK2p1ZLl2SmImqpasFgsGMLIRKKyGlU3pHpis7mk1rIg/kQomtiwIeWR5cUzqvkhlZ3Rb7bfKcbmyrD3/HVm1w6ppSTaBi0sMgSOjyxTKOVLEUsLWUrF5CMpFquYsgafIiFlgk807R6qs2VdLhNTcGghGC8vC3IqxmIpC1umYSIbi7YVIMkemm6GsbKUTVOhdcSoaKwhuAmhLRbFJDJpB3ufLeaoVAogOUkaDZP06LjD/KiIFHJHwpGoOuym4IIQwdQNYXAM256UAxmo2wajaiZXtnTKGCbvIMJEROiKyrblMN42TNNEDAGjIzkYMhVxWJMY0U3ZVIlcMpxNtY/fFP6ozJHgPI5iImsyDHnD5DwpDmxFoUYoWwQ0ftiWAmLSxOToGsnQD8h2UVbVm1z4xjrjnCWnUCD2phTv2tmcaZpIqcE0LTpSppD2Am3LlrLrZowhonwkr3pU02D0AlFALHgXsFYXKH8OhOSRaMS0IcRA3MkGpLZlUuYHtFL48YK0HckpEntPL0uxrbaGOAxo9kkKTFWj2oCoMrVW2P3baFtwdudnZ4zTkq9/9WewTcvBc9exsinM0wR3X3yB5cWHZHGfX/9b/yPSHiEVvPD69yO84f/5hZ8jhncJUfPqZ/4QtbjDN7/0c3zw5Gdo20ccX7/Lr/70f8/xfE595SPM21tcWRzzU3/xz3H7tU+jpGE7nVDlQ4L7gNn1a+R6xvMf+SRH3U2+9At/l+/7wR9hoWfEDN6XQUfwnuQzIThiKjSO8wcPWRwt8G7iwbN73H7x+9luN9T1Ajesefr4HSSCo+dewhoFObMNicuzU4wbYLGCqqWSHVv/jDhccnD7c+xd/RhgufzgV9m6S7rD25is0c1VVLdgDIEbtz7KsN7QDytWZ0+oxJbu5h3mN76PcbsiOc/s+gv4ccTjuPvpP8iXf+Z/ZlIr7n7qc1x/6YfxvuXz/+wfI8Q1s71jLlfPWBxcY9j2iNjjsyWExI07r3Dy3n3Q17D2ENc7bN3h+oEwfcjiaMH7v/Fr2JlHe/BTQglRnoISmgr8qEBG/ARCbTGmps4zwvmSwdwjiGJ+lcmyuP4RWnVAlIVX61MmasH1u3foV7+JWylSPqTpboDQuK0omEKZsLpiHLb0o6OZNXgfqecVQSf25tcJPhcd9Ly8R9LkC+VmYSELUtbotkbJGqnrcn77PXpD/7iv74lJ7uuv3Mz/55//D1EiAJK4a/FqZdn2K5S0KGFJeUIri9wdMEvZZFYap1Lu2sy7W3Yuhz2jJePYI4RCyZqUEpVWuGlE73h2SeUS8heKaRrRNsMEdTPHe18mum4qDXelvvOSyqEwFHMsk1QXPFYJUiiFuDxRJs85lyysaovvHYh5h12SnhQ9RrckFwnpMetnAdNV5LRg/6M/RnVwyLgaSnYl5YIUqZoyUc4KqYu+WOSMT24X16D8vlNEItASpjgiqBAyIiiBcGTe+acFcfdzbRb81R//N77r7+jf+Xs/RYyRr3zz5/j3//Of4mzYUtkKx5xr/ge5/uItluN97g3nHKs5h4uG423gkUx0UbJlBLelO76GD4kkK/TUcHN2RPeJj3L12S9w6+//le/6zD/39CHebfBTj8gglKY9uIKWknF7Tn+25uwbf5sXPv/jOF1RtQf0qwtWZ0+5/PCrqP4C77aY5pDmpdcZTh4SxMh8tsfy4h45jJC6gnJTArfeMuSGVz/741QHC7x3LJ8+ZKYrTh99DSVqKlvz6N7bhZWpylra1i0xyBIDqWpaVaaapjJce+0ztItrhNiD8Dz81lu09pC9569RqQXv/tYv0589oeoWNE1DlJbtGDmYz8kahuEStV3vsrCORhmWZw+YpOb2xz/B+uwEbS1imFhdPGTRXQUp8H4ARkxzBTdNREqJJmWFUQY/DFTaUO3PcZse090oVx/pCH5LGgMi11QHh+gDy+bBI5Qtlx1yQFJybaOf2N/fpz89JxjJ4Z2rHB+/xFu/8WtIOaIoKBohC04pSU+Kgkp1KFOjjYQYSdGzmXrkNKGsoWr3CW4NPpJGx1iZMtWSBZ4vtUXiafaOiilKNcwWhzx+cp/gJj7x+T/A4LZUOrK5DLjxnO35CdFvsLoCUabbmaLdnO8f8ODDN6nMPkrNcYDUxQQo4gZFTbQNdTUnhh6kxscJP4340dMtDgBoupakBH4sk+UcMlmn3SRdo4VlnEKhDARHJevSRlYKfEKrYlErMSxIMpCcQ4iyFkckRj8xa1qiT9hZzTSs0LYmuYQ2DaRMtqqUd3yJUsQYEXVNCAFbNcgokN/OtQlJiuUgilJE55A5gZI75I8uFj4FiYiWijgVM1sgMPbrQkpQGWElSnXEMNGJiqxhmsYyZRt6mqZDqkwsD0fGaaJu2102uiKlCaUFtW6Z4lSmbmS0tugEfYxoI8lpJPYemQOD97jlScEwNor5rMFP7NizmTgGVNVhlSqHRZGIfqSZ7eH7FYxbfHAFsRUTphZFOUpEiwrbHqHripAK55SsaLqC0ZpIoMqa1poZbruh2XFdo84oXcg+CIMWNVPsCVpiRbnkyJTwBLybUEFyefI2tgGtZ9jZARF2MQqDW10ghEIbyJVEBYPeDS1kykzeE4XADRNdq1FJkoY1MUdSpVn1W5qmpVsccXm+RqpIPduns/uMblvoDkZjfWYcR6IfCh4t9qAVcdownrzP5DNVd4V+OSFcwskLMJkwrJm3c6weWccNyTRM+QILZHeLJAIxtwipmc0mxDRDU9NHjxdPUArkNPLpH/pjfP2b7/ADP/av4YdMO8uYbo/F/i361YqqKYKb7fKcsb+gaWYEBP70gq/84l/kxS/8OHfufgolKx4/uI+REbc9Qasaq2G1vkDP9kg+cXzzVfpxw/L932Rz8ZBbr3+Wbu8uadVTX73K5ux98q5Qurl4wtQ/Ybl8i/3nX0dnzeGNj4OuQCnS6BATbC5PmYZHNLUizfe58eI/g3dlXb9drbD1HuPmkm6/I0znXJ7dw+iOg+uvEVJkWJ5g6o66mtOvnpJEZr5/h/XFI7KfyPWMSkk2px8w5RG33VDZOa5/RC07XL9E7s1542/+Nwz2KSoGVN6RPHwiaVGKXSSyEOSY0W2x8FnzOpU5IInA0BfylNDHHF79BDFpqqu3qZtbCBl3RAjN+sIjTSKkDc6N6GqBQO9MimWbbEXG+4gfByKi4B5zRkeJrQ3D5hJjyyAihICRGiETIgbWfg1BEKNAmw6pDX/mv/xveeu9e//0THJzgpi+ndMTWNOR/YifRmazGcXipkm+5GfTrj2WsyTlEXLhKuacqeoK70obXWuDc33JzqZcHnK7G4AxZc2fJKQwgVRIGbCVRolAVKHA6yl8SCErjK4KaLkyTHHCVg1Q8q5aaSohyi2/NqUpX9VlUosqPvIdy1bIBFFgK8ngHMbUxOCKIEBd4eC5gtqQU8346G3CcAthb1JZBTKgbYVPvgB4RED4TJKKKY6lgJctSMhCILJAxYQ3Ei0sWYLMiihLOJxYXNhCZIw0xCwIv8fFZwoRqwVffuddqkpge2ic4/rLf4TZhyes45zRzrl59RbPyYpvfPAGzbwmbiO91tTS4GTF+WpLJQRHGC5mA1u3ZnE+4OLLv+sz1+cnDOPI9bsvoEQmIVkvn1FnyTu/8jc5rPbIcs69t77Glec/xtNHF+C3JF9eBlOcaPzE48ufx64uaA73OL79Mo/fex9FYvX0FFs5Rq3puq7A/psF9772a0SRsPOWKho+XD9m2q6ZdZL3Hn6ZyszIU83B4ctEXaIBujX4MUMYCCGWrHSIvP/GV7n9ic9w8fA+PjjE9pLq7g3WJys2ecPRzeeZdR3r9SPOLx5RqRmmabk4e0Am0M4PGacT4rSF0THWDWZRYZCcfvA2IfTo+pDsByqrmeIJOTUgW6awYbx8QJwyspmj5aLc3EMpCngfUZslYYx4/05pGUcHKhU4uYXh4j5V6JimFZUUjEPJ3spKc3T9FbTWrJ4+4OUf+mHWF0sObn2MD974CtaGwnj0FmHKi3q1PsMKhVKQRZkiu5BRymCyoqkN0UKlK9xwWdBqIUFKNFKTsiSQqA+OUTJipcJlQdYVOQVWZ0+xStAsFjx4+02uv/IqSVesl2+ihKfVhl5UGFkOVaZq8WMiDFueLM+plKXt9pCilIVyjujaEpxGoVDZ7DSnAaUywiY6u49cQEyCICt8BkaHEA1EqLXFyUIsSCESRECJTMjl35swEVDk4AoLIUWkKrIanyLKCIwRSBkKz1pLFlkRp4mYJsI2oIRidXZCW9WF5BAT+BK1yjlhrC6iGwnKNgQH1hjcOCBSJKVAFIWAknOmVqIYH4VgHRIKgZ7PSKIlSUFXlbWjzIkcIwddx+QCSQmyUqQ8YW3hlLt+QlUSIyV23hKzwLk10gqCDxjb4XKgUnXJXKaaEEa89CVf6iPeTfi8xLkLlAhs+wldlZ2z1CUaoOctqjpi45Z4kbG22SGOElmvUSKXEZaQtE2Lk7Ig0ZCYRUetD0EtiMRiqKprRLCkHOj2G1w/0NR7RbiAQqiJ6CdyMqickLYpFqp2xjT0CFle6M6Xy3lOjqQvC73BT6xFppHNDvMmaGcNeYJrL36KIBwihnLZoUQpfIrMj6+Rxy05jKxOHmOzpvc9OWyYXGbykfnckpLnwZMtlW5Q7QLvA2Y02EqQgufs0ZbZ/oJhe8nZwyc8iwojHZt+g9SOEcF+c4i2B4ic0FKTnEM3B+jjFtKWKWwxNwVGzOhsS+7X+HiANS0hJ8LlKfOqYSHvIq3EpQEhBM4XPmu/zRwuKsJ6za07t/jmh2tevvMjGNFyMVj2jz2//Hf/DlY1XH31Jh9//YdxQ48ykn61pJ7NMPUcbRW6rZHrDbE1XH/lOW698lmEMcQBgr/k/HRJYyPVwR4n50v2DvbZrJ5ycPV5vvGNL3Pz9vPsP/8SzdGc+cHLSBQnF/dZn7/Lc9fu8rXf/AcsDo955+2f5Y//u3+B5cl7KGNZXpzSXw40Bx1SCrarp3TtIXVn2A6Om6/8KGH0DJentPMjhs0ZurKQRrrjPdzlkhQje0evYkxVBmwS7GyGtQumfkO1uEpKkc12ien2IASUnRPDRHvto5j1Y44PMsN6i6Ql+sy0uk+z/wUuNg8xtSixSpnYJglSMHloW3ACRMooBCHUhARDfAs2LdDR6JaUDZWqWT75NaxYMEnHsrng+OrLGCHYIGip4gAAIABJREFULldUui7ElDRHkNCqQtuKNGwRKYPOiKBJTHT7C4btiAwTw+RwaWKzmVC6ps+OKnqs1WzHkZwFldKQJZEERC63T4jDQI7T7/t8+b0xyX31dv7b/92fIafiTE4pYEUx5aTgi7Qg6zIJig5ITGEsMO9sSlEkjAQVMcIic8vkt1ijSKlMTEmFIzpOA1kKJBEpIlKU3FmMuaD8hGAKawwGKSxkg7aSYbOlqttSqBADUiimKaDYPfARRfmbHFLmwgjUBfysdY2PmRQmrFbE5BEChIwle5zzTk/ZEOKI1YYxrtkMW/bNMT4KJAvy4hayOUDUEKRCS0EWspSRhCq54JR2GuIyiZFZkmMiK1lc0lki4kS2xVwFIGMGk4k+UdctwzDwf/zzf+q7/o5+4ud+EikSj579LD/6r/+vdDT0YuQT8y+Q9q7zdfshhx+eoIQm+4leKA66G1RyRk3LuvLQZiafuCVmuOVD9PEelTrk5vHH6R+e8IMP/ofv+sw/+Xf+N45f/ViZlhpBdp5xfUZVNXSLPU4ffBmjNCfv/D3Mlc/RHT5HZVuSsKzu/xab5SOuvPpD9Ktz0uocu2h49uwZVQ4lQzpGUiUIkyfHkTQGVD3DyVziBndexifPcP/rCHWNbIo9qatvMaWB02f3absDhqln7+gYnzSLw2POnz3gyrUXMPYIt9rQTyNpe4ns9gjTOUJqjNakaSDYCMFBrhiHM6LfoGJVmJe7B5Axisn1VDL/v9S9aayu612fd93jM7zTGvd49pn3OR6PbQwesA0FE6YQBdoSJaTC/dCipioRTZSWqkhVQ9RGqtJWidRKbVKRFkEhxS1J65YEXAMuxgaOjY+Pfc4+056HNbzrHZ/pnvrhXj6Vk37gY9jf9tbe+11rvWs9z/38/7/fdbFdnlHtP0HUki7lItJE7yIqGI0m2KpmdXyfbbfi8mPvBVUhjaHrWnQfOTt5wMPj36co91BeMj95RD25QL23jxElMQw4egpZIFWB1RZja3RlcEOivHCR1dkJsffEpkOhkSogJrM8Yet62u0pfXdGGBLPvvsFHt67z8HlJ9ksjumaY4yIDCj6fkAD9XRCaPqsRzUCLQ2qUBBU5m+eP8AKoeljjxQF5d6M5vQIkqAfOvAtVhrQWVdZ1WM2g8OoCYU0dKsjLj35BNPplG61IQjYro7pmyXetRRK0GMxpsyq8Ei+vihFlBIRAkTPYn6GnZQUVUlyCXGOZ4sIRGHOjXD+vFCmqa2iZ8CFAVvMkNrkQKBUyJCQqYWkCS7i/QYfOgiecrRHTNnkZqQC12NCDhq1vsVMagLnRSgUIbi84Yqa0K0Qknw4CRIXwUwmNNsV2TOUH2LKqkJKi9TnqCwCIkWsKknRE9EYfb4BioaUMjkCQBPoG0E9HmUjkzL0fU9MglZGqjJjs5Sd5GuiUqQYGWKgKDXOOazKhq/BtRgzInYRU+QC2DDk3kNEEvqB0G8wMoE0yJQyolBbYsg4NEsiiJ7gBaCwQuFij5aCYCeUNs9zkhQMbUNRFPThXNohEvhAlCpHB863d92mobATOrcmdD7b3VI85wY3GZenKobY4YNAmwJJ3o4ZK/Bdpm/osiKlbJ+SUtIFR/L5PmfQSJPjUlbbLFmJARE80g8E51jPj4ANDA1D12YTaNiiQoGQjnpkkHrK0LRE1+BNRM/2KctL+Oio1JS2afCxRVlDcppN2zAeV0Spce0WjMANiZGusCNNv23QZsJ8tWRSjYkpa++7fokXjkJp4sIgZERV2yyrUYq+y+jO6WjKZr1ESZFjNtph5AhT5Iy9tAX0Ahskp8shRyxqweziFD3ZZTnX6KHhG3d/l0/+yL/Nk49/EGMUrs+Z6xD7fI3sPQ/vvYkyOnNso+Tk1iP+6P/8p4jS8p0/+jHaBw1v3XuLw7LiDz73vxKemGLnJc+94ypPfvy7uXD5HYTtFj/0aKs5PTrmq5/9JZ78wPuYXXgatzmh325pzx5w7X1/jrTqeXDvNU7f+gq7736Ww71nOHziuTxxB7Ynt9ApYfeeJLmBrt9SlyN6t2C2fw0lK9Ynt1Fl1lvX9TTj5WKWkuhyxHZ1jHZb1ss548PHePDSb7L3+DtR5VUQPUPfUoz36I9vslkdI0X+/vLrhjde+X2eePI5fvsf/xx1DaWGAfA+UY8kUkTQkrYH76EsI14UWLGLLXTe6DgDYsjDNykpmBJkwZEL7E6fotC76PEzXLn2frzJ/Y2QGkajQ5IRJEoUjle/8geUbDk8vEYyFb1PHD16hYO9KSFp2rZnYsZsu5bOO6TtsADJMt69iIrQdlu0llSlJaZE053x7/3NX+Abb9z9E01y/+U45D57JX3mv/qrpCTwIWKNziuuFEhSoFVFHIZcovIOoSM+CLzvsuzgfCUfCcQUUFSQBCEOSJWnDVoY/NCjjCacH0JF6JDRIkXINpKqph0WSBRWGFKQmbPIkLWPCKLMulWhMrRcJDJXVUiQli70GJHh7UkWkHqSLEhDRHxTOiEEUctsMHHxPIw9EFM+2FgkQnZ4BdJrpE44b7OCVySCnWFmjyPGJYpcqlCFBVHgYshg+yRzRjcaVFWdW+G6XKLzuaXrGDDKEv2A1jq7f1KOcPzS9/+Fb3mPfvK3P50bxyJR1C3Hp1/izdMFP/szL7HqH9EPhrVfsq92aGODTFOwJSstsaLiiTijURvulj0TNeOKA1mMKALs77+LZy9f5NJv/Uff8pp//ZXfo4u5nLFdzlH9kvGV6yS/RAwSsXNIdBvqaoem7+mWC9g+IkbP4uZLkBS9nRL6U5RvERefZmzGLO7exM7GFPYCSUOzPsnGvX6LGU2o9q8x0okHN1/BGEOhdpFVtur5fgNpB+83CJVLJ1JKklB4UfDUCx9m8eAtFg9uo9cSlAfZImSFqndwfo2PuVCFTITYZQsaY6SacOWZd7Bz6ZCoJaYYITrH7Tf/CBs89772Zcp6jJcaT8CYGaYsMrqp1PTDhjScF3OMoHcdwuWMumsWhC5Hf6aHTzDfrFDOoUeOKDSxryiKGqkC25SNTTIpbLIUkxFte8b04mNsTxYInbcBSQpip0hyYLJzkbY9YVieoEuJKnch9NhihI8QYmLwa6z16LZjudlS1TWj+oBtcIxHB4SYNxOkmA1L1hKHACFQVxXb5oxqdkhRTeicZlYpbt98lZ1pSYqRzbrLNkQi3i3fbuBqWVCNr2EPd1jcvcmFq4esVz1+c0ZZzxCTiqq0tMsF/bZl6PPDX1HWDMGTRCSFSDHW2GQIVjG0A9aMaDdrZEoYDJ07QaoRpqqR0RGGXAjz57tCOSikKkiloR8Co9KQOo8p8iHIDfMcGwgtsRWYSY5L5ZJGYlKV+WFWWhZiy+jcJd9vG0xRMbRzilrTN4m6qBm6HmEsqBKhwFjQKZeW2qFH2AqNIMTIEAeUiAgk89MjiqKinI5p2w4lEwKLRuNFm/Wz7YYkDIuTY+rRBF2PKOwIRJ5ASgLRgbQZ4aZjRAwOv93SujnN+gS7e4iq90BmMkGRLE23ZTTdxdQ1MSWkgrLICKngEyJEhFb4mK9FnQ9oL9AqZYOS0AidNb2Qy25KffP3Ei8yJSIlgXcRFUCQJRtSK4SUbFZzogDrWoydEIj5QSdm3FtIWS6Ts7kzjJIkNLKypMETJFgrkQoGD1rIfC9RgEsEIUh0aGnQQtJu1viuzQIk0XC2PWbEmEIotnFNoWqsCWg5I0pF0p5SdyTn8bJG2RmxA2k0SgTG5Q6n2zm2skjfEb3Au0QSIESkbRqU1MgSivEOUNKvlrksOvSIokLb6u3NnkzgokNYEL7J2eYhgArIJOn7AVtXtCYQm4bJaEq/XSBFnR/udIFQuVAZ4pakJcO2J/USoxLaVBkzpTQiJuabB8xmT9NslvTiiEuzdzKePMtod0S9s4OIgubshO1qyUtfe5H9gxm7165z9NZXUVETz1bMl7/JuLqMCAVvrb7E9csfYBkH9p5/luevvcB0ss9ivubC9Q+Rmh4fFLuHY9762qs8uvkHDOWc97z/L3BycsTB1auUpuT2l/+Aa+/7MA9ffIk3X/pFOrnk/Z/8aXAlB5cuE0XeQtliwnr7kKLcoSxLhs2GbntCFI5qci2LQcKGorCIcpfkE8oUCCFQRXmezV+yOr0HIpLalmJ6wOrh6+wcXuf46AGVdURd0C6Ome0+wXxxB5VypOfey7/FO97zIX75F36a3Z3I4HJcURUJbbMhdhgcjRPYIjEtJU1SFOmQnXrK4uwNorDo0uF8pDQa1zmiSrihxIx2UWKMlLDoF7znXT9Pbzzj6QUcC9pW0Lee0XiCqScIvaRr8tZpszzCxzWXDvZp+pLkPSFEtBqQ2uSCa/SkkOjmHbaukVEgR5Z2s0F5j5lY/q2f+7vcuHn/T09cASHxSGLK6xnvYp5QSpXNN84RiGgGXOgoTU2MAyLlHGQMgYDGmBIflgg8SkZibJHC5oOg61DG4INDqEjyAwJBVJk2YFS+0GpVIoQhDuFta4tSkugzEkhqnQtmg8s3BSEQnBuFXEvWQ0e00JjgiLIi+gEXe0ozxuNJyZ0jvzRCpGyKkQUpOGSM9O6cmxASaToQnUBaSRIF2Cm6HKMKjQ8pt9mtYXAJIdtcxFCSEAeGYYGZPobwAz45pHAMKWfxVIx5quw96ps6v/M4Rfz/CXUP3mGxIAeaTc1o+j1sXv517MUCf/sAb0uC1djesMGwmQqm0ZOiYVcpCiu5sZ4jqgnFyUOqyQXm7Zw9tU8j5yx+b8Klf+41zWQHsVlii4pq52mEHrN6dAMxgMTh7r6FnU7pmodsN3Oml5/i4aOvM9m/TB9h9swzXNq9zJ2vfYH7r/wRl+wBi+6EelyDkCw3tzCqICVFDIEYaya7z7J89DVOj97E7B7ifE8YtmzvN4x2S4TaRcSBoh4jTcUzz7+b1fyU+f07FLML3P79f8ZyfQfLJDOW4wZHi7QHRLfIhyCTLWP4PusbRcoTpNDy1qbDvDpjtbqH6zv2Dg6zylAOjA4v0Kw7di88zvHRDVTfsdksiamn3Co6HxhVE0RpiVJQKEMMLWU5Yita7PgSXiSa5T2s0XShoRIXGJotxV5JUdbIriXEHSaXLjK/9yaq2uXx938YOx7Tbjccp1dxzQIXt7TrDUZNKW1Fv3pA222Z7Fym0IF22KCNpj27gzIVw2YDNrKZN5jaMLYDzbah3yyx1ZR2G1D7h6SQdatROGQ0ODegU8Ny5bj4zLs4enCHlBLt2YqgHaNSstmc5MxkcsRmi5YSITNiz6qK0egQaQUnd9+iLGtO7p7hfMO1Jx7j0YM51pZItUcsZ1jmkBbosmS7PKVrzzi48hxiNKadL9luF5wtH7K7s08n+1yq0iClZ2RHpCjYNkcI8iRTa4kUgczBCXQ+UPYlhkC/7oje4dttNneNdxDC0yzn1LMdUhgoSoMVmtAFhtTRrhf41Ypqf4+t0kzLvYziITHevYjDU5ieGBTa7AGZaCBlfhg+vv0ai7M3MMIwNFtsobB6it5/jNHeBXSlmI5nYA1tM1DYGcoaosukBhkLkAKhKmRKHDy1h48BpQQpSSQaLc5V6ypvqqrRjOQdcuKQkxFaTtkprpPkmLZxVFWBc4FCwU6SOJdQ+nzyqnOe1fuOFCVlaTItRuf8bKkTmAic55xlngQngChAQyIb18Qg8lp0s0HgaVcNWmuU9vimy9dfWyBtRQo9vVEktyV6AyWZB2oKrDZYYTD1RTwSZMpbhCihiNkKJjw+KKxUeNfm+JrPWUgRNgzLJY07Y7OeM6l3iEhMGvDRcWl6mB+qyim78iqeJTGWeSoeIqIo8kQ2Csw5ZnGIDh0TzbbleH5CcHmKbSEbFMsdhi6CDFRW47en+EXDhpvZclZN2Cpwg8doRx8SVbWfi24BpK4wtsSYyHbTUgiD6zwMHVF4lvM5+mKN8z1uPqCTIqWGeraPrUqW8yMmuxO6TiCTJ1qFkgO22M3iGSsptGLdbJlWU2JzTOg3jMe7SN1yfPMPCc0TVNOnePOltwjuFvfeepmP/9l/lT/6jU+zPvkc0luaZs6kvMCT3/GD3Hrpd5GN4vL4edresbc/YlxdYefydyIl7O5EinrE4mRD1I7NWS4hXnjiMcrph6inu1w/uMp23bBedjzxwe/h9O5NmvkjjlZv8J4f/wn299/Bm3/4GYRq2T94nqFfE5JEFlOEirTNCq2zwc+OdzHjEdvFCbOLj0OI+L7BTi6yuPNVhsFhqxnjvSvEEPJD3OKEVCnc/BRbP8FXf+MzBN9w8MzjhNiQRvsMm9tM5ZP8b//9z/FdP/r9/OE/+4f83m/+Q8wu9EN+kHAJrBF0zlBEybh4kqLc0jT3aZ1lc9rT6vusFvdzRMp64kYwhEgqQhbSiERRtiTf0CfBphdUo5L58ec4PrrJwd7TxOIqu1eeotipiestXbOk7xaog2sUuzMOZnsUVjEMCwo8biSoTI1vHaYs8c2CwS8o7Qhdg5aSQQrEkJCVZlTuIlRCafMnP17+yzHJfSx9+j/7KyR0HpcLAXHA4xAhY5oyySDz6bP+czhv1nrKosb7PHERQmQUhswHPREGSlsw9Fkr51Mg63igMCUhZui0d9mjnjIVHCEUhIgAlMzrf20kPgSCjBlbllxmNcZ0HoFIJJkypsxHtM6lEmTI7DeVyy5S5DJCigopJVFbXLshEJA+4p1ENA4XE3sf+ChtFNTVDn0S5Bq1y58jipA8UttcsiCSAqDzFFv0Lao+wMrz1xPn5TihzmMS56s+LYlk1a73niAj//MP/MS3vEc/+Vv/CIggCpL2mCT49P/43/C/vwhfff0hY22YN48o4yiXbEaCTdcznu7TLDou1zPmskP0PS2OixyAW5GmF7k6u8ZjFzzXv/Qr3/Kaf+3Vz5MC1LsXWfctxhSk9pRHL3+OePQa5bWnMXKfk6NjJodPU892kVoRfM/eOz8K/ZJHN17Gb95gb+cq7F/HdQuOXn+J1dmbzC4+xeZszmx2yHq1wPePkFERREdoG6IaE6KiVmOUrWjDGSEW6GKCERlBYzVs2jfPH7L2sEJhqxIjCoSyNIu7xD5SX3yCiMaHSGkrmqEj6azqHRUl/bCmrA/o26xcbrpj9mY7BN+dR2k0ha1Yr1bsXZgyKOjtCvf6nHD+86FNyOWjNEHUiT6t8W2kmFzhmfd/hDvf+GNiM6ePkp2Lh8xPHhF9yXs/8BHOFkcsHtwm6DHv/q5Pst2esTg5Znv7HoMvUFpy8Nyz9Nv7PHj1Js997OPs7+6xOjnm61/8PJMir7M714G22NCiZE83eCKJAkPTHWcMEArpArqeotU4T5w9OC0xtkSIRN+1SPLhbAgDuzvXuH90i9HoEoXp6Dcts3HNZptVn89+4LtZzl9jM1/QbJaklFBK07tEPdohKU1CIlXMQHkX8K5FSIswhtZ7Sl0z2pmyna+zjUg6rBYMPuLaDcJYlFIYY1hvV0wnhziX8VbBbbKGMwiQhiQSVZm1sMlIhmaLSJK+XWfVcd9ncogpadoNutAgxyzmJ2jRoxCIMGBGI4SCvgtoldAicHL/DlElrBlTTfdIfY+UGicUQTuSLCEmaq0gSXyXiJHMWN3dYeMaVNwAG4y2CLuDcPXbrFgfYt52CY2WRT5Unl8DrbW4EPLKOeX1v3Pum+JIlLYZZ6hyzIBzlq8WCkRm/iLt+XVaEJNEiIQfApVV+OhQQeEg4xhVIEUBKvcsrDDZ0qQU4u3CcO4WAHjhiIhsqQwRhoEQtiAUIhpct6RvN7l0aRQuZMlGEiUjWxJVQmqB0IDIZjSpMu1ClfnrE13EaEmMBp9ipmokSbYaZXkDgJGGOLg8oeoHonTZREmPdxt8t0RIRd+1aJ3tdH17jOsbKnU53w9rnRFsKJRXlKZm6B3BSISUjCZTXFTYcpw3eCrhY3p7cKFEwqVI3zu0BkQkBk1yHUZnepEyBjdAkjozMcuM56p0ZtLH5ChtyRAFvst5SG0ikEgdBBcxxjBEhzI5imKUxfuBtnPUdU3b5QcLH3qUNAyuoa528V3LMLQImXD0PHH9WbbzNdvjBlNYXILQt5Rmyqi2vPLqmySlce4Re9MDTtYnjMbZViZ6TTcsKO0OJ6sVte7QjUPYPcSB5PDgXbz4B5/nnc9/gtGFd3L7K2/y3PWneP3mq7z7Ex9l7TY8fu0S0fusV286Vus1VgREqkAkfuOX/1s++uc/RmUF00vvxS972uUtnnnhezl7tOatV3+bd37bx/KGIvY0mwWafE8txnu5YB4jtihYPLiLdz1mVFGImtM7t9n0p2w3grr0HByMGbo1y9UpcmfK/NUNX/t//hdivMsmnPCDP/o3KPcfY31n4MbvfAYz+Txv3HEc7GiKIpBEojBFtlLKvCnuh4rZaDfz2+uW3g9EOaCFJKRIZQSr8w31WGa0YkgCYxNBJFwQ1KWgGxJCaSbVPr2rufD093HvS1/g6Y9+goNrH+H0aM2bX/w1inFHsBcopgfMZtdYblpmO1eoJ3uYoiTJQL/Z0GwW9IPn4Ooz6KLEhwEx9LimgxRYbk+xOtGcnKAl/PTf+UVu3Hrwpyeu8N5nrqZf+/mfIkpFVY5xccD5Bm0kxge8LlAJIGQEjs8XNiMlKbR5XYwmtFuSqRDSEYkYkR3XQiiSG3ApGzhSCnnSoWxWMrrsv1amwoUBg0RZlXWGqPPXkyBjttEoiZUCYsi2FpsRXVJKZIzEGDG2IiRF6BuETsRvqnb1GJWyZQ1v8tQ1BPw5NJnYsV2uCJuK8bMvoK9cxRhD07XnNISMjBJSgsm5PhcDEJGQD/fBgRIsHt5ntv84SYZc3JIZfaZ8XsdGXUD0+cCuyIcpBElJfuX7/9K3vEef+uyvZh2lkgjR88Vf+Qx/97fOaO0D5jdX4CVJd5RMkIMkKs+x31BPC/oW6nZLVyWWQ8/I7qEHx66ZcRj3eDD0fOlzf5u/992f/JbX/Guvv8hodonNsCJ1a4bjOwxDh/R3mb/2Jba95rEn34UoH+PkZI5JgVTUTK1ifP3jTC4fgix564/+J9ydMxZSM97boxISb2oOr15lfv91tsc3GLb30MDp6k2ef99P8cYrv4OyF7FJofuE2Jm8rVCUhaEyNZ1P+FSi1AipW4TxKA3NJmGSZEgSETYElRiLXTb9Nt8k5YRLT1xHlS1TKbj5jddp3SYzX72nnOySkkNR0XYrlBZYWaKURRiF65Z0zvHM+/4M92+9hju7R/JLomohVjz+jk/w+itfRNua/ckYH1rOjk8RSlKOJ6gAUdnMyG09XddQ2QJV10wOLyHHU9rTM9zpLWIMPP3xH+Phy7/LGze+wKzY4+DK85y2iv3Ll9m9sEM/NNhizPrkAf7kZY79mL1xydndr1PYnAs0UhLlwOhgn+XDRyQniEPP4HsmhxeRRLbdGlGMCC5fdI3WJBEJzmPtHl5nvrOWjtBu8U2DKcb4vmfbdVTlCFUokvIYM6IbIloX9MOGUT1haNd5BW40pd3Nylmlc/k0JDQlQeXoUXARIz2dazF+IBmF7z3Yin61xY8FRo4oUCgt0FrRNRtMkeNTShi0EAihGVKHtZZu3aKqGkVESug2W0aTksV2TYyBSo/yQVpGks9tdR97vMuiCzd0KFqSMehCsl5vUdqi6xEyFQQ6Ygw02zVlNaYaTTJD20ticEhb4kPKEyDCOf1FUJsxSSeE7/PDcjHOmxtTQVAklQ/tWqlsUxOSFB1JFm8/XBs06IJ22KJtPoT6Ll9n8gPHuVgneYLIBVoZFELZfF1JniF4jEr5Y5U1SlaE4BiER8mAtgZNnipHmR/ktTTEmHIxUMCQAlKDVhUhduebtkgUEt8FpChxocEqMhYsCaww9DEfRJJw2UCWQPoi/1mUKCWzmEHIc/Ywb3POgbe55wBWxjyNTpLCGjbbBUVlcduWJBToBhECrl1B8lnRqwuC81T1NEt97IBOmqaT2HpKCJ6yqumbNVoKmm6LFBHXBExVElO297nOn2ubgZjQpibVU2KQaBsQIW/BbJVzuil5lDIMboPwkhAFXdNSWNiuFoCgG3JXg0qzNx0RziN2XedRSqCiJJWZ1qPKxNB7rCyRKlLbMc12TdSSUmdKRNtEBA4PuOaEqhoRBaAMWiSCTzSrNSoaVFHimiXdkO+ZKoKjyTGvoDCqpJdzjEjUcY9BOlwKJDqUDKSNpo8rrBzhfcemb4kikMIZ0VfEpPDpjIvXvoNnnv92hJjw6o2bfPDj34FqOtR4RN8ONMs1Z+t7+MUNPvYX/12snKJFTdM0LI7e5GT5iG47p64KHr/2Lo5u3snmLpOY7M2QUlKPLrA6W7D/2OOZDXxyj/zNKnnwlZu8/uav8+y7vovT1/6YS9efQk+vMJpNIUiOXn6dX/0Hf5OFGtiRW2Ql2T2MfPij/yFDlLz02V/mYGfD3c0JtoBSWZK+xI6WJO9IAqyqc47drRCmI6aWpu8IwiOixZj895RMRCGxRKTUSOFpnEAZec55FlRGYtQOzdBTppon3vcTLG5v2Lt4iYcPHvD4Cx9kffQqD+58lr2993P08MtYs0+wCucc9f5j7Fz4LurJTlY9FxZNYru8i9AThBkjRXme509oEo7zbkbX86m//rf4+mt/iji5KSXK2YRmvcKlLutXpcWKgkFkkHAwEqLCaEMfeiSOpnVoqVAqIYUnCI2RkRAiUih8CriYqJQAoVAiiw+UyGWLIQzIQaGpUEaB36AiJJlo2x6tLSI5lCkIQ4tQCSsFwQ9Ia/ERtJwQXM7beucy0kVAdAoESDmGFAje5YtBcEhr2boEQtIvTqknM6QI9O3XWZwEElAU74FqinQSJBSmJqYskei1IoaICQqhBZZs/5EJRMwECUWirvdQOuF9JIgSiSTEniBzGc2EeI4mykpQkQJCQvTtv/AehZAwRpPi5NpVAAAgAElEQVSSQFBRvHef+AXD4s4x4vCAB/MT9oNgG9c46Ugu8EQac6tpYBjYmJKlHiiKMaUzeKGZu55b1wb+0w//GE51/8JrrucPcfOH9Iv7ODw71z+Cf/QWs2sfZ/bMJxg99gJf/dW/xfprv86FJ95H1JKjt15n9v7vRppIv1kQIxw8+X3odxoubBp0OGMQAXd2TLfZsnvlnVSzy5y99busVo84fPwvsWk7xrP389j199Aub9HNT9k6l/E6ezsM6w3NsCWtE8GfoaYjhJySusS23Wa8lOtYPbzJ4aVLiOmFbAarx/SuQcuG9YOvEbqBI5no2y2iUAQtseMLiBCJQpG04sLFd7E4vkuKkaPTY+qRwNiKUT3h9iufQxQWxiWT0WU6NyCl5MHJ6+zs70GQbBNQXKS6WrC+fYSJCk/CaEMKAnRkiC2V1vhuyebUUcgraAIbbdDlDLd4yIP7j9DDITvvfoFkJxzuWuS44OjOS+yNL7FeB3yrcaN9wp179G6E1bvE9hTnHWIyIQVFt8pRn2q6i2snjOqKoe2pr+5TCc1mveCJ5z/OZr1gd28f7+Do/ss0ixNYrlGFJqkCYXeYXr3M6sERUsyYVDv46HFpYL1qKUXPaFxxdnSL6fQKYbvFFmMoFck7RArEbYMTKQtdBkcaK5qmwZoRwjt6mXFUUZf0voVCAQNyLNDNFr9dk+oio8FmE8a7NcuHc8zuHs16zkhbolSQAtF1SCHZrhYURYk2FcKOWW87SjOi324YvCP2S1KU2HpCNCVyDVrEDE4fjrHjMUPv0F5g7ZRkDNEJtAyoYowbIrv7+1kn7AK49tzsKGHIekytMkqoLIrc4o9rYrsmpUSIJSJEiqLADQM+RcpixLDp8LrA2CJPLgOE0GCUISTFIFpit0VamWUMQWcNq8y4xkQg9VsQmcWcdKRzAqNrCl0Q0pAnpUkinMT7Far0iORzCkEJGHqcsiQNsff5uhwCUioUICSYpAgxEhlISRNTgpQQSEpdkOKAVRoPdN5TmhKnBESFSAklS0LKB7YgAsLWWSrkPRpB1Cp/r0hBoQ0xOrSUpJhja8ZGwFCcH+yFlVg1Jfncm6jqMVHMGAZPqCpkUNhYYHUufXZdl3OxqUYYhS1G57i1hEehqxHrxQJjp3g/4C0kZTDFDkJJqpFG6Zhz/rIG77KsZehQWueP0YxIIovctZ2QtMSIEid6rEoUoxojJNXOAcJaIgNySETREyJIoxBRMioUCEFMAREVCQ1DQKWOwbcUyeLVkLFywhCMQUVFNywwNhIGhynGpKhwySGjo+l7QgNWgvctbljnBympia7FK41CYMspy+WSoBwjvYMm4mVAJENtx2ibsY7z7iFjvUMaQNUGW+8wxA2+H4HUxORZnsBkorjx5S/ygY9+H3p7n5e+9EUKObA6vsN6vqY8VPzrn/or3HtwjfVxR3t8Cl1gETYc7Ex58MrLfPsnfxQjJEoW7F4N2DoLKt545Y+5/u6P4A1MLz5Gc7pCAOiCu1+5wW//+s8z2TvkB/+Nn+L4kWUj7nH/jR7JfWRXcvrKLT5785/w9NWGsE3UWiBUIjaCL3zhC5RxiU83OWk0RkyYlge0XqDTlM5ZqlrhY96qdG7NpllTTUdUdp/gH+BnkfhIY6oRpUi07gTEwEpCajxlpRAy0DYRk6Co88B/aDdEYei857UX/z4hFNw8WlFXieXv/QoqSYRac+ve17HmIiF0+C2gZqQwZ9h+mbOlYf/wBaSegi5Bz/I5Z3AkEYEIXqDO9djt0OOEJWUWzZ/o178Uk9wXrj+e/sl/+e8jFRAi0QeGKPL0Viv80LDdrklBUs8qRqM6r5NjzCFuH0ghIomZNJBSPhRqSd/3lMoQz804mVkbsu/cJ7Sw2QufQm7javn/2c7CgJYq97QKTQjuXCVsQORcbZY6SIyZZWZmGDJGJWaSAbrE+x40RK9Q4/1cXJOKbrFFWIE/voU/+gZDt2TpS1wU9OkiT77v+zi8do1oyJk4qQkJUgqk82kCKWWJhdKIKLJhKAakyhcRobPaUhpL32f/uowh8+9iPjQrKUlIhr7NOWQC/+iH/81veY/+8j/91SzU8ANWa/7zX/hlXn2x5Qv37jJpPdpviaqlI1AIgQuKszDnQnUVG+CBW7JfHXAs1hy6ESSDjAn/5AX+9g/9Rb7/xy7zD773x7/lNT/1f/wSL3/pKyyXW378Z/4GLWcsHtzn2ns+St8sET4R2WDLAooJcfGQrmuwdoadHLB8cIPNw9ucvvEK9d5j6NGI6uo7mVy4iEiJ9Z1X6dYPUeNdhPQ0J7fpFifZcqcdQwP1aMZieZe9y09x8ugu42IPO5kxtCeAxrk+l/WUITpJORmRUkBIzdniDtLOqNSM1PdZbJI8MbQMXYMViigSVT3Jn7CCLkKKGpVKkhIkAkoaQmiQylCPCtqmY7Z3DWEsxw9uZKVoP1BXM9qhQdc1odtSywpvJFEKUoLgBipTMixPshDJZBSdMpLClDz34R+imMxyrGW9RgnLgwd3OL31Dfrtiuff8z5eu3GDNGyQRPq+obKG7dkR9d4lnvvo9+OjRmt4+OrX6FYPEVLinCPGLYGG0h4Q0pQr1z+EMp4QI1ZJ7t94ET0eIWSNX2/Qo30G17F99CplNQEFoWuQxuAkyKTxTUc5Kzi+/RJxCIz3n6Icj9ksl5AcgxgY6RHVeB+VIiFKBp+wlSbJgO8jhU202zWVnWGqMY7cDhdGoBH0bUuBpUkD3i0Zhp5SwWhymcMr78CFDfOTY5LvMSi8dyhTgynw2zOuPHudN1/5CnFo6NrAeHeUUUJqgkChLChZMSpnBCXOJQkah6dvW0SzwG03iFIw3nuapM+NZ3FAlxdzITY4lAgMfkCYIh/wXEQJiEMP2uTVYARraqLM1wZiIPgeYkSbEbZQhCjYhA5D/TZH23ufFevfjIIliTEGkbJa3Sibb0hS0YctWibCEIjnW60QPaOqpOna/O+IiMoS24itpgyuQUsBUuK7nFsFcl8BCMnn6XrMhd+UEkIr3JBQRp9jvUQueCmDRNCFnqIqzmNd53hEF7JC2UNUIkc5RMpxCT+gjCa5hC4tYejP4yeehCHDns8jZjrB4NFSMHQLonP40BCiZ1xXKDHJXFyt6foNwlYUdZV7D9Ejhc00CxPRqiRsWkSSaGsZui4ze+lZb05IeMLQEyMoa1DCUVb7CDkl+oQuBF2zfZtqo0S+P0ijsaMJUhUonRj6yNDmDLI2Elw2U7a+O7cdZjV0ip6maTCFyeg2n9XJk3qarY0i85uHYcDKQNNuM51HGgo7IUmPdy3SBVbbJSE4jFcEzmU1wWPGM/x2QCgJwrPttkTRYYxhPNpjVB1y78FraBzaSGQqEDqz5HOm2bPZelTM8iFtFav1HG1rohuQegSipdtsKZRi0+ZN1ap1KELuogwCr3z+edX5e6Cwlluv3GB3dIgeWdr2mJACy2bFD/87/wGPjT/Bat1yducNbt+7wf3f/zSNuYKZ7vMd3/MddH3D48++hxe/8CKrB3/Muz/4IZLdYVKOePO1N/nuH/kRXOvZ3H3AH3/u0xTPXKD/6j1u2b/Pv/Lt/wlpdInoodCKu6/9IfphzW/93/815WOJD370z3Ln9S8xf3RKUSR6wJR7pH6H2nqUmKDMwLi4TNtFyomjLmuWTUs1KnG9B9cjU4/SmnXaIoNiUu5hdSQqGNwGnXrWq4eYUWLVLdktpgShSMoipCZ2DhmPcL7AGkkfJUYe4sIZSmaClRcOO7JYI+ibnoihLC0CQ9d5vJcUZY/zksPDP4MsS4gjzPRxZleuI5Rk6D0idhATRkmc61luzxhPdpG64Cd/+mf5+o03/vTEFV64/nj6x3/nZ3LZREqkUpn9Gj3D4DMU3A1oqRFKYE2RMUNG45oNpHzx9cFhlMZ7j+/WqKI+V9UJYMjNc6VJwiAYspIympypOzehhXMCQRSRpDQyCJQySB8ZYgdIBIaUGoQAZMhxCK/yoRuNNBotDC5KklQ4tcN4vEeoakI6n6xuzqiUIZ5+g/b+b6LLPYQecfveHNdLErtcfPcnObj+VD6QSksUPk+gYm4wQ0HsNyQ0QiikBJ/Obw4x6/CUVRA8PjcxQIosjogi/78hElOGrQ99Cy5gCs0v/fMIsc/8IlZLlDEYU/ADP/9f0L8KHslsu+FItyxO5iSxgQC5L2JxId+YqijRKDAVB7HmptxwWdZc2HuSv/qX/zW+83sK/ocf+FYBxad+7T9m23+V9uQN6id+lqe+/dtIqaCazAh9SzIFSMXm7BFhu0bgCM0cYab47RpbxnNt55blYkO9c4HFgznlrIB2QXP0VSxTfDGj6Ty7ly6zOLqNEhbXHtM2pwwxYVKFqqbYSQ3O0stIDIFhaKiKAqEMm80KE2d4LajHEqX2ePdHfoizszn3v/5FcA0xdAidVZRKOIKTSDlm262xlWLZLaiUIUSLjgVB5yKgLkqErrBSUFQTLjz7Ae7feZ3u5AaCzIjVdY10AmFK1uslZWnxbUBql6UFdU/clthYYsuBKCKbxZy9a8/QrOdcffwDXH7622nbBl1I7n7jJdywYLZTc/LgVVZnc0p7ATvaw1hJt1mjlMCHHh8d5egxGuDK3iFReCazXZp7N1huG1RpafzAx773J3nxc/8dkSkxNrjNBhsT/bbhwjvfy9Hd+6T1A3wMjA6fg7RFWkXX9JRhRRsMZrRLXc7YLo5yzCj1JKMIoWY23WfbzIEGtEUqg5T2bZ0sLj8Y2qJCaAMyMAhPcilnTTuXf0bSQOS8xOQFMXmMyYzJQMCIEtCIBOvlKZAox2PS0CC1IgVJtAVKlvjNGXI6ZjzboVksKIoJfdfhh0f024bxZMYwRNi2kBwUClPO6CPYqqAsZgSX19wJde4dEAwuHzq1lgglUVriYyKSznGwEukjCn1OZJC4fouUmiADJINJCaQgnRNfVJJ0IsIg8EJkxJkWEBNOJpQSkAIanbdTISBtQewDWoSce05bnHOURc2m2eZ4WIRSZqpN5pI7RDtgzIg+tSAKMAkpFCoIhC5Rlcy5WpFQ51lR5yUhnTPORf68+75nCJ5RMckxiiTOD3m5SOxjwGpDTB6rLDIIvMuFuChy419In78OKWRijkwEAkpmMoQbtsSYi2cjpZHhfBjStSg50IYl3nVgBAV7JDHNPRGVs5eZGmQwVBSVwemUqRhDi1SKdptJCaYqGYYVyXXYQiFKReoVQhdIEZGVRnho2zPapscHiU89E1uhlKIspnm6LiUpQRMUZVnSx0hdTdEii5F0KbFK0/aJkDIrmBjyAViSiQoKlPDEkDPXzg/5MK3M28OdsqqRJk/uNYbQ9xgkXfTIMmF0weLkmNG4JPqAMQU+dCQnQVQ57ykzylPhMLak2bQkDT7AweQAhWA7dLiokAZ0jJS2IEbP1p0hgse3bY5ZbNZ4BNokXJ8FHVWl0KYkBuiGHi2gVBVt3xBkpI89k1nNfLmikJawavIQTHtwDqSgcx2meAen9x5y5bF95o9uYVSDMQVhiCxCy9V3PM1z7/9hwjowGhV8+fP/F2G24kMvfIqXfuezNMdbWiwPT+dc2b/Kt33oee49eolbr/w+4uIZF/d/kA/9+R9Duhl+Pef+i3/IK5//dc6Gu7zwnX+OO68esexehJiLslEKernGFJoqPAVDjp1U4gCHx4w9Kf2/zL1ZjGXreZ73/OOa9lBVXdVz95nJQ4qUSEqmLFlyDNtJNFixYyAggUASAgSGYUROggy2bxQlV4GRi4xwENiBZTuRLCUOEkGS4VCxAokmKZOUGB7y8Byeuefqmvaw9lrrH3PxFwmItGVe+EL7qoGuruqu2r3Wv77vfZ8nUjUtY0rMpOBs9ZTDxQHb8YK6a/A+4Lee+cFV+u07eL9G9xWpGRndKY29VYhVbsdstkRUHmMOmfotQm9gqvDS0dqGnCaccOTk8MmC0Gz9E/abu0hpkCqSQxmc2Kqi975sEuWSWXcbJxoODu6wPeup5nc5uP0SuusQOqK8RmjIOZRK0uD55F/+a7z69e/skPuHIq6QUiI5T9PVODcScibstgihSFJQN0uiGUsBjdLcdH4kuYwG6rrFT4VBG4YNiozpZvhY2H5ECpNRRzIKKSLjNBZosc4IX7JjPgzoS4NZkiCEx4tY8rbZo6QgxIjSghAzhVB5qQ02GhEyyHw5efMY27KaAsbWpGpBkeqNSDExnj9Bpcz6wafoKlMgzwpQF2xCw35riJsdgYhUttjdUi6nxxQBAUwltyYT0l3+vKdArFNZ34lI6EtwG22QykCEkEasagiTA63wYYJQcD2m1YT07auAuq4JbsSNOy42J8ynlofDe9TUfGl6D3kimB1UqLUkCoNUkqvLjjc3x0gnyaJDa4gi8VSO3BZHaKURT865frc8DHzr6+6P/6ekzURIERdWOJfIaUCRGfoLBBahLN1sRn/+mOrwOrmZk5Pj4cPXmJs7jOGc3ekFrn/EOAwgBKa5jljsE7RiHB4Rjt8h5IZ1PwccIo6MbkDXDY2q2WwmrDEEp5h2PTQGY+ccHL7E8mBO6h+iJhBmSZKRyQ+4cMzv/drfYtffw0uHFJFWXCFYi9EdUi9QtiIQaJb7rNdnNNUBle5oZx0Xp2dUumJ5tE+/uqCtW9brNWG75vWTf0hOEVM5rJ3T9x4TEill3PkxVRiZnKBeLPCrzAsf+SM8frzm9geu8+TR19k/vEWIA4RD9OEhN67e5OL8KeO04f4bX6HSNbNujxsf+RiP77/OfHmTkC3WS3xyhEucjFGCpl2WlWqsaLoGkQLT+WO2x2+z7XtuPfMiwhhuHhxx/96b3P3wj/Pord/l+OETZvUSZRSmrRnOz1mYlt38OjMt6C9OyWrH1u24/fwHuTgeqOqWhKQfzxHSMPj3cLuM6hYoPXF2dh+lAzGMRf+bKtrZPk7uEFJy/Zm7+MmxPn7Cw5MvUAeofYdedgyzKzTMmFyP0IK6qi9LUQahJNv1hrpNCATjNKGVxDQt3f4hyUV0jiRZM44jynh2q1Oq6hBBwObE5vgUpQRnpydUppSgbt76Hs7OX0PQIJv9IhCxE9Fp6rYhJRhXG0JOGAE6Z0wzY/DbsvXSHePUoxGMYUDVC8piXjHlASVEEcBMkeATy6v7XJydF+Uulz0EZQjZkaZcMtIktCn65ALzVmSVsMIgsiRMEERESo+QMG5X1EYy7HbFUKSKQjT0faHS7LbE0XOxuUBIR1QD1IacFGhN2G6odYOq9vBjRnb7qG6OGCOmrdFZ4I3DhxHGiJCSs7NdKRS3+wgUIsI4JmKWKOURaWIcHFbqwqVFFrOlyPRuACGxdYuoC8JL+0AiErMo00s3FlqE3WFNTaMbpMo4N2IEDDsPAayGoGdIWuZHM+LmHK06vC+qa+8jKXlk9qy3F5imQ14ITFVjZnvIrBiSRFmBaDpqY6lqQUxNyaxGqJpLlW1OME1YW9PUV7EtZdiCZAgji65mGj212iMKiXCBA2OJOoN3iOxJuaLu2hKLSYl0eYgTlS5M5hBJSZBcXzajKpeHNqXQZs7ecgaULULOGaHE5ZYmICoDsUzhZ3ZGkmX6trxyyDT2CKnY7ba0s3nB+oeSY1ZCUtUVSgT8FGi7GRMOncoGaHIeHyErAUrgNmccnz8ixYnl/DpW1WhtiDlRtzP8tKNazEiqIvUj07S5tIIq9uYtq9UGWWWkl+hKoqVkmjxWaYTz6EYRpokobdn4TIraLBAMHFybmPpHtEtTzIBJI6XhYHaFR++8hc7/H9tH74GJ5cFGHHHv914jaU0f73N28TmkqHnuu/81fvMzv8HLH/hTGJZ88JkP8+ix45/88qfobtzGjgMP3nwFrCSsPJ/71P/D8kihTcKLBqqK+UxTT/s4J/GyZbms8GGLNAqrOxCeNI0o0TKvGvqLx+zPbnGx3TK/eZXp9BGeRLd3REySw9n7CTIx7CkqsWEaRjb9jq7ViGoixom869mOPcoIarHHEEa0sgy7icViyTRusLIBZYlhxo3ZC0x+pJKaXRipdYVSkskFKjsrRxhVkfMKqyTD5hFKzhDu65y++np52Di4wt7eh8i6xlSGKQvqlEjh2+ON/7zXH4pDrhSFVDBsSvZIWYPtFnhK6ST6UKgCFO4hMiC1RORIDJHdOGJtDcGj64qURpwr1ICQFEJrcnQIJEJl/Oip2yVuFwgStNFF4SgtUlh8COSUyUIgjcVEQ1KKEMrFL4mENJqcSgnDpYTNqjSHVSalCS01ox9pzJJp1zP6R9C0YCTTyQP8+pTJXSDxqPo5tJ94vG149v0/wOYLn0OLc44fvoeYX+PK3WsIW/6TCwT5UonnXaDRDTHsiCKTKKgxIxIplaKcsLJg1HLCxakEvIMt6DAEIXqsssRQEGIpJ6ysvu1nVBSZAmtr7h9/kY/fucL7b2U+979+BnvjOqvtRN3MWCnL0ZjJi8B3feweb/2KIoqBc6E5v3sNtk9ZyOs8PfFcMfAzf/VHee7lumB2vuW1fuuY+cEcIRxKGlSjIPRszx6zevAFtIvoakZ+/8cxyz2Gs0ckv0WQqcwew9lj+vWGSGT/5oeZ+i0Hdz6IjzvOHz3h2u2Pc+vaPsPqAauT9zh95x38uCOELUotkHKJrzWLPYMfAnKMNLOWKUfGaU0Yjrhz+/s4uHGdW+dvc/HOu2zXF6yHYzaPP010ByxufgTV1uTRMa7O+OAP/yjO73j45VeKijpE0Ir54W2Cc0g1w4ua5z76MqfHb9NfXLDd7giTRGvNkCeatiL6oby3c0BKzTRMTGHASIHMosDop8Tizi3efeNrWCn4+tfexijN6nhFVe8Rdw+oty/x1he/iM4DaXiENJHVlAlyx+LOv08/GVY7iL1j9FvUYp+QQemKwUFjanLV0tkGMzvg0dtfoWOi326IY89XPv9pDm8/w/LwOY7vvc7R4U2uP/dR2uURw2qDspFbV6+yOduyOn6MlTsQhvnBgiy2KD+wWw80y8NSqnQeqRuS2tJV76Pam2hVQ9aSECYObr8MJJpFg64PaZsl/fkG01XEymKl4s5HKp45foq0ASscpyf3ePDOV+h3F0xD4VofDwO6rjhaXiG6mqbdY7V6hJIOjSRKR/BrIpG2W+C8xNiGuqqQNqJnFSq05Fix267phwlbQfQjQs45P3+P40efLXizfICWSzozx7Ag2oyQFTInqnlVFN4xInMpWlWiLmpMqal1QwqZsFVIpdHGkrNiZveZxm1ZkZtCleiHNcZUJD8CA2NIWG3K9gyoFjPyMCCNROAQ0oCQiARaCdyU0AK8XzMlT13XKCL9xRnaBJyf6AeHwWLriJsSVbNAVIauPSIrydavsKaj4lIwMCvKWCMVnVgwjmOxwil5KT6oCG4CFSEntGrY228JrijVpS73hSQAUb4niYZuJkqBWJZIhb/cYnXpCin1aMAbz3a9paUDMijQBkzTlBhaDFQZdn5kjKtSrssZLU15SNitGbeB5a33MQwRubzF5DL1QYeRJSphVJENtWEgTK5g/XKA5JGmRuZYxBAi4vKEjKJsEGSNVJasBWmCupJATYgRlC7RPCLKVMjY46ZQNNQqluiFbYkpI9AYLSCXSIYMl1s/Ik1ds9vtSvTo9JgkoA8jTTsvvpLLiMc4jeQ8sGwLGcBoXdB+0oBJaD0jxYmqahjHLZVpSEHRmELssLpBIXH1QPSOGAMIT1V1uFQwiG4syme/64FE180ZXU9yDh81QnqUV7TNPu3ePpNIKFHkB0bN0brC+ZHZgWIMA60Q+BpEirgQ8VPAuZGmafAZZG0Zz04KLeXKFaLbYI1kGHoW+wfEIMlxR/KZLCSCHbaaI2eSyijiVOFisRmGPHF45Tqnq99DiY6cE3F0pGHiq29/lawm2vk+++q7GHdv8coXvkSdDA+P3+SP/Jl/nV/9+/8ze21DMnBz/sd569UNxxdf4EYlsLNEuxyRk0ZV5Xwyq6+jqz2ydrRWsJ4mkqgQeIxSbPxIVVU0s2uIqiIDdjkjU9EYTb9x1Ms72KgwlaZNNc45+u2agCflCZSk7TpScvicscZS0bB1qhgzc0tlLX48p20qtruRpDWDkqikyEoyJof3G5rqiE7s48cNpmlQ9YAbJmrdou0+Lnvcbg3W0TYOl5cc3rlGHQXnw32G098l9Z6sNObggONpJKf0HZ8v/1DEFT78wq38f/31n0FrTY6ewY1Ia9HaEsYBn0vWQ+TLtp9WZfqQIlpKtNQEn6hNzeC3NEYyDENpEl5+M0Qs43t0JmVJnEy5KGiIYSholZSoVEsm4EL45sU/J3nZ5L3MiKUJrYuBLYSCOEvFzkmkfB0jW0KEkDQxVyTVYbt9MJnNg6+Qz54i5RNU2KBFh9YLXn8XZlcTqyev0DQTm9UzeHPB6bblB3/iL9LsteVmB0UmkEreNsYICpJ36LphChNa5lL2iJCyxOqysuz7HlPVpdGMQlCmqwqFkMW+lpPnl74lk/tTv/H3IV1yL9uJ/+iv/y36pwF12DKut6weeE52T/E7zf76Xf6n/+Wv8f4PfYQP/uhf4dG5Q8lD3GLO88+/iHnqaPYtL3YD/81//tOMwykCxc//6W+JSPz6LxA9HN66S9UaXH/O8Phz2NkNmu4aJ/fvcfjiy6jZTaSIpM0x6/Uxmh2b7RYhZtR1yy7A9TvP06976uWCHEfWT97Bj4Lu+hXO7j1i79o+u/MThovHzA8WnJ+smHctOwUm1OzW7yLUDJU0sytH+CihPiLsILjCsQ3hHMEEeSp4OxRkXYD8olxks9gn6g3b+/dZVB3N/h5OROrL5nseSknQ1hW7foVWI6uLMwSJut0nhoFKaZQSuFjeo+NkmO3PCW5CsqOyHc65ggWKkMQOlyeSh6YubdZpGFl0C5rlHU6evkpM4fI9FemWd4im5myIRooAACAASURBVAMf/QHe+NLn8LtHTLuBWkdGKdB1Q/IJLWpIZfXZx8z+wTVyMtx9+RZalEOOUobV5IqSNjm2T++zfbjhYrXj5R/8IZSsEDpy79XPUOWBYX2M223Zu3qXenGIsAuSUQR3Rhg8IUWC85BHko/U2uBExsbysDxMmTEkUJl5PWeYdlRVQJobVO11Mp7Th1+l1Yru+jNU3XUOr18j4tAqMYVH3H/zn6LkrPCH5YJ2cR2jG177wq9Tt4bgd+wtb3B+uqNdLHEhlrXwuCZFQ8o9ORmG7Sk3b303/eaY0Q2s+zMOD6/jUeioMJVFqgzxcs2OIqVYcquixCSUUgg0MZd8NlmW2NJlJl8SIQmENGhpig5cK3wocYbgekQq1JVA0XdjJF3TIMnEDMHHS+OiKIfBmErnQJRccPYB7SaSF4xEjElo0yDNrFjNlCqTI0pszAVHch6r6oJihJJZlxJhLTJDTK70HtyEtBUiiMIWNpayJCsWtloqBI4xjGht8S4itCJngRKx/HtyIisFSCbfo6uaGBI11SUmLiGFIUSB0oLoJmIqxItCTKhJzpEp5dKUM1MKdLZkGavLh4yUMsZaplSMaJZLMo4oB1p9Gd1NKiJkjc4aKNE3yOQQUblMMEMOuFzwWjGU7PM0eQyqfCyRLAQ5ZIwyiOhLH+NymxiiuyTpXH5czjSVKlg6YZhCLOKJXPBewzAgTbHxsdsQhhWjWxFHR123KDtHq5p8yVRWypBCxJNIVjENW7RS5R7bj1TSfPNQ6OJIjBNKQo4e51ZIpYkCFvvXkXKGFA0punKvTCWCKG0FwtN0HVPf411gCh7pPG3TEVS+lOaUKKBb7eh3FyAC2UwMY+BgfkAKtjzMicQYJqLbIXOk0TVIwZQc2lQF8xlhGB1NbakrTc4NLmqE9xhdipbbXV+QoSohLnFwzjlmzQI3Beq6ZvSuTKuDK6VJDWMf0NqiVQVpwkWHClsG12PlgiAh9JEp97SpxUvJ/OiAd598jaVZkk25vmnXkOUpeVxRXX4fOz0DZmBmVHbGkHdEQTHwSQsxUdWWZb3A68KpJmuG1Zbt7oKmK9l/jGLKE60ITBcDIUtqU64Pm6Gca2YNpOwZnSPGzHq6oJU9rRQkeaVEjoDoPct6xun6HlWXIWuS7ZDKMm9vEaZ12QYEiVIVUwyl1NrVCEDKGi9neCEwRNy4oa7mKGpAEMYtQzwt4hcEyY1ERoTc8XM//4A3H0z/8uIKQoj/EPh3gQx8Gfh3gBvALwJXgC8AP5lzdkKICvg7wPcCp8Ancs7v/IGfXyqkbcokSiiMrdBtTdg5YpJIJYtyN+uSg8kOY6qiwdMCP5SsbD9tSmA+F+h3SL74zUdfSltCFMYsEmUvbw5BoIUipozG4GMBiRtjScGjhKUAugQhFQ1kpTomt8Y0GhENSlt8nFBKFwZgdIUQIcrfffAjOVrcrmcYnnJ6/IC8PqGzxYZjdQngd7NILebExQt4dwzd69w5/H7+xEc+SW+LNUqKYoURAaxpcH6LlIaQI6pq8b647HMu3vooQJLxobQrhVUlE6c0Iid8DCih8XlCyUJokPnb3zvJp3IRTwI3VnzyT7zM3/21d3n6xlep6iM0A0ubGR495jc+/X9yEdaMu8A6Sq4vPszJvOTq3nnrVdSjC1o98kM/8bOMFxO6siTiP+ONkbj+zIsFQYRgWD1g77t+hNd/429z9dbHaQ+ukGRGrN/g5K0vcfjMSzSq53wVufuxTyCkZ1g/pB0y65NjsrsgiEQUkXZxha04J/QXyNAzXki0qpjdeBYwfPAHfpiLiwsaZ9ie/Q43P/Bv0B5VvPHpf8z5xSnePUXGtlw4dbkIa12oFsJUSOZFpZwEu80GN5whtcBXj6jiPvNFgzEd3vvCBdwNCK0ZfCSnLWdPL2jnc9CWg6vX6Dfn3P7Ah3nny5/nyrW7PHrwDlJLRjfQNItC1YiR7XBBLbeE/tLuN3hyY4mdxLgJ505p6gNMXdFUirPXPkvuWpp5h/cRlSXD6VMmoZk2K9id0+gZ03ROf35Wikk5Us/mhCkQyDz30T/JnTvP0p9vCNtzvvxbv41fvUelJDnCwfMvMOoD9jpLPd9H3hh56Y/9MZ4eXzCrlzx9+xXE8JgHJ1/FKMPc7HHx+DXE04fY/ZuEKJFyRNQ1AousOkLqUAZmsz2evvd1zsaei3CfpZmj6xqVKvrtgJIRP1q0v4dzj0jCcuvuTc7f/RIXb71Nd3iFp+8Ynv/on8I2MzKH3H72R3n6+F1y2DINK6bxjBs3X+Tw2Y+jK8uNW8/w6Otf5vBaKbmaOJBUpDaWnXFMF57rR0es0sTm/BHZaPau7dP2s4LyMRWKQovJKExVGvs+J0LI5OgwShJ8T1bqm2pNZCYgULkFXQpgUXpSVOQ8cOEd89lVMhpjI9M0UC8PkCrhQqCxhuw9425gGkZSSBij8W5EhomQI2HsixAmOfrdmpwjtVVosY9SFQfLPQYZUcZiUn2JzkrobFBWg1SIcUB3ln4KVFVFErAbHE3TAIqQInXdQJiQti4HOdMw5kxOGWHqS9ZqQZZFn7CyLiQDIfHOY3SFi0WmgQjIYEBJEo7NboetGpwM+FA2VZMf0KIjIsoZXmoQRaiQlMA0DVJUTC6CzNSywXmPblqCj2hdYZQhS4GJBisDOUKQAplASoFQlmncoZJC1ZIUI1nnwg1XppSbTM0QRkxdk1NhN4uUSFlghcbHjBZFJOKiJ4qMTAErRBERZUmIDiUkKSakkaVQZmdMboeSkiE66mqB82VyKnIpBWZVZB1qeUB35Yg2TORcjFNu3CC1IadAVoIkFBpDWy3KxunqDdwwFhJSly9LdZH54ohsRXmojxNKSGIKKAMyAFkhMEx5QGAKhz0UIhBClPuJcyAV2tYs9jpC8IT+HBkzSSWGi55muUe3d4jpOqBsMq8fVoy7NV6PiCRAJSp1RG11UcBPK0LccTQ7AF0xOYeSiUWWhfMsJcPOs+wWEBzTbsKniaZpLvXMwyUHe6CtFZJyvwZomyUjHiEDQlZoobkya9hNiXHyKC3Znq+LPVUdUhmLSJBt4sDu46bAss34fsWtgyMsM1bTlhv7S863J2R3hD06LCQE1aCFoG46BpcJeUJJi5YapRTLdkE2nu35jjg5dn1A1Zrt6Lm236GqBWPalgeyIdI1HTGfckLPTbOPl47JDaQgUMaQZWTb94hsMFKxP1+QR1MewqPB+8gYJ/Zmc9bOs7zyHFZZcrREmdj5gXHKmGaOUZLoBSmDH3Ys53v041jY6VqTcqRqW6J3WNXSZkPKZdjpgsSJQNMUeVLbGKKyGL1A8Pg7ObqWY8S/aJIrhLgF/DbwwZzzIIT4JeDXgB8D/kHO+ReFEP8j8KWc898QQvwl4Ltzzn9RCPFJ4N/MOX/iD/oa3/2+u/lX/uv/hOh7yJq26nBpwLuM9BFPIKeSH5VRYJVljAN1U6FV4WAKVQMRmQVJlEOZvWQpkvLlRAtSHokkpKgg9FjTEFx5ypVBIqRlCh5lAiKVC8/kI4h4mXcTSCWIaSDETKPbojX8BtMzRjIRKSoKACMTMIyuIqNJ6YKzk/eI6zMqlVl0lhZBwvP6/XukqEjtgoPlh7j9A9+HUUsUChdTYYRWFhcmvuG0EEoitCCkiYREZYmPpVSnlCBxOcm+hINLbUkeQg4YWRifOZcmdMgFVaSl4pd+/Kd/38/o3/6Hfw8pNOBKoeftf8zf/uxv8vlXrzGuTjjZHXP2zjmf+j9+AdXuqM2C3/zK7/FT/+1v0bwr2T0j2L/wXL16h1fFBXomed+9iv/g3/ox/tUfk8So+bs/8vuLZ5/4lb+J9wPG7KNMy9UXPkB7/QhLZNz2ZCtx5w/ZPPksrT1Ed7cwi2usTo9xwxpT77N5/GXidI5VuvysxQJ7dIv1k7dptCCExN6zL9O7SKMFZ8cP8X1PjrDrH3D1me/mxosfJyoFvufeO5/h9NXfobId0lZs+5Fld4OQenwE5y7QqkWr5vI/kMH6HT4GoqmIabo0RiniWDQJ1irS0COqhjF4Wr0gaU/wILSnOVhim5vYCt77yueRcaJprqDrlsXBEaf33mEcxzIZUYFZd4WDgwNctWXVP+HOzY9w8ugJoU90RxWvfvYLHBxexTmH0plGdeyy5u7dZ7n/xmtoXS5WVddy8uDLJKloZx16Guj7FQf7L6Hliu75P8r1K3c4ffwu6+O3YfUem7glpolKlKa8NG2JM8xvkbJk7+gmRgYePXwNyRwvPa1dQpbYzpP8jnG9o9KKen6LB8cDz3/f97F/veNrv/WrTEMkRkXV7JX3r6xK1EhrVBqoqxlJwNnT++zdfh+78ydImahkTTZzjp65zVuvfx7re5pWM0aor30PL33ohxDfuOEPPRcXZ5g0cvL4Tcb1ioOrh2w3DxC54ez+A248+0LJptqKO899iKANU/+QJBKz7jpZWMJ2g0ye9elTxjwy7c6JPhH8FpVrbN2hZ9fBX7A57pFNJgSQQiH9FrInpEwOnq6dM4YRqxs2223ZJNlCQPAugpIYqYhC4EI5XMlvSAFQaAXbaUOjLXFM5WCnNEO/QQnQlWQcR6wUCOmRsQbTMLgVurKYUCEt5GTAapSRMGUiCWUkOltc8gBUVUPvemrVlg6DNTgRCD5RmRYpgRSwMhN8mcD7NODThAyJ7MtqNmVf+MXSIA2QYhHHCY0goqoyOs3eMfmIVRYqiVU1zgUg4ZPHqBYpMlo0TLGIBwCUUvRhxOQObQRSJUIIZaBCQ84e5/uyYZwUSWVizOS0IzmHlYpsGwil7Jtkoupm6KhJ2THleKmiF+BjKZJFiNmVaeAwYKkQJExjSnFPWFQ2ROEhBHwcC+4xZ4axR1SGcXRUomyKQnYILUhRlNiHklSyYuhHrKkLaWhuGX2hhWSRyNKXfosrsbsoE8iMjIKsC8XCKFuqHAjS6BBaFXaqlaXDITPKakSUJGnIkyfIEUORgCQpqGSDEZQcf84giiTCubGUOW1LcFNhHRORGELORDLaR7ShlLT73Tcz6MK4MrlMgr7vaSrFmMvm1VpLpRTjJEnJk3Y76saQtcN7D8IQwwgil4m5qIhMhI0rEqcsyBZ2uwFhaqTf4POGLASVbokuoW1bvm/Wst4MICe6rtzbZa4Lh1hKnNux3pxirCK5GkUm15khX5AmQ20KDSMTiNkRZYswljRKKpmIYcfOPaVul8gRtJG4JKh1U6bbRpSJZzZkAcNqQ7aBTgtiTNRtzS54hMskpVCmbCdk1tSGUhwWGikTUzqDybLpA62MOPUI217BpBKPCTlhrEamcv4S5UDBEBzGgtAds9qgRYMbt0ThmVLJZ2vTMOwc826BH3pSHktRXgvGKWGlZN5dY4o7umVDcsUWe7F6SpSJ7vqSYcrUwwTTiqQDUVf8lf/un/L1+xf/UotnGmiEEB5ogUfAnwS+ocX6eeDngL8B/NnLXwP8b8B/L4QQ+Q84TeeUkSHhoySlxGq8IKYCJm90XVZF2aFyiQjENGGlIY6elCRVU9GPA7Zp4fLLKFnWdVJopJJE5SEUpiK+rIeE0ExuhxZ8cyqcCvy2FMfQ+ODQ1pKiJ0aBMBqXJpRQCH1pOJOyNHLDRGK6bBQ7vKvQdUOOI22TGYeRVb+jioakZwgZebp6ysI2mK5l/+gqJw9WxHpBe+cFTHOECBIf/SUyTJTDjJSE5NDGQvQ4H4gkKm2JJIzu4PKILUUqK86UyDITvS8YHKUvs1HlfZJCREpRYJP/jLyLFLoUVaoZGYfYm/HTz93kB82r/NY7T7i3afjIM38GsycQcU7TeP6zX/0Fsvx+xo/MEDjC8BavfuUfYa8s8Rne0DPmVxpC6KnUt2v6BKEg0vwK1RR//frxwzLFyCNGtcwOn2Nx48Pk5ElhKO+ZpcdtTtmcv0e7PKRWB5w8/Rr3vvL/EreRuHiWo2t30Xv7dHtXsd1dNhf3eXj+Nm2zxwsvf4x+e8J2c5MYdjx+7cuE0TO/eRez6bDNHmFYIX2iEpL16X3QimpxnWtHL2Nnke2DU9prN9mc3scurrBbPb0UkwiEsMicy3RJTgwnZfKS/EQME7P3P8f27ITlsmHvhe9jeXSNVz/765ydvsX+YkZMB7hhIGxWHD98kyt715nP5wzTCFJzfPyYGFt8rBnDHd44eY8wjRw9+yKb854XP/LH2R4/xBiIcUtKEZsU9157rVxMpx5V1VxcPKaedWQqWnOFYCPt/h1c0Lz8R3+CYed5+Po/4fz4Va5e28eNCu0lOs5I0aBqzTBeoMJEFhoXFM3tI1758hfZv1KzevglkhqY7B4yWFKCZGYs9xb048C4ewc1TXz9U2/w4T/7l9g7vMmTt9+iEgbGDY5AZRdsp4EcwSpNe2vGyXvvcfjhj3J44yXe++pniP2GankIpmHT73j+xY8ha0vdLPG+Zjv0rE4eU1cdzbzD7B8gdcZv1jz3/u9h2K45OX7InQ//OYbdmtvvzxAnzh68wzj2vPXVV7h15znOHz4l7kaO+zcJszkHt66SU4CgmR3cJcmORhqqbkbbHpCSYP30mP3rH+T6S4qhf8LTe29RK8H2fKSrZwzOYauOYbcmavBKcP3uS6zWT6jqObbtOHn8gPl8xtnpU8SQkXnE5Yhe7pFSJieBsjMaWxW8FgEjDRnBYr5HzDty9lRtRRgjWu+hFkt8GGgrVRr1Ll1mPIs4AylRqtgdBYIpTgQSWpnCUpUGmQ1W23JdIZPTRBrOcX6AcUMvNEbX2HZRirtiIKLQNuHGLUKVh3qRLokMCrTQ7MYt2hrS6FBJIrJAVw1aN0zDjkH0RYgjLEY1pCBKeZWeLCiGy5xIZHQ2aCNAaPpxorGX5SrhydmwXN7Ei1JCUwlA4lOF6Bw5TIyDhyxRaNw0kuIFralYOUdnLd4NtM2SZBti3jKfH7AdtyS/o7UKiS2ZIlkjhWcat/iQqdoGHz0yZrIsU1ulLZN3mEoRBn95T4qgBfP9fdxlxpos0XWDqapyD5Bl8mqkIsZEThlTN2XTh0drTUwSaywpRnQl8ZMrCE8Fpq7wkyPlxOA8otHFcNd7whjAZ9SsMGdV25CtQSlJdh6XUslup8gUJcM0oVVDrQXOFVnH5B1SZIILKGnJ0RFiZNhN1G2HmHdoIQmDJwZJEBFSIitDFNA2c9os2cUdPiVIHmtbhDGE2BN8pm3mjIOjrWalhI1i2g6QFLKryTmidIOfeuazfXbjQNssGHaQtcCHVCbdOWPmLdEH2rYlpAY3DVS2JeaERJFSJqQi8bBCEbVCqmKdFiiiKAO3qpqRk6erO/ppxZgFZtEQx6JE36v3ySGTTSbbxMzOqHPL1Hs26zPa5ZzkIiZdIGyNaSyClq5umFzApgqnEplM3baEcYcYy+bcNgt86tm6HXuzJappUHWgkTXB3cXLEZkHTJUY3YQQtvD6uwaVOoZxzf5syabfMVeH9LsNIaxoK41MlpwGsqgJTtFUM/Juwo8TptJEnyFLpCgbc2JBUY4uYZJiNWyQlUIJx+rpMclrutkBSUMlA/04lOjpd/j6Fx5yc84PhBD/FfAeMAD/iBJPuMg5h8sPuw/cuvz1LeDe5Z8NQogVJdJw8s//IjAFD0iMlAij2ey2KFMxuoDKZa1mtP2mjlYrQ5YWJVt8LCu2MLmSiUGzm7YI05RJR/QooZAyknLG2BbvRmKMVKZCoC9JDIAKKEG5CMqizs3Jo4QocgnvL/mJGasrwjRRVS05RhIZJS1SaKaUaTuLy4pWGVJM1Euoq4ahisTdyMXqjKQVG5fRGWyzpLOOq7d+mPntl4p92KcC/M+BlC6talKW9Z1IxAwadXnoVsW4FoFQED9KqXKOzaqsSGXkUkNf0ELCkpIj5kirLj+nD9/2I0oikTX4PGGzINkDtG158bkXaZrIybuZYReRsiW6DbtJ8O99dMl/+elXePzalwBYySXc/V7S0PBxfcg777/G2ckpWpSs8be+REioVNrccfOIr336vRLgv/s8h9euEpNj2PWE8Rjb7dNfnFBXitXDdzGXrNrF4QfRewc8c+ejvO8Hf5Kzh6/x9NXPY7qGdtlx8fgeT+6/wf7Ri5gU8f41Xvmdr9LNrpCE5cZLH2Oz2dB1Fevjd1htX6GxFpcX+HEg5Za9/TnTsEaGwPbJ2/Aos9s+5uLxW8wOnyUqSnECge2u4H1id/qAed3iSajFHsl2TGFC6o5+dUEYJ5wQ3H/li9xjhzKBWx/6EY6OrvO1z/06um5J0XOlfo4kLGiBkRIlM2K/Y7V5QlfNqTJM24Gm1Zzde5tkDfPu2ZLbzjvc5Bn6E6ScUTczXKxAV2V6a49AeFQyhBiRNnP7hY+xd3jI/dffIG7eYnP/c+iu4dHjLbVqyapFGksYA1LUzPbuIon04xajNW/+3u+ynB0h633qgw1K38Q9fQg5srzxPty0IqTCdpVGURuB1Zazx/e5/fy/wunbr5N8yap2ywU7H6m7BVo21E2H3bvCrfltHj98yOm9L3Lt+ZeIboNza/onbyGoWYVyWJpkxNqGDNzfJWZ7+xwdXePk6X3azqK8Y9ufUTUdxkrufeX/xsjE1kWaxVUciWZ+hWF9wvF7X0YYSzVfYBpI8YzHbx2zXL6IEILzr7+NNplHj19HpRayQOSAWiTe+lKP1JbF7DZZwezOXQ6X15j6LWp05BzIWqHrPSoRCUmizJz58jbn5/fQRjCGC6RxmNqSfIUeLjW4WrDrB/pNiekYqSh1dYEXkaglxB1KVDg3oowhxIE8xAJ8DztUzAShELkUUKPQkAJCGZqmRYqakEeqpkVlQZp6wrDB145MLNNRMklkTN0idU01Wxb817hl2J5gbAfa42OPUh22alASxnFEGxAKghfoKhfyg5SonKl0zegmUogMw5qcPAiHUTOSi2z610lJU7fdpV3KkIKk0oYYC5oxbM+Jw4RWge00YazGJ4HA8nR8QqULU10pQcoGWXXFWuknTF0hUo2uyqYsx0AUmXrq2e0mZssZIVyQREKoGav1KZWpkRjWmzXBP8GaDp8U2ira2R5SlaGEFAZPpjMtTgfabokMPTJFlNakeElJkAmSwEqFkpI0RRpblV6JziSfS2xAVaQw0VQtfghEkbBKE0eP0qbIGRqNHyc6W18W2GqyD9iuKUZMpcmUHvjoPe2sKyU1ZXChv7SEJtw0UdkGIy05JIRWqCCxlSJEX6RMSVLNZmXKmiLKlDxwCIYsE1bMCxLPlTijNRI/fYOc4rHRIGTJZQ9upG0WTG6DnQlE1PhQRBkiC1IwWFMV218IGKNomjm73ZoswY2uYOZs0SPPZi0iGmxdI2Qk5B1V0xBzJKmMiIbZ0lxuIvaYxoRBIW1GCcoDhWwASRw2l1vejFELkJ7GWpJ0qGgRsmV/1rAdN+x2AWk0pEhj9vBhh3MlvrTdnGFmgjGM7M9bxhyxzQzEkuVCk2OAqBCqQSiPFRU2TkRtCXFiVi9wqceYRAogc82itSgVEaOhMpKUFUiPIeJTupRbSUxTQVb0Q0CyxbtCmjF2zqqfqGpLIOCipq5r5vqAKY3kpPE5Y2YdRmpgVw7KckFjFNN2RFSGMFOooCEWbTpuxPoFKa+plgbvAiYZ1v1EpQUFzfCdvb6TuMI+8L8DnwAugF+mTGh/Luf84uXH3AF+Pef8ISHEK8CP5JzvX/7em8D355xPvuXz/gXgLwDcOtr/3t/+mz9bCg8URElMjnEsqsJaz0jZFb/3Zc6xPDE5pO1IeEL2aFGeXIVQkEswP6WSM40xFGao91htyWjy6EmXE18fCoSd7BEiQSrmEhm3hCQRSIIQENdoIXFKl/VGBBkFZtaQcyR6hw8TRldIqQuyC9Cy2MmGsCMnjVQTw+4NTt7q0VET5IyL/h55t8Lt9vneH//LyCtdYfWqREixRBAQl7kpS/IOlRNZFo1jzrE8JSZZ4N5KEUI5sEpZckwhBKQqWmNjy+9bq4nTRFIZnRUJ8W1xhZ/81C/ivb9kc2qqeAaf+WViNbFb3+e9+x0PHt/jT//VXwK5JkeFqld87M/9F5z3S2LdkuMGsW/JwOGgSV3FB889f++Xf4YYM3/nR37q933NT/6D/4GcRVEtG0PK5QFjWJ3jfeTdN5/y5//jn2XyI5uzY2xtsErhtqcEFwnjxGz/kLOHr+N3a9przzC/8QJ1W7E+fpenr32a3dhTNVc5O31MPc88+11/nmHccO+Vz+CmU6xeYu0OK66QyGw39xCypV7u03Z3mMYdwp8xnK/Zv3HEer2mmV9lGlaQPDk2xDCRdUKkwki2XUWcRpLbQdT0UVA1e8imcH9FBhVHfCxu+ZASxkLoR9wuEd2K2eEdgkiFnWpKvtoIQYiugNdVyTqmHBFZYGtNlv8/c2/2c2uW1/d91vgMe3qnM9apqlNVXTRN9UADNtANGIkgSGK3lEFKcmUnubOUSNzkH0ik3Fi5yw0SUeQoUiJZxEbBEZKDiTFN7Aa6aXrurrnO/E57eIY152K9dEKaRB3Jkdh373DO3nr3s5/1W7/1/X0+Cx7+2E/w5d/57zk9WbK4/TrP3vtTspgQZQXmuALfpULITHQRqT3t6piPf/pn2T07Z04SwTUqRLrTju2Tr3P5vS+SxIqQNZv1GePsEFjSHJkI6Nmxur3m/NFzbt1/gxgDzg90x6ekPLJojinRIkTien6f5XqDMg15L/Fz4tXP/jxStrz95a/wiZ/6cf7lb/83HK9fw6lI6TVdc0JJVXAQs8J7T99acpqrLniaKEWwuXWLabyi71e4Qx08klLjnKDpDTmMtVNjDN7P6IUhhVKJLspjVcW0hfkAbo+iQZg1pmnpNhuKUExuprUNk78iRc+iOWG4fkohkYtDZYmbPTnPCHNMvzxid/FNmtUb2O4IdINoDFoojLJVNhJCzV6mCas6NtZSzgAAIABJREFUEhIlJITA7HYkBsZxwHtP251SSFilcQE6oxjGC2SzrhvwXIhz7WCptiMpT9dVy1kOktIWRNQUEo3qSbKQZB0YEkkRU6bkVIvOnBAhEVOVPKTZQ2mQeEwnmaeCaiymk/jtAaUTSSlEtiAsQhfi7OhsRyJV/bEUlChIkYr50oWQElL0iFaSYkEWgQ+OkBIGg5wdZqkJ04zVhusXjwjpmqAu62CuMWjdYsWSkjIlNjQiEIqv9zMMjVyyHy9w7DFGo5s1Ek9JYBqNn0OV6tgGNyV009MKw5D2tKpnniLdrVNM29TIV6lmr7Zfo9plZcIWUfObqc6YhBCwUuJSPVlUja0impygWFAgpKkd1+hBVp7zPA3ILCHVtc6nm3s4BVESSkgm4THRIHRTdazB1wEwGSn5QBoyurEUETGqRUhLwCMag0oFt59QqtYGpmsoWdAdnTAPYy36fMCKqgPuukUdaiwBQaAUQVKKUqARN8j2WHDZoXRCZI0wCoUhxQA3AietJUKU769TuVWUALJYRJHkUtdvY6qYoDKcRf1+yVhrKXGgZElwkZI1Qk1QDJpCCg5fZjIaYyx+P1BKJpZC21er4aKr3OdpqLQHKeq6M8URoqbrDdO8R6qe1tTISWNbZh/qfbpV5OSwTZ23KMFDijcqbYkotbGUmAmHOnjoS8KlEXHDb44yU3Jmv39CZyzdoquRyyDpVmvImqIDWhjwCicssribgUFZh0+R+BLRKTCrwqpb8Pz5c05OTojjgSITi+aIeR6Z54onTcGjukT0Cp8murYBr8E0lEYQY8IWMELhfEEVyJ1ApQbKNbkBGTqUsESRaNZtHUjMmmke6GVFm2lrQDbEmOlXmmk34rJnmBONtAgZyfMOqTuMFdSyy1FkHaK0KvB3/+vf453Hh39lcYV/DXi3lPLipjj9TeDzwJEQQt90cx8Aj25+/xHwMvCREEIDG+oA2l94lFJ+Hfh1qDIImbiB4luUMigpsI2klISfR4QyiFD5rzlXzVxOkez3FGuwypBjqROwRiApaLMghZEQJ7IQKNnW2dUoSSKQS8ZIQ/QzQldSghX1CCcng1SBlCtUusiEjpksK5KLHNDa1FCAkDg3QC6gSo0LyGpWK9x0gwV4D0q3IBMxLVk2n6F5Y8/u+jGPn7yPUgPC9jTrDWKxQJpCTAXvI0Y3EBPixgGfgv9+LKKUggyFqARGChyRIm09slN1IEkUamdXi5q5FAofdmhV7TsZhS6ghcDpH+TkOj/XHGCplrmkFsgUUJOhl/cxzQUn6wXnX32f1WfOMHiij/DqA8pXH2MffhYXB14yHeePvkZGs/1A8Q/+2a+xvR4Q4gefM8Z6URvR1L9tqZEGYzZY6/jM595iP+zQbcfq6BYpRy4ev83Z8Rnb599hunxGvHyX8/e/Ryc7yILD1YHTl96EWOgWZzSb+/S3PsFLb7VcPX7G87e/Sd9K1o1mkKe8/MaP8/jdP0aUyOblN1nzKpuj23z09rdoWoFmQaFwfXjOR9/5E6QWxP05WViUXiCbxOT2pDEhisXHQlMCvWluCg3DpmuYxgMpTaD0zSI/0azW9P2GqxcfgOvw00wjW/TRgtFvUWpFUYLD5TWaAqs1RrXMKWNEIBdRAftKYro79HeXZO/57C9/gd3zC9rVKc5fMu7qsJMPA+MuoZoF7cbwyb/2eYxpCLPgw698idXpPaw27C6esTj5GO3qdWz3Oq++9nP8s3/y32I0LM/eIJ0/RVtF7DWbbk17cpu2WfIjP3PMOL/ge1//Y4yfCMNAinB9eIHWR4TUsrz707Sr28SY+PhnPkGKjt1hSwoTon/BN7/4e3z+3/k1Lp59j/OP3icVQfR1yE53BpESx5tjJlfQpiW4A70VZB9x4wWUmZASSQRCbGj1BtNqpjChEAgpcWFGGJhnhy6Kpm0JIhJDxQrmYumXd+sm2QQ8nunFcLOpzezcgGk3DNkThitkGHHbd5iGkf7kZRYPXq84nPYeZMmt9St4f06KEikTcRrrUasvJB8RpkV4WQubUkgiEUtFK1q9BtWgzRqdJAZTT8DiNf3CkMJMr27x7PEH3D57lW55SuprkVVEJjNVFJSCog2xRIIKNFKSRSCnBFISXEbKWrBMfkAbwDtSCEhKzfarTI6ZbBI+O+ZxYBF7posBs1hQUo2HGRnw8zVKmWrrE6Liz2xHypq2bSA5Rh+wiDrYOzjmfUEwYrSk+JFmuaHEjGoNzs0VFC8ldx5+hjlEIFbKjAbpJ6bxgJQS5wvdugNpSEWjZMVOntoHpBQR0SELzNPANO9AWVy6pCHRa8NQrpjGtxnKkq5/BZfg9msfZzh/m2m2KNkwa4WVx8xzIoxXZC3rawmgZANIvNuihaXr7hCLwzmH0QUXBO3SEGOmxEyjFbqt9JQwDdgERVV2rkCAjuQsERrS5HA19EwRmlYbyjzhp4ksPM2qo8SMbgWdVWy3B3RrmOI50zTQb26hjK3UhxKZp2vc5XNQictnj7C2w/QG065A1iIz+sDeT0hZSOmmELGyxuHMkiQzpsm0KCIBQhXiBBWwpkV4yDLjfaiUpEVfIyBFUVRBldqMMaJFLFtEjDg3o1Id+JWmrh1x8mijKUJDSqAjaU6gJULULj4pY7VGF43s10hZlfW5eAyaOLm6ITKVClLIDLtztG2ZJ4c1CqMXTNNE0/TkGwlV12t2YayFfdsy+1zRexoO84wVNZ/qQ2LazZg+sux6hFIs5YL5cMCKXDW5odDawt3NfdIN9ck0tagfDjvmYWR1dsbhMNIpQzELGrOoEQElMBr8MFVij4kYJRjdgbNbJ8x+RsjCQi/IYyDnSl1KMSCQ6GwIOrFY3CHdDKI5fyDuHaIxFClJGrKwhOhoG0HeDbhpwqYW3SqUVsQIzgdSyBAnjLIgBYdRYFJEScWYZiY/cdSfMe4DR90RwzSyuHdK2h5RSkaEgRKmKrXWlQA1hPSX1gv/T48fpsj9APgZIURPjSv8EvBHwD8F/l0qYeFvA//o5vd/6+brP7z5+e/+v+VxubmUiijoVpB8IKkCZGy/wM0jTQMuCLTtKDLW0LbS2FKzVihNKaLuDnMEIxBRE9yBRA3/d6YnuglBLZ6UTmBaiohIaygFhFJIZVBJU1CIUi1HUUREipXGoAShgNYtYfJYYxAIotLIHFC6IcwZZUVVicqIKJoUA/LmzfVZsDCCUlq0ETS95ezeLeLkeHwe6Ls3KMkTphahM0JKUnY1RuEdAkPWElJACI0xNbdrtCYWR0mhIsUUlHIz/ibrgqIokAuCjKUn8+cbA0NwE0lLkgs/8A5JoZBCEZPCSsg5cDjAyYlhyp57jWbHC5595c/Y/MQvklKk0ZHV1SPOT04I734XNbxHOH2d9nTFcbfk177wE1Uhadrvd5z/r4/GdhRR7TrcdKp9iJQUEMUyn38D/Y2W/pUHdF1Pd3rCcTzjgy/9FouT11ke32GaD9z+9M9Q6Nncf8h+d4Vd9jx779sYe0RULcPFe7z9/jdZtDPh8ppD7hAoXvrJX+Dy+cS91/4607jn/PycpW15tnvEw0/9IvuLd3n25J9j/IKHD1/hydMDSYOPexSGw/wcPS9RixM6u0agsc2aYTjwyo//JM3pLeQMH3z4VR6++ibL1S1yUqSSsVoxTRPD5TscLj5EFjCN4RM//wW61RLinq995fdp1JJ9k7FS066PUdqyEIFufcZ8mCHUTtT540fEJxsOp4noCra5g+4sd1/+Wb72x79Fv1hjSkbaSCyST/3sv47ue9zlNRff+QNe+eTnwWpicBS542jZc/Xh1xmun7O9eIfN6j7kwvWLK1pjKLqQwwXu0TlhPODWx1y+eIwwklv33yI1z0Apuq7jMB7YX7xAGBivL4nuEpEj3/zi28whY40hqQVvfPJvsbva8c7X/neG/RX+cEm/WhKzYN7NSKlRTebF5TNWq1vMw0y7OqqT+DIjdEDqFWXODOHAYr1gjhONWdB2PaSxHjdHSckSWWp27DB6pBF17FQYzNGSgsbruUanZMDeKHlD3LJYVIC/ipLLZ29j0lNWd96guf8Qu7hdu2uqkGKghEQqgVyOMU0gZk9wDisF+/1IszIoEUlZI4qgBEgVe8o8zyQ5IdsWFRIpz4SQKLLBmkza+VqIN5nTO6+DtAQhkGQKgZwyuSS8qCdEMYuK0SoRbxV4hxYNShhmWY/FF6sOHSKpZGgtJtclJMUJozRzqFGocXuFXK3QqzNUOkEFSS6RhbGEEFis6sAVRZLcxG6cKdJh25adV6xWK5ruCDcc8IcrWmVYtyuivgVuy5wcw4sPkSg23QlzfI5pV8hgefTkvYrUc4KCITYFfdghVES3p2ghbvB6E1F6pNMgMl4OxNLQLRZYqbF9j9JnJBVY2GPa9ghy4bi7VyNrKuF3qXYJU0IsX2bdNDg30SiFn0em3TklzpW/biVds0TEKlLQpq0ynnCFn3bI1leSiLPs5vcRUdB3J7icKX3PNO5RsiVPhX6zYHIHtLbIIvF5wIqmkmq8J4VMsA7hCo1QrNaLWjDFiFQaqQS+eIStMiCtOvpeQJrxuSARpOLJCPrj2xAFbRuqkjzPxDFQmhUhe9zsak7baJTuK/vXR2Y3EjQY25OJlVigDUJKyBOpRA77gd6eQEqkrLFW4UMmxYicCiEH+rYhUwenRJYowK4aLJroq4wiTR7dGtwuIAyodkHxE1FbjNL4EGkWhrYYchRM01Q/RFqhDVhl8dNYP2Mx0N3gB/u+oVktIGdWssYOc8703ZJ5GLDNzfXvHTYlpLTkOdNpzehmgpxZ9A25xLppKYnlWYfICZLAETAKVqsjshvISXDWWIrwzN4jc924aS0RUdFvOo5e2SCmluPmBGMthzihREHEruLkyAgjaPqGQxLknFDUDTgxsFyd4HcBazJWZnLUNN0GQhXBNDrjXDVZCqPplytkHKFpyZd1CLFRDfuSSNGT8kQjFLbpSDgaveS07SrJKQkSVZ/cm4ZMptX+ht+sCR6GOXB2dpvgazHPdR2YjUrSNRvmnWCxapnDxGHy9KuO/y9xhR8mk/svhBD/APgT6sn7l6kd2N8G/gchxH9x873fuPknvwH8d0KI7wGXwL//w7yQJAJGtWSZQFQ/eUrVRCJVg5KZREKWVHmMPpCkQxRJMZUnSInkFBCi4GJApIywkIKHVL3jpQiUrmaXlF3NnWhJ8HXiNbrDDYNRQqlYEiMMCE0QE7VHHBBBokxL8hHVCFTOSGNu5GKKXApZcBOdqBOXRdmaXROSUhY1dlDEzXTsCtke8+oDxZgz8/X36I7vIeTq+4gwEHUaNkVkqYrRkjNxvOnSFk/ONa+YvK9xhZwwUlNCVXdKo6r2kkiGGvTPmVQy+s9zWOIHZRCFWuRXrXw1mOV7GXfwGDOR14blVuHefwcbf4VkMn4y/Mwnrjn848jZ3Vt88+iYt1gTTzbor17wH/9Xn2N01+ToKy/0//bwOKSQaETdPQaPagxFtvg80ZQ1QexZLlfItmO6viK4AS8L9+98jCgl8fF3CcOE6QzjsOfktEYTHjz8FBcv3mf7zh9T4kCnCrvrK9ZH9znbfILu/qf48KO3kZdPeefdFzeZrAUH2zNj8H/4O0T/AmkTo3tEdAsW69dweSSJgBINixQxzZKilsQITdOQRELaxKN3H/Hm5iGpBF7/sV9i2u/IwpCzxw8jj5895/TkLl17n9c+8cs8+fBbnNx+lfe+/XWuvv0l9rtLun5NFi1HRw/YKs+jj96GPGNkwihLY27eT1MZiXu/JV0Uus4gz97k+M2fIrsdn/83/yM6o/nn/+R/4qd/5ZfZPb8kicT8/BmHi0cIU3Pyu4/e5frFI0yIPC/vIdqZMh9YnK4womO8mhh3T2lPX6Oknv74NrkvnD74DLuLPVZqlqe3OT6RPBHXDHvN5bML6DS3b7/OnCVFDVWhHQS5jBwfvcr67IxufQa5YXPScXr7FaS29J2oqmShqtt89zbhEJmGEdMrlChcXyaslagOZrelMwafHcdrhW4sKE30e/IYKyfb9jWnnnzlraaKq5K5RamKEgyzw2VPZyQQST6BhJAFXX+L2c8UZTh6cJvNSw+qplsKrG7Q2ZJCJMdMkh5RaqSJdGAYDjVvCkTv6RuJForp6hptDcPsMe0CZSwlJlqT2R92LBFYAd45jBVoUShZ0vZLSu/xxMrXzpE87kil0LSaMB8owtA3Fp9LFSdIgzWWyQ21IyctPgaMNUQPh+0WRKx5YtvhnatGyUVX7X1LTZCKxZlFYPEu0xaDBHw44OaIUDfRqpQoUWGMpVkkiuqQQtCLgBsvSP5AzgZZaqwsJUFJid1hRClLv+7RFHIJaKmQMSOVo1tZkAUfBqbDwMnL99AiIE2Pny+52A10XYtH0C8MLiXadgHSIIUkh8I+7ml0h20b/GEgFZjyC6btC0zTs+zWhGEihC1TFiRpaBamFgcuMYc9bWc4enAfwkB2I1EkVKyd6Wk8UAi4qbJVU7yGKaK0JWeJlorZeYLziMEgF1cooWlPF5S1QZeBVhVECTSLI9xc76F+nlFJYgyMhyvKYsNcHH7nEEJgbIdsezq9gE6zWHbMh4DRHSo01agZgRSr5rmkqgLWFqNs7ahaTclV6W26NaLkeg83Gi1qRFBIWNsNSYIbJ7TViAxpGBnmka5R6Fah7YJEoOt7TKnEhBAzgspT1UmQUsF7T9fXWYEiQMrC4XCAG069NYYSRT2y1xohgcYyjx7vayE2nB9oFxtas0JpQdNVuUVME4F0Y0DTMEVCnEjZMcyOlB1GtfV5UbR2QYweoTMxjbR2QyqeRkmksaRkcG6PRiObdR3u9nsaIShtixMRrXuyoX7es6CJgWQV2UfCYUB3Bm0k8zhhREuaBGZlMfqUQkcSO9pVz7QfMAvF9eU1x12L9xJlDKrTdRARTaMtu3lAe8miO2MOCdVtuN4/wt7wtnduz0JbjFX4EFl3K5yKzO5AE1uGlAmXV3SmZfYzSTg2y5ZiLH6liLtdZS5vWq6ngbPFAisV4zTRdpZQBsYwg7WEIrBKsVk94PLqMUUoYtRM+33lV7eJOUS0XnF1cGgSohSkNCyXmiHMFWb7Qz7+SsggPv3mK+Uf/r2/S6ZgtakMQ8Blj0qC4AsizLjoWPQrxJ/Pu4lYOaVZU1RTPdYh32RNHUJZSnRoq8ixZk60Nfgw0gp7g5mBoiQFj0JSgqtHtHEi3iDDlFIYaZjdrhrEZM3zNrIlqlxd0caQQsSUlpwr30+UGopv7YJhHBG25g6FEJTU3twwKuYsYyoXsLiaAxItpShyswS5YNgOLO/eQtgKh88Usiz4uULXyYI6BD2TQ8ToypgshboRKOXm+Lp2HazpycJBVqQS0TFVADgFHxK/+W//h3/hPfoP/vHfB6mROSKtpZBR6c/Y/+Zv0/UWJyJPLr7Li3dO+Wv/6d9DLDKyESQ/EB79j8yPFOfXW6a54cc+8RmO2xP2r9+nOENWNW/893/17/yF5/z3/uffqIMVyQF14C76mo2b3YGzNz/N3dd+lHE/oLVCWUUMAiOg22zYnj9GBkjGoBrD9vwZtx68zov3vozbD4Rhy/rsDnOZ0cXhLj/i6tF3KuptUkS94PabP0b217iLd7H6iMvLpyjR1+uAgjAGN9aBQGUNISf8dk9/fA+PqGgiLXATLFc9zk1EndFeINseEcH5Z/zU3/pPsKplu3/O0+9+DaU7Hrz5KleXHzB++C6PvvMvWZ8uGMqabtGT0yl9q5inHfurj2jUmmm64DrsMELS2Q29XTHsR9r1EVlooohIMjLNmOaU4/tvUq4T/Z1TVNezPtvwtS/9r/zEL/xNQswU50hxxM17GgXusGO/f0IaR0xrEGJgfecV9oeI310wXWyR9GSpWd6+w+78kvXJPWSemP1QcTha4mfFw7feZL+rf3fdL0nJ8Z0/+T20UsyHa7RsiX6PNEfs5gPr1TEpFfb+OafthhgdbX8PIRRaOWZXCLuB1mR0lwhREM1LLI9NPW61luXmIdNwTphutLghgVSkfKC4REiC1eYM5xwpB4qMKLuqn9OSCUnQti3TWPO8Jc9IWRBCknWHyJLsR1JKjOOIiA4lBEkLWmuIpRZ6KRxYL19iGK+QqkNqUXFPWVfuphZIZYkqkVOgwSBFRwiOTEEUSRp2FJXI7oIUIUSNsoIsJUr3pGlANxqplxjTUsqAEpoUC61QHFJEdYocJrTqMU1HLhoXMtpqlNTE5CgpI2yGkiqlITU0rWF7uKRRBpHqEWIKEZUKc3JMecfSrAnJ02uLP2SiiKyOLfvxgNAaVEeeJTntyULWzmF8znrxClkqhGgQRPx8ibKS/XCoefEomLJHJ3PThRqJWLJUaBMQRdJ01YjWbjb0+hhKxqWboWRhmMYriqAON0qJ95E4jlVAIUBZQ/KOhV2RRUEYXZt+wiJvRA2jA6My2QbKmAnTJaRIs1gRS8N4eEprDd3qPmkOyKSIbf05UqBvdNFKKXZXFyyUQZSM6Cz7/RZBRkRJ0/a4GCgxI7XF9C3Oe5Z2ScAT3IjIhka3+Gmo11LT1TmNIsgyIjvFPIzo0iCyo4g1fat48t4XEVnQbl6nW98iJl/1thhCHAmlsGxvUHdao/SCeZ6YwlWlNeiWLBpiicgS0AJySSip8SkQZk/IiUZ1GNUSRYBUbVy5zBWnlqkCkJTqCWPJCKGxpiWmCWkrckwKgyia+bCtmW5rWZwsvh8zcNMM+YZJLGGaxvoZFZZcqhq+71bMaSb6uTYA/P95YmlVIrkJ1cpKsMiKXBxJRhrdQ67rpxIWoSRSLfBpBGJd36cDGYkRi8oCnhzKBAoQikDagpWJlC2onuz2+OGSNByIoiDLiJKJ0h5hsAgjyWni2dWHnKoF+6vM+lbD1fYxrd1gVktcnCizp7QLTlevIkrGlUzXbjjs9kSfWdtCCZmxeDKRuXi65qgOzYeJHANSBKQSBDRhTghfZ0BCdlAmvNhSsia6yjSWs8MqyWEMlLIAsaU7aji98zGa9UOKc/gw1muks8RpT04DAYtdnVLihIqSGC01aSIJsaCMpusSKWQur0eOj46Yrh9TVESUlml3QdPCNGf+s1//Hd59vPtXihD7//UhELTdBqUEKfp6FOjnmjMR1TySTUFhKbkeVcfZoVpVA+gKYnQoKWtoOWaIgkYIojHEOOMCtKpe8ErUG4AkIG1LKDM5OSQdwlbaQDUNVQC7lhYfHCUrtpee01sS0Shi9pVfq8UNjTYDE7koVDYVt1MSLu4RBox0xGIpBaZ4TdMuCTHWo3gRKULUolMVEIkQA8ULihSs1htyTrjZoVWDEIr0/RvLjMyBFC0lRrRuq/VF19eglKlKRwU5VhSND/ONwKCgRUQCKQXmEv7yvIs2lOCqzr6UupNSn0bE/5LMZxFacWslKfc+gL0nr2teqmkW2AefY/7W7/H68hh7/5S2k+RwiRheJrUjZE36SzZbhUTKESEFEknOsUKnjeWtn/8CxnboxuDGmXm8JjybyVJxfOsOLz56n/nifQ4XA8uNJag1Jw9e5qNvfxXhz5mvL9EWLt+/JIueXM4J85Y3f+lv06oWKRx/+od/gNuOuMsPCIcrJjPQL0+wmzOIjsNujxiuWTQLgljgQyAET7PsQWREASEtAknTJdwQkFqhvSDKETnVAcDTux8nuoy0mXl7jWk3PP/Wn/Dun32N07Nb7Hfv0p38KKs3H7Ixa1brM3Z7h7q+5NE3/hf0+mVcnGjMfe50dzGNIoQ93m1ZbtaEnDB2Qat6hJq596OfJ4iGYbvl4uod0pOvo9qe04/9CJ/93C/i9+cIqZgOV8zXFyzPTpn2+7rYFI0+2jBdT7z6mX+L6D2LeMBtL7jevsdyfQ8dDXmy3H/9FtO0R4kW4Xqy9wzP3ifEA496TdcYvvPlL6NPLavjj3Hr5c+hTYcpe97/9v/G4viY6/2OzXqB6RbM+y231idoepQ5rhlG25IkdH2DUY5h+4S7Zx/ncvscLUfufOzz5DTx/h/9Dk8f7Vi2hrwfyE3N1vl4RWlaSthj1IIQBrSWqJKZ/IgMnpIly+UZlxcXHOJcm115RNt6JL3bPib5Awpo+2Pwns3qDkF4hO0xWrCd9nXAy3RYtSGEwObkFC91vU6iIOe5gvBjQeqMXRwh5ERWfc3NigZtoYhEWhpELJhySpsjKdRucnMjXMguQY5kA/Nh5OTkId4NIAtpHFBakoWh6TtKnonOIxQ0BhAQvMOogis3m3+dIUFRgjk6VssTsnd1A6oURmqMlVhWdOkEhQH/DK1bVCcQpiGmif7YIoSoGnFbiK6h3Mhn5snw4uoDRK4kEqMMXWswasPxakmRDUJJVqIlaoESGi0VOSQWreL54QVGaQwN6ghCUhyGLTIrhJZ4HNM8VnxWDjdA/YaoB1anp0whsOxbih9ob98ixTqnYVRtbARfsWNGtbRiX+/4eWZ9ZEnLY0IC22TGMnD79PWqpI8Ku2wqEzXFG7OcQbYNw9Ue2wmM7hGinvT4YllsljVT6iegMkqLcDRdz5QCgkTKAmk7WtWh2jo9vzi6zXwYKanU6I40jOO+rgVSIJPDtj1BCkbvObr/WaSU6H6B9pFiFsQgyd6B7jDZU6TE7XdMohDDOa2xuDiTtMKLuQ6lYTlMjs3aolRhnidClHBDGTBagwxIn0FJfDpQcqyFaxK4WCUTomSsVGgjCXEmh0zKAW06sgyIDO2qJSfHdnsJV0eYZUcMN0YsqEN2CJRpKRLAoUtCN4oYD6QYaBoLMdX1QzaU5Oq1uTrBzyNWKZQIQMucPTFpNApUIqMpOSPKiM8erTXj4RpjF4SScPFASQWtQRRBShMlZMocuTxsawa+iFoHGMeqXdFpUKKl2ML19SV6ccJ2e0Wj4NZyw9L0tIuI0g1ePMCyrsAvAAAgAElEQVQul+SYOG6PkUcdKUqMVYTkiQfHwV1iu56cHeNcN6qX85amFdhlw9X1U1q1orcGRGbaB8bpOcO8pbc9S3vMxXYLKpOTIEwDAo/RGRdHjpdr8uxZrlcsz1ra/hYyLSh2TXLnDPuZ9dGKmBKtWjCpmSJb+tUpwxzphUXoTKOrDbRr10SfGOYrYirsDgNN03F+8ZijBopekBKszu4h08RyIZHqhy9d/0oUuQDZBWRj0bonhoDVLbnEiuYSYLslpqSam0HQrZY1MJ49QpaKxqECvqUQFB+JyVf9XCp1MC3nqpE0Lcl7tGrwYSKWWHeqQhFyImUFJSOpDMSSPEYqhAncOl6R3EC+sY3Y0pFyZDwc6JoaX9BNQ4q5FotS1g6IKJQIRSZiNtibaIAo6kbJ2VSln9X46CqjpbGoEEnJEW1EoDBCIihQEjLVj7ZQ1MldclUeB4eQAnKukYUpkIkoqxAKhKyTrFCRuEVYQqkmOKsMWfxgwZncjL7hPlqhqp8sC/KPfoHwzjfougdEc4ec9/hxjxGnCASlBIq+z3P/Z5yc/ipGacphRJuW5vIx/qVXkCIx+/EHnrMmBUVVQVIxOAGJjJEP3n6flx68TLjcUQhs1scMZsRfveD5N79CZyKX3/l9Stjh8oKjj32O4cMRDi948eSbtELg1Ab9yms0XrB/+ow7b/x1nrz9mGkMGLmmOXmJ8cN/wbJL2JM3yNdPiO7AsJ0RKSODILDAtsdYDEonuoXEZU/JGWssibpZySLSLBqm4QWizOimZb7yNMZw/v67XD35h6jVkpc+/ia26cmLluOFJ8aPKHJBzpkn7xTk8EdcHt+l2Zyyffv3WZ69ge3ugI8c9oH+5JTdi/eR2vCJn/5VLl58xMV7z+lSIMuGo4efwyzOKIPj6um74M8Zwwt+4d/4O/SLI+b9FToJ1GLD8sFDxN37PH73S+jmiO3TZ6TtFbFTWNnw3T/6R/R2iTm+y73Xfpyj+58ih8x0vWcctnzwnUd1k2oX2AIlZET/Eg/f+DHMZk0oI2/94md59uH7DBdf5fCixarCy298tootMiifOAyXmNaxXh8hZODkRz5JvD7n/NFHzMMT7GLNbgrcffAK7dkJT59+QImKtuv47h/9KcvNGbdf+UliGNmef5fFLcnoIUtH0xxhmhWhCO5+7Ocq9J7Ch1/5Iq22uDSzaBdcPP+QIBSnd1/hcP4RZE2YHKZruXvnVbrFGr2pRevw/gfM2yfMbo/2hdQbGqWQqkekxOwLoPFJInSDRtcipz9G2Hp8qqQlOI3uNjeccG4Ge0bmNKF8IUUoxdE1mkzEsEBEQ4gFkQrOHZDJIpViHPaE4PAx0NumFjshMIdE0y5Q2pAzaGNIJaNyS04jUhlS8sipIKRi557TNgvm3QGlDIvGUoqmyMT2ekvX9YjFmuQci+UruDCjjYLksXqFj4GYAlqByEBrvs8kb/Q9WhuQJYOo9y7dHtXhMCXw0SFkHcDVxlJnDRQpRSZnWLcPESWhdWQKjpIz3eYeIqUbgo7D9gakQFpTFdHKsrQrtISMp1GWZDp226kWwkrhc0CrjJYNQqXapbKaFGt3d3IO7wOqFOKQkNEz+UDRMz6NaGEx6oisGqbr60pOaAWL/ogcEsYolFTs9xdM04QwBtu1aGtQymCTxHRLrq5foNoFSh9j+5ZpGm42CJ6SC74EbNOSnKNkyNOMFqKuQ0KjW40PAyHPbA+Bo6Mjwi7jUkK7qoaXEhIGVVSdfXEBN16jjUDpI8YwYZqWKEolOsSMkDNWJNwUa8fUnmAaQcwDpulppCXnCAbmcaIIVde7mDGNQd7Mmmht8XNAZvA50Chzo4+NZFXqnEmUpKxYHt8iBrh6sid3ARMLBonbX9Ic30VrzWF/wDaKEvc0eYmPilA8IBEhkDggCyjVII3kMA5oJZCpWuWSKFi7uqkzCn6aQWYIuZJBSkQGQyIRxy1F1dijKgYojH4PEuKc6a3CKo+2EkmHbluSb7DtGhcCgxtRjaE7eYlld8Lm7NV6ZB8FRjX1OmTH6UbgJ4/q6yS7oiFrRxA1omZJlbw0R6xRBO0wRXC67DGq0Oklq1XP4RApPjPPVxV913iWcoExCpRjISyiQJSebtFhzJqr7Tmms3j/Ats06FVLTobLRzPr45Zp3tOkxOq0q+IuYZhjJkVN027Ybh1K9Mx5R99a+qZhP2doTG1SYkBb1icGaTTqMlFyIuZMt1qQWo1gSZwkWrU/dG35V6PIFVSbTInEUI+csgAhJa215KLJySOzQGII4wRLQfYF3Upkqf+HkLKiMErEtKby91Ki0R1CSUpIKL0khoL3O+ab/KpShZQ8WRtCzGitEdmjhCH4CdU0+HlGm4YYAlJ1iOwrZ1cqKKXGKIhkKUCImwxu/fPGWDBWk7KveJU8I5XBqAbvKuEh5dphHYcJhCJLCW2PWC8hG3SzJKWAFAJKQohaB2ckqRSUrUV8CrEqj29gyTlJpAaEBhTgCX5CFihKoaWq2BqhKCKSCkilfuAtklKQfEJIQxaCnBJSC/qPv8X+3S8i3S0au6LfHLHdbrkr75JLBCSxtNz+7M+xe++a+/0dSppBCsz8guweUGTC6P4HnjOEfBMDCRQBMU2kDI215HHHbvecvl/StC3bacS//YfsLj9A6BVycZ+jT/4KZ6/d5/k3voy1hkff/qc0zPSlpbQNmgiPn8Bmw+bkmKtnF5zcvsW4/QBZWhp9xOpHfprtt/8A517Qt0tMqkc2pWiCELz08bd49Oh9yjyQ5h0ZjezWSJGIobIbS04459icvEK/+TTrkzMSF5yXL2PaDSmGmiW+POcbv/ttVif3MQ2EudqWurYlIzDu2yQkedjx8qd+jnXfcbj+gO3FJbqRdKLlMJ/THd2FBNvHI4eh8OCtH0evNwwffkh0I0/feZenX/8qT5//GW995hP8jb/5n5OmiXm6YrFZE4dL2s2Gw37Hql+ixh0vvvcturNbRNMxXT3F+0zT9pRe03eF7ZMP8dtzplRQacBfPyaXseLf1ncoWMYSUELz4j0J9KzvfYyd32C62/TrAZ80w9UF3/nK76LYsw/V12dShMPIxfYJhJEwtwyjRAlDYUl0S156+UfZTTN9f5euLUzpOWevnTBOW8T8BJdu0Z18ktvHnyRnwakeyDHx9Ok5Qq1oT+4zDonk9ozDjntvfRbcRDYN18/e4azZ4OZImDwpBVIcadoFKQe8nzjfPcU+E2QWkEYQguZowWJ5D+cmkJaUJaZf0NyobXMsEDNYwew9Ckd23GD6IvO8o80dYT5UuH7k5sTjkuQKFEunFeHZFT5PpM0GHyrXVBVQyuK8r4M+pt7rGmORTcswOowwNI2ubFml6qY7Kerx4QUKQ6E2AKSUJJ+RuqCMotE9VliMkvhYQfrtIlKKJPsBnQXjxVPEYolLhVZqAgGhBX2zJIypWu7sgjmOGLtEF0+7sfgwkko9vsZWkYAQgkZaKAXZGpTRhOwoBZRpKEIgckUceu9rN3qasGaF6nri7NGqI4YZZSwxBUIcIDZoYTiMA4v1KXGcmZNDKYmyghACR5tbzPOMEopIoe00WgpcEmSnKGiavtSOfKm8VRcCuntImzJdY/DRUrKn7SqPVpmWcZ4oBUIsZO2xq9t0a4mbqs3RzVVLrVNCdIamP67WMAG7q6cIZaqNLc6kmOumZTvTK1PnWwJVlDQe0DYTkke3PcUlTk/XANhlR9KCLFrIHh9mRKmipP1+oIgDBE8uLToOTLvnJKVoFj22P2W8uq73aDHjsqdfrrmanmLNAv4P6t7k17Itv/P6rH43p7tN9C/ey9dlY2fa6UqbKruQC0N5ApQlJAojmNAMalASEuI/YMyAAYgZkhESJQESSgmXXIUslVyUXbYrcdqZlZmvfxHvRbyIuM25p9nNahms60J2GglLICV7EqPQjTh3n73X+q3v9/MhEnNmSppGao7+gGnqpsk2PULW064kIrppEUrdYvkcTTbkHHHaUZAcw76eUtoWKx2BCKJw/ugcERKpeGQp9KdLBJYYMufnHTHNxKhRQtM7iTRr/Dzhs0DkEdPoisTKEWub2omIM8ppZBSIZBF5RliNtj1SaBptGecRmTMSWQ2o44CzDf3ylBITMXhWvcPPB9TSIAT06oQYQ8U+2hpBykmjvGe9EZWSZCRxmlDK0ImOOY8IK5nLEVMWtTDuHJlU+Wx+RrgaoZh3e7plS1ELGqnYH55SAgzjgUXXIbLg6WcfMMQ9re6wwtUTjvkGbRyNchQ5Mc17Fv2Gtm0xzQlyjmSVaNeP2V5doJaRkjLarZkmz/peh3Kae+aMpBMJTafWzOMAGdqTh+y3z2m0IoQD2kpc2xPRzMdLthcvefTGm8gx4xM03YJ5DjSbO1UCEmZiKkzDjpwCbbMm/yVitj8Ri9ySM5RA8DMoWXMvJVKSJipZsVfdOeHiKWWekW0hHK9RXUfnNkzeY7UgioLSCllsVZYaW2kNqRDmI1YW5imje4O0PQ01puDDjBKWnA+kLIhR3R4dSXKUeCLCWmIo2GZRXwbGkucBoRM51PKJUQKlG0RRBER1kisBpjrShbLkknHGkZLBTw5BYNwd8N6zPx5puzVu+QjONX27JBd9azjLWNtR8gRolJKEVBurOUMI1C9mEnDbWq7I5Pq5lFJqcSYZ0PWBnFMg+kBEo/XtrSASUvz4IrcQahbYV6a6lLXird0jBnVFKw8UdYbhnPYqMeeIToVkgGI4u/srPPnu3+PErWg7SwkjRs/Ym4F41qDkX3Arlhq8zxSEqbt/IRPt6R1Ut2B59wyVJbpxLPsWtf4VmpevME4zvnxG0Q7kivOv/jX8PPP1d75JGl/w8qPfJ1xfMx/3yCYzvLrBLTZ06wW7i0+4c+cOn/3x7yFXD7GHM+QtVzGEwGE/sVgsKPuEaixPP/6Afrlgef4mN68+BG04hpm+WRFyIMWENJYz27K7ep8SE4cXK+J4g2s0Pu2ZxwmdPWjoNpZmUWhW9xjGnjyNzIdnLO8/4MHjX+DOg3e5fPEJN89+SFCem5fXBLNnnjK9crS+sLt6D7GwbC+fsVy/ztPv/TO22/d4/fVfpls94M49T5MbVidrfvHX/i45RnR3gj25B1Kjuz2Xr95DZ/js5gkYxenbX+XmcEmYCq7dINZL7jz+KV68/8/44B//Fu7+a2xWS5bdmnAsjMeMTg1ZaIa5oPUV5eDQVnIhv49pT0jPb5D5hsBMOFzhi8EVyTRtiQHsukFYzdndd7m++Jykd8Rdx3jzI7rmLrO4gjGj5Akf/fAJIQ60pkOoUxZ33ubTTyXvvPE3WD1qSTLz/MkfEXeXHA6voDSUGImicERjtUGhcP2a1WbNJz/4Do3MtYAizzg5Nwzbge7shM3JL9AIxW77BfPhJd4azk7exN9cgGkpskMXgY+BaRjxRVBSwFrL8eIFbrEhFIkoGUWsBJRxRDSunn7IUp9JWlKCR5VCCmMVwVBwKLw4ooxAdR2y6zC5nuLkpCgyI/yOyXuaxRIfE3OKtLZnijPH456uXRPTfKvIleQ4I01mmkaG4cCq10hTp5Jatrx88YretvRdT0mFKAfm3Y5+vaAIRThOGG0q2lDXIqDSEH1AWkGgYLUmS0EqCm0NoUh8OOCMhjSTpWT2M027IORUIz8xU4SqBV5deas5R+Z5QChQudTjeVEoKRNJZJGrFcxY0rhl/7KW3dxmSde2txnSghIaVeoxuBCJ7dUTZLYoZxFCIoWiMQuOx/1tPGxGkIklsZsOWNNXUUocSGT8MNBqy+hnlLXsh1d0mw3DVOg6Q5wFh5sjfjhUjul8YLk5x6x7cpoIfsT1a5JtOI4X9N2aMM8Y6Ri2ryhSoG3DmK8hRlSxTIcrpMlkYZFNx6Y/JVrHQjcMxz1RZJrF3co0vcVfar2p0cAc0NIQfSDZVBvvqzvEYUJJwaQVjVozzUekyswhsXrwgFwSwQ/4nFhuNpQcGIeJ3nZIpWrBzQi0rnQcISQ5eVyzIJfIsr2DD2O1P2pFmDJNp8gxoYSoQgfZkELG5wmEgaLq0Chlos/ovqGIgIiZOQw0t6dmVjUMh7HGFm65+8Y1RJ8Ic0BnRcnQ9z0haKL0aFWwyTAPM9Y02E7jwx7bdFXwUFqmPKCMJHrPXErNz+dS1ytZoNwabt+zElVPGqKiX53VSalSpAC6l4gUyCIz+2oZlU6hnKT4SEHeFtwMMU0oFDFA7xZMYbgdnszYtnaK+n6DM3Wg1697jscL4vEV11c3NL3i1SfP2TxcMm23XH7+OaYVBHbIZkKUBtF0RC1YllOapmHV9gglmRJkW8i+/l8P118QQq50qZw4O3kbaSybM0PAMx9vCHMkN2vc2jPvJwoK4yTB78BU65tVik5b5iGwvnsPIXvc4ZriDygjCccdOWf6boPUlvG4BxRCaRoFxXisaZB/CYTYT0Tx7GfffaP8/f/qP6MgySnUEg8FZIuxMO4OfO8P/xF92tP191g/PK9t0OUJh+2OZn1awe9S4oQhqbqbF6IgEiSfagPXQEwKISpGK8UDTlWFb4y5iheEQZiGFG8jCr6QRSCWuviLotSdgdUoCdkfkcohMZSYKv0ggpAOYyS5eHKSpCJJ05ZX15kwXLLzn1cmcDLkvSKKhFCFOLU8ePNncG+8wbLRZCERSteF7BSQBtQtQiiMM8reNk5LQVDd1BmJ0Ykcaqksimo+K0KR/Vz1gyIjU8EYyzj56hk0t/lc5fgff+0//jO/o7/97f+2Hh+menOFXF9eiYK6+h2O//i3WfavM01bxot3OP87v04Morqx/YRRiavf+S941P88ratItCItU/MO85tfIuTE//Rv/kd/5mf+O9/+DXyYiLmyeY2QIBKXF1t+/ld/jUTBthv0LZnhyY++x8IUDn7gtcdfYXu55frqgtP792nskmICSrYcLj/HNIb58gXRKV59+n2MbTm9/5hh3GHdXe7fO+fTH32PprVcfPBHbO6+wYvnHyO14GY3crpesbx3n+EYKMc9mCXTdMAtOmIaIQnGOdC4QtQLTpsluRjG6wswBaYtKWlst+BiuObR2z8DznH97CktLQcvudkPnPaBR1/+Fidf+hrb5xc0XYvtFR/+028jkqRIWJ+9S9NLnn/0XRoU7eNvIowmHPcIo/ngj3+LuL/g0YOfxzjN8q1v0HQ9toV7D96BEkEb/OGGebrBmh7Tt4hQEK5mq/1wybS95Iunf8JicZ/1o59mt71GKcvqzh1IEy+efIjKPeZsiZQwTa9Ic8LfDNx9/U2mOfDqo9/nxUffYWF6ooQkJPr0Do1QWNFiXKbg8SFRsiWOM9vtS1q9pl2ewm2Ga3l2D7NwjDeejz74TU7Pv8R0Y5gvXkAZsJszRNshhUZbx+xhnCekgc4kQtSMfk+nHUhQUmOwTFmiJVhbc47ZXxMmgXASq+Hy6iXr069gM5UAkHZ4l2kw6CgIRJIXLB+eEcdCniZsu2E8ThTtoQTQPcI0KCEp4zUAjetA1ub9IUSUkZUVKiwyQRGFMN0gly0+ChpjURhkrjzUmCorOMaIlrpqaX3li7q2lmRyjsRppnE9pWSUihy3A9oYTGeRtmUcj8Qwo7WgTAERJ2JWlKXB6gYopNnjpAHnGIYDpRTO12d478khE4Y9IUdcU/XiKUekaRBZVPWrdJRSN8pFQwkTOQSEs4whQoqYtiOXgEwgRUsKkdY5Uoq17d4ZwlyLPW1bUZONsfgUSXmqBJkiadyCOdeCsRAFnSWejLylZGitK9tXa5q+Y5omGqEowiALaNsyp1wRq94zHfYU5etCfPQo1wC50h2kJc0DISQmP6KMQVtXp8vTWBW1cyYbhZKZErZIccpxnlmebjC2w/uBHGuRdRr2lJwrQcIPSGsJKdK4KkciGaRTVUGdE2GeIFm681NI1EFGUYQ4Mg11UZl9gDAS00i/7FBCopRgjgqr2lt0VEYaMM6hlMFIzf6wpV2s62I5JpRRzHGG7FE5AxZhdTUvFsE4RbjNu9q+QYRUoxmLjmn0RDHTygaywvSufsYIpEggBCEk2rZOgxGuvndtQ9ofKCXVwrnRlc8cbt9pCHISaNWQciDFiaw1pEhj2xr7yRGkQopMSJGiM3nMGKXpXMvoJ0SKKGcJIaFlRgpHTEd8qbl3ETWNW+KQJCEZxhucqwW8HD1Gt0gFfqzT5qbtmOcZaXqCr6eYSgaENMRckELj4xFLJbQYUYlLcT5i2o6kBPN+T2PNbWywIUcPRKbdwFwKRii226cYLYjHCeVhiB7bdHg5s2w1Vi1QTIzhKVd+oheSkhU+d7zevcv1dKy22fkTrF2B6hDZcjgcOTtt6ZcblG6YpwONW9QophLVLFsmLp++R5CRpXqIkj1Xhx1SVjGGMY4sC1opYol0iw3TDFo5Wiuwq009JS6BGCNxqpx3TUWQaWMrQk9n/Bj4u//5f8N7nz77/0/xrJRM8gklJVZrokjk20nH8eaa5F/x1Z/9Eo1dk4sFYRh9Vfn6OKN9oF3UBuQ03hCHCejQWhJTwGpFkRIpBdEPFOEo+JqLDfF2BL4ieE+kUPwMMjNNc1Ul5pprStSAe5KK7CeSFuhiQVpyEghhiXlCGE1KmbSDi/GSmGC6ukLoPePBUcKWJGake5fzb3yLdPFDPvzD36FtW2T0vHrxx2zyiHn8LsLIOlHRBtNKRL79vEpB2bY2sVOgpIRxFfGiUcwhoYUilVJFASRkKUgtcaLucqMIpBQxupYUfBiYU0bK8cd+RylVAoI0mhQ8TevwKWIkyPO38Onb+HSB0XfYji/JqSBkIEZQrSPHQHf/XfJ2j/c9gYSIR3x+RdJvo+KP368xxtooThMJ8HnGhpk7d7o62TKa6folbr3hcLzh/N4pFMnJnQcUnwjiBXceLNhdfYZcnHL97FNCmJGtvmXwZu6ev0vXn1GiwHPg8P6foM2W/ccfINzIfOMrqWHyPP65X2W+fp/pk49plo+RJbC/vsTKgkWizW1pI0PTSsTiId/4xb/Bsw9/yMWzj/Dlmnd/4ZeIIvPJP/lNVqtT9nHmW7/67/O9/+U3kE6ilgte/8W/yfbyM37m7DFBJKRoGZ89Ix62vHx2RT5eEVNgThc0seHy6hlzSVzuP0bISP/kO2TTsnn4VURXCzFNv+By/4IyrHn1wf/BL/8b/zbtYk0aZqQClRMx7WmajjCOpDRy2F1i2p6Tkwfsry85XS3Rb/0su6sJfzgwv3jGyYMvM233PPvhbyJSYp4i6cOq6Kbd0N//MmeP3uaz9z6hMTsUM3fvvMnsr0iHHUFm9OjYT4FWLIgxo5UjCU3MA83ScXr+BsNhx+x36ORITvPZxz8k4ZjjxObO30SWmf5cYDeG5eKUw3BJGCYOwxE3HUEImqRxtmEeRmLasuw7pv2eZtkjjWU67LHW4ccZkRtM23D0iqy3WM5QMnF6/gbkGVSh39yBsuLh177JxctLVnfus1x2TOORw+4zrj75lCw18/GALBFlLSF6wnRDGm7IMdE5hXQd2/0l0nja9j6FhpIVrWvIsr4sW9citCaLgmIm5hoZgowqkZQ807CriztjySScXkKcyFMi5BpFahXM0x6h9C3aSSGtIhVBmEecM+R5DzTY9QJKgzx6AoJpDjQLh0TVRfu4r7ENIbk6XqPaE4xqkNZh0BibCd7fHmcHcqkT1xA8KWakAKF1jRuEPcP0CuEcngxTQAtfUWPFIAocdxXnlihwUEBDUoV52kOCw3xAK9A2k/LMPAeG4w2SW1UomUGUGuvyiaZZIKVF9oYsBUU39IsVMR6wZklJkRAjpSSmMaFMxK0bQrTopidTAfch1NiEFLUhrhuDDYrGOXLwFKlYub7GE4y41fiOVfXeClRWHHc7tB7J6taOeVsKnMOAKAXbLhC6R+ZCAfwcMC4yhZF0qMUtozTB3zC8uCAExaI/YbfzJBLNSlMEmFbiNiugUkMSCSkarNJ4HzCdwVmHljUPl1KiIGj7FSlkpFUUo/BKYJoTRPJkUWiNhqIqu1lVTjxCUoxh8te0xdA1Kw77C5TuayGRFlEiLz57wmK5QjcNxhpm79G60g2maUApiKLgp7EuQIVEakmcQUaIuXKHExElW3ycKaRKQYoJJS0xFVKsjNumbRnnHcrU4ptWFklmGPeVMhIC036iWy8ZxhkhEpKWnAZ61+BD4nizZZSapunq1H63paj68/SmYwwzwmlkyNwcdjSd43g40PaWIgMUhda3TOOUMELViJMQzPNEmSNCCeLsiWnElZabi2c1n5yhdRJpLVOMvLp4zqq3KCUwMUATCQ46XelSPZLG9rcUqgZTFjxsHyPmgmwiT14844vLD1FdR5gK61PDoj3j4DUtjnsnZ1xsv+Dy5SV9t8SHiWG8YbM4Y4wjiVNUnNmsHiFbzfWLG+yy5d6XHjGPIwLFOF4htWG9uo8vCaWAUujaJXM54mOgRF836rkgtEFVRRUpwjSOWK0JYawn8fL/XRnE/+dXKZnjzSXtosdPAWMXZEzNGXZLvG4xbUP2MyqHOqWlgZxZn6xIqU4tCnUXWFCk6Ak+1mOJONcXcA211WN9WdvJAonUze0OtirkhKrFB6dEfWloR4kBcfugTNmjdUcpFcwuhSXLSPQDT15c0+sePxdynNjNH9cFfEqEfA0yMR4MrT4n8ISP/8GHEAc6aymz5s7b32L19hv4mMlKYZQipfpCKGSyEKRUJ5s5elS51cBqVzWCJZPJpDkiLcgiassVife13R9TRmiJDLIixoS4nZgmGnub9/lzlzKOkiLXN1tWqyXjVDFKuSRm7mEeN6gXDa6bsH2HnRODDhQEeRqwwiAWdxD7HVkWiAkpE+F4wIhaiPjzl/ceay1CNcR5z/njr9CsN5xs7rLfXqFDJuxeUYxC58zu8jnzDOtgWd67z2vvfGhKvugAACAASURBVINXX3xC4wJXL1+xOT1ndgn2R5qzL3Hn4UMO3nNyfgrCMO23XJTfwuYGTOSwv2R99iWG/ci9R1/jo9/+NpvX30SpNddXz7HrlqY/RZLJOdGtHmGWaxaN5urqM+6fPea9P/wup49O+cpf+7f47EcfkdmQph2Pfvqv8uyD7+DcfS4//5S/8ut/ByEUw37m8uI5Lz5/web0TY43MzdPfo/986fc+fJXefXJ76PCHr/QdGVJ7hqON69o1j0n9qsUsSaHHa3dcNhPFH/N2cnrxOkIRvH6V77O/Te+gbUrpusDbtGS40CYZ9I04NMeaS2tXXPv0TvcXL9ge/WC5eYen7z3u/TNGWG4Zv/qnyPnyMff/YSsBK1tKEbRrDx+GBGloVk45u17PHn2PeIwMXfV7OaEwrXnSH1COo5Yt6FpPbtX11jXM4WIbTpW/aoei04DBkEYZ2IZuLl6TlKOu/feoJECZzMnmxOGeIVIHX70lGEkHAd6I7nz5r+EVoIQLnj+g++jKUQ/MiSPc47rV1/QdhuCH4jesFptOO5uiPOEwCP0KSlOiH6J3+1Qdslxv2PRjWQ07//TP8AD+xvP29/4WUxW7F5lTNOzvvM2n/7gD0AFyjZTlMPYjr7vUaIKJmYipuswxWFsg4oJqRU+1owpJTFOR5zWzNMRoRpIGVlJnPjg0cZRjKI1jt1+S9MuKKlgmpbp9rs6+pHt7gLTrNFFkIQmiYwVkEKox55zxLUNRZla3iVgtab4imrKV0dinAGQOlNEZs6Rpt2gtIZSp+DeT8QkiaGC8pWqVjSpOoyINI0mZ8kwjkht0e2atjhCDiyNY94fyVqSMrT9GiMy0Y5Ys0aU+pyep4gkUWLhMI20bUshsr+5Ybk6oTEF0Xf445EiBK4xOC2YU6S4hNUtpagq7poCzDcc50yKguVCYru2quFVwiiHFj05gbaJeRpBZEKaIIPQkuxHlNIkn9DKoBDMfqRfnNGvH8LNC1IYmOeRFBXStfUEzxR61zFMHqva26ef5nA8oDSEGDgcPSkdEMayXKwQKDLQOAumQ1tT4xqpJ6WAs4am2SCNJxVIeKbxBmUEOTu0ahHSI5WkaVpiKChrSSUhpGIYDkjjsKYjpURMEdNopDX44cg8R0wAVcCazJwLZA8ik5NESMUcp0pMUAtKKEzzAaEVxlrwkePwjGk8sj47IYmAD4k4V5PpcrViOOwpceTi5gNisqwXy8pcjUCuU+B5mJCiVM6zNuT5ogqdhEFqEAvNNHpc22CtwU81Zz3MBxgynXQcwwFlGg77ibN1oISMaZZsLz+vXaE5E30CJHnQdYilLE2n8XmGObPslkwxVPJBiCjhELes/841xJyqnpeIF4VWqWrgK1QShpCUEgmxFuOkqnZPSSGnmRBnyII79x4zZIUqERECzWlkc9rWcnpsULoQva82Nj8jRNVEpyhQyjKMNwTzOk7XwnyYPqJtek4fPWY3RTplGbQmzxonBK7vEMqzLJZOLChqBX1ANJYUMuXVBcvl25Q0EeIenKF7cIYQmigVqlmjYqa/e04Yd4zDgNKZw35iuTpjf3NFt2puI1BV/NSYhjB7Ys6EkiELGq2JcUJpiY++Fuv/H14/EYtcAGMM4+GIYODlk8+x0rE8XSKNxfQtfkhIITDSYUwtP3g/ooSgSEnJsVpRpgklW5wTUDQhBzIV8uzkgiRqKSSkBD4idcVrZWXonWIYBiQGZK5mogIxBExj8NNM1qCypigDWRKVQGbF5WGH9BEjDT7NjLmQ/NMKekeTc0LLJQJPMQJtI8+vvodNAqt7YnREcU16L3C4SXzpl74JRdzmcySUGmYXSpFyhpwr6y8HlHRVoauqflhEhXEaLUyNS1A96VIZSolVDZwyIleEiLilKbTO1dyS/fHbQgtNNtCZNQhJ2zpCjChlkEi6r/+HXHzyP9Dc+yn61z4nXo7wcIkuubrLS6Y9e5fd+7/NarG8RaR5GnU7OePHc8DaNeQYMKbl/r37+GHH7uVzrvP7nD94zKhSXayXjJ8jJw9fZ7i+5v6bb7K/esFxkKwXd/j00zrhvNm+YHF6zvLdX8D1S/bba/YvPkc8MEQ/8vHv/c+00hDmHSEJ7r7xU3z+g+/R9GfsP/uQn/rVf4+XF1cs3DO2Ny9oFj2b9pzd4YZ2fUYMDU40+Nig8pLtxz9A9A/4/E+ekuKn3Hvrq8SUySiOe4UW59jNO3Tnr/PRH31Mc3qCH0Z6u6AZtvzz3/7viFnSb+6zeO0Rrz7/gPtvfo3dzQWNUIQ5EpPlwVd+HikLn3z0PTarBdmsybmgpcGXlmax4O2//q9x3F4QWSCbhhimasaRimnaMj/5jOgM/cmdutA8XvLk4x+yXq6Y5sjq5HV0s2Q6foYpM7l8Rt53tDZyebEjyoBaG8zqTVzbsj8E/MUFrutZrs9I6sjV9Sushra3xGuPR3H22lc4bq/YjdeUviVFgbCakkItYeaAUZYANIsePwaW3QIhI4ebF3RuzfbZj0jjaxgriN5zHCJ37z0iypcIH3n63f+NKDa4u2+z3Nwjh6tKTVgvuLx4idEGLRW6d+ATh5sd83Tg5PQ+PknUuuH+a9/g+PxjJgpu0bA8OeXw/GO6k1Oi8YSbLU8/+4fsP38f6wyqt2QpuDk+4eS1LxFzQqZKLjBaVPxZq/Ex0LiWlGuJK6dSm9QpIkUBoVBa1PxiBqMdStYJ3zRuWbQLlFIEIVBRMvsDnXHM0x6jG5ANpm2RJDqzYPPojClNQKyYpRAoRdI2mjCPtFIzxAmVYuWtx0QRCaMtqUApipIS0hpC8RQkbrlAYZnHI1I4ilpQlK5l2maBkAKjFCWMpJAq9UEZpFYIrUhxIGaN7Xp0rJnFpoU5FZRQyCI4DkdkCVx88T6uWSPLTFES4xqEECyXG4YhoYxksTxFIglC0LoVtlkTfOKw+5ycM227YLU4rZz0ovDTiNWKkARuVU8ylIjMu4uKllQK1a4IacJ1HdMcIWmMk4iYCXlEpsIcM8HfYM0SGeAQPEq25NLy/LOnrNdLcthDmHG2cJxhLDtMHNlfzkzxwM4HupMTrD2ldUvmVL/H65MTYoy1HCxheXbOfDxihKWUxBzCrUC+QbpMHhOUVBnIQlBES9+s8HFLKtCYgo8JaxXDzZGcKlmoWXcoQSVu3PYQpAKkZhxHwv4aqS22acAXYppJPtQp5+IMIQ1YddvxUMgk0Viinkn+SJxGDrsnyLkglKR1kjBsiTGzPLvHNBVcu2Q8jrfkpMjd0y8xhsqU91NASgjziMiilnOtxnULXKMQ7ZKZSBohDzNZJxaLc45jjTm4dsnsj/SLVZUmjTNLtyEJSdM0dXIdEvN4pHFL0BbsjFY9ISXmw2WlcsixQn6RtBiO+wNK189Ua0lShcOwp9EN+6srmqYhzAOHqy2L+3cpJt9m4iVaF3zOzNOxerNvC544XTe0JVN8wGnHHDJueVqz+iYxTwe0bJEiIXUmlRm36NDZEp0gzyNBasiRkmaUtDgBMBHiiD69Qz9lcGs6MidtA22DtGu8r3EgaTXd+i3MPOPjljlZsqz0j8XZO+QhAY5h3NE6cRuHUhwub1BS0J8/hFRIQRLSFcoX+v68xptSYpwmUJHzB2+wvbkhxkhI/paUpUl5JgmBVZYpzXTN4i+1tvyJWORKKXHO0a9PmP2O/mSJkY4smqrHRKCVImUY04zIgiIrYkZkjU6eYjR+vMBPM6t1R4oVuWW1JJWKBfHzhHKGMI0opciyHonEVGitI+eAMbW4lUIBGYi6tifzNOBjphSFsz1KKYYQCCmzD4ExRMJ+olGFmDOULbJ44nRNiopjiJjgKLowh4wYXqBUrFNa2ZMaxWuv/as8fPeXkKYlpIy2BlkkwkimaUJrXcsCorJvyy1ibZYeeZst00ISReUoxDIRQkRoUNrc/l1FTH9qEKnKPykVUlOzeNQNw5+/cvGkUifIPt/61KUll4gsCWMec7QfIYYzerlmfHqFuttRpEbZ6gwX+pTkP2WOP4dZKKQvlChRaKT68Z9J9mQy03jN/uUzlFI8+vo38FNEnS9opCT6AakVfbtAa4Pr4cUP/4DD+AQVV4zT54jSceetr5LGkfbsddrVGSkEVN/ilpbv/qP/ms62IHcMxwm7fIx2lpvdkbuvf4XDzUuu5yumpz9CxIFkT/nyt/4K3foMIWEREoeLTzH5Ckfg5ef/hOnygtfe/XmevfcPMP2KpnvIxaf/O1/9lb/NxZMRu7S89fW/xe///d8gHn/A6aO3+OKzH2Jcx6unTwjX1S7jp8D+cM3JcoltF1x88hnZWmL09K0jZcFxu2PMV3zjl3+Nmy9esd/dwPYZxp7Snp5SpplxLjx48+tEH6rpKxf681MOLz4mTEdeXnyfB2//K+zHG+QYaKxi3S+Y/UgYRp5/9AeYZsn1q/fYvPYO9qmiPLRMw46zt0/xx2umSQILtuMXDMMFK7nguL0khHtoKxBqYDjsefXiR7Tmp2noee+z32V5+lP4Etm88Rbx+AoZE1rXnCk+MXiPW57UfLkqGNkT4w7daZKUaKUZ9y8ZcyGUwDzPfHHcYhtHLoXVw7toe858vCTICEnhmg6yYH12lzQFtDGVDJAr3cS1Dcfhui4ur2ZeFcijx/YnlDAzjjPm5A6BTFGezcMzVukurmmwemJ7FRjDgbe/9nO8//3v0diWkAcUimGacO2ClMCYllzqIvZPgfjDfKyLv5JAtfV7IKCVHU46kgARCqu7ZyxOlrz48COOQ+be3dcgCXKYSLeWvRwnSq6DgOwn0jhyCEekhKbNpPmKadqSSmbpTkiqx/ZLvPf8C+CJEoQ5Y5sWiqbp1yilOJYjsmiEsogJ+nUmi3zbD3DIGNCiLo5TzJQi8WHL+vScySeUW5HyhJgnQOCHI9osUUpQcovWGWE0c5xolyc4aen7+/jbxYVUueLNpGKMHqcVAoO5zYuqUg1tqrF0QnOyuctx2OKcYzfuURFUMbhmhc8J62CKnm7Rcthe0WgBIhBCIOcJpQTDfo9UHY1umKY9KY44ZbG60nO6xV3CeACj0bfTUT/scWbisH1JIzNpuCJlQ696vLwm20RrT8izor27IBeLcZYwz7TtgumwJ88eUSKNW5Jz5HDxCoVjn7YIIklVUQG6ot6ETYx+i1YtCcE071DR4OOI0nBzFKhG4rcv0e0bGClohCHlyGGoC7Z4rBPB6WZAWkW36UhxiVCSGFKdjIaMNAohVoicCCkCGpkcIhYmH6B42kWPNtDqhuxusFIifcaTsW2DLBNJFrp2gS+BZr1BpkKc9oQMSgn219c0mzWH/QHtGozIdL3DA8ewJ4slyCMytZSuQ7YKXWC/q5FBqRJhkiAzKWREkWAUQipk5nZdkdCmq7r06VDlGSFC8QzHI6ZZEMIRKMQYsMISSqZpG7CRaT8h1AIk2LaB2dO0ElSmbS2uvcN83HN1fcXp2V1s2xNSAZ1p3ALjG1Q/M4UBrwcmPyBcy93NQ4ZhQAlHnGamcEAjSUjIElESqoCPghwS0R9AJowYQFriNGGcxkeBNJJ2uaY5s9WMKgJGGVbLnmHcoq1GhxkhJK02JATjPIDWaLUhxkAedkizZLrRTGpLKxpamyjTnosvXrC0G4IPuF5z/eJHNN0dum6FMud1gLGvEYV2uWEOA6ooLj/7ENOtq6YcjVGKHCK6W6BVIPqZhXBcb58R01+wXvi/uX4iFrmlFFCKFOeK2Co9smRSTDjTkKVinkZizFjXEvVMCTOyQIgT2kqm8ZLGdVjTU5Bo2SKUr6PtolCqQamWNM8o6SAHcqzBcKtbcgyElGoDOGeMkrUVGkq1KklDbwtSO3zJkAvLpmM3jJQwMPoRReBw3DPlSNkeEMLjc1X8Wq3QSpCjJspCa87Q9OBG/DwiD5GrL16i01PWP/MVOtOR0kwWCTELFAohbqkJt/+2EhNaanyOKCUQpsof9K2BSAjqUaOM5FJQ3OZYcqZQywr6trTmjCXEjHS1vPLnLz9VjmHKEds0VTKhLVEUcoR9s2T1xs+xCCcc+YLl7pIkX0eWTCmOHCa0arhWB5yaUXNLjAWhQ1Xd/gWePiMl4RatplcQ0sT20/fYX94Q8jswe0Qe+eK7v8s7v/Svc/LoSzz/7v9KVGtO3/4WXF2xsa/TnC0ZR49SG6Tt6j0mwJgGd/4G//Kv/SdcX2+5efmcR2++yccffJ+FqpN8s7rD49NTxt0Ac+Dy4hO++OF3eP/7/xBtetRqQR4bzMmS1772LtcXr9g8foejWXF9uIKTd9nce52UMp074bOPPuL+nfuMQ4vfa978uV9nTjPnD8658+inefrkByxXG/z2OdtX77O0HY0IhBwoxwvsomMqEdtsuPO1L9P3Sy4+ueDs5JswLLn+4oNqmlmeMg8z77z1NZpli8Lx7MlT3nz3HbJUoGB/8Yzdxcc0jeX+u3+VuUTKzTWRPVdhQDcOM93QLHpCjKzvPGb+wvH8B99BJYu4ieQcKcYQhEabDhESTi3plj1ozaa1XG53HK93nN15F/Egcv3BgSgluQgePv4mUTuWQHq1RWpBDInF+g5pf0HUEzEEmI4kf6DtH5GFZs4aIxw5gcmiIu5kpFEtzjSV9zlVycn47IYpPCG7CaV6tFzfHq0bcphvowER5RqmEjBao+0JKkMRmYhn3O2QalknlbmQSkCMhTgdePz1v87N4QXxMHF2/i7XL59x9nCD6Bs+//TjWmLLM0oWSqqnQn3fMwwDx+2Ac64WnrQhppFmtSITSEFgMIjGUWIi50IohRQmcgE/BF4eXiJdz2Yh8dNM8Vcc9gNufUqjF8QpgBIVGagl7aJDeFM3+Tmj8hprm0pfEQZlHWPySGNIs0BSObtKe0ICJQUJkEJjy4I5ekzJpBjJqfB/PWIyRitQsm4gpgllW5x7xBQCPgnSfoeU9a+UlGi7DZfXFzS9RaeMUpYYUsWmWcU4HGmVQWsFRZFEuS1iNehsCD7jjGScApqGprEoWW1l3kcU9VgZkYnxlisuM/NwrOimUpFbMUasq6cdyEz0BpE91hmmaXeLo9wTUqJdLyAmpuMe3bU0bc/sj5QUahs/TSjrOBxnhC+4vme96aplMQaOcUnfKNKs0KIw7GeKDux2R6xpaExEGo0QEZsgDNtaEi4Dk99jmxapNU4LphgQWTFcHehby832GU3ToMwCaXum8QqrJ/bHLa20hFd1WtatHIfjFa5ZIF2HFYEUC6VAiQ7XL+pGax7Ic8H2LfPgma3BKEX0npgKmIZcDPM4M5YRhSD4ASMDVweDD4nlosE2jnH2GCnQsmG6OTIO1/WeLBqpLbnvKytbKvw8QUysNyckZVmf9Cilq+iieESBrl3eGjEdMUummx3LtkErh2wsDslw3DL6gaIzskiCkfSrJcdpRItKMXIoYskcwx7nGkIxuJVjHic6YyAnOucQVjPFgJSqIsNywWXFot8QAV+gVS2ubckiMoZIjhKtBeK0o5M9Ak0K9f4Pxxm0xerIYRgpJtMvzml8pYbsd0ca05BR5BjqBFhpTPLkOCKVJseqy0UIrJIMk8eLTDgeceKWvbwWSNWSgsGgGKYDKUzMcsR2LZoOWTw5FkpjmOYZ6UFpTRx9jXyWCSEScZpwraDEhpwjKVTc2dnr95HNKUpJVrLBb7dMu5dcbZ8jmRnngcZAsYnnz1veePAuom0QpeE4eNI4U3wh5AN5TnS94HK/4+z8LXb7S4xR6L9EJvcnhK7wevnN//I/JeeIVLVZmGJ1FYucULIQ58T2uOWwvUEcBbZraVcdzbJBlETJHj8PlCzQpk6ApYik6LHSUaKunECtKQQkihAnBLq+yFJAS1Ef/KJUZasQVcFXErKMlGzqFCULlF6Qs0D1lu3VNUcC4XhkPh443uwqJmQ+cCyZpmk4TDtMtHS2UIzCLhcsGs0xeYaxsHD3Wa17WvUIvT6l3awpMvyLfFFOEqVLDaXf5mhB3j4YZm5pKfWYWut6pPinnm+jKDHXSRFQRHXAG1ULKjX3F+ufWSCk4u/9rf/gz/yO/t1v//f/J3VvEqtplt9pPWd8h2+498a9MWVERmRWVtbgmuzCbbssYRqLbtGITbe6LYTYsDBIgAVG8pINbFALMQhES2y8YNVCwsLuQaiN3WrabZXH9lTlqsrKrMyMjOHGHb7pHc7M4nxVkmFBb2jad5WLUNyIvF+c97z//+/3PCRX+X3CSoTUpBRAyeoMNyBe/Bry5po47WjCp5A/8ZdwJZAFyBSQMrP7p3+XXjrW9iElbDjEFeHL/wpawt/+N//s9/wb/9v/VDEy01j5v63Eyci7n/9xlvfeREqD0plUFCEERHDY1QkA2+sr3OXvUcwFq7NHaGB3dcX60dtMhw05CPSyR4XAd3/z11BCYc5WiNmjxZ548yGhOWfxztc4e/qU8eqal9/7BvP179K391lcPGQ/72CcCEUSpkwYLlmslpTSkVHIboFKE845tIy4yVOURoREv7wLtgXVcu/T72Ky4tVH73E4XDKHaqxRJZDDhOnu0DYr1ErTnN5nsXzM4ZMDsS28/zv/C/fuvsOdz34ZRcCqB0yHPc8/+H2iv+b+G1/jUz/x40Q3orSAVHmatu1RouDcDW5+SWvu4TfPGEdH8nuQS+49/RyxZF6+96vcufsm3d0fIg4f88Fv/x9k3VDcSA4OlGZ7M3Hx+CnzYc/q4j6Xr56RY8F2PZv9hjfuvY3AcpgOnJ4mtpdXNW8/B4rsQLg6hYyJmCaKbFA4/DhWSoJWNIsLVqcPeX35CU+/+MNwGNhdbXDD5qi11hz2W5TWKKNQUkIxFBGIccAYhc8F2gYTNSJlYkrYRuNjqSt0RC04pISJhUgEJQlKQUmEmDHKou0Rg7UfccEjTK5IIXGO1R4/7smloYiCNhkhBMp0CKHIOSJEfTgKIdG6UhC0bgh+YswVN6aERIhCTqF2AqyubG5jSVGhkEgj8D6R44AomuS3CKGYwwYjV0BFYVlTkUeHYcfq/E1Ko38wMU9HEYvjSDHRPQiB1vYH1AZZdKXf5Kr4Jaujfj2TVb28q1JjGE3ToFSDKDWvmqWipECxuTK5c1V0O+eRSiGlxEpJjImQClYLSs4EUZBCIUqF88tCvbBLCYgaFYi+GidFLZOZpqPRhhQSKY4UaUFr8JnCXIuhIiBMiyy1UFuiRTVtJfnMHqE1Xd/jtzdgPcb2xAxawn63QeSCbjvO773LvHnJfrtBSYHUhYhCtgmZW0JKiJSxpsPHBNMNAFHKakPzI+tVi08abTqa5bE4bDp8zCilCSEhUER/Awka1ZF8oFlpXJTEULh4+Ca7m2d06zto2bC/ukIrQfJ7onIMe0dIHXcvlgS3YZr3KGnIrtB07ZGs4Jm8AzLteo2RFu8gCEmJEq0sIV7T2YbkE83yBKPb2vE4PlvcvCW6iDUGT8SFBDFhVUDpnpw9pjeMm4HV8hzTnSIVTKOjbXOdzGd9VC17RMmkLFjfuUseAtN0QK+WKKEYdgP9+oTD5UukKaAVIQcau6KT1ZgqyEShMfYUJQRSHuUmrpa9tG1IGqZpwmhLCbFuUJyDojC9gePgrU4XTd3syIL3EzEHgov0i9NKMdGFMmfMYsGYJtx0YGl7QnCkJDB9jxaZhK+DvaOZMPsJqTpmd0CbQnd+By1a3OQRQmKPvP1URLW5HU1rKQdyclilyCUS/JEwQWYebzBNS78weBdpmp7DYVtjj4eISB1FF4oaKnUjz5Q5knAYZVFqwb5kls0d4uElqpEkr1ieXOCFYgpbyjjQ2I5mdZcwDnSdIVlD1roODLaHOqATkfbklDDvK/EqBFyZ6gbCOfY3W6RocdNMo6Bb9PhcELJhsbxAdYneLPBJYxqLSDt+5hf+a7798fWfH7pCLpmIQ5SImyPGKA6Xl6Qxgkh061PWpyecdy0XJwuy1KQoUdrWB8+8weUBZdTRhx3gCAUXMhHzgNarmnfRghIrv7YUiUgZnUvNnGaJFrk2gMtU8yClYrmSrIe2NJXZR6wc3hwT6/UpYXeNbVrCfk+3bCkDJLFkGmd8mDHSEHJhPx2QTnN9GehWj3HFkcsE8ZJd+w5f/jfepdd1vRFC1TOmnCpH2NVLrKrVREpJ5JwpQiJKQdv6EPJuwNoWSqmEAuphroSoUg2ljnnXGmZPUM0SRzNQ8v/PSa6bD0htgIAIglQ8tmkgRoSswgvtOuImY1cP6E+W3Mwe22tkKSQpCRn0w3uo16/QiCrj0C1RVATX//2rf/KItz77I7ghYLsON2zJyeAOnvF2IrKhO1sz7/eYpiHOmShmlJF0XYd++Bcw7QlGRV6//1360zMO1y/olgtuby/p5Jr95ftcPDkjZoPQhpgn/Ki4+OpfQfV3mPcT880t1sLZ2Zrlj/xbHDZb5H7EffR1cj7Q5oKIHV3bVh7zeonSkN2WmAIdhlxWdKsKwDf33uDBpz7Fzc0fc/PxK168F1ierRjnl/TnK07pGedAxrPbOvw+0so9Wl/TnTUsfuQR83TJ8vQxb7zzkzTqDocXE69ffw85/zY0kidf+kk+/UNf4dd+6X/mYvM2J4sFsUhst0Zqj2kFAo0QC4jnbK8/QoyZ1Z0LYjrBuYHd5hnFe66+8Zusfuwx3/21f4AxBtW2NPYN9vEjkhtoZMPJm48JPtAt3wRlWN67C67QLNac3X+X6faa0/O73H/rHT7+9j9isX6T9uItyu6a+eZjSkh0tjAbTRIRkfcgemRjkFqwsJbgR25efJPeFj78x3+beVeqJSw5FhcPmTYTi+VJRfmUQEoGScHniUYtEELi5h2tTJUiEgSLVcc8ztx79JCXz56hhOEwz0AiCAFFYPUpxFt8CGi7ICNIqeAPA248oLWmEfWBqUpEa0lp19x94wkxjnz0wTdomg5/uMWYuiLslkuSFAZSWQAAIABJREFUAJ89PkTiPNCpDlEC7TSStWG/3dfca5opKbK+9xC9uiD4ESU0kgUhZtp+SY4W7x1CLen7JTaeIHOpWumSMUoxuj19ew9UJGWY3URbNDF5XI40doHVllyqyStGD1ISJkchI6VENAYpBNpTUVeNwejyA4xhJzXKaNx8+AEHdZ5npCqYYiipElNSKrStIZRCybW81tkFJZcaeUiRFBzG1hd8267xYSSLhAoTShl8iEhdMYUpJ7QqGFXjVEoKUC2D28GY6JoWIyQuB9rFCp/Tkb8aaHvLPM4c9i9oTt/GHi9Cen1CJpGTqL+fKCxPjhdRLfDbW1IsdKdLii2k3HKy6gn7PSXO3Ln3EG0WvProfciC5dlDijCUMCKoZvRYZhpZjVYYDTGQg8BKCyETXaZIT0yCXnVo29cXQgFWCVKE7XaLi8B2wPYZd72HvuDmK07e/gJvf/opL17/CdtnLzmEDd2De4TrLauTc7wrnKwXzOLAqT5hmBNZVyRY1xosidwIgs606bxSSlpdKUQxQQx03YI4OWzT01jN5A60fc8Sg88OIQomS5yPlCQ4ubhLcJnsbtFmiW0kTdsxzQcSkRIk8zTQG4kC5v0tUjTotkdEQRGJ1WrB/rBHLRsaI5jGPafLNYfNSGg6rK5KdSU00ddnrDbgph159IzTnrZb4lOodIRcaLoFjTS0XY+fA6lkgg8YJcG7istUipkBlQoKQRGa4fqStjEMaUcphenG0ZsFqTg26hKtOhbtCePmFV27JDiPaAzCqFpUy3Uod3q2RqSZw2aPbRLStBjZALWclqYDItehTpodpgORM6HMyKxRSrNoFuyGkcXJG+To2B+25OTZ71+xUCtCKnUwEQ/M8wa0ZN0/QC0uiM2mbl3nXAdxWeL3e5brNaVpaYTCJ4XIinZxTilrjJlJY6aTDcPtltRkaAU6WE5OPk/0CSUCUmRikZR5IseETC1Bv8b7iJEB1MzijkGJGskqKpHdlsN2jx8OrO058xgps8eH+lz9Z/36F2KS+6V3HpX//b//BUTR5DIjCcSSWTTV2KNlR8w1cC68IAsqWkkaUphBB5z3qAIpZFKqk5M8F6QOGGPwc6grF1VQ1EO2KElKiV4ZVJFMzrNsG4qkXm6lJFLQwiC0JJaJGGoeVifq2qpASAXZd0zTxGG3Jbhbtq8PJB/YeI+OisVqhVRwtkiI9h59v2QcL5mna7a3A8Yt2eg9b731r/HgM++SREGKXNlxMdY3z+PbW0wFkQtN3xH8jDyWtuJxAiQKP2hmS2XrVJyq9M0pUIwihhldKiLG6gqWLkZV/XAp/K9/9Wf/zM/oZ37lF4m+tnbDXLXDWltyzvRKkryjHb7Js9/6Jzz9oX+ZfmF5+WpF/NxDkJmQE0pryvwK8Vt/l5PlU4qCcvHjPPm3/x1KiPznZ3f/zPf82a//BnE7EFYdF4+e8L3f/VXS/hmNVcyHV5x9+qdp1uc4f2C+fsHp/c8i7JKzizX7m++hTcdi/SaladAGNp98SNhvsMsl7vI5m+fvQXbMvtCtHrA8PWX/6g/p7Zowgklb9uPHhChZPfhRZKP48Nkld999iFSG9jCh1ufoMvPivd8kBerhr8GaxRGxtufiMz/NvU99EVFmtoc953ceIBZv0EhLSgPzcMVH7//RcaIlsNHyvff+iM3ta/rVKauTxwSfePT5H+OT773H5ru/RbN/wSw8qZUszz+LFi2iaZiTI+333P30v8q7X/oC3fkdpITxMDHfXnL17Ds8/fxXKFJAjFCq+Wc63JLGPZmEInB7+5rd9YGT+/dRdmLeJPCv8W4EeobLP8H5EWl6yB1yfYHfvQDncOaMzt4nKUd35wzZrJj2zzk9fYMHb38Fd/ucV1cfk2+viFOkKIvQicP2ktDOnK8+hdVnxLgjx4TzO25fvkQrhV2saZqCcxNSNHS2q5O+nNA5k03NDEoKcZ4oVpF1QdkTuvaU83sPuXrxMS5OaKmZDwe6/g3uvvUGeRz5+KMPOFt0hJKqjUlVs5gxmVwcJS7ISpCjQ+m+6mcBqTI5JnysG5Ld/jVCqlpmyZGzi0fcefiUw/Utu5tPIBTe/bGf4rC94sPv/B4PH73L8w+/hUoOLRMuRIooR5NYj5Q9QdRIhqBOyLq8JGpFoNJTJAYhDSEFlIwUPEJ0dXqTE7k4rKnlviQCGougroBECnA8ayKC6AOFQNf3xBIxsiPl+rCVKpJjwM8BJQpTHJCxIo2Em7GLJaiqknXZo7U9ss8FqQisFITkkVJDTqSSmUdH3y9rIa/R9ZKbCo0wZKXBGPy4p+97dvvXLLoV8zxhrUYSSS4y+gBaoYymMz05V2VujjNZgIrgDpvK4JWSlDTDYUdjKydcikS3fqsWGrIDaXFzxPaWOFfzW1UL5yqniKHq0RWUxiK9YogblsLgwwE3JIxckpuKaEx+jyqZ7Xig6zWN1GAEujklh4jPpcp8SkJkUQUXJTBMN3WaPFftuyAicmIcEv16xfbwGhE9y+6U15sr7vZrxtnh8syqO4cUQRaa8zO2/hrEimVJoCHMUFJGmkQMklAESnYs+/pyVI4DH5pFLdSJilvTKdaYUCM5zBP4iJIWVCaJTAiJRbfCe0f0M2IutAvNnCNhGgnziNaWEmZElCRBNchpkDrQLVqKm5HW0tBhTMscIqbpahmcwjjtaPuWGGf67ox4CDStIIS5Em+C4OCm+t+pZmRjDtimR1CQOUAoDL6yjYUQtVQnO7qu8pH7kxXDdEDZjugTokQi9aXN0lIwhNGT0w5hZ9w805o1jVmjrQKj6GxXsXWiFsCFamiahhATShly8mhrjmZEBUHQSkHMNbMsiqTRDTJXWoNWgqI0btiTjKdkT6dbjGlITlNUZg6ew25TI1JlT8x7mtDgiuFOf4JuJUMbOby6xgZRC+w6V5xi02FUj5Kaac6UOCLsghhnGimIGfx+T0qROO3I5UOmzQhGgZbY5V2U7MCcs+iXhLkqyNuVpVkprG1Re8tueEZmoESwZsWMIltB2F0jhEPHhjBNLE8aZndLSolOLvBh5Of+1j/kO892/0yT3H8hLrlf/vTj8st/8+ewbUc8rpMQGq1AFVkPdiEq0kdmcpDHC6ZAlIzUgjk44s0NIULTGGTXH1cenkRBzKF+qJUm+EIR0CzqukUXwzwcsM0S7yeKyAjhIDqUqrkgqRVBZLRuKPn7OdYqaUipIGxPEAWlDGncMtxe4fIGQmGeZ65vE7JALjMjjk53iJLRLDCy5ck7X6C7/4SiLLLtiK6ujpClfj8SQhRiKkghSEIeMUICJSWZVJmSqUBO9QJ6LLSUAkLqWgiRFTSeiyMnhRGKEgVBCwIOqzRxnviVn/m5P/Mz+uu/8ouQaywEZL1wi2p76Uqij5my/ZiEx1w8Rm92vP71Z0z/+l/A9II5zSwu7vPOF/8lfue/+Ckenf8kqgjmBz/F+V/6adw08T9+9it/5nv+/De/zuvn30A4x+3lDUIp7r71mOWdh0hjiXQcti8Zbm4wObF6+DZRGBpVmLzj7P6bvPyDf0wcAmePPs3t6yvK8IrLl6/Iu2+idMQ0K07PLrh99YrT01PmeE2JjoN/xnr1eaxcEotHrh/Trx9yuLqpOUFlsPLAdveatu2JEvpuzZRGFiennN3/Ev2dC+zZU0oSpDhx9fFz7j56SD42ebMAoxJpyLg5k1JlewpxBS7x/NnXma8P+KjIMpDTBqsaSruiae9Qcs0mRnegbRZM8zOWF0/5zFf/Iv3qHm6OTCkQdrfcvngP5le8+eWfZrE+Q0rN4eoVab5EN6dMhyuCO+DmK6RqaGXPy4+/RcoHWrkmG0j+FePgWZ/8ED4dQAeMUuyfXyNWC+btNbJodGkQrUQpTXv/EUGuePPpO4zTgauPL8mbLf29ltWy4aNvfZO2WyG1wKUdQp3gp0DXNaQ5k5mYpj3CynoBcDOiaSmqJ5S5FlmoVp5F3zIPM0VmkszoKPF+hzFLihQoUYhhgpAQ3ZrWGKLzLE8f4LIjFsH9h095/a0/QS5a4uiJZUezWCOw5DQgZI9zE223gixBGIrOlOAwtpCxBDfx+OlnePXyQ/w800iN0IaQa3xI5Ak5BXwJLFdnRLHlMEGLOao9NZ2xFUAfElkIRGkpWlBfuz1KVZC81ku0bgk+kfKE1AYpLFLlupYXikyNqUhjiUkglIHsKYkqvHERZTPz7ImZYyk3oqSoFyApa8teGAqaokHLerlP2VFEQtHh84wSmhwLXb9gnqYqfJCyXgqPsYPG6CoSSBwtVZU3a82SME+gIgJN9Imua5ijr+v1ECmyTgbnw0TOmaYxHMaZu+tzZmpJJ2XQpXYWpFVc3H/C5Yv3SbsrshL4MdGeXlSDnMzMU6IxPZ2qGuFp/5wSIGlLLh1p2tI1LeBwbiTnWpaOKlKKAV9lAlYCOTIRUK3F2nUtPRNx0bPq7uDcDiQ0XU8JgVgmjFqyH6fjn1uhjWQad3Tacpg2tbU/O9bdGSElQp6xUlCiJAhV7WLWEPMWoxXRZUxjKTkSdzNysYT2FHIm5Ql38xIRR6Ywc9o/BimIEqRtKaWpQolO4+YNEcFqeUpuLCIXdttbhFGV3Xs4MIcd/eKE0l9gGk2IM1k1GNMwbl3dqilDjBGlIZWMajRlcnVt7W4RjcFrQyssUDexUiXGzQ227Whzw37akDVY3WBEzxQ8bd+x39zSLXqUXbLsVkTvGMZNFbtMA5jE7B3L/oQYMtGPiMZgpMJIgygZ5xwhZdZ3zogiEQ8HivNo1dRtDjMpKbJsMEZh2xpbULJldAXbrmitwCdftw9aQtEIJeu2o0BKAdmYautCchj2GNtgjGG7uUYYixKFrDVr25FiRrW2dnB0gZBR2ZLmG6bdDWKCEAaGtCGLyJ3zt0F4ilzgmEkUFnrBPO4QeUI1kewVURm8C8gSqS0dQdevCeIaoxtyqp+/TAPJVOuqGNjuhtqTmraINCDyzBQHBFXZbJc9QggCiRwkKSuKrrnnu+YNZJDENHFwAzIJ1OKU5eqEOcykKAnTSxKJIgPK7/Exctg7zk7WuDmSGVh0PdMU6HvLz/0P/5DvfHz75+mS+2b5e//Vf4TQCu89xkhSmDBaAw1Fg/C+GjCsIIc6skcaSh45ErBIfqKUQoweZRXZVaWvbfq6tpe6ihFSYvYDOTmi8xTjkTIgo0XQolXPLEYao5HaIIpACs2cQavEzctXGGlZ9icEEaDRFNmiZEtje3JR5BRIaY+79QzhBucTPjhEHjgkj58nTOkQskHqGsZ//PSriNNztBIYZUFIUgyVW1skOVeQuVKm8imnqfLwkkc3TV1lpESBY86mYKzEzaHm7IBCIqfqs08pY3RHkZGQ6kSlTjQsv/zX/93/3z4P3//6z/Zb5nlm3mzBFGzbomXF9uTk2R9uKe4a0szhdkLZc84efwq9XBLmHS+/+Sek2z+sQ6VkoAG/3zApj8yehTlne/WC9eljfEhYVbNdxa4Zb1+Q0YTiWK0/TYw7VKtxYUbrSrtoVQNupl0siN0ZD57+CFnA7vUrXrz3HquH9zl78236Zcd3f+Mf0LQ9y/sn3H3riwi1okyO7/7e38eYQHdxwrC94fzBPbr126SsMLpDCMH22fvcvPgIcsbF6mKPYcQ0GhnB+cTdz3wNfXIfSyZNEy8/+jbkwOuPvsmTH/0aT9/+AosHjyluj9ItpiS215/QtxUNNOw+RmSJ3z3n8oM/5fHnv8bN82+hdVWG+jgR5oFh2GOlZtrVrKZadogsMGrJlDPT4ZbT/oKzB09w8YCWhk8+eYEQZ5ycXzA8/20a3VHsgn65JsmWmD1JCEoOLJoet3egW1Znp8xuj5YSlx0hBFAbNA8p5pa5SB6ffwU3X3G4HSqIPlTTlGkViL5qqNVYRQS2QyUBxaBELZl679ELSwkexISLiTc/9cOEnLh68YKzB5/h/MEDPvqtv1MRQqVwcvcxD958RNu2XL18ye2Hz0lhIBzRhN7t6/ePqYLmpQYk7ema5emS4eUlMXpKHMjZksKGqAMuSJTsaRpLo1ckDEJmjBWUVCkFWtnj2RIQshCyrv/OqblOIUNlmboZYxRIRZwcKdbzM5aMlbVvEJNHL06wLhMnj7AKmqoQDyFUG1jONMrWPoPtSDmTJMhSi6HKaHKoW62UAkXIo2WuqsN3V1doocG2KGN/UJytxcADxli8AlNarErM80SS+VicPQ4TtMXnVOMGc0IvNOiClhmRLSnKmllvq93LjQNFVuSQbjTDVSaJPbZtkD4Q5i2T87S2odiMGwUGSS4z3mrCKLl7fkbYv2SYt2hhGeYBbSX9ckmr7yBzrEitUpAq0SzWNLZn3G9IecBPBd20zPGAlYLlcons75G1IaaGhirGiHlLnBKm6VEZEgGpLeP+GoRDyjXKtMhSz/5iq7EzuJmocr0U02AUuBLQWRJDRjffV9tLjMiEIwozx4kSNnRNj2nWhNSjG8XsIsK0IFrENKA7wZRntLboFGpRyydCCES3g1JX9/ZYcs5S4UNGhYgqGrnuoWjGwyW2OWG7uyTFAaM0KYMQiraRaANZWSZ/4OLBZ5FFY1ULUuGSRypBGl6RYyBTQDRIk9lsb2nbllZZvMvkWNBGomPh9c01J6sFynR4CxEPQ0Jni+5b2r4SDaxUhGnPPu1pdIdRlsNhw+J0jXfVwJZxaNkQ/EgKjlIKqShO7pxB0qh2RSgC0gxZUHQmZYnVinkcWTQWrSyjGxHSVrRacuQkKbIQiDSqQ7UWdcRw5ThhiiBTsN2K/XyJGROthCFf40QkzpETdUIOE5O7ptWCiMIn6oueH9CqAVcRe3YdyKohxQK5Rj2bpkO3BVzDwQ2YpqXxpeZ7Y2RyHt0okg+kNOKLR5VIby2GQFGSg/ekELHC4/yBkg7EkhHJooVEJkl3+ga+gNTuyAMuTFFShCRNmuVyibEdJQek1SAi2YG1DTE5WqHqJlJOFduH5ef/u1/l289e/zm65L77Zvl7/83Pk2OicCwSzB6lj5KDnCi5toGNqMWPEB1BVLh3jHUqkZODKBFW1jV+joikycWB8KDWKCFJWSJkojgH8gAiVIVe6ci6IbqAbDQYRUoBI3o0mkiFEcc5YBpLOh7omUIxDVr0yFLXVz5k0rzj9vaaeRpxc0S1ihgO+OQwKEqwHKaB1dkZSvecLd7B3L9Lt646UqQkh4DS9c0xlYo+0aLyNhH5GOOQqJSrIlDqmiMr3y9ozJRU+ZvS6mp9mwO2bwhTRApLKlXt6VzESEsoiV/+mZ/9f/25/X/99Z+8/x2EyUxXG4Zp5OG77zIedsR5ZtrccPfJuyzu3iVNB4bNCxpziuwW1RXOzOH6ls37f4g1gtltmW5eY8yCx1/6Clev3mf70Qs65RB2hZEt3h2I0TNTD/U0Ww5uXzcDjUHkQkmAtQgtKGkG2WBSnXSVbsXbX/0aJQ7cfPxd3vjiT2DaO8xTZtlHfFJ0tuXl60tMsJy/dY+ibIXJx0wptdQTnWc8XHL90R+zffmKZtnTd5nby2fMt69xgyLJwPLB2zz94o+yv9myv3rNzYefUKRhfd4h2xXru6c8/MwXGQbFgydvMd5ckrXG5MC42WAbhcyJw/41cXyJ85nFYoG7/TaXn3zAol+y3wWGOSESnFysCfGAm17jd/XBPrkbgvQ0oSMoiV2d1AdtzGRfGYdCLREWtNxzfvFDVTN62BGRuKEgkqU7XxPShugdq9V99OoJfr+ltwY3zZw9eUISmuX5E+btcxanF2SRMViGzQd874NvsTq7QM4Zf7giFkB2VY8bAzlX21EeR2TfMLjE5774Bd7/zjdq6VAkcrA0LQzTiEodqWzoFo9JwTO655QQaRZLgmtpzJp+WZnVZ2/c5/Z732BOIxz/rWZVLzZxLmjVUkgs738OADO/jz2/IPvMH/3Gf8nSfgF58jbFZnRzQk6eBWumYabpWrQtxAguZAq1BNYYiZKSIBIhJBpM1ZaWGa1qMSsLEE1PEZl5vwUhfwCeR1U+dsIQD7VJLrQihZm2k8RkSRw54bHSW5S0iKYhi0iMEZEFlIwSqQpxSqwlS+pGSalaaDWNJUWIKKxQpBiRklps04YiTGW6IkjBUyw4F2i7HlEkVhuKVORY6gU710u+1IpSBDFLrDC4PFOC53RxwjBu8D7gk+HhO++wffkNgodV11a5SNcyzA5tDCIqrLVVLbtQpP2BnOrZOceA1idV4V4c4mgkS86jiwJZCGWkyI7sJa3KxDhiux7d9NU0R6XbGHWCi4lWLwk5EP3IernAjw7d16z2tD8Q5wFhRQX+2yWmWaJNU19uREYrg59mjKmVGqkVzqdqccwZVKSkKuooCVJSNdseHJKEIJBSrlNIZfD+FpBo1TAMhbZtMZ3ChYkcJBSHyRolE7HMKJtJKVKExWSFKpnZO4puWeqGpBTzOKGbBVLskG3L6B2LdkHaH+pnQHgwgjmOhKanby9os2A6DEybAZc9sVOsmxV9k8meSq2IBTqLsX0tm08jJOqWYtgCBUpTc8vRsZeJE87QbUfQAiM1wXmMLBSfGUJCtBIjFeNhxNqMURnTdngXaixPGqRuQcE0DCA8Ri4xxhCKxXYdWgiiCJimx88DjalqYaEKh9dbGg2zn+rL0BEDKIusxjkFFMnh6op2uaAojfSO6ANJWWQ/k8YZ5QOT35OlYNWuyB52u4/olgta1VKAzWaHNuDLhu04s+7v44Ihm0RvJJ3uiSGhm5aiCyJlypwRjSKUnovFHURrCSXQCst8eEUugjFMFD1i6dGATgNz8GhjmCO1rJxrDKlvF1gN+82MWhqMXhL9xCHvUElge0ORlQ2+0j0SRSzQNT3khDQFLRXXl6/RrSDlmfkwsFgsqu3vMPILv/hP+M4nmz8/l9yvvPuk/P3/9j+usOuSUbpQg7GOFAW6bShe1EM5DRipaqM9ZkROSFsnW8UX0AlReqRwtQkcajZVNxrvM6lkGtszh4m2KAqenAFqBjdnKCLWPKXVyBKRVGEEqDrpK9WEVoqo9IemJ2tJIxpyqrkzZZYUPMErwnwgTjcMbqZQVYo2W0JURDmzNHcZVWSpH9LevUt70ldNsJKkFJEIoq8ZvRgjjbX10qpV5Z4aVW0gypCTIpX6a8mglKSkCt4uWlBi4Ps/c1VqhCHmYyPWz3S2Y44RqWvO95f+2r/3z/3z8B98659itcHPntntOT9dIWXPfhwQ3uNjRi+WLBcd28tb1m88qJg3CsO8J7gZP+zIm1uG4RVWCjYffAdz9w7ruw8RZcXytOPm8gOyh9ldcnj1Ia1Zk7zAmGN+uW3oWsvV5Su0tYSQse2qRkVCQXeGd770F7HrDrU8oT29W7PeImNkSzh4Dtsb3H6LXRlSCBAGXj9/nzv3v4RQmpNHDzHSMt48Z3f5CbvtLbpZcvb4Kdff/i0O47cwzbs8fPurbG9e0GuY/JaT1Tnf+ObvszQN+90tnbmDTAN0mturglytuHhwxltf/Am6RU92CRcdefec29dXxPEl7fqUxaJn9+pjnn/4B/SLNWjDYfsSLeqUaHPzLaRpUOoOBEm7vM+kJk6amd3tTKPvMvgDmlf4HEhqhRQLtKyYKhEjWluGOEB2dN2j2nbfe6LUtItThFAcxi2SPUlmZNMjc0Pxt4TpBcY8pWmXBL2g6ANMGdU9ZJ4C880Nq/MOF+Z6eaM+sGSnkaXFWo0LG2SzrJisCGNyWGEZ3IYHb77NcrnkcHONNpLT8/t879u/i4yWOE90ywsO04F+uUKoSgeYnGN9+ibu9hlu2GCtAeFrmVUYSkn18ygMum1IURH8SEiaL3ztr7A6eYDqe66fv4+1nvf/8HcI6bJyN+mxTY93Dl3q75eOiuzJDSwX54CkZAhkrBYEn9AYTGOZ04yVBaUXxJyrBZJE0bV/oJKhEFH6GI0okd3mVQXba4vMHikNUmh8aDBtQ9MvanxMCkQpTO414njWGKUostTMaaq5yzQnUkqIVlHiAZ8MColuLCILpsnR2EqVENKQOJZrqMSKJGONRglN8RklNCHN9aJJS5aeTMRQL8jODzVTDLXxnwRKZqIbwTS4LGiEo+lrmz0kCDKiTI12CF8wxhKDx40DIUn69QqfbhAopKznv1JHzW3JkByaOv0TqnYOJjdw1t8hF8fusD9SbjJxO5ClJZuCMrDs7nC737E4XWJEh1WFIUSktWgkRIfA45wjF0MxpnYqhAB9zEVTaLtlzYkqiSh1+q6kJJQZragXVCrJIvgMOWIspDSgVIfRC0Z/IKVa0GYsRATT4ZrGapIfkCUSvEeL5lhyhjmP6NZgaGiaE+TyBKP7yjz2GW0URVbs3TBuyDLiZcIqSW8WGNtScjwaOgPbeUCVBa1q0FaR8gxGYWnxNzt284ZGQd+0JFq8HAnO06/OMaahVZb97hbKhMyJLAW7aY+lxnHIBoEk2gbb6GPccKLvVhTdY03LbrejaTU57YnMTLuRvl3U3HBpEKKr2tsQaTqFLHONluUGQTX8pZJBFTKJ4GqUpu+ORlJpKWhSnqBkFt05PgxIBYfxQL7eU6xAKcE03UIweB+RNhPlRNcr5LwghpFYZnKIZBLdomEaR2SxaCtQSmFMw3be1HhR7GhUj1QOrSo2Mwlw0wFfEhLByfoeQixIWSOyY7N/jbWKMowsl0umOBOzJxtVjXQZ2uTqpofIFAKChLUt0c3YdlFLp0oiIsSSmd0e1RvKkcbU6I44enq7IktDjJJSJobdNVlEsnf01qKVIJaIuOiYr/csbD3H/9O/9et898X+z88l90vvPCp/52/+hxQp0EIS4ojAoGRGNT3ReYwy9QJKZeZFBMiEkGuiH8h5IqWIVgIl+uMESRBnh6RQSv3/UbO8ddUnIpAsMTp0Z8gho22HD1MFkouZ6A8EL7CmQ8iMkrZmfHOW7hXRAAAgAElEQVRBYBAyIbWtmVkMQjb4mJHduk79omMeJ1IZCVNCNQ3TMJNj9Z2/3j7n0cWnaE/vVjvN4g7ZHnNxRVJyOmbiXJ3CHPmWBVFVv0fESSkFZVq89/R9zzzPIGuHN5cjz/Q43f0+eN6oOoUWUhJ9wmhZM8UpEVNGGcUv/bV//5/75+HnP/4AfZzAxXHisLlle3PNw7cfIROIbk1zdkoKkZff/VNULNg756iQIUZycmQimxffQ8hEGG+QZebkjSekYjhsbrFqzTxuyAIWa8l8eE3Xr9hcXbE+fYQqsH32IVOeuXunY/YOodZk0VJEQaJBNOzyY9764S/jN55pOND1inmzo2k0ySpSuqVMijuffpduvQQi16++zRtPvoQ03ZENXep0QRVC8DXLaFeYxtL1DSUkYg4IBT448lAPOK92SCl59cffoigJwdGenvDona8yDyMvPnqPz/3IT0Kun//95hXjy/fYXf8JcxQ8evOH8YfnPPvG/4k2hTsXX+KjF3+A1Q3jYaI1DbYZOHiNSolxf0V79jk+9+N/mfUbTyjjLR//6XsIvSe4gXGzwx1GlJaMbiaUzE/+xF/m97/+65w8/QxxuiKN8PCzX+Hqu+8Rxg0hOBrTMYWRzEC3uEtE178PksasaxlKCbIMWFGLGKIxFeieNEJmitDgD3XqaE/q6rAkoh/QsmY+ha0a3ZJqpjL5geIz1iwIOeBZsh9G3niUCLs9MgdKEQTVVtJKKnSLnuhyLcnGG0qsW6b25IyUJ6xtmSeP1eDdRMwJnxSrZXs0ky2wZU2IQ80QixuKC0QfyDKjikJKjbF9tf4IXfXjxpKLY5h2WN3VSao4nmmivpBO04A2dSPQdRdQqi57eXqKDw5ZbN1MKcUUZqZ5xAjQovLHhbFQOlIKte2fY+0BlEQaBopzGGs5hCtM0yFzgzIdTb/GH+Ngk9+jRbUhKVXIJVUFOTCOI0YpOntC03bkMFKUJAvLnOtLVfETJSdU14CqmzFJzVJrCofNy3p+iYS1miIs0iyQUjHPE4u2w5hKRzBachi3oArDuKUz9UxenC7Z73eIDMvFHdTRHBlLxogGH2aySMQ0Y0zFWOYUSW4GJHEcK1LKLtAIpITZDVAcwWnmYUO/7FiuT8F0SB2qZKZZk5Ij5UjXdYzbA+1ydaRYdCiljhEAScr1wjMHxzgPpHDULZeEkhDT989yhxsHlDVYW89EbQ2CAMVAqgpkKRJhDjX7aVUttpn64lOUICORRGKY61RQWUoJ7G6uadvlDz4TWSQSHiE1qlgIhTgFsnXEaYc1PVI0rM4uCFiysBirKTGQ40iJsQ5WSqxRl1jw0eHniFWqlhpLwruB09MHzMHj4039uzpByYqmPVpKBRQ0C11z8l3fkrzCh3pWqqbSiFKYMbIhpIj3L5GyY7W8w24/VQV1huXiFK0Nc/CYRkExNXcdXM2eF0k6DpYKnsVCEDzEUBnUUKey8zyzWLQkpSgu0rZN5QW7UKOYVrLbHeiOU3iBxscZowpNd8J+e8mw+bASK5LBuy1RDKTZEwioUvPfSndIqRGLHpUNGdBpwpqeOWaQAtOvSR4EGZXc/0Xdm/RauuX5Wc9q3243p4kTfdwu897MypsNZBUF1VAu2UVjGyQGCMlIDDxCBiEQAiYgJkyQLA88sECGITABClFCBkS5quRqUtWlq246Mytv3v7e6E+cZu/9NqtnsI7TnyG/QCgi9t7vu9b///s9Dzk59lNkWHXksGBMQ9N0OA9KNezDBY3MzOM1oRyI+4lWbhBGIwzY1pCzJWaBcVXtXZRkCVWPXkqiXw3MbqKkXBXRRWB0/U1GHVgmUBlUqJucrmtYFo+fL3DpGoolKMmqH0jlAKEgFeyuXrHZbBj3E+v1Kf/Jf/87fPj08FN0yH3rfvk//9u/iZCW6B1aKQoSKQpJqmr9EpJScv2xhgBS3Jh16sQo+gkXHY3WFBIyG4qF5AIESSoRq1VdtYUZbastLScFZa7mmu52LWYIQSoLQi4omcnJVPRTYyEmvIsYqyozVxZIAmt7cogI1ZKEJCkJXhPDQopAjIz+QMgFVWp2TGpFo/uKBbMDTbdC9KuaXdMWpav8QeZEFrXgJqXETw7T9wgSIgciESlacqqHGUlCSY3LAQGgMzlH6qykFhIQtcyWUkJIWbnByWOUrQpSRDVMhUBjWkoJgCam+tJavIcIU7zJbQmJLQXZ9AQyAoVWkdZakvM8+Npfor99RFGef/L3/j3O1g8x84rjX/kryJN3EbePkBFyjkQU43TO9MUHLPsL7rz9Ldw0E0NhfXKCXm1Q2tBtTpE4Hv+TP+Tk0Zf47j/8X8mPP+Lk7KsUDf3xfXKTGTZnPH3+Iceb1xmvXrHsX6DaEcKG9VH9fDdHW/Rwn+HsjMunL2ht5Nlnf45aRpbxikYnpiQI8g5t09NsV4hhxWbdUbKtEPRnL3DZ1wKlG5mD4/T2I0rXsVrfRq/uYGxLdHsEDW6ZECqz+EwYd7S2odErYk44d02Iif3jJwQ0cbrgxfNz7ty7y5IPLJcHhLK0rUG1mkZJnn/0HqU1XB0K3/5L/zp3Xn+dMAWG1QnTyx/wwZ//PncevQ1Fc/zgjDztWZ8e8+H73+Hy0x+yWf0ssZyTSs3dnd55wPVBkIqlWQ88evMN9PaM6CSXXzxDGMf86lPcdMk07rFijexXTDFx6/YtPv3B91it3+TNd/55nr0cUW5GmcjQr3n++D0oM27/tJY/uw2RglWr2nI2llBUXa26pV5SZQG/EGNGCYkx63pAzIFcHF0DOz/THN3jdPUQV/aIUPCHWDmP1jFPu8oHPtQJWGckWq6RRpPKgUIkBMXszllvTlBFoq3GLQuN7WuxE8heoE0lkYSbCwiATAofDELV7HaJ6YY0UPOubr9n6Hqm5QKje4w9qUWbmwJtcB5jW3xMN418B0JThKx/nqQimW4Y4i6PaNUTQiCEGWGAlMle3EzXMrqrZThrupsnrkTZmvGnSMiVHCFKRpseP/lK2bC18FRIbLdb3CwpRGwryKmyT93o0KbFhRlQKJ3JalWxQTkjrCanQij1WRnGA5qOIgVSC0oZ67Mo67p1Q9IpTYgF2Vam7OQm1t0GmQV93zPurhCNxS8jIgl8dLVVHyXSNljVYqSpl3tDfTfkyGCOEVoQ/YzRmVxMzWRnC0ojzQohKvM3iYkioDcdPkInCj5NxHBD2NENttvWaFRcyGEmagdaI6Jk2x0zjmN9Hjd9zfM7wZ0Hd3jx8nOEs2w2K+bDK3b7V/TDCUVUnvD1+bMqK8nQrrb1exEXAEKeWbXHWNsy+om2bbk6P8e2A0O/xfk9pQiMrtNxIRRSaRIOjcWF5UbUIcmlkGKEG3vo7Ca6tsVdnqNoaYYOj6YdNgA4vyfHim1TRWOwhHmhtAphwI0zuMzhxScA6PaEIltKWFidPkKriNCSJARS1Glsq3qKUZXtvByY5+v6Hs2JzgqikCQl6JsjhGnqRso5hlWLDwek1ETnafsVWjfkokihRhTDsmPoNghpOLgDfbeth/SSyQVyCSgSiAbb9GhpyGkh5gC6RcbKn9WydoXcNCMbUb/jJdKbDmXXIAVaCpxfUBmkkUyxDr2yd4hQUZ9hcTRtNZW6eY+QFmM7zs8/RfpXTN6z3h7Rd5akCkJ0lORIplCk4rhtyLNiv78EUSeq+4sLGtPWrojoOb61ZvQLq/6UKAX5cGD0GWstxgRyMZUYFTLRR2I+EKVDzIWhsewOUzWjriWNMvg5EpUHFiQChCVPik5pirF0/QqXYmWSC0XAA9AoSSETYmXzHw4HmqZDljWDNXTKsChFg2Aer5ndy1pWTAK9MRhBFdmU+eb3WvBzIPv6jPov/6fv8uPHP0V0ha+/dbf8+n/zN1DaUnKuQgfdoE1TJy61FkCiZlBVANQNmFnXTJgLBSOrpSakhTzvCIBY6iFYyhsQuajrZK01QUpSqlOXzEyMHQKLMQ3LeKDtC4iMyYL5BmHGP5UtRIGXEi0SUrRo2yByDaUr2xPCQo4SNy91jYQgFg+Nxo2JRguSKKQoMe0RutFIYTDtgDUtSQioZdNarAsJofQNVzIhtIJ8Y4uLHmsUSwBlbL3Na4lLC1Y3NberMjmDob0Jz9cfnrwhMxhl8Tn/M1h9iQS/YFRLzhnbWVKoK7Flf0F2hSnMWFEzN3ozYIExLyArdaLR9SEhMFy93PONd3+RWVzyxW/+ffq44t2/+bd48v/+Dskqbv3Lf5VgMrrpWffHuDiSQ6YZLGm85pP3f4ej1Wt4Fyhyzer0FGPMTS5Zcnr/ASIeKL3Fyg0vPvgTzPqYi5ePefVn30H3AwqLkIX1rYeYO18i50vGH7+HGTrM5hGnb77L5x98lzYV8hLpjk9Ypmvs6hZN39AqyX6c2V0/Jl5llN7AcMzu4jNaIs1xi+giKhX8ONOfvI49fcTh4px4eY0PM7fffBeL5/LiOUcPHxAPjuePf4gJmawM2Tnm8YJxek7fH6FKprn7JeIyooTGNj3u+gmBiHMCtGJ//T62EfSiw/YdR6//PKd33uVw+Yzx/DEyV57k6d23iDlw9f4/oj16m+2j1zg/fwnLBVef/ZD9uYc8cvC1uOkOB5r+mJIU6IG2uUfTn6Lb0/q9TE+Yp+/RNQNPn32CbSRWP4DhEW/9/L/KySrz9KPvc3j2AeN0jW6OiOGmYS3bKotoCm45UIyg5AmZOnIy5KKQJnIYL5DcrO6LwsqmGqBMndw++uavcfboUS1bRMgq8/j9H/Lqwx9ytN5wffUEM3SM+wOZuu1YH9/CvXjJ6nRD8CNxZ0gSVCswVrB7/pLueEXIhdZoJGuc8zRKsLiA99do0yBSJCQBraG1Tb0ouhndrYlyoRHqptGdEEYRS2TTd0yHPYVYjYchkEWG4hBaEOaELYKiNd6B7KqBsOiM1jUDaYY1wWeUESzjgaFr8MljGs1+GdmYDpJiN18jaMhF12wy1dhorELkm+2BVoTgOLhLjs/uEJxHe12h8psjTk4eknnJdOURaO6/9iZPn/0FZXcgp/rvux4vWK1b9vuR1WpFUX0dIqRUhQYkEoredkgKSjVMy74yb/NEVhmS5vrVOZ2xDI1h8oGIRCpD1w3M44QQiiQEWgs0FSovNCQliIGKlzKW6VDNaNIYXJHIdmBoVJWx4FBKkvyBnHTdHC4z2ja8uPoMras8wmAZVhuUrMWd24/e5uVH77OUkbiMtN0pumuQYkPTNJzcu8Xu4hWfPX/MV3/mG3z+3h+xtkeMMiKLpFn1LFcXBLcgO0G/OWG5eIJzhWbYkrMllAUjFfN0QKTE0fqEIlqCXwgmImxkbQfcIVCsRkhdJ81Ks4wLfd8TiyfHhDAWkWJFSLqE7XrcvGeeD0AVCCkjsXSkPBN1oLEDYdmjRUSMBaEss59rpteATZbSWUASFkcSnrWSeOG4CgtGd7QMiLggRS2KFg3h4DEqY/UxphO4lFGmblQOo2O7PSYGQQiB/qjH2pbZT7jrSyb3gq7bslndZ5zDT2IRcXmFbizTEhFCU0SiNSuM6epdOC0cDbcIIlXkXgNuKTSmYfEZKQRCO9p2hY+FEEGlRGMHnL8mhELTKCI1Bld8LXWbTiKtxM8RP441UqHqM227OmGaZnKYiLsrpFZM3nF0dIogsX/+BHPSwlS4DoebyX2gG07IecfFs/dRRRFz3VT3egNJI0wlmHR2zXSYaYaOkP4ZPWUYBkSSzCXSGktaIhSNbddYqdjPE11jCGFBaE1B0xnJuD/gmZiWC1ZZMy4TQ9ewOIXngE+l/nvpfiISgkKLQgiNsA3Ca/rTHhfnWrCVgmnv0TlTkmfOjratbG2yoWDo2zXTVaVljMs12WdWDUitcVnSrFrm62sCE01TkKlgTEO5+dzGaea//p+/xwdPfooOud/40oPyD/7uf0TJjiKgpJtJBaauNWSLLBKfF5L31YwzB7RtkLqAqC1mJRQuOKxRiDzz2bP3KXvFo7vfYFp2JJ1rrit7XKklLWkyioQkkpPGiDUhjkhV4xAgIRdQCa0l2VUMmabFySr/kamgG0kqEqP6eruXhXk8MI2BlBUiBZaw1DWekQhlKilAd6TcYjqLVh22H1DSkstMUQ2l1JZnkaVifKRG6Wo+4qZYoEWmyJqxywVSiPVgbzUpRUSBmG/sStIgRCYutSiQcjUURefIQqCErvzaEMnJo4yGpMkyUVI9nBw++y77j/4cFTS+ucXm9D7i/gN0Z+stOMuaXY4OsIQcGMSO5x//MdeP9/zML/9l3vrGLzG9OGe5fsrJm1/n5RQoXtCebCEsRJFJQfPwa++yv7omXn3AD3/vN2jswK0HbyGbAd0MnL3xNYbNCRevntGf3EKnQkig24HHP/htpuef4y6vaI5PaPotlEh7dI/++DZpvyc3mtXtRxi5IopIjjNxuub64/fxJVCcY7p4RVciPu5BtWzf+QbRjDSNgXLM2Z03CULgR0d7dIpKAp8Dy3Tg4vEPWK9O6bt1ZRIf3UOkiYtnTzi6fRdpWjASnUaePXlKSZeYJnO4fkHJa+aLpwRhyO4VJR9omnssQZLjK4gGISXf/Gv/Di/+4s+489rXccEzHJ8xPf6QTOLq4jlCGlZ9z8mjr/PZj3+HvPuCh+/+dUy7IrqRT3/4PcLhA55/9iO0PSUTEcITw4LSmkavUd0JeZnrocJnhCyYRhF1gzZ3iPEV4zgy2CNKiBw/eAefVrzzL/wKtm+QFsar9/noj3+XZ48/QZcBEWSNDclAVArbtWy395Aik3zg/MnHCFWnGIMcoF0zx4W28eQl4krgsN/TiAHdvY5ut2izQlrP8cma/uSYP//d/w0pNcXDRrXIpifpkXH3Ccf33iInxfH9t8llxkbFbnJ0Q0vXnFSZinLcuvsQmerLeXfxklVn+Ys//Yc0CnIxCFmnb0mvODm9S4oK2VaSi46ibickhLAHNzMt11jTkxZF2xr0sMHliNGlTrfnBWM7tFxDTKAN97/0Dp/++Du0+piYI0V2FAHKVA2tMnXyV5K/QRNWCYZ3daqnlGVZnrFpbuGiwwWPtS0ppcryFZ6UMzkLRJGU2RNlqAUstcI0Ej9e40VHN0B4+ZyiBL09Q3aFkiP9Zk3MFi2r5tNHV19OQrG4PcRUPTouMtxg41rpmHJg1d0i+lhNUm6Hz5m2W/+EiqNyfb4GDjcbM+pa/wZNZla3qh7Yx0qjKAJ5M4wI2eGXEd3WuJD3kWFYU1LNiwuZ8OnAMLRM1xOllCoxsC1nr32Lo+0dvv+d36RrFbK1lCWzzNeM+3Oa5piTo/us+oHzy2fENOP3gfW6J1vFNB84vfeIF59+gC6Fszv3mZdrFinY9B1uDBS1IscFZQ26GYg5sSwTMkqatsUUgZCJ2S3YtmW6nklNIZaJs6O3CNGRkkabasiLYamIzMbU3giagiXnSLal/l8uI0Y3NxjORNZzjbeVRAp1m5cRKKOIIlH8gWZJ7HY7DJ6ma5HdFnJmypkcRrp2i1CauCwVZykz0/VIr1uGtifEicUdWNKBsAj61RmroUYwSm4prmBXlighicwwbCksuKjxu2uG/gitLIs7YHuBC55G97jdhG4krgiMbPDjS0KIzOcv8H1Ha3qGrUKLBn+Yahfi5AznHEn2CAWJQvYLxgTiwaFMLYsKo3ARNJLkIjIHQjwgRUOIEzKLioN0dfuJ1Zi+Jc4O7/ZIOyBHhxxWJJFw8wu2R6ekUnDOMc0j1/M12kjOjm4zSMP11UtCEVhhUMowxQucL+hiaAaNEZarqwvWJy37eWHTbVG5xRjDkgK226C1JaW2yjySJ2RPIpCjrxtsH9jPE5ttz1JmbBCIFBE3SL2U9lwvC21jGLZHtQjoPFo2FAR9e0puJIiCdaGypUNi3h+g03X7TibnAHiCk2ihEE3BZc+RXeHwLCGQJyhxIiLo2nXdbOPITST4ma7pKL4O9HLOGFX4T/+HP+Tjn6ZM7jffflT+r7/z71NKrKWdpie6CKUgcv2R5ptbqYtTdYurBnJBUCqHkcLidnRNA2Uhi4xICXnL8kd/8Lssu+8yiLe599rPs1pviTlQUsY2FZPjlxmjB6xUlAyofLMileRS13c2SVTTUlI9NOYcoSiEVnVKanuUGuqXbbkizjMexTKNN7pgiySwFI9QFq1aKIpmOMLYlqQr31aZFm06CpLow01Uo9T/A1OLBQp9g0KSZL/UCbXSFAE6WxIJRKovCa1AaFD1ttwqc1M+E/gSkQm0rvGFkiU5JjL11lREJsWKi1FSoijMz36b88//gN3zc7K+z2bzNR58+9dwCpRsmefrOjnKAh8yRhe0jpT9S7rTDfe+8Wu8+tEfsTz5gGb7Fe5881fRq47kzlkOI1dfPGaKV8Rzhz15jdM37laPuPOYvkW1A2EKXJ9f8eDLryNKoRvWHPxE9p62HcgxsL/4ENt3uP1IowaidIRocbvHSLdAM6DVANkTlmuKXqObFaob2D9/TmMTRQ9YHWhOT3Dzwnp9hxjq5Ci4REkeXQLT7LHbFV3XsH/+lO29B1zuv+Dii/cRtFx+9l1un97l8V/8kLMv/Ryvf/sXOYxXHHZ7js4eMbnI2Z0zLp5foduekjKroy3RTfjlwOnpETQt1xfX7F49JozPCXEiB/iZX/rrXD/7nH59wvHZHZ5+9IdML5+yuXWf/e6S7B3DySOiiJicyeWSOWYePvpZvvN//y/09ibzZSqvN+4nGpnJKoBcc/zoK8gg6bbHvPj0PWr7ZGR48HWa4y2vno6cvf6Q6eoz1qtbdL2ku/U1us0RJUmGoxOA2pIdr3j6oz+pFztVCHFhuzkhxIUvPnqP8bnj6P4dVAsqena7HVkqZG5u8FmOzdGWyS3c+do3cVczQ7/i+eMPmJeICFfk2UNpEHqFj5eYpiHsn7Ldbnnz2/8KzfE9DtfnlLjn8vwVQ79lf/WC5eBJ2vLGW1/BTy+5Pn8B2hJGiwiJp7vPOLu14fL5U+6+8xUuP36PZX+O6RQhQtucUlxicl/QSk3OliwLIjb1wq4TykS6foNA11ZxzGQhWdyOJBMnJ1XpWUohjpGjk3uItkObU7rjjic//jMOF1+gGkNMGklGtC05wWbYItvM/vIcESIkh5ItPmgO4wW3TtYshz1GtSzO4ZJD6x5rJImlbqGkrWbEnNCdIlIwURFLRgtFUQPzdKAbeopQNKUgA8gOrg97tqcPOVyf0xjLtFwSPLVoVBIxeYxukBiUrp2IZTlglCCUBWtPcKFSE9pBkZHkHDGmI86JsgS6k5bFTUggpIRUPZ3tSHq4wTN5RHJEqmiiFEV0V+RYMM0GVF01h2Uhl4gS9bkd4x5BYlhtmELhcPWcoiTv/HP/NvvHX/Dy5Z/TDWussgQqnzwenqF0Sy4NWnVIrSoaTQWKz4Q8cefR17geoZGJy6fvU7JnGi8RIjNocM4ghjOUqPnp5Ba8X7CNQPTH6L6nuEBfFEXVEprWmpAD0kpEauiaTY3DqEoJUqKQUmKZE7LRGF2TPsFlcirQD3RGE+eAbtoq6lEzWSRK9AhVYzlduwLAicquN7GQloCxGre/rmz4UFXMw3pDDDNSw3jwGF1Lz6qxSK8pOVftsQ8YJaqdT0h8iggBXddXcohVFCrDNeSEFpaSJUoJtKyKZ4evm5eSkaXmWsniRoncEP1IY+q7TjU9qWRKSUzXz4nLKwyW/XyB1huUParlQ2MxJRJ9QndV7rK/ekkKmVZ3tJuj+h4Uqn5OfkQaiVCZkjSdqEWxkARJZmxrMEoSU0DmhhAcWUS67ZZBGsbFEaNHqx5kIuWFlCZyNEhA2IboFkyyZJHZuxllEyUGpBd4JtqungEolsZuaIyubNrgKFhKygjZkJYd2QVmdwEiMvRbvI8oqZnijlQSSllUiPh5oRu2DO0ATOwJmF3giisePHjA4SAxoq1CCDmjhIZ5IURFWQpRXSNNQulM9AVdCjkLmnbLsizoQdNKW5GORjAeZkwpTPOBprG0/RotLCEeSMqT5rmi16RBC8noFpJc+K/+x3/Mh0+uf4oOuV9+WH7z7/1nuHmPtIYYAgJFpW7rmsmjBsGdP9QWda4AbFEyUlmKKIji8N4jb1rAS3TM+x7nPqeI73LYKe48+DlC6Wq5LUWapkOgsMISosdqc3MwLIgoa7hdRJSEKXp0MahkyKIiu2w/1PxIiUjVkYVGoZAq45bEHD3BLfRtC8WQYyLhqroyFlIpNLqnKIGwIIcT2mYNVId3gZvsMTUnmwJF1YlLPeSKG0RSrMU9XaczAChFybVx+U85gjl5Ykq1nJEDhVo+0Kq64JNQLEstr8XsKalGO4oQNQ+jFiiKUJ4xvvwd3Is/5+rTr3LnZ/5N+jdu0Xbb2jQVE6pIostsvvQa7uo597/8CzTDmmW84MX3fhumS9pbW7zoaTe38a4wHL/B6tYZrTDENnP4/NNKJ9jexRiDHiSfv/dH7J8+5eytb5OajmneI3NitT2tt9EC67MzDhefs3vxCdp7liXTru6Th4bVyS26zZbl6gnp6Q/J7oAoNa/sGkNRLa0ypKtPSFlQoiGYI5zVhMMLwvgS23QY02DaLX72bI7vEsi48RrTbEhiYNgorp59n1I61qsjzOnrpOWKeLXj4GYur69olSInRTccE4VhM0QOux+wO0A73KLvzpD9Pc7u3Wd75y5PP/yYowfHCCWxXYuQESMNKc5cPXsGxXF4/iGN3uKWJ0it0SogjSbsF9TZPXaPP0Y1Lc8/fMzth6/XEk6BpteMU+SNb3ybFz/+U/yrVzQnD9kJw2m/5sVnT2hswBrHYbwkc8Zw621k2dFuv8KbX/1Z1KpBa0vII2EciTFyOOwwVnM9TRxv7nFytuHT7/0Rlx99gj3SDGdfRuSZ3cUn5KsrNrdO+PyzF8ickH0kFIEpBXzNko9J0v9MVBwAACAASURBVHSKo7N/Ef/qewglGA8L3dZQnMA2Az55tNzy1V/4JVTT0q0GkB0Zxe7VU6wWxBDo+gHvPV3X8vKzj5DdKSVLGps5vHjC+YvvU3YOFyeOH77Dy8cfkf0l2V+jSuDABqkyUjVs+1tEF2kbhbC6Mn61xNiBSEGa4Sa6pBEkdIkUICVBVJ68VM73qj/i1eU5m26LWwKvvfMz7EaPsCv89ft454jakueZ7eY2OQaury65c/vLXBzOwUrK5SfcevAlnj99UXmyBvaXLxhsjRJkWUhCVLxd196IHFZI1WFNQ2HH5HakXMhOsT26zfXVOWkaMVJgpIHuCFsMUoFqDD7NhLIQ5gNStWjbY2zLTTOHzq4JZEqx5OKxIrHbX7Ba3WJyC0q39cDcGJZlQRWPbFtEASPrxLrtBKPbI2JmnmeGVUeaHE274WL3AnQhzJleD3gfaNY3Le8i0WrAh0C/OcXYgl9GZPG1J9G0pGVEyQ4lLb4kmsay2b7J0RtvsLz6gs/f/x6NaZniFQSHjpBEfUcsy4RpB0ROkCNNvyHkGoNo9YZlTtUIRUSYujVsra3DBtOxjAdYZgySpmlYgscOdZKNUdVuFSWyURh0JQ3lQMkCkRXCGIJ3NJ2tSuZlYTnsGW4d33y/O8gOPztiDCilyaXgkZimQ7iCavUNX1gx+xlyrrlwt1CKI4mMsWuGYah2vCJI2aOlqJnTFGskSFrSvNThkxIobQjLglECo2Q1u6meEjxdL8kl4KbA4mpRKaSIsIoUIpvNlv1+x3a1puTKTo4JMhLTCoxua947JBSGUqr18+B3SKHwoWDaDiNspWP4V+wvnqBMJPjKtDXtMRJFoy0he/q+RUjLnA4oHMVHrq4vKM6TUqbva5FKWcFufkVvz0gl061XCDqCk3i/R2tF9Afc7GlMj/MBpnPGOTOcrthutxQsry6/oIQFyRatGkp2dJsVRdQ1f1o81lpQitkvyCIw1iJNZvJ7rBwwqSX4EZ/3RFeQsqcUwc495XToyB5md8AaiXOuvs8TtIPG54I1hpgy7dBj9RY1zVxefER7fJvGbEjywNX1yOn2NQ6jR2jJvFyipUMsM6msEEWCXojCI0pEK0UKnhRB06EVkDNCNyShWeYRIQrWGkqqn9uSM6loGinJZU9nJGjDxcUFg+3rsNFa/ou//wf8+PGrn55D7rfeflT+n7/zHyKVqcIDQQ0bK43zN2ivmKFkSgwo06JMxV9xEyJP2WNKqaFyKciZCinXCcU1qXj86Mhixeg8UlXWZIpQREJT6LqhlsRKvXXmDNlllEwgE9fTRD+ckItGioJUDTkGhEwVW9T2+CBqeU0WDtcTskSgosusNpTSYEUh4Gmajiwswmh006JEBNMRpEFmiTYdwU/1ISZq+azIOkFWak0WIF0EuFFtJuTNhDuVCErjQ0LK2tqNy4QZBpIPqAzYqgkWN3gapQUla1IsNwgQSLHalVKph8eiNKpMiJzxeeLw9B8iXj7FvbomuC32zi+Sb91le2fL8Rtvc/bo68zXnzMtgd2nH7M9eQCDYd1Cd+shtj3BT2OdrpQ6IUEKRNG8eP8PkbuJZZlo771BTBPD+i7OHxiOVkyHhJWK9Z07LMtlLdmUSJpH/OEF++ePyfsXqOEM3dxmnA8029dI+YroFZu791me/4DV5pjk9qzO3mAKI3kaOXzxfWb3Ga3q6ipKZsT2y5WgoTVpOdR1L4roX1Fihdhr0bAsgTe+9nOcP3tC8hUULmxPuqFg6O4YFQ6EdEDIyLgLKAFaOcJUyP2AbBRIgZ80OQm0sMxC0BiLVIbh5CG37t9ldbqiuMCrZ++x3dzm4w/f4+H9u+xfXZIOlxx2n3P64BFuHnFp4t7dX+Gj934de3qHMmtiHsAoyrJg2gbVHZFyhzEju2cfIM0GiqK7dZ+H736TJ9/9PT7/9E846Xpm0zJ0t4nLnoSi7b6EfvAmb3/9HYQQdYJuFLurSw7TjFGSFz9+n9fe+SaPf/ybjIeFdvsm47inRMd2nZhfXaLNnmH95ap4DInx4Fh1ARFCvaTphDaCbDb4XaHpjohK0N22KKm5enxJkQoTVuj2GK09sj1hfXKbFEH5kTE8xeg1OUTGq5dICaYs0PSML55QTKAtEruS7K7O8S7TNh1uWTB2wjRHN1m1Fa4oEIKYFpSwJGnpbKEIgZ/3GGXJGvYuVEOQrpQUv3islChddeYxVtNiyrWRXIJke7rh4uKC28ffopQ1V9d/RIyBh+9+k8tPfszu5WO6dsX1XAtom1tnzPNL7t/7Gp9/+secnD7g+Sc/xs07tusjRg+rTUM2hiQKjTmpEzKjCc5RZIFQsFYT04wQVRccgqBYyO6A0YKUCtErmtKRRSDHBaEVqqsIPlcSRqyIcUEqyElTkqWkhYKg6RoiHk0iRkMWQCrM41Iv1UUgdaYUScgLnVaoErlYHmNEjy0rtJGEdF3FFrlDGY0dLK3p6M/eIE+Fw/mnkHcUpQkzFFkPCMpIkisUo2i1AWC3P8eqNapo2rblsBwI7sDz3YgZPds7AtluCCrTzYFljOTOIKVgWG2JecSwQqgWpTNz2KFND8liuy3ZB1SjyTECEaEakq/FRKksIs/glpu8tSYpgcmm4vFsUwceOUL2tKrG11JeIEmyUoQYabqey1fnNM2AKQXZCFKuhsoQHFY3rLYdwQtiqh0LqxtKylwdrqBkuqaWQbJyxFkxdIbCQska0Q2EFDFaEkQ9ZHZ2gywwxwWpLEpoYnIYM9R3FBklIIvIMh5QskGh0U1FxoWb97ErqlIUSkbIhAiRZZlIrsbm0uJRCBptKLkSZrLK5KQQsSpzs4xILUnqxuy5SEoSNEpV5fbG0G/u4MenHJ59QQ4JqwXX8zXD+hYytxQZuBxfcv/td5FR1y6QC3TdmhI8JUViqUUs09bejTU9IUVImlgs87Jj1XZkCtHN1YYoM/NBI+dzdJdJBULRiL6Q3EKTDUpXOoXLkpANoon0uiHGgC4WtwSyO9AMK6SBmlEyZArGGDpryFOql9joUGrC+4XFZwz18i2tw4drYqFmloOhLyc4IvNywHY9RIVtEltzwis30hhLGGdGl7CmJfqZdgj45FDBEWZLFtC0Fbvq/ASykNPCFBMqS4bVEcVLyLFGJf3C8crgk2eOhe3xhs9ffkFrLEerLVfn57SNIEmDRQJQSqLpO/7jv/tbfPjk4qfnkPvNLz8s/+Bv/636Qd3YdYRSlFjqql4oYthDiSilKdKiy82kM+XKqzMKmcaq2Q2xTiuYSUKiS+CwPGF/nrhz7zX2s65FtKn6s7Wu2RKtNUpqYhiRtqkZpiBRIiOoTd8sa0b3MHuMbqFEutYiZUsiME+O7XrL1fkrsmhwfiaTEAW6ruNw2KGEZt0dIbQka0UUmbbtUEYhulVd2yAhShCRkBOyFKSR+JuAf4wJhEEnSdG5osCKrKq+HCur09TSGCXVaW6IoKi5nJviXhEgMKQ0oWRHzp6CBgQSgVSl/pmC6ldvDrTNpvKA44ewe4qfLtBN5uAi97/yV7n71q+Q9V1KXlh21+TiSa6W4GS7Ic2ObrtGScnV5YuqO0ahTYXrK6W4vrqg3X1GuLygDPdISqE3A13TI5Xm1fNPkMXjph1te4IdjklK4Q6XGCV5/tFvsM0bYom0zRphj8BKXox/zK3tv4baHNM3is9+9B0evP4LPPv418nuYV352mtSSrh4oOk3GL1iPgSUmpFqwPn9jWhDovUKeQOKT9GjSoOTkOUaS0YIgV8S/bBFWoMLAZSgWQ/4aUTmwLDZsrt4iQ+Fpm9YlokYRoRqKMkiRUffn7AkxxxnlLQ8+vJXOXn4kKuXT7G6YXN2mxef/Khmvy8+xe32xPYOUgW6tcadP6FZNez3I62JN78Tw3gZOJSF7apld/msfp9XD1Gr1zg6XnP55D10f5c33v1VLp4/5+Lj38Wse4TRqCQ4XH1B23fIaDn66q/yzs/9FUpYKFIwuSvG5+eVU20y+91jPvqz30LsF87uvcZ+nrg6f8m7v/TXyN3Asnia7sDl5x+z++Ka04d32D1/grEt7/zCv0u3aVjma6QaSMXix6eosuPJ+TmWFbcffhVhHJ99+H2OT1b067u0dksQAnd5BSLi3IQqGb/b8cVf/CN0kujBcnn+mFgiBolWEpc19x9+i3F8SUyBJXviMiIZWdkj4IgiI0qsCEVSdEBqQ8kLgharCymKm3y+qEraRiKLrFOUkEAUVBYUJTHUjZREEERhCY4UHIddjT50aktMczU1JYHeKJTs8WQaaXHOEQv0fYtzLwnjnsPuKWn8mG13ir31TXR/gm6OMF2Pm5aflJOU5mYSV/DTnrtvvsPn7/8pRkiUjhAD+91IMRbZ2rr6L9e1NGuPaOyqGsmUxPYdOWak1PgYyEHRNAYRBYmAp5ZdFYU4nSPFwLIs9Ee3ya2mbTtkziglyEWScmQ/jnRUxNgsBE2XEApivKJEgZHHFCKr7V2uXn5BY1qUGhjHgm1uNMEFKA1KRlyccbNHyw1x3FGkYru9h+klh8M55xcvsdqgbMO6rzEEgaklGnOGlZqcJqJPtKv6f6dkLcuJBNJaVGNIHrK15CxoVUOc9yALyhhCyFXMYCxhqUVkHx2qxBrDUxIXF0RIxBtmuWp7kg+IXGhthxaSg98hggNR/w7Xu1fYZsB2ljgHuvUGqSJuLKz6DaUUXKzvt2rybH+SdVQ6kWIhLCPGdigBwlRCR8oOZSWaapyMucBGo1WDTILF1WJkCKFemqQmxYg29UAqtSAGj9IG5xyUSJgP3LjNaZue+TDjnKNfbW9wZR4pNb1pyaIwuwmNwB+qvCgXX42gqaAK5FwRnVIJ5rAQ/B4lblitkco8FgWXMsK2nB4NhFcvGF9dIVfUUrNRhCzotx3jbsfRrbeQqkO3A8vVFe1qQDUWISP+6ppyw3QOJVGipLNbnAsUlRA3LGOEI1EIpWL/rGgoKSGLJpWEVBkf62ehTcs8PkOgWeIe5w9oLWlNC36F7VZoLZnHa7x3NIMhx1ItqGFhHGesEMTgaBvD4ieSzKyGo6rdDZEkE4u7xooW1WrW29uo3NfCXrxmSmNFCI4dS7wkx4xRa4pWZOdRuqB0YV4OBGbWakWne/bhiiXIepgunlAWpJGURhPmOgjMLhJyRbJaCiJcIYeGxUl8nonFoylMo2N9tmVoVkyzQKPoFGQf8MLzn/93v8WHj3+KDrnfevtR+Y2//R/Q3Ch0U6FOFoJjniaAOuVUEi0kSthqFYqJsLiK17GGHA4YY8gp4vfPsZ0m0xCD5/rqA5598pjt5l/i9r0TlF3jwoFmbVimEasVMdcpYuXjLWRV6FA3E+aMy5EUM1EUsmir2tJolMw4l2hsjzYC/+RHLC5Tjk9RokcYU/M8N6iW6+uRknQt0VhL13W0wxbZGNR6hcgGhSJx8xkmX1FlquJtpLB4X/WPMlX9X8gFhYCcMLa+VJXp6trPWEqKP5l+S5XJsoLRi6xNaynrlKCITFG6ZuGwhDijVUsUHV/5xV9B2YFpf87L8w9ouIVpelbbo9rGLpoiMyIKRITSCUqKZGpkrCSByDN+vqooJtUQokOlUBE/29u03RprDNPukhL2nH/xe6y2t2seWay5Pj8nuIXLp59z/+4D9n4kvZpRwxGv//K/QTO0OD8h4sLu/T/gs4/+P+5sH7IPls2jt3jw5i/x/Isf4C52ZHfg6GTLh5/8Y0TaI9IBvEXLmu9LSlOUpmlXmJIIYV9zYm3H5BZ0bjk+ucfkDjULLQe06ollRja38MsLZCqEIhFRVhJlTkT/HCFXTG4HjURLQd/VhxBFEb0HBaE6GpG6qyYpK7j/+iPmuXD/K9/i+tUFwha2mxPGeWTVWz78/f+Dxq6RVkLomH1iv7ygy5bgL9jev83u6lO8m5HC4opgu32b3fxDDpeXrLsTYjJYYZH9LdZnPX1/n6a/R/Qjn7z322RzE2HR9/jlf+tv4OcL/uQ3/nea9ozje1/m6Ow1Qsr4NOGXVwzHp6yGNWqz4tWHv8/Vsx8xPbsgxsjxyUOOHn2by8tzLj7+kDtv/yLb/5+6N+mxNMvv854zvtOdIiIj56yqrKqublZ3sTm0SIrgZMG25HFhrQzD8MqGBcPQwhYsfgEC8sYLwQO0884DCNASRYAUQUEUZ3aL7GpWdXVV1pBVlWPM9953OqMXJ9j6DL1ORCDixs33nv///H7Pc+MB7aph709plCKoHS///Lts64rXv/ZL3Lx3zO7iAt+P7M53nD9/xsGNFXphOT/7gtfe+nmqqqFa1IQwc/LF97l69jHz1RPyX/OhI2Q3EZTGh4EscykuUZiNUjfY+msIHFLPSDmDbKg3HWcvLvnaO7/CyfPHpP6cYRoZ58iN9QI3Duz356R+oj26TVIVVhsSoghFosPqismNCEDnQjc4v3zCutVMbsKnyJ2Hb7M8WLNaf4Xz0084//gDlNGMY0/bVBzcuMPzk3PaxYb9+Tm1EAzDJUZ6djgQiaZeYViAarFN2XQJYZiGMihtju8xDjvqtmGctpAytam4vDjn1W/+Ioc3D5iGHnIm7kdImY8/+jYuXGBFS5xHYtYYXWEqTfaBOXhkjFRKsx13KCXR2mCaGiUS+77g5ippUd2CzfohSdgSRWMiZY81CnxmnHZkY8jOEcLAjaNbOL8jZMvh8de48cbb2HHLX/zL32C5WVN3C+aYmLZ70uyx9Zpx+0U5xOmysGi6FikS1q6YUuLwzhFIxaM//3Xk8gClawgdwra09ZJGGXx0MAdmv2cIkuMHryIGz3J5wNX2OdvdJTkmum6JVIoUI2FK1FVHbNvCLEUVjWxy5CR/iJtSNiGoCH5Gp4ALjqwUSplSdEYgqqK5jTmRQvk3GUeyhuwdQpQ4mxsnKtshjCVAOfiEHh8Fi0WL6x2khFlUuGkoV+CpaJ+1qUkCNIksHTEWU18IkfDXKLhxILoZW1d4PHEIVBG8yGRbk0KxwblUMFLaFlLR7IqCPCtZmM4JpBQYkRiDQyRR8F3XaMuQys1jzCUmKFMhVGRVlkzWaHIs3NkESMAI8NOIyjNTLHzrJOS1VKS8FiWjnUkIkpfYdYuNkEVmuPyIcDmTq/L3SiogUwYlGXcVZrlkYWquphPadUsMNZ02BV9XGcZxRCmDcpmZRKSQHHJKIKHqWirZEGRicgHf72mbFVpVjMGhBLhUtrFGQCIj41z49QtTcG+xJ+eGJMCamf7ikjhlvJ+JMdLqmuroADd44jzSNYbz4ZTN4pj94FCiLK5U1aAQCCfY9i9LQUwqEAZrBLqiWAs97PfbsmhzAkyFVOVvJ3wp4mtrSK5GCInX5/S7jNUVSkREbUsUxRfFuIwKN88kqTBSMe16rPHkHLFmiW0VIQwsTI3Uil0YGZymMwc4FwpRBY8XI7/6v/0uj36UDrnvvHkv/84//h8J3iGTQVnFOLuyuZx6hBD0fc+8v6CqW/rdwH6/56hpWR2v8bEE0FP2CMoWN7uBlPY8P3/Etz/9C8atRPaCpYhc7g27nee/+i//O5KGEEumVebify9TpCQzo5O73q4IQkg0tmMMEzEbaitBFHWlsm2xjkiBZ2K87MlZcjFc0tiOEKCqJfvdiG0A0ZHILBYtRnaYdgNaga5Is8e0NfM8Y3RNCjNSZCARZSyH0yyxVYWIFNd8yEXnKUKx/wiJu25+hmvn9g8zvgR0VZGTKZnAEJAyIVNRcQYpy2bYRdAQYtHZVuouB6/eYXHjgBSvSpBcLjk/ecru9Dm11cRxoGkaTl98yOLuT2PqjtXxMXa5ojIWoTu6dQsIYgyAwO1P2J6esr865/SD9+mqJcM8IW8ccvfVH6OpNCePfkDWgcXBEru5Q2d6vvd7v81ydYhtNRdfPsLe/gluvfnjPP3+d+jainj6PspcMk2Sen0L0dymH/esjWJxeMjZF5/TrQ85f/kIjSD4EWPWNPUC5zNONNi6KTnxqUdKRwqZQCCJhFWWYbvHmJop9IAsPvOqJmWHUgXPFmPEZkPGIkhknch+JCZJVgVxp4QkhoEYMpVpGPwAwePmSKxavvKtv0mIPfvTTxA+s149YJoHgrtCbr7C4f0HaBX58g9/g1zdYHXzHt3mCF232Kq5LqtMTLOisjeRVaLpFgg8L588xgVP23TEKWE1PHn8l+yen1LVGy5PnqG0oFtWbC8+JwPKGlLsMOYhOozAwL23vsH5ySOCmxmHC3QnOH7jl5H2kPPHHxJ3L6nXhtXBgv6LjwjBMu4uGdwVXr9Ku4hIIQjTwBz3iKBQQZCiQSw2ZKCuW0LKKF24sVLKIhHwEz5MBb2nFgihmCfDnYdvcfbsPaQs+b95HKntuiho01zsh3OPlopxd4mUjtqObG5/hfbGK8SgsM2G7eUVyjimMdJ0LZenJ4T9HqkOuf/aLfrtp2QszULz5affJY4jigO8UyhtSKKocbPPhJRZ37nFa9/4Wf7Nb//fVNWKWw8e4neZ3dVz6qqjdwKfE8tKM/cvSJVGa4MViu35E2JyKHNAVpngytZGGYkzkH2FMgbhI0Y0+DSThMPKkuNUWJTuWL/yFhdnL9hsNlyePCFMV1TVEu8zuC39MJHSQE4L3DxiTU2WO5pmDcKj5ILgM1VVsT17glGKmBXW6iIWCJ6efUFV6SUEQdPawmyVC/b7XSl+hkiIPbWUbKcZuxD4KbJcHULdcXBwDx8GDt/6KcSuSEq2l1c4vyTvTnj2/I/xwyVaaESwbF77GosHt7h5+01OPvs2Z4+eEPIp894xz2eAJDqougahNIdv/Ycc3r/P+bM/I+wHxD6w318RbUAM8NWf+Bvs93tuv/46H73/F3zxyQ/oqliWMkIWUUeimDYl2EqXeBcGLwwqaUzOjK5nGueiM06Rtm5oF11R4GpLCJclTz4FRLAoEwhSokyDH2Z0XRGiR+uWFD2ykkThMaKA+GUSWKVxcQJdEWPZ7uZrwocilNhG8hgh8KFHC42SBjePKGEZxguEVohcSDtSK7IApMTvT4kUC5+qAyJldCo2Lxdd+WwcR1QtMcqiMAzThLYVPmQiEWNbrK4wUhCrzDhPGNmSXUBmz5xmpK7IrsiLqqoix5nggSRJWmIbg5RlCVY1NTorggtUxuCZSUhAoXLZ/sY4I2RCREfKAaMbEpppmpDW4p1kvTpA4NidfMo4XFA3Ch/E9Sa84uxiy53br+FSoLELBJpKhrK0SFzngWH0DmU1SudrE6HC54CMEiRkLdHKEEIqUiJrkCqBtOW19q7E9/yMNcVWNoQ9XvTgi4Y9jw67WSJub0i9Y6Frxv2ekDOqMsiocfMVFabEjyIEQChBDI7RO9w0UOcyePrQE01EqwbhEvM8IjNoIdGLRfkbqYGu6nDZsB17NlULlL7QeH6BV4nV+giEJGZVbK1ZXZf2HU1byvk4TYiOYe7JeHKIbJoVsxuZ54nNptB5UIkpJeY0UeUakSpQjhCLhe8f/pM/5tGTix+dQ+4333qQf+N//nvXH1iyUA0UeD8hSYxuRilFdomUHc45lt0CZk8U4POESBlrlsTQE3UmJ18c5/PIfvwOf/7Rh3zvo8xuL7j8Ev6H/+KrbO78LbwaiGNPo1sQlikXDzsioU3GqIzIiZg0aPNDgHoKAitLe1UIjTQackVlCkZrnkaCjzg/40NEKEMMW5KQ1KrB2nLlH3SmMyt0t0Do6+k9SpLKeB8xuiZHd+2ld2hpyKkU20JwpX0qM7NzKGsQMWFVIUYkkX4o1dBal4113UDWJOGJQZBULhmq4KhMsakFnzB1Bdft5sooQggk2XHw6m3a5StFZewdF8+foFVk//IZmoGYLSFINkcL+gir5RHbIWMbi99PyDEy14L14REmRPphy8G923zxvT/CnV9iukwlLbaCpG/ilscI3+OuTpBC020WuFFy9PBVtMyk7BjGjEkTQVS4nUObK86ffMZCT4znL5jOP8EsygfBG3/7f2L37Ac8e/dP2axvlClz1ZFnQQ4XoDZkUZjKIQSELkgfoywilg3FFCeUrkgiY5QgxKpcOafysFK6KkWN6MlKE/1ctirZQ1LlcEYgikCK4HPGjxMKyCkxzTN1VWGURLSC9Zs/x+1brzHPM8IsUJUgXj1m98X7HH79lxGsQddMl094+d1/QVJrlrfvMfWOKTiUrDDCoGVi+/JTKp0YYsQ2dxGLFXK/o73zOtW6w+8H5qszunrkow8+wMoZpWaa+ja73QlSxTLdm6YIAXYerWZyd0DQKzbLI6y1NEeHDKPAxEsef+c7LA896WDF+vCrHLzyGiIpTj75BCUrmoMVJ19+xt2bD/nBX/xzDlZHPL8cqepEZRQ218jOsvee5AKVqtBVWwodtqXebLg6fUIlIykEkmhBGarmCCEzYRrATYhOI5REi7oMfLnYlqRIRRazvsHxrVe4ef81guhIo+P0k08Q9cTq1a/CnJj6Cdc7wtiTwgX9+XOa1Ssc3b9He7Ri119htETmRMiJ8yefsHv8A6If2F3tkKpitTwiK8nQR+o2oHRNXa0xwTDsLxAWxnCJd5lGapIVuLinMiuUqoj7nimOdM1NZF0zU6QrzvfoxjAPEYRCp4TKknnuEcYSw0Db1igJPsDxV34BYuLxD95FyolVG7g6OcVmj13dZzAji8WiCB9yjXcREQNoQx9GpIqkfU9VaQSU6AUaUR0j/BMQGh8MxhhyHInTQDg/LfhGGvJyfd0XkHTGYw4qstrB4nXu3v9Z5v4ZH737r1kc3qXrOtrjr6J7x4sPv0O/f4xPpyzyXbyUXA7P0CoQGUmjoDv6CW7cfJuzp48YhjO6o4RKAnHdQp/jZREJpIplfZf94EnskMGT+onZjbQHFQLY7wOmqXHDTCBydGPN/vKc4D2tlWQtmXNECkNtO8LsCMKjaoXIDZuDY/w4QZoZf3T0QgAAIABJREFU9hFRd6wqfY1H++tFw8zcFxuV1JYYIzEMzONACmN5zg3naFVsVlV7B1VbhNR4JxEo2rolZYtW0Pc9TduSY7FbhjyVK/Zp5uD4Veb9FoQnioRIshSRg0CpSHCenEEqRUxbxnGH8IYse2Iqm7rz89OSnbxxwDAXmUgtBCkHgrSYRrM0h4SUmbKCnaPSlqgiIkkGP6MN5bM2GKQEYRJYiQueum5JcyBmQddUDMOEMhUmelIc6Puear1h2E1oXZdNqdKYpiVpGPsZLQpHeAozxmpinAmh0H6maaYyGlSNFAbvHBnQSmElTOMFUhhmJ6lqC8GTFZjKFsRfVeP6C5TZUDcVKaprMUyPVpaQE8pacIGQPVp1zP1TkrVY0xUFtq2JOWJEKhn3EAjOU7cGqdvCIh7nIuEQDiUTTdViZUccZ/rksIuO7W5Hd23PnFKk7Zak7JGhIs8TtRZ4BZOf6Ycdi0WLdwNaKuYpI/yAtJmUNW6ayH3CtEWpu+t3rO/ep27ucXX6mJwvkMKSvYM5Ua8tc7NB+ZkwDWgy/ZBYNoeM2bNedsxhZD9e0jYL5iGjs2J10DHEnuy3mAgqW67GkVpbVosFlxcn7OYBbRLkinGvWR8khrhjbRv+/j/+PT55/iNEV3jnjXv513/tv6Y2Fi3tD81b8zySpSenYgjTlcW7TJ3h5bNL1ocNsvJkNH7wWNWglSepzBRHlGwQMdCalpR27INAaYvIAmZHlqVchvIIZZjCRCNL21oYef3g5no6Dvjwb18rL1MpEQmLtYdlYgKE1ATviZMjkYnRExGFfZsCqmqxWmKaEjB3sQDordLFtIYgI8mqEBKEEoQ5kZJE54yWkpADyIKJISqstfRhRqlMrTVujqhcGsU5R5S5/pprnWkSpQhnZF1ea1MR4oiUqoDEkyjbyJAL7zDN1zrTTMqWwztfp1l3tLfXuOmMT/7gN0F0OCaO7z3k4NUfI6qKsw8/oLEVpEvcDKq7gRsnVPRcfvmU42/9DBcfPubw5pLnP3iEMZEoPAd3XiFOW0y7ZBp7jt7+Oc4+/SvmZ5+iupuk2RPHLxDTTFcLeqc5eOMNnn7+XrkGrVpUVZEIxJhZtTeJVcO8fY40G9x4irI9/uycWq+Rfssc/HVezRB8hLoiqUBXteQpIKqGLIq7vjEFeh0FRAlCGuZ+R9PcguAoF2iJiYHgSmtUxjJQSCmZ9xPaiOu8mqO45CqGKaA15cMtZr71K/8R9uhN0tzz5Ud/yjRNHNx8QNrv2e/3mO4YFSNXLz+gbgN+v8dLQ90d0u+vqPQRzeoQJwV3X/km0hSGY8pnvPcv/x/a2rC/EOjlISLuCcGyvPMVLk9e4OM5ef+SbF7l8OgB93/iHXb9KXWWVE3i8bvfpl0lZHPM6uAVjPGMsaKqj+ifv2B5dANta/YXhTt6+eUz7n/tdT7+9h8Q3J6QtljOmX3AHtwhe8U0ON78hb9D1xpO3v8DpmGkWd7Gh8Szz7/H3Te+xcmLT7jz4BAhBFcXX3D54grjl7THazyBMIvr6M1IimDNskD/tWaKRfGtGgFToumWoBKmqogRptDwzs//MsPJBR9+933q1YqDO/e5eesWqpMYXePcjBSR86dPef7+H9NffkGUkZAMB+v7DP0OEUBVFS5LbLsiTKccHt9l9eBN3HjJ1dlnxGnLfBFolguUkShb4XYn3Lj3kEfv/gvImkpWJFFhu3W5FjU1MUiErDFGsR/OMEoTcs2tuw9wYUe/uySkRH/yMbWqSCEXQoFYg1Tcee1NtpPj/sM3+Mvf+784uPUGWmyYoqe9UTG/vKAxh4xuT1aabnnM/vIRMmW6w5s0myXjPLC+cQufIsf37pDcyPkXL1AxIk3J46ZckXXC+UyzulmGfzdhuhXzMPHi3W9jGujHPe3yZmmk70pGleToOskcI+3ikO2+WN9efvYBR0czxkR2ux2LpmbsJ7DXYiCZUKqDpEizJ0jFatWRiRy8/pNINM9+8G/IYsucApaOEtj0zKNDpUTsT7n0O9bdDXR7iEiZpCdCFNTVkmG3pe3WZBHYjxN2uaY2RQbgfSSPe5raok1BHiLBuzLw1LbExXxQWC3xowc3MqbAwhiSlSifiNYir2NySimGaaQ5OESnqshvwrYU/YiQJSFkxskhJdds66Y8f+JEo2xBTC3qssmMgapaksZEs1ow+h4XA8Z0zP2A0eVKm+CJyqNVBRHcnGnXRRlbLTvi3LM/O0GGCTcnqkVNZVeoelEkH8pQyYp+3BcigCokCZkibWPL5nrYE6saqdeEecuwu0QQkErRLjrCuGMMCd0eEsKMyiBSMdGp3FHVluA88xTo1kvcNJco33W8QqCoRYkayqYiKoHAYRQIUUhH3nsyihgcImVIAmVEIQM4jwsRKS3LZcfkznHTdRZ7fRNjDH4e8DEWmtNwnWUFatMxz5eYFuKsqLuWKHYoGpzT2EYxTj2VrlBCobPAJUHSEmkKvmxhbyBypK07hmFANx0pTiQPob/Cz3u0jMQgWTx4QDi/4IsXH1N1S1bdzR/SL3LYkyXYRYsQDWEaCS6SxZ5aLpiDR6SZuN9htGD0BrnacLhZY9slfRhJ/Z44C/bDFSppfJiBxGKxYEpb4jRw0NxgkrKoqc0Cqwy7YUt0u6KkFpo4j+AlphLMIpHTHps67KJltz9jvLpk0S5pD1roi2b7tN+T00hJVGTcDL/6T36Xj3+UNrnvvHEv/7N/9PdKeF5IUhQoo8v1Y/TXHvrMMOwRQlFXilYnZmp8GpEoclCoJK+viQVZeqLQyJSRWeLTDEoSkseaMiWGnNBGEOaSa0IkEDUyKyKB2e2otSnih+CpmwXTNCC0IouIlAarlzif0bbBNAtOTy65ODuhUYJMBJkwWqPQJGuRWtNVNXWzJMsGsidL8cNsrDIVLvui1tOa6FyJFvjrn09JRBRwza8tw4AHk8kylgkrS2rbkpMo30dAEJmcRDG9oMgxlhO8FAUvpjVaZlycSFGilCVfH9ZKVisjpCYJjYigk2SabvDw3/8JdLYEvyXLqrSAs2e4eMaLDz9l/PTPaI5exaxXNKsb9BfPGC6egK2ZTz+kWt7GNLc4evUeX378HW4s7nDy5Ixbt2/iY+T08QfUD7+CMQF2Dhkc09UFvfuSW5tjthcvUaaCdkW1WLHbXyJiwQWNKXH79a+zu3zB5u7bjC++Tf/kCbWSxCQRdcLPZ7jJILXh4HBD1AqREhfbl2XrsXmIUJsSOxCSMM1IEYjOk60pbe3G4vqetjpkHiaUlSB0ydOhQJWtjdWJae7xsQxOSslyLagMF5en/Pgv/Hv81bd/n7tf+QYpG26+/g385QsuTj/g4Og2fpzYn3+CspIwQtWuefrJR1gVysAUEoFEylNRKKYF3gXs6pCmLfgmqRRZrHnznQe8+69+HUKkMhtu3PkGL148JmTB8Wtf5fzlCw43x9TdgpPPPgZTs7z9OlVjyWHP4mBNlJZudcBuu2X78gWnn3+fx1/+K241r1G191DtgrqrieGCWtiS/1x0xH7H9uwKazIBgZsFtB1v/eRPk33H6tZ9nn34J1x8+T3aw1d4+NO/yOXVCe1qw/bplzSHx1yc7VHalN8dz4svP8DvL7l5/AA/7zl7/AXb/pQcHO3hQ9IcaO/cJE4Fb4MCU695+PW/wTDu2J0+ZXuxJ6hEniI2OkR0qMUGOKI7vMHNNx4iY+bZJ98nTy9hPuNyf0ZMgsXiuMgM/ExdKbRtiYAWAucHomqQQuP3JUZlbQ2mYu57JJLoRgICITLdokJK0KKj3ay5uniJFJkQFAf3jrk4G0i8YKlWRA9Trxlmx8Of/lnWmxtcPX/M80d/RJhOGHd7jFii1w2re99kPs/UreP87AWttoR2yc/8B/85bthx8fIpf/X//b9E9TFGdzSbhuQatu6USt9C2AWDzzS1wWqJCJq9T2jVYhsNc2axtIxqRYqe7Lasm4rJeerFGiEUVxcvaJqOWsA4PAcFVb0i+BklFkxhIsdEXdf0wxY396w3G7bjjq7akENgDntk8Ph5ZLE5IMpSsFXGlI12KM+67B3r2w/JGsL+gv5ihzITu/6KhGKzuEUU1yZIpVFGk6InTSNGB8akqbPGdC3D5LB19cPPI0XBSEp1LT0IE5VukCGAn8C09Jdb1LIqt20hl813KExSLSTIGpEn+qlnsSz68/7qCoCuWzCN5eYoVZbsS+FayEhdW6y1pa+iJSIm3LSnW1REHyAVruvYD/iwRycFqqiYU3YFNznMSG0xdYkGNqubaCuZRo9KCRkzUV4XpUxHTIF+GKnXS7KAEAKdrUkuMg1nVFIz+BGhNKSMrRqE6JC6IunA/mqL1RVaBEzdkWdfCCzRlzhYThgF25MLus5Qt5p91CVGdHFGu+hQpiJTblpzSKRYNtCCxDCNhcWMoGvXDLGQS9LoCS5gbI1XESkltZVFEJNEQZbJhBQGIUrsCZWR14XQ6DxjPyJVxlRFq10h8RGIlMO6iEAiTgEhI7v5oryfREZKQUyWuq7JMhQDYYjIZHCpJydN25lC2QCkVAzMpHlmZTtmF8mmwubqWrCRSTljtSN6h8oQt5dMsgEjCGGLd+eEeSiiGqXRsgxLKUt01SEqQ1sphEhkX2QYEoOK5VCf8WTRFWJQFoXuUXWoHJEpE7Ogkoamhigg5cA0g8gRYTTZJ7xLZJmwjcLIlhAnalPj56mQZeJAorCNXQrInGnqxDBGKlP0yjoqQvAl658cCIGuyhDxD/7X3+XRlz9Cmdwff+Ne/s1/9N8Tk0MqVaaEVMQI6bpo5WMgJ1doAQAxYJRmiuUaRMuqMChVcZNjClk2pes3sExEYkFr5YKjcC5QGQvzDEbgU0YrCz7hcsYYhUKhcsLPMwnASKRQqEriQ1XKX6JB1w1hLq31LASVLs707e6E7Ivj/mp/htJVUXeaBik7qlZTr49ZbA4K5ksIjBTM80hlajwJlUO5yhCZSmucK1vrLEAlSYiOqDUiebRSpJyvH9yWyfVoI8uBXl3/rrpCRIdpK5zzJAHCS6TwRX5BLhnRnIlkZM4kIbAx40/OmadTLBNhnDl++Pc5/OUDfFBoQ3GL7y95/vR98ov3METq1VvE1ZJ4vkdwxXj5nO1g0Mlx8FPvMDx9wv0f+5tsn79g7B+TTmd82NMc3iWJnnm/Q4wvmC5G6CxY2NxZMJz31I0mhRGZKoZwyeQqbH2DGw+/RRI16+P7hHmHuwpcPPst5PmnuL5kSGVzSGeOmJKjOThGDJ7RDxjdkozBdBXTKBhHgalNOSAJRVMpvHeEJNBNRZbFEiVERoiC/MlS4MdApddFQ+kTUntiDuhqQRpmwtBTtYckEZj1IT/zn/xnNLVlmgb63SVjP3K0WbI7fUJ742bhjco9F49/wO7xe7jQYoxiuHhC7yTHtx5yOV1AKtzKrl2BF0QfmeUpIQ4crm/Q3Ph3qFdrbt8/4C9/+39HxBXj1ae4JAiUYkZb3UTYFcIEwv6Cxh4gTES0FTlU9NMBP/Mf/110ZXF+QOmEtBWV7ojzxDj2nD15BNnz8fd+h0rWHK9v4WJgGPbkVJXBIc2EaSx63KEwZmfZ0ZkiMtmPiXsPf5LKrkhz5tnzX6ep3ubez/+7RNez+/wDkt8j5JLjN7/Oi3f/BC93NPUt+vEUJT1uuCjK322ivnMPXSliMkRRk1xDncB1ioNacDW9II8DdV2Du0RrzfZii+huc+/r3+Lpe+/SLouh8Narb/H0/e8SUkJhUEqzPr7N40d/imekEkuMapDKo2xDv70gpgFra+YpFMxStSiWsgQxZ7TQ+Guzkm4q6qoliGKf6vvMnbd+iReffpu63dLkBZcv38VPHeub95kjOF9QhXJ6xo13fob1wasFnRgF7Y2GD3//tzj4ytsQA2FnGfqe6eICFQLtwzt0hzXu7AX7dEKrb8DVwJwnbtx8k6urM+Y84sLA0m6ICc53z2gymOromjgCVjYkIRG1ZfZbagQxWGY/Ya1ECEPOkbpOJCxhLpgxERXxevP21wjDeRzIKZLwZCraZk2SMzIF6rZiP7iC6QIkmX66wGRBCoK6WRCDJIkKYwVaS3xwCFFMlkIWLmzCg3fIVCJgbhjRXqJaySRmBBVNt2TMER0hhUxrOmJOxDARVMQ2FjdOxVCXIrlqUFkwxQmdFTJCShMpTLh5j0iZdnODzfEDRl+QVMqVvO44joXbLRMpl0Om8wKJZk5jIWLI8mx2fqJry2urlCwdDedwUSErVRY8c2C9XBd1rRJkEYl+LIelSuKmclvaLDbkpLCVxNgVV9vTa6mBJjiHqGqmeQ9IKiWZ9q48+2QAF9G1oh8uaOuO4CVNuyQkSKpIHfARISNRC6QHLSqSitfWw/JzStugvGfwW5RdIoSgFobZB8Zxpm4rKiVw84wwlmmeS9lcCGTOTGEo9lOdwQv2+z21lGUYUIbGVgSlCH1fcuZW4+KeeQykLGkP1temsYqkr9n0vuShhVCM/UCOnkbX5BCRxl6TSRK6XgLQWkXWkewTxEQKiczEOIVCqTAdMZyTsyAKTZwiVitkY0huRnULpJsJvceqjlFHTNKwfUIQlsn3RCFRqpT1DqwkBMdWGTbLFWm+YN9/SdWtsIvXUEpR6QaQDL58ndSCOAVkApci03CCFhajO6TJeDcidSzimHGHtosiP/IzURY8a3Izi7bB5RnbHRRZjwxgEkZWTPtd+Z6mYvYT476n1jWIyH4+Q0hFowyzd8QgWG8ku11guLwixVLfS3mmqjUIjVY1JkmikPyD/+O3+Pjpj9Am98ffuJv/6a/9t8SUi2ksBsw1108pQ46FuydMhlCydMSJlCVClOtJERMxlN9ZyFCKZCoRRTF/KDIuDaiqhRQIRFRQKFnC10FkQirTSHlNJIOPxRVvSltYiIr9uMe0LUJklN7ghMBgQGbcnMqkGTOCSMgzRpXcT3kT1+zdRFNJhBbEqXjJhShTqtWQZMlQpWv0SbHopPKfWlGsPsA1HJgcKZDxWNzPOUZSBFXbMjGaSLxue0opkSGRkyarWLbDqqCwskhoqUAknO8xosKngvtBlN+vmnbsXn5Izue4F0+YwjOa7U1+6h/+No4rxu2XZJd5/uhj2qOEzo6rT79Ptb6FnGZkivQxQJs5Ov46o1kVwwqG+eJLpukls4Pbd+/w+Iv32Ky/ydHbr7N/8icF/1Pf4+brr+CSpGkOqNsNl/sdy3rJOF0yDS/YfvYd+qsKa2fyVUatF+TtY55+/0OOX73JePkJhkBQN2mXghwzenXA6ALz+Za6WjOmxJQcOE21XINW2EpgRV2u8dKEbTtINd47ZGWISZDihK0rZLfi8uKC+w8ecvHoKbK1TP0lUTpM1RQkkhGkqLCqY/I9D37u73Dn/psFwSMc0+RYHRzz4sknLBYLqsWKHAt8XhnJ+ad/xsVn3+XLL76HjpfcefB3ef7kr6g7R9Xd5uziOeAwYYEQLc3NFQcHR8Q5cPcn/xaPP3wPqRZcPPmnDC8f0ZkD5mRxWTNHwWZ1D2RpvUqhUckTdWIKES1KIUznY8R6w2vf+BZZCnYnn3D54pzFes35+XOO7r1CTHvOnv0FPjjc5QWahiQi43ak6zqkbEmTg6ZGG4+YBSIFsu3wOF5/++e4vNpy8fQz2hr2+wkjDUq0jFnSHh/QrW6wqQQvL8+IbkcKlwQEm3aB2txkd/YZVxef4V6aYiUMI/vLHbJpmeYLmiPJV7/2n/Lp+9+lWVuUaGlbmKaAaZcYP4HdUB8/4OLzD4mqwvd7tBZIAnOO6NpiyTjnMMYwT1sEGUaJqRWyWWFXG3TccXb2BcpY3G5LI1oiIwELQtFWC0ie/uIFVJZlt2E/XuJDT8iRGFq0atBLx8HBQ7r166xu3OXpx+/TiIlnn79HtTziwb3biPV9xotzvvj+n1NtOmKfqV85Ik+JNJQrUm0kk5+ZXE9bKbKEKTasbn2DVWc5ffwBy5uHzC+ewa01Se04aF+hszd48ukPGPuPWVU1V1fP8DrSHHwNLr/ANq8zuhnbacS4RYkWrS1u2rLtCx9aiI56sYQ5kbUs5kiViQHqqiJFaK1BRs926FkdvsbV8xOEzkhdlh8ozewDs9/SbFqy25PnGSVWhNEhrCYbhRaSKWSsVpimRMamqbTztSzdAyEEkPDCAwkrMzZWJbqBJlWaSha7lgyWOA9kkwveUmScg8WqgyTo45Y0RpqqRkZDCI4oNFomNJEp9EgJk/M4b5B5oqsKV7bqlgQ3Ya0ljDN+TujaMPmBJAIahdANRhrmGFAqIlQiRIHymSjKAT5KqPV1YbpeI7BAIGuBEZK+36GMRlFhK8U4ZoarU7JM1O0BytTEFECWkpxL4nqQERAjtYI5JkIqFBjDzDwPpJRolwvmeYQoMEqX285GFX6qSjjnqGSHrRp2+1NW7ZJMS1SRSijG+TqDW1nyVGgDBZ0ZEGliHK7QVhFjoS1bVVHXLcO8Q8fM0O/o2pqgKUKCcUaqxLDP1KsNrTUokwmyxBqjz7jrvs/x8oiEpR8vEb4nuRmZIcgKWx+SRKRpShSSrEsfIo4474nB0+gKScZ2FW4q0cWcAm5+Rrjech7evl+U7lKQ+gQqEGQizg5MYr26QXAZ1RhmN5YqnbJkaUqUTxn2wxY59/jhgiBnRHfIUt/BJAU24GKGqtwiC6EKiUIrfC+otSIriYyKIUeEGrFWwuSYhh5jGrzIVKJYKV0qCFFBZh6HYmIUAjcOCCOYpx6BxkeP1oYcE+2iJblyQz2lbblxj0VgMscdqEQVSv+IVBFdTz9eYuoG74tlz1pLbTXjOGL1siwSHfzq//k7P1qc3HfeuJv/2a/9N8WYojJWKLybgIL3qG1L9FMJ0M/FFFa1/7Zh6aID79G6Kk11pa6zXbHw/KJCS0k2M1ler8idK9BzWSFMQijJME9YVbae03wNyXYRVUV0lCAKJDtqWUQA2eIdaNuC8CQfCc6VqwFAGYPzI41tCWFm9IHz3YhC4T5/Sb084P7X30I1DZFYCgjJl0iClIV/l2H2E7qyKMqGLoZ8XW6BnAVa1UWPqEAlhdCSeR5Loz4WFW4UBcJsrrd8PpX3h6l0OSgrimhDlOuIUpSKZFG4iACxP0Fk2D39XXQcMLJCDhv8rZ/CL+5y5423WN08QjeecX/Gi0ffpRo1Wjh2l2esjm/hhpH24Vc5fXFJFTN63hYWX2dR+xGfIlBz89Wv8OjdP+f+279Ie+824eo5z/7sj6jW32D1xlto2fP8i0+4cfOI/fkFi82Gq+0561u3ePnBd2jqCFGT41OEzGz7gbvHDxH7PaFSsL1iGzx2cUi/O+P8cqCuWtpmjawtZnGA0S1SaNw8YozCO4Gp9DW0v7AChRD47AhTJgVP03TMLpFjYnIzRkuUgNntMXXLMEzIenl9RaTAR776K3+benMPqw3D5Skhw3KxYnd5UsxzcaZeLBjHseQBY+Dky0fIecvnn/4hB0dvE5yjXtxjcXDMxfP32F/8JSpplFij10f4vC6WnjCQlhvCNBPmRLU+4JM//F/Io0QujxBthZZL2maJajXDdqZtjthdXSJVujZHlQ1yDlBtlrSrO+R2gRaZcX9JnCa0URgOGcYrJjdjD1/l1Ye3mNwZtjacfPYHfPxHv0lXP2R2Gt0sERpSCMg8gzbU3YZ5ruiO7lFXiosXT7A2IVE4IUuLWhkqXWIAerHk9Xd+EiksoxsRSsJkCqat0Xz6h/+cu+98nTo7rF7z9PPv8eL5e+zPnnNj/Tbbq7NicxKW5qDjzpu/wtXLxwwnzxj9JcuDH2N/9hGJiNQduj5AasHR0TFSSp49+Su6o1uEFOnshv7yCSIZctxijGU297nz4D5Xn7+Hy/tyUG7W+HlLciPaLBiHmWpRE5vAar0AEm1zh+NXvoUwhv004C8HRKoY+4nh7EtOHv0p8/Zjmq5GGxjDjEiGlXmdKzEixURra668Z1nfxucRY2vCvAcS3eoYiSqvfW0ZZ09VbZhcoiaSo0CGPS+c5/bttzm6ccBn7/8+P/bjP8W7f/k7HDVrUr/nqoJXvvpLbI6+iTv7kOdPH5GZkKlnuNjTNA0xCaLM1FVHiBUpB2RypDAxVx1ZFnrF/vICIztUglpropLMWdJWHSZFnJvQVc3oPDKD0pmUPIOfqYTCKEOIkZgCWUkq24JI1zeBGe+gbjqmaaauimY02VDEA0qhjSwD7dgzzo66WbLfDdS2LcbNv17G1C2RTLpGT2oEJilmPxGix1rN7HrCNBBCZPYeoxQ6gw8jVbdiCp5V25HnudgkRWmlSymwMiNtdf18n1DRoJS5HjZ1EUc0AmQpSGtZlc8x3ZJjQEhLiBqyROmyENImFwFDZRBJELPETTvaxQFW2dIjmbZMu5FueYDze7yM1O3tUtwyqhyww1yMdglcKLesUhV5UpjKgqqqmzKc50Q/D9RdUyJ1KMjXV/p6RMiO6AJTiIzDnm6xYhxHpMi07QEhlyWSlQKrJTmNjLGUvIZxy7Dbs1wuQSwxsvRUtNZgBOPsES5QWUlSNVleW0L1VJS/MWFyQxIBoTR5CqSoqNoaKyPD+RdEVbTKUmxQVmGUIBLIyeLchDEVSZYhN/tEjJ6mUtTt4v+n7k1/bUvz+67PM65p732mO99b89TtqupuT+04tokixZA4AQkRIYFsCYHMEEBggQSvUBASAYEMFi9BAgnJwbEVcKIQBwEhdmPTbceN29Vd1TXXrXtv3+FM++y911rPzItnt3mXV3nTf0DVPTpaZ63f83u+38+HnOs3XChDCIHDbsUuVXNgybkygkPGT2tIELLHiBbTNqSSK6pLCWKO5GIgR6RuSSXSthYXtijbUTZrenudcb1DHRgwTV0i2SqIiskxx0DcTeQQ0dZgxECxCmk8UVTxhEJBkYzjSNM3TCHW027kAAAgAElEQVSTlav/1i7S0jKNDq1ASUmRhZJj7ZqojC+BsN0iyKToyGlDNJK2v47Kmm5osbZnPV5C2HAxbbBixcHQkdMOIXvmsCHGQNsYdrsNTTMwjRFZwCL59/+73+KjHyTj2ZdeuVv+5n/yC/VUQ0ZkhSpVlStyoZK8Yy1PBYVVBqElBUEuqW5qDeTZkzIo2SJFwYeJnOu6PIlMTDPCKjZXl+QiWdgGgaw4HFWtN6hUh+ZYix+NMRTpyUmSiqwxgEZXHqQekGpJSqW6p30g+EzTVGxPSRFh6xWeaTpESdhmibF9BUDn6l5PzqHNUAkGqqDJpFSzWyInkhHMfqIRHVJW25oQghw8RRi0lISUKFpjkHstcSUliFLzuFLyJ9eApFqMCdMG03ZkXzCy4JNH6vrclKwoOaK0RVmDipnsNmzXn5J2j4mPPyUGyeGNa6zVwOLeV+nsQJkT4/oUPxu++Od/jjR9wsWTz5jHK0LT8fLbfw6kIFxekaUmXD6hSFB9vfLPquHZe9+EmBBCsDh5g10ILHtBunifWDT6+Itce+k5Nqf3yWHkyR/8HsIuiNqijUGLR9z4yl9gXgcOXrjD5Ue/z/bj9/BecnL9kLPPP+DopZ+sw+riEJ0Tzl2yu7pC0O+vjR1FVX6nCImiSs0yF4ExDbt5t4/GjLS2wYWIaiC5hNYNPtaSoSoZI1MVP/Q9u/NtZX+KjL52nVe+8NNIs6BpNX7c0a2OKbpw+fgRTVrjXaK0HcSA7Q45vH6non+04vFH3yImj3CBs6ff5eD4mN3lp2i1QJTANJ1xtHqBOUmikfRNy3z5CB0bnG+Q6gC9bDm8cYOYN2wvnnH25CG2O6qn9DSy22wpqWJ/mlbVjVFOvPDFn6A/foGPv/WH+I1jOLnJdPYJU3D01pBEVVumcMrB9ddwu0CMW/qjG7z2I/8kB0eHrJ98iwefvYc/fUaYZlKcUcoQYiaXBm2biqQhIpsBnUzFAuVpz9KULNqO5B13XnmenRasju6hS8/67CFP3v9jzs8eYgg0jSZvYoXDDzewi8xuVtx85W1kLohyyfHN5xh94sbte2zWa7779b9P2KzpFkNFJqWOMl8ijeL8YovtO7QqZFfoTq5hu7v0B8cUn1keH3K5PmU47Ci7bX1Ov/s+3ntuv/k2B9efYxgGPnj3j1kddoynj9nurjC6waeGm7d+iIuna2IYQRs6KXnw7v8OcofpVxgLykS0PcYogfczIgliDnTLQ9rFiqhH3DjRd6/g0yWbJ5+iMBwc32V0W3AzKWXGLLlx+3nGXUAbBWVXW+tKMrmR197+KuePHsM8Ixcrbj7/JXJ5ymcP7qPnMwYxsHjxBYblIe99/Xdo5jPEtbcp4aO9wNgQwobpbEt/cA3PRMwSI9o9y9Qj6WmWK6bsKK6Wt0qpmy3bLpn9BFSbpcotOWa07dGNhuAoAkLJVVcbItb0ICLKSkYfCNNI0zTVtpRqllC2LbhCcp5uucCXyl5VSiJkQpPQUpJKBz7Wa3FqTEB0ljSNWNUTiPW9nBPOVWYpcWboFmQKPmzRUtE0AyILZp9AQrxak3Rk569YDCf4nSPEuZJ6hGC5XJJi1Q4vhhNS8OQM1rbovq0FpFjtnCnX76OIYE0k+lS7JxlGF9GibqpDnEAWDhfHZBcIZEKaQTQEP9bNZJaI4rH9QNxeEgMsbp6QSwN71WwIjq4byDljbMPsdtB2yKyRKIyQTH5H03TE6GupOvv9tlygiqklMyohR+u6aTdNgw+7qprXDRrLbp7QxpJcrD9z2CH2dJokqmRJELG2ZRwjeDDtgGx0VQQjaW1XF1CmwYWRRgsSdZOvlCTuElaDUE2NsaSajzfSVIZsKYj9pjeRSKkQ3O5PtuD9omMTR1LJyKywStdBOiaSBqSiEQOyU4y7DbvvXdIqU22TC4tQkKOohlWd0d2AaQ0iB4qPzGEEMuSCFB3C1CE+ZbcXKBmKLxAjSraV7iEbsoDkRiSJqcxoU7BFYVVLjFQOb9uwmUYWC1OlJtO6bn+LZnIjTTdQBBTjaOWK5AtpninMEBKjPyfuLkEWYrRcu3YTN40YIRn9JcpmkiyMu/ozC9Ww6BZIbWmbRI6Z9W4LaMZpzdA22EYTxrrYlL1G+ZYpZ5ou0ouBf+2/+FU+/PzZD86Q+/bLt8vf+Wv/CklCYS9icJmmMcRSr0dj3gCFLHpMlhQZqv0kl73oIJNmD1JQhK4YLNNQYqIUQdSGmBy51AbqxfaSw2aFaav2sJW28gCJ5OKw2kKoRjApJSVWJmc2khQiUVmadkWIglJEbWlGidSmPojakXOuYG9pqoY0KaSoSkmQSJHIRaDbfaasSLwo9XSLIJDqAyo1WnVVHZonZJR1yBcCWQwpuxqkL6m2yF2gU4ba86/IKpdjbX6GhLYNLgaM0ERRWYdaC0osFNwePwZRRHQQFG0Qu2fYXHjw5P9AnP8xXV5WR/vxz6DvvIAYTri8OOf4+F7dyAdPvDyl6BukaHjzL/8zbJ58hzgZRNPAfMX2/Y9YffFlxu2O41sv0xzeYHvxGJk8SltyWhOmRJEdy8OeaXuJ221xu5mbX/wigplyucGPnrloXHT0rSO2N5iePkC4mflcI7ki8xRhOnycga4WKmRLoVIwxnlX3dk7j9SmRjfIKClJIlb8l6cWWmRBqhahClpIvA9o0+DjjFa1nRzJIDq03G8KjAFtCO6cVHpe+sm/QHdwGz9XlMvm9BH98TWi81zefw+ZI2f3f492ccLdH/1ZlB0QHnbzSKbQ97a+aNNM9Bt0clw8foc8fg83z9iup+1XrJ9+Su5f5uj2HcbLT7GLe9x+5U+zuP4i8+6UT37n/6JZrXj6ybdRWXDtlRf5/PPvIkJDKVc07UBJPSRH01e82m5cU0KD7iGVSPCKRhTyPGP6Q0LRdLZh4y7oO4URPZfnF1x/+S1uvvom/XLF/W+9w63nXqA/WXG1ecjpu7/Lww8/wcpClhZhVpiuheRBamLKGNWTtw69jHQ3blDCzMUHH9L3h9gbb9AcHfHkgw958bU3UIPh8uGHpHLFxel9dJSYxQHuInHzpbdAK5zfceeNL7NZf8720efE8ZywU+x2O2Z5RSssul0irSHlHdNm5PrRIU8vPkWXJVaPzKVjYYaaR0PU50lkxrNLZBJ0B0ukOaDYzOL4ZfrVktMPv8n0+Iw5JrxcVwD8YoFGEdWElYahvcnpk/scP/c6JWwpO08slwhpObz5PNvtiMwJQiSGiaw1xh5DyiiZubgKLE+eAzLz1XvkPCJnhwsT3cGKnJd0pm4w63XoEic8RVZc40J2uJAQzUDw8NzLr/Lss3cROrO5mtBNtWcdP3+P9OyM0QeG545Q6wsuzt6lbVu8n1kc3GK7mbHCkIzFKsuUHSE4hvakbtByJiNJcYubZtrlwOhO6duB7BO5lAr3l5JUNsjSIzIYPWBaxebyGV2bCUqh5ZIwa4yRZGHq0mIKLI8VYQ5MO1fVumKvQ4+WpV2x3VyCEWQrmMNcWb6iZTArBLU4hrjCzzU3i2kZhiU+1BQ7UjGP64oIlJF+uaL4XG9fFkP9diRRLZP72FEJV9gOLsctRveUXEUPRklmB43MqEVDQuDWOzplKKLB50TXK+YQ0aaDUodjkasMSKv97zTt8DEjlEa6yzr4LltUWZJy3czl4gh76YHSDeSGHAUlBbS1dHbA5RmZEz4pjFUgDTlEGivIAWw7EKNnToEQI11rkGLY6+drpjWLguxbhE+kqFBSkqdKBLKNqiQKI/A5VZKStqhSdc+T22KKoD9YYgRsdxM5g7KKlEecmzBKofTA7CKL/pAwzsw5YjuNSBJrFng/VnGUMUzrZ7R9Uzs0wWOUJERJuxhwk0MZi8xVaDBPHmMVMgn8NGOsZJ7PMVoSp4AqkSxaUqvAmnrobSzb9SnKZqxZQJII05MLyM5QQuUsoyTjuN3fnIIUGq1SLakSSSmxsC3FKGJwSC8QRtdtvW1IKVFEqeVVkTEJZucoOdMkSZSR4BNj2NH1lr4fmNxETjtk7ujbZV0M9haRPc5nUp5ZtgPRB5C1mF8kzOuJZGa0WLGbz2jIOD9im46m6+m6gcmNmKxIviDbjJeCMBcOFke47EjMzJs11ltS3tZCn16QhWaMIxFP3Hm6VY/WmuwKoWQWzYIkNNZ6iIZ/67/8Vd7/7PEPzpD71su3yt/5z38R7x3a9JTg98reSBGQkwdZKIQKG8+GmAtWCLJVhBAIZUerZeWbplxPMTmTQq4njKAQGkIslJzZuS2daas6UFgUnsYuiSViNfscr0CYFnJCQnWkI2q+xQyEbMgYUhJspy1WWFIIKAtds4c7F1GvBmMVCaisQFSvfSGgjKbpDyCBUS1FgUdAjEgJUiQIhSIqL9g2FWMFYND7zC74FMlFMIVd5cxSLTklZQoBVAPZg6xBeaRAoKHEmrPK399GQCx1G2GUZWKCp08Zrx6yWh2zG99Bbt8nj5v6u7r+L3Ly+heJ1uDDTJ4iWSpaMmH9Pc4f/m905Uvc/ov/Lu2hZb48o8me2V+SY2a4cRMhBFdPTlmfPsEuek7uvUK7ugZXF2zOn+KmK9z5MygTqxu3iF5hlocVuB4umC/XdMevoZTCLAwnX/hJzu7/Iad/9H9ycfoRrblBuzjan9tBy1Vl3WYBSpL320IAI1qUUgS3rdfdZKL0aFFZfkY05FzwKVJEQtGQo6+OdlnjDFJKUqTeTASFLI4oJaJvWF67x7Xnv0xpFnRGkrxk2p7j/RUH10/YnT7kk9//W9y69xoPP/o61179GZ7/oT/F5uKcs9M1z738KllEYijY5oDN+gHb++9z/ux9XnjjT1U+YnuLw+MX8GFiu37MwfW7lHxFEA3DwT2m3YQ7P0eYwid/8JuV7ysCLiZKarn1wou4UDC25WLzDOFmjG1JDlIq2AOJWpzw9NNvc3LzBi54zh8+xqoVkfrSSvM+E18CIUnuPnePORpuvPA6PmZso+l0i14MdEOPMDOP3v8aF49ntvMnTBtBG8GNEznvIHp013DrhS8jFiusbvjwG38T3Ryi6GjbVaWnOI/LEUTAiBktE2hdNxrJQCNQrseTaA6vo3aCR88+5wtvvkEaL3n6vXM6Y5nyU6QYoBvQpmb+T67d4NmD++jsufbKj3H9tdf53jt/yNmDb5MzLOwhV9s1LgROTq4jWkXeRoqW7NwlITiOVsdMHtrhkJA8KmzISlNI7C4uWZx0uO2mHmRV5PD66/hQiMFhdGa93tC2h6QcUbLedKUcKEJwcHyTaTfT2Fru8OEZbgaiQ/mEvXlAWl8Rwo4kD7G24enjxzWfaAcYGkZ3yZIOmweSkugGRr+m6zqkqcjDVklEtoQSSC5hm4ywS1a3Xubh138TqR3tQU/wBUrVDycv6Q47RgpZzHTdwPwsoFRDkTVvaVQhe0cSmiwdosrN8bsR7z3toiXLgsDi/Mw4b8hFcLK4gVEjE4ndbsfRcJ2w21WmetpgdE/wYFVPLo6Y6s2cEyNKSHpzhOmGanMyS4y21ThlDdM8ViHPLNEC2r5l8mtAEN2EbDtCSSij6eUKbTVXV4+ROZGEwTZDVaUXgVGVf0upTNXSOqTzSNvts7W6ShASSLkge0cUO6RSWGlILiBVsxeZzJWNuzpAlczormhMS8yJ7CJZFGyrkLqj0aZuqM1AnneIkpndRKZeBydVyMnWreZ+0BYlUfbbyERCSA1IlDSEnFFK1mhHoyFHVBI13pYdG7+mM4fEGRJQZEbnzLy9wkiN6VZV0NAk4hywqkGLgMsal9irtRvMYLjYbeiGFTJHhmWPcw7V9GjTEt0EZaKxLSJlQpGoriPHhJ8dYY4MQ0cIgewKWU71sBQihUAz9MQIQ9ezvTxD9wtSLEg0QoEoteRUkAhb44GNUAhZ0ERmt0U1lrh11dLaGISR5GCRBLre4NyamArRVSKFki1aW4xRzN6RdCVKSQSqSIo0WGtrjEKAEQqKwxOxskP6QiATS8CPEweLJXOs3y4fdpSUEQpSSiybKhpRQ2Unq9JXsoYsBAJ4R9sMXF2tUaZFiVqQxy5wuzXJzSAC66szuqHHiiV2KExXAdNoUlYcHLZocY2QA3F3QfAbWrOodA95SowLcJ7sCzHsyHnLOEZuHh8iFIS5MBdPSYl+sQDRYqXi8cVDTGMZ9nZRisKoFqMz8ybwS//Nr/HB/R+gIfftV26XX/+Pfx6tFYrmT8pfJUVs2zBPVZuay4wULWRBimCkomhJLoFcRrKrOC6Bqfm+XA0vhDqYhgS5FGoIlSqZsA2iRIwtCF/IYo8liRGpK1ZElEz2E6pZkEpGWUWgR9Az+sBmri8mKxRGy4oNa2zV/ClFFhkt9mgVM1BE3G8Hq8avbp1bSqjYMa+oAoqUsErTiKqWjHqPEMsFVSqPt+ZyU/095HrFLwrofX4XIQk5oZRCSomPvp4A54hQpl7pof7/31tJoNgPwgVRqH5trrDAg2/8Gio+oVMzPrYcvf6L6Dsvslmf0x0dUnKgBE+4/IT58XdQckeeJpZH/yzi+Db29jFpchw+d5edP0fRsDwcWJ+vObn3HERVmcWXFxBmdGsgjVw8+ID+6ARzeA9F5snH3+buC1/g4vPf5/LJE5y4zrUXX6Adjli98CZu95jTT7/G5oNv7s1x+5Z1UujmEJc1c5ZIpSp4XJWa5U6q0iYi2LahlEIsEaUz0c9I0YBdYJUiuLB3hjuEjkzTlu97JqVuqspVGoJPHD33Jmp1l+vXD7i6umK3u+Lk9h3G8wuEjKwOrvE7f/1XeOntH6XTgtlHzu+/Q3f8CquTJfroOYwRdIcndetEpm2WhDhzdfoO5ekFhy9/hccfvUuJj5mnR/T9W9z5wo/z6OOPuPb8l9hejqhO8OQ7v4Hwkls//E8j2fD0u98kXJ3RLE8QQ8fucmbol+ijW9jecrn+nJPjW5x//oS27XHuglQc43qm7W9z7aU3iJef8vCTrxO9QekGKQAZKhGkv8McPLbJxKstYe3pDge64TaqP6FddchwyMX4gJIdhy+8zLCwXH7+PaLf8OzJBxyaJefrmeDPOVo1tJ1BdQdcbUYECq1bICJVwzSOdL0CEapOOnpKTISiUE1L013jxbd+iKf373P18D5df0icN9iuh+WAXijyemI8D4xhQmoYTIP3EaMMu/OHeNfhtuesDhU7rrDdIa3uufvaV1lcO2C7ueCzP/gWdtnSdR0xFIyQSKNJwuNTrNuaMdEuBnysKlJMppEJESq72tiOeRqRssZEtBJYe4ifL9BNj9R633iHEhQKSdEN1k7EkLFqQZzWRKlrbGv9GUlqfIJYNIvlAU3X4vKMz1W1qrJE5VjflxSE0WgzUMKMm7cU5yuFJUmOX7pD3u4IfoLNRJ4vCclxPo9cv/08Almb50XgokNNE8FEUBZrDxGqJUdP2yxo+wOgUFAEHLMbUVnivWexWOFzQhVLyAFtFVmGCrqfNDFe4UShEUuym2kay279Kd49oRluIRmQBcbxAh82DIsXsa3BJ42xVWmuc1MV5I0m+ogUmSIySWbcLjEMC/zokVajjMEoRVGQqMx0VQRhSnQHDVpqwuhx21pOa5ZDHdBk7UAIIbgan2KBfjhiSplhdUz2mVYbUsyV0aoCcT/ECKG4desWGVlJB5sN67NH+Oh55as/hUqR+x98BzWmekBXheQzAsMcQGhD30pEgWJhDK5SAvIM0eBTQBsovr7/pRYENyN0oe8GvI/VJOgmdn5HowMZhS4GY1q06Ui+sFoMTN6RQ6IYi20N03ZNCRXvGVJBm0KKO4wwpF2NADSLBbmoqkaOGWU0m82GbnnItLnC6ILpV9h2QUwZqermWmSBKplUJLMPdF3V0QvVUoLDzaBERIhd1RU3K5QSjPNUGfGTo+8Mvui9JU5QYibFCd0OhFKVshlBIxtCSiij6rAPhOgQISGM3BOiWrQGbZcY3eFTjRp8f5MvckCZFhGr1riUumDqlSLKKpPqug7vZooXlDAhmkwaA1loSmvIMbNse+I8VxqSsXUbrasZ1eWKFyPGuujKmpyhkZqkC7lMlRwRJf1iyTzu0BSQis2uIvSiCBA9q7ZnFzzCF8xQxVB+DkjZEnYTp1eXLBctomtpMYT1JcVJJnZkNQEtjTigXXaVjNJqutKissUIy+V8RZY1DjTP1fSJNrSqqXzgwWJWHURN9gGjJP/mf/rf88HnT39whty3XrpR/ue/9guY0pBLLYtJqdFCUoIn54Bpu4psCRXaLHVT4wUiEUskhESzL6JZrQkpgaonE8neJoIh5foHvPdB1E2oyhQfaaxB5lQf/OhRsq24sRIwtmbHQgF0i1BHxJQZ50zIhXkeCTmgssTY2uCVWtAoSZwzhR5rFMZWeYBSCmVVvb4SZc9PrOSEKApE9umvWOMOUuFLIlPQWaNE3QqIEqrUgsqGVGqfxaUCqoXS1VmvBEJrSky1OEEh7ukJOWfM93FrxiJyQWgBoZC1xo1rwvYKcfYhMj0juG8jpsLy7p9nMofY63eQwyESQ/KOzemHxPlT9OaUMH1MUxYkcY98fJfl8tWKoVKJ+elndHRMqWW49wrSLLhxu+XxZ9/l5OYtHr37Nd74c38FuzwgT2dEFZg3M36eiHGLmANaTkzbMw5ufxHnM0J3ZNvTDj3+8hPW93+f3ZNP2G09kYmFXaHsAUq2CNHhSgHVUqyqZiYHeV9erPByV0/gpg6tEUWrLFAAUQ8WslDCTCoFct63WE1F37lAUbYWElQiELh283Wee+WrhNNzHnz8u+xOn3D++A/pj4+53HhuDi2RCTed0l5f8dKP/RWu37iLPjwhBYcgsluvq1HNTxzeehHimj/6e3+dflkPg61umc43eLlkdecNLp48YdEaUpix7YSRJ8xuYOoVb/7YT7JzH1NK4fGzLYOy9KtbXD65xGqNtAa/nbHLFW0DDz/9JnG9QYrCc69+lYcfvkvbCXx5gnQa5wLzFFkeSOYguRhnBluLjKKVtLJHOsmYzhDriO6WdF1HcJEgOm68+qPM0yW5zdw6uctuPGc3nTNfPAYjUAjC+gqpNc4ZDm7eYXP2hBwD0tZDMikiVCHEHbfv/AgHt2/w2be+iW4alrfe5MHH79CuMt1iSdco5qdnrB9/k7P1Kd5d4FLH9aM3aPtb1X7YUNFqB4csDhZsN58xnV3QxkPKIrHRnmFecrUtWKkooqHpC2FeE90VbbNE6A7vt6iiaBcr3LwjBYdpl6xPr1gcZMZNlbN0q2uYxTGH165zefGQWDzKdrTGcrU55cs//Zc4/fRjPvnw6xysbuB3E0ILxrMrdN+TfabrG2KcYS7ECJ1V9MsV6/AY4QqqO2D2jqY/IqbEcHSd9dNHQKJJEdUkYpgJaSZdCtIgufvDP8Vy0IhRIkzHx9/4e8TpCcnpyu1sLbZbItoBIRQKg0+O4jeoLrEetzRSY4fbCNMhQ7X8ufEC3USKMMRZ4mdHf7xACYOSHSFmhLJ1SBACaRtymCg+EnNheXBSYw3U91sMhdE9RSlBLzRZtBQrycUgpcFIVb+lIuFSHcCkqvE2pCCngm0tPs9QqhFSysrSFUXsCT+QiAi5v53zCUSqPNOU0NLQNA1SK5KQpDhTQrV52qav9soU8N4hTEOk1Oy97aFItDakFClWEPxIyYZ5u6FvB1IKGAuQISluvPpjTKWl+MdsnzxCH665fu2HYMo8e/C4Mud9RloNOKKwaGtQRaC1xkdX+ya2kIMgOIdUoZbqXKiDpAlsN5eY9gDddmhRaT9ZyEpuKBqFZLO9pG8HKIIw7xi3jzi5/SrOJdpW4/xEQaM7BT6So0MUCCEhdRVJSNvhxx2t6dk5T5gu6fse3R4Q9ptw22pEigjVEEJdGnW2I8RKeEgpkVEYURApkix16N8vglJKoCtOSwDGWtKm5pKFgpwcJUGWNc4XfSRSv6+FgJGGpDS97TBIRrdBKUik+o1RuvJvFZimHlxyrhG4kBxhTGiVkVQhjTEdwUPAV0ZXkcgMbrxC2YIUhXmKKN3g9kzfg8M9p18apLL1FhaHz5lF2+LdRN8eMW5GrKxzkR6aqrBX7A9dcr9yD4zbCdXAbjqrfRSlkFmQ3Y6YCskrFscrUiowjyQBZhHJucPqFfg6s8zTGp9GpASKrhG6pscOC5TOXK0DQ2lASUIKCBkQBkIILNolRbRYa8lU2+DkZnIKTNMOlWf+vV/+TT588AOUyX3rhWvlN/7qX0bTY4ylSMsc5wqkLpHW7E+WWdfCjjGktLdxUU+t1R1eM4LkgjayPuQ5ovbavGw0KWcm59GiWmF8BpVqtlJncMqjqANPToKYDJKC0RltDSEnTLOqSlxZW9yb3QXe1Wxuaw3aFWZfmahIi+KMttM0i2OWw8uYrrrLpdHkHPDCV7ZjUZALpVTT2ffbqiklVDLkPT6qZIVEkVJACVEh00LiQ8KYrnIg92KNOQWk0JADqciq/C2RUnL1rguBFJqUw5+oTkWpTuoiIEwbwv2vkXcf0C3vcnXx2wS3wjS3OLj+s5RhwLPg4PYdnCiQAgZJ8iNFXzC98zcoOWLMa0zL5zk6fo5iejIJoXzFh7iCTpns1lw8i5y8dpvl6oDLRx9z+NLPsH36hP7mSwx3Ok4/+YxWKhY3T9itL+kODiBX9zpBcOONL5PyyJP3/gi0Yto8gflj5qAR8oqn3/2HWH3MUX9c80zmGLpDgiiYxtY2vKDi5HRGFgNSYIzBu4TUCiMM2deSitChbtJlBcSTC4oCrcG7QtO2jNOOnGtpBMBPa+L4TUqeGcw1dpsNTX+AGxPHxy+y256jyUx+y5/5hV/m8cVjlE8M12+A1nVDM55D8LTXX2K8eMK4fQbjI1zqaG0ijlvWjz8jS8u43aBQHN+4g15c485bP8Inf/+32J1tSWpmOHyT5XOvI3LSckMAACAASURBVPqWxdECnSauTp9y6T5GRk3TnLA8XnHx4DG79SWbZx9zdOcmbXPA+nLDwclNPvvwPikptLjE6G2Nu5RMMrVUkfIVQh7Qt0c4l+iaHr3oufPGy0zPJuZxIhKxy4HTb33AtdfeZE6G7M6Y1s9IbgQ50rQDOXgqJw92Y+DOS19mfXbJ4VHHxf0PmXcb5GKFS2O9tQhbJB2L5jqjH7l17w1CjKhrh4zPPsASibsdDz/5hyyvLRDmBs6d0pgX8WHi5S/+aaIyHL/0KsUl3NYhjYL0GP/kM053ied/6M/w5J3fxoVEazWff/A1fL6gMScEX0jZodWMLDcwdAilULYKWZRQlJKRyiJCYraRzvZsLi6wYmA+fI6v/PTPokVi3lXOrjDHlBSJ6zM24RSxPSeZlqPDm5x+/j67eY1ettw4vMezjz5iffkEQ0E2HpHqe9AeLDBtx24EKwdiqQWlaTdju8LsLyE6Vstjdtstelhgjr7Ea2/+GPc/+i6tOqdtJNvLU2xZ8/TZZ8i2gWmFUA0xahCWXngmP9E3PaFkfFyD6evfTNHIIpHZ4/IMSRIECBJSC2SG7bylaSzTLrCyd3HzBt2ZSmZQilTi3oZUb+3IgpgK6EzXrkizR0lJlGUfLapcdSVrtKiYAJS6xUPU7KrtK5s3BwQWoSRFUT/WEsZxojWWQiSqQvKFVhlsU4u/uh1Io8flwMn1a1yeX9A1ghTYD+ltpeQYQ5xGCpKma4ne1Y++1oSgEClRdEZoweQmjobrzFdbSqsoea7iF9FTrCaNa6bgufv2T3Hvztt8+3/9VRwRmQS2EWQRyNIwT4HF0TV8ijRNS2sMZS+vKSkS/YRGMPo1RiUmH+jUQBaVVf99vrD3M0IoKIZWatyc6PsFLk1o1dbDQJiQcmYuEWjIKaGUwLQHJKHorSWmKrXIuZAy9MuO4DxKSJTc0xiEZZ53e/KQwfsZlSPKVItc2y8pQAweqMuelBIhJ2QBkTMpTzRmQUqBmBO9afBKst1e7uORYKXal77VPo5Rlb6YTNsOjLuZtlvWpQUJIQo6aUxjSUKRpYDoKWImR4MyLVLWUnGNBQZKgOZ4qAVJldhuNzR2hdEtzjm2V89Al8rOdqlGFOWI0gYTFHME1R+SySwWlZDhQiELTQozJUeKMRAd/bInzqClBm2qyjxlDInkrwjzVGOYVxuQE56WEhXtQYdQI9udRytLIwVlDoQCPgaM6WiGnhB3eL/G2gNMGVBCV110cuz8jJ93HA7LepOqNVI0TCGidOSwO2LnPNNmS9NCNpmcLavmkAh4n5ESEo7kC0I4REmkPPJL/9Xf/sdbPBNC/DvALwIC+G9LKf+1EOIY+DXgReBT4J8vpVyIWp/8FeDngBH4l0opf/iP+v+//dKN8pt/9ecRqsWHHVZnMi1hnsgy0nUdxTmkaCsgWGukMCBqSN0KhTA93o9QKicWVSg+Ymw9Qaumx6U9rUFVyYMIVYVYkkKgEAqQtcwmqS9PtEFmUU9kNkMxCCXp2gNKapjdmo0/ZX0JLiRUFvSNZjADXknUkBmwoBuKWNC2LbFkBMD+j0KYDNEhtamnS7VAUKUVWXgMmuLLXkNccWeyaioIOVBkPfWlIuvDYFKNNEhTwWQpIxWUKFF6ryPWpobuS7WdxVIRJ4Rc0WMkrN9x+vhj+vyY+fHfYrs9osgLtB54/gs/zxhbupMTSrtknD0lCrKPMK2Rac1m8sjzb6CkwCpD9hZx/afpXnsVnx3RjzS2I0dP166IGWTaIvwON+0Yzyr+ZDd9lxiOGO6+zOGNGxTZsd5uuPX869C07B5+hHvyHmq4zebinFs//FXmqytC2NBozfrZ5xy99hXcs3eYH36Tg9tf4OrpKTIHaFYkaSnKsDq+ybSeCCnvxRzVUGSbjjglhFak4GqxLFZ8S8quZq+FqjzhaQYRSUIyj/urxhRJ3tENLZKJLDzaXEM3BrHbUmRBNIbp8gnBSTpdmOPEPMPtF36c0vT0107oT+6wvHaDy2ePsG1B+cSDzx9x/cW3WBxonn7y/7L7zjfxVqIbi3VbruZqlrG2JUwZ7xcY06CYSHHLZXyEsT3JLenbW7T2iOboCLsc2MXM8699iX7R8vm3/gGfv/8O7dEKffWAxZ3nePToOzAb7HDAYnWX07PP0CSG7piQrohaoaVC93e4+/wLfPDe7zBvNbeu38YeLSnqiOdf/hLvfu1/4fJ7n9JZQ9hc0d24QyAjdM2G55g4HI6IMRF8ImRPaxtGH7DDUcXjhQZtdth5ZPIbrt1+nY2bMcZw7+7znD56n/E0gQ6IMnOVt7z1k3+JD/+fv4tRnhQs2AbMQAoR3SoCESOPwEf04g6rwzdQTWZYLtmeXXF4dEK3WrGdHvCdf/AbiHlDwKFkZFgcQnMIxVGCqwOCXuxZ1RpMLYRKZXCTRw+2Hi7HwJ0v/RNodYRcJkSpfxvzWAtYu41jd/6AmC64OlvTrB/Qf/FHEeOMXd2FJDl6/hZpGnn63geEFBlOqoABoDmyyOw4ffYY5Ucunn4H7wopHmFti9GWtj1muHvM3Rd+HIFDNQMheNZXG1IMJBpaWqxYEkLAtgplJLbNXJ5+wiff+Lts4pq+v8fy8Dar3vL4848Z+iPmuCWVLUUKkhPoFubgWNlrzPNI17fIrOoCgILW1e44pYDUDcx18I2VLMwYLlgc3yTEhoRjujrlYHGAjzVmgas3eVJKYq63XUJIYqofzlyqCEg1bY0coOrVbgq0vSbOEzkZoCrSsyg0stQbLzRSCmIpGN0gYU+xqQl/N9fn7+zqCa2KHLRLggekJZVq3NtsLjApgtas+h5p5H5o74mUusTJhXmu3NxxrCSCNI8sjga2Vw8R0tAsD7l58yWW119mOH6Z80cf8+zhuzx67w9odI8dGkryFNkx9CtiCPiwwYUZcj3EC1szqQdDT4qRlEdC8Yw7h5ED/WIgTTNRBOawY9EPaDOQokRTf6c11tcSXURlQdEC20tCLoi2ZnyFUECPFi1h3iBUQQkFum6zpa1KeiOgCFMlO1lWsoZqyKXi2ub5kpwc/XBAjB6Kqu9iqQhhxu5tkyE4NC0hb+hkW4kLRhO8o207coIiakwhkskh11uIkglC0MpqDp3HHYj9oqvtyTljDex2U438jI5Ff1Q34GGHFB3sD1J4h7YtQUZ0qbGGHANGQRG6DpPa0mvFGCYur85YLRekiwndS4rONG2PEpaAxMWK+ZzPv4dVmqgColkiQ6K1DRvnsEbQqobtxRo7DGx3O1aNwU8z43ai6zVj3CBLjerFNOMmi+wMQ2c4O38KClSBZT9QSoK+oGRPHDVITb9oSLsr1qFgyj4+oiCFTNcuceESlQVKaJKRNMaSUaQ8oTxswszQ9biwJcpILJZOL1BNBymxXV+xWlhMM+D8BoQhJ8cv/fKv89E/rk2uEOIt4H8Cvgp44LeAfx34V4HzUsp/JoT4D4GjUsp/IIT4OeDf3g+5PwH8SinlJ/5R/8aXXr5dfuM/+hfqtUGpQ1b0I1K3CE3NiwiJ1U3lA9qG6PaN9eKIxZOLqoOhKIRSN4NC1K2kUooQEsLUk4xQEqsNYXb1ASyFuD+9AZSi9v+NIwvqsJkzytj6QpACZQ8Ipf68Qc3sXET6ghJ1E5h9Jsl9MSwXfC40pqVoUzOgRVJSBiOQlSRSX8xKUYok5Up1ECmQU6jsOlkJDymlPdhbog2k5MlFYm2PmyMI9ydb2bLPICsEiYyysprhBIhY9bPo/WlXtQQ3Yq1FSV0NPeExu/d+i3HzDolII14nLXu8eY3b994mFk3IhTStMY3HbZ7A6HCToGtXNPLbjGGkiwvgEHnnS4gbr1QzSi4oWcsM3teyYYMki7THnyVMqxh3H+B2W0RquLq8YrG6hSiFpj/ALA+Y159xdv+3MWKBLIc892f/DVYnhT/69f+B5rk7XL/3FovbryLTE/L6GWMQbC8+4OmDR3z5z/4CT8/uc2gtyc2EqwuELoQQMLYnhBoTCVuPzwkXZwZbMWNCZvJYWaNFNORcs1vFObxU2INjpKhaaRSUMJH8jjCNtbBWQr32UZYowchEcWmvpXZsN47s4Sv/3L9MTpLteqa/fpvFsuFoWPK1v/0/8pWf+Ys8fbbFT1d0i8D6yXvMG4eMDxBCsn70GY09oGwiRQlK15JLV+kQRqKNIGSDaRrcvKOMjiQVoQRWqwU7t+Jw1aK1wG/PCVah1CGlucEbP/KjPPjOb6NE5MnnHyGSBSW5+8ZrbB5+Ts6Jy81Mu7pHETvuvPJFOp149/f+b7pu4Go78r2n7/LGy2/y+MHvUvIpJ3f+KZSVuGlDsgLbdrTDAYuhYxcm8hy5ff0tusMWpQun3/sMZV9BFEmYnhC3I8cvvsT3Pn3ItH7w/7V35rGWZddd/tY+4x3eWHNVVw92T+m03bYxnmJQiCPHJiFGKIBRBI6xlD+wCEFIgGWhCIkhyIgAEgSsGGIixwGcQExEAMeYIJA8tIljt7vd7uruqq6hq9583x3OtPde/LFPNZXGDlXtqnqksj/p6N1z7q5655199rnr7r3W78f41GmO3fdWjpw8zvzKs2jTMZnsknrHpTNXyHOLb69gEfxwSKoZ4jyVnZGIYFxJ28FgaRy0S5MEdSniWtq6A+OCo11zGbE1xXCENTkZGd4UoXBOq5ASNFwlLwckLiEtDfgSNMNkOelAwoy170iPPMhrX/82djb3aKYNh0+c5Kkv/CbdfJuqm7F67CQPv+372XvmKS69cJ4sy5hun2ft+COMVpaYzTbYn2yTNBV51tF0gl+8COk6VlOOnHozLZ4jp04gogyWh1z+xm+SZgMG6QDN1rl87hnSvESs4FfXGS+t0bUzyixntJSz8dTj7O+dD370XYOxjlYPsXL0ENJs4EpDVe8zKA6zOXmR9ZU12tqwtj5isrvF+tF72NrZJHWObrqHyxOKfEiepEgr+GZGKw0+d8wWDWjKioyo6MhNSZaC5AkqNU01RKySD+f43ijBKYAjzYaImlC47C2DMqGqW0xeQmspyiGL+jxNS18ALJhyiWzQUOiYzYsTlldXcAJFUQS7YsA34dlFmlAvZuSZYbY/oyiHdK5BxOAF6rpivLqObeeUmZL4IgTveVBb0dbSzCYkRTAYujpTmqYlLZ7EZdRdS0JK1+2RZCl55hkODrGYzTn+8OtYu/t+6p2O2fkNtna3KZYGTK5cgsUusrrNMBuxqCzz/QnWdxxbuQuPx2SGtuv16IGkHONNxtAEDWrb1mTFCDFJSAHQDm26UCszLGlUcIsaUYNJiqCGZBd4XQTbdJ9R5Dld16KFCWlUZMwXNYOVFWzlg+W80RDQYtCipLXzoNRCzmCwxrydge1IipJ8sMp8EdJ/kqSXFzUSzJHE4EmRJMXbjjxJMSbH0dHVHSaxZCq0zpKmCVUzw5BgsoI8Dc5biGI0xXUW19ShbqbIEONI8oTFpGI+nbC6dIhquk/V7JGbOZqtM15aR8Xhs5RxsUJrG5wKZZbTNhXWWpwJzqN+1pBmGYojHSyhIni1LA0KqqrB2zltV2M0xdY1znf4TmntBDNIsU4ZDYaIGTAo1qHISTMHnTCf7OFxNLZl6EvEdmg6pzMJRT5iMdsLpliFh05ZGqwzbfdQ11EWyxjJKHIJn02UIZffLkCVTlvqtiXLSnJjqOqGcrhKU+0x359hjOJ8KPAbjkcY8dSNpSCklzb1PsVoSC4F1noaFoizWPWYJMMkJbYNBZZZLphGsG7BZF4zGIVieiOOn/zIJzlzs3JyReRPAu9S1Q/0+38DaIAPAN+rqi+KyAngv6nqQyLyz/vXn+zbP3213bf7HY+9+qT+h7/1fjrbkg1K6mafNM1R1yBkdLahSIc0rZIaQfFkJDgB59uwbJ+Hbw9iPM57hlmJdWEJ3nWOPCuDDi0ONQneNmRmQGsbsizBOxuWpdMS2zq8h2E2oO4WJJnBakuWjPGuL4ozRZjVNSk+UWrX4VqHegn5xHmBZCVi8l7Y25CnCdYJ1rWQDEkFOu9I6aVlgA4H9M4yXoEgKabOh5nezmKSkAflnCNB8c5iTBBwlmxA5xpMr/F3VVsPwCJoCol4TBDQBS9I2t8D1vdTEUrXtexvnWOUTZk+/xvMZwNWjtcsdnOW73o94/WH6cwSZVlQTc6x++zztBjSpEPcJZyf4W3D8vAUycppzNoy+XgdXAnZGmYYZq29M33uXjBXsFVfDGhSlJY8cVSdp8yXwDRoC+PjJ1lMNllsPE9arpAPR6SFoZvtMtn+PL4aMWtXWVlfIu1mcPztrI0PcfSRB7n4/Bc5/ao/yMbZ/8koX2ZzMmc4LJhe2WD1xBrNld8mGxxhsTtDbUJHGHxNvUCBfLhENVtgsgQxwSs92E/3uW1uEZz61FNIQtv0Odgmw7qKIhPaxRycwySeul7gWcEnnsR3wQQkzRFxFGmBaxvsykM88ta3s787o1heot7fYrEZArT9nQ0yWWJw/BD7F8+gbsF8uoPtrmCtZTU5hjl0AqM1i60NinSJedfR9d/4KQykI/LBKmuHDvPc01/AVw2jlVXqqmO0cpLZdMqJB+4LRWdVzXy/48jJu6n9PovNXQ7f8xBrR9bx2pCI0FrP9sWnw/MjWcNZcKki0x3a6S5aVcgg59iDb6RabDLbPUuaL5OVK8ym29x98gFWj93DrK742ue+xPE/8CjddMGhu47QNS2Lc5fZ2XaM1sdkywXDDAajAemg4vLFs8zPbZCvrnLXfa9D8lX2ts9iF5a9F19g//I5BmWHyVbx7TaVTsjLFdJ0melsRuohH2XkJsGUJX46p7ENajpkMAabkucDXGdfWhJFcpAJaydeTbUzYdGGPNyCkEPqko58MGLRhGXUREPeircFnVYcOnY32bik9Y7V4ZgnH/8Sayt3kSyPWZx9muHhI+RDoaodaT5gMb1CNc9YHuZ4G+yiWzcj1zFeQI0DFZLeFKY0YLUOOuGaIkXCaHCUuoPO7eN8jal2GC+v08wapN1FG0c2TJnXghQZWgzIkwKTrlPXntZZSmkwZkFbV4xGJzBpRuMXZITAoulapvsTRoOMYjQGY/HWk/dfHk02pG1nLC2XSOuZzxuK3rQgKzPaxuJyh7eKSfJQQKsl4+V1quk+tt3mwe9+jPNnf5vGDkjzMe3sIru7uxSjZdKspKs95XBAkaf4JthIl+srTOcVeRkcNO9+9M2MygG7e5exjePY8Vexd8VgFzvsbz1F2zZ4Orq2ZlCu4LxgvKO2HZ0aUuPIEsd4fJTJdJ8kK3CtYzhKmc9DUaDTOZiclJwiz4PkVlsxKIbkRunUYLsGJBgE5J0jL1eYVTUnHnuUlbUcyVZI85LEG848+01s5ciKIYdXTnDhyd9isdhhmAPJFF9DZ1tc0gd1ZqlXKKrJKChHQ5JkQN1UoahRlLJcxzCgtlPKtAi1GaajFcE2lmqyy8rqGFHY2DrPidMPYfualCLNScQESTJjSXLB1crRo6eZ7W4xnc+QlYL19eNU21tsXblAUWRk6YiqqkgSG2TnKsVnliwdkEqGqxo6XWCSjHy0RucUkRTvHXkxfCm/VkzQjq/rGrUWaxVjO9KsxCG0jSUtBN90ONuS5TBcHWN9kP1UVfIsOJQ1tiI1CWKD/nBaFrQumJn4zqCdxaRCqg7rO1ozIvEtzf4uTdeSJBmj8RKSC+oENH2piFkkSJoak9LU9qUUNlWLNxbjU9AOQ0g31CQnl5wsFRpqnNpgo64ddj7HJBpWlDthMF7FaYI4y3g8xGtHqsKimjGttvBJAc6wPFqlGBRsbVwCaVgaLqOJwXYLmkXXy8ZZZtUe66OTSJKH1UcpQ1qGT5DM4mrb21BPGYzGdF3DoqsZDweIUfZ2Z+RJRiqCUYE0oa32MMZi24ThaBWbVhRZjuoI23nmbo5kLYlV7MKSieuNtQZUXfhSNh5m/MW/+3Geu7h104Lc7wJ+FXgrUAGfBR4H/qyqrvZtBNhV1VUR+TXgp1X1f/TvfRb4a6r6+Lf7Ha999Un99b/9fnza4FpwafAWT3qHsa7tAz+fhhtBCZaZRvslJI+oYmxCS7CDU2/oqorhYAQ+RP+q4UOm7brenjBIRHWuDQLczvcFXR2GgkEaKh0bv0+SpkGGpRgFabFkgHNB265TT5oXqBds6ymKVTrvcMbSqiHPcwweIx2pySDJw7dzkyGdI80MXiVogeYpvrU4wCSCtXUISDHBTjJLQ36x9xhJMCLBzMKn+NTQ2mD8ECTJ0uCP7bowM43H5FmfrG9CAVqnIZ+NBOuDWkPrO/LE4+oJ2xe/QbP7q2TdYabtJkvDkywdegtqjlMuHWImC7Sb0G69wO7GV+n0Um8HHCo3D5dvZv30q8mOrlIM74EkRz04BNuFEZ5kQSHDaIPRImgZW9ACZNJw5DWvYbG1jRQZ7WyDYvko9eYmyTg82NI0p5o0mMSRlCnd3iWcseFhUjmywRGq6YQie5j1Rx5AE8vSsaNMLj1DYhq6pmZx5Tz3f88fY2/zGartHab7LzJeup989RjTy2eoJ88gXug6h+9y8qUxXbeBdwlpCnVtQDPIfS9E70m0wcgI1Yym6Rgsj5nublAKiHbY4TjoKNcWJzl0CxwesiToJM/ntPuWV/2RH+T5rz9JRs3ysQdZO32Ey998klP33M3Gc0/g6MiLEdXudtDRLSyJdzg3YW34IIt5TTPfYLi2xOTCBqPT91DIEs1il1l1KUhZ+SFFscbyyaMcPnyUC2e/TqIj5l1DYQYYsUxmOwzyZbq6Iy9L9u0Or3nrnyYbFSxm+xw5+QBJXtDsTNnZvsC5r3yWY/c+xsLuMhotM7lwEfEOEotqRr3YZXmwyvGHHuHc018BIM9zqrnHiWD3dlg+usbSkdOYrGT57vuot84zGBVUzQ4bF75IszOmLIfUiwYlIRuNGGZDBkeOok3DcO045XBIvX+JF5/8EvO9DucrGE5ZHR+hTTwpS/iq5uhDj2GnNZvPPIGzM+b2MsdXT7NRnydPj7GYd5y67/XU1Zwkz+jcPonJWBqdJC3uZu2eZbKiZHP3BRYvPImbG2R1SD25wsKCNAtEUkbjnG7qWT50immzDY1QVznZSHCu4fCRdZqNitneJcqlDDMah+IVk2HbORbD4dOPsHzyNGWecvmpr7F35RzlaB0wNJMdBstjKlfh/YJT3/UmVteOcWXjKfaeu0K9exlvFcnH5MMRmmXkYnFpSZEbqkVD7i1JUeKRYLKgPsgkDlbITI6vW9Is5IuiJcYRcmHxIf3GGIxX0kywXUXTLGi8Zak8Qprm/QRFjVtYEjrSUYq1HTILFqweTzlaZ/Da0zSX91lsvsCR06fZvLBBPVMOnTzM9vY5RPdxXU7CAFOEgjLjHX4gCCWihr3NF0mWhpRjzwOPvZNsfG/IW5xZtq6cJfeW3d0tXv2GtzHfusjl554nLQua3RcpSphXM5I8FNFpV7DoqrCChpKmJd6FWbo8WyYrl/BdyBG23oDdxrYVCQWVb2i7KcvZmNoTCszwiFcG6yssHz3EYLwOLshfXvraE6ydPMberrI0vAdvHVk+YmPrSXRxjsEwoZ3N8G3LxG5Q+ZLV1XXWlhPmtVAMB8F1SsZkpFg7J00FJxm29vi+EIrEkpgCk46QLiEZBcWexAcVDJ/mGHFY25A0LR5Ha6cUxRLogM45MgHrhLIcUbsdZGA4evoB8nzIpbNfZWXtFKmW7Dx/CZWKJINqsQ8YRqOlsGpJFpSHOlh0DrWKwaJphbWOQT4mH62hSYq04BPAKYtqSpkUQeO22weTkBJmUBujNF3vrGegSBSxHa6rsW0SPldHGaYNBciiHUaCDJdtQHtXS5c6kn5VmMaE62L3g6QbCYqhbWYkEiatOjfh2MP3Y6aGrcubzPdrVtbHGITKVpTlgMYmJJphxFG3NUY7mja4vA4TCV8WfBKMqGghA5OFtBp8sGZOkjBZpB46W5MMCmzVUfgWEgMa8qeNCfU5o3HO/mxGRhasgtOWRa0U+RL1fAOAk/e+liSFi2e/jGiJZgVpnuOcJ9UMnKCJkJdBccNXkzA7rTWmhKoOCv2D4XqwApYKP++Y1xXlMNxLziY0tWW0mlMvgjtemmS0rsaLp6t3MZqTJx5xCZQrvXV1BnbOT3zkE5w5f+Wm5uR+APgLwBz4OmEm98euBrl9m11VXbveIFdEfpyQ8gDwKPDE9Zxw5JZxGNg66JP4fU7sg4MlXv+DJ/bBwRP74GCJ1///zT2qeuR6GqbX00hVPwZ8DEBE/g5wAbgiIieuSVfY6JtfBE5f88/v6o+9/P/8KPDR/v98XFXfeD3nErk1xD44eGIfHCzx+h88sQ8OntgHB0u8/jcXcz2NRORo//Nu4E8Avwh8Gnhf3+R9hJQG+uN/TgJvASa/Wz5uJBKJRCKRSCRys7mumVzgl0XkENABH1TVPRH5aeDf9KkM54A/1bf9jwRlhTMECbH33+RzjkQikUgkEolEfleuN13hD32LY9vAO77FcQU+eIPn8dEbbB+5+cQ+OHhiHxws8fofPLEPDp7YBwdLvP43kf8vHM8ikUgkEolEIpGbyXXl5EYikUgkEolEIr+XOPAgV0TeJSJPi8iZ3jktcpMRkdMi8jkReVJEvt7bNCMi6yLyGRF5pv+51h8XEfnHfZ98VUTecLB/wZ2DiCQi8lu91B4icp+IfKG/1v9aJIhDi0jR75/p37/3IM/7TkFEVkXkUyLyDRF5SkTeGsfB7UNE/nL/DHpCRD4pImUcA7cWEfkXIrIhIk9cc+yG73kReV/f/hkRed+3+l2Rb8236YOP9M+hr4rIvxORayVZP9T3wdMi8gPXHI/x0g1yoEGuBBPrfwK8G3gE+DMi8shBntMdigX+iqo+ArwF+GB/nf868FlVfYBg8nF10LwbeKDffhz4g21rdwAABClJREFU2dt/yncsfwl46pr9vwf8jKreD+wSnATpf+72x3+mbxf5zvlHwH9S1YeBxwh9EcfBbUBETgE/AbxRVR8FEuC9xDFwq/l54F0vO3ZD97yIrAM/BbwZeBPwU1cD48h18fP8333wGeBRVX0t8E3gQwD9Z/N7ge/u/80/7SdHYrz0Cjjomdw3AWdU9TlVbYFfAt5zwOd0x6GqL6rq/+pfTwkf7KcI1/rjfbOPA3+8f/0e4F9p4PPAqgQt5Mh3gIjcBfwg8HP9vgDfB3yqb/LyPrjaN58C3tG3j7xCRGQF+MP0mt+q2qrqHnEc3E5SYCAiKTAEXiSOgVuKqv53YOdlh2/0nv8B4DOquqOqu4QA7eVBW+Tb8K36QFX/i6rafvfzBE8BCH3wS6raqOrzBKWqNxHjpVfEQQe5p4Dz1+xf6I9FbhH9kt/rgS8Ax67RML4MHOtfx365NfxD4K8CvWM5h4C9ax50117nl/qgf3/St4+8cu4DNoF/2aeM/JyIjIjj4LagqheBvw+8QAhuJ8CXiWPgILjRez6OhVvLnwd+vX8d++AmctBBbuQ2IiJj4JeBn1TV/Wvf66XfotTGLUJEfgjYUNUvH/S5/D4mBd4A/Kyqvp5gU/478triOLh19Mvb7yF82TgJjIizgQdOvOcPFhH5MCGl8BMHfS53Igcd5F6XBXDkO0dEMkKA+wlV/ZX+8JWry6/yCqyZIzfE9wA/LCJnCctM30fID13tl27hd17nl/qgf38F2L6dJ3wHcgG4oKpf6Pc/RQh64zi4PXw/8LyqbqpqB/wKYVzEMXD7udF7Po6FW4CI/BjwQ8CP6v/Rc419cBM56CD3S8ADfXVtTki2/vQBn9MdR5/H9jHgKVX9B9e8Fa2ZbxOq+iFVvUtV7yXc5/9VVX8U+BzwI32zl/fB1b75kb59nG35DlDVy8B5EXmoP/QO4EniOLhdvAC8RUSG/TPp6vWPY+D2c6P3/H8G3ikia/2M/Dv7Y5FXiIi8i5C+9sOqurjmrU8D7+3VRe4jFAF+kRgvvTJU9UA3ggXwN4FngQ8f9PnciRvwdsJy1FeBr/TbHyXkt30WeAb4DWC9by+EKs5nga8RqqEP/O+4Uzbge4Ff61+/ivAAOwP8W6Doj5f9/pn+/Vcd9HnfCRvwOuDxfiz8e2AtjoPbev3/JvAN4AngF4AijoFbfs0/SciB7girGR94Jfc8IW/0TL+9/6D/rt9L27fpgzOEHNurn8n/7Jr2H+774Gng3dccj/HSDW7R8SwSiUQikUgkcsdx0OkKkUgkEolEIpHITScGuZFIJBKJRCKRO44Y5EYikUgkEolE7jhikBuJRCKRSCQSueOIQW4kEolEIpFI5I4jBrmRSCQSiUQikTuOGORGIpFIJBKJRO44YpAbiUQikUgkErnj+N/dPnIA1qfgNAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for image_path in TEST_IMAGE_PATHS:\n",
+    "  image = Image.open(image_path)\n",
+    "  # the array based representation of the image will be used later in order to prepare the\n",
+    "  # result image with boxes and labels on it.\n",
+    "  image_np = load_image_into_numpy_array(image)\n",
+    "  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
+    "  image_np_expanded = np.expand_dims(image_np, axis=0)\n",
+    "  # Actual detection.\n",
+    "  output_dict = run_inference_for_single_image(image_np, detection_graph)\n",
+    "  # Visualization of the results of a detection.\n",
+    "  vis_util.visualize_boxes_and_labels_on_image_array(\n",
+    "      image_np,\n",
+    "      output_dict['detection_boxes'],\n",
+    "      output_dict['detection_classes'],\n",
+    "      output_dict['detection_scores'],\n",
+    "      category_index,\n",
+    "      instance_masks=output_dict.get('detection_masks'),\n",
+    "      use_normalized_coordinates=True,\n",
+    "      line_thickness=8)\n",
+    "  plt.figure(figsize=IMAGE_SIZE)\n",
+    "  plt.imshow(image_np)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's run this using our webcam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "autoexec": {
+      "startup": false,
+      "wait_interval": 0
+     }
+    },
+    "colab_type": "code",
+    "id": "LQSEnEsPWKMj"
+   },
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "# Using OpenCV to initialize the webcam\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "with detection_graph.as_default():\n",
+    "    config = tf.ConfigProto(log_device_placement=False)\n",
+    "    config.gpu_options.allow_growth = False\n",
+    "    with tf.Session(graph=detection_graph, config=config) as sess:\n",
+    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
+    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
+    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
+    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
+    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
+    "\n",
+    "        while True:\n",
+    "            ret, image_np = cap.read()\n",
+    "            image_np_expanded = np.expand_dims(image_np, axis=0)\n",
+    "\n",
+    "            (boxes, scores, classes, num) = sess.run(\n",
+    "              [detection_boxes, detection_scores, detection_classes, num_detections],\n",
+    "              feed_dict={image_tensor: image_np_expanded})\n",
+    "\n",
+    "            vis_util.visualize_boxes_and_labels_on_image_array(\n",
+    "                image_np,\n",
+    "                np.squeeze(boxes),\n",
+    "                np.squeeze(classes).astype(np.int32),\n",
+    "                np.squeeze(scores),\n",
+    "                category_index,\n",
+    "                use_normalized_coordinates=True,\n",
+    "                line_thickness=10)\n",
+    "            \n",
+    "            cv2.imshow('MobileNet SSD - Object Detection', image_np)\n",
+    "            if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "                break\n",
+    "            \n",
+    "# Release camera and close windows\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let's open a video and try it out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "# Using OpenCV to initialize the webcam\n",
+    "cap = cv2.VideoCapture('dashcam2.mp4')\n",
+    "\n",
+    "with detection_graph.as_default():\n",
+    "    with tf.Session(graph=detection_graph, config=config) as sess:\n",
+    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
+    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
+    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
+    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
+    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
+    "\n",
+    "        while cap.isOpened():\n",
+    "            ret, image_np = cap.read()\n",
+    "            image_np_expanded = np.expand_dims(image_np, axis=0)\n",
+    "\n",
+    "            (boxes, scores, classes, num) = sess.run(\n",
+    "              [detection_boxes, detection_scores, detection_classes, num_detections],\n",
+    "              feed_dict={image_tensor: image_np_expanded})\n",
+    "\n",
+    "            vis_util.visualize_boxes_and_labels_on_image_array(\n",
+    "                image_np,\n",
+    "                np.squeeze(boxes),\n",
+    "                np.squeeze(classes).astype(np.int32),\n",
+    "                np.squeeze(scores),\n",
+    "                category_index,\n",
+    "                use_normalized_coordinates=True,\n",
+    "                line_thickness=5)\n",
+    "\n",
+    "            cv2.imshow('MobileNet SSD - Object Detection', image_np)\n",
+    "            if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "                break\n",
+    "            \n",
+    "# Release camera and close windows\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "default_view": {},
+   "name": "object_detection_tutorial.ipynb?workspaceId=ronnyvotel:python_inference::citc",
+   "provenance": [],
+   "version": "0.3.2",
+   "views": {}
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/21. TensorFlow Object Detection API/1. Chapter Introduction.srt b/21. TensorFlow Object Detection API/1. Chapter Introduction.srt
new file mode 100644
index 0000000000000000000000000000000000000000..31c03a5cf244fdc89cd7b1be0f43ee1af8756e81
--- /dev/null
+++ b/21. TensorFlow Object Detection API/1. Chapter Introduction.srt	
@@ -0,0 +1,27 @@
+1
+00:00:00,990 --> 00:00:07,630
+Hi and welcome to Chapter 21 where we talk about the tenets of flu object detection API.
+
+2
+00:00:07,890 --> 00:00:11,050
+So this section is split up into tree bots.
+
+3
+00:00:11,070 --> 00:00:16,890
+First part we deal with the API install and set up then we start experimenting with the actual trained
+
+4
+00:00:16,950 --> 00:00:24,180
+API is just one extreme and resonant SSD and we would we use it on a web cam and videos and images and
+
+5
+00:00:24,180 --> 00:00:29,980
+then on twenty one point tree I go into detail and how you actually want to go about training a flow
+
+6
+00:00:30,030 --> 00:00:31,230
+of the action.
+
+7
+00:00:31,500 --> 00:00:34,840
+It's not easy but it is doable if you have a GP you.
diff --git a/21. TensorFlow Object Detection API/2. TFOD API Install and Setup.srt b/21. TensorFlow Object Detection API/2. TFOD API Install and Setup.srt
new file mode 100644
index 0000000000000000000000000000000000000000..1a642eb678c535765df91e8465dc932520216f19
--- /dev/null
+++ b/21. TensorFlow Object Detection API/2. TFOD API Install and Setup.srt	
@@ -0,0 +1,255 @@
+1
+00:00:00,420 --> 00:00:00,970
+OK.
+
+2
+00:00:01,020 --> 00:00:06,500
+So in twenty one point one we deal with a t i install and setup.
+
+3
+00:00:06,630 --> 00:00:10,060
+So let's talk a bit about tensor flows object action.
+
+4
+00:00:10,150 --> 00:00:16,380
+The TFT API is one of the more mature and relatively easy to use object action frameworks.
+
+5
+00:00:16,470 --> 00:00:19,600
+Most of them are actually quite finicky and tricky to use.
+
+6
+00:00:19,650 --> 00:00:22,560
+Typically most other obligation frameworks are finicky.
+
+7
+00:00:22,560 --> 00:00:28,140
+As I just said and difficult to use and they brick quite easily as I have a lot of moving parts tend
+
+8
+00:00:28,430 --> 00:00:33,540
+to flows of detection attempts to solve that by creating a framework API that uses tensor flow.
+
+9
+00:00:33,540 --> 00:00:34,760
+No surprise there.
+
+10
+00:00:34,770 --> 00:00:40,240
+To create an object detection modules using both our CNN family as well as the SSD family.
+
+11
+00:00:41,010 --> 00:00:47,550
+So while the TI FOTA Woody API makes it far easier than until the remittance it still has a bit of a
+
+12
+00:00:47,550 --> 00:00:50,100
+learning curve by the way.
+
+13
+00:00:50,100 --> 00:00:54,890
+This is actually an output of the tensor flow optimization API with an SSD.
+
+14
+00:00:54,990 --> 00:00:55,890
+It's quite cool isn't it.
+
+15
+00:00:57,180 --> 00:00:59,350
+So now let's talk about it install and setup.
+
+16
+00:00:59,370 --> 00:01:05,040
+Now if you're using the visual machine with this already installed you don't have to go through this.
+
+17
+00:01:05,040 --> 00:01:08,600
+However it's not that hard to do so I'm just gonna go through it step by step.
+
+18
+00:01:08,610 --> 00:01:13,020
+I'm not going to do it with you because I really have it installed on my machine and I don't I think
+
+19
+00:01:13,020 --> 00:01:14,790
+if I try to reinstall it I could mess things up.
+
+20
+00:01:15,360 --> 00:01:19,580
+But this is how I did it documented everything and it worked perfectly.
+
+21
+00:01:19,590 --> 00:01:24,430
+So what you do basically activate activated computer visual library.
+
+22
+00:01:24,690 --> 00:01:29,670
+We're not going to we're not going to install a disk because they can have a lot of clashes with packages
+
+23
+00:01:29,700 --> 00:01:34,480
+and libraries being you know messy with each other not mixing well.
+
+24
+00:01:34,590 --> 00:01:36,470
+So let's clone this environment.
+
+25
+00:01:36,490 --> 00:01:44,820
+So we go into the terminal and go condo create and let's call this TFT we named this environment that
+
+26
+00:01:45,070 --> 00:01:49,860
+so in future when you want to activate it you just go source activate TFT and it's there.
+
+27
+00:01:50,560 --> 00:01:58,200
+So anyway copy this line in your terminal and clone your directory your sorry CV environment and then
+
+28
+00:01:58,200 --> 00:02:06,070
+run this line here suited apt get install put above compiler Python pipe Perl Python Alexa Mel and python.
+
+29
+00:02:06,120 --> 00:02:07,400
+Okay okay.
+
+30
+00:02:07,530 --> 00:02:15,770
+Then we do pip install system pip install context lib Jupiter mapped lib and then go back.
+
+31
+00:02:15,780 --> 00:02:21,930
+So just go back to old man home directory make up for local models and then get clone.
+
+32
+00:02:22,110 --> 00:02:26,430
+This basically from here from this getup.
+
+33
+00:02:26,510 --> 00:02:35,130
+Link here fanciful models and go back again to directory and get cloned this as well and now as we go
+
+34
+00:02:35,130 --> 00:02:43,350
+back here go into this directory cocoa API Python API and end to make this will compile and build some
+
+35
+00:02:43,350 --> 00:02:49,050
+stuff that you need and basically just copy this line here and then decide comments here so don't actually
+
+36
+00:02:49,050 --> 00:02:50,850
+run this in a terminal.
+
+37
+00:02:50,910 --> 00:02:57,240
+So this is where we get the intensify models from so you use w get put above zip and you download this
+
+38
+00:02:57,240 --> 00:03:01,720
+link here and unzip this file and then you can delete this file afterwards.
+
+39
+00:03:01,740 --> 00:03:02,450
+It's fine.
+
+40
+00:03:02,760 --> 00:03:06,840
+And then what you do you get to part that we need to get everything working.
+
+41
+00:03:06,840 --> 00:03:12,540
+So we defined this part here and then we go to our protection bills and we run the tests and if the
+
+42
+00:03:12,540 --> 00:03:19,740
+stuff's tests run run successfully by opening this file we will know if this install works correctly.
+
+43
+00:03:19,740 --> 00:03:21,410
+So go ahead and try it on your own.
+
+44
+00:03:21,420 --> 00:03:27,930
+See if it works if there's any problems don't hesitate to contact me and all the times things change.
+
+45
+00:03:27,960 --> 00:03:29,850
+We did the updates.
+
+46
+00:03:29,850 --> 00:03:33,660
+So maybe just check this link if this isn't work first before you contact me.
+
+47
+00:03:33,660 --> 00:03:34,870
+See if there's anything here.
+
+48
+00:03:34,890 --> 00:03:37,620
+Menu maybe a new vision or a new dependency.
+
+49
+00:03:37,620 --> 00:03:40,330
+You never know.
+
+50
+00:03:40,500 --> 00:03:45,190
+Running the demo so download the python file in this folder.
+
+51
+00:03:45,200 --> 00:03:50,420
+I actually do have this file in the in the in my Python the book files here.
+
+52
+00:03:50,820 --> 00:03:56,540
+However they do work you don't don't run them from there they're actually copy and paste them into this
+
+53
+00:03:56,540 --> 00:03:57,310
+territory here.
+
+54
+00:03:57,660 --> 00:04:02,380
+So let me go to our virtual machine and I'll show you exactly where to find us.
+
+55
+00:04:02,400 --> 00:04:06,560
+Okay so we're back in a virtual machine and directory I want you to go to is.
+
+56
+00:04:06,750 --> 00:04:11,940
+Remember I told you based on our presentation here I wanted you to go to models models research oblique
+
+57
+00:04:11,940 --> 00:04:17,070
+detection and put this file here or resources file here that you don't want it.
+
+58
+00:04:17,160 --> 00:04:21,720
+Oh it's actually stored in your folder as well and your notebooks folder.
+
+59
+00:04:22,230 --> 00:04:29,250
+But anyhow let's go to the surgery and I'll show you where to put this file since models models research
+
+60
+00:04:29,400 --> 00:04:32,620
+and object let's type it in that type of it.
+
+61
+00:04:34,540 --> 00:04:38,500
+Object detection and you'll see it is a notebook somewhere.
+
+62
+00:04:39,280 --> 00:04:42,110
+This one here that is a file even a run from no one.
+
+63
+00:04:42,160 --> 00:04:42,430
+Okay.
+
+64
+00:04:43,060 --> 00:04:46,540
+So in the next chapter we're going to run this file and go to the Detroit tutorial.
diff --git a/21. TensorFlow Object Detection API/2.1 Download the code (for those not using the Virtual Machine).html b/21. TensorFlow Object Detection API/2.1 Download the code (for those not using the Virtual Machine).html
new file mode 100644
index 0000000000000000000000000000000000000000..36dbfefff11dda19363d46501fd19f91a8f81623
--- /dev/null
+++ b/21. TensorFlow Object Detection API/2.1 Download the code (for those not using the Virtual Machine).html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://1drv.ms/u/s!AkTkTuTv8A66da5SgRE3zXCrtQA";</script>
\ No newline at end of file
diff --git a/21. TensorFlow Object Detection API/3. Experiment with a ResNet SSD on images, webcam and videos.srt b/21. TensorFlow Object Detection API/3. Experiment with a ResNet SSD on images, webcam and videos.srt
new file mode 100644
index 0000000000000000000000000000000000000000..dcc111e4703df684d432e566d8a7794060cfec0a
--- /dev/null
+++ b/21. TensorFlow Object Detection API/3. Experiment with a ResNet SSD on images, webcam and videos.srt	
@@ -0,0 +1,471 @@
+1
+00:00:00,650 --> 00:00:00,960
+OK.
+
+2
+00:00:01,020 --> 00:00:07,680
+So welcome to the 21 point to where we actually start playing with our SSD inside by using sari
+
+3
+00:00:11,660 --> 00:00:18,240
+Hi welcome to Chapter 21 point to where we start experimenting with the object of action SSD based on
+
+4
+00:00:18,280 --> 00:00:23,080
+resonate and we do this on images webcams and all of them feel it and videos.
+
+5
+00:00:23,100 --> 00:00:24,580
+So let's get started.
+
+6
+00:00:25,170 --> 00:00:27,000
+So now we're here in a virtual machine.
+
+7
+00:00:27,000 --> 00:00:32,000
+And this project is going to be a bit different as we have done because we remember we created a new
+
+8
+00:00:32,010 --> 00:00:38,120
+environment so we have to launch what I put in the books from a new environment so fiercely that school
+
+9
+00:00:38,130 --> 00:00:49,140
+source caps lock is on source activate T.F. the API to get us to not use on the school.
+
+10
+00:00:49,140 --> 00:00:49,680
+There we go.
+
+11
+00:00:49,860 --> 00:00:51,950
+So that's a environment that we're in right now.
+
+12
+00:00:52,230 --> 00:00:52,850
+So that's good.
+
+13
+00:00:52,900 --> 00:00:54,010
+I will have to.
+
+14
+00:00:54,010 --> 00:00:58,450
+This will probably be any name you name that previously before if you're not using my pre-installed
+
+15
+00:00:58,710 --> 00:01:00,020
+with your machine.
+
+16
+00:01:00,180 --> 00:01:07,080
+So I put iPod it's on the books and it brings up a point on the book browser of Jupiter.
+
+17
+00:01:07,560 --> 00:01:12,950
+So now what I want us to go to is really there are two ways to get a set up.
+
+18
+00:01:13,040 --> 00:01:18,540
+You're supposed to download and I put it in the book file in the resources file that file was basically
+
+19
+00:01:19,420 --> 00:01:20,090
+this here.
+
+20
+00:01:20,240 --> 00:01:20,940
+All right.
+
+21
+00:01:20,940 --> 00:01:26,130
+However I left it in the artery here in case you wanted to manually copy and paste it into the directory.
+
+22
+00:01:26,130 --> 00:01:28,640
+So let's actually go ahead and do that.
+
+23
+00:01:28,650 --> 00:01:38,940
+So copy this file control Control-C and the doctor wants you to go to was the Saathiya models models.
+
+24
+00:01:39,450 --> 00:01:45,240
+Research sorry and object detection and pissed that file in to here.
+
+25
+00:01:45,430 --> 00:01:46,160
+OK.
+
+26
+00:01:46,470 --> 00:01:48,070
+This one here is actually ulo.
+
+27
+00:01:48,180 --> 00:01:51,780
+It's not actually going to work to paste it into here.
+
+28
+00:01:52,260 --> 00:01:59,220
+So now let's go to a Titan notebook browser and find this directory.
+
+29
+00:01:59,520 --> 00:02:00,740
+So find the file.
+
+30
+00:02:00,740 --> 00:02:01,670
+I should say so.
+
+31
+00:02:01,680 --> 00:02:04,270
+Good models research.
+
+32
+00:02:04,560 --> 00:02:07,630
+Scroll down to object detection
+
+33
+00:02:09,980 --> 00:02:16,610
+right here and let's launch this file.
+
+34
+00:02:16,610 --> 00:02:23,220
+Ok so here we go this file here is actually not a file I created I just modified it slightly.
+
+35
+00:02:23,260 --> 00:02:28,900
+This is a father comes intensive flows observation API and it allows you to basically play with the
+
+36
+00:02:28,900 --> 00:02:31,850
+different features in it it's in the official Demel.
+
+37
+00:02:32,180 --> 00:02:33,770
+So let's run the first box here.
+
+38
+00:02:33,780 --> 00:02:40,990
+So imports this plot of stuff in line by the way in case I haven't mentioned it to you what this does
+
+39
+00:02:40,990 --> 00:02:47,200
+is that it generates matplotlib Matlab plots inside a phone book as opposed to having it be like an
+
+40
+00:02:47,200 --> 00:02:49,080
+open TV and a new window.
+
+41
+00:02:49,540 --> 00:02:52,650
+So anyway that's duties imports here as well.
+
+42
+00:02:53,230 --> 00:02:56,340
+And let's run this block here.
+
+43
+00:02:56,470 --> 00:02:59,830
+These old directories would basically point to different models.
+
+44
+00:02:59,830 --> 00:03:01,230
+This is the SSD.
+
+45
+00:03:01,240 --> 00:03:04,220
+This is a resident SSD will be using that street and the cocoa.
+
+46
+00:03:04,420 --> 00:03:09,840
+That's a common object data set and it's going to download it the first time if you didn't already have
+
+47
+00:03:09,840 --> 00:03:12,490
+it saved is going to lose here.
+
+48
+00:03:12,490 --> 00:03:16,680
+Actually I do have it saved so it should not download.
+
+49
+00:03:16,690 --> 00:03:21,960
+I hope it is doing something so maybe it is downloading.
+
+50
+00:03:22,010 --> 00:03:25,750
+So anyway let's run this box and wait for that to finish.
+
+51
+00:03:27,520 --> 00:03:37,780
+And we load a little nap some help a code and then we do our detection boxes here and don't mind these
+
+52
+00:03:38,590 --> 00:03:41,260
+red things that look like it's going to be at URL.
+
+53
+00:03:41,450 --> 00:03:42,750
+This will still run.
+
+54
+00:03:42,790 --> 00:03:44,360
+So it's fine.
+
+55
+00:03:44,430 --> 00:03:44,920
+So right.
+
+56
+00:03:44,950 --> 00:03:47,010
+These these boxes have from now.
+
+57
+00:03:47,320 --> 00:03:52,450
+So let's do that we do this one and remember some of us do it again.
+
+58
+00:03:53,220 --> 00:03:54,370
+Let's run this one.
+
+59
+00:03:54,550 --> 00:03:57,760
+And that actually was not up wanted yet.
+
+60
+00:03:57,760 --> 00:03:58,720
+This is what we want.
+
+61
+00:03:58,730 --> 00:04:04,020
+So it goes through the images in a Test spot and it still takes a while to run.
+
+62
+00:04:04,480 --> 00:04:05,110
+To be fair.
+
+63
+00:04:05,500 --> 00:04:11,980
+And what it's going to do is basically it's going to take that image it found out and basically run
+
+64
+00:04:11,980 --> 00:04:17,340
+all of these SSD functions that require it to classify detect objects here.
+
+65
+00:04:17,590 --> 00:04:18,590
+So here we go.
+
+66
+00:04:18,730 --> 00:04:26,440
+So the first first test image it picked up this is a dog you can see it clearly says this woman.
+
+67
+00:04:26,690 --> 00:04:30,740
+I'm pressing control and moving my mouse button and we can see it's a dog here.
+
+68
+00:04:30,770 --> 00:04:32,080
+And you can see the probabilities.
+
+69
+00:04:32,260 --> 00:04:33,310
+It's a bit hard to make out.
+
+70
+00:04:33,310 --> 00:04:37,720
+Maybe we can actually change some parameters here to make this a little more legible.
+
+71
+00:04:37,870 --> 00:04:38,560
+Insightful.
+
+72
+00:04:38,550 --> 00:04:39,790
+I applied in the book.
+
+73
+00:04:40,080 --> 00:04:41,500
+It's a dog here and a dog here.
+
+74
+00:04:41,530 --> 00:04:43,720
+So it's quite good.
+
+75
+00:04:43,720 --> 00:04:45,790
+This is the image I use in my presentation slide.
+
+76
+00:04:45,970 --> 00:04:51,940
+You can see this is a kite kite kite person person and these are the probabilities which I can read
+
+77
+00:04:52,180 --> 00:04:55,260
+it looks like 60 tree just looks like a hundred.
+
+78
+00:04:55,270 --> 00:04:57,400
+But what it is it is.
+
+79
+00:04:57,400 --> 00:05:03,010
+So now let's try it on a webcam and I'm pretty much looking like a bit of a mess right now because it's
+
+80
+00:05:03,010 --> 00:05:03,630
+quite late.
+
+81
+00:05:03,640 --> 00:05:07,710
+And I have not comb my hair for a while but I'm sober to try this.
+
+82
+00:05:07,720 --> 00:05:12,840
+So let's run this webcam should come on any second now.
+
+83
+00:05:15,680 --> 00:05:15,940
+All right.
+
+84
+00:05:15,980 --> 00:05:18,470
+This is me in my natural element here.
+
+85
+00:05:18,680 --> 00:05:26,040
+And you can see that actually went up T-shirt on ice free advertising for Apple.
+
+86
+00:05:26,060 --> 00:05:30,520
+So this is me here and this is the person box that's affecting me right now.
+
+87
+00:05:30,560 --> 00:05:31,780
+So this is actually pretty cool.
+
+88
+00:05:31,820 --> 00:05:37,640
+So let me just close this and no let's try it out on the video.
+
+89
+00:05:37,650 --> 00:05:40,880
+So this is a dash cam video I downloaded off YouTube.
+
+90
+00:05:40,960 --> 00:05:43,130
+And so let's it it
+
+91
+00:05:49,000 --> 00:05:50,170
+it sometimes takes a while to load.
+
+92
+00:05:50,170 --> 00:05:51,850
+Oh there we go.
+
+93
+00:05:52,720 --> 00:05:54,630
+So this is a tip.
+
+94
+00:05:54,850 --> 00:05:56,150
+So this is pretty cool.
+
+95
+00:05:56,230 --> 00:05:58,190
+If I if I do say so.
+
+96
+00:05:58,750 --> 00:06:05,170
+So we're running it here detecting imprisons cause I think we just saw a bike from a mistaken call again
+
+97
+00:06:06,610 --> 00:06:08,090
+and the frame rate isn't that bad.
+
+98
+00:06:08,170 --> 00:06:12,010
+Honestly it being on a C.P. you not a GP.
+
+99
+00:06:12,280 --> 00:06:14,210
+This is actually pretty sick.
+
+100
+00:06:18,900 --> 00:06:20,460
+So let's close this video now.
+
+101
+00:06:21,560 --> 00:06:27,150
+So you've just run and experimented with SSD single shot detector.
+
+102
+00:06:27,550 --> 00:06:29,090
+So I hope you found this chapter fun.
+
+103
+00:06:29,090 --> 00:06:32,660
+I found it quite fun to play with this as well.
+
+104
+00:06:32,660 --> 00:06:35,680
+What you can do is put any video you want here.
+
+105
+00:06:36,150 --> 00:06:39,840
+Another dashcam video as well and any images you want.
+
+106
+00:06:39,860 --> 00:06:45,590
+We'll go there to see what fall they look at Image Pat.
+
+107
+00:06:45,600 --> 00:06:52,170
+Basically find fine image that is obviously not defined there.
+
+108
+00:06:53,410 --> 00:06:54,450
+Keep going.
+
+109
+00:06:56,320 --> 00:06:57,510
+Testament.
+
+110
+00:06:57,720 --> 00:07:03,960
+It is as if it's about OK syntactical test images.
+
+111
+00:07:05,850 --> 00:07:06,590
+This one here.
+
+112
+00:07:07,020 --> 00:07:08,700
+So this is the directory we're looking at.
+
+113
+00:07:08,710 --> 00:07:10,650
+We had what else images in it.
+
+114
+00:07:10,700 --> 00:07:12,010
+I'm not sure what's in this file.
+
+115
+00:07:12,040 --> 00:07:16,950
+I guess it's a source file for you want to put your source for images to sometimes.
+
+116
+00:07:17,020 --> 00:07:18,300
+So this is pretty cool.
+
+117
+00:07:18,530 --> 00:07:23,420
+So you can experiment with a web cam with your test images and watch your videos.
+
+118
+00:07:23,520 --> 00:07:23,730
+OK.
diff --git a/21. TensorFlow Object Detection API/4. How to Train a TFOD Model.srt b/21. TensorFlow Object Detection API/4. How to Train a TFOD Model.srt
new file mode 100644
index 0000000000000000000000000000000000000000..bab023bd9b4918e33af0916a9a75f23598b31372
--- /dev/null
+++ b/21. TensorFlow Object Detection API/4. How to Train a TFOD Model.srt	
@@ -0,0 +1,503 @@
+1
+00:00:00,700 --> 00:00:01,040
+OK.
+
+2
+00:00:01,050 --> 00:00:03,310
+So welcome to twenty one point three.
+
+3
+00:00:03,510 --> 00:00:09,060
+Well actually talk about and tell you how to go about creating a custom tensor for up to detection module
+
+4
+00:00:09,260 --> 00:00:10,220
+model.
+
+5
+00:00:10,890 --> 00:00:17,490
+So training the tens of the action the training process of tensile flow of detection is honestly a bit
+
+6
+00:00:17,490 --> 00:00:23,970
+messy and this is probably one of the best of the detection Well most mature object libraries on the
+
+7
+00:00:23,970 --> 00:00:25,600
+market today market.
+
+8
+00:00:25,640 --> 00:00:33,540
+But an open source market you can say and it's explained fairly well it actually is bit tricky to use.
+
+9
+00:00:34,020 --> 00:00:36,530
+So this is a step I broke down from looking at it.
+
+10
+00:00:36,570 --> 00:00:40,310
+So first we prepare a data set in the T.F. record format.
+
+11
+00:00:40,380 --> 00:00:41,440
+That's a specific record.
+
+12
+00:00:41,460 --> 00:00:43,500
+I'll show you in the next few slides.
+
+13
+00:00:43,950 --> 00:00:48,570
+Then we need to create a class label file that's a dog P.B. text file.
+
+14
+00:00:48,570 --> 00:00:53,760
+Then we need to download a pre-trained Kokoo model and we need to set up the correct file that actually
+
+15
+00:00:53,760 --> 00:00:58,490
+structure configured the object action pipeline and then start training.
+
+16
+00:00:58,500 --> 00:01:00,690
+So let's go step by step.
+
+17
+00:01:00,720 --> 00:01:03,220
+So what does it T.F. record format.
+
+18
+00:01:03,270 --> 00:01:07,650
+So it tends to flow because the action expects so it treating and test data to be in this T.F. or the
+
+19
+00:01:07,650 --> 00:01:09,220
+school record format.
+
+20
+00:01:09,300 --> 00:01:11,460
+This is pretty much what it looks like here.
+
+21
+00:01:11,910 --> 00:01:17,220
+Luckily though we can convert existing data sets that's like in the past SQL View see data set which
+
+22
+00:01:17,220 --> 00:01:22,260
+is stored in Exod. Mosha directly to ATF records file by using the script.
+
+23
+00:01:22,290 --> 00:01:24,300
+They provide this for the here.
+
+24
+00:01:24,690 --> 00:01:26,790
+So it's quite myself.
+
+25
+00:01:27,510 --> 00:01:30,030
+And no we talk about a classily files.
+
+26
+00:01:30,030 --> 00:01:32,910
+So basically this is what the castable file looks like.
+
+27
+00:01:33,850 --> 00:01:41,350
+It's basically just on a dictionary Shukria where we have the IDs and label names.
+
+28
+00:01:41,350 --> 00:01:44,660
+So that's pretty much what we need to do with redefining.
+
+29
+00:01:44,830 --> 00:01:50,560
+If we're training a detector to detect let's say London underground tube signs you'll just put the name
+
+30
+00:01:50,560 --> 00:01:56,740
+of the class here and it's I.D. and you keep adding more and more items all objects here.
+
+31
+00:02:00,660 --> 00:02:03,510
+So now we have to use pre-trained model.
+
+32
+00:02:03,840 --> 00:02:04,250
+OK.
+
+33
+00:02:04,380 --> 00:02:11,860
+So we don't train this model and this is like the resonant model that we use in the previous chapter.
+
+34
+00:02:12,240 --> 00:02:13,890
+So we don't have one here.
+
+35
+00:02:13,920 --> 00:02:20,490
+So tensile flow who has several pre-treat models and one Ko-Ko and Coko is basically a large scale object
+
+36
+00:02:20,490 --> 00:02:26,020
+protection segmentation and captioning data set and basically has a lot of features here.
+
+37
+00:02:26,370 --> 00:02:31,320
+You can go to the Web site this Cocco decision it and you will find it.
+
+38
+00:02:31,900 --> 00:02:33,950
+And so you don't live with the models here.
+
+39
+00:02:34,230 --> 00:02:38,620
+This link carries you to all the models that are available on tensor.
+
+40
+00:02:39,030 --> 00:02:44,560
+And basically just use it downloaded onto mental health and on target here.
+
+41
+00:02:44,730 --> 00:02:46,470
+So that's what we do.
+
+42
+00:02:47,100 --> 00:02:50,180
+And this is a list of all the models of healable here.
+
+43
+00:02:50,280 --> 00:02:55,620
+It gives the speed and the map scope which is quite useful helps you choose which model is most appropriate
+
+44
+00:02:55,620 --> 00:02:56,590
+for your application.
+
+45
+00:02:58,530 --> 00:03:05,310
+So now we get to configure object detection pipeline said abjection pipeline configuration file is composed
+
+46
+00:03:05,310 --> 00:03:07,780
+of five sections.
+
+47
+00:03:07,800 --> 00:03:12,610
+Basically we have a model that we define here to configuration here.
+
+48
+00:03:13,110 --> 00:03:15,310
+Then we have the training config here.
+
+49
+00:03:15,480 --> 00:03:23,350
+So we add this through here and the in part Real talk about soon the valuation config and evaluation
+
+50
+00:03:23,370 --> 00:03:24,090
+in podrida.
+
+51
+00:03:24,090 --> 00:03:26,760
+So let's take a look at this file in more detail.
+
+52
+00:03:26,790 --> 00:03:31,400
+So this is a sample of the model file the model section here.
+
+53
+00:03:31,890 --> 00:03:34,900
+What it looks like is this is the model config.
+
+54
+00:03:35,130 --> 00:03:35,720
+I should say.
+
+55
+00:03:35,820 --> 00:03:36,340
+OK.
+
+56
+00:03:36,690 --> 00:03:42,330
+So we basically what we have to note is that we have some templates already that we can use inside of
+
+57
+00:03:42,470 --> 00:03:43,240
+of flow.
+
+58
+00:03:43,530 --> 00:03:49,530
+So we just basically make sure that all classes match up to the classes that enable custom are being
+
+59
+00:03:49,530 --> 00:03:50,910
+attacked to Dexter.
+
+60
+00:03:51,420 --> 00:03:53,850
+So this one was taken from the president one on one.
+
+61
+00:03:54,480 --> 00:03:56,940
+And those Treen in the Pascrell VRC dataset.
+
+62
+00:03:57,330 --> 00:04:01,950
+So as I said you don't need to rewrite this file just edit to one belonging to the preacher and model
+
+63
+00:04:02,070 --> 00:04:06,750
+to be using Dymo defines the model defines all unnecessary.
+
+64
+00:04:06,850 --> 00:04:15,300
+Our Or our CNN and as the parameters and when using pretreated models as best we leave this configuration
+
+65
+00:04:15,360 --> 00:04:19,170
+configuration file unchanged unchanged just a bit.
+
+66
+00:04:19,170 --> 00:04:26,320
+This red box here with the classes and notices that trade input Greedo and the yvel config and involved
+
+67
+00:04:26,360 --> 00:04:27,900
+in podrida sections.
+
+68
+00:04:27,900 --> 00:04:31,990
+So this is what we have to try and change these in red.
+
+69
+00:04:32,070 --> 00:04:33,840
+Basically directory mappings.
+
+70
+00:04:33,840 --> 00:04:38,010
+So we have to make sure that they're actually correct and pointing to the correct files that you want
+
+71
+00:04:38,010 --> 00:04:38,780
+to use.
+
+72
+00:04:38,820 --> 00:04:40,740
+This will be your label file.
+
+73
+00:04:40,800 --> 00:04:44,440
+This will be your record file you're treating UTF record file.
+
+74
+00:04:44,670 --> 00:04:46,270
+This is what disappoints do here.
+
+75
+00:04:46,500 --> 00:04:50,930
+So for validation and for record for training and for validation.
+
+76
+00:04:51,510 --> 00:04:53,990
+And this is Leavell parts of boat as well.
+
+77
+00:04:54,110 --> 00:04:54,680
+OK.
+
+78
+00:04:54,910 --> 00:04:59,450
+So remember the labels are the same ideas will be the same it's the same file.
+
+79
+00:04:59,890 --> 00:05:00,350
+OK.
+
+80
+00:05:04,360 --> 00:05:06,870
+So now here's the directory structure of the project.
+
+81
+00:05:06,910 --> 00:05:09,560
+So this is how and where we put our files.
+
+82
+00:05:09,850 --> 00:05:14,860
+So the PBX ex-felon label file that goes inside a directory called data.
+
+83
+00:05:15,250 --> 00:05:22,270
+Then we have a record files which is a train and evaluation of record files or validation whatever you
+
+84
+00:05:22,270 --> 00:05:24,230
+want to call it same thing.
+
+85
+00:05:24,640 --> 00:05:29,820
+And then we have a new directory here models and then we have subdirectory model here.
+
+86
+00:05:30,160 --> 00:05:32,360
+This is where we put our pipeline config file.
+
+87
+00:05:32,500 --> 00:05:33,990
+That's this file here.
+
+88
+00:05:34,390 --> 00:05:40,330
+And then we just have the trained directory and the evaluation directory here as well under the model
+
+89
+00:05:40,660 --> 00:05:46,340
+inside the model form which is inside of the models for the here.
+
+90
+00:05:46,350 --> 00:05:48,820
+So now this is how we start the training process.
+
+91
+00:05:48,890 --> 00:05:56,810
+So we go to basically terminal and we just copy this line of code here and make sure to code the lines
+
+92
+00:05:56,810 --> 00:05:57,970
+in a read here.
+
+93
+00:05:58,280 --> 00:06:03,090
+These correspond to the model you are using and the directory that we just created here.
+
+94
+00:06:03,260 --> 00:06:03,710
+OK.
+
+95
+00:06:03,890 --> 00:06:10,010
+This is the day that actually it needs to be pointing to and it doesn't do that.
+
+96
+00:06:10,010 --> 00:06:16,200
+And then you can actually bring up tents board to monitor your treating progress which is pretty cool.
+
+97
+00:06:16,700 --> 00:06:18,770
+It's going to be district directory here.
+
+98
+00:06:19,030 --> 00:06:22,230
+That again does your data directly at the specified here.
+
+99
+00:06:25,340 --> 00:06:30,590
+And then before that I should have mentioned this earlier but it's important when you're labeling it
+
+100
+00:06:30,590 --> 00:06:35,450
+images use a software that actually produces it in the correct format.
+
+101
+00:06:35,450 --> 00:06:38,120
+So this is how we use annotations here.
+
+102
+00:06:38,150 --> 00:06:43,710
+So this is my wife with my dog Samuel and software we use as a label.
+
+103
+00:06:43,800 --> 00:06:50,190
+AMG I think this is two elves they have here this really label I am the label image obviously.
+
+104
+00:06:50,540 --> 00:06:52,200
+So download it if you want to do it.
+
+105
+00:06:52,220 --> 00:06:55,910
+It's available for Windows Mac and Linux.
+
+106
+00:06:55,910 --> 00:07:01,690
+And this is the format that Pascal VRC Exham a format that we use now.
+
+107
+00:07:01,820 --> 00:07:09,380
+It is not what we used us but we generate the image anti-Chavez in this format using the software.
+
+108
+00:07:09,410 --> 00:07:11,160
+It actually does it automatically for you.
+
+109
+00:07:11,630 --> 00:07:17,420
+And we can use our tents for a script I mentioned earlier to convert this file directly back to the
+
+110
+00:07:17,420 --> 00:07:20,170
+text of the record files.
+
+111
+00:07:20,270 --> 00:07:21,910
+So this is a summary here.
+
+112
+00:07:22,010 --> 00:07:24,890
+We didn't do a full project here for the following reasons.
+
+113
+00:07:25,160 --> 00:07:30,390
+Training and SSTO even a faster are CNN One is you is very impractical.
+
+114
+00:07:30,440 --> 00:07:31,630
+It is going to take forever.
+
+115
+00:07:31,700 --> 00:07:38,840
+So you definitely need a GP or a cloud to use to effectively train this A.F. all the data sets are there
+
+116
+00:07:38,870 --> 00:07:46,190
+huge there are quite a few gigs of storage and also to setting up a GPU when a local system is a nightmare
+
+117
+00:07:46,190 --> 00:07:46,790
+sometimes.
+
+118
+00:07:46,880 --> 00:07:53,660
+It's a very scary task but once you get it working it's good you feel very happy because it's so much
+
+119
+00:07:53,660 --> 00:07:54,950
+faster.
+
+120
+00:07:55,620 --> 00:07:58,150
+So I've of all time general steps here.
+
+121
+00:07:58,220 --> 00:08:05,040
+The Trina's model there are also some good tutorials I found online that do this as well.
+
+122
+00:08:05,090 --> 00:08:10,430
+Basically try to make it as simple as possible if you going through all the steps telling you what to
+
+123
+00:08:10,430 --> 00:08:13,210
+pay attention to and what's important.
+
+124
+00:08:13,220 --> 00:08:16,880
+I've actually tried this on my system as well so I know what works.
+
+125
+00:08:16,910 --> 00:08:23,150
+So I wish you all the best of luck when making your own object to actual text detectors.
+
+126
+00:08:23,300 --> 00:08:23,610
+Thank you.
diff --git a/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/1. Chapter Introduction.srt b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/1. Chapter Introduction.srt
new file mode 100644
index 0000000000000000000000000000000000000000..57070b8d2532f1ef328f3fa1695c1aeac1927d16
--- /dev/null
+++ b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/1. Chapter Introduction.srt	
@@ -0,0 +1,23 @@
+1
+00:00:00,280 --> 00:00:06,920
+Hi and welcome to Chapter 22 where we look at object detection using yellow vision tree and dark blue.
+
+2
+00:00:06,970 --> 00:00:09,680
+So this section is built up into tree parts.
+
+3
+00:00:09,690 --> 00:00:16,440
+Firstly we get you up and running by installing yellow dock nets and outflow then we start experimenting
+
+4
+00:00:16,440 --> 00:00:24,750
+with real and still images webcam feed and videos and then we build on a yellow object detector and
+
+5
+00:00:24,750 --> 00:00:28,350
+we're going to detect London Underground signs in our project.
+
+6
+00:00:28,740 --> 00:00:30,200
+So let's get started.
diff --git a/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2. Setting up and install Yolo DarkNet and DarkFlow.srt b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2. Setting up and install Yolo DarkNet and DarkFlow.srt
new file mode 100644
index 0000000000000000000000000000000000000000..a8f3ccc3b7a9004397d1b390d79cc5e99fb0e4d9
--- /dev/null
+++ b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2. Setting up and install Yolo DarkNet and DarkFlow.srt	
@@ -0,0 +1,363 @@
+1
+00:00:00,380 --> 00:00:06,570
+You say hi welcome to Chapter 22 point one where we get to Yale and Dartmouth and Da'ath set up and
+
+2
+00:00:06,570 --> 00:00:07,770
+installed.
+
+3
+00:00:08,340 --> 00:00:10,010
+So just to remind you.
+
+4
+00:00:10,010 --> 00:00:14,100
+Yellow stands for you only look once and it's a pretty awesome object doctor.
+
+5
+00:00:14,370 --> 00:00:20,190
+You can take a look at the Web site here it looks a bit shady but it's very reputable and very very
+
+6
+00:00:20,190 --> 00:00:20,520
+good.
+
+7
+00:00:20,610 --> 00:00:21,800
+It's very good technology.
+
+8
+00:00:21,810 --> 00:00:22,860
+Those guys developed.
+
+9
+00:00:23,100 --> 00:00:25,790
+So basically we have to install dot at first.
+
+10
+00:00:25,800 --> 00:00:27,170
+And what does darknet.
+
+11
+00:00:27,300 --> 00:00:29,530
+It's the official name for the EULA framework.
+
+12
+00:00:29,550 --> 00:00:32,360
+I mean these guys are kind of awesome should read somebody's papers.
+
+13
+00:00:32,380 --> 00:00:36,120
+They're very entertaining and very informative as well.
+
+14
+00:00:36,120 --> 00:00:41,760
+So to get this installed that Cisco to we go back to our terminal here.
+
+15
+00:00:42,000 --> 00:00:44,080
+This is all commands we enter into minal.
+
+16
+00:00:44,430 --> 00:00:48,940
+We stay in a home directory and we make a folder called dot net.
+
+17
+00:00:49,110 --> 00:00:51,270
+We get Clun repository here.
+
+18
+00:00:51,660 --> 00:00:58,170
+And then we go back we go into this net folder and we go to make and then we use this file to get the
+
+19
+00:00:58,170 --> 00:00:59,130
+weights here.
+
+20
+00:00:59,400 --> 00:01:05,190
+And then we just run this line in blue to executed on the test image that they've provided in one of
+
+21
+00:01:05,230 --> 00:01:10,330
+their sample their trees and in their dark directory you'll see a file called predictions.
+
+22
+00:01:10,500 --> 00:01:13,890
+And this will be the output from the test file.
+
+23
+00:01:13,920 --> 00:01:21,130
+So when you run this line here I said you basically run this line and blue copy and paste it.
+
+24
+00:01:21,210 --> 00:01:22,130
+This is what you will see.
+
+25
+00:01:22,120 --> 00:01:25,430
+Takes about maybe 10 seconds to run a little to model it for us.
+
+26
+00:01:25,500 --> 00:01:29,470
+We just see the output of the model here and it basically went in.
+
+27
+00:01:29,490 --> 00:01:34,530
+Once it's done you'll see this I didn't paste it felt all the way in because it would but it would have
+
+28
+00:01:34,530 --> 00:01:35,470
+been too small.
+
+29
+00:01:35,880 --> 00:01:42,210
+But you see this at the end it's a probability object probabilities found here.
+
+30
+00:01:42,930 --> 00:01:44,000
+And that's pretty cool.
+
+31
+00:01:44,010 --> 00:01:46,300
+So you know what to find this file now.
+
+32
+00:01:46,380 --> 00:01:50,390
+It's basically a file in the directory called predictions.
+
+33
+00:01:50,640 --> 00:01:53,310
+And this is it can enter any sample image you want.
+
+34
+00:01:53,550 --> 00:02:01,280
+Basically it files to test files a document data and you can this is a self-hater recently when it's
+
+35
+00:02:01,280 --> 00:02:02,470
+over my friend.
+
+36
+00:02:02,640 --> 00:02:05,140
+And you can just try it quite easily.
+
+37
+00:02:05,310 --> 00:02:05,790
+You can try.
+
+38
+00:02:05,970 --> 00:02:08,760
+Was your half horses anything you want.
+
+39
+00:02:08,760 --> 00:02:12,430
+So feel have fun playing with the yellow.
+
+40
+00:02:13,230 --> 00:02:15,820
+So but what else can we do from you.
+
+41
+00:02:16,020 --> 00:02:17,970
+Was using yellow from the command line.
+
+42
+00:02:18,240 --> 00:02:20,790
+But can we use it inside of Pitre.
+
+43
+00:02:21,060 --> 00:02:23,300
+Well yes we can with Dulfer.
+
+44
+00:02:23,580 --> 00:02:26,040
+And I'll now introduce you to darf flow in the next chapter.
+
+45
+00:02:27,780 --> 00:02:28,080
+OK.
+
+46
+00:02:28,080 --> 00:02:32,320
+So just to show you guys how this works without actually showing you images.
+
+47
+00:02:32,370 --> 00:02:35,550
+We're going to actually run this in our machine.
+
+48
+00:02:35,640 --> 00:02:36,210
+OK.
+
+49
+00:02:36,510 --> 00:02:40,570
+So somebody said we have to go to type in here.
+
+50
+00:02:40,810 --> 00:02:42,670
+Dot dot net Right.
+
+51
+00:02:42,690 --> 00:02:43,090
+Yep.
+
+52
+00:02:43,170 --> 00:02:44,940
+See the dot net.
+
+53
+00:02:44,980 --> 00:02:49,430
+So actually I didn't see what they call it actually yeah it is.
+
+54
+00:02:49,430 --> 00:02:50,300
+You don't know it.
+
+55
+00:02:50,310 --> 00:02:51,080
+Why didn't it
+
+56
+00:02:54,890 --> 00:02:55,350
+go.
+
+57
+00:02:55,770 --> 00:02:58,040
+So we can we're the here.
+
+58
+00:02:58,050 --> 00:03:05,940
+So now let's just go to the outline and I may have done that a bit too quickly because it actually pressed
+
+59
+00:03:06,020 --> 00:03:08,680
+into soon as I pasted that line.
+
+60
+00:03:08,700 --> 00:03:10,890
+This takes about 10 seconds or so to load the models.
+
+61
+00:03:10,900 --> 00:03:12,150
+It's going to be done quickly.
+
+62
+00:03:12,390 --> 00:03:17,170
+So now it's loading the weights and it's done and now with what it's going to do it's going to classify
+
+63
+00:03:17,170 --> 00:03:18,390
+that test image.
+
+64
+00:03:18,630 --> 00:03:20,730
+So let's go to dinner directory here.
+
+65
+00:03:21,010 --> 00:03:21,640
+All right.
+
+66
+00:03:21,720 --> 00:03:23,600
+So that's going to be in the Dot Net directory.
+
+67
+00:03:23,640 --> 00:03:27,090
+And this was the output here that we're going to generate.
+
+68
+00:03:27,150 --> 00:03:30,840
+And let's see if that was probably my previously saved image.
+
+69
+00:03:31,020 --> 00:03:31,850
+Yep it was.
+
+70
+00:03:31,850 --> 00:03:34,630
+It is going to make a new image soon.
+
+71
+00:03:34,770 --> 00:03:35,490
+So let's wait
+
+72
+00:03:39,510 --> 00:03:47,620
+spot usually took last time I think about 25 or so seconds when the time that search should be done
+
+73
+00:03:47,710 --> 00:03:48,850
+soon.
+
+74
+00:03:48,850 --> 00:03:49,930
+There we go.
+
+75
+00:03:49,930 --> 00:03:53,140
+It took six seconds and probably have something running in the background.
+
+76
+00:03:53,440 --> 00:03:54,500
+So here we go.
+
+77
+00:03:54,550 --> 00:04:00,420
+So this is the test image here that it updated its very nicely and neatly labeled here.
+
+78
+00:04:00,760 --> 00:04:05,310
+So if you wanted to do more as images go to your data directory here.
+
+79
+00:04:05,620 --> 00:04:10,960
+And this is actually where I have the self-pay used in the test and the presentation I should say.
+
+80
+00:04:11,200 --> 00:04:15,720
+So let's try this one here called person so let's see how that works.
+
+81
+00:04:15,730 --> 00:04:17,650
+So let's go back to this line.
+
+82
+00:04:17,710 --> 00:04:19,410
+However we don't use dog.
+
+83
+00:04:19,480 --> 00:04:22,750
+We use person.
+
+84
+00:04:22,790 --> 00:04:33,860
+So again the little the model when it first folded the slides for you guys no.
+
+85
+00:04:34,130 --> 00:04:35,520
+All right there we go.
+
+86
+00:04:35,900 --> 00:04:37,840
+So this is the up it up to file here.
+
+87
+00:04:38,330 --> 00:04:42,170
+So let's go back to a dock and a directory and look at our predictions file.
+
+88
+00:04:42,410 --> 00:04:43,520
+And this is pretty cool.
+
+89
+00:04:43,550 --> 00:04:48,200
+We have a person a horse and a dog all accurately labeled and very neatly done too.
+
+90
+00:04:48,530 --> 00:04:51,290
+So yellow is pretty awesome as you can see.
+
+91
+00:04:51,590 --> 00:04:56,390
+So I encourage you to experiment with doing pictures and get the hang of playing with yellow.
diff --git a/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2.1 Guide to the MacOS Install.html b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2.1 Guide to the MacOS Install.html
new file mode 100644
index 0000000000000000000000000000000000000000..8af0d18b0fc06a8c66284dfa698aa90a3cfd0ee0
--- /dev/null
+++ b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2.1 Guide to the MacOS Install.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://gist.github.com/simonw/0f93bec220be9cf8250533b603bf6dba";</script>
\ No newline at end of file
diff --git a/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2.2 Download the YOLO files (if not using the VM).html b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2.2 Download the YOLO files (if not using the VM).html
new file mode 100644
index 0000000000000000000000000000000000000000..5f63b7e93b8e40b2eab27f110a3f51756c6e58f9
--- /dev/null
+++ b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/2.2 Download the YOLO files (if not using the VM).html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://1drv.ms/u/s!AkTkTuTv8A66dAvPsd9zbDSYLeI";</script>
\ No newline at end of file
diff --git a/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/3. Experiment with YOLO on still images, webcam and videos.srt b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/3. Experiment with YOLO on still images, webcam and videos.srt
new file mode 100644
index 0000000000000000000000000000000000000000..b934a1904d41d3906767033a01c59bb24bbe4b46
--- /dev/null
+++ b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/3. Experiment with YOLO on still images, webcam and videos.srt	
@@ -0,0 +1,547 @@
+1
+00:00:00,470 --> 00:00:06,750
+I will come to Chapter 22 point to where we talk about dark blue and dark blue is basically how we can
+
+2
+00:00:06,750 --> 00:00:13,150
+interface with Ulo inside of plate and run some cool tests on images videos and draw web cam as well.
+
+3
+00:00:14,240 --> 00:00:20,150
+So basically this is actually is a Jeff of basically being using the video.
+
+4
+00:00:20,150 --> 00:00:21,360
+It's pretty cool.
+
+5
+00:00:21,380 --> 00:00:22,990
+Hope it's distracting you right now.
+
+6
+00:00:23,360 --> 00:00:27,850
+But basically this is how we actually create the story and create.
+
+7
+00:00:27,890 --> 00:00:35,360
+But this is how we actually set up the environment for Ulo Darfur I should say as the install isn't
+
+8
+00:00:35,360 --> 00:00:35,620
+dead.
+
+9
+00:00:35,630 --> 00:00:38,950
+Hard to do just a bunch of things we have to do.
+
+10
+00:00:39,200 --> 00:00:44,490
+So what I've done here basically have created a new environment using Anaconda.
+
+11
+00:00:44,510 --> 00:00:46,030
+Basically you can call whatever you want.
+
+12
+00:00:46,030 --> 00:00:51,830
+I've used I use it as my tensor flew with the environment previously so you can call it that if you
+
+13
+00:00:51,830 --> 00:00:55,520
+want to call it you know anything you want to do.
+
+14
+00:00:55,520 --> 00:00:56,930
+It's already installed right now.
+
+15
+00:00:56,930 --> 00:01:02,300
+So if you're using this virtual machine which I hope we do which I hope you don't want it you don't
+
+16
+00:01:02,300 --> 00:01:06,360
+have to do this and tedious install again but it doesn't actually take that long.
+
+17
+00:01:06,410 --> 00:01:12,020
+So just run these commands line by line in the terminal and it should be fine.
+
+18
+00:01:12,150 --> 00:01:12,660
+OK.
+
+19
+00:01:13,220 --> 00:01:14,130
+Except for this one.
+
+20
+00:01:14,180 --> 00:01:18,050
+Basically you're going to have to create the environment manually on your own but that's not too hard
+
+21
+00:01:18,050 --> 00:01:18,340
+to do.
+
+22
+00:01:18,340 --> 00:01:21,780
+So sort of duck flow install.
+
+23
+00:01:21,790 --> 00:01:28,130
+So now go see a terminal and basically start with your home territory in the terminal and make it an
+
+24
+00:01:28,140 --> 00:01:29,160
+article of flow.
+
+25
+00:01:29,160 --> 00:01:36,690
+Go into it install site and basically clone this repository here go to that for the Pipp install and
+
+26
+00:01:36,690 --> 00:01:43,950
+do this business all talk by the way and go get this get the weights here and then basically start to
+
+27
+00:01:44,010 --> 00:01:48,070
+this surgery hip to see if she can.
+
+28
+00:01:48,160 --> 00:01:50,860
+And now let's mess around with the all in Python.
+
+29
+00:01:50,900 --> 00:01:57,460
+So now he can actually import duckbilled thought in that build and import from the starchy import this
+
+30
+00:01:57,670 --> 00:01:58,660
+function here.
+
+31
+00:01:59,110 --> 00:02:04,570
+So no I'm going to go back to this and we're going to go to our vision machine and actually use this
+
+32
+00:02:04,570 --> 00:02:05,070
+now.
+
+33
+00:02:05,370 --> 00:02:05,680
+OK.
+
+34
+00:02:05,710 --> 00:02:11,830
+So from what I play on the browser you don't go into the deepening C-v territory you go into awfuller
+
+35
+00:02:12,520 --> 00:02:17,840
+and go to the Darfur master directory here and you'll see a file called a tutorial.
+
+36
+00:02:18,100 --> 00:02:19,520
+Open this notebook here.
+
+37
+00:02:19,570 --> 00:02:22,360
+This is why I compiled you guys.
+
+38
+00:02:22,680 --> 00:02:25,850
+And basically this is how we used offline side of Pitre.
+
+39
+00:02:26,250 --> 00:02:29,840
+So firstly let's run this block of code.
+
+40
+00:02:30,000 --> 00:02:37,020
+What this does here this actually loads our model All right using tensor Flo's T.F. net and all this
+
+41
+00:02:37,020 --> 00:02:40,590
+stuff we actually load the model we want from the CFQ directory.
+
+42
+00:02:40,680 --> 00:02:42,190
+We load the so we want.
+
+43
+00:02:42,420 --> 00:02:43,720
+We set up trouble.
+
+44
+00:02:43,980 --> 00:02:49,770
+And if you're using a GPS you can specify AGP one however you you use false since this is a virtual
+
+45
+00:02:49,770 --> 00:02:52,810
+machine and we don't have access to you from here.
+
+46
+00:02:52,810 --> 00:02:53,380
+All right.
+
+47
+00:02:53,460 --> 00:02:58,570
+And then we passed these options that we created here to RTFM that class object.
+
+48
+00:02:58,630 --> 00:02:59,600
+Right.
+
+49
+00:03:00,420 --> 00:03:06,750
+So you can see it basically updated the model here loaded everything happening successfully around and
+
+50
+00:03:06,750 --> 00:03:08,060
+we now have a model.
+
+51
+00:03:08,370 --> 00:03:15,150
+So now what we need to do is convert our open C-v BGR image to R.G. format.
+
+52
+00:03:15,150 --> 00:03:21,990
+This is something that's pretty annoying about open C-v with loads images and b g r by default.
+
+53
+00:03:22,010 --> 00:03:26,160
+RGV said it see if you can be actually made a function that can get it for us.
+
+54
+00:03:26,160 --> 00:03:27,340
+So it's not a big deal.
+
+55
+00:03:27,660 --> 00:03:30,600
+So we're running this on our sample horse's image.
+
+56
+00:03:30,600 --> 00:03:32,850
+And let's go and see what that image looks like.
+
+57
+00:03:32,860 --> 00:03:38,100
+Go to darf flu go to think of a sample image here.
+
+58
+00:03:38,430 --> 00:03:41,760
+And I believe the one we called with horses or sample horses.
+
+59
+00:03:41,760 --> 00:03:42,540
+This one.
+
+60
+00:03:42,870 --> 00:03:50,780
+So now we've run this image inside of our vitamin book and we got basically some bounding boxes here
+
+61
+00:03:51,360 --> 00:03:57,410
+and some confidence scores and a label but we don't actually have an image.
+
+62
+00:03:57,450 --> 00:04:03,190
+So that's the play our results using open C-v Tado.
+
+63
+00:04:03,190 --> 00:04:05,380
+So no this is actually pretty cool no.
+
+64
+00:04:05,620 --> 00:04:11,200
+So we've displayed at our bounding boxes here using up and see if we would do labels.
+
+65
+00:04:11,440 --> 00:04:13,450
+So this is pretty neat.
+
+66
+00:04:13,450 --> 00:04:16,080
+So we can encapsulate that function.
+
+67
+00:04:16,120 --> 00:04:19,470
+That's actually the function we call here display results.
+
+68
+00:04:19,480 --> 00:04:21,290
+I'm actually using this I'm not entirely sure.
+
+69
+00:04:21,330 --> 00:04:23,170
+I think I was using it.
+
+70
+00:04:23,170 --> 00:04:24,290
+No I'm not using it.
+
+71
+00:04:24,340 --> 00:04:28,430
+It's pretty much pointless unless you are missing it down here.
+
+72
+00:04:28,510 --> 00:04:30,530
+Sorry my mistake.
+
+73
+00:04:30,780 --> 00:04:32,320
+Happened to me to me a lot.
+
+74
+00:04:32,320 --> 00:04:35,970
+Read my own code wrong so much.
+
+75
+00:04:36,080 --> 00:04:41,630
+I think something is wrong with me sometimes anyhow so this is a code here to load it to run your little
+
+76
+00:04:41,680 --> 00:04:42,970
+troll webcam.
+
+77
+00:04:42,970 --> 00:04:46,100
+So we basically load them all exactly the same way we did before.
+
+78
+00:04:46,390 --> 00:04:49,320
+We actually don't need to do it twice but it's part of.
+
+79
+00:04:49,320 --> 00:04:55,140
+I just posted it here in case you wanted to run this separately from this on top and we re-initialize
+
+80
+00:04:55,140 --> 00:05:00,310
+a web cam here from open TV and we run this so it's going to take a while to run out of what 10 seconds
+
+81
+00:05:00,370 --> 00:05:01,740
+seven seconds before.
+
+82
+00:05:02,140 --> 00:05:05,910
+And now our webcam for him is going to pop up here shortly.
+
+83
+00:05:07,340 --> 00:05:08,810
+Well no results.
+
+84
+00:05:08,830 --> 00:05:09,660
+It's not defined.
+
+85
+00:05:09,730 --> 00:05:11,590
+That's quite funny.
+
+86
+00:05:11,620 --> 00:05:13,500
+So let's do this again sorry.
+
+87
+00:05:13,530 --> 00:05:14,200
+By the way it's
+
+88
+00:05:18,120 --> 00:05:23,580
+now one thing you're going to notice is that it actually runs very slowly on a super UBS system.
+
+89
+00:05:23,670 --> 00:05:29,180
+The frame rate is nowhere near as good as SSD.
+
+90
+00:05:29,290 --> 00:05:34,420
+Any minute any second now I should say webcam for him is going to pop up here.
+
+91
+00:05:45,750 --> 00:05:50,540
+We have so you have a webcam image.
+
+92
+00:05:50,540 --> 00:05:54,410
+Right now it doesn't look smooth at all.
+
+93
+00:05:54,410 --> 00:06:00,770
+And we see multiple boxes here now the multiple box problem could be is actually because of the trouble
+
+94
+00:06:00,770 --> 00:06:04,960
+we set if we set this close system because it's very slow.
+
+95
+00:06:05,030 --> 00:06:08,290
+If we said attritional And actually I don't have the Trishul parameter here.
+
+96
+00:06:08,570 --> 00:06:12,550
+So let's go back to the top and set Trishul here.
+
+97
+00:06:14,110 --> 00:06:15,640
+And DUDAS here.
+
+98
+00:06:16,790 --> 00:06:17,200
+All right.
+
+99
+00:06:17,240 --> 00:06:18,720
+You can send us to point five.
+
+100
+00:06:18,710 --> 00:06:22,570
+You'll actually see much less banging boxes on that image.
+
+101
+00:06:22,580 --> 00:06:27,480
+So now let's run Ulo basically on video.
+
+102
+00:06:29,740 --> 00:06:31,400
+See how it looks.
+
+103
+00:06:31,430 --> 00:06:31,720
+OK.
+
+104
+00:06:31,760 --> 00:06:33,140
+May have seen this in my entire video.
+
+105
+00:06:33,140 --> 00:06:38,890
+So this is it running frame by frame on an elephant on the couch and a person sometimes.
+
+106
+00:06:38,900 --> 00:06:40,820
+So let's stop this for now.
+
+107
+00:06:41,300 --> 00:06:47,010
+And what we can do if you want to experiment is let's actually load this model.
+
+108
+00:06:47,100 --> 00:06:49,160
+All right separately though.
+
+109
+00:06:49,550 --> 00:06:55,430
+So let's actually we can just run it loaded from up here and it's said the Trishul two point six.
+
+110
+00:06:55,430 --> 00:06:55,850
+All right.
+
+111
+00:06:58,450 --> 00:07:02,880
+Doesn't take that long to do maybe about 10 seconds to run.
+
+112
+00:07:02,880 --> 00:07:03,890
+There we go it's done.
+
+113
+00:07:03,960 --> 00:07:07,690
+And now let's go back to this video.
+
+114
+00:07:09,790 --> 00:07:10,660
+There we go.
+
+115
+00:07:10,660 --> 00:07:11,920
+And you should see definitely.
+
+116
+00:07:11,920 --> 00:07:12,780
+There we go.
+
+117
+00:07:12,790 --> 00:07:14,200
+It's just one box.
+
+118
+00:07:14,200 --> 00:07:16,570
+And this is actually for me it isn't about.
+
+119
+00:07:16,890 --> 00:07:17,970
+And actually no disappear.
+
+120
+00:07:17,980 --> 00:07:23,260
+So maybe that threshold is too high but you can see we only have one bounding box here and it's getting
+
+121
+00:07:23,430 --> 00:07:27,480
+it as an elephant right all the time except when it disappears.
+
+122
+00:07:27,700 --> 00:07:29,310
+So this is pretty sick.
+
+123
+00:07:30,400 --> 00:07:32,330
+So let's close up now.
+
+124
+00:07:32,690 --> 00:07:37,200
+So that concludes only yellow a tutorial.
+
+125
+00:07:37,520 --> 00:07:39,710
+These are the staunch options out of left here.
+
+126
+00:07:40,070 --> 00:07:45,410
+You may have seen me scrolling back and forth trying to figure out why discoid wasn't working.
+
+127
+00:07:45,590 --> 00:07:52,130
+What happens in open sea is that if something opens a webcam and then this code it crashes inside like
+
+128
+00:07:52,130 --> 00:07:54,500
+this could disappear results were not found.
+
+129
+00:07:54,830 --> 00:07:57,480
+What happens is that we now need to run this line here.
+
+130
+00:07:57,740 --> 00:08:00,280
+These two lines to be released a webcam.
+
+131
+00:08:00,320 --> 00:08:05,210
+So what happened is that when I try to initiate a webcam again it just got stuck and it had to go to
+
+132
+00:08:05,210 --> 00:08:08,020
+kernel and restart the notebook and wait bit.
+
+133
+00:08:08,350 --> 00:08:13,820
+So it's good knowledge to know when you're playing with this and maybe something you mess up something.
+
+134
+00:08:13,820 --> 00:08:14,790
+You know what to do.
+
+135
+00:08:15,050 --> 00:08:21,790
+Just run this line to basically recapture your webcam.
+
+136
+00:08:21,820 --> 00:08:26,060
+All right so later on we're going to see how to actually make and model.
+
+137
+00:08:26,070 --> 00:08:28,640
+So this is a model we were going to make in the next section.
diff --git a/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/4. Build your own YOLO Object Detector - Detecting London Underground Signs.srt b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/4. Build your own YOLO Object Detector - Detecting London Underground Signs.srt
new file mode 100644
index 0000000000000000000000000000000000000000..452f711d74ca04bc305fdedf2eb93c7f7bc00178
--- /dev/null
+++ b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/4. Build your own YOLO Object Detector - Detecting London Underground Signs.srt	
@@ -0,0 +1,1011 @@
+1
+00:00:00,580 --> 00:00:01,190
+I guess.
+
+2
+00:00:01,200 --> 00:00:08,970
+Hi and welcome back to Chapter 22 point tree where we're about to build on our very own customized yellow
+
+3
+00:00:09,060 --> 00:00:10,180
+object detector.
+
+4
+00:00:10,590 --> 00:00:15,980
+This one is going to detect London Underground signs so let's get started and see how we do this.
+
+5
+00:00:15,990 --> 00:00:17,380
+So just something to note.
+
+6
+00:00:17,380 --> 00:00:22,620
+Firstly doing this without a GP who is going to be very slow and we're not going to actually make a
+
+7
+00:00:22,620 --> 00:00:27,100
+very good object the doctor is going to have fit quite a bit.
+
+8
+00:00:27,330 --> 00:00:31,890
+And also because I'm teaching from my virtual box I don't have access to my GP so we don't have a choice
+
+9
+00:00:31,920 --> 00:00:38,610
+but to use OCP you and also the Trina's using a GP who doesn't require a different set up than anything
+
+10
+00:00:38,730 --> 00:00:39,480
+we're doing here.
+
+11
+00:00:39,750 --> 00:00:43,640
+Just a few additional commands but I'll show it to you in this slide.
+
+12
+00:00:43,650 --> 00:00:44,570
+OK.
+
+13
+00:00:45,460 --> 00:00:53,310
+So first of all to make a custom image doctor you basically have to create your own custom data set.
+
+14
+00:00:53,340 --> 00:00:58,880
+So this software called label image which is found here available for Windows Mac and Linux.
+
+15
+00:00:58,920 --> 00:01:00,480
+It's quite easy to use.
+
+16
+00:01:00,510 --> 00:01:06,600
+And what do you have to do is basically set select a form that will open a directory that you want to
+
+17
+00:01:06,600 --> 00:01:07,680
+use.
+
+18
+00:01:07,680 --> 00:01:15,780
+This is the directory I used here for my TEFL images and you basically said to format Pascal VRC and
+
+19
+00:01:15,810 --> 00:01:18,010
+basically you just drag and drop.
+
+20
+00:01:18,060 --> 00:01:19,860
+Actually I can show it to you right now.
+
+21
+00:01:19,860 --> 00:01:22,980
+Let's quickly go to label image here.
+
+22
+00:01:22,980 --> 00:01:23,850
+All right.
+
+23
+00:01:24,180 --> 00:01:27,960
+So this is actually some images here.
+
+24
+00:01:28,200 --> 00:01:29,210
+So let's see.
+
+25
+00:01:29,400 --> 00:01:35,220
+Let's clear this one is to leave it and let's delete it and actually label some images here and let's
+
+26
+00:01:35,230 --> 00:01:38,050
+make sure we're all with it afterward.
+
+27
+00:01:38,280 --> 00:01:40,850
+Let's create a box here.
+
+28
+00:01:41,760 --> 00:01:42,050
+All right.
+
+29
+00:01:42,090 --> 00:01:44,960
+And let's call this London Underground.
+
+30
+00:01:45,060 --> 00:01:49,560
+And now once we do that it's going to be saved as our class here.
+
+31
+00:01:49,980 --> 00:01:50,700
+All right.
+
+32
+00:01:50,700 --> 00:01:51,880
+So that's pretty cool.
+
+33
+00:01:52,080 --> 00:01:56,740
+What I want to tell you is make sure the format is set on Pascrell VRC not Iolo.
+
+34
+00:01:56,970 --> 00:01:57,910
+And that's odd.
+
+35
+00:01:58,140 --> 00:02:02,470
+But you'll actually use to use your banalities as possible the A C.
+
+36
+00:02:02,580 --> 00:02:03,910
+So let's create another box.
+
+37
+00:02:03,910 --> 00:02:08,050
+Here is how we do a second box an image and press OK.
+
+38
+00:02:08,370 --> 00:02:10,950
+So now we have those two boxes here.
+
+39
+00:02:11,310 --> 00:02:14,050
+And all we have to do is just complete this.
+
+40
+00:02:14,050 --> 00:02:19,610
+See if it saves a TD or a tree here in Exham or format usually you want to save it.
+
+41
+00:02:19,680 --> 00:02:26,380
+You can do this manually all use a script to rename it properly but call it like 0 0 1 x amount.
+
+42
+00:02:27,120 --> 00:02:28,170
+And there we go.
+
+43
+00:02:28,470 --> 00:02:32,340
+So let's go back to our presentation.
+
+44
+00:02:32,340 --> 00:02:40,490
+So this is what we do so for and I've actually got 100 images of London underground tube sign in various
+
+45
+00:02:40,940 --> 00:02:42,390
+parts of London.
+
+46
+00:02:42,500 --> 00:02:45,540
+That's what you're going to have to do even to have to get images yourself.
+
+47
+00:02:45,550 --> 00:02:48,340
+It is true web scraper or image Skipworth sorry.
+
+48
+00:02:48,680 --> 00:02:53,000
+Or just manually using a google images and finding them on your own.
+
+49
+00:02:53,030 --> 00:02:55,640
+So there are some things I learned the hard way.
+
+50
+00:02:55,640 --> 00:03:02,300
+Make sure your images names are labeled like 001 much like some random digits like I did here because
+
+51
+00:03:02,330 --> 00:03:06,580
+you're going to have to mentally adjust them afterward so you can easily do a script to fix it.
+
+52
+00:03:06,590 --> 00:03:08,070
+But I didn't know at the time.
+
+53
+00:03:08,150 --> 00:03:11,090
+So I actually had to relabel my images twice.
+
+54
+00:03:11,120 --> 00:03:11,840
+It was a bit tedious.
+
+55
+00:03:11,840 --> 00:03:12,750
+It didn't have to do that.
+
+56
+00:03:12,770 --> 00:03:17,210
+But it was easier because it just it was a hundred images it was easier than doing a script to do it.
+
+57
+00:03:17,210 --> 00:03:18,840
+At that point in time.
+
+58
+00:03:19,130 --> 00:03:22,490
+And then we use the saved files in Pascal format.
+
+59
+00:03:22,550 --> 00:03:23,910
+Just remember that.
+
+60
+00:03:24,090 --> 00:03:30,570
+Because you went out in Jess's and just the dog axonal files so what else to know.
+
+61
+00:03:30,670 --> 00:03:31,160
+OK.
+
+62
+00:03:31,450 --> 00:03:37,210
+So we need to set up this file structure here and I'll go to a visual machine very shortly to show either
+
+63
+00:03:37,450 --> 00:03:38,770
+the file structure.
+
+64
+00:03:38,770 --> 00:03:41,500
+Actually I can do it to you now but let me discuss it first.
+
+65
+00:03:41,710 --> 00:03:42,060
+OK.
+
+66
+00:03:42,160 --> 00:03:48,600
+So this ensures I will tell you why this is important enough to will again if you don't do it this way.
+
+67
+00:03:48,790 --> 00:03:55,570
+But it will come in handy in the end mainly because when the actual files have the folder name that
+
+68
+00:03:55,630 --> 00:03:59,880
+it's in the image name and basically the pat to the file.
+
+69
+00:04:00,130 --> 00:04:08,740
+So I did this in Windows initially and what I had to do was to go into make a Python script to examine
+
+70
+00:04:08,740 --> 00:04:14,860
+this XML file and basically make all the changes for me manually or probably a blow the scripts if you
+
+71
+00:04:14,860 --> 00:04:21,150
+want afterward I'll clean it up a bit but it was not fun to do to correct these mistakes.
+
+72
+00:04:21,250 --> 00:04:24,010
+So let me go to the virtual machine now.
+
+73
+00:04:24,390 --> 00:04:25,100
+It's here.
+
+74
+00:04:26,440 --> 00:04:28,850
+And let's find this territory.
+
+75
+00:04:29,350 --> 00:04:33,120
+Actually for Call it actually I was looking at now.
+
+76
+00:04:33,340 --> 00:04:43,010
+So no I believe it was in top nets here.
+
+77
+00:04:44,480 --> 00:04:46,800
+Actually I mean distric dark flow.
+
+78
+00:04:47,060 --> 00:04:51,850
+So definitely darknet Darfur Doxil master Treen images annotations.
+
+79
+00:05:02,560 --> 00:05:06,770
+OK so ignore all of these files I have in the search area.
+
+80
+00:05:06,860 --> 00:05:09,840
+These were just a backup copy I made of them.
+
+81
+00:05:09,830 --> 00:05:12,800
+So annotations these are the files are important.
+
+82
+00:05:12,810 --> 00:05:21,080
+Now what's funny is that usually you train these doctors with hundreds of thousands of images.
+
+83
+00:05:21,290 --> 00:05:25,220
+I actually trained them only using 5 images.
+
+84
+00:05:25,220 --> 00:05:26,470
+Now I'll tell you why.
+
+85
+00:05:26,720 --> 00:05:28,150
+That's because I'm using a spear.
+
+86
+00:05:28,460 --> 00:05:34,120
+And every time I tried to go to maybe six seven images it would crash during training.
+
+87
+00:05:34,190 --> 00:05:38,320
+So I actually actually had to do a lot of trial and error to get the thing to work right.
+
+88
+00:05:38,330 --> 00:05:42,520
+So basically I spent a lot of time collecting images that I never used.
+
+89
+00:05:42,740 --> 00:05:48,030
+So it's a bit sad but that's OK at least we've learned from my mistake.
+
+90
+00:05:48,050 --> 00:05:50,320
+So let's take a look at this XML file.
+
+91
+00:05:50,420 --> 00:05:55,620
+So as you can see we have annotations annotations being the name of the folder that it's in.
+
+92
+00:05:55,760 --> 00:05:57,670
+We have the file name here.
+
+93
+00:05:57,950 --> 00:06:01,130
+This final game here corresponds to the GOP.
+
+94
+00:06:01,140 --> 00:06:02,260
+Finally I'm here.
+
+95
+00:06:02,750 --> 00:06:03,630
+Sorry.
+
+96
+00:06:03,670 --> 00:06:04,420
+Images.
+
+97
+00:06:04,670 --> 00:06:06,750
+Finally I'm here OK let's go back to it.
+
+98
+00:06:06,770 --> 00:06:08,630
+Get it she added.
+
+99
+00:06:09,140 --> 00:06:14,260
+And other thing to note is that it has a part of the image name here as well.
+
+100
+00:06:14,360 --> 00:06:18,120
+So you're actually the training gasifier training module.
+
+101
+00:06:18,140 --> 00:06:20,720
+Any low actually looks at these file names here.
+
+102
+00:06:20,810 --> 00:06:23,360
+If you have a mistake here it is not going to work.
+
+103
+00:06:23,490 --> 00:06:23,770
+OK.
+
+104
+00:06:23,840 --> 00:06:25,760
+So it's one thing to know.
+
+105
+00:06:25,760 --> 00:06:28,430
+So let's go back to this here.
+
+106
+00:06:28,550 --> 00:06:33,300
+So this was a directory I told you about just in case you were wondering.
+
+107
+00:06:33,320 --> 00:06:36,180
+So yeah I just mentioned this this needs to be corrected.
+
+108
+00:06:37,540 --> 00:06:40,410
+And now we need to go to see if you tertiary.
+
+109
+00:06:40,660 --> 00:06:47,200
+And we need to go to DCF to see if Sorry lower to find to see if you'll file and copy this file and
+
+110
+00:06:47,200 --> 00:06:48,090
+rename it.
+
+111
+00:06:48,130 --> 00:06:52,440
+So since we're using a one class you can call it whatever you want but we can decide whether you'll
+
+112
+00:06:52,550 --> 00:06:54,340
+under school one.
+
+113
+00:06:54,400 --> 00:06:56,490
+So let's see what that file looks like now.
+
+114
+00:07:01,080 --> 00:07:02,310
+Let's go here.
+
+115
+00:07:02,350 --> 00:07:03,770
+Good to see you directory.
+
+116
+00:07:03,780 --> 00:07:13,490
+And look for the file I made previously that would be you know where is it to search and disco.
+
+117
+00:07:13,500 --> 00:07:14,120
+There we go.
+
+118
+00:07:15,360 --> 00:07:19,230
+So let's open this file and we have some information here.
+
+119
+00:07:19,470 --> 00:07:21,440
+So no it looks fine.
+
+120
+00:07:21,450 --> 00:07:26,760
+However we do have to this is a file and we copied it from the original file.
+
+121
+00:07:26,860 --> 00:07:29,160
+The awesome changes we're going to have to make in this file.
+
+122
+00:07:29,250 --> 00:07:37,880
+And I'll tell you in addition so you see this red block I have highlighted here that is at the top of
+
+123
+00:07:37,880 --> 00:07:39,190
+the file here.
+
+124
+00:07:39,310 --> 00:07:40,810
+This bit here.
+
+125
+00:07:40,890 --> 00:07:49,180
+64 subdivisions with in height Let's go back to the presentation we need to make sure these are these
+
+126
+00:07:49,180 --> 00:07:49,860
+numbers here.
+
+127
+00:07:50,110 --> 00:07:54,400
+Now in the original file there are not a naturally or could these are commented out I believe.
+
+128
+00:07:54,400 --> 00:07:59,500
+So we just need to make sure that this looks like this and Jinyan the heightened Loree reducing the
+
+129
+00:07:59,500 --> 00:08:02,310
+height and weight makes it run much faster too.
+
+130
+00:08:02,350 --> 00:08:03,540
+So that's a good thing.
+
+131
+00:08:04,950 --> 00:08:10,540
+So that's added to configure the bottom part of the configuration file also needs to be edited.
+
+132
+00:08:10,560 --> 00:08:12,680
+So is two things we need to edits here.
+
+133
+00:08:13,050 --> 00:08:20,220
+One the convolutional last congressional league here it needs a number of number specific number filters
+
+134
+00:08:20,240 --> 00:08:25,800
+here and that's basically from this formula here and this formula depend on a number of classes you
+
+135
+00:08:25,800 --> 00:08:26,510
+use.
+
+136
+00:08:26,730 --> 00:08:33,400
+So since we use one class is just basically five into one plus six which is tity.
+
+137
+00:08:33,420 --> 00:08:39,430
+However if we use trig classes it will just be five plus times nine which is 45.
+
+138
+00:08:39,900 --> 00:08:42,730
+And the other thing we need to do is set the number of classes here.
+
+139
+00:08:43,020 --> 00:08:51,080
+So let's quickly take a look of that at the bottom just to make sure it's what I said it is good.
+
+140
+00:08:51,150 --> 00:08:53,710
+This is in the conference last convolutional league here.
+
+141
+00:08:53,970 --> 00:08:58,340
+And number filters are set to Tuti and classes is a 2:1.
+
+142
+00:08:58,440 --> 00:09:00,510
+So we're good to go so far.
+
+143
+00:09:01,050 --> 00:09:02,920
+Let's go back to that presentation.
+
+144
+00:09:03,000 --> 00:09:07,910
+And now we also need to create and edit or labels or label textfile.
+
+145
+00:09:08,160 --> 00:09:14,760
+So this one is fairly easy we just need to remember when we were labeling it in Lippe label image that
+
+146
+00:09:14,760 --> 00:09:19,360
+program and we had a object category named London underground.
+
+147
+00:09:19,380 --> 00:09:21,130
+We just need to list out there.
+
+148
+00:09:21,210 --> 00:09:26,610
+So if we had to reliables like cat dog Donald Trump would need to put each one on a new line in his
+
+149
+00:09:26,610 --> 00:09:27,350
+file.
+
+150
+00:09:27,400 --> 00:09:30,470
+So let's go to this directory and talk floor master.
+
+151
+00:09:31,230 --> 00:09:32,800
+And take a look at that.
+
+152
+00:09:35,150 --> 00:09:37,580
+Just to make sure it's done correctly.
+
+153
+00:09:37,660 --> 00:09:38,180
+There we go.
+
+154
+00:09:38,180 --> 00:09:42,310
+So you see this is London London and the ground is level there.
+
+155
+00:09:49,310 --> 00:09:49,660
+OK.
+
+156
+00:09:49,770 --> 00:09:50,810
+So let's keep going.
+
+157
+00:09:52,680 --> 00:10:00,650
+So now we have to do training so to do training we have to go until it's aminal and in Darfur dockmaster
+
+158
+00:10:01,190 --> 00:10:04,980
+Darfur mastered archery execute the following line.
+
+159
+00:10:05,040 --> 00:10:06,530
+That's this line here.
+
+160
+00:10:06,820 --> 00:10:07,470
+All right.
+
+161
+00:10:08,350 --> 00:10:12,960
+So essentially this is a line here we use if we're using a c.p.
+
+162
+00:10:13,090 --> 00:10:15,090
+So take note of this.
+
+163
+00:10:15,100 --> 00:10:16,620
+This has to be correct.
+
+164
+00:10:16,720 --> 00:10:19,900
+So we have to have all of their arteries identify correctly.
+
+165
+00:10:19,900 --> 00:10:24,110
+So it's CMAG and the school configuration file.
+
+166
+00:10:24,280 --> 00:10:30,920
+So you underscore one school class and we LoDo we it's we have previously.
+
+167
+00:10:30,970 --> 00:10:33,910
+So some people put the weights in a bin directory.
+
+168
+00:10:34,080 --> 00:10:42,270
+You just make sure it's lined up correctly and then we have transitions folder and tree and images will
+
+169
+00:10:42,540 --> 00:10:47,540
+be specified in the box if you're using a Jeep you can use this as well.
+
+170
+00:10:47,770 --> 00:10:52,320
+It tells you how much percent that the GPO memory you want to use.
+
+171
+00:10:52,320 --> 00:11:00,300
+So now after some time takes about an hour to always want to see the train of five bucks but that's
+
+172
+00:11:00,300 --> 00:11:05,370
+just what five images you can imagine how long it'll take to train if you have hundreds of thousands
+
+173
+00:11:05,370 --> 00:11:06,370
+of images.
+
+174
+00:11:06,600 --> 00:11:12,420
+So after we do that training is complete and then our model is saved and it's actually here checkpoint
+
+175
+00:11:12,720 --> 00:11:13,530
+file here.
+
+176
+00:11:13,860 --> 00:11:18,450
+Now when we load it back we just have to specify the checkpoint and it knows basically from the model
+
+177
+00:11:18,450 --> 00:11:20,790
+name what checkpoint to look for.
+
+178
+00:11:21,360 --> 00:11:23,600
+So this is how we load it into partem.
+
+179
+00:11:23,910 --> 00:11:28,610
+Basically the same thing we did before except we know specifying this model here.
+
+180
+00:11:28,890 --> 00:11:31,680
+And oh a checkpoint that's all.
+
+181
+00:11:32,370 --> 00:11:33,650
+And this is it.
+
+182
+00:11:33,690 --> 00:11:34,570
+This is what we just build.
+
+183
+00:11:34,570 --> 00:11:36,770
+This is where we spend all the time doing.
+
+184
+00:11:36,870 --> 00:11:42,170
+We built a London Underground classify some of these images on the training data sets of cheating here.
+
+185
+00:11:42,420 --> 00:11:47,860
+But a few of them are actually on the set and actually put them out quite well.
+
+186
+00:11:47,880 --> 00:11:53,160
+I was actually very impressed with how well this worked given that we only trained about five images
+
+187
+00:11:53,730 --> 00:11:58,600
+so let's actually go to division of machine and execute that training.
+
+188
+00:11:58,890 --> 00:12:01,000
+So let me just get the line here.
+
+189
+00:12:07,230 --> 00:12:13,620
+That was not lying it's just lying there reading these comments that are relevant.
+
+190
+00:12:13,620 --> 00:12:16,930
+So this is the line we want to use.
+
+191
+00:12:17,140 --> 00:12:20,270
+So we go to this territory here from two no.
+
+192
+00:12:20,590 --> 00:12:23,800
+So let's open up with your virtual machine.
+
+193
+00:12:23,800 --> 00:12:26,230
+So it's good to see the back.
+
+194
+00:12:26,280 --> 00:12:28,740
+So it's really dark.
+
+195
+00:12:29,080 --> 00:12:31,560
+It's not always mix them up.
+
+196
+00:12:31,570 --> 00:12:38,160
+Ellis the off flew again and then we just run this line up.
+
+197
+00:12:38,170 --> 00:12:39,350
+Something went wrong.
+
+198
+00:12:39,640 --> 00:12:42,330
+That is because we're not in the correct environment.
+
+199
+00:12:42,440 --> 00:12:48,300
+What I do with my environment service activities are the answers to API.
+
+200
+00:12:49,000 --> 00:12:54,220
+And I don't own a pistol but I want to piece it and have it run at the same time.
+
+201
+00:12:54,280 --> 00:13:01,530
+So I'm just going to make sure we do have that character courage and so let's copy it here.
+
+202
+00:13:02,660 --> 00:13:09,510
+Go back to it here and let's specify Let's see 25 ebox so you can actually watch a run.
+
+203
+00:13:10,040 --> 00:13:13,290
+And this should work fine.
+
+204
+00:13:13,340 --> 00:13:14,150
+Let's see it go.
+
+205
+00:13:18,590 --> 00:13:23,710
+I'm actually going to leave those five images for you so you can actually start training this class
+
+206
+00:13:23,800 --> 00:13:25,630
+this object on your own.
+
+207
+00:13:26,640 --> 00:13:27,860
+So you can have fun doing that.
+
+208
+00:13:30,370 --> 00:13:33,440
+So it takes a while to get started and you do see some warnings as well.
+
+209
+00:13:33,810 --> 00:13:39,060
+You see some statistics here saying that there are seven objects detected in images just at five.
+
+210
+00:13:39,240 --> 00:13:44,360
+That's because one or two of those images had more than one London Underground sign.
+
+211
+00:13:44,670 --> 00:13:46,640
+And you can see it's going to e.g. park.
+
+212
+00:13:46,860 --> 00:13:47,990
+It's actually going quite quickly.
+
+213
+00:13:48,010 --> 00:13:52,110
+So draining family parks isn't going to take that long.
+
+214
+00:13:52,380 --> 00:13:53,520
+Maybe just over an hour.
+
+215
+00:13:53,550 --> 00:13:56,370
+Actually And there we go.
+
+216
+00:13:56,370 --> 00:13:58,600
+It's going to get it's going to be finished soon.
+
+217
+00:14:00,810 --> 00:14:02,330
+In fact that's probably finished now.
+
+218
+00:14:03,780 --> 00:14:06,010
+A Wait how many e-books they specify.
+
+219
+00:14:06,160 --> 00:14:07,150
+Twenty five bucks.
+
+220
+00:14:07,150 --> 00:14:11,380
+Oh damn those five got so used to treating five bucks.
+
+221
+00:14:12,940 --> 00:14:14,520
+Anyway while that's running.
+
+222
+00:14:14,740 --> 00:14:18,030
+Let's actually use the model we created before.
+
+223
+00:14:18,250 --> 00:14:18,930
+OK.
+
+224
+00:14:19,270 --> 00:14:22,420
+So that would actually be in this file here.
+
+225
+00:14:22,420 --> 00:14:25,920
+No that's the tenths of one it was.
+
+226
+00:14:26,020 --> 00:14:27,310
+Yes this one.
+
+227
+00:14:27,340 --> 00:14:29,540
+So that's test only object detector.
+
+228
+00:14:29,950 --> 00:14:32,480
+So this is the object we're going to load here.
+
+229
+00:14:32,650 --> 00:14:33,270
+OK.
+
+230
+00:14:33,670 --> 00:14:36,800
+So I turn them all up to 400 ebox.
+
+231
+00:14:36,810 --> 00:14:43,030
+I actually did it once at 500 but then I added in some more images and it turned that 400 just tested.
+
+232
+00:14:43,210 --> 00:14:49,030
+This is the trouble we're going to use so let's This shouldn't take that long to load.
+
+233
+00:14:52,490 --> 00:14:55,910
+Almost done thanks for telling me it's funny.
+
+234
+00:14:55,920 --> 00:14:56,750
+Tell you
+
+235
+00:15:02,030 --> 00:15:02,480
+OK.
+
+236
+00:15:02,820 --> 00:15:03,100
+Good.
+
+237
+00:15:03,150 --> 00:15:04,540
+We've loaded our model.
+
+238
+00:15:04,760 --> 00:15:09,800
+Now it's actually cycled through some test images here from our test dataset.
+
+239
+00:15:11,520 --> 00:15:13,800
+Damn it's pretty good.
+
+240
+00:15:13,860 --> 00:15:14,580
+Good.
+
+241
+00:15:14,580 --> 00:15:15,690
+This one got it right.
+
+242
+00:15:15,690 --> 00:15:20,520
+But then if I do this as a one London underground tube same it clearly is not.
+
+243
+00:15:20,540 --> 00:15:21,870
+So let's see what else.
+
+244
+00:15:22,240 --> 00:15:28,200
+Again looking at maybe some curve here perhaps got this one right.
+
+245
+00:15:28,520 --> 00:15:29,620
+Got this one right.
+
+246
+00:15:29,630 --> 00:15:31,060
+This one right.
+
+247
+00:15:31,100 --> 00:15:35,090
+This one I got two boxes so we should have used them none maximal suppression.
+
+248
+00:15:35,110 --> 00:15:37,450
+I got him still didn't get any here.
+
+249
+00:15:37,490 --> 00:15:44,230
+Oddly got this one surprisingly And this one and this one too.
+
+250
+00:15:44,280 --> 00:15:50,700
+So as you can see our model actually performs fairly well given that we only used 5 images.
+
+251
+00:15:50,790 --> 00:15:56,730
+So I encourage you to experiment experiment with this make your own optic detect detector and maybe
+
+252
+00:15:56,730 --> 00:15:58,270
+show it to the rest of the students in the class.
+
+253
+00:15:58,290 --> 00:16:04,710
+Upload it to upload your model wait's to all of you Demy form.
diff --git a/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/4.1 Download London Underground Dataset.html b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/4.1 Download London Underground Dataset.html
new file mode 100644
index 0000000000000000000000000000000000000000..056de201e153c91f319d7524577ad76c11cf2094
--- /dev/null
+++ b/22. Object Detection with YOLO & Darkflow Build a London Underground Sign Detector/4.1 Download London Underground Dataset.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1de4CDptnz79G82M_0Xfdddnvler2oMui/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/22. Object Detection with YOLO v3 and DarkFlow/Go to the folder speciefid in this file b/22. Object Detection with YOLO v3 and DarkFlow/Go to the folder speciefid in this file
new file mode 100644
index 0000000000000000000000000000000000000000..a594c42039028433b07ac5f1b5c2913dcb2af5b7
--- /dev/null
+++ b/22. Object Detection with YOLO v3 and DarkFlow/Go to the folder speciefid in this file	
@@ -0,0 +1,8 @@
+INSTRUCTIONS
+
+
+GO TO THIS FOLDER FROM YOUR IPYTHON NOTEBOOK
+/home/deeplearningcv/darkflow/darkflow-master
+
+OPEN THIS FILE
+/home/deeplearningcv/darkflow/darkflow-master/YOLO Tutorial.ipynb
diff --git a/23. DeepDream & Neural Style Transfers Make AI Generated Art/1. Chapter Introduction.srt b/23. DeepDream & Neural Style Transfers Make AI Generated Art/1. Chapter Introduction.srt
new file mode 100644
index 0000000000000000000000000000000000000000..333cede9badac5844c9507b92d2b9e1a5979af72
--- /dev/null
+++ b/23. DeepDream & Neural Style Transfers Make AI Generated Art/1. Chapter Introduction.srt	
@@ -0,0 +1,19 @@
+1
+00:00:00,570 --> 00:00:06,870
+Hi and welcome to a chapter 23 where we take a look at deep dream and what old style chances are.
+
+2
+00:00:07,140 --> 00:00:09,900
+So this section is built up into two simple sections.
+
+3
+00:00:09,900 --> 00:00:14,350
+This one is on deep dream and how AI generated at all started.
+
+4
+00:00:14,520 --> 00:00:18,520
+And then we take a look in twenty three point to 1 year old cell transfers.
+
+5
+00:00:18,720 --> 00:00:19,840
+So let's get started.
diff --git "a/23. DeepDream & Neural Style Transfers Make AI Generated Art/2. DeepDream \342\200\223 How AI Generated Art All Started.srt" "b/23. DeepDream & Neural Style Transfers Make AI Generated Art/2. DeepDream \342\200\223 How AI Generated Art All Started.srt"
new file mode 100644
index 0000000000000000000000000000000000000000..fad288bca587bc074c93779488b0e696b63b7d9a
--- /dev/null
+++ "b/23. DeepDream & Neural Style Transfers Make AI Generated Art/2. DeepDream \342\200\223 How AI Generated Art All Started.srt"	
@@ -0,0 +1,487 @@
+1
+00:00:00,580 --> 00:00:04,630
+I welcome to chapter twenty three point one on deep dream.
+
+2
+00:00:04,630 --> 00:00:08,820
+Basically this explains also how agented art all started.
+
+3
+00:00:08,920 --> 00:00:10,420
+So let's begin.
+
+4
+00:00:10,680 --> 00:00:12,340
+A deep dream or deep dreaming.
+
+5
+00:00:12,760 --> 00:00:20,780
+Now researchers once oxter question what if we ran a pre-trained CNN interface and this was the result.
+
+6
+00:00:20,860 --> 00:00:22,460
+Pretty beautiful actually.
+
+7
+00:00:22,480 --> 00:00:27,370
+Well isn't that exactly how it started with these are the results of our deep dream generator on some
+
+8
+00:00:27,460 --> 00:00:28,580
+image samples here.
+
+9
+00:00:28,630 --> 00:00:32,260
+You can create such some very nice pieces here.
+
+10
+00:00:32,560 --> 00:00:35,360
+So let's see how we do this.
+
+11
+00:00:35,410 --> 00:00:39,070
+So let's talk a bit about generated art content.
+
+12
+00:00:39,250 --> 00:00:45,100
+So before Google's dream was announced very few people in the world even taught AI in the near future
+
+13
+00:00:45,520 --> 00:00:50,900
+would be able to produce art and all that's pretty much indistinguishable from human creations.
+
+14
+00:00:50,950 --> 00:00:56,260
+And while we aren't quite there yet Google's deep dream was the first really sure that you can generate
+
+15
+00:00:56,260 --> 00:00:58,950
+exciting and enjoyable content.
+
+16
+00:00:58,990 --> 00:01:01,590
+These are some of the results from Google's dream.
+
+17
+00:01:01,750 --> 00:01:09,190
+It is an artistic modification technique that uses representations Linda by CNN's to produce hallucinogenic
+
+18
+00:01:09,220 --> 00:01:11,520
+or trippy images like this.
+
+19
+00:01:11,650 --> 00:01:17,890
+And it was introduced by these guys here from Google research team in 2015 in a publication titled deep
+
+20
+00:01:17,890 --> 00:01:25,270
+dream code if we're examining a code example of visualizing neural networks and it became extremely
+
+21
+00:01:25,270 --> 00:01:30,570
+popular since it was announced and it has produced many iconic deep dream art pieces.
+
+22
+00:01:30,700 --> 00:01:36,120
+Stuff like this which I'm sure you've seen in many blogs and online magazines as well.
+
+23
+00:01:36,160 --> 00:01:38,530
+So let's see how this all works.
+
+24
+00:01:38,530 --> 00:01:42,730
+So remember how we created our filter visualizations in chapter 9.
+
+25
+00:01:42,730 --> 00:01:49,780
+We ran CNN in reverse doing gradient ascent on the input to maximize the activation of specific filters
+
+26
+00:01:50,140 --> 00:01:52,600
+in the top players of the preacher and CNN.
+
+27
+00:01:52,600 --> 00:01:59,260
+Well Dream follows a similar methodologies but with some variations what we do is we first attempt to
+
+28
+00:01:59,260 --> 00:02:04,270
+maximize activation of entirely as instead of one specific filter.
+
+29
+00:02:04,300 --> 00:02:08,080
+This allows us to mix several visualizations into one.
+
+30
+00:02:08,140 --> 00:02:12,540
+We start with an existing image instead of a blank one noisy image like we did before.
+
+31
+00:02:12,820 --> 00:02:18,400
+And this allows the effect of the convolutional filters to work the magic by distorting elements in
+
+32
+00:02:18,400 --> 00:02:25,060
+the image that produce pleasing visualizations the input images are processed at different scales also
+
+33
+00:02:25,060 --> 00:02:31,510
+called octaves to improve the quality of the visualization and that's it for deep dreama algorithm.
+
+34
+00:02:31,570 --> 00:02:36,360
+We're not going to go to what I write in the book and actually implement its algorithm on our own.
+
+35
+00:02:36,420 --> 00:02:36,990
+That's DO IT.
+
+36
+00:02:38,990 --> 00:02:42,110
+OK so let's actually run deep dream now.
+
+37
+00:02:42,190 --> 00:02:42,490
+OK.
+
+38
+00:02:42,490 --> 00:02:46,740
+So let's open the primas in the number 23 DB dreamful the here.
+
+39
+00:02:46,930 --> 00:02:47,530
+Get to it.
+
+40
+00:02:47,530 --> 00:02:51,720
+So you know where to find it and have it open already.
+
+41
+00:02:51,790 --> 00:02:53,200
+So again.
+
+42
+00:02:53,290 --> 00:02:58,740
+Well not again but I should say this code came from Francois shaly the creator of keris.
+
+43
+00:02:58,930 --> 00:03:04,240
+And basically what he says here as well as the inception of version 3 produces some pretty nice visuals
+
+44
+00:03:04,270 --> 00:03:07,270
+but veejay 16 to 19 the others do as well.
+
+45
+00:03:07,270 --> 00:03:14,080
+I've actually realized inception tree is actually the best player of all there isn't a huge difference.
+
+46
+00:03:14,080 --> 00:03:14,890
+To be fair.
+
+47
+00:03:15,180 --> 00:03:18,340
+But inception 3 definitely produced nice images.
+
+48
+00:03:18,340 --> 00:03:24,050
+So here's how we implement deep dream in course fiercly we load model as we have done before.
+
+49
+00:03:24,070 --> 00:03:26,220
+We don't need to load resonant system into experiments.
+
+50
+00:03:26,230 --> 00:03:27,620
+Prior to this.
+
+51
+00:03:27,640 --> 00:03:34,780
+So and then what we do here by doing this variable here key thoughts set linning Fiers underscore here
+
+52
+00:03:35,680 --> 00:03:36,530
+to zero.
+
+53
+00:03:36,730 --> 00:03:42,790
+This setting disables this setting symbols all training specific operations.
+
+54
+00:03:42,820 --> 00:03:48,430
+OK so we're not going to try and we're going to use a pure Pretorian model and we're going to put out
+
+55
+00:03:48,490 --> 00:03:51,480
+the top we some images that I should say.
+
+56
+00:03:51,970 --> 00:03:57,500
+So now we create a dictionary of coefficients and these coefficients basically mean how much Julias
+
+57
+00:03:57,520 --> 00:04:01,070
+Activision contributions loss we seek to maximize.
+
+58
+00:04:01,090 --> 00:04:04,450
+So feel free to play with these numbers and change them as well.
+
+59
+00:04:04,460 --> 00:04:07,000
+Is this a random combination of numbers I tried.
+
+60
+00:04:07,030 --> 00:04:10,010
+You can try any numbers between maybe 0 and 2.
+
+61
+00:04:10,380 --> 00:04:15,720
+And you do change your dream style in the end.
+
+62
+00:04:16,030 --> 00:04:19,890
+So next we find out what tenso that contains a maximize loss.
+
+63
+00:04:19,960 --> 00:04:26,340
+That's the weighted some of the L2 norm of the activations of the layer destined defined above.
+
+64
+00:04:26,380 --> 00:04:29,660
+Sorry that should be defined the distance.
+
+65
+00:04:29,660 --> 00:04:30,330
+OK.
+
+66
+00:04:31,060 --> 00:04:32,760
+So that's what we do here.
+
+67
+00:04:32,760 --> 00:04:35,270
+So actually let's run this line.
+
+68
+00:04:36,090 --> 00:04:42,480
+And this bit of code and now we create a gradient ascent preprocess that's what we do with these functions
+
+69
+00:04:42,480 --> 00:04:42,860
+here.
+
+70
+00:04:43,050 --> 00:04:46,270
+So this is the image or dream that is stored in distance.
+
+71
+00:04:46,650 --> 00:04:49,390
+This is how we get to gradients with respect to loss.
+
+72
+00:04:49,500 --> 00:04:55,740
+This is how we normalized brilliant and this is creates a Cara's function to get the value of the last
+
+73
+00:04:55,740 --> 00:04:57,930
+ingredients with respect to the input.
+
+74
+00:04:57,930 --> 00:05:02,080
+That's what carries that function that's what it does here.
+
+75
+00:05:02,490 --> 00:05:08,100
+And these are some functions we get to evaluate loss and gradients and implementing gradient ascent
+
+76
+00:05:08,370 --> 00:05:08,850
+as well.
+
+77
+00:05:08,870 --> 00:05:13,580
+OK so now we know we can implement a deep very moderate him.
+
+78
+00:05:13,590 --> 00:05:19,060
+So again we have some helper functions here that we've used before just to process pre-processed deeper
+
+79
+00:05:19,450 --> 00:05:23,640
+deep process saved image and resized the image.
+
+80
+00:05:23,640 --> 00:05:25,470
+And these are some settings we have here.
+
+81
+00:05:25,650 --> 00:05:31,320
+You can leave the settings as is for now but you can feel free if you want to create some really cool
+
+82
+00:05:31,350 --> 00:05:32,310
+original art.
+
+83
+00:05:32,490 --> 00:05:38,730
+Feel free to play with some of the settings specially octave scale and max loss and number of iterations
+
+84
+00:05:38,760 --> 00:05:39,370
+as well.
+
+85
+00:05:39,450 --> 00:05:42,620
+We don't and it just goes hand in hand with this.
+
+86
+00:05:42,690 --> 00:05:48,900
+So the picture we're going to do we're going to modify is the Aurora or not in lights image I took in
+
+87
+00:05:48,900 --> 00:05:49,580
+Norway.
+
+88
+00:05:49,920 --> 00:05:56,160
+So give color this and we preprocess image by loading it here with this function.
+
+89
+00:05:57,030 --> 00:06:01,410
+And then what we do we initialize a list of tuples for different image sizes.
+
+90
+00:06:01,410 --> 00:06:07,130
+That's basically how we do it at different scales or octaves and then we reverse a list of shapes that
+
+91
+00:06:07,130 --> 00:06:08,730
+are increasing order.
+
+92
+00:06:09,160 --> 00:06:17,500
+That's how we actually implement this algorithm resize an umpire array to a smaller scale and an endless
+
+93
+00:06:17,500 --> 00:06:18,010
+loop.
+
+94
+00:06:18,010 --> 00:06:24,610
+We actually basically start creating or treating or reducing the loss so that we can actually produce
+
+95
+00:06:25,270 --> 00:06:27,090
+this beautiful artwork in the end.
+
+96
+00:06:27,370 --> 00:06:37,690
+So let's do this so we actually did not run most of these functions here believe so let's do this one.
+
+97
+00:06:37,810 --> 00:06:39,040
+And there we go.
+
+98
+00:06:39,290 --> 00:06:41,430
+And this one should be fine.
+
+99
+00:06:45,040 --> 00:06:51,820
+As you can see it's not it doesn't take that long to create these images in the end let's wait until
+
+100
+00:06:51,820 --> 00:06:54,820
+it all fasco this well it completes
+
+101
+00:07:04,090 --> 00:07:04,480
+OK.
+
+102
+00:07:04,520 --> 00:07:06,920
+So deep dreaming is complete.
+
+103
+00:07:06,950 --> 00:07:08,330
+So let's take a look at image now.
+
+104
+00:07:08,390 --> 00:07:11,810
+So it saves in this image pattern right here.
+
+105
+00:07:12,230 --> 00:07:13,270
+And here we go.
+
+106
+00:07:13,280 --> 00:07:19,420
+Now I know this image was here before but this is a freshly generated image from this process here.
+
+107
+00:07:19,910 --> 00:07:21,450
+And take a look at it.
+
+108
+00:07:21,470 --> 00:07:22,760
+It's pretty nice.
+
+109
+00:07:22,760 --> 00:07:23,870
+It's not amazing.
+
+110
+00:07:23,870 --> 00:07:25,550
+Some of the individuals we've seen.
+
+111
+00:07:25,760 --> 00:07:29,220
+However you can start manipulating a lot of these settings here.
+
+112
+00:07:29,570 --> 00:07:35,660
+What I wanted to show you was if we can set McLaws is really something metrics metric so you can see
+
+113
+00:07:36,020 --> 00:07:38,610
+it even though it ran this loop.
+
+114
+00:07:39,080 --> 00:07:44,840
+It stopped right here right before it reached 20 because it was going across town here as well as here
+
+115
+00:07:45,860 --> 00:07:46,580
+and here.
+
+116
+00:07:46,850 --> 00:07:49,850
+So you can said this to be more stringent or more relaxed.
+
+117
+00:07:49,940 --> 00:07:54,740
+You can change your octave scale and scale and you can even do more octaves if you want to combine even
+
+118
+00:07:54,740 --> 00:07:56,140
+more images together.
+
+119
+00:07:56,180 --> 00:07:58,150
+So that's it for deep dreaming.
+
+120
+00:07:58,340 --> 00:07:59,880
+I encourage you to play with these things.
+
+121
+00:07:59,900 --> 00:08:01,910
+As I said and have fun.
+
+122
+00:08:02,240 --> 00:08:02,760
+Thank you.
diff --git a/23. DeepDream & Neural Style Transfers Make AI Generated Art/3. Neural Style Transfer.srt b/23. DeepDream & Neural Style Transfers Make AI Generated Art/3. Neural Style Transfer.srt
new file mode 100644
index 0000000000000000000000000000000000000000..9ce66073d972710cd1ab522ca127a40541c9b3c4
--- /dev/null
+++ b/23. DeepDream & Neural Style Transfers Make AI Generated Art/3. Neural Style Transfer.srt	
@@ -0,0 +1,703 @@
+1
+00:00:00,780 --> 00:00:06,390
+Hi and welcome to chapter twenty three point two on old style transfer it's going to be a very fun chapter.
+
+2
+00:00:06,390 --> 00:00:07,730
+So let's get into it.
+
+3
+00:00:07,740 --> 00:00:14,430
+So Neill's tell transfer Neuralstem transfer I should say was introduced by Leo and guitars and Company
+
+4
+00:00:14,730 --> 00:00:21,870
+in 2015 and their are people titled A neural algorithm for autistic style and neural stel algorithm
+
+5
+00:00:21,960 --> 00:00:28,350
+went viral asdis resulting in the explosion of the work and even some cool mobile apps.
+
+6
+00:00:28,400 --> 00:00:34,950
+So neural style transfer enables the autistic style of an image to be applied to another image.
+
+7
+00:00:35,070 --> 00:00:40,900
+It copies the color Patten's combinations and even brush strokes off the original source and applies
+
+8
+00:00:40,920 --> 00:00:42,460
+it to your input image.
+
+9
+00:00:42,660 --> 00:00:48,540
+And this is one of the most impressive implementations of neural networks in my opinion.
+
+10
+00:00:48,540 --> 00:00:51,300
+It actually produces pieces of work like this.
+
+11
+00:00:51,300 --> 00:00:57,390
+This was a picture of the Eiffel Tower earlier this year and this is da Vinci's starry night poster.
+
+12
+00:00:57,390 --> 00:01:02,210
+I can picture a picture and this is the generated artwork coming out of it.
+
+13
+00:01:02,280 --> 00:01:05,500
+It looks very authentic like someone actually painted it.
+
+14
+00:01:05,520 --> 00:01:07,940
+This is a picture of my wife in a carnival costume.
+
+15
+00:01:07,980 --> 00:01:11,700
+I think two years ago and we basically apply it with his violin.
+
+16
+00:01:11,730 --> 00:01:15,010
+Pip Pip Paignton here and look at it cool style.
+
+17
+00:01:15,030 --> 00:01:18,300
+It actually copies a lot of the polynomial type structures.
+
+18
+00:01:18,300 --> 00:01:20,990
+This painting is copied here.
+
+19
+00:01:21,330 --> 00:01:25,160
+So this is very neat and this is a famous painting called The Scream.
+
+20
+00:01:25,170 --> 00:01:26,780
+Try to apply it to myself.
+
+21
+00:01:26,850 --> 00:01:30,810
+Not as impressive as the others but still pretty cool.
+
+22
+00:01:30,940 --> 00:01:31,980
+So let's go on.
+
+23
+00:01:32,430 --> 00:01:36,430
+So what enables niggled style transfer to perform this magic.
+
+24
+00:01:36,510 --> 00:01:43,340
+Well you tell transfers unlabelled not by having a fancy exotic architecture or lengthy training process.
+
+25
+00:01:43,410 --> 00:01:45,440
+It's actually all about the last functions.
+
+26
+00:01:45,510 --> 00:01:47,340
+So let's talk about them now.
+
+27
+00:01:47,480 --> 00:01:53,370
+So remember Firstly remember when we visualised our filters you looked like this.
+
+28
+00:01:53,370 --> 00:01:59,880
+We had low level NCEA mid-level sources and high levels of Tilsa which actually picked up more structure
+
+29
+00:02:00,190 --> 00:02:02,740
+and see a special relationships no interfaces.
+
+30
+00:02:03,150 --> 00:02:11,160
+So essentially it's established at convolutional Leia's store hierarchial representations of image features.
+
+31
+00:02:11,430 --> 00:02:19,730
+And this is the key component that allows year old style transfers to basically enable them.
+
+32
+00:02:19,820 --> 00:02:22,200
+So this is the process for this.
+
+33
+00:02:22,250 --> 00:02:28,370
+We start with a blank image of random pixels like this and we keep changing pixel values to optimize
+
+34
+00:02:28,370 --> 00:02:29,500
+across function.
+
+35
+00:02:29,720 --> 00:02:33,140
+Keeping our pre-trained CNN weight constant.
+
+36
+00:02:33,200 --> 00:02:37,940
+Remember we want to preserve the content of the original image while adopting to tell if the reference
+
+37
+00:02:37,940 --> 00:02:38,460
+image.
+
+38
+00:02:38,510 --> 00:02:42,060
+That's a goal of neural style trances.
+
+39
+00:02:42,320 --> 00:02:53,560
+So this is a class function and it basically consists of two parts a constant loss and a stylus.
+
+40
+00:02:53,560 --> 00:02:58,180
+So let's talk about these two last functions starting with a content lossless.
+
+41
+00:02:58,250 --> 00:03:03,880
+So remember how all filters and special decompositions of an image that was the high mid and low level
+
+42
+00:03:03,880 --> 00:03:04,710
+features.
+
+43
+00:03:05,020 --> 00:03:12,250
+Well we can assume that the high level features from the upper convolutional is stored global representations
+
+44
+00:03:12,340 --> 00:03:20,470
+of the image that is basically true for all CNN's and therefore all content loss can be defined as it
+
+45
+00:03:20,550 --> 00:03:31,000
+elde to distance norm L2 norm between the activations in a police as of a pre-trained CNN computed versus
+
+46
+00:03:31,000 --> 00:03:36,360
+to target image and activations of seemly a computed over the generated image.
+
+47
+00:03:36,370 --> 00:03:41,650
+So basically we're trying to get basically the oh how can I explain this easier.
+
+48
+00:03:41,650 --> 00:03:45,630
+We are trying to get the distance to last between the image that we are competing.
+
+49
+00:03:45,650 --> 00:03:51,070
+That's a generated image versus a target image which is our source image and we're trying to basically
+
+50
+00:03:51,070 --> 00:03:55,390
+ensure that the content are basically the between our target image.
+
+51
+00:03:55,390 --> 00:04:00,730
+That's the image basically that you want to change into a fancy painting.
+
+52
+00:04:00,790 --> 00:04:02,650
+We're trying to minimize that loss.
+
+53
+00:04:02,830 --> 00:04:07,880
+And this ensures our generated image retains a similar look to the original image.
+
+54
+00:04:08,170 --> 00:04:09,700
+But how do we get the style.
+
+55
+00:04:09,760 --> 00:04:11,500
+And that's a stylus.
+
+56
+00:04:11,560 --> 00:04:14,440
+So the content loss uses a single upper level.
+
+57
+00:04:14,530 --> 00:04:18,100
+However the style is composed of multiple layers of CNN.
+
+58
+00:04:18,310 --> 00:04:19,010
+Why.
+
+59
+00:04:19,390 --> 00:04:24,920
+Well because now we're trying to capture the styles from the reference image reference image being of
+
+60
+00:04:24,920 --> 00:04:30,270
+a painting generally at all spatial skills extracted by CNN.
+
+61
+00:04:30,400 --> 00:04:35,240
+Let me just say that again so because now we're trying to capture the stylus and the reference image
+
+62
+00:04:35,240 --> 00:04:38,640
+at all special skills extracted by the CNN.
+
+63
+00:04:38,710 --> 00:04:45,550
+So for the stylus now we have to use something called a grand matrix and a matrix as in layers activations.
+
+64
+00:04:45,550 --> 00:04:50,520
+It's basically a composer of the Independent of the future maps given earlier.
+
+65
+00:04:51,040 --> 00:04:57,580
+So now the product can be interpreted as representing a map of the correlations between the layer features.
+
+66
+00:04:57,790 --> 00:05:02,440
+That's a cool ability enabled by a gram matrix.
+
+67
+00:05:02,440 --> 00:05:09,570
+So the stylus now aims to preserve similar internal correlations with activations of different layers.
+
+68
+00:05:09,670 --> 00:05:14,930
+So we want to actually kind of maintain some sort of correlations between what the style has and where
+
+69
+00:05:14,930 --> 00:05:16,980
+the generated image is going to have.
+
+70
+00:05:17,020 --> 00:05:23,200
+So as it says here as I say here across the style reference image and the generated image this is what
+
+71
+00:05:23,200 --> 00:05:25,450
+we try to preserve the internal correlations.
+
+72
+00:05:25,450 --> 00:05:33,000
+So this ensures our textures look similar between the images and know what we have to do is basically
+
+73
+00:05:33,000 --> 00:05:37,060
+we have to minimize the total loss to get a total loss.
+
+74
+00:05:37,060 --> 00:05:43,760
+It's basically this distance here L2 norm distance from the style of the reference image minus self
+
+75
+00:05:43,810 --> 00:05:50,130
+generated image plus the distance here between from the content of the original image and the content
+
+76
+00:05:50,130 --> 00:05:51,770
+of the generated image.
+
+77
+00:05:51,780 --> 00:05:57,470
+This is basically the content loss and stylus and this is essentially how it works.
+
+78
+00:05:57,480 --> 00:06:01,540
+Try to put together a diagram to make this simpler for gay guys to understand.
+
+79
+00:06:01,560 --> 00:06:05,460
+So we have a content image here and the style image here.
+
+80
+00:06:05,760 --> 00:06:09,360
+So this is the two images we're going to use to produce a target image.
+
+81
+00:06:09,360 --> 00:06:12,660
+So now we get the content representation from BTG.
+
+82
+00:06:12,900 --> 00:06:15,200
+That's from the convolutional of the players there.
+
+83
+00:06:15,630 --> 00:06:18,130
+And then we get to self-representation as well.
+
+84
+00:06:18,240 --> 00:06:25,560
+And what we do basically try to minimize loss now generated from the target image here between the style
+
+85
+00:06:25,620 --> 00:06:29,950
+and representation here and the content representation here.
+
+86
+00:06:30,630 --> 00:06:36,990
+That's effectively all they doing it's pretty relatively simple concept but it's basically minimizing
+
+87
+00:06:37,020 --> 00:06:39,390
+the loss between two types of images.
+
+88
+00:06:39,480 --> 00:06:42,560
+And we do it a bit differently but for content and stuff.
+
+89
+00:06:43,020 --> 00:06:51,850
+So now let's go into a pile of books and actually implement some cool little so transfers of on OK.
+
+90
+00:06:51,860 --> 00:06:54,150
+So let's open the chapter 20.
+
+91
+00:06:54,230 --> 00:07:00,620
+That's Number 23 for the here and let's open on your old styles notebook here and first thing I should
+
+92
+00:07:00,620 --> 00:07:06,080
+see that this notebook basically a lot of the code was edited and borrowed from Francis of Francois
+
+93
+00:07:06,080 --> 00:07:09,940
+shaly the creator of Karris.
+
+94
+00:07:10,550 --> 00:07:14,920
+Basically he puts up a lot of tutorials on his get up here so you can check it out.
+
+95
+00:07:14,930 --> 00:07:17,660
+It's very good to learn from not all of them were.
+
+96
+00:07:17,670 --> 00:07:19,120
+He hasn't kept him up to date.
+
+97
+00:07:19,130 --> 00:07:25,850
+So you may have to just edit and update some of the code because it's now outdated but it's pretty good
+
+98
+00:07:25,970 --> 00:07:28,200
+and very well commented as well.
+
+99
+00:07:28,280 --> 00:07:32,300
+So let's load a target and sell images.
+
+100
+00:07:32,570 --> 00:07:36,890
+So we're going to use the Eiffel Tower picture I have and the star in that picture I have.
+
+101
+00:07:36,950 --> 00:07:39,280
+So let's just go to my directory here.
+
+102
+00:07:39,440 --> 00:07:43,730
+Go to the planning folder and let's look at some of the images I have.
+
+103
+00:07:43,760 --> 00:07:49,390
+So these are some of the pictures we can play but this is a picture I took in Tromso Norway.
+
+104
+00:07:49,610 --> 00:07:51,090
+This is one.
+
+105
+00:07:51,230 --> 00:07:53,330
+Basically this is art.
+
+106
+00:07:53,560 --> 00:07:55,010
+A painting actually.
+
+107
+00:07:55,370 --> 00:08:00,250
+So is this a style reference picture I took in Paris earlier to see if picture.
+
+108
+00:08:00,470 --> 00:08:01,510
+My mom took in Paris.
+
+109
+00:08:01,520 --> 00:08:02,300
+Not a very good one.
+
+110
+00:08:02,300 --> 00:08:10,440
+In my opinion some Japanese art a picture of my wife carnival in Trinidad two years ago.
+
+111
+00:08:10,480 --> 00:08:13,300
+This is money is what it really is.
+
+112
+00:08:13,580 --> 00:08:20,920
+One of my friends as well here me sorry I had to scream a tiger tiger again.
+
+113
+00:08:21,150 --> 00:08:25,620
+T-Rex venom while in and a puppy.
+
+114
+00:08:25,950 --> 00:08:35,170
+So let's go back to this book and run this code just to get our target images and loading tents afloat.
+
+115
+00:08:35,630 --> 00:08:36,140
+Good.
+
+116
+00:08:36,160 --> 00:08:40,480
+That's them and some help the functions of we will be using.
+
+117
+00:08:40,760 --> 00:08:47,560
+So now this is where we make our loss functions and basically Ogram metrics and loss and total variational
+
+118
+00:08:47,570 --> 00:08:48,020
+loss.
+
+119
+00:08:48,020 --> 00:08:50,210
+These are the functions we use here.
+
+120
+00:08:50,360 --> 00:08:57,170
+If you remember correctly we have we need at a total loss which is some of the content and loss and
+
+121
+00:08:57,170 --> 00:09:02,730
+grammar tricks provides basically how we get the features out of style or the style image.
+
+122
+00:09:02,750 --> 00:09:09,230
+I should say so now this lude BTG really produces some pretty cool images in my opinion.
+
+123
+00:09:09,230 --> 00:09:11,360
+You can trade BTG 16:19.
+
+124
+00:09:11,570 --> 00:09:19,510
+He uses Viji 19 but I'm using BTD 16 because it is faster to process and see you and you will start
+
+125
+00:09:19,520 --> 00:09:21,320
+trounces do take some time.
+
+126
+00:09:21,320 --> 00:09:26,970
+So let's load this image and let's do this here this is a final loss.
+
+127
+00:09:26,980 --> 00:09:28,130
+We've been minimizing.
+
+128
+00:09:28,370 --> 00:09:33,930
+So we can actually pick differently as head of we want to use a style is you can you can explore players
+
+129
+00:09:33,980 --> 00:09:37,020
+individually and change them as you want.
+
+130
+00:09:37,020 --> 00:09:38,890
+I encourage you to do that.
+
+131
+00:09:38,930 --> 00:09:42,700
+You can leave these here or play with them and see if they make a difference to you.
+
+132
+00:09:43,190 --> 00:09:50,780
+And look let's actually run an unbeliever and somebody who has run one two tree didn't run this once.
+
+133
+00:09:50,780 --> 00:09:52,200
+Let's do that.
+
+134
+00:09:52,550 --> 00:09:55,610
+Content lost or defined means it didn't actually run this one.
+
+135
+00:09:57,090 --> 00:10:00,870
+And make sure it run all of these as well.
+
+136
+00:10:05,590 --> 00:10:09,460
+So it did do this as well.
+
+137
+00:10:09,490 --> 00:10:12,700
+This is how we create a gritty and dissent process here.
+
+138
+00:10:12,970 --> 00:10:18,610
+And this class object and now we run our style transfer loop.
+
+139
+00:10:18,700 --> 00:10:25,690
+So what's going to happen here is that it's going to actually take all images and basically actually
+
+140
+00:10:25,690 --> 00:10:26,720
+show you.
+
+141
+00:10:27,160 --> 00:10:30,380
+So does the Irishman's head start saturations here.
+
+142
+00:10:30,730 --> 00:10:35,880
+And basically what it's doing it's basically trying to minimize a loss between the content loss and
+
+143
+00:10:35,980 --> 00:10:37,750
+other lost the stylus.
+
+144
+00:10:37,750 --> 00:10:39,410
+All right.
+
+145
+00:10:39,410 --> 00:10:44,750
+So as I said it does take some time takes maybe about three to four minutes to do one eye duration.
+
+146
+00:10:45,040 --> 00:10:49,810
+But luckily what it does in the style transfer orbits.
+
+147
+00:10:50,080 --> 00:10:52,350
+You actually see it step by step.
+
+148
+00:10:52,390 --> 00:10:54,030
+So this is pretty cool to look at.
+
+149
+00:10:54,040 --> 00:10:58,830
+This was the first iteration output of what we're running here.
+
+150
+00:10:58,900 --> 00:11:03,370
+This is probably going to finish maybe in about 20 minutes so I'm going to wait three minutes for this
+
+151
+00:11:03,370 --> 00:11:04,330
+to be done.
+
+152
+00:11:04,510 --> 00:11:05,500
+Let's see how this looks.
+
+153
+00:11:05,500 --> 00:11:06,800
+So it's done here.
+
+154
+00:11:07,140 --> 00:11:11,700
+So the second iteration gets better and better and better.
+
+155
+00:11:12,070 --> 00:11:16,460
+And you can see sort of stops or maybe after five or six is not much change.
+
+156
+00:11:16,480 --> 00:11:22,610
+So you do need to run it for a full 20 ebox just want it for maybe 70 bucks in my opinion.
+
+157
+00:11:22,850 --> 00:11:29,460
+Is that one that my wife can see goes up and then stops changing like maybe 6 7 e-books.
+
+158
+00:11:29,490 --> 00:11:35,250
+This is a picture my mom took in Paris pretty cool actually fine.
+
+159
+00:11:35,400 --> 00:11:36,990
+If you want elite ones look nicer.
+
+160
+00:11:37,220 --> 00:11:39,060
+But that's the beauty of neural cell transfers.
+
+161
+00:11:39,060 --> 00:11:41,400
+You don't have to use the full version.
+
+162
+00:11:41,400 --> 00:11:48,980
+You can use any version you want and basically it is a really cool way to get auto generated.
+
+163
+00:11:49,290 --> 00:11:52,700
+So this is actually how we can plot the images in the end.
+
+164
+00:11:52,940 --> 00:11:54,570
+A source image.
+
+165
+00:11:54,570 --> 00:12:01,880
+Its own style image content image you could call it them just see what we call that here Fishley we
+
+166
+00:12:01,880 --> 00:12:03,210
+call that a talking image.
+
+167
+00:12:05,670 --> 00:12:08,000
+And this here is now generated image.
+
+168
+00:12:08,040 --> 00:12:13,100
+So this is actually a puppy took me and I took off Instagram actually.
+
+169
+00:12:13,320 --> 00:12:15,010
+And basically it came up different.
+
+170
+00:12:15,160 --> 00:12:18,070
+It was it was this image here.
+
+171
+00:12:18,130 --> 00:12:20,560
+This is one of these here.
+
+172
+00:12:20,570 --> 00:12:27,600
+So in the beginning but I think I may have actually modified this or didn't run this way I should have.
+
+173
+00:12:27,870 --> 00:12:28,680
+But that's fine.
+
+174
+00:12:29,570 --> 00:12:34,870
+You can actually play with this on your own and create some really really cool new old style transfer
+
+175
+00:12:34,890 --> 00:12:35,640
+Art.
+
+176
+00:12:35,720 --> 00:12:36,090
+Good luck.
diff --git a/23. DeepDream & Neural Style Transfers/.~24.1 DeepDream.ipynb b/23. DeepDream & Neural Style Transfers/.~24.1 DeepDream.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..48e53c5a7571075a658cd8598c96f93a97d9ba66
--- /dev/null
+++ b/23. DeepDream & Neural Style Transfers/.~24.1 DeepDream.ipynb	
@@ -0,0 +1,309 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing DeepDream in Keras\n",
+    "\n",
+    "We firstly load the InceptionV3 model which tends to produce some of the best visuals. Feel free to try VGG16, VGG19, Xception and ResNet50.\n",
+    "\n",
+    "Code obtained and edited from F. Chollet (Created of Keras)\n",
+    "- \n",
+    "https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/8.2-deep-dream.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.applications import inception_v3, resnet50\n",
+    "from keras import backend as K\n",
+    "\n",
+    "# This settings disables all training specific operations\n",
+    "K.set_learning_phase(0)\n",
+    "\n",
+    "# Load InceptionV3\n",
+    "model = inception_v3.InceptionV3(weights = 'imagenet', include_top = False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We create a dictionary of coefficients quantifying, how much the layer’s activation contributes to the loss you’ll seek to maximize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layer_contributions = {\n",
+    "    'mixed2': 0.7,\n",
+    "    'mixed3': 2.2,\n",
+    "    'mixed4': 1.2,\n",
+    "    'mixed5': .2,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define our tensor that contains the maximized Loss (the weighted sum of the L2 norm of the activations of the layer desfined above)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:Variable += will be deprecated. Use variable.assign_add if you want assignment to the variable value or 'x = x + y' if you want a new python Tensor object.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Map layer names to layer instances\n",
+    "layer_dict = dict([(layer.name, layer) for layer in model.layers])\n",
+    "\n",
+    "# loss defined by adding layer contributions\n",
+    "loss = K.variable(0.)\n",
+    "\n",
+    "for layer_name in layer_contributions:\n",
+    "    coeff = layer_contributions[layer_name]\n",
+    "    \n",
+    "    # activation gets the layer output\n",
+    "    activation = layer_dict[layer_name].output\n",
+    "    scaling = K.prod(K.cast(K.shape(activation), 'float32'))\n",
+    "\n",
+    "    # we add the l2 norm\n",
+    "    loss += coeff * K.sum(K.square(activation[:, 2: -2, 2: -2, :])) / scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating the Gradient-Ascent Process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is the image or 'dream' :) that is stored in this tensor \n",
+    "dream = model.input\n",
+    "\n",
+    "# Obtains the gradients wrt to the loss\n",
+    "grads = K.gradients(loss, dream)[0]\n",
+    "\n",
+    "# Normalizes the gradient \n",
+    "grads /= K.maximum(K.mean(K.abs(grads)), 1e-7)\n",
+    "\n",
+    "# Creates a Keras function to get the value of the loss & gradients wrt to the input\n",
+    "outputs = [loss, grads]\n",
+    "fetch_loss_and_grads = K.function([dream], outputs)\n",
+    "\n",
+    "def eval_loss_and_grads(x):\n",
+    "    \"\"\"returns the loss and gradient values\"\"\"\n",
+    "    outs = fetch_loss_and_grads([x])\n",
+    "    loss_value = outs[0]\n",
+    "    grad_values = outs[1]\n",
+    "    return loss_value, grad_values\n",
+    "\n",
+    "def gradient_ascent(x, iterations, step, max_loss=None):\n",
+    "    \"\"\"Implements gradient access for a specified number of iterations\"\"\"\n",
+    "    for i in range(iterations):\n",
+    "        loss_value, grad_values = eval_loss_and_grads(x)\n",
+    "        if max_loss is not None and loss_value > max_loss:\n",
+    "            break\n",
+    "        print('...Loss value at', i, ':', loss_value)\n",
+    "        x += step * grad_values\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing the Deep Dream Algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing image shape (353, 529)\n",
+      "...Loss value at 0 : 1.0304297\n",
+      "...Loss value at 1 : 1.2510272\n",
+      "...Loss value at 2 : 1.6979636\n",
+      "...Loss value at 3 : 2.271832\n",
+      "...Loss value at 4 : 2.8770106\n",
+      "...Loss value at 5 : 3.4737113\n",
+      "...Loss value at 6 : 4.0769672\n",
+      "...Loss value at 7 : 4.617928\n",
+      "...Loss value at 8 : 5.15317\n",
+      "...Loss value at 9 : 5.685014\n",
+      "...Loss value at 10 : 6.1906824\n",
+      "...Loss value at 11 : 6.645993\n",
+      "...Loss value at 12 : 7.0928683\n",
+      "...Loss value at 13 : 7.5481906\n",
+      "...Loss value at 14 : 7.9534693\n",
+      "...Loss value at 15 : 8.386046\n",
+      "...Loss value at 16 : 8.765054\n",
+      "...Loss value at 17 : 9.139767\n",
+      "...Loss value at 18 : 9.5182705\n",
+      "...Loss value at 19 : 9.86855\n",
+      "Processing image shape (494, 740)\n",
+      "...Loss value at 0 : 2.864695\n",
+      "...Loss value at 1 : 4.0144286\n",
+      "...Loss value at 2 : 4.9185443\n",
+      "...Loss value at 3 : 5.688157\n",
+      "...Loss value at 4 : 6.3830137\n",
+      "...Loss value at 5 : 7.0187526\n",
+      "...Loss value at 6 : 7.596686\n",
+      "...Loss value at 7 : 8.150836\n",
+      "...Loss value at 8 : 8.67249\n",
+      "...Loss value at 9 : 9.186928\n",
+      "...Loss value at 10 : 9.649975\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/deeplearningcv/anaconda3/envs/cv/lib/python3.6/site-packages/scipy/ndimage/interpolation.py:583: UserWarning: From scipy 0.13.0, the output shape of zoom() is calculated with round() instead of int() - for these inputs the size of the returned array has changed.\n",
+      "  \"the returned array has changed.\", UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing image shape (692, 1037)\n",
+      "...Loss value at 0 : 2.9274173\n",
+      "...Loss value at 1 : 4.0277452\n",
+      "...Loss value at 2 : 4.9098253\n",
+      "...Loss value at 3 : 5.6878753\n",
+      "...Loss value at 4 : 6.426328\n",
+      "...Loss value at 5 : 7.1218004\n",
+      "...Loss value at 6 : 7.773466\n",
+      "...Loss value at 7 : 8.408107\n",
+      "...Loss value at 8 : 9.042133\n",
+      "...Loss value at 9 : 9.666492\n",
+      "DeepDreaming Complete\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import scipy\n",
+    "from keras.preprocessing import image\n",
+    "import imageio\n",
+    "\n",
+    "def resize_img(img, size):\n",
+    "    img = np.copy(img)\n",
+    "    factors = (1,\n",
+    "               float(size[0]) / img.shape[1],\n",
+    "               float(size[1]) / img.shape[2], 1)\n",
+    "    return scipy.ndimage.zoom(img, factors, order=1)\n",
+    "\n",
+    "def save_img(img, fname):\n",
+    "    pil_img = deprocess_image(np.copy(img))\n",
+    "    imageio.imwrite(fname, pil_img)\n",
+    "\n",
+    "def preprocess_image(image_path):\n",
+    "    img = image.load_img(image_path)\n",
+    "    img = image.img_to_array(img)\n",
+    "    img = np.expand_dims(img, axis=0)\n",
+    "    img = inception_v3.preprocess_input(img)\n",
+    "    return img\n",
+    "\n",
+    "def deprocess_image(x):\n",
+    "    if K.image_data_format() == 'channels_first':\n",
+    "        x = x.reshape((3, x.shape[2], x.shape[3]))\n",
+    "        x = x.transpose((1, 2, 0))\n",
+    "    else:\n",
+    "        x = x.reshape((x.shape[1], x.shape[2], 3))\n",
+    "    x /= 2.\n",
+    "    x += 0.5\n",
+    "    x *= 255.\n",
+    "    x = np.clip(x, 0, 255).astype('uint8')\n",
+    "    return x\n",
+    "\n",
+    "step = 0.01 #Step size for gradient ascent\n",
+    "num_octave = 3 #number of octaves to be run\n",
+    "octave_scale = 1.4 #this is the scale for each ensuing octive will be 1.4 times large than the previous\n",
+    "iterations = 20 #number of gradient ascent operations we execute \n",
+    "max_loss = 10.0 #our early stoping metric, if loss is > max_loss we break the gradient ascent loop\n",
+    "\n",
+    "base_image_path = './images/aurora_norway.jpg'\n",
+    "\n",
+    "# Load our image \n",
+    "img = preprocess_image(base_image_path)\n",
+    "\n",
+    "# Initialize a list of tuples for our different images sizes/scales \n",
+    "original_shape = img.shape[1:3]\n",
+    "successive_shapes = [original_shape]\n",
+    "for i in range(1, num_octave):\n",
+    "    shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])\n",
+    "    successive_shapes.append(shape)\n",
+    "\n",
+    "# Reverse list of shapes, so that they are in increasing order\n",
+    "successive_shapes = successive_shapes[::-1]\n",
+    "\n",
+    "# Resize the Numpy array of the image to our smallest scale\n",
+    "original_img = np.copy(img)\n",
+    "shrunk_original_img = resize_img(img, successive_shapes[0])\n",
+    "\n",
+    "for shape in successive_shapes:\n",
+    "    print('Processing image shape', shape)\n",
+    "    img = resize_img(img, shape)\n",
+    "    img = gradient_ascent(img,\n",
+    "                          iterations=iterations,\n",
+    "                          step=step,\n",
+    "                          max_loss=max_loss)\n",
+    "\n",
+    "    upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)\n",
+    "    same_size_original = resize_img(original_img, shape)\n",
+    "    lost_detail = same_size_original - upscaled_shrunk_original_img\n",
+    "\n",
+    "    img += lost_detail\n",
+    "    shrunk_original_img = resize_img(original_img, shape)\n",
+    "    save_img(img, fname='./deepdream_outputs/dream_at_scale_' + str(shape) + '.png')\n",
+    "    \n",
+    "save_img(img, fname='./deepdream_outputs/final_dream.png')\n",
+    "print(\"DeepDreaming Complete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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diff --git a/23. DeepDream & Neural Style Transfers/23.1 DeepDream.ipynb b/23. DeepDream & Neural Style Transfers/23.1 DeepDream.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2a42cd5483d599cc27f520603809f048b21d3c32
--- /dev/null
+++ b/23. DeepDream & Neural Style Transfers/23.1 DeepDream.ipynb	
@@ -0,0 +1,1206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "uLzHIySHa1MN"
+   },
+   "source": [
+    "# Implementing Google’s Deep Dream\n",
+    "\n",
+    "We firstly load the InceptionV3 model which tends to produce some of the best visuals. Feel free to try VGG16, VGG19, Xception and ResNet50."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "7hqGSA5bHEIl"
+   },
+   "outputs": [],
+   "source": [
+    "# Import the libraries we need to start putting Google's Deep Dream together\n",
+    "from tensorflow.keras.applications import InceptionV3\n",
+    "from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
+    "from PIL import Image\n",
+    "import tensorflow as tf\n",
+    "import numpy as np\n",
+    "import imutils\n",
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xi8VFyeCOdda"
+   },
+   "source": [
+    "### Image Loader Function\n",
+    "Load the image, resize it to a width of 350. We then swap the color channels from BGR to RGB, and return the image as a NumPy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "o3B3JqDIOd2X"
+   },
+   "outputs": [],
+   "source": [
+    "def loadImage(imagePath):\n",
+    "    '''returns the image pixel array resiezd to a width of 350'''\n",
+    "    image = cv2.imread(imagePath)\n",
+    "    image = imutils.resize(image, width=350)\n",
+    "    # convert from BGR to RGB\n",
+    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "    image = np.array(image)\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "wL-9AG8WOhQP"
+   },
+   "source": [
+    "### Deprocess Function \n",
+    "Utility function to convert a tensor into a valid image. It \"undoes\" the preprocessing done for Inception and then casts the pixel values to integers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZNepmFxgOfbO"
+   },
+   "outputs": [],
+   "source": [
+    "def deprocess(image):\n",
+    "    '''returns the deprocessed image'''\n",
+    "    image = 255 * (image + 1.0) \n",
+    "    image /= 2.0\n",
+    "    image = tf.cast(image, tf.uint8)\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "YJiQuQriOoTt"
+   },
+   "source": [
+    "### Calculate the loss after a single iteration of the DeepDream algorithm\n",
+    "\n",
+    "We firstly add a batch dimension to the image and then grab the activations, from specified layers of the Inception network after performing a forward pass."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0fLK1GAYOkZr"
+   },
+   "outputs": [],
+   "source": [
+    "def calculateLoss(image, model):\n",
+    "    '''returns the sum of losses'''\n",
+    "    image = tf.expand_dims(image, axis=0)\n",
+    "    layerActivations = model(image)\n",
+    "\n",
+    "    # Create a list to store the intermediate losses\n",
+    "    losses = []\n",
+    "\n",
+    "    # iterate over the layer activations\n",
+    "    for act in layerActivations:\n",
+    "        # compute the mean of each activation\n",
+    "        loss = tf.reduce_mean(act)\n",
+    "        # append these losses to the losses list\n",
+    "        losses.append(loss)\n",
+    "\n",
+    "    # return the sum of the losses\n",
+    "    return tf.reduce_sum(losses)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "JbeicnOOOtVM"
+   },
+   "source": [
+    "## The Deep Dream function\n",
+    "\n",
+    "This function performs a forward-pass and then taking the\n",
+    "gradients and using them to update the input image to generate the deep dream effects.\n",
+    "\n",
+    "We input our model (inception network preferablly), the input image, the step size we use for our gradient updates and EPS, a tiny value used to prevent divide by zero errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "rd89e5hKOppR"
+   },
+   "outputs": [],
+   "source": [
+    "# This is new addition to TF2.0. When we use the **tf.function** decorator to a function.\n",
+    "# TensorFlow will automatically compile it into a graph which leads to faster execution.\n",
+    "@tf.function\n",
+    "def deepDreamSinglePass(model, image, stepSize, eps=1e-8):\n",
+    "    ''' returns a tuple of the loss and the updated image'''\n",
+    "\n",
+    "    # Tells TenorFlow to record gradients\n",
+    "    with tf.GradientTape() as tape:\n",
+    "        # keep track of the image to calculate gradients and loss \n",
+    "        tape.watch(image)\n",
+    "        loss = calculateLoss(image, model)\n",
+    "\n",
+    "    # calculates the gradients of the loss with respect to the image\n",
+    "    gradients = tape.gradient(loss, image)\n",
+    "    # normalize the gradients \n",
+    "    gradients /= tf.math.reduce_std(gradients) + eps \n",
+    "    # K.maximum(K.mean(K.abs(grads)), K.epsilon())\n",
+    "\n",
+    "    # adjusts the image with the normalized gradients \n",
+    "    image = image + (gradients * stepSize)\n",
+    "    # clip its pixel values to the range [-1, 1]\n",
+    "    image = tf.clip_by_value(image, -1, 1)\n",
+    "\n",
+    "    return (loss, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "VAgK7I3KOvdp"
+   },
+   "source": [
+    "### Function that runs our Deep Dream Model\n",
+    "\n",
+    "Up to this point we can perform a single forward-pass of the DeepDream algorithm. We now need to design our training loop that enables us to do this for multiple iterations.\n",
+    "\n",
+    "The `fullDeepDreamModel` we will be making below will allow us to pass our model (inception) and input image for a specified number of iterations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "IjrdcPLmOrzR"
+   },
+   "outputs": [],
+   "source": [
+    "def fullDeepDreamModel(model, image, iterations=50, stepSize=0.01):\n",
+    "    '''runs the deep dream algorithm for a specified number of iterations \n",
+    "    and returns the depresocessed image'''\n",
+    "\n",
+    "    # preprocess the image for the Inception network\n",
+    "    image = preprocess_input(image)\n",
+    "\n",
+    "    # We iterate for the specified number of iterations\n",
+    "    for iteration in range(iterations):\n",
+    "        # use  our deepDreamSinglePass function to get the loss and the updated image\n",
+    "        (loss, image) = deepDreamSinglePass(model, image, stepSize)\n",
+    "\n",
+    "        # print updates on the iteration progress\n",
+    "        if iteration % 50 == 0:\n",
+    "            print (\"Iteration {} with loss {}\".format(iteration, loss))\n",
+    "\n",
+    "    return deprocess(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "oHbfAHoNPHN3"
+   },
+   "source": [
+    "#### Define the layers we are going to use for the dream\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "53jHz8SEPFh6"
+   },
+   "outputs": [],
+   "source": [
+    "# Chose the layers you'll be using \n",
+    "names = [\"mixed3\", \"mixed5\", \"mixed7\"]\n",
+    "\n",
+    "# define the octave scale and number of octaves (tweaking these values\n",
+    "# will produce different output dreams)\n",
+    "OCTAVE_SCALE = 1.3\n",
+    "NUM_OCTAVES = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "22wweGHFPKCn"
+   },
+   "source": [
+    "### Execute Deep Dream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 69
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 5201,
+     "status": "ok",
+     "timestamp": 1590903136556,
+     "user": {
+      "displayName": "Rajeev Ratan",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhBYJnwdIGYSJdvGmzIt64rYSF1dDuiFyfRw4Rpeg=s64",
+      "userId": "08597265227091462140"
+     },
+     "user_tz": 240
+    },
+    "id": "3saPnc7EHVls",
+    "outputId": "24079aff-0a89-4d26-bfd0-c3427eca58c9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded Image\n",
+      "Loading Inception Model...\n",
+      "Loaded Inception Model\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get our input image\n",
+    "sampleImage = loadImage('sample.jpg')\n",
+    "print(\"Loaded Image\")\n",
+    "\n",
+    "# load the pre-trained Inception model from disk\n",
+    "print(\"Loading Inception Model...\")\n",
+    "baseModel = InceptionV3(include_top=False, weights=\"imagenet\")\n",
+    "print(\"Loaded Inception Model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 400,
+     "status": "ok",
+     "timestamp": 1590902969580,
+     "user": {
+      "displayName": "Rajeev Ratan",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhBYJnwdIGYSJdvGmzIt64rYSF1dDuiFyfRw4Rpeg=s64",
+      "userId": "08597265227091462140"
+     },
+     "user_tz": 240
+    },
+    "id": "h8PcVoyBMFba",
+    "outputId": "3a012970-df34-4136-dd71-85679439a9fe"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"inception_v3\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_3 (InputLayer)            [(None, None, None,  0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_188 (Conv2D)             (None, None, None, 3 864         input_3[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_188 (BatchN (None, None, None, 3 96          conv2d_188[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_188 (Activation)     (None, None, None, 3 0           batch_normalization_188[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_189 (Conv2D)             (None, None, None, 3 9216        activation_188[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_189 (BatchN (None, None, None, 3 96          conv2d_189[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_189 (Activation)     (None, None, None, 3 0           batch_normalization_189[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_190 (Conv2D)             (None, None, None, 6 18432       activation_189[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_190 (BatchN (None, None, None, 6 192         conv2d_190[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_190 (Activation)     (None, None, None, 6 0           batch_normalization_190[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_8 (MaxPooling2D)  (None, None, None, 6 0           activation_190[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_191 (Conv2D)             (None, None, None, 8 5120        max_pooling2d_8[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_191 (BatchN (None, None, None, 8 240         conv2d_191[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_191 (Activation)     (None, None, None, 8 0           batch_normalization_191[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_192 (Conv2D)             (None, None, None, 1 138240      activation_191[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_192 (BatchN (None, None, None, 1 576         conv2d_192[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_192 (Activation)     (None, None, None, 1 0           batch_normalization_192[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_9 (MaxPooling2D)  (None, None, None, 1 0           activation_192[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_196 (Conv2D)             (None, None, None, 6 12288       max_pooling2d_9[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_196 (BatchN (None, None, None, 6 192         conv2d_196[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_196 (Activation)     (None, None, None, 6 0           batch_normalization_196[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_194 (Conv2D)             (None, None, None, 4 9216        max_pooling2d_9[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_197 (Conv2D)             (None, None, None, 9 55296       activation_196[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_194 (BatchN (None, None, None, 4 144         conv2d_194[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_197 (BatchN (None, None, None, 9 288         conv2d_197[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_194 (Activation)     (None, None, None, 4 0           batch_normalization_194[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_197 (Activation)     (None, None, None, 9 0           batch_normalization_197[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_18 (AveragePo (None, None, None, 1 0           max_pooling2d_9[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_193 (Conv2D)             (None, None, None, 6 12288       max_pooling2d_9[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_195 (Conv2D)             (None, None, None, 6 76800       activation_194[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_198 (Conv2D)             (None, None, None, 9 82944       activation_197[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_199 (Conv2D)             (None, None, None, 3 6144        average_pooling2d_18[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_193 (BatchN (None, None, None, 6 192         conv2d_193[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_195 (BatchN (None, None, None, 6 192         conv2d_195[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_198 (BatchN (None, None, None, 9 288         conv2d_198[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_199 (BatchN (None, None, None, 3 96          conv2d_199[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_193 (Activation)     (None, None, None, 6 0           batch_normalization_193[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_195 (Activation)     (None, None, None, 6 0           batch_normalization_195[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_198 (Activation)     (None, None, None, 9 0           batch_normalization_198[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_199 (Activation)     (None, None, None, 3 0           batch_normalization_199[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed0 (Concatenate)            (None, None, None, 2 0           activation_193[0][0]             \n",
+      "                                                                 activation_195[0][0]             \n",
+      "                                                                 activation_198[0][0]             \n",
+      "                                                                 activation_199[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_203 (Conv2D)             (None, None, None, 6 16384       mixed0[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_203 (BatchN (None, None, None, 6 192         conv2d_203[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_203 (Activation)     (None, None, None, 6 0           batch_normalization_203[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_201 (Conv2D)             (None, None, None, 4 12288       mixed0[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_204 (Conv2D)             (None, None, None, 9 55296       activation_203[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_201 (BatchN (None, None, None, 4 144         conv2d_201[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_204 (BatchN (None, None, None, 9 288         conv2d_204[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_201 (Activation)     (None, None, None, 4 0           batch_normalization_201[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_204 (Activation)     (None, None, None, 9 0           batch_normalization_204[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_19 (AveragePo (None, None, None, 2 0           mixed0[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_200 (Conv2D)             (None, None, None, 6 16384       mixed0[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_202 (Conv2D)             (None, None, None, 6 76800       activation_201[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_205 (Conv2D)             (None, None, None, 9 82944       activation_204[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_206 (Conv2D)             (None, None, None, 6 16384       average_pooling2d_19[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_200 (BatchN (None, None, None, 6 192         conv2d_200[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_202 (BatchN (None, None, None, 6 192         conv2d_202[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_205 (BatchN (None, None, None, 9 288         conv2d_205[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_206 (BatchN (None, None, None, 6 192         conv2d_206[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_200 (Activation)     (None, None, None, 6 0           batch_normalization_200[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_202 (Activation)     (None, None, None, 6 0           batch_normalization_202[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_205 (Activation)     (None, None, None, 9 0           batch_normalization_205[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_206 (Activation)     (None, None, None, 6 0           batch_normalization_206[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed1 (Concatenate)            (None, None, None, 2 0           activation_200[0][0]             \n",
+      "                                                                 activation_202[0][0]             \n",
+      "                                                                 activation_205[0][0]             \n",
+      "                                                                 activation_206[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_210 (Conv2D)             (None, None, None, 6 18432       mixed1[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_210 (BatchN (None, None, None, 6 192         conv2d_210[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_210 (Activation)     (None, None, None, 6 0           batch_normalization_210[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_208 (Conv2D)             (None, None, None, 4 13824       mixed1[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_211 (Conv2D)             (None, None, None, 9 55296       activation_210[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_208 (BatchN (None, None, None, 4 144         conv2d_208[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_211 (BatchN (None, None, None, 9 288         conv2d_211[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_208 (Activation)     (None, None, None, 4 0           batch_normalization_208[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_211 (Activation)     (None, None, None, 9 0           batch_normalization_211[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_20 (AveragePo (None, None, None, 2 0           mixed1[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_207 (Conv2D)             (None, None, None, 6 18432       mixed1[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_209 (Conv2D)             (None, None, None, 6 76800       activation_208[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_212 (Conv2D)             (None, None, None, 9 82944       activation_211[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_213 (Conv2D)             (None, None, None, 6 18432       average_pooling2d_20[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_207 (BatchN (None, None, None, 6 192         conv2d_207[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_209 (BatchN (None, None, None, 6 192         conv2d_209[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_212 (BatchN (None, None, None, 9 288         conv2d_212[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_213 (BatchN (None, None, None, 6 192         conv2d_213[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_207 (Activation)     (None, None, None, 6 0           batch_normalization_207[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_209 (Activation)     (None, None, None, 6 0           batch_normalization_209[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_212 (Activation)     (None, None, None, 9 0           batch_normalization_212[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_213 (Activation)     (None, None, None, 6 0           batch_normalization_213[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed2 (Concatenate)            (None, None, None, 2 0           activation_207[0][0]             \n",
+      "                                                                 activation_209[0][0]             \n",
+      "                                                                 activation_212[0][0]             \n",
+      "                                                                 activation_213[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_215 (Conv2D)             (None, None, None, 6 18432       mixed2[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_215 (BatchN (None, None, None, 6 192         conv2d_215[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_215 (Activation)     (None, None, None, 6 0           batch_normalization_215[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_216 (Conv2D)             (None, None, None, 9 55296       activation_215[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_216 (BatchN (None, None, None, 9 288         conv2d_216[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_216 (Activation)     (None, None, None, 9 0           batch_normalization_216[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_214 (Conv2D)             (None, None, None, 3 995328      mixed2[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_217 (Conv2D)             (None, None, None, 9 82944       activation_216[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_214 (BatchN (None, None, None, 3 1152        conv2d_214[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_217 (BatchN (None, None, None, 9 288         conv2d_217[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_214 (Activation)     (None, None, None, 3 0           batch_normalization_214[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_217 (Activation)     (None, None, None, 9 0           batch_normalization_217[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_10 (MaxPooling2D) (None, None, None, 2 0           mixed2[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_214[0][0]             \n",
+      "                                                                 activation_217[0][0]             \n",
+      "                                                                 max_pooling2d_10[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_222 (Conv2D)             (None, None, None, 1 98304       mixed3[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_222 (BatchN (None, None, None, 1 384         conv2d_222[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_222 (Activation)     (None, None, None, 1 0           batch_normalization_222[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_223 (Conv2D)             (None, None, None, 1 114688      activation_222[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_223 (BatchN (None, None, None, 1 384         conv2d_223[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_223 (Activation)     (None, None, None, 1 0           batch_normalization_223[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_219 (Conv2D)             (None, None, None, 1 98304       mixed3[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_224 (Conv2D)             (None, None, None, 1 114688      activation_223[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_219 (BatchN (None, None, None, 1 384         conv2d_219[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_224 (BatchN (None, None, None, 1 384         conv2d_224[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_219 (Activation)     (None, None, None, 1 0           batch_normalization_219[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_224 (Activation)     (None, None, None, 1 0           batch_normalization_224[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_220 (Conv2D)             (None, None, None, 1 114688      activation_219[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_225 (Conv2D)             (None, None, None, 1 114688      activation_224[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_220 (BatchN (None, None, None, 1 384         conv2d_220[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_225 (BatchN (None, None, None, 1 384         conv2d_225[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_220 (Activation)     (None, None, None, 1 0           batch_normalization_220[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_225 (Activation)     (None, None, None, 1 0           batch_normalization_225[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_21 (AveragePo (None, None, None, 7 0           mixed3[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_218 (Conv2D)             (None, None, None, 1 147456      mixed3[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_221 (Conv2D)             (None, None, None, 1 172032      activation_220[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_226 (Conv2D)             (None, None, None, 1 172032      activation_225[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_227 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_21[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_218 (BatchN (None, None, None, 1 576         conv2d_218[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_221 (BatchN (None, None, None, 1 576         conv2d_221[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_226 (BatchN (None, None, None, 1 576         conv2d_226[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_227 (BatchN (None, None, None, 1 576         conv2d_227[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_218 (Activation)     (None, None, None, 1 0           batch_normalization_218[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_221 (Activation)     (None, None, None, 1 0           batch_normalization_221[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_226 (Activation)     (None, None, None, 1 0           batch_normalization_226[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_227 (Activation)     (None, None, None, 1 0           batch_normalization_227[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed4 (Concatenate)            (None, None, None, 7 0           activation_218[0][0]             \n",
+      "                                                                 activation_221[0][0]             \n",
+      "                                                                 activation_226[0][0]             \n",
+      "                                                                 activation_227[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_232 (Conv2D)             (None, None, None, 1 122880      mixed4[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_232 (BatchN (None, None, None, 1 480         conv2d_232[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_232 (Activation)     (None, None, None, 1 0           batch_normalization_232[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_233 (Conv2D)             (None, None, None, 1 179200      activation_232[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_233 (BatchN (None, None, None, 1 480         conv2d_233[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_233 (Activation)     (None, None, None, 1 0           batch_normalization_233[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_229 (Conv2D)             (None, None, None, 1 122880      mixed4[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_234 (Conv2D)             (None, None, None, 1 179200      activation_233[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_229 (BatchN (None, None, None, 1 480         conv2d_229[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_234 (BatchN (None, None, None, 1 480         conv2d_234[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_229 (Activation)     (None, None, None, 1 0           batch_normalization_229[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_234 (Activation)     (None, None, None, 1 0           batch_normalization_234[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_230 (Conv2D)             (None, None, None, 1 179200      activation_229[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_235 (Conv2D)             (None, None, None, 1 179200      activation_234[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_230 (BatchN (None, None, None, 1 480         conv2d_230[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_235 (BatchN (None, None, None, 1 480         conv2d_235[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_230 (Activation)     (None, None, None, 1 0           batch_normalization_230[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_235 (Activation)     (None, None, None, 1 0           batch_normalization_235[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_22 (AveragePo (None, None, None, 7 0           mixed4[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_228 (Conv2D)             (None, None, None, 1 147456      mixed4[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_231 (Conv2D)             (None, None, None, 1 215040      activation_230[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_236 (Conv2D)             (None, None, None, 1 215040      activation_235[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_237 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_22[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_228 (BatchN (None, None, None, 1 576         conv2d_228[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_231 (BatchN (None, None, None, 1 576         conv2d_231[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_236 (BatchN (None, None, None, 1 576         conv2d_236[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_237 (BatchN (None, None, None, 1 576         conv2d_237[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_228 (Activation)     (None, None, None, 1 0           batch_normalization_228[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_231 (Activation)     (None, None, None, 1 0           batch_normalization_231[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_236 (Activation)     (None, None, None, 1 0           batch_normalization_236[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_237 (Activation)     (None, None, None, 1 0           batch_normalization_237[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed5 (Concatenate)            (None, None, None, 7 0           activation_228[0][0]             \n",
+      "                                                                 activation_231[0][0]             \n",
+      "                                                                 activation_236[0][0]             \n",
+      "                                                                 activation_237[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_242 (Conv2D)             (None, None, None, 1 122880      mixed5[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_242 (BatchN (None, None, None, 1 480         conv2d_242[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_242 (Activation)     (None, None, None, 1 0           batch_normalization_242[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_243 (Conv2D)             (None, None, None, 1 179200      activation_242[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_243 (BatchN (None, None, None, 1 480         conv2d_243[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_243 (Activation)     (None, None, None, 1 0           batch_normalization_243[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_239 (Conv2D)             (None, None, None, 1 122880      mixed5[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_244 (Conv2D)             (None, None, None, 1 179200      activation_243[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_239 (BatchN (None, None, None, 1 480         conv2d_239[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_244 (BatchN (None, None, None, 1 480         conv2d_244[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_239 (Activation)     (None, None, None, 1 0           batch_normalization_239[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_244 (Activation)     (None, None, None, 1 0           batch_normalization_244[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_240 (Conv2D)             (None, None, None, 1 179200      activation_239[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_245 (Conv2D)             (None, None, None, 1 179200      activation_244[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_240 (BatchN (None, None, None, 1 480         conv2d_240[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_245 (BatchN (None, None, None, 1 480         conv2d_245[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_240 (Activation)     (None, None, None, 1 0           batch_normalization_240[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_245 (Activation)     (None, None, None, 1 0           batch_normalization_245[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_23 (AveragePo (None, None, None, 7 0           mixed5[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_238 (Conv2D)             (None, None, None, 1 147456      mixed5[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_241 (Conv2D)             (None, None, None, 1 215040      activation_240[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_246 (Conv2D)             (None, None, None, 1 215040      activation_245[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_247 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_23[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_238 (BatchN (None, None, None, 1 576         conv2d_238[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_241 (BatchN (None, None, None, 1 576         conv2d_241[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_246 (BatchN (None, None, None, 1 576         conv2d_246[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_247 (BatchN (None, None, None, 1 576         conv2d_247[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_238 (Activation)     (None, None, None, 1 0           batch_normalization_238[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_241 (Activation)     (None, None, None, 1 0           batch_normalization_241[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_246 (Activation)     (None, None, None, 1 0           batch_normalization_246[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_247 (Activation)     (None, None, None, 1 0           batch_normalization_247[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed6 (Concatenate)            (None, None, None, 7 0           activation_238[0][0]             \n",
+      "                                                                 activation_241[0][0]             \n",
+      "                                                                 activation_246[0][0]             \n",
+      "                                                                 activation_247[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_252 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_252 (BatchN (None, None, None, 1 576         conv2d_252[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_252 (Activation)     (None, None, None, 1 0           batch_normalization_252[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_253 (Conv2D)             (None, None, None, 1 258048      activation_252[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_253 (BatchN (None, None, None, 1 576         conv2d_253[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_253 (Activation)     (None, None, None, 1 0           batch_normalization_253[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_249 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_254 (Conv2D)             (None, None, None, 1 258048      activation_253[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_249 (BatchN (None, None, None, 1 576         conv2d_249[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_254 (BatchN (None, None, None, 1 576         conv2d_254[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_249 (Activation)     (None, None, None, 1 0           batch_normalization_249[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_254 (Activation)     (None, None, None, 1 0           batch_normalization_254[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_250 (Conv2D)             (None, None, None, 1 258048      activation_249[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_255 (Conv2D)             (None, None, None, 1 258048      activation_254[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_250 (BatchN (None, None, None, 1 576         conv2d_250[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_255 (BatchN (None, None, None, 1 576         conv2d_255[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_250 (Activation)     (None, None, None, 1 0           batch_normalization_250[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_255 (Activation)     (None, None, None, 1 0           batch_normalization_255[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_24 (AveragePo (None, None, None, 7 0           mixed6[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_248 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_251 (Conv2D)             (None, None, None, 1 258048      activation_250[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_256 (Conv2D)             (None, None, None, 1 258048      activation_255[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_257 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_24[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_248 (BatchN (None, None, None, 1 576         conv2d_248[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_251 (BatchN (None, None, None, 1 576         conv2d_251[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_256 (BatchN (None, None, None, 1 576         conv2d_256[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_257 (BatchN (None, None, None, 1 576         conv2d_257[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_248 (Activation)     (None, None, None, 1 0           batch_normalization_248[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_251 (Activation)     (None, None, None, 1 0           batch_normalization_251[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_256 (Activation)     (None, None, None, 1 0           batch_normalization_256[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_257 (Activation)     (None, None, None, 1 0           batch_normalization_257[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed7 (Concatenate)            (None, None, None, 7 0           activation_248[0][0]             \n",
+      "                                                                 activation_251[0][0]             \n",
+      "                                                                 activation_256[0][0]             \n",
+      "                                                                 activation_257[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_260 (Conv2D)             (None, None, None, 1 147456      mixed7[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_260 (BatchN (None, None, None, 1 576         conv2d_260[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_260 (Activation)     (None, None, None, 1 0           batch_normalization_260[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_261 (Conv2D)             (None, None, None, 1 258048      activation_260[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_261 (BatchN (None, None, None, 1 576         conv2d_261[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_261 (Activation)     (None, None, None, 1 0           batch_normalization_261[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_258 (Conv2D)             (None, None, None, 1 147456      mixed7[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_262 (Conv2D)             (None, None, None, 1 258048      activation_261[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_258 (BatchN (None, None, None, 1 576         conv2d_258[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_262 (BatchN (None, None, None, 1 576         conv2d_262[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_258 (Activation)     (None, None, None, 1 0           batch_normalization_258[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_262 (Activation)     (None, None, None, 1 0           batch_normalization_262[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_259 (Conv2D)             (None, None, None, 3 552960      activation_258[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_263 (Conv2D)             (None, None, None, 1 331776      activation_262[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_259 (BatchN (None, None, None, 3 960         conv2d_259[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_263 (BatchN (None, None, None, 1 576         conv2d_263[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_259 (Activation)     (None, None, None, 3 0           batch_normalization_259[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_263 (Activation)     (None, None, None, 1 0           batch_normalization_263[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "max_pooling2d_11 (MaxPooling2D) (None, None, None, 7 0           mixed7[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_259[0][0]             \n",
+      "                                                                 activation_263[0][0]             \n",
+      "                                                                 max_pooling2d_11[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_268 (Conv2D)             (None, None, None, 4 573440      mixed8[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_268 (BatchN (None, None, None, 4 1344        conv2d_268[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_268 (Activation)     (None, None, None, 4 0           batch_normalization_268[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_265 (Conv2D)             (None, None, None, 3 491520      mixed8[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_269 (Conv2D)             (None, None, None, 3 1548288     activation_268[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_265 (BatchN (None, None, None, 3 1152        conv2d_265[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_269 (BatchN (None, None, None, 3 1152        conv2d_269[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_265 (Activation)     (None, None, None, 3 0           batch_normalization_265[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_269 (Activation)     (None, None, None, 3 0           batch_normalization_269[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_266 (Conv2D)             (None, None, None, 3 442368      activation_265[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_267 (Conv2D)             (None, None, None, 3 442368      activation_265[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_270 (Conv2D)             (None, None, None, 3 442368      activation_269[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_271 (Conv2D)             (None, None, None, 3 442368      activation_269[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_25 (AveragePo (None, None, None, 1 0           mixed8[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_264 (Conv2D)             (None, None, None, 3 409600      mixed8[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_266 (BatchN (None, None, None, 3 1152        conv2d_266[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_267 (BatchN (None, None, None, 3 1152        conv2d_267[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_270 (BatchN (None, None, None, 3 1152        conv2d_270[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_271 (BatchN (None, None, None, 3 1152        conv2d_271[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_272 (Conv2D)             (None, None, None, 1 245760      average_pooling2d_25[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_264 (BatchN (None, None, None, 3 960         conv2d_264[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_266 (Activation)     (None, None, None, 3 0           batch_normalization_266[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_267 (Activation)     (None, None, None, 3 0           batch_normalization_267[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_270 (Activation)     (None, None, None, 3 0           batch_normalization_270[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_271 (Activation)     (None, None, None, 3 0           batch_normalization_271[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_272 (BatchN (None, None, None, 1 576         conv2d_272[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_264 (Activation)     (None, None, None, 3 0           batch_normalization_264[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_266[0][0]             \n",
+      "                                                                 activation_267[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_4 (Concatenate)     (None, None, None, 7 0           activation_270[0][0]             \n",
+      "                                                                 activation_271[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_272 (Activation)     (None, None, None, 1 0           batch_normalization_272[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed9 (Concatenate)            (None, None, None, 2 0           activation_264[0][0]             \n",
+      "                                                                 mixed9_0[0][0]                   \n",
+      "                                                                 concatenate_4[0][0]              \n",
+      "                                                                 activation_272[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_277 (Conv2D)             (None, None, None, 4 917504      mixed9[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_277 (BatchN (None, None, None, 4 1344        conv2d_277[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_277 (Activation)     (None, None, None, 4 0           batch_normalization_277[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_274 (Conv2D)             (None, None, None, 3 786432      mixed9[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_278 (Conv2D)             (None, None, None, 3 1548288     activation_277[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_274 (BatchN (None, None, None, 3 1152        conv2d_274[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_278 (BatchN (None, None, None, 3 1152        conv2d_278[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_274 (Activation)     (None, None, None, 3 0           batch_normalization_274[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_278 (Activation)     (None, None, None, 3 0           batch_normalization_278[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_275 (Conv2D)             (None, None, None, 3 442368      activation_274[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_276 (Conv2D)             (None, None, None, 3 442368      activation_274[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_279 (Conv2D)             (None, None, None, 3 442368      activation_278[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_280 (Conv2D)             (None, None, None, 3 442368      activation_278[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "average_pooling2d_26 (AveragePo (None, None, None, 2 0           mixed9[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_273 (Conv2D)             (None, None, None, 3 655360      mixed9[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_275 (BatchN (None, None, None, 3 1152        conv2d_275[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_276 (BatchN (None, None, None, 3 1152        conv2d_276[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_279 (BatchN (None, None, None, 3 1152        conv2d_279[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_280 (BatchN (None, None, None, 3 1152        conv2d_280[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_281 (Conv2D)             (None, None, None, 1 393216      average_pooling2d_26[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_273 (BatchN (None, None, None, 3 960         conv2d_273[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_275 (Activation)     (None, None, None, 3 0           batch_normalization_275[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_276 (Activation)     (None, None, None, 3 0           batch_normalization_276[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_279 (Activation)     (None, None, None, 3 0           batch_normalization_279[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_280 (Activation)     (None, None, None, 3 0           batch_normalization_280[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_281 (BatchN (None, None, None, 1 576         conv2d_281[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_273 (Activation)     (None, None, None, 3 0           batch_normalization_273[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_275[0][0]             \n",
+      "                                                                 activation_276[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_5 (Concatenate)     (None, None, None, 7 0           activation_279[0][0]             \n",
+      "                                                                 activation_280[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_281 (Activation)     (None, None, None, 1 0           batch_normalization_281[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "mixed10 (Concatenate)           (None, None, None, 2 0           activation_273[0][0]             \n",
+      "                                                                 mixed9_1[0][0]                   \n",
+      "                                                                 concatenate_5[0][0]              \n",
+      "                                                                 activation_281[0][0]             \n",
+      "==================================================================================================\n",
+      "Total params: 21,802,784\n",
+      "Trainable params: 21,768,352\n",
+      "Non-trainable params: 34,432\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Explore the layers you can use by looking at the model summary\n",
+    "# try using different mixed layers\n",
+    "baseModel.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "SFaBiZRwUf1m"
+   },
+   "outputs": [],
+   "source": [
+    "# build our dreaming model\n",
+    "layers = [baseModel.get_layer(name).output for name in names]\n",
+    "deepDreamModel = tf.keras.Model(inputs=baseModel.input, outputs=layers)\n",
+    "\n",
+    "# We convert the image to a TensorFlow constant for better performance,\n",
+    "image = tf.constant(sampleImage)\n",
+    "\n",
+    "# We take the first two dimensions of the image and cast then them to float\n",
+    "baseShape = tf.cast(tf.shape(image)[:-1], tf.float32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "h1zC1hhpPSXK"
+   },
+   "source": [
+    "# Run our Deep Dream Generator!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "executionInfo": {
+     "elapsed": 95972,
+     "status": "ok",
+     "timestamp": 1590903411701,
+     "user": {
+      "displayName": "Rajeev Ratan",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhBYJnwdIGYSJdvGmzIt64rYSF1dDuiFyfRw4Rpeg=s64",
+      "userId": "08597265227091462140"
+     },
+     "user_tz": 240
+    },
+    "id": "G5svaIpwPS7E",
+    "outputId": "c347894b-1486-44d5-ee5b-e7867515041e"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Working on octave 0\n",
+      "Iteration 0 with loss 0.6881672739982605\n",
+      "Iteration 50 with loss 1.2937997579574585\n",
+      "Working on octave 1\n",
+      "Iteration 0 with loss 0.7825002074241638\n",
+      "Iteration 50 with loss 1.489501714706421\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c683080f08>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# We iterate over the number of Octaves (sizes) we are going to create using Deep Dream\n",
+    "for n in range(NUM_OCTAVES):\n",
+    "\n",
+    "    # get the width and height for the current octave and cast them to integers\n",
+    "    print(\"Working on octave {}\".format(n))\n",
+    "    newShape = tf.cast(baseShape * (OCTAVE_SCALE ** n), tf.int32)\n",
+    "\n",
+    "    # Resize the image with new shape and convert it to numpy before running \n",
+    "    # through our DeepDream Model\n",
+    "    image = tf.image.resize(image, newShape).numpy()\n",
+    "    image = fullDeepDreamModel(model=deepDreamModel, image=image, iterations=100, stepSize=0.001)\n",
+    "\n",
+    "# convert the final image to a numpy array and then save it to disk\n",
+    "finalImage = np.array(image)\n",
+    "cv2.imwrite('output.jpg', finalImage)\n",
+    "\n",
+    "plt.figure(figsize=(20,10)) \n",
+    "plt.imshow(finalImage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c6830e2d48>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Run these lines of code to show you plot \n",
+    "# In some cases the first plot of a matplotlib doesn't display in our ipython notebook.\n",
+    "plt.figure(figsize=(20,10)) \n",
+    "plt.imshow(finalImage)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "0_7K5eW4OK0K"
+   },
+   "source": [
+    "## Please experiment with diferent Octaves, Iterations, Layers and Octave Scales!\n",
+    "\n",
+    "Start making your own 'Art'"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "5_1_Solution_Milestone_5_Google_Deep_Dreaming.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/23. DeepDream & Neural Style Transfers/24. Neural Style Transfer.ipynb b/23. DeepDream & Neural Style Transfers/24. Neural Style Transfer.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..907bc892cd533ba15df0334256b1a11d82ab7cf3
--- /dev/null
+++ b/23. DeepDream & Neural Style Transfers/24. Neural Style Transfer.ipynb	
@@ -0,0 +1,1469 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Neural Style Transfer using TensorFlow 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "g_nWetWWd_ns"
+   },
+   "source": [
+    "##### Copyright 2018 The TensorFlow Authors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "cellView": "form",
+    "colab": {},
+    "colab_type": "code",
+    "id": "2pHVBk_seED1"
+   },
+   "outputs": [],
+   "source": [
+    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "# https://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "6msVLevwcRhm"
+   },
+   "source": [
+    "# Neural style transfer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Ds4o1h4WHz9U"
+   },
+   "source": [
+    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/generative/style_transfer\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
+    "  </td>\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/style_transfer.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+    "  </td>\n",
+    "  <td>\n",
+    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/style_transfer.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+    "  </td>\n",
+    "  <td>\n",
+    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/generative/style_transfer.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
+    "  </td>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "aDyGj8DmXCJI"
+   },
+   "source": [
+    "This tutorial uses deep learning to compose one image in the style of another image (ever wish you could paint like Picasso or Van Gogh?). This is known as *neural style transfer* and the technique is outlined in <a href=\"https://arxiv.org/abs/1508.06576\" class=\"external\">A Neural Algorithm of Artistic Style</a> (Gatys et al.). \n",
+    "\n",
+    "Note: This tutorial demonstrates the original style-transfer algorithm. It optimizes the image content to a particular style. Modern approaches train a model to generate the stylized image directly (similar to [cyclegan](cyclegan.ipynb)). This approach is much faster (up to 1000x). A pretrained [Arbitrary Image Stylization module](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb) is available in [TensorFlow Hub](https://tensorflow.org/hub), and for [TensorFlow Lite](https://www.tensorflow.org/lite/models/style_transfer/overview).\n",
+    "\n",
+    "Neural style transfer is an optimization technique used to take two images—a *content* image and a *style reference* image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.\n",
+    "\n",
+    "This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image. These statistics are extracted from the images using a convolutional network.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "3kb_UJY-jCEl"
+   },
+   "source": [
+    "For example, let’s take an image of this dog and Wassily Kandinsky's Composition 7:\n",
+    "\n",
+    "<img src=\"https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg\" width=\"500px\"/>\n",
+    "\n",
+    "[Yellow Labrador Looking](https://commons.wikimedia.org/wiki/File:YellowLabradorLooking_new.jpg), from Wikimedia Commons\n",
+    "\n",
+    "<img src=\"https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg\" width=\"500px\"/>\n",
+    "\n",
+    "\n",
+    "Now how would it look like if Kandinsky decided to paint the picture of this Dog exclusively with this style? Something like this?\n",
+    "\n",
+    "<img src=\"https://tensorflow.org/tutorials/generative/images/stylized-image.png\" style=\"width: 500px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "U8ajP_u73s6m"
+   },
+   "source": [
+    "## Setup\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "eqxUicSPUOP6"
+   },
+   "source": [
+    "### Import and configure modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "NyftRTSMuwue"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "sc1OLbOWhPCO"
+   },
+   "outputs": [],
+   "source": [
+    "import IPython.display as display\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "mpl.rcParams['figure.figsize'] = (12,12)\n",
+    "mpl.rcParams['axes.grid'] = False\n",
+    "\n",
+    "import numpy as np\n",
+    "import PIL.Image\n",
+    "import time\n",
+    "import functools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "GM6VEGrGLh62"
+   },
+   "outputs": [],
+   "source": [
+    "def tensor_to_image(tensor):\n",
+    "  tensor = tensor*255\n",
+    "  tensor = np.array(tensor, dtype=np.uint8)\n",
+    "  if np.ndim(tensor)>3:\n",
+    "    assert tensor.shape[0] == 1\n",
+    "    tensor = tensor[0]\n",
+    "  return PIL.Image.fromarray(tensor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "oeXebYusyHwC"
+   },
+   "source": [
+    "Download images and choose a style image and a content image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "wqc0OJHwyFAk"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg\n",
+      "90112/83281 [================================] - 0s 3us/step\n",
+      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg\n",
+      "196608/195196 [==============================] - 0s 1us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "content_path = tf.keras.utils.get_file('YellowLabradorLooking_new.jpg', 'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')\n",
+    "\n",
+    "# https://commons.wikimedia.org/wiki/File:Vassily_Kandinsky,_1913_-_Composition_7.jpg\n",
+    "style_path = tf.keras.utils.get_file('kandinsky5.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xE4Yt8nArTeR"
+   },
+   "source": [
+    "## Visualize the input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "klh6ObK2t_vH"
+   },
+   "source": [
+    "Define a function to load an image and limit its maximum dimension to 512 pixels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "3TLljcwv5qZs"
+   },
+   "outputs": [],
+   "source": [
+    "def load_img(path_to_img):\n",
+    "  max_dim = 512\n",
+    "  img = tf.io.read_file(path_to_img)\n",
+    "  img = tf.image.decode_image(img, channels=3)\n",
+    "  img = tf.image.convert_image_dtype(img, tf.float32)\n",
+    "\n",
+    "  shape = tf.cast(tf.shape(img)[:-1], tf.float32)\n",
+    "  long_dim = max(shape)\n",
+    "  scale = max_dim / long_dim\n",
+    "\n",
+    "  new_shape = tf.cast(shape * scale, tf.int32)\n",
+    "\n",
+    "  img = tf.image.resize(img, new_shape)\n",
+    "  img = img[tf.newaxis, :]\n",
+    "  return img"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "2yAlRzJZrWM3"
+   },
+   "source": [
+    "Create a simple function to display an image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "cBX-eNT8PAK_"
+   },
+   "outputs": [],
+   "source": [
+    "def imshow(image, title=None):\n",
+    "  if len(image.shape) > 3:\n",
+    "    image = tf.squeeze(image, axis=0)\n",
+    "\n",
+    "  plt.imshow(image)\n",
+    "  if title:\n",
+    "    plt.title(title)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "_UWQmeEaiKkP"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "content_image = load_img(content_path)\n",
+    "style_image = load_img(style_path)\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "imshow(content_image, 'Content Image')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "imshow(style_image, 'Style Image')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "YMzChXSlKTA2"
+   },
+   "source": [
+    "## Fast Style Transfer using TF-Hub\n",
+    "\n",
+    "This tutorial demonstrates the original style-transfer algorithm, which optimizes the image content to a particular style. Before getting into the details, let's see how the [TensorFlow Hub](https://tensorflow.org/hub) module does:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting tensorflow_hub\n",
+      "  Downloading tensorflow_hub-0.8.0-py2.py3-none-any.whl (101 kB)\n",
+      "Requirement already satisfied: protobuf>=3.8.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from tensorflow_hub) (3.11.4)\n",
+      "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from tensorflow_hub) (1.16.5)\n",
+      "Requirement already satisfied: six>=1.12.0 in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from tensorflow_hub) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\envs\\cv\\lib\\site-packages (from protobuf>=3.8.0->tensorflow_hub) (41.4.0)\n",
+      "Installing collected packages: tensorflow-hub\n",
+      "Successfully installed tensorflow-hub-0.8.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tensorflow_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iYSLexgRKSh-"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=512x424 at 0x12CC7991648>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow_hub as hub\n",
+    "\n",
+    "hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')\n",
+    "stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]\n",
+    "tensor_to_image(stylized_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "GEwZ7FlwrjoZ"
+   },
+   "source": [
+    "## Define content and style representations\n",
+    "\n",
+    "Use the intermediate layers of the model to get the *content* and *style* representations of the image. Starting from the network's input layer, the first few layer activations represent low-level features like edges and textures. As you step through the network, the final few layers represent higher-level features—object parts like *wheels* or *eyes*. In this case, you are using the VGG19 network architecture, a pretrained image classification network. These intermediate layers are necessary to define the representation of content and style from the images. For an input image, try to match the corresponding style and content target representations at these intermediate layers.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "LP_7zrziuiJk"
+   },
+   "source": [
+    "Load a [VGG19](https://keras.io/applications/#vgg19) and test run it on our image to ensure it's used correctly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "fMbzrr7BCTq0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5\n",
+      "574717952/574710816 [==============================] - 4197s 7us/step\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TensorShape([1, 1000])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = tf.keras.applications.vgg19.preprocess_input(content_image*255)\n",
+    "x = tf.image.resize(x, (224, 224))\n",
+    "vgg = tf.keras.applications.VGG19(include_top=True, weights='imagenet')\n",
+    "prediction_probabilities = vgg(x)\n",
+    "prediction_probabilities.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "1_FyCm0dYnvl"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Labrador_retriever', 0.49317262),\n",
+       " ('golden_retriever', 0.23665187),\n",
+       " ('kuvasz', 0.036357313),\n",
+       " ('Chesapeake_Bay_retriever', 0.024182774),\n",
+       " ('Greater_Swiss_Mountain_dog', 0.018646035)]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predicted_top_5 = tf.keras.applications.vgg19.decode_predictions(prediction_probabilities.numpy())[0]\n",
+    "[(class_name, prob) for (number, class_name, prob) in predicted_top_5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ljpoYk-0f6HS"
+   },
+   "source": [
+    "Now load a `VGG19` without the classification head, and list the layer names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Yh_AV6220ebD"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "input_2\n",
+      "block1_conv1\n",
+      "block1_conv2\n",
+      "block1_pool\n",
+      "block2_conv1\n",
+      "block2_conv2\n",
+      "block2_pool\n",
+      "block3_conv1\n",
+      "block3_conv2\n",
+      "block3_conv3\n",
+      "block3_conv4\n",
+      "block3_pool\n",
+      "block4_conv1\n",
+      "block4_conv2\n",
+      "block4_conv3\n",
+      "block4_conv4\n",
+      "block4_pool\n",
+      "block5_conv1\n",
+      "block5_conv2\n",
+      "block5_conv3\n",
+      "block5_conv4\n",
+      "block5_pool\n"
+     ]
+    }
+   ],
+   "source": [
+    "vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')\n",
+    "\n",
+    "print()\n",
+    "for layer in vgg.layers:\n",
+    "  print(layer.name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Wt-tASys0eJv"
+   },
+   "source": [
+    "Choose intermediate layers from the network to represent the style and content of the image:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ArfX_6iA0WAX"
+   },
+   "outputs": [],
+   "source": [
+    "content_layers = ['block5_conv2'] \n",
+    "\n",
+    "style_layers = ['block1_conv1',\n",
+    "                'block2_conv1',\n",
+    "                'block3_conv1', \n",
+    "                'block4_conv1', \n",
+    "                'block5_conv1']\n",
+    "\n",
+    "num_content_layers = len(content_layers)\n",
+    "num_style_layers = len(style_layers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "2o4nSwuN0U3X"
+   },
+   "source": [
+    "#### Intermediate layers for style and content\n",
+    "\n",
+    "So why do these intermediate outputs within our pretrained image classification network allow us to define style and content representations?\n",
+    "\n",
+    "At a high level, in order for a network to perform image classification (which this network has been trained to do), it must understand the image. This requires taking the raw image as input pixels and building an internal representation that converts the raw image pixels into a complex understanding of the features present within the image.\n",
+    "\n",
+    "This is also a reason why convolutional neural networks are able to generalize well: they’re able to capture the invariances and defining features within classes (e.g. cats vs. dogs) that are agnostic to background noise and other nuisances. Thus, somewhere between where the raw image is fed into the model and the output classification label, the model serves as a complex feature extractor. By accessing intermediate layers of the model, you're able to describe the content and style of input images."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Jt3i3RRrJiOX"
+   },
+   "source": [
+    "## Build the model \n",
+    "\n",
+    "The networks in `tf.keras.applications` are designed so you can easily extract the intermediate layer values using the Keras functional API.\n",
+    "\n",
+    "To define a model using the functional API, specify the inputs and outputs:\n",
+    "\n",
+    "`model = Model(inputs, outputs)`\n",
+    "\n",
+    "This following function builds a VGG19 model that returns a list of intermediate layer outputs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nfec6MuMAbPx"
+   },
+   "outputs": [],
+   "source": [
+    "def vgg_layers(layer_names):\n",
+    "  \"\"\" Creates a vgg model that returns a list of intermediate output values.\"\"\"\n",
+    "  # Load our model. Load pretrained VGG, trained on imagenet data\n",
+    "  vgg = tf.keras.applications.VGG19(include_top=False, weights='imagenet')\n",
+    "  vgg.trainable = False\n",
+    "  \n",
+    "  outputs = [vgg.get_layer(name).output for name in layer_names]\n",
+    "\n",
+    "  model = tf.keras.Model([vgg.input], outputs)\n",
+    "  return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "jbaIvZf5wWn_"
+   },
+   "source": [
+    "And to create the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "LkyvPpBHSfVi"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "block1_conv1\n",
+      "  shape:  (1, 336, 512, 64)\n",
+      "  min:  0.0\n",
+      "  max:  835.5255\n",
+      "  mean:  33.97525\n",
+      "\n",
+      "block2_conv1\n",
+      "  shape:  (1, 168, 256, 128)\n",
+      "  min:  0.0\n",
+      "  max:  4625.8867\n",
+      "  mean:  199.82687\n",
+      "\n",
+      "block3_conv1\n",
+      "  shape:  (1, 84, 128, 256)\n",
+      "  min:  0.0\n",
+      "  max:  8789.24\n",
+      "  mean:  230.78099\n",
+      "\n",
+      "block4_conv1\n",
+      "  shape:  (1, 42, 64, 512)\n",
+      "  min:  0.0\n",
+      "  max:  21566.133\n",
+      "  mean:  791.24005\n",
+      "\n",
+      "block5_conv1\n",
+      "  shape:  (1, 21, 32, 512)\n",
+      "  min:  0.0\n",
+      "  max:  3189.2532\n",
+      "  mean:  59.179478\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "style_extractor = vgg_layers(style_layers)\n",
+    "style_outputs = style_extractor(style_image*255)\n",
+    "\n",
+    "#Look at the statistics of each layer's output\n",
+    "for name, output in zip(style_layers, style_outputs):\n",
+    "  print(name)\n",
+    "  print(\"  shape: \", output.numpy().shape)\n",
+    "  print(\"  min: \", output.numpy().min())\n",
+    "  print(\"  max: \", output.numpy().max())\n",
+    "  print(\"  mean: \", output.numpy().mean())\n",
+    "  print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "lGUfttK9F8d5"
+   },
+   "source": [
+    "## Calculate style\n",
+    "\n",
+    "The content of an image is represented by the values of the intermediate feature maps.\n",
+    "\n",
+    "It turns out, the style of an image can be described by the means and correlations across the different feature maps. Calculate a Gram matrix that includes this information by taking the outer product of the feature vector with itself at each location, and averaging that outer product over all locations. This Gram matrix can be calculated for a particular layer as:\n",
+    "\n",
+    "$$G^l_{cd} = \\frac{\\sum_{ij} F^l_{ijc}(x)F^l_{ijd}(x)}{IJ}$$\n",
+    "\n",
+    "This can be implemented concisely using the `tf.linalg.einsum` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "HAy1iGPdoEpZ"
+   },
+   "outputs": [],
+   "source": [
+    "def gram_matrix(input_tensor):\n",
+    "  result = tf.linalg.einsum('bijc,bijd->bcd', input_tensor, input_tensor)\n",
+    "  input_shape = tf.shape(input_tensor)\n",
+    "  num_locations = tf.cast(input_shape[1]*input_shape[2], tf.float32)\n",
+    "  return result/(num_locations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "pXIUX6czZABh"
+   },
+   "source": [
+    "## Extract style and content\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "1HGHvwlJ1nkn"
+   },
+   "source": [
+    "Build a model that returns the style and content tensors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Sr6QALY-I1ja"
+   },
+   "outputs": [],
+   "source": [
+    "class StyleContentModel(tf.keras.models.Model):\n",
+    "  def __init__(self, style_layers, content_layers):\n",
+    "    super(StyleContentModel, self).__init__()\n",
+    "    self.vgg =  vgg_layers(style_layers + content_layers)\n",
+    "    self.style_layers = style_layers\n",
+    "    self.content_layers = content_layers\n",
+    "    self.num_style_layers = len(style_layers)\n",
+    "    self.vgg.trainable = False\n",
+    "\n",
+    "  def call(self, inputs):\n",
+    "    \"Expects float input in [0,1]\"\n",
+    "    inputs = inputs*255.0\n",
+    "    preprocessed_input = tf.keras.applications.vgg19.preprocess_input(inputs)\n",
+    "    outputs = self.vgg(preprocessed_input)\n",
+    "    style_outputs, content_outputs = (outputs[:self.num_style_layers], \n",
+    "                                      outputs[self.num_style_layers:])\n",
+    "\n",
+    "    style_outputs = [gram_matrix(style_output)\n",
+    "                     for style_output in style_outputs]\n",
+    "\n",
+    "    content_dict = {content_name:value \n",
+    "                    for content_name, value \n",
+    "                    in zip(self.content_layers, content_outputs)}\n",
+    "\n",
+    "    style_dict = {style_name:value\n",
+    "                  for style_name, value\n",
+    "                  in zip(self.style_layers, style_outputs)}\n",
+    "    \n",
+    "    return {'content':content_dict, 'style':style_dict}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Xuj1o33t1edl"
+   },
+   "source": [
+    "When called on an image, this model returns the gram matrix (style) of the `style_layers` and content of the `content_layers`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "rkjO-DoNDU0A"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Styles:\n",
+      "   block1_conv1\n",
+      "    shape:  (1, 64, 64)\n",
+      "    min:  0.005522847\n",
+      "    max:  28014.559\n",
+      "    mean:  263.79025\n",
+      "\n",
+      "   block2_conv1\n",
+      "    shape:  (1, 128, 128)\n",
+      "    min:  0.0\n",
+      "    max:  61479.504\n",
+      "    mean:  9100.95\n",
+      "\n",
+      "   block3_conv1\n",
+      "    shape:  (1, 256, 256)\n",
+      "    min:  0.0\n",
+      "    max:  545623.44\n",
+      "    mean:  7660.9766\n",
+      "\n",
+      "   block4_conv1\n",
+      "    shape:  (1, 512, 512)\n",
+      "    min:  0.0\n",
+      "    max:  4320501.0\n",
+      "    mean:  134288.86\n",
+      "\n",
+      "   block5_conv1\n",
+      "    shape:  (1, 512, 512)\n",
+      "    min:  0.0\n",
+      "    max:  110005.38\n",
+      "    mean:  1487.0381\n",
+      "\n",
+      "Contents:\n",
+      "   block5_conv2\n",
+      "    shape:  (1, 26, 32, 512)\n",
+      "    min:  0.0\n",
+      "    max:  2410.8796\n",
+      "    mean:  13.764152\n"
+     ]
+    }
+   ],
+   "source": [
+    "extractor = StyleContentModel(style_layers, content_layers)\n",
+    "\n",
+    "results = extractor(tf.constant(content_image))\n",
+    "\n",
+    "print('Styles:')\n",
+    "for name, output in sorted(results['style'].items()):\n",
+    "  print(\"  \", name)\n",
+    "  print(\"    shape: \", output.numpy().shape)\n",
+    "  print(\"    min: \", output.numpy().min())\n",
+    "  print(\"    max: \", output.numpy().max())\n",
+    "  print(\"    mean: \", output.numpy().mean())\n",
+    "  print()\n",
+    "\n",
+    "print(\"Contents:\")\n",
+    "for name, output in sorted(results['content'].items()):\n",
+    "  print(\"  \", name)\n",
+    "  print(\"    shape: \", output.numpy().shape)\n",
+    "  print(\"    min: \", output.numpy().min())\n",
+    "  print(\"    max: \", output.numpy().max())\n",
+    "  print(\"    mean: \", output.numpy().mean())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "y9r8Lyjb_m0u"
+   },
+   "source": [
+    "## Run gradient descent\n",
+    "\n",
+    "With this style and content extractor, you can now implement the style transfer algorithm. Do this by calculating the mean square error for your image's output relative to each target, then take the weighted sum of these losses.\n",
+    "\n",
+    "Set your style and content target values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "PgkNOnGUFcKa"
+   },
+   "outputs": [],
+   "source": [
+    "style_targets = extractor(style_image)['style']\n",
+    "content_targets = extractor(content_image)['content']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "CNPrpl-e_w9A"
+   },
+   "source": [
+    "Define a `tf.Variable` to contain the image to optimize. To make this quick, initialize it with the content image (the `tf.Variable` must be the same shape as the content image):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "J0vKxF8ZO6G8"
+   },
+   "outputs": [],
+   "source": [
+    "image = tf.Variable(content_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "M6L8ojmn_6rH"
+   },
+   "source": [
+    "Since this is a float image, define a function to keep the pixel values between 0 and 1:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "kdgpTJwL_vE2"
+   },
+   "outputs": [],
+   "source": [
+    "def clip_0_1(image):\n",
+    "  return tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "MBU5RFpcAo7W"
+   },
+   "source": [
+    "Create an optimizer. The paper recommends LBFGS, but `Adam` works okay, too:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "r4XZjqUk_5Eu"
+   },
+   "outputs": [],
+   "source": [
+    "opt = tf.optimizers.Adam(learning_rate=0.02, beta_1=0.99, epsilon=1e-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "As-evbBiA2qT"
+   },
+   "source": [
+    "To optimize this, use a weighted combination of the two losses to get the total loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Dt4pxarvA4I4"
+   },
+   "outputs": [],
+   "source": [
+    "style_weight=1e-2\n",
+    "content_weight=1e4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0ggx2Na8oROH"
+   },
+   "outputs": [],
+   "source": [
+    "def style_content_loss(outputs):\n",
+    "    style_outputs = outputs['style']\n",
+    "    content_outputs = outputs['content']\n",
+    "    style_loss = tf.add_n([tf.reduce_mean((style_outputs[name]-style_targets[name])**2) \n",
+    "                           for name in style_outputs.keys()])\n",
+    "    style_loss *= style_weight / num_style_layers\n",
+    "\n",
+    "    content_loss = tf.add_n([tf.reduce_mean((content_outputs[name]-content_targets[name])**2) \n",
+    "                             for name in content_outputs.keys()])\n",
+    "    content_loss *= content_weight / num_content_layers\n",
+    "    loss = style_loss + content_loss\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "vbF2WnP9BI5M"
+   },
+   "source": [
+    "Use `tf.GradientTape` to update the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0t0umkajFIuh"
+   },
+   "outputs": [],
+   "source": [
+    "@tf.function()\n",
+    "def train_step(image):\n",
+    "  with tf.GradientTape() as tape:\n",
+    "    outputs = extractor(image)\n",
+    "    loss = style_content_loss(outputs)\n",
+    "\n",
+    "  grad = tape.gradient(loss, image)\n",
+    "  opt.apply_gradients([(grad, image)])\n",
+    "  image.assign(clip_0_1(image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "5FHMJq4UBRIQ"
+   },
+   "source": [
+    "Now run a few steps to test:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Y542mxi-O2a2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yQs6GAOR5QLiL4/x5fnnltKr08WARPdCV43D52pDh0/96m+Pd96+qWUXeEoVPn8g7RHiYJoN2++nTkOIDKKIL7zjgB3/8k71jb89PJwKryovtLoBRVlOALYJym0HQ4MfzFyuzguJ1KhnspFEFno1WlJ4Un04rCo4cU+O4QIzf5QfQK0T69njDRIvOzbaomU9O5GQ62DzCBnoOq+trT1z0yC694xndO68AFPybN2+VTCzKOAZC1uHM2pXUiSZNzlnxH+/ZlwCmqn/ffNEZiBCQJxknFQPVkmXndTmb22z85utpAZOqu9xta0CYTb7Lt7zPf3Wft7cdEqFrNp1Z5ZSz/y5EDmB1/bIGTt+tRx4QgAx9fm/R6jdb+9rn4d5Vm4Ebk67bAC/tsMZe8sP5SR/2bs2Pxf5AbI0VQm7vTcEjvdj3f59uyUzCjuwYN7Z/Po9i1IWZEXMuL66pWyEAxoMwYr25fvnyNg5WAAcXl8KIW1t0syp93S71yfbXr/ISF8U/0w8rirD6wMek7Fny7LYXzmNMcvz2be0udBuQDRDJFVVtFx/fMDtTyTJJXvhNRYQI6iv//t2HX3nMN0/5+dEeHlEVANuVJ4Zn0xP8VLfF+rr5vaiE4PPfoOa8IzIrj0IMYYUIhzhk8OXlShwuj9/A5Pr925gkx6exHnmgD59+LzwhMIH7BCKw/X/hVxGCe/FS+v2KKpLunJ64Kn/3mV+8XebP0eXlcMfeUiyi9HuWVajKy4Ic9PgMo4TAkEnTSM/z5pK5rIF4fbJiVSM7ADzCIxDa6GhTVBb34K3HXbFKKeOvc11Ws0yWpXHVCx6fmqZ6VS+nvFh63WwAAQagiuF6k03XwcUXlC1ajhqFf5xxZ8QE3QsfigNhwjpUlaZRfEDA2yVjgG3jYY/2eNBpD4jEXJdh896Dc5qzu8BNzkcx9qvavOoStfA/497FWZ7V1MGbN955E1B7ygmy3H0Bb3CG2mjF/BOVImNQ5yZTA6oXwfV2EuXBZTpCTWVJJxAVE/jxHQu0gKe8V3bJGWQHskHAemU0wDkfzMcWAIoHm2YZrS0QzoIroojbEF3OtJUJrVuHYQDe6o6iqmmur05YF2aa8+fhgZ69Qyttm5F2xkc8Kb19dTnkrOo6T/cPHSZtE4/2/j0SJyQysWTzESu4pOnp0E+OCJ0nrkcI9cl+YHBC+PTEpHZRGlzbddYl3S5U7o348ZS4GPDE4/UqBBdaAPp+OAAAv9ixRwQS097ihOdXmM3kRjWL2elEkHFIxOv1FQxmgkooPSAZ8KWTMRtWzBOAigIQIQBkjIFoIQw/dKHL928wZXZyplA6MBptXJ5CinkSgW2z1XMqPCCqVQmOwMQf2eXNi9tbMnoi50brNmuMAvtvPy337NiXm3GZ0Utg7ozGYv14M5KL0rzSOvo/GhURmZleIjBAfng6ZXJm49lW+XwEWEABOwam9pMH5PufPRN75169uk5Gw5LZI4GJKpnOKd/YMRhXFxTz/NoiCWqljzFFQ3g2BEJkhECrS+cqUj6R5b3g7icigAtWUQdFQdjTomckAXRUdDwpiYc11EROEMkhXxAS/Xh49M7djSPXeTj0rVPHK4Domtpvr0z5h/y4xuwI5uWOWX48CoBzJWdwpzV2R1MzrEsFkKwTJFBJICpH2F24trkzy/ufWkXAkjZhm9uwQ7d3WjujZNbHOMmU5XfauuYByRHq6YwdKCTMjmU8zdgVdbp6C0d7jsuS3KYpFhy+3M/4IAbfVYXehSL4EGGWBRdNI+GKY8rLQuPhTtXVqzK2Rd9XTnhoQLn27hSJq5wVbDmf6/PJkAqwqQjs2bOP+hxjDoXXVB99sSpIMO3LJxU+9i6EftR5/aJ6sX6ZE78Iz2iT5aTJ8tLTzePZrKg4yXx/N9K6hYrKV/9Ihd1EpcN9AdBUY477n2qzZrnQWxyMnmOcv9zddqFg70pRIsw5ztOR1uYYJWcDRnHJ+wKEEXnNOAm4gG55upFjNfm2bcVmnG0VaQ0B4Wm4k14u2t1m1wanOd8fz4xd2HDl0P/nf/3X3Y+fjnz0hSIsw/7oZmTLqLb/2kulXUNZUJTz+MvQ+7d+B9Hmh4dzXRCYltVRMoFzz/AwHhd3xF1g8eKLxjB/l74JnpGPKWrb9WpJk2tc02bOqIzoLYPo0dDR9qJs6hc62cM4X1z9Bjs3Ed8vZx+qmB5uDFZGEHVR6arghYP4fMqADlXSMp8XKkq3TNNUi/igojgemMN9VE7P2zLjQuSIK76MjuprEdIfP9wzDnfZvMYPkemCRcAUR7MshgnDxRsEnqYU5oXdTqsO5KuaqTgzIjDnIThXdhc09njsLVQeopRrzkkRiAuhp1EaUzI2UMAJUAQQt6EgUkPuUIiJ+GxnGsAbuTP0zehZSaGyzTrpwPLDuyu/eweS9nLaMtFEgEi+wgpyEkSfsOR8n9NBaRm5LrGY6wVpx+QVEFfg1YAAAMz0YruBECjFiomZnTs8LVBpyKAz/OrsqlnbI9C66sZMhP0RlI4B1rsxT8AFIqgNahu1p6jwjCMNDpHmVDE1m2u+P1h2/EtRstraUR0ABHs3wcSr2YM7YirFkppYwLYGJMfB2mofZ+xPsL3gkAedvcp2vTk8Pu7kLOUl5qOCC4zoUbdV6Xou9KsatfT8/Gh4qcO5qAgzEjMy2QGSX675VUf1qfi5a9AiWARdbI0U2/Ucp5jm4C0wloynmefjnCWXxa55fdCUoiXXWHaEaE+nNkCPUMJy0Jhs4w0IFKzspjjvRSvfkNsIRT31Ujc0RIPagHp8TgdiZHTmWndRvpzF9o+PQMSlT5ZS11yICphIO8NjED9KD1Ag9QR53VhODK4pXjV4fyYLRN7ILdOibpZmx5qYENZTpZWN/aK2kG+4glDoNLMyAvAMh7rYEJydM4QU9YCibmtu1u0m5ye3bUqtoy6T23k3n/XGpEKm2bLmME6pKEoL4ogsZZPUHFXQmSxTM1aD3x+1r7HKv/a0CavLDj1rnsDnciEsNo5POlvOhqFPU4UJ41mjnYmqD7yDt0ZkhhL3gWp1I6QOxW3IZdJhWixrrP1wdl1Xmue/v8Vp+m9VIQYO3IzllqZBfF3OKRv6VpPlzQu2CcQnXywmvUWcyNVsjkwlJSym8yCqRd1mpRev3TzRNN49PA95kcZ85UtX8mSzU8nHU1ivEorYfKE8gZYeloSoSZ6+jNOVdp5PZUaURV149h6zhdAtdorR7y54xossYne3kKwsCgAbptYA1g5br+dgy3L7qnr7UP4qBC671TScPUNmBkMsCY8eNnuiJEl+DLhFaphMzc5GDodlUaVEU5kYmu65f2YVMlvhK4EZF2sLd7GbUEuLe/Czwwvo5Yc9b6q2dD7n/Pnmq5TFdVlLo7rg9cVuxPlxuC8SOAtoUKcFV59eVQ4dPcwxx2nT1ZpGbDIYIEi3TfMYvEcfwhlhmGa0+6SGPYivhUhjzFYagBqkjseoNWhZVqIxtE4fKEMuyqvzYZ4TOXe53sncn4+IbVmHhmcnmp+mpWqCq7lGsdGVE6IRLUbD03xxtW6b7tQU5RIddeQ8nPcUU3i5CyPP9sjNinbki56A7n5a/HprWcasTszIuaTpr8N8deEuq7IZB/FvHK2s+7dcJ/PTftHM/pyfBrtKW7oW0LOpiS6bsdSsp+eo5uracRhd1UzlgMUsT86RNg1DdNi0OQ9h1NoRXF8kdEngDr7JsmHkV4BIEiGTSEe4NKhI2Rp0rPAMqyyt6gNRGUzYpmPVXZR+wC1g5KUZMhZOK4CnJ33zYSsyjLbo6gWd9tT2WZER7USpvdtDtzKw46FAFKuPx5MPRIiqGUYelI7tY7fsCNLdADhNvLvEEyTJOAy2uVwvs6hSu7H+dJ/ownrn3eJX2+iOnVR1e+EJFNMx98t3s6S/xiwVXZQqkUYMmBwY9V7N0qVCavT7A1wVW1tL0r5hJEUk3jA5BNwjMhCtdHvC5zNBUYEeqqKuDXo1kfNz71BOjkUvtx1DcYrZ7TpyqIfnwXGW5Ev66D9m0WWMvLSZOcdT4PXaniVKkJpVuxWh5+M4LNNTmntCaOoqFJh7HmxMdVogNMjDXBRbyv4y3mdaBSwDmqwgUTAFd59w58pxVUpcTM3HySv1VUMOwfaPt8KJQuN96TfrVznj3d0jkiMmSxE0hRFw3e5z9ANy9YbpaBrBnOaG/GmEovUbQ9Zsx2QXBdFc6dYDEzEZMGiKGlUyEIMvKGuTMwI5U6q6aRjZkIg4eEHoGCOykJVI507V13k+Z9wjto8sxs9FKDOYqSMOBpuoiyPdFReCyKU6g8fDyWQ263vv1xS2UDbbpGorsknrQjOeTmO3yciUFuS2AyBk0bvnHi5piy07bCbERQ4lUVOwAt2laLQAbxfe8+LZz8rymGeMAWEL47zbhsJy0YRhnEylDC/n+ebit09l8TTeLc9jztCnnKBg4GU5Zf/6qWg+GGSLtmFKnuFNo32vouRWDCuyQ0CkcWzcVT5nNpXFW3yum4t+eoarFoMbf/4qrkozZXaYpxCglM1cHUvfBhKPVvjmXTn3xwGkXcSb0vo0ZCqz5W9PJ+ywrMqdag5+L5P67LFKlhaNLRVZYs7iXJjj4/x5Ofz2bbXm/Z2MpycABqzPNM6neW4LrCMcMrTZ8daXw1nji6LULGoQ23YV07OpgTAW8dY4PFt9geCR7pcoecnVtgMytIM7BinI2VIbkwKOMxThtz/++PvbV5EMm1aHfdnWA+R5nhpq/G6G0YdQAVzGLGXpDmbT7U355iXnuay2nqJkABhB66Ye05T86qXBISELeORQBE2ouSEtdjLHRuQskKb4GIrfytCn88riOEBJvpDi4WH/kLWqairoyUCc65omxbifeuGjY098PE56Ksq132YEMy08F67LiwxUVTjjebKqLqpSjyeYxqYKVU6Cg7lkZZFA/jr2//fVewm3atCUpUyL+8d//CeiicT9+ZtlSbJkkTv69dG6JWdk9iWaOG6vrlpLy/lxPGOpXtNKpjOBY1+KEUtbuCbbgKySh2xYLM48EeAE+/p5l0pGavNqYN9mGft0uL/VLAC0Zg/8SsYHGXL7cnkwoGcdShOrCvKm2iH0gAj/dqdRuSi5gHrtlVMWAyvQfOcqyfdPSb5+yVuuJ8jpcCwQ9Q3Tox8JiXCsgqLlZMGXOcMwOUTz9u+wChKDLWvbBVemrPr8zVef0JnqPSF6C42RCiIAxKVfsT/w7V7aFkzPDvkVVWB5Ps7naT6cn5GAXeD2ZdvO4HBgs5WzaYGsaEjmF7Er2z7i0Z5OqVtbWsCEinLFNoYCcY+WFXI0BrBqRf1ROyO4bo0g41oPj9BJkbwrNWWL+2mWdDyNV9tOuJIidEVBSMggMVkGBCiAvXI2VmNr1vL4YFOGZdC20qzHw0I4Bv82EzjvJf8QEEYOI5jZ3HoDFZsvE97X5kybuBzIzvubQEy7xsxpzJ2m52xEjACeaGt6GobOrfBiOGu+Kvl4sMPTPXETgk9ZxVRRkVGTPD+dQKNBBtHeQBtr0IHo7Did3GF4Sr2YEJO7bi9c3eVpT9qlaWwop01hogh0zJrB+0IdYGOMACgg3YbPk/F6pVY5HtHk9sm/ui492ig7B2J4n8n7lnjCI+qFARiMSVdHsFLMHSS/UFgko5gCjEuEYdi0axpG1zUIevPYB08WtPa2XZx4rwhnBADMmZ5k9BzDWLlCt7B5lLv9dCiK6HmLbIvMB79HWzcSSCd1K3fulRmRLEMWbdu0bLpK7dc9xGZ5vrkDKAHKj58+FQwBttfrg2Rn3ZyyBxC18NfhoF6uiW8BACAyCYqBFasVEQGOWTQv89qXpS+Y5zw+pnE02lxV5F9t/nV3oNigq9h5wppo6MCyqahY8AweBIazB0srOhyLuN0W3uX9o2gub0GJiZ0H9E1ppyrfi0gGiKNBoBUIsKcXZ7kdh5jj7HJhaPZKz1lK6nbtmXo11r7JJVWIQhoPNFBLBpbSU1210aYpxYLZGc05pf4YodRAAKtpuNNzcqUQqJEwwfo1XDYAYOcnbXIUbtvzyaqixM1wfjyBXL9/311fPf788nj8tekaNJuX/hwPa7eBZXWEszss293Lm3jEPLpjT5I4+LQMWVLmxSEUlc8TxkWkXVuSpEWoDNWyz9NqJORWigMs5GbHq9Qflpub8P59Bl+1G03O65BH/KlWGL/qXm2urh4qzaZnlZKZ29bi1LiLbBM+YLjYJHWLJLh8CT9/iqCrgs0zMaM6ZEEAmKK1LS5Rl2HM0eZ+vb4UBMnYGaZcvXpxZfpk8GqdszsptAiKtD+I1osnq4v2PWw0P//6NjhXt59+o3ixJdUpIQALH/KvDaznrAFplc0DIYIY6jAnDxvbsNptXJJlz7xdr5/SbcYIvjjZHm7gOSMIEPHHysHL35A1xUXzKleiI8c8sREOkNux59oMCYSREI0RG1wSULa05CeAy3ARloOmeJLcrQhVshiw30B9BCOASzQV9QqZEAojVjEDRBJPGKW9QKs6RASbV63E1IkEU/3x92cAfPuawBGhEYBJ7lUDM5qCCmVyDi4am8cGtA+6Pz49LoZnEUICwAIqdt6jBAKBtFJAEy7DEa0YitAYnZdcAK7fwc8vpw06XwL3LXuwyTQpngAZYNPCmfnuReG/w/LjcP+meomm2Z5pV4C9NMg5kplafCLxnxwIjRkCEnliBAzsH8/7VbtyirhMOUnVK24xj716j3MimqHnvPEvN6FothZoFk1yfl5ibooG56OkzjgDaiP1kn6awa+n3Zt3JbtK49xVLRdNsbbhiOjP6PDwaGU5k7Em1LwunLlkKjsDsDXOz1HAF4mRE9rFtvrFrjidLUcVxbBByyYHlAgHONQ9a4Wzt9EgO2Sqiut373a+rGG0J50pZXNqmggGA0FSR2s2cVSbS8NxQiBEcyNA23QoljWpdC5MIsWZThcgv77dv33TMYoszgFiB+uTgYGiVWoiZr+SvVLABJGyqFjO0jHQerPbrM6n8wF4o0KKBAxJz3CuYDUa5D0xn2y7QSTEE43MjlzG/fQkks3sfMplc2ashmzMvDZa0qJq1PFkCUUISNXAucNwmJ6/pauPzPfC7+pXnXrTRVoW0sXiM06+dJQLzjqY6V4HM7O93bwQYkaEHs0jIEqWM9xNtLlg4FW92ZRepmV6njSPHkhWBJOXZY5Ps7Ujz8AioMbHYV6tBtJOkSydMcajls6bwrdlet8iUZcjFXoacsgqTA3SBARYwNpsBgumAxdmOeUAx9vBH5it4CpUpWE3LoMv7pgoLrEtO9s0IMqCwHYL9zAmcIx+DUmXcWqxcut8ehpdVdYpad3A5mKt4fHIJYq7XB9zuA7h5709i7y7poAUY42xz7PJglrI0rA9PYum4Oc9cc55E/CuWiylqVy3KT8tOVTksNAkjbaxjQZWgjdFfPmOJL8BHkKZRGY9zEdBBdA0z3JxIaBihij4aHuw4NAB2YAHgM5yGn2g+z17F06Hn7A3SwzU9rQ2YA6rqyu/u1Cg4RixIzE7I4AeeYSiCpBiqhNzWCQfcnyrmpsa1UpfPJbSUE6OgUtTWjMF0b+nI5b5VX1xltnMslhdNuZeppwHe2yIAiMF755/fH8CBcjkCZVvzvF3EnKE4zmQe01vgvF5LaeHB6WEKNuL9T4fOx+aALOcAq0LpJwzGuSmqoYcCYx0STFLjgydVqZ2gAPuC4xARlzwm6u3oBB8mXNOcU4JVVxW3B+OvlslW4zJFZegUWFYY9NjREqEBcFcZ/DO90nj+OVi8480Sl7OL7VVE1GdwSqqyuZ5DxHIC6hoA6USYhhGUFmmqTZSXaBbZyEcAP1I5crQTofH54Oq2b8DmoSWNRkrIAGZG8ZZNbfQZTJTywzaMaijBky7rteTXLJbbV4CcJ/dYgeRPaAXeAIBttZMOnNq2UanihLcFukJncv+dQ234xhJUzuDFgZlSRxAB7IVQb1+8+L+809V4PF+PEOW6kD15nEYhLhFzTSkVc3Ok88uuckXzoXu9uauKhtHayKEAw7OVHDTdDpMJ3CG0KCjsAKpCzR0Sp4TwBxHQGvdylJWW63ygSpoudRUGzPas0Gy1Bs51lxBBsXxsOdko7bNMiwu5FAhW5/uoFDvdiDn6WemjhqKdVfl+77ZXUMPuoxLSpeq+21jWmoUnHspOe0TiyABPKJdMI6OPfPrN+8QS1eqmiDcn36JSeOrKfcqUaFRODFfsAs5CvcoywmXwq9LN82+9DJ3UA4mhqoelUBoti3gk0HWNJEHSgrJVEFF0BxdAJ0ADMyYIlAaslamppDzjGje+8PhoDrb009Zrw0zEzJqU+kyKQ5NcGc1VRmdz46DaxAWTTkvSw7Mjj0HC1JUFTsozzYzMTmkTNAPFme8euvSMvS34fJ3PTzbknak467GeOLxEVO+aFrnfKBFchGYUEQN4LvhSrYdnhzooApfCD3SRbDNRvgRqViy2764CMW5n2Kcosjx54+N27pmo3pCR+ncY7f59OFDJPwWZ83oIJoh9aOrtuQygXaOI55fbVaAfJq/E2JKy79+PcHJf/ptK792YRvHueYgPUpcxM6LrUw8suUADWPpsZhBFRzDWeAMeOc5SLabLw+uxv98/R+MtMzT0/lAjEBOaxji4bLcOdS93XcFoEnKKYdg7AhIQLLtT2L9r9Esxu63yjcoY0U4pyWme1VwntO2YQnTodfp7P2C+umFnE0WdXxx8fLx17c5fR8zCDWb5p25gKcTImQY74czwLgKl57Dfd9/97MAbBHq8NqtprxMKc0Q5el5QBwvXkgZNsbgVA2UiMEI/ChJAH/UXdO8fvPRhZgySPw+3IxkfLVecemtgVPxnA6Ei0fHzpz30/3JmyRQbZMe0xhPGRhxyFIBAhKI6YUvK4ORXXM+mSzD9k2dFpgX2AGALOMYXVg8f+Dm6XxcmmXqod2BoYIN7sUffzxKNn3oIHrb3z3K8Zgv6z3Tyw/bmguHSjU4LF8e5x/ObRvHKe9QIXBMDlcuE9AyRpBJyiJiOOfTqltv8TJpZI855/w9OwYj9MFXXftye41GKiYKh4c7gQJVZdYFFu9rkZXSoW0rb/CAifZnK41XiSgS4KAJerxoQr0q5vO4Vqh3PE8viSyfk6jcIv4OGPvW1dVOYwQQzZiFmcZqdkdZtZ0gDidpmQTRTNGAFNEVzwcl4k/v3qlZVjOzHHPebkjREKU/m858sJNPpThQdzrEVcU6nqw3anC9Ia7WoLhIrtlJ4s47ASixATqbHHsZ0LZzfdGSO/yAdee2JTwjw+PP+eK9T+y9Css6sxqcaHBAhbh7sA+Yivdv33z5zE37qvBxiQ756eHcR0U8XTQarlecs04AZqjAMyoeskghm2JtZmARQLMz5phyTFHNESMczuaQJ+7TmtawLm5uboyZL7rki5imJptZI6kXPJxMRZyfqg+fXhVxYU0qi/KodrFMSZJW7gToGBBPUr3sKs3nYR/7B8X07nWXEknMGg9lTRD7w3iaSDvZqFAbEciJiqjCZM9Vy33vnQttVeyaVERQWpZ4q851rDcJ2ik0nfZP7y7Xw3nJerC0MrwAe8RjD30GcJ6Lut1uWncyBgAXBs31GqKBIOAr6L6Of24Onz5+/GiQkPm7XK1BB8kxplrXBeKJCu9mN/hCJxD1SCZWhrJ/uF+vd8EVmuTtmzdINI/HZmXDPNWsAGDwfH1Bj73LugZY1PYIDjLYWdM5q1hoGjMQxXrXDdPJ0Agp5qGHtHGlJgBvzXgWkQrKOD5cXex+VTCT6f4pgrFzXWgvmnoWZeOwEJEJCBj0BJsLrpz74x/wLQdFYmaCfun7Z1s5d+HrV29ql3Pe3/1wml+9/p3Lil+uGUzvj5zFiobJGaJHfI9hWJPDOvPknLNliV1bLjMnHBv9IqdrW4+jQLHc/us0RSk9Wa4qP5d9FX2WWePxTnW7XhfrCpLkc47D6Y5ousYiQJ5zb72Zx6K9REDBYE0vR/1p31WNKQBb167HNXaZ9nk/x4NB0ieLsyGjjWPbsYGImtw+J97UIXb+lSHSOLXol4w/7k0MfKdoZgNuFsDHPx9i7JE+oQf3MGA+f32Git3L1/SGCS93dfnYjwuSOzzHnM0cSydTJMO5rpTYxzqMRSxnr4JmPrwInItytmu7vblB08fbCfkkW6CtOqVk6tAhUOE5fovF7+//EdbTPN3oueCwtpfnILvyMmW4fX4qS8GApGi8LuQZU+J25Que02JaYas5LgVvVyFAXggsIDMySJ6n2N/fERs7as8nKwowuKDW1Kq6qLvdeThPw1haGYpiUy4ezOynflfXMzOIAIywrGS3LZ99Zjg7B6PZNEzngssjlnUoK0+Nc0XBxZCWRtacdyMPiyLlxADOb1ZXZVfWQ56zOkemfLp9OuQnALh+d1mFjXNFT+55f87LglDknAxccBxBslcARsaGdLbWLDPhu6G5gYMzbc4wI3QbMMDsrA0VY7V6Xyos7DA8LVnlq3wxgx0QjTFnBg0mxmBkzszQCCZrVgpgama+XAS60J8l9DNtikjWlq58Ed44H8xwHuMwL4TaLUlRTv7A3GGoijk72BmMSZaY0ul4VkA2fN0Dbre6YMRk6PA4SGnUdGYLUwRYAS28wDBMxXLAcLlq0BnAAQgMwSgdK2QwDM+OGoIMTjtCntwEkSacPZS2ciKSiThUjLVTeaNIu6JPk6pLXHI+AwIQWeWW3lfoY52XmY9H6mAui66seJnGae5n5jqUe9MrfUklAc74qlK1clMquaw19UfO47TZKNmlu5i1R1woxNfVNT3dH9HYOzMql0J8MuPAzgjU1FRKxfP9YbdZXax2/zz+j0gO/NLpImkxasiTPxyKUGA2VjAwAQuMRjQCqOSXznfX1+fpZGzj8YmpZnTYdDxLIEwXiOLXhA85zvPciTZ26ZSe8WFQ0YhM3PpuFy76bMPBRCIRETHAEUNjKiBZ5cmLDsMzDvMhA15umG30vNLwpDuvTQQgOgdXe/SECIZguKIuH/IVbKhsfOEXjPl5fxj70eDN1tqVqWTbK9SwzHPpao+nqC4mY1uEVM5mCh1crlblPM5T3llOogCGl3DZ29AYHeWpg67EkOPsQiH+uNzp/njWfjgWoXDI6N+9f4P7aTbIU18o9o4bTdks7X1ChgGWzt0WFKYiL+QuCnZNubqEyUl/n45PX1REc+Duw7sdBW/jhZyyn5/InJ1ODoBFFwV15J0LQOMBTcVRQAWeU2/GZWhgnXM2CyVd//Xffy1LNsOPV69xSKaMGYpkB/3pENckzeTjqTfWljlyFbCZlhgtUnDe+8UZzGhmK4Lry9/CFZNzhyxFKmPei5g/ZwHadpuzPV3S2li49WW/Pz+KxEVNIaVftOy22RcFshHmmNI21O2Gjpq9qyZdSiqe8+23836ZIwPtvDepxarWl1VVNRcKbH9NMtOXI/3mqkN8OClVBkCWmKGui1C1xao6IfoQC1KONExB7akIMUlUCUVVX1/9kZM8Pu5TvtVHoY49khrUpc/g80mF7ZNbkQs/f312ChHnoe4KFx5DmSSRwyxCDpnY+mNWy+PoCgehTWOPFr3fOFhqMhv689Nx3a2X4/MB/EV2/OZ6vbqGkZCNW/7x65/TfP7x5/nNHy40bklDtoyuQoVlmI9J3+2QAegduvTjX1orAMCGgXO5elnEXkeUtpoRCR2Hai2cbPEeBnoenjIT8MJQgitRkzDTblfNyxQ1B87m6P7hFyCJZYUMjL99+h0RwUgW7dMzThkJmTOASk79Mmt+wd4QHhXyAoeY43Q/ue3lfjo4RCJIYk459nS5olgnUxGY9Oevo/MxpwWyghDZb25HLzbzE5xltLZcADnNa4AZCc59YD5nXK30IEfMhQW/pLosPKL9eDp/evMqk7vTsXlqweEhCkDROWFKDLCaV2DqzLLqsjykFHNWAwPgD+j7tnXF2nweltnQIfO4rtN0/+v+jpCZiZnXXVu4mjubZ8kxzcPzz3MCsDLge35pF15vj/eAxnCwXlppaEXGdWodzVFQHae8ckt7l7+RQVMsERR4aav1+bSnpgUj6zYESMD4S+7tbHxhz4tzMzj3nNKLYjtJH483N2AfrOwlZx+e6uNq1U0p2/mwlH1UdY4F782AAPRwAJBHeVa1PE4vXr0mpPucHVEJhoiD+TBE4PVCk0MPhl3yuTae8OHu6d379+/DxdfnO0s2Hb5yTezVw5Ygs7IHJ8dUtmsAODznKR9kzS/LsiwCma5oPkxKDGITXK4pL0IMkubz7F2mquG6ITFUQzBNqcnN2rHblGkSPnlZ0ownMES0KOM1b5GQWUEM9slR+Fi8JR0PkBxaftyDQwgxr7db8wQwAfhTgY6WefZQOGSLdlyO5+WIll8trcns9vc3Kav3H5jyc4ayYeqhzpoEdEHuCbiG1YodLbRMx1wKwYYoR4FsNXqAiXjq6ooG7Zl5SkPnV2DGzIDAmBsot2s9z5mcI8GubHaONC1pOc0YqKkMXX3Oc0s5LvePv0YftMLbZMzVqr2wxLvwsgw1ZLKAfLHTOd487HW5vVDEBeIwkAB771yIeSHHlchkzwex/ueALoSXL2W4J4eCDUCiKU3BV9E0ozZbFfGuJfGY8m/Oc1VDFO9R40j5+NF34CtsmIyJXUYTw2qcgZjIlxj2Y2k6wzzJPLo19xBWvogGSz+O7OKUmJDZ+VwkSaew92SPsm/TyiNBwgdHB8Pq143WCN770osk0Cwp98Mx1eUqFK4opiU+p5wFA12Tu3Vs78I/iku/5MTBLabIY7Q6ycLLznOv5aPilV3cMna2T6oQvMtJTNN0PMbjHlTMtWlZDMAxEzFaiYiHx4Mjp2n9+qqZllfZ0tvmdcz9t+PD2+rl99NDWnBz+Ufg8DQmtqIor/Mue1VBMpuoILdqbL8nDgUTELp61cshW5B+5nJDcL7UtBQoh7v5PHku8Di0WL748A5AsiSEudhSHI/Ln88nWchnIHygHzu5rqpNn6bzciZQSco7AugUDg936ly1k20n+292wr7xAS3A1VgOgagjpzFRHqpipZbvQS+JZ42JdLPdAS2ZVSJjw1GKJEAo0fJpHNhxykoEv/32D0A1yyJ6XKZzZuQWa+L+0cwATCQ17bbwiwrM0bG5svLtvprItViMAkbI5g3UAH1ycEhciVgG+3U0QTVnsiPzPuD2tTrMkg+6qAmoeLO2KoOIkziQDyVXnW+gxaXwdXDs9/1pqutgU7AGiL2/VFv6+VksmFZFMSwpp123E3GScxwsp9PhyMQG4Cl4F67fXplYd7JTPs/zYgDZr7Zl9hxHe+Hjg3dFVkmaEm9UE/uiJmOweL50fFuTW+PKNmDjaE135UG4PJ4HPQIiRchu3Y7TBrS/JhzKcpEz9kRIzAWZjez0dK7rxgCDL+OSBUyQDjoBr4titaSxrC98wV1rgOlf//N/EODj62sKmw0y+WKc5j4vvZ4rLGEp57qvqxb8jvZnWeaclsoX2fLUnzyHVVVrlhzzG+fYRAAESaCLWTqsCx0B3aTokTd6cZtuhscZhxXBw+fPf6vauw3jVoz3ItQZnpiNbTwcexgTZHTwhl/6kgmMLDPSC9TsIa+2MeaURZXq4IuWCqrAoFYzlCwiaoHHOhj6JqfJlh7XrxEJDmagjEyqTA/kaoxbMBY83lt+pTPQ89Y7D9iI3oVyFKFpohBANJm5CPEmVCWjWGC35vVfy59otq1WYYrL8uice7eqy/ef1Gz/3/88/fX8ggARLCviEJqqH+cext//uF6Oh35KTG1ddUbuvHdFffIMEzN1MImZarTs1Pmympc5kCINAHL7NJforiVvkcNL78O1nO7zcW+hpmmoXRvLUlFsmsG3Z+Z31+9c6a41VVWzeCVzGe3bzUGnHtT++P0383gtx+LDJzn3KhMQOmWIQ1RJMbFjZl9X/MEVn0nfdu8oVPNmfS+L+Mok2Pm+rC/odBKzOE7P01BbdVGULTrEEh960IxwkeCx8Y4Ky+lRB19SZphyfQ1qZOK4nGJG4I2bpzh7X7m6YIMMObHz0cC4FRro3+K+Y0RiK/AqDAuUylSIGjUhnyKSHhCuql3rfZap9pwycvBrXBm7s+TzAoU6bBqfoQ6ysj+yT6EphzimnBCYWbuqTUmyLGVVRj8Ow6mtLhfbzPBgCxabtSs77ZfSb1OS7k11wXg2iydDPUkaFvKpKcolc3VN+ag6G3NZt0XlTXOW+MaHOEeOJdjcbHmJ89OvuxACXHA79Fj5n3/907KUL14i8h//+fvj7XkYbin4TGElzWNeSu9KyGfFk9m1o4OcSdz7dUdWMHgaxtQf4/lgkq0MSABgnz66JPqvecn36T7c7jYQ2iJyRbaHZzXBv5cnvIPA5C63za2OOiu/ZCTKwDWjnYhjBCRMkwKkHIn4svS8WrnDYpNgSjF3WXLtwiTFY/w+5koNbHpwY8HOf7h8Dy0iQpI0yTha7K1dW0ZZEAC6dZwGEe3aFaFatpwyaGYmWRZuuLRgjJf24ib9apZuL0cBtqx7kNUMsCWwbvtC8bgAI2dABD3cPoh4qY/aA3I57rMukS+TZIRT8FQ4r6OdCns+9xdV6z0MkLs4Fa4c7PD34+319RVD9/gEFY1mJ7BkJuG4P7PaeVzmZdWZIYGhxzLAJsPx4WdebehhODpngIiIJA/GWwJ4ubb9qUhxyWrJ4u3hCTq50GtWLZmvL4D2V2SU7aiPz7mxySoTyu2YVFOOhITIy8MMrmVLx5IX0TQPhOsaqKMSUb0tYb07D8csMsWoWVe5yFYrnglHJAUbsmSJfLrR8m3rfOBXHbvWzNTENC3Vgj1IZzqpKVTaDMtMUw9UWFWpc7lsdLrz3iPiz6935/EZc17Kwl9cwGmEUGPn/SKUS5GAcCj0JPvNwR98KGQu2jp3dpHy6YpnvmxpPOEJkEANOtFnlUiKRCWVVVGVlzVqBkOGpSBMGSGaJDUlAmIfFsPewRQ8/LpJKV1dXHfd6nxz3xYOWbJX68+8askFU1vihAgvQiGoiADrRvdRFDLhFJPYT3MbB3MLKExlnGaiqT8BUtV2r5tGu4tpXmQRBBOSw3AwsIbDdvcaR9Jt6TctjyOzA5Ht+qp+vtunuXN8MjhqglMyQDXd/+gNcT2Tf3ctGYdJDcWtOzmp6agZFxmRgBwb2P75eTwekW21gcLji5a6gfXKmC+QO53ndMiprhWIC4DCAOh0PsqkKbjgyrJt55iXHJ+nSPshUIA8MNVdvR7mk1PSu/4sqTJRjEaK/XSQnMBKdtTUSaxW6B+XnnO5CUVwylDAsB7TaXlEAJiyLffTMgfnN6vL+cmqdeX8y9JVYHszgEDsZp9eMBVUThU4BZWCAdmNj7oAli04PxF3CVTFcTkNcyiZAUkV+nGRtaQTIDrfkNtednheEmZU10I1CmgSsZQzdlteTe7ROxeXuXGWy2V05SrL85R2VXEyQGa3xKluL8vydNSnX4e5Lkp37KdJU1V3zUiBcAEKeXgSztSBWCAORj3ZCQ/ABa+sKNLclFQUh5/z1Oa2z3ybFmrYl60Np4y+jUlNl+FHU3cOdDhvuhXEcYbwjPNQVKvTOKrOFy/WK4IHzQXX7UUzPpxv5ruL6x1eG9P2sqz35/Pz/a0uY10UY8zVMKcspQMgU8/6fHMO8QV9LGnTrTyFgo4TEiAGyleej4pOAcEUFPWr9a+o9j7tjO/c4bQHIXex8euGrwJg2cGpWNP2cu2cOY6s4q7wfTI5DzcnbX1jywBlSdzuaBqtaWkcwUTTU04JGvBGSXBJ6BiqGTr30bWkan8/3uwu3qb87z+PVfLP/lbbV12WNgCrqoqaBc8Z1YdV53DJkZASqEd0QGYqlhFRJOMKL9MGYO4H9QSebecALl+boEEFiLhthsdnQY1ZFkVRYJ/ReYPFaCLLpHfMbEZmkCQx7fr9gxx612wMbFOvxGRQ1Y5hWJ6G72u+tmQzZiIKocFKVmSO8H5/8Aw64LvVxXBmt2IgfrZ65QQdNuvqNFrJi1jMOhsomIjkXdfePy4IQOy0Hbf4wklmQCQ7Pt+CLm1SA1NQ0rZgWpBXuaUdIeHjkUh7UYxSpflnwHyOSVRfvWvzj+k83tZ+szQrQOlwdbBjJQ0gJg/P8Tlr2rmXiVbsTq9ftQZwTtIhFL//drIfd3L3AnZgRcZ0sud1/Ubg3Ou5tAoMbKHkKwNDkRYA537wV4zPavPF7hKn/UTOb9Y+OEVKkMWE4mgwKbVkHcOJfdz6sBSFiXuYZ+PsAHqB9W2PQIampprSncgoQt6Rd294xZ7p+DO3nSeHcLLZG9FI0AY/R6bgJqT+6Ykkl67IPrwqVzYAWL1aXaL2T6ehGTNgA7jKMaYf52gjI+n2Ek49Cho8SzSBDWya/PMxE/NFQswMzk3QeURfnubFzMZzHxFbLBw5ZjaBlNIx3hPx5du3shjYreMaH3+ezeXzsQHF4sp9rOu//huwJl46ywB2BihcseYikvFlgd5N0xkcdQWG0SXNZagz5/msaBRCAIdO5dU/rgQt5xml99zrSv3sSXtLc3oAE+NQG2qAC1L0ZqgUmtqx4/Bmmqt+0KrRLXK5u9oXtT9QYQhpITZzSdrYTjnlg2l0pxkMvHeeXCmV1hjiOMQ5R609u4QpnZnH00MakhGYtagmyNQ2L5PR8elXtCEdQ0tKeGZEg7WhFbyAO0sDbp5dcAmj8QJoKHbvYDqf5HgE4778uFqjM2IOiObqkIY9IKIMdVX7EGaJiNYPOo1T29QpRReziAxYtAw5RmiNFq7LUJRukKXx3Hh7elwqM6kt5Mo5N/pkKYtBAAituIJUvILDoprVB+o1JZonBChckYaIjbbtblmiWaPu7LitihIkljiDFdL35jmz95BmyZoEvVO1I2bUTM7ArGC3UTlMD1cvr00uqH0BzJ09ysjB6T4v8/EuFCXHFit5sd54MgqhgOhYdpUrCPoxe4Ori8vj+bQQwPHAPeHb6+vuLdp9GWob7gMqK0m3sgwQkC6XQl9qOsbnRdUU4YgoD3R1VRX+Ir7VB7B2akB0uO8BAZAXd7lpSRZzX7/+ADB2DgjH+ASJ66byVmh/byaqJvNYqPz7ujWA8JaAHOC61Pkx9S98awo/z/1FUSzZmDcXF8Xnv75+G9H13jNX1MXnU2aSZfbtdoigp7HqKm5a68+njJKSmaW4bLoXjKomqsqemNgEHDr19J537Hr0b3QbbREENUM1SSphXh6IREEUFYucVTsuqWQWZLCcGVENAGDrKqjp/m401SJ4AU05mSUSbRjnVbIIQLBeh3xs/CqhLRpztgyoc04FMXvXx6nBTW9CZpvAzKAmTLLZsLh13D/o9sIZRJVkdoAJ0JzzzWrdICCAoJCCiakshnZQIYPuYgXOGXmHtKiAUIUwZ/GqkmqJg2nWMLdY9VEb8mM7HxfgSmLOuR8BKItpZQYqjyqmpV2118Q+/RxKlfTPPweVn9d//D6iAb1z0+2xHncY7u1XgAJAQVRdHmndDKcVqpEcsQfN0WoFKDCL9xoacnbx4Xf9+2+IxH4kQInAyhxK7AddiwJJ2bqyprYq2cWHNAcivijbtRxHjfeYzTpL+yyKNXL07ir81m54inKCp6y2ocFjPu392rGSlQB6XhbFJFb57doVQp4MCXASKZb97Py2ujTePj38PzMuV+8/pP5oLtB18Pf20reKg7SmiHrEhQThHHKzQksm9HCAK0/MxDQVhKCBSc0WlXjYT9KtXzZW4DzTeRiut781NaJDHWVwrrM6wkm9A8V/dy2NDV0V6M2aU9LMl7xRDKFAJrLoJYzj6LXVEEsfckqaJwx1wbiryjzxfjgWZWUOmDhCfl4eXzTigBFRZoZIJnT2vg7euXJJoyBMOY1pnjQFLQxnsZQMQykTlxs2EYXzcemPVqxJAUjv7m6CPrs3V1G+U33tNiv49ciuELE4TafxNDnahbB75bV8RXQvOcVlaFZam4mVvfIY5g3XSzxO4yhxceS2wuh9MCZUosWwIYzs2PJi3KmeXckcQoaYVcocCDkqIheBzhb9MCmUXKmdx7k2i2mC1C3LILPPuq+6ZsP+6uWr09wvXjUiNITCxz6mh18K+dXvv229S/mxsB7ydopUtY6oywlSSgiUJEF/inWRUy7qwocgs89QBUyeRzAk9r6+SONs3ufmpDlb2sZ0j2BI3Su/CsxiE/oCIb/rqnPsA25fFkFIe5EDNoCmeoe2CturYhazSL6+xtZ7T4XL+m3K/mjD3LnCu2F5VBN0Ni0Pael1mI7VuHLKVh2nz5UriuKFah40V2nxhAV7rBsOrMN555vHJ0123xNfF6UrSp+zUkFLSvEMpAzkfCBaLTGvyzSjyhKTmivmF/Q6kagZ5MUsmSaFXz97NDOnAsT88sOn1ofITYQDs7v7dQNLfGNK2yvjUQ1B1QBQmHoPVPV2HMbokxyXUvIhL/vl4iLnBVcloFHXBRQPNMWRLDCgc5d5+ZUWgHlhF+N42ISWQjlOo4IAAHE4HtN2i9TQ+enImRkwLvMi8S295DqAEsbvdmqFANWOp36y2K7XbrUO41QFtxStQjLQU5t4BCINbY4Gu2Tgd2oluEJVERiQn0QvHJ0ckavWICeZkWst6QjI/Zmcjk9iUrzYkQrAPJLZNQCi9oRLJ44dh8AsgJJFDKwg7wmO6wvXG9W6oJSGG14NO6eAanDsT5u2Hcfz4XTyZgVRwVy1DXQtpxSBiJ0YLDkPcuZ2l6eD0Rpb2nHQfA1kTedXuDv1qm4i9UbsyWdRyWpK8Qjl4n/pjxf4Rq9Es3z7dQB4/BrfzNOfKaX7z1/57WUTPCjmk+buvlCdOE6nZ4jg1qsC9dSfwu4qtG1xSKxYO/fgSENBneoYvzwdux5PKf3X+ZyiRwNQo7SnYgOE7jwjal6tiMDMpuN4OJ+ri8KR5CkhZRBWS7JXNYDtVVt2DWLZETA+H/sawq6rAq/O5yN5MwCoHaDYSGBmohHOMaupkCEZiQFGWFnznI7rlw2iO0m+OC/zPAZaEPLrdeVcdMOUlSzhtEJEn8+HBEkRAA2cwWT4UqHCerAB1TNZ1ovN6mmeT8fH/kbfvtw6RwC+u2QwA4BBnhrPkzvO4uvWAdjppG7S+b4fqW2YSvKsSBOOBBEnPWgL9bkZkCnBAMlWq45SRjpJgQxNqS47sxOK5LzIt+N31YwMRXvN6JBchIMxzWZIPADJ6cRVWXtXMmaIYdOM1C7PX3j9GpmNjlbx83KGnDWfGTlDCkjiVMs0Da7VxTRbfzdHfh6EfC6obF5tX9JVAqlWFZGO09N0u7eNGKOBydIonOMxJCcnmfp+9M4V7F+U19hgOUccmWAAV6CO5gpA8YSCo1wau3c2fTueLWIWIzUAoLasVEVSDgFTlNXlZrj7e0G73FzO+3w/9yHE9dZvOpZhcfNtBG5SmavOVB3a8fGWmYMPu6J0LMeHodlsjJxaPp+kW52zlDFFZkdsvPY1835tyRjzgJXHPM1GKdaOJzAFzmIodQNgW1kg5YUo59iiARqoMbWEZ1AM5Apq9u4BuTzBlGXjGFRvUdQFt3M46xiYfT3yMtjjUXdqVp+e9mdC3uEcMANqu45EejhazqpCT7B9WxZ4mdPJhE6HsfJ+AQ/MoxkAarvieIYs6GEcXcC8VSYiZM9FhYLnpE3RZs1xmQUFAQl5AEF0+QzrbTwem0wDoXe+ePP+be34oHb8qohnAHV//Mc/CD0yfn/6TtkRuTN55+vN5mKe7nl6cJuSQFU6AIP9qARQ0KCWkJEdFYN5vVi/32z46TClhx+3FKfn+/zh8jVt+scTGuWsURYZT1TKtV8B1YkXSbMiVEVz7o/MXHVrTzlL5iOD6RJT5XxgVgHEJ5tzD9ia2kmsYzjtByQFjkbouNxsFADNQAnQACYBp5UGUx5odh2LW0QYoCDH7qPa5+Hnz1Q6tIwrOG98tslBIF0p3TgoEdnzZVn1GO37ww8zfR8cvb7U6WhJFTVbbkITLYOBoiZ7YHuXRO3b1xTg11Lkbai0ULMZQe/v0ZQRb0/nDvHDdocCetoP3o2am37Y27IoZxjaesVoG6oLo3HX9qqWfma69KgpzsguVZs8HbnbuXFiX3ryS9IJYhucLPpt2CNh2PmMRmo53wASdxluDQBScvrj2/L+fYtWeWTYNDYXgkNjeTI/qMHZh1AkhfOAA4ZtqLh01rOG4MvUekRXVObvjsROcYN4JlPjQDqYL4fg2NGq2EkD5nEZR94yBZqXxVSVmakb5Xk0U3KvgheTBPArezcco/SC4dgvQU4QoaKwH6YLIEErQxmS60/n6zUPC7FqUAhoRpW+3ULP/fNDp9VH9+mz/rcIuNPRGHFVkK/N8NkhABlDFUrK+GjDioYm7MptTe0jMEIgIASFrvPjkuKsk6S2LCr3+vF5GhatAhq4Q5bTzdHXFIJvuo1aD6ZRQVSxdFzo4efeh0shf0BQC9evDPY6zsmIXUliihk2ftvn4+Hu/nkeP324QKiZBKk00fevX7uiFJFvT7cBqQwFYZAUbR44KY7ccB6CMyQjWGKaUzGdfyitt9vKcKLtZn3hc5p69SpLxuycQdjippH9Marqzutkp35oyhMYUI20gqvQqBVqOE3jz8cfjWc5VjSc1CK8VQCCLEAGcMyzyYTtigxp0wDzahU2UCrknCtBA/BdMU7A5IIBIHjDizcgWdF+nm1cgK+oP0QbZjC62l6zK28ftC2mMtBCPZo2TauWT3JkpI8f3jkWwALhB9HuHfNTjGkZJI/lxauyrgDs999+Q7Lb2y9ctFlyKDtCaztfEC55Yc9VVa9JzglqwMA0z5ggjyBIyoZQDRnXCtK4ZKvuFH/UlFWWu8fZXTPN7tXmigxTTKUzjB0WDvJpFcI6fIoah+ke4h01BKNyt/ay1jRniCuqZi+VgpsR8UKsvFifd+EDefd5OIuUGzNc4nOdcUjMtK0KtsZgCj7LsYyk3vmcUsLMgIkhpGGsVG73/d2jqi2eV0RU76BbUVFqUv7rHltPqgrMV29zXCA5Ws4pZTY3Lc4hO3InW60wpjn/dEQDXH4Ka7pCROe5YSY1tcm5onLeLeOhLq+KVqd0lR5+AoD4K8clQwZKdvUaHnDC79v24+alS0f5ejyfIx8Kzo/fAMBqwNda5OmH2zv16EhM2jAdT2kajz90/+b6DRsAUpKsuCzVZl1hR9rPWbNO2iNYznHFnq537Y+fIGJkQngAauCUjnILSAiuqKwol2VCEWJiLsmBgqB533CJC8uGCcCC4+KOPS74pnYv3vP998tQgK9Wy/QFoiKvX+nGDIG5oe35AvQ8tkG//fglOakaE7q3F+TweU9F6YFAJWt/vqnvX0MTsE10oQAlgDmHqkhFyVsiTUusYobVVT1D6Zfn8RxVy+Iy57Myd1D1OPXAxG1ADc7vVquYckp5Snp+uMUXqxCbXMxV8oj6GPdhPkLjW6gHwUEVSt+a1WBgCICA8xt4o0eBFnStm4vt1cV2XuLp49uK2NRQmldGod4h7LPemySRuiTfd1Kkmmxh5CzigQwHr1sXfN6PXgW7NbHfllgzPlv3Vz69Rqj4yrORO4oY+botiEMwcufhsGgcZgTADJC9qzINiSw9jGpMbu19Faoo8CwRBaztDI5ZowuhgpfLzW3XvHDLkwKbyTAp6LJa1alqmYWwNUF5PqBl+dJ/t4MByrz5ZQkA6OQm5pUHuhCC+bwPBg6BDbA/TMd5QqIt/8YbBp4Q1ogDmAfYQntSvaxwKmgZNDtmIpzilB/nprhGLrQHgeh8FSHf9w/Y0IGP13CpCM7w9vAcJb99dXk8kaMD+u44wbxY10UAJg0xcxa5Tw+Fo92Lyw1YWTUi2Y7fsbmEIQ/jUBN9SXtIsWt3zrvHMTZLJETnvDGiVPXQUecPPAJpjGcz27272nlmlYPkvXxedNFsZpukMdtM0DKwq6Ia8EQjTBTo7im+6CBb44WxqU2cKnCEtbtsivX3/Z9DGonl/WeT1xkj2B4ekTJSXRYgCY0AVw2R6TiqtrwxqA2zWLa2a+aRPCkmKFX0R5rGOemcyV6vowSqck1ocYrTkvKpZCUrh/4UtNgCI4JDfQ3wHNiHMsevgKCLCI1o7jzFd6v1qblcEz7/9g+RPCM+//quyTJYNkwygUEd6pSTrwtEmN0EZwwSzi3UCoUrEg1dNCmrc7ZqifPpGetimrhzdOkvMzz86xtQhMsr37WvPNcmpmA0PcQlh1C50cT20D5xd/0qtM+qy4/Fk6vXF8EUUrpqm9KPNI1uYRSHSeXrj69g//F/1WKiizp2DBDTkn4+E0K4XLUXJeaCgD2LbSSdsAZwdRVzBE2SFgbqZqL1lVW5groYRoToOJCwJdSYYXc5F6KiqM6AIpKgLTO5Tb12dTrvqXLkw9aWoloNS7Snsyt9mna8IgB0f35Pf7wvvzyrZKMmIKIhjVnLKSkaWZsPM/AzO//NtETcieEVXM4vwlqWJF+fcpr3tMOcyED4QzPQ3MGrymxDq6mdUF0R3Kop2+KNJ/r769+n258bYN69LCwsClVv+QTTJpum8/m5LquLYrekB+lWOmfYXJoSnu4MKZn9Moj2by4TZKDlAFxaC2AIyGYoxs5rUZtnKJgc+Dan1KNrBMaEI9LplAkHpsLbYXN1fYqx4bayvMQIDN/sJ96DRHmcH9Dw09t/SL/gcANYAg4uYMuOiR5AZrLV2Jxr8dT3QcDSdGYfCvPlm+3qOAwDNpU35ywwqhMsaz3vs+nT/tsxzmL2Lh9EMRfgqAJTlfi8f+5TlGH+N0q0mrDZFi2WySbH9Jr5iTFmq8E3q+oOHT/3c4Y4qlmjyw9Q1StFALWZsZyG8/kq/tIz3C3GlE6xqbrxes18PmvOslSmjuysihZ8iyaVLMkBFc7H4A/ng/eFmxED0hKLbPNodFHj5Uaejjf5fqduw+5MHhxdlGvckiKJ5H4/gEOrEkRJEGzMp2mKIyUQRFyv6/WWprgcEQlPCBVxoPQ6G+REi6gUpVga6+lF8YoczOdTJkeujIss45mN3PE8ijJihWcmfh8+Qm1ke898wBtCZkSbVpOy80Ml3WM/DXkBQCAkduCO8TQJNQUzYScUAPSUxhS/VNh49MwOiEzh8mJ3Pk4L3KAiVL+Te/lihT/slkw8vEDCRAAKs9M4DEyubPF+r8EbsUrqqxb7sQUYkEdT3fhrDMfttiWknCfVH3inx6R6vh8Ektl+HDkmfHNNS52Wkw0xBwaURYELQwQJS1EVa2qfe1NnxLTzbKa//vrv8uO7lKIJqIrAPdIIUBh2JlFdzZBxzAqzgJWO3Ey2mk1Nx2PGS4BCiOiyghpfprdf9U/RdK9LeZ8leVOiOjRl7V3RUdNnZRfQl4UrvIAxgSOpIYhH1OViy9MJIIlwivOyXwQtrik9i+MNGPybcFEIAfgMo/dxPS6zWldg7nv/qlWHWwRJ+eGvZYuoqrzy1GzeNpOvduUy3cTz+euDvAhh7zGDJKXNdruqK6Dbw2MSWXIsysK0PzypqfqiQM8JcwXOzgusoRphyqJF9MX1csrjeeq62pUNIf6HvzkSrqt/iMw/x3sasUz5384G+Ip9xKjaC08HCVxoM8foq+KdKx9+fVkt4jY+ZvUIXkAz0q/zrQKgY3T/PPaaBEL1mFM8/wKw4uPH91zvn5/XXQMItA/C4g9zfpktIkOuCK4rGEe7e7h7e73phfp4+jn1l+Xqumshu2nqVSWzCdgkKSA7sgxkzsqrrg1BcwqbtUd+Oh8ohMyeveKrTTmzyHg6dXkGt5w+3w//GR+/ofeZ1yIn11zFcvjxfXlTOK5338afHsKnd5/kWz6kv+1WNi1DgzEtlnQefhhEmBcNQC8KkEXkNMN8IiZxTVu4Q2eajzLU2CD5D2/eILH9/Hnz/N2Yatw4h1EwJmDCS3292N46Rrc1EVNDBRA+Qi2gAAqAiOzoDbgBGNB72qznlGofkiWZokMNEhYKinqx2pyGc4o5Fc6fpURLR4WBN6uXwTvEs0O7DPbz69/LkrVR5hX0kylQqJDANZ6qgir+Oftre1a5gOJmqbaFa+PpV7Xa6WGYz9FtmmagJSaRxKGsL9YDYMyJ8bA0myIuc5LH22+gqmaiymUFpg4BNx0Dcslrtzmcz6fptMzRVAGtCIVzDkVO94PJs19j6TPy5to2X26+5lep8Pwa9RnrJCeixXRABqQtF5glqWrJoast5tzOxfQRXsLVz6f7S76sgQiKlRuBDv8uso0zQMz7dPShxKbQBRVQs1S+7g83G/Cr61eeaX+2kxeG6UXn//80/eeurtuypQlFRDevfz833BzTrbnW2uZknkSVWZkCCqmkAsQNcLFI8ANBIYxQClN53D57memGH597fe89Ivixi7iJpojW4mn7sQSVg0JL122dW++kQUU482nq52PqLrfveHkk0CRJOSpgUTsI2XZj2vV7lsHapGDjQ6Swh7MuaeaqNT6bw6BpDGRg1P3y4DIXQkR0u7KKiefu7FZX2cVNFYSmM92+LQ0YTyKx8gSpmLhHZrkhCCqKketZ0jkMCpBnddtUG++IalWFGkQFEI1xSuCtRWOsgoCkuACIIeQsXV5kh/MYUzx/27urVUokXRCU1Hy3VI9EBcLYTS7LSD6QMYSPRJ5XFL7Hcl34oo+JCQAora9NN5OKoDXa34cDToZqD2IqUAVjF2MTsoS6zxO8eLMsfW4LkBRjjsY64623iqmTEfHTRcY7L6qc0jxObTz2uggtiguoWuMQMgdeUAkj7U8szBvnpoCJz7ays80WppnjmgESQC4+O4e0l+Ol2bq1XY6/YbaK+WywrdSM1iHZTjTLq6qoM19oUgY8ApcgIsAZAlkCJMlhPTHexOU3Fsb2zQX7rw587o1ISjzO03z6dn1xYcF4N7uGVikqoIrq6xnJaOAU4xYRaAv8otgi5EhmnqZf//qvRFZV7FIgRpeXur0qpjEscuoFhNSqIQs4glZtIXOIq9WlOgjKASQ1ULLPPBGcG2MrML+8fjkwzOf8D94rMIHZbRy5Ynm895rleTbN8+5tYw4HXiIACZCimYz16Mj4Drsf334yQDWjWAdVnj32tANWwbMl61dl0765BLD48IyrGyun5f4VRvr46YdVUZO6zMkyS9PUlL9T5Ww7S5osJJVXDOH54A7DZI37dneMUYTBkLv4+M4YPyqLycjrKb6s3Kbng3EgWnT7b0V+uStqJCTjlGMEsHlh87LANKKYJKEy8WUAp3EZLMFqGAF371YlZiT7UQoXVpGyq8wQTh3h4gCt98XPP8uvv/1kK0EnxqMAfrvjlAYkg2WXkjXGtxQEssmYFcRxxmWUUU62sMLQ7Yd31xdkkUjxhw8fv33/LAw0KzrBpKN3m8q/BX+g1M2cojuV3+CLAljyHguDZMWscYLdVcLQ92ytAZrcVAF49IiLJ8w9GYML6mqIE7ItfT3KtFOcCxTQqT8DjAbLSk5nSb4CUZQkAIKDrnY5167v5m3baO1CNI/HXnjRbTZzbVhVNtMwpExX+aoQCWW+TKPB0jV+XI6yH8z1tYmLxCVr89gNcf/cLQuPC4Ds3rwFjgia+4wIjN3PTBIkzmHI5pi7cJiJTJ4V8zIiqCUSURStM2dmPvZ2XQjUKKxfvry4y40ecpTvxmTWl0WRyTK1WpMBjhMoJw6A2fIYs8Jd58gLf1pdp06e9+fylKpbRiiTdh2dhdGohVTZtFhfTmX1cnihpqxMA7NFj2DoPM5L7ElxdGWj6HbXDTrSRFUO1ggBgHyfOw3qBQpXCgdhX1WZdfXz6Td+jJuf1unrst7Wqvg1PPi0S5j4qrZYq+oVNiGmZJxvNrFflxZNgyoeFDPnVO3xdJ6XuMvXtqhwCTAerSHyjseRk8jj82q1Vp/ot72zBINHirnfPk5fC62adutdWdZ1itNAqLBsNq2qAqfEfGA5d89cAhpLAwBRxkwOmSCeT1KWze4iPp56PoTHk9vtnJbqUGknw1cu2yPn+vJojSXz+nX/DpWXmZsh5KuyQ0KJlPnKbVS6rtNXeWVM1QRfB7ZqChYfGIuUkFji/4jtlheYMTSFW/KqS0m6Kizu4r03oMsi0WeZ/wHQOhfnWTghQZbbzBpiRYMxKWhQIIOLwmORHC7GOI+sckgxRePX4zKxBEmamayBLJ5FavaaIGWg+sjdh/NlaVfROLQVDG5vA4q3CEl4u1p7ctP9w6ugId+srmy+9CINEqLi60yVZ92myL/9pe+XmZ5+W7//Cbsz1tUiA5Uuc8vqwhkHmv4GSQVVtZRxXnDqlMGAyXwWbz6w6u+f72//+ov+9NNvX34LaRaVzery4mbbTcPoS5oWjpFZ8iTJMmHeltXz4RGHXI2i0pgWp9MomCiSIQOu4W4yeXVythiBws3Fjo/DjXVEKGC8Q8fZxFB+/PPP1oIqx5jC0o3d3di/LWvvrMa5yEq0lWilOzJk47x8Ps/vCeTqyprRDBSDBDJ9xDEN09PNm3cp8x6O3/jIxFC9uyx8JqL3z12ZG0c4nCeQhCl6Q6CZIe33NCUgk3KfIZnbzH4f9PbjB+O9Nc6Q2R+PhSVL5FPR6WiCp8CIU3ZRXtHakXkKD7JPu/WFMz635DO3pAiS1i4DoPPl9spk5mJnlVahjoaICFmS955ZOuHnEyEgQY/lT6jVZ8o0pmrLvVLHc9nPLzPDkrC+whLRSq6xxAhcqZxZxdmbBhcGjXyiWORleX5zfOyOHGONerHb+j/9dPGv/1oYeU7DDV2e8WU5nkLWjHFuZuuS09zIkiEA4/VmZe+GsN4k9Q5JUDWtyixFUGSmcYW7KY29QXNCf0UYEHqn82E/bnfbp8ejBm3KApxUm5woVx6TkhwmeUy0vb4FP7Sb9XYbmId+2a4K4TZwB+RYNW2v2zhxSgKiCMwHqzmJZU65Fuo2ZzhUaeXnZNp6pcII4oBUTVs9Pj1l6GYgAKicu7N9tRjQZJBOyFs7I9acpgjhlHqOs7xrd6Y9P9iRu9bczLJnDiOVqDRzfJkDhmdNCbCTlwkARJEMZOUbTvsE0wHHDW9e4utFdXU8H63xKUZj8eELhum1zEsHRemsoB4fJCzncc3rald66HO4oCsTeYmcicylqroIPPGBMrPG9bkc0ssRalnktJyAwA7NNUmohFDoTu8zvEr+JBBiXrolSAylKoJtRRM203puxNoGh05DeLETrFo3yVyJwwwngQwpaBpPh9kUHJ6KfEcoopNBbRFeQ5aSKsDBMJyeY4jW2vfFhZ44LnugKtjFaK8z83ZV+4x5OCSY+nuNMpllXW7U4OM8O1kANQ79QfZN3nav55Sir2tAyBIBCscZ0Qwyqde83nEbCuuUO6zUL9QhDcez9ZmyhYpN+dYApt8+o8Kbmw8Ly4yRQerakEQArTjNpd1WFff7mBbBseFU+jdq4NJ3vinQmRCm4A7TvEqqWuYAavvkAHw3LemcAEXEWY9W0ulsbWZLsHTBp+9g33/59htC+fHHbV54HM7lqkyHBVhsZUVAMVVgDdkJlZMaRmZALk7jgqKELsuKwrcFtcWKRrMklnbtyFxOY3HuX9cI+dKep3E0s0H3rro8aQ8ghXeE5sU7I6DC4/C4HNAR+GqNlchFnkD5lz6ZemYQAUCcHzvGWauc2uTMVrJBR0YYrE02R3NycebIHG2W+hcD19Z5+KJABLWI4JcU7b/+igaVAUAT8+PdA28o7xwovMtsyIiQdtaokfN4Tq4IXe83uM1ugoXks2bxMz45NpsigyReU1vQeXEul13TRluaYRBmcs5lGUU0feauM2sdqMwKyHzx0x/qZbbOgcjy5TNzTMsUHV9nmzksD7/8ogqJ1m5aOKJklrxEpRzcOCzMvz0+v+MUM4PtZrVZX4h0LBSF6wqnqTD2DAL1mMc8QfckMQGHXWbQFXHDPsvsQvOsMEtW1YtqUnw5JFJhESBEJEmRRJxzhiyFumqtCuRLYVoTtxdx4nl//269exgG4fCmzBJiqRHRkRH79s8FqryA9jHOnKyznjy8nKw7KdRkCrcto8nD80CcBq6VmQh949+7+FprBIhx0jmpCBqkXFq7m+xLhBxJvbXMZ7IokHChsZnqqTydxlafNZTVzvj8cvP5SzSPrc8SYlZsfZgjpCfYh0kV6Yf1D7zTRRny6Wj7N/6COYJAQwxCmDelou8CoMkLoamtxQ4IAAlAjNruEcisNruGl4XjRMbJ41coDZY3zUXDA0KAuL7o92cyYYgc+57MinaWY3J5KRjEmBPVZt7ngNTYYXYXfgRGUXZQPZmpNJVzNEjauHXiuz7G8SEMxcyR8YyVrxHAAfRDr+NQXN0ycEau0HxAONKzmnnQtSxHXOGW60Ui1NLSul5ptjRVYYtyZbvXI0cBaKRebVeblqw1gDCnCIZKXL4/dWCoruqAsXTNkpaizqzJUzdu19fLMPTirPcc1NA1wDOpj6zywlg5UpDn4bw5VFDvx7PP0goKQu1UXF2vfKsSGyzhQ94YM3P8TuLOo02DLOmrX7mYIBcLkLObaLQ2DDplsfQ+P8rD6++BA182IHFb+1qzej+fyXoimrrZkh2XpL5Aa9hXZko7b9lfZT6Lrg+DtlXdB1N5N/TGWge0cBQ18OHdJyQ6HvczmZ2VVNxi7IGX0nGsiffxMLCC2FXbXlw16s5Tkl6kBLRr3RoJ0JSrvlzDt9+ILBODgCcUnwsga4o2bkzubZuiZIkNTRY9KnTMKhgnxW75UGaG6FfcAUy5syEiGkQsRjANRINoEUuENB2WaQBgIgNR+66rm8YYL4CYFCjz8M5XFEETJ9G4lFV43eschOMCYAGKPA/7F0DjNDgmSXt0BQ59Yaw1Sq9olYZBmrN5mlUyUxcu8Fxr7DJZTalIDSdKDN1ZnBPbbuBU+tJ7n6wtRo2gCEIxxsjinM3XzRCPnhExr0pfemu8w4rW2uxP53Bcvh+/O5eXRXVd1Ng04X4/vd59OT001Kw27XP/0kt8u1r/PP/pF/xrad9fffQjmYs8V03eYpKa7KCSKydSLwRpFrIN9mOI6KxAiQ/jd8J8M68u7btXfmz03UZQbjTEBKOnZsEw+8KRalomZ/08D0hYVmXik+kBhEznIorMuq3GNlufppI5DcPStsBn8a0pn2iYzlYnE1XIEBhllAgpcE7p9DT2BIogRbOxGcawRA4xgeqSUrW5dWT8i86FaK8D0ltXZpsL2g9SFpwMosmVcXJvq+px4V0zr2Ob+Whbm+Ljb8/HN5tadC1hqRyAMgKc5dW9pBNOlkxtyBD6ojBWRRnJDZaKVvOsuH8aLRGI8rxOcNLC+QqZCsiOhVmF8zFN/RPIImNJK3QU77+jy9G5l0OvYcLNpnTulzDD97vh4wer0T7fDXBt7Uyk5NXZxhMhXhXGra6VAOnJ+IzBOEUSdN0yUUp8jCk+f0vJYZbJBlvjBi+zzpg1k3rXc6fPLFxRNidrpin5s3hFWnOZMNIg2c5OtskAM2PBEfgPb6FDydU772ZfH9af+auoksMuLioz1GNNBaqxgKIjsY5t7hZNLH9bX0gRG5UsYlcCjplx62IX01K7kgymYf56vLeomcWrJFQrL1UAIcVBo6nLpKigiijv1mvEvfMpnwwkxG+QFBKQzRxCNQFVgnrkBYUHf7QXtzmnPKTOsI3HuF7QYHbdo9mA3VzH/glHxAtI0+mckjffvy5x62AGkwQVIwAKdGAc2QuLRhgQDAAtS5qW2aiKSsyL9jyvb29FWVgB9Lh0Dg2DoLFBofBb0Umc8rQoqDEWyQrofpnD0+Mw9Nv2vfXZ0s0MKZmokF1dvutCh8Fvqhp2EZbhHNGxaIh2tZFp0WBtW7Gkv/zLv4ABbPFrSLvtu5XJz+vWjjMZrRxsmJ55m9xZj8kiQjsTLgPGN1m7wp+fVn/ZP+lLh6vrvB85jNOZs3AAyvWIdpMIEV2ynUGCwYS1FhHIviyG+pwwxoBxSpPEoqjIynkI3lrKCAlF+JS6wmRj0xzP+9yoglW1mcjDiYXg4ofL1qwQ6B4RnNo3hRsT1wCCyS4g8S2ZL6VnDtxs4HwXRHKzclQnkLK6ABUmDjwUqDQRWGrQKiKbFLkId59/pauPH1sEMZnrR+nHznlTlMAS2XlCtYQA4hSLFfVnCNM4BlA5vu73htzbt29ZIUoEgsSKaQTAEYQg6DKBqiFTIu6QAHAEqlSl1DQFZhoWrHA2hrIsV4Rb/xa2GiS8CfOXL19O59TUmq93LQ/zqEFSjCJIbVl4nxGY/JIckWIVowUYgIiIIrVJxBrKvPUf/vD85ZcuvdjJXjdvgqTTtLwcgZdHev8DHyMnsRnp6jqGWTY22/zwLoT4+TNMl8YaS2tT5VBxfvcH5mGyfkR8IVp49HzWJb5xFpXkZEUAB+CqMc5AGI7W3K4z58sLyCYKSfzmImu1ZpGYMMmshJNZ2hlMm3vrENikJ4aa4+uhkw/1O9PxO5ffoxXFtlm58OpBnI92NPM8rlDy3AdQFcpuqJqLlAwvvTEXtCulE9AT2jaMmpTbH3YDjPPrw73zssx5p4QWYcp3lwYNKE54xkSmd5nzpa25G9CMBIamJRTlICTlQGI2AOdTX8bleOrc0XQGS7So2zRFQhReyiU7pn3kRXDZAvi1FWLsAAAckRTA6tpj1pQbAAjHpf1o+wGklF1ROoqJ1YIqrEqASUVVc3IM+fX60obsjh9zkwdJqCkz5Jw5keL+CyLq8QsHsj99yEGFSvuogA93SLfOeXl4lMsdgUXADeMxibM+zXtAARne0iWwfMGZTJ/nDc9haAr1lnpjjkcEC5Abl4FGzSSrMmONqKDRxMfuwFZNaz4YCqd9bO3eeocJ5Mu3L6wSmZOScW/fvf/AH+6f7k9w6k8jaHKjiX6h9YK2kjA1rlxD/jL0rq7VoIIQQpI0hvHcm1VZAPZnTs+v31Sr9+/Xv+0fVOXdFeXbD8s08vM4+mqYpuymJsx4gKjIHNAaA+cDtGrPScUiUdKK6Tjq7n0gfA+7E6hRTJkHMldFScyFUV4/eMK9j54sovAp2fWkagrbk1mvtGxl/LIuMKM3G/etgjKT8Q4SAqAaVSJPiIUFnEmTzzLmxEJAABineQ5Ltl1H1ruHfrvLJ5Fu0Xeb6v7xmyYanDOI1mxKg5OfhIEsvHavOZaIiGBW63VZorHJVYnRvgwNIEcQ7wqJgYy5Mm+/vn6pa4fkU2LueeHQNG69oFV5RVrdvDkdPk8P4+Prb/n72w+by9PlVr59uwQNGot5PklwaTHGWLDxmKyhVzm/Xd9e7H7etvr582cAm9XteEqROXE/nIpy1eaLlKD9YgEHVQTESG7ca1YHMjVq8FAE7RBJRCQuRea37fqs88zjXXiYxvHy9scnjCQAxqYYhlMolgjWfvhgnN1OKcxh5qRiMDsMDLrMw1IBnE+fN8cfsp99zf1zZ2Vo6xYj1GVz1G4+d9zzICmcuzYqXhPOoDJD6UuxCsHkHKli7vtjXmXBmnbdYndeJVLrOguwhMkUKzKMgKCK0Fb87SUiASFZVFHQl+c+StvuSBUGgJoyA1KrorEdWIT43n6A1QyaQgzFPAGhgICoLjYrVzNoswAwHKknD5lYZfA2tzYLYTns45ZqaFe0/5akc7hym8KZEsmgJUOWAWKa6qaaxrJtvaLxql3UNRIiKcHFhz/MX3/3WXFclsEgQkyXllNRWHAf3j/pnfXuL8u/dP94gsh/93d/NsSfDWr6Yqy1Lj4eJMZkTWbfNY2nQsLknAX/lt4lupPXCICwgAYRtTLReH4+qL4hY12uHVJpjF2PSyevDdSihGpIxE2pKOrzogmWuK5y8f7lTGVdlC5fOEh3KFDxCm5f5AAQjs8dww4gpVAB1CXiKlcpw/wAo849xRyVeetyuCwjqE4PmHvTrJbxeLLHH2lbcvn78ZX8nIrK5UyI1lSkQR9fIHd2mDxdKiyoohDRRSZSbrBhi8YxB1FQJF9JXJYeFGrS7DY3UqyjSSwKYEOM03EfbLCkuXcZWTSgAU1rdAcxhMwUy1j8/jze3g42OEl3YX8thC0RRpMgReXECyCM1vo8X/ySGzedp8e+L2nmmMhV43LvncszD5pOr58NgF/DzeqdIWO/fvnXt+/eW8S3ZH67SiB3Dm9jjTictdgC4CEGXb4gXUoYqjx/42q5oHg4EyqhZOuSjbhxAW8FDCr+DTQmGNC4xuQhTUYMan0iBtN7l31a/2gBYiJlEGCHpKBJYqnYKRCR2b4VQz6jj5/en06n1hXTMipLGtFu16jqqcx9A8Y4M4/jXDVlULAEC2CfAFd87hZnJwghcbTaPT8GVQUE07wn8q+nZ+tX3tisqha7blJMpU32gKTUGIKU9PslrBZ+8NoC+wyr1RvWBQOP8esCwrMj4zJn+iGZlE5IYMha8pmtV3WVHZ+fUZaR3fj6RajZ97vjYNGsnBf26+TF3jjgj4gR5kT8qmfTGZuOvbUI4GsXbZGrepNZIFB4iftttTuyKJy9bzkxGtUkEsBa05Qr0RRCb2hb5wQwAlTH7ghG282aqjJjTZqGbvEuYwWAwDwkdiYrhv1+HAdT5ZI0hDm3VwHExrMBag1zlLv9g21wbalcvxlfPhNiHJxtksCUcqca5zDMU58nOLm+KC89lg45gyxO+KX7bkivtzeqeP/5OPGZzMqsV+hLnZP2T4fqwjuV8dx7IKglYh+S8jnNhUFMKiJDjIsUsipWqmidIyLLaEAv4OL38teUJBER+dLZnrJwOLr1pZdjMK0RfWbO59ErnH2bTSg4zblr0zKtkFkQ+Y2/Sddf71hKLmaZhrgMhw5BjCdFUzfVJSmVkTJM34NUrB1DNUI0RbmahnMIkubU63Ejny5aWU6pD3NZFjnReOzrq0Y5EllJ87GDSdkbg4QWPYsyBKLn4dyudnbF0RApUgzzWREhkXMh35uiglnVGSKEGsHU0iasWg0kCsfYs5S1IVYIDAiuyOyPH35afpu/6K9fX+70RQEMGfdpfQ2tgU4HXqxdgTAj+CwfPaKBA+EkgscjhfSKaDcXdQ6ty3/+w9/vz/N5fJah31w0KuE77/86m5usYqz37HJeJX60SNPXh7swGGPyvDSuYHUxActozM4Vvh9HkeCE2QmBcfROd8mgwa2RL4eUBPn0L4lLZ/zVD5rR2EsnM88L8NzrVO4be2GhRBRfOdIBHeUerS6BJw6iJUUaZiWYKa62OHUTI0XyzOl0DquNsVr4TauwC3PP4T71yYBTNAAGAbPN7QKh+/z4rOEnfs95GFVscF/++jmIAmHpcjcotLmMSz/1hDCh2UQlEaCg/MycKOOztQ1IqhUnUo0C1pE16BeavPM+z5pNo6zx5XTsT7zPy9w+jz3ACKgZurbUpNCHRWeqcucuACcMyizXInNVore5y8zHm7d5UcXAYxhiCokDg5J1EwGnUKCuzVoK0IOk/jhQznEZSk5NSYZ2RTGGcc7mq3rToEW9B6zs+XTszqc3f6ousz9+pFq1H+KTrixgY4L9ejjK6fXjZSOW6Wrr0Sz3X778/kWEF4lE2pqk4hYXvD0NphUxDKDCKQQo5MUtVXBpjqKwKvMj5JwlSArGEIDvWUyHYhHgiMYWpWHBdd1s7MvDA13mlfECiRAzY6JCgzh3XeGr/eF8Po2ZK6dltt6/DgOvChJBldIZIYqZtLgRBnKmLRsAGZZRFfYw5eIFhFESpaQM6f4kjvmgi5IjEYgwN4IGXQ0O5BL4jmR5fXEswCxrv77wrl41tFpvaEX8nm6xKIs8z2jEmIVx7ENY/pQwmThP423XyetJ5XwQHmM4fOsCl6IvokIWbm7XjjJjxta5aWHVoBmvzPKAhzVsAEtAAcVrKVm0YH4pkoAuyzw/3Ic3H/KsLIpq1Vb3j8+qfDgdVVJKAeE8TYutM2vc6e7+8vrakIkcydgBIStM0psog/qMAUSlEHV1yeYi1AjfHl3x5qKV6e7c8cFq2C2O+E51dtayqBvuvt5Xw+lUX1wMVf749K2+gMxuV2NtlzgaWVgJUNUgY5yF12DILzoa4+pP15eI942bn8/LQfh0L9nlBW5PzEgkxhQep8VDwgpX+Wb12j9kPiu2ZU6ZKCDos6Tx+0N4f1Gjs4kkpA3gFBQQ3+Tmt2b9Jm/v3vnxNI7hhDGK8wTBAGOlSMWKOBe7o5u76a7jl7puALby9UGumBOf0z0Eogar1S52JxRV6PWbcmWjFz4vCglB8kwR9xM7QymPwwNQSsGobSDl4PtlsnlWZ5DN0+JJOKmAqcs2aakbQNoHj+ZJga0htGNh/BAZQAFBOFlNdJG3thWfGVHMJ+WDMEiP0hZn7eTUeVfGxDEFZ2PiJtREfQxBDfTVlPu37k/h3yx3fzkyb52z1Up0lAMOIqYoE2TeizeUnB4h4OHV2lxW5cXVVZhmUpy7U3fWHqwSZUWVsEzh6ZnOiTOWIPz1FW9DeBGVgO/tD9uPuBIBuDtW7dr5co4SVVKmEDdgHCMZFg9gEwfpQ1F4dEu8zydHaihPNDGry435cPXBrvwwhVOOWWZSKJ0zaJusmMEop8jkV2TP0JJdjPXnNEWI4HIy8oou6LzmsBQKE2VZXrh2wbJOr3y6YcfzmOKX3zhGXDnB95RleYFO3O+vX4blyzQPKcXr/Mb9mM1hmfrQ3FYTjOXZMYBIZs2ME4eUvLUEOM4jrxtxiOcXTqk1ZKxegEZpOQmQCDpFsGiWuABgmhdQ6H85Ob26s1GwzYVeT9OCflWhKdDBSTABlJm32TRBpnLCQzRzsLsMnC1ubgpVwynpMkJbGEZ/WvJtpVX9++lEQKBWzsNppWsBnbOirn2xmY6U0pcynS5dBryEMRiOt9I4qg+Hr8deDA2WJRZZ3v8yFx//oSxqY5x0kY1eVN5Z2rkquyl8Acs4hLlPkogng1oauFFIZXLeGMpCP8XIvoDH7uBMa7PLCE9tXWHKLUafm9MyHMXA8Znam4FiLrGweQopzx2+vwm/fxsBW7K0roCiQcTra4hzUa0Opy+5KS98SU2jCe6/fu3zBQGXGMnE6/zdHg7WlAEk8Bf5PtPNG0q4QgMWE+jiSBxa8L7wRbWVLk7uLGWuq3RrNw9yoGnBbDSZjWG6wmsDDhkQyVH2+VdX0V6hyvLyYrNti+raOH95U5RlPseszAC8KdpECiLT8QAmw5Gu0nVYJRAuxhI+QuZ8DD2Aw95xDbEOYVzmYfrrb//07enh5dshpHNeZhdvsuRZOBcNskAb91Pbcz+wLE0RhwlK0xDZpt4Uxj8Pj1lZLqLeZ8aY02k4Js3r5qrKD4cT85z5zDru0/DTxSd/uTt1J1BI0zT0g/cbyCS34NsGknBZAYCo+vXOQBrZXl+/K3OIh3AfHlaWLg1iSviIxVX+h481J3Rlw4DfVT7stqjia3+VNz3ovCyQB5OaUG8Ey/Lcs3CxXaGjzZtdW7feOERMzJcKeum5uTl/e1jDiXarYmbu9dVQbgCEu3jIL9p6a5+PQRxlbn1WnSBGuSvhrSvNEl5aWCMDiwnLTKoW7Osc5+fXfz0NWe6IIJHLjS+Lcl7ognkClXFQjl0SKJc6+BFem5yybAciEkWYG3czX3v2oY/T3L04UBfplNKbiKzAaaE1kDEIRhM4A5ttPXdTCjH9vjj7Ha/eF9YW+6lTQENPfX/sziktt29vTObBaLTJu0KPPSmBigIg6MvxhHVVqQQF4/LWGOPM/PDIq1VbN8swI+9Cu6ikeDzGKCynq9uWI6TQOOhYFE/J2YVcSqyvqs3zdPF2Rfn7HIJzW9FOY0f01pklv1kj0RxZQFnnS8L89q1lnBPYFL21mcv2cxAw+bad93Kxk1XK/1q9FWBvkrgj4BLTi4IoSzTfya95YULaXF031aofzSLHtN3yfjGNUFHgCHHqwRIoNHkzzh35RiVisK6ws7GIo21Wn9paHB3vu1c52bINoiwdKht00DhKzDER4hhjXmpdtv14Iuf+Bju762duU8x1Ude4N1A+IKzVNzLwq8jzw+/0RMYgsNy4hro1FWY+Hb+ns7IaQpN5Vvnw9lO5KpcU+4Gjm5fBoxYTugnA5ZANSiFYYxB0HsZrJGU8fr/3zNXqYmVW9jKX7oTikBgt2SSuqFlTURfGECgU1g/fH56nB2tycNjPEUWtzxSJAAMUwFLnDqzhqtJTB5lhYaRc95R8cslpOP7jy72z2Y8Jyu2td26JFhRrWIUlxQTqXJZVHKfSuyFN94dfrN3ZtlFJEIT1OfesKAKShufx6EQlSbTIXKkSIp4ACxg7JdV31e0c+dCf96f+pqmJXRy+qCSwTarzm743uw2gzKe9yjQH6Kd91WyQrC2Is4HRxuNwro3uT7zE4noVXrt8vTbNh8KQCJEKAxfZLb4rBBSJknCFcEwh6gxc3gB8n8eJF08Baj4PlR6PMTHvbObshrbfH+5W7dplqPt6W8o66e9fIgA2LqsdntEQkvSzVxmXKcaISgqYVJij+LkeUsiLHZoBln2IpeoiiPjQiYsdECrPdVvf/qd//x83u6s8YHN9g2eLuUKKBmQ6vYDPFax3Hgigp6ws0SizHiiVS6nCHaZl3zmlRtWY1eymZmmyZYDde1hnt2//HcB8mo6vh2//1//b//Gwn0IQG05FtVB53RbGKA6ImLmmzXJU8a9R8kXGV0GXW2vsuir6cYopvu5fTKFFNMtCbVMRlrtdsQU5T4Ooxpik7w0aX2RhWgR7xIK5i1MGQKqKACIa47KPugF0FsPT/Ov+V9XUOmsMkiUoyGQoaUaoLYJB8+HqDRGJJAlyPIekYpyH2RprVDlCWoqgc0hWLMSOj4vza7AsPJwfMc5tWWY+XDYZm8SySHcivyr1BSbTCr6E8feHf/zp/HOEUPhGYDifXqBuVJrCptBGBu414KBdtgCZEm3mrEZ+Qvv+3acQiVN/PE6neDq8PKc4K0ieZVlmrMsMWQvOOi/HyPZIV7tt61CBkEBDkfQV0oSPgNLWLSVX74+4cHnbRj3HoKC1Amrs+Jg+Hx9HZue9v1JJNWaIzup61U7xLCIqdVVW9aVxhtSxzAqamCG33DMigKg3PmnivjtSnWpolUDVoJHcphheT/u6yqf9KWtbMsWMKT2O49C96E2hMWma5t6Sqwn72QNOQCvjpAuH9WMtukcAgIxwUCJjnr+BtgeKm7rjQEiR+4rji0vm7KMAqSNyhQABNG3j5uHUz//yLy/rH26892o5JZZ5QVIAl7lrMYnTK3ztvy0nMma1uziKhHB2pnEpda23gqtSB1kkBgUytfeejstDpdEZMTadVBJwQ4aihnCaqDnhYG1mCBTJ+kZxlDl1x45Xmze2FMyXcQLQaRwFqciz2RqVSNZWqXoYn7/eHaz5LTNu5QfVOC+DsLLIR/fj3pq1Bt2kbhwhEKPJ3K5aW0u2pWJeb3xhVXXgdCzRRscgr3FuwFQAcBYUQFdNc18YVzKjzcLzC5Jx21uzariPy/045EIuOZqX4PK8EFl8niHQOA5lXorE4nr75u7xAWGZTinlRVNhYuTAThBJUTX02eomLDNZpc3FFvD+oTuTdufn27w23lyX+VW5EmEYX8QEDWzNrikzWemXf36Jofi0MyM5TGEyY6GltWZ9cYkDazib1JhFoOwlMA94jaQKgmQ/GpeTNStDjQFgCaYoSwLFtIBEbzBJeO0PoZMNEqyGoCHVSaZx9ib5PA1T0aRpZTZ2EKhZWQXIDH7r1va6L+41u0Rf+YuVGlOobo0jwNfv8eJCeHxY/mmB3ALSrbUqTMN4tV3XqomXRlliQpt3qffRJZal5EpKBT0ZgdtmGaelj3M/n/LMqnASItiiSYRy9xC9t77w1pnKOG1COwCYJAGW1LBkxvDcA1BtVhu/AuieHvosN4feVWX1w5t373/8uya/aJp1YeuHw/DyvLLmjOcWYN5AdKZSV0BmNVTk5jkv8hwBlRPIDIsEkewG0zGct+4KmIEhQj8JHWGiQ9R4ylpvq4yK9z8WN5/+tz8fhpf/8j/8vw7Pzw+vj/F4AuMqlE2BdOPDuCDZzphJZyQlCN4gAp7H8yBjOiVQvV199BYAIITlcHw4WSMZN7qbYzzJEQDJWqMpKzNRMNYsQXgKxuYcwW4b7CP0YWtsZTUu8bfn30TTRxK9DCIOA4JR6UFLB75DLAXo7l5a1X36VmkwzZrtPo6NQ4eCwGo9MFppR4GH5xnNCK572dcvjAIYcyq0fwFZjDPSCfA3s9rA9NIkUeK4bq4moJ4f579q5q93N0T4Ur/caD3wOeo0wuwB2CUAyTk5KOPC5zNAMu/e/lDkXinFmF9ssv6x7OHl0x/eyxBc6WiYTsqEiOKdJYPEJ/VXBgcM+kLrFk32hFCiG+EG2qTLxHIyoHPS7KQ8UsrikA9kS+mLTqYobMn+sWh8U8dRyRo0VvICmqrYn5DIrTZ9F4xHY1zXPbWrG2auNEDdDGMHIN62RscpLsR9Nqxsa5QXRBRHwCoozqyfYZjPvaLsVle6K7/n4+n015G2u9XK+gsOS5JY9S4ZherQdWfjLBVokgcQsDWrKM9sM4cRfN3zMktAINb7Mqz0OJEkFAszsJ5SXhJpmlXJrVZWhms5nGQ6ivXNLp/NxhnCc+L6BbgywRQ/rNNvpzfXH8RiZDMC1Lt8Py2U5nEaFw85M1KZ9Lz2u/3xblWjdRuVjinEABDRmtVLPB6UAQlwpxCEk/FZvnZFrLIVdVrCOM7GTXMX53BxdaUaQXgMIbdk2u3dyy++uXlb3nyGMygCmaQoksiWN+4GcgUyaxAFyKAMjpRgOzV9M2SuANQpcWIsnZ1VwnnICROBgGysNUqMhuPknTMUFTgzPsT5PsxgMPc5GBenhDMFPy4s2xaN3ZLrVDSvalZVkVKZpu8gBpUmI4qJ0TpyFqPlkfNm6+EVjoXJAzh3HpM1WG2sAhoyOCnH2pRDirssu/I2L/NwOskYIakTDKl6heXS5ypqzULgH/a/46DG2osPl9Lx8PCgkR1IZSAK05P0CqWgcVZARMR+Q3OReNsbKGtehkKTL9Lz/v7lcPz4/ocs5RF64eC8JQQaRMVpu0I7zNEpujnG6WypIgaJ6W7u2Rif7VzpbvYh4iE4e4YRUI6ExdReviC9OWMpdxS2QBQTULh2N5mZDiMZS1RlDoTn4aH2JRtigoTJVcQila4MylkWxaNKLQZBoKwtJAkEhJYQEAyrKAhXDGlWhBKbFZluMKxnE7G2ZFEri95dCpg4fRl/Of42SJHV/+nyzz/96f2PV+/ay/cAVUrxcJ4P4Qltba0hk6WJDOqR6PLyGpKb5bTYfhqQzOsQrk1GDAL2bHS0aF81gVu9TrhTdwYBRaMrY2VtvNTW5W5aNKXpocEbvNxWl//t/+zfAIyH8/3rX/7hHx6+3T3e/eX5AC8xz9y7HyCfOEdBAI7TqxMcXEa1nfwSQ15UbV2chxMhosFPnz5Za0X1MAxJNGJLK0TD8HiwxoZ5zH2h7dp0J4My+iwbEkoClBK9EXy5/wKiHxHcO0NSGjebHLBA7U0/V6pVaprweCwoMEmDVb67QDBpbGvGSZEWqZfI3iVJakpIkS3ay01m7SAPgJU21mpvD0n7JFFUIqpQf0SRhAoswNa70uyK5/3LRbUFgDMH4aSSpjisqL6E3USvahgIxmms8xIdzTx5tejyb1OIHUuPt7f2ssZx9OnrABLA5UcY2HtFMw/DdnfJcv2NH36+W44ANZgiq9mA6Y7SFE51i7aPfH5dPnpzD3A5paFYx8Q0msrmzmUn2HvTf8p+pg+59vdZZiGrEJFFxhBfUmBQNw8zpKpTS5hX9SxzjoUCIEhdlUPfI4ClCjAIJ0inA2ytPBO8h2oC8E0omNUazSEbQvctfH5D7941Hx/HYeJ7oMJ5ne8HypKT0TKyy8o01q60k4lGUEWyY6JaNAci8uNQHeavT7y9uFQIeJXJVBWNHZ3Z5imBG5tDOngjuUXlKMAf3q6Ox/rAJwMxHbngZDATJtlDUbihb8a+0xLGtKCa5/AS0BST6jhkvghF5G+/Kvki95Li9+dfHYmzlNKegDlJFWDGbInHfZiTsXWVwMBYNDFizEGn8bAsDpVOvS+yMq+L3Htn5mUqy4Ijc5IkKoTzw/zw8uXTTz/mb+qfVj8Q4uH7WcnsZJeuYA7zEqIC5i576fO8TNbZ0WRzWLK8AFkE2LrKGX3en1VFE4DWM0kuCRDY2/E4GT5T0+oyOZ8j8qSApjBYLZoEqLwtmuL9jqzPc2ZW3cYYw5cH0aMWQqa0CDAxcywYXomcz0RPU+Ay5FWd5hKyfgCzEeY5LOLbwrlxPh/D2Vdu2o/WmDGFTZyXkPCw/1sXOSAqGYgw80N8/HCT16/Jp+PCR0ZUVSklG0JHzhtvLYCf64R7QrMxxpiNFAQ9WsktIXUs6+trtA5hZVZ2WfTZ3aub++50OE55Lk7SzlkA1pENQlKc51w4KUBlFrNaxzR8C8ElxwUDRX7pB8B4Hqm9zVd5vBusyRSNmcTG8RTSDNoMAsCACG2Y4jwPR8/tBVlUDC+fTR/lBxVIs6AsTss1nruxhiKoGnqjVRIgY5iMInjvDSBd/nyaFBBftccqfijfR4DH8cnwWaV/67YiRgwjmcev3xbWC7x7TsLo/7i++Df/1c2bH/+b6907gHJOfhEjCGJN3frzvLMwIHm0HltBbNYYARiMYkTrmk3BDCVI6DtWaKwliR5AbLlpXG53FoDcq8C8yqto0VoPswQTY10WLJhO1K2zaQAd1Pg1tu3P//HHH6E7d4fD8fmX//z/+Kf7u/TrGd4ltQzwIcZfi0SEejgNf4Nfv7m5SSkd3fM13T7xEyW3YFLhqPUKvMFEQBhFirKp23CMVW5XFse8naYexhCyZuX9zBEN7L8/T6qNc+5yhRKtH9RxAqHYQL5In6pqZ4T34xEhzzNjrHXnCbdr7Uegbe1LVpyMKmhStt6TdR+MMwZinPbYKw2WTZyBjZi1hLslsYAlyDyCzii5Jlp6HOJ3OCsaH+awLA93X7GA1+rbGeYW9Jwmaz1yA7abxhmz1mtvnIyYw/EUlhezbu0qvh7aN1n2May/jr8k5YqmgnYMWQCj7uGxn5O8iMJTeN4Wm7ZcxSw+9/N8OqcwJuRxVZmCtpsGUd2UEnkEq6qJ9fvxUUQNUY0Gc41ffld02Z9sSgdV7J7Hc5EoGq0crOkyrMahZ0kAYEQGmCpfSRy74xEQh/BsrFdUQVJh3L8oGZkeQBBWiYhQGYR3m4utXrzGB13mZRmvivzLV7l/+JcYkkX8UF3BMpAAcZ811ud5pOd0CmjIWgDs4XiIt9ebvP0uGsKSITykIM+/39TrcX7oOsJna8gXWW5R2ts3zIzDt/uzwefXm9tLMy/5qvJIUUt0lauCtXketx3/VmHV1g2Ri5LWZsVkTyGJQuvtQhd8/WyguShyCxznGaHjuIg8OrLQJGPqcqy+Pj4E1qbdiOqg4EDjdGi1tEDgsjwrobnszvssywQAEFmSNYBgvzFDd14XZd2sQ5h/+/Wr3yIwffn+LTO5I/dsTvMjcaUr751zTwQ+dBHUIJp8KGzi1BVZDajozAzMnDwSg7DOYKpesFERRWeudrcUYtTDUV8evsTQ+KbxTcGr1EabGeEUQjTLgw5Wy2vrrIpoMrB+w0+/qznLzvIkxtlYZOPhVDmX+wbsgheCDcQYHqbpCusuduuLFZ8myjMPRRW58e2ynossi+cjr1by229zSohvjD0jNKhu8nGj75uPWfg13MWvX/eS58WPP1SaMiLyZTwNCA5F9MTP5TiIcY5a1V57spt3rrX23//hP8xmBgCVxJxm5YkfdybnYmsM1GsnS7TGks9SXBKMGBVTF6Woyho4DoowyLLM81MILVNh8FHEp+LtLtn2xmEO+L01lgttywsRazLz7SUxM/sUVMC4rkrrwdbrmgtNp/j0Jcps366NsSqHUTKT6vH+u7MVjqnXATb598evHMRQtrZvVPEln0vX2DX6lYgqTy9wkF+r36/x5k22snYl/ACqpGc4TXKSqPa6qH+8uP6vVluXldu//6/L/BKgmCHrXqdsZYiBEWrrjZWipvOMnJRxrnlBS0knQXEgmfMAqoQgSUCqSudpQhKRCAiVUzRmBA2AZGz7FgHQTjNA8CYxCO+HxYB1tYHGVsAVCgMGeskzgmXb/LBu3r69+fTn+//yl//vf/n19G2aF+anyPn7N9fe1eN0Rwlv314bY+bll2EJL9nX8SRNk8GZsKrarPKJWRD6DmWmpS4u8/pFPv/+S93uMm/2r91qVYLO55EJ9BSnRXoLeOsc84waTEOMoqlRIZutDHlnzeG1MzHvFdpYjNBHXsqLy74T5mdwDXgP/Uk9axjdT1cZrWKK9zIgPzlFB96QFm5kgbiXOA5EJnOZhjmpJhWDSGQxt29Fsh9+Nj5j5nbVXqxWIYYrCoikj/2LjnQroUvG+NutPg7t/PqrwIFWkL27uPKXFeJhP/fToXlfv9//dD7sOzNVUNJM6yt3Pl3FZBHqdZU160qVD3x+vttHQ3R9mXlD/Rl9nfc9GSDnKitz0MOhV42IQMYQ1e82DUSLxrjqwmbeyUruv70S9sIwYfvh5nT6Jq8xknhyXbeAA2uUSJcCo4VKV5aoD0M8K9xUFbkuDCjnttwo7IcO8z5beISosU/SCsz3bYia1WBeA3cfPlR4mn59Uk5p3j8UxjhjzUx4rUCJBXVVoTHgZoBy2q4ydJFlCiEtARHm3z9DmJ+HF2v8W1MNitGTAC8i0n1OWYzPcWucv7lBVFOXhXeZs7xMxJEne3o8jTTa5Fdvd6ICaFmTd5mxNn0J0vp+no6ff0GCyz+sNMaFT9N4UnYI88VlhrKdzmfMSB7uupiMzcmVR0VbzZaIp9kWZYhxH/kG1BL1GjTbxP2Tt1mCUoTP/UCnMxKKK7bXt6+Pd7u3b6+dDdN4Y/LUtK3ZPkwvzjBHfq7KDXBSaLftdDzMKeSZNdZzktN8ztYrxzBPoT9pXjNCtfCrcsGIoKQH0GWYvmIYv5+J3mbZW2xXzVvKfYSoBCiiCj30pGaah6H/1x93P/7lX/8S0vhxuRJejFJWWn3heVzAyGpjBBEcK585lZ5VxexWFxfNlY3ORX8M3+eQV1k5mxmIrDWCFH2JhzO5RnhG8ZhQZBAqAO0GLQ/6jMdVURGqDSeYZjCLCETALLduz2fPIpKy/Mp4vN4mIu8yJERA++vzaC/EZwAyDeFYV9Vz36Eftv56DNPZ55d2E4eXxyHW85iRV4+4VL6BJSyH/qhGbYESF9wonUWOqmyvAGsyc+aeFKsxgnD0Nu9fsMpJTR/OrwKtH9GgJc90MgfwOkfsO5q3DvwHTOk0TXxPrM+8aq6TkRg480V7cTVqCmmo1p9EQNEZGook84RqmROcPLrq7xbzLxLxuzyQar3fR6Zd5pOWoKs/Xq7//Wb74//if0r33+HqTzDtwa4lcSedeKe7IgJYAAt/GwKAJm8ENIIDlREJINWgAkjwN2zzYIAQgAjC/JCXhTW43bwDQABKgCLMK/MCporz2J9aTZIRIo6oBWKorAeYARAADJABA8DQJhAD6t3lu3f/4V1x+x95/s9//eX+7vuxOz7cz4mfwMDl+hadLGkBlJ+KTZFf/J59/VS9/x8O561xwvJ8PGCYM1+0/qLNyuVeJz5w0uF83H74mNbc5uvXCSw9pRhsMJLRVg0jZotH59NDhCY3vooRIlJZ1NBhOMQUUu0outGmxZFmmf9p9+nr4WEMM7JfX7+7assocbEmxPm4TPkyGqum1EqHZbKMjKQm15NCBprliF0agdbG4eVKjclazvCtKsWYJsDM+5iOpEejBOobV3KA/YkkAt1uYwz90KvG7McPjAlMvjgTA7dNJuX1Mp9wHjb5ZUy/P6TflY17fotmUXwom4068zz0IINYq0b0YrVieQ0R8sKcXuMwjo2VyYw2ZzXWVqqJnbrSfNhuOKkhsgaN5kZw6YYeyUtt/AhOjoeDcAQAcrmmkJeWbOa8U07CoOeIxiHaNCcss1bUEtTCmtyGGmTul2OIUZhB9NPqZyRV3HmvYIxK7S0rHnDr/1jr/IsSADOLAd/kKKhlB+hsdN5fjIGAqCobVfjnr/80PY3Oe51imZVgs3fvb51xzmWt84ZRpq9fHobnfTynlFD+9PYWHIqk1kCGaoE5zt46AiJFZaUSnCvGZURrAbOZaOnIZUM4ldkbY4CssePx62/75MB+eLt1bTv0R4AbwKVfDjqnk7ggsmnfgLWWwPbO2pjyBtZX9TTi0IsAYNp5+3w8aezf3Fz90j+hbg9Pz9bYuiinutoY+PCnP2yIwjImUH97PUzza3xOtJTVal1kQwjEVEfOjLGrXe48IJxPL875mbN3ZfOPd48phJRLXDKkbPFtpkoW7Yms9TOF7+F1mOlN3Qi8c1vlpX6OL9bgOPoxn1tjZOml5cvQIh4jL0niPM+Tuwsgdon46i1AtKbebWhIQz9ui+I8OgZpWIa5h4STF2ACzI2ujsOeMny6f/34I/pspYltNzOjkVugHiqFcZNlaDZNCWwaM04zxsXnCBC3l+7L/Xz7wUmQZTIKYYJpGnS92u2yDMtL9kKoalEJkNAC7b2/VpSjHVNOmQoW1PqL0xJYAA7n5/giI/7Ufpw8B3GkYw+hGApUilEQTP88VdtiRc1oUwbaZrYoVoevd8HeGEvBWka1lnXu/dJrUQmnmmWhxaKqpBdlQFuYbJeZq7cWjGM+InGRNR/N8rj20SYIYowNcc5CFpZJsW02pt/LEr7nvgRjV5uUhDows+2G811WbD/4zTzHx6932Wr9X//h337a7rLdB4NlUW0ASnjc91jWhydRXMxisrqwhEA9AAIoQACYT9+rQlFVwebZjQGa0TqwBcQA8zIlyrmASZIYE1Qr0CEjKjPyzQaAACRBZ9RGKgmgULCu3F2+B7iDOcKUfCuqcDo9N6uLBIAJ0AIIAIEFMEARXFwOmsJCZbn7+N/d/B0Ad/N56I7Pjw//+u2s8a/9/qixW711FsuYjsvv6dc/Pqbfn+7flvB9skCmKgu7duhe0muao/duu7n0Ws0xTVOnpMqLEpAjkgQiDRLk2QGhIrCgrnOyzU5GVyYDpf38Otgxg8Yz26k3mIyj+MsvWqxXWQVG39zsxpR+7wYdnguXcZpTNZZaawAJ0jkt3ASgLyNeGyEVZJQjo3GrunLNCkoMC6QUXs/72ORiKjifK9Sul4KJdQaOw2w21hYu+7q+eGtsD8pF5jZXSWLCL2SK7uwgEpZtTY21mUJv7OoS3/fye03bZkUPXWXb0q4g9XNMo8GW8ugYL5QmUpBkyNpVbjcVop5fT9J3uhiDBG3tjTaOxuk8jP3OYFILpj2iBbDsvPVV0pL4kKkorhSTesMRABSU4yJRdJSe+9F5n/saADUsnd/sUJBI5BQCHV+DzOg9WldcZC1bp8ocRJVpPCoDrLwSYUemNO7W6SM4n7OyokEhHBDXDficbfE6fl7UZO7dxAk6qJvbP/zhLQHsm3ZrDIAyL6o4zqPGtL74+X0zpNQ1p4fPz9PrcLrIbsdhAnJZnhPROMxhgvrm1hgqy8rbIg6UJIXYqyQErKzHevO6/Pb8NRhE2lUfmo1bWxBUMBADTPEQ7s+ncVGpV0ab2llPdbEsshhrjSo6wgSHc8fLrPNNuZqmQTR6TEXTsCQYTyOMmXc//vAjII6gNA3S9/s4Sh/ztl449MPIZJY4YOZeXu9jtbrEyhKOwhlQkFhkGdlKEVer4tvLzMdXlNo2BZCJ8bRx2SmhfT2MKXJK3jky7o8f/lxuGxFIgR+DEpJ1Na3gMqTZibD4czZUOsB6LfDpxx9hluV7//vwF0T8cwSsyWcmCouq894ZKi83PEQqS08uDecEePx+oncdo8KZoLDW54dX1fC8gLkQwvUG0fDzbMYKAUQ74FxsQnRPPN78dMHMx/NzpNrl+7A0eZnMonPqsabr9r3PqknBZ3QYh82q/Roif48qg7Wr242D788PwINbwT72THEgF/XFlbty98O0PzUFd/Ys7KgToV3EwxiOp+mkoJ5y9KaTkuN5Xa4vpPAut+Dm8c5YmLUD1tqimdjZjHmR/YkUSiQFGefZGNu67er9tS0skbLMzL/GqPwqsOmM/aF2CW12mA6u2WAKfZoTD7KcnqXC/l5VMbPimpii+oJIAZtNdp2TquDDl4fd5uZ/+b/637zd/mBShAnA5rAATI+QFbW7BbAUqSgLsLnIQgAbgAGAARDAlDj2UxYZnIny4IorC9YDKSCpS5pgBvCIisCF5V4W2lx6gBsAC6AAiED0t7B3gCj9kpK6bu0UcoAcAAQn4ZF0BSYCKgCAIbAAJQACWECbbaM5chKDbubp+OqpMHj56d9efvh3fx8A/uMpvPz2//4/ffnnX+gdqhZ2Nej9bLx9Y69Om9NFu84cpRDNyF2Yk6hFV9d1Vpg5zFg0CDwp+8gE6DEvtGdhNK7gKEJgTZfnaQFfImG+nNMcIqlm7gSujCloTE5tZHbTGX3uvafBmILN631J5WwHi9bPVV528UUSADQ6cCHQG0fxGzwqEMInBJdnmFedKj90tl3JvOKNbWF9kmRUWVOtScuICyk4xzE2H1UlcbpjhPGEPC9ZaeQElgVSLNg4M3fHunr19k2CXMOrYv0WSoP+0KMjyXJVxX54MWBSvXAcjeCUsgVA5N7Yt5i0ye2IR7AG31znyV9YA6h7PsjEU4hoSRuP5gLIF0pDb6KmOUZGEI4GAABBdDyelvHUbC4MmZQioMm1popsVgWtdDwRkdm/Howu3OGJr1ZydUUIb9F4QtT7w3F65P3SCiPUWjdqBdTpUtJ0NGrtwXGGVqFLYSPegdEVKF2BIQDSuSza65XzN15/v/nQbBpQ/OXrZ6qyq+u3Pd6pkwben08Bwb0+3WWZdZkrNh/z7vd5SSZvXiIYPG8of356KPISVOen74jgKBEy0NmiJmW0iEBLGFPsEi9k8e/+9KcUZk4zsHI47F/Srs3aIn8dI2B6927zpT/KUfKfb4buuCzi/Y1Z19IP5KwBk5HLhIZ+6scpTOd2vUKSu+7AL7P5RFgQknzhb01XkIgukzewaXNumrhMm92akUbICypDzHJTjAb0+cQrm5Fb5mFy5nrVprR0ysP5XgGMj9F6moEP5/O2DvvR56DOsFSfLm8116IuY0z9uQeXL5fs5iY1qCacnaqd0e6CW9KymECfbeZfYnPmX+e/EtKfm8ZevcPxDiea0uGVt95kj0PEOVpYXmqFKSKCziNszFv3QS81cvLe+RAJjM8lP8GT7Dcv0WV+MN5S2qKH9x8UxCAFkPHw3OdXhsFlbVYUsuz7bnh+GgPzdud2u3cCWSB91h7TyUb77bXH+uL9rf+Gmb2qeQAc+xM4KeacF62u1oB9WgDmLk1pcsct3KYlWGYpXDi8HrAz1gJY1QuGQlTQBdJd4W0ijM+zmpeRcOc4choIrwoHPGS8aIcq0FtfsSikrMh9+Y4urQCHIKeH35tMzORBqY/T85EBfjXO3n74s99dcN0AdIzFQt/i1RynEZrO+UyKEWm0xgP+IDHmUWf70u/52J9365tPf//ffNj+O4DVGE7OWpwryKdOB449GVtb6wxyCqSJVRKEmTK1lQMw6cFTLDcCuhy+8hTiRbG14AKgiEYWNprbSuOQQrBgKCJ5Atj97WoE0HcQiZUD4KKgIFTAitaQA9zBCUAB1jUUznc9AlgFmAECmBoE4Pi3DQKggpFJVqsbAAFTlRuAQONxecjmW49gdOWLP/z9n+NT+H/+/lWA/U+f+NtdtqmpKE7d8RKSCt2NX1ZoFYxCPcUJLPSngp2gSbZpLhbbpRRCRPLFWZgRXI6gjkg741cUUWssw7SEFID/Vm8COj8TN4p9CMFlFwoLkXFkTmlfnNP7HUlVLmHZv56sdf2J1SAYi8GRmQ1eOPaf4Z6st8aay/fUluPkBDvNMyHzimRDji7y+cynLjqoNoqaUQUqmdOdOAEH2g8+X0KFOCW0s1DypkxRVHunBri9e+5vt3+Va5LfDlHvCQ203tI8GcAZNCiuGldWpSZFO1BRiFiR6K4uTC5AKKC9EOGF8eOyBJNPCMswYwkGVRY8KbcqZ+Y+xesizxASiRD1sqH+rKoJlFOwNhtmbitCUI+qnNDngFiXGqe8LnIiWHim1r7bXhvMMTKgIqmcROdzEmPAGawANCXoRL0RUF3AbUaDZBwIoW68KX74Aw3EU6erbOTYqwBsZHCvp+PqYuPILTYfE+vCqvELfuVpLupC3FlEfVMPL8OcLSXm034oy3WK6bDv6yXwtuJkQkpFVi7TxMKWRWQ0nu26AXD9495cbouEp6dHRIsqlowzCPbu+CTNhxzm7bZ8QlcuSk+nl6wwQAxl0FydqdB2OPLa6QgCeTC5TqaQlz5DQtEmKyDLt5t6wXk43Psd/eTe9xQCz/x5lhKsdYRmTYAIfH5EE9Bfq8hCLh4OmTF5Zqdx5NxmU8hXpfO+sOhIT+NwDFHGM6oCCDxOYAdf+kRoNt4lKtqLMmNbmi+fP2/S1Wt3DEp2lzNqbATTDPOExUn2aOsoRmkUspnzPkjfV1La6ze+zLZVeH49o3FVPUeuytKgn+dh0+6Oy33ZJwBFNFIW5tQ996c4zYMdy837pbTkKz2+QOml05M/LhTVGWONXV2B15RC10+rzdbX2XP3iuIJCRGvVm/340lsdM7SRY42f45pwpNNC2S7VEXuzm/cjVP8BGhDGFgXWSJcytrrcwHL0gOTU5cZR6AXZmPEoGUAFIEHlUq3I3fGZj6rk0sriMheXQQwFMx9fGjVG4lJek9iEWISEo5RkxqHxhomTVyhmzaaESCRxXmJZnNlysYYMiqbMNdffokMX1KMz6lscdnvjx7FnBgGwTTHB4E+x/pdWn8zg2owlBTuHNod2Dtr31+/+ff/6X99c/kDwKoHSGlFNeQFAGRe1oeBMv/a42KdgXmsi9yZCPbKAwEEgG9qGaED2EGEqjx3IQEAgAzdq81dUiyg5MTTEkC0QQDroHkDoP9/AShLkMGMRbHCIiWOiyxwTkPDFTAAgLEwCcwDAqaFZTBIgADIM5IWWBj4GwwOLDkAA6AAKTeLWsxcyoiAI4wzoK3q/8lP/2n36//lf78/vuiXIYVo1J7nJ1t0r7yAiGaYNLqcp+wMM05xBvLWkQ0q82icszGAKnjE9gb2e316Nsi62UJraRpMlseQUpIlMfSVkW4mRCiyhrHTECOZE7pVIWuGWBJrW0folV+Ovz8kxc3OzajlNjsfaYkyzrTdmrQQMzhrP7z/BNYG1n5+ybJc0Y4DCvUJ50PmwvlE283OWzzvJc4kRuIgpTVUIBln/HiMTa1DmwFETjHiKUUWVjEmRrcCTqdFfZ2qRQ5CbgMm5dYbtCdYSowrTqPszxOUXKmZe+5JTeUaWmcTLomPclTYFnGaQr+c/OzbNTRxDYzo9qVMkjr9Dkw4bfrSlM6wgrAUhBPWSUUxAI+ESLHj0ADiET0cvq82706JfVBd8ZAXbwG/80uLoBrT/pU6g2TVlRjXChd1QiIRsxCeZ105NZYy4ZRzEKqTC6gVj+dsd8kJ7g8PE8c3kD2EYxL1+aXM92kJqo0weTDXZd43H5qboiWOMjmTK1Bk9cbw5nKeaTmnfjyUq7eYTSkEZ11EmTisLzAH/XU/3WzAWWUCW2mGm6TZw7S8SagsBPD29o135jx93x9+zY0pi1wOFo5Hm4yppoSUZ9nlNd7p2SjZfJ0Sm0JtVczIyq+cfENGw37YCB9k6s+aOHGwEI4t8zza6w+o6fHl8666qOpsc3Hhh6mXtA9JxxhCVITVzfjQv3JRrKpcI03zAdFd1m+79NydXlabCwH4uj8t+4dgvQO25PLmorlcyG1w7FPJEtyk/Vl+PT/aezHG2j4FzDMrgMMxNWUUJmCk6LDE7TkxWgkCqtbow3cNNlD5Q7ZC4vByGh3nzWphYWa3dN6tDcwxYe3rqvbIoEIWpBO7hBkNqlmNAIqh1z5AcIXL0o3TaK2dw9LKjmtOKX1//koOR+ZoxUNBYoxxhzN0p68C0RD+/PN7JfeddHHfyBhVCzAzWq1kz2OU7HQAe3h8QtLNJjcJg9jKdsAlRCprHEfRFEm8uhOQW2K0mfn44YosHc/l+WRyF+aqsfMgwCGmpBFccXF9OZ7PF9u3cTlaY9ES2Oe7R06jOA+Nc5Vzi7E+WrpyprDjMCZhbNqUFmP83d1daV27beD2R+PB/fbX4/LlQ/PpjnPDIaaeSYx9H+Pv8ZfF/OHpVzwju4juwoTRQONxnTXX6/zNzbvbqw8A1zMAA7DAeIJhBeev/fn8D1X9k8gRSD5s30DTAgygDPAI4AEMQIOQYLkHKKA/sPhdswbAGKKGSZwxf/s4IFM3W6ISACCNf1MIgEX/5v8CNFAiKAAYY72BORy5C1C3sDrD4kEUYtjk1f3TX727WN/sxgCIOSI4AARNEJY0OSM5yt+8CZNmFiUCsAcINTRb2B9gsdvt2//23X/3f5j+d03c3q1zX0/T0/MPNzeWjbAAxyksYxwr47s1tezIJGN95+hsLIYTiYF2JTGNPEpT5v0pRKaXsfjhmsZBRdLMiWxKvV0lPY8WMrskyZxpGt2/xMSigVaLUQoAfpk4HLAJhcTq2lFF0kGcm/vnOyLrfP34QPPyOxJurj6qISVCMFG3EkZIFkiM3bibJr48O+8zo3SOoTtTCvnqimnBsOYeoxdlEvh8/My+cNCmkA/MhoCatJ6jHsvnIhX0stgLVmFVBdsT26xtg8GqG6qqWeTYYHauwMx2nZUlbV7Ph5xo2T9aDVZhJqyLDc5zkeWVQVhGkfrIp6z01gZEbUJD7CkrUVRVkUET9zlqOEBKUFcsLeIZQI77pxTFkEGk1PcOx3r1bqb6EgKoe0sXaJb+2yFFXCkS3qhRUOYhEu4RVFjQOJYRrIuHpEDOl72EmOJ2nUOdk/H9cTiPAxGh8Uo2t1gUfrE3q0uoyJxbq8/Tcy4qp6m3m102Lqc+ueV8BJWyXi9956xBX1ql6DmFiKu1sQXwbwRbo80//+X3GEMEV/3oVAlIEODX+ZcwxtwUaoCMczYjgovVh256qLKtHg8xLcZ4koRIlBSUymLjzqe2ukDr7s5/lZPdbN81nl9gbiSQmDGJ8qkXzpLJPSWTV3bMofh+4d9baw0YRqvgnJ2+fxvOg8tcZbxCalDxAtnVFQ8U3PXFrjf25dvnrMiNGdJ5AsWpO577KfSvxrUtYp9vsH/S+X44D3lujXXLXoJo2oCn9vLNeh8l9SNbtesd398tmhjzqIyECiNooKjLkizbLPNgGuG+vvpY69me6JmeyqJqb9z8+mS5WVDznFmJzJQ4KPuhn6uiJUPncfC+mAO7IncG1eI0B1lGX1fZOJss+KxJNp/uY38x0ygnOPYyNU2z2DnfXauphl9/VVaDLi9zFvv+/Q1Zv+d+jE9AwqwCi0wGIFmI0/R7eDaoZEsRUzqwp9M4O3P53E+7Wr1dp2DqwuDQDxymIGUBxiM/xb/M/3hz80eGhHUMUeC478JMm2oyLqpcFPnznEl4RQs8A3N0evz8EIxxxnqL/pLeduasylmRUV4qySKz8xlr2uM4pxRiBOf9PQzZ+cJs3lPxfdgvT72neNk2C9nfYcNJ42+BgD7gx5PuR4YP2Q+FyU8hPXm/fMv+dPP+qvrgoQBABWAAqQAJlleY5t9F4dMPpk+blhjguJz281//ixEVVvAmv9y5tcXqz5AEXHo9ARvMvNIyAABimzlEEGNWBD3q3ypVPUQPFgGMAKuKQYf/Y4kxHKZHEDHVZW6prN4A9DBasANMBliwvbrtpgF6C6H1dg9KrNFYAGKQTJHUA+rfcknGr2F+1ZQgIWQWosBpDcsLRLO72P2bu7//77/99y7R+6ufftvtS+efp1+DRJF84tQUa2BVho5QT0dr7JyLqm/8dsKUzcG6DAzCkYWsNTiFvb9nMthEPACFZczz1dB1qcc3VxlrXCiid1meK2RIGQdlow6EAVHTMs9aJsyNgh5O0JavogrCdX0zlwjf3++umyYjVJoSomq9oiM1tO/IQgjL8m0CP2DR4BhGjhk2VTamOKsq4PlkGKH49OMHgUkwHYFfTr/oo0wy6U57OeX2wkpYzsGI1YPhOamgblYo2bLIcF5ilub5IdACA5SFpHnOXI0gF3VrZ/O6nAsAsNYZ1ucXqlpnszGOwqn0LiaMUUGBSU82rTRz1oSYPGXIyYC2bE/WqQ+NtXqh3SuQgiGjpAapdW59s0IpCBjVAFlAy4IpHWICVauqHX+thxrqFcJsHHJkBSAwOeEAQsbWZHFT908vQKiAvz7/piJlWeZFWZT1kLEB71Tn8UEADgLLUpYh+SzDqqAzgZ5fO3t6eCrebfOyjtNZ6xaGbjr3Hz7unj+P2H321fbK6sNyb70gaejCRZadva93mkRUZ4I3Ccx7+un+50cNZpgGYq/fX+AKJHvf5pXTlWxy93qQLFNdYM9a66f3N6iFPI+nYiT+mL8c/dt2E4yZ5zfovXeEUsIicTV3j+2bN/Hrrw+A5XW56MFwBJFpmiQsTpiTGmMYdBlQNjbfreK3O95r/kHJQIxzN/aPrw+QLGzS0/mwxOmiWktcBHW7vglhsITvnBm6HC1nSxr7sFjrWuNWPnI6TY+V80tWiQ/L9852wTUZeJ9CKDWOIGRbhoPKYk12iSUG+f7tX1zKOntcwgHwNmllN1mMuGSbqlpDHM0YkxkOS+Zu8htux2GPir89n9a5SZEDL5h5oFFNmxUYI2d+XEJWJViVHtGYXZYUzmG6ypqqyTL0Xx+/r/JqXeZ0c3v8/M3ZHcO3ssizrOqHw7f5c2bbiQ82N5nbqJ44ZLt8e5Gv8ZZAwDrY6mkwAVdv8ry4dNZ7X6jKMo/9OPjQNTaf0JnD2V1c0g+Z/rOYxKbwCwdisQTGV2HuzTbXx5cDVw72IXfn4yl1HW1Qdav6kLj6w6e3Bt350HEC67xdt6rp1PUKFCkGzMjMiRDfvZk7OJrglKbl7D9efzrtYO1Pv96lzFuP5tTH/BkTGbIedQhJOmMvLUfF+7Jst//h4+2Hf/tDXfw7AJtCH6EQb9IZUoKUpKrqn/70JwO9S4Maicvx+T//n4+HJwihyArvqvl8LDZ184d3YAS+HAbG3BiiS8jIAbsM5jiARuHvGGOdV854AADdA1fMOgrkeWHA7OEVQTdwmReQwxYgBElhfPQ+A/QgBeSitMfpALF/ODza7vXdj5+2pjwZq6AASOoACcnN4DyowsJy9rkFQIAcjg+Q30CbQXgXysmL++l//vP3//uX+/33X/7yz9XP7pnv9weGFVboKuOdqWZKJaecTLe+cMaWYeYkNsuHpd/PoTMOj3uEtAEsEO8A/pii+fnP8hIh7JFoOhzP00vjzZRd83zKluSyEPMSTf6ytJURO5cOXqjeDt+ZwKpiWPh4YgR3GpXQEJqpH2GSrIKpyfzEyKLAghAVZ0tWvSk8AaAGWRzo0g/ZZUKLz0zIy2Kt8y5p5qyQAo7nwebEdF5V61N50CQyMsckm0Mp20wFOQEizhgR3bjmzPqRyPhV1nJ69ui16jWsOcrjt++aEiH9+NOPGy61632IydAjL9Oyz67eLNaNy2vhLjPTJlULm7IfUXDENNGUTufRhu31mohYpSmbgTNgJhU1ClFAxQG+/XCLwzhPfV0WIihoCUhAT9Lt93yt6BQQoACD4CRHUH/unoo8t7u1nHqnFgXAJgzIErO2zWJIRTIG0FppNwiRJfAQuQU+s0jMfLHNK1U2vhbRE4D1GYFYEPOBKizmvBBz8jFhu5vwiyQWQX1K5fsXlzbvzQZl19l+EF4odwiPZ82HtCFv35bG1N+/vJBUd7wHhCLb7W5+OoYjaT71NES+3L4tVx+oLML+eOrOIYUtEVNbZruf2z8i4l/K/n32zpX6T//w/7nhCP8/mv5rabdsSc/DMnOY6efnfr9s1arae/fubrRg2AAIigBBQiClkCJ0pJvQge5NZwwdKRAhAjRNAG22qVp+/f6z0w6XqYMCzsYdvJkjnnzed9/54RNDQNXobnD0BxR1lXiYVyqbX9CMrOcwhzHIaoK5+OPDVwL1NodsEryf+0Kzomn71I/DcnMGKXLOpjhbcuZMKtW6tPr+dIAotMzTGCRJ8FKtra0LWtiF7J5v43phWMpPMW6omZOPTgEt4VrzNM5+9mgluaAlcUTpIE2USOD0bZ7j4WiCLW4u3yyW/Um3a5OPJqbZBUkpncZDkJOG1iIoleCumxvoTmN9sTxfLMdDZjIrl/DSKhfLR4HaXO/UGLc+07xuzgTk2/0pjE+oGqK+37SSPAVKIe7Dcw25QlneXG2M3j9sAh+/3v3cFbMhtdAl68kYkjQxxO/yM4tF8h3tnmFE/dPuAyEW1l49K3yd7Z66tsQxjDG6uswga2EamRAZ1d09qDekwFbsU4xdlteBUyRKAKl0iIt3JolEqm2lEHqbndmVyg1sn5Ve+MDjOByPaVGSUS0DHbuh933RVjkYSqEfiyR7VVZxuk8mD17N3ZM5qg1EdfZ2s7G6LLQ1ti9iTKCVsDz7px+z8n5lFZvbrw+LanVz9vK7d39ZFEuAGoC1LTi4OBe6hjiKISJ6LRG85i+fvlyfnX378NdPx/1VOekrSF+6YRindpynUba76Xlw7Tqv29XiBdQFAAjEE/gb0yRQDraZzRQoAA2AkFUgTEiGeeg6m4UczCSzZFuCIcmIkseYYkjj4FIMm1ULiGjXEDzwBOz70/1Pf7P79Z/86cIUDOoEVhCc65J6EY8PtDgjIBBMQJK8VhnnbX/ojJh5GGatr1VYXZ//q//u//TTv/kff/f0ZamuvDyezudrvp7QpROI14PhRvAASh0PrPTJ10H3qtsCO2ULnRg1JYYDwEE4ibyP4Tcx7p5/ekyJVO6mgQiy7Jw7VL/YdGJiQhhK1XRAbcy5k7ydoAdVIulWkYFybYeULxc3XTe7iIFyzb0SlG7suwbqIQdJRExyntIDkA7Jakyo49EaI6VlpuRhAWZWfkIkJkIgBto+e2NVcglMzRgvKGeT7ttv8gF4DwfaS6KCyGB4AADS75wim3VqQvBJhMhyQypmHgIV2dli/fz1NnLY/fxzs9AQIRGhUggoCGrITa1k0iIEmIMEpl/aXQQFlZAUjQLOC0uc9tOAIElNW8kuWG5Wq/3jvWN4sWmJCIkxpmHal/llSI5YtnjffQkA8oxAwCQEECFt27unxOlZ+B0nMMSLhg4nKg0mxkZ46hmINcOUJjcUN9cAQzqd+qZuKPdjIo6Rx4iTO53inoEMoanqpakajqdsad+G1ZiYwrx1ZnDbFor5do6lawgzlS3slTD2dOwePQSqi3VWMyKWRVmVZg6u6ybnprI4L/KqXC4yWywWC4vGdl3TLrU2iHVpXRwHhGTWr6dD33NcTNO0Od8sXjWbDQr2nby8fllm6un+MAxd+dj/fFTfZyZ/+3qfDt3dTMpuri6ratW5/sSHBuWev0AEGvnT86fkw1qjggZFd+HkIVmdpxiLwoxyumedUbVCit752ZGpPnQT9F2Z16YL3clvVpnKARF8f8C0z9aX129kjLv320c+pPjSCNYqaAURspwrEFcoEUsyhVngmwDISInRkDb7mbzd3FzcrNYh8c4PeIxT8hScMTHTtl4sfMyGY4wxisRq0RrTsgz7bt6P7qw9uzwzPnjv5j/+9HuVqavv3y7o1fv5p2VTTcMU44wQ8qYVBMYs5CUd+8h0lp/1x0lKcWGepylXujVh3ztTw8ZkhxT9rFlZ1MxPSU9QfWf8aT9/65QAAmoF9MO7H/OsjPtIRyURNeU5ciUpK1qBKalTuYuodErpq/sjEWRaDQ/brNGatIdcjJXQScsXgsddSilkJm9IRVuUTqSxRbHMi/xxVAX71TqrcqsVsKQgSZMRJ3mTn8IcU6y8cyAiHklp4xlIKXVI48JNDlnx1+hfcmCb5b/51dtduj3XV1ovsof7J787W169evX3/9k/+XMDZ7x/JHmAxQIwTFPHqFivIZDSlpQ+HaICai/fttn4fpzeEGeXpfj4TX3rpkvzVTX9sdrw/qys6YZsHBPLfozpwMwI8fniEuGTAlRgSIzwEwmCUEhJ0LjgOAoT1tVlCQIQMZzSiDNGSAlAUFJWFpCfAzCAwP4ONvbd69+45/0fPr//3d/8x+zt92/aZkmyBQPVZQ2wh1qBAcCEswXraE4h+MCc1c4dWdsLF05pzPs7vV63zfW4v50fPs2XpER9E9Enb6JB6U1We47Ki1FaEeWWZIIUZ6X0OrNK6fniovJhiZiK4uOHD0SYUlLNyyYmszbh4I1lXZbH4VhQEdipwIXVnXXIpBTMrjdEUsJTYJH4NqppysnkndApHXOdcfKBfYpcaA0Mxh45oBCyVhknAdKFYCYFYYox6QBCJQtnUSQlk0EhnjItWeZtNLVWCThL4Ni5I/hDqVCrZf6r+Gbi44GnyeoyHeKDnADoXbaml9k4htFFpZVzfg4nXNYNIgdf2KpRpn77avv+w1MIzZGURkRUCV9Sfq+BZYIxs+aS4Rdtme5gsiIsSClq8AqRiI47OQPePz0moNcvN46CikElLoRyDUrPKLHbj1JUHMw0n7r5GCq31OvFhgsyFk0IngYzZFwDCZJNc348fEvp+tsjnq+lxVy8LFfdOCYlmcoS4lc/6ZvNorD7NHHtRC+2cVSEVLHdG9NeZImqAqZJD+NT9IfYXrtTd9jNQl4SXpmXm6wIYTakNUB0X1rPeWbU6fA17DJcLvOL9ury1bvfmITWZou2CWFeNosEKUWZHZRZZnU+gS4AAkck5UNpFCkCAkmpU5iEhVbR+SkzWarKEAMoQ4jrs7eVthr05T+78Lne7x/3/7//b0huZf2n6mKmiUM8P0T1qtFmePqgs6sYP6uU/M+3z8zeErZcxP5Jzi60bXL/LavPReRx9yV0srparauzfkqPt7fexvPylb+/rcuyyjId1I3KszwXJcTspy0fk2ryxNsPH6cYowDgZxA8yWWdSNPuxMuWvWNNXquMCc2LN5lxat5Oz6azmvTLH98YY5E0cVyfnZnga1MgSojh7uH5+fBQlGBUIVqfb96yc+Di9dm1MPiJ20x9253GR5nD+3HqaOaf3vdvXqo3F8bObjf6hmHWhTIEMSEo1Q2HsqugVIwkgImXaNCqXKOxhfXOoxtiUXi3DXtRR9aVSTYvco0x3HUawSoiQC0xiUhi/nT/86vvfyDJnPeSkmVh9xTTFLRTWpPjFEIUUaQ0IxbeKNRUF0UgSt6U7hA7cxzmQRIgQ05mmMbRuPTlmCSgMjk7511R5j5GEgSk2Gi/HcTQ07Cfg4NyULq2LCfvPZGEgaNbnl8eT04EwgJHaWpReWOTB0MlRaWxzwdss8s/vzw7/4t/cnZ+bWABXg/eNKqE6OfoiKSqs8fnbYoHVG9UW6enW+927YYfD58wweJXrYru6f3dw/90//z0c1Ys6fWvrkeoF5ti0UxYKVuYADiW7QI8QAbOQZ1BLvD0i/CPTA2gOOyU0QqRUerqHED2x9tVu9FGcS14DMK8OPtu2t8WixcAESBAdxrnSaOzFWSN+ZNfvbtnO99+/vr8Q/aDpf2z1E00LcIQpSHS43EOTfKzM1WVs0Qe+25m6MYxQwlf3VPb36W0I4BxADUCPEBYcSoJO6Wh5CxJiMIRA4tS07DPsoVCR0oJQeAQraCGE/DCNG+///7Ll88PT6fT8TFlJbjWkq8FxU8IjAqIjACPPiTmlT0fHeMEepFxkhgFUfGb7yCz3fNOa5OSnLQ0wBVSb7MQM8VeUkKlAYg64FIASD0OtIZ1m3/66hDHVdlyjISIRyN0SsVCxEjIg5ge2IhyIxWN7tNEiAobCseoo9YZ6qJcNtP29JxOoFCTpjdnoFQaM9KcUhJZQjqRYKurz/Z41oPUKQwpCZ8RkQJqCQlhSgyQWAe3jahj8GFdE1HHRxnzRColJgaGBEIA6MfHWyGtERMoogo1aCaJLWBPAD1ginmSwUtQIgLWlFBd5SmxpCQ8y2xsBqXNfcoBfK4zKF4tF+n+4+3M2Xa/ublKgD2E0hiduAvDzj9Jl+L1+kEMUmcWL26Qjr0/sbiYJ7pTYey2siNs9TlLTPFyxT7Lm5XdENA0zRJQK3X7sMvPas2QPeXtpl2tVkVevL35rmk26/VlTkuAHEBHsH1whY5d5Fq3VkPxi4YWgGTuMC9RBDAhJOcJhTkURYWokJKAS2xm0nGAPF+fOqQcPDbxtM+NqPK6LZZt9WL93zSRv7q5Fv/+0s993384dfTtqa90U62rcrNaPpayqpcVf/ssiSmcx/BVKWHkEIObeqXNaTvqTFOfDjLv7p85pZv2TZlbXiyJWVI4hVNRq7I/jSGYVpiVrAhYfvd396SJalJ6gcMEiArIjORiWccYnRseD4SQwOVK7ws9jzPHJJkt2koZLSBf+32Zk+r4xN3Y+xfrpc2KlNiWWqvMSwzzKKzvb78uyrNmc7Gxdpljd4unKYDqJQWQJJL8BO49Zj8arKEM4nDRZs0eddjtM0ylpurYhixE4DwvGIRTUoLBRwjwefT2FJMcs9XmN5vF6M69oyCHUpnw4J8RL5BodYmN0R449TLt/cn7hz0X6hXH28oQySL2j7ARkzK0HE6eY0StK2GuZD3VX6Z9oRsSdn7QQJkom0SXtRkLFA7huDQYF9WejzyoSOxdpwgUQSr9KFLHKndK121jK6W6h4PTfSFVPAGkGU3pOzcs15jScCrGgkYOxSxzpdQr/Zvb3cPz1AOf+3b9Y93+/avLiz/7JwA1zPzh2/tF9Tqw6AKS9yH0ZZGd9vd12Z5cojYs8HjI4qJo0I/b+2MRZvGh/5v94++/nh723cl/heb/Wp+vr266ctOrSpHOAY4E5B9muYxyysgEuM/gNQIOgxOW5UIAOCtawLI2OiQBwCTsxq43ODMYSIscB2YATuABJnb9PM+lzk5Tr1U6+zIAMCyysv+l4vazfj5TVRmNMQACV30HugJTrhTmWkno5g5NM42N0WVMA8RpOORuh3WevT7/fr77mnOvbnD9M7JexdouihNA9LMtitiNmCi6XYpB1FRURp8v7MCI9Kge5t4iYj9/XjYX3sZwejRFneUGZwqbVRqHFANkBiUgwmpdp8inb7tA3lRaKqUUeo6AePbj95k1TlhKq1HpxLrIc6OViAXKK3oMjXpKrO2ZRAeej3g/3ZKS68VKIynaApTZCo8Hr4BtS+E0F6rySqIFgDzNVWa4qpFZQddbq4vSS7AYll4Plalwnp77EwBopW+Wr5mxn+LOHTQtgp2uco56XVoLMaGELnXpIe67AxF+b3O9rKEXooT0i+/DMKrcYp5TVRaBpZkFrM2qJgU57QOzh8SCIbEHWhpbKkCtCJEQAAXprG2eukHZqusE6hRqJwcRKhqb6ZDQnuZUCU8CeurA5hnpR0mZD1FUrq198a66/9pP8dTpusX9ly9biCwSJeEC8OV5VlpOKE4wbrfUHNlnWVlD3NqcwqwbJYlYAuWaa/8chPF5BWuB7MSH8RAru8liOR7m71cvr//0xxrMqrVZ1PbtDwAZwMKzd5BlpDsBzZqMLYnUL3gFgAHQkAStB0gxaaNbDZEsMJDOAAQAABTqsqhLHyIxCaE0oAAIoTIrCyACAcBA2Zz9PYDfAvgrauHlawD4zdO2w+l2f9dNh52F6acvu/H0243N16vx4YTpToHSz1s5A1Oq2X2DgDHGt9emuSjvHk+WMCrrGdOx707HTbNIIRmbmazYjUfKs5w2yT5BRsaSznV+s7hUK1IGzxWR7k8UMPnpSJ0+a+urdqlQJDkauE87a0rUdPn2pjtO/eF47LqsKObZ5SpPZ7w4qCONF7h6/XrJqXIxHLuBj9Pqh9V1tVCdDmGOEocDA7tykaYpqWb5NlshCtG9PCAwgbrRtfPTGNxMwrbQo85LzhG992KVndEf+9hUCxH9bX9bm3q1uFpk9eenD93xcNy71qzLC2G36d23KbETQcw4IRxnjUb75wg2ZeX32+mpqJqFqwvo3XzHmOQxhMbpScWIj4K1i2xM3LGaFgmOp9Pdcr0kRAuSkkNGkpwa0GW5v39s1pXgUJGStp7jo8xpvVwomYtYMIwpMQcmRUEScS3Y50rlkZfjWFxWxmJltoljTF7QVqmYk3/ibiHmoNsiN5uzyz9f/3D262VbNACP8PMXuLn54x9+l4gga2JR6GaE0bOGodsTEaSHslnW0nNgGzstMYzH/df3y8tpfzim+uSpK4E+ziH6VG3+XA7lfjjVreXFitk1In1bjv2jxEGvdA4ZAAMsF03HEv8z+4/ME4vZbztJbVE50qouz/3xaxKMZZlJClOHxADusHvwhU1uLNv81B2OhUmJQh8kpdzA5E7ziV+c/WqcT5ADmRpkoqCRME5jmg37GcanwjRenxyr/vBXPO3u+8NLWYY8aiXDR8DvTLKvxY/AaY69Q5AUr3X5IQZTZlZdnFFqF7UPfvv4NV/WRPUG6iGfjMoStUJyfn6DZb0LiY6sW+M4gcJMq5hrGWa2I+hNnll8+2K6S4fdSaer+rU9StClGgpxyjM/wRJ9V4pglJ2PUDIIGI9Z3gdT2/0w7/1WxanQGomVUjnpENw0j8qywBoQBXU1Lz/hoZhKA/akSJVW2xgTWEwcPUDQuETKMAIiaa0u6nZ4nLVWPyx+1NcWyfikU4L1ednk8DRq748ix8+PEx+jqk3dLrGUajK56my2UY4ETwyabIURWWZjEZRWqMa+V4TOZZEnrESFvClYY8mdTxKL1cra8rk/QPAPu1BQWrUEKeOoZLkqYn1yd0mRiC64IrJtY+8PBw49IXlYEZwkyw0RohCovjipYYEx2aI6f/Hd8IeH4+Ee1FtO+rsff0XEP+2fbpqiU/EIPZlVrs/dbhf4tNAasjFNsNZDZQzE+QOHULisrzUFZUUkfnDPpJAfNEmpdPH2xet1s3n38l3ZVjo0kE2ACqD8ZfC3Eizh5IfGli7TwoC/2KsgYvSgcxQHIkgGIUwpVKoggl/SEwABOoAGABIAGZ3+MyedAGoAAggABv/TJmEBAHQnmhavyjlHlRebpiBtzn4c9g8/Gun+mzffPv7emoH2O7Fa5ziDYz9n9QpLCj//fJjn786NKm/8rLvTPWAGppo8+/2tqvRqc9Yfd6R1ABFrEvKTfzz1eyN2VetIvsfuXF98jVwcjst6GaN/2V68uFl0+36+u2+adp4A01Hlap3IJaSzC5hwidXe7ZelJQNH7TBJ6V2CSk/z3v603wk2pzfZGyrbQ9ZMQHKScfsZiHy+ANwtKO1nl5XZPh0nWUQKIMtLw/17Kd/GJO7L8SsXsS3eaMB+HA+V2tDioTturNUIHiFbVLdfZtQLqs9As9TwuvnR+/nh/nHffZbD6/Nr+/gFLxbnBYJQnXo+pFHPYX6vJxXzF29Uf8zr2oz++ePx2bhppVAppFZ7iRI06qxZvorD7A9T+f3Zj/ZPQnTDNKSURCmKjtMExyQVTn3vABIOBnTgOPCTPZlVVpcVxHEKrvdRkMzBHdWESiM2KvQupLS8bL59+5q2gZYQXJADG0Px0k8SEghHeMyzdrDtyt5cn706f2W0gfuvEGU3T8upPzdlLLL6LIGe76eU98E0FDgGiNfmbADcPd6VlbbEED67Yc4yNx27ebtzf7tzD277sOfD9E//wT8/f/PSqLKuTV4VbKACIxAKIMoqgBpgAKg4RR8PhkoBQ4YAaOtmt30szq7bVRnSbLUd9QpAgnckiXO2ec0uWaOn05Z5Tv0ey7VSarFogIPRfl0FgItxdOFpN7nBDx2CfgLWvAfKhWcSkNTqNsKhqCqwbuZnFXh/iu7DYYfIm2lOjqq6AZhrxmnfqflwOF+zpPjpg700z/HG393ZtgZjDsKZ9piFusAUAbNBC4I4EBv3h7BcKZNtOfJhL6TipCSLndKjDnU/CsJQltq5RaZ0tspXzj/EVHhbNOdktsYaYzUaD9s6XEgpHYgkIus1Q6Fw+0C55sOpFzmkFIWLIINVSufm9tuXbuhJqR/efucQhhElHSVPLgW2HeWLgjLBzgdOTGRN9Nu6yDdFCpAGIbsKi4CR0wCnyyzLXhTjrd5JJxrTqqkYgnf7b++1VVcv18bGtITr9CpNTBMC9AAoqVfaijbIAikygFWWSLmkRecG9iKAJEWrGLk/bOvasCx9HIwGUBiA0Rit1KxKio/7A/M0rauKtQUtR6gglXmODoBqVtOUIaGxRJppTmwY08wBKxBCAOFSnr9tJ5cyk53l612cN63qT6VGFYXjdvel2jBFjnvVj5JVmZUUERnkGMZiWEAtaWTkV6SSbw/ZzIdkNirDDAjlG9mqPluc/fDiz0Xo3fl3sCyBcZiPegbxLn3awdJk3TAuFy0S2xkluKxQBCHKwJjxUSGyuxNVKWWatEvQpuMh2pyMgqwEAACXAAVGghIA4i+iLYBfKjEHgApgBvjlFh7+UwBAhuCbDQAkSZOboi5hHiqdF1V7VZ+9ffnGuf549r8dPzz33Z7cAHkFSjCrrSqXFamlScmP3RhGp89uJJLfPdbtqs1r7zzachz7i/LiaT5k5oVP90nZYnV5G73W+ZpeziFWzjGATy6IM+CyqiFJ4SmocayyrHcGpjwiBU65B0CZ79+n3KIlK3MWdIQ0UqAwC49VKlMBMdp7xdCr6dnxYyyyNapmoZQbuhkGa2gFMhbeQiloHWQi6JY2BFehUlQap+RysUL9zQMxewFCEmm0Wkzp29T5T90XYaw2Ny/PFyHFdHSSOUR1dX0ZnR3drfBb4CRYReyf/aGLQ4iDtuY7Uirx9jZehNhPnNmmcCc+e/kKVUj9ffoCUOWkr1gHVvkIsZJzpUtSpMjIMCceyDSQa4LiGI2QqtfLMumdGVIUQiJRoY5NvvK9i1E3gGl2UutN9WrCzhpa1mUs1qdp33sHhClI2oswxSTexyuzELC7cZ/2lC+eL19uXrz+R7/91RKA4k+/U27CELOuH0t+f/e5qZdNuRyDISQvXdqmkEKVFfPk3KC8G6u8RYLxSN7d9aenMHbp50768OHjl/fvH4+XL/4f//2ftv/gGqqi6hvmDnu3r3WWopKUaQtAACeAG4n7095fXF0ADP9pzpkCAvBI2coQRI7zksRNj2HqAdiucoCp7x69d0opTZirRaZonAYhhjQVTfSzJdqX2eZss/5yePi6312trwUghqTrPM7bwKluCDkd7ZC6w6QE1ROxOa9qO5Q/uUkIlV4gWoWHYY9GDGn9qqqdm/Y3L1syxpij1q9evTDT/LU70GFPN6+AKEROCNpkDpIGZiN77VCVwsms2pXJemEjidyMpTURBaBVWVfGAeAG4JG11lrpyWcWkEEgpW+Aa0ggrEwMS60ZANCGIY7xyCLOLVgORFgu16bIkNYvg83LdEwRzs/WSKgIMP3ww9VuZ3iYtNZDjJSGMNqkR4RlVbL4bnLTqsq0wgKZs1AOjqosMS8IZbl0p/icdjrLAMl/47TikFJMYgTa+iyX9pYfR8D93WNB1olcc4a2RgSMEwJwwj1Hk+WgqgKJ8wFmRUZbEBMtBrKLAlmlBKOIQaXBpMQOm0yJqXmD5wrM8fiNFisUZAE3CiJbpcAjgSRhRFCkBMBz8imq5ZI5pnRHvq5Crih6rUVonFykOSsuyrIAjp/ffwKVFChAMVQyvIX2FsaYCkm6Cp1n8UlYgXwYZ3kAvDZnElpcHPL9TXxpZXjgKc/KZd78+rtfrZsGPQEk2PYjz91pBnJKBOOkdwsNyGPf4Zxhc+oPqKtykx/60U192yJShbsOrkbu1NOYrB3LyKxIOgzlKJiKVhRsPPgT5AgkAiLAHgBmUBlqHAEIIQkohF/OajSA/c9JIMDGZH4IMa9IF+MR5iU0qi7Kbf2r316+zb/+z/+zG7sQw47n9RqO7XVrTymr5+kLyvL8xfdVkX17PHCNi+VCRcUJELGqGxGag+isr/N1nM7OrJw+3qGYqqkgcJHZxc2ytHl43hIBpwQA67fv8olo3J0YUGxaak4+uRimPiWp1SwAUXNhwjDZBqqOkaXecj7QLdzSqext+YpWyt3C21fu+WcvKHfxvja0IEuZIolPUPhsKkPt0B4SAFLZ9UVTgEDLk1IrnXbgaU7ydZ7T/nlss9EN2Fz8cHn5vA8Xq+JrN+C4V5IaUZQVpORNsfzotv3Y1VUl6LquQyLEXOtLjW9OjEP8kgCjlDka5zObWnPqHsPJHzgpMtLRa6gWVTRjYRYipf7p/SejCYhdCKDtsq0j++MOA6oA0OpCscM0MuwE1pTUItUxSMImxNMBQWzW73ZZu0CEcZ6bpvIJM1Nl1rz5rpim+Si8IfUlfXE/x+5Wb6fPHOXFzZs/++6//Pv/6J2pGgCf5lk4RfHcd2nsfLebBlc22RhHDTJPqK1wIAm6K3TqB06Qh1wDGutxdxykH/rx+PjQ//QI5+H2dotZ/n/5x//lzY+/9to8nISGT5qUUmKKVQDsU3+hmzg/T3NslqiybHN2BmAYinE6JYmzG7KiAn46HrTzKeVmSQjRbRaFMYYTsOuHY4eUWClJISKetpMggEaWKLswgeS5Xl8urLYv2+ufP357Sss3b8qnOKEIA2+WK4AgKIvF0PdQ5PncZxg7m120y0E9fOEiI11CpKpop0A3l8uv2CriT58/nY6HQtsffyzWF2jMiH4yBhMpQ0NV6/3dLjsvFdp1u9KqsdZCUj07IbO25uh9evpmtFKg3XbqyqhXrzYsjSrvSX0RwbXV+BrjKRACEQDxxwR0IKAB7qNnSCIpaU1ICRVlaoNEm+bFcqVj8DE6UMjG3w7HFCCeX+69Ez8FS2nss8LmCoPvB3E3czW1c8IiVzq4MfBci7RZNK0zAaq8EJBcxPsTk2Lp56BQzrQakyCRzowQgFLm5ZvvENTdlztA7MXYfBVlQDIsCVzrVMRoFEkogdDATL4gAY6nMI+w2ChmDCLIyaJV2mRa92q4fPFGCO4oCYsBOEcJcRaMuzHsdp/Ui7NKbDxsSS3al3koshDD0TogzlCB4IQNTiNPTpQCOAecRuwXSnkndYKJ3CW9yK/AP3iFmJFZ1Wcf0hPMifmBNOJsk2bvqivIumVVuHmO8AhP8YMA4vdaI69B6JpfqIi3u74A/eObyxevXjb52kRl5gDkOaQYxkKzOtKUjZUllZJJ3TiaMabsKFSxl4NUl43m/f4JlpuYPjyewlJBUuB21uX5CVC7wVJTZVYgQFwIpRhjpnsie0TSMyH3KYi0VEJCoAky8pPWKpEdkBsgARCICAkRjMKmLRQAAAxLEIAJEMAkuFjY9et/UoEEVOo0/NGF2tn/VcbjNMf3Hw6X58vE/P6P7wHEblY6Qy9oURsCpelw2kdmlZFImofPnz9oTWZ9fb4wVYAAIIAxHD4CG2FyzyFVpckU7J92DM7Yae6qw0KJloSc51hyVA3LCVNwPR5hjrMHrpigzOQaX+3XXqLY0peR8g2nyR/ilyNKI2n5QocMCGB0baUypSWZWmmfndrxtA8q2Rgzn7KZt+o5PE5KFa0PfYp6s2LroMVS5bvkDrsv49FoUtpQnmmN4j592rG/ef398hvz6VllmeBYlnoYR9WenRVagyxadTa8/ar1RYRAhlBbc3G+/enb6L1WdF1W68V37CQqN3Afhzhkjjlp8zK/glzScPqSoBqxCHoCfQb90/bxyaVBVorUEkFasVYVnCSGeTiprBWDpPM8QxVIUkzfTknrQAqDcC5wIn3afRyX+kr/sH+H7tO39sWfnCv8P/6X//jVxbWpFtvP83T8uRnukwQtgQ7zoPv+sTo+3/961axW1advkyl9mrwCQAYZBoYQYmFTSIn8HLs4Pz4yfA6Hh93tH7/G/4j/4ZD+7//qX/63/8//V3H18ncx6Zx/GGu43PynXgBg4DAeB1RaADiiD3leMMDcjUeRWOS1vmqVL8buHiQZg5elmYYh87uUA6RmHLyfnVaw3KyHw2mWOQmXbb5oNLY1OBTfNfvnh+fTUd0vNi+1LRBw3H+Fl6sUJ8y8nA4MlsAIPVmg9XUNYOgQg8OUurxuX52qAFZLVhXZslq4/W6ebff5p58soaAxRiutNDZlLXLqgWyOMxVz7xFoynU94yD9IYamsOCdAxWPRxTaG+39zMwmyzmF79/dRNEphlOYv6SOeV8OFV5+z2ca8TJgukI1/vY3myQoMLpRTqUoU5dBkEkhAhOhUsqFGZIexy4MvWQxqAiDjklhowT3HjJLBH2frFVI0JSXnb2f50MaWyoBNQScd+NK9dgiLTXphgoPB4+krFbRh4PluhKZqG7iMCEQFDdiPPMkREsMMA9JoS2aVhByc9EaGPbeH3bcSJIIIkIkZAiFKxoDlXowSlHDktlC06yU2x88wnpZE6vV+ZVPcifIT19FOBAcjKTOg2ZIDMSBv57JWwGOYTeMiyxXnGI5iNamL0ccLJeyAPTTSFplRs08I2Jrq1m5gG6F63wh4TDfPn8O0ZU003r1xjaqMEJrAX2Eg4pgQAQD887GTRWn+UjWFqRxopetWEUEgo+jKbl68+Llzc13m+ZMb+ckMD27MH+NzL70jKKOejLjsAORv8vztTK6F4/rSCk/7D7mmjmJmb7un4M7PELkIeQwraVwbkyRhRQyd4JvV2eFd560HA77anPJwNwF1ynUO5tlevIDhcouWuLjEKBOhkNi57N1BM4gCCSBBKycH0ddg9L6F8sugIIFwwIAQN1MMGY8NvVvFlCulsWp/3T/u1tr9vcP+5hcCmjz7Pvz62/poHL7As84hph8kfdlyptVe3v3LfhRlFKGkj8+744hQUbUgP7q8EXB31xSUVqSzercfP/2LFH7mO7u/3jk44bPHM5CDrDU6cgH1zVWtE2Frv3lnAYgnJRW2phF4URoTpERRD7ef5n98NaabEPaojYKEDBgSqC0RmVwTChDWQIzy+FEAgpvVjjNJfu2rKJOk1OF8QliHw7NexpJv7Bt+VpNx5IY+xFyp9fSxpSMD96hMa0aOTMAUFc587B9CjrypzP1m6BvlGJOxyhNJaDNavNj/fMffw8gzevXYeK7rl+0mTba5cvqjLpyebUSRnkMYTj2+3o90CPNmS1mKtZ+PihdqRAoIopAsxw6yjNQCuo1YGb6rlPAAmxUVVYFNqRYdUqIolH6hVVc/D0OsA99+PKUm+Iv/vSHf/D9u+Viqebkfnc/hW8LP8PKuuHR9aNo343d/tP7fYJ0/k+874YpaRclK+JA3s852y7t8tynTHkXNcQTj2GaTofT/veHv3oaQoBy2f74J3+pm+UfXczL5Rso4eoXegGc67MsZ1BUlBw8KQzp6OPsB2yr0pqcUZJSw9OdptzFoDBdLBZh9zCnGJnJc5E9Bp+IKqOy493X6Ofzq0oVLZgWZO+f+xRTljm9eL3R0+0f3hflwm5eLBetT3D7lSMva5iGJiOIALx/5s1ZBfTLcWmeKDkoy7ZpxnaXPXNSMB/X9uVTPH358EUaeL05g2LLUiteKjUShZSyqo4zDoDhcGQlGgSz5dncdyalbhhWtsipPJF/aa1+uYkpgoDWxBwSB4GAoFtdIDUDXkg+cmJEplOYFvSo4vdl473jxIVeUpMJIDuVJMXoTtvbZlOzP81PUchMKMoiq1QZ8roqyHYMCFAYMlq4oDTj2GDLoL5/s3o4vb//sn8arC0RVTRxs1oqGrRCUqMIlSR6CUBZOjoDPvBVhOTjPFMFg4TD/iH0RKO2Bud6zpM2mdY2Mk81di5N3ReBRLtDhbhW67QgRMoqtT8OmseQlIAgsj+dTm2dkeIQWeqHkfnwiKuLs9zQcEK0YqJWumzO98NPdL54jS1LZI4K9NnFzePd18eHD+Xrd62uJd4SKh0MAgkw2kwDAPAIQCzA7J+OCBmQKIUxeoxyqTINgNTI8xQgqlf246fbxY+ZCcEwdPs0pcSCT+E+gmTG1uusOLvOunTsZVEVw3ha5eX68vrv/enfy5VO73/+HNKVD66fOBTCLfYphTCH4CVRUgTVkCIpNqLc3KXLY7997FcNKiWh4ynPupSQ+dZ9Dt9UKq0p2nadFO1FPW6HkzI3jd5uH6JPuWonf3TTsNycAWDMyUjSgjE6RCVxn/AFwSggBBKgzyAxxxP1mStjQKu14C+fQsD/ua1pD1ABBC4IlYGMYDBYbprv87/49ceDHI5Pp27rVP+b3/46zzV8fFDnbBQlFJEklOmzrNs/3d1/NNb8+MNboBEBk3Q450ZpFLw0TO3VBSdC8t5/3H82s7qp10zFptk8jFuKjucUA1jsHLt7SQUXgplx2YxOTUgqCOOUIzwPTZsLEHH0+6OW0NRlc74U2yEBc4yJFy08e4jC6Aan9TxLzpIbJeNo0BhloO9pSk0ppSLM8kyZjNR93d5UyxDdjvbj+DudxsBYJaR7o9etSDbvbsfgr4xI22qcum6OEeqyW7U3Ov7sv7Yf9ZmRVud5ZuKZGicXTm29OPvx12sCArmXsV3TlNkYiXH47CGl7ndzENARPksZagJHN4m3KSStrZcMuCzAYdojoDp2cZjH8jURH/1cJvZpp8RMc8dUs0IdmgVpBhxlOsjBqAuL5Xz4ojs8u7i5uXr5T3/zJ3W1gENM3+5oPNT+NOu+fwrgn5X30iX+NoXB6arMTeZ94BDJTjQPLnpQME/CbTC2MCnGFE/ipjRl5bDfHf/q+fC4hcDwl3//h+//h39JdZu5xhfUiTt0riUUDoCwO0hdJ0LIqjY6iimFNAGpAyAS7tOjuRdDkOKskF0x91MsyhMerdUQfNp3yZp6nIcYY6PmBZDKHITGPf5Pu7/dj96fFEJe1/rj+voloNx//nLuTWUXYdzPY2dXeQ4KAUASIG7OfmkuIMCI3xXw7Xj38EH3wJc6bXWel9+etz++fDvyxfDl6YfL12i59+HRHG1/D1EtmjolCI6iSJTM2FNKxIsQA0/9bEAp1iTWumhJUFz0jjkyH4M7kWKHtOttuSLmBnCT+2FG45PPJ1BxTFCMMP/cH/LgPFAQBUKKVAjx7dWL8PH9kWB19YIP+zpjKQN0kArkEpkrCeCBA4mAjBAgdqxTyq0BuA14WbwoL5btwC4cL979uFA2pUhpMnSr1AJAMyGt6wRBEntLzlNJKq+q7TFwmglOAL3SotTyzWpBViFCKnnmxIXo6JON333/Win9+PEjCwggaEo6pSRiGkr7Yr3AELuuA4ELQPH+UNVZVsdDjChrXRkSsmaxuulVfEmWRLrr5RoVAcw8n8KpUZtlXXbV9xy/pbHTZZuIJoSc2pkmHAbXLNCaKfqS9iVBoc66fl8maElB1EJj2k8xjtC2NCQFpoOD3+aG7DW9k3VA6RZyTCGxZ3r7IjIBoiBMRxtm0nQ/z77OzK+qm5KyME4ppf7+Pg9xNkZcYH8I/pPz3qWLKAoR8dykLe1nH8NcoT7gUM15N3XLz39QFyXI0B9ydepYcNCrEvISin3E94dd22LZLJoSdnfzpLULQuJz3Q/dyRBYCYmjdaJstts/hIDL5euUrY0bUhZnhQsYntznXKlRYT5YqDwXTRRAAGTQAqj+0xsIBhZxQMUv2VkAvACYK5v/i//6fziddv/uf/03p34HRasRF8V6o1ZIiZQnDSgE4n/+8ncs/vVrzeBwfqTCEmjVMPdeaxsIT/FDSnWgKmCkrArHw15PKyrUeVv+7j7APUIlXnF9ldLzIg0DX8v4BEisIIjYyFp8VEs6zxTzFCFOB2MhC3RmLMoIXqlSBMFo61INKfh5zhZF1ZMXB1CE0A0uLBOr54djQPCiB1I15Fp/5q94O5P4j/k376MttGBqOcakuILRgRqOqRR/6zNloMqQvbA/d5DZDM5fC4C2pAzgxVJbne+jYdzHATJtJ1/w6fBkETMX5TAU5wAjttZGOm0fY0V+fmBIiIIVP6cg4tViuTDt/imFKTvfeEwudN2iaoxeWMUXbfbxZw9xV2w2TXuZMIyz72JC0pxcb4xWGvEsh7NXQoKyf/X9ZZb909/+09XyAsDAh+00ft7PQyZRifN4MOBFPH9z4eDGfvgSY960GNWnnyYgFaEC8SkyrPW4e7wKVQa+KAub22H85uYYZudmv9+Pc4Lzl4t//Kv/ob16ceA2jWjoeAicUPfORO60yQytRvSFKB/Fp6QIEp6Fak7i4vGjpKiqc+56zBQIt2mACIzBJnHTRMYQ6SN2Fg1i8Cm1mwwQDg9/9Yf/8W/+6uHxN+vN9V++bPeRZw9P3y4g/zqN9tHlr6vTJEofS5rHzmhgVjKzN0kUPQqSTyKCJwNFU9zPbmkrlU2J+JxLjmlTrJDx/cdPsBbHc7Yx1GTQ6UG1LXEfD4lF1JThuVF2Pz8/DwdhMNhMvkiDx6Ivi8LkNgim3WcmwSa5mY3DDDTuDKtZ5f1ucEW+IBYlQCBRUgUxb7ppUFc6ew6UWJM0ZwyRIhFdRc/eaYPMyF3ggmKOgzBC5ywgKMQF4jIDVLAcoSdggE4yufdfX9vv7Xdr/BLO82JAvbvvq9V8Yc4QyUViMJry0XuT4P6QwozlCiPKWZ3HIlK6hhgL5dAolWc9RA2qwnNWNCscdp8QOLz8dUlw/vZqODzd73fpOb16+x2QUAREXRM6Jfl6Mex3qMAi+dsnbTtGa6y+uiwen7bXtkjB97fPX2t5ubgyd2NalKpWcuIyNS5Njri9Xg4PWW1yo9RxLyYziN4qYlqlznPGdQhxzrME/thDScvqJSeklVDMQG0lEgJAEqADG7ukS84KRZmQMUpjGbkQ0AuRvNvtxlF3vtT6OTcFsv7R/pAv9OaHRfqYTn/97xOZ4evnue8VoRYkwBBCTACgDyEcRue+iGCelXLZ1L6gMDfsh1Vu8mZBio5PcyHbD5N3UUwIR85sdm8yBWSBL8b+6E51oMM0rmH2Lsx7RW4+GZVNg5rGb4FVf+QkqG0W5qnvlalMXmVtU01Gbxzaunf7HNpg+kq4HzuloCCjaIEAQHGWFCEKaVIKGKr/vBVkABkAVNmr6vzqX/+r8w9Pt4+f/rjvt2E00nTarFMapuf303OP58BxviYgOY/uCZ4mvOanOzNnAAlSHQQhVpGkIuAEJre5z/e747PyzypdcpqQrhDdx/CR92LN2eLyZTIMaUVzReTwomRIsD9k8ShRTifWHBTJwhoS1GhwAKmAJSJCINNPE4ihtgnUO18aP1sM1EPtHFOpVXtVl42l1OHHjx+SIDVYXCwk7qcwuDinSW+EFS1iGmYXTjOuTKYTJYQFM3LGu3mIaqkNvd5wIuGkfzyz9vyVEVB83N5HKBoqLE/FcDpGN0MwyqMuz5Qx4f5Rf/dDQTKuz3NUB9aQCEgrehjpXjuiUCq7bsAlFWBPQOOyqU3bgHi3PT7/fOvjCSSd+LFMx24uy6xRjW7YHlliuie4DsGx83+c9mtavbp+edNerVqE09f5y+502hNeON+n5COP2E+mTN7P4AKKhNl347QuFwkBQWLwmRl2zyEGp4hWiMjRO2+0gMz7R85U/vAsY+tZVL2Ut7/577/77/80lsX26SPz1cu1VblWOrPKPo1tXVIB8PV2z+2VTii+V3mThkiN1CntqL0sCkKYKmMAlRiRBmUMbiDFtmREfNzqbkzKWkp8tmrGYNztp/t/d3+c5lcrxZvouz5MpI2WkxvBlTrz66GQVmkwNPt5FtHFogGKOKNPlBeZyhrPSBMo8oBm9ltb0rJbP52OKi9kB3mTv1tu/ub5i7ufzHdG44Kg9WUSwd3hoNZ5y6sETCIppRbWqjXdo4kcNgs2yup8XWiQYTjefZumtCzYi1KKOUaMY8KwOKOYVudoOUCfojfKuIhe27KP6PQO+EJFNAF5Xccw++Q0RL8N7uzre5XlkkWBmBCHoGMGpNAog3AG4DAN2CdfTsCqIkVckdK9jls++K4jUgfEcb+V+ZR6czApwTBncSk3AIxEwkmBBgta6zEkowyHYA2JewTKyPMoA+lsX9bLkDJRS03f1kZQ704fnoZgS/v2xfe2+9vPLGQQMTr5aqV9eH4YxKsYxbn70/bNm9dvXrc2v1Ano4JRGuK4a2cFpZ3IWzH9/llSXOSNzLGWrIuekIAZe04xHQ/Pq9dv86xIEqMPLBAhpTRknOdKWVLqSECuBcN8Qkl0yBERkQHATJmDx3vRl3S++lWdeRucnx5iLw+1QZzikrcpUerdixe//ngIgGLtNadv5XeZeaDjfjfvh3C4x/Way5OjwX8c2YV0VfgPHdO1be+Hjj8PLqskSWx9cdgHWYTlahJlPODD6SjH7tvXAxjLaOpyKaYppbjJtcviy/IGrtQ8+MzJgZcSA7JPcQgzFNkOBjtPD8OIF2e5XeWVUlBsYtB5oaCxMEyPnz6Zy7PieEzOFDLIzCHqkKS0OUdJyUcHGBWgQeVEAQSntAEe9lS1AOo/Y6aBAclEdX19VdurV/H2/cc//P64vbuGXXSjnsvsBU6xqGGobhRASMOOViomyTfReh01ZVl2C4FAIWrvZ88MAD6vrouMBIMP/OlcEm9jH1NUWr9ar26tNChEBFnoRYFwIZKQiIgTk6KbzRoBeb/rUArKgmYpMwUBJM6RQunn1GJSmUdITDpLs5MYtDU5chhOYRhFLRX0xuZX3GTnbcJxX9iY3MTvQ/B3KbRpMCZLYlaZIsb4NY55SbrM9gkVt8qoZQayJCJC0Xb1VmLibnuIWnzIVBEZT5G5T7TY5JsMkfO5P6UkmUmH+x6uoeijvrFDnzECYs8XBHniGB4Ph/oxhnytVBlZ1wtdRA6TO/TbaZL5FhV+9/b15PYuNBr5abq3qhxZcx+ll/TmfAlqSvjD9Z+8e/Hu5uKa+/D5D08wDuU0eW08PjLH7OBnPWoJTia/G7kPFIhRdICS3mqdVShP2gsMJve2pijd0IdkM23lKksu8Tzv5uHZT88xYr1e5ar+7tevLr///u5QJ+oNkQA9BpLZLyvtjzCXuHt4SjGeZVprdZpqm+tIQ3SBMATcCV5v07Cb33+X2RDPIWy1DCZTlIwLeva0fXygvFZVbUwaPWnSh66dFrMZGEyrzWK8BY1jU1XD3PfjcFgsz9Ms8x4hksyRwZoKQbzzwbNSebfL69UoVKVKSxWpUwK7u/tlmUM4+jYto54xxpd//tv0b7d/PXrNJkrHdz20i4W2E9kMFIG6vb+ryryuGgKllVlfEE4FGkRgHoNXzHffnqPPIPKmyXYd16CJFk2cPvtDsO0FDWEKiC2ZU9dLMgV6F3dKsqnsTO71aF2UITI8fKuvX6QUFiiSOISZz1PCBG5Rih6hUKTQVCCVHI5JJFIEXZ0xoKRHfgJGFiazAGTQWhOZ7Jyzsc4L4smwXqlrEXoEOQPtICyI1t+904ri3V1a5FmtYDpIrkLX4aIsynKkKIiE5EM4Ru/VqWjeKb8HBGINyOb1i83+q9KwD8fQFggDHrRhd70ucIpQYvz0XoGWd6tw/PrAcHF62xKlTQNDv6FA9Zmgsg+P4GJyPqRoFQ39iUlVUqQUEGn/tCdFBnOCdkq44BT1qsQwnHiENNOwpJby9YjzwuQKFKSSr0389NXHp0HR6+wG365C5J9//qay8zJjc7pI5oiInGVcVxyQFAIK8OrVK7V/epdN5r37af133k8j9Cd++OrH2a/GcBgxcate6EuTdjpTWVXrRV3lNo8FB/SYIGvqorKgVTyked723RZHWZ6t6LQ0XAx6nhG5zCY//t380+J48ZTcK2X26pQS7oa+bKjKdBiSMU5ntFq+jqSzYQBBq61KklmtEBGroozVMHZEDYCcvD8yLR+7Pguhs9nrrM5UVAp2glxqHWMYC01pYlEBTl5WE+YVgAaICWAAozAU7Rus9M3q+5tf993t4eF//+v/8MkG0fUiPX5+HGPzVanrbxqR+G0KJCCSJaZu5joKZqZIEAL4mO4l5iawxxA+PW3d/pgkAYmw0W++/65URotPE/BScIsoMGZHQVIoKdXrRXQ2Z0LkXTxx5wGQLhgceqDeD3kJjKYmxNljrQbhFIMLZ3XlnnvSwrZm6QHE6tmsv/vxGI/j3jn/6e6jKA+vbhBfs+yFd4cT6CW2EU4z1GwiGOwmXLYi7BhLpUjl0B+wvQAijc9fg0tHU4Cybbmck5BSoGBZ2MLQkaGInJJm0aYscEbWI53y2IoEgwIDA4EGhTTTcnO2SrVDX44ZXjptWKbP8SlKkLft6ut2O2l8+PgTNVjnm7zJmtQKAyjVrWenUjjMd7f3hSni2980bfvt/sBhp+apJRnbzG93U5wVi1lmtYif1BgdGfDtnBLE8SXlp7xoUsLbQw/kUjtFP2xsVdjltnySo7d1s1d2nE5zz8MxSTAppDJfXrz7r3/1D/4PB2kzBRxI83g8EA2csjjOlyDPAq9eXC6/3R6t1t7Nw+lrWr6O8X781s3nbaGh658MpN9UFQgEYWQ/7LOVT2Pw+6fT8xxIvWr+5MoWJT9xvzsR7gWpbv/sw8d/Vz2MVKGtllzMrtlMKvzx/i5XhhdVLPKLurZl4dMwjKdTH5MMENuU/DRsT7jU4c5svrsoAd6Vn/HH0+EI0HI6OheiOSVm/3zc/MW79d996LadaV7beqD2rAWo8hYlCaXMVk11Y+zEnAYycoRaAzKwT+n5kywuOHCBuCZFQfXNAiAsupmYRVh1UlxgnAK2TYaK575ZUIMyhVaoZ4wxrWLulZOCWC6vY4rdONSZkfOL/ssnmRpG7TmhxiB76hqhGdIJwMJq3aRocNI4DHLQhIJKgXpJL761yM09i/B8jxE32vmuZxRjDALl4QPBdffl6z7G9vZL1NgnXLiDBRqVYvG4kKiVo730RXGeW8L98XRMx02+0sNp9DHLMgXycP+t3B0WL4g5jqf9WXMGYvM2Z45EJHURw91XNwuZ8uefgyAnWPRzvhSJX6VqEhTSn1CpTCm5H2VREWCStCkXPOjU8pV6qYAKsJxERE4uhvR8QDAMrNUMQ0PqfLmBpjVFvt6V936P4K8lR20PkOoYlsUr82bpvslHu/eRlzciY+JVtKmcZUhQz6cpFOpTr3zcKb3226kb3g/m3YLF2hu69DLa/rnY/+//2x+/nKahh8i/3h/V2Us9pWO8s3meK9t0k/QvFD0NRa3wbH/qdo8fd0qfGwt1WReZyvItxGY/HNoJpnnLZlAuKy53djRZMUgKlEeOisSCED71ey/UFHUG6vD4+VZIaa1fPDzczrFaXz0ffJHLX7z9MyKs+ahWK7Uc+08PcR9YZJrG4A8pXrTrsyQTaECkxL5MqbcjAjBowE0CYAGHEBGyHEggRdgZMqAY1k1zWjT/3W77b+53X9Npd9+VVb7IX746vP+pEIivwsPPPxd14Vv0No/wgYWVaRMyLtfaviiUPnbu4+lzmkZJrJX9bvX660jnZ3DHovaigaqFZpY1qkTt0Ahwbceu5EAoGP23/R1zAGHPuMuSwiyPwNZJYDJUpCIyKohTCBK5TIWa9SgnxQuMpV6VNivVsEfGaPhbfNC3lcgbrndpILKhxavZXCX4iB4iymCSHUyICckO47zGnLX1601pN4fQN0LAqD8OfIZLsRmaPElmtM5sVgOnOIMk2O2DJaP1uTauWEk4EYoTlCRdVszBZTbXO5Cn6cqU2bmxg8riEBVSknS8haAIBTXDsnLbR43m6NgPcavvs0IJOD5PSnKVV+GZrJLv/uK/fvXu6h9eXJTa9E/366xwyD64dHe/60+50uftyz6eJj+dJrE6Jcak1nPIh0BgLrVt4gAGKCX9+X08HX7/dJ5f3eS5yq0uLFGc3XgcwhRtxBD0bjCY88XLy6y41C43hYSEIPWLs+xuQ8ZmV0B7qAqIAObFzfcC/un+fYjhRZofC3h7/QbYayyO8xNxskbHedTs7FCGLAt+mLiAZlOvz/RFbiBPI4/i9Kt896EN+4c0ZnnxqnfZijJPnke7fXq6//f/drdsv784A0o+OAjhmLbjvsOCaJYIdrNilKkp18GHiX3cffy6M+15Xa3i8XDK7HRxnj3df2tUToLV7GV5fbPaf7n1Jh2b1avxeHhsq1WKAAk13ry4QsIY9Gwyfu6jFzAF+xDGJ4Wi9FdBv1pCFMLdKTVNPUcvSfZyj2pDKoowx3YcTd1Iv4dVxQjPp0OOqViZSWR89saUcoR04DR/eUhUOo7vTz4m+UnwdWmtMmQwqbE8qdEgKY5z8nfd1PPBv15jXqo1bqzeOPCYcBH8riv1FRjZFPDEGaf7GYVg/WHsIflkzszyWsOXgMTqHDZb5VFGTAKp96HMaiJKE2ljC633KP1hm11cVtqH6CDyyqAq293zMzalNhcCDwXWBdZD6g64PcqBbpEdXxbpRanvB76iBKAHCe7hPbaCS1TcZAWq7SgAbFVKx9A78ZNIwqzKss1uOJZ1E1LsT1SJzDJ3gDYjVHAOa6f7qirq80sRACRm+RIfkcWQusMHuJsOkpbZmbyuXB/v1JOhrCyuj2Gc+1kR7DPCY9zL59ViZasyFKL1Qmn+/fazlnjM+lt/1HC4VFdMEKuMflvxf4wA6Mfpdz7+kBta4EavVdTyyKzM1OxUEPW8/QKPz3OPQKssd62ulE6g/XMPwfmsushq1WoCykKR9Dh3fT8d91CebxqkzCaXhzP/KCG4Jod4Ssfp2cWUK60iTFNb1qM16uoif/j68Nc//ZUXX5O6+KSKX/+qyAlRm8GdbV5+3n8Zx69XZ4Zsxkgcpie6faFuEnQSAEx2AqHpecQzkpApbQgRgTUoQAUwAYzwZgn4D//x/3kK27v/9X/ZjQBcErfPbN9QpPsvJ/JDoLTV5t3ZBtVA2nrvk/DjoKIfiWUe17y6+O2vkpdx8qQRGwpRclBCmabDICX6eWHsjGRwr5sW52FKRZ0iZ9k6NNQ2kODYhX4XGkv0rklQ5PBlu88rBdHHmBKI1mzJhNN+N4OrMTq75d0ENhsoM+NTruo3i7dcyZTCnawai6evB39TZsa4LudTGKtYNAtYLoZPt5nOorHdolKrJbDcVqpMS0WKAfX1d+8SQ4XZ8eBTRBDZbp8RbMpj0+hKgc1sSl6TeCZSKdfAzoNKBT7DAOYUjdkErRaNeTodULwlKVpOykKETGWo8zAOPMTCqPXZZUzFgxsTlhi/goriIiiOH1lx/ebl5Q9/9tu/vF7fcdr97ieen8Un4dAfPvUHEbTN5nud6f44+k6BCodjofvxGH0Fi71KksBYvXvguf8DrF++fqM+ZNdv21wNj3uXbopoFmLsj/R4GoPSYxiH5L4ebl7+5W//7OLiisqFUTMAB2V2T/uKoWzOlCMzj9N02ptcc3JUbDabDZmOFFQqGXpIMSFGDUKcYOv1kbbTbtBWiCcfctuq6sfFZV5U1Sgzh0nUhKAK04gpBpc/P683p878gyJ91QFmt/3d307Dy4v1q+/esIhwDAHEi8r6zG1svns+muEQrKbZ90qXChVJisHLyQGHy3N7eJ7evTlP035Jgaq0p9Z345vN26V2f/U3z7D5xgrEj1wXQLxXeby7TSnGEPKssioLBNyk6UmWNxsCDM8Pwh4YE6uxqBXqY1EXqoC04y0UiKnrMQkgHLsjWo0TJkmCehZVnTRUusqb474bIsd5q8AUCu9jNKHvlIl0SB93hHh+fl5VxXIlurQE8/PD3ARYLBgvos0zQhXDnk/D/R552SbHWbaqgi9HP0+P3fsxpWBUxl9iFBGQqRyQsH0BiT2ElWpnCEEgZeyqghgnNO2Q5xf5OqbkPn1Kwq+UcBIro4kqGqUYgcUUjaFp/2wU6U7ogD72W/EikYBwpHK1ym/mARCxAZMapQYGwmNS6wfUuWRGZuaYhFHDBBadKht70cMAXnXBW4Yoh2empswvi7wwq2zymKmxv1+tLWg1+W9aXyY02VL7gw8cLmz5vj9qo7MfrxPzYSZjMuaElRcXKOPzTGkS2ZTHbQxU9bN3DKayZ5n6EhLa0hvuZMYcSKeuPn1X3bTtP/uHF85P03/8X/7KnYYPT/fJBXD+OoqkZAFS9HlekDL3SWZFV2fn1dmqH051dAyX4fEhFhXDIT0ZJjXaFBUVq3VCGn1dVRjn8bHblzZuD5/3kxcggm3G9U46YwzVZ4CizsUHp4+DWmaXm5Xv48P+26b48a/Tp1/9+9G81iZ7Pe1uza/Pm10pGe4e9pihzVVez7iPp+u7LKUSbgws+vkRgep8nGJAX+lMA0ANgP/JvQ4acAuAUK5N+eK/KP9F8+b/82//39++flOqXP5oMF/8+Nd/I6Q/MtDMyaIjVD6wj7Jc2ZIqSRtgTr/IcPH5y72AlDdvAjs5scr2pDU5T1mWATrnkW0VD5iV4zRMnnObwSonrXvH1FK3L7r47fXHsfjuwuhXwM/z4FkQEueEse+e015QDCVlFkEd+GURTplNmM2BALmg6GD0UwyzKZscqiys8trI2VvBMcHgT8/z4UTKlotVs1w6YwuAqNUcH9xOtj5BAr3dpq7rM41IiiMDqKp5lYIr88k/H1sdZr0feoTZ6LyO1eLSZCeK3J/KlIPgmFHoTgCHZ34NiEBEBNCz6MeJqFQ63aTTN9Muy5fl2yHyZOi6OMtL6OhVg+85XUnY7hStN+f/1b/4V6/PF8C++/3+Zqzu+FGP45xH77pF1uR5za57329DmEoTaopHk2mbDnHS6ZQk2kxy+w98jPM8632kG3pRN5GDQLvUx8Pk+ju1LJ76eTQgT/3w8PvfP6m02PiLq7PFclGplIphPoztYlWXyyH2E+Uc51qJPj/TSu7vH3K9ZYsh6py/NQoBtPgipn1V+ujmkOIOzGTydWbaleH6R11sniJaXROgwVDYIoUxzMOcHbvjYdXU/dS7OCUIMc2o7nZle3315i/+4h/V1YpN2fmBAicWkCbB3HtT50MKuU/jNGofwRhX5CVzOIXBWD0+HyLquGuTuDGFXClThLlLRuxq+Zs/+dXtz9++ZYvifH02jIcAcX68BVuizcQvUzoFwXnOd12YJ1o6x8UytQ/d3i5PgjVnhPo0eB/5spgeUxTywFLUvDsNhVgAzEqZcUJAJkSOqoStdOxAWOt0Ub0rFiKUcPKwH4Ikn5kvszB8Pu17N/rzPbn06GJqCfOzAjkBQYqBTxAm6IRDBJHyZa0gyfzcS3+MaXRuBEj4SskWQ5l5VqfuZ+0i6eQHH/EZ2ZYZwIUbj6BBeS6GztkkzUv8uo8psl6fz8GT+GkUAGYvVCn2kQvd7VNwvxAlJsUg5qZWQhVyDvok25OjtkVAAswt9VEbgPkIFwOljWhmKBEyZdSSslZOaPCkKYscqtVKZAYyVUM1lDMlhQk4TXbOh/2yEHP+CoB+SiGFT8BZlr/w+VYh/QGAOVxeXxDhl+4Exdr0NkGYR8/HuHypCoLIYQudMaoqIE6+EmU4zhFrW+eZBoDF1dV2+LJlMY/w++wzHVL5Kr9pL//8n/1j7ZMfptv7v0uH6dtdPw9DjvTKmL9NQZLXxmS2oEU1I9ebs5mRg9NG2cOTgmtfB1RpEUAEJzeCpuuVCRHhGVl3Y1DZoe9EjDFteeaRM6iUWY8xdim10YceduO9GZvG1dDIy7OXtBT5GA9em5/7+u3kNR8OB9UiJ5FH8Od5VVmF5epy30OY5yjSGZY4T3VZGXjSmhmTgErc+NSNo7ZWfFEjgABgDzsLVbZ5+6fVv17C1y+/P/7dv2svXrr590ddar20PEKYyNYGmJLkm9Xa6lyJEkKR6cvdV+jLsra5iZyWik8xBqyM8qREEUJb6dHllGesrCJOyQghJmEJiX2MgWetabGhw5E+TM+/SRuToFIqCRycq20ZXdy5ZwF+Y+tjvpHlBPMAwxoVa59J0oF6fxKoTGVh53sPbZ4bNBBSUIv8JanRq8/dM4BorRaLAjUD+IEFQXks1NLJHtiNWmRvbQJoqtWVrfXaWE3KmBKxiVet3z/icU6KE4ifx3g8xrfvFmQ8uEgKSElgEQLkw5A22fLE36JQniXLKKTmJme/S0gMMijZuxM2Cxm47/2on3OdMp3//D69vVj/y3/xz1+fL45Pw/O3wYzf5o1ZP87P84h3+yzLiuvKZNfDvhOf3rVVWs7jUa+b/YG6+lvni03XfyvbK0vSh0dlLPO3jz9346kjxeff2YviElwCLH1Sv7CyWUrn333/XxXNu3d/uV6toq97UDnPy/XZ1eUqgFIdZICl1pOKzs3THGnRFOQ4TBL8cMJqaSCY7u6RhMuFSqylVzaTy4sCNhuAKwIAyM617QAcDAx+CUoFH7vn47iLxj/tvs3+qOqIh77Pd6RsaZof//TPq0Vri+XT3XMCNrWdh0FJOl/M8+Q1IOEtTQ0HKTM3zY6VAnExmUygcuVRjvpPyqvlb37/+5/kLl2Wtqz7aXy0cP79m+/unp+HcZrCT1BmCrW5OCckQsyW2j1fhITL88TJ123Yu7TMrLU35/k2zA5OQVl4YHSIzTOeMAcW9eKtJGnac1vVxzEuSiBxImFTnPOWa5OelCderNuLLNgclJgJUJkCqFk58oT+N8rAfh1P2xAnwrbr33cxZpkBi3JLkLFfR3YJsKw5d1nWoAgzs4xuFCtwKlBOhMBKSLBzLNoquhKdAibVYObuYYpcR5mTFmRBJaxQ23axEx0OP5NSmlrQnfhQVW0/9EQkDCkKksmOgwdcLK4/HI7x2TFMp/UZKpMdgyZNRfGLjKTImtH08QRNGVOCb318EYlXiGGl87UApAQ+xAOGwRyCYFKhBmKlheMxnVKqS0MaEirC1w1B5+JD4hMjL80rG3ySsLcVAIZ+l5Y+L7LIyRO80elbYRlVfJ5j+LD/iIeXi5XXxMDstiHkmlSWnRCK6A/zkHMAS4Fn0st1SqfNWeADD1O8c4f1z1mt35k3etr+ePHPOMSz59/99L/fjuPwIUbvPJFWmR1i/OsPf0CtSJO+VC/4staNXdUqdgAaA3mrqSASh4Eev34xWs+Eq0ziqu1VnEcXMY/Dbgrerq82uZn7HhQ893GcD0VuMbk76c9Pl3ZtwLOkRBF7dkUI4mN4eh5SPNevd+owPD1pOrt//3zzLisWlUqFtpjFU1OutLUAjwiklPLBujRr0FWJ3rU1Av+inCsAFbjZ6Tx/9eLXTa0e5mHYPY7bnIuLzg9KG3v2YqFxqSitcmt07oTjHNMd7+YtGmVYG1ZIvLmK3rlClUtJ+wgMTCaPbtAgu4EyE8OpQu0kLcqLuXuO46w5OyhT1axR3pav/nD8w/zzH/O3byuZktjS2u08PuwfkOVN9naQCUji6LQymiyejkCOoZmyERJUXe4ahNPuvL7cLy7IEAbJDI2A2+GLnlVxXrw5/54IO2bDnjkJ2CKGUaMcHkVEi/Cbd29znaWIChRadIBxxM3aRkpqvSmyvnTCqO6nGETu7+7OLy6Z5aiLqusBW2hTOG4E97Ber/Bi+3T3bE1hsmPXtcjsl0WjE4s2OTdlQzpRT0g/tO/i9LvthweO9Kftclk36afjrv8g06JpM98fPz9sY3BI+lWzNPY6JCh5vagJJUvPf3TxeR5Ou60ft0/6LNsf9hf6x7Ca5uMBEURfm83qh6vwHKbx8enedgrw+uZKQ64pqtI4g6jp/M/+dPPbs7Iv4yKAH9FqQjieuv6wS8DalB1O/VylKUCpzzPIdSXZMwLDEfkgh93+KKRINUPtKle8LItyAzACrAD4EZKBJBA8zDw7dfvpUYe7Dx/OibEYDD6FWf00/W2lF2pMZWHry7P5GHb1+ayzfmtKo5k4Oq+topC6I5VlnqSTCKxOHComleXkp14wFHkNKXiLYX+usqKuln9/ff789Pj1r//9Gdicypg/xHn/6/Pq949zseIIuWNBjnfjCRDN6JTYlJ0XmkJSyRiFaqUtpGX0T39wURgtBNDKmjLBy1xPEPfbx0MSFpFs6lGiy5oyNyJEXr5Mnwouy6Jd1Am99TAdQl85qpMXigx7bi9NqJ/iTEDaZGqc2VIu+kEEeSG+EbfTxvoOo4/Y+T2cZpuHVFiKw2mfMQOLjqBVJMDgi8HkwQtI2Tm4u/u54WjzbCKsVajmkTJS2qiksnL2R1Kgwu7AYPPivGpw3pkhm86Fi6pAVn7yebsOLJBXTVaRzsIpMRiyaypred4FhKNa1ubY95qln1MqCz1U1Wm3lU5IqZ+fUJ6CwPTr7xaS5BBSH5UtDFqVqAzEEWgILgMwwgSnedK1AcKIUOjs7Cn+8fazwxva228uujAFErRsODA1xKgE6UXesMAaAxHdXWjXCYLIl5364V3Zd0GLt4U1CgABAABJREFUDc6LiCJSo7arxmhKNHXdIMIKfHCOE9Xnl68wBvcUfbJ7MPmnx+HF+ajlxWFb/uZf/LMMgV2vdKZV9ofTvuoGFNgjrQSjDz/99F7cverd6DyRyqz9sSjtRW64LF/9yuYZJITuMATnHm9D5LYoDlNR1i+3M+qsu59Umo5FUWWKfRqb6bzWKBM8wwPcyeSdlwESnSCcMx9Hn7e5KHpM41JKqyujbXVxlmb1sDvUNU1agoKVOkBcAQ4ClEA7s51PNgVoFqt5fhBqgCrxDSHkDUTIBoAWyuXiu+W/iF/+l5/+w3bqJYlGa2FTlQgSsTvX9nQYdX+a3eDFkYKbhd4LgoqJUkPOo+fEBnRY1ey5ZNYxGtQdLNt4DCGhpCaGYbq9HY+GrMqcxuG44wRdYVsi/Cxc755oYw6f+5G6XYgIkudZdVm6OQ3dAVM99/N5GRFqTDXhLYRKg0Y6fjncs4DWuMYZg54d2NxllocdV7k+r8/7sU8onnDMC46URb/3e303IBIR6nfvvg/OP/lpUk8gr1Xom0nKNNGtUedKj88qGM+9YLpcnH2YpjRNGqHJ8nDsBMHiaUylNHrYP3445L8+K2JTx9KnqMMx7Yp6o81zCGc+hsgrrdnq0UadIgAebo3o7F//6sWP//xfUopzfawfBuu1P3wWml9dkbFvtLbKZDzQ5MdhipiCGd8/uSkf/bQfU9F577HOJjfFuQ/iVuuLx1MI0eE4Ql1eKHhUOkUZvcuOuzzWNo55c0n9MH18b9YDnV119WqBqykMp9OclZu8iM2izavMajiOOqd9ypIf5e7wvGpzmxujFaW+TzACWG1u8pf4Iq+jB60BzBaeAOYKLgOIh5lA6e6kXDyNoQb/SiHFtP80zttx8JgVRXf37W6aXv/pn0QuITdZXGJjtS9QAWemyU7D6VFCaJqUUgSIFDBiFNa9D8yD8TWU0cx51+3u7vZU3vz0+5fln5obMvVy9eLP/uzx3/+HlSO7LFyc1Updp2wg/vnDnYOgtMI6lWbtN1ncQfK7fVxK3zd5oYW2yW5qUm9+lf38BySy2iTANr92RVlKvqjX44i1QpGUMAFIfxpP3cFbHXa73JoWXxa6APIpm0PQjT5fgRYbWPViq3yRqV0hhNVVmMds+jjh3Jv2V9/3P2uawS0gSXDJi8FeUJ8TI0K0FEQ0QLI6IxC4AGXfypfP/CC5NJMSntTj8MSMB0ZyjhA1OPPRwyswujA2ElSQ2AeMThIjVGnazsnvq0TVcnGcxmdmg1o6WFzW6dn/BHev+U0KJ6NL8/0NIcSqTHNYpKBSbWqdok8hdEfnXY+ESmnSunlzc3i+T6f00cW1VmWTxZWK9100i8sVnhJAEJFJZoMANbUY2B4e8NKSeKRmY988vdr7tE0++cjKZBxTmtGsswZfap0REGLyKbGIJI7fvtqiefvq+umwy0WSsqwkxaiUAkQvgiwKQCklDDmnojkfOLnPH3S2fI5aQlxn6sjsJQUvrC8YVECbb6pC57//w5OuvU4s/Wn57k1r83ORFBKMzpXl3R//DgprZm+N1aRJWdwsHvbuMvQxRQWYVXl083zaZde5VGf28WhvRM2jOBTM5i5rGwV8MJPVF92o8evpk4FMkUKkCoM/H9chn6fBD10d4iOJNb3KN0Fges5NYctzmqcle/YweElN4OgnbazResDtkYmGRKSY5ywnpUUb8gCUQ0qQFJQAACjQI/zDl//o7fU+Pu++3byofRoS8jF8Ps/a3OM4TiRAgFCzmAsqXdhC1ByrkVOFWhJxTweME+WXGIWZXfDLpQqySrspuPnAo4rlOV0Zba1ko1pW7eDJJO++qzXN5NceVYv2qQ+wpGXxdpEZy4irtrXNMkxh5w73qb9KZcAxWFKoyNhhF6K4tiwri4NPSpSyrBWhTBtl6JwksDCR0uJj5oahrIZE6tYDaq0uvrtYao8yCx/SZ4tnBF85spIVS3Ds6BtDlJCH5B2Mks5P37242n57+vTh57OzS1TkQkA0qhEVw+lZ4PEurl8nyptgtuFJkZL7xy0Kc7xdXSTChcgQHaAQLFJISfR/cfHiL/71/63Sefcf/jZszvz23mSJ4mghKq/iYvA89rMMIfExT1lUHez5TOsnbgIFUo+q9z7u+ml2n/jr3xOI4t38EOslHZ7u9/Tq9c13b964eXv7LS5WC/w6u6oo0qlqLtpzk9HJGn1us2N/HBWTstfX57ayGlLk3vGch93zrFMIs5vrIrcECreS2ujCyCwARll80QDgUf//afqvXlvb7LzzG+POT5p5rrj32ulN9bKqyGKRoti2WhbEo4YBGwZ80Af+Mv4sBgwYhiFYbcBAA2251ZJIgmKsYr1p55XnmunJdxw+KHh8iHFwHfz+MAW8A58CcBEHiAo+yZHX9z2hjx2fMb27G8M9//vbfx667tAdq9Q2Oxh8TI8umpuv/mCpZea2MSRv1BB8GDQoF7kpFN85Z1LakEuSSRQUQyjVHKACQTzsm/Z3v/u7Q0C2upDNsXb/RPvX6us8W87Xn5Tpm6b4APKFEoC3NfaOAFBpHU7mlzL3IUrkuGIUIXB+FDOGNgQWPZqFiVz+7MUXWGRMCgBAjrZLFFEKXlacUko2ODuSoHlWzn3pR2uXlZlkTHKT57yZft7XXCBjBKXA44FEGZM/3rdJSCUEH7K8rLpMeh8Xwlljfuiar5seGPM+pHIFoQkVCWbkMBE8OttWSs+Y8mGbzCwhwGSaQLFMdnsf6RaSvpKvRrJt2nJGk5j52MO7OoojFYVYZSu5ZLP80IdBc41YW9RC6mkFAJk0FROUcidGvqFr9sgIObLnL7++722qIcQjTCoexcGiGZ4EQ6GLxPNot7A8O81jOcn6HgvBlhdfPpxByTANHJCvOb/H4Gn7iEuHGxLrzCzzDNz2sZpP3373o6F4qTS/o7h2h5ykXDJeuKg0eiHApl4s8oxXRQqCq8GOXAinpd/uhqa1djAohZAWYByG3GS7/R5Rnj67ECl9cOGExaaSwjKGLI59OB5zXlE5Eb0M0dFsPFevF774PbOm1RRSUMYwYUhpy1nyHNSSV+2YsDlso7WUkvfex748n189e2XdMCumfnTWOucebcv++ebHYL0AhgnTOK4QLwaOBoQW3XEXd130eTEfiymxfnRDz0c+JAjxOIzNs6yARAfmEqfUDZFG2uyitTUvs6FxOS0m5ed+w931/hAys5YMciHzfM64D9FqOTg3tg7aMV8o+RQGzeUkZzFELllLI6SYepWcwRwagAVAn1TBAGH5b/43/92hfvx4/U/3Hz4UpyWkNUfjH94XjDhDTBis5MIMrvLyUJgs6on0lAGbMGCcP2xbovuGklg/mwNRjB8ePhJBSsl7f3GyJkxDGMMhBTChcEpoWXFRPAtuu/20n+b3Kl/OUiZWLETftM2j10VGhTFVqWI6G7qak8J5wSGmQPbQuvS4QljMCl7EsrcuzzmknPzYHpAAeubiPvfZbUSVlaCX5Ea6bRCXy4vZ1Egupej8AZPKU6Z9bJCS75yQ05x7pqndp6dAJ4FPmH3nbh7bq5djdbmmu8NsMmkH0TQfvCcb5iVLoCfj7v3v/qnWp+fm/ORZ9nxTSN40AYKn6Dmj6LeYGCQp90swBIISL4tFYWbDb35smyZwnXNQV9r1wJHEDlKoh1F2TUxCNQi8dnXkig5gg954wTkBPAn1ps+n08VkOhexKOZmQ/5McXP6lUg0DG3Xvy0ny9VcDvvdmMazVITepqFNTUgVaHOZnb1gSdb9/csXr/OCNc2THZoxOJP5tuMMAia4YGtZMpVR2tSubIJi1NJJNdXrFwDpKSWbPCJ0wRX6PIXR4jsEePj+09jFCpgbzF/98P1hd93sK4LqNFtV1U3TGdfvr2/9yRzrm9tKf/nlr5aTmdsEieiZmFfYAgARi8AGmzgupQicBxq9kOaBsRMfmrq+vr5OIW2eHr/56tvnz59N1uHgldmWQUYZ8asvvv2w+48f2vZnR4PTE8DDy/UX391/d3n17IddFyrqiUKIjEFFGELwoAWS0Sxn7vDYVsHzGKjMCOH2/ppxJURBNgY7+EOHsS/IdUJwJJR6LU7FmqMCiCGNo+sZ1H4NpldpH+7CcYIUqwkAcHzEwpTEIreQrc35N1/b8SN3S7o7wsg+pO5Zip6dUwN4NgXLU/L9yBMcgJKQksIOyKmm2wF2gRP16D77GBF5ni/ZPIhBXvbPar5l3V7hFDRrKB4twua44kKkdHsf1s8N6xLYBvM5CaxDnAA/NJzcvYMAXJyKFWNGGTky5JaJrAuICZlMA47EZAlpQAoM9mIxWWECxL6jwbe9QwgxhvVwnrrrt0ycn345E4yBSIp1jFJiG6RZ97R3e0vTFTI4E1qysy7d+KD8EbHcI1IKuxijlJnhE8lyJp4aIh6WyLBvamRC6/miKoLniQ77wYX9QZyfpBRNkQtpnLVNTDA2llXJB1VUSqgnsVOUSU6sjVrR1BTBSq7IuqfhwCTjjPDTx1vJpZTqB9fK6TzL1vOcjoc1l9g0qR8HBK6kUhMCI64rQR/qw2oSNpvsYnouvu2K7Re6oBSf7t9+/rSPgWpky43t73+bQJTfTKqFoLS3dce45NFo5Na5LjlY4jwWj007+E2Wz8SkSi2Bs2ESg7WLFyXF6nB72K2ezF5Ov6rcwx16kpo+fHjQ8wbFfZbLhc4QEDljAw7HFAHWiIA9F0PyWMjVPjOpU5BADEmDT1oRWxEAAvTN/GRZfvfdP7Zjb0IBqYgx+NEjuMjYZ5e8ZReVeYLkxxR4wDA2BiPAlVgqrcpXz57C/fEmJvr8gOeceKIk8Yo4xPRe56p3Tsos8NTjoEIcrOv6ELYfnPMA1CSV+oMUI6RVAZDnJmHh67sGrebz+XJy2D993x/ehC9Q2GMYYxwWTGDJYbJIseDLEhtHSFJxG+UgeMVpCEXMS9FqRpP60Q/dXuazqgzHR95DN5NGHA67U708hqeKyhF6jlilmXfZGOz409M+pWWL8jWHCxY/p7a1jIftOJjQl2U5MWe7962oE2bxmaRPsSRq852yc/fIArU2Hh2VyJGS4tMkiKUOkmDw5OpTV66FAXppb1N9rMfeSSRIcXj7tvGHWFok8p0d2zgMlUNhans0oxvzJGQYrUtCudzHlI2b+/HaxciYusGQIQ73d7uXz8T+MdSdb/aCgr5w+/smsm66OhfLZ+nugxDS2dobr09nYPDpOEJZyVx3Q30MRwlxtUzeaz5td5ukGf/YbLFOWqtMm1XGEhRV1kbJ28NdLCsLDBhPAIyAIOV8rLphv7U8DhN++PRfH39709U7+PqPL1qLmTZ/8913f/Vf/sp5++VrE0H2o2VN93Hzj9N/EObPDZ/nMKyhipopIdXm4XNRlDbWsyIJTu3A77bOLDTtO7Oaf3h4SBGutL76b/48EXZt/eETm5+wPe0eNkws8BLk6s03t3/3V/utZ5VMEVKGCHy7R9zfuckVxxjbnlLYlVlOIlUhWIhoPAFEWDx7jgieHGBMPBILrZZrIEZsdrlIOE9jP0uB2r5Bekyb5R0AxRB9SsGjuCd8Jl9Wi+lhDwChvKyIEgItXkyVy/a2q+FYQYYSJGeUS56//uLH7z54/z4RxFvq6LJ5DkhhGnRxjN6WshCZACcYGVRTGo7Od4ILhJLz+YzLfKkTjEL47ETlXeFPT0J0wl4WTQjx2gXCkD1+HLrw4evy1xF5jDHXKdTuuLurJ3PZN8aUBpTjGIVVLO12PJKTmROcO2LeuxJSP8GUJtoTIyJQc6kH3wGkvh4TT7lRSTCK9SpWTThh+OCfhJSGswSxV5SmejaGsQvcvNRccAJoUpQp9kBzyMZEqtY2WTYEpaRouNQeZc+DkMj6iclcy5HHGIeYMs1evj67vRtRM1WYTrYoqn7T4oXgbCae7rJprqQexra3rSdF6HXKmG27flC5yFQxWh/czrCgq9O2vbWd984xzmKgdHgqZmfUNVtnIEQLcbaYlWgoj5/vU/KPSc/C/kAmKZ05xViPm1PJot0VxVUxl9W3k9OW9Va9/Rx8GvpxE6P4+FMKCZ+/4gIQyHfhPtwZRvIwoyyXWmVKZ/x5lhs8doP9EH0vWt0V8ub6EwO27Y6yk/3wkA1/oETXtU+VXFa5E1Fn5mr/9H4raDabwKF/6L3UWgp9TGKxG3EKLB0A5hULsUSG0rogEEKMmosA0NXAFADI2fLXnR2Gu+vU3DM+hsGZjKdIKsHp9DShqCLM55dZkR0YpuDScV+PhwT2zODJ5Nn8WRNGt3NDJaq71flVJQno4+Fyk8nYNjo3IpfgQrSKc2KMTWYLZJwxEko7SFao2lkRYKtQxj0r8zjJQoIc1Osv3/zun3/3Ga4lKJQ4EUperVj7oIRUQoVjJ5goLSbRQu9ywKRy5mT0sF5lzRBj7CbzcjUXx4OTuEFkC3kqzrNVSil9TofT7XPUQphEPPnx0/sfjt3IGCsSHxvM8sXpxRhC7gY/mOHDT+++ePNVAbif5uzpodkXpzP8arm6adXphThstq4/pMT9rODOSOlClBAcSlYiRAxoGxsUlcuB0Anmw4uUmlhdh97FfrR29HwIzgZnKS6E0fHJD20TGCkGTc9PZjzmBh2xLb96/of394+Shdnsl2KScRS6KKVQPqG1bpplcjL3OjOzMR77SbUMORzaRsxfcdEYsVZLNdhxtz+cvrmKkD7vtwWQqfJhcNEP1saB75gtgh1PivnJfIIhBpfa/oAcdSXiaCUScvSD37jAJY1tLdj9WB+v398Px8O73/71zV89fOYTL8z2b/Hdh1tAfv3x+q/f/jYD3h6f/+mv59mcxm1W5HlXHH74fre6PMbLVx3KmJl1ps3+wGMcU39omdHoB5hNdMvKrBCMcTZ6IUT1ZUFxX9eL+5s79/E+a0NVmnig/VtjC4FpRCm6EEpEH+lhG2KASCNn0gBnMreKAeBEVwONjIdB+tTXI8kqKICYkDs3BgxWjioS314/UJgK49IjxJ62IFYzPJlMOtv5tI8JkQcYJ5TY2er0bhPomjCbiuRTS8kkCinG6MLn3ac8z5Hh9eE6oE+1ny6fJIj8i9dv3vlIH++Z8CE8ugcGXFpzMltm0zlyA+HAE9gy68d+1ySAxOUrlalyBRDZ5s6h7icpd93eCK6bKY9e8hYVjfbM2fuUsE57oiQZ1xyvu+0clzKPZjnjxpiTedcMKZCNPpKPnGdFqyeqOwSimPIytzYBRa1yCBiRJRGi66yqj3s1L+ayOLLEYkImZMLk04vzclu3rj8Co2ezUz90ke2nsgjUSUAZNQhyo60Fy+8fPEAMjkKUjLtEzAdRvCB/xxOjGFIFwEtjnxIoQowMQ5kGaydZNn++mDITKh19IN0pLagJyV7HBExyxviQjUwCQwYJIKTk03QxWVVLJVQv9j0aW/Mq5z6kY9fFROvL06Ef52zBIm9sTH2jldSCt7fHz/Wnc35Wj++Iorl8PlLCSbkG/IlHXpbWOaBEm+33j0+Q06Jks0lKcqHEqkA5fffx+6br+h5u373ir9gsSKHkojB5FcYUgwdI+8dBm7jtbI/NRKiFvOJXei5tb10FqZVcS/k5+kl/OGzGrryv0164eLIekVaFFDqn6OsPT0FK/YLNO0Qdle+tmlzB0x2cP+52KklTMuXGMWVZClRNlwkBNZAAAPjlNy9+/s389h/+55sPfH+4jTiol1+Fz+9SSGw29zFRSmOg4XAfIBIL8/miaPzB+3bwE/zEywg8m6mMgPHEbq1LRstozVhFzceh7WPMdUEyQAJE6rpjyE3JxMF7So3hC5HsXjAt0AlXePSJPQ0HymdXcvpl+fo+br788ivOpLVDjE4sTjITOWxZG2E6S+vCDwPFyI1EOSPWppSub7qYasnN6WIKkNrjw7P5aj57jowEYhdTTzE858QhEcEQ3gFAXtDJqeL8FXKw/mnzZAfrhmm3ovN5fnnM+PV2zEp2notHmmeQskf80b5lgulwIohpfVJMmafYZoa1Lh+dt67juAyj5mJswgjWDuPDxP2RLrIL//EnPvmxxucUxpFOAtmYggsh2N2+PjyZYZ4WnLWzXXA6135vVOLkHBsd5+q635A0X3yVtzfMVjB2nUrx6uTk/dDdPW7Os33X4cfr4cXpQk+M1hoZGzNrjC7mhRTi7Wcr1tPK4OdxsE+b9PoNRtsefuDeYVpM+DzLNNh+ejLp755+2G1STKtX2dXkVXSHOusrPmvvriGswENSaV0GqO32/f3mr37zt//lr3/3N39fVPPJ2Z99FMKcbBsX/+hXv/rj/8P/6vh/th8+f09JPtyytRS5Bcgir1Im2xiEELUJue4DJBDCcPCJkC9GShmzQimd+no6nacUrbUvBZeS19/14JruLf7ut2/jbz6spos//xd//s2JVoLfjJSA9gwrxkQlnWaclmfr+dYgJHBcATig9AhsgqgU99Jk4JkHiHVPLFm+aTeWx170yaHRkixBBbv7EB1aSudEyTlussxAd+wSxZh8y9m0e0JMjAFQN50tUvKBM4qsj71ScHF1XpRFTWmo9zyCy93j4VAWpUamXmqtvn7dprgZk1LsmeRCsIg0dBCOybeUIj26d4dPXJzz1XO2YPjU1Y8OhEETpkZDdL3jGefqJNfbTAgbSQZfX+NTghupkIKKHwMsWI6OYCfkPHEJkIaPb7fIgfOEEiFZcKcna47MVin1NEVn48ilzmOQyBLlQzMS1WloCHyBorjI3aaL3q2n0072wQ6AfFJWIQYuWBid4Dz5Sd82PJhSs9CGMQ6A6fzly4dPnzRLTWhicOVqtds9jM0g0KrzKyuaWZpMQkrUETAs5ICznGB0Q40boCk2VsynF0tx48fcp42hKnP7rReZPMULwiQ4Rw4xRpOQMYjYlEVGHDrfaQ5JRJ/Y5rgLikYaWCHLrGzaxjDFiA1zRQG376+ni9nH7WfkKF979lFFdC+UeBdjHnAcRwbwOs8hpk8xTa+ex/Y+uHDYpmA09jDS8eWbl3L5h3/Qj0Pfv/v+x/fdRzqQ4Bz2mEYQMH/2/JTr7Km31NbPs+lzOTdFJrjxrm+tQ3s8BtZ6Nny+iSEkSkN0KZklm7MMd9sB3LunjkuptJQppMvFJS7K6hET6v1xe7qmACTYxekqG8mBd6o66Y+HaFSMvgfJCCLBE0Dw/ETOL//wX/HL0/F//Hdth+HT9TEBcuWcTxMdUobdYyXPSoHAgQOIOap6y0Kd2gBHF1lsKRVlpcZRKxnqgSQwbNfLZUzxU/zM25JhLaSyQ4cMEdKYZUA7vhMQBz7RDBjDnsdcqBi5RYQ47A8q66pa9EpKgz3bPN3Ns0xMlBCUoq0BpxNBgEM3hJQoEvmYGCDPl8/lYYPFiXR9b8dOGr5+uQQk2n0WEbeQhnISOTIGwcZDSmZSiMnlVYo8jLW1rBnosO+Qcej5bmKB9qmDBlS3IXz1ug8B/KchgQBcVNPRDck7ZdLecx3CgIGC9UqeaHHCeUNHP5CcM2PBmMr7cUg2zDJeTLah2d8eh3bkm6EqevvU7u7H3eifiRUymzcpYpS8AqBN71aql3x0gYQxC5X5ybQoy6fa4SJ66+qndx/PXkFmcD6pXXg6bhkXrJoyjkTEFbOf77J1WywLQHLx03L2TQzWxiAuzsvgj0+7w41bnUznVUUh2qHbHBojNy499X3z9ReXuT71vu391oEJwtmhi2jTaHnQManD4/jP/5+//7/+h/8Qvv/+dPLL6WTVtv2Q1Y9bV1Un3/3wQ0GFfA70mXtPVXl6sDHTyjrTDqjB9+2+iJc5Y00+5dZifl5m/X5soctzyUbfJ5PLLJtUE+tGIaQWIn5MEcBaJ3khufp8ffdff/v2//3Xf/ff/7f/8t/8d/+q8om69uSLFcOej1zG2ht+BzhIEpQQ0DBJMTJE7723IzVjkFPmQ+AyNSPP2NEejCgrNevLNjwNxDTfDbv9XmrDheCmiiEQYQAopuXm7rMWqTAQkYJJgwv7Zne5WHVcaCMPCfqAl1pwBYS+924MHZuwGF1i8pj6+GlQUp/xVRis58flmzdCCHJk+x2kxKyILl2H6LHN87m8PCuBPHFhZgLr3Cw7FyN2hCmDSSgJ0WnWAp+yy4n26pu34ZCGBlsRGBGJmViZc4Am0bX/aLsEe8YZ49xkF+eLEUIDdohtxrKFUjd2py0pxjPO7xKBC+nxM+f8xfMXHNmhP3bkpz6FGGachUQxxNyUw2AZF4zx+rjn/DgvT7XQKYEUkjG+Heo5mwjOIKQ2jJxS2UaxLDjFSsqB9vPl2RzttbsrGY+pxjiU+Zc23DVs2wWW+5wzfqIWu/ETxc+CwxRUbMdsjCwlBpALxSj2XRcpLiphY8yCEaLkxRR5n/A+xJK8URG2m2OcOFNJX7bQ4JF2DatP9YsUaHxoovU88U7l+Vm+ztZCiOK0SaXySvN+W6o5Is9OTpDxu7oOk6yISZUXRP1hP/JCZctz2GyD7ynr+STk+cXX1S+TD5jo7TiwYzd8fArt8fqu0zp/efVM4YlQwl+7VN9xdk81xYuiHUOWF8uu63Od7Xzf9R0ktPi4P45979pmMVuMbeNkOJ1OI88op+icxhwJXGRwACunx6dHIc1gh3K5zCECegEGUkRA//vgTALew0GAosOZ1P9YLZ+6Q0UwEapHzFZlGLhWmk2vZimG5J0bt9sdtMeVxAgJMJIGHrHoeqBGhJELYIws+c7VCzCC8+d08Wn/WVY5mMJp4Iwp4T01JKR+nqWdZdwB4yk2yNfEmbVeKDVFM6SdNCfOPu2aZH2NkjNTKGPb/T1KyErF5CR4t+kHmZXVAixJaVgrct9ZkcNMsyF4TvHlMhL/TI+w6VEcHprJBGcVjwG6PTCvJorLSsNYyx1ybZoQtFq8fH1JwBB5XTvGxOTMHLw/XD/NkS1e5bF/JY9thBiY7oMXTBDxrHOiyE+IE9eZFtw3lK51IKOBYgDuHx768zPN87jOF/eHLyaL+dOHvyHPzy6y3c2uv2lFZ5/x57NXZ/1nZ4+oykwxzNGFku+tWVoWpQqKQiwms28ZMiInGMteXjFm89HJfLYWWdd1qRS44PP5GeOcS6cB2aztbh/nctIPH4INx831aPIodAzSRUp9p5VRugz0gJgBCxdnPMLe4nByaUE81f0GEdQkKxkNw+3u6frVq+X72/2z82dPd3f/+O/+h//7f/rPn9/f/otn/7aa5+++ez9kOdT07np/qH/TdOP8vy5HOyhwMqPpsptOdVFAbfsi4iIaiaY+tjGB94fW6+U54xCKKRnvY5qwtTEmh0h5OfG3TirOgPq2caOr98108iyld2ulyhL+L3/z74X+/Cd/9gt48fCi10PatUozFCFaTsw9fuYUaExcND1LCgwfd20GFVcButHkmdtzipLpw/0THGN+XiiS48YDDDnGFICVWuBMMbbddYnoZJUH3wffD97NLeNnMt4TzmaLs1PfNUPEbbfjwepJyaLfcK7H0NWHoPaUFTG2KUKKJKBGriPB42J6sUn7aNedRNvvwiFRIusPISZCkBnHpSzZUqGLjFiKa6KtHTH0++/l7HQ+zUJzTDa5IW0YX3BePVXyTE++vcxjIIjeOSV5Ike0x5t9561HLAxLZuYTtto+HA7acKeOdym+Ea85h2nJdgFLL3rnw/WdvrhMfJKgAQHet4xzHSn0sTBzobWQ8sFvZtn6cXfgkieiQKEZ1Go+EaJ5OjijMERXAmNss1IpjY9lGmdzEejABKOk9uyeI+TYkGdVSsSs5HMpgXOl6OIV431sO9+e8AZimmapyL4BBD7e2/yQxp4ow3SYg8rACKmmfE6ehvDRMoCuBmFAOCakkhmj0NdHF+zE4Wp2zvkA81ZBe5Q2y9Lvjm9DIDlT6oU+y05+d3eTr6cHeMe5ysqiRyXcMTdneyJx39lybXJTJ3dEtkJ98+meIp1MlxFEhywTilm/a+jlS/X9P/9uenHZYEQznE6KcDrPkvnQW9sP19GFTf/8rIzDg16suauZbXQKmhdofVR5/6FuBkG7bgyIUPrxGEOLjJfZfMJX2ov51VxuO7dpoiMrG6XWMaWhHopnVSaVT6kbh2gd5UWkJMH3jqGEMgEg7wXSFAoADa8B3J/+a6n+X/+33tXmbHWCIefFUXau2wEXd0293W0hT1rpCbChG5GBFJwHCVPBc8MgZMlCcLmwoVLEpKefRLrgXHOFUSsRXE6xQz/CFg4gc9FjHRvCEVnFmea5zvKx5sH1ZrgfjpKrn53PrhM3p1nYax+GHobhOHQ1Xb068Vm0adylYEM4ffmSC0wpHruWdzufQpEXXd8d8u2kSImsS4mfynN5KnY7Wx65OmVouLLYAHGNos92bZpTYiMoiEkWKSbvlfe9EjLE0LV9LoQ+XUjFx64L1287IZngE2ScOJsU2xSFYQg08ChQPB3ulpyawU+mk2Bb5wJij7yqvjlnMg0Qihfnr9nZrqDHm//oanu4IW7jO2vfsM/b39z7QCZ7w8MEWbpNTljOsL+Ow1rk4977Svxstk5EIR5dqysawqQ6XN+Sj1GwlvaQ8HT55R0+dMP+GZVJQGw9VDTotm6uzXqeV2bQRZaV9lONpLQqQ+SlMTurZwKJiePRzxeUo/dBxaRDOEghgrcuDhwz7btgL5azSd8cf/P3//Bf/vZv2V5k2eTs4uru7vYvH//eaP7r9a+3KVYCiyrj5LuuYUCLZZHz+eCGsqR5JbjNRJ3pCliiSVFWqzlwxlVqR88tj4xHJhjjHsB7y/eDOctfflRjW9vFuH3/mMKL41j/+3/4H562ZMzll1enz55dTH9e1c1PfEFVmUeQPsVwsrCxSxmT+ozqmlXTynNoegYSSqa5H0LIjk9ci7apg5O2HxQXA7j2aVCRM6mOSpm6JirUfA7NwJAjiyGGmLz3Q1Z6zzI24o74DGUITk0qq3TqSU2EFmpQWvRWVaXTXEY1gg/UEECIfaRRX+ToBBra5Zhq2o1hKgJDKZiUIXmIQURmVoPr9cocvDOKaYaNb7GvU6aYgNVc6xg6QZk0PGOVhUxmrb8r9gCLU6SG8W48crQNQYL73jPUiumS88mcRz7grBxcyyQJjnKQyR3CvcKrEifH2HU6k60xeb6aVF1MY2MbxgbRV2laykxKdbf/mMVJNV2yXDdATHHGCAmRIUc8xj1w4oZLxce2U1qGmCZkY9ZN42gfQ8yYnDWcS6MXUQ5bjFfoWauHvJMs2IF5+4FxlaLmoCdqlbzof/yYjDavHsbb/uCJYWa9DNEqmR0D7m+fxsFSIoIkpNCKmSyTIAxTcQh+3Lp9fR2jIDl2TFw/3RFcvVRZ9uqbDPqhORXMv1wKudzZ65ufPhT5pCziQ6yUiYYpFx5maWYyl9ctCX1g7MwUSpdDuBlhIoXiCJWZfnzojc4rcYpEW/6j0IoQt3DPuhGq+Mhe4DgAn8oMY8ZYH9uH27/77dvkw8/Gh+zFa2E8faIDHwQzM1JcCTQ5Py5yFeczbB4k52w+WWS1PIxHCZN6d9C7p3K6Irn1IXdTW7ZlSil0aWCeS1xWVeI4+lrnuWaCRCJkwdkQC0VAPnrgnYgLLWbV6fNn3/5/3//T8zHky/nojvv+vqym424MKWilLteXlJIvHGNMMITus934dGuTHIVgU8EgEmAEbBJiQBzwtmJv2LN1bKOPEYgMB+LndDpEavOwLi641tkRorf7IXysaQSGImkjVcUXm9E2h5vybOZ6ThBMOQsuxSwxVnWirnEADlKbW5bip8+YiCnGuWAKGcdEYJjJxehoBlwbNC7mIjm8ZSA+p9U0Y8Hz4MLFgjK52HURFRLqCmIanEcIPUsAdaPjLM7iMAwA4f371nu/NLg4C0wCY6Q7du96ijAv1WBDGsZc5BINkU+oIwzUD+8+h9MV73g3He3D0KWj6p/2v4PxeSaL/IWG/Pyi2e3rfhi+59GAvDKVyqujDKPaiuFsDD2fY/vAqEgj1avVafVl/r4ZIEewwzb2J9W0dZ4i6GICt/th7GeXIskrBXcokx8HGRQ/iG7Wtq1WVeFje7z9pF78mrLEbCbzjOxjB5yPy4PbxND5yNsbJxdJMorjk7eOGe2j7+PAMX+ou+ru3bu7lm4ePfEvv/3zRbXd/bj7x8P211fPp+++ud5+F2fuZ1nGy8L5ULf9JvQSmpnOWTED+xju1/EF5FOAvNesGprDfsuHvjaFPjs5CeV65nBgGNvgx5gIpcjgfG7CeDQG9lvrXX08tsebrg/j0OVGzuaT9emrPz79eVFk+7dsOPZmyTNOzvhLYL/t+4XJKwOt4j35AYLMuOg639Fmqn0/KJ44p4zzkVhYsGlcHtpgj8eyzAGIbm8bC5jPsXdmOsVxdCmmlHgKnfUhJlGUwJwqtS0xWlcf+7I0VMTA8g6QTD9yaSn3fl+gkcgiyyONKbSAKvlZso3d3wcQIcBY2KE5UKQC2ZAZAhDMHJsBsBOYJyUGxJIS2oFN2CzL9zO+ccMahE8xA7TJU1bINHgMR6Ly8LYBRcFFYJRk3x6Owc0VKxY6aOiiOvRUYRcJMdpCGKRyr54oBscbSnIG1eHpyHjgAmzHkLPqxesZUIxZg3SamxG5SCJyVzDahVkOyS3ndvOwXKxu9tvBjecnksXQaZl8AhilypFq5wJ8Jq10SDwE/vCZ8VfjUsz3z9BEAL4us56DEjyTpHjOE100TfPp02dETJESwUxl6S43avrs4tVioW+21/NigbqUkEdUHuM6Z1IgALVtKqUfXc+JKPt9YAa+jcFbxxjzN5/j/rC9GwTbjCFGDMWZ2Q+NCJaJF1rcsSvM+bPMW2R0yuEWKFjbxBaPCMREd7+vZqKJvGVq3U2WWWwd4zwTg5ZmDF3WE0bmbWIgs+wFlTcheUgy9Id6/iA5CBEvzOVSfvn++7eu739yjn/86QshzJdfFLGBNO3bxBs42rbTDkScHc5Y9MtqpjNxt33nOFvoIl2zhsbImzwTiMk9Dp8onuDFRfFS59jbQfE+n5t6FMBN8AGAMZ6QJZGsiFlI0QcDPHovpVy8/l//m6Gvn0pi6ATwKaoc9Gf75HuIMT5EWjC4vr5GgNeXVXgK1geGjKfofaLEAQl0Sh1aCShxi/BEe2g09tEXWkbXaTlpe+DoFDDyMcbdcLCyEZ2UiUnOZYUD0atsFeN057ziUjPWjc654cB5sJvEIwHVlFIaGHGqshQoruVUFn5MgY4lLwYaeQwMxQgCmE+p8AQ5gKhoXRJ+tNuZy9j0pD6+UzZvrRDNwLmnJBLVLnUEKY95D8LrMtGGxYJBdH48y4Naci1mEHlKlgB8iUvWW5cRZSl10blt27DECLzz4XbXd9bHmO43AmLzX+x//rdm/upKZ1P5D3/zd4tVZprGHhznQr4+4Y9PkdLl5I1+NYmOHccjA2ndIxiCfcpya3LRjWTKvGUYHx/06+fj0wcjYLCQldN5VU2rImf04NzBu5VTaBYA8+RvmWbtU+1NMz1N04XbD8ysSuK87zvesRB2wXVhninnMRIXOsZI0sEddciq0qdgQ0iiWNPHdrQfHLH39xA92Un5y9evfiN/K0/FbPdz27fpbPoXX37x7w7f/fX137+QoFllffMuACG81tWrlTBsW0ymQ7cvImm3ZGOsHDupZO8DE94G/flhJ/IOjOrUTLdWyAaRSS27oS8wrKfTH7ZbOdSsgoPf+Rr+5fmvCXVZTfqy1HN5fPfTI2M5MkIanXPeeQBnP23k88PwKQaHTEC4dCEKw4AnFkaTXDebFWEc3X3Oz0uoBttr51Sp68N2upoiE4g0mVScCylYAMiV5oS9x3rfAjFcTAKNolwontkckrOFzgVOGp80OhryCYqUWh9Bm3BktyxgCB0nI+XLSWL7wSmYUqz4hM+N0JPnj+PRBBY82URKFj0G0OsXSr9NiEB9YhyQqcVEykatSbKWy4HlxxD99fXq5ZusNGX71BHQzGV+7DqRYX3sxFPfSx7zFxIFgMsgmRADSs6BYbQQAYEJVFaITWgWci61oiECy1Khu3BMrT+fBmIYZRXjbkdJsilopFkBgAxaStUC0Fcld3ZaQqKQ4hPHk5IM40znHQtDwPD7hBRjLEVWXH1x2e2Gulgtn7XC2+31Af2hMVNdtUxLqWnPgI4A7MXlt8vizaUp8i/yspoIzAE4AAeI5emvzowhYAjYApOUOAYBySfI1T75cTl7wdXvjWQJwAE8UA/I4cWffAN2bOuIbHz342+anVz07jf70brsuR6CNCLnSbOgXkrktH7Dmni6MfokXEU7ysCxlbXtRy5wxss0228M8BhUAk4AFA7UnOfZOHjOdO7C0GWkkU8mXB0W6nlDNdx32fPK5NlyNccIv/un3xx227fRq0/vT+ZrcgcBpjjNz2OlQrYXWiDgZvBcxhia7f35ZI5GGBNoskwqc81W60IvX5wfbjd2ox/U5ZurWQp16wPYslgzycCHnoAjGLUC1pPjnmyeI4DoOpZrp7KT1y+/8Z9+c7N5J5/Ny7xojw/NfhRCpUTHT2+dMcZkKabPrbAxnpULvpxwxtP1+xgTjASzKhEEH0WaCjrSEICBLIo4xkEU0CYmpxI6JU5Dv2/tEFPLkM3K7NhEwbR/8BhjuDj2kfYHYmHdbcfgUtA5cz4KH4PzwQ3jHewJphUYtohBhqkXgkGT6giTXS5pMFzG3EZluWdsuIIXQnBhTqbE2PnbPD2TKNiDi/n3W4EFV0xQjPEQD70PLeMcspQK4gENpjjGpGRhBFdDStH7PcTAkBMyH84i1Tc3B2s31WTCEOflPPjgovN25vFzZthYBHSMtaKuj//w3Y9lPquK2Ytnp+PuToT4qWsAB6VPfvYz1j+OcUkPXX9MXttMwzHYsjDeQWpSOhw3qlKnp7OFkd3V5SzRbSuqaWGfNtv6ybZh34rk/UVe1C7sh9FvDpOpyPOir8t/PrQnrYjfBHAZEOodH9rfBWcvz6fTYnroi9QMwEMknmIoJzY4Lzq5BhhTxBBc9N1PH7+3Pi+KLMu/+foPOOM//Pjj4/ZRn8QTdfrmj7fv/+Efbv8Gnv3p+f/R/G/f/u5/etr1D7aZAbxCWJ9dzv7ozfnFMta0qY8IqdKaq1SyQbC271Sm5fLsOc9yFKIj9FHwewrcc6Z9DJRKUcoCcfayfrnZ3Hza7W726VjP1PwwmTw87U9OT16crWans6ckfPDm62JSvehwzib3nKEwRik9emDi7NJDJ2JCLEL0KYISDnx+fJJVq3jgXKQUH+6v5VRN5byal1VVOfCJ96VQgxCRcURAAu+cbWsPfC5ngbEGp1qI1PdIkOc5Z8ynhN7tqVEwMn6KFEfXt9KlBJbGGJMWZ6fIY2JBrFlmMPSxG5qRo8CCp+CD0rLeNE4l7iOF8pir5B4FVzlOukOLgt9ZkKlNXWLnzxSD4AIsVwvNgREUWe6efE0BfSJsQtz6ejLjk8k5sHKMveATlxJrb4/lolQG2qdWX54aYTkIdNF5lx0ncBEqIAYXTI0etysYgcZEfuxDEpGRZI00JrHOQV6ljvNcCwHbHa9C3kHjkzdg1g3iJIQYAlAUyZ9S/ITEHaWf+vjy/eYmclUwb2uVKOMnq/XF6cufT01e6FwZE2NYr05megGQAUTYdDApAAiA3T00jBUoFDcMABDi0SZv2agdIFRgGKAfXUqwd2lVaQhqsNvB8QWb+hg47pl6AdgY0LC4Kn7x7F+nH4A9+/XXvQ0kCd8eHo6xPnWmHwag6FJMN3j9wJ5NNqGcpMS1Fkpf7MxHzJkPD2EMC6ogDvO5UVokHA97Oyki43N+ISih94mJSjHeN3TU+2RH6/qNb7p6w2r46ouvfvlnf9zWh75pf/j+u9un26HzACzbZm/kiRQnZm2iK33cqqdWqzVTRqu5p82h6xZR4fJyGBxVzwvJF+XraaQPw7W8F2en08lZjMfEggdEyCHfB2BE7gGdd1FIYDQma6TIwEelAIqv/uSiP/zm4fP6w41T7u3+EEm/efMlF/xpaBsui2HQnJe5PmgTBseHGPa7Y6CDtz6mqmmnF89JhENzGCYWipUUCkRGbS2jEqXLc5m6PnWdwR2pnLMFJkA8iKRUFHln2XlGYRW7MAkeM9/UQxDB+FS0tUsJY0zxY/Re6PPRJp18S93WftZHUTCtBGfkvPO5Eh3AWJNcZa/4a0RkDMXbnz4LNgl+98XDV+pKSHi1GQOqB+NUGQiiZDwm7zwniCl0kUTD4xRFCXzf28yPVt8iZIgExvJO8qfhkw1BCmVk7oBpYpvtLow+gkPBtMmlkSar9zfJXJ0kP338fPd4+sAuU3zox2EnT0/yGPvjKDGJDPbGrkBwRoWIWekOmyr6UY82OFtEIIQ3l88mk5Iotfvv20elciUYts4zvEipbbYf2i6rXsUW8XVVWaXbJyZCotoP/fDZZD+rne/HZzN6xA4Psjw5n5RZ9D65EQkAoa2bRKEsTgn2fXk3jsH9NITe3QI4rpH4F6++ZFK++zzQeIOMrU9i1b8262kxyf9W8/ufPo0//fDsT2fP/+S/t/fD5uNDn9o9DNV8ludVjH6UrhJlNvAroyuJrDzlpajd2PSj+XyDDCZfX0hxGlGo85zvZ7HAxDvdNqXloNEUZyd/dhxPhun3y8eeaDabHW+azjx7fgmSF3JGAo3Sbo8225vpyuQXglDrV5eVedguRQrv/U3ZagaM6SlFZ7HNkHMmm6PUWYUkPn38IJhkNmtFbSDzydtyVKEUjBWMMQa2KqBuYoj9EBMhX2jrfSylZUKGQXHBh+GuaXBSATJOjPSUMf1w+OAOVuLUZOspCxESi7yLoRNGOOe1QEwsjBuq3aHKVmylloxPCs+CQyXzkGVeEN0PZjmbcxyZqKpnifrosxD6sHGUiC8VmNKmiM4lWwMisi4k7wpW3yR39McVTJC5EB6gfmlOCgG9yX1zjEXMs1In27dcUtJ50dGBmI4pRkyJf35SX804Z1EELogx7t0EZPLJBSuQDJQFaMtQCzRyuE/idI8TYsCZzLHr1gwiAkN4wzZI7Ye3TnLkRWaW82yZzU/PrviL56VaYiXOVcZR16AF8AjEYy85m0ABEADC0+ZTSlQ87TUuGJ9IRnqtKsBxiADBO5Fz5TBpRm4cRn+Ucpp8EFwEQHsYCTvkwtDCItPyzAWr0giAkOD3eP720SzPWIdARzdZvfr2ZL2F7Rqq1/CGINrRHk8evxp+++4ftvu9LbPu6TEsXvav5gIIbT+MlvJ8DEDWWQYTLhkwGpDJOILXdkK8RazK4ckRkFLLOIsuvicCGFFk4vPjZ2Y4zYaT8qQyr57Z4vvffe9GT4nepafn3iZ+LgaUbdG7yPI60dDAtXImpnhMa0NHyTNP9LtPv51OFi9Xp0Ujt4f71fOVwCnvD5B1MDSxZ3x2CSBdXQPnSWEgDDAYJT0CAkACiOzk53/2p4fb+8fRGqf7zEX+k3/LE8dYzFBbDsH2u/6uVHNSdBi6BamVPKti/xA2+4T7zzeMy8mbN5oDEeWBWuu4muQTUUQ2bLfb9lGzYKQsqyMm5InClhWcuzTYMszkm5SIR1JUpBAn2DMkl2IYU455jmO3DapPokqayeSCSOmSXeLiCZ8Cml5J611Az6kDHdWp+UoCDyG8fbsRnEmCgXF1TZZ/TvlKr2f6eG+69ukIhwpI9EMd47YeXmUBc4wDWMmlQWZNysTYelROwoK8vU5SgpZSnF+cImJMvE2RCXaqZVES8DFQZAzGgQvt5Itt649y33ej/+0P2HZXPz//g+N7Rp6EyseQptPgbLA/PH1/c3fxzR9sPnjMqVIHiEdGMwZhTHE6ma1OkHHbde9eV6+s8AMQAIyD/vqrzB/L47B7/qwYUwpv3zdGF2URlzHc5Zl+VhHbW/fw4Ydepj5EkjKfyq/Wp0B49/TYbo9ZUcUQinJCwdXHHaYE5G8+ub6zKabXJ2fF/BlXOUpuvd+1n7OT/NJIYIDo8ix7M//q8lmqf/ftf/yvf3v74aYav0fBFlMnhs5gwvpIff2UlwnV3JzoleTmxXKJ7DwLLC1WJ/bzra148fLk5uZo0cnJUrEQbM/Pns1jFmMYQ0IMulDG/PLcV/S/d/CX95Nu/Gdd/NGfLdfmdH9wtATfHHShbCSxz4Rsvj47/8e//Btwxxtckx2LXM4mWdFnwcWcswCYe5NVGWP7w5bZrb4/fIo+cSGUEkgyK2PnO5uiLubAWCRIY0+ji8iOXe8JGcqimDY8JeQALhTG9VwgF1MmgRJgeKLkD4/pSAL0TJ0uZ3v21H7uYpVKzJDgTIv3mQYXTzJ+YHXWTmXG/TbSYuu9QYxZnk/E5EiucaEsp3PCZB1NzCxLHWa2UUwVgrOII2tlSoapZJ3nnBEDRA+QttGTYzIzqjgbuDoQaX2ps6IOnsq1EvswBo4eggTeCMEYAcOUvNvhXZkLzgtnHwI7rVjBqQ7B4TC2iuZq4cJIDPJA1vuKIUUY3NSMu8RFSuzWkfigBatXc/75rtcqK7JCitVfXH6zXr2ZLqbizQRAA2gA3gM4SC0ECgk29TAziaMaHa8MIAHQ492PJ5GA8TEGcaEg1qud/z13b0QfR982+1mkoO3YVvlosci16nJdglrAeIRBRhxRGSfs6B705AofjYsHxTlQBjBApBgQYMphQssGAAOw+Njvcja0Bx7g7PJMM3W+fvXllRtD37THf/6bv3643c1J2EWPEubzEoDA97MMuJTO3/khlJeXTEmZhkgzXERMnsrEGgi57x/fhRALY9L5s/B4iwJSDO27zr/qM+Wrqvqjf/lHw9D3h/6H3/7wu6GW320k8W+LrzFlSF6YcrQFf5YZwSVXbByovFK54j2n8Thkpydt1bPGP37ErOgYmzQRJoZjBcABSOcuhJKQCWCMc0QAAAYweoCxN9OT09Nnf3n79rQWP/s6C3CFjA3jYAtkAEykcdyq6Wxi5l3XA4BYlKm1qfdLvtzbRyEE4zIcOfIWq0WpuPCuMMJ26eH2x5TJ6RjWJ8Yrr410g2ctT0KJYMUhyPNV2h+ixYzU/XE7BxFdSJhH3AdoIwarJeMwhISH+0immGaCIldc+zMPN5Qw+JhL4XujbK8WkyIebn/aLhBf8VJ89fU3nLOUIiI8AoBz2zC6iX5+fhrdOtwMw9js4g8JeENiZhkrVUgmjDm2ufADqJJceOoaZLrQPAOVLVYg+WYYschVCDkiM+RH61OOZu+ss8HN5Wwl12dKiK/U5skxXz4dHt3ijS/LrusotuV0UePWFFdS3QL03XVgDBmg5MloQX09C7G8WswXFTLYfdofCNaL+tD1ThM0xNB/eKu83RiXfKWct5Py4ub9xy9WZ8Vs0vqjtQ8LZBogjM4NNsW4+HJ2Xl5ZN95+vkk+mUKdncyO9dGNA5JnXPnwdHhi2mRTVU7PzxlyZNn1Vl5UuczIaBt5HkERZWEYJhcL5Fzr1fm/WszX+PTjs5sPn35zPGZDnwmRKd3oPHhaZrnKK12sXryYLFYLUZS77d3J0h1q6lhbFKXR5+cXkmQ25tOh4zR2eICmBMd2qlde0Loi4Otq/eZrbYfNW/+0P3+7Ucvlh/vP6/OTyXoCPOj+QAlqF749/bqaVis5+w//+X/srh/gzM+y2QqanmRWpeC00QVAcp6cNdrePXWQomBMnM/fkBoiRS7GWV4x0mc4HQiBAkbIMh2tjZGEUKLUjAmrODKJABOCocAiUfK+ZRg+3iAEY7L8ZKUoEsSO1bBDIY3xik9yJ2RNAPc3aS3IL0Jj2/xgxwEi42a1zGLKoe+6IU5d1wx8jFlxY7vkbRqOI88fg2NayVLFnkc7Ao2Csw7KhoVTigl2QHGTkn9MGEAoLkUVsszYARj/GIMbtkmGPK/qjx8S5yrJGGNMuFwuQqoFqQHaaIlzCiH2EjgyAvLAJssKWsaorbCcZJhSCQwlXmDrIWttEgdinOsyr7Jiyhh/8ebil7+6XM7X5WRGg8esBBLw6GHUYDgAAtAIkYAQmBQZOy8Z8CmwWs0zCACHaJtpU8OrCWwt7Ts4OwPGoBIADvajE62UaqIEFjrzGoCgJNYPfhh0KdNI0VqptD0eqIdsukBUADbgfmBsIQSAio93/OSSyxxAVxAiTgBAAK5OXlMEGicx1E93zPldLsNsUZrUmdnLf/0X52P7T5vNw/u/ubk97qfJwgue0jlS5B4OLs2uFIMs2OjBrUd3B1WJNAJFGlP0an6mb56Ojuz9x1RMyyrrpCC2XzL1Y1u/0+/5BhPF2XT19S+/Cr3/+ONHiESInlISCNJT1YSBqGkgL2suDo+f3ogvlpOTXGfkk9UPCacOs2waJ3UGgLCxIBOcZUAIfCGkbo8HoXQUMgEAgCQQEmzgALj42b+4ur9+2nzI7UEVD58gkgEMp7OUBKbVbN5Cc+f3kVnoh65r/DBGSS9fvcnfMqROrN+8WetHMZlETMG3gbgb9k83ROnlyTrRpPPfK6acS/QEnife9sHjLqRzPo1N3RCcZLlgBJSy3Ie4ATcAxBpAE4difVXnB5GcC9s7F/0nBGIMJkbOpHHWmeKCE7WDfPXI/OX2VLN0fhmSF28//DT96qupYJsEbN8Ay0Bqzu1HjyvwdEriJP+Kfvn+w48tY0dI0PgVLrmUneaxC67ZEDEp9CoT0+pFHXbzsrDkktNSqtBsmwRaSsbBxY0KUoE28wWLznorlHhIwc3ZLHT3d3c32dvXP/+Za/rvfvt3X1/R451YrpZ//KuT//Sf7SKEW7hFK4fIrjJspUplVnqFGtKjHQA0V8EHgWKmpk0xKGWoh+14mOpLJXj9rlmuVzcuPbj2FRYyl865lEAxtl5ffPHF1cW3v2RaJ4Tg25hiVeSC4XZ7M9ihUBqSf+y3LMZMm6hkYuLYjhCkgrVU+2MzxwnnENz17U/ZnEsjhgY+3EyqfILc5Wb2xaK4KuUntW4qP/rdbkPWNo4TZsJMLKjFeXY2KatMPfZ3XOKICuBh2xDchfllzIRhRmlB0ynF8vkGGA0xaw0DgMjaoywXCIpgOsPFcp8wRfD4Yn5y+eXPzhcrsT3ciuWiGHf1nhTTENjF+uLf/rd/8bff/VVXHx/3u1yBYAkSioj9vg3Jm368tjFGBsQkisX0DAQ6xwmRjeXTw45XvJ3xAWtHcUEGEmzjg/NotJmVs1FHYj4RT8Fv4h0fCyUyaUo3eDc2aqqwRICm86NkOjWJc8ZNRhGw7xVn9dhHOyJqTqO4wIJTTuNTECFEQjHh034uopLpmOkkyXsr1TSxmPMBicYhp6gZJ512idhgc1MADQDxkYCBQqQu9qklrBjqhQik+sDB8vwqUkpmRnSYMt5gitFPRxzKUmvj3JGoq7g+BkouvK5eC+05zgAwJZYgMRQNHMEzKfRj00W/TUcSXJZVkaXsD/78V1qaxWymx1RevQDQAAxAAvQAbByH7EAQN41vqwFAZXBxBsAXIH7/gg4QALCA+Agu3e4nFzn4oX74UScOgYBkluWAAMi79hG7mvdjTaEs8qyvI5YEkbMWM62RAXlIZfQjMAANOQvkCTEgI0hdz4ALFhgl7ZouTkEEqBpADzwHkoCJRhr6pj4gMiku5mf5ca+migOvw3EU9gbmYPKz569+OZv8bfV39293292nQ4ob9RzP06WUpwu5TADJDwhaMb1wAQPFNADzyb6NKVGUqrml6HPNres18R69DdbqSqEB3YOnjbhJuT9Vl6++mb3/Yf+b8bevUecn3ygjEZPdPnakpTqdXM4WXsiGICMGFL1PYdTFOiAfutx0XW8My0xEoUY/dmOxmDHCcjLrMJ8COIDBghcgGABHAACY/vnVN/cn1Y39qY0Q05OSwsbPN8GyGxDPODDpt31mVjjPgh3JD0ipPtZSWcHMs9Ns9Awsdj5Gd+DjaOHSZBKA6UxZ68NjEicUOi8zlVLqdMGMtJsNRe7GCbINZjRjFAOhIIYgqCQjVhxFceoAjon6cEwxzdi0l298vEaKdeeKm7xLvJ+1laB85HAVE5361U3yTfx4K4gTx3hPlHZ3IkbwaZQ5a4lifncqlygLySi6V6+/Dfe2ZTsVq9aHQ2yk7aQEI4v5/GQ+q0ShJIfxRm93lMuoGJ3mrvFaM2yalmmRZxn4MvpBxjSOvreuJXa8/pytMzF7Lvh4vf1xPb4E8tW0co76toFFO519mZnNTfOOMSmlWgPgPJ4GPl3Ns6sKhcA6w9BnJldMe+Ysh73t553hKn45+VqW3G0dypEnninTed9u7yfT00m1k8hHb9NxG/d19/6nXVYGIMa5YjIF75JLKUD0DR6nkAqOxixZxf2utvkUOONcCcYxnSaC1Cmp37hwF00mKqbSBVNidMA0K7GJFBMkobOZ1uMIopj0nfL7IZAyeclsLvJJm5hLjPs5TfYg88MhLtfmZ3/wM0S4Tp8m6eLw6cAL/XzxZhnT8emBm9zZ3vs0HmXNdxfTCchvf/5nZyePh8+fDpLD46O7eD4dxrbvaFroIZYnE5sOO5sqU1Un89M//8V/8+nmOxnqwIbDNko71lZ244jIG8y0BOLAkjBqYvKSBDCJXNjVeraYdUSJMZzQ3Efnk2dMDh1CwqrikGNNXYzxIul3bgOfh+ycJma+sbf24yGEcKovJEDycZU7SjPij4+CcabTnnCWhURDPcTzLONrxg4lBw4JUwoMd+1uT/VL/aY3gsI+bo+MobUjI2fni0oqYFyWmUQRo3POKQrjeg4a/GB58BJZj5RiCp7wQqJUScxSmmIaMKlYP8RqQJ4BbihKYAEiQehD7SanaxHhxGjJTy79067fWWEFm/TjE7AUk+8HGg5CGyOZDiFW+aKYls9/8abQ+vTkdDeOU7PmQAx0DS3AlKBDcADCd0dZnGamgIxTy4zMAQCihZuPcPkCNhsrkp6vNEDTNg9F4TdbDi3AlyAztEWuGOwiRAbSwPtruLxIiRmp5OWqetobxgH6MNYJ/KZNJ474QkKQZH2MgIwFZ30pQwilYIDMQWJSAsctWdc2UV4oGmlRIoAC1ifs9j6lm6oo8zx1PTD2ZI8XLAUoNMBMhAF8DUECRFjvquUf/+ov6Fdt90/v39f1sXO+r2vv0uexs0OHMSosb+edGFme6JJpXpwK5679wZWWsxUarpXYBpoig5Y95qhoj+zc6zFhJK0YB+cB0uLrX579+M8/vbdxffdhLWfmzUpMhevgcDj4bCwo1fkk2wmjWzbhRk1kdNBAysuxnBgbOs4CIiPG8sxzZiDjkAqAGkAlQKA4IiuAEgEAgM6vfnYWr44fdBjuX8dyqzQHC2DxdTcTgogeJx/JTFM8JB3nNIEATXRSCikntmVtTzF4kxNgLHNV7sZBpjKH0fUh3DwM4nksY9rxPcWQnN8noZpEPsWUH1njva0JIl7M3GODiFL7qHXjkY5bSrqOgSE3spit9SJxGb6gQ/PO3dnUD6zSHT+SP309cW7u/Qf7fn/HNkIIcf7tVxOA0VrLY0QKOC1UkppZjgUTm+PQ2MTH8nyd+amNFr2LmsmJXFJ6kJhNF5fzqUrgUnIf7zCGeyqojarZ3CzwaloyIlCzgkt5HPrt+MlZF44xeA9lsdSGvZjPzNIomV9MDk/+7sO7iPyLqyv3dBMJ7P6JKXdZlK1Pc6H1ulCLGXEjFc+WxI54c3S5ZqML85Ni/9TsnKqGDbTtQTSatMxeutoPfQMcm3ZXoZrIYUDMoZbPX2ljhpGYuIIsD0ICgBImKzKIfmiHSiYmUgxDyTxKNExKKQef0nKZ6XKz2RnN82IZo02eEqn8jaAHGp4OQImflF2Prnlgqsu6Pc/GLLibJ3jzMlzfj5lnUnGhIKsW82n5tFHVsypDgKcDr4QftAOntXx2Pqums0QH2qTxpO1DzTZyU3x2154DR8esU+VUMgmNx7swrhMkoe6ZArm0IZYTL0J+ONxJoQBUJFhPz/c7y0dkh3qyzDOlfvbFxTicxRSeX7RhLP3ACCk4F5+eGsqy00UEYFzYIdoYHvZj3717vBs22/cE6ezFsz1/EA0b9uN67aFFRFzMXkilAq+O46eUEu1rhvxNnmW5tAP0QkhgE20Sfq6JMyRiRxd1PgpE3QjioQh+gFDrUkm+3UIzQQohbgiQkfUj8+qO2SkXGsrjRQnbDXlLRJ7GI+hEA4o4leU7N8b0VJjFHFgdGHlEanreAoBzPg0gJpzhbEbcGOrZmNCDOKPQPk/FtRDCs8sUryla4C3hieZCHuMusflHdh/bnrvmQcqjFHr2avZick5QmWwB1ueTWZHn06IAML+flS34RKEH7YECJAFmB5bR0wynAC66QRZ2HBvBe2FrEgw4ByGsd/rxM1RM9R0NJtNqn2t13OVGFPqVG7b3159SoNm8AEeOMzVbABFZkqJEIT796OfV1GQGpktJH0JyZeVq6yY18Vg47Acm55Pn+92NMmViYdf3xWxJPUQ2SMEZQmkmQvJEpZQqAHAIqX8k1tlxnOdFwnxJLUxKMKTvDEAC8jAQCICzS7h5Hz8QlwfQ8gBwefHqy19cOUh7cnlwebR711vbX7eP23/8p2O933etd70y/dnazhAzMacQt8qUlFi98bMVEEG4V/nCWZ+0hNxDqhLcPd18gBy+WH37iz/9Zerpp3efds2D/v6pKKdfrL7k51K7PJ8krsVcMsYhtsMhJ1aWKq/OIvMxhuks1c3ISVAS0kiYAgAAQwBK4AgQohREJI3O4PcnT9HvPr7fbsXbdXFyCO84MsZiCkUbIvDAWgjiQwrejp0XeD694jE2D36aLynVDGwEX6a5A2352E9bqSYjJJ042BmXLnjDshf3u5uOGHENiZgQ7z6+fSVy4mVMhkkPdk8ZB59SUcTWPLmN5AYhzKZnVVlgFIg+RSckN1X1szgLm3TKJUkW+w+JJuPQDj9dX0NkjIcoxIKT976//UmAYEriJNjInQu03zWzEyYnOsUu3tvDKTlmdFHOZRT+REpyL0SuGZOc2OBiOzAuLPm5aQVfx+r0BAo3xBg3H0bA2usheq6llqrIZgAki4JSKuTC+uOeLQbh31y9eXdzN8ly8/zsseuNUZoZUsPVL75tNntOUdU97ux42vqw9HYJh24kpKjr1let7Z3XXvB8dXY6AxQFGSX4T41loqRYRDp+atObb9Y68n1v5RT05Nywp5jH0ToxOEtqevaM2q13qaryrG/IDsBCUVaREhujC9H5ILMcGZst5XRykSjkd/LOX3O+E+OVmaBRxj7WzV2TDk7w4qs/WN36H9QRfOQBkUB4wpPJhKusdhT0BCsDuxtIq+cTda8zvL+JEHONnGvpbXPz41OQXLB50Dxbsozl1j7FPgQmF7NZliJEhpwSaEo1wCElegJjTAh+CD4PeQg+UVQmC7uRspIRQw6j9VVUWW42206yh/119Ew4P8gIp88vg7GhndqEUxR1qzMhk3RK5V9/mZx76YOrd194Gnp7J2/WI1heKN/DrAzO+fqxrlbz4XDQKbsdnmhUAMkHLZNZTZ4vJkjuJrkxOpwRoSOkVlbk05QYpLCP0kDcK6eYXiXaCpAAwFgKAQWsgrGU2KH/7EL1Or9YAXsr8MWsQqhuEVUiam0k/2EywNHilCU1r1Ny/RFjStyajUEEGhJkYICviVgICryKJTLg3IU062EI9+OHdvQ+EcEtkGCU3j8lJcureTWZVqz6o/m8Khb84sroAkD+//ec9OiBkRjcUEKWgG+b/TTLXYzDYRDsKHU1S5GxBOBZXQOzYOM4ODMLFCJwHsuK9z0cD4RwtHbGmcCVd1I3N1AuC/Cd7YrlGpN///6dkWbKEVZnaVNb7xWXzbH+fHOvzIvTZ2THeEQ5RJJCTOcXChxzPdPeOZuU1kFbn9rtECLjAVJEP4aoekhgR8dQM6kA0IWQbFtVix7SsLunOCgOWmmXRs0NnJ4BKyEMn5q3V/w1dO4QtzNRABC4nMXjEVDAaVQ1K88cyABygsBkkjJemoRg36zfwKt/AWC74+b+p7/8m++++3zzqW66QqfLV9OzFMVggx94rJPx3DLPiq4/8uREtgCK8V2HJWZl/pGu1V7p6WLyh9/2nz7irutt/93d99/yP4LXKpaVfrgnWarXL+ju88RkiZhi5sAwMiFGC4DTalaU65DGRJajjgAIAKMHSSkG4CwEQGDMARgABMEKxarxEeorWLn1odgiF4Pd2wCyyTibJpxXVGeoW9G34348jrmZTiQcOs8oncg5VdTua46iyKdDF0ZqL1fzQUF7/alub4w2kVAZ89VXX3kbfvj+Ox/iLtenp2suuAjGd9uYFZqNjVCbfpt45HKli0VZUvSwq/eGU9Hkd/z2ChG8Z5Qy8zzw4oeUxp8+XmLwTF0hB1onlsSx2TSphzDS6TwxpXxwdqRkJ/M1TxT6Tcq1miqx4qaRm/AwG1QPQz90UzfRU+l30eEgtFzmvBGV73d7u502ucml279Nu5EzUVzNdSyDIBcplyXGIhJwAdaOzuU8pX4coloMFFsM6bj96fEhzyvv4ru2FrtgdnfxuGOKo9INE3mfifU6JGONZKMLSazPzr0PQhR9f0h9c/8wXL164zU9DjQ4l03l6UU5dHxsOs+ED5bJQbgqKXSEMadjlEIs7cEdXNOL46v1Smjubh93jJZSPD35BGJ0MkWXQFaLDDHLsjlHbjSn50nfAiXX3v52FD0zFyeL3Dt+KMzM4f5uy4N8GjdZUBiY/RiRZ3s0z/halwRmUio8ZLkgcbAgXLc6WQ4tPHU3L6dSGZC5zbfs0IyuaRG5ENx3Yr0yzjHEn3xaHyPqLlSTedr65ZlaILhvyt/87pBzJjW77w6QghKQfJvn2qcGdCaEjgHef/o4m+dZJqrivPhmG3qS1tztxP3t07Hbx8BP55e3j07ALcpMhTBa29mXVcYnJ8XyvEgsMf7lz9/srdXju49BwrDpHmyoTp8lDs6twrgvi+j1SBS6beT7J4Nh0KziJhQG81NUT+yafAgxeC4tmuJUn6WEPT+tL/spFgi5BkmpEcjJ+T0LEWaV4FZtSHURBhu8TT8B/wKAPWO8QwbTrMAT6+JjuFcgQ0hEu0ydrMkaPq/NpreJeG4m7lJMEJcEmOJIcXvYBoRs6OK0Klg/+aPidFIUucny1cm0KNTFOTBWTBfgb2kPSAaYgJQBlN39fXH2DADgJp4sOBB9au1dL7iv7djOL19MTU7nUwlYhMGOI7IIYw8NAwyRsZIX8FnEnum1cIMdO1dVpzyYk3QDPIFLuuUQRxi76et5+/iUEwEfBNHVy5fj0KXBMY1d31fmbHe9q1aLqPnIivXPjJe+e9/7ZtzncMqCxVZZjDFMvYVqlhO53o17XJ5MLUD//h0/XUGgNAzZtAIp6m4rLOL5xKT9MCbqgbIshKHQhdaGqwxiBAj13QMTDBYExuCWQ6bh6G9EO9HLoJVZ5DNZtRBZCEhNkFkZR+LMjY3gglMAxQFMMX355tdXb359eHp6//Y//vVffv5482H77NUVxXRmTnCIJRti4O/te/XAmcqEy2z9gFG9WLwmgnuSXB6SZEkfX755zc7Cb//ut8G7T7f/rLskfvlswngIcTx+OmrIqqnWVVRCSdP1VmZqZKwoCwAQzIyudtGO41jOTgAIKWjN+sAKYlzwlIB5AAXS6H/9r/7tw//zp8fffWBroiZwwyJLCRmFJLlMT7vt7lhN9bwsDJrJbJVnRXSOQVgWi9b10FFhptaKutVcLMKImx5yzADzLFeI9Gb10kgPiQOkL9/8wmSGcRZisF0PIXFd2dRHjh/v+4iWX72+LCZPx6EdU2/zk7KI6Ej6hZvHEEuNDCLxpXievuq/aQ9NQFts6fP40zje+dYJSiSTgLMMkSccITCGQuiplNz1XhTZi6vLu3771G5s10AiLJziGeP8rrt5vo1iPvd1ciHEEJCPAB0JQQldf0x7zxjjLwpEDsASBDBsCEO73wquOS/69pgVI0mGiDNUg7U5oi6z++3j8/VpuVha7jJ+Gidt/4CQl0xvJzUMPM+1dSG3gbxFT23/eJ8WZ3EH2oDh6jSbK542j4cWASA0NtKeTzXPitxr2QQIND4HyKblu7u3+Y+7Xiu5WhXrk0Ue2i0KJQ4H34PLEhfzvDLx4dEBy7lQDEV0sD5Z+s3NrunLizPBxMLkq4vzsbU/dR/IJZXgiMC3bWPFbB7RJ2HVselNUXZas8G7kDophaziqA5ltjo952eytiN3R6EMF2zCgbEIwIHYdBWhjn0/9OjWhitDKeRSgRCnmTKhblBJ8g4EjWMrEJEzFhhIyASONnAhAENO1W13M10umRbayOn07MPH92PnGItaTRgudFao2fzVUnWb9vub3TB0K6uCSpKUiJFB4IhAT8f9UMTVMMZdPJRCRIYnQuSv50yesK8nr5ElAIb8F78QwPg4dNYPlMLg2q7dfXr7neKxhdEQSLK8qZKxrg/+Kb6PW4bHl4uZODlbymzNJyEmOwzIZX0UeZaxMHBuKi0HCvyW+DkmvaM4mJoNxQ/E1l6MI1cU2BB26KcZ5jmWGa84m+eCRe/HvhcxX7nBq/lYhyMlHw/O+ymlwWSz8nKWzcWFfHX1rChnZVlppVLbMS6hO4BmEEZ3d8taJgQDzsil3vWC+WPfFdtd0zQ+uEX4AlioAOZL6YazTj4BSRfiiJ7GDgiCdiayWB9YRKM0B9aP7uPuHxPiufxyP+5OoODCQFdDiGDd8LTnYiFU+WQbs2PNfb0vhtIYthSAjIxgyoCUubf9duu9PX/1quU8fPhk5xfeeze3/sYWj6ZeCNb5cl750e+6wwKCWL4Idw3FgdkRrCMEf2i5kGVZeGvRu0qW3URPQAB5aQfUAYiOxz3lWTM4ZcYJ50oouV6WIYGQgLUssprcMN4hxwM53/gGx+V07nQu45FCjMnHYLXJfIgMGTyNUADsd6AFXCAMtFr9cvm/e3X24cf/+T//L5v7pxDHZ5c+wXQcTCKPGPTlCe9mEpnUxikFAIHilLBbTEw+PdZPvbJFZl788mv59uOOc1+0QkKIsYt0XotOSY/iVChEmQiNloduyIyG34eBARgJhiCKGQswydVh6AVqzYmIMYTDAFMOsQdBUMxWf/LyZ//xt7t1PiHZ7HVP6Sx6xsIRbOSYsNRFWbDoKHgtOTli/ZMQeWN3LgQJWd+P0VS6kuOxY3yBUeTT9O3P/0j/Xo5i2LZ1ciMSm08WwImIDsA5jpGJFGMguQlazENRnCzyaiSR3FEqneu6hXnytaB0KSqbdj6SkZqJGwYvdI4BwaUMLFPpAZnSKYja3RqaM3KMR25FnApmFUYa29rbFM/nd87uP31MLJhcZtKAJCz8Ak6Px7ptdrP5lBi1EAvBueBZXjAMlHocSOlcXrzgSpK7SaJzqVSokmCm4H1nJuVEJJeWi0RUCjDRU4xMIiAehh66h6/W8wLg5p/f5dBoTIL6rnbMcOmzzc427SchsnF0XOZC5WPjg0dtqhmp2+PAM940TzKbXbycPlrnQmojI9eHQeTcmNxxr3p7qOvjJlz/fPmN5jgO4+0WqzJRiuP4qLNMy5Aw911sjna2TllW+RQYi/Vx45jiczOdzx4fb/btEHYjo3yCsw4GBaAQVaECKmVHN6CCLD9Z9L6pXWzb7tn8G2Fy7gsW9+HRJEkSQWrJjSJGD8PtqTIhFAxbyiYMs2oila58jNJ1sfOEXhis/b0PhkgBRWtHoTM/uiRBYpblMU2yWI9dvymymZChFWNRTRKgH20T21XOzk7PKI2K++OxRnqvVbFa/AmIPJubL7rqx2jbxGKkftyu59Nllo/1hgePEXxnucymaqEyGZPtuTdh0raNVD4302NXM6minHfaTarZqVyC7ZhYCfHlL37+q9F1TV8fd3fQP7WDz8Yh6Wi9f37TfDeMOx/i9b33gQBOpNRcwuykUmVVlJnKpi6E4KFuEPVzV727fnq+otfrAoExLPpYnrK579Ln/c3pSUXzhIN0BU/d+NT0+809JiCg06wgluk8Wy3XUsjVZDlZTqbzWZ7NAH7vKDAAP9RPu8d7EpqBM53jg2bLC1UJmAhQDLwPPsBgGeMXeALWFPaOmIA2QO4BwlNjS3QAcf/kRN75UqPvZSnhEHrrpnyJxqfI4vb+JoSQ4uXsD8wiL+tpEFOtBYglCAbKZAYobAiVs93hvpPG8CyXSpXFuRfSB8Kmjc4GH7vYpxQyovYpDUMffMdOwUhVLoxmCtwnGx2XjLPCt23TDxXe9+3ex6HvZwHIGKO0CowPQ4eBVfNCJL7yCe12HHxzhHyipZbV+kTnzvaJJCcahnHgyAzl9dOtbwZVZtaHofdCEkFA5FzkRJG5WmiTCT/yLBOcca1kQF3CWoFiwKbQbeE6s/0HXXzAyzcvXv/h/+nZ64Dj//Tv/x/j/af9cF9cGszSacclm25lH7qBM5hLqcxIQSGhE3wi+b4d+ovQpwvAXfnls/hxs0nuQjBaVHk7wGxZKokiG6t5f/PEOAnB1xdrzRcAAEAEqW9qVJJxmVKPKQPwAFIgizGkIBkCRggAAgEcv5x8++WXW09t71zT3vu6wSCMzJkTeW4yPVnMyrE/ggMlVegd9rLMxKFreK61zkbrFqY61CrFjdLGdfG68UTq8lnVCO7rJ4ikhEBi++Nx9DGEHoCC0ZqBpUSKtMq48xmH4KNnFHItYiIKjh4YIvjRzhRu+gQ8ARNqgbtWrmeqYD76ySv81v+hux8/1I+i/9ziMlWcPEc2JTMyH1zwPkWmL07Xoujj2L+Qr7I3PY47++B3odFDgYvTs/XYH4BS4oBdp1eLmGKCmCLZkEZyHPVKZhgf4oMQFxOOEkn4lAj4ZMZUAWl6rr0Ntj/4iGNPXhMfdT6Ryhx2R1q+nJ++6tr/9Jv/5f6r1ZQdA86QyyvK5FSYQ/2ZxVJLU7d+DKF3SRJTojz04yE2z/LlnC0jsoFL4zmnYzVbuzg919w7Hnbo1gONrTHiV7/6RVYY8A+p2SpjOC/2u8fj8bCY5qrIuvbG0TyWlJsqr6bK1Eq9IILdfrvOjZST1cqNdt+xIIfg7XjoHx73jrsYpeY+xKG67T8Vea5RBW+ySXZ+uUicAoTFQmx3azQMT5lRAghilW3vbnkrJlcomUk0pGjHj9uN1NPp0hNUZTZZKWvbAwVITJksuMQYF4lVywwJ+zgKlZtitVzDP32oG2ClNg10Wlf+uGud02cZ7IaYVRRCjDa52qjC2xPvd0P/Q6a/pfuRcaVlV3lTnrrjdjZ2TWcYMm84AylGJIEFy/VEcgKIWSCgCeBxHA7HY1mVFFOX9yuv/NHtch3qgeaJgZLCn6hZpopJVrHbeSgGdC7F4E6DfbO+cn58//Zoh13XdX0PFIlxTABMtfu+LDLWNqBZDmqM/qfPu74fvj/iIkNm1Kk+QlmNxw/3TggmReDHh+7kdC6i1pN11OUvX4+vXr9iwISSnAuKTFn1uYZnF4xxcYDjYXxYxdOUEgLcpaFoY1a8ypRnyPA0A+f8zvqMQnL9E1UUXceiCCSCRAXtR6Y0KA7uk2Wz6KwbdS27fmjXJ4IxWGBsgahxOuObfTNge3Hycvz+7rvu+P9j6r96bVuT9Ews4vPDTrv8tsfkyVNVdBDZRiJkmlBD4JX+qyDoRmpCkFqUSJEssiorM4/bbq297HTDfj50kY2G4k8E4jVPSKX/5pt/Cts6jpHrwjMzdTYMj4zipS6t8/eYkWfevD27Ql3qUgrI0bv4dLxldT3Mo6Cwblou5Xa1nhmKdJSqYJxLLWAPPLFJ9NEG0nOOniMtzxjsHIyH9bJYszOoy+BmI0oG0cc5gV/IBqdALCMfaFBm9LWW1o59bMhSchNjIiSRRNLGPEXWhhPk2NR1P9tIgYsq+BcCz7nm1QYE4zkWwVrO/Pg5jiiTzjzXSY9zqARCegF5CSXqdAU+wMenoBZeDtXZxX/3r/+PD/f/9v4/fJmm8VM3dgHPz2iRoDcBSRSFjj7qlTvFIJEd+WVRaOYXTncUyDVlVukwn56Pu8LAo80XulieX5ShoOMotVpuzupy9Re1HwAAEIEvt5cAMM0zkDeMGG8BAZExTuOYlEYQzPQAADDAaf/Hjx+/zPYw+YNQKJfI5Gaj6lFE8BPx3O2mZG29OMMsYhrm7HMSQckwp3YBx6RSDik8K22MvpzCzChtvi24mwWlMXtKNFGus+RQFrp0POfsoGkKPwnOB8lov2MkUtGWmSgDAtKylUKoaZTcmlDmlNXrVjzNKDJdbbr9sU93Xm8Se9B0XZgCr9m7dClWq0vvZyhZQZICehF40G3TSqk0V8HPn/wA7PhrWmT2IEbGFKc+v5i94HLoY1sFJflfQhqIlZTeu2DFStgOYM6Z4V4wxrg4e3n4GGSEVSNUsWTqJRHbPw4MaiWKHE8i1wsznDphpEn49Lz7w/Zv/+v6f12W9WbRhu0Nd4M0Kiay7oBjMc2hnaItreLs4cgXy0CeUTnlWXPWCIGc8Uy+v7uvq4oAlBTCoq1E42mAp5zOxFs4v+fW/fKf7w9qKK9f3ZilqKpMma3Xi7adnh6ml9305lovpEwZiSLDC855zHSgGUHEu4+HwyilbNY3R/yykM1ZHXp/WsFkAOZs5xRKrc/TeaxQEc6RV422PtokNRfAUF7odlFtkKJPrhttcEoKqZZpemIkRemelSpNed5e3Z2+EmdcrskrhRkEc94bZaRQlInHuyCqYZdiyXlTGy7Km4Y9Ho4o4uNcna26tpGt4N5wjt55QcSBeWuN8aJoY+jCi2fiU7ALcd028+Ip3l/CetG2/ecnt/eFzHEVfO+GoCn1w9f0KIQQDNjXt2/f6Galy8QEztNccTxX3E4+dSNOi6YSSi1FsGNQn5AWTC755tj4IvFcjoSUnrJYPWurklmshbmq15yjfLPlpJlZ5Z4BQq5yDD6llE8xHD7YcXyYJ8p5uVrfHg8VY0jk1md/VdTXpl397l3yvlisuOUxgBHmYXe7rNuPHz6+2ryFkt3dx+osRcM+fyZZkDnbXLN1JB99AqAboeel8seDcsXT9HhZ1onM3u55phgxUZ6ZkK3AHPf7p2+37/M484Di4n348BFXq5ZLUVFKJmcuWSKI1s7T0EkpGRgSnoGBPtyrsRHNd+9/D4V8njs6HogKZLtDyIvqMgb/IaY5WGQoqLBkxxNUCbLJx0Nmqin0pFEGPC+Kk5AyRz+N0/wIKYWiKCMw/yXO0wNicCoxkbaFARozjZBzagN2TtgEpezvb1WrU0zAkCC3i7pR1TR0lAH2GbkGnxtxXlASGOAij1ORlQo717eMYl94H4kYT3PoMkTnIshIxTmLe71sLxUCAnANEKuYKtaedGBjE9JhFHKWtyNFn1yThsXcAJcAi4E6HgeXQvXpg0j21eU3Z//t2bN/qP/h6c/7vtCDECsRBKuO2lAaAYBLyVLiRJRCgLaJyfmKrhHaH797OZ5mjEqs8c1iyUUMNL98KaqlLEpVrOH/b0bbV0YCGAAoC9YNGYBSjjHnUpaQSdCsvAHGgByghjXU1d8si9+67gUz+/F3P+yctXFcyLLNqVotjFGH/VMwHIdxHocZscz8RFlphZysFedtUUr2kCXUC8V6YvN207IYQs5P4UmqDJkS73ZdUjADRom6qstFcJESIJDfY2alLlDKU4QV5y67OVtxmIK1JPiRn86hRqZtzZhgLQ9h6/IxoD4Q84+n3yjDq/q1eqPF7dcvDOHt+7eCqb18bng7joPnqGBt3fRb+BjDkJnX+Jss6812U8Xiw7xLwATUIe2P8Hi1+LafXoACsiHlmKKXZqSCdyOeHQhYIVkQuvzdux8+777a56lslyNjYRwM8loJB64DF9NTojJPo9wOhiob/W6aHvYvh2cmzl8RHz7u5m9u7Pyl/2Ln83pZMa042JBPh7EMBo4minh4YkyaDPvP9876HjgumgXngMhzxn7/ScpvirOyPxYp7YlycZ2mcbjcnr/7/XVdvyJehxwn3x1P9mtnoQeu5PLyWyaijaKsV0JKAgwxVVDVekOlqtesANh7h0coz6oFFXel1wUXFPGeGO/Y6Pr6uK1vbAAlZF2Xc6ebM32auNR9Wax+bJo/HZ9Vui0lREwhRn/7rBJT16aovn/VPo7ARYthF56fdkzah5f+7PySA3vsvi7LTamFgl1ixoyyMQXnfLZ7S9v2vFXrbw+/PDq0lFEt1gA54sCZguh1UXJk/sCT78smFarBKKEnT71iC2W2bIw0nlRNqao0S8c01lOMeall8XIUSst6UZ+t1Wkovnx+XK24YEPFyoiMOFNlq4xh02POndtHnFMyojcCQuqGodcrLVa8KYyvMQ8B5uo0p16sBXFT5uwjRPZyFFFKHTpeJmANiVSbTLZtRVZv6PrmO0oxzjEx/enLm9Wbbrhzkr/dvMUFep8yBoZink8slnH0CVz+/BCs+3j383v2DfFjGdZc6cMi0Sng09SdVTXLZSmAMlEu4zSCVf7EkE7+2EgNNEePLiQfM3Bjo0q241x6ZqPgxvk4z9HZgmDMgXuEnGPY9yddKOYyIaS8dDjLGJwuymOKnAFfvAMe/QTxZdBGAEQfuSmUPTlrLWPIpFOy5EJffFvter/EPNgpTy9EF/3UBT1RTN3hJIh1criozjlkHeoAoR8+ez/qalmsFmebciUZpBDtlPOBI8pHDkyCZBB74tJOlmqSyUQfFk2bYpTSiJkDCmDMOUT/8qv/NeclY61Aevuu2M1ooA8Kc86lcKcxppQIwI+zNBKKqLhs6hJyPLhQI0rBIcZoY+kBq35+kNVN1/cbVaGQz7U7t+NDiMhEVwjDPR/5MzBIIXKpNFy8um4prf/h3/z7l0lvzwuCLMOlFgdmiKGZkQPmRaZpu0bIGSHQ02c5lUqrRLeC9qfHpcvi7Q/s0UhVmptvzwA8wMl7GjvknHHGEI6nqW0ZQw4kUwiZFylnSQYYBTvGnDkE5VYeJwUCHD+7vPnn45tuOpxfvlGCrQ+h3NwIru1scwzRo3STljrylIqiVmWOJtgXLmXMNHfYLhERldLvz3S/OxmZlWTO/vznuz7s/eu3b/alumBLfoaKGy7Vy2RVdpyD5ECYug6gYBfNxgXf+UAChxiUZYYprKVommowmD0h2Ti2iyWKMMQxYwA2UbLUkL33ubJCCqGUukJknOfom6CADeD5Si4Q4DgNOrav1IbQG+lZHlSqQ0yXqfFcGcQjQ3sEXFJh2jlbxrlUOkQaAy61LAwd/EErSSyxh4/Lq9ev19c/7X+meVaYPVFcNnE8RdvLPOOaLrK724Z5eBjUFavEEq73fb8fxuvl1ndiStbjSn1zufjTH6GnB/90Np3FlCYyhdEOI6sJbN4fvpb12RbbB7Wtq/5ye3Y67s9eXcqch7ou0cYgA/SapZxzVVd1U1+tF0YJxvY/Dw/pCVLGxWa9LJflWT/NhjHxOPS4WC2J7kKXfBnHAYUgpYGLl9lGpIpyzOlrtM3cx+CUBAjEhCjr+pvvfww+hkhAHIgnJiFwYkwhKy5fQ5hCiEs9Yif6l+d5mleFKq4V3c72yUH1q5PLmNjj7ZcESW3XXNTrbc2Y1CiLTXMm1hQjFwDQ9/lEKQHzMMNuqPSySTPTJnIvZ4ZzPyykOqK+Wi6Pn+5GJt815RDj9mAy8xC0mPgjlwmYewqE3erdenw4ceQbTXaeF4IRoUsgWeSMI1COPkYsSxanbWPc7CZ2ClxzS+S6T+3lm8oHlCJC201DtV+9/rGBphgXznLBgNswNDIG71X1rHSRSoddwnT/gNRk1iZijOcElqPg4jDFZa38i3vgdkzhm0+BXZvT5y9lPhcgUPOnB8sZ+HPf//SZuNi8eRdjggRcCSWZPmbg9nvNYsXSyzPHOJ7cNEF5yTgfwxTmT0/Vzdqn4MNXKbjgTc4DLNvtvnfIuGHLebJkVNWcOk+pFUuZXE6U59O8CIFJCUJ2XBx+/VBeXGeMiXKxWWsfnJ9z8t5NxTMb+TNFsn4mOMToy1Kekn/e96uChyCjG7yUMvjBF8iYUMLoV1JJcYM62MQCy+DsxJHicCdlMc0TxJTDpApzUa5ZDqXW++k591CL9up3r6EiEMyOx17um+wQrGCAsEirI+dLeGHATYojudozK0SkkD798tts01qWNZZxzlJ6KZc5J+9mgCLET4W5pvv0bqvTZIKNA07Po43eNwsTYq4qrauCVqwR58Lzl9j7mQcYXHeKzsWYYibJ3o/zn397pqL83tgi591L3nHOjFHrcD5Lx8ZsKIMAvmhhLAH2MBev3/9X/1o1/7f/579/ftilHN5/YzhTQnHOxDfsoksOkS8EOG5iTliUWiImSoykUbQfpWHalKkuSlVtAADAAgilUG0hRoVARFlL9hdBCHPVNBy1AANEmVIAQs6IccDIGAcI4DlcNW+v/tV/dVY/Pv0Sfa/Hpyn2gmszuyE6UOLJz5slxLLOkwejU84b3FqmlORZxFFLN2Om4XSseRxBH5KPtz8P5JMP9iV+zR7vcMmpvQEROQPqDmk65yU39vFTIkJzzjkXT8ej1IZlwkzQnRxHQMr9qaz+snn5kfxCtQn85BxfzUKgC6NBLK4F5V/sxyy+0wUA5MNTXCwYQU6+baVgLGZ/2L9gDWD0S3hImanHguKv1nof0vLygtp1ast5HI8UeYHSM845ZGBcV4IREQDknABEwwXMNqcgpdKSZzcybpBT1XVMwCl3HAmJKN9lyFzyzBK7kTHRfp6e+u7m9esff//j+dv/cv/xkz2Wqmj01fX189C9HIpevyZM4H8ZwvictMpcxYstDaWrYU4UpzjOlXt0t2u2WGmllIwuwMhyHSVXUujTEW73NEYcxyEDykJXi3Je6iKxDJfbswUTwrtMu/Fw0finPV9rDImsfwj3nK/m/o69vpFK1nV7cEMT4By5EDyLmnf+SOmybsehOx1PISuJPBFnyCGVmmt1WSZSWkkb+tYOMuUfqlK8uojhczqj9ALzQwy1N3VbtGK+H1iwQzaRAJnIlExUgDmFjzECgzQP0pg+jqvGFIwBRXetxO79zfNnHKWa80vNL1aJYHZUlmdc2HniwvozUFpan3cumIIpIWN8FoINwykKbkDTYgmiiznnsAzOE4niLKRTOr4Mx/2kdKvWFLMRvJnZOGSSCDpTfhhyjhImvl1Wc1Gs1O2H8eLGa8EogWWJS+UzYrYIbwC68XhI4Ug5mZQXknPBg9RRr2qPRbnlFcYYcxqNbHjMkCHPfGfzS+obeZMhQ8pv2DUHAM5jDIKjQJlifu7Hs6XUSDPuwSykWorCVogxjyYdeKerwtg+lmsW54PQnTE0kK/jWFcYs488seMhtzqq2U+IUulKuHgSgYFdNaXiheuohXle9PFcbb9K6+wA8VLqgc0BlfC9LWpTmEyYwYyQOea4XVSn3cMZ9dnDlU4EUTK7cxAhF1zINeQTZR7ddoqCiZn2t13//Ckvl8fjvipKY6REtagLEGTQ8OXoByHCPjp6LSu4uICGQ6b943HwT0iRKIzCL8qUU5yeHunAlt+uhKxt19vshMBEwy7HaRikkIixy+GU4374FEKoi/Yf/fhX6mOxoWbgqvc//5q/eddOWWfVW2QRKQiFMcyKs2q7RKYASMdPL10BESSrCrM4u6pwGikX4ekjAX4qvrX2N0qfnOM5AWNCq7WQsePHaerP9EafX0FVAJtgCrA4hxRhDps3/+Rf/Wj+9vFP+8PLy+PIhagWYLT14QjYZ6axZ8owLYoxvb1UxbP93BTrc64fueTbs5LzU8ZI+ZgA+wzUJ2SsavIsSEeiHAEyAwQCIMEMAfa+r0SNQCEnxRUK6ZITrAZg0PyFE1eq0xEpYwrifFlOgXMWSXGb+WazhcCUTiHG2KV5GgcIGer1pRcsHRMkrq5E3Sy9ncp+LwGhOt68lusP4fRWabl9ys/ORQqnO5qbtlbbRnTRcasYEpc/fL8R/OzD42OaAuiKI2mlmFlwa3OeObAJVE6UC63F8pjSJsfAUvDlOkyHOJQce+D2s4gEoodcA/UhGxg5B81D0WgAd7t/EIXKiCc+XmTBMBXfyOhStO++xNxn0kTUNjn44fCyqqocg5JVJha5911uSsYmGrBfhjmxAoTnn3/j3119vzafn8fJnzbNEiG7eV6JNvKvz73/MiJDE4N9hK+MmbvXD6/wO/XD72W5PIWv/PJNddJ/+uk/di9PV/fPZ0A04ugi0YIApnDkilC8sRMxmb7e7coql02NgDfmbJanp2l/vXxV1HW3608o1KGAF9Fsl9vzam5gngMSq66ri6sfQpdniEIhZ2LnpvH+iyiqsqlAFqmsAYx8tUiEfncY40uwvbn/Mi8rafglw+RxB9L4VM2ISqNgu6evp36aXbq8WkPZFJvaDQTKyFZwyeHL3fjtO+oilmeLpz4hha8f3G7gb4R+/QpEwdM4jJMlwRUHgtx1QhaehUpylsuY9oki5Dhbl4O0CSEzyVM8pQmmQRjSRvEMhs8dn0xoVfUydWKwcnvBMVgpu66s6iKUL+GY8jQylEyUMeRGucS9nag0EqH0e+fZsB/S6iyIcp7Q99kLK25uogd0IVTavNAoELmUnmfGnCUuN99wHeb7W8dUN/18PLHvf/hBMNvF+EbKl2C7z58ZwC7nnN1ScM7QrCsvGzHdqYURopxtmlOno4kJkE8Nf+WzzqCB59c33wgjJGMpxVqfd+mR707mdKokj/0vQmnNFhznkIRZnu33v7Jpt9naFAWGiJSoAgSgyA1n4/NtVti81khXJtzPc8dQKU1SzkwJgEQdVWGeDPbHGPSiMpwZeBzc1rEGylvctfsErxf2Sy3roRDhKHnlAQGPdqmaJKUYhp3wtpBivXyVY0w+fP5pPL9h0x6XIlgJjRZZVoNOepzHpSxXqmQ4B9d9vkUXITud41VVLtn5KEfhCE0M3nrBcZds/NKoDSgDywpO0/wxfKYvqpCJQrEKyyLOMx/3anGGvue/RC8/fECmbnPIKLb6fD+/ZPIxw0ItyqIEYmebRRo2Azu8FQtVCIZBpd2GL0ReanBgta7lAWZBdK4stoKIrGs0QAqP3WA8mTDZEJOUrpv3mjGpOW5atfgWhPgdr2H8R8MpPPXA2XhxfmkqNvQ85ydzua6NgJCjmxCIKw16BZDgfgCDm3/24/9ufj2O/fzLH7/YwYZ5uc5JpNxto9wKPLlhcOVEvu9USV9wqrsHnaWosJ8h5HTBuKnXAMclg7jADAbAAsSMQAmpO/W6qoVAjgxG8BhpxHGp67ZZjMPEGXPRa824+p+cAw/0aQq7+/vlRYsiuN0sdMoB94iXXFHOmYhxLoU/+QYKjroCoa803AvBFN/ElCEf2QNBXnQ8HC2cYV6h9+DhVudFwaSrChht6Dq2fN0UG0ZTJsAzy3Djnbfdqa7qWkk3zSXwQjNXlHksUqI8dizHec5cXszkDhQBOwI6Qqwhl5ECJYGolRIhEwLUkKkbmUBxwQUTLtwSCWmUJ7vIVOkF4pAzhswFz6+Y+uROkwfhHXA22e5yvUxB5+Q5Mw3jVlsigTXRmIYYVQNSWlAK4JGv3t2whRtcEMH2/tOnn0P2yBLlpIwWJddtxRYtLl4T0/ck1X7/6+T/5fXvt83b91t4++7Hf/N//j89fPmsfKjX5yJ7+5KPp5+mmC7hrxZ1/MI+nw4KmOe8VUJVdQkMOTUlzRkSSnE4Zu8812fehvAQ/Oq6iFu8vhZKLnQ1JJbTkGboV22c7WincnuVigqF6VOIs42L2GTZMSTtdVFie83gGLo5YaqmJBhfm/KF8oiZp9owXLZttYxSaWD85aRSJ9tlKVtNht0w9uX6rCVQzZuPf/d3z2P/lmXdMFYgEwwYJ8wA2RPlDLKQkGWImRgDO82m5ZwxaBMcUuR2PlAkoHMhSA5EZr7KjVeidtXH/PxOS/udhDADSOh6SvzAgaZH7ZTZFHa2T4MrTSXk0uRimO9qoyNFypqSJ6jZiUtuKOeLol4uayXNQfWbrTjVRgp+bz1/cd+8M2p9bp+f1+xtl+7AsAgZfKb9yzHBzaug77b9eO/swAQ/50hIc37JkHSxTf6ISa+45DTzVe32NmSNlquz4tA9xZwWAl/cPHBomuRZyL1IX8lWsDICgD3cJSHslLF+2ufC6zNt3SC7IOVrgP3+KN6+VVv+O/V0RF85F/lGKcnHp47XgPOJCWiSePFx/HWoL56l1OlApIXsfXLgXIAnQCj2AMOzy8TWZVkbkV5iZDALuQhpHqc/sZ9/H3+/XscedV+CSjoxDBM2i/E0lorZ3cv+fGsuNhfp+MnvorcOxMdK/1UtH58Azl12sT0Wo+hPU71e8lBGZiWyw77ABJJIi5URAoRQzywaUdHsZ8kF5Si0WpZXMOc0h273/Nvzbcr59eobJ6WP95ghRdLKksghsOK8KF9EYrwxZzdstIm3l0a9UHFWSid+euol0wmuQrJX36yfn0Fv2hhd8nZSTKm94HAGp2grbIsNBQTCDQdA75UUwtm5H2PbDsxGi4YHmWzWOOWygmmEcQYi0AyUhNV1tRZvV3hIV12G/dQz1wsOojAnHioRBbQAkKaJuw6AjSwWs2IlsL5uwqG5eX/+KkEOwNcAEkZLLzSfhXQmI4Hzc6aH39ZPn/7LUWa1Wq+++xf/bFNvF1D+ha6HACxA9mAZMANA+JdmQMpjZmsGAAANsJ7zMIUsCZkom5oz4aX2AAEgAqwAGBhRVsSFHX8DzjLLvCbY45zw7vnB+pkXotrWk1WcZ45Ic/c8fB0urrUyRoq7cfYPn1M5flMIHFQm4go55umI9YWEhBP4HDi4Odkw3/Vi855kZalnRcqZYkoueFnLcZzC3CGC8xMWJkvBI+NGU+Q5BaNX3PmeBoI28iMxZlGGGKcuT5muGIrofWIcMHMCZ0QlVUzz7QMQJt92+hgm5+bjLGWeB2TMxPSidc0TG02SuRDTgZSY7MwoCVYKIRlw7xiwCMRVdNi2ITsWet+bFB0r/hAfwM3J5/i3z3cxeF1IJFpds7PVexBctkuF8jNRygnnW0ol/7aoF2+5LgTh2dX1/+a/+1c///v/4T/+3W/u9pe/NiWQTZA2jIprF19AaFyi6FlsqrKuSud8iB4FMi4QGALqUhdjNXbznw/zYOdr9eu2CK8WWt7nw1kjZZtDElzEx6cuZVaVl8jIuTsXlJsYBz7ODy6FRGF/5JsVMCdRsEVhQCkT2WSxquTUocJNUSKrZxpc7jLwvvOSWcFA1ytEk2RihCuXT667//njy9g5b71kxfotF0RAPkyUYfJaaWmDd8nLUjNVcS7Jp3B8dvORQ7p/Hgvbz+bAYsvTs7QPL2CkNIMQC7NFfS5xCseXV9RwKVIq0taMX5+TdSQ2THTOpPuHe7aoqnrLLSToKfvToT9fLSAeRQq8o5MpGErv8sIuw1e6V79kyurVK3U4zTevLsr6Zfdrx84rH/vlVrZ5GbZ//PIrMHAznGBAAfKJR3pGlndf91ffnnGeAWNmA17AslTHR2a4HgSylJc2WgwBeQEsOT+OgzKGLaXYWclQ+G621rQ4zkliSUSn01RIbzsNuXOIy1TClCEiZhzrk5Yc6HDXnc+PD1fquqmr5/k32FWqKD3necw2pXNNrH13A50/HYOX2cIpQRXwGEGOIZoEvlY+Apd1XTBebpcGWU45v6orSnluXB4SUt599JvvbsbHILTXYbJ2fnxIQj57P8TgYwpuB/xi5ldLqKt3P/0MKcSPHSviOUH0G1r6lruglqhxECYEHyd7CoG6U6HNeaVNisA9EJOSKHhEIDdhUU7jJ/aEnNiv1mdinC3r4pytzDzfaiaRnLc8Bch5UJYMGMhRYKYLk0+RM4opvlj2fZBM8qoui6rxdhiDrLQOwalK+sFHCivBc05VTEGwdG4FK3FTIDsyYvPH2ZWZaQDkhSzISWsBxlEsFhJMLRekMR6OzHkQnEUNQPDyFYtXzzNS46PiFVNueU7oS4DTuEuRtQvnckoOk4PF+eV+6Iz3G1zt53sfooa0uaMYEttY1p16ITInxTg6XLYDLhTAm+vz/+2//OH004cv37//a4QLAHkCSAACQBEgAghCjgCQMknBGWMkNYMMwABgTCEBMZZj3nO+QmAppwo5ImSADDABCBDyuwK/agCcQ/A2GNuK5aZMstmYfv9USqlQp7NN7ufFYgkU6kWA4EXwj1M4fflz0dKrqopsng0wJos+ioq3nuKsZNlmHPhs83JFu10Y3GlzZ/iqzKvE3cR7ithUDY2jJymVYgiZIMY4ygmL5Uq3YZ+8ezIgUgTyIBrlogzOhSF1O1cUBTLxEEFE7xznhFS8M5XiAOLzUygLTLoNXjMRlCdVDJgHw93oATzFzCpUpRFTgXREMAvuGNcVIhumRJQZl8C4VpS6K88jAwmzR0ExE3tMQJCZhyx+qKsAZfFqRRg9LRgozvTjnOYYiCutclFev705/2Z1Mc0nSd2n+zFKc9HWP/6v/vv29b/7t//D3/+567rTGJy/1iK92OPxJ13W7HRvlQ7zGCTLwQXKy/ebOCQoWfAp2a6yRbSOERlZbOrFGVsRX6dXumxahjy9PEfvEl+KsUMdc4yfCFDMLZ7ZppVaoyfykXF5JfiLoy3Xmsvs3MdPnxpiy7OLchiqeikZTzCKec7IlS5EudauaC63eiMi5JjvVV4iBdufXl4+u7nXnNGCDf0d01HqG8Ebgq4q/eSEEJAK/m7zPmV4DtmNR2iSNChGKDLympPF1Hk4nFCayENwBwt5Znfl4qa+vh5szpC1Kp3zK75zivnD7fXm4vbC29BmijfYKjceGYfTASgylm13YI4AxUHEKYZWGYmI+VblwkerCo37nZ3G8NG9f/PtYwzD/Wwl00g9uNVKfr/4bsB+cbFofmuP7JkV8FpclVUDNZDyL7u9y2E2fkWb2Xu/Alko1rnltoqu64/xqq6hbeY5NM1Saz2PxBlv6iowBkhS8UaKKch+hjDaRjGTfbZcXMjJnrSWLMjgMl8jRhXC/m1z9ttjmsNYFfrN+ruPRx+A1YvFuqV4FxJOYXgEpgWcpxgT4aJQXZdlnhUgOi1Wi6hYM2LN6Uucfvr4xftAOYtZANB79f7Ht++l1M+n/WEPkVUhhjTN/ex545nNq81lW5u6LAEgxy+UJ0BT//4b+PLVhy/UZcQVrSwLzvrUlPbQs5xzbhqyfgkU6vJ8u1ZzB9LP0CFUSo9hiDjRkbMw9BgY54JAEsxC8oqtsNw7L5RADoSZvLBtwxhrnkYwNa33MUjKzkchAxLBSSj96eghZ7EsbMjBx4KrQFic6dn54A4rSGnNKDEgXlQcag7s6KduHIaU8jzI81WDZeO98y6BlD7HnbM34tJIZQerNWcWEyW8ALbPPWYhVOceKDfB84ZxDQzHUBaC8wLKU86YYdG7xwIWwAJjplk0y+3SDX3VLnKfKoPA5zygCDyNvqgZj3EyJrvU3XGhs/KTfP8B4P3v3i8BDIDdnx4hXfPMYAs1AhiIgAwgJhjnmBgnStlPo5SA5djvuS4rqWRRAjACyECUM0NigH85EQjAwdzMK+titX6nJEltu9yPzGfskl9HPyNASNkdjhloH04ppVE0VxULPjYhhovlda2ADZzTODPBUk/J4KqQ95/68Y1puWDcFDFHWK/yaec+z+E6LJkZirAPT3XBw30QKCqNYbMSw5wp8xxV1EMceUxcJG7UtonDwD+8dKxnFAgazovi7JvLBVtyIRGZqF+9/eXLRx/8XwUeKRNKO2KPOeMEzmYxb1F3YS7AcrOQYdAeod7Y+/i823F9hgn4Iz7r3ebVUmWRsyuKs3HnD2lXKTHgdOZasPcHhDjGdRcnwbUUrWCEramYUhS72ImiKpSLpfUxNsUZpJkJAKz8OO4e/j5+rML6h3pdy+ZxmD8X85nU6+t/+i//D/XXz3/4H//v8zB2P9v5n+RP50ZzLPn12Zs+tuuV0oXvs15pHBjlLFxzHOdgY6Xil6/PfL/fFkVMHhldQnoWEHPk/UurlVLwtP8kQdGe7WSSIQVEcXOWYZx//iiYrK/epGnrME9Ppw9tczP5/X4/O39z+b6oKhgPRl2n8AScMZbHyVVni6WQ88qQVnPOKQWwXZTScDfdDdkNLLqrhRI86foMRUVMBSKWV9ENpYGyvthjBxlTCMe7DyzkmswpZk2huMjRXhReM/4S2wzztJhlMCFjE079L+6nb8IjE99Lo0whtCq5wKKaxthnii449E+wIg7ZT8cyw2E8mqJQSnYhMCWXUqaI1g1vX63c/fDbNGxbeaVxl+KyVVPLT7noh17IK76CqtACmT/0cVa/7j63pJtFy4EtUwEhO7+T8GRHen7OQohqURe8YlwhEwVeZkv307PSVz6Xfdpv2203dhjlxKRmPMUEwAAJkGek4D0yrlgPPhLMkjVZ+Rzn2vLDpJokQ5vmUunkyzICiO70tBBuCPfNsyqXayE5IHTDZKOd8wRDokDKDA2MdKpYzbBmIaq1XjzCR+Ht0vHj0y8xr3qpvj4/5BzratG25hpZXxnBB4/I5KFcyvbyDJA/9P3pti9Xel2UGRfJ2RRTCo8uRBxnpnL2v0gUAgXxHF1mwsL+WRZl5myYpPZsJG4YmyJ/1RQOsx+fYjeigtSElNAFBKUIrWJKZLTHPmrQoiTIAImaGUtWSd5NI9fCzSgq5sM2sJCTdbip36mH/aNiSnI88MxYodrnSqwVK8bZ5pw4V3O9kJg9bmyYQR3Kd4hiVChjXs9M+ONuv++SC+cMhVFNXch6M9s8Px/k8owL/bh/IWJlaWIIP9/+8sqXt4eXb7RpeOtXHsmDudQD9ZLAqz6naSNTyD6xLUcGlxU4mumy+A5Muv/0yY3+tH8aerU5D1nk8+sFMIB8rtoDWQrbwpgtHCHbo7QylUXFuHMD/Grh7BGQQ9FQIpEjV3featUlaLcAUkAAkIwB54JCSJQJOKSKOKKsGl385bcaQR7iRDlrUUj8n4tjf3nbJs7bhRD8119+vnpXHcUBXZHMCfrFob/Tc6JVudGmqhcZgZjI04DAjGafTs+rFM7UvD84UYg+okGfilzPHJkeeWEcWSbS8Vm3hQ7zyTnmZp8hWeu1s8G9oksl1KP/1TlnheCnQ1ICOXMqLMyyTlGzlCkZw5LtPt1+CinxxAptxPISnBUo+smyg+tgFAlyTDGE4OxlYHeUb9z4lZTiHCCxgi9EZAwilMucYz4dO6B4+9tIKLjM04TIlIZVsdFJ8Fl4d5Th5BE1EyEAUtR1qTbf1TKLQqYUKudx75GCSAm9jrwJvqvkGTLrrE+MsyAEyy3iKadTvJ1R3eSzb884F43Qlxd2Cl06wm0Nvm7rt9/8t4V+++//H//j89MzcMY1ZzfCx1lq4qUML/NESSTwQxJa5Wg5zzzkJIvB9mnoA4PD44eoeL8cX4nf+xxc91C3l9Mc/TC93babb64Dyx0Vp9SdKV0CPHIBq5s6ib37fPpUxvEEXfe4Og/juF5es1JzrbncUI3JDVJ/k+BjI8pMUyzOhFTMYCZqKQ25CXaMp6dp/KAYe7U1anuV4BH5X/rL9WnopSiEXCAURLqOa8vCXRhyArZowzC0CODARRfHu3JSacQ4Zru1+UAUmBYpp7xIeffiSufr5WJ4eFr/4/8aWaqEP/pAmapIZLGNTZA+kxXE2lIjEkdSbrZSzSBTJB+jDfb+6fEw7TdnrSwwhdmHqTYFV/zIBnWzKhiVOTACz/x49FvBQnBw/5EzDLgeTwei0yfGF0pLzl9X8qlJLGsfMyQCgtOcpJNG1cdDX9YVzmyYptfnb9zd4+S8lDwUkVvL2AIAXYgM25QJ2JFx6mJnw0zIMFEp4rT0MSxiJkMEiEKwTF/l8nKVcHzokMsQUCqlBEHOjIvyUgmAekceRGZJFSZmiGmaZjrMJ+dc4Wzk6vz7mym7C1GsWlnXBVFnfRZ06ASVoUxYVs3s959OJ/P88Csgyvrbl+i97VqPSJmRn701TPKM6A/26GLKwJHXLfhjGN1Q6JhiPU4o2jAPbCjgEKEmVcjC3ITmN3hOuJdDfTj1jRQ5xyRoFlH22W7MqvdhHIerq99pBX6ex+Sl4QynbhUWsATPb31m92N/vlwoX9QbgNRbraGPvAzCWBtn7EOqBIwpp2keRsoURyhUP+NFJaSWwdoUMyaf4pEyaS5HwURicwLzOO+tzRnPvRqtk0poZbikD7ekjRiU5y3w80UGk+lEg9iZYy7OJArvA/hR8JpzIJ92+z1bryHh2I2Q2IMlcIGSR6CIJ6PbySXro1HF2D1UbUK+1Dn63WmMabATZWC9LvCM16+y86xqodvB7YnapSae3NgQhG43zsfq4j3BAeGKI+iC23FGAMC2BIKUGOQQPEGSXCFjggClVCgBAHIGwIwYMGjARb34p//48u/+7aObjzShWhvJ1/VSeB/ttki2v892xXOttPL+ix8vGuBYT7vHzGC1VevtKgHTEF9wzkSj8gtmgNVNVSxR9gOvcCLGNqDysnSUDvHlDh4hp1xz56Kd7Nu3b2gKk59qP3VaGcecP7p5aM+1jsthPuKQK4Drm1embh88i4NNdppCQm85k4JJIaR8r7YfYCekDPbaYq7OrpziHFTpiFLOzKKvdg9fLGQCEpzXgK9xUW2voOHhRHIpRQYOBCrrMQ7z4Z05V9/eeB9CDIwheozoh26IaVwyS0tGRxsgm4vFNI99k4rKRhtmVfBYhtlP3M37Ryni+tJcmMt1samaNyyXBLdzP8hEa2DHHcl0EoW7vEj//b/+3z99/Y+Pd3fTs2utJc4TLeyzlzQ4LjXxRi8HhhKkc2PkngGkEHtrdVWe3ximOFi3RF5r8SkqgTnjsxGpvmrzuP/0ZJfLIg9uuGlub++Vqq5lfrm96+wReS/ZYrm8uLhWX9h5+4rnkMcsfSXXdApQq3yYQX3z3ULp9x7Qh6ySO+bUUto/3P36/DLMXXQjzykDpfwcc5aSZxhympg4n9jUomGCCy4+2JeVszmmRdEqAlDakA2Jy4nvDzhhVH4K2ROCqHkmtH5iIOv3l2HOOM0Pyed50r/8Zqry8tv32/TovXk++EtkGQIkYDAYjOSVV8ZZ74HmYYgqLtqt9ipOjPEFigdCPs92WyaJNOWDMWzVfDMPccDgp0lG5DkXQi7P3tKx5xL6PHs6MJ0CqaWquRLLwoxyZfo0595WBUlOAwhMhHj7MCUKTbWEguz9nNZRCAY5S12obPaz17WsijamJPiYck4pAYpSS5vyyflps4lJYuiQHQuupX84TirlDB1ur47Ez4/YsYYVhzQmqw2dIisgj5NVjE06t0yrPE9HmlPu5h3iggl2tjnbbs7bbrk7OctmYiykdLJew+xiJpMEU4zlae77cXf4+nBWGMrp/OJ1Q+EhzpAt5wIJhyFCHaMbc26EKc0bySjawZ5C4mUlBfeU0zh9zv16le1gn6142f1xt0PG2HdCsncoR8ZRxFEusJ+JsaR87o4j58okSk8vH63zTFguBOcFYYRMkOttYo7g58fbGEGGRbHLp0v2OqeyKLfbuj/Eo5JmdoyBYDxprkYFWCgeWDdCmo/lrKxgLc9xkdKyrMrnw877pq6FUcf9LjvAENJlUZe68jAc02jtWBSaCX73tM/5oDR/vXydmyEjzNPjOCmuynyUOSdYI57J8LnoT4emLsPhkUUw0XsuEaAfekXLAMBVYYqlKiQKFVlf0QKQTqd02sf1woPnMwAIUXDDFbNu6NRxyRfZhzQMvMiwAPYwy6JkCYT2wuckEdwTavMXQ1hzcFAjpowwZqw5Q+AMGQIwxoiyEoKjAQDKmTIhIhFkkMBp2D/1L2VQ9aJQ7eq0O9qp+zBupWBnIvNSYERbZ3w4HtxoAWE8eTMIo8R3l+fULHxyd/6XOQQh1pytaPj6zGM+OClYfzpyEu3gDoVmQqQ5KSUKeqVFIbmJIX349bcQY3LEkBllpLecMDpXutgQxwMe3LGL+d23N29YkQhONtLxISSpN6uzomA5aQQEEIvlanrQyb789PMvOadXb77bp6TmRI4d8xj8GiidM0hScKBXWDsZ6ldXKRHnMkRiJeaH6OkWlCwuf3e5Po9TB8iV1NTB5CZieDzNIcrIO15g1DXPwVfTPKUzqTFzFogLBaXkIaI+ccV4DIWCYsVrCJ+nu/0LTX1dLCpuWTthsZmjbUzTZR/FkT8m35rdcrsRUgybYbzt0We1AGwwDq3mWWSODYlpnkMMwRaD5eti/Hp/n+LrM1NgMRg6/NT/Wn/4tvr24mpd8gLpylxLItXd7XyM3mg/ucPtHjPxvHaf7dEeBWsgGpRisGQ/USbaPZC5qXXUomq3797kw2cuOTQtwDZDZpk4MqRcDP394cl2PbiwyHl1/q1/+TMsNHGOYlKqOA7BRqE3aEZFi1JyMzMu936qTUWsreqhO8R5GuYAnlpWnZ+32RgtX81//odhN2aV8hJtWpoLzZ5GKrQrF1ftTZnW2Sz90uhYCfODUL4emmMYyLn1wkbnJg+W5dZFrQyXRVGtDt1+fyDvbFjC8rJ6+qz+8PM/IMXf//gd18zGcZ2F5HnyjyptdCUcY0WGFP0cRmbIJU9dbwTvlSLOgSEwTkwgTIkSIpbW+abR4DPKzrSLmh+OZrNcIoISUmullXbjCBUiw4KbCvSQEoP8F3wXAGcsR0p6s2G//Bx71a6rMPKUcir59OxJ41VpnjQTnUphp/LsrNNtNkZ8vY8EHVsoEiLGRD57Ey1PMoNe8Gu4bBC1/ivdltmFP5z+ISUsm1fyzVJg7hX3XF6zmHPbpYmzToqCZ7x5K8vyd83dlwON8+zL6HkISThGnCCqaCJP0QYkFmFWhmOreAdSSq5PlVXYlJSODJMP83VZd/wVyzTPv/znEH6YlFCCeMZzRLXhY5gJFDT1Qp6Og9NByVKpYrVoH2cLwYMOhWwlwMG55+OBjkNVb2IlKDD78/wP4bldvN5eBCjSNbFgyhRiTEnmjFnrKsZMnEEy9cInvWwK2Xi7d459vv183B8RUdRohJbAl61JA5vRR4IQBZdC6611R81iCnPOwbnxT49/fL3URGlMsTDGZzJSdGTrCZSQfpUhM+1sUryQnBFY7zBBDH7RbjrTgmJCSEZ4Ovjh2M1iaF+9qpdrjmSk8Wxe5eJgD9XKpJCIRDzto82OTsLNKY54FHKuGCTGCziOkCPPfpyG6lUNsARQAFC1EDwjgkgwZjDMcE5IHHyIYRZl/RdoUIozkciZQlJ1zQDwYvNPePvyQfwyfHio3sWSLaalCC5bOiH5mfaGmlyt6t1QMHl29s4Pd5hf3pRayrAfvnwNk+2ib2LMOyXbFVtoFx1xrZbeToIFqG/Y7oOvStDm1OtFTRLkjP7D/jb4yFHe3d4VUCy25ZEXlHOdKtdmyZFDXCNtMwrZzNbePbz4QLqQxatr7UfIuR8PD7sjZRKcK/M7aP70Nwjg/MdjL9haswx59rzYapPtbI9AF8UVZFvXFwuGeBKptXOyGbIxGm84fxQ+JvPyiW2+06wHOOXj+fP+M5MFk0pxUS6UWbxjMg5x/mo/JBZZwMwxSxUMVlX5jDx/ffCLMjonOW6alqX9nMJKYG/ncRx+992FYNXuP398/mlSdh/8DDwmlmJyL+j3SBVfbrc3ND7vH1/WegsMD7YzdYGR0jiURZlTQsy9ivX+xefdN0aw7U0I85b7PZbpLvSiRw497QXnEtXxePy0P5mirNu2ODQaygcBZ2d5HIi5atWW9pCnlLP3WWQhFZcKJDPLCvUC0dytzxcwJheifnExIDRVEKGbs5vuP3+SLLg4u3lszyNWFeNAlE5HNOYYnaxro8vFNA7M5h6tlBqNEMqgd8Mw9EPPKRFDCZy3gsuaBVKlEv/0nz/84R/2L4ebhSxSkjHdgzyfS7psLaZUTQ+fe/bVayO/+eGbxaL+/rvF84TPf/Q5Rt4mPEJDDIqYgs/IQ5qVUpmOs+MvLy4SLMSrjh0hpI+/ffzd73+30hGSFnPHwPMqhGmyhyEy7cP4zmxiyRx5bCuhy4JL40tYSpfMKLTMQ0o5cYYh+XFKs8MEmmDyeehenpZt12XKGZHVbT25HhnEEPMp72gPsaha5ZMDyeZ92SwzYFDaWClv98fv1YzI2ShgJXbjmIkuv//9xe0tJMyYAYBup6+un2BQWiqlnW35CtsKF1vTH/oZHRe1NonI+x5YCmF/+s3+GkNarf+peV1kk3PwSCllzKS9HyIFqYUQISVFMfvhtw/9lAG2K5nBUHJN48eOh0BNXWYyoIbgQzeHo01v1DKonFjwdtZgmFKCFiHHSspEuKind2/Pp/53v374pSgFGZrnOQzBjqNMpazqaEpv59OBHnc/62KtyyZ3ITkPPBVlCzl/9W58eiSfRFFOU8HmPlK2dmftwzjen06lLqo7g2ll09xy37OBmNYxlZL1ddOUvISEKcUPd3/fj9k557wzyrx++4YhS+nos05kOjLdywDUrDYCGCFLRVvAzGbmzXIT9mNVJYr+5ZQl48bwoL0HRqQISBIhkWTAc1RVPYwT5jid9u3irOumqxsIaQVEyBeMoytsnIo8j93nB1lKX5JHZk949a5cWW5tKBU/7J6T0CtNOjNZZMgGGILMcPLAMhASpmxod3TPv8ZXl4+iagCWDCAlyBEiQKHA+6QFy9ElBKYNABJRSnGyqamrFEDr/9kLkKtvf/c3t3/4N0/8w9/v6mpYvqtc2AiUHCmjWap5/zKQc+UZJrW/7bqbRcklPg3zDAkW2bSvIIrodzE+HcXZkkk715eb8rbPRRhzgJOpVlWNnIVqYSAdPAq7U/Li202jljp99r8Mf5YnZAVHgmQgZ3JATAXuWSKk58ejC6ZcKF7ZBc8pzYdjii6PPQNgDERywJX68cdVJPh81O4p8v2tkOeZxzkNAolJSs5FLDJnx+kgpESJ2UFRFolioMjdxDZrdRg+p/zu9FkoJRZvrJxVhd00Zz9dXNz0du7HzvHwnD4n75ngqtjMPnLUjVQ+Zk7EMbNgOfig6/t4V4RQpkzy5nQ6SKyA/hvOKhV+BmepSOho7ma48jhaPl0zyrv9Q3PTbl4vO/ZhF7o4tTVvdFaSS7g/8vdMGb37+rSksEt2tP0uhNr+lh9quFivzs8Wq5oBuDkCgY3DxeIyTrMpXaGZn+eQxP54GJWhXp6yXSxEP08cdV0RmvmUFJaIVoiHVL0ny9gHiBnKPUxa8xZWjSgLcvvxeDq84OjMPJyp17/5Py8Q5+MJ51k3Cw5zu5CZikQpJzHvRx8y+RkE74855CzrAvY2pVgVig2zuaz9pO+Hu8OXAyEB8rI2r7/ZAFKkRZ/35FjRLPok+ENzl7OPX5lJpijmmf3n//B3Fz9+cyZ1A6/k2WHeOalrtRzYKUbyzjEHfSTBuASh5auz8bMV4uXt5ZtF9Vc/ff7b6Ozjl59Y0759G6O3q/rs6XRvF2c8azaMpamecRwO4+VSKrMRhQn25QWi9q3STGKKXJy6E+OMU/bdtGgXtRFGSy5ZnL8Ze2QTl/rqiZIPmW1vuPDx6BA4Z0yYXEGbBXianU4qKZhTVaCSyvsYZNLl63y8LcwrL349DuP5x79jI0NgQOye8Y3IC9kg3ixXwhTRpTyNh2lc95xqyDamMrp+9j4kVUplMO9Syknr7775vng56TC/8FavgSCaoz+wdPRjdjObyvSyc+9N4bwrnENlgme1PBHDlNhyBeO9H4aT5KbvCCS/Xq/nw65oSmK0252Wi8rOsLdPqpFVXIAKMx3n+WjHBXKqnHVXkvpsNqbiJRv5QWVhu+k4LLbXv/t+ue+b5brYH+JhNzDH8Yalfpicjd7Ncy6ENIJb18sFp5Fvq1eb+o1H/9PtowAETCk3rRAelvpCX1bVMJKSVYpx14/T4332NqSslDZaa6E3y2Xwk1JJCB4dSVn98H177OB42FloSiLvvBKgpe9OMMdHm8eN5w4SeSGM6CNRYkKQpGIm9DelneYN5GkYM+YBYVstpGLrVVsomZ09PMfOzjBKRMQ1KVWiMou2xZyin4ViqYBh1CPDEoRptNQGo8WUnhNePYZcccYQHEDBQZQQEp4tuUxv6uF5l1+OL8ba5WaJAFFjZtAIoIxaQ3CJEYDwBJr+Uh1gUDclIggNAJAAwgym+8SX1fKv/8lfP30+hGEYBvcSD/Xhip1zRgINZ6KCByASZZyGuagoCx4jakpg+MTlmJhgSciltzbGeQq6LMKITB2yD4XTE/lkA0POwQ+9XWCImeXCNKUt0SJ/Dd++fLfzL95b720x5GKzyAQp16mW/GkniwuRc17J5F3/MBcGmaxFU4vNZosgOIiPX4+MF5xLwaHmi/KCptMO8ACU2flaJZJ5jAfQVRksSm6EURljzqHrZ204IO0540TNaiVn+xkIU96W1kRWbc6pcgebv+xCt/sliRyv5/AcTHW2jkIqGbhGaSJnlIEzS9uWELJ3iYb4yVOh2nX95z99KotFcdWeba84k39gohxFqkrcqFS/YMZHTmc+hpCd3d49m2/e16sh5jD2+esSBNdncPqcDQkVc6iK6NNFOn76dD9Pay2NegfycPoS83Y2xusCC9WQ2GT/ElNWUhfmcrbDl98+DZNFrt6uv9ux7C2dgOUcpYp1QsiAglfkJysm+vJw4pyK/HyUxoiGUz0maiB8nT7e226y81ScuILlNE5lSqtX750fY1QKm3HiRJ7DODuR0NVSZgRZXsvW2ajmj0/u5XGebSuLDJEv5g+/nDrqZrLQcP7EFstzUsaoa11U++fT7cfDomkZz8N41MYqo9bn79uWucx6R1P39Pk//fGxkFWlBTaHbnx1OtJaYXCJgElZ6fI0jeM0xHAscXty0IrzEXl1pf/Z6q+H7ld/cC/H0S0fhWoFaygkvHtqm3Wf46rdhDhVRbE6i3aaAJv9wNfnLaKeI0TrGGPIxMosAG0SgYYSS58hEsGao+U8r8U00vw1IT3gej1Ta+ejYCqExAQfcg5dFC1vdUV0dOgqZOdw8zH+8adf3Y+/DwAk531rdBfcx0NIiShDDeiYDIxFRs3GVSb7pJ0/ROBRg93fIWfOjl60RQH7PSWKWgDFDATv3vEcwwwxRG4Ox0/hyT100zAoJQvJXpDCKacc/r+nTnCuhNh4bLj2ZOycysnHCwUQ5uDWq++tdaQQoJY4/Hb7slg34BBcoyiVrLaxSsMsUmIsmaJwx3ke7oAxRDl+PcQHIRWX39X1UQOWSoyMK2LZYOQMaiYBuG/6dLKjc7zYCKSrpfaekrMKWTkaYSzz4+Sl9vmHy7rcrH5zn7ZUhxx4mgvBiR5zYCGxYz/NQ//D+SV0B2Qo6orcHBJ5gSnM/Zw55zGNz0/Hqrssy4LQpuPQt+cph8Zil3J3+FKvy1VbKFkhdVdXXApIRzfxSgjEOKBU8BwHF6ZSGiViivL41BRNWSk/9f2h+3TsBdusFmflFd8FxtAzy4KGME0IERECgdQq59yWMiEDri7Xbz4//PzAGaCkIiIDGDmUDJiETBAcuAnyCVK1XpjRJuvGePhFrL5bMAANRJARACDkIJWKiRA9ISKXjEkASgDWgx1h7KH2c8AuHR/kSrV/81cvvxF++K0fCE/+kT/A2l2gIudEJ5USKCBaUmYVlEryxHRNOaNMPM85tStgxM1xGHbANqtNy1W6qInXZPh2NhrkMUgmiZZUHFigm2UUz/g57+C6eQ2FWjPD/GpYnMTphPMkthcoVE4Z+WvcYh5y98mzK0ROJFW2LvWKwvHe9w1biKurtcswDxLZC7KJMVZtzmzqkzlxfNa5xsRQquA9Ss11MVlkPOaqyeuUHFaIc4Gwz1NiQJBKE4eTfX4gzd7RQgiTSp4OH4Vki9+9n93gtt05FgiUEIhxwQRXMxcFhm2ahsxcgpozQ2x6fXHld0TUGV39k3/6z7WuEJUqqp/CH/d/+/gmOn6TjOarWNvsWaAYFaDkc7NpVT/Nh/GPX1J4JwGqJJPAA0Bz4KWb3Dz8abjtjq+RWe/3Cc7z5dB1VTkG95QJVaNjLkZpo308W7YYeVWo7njMyH97uVXlWusFAKGcqqUeAHEiEW7p/IouVRqS88O6989zet/AQprQ04N9Do93eXbJBmGRL3kKrt99GRhtpSYf1fo6JZciB2TxmUdGlwAgK5umXNICqq1i87nOikUPwJEAQqIpuFnGVdqIyE6vMk2at28/Hbpf/3ic+3u2eHN2feOOT2V5s3j15notvedFGUNmC8Nrfg4cGQcpmGB0PD4+333cDyMCLA3l3JdsKcFbdJvLS4y+OI9KxrunfXX7ZbkMDJxoSD3H20+ou1//5p9tL87M3e3MMy2qxs22d+Oqqdlg/OMxng0hhQ5ilcV82i/Orjjn1ite5sNDjykqsrdPh7NiJYTiUUedE0Q7AhoEpYD4erKwPoPA/WmPUkYYTrBvqKoadzx4JoRXvPoGLj9UX/vu8dePl4vqOHrdmH+8qtT5q3E/Dm4siga5EKocRotCMmMYUC9MQ2kr2ddJt8UKUMzR4qkBxo3e+3qzxVaNP1SV9m7uomOMhZTn28P12ZlpK7k+o7GPBJlJZgB8yF+/3GVywWWVC4X26AZFTfJVpcfEettPRYrHcVMuFHIrEnJgDKZpJABVCunnwIXhOlCRRTpy1zEpy8qUb46s/2An34f1qbuq6/Lt71N//Pzr3+6tG5xfNOsIuNYrKDTIAIj+sJvdrqoqoKEqNkYa4dEwSVrAaTxBv8rMKpi/9tWVklraHDHH+Qhk92ogybmRUk2zz+ids6uWxECzFhTmbpR1KREW9dWqFsPcNHVbVfpwUEGEOkIfZ3+YgHihWtn2PDIOZ5jQ+qTaGXur/OxyrbhOSNIVV5cFgc9JcK0QhnmYSp5a00bery82Zq3SHIxPVgSWZxnUsSyuSEklrffMFKOiKtEYAQDgrK38zTQ8hegGvhUwF2sPguDkvY8qhDxNSTqZEFhRKIkxumkUyxfADQACAkcA4plSTEFw5gMT6AARuARADmAUGAVNBS8v8HIk+/Ufhn94ZNwpoeib91cP9/ecAyIc5GCmusyiVUyrNIcYgSsTCQA2yfPpdLIY1erCxdx50KbkwzPk+eTXJ7Dg7EpW45DrNu7Be58yVmnnhvSglC7qdw4cohpoV4IwZ+tJ9QVUws57jNVgQYQpA1cLdQcJcgq+8lpiysEdIWkgpBhSOiQvIoU9h0qNjMkMlHKGVXOdzUsoj+GTZKkwNQjOvWRc1uUyFMgkk3lMedZMJJZCIN7WwiNryowpVhij48B+js9lbONwytExIfzRenvI5nTirWbtQKASGZH9zAQOsl5pQ2MEIUQlpVPyy5f7sZ+rcnm2/Wa9OufccKz/xb/4b8z++f992v1mB/rFKgYpHpl4vX5VS6kI8H7OhTGvAPcjcjtFxpRuxGRpjLEMsxhPp7o77TOkj5Tln369vH6PmzipzaEoX20uAi9Ew/0uxkCYFwVnHnPq4mLelqZ4iWDHuN7Ep5cXgiSq2kgOgkWtp6ZfiFos6uiPzkn75f4nvP62l1+S8x8/FpwtdFHFpfW91hK4NHo7kyVSTwdfckzpADleyA2ZgXwvz9dcl10xDV8/DBeXPMG476hpaob98ImxjDGe9OimySQTA0z3HbtcDX98ef76d/MMjN8sX692PjVv3l0umoTl4K2f53SSEzBTzV1c6cofE2iPb15jUSyv333/2y9/iinvT8H5VDV7znit69aYYXCMEVBoq4GVi2lyWo4CxfIix910v4un417o6myrj8eZkRqGESXvfAAOqmVVs0gADVdKipGdaZ66YRp2n6F+Nx76mhXrqjlTLsqaIDM21TlDWzEMqeCnSHT74fPVmvFtcnFdNqLiYQiLqlJa+nngFBVWJ+dWRr35679e/vwLXpHS5vDoMkOveO0+zVgJpSLH0egNslkxDoDzNM9DkX1m/ECBxdC0bEHnd/2jIFlVCZngDEcOj+Hj2fG8LCv3609KqVOpFVcVYG4X/dBb62fLpHJlkm1t6P37V799+QLhbBiSXgbuSoHzuCBk6Pyc47lq7ykGF2rTZEOlaE+LnkfzaN0ZcQbIpZoT5eE4j6NtyzJD0GK2+2fOvmtfj25/56fb+/u/GialVMvYqlzkQpf1Kx9Szv1TnpbERmqD3wXneVuu28u63Qj+PD6qEF7KnLw6S9M+KwVdIMjW2mmak4t8qZlgslxAezIvWCilm3XeP/vgEiFDzqtRpCiUAKQi2tnJTPB8uDtZXbWv+3I8RxoUc/cgxFYvzwFGv+dU9z6KHDNlLTAXzuV6WTOjg7kbjs4dvvzyarsY2TRulMgni57U5SUwWexHEzPEcP919vE5paZc5LYk4y0Y/eXjb1JpQMZ1MZlqTGzq2KJV5eZ6GIqQ/xwjAbIhap44V0ZVGrhC7oVaghAcAwuxWeDzKY9PT+cXKwA+2H1j1pyDEgogEYGWAjkHzv8i+WcAm8BxCCPEjFUqw+J63j1BPHEZmcJyfb4FRoiNggyBhETuujmbfcy1UaIY59mltEHTn2IIWC8k3Y9cciUrKC4YpbYsQiQSJjElkXyySRjRnib/jDRIId6sStwwnQQfkYchWpxiDJRaWeH67dIe9t7Pzl7zy9Tw0xCY4rocWClPc4IU6+xcI/iJi+rVagEiAiFHYghlGTjCAbybEqMGcy25Fn6GvP+pEyiLchkkm0/3XJ+NDUlArXlE5t0oRUZUIQfUDOM800twE//A4Id6cfajr08YuSRL0vBEfAELRHbfQ7J5wXlmmKs8u1rJkcx8ezszkFwuz35/bd1stv/L/+ofLRebTx8BaHe+wfaHNz8qP+2/fPrls8wxcZ7C4/GLZmqUIcfYZHhkr6/e9N/9afzbq0RsmGJOLAMjSjGcjl///fNTIpBSSdm+erd9PPbs5dD8rshlcCdORmRiUpeMibtPkz52IfvSYHbntc9GEYsiaCl80z/Z0RxNrbTAvKNBPl5WauinzGs7Pbv/9OUpJ1NpJfjbswuzWsTfTtkfwuEs0b5oF0tezo6aGnTWpyGH4Jx2etWsX6b5YZKXciOXj+mQrM/EquKiyRDByOo9gzQO9zcsJLMBpM8p8FevVij+iPvTsTxbNMfjYH+b2raVibl8jPZOtesta9J6rPf5eXCjH+1u3gc0Zh67tirl+/fV1dl33fR0zC8sA7GCC5zDdPjwIbrhlYKn5FmaFi3GQDxFEiEykrkypexPf6erH2KIx2P89vqH7fIy7MfAHVNSZKFkoUTw3GlU4+1volo4opRiM3u5ulgaZbiijnOhGE4xsoQVCyhEGjkwJUSvzOKmQnUSoeWMp6CKgqFkMRHEel0d9j48nj4ye3O2pot1CAdki7PLl6cjG2PcP2WxOcpRc3B8opEkOWu9H7a5FKKQhUCMUwKK8zz7u/6Iu61ZgqLncYhTVbQjY7Db77l8JExXJZ59/8Pt3e3I2DSOGAlkXS+L2M1f+rk5+Gstyt99+7vjnaM0+8RYk0inlElIlNK6OSInjqdxiMHPzILgMxeLptmiyZmOlIKP8NUpmbhgi5j9ag25T1SJ9mz9/qZy509///dN06hmy4ITioCEK0rHjywarxPD7F1w6ZGQFqvqzas1EUxzl0Mq5MAsT4AUU0oxVVcv09NfolbDMGgdJjtzzpalZEZNuzhOw36xhnYNfpRul8h57SN6zkRv1TymXFibowto3TyODEq4ry7MSspVxErA0AUfpVBdzyAfG89xGVNYMetQEWE6HXYeEhAtwue4w7ksIvDU2y6mxfMdrC/KXP78/MdvT9/eCPwI8fUrQ5pnO3JgaYpSMmOMPr/wh55xXmo9BRwHtq7V22/KP9+eP748bRetZuQm21SLyQdIKeXQGA5Og6bZEWNyncenTOP4pareaMn+J29XCsoI9JcwggRAytF5P6myJCIXZyeFyfdgV2vdyov9MWLfof/paaxAl5nwNpxWsqx46E4JMuZyVegmELOeUibiVJV16q3qRXG9Oo3gX/bBeWUEcA7JQeIN51TnYGPO0/HuJef87rUomtdKVUCnadgrJSQrY244VxByCtYi1mK1dtjhwVXAIDk7ZRYEozKHCFlwx3Iqx27oTkCHoQNRIuMZjhJKNxUpOo2rSF5AdPvCnD8/DhBd1a7evv9WCsmZxOv1aP3Jniz8RQrPXJeayXgaI/miFFmsA3IUd7SFBV8veHhhwCVOIzRlkUSdjg8BIjDOpBIhIsgcQ56HA2vS6aW6Oi+DmPrDN+fGz+pFMOv8H0594+ecoR/mnKAsa5m3Py5PuIjEKOULHxF0NehSl9Q/LG9es+Xvm/Jvq+fd87ooVKHCwg/jdPy743H3lFMqzPbb7y5vgX2cbbr9sDm7cLvcXWeRvgR/5ryPtsXUQcCv/nQJDGMzp2mU86LeejtdS63X+fYraCW/efU6muDGIGKabM4hl4YhRM5yaVTI6c3FDZRFGKYUHnm+AIAYE8Aq5W6YbUp+nl3wCSDi+JLyFRPmMO82zxO/KMFl/rLjXCatv369SzEgQcqpOx17GllxWq/X2bFNDrvu6T/9X/7Wz/OP//pfvjwnO00/Xm03xS70Vkpl3eejOYtPFh74I/RTHDcN36zZ3R1JfpyNXEhAlrbbzYY3v0w/hzAzJqVU6xT062306vr469QLPg2SsnxSPkdQWRiETMfP+erHWafAmBYNzy7fx71Whc75JYwX/jTY59hTffW6kOw47d4wfl4p1pjVQp5Os3UDcWFiB5BxMqwm6rAvAgrBecaLqgZcAs0MDcOXUcS+K2by9mSN7+/mOAtiDYjw5w/D2bnfhxmmz2XRMC43ug6r7DiPIseM0PucPKRcVtXZ6kY4+5v/AHMCghSSTmascGPAlx5SWuo1F7RZF08PlTZyef7qXyw+x+zG6UvfHXOmsC1Xs3hRuBxcSuncMD/Hz9P9q8drVp95N2eiSN3QO66UkhRNghAoBZXTKEkxZsy6UBrmdIiEx27YH1OIwEsuWKJsVF0WOivW4NYo+fZa+Jh2H0OIKRPB5YL8EwAgRC45TIGJLgtaM4WyXKt8QluUnLHeeS8ECBDo23l2MWZfPgVjZgyyWB9ePi2aS0dP1fqqSnKa+m6OPBR6+bJ/YmgtAvvKeTmlcoM5EgPmTc5DMOtFSnK58a/w6kPOjZTz59s8fDHiipAhy102lOboRy2SZiLUfMVkTB0lwY+nL+kYCba6ODO/y5uUkhN918UsUXIEN0UfO32lLz7Krt6Xy3r52yBYyLMvgj1SlkpvV0uxWEEMSfBMdMhTDdXs3L6mraxeX5597l+O09TqQpWVtZ5BSqiENsCod/PU2aYWGQy/XtBtN05DVR1yisABAJAYhBxzkDmDLgAAEsXIKgl/STOzSwIw+PT87/7Lv3t++KXtHoxh/EK2nHogxqFGzDnFeZp6LlQBnHVPXc4zV5XResJwIZVk4vn+Z4bs5uL1AHHRLg7D4zDF5OdFvA5uvj18Ij9FCsaU52vJBE/+LoE2Vslnjjc4B5mQM4VSqzQIH4YxfOXNOe0TnwJxEoF0VR2tH53l6Q4DH2KWXKaQOBdaaSE4QbIYneISeFZETJc0HA7HsNgMMiOT+t27V1IqxjjjogfMAtaqPaUiU5Ip3uZDJcW8rFI3RQZKEgcGcD2GXYmKe5ZGrxGXTQ0pe5+FuQqA6IdieeZmyxMAZkbhtYEnq5YGRcmeuxQG9/XxoW5uuuXjsmyUvpKxgOMsh6qMa1ugXA1ROYspG2bShjIYqTIDV+IvXf6mKq9W//joPvI332PBj0//4fnl+Q/dMIUsuPrub775nHLxKNMpMdmcff/topGJzdGuJdcSdFvGx4fwSrEKjBCYuORXbJGWfMFSzE/d4xleYDrkUN7fflGlAcyUAAGBaHOul1XZvH2tlfbPv7L4hPGKdscEgKWiAtR8E5e+7HRIKFse+bFarcbD4BfexCixj0k8dvOrNVxf68PjGKmWgZf6u1ZHda7//OmXnIe/ev3PXvxzfgZph07Ll8+7h68/n/bweHdYOdVq01pM9sgh+cvj0NfTnz/tftkJLgczn/LpEWolL1AqpdXX6Q/h/vdUP/zV/+JvqlXJpI7evyrPl5ci0Ckmh/Hgdaq5x0HEF6+0iC5yz+codt3+fHsu2QoXisWH3fHpcDoBCkZAKaw2AADtsqH9UWi6RvhMUL6rD4cA8zGD3B9M3UginjMKxlGSxxElLEpTViVQfmCxCTYmmLz/KWX360+UfSG5jSM/sTk5iKaq3JiE5oojv25ffZrt/dyVIb403owCIPCFyQ6Tnoq5WbfnoHjvD8/3t2G2V69uVK2ep697/wx1ji5MgcdIQiLwOM8nG484GMgfX8b58XbsT4Naqvff/+OX/uNUbaDvM4dKcymLUNDtjvVgj3cnLcX11bVJ1e34ILKgCHGfjJlQNVW17OOkSm0zuxtOIo7d5/ucQSkllRZiWxYWVVsJD5DPvF9uVj6EP/7p56BKFQoui7a9SQAhM88Ny4zcnIU2VCG5IXmAHrLgSuachx6KzFP2MQXeB8Ght/y0S4vLs5DZOJ/qZsUKhKl+eej0YuWJWEal+F6dM75/mE5ny43RiljwYTx4tdUXTND2ouZsfj7NQ/RJnSi/pLhhrQmus/NxNm3lRp7J1ECToGJoBCMKbqbOg7VeqMLH1CrV8DNLPXWjpWRDZIx7ZMTxCFxkW6sazNayKc+zUilHzzNaokWbu910PB7x+Xm2Tq00FwsY6850jPT8Ep42+rxYLbfvT/svCQUIZaNFlpu6IoEhsdkfOZOnKdYlxGOvjZ6tG46fqmUNgADnoACVkFCDB8g55NzNPpHJCTIH71imdF3RdPH69P/5v14SnaIJp+e6bANFWRVl02FsYqQU66KVidbOnwgh0huG+5y8jbZeNMvlpfzw4fM8jQ8/FVw0m9+bYsXtKKLxcrdzo+aohNy80rq4FrwnKKEfmafMMieOt0iLIoHkVs1DKmrWYg1hR8exxg4x+tmJi4UQxME+n+4aDkZWXE5VWcHiFWNKCCW0kiLCay0ipZzxZe6LeeY5tVqWySRyF2+3IIwWjHA87bvJpwlCY1pIiSh7jZug1hXPLPll02nK3SGwea1E8CIT7/qhphKBHfYTk1xxNgc4a83TWhNDERNH5okKLYZ+lw8vcy5dN0+IkKIGu1pGLRK3GeSzH+XsbEQyvk0mfRF1iianKDO3eSoXW9SKjaxpFMV0sujqOgf+h9sP4+mw2z25eV4Z8/b9t1BccGaL57JcFGab6qKpk3t6ORV3jInOlVV/O6bLYg75S6bEH1dVvdisuYGcJ0ggc7cpl0WlivYV45IrX9eNUqWNX9tYjXOwwR1mtxgfmHy9H/jZwGLJQMfsmVanIwqqIE0sYgwYt7kh13khcllJAqTbwFGP8cjATyke5moQ/IzllGTYECgAxnBVFraoqvjsUuwpqZe7r6Nttuu/yfbR2nAplyr3p+cnLcjb4fm/3D3e377sj/+vu9N35XcXF+2iLUh9rMx88OKsXqhYuu7jHP54e1Z+0/z4+pvq8U4vv1mn8BgP+zBPcexpjOPzMB+H6HxRlr+EKHRxXpjSlGJ5NU1zghfr0ygGVPnN69cp757nQIJHH/wcirod7z6lIWlCSJlySOQBWNnQNJYZbNAihSTDaEpNzIfDfUsLk9iZlEcBMUGKbuVcVxZ9r3B+zMmDE9nPtVAcY9rLVH25H9tzpBupZHVzyoTIGiGGyg5+R8guxeVisza6ut8/UJ+Zg9iFMNtCMd866lAJpeutVsH7wJSRElKIZYVRd/MYNYSbq/zLAsffhvvmFwID/KhyNhxfJAv75+vt9WJlXm8LqR9vv3y0X26NFjHDJgbZLFlOLWtn7Ocx7fd7QTLHnDTp8lJr5Oy6rBrI1JZJiIEJ0qpMdsygg/cvX71SUi8uqpI9p4JK06cJZLkQQA4oGsyMDIITBoXSpdKSZ2LzgwJIVZ7myUcrS63jNyuux4B6qz2krgcpUI2zlCYx8rp0YoBnlxlvrUjlchxCiA6FdIkiO1s2bcF5P/T39uEYd7kj0bAgjxMHIUrgUOoziK5gHrLghAyQ8SxdEQCj3yF6eWBdZitkpffV9VtKUxIyhOwZulJKEjGmGIHTjJmOH7uzas0yO+6e12WrLIeKpRhSL4Q/AlFhtBC8VDUvFN8oyEjAjh3lg4O1KK63x91tpqWdexfmutY2TEoVn8bPMLG2UinnEHNCXWgp3JhiFUaBOjEROfwFHQ2zABedzQxRJ+TJQ9bgIbuKEHBbmUT0p2EUfs+zl/sBlXoQj/wZi8s+9LbbcVm0ZbHJObFmtVAwnDaqcEzEYXhZbhbt27N3H+815z7mcPsTIkvAv0pGIaSYlCkuLgumNwwBLeNJsmKZxz4bwsyJ4ZfukAGLoiamKzQ5pcSQ0RMQRddTproqQoovz3vd4mZ9SWSZh/8fSf/Rq+uaJeha4xmPfd1np19z+e0iMjLLQOU5BycdoQOIDg2k04QGv5AeQkIIShSoVFlZmZERsWPvtZedfn72tY8fNOpf3K3r5o9dCh2TmolG3H77UC/IGM7FueTysuDHg1cCKYDAvLwiAB2HX9FwEKwBbBiLq9C2dnKwZDM06rHbPra7PAtncjEPMRN/QxWoVefuFhO/7TtgjKNkKMKERSMSiSGDOHRstWQJueTLqtxuHgI5wFC1h0WjTldviOjEoDGy2z5rXpG4BCmjxU3P++zLrNCcr5eH6IUTKEtVUdNbjuoA0mg2AsC7d8WXT78L4wdAhpx/9/33bb+LnAUlQ1OsFA+Up3As63kXR28f3l6WPUqVLJuFQjax4Izvl9XJoqyU37iH8RHw+rR0qWrWASQKrRGFlFrKknMZ8nqqq4enTzMU2Ghx8gqZBCE5sURdTBIKpsRK8COT0saoBJfI8dhWkDIGpUeIKdgmkQrqaRgwtioNvrgUXJ0GG6lxm6cH/22eac84fLm5O7Zc6nks4nh3049RcDw7Xx4enDj15e5XOav2dnx6uHm4//bl28f/x8d9m/Q/ivt/t33z4//i5Xo2JH6kx/td/DHb8PT87dj95+oP67e//6lSUFyTi5/bTcyWVPSRPBVZ1gp9HaVHztcpZaI95UKUvE9+nhKWl6taNTOgxBDbmIgJK1WVWciWDj3f5eb761V38HlQTZqsJqIMlHIUUjEnyWT9fjHT2CR352GJDcuZKDU0HJhTNIxK16UoKPitycxdFRWXlyndjyhmPE4p2OfHv96MpizNYi4uTzmII9z7P4+M8dP3l4uiYQTTOGzunsqq/PH7333qvlapBALDliCVQMGozwmttkknTNMCGpYrMbZDypwzzvn7Un9rlCIllIpjSDmiLkpKVgjbj37aP8KcV+WL65cZ82QH7Iav7fCqNoX2LFPlctTprEh5UKw837WHs3o+Cbbk2gj+2e3sBBonVBkx0tPIl4akfm4/MlYqJYfRq0o678jseWBPGEsnWE5KiiezacIMGKc0+cEx5CiQKc8z5pQZE4AnE/cjy8q4/hmFlo3mmSikkHIkSBZSSQs/D2HokGfkyMswEp9c76pqjuqAm2/fhklaFEJnJk5ZnRoXEhcEbShMmbJDxlhOLDKRbDpABia0jGGAnqCA7GmBSV1eZK5kwboxESNQIobAk2lLLnd7LiUKFa1z3rsZVFLFIPFs5Z4sHcl1U4Jxq7xQqiyNFlILSSmptAs9V7Q4r/iuyw5iDbquX5Oi7ZMXjVhoFUq/3x7Qgi60lBoCcs4pMaSQOI+Tbdvuxeu3BKkD0QBEgD1AgQpGpIrlEbCCwMCqaB+HDxHfv9B/t1z9325/7jMwxl1GyZVwysfYf4hSqeKiPK9eWo3+WHtR73MkMwaWlWBGDXAMCLJUxhSVmKaYEhBDBudcGZTs+qwo0WNIASgHNhGOQ2jqwdaSDE/P5v2P3yv1cENC6MH+GvdFqiJlBwyAISAqLbgZP/31kRBr04TgTbAYAJDlnNAnSqMYou6fkpCM8C5nj3V+u9LiCAcfUEBugenA2iFMlKssoxSlgtQ0ZVEWcXz+xI/NS2GNMZlUyEbYR+E4SIhPT36KUGaWMeUqpFzVM+f7YZd8OPbHF4lckZycgFX9+HijEbnhr1ZLP1vlxJBxykfIId+PoQ7d1FdVRAFBhMiCWKIjjQPtDrSYx0WxRGbaGHm2qJTtBz3jwHFg7PRtscC/Oew2v+7+0UzF42jXs8XsakYZsOHTp18DTTOcDeHLil/Zbf9tlxXTxB6Ws+XJiuFwGoNoD7dbPyXGLs7P1m+b3oWyaY7H3jpbXLwsuGyT26U+5UMlfCShlusXFc/AHp5vWcqeog5LpgNRzE2uWpOnnqU8IFvlnFmVWCvGKLzwBtDI8LC/mxLniPOSiZiFcuFxmCw3V16UYeoDhgAmCy10C7KYtJ1Wsznu92k8G0+4HiLdxth93Tw+f3nouoMwPOCLh7Q/gEkxu4c/NYeGKy93f7YPdt2vvZvu7/9/T5L+Nw0vyx0wZJkcFDa2iqntoJu275UD7sprzr4iASjEQrxtaWBZqdNFEi5yduj37qkgxuuTBUkujI4HOgRLnmdiciWkblzeB699YsS1G2QGylWMIHFMbKTwMG3i0+a0ycP2a71RB8LAJDFd4hLheNhnUDIxbACOefP86/nyhYBQtc+V5Nwoqspc8uzl193ofvmGUgKD6rS8uPxxpStIfn/cS6xCcv3sPHlrQ/jr9ueX5ZVCGivQDmPKRBk9IoTupo+VrZYs7AplguAiptgNwzgCEb169fILG2kMSciGl8hGLthcG0peEx6r7YXAildOupuP2aDLnOKuRYuSWzNf0ryUSTEWqB8wBqDko2Nxu4AZ0zlFR13Ohgko5HbHgM2WC8jOkZNixnWBeETN0YtEpAsZY5KT4bMm25SirevG4TDuWE5YlMm5EHwQIgL4vc2g0qpeORynKTK5CDQGUwFlMwwRiFPOKjJtjCjbhxYoMF6QMNGG46OlwTW1FifrKidGCXjq5VinuZKSMxajA4YoVAF5CCnnpIUct9vF0sARh45VjLDGzCCFoaOcMuWcY0qWCyInephUabLjyJmS2SVA2SWIMQZvU5d62Ac+yEQzmlelijkywbk9ei1sv+c77rIXUC2O/ZNbyvV5Ua2cHBm0KWsGPHd5gB4qLElRjkAZGSPODu1QFAVQGkJ+3my4ciBNU5/uAWT2o1CgA3hJCdgIzAJ1OY/TgJ+jPnnx3/z93938/OBwRh4Nf0BVwSIt+tfVC62EkDoTeiGEkUlRACQOPcXSTrVc+pwEGmDEuVKaKzYTRcQYkYswr7MkwgFFIDK8nTjIyLI7JFZze3ADhcsYmeIFcgR2yCnET8nMSSc2IJDgQsaYDr9tQ06qLIHr3nuPhVAQrJ0XjCUGrBK80a9OX3DRUt59jew1axiYkL9CyGgEHTLNvmjH05Tt5GxrOcM42+LyDRHB5Mx1Q/fJwwAVE+CT48goPKbPKbHMupwLrnxwxplilex+QiEFI6Qp+NnYfVkWqHvkLKnrlRGauBf8JuY6eWQ8ZxWP0ydWnDQUpeGZ2KHt+6ktOWitggTGFqdn533fR9IFd7GUnJgwUmLacRi2W3DDkag4FeibfXpOnmkuviviX7qnBZ4NZ44RZdqb7Oqm9fdltnf1/PeJvasb+nRvIT4vqgbQrVZxvp6fnlfAeYjjiCTEHKXgSg39mIqyKOcXdKaV/Pp9aKR86obYdftNXwIn408iiaKilIgoVEypVel8mQmUzn5U/BQe7iMDD0V86u9c4EK+e/uWwIj5Uw7JEwtshQBx1vcHh6tZU0pnJ1fLAmzqh1Xy0sgMvusefK7rKrdNEyax73/G14LX76ebXyaYZwAGmBFob1HqzZB7gHWNw24QnP+7q/Xp2XlZlonyBYBzVV3t2OPYIx/qRqNwo/fhSSDPmTuWMscjda/ZteMBoBJSz4ur3VIxHIQvGBIjniaXYxJcrs++S1NrnXNhlIqlEP1EjGRjUu58141CIKJOB4rpWYrIMK/ijMlJglo24IOL0i11zGIajtFy9rao4ym2m2dthDKSyxzFlEKHKKuTkx9WHNhiJ8MS9LxZ5wzb3Wc/pbGP1YzqWc0heW8pdQKRcwSgxgcm5cCKU5m7qdVTndNzdlwrEZLsn3KzUrKWKSXBg7WTC/GciqnSOQTnjjkDSko2aZNStGeYMOeiJI5GyYzPrZMTC7zgMoQ8fX0GXNCSSXcmKk6Tc2IssbpIEHNytRc0yqWKBwLkv9nWGOMDS2clep2AKGdwEaHkapIrgcA1FFrqab/rSZVS9Nn393uOCAlihBSrsqiftvD+3cl8CTc3N2EuXND+/kYtvIiQy76Iy5BHkFByA6PNE3PkshsdYaGF2xwPxPwUFtXMAw13Nx1Leimk1ThyqChTnoyTjqeUeAgBuTyIAx4XxQtikjb2s7NaiEYqVCVKOcVNJO1zTlw4zjzLC8ZyyIZFz5EmxjFIhXtLBYSiKcZhQodbNkgJDsXZnDMjypqCZRazQaJRYq1YURLXdF2eOjHFsVgpDthxTp2Ta/k07cqUDay00tbamClrxYVKOZgpjFHkTM5ZCIgqQn0qAabgJ8uwF5wDAaQM3kGOW8jPlOCvH/755e/frl+8xmdK2ebVyhw9NqZcvdBxTCymBAwx9mMmkDwjp2wKDdL57r4fTp1gizVjNkTIiSEqN05Kih2oUxvY/JUQrYuPlHmKvBfKyagiyx2M2XF+nhnzB+gzXb/MD78Qg4aSTTnnpTBHeMhZhBBMyh4WyxVQDjglUdQZEo54yhi+AORifnEBhAyXO3aALBgO0fvnx3BaZpj9kMXW5yoOj0D0X8Wo4MPHTRTyTxeIIOXi5ZrOp+wDBRbBpWFmgVvfLYSE0pSRj5MtWEWrjEireVMW5eMxrOu5bFz4cqpxVDAxLbih2LW7HNOBjDqEWBpuGWOPyfr7h0C6nJ+VzQk2CMHthbjQTJ9cyCi44IAeIXEsJVIKcb6aGecXkFtzMXUHa1pM49uyeIhxME6JYVdOr7Ig+8QZxBwJDkIqmPxoPy/nr0/WOAU7Daw/3ijJ+XouEIWeCXXVTQdlqtlsxqnexlw2JZ8CV4Zzfjk7Ge1EANxxl7KNSyPbenl22ZTb7f2nu9tZaTiHpWZ6NY95KtBPMcPnnmoRaQzJkec37ZQTcCnfr9/Xy/loh0QXCXPKiss0mgrue3UlOeo2D3k/kbVRykys58UixcV81d0dLubIkQ92sIO7ObrFn/1tc/8fvm4RcgGFAKj0a37SjEoM7f2MXj7e777e/Ys9E9ffv5udrEIMk3PfQlCH/uluXDWV5BQcOd/qsk7PPRqYJtqTlfkGhDzK7nr2sttuLF8rQQvOMlWyMpnREJxGxTDG4A+H/WoxAz+aQ805jzOeEx6fpsfsU3rImZT8rqhRLgTWrxbJxdTUaEYaD/0Y+lieJgTMRrIsi0UvHnznMNE4n1fBOzsOoXEJASfMjGIKPiahynXzmvGqa/t9COPmUJuacrAsE4Ygt79ZhHvXNIVX3rMmeUKZcX94Fiy2+2KZF8uZVIL3RrDb1bVMXQzbqJvZT+/Gnz/0N8cPp/XlzDSWUqbIlB79VAg6dH1ZBsVzSoUUBRKFFHwAGmOKvmUhJ+pirpXFTgDLcuQkMrJbyCYmEc5yzFkkQI65CJln65ypmkhDuD0ylrEsxWLm+VVBlHiTmEux7bpWZqOlXnF+sFPqhxACE0KYMrgxUeH7hRTPMeanbur743K3U4L604KXhu97hFMi61M8qWZLPUsquylFKwSdylhNvXu02xDGnBxWtQqjFHJZ0SikEeVBWJ+AUgxsJWDKxAUS68C81BV7EYf8280HYsSRvS8KtW6e+1483I+FWlW6v/eyVpC66JCWWqLMKUtEteT9pGGYQuh44n7inEFKwcYOmFGSqaZl7AQzNwXQkBgiaoBmBSOknCAxbnK/BVfSWgIXi8x2+7x1oysKwSgEHwupSFa77aEoCo7ChcCB6pxUblVmN9b6s2EBRS9qrmL0nnmdM4SLZA9+eMrjmN68Nv/pP91c/fiyev/qU/94mJIcLWjBpMZxGJsKh30C8lpKzlKKYFNUm4hVzJR1ZUa67Y+vZ1HimnITjZWuYNjJprksZ1Q3wGCfhzGrwhc9bAUWyogAIaWyzduX8oyADx0liJDFazbzy4CwIckynd3nh4Tc1XXQoix4YsjiVPGGGATFGjnLOQB8g4yizlIoeYfHHJGIQphxEM/2G89a9DfTh8Pid3+Ty8bt92k+w9DbPXnn3BS/SqEk1P/4Qb5FYlGPRYgqhuPBGT4EXZXl+bmPsSrL6CwR88MIyLSSkEPX39vHfk3TlBF0YU5l/LY/iIyzmhgPCiOOWBDy6+t5utl/G8d+6LaLciqlHngKbeph1ghJuH0+CKW0UTyEHClzrXYfPyspMkv5VIu1WNrTtvvrRfX9PPxlKz3XedjKdSWYmBgLIQBQphRcTPPLkik+brbHjrRZ8FydnJirmr7uvGlyyhPGNAzDc55YFQt7ai7o3dX7cbRa626yTwlYcH7zeJeyKiqujMiMKXX98i1jdNgfKNvAEg1ZdS2hFicaLqv9brAjLSPfUEIU363OxMWJnOR+d/A5NLPlcbzV5iLIYnreo9CyYMF2MqVxSfxYQsqUqfTTZucXpPgS7u5u/s3f/qtyt9rWdw1T/+XLt9/2H0aYL3Be14v1slnMjt3+6J6G29Rfm/XT4+M/bu/+cHb+v/4//P3lT/9T55888AWetcqWZTdZHuwjSz6O+fJSW8IdUdYsWeKZ/f78b4rVzDoaVDVTkhF7clPDRIoOOWvDU82X5K2RigvVjY5BAiUCI9HiYX9sUwvAgOj14g9iEdrh2G10Go7I8sHHm/ar4ISMd5KdjMFcIqWEI1BZyhMD1u0/+ZA6o7k8Eb6lmH11WSZCoJxyenycLL8Vuo/uZCF4uTwJwSMymQ5NUYsoGbLHF0PFShpzIayNMBHmVaX7MRLYxTL2O97f8fIlgElTc9w9DgCnkUMO33///rcP//zxy6/n76vV6t2hPQ7ZzYyiMZdl452fr513Y3vc7j5vOx/4+iTF5JyrindlZSpBESONcLCfD2NcFgWXYhg6prhATmNvx6BP36N7/LTZEhGKFfnt+emZC94uZrt2zzD77OPYpdQyQfO44IIzYinIEI6NUrwo4/HYzJqnZ8dQqwU/3IRjiqNCqpsUbCYs61OEfQ7hVOqc8+QsTnnbbfzH3zhIgrPBfdrKuht24ziV5Wy5mOkuBdWv568ydcfN3XJxlrQ6GINd9sfpydrz2erubj8NW7MtfJxiSsgJObx78RpWyzxuGMB4OLqO+WIW7RFAgkQlmuim8Rg4885i2RQaA0fBWB0h9yMYZAMDLgQDevP2NRfDZAfrwDu1WOTJAhd2//U5xGydb5pGiTkqoafS5qZaiW7H3MEDQbJ+tTh+vgmXF+eAQWuM0SOC1RgAPPnSLDe9N4m5qXc4GSaMBc+iR9vubrR+JzGFuEtxM3kjFH65+aIK8+53P412CpnumSiGUQH2fawQtdKNlI7PBBtScL0QEiU4OwfCsrzft26HoGsE5F6SVOL6LSs0k5KO7SMft2MbBf++Wm9cIe1Uqrnt0TvLxbmvzeaR6qqdaZ22EhqklLe34fS9SkT7zs/PynK53nz8WLx4OYUpCBBABbBMcWJwQwfTSRyE+O3mVijOw8CKzAV0szxP/NUP1waKMLKv0JZ7YGzBcMz7dYCFOGPvVsHepUywz48uYH4yfA35mChaonlZGP32VUrp6Qh+OKDg57lk50xn2fbb6evdmK1utCykqq5csmN7w9hMnBfq6UHNqptHy5hgxI/H2KgvVXleN2U8hL49pNWmjuVjGAUTPCQSiui/YqOaQBB6N9Sysq/+7t8wilVKIYRj3AmZfVGF6CXUcj5lmp2WeyPU5pgZDhsf55Sl4DGFj78cGPtnzGoucBy/k9g/37I4FTYErter07T34YkCdkVl+er38sX8bc5sZNx3Y0gBjy3MFvPV1X5LQo45EzBUzSxYZwXKsprXZzlaJAfr0D7mbG3KeSIpChAxrysQeqWXa6L06HdVXWHE0U7PIGaOtfs7zXlO2R4s10KU3KAgzSFQysz7WBQyT3Hy8dCOH/9qy+r496d///5/9+O/efzj//Wffn34uvVCv3v1ikVXNymRO2ziQ3oWr3548/aH93//++/+52+qd/8OlN1lPTyA67eYQOvGvGyELfd3N1IQ+bCbrJRSSVlX9bvvfpCVIAXHvvfAGCEwWKLKXIIwWpm30CglaU1j12bvEJAyZUgzCbFaDLvdmdGnr97HPvB53j0dkeWymNWzOSNaad28M6N3BEECEe0KZQ/7MLJU9M47YsBny+8Pj39MUxAHzlzigudMHDnnohFqOauq+vUjR98dCGbWO2KgFbaudaYTQeaIkmEpFUl85oJlzEMw2W59xGmUnz6lnFxi5wv1+DjU88WSS1FwAHSpXLjN+5dv77cPpY986hKFV+VZCElqHvxIhMcnEQZxukqbolY0pHDB8Xk2W4UA97tD5r2qVMF5Lec+JJ9GYkAlISRugWflkTHGslmY1MrFUpWsG0TnRb9rk53KUsboI0XJ1Xn9Ulf1zt/CEb33x/4h2jzVIgZKOU3OM+Rc6WHTZ8Bx16b2IOvkZ6cufZI5+bt9clmHsKP2ub+XUwz7b2yEsuSMPW8BOYnSNKWp35y89NP9epaHLUuLb8PDsHns6+8OpqzjIfjYUcZ02O5tzxlUldHqSoTJuaeXl1fCoBDMT3v/0BbLQi9Ovn67nVLkcpnGLdWrbEfLsIxCSVbybBx4mYaxt/k5xgjAAl8T7GIM9fmq6zouuEugZNnMxOM0ed9XvY0uCdO8uXjtibzP3fHZp5W6NvPVggTdf/grD6F8SakVJQfOvfPcW1+o3rjmELhQKoUQy1bfAYiXXz5+ff/TdyynP/9yPDkL3uNkd5VvULAoc6ZutLGl5ji0jSieGfX50LBGHR9R1pR8pXOlqsQAIFvtWT9Nk821JgY0POfi7PnhA5H4Qp++Y287Tp6ly4tSaRkP/rP9EFi+mr0U1QritJ8m7wF5YWNObPKkGDovkhYTcBZienSbV7MTVcNh/xxzIgBcCXP6Ih0tY8w7r5RUiQEPk07eDedQrCRfrRQ/kWKpF3zG2q4SDRg4jvt2Ni8XzcUvf/mVMTHX36dGYeBjeR5tsCx0W1+l2mt0hALVPeZXU0pf7Z0yKYtzVGyxIBDSlJeVeMhWYN75fv5c6FPNGGPL1Uk3rOtTv/NsBSKJ3zYTfv72/t27sS5bS24eFvJixvunOGIsQ7Y5dHXzwrdb74dZA2aPxoII7aIserFWIkTAwhRHi1IekpNClZO/m+s1l+nbr1/FVU58hq3U+uXLU7d7Oki1IIxnpzh2r85jzNEyYOHRd/3IuTpRfAbXfhxZSKsTU5Rt7tm8YPYI7fi0f1SnL5r5u8tXi78tqhlwrX032MbHTSa55Ip4MFcUinOEclYkcFPfDvE4VHXZWjcr5GH3AKMyFS7mjDtm12zWVN7rYf8lYYmYxzFWVLsYnIuOsbP6YlY2s6Z+6iJOR83Yu3fvY8jP3bOTQWpTB2i7PLbDeo2CxNP942H/7XDsH9KdNpvv37/+PxblL/92sA/5aNN+c4SblGYuzIPo1OpsbV5+r5rny1dXrdz98c7w5ySVKsoaMY/drnPBjKOdN1dejO2+qv1Cs2KSk6m5ENb73S4DR+L42QbeHQVDOn8hoHfDEEr3Ai8ok41QlmfzIjk3HMPBFxWndLHShqdpvNvcbWa3/DkGXVYo7ONtyNUMhBOVizFc1kYpGYFzsViu+idyhV8c7zd2CExmqM/ddEdtR5AwMjax2bxiwGKiRH5s+y5LWZ52IRGLHCIT2ytf2MB7FquaMJiYRsHLRYyjTaPzgHBi5KGu2XldqygANApj9MP93QVl5Jx4rL0Y7i2/nK0NJi4o6VNe3gz79ExuOsZgheQSRU6xsFn7eKkaytn5VBc6SMVkkHw96ilSntdziQ4QCYfEDEfv4x5mLH2L4zj0/TABw8zc1GelKxM6MGqpEUEMlIWIzHXBt6Nlx+h4U7GsSfzup5WQfHd48j4fuxZlocRsoruirKWcEW25mUueORSyOuU/zGSSEhjfpss4LwTnlpOQROsY95dsIWBOXNJMcO68c902cGCwJ3P6rul+a2+/0cv3OSZW1+e66NZnWurYeuV0IU3Url7PWRkZeKKAnHCFk8RF3Sil/G54/aL45SNk7xfc8sw9YypSBaBEkDEKLl6rJlXzMUaf0m4KaoErPVOKOW45cpcn26rxMADDw0y+uX4z9BNwLpFnzEt1xZXgYy8EW1XlMzAfozgKtgrNs8thpDQK3ti00LV2x2FWwYTi6Y4W52fBx83No/rjydkPqoEw3Cc3tSPut08n5UU2RbaHsUPJz/lXiu+s7aetRwCieb2ILnIGUJW9az07BGTQRhGk53zmohssHCmKvOCqQyY5cKS1RCErLPhme58hoRBqkZ7YA1AiOduPHqWihCElJhRwIiYCWIM8ZeWc1jR2NDKHcgndkYrZbaZq1+75sUfOy6Y6bu44ktCIOnMVvAyOiHjIDERop0UpSFnhkGe0AImZmy83NkBRmJPv589jZgMszLqeh3Y4Oq7ACWKgUCr+fgWZL1hO8fzYI2dcz6Zne+sGVIqEcK6/vPqpLBO2LXGGsuAzWajrBPnXsH+9F/Kknp2ut7vtzx9/hZqYRV7IR/Ph0OuKyXwdbQqOq3PZHJ97Gvfl7Py8MNvgIPeyWl1Vsy+3LUNxfnK2FzUTemlqk/Ljt0mcbs7m1/XLQkPudjRW40JetrkerX85P70ZvpjQMOarqh57dNaqF7XpsuDylJlkv91/DHOEqfqO85XW99vtt6///HXy1KzWZ8Bemgti+MF5Shi7WFRCjBdcxH5siyKX9VlZiAOycOxtjKas1dlr7VvDQTB//fL8cExAEysxc5LS74+3tzdjauKZXgtQvHCyrNHHw3HQC1Wpwrvw54ebmTn7V+/f23Fcrtbe+93xrt33yCbLZFkL6207EZN0fTKTT5DG+A+3/zGmTxdX13w2p0e7Y9Px0B77zwc/qBt/4Gm5Wowr2u3/eFpcyGq1fP161nNbTpREtz1YOx2PGx0Wm0x89/S16uoYTcFs5xOwPVPnMYHgV+fFSLrvTNpp5DNZEHHB87osg11UPCVrx2D7p/2TW8455KqcF1oQJaUoSZ3u95Kx4hW/jpmJc8Y4Z/p+w+NEfpqC29zD+pZl9+EDW2a94OCTqsr56qxtp+ScC5Rx5uI+EfxYlnVRTZOHIEMUR4sgB4LFVI7ySVQsx5J4nZ04GosDU1XNBa+4d12GvjVtf+uc7zXNtFbKmImUx8N2J5eYvC0KNSBhgKhnWKWMU3QBWNrcwzH+FnwIMTfNSknJcgZ4+f694XecMTrLoy+mQKG1bHAuVcmURWJeIUeAAw2AuZSBUxl85CybpPnIc6lkVQOw7a6t62WGZJAY57KBZhL79gaD12crIXWwwSWUx5qtsUzpebj7+dlem/oZNq9n7+5vf2Ym0kofdvL0XWmyndarUyELgudg/fEJw4Ft8iaEZL0Q8nGzOUlkOMsBU5zhQmakxETI3qYsozOXC8lEilEVxY+/++nx6SllsnZKQGkM7j5rhUpqkuT9JiXn4gpjqKtdJiGo6zzbxTFj6fzk9nfupFrM69Vcjm3gDhkO1KXExo7XxqhVyjTzGiWvMMb49Ovx7eKtVPJpuC1rVuMr63fd1EFOXC+v14sjQ4J0f3iGlABkPT8BiDFFIYTR5v0Pb//Lf/yHzUGtbXKjl1/6lDO9IO/LXXQG2Ri1EKE8Dliip+np6WGzffpD/YdvbfThJjKuFGL86B8x8HIMYqSkjICpnUgMrFxixpSObmCTL6VoXTJ2sLVZIsZG95sNMuAJ+DAxVD+dXbrFanR9vAlTdDDDURV8OnSuXcxlNklybEOMIaHYe5KcxlGfSR6Y5gEScMzZ2ygYxqrweiitDxL5fPWSdvc7yMWsQSbHxSI83KcwgbZcMK+TyyNBGpiRMRJDuE+i7z+Fr7xasFg2UpQWp4d+z1CYqnr3/ntgHOwxyW7LUpNN1ny1MBTpJLBd5CzzNh8lSiFlsX4HXAheigNbPe61rssr9bzfn56I7Sa1OJzxVTh2fohD6nO1mhVNmiWIbn4y37KDAydHGY9OLEhwCowFh+zRJ271vOjyMTHWxvDCHCoBXfbJjsiYEIYBf/P6LXLJtjsSvFBcIF8tV4ISEb4wP+Q4tOkDdqVeG7qv53M2cW7SyhQKsQ39uoFG6ufsl38zR0q4tW4KPig0euGnbTvdtFtqQfiYQeoZnLx4cVUY88SZCCoWwm4xilHJoSi51gujhOT8EPYYEvBanpzOMhNS+CC5G4G4NdRgjM4kf1RmLhXTHDePCEXQyqCwfRs4sylXpuRjYGLwPnj7bTN/IXK6+O3Lrbi5my9qT+b3P62fgbY3dtz3/Pxi9/mDcO0+PpepDb6Tp6fjb7vfPrblYvHnyRFjNvsiVCPrga+w6lGZtD983W7/8Hd/9+P/7L/n8fht/HIKC+98pHa5vG7KWQ7T0qW7lsfghaCqfMUXChnLbogAwfvuU0w1FPUycVFdyFKXoPGwyyAIHx6n+SzHtFhfcM5zGIk8w4y8SHFvARsFHzNULNgYxjYJ/iH4C84KxqrCpPp8Nbqi8OPonZXpdCUqyp++2l9e/vVcvZe650DgQgxX1h2IAOQyW6eqyoU51LwsZy0gcwBPjEFSidSCBJ4z3mexr8kftz77jbFlUTRLO/WM1dXaFCRlFjwzxrSQTvIc45wHVdSRQHOiMLIcxcQDY3BgxFmlqmXz+kB3ovz+99/NfYgpBAT4yNsGFK5QHmfQTNfL1bR0kSgT+ikSpLJpghtR4KgpOQ4SHANgFgDun9MbO6WYQvRfvvaLE233fhtUtM8B60KKJGvrLMWUkZ9g3mZ5VWSMOYVCH/xxObWH1l5Oq8tTJrXHoige7U7zlYb9jTw/TzZdsHNIsBtlPx05YlOWhqFBwZmU7MxRhDCwKXbU1U0BSiZ8FNfC8uX+t98+DBOwW62VunxTcrbQ2gKUmUe2B2KsvMouUhJCeyEoyLid/ELWg6XV/KTOUaBcXb94+nLzyy+/vLr6/reP3y7WS9HIJc35SjGL6J5cjo/jZA+upaeSsfViRizD8C1OS5mC97WtNl0/gUelT5pl+fgAQLfCFKiNzUTA+DBM3jQ6TVNvlrBsZuum8MNo5/PMPHLeh0DPz6q5iFBkyjyzxPgjCbUPxZwRwTRNbbs/Tr7fx/c/qr11ebS4WBSiHdcUhxYEsXb/dHUp40OfqA+ySakstUQhcoxCXmZ17NqYeqS84DyFaKReNRqBcdZNt18fgmvEXM3Oc+GH/Z7X4lGOLPkVGsYNjEnIAkLwHUnR5gQUKrQCV3CMuSbGiU+WpEYpGIMseFnUp3t3l9qjODkTOtPZSiWv1YyxmLOfca2RG0idf3z4Al6A+O7Ndz4OnX0SxGxwKhni09nrKyObGNn+0KJiRpbT1A3RZgDWe6OrvaqlYtm1OfVdWp1OebCtt8OyvuR6vaiaHSO3MW0fpJ7stBOS+e3zpAUWIFQTRPLRtXr23D6XZSjXy+P0DCsOLiWKb7iIleSaXGMBWeQn3IlePo2+7adsGNSCjSTo/oFmZ0rIEQURpbFPmd+phj/frGZmyKGJR6VMRlK61pKNrjbl7NS8+PXhZyFqJWQKA8cmid7w11O/L80y6ZPF8zi7JDmZjemnzlMKGuJrRqop5//qfzI/ORdcANH45SMrv+csEUW8QPLzaHe6aEI+AuCsLHXVzKghgH2Kd8cJnu5X6/mQMjyO7969BpPdIFpqV6xh8kK/8iEIKTfHAGMHUgXKB4FXRaKWnk9M8Yc//GG9Wtmn52Ec/tWba77S87Am4gvvHjF6yzCFUi8Tm1ZnZjMFY8ft7hf1Qm27Qzg+7YaiWMNDm3bbnQ2gxYFvodSpdsvvfnj1P/6f/s/XF1dfNySSJOIs76YA2G6Pu30KY/Ijz3C+vtZjy7F/2B6j597my5coOOvlxIFHalPCw9Pca0MFLituCplz7PZ7N47SCGxYI2oiDD4iZoVOFsLFGGPYh7DIQgIpxqt6lg68Z0+UIX1lrIi6YAVDc3KFDriUsvjEpTAAvZZOY520nKbNwSut5NlqCI95g2zGY8dEQaWz0bL9rrNiu81D+C+TTk4yOKmK5n2JBGnjO58e5V9sxpSZ0n3fpZyyFMmH6fVquTKVGCYX1Hi7GSullMbMU8IFTRZERg6CCy6aK3qazq4bbl348KuXkhelIFKsIaF0LIM75h17xg5QKsaRCVLKdMVQg/bB2afIG1kURZat8DJTmdlB2JaiMNog8yE2XHBOgoRimSDmZQ2JaYJMXM6lfGL5YRdPTUqxpfKFRp4xfR2OZHDFyW9GBiShdduoc6r4fcgh+pwj1nl2uloxKBE4wJhigULkBKlNCLwoVWePOjo81U8PnM1MDO4ujyFExiq4OpsrlkJE5wkw41g05qRShWGpiMATY0aIzLh8Hnmk6CF9fv4SWOBLeabOtZZn716eLxdg3WI5S4m8td6PwnGbA6AoWVZLpQC4KEj5VRvSgbJ61IzLU2DCzgphSYR4dF5B9kLqsjibVeC89GWNk+0fO/+uziOXYw9z+f3fnHz7/37qPt7lTHS2xHZPq4sUg5DV/rjTlEXLxCCqJoc6hDjGqGNyUzNBzvdjGNo75kMlp0RlDbFh6WjHiRHf3keLTKeQ94vqkgMb2sFnF3J8ONwwL2QhtVSa9ePRvl6udcHd9n4zHfULeJlXo5grrQrG9+hQBJYCCUhsLgBcdJzxkHPwqZifMc+tf1DFnFABmzrBROJlAJI6xwnHMa/mxjTqWDlIsT2kShPknnni0tIdMUKA2oVfN549cym4oCzkoslW3n+7+345v4ObdI+mZFVRfaU7RuusRTpuk45SoBFqHKek8pCSkZ5NQhQn5AvpiyO7k1Dvme/s82uueuoOw1gWB1OEdjcaMopvBF+diX1AFJXux1Encg+PhnmYv7qQ0uN8svuyJIyHowLtQ8z6Qp/d5nIaD4VYgKwHGEWR9Ksi3m2jd8SIsci1aA/7+fkpV69TERdz0Q5Qz5cVFRINEQOmTTWDlPt+l6TWi7o4NAAqdsc+qKquvc+EeX3STNmOIuOVEpqHmWB2UfLy1YxIxpyjUqWM+eG//OPTj9fczLpuXL304phlDsQhmoi4CCUXXK6Ws5k4C+AlcBfHtk1FKej0isW+ROmUvj08vJxdcrl0MTHJfUKPIgxsqDX4UZeMgez7nuG3mAkymXrpaYLNw5eu++5FpS9e+LR5PHzaepfi2fenqxOJ/+//8M9xemA52oEfc96yxBCHQxim1EMaZ0Gy82N4iHnOzREQbIJczc8u3v39/+V/vP7+O4Dxcfrz9/rKdV2b2qYqp22ffF9IMQUiRUo3mkWiaihvIYCj9C//8k/r1WvGgQeBWKjqPIzBut8K9h1birZ9SjGcrk7mb98+DaMb+q1QTQhCClEU3i2VHMM0nMwSTiFGKivDregPT8ecIwESh3wXh/h8uhq2D9erJeNeaa2vdOFXEUKDrAw+FympSJv5GNvuq9Pvzp3bJhdgjrPSFIKj8Ce/M9v7083xQDlaYENM+30v/nGKKb2SclbXpzK3pS7lLJewv+8CRIqRIwvHbuh6JUShGry+VkM7JjZEh1x8psx6m0AIpkzZcFmeccim3qYUiOA8iy4zoY5Tv5BgyiKBaoCnnAFwKovSTjmnAqsIietKGxb6HIKfMYyAwLqzJo1ysn0xuml5+dIioBu9U4KDRM7LwuZQSPXUHYeUx5Nzw4tx08XXZTV/c72Ya4NNrZ/GIYUuBAxlRLe6LE1wX5ecaX5dmTHnNPR7Po0uF2p8HITACVl+GuPZOD71mlZ52Y+3CMfxWoevzw+7PtxLP78N25BS+v3vv3sYsQs9YXFeKplSF+F1RbTrXOMSDNXy5fP+YVYudFXELoYFJRCz+oQp8HJEgut3F5Uunja3N4e77sgVMpFrs5yD5J7ISOC8glKn/lFqWRaVuupituMHa3RhTubJamRyGA56fVYv6vv9ZKI9jneHLnHk4cFzYRRn4mmUxjDGj9vjfPmH1b+W/+Xf/8PWTT/99mtMHqwujj0zO6YK6+3CXPvj9rC92R0BecBcfkyDIxdKPyuoFDVo6J5CxBHb56Mc183i8diWRnDthm6IXbgR34bW1aUYGTDRLNdvT8oqxpBjYoyWsyDqZT7eZgqp9KZUX90Qv47T+ctVrayrFsiIJ+/ivlQSUxJiogXEXRIzTX53ZJwtqaKYx5xcJSV0krHOequk0CAYKomsEI33bYwA0iQc0W/cE/EosWTsjB23kB6DEBKxUMwI92GkCud1PQwWJy4LsThpnEz8WeTofIWcXZu4JzaN0jdNE0PKnIkEg0rknRZFc6pSOCu5KqZi3HUWWKN0lpwEDn1vKJqalc1JspMffRdi2llTqEMiGbLQhunDE15zFNon0YZs5DClYGhp5K2zliauq4oVD6G7jbeTvyz0jHOUQkyiFt5aoeC42Z2t6IShpwPynOJgD8g4N5KzkvHV6Wnx/Pw5EIPYcF2q6qwo7YHgFMvBdqMIUrd9L/SlMqGMHnMXPbNDr9BOf3z6LYT+FcAB2Fkzr09Ol/rc9X0ImS0yJN8d/4h/XPzwh/ehoY65H1YXkmOElHL+1nm7+cxShlaSQEJQRXEMDp8glJExJUZhZ7LSGfsZZLfZWc9ppRhnSqSmNsV2p4luD92hPWy3RSGVlNL0zze/bnaUICq0T/3XsvTC1QUbLN58fXi6eeI533zZTeOYKFczTLy8/7b7eTocLBiEhgEToMr1+59++Nv//b/5d//m3wKoT6n1yW/tg8rQZmdaui6LJ68v5/Ox4I7duf7xw/Me60hCXF5eFLK8u7nhLE2T80ld8K65WALXpr7mBXvY3jLmidJ28jkfsg9AsPAGWeHddh/6FPtauwy7WaGRa5Ihp7OMMD9dzib76EZtitJcbfa/Cq9G6zgiRfb15qMybNHoMYSY4Kjm8XG8uiiqSvXDz8/GnyTp5HysZsiIOz9n2EkeOJ2/0hfp97613c2vPqZKqUabXEYmGmROeL5UIi8UJXZ1XRkw2R4pltl3Mbc5Uz/R3c1nlAYYGd30Q6u1GkY4vzxZLYqRO0gI3gdBUxLFdXFdwF0BL2OmWNg4hmxRJoG888F7N9AzT3PSFGLhmGuyiOWZKMcqcD+0wILSM7GMwRaP+T5TnqtYRX9fSd5vmZDOO5sdRxoKk6SoU7ho6r20+XNiUlxfVN67rT+0cl8FZUgP08DCAlnauGfBCmX4fvMn/8WSLpXQTKlDHjlRM3jnfEZgOXZNUTWQdXYHJqsVpB1DXJ2u7ZiX6+u4pNuDN7rUw1ioahy6QSub0stFVRK1FVWTzUIJ8razev6k8DrHLIVq+/s0e6u155POVYMx/Xr3KwQ2XzS1Kl2Ih3GY1yZn6AdH4EfILKRCJwp9HzaCgdZG1nwA9Jt9YIVYnNen57OqSs5riPXJaRIYDvvgg9SGsJR1I7MzRvlp8gDAYH75+3/9v2r+/f/n//VP7eG9QpGja3pHhdAlHcyf3B9vN3eCubIwQppmnQ/thihR9HeHbdCca8n6A55enCvMUbIY1kVm6en2Zow5ojBXV9dD44H4WgolxOa4exqHCforM1f1IveP3u5sl5+anJasBTLlWVyNwbZxdVacnO76J8EwCyWI+2BS1vUYQmazpvr868/OW87xpHgPAssaOIfY2NQNmc1VUo9uu3p0ur5e88U2mrOzhJnus3CPyZhSFfLqep4y5suUTwUQRxRiL8RNvnvj35RcsSnGMVx//3rkYzcdovUcpGnBLSDuE0XmBg9mZjhnmXGGDglCb6dwHPK85HJREwC3R3vSme2BBxK8mav5WFmILnRjdt2WMnKGKK0vtGIUi/mrE9IqEqnqAiqF6RlwbropxXaXvdyhUDKUwyfWCGTVpPs2LeaZlxL5rN34vJjqZjGYpslpr5E8S73ns9ND1yrBKm1Czm27nc2krk8p94nUweZyOZuL+dEdoGCSqbPmUqj1ttuxbkAhUfNjIpGqpmzGdLSd984noxdSCCHjUu+enu++7KvLl+uPT80Mt5unsvaF+mlWqP7+pjp/5alPlL6E2H36k1JziR6yDzQxs9CMv3vx7m5z05OknATTyifL9Kkp2kYOLYnAxCzl8XB4Pmx1+ersnOKaQY6NWUt5N8SbznmYiLOY4/zse9PE9uNUVfW//tt/+5/+YSx/fH6n3u4329/Vf/j0+cPYD2HIfL4EfuC60llnH3IOSuKsecffnBbzuZotbiDa8ZZlHNOmwOurfAX1QxDN5uvXsTtKDEorQsxNcjbb4/CX8S9/893vpdY8Q2Pq1erMkZ/aVjcLxggBzk/qoqpCoHvG7GBDjIO3aCqulGDIICPqOTd3fW8HP4uJthSW3o8huZAy3D49LevZUR58sOvlom7mBefAdK4bjkyKKY75GHIhqyPlIyBDXlXzspl3QUQM0vpkin2kCRL0SSBsD35dBL75ipyvy7JUGhnEtRC+kJMQpXriYrbdh9mZ9yMOj14VDXgSK55CINfGXsWSCi7FXBkzk4phqhemMCIB64GCc57xsCFGwALbojfeTxClZlKBNOPzZJ2fbCkLUZbhHDjdJZCCi1J2wVWWF8DdlAVKLuVxf/u8j3ZyiKIq6sGOFKkm5ubNvJmn/uC9JxeZ3XHnM8HN9um42yMMzIr90MoixxDmITkXAmOCSYETstNSCee66bfHnZ12KcQpKGWuZ3PLoNSaGTUVGqXwYTD1ArOzkSxtdVq7PvQ9KcNtZt++PddXazq2H1HQvmepDG5fmvV5VTCiru+maayNJufs2C8EP9FvuZAv3rzmgJsjKeuYZXbbOzVunA0hFS+4Fg1x7EIOUoyDFVIsmyUzxoe2awdZzSpyVBBCIqKQE0+JOr4R4YVS4ySmikwM02S1wKNgIkyEtDy7nJ5sSHnysszJNAX60I9TXc3nF6//zYsf//1f/yFSTimRuiCS3vZ/efwXF1NI8W9//D7DlAm5EugzkNx28dge8rFHwZwP/K/VMBuRRQLvv9wJxYmgKky9WPZ9ZwMAoQ3gsim5kBxzd3zcP52/LSA6aCnP2KzGI2HOURaOGZGHImwnNUdrTMz3yCsuBHVbynMrhSq0jm6ylii9U28BQ5dan8CzuRoYgwpZHuAAPMfsJ9alCi4ChQm+3I0+fzOmaF6/KUVyWOXYEWOVKjiTORGdMYERntldxTIlH0MWHiABWR6rWlqBRLA5jFM0i6UwZ/S8s0CDkbMcZ0LKerEHCiIcwzc9jM+fdwOJHzHGUtkEiLEbRxZTTC7FhGWjWRWiI4Ykw6Kq1cqMQtG2BQYaooQ0ecZOR8dNkdanvBjXmtDZCbkA1Jg7dvs4ng0Vj9L12/uQ5ndT9XIODW1DJORUEAEqP0OREgXgDQBrZnWIXIg8M8rH2KYQZXkx00v4fnAfacjd2NscXi3WXlfBhccvvpnVlLk0w3lZ6fq/89OAL5EBU1olWQpdCtJI5eF5I0gFfxLDELy/Oz5L1ATxt/svL2evpo8fxq7H2TUTbcNiiTXFkKLPYpxXC3S2MKat+cGHZZmaAtjKP49DiDZGVahpvrRIK8rp81fbssdXzfzAJapnUqeFmn/quvViVQu1n8YQfZ+x3XxbN0bt3/yl/+Pzdhv48/P2SEBluXg+5sTgh9dXal483T02Sp6szqqqmRfzRb1mKA2IEUtsMS5KCfHnP32Iwf300zpT1pTLhm32O1POz/j1g44/vjA/73d3u+jHTsSccgbMZc64vmBP97bdhxkHrTO9TsBYC2ymtFBFvbSlkmO/PTChbIZhwDWhII2b2M9yFsIwSULVUwzXl5ez2dx1eZeHy/OKd2vOsgsWcjAGnS37w6MUSrKuroZ0WAPspVxHX7k+iBeVHIVJlXihEDGvnBvbdRFYAMb1vJNFYSATAqSnCDDGhk/9ZFAFueyP0TDFxQvtNg6I4U5wo1DMmZ+tr8yiut8e+v7YzBVDVZQKOY9EobNBAxfnbuqY1KjZ1BH6/OL9KVEa6Ks7moIxIBlUhMADYGfiuoiZVjnVbGi9z/0wuONBcGqy3wbHlWyumpfzmVBnUkjIEHzajUMc2xEASCQ3YsFxxpEzjONppVaMkzsI5oTzMxMSnXIJAHicpIpsf9w/hn3fP0TfX5fFhZAPkUw5g4sLuH9U89lgLeNs57YwMCpr4EjPrapPcwZsoTIVMClGAOVYp+uCV4Xad8hpkIivLs5yTpS940Ki8CkFQXzcNqDaz7+4pshYzqqGhqpZCeBix0vaPqrz5ctSSdU8s62OiitdNTMeUoixo1wwUFwpDAl4n+XpQUDh4z4xIADKmQETBy+4YOKATGD0RkruxymxvNClyMTynlrOAVKUXM1DcHugydvTZb3+2x9+uP31U7c/CTS2N1+OfYi5nazQ5er05CMc5m5ExiVpRLmcL1hdVqqRLNw/O/DfwvSpb8WA/RXPOQOs+PUQmeFdpJTylEDPliE6ardc1UqxVBQsdzknrAmKGuEofer+61vNW6CGSxGY8NPRNF1rfU651HuWkaeQmOwz05mAIgNAuM03eRIoVxLzNnkEjQlnnFgK1imNUyeCamo9TsTyZ4nFuzc/lGiOwwTUB4q+8Ud02mIjzuDZixOfXBpQLXmj2NQmG4RSBGIZ8FAURTsI4mVzEieuU/AoBUcjC5EjAoyMzYERU1Jcgx/7vF9IJYWMoQeUKcEIzrbDYj5bLeeJ60iw22+ZAtEsFCPBMB09dCM/O2EoqNvmLNihEAnGoGspI1dEGkXkLFJa++pewpNbzPmkPLAFXxC+UClPz4kk0Ir+6zA3TimLpUA/MlYKfZucGKzhemAqF5JHjZ25L0fSRdbvzs3hE/+8Ht3gJ854NS9ff8+PvYGJ1bhgSz/r/D56SC4pjWh1UTweI6piuP+Xu2/2p59+lCcSbLBD92J9irgZ+m68Dx8e9sOQBb4lGx0rlmeMwSTkgREeDiSVGuOQ5BGhiBByPA1OJmcuzPRwDHyLJMng6Xy+3LeQwgeYXJ6vE+C+FafXVeTplIlX1cz3w9eP30xfHL/s//r1P2Cqhqc//fovv9h6mPqRZ84EF8WpyoHhVVQX/S5tN/viotjuymGq5MuSOud9CJCcfcwqGQZ33V4IATnmY34vtHzzwtmB9rZtH1wowdnw8tW5MoXAIFZ1Kh+6mzFOZVnrs4VWFR3uB8sXwGOIRwKslJ4v3OHokuvHiWPmfOJXb06nI8vh8kV9fKKnQ/ttGM+dVcil4TxxmROwRCwZ5e6fNzfbT2ulpUqGUcpNCOPJcvW8OwYmg1+9uML9riE6njXMBkYhp6RZyOE2AYtx5jJFhsgNT3kWUsvLOQ+BZRBCIGP+KONcqfkKpjS00dSiZ5GbFe7UKB5OpZDFG15EmtHIcbJ7bowPfnk279pjVdZSyIUwlDlAzipBHFHOU3vHRBbyApiYp3e/7v5iLjPzAiK5SgL1OrN8D+P4q50sRyOwYJTLqryYn5D0S4CqKkxZ+uBjfr5PXQphFi8DhUNINFqjeDJipKaipGNK4Bn1QInKMLPMA6NIme7ylHYbExEO06m1lrGtwXqJJzLxaHw1UjuMuh1kNQtTiIkZLYQs8hjRW4UYK1Hlwj89s6wBdAphdrq4ue/rejgOezWv7LQvaXdxqSZ7u3k8FNmRXBfc+g74TDIssolh9LubXpycYFGaqnzY7YXjuhAnr96Vpcg5pZhw5IlbzCIRD5CZEMn7bUwrl6TU7aFnALM1R0k+xFGIUqhbz/xICmwf94bUcnaSU0SUqlCrLARB7A4xpaKZC46nJ+ucaBzl6aw89nYCKqr56b/7b/74//y/H799gfMXPu6B89l3P7xsdNmUH8Y9FVXPjuyYsPXjsKv1vFESAH+6nN89DLUtCJ9O0QGjcFYzKX14AgkhJ4GSeJEwnpfqic/yxGN0yFJkOjw/FiBozgMWOe/ABxIsxggx8TCJ7KXoUw6QE2eSh8SYYoLlJ89N1kbXVQ2QC23wol7WRjARUw5zN04ELFGgslREjA8Hybms3g5Tb3RZv3xbmcITC1RI2kvkkq26lOP4uGF0dSaFElc8fcMomZJNiKzgRukb58oxYW9tAiXnAHyaDikpI4uy0DprUYS997wfWswestECt/vzanH2+hXnZnfsn0JYr0+Ker6uXoQ83ocYSYZ0lOf6Sp5NGQbEFBzTSKfrc4J9CGOcmJZpGBjKFOKdYMWhRQViNk9d59Qt38OfnXi5DoU0CfkSzkrFuoASMUBOtwFmGSRjwQBAwnKT5SWjBE22Y2w0n+5zCAzOmDmQS7ToV7FRTL5bTris7rv+/uep0OocMHtvAh8ZT1PcwqhWDUINiMD13oJScnGyWGX+4cOzt92q7Hd2sX/8R6X/W8Ayxx6r5vD14ON0fn7l3JgzdQ88qKNQ8rwgow0K7mzIUwLmuog87wopagmWcfGkvHOb54PEQ3+cMvC6rF68/h5Q9P2uqhcc9CiZPJ/f+/D5/uG42QPjWpmXF3/w6dGJt0VgxfF4qdztirC3LQ02yWb2cnV++uufPsbIjr06X+lm0Vy+PJm9vFyt1gCUO7+KrOyH/hG/f7WQsv704aP31nz8hSU3oDufV0G/+PKnf/nzn3ZKpNdvZjcPm9+dvp4XFde699PNh78KWa6b+uL9qly+I+KVoynl2A0Hruc5mXmZWZKFCcSmEAzLrit1wVikPtPpwy3T2g6HHVAALExZN/PIFAf/cn3RFBLZvR+maYzOAnea+1l0CCzoqTY69eMXsGdExKzI55QnoCFAhNQHXNtnGtB2fnTLJsmyYZy7za7PudQaFnyi7CdvreOiiUQ2e8mRzUMxrrg84dcSMv7pz/9S16VuhOTSFMbaAQTJqqIYkSFk6v22YmUgQm8dn+fsc4yoBTPl+zdnXNdTxY7H35A8MBgtowoEF3O+ICbihGeMravLuE6ZQpjGYWoP/YdH72WQpigy0x6CqeczKaO1hJBDWNkpehfDJJFVtUzJeTtQSzE4WzjOCsZlZIkyMtoIxtZpLWaks/TdfUFFOp0h1sGlhIGSHlHuvD/JaiokchVZjs7KIL9Mz2dEOo7fYnw/X56dasSh7dpVmJZn86IQWfKhP5rRalUkbiOUvMjoZLwAAQAASURBVGay8r7ruSjqi7fVJXbDVDA0MT75IE12fZcUP3aHlDMwLoQKmWWloZ8YZ0wrtVjifpdjSlyu15elwpD8h19/nSarjTFmnlnJURUzuX+q3ODZImbYHJ4LIQPVFWNy6EZe1RfL5tC294+PmBMR8bqQXHQbV5w01y/fyKIcu4OqpE1BvZ7PavnFje3N08lMFgCVqEYzjUbjkK3tGIpCFrwx80ob0EQG0ObLNQzHdLfbh1xflryowSWJPHnfhSztoAtjpCI7ReLFrIZaUGIi7yJT14ZPXPdMrfT8DtOVgQd15qY9sg54OjrLEtXCsBrtXfiWNlLy76vZfDXLkCYxjJ2yFKjK5EU79I0EZLUxaW+Tzvl0lZe23uti0dSEYtf1iWMo57LfeaJmGqp6NSJFy4Wpi2jNMH4Nn+KqMsBY27YliqauIsl+X1ASU4p1M7daSKNhmjhjgaJUkmXFWI5xcvuDTLOlVmW5Zqgv/+Zf+0+fAgmuT++3z7yeMm8y1wGWlZk/OOvDjWlBnOA6nUc/cmWG/de6bmbF6nEp0xjnMVxIfiCKUCbGF82iwjWafnefJhcqQlHVJWgPW9vuJwfCvBTK2DhJpdzc6smwRODwUCkAYlcvqhSHkwvY54T7HDEZVU6cWC5qY3054+pd2UDrIMTFAudbgnwRaz+FkQmi41MH2ZQViaaI0aKx/VRJ+dMPS6IjJLde4HgD4dzOFifeTZWdPu6OQog2HGjMWusgtdYFIvbJyRRzNrOlBJgxAE0R8XhoPTK3Ls/0i7vNc+QT8OWLsqj2x867IUT/vHk0pqgXpus7++zGyRqpVi6wq6tSVe7Ztk/PoTHd45SP7JggoWj7ThSiOIjXb67Ui+r++HQ8HOtllU6DmMvF2QJGVTf1gogAuSOeMmPnw/hZyRkaxXQxN40Lo57PeX/rMn39y5+Cc8bocfzIaNbMz2lOi7G82e8Vb7yKxvBVk6v60oecuv5oa2fiTPITKQ6poCExFXJO2B1ny9M8tdZ5hrg6fbmZ/tqmNNiRs6grpbkMYUxBQU7OW6EP3cGJPlBiSQ80zbIWM8GdECkeaVh8d1b85e4kbeP6xfyxj9OvE1O9BC6Ezdml0eOChCnPzy/YuFO6ACLmPfJCJJQo+xRjiCBQCT4GmmyxqEQemeIiVjlNLrYtMcgrJyujmA8tFaZWWQzbZ+e8aMTSLDvhh5tWSUnMlpUO3n/9vMnTxuYktXzzdhFyMvpVJorxtihKSIbHXixQtmEh58l4J6a8haftXWbMLiYfSVONiNblJAB4bLf33OjR7nMqiSWnOiPipV4IXo39t+lhA12UXDAA0cuxicfM44R+KCXnymThNAwDwfFkXfmAnX/k9UlPiMTcdFBc8hyfuUHmuQfJqAtR245zKYmkfqPoi/vk4JJ8SsjKSQrF4OPdb+u6wkPfcLgPjDl5fjpHBD+k4A4w2pRGWrwqpXZth5oXkCCnulIAA8UAJXAlTFKTk5LQ0pznlnxM+9RwTYSVxv3X8XP8llIiICE1UPX6/KUs9OYQUj4ak1mdObGF4lrzEbcFOOSr1cJE4t82tzwhEDgfuRDffvlZN6ckihtS16z+/n/43/75r/9sw8BfycRgP9xFkpcFP8D2r/0OeVZcEqyCS0WWrnexXDZULMUqnYW44yky/+WmHfoO6PS7NzHT1CNnXkCXVa0pI1d2mpR4xApzjhNk5lS3+2YtvHhTHDNt7RCTO+OXFxliTCa1hQo2y+NwvFJrRMUI49QmdWQpv1aVPjsJbdsiS2Niuus1ciKKWXCFUk3jWJhcQ8xdcm6Ul6W85WEvBkax1qvg+v0hCkjTXpIMo9/c3mTwQvPnhPlJaxeRE/2gVBJShtRLOQ3JyGdtau+zKU0jMCbbhymlo98PQgjSTNYzvTq/WmtDUisltQLgIYXXzeJTf4wJ1OuLNIYGU8zEtbkE/EZUy+tq5uwUHPWSEqDu2uNkR7/WaRoLlCmlQaNWM1aj7XZP/pkXuJKK4ZJXsrioXv1KI4wz9qLdP2qpYqTOHa4WakhktzlzzpVEl12RCCAFaHcRaiZfaLlXIeQhRtSqFyqoh5JpyY1WZ9/9+Cl8C7C+JvuNpa73djeN61VI6xxGkQK5wnML0/bOuulIOefpxRUJruPRfRoG/fPPq//uf2ljaB8Ou6dns1Znh9j7igPPMkeupdYhz9taqs1zZqapJQK31m50FIGdzWOMS4bXujh+sXt2vyHcwpK5JnE7ycKoukBRjuMzLeZyfdH+9oF8EqFst+3uaXt7e2/7cbifDneb0Qf1Rs2bGe/RVOVqcTY8j7f/6ZsLoX5RLkjkhvmJ81yXx2biDPxvLETNj0DlcpHut4/TfmcZnOuzW/9EqA0MqMqLV03J3qTMvt2stDHx+bHrlt1T6sm/+WF2Ws/qpomMbz9+2PJaqVKIBBO2qEpKIMEsV3WamMQEaMO2B7HSlXNTUGi0vnpzjuxZcDYOPGfiDFkfc5rCsMsIjc5SqCGNWp4W88pHNvVTZ3eUwpB6v/Uhb+9gz245YqPLbIo1zWvBG92UK0GILFMnGNw+70v0fXtkxuh+Cl7AUgOPxNKERYMixL3ggskqVMdNSnqPK7PkU7y+nmutXcyBMhUUZHCHiTMhTms9pr5vdcioMWuFkwVgWi3nDXv4rR7z+Eqett2wMcYUdUw+RvJ+ArJsz1+SkosGUoYdZmSJooGGzQGiAF5mdH5vi9JY62SVvbVKKqHXgnMhOg7leUzBqf7+rx+fpxMivUAYABlHoYl76EjM1AtWhFkQT0pJhrJmVCY/UCr2fpMV1yuNR6A8hOyIsvAOEZmU7fMkRWrB4sCzWeI5nv82/0v3l6vZi2wEOxHeubE7huM0dG5eVVEVa4Kei93uoPabsWT1xNAUT6Ntnj9o0+RqJub8bCV9DNy3PItcVyEwsIoksTzF5ILQmTcq9tLtd0NHOW1yzpQlF7Wszi4uUfEEyeU0ukgSEhViLmpoKcd5k42pp93WUlUUdtf2RV1MezZrGq2LxXyeM50o05KYTJ2+hofXOJ8vmno2bR/r5XvODVB00anheCJMMJejOEDMYXdox1Gfvmsf2/3hwzjrT6LUL9ZJxTjYLbAD8rOryxDhiIzlIQExFCZ4UxoGRXZZsNOsq/sPfyYav//+h8XsBNYNZe7zzSlVLubbeGe/xOq8lGeQkiMWENiAo7vvKKOWnCO+fftWaYlIwRyaSKDEGFCyjJzxwrhoOZdFZQFFqlUaD2n7pJvXSmkK+0wAyAkTB8hV2Tjvpn73cCAgxlBUv/9dHvo/YJg+fP6rHS3rlZYWfNp1PAhQp4klXQsSLGavuCCNUC5TC+CcbBSreYJHx7+rsuRcCiFhOPB6Uf/uh28f/kkYhxJDfDyay2BHJaovbnAffhE//pQEgbfLZoi2JJhSn7gW0CSNDFQGqewgNCWb0wrQNNeMhru7HXfKWc/whXzzOX44ZsAz5F0M9+7XcZo++5dlua4XPOcYUohPQWUmZxVXVTTZHb/RkDlnyAlJjVLgcXe5vpryc2Ip5kdgyla86I4HUEfwiaGqmz6Ad7Y4CSIu3GC9t56L+dWZibbSpTGDTwQNw4ntUxr7TunCJsXYfprgHz/dVPBquVyGEKRSQmW9lvnomT4fMFLGU6O5kF6sdsOfP0d4sdjUs6uyrlYn8PPPm93+OG7G6vzayKKzXim+DYGVq3NtPtn45BTDzEK7b/dH3Nlq9/X+2XZTUSz1D9k5Vziljfv++3dP6fjX//hXAb1eL7WSLrCLqrp4efn23ZuX/+3LE86+7rw3k4k6ZfYY4wt1Va2pf3zszTC5lMJxZlSOog0TVH53n0zZcJTBZrmGoZ765/bYtSiei/IFCBPqGfWkdSFEmYGdnFwApTXnQuv96GM/WGPUIZocbVHonJ1UDFnOHJFGO6Y2EVHAwoiq0LoszzAd2C7nmArU2XfpMCJBx+WKn025b65Kmpfrm3+LDR+hAyN1XQ1OR+l5jjWlFpgbh930yUyl34U8w8mHUmi2mEc7MW6BiCCVYBlAzD0XS+A2C8YQtSkLXj2Y/dSFpZQpxsyYkpIliLMQU6qtokycgTHFYeiLlFlR9aM4XTGOYraseF/O3jQPPjjrgQFB5vyF0glsQvkwYSx1AzmBuhuRBaARgz0uVMl5JK7mPsaUUTJZFCW40UdvY1YClbT0fPzLccrW94NFjlTQ5C7crC93US55csz3wbB6I3D9hDlF4pax2dT+Ok0DIgzIKz2hDazAMNZaq/3xEy4vOeeJwKfDFGYnOqWZeHw6Lj/l+9wyrY2Sd5lYQtt3ZCfQSopVzsMwDK3SvLdRsCHl4pg5pRDCiPikdYEshu51a9z7l6YULAXPA1InODg/Fvqlo0xUVpkB2TaLOk8zXbrsAGAGWJ9e8ZIDypjJp+ymBBCtj2FwnI+b6Bh7IAY/fFfwiBJCVlERGDarL1SIOaZAIJ0NvKxPmEbEb5dpClMhedrtgUUAPY4/5xi1kc+8XmE0BAWu+/KJc+3sUCXHz9XtjSm4PLRhtutZpazzXgpRFKjU4G3MueSMCGIk66zLhznnbeeUYEWxrKuZc1GrCotZCnEYRmlXs+WCqXxBCn6PVV0lTJ93fynFKaewf3ApOKPKn86/4yvBgBijsPkSSk9Yh8kyEHMGU/LkOQU1gctcGgjIBwb8MHYFPl6injLdB5+DGufQTUM57kKKUzcorl+xa/5ai+njb3R+LouG/f7H9/0NcM2ZFs2qn/NzXWvG77LHKY3C8XZ89PflaETi68XSFOtKlt8gqphHoOG333RRvSplb8OSD6x4IdEzGmjkjCLTSdw8y+CTd5DhNbK2dYuYpFNQtMGLF5yaN6+YVsF7grS3UqRDzF5uQpbeen/7OFECCfpPf21fXNvSVEU5kVnqOqnA5nhhh/Tnuz8f/Y2dvhdXvKhLPciUfBO5UsVqJfHkhwQ5Jp9SJMZjoG9H3TPcH5kfe0z0+tV3J1csxXRxltYpDDkWnDGgkFNn9247lQzlvKgujJts7Efqjoe2iyC4WVe77afRNcf+/O//20sUn9TiqX+8/2VblG69XJ+dnc0W87KuMxCfc5GIEe4Z2B/wfVmehly/fn932G43x6m8K7UBdvryx1l2X5vNGpH3ureODupkzXkAunXp4R++uinWb40bA7VEO5j7yxlGtdSH9vCy5K6u+MFUCeLN/faPH+53m4qXV+8Xcis4Cn0weZFjiihwABgcG/3xPrmK38hWptVo0vzl9XUhhMcX9zRy2+3cFy9Rq5WeA4lonQ8+Bh92+2eldde2NHB/OChjLl42l69epoiZsG4aJVmKyFFs2/3h8SlFL6TILAmJQulaciN5+6T/5c9/Sd5dr0mUKDTyzls3gScb7GMmIXAOuEKcHkPO6NQcOKpC5kIRj/4pP0+fS7UkIedKjX5oZSqGcWyaXfrkDjamJoa6Keb9GcUQE2UphAQBMfK91A2jEICzBEFIU68LFMi1nja4lsqrgOCTD8fUBQilVJIX3geVFQJHCVpUQ9+VpVrkYozJ2bi1YfezkSqrxTwltB9jscTagLOuie4u5yWBYjhdvhbHnMocWuuGiuFuQkwMAMfe1arogx/drOSJvO0fY8zpmeu1xL237ixWN5vutZLi7Xv327dkztiEOYJkc0jgIaS4J96dV/i4OybGpDAMJ8ZmlKnzXsj5alEd2ZhCIxTz0iNL1FRlSt66Lgys0OxwcGgg12OVPXXaCJ6bXcrW2zpT5ny35yW6p/BkDJ6XpYs+2r6NEQU/1zqcnyLHC9nljaLgO47fYDr/9qX4sWYzBs4wsnwSQ4ztcNPaikPyzHP0yLFkRhRScbkWMs5mEcLxeR+4jGuOLWgluTELCXo5i97w6Tiw3vkMUGtx8OgWncBsWKbD/mm0TBv+LJ7SPavaFkgkkhfvX297++KseXd9rb447dOvIQlivAXtt+2JFZZ17lBMOs+64dfun4b/zACVLBOfJ5biIooRvkW3ahpuZMu5LQvoBy81ESwaPeY4ZcmkQrHbDfl17Y2upCStzTTk2/uDFgXD0LeJwC5mxaMrZd+N3YM5WbNA324+K1GdLC4uX1wYo9iQYjgm13oglhqio7NHLjgDlmhWCN37pDigl0lNLGeOANM0zqMq55hxOS7mLzlhPDi7WsrN3TMJfQWc4zO7lQJfLgWMKT1mKpCP3552KGamfHnSUCBrlfLZWz6K491gJ14wWIVCplHu57D4HF2GG4ImxBinMcWSZsaGvbWBTTeM+UjJSi9Panf4ptS6OlmUlu2zg7YbhtYkT0KHoycZlcK7zX1MuamWoOpCMDvWpTkIxg9dPA5TSkFJY7jS1wiQlH7Hx729/9wR80QVR/ny7e/rn759HVjoSytQQk6Ao7+N+3pz116q1+YKWQF0PKRuJq/vos/OoV6yk1NmixdcMICYco6JEYucTyzeB092iyOEhmZcGgNx8M/7bRi2ojuKcxHD3AbYf958+OuvQVb0fvz95YvlfDWvmzBCb9zd8XbnDk/Pm+VqtVit6ro+uzgfhZvNG+Ri/MX+8h1cJCZV8eb89DYcrGObfcehay650ud0Pvrsi04s3373oiiHcfhtmOKdb5r1+VlRnsZPnx7Ozs5KpQ/P+3nVXNdzewWMA0fm7vbj8OXDb2FvPzcwX70WrpiUqwtjVCGF4ErJEP1m6iY/rPPp4H6rmnNTHPshPnSfnJ1KdSYFnZ3IkLEfclbh4eMvQKYP5exUKVVURfnDTz+m/a55Nft4MwglxFw1i4UUxgWHDApdxhisDVLDeBjKoj45WQjOOYcNS00Mx2nYA0HZ6OKIilfrImeec4Rmd+yFJ2hSrrnQpqgZ5gyy4ExdzTzesXGvDI/ds91SU9gRmO6lqXqjUxw9OUkhH56zjXwhVNFw4BdMmj6GFDmXIHgYxp7hehlFVFJGpqXUKnnJNfM+UwSapT1MsPGSm2ZRNoV43u9DoPnyaFFFF6XAzHPXHinnrm2H417XC2OqV808XOD4jL973cREf/lLK1tBk8I5X830ztp5SkRMUKgWnPXght4XI7KmmERSEzGBQvphFmd+iSll4/s9Pu60lqrUMc4C7QuSQKAXtTDFZ6RLqwVjPVezyDbiW0XLWTM7uIOjvmSUFGND9r6TeUbmqhjaSGS47saEyVsTZJw8F4lFpfPTfhvsYTW7OI7qzqWVsVWJo20U9Ar5EJkGWXE8TiP6QS1mWqfVvEGLyIaOMWD8nSm1FsIegQCCCHEinK3WF3582MVU/zbJV4LzBD3EItbJhIBRxYyiQpN4XM4aDZT9WBvNxH9NakiclWUBHsuTYjNN6KbJO6CcU1RzuRRv+naX6VCmbLROh/gYLJu8J5rX7OT0nZRF+iFJXTBR3H55rjkM+z6vC00sQMdD/QJOtYBRTCpwOioq2ipeMZ2ZzRv36ILTqnBhOk4Pi3UDVI5hZIj1Yr7zKMZxWTddzZ2PpyzxHE1OjFBxQ2dMHGQ7JDSoxTvBcfJJ6/MZEwffBLdNLE2dktLLAiqzUKl6fDhwkIX6flEgH8XD4ycmuEDMjM8M+gwsQplLNDjZVEGROYmYgKGPkYhRznkZ4DNEdzDlmkGQ/OC2FVKsfVeevT01exRcXX7PUCAKkVmEm2OIEd+Eqnj1+izf3G99fPr1tk0xqO/Phajy4a98ZEnmt8vzpqwQ6xCfbvdfQoIpTGzeC9I5u9mJigWU2cRSZ3fPcazr+i4gysyPVopj2AxqtmQsHw7PJymglDB55pKUDJU0DI/OB5Eap/UyNTUYvkYcZjZMNU/aqdV7ifOazzKWpqjnv/u9+OdfmbMlZWvH8PRQzxazJj88uf2nWyWVUGZ9drJYm94f0y7/E3/iXEjHBchUtW6KgtQIAMCQK8bxKcXe+/TwlRcvUkPe+yKGY9LCHv2+vz30mk/93i8iEjNDWT785Uu7+W37p7v/+LRxgc7O3v/2+Uv3mf30P/z+rbzCk2loFx8+fm777YPgZVlV9Ww1a15/9x0yfPP6ddE0MSZlC7tsrl4VMVIEqqsCeDOx6enbGEPQ5uz0eu5lZAyecvry22FqXY5ZK2OU+fyfP3/69PlsfeKmcZi61XQwpug3mI+Hu7ubx+GXIiUlWcz578qT9dv13X6fl27SbPz/E4Rfu7Zta4Kg9f/Nt+5GH2b65bc7OyJOpFNSJJApUAl3wS0IXoEn44IbRElZN6gQKpSFMqMyI+KcONsuM9f0w3bbfOP7Fn1Oc9vI1wXshp3pf/J0bSEf3YgYJJekfDcmk2N03vc5r1FdVW804Q/iwPU7uj8KRa+1YBSij6TvEMsPLQ9teXb1HZO1TTlC1koZa6bZUMoSJi3pZr1GgOit9UFC6hG9dWVM7cXVrRy3//Dp7/7HhxiD4PxCFwuceEsokYorwukYgoe8MHwkvSfEOKfZkS7bEMOVVM13lwdjxrAYhylCBpKdUJAxK75IrjfTjIfnsemHrlJliKFcrP3umMLzGMDY44sxfM1bxCG9GHjLSg4jyS4co027r4XWr15fESQhPSvCOsheuQBBMu0GCxEQEAl6ZGEa22VFKU02Oftr139fSoX4NbjXgm3T3ji5EYzWWj3n3P1i5yKiOe1OtuaJs+Cyo56NmSLxIQHsfF5RK33murxYzpPzNnX9oxtHhL2fAZszPruNKmc3iZlRM81LOfrYjohnXPZzj/eUL5LLUxzs4ZGTZyG493aXxnXXkawJb3lSinAPKG2e4+ynnmI5c0XzTguRQ5hSOsLkBibQWAcXMn2dQnzaSSFoZHXN7GA+bbdnm0VTNFVZM9Q07DgoijRxiOCSuqTL6nV5DWbUF2v3vJsKOvcJHq0UtuBvN6yeYEKGCSntTxFirTlVPCST5DHMNRF2LpkOZTeOIibKGPGTqGW/23UHqF4N0dj5saeOi/XCP0299z+U0jZcL2OMd4Te5JxzIuYw2fkQXH2w3eXu4rW+ftr990N779B+sZ4zqmMzTwM9ZaEtpXiyuxSwKhbfvP/gX25LDkQVkPPgZmDEZXTQh+Cwl8TvS8o77da4Ooy3Q8iZXcpexRgdB6XXg3c/3YVwtIRwk8d5oudnCmpKeDjepgicEv3y9GuMHWfq3Svm72wwn1saRYzPGSllx5HI04HWVKqG+mSxy5mmmAFS8CmY4AtFMrBkHhDeHxy7cjQv1bFLpuMUMgJ8vc1TzpgZ5wgUkLL0+YgEIAfcQ5SG0WrTnMdchnm0MEF03m9h6hOB79YlJcdTHDEdYirKVVlC8zjNfj6CcLA8f354BFPUiqrI/HGui+BdXPo0ji8ikaqtSEohTDUEiEGwMvkxxywZzSkqIt6gHjavj2Y4mtvltAnJuOi995SzTVv6RqckQoafto8h/bSsz0tdWa7s3Lm6zNVJDKd9l5fhavEqk3Sz3e2Pbns8PuEEYoX1tF6IgZGMmeZI4dgtQzxVxf7hESATzD+5OT18wuBRQfazj2N0VRrmQ3pkRwLGnBG3n6Rk8Pz0dffz4+7lt8f7+98NjBNkgLKhmxN/mZ7NZnf6u8ft68Przffrs1z+szf/9//u77rj9HX6KIT+Z6/e/GW3/8P6zBl/Wm9WixXjKtHdmr2xScLFRZwMBVakZVgrZXjWwP38kpHsnJk+mnGkZ2z8LSTrYc0e7+6dsdX+cHfYbuJwgOEvd/+0tduu7w7D/obAat2slkua4Bo3n387/NPtUS6nD9+tL50J7KE+z2sYHs0n/hzb12y/Z+GMCYIkhc0qNEU+hkAmxD4CR0m14JLGnRu/0JIdO1KSKdx10c2fM/zw624u6ywcnh7n6Zh8WS4aJOCTo4J46+umZoi5n8100DENhTrkU1TrhoBPoZ+GNjfPhHAhK1bmEEqZoHnlgocCjTEkp7KqGJBabYhINuS60p6RAsetffpzNxKkii9sdhAlZZITbmOmwySUgZBV8NaHzHJV1QAEE97dP3BiS7EJUSaCWnLZvlI8n0iXE0kBI4tM0HMsntnV9PS1O40UgUe2XL8+HT6phrqcFIAnMVIRQ3DeAwClfFHV3TTGkFKph/F0OOwJTdckjm5MaMdu5/rT3eVbY409QzlRDyFm681SLRLJc6aEd0AFm52H4NM8x90QtZ5t9LC5iWC5zKNfL2HzLQnOP/3yc1K6gmViaVhNWadib4fiqwzni8v88uv+/P1ynvaWWE5jCiYnQWleEhCbt2c49/s9OgWynOZpUdbOOmpTWQm78yETRHGMc0ReSa41CSPVnIWU8tRX5VVdsKfHn0xfIYWq4Ode+kXFpcQTsyECREYYU5rWjSjO/SH23KzOXkdV7YpARW3ZOJMu+fSWxPvp43WiuVgKTZQsZDQ9ACPJ5nQ0sJAz+qKKq7kbhJRUYHCONg2XSrRrIXh0SZWZUvS7/fhpO7j8jjK/FrqMMjaRz/MwhkyPhxeg2acYfGKEvkyPq7rJBX+63V1/R/zseLl0NgpZKc6maXieJ5hyqVqesh/vtS9V1nnX30Hf26ldtkhjzoTqBSFAO8IZ8fsoX+mrxQ+H+djgamhOuU/GpMtFYZJJu5RC9skR5IsicTmTSL76mZc8K2ZPMLu+0O037y9ccLixlOuMc7d7zrMac6a6rmtJLGWB+CohvE4+BAg+EF4sudJ7+1Tn5vDkM8Ij0lUquZ+JBDtPeX0Jyc0vBwBkXKWvOYXg4BMLITCO5DXJd5GOp75EzBI9PV/xpxmHn3+e55Og+O33mjOgJJHoxox+djEmWJXvOfnds/T1PlofnP085qvi7LyU1poTAYTnLiPnfHmx2lSLfUraB27o5/0nGbwviiI6mjM4kjJspy4hTvMMgFUlx8EThgXkQHA/Oz872U6F/mZzdcVlBZRRXgmpikwzjZNVsdD0SB2Z0gSq1Su29nEgKY/B/f5PX3hxR2rYAEioOxzJftV303pzk0vmkjPkCzc8By/XtNUiTK46mu409PNJeAu+o6N5HIePL78Nf+r+4XAY+hADVB7elyDOGQKj/ENG+prANP7D50+fT936wze6bpd//Hd/Xbwu/u4//t1/89/8KYbT/+vlP7VN2/7hf4shbIZu+W/z5oLxSs/98ZQyIVW0tptSHMeMgoscTiEwi0Py4XfwsWL4uqoP39ruZUC3n0+HD5jwxrQPw+R7COlcilkUzaL8N/+z65yzEN9opf1vv4VH+C+f/jIDvJnevHy0Mpm//RenV42fxu7wD7+t14scQsWxIe3LtPPGmdmEFKaeUgL6gs+DD2Eu14XgG8RJ65KlIJHO9IE7lNamEJavkOlGl7BSdC+plsrOjiKjRESaXnaPOfqJSaVUF8FaK2TVRz/l3BNiBbsC+c0P13e/PX9488bPLrvMhcDkUo6tbmA8JaTDPI/+ISoxzcHNhs/z+Ydrmj1BUku54NcPbpuM16LwwwxjEAX1p5RUhpyllmSh48F64ykhddV6629fnnjkTLcxC9WoLHg5nyXKQj+A0xEGJ2WL5Fi/madJMBTIGaVapjDZMy6y8xS5sY5zwbmSqizK7KPzwQpGL2idIRIaNytI8Sm7FEZLbWQIp4+/Fa/fCMBuH1Mqke4dJ4MhMRcx0CB3gqIQM+gmLVkaFe/B3pksuzs6kxP58VUj+LuUkzX212kms/3x6vrWpVWiJNBt6s4DQD7Lt9D7UL38bnIWyeilZKWORZzGCQG01gBVPG0fw6wbShEeu1iii1PrsEA4Zh9V4JEIpWRlGaHOe2SRHfoTdWG5YvZwIOAv5GpgPVIyqqymMYPAtOe6ihFSJkJWXbOwcrA2leWmaPXDmLW8qBc6rFY4G5YRn3GZ+uLVOxqJmp/x+nLa/i4lJ9JFP8/ZUV5eLM9dhyfRS0V52mTzNYGuG2KCCwhj7heUMqhyBkXUXqQZYFVKgLELmVutpM5A9LUjbGk6SCZHn1JIFMVCahpzTula8i4jFYQYiFwkpl5dLjmAxhROx7wfldYcxVcfQo6Mc12W1s3WzZqA5lzVpZaFHe1o5gM7uphWdON7RwNr68Wnr50d7iFImYsRUUuaAIJNBx4zo8gFx/C8PzWX7ZvNKuc42YNy4ZSPfhoQFZdyKc+SyD6nEDs72jymGV52gEIporhzpzTO0fNdmn3ihBAqaq4qCCZEYq3H+y9hswpsElYSoXLEkMMA14ydrX23tb/04QxsWkvE4MI826qMb5f8l/1VYCNDoIRkT8yUaZELYnaJBZ+CmTZCftusPo8LPE6NpJJQecO9c1+PiWSoF+wGLyXXhOJx6k7TXhQrtuSwh9l7xTUUMhwPpKhMiiEF3hfcTwMZ/WkHjPKjQe5xwSOPjkhb0RNkRSfw5d1p/6GpUk5cKX3ooxvcORhJ8GTBGZJD0sU5W9uKFBjCDJPrTh+PPw+DYH1TVe8E3NTrdrk63d6+4Oeces7KnFPVqf5h/nL4+WT7+jh2J5vSITybxwEP+93DQwcZkMAfa1SXOjWIpAag5M4QtoPXb6efgWmC1E/z6fHlT/2My/CHN5dXF//7//U3l+//wz/846ff//7p8em//af/x4fbv5rK7b/7+r/68X/3YzRzVVYWsW1aVl2sSulofj7t5phDCPfTjDFAZpei4Nad/u7pcNjvusN+dzjt/rOW19U/6c7+JdvWrQwg+04pXWvO+F1O75sk2OV/+U//8fPvvwWAH4GuCjqkl1LzH85+qAp+2m3t0DdXVzn4gldCsM0CyebdsT9aD0OMnh1bU3Ei8mA/bb+gelWLJfHZTX06/sJbLL4p30QtmzNKNymBswslL9qoZC4e9Yu0El20k2GCF8WSU6qkFhmymQQpx3lwgy/rLBuDVvIMiHj/9GKGfMUYkSZ7byTzJLuc0bsxRFUWFLBQnIkSq1NrDkLITGnMOfqdd9u6eksU8887pbjkfBoCMFVkyrKIx3h0R1bVbCLHvu+PB+88sGJ5tRo477ohdcnv7+jbNwIBBSYhvUkTQ8YsyZpRfNk9i2rhD6k8b9xppLkXIzUCIqBzAQg9xinNtwwarVeMEkqmfgqM5ciHdlXQVFAtL/Hmz3f/FAjRGSykGGNM5+06kJyfJlSexxBGI42LmTr8aZK1YqKT7+rS0UYu4+aJ04v+1G//x79PiCEEIcScXAj+OKcaOVKKFQzDX2KKFUFapBpZwDax0aXgZu+2nrS4//r3yWGs62Yq4pgJEG92ruRaU0VhkWvTCmBpeJkn39k+Znd0QTNEZx4vmQhPJcnD+7cfHHdiu5fFZbXk0UsU6P2C0oljAVJaXfmcMKey1mVJfRCc9rMS3g3gAsbcCH5rvxSqeL3Q3vqnvdnEGGU52KgVZeJa5n2gYAKds02g8BiKqjeFttPpxZcHe3rXvPVzsBmNmSOBwVmgdNU0Wm2yrWMehy4R3hOCIo9ESRs4oZF6LMeQ+G/vN2d/90n23YnpiylPhIXYO6Kuk2AVYc8PrsCHTQBy1rLJO2eO40wYbzfnQhTDaSoRMdiTn4RkyWXGmTOWeIpzvMM7NhG5WnlPo3tSVAhcscqKMSaUVRMJFwmoxOzRvcwxhu1l89Z7d0iHtJvmFAVOVFMkEgM87e9SzJFSllLIOcYksDhftTOJMQZMGekrwjMYsnyLK8KaumaiJusFLFbit39wZnB3D7hkZH0RZ3rE5xTbEgm7Ozzn6P2GNaJlRAmuCKHc52MfzwS8v26HYVHwyADjmBMmmkgf0VZWhLq3d7u4qovm9YWqRcp1CQSCj4/HI1+umlWzoCRjTpBtml1mCrX1ngq6XranhwcxDD74HC0mxSBECgtxcqmoui5MNhvXLaQ0JsTIVLMqzjObfUwFqcnlle8HLplUqh/Ai0iQ8d6kckFrotwF9rF3ps8+HCe92tTXP25KL+1kphEhK85ESpjhdHx++PpnvForGilxjI2Hxy/3/7+7P51OJNnhtCWqwwlOx3zDydrGy1dKXlTMFXJzGeAxMRKToxmVWqMTcNEKUVcX75Uucu5cEdMePj/9qbGPV5frP/zL99/98fqw/6/+/b//958+/fZ5/rvcp/8v+W83/1FhO8nzG8rOoqVt7o7jnBD1mO8ebU4Rh4EDBuM+m/n58PHX4e6almVdVLk4E9BQCpWVEyCRZLgjr3FflELKQ4ZLcfbmX3//bfM3/+F/+A+P9jcP8AXiK/b1ct3+L99+88O//JGT9Pun37TkjKQUXw67YbORYmSh8SnHui4kn/uTjrMvmVhoRTDdH386YgQkDRUrRthI55epftXm3J9OcJzqi1cLquNsp5Cx5Quu9aHs4hTeX3+DhBzd1k5mTnkwA1IjUsjCkQlzxi7u1gTlexk+upqVM0lj9GOyaeoXTLKi0qXWQowxEKkIJRSmBtJunjYQnXVftuZi2TFeRFRsGrCgolDEByEplxGSNt1pnmdFhCYi5HRwgZTl9auLi2VFKKPOTyH0NMXFeR1DN2NJjuiZS0j6ZJVMbvaDKtli2u3tEJZvCCVn2SYiUI/GQQJkmbCNuh7SRBAozc4ZHM3Nu3WIYhq3xCLGmQTPxO/BeXu8a3gbz4AMSMfd0F8lHXPn4CyFbakra4AygT6Nfp7ysrwIFila/3V+8hD+ZB7mnXMbra8B4ixepvE0HFJ3PC7P1TyG+MHOPaltc3EZjqekQ3QvDqPb2RxSdkIGOI7OJqzw2bhMlhtV1qtS5cAhERrd/ji++HEpF5O1YmRa5WDo+SqSIxDGZcO96Qh6pd0wvVBK+BlhmvLVEeJmjpGoBmlFF1xrrZzYBRU5d8GC2AmqZIouF4vC+5gYAhKc5tF56z89Pdpp4Z3U6yyOLkEMOQGPPj3xZ2EzlcsURsrYQikphBsGVpxPnA5Mn0cK6OJsV4xKKVl7M8/B3famYB6S5Cal6HJGDylm5Klt6wlcTnT919/94ad//O9+OWzevowC7MsB61zNbXza7gRKrZWU+8m13hrsD4MHFIt6gZi7eVCF4kxliCkmCOmEJ5pYLiMsaGHOog9lQxcJds82Ol/Ssm5TpHwhq6xJSOlrB6JlLPt+N1JGq++uIeZD+hT2nmUuCBXAGUggaejnyBgyWmLjoTsv3jdLRhU7zpZjOM3Aoiy+Sf6QZk/XEghhX4koXuhiqTdlIj/8wf3j36WY6STSgs77eWYsJWOQsqObWI1EUpOsG81MbMHbVb3gex+8hRCrmUqHqcu8hKlgvieZQe1KQoUiysxj9Hk9DTTlcNrfIkQEGz2/Ofe5v0vWkQmBaCxpXqBgkWJJaLVszPGIYYgXV3y/T7V2EWSYgQKPERdlQhUmWzGwteBlkFK64LnRkiYFceNcudzYYpFvIk078pXQAHEV0iePlFsZttEKEohKVL2dbfT+MY83y42QWvS/Hvrxyz88/fwm+2Ge/rR/+cO2FFdk/7LNh/zLsXs6HLz3HHMM5n1RhLN0dSOq5rWzO63fU6bz4SFyGMbz8kMJOVwl3Pd06qFkEeX7xTJlbu5jfMOLl8Vehsq7+Ut36KYjXJI3G/Z/+j/+H77++dZL46yTildrEmbNOKP7kyfdx8/mP2//nrEPlPGu//MreMOEkIQf9kfjHx9xeK34+Y8fXLJSCn2/yPFkAkmbpioI5z+UH/i5Um90YaT+w/W7v33zXxEk/+f/y/98ReB/+Ms/hXH4qx++uXn7+of/zb9pGk1uP0qOgjAcOueRQLKfdi5nkQCGcQgpRW5NGPq95el603yoP1QR/uP2I2Hk7OK6ajf4cGeTma07DZAEqRb15uIVEwX6zEUBABjwWl5OlyHnjDFnk7QmAxohBGOcdNlnNtvnHHx28UVFcU+qEFSUhKHX8UxuRh+hicd+H/rY1nXn80RATMfkzPl5pYEzpBbzuswQIc95lfuZ8QvJfHAzI0gymcCl+cROJW+kkJwRzOmsVFVRFEqkFH+9vR8ny9S5OtMMwRJGqxBFy3pOKaacszTGsZQGQWFR1kc7+72li8KhifOJ1evnly4mqBbr2aYcs0s+yoUzx0wwDzmKHeSj9TxHG6dULqpvzsuneQhESiTWTjFyyaiKItE9gqYN54TQCUhDWlq1N5yGYOewjx/9XZLJwhx0UWgkEtHHtEVMQy8YBH09z93u2O3/cjv2j4BY8SVNEwhWDo4LFjH4ECjLzrCScCwKUMVCKwBMQ7rbPS7aun9MKbqDf/DeFeV355BMyUSYG+7Vup32W0g7f0RCECHPv34Sq9LXpe698JOLjPtOlqpeL2ZjjYk+gShS3cYkQaNmZDHIiNueENexwjy8QLaFVu8pF1Lid1fsl/F4uK0WbwCxd54JtxDtpBx1Ms6drArJpeXJTLOzEyGc1zwxrAYzxmS7+XJTg1omJuJzuA133KmMCAxD5pxWmzXvR7Bz12QtjujPaXjZw+sfLv/FTXPYHh/GkFzwTg4svnrObZmZKEhJ6WmFzuhkJuR2Xp9vlleb+6cHyWUMgdd1dh5z0loveQsVuX15imVochEzNEw+3t3lmTREr6Q2LgLOR5xW+cLwVBENEQc+kOG5vX4tKWI27CgXBWUGpWahuFBAaObNhzrHRDVzzsVYlaV47BLZ7b13ZLOgnDA5dSdKbKeY6mg9B9STS0vVnRzKXMbIlg0LAYJ3sx+0EamkGBgkdnO9ZKy9i5+TC5TTFP3h+CzXwje25sXJjVi2yZ0QcqYUMgcfucKQIKaAMLHjiWcd2gYDpYx+QPoJgKhCc9H1QAhlS4mGpC4K6WkgAX3INCc8Zm+QrE8dyTlEH1PKyR0xaWuCkAaOBLuIlC1EAhlh/zk+w2cEVJw/vmyO/+Zf/etQnBHZkuV13D1llwnVBPstiTGVhNdNiULrVuvPe1eXa4Lh4RjbfrbG9R1Gb2N7pVZh3e/N2TjczYkE3/izrT/77q+AES7YYff7xeXShzrGe12+2u7l5fnmoZtye03Ltn1t+bKYdocDo2EVpcNqvZIymFYnmqRdPAGXUE+7UXC28PPJGtrD31vTFluv/MW5O+x30zB8/v/4ZNOx+iupDqvVj5BNsMN7nKf59HX/eCKqr+tvdaUKft7+8V/9uK5anSQ496us3qQ/aspZTKn9df7n//LqgdDX5O2X+PnH4ocoISEhFObp7g8/frP5v775t//vvwNnb/71TbGQupIQ7p2theQW0mCnFWWGUbKs+cthMiNpGpZzzJRRupLnp3T/uN8TyQlZnJ+fvXv93rkxJe+dHzVvRkmIXrbV5mq92x2QmxBTe0ZDjMEHyocZYM/ZhayjQQ+04ss5TN66qep0pzcX75c0+vmnqbPjRbS/JMj36F+xnj6bP5HNNYt8mkZqjbPeuJkVMuqU+mSWg0aINhOg9I7+Fqz3483fXi+QJC6e91sghBvryjJ6z61Oi0oJ8XJ/R5Y3bxpJKR2m8W4IRXJNJY3dzp9nqTWsV6+blhE2VaQ/phyR9zKZHEJvzT5nb6376au9CAdKKeWU8mpNYYulcYHn5F1KKZa0zzkLzrfT9PR5d66wfbPK+RRFpHTF6IrCl9AdF8uVqtjvU/TmdwQtdCRwoCr2Tqd467+gq1PvikVA7zpGuF33ckdOlI8ujMG/OKeTJppkTEA4WXDhStvdNJU049Y6M+bASCp8y+qKzD0KwiRlUXkWQm6MH5g12yc6+U/OO0JYSPHQP4cYKCUxxofwOwJ9P73FCvruKXxls733MS4ILUtFGXMpMJUXBZJEoojHIYyRLRv5ZrM+DF8yXmykpJIFmkEwyaizBnvbsy6NIvUHClEX+ttvLxF4zpRQ8oeb6uGQpVL38zFCikxlXtHdY3QZaUVPQ7mop7H3jO5fduR1iy7KFJMNlJB2USnVppROP93dQ9R6UaqV4z5DDHHyfvr1k0GCMaZ1Vowb6s93lOntqVn8879tH57qPgD9fHu3fC8Kt0JIupQYtqdDT0h29y52YZfw3M9+ntCngA4wv3T7RrEUgu1MVKmgLdLEYx0BEGjE7FMUApZ4KVmsqDVsKTPgQKByS5JeDrfM+7qq312sEZI/umq9jjpQimQYkCs/Wktg3D7rQiknf7n9TUrx7Tc/+tMzZ4qLcgBk5JgSRjuBxaLgaLqyadHlLg/gu+6XruBlY2d5xU+9QegJZ4nEfDSPMbPPx71sBk9N4KJk/GSftf6ekESRsoKzGQvKPFLMCIlWo/dADOG97SmL/uBbxpJKczcDL4TiYUpncdNX3ttwEpaKlhsXTZcDJgiZijwBLFCg+GbzHu1DcJOfXdaSIxLwASMjUUEfsYlaPJ7GNEKQhGdJAgmvAHxOx4gqRzVQ1mPqpTxhYFSnMDKSRUuqSXFkEok8jd3XXUDG+AUFF9sNllanTYsPnv8kQQ+ibK/r0qopXSANgiSGKyEuzj7+/vnNh3cP+/WxlMvi/Hgni/b8bXtVFssyPlEheNFkOmtxxqs7IOlt9frp+plASb4ekWzs/AAkUipX1WJfzGYazyGjAr6fJ7/dbkeS8Lizt//9r966GKKmRfafpTSVfq8a/R0n8j3JFv8nb/5FUZcMZLFuaT5pyzmZcjfgm4iw0rVhYl3WZcpF8220WK4xbsjFaTyWsvzN/go5L+E9wkkHtin95l/8kaYg3vLwNfIi+5OMN2FxinceGKMHHE/TVI2pZHLmIlubfNIZLI+WxOLZDCQHkfZ8sJ2JyZ9Gq3DeOSN5iYFFylUQ+2MgUhZcKy2aaoEZ+2mqy4pQFiNiiqc4DvNUW0AoAPHMnns2vvS/5GjsoQdn0gxSKW9tUAEbEp+gqDiljDF6dXXlfDC7U9XUl/X1l/mzINS5lfv1V+vdF0DOxVVZKaWiDRysTKClPmZCI9jZEUIU586ydn3WVqzrDlB434cV5YOZEBxntFk0l+fXSCjPcZqdMyxQnliMPDLtpjsVoz2F480ahmMoigXn58Pwwji/Cx3XMvPGEcQFcXMloiRkaKuyXq7zNK1frykr5/0+zenx9KgXJxSJLZFh5px/e9U8PCuSfM55Ph2CcYKRb9+vgVDIRYpj13+kWAUP/GhHQt0U5tkySs+RTBUNgD4Gx1Q+GihxuQIPfP3mw/Pn31ICH8OQu2yp9g7HWfIm89OJicE+ZrZmthvcY/CBUkYF16VYLd8Hb4RQHz99dvPMuLgLL1eXy9/uOrg7IGRMiQKG4MqqkEUh1xvIIg1BZSELSQm//uaVVAWjipJVxxCpNX4iUTIMPjidcKFvTtLpU3d2rsuCE5K8ezp8ie2KzYaByM65+dFyzr2i3hk5b3J+kLwgCUrBYGBIkG3OF6KmjIYw5UZLxYJhzrqn5yeP6RW0xssXN3ClWSN4XGXsWd6/Ll7LVzK6gHezqKvBl/HlJK43dbk6vhzUW/XjDxXnr7wMKWXnJmehbZdw2I0GyIKfk4q3KyHk+/cfXp4fAdNZu+i7fUY2zzN4dDooViNwJJKmdLw/YEpaSZaeMVNEsaDSQ+4onJ+vEqann70UXOqrUuv9blcQeoqD7ks6jqOi4KJxua5Vlak9Pqfm3LoJCcTkyvrKzgOk3KaYwZuUBPpypctp7jSPp51HXYS+Z1lcrSymcRcX+iyHj1Ieh60iWO5yF3BmyeZx30PrKnme01TOlYSTd5UbB5YqRRlSyYLAIQeEuWWM1xBDo0SjS7JholDBYbr7ig4gJh99Hz7HsDLGUsarYCaIAIlSEXMiKaWUvka3IaloaY5LhoHdddjo7I65STh6lgWkNMzTyEQrxeI1Ulo7tWShSCYkRtVq8f76Dxv9HdIWqOYyqXI7url7vDOOK/E9YEaSdvPgXJaF/PYbzTh5tnH0/vhMgssSZblon4fh3Vp1f1WvPX3CfE0YY8WX3ofpcnXm1xevu0DeX3/gQlDyUL56w80cfGKiqjftoloPbh6zkb6ykmBZho+IOun1OtQLA4TFB8nQT72x44LgE8PgPp/5VJJmqeZ08rvu+T/ef70JUatydblUapnz13G8g5trOET7+ScffbNaazm/BLGuwBaVLdXofeWTLN/RwlM9vXRoap4wEKgle1URrgiQe+pfxxjSa/Y6J/ds7GFvr31mlZCop/vpZKfliRBGWUksIf7Oks2ZQjKZx6+Ph2aphc/LcLa1u5GZDEiVyMvr7fNWJBkg9103Df3jydwUXje6rM9O/ZCifxjn9zcLUS6pLz2LfTe8bF/ESmzU+vPt50WzLHgphHzNzyDZLjrGOUUg2V+z65wPIAWyIqZtJCYA8unZlTzgbN2MyJybCYVSq8rJhjYxxGDjpy9e5+e9T5Krv+bfMKGqv67s1tocInPER2B+fPa5QR/jcnMpCe+6O4xZc5r9ITyEUiZLWsVZP/eL9eJyE73pBjzgpFK6iCxljDW6MBnBsdtkMl1V+IQEKwRCwNrT16/j9997gMucpWJgmF0SvMc0FPYN3+wOz4OdHPcICH582OMVMMFBEV0UTMm5c45QhVi/e2s6mloP08wAaAozl0BpnMevCFFLYXwc7f7J5dJTH55kezMdu3R+rhH0CKFMU8o5YgcweR/NU7loV2fvWU3mxz6EyYSmARF9jrXwx8MogXDG+ZwzbeUqZVCl6sbOhsHyUchlofLldx+cDV32p98+bVKLlKUMP5bfMyApfso5xkByREar4HJsIpGiLAqoysv6BgilsE4ACZATEFwA0hRfAriBNd7u8flQLxZCtBmCdbP1lmj/bJiUKpziw9O9mV1K1YXQHMm0Tr57uxDVYL+Md91t9wUAnLPMLdVySYkhmfgpI8HPXz5ba6QsHatSiRtDSZ18tmEl37VrH5bMIrt/TmYwzsZBkhh/t/ldKptqg+6gVe8shflxFNFDsR+9dmG/693sl0xUyyvGCsoEZooA1o4xRyEYRcIkVxi6TGgqXZ4TkUU0JzcEe6qQtQiCC4r0iRCGc0vagT3XdzJWnjI4r1esit3DfgojK4tikH04BpoFlM5nQsip7wrBKiExjZxhiK5/GZhOjKjU9wmnikYZXKKExOTDSAdSpCass4W4pilkf7j9PLvQrqiLpZ+ZofFkDzY4Sgm7eP39FJ8neOI5j2kggZJMLDtGcGBnUUhWxr6rHIuqriTnQEmcB6qYtgprJaSKlMHbhmaSDpH2j0BIGtKcZvX6zAYLEFFQsDkGP60XObBqGEyGgEkTI6eMjOaxB4s4iNDGhKmbs8vQEHbxrU75ImZ5iHsXTzkpk5o/5NwRREpyTjFxJJYqmnkkC9yw10yQ7jQ8WxPCTup3f/vHBikfp1nEfEpAc07gdtHIYhEg3rKti+w+i/nIxIfXENtIf9dFtVDfbc6uPNHNckUYF6cOAniuGo7rP6yWdZsH+Dh9WbfnD/vnHy7/UFbFZ4xnm5v+9JW40aUZkSQSn9wY/dOJNuLxidLEFwFTfnm6/+mnaR7GHFNZlHW1KApdlOU0ve2HO41X7ft2Os5m79AkwsCC2zl/fsGUolFT74KdXURUqlUY2sR/Dzb2h7oKnr8vKIkukQyLtBGYpqF//vWZsTwJqF2OTDDByijIu3eUPVHxIYKl+Ptptq1W51fXlC/3L/fOTR/eFqJQ22cMhk5+aNdYTFcnF5rFeHHzjUfaoiMgBVnHwbrJslK+ef1WVxXhMgqcesslp4y+0++BiJYsKZGASKEFKcFTa3cAFTLug6OYKWegmbcpDIEwqlWBOWHBi6iKop7dpxRzjv3HLyefHIGfT3xNfUAkkRQ/LG7KssBMjbHmV/Nk96pui1Ua8gyAZys6+3VVTSXX0VotdcsKoqR4zs8+W0HzqS8Fa18LRGttcrYv5RkpzwhBRmXn2IvPgSAzWY20LBzBM4qj0Hg8nnLOOYWn7tnYUaFy8b2NcLyWbDAme6+z9b4qFWB04ZFGgQAEyZkSU4pmjKcBBvaioEBCYBi66HmpQ/KcYEzG+cAyf9iPpMjjo325O1jpJec9mYeJvW+KbT7KIWNDj/4AEYempsFXxndNjRWInJ+8IQ+0Kqo4eRkTecXIbZvR51hdFmcdoSfznCltlTBjP/u0rCog4zYXOE+HOe39Yck2Dda5qJ56IOQd0zlcQ7oHZKtKWiGVEEwdqW8SKc6CLp9fjqmbe/0oyooLhRSttc7fQfYpFzGOhBJBJ46av64u5eucu+fnh3m26zOdNCj0lBYXp7gVZ2JN4qIpmHjyIR5cUxDG4mLO1bKVJRZnC/fx+ef+4QopNIkxHqO//fg1p8wYX9CbIXg24MSmwtSiCscv408Pe0YdJ0gJuUEk+dxlK9MqxRcp6sXix1V/29seSHr4bEKpUjbUOqIrwhcibgOXZBbYUEZFSslbF0KW3nsx0dBV0JoY6gAzzymk1CRmoUwU60XNJIy1S/davb0GouiaveHqa/lpd+u6kRFYXrfTNJCGXfDzGFOSUc6Cp+R9EpJyyt3c65NL9ASobyAPUJ5e/Oatp4ZjBh11dlYQgaryxqmI1eqVb4AD9UPo52l6eKCUJIgv+8eUKWPoomecN+rt+TvBppSiMbRmo90jM5zqYKwkJAAjCvTsWNQDBRoD9U0/HLo4Syl0os8w4eNAOa+4WOHrxELARPhK5rC1R1UoPk4nHhOCiCtXZ4gA01TlSCjFnJEQGzMgHVkWADVmSqkL2ScfLNWMX3x7Nc/c5M5Y1cchHyFIEIX4nKbpz/+QtqatzkqhL682Lj9Qzmhx5dw0f3YvGeZiXrWv//jPzyZO7z5+tVSlE7KMhBlQkswEVmd47r2hMsUazras76niIGTZVO15Jfz+geWAsHWW91KIRquS5BwSYwyj/+n5p9PuxA9PRMLt8PT8fKhWyvqHrf8Uj4ucLIXgwqNzkSSPtBNSKUamUZyOT/v/crtPuS6rZds2utZSybrZl12TF38+usXH38i/ulSVtiqipvziNX6+G62Pso8iMfKGsHacYz/OJKkQ2NivVlSOnLt94GuUUpfnLSN0P+3r0D4ebr0c353JwZBxl5qbLA86f+CZsN9Ge54mxpS6upkOZpiYotOby5Uq8C//+Pefv47rC0mo1Cv2/NyNz1ELNwVGCTlKzDtrB1cEQvsvGYCV9UWhdFERIE8P94PNsiy/X3xHuTj6E92OT8PDTfsuAbjDfjdlKqKfmCcjpaQ7baPrcvQ8zaV3jMqqUZ2LtO/ISIrFRisF4rvLtxOwSipcEAIAG6Lmdxq3BDzHTZ4OJo0JEgSPGaJM0jzvjuO+KjRjjJOZM/p48A3OJS/JQtqj+f1wKAHqCyWkHEKeemwXmXHquUqsiImqHO86E3qGBBtceN8nNs1zbmSPXfCSQJ9ZKwoiYB4pRUIZuBNnlPharDJABppzW6Rqqc04jY7ZqciMUBJDuBsnktPi4owDp5RpoT5FNm9/7Xc9kcgqKFJqYqYutCS63h0HJxRvy9oHagZbFhg4x6p+yeEMuUcolapm54bT5Hh1xhCAhVwKf9x+Olu9L7R6/OKaiJjh0/Y+hHCZSuP6+XjgTODm3fB8v5KQ8yUhiTEmWE6zWIg3p/3LclGosrLgoWGxJ3srT7YnaboQ+q3QshC6QOjmOY4uX7B3N1rULdAX4zMyAilnoESlFKPnkKsF5SoXikvNRAiHp+eXGLMQRT+kZcvnWfkgn90s4ICE8rE9EMqAAkJ06TgdZN8zNyGx4oLhm7M/fOocS3lrSGkhqxqJFZTzcvQ4h8MKVnOaqWQiF5t1K8ukAohpMuAYoSrjtPdAy/fkPT6yRrtG1i9fv8DV+eI1Wu/7I+fiZR4HY9bL9ftGegdII8lFqYMjFF7d3FgzO29IINaOJDkQQs3biCy/SLlK3ZG3lKcN8a0l+SIs2Pxw+nw6ve6vaUs/nL0F4nYff0Ucg39hvEW0IXE755CBAB2siVOqq1DkmDYQJofRU5LLCk/d9uu9bEWzCDQB5VZIjmHbk7QkzTJrEmb76eOnTCOlVLdNIXA2Y+esTXZVcd3CGONofz7+wlj/8DsKD9xSjDWQqfapy1ooc+xTVkh7knEFxCH2dl6z8urbt5QxITjlzHmfUx4m8vtu751s+DBFu2Ftpeho7WJZhan3usCYlLUnqsHHDiKFtJIKM3B5pmgHEVOMifmAMVMao2oby2TrvDz1/eyzLFiF56dL8SojZxQQx3o1F5vq+uqCKUGxeS37gwq///afHh44F8X6/AzX3357RjA/fuwpJi5SYJDsJJXJyU0Ng5QZNi6cCFVSFOqqOIFcEkr4WurCp+fev6yKhYKIlFfozqvqcf9bHkEL/vllO45DrUp1c9GW6svdrZ8tXdQ7M4CuyByWhI3JvYyd5kBjqAEA0v6hTw9PfehjJq8pXZ99OByfC70oy9ePZBAxv5Sce/XzPPPDsawuQcBok/bp9bk0kox9Nc+8rgYlm6piBETBaiOFm8IskFHBFzrG7FwgjuxOg+rCAZ/n07BqKWY3jpQxfqbewjX7uv30qkU5V0OcRCMiEllJc9oO1nx2/ZuLq3Z1MU/Pp+48y2h1VOcydHQeXZL+eWCk34JoFoEP+0cDXmp1jbi62UCePn06ct1+qz4kzmPOpdxMdj+MPWWCU37a55RzTIED10LJSjIzifVFoa8JeD8NYZ7S/d3R2tGYow+hc+Y0c34XgxecHyMrVloyZoPbBicmiJQuCYHMp2lAA4Uqc84tTNp+efRGhFwVw3EuMKQceUt4TtJOg8/2T3/5R0LIh3drwipPDmUpdFMKLkNIBeT+OAH6oz1NA2fiNbHMEYeAlCWWswuaCksooS7gKTVFLuSGp73r0sFseXXF7UlILDh5SX7O7nnaXuZynFIFtEqeLjAxrAylQtR1G/bex8AK28boV6+40FwwHyxLtxDtNI7EYIypXfiNFSPR4zAEIMiF9xNF1pSKUkLPztzpiFUVh45UpCX0LqcLzl5tNmm2glFGOZeHprmOhSN/oZXWy/Xi8HnLGC/er0uV4uVaEywKu71ndUsmzryaqVgjk7FamN0+n28wJmQpKVU2BHOghRSqYgWbrcmEuAET697iG1kWRCo/mxkxR+v9LTeUkLwU04JIKHQY8nH/csJ88CcY8PxbVaZiFnEOoR8DkgkAYozfffvD0wv1fdZa67K287zEPonLFNKYD+p3563hDAXJPmKcfKhmO7uZsaZZRegWelk11N+yoT95awkT1Ym4RazsoqdPPfGBjIb4N/gDV3VnT6s//uHqXPzn/+dP7Odf0lJ9PQRCF4K7nLMUe5b5aI1SRanbPKa7063myprTglCxFChTDA4R0uSzqMFISPnl5cWR0amm4itvZ0IxRJ9ETLO7/3KXMH14/55wwuqU0rOQ3tphmAlbAK1Wi1yN2dxUN91pv3/eFmGXC4zKyUBYwZ3MzbrojDc4LsmSvEjBRpoQEy3F7Lvut5fJxAAkL3m9WlWWY0h2vTyvSTr1x+A9knxWislb1yXWXp5N8Dnk9Iq2NOL2yxONAklAnT03aAqfDKWcUWzaVrY1cqCURO/vH7sUe8LZMRc1NwoVUQRHlljUjj92u7apRCZ0mmNZC4JXTDjKIUef8jx0OYaEMCCRgmdvIwoGPbAUbZeAeE78qZ9dpEzsKVfjFAz5ojE9HGBZfxd3Vfly5heeIk0MQXMmilrzLVGS/PGvfpRCKIq7x2d7eEz0dcQooZuyM1RXvMpPudRDgQjWD8IKdka5rkANjCGesjb93T3hr8uGItBmKeN2PG2fuqeeC0vpghaDVEQzun/6pwOVrpu1VuPjn7DLgHG2U9osy5jPZmmWhlGuhrl7ON1+7bwNIkrhLH37dmB20tWT1udLmSzZj927lW9uLrf9kwu+AO2yS8Ama4U8s8KXUk6EdMBeA/Snk+R6DHGq+CrzzBnh2UOMIZz6seOHYhcVpmB7wVIS2SYkBW+rOnOdMxIhuJDt6tJ6JzmfM1JhdFXB2cb2Xz6mWZ9d2s9/HrIRKo/jFENkEVLtVhiWFXue4ZyOsz9QmBuGAjG9rhKXiap6maubd4yq+BjvhpeqvgLSaF2/1QoL5ebORy1Yq8ch2pkmO7O0oMXkeogmuuOuD74bc3RTZoERF2xKMkTBeAmM5NgdU/vYd+x4KmRJqFNCO9+kp0hszJAgO0Ig+Gmc6CVz7IcGheRh30+KEW2E0ofR+GObb9p25VwAINnOkSBg5d1mnjCmISEBHsejSYEppV5d0HuP0fg0ZEqq5LZKaJuAARSBxE2NwQXzcv88j9NWqUKyuqQKM/hMiWIULKbVIXlBJSkgNLOJIzJOGPbGDR8/2eh9TLvdGZaec2SSxBhj2O5T+Pps0KZvpUwmzU8xX7oykHE0GM+sjMOURxMpSQGciGGwoYjd1PfV6uzEBDx/ce2C0SLncJgZkRwJ3t7F6yvGpWR8qQstb8p3/JIgPtk75oUoufSpECpjIgHQpjDtV6xUw7DXFbdBJw6c5LmDwnJJz9Z1riu72+4suy5kKUVerAnLT/aYn0fHFK6WS0Ymua7jTtquf8EjknEmKYBWkbG2jDJri6emTz5TMSWGaLyjRVl9+/2PWunjIULKyeeUfYodCk2EzSW02w8+7zM1VAp2XdvD1zAEHEFzfvXmLfNCrQwlKYFvz1gaeJ1Kch5iJimiqwZyGCbOKdOvq3fFou0y6ftpjc2iboheYXekkAlNNzftbqSKqbblPOYYC9lUFYOj89op6+PggyGRb+fLVUZWe5sYGwgR/sUe0s+IIDi/gnOYg04+TqMr5vLqYilUihBTcv0W4j5vzcwSoVTxSAmJVlOWe9L3osnZrzar1aIYPzt46MBHeJNpQRpZJLUxT5DgOWaL9JnKhnKCUZQ//vU4zO7P/+WNesPesMyRTEdjp5nFyTnJi0I21n/dgn/+3cosCGXsupA7++bZ//Jod8Nk55NtSk4FsIzgDQWN9QW/rP0w82BTfzwctrPg1hmhJKMUY26oCTHQbMZpzGM/LRZcckEpjRC6aNCGw3TiMg4ngkZqXuhKi3UBcIojMHAGCUQhZUI9PL8Mwa03r3hRINIROMaJvPQDJZgnt3XJ79l4+Cnf//KX39bt1TdvvrtYn4F1b27q5d+++5vhZdwNFCE9Pzz2rveJIo9mlicfgsEQtGnogrHaokukyBmjdnKodTOtFePD7B0jTnRmPF18uOYlub3/8rq47rs9pxTBVyvIlnBaQbM/RhP2M/dGUhYFmdOC8k4rDQDBnIwzaHQF4nF6+frbbMeJJrhJLAZPxRpV4Qm7vDq3SIKMHMKqKrH0OUSTDKVVTPN6Zca+nXFeqKtx3O7pEZARMh0XZ0zpCNzOfZzi41ozsC4SxgsCKUWztsrjkSZzOGyVilVkWcjNSictX/zX4Q68jUPhCiWGvKdDXghmjDNNuoxwIiWfD4t6xa4uptmQxIOUNs1kzHTFaDaHw4t9+finnIexL0r9B7Xg3/CiWXBZhBilqLqnu49+vmreRmOO+99DTJSRYeLsVCiularaSueJwpIzZYvH51SrMJunl6P3gVETmoWdBz8Hwup2sWSCTQixhzkMQDRyfhVLeHdRL5dmSNnldPKz60neR6d7d0R4AUrojWE1zxh92utCXp1t5j02SqZaiHRFNX7z0/cf8Te/T3RJmZKMSkIQSSJM5QB2jHNTiMNsvftpHjmlbys6kBEBDnOjid6F3mXPKy1MePjyeDfPiPiN4J0z+5efrWvE+/eCcy6L+cRCSNEcoaiNSZC5SR2OGU95Ivyi2tSLTcyQSTkjiTWR3r/MpxyVLjdYf3qzAOJAGp5VtES7gChIOlmqlahbLo6BawbzyU6hWCxLETwISlJPOFA5SR8cp0ITA8FNWVoyWyuRtV6TMQVSLmew0udqrHszDsY6nmpFJxScpoIVxzHObs4kSYYiZ4bAIj1tv9DzqxUnzpqeeHLqDryWjSprtoxz8KfhfgSAdtmqlLl1ZthmTMhsGyhB7n1CrtGwRVMHhSA449w4kwnlFCiri0IgU/M0B5fn/pAzRiIoWRJWPgbQignq4RXkY2XsBGxmvmZnnOzRB9tSwgT31PXHFx8GY72Zk7Fey+rdz9/Sa1Hw2gTHm/XaTQTVJPPt8+9lWWUa3MtenRX/02/+9s/HUz5bvGqngDgdP5LF+csTSELazdK6/ik94ClnQi7O3796/cqQ+PD7P/5y119vJikh78uU8Wl8QuRlUXwv3sCGhqcnwhMVUBxmVTxGkuwn2wdIgGJr8CIjpByzD0OyNNSdJRxS4dP2hazHXL2Sar2u572j6OE++BifWQccFWxadoE8RzhkHkShSVXxUvOcZVGNdaGS8afjzKxmsD67PDxvEwDRsmFnVdwONR0mGmJkn/df+rsTIcTcQLojVVV/uLwEINh55BnVGS6lCyZ8/VVIHkoh5LR69aN/+ngECOhtcNHPJNOUioYk1zTeTRhVBRWXrHx9nrannDlhjNVifwTBx+hN7563xpZFkzyHSOw0P+22KcdSUImtaDRB4nw0KvNB+XXWcyIu+krkYwBClND6fP3mwz/71+9/JJmQ8Ik8Jk5d9aE1neu6jrwc0qJ0REYA40eZzTwzpaIQDCDl2MXlujWTGeeYYnR7MXK7likGpwrRT86FHZhVIs64j7/+lrnPPmaXnj7nafELTH035gwZQi6M3qk7fdCjLwQyu93TlICmUiYHzJ7s85+fQ8IlY2eMphAy0bqQPFdDH0hJjJuAiJoQzAlyyn4+B8nyiYBKoWkWaRy5jVN7sQlnYp5lOO77qWMSmSvrUrKcrSXytM055KtmBSUEg8FFO1nbSyFD8EpwYNpH3sQcJ13WVJVQ6CLnhKcUmBFF7U4d7v3zldqFL+tYc6pRoJT1fpjTKacxJrRvOIHMxi12KVEknPM3lMWraJxD8+iG8EhLZmp1vUR5Xldls6gzISfGbwQnGfYO0mky1j7Fl3HsVOhF6V5Opu33BJOPPggQRAvKUJJ0zjc5T92Rk7ikdIfx1bJknCEQEOIUYn/7xfZmnI4Vau+Eh3EjO5pzilQUmtY0MnCujz5PPKa0s9v0uF7k+8BxWJJ3OuZlfp1zTGPPG46UG5K2+eBCAuPlqOPkvBOcJXi48yG+vHplvVdKzm63JzoCXBF6GGfHSVTqR0r196qu3yz+co8r/vvWud2vbPPdGCNWlwiEDa9GOlG3LaRjVMbg01nexIYJeex7wmUmIjND+vrIfc4QvJ2dD8ROwOLTwJGa4MLdlN1CRYUlO8WTGbSbDzHMc8H8/kBwduwtADHWaTW02PAaksFSX2IGY4+Cn/G8PYVWii7p1bE7LRY6Ow+xLxUWQgvhEnGEAo5EJ25ccn6qLj5M21ktZeOC35pzdE7xb5lOKnx92i3XtLxYf79ZL9cfpBzCafCHgx+igxzd798svg/BPn1+CtZQiO+k1pe4JHwGZFVpia+TipmEGcwcIgIr+IhpLVhZqBijs8NSSZsEhZQZManI6dHzqrfxhMket5dvrn/6/Z/K8fc3500+Y/5IqwqduwtjLErJKJVBE2C3d72hAtJ9fnqLFRQc6YbI9ipQTmx+c/OGl2zssfe7FbAzsfjow/RPf8nryBdneE3er28G75PUNaZdt7WgizYPpr/tbgVwdgE3N+/N8oFhl57ZfR/K8DUje6PfS8ndmnCDQHlSTl5yMmPcRu9DTJgJFko7qKOLPj5l2LMTFxccBYnAYrIZS9XPkOZeNI1qUnkikRMUPPOCN72DiUbp/cD2G57SUqWrKymrAC5i5JqN9jEKjZBk1eI0TIdEdAUxjW5um7Wb8fySblJpf/2FwaAozoILSa7szdSgSoGG2wSEIJGZsPh4HLuPCJm9V87hlE65f+igtyA04VVBQ1ApZt5NmTHUgQzs6fTbPgX2US2+54QxzDWhWTJ+2cRitTnuj5TexMoYN1Okpx5yTowKn2KiLBM2AY0x71KWmLNy3vuJAG7vAYFyga9uLoR+tX51WevB3BfM6k4GmyMnZfMhbvJumrFarFoNXX7sXKJeSkUqQ3Sp6LQbPdixmpV3QfP6kPf8UJBsvc+SssDV6ELEK38/WQ6bun24vd1ctgh5CHeZkRgcj145t0+BxFJDzv7KALBpmklMQ19QzgA/3fXTsJ2myXn4Vr6r/7CAjz6T27p67+GETubaulQhSzc17YDb7kmJBRcmzlIqztlMGYqi9J4kdLxcF4FqTrfLZjY7QteIfVu0jjIisHj1Pn39bB+2/UW3jjK6Mc5fFWTuKBIgUaTYAHhUcoHnT/GZFzeawS/TT9HGWslWF/u6vjhrObJpLOck47zPU9ZcweHE4oJynV38+nFy6UsIzjr/V1XBXi9oxRImwTnnUgj5XVHRzR+kboApzqhP0Thvc76NsXL2uD1x0oQUsSDV4opAW6rQnlEc/NC9FGq2ysIMXebeogu//PSXMcwj5kCQciltX7XLhXfWGTdNU4pJcY0JGNZYu/rUJtwiIUAhI+Q98kLwNfEpoosfj0ciuDpIoPl0bE55B3CXc7pUl1opYReJzV/NQ9gFjjotSwVVhVSutF4tU58HYwvqnhp1SXXPGGU0RdpTMp+OjZRtqTPozJI5PmACKK9V9+dZqlVOpxAJsJAh8AzTeNhO1TuWk4sxq0Wp0nqe3WB6gesswVnGwiHev+DleQDO3VjkcgIm7UApYgSSkfLZQ2MDoLhG5jMwympZYcWukk4pUm9UTF2lJdY5hAgxlzpOFgYiN0tyOjVhSIQXUknwieSZSpwz1pjjHAJEQIipjHK2PlGpN2LNLY5+l7uaujCdvsyUvWGIbse3tCrrd+9eIWtjAkapFbE5v0RsFq/T02+/P1nbPf0JOL3gXGE2wVNKsWYYaX2EKKRFPOSANoeUhJa6rnxUmuBqVTLKgk9IkryU5UDTMQP1HpnIkVdFjjSpJBt9vijzt9+9fPyd8dVsdlElVJvteFw1OcGUAYNKOIUFwDmlyAilL4w5VbBE8u5pNwCPiVaLlTblFAcE3N2/bN7/8fKn//TTlycsQcqZZOoxznrmxh91OYWxIbzF8giGLhbu6eh+i5+uq0im/IUqpExJldr3sRJXBXnMbM/lksZqMQ8P9reJviLJ5OwoZaIoIGRubP/73ed57hJJuuQi4cWGB8GBldabpJSe3eyJhCISwrkUXANbplk76NslwQwNqeV5GcM829sM2T/CpzFFzJ4Tr+gi0IGYhrEc5qnT7YrGUzeAiYcJQsyFIYqzuLqkcoVA5n0KhvsKRgsxQo0Lj464bbTbmKz8sHHhZOz2p5/79eZnlBUv7H7GxRSmoHMg8zg77+ZpAgKEUqTsLtwVpyaCEs55J050cv6lmApBqWCTqBXjjDB6cQaQfwQAnwJhsN/ZBWfWx57nUjKzmxPPBEMkLqWYo8Pb3542Va8Oz2z7evvt9z+0hCFVQp8qSmm6fDv++rGfu6Ojv74Exqe2fR1CzqYsiwzJFt7oRR3NGIKNIdX0DCQJxkYoANmMYTdPtKCu68xAsoloTyRVzhn0W5Jet5P3ESGM1oWYp0M4S8nkEPJ0klzyhZzvB2/N2I2U0rZsutEoqbTSWbJOFJf1ZkpD5x8Ia1k5kgSEUk455cy4kLxaLRhIASMnWXBUKEgX7crPRDS+qIvQdyH54NPd5y/2W+6i4Pr19+YekgK/OAHOx0/3t69iUG1FFBLGOS0zncYDm5gE7oyZgx33BIt02bymzlvvnd/bfXyE9QVnnPMLOxlM+xgjS976TIFlwoC9oMs5JcZ4ItRNEWRAQmqlEAmFC0YuMpI+xmMaMMToLDmeOBP55jUl7Pr8gnEFhHJBdsY+v/ilQplJd/ABa8a05jaliQVPQtD4vfirvX3+Eh7HYhNWOm0fxvuP21WQZU0qeUZqomb9mHdVxc0x23r2oRRjInRkgAmgTyxNiDQYU7VJdJJzF0ldFSzlKdwfLQLWVCxws5uPLzBRWZcbK/QSI3BQNIF1/uvf/xmSp29e0Rnj8d6dv091ITwxKQCTUGZTOy9dfkr5cwYP4Ai+3KZjDlN8uLwjq9War15ioIC8kjTJGaNM8HiyFwnLEjkdGVPv36hTlw7jQSANS3kjeSCq1KsQfHD+kTxrbCiZKSAldRcjlCDpEDKVgueCzWYev56W36865xZrLGyFfk6Ei7LaHp5ktUjJk7kLfBF8B4C00VWVDkkq8JC6iiitvY3cGqdbsY8WM1moajcEkjHZZxH5Mjo7TJqhQKKEhkxCju+EFPI85kQYO8126N122hGyu0JRnV2rcCSkFmlXthqbwr5ssaWMbFIc8mrBAQlqGqdJa0FZBrTWA0s555dOwUaElwxGFGk0c2eMa84vymURRkrsYGMklGZjAfJaSVfXojrj8WiGMEVCpDnNuRKQSbYHly282HDJCAGSsrfO+gKSmcprJYxqi5UBTRivdUM5s8cJyurf/dv/+tf/2z+8pa8CTwPpFaUmjBDpYPbHOK69fs5ucnueVFpHOpMlZE9uTmeP32z+RjIdT5gcBgFUJypzmlO+qjMd4nFnJhN0YBU3z2h7fgx3fX80diAEVV14aw5fTlska6194a/pIuDuyJmkucpW0JqXMhzI0zgatGy9rhpMOYgCiHQhP44O5yByReaClPhusFYy4h3HLhiROZM8vnh4i871ECrOO5saXMm31yx8OZLcBG1gjJKVnvGNimImOMGcnHNzTj0vL4Co6ek2HiPhkJRf1ctJbO29f8mIMEmQK4JK8oDSxmQBCmSY1yF6prOnoFdi2hWEibfr98/jnWfQD1272jjgSD1iJkjzw/MpTZ0ZV2cfvJlqOxrHxnHHxCw2Vf72bRFhQahggrOCgSZJXr8KlXyfFmiff/JT9uOJOU85/LIfrm7/ca2/52LdVGhilMVe0paBz67fnDf9QB/uJqUqJMQHAlCKWaRd5I4GRgqVt3uRXMqz74Lf3H4JZfenv+ycvW+9s5R0gAlT5BCbMfvM9oxSChdr++yhmwUhkqoqX561xaDkNh7ys8TqWBkZiUPrSdDVqi4p71w+IjAyImNEiZCEqplQkhj+kuQ3VU0jZzZXsq2ZHrhAvZK0MqYaKDVPf4mMsQ/fjJ1rkkvosD/e9kOOli6yUIAeGWfMaReAIjKmBm+1KpRW++G5ZpTJqhtmutD83Wa6v8e7r3lMcN0JQmS1SA4p1OCtjzm62zKltbzITQDrSdE7QCF5ytk4b1nO9vaMlKeQZrxXuBDtK84VrItchURyyiQhHYwbjZ+EIocMyRwPwnWfsjcUM6EQTzGuSsIo5YIhk7BsLrap9vOccrq8vEoEFAIE711Mbhqd60wY74cEgtSyzJjzRSa6isJT7030h9GEGCG7JT1HWjA6gbchRqX135z9zfSQCODYTLvnYVJbnm9SSsnM/sU87b/iqnUuxeNBiYtNTKPL08u4b905lwGT2RtRza5PydWJHE2YKeNlpacny7t5KZWlxO0sOW7tFVMIQVdKF46edt2cMedYMom7cHt4yGy1NMHtXr4mHdtinQ3t6COzDEbzpX+4CrD3Hs0djRWiDgEfXHe9LMNsCKXzNAMtIEfklI/O+iMzY7loTjZVPuQdUkrs7CPaQktrNedPnOvr6wuSopgDpN55kUiarAeKalEoJcqUfODbpHzpUibKhJuquNuZMbx8w1XBFEDFeYvXkjYto+Lu/kDO12AT5CWZKeTuZzCF7y+IYJsCGfXzIOZMmYpHH+AQoY6cxgRERFYvct+PrEL0XKuzs7MRsUcUSEENaNtYKOXk5pzySza6SCUZChrmHXONl8PjNKcUunk6nU7TpHNWSHExgsEFQaZ0HzlQxvjGC9yk4RCOIcVJnBVMKCEktjjvR60NsOt/+umnb7///peHT3/T/nW7OGubMybdAjcbKpBC9H1C5WNoVE0zxBiuyCtCSgbLZw0RgVHJRcGEIkSE4xfJvxVpgDX3hXWPj2W8pEwHXaevu9hkwlY+R8epzG8M02/evyM0b+f9fDysa78/uMNjKq/T3NI6cwhCVqPD6B1lUPrsR6ykYovNIsfTs4uXYZ/CCQhEhOPg2moTyYzZS3ACM+jo5ik6gvQ8JVflNMqCK1wtr8J2cJoAIsN0EjxHQ42Xi0VgAbpE6SHWlBLKo4Rfj0MyWzVyLgTljAZyOuSwZpSt5c2pio0GRWJmzqEQHKGAbvptG1J8wG16Ob3ffDeN4iXcAuZSa7Ki16vL/aEXTJnAjttf/fJdv/9MCDRVRVnDISekXXABUvaTrIIPhAdLC2+9u0+ORKRTxbqCkSK8kP3VPzKQa7fsMSy1pLSnhEopKqIX35d+5oOZifXFRSUVyykywU6nOfp0ttRfnrvN8h0BqoWe/AQ5E05gJkgSgLeGrdbVh15G5YCXr/PLz96gdR4QlKrqelptXgFmEeOCn2k99aOlUdy8gmN2k3U0JoeGhkIDyJ2bwka+GuNIqlYI6A8nc3FdkjxNRsmYxuBWRJQq30gl13lh9JF+lLpdXdWP3cH6p/xYQrNcnNe4fKXyy9/+TffbSAi+0ZBSkJL3ozl2n8aRLRglzbmjjNMhYICUA1N1WbTL1e52n1K/WceUOKVECz2RqbO6yFicnZ8y5KapIQrOvrz80j8eWYKcIvVQgyqBzcFIGkaSy0hZQwiBlHB7yjhxwtlHfge8UueXYXMJgEVRSKUYYypG100xgXcxhMhKzS4oyy1qpJv3FDIm756mFK2K/ZgcobMzj/10vJtON94QBIQ78xLZRBBxmB3JOHgvqkqWywB8mvU4nMxwRwg29WKjjDirXRAXxbVQKhA2d5Ocnc9xdgEmKFfEHOzv3c+MrRY/N+p9Ici7Gcbo8/x0gISsUct6CdYaIXZIrLURSPn2m4MPtdaKUN5/6e08E35GaZwEb5EymGZFz9rYpbP330yHW2ccMlYcptWr868xTbf39IIRvXJPu/PXRVt8V4bDrLaJ4+3+uV7ockGnoddSpZnlyrDZhjk4dnm1rnlPYw7u5O6jR1QPTwno3NZSblQY58TE5fVSYJIdEZR6OcljgIsb8/L8PBw2egNUKE6IConcvDpbMUqc98cugIIUEkGGmmVPaCGfA8TcEloJusMY6tyY1B/noX/6AjHD6mruDhwqjJQ/MDe72MQcSXo6ZSxSmdWavanWOWdK6V9+ub8pimVVmOnUYwZeePDFTFWli2Y13z9kJLxUhmclGeQCkKeUWqlGyN77qtTTIrvMXNo5zq74hlDapwMTRa2LEpuhflUFDAm7S0SCIQZOWHBdqYqMGnB+eoQIQDFBw5qxg7ry4+gB4Mux+q5ASgFRb2qIS+Dy2+++J4RJUQDQWl7+2//Ff/37558wJ5e6+3BKh0DqmPv5JA9uUlOYKCuIiXSx1kM8hhMJSDw+n14uiquT1Ku8mzhfUBuQ0PUGC06GTEdrfSCmmjRuOdaJBtqtfrgRSBKGkog3Z6+N6S+T//rpNu/gsB/HVRKbVTCjZtUgDm4SBDLy4DMe93vInV4bxnJwMeY8W/cCQso5TK5LU0wHgrpszylHkqCPY7HkJhpkkSGfzAwFTftgho4Jtk6JIcrrK7ZoeGcRsyPXMfaeZlSN+ja+u4ufU07ntKo/VAnIrbU2TkwYPmqfYsuHOOYYE3FbVhHimbxomXHnFp9yfp4fxOac7DPLeP3m2rr5dvtJEIUBcj4liBdLPJ0CSMlk68NxcXEpGcR5HwufnRYFoyVPEFy0LtsUIr3njCd2g3+9+fHm4n2hFoIosND9ftWZXQm6+YDfDocv3SH/+Zd9qbmQV9eX67OGM4jHA1ACFFGZeAhCyWWdn2699naaEymRpQynUFXkBcVzN1+x9a1S57Ojy8L/qL/7R5eVlLODilhZqxF5Q18yWRj5tMuEDZwwDARiKheLguZM0UwPJf9ukfiX+Vd4hyKIclmTcey7UXTgdUYuzqtmLvv6jPcx7ffeKBN5TEnk2/2w9fWH7xeCvjhtA5ttWUJ+3D206lWxYhcBIvjJH73tq7jvZvr+LTKCAHsycxUXDnnUPMTsjdvvdmcbYdI6xlgWpcnucZ5AEYJsMNZ4zwBit3/W1u1mv+u1Ut/WbyKOFGnUYMYTRk8PL4wKWpUx68PxVhb1+XJjo3714a/EYj2AQKoDYScgMAzhdJp97pEDycwTVVWItIGImF6c88OIGNCb3DlyrhvvyexTtJxlTIuiJXN2Y7DlKcMx/TrN8zwLLjLg99V1Fm5kPFs7u5iJZGJVt1OwxodQvfvwlCNTMQI+ue3ozcK1iSYi5TerZrIJLb3ff3E+INBt2K3MWhUqx5k6KjbIxpoB68chS1VX9VIIoFQM0efAjJnMCGJVtGfh2AXTeZIwOxgBFySnpvdxcH7/l7+fKVZcaEZpnAjgRorTag1CckbFmRqROecfklUrxRcX0L0IykkGVMc527kz/hjQ0xQTUTQ42ocpHUJnrUdKKU0xlVozTmpIL32vqpaSmeRYlRwDz8/eT5P78jvnreRi2dR9Zjb1CTMh4YjYx5iOTzb3JROspjgVwUtWhZiZUNT3ImPCE8M57nSHlFEul+/evqna6MLDy9NlkdUHrUwR5uw+uURKAEK5YXMZTfj9ySUfb96KGE9h3OQmerGQhfIglJp8SiUT02R82yLhp+1psu5m8YoWIhYohAwh7P/yl5TS+O03NzwJJI80Msbv98/Tfg8rFt1TwhQ41IIlUeTQLd2C5NTactYhg/C8m+Y9IVQ2lMQi5hxAHLURM811HWI4evduSjFMFMJ8SMe0F1K1b78pQHz3zR8AYgb/9ubb035/OD1xsvL7h1T7/uDsEGiirnRa3azLxamjOFGSM880iTXLAXImCs7JeXChwunkfFO0RHMf/OPhaDNw/aF9Ve5OA8uWguKYCTRzGhGQxMJYyiTRmP7412cI5MuXuzCB3pOquWCAKIvm4owkWUdOuNpBJHsD0Zp0zClYE3wKTSG2bmaCk2w9mDnPaRhUrMdoFvYihrlbtwKEyHE2EyL24yGGjv3h6upxeAlxonR56vthnnldbCQ92c4Hxx3lrSBdCyHvMJweTGacXdWl2FDO4nQbUnyySaSwrhpIM06SaSnyzEhZSzrGRBgVG7NsL5eqRcRj8tjKTNssMAOPVRmyy8sCGT2qGJ73MTyPVZHQa31mlIs0YXYJYkq5wWYjl8UPVUZ+TKyH3A/dL0P3TXW1ZOUUe0hOwgCCcMYroarX7y4v1lQq50fGyUO/16JcVWJ0Wxnt1/Ty4o83U6nnh6K8Ok6hNITbfOrtLnNO+xDdI3ZI5aeM7x4H0UixUZAxPOQhtwi1cWxpoDKGCxdwsrNdLFskmHKsqxowWeNoUldFoHP2YcYI/GBwOAVJayH0pZo9IULuToeqKq2lK9ksry61rA45V0W5ffi62x6GL/fV9ZkQUlBZEv6qKF6G3vf3xPlHyOFxEn54sjPPM8mxyJCpkELxQT4hEULxzHRdECo7Cl0OgRBOtpxQFzlP5lrcKMYmwJ9P3jsr7EwZO1erae3P4jqwyT8+IGd551GPul6QhkwWkASwz93AYHLddIo4WfhN1UdHFS4uSKAxJnZxA4B+3mIyOaX21ZtVoXofYohPMaTjTBpYEb6duskf/cshRB+V8BCWp2lmXZhsgURwabxFIFXVfNe8pu1isjPWsjz5Eby1lNCKUuRSvX/z42xjokdJlxBuo6Yzp4zJVarLuZGE7gC287C9PTGi61aeL/8gpPh82OE0y7oOIHNKaco5R2sTISIP0KuUh5Czi9oYlrCgMES6OCraTOHBuUwvm/4Y57HvjyGE25hBp+UBouT8nEVqDoMq4td7fnXhKIlTiDERlMTTz2b0p1krSYf9wLvKFTlEh34kKTQhdpE7tiBtgZ6WrIR2NN3k0yIB8/E2OqA6R+xtQl5xNASIN7OHzJTiPhRlhVQjPfuGLZLGTJNwHKXKjI7TmIOPKHJ1xUhkUPfNiTORqVJ9zV0+MSwpNyWQshoki37O0dsk5xDBuodhd4rmn3/8HpaKIf3sn9fr6xOfKqYMAp254ISHKAt60/zh/FxmBJiiK33Z6HHClDNMUmbsxJjQOcyyKWix6sA0NGegMQbvAgBQmGjiMQU79Ksz1bRFcIO8VoAJgdGcfJpm14mOF1AeZxPQrwMSGUHKdU7gshUxxuy6yCkT2LDCdF1KKaUEMOmUCiza9s2i5SLO5uXpbi5fDSHDAIzAJazhaMbfP5fvX12BzMb9nA44CVVwyhUkctruCBCkJJIypbNWhXEiO/ew3A5y/Y6siOoMixEZt7vpt6e/xJwVI3xZHIZ+ngMHyZbnAnx0Lqcp5RR97ucnxiC40DK6uH73/ftv+u1RKQVFkcOxUe+YUONgHhHF/jEH35kDE3YyWk9gBmjPdUfo/DhCSRhzC1JUmaeYGJgMgWI3W4M7K88urLdVCt4HbfgpO/bzwz/lmFdVXeu8PQx1WboZjv7AJfM5uGAligUrB5akFLms3BHx6wjrQyDcjpE3SlXLsv89O+Q7louMLjBDmKBw2fzQCCDrl9OxUy750zEOCRACn8OJzg5SYR9+f3z/IXq7rJZ9doRnbKmNA2niBeN7P/tE7DHDElu2dtZ+Cbf8ThHCIfJQdXt6z4ie/mq5bKsItUjEQaLUND/+kT89iGXxvH+0JrBCLNsznMEwu6iW3kwJCUUf47brDrKt9QLl8TFQmT24lG+W2OvVsScelrJAa4Y7HzfkHdF/iS4SriBqSnNb+MAFmDaELicQQjI7ZR93zEEyCgFUfZU2qUinU2YUiSHBHyhVOYAgtMmJ5VRyfqrrZd3IZplZ2BPpfTBhbplavvvB8J1/vrVA6OLChpfH9Hj2h+8WTZsTSfY5RDDWmWkmxBJBaqUR2xjAQmMWzwvbzoRFyNM0162aTk/gQ/X2PYwiuEAzjTHf+h1ghQ54/+yDjxDak9BqzymheRKR0qJgjKRIErOZnEYaiupsGEfO6qtzgLGLxeLs4s1jV576E6l0IZb0QiuK7wS/BeCdD0BC8oNBYK7b4fWGvwMKm5z8gDlVizJXwlpjp24XHKdPLCuaRwogvQdkyPBzmJAJvjpDTtxskkuWodyc86hz90hJ4fzjx49aBTvB0PywbjT3jCcYA2R6SB3fhbmI/mgd4dxxmk7DnIhFFxdEzcE+7X/jbWGsI7mSbATakhAcSdBHQERERguynfKVZNzOu2F5Vu+FpsyGEFxvxxyk1m/h4tfh8dV3FaZF0UmemMkuldHMJs7zuZRHgCkDBUY5z5yrjMEZaoNIfOZDk4nnATMCoE7FJEYxsyEEOpl4Mr+bHffISCuXshy59hQT0XkEhYIwCIEyzDmRvg8hZACEscvzIcfa//9J+q9l25LtyhIbPlxPudSWR5+IG1cgkVmZVUmzMpqR38w3fgCfSJqBABJXhDpy66Wmdu18wGf03qy33oAHFHyJfALbUKrLEiH7ECaQZfI6o3SUEaQ8ZU/pkFMR7GQIIfap5zclRMiM0IDuUa5XG1ymx/HzCg+7zT/FeCTpBifEJsC8dJNQQmlMu32jJcV+IlRZflqZV0Gmbh4IUr4KKebscfG8bl+9u1p9P0Uf++ejI+JScrr508ek+pqoT+Z+neU89l/F8sfyNa5Q2NlJqZRmyLjBw5CUDpvqytwdCaeWxAKiTzZxoI4xy5ZAOGOM8ZBygkoV8yo1X7p9eGkzjvCui7Ii4TeI197Ycfg55Supao2Wyu1/ufzw/fMX9/PnX/0ZRKDIsGhxc5PdklAQMzKmOPIUIuS09HuSmEhpPw87+1dZ/RjaKksTHr59HcaQ4fWr11OwpBL+BLxijLElO+ZeEuE+BdDUNRa+25KyEN3JY/31MxflhojJemeH9UULyII3x/6ZJjarKlvHiFAjgwiJHcqdRHk9Pf2aDBJG5+m45SGyHaWdzFgkri/etbV/CofMD+N44CD8y/632TJO2Bpx3dLYbEMIjBjF681afz88g1p74oSjZ2pJdWnuTsFb3Wi8pERf5JnML1+Di+LmYjBz7OuGEltzuuQWKRW4OHO+N5fla8ryeDqnFXnBJQQH4YZl4zHn2cRhMWZ6eLlHnaTXjy9f9OXlB7G7Tw8mpof47Ea3LXYmzXK6nf3TEowsLsV1KPmtmwy0l1esKlTbFExq9vZjOtyB8YTHpH3/zfnly9e6bbTSxdXGmeiyyWb3lJOpNBsHwZTI+Pm3n29Slv/1n2+JCgAnCAsFJkir6OPRHuP0ett059t+/teiKrPeCV486ydCiqps7+5t05Dkk01aiaKgXGcY4ElRaAgeottcyWVyCcjFW775+iPj3AKpimvaXJT2M8voGcuQL1brpm5P05I455WEGEjCLyFuGF3d7nohBK9FUG4ZxvHl6XPT7NTy28+QPdut8jgsxG9JHl1arZo4+ZwD2a38chnLpjiEWU4EEcjmdndzzx5eMQb1zaeXpxwDAQJlK56XtihD+Sa4sZEQm8V/6rAkqUgt1uWVCgcTG0ZCtHH2/AIiAeQMZbDR5EZHfugPi3smpGEXu6xjNAtq9etk0v0gxO31jU5xQEL7wzlM45N7S3AI7hHibM3snalE5DJpbS6SXghwcRQ5QUyneaZ4QVcFnL/GTOzZOyrHWL+5YfE4y8VHyE/dvilvM+DtbRmc8F+P1h1qaA9xHzw4AFNYds9dXDjBpIOuS0nFqm5k5rS3+VbNZga2Mkm0xM3BZb4dEIooMqeiZpEn42yIoXhfBBL9KWRCvfdK1AP6vhuDnTLCe6wZlzqJPOLSpUf49ToBhDj3eBbs3aYglG6EsJQVLp+nJcfQBk85N1LWSHLmNEIR8Mk+5ymN++nyzSrKc8WEWfzBDisudCUHzzuGUub/hFQx6CLn0vtEKBNRNcV/fhGEHLsYsWwvSqCifgKbiF4hlFnOBD2hpbdjtCpRJAiNXkGdkiPBd1MPJLZkzVdQavWtcDj7PM3UN0kOX+e/phD+WP0pwvdsbbyK9repbty4Z4rlgLC7pNSn++6wdGzmh4FjWWjgYmKDmYbsA+E0I4k5U853q7pqyOjnxbwwIbgounPHFJPbUOFFSPEKVxu9lm8jEuAoshs2jUwh57FfCHtOpGCJp8uuc4X3JCXGACkQgv7RxpCXomBMqapEKU/9mRIaiegMIQQZCTOB6j4GcT/Coi+ny4uWywapyA9gDYHa7/783//86fDbl//XD1x9hYVxxgRhibiqvg7Zb24GJSVlw+MzzQKhUDS2tcqH41MO2//4V/vhFs5jGiQVbNNsqORu6pNdIgFS1VIkZDYS4nyzmP4yhoPrzXbBkigXNgsDoKiKPNjgkqx2lNfWjo/mNIIXdkne5yXqtj2T6J9PxerthdI//+2X/bErq4KdMHr3c35iF/trWSw95gxvnv5Nvn+34+a4DDDEVNIAuskjp4pdK5mu3pBg8/INII/zOLvJnMzFmmKo9uoZk7rOlq95h+gWG7mQGm5W4kv9jmX/Trf3i42EEi5ToDm7qDysK4Di+OW59f196M00YZK8YoEJ65acZqK2zWZVr9My01MKC5gULSX2NVllnxc3pVgXqkJ3nNyoiCa2F9hUar0p6+DjKedg3DI/4rs/pBSPzoUQFCOEOYo8LEDKdbvxw7djeUNFJbewmiazLa9pVSfkuWPn519dRXdFscfxl/5M/+Vfdpf/W4gxxhkJPD1Cagmp8mJnQtXFq4LKd6MZTiZvc2BKAAChoSiBo8/cJzCFXsFCjBoJlSta0yQv7aPzwZ8NbLWj/Jl3P84aBE84KCYPHoVzCwSjFCfUMRFAiW3JkSNyZBJ6GtdZSKnnAiBgmf70h1fBbu08W7v4UgebSoZIcqM5rLZqeSCrYtiPLpCLxHNyMYS8oVKsEEJOAwVlz/Yfxc81eS1Ik9c0DektdUfECqiFSMgsEix3Z0vZOotFi67rVBL7aRazPWe3ZNuUQACFVIzRae+6fBizZLFoquv49v2GwjycSaX9OSrGoIoe9o9HFBSlLhJpkDrGjoUUNoubosIfxfL4+/TcnWc/GxSq0zalxJFFQkihy5Sn5ZNyKV3on3zDnWcKeuOrw3FZXWTGgLWXrCntg8o5IpIQw9I5tYoVTQ+0uEh178ZhGTjjxbqhGcuiCCnmeZmn3h+fsbomp+OIDIs6rNaQPM9RMMUEAyBLb6d5DoWnBCFmNxgSgKA9p5OSQgSt6zIpIocneyFdd4A0k24TdP8j1MlhYjPzYc15fzhSWdQuOxJnF0NIgtKUGczeL8E3SUcxm8OcYhObUzrhJTR86130MRsX52W5rDkWr16en1taW0maOqQY4hhjl4Ei14ztNqYbf+3OW843kutM2E4OMTDuasqmdMSwXoSG/gRArNIFLaDOahI8EAvG04EbsmZ5g9s+Lcs0eZ+V8yYB5pYTYu9jiEFwSl4hf5YRrHNjLabM4m57Hs/YWFpdbWxnGoXkIq/oFUOjAuyjHW1nCG9bQYgc+y4io/WuFNyGGJD6VXUh6PFMKRu8NenO9yKCyg0Ref4+z/QPO025oJQrceGjn31PEqEeGwKUCf/YC4gEwNMqIIck+e6smEojHxe3+NSND8nHmz/9sUAwNJBD+k5mRnJD37APao0ZuUrR44iQSF5n6DFOgV7WdZN/NQsv4BKwE0Vd1qLVownGLsGHOvoR8rDA68tlISt7/Gq8w9qYx8koVTzdEV+FFNe7S6bUl++fZaEQGaVkzC6whQVYYM65sOZ8SjDm0bljymiFUrDYaONCSmxWWaW6Gvv5U3oKRDLk1tBMbcHlLBV8/hKXNNPyjqcQc9OuhKDpJpI+j3djfkzkLd6+hfM93jnzyn1PViR3GjXsfw/OeWOWiiFLIVFC8v39GBJZR0/HDEgkdP0gGvHG/3jsphNJvFmvzGJSIhDAz7/FKQSnK+2ysxzZphWMHZ87VqMD08IL5W8vblfeqYIXkUZdb6bFKkChWZE3C8KUYCHZusI+/gwpfuFnpRRm/jD/Tm11xdd33Uwmv1pfxWg4lyGGHOgwztYsFpzSDRz9r8/PutxsT3O+MLD6w0psDo+/qVVp5kPdrHbbR3LYB7L7nv5+7Cdpy6r5b4x4AEt9Ifpyp+vND/34H//+by+P/6f+RV59xA53uqCy2F2zeeCfx/npZeKalFXz68Mjd9PuT9fjP2A+f6qrt02JWq2fD5NWlFHaka7oFygSMmKMGc34yhTPbCCLdXlXN2vGqmv1f0GGxrskE21alSNhxVivdoTGLa+1nqbsQyq3lRM5AzAmmlcsPi/ELowke57n0x6bAvvnXOlgx/u5f/XuFgAcxMXSshE6dfNwRF4wjN5ZJJwqdh5H489aquloZjZG469wezqah6RyJr6LMQYK6EOQQs8h8A+yTLvz3pz6cSLQbrWkuNCYc+WmF2NL6lG27AN/G+3SfHirWh5YQIxmHuzhAGsWGBNys9Xt07evhjI+dCFlVX+4vFExpeU83tv5Yo3YlML165FNOUbv7WATkBijLsOQafdkM7JS//H6Un7vMYY5w4iLaDabuIlp5quQcJiW2KmX1wjTq80lDiY1eWHvwvL9F/vsU1zvtnX9Gn0UGbyS/dN35vxK0XnHuMpalzSTMWY+G5slcB6HfrLGu5yXxCAJqieGwXnyfM+alQi0V0VeJsJDDDM5naQXg+ngeVhhJuVzQ2NIGTIAo/Xtpvz4Knx/nOXjlF+lxXBLeE8J0BOd15I3Xg3jU3F5eaA+W6UBtnhRFJQRk2cTVcjdKLTCmzeci3RYiNQbUPhASE3cKTDBhZDWLeMvPw+YUfCrj2+JPk4pMWFcjn4IAAL5OkUqcopFmRIZjF8pzj1PMnbUldaQGWMOU5bePA/WlapGFGE0ZWZEGTKbX+2viDmmAA8TYY5CnKen5lKY+TeKuJpUqkr/5ecX7yspqb4I8Tmw7cjuHQHBtdkHziVkiDG1TV1ebQrGRxPMPJUMPRWeHi6qlkBGQsw4He4nWSFBecFoihDCJM5w2gCBdW8WjL7IlOkWbNyjv+GFi9E5K6ScTp1NLNSVMKPPwIGXmysyEQgscz6iZ7LOpLOx+9388uaXndtg2TSnYYGMkORlecnWZf7OwePu+v+od//vh5cv7z9+zEVBRTXtzTh2KbndqnDzeVutLq/K2RpSsgwu5FlUpVBzgmBHLy8IV7vIxP7UEV3Rqop24gIECTDRnENkwzhKnXLMkfk5cl43TRkJcZbuVb5qTHVBgaZ8+tvnb7lkWBFHqJKZzKXVzn/9jWSu61t+XdXxvP7wQSrZCRDLlGr79NN3d5eaNWtXf6lq7I7/vy/fersKmWQyorEWgRRKvAXO1I8/TsvT52XiN2RV4XkwZXVNLQHO7TkpRS/K9e/fH4l0DRSYYsxozcwr+bFcG2RzyooJRzrCRfO6dechppnJtylnl8d99q1XKXl0HgmlQIWnRDGtdeomJHiz5XT15xx9jh2SNZkJvBSFVoTSXVL57ccVwe5cpcC8Oxa6qYSzgCuxqZuL7zIgQ+k9r5pqs3m7E3aul7At/IihD+pipRiBWGRUxU2bvszkDWd5NIOd7yiA61++PecbsKq4fkP3Ly8PV+3rTdvOdj/lubtb3Z1fBBeFbv28LFeykmJZ4NdPP99cbgWpclOeXyaKebtSOdbOz2Z6eFV+wBVJHGaY2pEIRivKNH2nbun0SDrdjWz2mazkRbm+FGUx7Z+qpi24eApx+H43XSIPMprvKb2HOpEcPOVEE/4B3ySI3nx9/h7j2VteXaz6p++uLqniyKSxM2Mbax2jgourpe+rBOUr9XDnmxpY2WbqfLdUsl5X1zniEseCijN5RJxzhJ6wzKOkalVdQePyp599iGjiQFnbVr11sMsQ4TwESb49nInOfEkhp7AjaQ6z/fSblwrf9qm4RUB1u37TbL/5HHxULLVvbldVfR4pzEHzuN8PUgTjlmE+4TmX7yQ4SN6LELwGoRtMg/WFMZbnORq4fcWOBzeAnWZUUgq+iXEap94cxmJ9Y5NREJigmR7N3LvkgNCvv6fdBw++qOrmnbokmUbOOvO8uGj3DxD8rWIioRKrcLTOea44tzZSmYhZjkkQFpmkMestn3JgstgW1Bqr/vznDWdffYo50qXJ3fdybn11KkhDQVtBC1ZwCsE9jNYSklEI9+Lv+r5LeQUVu5rp7PO4IhBzziJXrIwomZguD6deep11WpwpmYo2jhFcrPAwTQUniWXEYZ5CiioXxD9JWqVBAE05xqE/fRp+rXdbwqsrbnrvz3Fmkp+N10EX28rklIbEZTmEGc8e397o8zlqDQVTcVgHpkWVSFzOx5xIjjn7lF1sSpwkDPN4KQsC8lZ/OMe/o5lzespAgSLgMREZ0wJZyatGDu437zMSIpU5PZ+7Ud3YiHFdi3W5LVVfaUhgjh1hnLnuvE+kqapMMhWUIQmKI8UUQ4R8cXkz2u+SUCRJsuiCIqOSNRvuFxCO5CITUq40wyqSOJuHb1FsVRPz0g/LaYq7dhUTm3kx9ser1aurjewb308PTLyPGsPRUdow1gJ52ossCU7DWRLFRHlxceFdPA4ZC7Prms1fLv5v5v/61/9P7+qKIZnTaIADZaxkshSCphjsjPk0WRFy0rBdvU12eoyPlPF/upB0c5WRT+PMBC9ULaLLQghGVbQWbJRUhLYEkjInwEr2aotk8PNAU8XwzqW3xJLR7H9/KBs2b8VHqbtILGImHFsMkdECGakEYTgAWZeQ8IkBkMeJqUqy1/Qn8RemVO7gZV7mBwNCU0xpzo6V7PYvl3XaYgKSM5uXJ+vH4g1u9Bsb/Uh+yfGZXQrW+RTCM59ewfv21asqhDn7QdHaM3N8ULEKV83x0691rYtNO5z2D/O+2qEN05Km2loAOD4MjIXp7ZAwxRiqLGwhg3Osj3R4Ct6KWmu2Dnag3seziWkJlL6hb3vei4WabJZ+WgIsx+X1u9bnXHGXc+Icv3z9TesXQgX58AcuwjKf5/6FXN0eTy+McWOhN4L0z90jlOBjvTep635z7dqxOvOCDqmlpA3h95zmp3HY1i1l3Hs73P3abv8pkXFycUUpI2V1VRaU9+OoU1Lv3rzw5tT9e8yTeC8ffv/eGdhsLr/dnbSaCIamrO1qYUDQc/L4LTBuzKAEJ/AU+w3dVddVQbn8+tgZXfT2uXKTLAtFGV2WbVF5Ei+3YppZABorH3NOMhozI1SI8I/7fZ71Mj8ixebylSowXazbdjdMB8q4XfZV8TpFT8iEqItaU96cjkwpmIKCvtOFn4ZgTHjztuWU2hl4A5Avc8pVzBzAkCKTZ+AkZ7A++dCipC0D/uqd/v1vKdYEqwo6zjc/vpqHvdDWWzcvLqWUhCoioD1kYx8sqba8DSpeMmZjWlw6ffni6pJEQimN1TWvqUZWfPy4zDSMLx5DjD4sdprst8fZ26PKqaDoJL0o4bpa55i7UxjmAJiXZGC10mxXgM2XzSpHTbQfLBAA3GUiirLy5otkZCul4TZC95J3cwhx/7v2KmPiF9tbQfMw28OJIs7LcnZ+k4oCmD+ysPJCuHluEriMuV+wEMHY0ao6pTwdlnvv3ctnztvyhxtjzawXPMYp/EYOSHEbiMyeAtFClLy4wIoROFAznUkWLTHBhtFyRVMund+vN6t5SLPNvPA0RCoZeAg2CC29GzkFZ445IecijCblGFP88PGmLCq/5/SIdjg5O1HeAolKqo1bGZbG9eaCgBK1qBX5vuSM+YIm58os17rwS5XIN3xBMAMpBSGHxbuat+hp8LbUrbn/QjLU8OoOXpzPUrLgc39K4zhzFm//+BP4AHRAMyCjZMms4pzJ6dGT5tQ5kQl9rXX744/W++Xi7np182IOCDmS+SmmDxmQrTcXpiy3z+chxPh4dMGeClfF6opPI9bNYejCkjev8I2SDpGXqhRljLPz1tlEZZliyoxxxrxrqoZl5wCJj/55OcaQYvaUspeh26wviFS8KFVNejOPw6xEJdGZKdMbpIzA80gZByH4Zv1KKGTagvVAQBYYRPd01+H3H3a3V2/x7h+rgSBTmsZouSdtRcEfJENBtENLS51TUfKH359/7++It4mI10yyqz8isq4bQ6aEUL+M5+B5Q2mmKaYqE+pZ7EkOCqVfkFBG5uzwfJSpurh6xdLPZilxcvfDk0q3kPA3GqDrldDsZh2i8yxKqeqycU9dhHnosKyIYJbBelGKSTpEb6aJWUZO0TkCKy7W263Q45Jk8nOyT9++8ZAFZezXXx/1GkdOKvrN5W3N+JJiDo1YOWFkPPJ7OVRABZOGpOp8FoLJqnh380ZwUTbMqqZELHfvAD3BY1Nb67j3pvNdUqm5ugVQ06VEVC1IiJlofd4ootq3OUpKY3/q+0FET+hripESmq2jLkzROs/ftE2uU25KpMioJxPt4c5DImBdGDkpkjudscX947poQ1xWa0i/TyrjtqgNZZ5g8IHzjwSIop9sPxtyz6VMy2l3WzmxW83DGX3wVl38YJ7+ce739ZvUqnd/mB5Ss+bXg2Dk1L3kMJ5HEW2/kv5hNrE6q+3//mb9hY1JVrRZOSFLPw3r12uSIfgQ7j4LreTVJVVqxyTP85QA3Lx48TzNEHoqrouyeNtsVquWc/W1e45D751djntSl5Rnl/bUa8fOdKFM5zrDlFhOD0DIq/U1aG8WG8HHFI3z4XSYMr9sw6nzLpJdZHVrqUeGfEwYAjpHFlMwvSi+mvuJ2HJvnupv+gZIoGyqZk+ioozgBgpgTHjBYzSjy0/xvO3LgRlpAmNrFzAD0VxWEBJLnAJpqZhlJkQVpX7zdqNWmRfG2Lvz2ZyOYitXxRuxbt7fvv5+99SuXuUY7eHxzN1qFZUWgRZD8GIVVPOTiqk528/P3+xyjJGuvOHqTTw4OzqFMxU1b/PduKzt1FNA+0Cn6qSgYY3IxbSkYZmWNG6ZNkBqKAHIK2wPuKZMBj8umRCkSGiy3qSI/hzXTlbv1Xz/OpZUvzZP9x0cGdmFfrHhQAgICknOQnKObBiBoF7KZfm7jdHmdLj/Vl0iKW+LYK/H4UwyaC2c2Sd7IoxzwV2vl8XiynNc1pqLuSGAB8FioUmO0QuIYfCRJoeOEoqcUWRlzj5aSwCtmSgqTJE22VBv1SGxKNLV0vd//f1nP1szTQnST1oju2zERbqlIcQ5+RdGrZsqR/FWeBe2btt5H3hOMWdMqN7rIi/YijlSpxhiIbQ/fh/ujpq0vVlW8r36Ubz5dftpOjTpRkqCjHTzKXizplfp/sGzZXRkU6spkJYCvmJWqqJ6q+K8LqRt5O/Hr4tyuq58CYVYZaRMisZ4pmRIcl5MYMtx/yzKN1XdRkkEQvLD7dVrrjSaM5rwMvb9HBpBIy9CWmJniS4okgx0jIEMY7FdaWq5rgHgz3/5y2+//VrqCzLbPh4IyRRqzrUs2Lbdaa2HmCb42vI6xohpzKZdXn7LOSktCIRzRyMfBBfdZLhr9cV1oP7txzfL4xYgcKNsUTJmSBZ7vPMpAQkpV2hcFBl8Kcdk5tVccEIUcqVWH1n3pf7wIQIMU3+aZ8ELwJwJEUik2yzu2Q+9vVz50cTJ1HU1zkrHwAXsdisqVqquqYtfXPK/fxZMILLqqs2t9F1CmeWHC4KABN94QxjlBExxncynupUzScoFLGsPhKbkvz7QjGy3zVdtgaLmIUAcSXLkyxhCSg1myrig7B17/9NPGfLJ/PI3Y1fsiamNN0ONhSGOXDhwbeH4YF1K/NbyJ3s/uvheUP/yD3nzl6qSOvrlaT/4PPOIwhOZ24I+u/vki7palUgP84EsLunNibEiRYY4f/vthzevgVLb9Scz+pxK0hAl0FBMNPCwATpnUyAL89Oxc7tMhLi5M0dFCSVp9fpmvXJUKMrEYA1H+nxtsczOOSFv067h3UOwPJVFDL8txtKf/8a5kIx7TgCWoi2fz3aZx/P9t7ouMycecrmq/LH6dekvjr/p7cULpfP5C+MJNlszjwQsr5tAOZ9SOIR/HE5lcVLRGpdOD9+0JlWJviA57c/7MDpvU9wWYsMCCX7IRJRUEpUjCZA3SvKLj28uqsFQQJin5aw9zaHZ7A7Pj+fTy6YQDiyQ0SPFRYHOClNc/EZm66VmhJaM4GTOM625tYsPgSIXmfZdJECAUlI2mdBUFInL49ES4K47EiTrzXuSTQyRQJdC7HjHOAdGkvOEVECYsT2XkK2LVZ6J0aKIoYjoY1k9zM7jc/J05U4g5GHJ12WmNQOC8kr7O4gxU2BImHfBo6iJBIGhix2xsR+WdiIsShyf9o+UcmMyKijEGKN7sWb8x3ijDlS8q5j4Y/u+U7cx+Of5FzHfL8R2Ji0k34qt9+GdLoBgMc6D9aIwHPmSWaMIN0qAnOOUCRZkd56+db+IWQHhLLZLogyraxAcDic681M+pfPcXIF7/tk8BQK5uGDMhwzo5mlOI0GCvi0vGikISTFRieNkM9tUDf0f6uE34r0I8/HOndv7R0VBCuGcs+aFEI60orwBxiEg+JwiJAxMcB9jckH7RPsUI8EMI09xMahYwEy8RRIZ9c6NlIEZO9mIBDmRJDK/LHZIyh7py7J8+9vf7GIk4bJQb7hQ7K3n3nISBhwLYJxRufAoM4C0V1dUJEILSZ9jGr98hYtbqiOAELxldNIsQ3T5fPz03E/G/EhvtvwDo5wRDEBvcku0Oy9GF8gFW4tLoeQ+JSGpHP3A/fOYn5/Tf7n8s6ool8W4qogCpziHkq2FVMEec0iZtHkfEhL6QBIVjvA3aybx1TvmpUS9e98e96Y/fbs3p4vrC+49UxlIc3EliQ98XnKeTzmvmWCU+xgqpvjNJUiwSJ1ZQgiU0jc3b9xCVFHaY64UAsAwhKopHs6ncpq8s+CivuK9n7hg5ab24k1wzs2jtUs2lUxUSFFd7YAWxDNipojRpEP1uNeN/vHt5bf5y2SXbRTPufeDS6sZQ4pd4DYjgI9P2F/Itx8/lJKzOF2XDPIwD9Y6peUEILylTCVAWkR/WhiTjK4QT4Fsa7313mx3Uio5jXEe9vHxxZvIuRSc/umnPxOkCTxSnhRVb24IxGc7AmQpxBiX6LAoqS43CS0YmzPkfihzQllRYGX1xz+8br7O1hwXuarm+TTbEyFImRI0RFFc/vGnay6YVCrl/CO+/fvy5Rh9seRMRCLMxU32doV8q9Xj3hH1cuK0LOSmnHnbZhim6T/wWWOaaUHLdcmo4dJMM5LcFLTylZiAviClNiHTBQoSfFM1Ofi3lxJIzsQf7744ySOKjkzz2PNcNJ4yBMIE8yFVqVBNPOxLSg3rLsuGc4qmS7azyxzmea9k2LWXmUcQD/7xzXDeNZUw+gB4cqGehbj9GD//Y+qWcoP61Ttw0cWQ7FIVSiidK+KtS5wv3cmZGV9fwi/9L/P0f/73//ZfCvPbl7GP45xmHwPUacsYZzpIQNqe9p/O+593uz+97R+Jt/+xP4vXu/vleVWl/Wy1KJu2LptmKuvo3e1NQfirdH4+LdYtk/7Tn/2Qfns2RpsIj7orLzaXZnLiHb76cDPYiXJvC/oH9oqI/OV8jAMO7tPJRB3Z9tWbDEoiFAPcL34lgKJFxKLSnC3cLEcbGdIh5xwSJalfFjFF8bFd13/CTHYb/uWrU0gbrT23O36xCDL7PaHQSLSMpOT72VxsWwlqibFllVjzq+3tDX3z8PQdvGvaVT+dcrBcHx1wmJewLGnBGfWwmPLrNyLPiRfXtx9f9h0V+P7jVcHpFzd1XU8zmecJALUumBLAQvTae/Kalb+/D1//5mL4VYqiQk3Aa95Qzky0vKwuo/kaLHSO74rzNBa1UCUz9QdtLMtkWpKUcSkMAZYGoCUb96hCISNsm22s0hLc6EKJ8DJOkHi7FvnIi7pSxJ3uTl9CXDEK0xNjm8LtzHop/JoK0l5eh+xjmI7R+rkvDUnI++82tyLEA6nyzWYzfS2AWOCkquqcc4yrnBXnMqXYh5FhKpqCVCEQzkUjpHhchiEVK0HHCEQBN57mWmLy02wFKgCIEWkolTofguqdoCS5zGgMhT6fu7tvjxEUUvrx+r+X2zR/mkkVx2FhmYqimHJuqgIJXVJgUDJaziQ5av3nF10UVfvW71a3q/L356dFClqJmOKVqKJ/9I/WGwsxqWuJI3Hmm//1lvFTQXxcVk0pqYR1bEZ2BDP10/Dupz/O64dML+r0Ms/47ZGkPJ3HAihXFajoQKl0nuS7teeZQaNcJJFVlVBJHfI0JRiju2L8e7bnr/9fgD+5+RC9aaqKRsILBd79ZzcTgUoMD0Ay5pcxhTDmgjZyJUsZIE0h2pgKyQlhnbL96SWnnMADFimlhDPFjUSUXEjOLWfnuT+G0yVd06mzpq+Kej9lH/yV1qtC9XUDA02DK28xAY0hUEFIZGXztrl5Cn/7neS7r4c5zRE5YA2omCjW8ZyIIyj4dreJyR5TGp8/0wX45dqYvmFNAh9jgrKKM5q0SOedtXxzuWmqvV3YleAUN69kkxEy9NNZYUnLi+1ahxiSygz5/nyCSjNCz7FT5IYnWJalrnhRiGxJt5+FxhmDaDlZCAGKBAcEYrJg4t0tx4zwtNeI8yRDZpxtI2ycM+H0pIUsuCKUsp9/+eX9hw8zFloqjNKGWjMUBJdgJeXLZM+1ooxwThlJ2y2PxbtlmaOf4Bv2xAwE65Hm5sBKvWISAHIKp24pMRWMz6jz6kJyBYMJxvb+eJ2C21ih2Ti8UG92V+xs/RLkKq1YLkiIhE2DXyJmyqNLD9pT0Uh0QYQAM0yYaeBCtibr6fwIk3kgXY6q0+FX/HZ18Y68l5uf3xZiOQlKJwEUp5hUYyEHQh+Hk8kp1nRllmUlt5z6UzR8s5r7sBYgtZqWRapSiKs3b+4Xd8sosRdz79x9fzo8/j6MIQavpPyXf/n+v63H7YcP0ntOQKuyjbHBrXofOF0ZeqSg9seuKOv7E7y67PfDXNeb0c5sZpQSlzG/nDCxfF3C5SZ8/vp8eCI5U58TeGbMr92n4eHAGFVKJgI1ljcX7xCSDWnJwSvZoNiutyHah/0x7YgOMSknWbpcvSlZ88tofuD8qpS/H7+l77uuiObwRNKld4pnBjson6p5epax5XkVqU9FBGeM9+vL4pxHOorozFN8iCnfPz8lmV6+P7MrpmH94Pb5EN+/+xGSX8YzUXuWWHKxQKFSkTMQrubp7OM0dmTdsQWINWbTNrNZFmP2L0/H+ayrivL5trl0sgzOvNbvzUf77blnhgmZrR+TH1oMjCaTTkNvguDqWp7MebFzxAUN5pR6QmgocjCSUxP9AqeRmLT053z/p6s/RjP4zqLhs3F+V4boUJUkgbU2er/4tCp43dQ/MZdFw9VKyMotJnjJlYe6PI7nFFx0lmAOgiBSZ431E98TRhmIfFV7+Esb9hYHiDlGHxAPGbEbGspEFjmtE1FW6YalmPxk/D48k+DGc6WEEn52y6DKqzgNs3Vdy4ocIEDiiWdWri+ycSWfhiXy7mxx8IOLgILf3sjDK1txKtOzGBqW+KUIAEy6QrE0G0DUSlChJ9fDNDFaZiGQ1TctneCm0kIPvNGiINhNU6Z86lKqVPl8NskbNsVz5MJxnn2ee0rqfC4X+I+7+9ksjGC9Ejd/2QjRhPO8VIVe/SGEg1ksbur2Rg7ZpnZthRTDpGS9PJqYibgkyCWJIbKcGUu4KbkIbmGA7xV/qCsh0NNam7xetdZ3rEokr2Ik1gZ7HjoqSCKCK2BsjLmsX9eKkQw2JkbJiSUSXcn5VtV93meoL+Uq57MJ81td5ES647ln3aZdz8tcFMXFbiNmmTLsauz70ZkhpfT45dtwtVPW66gli/3dlTETe9VyZ8j1bYZP6Rv98qszkJCQDCmKuJarDF5Tsb15TWLBaGVNeHK2z4kj0jX1zHLc+EJGN0ZKiMeQphTDfB7evn2ntViSC+fnet30hefHeR+ps84uvpJFAP8yj1rv0NvFz4ox8NE60yaVp0Muy0LKBPF5ecqRoiQICQjxCzjjkBFGGPcBUqCc04USQSghAIiSso5FATG5HHMqr95c1dS5/UQZp+zLpxPbVbJsUxbIGPFp4ZR7LuoqPfbZBYKLErRmkKvg7Nenv56MMTXXhpUrUKmqZ7vwQs3GcsYCrovbFzLWjooiRIBkRs9TSbNVyKTSJC0ZjP39i/UWSBsyK7SKgoVgypamqJku+fDoyDT5WuA4WhZnFzNBwhJmSltgXkr1w2U7QOpzBFgVhJnZDNO4KkRIOaSrnPd8clIXihISiffP5y7HJF2wtBjYoRUtFSWzL2eaCTIYYaKCUi6NPXwJ38eDTSsoCC1hpUiWihdv3tmnlx8n2H/r7ol9GqfNqdPb1bskkFCOBAAXMhPPRmdfb9ZWgNJlZsrlVfmmvtxcDsEdADNmTQX698hIi6SfJ3GtcOIx2Xq9lijr3e7xU5fiHiiJoVakv7l9j+al81QkSkvuZguseDmPSKC5WAmnAZcY+oq24PKjeY4P3e9vP/xA6bLYnGdZaiF5BGyadHd63t0X1vdYU6pNmtODO66mFhFLLYXlBzYhYxWvcwrEAyTwYwoh4oEf/Vi6S8fnxwfb1IlTFfJu8N3Vq7fANyFzv+SmXFftei3Nfj+STL58HZGN3mzG6N/uLisoCim+zs9hzlAkXOIz9HVuSfaJEBdDdrQotFTo/ZRyFpk5QY2xC5lbXgVwIAu/adPhhVcoZH3+9dOs3q502YeY61SmNC8y5WTNU0hJq2KkmIa11RGXnHiWJJH1qvALVEYoyYAm8CmkhG4x2VGTEl1O/czna9oyzilnGyXRJ+c7seJq294wEFQy31k78q0Q19R7n/aCGJsdyeo8KwlCAE2WwLQ/62IBJ2mmragnn9HXiRMCkXOecw7eI/AZMssJMxjjmLQxXWa3RNLUfiGETVQgS6Ko+VIlfFoOwd6u2E3BFbWLpcvkt5fuy5d6tULGUMhW8MRWMT3QNY8vQa3y0+FpXE4dL8fD4frjx+gchRiDDeczK1cxhhgjszxS7/zWpYVyy4uy0uLfD89KC8rw9YrzVW2e+mN3Trywk1W8bFdXA4AQ/PyYRGXZlqaUZN3EogiHIScsewNcpGJBpZewTPF0ma+M9x3HClEXBUPfQjiFmdLeTgECVHKeBrHYaAnymIFxSikTvGiKesWepixDlDwbwdjM5jRVu0ghcc6TH+dot95g8HOx4s7vdLF6dcssP9CXTGB8GbNIBCjwullT3dTG5uN5RspDoeej5Vmzt2wNuw0IuP0DQCDw025zajdXN8M9vlkzrV1IhvqRTE6n777bAJ+7ZTJPs9rHqLIeiJQ4cDJH9HNGLLTULCyBVjFUtRQMhtP9uX8BGgqWa4NT9snD1faaohJC3X9/fn/5Iep4fqFaItA8LgvqMk+OCZmX2oYzrDlCEpaLSoHlWgnu8VwDzTEEJyXjKKI1BBLsLeT4it100X0xnZsSKXiVoCYQfQDE06FjJENeRHzJrGtYRNCYU6QWSTqzsQpUUTojxXb7ysVDcMM82fsYE6FP1iXjASidz7nViKeLtnojdwzlhlweVGD9IpjmTPm0lCUryE6XNPA5fw3p+O178u9+UJ0JJvq5i8FAnUrWau6EsM+caHpieUPpzTUSm+8KetlQJsf7L+b+pZDFd1ygUqFdOJOvkeyDHO35VzP+N9XaGsPEhSlzUWLYuvmUwDMWLirZJ+pDDv0YeNO9wIcfC+kDReC0PIXp9ZvdM5lDin9Y/eRqO03ue+6+u8/x2YSzpdu2lVVHJkbebNnvPSSCnJY7OJ2dNS7mtWD9d8T3qYyvyrId9TqOVhbCcA4ZXYpfv3/2ZM3wXLz+KaTIMkmqDI+fYxlp8VFDJSDfDQd+ZHVZZvXjuz/WMcbx5wKRdhFCBi7YcR6bsg7KVVkzJBFhWkaBRLI1ktPn+Wy/IjKW/v6//kMWiPTjD1xquqerlAIk9OE0j3mzXlFBhBALXVhPhWKCy27qT+OZSjqUXRm1nax3xoXknedKcFlmvEDEthXLcnh8+qzczEiClOYLh+BImPdJnl5+a+1Vv7SEHgp/dXstvX9rvZv2z1+z9uOdUFWwi+fxcZ68s5qh8ceYpdJyOf/9frZybN8LUwgdWksZWf1ZvP5kecERiX/wknulZV6XPNJ1zvPuVaJ6Ybkbu8wSF8U8LRGjVpIiLrJuGD/7RagMlqcUxhBc9CfzuH/ABBEhOZ/LZmLNFnDNKCZInLGK1Lqux2VyzhAE40y7rjdVAZnmPDkYjBnL4jK4o/M+hpBLBm1b6FVluz7ykMMCsdAREjIaPcY4AQ/QZD/0D31fUE1FE6ONJCuNALOfYmbIF+/L8YWKnWDB5zjp2g7+qX/JyEFcUR4J1VwHIiWfgXqrORDK83SibUOFmrjj5nBwOFvHKA/ZEQqUlokNUnFVUwJCqWUKdxxj9PC9hx+rotLFKYTx/peMRPFrrhiTrxSJi4uXO9VuX0/Lb8Ozd4+jnQOUUjBuMiEYUgqhP2gskTNwKS/OpylI1fkX1aQmNJftKkVcFlEWAjjzIZS8HtgDCyRbIkVlZlNpdbl9SwhK3IcBniwoOBf69aaizHKBm4TpmIeUYW8w9qcuulQWqIPgFAPdE9iBaH+swn0MNj2bXJc7nxZnFlKUw7lTlBo76aJetxc5p5xyjDHFuJglEUZbvmR2Yx1uSyNLBBIBRwjoU9EF2Knmh//xP4eXrz0E4ub08sxjoVaKNlXeKKYJE4cwmfMB9ED5iQCktAFFyZyAUAIrWJLLsUzEzKxYUSRuen68fCdimkK8X2wOtpKqYTIyZCTnsm3OcezPSyDupqrMbLy1u83uYX8oGy6LszlN7+urU1cHGiDQWOq+G5ZgaEw5F6pMTb0ihORCRubIaKpkU16OT0McAt6oFdIpGK1KJIQm8jZy5mYXvU/kRPCSgQKaYgCSKZJLKdllDAZpRahLaTEQgn15ziGRdnU9jIECoYpl0TQqaW6O9/3Xw9fXP7wvSX2N8Ws8m+S1qDEukhHBiFkmHw6u744sa5Ui9UmkWdlyqnJ0L/1+7sRHqUDKGBMwwroZiI2JneKSXvyqvSLVpi7XtAJ9uEtKcVXoGINdgkuh2RJKOWfLRp0HV19K8l0mSo45Lc9z63RicFmw6vJiGExxRqb652dBIFW67MdZeyJ9FAOdP4xv69WDe3z0L2GZCCLj3CVjH5665ylwKXVoyUfrPh33e6U/+owcSCVViE3VmJ82/+O7O8zI/PFE64uEAXMinM6ZsGr1frv7Mp8YTks4jee+Kf+L9DIf3HKzNC5FwGuolmu+KaoqIUZ2/DI/x4dm2VZQdOjGsFjrJrr4KbHX63VOyRpBqGIsBBspuUTZvXeX4t23bjGHF62K33+fc2OKOHHeXl2Uy/xhV0P2wS9LCsnZkTKqlRSqMMmGxUPIhS8JoUj2SqV5JO9u25Q2ZhbLzDMZvRU1bTfqT4n9jgx4SZfkXPzdQnFb/NgNthv2F5vN5FQMIRElteWMbW5uKh6/jRQBMoVc6XMOshARwrwMIXSvy5vvpEnx6bYmcQ5UUhU16GhtGAmY+/vt9WsXXHbM9/fm+QkILuoixdkPR2ANHJ9ht8oAANmYibM2xxe+uFiuy3IdJjueg3G9t4ekB6Lg7TUjE+pNSSlB9s6H9OxOSJrZnwBCgHivREbglOmcgVIODDL37nRwj+nIkcV5+OaNSYboGgFTPvWjHzshROOwTMJnYYKDwjuFQ09mjDApcjNG4eNzkbeX13myCcocpciT9MvMtjS6degyXeV8L9CnLnandKiqDVdrVqaQzIiCLUUreE9MJroUwcfQmYElyqgWp+X6+nXB2efH73G7RoExuUhjWhuRobYqlOHUHZchpwhNbQRFysrt6x82c4pF6H/rCZUjn0ohHTmEYO4X+3A6LHceBX214eWmnk9BcnHTFIQw4um8dJin91fVwzFtk5uSK6hssGBBAtjTviMsRUhAMhNbuvA5zK91O4ekCMmEAIGcM5J4enigoPJKXDGZ9BXjYnzutaIgLCE094Sopq3JDOtQQV5ITtkKQEPTOB04K1NJV/TwLYM/HuxctyImjwicMRfC2E1srWxmANmejxBgs5KMC+eitQ5jsXDXmcxSlMsdF5jk+yyTcJk9fEswPwzL17vh8lrW5eVEJjbVvkgpCWT00Rl7+JmuKePGuqmCRse5wzUo6UOCjBx6ILCksFGEMOrcfOif9c+Y37gUQeuN8EwXZDZ+Di8hXeYIYxxJ7iglAMHJ0GwUkpB2K+IEIRSU+OqdijHFmKMfzUhzw92Sc24vGpIxWgeQkhlAi8ypCnNknXUPqhSKagchdcf1rgLMkDjhj+w1uQHBjkLYhfjaiRiqQlOP7UXmJIR9LKAjBO0yu0/3L4iRKlVje9GoDTNFSQhWixfMSyHMvIrx4eHzw08/fiyL1V9ui19//yoylduLzlm3jHn8pLZ0SQ4KWuhidkM3LwWvE3i5LZqyef72/R8+/CS10JQJTs/ehwDIWdlEio8DKyRYn7tvj6hSoYsddUtKI+ah+55f/Oj/9vI/LqyUgdNMyYPT61VbwMElaWMK2cbsvV1CTiM8K9XkdGaQV2udTpNeQQwXqbsvckkgv0wvSryvwKY8UZ2/Vufl6dEfzVrjc842Re/9v47TarUIhugpYbsYEklMKqU77T0rrl4rZG78PlHEMU3D7A/HuLuS6hLZ6qMqfvZDy9sjP+iP7/wSf3cvBZY3SPefHb2cjy8dvKBSm031z5ByrMpd2j7Hx7KoYyZvblcEcp/i8eFhWaacQkyBUvzjuloXwm/sm7YYxPUyYYxfV/KVFuXslmNndIkGRcbA9E61ZDpwRcbvhwelpMneRVvqNs0pBJtTqSQrWxY8TTmdz885JQLUu4IzliUIXHt/6vqJO2EJba8J0XzTVIfng0vHee6ts2LX0kyA8odxzJ5ho+uyAbaOZiLxE6LKISH3hW+It21ZJZXUxTqZOQ452+CJmZ7tOIZ3242XvCgr1IombcubNsVC1k+TJF1Pm1y9fV86P0/GueATYKII14jqKuu/nRbnHcAL5EbvKlmsVgUrGEtqz7iYiV+r2QTmWkullk9dAo0NgecnXZS42XUPX693N0jovJj7b3cYQapEKHjPpGZypSiDFCRt3HGCEIKZAlkgxrChBgmTbBKJG47Zupy/rYqbfdcdret+USmxWIumAcwCSw2RuePh96WP/yAZRgAgJANqqVisUo6ZAjSOODtPj8LmlLeiEoWPR1YK9oAFdXNIewxrYMEFRuhb1c5m/G4OMiyJQYLPxk7upAqGLqVhTFSoJYaH+5ec6CUrrKBVpaiEDqZYi40q37vz37+e2S37QG8GOyk2u4RNUYzzpKQuZIME3+KQ6aZdTYptNeOQjxTUvKwp47vLWFbiOJZCXQi5I8unoTcp6RxJZQ2jNOVk+2HKAMi2LePFNphzZDxYr6uSExlDyJB0KVFGuyzHx2+03QjVkv/M30VigS9abwimRKE4MaYwo3Ge+NhUAjIgkqury0q2z8mSyWjVeLOIsKbSw9lG01Nuj33G5obv6vL1B+XLkR53xpOmBPoewf2PmY3Pf7cOKBdpnJPMQpRJ14/Bdb/91ZpurUUMpx1xBANkC9FreB2CWLd5f7hIeM9JFpwSTuLnfW/70XtuQHLtxwU7Fi8UzolSkckAjGcbs6QNxpxCCd4TRrJXYw8yFVqZhGQeM0hSah51wWTsGOcbgGZdWud4Sh4IEPafzoa+51PNKBMZMKKZZM7QUsZLil0OS0Jg/374t6psy+GiFnrAli5pNI+I9tTZV5CIN1Aq+u5VuL/7nhInnDEt6WapMIfIIaO3WdnkREryzRu9f2LOv9zdP1X6eHH1oZbc+zNlt+x04o0g+ifKId38GxBvGVmsJiRbY0IEwu2o8eb9++fHpztr6kzbosqapX4UCcuimXG5Xqe+b07D6MOkFBPpdH/u73//TqiiuYrCIqO3TU0D/lwvCOL2qrGzX4VXLnzmJm9qkos6xcCIfzBhV0qIppJEiH2evN7peeLET8Eyhug/ubz+ZjCNGYKl1phLXeuLCxbsI3Ytobx++z1/STnkfEX2j6SuaU1dHyABFVzJaJfT3A1AiagdIHJPhBS2O/n9M21HefOhGDglXVwsJHaD0D2lJPzDCub5KY4fL9R2b6OMkNV8Z+FNKZdyHO66TXvjvfHO3X3/Gp0VSoL3AlFKuYlJ3bynw8vy+2f8sQiOKb1CFBBGnzkJOTOZCcTsE4UM3bhwwh1nmhHHCy18bZNxwAlNDDgnSTHpbe+cXWY/dKPgW0q3gtIlBh4sSTJHWuXEXAAuPeVL/9UtzEd6+ep9wbgBOPfnZ3aQy7aq9OjsMpiXkARJMfsMcvI9BaK12IA/vyy/fPoPClmmNxQmDRIIoULJC3KVwXDJh2GcBwaBklCWzE1AlnOVuIVwPjwVthxSjkss/QZYlkJAAueDnUawXRJUCcqEI1pEHJ7DEqzPnpRC3ffxwI9pS0KWJpj0n1tFUgVCMAhBqbfZUtpS8vjQL6OTSqU5NlNkF9EsCubg+MyYWhfNpl3XUT1ayyAe7NffHm0DY/ZxjaEuKBTIHIl8fO1fDXMCYDOjtxwbJsDn/SGAmjIYXRU+kBBds94orbiQkfHleORCCan6FFJKTBaNiIGLntxlBz9W6vFD0KnZXjOLU6AqxpgeH561GcI+mOWNYCEz56M0i6S+VHrpl8lypWtUHBkPwb8ccfEPqP98fbl+eDrF7HJOL0DdIYlNpaqr++4fhJdFkwijQgpVJIH+B045p/bzb8bY1X9VlInxxJSmRRX06lKU20zZahOVuuaM324+mlWKdjk8Pg1McKAuuEzlzcW7oDMVlCK3WSopD5PrLW+BVZywDQ8JooC1YAcscg4ZImIoPR2El2MSmQESAEI39fg1pnTgjDJFJ+cxAyTDMAqyHk53FXn7qigf4/3ZnXEOAVOjZGaMMs3ai3UthuT9vmtuKsFnSgXcD0BJ9cPq3df/+f/86/+jqj2uVrxIhK7O3Sd/HnOMH3/4mGPfnwyrkaGws90kwpY8m4OBzUpbMbWiJLAK8/fPT+Ojt/YT8e9/Rap4W0m8YtlpPo+YV1kS4ywnBAyqYl7MBGhjIC5MhdIhdGwywQ3Zu6ZeHcbIBHUDzRZRTlqm+73r4ndJeamwAQtsTpE6DF4FJfl08pnk7Wo1Yjz7/TIvdAQOhP2x/lNMGdfsPI15OQLoS3K52bHsbCw9gRTPj2a24KMGvNTXd8yXwUO3jToRHHzbksdnrJr5lBfo2u218y55syKRwlFEb8wCp4tSlz7YalP/4+//dj75ix9pHeGFGaAoOCWZDgsWEO5oIqVegMyLpXNsdTnPZslxGe4yRS4olYfdGrm45AK74SWO/e493lY/5qgo2RY1iyEsRMfl9JwU4eLg+xL4sgjjhmVvN/HMOdbqbfu2Orzsm7pel3LfPfNdFoxjETkrjo/P48VYrpVZXfvuzE4vwzAvSOeQIBe+I2VFWk5Cr2XJn47/crHdbFfbOx8qwbXg/fE4jGcUhN8y0JlAeyHr776n86R0dT7uIScN7On77zBDH58xp/4fPx9diM6K8oe/XLa/zGiznSlo3eXcror4t69nO8qrphi6/SVvrrYizSOb7SYsEnJGVrC1sRzyo+ZFopqvIYdrjg4IdcbOk1n/0BASOd/tx7l0oSzVIt2yDNYBj4wJ5YKK3pPMuS2jpZiGhmI2C8YRsYgELi+E1OsXDzZ5zKkYUXrpqKS3WzOdeAI0TPKq3SKAclPnCQmQl7bgg6FrPsVIRVXLOvkQ5qlWnIXCeFoXHR0HY5aYoaoqb82L57K4sXiSOJOuNlZMZqDJFnXbrNq42SDjdjGE2yXMmvim5FqWIXop6tC5WEVKSTecOV8IXbwtr68ai+kYSsV8JeR+STuoIsVF3GWBrGYP5pmf1lfN5X6ecrQaMZGM220WUjq7ny2Mwy+P4zg+hRBe83ZMYxDC7ZOVRKw2it8EGb4b4SHtdLHVDYNc+/WpfJnii/9q7+flwgZKqDA0hoXV11e3Ko+QOnLu8v48pTxwpBAJoVmVVamQVBfEG2vGs4jFnAh4K1Y8pjxZXRLOQ4ws5T30JOSYc7MKj4m4ur1c/GMNSN+9DpJtKVvnkHmRQiLhBYKnIQOJ89TnfXZ4oNu3L/seiCiqV9td/P3XYh4f/7ofrV02m9q8I01W83pHNM38xFWbBazq7J3n5Pxw70qUH9Yrl6N1sWk0eoYzlwWdI67Ljag3jpCv7oHKaxwXJAYRGu9KCjzvOv/QROBse3V5nVcESeoO7jR+i4HzdckpTYoYESICenY+PaboaVuBJvxCal7xYbL9oIRo67YEmmNEoC3iQXsdW61JOvpT3lelnA1AZof7e3RM79IUZ5EpkKQ4T1QJ9zIwFQNrW9iPjqSoiJ2+DFAXa2lBbqFKHBhPA8vtdJ7+6c07TuPx+G0nFFtXz+Vd9zjQ5DezjNhkAJ1IpsiJi5QKDAgRKgg8wymFOKxActB1ll9d4oFdjdObrZR+43lONqLJa16ch165ac4LwJYyl1MMg40qkRSjHdeBQFk5Dw3VAXE675XeEXoVyAzZi6FgO1qQKoBPk6F4ZEX0hGWbh2bkQlbMU1Bnf448i+vdTChTZIjEjFO9oaGlH8hrRCDZufn+d3j2jORIkjn3vznHuHj1Rt2Q+nCwCRMpGaWFhiLevmMQNVJCVAYTCcaciEekcP3+lf371+X55/r9HzAlQfmf//jnf//3fzv+Pp0/5GWxSFmTVoVoeLIBKkMx8xKyzvn8sISmFCHQsm1EMEKIzCnylsnMGAnp8zhGCpAgOdchhH2YeLd10RRUbV+/4cvkTKrI+2U8vzV9PBiSMxw8XMTs7/s5yiALn02OKXmKhBHm+l7JKkVcrCnbKxKDLYpq9ZM+n+PL/jsYmlxZ+Lbmo3IwdCRnyjKSHIPbhBzO51c5U2tfr0vHxxw5wb7SMifM9pw91a2qhQZIiFnZIHN+teL3mb98e4k+cfEu1/nJe0zMxSw5BlektO/pZn1dvV2jKmiCLW+zs8vp62ceAqWMxQg5xzhpif3gD//678iY2Kw4uvNoUWapEssqnojglZDulqk5eUCa5kx0SUon1GWLZYjx8LTY7jHF0BZCoJ7NomwmuaSF5vNZ7LSsyFWke0ekTSb1/XJy7uXKivLDG3d8xKwR5TIbn4xU3CeclZaOB8a9n7iKmCszjzhMfJjm1+GWXRK7L3JMiIcYmUk/Xv3Ia4SUQ/AxMmox+K7oqpfQBTevN/5AZzodookh6ht9AVVD7QDeRX8EiN6L5+nTOca38n3mmUBJ0QVp4DnApbqgsA+hDGzM98StlK4Wz/xCcCUq/Mn6fgU51A0XcjzdLXO/pZK6+BwJSTkOzPuT1qqUTbWt2Z4b6up1XZXJzdlEM8URg4iJ3w0A/VG+u0RWKbFt8op+GGb39WRCfkpSoITgXj4d9sgobfV6y68G/4QEpFIX9PVkLcrUJwv0UYeKcGzKnWLLAnOYTz1nzZqqyzfnT98Y5/lbpK8IE/weniXmrQBK6fnnAV/XK05V8hDvBjTCjSmGU6Rr74NzAARCGqf0hNgIKDfMZbnaCl2yVx9/3LWFWea/3f+7K0MLha1L+yPfICi5e/eho6nwPtg4MgRVqhzJYKYUIKakdUMXShjaXJRNrde3MeFMYnANH1tSkoUYQpN9wOFH+fGD+vnXZozdq0aNtj/9Lj0+UUFQSifBT1OyWVH0FOW6fbvhZv0ufv1CCG/a2h0WszbETZwhQByDvTNLC3Dd3gqmdTqHENw5BZXTdGEiYRI4o0SEpZrs8xAi0ujXjKamMc7ZPlbo/zY8nv5+okKoix+Di4JrQQEyQEwgCYBuXv/Xf/7+W3j1EamnSEqWJjfbMZqjqaoaUu5o0dIiJ4xIi3PicnFStaHxrRnuz4l7rlhWayb9+4vM9KbcfztFYqnyX+OofiWiwfXFPNmnu+eNXmFdQapSTkAoMlRXpeqO4KNtNHnusV1BSHl/GPESyMjXt9mCnUep+ev3H6QU89RNJvqAMbeRBMliAFKT7EjqsZdGZ0uvy6uwhC9fv7J7GkvCV9moVz+KDJhCd9xDMJwBpSJVEbtYi0qkOaX05ffIbliWHEu+iiRC7ZEUhJJxQYCEZM6FatL43H014fblTq3erqQcHcTew/hkLJUX6p/+/M///td/818tr7V7cp/9XfsnDSgyKxYbFRek4GlYm8eHbzavm8JDMikQpEoUKYeUjXPz8XFcYmIs9/d5ps+QSi1u3v/fZana7KEhMI9h/3Ln7Vg3lW1Waf9MEqYEOUMmrfUvXOaFJjwmboViSqzlYsNGMcLF0J2ai+0TSd4EiDjajLqsX3TCIXme5ESzTXn4QDlS4r0b1MQGUYUQGadjjzuWDUFlC62f52nD4it1QwTlJ/E4Pax507y6oG8QF0OKzZUP8kb6nLQU36bl4a+zFAoAV+v4/DI5t1QuwZyNmzI9L3Zuov3ad8K6hlJZMC5YTNTYsF6/LUDyTeO780KlHZFpyYVkjDIClGCKkTKaCZ6Dv0YlOS9l4UlZZcZzyjmnNXs5y5B0dHJ0B+5NSfnZ948vewD4oc5UgI2RM7pWSPRqfqG/3D9vbCgSoTN11MswJ0YpZYng0eRsA1MjcEROp2EuVCh9CFzIMi774W77sILeWT5FQp746PxovrOTsA6ZwLpi4bxoG3t1UlFNaYoxbVMdcZWYd2npzN6/GAm+KUNaMitZ8MuLmTMQi71gdS4ypkvgB7bihXQnQ4T12IA4q9D6keVwoJkk7BZSNtaYf10euTytm3/yUPiwVIKmnHXXJRCH4SEEcbGpMhaLNU7auqpcCMPBj5xmiG0sRwbScY6Q6Fvro7fzCklkJBFJCl2VmavERh/HQIPWiN65YTg2an5PJWItKKP8EEibBKWFzwGDpFG6BN/txNF51VQrUUzjUNlpYFBs1xOcGyx3stnHr7flP323v9nlkGI6PzxPIhYbwwBgQSQ5+tiOOR0yAxp6+ykEAoTqYrOupY6k5JOL5y/Zh+dd9R4pe93IhPTB8nQIqq7ajaCkXqxxllA6s5a1ZF1quwxTvw/JMxK8VmsfcMIsmMhqZYnoY6SJO1CE+YIIVUhnjbGzPBh/UauoTB4po330TtFt246QFpz5FKHRAK0kcnn8vX+4mwp9rfUD3QF1EisnUs5ZlA0mDz5pRktUZ9NBTpDj6hV9/prO1kWbleB4DbPRdUq273SQmS2aVnWkhISMAOB8XMCH4K0Pot68XqEXa411qZ8cgIX4DE8ZxGq3W9f/9E/T/LL4ruseUmBP02gXw4WguKZ8opKGeQJrJbLk4xTNiJ6Kcv/9wXojs6hyc4p99eom9alatWytK+Nb3RDA+4cnMFM9SAL06ubG7ed4GCI4HkxieFScMSaJW2HK2LAyxZe7cA4vVLy/oKb94fWm+j7m8HLByWDN2RJy2o8JMERVlCJG4ykN/ixQXxE/5qBpV/F1sP7XX36eg2U/qauYSC6QlTzf35mcBEmOuXSb6AthkVItyeuP/53y337/LaQTO7/jWqZTIKuMdvFloeaZAPMQifVMBBFJEHRX2Bl8miNdb0T/HKajUKs0nVJmqSgu/vinl+GF9C+OZMEZee7NYrFo5MUtUg4BuJnYhw9RuuG0ZO+UlqKWznmfbPbdPD+Zkmj03oc4BV7nd2tRqY8Ebqb556+fE0by6ubi9aDG5C16oTQwvgzzP+b5zcnrW1Vvb0luY7BDPLbICFB/F3mWIie/78TNK6a0coicucH575/Ap1KJRJnCfNqTumyOHDvrt5RzyviBSSmkUAGp5IosDGNWjRJSPz3tU7VIUi29TbAwRF5kM77gTM00jFU/5RBJ8nWjmLgC/O79ux/qGDyia5DFplTUaBXAn92YWozepwsS+AWhLQ0mUhYkV2ogeX5SmictbGyBUM15BhJCGqauKVTTJutCD4UbK6RIOH/6/F2+1YlknhrieTRhW9aKrN8pJjboXjbGiyDi5KPg/DXjQr0j1PnpBAvpUF40TXFZvN+LT95//O2LXrUZhV8cFlUA0lsbh44WtVtS1AQGEk3o/cCN0Tmj4Dnl15mworLOdJ+Gc780VHHUQrMCWefpPAw6U7auYTyRCDYYMpP5eF7auWDlQkHGjCXZ8BRiTjbGRFj78mOhv8RUFass+ZlQzQfMSTgzqQ3zWZIDZBz7gc542VxHxiBBWByUs1I1ogAyQO4aB9YZ1OX8sHQvXyRf0QI3TcMV837ykTDOjXUnZ2XKKgouVSU4pJFAJgSXkNlh1DVHJc/JM4wkIvHzElxJEmZkjNDNKvdHPXiSSqAFqhVoDoRGevJkC8vMUFw0q0OUy3xfVYV7MSxGzijBHGIAFty0J0uYt5OQ1yqXD8ux/2p/fPf28c/VDZbBP9PYu+WZCmKW4K31OaLJgopMiGDsvdb49ide8X6mNFeMCr3xYbzaL8+lyEW1gzxPNq+u2HX9x54+maNLUNR1DAunhJ/dlGaFpC5uBFB2+vJt7F8afbVp39mqTUT7kEcHiwmwT/iBBe5zSOOn0Y2/OMePd+9y/HZV3DLFZh/ZfBwGYbnDqoViT1mLjAEnOTcAe0hpMaafYkXtFJ9s8FUMjLOck0uhXCwBvSNFcB4gVYjxDXv6lTFqEIvwPdl8vLq5rDbb4HwhazRP85NNgjLNgTxRSgK6twQb/b5z45CM4LO735dKbOkO8gWQDtKLff6cv31Jy3P2HWnS6royj765vgVKzs65s9FSeUBZrIMQ/KJQhrGc5tkZSFQJKOXe995rzmAq9ACgY6SC/5b2N+UVW5d+tMdxX8WNsu7BPCPJjRS1LHkMDEWwbsnZx7Sa7xkyTRUt5YoKwihdnB0nv3+klEDm1uQ8e4qMU4Y0kgkJpxyZ4krTI0s8PrmJ0aoOzljvQo6JWXMklHDLhVdUC4E5kwTdHAB9k6VEZSUnRGw2ZdY/v9w58z15lgE0uc4kX2Be2gaJNEKExyMyJ7Ss3IWLtswhz/cwMSFIsob4HBUbJh/dsrvYNZoe+S4c/1H/9MNKFF/+7d+1KCTdRhUgZH1z+5ZTAWXaBYKkrEof7P5wLzTMfnZRZhFjYpGZ5jZcqEvBXmulq3JVlR9+fB9IBsjRvXrDu2Oyx5z2SWggAyH0npE/zfrpOJzhBUhWUlJdU4Y1QCXzV3eeiN92dpBnlUkkouVYvf6piP7b18WG33CQMJ6ZriIhZ4ArSoWQhS6k1BRZUVS6KqLOw7jsn3LGZRmQrnQ/DAHiJu90XRaiHJcBSsayaoEyGMTl9gyQ3IPk16x9YbgzMYIPWLtKLDEFl7qYCTeQabZ9bxG2WpNMo0zgieCJKJpjzgzzZIQpFpH7uUbZI6SmWREK5zMAYF7mSAwCnIUUjVwX79eUpRDt5D6bfohjP0FBKKM5Zuvdgi2OZ885kx8qLtiRDKiKHM00n4fD4YaXhCDkfAzhNUi+ZG8rvhVuxLc3zUltLJnnSK2zzru37c6ZueMumGns0IXllOl2BWDJRSuMS8wIIWSKHkWuhadJEZl6d2aMcBTPS8SCbIuVI9qTIJBopipSp3TOcA7SAxDED/hjFL/8I/BY3ZTtLICXlNDH/X8o00fPRap8nbzz+gp1SNMWSE9zy63jIXpFMUZm7ZBQuKH/fO6DcY0U69U11mmyCyXS5VwpfVgGhYIZW13c2MWML/fF1e3zt3vBm1q13sZp6FfVeogBeZJ8BqIcyZLLFognDkSOaU6QXa3KuqIomJdUzDPF4JIWLi6BCEoJK4F4IX5o3n7yX+Z+cvNonOtyRyJGMLJkXLIXtxd4tQpub5K3wb483wuafd+2lue4TEvwMT54ux8DwVIigvihuJE/rLNQAbjjoYJyMV8VVVdvN/bUeWuJyJDQHtPQ5NbP++wqWpS6ntg866yCF2OcwSi1jeGYrDzG/DyOr5ksV+85WsapTIqV1efPc869wBZInp7m6fwVwGcIMf6voqhW7xoLvpykJYv3PhS89d6nKvtuZrogxJoXIVBoTShT3JO0UE43pRSM1xTnlASRPCWczvx6C9RDIpkIGrMQnMDGercsTymcT68vq+Rz8l23cCxz5ZFLn50NKvqjQBohQvi+4WsvWGa4pnaIKcU7ZApoBUAYF6KscjjIN5fran74fpwXW5aNEHIlEa93yZdmegLGFeBsXXSLw5jKvGq3BxkEEm+IbAQgEB+Y7YdCrZlKadnPj0KIXPNAzGF6ejz5UpVvyvbyDz9RQiPEC0GXeU7ep72L4NHdM8KLH39SZbE8LHfnL1rfLMupLYoQXIiZkYCokESSnaOZJqgMi37oT26LcK0a2UiTN923vyouP+AHRt+9Q44UEaRIYxeCAwTkTDSXyRpSctenl/mgDvZCXP7hw9XjM+36ow/u6fHztq54yyXNSCUi+813gQlI8hBo5UgF4vtiIcd3QEOu4ugKymLYdnQ+5BferN6smuGnP/9YloTAvr7+r//8p88mM8ogeIa0WywjTw1uQrIv5/Pj855y6nLq1ZBBB9+VKBMGVHIi5+/zKO/Py6Le/u/pUvzoF3t+fOgOz2boVFzWF7fyQzMXF5df/814s6S4yEQju0nX4laEybpoHWIbOVlCSimISLMvZDUuofdzQ9NkgaTwkX20zWTcupTqanXd9w9CUAAAIMgEVSpTSlomL1+n9X2JFz55u7xQLuu1YgOJPoHDaRz7NFa0VRcFuyxEJ7hmpTkcT3lPH5RBzANkDxEZcRRiiiOBNtuzfH2JlMXPrtEk5swFF1KiY4G3vkhMKaFrHfK4GMoYpIPgKiessR5iRynb6asXP4eYEmea4Or68kaXk10OMFZcXTdFQ7GlhBMKyxKnDBnIC3jvZQE+s3N4PgMRjap3axgwziMJiV2urvZdH+OQvCa7sIoC2Zf7n7vpJiuCgsDVhQoU6cvjRACmd3V7dfXam68pVPNivpzv8jls5TYTU1ViSoZFGojlgbHxQCGBczmFlFfGmNPdXXW9rXVNqe1Ga8fZ5s9KrpWGFEutow9frZVS507141fSL/1ObprXclV9yP10SiamrIbAOHMcYwiDP0dIOL4hORBIzvvgyhXnNS++T88bRnd1JZVIbIrzIlTmLRNu5ZalkGq32v589jQH7Was250SxQ+184XQK3ig9+lgDwT4gQiGlQ5q8T6QlCfIMwttYFxz0mIthIA2u5yy6yNQQTHv1Kp2xGftbXTnYc8kvevu3Dz5YG/e7jDLu+U5tpFQKtg6prh/fAzq4ZbeJp3GYRi783ZdKqTzgymSX4gLPt3N4IUKIb6Pl6hYxjK8pKPyveOSDfxy042eELCWz4xZH1cM+0HqEn7a/jek5CO2cqV86sf7ZSXrSZmyLpHuU3SfP/mPb2/fFJtPy5dTBPG87PdDpvb/+J/VbVkfr8n0SNFiTpk20LYf3v9Bcoi/7Ze/vF4Jrg6fv1XNJidgiNIGUiJnMsbQclpJtVRbDPNDnxoS2zZ03VDyWjChUggxqehISDnCrJAvuShJjIRgLEkWr+Cui/7FIa2rlZBMHP1nLnFNWmscpVXCJCjLlNENoxnzyR+8L9myoiWWFb+8lQBwPOeuJ1aBSkmXHFN/OGBnxa4oMlSUIpLZmudnm8K5Kurgg+b1PPUzFxVtT/6YGSnWtRlGWeia0tFODpigwIWKwUXKCS0DhzyfxFa+3775/ZdvFZf/9Ke/KK4RKV+Cs2lGy4EoWsBGJpYJ1Pj0qOY9rd9/Ofx66M5S27bMJeyR8ijqSFQEtsxLDoFTkpO387kmVlNKEykIMU/T/zo/MqZ2QCtRswCBJkzOOI/I3dkHl5PHrPpnyjhbks80ExjtcfSHK3G7uyyalTPdy93B7rtpWfaB8Xfv/lmAJLsLyPnBjXzPWZ1PI0t4hlwuscKEVMDiZ1fOEbJ3aXgkZ4zwlPAHHpxleYHoyZ6W2i0L5OWbUNzZROoFMdr5xUfDdUEINL62GPtUcoqRhbSP+zx9vHhnbiv+hr1hr18yLCkZJXC3o81m/jqkl+E8PreLr/W6lpV4xW8fQdY8VjH4IEZ/luktxXi9Luwx2NNgDyVfHftzHGZJL3ct/W0/RcwHDAN9GoU/u3HRRAUlKpoNciVRc0DK392QepcyeIdJLd3BTNPycBzNTP/wpiYn0i0WheB51R2WF0k2UXTGvtmqy+0WvV23nNML+3xsY7B6G5Yx7LuwLHk69Ms0PTyCUD0hfyBVEpDTGgK3CRnhKMlsPbCFFG12DhEl57OxEG2/rknQgivPw2KKYk1HhAERnR3ykOL3/OjGQBl9vdJ46tJVG7li/Fb7QIeX00Vdtrs3jJZdfjDOZ7h5RYsSmGcmxSWDKRj5NphVpdUaU8rBkBD86filqpviT3+I6VuynpCE7Gke+l+Xx7jZTF/hzdsVyeL86fNiDC8Cz2mKllKBAKGLgoWeUhJTZBgSF8HIEMPxMEjvLDv0kxZMiCYL2vX2y2FRM5UIqkyBeaGUpMV9TmZ6PPcP2+Hd9UUBlCSbU50dd9DU+WE+//lUpso5uwNufbCCMFU5w5pM0KUbIVZKMkptP/kQWEUPfZhfFkKfI1CgrCJ+23op8yRoOnVn0jln/anPOzPbQBLN4ZCsIbvNohbEwKROCfqYqyrGmmqAQHL2GTDmBJZwXa4mWyB7Gg425kwoDPJk7NLEutBFuZIxuRSRc+GtIRllVR/HbynVjb6aX76IW/62fUUzfbO7JSRLeheUNcsUjP/b3ydCWV23yGq1rlFgqAnTRY18rdrjvtxEOajdFhllseKX8fDp01fYblpVMCZlzDkmsnezMZNSZUoxnq3e8aesd/G2XH8KpWKvaf30mkucFgZIKd1//VIdTiUNqLVgQPxLwIrCKi3L7BBy+O79APAOsmcsa0+ehh4wN6u1DPnsJp5gyJBMp8trOMNBnrdaV1W10Ypp5qclHDvBRZKEvXnTYjF1++FlX1+/zcFPI7EuTI/fQ+gopZCKuHcisvVVSwt++jq8vi2cK2Mww/ngT/6CsdWuYbwmKP1yiNGVtmObLVQFtCsfA3s5F1AKuuXITZjPy3JWdPfu1WLCYZwQl4yZFbd+fhl5nfhAGYES0zm320u5Loe96RERGUGefSCYYgpRb6YcENIaadeCG+wTf0xpQXmRMfvkH799k7wCAsFlqVhhUcGg8hpV7WGfBsiryKwnKYfMAJ3LMz+xqGePbQ4wW8eFCD6kHBtIua7+/yT759ZlV3YgVq61/bHXfjYsIgAkmCRLLElD6n6Mfuo2ozWkKhaTmTCBcJ+/9tjtV//o+R6TFdl9GXPo5zlthbk0r/HNwuhCTL//3RvRItItZV1PfhznaLTJFfR70lUSnBst2mbKSSf4HWATIcpm8VGfnnYBlaUev979ebVF4vy6VCHUX2PoPJAfGGaJq1zFpBKdB1Y0MYbEFeNKtJ5xbaMO3ivGftp+8IeZ5iLmscDc4lr7fSSf+/nBjjHN64uyL7u8V5hQELvQMrrUiDkpKQQpsW7F9iqtQxhrIadztjFIG1Bg+V7htBSWleWsm60txuBcTPG5ewz7wpjEIpKkjJhZ16zD05/4vHt69f79ojWACMROPiJ7YCZiNjyqqtgUFE5LjDSqV8xlnleSZW1lXUNiKoaoj98Df8O8WKptq9dGWttTnM6h1EUrNEkYRxui9Njb+DJObr34ebk6UIqMySGzafTVy6egnPAEKYpMC5Qe8dGFSJALbMx1cmoAQF76kKVQKCUr6pCSqXRhKq51/3JQvvQvp5gKfoFTCvEURmZSTrZEVUvDp9Ql1CiY1KzvI9fC4nHh/I4oIfJdOnEnFkC/uk9cKxDLwR6/2Alzp3iGWeilgGr1OraJMR/d5NhqZf6Xj/+3f//+f3oZX827Q4uFNj7E1/TqV/mfOiQ582HsnB2UKD++e/cff//1YDvNOXOz4pWSfO/OPgJjuaU8aen63WkELuSZ6ALkPPvCTlNgwXN1wULMNabYEDk8H1C+pcfDsDQXuUns+Cal9Nj/PtDPqxUzDflEzhMzdeqHeXTGrCp1jZMTzpskp2j5w+5x3JPzKcZUFIVWDBCQiaviTVTHHtbvXiOxp4c7j5OwY3LM4xwLO8KFWZTQfvV0LnpeFcu78TyldF29T+OJUAgGivGMqWxGP/EZOQGvFm8EqaPNMocEfOwc4MjLqipMqprBf3pbv5/jOIfZCJlp7PxJOm3nOT3S1Yerl6jzfFwWy6C2AZQ/H6sYVSVdCEMPfoI0e6Hf/GhOX4neq/cvhX/hOcfyUhayLPqBin1pSnvQjJOTqRl3f5jXP6jFa3+621xeS2O4lC+HB0CtuMZQEhJBSgEHug37+diEqebCRTchQck4ErKVyD3Us+222PdSC8Gxz0I8l2Bc7J/pHKaIin7rdq/l0Ny+IVYosqvl0kWPPcVaxHJ9IZSRajRlLgtYpvKoORda6MDJWyekUD+84apKIcQAQtN07EJODTCc8Hzs375tLhY//vblEZn48MPSB/vl61eV+dTNRLnrufOd0erUpY9apxRcb088L5jkqxUD/jxFQadFsdRqydAOZdvUmh4L5Ovq42XdYHf3hVK2dq61Vqv1nnKWwf1xyKorXr0vg5/GmSArs1Hoy5gmcph5ZvUFTxNCEAqRiaFLBmMIJdaTyKVq21+uWucDuZgiN5J07cdTdbWO+7OPLBamztbHKrE3gLPIbPn2w9f/9n/9/R+/3a5Vtb7Cq4ojlxnI0kVVZ8rokxYZcgjji/vT9bNvy78ytEueKUNhaqGkAAFVDOxHAXAZw3gyg76sKlbmtJI3XRySXxbSx7OtFM+Cc0gp5azkGQ0uVRqnNerw+DTb+WEaZfuheH5Kdv4MVgCuJHvFSz6ryBMoT8HPkXHLOGV2simfT8KHf/wtX7BwcbtBz9+vWLYlX0IMfjpowWyeJ5n8UpADrjpFnCebASioUWYzjWVdDhRxf/77vr/f7Vy9vO0TD56U1nVBk7O7gwvPNgX3bFhOZnhxx9NXHsdCG6VdgLS6Xq8WCkUPcagWVV3b0zjFcGqai8lCP9znLJiofrxeuHmoHzbjGg/wAJaeJ79hxmjDtcBKbhbGWR2cHafzvNA3ZtPptBasDP53SMPEE5LVYBkQSmUKtpDFYjF8PY2KGOda63mcpvHk7cgBFUNWNFkj7F+ISEtT68UWjYsnTLcRpcuSBW5M7X0EEpwDQ5nQc8YnP819T6pErWCMUnHvdByZkFxWEnXiKUpprnWxX9dlSPF8EhlTDJwTsB7yAAIGgZwYKHyeT2yxWqkF5xUlCG5K2QO4qXZ8qnwCJvlLPyZXd/kodqI05b+8uf3qT1/3L///y8UE/9P+gRy39BpBQzrY/QCFN8W0aBfepTnEo59qeLpJ1YJWDJD7bx4pqcJhRT6Umc+so+cgSldc/aWd7h6HGB4S6okDVhzVEnxN3oEuTNCKfTrUK4Wb9fgnA4Ipu+xL5yDll3R5mS+2yXGT+YjPL+OQ7Brm73QagGETQru6YaNOrKcUM4DzkztLk+zulMYqNMJ4fySbh4Nfzue6YbGqYniYpk1IgAGUsLIQ+lwe/Qt055PZm6LkQqYYhLCja5a1nxxCrmPOx+CcGyu5HDJKcUysAn1RLJgK4eiDZIKJ8uv0qeIF52IKaZPp7fKHg/hTc/0Tu/n07c8HRhD7DGvyaQg+HXdyPFk5512MMaGWE9pMFK7hwq7KevnkUGiGgj09f99sLgsVlcmJWopj8ogMJ2uLotbVynpP48zLldXmQojjo6nFkMhbi/tv98nRcl2i3spMHSNfeYK2P3/bBVy0TRNbXsybCAc5m9o4x3KaYpobFNvFqxhtjM9SCsaGw6Fb68tGNJ299wzUNIrgDrq+Xq4pQwiRnYmQ8UYsAzHOEZGhTMHHEMf5cNjHtx/eyMWC5wyQEw+LGsPoIBHiwCnniP3hmGNmaZ91DQwzI5fccOokl4AAwHLKewompzzPuRtpvcmJjtleA3Gmi3YZ4wzn0x92t/3bMVTswLxdyJfPT/lGGbjxfjdKl11WlQEYiBGKoNYLPz0iFdLUykevH3m6csgC5yy6ag5aNREpY6gwbQVyIKBwenm+eFefXp4jKjBWipSGIzAGFRWRjiiaNNFa+2ji7MEB45KRvD+Nov/+g/7JFicOiuX54uK1nUM/zsk/pZg/Oye4utUNaTEcDw9O1I3419PbtCZRG+GuGgIV0/HX4T4j8aifz6eYOlO/qgpZzxiJIoPjrmMLsTGccU2Yj2fLeUqJCHRRRE78svJ2oMPxS4yh1R/hkmmFEDkBkmBhUzhvM0BjCDQHm3uZX79u3dgNn++81qatcxcSe6A++zFb98xvDDZlmTqvMGYXgmVgcgYEJm3JBYQ0Uk4hRiP9+02Tr6jWPKCkUyAATGkaZzfNnEEi6KaZ48hZeFW+0pukC+WtQ5BFjZwk2FkUAAxvX8nD38cQdiMuEwTBOUipeJFynI9xwCmlRJkgn5BzU1WiWfGiBK6ZeKfr3PUjyGZ7+W6ugD33R/fwGKv5Zb9YXKp6lbgPwBC6UZUCYaAIrxnX0vqXr/NYx8gjFG0SL5EZYsyEz79nHxlwQKI86FqV1RplkvNVwU2nfcycOADnHtLEWYK5kbrh4iyGdXHRDQF1gdOiKCFq8oOnGShDEsAov8QhnZ/GCJDIiNecjcQ4UwWWhnIHcZZZEMdC1KA2A0TBcCkKpVUOkgKv48uYS2Ix5JCJkpxYYOdhkoPiPy9/YNOXjs0wO2uVko1UfQxf/Cf2iUFKx/35HOaibFm7ZkHGw8x5PtnzY3f3bvmu3lZ8vMzn7+ikT2QxNrEqRHkI43YQUFLONdKcCQdm/ZRY5Fe+9n5XqvelVr3zwUWCyKYSWiVnFiYpCs9xBmkSA+gfsRaTfMOB9Emnak+6qqooWGGWS5FZQYZYLmQLhDl9Jmr6p/tzTOxZUkmxo/UbZGyj/MwA1eTJM4IXXkSeBMM2nscDgNJGSslzm7P/wN884XPK2JqKaKlKnJI7xpiJz7nCaaLsg5a3V5fJuu/3fzBisY/HV/u1WG/kJWfENPcv027j3jHWmlc5pk9/fE4hKvV0sbwuVc4j9cE3lEe/SdrllBM8AOc1q1q51PvqT3ipGTN6O0zi4rpYLH98c2MOjyQz6ymMitH2Yp4HG6hoysFaa13ctFugBngi3K5kcho8tYtlZSp/nZglAlBVw8Q8W15zV6U6+v3pvLfFVPL35Q1dBbW3ljKhxBTC+ZDt9aNJbiWlMXrCPNl8eQk+prxP1UJo04bDfh7Gr2VNEcANyBeNtuWUVdkwBlKmlOhkHXiwDeemHHM8l8UqU/Y+I0nF5uP85fhJSCkZupdj8Klp5czqAlRYplKaXfeMmBljJAJLwF5d/xgarHmMGRicQhJzZDqOMVaiEFwDL67/1//tvzz9tjtZ9vYy338+fN8zBdIAlyK4o58b9epSlEXgeYqjEEJnFrvZthVjRglOkUKMDkDGxFAJhUZqyt6CE6QLFmLeZ8jnCBf5wDEHiCZNTnNurWSqSA13nPPRQqZHHkqkKfBK/+sv/xbu7/6MT96G+/yivGIsIKpht5vnYbazkMgYl6r+uWqgfRdUvh901f7TD+8LbKWQUuz9WGY45/Hbr5O1tvilGqfgHwfvCPOATDWL1ds3l01ZFbwcrL3H42KoOaqcmdRRaIRcVNXZ5JQBz/cxRLdsb5sKDjGAKco8jgQ8ucm4UvOQYffyyC9uW81e65a8ExxXyyVNB9CJLRrt3iKhfm3Vp97uetoQGsPTec4T41CZek4q5wSAkc5Ny4SQhkoh3ki1vuSvnJ9eF8svQHbffevi+HJAKMWaQxm5FRQbl0YWQSjo7QlzMmVBaqU5iqpgLMVki6owej4P6dVqSBJFJQt9Jee5exl97UIuRxzqqsritqvvT8ZstBKmRVYwwfre1ov109M+TvewJ7xax7hWXM155I3SWXdlBCJ8Vipbr/TJOqmvZtr/5/M9UtgqHUkArNIKkntxf3z6bH0LtGGC8RaYiBklXSlVPPEsOA+h5QXWTKV4UqvCaPWYpoi5UJtGGcOKWHLwSWzS6eT90GU6xcePXAmUkLZ73zrRCgYsneN+/4JpoEwyp0vKztp9DqB4IrTymPsBDkKvy6f1G6QO4jPk4IdcxPvnu0DEY8w2FDK2ZsEtm0/70DTweqFdLHMM5xgL2Rpd8QgPF9+mbzEaDpnPBpTLhQjDJS7wSgzNr39//rT7VI9XP1xukruAAqy1p7kWSyCIEHRKVQhp6IW6bAPOG7YZ691atNOojjNXmBzY8TiKorIMku5bwaxlC55Eh31RlwVr7fztJchTgpsj23c+5jwEXk+twTkEtX/OOc75dXDcql5LmTPXu8zM7QWmNV/b9XSebIzRJ3LBsYglABOshZaGPEM56Xzsh0yZI89E/ZdndaXPq3MQUx5QbFoX01NIt3ohkeeclwjWqcmjpkTWKc4p07uLN+KKWd8hsgEY3T3X242UOj+65zf9eNyxUUCEbbm9pivAeTie4jzS7r4j/2k820MuiV+btrxohbsoPtZCmk9/u5ut/2lVPcAjzq3eADcCF9ZKroPuXFdVuptGFLy+acfJCabE6XzS620piXKmwJWghlhHcxjP3w+LRTo8jfUHqkUrMCrG5YIvxPrwzI/zw58cPyzesy2VuZSmbFrzLU7mihYCM2NSyZQSC6lq1uMYUTpgkCGOUoaaC1fXY5jXCxDFx5XiokYcCXqi7j6LALiQl1mJzHgtaOd95YYgl9kkxpjkght4py45K+U7k1la+C896DzRgY4+SgU+arRfu5jCtwJvEw13f/JZ6Oa1vqnXq6sisVmageSJyQFQQEpPWUfxwPh98vjb7zZOuBZvXv8oOGOKm/SuzUwZKZR8jseQbeSMySGXFzFMPuYlSYZaMioyokDpYQLFZ6uRbngBVUYc+QOKbXu9GCAXpnGYGFeQk6+qMs2eyRm5ZNF0NnA66fnma/pyHW8WH5d5XZb2VYiRMyGkfr7zPk7Bv5ASHz78lHJQSgmhTFHFRMGn8ue//FTXSumU6YFQfPPsR3H9+PnPjLL4p6IofqKM+D7SdMi5oaN9ufvSvXxbNc3bHz6UEeG4YIKlHAjzDnjJR8272KMUbnjoHucnAL40yuWQlV4SuaYpYwI0uSS/33OWilrOx6+npulPI4tuOzlRATqeOsCYD0pfsqzKN3D78/jwP/Dcu8mPBUdBIKizzwkKYEZwlnNxPEWA2c29kH9P9rk/xYpMKAgm7fsgHFNyySC5/bRoQbeGg/t+749uz9NqbOIruimKelyyKkJKDPJecS2ZkMgpRDvrtVFG10BwcD4iqar0px2fZyh5KEQz1+5BnbeiamUutVKslVfI+ZsP7Z2Pp3/8zR9PTLeyXbJymUXlGUBE4lncGlVr8oFiBB8Rrhl2r8TEeUl4dFHn0FGKp5QiEDIWGUiehdymks9rB5Upz1IusNdY5qxM3s+iLGXKCZlIROcvT956uv6Ws7FDvVapGFPiyOAVx0xbv23KbC5s2lGWt7AM2s1rX+rV09PDuhGIRRruLxG5bjL0iDdcG/tDSTHaebJKS3wV4atq5exatUgYlPOjc0Y2OXpQSt8/+U1S2gup2YnN/gxTdywXVSv0u8XHv33/71+fvmmtCl0P81HpwtUiRGq1+vGX7ZdP8xj2f78vri+YVMyTrGvMQJTEMA98IchTYGOd1m4KVMhziKm1RSHWYbNZb87n00mBbjETpczmM5vc/OoHgwyLO/8w4/f+15iSXi9FZlIoyUGv60VE8CfddVkUkJG1jF4gBJEjIF7lWY7RP+DdizyLDhO45WZjalAoKQkf2xyHLtoXGxjzYUxIrNSVrquoi7poP24/SKUWuH4Qzz3lBGDP8VOOa8GfHl8Ahqb964f37de9RWQAJGYuFAqEuz/uAXJIluXkc0DJVV0UGRaLjVkV7HRG59z4d7frg9OTOBPPaep7OyHT66qp2/9JmwUXAfrWJ5Z5tSg3tCnpxLzMcT++EEvJloViLK4M46I/ei+yWch2UJGzYvY++72NF+tmlShTioI7O6umcaW+Frxo6vsMJPJelEvI2Q4nR3l1UeeTy2F8ePnjffOBEjGmjk/H4354dfkXwWQWA2fpZT76PhV1OvthWy0Wlws3hKG/47IVzFOApdaLFhkkin1mO3dK58xC5jHD88XUoHYJdaYFExGTp46xKkyEPrFRSiEwY/6y84WfmUqSjaxPmTmdTqhi7ylnytk6/NX5BXIrgM+P+In98eXb9V/+S55mxmTkkEWCRuKWyVC8uflXl8uLV6c+DQLAQjyS5d9nADpVWdYmIzm0GReUbEgOyxcWbsuUVgxBioNAcx5T1ERzy6VCujBVUiJkPzui6Q7iZjje5XwQZc0BQ9dJrth8VmyBIAJMIcIppqKoQA9wyA/xy8sXJl/DZXVTkv30db9aXYyF/+HiwgcTQmCMMHJZ6Bjjt8fv/N3HC+bJHT5l/Zro7tss8k7I7Tvi5bBcyONYL/+a5j8LXqjqmoO8ioRXZVo0Ly+fKfn7L59W1TYrVuv2xZ1A5cTjLJxjWM1Eq17QTnIw6rWu8LwTNYRjITlDjZiRkWCRM4VgNGNCS9RJDkIbeX3BfDIbpoJyzl2sr1NFo3e5zkbqyGiIVNsQNj5D2B+QsxiVA69ST5ILvJJXBpVgCuPrIm0L0NOYgyRvL6Gclrke8T7loU9FTX98vVeSfnz1I1Rpieuiqvqph53fC1ghgeKKySCLj9x8O+W03P14s0C2CiGNSAPn2s9RTTLl+qZdjLQrMBH7UG0aesNTMSdTN/Xk3W/HX4Ev4IIVvLZxzpGASaaEnX0IEWtjipENjpuTKN4Mp1PLpSzK0ANNHfdQlJ33Y1SmKkxAagCIsVklMLPEja4NL2QqBDPoFdvqslHqWDLOWMN0ix85QHqVHk/flnojhTj4M7h6Z38HpHer/yIX4uFxHvrv1OSEWRfmLFkOkGI2GsvKAGKAAKKUEjmTwwjaHDDX59OuMKtCVxQOQVnrQggOx5ydj3ZiqBlwAZwCJMhAcvfCqlXUxbI0lSpyJ+Bs+3Ee1kL9/Jdffvvtt9lOo+0M4F6mZj8Q8U7XpTJi83p6+Pvs/xy+1G9frxLyfpC1DIPtq7owajVSR0xrLpwojFSsZ+4YnDq52UUW3ehVVizRUvDemcLEOkXDxfkf59+mP3RRMsCb9V/qNQqSRz9wYGVKCV8gYkbw0Z0DlPefRj8vzY8B7rm4tpbmVVjYD7nBxDof5k/Hfc2MLPj5OFEcJLLRJaE3YnmB2S+laZqKalxwNi8XExdVTF/dbv76fL6m26oEkdJjrrYo2Apgfldwhix3j3dnKtevpRT3D+dXlxWv2mxEodhK8oh59/JbHEJItBQuuBWXR3FSLINEPJ3/MVvLrwp2sXivnXB1+1/+rWo24jnZerW4uDjcdQ4Kc3GVNbiUioXeznkrcPZow17okTPhITKGuGIPYZeCuF43ZlUnnrb1JRdK5xB8hK7MMD+9hKYaI2MP95NBHN1UlBNmVvIQqQowr7g6xzQO/fxrX1Zy9U8VDKIzL+l0/mPe2cbJoDBKWQtaCCT2p398w9/vxIEdhVpl7IdmudZacIgxpGj3nNscMrCSI/oY0sPd6ablUYfMfdVCsElJYsBqPn4fSZ5JhEVyLIWsDUcKQM6T0KZARUBl5Olt/cpcpxQQCRBy4ghCKh0SHbyTXDSLNdfMQYoj0Uxfu/3kDs5/PmcBev6t/wSZ1WVtZFXpNkGmibnGJ1KZnTPVHCxM1o+/ocXw+hUD0Z7IOycwXKMW11vMyU8uPv+eXfZu2meUj8/DLJttJZkOITKQbMQsWm8ABLpDOfCk1zDN43k8nGvegnPk0hN+0Sd5owni1dXyxX/92/4LC1YwJi+3F1kSk1zw2NSQxxMAckd87nLwws6RC90oAGT7Ed/cCuTyrLBMWGTM532s25wB4U3z/svpT2TQzcPl8ibztFC1lFlqcnaeBjKFREeRlQuWr0T5suNSTJpzNeUsc5QiUVwz2AmYp4EnHoJjqV/VelaazW4GjEQuDZbRuyZO4/Tbr79KoCsk9vZ2Kb6HMSZXK1OXF4dWbTNUsU9Q80rIHIdMLiWBagnLFa2uysvb4n462ijH3L98egB/U15c2ejP0sfdD5u/mFLYnANkERLCUjRqq0CICQ9HyLGqlulf0svvjo9Sm6sMtKOZV4ty3olCWiObWpXAIzct90qUYnFr1AZEeDmdDufjdrv2jx4un4rSmKY8ffsuZSwKzRgyDnnwKEgsr+Sa437ENgrwDpVOFtIUqBZ1153V6dT352cffcqpRcYZRkTFJTe9wAIZjwyTFuLlfnz38VKbhmOP0zG+5HyWKArexgV+sZ/hpSCLv7xfTQd9cPOx+/tN/fNP6xQXV4D3T2qVI50z1kwksMM8vQy7rUYEN40TZyCYejnDdjlCzHEQbtXXm5ucDcvfU9RXjqfSz7cRp+m8Y8NMCFEISTl8eFvFSe58HPvdOEPEerXGcYZIOE9z31RXr26+P3yf3UBSlccpUQktY8MxUlEbI/7yr8U433965ET9sy81XkZ1jk/zQMUr/3IvMzydRKSQUgGswM1WTlPBQBRUyyI5m/IcvpyIeK+YSPa+HqDz35yzW3mlzUVRmN0QNou0XK5yTIxLoiKrO/1G4gOvCq2XH6hDfWHsLhBt4jWx2emmgBwQt17fHt3nxhmK9JYnkCzmK29Yc2uCi1NUriyc99P3F2s2buhPLvZKQvAgdBO9czVa4MyyRn9sjX34kV8wZLBaLU1TNWX1uVgyyZDJVV1oSl+67lBIk+M0DnLN6lK3ns9+cnMBxejudi92jimvqqpcLpxCcXu7WL4v2y1/9s8GCt2qi0r6ZMbS1G3RMv6ikRtZY6rOfCRulTIxBcZCvKloKoTfSZZFXRVEp4ceVulwBpjHvqmLidecxaY1kq0Z5uuLB6VYyFtA0Z+fZ29MhZRrMLkJgT2eH+l7hl15p+rlzcJoFGyx2bYRz63UwXGm3dNRXdxIvuVFtVE3Hc6LmTSASMgEB6KcIsQOucI5exGFZJKjYJLjsqEQUsj5DhrOn+3Du6o8T0xD9phDjjkyTIRjJB1RJS6ylA2lzvscAu7V3avjNRsykJvKGMDZdHn1JhHG88TXZcR4A+UR8ii9kEbZHJMdp37+czDvkHO2FGqTL1/Wh9oWAlIqyhgSETEUwGbsBKHJuvpQ6oq1OaFYcexJ504YNf3xh3fO24mIUDAXIQnOsVBVy4sGEPxkcyBWFoFjBvKd6yUyIAkJFJwYVDqWgr+ImcucMIj5ArajS6MUk+Zmri9YcWJieky1PL2USrE8+SQEcKFFih1hE6vezVGcPv0JP/8kEDjixk53K065br3rwLSKsYD9OSyrYqnf9e75OHdyMssPy5enl5uLbF1xOsa1Frqq+IlFwTAwC2y5jcOkhEIho5ubsppztnmyF5WIKXnEHEmu2hGQch6QK8vF2gw0RgWzn//9tJvOJ85ZJ+QPXz/L17dM3Jvkic7zc5TreIgnz5aXHB/P9y/xTglyvycj6j/Kh/2n/n/9v/8/BC+K4dwD7bxdGrGS5sU/LtT1ZrUsGzPbCYUKXKxkMY7BjNNL59+0iL6I88QLqXZF4iSk0foqJvLuzL0PDBAZ12q9qBDm6aWPUl6LQhTFnK3bnfqUGs4Y15fvL3WprRiHUSoujdaswJzJVI2U1GZ3xXEWKnqA7oQAt5L7LlsvCknjoKw9I+dFKVcIuuR1rQFE+axESyNI7aRYKB6DFGdVqfj07enNtRAosJ6yBWIrtmipeInTm+KjH9wfwx+Pf8hz9Eqrk3Wb/c4sChhHrBfv+dIy8MxUgI4roJxCzlsTj0iQi8JzpDcdM5U2qw8XN9rnzGI85TOKhvHdHR28ncaXcauuIJGOMrhAwlBPM/HUmkVZWKi6EHre4zlChgAYkpS7A0H68dWb3WnnWfbn7ANNu0NOGZ6wbvYAQja6rtarpB/EE8zNzIR3B46cifzDW+bspfDjo/eGra7EFY+SzyIEb2Th/IwSvNdtaQjzRQx3CXOilfrB46fNzcZCtJ1HPbppycrkUypRwv2TdRnfguezmBN7HfzLF3defpm7ylj/wJ2fm3A1+GArdjFzPie9WMTHzyREKDc0zSOD0VbWpSWPTZySEQ1rykrOxeVCKB8DMCzqSgpDMfmDT/HB/iZHdCe8n/9or67eK+UlayY3GWVj5765Z3ualjfrdHyax+yauJZsgUxYl3PI4UihDF04jMN358uqXl7eVm/eH/bfycnr5vIYmUWv2GWzaYGJ+rpdj5fVbWM0K8sWEEuCdEpwzrrlDIqYB0COIGME1MqYwkni4vJ1wwtejLvD7Ol4v5eSM1JGUs5CGCPrw/Fo60rE2M0+a6HHYWpXx+xbhiBaVU/p+9QfXn4PG6lWFXRnU170ydUhezApY4hGZJ3h4f7AChArUcu20T3LpUCENIXQf03kY4q5xNB5BORCMMbz+JSgRkSGRIxySv7zl/bND9qAf0Bw5LYlMsanQ9fazFfKRQWpiuHhacrDqLTH73ioeFui4FoK5Xx82h+qdt1cX8To7Lffv5hGvXpXKzN27u3lm/njT2l+ceYzUb/mQuBtDukqh8USvDPe1cgWRzHRELCoLA11yP7LH/+Z2E8//oVzz7HGCYnObGD9OCAQCp5TTustfaMgBhRcSX3uE/HCyYJL5k2pQ9QUhwacE5DyMzmbnSoEKhrDPE9jknNC/y6svsan/xOsH9Kry58bDqMrwzgmw3kKwZMAjN2xKwOiR+ZHXuLdM9hJrNtf3pXiS1VBVQuk7GROacHZLMSCq0MphD0+zWLZan28+Pv57/fmobobI9H3u3zb9BpVtbgkEVNMQZY3osJL5TMsW4gMIVnB7pJFNnUdzcsmQLlRTFWlHzLEQFCWLERn8uPD1xgzSP53hvOffyaijz/8EOYoEDgW0ht+Nl/m0Uc4X1TDH585747anF6eUwhZ8e7UB40I5hCdDc5oTSUnDa+71cKspGz28cuYvpei0E5PauZCM2/7OeRCIJYcziOppnTZeaAshGka+U//8q9K69PUh4FcVZHGuh9Jn0ww0CDdpts7UdYLNEUcsVq/+dAP8HY9TlO/71nFU8p2fJSCEcZIYv7jt3p9xSgsbhqRyT59nfuxaMo3bYOnA+d8UUrOxv18HF1I0ZlLqACTjyExXRtbVkGKTNjjzPtyTM/r1YcP7y8faDTQTHDvwzGmGQkWuMiZTnfT8sarlSrOmrEeAN6xRTLIQNl+5KqcOnzp7p2tpIaTKVLMC6NuXv28gHQQDxzYOI5V7cTV0js1Pn+Nnte3ay43r3ApgHteTrr5HH+fgXXHHXp2xW6CIJZxom5ne80KVVwUa1Ogqn196o7jaOPhqV2udMGCUDG7ujQ5QrhaloDnXo0xu6QYm61133eAw24Mq5j6dTaD76yfuu439f+6v6oqLDlsaNFy2/e//jmWBBOgkkZF5504ns4l2/CyGdkj5riCSPFeFLcL1O4wuBI9niQrM02TaWvGY4QHYrpq1uWbODx7Onpnv039pYQYndCXs30Rkk/ZkadKw2SDO9urJfLMy+VHpvlvx3+4JK+3VzFwZCynrHwOGJ2fr00ZQpiDT6nIjTCjxzA1xt89zX/KLyDICLVkBPTE3OQ5JU4z74jxiIFrqAXKmyuROM69UaMX0c4DS2H+PHvWe5cksF/apSrquagGTkVxk8ogqgucTERm5ObsTKOMUrR89RdCDkxUl5cgFFtesbTLIeIYI4uhJ9TFRAkhVZcsVPIldHyUy4t6oW6uLhfLhZ/nWSnJIH37+t/v7p9WdaH18tw9Ldf6y5ejELzUFWPitPPrJpzPZ6N0o+T1PJ1OD9RdNqylw/HXl5e8XrKiFkXNZkGNCiEhquxChozFSjE4hHBhVXywv4//aCivL9Yxs4xssbDWBcjgffTnlFtoqoofkYEadUhJQfIZWVpidCglB8AXYZiTqaAs6TyEhWRw/haCh/oi9w/ohU9sqaui0KvVxvmOfCijd81CMiS1vcieoahkmPw8T45296SnHZzOLrx+9zuLqEWVCUL0MXZarY0r+2EPp8N0enazy0R2jv/jb/+xqWtBtDbmJBXkUESnpSrrahimtNt9Q7lZrH0QyAl5oBRVBsUKcmxk4FmarE1RiXBWIcHl0oXpwf0+2R4xScZjClM+JecYJt7mfnxKJfKwRlCFQ6oX/fFJaZEwhTiprOicvP6MLGutxM8/lSlHjkzqR4Rr5Y9MsyjeqvP+HBPLPEvZVMlOnhmmtdmqi8WP7zcCc8pEueh3whkeZYBz4Ry9XjDBpnDCOWkpGQ/BOuCiBI85hDOR+R6InSeTNBTlwk1usm6azilGxpneisgFvpM/4GulzGE3lVyV6Zx4Iw00Ph0vi3VS3UKayx8ls0VM0/jEr5Z6CP/yL/9qdPXm9ZuqLmpT5qeSCh22lZdkTYgJUBgu2FGPow+bEgRJoRUHxcABaBtSTUze6JhBvxX/VGkhC0JxgBNzhUZWWpy4hKzKWxH8TX//f8TBm6ukPMy7r/W7H9m7zePDY8lK27kqr72fOOPE9giJI8+44GAUDsfjXNSvFYyIuKGELjAjG8pAMPfpeAqbjTYVP3qXJVtDHVIAxLqmnnEhihLabDKzjQD3Mj0nnJ08+zxLIZ0HH/wh7L0L83l6mv+4fvWjFGKj9ZovRbkJ9kmK0SnFC84iqmJpmUNSyaUwfp+hyZPbpT5ZV3PpPRsf+E01OL0mT07w4WmIOOir5XVS0RND9a78+frNeN7tEBCfzjrXWYeCSxdtmAepRJwqpgvBcL1aMpFTClO3m7ukjcgSEaEszvNOW+RWFzmieYd4XicRFtGp9dI99Vs/FmWrxPkXVZ2UtM5aBiJK/2x1Uf7mHAhem/cRR8YUhzVnQ1vdlCAP2XvHDErG1yk+hMLj3kt9FPLty8izW5VbVqYkpMhE+tVCzzxESFzx8tKnPFC+Mi630UkXbDDlUjOiRVFrPnVDBBa/j/M45qdf1dsfXmP7XG85IpuGiVEo6pYnhdidu7Q/Ca1RiqLhzEdiCRhDL6VgCfwt6rq95q9XwhhOI+O1x7SFaS+oqs3lVgSbpzBL3rmohiHXTwG2+fjlPB1mAIiRMjPN6/+pXDVyzgLrWcwMSJqFHH0Eg1oyTQ9ndXuFcMWXAANCTm4YY51mLAT6nDIGrqBsyXkppBKXMlalb2d/Tpp85N8hXmRELpkiG4JEWKw+nE5PhQjAsKxba6le6kW5kYjPfv+qbJQquJwCgZCgEp+tG3/7j/Lf/mfGsxC8rBvk8hR8Zl7nws9e6oa3JgOb0a+RdbNHv5vSCZGVpqrWm+PpG2JDWa3EOEGWim1qSmhEHoUHzpNGDHl+yvSDwK400c2ay4ehk8CY92noCVvRcf4GGedSqg/b6tvAkgvi7A9Kukd7sXXaaGtjmIfARCUYYxCJkBIvDLNzPs+fQ7bTTJzeVUwAy4BykmINgJaLJTJ++vrVW6+kfFvdbt6Xbvb2+1OhbovLhU9RrGQVUzg/QBnGw8Q8/MNNkjAyD/xKSm6rGi1AGogpxl30NNNpYMQQUpl4H5mATMHlB2Ai8ygYCSPDnL/mzxECIqqdGeUpZMN5wgQiWoJZKJI68Ri0xQJPXw6zACWF+PDjT2L4vHucn4kzvYNz9U0S00Imf9aCp0gZAKQutD6+zJKJv/7yzwSUczJFk0I3f74Hmzw60/4EsmWXCVgiyA0UasGnqaec64WAbgw+FNeYKEaUnN6oVh1Pp2/9yBgAMsxUvX5ljPRi4qQkLU+VHh9kpmE2C2AFQsrIZXNJD89328TbN4IJYGWxvKpXCx/OQZZ/zJ/xM3t6PKJc/W///G9HJfrHF5akswllIKJIquGXgXcZaJxdopTFQoaQGSIyH+I50aIQ6IkwToJrqe8ywLmol8amQhHTHsXFNeMy+r09Ox9gUV+RfWGSs+TH79TNoyubnPG03wnFjNkAX2RErXX9kdzoLhdv7h5/k0vWtus1O3HG+uBDJlHw8XB6GSZCBMyzBSs4J0icI+ScWWY1YwqYogtdCtlFv+8eStU2psKMLtnX1bsVs5/hswas5OoZXxig/24bnDNizi50ozYChSIhXMSmXQKC4UVd8D7T/vkdkzZn0XKdcNB931mnlTlHvolM3b4WqwaEiBEkF/M8HIbd8eE5Jx/9dN1q62UXz3V/gFMOVVUJFBcm+JmJchQsOacCW3Dnq8oOez/7aC1vJJejnHUZuj6DezkEn/W+zklwvuY0gvBccB5ZTnvk3HDxvq6ovkp1dN+/6lrj5vb6+29y805oVOdywSTFzL3aXlUux1dz26jLU3o09hwCZ5BySt55HVLB+LKKEMnn5ICPWp6/7WP046sJHWAmk5LWai9F1Yg2unBN8Xkc0JoQpnMTmZ+S/R5/f/KudvZDvDQXC0XCDqSMz1EVMJVle9lefN2d4/mQoWJku/2uksVpLzdlUWYodLuAWMq1XkkY9mGyeqMJjim7xIgED338tBsa+4QCiPEyz3mMzvjP/+3UjdOWSP/4s3zmTgw1boC5Ew8NSWEEb5UpFl3fBxtkKVE3Eemei1VhaqBheCLGMuN2ShKIcRYjwxdhQFlS5asrwjTHcAinDTZK1C9JCPS///mMUEY/edsT5R9/fHvZtjZ0k+0BhJunS116xMm7N+WK08nm2TubhexVoZFxBi/Zy+9ftmXVXJHmbARimRgh5cQRpkNfrNZSKkIkKZrVVY207BfP+puUjR9oOE+rV54LkQY0ReB8Sy7xvN+dpVIqJQsIApQ+HcfigqfBUb4fMqU0I6aUKTMpOGj+/BgEYwmBXoZl1bSV8ghs515yP5wBcn1ZXQ95qnxHzAAMPTQ+5MLLddFe//jj+2//X3vY5Yx8xRkg57qoCm3KsWcEPGdkRft6vb6+WmCYo5s0luU/LQTXnAn/3bmZbBg1Y+V2lVb7OS7n3eH67c9aSIq5KioGOTdqyrUQkjMuU/Ck6pjybIHSqaizO9k/f88LDxW0plqif4pJCEEpCyXiyVOZeJLRiiScJM5AWmfLkkkXDvPnuZNAU3b6pjaLHz5qZUSa9je3JBQIWWSaJ00W81w4nDkkBjFxzMd+CLkHznT1KqV8Hp8DjFOfx8Qbs6QELbqMs3ocJsFP4IQEO3lTcGScJZ7rWhXncTL5/Ayj6OmuY4yVRsQGtiLlSC0sOcvBn5NlUymGGp0gTUQXlAUxrbhDw5KDHB5hTOZyyYDS1+fsTkQsB1KyHP4+AuRa27EaYqxVMSZtW1ac06wY3xbvMTPBSeXtjH2kSAz9NLBlW3LBM5IQRlkC5lKSki3UB2lKCJltb9iJy2kXY13LlF0IPox/+9tTjO+X23JRUze3b7fQrOY7iESXbxZOvW8Z7nJImSpjhj+eHWLvn6dh3H78r0oVKfpxmrVcymCVyclRsPm5txzZP/3S5pT3nS0KxTr2Evx6ZSitxWIr7LxS6ymECHDVthHKQleNNn16dl/s3Q+frM18z3fUv2mXf7155w9POX3nmbx1jDOoyhlymQIOUy5Xzn7dCeEek2CaGGfbzaYwMZrpfAyenGou5jBFMnUllMzd/L0/qHaxXl465xHEplnXUo5DF33BmMBom/WWZcLTS44+T5Pv8DvyVg+v1Md7shSB57zBav32p3ycZzXMds94RK1Koy7KOnwJ3XmIAGEeGPiBDrIiaC1m8hhUAq716dxrFvM+5EjcM58fuWhVqVwuQKeJ+ql/KYQs0Xg7KMnGId9Ndz/WpdZaZYKyOFpvilQy7GLvXtZpEZhg8et9TD4U8WV4xly3xKRS8p26Ea8Y4oRdJVbiMpItc4oRExsWG1qd2J2UUgE4t0d7gSV2dhS40Rc6duf+cOq73k6DKi+qqonWy6ouy0V/ejzYAohJrFZqgxvPg8cwDoKN352tXWUAJK00akm5jNkiUgYXu7jwtaOJHu2ds75X+pdHZYpBLpbN5pLUaV2oENJmc1WUjc9QMXVG2S703iBzEc8TbpYZUKUk1w3z59G1QFwWDO0Dl6Q4Tl2g0QpBU8rXq+tSr06zDeRooHFoGUqG1ebySpIVTMvKLHF1//xbkTOI2QtBdMxMBRCzLbXSby/U3jOWAAC12gCdcRqHq1t7fDjTQxydqDe8MBRF1QAVFevGNCIs2FAhYMqCu+x6N4R5vMRXqwuZc2ZEgJAyEDzloHFAWRQAkEPQnBOCncbrzZaoOiaFmcELh3WNMbMdccWBQU57pepXVGE8CQRKE/KaVqMJlZ99152asih1hRymueuKgmMyEQ7jUylLfj9fmeJZFD5acJkEcM9Qy91jGqzI2YXkhXXX71YGBD15mB8T1eD7zAu5eD3MkdWUeLFPdhrRgjx8/jzP9rdPvwuteb0Jy7UNgwLgsE+2rBHtPPn+5FvmY8qpWjL56Ky8rqSQMU0tncDpAAEAAElEQVSO4PuQzk8nsRKlN2VTtC3UsQ5BLSXt6byx1ZmSC8+NEN+Pu+7gtluoSt3eSKMXnAlEJjBGztp0HHGZGS/c9FwX7wj8pIVmJetGwguPI2dHZUQonCGNeul4NZ3uiGkH68DECvNG6ggXJcKlfkyVzpXxcYzTKT7tZl2QPcW99R6eOBEjrsvC1MVyxUo6TM8yw1G4EF+q8YKTs5p8Ql+FBUeWA7CUUR/PAjaZ81teNsow6GxKJVGQPNfbm1ZUyY37eXq7Xi/9xak/zWHBeH/XMTfx4k0eyYDsUTvAM0TkpUkmgJ+XcqG4yJ1LgWHTzvk4AxbyvRDqM2VgHLmCNVP+ihVTfPTtMgd/JoD3l8vi/S8h7u9yNGMaXr4kutTqx9PspBBGSmYphBAYSTbqi3dvVPuff/t3Tukv9eZ57AWXDJm3cZ7y8bBftzWm/GYrmRAjJ9ZAKVgseDf5hl6lHFMMx1PfvtuWQmljEOA0jw/3z/rHdqVei5+8TVqGgAJYTDDcSalBShAixRhSZIQ8jo6Aa9UJqqnbnZEv9cd3745MzM5N3v9x3OfnoW0uBRpjOJpi9XyK8/QtOCGDaeuc4fvu8XZ9IwXkjI/jCDnGlLq+M8xEFhUhB1Illhvtdbx8CXG0rMnLZjn7Y0llKG0+zfGwc/OjhEiUD+MLIuC/kgx8eVV6vp2/9PN4FGGEKOkCMPIYs2eKh4TvWkd16r6TkKooOGfk3DgfgtxI5Fzoqs0sh8N84OdjvDRqxW+iskwImLngXLIlFKos5MSP8yPgY+mvp9OMC3jz6p8Z43v/aaM+sDLvT27ryiG4mTGCCH3wkE10cYowAOVCJTxSNsa81QoRhVO0YCpnL+MbI77vVeDqfVE8y2iqlab41L1IbBVruOY8RYAFE1NcR5ZxIGBGCDcMRDVgjhvvvj2f8vuNFMgAKISYQzXPtvvHrxOYEGNRLX+qfil/bsJZxXbBrtUMWz16ZLAoK1E3xHjPvqja+IRtCn0jmdDkDxFrgwlAdSPf/fb58vWPShrANoaTYLyuGxA6s9RWMnvlsv/ty3/WuBDFdV4i40AHGgkWxPMBDSItxBZufRjn/gVZpmy5OOa0ADYynFnmLGPjcQYUm7Z96kaQfjiT3hZNPI+JyGLmOZ9eJqPniKYQfGZzdbIRc0dQlRCv0hW0NrEzzQarCQlYhRgTZMYoCcFljlXIEINYlGxkXcmC88QQnCNt/KYO1jECtkE2ZLIU4jmnxLlk7aahkwfDGEwghRY0+pjiYbe7ua21Kos0nseJg3L1sqpXAnp8VeA3DgQp5PwUkXH8SNGlwJNSTUqUYnxV/sxzdl//gDx7F6CkTIoJmTa5Xupux7M4yaoQi2LrzNv/+uOvvz7O9gS4qNfLsjBVWRhKPW3Mfr/rj4aRZlL13LEYyzgnwxJu2Y3jL36P05LmuzOdpVk1jNA/5APmSe8E8uAKwas9cyEzaeh5fNwfpkrJlhtZcCBGXRdPLhAT5Q+vtV4+ffnPdmEFl5HxQi+id6ObI0+gCpCBJlc19eVlW5pidl3vp0T+WJgCjI9BoL2bUym1FFOKI2wvmLj3u8dpHNzJHwWv+5c4eZXki42R08e//MhNcZB64il6l+JByWtkho+IiDlk5j3NZS5Zz1Ew3laqH0NdOZYkX2eoKSaX9n+GeC74Dwx76udJULHdvjEFAf0f/+//58PV8+0v/7swJZ/OZbswOgBGjgXJwucQwvGat5AML844zXWx2uEkajG4bvQxBhyrO4gG3Q1TW8YUPmdG6DK/vGVoWfiSYqLicm2n/vfzyZRNuV32/+MB+Vyvfzn+o8NLspiJCHNOPsp6seKsMvpis57Tyc55t9tt19tk3cN+3xaLRi3X7XVTLsyi5upE+6ngyBNBvQUoBUpWlkZWkxVPxwMB4zm8/+GHumhH1j+c3c36oZLSk8Oat41RD3MKfp4mJSQiSim11kSUXCpeG6FuF+Qy40KffOf+xPutvNpw0WoUYvu1SnEeikqfvg7P/ntZFpLpDWMX796ClJmJTNzO4mk+2sOjnwfnxgBzCmlfmlVK3A9Qcc0zxSw6TEDf/jzH9H2zuVKAU+y4UyzkwsQcbqbxE2d55+wZ4C//4xxiNAcpeGDACmORF3xZ84UteX2yyXmUeQZa+uCs9sWiRu1cMJc/bQ/zPO4D8oMyG4GihgKqhjPKNIpla9ICo6dssqMIRFIESEE5PlWjlWIxnsKM/amIVvA5x9Dzr2wSmeyobguCmSXDb6R+FnnCvRABhICQBES6yVjCDWX/kPuF7/qJs7wiB19F8jIwDqHS0xOz+RTanBaXG4Yx2xhBIBdCBiYec1pnIsoTzGICQJZLaGiI3pkSWOaZ0nRngULkJs75Gfgl3/61fLOnSb1eCFYsG14t1qpe0OBPOpVC1tc3Spq5c4JpVUtdFpGFlPyaCQaKWJQiCXaYnrsnP6n9l/Xmv2bOxmkCrDhj9nzIG7FaLT89PM5joB7wn9+hLsVgJagSsgDcHUWsOwxuIX1GpspyGCP2XjSaz4bqnhmMFsZqliK64nXsGFJmdANnBwseU2TK1JdmeHjKw4lVLfW70HyUasGLMs05zXGzaG2/zxEWHLgUkcWQQEEDeQYABEawZJpNlFgGTgQVy0mkkjhB9oGVmpjwOfoUcs4lgQCYPInQhxBvsaHKBDYCX2hOohnSuarKmPTGzjmc+/DlS/nDa9VcVZnPPmLqnRIgGVBOVaYIDHgKCZVwf8K4je2yqqvt7uVUsbJopH05JYZl0pCnvChS0GA5uag6pyTOmfmaZ85sxbbC/PWf3h86W2plyuYhBnbcuTCL6Js23V4ISLoq9Dhd+uCVNJTpD8CqXAKf9GvTuneh+Hcd0i/tL27e56rPkS2ZH2Ieg2CoWBTZhY0qMnIuxYVUkisCSMcQesYAEaUwshXC1LrkXohGLZgILuWMOSMhclM3mU40SZ5ySj78+ad1ExmBPEHOK86J0qjfLjmXBVMsuDolP/953luLfk4+WK37RNvMOEgMqQZuWDE5Rs42RmoKB7haEAkfzhV6LyBgFIyusE0weRv7rGSJUXWVa7JzLC6tPdkx6clUl7c1M+Ld6cG7kIb7F37ZVEKuVuUsk5iejVoOOKfURsY02cRSpbIOTldFSKMgGaxclVmKGREAMSWOqHDNOLtkSrNiS8gZI7rNYMMri9nKMOTf7G/nEH/eF4f2USwWdfW6jFm9ehVd0Vyz8wH4CXza67ebdN6rYpnS8fvT729fLbXISkrIWdEWLBhp3pQfRFVOdicm6c4ynL+qn68WbXM6nlFrhkrxsPNj3LvF+kprErxg/GI8/daPfWnKy9dbwYRP3YtP0bLZzYpzVpZ0DDYlG2MlZGGMKQwABplYpRH9HDIxtjavQLMj8qkfj8OADNvXb67DGnwqM79j31JMy3J5dfOaad71T4eeLt68Z0ZdlLqe6Lud3DRkoqkbgaUk50zV4WQFpzrafYybV+1m+fZl+D5N1piRSwOUvT+JRNReCTMp9RZLvKrHm27HsYxxnxIhJVlzmPEYU/VykCA8yRghhOwWm+jD/vGoKlWwxeR6gMH3i2OWUJs4OMVZ8v7QuXJMt69WwQHywBIx4ELpDChSjMUNzTiNhCJVSMyKwlSOVofzsTAhs+CDoOilVFzIPhvJ7jlDH+vMszREEJP3araOi+4svw2/5kyaV4uPrC7Mw2lUMeFxjSUPIe0eO++zZGm6YA0zIsRDd85+mrCSxcxTKRUHZznhhb50rfXejQ/HuOgrThvFKKXogjNWyVfz7mznxKQpFmyzftUNX8HXOeU941EwMR+bRXtJK8a5KcoUsz1ZKVTbbqnRMPkmTJ5rloMQCDTljOq2lI+cM8a5UJrEXDComE6xQWGk0h/Eq08fw1tiAjL7+ptN/qlcfDTLJBHbWrTtMgaLCDL+RnC7eSy/K3bt4YjK2ERF5DxS3iA/csrIkReGvU3d3bg8iH3+BDu8+vBRX2w5xE3bHm16u9bfEezTc7RJcHa5fevSOg3hLv5pzngtQF82kTMiTIkgz4AcADPFTAiCC4uZpkyiRPswTTfv35IRdHQyT9AsCh9ESALpnLuCeCpNltFai4zKsh/2FVAIYwFERcGKUQbC8+NBncej1LW+am9rEkJAlrrhxXtGv3JUtHqNTKQuNc261uvk89PzE+Srt8PzrI4bZWopMJt5sgEY5rE6jacQtu9eRRKZZS7FS9o9cfBHaouNRHboT8JFBDJa3ZSAFxXBRLFjTwr3XxCg/PlfuJDN03H63qXLJYL0fi7WVVZMsMqSXzRXhbuPglYDXRVLSAISPyGxh6OLvZLRveZJTGFIdC8MS+Xlx/J2Idj3Ha2KRmi0GHwiqk7AshR6XSJjhhv3+F3HsF1D4vPu5CBkUU0Sljlwfj7Hogn948Msm2rRjc9h8MDg/nQQ0YEA4JtVGAVJVSrNLt/gPqPgVp6HAVelMeJu8CtlOeh5fiae2wRZyIeuU0YtpOI+hTojG7NSizhH7RoVCswp9gChYTvWd3a/X/CFLReiqlUBPPMUcwX9y/PdP39Y0XLrz5gcKC48Y6ejVcoqwVlwqtDOh4g8Fcrv9zHFbDAUIsXmnZagm+dEkZIQAjlu0XuKdpj/cff3ydoM8MdpUsWFEDfPw4OXm0LKuWIXUtr3OnLW//lUT7g3rIrzwH0O1tKslnw4dgZUgN3jIb7fvC3BjFMY47kyhjeSHMNeCI6tUXsbIFKqm0o1U/8gmYgEdp6uroTRr5qqzhwJw5yeuxeUW7gsb7OISMrajMVa5SUlK+WMiM45xjhn8vE5UXFW0gBSn58hM7CcCwMIQHm8+0YhUogqmXei2qmiWixscIqbqrnCSow9CXfeNYuGs1c3l+ulmcfzPK/9NM3TGKNdbG/6w4MPWVPoJtAcMsE8THktBaW2rjxnKIP1HihxC/Pe+/ykZcuZCEKwAmY9QfJKMZjx+2gXp6zqZezkzC3NfJzcmKjQF6bI3zG8VjezFwInoIPnw7XahMVVPz0JHQeGpWYppVheqTxE2w02QDYv6Y4JSYKHUJIsw3xiplRsEaO/TPoBxdpRUdYKVwjyzHDuDXLjeEjBimS4FDWIlEYSq8VUnNJkmNKLlhh03hLJkFWYB9OapeTWZX3ZiobHeY4yHnwgGpkQslzopUDIOac+pRpg6MaX3XNZ6Qh5wZPgQDH76KN3MaKdhjRT0VYfcXOsTqV0rzZXJazzJrXp3FTFiCyjN6zogvfec+RHHVAwvl476vdprHPQyDM/ysRoykxlwotf3lWQw2wnDpELnJlrlJFjYj6/iCcaeXrffv/bMTqG+FhWV8v3JZzO88z2fVxvVM4RKOWxTuVZfqj5F3oMMSVpQ2hVzJk497oseC5TQMzs8MfLiXX16vVCvD8O3xqGmfPB2+ic2x2+zSwmLKq22l7Y48PpfL7f378rXwlUQrCOi+7+eMMg3qQ0UYi8KENMcDp1i7oEpTIS2CxxotXVFmP0fqPVcaVhCJFjoDx/P/fxiVKeKMH0XJ0j1TrH9PREXNnLgtC0sp+R6az1RpYZ4Iy8VFVz2WaEl30OeGCS6boQ+mOY/j3t7/9ICYX8J9yO/fy33/6RU/75x2sCWHZtisMzACMSw6iUHhEEBDaPL9//jqrCwpi6sGESZzU+dAMcF+2iLgOJS2nKqnuBHIdPn9M4SaYm65UqK6nkxLgQF5sfvt/f5c87hgjRq1zxjRrPY/A5T7sRGBUR0pYNA2eSAqsTBi4oMcE4+wY+xd9mD5iMkv+cCUEIwSV5IqVJFmkfNlfXu9OjWLZLLn0OcHjUGnSlbAp8fMlQtiD7kVsEkbkiLpkoLl4L1/3t5c8FwbZQu/MpJVsKzleLKA3jVTl7hS6ns1kIZdrgUkjPP6x+IGafTt/bH2Rhro7PR3fG1RVXAOzhREq/CG4x8P6cHV7WdUcQ/Cikj2zw58OmjEtzbXiJ6wnHUIZuZtGfg1yupYycte7wub/drC5u3Ji70bVbk7MrUEJZQ/Rp/myKa/98ZMurZFNdVKKoHMPnCET0eaY0n+jkpFTAl7RJ38IcPve73X93ccwIxFQWxulKEJP8cqn0bK2/P34zB+e5uCxNBcfTH3Gc+V/e1isz8rMIJecs+XvvMgfK6J8ev3vuN+WiVqIIPScZJPd+ktrY2RtpQspskKYReXNRrDeuG8ahH+yASgTAJISaDM+3EvuFXzDOAWVVFbML/uwgZyFWmXRIXVlUgaUj40ILxlRZNgJVyjHkGCkoIZrV1V1/L3xK0W15woZwvBC0z/fUby0EmB3nIsRcK67GTIRSQ+icG8N5OLw83z2k4CFFTPhBqvqv/9yN48PzLs99DMkUYvC29FkxzBRzDpyJbp6jpA6Hg6NG08fySrc6koP+ETnnb4vbxMt9HMaJsqyuV/H7FFkKh/N6W7dV+7Q/VVif3rgQ5xzHn5Y/f8vP99OXjXpdvZNGbqaZeXfyKCo/eSCa/UgFX+uVMaoqn74FJJR6nGwXsygizR7vJr/Quiv0092zCGdqkaq6qi7roiko91bN8FgzlWIxEwRyuIlX7GpZLx/78+OQu8OwMpesFOkqyjg+WazLhigmN9k8rzPLMQfV6M1NYWc3OB8VA7Sj73xsy2zKZXfa15UaujwVrkgBU0gxQebz/R9WX77SN6Jtzz1U4pbJ4ug/F2fdvGn0VU1DiEYBqkVbREJkfFlo++YyVymLGoevR7O4lu1XfxT3obwS8xwT5OJiNe3GvFCsYyABzDTOk4t8Z8XPrzdz5sNXd3j67fLif0b2U1tV07d5GGGxxnbNXuZwvH/IkN/evpVoGVwuX/1++EylKZLvSx2G+dzPDhHO6Xvu0U7PiReUrr++7DdrjjF++vL46qZ67HaNeFU1l7c315+/7nJKKXvI/d197fwZi9vZWy2q7Wpxsv0zpyt1wbnOcwCukEGzXmopggSlV3B42kUSZJUpISfhPXBun3pvv9l5YJCEwAuFBsRZiEnwqloIzdjZlFXhezi8PDXtxtkYYmoKzYoq+8R4yYQcXMCNrMHsjr779Pyr/2pRJmJElH2VrPjz7ruG2rz+oBVSTnglwCq7e5Hrq+G0LxUvchpDEK3mOCTbZwbf9VOO6UN78fDl+OHiWnGI5+D0MH59/iaEib6YJyFYqhB55piN5uiJGG03pmne7uLtPgQzcWgJ7KCnc82xrreZkp0uUaKdy8o4yQ2Igr0y7b2PYBMhqmpVKDund8WH1OjZkUg5k80xZu7n3JTWp40qAuM5BZz7UkulQjxZiKHLJqUcKPGUW8mtQJavmQW8lR9lOW3U/PzoFqK+VtVJi5HNTUoTF0yBzJBSsy68z3Lzk0pYH3aUMvNJCFYw6dOTcfVi2R77U7lcypJx1TE2q3PKwJu6SJSkneKcGQsk96rly7QVXPi4C49RRqNZoZlzksX5xBnL6SJ4cC+f5OWrXr5E4jZHDDaxzPoRKErf2PL4kHITIyWQUrsQiMkFE6WUXMpnIrqpxczS2U//6GJ8wOwBUqYcKGPx/uNfWi+KiUmAPIX4Ep0M+8hMDqEpWvb+h+//+I8A9hQfMHshcnBTZNCsC/9ZMx7KRcuSFvYg+EWQLuWRM4sIk52XMegMxqlozODD/O1Qb+q1UPPFBcDCu4lHHWU63D/IqqqUCjPpdStZvrt71G+MPedmXaIEe7Ynd/SUb8ut07aqCusjFOVw7pCT4FIKkcFO85h5t1KdKWXKcT5NdEchvjyl1Ff2Un3MrQEraMjEMEaRifZVYhy1UiaWWdd/+eUv8+5kTyc4PEeg8Ow/v3wF8Xa59By31mp63ptNY90YQ/DORa8YRlCmbN8EJud5OMmOlVyg3nX6mPO160MIdX3lptPL82D4vJ/Gm/imLKBoljnGGvmUAhuAC6o5EzytpXzYqWM6ZpaK9BhjtjWJvbHAq6H3iIubWtV1BhCML5Z2EILZguxiqczhOXKEjI8Bl6t28/P17dfum1ppmRl3dh88y6ypyyW7jNYTQ1VBssL6ybmQKHTdsa7EtTadGzBG6Nv+Bv0+WGv1JQejCtBJBd/bCiC+jEMeUHBkQfCizRpvVLYhRF9tL5yfIaXujKZEyjk4775CWlxev3l/mPpWadMuejlsNutif1lclxxDsAlMS5mkrrWqEEXI+WsIoLljkKJHy2UVT5mpYdFJKzsV1UV4duq11OsaC+EqhJERpJfTgepaqtsY0yml6dvvTfXX1Rq+fu4YMs47AluXFzOJ6AlFCRQJmQ9JcNbyd/bd11fq+rn/B7khAGwvfDdQPU/wRl35dVM1KYXxHz3nzOaSn5O4LBeMS7YxSnaHYRpeUrxY67MtNNapONfyHS+/F1IqH4RPSZcVm7esmfcP+6qupJRKqczy6XRamXBKBJIzgTb52G5cyP4UwnwEyk1ZvhevQnt2yRUqRlfZ6LlVLAMxvHt6TDEpU9hAPiND+ZvzF6YMKCDl8xkmxm9L8mD7/vybffAc34mrur0OdbTCM66KNx9LVdaV8lP3ZfavZRQc4mbDtYHN1oqMFHCMHoLiNRch8LENQadwOE3H477zoVIFBWbngRlQLhrI2Jqsmikn3fBpJMF0mh93TlC30heGIy4K7buHeOZVmnQ8R2D4eAyzjNO5lNUkqOQm5WAkVuFCLd+GizI99urt7ZUqvQ9EDIDOXRD6l58AWfQWPLm+V0rP+xcRvW9Kjai54CDhNAWWgBdAyJGlzcoQN5SnZWZ2oKf5H8Vc6vXcwgXvK7VwhyQ1dkeARjElA8tGlEglsp4nI2vFz8XD7oWJrMrqRQn+YgBA1iu2G1IIYATlbcqC0VEhSue5FAL5PNn1RjO4KgJ5XsT9QGfPlMSyisCyVM1Gc8FlHhA25fXpyYeNd9sfP8hHsOdPGhtWxTC+cI56rWIfSyXncXQeVkLQehGAU4TRhnF0zkfGi+gYU1asWc0vtBs6fv+j/Pm3P/5O+XBWW95PeRxM05x1sVElrBIC+bPff/2zfPtWvtrKaY5hSekbY3AXvsZjerd4de/+XEmRTgwhEZchfAvLBaiK54gFq2XATjWQHBOZO5g85YFCe5jn/jBppVPoF0tdVrWNG4kgpQTsvx2fFsw4b/s9Dvps83UYXm7bZbH8ePryjQLe90M1M23UIpGXJWCWgnOGmufT4Is2AWGmLDivN1U4pdgNItHrxetqWe4JsGB1WbASo0DguuQMYlam+P10ODw+5eAuZyG2pVx8pLvTt90nyunDWzaNmUxYtfhykinwwHxOIecgZNlg9edxZpQ2YvvVSoqktebDqShN9JMPhdHvMKfVtljU6A6uZd85nEYScjjFwkhTrIplYA82hDGGTy+/1XV9dWEqWCCHe7vnVlV8nTecTqNavK4vDUeIMU7TIJnZ9+e6rJhgWXIKiYuzlkVI6/14P/LKn/LsJwOlkAwQW8zSrAbbWQqGp4wHf+ZGy2YpWwycG6WbwJmPWeoMCIwrNQhRB1Mq3TKRoI+7jDmxuWU30yKH58QZpqSMn17mcXWSX14Oyb/88O59oRRH3oiWUnj49R/7QETUxPR+sRh01oWqyHQJimVx6lxRL3EXvY/6bZnCE2fXmdK3u29hsTr/9vviX/9aoXi+e0LUair6RDk04MMuxAtuOmOHvUMmVXhJFN2qFAP5vBpGnlFcAvpu9nH98a/l/d61V+zVJbu7FxkWBPQQfOE5K7Z+/3kI9uHrtzevXyPg+I3s6/n5K1SGa1NwwxvrsjGlKv749FBkeuz+Iwe3SO/fiNaWhzB34SXs0qfBAkoNyEqjEcEwscHItis7TiHlAaztn7jkw2gn+0mcOABN43jx6p2ggAAk5T44F/O7t1uf1N4FmM45LxgJbW7fLGvVQAwOOxBcjtZOfs4IRzyXILTR79v3GbEbRueDZ7hwjc++RD3xKvGc8gCpnCm87I+/fvo7svxL9RN/B2VovqXUCpsTXCP2EP3hYINZrgwk52N+erhbrDazn0i1Fy04ioWwQmJGE7IS7hRSCPEP52xnSl1tyafIYNGuNRCrAfo+xOxClrKAElMEARhyBsD0aX/K+ObNwtSlVADTVHhJiCHpjA6z4rdv6nmomqv03KGyTh1szqZjwJUfs17V2aTHaXSfnfPPwkavtQYpmRZ8nl9eRqMKWVxwbUWK3s6h7w42EYCLI+fl4qJoUg6UbNEuMR7aErNlI4sNqeLiODnU+UBDXVS9B/Qy03ayfbnJXYqEFYhctuYvzb/+/uen8rp5cP+RMmONCGPc2+CVsv3UY/VqmLnSpxL9U7+q2hQzAiu0sWPWQrres4KL2Mi6FAyhnzogUmFpvKMgJQn+6fNnxlkaxv/Pzb/973+5XJfFTb97zLOvTMPkkHKR1bNRhmvuc4B2Y3SVMmR3RI8lU0VpwMj+4BnAApz30YUTO3L9xnz48FPMeyb1oX+izDgC473GReSISiYI8SWR98gbxV6/AfwzkObbysx6bUpdZ3oo0wXUgmtblEVmEILHHHMayS0oN6LgLEyJz00m1UDnq5uitjnIVanbdtqzwmg7j8M4KGyWtwskCcSyy0YaqZ1IlPKdncfP7vRKvOJaMVlremSsHMbdueO04q91QYgEUfHFzSVXbk8xzGn2MfKRQYncLkrhoYbvLs73iS/9QDmfMjISgllI9cLUWn3c/LS9CP/tjz/+czhjl0spgDIAvl3/GEL88+uMGX54p1KkwxAaWaOaBZJAn7TecD6MM1wI7OBLd7pNG6dtLXHO9nDsgnrE05Ihr8ui3NbY3tgv548r44GNsQhljOmrjPRutZjnaZpn6Z2kpps6q+Sp90UtYNrHXWpWaytYI2VOqZtOQiomZLTc8JIn4qLHURFwzqWUTQ46+URzt++MqkZRFVpDL2kreDZthsGfXmLPCcgFG8OMddzISalIlM+cmULwtKAy0ZLJ0RsZhodzbLcKdFMsNDsIrtdI/KIljefPxyPr0KSzFcHeSy7qQgzD9Pu3L29fv3Pfj88uSiUbU7754Z0ydVn2POt2tfYv0+TmLBWPblSiaVlZbuGQhZSH0yl4MKZkrGCERMwdnTQX/FqnOXkKoxNiFtvbQoII1kvpQ4ZeuGTZPIdO2O63fvkvl89M9A9n1pZjdKe745u/rrp5fHr6Wr95/2eO0+8j/YCaKGeSnFPDc7KcmZs3t5mJxSte4w3Ou+EQxkNqJOIIUprLbKy62TCdqxD5HKbH4M5Xt0VZz8U3dkTetD9xxPMOxYVCJAHUD53R7LDfhRDMZVUur8ZhYMhkUQrO++7U1BWleLnZ3uedsmqHYnIj4wJZi4ltCpZZI0DEL7Yff8/ZqptrJqplSgNjkgNARoTZzcBhZEkvF9ucZcvzed77c90C58ZmP0vyY4Ds8vXNe+1dOAPG+/67ZOywuF5ZO4Tg/HNwKKR1fb4LdiWL5uaCmyU9BGzRQTwFj4HU6NJiyLnoD7S+NCPKopjX7z7kCAw1HO2Rv2zqazW5U0gZUi04jFaWVS7acZg6V7165abZT4Mf51kI0CxI6UVQjjt2K4t4xV0kApZZNtFRf0Y0KLrEr1BMqjz1x/7znFDN3TGmKoEV//jtH8Xlz68XrDRSrZoc3JVYShkff/tyDuOmLuO428yY65ZEFUtZVeWsOHW9OD3ujfDFCagkPtuuR2h4suX5styoi81NeuocQ+ynquJ2QrkUKuJ+nrtnzpVkvEDD/RgHb7W+YLcnBBkczDEi4hM+F9xcYAUXl4FStz8Jo4BYjEBJCFkAKs107o5HOq3U+5Ux/L0MkD7d/6fgIBikxDHwbjjV00D1QlaF7Es/puASt8HjQ2aXpTgWLIYERAWfZx+icxGLqgYZEOw5ahgh4TRmrVwGMrrIOe/D/casWXKAbP3+7YBPYNmUjoehXypTis14c4AwRVLj+f7veaAuuOEbZ1wxHJY3H8qP/BKcm9M5eOtj8lJJObu9lBshiHiOxKVCXSBf6gCt3HEhhEQHOU1zWUoEFFzKK2xmMXTdbKe6aBdGwnxZIN/b8MPlD7TMB3uollUYYsakZbmq627ygVgjTAhW0unhPF0u6mHydgqtlIu2iBBDttghI+58ePhuUekM93Q2vFqAaLBZ8EK24GK0h+4RYlZXr/71DP9Ov05DTyldKrH+8WNmcv91uFgiOfLf/EbqnXeyygXDRICYgaJYZn+0asQN5iNvQ04hxk3drK5uXlwe5tGlkCg9PHUC7zaZZwjnsEEGsiCZpSkqE0U4enJg+oJ/kCmcP40zD+2mvrgoVzsha+Ubvdjtvj7sp1XdnI7HoqqD9DrGzrqRcZ1CNPNm0E/nXMO55IIxnlfyqo0cU0iwdw/uOdFFQIVRFJSAFVKSzj4uCjVPR9BstNxhX2wVuRpgGH3kU8Gz5cSA8XdKE0Xj3d9/fejz4xwc4/jj+5+0vUOjm2K9WOm6ZKVWiFkwyikmD0FKrsxmsfrw6p9nOcmyjEO12FzW9Xa3/0yKLa4K3i7WW834MsT4EudVynd391Q25Xn2YQgDnXmkHAN3zYSWRApZkdctG6yfGxYDt2WoAhe9OpuBK5En4gvBmQIAAs0kO54jY49D2ny+2+dY3t5UL/9+inRk5ZIlDlIIbWDin/uXH9/9wNmsRMUOCykRVysdEEvOr7J5FK8roXRZzzOkYL88em6/e/dDE4RKwb+qXhsDWs72ZegQuJGvTxj7/U4psSrKSKQuzOX6VoE0sZjjmXJKQDnjOYeWs9GHli862fn7ByS5vHmfdJEcDjHawzxAuiYmOfeJRx94xclHoEyUffKSeMw5uawQyNFsExUlszlXVonNnGKf4utNE4SCwutzCtGHFJ7xXLzBPGaUn7+5zRxt1W5Q3oUzpJtrPOS8XInesq4TvGeeMikuc2LcRWAyqS5RNpQVg1lfFIzzU3/WvKQ1syGNNGnBSTCiFJQcu+k6AdRgUdh934/Ma78kGKZR6UEygFRilN6lvI+xPfvouV71+2fw4lvol9WKGCakdNmgtxOYWCjCnMsFYszfWhFyxMPz35+KX36GnHOVUoQ07PrBj4SUT/t0coW6VpGfxYDYDP0QASY7qFIQ6xO5mFhOE8+tlINwxQA510U5jCNDFcvMjkBEsp2nURVackecq5W6XDU8TUIjhascggeQwrdNKzgqnCIrBONaLvbuHKaAhjX18rg7eJeX7XLoFIM0+7CzT5wxtqYodv23HtuGkMm1SnOKOfLXNFfi6df/67dm+WPZihRPMdZosNzwcIyaz9CEoq2H4Tg8QlkSsYar7OJklBytC6KSQmEIPEicMl/XCw7IaSjMcuUmCzXUPOezLIRMKbaokURPEPLKz7kAy6B1k7mSdEj3SHx1/SHGMIVonpEK4stauwmAcSGtLpQUlqAszOwjVa0QaNG7HWpcTcIjkzyQj8FNsqxiiGcBXBYycnmOx2EO+3kUYlz0t/3UPU//P5L9Y0nTZE0Q81y7f/rXITMys7KqTh3REsSMgYuhGcxohgV2vE8saQYuZkGIYc+wdZ/qElkpQkf88pOu/eWCz308rxQR/Lp3dcVjCs5MtjWeUuYYKylOCSMuFpdqkVLIVUz2OGhLjw6dIb+1OFHAGSVihWjxQd7dv4s1QouikGzBiI+BE1rls/GUQhrZ8+MUhw/yQ8AdqRP4x+Sf4BSHCJtmM4SxH4ZqvhZkUiSHQIWgGKPoPLyELrbmWQCCtVIZIqeQXqcJWUrX+Jq/9yzsd5qpo+kPn/vR+MC0Y5ReLq8I4vZpkLNZtax63acmYVwRMX139Zc5wZzLcZx667OTD5WIIQIhjNBqUWc8d84jAIKitEOVZz4ycQHuk0WCCKm0tv7oVaVeUSwJQlLWq6TEaowpIYLKbI7i4Lw+vgZPrA4V5zbS5GPOkGCEAsKFILmSITFZYzNQgN8+HQgM3oe3/ManY6sfEhiUZe54yAidBjWOXyUtnI2EEIxIluWYw1v/bn11WcwaFmuZl/xQUK58SovzJU2Uyss+dks+Twm1w+NkqYwEgACpucCAARAEF/Gby3Cr93koMgVHNgATBbrvxxwXqEx8HKXinVBuf0ghdMeRzopT3wFhQu4q+aF/eRRyhlDSu7Z+t8icjemVUMoUTfdJ6+63F0s5DZEGAg9P/Ye3KyqUyktSluj0Of+jiAG3h4clFqmqllK6aoZgN/ax09PPP0Uh9bfNIN8XjH8PGapfhbpUkx3bdqSKSJXtX58pI7NiaafxZA0QRUPAjGOeoXnZJKAkWa8Z54QDsVSwnCKEjycUmUA54qWARM+DePieqlu2mgFADAcKVFK1Klb90GLnCIvrunCueE37gvsHPPghXlzdjEeHTR/Y5Rb19svn/rdPTy8f6fucS0F5cJn1NsSobfj61r/7Siqyfq4ZSvE03u85ZYJxnwqccxNsHHt6Ufstiq/qmlAg+PnFIUZp/faJktCTbFVGMCh4n2xQcwbaOUApOpR6Py1NIpEDD5O3/eAsVZntRd5xhyeqMzxDEUeOLfERken+0yGlnOOUC1otqPA9Q5Vtn7wzBHBUlLNEUwjRhj1j7HqchlyRp6EP1p5V+enl+Lx/oJTwaEOCcr7AS8X3oomkc4LMmIcwOmS4tTCNnZXFlaR5NB6cN0Y4FwqeRJXJjEST0qMFSLyUQhtW0/B67LtdWd446r/Ern/eanEnRSXEJaGJCk5NGF3m3OMH/oEq2XcjHSid08HF2Wyx32JJS5wHwexp5+xkGGcy98fRE1U5oxGQYL2gnL8RiZGEk5+hY38MjCOlmNaagRxGiQWz02CtRRwA6RS585hQlDAjDAmCJjivSFPUh90BEYJCgzteLWnGSJ7NOc1xKVU+AiaTmHKxSjEIqSJGISUpRN93VULXUqWEeSQP0/S72XtynDRzYi6AEiWyFH0bfJBSJtyFkGXKhOROvchyBNSGPMc4NTj0hlKKY/QR5riYag3JCkaNC4hYZDSz9I8XKwB4eHnoRD+vGo55ShGJ2nze9amdk9Pb+q2U8mO7naciBodpfHrahuSznM9JSggDTgkAfIKESHWDKaJHeIj0qrcoSVEKZHC73bcZBQZKtqVvFBUZEP7tdxBWt/3/+dy95pp/d5EThyxJZ1WFx1ikUpTTnfnChYA2l+f05fmRM3aZVS/iwPl8Bf5rMN5bACdx9JyHEMZf4QHdJotFwzbzS7KYXwL0+8MvXz63vZms+d3l7++s+/X+7i/aKq+zVCUS0udb11yPQ5Z7EkBwiGbI6Ypp7yAzHb05W3TjvnfAGRcCaMpk5kJ8fHZXRDJMZpkazbjTthzzoJMQftq/Uoby78qT/TWyTZ4qGlP09piCIOhsnacuYYKXLKL5PHnmUYrJY+mK0S/ny5Tcr3d3h8h705aiyAkeupaWqQ7U/PI5//bNdYtxshQrsMGMXUw09FSoTILeGvvN1RWhDFzbhzx2ExYK6J7LpfUDpgxjsVCXxn8G/Pb40Bqab4eviOBw6tmSy+tvl5I+vvaLBX8YPy2OG3NCKKF4emgXb1iM09D9cdn8w8e7+fvvwdmLYv5snw7TUaIyU9rgQUZvTu7pYVx/WBVdly+FHfo/f55qEiPC1NlpiD6u9Ok1T1N2tui4RMAwF6sLIcVViuGZiLcFJnD+JE4Eh2WTl83cF1jrKjrzfco+edO2R2OKH1wg7Je6vuHvN1leZhSfMnzGWUph9CFtNhxzzMnxda+KRPkiQUgkUwhc8JHaMscs2mGyqr6K3nFuLAJnJppKwujkvuTbK5YftSH5YR+rZsJgwXCKR3qMMeZ1diTHLgWJOsIO48TXnJ1SIpowQbBlBCGGeGsNAJC39A2fPYRd1xNI06TRJaSQvfnsJ+M+EcIk85YLok1ezWLN4fWRm0xyakpZghkqwkF6F4fJIMBZMVsQkaZgS6ZdT4BylgWfKGdpSpwySmkG0TPWmxFzxVXfnuYekYSjTUCn6jV1lXTJmYLPRVbc3d6hrHMiy2YbMZfZ/HJnRzc5G0dUVDY6RB6q+hvnt4BjwptMVozSUYkE8ba7z5iMt+Mxx7CeVTkFMUTIOHNA9yNQjSBoQsOErZtCcFACpRcFcbSdEAVHM7AZZMTuu257O3z73aIs35f1w+Wb6f43jHeMd6mv5uvVbPd0evn13p1Co3Hua6XyLKeEEzR40xMkFhS9IvJofgUvkeTF/C2mojQ2+uAmdvKhxOnUDVKHBaFXv/9e5Rl3p0KUAPme6xrnUezVrDpn3yhUQGQ8OBdsMavHp8f1bM6X57v9YwDUEyoTDkWhuOJeci4niYhgJKFlnUPEw2tnup4y3CeLmouSpLtdYPwiC/TV7JSXtmyJJ9af9Dg5MfHsHXr8Eps5Se4I/HC7d+NLofKzvMkulsPQz3kTsog05Y4C9mNWUMFJIlVQxntCmBAZRgLAi9QFVDjKZIFSwIQxCjY4ErjNKC8o96lTpMIlsafp8fVeJemTVrg6zy+TTyklwgnBY45FAhJCiCFe8QZQsC5OYSrKEkGUDARxGWXcJysdeOidFbuWgNj7B5XNSslawWAP3u6wZNnF5owgla4Oxxfr7MPpdaGlWCF45ozx6E3IQEo1WoxIyFGPYxoJphZ6sz9mxUVabxiKMI2chm7r05bxt5I4E+McNYSscRR2kXAOEViy3tnh8+sXReO6Weez5q/+6q9+/PHHGKP3cTN7n03I2jvzcoABsjermwtcNfMv4yNuKa9qEDyMI8JouazMU3v66bW+vr4U7bQFmKs2uWFvRFJFpqumdo8HO3EIIDgrCbHQyji7Wuf3r1/aT624yYWQEOkUQpcgxWQKH1O+1S8pIsB4gXxgYhtcbW1CgCgYPf3yy8cU47flpY9kUzS+9VafpPeAJoymsGNIIKtfeZav1x9SNNHvDSV/OJvnb1eXbSgTOClZ3qDJECLfbuZIDj7EvNxEH0JEPqbbr9DM7YCqFCxVN3yGp+ePn1zDT3zHwvT082LxR0rfdO3T8vvlcGu8H5S3cTcR2tsyKwW/+/Kbl64u1SIsLoX99be7b/9qYZ/7Lpxubk5nm9CPvZAUEO3b9ionxi8TvPS/zcbOsAtRcezU5VmZvboeYRBc6li96tdVdrZ4Kwm7TiG+y7OTj3BqeVawhvhRCKJwAX9MovuZFt8JKjj1FMUh3I1Wckx5QVLGWR8jXq6/KysT46kfi/VZk6kjZ/H+IY5Hd2KM4gS+uBAIJzKKKm+dqGTgoqC9H2EYMMoZfzOkaM2QAXOICDJQXmQpSkqHEyRIre8ZxcOxC1JdlD/oqOu88uMzrRgcqbXbu7Fb57T/8C2+/ynFK+y98XvG3gxjZ5y5y2abMktJHFwDgDHBSgp4s+ZuRNaCkhhiCJExKmxCwZLkRxSH3jAu331ztTuN1nQBUcw5w9QZA3Hi8SofTWIUOZOjxGziktOWJ4Iw26FYEsVYPktxAlYdo5isp6cjgl1MXs7eNQQHTL4Y6067VCLG1kUcA/Gi4JBoiAduRXyO2XWcnV0yb16/XXx714t6UdZzuu+2AgNLOmCCskq4AUd2olQZ4xarRvB2HDwlHFgQq3I0HRAf4jgaRJLsOikX9HIeQzV9fKZnxae5B6/rbGJIJMwRP5GBkayupbx63N3d2uUFXkp1CiIeuoSYICxWtYWwoIuCJA8sg2IE4h+0zSPEkAG+FuJRT314Vb6ffUP99AlIyeecRsJQJibHK7rEbzRBiSGXIrLw+fYf5/J/mJ/NS5nHqkkQhbERs5px6ShyXKxzWnBMKXaAfMwoxRj3fdcPrs4lIbTiNRFgA5E1pDZ/6K1DjgOEk+WMRh+lFEl7ETpLpaQKUORt56kjUuUib5Z1RM6BmTACgzBGkZIc5Rk4hhFeLqK1M1JHwJ4blxVs1FHmJQoFiohgRIsiT7dbI5MmeVyXi0DVdvt8GP79YrYpmE5U+skg5ByaHtFQp7lkjBEaggs+D6FFBHMu1bcyjkHsGaoZFQwSlMwH7wRyUCqZIsGr8PKbty4AHYYhoV/3Dz8MQZa/a7hZFBVGzu0Pe8fRZb5kClV5Fczkzfj9Dz9MHXg3ADIpBCJy8BpKi3u8Zmp58/b29e51e1cRoTItGU9snEu87+3H9DNO7FzdEMmNBesC0oAJFpygjDAuZ0i0Y3fbPQlH31xc5cXvvNtNJKBxcihs8mWwW4GFkGBs9O7AfMS1wDFJrXnyNhA+QV1fQmxDT2G2PB1+XTTzGuEwz9dZjX/dua0l/pGTFScFddFtp6N4lblmxc3bb8/H6WEwNmKt44BOrVhebrDAXhJEFosrxjnGU3ABIr6R+bGao+fhkfRnNnR9+835DSyi+Bx7fcyq/OhHju0a58G2jmqqBYqZsza09r7bFnn2/vucljJCj3kz5vmsniHZ9Puvq3qOSnH3im42nFK2d/cqFCHMAGEsinLF2u0uQqwXbHc/xc9GckEFMmaK3Tifjx+f3TcxPuOu66s33j6MnVLh5flWCsLwzp7oy+DzLCuZCvstsmbB/N//yz9Lyuu6frm9LX0QsiHEY0zndf/84jz1KX5O/puDDyy4+5LR7Z5yzoV42PkYXJRx+8Xmb1OmsvThh+V22u1/C7lcz4RCdShsGpLbx0TAf0ZAHWLD2IgIMXv5rZcCzeUx3Oy+3Ip371IMtyaYcbqi9Nh3p6cXQvBqszlbrSghzjlw90ETa8Kr6ma1CNFGoDWnqcCHw8CYujkjX57wGVs926fpCKoiEQ0CNYrQQDGGkfGmmUcVBPHul8/3jJKsKLefjnTViPmNOSKq0Dci+1hi868fX96dDWbg7NfgIZdZLnYjJGMtABDKwd+zVvRPL3ROWJknjLnWPjqV94ehRonyIo5+Zc3XS1RIxapV2U88mwJFGAAjUqd0F06RMkoYaX1KigoWtW/zQgl+Pk3giAdqLTE5wERVCFKIKsDB7Kd8XhWZfLE2BE9TyoqcolkKMYjsESLDGNBlPzzLbcSRfkMWpOHsHaJ8Sd4Nh8FMps/DsM/XF6/toUhTkUdERKK+TqivJpccskFKKbL85BN5tE/9E0hO56usyCSjxK8kPwQXLSfZ+2XGKaNe2GfBG0p4xIBHxDCANnhdlue/53/+B3/n/bWgHrVeo+q64Iru93GaWIZUfl6QhLECzEbEVHQGxdwEl8Z+ePZulJyJgKALSEKB0oFt6biEhChOfTTDYAbZEqyir/WX02/nv96cvY2MWhusnhJPftQLRBEDRyx1qvMOkwSIVKvlDMBPNsdksaj65FJKnHNn4zOkEouhRnaXCK1FMQXk1oT9engqZpJKtD/eJeuRV91PPyGEi6rMJD0vZczp7riXiAA4SAETBgkxSfsERZ45a0KIhPtM5IqXhMZQ8hgtISwGGKasLqbo6aZkzluY9El/yTffDRgDQM1nAi995thMzacVSeRhmg4xLSb3JKECxQFZRChoEpL/zCnFCQ4SMWNjZkb0ZkXAmxSC792I5rXnc4l3BuOGEJJSuNs7lpHMpjFHhxBTO+TGEI0f7SNCCcK8AtOdHjJ2jNFRCoQyBNihMCuCdw2lfZCTtfpydXH/FB72t3PFVzkhgPN3iw+Pp19feu2Gl7Qt/fwALxgJcqgQQpQdU1NnDAWM1UI5snK73WPXzRf+61dJNeVSchaM7aOzlFKYQE9IqhZpDoVtUNEJnyxaJ9G7VszKsLUpCorSMmcqL37aPk+dPhGie83jcaZ1iknwgiAfidJ6+qS/5v/e1X/4veDfzJAOUGIcJ4WC8QcWJ/OwhsVoRyqn/oBiiGQmcH2+0OMOJvf4CufXDFXqpDqTXIx5YiTDl/Li2X0l4GJDiA08KZLw8OXho9WqKN6el7LMy+pqmKb1Wep7jDm2eiSEVJc3znmIBy6l1g/tHW6jlt+cJeA77TU1YToYV3DNAcXFddx//e0yvHnB1aE9KBLD6XXf/T5ob8xjgnJyndFGcS0FhpQTFHbPd6hqZtUKB2Q+3p39d38c2v5/+99//p/+73+cBnKkL0VBEMUax5eXF1EqEC96T6tNDM9xCAMruZh09hYHZ7r9ULz7lhCidfj89e6PP3yPKGXrfEPPXk8PcXRMyKJecC5CadhCQlxCkIwa5WWre6hRCOMiLpTEJ4JZT3/bPXs3CCaNZAbH+XpOCGnKMoXtr7/t3xDEcpbfFN+24mUiSrVYKesxoSVUyD0dQsCfHxaIJH4zuxjFl4dPGIhEWQ+jSESkTmKkCHZsPI2a0CYvSwqxobxDw5lfBAmTnRKqKKZcZAST03Cf7uA4HdVG/f77760KjtjkwQcfjfNjEEopgOOxV/tXLElWFZRIgbgJJjnMMo4fXimVT9hx4KenU/R+r7LS2sEhYXkKq/2cLMQyxQGP+zJnEMgrJnmB7R7icHBFzpCh3uiVQCTHCTFVs0y6+Lk8P3NCSPQMfuN0klBZaiMMEidnvEWJ0j2KQGbh98U5BcCnEyMpTD/98jX6TKVlE6IQg7SLzUZO6XDaViRENvnSpTAnyLkoABIKMLUgYjvL68AkY0oDCpRVkmZiBZV8KwF7CxAQirQok91FnZSSuJcIExuQa62oxCU//3m4FT9t6fml8vlcUTv2YDNKArbE6JfoUoGvwRjHEUbUP9hP46d3sOjD6zuaaCUwYME4prTVY+siVZsSUB/b6ERUU3IVJRaBVpkg/A1CqF5dmHZIEQXSGPFq6oIzfho654Fm6ursimKCI3gfMMWYkEn3Rg+UMTD0xfRQ5APGqUOicEC5nan6dIxoZYWdIZqhYCMnuTvHtMV4RvD12xu/v+MSgx6WmcSE5MiggiXtDMKQwbIUiewJvkqGIzCMZYB8SsAZHodJqgzjclYhZ7TxhEWzUVXCy5P7zPb3b7mg774RuUqP7vbwWITUQaKUc5ZTtrYUnHOE5yUnWZIxhJSiDS+5pDjFCEQ1dqBZ9H5WrnLcdROEZE04pTzdJ3fVpHP7XY1V+W1msAiT80OAWpAiQ8jFua/ZjbH+LEU/yfryWzMd4+d/3HlXbgIKjFofMCII+wqnXeiHX5brd+83756OXwCnKRnoA20wX998GN3P3c9ttPWCXaj3ibGyIg8IEYRpeyTB6GRHNtTRBIJckc09V8UO4VHJMiSWlfLkpi9av3eWDehIICCLjyCXryuG7p0PtCTAnPeO5UQbBk/yKgPo4i6evy9X7F26ioKy7vSvbuf9YUQAlbz6Qa4+x89bY1D/Ixc3x/CUERQ811MoGEyZjFFmVaVRLGRTXuLX9uXNN3/x84//uM9nRLAQI6L4Op89xocs8TqTYlSeO+7xtboiZzrsnnzyaBwxFZjRd6JZ/9DUZzNCOaZdAITimdWa8Jwz/s38nMlM+xCBxAQfP54Y4fnFWwDQOsBiqr27HablD+FpchFBDnCnu3//ta3W+cefPv/x91eEwsvjv/FZFQHfPh2qRn366TNGc8WAsLRaXOsp7uJU+mOtsl9++ae/+Jvvv1v+/n/9r//L/ybk72fzvUOwvV18WyVDzfAK6Z3r9G5nTXolCkcD4+lo2pb4K2tNf+rTvSHfVIwWKfqhN5PRi1rS1WpdZMl1MPTeaUwoY1KI3NtCf70zZbIxe7Ftsd0ct/vqTzRDiMxJsyS7rxpjPFuvS8U2nDNCnDXDYWfbds4YAng28M09eOoN2FfIri5OjDQyo6djKusc0zyM3lv9/PDASTynmND5iLo8zxNrwUwYKW9DgpQVBZ8gm1WKLB7bF0pJnxzaq/y82iNSITJf38yaL6t56bPxp/7niqhtOuUPMmWBF1RE3xnvUsLDidJiqXKZzVIgTh/cNG61k6rI8yoHGnK+IOy8+WFTFPoNSy/ak6l3PPPHyWwlL0afHKRNhOBhdAmnWdUwiCjavaAMlw1VDSUbQr0HlzyixzakiZUiCk9Mq3iOiTLTzkeRSC0gR/uOLSeEZjiZXDZvWZUCii8/71vPZn/80/Hf/sVac3GVCP0W5i92a3klbSb5tAJ1aCdKJ1hUzUF2w107m20IsDh+DYRW1SJdXp6FmAhNCeU3Wd9GDN0+EdCjMa/YQc7ImknZ0BCyESFEOMa5TyED1WzeZ+m4HYfwsCVUmtERTFbzjQzSMppj6RU97l9fh5DlEbk39+jJWvMlPVwTFDEyK0tQ4iwVoDhcXEixLKvdpG2fUkEIWROGIY0wjMlj8/EXuPlWiFyPgdAIAXddOLvM8O7FOcyY+dO333POkw/D2JtfTZseiuQjKZY5SIYnzCWPdS4Ou3sMmCJqxhMq/NeY3tMjRBgjjKOnMCUtPfeEACfY26eIEREI5aSKnhTQ9UMjZyYGPFgfOGXnp4N1/mW1AkHWeVMwIROklHofYkLJmufl4lrrOPdZJ70TUVZyPCkxjuT8ipBkvnx+xgRTVtelcA6ydUzWGUuaRnjatxBjStE1hHLuEWZJSe81lYiQxWwhdRhP0RHGPZuLesQYceyFMkP3qeJXd9acfVVkgXqSRFl8kwtAMKh8LZuDFmj78BRicu6we8goWl43s6C9TSIjBSYhseCcIFi9W7LdbDceV2lqnMMUyBkHwMZYQPbRtxgTIZYph8d+VwreqwYTYFkmM16VVaLOnuop2VyisW17nuWyCGHQgWRjNlwlfCj7sI1D8CrHDmV1ikFPIyvrYiWVM+N2/zJrlsDhadDsKyOMRWbwHC7L7733O/PxMl/JTPIr4ebW33q6Jvp1+kZQTCh/YcX37elOZKU7m1143yGICROOqxYho4olI9roKZ9ao/EUbA2XiOWXZxiF7EqMj5DAUyFP6enl8YRwpCN8N2QQE3/PqFMa00Jl6GZDC4wZllmhVPXEvZWIasWzOUHURdT1/dfXJ4i4by0mZNDGO4dS0NZyBFSwdJ1dCH789c/G6J2e9NRR+suiPD/1X71fGq2fXz796Q833tIvv27/+q9X89ns06+/Xm5WXz//+D/9z/+P4HSnJ8GYxMna8e/+1//nX/2n//Tnu80//dP/gv7qP62t+i/d8B+qa6Xyl8d2c24YYXrogjPsmtKe7e5HOqrm4tQB7tsTE88uZM5HkuY2of0Oje1rpsi8Aqnq6K94rABu+9vu8YiSOzipLuUCjrYzzz/+9GcqxcX0sYNZd2cvK4dQYsVVoWbanBwTuXFde5wRWy44yi9Se78cHWwkEYx8DWYwwS24oNH7puajCRC2QkKI6LR7mlAqGKHH34wzBEGecNEQzNqEolQK4UJ3BLePKHODtaXMEVYxBB5lRtUWsWW1fi5nX7vP3vSoD3o+qTseZUwWWKD2IDnQd2dliMjb0gMwhHmCl8FILv9mM8eEi9VlwAxV6bxsZrNZIPF3BHUpjD0lSuTf3QTtlaDjcer7HeLrahbb/b31v1hdMb5g2fvZlcScWe9Da5PTdHqhvIzy7GC3b7KVFIXOoR9ba25Z8EqsZAw2IgQ48wu8XNojfCNySjl7ODmN5giYdi7evC0+/jzhCqfBQCPoFCIMHVs11agRzdPwsnX+QBHjTGY8a2/bpqx+9/3vEqCT98HHF+sXMT4fXiiDmELwllC8KFbVuowhWOfS5ENWQIkhUIKo66yTlnK4IZdfiy9n4tJxfNFcVasFRgQQsHF0p21ve16czS8R46woqv8+/sd/+/u/N10rU0QI+TvKlpzli65HnbFjRttxz5JDuSpJiSKaIuZ888NZE94U5nHyUde4eY4DirgAVpYFAvTFDcmr3317g4IbxqPf69vnr1c+FUFjdq1WMXmno9Opd0Ds0XBOJi7s4bF6K84RTFJ2XbdAyBmNo5UeH47bh4etT6kpc7azbF46KEiAJHnoe4pQCLqoNxHf+3QUeE14mpdlvWis3Wvb03uRLiOlfLEqAyhjw85/mjiZV5J5OkF0/uSiz3/3A2Ckx2GcATHcKfPKDWOSlzFF4RNug1Ei443JmZz2mBKcAhXiBayXKZECIeatX1vWo2Gm/bC8WCfCnofjTbY8u3bD8xClN19fn2H4cPatEuqwPz1TH6YhgD36Ueosy8+rnHurQ6xJeJIZctsRBOr7LqsuKG6c/fPTKVOjIWYoGXG6TTHAIhELdC9xWWGiZAWR8TeXjcN8wZTKcI+h04ciWnPs2mPCa0AUKSZJIFhmkTBgKpnWWl0VApIYnSGM/rR7CjFuyrwic4bHDvnJtHmeV/UFQ1fawngMHuK333yfNTki2IfQTeMe7gITx/GZAirKPKFwkh0eHzhyMUbFSHc6jC8TRYJGHIPTL1+iuOAjTdI5nIhoDyZ73j8GGg72hQuZx3R3+tiUJTJDLrPodjFRsV6wah1eU+iOlxRDsAgzu0sHjMvF3HM6z3OseN7MCNMJX2F7P2pfzOaIsOB8InQcBjtWGYGXrSH0LDEfBCMITbaY+XTsT6EPT+a2e3pi75f3p6cUBzPqBzUmezrun8oMf/58fHoVklWnw48//vzDd8vyl+70r3dftDeffvulVrY3duxw2z++XTSPuxf17//8p98vX56//Prr/zs0f5pG/V//j/5v/vZ83IfP+3/+3XrzdkH++ZenaqhEJhbgXvasLtHNn8Q0Dt52q7Mto5vg4v3kSi4wgKRy6DUpEoBENGPZRbNkiNYOshCcf7671TuM0JzgJaMkevCnq001WJPXa1bhXjuvjXDBBA+DxgRMH0TzNSrPgFpr3IiiI1SQadhlTSWV6CPOJANGUmSLvKQ4rGlKQxcELXCtXPDSI0KNMSye7McpxS8JoCc0JOei4bSsysHn4I5joa42ufpzyjlidOvfvH87Zcc8z5qbwrsUYy/FBVpug7NCcbtnrbHYLdu0DwF9uHwnZyohoClJmdNIIKd5q3bd1KHbtmqFOK8arvLsmWgOYIho5PwN2ThuUeTlMfvY/cMfNsvFWbkQwgjVv2BRYOxTRVWQiJLi68NXPx4OeC70lhuSER7GMVm/vNKeEJ+8LCXBJ/t8xN0Qqzfh04N1TlDByIJ9+vzpsrk2SzaQI6O1FB60JogJ1/dmbVrPrmeMHAnis3pxGg7Pt/eEsiIvpZSTNi/3bjMP15J7TYxHlELBWXm+cNZqa8dpShEowTqRvDcpKIIoI4xR2vf9NT+Dsiy8qlXF3+azvKaGQwC3/0oAC8mzarN7ap9f5Ye3NECqyvyv//D7/sc/QwTCaMtZjYl79M+EoGUpAyaUMMw4FwfECz1aQcn+6ZeMV6GmKP96177Pc49xRrHBvh/6vD0mPi42ghFlzHT47WeS5k1nOlchIrKzELYvSXDImCGsxthyGVNSttVNsUEbgANGQ0oxeldHaD93atlUHWKUz3Ck1zc4Bl4sECJ2GuIw0iJksgxxcvbVB48wRLgNo0wsHJ/2tSDg8JhQMbJUJBuckE3FFyrErf8tkG7iuKE0RbWaLUOKd7e3Ng+Lcs6qkTIRUaIVdiGkQs3KInfpOKQpRDSwHg0NrhB5wJTRGscCJRoNoRl3GZ+7jHjPt7YtOa0pix4hihOKsb2jnEQ43D//1Mz/uqwzjmgEklff8MLsjJ7cxJmkGBMlecgZpcbIciImyZfHE+HTOHAuOK+K5rIw/S7dAgJELact21PKeW0cTNoylo2OeeyHFBluuIpXdMEwmxguJMExeji9Ul3immyN8bKYpUO3TimgksYIrihuUIVUdnh9fjie/pSjVIYUkO/suHUaJnpxfv90igCY4sGO2/1WP3WuQcAw4ZSksddWUqADZgteVuXU6+AiwkkuhAoykTLZw8uPsPrQEUyIBCY0pcT0g9fOLsWmPE+CpMi2rl3LJRTzjMsp7ROB+bXCp6DtZwgbLlS1ntMMsb0FxMlF9jZ/Wy3y1qD5cploiqgCWMbYMs9V3sxnZ/vjzsTQiawB1izSuEMUXo1ZB2DIu9g6ilEJ/vV0FF6/jF6rYTPOtHshOX/0X+ZDNvaH7vAaffT67tPPTJXD2CkzPtwsz+pUPE9f+6G//+mX+eWaYtp2h95OkVE1nfruR0Jvrsvirh2fdy9jvHVW/sPfPwOU1onn7fNf/G7OfYDdODBUEHXchcdftn94u0h81u733VGpqmhfaLaVZEH1zsaIkzvVVYYpMXo0OlZYFps1N94bGqiq8+KMRPV+wTOX1xlJHLKNdVHkSk9umhhNKmaZJH21WAqBsT8ARZRxf3K/HXtKcCGyzVxEovrDVuQVwTRiHChHxlIanHcZ7YbE3KgJoUMKjdMxhmhswoh5eCFkgfEMkKLnN3WiORpHM8v4S5iSPUJeXObsXsjiqqCEcE4I6YJDGGXBk+BHKVN2kIywaHQh1aLkm3CWgpVr4aOOMRX1Iu7Gl8FJqDXRAw56WYIlifjJm6BNxBoiz52NlI8Hl1wbFvNgPmFMq28/5EX2fEjd0EFic0qmiN8WBWJZnLsqP3++S9pa7JkohKJkUbKBxqPzwTiWa51nnFna4urDnJBaU80RTpCbaBkT/GG8IxsM8WLh3EtC5dk6DhzTlpSCay2ri7DlejQ+MzSm97PL+u1ZAoQx/vrl3k99zy6Px1+qqthUHCiuQwgvT7EkKcksz7gVY9LO2UAQB4oZRih5bIMzlhwoJleAPp9+Pnfr8dq3dwMldL2AxnJZy2y1Wld8++nTUX0jCNZHcy0qebb2JmJar7l2qGF0fvWe+5AeuiiQGYuMtoeyPY0Znwfniiq5SSenQK6XIl+VqhPj+Nnqosjno7fIV2icHQ/7srClwqe9O+zvo6YIdcWQ1c1MoMzipLOas0hsMpj4qXv/7oZi1h0fdNdrY1jwJl3a/nAc2ojIJa/obMlJTr2OrzvKFzLkWV4m9BpREWJHGVNcaJu8Uyduzmby9g5m68wZbyGUdQ6IOmdj/JXxkrCKN6SLKT7gMeNcpZDB5PbWezLSfgHCCYl5xCiogioCPg2ccjSulxfatPzk3H77JR0BwllAy0JxoFqyFRcJsqMNQm3OBd2H7YR6whkHEoUgBXk8uPfvvmchQyBrFfcRB5cwRmWNcsxFBikio8f2NOFY+uQzRvbOZRQRLldllaaxqLHl2cl88SSbE8lveDIp0RQk2AmzmBAIHwMXVYdwEBjnwg1b2+ljSc5xhVy0EYRsJec8EuQhMT2jEomS0HsUKWULyelScm85prBcnEGr/cGhA8EKoQjW+RQ8mfYmOC6ym+bGtf54OOANJpwDAEtUYwyAwRg/uSIWIjG5Ohtd/Nr+++zOUcbqiGjPnpxeAHl785Y5tvUjF8rFiBbNMiQAAEyDUhyzYN1TfPoQv4kJeRSzshyITS8jKxMnMux3PaOICaGK83mFaR5xEdO4709vrr7lQiKCHp5P3hPWCJTwqW2FLLG1J1qMqWcTz3Ic4xMlawTBvHac4cxPvjtExbfBYr7yevq4+7KMQunQ5ccO7487TcgViqHbfz5OxaaMnz6644qsV7OXLRyM7n/7ScwK6dt+1H464iIFZI97E/wzQRnCeG++LuS71h+7Tvy+KX407ePjfzm2i0X2/c37DD3CYAoX7+6+Fp8//npWVD/dfv7lK//D9TzY3GAk5zUXZRQ0jPZ4as/mC68MgT6L6evT8zj0eho+LOkNvyEUEYLj65Pd2WfwV+sdA1ltLrohLNcZSTJFOB2BqxBdVBapsznQZOTpOwB2/QZ8n7JrguTW7BcJSoKtlDoQ2sjJH5HzOGtkTvw4xAjeIeMxjmRJa1SWuGTv2j0X71JGwAq5xjY47w1XBZsrUBxQqvJS5nn4t3G6IijdB6D3OsbugBKKfr90AdczzkSMUZWSTCeCMVpyyqH/9devk363vnC0QjwLJGLGeCBsMh6hIaMDz2qGFILMExtNIujRHdtpvBlO9WyuYvRfvkx1czpuGbuoVAw2KscfuyeLTmuLq81l/rv3HnmisTCvgjOHfFnUM21u7R28ovO/QBpn3qYFrzkV/P21e7X4RDw6MUSSc8YfjFjaDKmN1xGTkAUrJSM9nvEFJH+5ibfPAmIdgKoAKaHXCVD5jl/57xPC6CzWcL1EEAn2fniJDvDII8N6iw/kRDCVQQpJY0yEQwqxch57ak2Haspubq79F0RmGZN2Hc8blZIy3dMp+jK85nndfJCE7hu1BgKtdllZpDAiZAj7UEqRq2zbYTP5aklKNM4hHCmpuPpulvd6CKxb8M3Lk/He//zldTZ/EwnzSZTL0RphvAeuDNdP+wfZq4vVO2L3o7W20wR6nJRi2JnBpYSuHGbgKUq64nnlAg1u/PXT5HLbhOzpdDKnj1yKWcwFW1d1RTEWWBymVmbLolzf7n8RlpjRX86ZCyhE66PDGCeQF5V3frGsRkvnPbwWN8r7TQdPTGQjxXm0CLySWeHla9hRS2ne7L/8SgjhQqjLApUItGxIo3GYQCJkAeMQ7+lJ6I1rgMzelMY1DBHtehcjMNL6ZBGcrDG3IyYsvxlm2bcVrbvQRgBKBUarzw+vdvL28+e0eIc0crn01UDSKiK/P+3L5RwVRTqJ6owsGo1S6qcNtePF8nK7vR20ec8WXXtkb244IBbPu5f2nqQ3jAVk7Gh5La6a+jUpbSbOZvN1pRumtAGcQEjBOVXTEEaXdOIJnxItoUkErJBywwCZ197YccEXhcsm0yPKM6LgkO7TrY/oqw+U4A9UyixHGfIeoZt3N+2YlTNAWNv2/OYcE8KleCHP0nPFl0xg1j/FyY3349b7jVCeOUOZpUQhHrWwHFVCZii/P54SqVy/z+aLCJH6LoZsCmGgbAHAENsCwCPE78LY4tPRNY30jpXzeXwhj+HAKG9yeXFOy9nbrJx1NoisXFebJF+FKBJqJz3iyEqVk1F93P42+fB2cW6nYLs2nhBmBKNLNTNjbv0hmv6jYvRf/tG+PN7WdWXjZEJ3XCxjOjzq6fD55buL8+Hj4Udyent1mFfv79tjO/TFQgb7+dNv/uLbhRq76M1XxxenWw3e7loVUzg8pya6X9xvzvt0FEISis8+5AuPHu67zqf3QkyZiFEf9Z83X77v0iNKxXmWfTxuf/rP/69v/2//49ib6c9frjI7DnNnB2tGlaWz+fx0KA67/QKHqe+V1fzyzXnsn5N8V9JYVX7q0+Tj8fjg/JuMBEB+ueHBeq+92+lpBWEK7hkToViGkibrZaTUty0Qyt9cEF6G/Ynql5FxwpTuR5bhXCqDMEUGs3IoTewQ45LLteQ9iqJCiE8acu6wD9Gjs29wwfnEfxkPM30pFeuNczqd1YQTFPwgOFlV5XOWYyJCqAGMSwEFRCParDPBNuG4wwC4VohgRJNXarz9hO7sIdpLweh6XdvidcxRGoMGH7WoGMaEDgdBC+x8tL4rUCFySmlKU5Hzsz/8NUXpv7//Ec1vMMVSEMLaQDmKATU4dtHYdsvp4uEXVzd+74vZvHPRMgEOCgGZLH64+O7z7ZdZApMQF3SiuEhxdxgkns/eYnmL2fc/fNsN7c+//Qx2e6sSIyIn0QHCEJe+0dFM+2cztSuarapZXL9JOKS9f9K7mhxEMy8iTwJZFiP+7J033rGE86bEkQMrHYsNK7lQ2PHR7MhEUYzIRNAIVygMgQzPdLWkuArpaB1Qig/jlKCdcZ8o9mnHWMxybqKXctZnL/rjmMnaA3TuWMWhos2jDwKPAZ/oifFl4316d36mMkLQS8494LkQy+rb7OG2DW5ywUNFQQPNC4KiPZ08AGfLnrys9ebAWlFRJJc+/hz1QEobnwy54pxn0oCYzeI21VKgqjgYffpyCwhJNacW9/aJRNRUNcEyrwoVi4G2YiJAG7W4FAyhHbIhoJQIF6TFPos+cU3cPFlt4vF03548JTumlPiMIv2CVnQ9yyhLguTGO6dx7mWaLyiTVY4PjGGE8HyWgkVPKa3CFC2nQqYEUCfCUuBhVTUxHjDZMCnEu0UcftYvBkN59EyJJf4DE6i7OeVipZlMkCx6kZTHBChlMW4hAUYE1bQ/fKUii/GM+tmbt800KsyQ5OL53qT0hLcLax2kXOO0EaxYVqJ9/2fzLwYHm+P4+lUQNHoR83ierSQidCQnjliTJbGcTaELI47KBzC3h8lhOhO1WrIS+NhhNGVMcczdmeu1TtCkLPqH16Pgle5KhJZnNWkNZr7CcyyZIR2Z5CXlkBRFHeNcyZxS4jm4kRBnzLFPqZ8oBUaRJJqfsKY7t2WWoxFwli95GXlE4XUXXlIAhMnZNz+kaUhYcAWZFD2iyI71WaXVqpKSJXn79CXMVZaQ02ZKFCfMpFrf3EiUraorZB/06JcoOFM/hCOmPMubs82ynp8xlXnKxt7whRimW+ZEJcNgTuAbhL0Qykx6NDprPhCKxsNoNUkpIRCO23Xmd7+egg9u6ufiEh96hWf1opow+7t/+UwIOqOb+/jv0RgO8s0F7J7i44P77vvo9YQxRzbiFGX/OnQYCrOemI/e02OMCQ1GZsiPPt6n4M0ccMflxfz7vX4JmL72btLPe1xcL5d/gPyTtdr2jn0qMI4JOY8h+Z9O2/znP6fTaav//V/M337718s6n1EpH59pSsBBFwXRhBU43E7uvW2FpFebHEVtYZ/2PVjACBeEoNnVjAwpz6NcxhDy5Fl4ipgLmUkuFtUa8VFbu3vc0potLxofEXt9esI8VwVhjCNyaofzsmaMJAOGoxWQgp7rBXQhJZysVcSxwAaFeXE0lEQMeSji1PnQIYRZStwwQiBid4xhLjNpnSU8ay4u+d/5n368HWx3czObVTKb+0yeYaZSTHhd4kiiovHkmDdjNPvDM2Upyzg9FxzjPkyU4HZghCZKg249yJqF8ywzKXpCCTHo9fQSXJSEfjNnXGrdnyglRA6dOYaEEfGdFxXC0ScsQlURacBWEBIkwSY9pSCDDzwheLV0Dqwpry4v9XFrg91cFATNOu1Hb3vdtrZ2NDIXLKbkuw8fPqNPkrKCZyi4VYIKQXJSJqSkLkVTVPM4xHjapw57JuZ+TijRfehZ39AIRcQkwRgsmwbvdaeN54ieI4U5qbrtCVzgVZJeOh4VYFCBUDLQnk5TNibB5DYyng+dG7KoSMJcYs6LbuoKLOc03HcRSssTCzmxKeHrBnbo+HrCjHTx8ZJdUMFjCMfnhwJpOdSEoJhC3GjA09Tr5NhoVFFpx6lMuaWzNH2lMWWCqxDsYcsCuHJ6Pp6K+ao4nz28ktuxf/fzyIWa2cXsGx5O2hy5CzG9O2ODeXx4wIkgwjDyT36ItJCrclEsaYQsFRhTE/AIKkHx+vh5JyKi9Yf14vD4U7J7mxgytKloN5FHfnJtmqmNzJh1IC6vWV1jeIEUX81W9hneyEQyHFTiZCl5NP2xZ96Hqn5HREDOJoYVk4Hzo5nWSQUSCy41OlJSu4iid12KUlpckBue37+OP3+eMDZ/+ONPXPDeQSE3hNL94y8RIF/OPfEE5c8tCCazUpRn76s6ytU8voz7MMZ9PJCt3k7n/prRBuibPOMJbN8+xDAcBF7OO/xtzv6J4ipVXNnWJ142GQGSxjhVVZlvVu+5sj7zMT4O+wRBLSiKRcZdYjSR0O8+T3caFUVWFKnvlJSYJO+HyWcyhUwqRtliVgrJSPBd35flmU6WCELw/E1ZnDeLkY6GGGICmL2PcQoo2hjiGi+QzKosz6KZYgyUWI5rVczNtKUlTVxsJ1Ouq9wUCJAUBelfJi0fTjsktMwKbnQ83RuLcxQJAgJJSI4TxmU+9hYFO1mVZ0XN+MGnnW1DfFo0jfDJ9HQfDJfqffb9YpHPNnNbWzuGw3NXzNczHoK8EUKlRLz1bNJUZ7Shh/aosuzmSsQEQ3cf1GpZ82WZII7PP5nty3+FJJzerRq6f32Qc/mP//oviNwM22dmx9163f90cOPoLj9LEc4VfOlN/3AncZyDhGSvyCw1rTVHUuOLlo3jdDphknAYAzXUjFMCCJCYlNbqg342XvtgMJjrNxdKUv1w1yP0hiJaquVf/6E/fhaCTp8PS1W8BHO4/ZnJWk/D1/hfdv9n9R8v/6+zv9mAo7vXnwROeGJ1k7F6WeuD6V+KmrOUfX4glY+uwOlszilprE0YUTfF8TPmktK/Wd/ku+MA1Vo5TCk5+sPYTt47pPDl+RqRklDGq3XVdT4GSkkmcz/YoYP5AmjJ8t7LTI7eIPBVoAgrnQpVjQR4oiIIaoklkFSvpszB3M9Sg2XiVPAyaIO01zM+o25iaCVE/qsOh35SkjXlZQK47Y9iHxkiyypnQrGkzR52DnkSSoHZelasLN3hlIpp9CEhBH25yPoRe+OVyAjxuMhZZBNPIpIQvHdeLPmHsx+G9jHcvxTWGu9FnHrE/ca5SbQvH4eFkmS9AIRQwdbOuWSNxpJZiipUljQCT4Smp667LEoMchhZkynhlyfbe6Bidh4SYg1DLbDbz5/nZ5tmVt30N5/tb1ecnxgdH0nhgK0fZUHZpFLp9WHi3RDyCvMwARGgcIHBVUXOUpNxPGoT/+nTLsX4/hseypDsUlFinbRjEDnJXdOH1xiTmUKykCyj7YlXgjDuH528EHzJ3ClKlFFK7DDgLEcocAaQ+BiGHJcT9Apljgyn8VjQJu/2fVYLxhJYFUVFvHRGfljjXe8HBxhQRcfeA1ljgrJ8dn5ihy4ML8/V+to4LSOfpoEzHmOwxuBm3LcupzzzplnP3v3lX/du/PjYmsP2byllzzLnGOW8uXpvKX389JB4THAu8zacBu7zzfX1pWTzurFPIZHYGp271T6OFzcVlu92vwwXykOW6KStoQFH5/ypR9Awxd8E+zjGqaoX5aKYUVYS5si1xahnAqfWDzQVlUfRUi6wszoVFUnxat6g11Osi4KfxbGNbDYx1p/IxOmEoaKE0oQo40sRcIhCEM744uwHIf99e49HTPw+9AyWGzVNI7aOFVleSeTqhRYn009tDC5O1gxf71rKvkU4n820jS/TPRBSqLzvWiKdykshimVG6+ocgXvdPx5ObFMXbxZz/mqmIWBA9M2SoMRoxlMHigSaGJEU5QEslaJBmRTiNDo76aYpM1m2df7dG5JS7K09FXnnXsikqbpkMVrjq7L8+unTE0Y5peexXq7fvQZrUOl1KLMeW/+0vQ9AG8/UUhjrKOeqmB8QJzmdNcRY69oDYWzQXYrxiCaeBMl5nAwnDPnCquRC0BBIyUiwg5nOl4u9Aa5UyqgbAM74zrtwOuxiJzZScH6Bm1OtwcP1mh5dFqZpuL13VscQbU1SgsrFWVldzG7y75ZKKezlfrdPiLI8X3K2PxrAZrGqGJfjUHIuyRxtt6fBjm+vrtvu8OnzJ21CRtcZtdM4eet/uv0nrrdn6r0x+uCfe9h1r9C4lr7r7x+DG/qX7UvKLOrjP/3D88014hX3+xi9EYwbn9QeaGqRB28tfUVhHYSkKEb8TDkl0zQSQgjGvbHRAkIUE7daVMGOnCIuiI8DngWnxcShWWNrPrpH7xgjnFY3aCXPESECL/5qcZO616/O/Mv+7//0T3+7+OOiOC1at93pfaV7/vaaiIrs+hCjA5uimzCvZs0IaPz1cWBDDpFKUWrKDUuzr1zkeaPquvE+Wm0BxTfvBePf/f9X/eScTfprek29Qy5JmV2eX+6HR9yNk1BzMnmEtB7VfNmOu5zwhDjGJpga81ZrHqSQHLTWNvNKLGTpESKvW4ciKQsRo5v09Lx/zUUtEFTV7Gx96fzIuWc8+/HnHwlKF2d5iOnp1NWlt1GzFrwqUkokSFkJhtKPffuhItXsnR8jZsxEmEkzWBd9kVE5HZ89SXiRFZzckx22yJfFcWoPwgpPr+aLyR4nmnCByjH7Yr+SgKvmjCIyGk2DINpRxjAhjEmaVwcvuNs3nHkhcsiHaQqCtW13vv5mUHka7oDXlg44ZfZwAsLYcRyffvxRfi8wLa/55s4czpW6mOc4JB8DP3CK6bRPsUauKMWIHS+2o07klvVvKPuS2eKMVVoHZ2OKmBD21BMFgZCXvWaFSpR4q8MuPNZjnCI4Wm0o9ZumzC4cN+Pt1yezRTs+4w0ET1hMwVs3TmNWFhAT5dRSaJwx9YlnTeObbupGFfaHnAsHAbs5IxiwFJUvD8RZ0riw9zLP0pj0Sw8XOO77SArILxSd+aDPZHrNyNC1SjHskLETZQhOYibt3tsw2STfnZ2fif/wH57/j+nP7QllbALEvGeQdHe6PWyBcyXPsiYdWUVxVIvLOeIEwQsijvQFVh45WgIdVVOwYM05ItNRQ3BWO0wzEUhxcRlD/PRyB6+/Uq4QTrE4B6EOOekn7TI5Jxhh8GRGcoIQAAANySphaNF+mShjAGRq+/PFQgTZZq/RBUl4wxcGh8EdcGxImvrtNlufyRTG1yiqE5qDzNW7P+RcEB9iCSXnygYTAiBcpZEyCohJTsNZvjr4xxgixnKFIUxjC5jmRZ4XVb6Qudw/7ntv29MBU54rbowhDGiRpmkyLjSX/Pmz3gnhvc+/fuZSnJ/V87ON1sp6RaSkgk4p0pKMx67dTbObKwpNYsn5pM3xQGZZlkZnbSKYUlK/VdFP6TBTFXJEQIo+NIQYMh3Bs7riwReSY+KZyLErMZ+UyxzRnJzLZTXARMCV1SZg0hGdM4wQgIhVcakgUucGIkQpA4CZIwPINQI6PCOwo42dk0BU0AMJflnPDqSlcWyQdBRpXhGiEB5CPPhBpLZ/KWq/M8dpG6y9yAoWs5zEE4TlN7+Ts7Jeb4TKwtGFHOd5NfWjCBZf1/5xJ7igLfvcf0WElvO32o6ROJWjk0mPT6/jhH939ea5N/0zji/H/3r3/wnOfMvPuuFl7O98eTbd/9suhAHhPy6XCMWLxfLV+hmfpfJ4Gh4BE4RRiC4Ox1K93YX2hO110YR6Sw+UMpYM+HmIDymmBAABYCKoACTnXM6uFqrrJ4JwsCbinBlnTrbb1MXlBmOUUVL6cJworS54VjIlCow5gj5YTPudkN/+gJ8pio/jL/nHi6v1ZY4K4OJwPMlmyEupMswwQWW25Hb/gDUhuh/3xPIgcskIDphgxAENCVSaFCw5fyHAkSCjft5xSp+cS0KKhBBuijN8wd+yCWGsH07HAy3xPNBpPEAucYwJW0VGLb0VhU8yHYe8SmMCNu8Uld5R4+azGXGm270cIM0RPqIkhp5bH66uLo1LFFOXAiPqb/72P2a1K/MScMje5+uwYDOi2+Fi3mDc+NMXdnFxXc64N87120Nkl1WeOq3LIqIXJ9CxR5gbEVFwUjmCQpHz03A65/Vkps4NxaJUgh6QxHHWVPnkwlhjGqlyShtnBl2cXcpsQwfDIg8qa4NlJ4457rOCnxi4Zx3SMAJBcbnI8lLc392VVVkgHLp+lHSWcBni3azFjy9FOWNY3AR7GP7lK3vXfX/xV99xSwGgTOkQscEQkosuIIH3uzFmD6A9Sov6opnfBMXa54t3bxZW676lzeIv/vIv/+UQw8E5ZLwDbl3PPFYss36ac7Ze5hMv88AFLZBHgANLFL1Zzh/tK4JpdDE5Ni8cJoP5eiVYkRcPD1BktGFZF7GeurNsQW2j5s9G15gzp8OIO2XRgO9A4nEYUveFPfmq2byMZhT0iEVz28fkb7kt/Y4De/lCzzaX72flcTfXUwfJcYp9cNiOI7Qz8cEncUTosiqdaRb/3Zub/wZmtJ/G3c1ivZBOmTERxoigypyQYpjQ9YYxoQGFEDKtOUY452GMx96npL/8RC8RogiTiP3OQ6KUzFLtrLZf2nsXA6GcvTv/bnE1oTRNHdwPrK5TCEclqA84y5mUjLHQWzd6PuMU8Zh+4/gbbOVVOVdFHqJXk5gXwmAsvRzQlJG3wALbjcBEzsXjZyjy1AHidz2da+EZLDhmjNEJ4egBW8QqIoP3OKTRIZhg10kuBKRILCIrFaulHjthcCNtsJhY2yciiKhRikEblxAiiTgKglHvvKM8NG/QDOUA0YcUDsg4SY/AeVKznNDchSjAn8uL/ZtZ/+U3f3SJZ/YwCkVwjIP1naMoEzNO+8Rk8MfUyiEO4RBOXSbF2bt3ar602yN9gS6Cj9NkKWcJjxFnIpcCUQaSxVKghjGrYhDa+356jC7gM6IEiYkJfhqiGUBztUbArIl4RBjpqqiHGgeVL29ye3S9GEnAwcJhaO3rS35+RgWLLKBMZwz6NDy8/mZRTqqsPkWc+hUT4ep9TgjaP1GuNkKWRRm5YJQzkZ/Mw+L8fQWNix4om4Y+2L4o36BlgXULMJMs3I06w1pzNJy+THrLVnWP2+3213n19ki+9P3+PSZG9mb/57Y7UtM7PRxCeCuEGTsztGHRnG5f8zfXDEupmrYdFw2cL5I9wZ17xoS/oWeu8HYvcduZkqeUsl7uUaIRc5tRque8jCgS7mywdrQjJOpJcIAGPF/NLueXeaasc20xKWz7V1Kus6K8AoIjPfAxIZgzphgdOH2GahaRMnp07Z4UdCnnvMJUlGTn9NhFAmzv8jcXir3F6TOLYr7IwQwiEAI4hEPHiAJ35gNPhYCw9d53p6g9Z4JSAhGyRjblmhS0oDMbNIpu1Jp4poOWVCUoo33JGaMgBpVQ8J47OB4ENgh7muVVchAQBmLGvpyLPBdJse5l/e5tsXvUb5iARH/0h2maA+Kn3q/KTYmYVrP+SMx0FOc4w8rzgZJqOc8wzilBQOkYXp3d90dKAEnwCTYgD4ferBDNTBA1i2lRNN3nX09imvjlhXOmLDgQONk2QmTsSqARn1rA6FhkiDhBiAm2MLPX+IoAnaml83Hatbks7DAVXFEuUsZYACwYW92sfepOp5QMFyRab5zO8zwGGdHAGWYpQQzKbnUp3r25YKLAZ+vm8aMwv01f6ue6iIqwmbXOC2oIm8yE5sh1PhKUOMpMmdV5QW0wzgkfeCBh9zrs+7TcSJQtvJ9I7GTBiBQS5zykxXxO41yygVMn7BbwcohsOrYh8VISpphaXYT9PZO8m4abUHg74kjNkPVJzcsjidKYFPpkR3O3SJec2JhSOprxYdqZNOSe863Kso7KpQmRU069c58Hs1w258szNS84FzPjpKiqlPdBv3757fqbD4RShtgsum6aqLYxh+BhHPoRk7wMe76JvMvzqqkXOkM343D+7Q9lU6kiX7CMSGYxygiKMieYhZSMNRCBzJZk1EzQzWJZ9DlAEYyngChlTjGgSPE3NBIW2M/TZ4RRVS2/+fYdoiLEcPSpRgiK0vSdVwWLI1icrnpwnHRzjghoHIgHgjDmwRvIRa0aQbkJE5hxJ5KXNrpXNNJm3Qp+kVZFCq4i+EBNhCZB8GG7yN4h9ehOUQRyilisBpnlnShE4oKy3kTkEkYkA38C/J4tKaIhSILsarUgp/3BUIH2ueZzNRdNnWFquQBKACVGM0F8hksLJztaiqm3PkSH6bna5AlxkzClPBlfZLhHxLI8wbTI81guE2UVkxOB5krYMY8AJ+dswgihkNTu+BsDmqsaO8sbVVMc5U7/uqNClm+voHcTbxhEnDNQ7LKiezuiTEpOCEHGQz+OMnNMCITh7ExA9PtoMykDQ5iJDGGCCfKB5wm5ATTEUUiC7JfXurpkCLPTlmdv0yIE5xTFcHi+hTEefXlRiogKwcgMzXsG0U3HEKPeQ6hRcAiwS1xRIZWYQpxKSDtK50JIRAkACJrINORx+cqoBA/70TrPhLjzyRpNsGYJTYr7hXnTXLpp9Em/e1ddn/8eP6R+t7f9wY4npycZAoH0DkPx7QfnDGVAMMKSDqfDlKZEIzIkLyCRNVvn5LS7VG95jszkhoTlfF6VU0fK8UgCNZmvDfNH3CLfp+gloudLHKy4qenuSGLwmcqXeIU4pomY0eSumLDFTshZc9DjLK9MKvZEnCEevD9kckYJHqxaqc35tSgryvmYUprCXAokCFGl4Lx/frR3z+ur9zVfu3DifF2uz8ZTO8XAT0RIRLWxVLAggUkA/H59FSjNpSxkhjHGiFImDu506nudJicGDyfJJKLDvI1RfGlkhsoSYUt0ZIm9YW8O4pQwBRQH/hJcJhEtBGYyFZKlFH79+aTd7az5Tgyvo6gVpqtyQTGxvBDTEF8V3ZDfrS4f356N7JHmWHhBp7RSyxC2B/887ZIb8ebiXPBqJNsQo+YFd0jlM2tjfzCcQwghhBH5UJFUKJtiRO1zbPJpEseXY/V9teJ8shx5khAeGRRZP74iI4ZaNJRMmVOzojwaBHgh8lmRTXjSkCyMpHMnabbYNLkqcilzWcuS/PTrbYi63Fy0ytGXkeHULUcd+/HBnr/JMTh2wNOlSfjiHH771Pu2QLUx3jgxTZT6wLteG91zr0BCxhnmgNJkEpbJdwErNA7U6AEAn562reVsKTKfqVQ6BiwjiIrX/XZVlUr4173RTFHiKXa4KSG6h/GALIqC4MtzCISX0mgdnPE8MLyjPs907Y54sPvL629eH7bBaCwRY7ia1fQ4kIkQwPl37+uB0CJhyhHWuJ4T2nx3YoL3p6C3u31Rgo9qb1vpaobzY39646NC1scRYIFTTzASVT48PTy8nLAo/vDhf1yo/OSXac5nf5nJkbpfjohSQGQMZNhN9AYrjEccJS68DaF7JdWUaKOnrS2OC1w/PH0pquqSly9uf2YFpHkYHzCi0e8cRKDrlCzhlKnrR0rLGMDjqHKdSMyYiInSismICo1hndouUg0kR5ULjsZTJDjVhAcfnbWt3bvYq6LmRKyKy1e+05lBOnXdJ6sXdvslv74eUScJEV5YTNh0nBUR14UJ7GzJfLjwKWWJHdEAOItbilPKA+rj8zV5ry5Rev4Kw+DyNeG8ulgtdyNSwHKwKVo9Ye3Q2ZlS+f3TLRJeZoOkS5AlQcepe/j61Y2jPr984rISalEUTUw4Q/noJ6WUJ2Du96fZRChzw+FUiJAlYcxBo5zHjFXJwagCc88p0k3TVModHs3N5ZxSMp7GAQZuAO9GtinnXACIjJKTSxN1RU0AWQPE9K2jNsNq0FPNWMFQJi4xJTHeMyURwdYZlHOjW4RzyRKVmbcuZVAreXSiR32muMSFMV08Rc4STYqsi7Mww2tcSvX4/JQCZFiloK0LFHnsLaFYDj2hlHKZ1WXOJM+g4+GYECYmB8DEECTY2NPZ6og7FrPjKJdVsH6OBSKdd6feVorzaKatfda/Zf++got44X6Zfvzel3W2+7vuX0tzaoJ7hPgBAackMWK+fHyMcXN2KQQrq+pmvRnHERDS0xeUFhCc9saH+OL35OSsmXLO6uVFZKsipX1sI4YtBslJCBC8wZgsrmomag4akXLetCZGmctXtyOWAuAU8UzNypjxheBVHlLwQGisq1mj9QQkLMpqxoTtjyTjgaYDtKU4p1TAMW49mEe9ytj5FaHo8uDv6wcriuLB7K63iBUiEQgx4MUc04BwPx0YY3uFmzxDnPOI4NZqZp7IDrFMslxhnCvCSjaLRZFBHREM0ysjdA+IeH8OLKEJ3GmyDFi03qcZRihwxJG0gZ/Tvq+5is6SDjOxZxGFrw4STmbqOKWeQ0biYcAkmtAC4otiffnub4db7I8Ttp2VDrP4VR/TAyREImBEifcmakelELQae+0BDcm8KwTj+fF+N88jEJBGt9LX8WzwZN0gImv+oViw98doQphYLklPYrKIYoZQgcosF2fu/Jh3nUsjYbKuxxNCcVgXBaPUY5znhAyll9nB2m4y364q5LGeHs8vNgUQqcO2GrljuYvHB6sYvVq8C8mxD3k2DJ3tB+fs+NOQ/8VKT+3jwdbjix/igXDFGyakPMuQQMkFEzylIFVOC84BG2N0GKapp9flvJinWLxI56M/A9oVaGbpnpKAqSGr6u3WjjzZeDo9r6nylBSLfIoWNw6Z4tE/vaFvJXY2WXgpAMUoAFRM1mYZAxRYnX3++380a3bOYf5NzNQ8VQiHyIYhAvIpiQg04xB2YzeUCXuucEZLThCqKDEznKtM3d09FbmK6AcECiNL+JQL4gfvzB6PIBN9z+pc5rf/+vXUP/WnPbMOodmpCnVWOMy6iRKWGNJBBhYBo2NCntVnhWCUe2l1ZCtJwuWm7AY7UAzRUmQwpYxhhAzgS6f3lOVSfpOorC9Ep9EOYVWUrKXgPeGGSSqoHaaJzE0KlOIJ09IiSRhNLymGJyHeXaw4EhgxQBplomzyaXBgppMDnfNcVmV6AcnyQ3UjBCmrkhM3YU7lTDvDGoEGQig59DjLBiDVId5FuKggDrIjxvZRY5bKXDNCJoywFNuX3Vybhl+zRsVmkV62I9FgwyEletiKaVBZLZpS+XEwD3fDQwMRcf7mB44sd/6Ckunku9gHJUqCRpxyQrDIiH1zU+nh9Vzrn1/T/qt89w5CMvsjlp2UhzgMsY0C8dXZuiCCErh+Ex3uCsTn66XkBSyWzmPFcmt9Ow4HN/b4VJsqIpcSACMIELLELhktit4XXrfOb1H0mRKYSJ0cQ5xzzqiyOmNeJ2wSQQqHIlJUzL0eaUqhWBOO50va2rEUQ5YCAI7G3T7tOcIUY7sbXod2rfJ0POZFJTkHTigl7HIe84U5HUdICWkcKRUUMAfIUEoPSfJuXlyi9iVcvgNMwF0GfIqx3yU6JB99naavDxFHSuVW3Ep0zZPwzSL/G45+/vPOpQIwIQIAI0ZoI3GzvgLkbDxtv4QeHvxj17XR+RjTO2pZ3fRPusaLrMwAVJE1qmhohoKZ2q47djspFJCJ1eUGX8lMSUUYJffovsG1NsYhjJdkJc8/f/08K2cJESkY5oIgYEwcrGMhsUyShKjR1njKhQd8AoB6sSkbWde0LFVRSa7SMtq21S+xjWwJqbmW9mW2Ffta5E2lJBmkh1DmMor2padzklAJ5li2EWfG9wOIP+xfDwRz+s279dtKyMwhcorJ9kD8yUdLEGYEbMJHCTR58M6SQSpV5e8TpCf/lHDlo6Z06aOOQULUknPz9GR3Rw9pIUSVfUcupYuCnhTFj973wx5hIWlYIzKZI2dzEe6ft/vdY3dnw/DmJkuJhlus8mJz8+F43EmhntvblCBjhQtOGyvqfEX5NIy5QAsciDn5anYw9HWv//L3E59nWKw0wQS5zvUGdWWsU17Qanir+WSWHp8EI6eXo+2MwnR1hLLVT/GrlDkmTFFhwFsmF6TCBUohxKSS2VZyGaNGwXGMndVamxwrXncCYEUcr7iewvDjj2z/7EKiJeFU5cm68fCUEnaepiznQs7ZEi9ZewINQY7cszGvC0FhOJ68x3VZBMomVQzPvz3dLfLSB+S5ojx3SGF0iqpY5yG2w5RV5NYjj8Y3lZQUSRozkU1+OvVmqddAuA5UY48Rz2DRmhYDSmUYcc95NoIyp/2Y6FhVlOe43CEbowkkJEoIajue16AliTRZjBCoPHmZaxuij5N3erh7dY8h2T9t/rpuPJ2+7g/Ps/OZm9jkJ5J5JmLEiFVofSDBOkZY275sijPigvf0SCjjBcnz30bXzInMShddhpxSzIeOGE6q+yF6nNyCC24P3uUIp6UcaPTIDAnjiAShBgAV5cqg4at9zOklmVMSORMcR0SJjCahFHBAvT7m83lQfqWrIuN7d0JRNuGYsXIHA6bFd4rjROA+xGjrFYqM6SkrC+XdwXtX8qXh2vQD5Vkl4WgkJGqt1jYIWaCC9XSCKgmDMOKdnzp04EJgILkL++kkMDBiaoYQbvHAikUROKPeI5k9D9oH+zabhQpmmcxm14hK5+LUE5FVNo2HcG/ItHuI7TxVAk8dlGWhkkk+WwiKs8B8wNFKfKKxZK5aRxUwtqEFhAL24csvuiZsU3O55lNfNQVTtFAZKynuvTVBNIUwLuQ4YXT/5M4RQYzeE6STj65llG2qNctM7JUTcaIFxRTsGInijntrVHa+yaqv7Zd5+YFiiuh9w6+D8+P4Oc+kT3s/JjqboYQPNk3aneVFdNYkXdME+7jgwAXzNoJPwVgwcdu2OaGAkkSY+AqQonRgOCYALhSTuU0+zJccNV5AxGkwvfVsFjHBCmMVC+AGdUKvEg9+PA7H2bL2Xx9RRYGZiGt/PeRlJTIcAabw5c7M2GiK2eL/8j/8h//2n//3OOnNPG+d5jygaokB+tjJ4DGqLhdY5uOGylQ0H399fen7OnLgiJOcUuWDYYJVjQgBXIgxBkaIkBJhGPtdVmQA0zFEtIsEiJZeSpXzRqXSuAl82Gw2D0/PwTk+X2AqTqWah4hwIi55xXlISSWJqA3eRp8V5as+1YDzhHKLBN8gQtIgqkVjxj//8y+Hq9V5mQviPCFxKblHniItQ6CYPAtHNKpqRV/HgJyYa5kL4h+Ix83b83M2C4lph19jpJxQzEJicWwApoSTr8G1nuFAULLmAaGVLBqCGKQNa1bGeYwxhiyTtE5exvBrq68IEdCAd+ItC08RG+JnUYpVHkeLYpY45nuYsu5ImEDVu99db/jrf34J67yFWW+NtzTbZEfgcZxeyl6qYtjcyfLd+MuTKTOVlfFwksmUqGQhAZZSzN/P/xjVnWiuKjkBL1zf2hNGtQXSnyDC9BuhqAXGoaQngoYMh8Qj5ZRR5th0Xwb7zcVa+TCQMAQezME7lTxIIRUVSMnCsdPTThBWFcF4kzBwPnImsHMx4DQm//Tx33vD1kTigADXv/DjoPWnL93vf7ecza+kQLttBEdEoA5BCNFxCsmFgI1HhOaCZyaGGOrlLJwIu7v/LXiHKb75w/ciRpqATmnKD5hkTo8Hl2YSDCII5t5+9WnvKfWUGW9uI0JQERYsTDOxdKNOwBLANOpU5lktj8+Dwca4RDdXVZVj1gy69LMH0lFKWILEIySEjpyNgw0pwanD/Nga7R2e9NR2h8l0wdl+Zj5srvM332+fbud/uqKU4w5BgW3l9WAJodY7S/T4y4N32o/FbmeKG+62GeNqyzj1Xhs9mSmXk8wSxiRGV5X0nF8PEHr/EYUREcuoSTjTgAkjQPWkUfIdJpgxCal9hikrqsVZNVhnJ618aJVIdiJLSiwnjIHmshTMQXDGo4QjEt4ACD1NKcZrOveZ810QRAiZYPQm+dlmBZAwxkv+1midJohYykxFh+3pQFAVo8ZuMiECkYiuZqDHjCCCEuHOtZggTPxvZgA90YJDjAXFGFAKiUkulLzOcrFZj7cPJ8ApopjIduiz8GW1+j4lejKPCrUIW92FtUrsgm9133rER24G5mWqEI0xxIA8sop6Ibi1BpwCSaWQs7K0f6ARYoojgs0iBeyDrXJQ4QKLHqg/xmnyGIDQ7NPr9t17TshqisdtGMe+9/vECOGXZC5LicnOHgqSYzwWgUeIEhhG2Dg/HFuH2PqbAsU6AoIUh0/a3vy2yr7PUDZnC47yT8ULi6Rk2cgQgeOEqWdpUxVqAsemw+7pNI1rYybrnnx4L76pZ6UMiLqkUB5ETJWRGBOgmBMuM9ZOYVa5hCwlfEokEZzhrTvmz6XOFwglrPEzd6DtDsXJOXN/H8lmrIUDQ80DI6/ehxg5F02dYU7HLlW/6a9/ZOX5zZVUwg8AjAGRJKtwSglTGCAgYY1RUkxa88QoW16QAwaqiqyNiRCMcCS4mS3oONGQut3hRZT52e++H7pnxMOlfO/00aODZCpyJIkkRGplalKdpm3C86qiIaSmWUBAUqn+uMuCZ2XFFE8JUbVYKBJiRJxphHGRX+UlU0os1/VihasGMKWYfocqQcl/+zsB6f+7O+wW5ENdzZ+OHxfLGWckIoqZpMEu6nnfn3BkaROmY0efIIPc8+EsL2uxIIg++egAAOOUotdAREmYBc8SihyWKfYBAyYJEezcEFFU2So+nMj7FQYcQ4L7W/zNO03kjBApRdDeu30X3PqWF4sFCqNuvdlmRdGpecWmR4cLhpHyrDueuFSzhL777nuE3WuYjvaAKY3lNYoJfAIowEzWkon2kaXqfKMHAGuqOq/IhLLMBIgJyQ9n73eGcx5SdffxSwqRngkaKWKlkiGEdyFGggYE4AvV1PP+0abQ5ZJnTIX1+fnxuTo/5xh9+vGXWK+WYimleHp+XTZzArg/TXr3gOJhLTl3dtu1xrMUZFWk4dG8dCTa+EMz+xueMWEFw/44Ha5MeiACKCuK78d4+/U1k54I7J1eOK5Cf1cXbyK5IKQtxLn3FmGKYop2CJxffP+nnQvTx18wiRjl3vZyVjjXZ6iq82abWjSkXK4seRnMy2D6kpclU5HXMU47yGKiGCu9dSfVE4Dv8+rPpwN/jZuCuomfhj0WionvMjMdk9cYX54LbZgNgM826CEAIzZoS4BIKRAOkYzBkHwmrjnrzWJ13nbP+398pVxcfPcDI8Y48/q6r27OW+vngw8pRh8wgugdntGxDTb1r05stTWdb4XPJ7IQKhNocFN41fRajebotGs4W4oI4VXF/egGERy0wWc2whFTEuPKK0myHJuMUgqG2RRcioqCEGLUGBeRBZWdTikGyvMYY7lomDAcakxZFFYycWzR6AbKZEgTYkTnSzEwisAEw3DEYMXRGNaPkzGEXl4ZRNzjbiyKOoau71lyQrKEKsUVk8jH6IQPgFnQ6UBMlmHFb2J0HDNGjq5K5fLSPd4SwphOwTo3JVSHVAIkEJvLq3b0RApxVRwOnSZWP4m8puVNI70QNMM8BcOofv34qgRNNFnsCZllGCNFnbFFbrvJF4OQNcfFYdTOiBvjdyFExjimKMUnRBuFuYwCu+nphIKLQxpL0iglEWJv3tz0wylfks35+bJa/eY1y0j5/e+4t0f3m0hilamUJBYUQX3SJ0AkjwipImcueHv/1E/Hh0Ner+scRdR/HsTb0aaKmNFDIlTEGPd2zIIvi7oq86Fva4GPwxGNaEOyBveaJGOtDeGWfv6OfjicbgvGMZsHGBIlEyRgjEilGcNcxCn4NCzW+ZgsojALC0bQAbUZwREgeuTtKETvfGa7Ls7zY7DRHQNhAQVG3wp8F9Np6i3GNSX0qml2sHiG+4us+f137z61/6qyuZ6S90HA/48g/GizbEsQw7rtzfHXhY9I90y98o1WA4REaiBNpJ+rGQf8NBE/EGii0dVdVc+li4wMe/2x2++ttdqE60IaMzICESadf9K/9p7vdYDwanYFahGeu3k5GwlKcBgnEtKknUJcVNUcU7DzimPMY7DJoQhPBTrOYexRcIF3FNVYkXhalj5whGC73xNGYofKogopEkwwIcEabltVLrC3DJASQXpEispUcYlYOrSFVrSZvV2sJBPOue9+W07X//iXf/7rgY5ZLdwej2OE2GNCSTkfD08LcJL0JtidbxwcIhDJVY7FGatOnFPrfvIWUr5CgsPkYxaTCCgg5AkIPvMYFHm739HTBHFmoz247p1YxOKsjMB5RCR1N28JwAIgAOD3v/0jeL7drg+PXVupEegFh44JBE5oMAJOfeKc6ZFnYLSHbgL9znz6uD8CXS75cXDhZY9vyMo6T0l79ercDb+OH3zwmPl0unQw6c3nJq+Lb0/BbnDUeUXsw0MHkTUo4fbx+YkJgTElLUNlkdKSoeNbeh0QxAw60xkxLWWTz6Zf3x+kbde7HZjUwGj+979TLqKLBYAY4Ahi4GLatjOBVsakm5w+ogxFd9DYYoQICsjeucceMC7eZKL+3Z9CCAQ4iMWsBHEk5gyAL9r4ABJirGJU8+RRAjBv0BOazbHSHEfENXEWMYp5ILmxKgbHYngjsvTD759gNKBTTyqXtSzyNGLPYtWUzk3DODgQVGASzWazNyn29089gLjkHmGUgbO2CSzSUiIkztTzf+lDOhmcJv7SX9LTAtTRBuYtcFlKqHCRTjq6nz68OHuBvhspDOMks1xwjiKZ3/xWYHZMyF8ALsk/JX0432LgS7G8G7+aD7fzk9+z+uwKPu6kD0dAIfdWQwBTft6KgxwK7w9t2/O5TBQHHFICTEkWvHfaOaBHqv3+LBcuspC+huiksyF6KoCxJjhPt8x1D91kK1nqkLoQBgh+u/zhavHbyJCyCJsDACXnMaAoGIcIG2sRSsmZ4H0CrTIuGIsCQwSRRJxz5+yMCtiblgDMrbNxfAkxxXihk0kQBe09oThlMoKAj/sAMQIo+ACjhQkQAnJdKO4DEJwxhABBKDKajypBazmisJG51YfJUgLxpQhDDN4zRoJMzmjKBCqzuNsPhcyXZwuRo6wACCcArInjNP3787+DZ+Otwhg7F1PwIQao7CMcT/MqmxVhBCHRaRh8dFgyKQUD9BiJs845RQhJKUW7diFLyaVorYYgwYYtCyB7P305+IaljsWQBJ55ihkQkb1+906KQdDxiQMYBSedVyBCBzvUTr6cwxTHfqwQpJQjgL0N7a8/p2++FRdnWQhEG9x1awCGAbLYGSFzEKWgwVlrRqxGZXfth6/vx+mPTYNAIjdXi5O4+emnaRqhGRKAqShspkynKrBKc5TrDDkSzDMWpct4F45cCreeLBkD1ADEfmyzKjMhonKcVPCBooeDV+PVqwJxvNW7NyD7aWDz0RB+uSMHQkjfdc74hsxykFM85bk4ef36p/ef/NRDCIL3yqLZgrqJzXOfMDBpyc5OmL5bNosEsEd02gUcjZeRBI8xK6tye9gDAkmWAcp6c+vz0PDM6E/WZRRjDzIOwh4oBliNaIw+tRFWCK7br66/zq97pkAMTVGs9cQopYxKLsZx1Id9XTVZXrZDIkVA2DFQF1mRFXlOGJNiotgAzUFMVd4HF9D0smnn89o3M03JoBW2/iwlJ3M2HBDLuHdwk1zuRji6R4qXj5gu47D19TVfCttD1CVQJgAT8LFEkFEaUUrJ69HFCAYVnsKhfe7L+ZnPHE3jfs8jQosc7yAAADjgPCAECnD+ugzzAnyuwaVT24HGnEIkAKJEYw7HHnpsDIohO7ts5LB5eo/We7vbaa0+lhd5VpdHy6PWvb2HYIo0wRAtSDTziBuAk81sgHLY3494Snv+gtkURAgxtJDVlwVeIoq81ZZiEBD0q3sQg4cc0zqBL78+f43PyXXRMREvJWcof8oTmAaFYb4qloFRlDAlYObCuQAIu8hShHsNtVGTiUnOmD46gKnDtM6r11Tm3/0AIRp/2hOBBb9h6cMMCkDcS7Jr77yo3gWgfY4CouD5CbAaIqaiKxwYMUkkWQTrlLBzelIExzbF3djDFAtvjFMBo8EcJ6U5EXDhuEv9I5jPmirVECZgXtzu4WGtAxJIYgrpF2P42BFQRgDvDl8SDj4AgjnlbzxysUrPevBdwZaBUGkm/hJbcKTlavWh78e27UVAVROkD/mlRzQhwKncjyPIZkWZQ3Lb0mhp6f1min6W3hyy9ebp6ebitc0F/DpD0Hig1fOHwFPwerfV7Rq/6N2kXnZfvseMQKnDXeQ8Q0q50ftR58ppOO7XhnnEBIYswswBFFxISmt4DyJCXwNYiewZ4YCxd847iy5YhXHXJxf6FJARMafmsN3y08u3Vf646WjOjlwvQbcBAeJgji56QjlGDJ3wHC+Rdzbee8V6x6myY6T0m+9liGkRYASnxnbbcXSUhX1iNAGSjKEZABNBvgu29plNCLjH0dW05rzW9gXEfXDT+njPHH07u3I2Bp+UtTZcRoYQ7eAIoUgIgziNnuYlFO1mAIQ6yLRtQVYERo8Y9maa7iaCfIIhK3KIGyEZhj43dDP2j5OZY5sRIjIOCxC6KW59j1I+Szfk7QP+0utjjL5AzCdvWpdiEoEQCAijmGKlk00B6pDqEMFCaa22z2GO/V1g3/ovh4OZHsLoY5E4Xvm0wQDiIoFdAIejbgoek7EJgFAu8Ry+7vSw/3oHUfPuqgoBYMiJoIXtMS6WubDOR4287cCU1PMxUqNA9N788vw4JbA8Hk7fvb0A6TY4bVVViJKz1luPoKldBKUlGkGZoxloJKvETHMfI1hJEaQuvZiEou7QHokAyR28MWikEFurv+w+XVycF79fvh7UEL609s0ckZgigBCfytPb4b0OU8Jdf/A2qcuzN1ldHXebMhcY0mLhouMeM0ClftnfvjxesBXGXMqlcso4NVrF6UxKPk4GwJQSIIyofiQnyWk7AJJnbAYv9pSJlDCkGDIQ/SqXAksMuQ+eBJyO6OsUQgr0Bp+Jxa4dlYkVJgCMymRlc0oCoISwvERZQfRBZk05+44u4WlZmbIEKboUd7r1Qb/is6vFLIuh+s//8e9/e7g7WNQP7OYiBXDYfymL1dhPJ1kZGfDjYDuNR5RMDLkdpznmWtlUMnUAKB48QhYyCE2CRFlCGYI5FznhW70xrDgvll8326QDqaqD1f0wJf0IonnpblIEtIR0hTUwBWAA0HYm4DF7gD0ikVKq7VRtt1HDICeS5ZwyCBNBg96wf/3x6+Phb1YfMT2Zv31XrWZYUqOfj/Z4akqIYpPlmnkt0YANoom/kh64dvi1DToTUFCaMcHBpTiNOQhC8H0UcErz88Ye2p1xZ6Q6DMN47CEt5ZzAsOi7Hymq3tGVkTZOUYpEMJ5GP9EpqQSea8e3p0Jeyjllo/UKwAifUDB2rJV1HrkGcOPiLOP5uQKIv+aMp4hA3JI7/+X1x1df4/0bQYvf/uH8wy+bQwtY68K8CtDAZPICKZ3roROTz88RPXqEhCMDXvlgaSZEtFlyCIAYPaToRHJtjEoqQAOCQYcsI1nKXrSS2OgwBTLJgUUus4Srl93toe+U1rEoGJkmFYL3EISyKJtiHjX2BaRFKFNlGJ8LbwyLnI1jcfV6QQj5c4yfbh/nVyeTcwME7wQzCTy0u9sp91+esu8KWWJGKgAGQtXQW6fuaviOnqymAF1K5bs/uuN/6T7pbvc07Mx9iqD/rLQCGIYAKMbRttqABNd6uiAnNGUJxZgNWgk3h0uVYsAptAmHfgfhCoDgQudRhSnH/DVF5NVbtgmizPiKKKWsD4f9XruYF2VZkSr6nPCWkBUBOkwhc1x1pUYuy3K/DYZBCTGkEKBsSqBM4NDBLicEyzpLSIODzzGMQUI4wBGK5fLY/mhGjAGxfoiQkTSbzRMisOIwVER7WJFyDTh93nrUoyJquyVZmcPYtwEGIs8LhtLJ7Opp/Qk+34qCUAoCMhCeIjKjbLKYE8zqt+dEcEAkdpMJEYOEnIF4yn/zXdRG3753AULqqYqHIxuylhBEgz206sXaV00laKQBRAe7mKw9nL86f1t9Y2n3cf9l/zwgiAXDFY+CRyFdtMIFJ/NiHHQmVfAgbUd5M/eO5j7a2aLWro0eYng6Xz6bu0w751TMQTGtjj7E4Pwha2ZsrT1idNyaOoOL5UJrc3zUT8+7ysIoI4gFJwgROI1j6sDQbSFt02S8GtKqyKi/biN/y+KUbp91YzbgPEt3Vo/HcvFq70wKAHNGhXjcDYRaiSexONde7w8wTE8n6AasAWJ7hC62W21Y75tmVeAUvDU9iZoE6ozqu78qJetpKS6vyjyv86JDOz4ClsE5rD2+STEE6jllYQzFCXvzp9Vf/mUfA4QphKCT/9qu1QjYELFP4N4eGGXtNHUDLAq4mJ8v6sohZ7yeLedDP3jvQxa9sVPClBUA6tvdo9+r8lU+HvpKMOeCeY4EEoxJiOH6+nshxIUqbtvWPh7Zt5c5zzgmXOaYCRdDcAohqiaNa6gdZ5jOVterCzowMSKY9ocwSXuWkNOM+18ffszy2hezcDKf/wns/rpp282vn9lv3p4cD+UYVbLpi++WgBgPw+qCvTxzIR4Q/W7CofTugEjhk7Ip+jgrE/Kk3SYG28kqQud5FUHyNgQHD/vR748pBTaELnWuOxJyAQA2+hNhNQIVaSmgOGUJApZlaXr3pvu5B6DHjBMeo0BodLSRiGCsEIWUXb11bR/8MwiQUf7qRkao+fRplzh4wTTHZV5oMwYPOkBiUtY72yofHIXIAri6LggosbxAh7hB2B/BuKTdC+VswrSfk7P/9unvo9Ln//CbHLnItTLtyyORWWT6bIGbvGETHRXTPLAghACLmKuQCA/IgSxLZ0M0/e2nxAMnWOIczrFT5vaDNm4MAVYF+PbiPEzb4O7kp5K8QvzkW4IB4pSfiyJ/94pL9o//9H+5VS/75+fEEQaggGhimcMCNpdg8yuqEIgxhoAA4TLDEVHKuVrjYHn0wLvAE55ZPKQMYB7CqHWMMaCYBmSRCx7r5DBP3kdqU2ueMRazRtSpubw5gwnmPEhO9WCrkm82rbZWCoqhxV4EA45HnBc+ThRjvd5AUoesqJp5Hssq2q0EmSbk7vHH8bYP2ZxZngLwNpSoSRL6kI3ds/RZYOGqWR52cVKaFZJmmc8Lrho76MJMA0yUFY24IMx3x8gW8rAbrJoeHn68Kq9rjBGGnfJwHHuGUQTKoMIXHBfnRaQYTnE4e91kzfcQIGecT4GVbJ9SE2Nn7LYbKMMZoximCCKIndUCQxjKSYb5CW9icAJQHL0/pkQhYYxgEUMwOWUZNusBeQNArjYPMqNNKcgrmlIbjjElIJCGqWwWwgk0HSEhlOrBqpgi7I4UXsMUEeayygRCjeVa6xEpeJZFkPBewwLHdB+fgfE+LeSJOtyh0qcIXYIck9AfBMtLiL0OxCtUnlLBqcipVTrGw6SNxgkqiAwtZ4LVIsTozdWCjiBGF5blqQO2O66nBMOgISKRERjw9vCy3b/AullQdCnO8DcUoiR5l4L20Xvntelftq6p/PlqeTxqzOjlTRKUYTjTNvbbCY+I0mhQijWK3o5BW+yLrh7DKIoiWlOWbBqUN3rFsruN0dw01Swv2cky29hDC/11vTLWOTUASKZ+OypDoxbBeaAxddZsfWuxRIig7OScHh/C06RCTJCk5hyJYrPdzqsF4Vx7TzIqeSM41cj7GKOFMfEWQZ8hhHE/Hh131pvx8HWGZwhDMD1npJyBtHj72tx+egLAK1Pylx++44gNrgeBibAlf89/cl4WKTSwNicKejLZeFJ+X4qH0E8ZCGBAkPkaYXH+Zubw/ePUnCClKEKWkJHLhjJ81LYbjhintu/GbogpLd+8M6rrGSGI6t0d7HT0oM7emIf35WqJSGrz9Um1JL78vL5zD4OJD4Lz39Z1BDN38ErbvKo454TRfnKVLEAqnF2jckEhb5oFQrTnIiFoQQACZSxh0Bt3nyDGAut+B/db4C9RCeRrSk/+MP70499HIZNHGI00CMZIEDga3Cnvg237+YyCKX7+dJtnZ54Y9Zyo2PLxJs7YzdU5wXAaJzIwMB5dllGaUQfMBFICMQJBze55E72L8a6u55RS0jCEjl4hCMkBFDWQHPIzSVOD1Pigoz85gUnlPjsIQtyTHW3kWeKPoT6v/uf/6//yz//8vz9tbunzC37jzUH7DaaMFH0KbH33+RC8qd5R5ABgDdJp2/7E73H6p+8Fn6tfPv9s/m4TAGRJ0nmFJcXPwZmyqfb7zTh2MVlovqzKjPF2nFbdNkEwOjr1HGhTzQOHRcLtwQTFw0M+NsoKS70gNRB+f9zuX17KqwZK6Gz/04vqBugiSQA4b0L0B3OsqEyg5N3EXmjMI/nm7bcsZyv4NkR/u34KagqzHGaNAHBHQuqekRY0kzQYkuU5GEEkM+46JBjCPoJ4/9QBt7QGAm+9G9gx7kHuOayJYRROEGLw9esXTGkAALNVJclNrNElRAi3Xc8zyXLhnUeMbF5MIQRMIy5J23cAk6rGAEGM2XEUjK5dEL1FWYIEOOtIaidjrJRZxM6SxpppNw0vPz/kJ5dvZ/MW1GQiiSYkFtTRgAwhOKc1RjxFcOyPP//a/sOfKH3z7XKz2ysLFqvt9n8sCUxNBpEJQa5qjDDJc/T5o9rYD5AictlkWaV7EFOKDuBB0JyELK0PBpp0jQCEJKsLGTqtCMoqgcnWYmTvXu6V9YVADQE0JDPoSJLTmp9W3RnCu3uVX/Sc8eNapYRKoyckMwNxm/ksdGYqAUGmAHqfcQ7BYI0ZtYaXBQ4E9CAlgCvedevRBcH9AF2FM5LxhKDRNmiEKIoj8MI9bZ+LssxTJjJ8wkVfUmea5D4D7Wqagt9CHJgQUFCRnw3TMJ85sw5evYyBlm8vUHkSbh9iAORlQK8zH/3tlyM4FcOkvLUw5Mj1IGEWobEAAjZ6RBEIqR58m2f07PwtsUcTGRgmzmg5O4PhQjibwghDNMZjNVI0GINjcEAAWEFSkdOtOwwH0I8xb4xSn4Y9ddZafXL8hgLsfQcM0nGAs1k8CH4GZqlKARrvLUo4utBPEIZ6niBzlxLqHq03n9NWfjNruCSNi26/gboNjE3WR9CX2OoAzaCDVzAhOkLrkpiJ9dZkxYQIpfPXw76jpAsUP+8Og3bLZa2jWT+DBF64zGPi8qQklJALYu2cM0BuBBnPU4WZV60epl/+/j7s4RDOi9lFxk1wgvH6D38q3r8PPk7PnW5HUcLBUXRjVXCCytyUJAUzxkN3yI7F48VzWTVM5tuXfXPmvAleW194/Xj/1Xob/Cx+1ylPsKWSJighQ0EryzBZzEvBhjhgSTv4aDyKiUICcHmS4BM9gjml+4YYbypaIQAQF0CS8iWBkviupCnjp0sXHMIkRzIFmlC+PnScUwXkSZNF+SoCxKwsVmVzOscxGWdSWRo7KKwQiAiexniXZVcc6RdMhFIh6YR9MRe2xAqq9cvtqLsyL9VkiuvCdpnc95Q3Ka1gSQY4CY3PLioQPVcbhtLJSfw6bD8+HLKrGoD6vMEILF0LEYDOieQdZW/CwsYYglMYo5tLRDAAEBAwEhzGirg00gEYGTkuCSBnb7Kn59W4e9o9wbmwAEJz75IBCCZrQnv8cFb+bt4s/tM//efD31A7rQe0Yavssqz9Ie0tDJH88CoJjJEoR1p9RipYa37Syvr/8c//8j/94X+667UD1TU/6RNtY4IWYLaSjYGxH6djarcFRzqyYLq+y40++omZQS/wvORzjLzH5gTRcbVCdkJghtKEoifAxtBOmzGOx6qsyjIDBr7Xhr7OlvSdAGzy1hpb5/VFtZABdQY93U7DwwA8IsOXTxDiA7YnBIGChuRwlyqEaCGTKIbWQskglsPUY5ywAc4lLzNJWLIvxLuK4xmGflmGaCGJ3jCjjqaNbvK6UwftgHnEmPDm8k/fv91vAkigmkuISPQxJjIgFDw/bEeeM+Meg2XGdgQhgjmkEBPovMuzPE8x2IVAKQSHqLIOZhyEVMDkxxFmVDs3QnubUixPs+uMQe8T9Q5Ow8ElYysxZWfkXfnbZH2wgBCSSK3hVxsWUjLOuLcmgf2qbrKLGmao66MPR5yarvcxtadX5cNd3T0NHzpweQYI8db7MUwQTsu08tinmC8cNzk+2Ls8iKf9aFzkxAK8Ut0zQg4iWqwy16IULcIoIHxalPZ4jDGQEwmP9rDzEgFPWQygjaGKPPk6zMFk87wmFeIhc4BfI8ZRPsmHhK4hRFCbCXaISAqngOoUFfMp4g0yQZsAohcEp6H3kHTAzTgSYB9jkfR6orgM1SQhcJMy74dFCAkBHDqMc2eDEETUtPVwr4CsKFF4cMA+OYB8Ks4AmyWVueBDihCJqBHfs1gbH3zws2XmCAaUoANAQO8na+oF9xPohp6SqSEIOjN5B8aBAQOssIRRYpI37skd4wtCQKQIUiQUSCfpvAavm5kh5sOtQZ2NKQ69R9h5zmvHndQKVYKl2MMEqDHe6O3LISsZbgJ33ivi/R4XNTGJqtEmgLWilCQIft09ZZmEICLnMLB+6FM1aa0oAcwLj6JtTHoI4YQkDGIgTBABGBM0FejleWedx12uoVrM3xoM7u+6osGjSwwBCO1EsAQARogoGbwXzsaoBF9hJhbRwnc3jJVq+riYgYQtQCNdqywvLcaO+EC1Kwc+Z6voVHu6g0Y8a2WOUdmw5DAAdupdl/Gy/OPNn/+/t1+NYdGgFKbo45NP3nuIwBFgq1wkqZxxhFQ7QkZAxTCHuk0R1pjhQoc+MQKABFAz0VDxre9//WT3TofH9da/0bsu0rjPIxwinIuSg+DVzj+1jGK2+o5T2o/UwT3LZAJI757AaikzEndRzjwtGoQgk4FnVEZt1TSp6YU8RhoJwZ391zCWkaztNlPj5LRF5zJ/vXpF5K/fvh7uH5RWGIuiy2BM4KSEd0nhr/AhoPl5cTo/br5sNi5HuGbXx+NTv98hgNNdIGTalcuMUtAJmLHZOX7aKLX5Nfb2PcEQ2jfvuPWts21IMKUKU8uFYPQ0BIeibjEXIGUAybNTYHW7Wx+PtgBoUAoixDnHDB18smt3coEo5YQwTkXKy5hsGkAcY0Dee8kkyXUReTWGNPf1i1CSZ4TBnHwDAZrDc35KMGGmJcnuB3MMQAgbAwlGfTi6mHk3HIdMVigxGEDuM00cr4XIYNrgWMDDcBQEkDqDRgWI8Bmiu3r095yQk5OT7GwG56eAPML7EJtmTKDDrMQOIcAQbw96c0AoC9MswcOz8YZsOZnUACGQANOgPc6hVZMybX9fVqvgbJ6fNdKrDlKKSSIQKEKN6lGVFXV9WuLo9ZM5xmmw0UVxckGPVMtIGaOaH/0nRpuzi5M8z99/3fcbVb5dnlEOInr60lkWDUqYEudovxmmaX95vmyaxaALjgYPSRoHIaSePEExQk8ph5LtgXGdTmOktPWABYngFE8pi/RtSp8xkGG/V6nNC1pUoLeWBDvpKWhYFdmLTcAFkOfNapbA/m/vv/zp+wv5zfd1t2+3YfmWJwQ6BXhSk6FO70cdvEeZIItGvxyeXCLwpf5O3jSvqs26ZS7f9V2SCRCs4HjWqO1dfxEe5ueNITwP8/v+69nZ2cNjjzIE9aRNOlth5T1NXltmCY8+Mmso5ONoMHKGkExkCLHBRokAIDA5YJx/iXgh/EHvSjVBnUM8snABwxYCRCmNnR+jd5FnGEbsIWQyk95HE0dKuBDUpxgpojNeNJlnPnCPQzsjrAcg7F4AgAQTiHCKXkIGEM2ATCnUMCoIIkKmoWELvoYtejIVOZuDGImBDgAEQAntfgAepPsulKxK0KcEA/bWLJgIV2dg7J02MSGWV2zqIEUikyyAdUSMWYiUNqYfhgVcFJflrFp6O3mtY69jHafnLd4pMm8oA4Mgk7OZ4tf0OnuTHdcehjT0ajDGusd9cOTjnTyZU15RPurJltyJ2GCSNDb48AiyCtoRIC6wjSbkVysQSjAMznqABzC5DsMLI2Bw6qCSdojh1NKPenr9MYhrFq2tpPC8zBH3ATgfI8RtLkdv88RBiqdvTnwzL2FMkkOlAwFDTPjuDr66ZIJLSgCvSfCBM1BXdQZ912oUJhwIZ8kCgkMkMEqYioRgcsZoM7gd/XT8zFmxU5ozzhnjeCm9IUO1f9gEbi/fLOl/E+hpD5ceREykPJfXd8/3zi+UcZSK2YrJRk7HDgU/pSyrfQIgxhd/dJHu0YzNQeYD6k0XEYmIBe3V4+bb6++6qaUJv/iP8/MrsGmZjnBKbvSPyVyV9RgA2T8hXjCRc1wtT19PRrftsD0acUakkILN5c0pZaI+vHS7KcBR1hwAdBHmR9e+jE/Wa+/vQ+87TNiKSbSCGoTh8DUzJ6viUH07bo77D185yvrdw2txczt+wRD9+fRbgIQaJziNtpanQjq7mV7gYnmSFeUoMeqmNtgYhIOq5phBtsj5074GcF/V5xjeBmcYowCCFAhAJWdC8rbr9pLPtLLE0pE0hjMEYHbzbb/vDmECCHmItvujFNkZybGoEkIhOO+898FqM34ZPNfkSA8BaGOZ6ACFHUneIogo1BodwGL259+8kY2opheE89bHqNeOVKLOF8CMqdsrxvKchi+A4ezJme3f/FXlNNhnIUWIikrMWE47kmBaJIWoxBjpIYGkTF5kU6HT17P5t/KGx4Mzu0/T/aZvapzlMJCJQtpbeCIpcigezdzhsqFkfukguz4JwZHf/+kP2igIvrgHO/iO08l3ik0AOzDun61HtnewmguIBJReGxgFGOta5bVYYgIGr/3j2Kux7YaUglAGoUtIwF6rVNGS/4bMMSH818GnkGIOQzs+Ub6SBT+JOMBJqVhS6I+75+3QP2eNRASkDBJArRSUQDzZhICPMc9zFyOEcQVwyJfGOKMm7yIL0FEgKJRFhGo+awK0YW8jQG0JykwyWnEwusHoPVQoAkSzYL0Fk9VUh90nf/LdLC/qJiKn/WAodl7LuiJuoDwSjLVHhFBR+Nfl66fnvh+LTbOWR1JWGWx8+BqyExiT/njo27H7w3cziK+Uvg8BKVoUPpkwYDKqlvIC1iWarAcwBh8CipRSSFAEYjJDigk1TaGxNzpQhONJaLRLadQphCmE3ebQOm8Zo4JC5ezbSukhIoSqQifREChABEKmmGqWs2GanDMpK70x9ak4rMtpGMfjmBi9vm5GD1x38LObYNwv2yFP4UIKiqHzIdNaMQKE9gEgWGVyUDpQWLy+mUFbDSBHrHjuo0lxFsGC0lSRbpgCDq5VcNer1ZIli0CCCB1CgOsOLJoSQhttVWZQaKgd5QidZvOHdHSKMl7xVX15Gls/xh4cLIcAec+S9XxlVizZ57TvCWWc8Tchjah4Djv1AW/iDiMsMg5ZgmCpx887hJYPQ3iXL1dn0fdDN0YwaaVAwKWGTA0uOI4NgJgUwJo+OC+zbHKaEIhXdDGaoVT9z2O0Dl7dxLsdvblkvbsP4bforYHDcXAZgU+bDSVzJvPT8z/GnNJRONTDRGb5CsGkyxwwwmbZK8y0NuuTE388yKpxKVBKJEEuBkoDIDzm17jvXTIhwqKZl6P2MPLAZcEDBf/y7xN6CHnWMJafXHxzSUklmTLm49f97ngUjYU+dc588/b0N4vlQU029cBI3Zpb/VdEGBOXQIKgvAuBKDRULE8M63Fk2BAIYmKMAAJDSj0MLqEIFgSyKqX03W+mj++7GB8PTwtxdnX1CgIMmwVUhgGJGXkTyKw+00OHocBkKR3qhn10bxaXc40zx7xTNr+ouCsYIckORU2KJk7P3nbbQPOhO2rxFKKmYzHPGYKUuAhpkQBco40gAMejzi4QSIsMZd9c8ojNuDr78+X58PrL+pa9WhBGE8TlzenCGsjncIIyMsiJ9oZMRJVcjBFhA5DZH8ZYnKwKyOanvu2WMxEdA95Ya5CCEQVQPGmd+aCjywM+TrbkYoreBrdCBS0BQn/+h6f/8X/orn/15s8JHNbmLsZLiIsAU/AJK0aZcD4Mxx7wSF3gAQpIdb3yIFXDeDwOzhetWUdvV/M3xsKnI8JpHd2qRiwU1hMnI5VYdmKwO1eXOSNvT7IvGYRZggwxS7MSX9kTX5KCjjvIBsAvEDTgtBGHLqc0JagF35vBsUt5k09T/5cf/0VZjTD6fVPuJp3RMvJS0MkOaMp9DBVIAcGozPGF8xmdzxIimcAIMmsheSXpgYxapeZMSlQxaJybetOO3bh1fdy8WZ5nVYYTE7BMMgMtH176w/afyzmdpsF5a134VQ/vxKznfIK7HNeUn0aYHwQHfiQNiinEEL96zUPqZvtptEFbmYhKCfJI8OnRbaN1osqZitIkQnInuOv2LgUGQyGp1gYjjACpavqkJ600jMabRA1mOyslh2elA+KqMyGA264tF40yxkcdcUqF0YonEgAY/CoF68G+NPcvn0lxevkb/WSNRpCIrDTMHLgocOx504Rtby0XzB8PXrAGo+HLl6fz00WX/HxxEhrUeUtjRAVFgDjtp/TLL78MTrs//ElLT/MsqJAINdFgCDGm2CRACUsRHocRQzxOCNK0XJ5JKRR1BC9zkYK1rYI2eC0R1RekcbC06dMnUCGcN/AprHtInGABKDUDkqJBlTDpXQz1RMRCMo4pRMB/HR7B8UXmOQP58eVJu5KfUn+a4TaGYP72677X3RnloIFhoL6cQ5EnrVXAjGCIiggpz1RI0SX2MK6rs9eS2tQgh8E+IYCpT5Ms6aEdYTljfs/GZxBdU8xUkUOlHYdm+9I1tQKAmAc7QqX0WUZxV2kWWEJZUcDgB+0d99NhQNHrYSwLsLGgPBwiIdFlQY42kMloiqWsEOjABNSSlV2w1hvOIsLoJlXY2Y5Y9/6X5WqJ4jCbn/p+65FgRofCeeqY5HiD99rKQAPddkfUdyNlgmXnzpv9+tZ8Ub8OinHyLZE7cHZKWUAMwpS6IiBAuVc6UtmcrS54/iavy030+dUZQJgZbYAHVKTDk5QEMdCfnsyzPNFs1EOYBq4HKplZLKiUfGcRAaDC8Oai235JahoGleclZWxWE3F50kdPqt1lypY//ImznECiJt8e/fZlT11W1GDWzFLQz+M2WXt2c/Lx4dk880YUH7oDQFHQCzEzGLu0ciWfI+ShM8uMPFH+raxDSh+GNYnk9OKss0n1jpuWhdJqlyr5jsmfeTYoYNemy7cAlpp6bhjg4pnYGiWoyIitQkr6vp7PKAHSVGa/m6/k6/Pi2OujjxhheoNuMFgDHxAkvI7i5fG518PD/XDgGcc3dMkk8IBgMBXmsH4AAAYfESsLgA7b9072RgTl+817gyx7/rT45k/f0r5SY3L7KOcA3r84gRaphVnZYgxfSpeRivMpFZTzYCdCEcSQIu9g9NPOaPPl7rZBoJrnNOGYAhshrmo/7UEF8kIn4tEkjBoSzgL3CYQE8AnA07vfff3LP3/YhvI3F+KjPFIqzgDAjgsGMuICxItrLrEKT4iVcaIIM8yi0uNajiEwD6LWRTHnIKdRIyQgpWcIMmsJZQg578NR08ZY5HBLtsUhb19llXjzbUxTFnO4PgBYgTKNfQ/VCJOy5m8BoTC8eFbk9TzBOUtVxQp+ahLyP/766ag0QOmH3/wG1jNKBfDQheSNSMKoo8ZKA4gcxYTENLxsyNdjeUEO//6v8tuzbMNhTnQipg+uFgEFjjjiLHpESXbo91HB98/vlztwQRsItZnetNndoz3S6Jm1o+oi4Ajz4IfP4Rb712fn31azeorctgAaXEFPqmKCRpPJtc93LsozJko8AD/6oF5usQ6XN6tOGZMiU4dBXhKc88mS2axGsTdr542QkTOMAOn1YJRcNksj/dB1qbUq7kHNh74PL/vF4hI0Z4ySG7BVpVRGOZcYY7CmeZHNjLszLScJYwou/PjMEl+fZm8QzzMIodNOyjSNCFMtKPJgyikwfXeg43DP+ZxUMMkZE2zzuLZM1lwMinoMZhRje5jiZEfY7o4pJTOI9Vb85rfFRVXs9h2kInnnCKpdhEWpXASJMibn1yVi1Hraj0fjiCjCHiaPsZ6eEpIwyjCLpKYc0fPf/XHG6BYhQjIgiunxeTLji3laKn7GRcorVpa4iAlTE9MAnMjcmSipigjlMCTm6k9ffv3rXx6KUjbXmXUgBEsxhueZx1AipnqFGMU0SkmJyEOEANPUtd4okmdJt2039syDEicMffKDCeP9bTQBY2Z2ayimqwIzGJJwMHLKIOF5gJoUjAR61vwQ6e6R7A1wnKhagAcbV/xEGzVN0xDb4PzqvGB15dTR6Z2SEgHkXcS4NAhE7fbOcYa8txElzubED9pVMA0+9KyMkXBhS5fU7cvWOfd6o/lKLCXDDR6Rb9uvWk24wo+H8ErO+5bmZVRKQkhfXtTT88cAwBwzRAPElPBs4tvgsqv5O6zIwJMKsDGzsXarYpaVdYTgY7vDcCI9QjMsissiryGXgIypcSHho3uAUrwu/hi+ed1DdvzyMUQN4XAa4JMLOBhI8tez2bESx6+bvtOhuaaMwO4TplkMPcnyfPY9ZuXzmuQk3X76KNkMM/f9D9XzV5CcX14X05cjHOPZD99dfdr/XX1BqiZc1iTWZ4CYde9CMzujNMQUwPBC67l+/Ph+HBzuIwakOXOwwYdnd9wTKCEO0Hm7923GT+fLPGMd5d99892zViuZ7ZmzY4xhWy1OpHhFO8rR3qEUj31gVF4sozI/rz/8/tUfQFVnfUAWIwK9V0UwXdRAf7V0MKptzi9n8HIz7czg1rk25hmPuEr8vJAqYrTExpjJKR1GBCYPRibBxbfl55+6j4e/nowXIcHDkGtn3pwRHQCwRiMwTBPNy1I05UISyjAEjCCcldvtFgCijUEAn65O9gCBEBEYn7aWQWJ9KnBaPrUhC8klACavQIIsxAJDAZ3WKh2EkJC9ruth8Q/BmzTQ2eXSjMqtA1+QKU2TOUyI2b6F+/tp6B7FECP0AQRryJwUpKHYd8MDmZ81tYggGRYzAUH0FCQVbPKMYpFLCKBNXkoAFTIBxI1kv1/k+n4LBwUwOMCn/adjNQ0hRgQUgCkR5A3wIrUuQjz67Lf0chw3GhTOBM/zrKSoVb2sG4rxNsLgYhTA9cpbm00qxTDlHEjstLOTiQ9/J9Og/C+PFFOkiE9xgDAap1McBs3hmZAAyuL8/Mzsd+HwMyeJ4zw4BMkDxyevyzl9ewGa0f99B1fUwUh//cUYBfx7KTNGmIMAZjgFzqV1QDOMMJM85sgabwLxvh9aoOU4m5ElA5yeyKsuglal+dhKAXwEGTFNs6A9HKfDy3M/q3JKYO68YwEiVpSyrvP00v90bOM0XED/gkkyihyeWSZtisQAH2N2dV6k8KIT88ZSHuyktcIYRJ/SLEVT/qrGC3pGokruEVtsZ6e0G72PFroiMbCQzTzcj28q4MLYx7HtiOp1n6OJljl5TsE+mlevhSsOrq1RvVw8Dy9xUvYwhN3Dy/xqJcMWcOoxIs5OzrvH9cEbJjIIw3GHaKnUuOaZzKSModNb4rIEZ9V8uZwQTMogihOII4YGA2+H/OwEQXLOaTRpvzt0nf1qdLKbzLQXeEWbIhJRqufdF1uexwGN0e0acH26EHdfMUjxorkSKU6dNsYuizqhcvKErKo0OR+TB8kmFRwBU2tpY0PSEV5Q1tycO5INu0AXGBPiVYsStYXyO0tmp8ofOPIxy13y3jxDwik9Q4QeaeaQHvBBTz31pCgzQ4bn6N6i65w9HtKah8wFPS+vDNhXeKbgFjI7v3kVnAYQ+skLr4mQ+HIefRzu9mUlIsFDNwIeq3zSw2CThTYVwnomy7JcVvLT/acPSuGvBge/YoFdkP3nmHmP5jxGDGen5PnF6ZJJ5gOOEIEEUIKXv/3t7JN9hC7sq/mijy2PXIEsABNhAXehY4jXFTdg1JiflM2iPO1y7lSXkk++Z8xhygz0K5j7wHI3pNBW+dWZLCfJ948ff7n9qF5/ezLP9m2P9k8vNOpqdXF59THdxgJ099vP3faPlycvTz9RkgSjCUTgn40jGQtX1+n4qL7e3ilrgYIgygjsZvf+4m3z9h9e//X29r35GmOUCYTHB3mOVjUGaA3iMsVNjRhKYM7qk8WZUrN7mEKIm19+pFCCgAEhAACCCUNo0rv2uL2+eiXrGiCoE4QODk8tIaBgr2WWIIyD6KrqRPYUYkmwrXCzq52YXLAeCZEtKI+UYRSiHYajNt3PP/57IfHgdZ75Q+Au+kAgTFWdz5dkgBBAAKHv1XF/v9kTM+ydLysWg5v6voYDJ2Kcxn7sASiuIP4MQArOhrSoFzQnSyqyUECEXjYJpoP3h5vzU4TxvNSHIQt+d1Qei4xVBOP8Ynl++3EDnEohdMmrkM4iQgBF71ICRW5eECA9T+EIU+msepLhnAl5g/c/HSHB9evafzaEo4JxDp3PMnDc6/bzSFjPm1QBPFhMSKAUcKEwUR5yQSnKDOPBg8S0QsWcEacso4gQ3HihoHfeMeEjBgwmszNXdZ2Xs3H63LsJ41dBPyHKeF4MOY/9HTbI+5AwwfM64RNEMpOr7doYuyWsyH+zyFoybSZ77B7CF84KFimbnaemZsxvTLBWuQgLkVcQTRMAwdSzGSGs6TPmHr5ImTFZJlGwJB1OEUIPIcfJ6D0ZsmKZgdVvhNJsUs4s8uqKFsLaHiBlXr6I0nonZHnx/c1vv+6/Una6WMwnzOLLllNugR9SMP0REixWdbm6Dv52VGqMAGLs2L6Gq4JQ5xNGeZWGyU6dh4wmCzuzPmSvrkkhT+ZYbvE0xcLBkJ864Z3zECF+OOKMXMSswZC8vbkWIgGKRy4k78bOUoNgtm8fXyJXw4MYoBAMI+KWpxATgFJiNGZ2eHRKZrPX2eFza8aJRE5ZCDBEEyMB3sfjpGK7b5tyZ76m7XTUpN+1ZhhO/+N/vr4svnzNRDtYjwYBK4ny/A2uv1i3sG49PsDyrLVzR1yEw2TjXM4aexxm86zgjY3KhrXSCPN5fVX7GLuBFyf5TVk7HwDGj2D0GICEgwMxaDMM7mVPCxkJW6uKz3BZ13VJpnFyft9N7fbjJ7Y8vbg8Z2yVv/LnGHmKfv5pb7pfKMAxei5yUbFjv0OReO+zmYzIxIiNd2PwIsIIASaUYNNDtqDCJBucf3h81Kz47qp5r5/BlwF+88r5+KwnNc19qUE3GK1slrybYRqKVK/7AOGTIYVGQVJFKAIEWWRdTr1nQIUQOp8i6PyEDhIV1Ie1mra7ffeywxi/uRZEbY4eYIiUDezQwdXcKANXBBMaQwClYy23oecQMIIIYwyHSLkbJ8BhM5/XRk5Sh2jN0wsx1Sn3iSVw8rr83BmfNF45Z/ThSMAcCVzI7xMcMl6Ec4XXk6aMkwUNKRoGS18u64M+ruqT169OXPCHYZqo9QiMXkHOa1F4qBHhuTi3/WeK6oxz5wzquD9s+syL8xteyPnFdd4/AxDG2JlCYQgBhwSFhPHl6TUgyCwQeB4P3bFt9wRDuUR9b0ECAOiz86ZpSuc0H2wq6/n1a4cmawwEAVJ29urkzc37Lw9PdkI8BeUtK0RM3loP9cbtU0B+Ex4mH/wyhBBPCVERtcUM8ALoqaSMYGyVjRNMwBCRx5g4I5vdHvTDfUl9PFbypDpJRjJuTVYUMQKEepRjiJImR0YLSKrNFvJZzAVKZsNYQ2D8+OGnrl+vt8+mKb6/XhxGd9RjtqhdDOrjL/VvzyCjoN3qOI16iCBa3efmLHOflgwRmH/d3vaiOLu5eLoD26e19s7O/oSi2qwbA7z1gPdjXbCNN5AoKQmVWdZcFEWdUkwxn1fgebvDMCFMgnXWd489j1CLYlbAxaF/sE4/dqaxkc9hiqLzkUaJMYgpIjuwCJnHYMHeVSX8483+57v+CSCCpGRLH/ZT3+53X4HZJgABQpfZa1LipXCIuhgfbZ8nj11E5Uqg2iGgO5sVgAHdH0UWLecE4aD6LTGlgsTEluY8IQ/g/PTb7yAmsSCgvgEJCn1RTCMoGmr34Ji5qI3x89N58eYmjPgl8mUJLEoFrFKVz/isrgt3xu6+fMGEX71+PcsXlGTtpF4s5lOchkxcMklFu+up6QlBYsqJSYN7HFGzoM0pthzyMVSy8M5ENETvDXR+6eBh3E7k+MIKTvMKAQU5jGGN4kZPh8lNO0R9iOBwbyLOcsFmghHydGgZSoQCF3YVzTQERxj9472bzeZsUSFcVqfGxJ+Gez1OAjUwgnhQZVEJkiWW+uNGSIRw2h8eKOGUEF4sARzBRPG0RmEFsoReXmCZNfObojxN11ibyTmXtmpDXJzSaFxHogcYTXvMG0HmfolD3yVGKaQixDg9EfiqXNqXbvjUv/zjxTdNVfcIw4Rc8gD5I26DDLzPnZpI03AQtiCcXS5wxMWm/eVw+G//9b/+0+/+o8xPQ1TaVBYjA+36RYXATtsxAjBw4Y9tcAmdLke/d8Ewj4GEnFFA7XgcIMWns0UxLwHC68Mjr8pTig/WteFLOEKECasZNA5Eh3mtkcIXJ+6wy2WWgI4dGngkSmRFmaNiWBy/Dr219uX9S/N66fpxfcpWOGPF/tB64Lzg/Pry2nr38rJmq+vFm5W2CRMJAd4Nel6WiBJAvfUUo1hoHwsIGFsI+fFh06039mSOX53Y20RiSAB4b4M22kwcIkxwUwprdXTDEADjuSiSVQoChyCvCclhPfqX0LeW0reUpcArnWuJ4trdbx+qumjXO0JISiCmlAIKIl9kC4w96vctpSQfAcVppyClbOxDdpaunewlMDZZQzF8HGyWHY2nQ7/GgrZ0jMfEF1k8OzcBw7NX/Xpvn2gfj93HLKbHvL6hTAgm6AwuS/bw7JNBqYMNlCsa79S+BS5bZRdNmZ0uKti8uXlFMFb6vpgHSPj752EyKt9aerqCirhhY+UouEA+YomQ9YKIenXhe2t3e5KdmZ0a2qEqmw73ExswYQmiYMci8i8xvPKLZl6z09UwDaBrr84bBKMfd3NR9WNnjoNTL2Gz9zNZivP1w/tu2hTnl5zsoxGL+t0//C/Xj/+f/TT0MAKS5VQVmrvxecc5ISlKTMooWtcOO2VhHErqQgQGeBSjiy0G2KugXAZTXdW55xH4k5Pl2LZFmalJQ4RoPQtAxP4QuPDCwR4dKa7cUKZi1B56PWuuWTbmHFPMHPPXGCICU7IXyRYCZedSLM9Q7MMwLkQ5KfU1prRLe93hkDKIsuwbbfQ1e6/C+ulHC0/HL9vu9sfH+az6f/xh/u33/3i8P2z3U7waoM0Zz6NTy8o/OBLTyHKKaZFcSUTCdXGIbomi8s7oiXDazFcHjNzDGkZkhwQRd8FiToSsvdcZ5GpSpAwoITckgxQXeCCEtwBcEJWc3r8/n795K3NF52o0l7XkQB9HLakYJkTI7E9/WiZi/tZuUQxfrXNGUe9zkNhyxkflEAdm7Q7KupqHBHAEcDdFtAgFQWADh7PTJBB4v4FuCpZz+u7EOt8fnz0JENEUI8n5o5rSZFPSoZotxg4Qkp3lXGw+viRtwr58dVrRyQeFx/a41Qp/9+2sSIVu8WnTSJ47C3785aHVbUJxVjaM8Onxo29bIgRBi5KUZE3wieRYntoU9mjvDI77sWMWaeVDyFCNi71SngERIwgxaWUAggQdpnHXEgOs6jIkuYyJDphJk7xmBIEE45xBUTWjnlDlOAheqVOEEoZ47D3InIB50FLUb8ibh+6D8u6knk+IOdtTCV2yWeEhTN65qR0xxjFmZcEKDEa3DTCwuBHHZGlE0SikvPkS3nsFbGsjZsvJTdo6C5lUti+rXCwyipO3BaNTU8Oi9CpxlgKpXebi5LTQoduF+Gp2fSre28e+r0thvR/WuQtpwEfJOfF6GxPfQTbfGjeCECXnRqv373e/+QeYILPDAA/jHaJeI8HoZpo1dTbh9OvOggR+uIxRUIEzs7bRBlhAYyfndFEWxVyHQK3qci4kqYHvdP/CIHXJG268jbxzFOI4bHB05/OzZzr3HEnDcQJ21OVJ7I8t5IBx+prNH6yLz+tp46MzBxhnq9PT4iUvOVIR5W+C87t1hxIBU1BFVc3Lab/LpEwwhYQoYT6iAIDzxLvJmZAgYpiItLzd/dtf9/vr+fID3paHGiKAAYKPTykvVtA8lHnwGCOovcW6A8KHQBqBdoMNg7VaS6wqchrDcwOiTaeH21uUSRDQereFICHAYkgRc7Y6t4ePf/vl55iiEE+X0RWXRVbzdhgBgCPKkLeWY+h2mS8ChpSxdjNZlBzlSk+Ls3OCcfAuINzjUfVxsVgabT58fjZ2n+cKkCzBMTk8W3BvpFYcHECMlkHHGwY7iCuAz8i3+3dddihqefXmncNO6clEA+kL4gojiMnVm9Ph/uFZa202m5NXN3vbSTdlRQF2j6gIeHSAMahGHGCETfDmePs3PU4TGY7YsdDE1CaEURhbkZO1G/m92jfdOAyb/Ytx/1hVwY7eDoHhmev1cAxIpGh9h03oXgAKGOXkLpL8bj/Vs2M+/+b3b7/+H+3RaFGXqzis/RhJpBCvUiIubuFsdvVYYEmQ9QjTfhvGI0KXKEGSYvJoqqggmAIInTUu6BzL1vUVkj4cMcZnM0F5XHcRcBcHzCSvqroUufPuLM1aHpy+hVGSDM2Xc7DxAGoAQZYMIen8z6+s88P4Ze9CCMPd2E/QitfMOW9tyGUZJo7HfY1x31UwgZXUz899uzUy41leFK8vuMjlkqnbnXIjgnQcOiusMWJFMSWSEoSQ1bnWhwiPWy3g/PUKi+N6tGfZCfRh6A9xGBmXHkUYWcxgxBBSzPOqjFLpp/5LX0ngkI6UUox5f0w4wK3EMCUM2kLVLMsFAX2PA4UQJBhJydA5kz5NVm/il6R3j+RUTS8+FcwDySHUVgXOIFJ6hAhxLiDkzjJKN5iwQvIkE9/JgJKzzqNEKoj7LRHXj1qzpHXitDtIRNNZ3dAbb2levspZNw2Hwj6gCsvy+9/+Hv33f/nXzcs9b5opKgNFLrAeVP/gcYQUSLf9bFxIPclJuqxOezexPD5+3ax3T1W5wsHfoHnOGLlGFJy+DXlwY0QQpaoEXheQGoGo90lwYq3DyOhJ5GeDerQIiNPLqQxa6S5ohX0lkfIWQUyd9SEBkGC0nIt+6lJ0ILgCsXbcMsIBosm6tvOScTD4PW0vGjSXtRTVbdv73fokYpOAw3xGwTHBwUw4A3WplS1s1CGNnvvlu3fRhG77jFgpuI8oTvsPn5Vy3gEAueQYDZpak3s+VJxDjGDkOSIpeXjEuMToWmYBJudD1dTtqN7/9DeEyGDC7b/+y+t//E+LP/0u/eXnyXQgeskkds521AoaDq3t9ygm+ASl4xqoc+8+u3in/3v5f/755JvTEIsYQ1A9KHD0p6JCMMuyuuoO6yovXzzpunb57nJSo0CQ5dBNx8t3LOcXFO89Ptw/qTqXC77e9P0S5UZIHsEB+GCTATZFTAnHHo5TL2MIOgjupySEBMFpKSjlwccQIjgryykkoxTDTBuz3q1RoHxxdXz4scqjv4sDOYps5qJnlCVEwqoGEyqKQmnbheOCzWGMo7OJs8y5BaMbtT27PvvwhH793/43/P/6f5MMff7/fVj+cS4ZDfUJuO8OS3P4fDdQfH0qBAGBg/XdhBk5v1heQ+rDbp/Azz/uJN7NEq6+rWG/32pLYqqd9yEu2MofOOYVkycoWEBPTt8UatqH4fDFePR1+OZVGeEFxrRKMqiXeXbVm+njpw+73ZZhejObzSEUhNt5pUJgLFJe2clUJQcYfvj6EIOxYRJ5NT+/sRqGOFDMKS2tPlYNwQAjlQgvx0O3I0fj/XwLTcFv5vPZ64vE4vuf/s4FaTsxTcflCiHsr66sLG+uX1++//XzMPX44WtTZIwkqBT0Me8sLrJJ2e64lavfKTnZv6qepLOz1fh8BOfOYELYGABIqGYpRsL1LLtC4fPH1gXTlCUdU98egAWJ+IjT4qQexh6QiM4LmJU3aRIXFYAXfau/e7uwaTc7Pf3u//b//Ovt7vbL/T8ursKnFHSvoZ8j/il8MpN17pfToozTBdkCr9PNUhxLsPM2z/JzmHSYoZrf/vILzzNrdF6BEMhqjiq06oYnACAmOAQ7L5eYkHK1PPYDJMynGCJ6CO0qnp+vzva7tT1OHL4gSqLvOKteN8Xm5RD2G0joDksHUoWufp6eSx8butAhlvMTSERnjww3x+MXmhU0gB8uLg1RQF2lM1Pk3xd54wzAElrf+S/OFDmJOiM5iXhUHWF48+IIZmzG0aqQ5YwhDCEDIYP9rZkZDooiouzyggnZdUMS2SgJMh5RkkePaz78qncxcSf4oqTOx43ibFAUDsO0ShdxNuLbT+C7H16fVD+r7Xa3uXlzJQD3Kf3bX//t4eXTdLLlA2QVlCVayBMQYZ+ylHb7g4VhU1ezEAUjpnwj+DBttoPEEGB8e7gbHyYE4/CMU4VwxnFJs8WbjLJQsNrZpqZu/fWh38OPvj694Ly23M+plEvafd2gAdrylpDXEfnhZf8w08nTypucS5gP4+gEZslog4hpp7hDrxbno3/E7tg9Nnb968xOp99/Sx43CD8rHcken6vuwffJEiQ4tv0OZAmhMhEGoYPHjUF0SilFoynY7dRbKQDXup0M9SkkznITYigtscxNnmflERKJCQTwsGtnzYGzFcL92Ty9dGsSZlBkWE32qEuXZ6yLsSeXs/zk8rt4nyDD9RzcffbteMiAw6mpaxPMxjsEphxk3binSQZ3TADo7eBvmFl/DN69aMgKSQveLM40pShuMa+YU2kYU0DR9gAROm8AoyE/wKnyzvhBPj9/3uM0eItxhDBQilI0SisfIV5QvKPAXRD8mCIUTGA1DVavGIrLyocRPBwyISiO38DTe/DwEL7u7zffff/9YImFDT2hJxg5SkbKY4jupMSnN5hVnO6mYwihg2QOxtTpVIIUU7DWUCquC9w6h4qqIjpNnnsbYgSQpBDLMk5bH0kCKTmwE3COIEQooX6kmQQoYEJiNBTRRKCGhNK5mm5RtcL6GcOIkSK4IBQiihH0IVSLXH4MRgIaHaxwplUbI4PorET9FEIt8lIu1XT0zndC6qOh+f4UXjy6p+GlXWDzbw//OrZvfvf7miBg1K778BRiuK5QdMakkjmGKRnHbrfeUZ4X5c1VLU//sHZuqz8o6xbAe+NdzRqNPcJIsRTjkQaCgJVliDC1wyQZKM7Pi6H9+Pj465cXDkAjT60/7kOHh0Pw/ry6el28gasEQAIp+udN23Ykz6fOQ6r2XZfXzTROzmtjLeFcXn0PkiLc+alkmZtsL5OECPqosATlcbAMvKtyJ09n355YoxECg+2noUen8ZT7/RjDq0DgsFataAeOYZF/8/3vfvfh51+7dueGbnGSEZzjaEYNU4oKSwzgtLkdXtpfzVNW0P/0n/7DL8eH4AfGGcUCORDQwY+Jaif8TafalGpm2dL3aZKTHrFCXvpx99zhhKCnTV6VBa6EMvwFKoDWOUE2Fo9uOqPj6fXZn//8P//vw/863j4jAQSWIjnnb1OMlJAS5N4QGjsiqTH7/boZMhyND0ndRhvcfnn5PS8EhEAUbLFsgnXpxYd5zACo5guCyOF4BNjFujl2Ax4mQuORln6Li7x7SY+rt6tVOeNhcMYSEtG6Q28GJGCCwDk+igJD4rPEYvGqPrdTv94e1tD9HnESgWPIKldU52pahwQnLq+XzclVgYinlIZx2B16Qc/lgj7C+8brgK1I2aRyiEBoVS44FcIrFFsT9Kj4qJtXR3cXUiKTGOAQOYcIpgRAdBjCFUTH6K0x58uFYOj8ux/ALz/3ft3efU0hzrkQbFUmmBCZsj1BNDJuf92yN7Msr43eq5FYG1ITVieLwW2SqIN8MuA4jJ8efdVAaFKbBVGUliQJOcppbF+G4Xjf9MknCcDipCnu1JpS+O3Vb8vL0gTX9yPGpAcxMgrJS+viSazZfPGuKr9+fQn+iLPG+el2GN/NBPcyepesRxT+hz/8+dfNUypqNk3AGwNsUrALZGnHEHyOypKWNgtatw4GTFY5pnGRqdGD8Utezfjb6xgh2dm7FCPNWXleZil79A7TbUgD8MlbCIgA0QJBQwKbuy8QJHC1RBhMu3UqKedk1A5wJFBJyia4NoQgCAXRqVHDgFZIonr45X3PqS0bb+yWdZXz6DDtmURZnNtuT/Qv6fTKbvZKKfwqkbks9r637qin590+SGcPGlwmHHh0Ot6hpVwF71zy1+9fPmi1OnO8RoMK3oHu2JJLFoBlXjLM6VxSL4hJAYqB4CoFZCuQ5XefPxIvEYaL4E9nPL8qEHodE4VAQH2P6Tt2lu93HmepDFWya2scxsAzQcZpwp0eD1aO4AUKViCk5vCm9217PHbH/jAFcL6kW/JUOBiBdLuh4ZDjSRkMPSBv8xkaemwVwdTXEFPGKN0Pg4oxwgKuP6iGg5DHwBIGKUZISAMJw7GRxVGrhCkllGNPIACH/cAxG/f7jEsAjHMaCTtNXaILLpYZLaxzGEWGj3pIsth4naFpgGHHgrH+HT0SyigUmHHCVuedthWdMOEpubvQlYfNxXyJUYQq7lMCCWVXHD6g5x9fTv/QYKi36//zv/0XIfiZYOMrisDNPKVcjweg1i2aEZ4p7Tymw9c7CO8Ihr97V/LiLc7v/OOzkvlp2ZQn9fA1QGQRhinBCFFHHVIWp4RWud+M66RP8ur0JKGI9t3xod8XWV3JBYLC+cAo5wQn63QaoE+J1U2EqSq3h260drRGWxAmbiBbff/aD4w2vJvCHOJ6Idt2YjC4UsOQSqPSokFyfkkY/ybbt5P2uhPHRCPw0caxZPlnv0GTCTN7z7yAUYdBmS8ALiOo3r779thtuvU949hbn86EAwbwmEwTtxNicyJhZu/3Xftvv/xl6/YR4dlKDjRHGBFaweQZZ85FNQaEyXLBY53pWS4OUjmDY3T5KoMugQOdlQMKw+ZFlqdDX9Fp6KP2SMfNpPWHp9PLs2/r33w51/fPtlf4XPqtCc4tECpWZ0TOPHDZ6AhDJcsnA9KgoieYSxcS6KN5sEKcFkJC1KuuPewnCFDWHTTGK5EFY8fRypxxE2dXJ+NMxC4VjQM06oMG0Rwf1surU1if42wnAnPay0cmqxlDL5Px6eYyrXdk8m0JNJbFsi7H8WimlxitsTQIkYEQW1TP0t6cXmZ5NG501k9BMwBJFKSAdLasp123MHN+ThlBvBt8RsZgc9QsSB6I4aGYn50OEG9Rpx8QCF5bTTmLfAqxPgxf7dGUzXy0WHubUngc4w0G1rmmbNRxRzHxwIdgTfvMiywQCHWJpPNj+cW3b13uXKanrz1ORQPb9QBwxBgw+URoDRAlWmLjXQjB+wn0IR4QR6FDBPPqRBQqJgAaZiZ1O23eQpjeldny1Syi+GLvLZkZ4lMABlh6DMqhI2gtjUgOmKSYIkw7qiEMwXgYMu4Mkk8YvXvM5OtsUbZjAi46gGoiRAZZDLwEw7HTRhHLI4rTNB4xBXgQ81kWBWFRnHOWQ4ReQOfJ7Oxs1dSMsWdjtNMyzwijMSG3c4ZoQ33yGYrDEKNNRlBBGPbRdn6qgNQqUOIW7HyfjO8HhW0NC8oL4YEQueCcZ0scUMb16Qm0CaHIxWLW37XB6YAOvq7Q3tPEguv0sd8ddoWe+NUVDMvzeb3K4+dgJm/psvWxn4YYoeX4aCKGCGXGjSd1jnsYL0BIpJKRHzjkiOPNYQ/0KFg2y/Msky93++vlBYOTcaBKEPh+tTwt12NzcWGg8fwZIQjRnWDXx65dd9kNYymdKK5rFBOiIymQcNRBFKYpeNgh6TirgBHGHvTnSWG8jxHEmD5+MgA+nlyfMMLNOMK8hK/fZlV1TWUnZUKEEogHDkCR5XpKTsgE4QIj9euvE0DT73549e6VB9SBAAg+jQAm2HnQOlOqZHHCciaBihwWCo/DdoQAQkgYv4JMAwSaIscSc5EF4Lpxn6Knk6ZEeOtp2yLBlpRD6IJVO6dOEjyZM4jRiAnHGMVEjOkQzQC1ecHiOKl0NL7KZz7roHfKzKx9SgmO/c+7L9/E4FLwwdkM4KouHnS/eDymch1Tk08FmAlrY4RnRYYCkf3T7fE4/IvSf6jwC1IegEZDmPHDoQ95yEAOomeIVLPKtiOm9HlSJUxlPeuGbiRENnXGMpzJw67PyypEc3JaOR9D670BOHiEuXMGY4Py+fPf//0hxiIrsmwBfVnepBgEIGRC+7SFBNekJAF6TH0BM0tthL7PyUkY/EzwGzlrZtEd2pfbSZJX1zefzMMYQsU6H0azhMB7CCbLIp9G28OMhEfo30CREdZvHg6He0gzEII9YThV5vB06PqY9ytZrq6+bX/9i6bDEAZwz4+f/wIwKH97dTObI146TtAIkslPlzN13D7f318+0hYdOt27Zy1z/JuscCcLkEmbxnCaJZSH4wSKjB208yhWZ6qmNsWC07f/8R/X4L/e3T4ZZIKzlNGGEW7tQJ4QWyXJvigtFWoK0cAZFv0AwoVgL+SSMmzHpO0BAqMnhwCWTDLEi0bmeTFqGyKcLy+Ks5PexyQAEpRQeTPj4GbRbVtlR+9D7HuUCDm7ts8fxzLGdLZ3Pw6Zxd3UNqt8f0CTAYapnF/MxKmvo0fRKM4FQ7jzHdKGiWQN/KK91EoWCyI3hEPQJcA8pkm5acQ7oUSMxjOaQ1gwyqa2d6WB9zHAw4cFE+L0m2+b709GbxxF06ElA8Yl58X5QviyqPdjS5uSRdBbNeqEMK6urxgMIsUUnDPWOW2SElO2x3oZkqYpMxPm7GSlfnyelicTBpjkGAwRkoQJI+yQknWy0xxhvWhswgAYsuppi2fYK1XGWUqurufHdpt8mtxDIqH84R1hqBt/Vs9Bz+4hpTEwZyRETuaVUdbRTc4zwI0l4xHhEggYOLA+AjYYoyU/23It9+2vX/TsJEVIqUDBKeUn3QFkxr62DkfdUSxSdY6RjoeX3XrUpvXerHQ9mif09IIBIxsISqUfoFf+iw+nBY4xJYxQMc+SEQ8W2/gwvn/wOsFIb26+d8EHtRMU51JrUwGUjSpMAGUYMys09DMCn/a+SUN37Ndgws2qpDYB5KxCACJELsrVL9370M+6rK2UoUwwTATCFABvjf305Se2FkNDz79vGu6xQzgHoE1h7R0tf3MhVJF13l8PyEwlvOAlTwgwTHq8IIBkqNqh5wqUYzSrWAmcrc1LkSXvMSPEuJ21iiOiUdAvLwn6eAEsNBGC2+nD+EUQ6q3ziMwRfhA+01R5LoKVIQXPGetogtw4E/YmEb/eBIB4CD4CQNkyxD3GIPiQThM/rFCGGePGxkdKKiIyxkhKM2pYzZPzURtvgs62ELz54Tet0ioECkAyz14zi4WDiDCxHFOPmFVhylThBtvZgY1MKcvAqpYhAwR07nP/UuLp6M274oytJPMycuAwjtZGr6cQJEkgBxghWBvGCDBgv/mYnXxHUEIBWGwgzPB8mflkreOFeFW8mwrrIXQOvYClIQVvj3//8WuMkovZL3ctI2/Kwl9+I5VWGJFXuAAgGYJsIEcWgTIJ1a4IPsCbxYycNJ+2yjx/0jofojqn8+wygxBZ6CMCAYJut5+M4l4nn8r5oulHqm1zsuymltgQYnLAzeeLKqu9B/etCg+bGP3Z/CQKa/ZOUIrQNMbUOH8sC2bd6fmFi/HxRfUenxCUMBZ5Rih1EYiCx5hwQBr5foiM2SbP6u+/IeKFUxZgLC7LBwP5+cljcvr2aWy2NhrK6TCGDKi9385n2TPhbA4e22cM9P3s9du8An/8x/D8IQQUknedft49dgExml2KN4ptM4Eufvj2ouTMfjc1/f5vn2xwbMronPqY4nbKcsrz9Ph8r8KuKpuibhbs/Pb5uRu/SgofwjhDmALOM6It712boVInjqo8HI7edMBCq8Nfj/3Z4iT7/pvMDnSrMYuMEpuxHrMIMKN4O07WeZN6AjiUCCrPrW19Q1CKEwKcDMM6lyKYFFKyMUSLsnp26HsHE23mVV1M2piNwAhTwVKJ7yU4zeTFJdvvaAghZvk06rA5xiGLz9QXpu3GenZFANnaZBenGA2yT9CN+x2maRRSYDOqRKZdJyAB8Qhu3oXeIiqFbLQe/cgSOB6JuiHozZvXn3Z3atKmneZ1g6B32GNGSeTQ2kIWBZISrV7CsUyQUaZTXH/+gjIsaK77DkJ4NOOgRq1UggAtVzQhC1JeNRKCZ4yXxuOYCIFQMFvqwEOzxTEDSbdPGqCP7eyME0qDwkSiggFrtXYTNSOD7giM9xBMye8UmFeYUwITiQxnIncl8JFRMnRbgtCJAGAaZEJ2/2mgAqn46hr5uNgob32qMR68cMlZvvZm6axPc4IGz6eEWTiKJHaKWS+7gUVgtP556JWayInUt3cehXsSEfAYeGdwkQ8AL5UwynLHBoKEv7yWyLFYr9N7Ez7/67+sEcZZlpMGImV0n55dazzpXYwxhSYTFcEe5u8ouQfSIIRhpFxIKaO1aholF07hPDmNQYwZ9m4EFgQMATIM1DzsfaRSTCKPz/eek+d7JYvqOGzqVbls5ovp1OmYrOn7zoZAh2NEoZgVx2qedocz8qZDQxzhRrnjyy+Y+OLbJfbaRx4A8ofUoykdY7Oqkp0MgAHgGBFQEVGiJS3zyysyf4Qv67he7C/KPHu5X6/OZ7IfnE7CexotW73u7x+jIMM92GsFlhiU3DmNE3beSpk1Zda1Y0A0JRx8FL6J40OgLFGqQrqL4crx2cKeLr6xwX++bW9en9x99ZM5gr8d059wMRY0JD9uA2YDlSnLyOz38pQQgapcRqv3T532lDidwi/pIThio/jotVwwOZGCRr5LB5nkoEjG/LJ8N2KNnS8JhpGd1ktS8f1Rfd54Zz9Oo9KcEQTaaUu/AEFZmtcZXkIyOIgjZXHskcMJJQYEnhMR/NFH30YhqIADtWe0ohFQrG1KcOyOXyGJfZfJbBCZW/eKun2fAESvb2qSEA1lAmi760XLo/CQICuC1jhzVGZVLrDFxWHsqTdPk20j4g1DOeXL66Hi8XGlM8wovH9KEPaMzwlPnAlGKBN8GAbjrVZaCVlMU91UJSCf2gPAbPv1qSgrjCALHgCUQMpl3m1tkM4xEug57dY71/2umX/VDo9MCc+W/kygBABhcGpO/L6PIUw+WjNgQTQgaJYuT942hfTREuQBxh6YrPjhj39I2gw/qTZFEMMp6kdnoY8PI/HG6K53CLAmyxBXhWvMYe0Xde4dkFXEKDXw2Nnu4emmeifPqmOHICKPfjJxBOzQhNni5jencrXZD2W18ipS5fOQZdH7YbK2F1LcZAVngsL+D79/68HVoA7b49cWtm3Ul/4Vj+mQQGWGqRs9Q+NuI4UgZOnAS0xpd9heLIsOcCpjDiAMIfVTtahwxbVH7TTO6tfNkkSQhMq6TCXb+aSSO1hEHRLLxXJW1S0/3t7dMZaL7GKUkZiKECcZGxN87PoyEYxoLgvHokfB9dmUEZo7D0Kcn5/X/HA89Os+t0uARkLOU0R1taghW5hp72QqMFgjQLcJIAArhOZI7FORXLurcslRr42zKWxZVZiBsywCynz7gNwZ8Oc3F27yXz9+EBQVMHfWUt3g4oDIiFEG6A8aTePQfXz+6ZuL3xqMYAIQ8RgTSoBiAhgFLsQQYkyo02C50F03dl2W5ziDWBDsYAowcUjpqXMAFiDDkcyANOTYPa8uXmfoXA3PZb2SgpIqpYPb2wEEZ9x44s92ImPgOB46V1nHKDvCrEc9DAtMYuApwlICaCDEFCRlVA91jKOFBEaoKag8TBv4KFOu/VBZOdAutvqsPvm7NRKHNaPkOPFCqk/WB6NGUAoWQsKvXv1+Ub/PfqtvP4isRuleUJpSJmSPgbAeA8Y1dB3wc8y7BECpUco+b442OgwTQIBACn5qD2izQ3UkGgYzXeQg7IwuC0oJp/R7+Wb5rn758hR8wgDvD6OwPOdsYmwiLGHSQgpi6GmWc07G+QhsKUuYwuryhhN4HwMA0fGlOJNGnT5tjj2J8zzvXg6nvDDUBpFDGiJxTOSSoYNczht5vD9EN169rm1+Y9x0Cukjv0DeCkDoUkaVot33E9HFCcaJUY5efIFG6M4H36OSTnXKvtTBeFso71yWeLGngp+cLG7a7d00tkwmfVrebUdlQ4S8mJ8BfIK/ASCgL93TDYayqg/rPiYWmXCUBWDGooRUmjaoQ4uICKeokhfH/vjx9omQmfbj6QV5eLwA8Kv6KJtXadY0bXYWKM3KWtRzb5G28GuWlTpBmkafxs4sl9S1ylNfLsuASph858jowXoc0YDQNxqzrLPrMAw0+c5F4WlBm6d+DO2gDDPaU/Lt736gH27HhLY5LuilSI8Pcd9nr64RZt6a9ctnmEEdfVmXACTxffMHQP4+9KL0DCsPoAqJaCehgzCFkM2r8uiQSBULpxz1G2RRSOPDlFJWFPzjj3sPCOExl/AlHPE+FqsMToxLMTkvldZiBkL55pIN4/T1swm+R+mUnkjghLGEcrMZp86DrPTO5paB8bCRHHtMmxARglkisCr01PYppDjyxXW5SRSGAaEE2GTUrD7v1cEb83mzOb0u8L0UTkzeTElUyXt7MplPHXgqxesMPBX1Rdt2zwC9o/Q5WkIDqcsAyILQr227LOdcsEfr1vYv+uOh+WZ5Vc3N9K8QvFLa3v33u267Pz//gRqx88/LIdvYh0TDGFNFmIjWk+Ioerl3D9GmkK6ARCy4g/nb3VMCNPtm0XdTBA8a+4hyRvPj0EzxJe+OXMoV6gtgMaKWhjnm1t1Pafu2yKrXr7O+/2I2k3VO9XXTuJjk9e+acfg0Hj6/7Iu6QY1kEBWhGyDPOS1my8QKcbn6DwD+cvsxz4v/9H+/+ef/9dcQknZRnF3jvGqP7mX72JzdLObUhehG2LpNIURNCwiUghVGY4TBaOMEiSmcnb9KAIsiDJZkgky5LGgzTgD6hBuPx96bOopQVCVu2wgZzq/7/dd09/TuP7zjDBCEtb7H91FRMLyMKjxa757Kc1al16UIZUr3Vy5DBg13h/WS+VmM9HI2q5d1WdMzokdltEKZnKL60L1PFJlP5uPsvo7LIs/k6rLvnxpEIa0ReDzagmHOIpqOH1KiKBPCvZ66nnF6fX3zxTm1ecyKMsLQtq084+Xy0g4uURkTwBFCJibGzHyGAIT93hhghQsOQ5yonDxAIZbzVR0s0mo8u0HtXRuHhHh1XUXfkEdNEU4KCmuAN54jiTDqjxOVPHgVBw0AUHMsHGSQ+lBI24cwEQ70rg14FGsGGZpmygZbL4s0ZS44HoDWxuTtEi7alyNM2uRLeBxYXYxT9/8n2T+2NcsSw0Bve3P8768LH5G+UFUogAS70WJrtUYaSC+qWWsikYuLapFNASBMFaoyKzP8jWt/e/zZfmvQb/FNvhPZUzUiPxySFc5TKJAGlkqK1/nZLMfwdXQm+EeIr4KjnhHvSXQkIQgAqDDlIgiQP0+q7mt9hgnZEXL6UmM0YVrZqOcRchzHYe89nIYmL6HxGNLR60uepWPTH4+G0cSAYPJyGgYsIqUkUgGcKhlXlCv1Oc82c44f6xbAZEiBAGWg4KB7DSAGCDtLAnUEg0XcTzvC6FJmgKRD+HSNnAujA3i6P3IWE25jAzcW7vcPTbImILDTEeUcxNEaDQASjCXWwMFSHIMsjOMMnTJMvH4ZrYfZNM8QCkASfmr2I+gweFFcLbLpSHD0GDnKLp8scFI8Ik4EBxAYY/Vk78exoiMLBFERnFcmC+gYEEIikQnR2vh1PtNd531zGuxpklJsNqUxRyGXr18mn74Yc/yTGmX6q18nwXXzudHT2PV4oDxLkGSIuBlK81ffvv3xnz0I+0fjraep8ep4UwcFQGwBIgQhGj6h5M2cp8tgJmckJYAhAHD0IYZg0nJOS8+jwxxvFmx3rHfusLFvMH4ygJ3b3WxWTyACj9o9z1MogsktwdK4Ej4Bxc16vkr2Ew4jK6RngDkXkWB5zo3xOGAQUq1Ni4dxN5mdGYaflA7Xt0uayiqNmFDBcmMBKk6BIFrO6qahhNEqN/2I4EPjN9UsuYhQ6VtrT9PoRSBc6WzGJmXrphlVfpHhKpcdfep913W9isEYrTFGns/hmhHSHBrMoSCBLaus45Klh5sDzdLFajVNiZpGZ8PQd7kEIuoxGLYkMIPhnXtxvjiok/XTKTmF4hz/9H6gs6ykGNGERFSk993IUsRSOpjh4/AIaRfoZP2wVSzcT5rRcRjD3MvydRypOp1KQmNEi0M4do5lfHpO7j18noExJnE1C12HHDqqYhze76yLkaebb60L91HNIGHLkLC2VzarZ8jmhaiNyMqzJ0WfH9wBa/Wu+9cI9EuOXFEM6sHWYxVJvqosRif6RZ341NonMpul5yxuAaDquNvni1F1FXc2wZwBdXj0U2RffYWjBRCl2Vff/VX949+fcN1iLrXWw9ivludljlxjD6dTxgzPCxvUnLHBmTwlXvLC4gm7th2sdgXN7gISGLWnkWdJTiiOyoduDUA47QPAgKfCc695sH7Ks3JD8s0TYcMnp/TtQwmHaQKE0Rf5MzpnExrZ2J8JCggCn/TD+EFRQSyv6xN0/Rl9E4oDzAqIYQSOELhlXf/uYRpHSmiJ52ghHhb3dsJbuZPhWTFnjx3UaiQ4GT1IBo2QtEhJXCIC1/AiheHQ9QufKg5yiE/5TOcpIj1JiXX8KZa3w8mb2gMUnaJPrxrjgIuAS5NmMdEpLaNJQvRNg6w23nWZRNZGiZCYkowCMvVaxaDDTJ6P20ODj5HhNp3857YFdORyTbgEuBHVWZVKgIGtD9YjJu0wTjiySReQAe31ewOxhQHpbTiGg3Yng0qeMMw71aMqWBCjc84jxAAYVLBWKzztt4ckRzFhAjURldMYP2eZaB69UvePJ59MayNw0wM07QCXsgyhkrnUnAxGAwoBKJS6PxI4ny9zhNGMEK0xtCTkVkcyWaN4AzRnCGFc910MMteHKBKNJAOompglJ+SDVtayRKAk1RAOx7sJDVJCrJVx9+Pofh5S/eULySih1BNvLs8RjmN/LALhUgD92A8iIiIynEy6GR964GvkEUFgASNZqDSQZp1WeXvaGdsG6BAdqLGEMwKDsYpaBzJKgR3bBoKIWcCyIAyziGA0LismH2IUI7Jg8hd54Zgd99CYP6PP3OrW6oEjGwkDwEcQEYZhGIzpWLXCaQKP0107vLwsEWMhTKHE2J2hOE6gxopwk2wPndbAB/90Wd42ShZZmvS+s8E6B+3FZh1P/YOefvrjH+DF5VdpMgGo0ciV5JjjD7G5hEtGekYdhh+tUyEWhAR21d7fW+Aheu69hzljnAQ/wjByMceoo3Cmm8dmSJaLCGGOscoS3rd6CAbhcKhtCLGOsbrbMpYbiJqhn1MQCsgQEksWIgggyIRPeuthmK1ExCAwxIKOHkfA7ehjJgVGSSqcpTTDGQm739vutp/GLoL43VdPXTCEYBdoM4wPuK1gRWDmYlf3PasuoD666Ks3L+xjrTvVE06fSrxlZnIwTTM47TMiBKuQhIsZbeqmafb3oZw7zsmTzbob+9o4dMTWPp6ieU2faQKIuWHRIxSg186RPC8xSZ1W3mNncOxwTyzmUwt8gNpkM6O3DnTp2atSsD//vA/7HpUnkSVPXojbB4JQxJcbsP0Etvd2cxGQOtg2Lysmyyn9zNglgthl0U+27posTZKUUordMMOJznf7f9VIK2CMgj6Vv8IH58pBe3PfZ6nbG8u+hGjnMBNXT3hCjozIOsxK3uI+RoICGGQROawoTXcpX4CRnOD16aiPcw7yNIl+1LYGyE6pBQmOgETU+47xJDSoPQmWNgeSUodxs6uj6VFeLEWFMZvlZYC7d/cjQSCacFIPX1+95uDp9Y/moe2XnIPHOthQzdjUqKHtRIZkniJ7Cix4kgEwBRezsHBQF0TSQo3HHBMsDOyAT2MWGOWSIOzgbkAFJ62DlUiItjaIHlmRRD+0A2QMcUDKAdxaRyBARAm2IOiR+mQBVxoJMmfdh9P98M7xJEH4zIZC5meXP5B5GFXwAPpwONy8fasQsARBGFZCY8lTjagUYPn67PnHjz8fyoR5Bjl51x4vmxN+8+2IMYIukoQYndBEk4M6TA5wW+KLc5ZT5KNw4zRxAhAOsL1+MEEDijGlMFJOu0aZnsUw5iCJ0k4lS5Kp32IWZ5AqkZQJsmxuPu1xePSYWKPpKsXRC8Ewlds/oX96v6cJKX81p1SY9bk6HT+4R347Tt3YnF3+5avXpgcQA+ecVWomy1iwqQHjsCcE4wjBEEFZlsN07IYjupuvuEzLxQxYrQ7uXa3N8/VfR6Ug9Kf9dNvuTJCMQcGxMh6uAho6djoV+axz22myKeDaR+hiTESGmNYawGZ3o5LicvJ7epkHC4ZxJJQQQ0cCzzglwRIMS6BdyZRnnBhs+lqW0DvaWoiGGKHry+0KX20E8ZOKOYw+K2f5oNSo1JGQ0JjF843mFFiP8s3Kxz5Odi4w9WqqpwUO02cuFuOoeVKQEKLjkvuoaQiq5ZJKik23LFOEp9Ybzyyk9+Ast0ybUQcOiSimsYcs9QnAznqYAaB0nlpvUZViAL0SdpySpzRn2aOxM9cuAq2XCXrojJ92hvCRExqOzqbTCKNFhNjR4TU0VuyHfmKWEkbKAkEPQbRm9KQep3mEiZIuah+XKGrU3vnqnOaiqtp5uw1TO4DYWKOh82RNyhREAMepk4KAdXX+sb5tNVb6lyT5zXpzTMCQYjJDcEpooPfOy+jJU3wFXqpXnwk4dxFvI0DoKb+kpUdtwL4kCFbAFz5AfZiQGXGEjKe1DosKdmMGo43BCUkbxxKuYfrtMgZcAEbwxWhvVLs3n/BOFj4ABylDlHFgBfI8BsJTTjFfcxqFMwBLQUyeAYQSziSVzc3OmvlwRqy1BD+WZQ7hORdyd2cBVsF3IU2z4JS6McjP52tMSCfijMoawGH7kGRZ/mItrakwHa5eHo8fR+jyvGi00YNSBGsNEyrEqkyarZ2s6rrTcvksS4pCih7Xe9K1Hx6HE0EIeIBhhAAB50bTSyfr7s4iuyw3AsTR2jxDg4yJxtkmjwU6bR+Sl8HjXSBLfkXP8/P70cXz/PHwoIFmVxe5hP3SsJ6itOsI1u1D1meyIgBZjB9CrCIoEgLLIqcMWT1B4sFGliqMKF3yxZTAcewgZN+lP7zTwBGSISii05WKNMOUW30muHaj1MjmgGjtm2Zy7op29ayC4vxJ5SxXzB7Vfrt39n5Z5fP5U3LpOn0NNWeMU+KMIwAhKFHr3Rh7RAcNjqEQjFwiVGSEqvFmViUpEof+PR/PY99r6z30F6ts9eRlKhMU2a/+7cz9v/+j7qxiIOPcde1Yn8ro8EDYyhggQ8CiuKjcow01RAp6qEc9dRrKUVQv1McHOEbMeP9oiNs8n2+AiMhRAGtjPLZeQ+8dMjKhEMcQgY/+dEflkyp2IMKE4whvpnHb+U8AwkM35rc2gnhVVvPZOSzPRhRk1wqsw+1D7w4+hVhOQXoZF2yWlEm1fdjpwQ4dvDZ/NJ+sfI1SVFIQ5NM5FL+yPx4+tj99fXtInm6i8VQb64Gn1EnvoUYB2tp9TobLghcIfzCtfr9DTxFh6CI/3+fmXMwwgo+nO4JhRgsfzJIsI3bGGaUhYktMU8j3iY5xDObuA6ciKTaAkfbhMxBLSlT35+jWiqSCEGqMKUd2V3Z2OI3jL+jOE0YQAvZ4CC9fJYwejEu0gpDpflBHpPo/j2O/3qzns2/6XrV3/ZFar9zJ6Q+ftxjfCsETKc8vZzmRAYDbSSdq1BKDgVVnIUn5GAFIRzZEZN0wHmaLTS/TOGrsog0dNFbHycOpKtbamOiMENIqAe9HK8foIREiMltghP0VWWF6DF4PJ3WgMTgIdYzTNIBAZb6go5xMzPlxHOWRpRVlUccYAto1p/3uFK0hs8tkVV1lxTS2Sk0IIQNsYhDCSGGVn6eVcZ9tE+KOkScSUJxTyAMz4GH3gNoWpMnpoelNr4x2zs5/qLKQS7bB2AwaQ22D7AOcgBMF82OK0cmRWMdZWXLirUeEThDG1NludAc3AXD3yy/3c/6rF7++TCS42GwfRs40T2nssG2htltnejS7QJXtGedJVnrEjGW6d5WYRmxUcAwSBQMAFBsykgH46GOKyGwpRmMIDC+evbKrzU+//5Fn/uuXibU2ugh6iMq6SrgzGuAeXmQbVW9ru/27v/vXH/6dzJTkGi2CsUmaZ9qODKeQEoAAUGLQ7XGrIEKwor4HNQdQA/SA2ZpjGBGhbHVlHjTUYfWED8d22AGY8e6o0wJQAic3siwJ7gtBAqDJgIxhmySJ1rpCA2YcoQUlA9cc6lgK5kkcR2ttCJELyZsRQssTaGNUChUkLYtCTE2XyhVCXYyOEqiOuCVNKr1Mk4euJ1NDGBeJVGVeP96yrOKYeg+gFLNUjErt74+EgGxW5Iwl8xfjuCWMFlnhY0Aokv2NtaJU0DM22IWNH+Ljw/X83J88vmDFojDxVdvtgu7L/BnyjyXhKwAfIkjPJDmirukwjV+6x4UXuASeAzfDEbY3n79op83K/OHHt5Rfh0Vc8mWBMZPpYwiSZxDBSQ1tz8F8TmAL3SPXGIkQ9g5WOPo1QAYTm5J8JgU36kc1YIBWKEZIqs3XV1+Xr0z73//5v+HnT74AHGOgjEZKqbfDYEigmEof9mbIdWbmg/KEhQJu4LNbO2LNREzZYzriBvdmAH2aVklyuVwQcOn77Q0RohIra7fGtdrWADkjGLhChZG0z08DpJg53ZUAnImqYfMci34YTFfISkyL2fcX2yzLZfrSezyo5ufp3fnZk/W/e3X9z9eun87OLvT1VroBJgyhAE4ONace0WIBxlalMsNZQtnRxTWCzolNCMH7nrGlYBfKvo9GT7GPYILQoYzrcVhkIdi4g6CEChsvdMpN9N45PSQR4dnKwWPojx6Z4BW0IDaHu65OEp7lIlyUfvfeHIKaye1unMzASzT2oSMTiVGoLx+3Fk8MLlGJLgQgV+z5xxenabXOyjk07uPf3/lSXTwJN/+iPuCHXw0bBgemNSlnTmkcT6icU5kjokM38tUTgklGVuA1eIZKF10EYI2k1mPwdmI6c4QVMtRhWwMQT6ddXRoPlRKMRxBaLmYE9xHea1UM+wtDozVaKw4FpcKjU/Xm7Hf7x8/tro9tBND7Wy4w+QoXxWZotuOP6t31T9+/2SRH4QhnI38cj0e767Q9tf1vKKfiqIw6HBVaVK+evvzqAjWP93174/G0jRQuE7hF7f6AO3XSqUsn4IIRKIFRwDh4qVIXWuuTjEazQeAduZuDIngKCjIObQxgDB1GAjw9H7lTpkKldYFC1QAP4D0enqYLTMnWPMTYxwAEvyDzlKtZCM+H4Y+qbbd1gyucJq0s5AgGjMIcLYGZhhGxFEmZy9k6X4ihfqh3fWvCMqGciY+PtxFJLhiKGjoEPFnQdUg7ZIux65U+0X28bhs9qoxLOGk1GWvd/4EI/U6N52NRqAs4IxTdV8DHBMMRFFMfqzABaxTCEiBQuzE2h0xmkBHCGeGYug7b4zA1+I7/qf+D3GQvV88FJ9aYtqtjJwEMcV4Ew9EMjS72djeMR0Y2ifUYw1N7pxVYofURRMBbzSLUeexPmQwMA4SRlNQo+zg+VgzPZ2dZfvf+59urMyNYSgnV2LWjLEuJ0RFqlmHUclRB2zpX+YaPJvY6z8OhxUm97NnEswEjeDvs60+txAJjzqsrMId276OOZE6ox2zggXlv7Wue/jL3w9t9ZinBNH0u6KQ/9vdFUkUfpq6bF/PdXnG8BrMKDvuTZwgiGgnmQmJIncGAYMYCiVBkAooM6n6aYpBlBdtpx4NhNDOC9rhexbVczD/GI5208zVEbLPO78MjwjCEGKJPkkRErqyJMCZ9j2drgKgIMUnz0zBtb7er77/pb4+Q0/dqfBEjJSRJs0/dHeKvCQYCRA8LburUBk3QxUbcPz7ZK+sPAYl+ryeRcH6RVodgG6xKCL4Y8r5VUPfB8mt+Zz8PZkqPQhakWjGiGN1Uyk6joeE5lDR9Hb/6Mv+CIFYf/U1ycCYpy/MK4CThISWFGp8iMXF4/HhH54Wdh8afAPPsyLjYGe7ZXgI5WZYDycspz1BjQR7tiAzTCTg/e/6//M+lJuB4qB+2d8fVBny5rgEElbwquBGpmzqfziJWjNI+AugQAzG2IP/qV3/1/KtPD9Pbf77hXevl6RwRip8STMCjx5FYQCCgGCVZhqnDD2ZsjMXQM5QnHE9aG8iN57djPfMTOoa40c+fiC0q8lmhP9b7k7Pzw48//Yik4+dCfQnHJ+6Hr35d37QPdlK9tngULiIbAQlg9DsAemNnn03jIEMsGAZaQZgkIBx2x8MwRJi+fLr0WB/2rHIta8IAIc1QQVkzjcPU90VMx4iBiyJx04BhnPskhJHSygR3/PIL2HhcINQGOqebsxf5eNe9r4/tXuqHw2kI+QI5oMo8NuOEWESdgc5CMKRwzs5pSDfzeTOod8O7GKH71D/u/kmvz6dm+Hz9tuDyza//spj9MJx+NPV7Unzj/FFT5iPAqzVjeVf3nhxZIvbjPMfYhZ7TMZGXw9j9snu7oishOEKgAnltGn5yVlsAJ2+sdYjOzrAP4BhMvE+9h5QUKTuHfHB20hoAhDCG0NnRgRHPnuSX3z6lHR2IHo1BCOZ5Yp0i2Gc80c+Mcq7tUN/2wFpus87lSUJfp9+08pined8Hk5U0aSpeRpqle6vSJU6lcRMZj7OwPJLxl1aVSmGo3M6LalnCpE8idoZ6wxQ94kRrbWLAlELGPh/rvdqW3ge0FIQx0HobTh2gn65dhCnYzGFo4gre3s6frnItPQuE0AVnKyZJIgEhGKKEgEjRr9++v29O74Vnp/FgTEIJ7o4mPRthWF2c5Yzwt4ejDxqUDEEPYBDITRo4i33keZJj5xAsovKYQcGQ9hOx7dB75EgMI2Ps1eXXPEMxRggj4qTrrk92x63ajgzt4BY/zMRyyXgXw4kswPiFEIM0VLhKSBndlvt1WK3FqLZKx+OnJApSknmJfvgu6+85l8xpeHfzBUBwXlSQAvQE0ums9fXP73+GCBAaiu//IiOdP3UnO7Fz+BQuP5H7AsPddDdNesUDPkNt6FMPI4jaDghHVrIrttp3pwyCZ19dzarJHgY0YsQ5RnCxjG2HjSbSeCAD4zwk6Gm5NNP49uchgt+///wtFfvV8n+43GyEFzfWhhjPLi5w4F2cReCMdRHA1SbBGNkJkLRBkQgiMaG57sQZDxPe7e6q9CXBAtOpPoK8zMpUAjidbzYm9hxnXlY5tVoBkRI19pxgPIJooruSBIpaQe1tUS36aQBJz8g8yVDUKhS+snkwqeOjoNnzqsAQE1x5u218wARijBFS4+imcaRVxdKEQ6iVitF2IR5Pp6fsMjdiArZ/W7MUB+PcNdoX94s5yzK0TMqH3SMyOeXxQgkS45AG88svj3zVciNpjn1qKOs6Oz0YVMBoQn/s53wdABv1/cSgj7oH7wqWzc8EUI1O4YDqkRj18bOx+vzpcsE2nits2HfJb7hIuwUwiO1+eUv3BG1ydxrc1B7y8nx5ru3YU14gwkkA5GLSynjDnV+WwlWFt2FoT36sypxoFTGZdw2aL2f7/QnEpq9GgDA6k6vFbw6HGqzOacSSeEYp8MEQLKOxfWzXpelPcH9vI8wde0X49vFw80vnvLt4k9NjFayDSeFBU84LXOVNfS8Ex/gMYDP67GGsie68d0eXRRsiJASlUz8tYHVF5v15zWOy2zNAwJcv7fbxcdTm/qBMsPn5DImV2NyHrC7wy1//m98+/K/3t/WXc+MejbsIkT1h4UvQgLzKcvb0+fnjY23CBscPo1b1h6vzqzFa4/Xl1frRPijdny+pt7Oe0GQc9UMDvv0uLKtD36R4rp3B1azDOGMW9wEpdRT9SnKuh7dKpR/71Xf8XdN+Dxfpxvksae96C/3DYeeTlJG4DQHsD8ZNqEqVQFJwrz0aUI4oRmGKvnGGsnNIGX5+R8oXUCR0aTdDlzx5mhXpr39H/rf/GP5QH344730UVCakXOX9gCz189UyjWkkuG7hPJEtq5vD7dMDhixj6XI2V2YcxpEg0qKxEMyNmM+qGUTIez0qgCCtCIVn087r6R5x0VCOLMMilU/wcN8nqU6zcrdv9dshGCCKtFUJCPtEpnn+JvHGjJ3ynyOfRwj//PZTame5ECRHc2pgXxx58uJZPhADAECA8ERCQE5+36tAbTpCvN68+O8fTv/4L3+fPH9JETV2WaQdz6zAC4A8GWPEJkTkAJhT9hjAjRqfZ4WclS2w9h/Qo3N5pjQxINUUCAs9KQspyym4cRgZx/Di2XlW1rU57LfkGYRjhpRFh8lnWFsNkbs9f3b59Zuzn3+8P1+HlV8Mg1cBRg/a91NZ3moi3naTm4A8MxoZ4CvOZbA7GEBTUnMztXgpjCT8RCANglYonlzhDlows0nP7oyc5Ru6Aad9LwiAQc/SiJZpNrhp6nIJPn4J42G4ePXkEcRRa5YWSAnkiooliOIJAWBToLsYYSJF5QgrX1cgZskxyyvJGF6zAJ8DEDHBjDFCWbtt7uth2x1WCRvGAWNIAnxlDUg3+HXZtbf3p/cn9hA+x0+vNHoQKAGZoNDHUB+LszPnDQq7CZLooEVCSNCZu0CpyAs84JgHAJBvpVdkMgbidBAGI7HbnbS1V1Wile66XzDGxvyz8+G//n/s7/7tXz99/owJhRCBEzyNTbq+dK119ogXOYXIO6clkAZkm9TsDzusoteE4vOKNfuooSoToVzCQYAODmNTFaXrO1qklHijjwDLWbUIIWaZjE3r5oWfxmhQ57QQC1hkkuLLc/z+07tOSJnnyZqCEBBBOVcOqAjzDqMFJlmCuyShlGrgfYCCLSCyaL6hU/A2diASpJkHQsVpGI7gPkkSTNk4TIRRoiiTu7F3faddCMv1lecknPYGUoSCVR2Z6vm8AMgdx7Hujgx6rp5QTGMJLtOImKDdM3do7oL9Cm/OcvcQWwIjKkFEVmzyVMbDfgCel7P5i5dnPkIfww48qghr+CBR8UZ+W5WLt/PL448/eZDCVY5A6nC/o90mlhfnYay/jNeKPoFSJIzpEKAbLlEcLWTnF0/7YcI4G4eaEru+YJoUSy7RAnNLtfGj9QlySlkE4CZPsVUAJy0BsaE+1lJQ2OS+3sYIXp3lydkPXBS//5dht/tnraM7v5DzBHRQj+o+tjiHlLzOqwWCPQS0n3aPh9ZiIuTSads7oIyIIeRYQLOnQiCEZlgZg6wK0zi2vUFSArTKElQ9fXMOvA/udPak+XQDX4azq/ViVT3qyUFiAIwAOh0/m8AovicSP26DjQwTNK8uKO+HoViUNCNLrSXPanXCcAaRDCRGGHWajBCjtiMy7cw4jY8MLfKu4QGU3gpGx8EhbJf5a/Qye739/GernqdfX331kydRc9duUfHrkrHNOMFH7f3ujlPOqwu9+2POqq6tbTC5TEVIXTSTUebU5OtNROmEapiQRALlDac4/eu/lITsYkCcLjbr3Zc7H11UmUo0i/Z6Yjy4KrUQC81D1IbtaClFF6R6PC7PNr1nIACMmRAARXTpLmwUpgrj9tj0HUR8HA7RKsZZFr3QFWXREYxRhBHeDV/yAWntSyAxJJTwyXR2hbsO7kcaybwA80zacWoGSCeebmh2ioM5nJL0MkihERCUy3M07vUn00TjHFzFiJPkNDgJlZuLMzTclq9fFGWpJt+2k/75/ezrX/Fz7OozSQ6paKdQeWOJHx0EbJnT6JiPxvd7MqfxNRP4h99cRW9rdzTeFHwTQlgm0mpBJgODHWWWA38hUz0a14DgHEEXZDg0AIwCr89XOYSobSqnVZbwp+dnRDjZ9es1Z4qBEJRSO+u7u0egtUheyiKJajRcTWACNGBIWNf5WUnmlHYFxWqyE/JjQi6sT2t/jRT8PFxHOpsvzOPNlkDkgez7TmmabXgCFyzDjNLTySRFFnAFHu5IAElJwiQhhFNC2GT64MgwYAIAjA/OeHt4+u3TGOnYtgTFVMi0r6a4HyLrG8/b/oiIQmGcEEtIkyCRc3xVsFRdwwhOH1d47mPn9ioCDp+AF/jp41d1NkgphdnezRhLkmQYPD5Y6cFovY1QsKsp3IohNi4/RsBqY2wI2CCUBBAokRhDKTFKJHIUgqiVijEEH3GE3ru768+Ukouri1LiGbjoZ86hvszSzuxA4EAJADwrYBJ9tshnuLirDgAKd9DVbBNAQEiY/nPvK6N1IhPMaYKzQKLCripNkswJtZbxukYZQVhmE4YAYTKrAoAuRhdh2wyrRVkWxWJ5BmMIScmiCdhpplWX6hBlchKAIyYQHcGNtgABAABJREFUYUYrplVVzrb7o2N+0Jr3KvAcYpbBFkTMJlyt5JHkozLjEB2ai2SYagXR2gBFEaCMWqPq/ZEyuWtVN55yUcaxg8EOXQ8QWlK6lM970w5TDYzd7uCBchiPEeksTb579lrZdteMhvcoIcw2Yl0Zb/bNNnKyeLXMp3wvusSInrZLvKKRWTJLYCGY4zT9lcj/8MP3j9ef5UQEJ56XqylC5ObVt2erm1bv1b5nT+kY6XFUIhjnOGXIa6NHnUkCg+ybNsik4gHzWACcIGJh+HE31DVww32+eLFe5jdT2j9+QRmFUhrlKUSSKS7pc5EWVxeMEcH5r/+C//0//HA4/d3tYy/lo+Ep11L3tx8O2x9IW1V/EUGltb6+bY7dVFtD1wWTIJOCjRhObbVZT1NHvJ+6n9utfzqL6eurbiz2pNvgSGEASYW8/VkN/fhjcMxux3Fsvn3xza9/+91N0/54bFMMI2RgW4VwWl2+OAqOprGAwbhwuN9HY0TC27vr3TRU1LvFDAg7UkQJZwFBQAKg4GCHYGZPZ6s89ZARJryLeS6U6r2jQ3tvcf5rQdMsL79fyuMAMcvYm2H8cqf65Xm5yb8bRjO6qVL6D7oVnr0QOVzNxq7uPtzbkr94uhIz1t8O65ev+uu796bVEUC8p1BA6PDQqGagC6blwjf3KV+9ev6rtqnru7u8WOJxhlh0KCUwdK1TancegJlTX9d883qzKgbtD3UIFny4u/XBRe+ACNzx8TRQzbQFIMZiuXEkdydNUaTGKff46JXValWtJOCcVICCqHZdP7JkhTGJl1e+Yz/9+HZCi8WTiuXnaBeYBh5pmjxj0QQts9xePL3QetCGRDBpG8oiGJ0Q7jpHQTgpJ4AbUomWYvqjgn9lVDJ7XpUVRPS7X//6oXVJiBMhiZQRCBl2hEAAGYqhbrxmwB+a3JTW7LyzOCF5PlM25m2crKpPxxAA6yYOzimS+apKGCZmkiExyglvc39BfDuEYIRYfPvVuVKm70YEgZ+MCpaeOlJF00XQBSgQoQTQ/HINA0v/9NOfrHUrP90CAAijFdIKJUoPIRqOTHcC0JdwLvDjy/KVVhPpx65pk0QyCs8vS8owTRKcZGAaImUe4bHTGEiOU4HYeeEUm9d7PQyPYf1k2Q+tJwJDs92ifJE6TaTEBFK8TOIo2YrXVvsjbIHjxt+ZLQfO2Wi9B0hxNvVD7xRAyM4qF1rylJRVGTBCAE+qGW4BRiEBLEYLI9bmFzcWkbkYbhAAF7OZnaC7v1eIAoj8OFHvscyDX3iqZ4INeYTeQECSLLUWIq4hKCDqlXFQUBts1PX17S0i0Grron3FCkKj8vbd3//D0xfr9NV6mAZM2FS33ptEFCwrnXlgFsqcoxCUOhUACgRllYIQrJ1KSVN2ns6SoiEEUeCYDjo6WBQpHFPHrWpp8eyb2HzyHpbzeTcdIYgMc8bBGEnO8/rY3T7E7BmIfKGEzZQCDCR4XMfSZtgAjZUNMvHWPH2y/Hi86IaeEszKVQA24TPPJwe9MdOcIiEYTmFwarle7ba7um7yHIy9opDg0OQWmEn7JQzWHfa7rEqj0RKjrVEUuiROJEOnJq3vbqz7rLADGB0g1M4CAGSWLjez2YaN+QS1YjnwGnEOGSlEluyGNuZ0JZ8u/WYS1NkvHdY5YgJDkTDnDHG7f/0XfXnZnV1+tZy6e9SciVXH0HI4ydCqqSV9K3PiJHDGxkmRHOc2UioClcaQJsTtOM3L89cvz7e3lqGJEKSOO5UszNSKIn+9WNwVgd5kbEUdzRNw67jUOb4qy+tT5yMU1FIdoe+tu7UfDy5bsMvv/81fb95++ffOmRE10bbpEqH7fFCnf/rnu98+LZdv3nApJ1Bi6s8Svx3i4fFYVrNlOes6Nd7eDLK9QiMjqEIgxhDa2/tH5drp7smV7lS6/9FF+zg+tLhJ2Qtxpsfa1/VPqWCbv/7LD//5f++sKQEQIoV2dBHK1olEUghTTIemhgQY3ROGMo4tJnaCjXQECz84RQkXCfIQzjY3+1/6+yQrGcUcRcOmviieN1376eOnhMvfMTo4mkOy3ryBPx/eZh+a4dj+uZ1tynJFru2Xzw/X/aB+M/tdtlhNb3+5JQwnTt09Ek+/u/iGs3zsR5OIpj2OY2ziac1LlL3OGCYR9h4ykXJDfM4gLRw0s8t0fTuHp3HX2oVkp8N9NUfRFRF6jmOEWZzM2DwCO4niGyPQqA8sodG74AMC2NeOzpMZTGkujghDiEiRyEDQhUkmBUaLD1fA7ayVk1F9N6LSfUe/7RYSlZilSfcHXb97/2HaCVl8+/ppkpUN4lMB4tahEdJzTicTJ53ma+fpaDqjWIzGJmmR8HSdDo9Gvoik0QzH0bsMxx3+wlkWjG7rE6Fwc/YbN5ulVhulOKcwRmssiDbJ0maMBFPWEVEE+uxrmlUE0egsiUFR4E4uoVPqssWGksye7i2iCQo5RpKBqe7pHLHtaZtChKAkyjxcbs6zIg/Bf7neVyWTMiEogu0NIgh6oM2QUWknGw12nrkShUkbrfX4Y12/GgngVkUb9di3ZcD8aZLyuYvWj1IPBdCMUq+n/YePbX969iyTUt5OLQ+MAO+b2krBzy9QF0d9INDc77vzEin+ZLFMj60n/fMYvXcQWiYkxYDmLFspxuGcXBJrVARJlknz7ieoVQJFMNLQOmgcKA+ciuAab2iK/UHzPFX+iwqScxzx4ENIEQj8BUiGIrhZVdnYBYSBCXAwlzhABMgCooOz/QOIUGLeORMCinbutY0njPIYZ+VlJrwl3scA/TSpAAmCwIbEqLrbjSmS7lgRtnsFCws66EchgNKuOp3e9QO3Zv7iVyRChMT6SfnhA4Mx+OEOQY8pTSR21gIHU1JE2zIboAjTcZBIEI7cMKrgMICp6alkc84Ica2bpq0bFMjbk0gLDz2CJM8XxioLrAXQjn7HXEkqH0/9RxjzmHqLEUzBCZlokIYsQFBpG6fmxLPsXqOvN9WXU6jrRlIbrd2slzBkVhsXLeDExRAhRJSDGDebNaPMubAo2Pb+blAPMOgmGNYWSVVoVY+7Adu0uZvI3DEexrHt2wY4l2TSBbpvhiHiJM844i9evsqrNF+i2/aXXj+ArFgyDiwjOGmpuw9fXCQi+zZJs8/NCSL2Eqwnuo17PbgO59D2BICKY9o+1DLZ75pb3G+1yMLx1I0DmUm/24+qNlOMyRTCiFrE0DxoHALqbBc89S2QIUiahJFQvx0jvUwRLVJ62DetqY/ApOj7V6/ugjLTQ9fvQ0CWzJm9b+rgnUMzX62WJ+fbtjadB8hII1h9utXeu9otKfWI3Pd48vBy8dzlf/7l9u/e/elvlishJAAqz2OEYm3iR/gkZHMjswEpmFAzNYEaTKQt8bZ7jMAHL08nifQRpxHaiTCSPZnJuDnj84P2GQ0+nOKWF7P1D6vF7+1hNChnKcTHYFE0QBZBDwGxsLqkx1uT5iFZbLr3tx/7kK4ivIPs9ZwRIZkEmCDkl1Ds0KXXp+guMOTOG58mcseOt/WpPj0toGFnbd1t4vry4i+ev9xuzucPu/v/dvFffvt0scqv/nT6OMu/E9QTCP+iyN5dFoAEhrjMQ1VspCw4j/D2xJfnPkSfpVepSBg6kP40NabxuGVlllNKbLjHcEoN7QO4enl1vGunm0dsnm34QgUdnMyCTQH3+MDjYBA1imBseZGPeuENhDO/lKgH0LW1KUvZjc4rjPOIgD5o1NcRinYBMwJDoVE9k1cBA3qvAA/7+/GoDicC6OVfPYvx7kPziV+uirMsSfM94l4TCzwlcxkNaYZHHhAMs2U2DMdh0otZ1kea+6BaWuvPdSTrwwQ55Al5dO0aLrWCT566gyfT4+cIHJVw98ddKsYskkyU4WAmsEPWuWEawVgycn61FpzTUjrvT2paRqBIAntIIuKEr1dzJy2E8fmbvEhWh/e3+/oxhnzhGGA4cZxywRglS4a9pNZqfXjvAkni2ef9lxci1YzugzxTKppgihBVJLEUeGKNtJx+K8sGxTkMajphSeEcieWcgplGhPohNymwQDoLtDb7X5A4vxACOkmRwgCAQ7jZnOa86P3QHB7O1ht3OjjGEirFhRynabLTeO0hgemSLThRBuS50eOQUCSzlJbCud50YXr7llOmq6fReIKJdao9tJxIqBTTsuOTih7AGIAXFGZZGjLPgq/w+qhrQpkyvfQghSSFOeUHTGYJpXY8S9muPJsJ/nK8e+u80YEFvBzBWBtDYeHiaCdjQMjkBng7hen29iHSGEw01hZpfhgOggo76St03nbT1n+OHtwDd8YKFM7xkuKHR3u2Qdef2uPhtP2ZHkL65nfB69U8EEyQKhNmsKBU9xEEQHIMonUJpQyr3IEJYIihBe4oaUI4ogEXMOJcTxbOl+eSLR4/3hkXPQQBYeN8OV9Osd8dtyTDYqRhf6LFa3KRga2BSywUThFEAVjLYLylA8d5zTRWIQ51GG9vAJFneXqjNYKhrw/2gyoLjglD04hpmaLSEEWpMKeTc5QbLDpJSlhdvBp2XYwPWre2eWhbKCKaTn1y5W0LwjaaK6j2TMxz7I1MBRZ8+dtXKCkiBFmRcymGofvgfp9exZQlw+ga7bFTeTJLvfi5h9afOLyuXSoh0l71es4j1qAuKtVNFuMFCmNVCnaaxuH3hGQCQascMoYvZxr6xhf99j2jgUnovCbrVDYIOKcLlOv5QFE149BnfXdUt1sW4ZM8pUYb4hPqKFHAd+2k3r9tBE85ZZiR4OE8QYZU2lnbb2cXL1O0buR77eOgEkQZSxOAVAqcXkvgRyoATukwNDn2JM2uvnqxfX/z4e304kWQlI/Q6xA4ADMEQtBtr0OaPWdJxDSGT8YBimhd9/b4pXj1/csXcNeZVUn39or7BloGK+9pneA4QX8O2QdsiuG4uZo975r2ONmxWYEgOn+IWzwU99utzCDNvs0uUxQsIvKXThtj06mQZ2fz/AIJSbUOHgHiOq+v1st6GhFISVxSWc+rTfGsOiabzX97G8MqdmZ6+Nm9Pyt/9+Trr3/T6YfR//kCTNX8DUT7XX23yasJuz90XZmX82cX1hBz3cTRLS9RPrThPkSQVYN7p1TwAY1y+7gHAIpNmlZVNA3jOCAznTQQnuRyQbyiKjT+92GU+9vFE1i0M0b3BsWeYUIe8qqCIb2ftjZMPyx/gzKhovKdQSIBx2MYR+JsX8JU85KjAVOAA0oXPkwVFxAhE42H98024TmazbNL8RWewf/2+Lnr7+fdgiUv8uz2Ylb9a3e6twEfa7KcG6tNFeXeIBzI9giW600x66kxmlezhdeMIerdQOSbIrTm8LbIZ3vS070i646xZa+9UicAsUyTNy/lH7eT75kf02qG9w9diu1sxq3TZzgXr15ECJqb+58+HbQNQ3t8+eKV06LIXQl9YRejrE//8I/vrP7tb5dx+bfyrFi0WxshE16ZKXmV2dH0Dy3BCGGOhQQ9CmcV1zgEizuDS6DXxk+pj/s44hqN54wffUAMp2l+yV9s1pWDzFf4rYt6UHwyIoBGaw06chrqixWNahkmpXez5AUrvv0BffxnwZkHlwDcS3lxwF6pXfn69TzJrt1nOOhPFOeQZEKGpvEhzAuiRt0TBkJPgZ8XWBLnwmN9s++vH9+PBnr8u+WZWD4jfjn0XwwwMeGdNy4RVrdhmLDkQoYIZLUyPmnIAaEFmWPuSJ63pXdgGtW00dLQ3hilhadBhHsW02RIgb+1fTwQTLOZRsNQ+xBcj6iGOGMgWS0BGL2/fXgwAcYsJ96IORV6mhbpTEBWrTIPTSLC5d2zO7qFFjXCQM1mIOB1HmKgl7ivD3efPy5SKd02epy0mJJOIYGgJGPbIkQornILeIJwFQGMD9d30/QET6iCCEIsphg1FpQxFtEcY9Q3AUqLLy8AYcxZAIPBEDiQEj6ILLQGYeRCnIYPYvXaB4QaQKg3LsL6hPQUgTPWyg5GSXIIw6LQb4ddc/fy+RlZzJgeNqgSfdRTxwvuKHZOG6BSU+qhZqmIKiHB79H9/eO+DIhrGiIT8JKtzwIJ0+PIILa3/ku4IZiILcq4KF4+TVJQLJa77cEBm+S7ZvJNul0mr7vkgR2wdsNoeqctPyQUp6EqC0Zzh0cDIEJj2HsyI9i3/Za6CQHdhoCQ9xHqpkk82kYTHjM5P4Sk8v0d67BJrf65/cPhp9NxEAxfgSAYmcfSnV92XUsIHRpDMfSKY6G0GpJNyPINwhCcrmk/7mEPGWYsZXnmeKIwTCcNvUsoyyTVoGIkHkJ99G1m+Cc7LmCQ9BAMaAclKKGDWxE8qI56coI6fTnTO71VN5lZJYlUatCjt22WFEk3Nj0buYDq9NYIPUy+8YtZWkI6Z5BU88Q9xtv7O1X3dt+P8WTZlYQcY5oATjnr+kets7b/8V2Wk5Ip4IozvMgq8yetb76QwBJOjirwxeyFPTzGqIN5mELFxBIwIhMqET+/IoQ6yn1jRsAxANR6ydN0lglVApzqgGkAEQbwhfOdJzgYdeMG3h9w+3iT9085Hf/3f/np5+vH51d+1/xvP+/Moa+7MKNsTBbR3h4dS+I8wbwHOyv1Od4b73zCuRlMxIQSDPHSIQSSdElmMDiVjJgDjHBhWOhVlcGqLIcddperV/f1vzz+4avrYb26Eq2czaoIRqOgQUOA6qTMMmSD7pqtkm+EbVsaaVdEHLHMZAxYCkJRaM0eezU5aa0KIJcMnq03J5zrad+2XVCdSdgPT+f/5t9891/+v9soDFzE+MV/fNgWDFLBmAgTdxdm8N7pVdIPJ4pg6nsXll/uHzlLd3VNKQk4mmEgOoqqUIvzo2rAAdua3Sfk+ZIDeguYx6BSB9ce6rSWjE5ydaSayvSLARBRVkqeilwdvujaxsl8fXnVh0nf5lykvZlUpxIp6rw2v/zhNGxPQ/NP/1B/u5g2v/3bcDkzekI86Q/H8cftz+Ot0opoa2kAEUw3Q7CkXhCZE25p7GkZU4DjGuKf0D5CSH3MEWkjCMNuCsCF7ad7ooG07kKnBiLkiQcXQo8TyBPqDoPqj1Hjvv9Idh+zsyxMYfhFmcUBI8bqVnedAAlNlilAZP5C5ijr676uKeVn6/NcMOtt8N7AXgcrGfHIduEx/GzHZvix6a0KBc1sXNXDQSQcQEmQ08c2eqzyCAJiMiOsmSYoRPP5GiCGWcKJocYHe3SEAwjRrJqVPD0Mj1lWbEo4ThqZKoRo9lMUsQHAIVR7dLAQjIkBIUTNEmItOsbtpeSEV2W1p/lcQn5zf0AoWSRVZwYqsHs82MPu83D0Nni5SM44G4GzLSDcR4egBZiPQ1cfP8jm6WH8KfnblaYtRMmSaGBtRGiOBVpUUTlwnABAanu8hdpDQCmN2oMKMohiABIJHuXgWoQWIYDDOEUwCrThjGqrVVBlshjIQRJaQWC1vrmFEOFCxY541MdYejWO8faEosIAxICcDGjOHAJR55vNohm6btRR0uAt5TavCrL14XigKLYTtoQk2AWOofeSDhCcS/Z0iRePx+M47yar/PW+2d+Y6AYMnA3GmBjgbD77evFKlvP0an4fbgqK9Jka3nWH3gAIoItt9Y9C8sCtdcYoC5QTueGsXCfZADG/+aBzp6dB6VMS4cSt800CrGfR1Z4JXpkHIBdTmAUnEAD1gIQMMeExsvDFf4F3QiYvZ2AtvlLm/ftp4l0Lzh2VsUhk0N0BBkjrZIIkOBkhB/durqd2nGbGIB9gvOdN1iGKINYoYflox+12F87OknzNBc146Y28I46SJyh0AzHhIVRxdPZx4ixrJ57no+qLomExvjWnDJbnby4f1J0Zd/vdjDuSb1ie8o8fb0kqKMvS4sq4I0HUal9/aKpU4CeXZvp07ezrL+8hxgKiVCYiEJLkDWoJIgQ/Pfbvdvs9YWz+qvCTHeqxulwAQr/sbn/u+r+KfQYAbBvO5dX8ooMEjX6KyR3ECC/Ik1lC6YBZaCyIpGTRES6QRxA4bSHm0EdOkZ/OquSCAj7lISXE2cdxKAliQ9+3/3rgv3oFwT+pIRwIz8d+mkxCizUVe5g7T2Zfa3yC3mU8g+/ibjzek2XhG21MmBZLPumkLO4bMzrFLdz6XNkx7QesEKdsP4znFEAUXdB93cU1X/7mh/S/9DdmV7C1PiqMYJqcQojeS5HmT/K8XFw6ZxRUaIzx1C7WawyLbtaP7YkA0uqJr4D6EqQUfOOQzyB25q45VhYzMKN5CK4ZzDi+8/6vhOAylfAuKtGP/XCxuAQiehwc9GR/e4NBUBPnvI8tAPrl7BUlbC0IT9nqyZPP11+8984NEFLS7Mt81RN6OeeuhKLsGZsd9P2kUS6njKLT4Lyly5IE5355+LFEIDp90nAxX8EJqsk4A2IAmEMeKGR0SRcg3Z97wdezh7sPbXus0vR3Kdfa/nh33+H/PgLsJs2StB9U07cIRiYogQXyMUzmqE8OlWSHXe0cDYZ6Hw+PLxbY+eCdgfEj9F8Teak9P9X7sPIm+ACjtyY0egpuJGMFsXdY9C56ZJGy2lKUeNXfHg5Fc1aWSzTDzS5oFgKatHZcMAjAtXF2MjKhgwt5mkPInW23p5MPnqAICUhjtOpWgdE82th7p90Lwkm1zpNLvhKoBjF0MbQHCjEA+6hKUjZ15yVt1VgKSSFcP8cmPVdbPbbjgZxYkG0YymKewBBBTDK+V+Oe2NyHJVv5aD5pt0kk9NCRAlGItsiUCkIIB2wn68uoWxwISYjohmMYJ05ddGgaBuchCgQe4KBPCFGCyeXsQgdjTWgRicrbHM6FVHYCoAS+MdemX0534/bqukuXyaQjYs868LCQMsg87FwPWhhk7nEXj7MQ0jQFGQ6uBzbwjMeIwAQ6ipyi6WowuoIQWmY02mmfrvK09o8dICjgzJOAE8Hx0yv16VAjN16kfooItW48nkw/8VjjNQBb4r2DXLgQEZ5RDBZFDiAcb26QtcgOu/G2SDIIgaOLfHgMPj35I0YQoucaHCm2+EXlcisXxA1kbB2+RE9VQTkJwXNK6dOXkMC8KMtZ5YP9+f2fTse+fJUayq+e5IRinvDr6PXQwcZPbI9JTDifhTn0cM6J0SNHnGKcB6baUwBmoIdSDQZjaSc/AUzso7ajBdS33Oj724nJpC8Bt2tuTiUqPCCc5F89/xovKEak3uffftZ2/HOvTgAiAR20LRBy5i+IaCnEGOq+HgxREAHICYYL7+7HwSi1S2ORspwQzLOkKjYEyUG59z/9mVA4ezbjQnRj/+hYoRhNyK7XTNncWURN1k9YT10PO3KCo3v+7Tl2g3EPEUjiUDMZ9OdF+V22rK7a8Xbom2IxCNJnaR5i+VD3//h4+Op65519EvFs8ZJWodj3QhTdOBGIgMt8cNbeWHMteDaNcR+bPKvaKsUdvXyypH+bfvpP73rXcGt1fbz1dLMUBCMxZ36HQA1ZNueMWohIcEhbGAtIITWeMbSkXDHweKqzvDwrM7wm2RwGGJ6EpCPMTPUormG3GLuma99W/off/up/vDt8nJEBIo0ZRVRsPVLKXBDks1jQWTRxc/Vqe3e43o+vEqC9kT7FqIGYDlNTSKrLGUuz0ho9jpCUVVloM331NOnC0RrVx/Cnvnu1slnx3auziy+Hnkruc6e0hdBizlFeWp0KxExjHCKcpe7WAahA8DMAV7SKs2IYt5yytVxeP314KtYNUB5Nhd/UhQbBORxtsYzRVSEaH432wJuu6/z8Y+HJ65yr2GqF+rHXfMIWYTR6nnpvYzOW568E5sa2vGq52Fjb5RnDGIc80waa09HuewHGQYbtof3V/DvORKlhQxULlcylatMk5Y42+90jGFvFSN2cUkLzHwp2FMiTmAJrRT/0botOZEdsQCBQ6hM3YfUJgiB4RiqSFkAaf4ITLEoGsq7r6Yb9n2b/Uwxea0XI8qX17tOkjPWZSCJnaQwYkBKiI3mOEZaLD6pnfz415/YP4/RtkZYmPBI7M04RQZOsVJr0qj8Q1ezIHNKcYjgZa0xT9wA47+3PQ78K909AVWVFekapj5jLFpL5cqm1cXUb6ocYZm6sleQY9hhrGABCgIIYnGu9YgOAGIlzASP0H4LGfgDTUuwmo+aJAIj1wJXKmkrHyQ3D6Lzzk1stNsUZIGDTKzUXxTgzuukp5UrremxVsE3dvvz2VybrIAJWG2Bhp9vHm9PZpqBZFVXL1IhHH7xHEHoVPbcZ9zbSbQM/PHz85usMpogH8mImY7J2I1P1FA5Wo0mUK8jp+nrCkiHjoPEL4seqFIR43yLYIwQhoyMYxy+3Sb6+B3ZFE1JLnHPOzxs8mq4lMKf4shACduoU4xXEeLXC/Q4b4qAHERJEIog8ulSIj62lbJitXjDXOGexnM8EHjX0oE1j3gdC8MhRZPzi5WYeQhzqNnbubnd9e/hixvYqBtJGkkiQIKCisS6SOoTg61YznCFSQjSJCAd139bMGmdvvfEDI4/bXRvC168ceoAgXoPPzz7EjyBlxaLkHH3zwzMhvrNhip+/pOeJ2CysCVP9efxwAzIxDQNZRkJAP366nzpKcYX+QqbBoLoZsFc2SUxeZBnq+yZV00nwK6NbFVEwmhR5kQhRO2Xa7ot9byc4Gy1jcTDCpDB13gmeiCQrQLHopjGejKyo93Gw3DU6D7W3MB5ENVtN8S7ByBirx9F1U4WeuJn+18+fI9DVcoZArG7cHYLwS0D5+3cfa+KpeJ3u676ks+32tKouB1wCNJgpiHwFz2cWkPjhCzaKpNidHEQwWDDoCRAHZgq00JWgSKHrYEKQNur28X4YmifnlW8ny/SdbtjHNxfflfzLIUGg/fAgY+AjTiV06yfZ/l4QQTGIMc5KZrQ9KRkm0znqIwmAO5u68Q62PF9wITMuZZpX0GP94OXl+tVvfu0f4OebBwgIPH9ZPnza7uuI8ogiBhoWS+IQaYnDnlED+JwGoDsskiYNWayyRVlWS3PYN1MKZ4hp42Re6Grz2YV93XzvHajI/vDQrVe/3vuLp89/983/fPPwH3BGKg5GhI01MIoGM9y1uJ0ykd/eHX64+vqP738/9QazMqH0l4OSKWWcecRmFCEYamCZsxVfAo1WBY5igkN0VoPopKQkzPJZiRYz1Gh+TDRADjm7uWBpSpJkVBPq9THdhyCsb56x+QOJVhuMOkqWEYA0WVWUEcwRerzFJ//FO2+N2EHotVKwgIntKx8GGJm3o5p4BDMELDBpwh9hLNrDVDkY0swPJxhgTfKlSsg8ZPSMUQQG03w2qgPhvVXZP/3Tz89evLSIASj7sc8o3WPuP8YQBxCwEMkAZ/HwGEM6HR8SGQMoDzWwHuXPXkI4XZ/2fyESKoXlXmujYmJQlBShDa7uOWt7CgaQS+91llEQSxCxb3kZC7weAKMjfu7h/Vz+xrvTPj0+Q1dpSMiglDHmVPdhEamUwNyHXtPVC0NZAh77avNs/W+bQ/PsH//5FsfUf8bmvEcQjjcKR3/XdagGKGRlORPr6kmVWpuJMLSjvYm/9GZU95gJF9CXpr9t/mnWrxE5n58BPJ/zLI991/QjenB5xs+rM7KaH043lKchiHkGXfBRT5MeCYawoLzijFP8Bft56Nr+QenInioyPUA3c9ZLNTKorUsLdxhHTni2rpIsCWNzhA82hn6nHZaLsmIQx7QYQOGcA6Db9x+oj2sic7zxiNbt/e6kZ0vjBNZCDMrGGACCEETrbRwhXdCpH2Jz7QDwaP7dmwQCDANy9mDAhAB11AYHKMtdHVqKSyAgjQJizjAz1gd3Gvp89N6ZGFGo3a1qCZud/vN/Pru6fP7669ZOkmWUiOLZi5zHqBwKEFhIGTMw5TZYErGnGCFjnB05p2LUHmUBKQwhHq0GfS/y9JwLY8cCrialRgAsNw644EFBBgURVfT4OLBHdVL34HRa6N4REFB0Rvs8EiZiJ0L3EdOnFYSP0BAKHSIpy8m8qrqtmkY1dBpQbaYa6GN9bP9w1MYYY8ryvphVv/ur3603i524Gw8K2GE4trFkBrK43/bZqVUTQ/x5Js8vFheXGKHZfniS9ofOtDE+MvoySYCa+tmQYtQvC8IpNGhLcZ5J2YXDpnFT6g/Dab99vJTkfuckYCjy+tNIeSQzbNc02rWgIi8KTs9wbqDgeF7OU+lQ1DfGDgGzFyGSnpucJjJ7Ko8aq8/Z7Gw6ewpKdNyOFoUXL74O06DN6POnxO8BjI234sIm6WyWPV8W6Bhcf9i23aSGXwjO5Ww1S2UbUb0/eSKIEDg4MPPy0BShIABS1k0O6nyv7Wy4n+4fjkmawLudNV7mF5Cixh6qMDe9/sX96fu3l5yZ9ctn+wOfpi/jQ88gKebPxj5YvcvTGYggBmht8Kus79W8qmKS1KOGNmLEs81XkLGiFCIx2kWlDQb83cfmh2/hm7/9/pf/6O6vH5+PU3T0ChWgpE2MmJcaRPfpCxKv0BoTRMZghPecY+i9dYqdToRjh3lSZLYz6ZML7wBhQshVUc0f7z/+qen+kqSH47Zo2z9+/PNX7Ner8tWf//gPWSEWMR0DSCVecSit64dVxpNulDQmycvszWh2N38Q8JA/eYNNSyAlUXYNBjhqcGuMGqZYEklGNWHTXw6jqkMUhLQmiPKSpXnxm9/9zWH7H+/A6al8sg8jsVIZh8aTK3OzyAgMFBVMRMXMRizcafp0fKzyk8tUPOWrhGXnyB/9q8ura3mDMJzJmdG9FxiiwVHIM3kAyNQfjkf0Zv76LE3imvvSbbztc5s4MGEb9Jyovi96pMT9+MHvwPZ0l3JMLcgXhBFlPXgS9XB3y2TpZLFerIIGX58t7EV8+05zkY2jiiOMWwCXIyQUAhhjGtNWzWdlLoTZ8w+iyfCocy87C3pC8mE3+cGjCPNZ5ptbxNHRNr5RLAZyDqdPnlLiZPrQ7aZ5YoafjTazebDev/tw+oDeEToj7V03GTtlGpin/UgwObjEAWtjBMYO8f3DQ2Eznm9e/vasR9M42bmRbQd12YYdSdBCbgihkLEQEIocBy/5CqRO8dvFkn6PnmVvUmVd23XXH7yUzgd53DkxPNrE5F9dJQGBCl9lokqm4eY+hQaVVYzK1Eo7FbxJsxyAAeoCBYj3aMti5Q3LylwfwyKWYuF6ExOAAQ1qb40jmFe8QFnBSkYj6UsIG8YxHmzEAPZDTyAjjIgw2LQQs2cBP47T4IG/DrdLsQ7OMca+//YHCHHwgWBmGGLRYaAnEnmm1GR21zfOxrzI72L7La0IwsABSi5BN965kwY2ozPL+j36woVkzAynOlg/jsEaXZSFFBLK9NPnTxfeIQwVo647eQjPLi++/dX3SVZQIRkXiUyUUj9/sDD4lJE8/066FjMYTzggjxQFGckFdSyzZsRkzeeCSwYhUlMYUfdFMGP0msqMk4fHvRQYJijQ0MYmj0+U6SVyW/X21DRpfWqD5tAyCtALGR8I4NEWq/7tB1dxuJKi1zFGhaZEiYOtE117yeH6TOJA3ep/fPWku7t58t0PlDHvPSEEM6KC6cHQTx7lreMMONzXrX5mQAR6UGUBlgti9bBvm6LgWX726+XLuHr5x3bfT0fnoRlvgxOG0jVLExzRPRAdp0/x0Hf++tELOCcLPCP9H+7Is2ROI0X57An7548jJqQ8/wYZFzCEeQonxZjCHQZj68/p6XRwGg0W1zc3IQ/rYt1qttNgg+YYt5i8Dudi/FOn7pot3sn06XJ13uyhl43FQGJoK/7UP8kjxBARTu/hnk+qTL9f4uT9vSvYwkn16Bx+9HFOrkC4945NE/NgIIInXik/Pp4GqABCzOw+HPeKIEozQlcAjVCw0wkgTCFA+KLE9fGX471S06v7A37+JIKndf+zE4ya/oO9Tyazml1QjkLAMV5yOkWLNBY8ohihd86Ec2UOMEDKBMaW0asNjHpt72NL+Murv9z836rn/6//x/+z/XgTtckgxGOFNS7eLNykO+DleRyHAGiUUesp58nAAap4cezqEdFifQZx5lXnRTKOD7lPuEz++ps3+/tP9f5Ra7Xb3S8IFpP88KH//jfPvsn/p3/46T9M6sZFR2fPbUmilAtaQIAkNoft8a16Vl5U9x+isSb0OsGJTGduit7UEAIG8/Pzq2I2h51AXUTweIi3zlvCsHODS1XXOwAbIUoqWH88+SezzS7vSfQ2WIwYxN3UKRdTMVWm3NkuL5jSWQTk2ZMzFwKoXGdbognJaPBRzC4sAtaqAHCGQQscro+IZHM9DjIjGEPU9QzLQ4uqS5AG9TjuU/35lxO6Cpfk6iKXNfAVLNTKcccBCCSHNjrbmqSAy1WJecqefB2hYFyOO5P3GX4mymoVB7Aj47PZ84dfbg1MeEqdTY7NiVNS5osBflIh0DUZEqWdoYgYzFpgLfIMAmCjjgdQQYToAMP46Q4HyImEOOPLmUIzqIehjYQm3n067KmUS0GncRrb5hP58/WftbWYYvb62hrJRRYCtN6FYG1csU16ejxqqGMEE9Fn36zf/bQ97D/MV3M16tn6jMToPdCRQk+hJUPjM+IIRh1I3pydLd985Vz4uVYzms1LxwSPlDkCTtfWx8/Nx18SWXECTCyaYxu8o7NsNMb7iSZIDwCLXFOsR40xXkU8uWkJsCGURm+FUH3SBq59n2uL7+4pCGnE9yDac3/BRKj7FsccFhNX3kSW0NHCHvFUO+C1kQpYFuHR6eUTv0DAMjKgYOsvPUnWnMkYETY41iiqlownbRKmxuBcMA5CwlhKOL8UV4wn0TT4RLzWzTgGDFCWiVXS76YFPz/5R0bmQipAvfVTjO6w389XGUT0EhOEzr29oRKnef43/8PfvP76W4C9DUaQBCBwjGH/BWCCQfAuWCm0maxQHrHET8MQ0zSkN+5Ehj4mkNBEe8MjhRCIs8uEEOO7gAYOZoyyPNH9NGyKM48IAwRFYJRp981wb8s4jsOgNY/LznDFb31SFQ7pevtw8/CJPHa2kmp0uCI22ttgfPCT79NcRHMMMHAokjRJnp+f1qevk6+tdf/6+Ac9GcqItiMhG+fEqI4s46iCbQ2ZVFjQw9DGY8YiKpNymJzxf8zjxUTnIQzOSTgNNsrEtykVOM5dbaGzMkllI4z51I/GKttxz0OxWT0rixUXN6omiBYvV1WSpwDgaxIvONeY1ZzABOpJmVkpAEWzme27deynrLxru0rM5XkyfbJdKlY2fhr/OPz3aVCmmv+6+uGJpORjM4JpT8SMcw+V0to5wAe6z8X6h+XTvAGRhJub03UXkQeT+eTbp4JyxiHP8HZy4XAarKzRQBEPIdz96V8nyb8vs5gmN1ZdiQv8nJ1dnFlIpgmBceQCH/cxrUD0YVbky3R+e7j92Pfi/d8XLLPa7m5uPpu31oYn8gdSXWh/2iuTor2y1EeYDQsHArjf2/MMgA5CrQaC/BBbzfFNV1yKbuxuHzshizw/f3r+f/0/f/8HY64/Xbf9qYBnONyYX7ZOZgiicoqDRl30VUIonhjGHPPHGFWc4SmPICsXK+pOw4DUCR7O0BWY8nKdF/PudHpv3PdK9109W2x2D4fhY/L0r1++/a+bNH3d1T83D+/EUBx9rCkFkQiRYEI9vE3aF9Xi3zh3jXC2rRtx3O9xLyTjCHkCb69PeFDjznGCRUbHycsYQF48dl9Waf4x1vpf/uHli9dItNoPH/78cyZeXdLiPpJFYvp+4gRiTiej99MtZ8QPQJuTG7f/SP1lnjAuhPdf9hRG+6fDn8F8DSOZMMwAtlSAU2eCjWy01oTghsmDRSEBRIEiGO3J/vm6ffFN7rWbftEPs4dTw4axf/36rESbIu+afoTQ2VFhAG7/8VYrl+Zw8V0m83nfjcsLjvfQKTvV9o+f/3V1tlByNTJdE8ZoaCKIZs/tm7RzO6rTXmayqMddj/ZorEaGc4NJkQLrW3BkhA/dBOw02zAw2+y649mzV9LNtC3ubm524+fwZPVNLt+5tbeT1zVH4Ql5EZkl67TYj4MHXtI5ITMEJGN7BIOzFmNAcAzBI4YnqzQS3dB19tYjazKL5vME8xopBMoAuIgjMWDUYfCMYTmiARK+ivx+dyt9OFbzKjoW7c5D1o35N6X9sgn+RJAAIVp7VCQPGc+zObfq43G7np/H0fngtIWTgzCqsyffRPS21dxYGCVKB2KEEGluuSXd8AWSN1ImjNeDBneq+JbRyxft/UcH/Yh7VlKrejetNx5CLjSGE+RcD2RMR/foxQKRXkwcez8P3LGMQ2H6aeo0DAIECIB32jIUBlv07R2Ki2+/v9ITqZvD5XIedgISCCLhSW6OysPx1AF1muSiXEWClJ4UljypB4uTpGnrpjbSkWujLrG9Aw5hki/E5tnMcPP/++N/TeTs6q//fSElCQGAEJFLBNFdbWFABPQYRYAJmU9uisa32wmTKJMzZBwG2oQYFA/jzSQxppyj5dZuE54ARFSvH2JLuSjKFFvHKGYn+qH+tOqKacwJqdExs1DHZLJvtL4Dv//wj70NV+xsCF2Hx5LNCYUU8bRIZX6ljaKc7yPMbI8c0k6Zn436tTrFXXvdslcsOIawFDyOygwj6McBozzPMm/p0D5qzYgPBQhSMICRtcOHw6e+v365eY2RkkadHHa9RbMk4RLx8cu+u8AwIRkCCGPr+MpPjfL9Po6gmU4NSYgIbRKi3k/SgpgSP4rK1Ke66wVjyqlggbF48t5DkMny25ez6+vH3d2OFEgkLU/Oytksd7/B7HYpL77/vvponWtdTOsYZECjOhwpiXJuMp/sJs27g5VZytcE8yB+ao8TOV/B2mA0YVIS7ketTvXWt3cFz8OuFtkKjE5E9+r7321WG38LKr4zX1cOg3ZAnRt5dNADLAki0kbbtA1A4II8efHN1/vdXqnV9vEB5ClW49iq365Ws6/mKihwY3MSAeGEIKsN44dpsnp6kKosq7VM133bPsG0KZ5wEmx9f7ith6b7h7//ufq/FLMKz19Xr+H/OP2v/c3NHVY/gQhD/lKuJI14bzpECg6JM2pSJcx6FaLPKIaZlsA1TZJWbLmGCQWnwtftA/XnVy/+9q///e8HFft2mKZ2vwcXr5DqP91d/+U3Xz27/Bt3urtx869evVktpo//el9ePY0RZFnJecYZpwHNLy+0W4+jSasKRnRBJQGArGjdn7quLmZPKvsAQ/KIOi+TvU9IO/3hvk8UePHvz5NvKUPs/GIz0+8fD0Nn36rkq3xCcBSADXFWWm2npoaMDSjJrIYJo/naInBqkSDWCowxBBBOIianwbDMU1qnJFGEiHOADgMNEgoUEAZAYxNwak7Gnnj02Fh398VJnqRyvvqLzeGnzwigz58PIh0vV5eUyKzd4Tb1K2PUoJT31GhnkXn3L7+/f5kmOgJ6Vx5PpnG756+ejMZmYp5M06OJeMRlnpX5DIRHjudpLgrUTY9KPUhUOmgoiBK4aJ11QHgsaEofjsdpYJ4nuUSz5aqp2al/SNLssvrrPjtJTC4C6FUzcLxCG9p09nAiz2cXZ/zRp5EV5x3SR9NHACh5QDAAuKbOmUXldOULvdLjqEeekWSxeHX+2hhvIsFjZrH3MHghBSRwkH0ERFkJ4np19t5o4AOAWBiDumZvi2g+xoTh+xoxa/up8e2ZKBwSGAUYodVTiFAG6SdreABDDCWVPuu6pqkHCkvmjxblIcJRFhlAZjqEo65B8bu//JtMZgzT6sf+7x7+/t3PH79nb8Zx8jNdBdgoZkwH9/vd5mkwABkfGU1ghSV0Vg7TCI+toEh4BDwqeMr7fkIeAhVQ4kHufQjJCQ06y8DQoBiDuoW9PMqUtrWNCBFAQpptGF9V8V19GL4MGttgpqY+Xvp58O5692j09OzZs0KKWboeh+2zvCCLNPkUJYO//Zu/YJU7iBtwwSwFIUwpzutxwByC4QTjLHozW6bT5DLG7083eTX3gxFcomJJeAU409ZSJGEg0UIAz2NA6vOtXvcYINuFKGJMCwiTww76IsLaFcjVMXob1DB54xmcUcCjGcCcHH+sa0JYKi7xunyWLsXihEKWEE0m6mFrLXD9EdiqKkP7weR0GqaSPIP+0+H46NwRPnUvycudHhq8Q1BwZjDK+oePZOYEzyBIluRVWSQ1pOFQh2J+kSeEoNk41nAPQOzseASWigYh6Ec9FJgwACABLKhcmVuCHMZID97EhpTLpRR5My0A7qw3CEA6duXq7GEcSd2F0YlI5lSwYI8uAEnRwxh8BJtNY02+2UQ0erizIj26R9rnvdsO5pCn64+T0ndbd8ZpyJaFYxz0jCENYQvyBeZImBMY62uE77Osiiez4F5SdyorOw0Qea3Ho9127c+y5LU+6ONRMHaOeZwXCajjkaj2WDOJB7AfkbNHKamOQWm6wAYhNGllvZmJxZdwKw98US0Z3dAFlW6M4aJM8PxiRvXQT5jBImXpEBS607AsAc706NDygqeSxyCLmXSRBsAxIV5Tkrz82x+4M9M0EFL0H6+7+5pm5tu//Qb/HfwvH65hQG/CO3n5FwQThyX2I2XcWooThxnDmLZaYBJIxg0Ew167hLAFF5drfzRN/wvum8X55YvV5XU/jd52u+N0OF4g2t3a/dvdv/vdm//0nz5EBzezOafo2zMHNsv9/jB0R2uVR+mH+w+Sc2oD6RRbrQuYEQyvhwc4oIInTTeopONPqvFTP94db1EDktiykY25Dt5BP5/94OFpAfKRfct/+XR9nIaP18n62di4U4S4VYh0glmASZ4gaILD2EMYGzfaXRSZ6hHGMckZQuUEjLKTY4AQjxMMYLig60QpBwaNo7eOQM7kbIrd1KEkucrz3auXrzrozua5TPn2TU45+nR6CK2e7JTPl7mdGTbtPrDoRka58bH+fLLPqvPL+ai0UP6u+3ny/nL1CgD/xXc+qo5B28t1Hhq82R0OghzZENEKrtl5Q5um7tZ8U2TV3gUTnOGsosQN9qbtwaApE1mWhygfxtDsrp0Os0ogdPDXj59oYcbRCvENKZYya0x8QIhg12WzZOrj7m6rZ0aBSbhlh9NUtM5fHwKIZuVglEOIOTqqPQOUX6xaQmIzUCRSj3uRLuzUqt5TYbka8WgPBkjWZJLpYIsyFpKHWBKcYjCf/zbSo/G19mf3+k4/bu+muyVczGgFB3RwDYGCp1X0zk/QMo61swFGSG7bPo+O89XUNh4wipEHHgaOSTa/yLNcSsoZYuArejb9uovvh6NaExa1AzLOHT11giw25B46Y+BKAjW1Stk0n1mYR0HkOepDgD0wY2kj2sB0uuRT32c6qhBZBA63AQHvIEpzNJrSBIUiYTlPI0EI0A5Wo4xQuQmSask8f3UaTjHYQ3gsy3mRlePhPYawGabMf4iOEBywhoaxVz+ULAc/Tj8xn2KYjKbdN2hW/i+ZMUnBH3bdii3em5M1ibaOEIRpNmFoIPmwN76+j6scpFEK7rzDMaAaoXN6SeDN0/OpbTAjQSBlnDkizwJEEA4wlba+r2/212YCIAUwg/4ULAkwrkFHavwuL78BK+vR+fycfzQ6YVMXTDvdFyR3p642jkEUlfHt5F6cId9H0mBIolXRd5eS5wl7NB9ZD8D/8ZERxJGGgxnRNCpTI5PXBZ8CidE149+P7Xq9JIxNVkNOSUQkYIXmEDzy2WKepsrcCK16yexURrFDYokf7XFoj37L9vPXi4JlyA2EUIGjERxPhJ+aXcLqV1d/uR26/f4wh1NEuMgArxZDp7rt3hmNZvAs5U04UaMPAGkA7CwJo1gs+oPWBh9MTZ3rB3j7pEpXVXX0oh2ndDQCx8K7pm7dnfcLx3FaOmaN72gvCznFXummuTsZD1O4YsP+sdm+SvL45iven9xjHwQWgiS6ULGvkrjrBCVjyhCIrbHUmLqf2jRLNs9Wn798saPbHo5lWZAZudN0iaI4wwp1kEXacJbCI1VN0yebYpEzIL1jTgKRdI22NuxOWhm/WsnYEZhc5Oel4IxnbTNF9ymMBlFvhT37YZWXCfgP4b98uHmrx7/4/JGLPF08751HhIbQBk8shmPw2O49SmlfIcZDGvVoR+ux8998TU7Hi/1tkyM2Wz39+eYhhF7f3tXln1bffmuk/Pj7/7z46v/+13/1b//+P+y7P/7YxenI0LM5+vz+8ypu4jM7jUdj+oQGCyPMARGBhmnSnbYj1wnNbj7s3p0e7/Jq3Q3jdjjxeVFefLM8PBTPZvjF06eblafo08P7/nB8rjGr0Obk7tXD5rbKrqpLLIdm6kEbEA1904x9uTwzu8jgEYB1BByTioDoQpiGhuoxZDPsp85ZskU2NXEZLBMQQEFoKbPHJhAJWUfDyCwIAIf5bHV1+dQhOxi9O/bDZKC1WcYhT67N29XOOZJBMUehfjfFr/KvDuyx3b4jPXfDMCskmcJUb1O+Qnnopt6REIEHMGBMgDagPmWZPPJCdlt0GBNOXj1/88unf/m0hf9ucXWqd2GYyCw5Atyfbiamvn/5F1WZm2Da424MwZeFskDzuTq2wYBciNksNRGHST3ubgCn52lJklyExbmvf2HMeIBACEWA1BifAgQQRkDkJdVYoTYBkGBoEcC7IT9bHm1ggmIhKKaQwsmNAQdqIvcQckA4LxKkBtIzS9zojVUTCu6Ujnu3mgwyhPdLthJz9n74eLw9ZCEGmhOUKK3KTDgUbGCC89PkRgcpF2Tsjchwkh6/HAnrMGEIEp6m5IJIQB67qfn8/llWZvLiq/lyZ9iMYWOktx9jVnRjOP30Gc1ZmS0SMGQBTYGYarnodP+4bYeOTe2SVlIG4ECczUFcwwS6MMrJOtAE7yMMGKFxP6UJJhU31pZFVi5KWBU4zryP1vnhHvaN8V3oPFKTA5HxpIR9DSGEEN51/lkBL6vSUw5iaG8+deoaIJhlMy+BB0Y7QvFTCA/j2HZDO/MREGStSf//BOFXr21bgiBoDT+mN8ttv88+7vqIyIiMyMgy2VXdVaVuCQp4QALBQz/wt5AQj9CAKCE1Eki0uqozqzIrXEbENefc47Zdfq3p5/CD75uziZ5GCd87pp1TzoN2HKuD9w4Txi2xRkJHAUJ6dEBotHd3MRNW2e1uXOAJuzTItVnfPHZ5UbTrblMN7afjbru1YwS5G4/YOoUXHu33Qg73kf3VV1nM2O3yrhGXfr+qiXNza6kcAhy8uMSjDSAV1T1FYAKAEgKLETOQzSJjr476Ywsex9WIw8DVRo0Sz/TsIiY8fVm+fHdsR2HiJI0pD6qewKjND2nIDLTVcTc5nUk1mn5QRgxJ/Tp9Tb2ot4qWACON+iVuA4+1o3qRPi+k/aQ37z/YKPYBm5JcHAcMtzSMwWwyZya7iXNi3CC6MOTH4aGzU9VYNUAahGfX893wgEEbe/Wuv3/80+HHPErPvkx470C5qyvoNHcWk21lhmbfRbr9q9lz4no15nA/iEH5JEqRF4chmmXzMjge+WQ4sGe+aw4N2Vr7aew2zh3lU+Ws/X7/GPiAQxtwEs/yJwf5jpgeoAydlR42WW1WCBqFex6g2fSaJWGvxBFXl7MvtZAAwRrHOKIC+NHg+Q566MDYkQSx/YRJHsZhSKPtxntj5xeYJCEcQeBR+eKlkS7gIUNE1tXxw4em3hPS+dSqGnOjB9mRLczO02/+7X9D/vv/728ft5UVuUBRCGgfCDmGGLMQiHpsgoj5mEPatS330A9DmvKABHLV6oXNkuB7v7qE8fkv/vJLhh/fvNX7++3m8fR8dn4VffT803ffvfjyi89+tvj0H/bw2ibbpqsLbIb8sg2n06NcPn957sEGVMQV51b07Tg2CDDmorP+MFqY6yizrh0cVvlPX2ckdowjwotXzxcXJ7UZxPDkvMbEaglNmPmrKPth/Y/VH0gdvMq/zs7SArE0CRNrRx7orrtiYWAvzKkGg2mXq9n1hEXhY4OAR3gcXDmhztlUy0bonYLXJiriEOPaQo8+oaeF953RGwyfBcATQsLRLr28e/yUl1FZZId6D3qvc4p9vkw0P+oQsjan58HNs28+O/M3P3xsGvV21+zW645RJqT/goS7dntZXqcg7INZ0yG5/vTEXguMU0JRJznhVqgjIWdxdFq+eFo+vf3xxyQpomDWukHf31KDgslXaTYZndtLbSNPKJ5wIjCYhGOv0BjlszIVFeCcq2M91EOZxTh7TlzVW/2jkkNkfJ5dRLTYATXaLhQh43UUB2EQgIH63mLZnoV4BQnqZSRVVM5WAwfNQBInEMgU8kMfhMzrwUs1P7lhwrUQKgYjMXxSwznQEPRPSrtD7oNGbPNx9mmO5mHCJDWbcUxR4L0yRldCTCMSDZ1tO+p7B2ZQcOmETcnUuHQ2BTgu5rx2YMRdCKK7+jhv5MSEgRixu5e27dRha1wpyfvN8fUJ2Te8aVo+tjdf03aIAJIZfV6kILUInsX1/Q//abcMSU9R6Qyh622IEt10zEnjNPI2SGzWKMfRG7WS0SLjQQecjVkRRrWWAWPEUe5cX2J/dIRi4LTRSwjodTwfFLpbfkBGQCf14QdjNCAzRHjDqPAymcSrhBu3R4A7izTyAb1Ssmza9mRyggx0DKhE61rtj/uUEwDwAFJgQRJPYYFxhudxKJUehMQeG+U0tlRIjDNwPHiv9MYeL5/OUWm8Ub7OA0KAOdQf9s2PKe5Y0i/rFAV9hxIW6Xa7OrLos+cnbJoobcdQBhHWyxFxao0ODCHWaXuXBxcTgj+CNCm9IazuD+d5OklyDK1yn4Qe9WABl2ROlSNyJyiixC/cAD+1jwMiaZqVGLR6BPIY4Daj5PRs8cObPzlouRJgbJOYr2T9OnjmlYAOczWwSYQxJD1rY1439X3TzeLn0xfJZRO3DUxZ6gkIenk3DvB4i+isYCw4mX10fWxHwUgUxAnolMJ2kkUTUqSFUYNcDncz92J2fYomev4e+hCGeXRyWUdRSGLXHM6iqEtOkdfavrPv5LEEQfBq7EZtSQCRP2Qk6pyxm4dlnKb8PC2iaLM9DN32tEShzb8/LM3tSG0slQJohj3zZTEa4ZstGM7dJR3bIRociAOT1sNyjZwTXmRlLE357f7bmTmpTVvgNWb4QbDUGc4FQJcUnkxhSCVY1UshXZKPXQdB5VbAUbLE1oAqKth0inRMiPOi9e64P7THCjPGgRGBZywU/SB05SVO5qDZdLgHk3P1+f/8Gvx/xG7VauGsMHwdjaFUDAYMGE0KCgiRFIFOjCiFYE510zPiXe57i0+COFHjD5+qLBJHZaKzSc3GY1U9PT5Op7PTvKze1ofFcHbxr/sXpqtX4yUajSj8EJ5dJrYV2u4rEYSKaufGH4WP17I7mcxAgW0VDc1RSpnD6yVvRqA4foDszFpnRO+TtB7tZvOxblrjURaBxqg/f/f7GGUvvvzC/eFt0zY/2H/8fPIFh5CMXUjYyTzaIYuHJ+UL+cZ8UhvpTLf82VcvGSEhwcBCHEKnrNUAIOQM0r5pJJuBLLuacYc/FzMbrchDdywJSlJ82ttjs8uK0FE9cFHho0XAltb3dqYTQoA8icCq7oEM0s9AaJc9mHyRfRn8s6pqZDc6D5QUp0V+v75dbx/9HG9rlemUzK8KlxIA50l4NsHL9YqwsN9sP02jxSQ91FSoNslO21H2SiFIk6RI+bwihWuqCcwa5RpVadsLESAp/7x+D2unX4TlKJLoqg+C7POFZ0xiSCh2LueYKfXeHT7U8lrGIQ/ZOaUes0sIHsfDfkTSo5iZJpHoMjPDD029qeMXX14UtBqRVdR4CEgmtKLQGCGMQSGL2067fhj3igatrJ+yxUQPujfOK8nIaRd17du+OhXP8dmtu1X9QFyYzwtC0Njs6w7EoSbBQHW4A0aPtXMNReWD1M54h4fN+njULU0TqZO2uj8lV+Glsx0aqmNr0hAUiIx+wefNCSomZyVcN6fDsHnzPfr8S1Dvwldpcxiq0yhLrz739ZO9Mz9a8XMXMDaJwiTAsWR9BkaWpMyavq6UA5CSwBh/3LbhFZACc0bSogXOsE7Iw3g/Qg8Jm84nmBiSxKerR3l3/FQdD4lX5dXZrHVKbjnwvdpbsBiddgyXv/g8mSD8wBmmxjgl73uHxPWC4fiLyQkBHnKqsQEEQOSU80b2PjRUcJdSEiIqsPQYEeMk8M5DAzwyUgLCTHk96UdVrVf6TXP/fFBjMNS1Lvg49vvlj7LeEf1IyClN7PbYs2JSbcVHIn/97LPZX3ytrJXYJFE6A7Zd4NPpi6d+NaN5RIP79z/mV/xhf4TaIsZwU4POJ+cnnhKht2oYY6ytxHaupG+0tLDBdAjIond4oYXNGJ0FzCKYq+EYMz7IcWzv39t2aM7OF1IPfdeojOQdPJRHK/d89L22QTtFbECk3wtrKCiLk/PJs51yUyYdBUCK/mDCuflJloX/9NeawMre26etZV3oyf3wnpnPbRamwVyPvus7pK23KkwYMTRKpy+zRRJeUByWhGIWrIzxljgcHrEONhW4kKZ+uTr+/fhbfxMTs9XkDOcQMFc1FQOgr6rq/vZ2sdGzv/5svph++x//8dPd2ilFJUTuxUV5evFNDEbTHBpVr3rRJpDzPEifoGAABXR62EhVPeeknMzVfNKP4HHdDB+NuYajk/22i89ibIjd9gIxk7cRmqRJoVRrwykA9pMyEo1DUBI/eBAFYaDH7WZcxsgTjs2nSiSRHQWmPgbjgIAOdbNtAQPpbFIkZ9RyGbUi+IScCEb4/N+cwrdu+bHpn6oSZgud9WMLE8gBw5D3YyWbdnZ+SoniXtRNzesSEjvHOaPR4qffrL8/ai/yi59C9XGwxtTtY9c9q4+zLNsvivdvdqcnWXb5s7t+zCtFyoPnpB++G275qrdgbgPA4JmDgBMIT+KyT8X+u6YJ2r6rxVZ0GaWqPEXkiChPGtHwLjF2u0nixD/WYSqd0Bsh1+390EkXEZryv/j1z3/zH/+gpXLeQML1th0CjUkd8wBjoq3wvjnz4A4iN+5awfVoSMjyghOvWuqY4SRr2bMk6tzT8s/wMDUXLyZNxaavoyKbrEcvmyDgBJrb999Nvy5BBMrFM+PsIVbSyKE/wrA94ZGHaBWt68bERbSskxal5FGR4gKEUxx6M6r28AM2JqL06vqFIXS8+3a2mLAk7dVgm+bDKIIwaQWeT0uK4WDv6zjEp2BoetBWbHQ2jUFxGgczExd0OWrRSYadGOggy2nRM1/w5Fn6anqepfnkqZMAJorozrIce4sg0c5qy+P0PP5mzfbR3qVGYByjiZJukABFD7ChVCUOpAgwABCeoFfg8bumf/Pm8ue/lMIa0ogsiTRACAY08Hgtxgx0dRjkfZSE/XqS5ciLSXGtwqNYHiiNPCTpGB36bVjj5Dxf3IR3fzh0ZD+KjGF8kqdUHgYItEiR3U8srAKgjxZDxYwpLi6IGN61vYRHYFiohgbUkJ4A3JsuUIzpmUOIgb2lMIxTRFEYRPzZN//F8oen1eYPh80JY6h2GnpNMOF6y+evXgV/XCKUl1/xQNMo0OAoDdIskcMoxDD2DeqeQMwYxqnzmMIRi4hP4ACQqhwlQHBW8DJIZC+aWhshdSenTulZURaJ050xa6X3dmvIJenutOItyjG/PJsTPqrIFWHW9Fmee5iq6cQZJMexNTqjMcMLh0IAPARQ+n1IwjSauAQZB2YQ+NBuuxWSgjhvhGUYxpw/bu7EFrRrPF3kEekPwJ0Nw3G5d6Y2Ju/aY8BWbDJ0O6vbVmrFItg0taDvoySh59P33Q+iMjbzkOUfVI2BJwhC00Qsl31rjCWEaC3PL6/bVW1VX5ZJFIXK7LTbOegBgE9+oIQaqRGhNAg4DRNbBDR0N9O6bVprh+ajENGXaRLPJ5v7DYHuzEVqGCBD2ijAvYvcDb5QTEEZMLbzbvCucKzQjUFIMnja59osSUfz62e9FmbGnBZ49AfEd6TMCORj1BOkfFpMmxODklRkMYmPw71sjxIkxhAcUYST208PGCECCfLYTm4YNdvv/xw9u8kmxW73kWHCdvSkCE+++kW/B8tV3Qwf/tn418htkiysD65tPMCXxn77p7vfPnPj5/+zf/Xy6heP6I9yHEP86uYyXD8kVaMtZCa2cZJFfFKBdhQD8xGeoGIQbddCoAMeRq+T8ABswGbzyacHksUhU1lllNiWs5l2GlobwLVUxexDKxMJQ1rS00mx7Hb0fpol9TgKBU5OCJQZhhi64bhZ743GPleDADToocSY98cWQAgBCMooCglDa9Osur5qe22whAIlWTQ74Zt3HySPorNJfHSgdR3YlcmcE4QoMsh3zgWDtI6sdZf1QjFVMZZESTdP+ntbTor1g9NDRzlXh+PTaq2n82g2r3u5q6p//s//mozrv3u3v6iC6dcLdeil7mdx0EW51rsP70cILVzQHC8eVxREOjom8Skf02DIu663XM0mFlhECiXumoYShUfhIMhQWBVi0RVNtA8mUfpZ4QMSBtFnz19++nDH1jIICuT3UhLbGi5bfnpGMI8mJxkj8bInNPLQa4knc2yUgBhPdGcdap1kw9KneXQSkwGNu7ujT8L7OyP2GJNBdVEOKIEgEw5OgrPOBHvh9XDYDSPCnKS4UNg/Hd/8+LQu5lOU4wOOmNUEeyXXu6agRZrQIJue6O6xH7qLq2E2fTG/mI84w9aE/Rgz5qSig+itRmNL5ZjyqZMNpiBjaHRPm5Upw9eR934gi9AeMur6xQj3URjhs+mwXNUHMS5gt0cJnWOUdveP5AzFYaw0ZyFOOCX3w8B+qPKsodNTnsfP4+T27g7U/q6IJkNPOboMPENw7C3QWOA2UDLJ5+cJXIpRfTyCYoqxK4y0XrKAQmilnk+4aLwOoZ1bqQNT4Jkwj3a/dIoBx3dbrqVX5sAIpx1NQpJHX+dffvfdt0fFRwUAg7EXkkAb9rTn4OD2FERgMUPGiHHFncXhLIEBdcSjjYmLEISN2w+P6cuIg16LpyNKy86OQ62cma53Y3RKTiJ69rOr797gV68WlGKOEHocjnJElUez5PyXvzpdvZ/86szeHhS0vdw5l2kPtXMOQgixhgkRYB6FLs72dw+WkvCaxNNQ7DAljFFovLYab/d7PVRWdvvDShl+NgbHemVUr3QdxhbOECGBo140klrCA2ahb7omR47Mi7iYmSB9BgIO2K1YjsatQ0ZAV+9PtFSY+uv5FUIIQij0I+JAgFjVvggiranwLQ+CQYqQBefPXizNWHgtB8Xw/Gy60yoyckcwCvtd/3gn7kc3jrfvDu82HwiPXsw/24Eln8Dz89MwSbw4Fumk7weWZs3+jqignR7daJRXSguEoXNqbEwQk52tOXQJxajf6GarrPK5q4AnFTHMDaOiGCHrTRkYyLSi/mDEwyM7WST8+sS1EPp+uUeugukCoiCE3eBZnnVgAI/EanUxOlf7w0l0pp2iPFgux7bpJsVnJAzVlumx50NT7fskgkXokVRbgDafWrTW/EUAs+kVzQjjQxHpbodcIPsReVxM8tn0WSv3EIC+ws7mzh0Y8cSKePvW1tP2uBVdPfsX/6K0ocEKGNOM3vnYuTotJz9dXM1/Ntn/DhuwDiNlHQ/OJhP6N9G393/YvJ/94ee/+uXXX7+6OBy3yGPkAxbR9aHiXkX5LAkLYXvb8KFRj+1jKnLqMzctThckn2Hc0Goz6knqbdf37dvlDw5plwLkkmHtgvmsaGk/2EBpMkm7DyZYpJKY+6F3YBEV2vp1VVfHfVSWAQeIEMC4jyAaAQhKtFv3RkkSGFao0M3iz8NC5I3+8eCN6kyQknGQel+NrVjfh7+e3WwuN+82f3y1/oonwPEiAFhroQ3Lp8GmbiNMA5jxkqLtDifhoFwmxyx7Prno/9OnPykZMX71bEab+frxt//x0+19kF/PTxNBQdstq647/dk/+QI+9OohSF9Z8AeSQOcWcRAsNzSJoQego9GP1bKujov5F8VLQGlG+3dh8CLO8ro+zrXUmm1tBYH5YjL1APCLcxfrbGwDxO8bij4np6c/b7un0V3Ozufrp/VHs//G55JH0ILQZU1oiBgj5oJqr8FpHGdH7iaG8UJIA1IgAm0hhgCaiddSIp2qGYg1QowEcRAHVwl41xOCpVMGWHQJ8RoAgEqI6rGzyFN6nkBItYWD2Mo3x33jehC/itEAudqikgNEAaBhwPzBE3YMYhrl53ff/vH3v/3jP/tXp/OT6fr9QbpoZggIR6oHbYcgBlvG8KD92FNuXWWSKQ0kOkA5I0CIoCyN6yjoagI81QGJLKz7R5N6CJWMsFMMSQoRhmnAU+ZKyPRJRgH0ZBqEnXF131H5GLDX9IwytkizIpS9mIQzPLihNVs7JRRSJMDMd7VvJAvTKQC1OshmmITPPG9RQIaqwtZhjL3HwBrcSd1XCDq/r46VUo9DmhUA0djadb8PEhSGLyalk4ehKPKAvX52fWjWoxabuE+iLKQeYArdaPXx/iO9P2MvyrToSwq9rPGtdJX2BjhovAQI9woVcZR/9Y2W9t3v/3MAhuj8ynfH4Czig3NgbJ366uL5bDY71pVVymjBzxcche3DAwZo+uxkyLYauj4awEwZGuOAWoQ7yAQGYajM0Gk9IABcSPdShGVyXp5nlFaZgT2WRg1CWNGKsYtZcHo9n0ySoW92y7Hrx4C403MqxclgVlVTYxYBAxzwNKrj6XOFPjWPy25FKpIlQT6yPP/mZ0Ub+vtb+rNTz0698mmQT0tjvTJqhKjqW1oUMYFeIFPrQ5AUatvlYbHv5eHxHpVFRFEImTKg2a5lI6z4oSP4dRwGJ9fk053uRpKDy+cFEfZP7UAMG+EdY+nF62eNr5GCNEKqa5LJaXaxcF5xa+EIJVMG++n12eB0MSPL1XasN2EUTaJAHY5empHi/uAFcMJI9sjJJCBkET6fPycJBkRLu5NHl0XEG+BAO9S+k6mxBYF6qG00QBuNol4dDc8d8tBDXw0D7pK7ApaD7ftm+bifPrt5cR1WK04CPg09bn3IiWs+wBEABIsgmnH0tu2GT4NM2z+BDSOLJJw5B8xo7Vh57zx3B6vH5V55F5IwyyiHszQBcD9YsInyyV9O07ejRN/fezyA6cKu96SC+7XS/vCqfBEEMd5BgJ98kvfgwD30/aONi/T1/HQ1/G75n/U/CDc1dS2NbpsgyzyZn02B9QD0FiHgDU/jvJg+3a53uy1mhjAvx9gNcIhnnnXlbLJgs9vlpWRD+9SyE/x5lr8fn9zQMBabJO8glYA6Hn95MalGqWyNniG3iJ24poA659pBjNpJWUe5L6jpRnd8GjCkScyvL8/LyWmS9R+H5g/LP42mfh3lvLAWZixL4hFyPD7ejclPv/r56eXb//Fv7bD6vpffRNNJQHAZC6mA0bGSgMERS+qgEsbHgRxHJaHWOuRBkuR9VzHK4/kE7d0yiDbaJvVqZo4Jz4yhq8fls5dnLz7/V999//9Yrfb7Pz3sOQBo1TGUomJ69ZOQtCYsUU2L/DlB0fDuHsGqa6s+/M3rf/7P8U5XYZOCGR+DE12MGju2c2CIu77vup2yRZYSSCcFbNcUZEE5Cb78+uvv//D90rYzXxyBSIwkntqRWiAlJ1Q89SCzBj8cF9kCA2SPwl16472DwFGKaU5GRtfH2g5UEuVDNz1/Fr0K7Ps/GGWwwfzI7j5p7X+T2RRixIIXOKIOHlq53qzXuhZBzoOLAGpc42Zg82cq3GcB1yAUO0cj6XhBRsLN+WX8tJOrt98VL74Iwk4NVeVlIhyHtYt5xAIlFIo4MjLjgSoficEezAtdaaUmkzg5hHu7w8Y3baVsRWCx77ZJlv/iLz+HiP+wHvwYUuLTRFlvxvZ+Oi0oXWitiHIuSJIWUGFJj/bVlkWTydfnJzocxqHhgaEsHETffteA0ROtNUllO2JMJtOF7PvRmb55dNgWuuuBrwdzSZIBYdUdQIlhhGdN7mL0jFyTOZVUZRC+3b43asyKL6YZzbLEqOHuqY7DMORxdJkvV/JpHLSxM0KsN2CSlvwSidUA2oP+BIHnGSUQqmrR9T8gPMV4pDig87nDmQaOhSybxM9vbrQSb5u71HnII+/B5sOnP1eVsZ6NEseOarQFNEsjF6ex9nEE2fRl1baCCLAjItRdwwYd+HEEQ3MYBjFIaETgHQC7xSQ4Pb3CCErZtYcjgti0zuEgX0yiPF7dPv3wfde1H0/5NJ2hqLjRoumOt5DVHvrbO+vAAE4oy1maxdNZSNAziLa5za7z+VHuUOOu3TG6YsdQPEf48TpZ/vldxhENZ0AM1khj2bzE2obCGBeMsCfHfq+B2/pMoy6bTXsAxuPh3tqxOXijEcRnl/nZCFECEH7klK7GMVh35PIlD2eg/6OmflJeffXzZ1FRfmreaDBYZpxzLGS5SG7lXWRJlsVJHLRysAHdd/sspEC28+l8kkYKa2Rb1UrGrCFyhUyqJoQFOLwqPIhJ0EDcf1pPZhkW/SmjLSK+05F3Zea5YISSZux9PTiuamlClFzNuTdTQjBTfHoyWw/dOmD2o8ny+U8uCyBtWB8iFQUsSIuYRMmYB2pc2bEHhPsieXV62Q5jA1Gz3A7Nht3gHII2CawrcUpSpd1hX2e8BDwJs95RKFu3d+L2x2VTl9Mj4+zaaBCNBeHv/vg7j/DlyU87Km/45+ycqNFUdtMF4RQ5OuNW907Gox593/1s8fN/3H132725mFxMp1yo9Cad7Jteu34cR++FN3z6PCc64Cj4xex0u9l1x6PaD2+a+/YjZi+obobgk7j+6uXpLz9D7vipuPPAjGw3iVIhmCSGZqHaEjKML//qFRz71W7Iv54YoI9VxTgNzqfnNBKHBhgnZdTr3dEFWokgSdIwvb68QR4c9vt/+P7HfjmMsjdn+g+Djt76MNAUh2ZSNA80z5KAx8fv/3aN9bNFKZ8+Dt0b7xY8HhBAjClkQpYnWgxRhjkLpbGqMVObqOBdGH/+q7/6699++IAa10VwysBr9/JP7x+/v/t4cv/yi5tXeRSOD4/9yTQvzhENf/Pbv9/ud/NFxF7hrmFKHHWzj2TlpmGWlBjH4ygE8tlFbNZ292bzy4pcX37+rn97qosuoJEVZP/onGLQaygTb7KYeoTK4LrtH0zvQ7mqn02mJyxIw313aPT48sXrTevZ0BDUJTQMoiSfJm3Xd577WR8rv6M56roPwJeUhN45pYFAVol4Sm0+LSCzwlaH48kksh7VEc6gLcqiLMsjPHoAjNHd7k88j4lhZXQtQ7IzH56xZxuwOlTtq/AFGbtV6Hv/iR+iNCloQPLAH+vtpNCEIErgh8PhhttBqCyccrNCfa1KHUblIAACADl+aI9Ztlj2w4skS0LyIyb86WGWFlYcc8fCzy8+Vy+6YQR++P3dDoDWW9wPmiGKpr6yZIjbS+4nL+dZnForMXak7gFGwwgZJYkW4UgHqod2E4YBJMMWXrjJ/C/CoKXffOePTjyOVSO0PUPsaAk5zb+ZKCnVjwYLI8U0ShHLjYsHXUtgy6bXNOMXEK1tQKrxWKhM3v24H3CXTa/PZ5mQoxiFdZ5iTAbmD0hDfQIuAVHL/bc8DKzTPAzni3lMoQVwGaCq3lUP+6IstKlXd8tkNkThM8w8RVMA+W83H7J4LuX44+FbR9l2+xBHC+Te2Faf0XwtD20jojAOFMPIOGvf3u5ZnmtVpyhIkcV6DvFl+CqMwcUReuWOBhELM08xjqEd7juHyE4mhhGOndPe2hM2cY6CE/pk9LIdCwDTMkC8y8mlpeL9j3eXKfmw3PRt/fzldRAg5/9Re88kjV9E5Sw36YBk+3x6XQDE8XKShmSGtVrtu0kSenthSgcPjJ2TmXMVNEcKDIsKiFLopJA9pEEztJAwQpC3S2wEY2kK4DiZiA2S8BhPykWK4zLBGDLGlKiCKPCU/alu+7v/6D0hYaTySXGpbJ6s9yvZqOD5RIkaAL/c/4lALqtRP08nsBS9qLtjEIt+eCDhZUo8saZrqqA5OOfu+v7+4Xh6FabJDXl2YhphpJFCdJ4k+ycEPBrKPIx+bPZpAlW/TpjHyhktOiGNcxBBQ7UdLOOs4qx3awg4g8Gu2mg04j2lYT+bvhIC73bVzEFDrdOfup741nLOQsZsEJsoaZoxTFGAI0Ojs5+fr542WLRjSJ1UxqiIBkgJD8zCW1/S8XgEgOWnC97IwRvKUF3tvfdKqZKNwbOfGC2jpLyeFevDxyb44pyZzbqdTDCJQtMd0GgIQIZBFqUV8FvyFOUYYhWmMJ9hIrUUbl7yVt9W0AQi4i8o7uABBKBbunJezvM04V3WJF2Iqt4cnAyIdcG79+93/r3yjnPOWSgYxRAzygFsmOUwQDw8YxD85nerp9U/XP3017p5qKhM04Xf3f1JKRjMzyERSs7OQXOMkri4uXzOeXSo6rHudocDwbPwOZdt4109LjcY51hgj8h59A27OSTWcc/faiOtcaf+S5nt2jaqZXRWEKP1wI3XVAljrLXcWCLbalrOlwiwR3z96kjIIoXwqA5ZHB4siy8XL4X48OHx4eHT4vTkJCu7cYia79PiL3/5F//7P/zu8disrfUXaBJepnXVWrn/8d2HDOfl7FRvK5wlgKXdaq2RW6+2f/fv//2//i//68/wjVD94tDbMFhM0rA/7Lu9Asry+Nj1XaDJsN3L+pSd5WcxZYZS9NOfvbj9hx/qvmnaY4BRnGa11E3dA4Crro88n16f8Jha0bvmQEI+jVO+37J41qPuWNf14GLHikVN6Y0DVhtDg4jgANYHTRyJ6EmYPD+9Ufb4o95ihqEsYMBks8mS/IR8RQBZyPN3H9+LGzkSmiXZNL0UUYt7MY6tVvpsEmLMR7LG3hEMD/1T026vvprZvbDWIgMgaqyTQi8w7KIykloCbadZenX+8yQo//CbT3EagdgOd5QKhLEtIMIv8382/as/v/k2ZNwaOMuU2K6CPPtyyhnHBHnnRL/+08etIWARUT4Xq3WK2BjKYdcddbppHsuvM/3Y/f7N48vwQxTSNE7IhHbj0NR1ZbZsON0gfBFXcUaKdALycd9sSZ4KHY0Wdh2xVvCAeifm7jSGI+aR8uvlshV4SOMvnl1Njk1AUw+M1ElAjnIt1kFOYReAyMmDOlg/s77p+1l9Bi8Kvmm3sS45Sil5I03b91oYv4M6MlqJiJ864PQomkY4feAp97yaqcAsMCKWEqP2TTRJX2hnr2Kc4bHzSQQYW7yieS9w11dD/djM/ZwOzum9W3hrVIyAxMhy7cYh1TMCa80hBlIRG2EWhQDZx2oZ4gxA0I0GG0wGXWk1DH6ULnJm+/DOe7nswOw8JetGSGCdxpgBBCllURFiRnStzUZtxrdHRCMef0Y9AFrk47CsGXkGfmKAR5fX5xgjPxbb1QZiMCkUDPDhUA9ShjCOowgCvxp8bhH0rt0fD4z1j9uQ6TAIKA0EsvpYlxhoCoMpdl9ef9F/zY/dH+QeQhoVxde/On3Eot6u8jkH2JNwIhs6mUPtYBLGq/Rgj6o2lXEqRkEusiy+IQQ/7A6FtrjfsjCKwuDSmq3gsyjbqp08IIDL0roA4XMMo4uLuDp2pNk3beQ8D6EEkgILNLHOee0sN6ZHVe2MMeYM2AEACIHmfYEdSKFP7eajknGWWA+qpP4k3Qk0qOuPGGIahJgRY6FWE5FKebAeOWtU1R1xt6BpQgOK/bAYuIF4rA5NX19cEQd9SLP23PghxZFGHvMgzIrMWaUDdbw9iErEl8eLIvvsF7/SLQrjLClAVYFmfMzBMx7xrvfQgjxhR2u9UsJYG9BsEg7DWNcr2QRY2PyqgsCkGP2s+CwvzXetqKrGlFM2oVv9GHAKownWgIJUkXh/lNRJIO6afYWYt5kP8hAgYIzFjCkNrRAkK4ASqFt9+9ga8YFNb7LT2C4um8c7JEY2vQy7DWw18270Fonk7PTseFzdSR30x+Wbd1GaTbOiihO+OpgwSQ3QZ8lJkLaCwLYJDYuz06soNMj9i9dfNCkXQrUclB92943JjCSd4BGnEKcs5DHtMTISBowtOzBRMorw7W3z8uXZ88sbAlCvW88hYWV0jopx//D27WUcTL75AgXx8gEY//H1sy//1//b/8P/8f9UPa2/fwDjlBYjg7te1VUdYzMP2dKguTV8flo5fz7MV2x52OweP95G60C4JSdFtOCqrUzzNAqhgsnYN42DdBB0ur9gV9n5+YvppZVgY34P6VXy8vL2T29KoPSoA4YzxCQIh8GexH7ErKmbfkV97C4yAjxo2m4YJZT3wsgoTX/y6lI7CCHpwREY3jz1Fk7Cm8/wbzZOQRxRbl375gd8TaZleQqyw9GZwd3ftQFVoADl4gKB2Ubss6zolOnvj32qaMZxHIbQ8JDnWdq161FUeACehSxY2Mb0XYvUGIQ0OY+UBSrChIZIUA9FH1rA6A/9MOk/xml5OskI5cwYj3fdcdj2NVImMGWYn3yZxXFbrY9VgGLPnB6PrfUn8UzIrmne379rGu+Ifdi4MNwLIfmhjM6i3KK42W5G0Nj48kw93f12uRtlexYnn5+chTdfxOZTbUCSzQiR3eAH2CBOM+phA6WqyGzuCKLOT5AFRoF+Y5pekDTEJ2n64hn4dmQ3k2dFB1NO7r3wlgCAdgjh6SRTB99hxxve+Oqb6fM0Xvy5+w9i85aclwnjUxBIS3YCL4xqPJou5tHfpNDfUMQChuDyQVrmKa+rHQDydHRj1IVhh1ArxQCxhUzLduSnJ11L4XiQO7ixnzqcMh65C52pWYm2KJpb3ToAkYclgAMDUCIVqaHqPrbGNi1lHjrgCGZc9di0GzV7mR7a7rh8YJS6HoEgIonNBT44kKUnxqwmE2wFD9Prth4P2w8seG0ZoJc+iSl89GI+4hCH5xfPMMwiE5LACU/t59lnEV5WUnwcPmroqbt+5vqmiFJ6suj7tl9vBm3DIKUIOucA9DlABHllEKPcGXp9UrSDN/ZoR4kXQUkgRhC0Vq9ct1xDVxPOaRB+lhSzL/7SZ5nfPpoLoM9n/sNWjtq3HQzCDiOJPYBGYyvC7lTOOYO7w21V996jpqoKFPOzKzIcIcTwPP9sErBozqt+GKAJQE3gyOgCOX18LzWz7UiHsVzMXV9TivC87D49QA/JJTWadttqBMRySmBpHcDEtp2s2o8AnnPKiZTl+WIYBk6ZVhPOHlwLjnXVK704/5W+KCDCsrFO8jE0Yy9JjtAAjG9zBzeftiGlXsdlkSwypzKdLj6XqnNQIu8qV49io96OHpFRmb6r3V71oq/Hno1tkJR+RP/5/T8yHrXVw3ReOD8f3QAx0GqMEEBB6EfPoowoTzGStqVxHwQuOFqsrRg1DbC1NgseF7P/yrvj/8u9nyFOaWh0Q3AUxlrhCGGOSnBxHg8bC9zTEPhFkC1CQONcKtM6tJNb7zUlnlA9cFitltOLv8DffKmWD0N9DFNcptPMg5gHJS7juePWai35s3Mh1f3bR1A9gYDHi887MHgfuccP2qjJs8/aMUa+8cH5omplSoGDzX6/iUPQ3pdxEljVabWSqDzNkarFh3esPMXeIo9Fr2wSiXrbeR9cPKN7KyYMgFI7tx9VzqMgny/vK05mDKqs0Gcn8/u2enq8S57d5CEkPDvshs2sKuLLz7/4q34cmQbn7mVM3rbBLs+zLI92ZnQQem13YRFQnJPpv/yX/83dx7vu3hHqewxTs83SmbFCVU1E6eg/7Y9HIeSOQ7uICN0c2l3iH0Icffhh//xVvDgr9/eRGJq2UjGPCSLxiE0ETZh6pw+HIyaQDXQrgw7a05BVfR2HIUIwiWDdNXEsdhUwIigyrija2Q2XKfDcSE/JbJJtd++fyCcMu8mja490l6nZyfQ0YgEOCDQ0iKNk/iUgKK5GAzvZBwVqeBg1tkh01S3vdx/fjOPgYg8bM0TNbreb5GqREJxn6q5ti2FlQBx7h3BzezQXYa7n2/3u36v35+zmqWrB3cdZMh84Y8Cmk9TUVff4Ue8es3SCvVw4fXBdWODTLFf7NdregqDT3TjH4JQQcmftFKGIh6/yC1iWre4whbe15a2EHp/4xNPCkw/WmFG45MBdlvlBPnSGPUsE0kz2A4R8QE+WJDCLOY5gbEEt7GDCltfjk8EL6RCzZXaj/QtmDFJixL3zoFzwoesylZF01TTIjEvPTlSZ3+TPwjNezNO/GF49dA920/SndKRsW++3cuWIzHAZsSRmOYQnqtt4NZqeE2dSHB4DHDddZSVBaZyEsFNDY7BD2uv62NHQtp3P0wDPYGTpsFejAMObbpx+MjCaBHeWzy6PcUCO+MAQrkfqtDQeI+9ckhcEg/q4CxnVOsIKIFLvD7tOCISK8+en+9V+2wpUw5DgqxTVHTg7nQ193XR771iRgf3uMjkHEEMICSKnNlpZTsOLaCoyp7V3tdXKCOPtD/4uTJKTkDA79f3Gwl4NytC8cM7V/WC0gRDtCZ5avd3tGKNjr2cpCgKKoGRADjss0T4IeZqXGeKMcIAYnDoh3kM/nzKm/+kl/fvfJaevs5tFJXsfBBRJLTstFewfm9GCKIVNB71PebYIAwrBoHvjYZzyp/tNnmVnk0V+euGt0QR6q/EeRbNkuTXF2XkAuNoBW+vJSZSkvN9pDY3Ie0NDDAXCBLvROySGkSDm7uF+u/22bljAJ/NT3I6yUa6gDnfuoxvHdyPEZT49DVJnYcgjnrE4CCCImv53t53Y7N7+k5d/E6fJeqiQUeuqP53hw0aWgbvJ0E5J2C+37fBnKX8xLoo0kaI3/HdKqwiEtfNBCji4rk8P8E69F92+6YCTSRI1bXvtGYzpn+vaepdmyfL+U5xQ+AwbZ4wex4DDEM/SEgPVRPGu13Mnj0M3LXLrwArAmVaiacjgCcEySJAbJvFiXrwhFNhaGhShjVwGx1lUKGYMOks9IiH18+tfAKyisId3gQ63zXqvbXdsEI/DeNZ1Q716QG74BYa/k++EH1e7Q3kH59/c9O+ejsb2Ur+az4L5F8yv+7vtp8eHkKcsypSSL86mu571FdiJ8nj8YzGbGkgJFcKsjIGezar6MMoVTPNVb2RmjHcq4KenszSxNj1tP3a+V0kZAItXQk3L4IjKplkGplecKZ1BZCwMH+rhmGGSlcXsmenWozn6zJTzwtd9hzbL5V2UXhTzqeiC99/Lz3/C/8v/4n+53e6b3fJb+lFKQTG//pvJxdVl3fzY9/nR42i1pDg4gA4kJ/l1MD6JLNw8TybciFyCY7WVE4tGbO5NW7PyOQszeRVHjJDvm97qIcq+Pl+sEdnlSXEz5dtN0wZoX1U8SAljTA5it5qdnAQRZiykpLQWUnREAJ7OTyezMoxJKNuDAcMQ5ZM0QDmjwb73IcmQ6XmQOWut6ebTi6Zr3PmZrQ9L68QaCrdGiMRxzAZiEsuyAu05Pg+ikDnLsKPG1c1ulzrc7++k3cmxSy/4SE6isIyQvDWeDBQQ7qr2SZnQzMZWEmspZx7mGEdpOKQ2Q5LLXttiMkTJOg1j1TXScOtoZuW+A54Jgr22cjDeBjoF220XtQcFiNgK0yuMMfSQpFHyKnhJgj6Z5t/t9jhCcxZHad0Np2HKq+B0wrqAfc4BB7hdu7VlEUtnh/GJg8T7WgcMhsXu8BgqlU8uDUlAQjHnSganOrRTx55M08kjVMPQKqMQfQ4JWxjakwC7HiJd9/s4EGEk7xD99V/+AkHUb3qwc0EZT2eveHSqwJaaSTnLrvLng+w/DU9I87Hv5dOBLn5sOheZrMMUQXiZwcgCHaX1UY+DYfqC+E9eYmCQWGoAiBWAEx6wWIpaKcUiQgDGEHk9CboVFkYVeX7NAwBh0NkoYOIAEYrC2KY4VEYb7XxIw0BKrbSM45gSGnGIM1NtttvaqEHqdmOBkMOxGeokNMCIatcI0SEQcBb0e355wTAjE4iO0wuftULKj6q7tlu3ibRUhhqAiREWo1hVDYCMBiXkyIh4XasI9SOICPQeOj5SEkFXZieSfnS7SV42VVsfK0p8DRorzXRySQhaK3FmLfAjNK4bKEOsmMbQx/hsTa7mnoA4YtjRHJOjbGEKi/IMAnMZpQ8DAaY/7lY9hHQG+e3xx6aOCbkOsjxPBApZ1XUM69rrhA4UIseVOzaHI8an+NzL9xsGT00DVS9AhEiEpTocOsijM0VHuQaMcko5oWRSlK+DaPriZZikCiFX6J4HP+Fclubt27dW+6+++kkWZU8PjVmTNsIDVc/K2eXN31TffxsnEzgpdlsp6hqxLGbYSBVJp4ddNdft28f1ME41v2Ye+RMxbA7HDa5BGPNwDkOtKqfKEo0Enf30L3+FjFZbI9bF5Oah+qGIrqwP9ts6OjvVgM6e32iggbPEe2N1nCZSftqq2QYeUU1CDzvOR4uPtmmWLLDWAgAUqtp9OSlEL4b2gZE5r8NuuzahCCkWQz/zMRDASRTg+q7HBaxliz7mp2n/hCWpWbUVO+tRPGHEc20O3dtb3asvv/6Js/IKoeeL58bZd8139u2nklCO4Hsx9tsDP+v90fVi00h7NplryO+Pt7w/DlvBr8ufXC7+tP5cOTvNZzxm0Eem3vJYnEbTE9+WxdnzV+eDgJ1olH5qpVeNiOJSmX45HNH9Vfpl2t2t0F4Xc9KYbN03EcWsCyDAmITG+/79mH4eXZ0u1h93oqcBTQAfoiwW74eWPe3yFMDKI4B4vt90r69u/lf/i//2f/j270i1HO3O4n7rxr7/s7GaMniGF+7qij61AEAx8qsT87DpleYYew9gr4ZOdfXh+GDd0eOreZk9e43i2ygOrFQvGI1iHoQrgGyzOsDgnZ2gaXp6+8dPyoDEoNPFsxFkbqztznduRyiluHEe8OkkCJj3SimF4ePYpT1BUEHYNlHhQ5YhSGWE0qs8XsYOWaVtkSVFXtYDJSdnL2n4/Ma2XT9uhGRd7P2YU+BBHBi4VCnBHWdYe2USCIrKOwr0ScC9wnmS0nXV7epjGiZlPJ1O5F3dA1FMIuHcwpoI4mPACJEUyw0G2kfIa+uYRwQQ4i0Y0DCd8nG7xznRWtSHzWa5jFg6n78EyvdNP8rBZC3aIQooZwGCWJuYnJ+ch2dpJyOBqSzDqTYOQkAcxPt+TFje7QevRzK1CuOwzJMGgvmULg8R8cZSTAKKqNkMdZCEEPtmPJxEz1sLhv1+hDjiCl7Ms2OHPBSi88ZDej+G51iYJJPKHPNCNcfGWZx32c8WcRQHbdO93/xopC4ecx9ggyQc41HJ6v3Dxc+eM8p0k6jdAzrL0elNP47eGmRdPHT3zncfVF7kwLH9fm+GeHJioc+IGDFCTsEsLt14KlxjUGQ2h+/3VSD38CxrqNP5iI2zH9rzebK4sQDM6LgptOpH6x+cVUYLXQ2qbd9GyauL82c8QFW1wjDsx1oraJ2u25FqmIRejwxLbFhjlNfS8kDcnMAfPnkPxHN+8V5/WO9CtqCOKQyd1LYXisH+SdIAEwc16QSKT1QZo2a0tYaItiBvjsqNkGGjiiTpRY8S2x6sXK42oxEt/vKztMWrpydgbW4aWp6WadzstnES9kq5Xh0WGOs4Aq4Pg7q6Tw417oZxsbiOY1wmK7MJdnKUjcu3bJ5639F6bOISMIKmJ7wfrBKX6UV307k/wMvpIr68xFXT61riIOIlnHrRUz7z51H0tjpayE4mAij/fhB4GOr9p7ZqoY209O8eq67vw6h7/vw1z2cR2hNElcZZepN8hfpRNH1/QIAJzUaxmpCz4Pyzn/2qfVzOimm7RPVxdXBnSRh17Wan2oufnXxx8VetGzNGtm6wzmrtBqpijNf9ikBxqjgCYMHY6fk3zLA+b5ofYzmKJEs4CYAEaqbNiIZh6egJDal3MItO6woqZ+odPIbLKDlpWxsCAdiYR3y7QxApRmlPDAK2VzZBOzQi1R5UXjBO6OBxj5UxJdJHrMyPfXQWAU+6VjafusmNc8qKvplHme33GGOM8FgZoaxnY+7CJH+OhBhh38tZdMLcbmMOxERc9Fqpo9djaUxeRmdTFPON3MMObLMELmgUDmOcUziNZ4+P1SjRxwNIiO/OXxexhITE7ZXJqRgUY3gpDwTzI0J+iSIQxydfxC8/QnAezbzRlN6I/W7cyEO0x5Dfr62DThzcF59fZG6x+k375O6f6ziOE+tqr2MEcPNmFZzn8kaxVoeh8w9OOhl8YOd/UTQnF1a1QRRGkadHv8XV/Yf3EZ9n0pm5m64mn7bQFIdn6clf3Xzz7/72+wUOgpDL6ul2uMvyklK4bWP78MR0nPh529WjcgaPnuQG+rpkw5u3hBtPkPf+tMyjiy8OzRtrxJ2SwLu8DpMxcECDxplCt7gxmpJ48vz5pX0I7sfb4jGKr/PKHrnoJ0rGSUKw/yAkrtQO4sUcA1s1leaNboMgCENKCSJhGmdLiEwU4hjB/BkB1oMlC2IeFOPyfjChctCYEMFyEvI5yvtJjyPf9q0MNRKKKhIbwpwJWUCmbHm3hxQGNGrt1tTWQC8wjh2eRPjY7zfL3+eM3681gLNHeZif/TxdYK1OrRyAqFg0MhARGHPCFCJNW184riVDsVEjUk4AZ7x1h+PWWUOTU7iugxlJ8tTEdjKZynFub50UA1nMzrS0ltFRjJTwljLCg6Nv0qQEJxzMn8eP2Jf2+G2dcKkJdV2PguirsPhU7xB1AQsfZcfyGEfxRmyTcvbN9XOzbWUncN/ddvRVIgzRfEIiHm8+tGtTF0frQQIlLy8n9mEfdWH6KgvbkF+GVnTfL/9DLVwRzxF2yUnR1tKrJo7otj1Uv9m+/OInr8rZt+td9XaTX11wzsO6QzZvQTAbNQ/z5xHGWbJrflJCkriWIX4cIY6R7MCb9V2Q6yhKYdffte1hFFfRN4hFVix5RNVTu25XUXaG0y86R5U2To859w1D4SUzK7XfVNjj+SIzymIIs2SOQPDwp8fzV69PovDtDx+CBI5146jxSGAUlhaMO6WFBMx/FqYWXolY9NsRUfjXLxbVNNLAWFiUNppDzCNSxMqMezn6bt83b+5UcFI12YFKi90IdMwpgaDf7SIWZcB5ke5MraVCgKinHQVuVEMShcaT4LgCNHtqK3Yn+0VCYTKsJchKmXBgQDCNzj4Lun0T94Mj+n71Y+cOk3najP2E30Tcv1dHYIFf78C4HzcwChMUJxE/D4PTXyz+KJLEe1t1zcnrU2ujY9VCRNjM62PzsN2NTF8Q66W2WoeiJ0n8oWLT5IxwTy9nn+XX72tFeJCGGYCk18fKSe6cS7k9wJ5C7Ql0gDHSdZ3vvX75VOIzjK3V47gbRXf8/Px0AJUeo36oVVvuD08d2s0G2siD0d6gVgtzGEwjmi8WCU1Q/mUSPhHj+tv+YdKlSnntCWcvKBHrUfN9SaKtFkbjwaPl0de5WSRlenv30TOGPTRtUyQM0Ji2fV2NAXEUud4bt2qLl6dgF/S9vzgr32GlDut4pDSdDENdAFTXQ6gxAZF4BLSJQVrcAcT79iQPxP1tNgu6T4fVunpCw7Rc3BQ/R1YZ6LarnfU0aAKLD9sG3//5kfHY6KXlmqfxJKDJRF/MKEdbt0yKkwRu+kgnC2w6aceqlqjpJ9jfNnS5nP7yJ6vhGERji6U47kOKYJD6du8gJQwuzi5GObjGHuO1Da9lNTZoddxtnm7rReptzblkYFK8mhoVz3dFezK9drn/7sPb1d26PjY8yvo+addBBiNDA69R2PUyxdAXVrU8jhux29b+89OTN/DxuB5A+tPoeZ3fjW/bN4cPPyyyr1BwzMtPPi7Vu2H4/PxscfNvf/1v/7v/+//5w8P7fB7Lk4vz4oUGTwdxy8lpOZuvt3fTHBUwsPw8TSOaVss//mc280YJlerz7Oro6Ntv/zSOcvYiMK69moWTyeyw6jPLgzDHTh9tL3RDLOD5KQ4Z/h46gyB0Z70BDkcMJSFhSfBVxPvwEpjWo2gYbBhwVYK5xRJCAMM9ihzheTYbgD8Al78M6+MR9/NRBHEQ6L6BRVhkyYfbW48elkNnB9XqJkoCbdVkNoWiLrIiVH61PY4CXO6+/n73h5+dTYUxx773OZtk075zRtU0QtJYoFIcZDv1BH1X8nMinFzxUXTnF5Ga5B6MowI8yMoo3Msho15bDYdO9Zw5e5Fd7t1eYmG2Ync8LMIoz2ZDguYnX2dpv/yHTbV9MxK2t5ogjJ1z3QpqRiOOQY6xHGFysZd3u8d7gBDnYSgCvojNseqbQInuWJaLKGB5CsGggZN4P4YRV7ZVj2d7hjD464u/EuXz7faH/e0bpbMh2DwdN/NFRq582KlRwN3+ccfCv2iDJAiQx2iNrbXAnT89vQMHtIhytwgCGRwZu717+Ok8Pf3mevYYrjc7eHDReZK//Kr749/fd33sonAUMQeRzF1lt/0/PH3SCHHgwS/ATxCL9rVqdj8INcbJeZleBHE+WmE9d+i5dn9ecswEIRCNSrszlw/lV//kOk1Ka5F9qsNJjJ0HWlRvBj0eMOyC8LpImUfYOC3EiJygOJBV/e5ptd0+XMVXfq29WB2t8NAwbcAc4j2FW+sg3rinouMs4l+/jqOIlATcY3rGzqlxZpRRGAJYK2mtdlLRH/uB2PYvtAWLZ3FCRgFEL7EU3HHvgXio1sMKInAS0GAxt84xqRLPDIIa4xH6PNDzLBqgnbvTjTlAiNTxqd9PJ2kxMvR2UIah53GhVCedA5JUZl+3bZ9sz2R6zZPdc5kLcruyRZTHLHLGr45HZ31FA9tJg3VyCfsBDuZ2tZacMn8GACgTCBfBSRBFBLqxX80nME5ixuZgkeR5+FEO0xScYrFt1A8/9tPBRzzCZdfhqwCOLeYxYEHsjkZKFYLTHI/S1cfGvgODB05PYrzZSGEabRRJcARd1xyWm7ckohAoHIPBAodgPkplxucvnmUz2sp1/e5oFJokkzRb5C60vVlqWircreIDPmYOTPPog2hpoJR8mMUpp8f1CgqlIxY1UYZrElIQI7IdSRhjD6IAQ6uPYzAO42ZV9VjKeXb21Sx5C6GuD7nqIGsInOEgtsq0VUVJwIkrQxTXfdfVXg3D6s+DOzwCNMSICAZB0NNg7C2AMIlDaW6VRVouVK2Bh/OrxbiNSGow6wss0whR2g8fXNU0NE6AChzyk3TKCWnyoxyjOM1A+tTR8ZxEsyS67SS6EphJBm0PNQsQYxHBRoMlOAfA6qFpVuv/2NYoDubaRtMzFgfTtOhFiybzYt9q34IkzCgPPLB/8YtfvK/f/Pj47vPyG0s1sFWSvDq/SEgsMXJs7I5ql2U4yaI4cH1TJQE9DV4exff7ZhNPLk5/9ctw/7hq27nZPFdUtzjWI4k68aZzz59fnz373/1v/tv/y7/7dw+b9+Cu2ttPWLp0kl9Mn/MoQDE3Rlk5osz2ej9unwxQMzJvi3b1fvnj+8c8mcTz629OMoCFUSrPir4f6/HTOqVvux44COacpPAwtuh+fH39GmL0oG4/01/2EDGrMcBCjm0/HBHlBQIa6t2Be4co1D4ep1GjVZbPOABC2dCDLAwZtG0xz5YHFEjARYbzi8ure2Ae7r/1Mi0nLxr6AYTgYn4umRlcXYAEL2QS+ZT9hL/dPrz74bDav07nTkOfBdHsdAzSjaTaRChAwtJ8ZJNZVMYv/mZ6cfb6C8n58dgL2//x++rx2w9BarJnAa1yC9aHw1SaMQidG4QPMCJ4lHYvdDUqPplFWUW0/vvtfaqaScUXpxTTl/EXFy7N40PIxAP59k31sqRnL1Lt+WBlI5a3bSPkZvPpqOMVpBSZ65PJYlGUoAoQBQxSXW12bJK0ziINjA5QIkOmAxeK5HB3+B/f/vd/ffMvy3ziUcZ2VNVPBE1b2CsZnfjYQxVGdJ5Pj7cPQA0WB8YZzigmGOKnp+VHzPMwiWvYjqOX3ZjGUcApXDfUkRDh+83bkiU3l6WZfWPhdiatW5yZQ4DgocGdw5qE7rDZGuOO3LUDeS8OFoEgy+c3F4iz6XS6lQSVMdt8QHuMJ6iYR7hfyOxiHqK2gM/yn0GANg93WwfLytvWan3UauOsAwBkmGURJ3wTs2ceAtmpk7MTTsloYUUiDRXyDKE0xaR2I7CjB2eA7eFZ7rcG9neYw0se8JQ4o+97ozdonbzVClgH4oCl1IdC1Qe7Xd52UsZI3br7soE1PY+MHry3hpeBGpZmJzcIgysUkgWyZm9s5AAUlEdtjZxWEEBuA8JiZR4Ot4AFhBDKphQFfd+SOccQvOQh5+z+tlZKOw84DMuzqUwGBMt+2B1ls7EhLsRF+rJzo+2A7nrvQRAlKsRBvPdjUlX3T1sxn80vrs57o7ucj1aHXBPtlBjcYD51A7pdUxKarlrKjoRwR5kPWYFYcxC7ZrxchEF44UIuvAaVVKXjvZthXPd1pIHmvCCzmNrvtt9N6vLs+em0zOBmcLb1gQYYbKtHDdXLVycQICRUWFUDplqJOAoJcA/yKTetm/be4OV+EwZhg1SSp0iQPx2X13hSRJNJST3e2tWT3Ljgb3gWhx+VrnRtQhR5ztdHKcme55papQOo6ixJuu6RtK4ZuvZxw6e0rsf7an05fc5y3hxMHTgoMNTaQwrywj7CZf8A9AW+joenJ+hPE9/0aW6NPYMwLD7TnE1fLcph8tuVUXo9IXlDQk7yQz0m5UnIwhAbEAQYGNV3LmtdZ/RAoHdH3/mnkdFUkJOiiKrNXcliF29hjUVEPomWfXrks8/CYGXWg3FVXRSw7iEksq80iQ7x8KzPnLZ8xIar/CbMWXCsBhKmYRA8rRoMrAKPu9343Y+fprNF9ErujvL64mrys5n9Dz8+yo9fJNlt13ftwXXIHNXF89IDp4LBe6YP297olTrm2V/FSfr6m5/erRrW02BenGfZt4fNVX3YpZRbLjS+MEo3R/nHyhTTxfXpv/nlv/h//32pvTif2jyKkiAwxj28e2cxPqcnT5v/6ex84aAdN0cpxXIcH+/75doSYjHilPvHGc3UbugaB7SU/vFxifHJZn9Iomw+n0/BSaE3+cl8Ed8sl/3q/rBf3hdnFw110bDTKZBKBsG861RAucOAGI8Id1ZE+kIV3J/nTgoBsFEapjFBkIVlm6albr1WOMCcBW+/++Nyt56daKKoMsoxU2YJCqCSmEjCGUTIh+EqvJn2+1Se8BSdrHa/w/qFz6daK8ABCjSASVBD0bRIVrX+XZKEeiibByOSsFgkv76av9Gm6Ve588FJXFcgRH0qgkBrkEZd13DtiOkNAYBzP3aNc56g6Ywe22aUuP9bML1pfnr1E8FpcMNOwxdk7N8I+iJ3SQShqj7IoU9LQoPcQ2Ah9+By6AeAevngndzR+EVK06IgRzAg3wLgvXWKCmUpwKzgn7Xn7bt/+LEemuez4jTkHqt7pAFoHDNC4k/KCLDOozyg+SidSuCIY9fs4iKHjT3+uFfKpVloKXY9UN5apb13ppgfyJIfKcuTRA27u+/T5KtnV+nqqX632UWTPNOrEMPzyXy+KAjYjZOyqQ5hMkJkvowXQfQsu0wcQD3W2oGDEeiokigNowSNIzi4JIJkux8vDAX0wW2eD7JrNCPhsa/bzmIfTsOLmMtexGmcMsbaIzJh3YgKBJ74OZajR2A2m4huVJmwJiYsLJU8HA+y006P3Ex87nl7QkB9EkaoSQ6AlFPdRIDKmIGUeIsRoE5gqDnhGnXaGEzRi8mzhpzEzBkDGIO2Q9XDw0H2yIJXZxcwiZz71I5Yyi6MY+uMygpUqchZyRCuj+T8dNGMB+8BwCMGhBlyCm6o31vnATAWPG5+DILSK2E90kfrG/h0sa/7Wu3Ul5c8TL4GFkUo2oYHLSNjQUQw6I7awR3TXY+TvHx289noYLd8tHNAPVIiPnad6JqryfSSEIFNOElMo73xqO0wp9Zy78MYsckFv7heHA8HZ7uOkegkSuWovQ8Y0RxQSkdjQkyB82Wb3ff/SZmfysqEpQ99xRAb3ZAA/M9fnLLFK+tx649H5I9//IPi2aufvLwoaX2nk+ccxgB5pth0GATkNriIX0dfjaOJYYpwcBAfKL6L02o56v12baSyLfDC9jhRveaesOJmEZihHfEU2nG27I4f//iJwn3EyTh0V7NFM4j29z+Kf3YeJB54DABKQSgTCDxDmqTx9NP4+G39569+myoAIk7mJ+U/ubxCYeyNt/l8/XbPD1Ezy1jYWDCdTJipQMwoyB2PiyYFzFRiEIyrxaKQmvRajASmvI5Ycdg1n9rDja7KqzLOs9pL/+h5iHzCMNDGVVcn5N07BIJopIp4ThOeKW2R3UWYbYbg9fy4WjlnhYvAjv3d9gPRwFu/pXtG0dA10FoSek/UYm7CkrDtbnbSZfmXf/jH349iUMNQ0nJxCu5rO0g1DtAAmoe+yBMW4FmcGJd44z88fKTn17Q/OOfTrPjFv/6v1v/dRistRynoEPWHY9eDbMKsJQDUOuEivEyzDkbnC76vVqCzq3YlGnd+c81DZrYeIQ2c8sCQRRlMshIEr7/4S0oyBAghIcBVPRgI/eHY3K+WEKfJpJzHN0l4DincuAPGAeW+nJ385T/56/9f8x9GA0JVWcsAjwIZQFIClxNqprMp8J76kAQEjZac8AvGl9aNHkUQ6H5sQ8rSEACEQjrgMNMee0yn/ALgNk7jzK+75WK6CJ7zxmbafRJ+oKKRsidLRguK+GDMQIGKF9q2AMYIjtwJH+UTALwHfmQdjA3w0BrhIW1NI0WS+EM6ZY77K6Of4laI0G9UjyuHgtiHCkG3bliREG8+NCLLC7aY2CwVTR1TfQPwtG02oNFr2TxVvz387uLsKkmT/CclSdPYiLtRGIyawsN4SqI4sgDt02Ttrx1AWkpnFB6k43Aclg6AKJ4WCA9MtsLgEwTU5NSQg8baiTmE/YX/cPf9+1s7m06MNRzNKPdRPRHCHyNO2Wsse3WQUVKiYqbePd2Nfk6VzOnh3SGYT2gYOGAHF7qiNB8/YgDjuOiaNbAKGEaKIh3dw6eHfDYJACu1dpuNQQ6lxAWDAIoMPWJllOGiiMMwwNfXRoyb+08iKcIgRuw84402Osv49Je/NNr7IHDlDDmReitI+Dx5HaM8YWBdPyhbDK7RgABjm95t7coCHRydpxABSBChLBy6utq2yLqurnNaSN8aa+CTVcwqt1Be4eBCIWyUt4gonBdTl+ieYu2knMHFYTwq2wTFdGhUNXQWAYyAj8IgEpgQVMIyAYHFA8bGWeHaAYIConx6ojECWmB8Wc7rw1ZJoVJGcchdQKQZjBHWtYM1B5mQKOaMTyI8GEMGUJveAu2DDBPKGUtj0j81PkkF2ILBITMn9cgvFpPJgpJANmNbNSksNMQAaYRxOj1TRmXOsKQOg3hTeXvYkTCgO08J8tM0UK6Wu/jkNAZXQjgtwTtJ0lnYK0gCJJVR795EYUImXzVMaggsyeJhwKHAwFlg2uYQBaHSMg/48bCZpkX+Ik1XObF+dL5qdnAQSdYJMeJXsDxJxfgjxPMyPztP+aOa9WEbthVgeTnxlMRd3ex23Xk2tV31sBrlOSJGL5KpM2Gl1agFb3ujxzNE6/0WOpfH6bYVpQ6rKNLHUR23d/2gI3IaT9rjnbUrwuvnz15FSXxYVidnszgbb93TWVLO4qvNwlfdR+2KDEe9tsw5VtJMpMPQawwzALxRg+gHobIgirN8Y80/3P9+S9MrEnRd76QYifDGNg/v+jT14NbLZnpxzsnlYXUHMsFYCBG0zmHEW1D4LBO3/T+qTdSm5bOr8P5tfwMEHOWYjseWnJ4EPNxuNulnl0olWtYGjASfkIiVoh0of7d+e0ZKzMJup/qK9fvgxeeXrAjCJIoT/v3jRz0cXyThq+vXFDFr2vRCHZdbFB9//dOTv/3dQ91tgzIajZuXQQJxpwSDrcZhP+SH5iEIk+T8RvfadPIUEVNMul3z+AienZx+/eLFx/fvZlOsvJRKSHLGxm4ipKVU6q64LsJq0Ryemk5V9YaTaYkzu8BDC0RVz8iFN6Rdv1XWJYsThFkmO60kYk5q64zs5C72TUrhp64lHJfFfFfVXhmTa2glwhwB0mv95+H7nqizv7gZqqH+7r6cnwjTAhFA4sKU7j1qtyPXPWckiPIoSuRonnYNDAs6iaxzstq3xmcRDQnpIKEBh2o0BhWXs9nX0y/bS55Gzyndp3SsPg38g5Sdd3qg5kz7LvVzbyAELPCdlrWoNTXc71cYtA4lglDIl7v1Tc4MkzzlyAfdMLB9FaWS8bjn9omgnuygRwQzMKp0kSEF9FGoHAxOFRIbCB1CKGSAIJe0YNe2Svs8BhnMIyJD38r20+/vdodDlESeeBIERBHLWI8gQYUzB6eIXDJmMYbqFiMculBGJX4ZkQ0Vzc5a1/VNChgR7vQEYbcQSFWExFEmNrddwWeTr8qk+bD+h7rbW+M5V8xc8b7P0zO5s9HzaTR7ziC8nk0WnFZ08ek3f3//qSrPTlXId4e65BFjnFS1DrJBiPDqmXe+fxqpxykhFFJpBFKWUfazs1N0deGQs24Q3QfRjIkabOkq8DbGaTY7YVMitk+P+62fnAblzHUC6I2T0tu2NbHyoGB0I0T8UGfzPNE+p7ZRg9fYGJdGheJHCwCwVjqJvDsl8SSfcI60UsXE1goQHVPjz0+TrrWsJm7QRERy3N6rBggZp6dRyL3qj5imHUpDE16GrCT73QMWzr1pXS5xObeIq6Xau91kNnFitMZ+/vKLU7YBwDtv+81yJ07yFIybTpm25EGIIIypao4mUAiXRCE1ahKFitBUchtq6CJIODtPESaThW5a64HXRhOEGYQhIQUPEXDDUIuhI0EMAQ2NS4sbm2ujrb/4sozTw5L37Sev/Ni2OCTa2kNtPAwmE3ioG2vq2fxLAris+smzayqGfY6jFla3nx7221lZxFF5ODTvv7+XPh9FC487D6Zy0TuCp5+/zqN57o11Yt3dhXrKE+GNLSMHY9e0ntBA4IbAKKFoL9flKPjiSyN13z0xrT0JwpBi6gfTHO+2+TkbbGU+9PLiWRlHN5PpcXfP9QAIoh4sV/X6QzP5xU0YUulRllGNwtb7RixDQ6dXRTfAdx8l9u2vT+acgc3QNm4EOuAQyDJOLApZZolchHqvPOFF9iwK42ArujAwGzFMTs/zLX2qD2OYnE5PdIBGqVErsRw7e5ZhNGOXEWom15eM9TEDHvtxZga4WbRYF0ER3Lx8wYNUC9Vvl8LiMkxjqez5yWlT11XfDT++X5xM8wC6wSrmN9tj17UbzFl4nEVfBGePh4fVP/z9wy//6SI4GbVQXd/++HajFYRjJ8bhu+++ezFPptOMWKo1XIuee3fCCGN0OAJ2Fuont9zskujk5mL6+c317bH2ns6D+FtINvXTIrgCvpHtkJEu5MGDPQRPNp3Hr1KmukcHboTopLSAEcPHFF1SWmvdKMGgh2klb+sWEc2kOp/k9yIZDjXA9Ge/+jcPbdN8t8uvSMg43t8BTG0x6eumwcs5Px+6xmibMny3lerMTuLIWhfn0dC3CgdshAPk3HiEsJaCneSOoGE4RknRdw3Du0YLq0wjRzSLhpwJ35OaWuehn82Dmdb2PA4eLGmevovYIuSQPr/YrfcJxCSGQy217UvCNfb+bALrwWO1GzZlOCeYKlmFjZZKIjShZN+P0cskasIAWjROE7RtpuOMqdPNxz+dfP7KYERHL3OcssI1Z7h/HNBRKR0EZi9gkcbhOTNPo4FqTz0kiCKawlC6AVFvTRPgVwq1mEBrHI5ii7m0yWpgyfEwNPX+2zrJwh+AbPv+uS6DkLE9PJ9f9GV3rDuWccCDQ380ESrsl/H1ZKxud6qnwW4ST7S1DFzb6+3haf/b7cefwxtyrA+ffxZCOB/HZuweQs77x4GFTthTmMwsEa5R0vUJCGBalIxVu+WhqSApAMZQjmaoGKeY9e1yV8a5p2yn1mECzuwNYkpJs1rtfOTMZCof7ckiZvPFDSN5nncIVlpBBAMEPmxXP40oTqISITUMph8xjzsxkiQMCNsfjnh25a1rG8Bck+n8Vq6GTx+Di2c2QknMUz3OFhRDdySSQHpxNmE0JuwZwP3t7Vt6GhOeeK26KEzKhV8+xJaXcfYgBj30bruynO+qnYod1vF1+XMbGgCmx6oPYrMTI5CiVwpos5jnnkiPcZgyp8PAW63brUCgH3xNRjBOk8AHDocXsyDrGgecrbb3FpbagAaNPnaph0II1+r4cu4/j6Lg8uL8+dCBjoALerU4me2edp6AIGQ4jL1lYZxOJsVRQik9zoCrYEJ5411+2EcnZ07d4Sht+woUKXCRlMazztUqNIpQOATG9BghCgBw3iIAlRgACwPOnGlctVMqPYPm+HS73e2Wx3WcBywKDscDJs/OZqzIZIQv5y9IyCKiqO9g+2XzeFfHWRTGUskPxoSUzNKZOR5343YzLfOQxqhHX/3NP2GUaQ3A3aba7i+iTGSnlDfCVKs3DwBC8PXVAuWHwPe616q/Oi+zmKJ64DlzwOXxgIgJrNawU+oyrfh7ccsOfzLd4J07L16f/7TU48NIhzO86OsRYTjlN5/0b+YqOpgf+vU8YfRx8/Ty8xdK6Cg4C1O0XR9fX35p6C6Ubb/uOp4GURKXwd///jdi3LGQxgTPF9nTcmksmLjrUfQnaVb3lQszYO3V/KJuHhP7FCS6PuiBFlw5cxK3jzXhCe3GiHTQ3okk3R+lOoxpGSRxchgMiEpsSTF7RvTY9XvUcZ7EhpiAf26mBVUuWszuG2DrzctFeXfcmH2vA9QSyoNjqOKYn8ZSdH33zddfPeyOp3NOg/M+aIe68QewHTfh6Tw+WkCGNohu33XC3H718uVXX7CynBb5xXqzPjuZMy2Xq2WeJIRSHvBquzMkejadZGPc3rcPxw3nJ2fRy7P03Blka3w87M+T6CVP4MWXa30sRsWQHtWkF25F94D6ifKz+eRHXaWdoxptMISIZtPR89U4Jlbh5LRIS1JG0c6nAGzbtsnLSTZ3euCfHuT1Zfazb371d3//P5xZ3/eDFKLMc4qcUabzfVx3FOtS6a7u7Rxz2q99YISUor44u7p9010Vvj+Qk9e/bJo9KUyGRe0hwbbpVpQSoXAYqvXBZqc88AU6GprNm0nEcLIQ0Kq6j4I/HB493DZ7IS9nXvdqmnbL+9HqvJtgk0Drqno7n10ioXRaAGCiLCFSMB5wlgLvQD9EMRTbUZ5ODcMpC5u6j5MJwp11/iTNaTmN4+y97IZR6C40+p2QKPYcE/LRwfXfP5hefnF1dUZJ50F4lRnqmq6Wo/3s8uuq25sRn5yc7mnDLV0OI+8VxEwHYBz16G/vK6EGM/n6F1fXFy8g/b/9X/+f/9PHv392dVWEebRLGi4ru1dG2xwcqiM5vOjG+2dXZ2EaAn30hnvgxqH1znzx+b+RN3uMy7u+JRPGs/wagYPY7ow3SqEYxo0HatShbXdBCBlCvaoAFG11TaGKC1zvrJEEKsqskd55s8BDkEYGqErbthMmbKKYW4sxt7NigUgCQGCLMPvqi+v5POz7uqp3Smzebs7P/OnkysrmUG19lveKwizlQdQq11RV6gji/KQoq6oWfWdCC+tTH8kIZ43o2t0KBKTJ0Rz0qT1mmKdZrpVklHHMnX8aNuPBWaYo0Ed6fuPrtlGDdkcHUGT0acANdpdnX7H9slpVA+Enp/Offn0uVlzI0YJ2s0Ogxw4C69yI1DUrGPEBTWCgjAGu62sN7d4ghIBPfBKslALtY9fXY6cIjIIkwQMDFJxPyWGPh7HZdMcX3sXlJA4BgNzt68E8jULC8xeo6ey0/HH1o4/xC3jTi9FaqJ3hDobG2dFUA0hQ6qjh9eHJ47lSD2tz88wd1B76JJkX/apRykMEa+xT5QFyYZRBSO0gfZ6SMKOwuAip0sJ82mHsoFPQOQb8X7/89SAGe26DLGhXQhMFHR72DKAW60wO/VO9oUmM5NnpNIAITjNU68nYj+1Yd52YTQgIAgLRfrexXsTL27PkTDQoYOWvf/6aZgHs7EHB8/hz+dMvPty+K+KzgAfH9cf15rBfL8nx6ebZJenH6oP0uWULxqJQWjI0sO3Xw2gIYzg8u/4y8pXJLwrfPXIdw5zW626j4HN/8f7D6rCWL+bH/W45yweQxty32/2+H9R8Np2Q0/vtY2sOQnZ95/z5jLZkVF3zpKI4zb76p58lBrkHqZRUSggP/YgwvxvHUhvfbwPOrV6G27vzM1iZI/Q+z4rq4ENlCSMEAsu9s1ILMKeoHgeXhl0HxVg3XSu9TRK/bwRuV0Fm5BkhNZTtxMPmsdraeOEYNJG/SGOjpaz2VRlkLOSh69aoS/ZQpce67brjOCbK3Manv/zsZXY4msaYEcbQN/sddQYmsT6Ps/j5Zb8XSp4wtuXcPt43m92uOH9Zq6p/98YGPP/6G84j72FgDLbuw/bWexyA9PXJsyDng+h2/6gMP2zFUO0ilCRh/2TqXuSGuKKzdYUqioM4iTar7vObF+jwbiN3I3UJvxgtZAFVcmTMYu62ezXuEvEiDON4EZw8irvV9vAyee4X0XEzBHF6/dmL+3X1m9s//EWeEooxAkfL4LDz+dnRSgf2ZJoFMbrEF73oLfOtszREQRSXV4u2ulNtFYfl7eq7fLTt5UzIEWHgvejQBtlq8MYAtSAXmPH/vFp/7tiYgGBT384Teb8/KU9Us2WnJH/1BfCEXJ2e5UXTktv3f7te71JSAnIaMADoYAaTQi+yBC2Rsrg1TxPsGAaUYBgFhgfiwzvyV38Fwsp/7GCSd4MJv8gi9lL/8O04CsoCuall5DChAaejxvtPNRhaEkecBudffL7bLldP2xt1bmXdpR2KsJ8FqNGnN8/6vtvsNqTILpMT4d3f/f6j3v++LLMsDdP5zfPcGy2thkEY/+Tsn37kP87yr2lIf/94i2/iy8nZjlmCnUY4IBDCqm0mRQZDyLaHewBLQrDQo9YfguAi8Zg4T4pn2IPi/s2y6v1zngqCDlGsetvBPICGWs+yOX0UxWXeBX0BffPwNAo5S1ggzb7iZbmBCEqJYh76Zrz99GizUjlg9oZi5gIQ7gIH9jR+DU6jM6P3XX/4859bRidOzybILT+Q82J6aqAvAcF6aOYoo95u2h6OKgmuGQ0HQD58aDNiIxseuQy8C6JIoQAUGhKLie0dZAr2XR+GFEFQVU0UegDhU6+aIixpGisbjcNoZBrh8vRmGPqP+i6kuL89pBF/ecEBcaHBbBW4J7fqrXGIB9NRrgihe02kYLMERDwDeuWFtwwNusZoGhojTzzprfMaGIwo8e2z4KIlshFPIopUryPqTd/J7VYj0p5fTp//+jlYLY9GTk+C2oAdCmrpplV95P44bnzGFAAeAcZjbzhE3AA8jsY4j1Ag8MF1yKWT1EDgmFKaYDKPFo91c/wkeeAgBA5MF3OzWS45oZZZRQevGOHeA4wJfuwcXHcek8LSNtaHd9zhvvhpGAjaNvd8kfCbfBxUvaqjQcigr5aHKEywslYJCz51VllnAzhRA2EzO8OR7dnxQalepfEEiEc00f1yvc2ORXbZNorYUWwKPyHUmrX8MFPhN18lu2MjpaGz7EUiX0w+h/UK1n3TNl3bhW0Qn6UQLgah1qtHGhUBj7LFZ2fToO7HXmzoGDRrfTqNYpfZ0/iGmvp4+MMf/q6u60vOR9lgN6BZkJyxJJ3UbdeTBQqb08vTIg1W+2XIlZGsF4ZGSRou4hdZZjvvu40IEAanJ1/dLj8eV/s4OY3sYGwQz8LkeEyhM0kkT8fjnUa4If5mvpCHamShxbJuoPYpiCio2r3cru3sZAQIovL0KmwGH6cL4iQ/u4hChhD0BYoGmCdz4SD2SIhd/X0Tv6CnMwpgQej/n0T/3LltXRADrTe/I48x8/zy+r611t577XT2OadOlSvZZbuNTKuNRAtEWyDEb8QFcA1cADcAf3BLqBGGFo2zy+Xqqjpp573y+tLMc+TwZn7wXMdjjTEOOh5E1EziaOiQR1i2WLrvf919/Z/+w9Or58J0ABCt6kk2B3aKsM53xd/++7/87IubyXg2iLZuhd7I0rwL2OTyNKoHt/7VLzziI9+zvYqqKrq+UOuibfaTyUWIZ4ELQeNsuc6WoQIeOGx+/Pq3p89vSAVCHVQyH4R2jfN0AC+nlC8R/u5tXUfzrBvs5nD46qOTV9uGNCOj4UXKPE58a3tK5FqZ09YEI05vrDiUhmMCIXSrFVhM03/wj/7w//P/WnVdFSDC4pCo4eVx++LzFy6Q7fdlGHYvX66aKo+S2JsuLUHlrn6bf1hOpw9t/mNT/b1T72fu6b/+/pvnpyzwKlmXDENmXSUD5mEOtqu7ZjZNp4AXfuBhGHoBs6Orq8nZydlm2KcIbmjY1gIB1VaH5bk7fki41gd9n1UJD7nTUClb6FwKEfQTbwTPFNJSiaFXCBHsIsamWmkhhy1Px6Mozl5bFHciS+eUeL//3dc8ZADpJJlAOlHNgbeKfvIFAPYpw11dCWW66OSzP3xe1vl9vk1dMoueu+oIp/BQrIIg8qLgw+PeWbLZ3rbCcY95z7Lx/ClQvRViow5hHZ5/+fTpL237uyA5GWc8kqHqcLUq3thOu/NQyH0Slv1LLZbF+OMncec9WoERSIPYqMYaC8uyDLygl6Rtx8AVXdsGlCNEirwGxybMxgvkVgU1CMzH3L8Za2NRwfznHn8kudi0MtU+1Uav92gca6REm7/Oc8cg/XJxmWWzh9WqF31A2a19K6zkro316KHrbK4pxJ9NZwQDelzLOFNMM4VJeLQo1FLtmzYhihxNwHQYjCKSrG/zdthkZFoR4MDQSGuE5BCLtnKsV9Ioog4E14akB+drGPMRHkW7xgxWJP0gA6qPhaNICrUfatcPQhWj4Pw8i29//kMGNIWpMvN+2NMLDhanEwG2x7rddkpJrSQsHCEoyiYD77X1AA1lK49NQzLsjCOGDtWub4jQAsyAbBpkXBCCo602j07pBvq+syo1dZZmH3/61I/SjXx9UCKLrs9OT6zmm/2O+2hE3AZW0/FiXT22YuAopsTzvTCgURczvceMCyvaDnUjPzF6MEYqJbu2oZwuwoSO2DDEEBqM2vsPG2M15R7sIWNkPD+DgDmEKojFtmEAYUCdaxAJyFPavdHdqj6qD0fZxT8eCi3n0anqazAjYWEIFsSZIHCVk147KLdYkbaousAztrQ7rcu7DSF2fjpKcU4+Z9wP3Z3jjFDutGn9IDaywm0SZBqbClTVUYuj1EeIxmbKIBlIPOEo37xpq5YSFoQZqpYf7vN86DFd3lw8EQHpe3ZXtMf9AIxvw96MZg9h4HU9Pn+CRRfHbeDRtrZtUJ+0YrqYhNNwt5ND2wov93GspIVDLm01RpZOnJbwPFlsCo/P5A9/u23abwGBdbkFwP7pnyzh8ElVvtFSRgHu8vVz+HFxjD00BKHHa3d1cfrhP8u++ZBk45h1dWO1wjNOxg51VfE+/5HNJmGATnEkmJ8lZ2FxMH7ky3LLSQRy6kpfn9Z9Dk0t9nl8cpEID8xK0623uyfLBV89dtMTg5y/ywuLjGJ4qEpCptb1kws/HgxC+5ARgsJusPmxZBN+zthxNb19+DvZFZNJEmHIUp6dXgd5RyIvLoBL6cdhsHVYK2uViYJ5hvDqWPRNf/70PBRA4INTXUgFOqxIbEeIAm5RmcfL+cPtT4l/0/cdgsofpRYlzTDw6ITREFF9YmgRq0JUJIx81hikDkeGpqhr8IKvSPyREWZbijhmZLhpfTqfeQ5et3lTIRqm3h/96r/+5uv/wR/5cpy8Xz+0HuYRqlUTlMVk+jy4Ho570eFsqwdmMZ4Ezaal3pSkZHl2qoxc/vznnzzeDgq2LeJsyuH+cNCQ9L7lSISuqGtcPj052x444v7Js2ezsde2rdNieLiTtK87YABCwaj58DC5vLoYW1uh3aEZohoxSbSnMQp8Tq3NjBJQce6FntcPxKiBERRyIgPVD30v5QBAAnDM+OGuev7V019e//Jx8zC5eXY1DqVTou/X07E5bVnV2ap/lcG8U5MkDTB9u+1/+Nu/htAtTp+dZbFWPdPOgKZzIBnFQxy3+RCpNhrhT198TJlzi31blu97T1rlI/P6Vb1+/MnzgiyOgXxYhid54ve9Z5dD0TSBN5mN0h6txatOwWoW2wtKEq3vZB8WYbxYXCwv3r9vug6SGEA9DEC5EYY9k2Ts6sazRxpmOGRBJ4q5hVCob77+zgAzn70Yje1gFsWxHk+pn3CHV60eqHXvNpIYcsJTBDBoEBEEAw4UPqHna7lRwxF6c7WzXbk78yNa1fK41c6wDPWqtFsdXgUCG+IQVqoEGMU+EEyFul0PRSUZPYUEFRakEJ8gqIOklbtCtYTbxtWtLJ7AWMRRe4TOoyBcqKp892otxxOPByPGzYuPMfN8JcdKvZabznROlFVLAh+UVtPqrsq7H36jn//TX1Hfpw4r1wOrtcuVro3tnA1CZLGoOmchjjn3I+swCO5lRQdGiALQA7FQG2Xs3oEUKTvinDpiFHVggJiEYTt/9pxysNv/Xk7ciHjrNx+Gc68shvuHu6svriVh3bA7pefHIDTcGqiL++NmV3iYNfuWMU2IEAPwAAgE2VVlACFnDO43R44gZkPfj8azYWiRk6cLgjDgXiaZlIhRSJWGRptykwNRL9C50u2gFSogPbB3SgS6W1eFg2aygMdNEYrBiVI9qkboUZpmfqasCbS0s5gc30xGp1IOAwu7UmBLP35xncZRlpXybc0Ppuv2CBvnZFUN+0NFb85xSmi9zoc+OcHa7lrZC0pszdc8RQ8oCfbATAi64fQNoxzZm+06bxHgNLu4uOEhX90JaN9ZNu6ZmECsy0Zrg4kpgpjvql62H0/49PJj5xTdPsZZnKSxtnoQ2+1BOUcFzxnhR1lIHyTzCCKTqxphHETKNmS7+w3CeDn6FXLfrNf3/+k/vXnx7MXJ5ONt/hB4vB4O3735vVQ9cWPfZygTIfGW4wXuCY8ZjZA4ahhqRsHL5vvmx1bP7eeLmyriLfY3q2O92QKIEGVFmjgLifIkjyLGlpOzw75w4MJl6VfLE9iE204MXZPqcYG3vh+YLZz7vvaZBWCaecio7W5LIEAz2798DJJQRsoMrk/PJm0bhumf/+lHVbE8ruvFNQujqK56gj0DW9MXx6E5tIJ4rJW66YTxkhGE+bZ7dffD9fVprauKBIvK415PsNK8EboLFxF6q/s1y2ajHwFnRS0oAJXzHPA92zdFXw/59vFq+rx0aL3f/fyjF9vVULuWM46gNmudLbCEGcaUJAgYACoLGej2+iAdGXvIapEbAF04ZmdPnlhdHKriWBzPri78mFU/3kGscnscqiP3SW/Kdi2MH366mK+n1Gp1+qvZMfe+/u6bySz9rm7e/e7rj4MnNzfPXAD162PdlkMIyHR6NppJGo/KzCcmToPFCHdv2uZw19QPWYysb6peXV5ct62bXrPIZ9jn8nRy8jc7lb+j9CPQgzY6AnuWjqCLTFLWghPIGcEJAj5EIncmcrCuG0LPpVwBgE/8yU/F43MAL768uvhw9mQ68wP0u91vUtlO0VVZyFu0YjW3qQuiqTXm/qdjV7+Po2w2mczmyxrpoCmkaGloi1URnlx3LIoDL37+zC1OWlVVchPXxXa9YkMYDon08Lpad119+fEy8Lrvfi8h+o732MS4G1SEkYm8zPEZOt9ED+v7u/gGRgFzgsHvD3R2PlPzw8Nu9fg6CH0yBmxdlZbY3OcUwMdtM06CjvdGk6nB79ff/6uHn4bGOAgvRl8Oovc9jPTU0uP22Ex4YmE/DEI2h2EHXiyv4qcv8F2TB/0U0K7rpOqNaE6cfVWXy3ETBl65LQ8oX/QZRFYMLWVcVM1xe/A4HryAEswJXq8RmbaTxSRlVopaKzlBQg4q6qTXxB300/MwiULafPvGNUt4clzjonLj1O9VyXFcFXVe1wST2GSNBEXuzOEnODq7ZhxyuvTnKAr2rqi7t6RGtIKvirY+aBoky89+RUFw7BrdDKYx2MUAADSmKbY2cNyDPqee3xcFGETZ5IWoexp/RMjN2UXeN6gxOlfzmd/2dZ+UJfG8vm2lKXo9FBScDw1Yi9wU/vLMD0eHfDXh4WQ5ut88xtwXCG238vfevq0e54vnKJLAosgPF3jyCoXc9VA1oai1Fpg3Dtlc9TdxGJxP/KrDLNqWVUD5Dz9964z+5HmESaKN9nQgUCtgrwQcNGgRiZM0mPjVJunbCo+R3EkNDZli21t6BTl+cj15iP0QguTQvn+zO/oMqxe8qWsLUu7VZRQyrABGsC+T1D+/OEfA5cfD3f2bhLLpi1j+3VEMbdfburD/42r/J3+jkqsvF1wSNRhLMIUcsYmDMrajrkdzlm9i5OHRdHp0oO9E339oYDCJnqU3vrX4UHZNUwcUhJj12+M+pdgxV6FJFJmDLdGeM2hRfP5krqsZ71s8In3fV99UdE6zGHrkFMOA4DrgzMrBn/n2kXm7nDw77wcYBgEiS5KldMoz/xfxxdP1qmLOckc/jT57+rOnqEAPd7dF/pvvHzdjpGanEYlwmo0hooeH48DFh7xXPSZAmVKnaXr1h88rMZGH23AZJ6PIHw9277wwzQWzHNkgEu2+Gtq+FphGMHRm9fCXwsWiSdJkQZb6AoRgTikxVaXSbGiOsRLWY+bYL6VEl6dleVh/+M3V1eXVRdqjjnt6j1lTVSMv4GHUolupT8ShKPIyozYEPQWddbZnNPQ9jM04TLUwAFqYqygN4iRO/FT2AJBHa0Un68YrOtnbl8dv6l7qd//rP//0ao2Olp6FXM+QjEIbOCdMuAgmaPx8fokb+Pj928O2nIZkkAOGFBIMicMW9Kyp2jv8jg14jFOScUYxxHN3FWHhUTAGyOewOi4Wf/D1b/6q2r6kBKUIqMMHNEme0LOj7/qmfvl6yJKxXB97WLzD5738EF5MAA+Szy66x+3j6uFP/uTPtQC3+32x/vF0vjhZXKTC2+V3IwAv/ujq/V9Wb3e/X5x+CkpTfPvdZr2zs1NyfoGyxwzFkQba696+3bAgZ0TcNbvzxE6m0btNpw9vr68/4c4ri852nE6J7RKEyrdd1Rt9wpjCQTDY+qAgbaKQNl0vOu0tFs396/7lcfn8dDo/b4qVMaWDLadue/jtf3x/e/XkyfSzX+zygxw6f5S4JzB0PztfLnkayu1jVbzLiR+Huq2VBdS0Ja96Q/yBSF2Butp6wUZJfX//OMmeGfsA77txOGUfPY85WXfl7JJs4ZxETLdH13eA0Tavfnhzez1afvXRxeHru6//7bcXf28xHo0nU/58clIqdPumAi5+Tq7I7dsP3MOj84uur/O2ZYHXO4OI1/bx6RP0vE9W2w3zCU+zp1+GAKjjHoxTr39kstMmRfsjHI3m2kgHXIkneFBIIdU02A9jgoQGyJgujRdKF80u8bJLjzbVm+Owg8aEV17erP0GWwM2sIrCE9wZVwVJdJAVp0EHRC4HZ/u+UbnmEhfgPfqeSzOtwtF85D27vpJto3pKsoPqosZA5B3rYjymu771NYz9kZZ701kzPRNSvTYOK0GMgrcbgKtwYR3TN/PLvrqucndioyRdlqpZF+3EB0XUKahTYkKhh8EoYlvjjbzMakzZXZpY35vMxqgshVZ6/34gVRMzOAy30GLcrLHDyFAIsNK4C9LLsQ4+u+nUMXRXaTaiPAjOQUuVr5rs6Uw6AyyHk+sxtlEGXJezYQaMG2cTp8CEtqapUNhL34GB2UFnGD50A8a8vt+V2iURwhC5ooqDIJCDtH3AiHFOGeA7ncMHrmfOoKdhsGqLTlSdUxaMELa/rd6YaVwOuVoOSoKjKlewnRTezbnXplF1B4yz2pxpu4coFIJZIK3zBmUH6SzUFriq+d3XPzzW1f6SGBnO2Qk5fNfWRYt06BF6LHN+WDVnT1RGpQBV1e6x9MsCgitBegjEOPIfdi8XUeZFTDnix8+nozTK+BaoQyMMaoIkP1Y9TkIfUptbz+2cluqxUM6BRUxgut1I7XzPjV00twFTpvAu42Muie3n49H+x9ck4KEDw5F2LTHBpEnnUf/0gBsKTJie9qxe2xXAeEq8FxcjhqHb8x5Uxx/XURjPvzxT39v72//OxuQ8SCiNd0D0ou1K5yVTP6DYtk11739yaX27qzvwuHan8N1DdUH6A+FcRcOqYicnoyBpN8f9T7/ZWyhbZwFgPo2m43Ec4hD3Yv/79X7cYOjgiXeaEIjbZkIA9eCu0bBfT0fj+n5lVnd/d3cbc6BPq4NWCSgw/3lpd1rNuElIPPaS+eNuq4h2fF/Ajijoaxs1bY8FIXibrxkiUaz+5vWPvs90Pz6iPWmsh1rDhmP9OKwUsBxjf5AqSbPybQWcGsGzgZlaK25MvnqvpSYAJ/g0iYPM6oD7UU0Hq6CEAxaBH2NC68EM2DHnE0cANs7iutrHJwnG+t4Q4kRmAe4VYdHQHNjm8Or96tOTyeXPppo6tzkGy4CHKgj+eDxZAzxNn36mgE5ilgPa2A6b4cnl1R3HQuuLs4t//E/+p//h3/74nMe//Gdf9W3x7ctfL8BJoit5b7tu92N9DPYT3XQXFxenS14SnkxDc7tbVY9JNpNz6YU18FCuD3Tca1zDj6KxYC/z7uXd1y9efDFPksd8o1ZL+ARWRwp8c0bjeHHheTFiPAjmy2WqjJMPQ1fks/Hs1I8Px8eT+9mfXX/1N29/A0H5hHXHPQZq8l//7PMkzv7y/evNr3/NOAm/ejoiV/MsK4C5X63A/tazXniqhxxQQpFAu9UdR6EJE5qlWJV9k58sP84PrwgcyUGjh3EYjqPJCzZDR2aL7VvrIahtsXmc+6Edn2jjnoCEjZ50Ra30Iv10eW5DuFciiFVfbde7v3r/jRLgSTbjThEIAEjOTLG7X9+BmEZ8whjXAgLYgofkanz9/FdfGYC6tnPAwQIQXSezqBtCDFWTNwbOofMhSXP9Xbe6+0rP9Bx7u7AacgycDHhtS9FRJv32XiKpKT9QMxR51dUNfoDAQeb5EHi2utoeG0D5xgzZyM0W2XIxbge3375Xsjc2DqAUfnciYTS6IEw+Vq3+5pvs4iqnPulDUP10Z0x2AjAdelmMqWuZFfLWGOnOlmdptpdVSMJUq6Fv905RAzwNjsdizzKCrH+QwfLU4+HqrvKE2Cjl+gr1nejF4WAZEdMoIhhZZa2Strd5KwKv8/3xtr0VQ6/EMbCNZWncOuYAA4EmwmgBbIJQ3fRNcj3Tdvj6268BtqcXS8YTY2lxrHajuDvuVo8PZPEkjCKDNgQ+RvG5kWIQDokHswmIaXFMrFa4tQa1yveVxEFsK9QByCLLvbYptKWeOHPO3VDnEELYri3z1Ba6opQ2fJwHyyDx4KbOHQ7ikxwUrN8iKw0LcmOUU4iMrAPYxr3Qbzcu5TQKIsg85Hq6b0HGJKQF8ZByi1HaI8cDzRnZ9w2KxPWYbz8U3vtmfHEDGbBqFQUvvgCvIUDQUaWkMkoiO2jCvODIR0IZ3mx8xRggkce11soMhDJ8OfEQfKhy1xlmFQgCDLCRcrtf+8CGwAbMdw4wRKQvZ6NAtKDr/asL+qgXP37zrd7IURylHr+/23w4HP7pV2HEYuQDmnpUiB1mWTyGZXO3+i4Zn5Ve6gffmO7o+deO0H7dtkpkDenYh2fTU2dB09xG+0+S6/Ts4ewUSxae+Jp2bz6UQTY9Hecoga4hMIgyiL1xLxTEIwD2zT69oAoqGVa1jOg8TsaB98Ov77er/2TQsPjy6TlaNBAvMehl76BTxtiALzzgtPOldzAHzLLUn2OmeNAFuq9y+P+PbU6Da8S11NDqdsjrdXtxdimjKrJZ3Wyq4wElaLbwmwohtveAclqWnWe4AnJtLRyPlwi54+G210eKkuxJhquwAfKxL8N6G/m+6o0BMP387J8l/zDLRk2bH78B3rTTBzhAOSLM8jPuk0P1fmgGZ45JiKLAr/QOEcZDpkwgoRuQhpBRh5q2PEsxt74rgU5Dohk+EMPtyRRXZYOJ7/k+wvi9EkpIriXEnDGh/KExI1qUnE/v+2FK5NLBb3/6tjt9anvtfRIdj3vYGo9474Gtv387jz79p//0j+1xMHeu2r1kuz15EkVVTAgbjb7404mig4dS7KYxqAZaF5Qa4dRoOlLGvnxbkHF0MX3Sg7f43LRdi/TC//jE++Z9XhXlqxqe+TNwShnt3u/dyC4cIpMFYrQU1gGN/e7DrcQkdiygPSe9iMJM16XpdOgvPj9/clflx5dv4qto/2r/06GbjsePf/d3bVsvz08mySmBI1z3+eOrMKIgHDHfi5VyvpCqZCERXedRYsTQ1PDj6TkAqC2HMbmJnv1cbMpqJLNnH52fzlbf3g7mdVeuMHYe9bnvaWK78gPvg4PUHMWHx43qzJMnF7Nw/EOzPXn7FgF9P7xdSHzx879ozW6/3pLJ7Lqt79/1uUGYeCM/ykbRuC6h9WCvBsw2IW3xCqiylZMMNT3ABN1HPOCZS37YV74yJfV9Hl2dJUXz8of8/qSOwyDEQnqcoar1VIyJHZwIU5PDCvZgED3w3LBVVdV+MHYQ+qM4nofP9qZ0rpp4V5hfRgl+3O2P690gegQAi/G+qG25WU5PTr+6ID5l39zed01bWDOIKHMeGefNwYsuxPr+w+5xOuZjNmlFKdKkPayTrlJBAb3xgylDGT29On1T/Jh/1969276ndRrNX/CT6E9CBLAcBCc4bk3JU187e9AM5+NoEeAAwEYr3bsSQSikBqD0vak1xhg1w0hPJ8BRHDk0Zu4DhhoBq529K6xaXGUwHr2Wry2w2hh6EqGOmh5IqYQzdsKijmCfRkYVsoO9rMYqcp7oBxw+kXAPLcQmNF1uoHaQ6boxPAYoE84SBFjIBlGP/SkNPaU6QjwAHkxlCwysNBpCj3tRgCmmRioAEWZssLXTzbaHCMI4HbVUQHOFUVMGObEXTLeQMW195+6dAZhkpdcBAxggodZW0dLrWCJPcYfRxGPeaexTevVev391D28Ix97FfOp7eN5hvzEfApYyGq3bMjydBpM00NGUCoExIO3JULn1wAATvQCc+Z5nCXD5kNC2JxgRqbDuZTSKEuI7UFV4291vvl2ORg3dDiY4PtYpYD6fyHo+H89P//H/pPy2fFfsG7MfL09P/FOjTiEp01HQYNDpO2nUPtd5pSAdOSBIxpMXX/A+h1CDoqMj30q3CMbB5GKRnCZ0/OGhooXiE//Pn/6Bxbgb1a+3qzoczaPn/CLRg7Wd23bHut9gY6VQTyM3RLOwLaVREJiO9FObEs/e3t+uV994Pru8PGPa06MiPbI97F0MK11jjwIZNhAuaPKhucM1u+/2jH6AxD7lrhsnpvFF29Jgudv9nRZWD0ALOzQ18oMi2sptX/H1mExbot/nv3XNMhoPDnaM4+6lhHNBwAllqtOmUW/f3jbVuiiqvJPdN3/9lxPz6Wjqx6Z0RirkpBoOh8OEXox4+eb9rhYuPPUFsXwIQpIob8+Jm5rpLPqYRX4vsIlGT3x2rGt95DwJC4p0DSB1pEOeR7VROLFuZVvUngVXpwHLTS6qY+OdAOPk/kCJ4tPxz+c/97ZvAbIIhbiXygzb48bjh3gr1m9W8CQcf/K0XT++iM37qmfvwygkHYWTyu+jXmvhpXg6yf79998x/D3nUAUMGO55tAGEBY3Y+oNs8qp1EttQGqRtdaiBXhJsLMB+UOimaz6EIafjpa+Pg+ysNpc3Zy+/627l7btXD8vgk1k6b8lwPb4CAWjFcBQ2SpYwCuu2CxDEWJ5SgJmxTsxD9lB7ZVm28w5HWfuTYJOllcvxaFWVuXcaXz59wpLkPJuoFsl231HmeTTLAk3V/v6nSjtCSJxhauPrbGk4z8umXu2/dzhKQqkVNO48M3LxVIpbrdtyRXb7H3gIYx+dJ2w/UGeBE9LxqYeJrCrgxvGYhelkvMjc7OzNX/2r7w/Vl8kn03GTXJ51R2sKIcyW7Ivb90PjoB2Nxni2RNIcqg7oAFvHY9bZtv3Px6aquccJFX7vGA8dehi1c56Nc3M85ntTmt4reXh57f380OsZwXiBSTm39kEofY/ZNaCOGzz2tWoVjYZob47g92U9tDKKUsbclmWkJspL4oiefZQBSLQBWoghZOPIqypV120/NBojS4Lqh/skG6XJqKb+TgUICD3ExuRx1G9f7ZpqFfiEINf1Q5zaTVceq0cjP8amHvnJkqYHsvrhw3vmxVYMDAcMhzenQSpH43TuAW/M2PYIdb9CLexF4UIVEIrZUAxK6+rs6jwExNnFyEoxWEK7yA/mlIvRtH+/GYY1zbhzcecL1VcEuhxo4eT56YmAkqFw/rMnkSAjOKsiDolw84CInoAQzSXnWh/d3MZlQiJNVv2dEKmUWqSJ6FvuQSOxUdBCZKxvLD2CeuqNCCLKbPVBe95x35N9sT898SAGhABtqAVAAYgxagccB7bsK5vBIA7zotMUGo2EEFnga2BD2dY2Y16lVW0GRRcTOJTGgNdNFzXCWUghFR3wfMhj0qD35w4r6ZryawyA58W3t61ogLOYmglHgcDR+WSxcY/fbaRlm6t0Fg0T3RqNqNQNoqHv8+f+aOjyHrxjglOfYU3YyYXs64FtYmQBxKVUFKfHvgJ11d3WahgIhJ+dL/gpdlwrNyPclMcm9Mj29UtKvGV8Np/P5ov5wUpR7+CZQxJVEqqBbIsHO3kqfWdkDUaIp76Nol41jthomnkYstMw7HTSe8HEOGvR7Yc9biQ0ZImZh2+9vq/36QEHPEY8m3yyBOF4+HEdxX4+YCED6pTa37/cFaPT3LMKBQPHQOcenm/0wB8f77yInF8u4wloi75aqVE4gtq1yoY2DLxUJR4WrtfgdPGEANq30veCu/vtLrDNYR+Mg6i7xHOMofdaAG9Xrf/7DTtnxQ1OZNc2ech8O+G6qvR9U32+8fvhMFRRFMs0gce2Ue+3FXTWNq0go/T86clXf/Zk5Hi5rYwdFghUjWqNBjMDS7c4mYfhzW/fbZu2my1mEGG35VmmPONwlw3jYd/pJQutttv3v4Ng6i1GXHTaqB5UFAYIsrbWkA8QypA6ch8b+xpCoIWv2UW/3eVVS2BQNv3I9xoBxm9f+x8F882st8ey+p4NATSOcSStr0b1849ZMdyCTSibjVZe9iw7CjmBwaC4dOsRzK6fXM1n/HDY/vT6355c/Wwe4z1JLgJvLfR+k0OHFlliFDg0rMPuNDjDk6Fqizw/juLJh33HwjidvOj7l9aRvpev8uaFd1qgcpmMnz9/DlbBfdfGhocxn8FntIGVzluFvfG0Y3ahZBQRz8YENEdRYyTT5BSBTMujBsK1zfX8y/YX8r/9l/+36zlKTp9N0jup+PknH8XOWof7QoTRuI/TydAGvs3zasSJPx11bUfqZlfuRZ+PsiVxlEAm95VTmoQsv31P2zlCVOWHIQLbspUSJ1kV8gDqboZ6DiMa0iFKpAQNAsBEaKqbvTjW8nSWfn7+2bF43ckfkvTaAePAhyAOsL8kpVYTjwezEPqjtm1rwD0DoBkYBatjlThhxl2aBNBgsGoh9VjXtb1W7jBKXpzyqzALCtMhkKTxANswzQLY6bqrD8WxM5VUwguCYxRQ7n2oNxoNSz/W/hyFfRD1F+P0o+ufq4HH1/4+r8Ykml+mBcHwkMuhVIMKtNSEYKS8xHPca4sDuzhtXz3UqPXiDJajcNa3W3RsDlmkDi1a5S+jkI9HM584C5joKbx/8IWn40qv+yE8zuMnFJ7kosSGhU8cEIRK1658frOM/IlQGijl1S3oNZBaiR5hbCzolcbIEZoiFO/3h8AXjJ8d9nd9v7fGSmO5YDAMfBjd121cPgjmyXREFW7soGOPBOTDw0/TeUxH8IhHm3IvPxzx0jNiXrcvla7mabJ0T7p6qIJuGGpE3+dviVGJ1sYC5ILYBdBtRlpgFWgRyJ5q+YAq004SiNGSzocO4MP6QWstgMPaRrFd74zvRUZqHHkY0IF4rh+CdAQhxhT7BgvQKaXU/QN09T7xgDUYCLgzDiIh+poN8/mJ04YoMHNxiy1C2vMDjPQpJ0xA7Ws9DMODaGxMbE+CGxpzCcfalLlwJ1aTcx9tvb1Q/pscnEBkCTXIBxhaWwt57Ord/W+6W30xNV7o22Fo7l4BKYmHFJDklIw6+nb7IDsAXVQ2jx6Cn4xHN//wqhuqtq8o2ECSnCGTZNNDQyIvNv3ju2FL4IiSwE95oZsdqCcZq4bycfu4/PkpKmSNrZJmEB1HBmLQPd7RWaPsBOV9E4WFkKfCo9buHAz6rR17FT7Kbd+0x1HszWfTzjHPkm1e84e2zF9KFUdx9HM7xmG8XV6o7VGudoRzW9Ho+swfS2Z70xML4Hm4VFbWUhEaXs9Y2Ts/8UijaiEf5SGkS+ekUEYmib+vW8x4SCcX0bviR7V6xMcALfYfvfhHf/Lf/G+yf/n/GPrq9e13viFzpNTLN1615NjzPOlT6M8DuGpf1R+2R/urnwXTyfibGstcxnFstYnPphnCYRCdnV4GYfhNtfXhKzOQgRUcemiHSewV1I/zrw97N3n+DGi7QzjjR8ZSMTijNDCsN8f3202G0+OmvvYIy+j6TR8EsQOxrVjtS4lB0IIO9ldpIrXwgtMoGckQbzbrfrMfLcfaAUYRpD40cVtsvNl4+Ysz4QILT8rwtRINlGlVvfPo5Obp1fb+INWHYb39dx/eLs4vhgEOz56MSaKInvrEuP719pv1vrSpbOO8TQiCRQkWQggvjDzkQUSi2JtAGcR8H+VO1bJrWEjsOCzuHiCWEZ6F0XOIdsgoTPyO2wF079o3mXdTApgEsyHwhDMVLgPqGR0sZiEgrFl1xSlHqkK+ZGkQ9qqDFBFJeUyIbHVtyrJC608/+/KPT1+uWoiwmowut8N+gF3nc62Ey7iua1AI6qm+bRDUAUJ+gJ0mrnGYgID4DOIgiLm/rIattHIMsLxYtjslxRufeVkYyLqceJRhSuRKtEoa3ZjGbLEe9ZutP5kTYN7s75nRhs3BOI3cyH42HTf1hj8fZ1ky/O3ByXaghnR9t3x2SX3aUGwGzZgJUKo7LWWtjBIwzHrU5dXQyEEojNjQK2lhFCeq3xFOs8VYPzod93cP26aI46x5U/exjcgIc396ehlQSvK8q5ojSCQA6v3mnUdYNDp5wien5zfGoe37tlxXTdudXp8Nw1BDo/sji6wHxe1uQxI8Dy92Uq0PD6aq87oM/akZ2igdq0XpNErO2fatgAEMN0AI/sVnN54QVQvDKLUAXiY/42kEeT0QAXLU393PTs/HF9N6AMxrTn4RVPn71R0dz84m2ZUctFivZdf6TjiPUT7qB10UIAxiP4ohww8QVMfazY4ePRlPkrKonbNKi3IojCGAOGRQhRn0feCkgMYhtLicStPLrqcucyuDRzSczGpv5iCSfdf3VhyaxjvjS59h/1Bq2/DhHCNLEfOdgwDjmhDozH2ssY9K6zQFsQPQGYXooTNM7zD22q69eXLTDl0UcWWacqh5GFjtMPfaAHkdNLVDwE+kq90AEFQarLdtFPmezwiNoiggBjTejD9rjB6PxQDJJPmDS49QpZSxdsSoLJUOBTAt9zjoJdBWK706VCzzPU5GC2gRr4pvqX9pz9DLof0kTn/+6S8Pw0FY9Xx6UrPatn7rY6m00Q/31aAGQxM+9MKh83r/A/B5kgTMWZ1qqWpTIgeD9MVHT0bT7fF0BqzHbNU+BgF32Nsdd7ovPOVX3/+Ht9+sLuYLP4xbxpIOVtNOc3/kmwm12qy+aV97SyL7hxGKYS3qMAvSmHve1Th9NPq4vaUnyWQSIKs9dYSi18ywMKFRKlvJAwiQW5z1SRLshnX+QMJktnfCDIJpJrsD8s1AuXDO98LnJ0Hy9NnLH17m+aH74UM7Ysno3PP7CBVkEfgZlQ0cBpkXfB9snuDrPmB72/Ee42yAMJX4Du2oMhgO/WPMe63OLj6npy9W3zwgAEIXx5Mw/fImtXuD7Vqu6x/NZtIZ9+7u17tZOPc/8re3u/39tu2qNI1XtStMVoAie/rl7OxMbIeO9r7Q611d5ndxGNe9LLf3UvvRJ+O0JYOWAFhM6G1nw9TXx92ddkl8YdKZ5EQc35rYXsEnP7rm+cklwzzA+6nIgIXMR070UK2tOsG+HkMXZZhopsWhqg9CEIIm8K0njnnvd13BNfWpyyxIp6cw53CqgVJG7Q19dlt2xffb2+1D0xTd0yfhzRmK0ziKPvufJR9tip26OF99WFMg2FRDHUZIdN3ruwo3oItOp9wHeV30cltbK01xQs682C/f13oWXJ5M+AV/a4rDYWvxQAbw42bbjnrPm+zyzWh0U2z3pye/enKZst5t+jvPT0bhaNjeYXDdO2ZiWJcRZCjgXZUfscwR83CuURL0dQWGAXLqAXJww2jq1JGZ9auz86Xs36Dd2T/88//5v/jrf/Hy1U/qJB7PBiPvuc+ph3l/knmTphV1UzO3rjvUATd/eyujkHg8gGwU+l3THx6rFlMYBJB0datOE6WzYHX/ePXRr3webOAOIkz3GGtsh9Yq0wztu6rv6Ob0/HPuTWRPARCEQnEwHcfMg/wm/v4/fRcVb+P4S5DZ7v39T/mWREmEuSeU4ADWEaIiqtu62XoYVcD0zrTsRKFoEm962PR11e2b9lYZb2Ba/yVG+GfVz+TIZUlkMFAKG+dHoTdZJJgSEca9ks0wHDkhCjM6m8wMdARoYg0TIX5dlOu3x67rZ/PZiIZFyBMPyuGDR42CTQTbfluHQ9J/1unvcyQGNPNvtY2oRT3qdT24ACGTokCMGqBMT9X5lHbdkHhBWfaxMkr7hVuxhuvcpNnIYzRkpNO8WNfz8yeDCgYlo+yrC/SBBzFotd2vCoegdcZKpbW0oKg143AyZwAq6jsqXUdJFl32fWu0zOKoaQZCIPexGAbgYtDAQRonWmlM1zYpm3KGBlHzC9rFAySu67u6Ncsw6xgzVo/Gz1x2kphus/kJ17M4Tj//1Hf6/JF2dbuwDj04KC1oMQSYq5SYSkCHYBBqLVU1nF7NDo+HkAPE0GAUxFBZU7UDQp5o+/F8DiGRmkBrOfMlt5SSgmGza1Woh+rV8g9+dsHkB/U2gRZjmmhYAtCbuu+k1Qa7ZnNQyuiQKmOw9mhX6EUk+zYPfHI8HJQCLNWbdsW8mRRttIh6hcI4p11LQXB73/o+/ursq6N5h1CcAreRedU1prJWOrbwF+c3AcbFAVTbpsrB6OIEylYb0XZdWR8s9/FZepXONSXeOENGrVe/1Zt1OOGTxSgKwu2x60UO1vBts098BEY2oX4eu/UHmE6bVVxL23eyZgRGIb/yZu/FSjH0JPbvikO73V3++Z9iBctt/mI8FX0PoOKjXh1B6w+ERvmwedD7odt8cfXZ7Z0+5jmB1Hq4j0Peqw6oMApmhObDY0CDHrDAQCnl7GRx88f/xZu33xze3jXvd7MxMF3dOnXCOtVGzIvRFIcdBJqYDljKmfYcMrZqguUpYJHsDALEG090IVTzGp18ldKwiu7PRj/Tld6sv+FBIrogGw/FbtBSXeEneL7Yvnx1enKV3xYhij75sy8XZ/MkDHoBHx33t8hBIGTbH94O/bDKpo5knC2369dX48X6t6CYVeKnukpxEhOSNK4kqEdtWKX+JK0whkMMS6PY9fUTwikm+KRgwGkA+tUGBORgrD25mG3eH/u2teYDDS5crQ7DOlJ2Y5UzMhZdPwxdNvmMcjs+s1q5iXMQNqZlIJWMi/qd3Bg9dVaLfYeigIEbvmj1+dRVbf3rX+/TTwYv4Me4qVcrFlx7PiiKRwjz28Mg+yF98gQjvjm8rvr3QRh4AVGmSLVEoV31w2JJPCFyqvRqfcjvh7TFPsQZd8BiDQlB3B/VbbH/+jCqX42exj/87rdKttPLADVHRMbTU7IvhHWY+FU4GXWlCBxaPLngyFNNmSktAcUB67USZlh6FygJPPzYGL3f7uazcbX7bfLpn/xXX3z1f9dFL45RBeLYPVY7ZEGK2u923w0dXYbai+ZX0yPaEoVV7HSZb73ZyWOllPTT+YlnMYq7PgdFs2OaebzyeC9FDQzAhMHcEEaqfMgi7nsMwG6uUXLzbHK6eFxvlLIQATNoOWxbjS6kKnF99kk0nY0pZ8nFLG/Xw3tJPv+jT7sBH8qdtWgBM83I22NJJ7S/z0aWgzE8NiLQ5TgYj+IJoLH0C+9wlGpHPI/xMXzKUWuagzIT/0nGHUI4CXuExCBJlecFJp62iIRhuHzC23pvtLUAlGLYdub0ZMxhNrDl3Pf5dBLFzfaHlXOCcdp/kHjhuq6mjF6ws5pXs1/+4nma+TCwhdiyRqY+LF3f9VlAl5NE2e5xfQsJ8zhFs9B3HEVxjLgn/EE2LKRJOnIA8cTzpLnva5z30qpHKa49H5Ano7MnAqe7YUca5JxBnMQx292X2+16PH6OMCmViXrlnAGjjHOfEnI83NMYFKDPcCq0bYbeEjKIVsme+VM1NA4bFwxhkLayrqta6Zpw2r9c9YMxz76kMXNAUF6Irnj4sF8kV1/G3vj5Z4QqAfR5PDFsCiAkFMMxw8C0zeBC53Irh8PFp9fr+FTAeyvcaBJ5tM1rLJwMKWnq1miAONJOt3XJ/BgZBRVwFhGO+l74BqHQNq0M56N0qDflKx8cYOh3rX8nW9ZwNdKuD5BGXWtt72ZcazaVRFtlkug4YKd6AYBtei0GhOgyTiFjwdFZqGoZizr1Im0qDaYKI9fLi8ErgTre/rgpADYi1B4LYJaAodpXAjvcrfd+7zkShjxsO3mrG9YqvIAhj07x+aprKSceBq4u3JB3rs1fHSyyaJJpAaqCxEv1tGPmFMPstKg2mKXp1FdBATqXpWDsx7uqCwOf801sc+nORF2ejCZ1344QLGJmhCprVeUbTnO2Y00WVBvpBzuQTKwzLmRxcvK4/c04zrm30AHlD/dae4yzkpZ9V6iwRL21ZhDUOmPzH3+yzz7/6Ks/7j/+xct/92+GQ3PUDRVNlj475K4pO5bRDU2sqGqURL62Bna97rtiXGSRdypGum6FtCr2dWTnVt/flWleFrsJ+VBUc+PjkARxPbp54l3PHlfdUaspDp7+4T+4enJ1//CglUYU9ggd5KZfDxhy1heSgFZcyhlTwqFm92TEHso3dT1cXFWzpxkZXD68e8J/7p36r3/8a+YFw1C5ug6fR3IUi2MOiYfddjT9e1KJ/f53Q7UNo59ps36SwNrv+zfHyck82PMfiyKjIFHwsd2P4qjSysg+wQhnM1XlgxDf5gXu2yfPn6Z9rxbTKMy4z57gT4TTgz0wn4cZaer70/jpttjHi6QqmofDuzRNrXFN7wMQBWp/+PBDNXbS9uMUtVSbUtvVA5kvu6Fs2lJofhKeCNU/9MO1jWI6h5OJFRY3fl7dIwAWi7E0EiFW96XSA4dra47GnARTjK4CNrqJwle5bJOgffzm98vrMXX+OGAEWdXVFKQJnJR6Y4pSorUh8QEYM7RR+MRPR9v1tsYqJdn0etI9PA5RW/jzsG4S0I4++qP/chr9P//F/2lVPcRjo7jlWm3lMdThzdlnbddDkB92CmIzVGYAWLjYO3BVHf0xrOq21yYm3I+yyHYDbe0W+NEi8LYIjQkL4eJI+pHm6SjjlHFM2zhDdnnaCdmiehqxyuj+4ZUnJ+jpiweEDsftfDmPknjQPSBjkKaYMPK4WUXx6a1bNZ1nXQStki74zOf6KS1bkhAhNVbHPmeSUNAK/93wyBPMXUbp6RdfLvyAv9+/tVZSNocMSyV0b6v1RhESEuQlgUfDeDmntuuDuirb+4ctG3w2XQYZt0iEFwvf9hpJSt4Iw+qu1kGnfN8YjRWUUnRt7aSAsziA5VbSar9LcdRyMoEgTBAmXts4zuwojcdZuCvrKR3qQlCihaVls6sPLWMMYTKNBQ5uEA/CZfYkTDuAOUbPpc4NWPhJEGf3CuzyKou4600EzEGLH25fQehFM1oWFiEixoAjq6t+57+lPSM4sJVwhdLJTjha28KZ0DDqkRDjnjJcNUPKpx5ihMf4FVQnwjlrpKKIkVForKWcx1HgwsVH/AbbhE3Q0N3++NNOKW1tIJsRib4aP5liRSBHGPV0l5ooVf3xrirYiOi10YkkRuWDMBIdaF5bFFgOHPCJ59EQBxAgNIDOOA70HZCnTdfjUXqagleVXNkjO8Ao7sIkkMOgNRrbSGADCPY4tcAWTUMkerDU9hsHHGKu00Cqu0mW3D7iooCh30lX9dyTD3UJnMW4b6VUJfLiGAAF2wSBui27/E4dpfOon0LoEcgE6Sm3CFJNeptcXsj7Ii95XYp363tlu8kyvR5P75ty298maRMFIXKa0z0CdjSe//Z2VT4OT1l8Z4BQ3ek81D0zIOqEWh8dm0HnyRhMgGMpAU17t1mvrNTPr6PAo9TKrsPgsPcI6hwQQjMUymMwTa+lnEdXeja4+zCAPASOHAA09EmcTSJ/LLcOjzUeVAVQg/qwP8JhiBMPE04S0TZpEIV9P2nscfvvX09Gr0/P/ujZzSd3D6Xb/00WheMM2wIEccq6oPSxYj6gG3Bksc9olEHrVUVumZukI4lAnufeGYkENkdrg/vkxfOAdl13kFkCjZSuMgam/klyPRoaPGStAfZvf/O7uqrOzq8x5ev1Bw4TDKAcxcEsgeV+0h0Lq6jPoe93dW4dSj4l375/iBWDZHjs3Cd0eXp69vbdWs/bc3NircSD8dVukNluu14CGP2Blx9/an8sPED60Q/uaLxlhOW0m72DEJBFqh9dH7BRjQYFajUZTSneK+kkTDCfTe1mL2bQrOvv9u8XfnStcFjX/uQiokgP85f9759Zuy/Lrhi+Hr471IfEpe4esiz1VZDUmfX4+Sgq0ml69tF++9huP7RiT632sdxtVAJYGKRFWS5mfxhGx6araUjXw2aKA7frCMQ+A74ywTnDopVCdCSUndIDGohhXpOl3vwXf6BrWRVvr//s8lfdxJlRQn4gdrZ5+66W4Wcv/mC3k7dSBSOE1kBzTrCntTbGlGphjSZCQTIWbgoAGiXPjv67w33VeZpLv/r2++RydjI+/+zsD3/9238tRXF57XVWGYSS2VWE2tqATU2yaCGkOnQrCHGWfn56hpt+HlKvH9xu+6iUkgr4YaokMaPdxHQZPcsy8mNzSBj14DwHr+5vd9rpIPmIB7ish04Mc+9UidbIA6EsL/aj5piNA4QcJqgd2ncCjAfSSLiML8mbt9vlRyOfXQPoACTO0ThiHLPDrYDu4ejz01GkOjsYb6/BgReRN4njNDt7cTNnANr3zfHk+ioK5vn+fYR92dmdkux0xiBEQYqPBZ9wjtH9bh2UQvbudPl8Mj/rHerMJot9z+J3uw+mG8an40U8OYxVswMUUebpwc4CZwIyDMMQTqNYBxIjz/d9FtqH7ePt4+LZJ0E2Ivmxb7vEj56kSS/yJtd8BjQgIWVSu9PT8Ww+p6RgEHf3738sRJSMpcPFOElm1+HYN3Vf0GTj+eqd0RC0tA160Sde9XbftsU4+3Ic2Lt8lYQZ1MjPxlPnI4W9od0IdTJFjJ/1/SZmKJrePMi+RXIQTYisUCoIonQcgcqNRyIO/B6rT/wL9Ats/XTqj8qufYDKO0LvUiY6tTJK4qcYYWAKDNHF5fNtE/PEeQQOWGvtnE0sMoSSkJ4a0HPOJfEwphw0qqeT8VQNRGsnbI8AZNpzzDnkhNIumkIltRhuxggtsxy4Ipfq4V1Ud9H5xJnWSKilkn0AXdc0nejEvX3rAB4PI0A4oQwRrmXrLOgIpyZ6vXlst+T6ZqLxqGsYDyLj+PPZPPLiZls0mwdDCeMcDsI52bcfqiJHGZgmmeAD6v26Y9rW1EJPSd9HzgMy5BSjY1N23TT9LDhdBOsmX3X70IHFbBb5tK+HuoY+4whmGOybW7HOKD0sfNfV2s9l3xmVRFGcYGKC2lmNXEPconN3LwVr2EnCu6rBHWmURbY2Ouh6+fBuu/7hKAdf8N7ruXFSHWaFJ73AK4Ok0w5odzi+vbu/FFpnfkr2A9WtF/OQchaFPg84x1K3gT/TBvmepwY0MD5/Pn75Qb55+VcfJS+GrE37VAYIOcccdrCULdGkTrtI0nCgg3HWaDUOifZddVw7Bbx4MgLVuPOLwEr/kUHCET8eNoMWlaFYCuswHmqQiz4WXng+icLb4lg1bjQZDaqpy4O3eB6pUiiALTXW2F69rzrp0UvGfc7cGUx6jov2d799s1iezLxova//v9/9x3/++T+XGubfFvFVixpuqehpc7580b9/IBFvuxoCnX0aSjUQj9W9/enDMQneGiBYheM4Gk+Suh8eTAfSzCxMSSlqAxRHxjXSuvR0EdLYnOlikMP2/RCMCsxQndIk5H6ilNpv9z05al8LIcMoEkKOo6kVcOsf60rRo5cfadXWs9EEcm+EOBEnxuSicU6G4fRXk2Ew7mo5v/n+zTfGNdPx3BKE4lECfHVXl8VBSKjf3+qlH8ej2Sg00eXMHN4OP/Vi0EPveaP+e0MTzsdxweqA7Z2TEAvELFCqWNkgmOEaD2RYTifQCyDjCA7YmDTJcGCVAmISVge59s2SJ+Mn87Yu4OERJqeypyA3IJn+6T/737ZF/t9//5eecDZCqIfbw/0hTIXGgZdWbde2fd31wfLjKmvFwVe7d97FC6q8IhqjnM4nfuyX2/XfejFBi5FgqJCr6Tjy+vnu8AGik6J4/F1x/PmS+unTbbvmAcurBvS1rJoGDda52d138+WlV/ID2SvpIQcpvVjtGQYeEVLdff+d/9FiNL7mFMm9DcxF38gkbdvhpBoe8xxXjFLGnaJj/PzptU8ZQQg8tLem1EYplCVK7cqHvMxyhZJBb3j8JAxLD2bGEcWC1W6969ubMRWNA6AVWKtBd6/a4Nw7u9BHpITcmcZTfj/yFtvuZY+5743FMCTzSV63g5K+RdZz0ssiD2Xpibc85auD6Axf3TPPjyNeKB2B7Iyu+rI4+Sj56dVAcG1dq83ocLjPoL4b/DNMThKkAiNGCLm+V3cAXStloMRCqdbvVUd8oAuPoCFvmgdKyc21jwBwRngUOqBNXbDmYANgHTbadCtNJhFi07t8G2bSFT1oWgTNAHhVVZ7P02y02txe+fGn6ei7vRj8Spbh8fjm/uKRcXgReC7Mpm7Es6Q6wLsPj2kWff75l0prCOLYy1KCudK+03JQw8RZ5fp+sAullNJ5Lw+l8v1aYw9YZ2wQhGVZW2eNtbtha4w9zc4a0dm6oDQ0wliltDMhdYMnleqzOBiNYH5nhpUahJLt23tLNdQ4xU7ZOEnNYIxRwmkeBAyIsjmuuiOhYv+79eGwq6qPTj/5nPQAhKMoCdk+zqt3Ipw1fAKPu4A1BDofOXXXGgniK29zVE3W89paXUuPP+Q7JgEj0dge0vPrKO8HQrIxyCZRrt4/1O8hjJ3o+u0DUaNAqcOhhoIzrgkiGjhg/Mxnw4Bq5jW1JGFEW8Z8PjStFySuLMNR9L7OiWL/5Jdf8tHZbv8akS7MXd8pNI+WswUwTVM1p1OD4NGYqKpGJ1nQdryv3glu2GKR2kivut+b+zoH0zRr6A4jkYhQBM7kuo+7/b7RRhOilFzU1QahSceQEgOL2/5N+GrzBmb9IvHeFLtlGSXTbL8FPDiITaSYVanyALTaNY0wssUQWgi6oUOOUoSHtmubw9Zb+f5CqgejSdzLCiHPSccbYMygOTiC8cgc675oq4SP58tnu8PvpR6HA9kqJKXnV0ZLAE8CEs0/90gYh0JrUR83dw+NCiJuKeN711hLk2QxnZ1cX36ChkIdN3HKQu6DB8/2HzglWsvv//3/u91V8ScxwohSOHqW5KucSx4lJ8Nj77f64+n4r97fhXMbBXEOXD80ZKAxSvsgcr6zwnOHA0TOTict9gutXZKSum6IJZR8+mn6+29W2Tkz3RES/HH4vGna7noy7a0VnE9Gk+VZhOHf/O13gVv6ppMs3ajy3e/uBmMW5+lgGdBukVwc8mG7PgapXm9WQBPR6lH8BzCmnvPbTfP7w/YZQr6fwT6JPe9vHn+q1usgY+4C6vcunQdcAGhiI3TRdRQDh/OpHx8q0R42cBHFZ9e0o20EFSZGdrFrQ0eQafpkbDDyfQ80uC0MWJDR06dqU9dFLhsJfGuEw7sCzBZ/+hf/u9/uV6/2Lw+321988eXzZ1cf7u4SfzpajvaH3fc/1OPx4suf3dy9u625bHEG7mU0ySb9xlBKoHU5e7+z/2T5lM9Z123rUl0//fuoA+vSnt9cjmaz/eu/IvhMT43c25EXdHLgDl3Rm6Gc/tj/sN42HyEYLuKVrEkWORncrx9Nt3akJ5yT3WFf/aZkf3aKUNyKnMDGoSpiY3Y5ne506HvzNri+/nnbroVHgDXKDH3T7Jv1eBz7DJbd3hnXDEOEfOT3QCpnhr4pKUS4o8au0aGennhjEu3i2lABho75vv8xlXX7eHCTOD10/fGwHY8wTnhbDJIbRv39oAjJOCWb1ePHo8/X66IH3YHmb19PoIULL7QIN8QypzzgIdXrUep5Tx9++pb+ZChipqww4VL1fS6GMJ7PKJ2e0R5w5DxjLYW094pqRVE0W4wDj7e4Np4lFtZ3hUfbwKDnoUehbprScEBNIuG9GPpOWNvZJcMRI1AOxhydbmhj5aCwI8CNe1aQVkIE4iwJ4kDf5TK5CUP+5LtelwJHLhkFU69RNus7LbwfxYdFFkRhEG5zWoitdrov1IvPfrXr86ivNIQxZ8iZEe4NdFIpKfv+PpfHdwA41FBgB4PBfugXk6Q47JIs0xZCBAiBnmZJguu2pxhCzpSQQBmktZZNWzyEY/bD7+84RpEEzgAa0ch4s5EW+GQ8mRZlvfFqDDN/PnMOotnyjOInddV1rfpz8fXf/RXH4ZTMKyvcpqceGAzJLQtQN898ycec6PXbVcEHTMnUDxjjnHMchTEcGeMgJmZ04omhzJu2M0QYEoTYxkEv27c/sthK1Am1HyA7sfMGbuIwzKnwvaTsFZ9N/HjiJcw7mt5X/KEsEf1kPAsZ6jDR0KcKOdPEbb/wOJydvvj8L+7uvyn3Rz+McV2y6XiX7ze/v1u7ahhU33nT09PDvhuPgmjJClXXasKY3w8NVYqdz1rZq84oaD1HHMVt/liuh4pAdgB+EjrDGtNW9SGMgLT3WnrQwuO+YJQncfDV6WQ4sMPhTX78yDu95OGxFbkfKAXXUTFgmihgL/3pAeQjOcmsjRB+/fbHVaMo54Psg9MwkXTvVO9k5IBUtYINESgJ5/PzvyelrFm7eVWKg6AjXhy+ib0QCBTog29BkGWDpH23ZkNy/clCFlI1b5ADeo8+msSMnNKbZwI44cCvfv6z85Oz5m4TeO701Lv72xaEmmQ+QlwJ56SxVm8266/zwx+D08UvLh72hy0wkjAb+LQf3varMwRwdJXiGTB8kIRo6wDUk8E5zgyDJeGR0sDHUEZpDHvFgY3Khih9p+qzaQD5iaO3FWzjflrAh9ficVeCKF9N4k8Y1N3uoX54OL84fwJIJwvx+GPr40406Wjy9OzMIF7sthyykMXlfj9NPvfiYr/fQYfLRrzsj1fp8uxmiWLy9NufXSTLze3+0d5l2VjufE6mXzz/NPD9hvRny5uAt8CQ4nBltAxDBvQjmvMnUP5099Ok/yzuCgwCegzDJADjOgC+OAzMQXMLMKb8Y6qlFNuVWnxFCRidnRmHm642TtXGB+t32dQLL67+D//7/+N/93/+v/y37/+vr243qCCl149m7pvf/rTfY0qaxfO/aHGytxQ4h6d+PElJQ0LyhNhe9GDYPs4QTeYJ8YiuYdvoh8cV9fjbQuyVSEf+ybOnaRpgDIaTEan8p2kldYYqH3Gy/m1fcPRsv2t16eqWHndvq031XouGzsbPyC9/9Yt/86//zSCUHIZrwrCPDKkJoq3fut41dUA7CzE3ZE1gL1tbd7WK2k5WTyANTTbk0ljqULCxpaKB0gwRJ/vVaJaykA4vi4q7qtin5ycr7KJ0bqGnqoLVdQV0kqpDm8+jmJOoq5QYCNE2S73jseDEZwoJ6EMnzUDUUGB9vBpdWX7ibL+uKuDEsyjWrLNNi2TcOxDtp/HJXLl4u+8nT6/v9rbdrRen5xCMTs9eOCrf3NWr6n469w/DIzQcskAJ8uT0nyyvZ2Av/VKAfC0gAsxraotNSyD2jCwt4LpX+A7bikLnTSEoKMBoHHtChELfA44mSXLsLMRBNiqr2myQiKMkToL+3d6nVmmN6ZReflodfkgGbal5YP1kWIuu9zRLJl4ZhGtR+ieJNPoJvdlOGtV3isauOUAHi+AyAU63RyeED9UIUjz19vmCIByNI9oRCCFGriiM7wdJHO/LDXQYYXJXb50xEGFsEUN06HXGUyV+Mn0VRmIym73Z3xbA1VKPMOMxXErKaVyu3n13uxJaI0aefXoO/KCrCslDT4EKRgZ7HgOnyz87Hm/Xt5pRDiAKGLWoSWNb38sBCilK9ukUESGUnaXhxScza/v0NKh7R8gYEqyVpcD5oaQ4pYrue63ggrEC+qSy05a0Q9Ea22vZRZONBHzTO2FVG1SMU4Csh0ld7EnXed5MEM8r2tHHyaClrfKTUdoQIwC6mp+KutZt/eYv/2OxqWBhnV8NQMC+6TbFf/iwTpJ5nI0w9xA8Pxm7aATe7/KVOY6cEn43OIci3qxfpb2cel4rNr9+/9Mfnl90fYeluc7GTauUIH0/SAUxqKyNOtEPlQTwbtBFJ5bK0of2/FDfbjaH/+Ff/cd//I84cCtEFUGky421DaEHjHlZtCLvfqzuWN3sIJyNpn90ce3d3Cgnbx82RykHZzzfwAAq7GfZIoGQZ+eZnYU8+u73P+KOnXw+kXfi/kPx7PMrZmS9+jBOJl2fW9+29YGN0vMRebl53O9c4gcEk7Z2XTgIRHGtcXDuLzFBw+HD7zIP0zT5QWiPBkK4OApPlk99dt1nXdaX54+vHBcYnz5Ut+ReLn9+AgE+YDw/PeHNzAmUhCM5+ILhEYLGGAuBTS2pKgcxwUuLAAKlV0qprbhfgZMF8ljb1E2go/FIAokOFDOL7znycP3T69IZ/zm9/uVJc9i6u67i6Mlk9rvv/rbP+gt+1qFgkqTLqyfHpqtXuxLx3JT+zrv6fKpoOkkvue/hYXz7+9XbukKSKzU6/3j5IpsFh+rHx28+/vQX2eL5ydQ1zaEu8qYe2rg/bIEW5kjWZVNeD5NiI5PEX06TuGradtfacTBMlNaCPKY+4kSSaOKSGZKIYub7Xi1qLeoBGAowPZmNFNW794M71GwfjFLx6jXGnn89/ot//s/+Zv3r3/3+f/z8v/zy6fz87eNmMT1luJ4tfvnp9dlPQz/KFgaI/X69rbaLxNuvqlGNge1R+/7TL5bRIijyunkjDrt2FkPusSjqWdju+qxXXk1rZnMLVVvzBz1mx1jZDx8ebi1LvCgs26EXivlh0eb7RmAZ+8nl5ZVPDu1BLrV8JcvyG0l+eZFlj3vNYsuNarhPKBQX44UWm9fvkBEoBj5x5Xooy6pw/uX4/cOu6nv80c9+6VO/ebOXs+SSk0fFywrWldDD1tBLMU1FHzD0YH0N0EUhpY8hoairKxAo7EAYMdl3Xdsy5gdc13lv5ICdHVWDq5t0AdzAosBpvDrk20n46Vk0evjwjfVzKLUvWOJDaREAW7A6Z5gWx6P50FPO6PJ8eXHeTye7Ih/WwkHoexMRKroJ/EXwcfplPuymi+kNoT+acuQp7DEMUG9kguABkd4hJT0rCqolIxzBKXBWDZCGuOl7zzgAAQI+0yFP8Dr/IIYH6jMGYFRX6dinQJvu8ZMnz4d8W1I21K25a+EsdAR2P4lVU7mF+S9OnwRRQrteKagdWw+rOjip3W5d7eguif7xi9CPXW+srrEzUeiDdUksKd8XQtwP1v70ve/73sn8vBmG8SiqNVKqTYKIMObsATaBTsaYEqcdATiGIcZw6KUa9qEzoh1SFVJMg8i3ahA7r4+Oqiavbjup82w8urm50VrGgwBhOnVAGicIh21TdcLzxmGwJoiEbtBE+NKvpffEm5jn+FBs3t5BvNmWJZhMTxYnc+ei9f4OUWQVPjBOEWycYXsx4JIz6k1n8m2p7YPCk5MbunrTPn7319Y1zMeJF/BPolXdcDHE4KTtLIAwplE1HHiug8t5cyjVJh8FvmrsMOTpeKQYHR5en8xQO3TN7ttqVaj7dciCKA4QNsjasmnTNPuHN1H0/Oej5WJoFI/OhfoBEjOIISQg4ByrgQjhjL19+XYr1SROptoe8h28PE0vwqGqWTAwjDISINW1mEOGeszOTtMxIWt7GWWk3aweHgq7cDfZhe32LYD9cNeu287ZVnZRFKPQNjmM0WjzWEYR9oPxGCRnFFz//X9wcXlR1Q+b/UPg1/Y0GJMRpwtb5+Om452604Ksf7BkgPMXVblOYnKGxWuz6xoD2w8AqK0ZPj2ZtceVsLts7J59Nba1yitpAApGGNEZ8CeRgjtKaWbyfEOOpg7Iu/a4qKXCKL2+njsUfvYZaLBSsmu/9zpvcXrKLz95U91uOhNnn/f+5hxc/1hvte4+H1+9vNtfhJO1XwBpUhf3hgmHrMFea4GxlGRGAYNwzMYsJXNMtaZVWwXZGFvUNo2D/ThNirYuyoMcRByOkyRt2mpvD1dbAqwNl1FqsDN6Nh6rSLAlsT91GuF3P70USpdtL5AfduCjj79KwptDUmFcFE1RVM1hs09ju6VeGI4ZlO+OpXAPlLmhr8vDW6d415YNq06SxTfHfWzNCE25ZYyyLc30zLhjCfHYCy/7/rZbCX8seOwDDKYacoQk50rKtmX8nAeeX0+bw85viiLOliCQ3idkYrvDXmslpZSUh1oJskJhFvw3f/q/KquVKd6TSTpy2oPJ1Z9+Aii7LQ2vVgHHtVQZdQ+3b8PZODUIShdD9eSzj9Pnp/1+271pq7z89Wp9yX44+eNfHaMEQtyTrY5sCjscYTuoYzg4pcrjlvmzX3zxhTY18e7b9h2EGlNYlVWz0n/40Rchm9e3jpTcX57+rNBv237YFQ/K8wa1PT5mzlcfZwichdQ5LXUUMZhdH/N9WeRK18wXno5bEMeRP02D2WjunmY/NHf1/ifunUx4QOsWObcHZkFlw12AhNC2vevp2Sbwm4qeQbjmmMZi3GlJYMipB22PLZj49NAc0KgIvVk/RdgDsitk346CjKxJ0SI2yl988kW03qrXG0stIWiwtVZeFKWU02ty9Xeuruq3Yz/BHt2bDmp/tFigGccQST0QYr0X82s8pN6IwOpZ14Jj7e+OPdTOmE70jtAhjmFFPLe1ArUGEBL5UWq0zPe7Bg2+CRtZgq31ApY3FXRD6OIxnQolJCy3u50vyDgOtWg5hCrfOOCy0fNoMXr77v37x216SauyYywMRVB58bf3q7Z3HwVxrvYK0y18JwWwd8hA5WFHoLHQaq0Igq5tWcVBrCchlJJeTp5EKChsiQTynCd7oRrjWBOnqTPKOQEkcII4FPteGO5yyLAZc/UwDEUsyQQbE1+eiEYk8VxUZRM3IZh6kf98esid/eSjp0lMoGhMDxPlIxJUtciIp3ha1j0jzphZklTYIuqQ9QRZv7VuvsCf+jY+wv37t5s4jc4vxsAhpV0YjOveMsI8DZVixDTR0lfVIs8L4KvxCCkdC1nkdkFKNMqm7NlXnyTeQ/ehsrCsmq7pz7Oz3K6JI4wTvNdhmCXMr4Zj1/eL+QLCFgJglMi3j/Hc9xEU9h43PB5nalBD01JpCYdikGmUCoU+ffGP4HlIveD9j7dBum15reFgIbIz96oRvTjULQzzfDoaIeuezryqKL6iafyxl3np/k4f9wfdS9mt2rabjBcPlnXdKlv+Ih5Nim4XRHOMCeBYlI+32+04Q6Pa1d+/XwMYp/PF+Lnn+13Xh5w6C5aL09DH0+Tq8pmn+h773Fj58uVPDu8IgVk1BdhWzUsl7aOuBunTboPByKFut17vt+8Wi+v9m63areanMXAjPyQfs1GvaoD24Rymowvqneb5y48/+pxSvywbir0HA1shnFYl1ZczdijvHr77EER0/ZArZZ4sTzhkXiWqvKjZecEkgpjlwhFtOkqhieIRRPtvNvm7v/6NdxZlp9M4jQ3VmCAlNEMOuBYoabI41A6hkHkWgFYorACNO+nCmCIruN9qAZHzgQPGSCnDMEAcrOtiPp/FUUopBq7su11Z5fk7LCfo0xcvsAHNoa9N4TaDmc0MABbEo4hnZ5euBGyaVcX7qjD1/d16f4covzi70T0iQo/jxq98Cms6hKdaA9gK4607EJ6kHphJh1RdN4TTUYYICcwIARLTJOKxAHYWKCHOAaqb5v1JdDKKOHatagVsdpWjhEckypQxWjlh+tXjintw7A0A4ODTKd6j/U/H+/u9vzwDjh3LtV7Vs0+n/8v6f7Fjd7MnSzStlZKyeKzbNghj62HX9xehn7N4GcbD0AzF/tPRMo4DdKrX+fv71fvuTiHDpRBKhdDCC5uKcFxwwVgUydC2x93j3zXm8qtl0j1JtamLw3Yy67um2w791RiKVvz+16UUMHzB0mj8ZviOfDW/sVP716RerddbWMyG13Ey4aDbrDf/7vuHxrYny9MUtb7nnYC3x0LXbe1AaIznfOw1fQ+phOanH38kfPGEklYEa0BsdX94s5oskgEJInUMuO3FfD7dRippOMJRA8uyUZASTgItKeWM9DPChraqlBWJhmAQQim3eswg6gXUXMMbyE+ffkUCgjIjTDjoBqD51Zkr7f64LQrW1XUYwTdDIWxH3ZwuLiZnnhnavCtHo0AUshf2eDgArTARxSGvDj8h47/4r37pq+lSr9q2boZa+b5SA2PB0Ps9jIOEgZoQP4aYPt63Do9Rcyh9PoFjmtHNsaPgpu6bQvVT7Kw3MI0osH7qwrpuawVupoHr6raz23f05maMqQD0ZvzRDocfv3j6rnvX1gxvAhi0FgJIQJbE4yxFVCZfBP17miS+7JTvtQTQruk7Y5A3OEz8HkkpmSReqIgzUAgdAS0NscZYpLWjXARtvHFCdX1CPA2VOzvBDDkCV06v3QMBivI4jL2q6lTXGQMRpcJo3feRw1sp7cMmJ3o6mkptBOpHWBPItBKCgSDufUxv7/Xwfgg8Iuxb+IBfNdtOFEEYEoZHnhqkd3H29NnTjwfRCyOQ0gxhY1ultmUZ9bg/kZG05dbXsdV0vtTbFtrRFQXyk/MUzUZDZaQQratS/Hx283fu9k2/Yz73j2QyHstUTAAa1u9Fo5ModM5KxokWUqmnH4eDGGuy9tGL9GLjlBZ18/7HlwhCYMF2s7+8vDbaIXdAayJM+7L5cSzS609PX69/UEYet1WP+oSO2VmS4NnNs6e7/rbMcV3BJx9fhizQPfcCDySAAE4kdLbeHzZqPHv6/Pof/NEv/vbx+PDyB+G2cqi7rv4kjb3utDBHBUyMgqm7gGlEo6mpqEMOoI006rOPLno8ymZREuB/+e//5edfngfpkruKpYQx3m13hy7YPG4N8hPMY2MmyXQUXMlWle/enqNu4mTTmkUyT8K47bqqbE/9iWVB6l0hdkC4q6sff3ozYPTWQtgqMvTGCgEdhMgHp2vRWNmL/HB4KhJsQXB+GV9dNk2jAZIljm7C8TA+FH7tC9lJhLDGuDr0fAg6PmDKvcmzAVJu63Ssp0366vAYT2OBJFxJ6CgFxiFtDSIMI9MTzI3RSonA93ivMG8JJ2VRxU5bpfJyvbhcfLScaqWGYWhJMoDKdsJnoebwu7p7rm36UXb86/1RH1rThiIqLOYR8rnXDa3q7O3+tv3wmtG6b/HF8iqqDT/zX7+SZVGwari5OJ9PprfF3buf/jaLL06mo0eEHcSu6da9CcYxRvRhd2/A4Hue3m8iL9iGYdpAZVUPled9IrqGtwYPjyjwOPWFsWcc6Y8vMSEOWOoT4vOtEEEtIRcjGAHA+OhiOmXrw7Epjr1M+r6MxvvqzSoOD31xGIa63WwgpWTy/HD7cv6Hn0cR7N60JSr6XTufnjAU3nyRpCUjUBfvK903AYg0UQjDz04/GZ0tyQoTDxtB/HZgZ1Qcmrvt/bu/WzGc/49xeb548fTZU8dfIUfzPO+rHk2CrlPAYGgQsDjyUyMVMQBqbZvHLiqjP/vDc0afIuDgiCyn03/7b7/u9zmMRrUbgCXfvNzv9+vKU8+mV+l4tm+dZ7s5p/5kcbxFeVMmieKCzwQj6LJEh65Usute9z95vodamnne2MzIMCBsEy/MSHp3v3qAD8Tzu2oNAUzq4AIHgwzomEcm7A7HdwPkjELKT6qo/l2/6f4qCLIonnBOq/zgMeyOtiyPqtceLa0N1nWhpTqNPh49YypwAn5QBGds8bB97PcdwhMptHz1xmjRUwQ0no4Wwc3Hei0opNgikbftEbJxHMfRHamPZWF2M8KhBWS7raU8wvkCdBxoVApxGBpX6TBMfR6JHmzbNgm0gWI8RCnvHtvjs5iCXfmIS1ejMJkyDT3t2nqg1jcav3xsD+9K39ef/eyT97uvf789xH4UwIquPY/GyxFzHOvqEJtFLxpoLdTOWNsRxyHA0I2YZ2CxaoFGQKBeDzD0OU1SP3JKIwRaCMOzJA5YWAhqfUQpM9BqPRzqEijpL2cKVEKCyIt6SZk3SqJGa+mA0aen4/x4q1Ri4BljzNrKOjMe0oG2boyMy7XyCPZ9jiejC07vH2+b9kicnVMMl1x+eG8FmAdPz08ub28fpFUOYUwgDUmzjygE+ESbQt2JLpPKQtgREx/XhyK/WM4JjkdS//7r371scoRdVeRv7O2zX5z0RjTtPoyeLk+mUIOqLIM461okhJov53XbW05YB+FpBG10e/vqx+9/eD5+/OTv/bG6vTNSzZfXGLTZJOFBBPF1NElgkM4uLnq/+yNGtw8vd8WDs4pzcjm7oEEEIkMVbTKtBqG6nrhM+0ErksBnxd0R+V0SjPVOIg9x2h/y4mKBPv/s5iD63dv/nM3Zzdl0vbNSNqKpQziOVFoPzXGShr6duPmuVMfiltNouogX8/jdbTMeRW/vXv1Im/Xt/dV10Is97nKKw71Dq80Bu45BPMtS0nElTdpmMD9irJYnozgclcOqh9piiNRc4iZNU025ZHHZ9Po9ic65tcHTqyxvosPxNmI8Dph1g+ih5h0CFrhB6u7ZdbLag2A+f/LxC6mUMA44pWZe3uVtJQnhbVt1hruw8AoiywJdnKdpHM6+gihJi7I3uigQpihBSJci5kET8TEmGCFMWYMp6AmhcZI4z/OFViECHcPM83w/qap9rXKGkDUmX293R4mnW9Swav2AiGtad3N1c/XZFSlWvUjKH9a/Ph7mB03OnwWLSXksdRZnPFw/ro4t1cdag+5JGsfPg2NpAo46Az4OQz3lpGJdW32//p2S0mNN9frr2d//6MQtBuagl8W618gvzMACSFjclS2ITlxQE9T1YRoK2OvqlDaOk8aVEGEvCnXRyCTwkthD2GCCOfMiEznjQdAMvd/RILQYUAI9/tHHL94/VH0DgPS3/mrlmv4+bx4VaEVja1ZxxmbpYKPtZlN0rTedMEGB++pKvVWcMC/wSrjNSj+KAgUlq4QJUAV8347Lsg/N3rVMR2V71H6/twu/hfnkZ5Mr/Pz09MlQZE3V3P/mw+QJaOrOGX+3HvMAXT8f372qfv/b95+/+Iu/+Pt/nwDn+r4tbvPLy2UUhdCsjt8cjZ+Q2fiPLi+qswSO/G2udvljXRZemFzcZNCf5wZqKftB3Up0Fck+DmxBpSRZnKYJJ9j7Q/qFC3nf3RpTeNxzW/7Dt6tZtM/orKxbj/uPDoZRolXEsfMmZwgwnzfruho6BzivOZu655/TAbn7xWxGCS7LgxpdOBdA2Fq9TNM4jqTomqqqldFBkMZjMvFmk6KppibHUjaP1hs60Yz8YHsoswQvpqNpd3EH0JlVTfXy1C1+8Y//GXRg9bCKvEF03beHfL3/cbG4+OqrEaGE++doCdRu7uFQG0JZDxpmSQJrkSOnpLXKqKL0Qo4gRNwLK92AHqM9DNNlo0fJaBBuKGBR13VneJUZM/40mf3wfX6UK68PRDG8f7teP+7jiJmuk0vYr9vsi+mzqdd3C+9cEY4N0mRdmHCMgDXKGmfquq1wezO74LG/byuoLUAEYwyrwiWxMYRQytgM6wEDbADipl3nXZKOjFEWq26oludzQEVbCe5745tseNSaOWuhw8TnnkH4ytnj/e09cNntA/tokaagbYxSEQt7P1nc79ukh2390w2MDXk2H2fzLLuyynu+8DDftE06vbE+q/u2qCDAyhAPmV0nwyAaRJqGPNBdAwg6eDJyGGGQd8L3WOb70Fmr7XFfOpQ+/cPn9Pv9bvf7zeCfnnzh8fe2aH1LmuLge34kNZSyJqTBGGejfi/AbHaCw+93u9ffvzwciqYdZjclKtKh0BEa8upgoO364a8f/kM8OjlLfr5t7fmLNL6MHL8igR3bEWgOBwCB1aJzfVuRmLViN3RQdpUFcP7x565p1u42LuNQMQ1sAMR0zJYn12d/8kfW4ur+7sPDyz/97GyJRODB08l4u/F2m9KjKPRms9Pn1kHcZKcjN1veeJSSpir2FQJNTLWiIg3oi3/ys4G2TZ0f/Y1HolEJ74REhJxkl/ntsOl+SqKYy4OTcjw+8TnQxg7DuGmOh3r39GoJepdn2flikY2WJd42D+3Dj9vb++Hi6dM//5OJxzJMYBD7212PSKlC0jQoyDrfT/zAnfBEQrSqu+PjI0QhZ4PnEcecl3CltaQygWmp+zAeYxIPuAOy4BQafOD+RFTAmK0P45S6Oj8WbLAGiMxxNh/K1jltcQKt7gSgDATcVxghi5WBKWqZs1oLf3+YGRE+5dJkzVECTF88P3FZPBRiMYuApTfZ06cXn//mPzeN+noju1B+aAiLIjSaZkjZdWNJd8/Gsy9H09k4oYq27W08HV9AfxsSMgrRFH3/H/+qru5uri+/+OLTTtdRdtpWmktPIJok3nt19FTpVO35dNt1P599tINFSrGkFQ6CUJL727dA0cnNl8CYoZem633H9Ij0ag21j+Q56jGshp6RTsiJCyov9bGWjoyN4RSgfWnznTJlmnadP0HDSlJu9P+PIPxqtm1NDMO6L84cV15r571Pvrm7byc0AAIQJUq0zSq5XOWf5io/6k3lF9tF0pZkUkCj8+2bTk47r5xmDl/0GJskaXs9/dfv3q8W4OyUAMAU4IYRsAw05arrG9t17fvHW2++fX6JCK4akUscBR1MTc1KwYmGyOIbetJRNKz5nacM3D2fWKMP05lOiqOjCSdE8OrR6ROJnP0mmfL4YeTW3f5Qj70AAQAASURBVKsk2f/LP//x7GJA8mxdpDujrjsuvb28aj7U30yXD0zz4uf/EPvNtgGHfb5PuDewzYtJxNVcJGFeeXFPhVaJWVW1m11qotpwfQ5QEAzVtrH7CAGkksRCpoRdb+A4pw66bIQAEKre0ThMuw9ddFkaVmRlthRSt6zdbCSFDnbDwI8xrHfQiGhlTr7qTEaEYCPLukpSy8SZoYOWFgNbzzWQtuMSKRzvwPKiCm/uwYonRGGpjiWvK6VVKe8Fy7qdfzw+6pRJnezXfXo66h0mm7DX6TqWk+kiT/y2gaeQ7/C2g6iSEmiBQ4oAUK5GAyyXQLAACAb0jvFSFwaNTWHglqcd3LZbMISgYo0WC+lbhq7RAyNrsvV8wUUdxQPbO2kEUWDoP/b09KrNPvn0OH4vQtb+4AXD0bElQ9qV4V25mgC/KLTiDMHCtD3eQOUHrVIFZ5BSXmmECKB0WdaIVRJDgrCBCYHAmBxJXrWsgbtVBjFGBjDAceDQ7iHdLzSESvOP65UXOLLjX+6eL3ZTfxoEfq/J26g/QcTAEMOGSQH68YC73vGH12mSkR1x7NA0tEG3qAqI2Me0DYeDg1eH8/kU6DTyaa/bydK0uS5SAjLsdynZAWNfCWoQbVkBrHmlDbOo0nxxsw2NlgXxhNeFTUSzzfd06Np9y3Jao0alFMLqP4JlkdTKGnTP45+efPakEwe7bedWftvzXV0VnGtNvct9YkGnrLjnGkma2wY0Xc+j8fnn/xjdJovZu1ZpEDiYO/FkYtxVuRdUm1ahjAmAus1f3s73jeXFZt5u+b4iBFLTpJQAg4KCYWWuqi2opInpyfGx43ZRo//88Vo3vcj1Z9k28FzidM3NJgoj0/A22+++3cyWm7t/fp6N0nQMjNCNnE1ayVaBh0e/fhJk0V2SWko1sIDbtumowII6LQHYZlvGUD0CqKNHy0WxrFaL+TIKBW8hnmXUDC5zjojV7XV/ff43bdg2dVWUNU72ABvBqEtOfONN+Hz3zg+jrx4+CUL3ap2Apjw4OXCqmLEbA+BXt/v33/7LaNw76Ipa0C5sWZ3by7IKqtHkGRfg+t1H7+lTJRHE1tlXzy6f/xHYTsdE7xcfLccWpBpZvaLwVnZOLSaUp4BUbSKy2tfLzPNJNl5tmCXA6OKpYgJgWMg9gpk/OAQSpkwDQZK6JYS5tqm0pgRYoCWAGqqltgU8SZze/O6GbcKDzz4LSTetmvv7XQCbu2pniWPhVCTZHX598Zuv/6/vF7ewrYNRi7nxMd227y93891vPvvlpE/OHp24ZVls6pPJ0bStk9uZmIxiZuebtQKJUqoocXVz28oy9qydKWOrX6y2OnK9Slsj6DKvIe3Dh4FVtb4dnWDFqkYsaX9w/nZ9X7VcTHltNYHtE8exDo44RL6KCpOGsBGG5jZley08qoRKKm74Ew8jgFso15SYlEJECDHN5n62S7ZXqoSqYPnMdce7XeM4XhC2VasYA/m6Pe/S+PDUD44oQdP729s3c7fXBabNd9WBcVBk2iCRo2xU5dzM37xZ6/5mGEHoQqOLNcAv85yv2jiyW7l1bYRcL+N6v9vgQS/Suq3rBw8e7TKSlekPP1aE5R/1TTMIPUeWxU3WbOp0vd1Ro7falWQL1vnSiXOa8j0nAUhrqFqZ9LGoCh9qTA2FWJLvD4/ONUb7m40wEJAUQ8gNOiQeVYbi3G4t6hleGDV1k/yYGhEr0DpvrAzipA3ZzpS28jr25HHsGsQ27b0dyFsNSGOYXq9nI9O6vb5hTPi2sbhXpn8v045nVSemQR89HD98UL59W5TbeVor3G1ZnhXL4zP9arErb9LwSfjUfZgbb2yliiQrLlO4aZ2z/b6IDns9zwt4zU3SimpBgGceWaep6Y+Hdd1CtPSsA8EgCiTnTPBG6gYoG2lHB9KhsgWwa9KcY8a1EwqegA1aAxN+1g3NM+35NPuwEgAoYnGNw+6hq/26XhY3Kt27Rnf/Lis7D6OTJ/8U9Lqo3d3XbZEmcKuu8hSB+tkTf3+PjS8LhQYFF4BgBQHBkJgmUNJ2AuxACxisbB3t7sVecEfTyiDasW0jinBTYtZCrsloBCFBGd6vt3ZsU8doRNs0N+1lAwO9m63NyPFHQ9jW0MS4ykovpoJMm7xrdIzTz9L7t/VmX/lIQam0djUzLYOzQulut0NXbVkUbwwSbHb7qq5LTHKNhGkmka0rYlvI9k0XaqJgSxzD8wJ68ESI63mxqzd5UgKlka3tZG8R1TkI5N1bsyDDwfAXxPjrN9ciV+E4NqyQWN7i1Y/CqBDPX/740kawqHkBCqhl2O9BGBnEjHCid8KwLccJSJ8e+ORodBz77ev79HziY5oA0/R6oTSE93Z3cfCrh8+OgocVRjwyWcu8TTkXO8642KVbHIUqr13X90mv240sywaWOZMyn91LGjzrxpPB5Ptyut++n2Dd+emB5Y61Xl1dS+CDJxO/pOTMMBCycEUGnU7n/PNgMip587vf/pFzvrIqzPbaId5WdwDvOPGA6vvp1D2eFItGmMIwDJ/76IYyG7yud1Bbz7zQj+OI9CMnK/K31bJSvb7vd2DLSq3yNo/aQRAQ1DZffvksjP0WIDNUJO5z6QZGp1PST784xEiP03N81PdjCpc3ZdPUsOax9KxnHz/cffvX148ffxX4h6vZItMwur3flInhmfNix/zGjRxQiQ9sNy9fpX9KrQvrqf8kDOHahCa1Y2gMczfLM0eSmdDNi+d3Qn5yfOL6vCj3rgg18fumxybeJl3XJuXS6dmAaTg8GIsmN2TWACO/3GV+5fd7qNvZ3m6udOP5jhORCD/TRhp71mwWMrF8tTTrvlDNtHMcm4ZT19WoCza31vlPPusM/GY/zT+g2+WykBWJh9mqNoTnimBd7LNm+/TZp0rF22a2nxEmr+9ezB8+GR53z8anm0HXzxuJSQaxuU+vA9tTuBvkSzuwpGjtkXh0/mT1dglt2hplqZt+bTQWpHsOh7RhrYIZIkPXNIU3aMCC8yYjIWpY6jYGcgAwuQmJbTqttdqt0s1iPp/e01nYMzcUOXGvqYc4nuK2c5fGy+LqE+/z3NiulsQwHdtSyw+LN3dX2HA7Qa9iBSeSmQXtmK1Mhq7dAq9F/EmhfvvuTzsijj5/hIjTDTzPc68OoOqgN9++X85fGmvy9NOvlGy6uocglQ5bOdmDjv74XrnBnmAitWgd1pYf9+lqrxX41HYwNYpkCXpuFQC9Zv0+v7eFlPvWHtZKEyGhZ6qU0S456ExeLJd1kTtx6LimhesacFSTQAvo+pr6YvdBWlBwQInJahVE3WSfGfQoK3aocwYDJ+h5RdykjXr/p993IpscHLmub3sugMQzbOzCV/kG8BQCqqXkRDfMx91G7FNVVR2rM0QG6saZJNe7lHZnd6DM7u/yym15naY5Vdbtr5SoVZovn7hjemzGGDd1GnLr4vSB77jrH6ZCihKYllrVH6abpjTSvOWyaQz/yAC7hkLQkXxm1J6pEKjaovUYtwi5W920XtiU1DQLJtqKyRrUX3S77tOHSqXI9shZ3p98zffb9brEqi4rpQ1lhd5F91Gh3s+zvcklA5TP10DxmGLi97Hw8oQPR33XPmuiEu2tUqttArpDnGkngkIDoLXChDRNC02CY2+aJloxGHtUQ4qJoSRtEhgqAKG+A3J1p4XBpMRCIgi1EhXisub+A68XfpL1bwyJTSJRChvdQooQ1CWlqJJrtVMs8h5eNDfvWYutxiAY+Q9c2tZaA9MwJr94EL3mpmfyory82WLcwZ1OI2ALGwOg8Nj3dIDKuwornWdZuQdzBBWwDDuOOmE4lkWRNNMY6RwuF++npo4DPwIJqd+9jM6f9Dv9PCl7T7uhYkfEfCsoqo1H3ijh08jtIkyUUH04GHQH801bbSFjWgfWcr8pnt/vdrA36Av+cYPC0POijhkwkuEsqHTNpOVFk8fRwjKm8+/MWiah7A+iw94R8xRrW75YlKbTEKvWawufe0E4Y4XaLksOo94k6tjbMkcQjYagcsajA5cGB3mxT/Km9pYPLFr3v2jqddsc1QUHCh6BCYLun373x+ezF5t59vnwkyPiOUFAsTZNBCns9+IW1n4Lg8DPG5Olu163N3+1eb5rJqPBheOMT35pjfyqYkEQGOvRSs/v6qL7YXfx639aFCYH2rCNHFU5NY68x71Rd9XyTVVEQDkMrndvIidCuDGMief4nb/5dZavk/S6uLlr2lyIhiJ776bf/OmH3Z51x/HfR2PYRpzS6zazgN3p9rbNhr2eCq8kS0wtOOl8QuJ3BBDLMlu4O+BAKpE35A+//R5w6x8v/vYwbFJRuIyrncTUVVpgk2pt5InpKdPOrEKU1SNDuP1QakoswxZthVQxNQmNho/7p2em7X//7qbbNinnVVpV2XXQrbMNHo6Pr6canzMMEclJW1YB6H918Gmye/XczI+lxHczExstK01qJETvtnVs9sNgOM/r2+18s5kVy8nBmRGYI/uZJvWT3fQvH95kD8+o55xEYYSrV4qSzXZKBYQbOG1e1GU14r22rlb5Cn38L0nFHOBwKDnATOtCgRJJu2xdIT3t5Dy1B0OSZYRgoQFGUCmZZBmJbA2gMzoicV6vIXDA9k/bbzebVVaan7ZMNKRFAr2qXrMHj7qbBrTNpAKtKd1GOVoH95vk7Ysr4kCgyS6pGrZoW3xw0N3VOLlJW5P3JAdoffBg8A/w7IZVMhBZnTfbwjp4VjR5mwIJs854aJwdeudDultFMtEC+ZG/q2LpVK1qiPAIFw1nVZ7tODIU5zY1GaZhEFPLIEljUjUXKZcmQMJ1jw2zQescwkDoZdNYmNlOl44t/z6/PFAT27ax6RucacOSPN9WsE4XxWraz2L/yLFEAE1T++DPt0vXnUGBHx1hK6BWz90bUc9xv7Nu3v3/voN3b6NO/9Mvfmq7bonZTVvPdytXNgZiUmDutqVBbCaa1iUBm1/dXfveg16oTydB5KekdKlpFJ2f/+wzYhg3m282kLo0Su+t/Wz7ocMf/7LrUtg2W2f4wJ34UiqT1KyJSNbWOhCgDO0k8JxG6/HYNppyayuo4SrJVZljEisipWjs1nZR7di2bUKlUimlw9Balh2LGOdnVcv3xf2Adgzn8WTsLixvPvt2vbppW+HZXZEtbJ9IURsNuxgMfS+oibUq0l5dYWLAmAz2GUZ2ntVSGPj4NPsoJNisMcaAlAKVpqVapDRsVe4bviAGQgpi/2zYVawBrG4Xy1ZroyF0QMFR0OpK3pZ9OkRxJCRb7a9BxIpqAWBpwpRJtlxmWVE6juvZnlXHRsk0Grie0+yoGQvGWUENU5oF4j0INNIZgMlmZT77tKnL8OST08+OLy8vLfqeSalsJNaLOOp1zy4kb/nsQ1VmKzIfsI7jFqCIq5IKKJazXRhajuMedw60St2esafArZFDjUK2cAw81+120XDSOzs6z+a3s/tpsV9ZZcog0kQnfEcqY3xwJAwfpwSK3O/Taut3LbD8cf3y8k/HR0+GB09UrgEdPj31404g9WUnGiWZw/bFadCf7URLMhrRftSdkjdZPbOI3W8OB72R1xuhWlwncnfH83Y5cxBnuRU7PTNON2Wh90GKBa7bIv38MHr087+/u//TdP5mOruv0tWPzSAORkUeSLE4mhwhbJDQ/k/ffX9z9zzunPz87AhMFNwo23VMA2b5fZPfaRVSatiKietUV8rtDvO82qzTX08ufvbTv6cPHog9nOab2+ld51Z/8ezJaHx6M/+f/7KeT3/3+4ePH4jS06YP0YypTTh+sN6u26Y0mCKuXTW1TaFJG0XF4puXWBf6SKY/bpbL+7raK9AipMLO5MNl0raKEBcI69Urznk9sk/kKNsXy/ssY/LWIFSqI8PSNq669Nx90Hddc0SKKhFVnRaoqt5uWdteHJ67j8fNC+B74rSuUQQpVqTMVVXn0peAA6A9N9IOwDXP8tLhY3gEEES8qIvluhN3ZGdcZvXlh5nhdPCDE57uJoBAvwHIEgJWZWHRGtajSBTzzpA1+cCOeV79r//Pb8Zcsd4IKXB09kAqXjHda4ErsY69BdfL+j0g4vDJ15MYrBezpKKiHQ4GPXs73m4//vEPN71AHJ52pVQ91MVq3imj6XQNEm30Ttaz6xLpvmFds3ridaUZEOBa0oIdo21cjCwHQQWI0JBggycFIRQhh1ktU9CUSjJeVa3pWA4wIB1Kcm85I4Fe1k2DKeWc50WJ79Hh18fZ+F4OT7txX15+v3l8sN/uOj5SaVsUvI68yA0tm4B2Rmmfq/ZeAiPZUUyNLoUYo41yEJ9cHJloK3Ash/j9j2/v//gnx4ttgcoev3h0eqT7SLC5H5qDsG2c/eJe4PL96s4N0FdffUqUnKBxKT8qiSUhFBMz226kQl17uAkMz/VHfi9xi3afYgy3Gd0l7KkrqTHwBs6tABIwcxQMigEGKt9vjV4vhGidzytKD7qR3KQFRF6j+VWuUdE7/omCenKgKe4EmISh9V4KsF0ZTvHZ6Df2yS+6/yO+/eaGnNIwMDECqWSOEhErdWCr7W5DXVNoaUKjrsyIN3Ub9dtihzZrsQJcKm4OxsMoNL740vPcMPbc8O/e3HMv18fHn6xnHz5udoepgabArA1r7FrmJJu+rStWEc5pJQgHNQihaTblXsO+S2pNAq32WqgqhxAzg9lZJS1qR7haw66BHIaSwiKWLslWmGXsHLV1drO+cSZov71u96pzFljR6POf/qKtBDEciOD7NBt5/bJ8j+Lw5PQk3Wfby0sitLLsV29fQgKrWlimle6b8ehcaFzDVokAAgGA1iIFWaLqQipJOZCHmCBNEw+ZcsPr7Ob6ADpg4OGk1BEQKwF7UPFYRcRygyVBgHFOSVuSsgi6g5EEl9SgB2fjuGRu099xoYIGbSXsIF7VDc5CEGRFIOSe2LZq1dqQpCz+/Jf3yb6Z3X9UN7OjyeTqT7t/efVjxDttTxgGNPoTcjBy8vye18Viypp81InbumlzHNUb5cdB2dMjxWpRcCYjaVLPtIJucXfbwHEJtvMVpA9IkdZV9bPPv+RSL+/vmqu1bVSGY5BGAa64lrg+ZKlVNNO6vgudE0OaJS4CHstY9Xv9/XbKyqXpHbi+1xTNsq2tsLDd8T4rVtuNYfnRYYgR3DT1ysMEE7cOKbPX03Wbtrv93h897nf6xoOfsmyhZhsx4FwHqtAYQ9euJLZrudRiX1vh1dWfXv/2L23edB9feO5kcvjwcPC4mrLG4KyVf/jjH7eJp4l+9Pgn5w9wx+tevpze6Q85H/RzdLl4bhhIYtrzUSfuAomFd0hJylnZjUfHD39uXExWrz6+Tsq2YX78lEPI+1Y8MB99/skg7N8km0JkWzM1SM9jscb2oKNzBffV2jSJRffQcMt8l+60D6Ntm/YpLV7mm/X6+T4/wcCyLDrqUOZ+MX78+By9K9Dk6YQlTcMY5jxXi23VdO931LB7RwcwazhU8+mq2z9E0L168U3v/AEFPt/nuDa7naP/y4VJjIMoGi6Dj1B0UGywpN2qPXW8bcsFbolqpPBph3jA4kAWXC3r0n0NggcQaphnmWGB+7++po8m1LQIQmSzrQ2L1fsocDTSWMKSta5jsVYU2T5M91Xszezmv/7+P99f3Z+OLxjtFpcvyvXeChxkm8PxpMCi3Yu9ngsho86xsMxZwaLhyKthUnsmzQL3bLuq37357TKwSv7TLz/t/+//7//43bt3Dy4efB71tBiI2/bo4NC2h2LssFpRGGSpZBUwTVzVkEEd2qIgDsJQaAgNk6W5bXvaMcxWAGEpRBXXzX3TDCEIMUJxHPQ4b/vPLn5po52n7rMlIEsSrR3/0+H48DjulPUHq9/DSlqcdVi9LpjgUwvFjm/XPC2YPBjZPg+2ixXEdrdn3d/sYQ9p6i3lMqmBQmWSVmcPH03GTxBJaMcNY8r3reu6r1avq3XTPzqeLiGZpb7n2XgRNPvDs1FRviN50ZrWE03Wt63YtfKo2xF2GNrH9Zi6yOKIKQWFOgicmDdVklcEo60TtHXbMWBg+9Mk16Wg/aCc3gM34BZRGisFescHnh2jx+TgXo2ePJ5/+9c/b+5+QladsPfV2U8821jmBfLxYLNPCK+z/R8+fHeM8ZEKN2NPcZWjvcTAQIhxt0AY16wOup2aJQ2km2ylFZ3uw76TpFF9/THpDTyfNiKqs10quaVFVtdstuRhHEHDRLB7dPTgJOZtU5dZmzVh9zwwv9aVB1Z1ne211xVuVRdZBdZVkYTWuUNAplFVZTBAYllapnscdzUTNVOgLBEAFd8RYBUqIHClhNQQtYK7T+1degc7jc8njTSyalbdW3lT9ombK57sZi4BqtpbsVhn2y8//VILcf27P7CyQ8RNa6ITrC27nwLN0WSnPxydfi24sB1TmZVOhGIN1hZQQgP55OHpdLaTewGAzXXqxhOktVCSgcSqaAWhec2oaambREQdja1FAZwYJ1JXLZiv3vpBgADTwugOOh0AOXNq3rgCzpAlRpZlC7ZKpGXsVK30nAEKBscHcQcBIZJ1Xr+vy7y6fa1y2jbjd8u3Shx2Hnn3GQtsmOP8xD7Ik72fbh3PhLSy8+22KjwamKUC6a6UJgGWB8M7CLzYLVlrx0HN6/GxXt7N/7q8O5TzD3LgmIeszK5+vPnw4s3Z47FrY7ThJsRO6QRuVzlOtSaR9yToaRf6jsvDtvXDqHdiBKe/vLkGg4EPQVtTw3JCBKp0B6GZTvdXhcGOBjEMBBczhQoAIo9EjqoYk04/kopMBoNtUqa6Bt1e3B/k+VZCnuzuQsu6ePA5IYc3N9vmfj7AoK7y5u2CJQL7tlnZfJ7b2Fy+//0/f//9r37+H06+fDLoPX74pPPjeqE2TcExK1j08GF1b5707W2+Oe797dn5Uch0DrjDa8mrxNGy6UYHZ93kmYaL4m533cpoeN4qhCFoWXO7/WG1I8NO4ztHty/379e5B6LAt0UHwswsKlBuVn03cgyIqylM8wBgK3ClQ8FtibudSTCcr2enNhLyIOPJoO/nTcjU2rU+GyFeXt2XataLu3WVp3fXPs4PLw4RANvb/cfZfRScJEU5OKKb+boo9I7L6m4VG/7g/IlpTF6//K2Q8Lw9LazjHMsY4cQqTRe1rCxq1DtW5dbc7zau51GHtJLIRI9hUcjMZwFrWya0ZGM52thEXd1dn54+3W/Xw4cP/5fvP/bDflNPP3twMZ0WrjNazr5TCEGq0J5b3Hk4/hnIov7wp6pqf5jv2jQZd/2Hnz/LWV0mOQ/tgUH/fKu7A8Cy9Wa18j554o7t9JKptunF/ePTx3l+sNur5bJ9icvX87I3fJSV8F+zjY1zqhEmTxCs+Mc9aQJrDBw3TBMcNRqGBm8aHQogsjaMAyVAlrROF7YFdlyMLZ5Q07C0hq0qqv2uDg9s4BrOgbn/eHMnXr2+ynpTTlNqc0zcjL/WhqrdT27e//Bk9NPr77+d3b+7Dp6Mx0dV1+h7UCY3Www6FkyqygFEQlQ2jWO7T59Nbua3riUYwI5XSNb94erdft589dkXn0yeNkNQoLIzOm2R0O0DwzcQ5kwmURARWpZl0w07WoG2FuTFNz9+eXBoDycDjYf7fI/MycWx555oCD2/rupyt1s13AAWFSSIfUdNa9BBCMLapE5gD2za1m0la/JwLK8Sliziydmzn37pWk6elaBdDTw37naWncFJtUU9agR2LeCy5KJdmEsMeYntXV+6m/zHaRB03eHRwZEQTGtR7HNCiUf9fs9oF4xWDQcGVAaohdsxusStIMuz3XXZjIPQUT0AmZVh1UOi1TVTrYB22yRcrdmW3C0do91tN54VO9Q7tY4pCou3q5qolpoOz4xkCyUrsjqDGHV8Z9UmqEAIqYoRTDjn19u9lPDEHSu8ELqVBjFbhZCmjcedklLSHYWz1d2a3vOEbQWHACrPr4HlrFut68I30L79MF9VxQchBK0SLtmHb/4yvb8PPNvGQImW9jDRhYXl4NNP9i+G46/OlAa8lUonFrS0QFrcKZaLtimq2rJRBRXCFW5x1QAPc85bGE+UbhpdNqZpG9QqNdxZbccCgJuM1rsSLGZdGltB7PvjPH++2ySJBnd/fK61Jl0TpJA/jLeZnZfFZ3GwkRS2xD89GVDndrHRih+4+Je/nCxe37JVcvSrf+wPu2/+gMcHxy1SVXPXdAKLWGWTclEbCEoG5JonEnolPuiGqcIG7UgDOZOgKsKRq2a7OjVrPstNI/ZcXsVOb/Dh8q7ohs6zod1u89Xs1cODI8ugeIcU14gY0OGLPfcB0Bh/++LbcTR59MQOwsCRPK3ey7A/PhoMDyElzk2jzMLMkk3klRq22816v1t5pgOP+wq2tEl14yT7v2LsywxXrbR9KNrScrtQJ+kGKWJZcRw9+SQvl0DOa766+8ufbSOwOh0FtKmMfmPPdTUaTLqDye3dLHB/8vHth29uf1/kjX7+zzts1c3sHTexKUx/d/b4F7eXVytBWURRYDMVsgasluRN/nqIw7f1bYMU4bd9YHt+X/a7lMGqgl43NEhbLJetVhfnp48f/uzm8j0SwqCm7fcjEgbIUw1NUcaX211rASalARq5s5Zl3bThaLRHns6YhaOXs0UYxXdkaPdPRkeDtx9+LGZ82DdOzg736xtVd6J+8erD1CLnSrCuY3Y7IwTAbLoETfHp008N11ncut2hT/3KNyIIZHgQ9piPIUkJPz7+u73KudWzKp27SeVYBNHGN0HKHF8hAZGlQC1RqyVUBq68AOw4t21n+tfNn1//ME3Xpz7fgOrbV3/VAnbOLj4sM2+c/tu/+/sP13tCWdDvF2UJ+GrwyWddbZ8Oe2+3dejyXuhU9WHZgmKznxacC90HaLZa1JwOhoNEb+/TunNCKrMmjvvTJz8xhdpuVuvNiuXa9VbFqtM6cjDsVrgdHXf+Qf+7eBSuf1xe1m+/PH7aOe73h5Pr2bIMjOt8EdT10IugqVOmzQwpFyGMSqhxLvKIWFpbUJSNh2uBEdEw0doiCCpllwytbuaTI5fyrtezhsGHwO+qgEfO2UZvGYeUVJEZ7T7+YFQoBvp/e/E7zltqorfv3sWDCe2eDc7CR4A1SlcAK7XpdU/uv7ubsfuO8anhUdKP2vam2G2Puo/+x3/3hCjClu3d/hXaC23BkuoyVMQaO2vPN0Be614/4knitP3g3ONiwCQjWdvcAu13oieHZzWTD6i93cuWL6ihZ3MAkKy8OFZtpaBg3ILrqgel4KGFDQLq9Q0iBqFatjsD22II2pvpvlt8LCNjSVWW95GVKImefzvL143nLUu1rlIoc43lZBAojkqc2q4fImctCSLmbL0sywoicIrcFecOBLia4ZT4nNODUFXuCpeidopE3Gey3GUYkoveTy8+6UHqJTkyO4asVNHJbS4MprEmpG2D1jEoMg0/TXYPQ98bHEmjgyV9m8quXxCsYZVzLDJRb6WAlKqCwcgwGlsUhesHaNApt4Jgul9tNilLdWITZJpGqRrVbKlNUFsmddI7i4EjhHNetosqE6JMm+syTXPTiqxH44PewelpCO20KI6d9vao16EUqzQ9PTnC4179cua6tPu4ZwdBvdBGJ47CdBz4NUau09DWMCXLixXA0ne9fhz7WlWCQyak45umhaWWlXLdGLqI7Uym9gYxGi5tewQphAgAiABvcl4yryems9yZh9HxcDgaCZDs6ffr54Ph4JePf/XX9Y+ee+a62uY7E9tse+XBuOsYbcPMtMgoEHHje644ifbLXfbh+5vrLC92XWewm2eNu4wag415scRAw5IrG9HC7fh1k9fF63cfTNv5ZvnXvCwf5b88Pn0yGp7kYmMOhsBC5W6T7f3QU6eHw85uZ2MaRPEyWQNEw8mYre5c6KVAF5jr6LRcpX+d/zge/WTBN6Jg4Ooub4fDybCoUpZTXcIaGTBAECDtAK70Mm0W6Z02lN3p4vHIJHi2zSWDroTYpI/GwWaudcECx9jVWY1y6Md12cKW1y9WZOIYkXVw9DTLb8p9JmFz6pg3SqatvVqvtttk0hvP7+FOaQuBu1QPBpP/5vMv/Idj27P00TMpy/LmgwIQsM2D88kiB+++XRfj0AndGjalYlXDmgNBHC+AwDEQwPEib9jqbuIGKz4tLRp5YwUHxWZjGCRNsqu3L+2w9b0HHSQxyqvKTIIpK3k8oKBVXKX1mtesZoKHXsBasCiXbc5Hw+N8Z3Ol7eGw2lc3qzT67BFrq5si70UxR9QM6wA4I+eIGtuk5C4CDcNWYD17epiX26apCAZOt5ref8PKKm03bIViJwgCkNXZuv44GX5BUs3IfQl9ahM/ENtNDYxaGsQFyKQmlaImGRCirjxopMjyAWhlTudXf+S79dNe0Pv6E//qx49cjfqTn44Pb2/nbVba9qPJIF+u7cub4vbduyfnjw8JSqDxzYc3GqRJNmZJEVp94vuPH7knX034XY49WLO39xgRWaUSlI3RHU4sE8I8T+7yTKrlbXEYNKHn6DwziDYsV8va1nWy49wSjQrVIfuVOOo+nVRl84c3P2zuZ51+r61STsya984v/Pl9S6yaWCTNuYUbhSot6H3cc8vGtl2ggPKFwqRFkCWGapvIBjppsnrmRIcFTUenp/jHf15/c5M8rpDJq1JE/tgzLanMg8P+KOpcHBwbvHn8ky/LclsWRpF85PmQ2ma72viWp8zUkdXTTyauFa7X27dXs7CIdR8M3M9MYNmlsd1tink66uBpm3CWYq/bXkttboCj8xJeftjHga1K6DievXOYuBfLkpw9PPvZT79uG17XDBG6zahUZmvBIlEYVciChwSkaeMaZIqBhrGiAW9KtNy5jw6bfaECFXixJVJHLjposjrFD52OgZ0NXXpQmaOBmm+yXPtWMOpNiqoFsmyqkOJsvcqcTpwWtdOgNV+XLbddZbnufrVDraqCbk9gGzau1UIFPNeiDCxmHzKGQOsWBaMOD8bIxkfHD4JWmYv7bVVLXYFqnRdkbvV6oahsbjMhuoGTpmWv13n8yeOwc6hrkgsImroo05HRATrnTVPbpSDCszVVkCMFlNJKUdOiiESILlTuGNwe2bGJZNGLBIWqWC43rp6Yhvmuvm10FrVkhTTIMnOIpREL4SF5QyBxzs+7fZ/keuVSezw+LN0sQZ2TzwW/nufp6fE52i3/5f2bLS//u/rp03//ObDgYjFjTR2TlmPTUGKnGVRV5ABKfUiJhkhWKaXEsGxhWaSFCMhSAEJMVgNeMQ/0DZRzKWGASgUxrGAFU9OCWlqIs8lRtxNfYOP1baEj1DVHgRd9cjoeDibOy3fAgaiFNDcX9YIwEh+OZQULU0EB6IoCVsE+whBx3abpDjiOaaAu1XciiXtceltYuxDosmEyHGwSRoj2OwHZuL/f/ms37pzEvalJm2Z1+aHtjaTAiNTYq2VbMYalb+/6nY4ei+1doi8v9604J4+TVW5JDLqHQGZN1mgIL8vbdbr99b9xH1z8H6yqXRTzsvZLmEuLYawYlwxKff2Wx6OBAAam+zLjLUMaYSxsL0F7aW828PCQLHb7Yp5gfq+YCwzLgCEV6SyLAgMw5TryhlO8BbhIdMdT69yiPVfC5rrQ27bGotG2Rww4Onj1n35obf/ZU+uffvErqZ55F4FUaKO4lc/wkAWn4M2HD0U6iWPTxg0BC6m7Y9e5Wyw9BA2XbpLNDqxi4C3M5oL6fuxm2T6+eFBM55vVnVvJ0LZkW0Csi6oqWt33n3m+PzGcm1ZxszUQhFCGujUstLm6FNQYdL2I+ocHZy8222NsZycBJubDR8H7ioWRB0megZwqbHmd2Gh3xc6xbdmIAsjBsG4babvm+DAq9sn726vY9wmrFh+uDr8aBA/Ptq/X3qPDL9BFtf3gQJuta25Ksd2rQdudjNAMNAiUpOGttscKExtrIYWs8j0F1HEt23V9U8TmUSX3gZACsv1+q6HsHHfcoIr+zT/8+8dnf529+HEzjZRIS6NZfGf454JNHz/pd+K/sy17D8j92xdh2B90urtke9I7XS7zfbJf3pPPO6E9ATayZ/k94LzS1cHpZ3R+F5r63U22XswePj4smzrsAs+2EErNEXRaqw1rrgziIoK54A1n817Xk1C/uXzRUJcpbT2KcAkNiHf7vdx+cL1PgaFbbvGCWX6rEWCGTaPYohRKrbSgGCmhvbpRqqK2UcOGaiorXlXbfF3wseE51t/+6lcjtNKkarplpZPIHRLQ4uiQCrp/813PCbsc6Xp7JrwVVk4c6CZDtts7x7istNDTmw9l0/7i4hf1YlFmycXj48ZQ27vs5eXis477cjPniyl6eGQRbZ/ZxOlHKOr1J1pAyIHeN3hNalfZCBbrqi6btZDkydMJNXAqkxWjcEMF47VuQl/vYoWyhifytkzbuIcJkLyGBNiujZkqEAsMi/aKOq8DtnEQrnOowHxEdLZbEDRwLR97ZVOpEvoWMZs2z24XhDLTGsVHFOfurq7LzbIqq8pVPSwrQjXAhLh23Cl2GaeYqKqpgY1D02lVhPJlyrL8xIhKVQtAAO14fk9o48W7FBNk2rUddHJd3N78pax2g8FpOBmboWHaURhiZkLY3oTdIWf0ux8WX//kN4aleh2KnLZdQ6WUakHLDeW1pXDDJPO6DzAlLWkxRDdVA/arXFIRB5YQsm2kj6kRGEmPb3iT5WWbPAms4NE5qD7uphlfoFZLLa3RyRfnvhuGUpt4w5hRzkmjCyWaZnp17VhmEnZMwPFiVX3xy5+oxX3ZVJd/uOwcHSgge0OGkWsBcpfhNmNIbAnQCEEFtVJCO1a7L4lJVNUyJaBpVrvG9lCzYqijNNawtBWBWZ3mhDh4BFTNCgl2u8ZTkFKA0Zv0en27K/dm/+Lk80++9EfD92/fWZbV5TJNdqIFISRGvzsMAnBfwQiZUT+03aKUu6v73eWWUBx/2utUx+ub93c3y6raWMfCJGityyA2zcZwLbsydaOszaqai8tS1Bavf/Pk754IkabptUxV2oLW8lGY830ncAJ7iYHLpguVaqgwtIxTawJCTRYYgJCVEPI9RNH1bMuhCAdBi9rZZq6UbMpt62msfGm0xXYZjYxQRjx0DFnVbcWkxp5WAkEIlAmqnCicScEJnEnNOvRY6HAo3s0p7TdSJcAACOdCAZWlCRr1bO5A1N8st1AG/QEGa/b89i8eoiO/HwX+Zp7c/PH6eHJke+HTw8Pf3t76ns1uMo3zxlWmXDtp1ezbgNjL2V8pcm0SAihjzRordNpK+l4rGfWo7U1klrSyrXkquHlV38fJ8B41bV4rTzSw04kBRFgqSV3PbapNlb++uu+MRsak55vNzfXr37+6qeb51+MJHXaU01vdLpLizfOq/OTs8zJtN0gFNlsne98ID/oTNn/fcX07MkHacHulFWxp6DVyZlVeC/0QUc+rygoILMrGVdXFF6HEA3ZbbtDWWG87diBL7R84Rkg9b4xWkJI7Dc3WtKhyYCOQiSOpNmWDYeVYniZIcVbkOaTac/2kXRyGY7lj5WpXNLkfOjYx0neLdHg1GD39Ff117A/m44Mqced306ND48MWzveJLEuNAMDI6WGubmvZb+ry/Wpuc0zxKDjwmU1fv3l74Y6jTz+ZPv99rdw6zQpXBwodPj3FAem5Vj9yQSsgT12LE8grWeubRPTA2IlaQf1Orx8gWc+1aSiLW9u24/lb11dkCZmHbVLebNhBTQzbNiohgeZNgSyRJ7Jo3GikA8jaAqUS961aM6KUqaG733ArqIwcIQBvP5AcYwP4Xffhb/6u0T/Ml1dG+KlDw/btvTveTbcAaho2je+5dg6kCWgY2pqts0tgLFVKgAKHwwl167LSqF4feI5zcHE4Oq+qkh01p1LXWd5rLu1Rx/HMmueO49uBv9urzWJDIB0MhpPzL4pdvttuhGfbyaYqGxcBghCqm3LJ5hIDC0WlbUBtlEtkINSJzizDydK96EEG27RKcQJsvuTjwf37l7f8z90oHGCD5QFFONQtlmGesuc394eTggDRsqGS9+UWRCO3bZUTeo7b3dQl34uxFxxH0TbZMK0A7iBRyry42sz7Pf7QeTwFizbDuz30sbF4dXW12XRty4GIM+7aGI1PQhO02CBmu7daarcYqFWpnKocnNvs5z+7+sPvjdO+7vVTSPjb1zcfy18eDDpRX9yr22JuO67r+lpLE+OGl0wrLVWDMJdAwg4QzULBJ4TkdUU4qWNCtgUhJh0N6rpkQiophRC6VJZjSCsDEB1DJBXbbhZ5dT2wEaRgXTl7YMDabuXsLofQEczEAYQSdZbTbwTHcZ94weME1PcsrbzVOCYyMnfJTgw47Rlwqy3LpohyxtpWSCwltmmjEAEQIwCB1gwhDJQtOVc6AlApImwpWrfBSBmlzFFjGBbQ5KQXth6c7xgRJWs1CFHXdVyA17eFRUJL4fVKAttrylpIYlseAkhuVSnap08/z6uMoJKJPLva29ZIuvX3ix9jnmdZ8eXffQl9j+1YGJ0afuf+TXr//O1F8DA0hQd7e7ZkrJHFdhzE7bGNtnp42Du3g6x4karGRsMorG92d0zWBvpJjIqRO8IgKutkd7v9UJbnXth3BqIw51cb2gkrJYALSkWNoeePSc+6QIjQ7QZQZhLkusp06uv7TRhEEMP1XWF02dgZEoyjqCeFWC6uHGIIJV3sYoEzZBTh3CxRSTLDhqFZLxS1VrIxd5CoEfdYXTAVCBKZmBf8GgdWjNrYcJZv3gWuEbf104sjA5lQNVYQ6nAy8TrXXP2XD++1Btf80o6c2AFmU8m4vdkuJ5AfH3SqD5y9fWM8POtBqbgJ1gtKYOk6pnDWVGLFar80De++yZ2yIoHxQcxbFWAKu71w18L5/Yz2h2edztHx6emzp72iIqPxn69eTlLMwurcQijoJgIHhxYmoszfVDtwdvG3n1qtcPjVm2sRw0KPvMiW9U19cNKkbJ7ejf0HysxGYaeqaw02qjaHmpuDkajY3Yd3CTT6h0eHtlFtZ7qq4uNuwkurGp45UVPd8wUvgtpJ/f6Xno7jdtE4dVsgStEO19z0ehipISCtcmxtQceASEEcMyKbplZKmJGtK0sUlzOCvohdSPViu64sMSVXBjHp6s7D4zgYWWdBBmCuIcxKQfCQOqmuh/EE0fsdVF0ZVqard9V+f7nZxMapQ6iRh/Ko2z2++GK93qQqD3sjnXAxT3G7ppNzaOvLu8uxSKraBaDpduPgwcOyyJNtm7f7OKRFBppmdRz/7LBzdklevdhM2S6I46qLelqSG7PYr+9aEcluJzLMFrqhAZhhwXRfZbkXYUIRV5SkqfRd3Aq2nZVQDuplTlHDW7NtADAK17r93b/+6+wltqqnES2sdww9JK53n5uAiXAU3G3uKMTxweF6l1rEZFo5PX9g1vfLdY+EZV5byGgAM05PhnHH3u5cxyY1waxX+eV+uYIYXjw482MrCajGsMiu7u4Y8M9x6a/X74b9A2A6vgqhAo55VKY3Qrsk4TkvOTcYhFEWar9iQhko4sFgWNw2bX4ZRp/yWq2dDWN8cNHBMqzaNYA6wBFe6rdvP/L6uYLo2PcLI0eu/7Az8XqnSsF8XyFkDIZOXlKpdZLPb9ab0HUb4S632+jkZOyciMbYpysEdaJFR3Re/H/f7B7QeNjxGxG43UEd7np+cTlf5eyI0GEU2la02SRpVjx++nTFy7vnV53jOB73JyZmdZ7tMhdo2zBjBfwsu/n4MU33TVvUsRP+zYlQ0CyPHp59BpQ1+1Cn+cLztRWxLad1LXBQp6umSowvDseQYEAtDhuZMak1jXpdBEtKt0K5ICySvQEhQrgJEDDb2R/vfsw2/Mc/OK595hjtIaRG0qhjoDngwjRsqy0GUbcbuNO76+V89tmvj0cDKwHbGdgE5MDoHm9lpcHMORzNNVnf3fZRQboXAKItmytCDBQZoIvgjoFSV+tS2UVeEpMwA2LbtZVdVElZvq+Bf4JKoklpaNM2Y4lQ1yURXdzUqoHQbD1XOZKZHA/jsLb908Nhsl9IVlCgGGMFc5Goqjnft9l4PBpLJddrRw0Mx3AMsl6/udwUJmCu5/Q+fUp6bvNOlSVlApOB6JxEsz/Voia1sgREYATUNol8wsIkEP7Tvxm4+FPIZ9tl7aPYNcrOVlLkp8h24T7NC1ovKy1X128oUQ8c7+HTp7PdIqcGiaEXiV06K+O4kAkCuRXaptlK3qztgh1MWabwzhC6A2EFbJflW+lYkTDm1TuriY7OgrB7fDNVN+nVUXwcxO5tU6qdMHyD5NClnoZtNqek4iYly/keXBYLrUfd3jBw9+5WisA2jRp4grd3dy+HZ6eWg50RoQAQaaxXW4B6MoppaN798FowMBzJnmFC3y2NPKvnau32mVrMlTC4cv2A2Osf3saRbxaNRAarLSR1axqL+Z+sAHqeTaGgPnInwUnv8PXLV9iIt46T2gE04HJTHSfJtGqev3q5VVoFzlHv6FfjECD08vn/ywXQLPNzjOSau67fMcI0bIHWds/6//zx+YML5/T0hAfRjvGyLEUyT9JVlrwRCgzHJE+r2KJIA0b2E99hGr77cK+1//DReHk3m2lliS3FXrLLZvt1WzAxOg7VBX5Yjw8GSTrbT7PG5orqOi87kyi9F6aFiGnVOgcKqB2vvRQpjzdNq3g86Dm2RUxtmTY6HFiajm5eup97tS20GV0cfXItpxRStS8bkmWZ5YR+sZqfG8bh4eTd1cv7Zb5bryejDmCJO9TMComs80G368p6tU32bVsVL97c96PO8GDsuPXb16Ler4PJ0GBVjDt7nuAShHXQP7C2q1U5L+EG70RhDI+5bG3PXGXXB8S4ei93zqufmk+iEf3Me8o22Q53M/7eaV3/yCx5EeJ+Xk9z0TlETuU6rufbSO4LWE5npNfFpsnLku5qKbjNnBhsBZBgt8d1s9Xag9r3At6I3WZvB+pbXs7nyygU/+0//YfLD3UupqayKmr2sX2ZZ8S28zJnSLgonM/kuXeiKApUEHidJ//42ZsPHyld6bvm3Z9f9Ib9BFOn8A3K4rDDhAQABHlwv5x3v/z6DAKl6Q6qMk8Zf8REy3if57sC7RaiwagmVWM4pgkh0KzWgBCbcK6KJK1T3GyuKcQUl8AQxU3ahm1d1tjCom53y7SYZwfCEYYtAekAbJ19SnMZEegHfWlZ+10aD4YUuArokhI7Kybh4/XmqtVWVjJWFmp2b/2k+ziO77nDmvZEy4eTo2eHn+Tz/XqZateSyi/MNrS7X53+o+Dvm6rC2HJtU5dWwXMdSFeZwwejXtCX5bZuWQQ86Kp2uu91O7EmLK2OfP+hZdjOoQismx9vg8Nus5zVKggefV60l4hwgs08rQHW0ETJBghm+CGhnjcXpG1KCYGSSrStyLN7Tdo2QDjTgSGkaCEftL2iFUzXCmtiYIhoj6LBo+GSZfP5nXFgOKxb1FWJCzSyqqYURZqmLOzEz45O7/fpfiUiHKC+mVVlU3GImMExANoiIbNiTzmsbZOtMhyrTSomc84wJSrJgeugIOxIGpiyXZRNluaMcZN2D71I8u2sLWxNbAMBCysbVnWZFzcIW1naWEF0O3s1ORq9f/d2s9kSGLeMmxYCUlluYDNQcg+xukc7xxCxtsEYmxYtNrllep1u7EehDrrcniJSWnsXmgANJxqXu81mvWziqHe12Piha4Wus3JKyRoTeIlizj4v2hLeOLCkVqsFzttEJ6UC26aGH83QhmTLpRcFx0fHJfWhD+ZZfleUUJ8qqFGbA9xWsNhkK6WRF3gq3RRp8ojis47ztskbKHsD8HFJmjoHTEWGzLMcYXtVrilkR4/Jli1Og55JzM313HTlQXi034OqLhMLtGmaJqppmyJhgxoCwTZK9qUIR8GuyIbKn3KAmjtMjcHxxcFweHX/JzCrybhf7lLihhuY0enrPy/sy6t/UdLKqwfuSR+k2x5uKfAydHu157DUGGNaMY0lRq0opeVP2+744MjmCN0ydPzob9fbqeO1vC3TfWEb3vXtNC3zLshGrrdZriTHT57+uh3aVEP/8JOKGka+eNWkbVsEfmXatgljHd4UCfMGgcxUEUC+0ghClrYYTtc8rAtRrtcawDB8hrGldRrGfYPwMuHGftHrdk+CKKlhsttwhk9PT7VQ5WblENT1A2uFZtkKo/jB0d+UnK0/XC7b4qvxZPf9bFsnwMsD/DjPX5EotM0xhHcUQJMKvJUFoKZj7fd5FMPDs15Vw6zMIcIOJbxtP756NdZZ1zal9qbJxx9urudOMeqfDvo9D59riP1u77d/+YPru+PJYDjsXd2CUwucjoa1KrdZpXws0FKjbrcRttWRg5hJoUjH5LaJLFdpq/dp9fjy7t1Ka2Da7mx/Q27FL774xZS/2t6Lfncw5sSIrM3tMt9svvjJb5b7t7uEh8ef/uY3Lrcbw8RTJRp4ZwJQtGs3tctgYyWoyOaTbg8DWMMmFQZcLY0iJbZ51PdFayslnapy3LguMGWMx1pPIYJ1kxVNXYZhOBz2asc7/OTkQTa9+NmDWqS/bX4Hj88/bNfL7aYXH5QcQdve2nQcR0XNayQxscoyl8rwvWE4chmL2Gp1/fs/tnluo8ebJrHOQ1xhl7fU6LgeLisbouPl+r3GcglIeb2saw0QHU+OPuQOkzdhN8oK6fj98+BipJdvqldkVn3sWEcIikY1UBJDCFYmATZCIa4galvuBTXjGiocRiGAJNmmUCmHe7Jha6BGYfx4ctQEruH6IhBebmdetZ+tMOhmLcWkqjG0uelYvKoz1I19oZuVErQzXV63f+n5Ezc6CssZrKurjQIDqEPTHsTRGu/ruhBzg4XrTmwz9iyFr5WETKbMSuOh4fi25qxjBrLaAKRNA2vYHkIsHtG+eCJrzVlBTA9Ua1SrcrbdIiLmXIOCorNPzmRvFInrsgWtq6pagMJpDNOWCxeCnPkI7JGWuukEsgG2KmBoQERF3oid1basrnFkEhHr+5ula9nW44vPgCjz67JsX692/fP+WIrIPDU6boe1jQNwWbkm6of9JP3xcOKvt/MFU33fWLO2XV/rTUUndatEy+q2raXo741Z59mRQQ3DDg0miaM2O6Muleso6GHLsik1BEV2jbwt3quWs49Qqw/r5ejYOI8GQYgwnrSMc8552zChLAQQwbzaQSDGwy5la9rtVMWsqLmvNUckndWEbiPulm1DYqMddEp3TREWB5yzOjz6BN3eSsYg3vJdaZ5ZNDJffV9APW9rsd22B/Fh9Junyx+/4VrJvgsl8CyNpGr2u5QlVmO1pISy7sZhW+3buqaGCWOCyUFXmxRaTBOvxAqts92M0Ph+L4Wi/W4xz3bV3nc8ruScWEdaLwkmYRg6mIRa2AYos0yiQEDtgiJSUSX0RgI8nx4dH2AMbrZLNN+NvNYkMEuXbmmcjx58f337p7/8GUBgmoaUwgu9nvfwOITQWhLF280qKxJ8/SJyoo/0jWV7o/FDLLfVPrl88c37xfrYoMy1aKHQZEi2O/OQegT4i1FdzoT84NrOw07v/dX9Pb52pVfVfDQ67GVGU27zMfYAbTnjbUGyFhiIN7WwbNe103wXhhe2sXr3+t18ZvoAZWVjwQYFfrfTsYRXVWK/XO+w4Uz8IAoUjD3Tns/uGeMEZVOQNJS6NgIKc09SAQKfTkYK2RcNXjW2yvIGZjY5o1HoFfXHKCyD8BFBxjk1shJu10ld5Nd/mb5fp784OYF2EFjRVraTeGJq+D6dHvjnRVb98e63TuQcDJ9Cc+52J/PV99ACrO7eLv9481H907858/2ImBVSCkAq/J6p1l7oYFQQUrMmbtsqCiKpdc9CQoiJVtr1zQ58+eIdOy6bfZUUWz+MqTGcG7dqa7tQ9r/6LCvy2ftLLqtksTKfnvoPDjf7lRvYprdK7jKxLvmReT/fGoYz6HScwEiSMt/e68DFIh74kyxivOYHxwcSnF+9fXV7fXPcHc10QitOLTgIOuGJu6ibfbXhwvbx1rUNblfL5RwHRGzEBTJyv3zUGHowvL2vKVgvFmrt3Q+sh6DYR4GJDNs0CNBsO31NSEcpSENU5E2VUcM0YcG3RWqZcM3YrKr+Zti3zh8qXtk0QF6otcEFOnj26aEfC0ZmooLH0fzlWxkhrxslqCxL5vlxQzjemxgThwT1lM1nz01qNm3jBwETglpGdreacYD0Eeokq7apIXHEUsfmms+ofRB1wuRmTgjI5ywvGAO1MoZCs7zlV0XTc5yfHHxN6ArzbGed067h6rYSwuJN7TmUYnTY7xnUcQ0jSVin01J5rAIFkZ5g/+Hf/ANWav7DSy0AtQdgWyFOQUD4ADYlN2zXCQdtwzKXQdZo35KrFGKCgNlSoXyguxBcuZRW7boIDz1tWIaYsPV0zyszCCLlxKF9PBwAuyq0NnlrKNuKHgO+k8qyCce2u9qC69Vr8+iw62jE4YmDvAPTIKNqs1MtJklSKV3uSmo7auzbYGhL436xioL49KuHnKuipMrOzToRpTR045aA2DwtZ6bnjQxcH8D0ToUt39SatRJg0SiECQUBYJWQ2jQ7suBpWqbMA9ppADDLyjJ8hxUZ36iGBKOeM0vKhisfF/3oQKfFXXqzWi7t/risu12LiRLJ/S4DjQuV79j5LtcAAmAyVEFlnNgPMcaiqASXqEQQctezLcOxAK10ibYNq9sVtwgoO25wFD/WjrBNByiAKVIyLbLru1kJPVTpjlDaMFzdZq3ihACtW503gyMrW5NON3Acd92yphFC+DWpdIQDx9cYJlkCRNss2tnLRWuMqOtWouoYvtkpPN/BFuzjput01s4Dz+OjB7iy4VS10JYlzFBHmUtp2Vr0RYd1qGk0htWxRFPA+jZnSoYXXhCEeREJJiCDrKjnVTXPZ3mbnYUdSCvv5IQWQHheSAMA2Ha/6BhRxoaF3Hd864Ghdov7XNTOWiaxbk3kH54YVaca5ZZowk73Or8LlV3zYr2NkcyzuTzrDvufj3IQGfcvfNN7Ghyq0A0nEUJYVgJWZeNdnABuGpjx1j6zoqNP/YLZw/FmU71/uUVo02QbrUHw6BMzad9KMGybOcShpq6Nnv43P8GrnxXpx4vT8bvXz2/B/YExaq340aBnEN8eWtff/Zks8sEDb/V+1lTs8b/7WblWt2INEdpu90guZn+cHn59KDjkstaeQ8yDidvnkrimuy3TfVViSgtZXf759wefPz374gE1bGrquqhFxdRWk2YkFOAbRRC2bSmg2qdp3xJPRz/dB00YudAluVgtF7t1Vo3tSX9s9PVE89a3nyip82wRRv5vvODsZ7+R83ln/ERtb2NWpdvd+OTQJM7mNi3Yst95Fnbb+WX5w/d/6SCpPB742Td/yX2zY5pHCBhKQa3APN34NGyqJgw9xztrxRpA4LkhEzWxzLuUHW+nTRh11Q4JtFgvnp0+vD+f/ub8V3O5UqpRAL7bLDp1FQcPsnKf6jJs1GBQbe/mH67WpqjwmejE4/6w1zA+e740sTUrsp0js7ypylocHTbM4NmmPz5DmG53W7A1MaRB4CXpbgLqkFTSwTVh2bt1nZdt0HXTJdfAcozNdjEaHU38cX6zOuoD1zGrZVksc+Lkww7Yb2PXel9ON/Th0ZphY18btLW6gWFQq3OaZzkAWqTc8Af+0LJc39gSoV5DIktEqrrJ87LH1Wh0OH/78pNouG2kZXm73QsS9GquRNPCvOF16nedVlddHpgDF2gjnWVx/6j+eClOzvabl+uV9txu3ClbJlPI6cWj67/+z64TG9397nk5l4vPjp8AZ+IdgMW1BY/1Bu3rkTAAzLPm4MgHGjdpw4AQTKfFBrudru8QFvJyXo6diLp2jVg3iqq6aHkxTwmvS9+2Fsv3yMSQgrr6oC4hVk02PPTrqjuZhCP+cju3uUcjd23UaANARyglAFEZXVZNVTPCGm4BkEEFNVN14fQ6GhhtlfIDZzFLgZqd7WJp+gGsnawmRovqTVY0tg6y+MAijc15LUrB9rVBLSyVo3cFs5Gl0cIfOiexb1u96d3th33bvVvcrr692+77jncxnLBeD3i+nW5zvHJSlkqKiP3FZ193u8PFdA0kMBBAClEfeQ0lsrnbccsyHz94hjFFSgMEKUJypwHk6RoCTwIENNAe8OxYrPa73W6hEXTGPk859aTlnYWYzhYfLcPxBa3Tkm939ng4HJwYVXWfLgLfxhjWwkbdXd0agthg1Bu6hLO7rMJVFVm29jziOGeuqXvdYVXVaGsgr15m1whiJTcUupZt2TSEhuK6T1m1yxqtM4IdZJtaQyYbpeR8U/OaIUTahLOaIS/2fbjdVYDoOHTr8gP1sesel/PLdVHe3d+KjuFHkdyIhjVtS2WEnwZBI9wkzbbT9Pv9ZvD8+yefft76Pkc7JhWss6opN3jjxE/x3bXv9h8eHL6cX9MuNnugqLZAA6NJoYwIsRteC4zYQr+XQteF5MZnR+PB6CcaXF5dv1bCEwm6XrOuO0B64jhe1A+3DPNmc6f7HVfJ9azGdZ60Va9sFYAa18n1tZD+fvfu5cawjC8eORa2p0Dv4LLZN50whDE9ZocJKOwmTfOcpGnMkHVG99vN5V+/ebHLPNsJHx1Rbk03MoxgKwkJIGes0sL1fV5INmfloDDiznpx990P3xXpdhA/Pjl/lO33CgJmD1yabZE5JC1GTdKgIKGGBY87vZXayG7jFqOuHUovpsp/vq28etcbnO0W34V2qrHYbZasLg7d0XyrS5vZoenEXy12L3frmtUicoqwF5H35LrJRA0nRS+1yjCOLSnv8pSedILDE438xXq9T3OzKJu6UzeZgbTZcVSacwSc0M/3yX4vIZqbJprPdzu72rEsVUACHobR6fg0l/n15sfiujRMY4Rg2xS26XYuLuqrOs9Wm7z68/MXv/j6Z/2zs9XtG94m3sPuzx79TGtsWQbC2iWCDsf7+Q+zeTPoxl+fP+jEJ1JBmzFlHFV1y3EjUZcL25Bl3ppKNxTpDuw11bITQOSN9Ytv7prN+PH4nBTdXsdmZIPu90VBNxhyPAld6bq79XyfrMvmQzz+GTY+BWr2kA4rWiyn19vVMrJjraA5jB88OR9u6//9uxc1JU/OThrL0LAr9aJsvf7gk9ksebfbjkZdgZ3Zx7fgQTcMXKwLQxQDLzJju4DUolLXfLuTi+ULzgU2AaCb3brZyu2g33M8I/nw0jox2PvV++nGd9FkfOoLJLipWpirqB9h2ACIoGNZhuX0u6NathgTemTr2aNuiL5A40OMXP9wc3vbJTaxfc+PXiwuo3PrYnC8mb7X7hkYH2zSrTMZhr69u7rmx0ZTqqZRvBHNNj397Keo5+6/T3v9Z5TixYYnyasLBON4/J4zmM7semN70RP6WffZ8XQ5Fcu6aXYot6bz25PTJ8fm4AYvMHCIP5jez3Bo9i6GaG4NTwOIIfms8+lGfFftC2DWKHdwvz7uGC/ebk3SOk5sEjsp3bOTiR16S1oVtxypW53u96w+ppY8OKH51rZNTjATqWsGTSSa3dYGlk5T7jpYa40B1YyErqpBsmbBiNi2ldf5SMNbkIfINSTh63dD20lbASjHEPc6EY5M2xMVVFnb8nBQ1RVZL1ohG1dUPAMWjqNjIaO65j/cz810P4gj0/FinHyU4sDzyEkcpcyybfHpM1oXxgFPbosAW7pETc3KAgdaepCmFGYAK5WDAhqURoZNLNw0dVItUAn3AYVEkHhg+BkrFWet4pp3iENYnheOadLzc+y5rFg3mjLODSmAj9tMwkOCGy9SUnruartul2ldIMOple9Eg0Ej7rWqEKmUVmnCN8tp3exMyw6jZ4iIlt0ZeLSkUFcNa2+g8IYC8NDqWI6wLQgV0EI2TV0mjWy9ei+4amWT5abpBqxVrev5pFMSqWERdt2mLNxapXlpWRCZFNre//4vPzoQPzN6iDeGQ+mpL2daVUXTMo1r5/Do2emw9O1ie8SaJB7HYVoromZQ9ruuVEOD7m7LbV5kH6ZTk9zWdYNB9efv4PfL5wpVHXGSFW8wIgeGA0FTcIMl2I2CfTozrV4QRp7tP34w2uzmdx/udcXOjr/4ZvtcSDqcHLy8/FiV/Gvf2TW4bqXt0KOzx3fOra8MEft1Qye2Ba2gSLa9ujx88NP3L/4f93ebX3n/XmkNIPJ9T0gJ86QFmgeG29p25xFA6Q8vrvpDbBvmX3737Z9vZ8d+tCjb2f088Ma2RQ2oK2X0AflYbm+oHB8S89aaY3h4df3BLT/c3ZpWGEPoec12ZxBC//TDN/3OowdPD/OmzHKHDnSV5iZKgPCNFm7fTYECvdPx0IvupuutlFhbx4PRenWXYlzMxX1WfXH6KHv94crft1VdpFZgmJ5LJ59c4HW61oJQ+Dg4WU5OVvPLwfhBWbyNynUOJe0f9yypwLDXs5l4naXTInUh5yFBWqqMekcnj25eXOF2iyEuTDxRRR1MJNCHMBJGX5IsBokEmzDAlC/uZ6uuSf0LX+T2KsusXMXxmW6MV7v3B86gQnWQZQihJE014zuLDDBMi+LFy+en49PI9hojZQVVVM2mq0ePvnRpz7ER3K5LbDIpRSUK7kRDQgCnYc9JakKplKgg0GM2MryyrZrA3VfXnnHSima9WUJM93UGgMryPVDI7XBj3+7wTjKH31Ts8GozlUZVWJ/mGutKIVjlovFrCJZ0B67YxrBA0cSejQ6Htz/e9iKmDX+9+NAzvOjQz7PtZrucRIY6Pbu/e6NHHddt7ELzkPN9YUzyct2YPbdLIpI6tgs5F0bPDqH1/Mfr3WYmhBAea39sX+wqqOgoHDiWvc7aCLitylJ0J7IhgLrT9bAJuWBXszslYxRSS2GFxUb6YccJjGeg0C7LUqkHpnmrpWfQs4A+fPof/u//0//N71gdIn73/R9/9vTLh2f9P16+JyE39gY2RgaAo0EsuHz+4mW59z77pP/2+s1iPt8astkl5y/ftUzfXl89OD/uxJ+hQyoAoYQS0+S4PIqO7xeXdb3cSz+7X7PY/mk8TuoyyzYrdH/86G+BAkxx4oDwCJ8seeqb2q4bkUypMp/1osHwc9TrKk277x1zzG8Xi0W5N7muEhW7lWcRup8XNS7LdeNPPfsBZXjU8bJo3wsP58msqQPAcivDsDGZ3ZCC7ViOOWN3YZbW3lFoxd5o/S48HkrATAIhkXmQNa+TThhbHk7v1/oIeyBY842QzPVDcGRC6PqoY1gNRLTd1zfpDVoJCJF7cIKrtOyDaPhz81/++U2effrmWoXe7f3bfB0cn/RHky9cB/34/QevG3hmaIg1sZDkBmqhZTh5lrbQmABknw2klhBr6viaGWBtKH4rdp42mGjbYBhAZgKlCEJFZTVd7AHEP/5YtwWMKWf7yvW9Cu2FKG5479gCPatl9WZdi+3NpD8qK6OHfSsXuXtEfd60TNbb+iMDW3M1zalRB//tDonYdyOQWy5x6Q20oSHzvR1oGzIBILYdze7WS4EINi0b1A23iQQ7CZTDuYSBF1iwRI2UgoBA27425b5MiG2g1LaoxM0+K5IkyxE+Sl5EvY5uJW9k00AdBTyrFDDOn0ymStPNZco2n5w9zrbzL89+uRMcmnFe8fHRWKruARzuitV7fde0VSsZFXC93IYkYKa3377Jqy3FIO+fgcZozMIP7bbJTGRQyyYKu3Z0n+ny9lak7JNH/yeqXNW8+OmDn2i7dJ2o2nz8T399HfU9bGjGFm8/9JuyPDv+hWH1hoJ8LKpTSYjdnaWvxpPD/+Hn//Z3Nz92h6MP7cf2+bKRAgNlDY9Aw6ps7zuD7SYfk6CuTaJo/n0+iIf/YMVx72d50wIMdxNzQOuqLupdWw7QsRMTXFJYEc+7XG8NywsEQ1t2fN4v5EEc45sPyZOnB9R2L28+/stv30/Gh0HYsVrL6nd1mpYa09a0rAkZBced6M1mQRxPt7rfDe+ur3dEPXr0YHl3tcdOHg2mSUFKv+cYlLb7V29rdHt4/su/Pv8+3+xu9u9uz94+fvrfYxH2o8LHBFK93VwPaLAUdC/39/PC2hVeBju4xhil6R4F9RE62hdZ4CEO7VWZUwPft0WweOVqVUoa4cgKSd2KsEPLdLFNlwfHQ0A4QQ7HfYLdbWvSYIgU4gJCSg9Hg6f/1t5vt2VExuMn2ySTzDrqdvCXNgAZV8VdUnXAHQLqq58/uQiOGnQ2nTJWNZiGrE5M22rr2XIfnvZpUhCphdDY9nzZtAnQRwhQ27z+7bdcrBvdSfJEBBECg/Qu8QauA0qzUVtQm6lHchHyZLHNeKa7pgGYvl6+0xhSFwsMb8m2TzRcGTOjTlEjYRY+ezpdXDNi46OoKtP76d2KEku2wKBcyGkFDO4FwXH74nX/wEO20WT7RlZgAzA2m6wyJfpyeCpC0tRmxrNkMw96TDHJixZBq0j1J+4DfPAoPu22SVtX6LBTtDPmkhMcjpzAUhbMmDBhiTHlkVK6hZbptQPhMAaJ5bmgbZIMuO2WhsYQ8t1mWmzIiy3VDf7j9//xwdE/+FbYHQ62RVLx3JTeNl8vF9Mo6N83K6mGdlLMrbbc/HGWzZQz+OTZV724HwXRfx9MVsva6zUQYGpaN7dzjPfxJNxlM1F43SjoURMoLFvZptnWWAyO+Gq+3c6z0fH4L/v9/kVCrPWirgWtyirTxg67saspqVi1EPvq+0vDitaiIFdWli44HMAYnVnYiwJ996Fc162nB5Kn2YKcHg/Y4Pn0jdi2xoBKIAiCsKAGQpI4rcNwXvSUZrZa8GVVtN59QEGJEB8ahpRCc54hjhtCLZtxZUEqA51nuT1xR87BarmtK51wxtK638MIoMwlyfXtXpVPBheuF2LHVRZ9f3XlhHPDdXVbZ6IkSSUUW+3WRVtPjj+LOr1f/vKXvutnaQJZ6aBek9awio2xZ3l50BrGwwtETaZkWVcAoZDiDUKm0RFW7UldJTeCPDM9KEyS7BqKM8gCJ88WyywcOOHkdH976brYLJHXq8u8m+xvd1mmsa3a6mjy5MCx6m1Suft3izTFLTRo4Aeh7Uef+g/dn/7H/8zWm/WwNxJpM6w63vGxOcf34qZWbY9IPBzTNuesFbNLDLFBDaAFNbaaQ0AytdO65EwI6O9lRTS0EbT91jcM2pR1ylIHY6UtFOKrm/c8aBzHZQ1DGColOwN3nxYzvteaBbalT4+RVpvdDkmDwuEPr64DbNfVTlDX6RuW7y9mq6IpSbcbd/qD/mBbLcfecRw8uJrtwtHhMKT/+vyHxZ8X/+YfvoYaM8YQ1jvGEbF919dMKMuwTIuSkiLke6YoN399/78u9/vTztO2gf0+6559vSjLOknc0GLYRXlLAlG3l/vvKj0cOTWqK4ZsEcfB+pslL7fH46OLw9NX//m5aQLWskARcT+zw7hHQgxp7HU5Mp90+m1blrWhoASm4XpBEi8YG5Ncvt3kaoRNOd/fka55+GH38TgOEIBl045sc7bfnh74TQGguC3uYl+W9etl5Ln/9OVP3y+mqirbbC9rC2yTbtDzMG6LotsfM0qqvCzKxrcsPTDadgpNYNt0VxbAqoQGWcuh6e6bmeX3PKnKHLYMnozBZqzr0iqrqlzkdafV8u63/5qG5L47RIaB0iQZd79ial5tWQmAPTKSNwvHseq6aHJxcARyJfJ+B3i24lki1spMB97jqNOd/jgtyUalKhfrEetv5b7rWgDRsqykJa2aEYCARgQhAhwKEU3XFmZB4L0EWz9LW3OSs6a+WkmQAw9RK98JTm2HSj3of10U1Q7qi4DCndp5IWw4qZFQSspC10GiAHERKS0JBlxKCBrLtEzDkvtZAYRvUJ2swzBKytLBuO9NSrHniQUNOqJ2qmXk+n/43V8+vL07w2Dy1afRyTlt94aR+86wKtY36wRbACIfKLLj3KYRrrjcZtB3N/s1VPzcMRXSW8RlrsqqhhpHxNCNEcbx7fsrZKhGlcaAOg+cfrfvusHQONg2zu3zj0KCXVtA4y52u2dmJwqi/+l/+eP7N6t//5O///Krzz4+/xB0XUtuWB75Q8cYCMdvRe0yrdyQG4bjOr5SoG11VpcKJSNtmAYghLStCDhri5kdBfaFlb3bih1d/OHF0dGp8K1+FDUPjv7144+9kLm+u+NXtjvqCePw9EybnbBn9e2LB1kCEfrr96uTE8PAZuoGDNEu7oBD/vq7twYgnQex65VpZcmKZUjcsGsmFTXdfb046Ay1Ld/U7xA3Hj48PxIqyXeEwvhZSPbZUisBoFSJblstGpWskiyp6nerBXVOukO7ezp82DOXXUL9anNZb69m68rY5qeTI6ulpmFkeVbMZj7uu1G02U31tJyrpG+hHCG6KxvRqIwApKQB5KasoLBbaiCV8vzg6DGT2JBV43tzVls4dF0kqBRAeeOQigc1ToFhDS7OQaGNlcCBIR1n/uHjfL7rGr1nR58PnXC1XECeN53YefwwdKR/bvc5T/dJVVW9QXwaiG+/2//z6//6ef8XBhKx2wFchv3R6iapTdfFrmyTRUuJQYdZZmlpjuxahZBgY+daNlASZqtqMAHu4WOT0I4JhOSLdgcgJCrtRfFeDszhAK5XMk2Q5bGaUXMQGogod6sbKlpDpf1Bv7j6WM/n4SfdMDTy5lAj1LE7/bCLiznFzt//zW+Y/PjJ6d8vV9mPd6+CHyD8dWJpKhALx77l2dzLwLUEXkTaliCsmAZLBYuSaGpgwm3CyhJkCTE9IAA2CaWQtVleFxAJwzJbpZLtRrHG8nu/+OVvdpsloWHd3oxhGHfjrCi0ZQY4rCjdtDVnzahpTIQyYEV+ML2fmQGqlzliJmAyIm7aTEvMH48e7W/SIs/2q6t1vurpi20Lq4XtOeG6VrYnsacd0zAIFmvW1MogxA0nCPPtuze8Sn3HnS6+ffHhr4PR0e3ND0tlxw7Gtn06PnQefTrbLbRIHJlUTdIsy+5R8MnJQb2e7lfp7no1mQyL3J/N6tEX52+fv/7w/NU5xQ1ItPL7nd5G58jW4lJp3zA6WnJewzI8fao/VsvddZK+Sb2oiK+ibufi7EKJpZ4E3d7ntbgm1Vn/8wloUTif8qIBVWZ2DE+1A9fe4O1RZ+AxL+pEXuhb0cHVXWob7epmIxBum9KN3KHlzffL28XMe3p+0O3otlmtd7sstzvjvq5b2ShsHZ8iYGgt1ebHv5z0/49Vup4cHNR1NZ0u0ldVWtdR3G25FgOBZoeDvl1UQKnVN2/3k9CxfgKUMhiy1OwdBGRprA9HXZ8YFBB7SIrWWb17Pj44l0Bev3pTr6p1UP7dZ3/Xe3BqKrVdbwa0NxgPw5Z0xk+2+fp69hJbym+WmsMkyy37ThCab4ptv9vOp+XWOXQdOji5XG89g3qGS4Ke1lcID43kfn7Fz//mpxwU//W/fNeLT0f//jckMqr1BiiUSy0SjhDEHcPHxMEop2EUEi44pJZvEDvyk4VJORe9mO2qo854/f4qyXeswyNyZCBOjbUJNEXOepW3DY8j/1bA2W9/93DUq2zDjONd/jaOu8FxT2fAdYZ5y9TNlQoHjAxgYIUQJVlChMhNxKsNIankcZDuqqJJzYoKzz96lszvv9/tRhgMnxgA0LbWoW8v8mVdTm0VLDmQrcLm6eX7m8GEnv7jl+M/vVt4Zdez0UrNPt76xxd2hFO56ukHyXUr3Jp29xQiA1EtmraFlNi+Y/saCJUjvbdq0zRNSsneD1yuUaP5PkTcPDr8tVW94u7RUe/UweamybuhN+jaFSrMGt2nO/zoGJvmHJbT7zanx6PWpuHQsR5l90zYFo63s7xqdxpdnHcPO6QWrLi9VWHXo1JWzYj76b6ChABvSK53YTdSWqZVvqq2Ubf391/8D6vFVjaNZTtkub2kEQV9CLEBN0QEGGIzOBmFuX/+JOr3B4yLImvy7MbElpYCdbTLahoh4VbFpSjyElEX30PQk3F8HpyGhotO6qZsikEmE18YNTcxEInmCYrxxKRGmm9bLc8fDqjlCrFYNzWlpLEMpcsCgkMrSCFCTdA0zfa6GD01DKaAMmyzUzf7zc12I9Fk8tCy7ILzu2lBEMcEMcWzIm+kEXbEwmCmR3oVlmr3QVv2E5u1epeuA8vYiNlF8EmRycaFzIXFlVBl0x0cHR9NIIYNE6jF3OLFXu4Fh5iYtmWYRm7CgJeYsxKPkCgRFky0COkyy1VeH07g7W4fOK7RUGlZ2TqRWZ4B6EjuHvZsK8gvP+rdGrvQry2TO4+/6F7fzLI8xZv7UJilmKV4PsKTZLW9ef2n65fs//zLfxMOOt5dlOp30g3rciqmDEAIAKnrMlMV5gw1TGuNNAAASamB4GqrQcRigEQjIGkohIrDhOemD1d7ltRraiCVNywgvoc91xV1TIiZ5dmiyC2sPDemYM9bT3OhhUzbgvO65t52t3189jNjYoMGMMPEbB9pPxBG0mxnN9cHnUOB2o4bk0jLfSKlb5seMqTfBmmy5wb2LCf3DMWK4OFpH6vr3UcFQBBJLQWx+U+eHLvewzKDB1bgBdEabnzDNEpNV+u6McpTUd8rt5t3+i6spw3eXHz1dQC+Xc6kY+FhH5qKrv74B0PqMk+xghUscDz81I1w55wfa7bR8Xnv47p5W/0+kDKphKhqRvPeOELC6tVgA/J8LUS+vrr5tsIJlOSHv1x9/dXn/viB1GIw6HsOraoPWWr2LI+QXAZRxUD9cUbOzfPJEWPvon40KyrYVsXdxnoSDwdfKqJM7e1luWGtaLJn549Xy+XM1Nim7Xo1mXRsnAXuw78K493N+wMVAkkY84p3ez46PNlsn/7815zKulbJqW2znhsAU7WfRuRFKoZJntSkZvetRjmCZ9HJgRfSTlkyUbP5epE6jrNar4Ige/Tsl9nRbfMeXwHyk26nSHKpDLs33qb5f/mvr6Lu3ckXx6T7LMzrxtcTEkMh23S3bIVvEtsxSYOhBbP7qcn3JtOO90gN7SzfVbUh5UfDMCVXSMe7xV0Udg/Hx4EfJYuUcSkFt1UFIzcVPgZwkdVQFy7StmcrBVnTOhBBrjFoBG+zGV776XnwyeNH9M3bhWa76nYzOB7pwq8Ss97lXsZ/PjkV3cay8Q9LblHn5PEXdtxdpNvlckl2nRTA5395sankyBnFBw9I0UWpmKNNhwAlBN7tIRaVWUfSaw5D2vRRkS+zxaaO409/Uf1v//nOBgdLz2hs+GCw3QSsElXeVlkOvdjAztnFJ5np18vF9lX6q7OHXx88CSY/a3fVoBuzae1rb4/KpVpzgKHbPYIEWYwgWhSlEqJ1mW0Io0ZEIyKgREwZBCDiSQgRVgUPFBoGB3Ulb78t3Z+w+79+6/hu1zUWm5ttWROz8yA4Kts2DuIf9umJN6nkfCUJu52K+GF32Ev2uzzfG/0YcJXM53NxExy5aI4KpBUruSxN2ySoS8sijOy6SHOvqtetHzrM9kW1lRLcpHUm7hdb+mmEiMatTLXaw6ZqWgYxdTA1sdvJ2wyAaF6lV+uXoT4kwMvr0tC874bDwBU6q6t6u82aCnpBlIW17faorjVQg8ZpTDP0PWzqT9Cg9nBTN0IKe+UnSdaVsvIBdBCigNC0aIHvh4TAosz3VnUQgdz3xG4PWHM2OegMRhhhK7I+vL3c7t6VlcBdz/NDHUSrxbpl+dNnz8S+EVJisySCaNy0QsCsKbxiDZTjOqZlIaqGsC+53tkquBfiE7WvGxaYHc6nPXZs9k1KLdu6uZ9Xdes7PiaW0yEpw1pr3zcZdvukSAKqE0lVWiCgNfM8S6BWCJ7xzYvbfPr6o9atY1pAkUOfjPxDSIlHgK4zUGRI/v8Jwq9l67bEMMwbaea8ctp5/zmc2Of06QSgGyBYBEiaFmhZYlk3YpVvrAdwld/Cd/aVy1d20S7JEikSqYEO6HTCn9POa+2V18x5zjHH8PdR0dCdSUutZE5wMcvCFB2ILC2Qb8iozPglXdvR6m9//svLlSR3j548Sopc6muQGli0IC+gGbKa0boGde1gnqtmLStNVeDdlgKJUo4RwYQJHAkYyohCVNZIirVaqkRJlRuaN4ZSE5GWmyBI5RxgtIswVtNsHicYwAnojRwHaaioEEsLWxITFuoM197tQbs1OVDDvNzyXCVkog1AuvLL+XffnLckcXA00UXTTes4jFVVP3YGO+aqqZEmmWN3DM3YboI0CeFgrwuh667XxWJs4yCMqjrHNRJlKcfXtt2BDStj0kYtuVKy+tbUDEsnKurpj2/KCgkknF34Yt3syO/yrGLMDMLQthQxLwnAH7W7qShVZSkIYpJSRSlg/h4Jx6lKu77Q/2xUPnO4qEB0WwwnoRcVc9L7eCRwOD0/Rxgd7O8tYDPqPJQTcvHmHyAmnf7w1atvjQnD2UA15RHqVznFOSpv3U25PN9uJjOr1XHlXst52CgpLGLNWwXRzLdGblFV3tZHIwhA0yhOkoZNkbHNWlKBIus6bEc+c/TpJ91OiNAf5u/B8uz+vXvEQh8d30ubLBcc1GQeCpjXLCsfNq4jJ40kCgRWQRHv6v1P7kuF7ygCa6o8W27d2LtKGodgcTjmxgzxi4uZok3v3X8wv3L9D+9vFB0nAkNj7pHt0t0udxhiBx4VCdQUjOM4BiGHwNRUadLmW3847JSlVk6n1n47KlBtC6G4O3/+QRKgboWcTdrtUVW+vLmZMto19PjTT790NOcyvKliRZBLjIntWFUidS2lbsSGQ8I6ioKjFKQgH4gEYlagsDO8F6J47XnraYhH4v7gjhe8dbfr1NR6NdtgyrBy7/ipcjRsWKPrYvcXf1PUabFdXCymzOnapvPhfA5BM7D3W/dHPzw4aTmdGYDJfCuYWBKBrKuCjMumbCSHB3mDQzmCiqrZojh7Q08lZTiYFCwLJQvYHaHIz28uKivr0slsthCFutMzl+9cpSZpwlk8b9ltjCRRJMIk7/bHN7NpUxsD1sfEuCVEZmXEShAHHOaaRGRRZJVI05IKCOAGUVq3DVLlta5WRKAbSkAjyZYom97LD2+vV/eO8+5H3ShVCvHm5HivYc06217lV7jX6o2Ge6JKViwftyJeVqaRzr/tDT4VFKyw1AtveFMLMvA92cNLJE26gtbkdaDUbr7uAi1SsNpqlykl4pGppJGkV3Euy0Zdtr/+/TNl6wMIv24i4m49QbcU0ZhjzAHrUzGWAUx83gbM20FWloUmSqgRgW50YF1U9A1NaZVH+bKq6gYgNYnyNV2OwKDV18KKB+mqYmlZpFmRElq99bYm511Jd7rjHQYjMsnSGhZYICJgFMliksSYoBTXVtndlH7fWokdbOrDDOKLs4vNdiPLyqDXaXfUnHO9KHYQkbTuDbuqNWG+e11eAUxw3VWNAsK9NJOT6lItiS5JRp7HRQVkc4txjZlZ0wW8/VL8AUGJxJv5tlJZsaMNEevzy2tV1yRJz1LKdkWuCoggCDEtG9VgRVSqWIyUtJEtvrwt8xQJTZY1Mq7uOCeyCQ5/3PfczaQ/MgyrqhNRKs2bTBDwcuFFWXI4HldpFs9Cv3YV08kESUuYcqedRdfhm2B0oPf2D6jeni3ef9kfLoWONWjXVJQybUuIzBNKKUc44hxQqsskkxRxU6Yy4EBMDKva7EjTKILAedk0DSFcVEUmwLQMhKKWI0gdWkUoCS9cEpkWQgXSGyNSIosN4qZcL3Kl0MxxTMjtuka9GnQqCkhroEmA0SVP7QmMd65fFAblEQDLsgarawwqDcKHk3HHBtuLmVqLQ7OX5dZtfCMBzfeC8f448tI8qvK86vdGhqoBWdkJkJUCKMwk3Wl9qxbStGhahTMY3Jmeh6IDWDad3fq+R0cjuxl0O2IvgFaYbSovwCmAOFFWSbYqT0+evn79n/+wir4YUD/0HNs+eHqCUniw/3jFmjKvpVzZVJRoxte38+NDle8/0O3h6M8ffWV2/8t/fB5HN6onCZbB7bD0BYZ01SQyrSsxhrjJeJVZSn94wMpyuNfznwdND8ZhnQdBVtYxBzdpOu4eS45+5i9tkJdlqGknyCKYo00WyqrBip3ZtICpx3F+W5QOYxLGhUkNSwwXG5HpNmqTHx4UxUhK6y1VPrnzVb5Ob4JbTVHr9JYoTbrbqQNDWZVEIBhaeRrQEt376Gk/Lt+trjJSFRGPi40m1zWuEiw48r7k0aBKkIOODm0ATjGC5DCMXvmb9+ndo6O2KPD5/PiLxy2ZWK02IiRZX4t77UaWDU0BBDHH6QpiZGvrwPPf7xbp7Yl6t3vQIwZfXa8gbLSu8+bVrXFyG8zXm3Ws2O/qwM5zqqqti9sGYUmy6zypipymYgmTKHM0E7B1DjpmgvEQ8EADwNBMIIBpHhtDRTJl+Qqu8oj7hkwVTeo1Kqh2SdVu3ZFVy7inPpiwBnrvruX2WNON6dIzO32lKt0w1wxlMrovii1BNAafTVqqETLcAWL+dJ/t8nI2VdsmlglLGw3TRpbTRuF85ValzrHltKAl/+XBv3qzeBGzqCrAjcdU2eyScTMZd1rDPzo86bbHlxfn0xelIo+cx5+SICNldj39XZqXSJDSLOOaloaRohDe6KVUEyFSRcQoS+qirFGLANFsFVWGBYwZroBQyILAMsCtpis0rIynkFbNsH+4v/bMMIuWmQA97ARxFZSBaqiOcTB8YJ5e0VrYVkWnCWcVLmOpZW49LS3O9w/w048+ffvmxfzm3enxIWuqwAN17W8o1zkuE4Jykjmso/d76mB2sYBDgqGiJgxWHRmkWYF4I0aJ8Kd//KW7cwnkROFERsJj25Q/fzDeO06jIi0SJLAruhQwaLWEomRZFhZQNyIfc1lgAbBxGTUq1wl20kI8Jp2g9MmqERwpjBKhNnq2EFVlImFvtd2xenJyv0mju90+40SKl1L3BPoLV5QVQ280KTWU2F0o0FO1siiJabTziq4Wy+V2I4mWeTDa7FKBs/Z+q8mKnu3ospmnvu8F6/A8SlIGeL/b8wteFCuCeVbQmlMvzNZcRLIGvW1LYkIoByuqP7REUbAN5C2r2rvghmPICs+o1R97sVtnlUFNJCFVaCJGs2J6JI5LBBPMGIAG0jPfC6qyoKUqQlWrsCJk2a4ASV+YXJ6fm36gfDQBCYxWdW/U31IcRSHzclHs3l5+27U7RUF3O7o6u54cHpy/fksg6Xfw+uLdFtJWO67dSuk//oFum5KdNnDd+MQwgySEIImLBiNBV1UK1MQLuNQUSZWnsi7LWncEaUOZh1BTQypoMG9SIWGQ8KihuV4oFGK4AGX10R1HzJOCZ3VptQQHsKZ0o1LIi2Uligc3ZYUYdIMS0NZ2N8uDWNhHRZkyziXlvIRCpRKU83JXTZx7SeIeiSHg4u5iW2SsLEoByorabakdRzP/cPGbO3f7uSTtzrx7j78YtO1f/OG7vpfmyVxqtyRFuJlGTdNSAC7mHCu1jPIgfA8DJgpSVZZmq9U7Poxgc+VPb28uh71hXORaH6msM73dGkrb9V9fNZwCVvKqPx6ByWS+WuvNYPduJY6ULZBMpcqZKJsxSNRlVudya3q9GbSJIfX+5V/+cJd9hktUSvnq3Tnq1ZW6A9flImqkvsZH7eVibhqG2hu4G08G3SB4o4iwqQ5fZX9o9/sK2H+yp44/771dvFtJycvVyjSlL/TNxZvUcLr9fm+63okiaeraEbCb7XjMab8vNqSV+WbtMr3s2k4tVG3Ywf1DTXKwKOkDaxshWLUEGZCGJOmOOAUotxBQAio5iHlRioKuJRsBGy+nW6poZbUDSP9u7Y4m8mF/kKfN89dndrd/Yh85xoGvyLqhqGSvs4eqjcaKIs6iuD6TXuwfyjVnrp9AGTHf9URNiPOMyNDWOuvdzXbjhtPCGCuD4R3Fsn+7nHEXI3LLR3yvuy9+1VlnuzEaTP5UBxmbZd4Xn38yGIzeuW4DSFGkNaPdXj/LCwBhFbC4ryFvQyUsYCKKEkaMyjxhmR9u4mLR1Sf6gTHwyHId1AT3jIFDjdv87Xq9Obj/J+v1DOySN4tzvwofn4yTxe3aDwpdaLfbfVG4SKmWlLLiSgq4ubq8CZBFClXticZmpNJbvZo3sz5vq221dsuqLlFat9tdhhVNAJFlqzQJkNe/MwK3uOEMQOPJJz97dK+tKtp1GLlJPnu2Wc2uZ8vzf/3F95IP5767QajZuTvdslab1eDTL1ut0eW7LRAV5gHfr+xG0XjURkrd1AJGMpT8qsoyREi9I4KS5UitYGMoGZBEnieCl4W21coq8YuvfuJVqWEnhw/3fE+P85I7ZahIKAK3yWJRMhNYDiK2bUVhUJZhp9Oeza8vL3eolppGLYo4P3sPLU3EypFpfdgVdQCQgGRVB1wqb8M4SmyzdftuVaskzo2Okcu6c+D0YR2Veo9wcdzfI72DkyYupUO93RmUdf1m9gaJDoKN1lKlD6xsJ2FRGHxPVESoeUItSsRUAAhCT+wqSJJYCFeAdzDvSMK3i29P6HFdVpSV8TaXWd598uiL3fr5dpWnvuEYy80sy94hjAanutR06DSar5ZFxRB2IjGGZSMIjRLX9hHMw+nb86naP20d7YsBJgOxrjKMILcsSpubF9fYKvaPJrI5KYssS1KT8SlTJrZBJWm3maVJJGBp9yaQTpW94VPb6ZcgzdHSsHsQ8vX0ukxSiaBB12Y1jRNv69ZYlso6lyQMdERR41DoIE1slwACruSFZJd5ndQipZVtGpJFoiAuq/Le/dOz63dMnCVZ8m3NHhbU2tvL11GyTQs1LW8EDvPNLm84vprNJVktxCi3WUiz6c3sxLSRbv56vqyq+M9k2Wq1ma2JlgIo3oSo0VWcpxTghumqgytMaQFpVVe05pTXvEYSkw2zSJI6yFWrhKUKtIZimwNCTTHLkjzdSAY1tA6ROq1O0Ok4gUdj3+12JUZDFnMiO8cY/M3sd4H/tN3CIF2jCNbSpldzxbIX727JBCOMQNPIidpqaYwChgJd00P3Jqsy0p4wlEFqqlJimQ7BQGs0NsTSTL54uz44uaOMtX776Otn3/lrf3R3rDYOaJs9Xdv/7EfRKpe799Jwm8G4zw1RVCUB6qatKJLeeVBDvr2dcoUuK8/KDKqSGoiroDy9+6PN+TaU9n/wo5Pw7CWvoo1ISByaqtK1xxfFdFdEdaUzpfBCcaSjxJREUCPRzJpyvdzdXi4f3rujKgpmtUyq798dcFxdlxk5rVAuC1SUJTvwIrmhfhggw9oUVG99Uhe5/bBH/wnoytFJt78g/P0V2+2iw49+9IXIluULJNJEb5bTS92622u1bhY7B+aB6w1Qq1HKZr7CqFQsgVdF22pB1pteJ516F5P09+8uHactiJ04WWlGv5HkHUhMgsWAikZWNw0mGexQUgjFLpa6R9Bf3H041kzl7OUQtfinUKeQhkTt3518Ru4aR+2H+/vbRdIWsnm2aWnWgSz/+pu3get/eXxXzvKiuVQAkThts3rvwZ1dGESQD6BamGS9efv+8sWefpBqTO/1J5rcMK6i7tqdzX/7Ko7z9GfQtHCxbXaG0213JAl220pncu/yKo3DvCp0LMtlcqsosiRLWaLjvnaoSLf9I1IjyvM8w+rRSHbUZOVifUcFcb14JplHXcNWEnuelZ6/zJriXu+uu3y1WHxTioZNOqef3oONOV8tyP5hr2/OptfLbZjFld296xOv34wQYFayw5KcpsitLzbXz0772siEL7fRNgWOgzSiY8kmTZRGGZJViVNFiosy45S1O22iTAKMf3pyUpTVTV4Fr4PI9yQi1pV3ePDpz372E5TD5cW3kNWVmuAB9GX3ly9uxN3uL/7F/7ZEyUjhhYiZAZiSFBiHu0DRpMaWt0TCDbZVXNEaQZBjEtTMAGKDwJ5mKqBiLM5JLxLWxGvujKR5qG+XrmpgmY/8sO7JtcdQlKdtWgsmqpmhKVq8LFKYGHm2p3G/gU0eyu6qV1Pd3JPDkjZZKjf9WipFInO1axJRRMuMrS4XRJNHg467TX706dPxeKRrGk/5+XVCAXj162vBTsjvL3ZNw4Rt8f2v7tZEnNc7JWnEtBC6B87DFgPDtft+vr0AsNelTIViwwhptJYkxXGGsZi01S5THxx87PTB8//Xu0217fV7hHdkMfPjdyCOqqFe72BEoF5Gq+0yS1PDMGrv3FS7jx7tT5iRJtn5pZ8vtiWAmzS4Y9u4tbcJFt3u8OlHd25nBUdbIhlmwyDihSgIDEknIKvFmqas4tAleV1TqTRt1RlO8iIXRj+sfL9EqBK/ts17omBmMbWLJmbtlmywKgMQ7ku4FECeBHkY1nGhYbGoKl3SiiREhQBsTLUMxHXuhmGVW4jo48H24gOipViVim3kubvzNu2OEpa76+vzkIhPP34ShcH0wn/64HMdgngdq+Y90qkvk7SqlHb700Lw270JasuVihbB8qPxH5scWcz8C1FIsi3c2w/yZVXM9u79a2gLIIWMA4GBSNY5JzprMpAVNRRqJuvtLISGKTQ1FaP0fHvBGb2vt0sJiiUjLV5XCoegLJusIKJqYkZfPnvtx97pnZWu4zB0RXEFZpVtd5gsTl0vz8uLixf38ajwGrtGNOaopkSSW60WjerMLmkjKUKTRzxPy6SujTBMY4pQvyppEtckXqtdY7lcrpJLt45P0fFBZ//r81/po9PD4cmr5xfr2aL/8NRhLFZ5fH31BjrS0BF0vH98vwUPLy++UxSz0/l4s/7GmOzJVussm4ZF2ijQefjwbmpG59cpkYqIrMNKM/ltLbTwvCUcy6fC+pu4ydDoxNxBY+PNhZ7JASTEsJBYD0sW+BooNrJMdivcbqGN55jG7HrWgNpoasERsBMYbe3YUHKoTJsgaBLQ4FqCYmcwEMzYT2rKuQrdhrUzUjTVlb9zTmR/q5Cu+HD8KdkfXSwup6uN0dcPetJ3LF1vXkiT7+/12u9WXoPhyMZpAle+3+lBU9NlyTR1Pd5xlvGNFzQOPt5/ZJrjGgnqYEDkMl7MynhXOIAVrGjlTcT7EOewrqL6e+2xjNH5tx8gl/NjtdWy87zQR3wae8FmQ7BkIUuRpOnZxYtn00GXFgSLrDoDYPHqhaPIxc4BdbvVAqJYrXEq2l2Y5TyI9ztyFmVfP38+MA0ScqUrPjpsKUZLU/FZlsMS/+jg4H95+S1C6N7kMefN37z4j0/kviIr4TqaDO90lVEYZoLYgtDDkEmSlBc1wgJCGYrUszpXYsXoipxRaMIBxhKrL8JVlEPHkIqi1ewWVIwledAVzQTgEXF28er1u/Mf/ehffvynn7x89WEy3L/dUCYTfWA7gmmb9vUqmPQQA31X9R21VcRVELiC2qkRtRCkqFVkVVVHumbgBb6dLg3HsnVT6LSLS8iSZSlCIFCxU8ioNQVXcq5f7LZx4ON2V/PDcldYUu7rhpaLXrwy3buSqPE746HWA3ALBeaF4mffzz6856/fvxZgfzhoRCA4jNKqzjeupMjiQECqTouGCoixCjGLo5QIiiq1BKudLm618YSzTLvV2Sp2p8/fROGdclKg/HCsQSzGfmCZKmOtUAhXO29fNS0yrfI9+5Ak/f3g7GUKIk1XJU9Y4Hlb0wa8B0EmlapYo3WVS4EptslqfbO6TRWFwLTpS5IF+EHLGT3482YsCEjYfrcK+Wz54k0YoKoHa7cmccB0Uy/K+tt5cKejIdVUiZGb9dvzRTU7n3zyPVGxRW2nanFDh7deuq/agUYBzxsRSqkmqUMlddwiDRfQaFvOqBW1bHGz8Ski7XuNwDX58KGCb292zJ0bLe3weJSmaXi7AXZjNnmtCy2n/bMfn+52dyrvLNnY+KjNGNaTfF0515c1IlXDeoQmRV1XCS0vd4bakgYK8P0qhvIhVhsNFlAbdZtNpuxWjqTt3Oh6u1h9WMZxZFjr8Hpt91uNc0DYlSAUtCgc3iSiW6wLXlBa6lpVM5kpFStElUu1LMgoE4DcFBaTzFZ750KBV0Xl1ZHemsCEuHnMeVnh3LA7l96rZljgkkq2QjHlHvA8/+DkUS087/T62r65/S6oMRMaZ7NcgNDD7YPY261rfq9nJEmZL95blg6Yn7KbKWA2h8PJAWOwMVCdoIpDiXGYZnEtsLwqLLUNQJLmQU2FmjShv64ESiFnQGC6BApkqDAX46Zut5zoepom+dFEskznCOK9+wYumefOZVkATSypTkzE+duLMM1Hw3GZCdez0mnbmb/j1rByt+vZO0VRNEtLzmJBZXNKqnrHUiA78utoU6TMgG584aW5XyiKl+I0BmGcSkJVpGX/uEPPqn/6xf/Y+5f/LcS9ew8fb+tk2kCcRBwhVWYAaADV/lWqyujg8C4sKG2yd1kuzMLSJoUzbHNZVrnP+PV0ane7iZ9q5fDBZwfbRYkLvH/nnqikSdlIHfLuJjlA3FSDIEoP+h+XMvvu9SvBsuJ317cKRhzKqtRGDWj8qghUyKqiRAgD25SwsJ3OvDhQDLnOSUrdI+vpEjTpzvOntyfHp16Vdkp57+PT589+E61DVToitJzPZ7rV7Wv6La6q6Xy5/BYXUux06sQFcfLWCw7TD72Tj/tko6sHsbdK4uRJp9/rO6U850iKX8SLm2si6AUFPhAEhV0uLvuDO6LOotVW2B+0ReBKHnCRHPYBDxVZrdww3HiEZomXo8ooy8K9miZpSLB3fdMI+5mYCPP3l6Wxl3yb1yAcdjCtSrnqATENwuDR48cqEl9u3Ltt2U9Lpc6KMHfbm+xXv3oeh1/uTyhGs+Vi9Pn4RJV05vXMrl/sokLoMsS9cNdqPf4Xj6vrKmKzA0H92d3HAh7ZyHx+9e7PfvqzntPOEhBVlFaFJAqiKAhEqGoMAUGENFXZ1GXDJMk0BIFrioJwWYFSEuM4qxqa6Gni5aWEKlO2kdWOa99m5pOHP4B7cPr1u6k7u5pNKa/6kwfLaCNatmX3uuZoUA+ysrBbvTzNY56QkhiFzxpOAZREoqliCpJWYhsj8z3N17fpmfe7MlcQgsOBaNjS5FSvMjcRk9uX+U9+/MMKQ7chwiIGMO6edtPbzUCXb/RaV/QwWa4W5+QIotaQRzihcQq3vcO9/TvHt26zeHm92vRxnQPDRg3mBGuKhCvAq8qQraSAmiljjLxQS91akfwWNnr9QwHLFFFZEyGYTwFVDGXYAWlbVAUMGOz3bFXfy5G6n3jtIpVUaectqlzBSyCLbWOwv3VXgDbd/kBmELxfIiAIWACEGarOQS+pd1mitp02QQ/3CVzSa+OeOewMJKj47u/Dm1RRlPntXNM1iBPcJVqZX6cJ+W//3b9nrPnbv/vr8pauO7AdxDXYosm4yvPVelVc/lK1CFCLcBt/1hrj8XB+vmR13utpQVAIAi7cdZFHa44bWnJMPUtTsjSpSqWjnUcJEVG7nWLFPDy1BkWQBgECQFqLWZrE01B1rRqu+pojDY510ldPj4VPDhe3q8vfvp5V+MDWa1xppm2YrSgNK00c6HspnovQEAxlF8pNnSdBBCOBV+jopHs1CYNc6Hd1I+NjZ6J2QphE+/3hsr5oWR+dECn9/ZIMEOhHKEslDTcpb0LO1aiWSUET2VIZrnGeIlBShDFrcdhwhBBBW078kjbckBqWZ5Ggy4qmWOqxoRKZtx6Pnr6/eH+dZKAqIQSu53d6y6tN46UXiql2RkPkoobhrILMo/IgknVN4OJyuxM5lu0WM/BK2+qsm2UfrCPp8PgIAFxSSjFkikwpxQ2ADSZI0XJli2nlU1GXQcMANzSNHOofV3mBcLGE1RCrKQcCbsE1CKNAFkWro0iSiveRpjwmnWuhqsIgYsWwgHXs+UTT983uvcenOc2ybdyX+s/SKXHPUFXO1ptEkfu4vw5DlVJajtXD3sdPWmdvin3Zjro+LUuzb+tjQyg1t6RWD37RR5BVH15d60r1Sde5KEG33RY9drHzEFJBWVFHH2tqrIlJlkoKXpU3agQ04nzYXTqK1pHHz4Lt3rxBvWGs6GXRFH7IUHR87/vqSgaV4LQHeErEu+ZHH4+92XOvIsIAq0vx1Zv5k+8f3D06qYPpL8+vuv0RsQ2dDawg5qJMVU6DGd6ltMF+nrTbwzwrv/v22/5kuA7S8CL9/Iu+irn7Fjz8aXvS6T6jSId6gooWJVF4ew7fb99/SPYPv/ry4N3NxWYj60MtDONNsFUN5454zOVNtEzSVBwfPAlTBigDqoABqpKghSVcBVFSsMuLpory3BexkiaZaY9Ma6+9b3cP9y4vb6Y+00J/uVo2adYBORpB3Igs4UUKGIdpUHkFUKNq+/tZxz6wUVXBC842U0Bbdw3dolDBndP7UVjgFerqCsu0Ui6rfJOmbZkOckUqAeAkW9Mwi4uB5KhKV1AzIJ1+T1kcfO/L61//cjvfVoZBbKJlrXQ6VQW18/jTzTZw7jn1zlcP+0mczN+u1veJqmlVcpvG8PT4bts5rqvCkFlCfMjlli0sGmJKUtAAjBtRoSMiuj2FQrWMgN3RRUnkgO7mm7OLpIheH8hNu9erSw55unN0dzdVoIjE7OlHzrpc3964zv7xQff0tnZDXHKmulGgEpXV7GqxRAgvvpuvN6tP/uLJRB1xmHZ5y5e2333zc1UZ8YwFeShj/XBsRyL9zy+W3da9H3/2OOexRDj1PzRNZnTAw0+74/4BzRdjKFyxumUqU8/zy6xO5wVDQMZunvFuG6bVxc1Zd6DJsZrAOI3MQAqGzihzjCeD47P1jbcme0zQVSKrqkCkCmJKKwgaf2cljYdgJAkDrtllyROEWk0jSqI+ekje//6RbZ25q398fo7sZvJkPNHbOYHKppApWs5X/vZ977BjaPU6LXYzX56YB4YZbLdZngk1xe6uKmqj3XdMpyxKRgHOEIKtu929k8nJ5F7v5fU3u81tW7e9xW4nMNkUCA2kupZP65rSdoUHhri6CjQWkw+38YMTs3v0KXcqSRPiUmKQ0ihRNPP+j7544Jxe0be3u7PQS34T/+5EeHhyeKSpQlNhzDeQo35bu7paY6e385PUEGGeEQFJulJahc4lyurk/bY7dgIW7OmypCmKaIhJQ0YqazS+baoMvAmK6/hiT151YqPpUP96s4Kg2zEEDfjrS9cVju88qNOYAypJtAACA3w3XTW0arf7sjSiAj1PP3jbrcE5D73cjyORaWrrUWvEhH7qRuu4MPxVAGVvtzMLSEQkCGLmJUVZ1nmJEkAcsfHKppAbyQWSAhhwQdNPEiKK+Xq74wALQEojCYIGgAwnp7I13bzRSFgp9fuXi8ffTxdsaarHDPULMEODLiLG3sjlarVbJqqkhvNb+3BiH5tF3azWF/vayaHSXbpbSTH7/V4QrqjMZGoMsSpjBUOSxXG9YKBDIGg4bWpJgwZAjZpfFjSvMNgZ9h26K3ORU84aC1U181nc0TRJ7OH8ViLEL4OGVoRAQDoc4uhNEk7OkncXVtcqMy6IRYmxmOrOnY6o9UaTwW8vL+frRDLEeZqCsnloPLp7dMdxHPVEefXzM7jXVcYtKVCCDLKjjWjrhle9WGSTCc4DlqYvO5293QLOt5nS4zez7+7r4MFPTprfrFavn99+KK9Z8MX3/5zyWoRSXrBV5pVFOmiPkOPHWVx5N0kZtQ7uHVjj8jotDUnWZBblSAUI10679fW3v9TVfpaEVSWPj+/Er6f/8PPp2dnfHwzqyelH/cFvXrzZPYLfp6p0e3ZtGLJB6zQKJU2xei1ExDQJiNOzKw4KzLksAs10hMnpo71xP6uTb37+H20RypLu+9PVbAkAsaeucCoW2+R7P/5iu/jDbpWdPN6LfS9+X2PCFbZJPgiPv/pcRyDLohpk2206mrQWmu++C0XRyduZd3uuagdqnkKpefDlF/70vZLzBoHlzdQwWhLpmpZ4zYuzf/xWfnHLUEJ4SzkQDycHSJIiDopNsSy8/UxQpJ4sO4LYnPS0duvhdvd2uUW6efDLt5enDxTHUKQ69wOo5Kn77DmzRz0rpiFPilQ2aU3ruoxkyXH9817n7qc/uOttp60JarLi27f+EWCTO3ab2K4Xpm6sY5K4ybF1wgoo35WjOCGuW1aNEihRulWmnsTb9+6Yg/ZX5+9//f4t/+qB8+///b/U5buB/y7bhpIkECJxTtsRxYrk6ChK2t6sSCeypYlCInJVAliTBAGgSu4IWCItqykzunF3nGJMxaDYDfsPWkr55t1F4YqShOs6vNeqL3evRb0NfdRkFbWrZLMINpFptR48vA/4oNNrJ+/Lc7z1vGc//OJHxwcny+UNElG4C1RJu4oCmRcfP32yXCyj0A8uFmHwrpAj+5hMxL6sapqiLPP3W49qrUeweIMGI1JlDz96HATeEGIBkjTYmrZzWyNB1CBq52HdpHYKa5EKPbszl2bt+yZ07r76w7slVo6BxACJiKTFUSSqUV5KYsOYZvUdaqpA0VBiQ6aE23LQpxqJ3mcZz7Pv/fRPEsv3Q3e+WtFKrdt190BIPyRO24q9Fl43c0lr9dpMcEBQEUvv4JHvbQO0UGXtaGKZDx/F7/yQJavbbU4uu+pDbeL8fvZ8tx3uNpswfG2eaYaRVnkVE8kZltGyTKVk5wKrXVWrAjvOmOvk1es/7HYTYIKyrGUoi6LoB2lVnumKITjWivsE6p9ZP4k09/3X0xVZe3O/wbTVNnSuejfh6UeHSFVBwNrd4QThLsbv47jQ1DoAssAwEeMeSMVYrcKdm8iyntEGM1FXzNs57gsETFq9oNqIYHXDt968CTOwLExBU4ZqULAG6OYdwxaDZJsNuqf+9HKeJVBRJVkmGEZJksYAQmAX7fPlh6wKRUuvaFty8wAtr6qI0kyQhKZqpJO8VRzVdTn87HOERMRXFW04V+oynfHmYA1yzGBZUdNBhlk1es1inwg9Yew3WwwZQ9KGxaVYNXVNEBIFuCew3qMBlOvrbIcKsdXqtcngapfkZWUoYkQDzjUdSOPebRpdawOVs7B10F2/CYjCd5tlp68PD++BcGOZOm30U/Sg5k3dYDOuCAOIUQAqAEmThlFeSBxzv6m2ZVn6jKUQQLqpy7psOMvzdDgQrI7CCouymtbbmlaWvE2LlQSx5gyA0OcC/FDT+Nnb1Xo5ZncYYGl+punyuDcZ2AfC0LlK6tXFTtX1HGWy0StRan7clXcaYryq6RJUME27pUGrnEOlvT/yL9PE5QkM5BBd80asq9mbN9Aak8zbXMQ7f8euLW+xhbtdSpvhZw/Hu6OivqQNeBelkig6sBRQGhavccWHnHWP+lmf395+o0YWBCCbCTLWJ3oLY38m+vMtcD1PEkxFMrbf3OBWgmAUB7kpAYJ46qVG/zH98J/eXl4enz4y9keGbtTX17PN1CpBiJE8OuZV1mlJ944eARK/uYpeb4OD1ml8u9gG1WQA890t5WJyAFV3G795tbc3bmmivzrLEfnw+u+KYnP90h+P+6u01hCQmFrRIlqvls9fi1JxqOFA9htNCHc3m2wRZXBgt++17yuSZGvqfLrptlpRulu+eTsZ9hVJGncP0f4p2cmJU7QCw2n5nOCiluUBNgiTiLZKwfbDc4JEtTT2PtMJw7gkoooV9bOm4VnlcaWxufCTg7sprN7upv2OpokikXSegxYobl9GwIpMRV1cQ4J1aPF19SGnDUILuhEKRSIeV0Xz6ARgUN16iS8hLLDO8eGPVcsxHcSEMge7GykrERSB2VEkTWUyTmPt66/fuZvoxz/oJOn40f3hn//VTxz5hw24vL1mea3Tws9KT4VdSeU8X8rN4bgnb2Kx3gJciYKGJGQhRQ1ZSUFzMuhXD+9ttkQSm+jZM0FUaZ7F86yndAphMt63G9Vbb9Y1anLkpQFLQqAYQ6LVIG44rsd77ZvrZrPkhqy4bv7u+X8uS7ko1h0gjcZ9WdJqQKUTBxXOXlnPAzKtcyztp/mNX9fthyeq7KfUC2gyYI7vxfvWCSRRXXJZH9eIEgqKpoqzdL5Z9kyn2MwwGSHVSfPSpvpZ4M6WN5+Mn1gt9e//8J+XV4u7+mFn/56kaKfCI4IwxXmJQYFREMODo5GqGllRY0ICDOu6rnAOqoADGi/Tdm/YEgQP4zy54tHw/umhEFeW2XEa2GE2tLfd/jGR4es3rzOvTuIZkdNyQ/SeZijK2e2rJAn++J/9FBO++/YPl1fnSDRfRfVQ/0Tp15tNsIsrI9sqmqKbBiE7CAxdVZtgiQC5hNQSZEkXt0YrniWogU6nR/aIGiRZX+3JhK3XqzL3BoenZd7SjBPbGpg8zitg6i0LdEZ/fOf1fDN9+w3MhHiVyoLgetuYh19+8eWHRUm6OIf8+XYzn89kxqEpHBz0Qi8qazOMwYGg+MKtR5N7qlgbGY5sQaW+ixbhNa27mbsGiD/+5LgoXd/yNFMtEZRqcvi4J1f0+c6tlqv2wQEYHUhJomjQtJw8iVWEVUEmmKZxoitGnfewiQOvplaro0hYbDmOJusqLcpgUaqqZjt2t9MPd2e7JEeOBf30vKZZkRZSY1oOlg8BZAVFLhUwVhowLigvvAboJQCFFIQsa7CcaZomy6417IwHztvnz/tFNcPbBkqp5+fn17KsftLtvPKfjZAQhrWmKU5rfLA/ePbyVektGSg7vUFdN261GsHu5ey2zMrlaiq2lVFnZFuOVHcwJoxVEmZjjDe8UUSoK/LZ/A3BiBI1Z0CSzKauHKfFQbPbNXVd5rwUFamjmoLG61WWSEKThzJnTncfCyKHdQM4Esj+0eH3Jz+OnGi7Xe4dDRlrZoiafjDd3krEHYx6V3WeVWmayNso4VxkjVvNGqTyLikld7U1DLutjXn+m+is1VItpmx3qxiAvcO7hVUfSqZqD4aastqD6WK1uLzNsjQKoyPFaNiw0z0OOua43MmagNKwqQOoaXLQpJt1nVa205JJh+DKkvDc26WvrqYfxWpAzY8GRQ13q1dXz1ZHj34kHMliS5/wE899d3z0E87Cm9k/Ga2+qvSuLi86g2EGMhhqg7ZWCp4mnazXwcX1VRImn93tvlXPX/3DB2Kost2fLd+XnqjGgUyg2e1FwZp+dzPoDghC5fl7wzRRxbfZu7dv3kBCbjOGIFCPDZV2sceDswiK22JN15w+v70eH01qGc3LRDeUR5/uK1LHNLsQotUmN5yWrGmv//F/3W02vc/7ciRRBvJ3F/KgczVjozE9HA4Ph5+fnV9GcfyHiw/lfOMYqqbolmU4zn1dowLQ8ijUjGZvwjjj6yVEWLi6edZqHwwf6vR2JNl4M99gShQRz0NXR2jS7TDV1NupQNW5FxNN7XQkqAhsp/SUhJuoSIGiYYJMXTMjfz3s9f08mt2uY7PqtLth6DdRgzW1YdKHd57dBlHLyGWxZR9jkBxPPv6jP/1kYPwQABMAQDlukAgkUXdawM9EUsE+gUkRoSXEqqo0KXMaXgGQsqZSxAd9hZ2VbCWZr9M3H2a/v9N1oGMDCsq1+3IX/7//b39/957z009PeOwDACCUytKR4g0edCHR/UAnuin7pSypjrnTtaKh4EdPHny0PwRp5nnPEsh3b241mMUQs20TyDcY7w2U7r7WMx/5Rnh/+uGftOtSMg3fjZyOWdqV5IzqQkcthVRbDmgJBBIjllO2XcMoUfbuymn2zXxxcrd68/X75rHywLnfbeRPP33ciKUiAsyqRbSQ48FQHgAdppTWwKl50tRMbbXMlj27pK5/ISntVmsiGJAgWmG5JUFKMwSRDAVEa9N6InneyHoKJ3mWCbto9/U/vEJlM+jHY9n40fGTb5fXWCVZXjuO1TMGsEn+4pOfaEL7p/+Hn938/h/Db6VOr5uL8lGPf/poX9W0NE3ZTr5pzj/qn7x9548G3XbXXC3XDaYci2VFM8mOQUGg0u0O67SlGQo56U4o55t0ZzHnYLxXkEmj6hRwbXwkKaK0rRllcVPzsrHM7oM+3lf+2LGdZ99eHBw68zdvMwwl2ht3GhjTDUt0nJ+c2v3aDBn3o4pSiRBVEq333reqJYa3u6Ue2eKdR44YgFyzFUSdOiTqXsuyBnlLDS+DtFDNvu5jQejgauuTsuREIsfH09kqJYLsmCkCGqewIgAXYRoVUVGynINGhUqw9SVRoXHhc4wZ9RdzBqqBOmQMXV3NPr77kSAqxdYSFZMSGZmamJYMLRE6pKZSmTxPdV1nHACMOwjibV1wMTFKlsZCGLqNpA0ALwhqgjqJ09+40S6OJkdDCWC3aHhNM8d6cnyvyHNCyGbVIACEerd2t71DkkX5wXjPXaUXW0+0nfjV6/zpY/PhA7+i4549T59Pqyrj7DOrTDMfJkq0u0x2493uNUHo3r37nFUCsSdHRhAS21Tmcxr6M0EyBbLBsIMwMFQjU2KRQNqiqR9ngqiygjcBEW0/do3W4ZdfPdQ0ZZmwdYTenb2O24ppAlNGBa8wQWgwTKJgsbmxRy3DLG+WN/udTJLggtd6B+9ElKaBiupCZu/82CjcfksAdeXynW4N1CYPWZkmNN7OXNSc6lrv+Ohk/zgJEj/2K0prEp2tPojsoVKLg5P9MHqbxfZjQ7f6naBrXc4WoIZDS6mBmOeisshBnYxc48y9TX/1ugD5eDQQJnt/cvBYINL1xbXWK6+vm7eXr/J4JcJtU0UnhyfL1Y6lZRx7hh5v+0QthW5PzGozTMuP2sNZQa3AVO6fHivWgiEGkDpQ0iDwaXHw+Lg401fRkgQoiePFbAHwghPUu9/biXgRZN1OS2BGLGdqLuk9ffIYP3u71b1dt9uqbCcIo/Uuo4a2t99r6jZXeufJTIkk2AiQkeX8bCnJT//FT9uj46aXilVdF+oq94iokVpYb3i0+v8t1v6Dh3c1dU8ZHIyO+7vdrkggg/MwxgLx6jAZj0WxbeabNcRVnqdBuH02/f2/6vw3GBbcR/f37oqoWS7XOjQGhyOnjYrrFYdlJLdVQ6+bGAJIZ5XesjjnYaqDJq40SyqyrmnabWu7nHLB/umf/XPHbC8X204f7xhKBbHTPegO77Q7HSCgdqsr/kAkSBp2JyKwAIAAAADAh6JTyhYtC14mDS4WcTIyZQWNmRRiWQS8oHrNUNbYbUYcTLN3eZou3zTwKi9jTGGyZaYMBCKYTx44xevv93pfjL/qPkmbWlj+fpqUbP67c/H4sa6J3766caP1D//0q8Xid35RDoGyeXflDHiwrqJysd/qWdZeEK4FcHPas1/VCa5pq9IUqScKYjLPOklbORZtMczypKrZ559+JWnCZrO7TiI9jY/vDCMYz65nZirDxgOweXz/J1g0NnQu9Scj0wFUOn7UjzPsy5XTM1+8f+FFU2nEHz16Eizj2fTSUD7NCqkk+cCgkmLEnOt9K2uExmZycYxsTDpUhEDS9T7DSewDwOqqORgeQxppqo51cbPe/pf/9Q0zG0ttH7bH26XvuYttw4+Pjsd73YbE3Ie9/SEM2YeLi5MH+5OxOf2n37huvL+3f9DS35cxzONvdzM0L5SyciwDNHwWX+685Y2EjEcHl7dnui7LpsQYxmU3idKKog4R+Z6yb5rE0XDdeAIhxfZynguiphqWtSGEehGETKlVFRU8NfIUiIzFXEwqbaKbpiNgLP/gn/2L3/z+N5vcG2jG1t2V8UY7QMQUknieh5kA9rqTu3WBCTGieNXrHzTgW1bqqjOwnM5iW3KnpXCWhxkxbN5Gu+sXGABFZ7tSJkwEmKRFVQdh1envdfqo4U6eI1F0vSYsSqHMiEBbvMVxU7XLmlWs5JZoF6pwJKhxXvicwojFcdVIRFVbeXpt9b/XALzICMP7iNS5Fsr6RBD7EHMuQg44kUUoAWgTumVlUWDMEARxXZU8EhjXGaNJvI9EaeyE6jsh9RzGOQebD2lVg7S/0fWBlyY3mfuIGJkB7vbaiGbPWRUXXLIeJII0K5dFuNl78KA9HPIGPBwMvr68zjQNVcJuHfVUpXNyXOS3dIvSONZkV5UOZboTl7EiShJpGr/ZBbdUfoA546BWQdoghBHPXL8ICzB2INsQyAXkNmVZOQ4yug1nv7u53ZYfsvrBVXoFEhliA47gzjtruNFuHafpUnDrfs/eMmhHxcBpSksq/DQGTMSCXPuaY0BIiOQTEkliKgsSGBpZ6aqEp4SIeV7gwIBcJSLW1GK3jNJKJkQUVUqRbXeXW6hY2sX2zMbv/KyJz6OyXKscGyPbNvu+u+MNKlZVEVFV044EIreV94vFYkrtUav2kqJiP/7yBzsxOD8/K9LC21SRv80w54mUcIwiQw0ymW7Rcgn3JgeTSuqICxoHVSaViqiaee1ei5Yehis9VBTxqs7LSsYQ76KVZbSTdXAVA0eypLYTbl5utlsA4EbEVRKpC7hgVNHVw2ND0a1cGq9LULgeF2nvYJQ2lUJQq98NFG8AtL59SCZasKMJlq2qY/WNMIiKipUADQ4OFGec1s2bty+pT7/3g0f9q+EmdafeCouGPex1lR5Q2ghGxHfdNXS9tQ+ktgAF9Tiugv5jS7AT//aKMUYknGy3k/GQjuWpPz843U+y1P0wF0yI/ej43mEDhfTZjc/rTVNo2i1siCpLtmYsStdxhmnuGBONAwEFyRULb85nmizcOT7M02VaAlKwUnGmUf7k4dPvf+/PegcjCCQAsJuljdoaAAgAyEBWga0MJAKsJQBJymudESYARySlVBbxreseKRZDRIB8NO4pQVbSWipTVdb0pkibzLda2cY6kY7Hd4wVdLOdT6LaHg3+6Kc/OTr4RBQUzi+OD7/8/eqvf319K+VQhOT9h1RWVGIYL67fgmz5+cEeLFmeFzcfVrq43Aq1THdRFASBr+nK3cH9O4fttzBdnXmdz4VH0Ck2BbYtQ9X63b+8+PB3NaUnh18xgOp05dOrD+FUe7k9OnlgG+D0UCXy3ddvp1l0JctD//yC2urh/V4Yen21W9VilnshK5okdh7avrveVR9M7WTrh6bpi4pWpzKXRCBKEsGKpMeUEQHoI6jyWs6KLYMOlweWTXNRFarkYnFyNBoO5auyLOIVDbFjtXSnt13tnq23I2NwctR7ef31+dllTN/fO5jodi+IP1R+bAo6Inu3s1lWFEQSKx3vZt6sdKEpW6YEGynJ0magaIUbueWTI319s9hL0lbHoTeNb/M6we+9jSApAOKiljWNNxwSQMNw52FBaJttG7Bm1L+5XcoIqLrlM2rCIqREKbdFrTMPQEehGj9LMkVTIcYck7oC0ItVa1KnN92ywZUuJJhiqdHK+0YvNyBAJZXGTus0cJPlr+ct56g+4LN4E9W49DdCywETAQjILXx3Oh89fAS9QKGMdUSMEFZ67f4AcIkFXgCJxZlGqS6IoxrLIjOUfl03cRqQHUGgqg2Wp1G6zWtFwwgJtmLbBuYsN5x8W49PHp+ejl4/C0RM+QEsYiIZmq0oDLCmqau6AY1AkIBQaTDumnkdoVEfFDfAj9c1rcWG2MA2FQTGKhJoEq6SIpI75e3NSpVA7phZUFB4ti0OmO9eds3RcBwXJQuiBumM4zCdboJi/+SBRPrG3qjIivP3//i7vEiS93Guzv7pHaeo89UnfdMURCnMKCrqHpDUO1p45ldVcixqjPFMBXpgCUkh6HFdAyBSuUKKaWrUibHbsZsiFnjMqxWo81Dt9U1Ew6LIOU1E8rvlrW54dYmJgPT23SpbzabnKurVcSBLRhSA2/i2rRhl3BAbqsNJUpVHB/tU3iW7NGDvpEQcj0ToVnnuqnInyINuv1+tc6WligYkbgNZIPmxaFqVry+pLxPSrIV2p9PvS1FTn3z8+bHdv85rkkTzOZRa4qt5bL35JiilbvupReAmreLpxS0wxX7PRIg2DcujTqevgm0WJpD6TUkQBCeHLYZgrbCO3lOkQfzuJs4X2c4Xmmb6/Bu7ayntuT1RhbxIkvjk5HuiYMe+5HqzR8d/RJnn+edSFXFKmKaxJq7FVlU2ZVEMnRIfne6wIBCr2xYsZ7gVuRpuRt1h1ukWWVLD0qyoaGgeAE3kFxXNsizQE54OoIhLV7tYz5Aoj9T+ggjbtOqJ0rYIdpvt/e9973ez2YN2Zxc0v/zlb1+cvf/I2huenDxpjZJ8WUMtSqOKRlVV1LS2JRKX+eGoBwf9ZrV2q5ARhiJb6yY302VWVXe/PCxK5/nPz+uaOpFualauRUeytQNSwbB/db787pWoCoPxICUUGIZSFh1FTcCFIBaVv4bmQZHnpKM/OTV2m4Zfe9Ey90mqV9pVCbS8+Tc//JPHX/2gUfsNgBSQoiGsEZOQ3bIkYqoOctK2FryU6wJlZbepNoogpUKGUkWSWLmfJ5duneMUykRmpaxKokSa23ShkrZpliqmQhS+f//Xq/St780lqTmY3IOwhHU9r935lVTmVfTh/cdP3YeTSSupvaiemNZGKrudgbGAy8t3497k6zeXRwftoSGlq4wNkOVHCYSu6xZFtlmvwyxUHhi/+MVlU4Hj8UE0BIWXB97rPBcgqr/b7u5o93e7KlEDvS/fLQe3SVgDpglQ1+TddKH1zeGoO3czzE1l7zRJvfoyl+qGy36WRmjSuq90ruNAjENJIZfX63uKPF/Xj/e/r1lyUsIUI6SIMmdIwhqjlSmEl1mgZPuGhncbwqA1mgDoyYQKmrXaLsPY3+uO3p9/CGtV0+92Hw8Ho0jg1be3t998eJcs/KpTiZT5TTpoHyxXszxJnJN9T/LNsnFaVpaV0U29bafzxGsr5mAwnM43jiQ0TRFEtQC3asu53MGGbmXJqSyI81arbwhZPTk4YjHebLdVQ6MsJ0VRtds909QZgqmdZHU26DoNFoBsWNvp2jCM1E1BUyhN0LBuVQJR4GXGBKkQ6Ww917Exq7QTqiaoOeiMULytbGoKXGq39u3uLjjLh3t1xWyndXPl7/fvSOIxWN+i9kdP2+X7i4u2hiwZL+ebRbAdn96xKLtO/NPTIxniD9++BFgrOoZelwghU5Jtx6ZFNlAKEPjTJNdu3rzw3CAI2qb9Sa8nMU3DWKQwgqBJ/P1xxfjgcO9ktQh+8+rNn//Ff2foTuLPicJVUb8oA2YYeoAqqQaIy6zhCagr32fQyDOZAcvCZVzuvHWVFqaiKoImU1FSrarOa0affff65vbDGGffBkHbVn74Rw+RclzXtDcc1v1OnsVG0yTF5jZlgtFH7Q4WZJr6aM+Jrs/j3/46K8syyYv21NAly9LQDw+967AoK95w3tQCwBN5mOVe9G53G89bEDqSWNNGNE6srrN115qqIowABxVE+dajglhVDfAVnjUoYxmlaSUNbPu2qASI5VqUJTvLPVnnZUNKWgwNQ5eFK6FFFEcWW2lau5wjWKpKj6EgDoQS1GJWlGMB1VzTOXTte/flQX/g+/V6VVTeTlTkqIwBxwQSkvOkCHXTgajx4qAF+KCebOq8zEES3z7Yf7CYzYQcKsM9BNkiDlCn5WviLryxzf3Qb87n71RBwlZruP9ULN04jisCjW53uivqm4qMxOnNtOFcV1QjUAWhvN1tiaLMvYUk9k6G5kkjCw8/iVW0e/lNUgTZLlmApW515eX8D95/kcSjprJu8kv+XATi3Pc3I20oYA0pDHAm9Kx4G9dVnOaYNkyUVSobq2CzbFSxI1YGE8YCABuPVQMdWqAfGaK9jHxNS3taHWUiGYM4Tmu4Mosqtaq2WlMuEmwgUgHOoNDu9u+OJuuavgl2j7/48uzq6uzDKtD9j7yre/cfpGnC0qipgWgZBce6sU80aCGCJH2XVRUU+uMTUXbd6/ksSUxL11TT9RJExB/+8K+evf672+Xy40c9WjdVmhIeOgmOVxGlFFcIjcZCNUOl0jP0MtoqMK+SC4xgSxZv60ANwLrJg1vSIer7NIdzWJnUyLPP/vKnTz77UwBoUTeKYACAywjUFVPEGjIOExBSaJHcgKCqo7KhWFVUlzBQwgBAwBFAmOKCpgqBjUgaqVYlCEzarvU8ma2jg55TT5Pdm795ud6dsQbs/VAQKrfhwrBlujTUTInQBD3uxzA9LyRJ0Nf5De3byut3mzhiljkajRNvDZhUeOxMTFTT2W3rPIsUoCIkDUedFBdhttN2kmVasqB99OTRi//5r795+47kpazIiqbes039jhWraXkFwT6fen59EyWtMvU/DDTzxdVLFLV4twWDpGqNFVETNIGoVJdlLIosZ8SNqeSt5CX16v3JWNakjR9gbAiOoOmSuGQ4IYpDaFlzLxQURa451FhR5oYzqtOQMZrPLlstJUm4MTyM9eab/3Rma9vbxXwptzCPL395pmr9QIXpyu/cPd3fnxiMF3T5enNzNIrevXpRYfJIzX0x3MfIC6O0o7ryYmL1K32yW78omruHByeVm0V5mHOZ+QooG0W2otBJwtR1A30jyIcNqmgEAp6krBSLJHtVXRNJlofDoXqvzwBV0tXq2QobxjxPSFkyCUjpTla1EpRHurINgyh2oakxykWny6rcGPYY0fQoXKf57Ibr+4zTwGIiSTO5Jpl08yaYx/PpZIL7ve5e5wd4iA/38XdfP5NZm9DtwOwudqnWVnuaczl9X8iKc3Jwsa7c9a3W2ITwTkfuD9q2ZG/8lSrqJa9EjKPLS7EoW5bhtDpFFD2HaMg5aKgpEFnpkgnGeSn2MrYor5NzESsiUbK0sMxhmLCaklFbQ6BpaU3p58BocMUR5GUDGrGktGiLppczQKmNaLJIaFMKChdqgTWQCRRyQDxct0ELyhcFlR1t2EP7WOBMPK/O1iv8VHAYhbStbGh9LJpRC0bzAOwifb8nMvH8wwfTkGWTyEhT0B0Sp37DGip9NtoTB6TAYafTCV76bgHzKieVQBg+JkSRjvgeyt5dclrSBjUASVwseMmrRsWC3HKUWr+lN1VZwFLkkAsNl0U5TEQCxCarmmgmygUFAuLquD+6nS92XlhpcodSMwhqg1e7SukN9+3HBDRuwghgA1n1qoo2A9wqyBI8/MTaG97htT+7eoFgBjRbwChPmwZhaMhlVWDGWHONmIJZGFUtUl02vhxibsJhh7R48832Kt8Nblvdjuslbu62VBvUApD73X0CwGGyWt0sXpAHzh7pJbOqRFxQJXmEc1IKTGm32lXT4KhGYHE7r/SWQyQJAqKKdBdVWR6bUZRbSNM6DDQIw/XrbbFPj1oP5VYX+/TaPQvDXRy/GI3rg7ahWGiZQj+6kqVeUzXZ+nZ0cqQPJucv3wJZkXqW4kG3rnIi0cLehnLD8ga0GBve7AJTGvdHhiuS8mY23S0Rg+2DO5ogXr9yy3zbLu8Dk0dugIkz0UXounlZAMvZP2A3TfBhPlf27/+l/UQq3oV+kKdZr9PLy2KwPy4lxQ3YrGKVH7hRbThDljFYAUhkgHAhWX3oaF2bSEpe1qVi3Llzr+Dg69/+z7Kwe3j3eD6bPRgM3PWWM/b1apcV4I+qXxJF3b9vu7JoKtqjj++XYovtgsB9wQQltwuacdVhA+Vxdfb6FqlPP/3kyeMvH558DACpwIIyvnIjxAXPEzGKo6TuSlVaUcroepuBCesqUt/QYoVp+TZpciTIhVK1hDKEch4UNWdxlokwS5SWiJyWI4QV59mcmdodURiP+oyv9Tvoi4lTpVVTUXOiP8L9lC8WILmnObfbZL1mkiJfe9sv8/Bgr73BIodoxeu7g4HI+eUHr6oWZqfbCoOdjwrdiEwlAcgJuWbvrXn15fcmjx/+OQdIagtfPHiQ5/ntYr7zdr3JQE4EihiVknatRIqxrF+1crlUHplW0D44ME0bIfHNbKvMzjhDjANmq2W7LGFWN2mJot99Mzfa9vHH99MoMG0zLKqqqYvbSP1EZewClycWR1tIKgBwypx2F2ppUaFmOetAooiCQDDQTKGIUaURT6DZ5v1ZfV3Q8amOW/t1QRECQpT37x4rpuldflCcbtu50/OBoR5PRisHT3r32sF8I9qaf7VtWiUWWFKGJoVnYfT+9z/fv/vjntPr4YGX50uQ5nXaMBKmkmXrhmFzAt31UjacIlqHAdQNuG/Ba48QRdfQ6RFWlPTNa3fj5kVWK7YX5BR66olV1WFAVyjhmex27Z7VKC4NXBrwMLQF3Yvkwg8QEnZV8elgYg7k1PM0TRAbeedt/v4f/gsRxO6ptVtfzX99aXUObl+u/45XTTrrtWctTV17y7wu/c5HnxwerJL54vZ2dgIkhnsq6Wnkjjoo2508L15cfydIOAFuPr3dMa4xNrSsxguJqWr378u/+kVSpDU1V9vlzn1O6/rw9M5gby8X8zEI3N3szWtvb7T/g49PPB8aBYaY1u5UrhlSka5Q2gw2rotRhTHMkkpqKjUJAOMoUJsqEMpC5CajjAgAE5DFiazKoiDf/fiLA+cO2Cu1P/xdXlWoqYxaV44mjiwhYIVuVNR59pEt35T9H35+eX1xdXXdaeE8Txazl91Ox85tSK/Ficwhu51FsDcaaV3b6KhEmpZJWDAsSTroVGpGfYUqAacG5SWJaAplWTahjECBCCEQMFrVNSswYBWHHoRmXCSVAgVOSI6EdJvuijxTSwZNiAsqS65FxK4CSacjy2lDsQhbgztAYu3deglMBEmnwrlExJryMIzXu9zQso+0u1UZXf/DFY+hpbQDUIs6yyuuS7ZaK7zZSlLchDWHIg5hagtYNipVGDSWOm7mi5kShtKkOfPfPep9dHLnUNqIO3dj2rZuabPZP3V7n8my4Ma79vxCuvsJPj6GX1+sqwqR0nSc8v1COdXL0CcilbB6zFUm44ZVDmoaJAaoSTQ5ZhxmuNPUve5+kEaWGatU/fbNTB91//jHj5f5y5ara0qIoabJ+tXNUuMHSaH19mxM9E0WnLlnP+r3R47zqmRFWaUAOY7mQy4IWh7nDVQVCe3WwZ425G7kQdYa9xVTQ+nIv968fP1y0j9VlOb2ZrpaXd/jf9H7vCfKJFfkouR7w9GzX/49Gu9DaPrBhVqo3aOGL+zrgg6qmghCGURN0bzZzNduJGmG3bpjHBSS3dbSKs6KNAuLF+tht60NHKFMc55DxNPl8h+vFqeH3fEPPn39d782su95i9XvCfzk43vDvS/+FauSNJJkqX+wZ3/y2Xw1Wy7nikbSoBoMJjs31yUV015gdzo551Xz0ZOv/uy//+wTZyAAgYG6AInr4bLYYt7NU79MyjQHmHizlDaNjAUsC64UaIbaKAqvXBoWSWVaklTJIaUQSJJUIFjmFRLFrEAarnARSZqs9Iz00n21fN8abH/6k+MoIcv1RZ4WsqCIMV7P3ydIImO18NNNKe2CYK/tjI2xrLz4cPHm5OSYFQmXjT1gXL+75LzXbUl55pQFrdOs8/huvz05xT2xjupWK+cxRalp6oxBEM6//P6XEY+iOO7d9tZXa1ElNWDlTcb1JopD4C3XIv54ohvG1o+iIFhDdN+vU7M/sM2hIem7JLjxLzVYn9zfp1y5WcaDh31Nsx60n06rq2n4VhUORk/HomSl52lX7iKW5wk0CBAAFBVBFxJh3CuTALKGYU5pTSlFfkwpE+taU3nTtvP4xnOqcStOsty2jgIveOTYlDarTcnTbKoXxiw0ZAHWwrC3x72NsgVZFbEgygUfRrh3iGoU+hW/e+d+VUxub1d5zg7NvbzsqwMsbNaLOnAlXHsYQ1EzxJQRlJdLutGp0MSYCa3PBmMSF2lPEbNq46cxkgUJ3KNKumtoUmfqLRQIOrIHmbPcvVnknVmLHChdQUE6VenNctPNgbZ3ABYiFIA0aGXYnVE48sjxnQej0dPL87+5nc/VGSpXl92Du8TgekuSDRuk3YdP2rYsjrw+1+W6KQKeihPh0fBeR27xj/hRZ2+1i8o0oNgPpgl3PX40KUuaaWTcGXatLhGI+/zZNk2VbsvudRpWu5nLIRcVkWOeZzEEjaErYAz9sKcq+RfffyRJsqVtKHLzXEWW3mJxVrCqaFesHA2Vetkq8inkcp3lDc1kgl0e4twjQAcqz1dpiWoExV0ei4Kgb7mbndkyK26rJEoRJjCHLVWUBCuLU0UzcjEtjRpL0m73bTCtHcfxNtcNrR6O9o0TSaPM3RRuBHumINNRCRKDtTdnHukWbE8FTVxVtaZrPgo0SaIgjtKUhpu4KVQMVDEFAGVFKaSx6HQZbcq65DIw5F4eA8qzOQd1mRNTDxnQkVVXM4KRdWDbRZFwIFekpQsCS/PdDHE1CsqahqKgK04jVMFYG+UwosjIRNtJI6CqiAoocrazUJHz7SpwDKMpAXeDuNVK0sKynargjOISZKqJwpsQCpISxEFYv0yCk5NPulWx9OaZuG4zPU2i5++etwSpFNrrzWq6uB2PjNXiIsuitvGYwfr82ftMMA8Ojjsdu8tTxkc3xey68Y82a4kgscQcZImsYi+kmgwJT9IcYsgxrKo6yxMFK07bWb27SfxG18YNJv76Jl998sd37v7y7FunIaKA0l2JCymQLxjuRBfb/tNBYrTyKKk47e2NJ7LmnYUmD8NGZ2ksMjuOtgvP63a7p6d3fJDrgp4HKQtjiJsaQ6Gn31cemYp+cbnrD77AUMjD+M3vlqf3Tx7cv5f02uv5bdbWzHfv605LarWgWua1oO7J1uUsgvhDdOG+3+12TOoMjg6+Yiht93pBEvrLeFEnd01VAqjaG1mGni/mGWy00RiQisN0/7gjnh79+OhwtL93+Xcv/uirHxIo8GZW1hc//t/8O6bTV7/9XxONv3j+a47YQa9zdVXU9Ssk+8FOVdQwjvzllsftyUd/9l8/uvcZyas3aYpr15TNIqJZKnOK82RTxVypSqGp1lmKiQBhQTmTtaTt3JOsNWKRAosSSWKZJ1m1pA0rONtJeV7mRVJlgaQoYNinksQlqHBDmMj47795s35T8S3g2SIAMCa2rhEglZnO29BSJEmWtitPIy1ItXVUdHU9mt2m945t1ARVpmj9L773o3KdI7DUjK5m9UKlIYJQl3m6pA0i8dovxdIa9qoyP//wKmvAPpjFWpSXaDDR9u296XldF9H+gZaHVhBWv7s+BwovFTFNoySLFO0gqFKWkQZnQX1TEgXLqhpzlaaNHwYw9T3P6jhtxVgx7zZcsmYiqIbUiFueN2LUNA0BolbaUJJ1UwpLf+1XmkSRKCoCp6TxkkqXMZWEskYcp8JB797t8fWr959MxkwE8DLOta2k4WfTKXNdwzDNR4/um8bvvv7tuHWwnt9eTK+O+6MtAAHljqCWMK0pr7Zi0WHubpdHFBONUeRQU2rEmlKL8W+D4Ha5FFV7NJqQMKON0LJQXRU8D3ZuAXizSp49OfwhYUeTtMqT2PWamnTaywSAVB1rQ7cWbtZXrv+dvzfYNwet+2avKnZuqGQ5gwd6jwC08kEmsCFuF5uvl0K3Z2vo8PAYb7bxq7AZDv/Fn/3b3/z212Kvu81zgTYFS8fH0qg/cTdFjNNoPU8g0Pwt5TVSLItWiMlp4lk2Cm5mOQTqHTVJ1OE9PXbVjKiJ0wwa2zB7i6XPELCfHEPQQEmwjjtBWbFMZkZuDQf8+rbwo9n0Uh5oQLAB54Zu/PD7f1pVNaUeZpCyEDKvKMooErm4ILW1vI7qGqqUmZKdqRHmphtso8THNO9JNWqEqCpD3/V3YZqUDYOGqcTEUzs6hQaAWLvXQ4YOgTC7nElGO7CXJqrC3e5ZGhWuK/Cz5uhw8dvvdEXQfxTCXaz0+t0e7inCLtxGNei3sYr3SrWSalqmBQLAxlbJdIgjMcP0YB+uacN8zHPa1EWBZIlpqpYWtVLkhq7XGuGcVVEcFjESiUCULfYNkRgS1B3wvjTaUtc2WYIWSlPkQVrzOgUNq2roEKIhpdM2pCEIednJIeNJkmIJIbtlWsNpmFCIDd3+2//02wedvqXajUiiKJCEY5UGi5xBUNKcRVG031W26bxRmBV0byi4Mzn+q6f/7PPvffR3f/8/ZiUWCp4kUZlUNWA6wDf5zPXdMiegvhyNHIQAZdeTYX9XuJvF62MobjExElrk3tODkd4IRABbd1szTnQJNpXRNJSJqA7KLNS0fkZzo92BXmGT+7kofHf+v0iamE1LbOrPvv3m7M3/9f/0P/wfj52ncfiqgMpyNVUkU1L29sXJPCzUmw1Ntlv/5v+z2H7vT/7CNjT9zoCXZLXGrzeXbcGQRw7aeev5GlxlxqPDB4+eEBEsVvMDCx3ff/LzX/5PkKPD1sEmKFBbGnY7luPM/3DDb+gbY+aYfVFXyNVVVZelqmhUbliNMEVESLPSMCzNeMSd12XBKrfYP6CX65zhEsBddzROb4Lm4M5uvegpJPRWnYGDCNgFGySiok+mVV6++o/t1uFj8+ADYPPN5sdffvXh7H2Rpp3gKl3Gm+uN+fHElB3a1GfRdF8aK92js1frzqg21cdVEQw6yh/9+Kdf3vt4C8Tb6ys86WJln8ZxSKs8jCCNs7ShuRfkhZAIWlNXpFie7dYC+d4fPei28sL3YihwgGrKyoZuGUBMZFEOyRZjCAEPylKrHD5gsu9hzrEcarYjj9p82VRV9frdGUT+uN8HXJCxTBm9+XA96nzeEeRKEyylm30o1LZ29/jOfDNdfvsivX8vz9IyiJjIxgfQyo+j3cssukmYLwqHcO6VilFVoCpAQ+DN9a0kcElScd1so0CxRgJodjcL1nKkE63JS3cqLrNbGW6wwL44nGgjxVCMCjLd1tJUDhm9vfIJ8mRBuXv3Qb/T9oLN7PWV3m3KOvcChOV2d83iWJDLKhWomsSaVTOCEIAIk0aKCBaDpoZQFzodR8IEbbkCZVFVpA0iDbQUSR9QmnHaLJe3f/vzv/3+f/O5UVrwWJgutprkCIpETTPLE+/lMzLow4oapq7J0nuWWaNu7XqGakYiLJeSvq/1nj7wrq8W620xT5vmst896uzZdZljHciqev/enYNHjxGWq7xalIuTuNnUubv19veJ0hrf3qzeXs6PH7nEabc839uczbBu10XJU5ZnIM9LLvKv7t314B2EKK0+DGgHC9Aw3+laW1SK66SRG0ICMZfPoCuND/VWv79ezAGVRfmAqTpf+aArN7XcoNZRG95426ZI5aZ0o3yT5kOrRVTGq0pWxQowFfGCNXVwBgS5vogDSYOsWQdxmlpOX0KiFZXblOsFIZvrsEGVrIlTQuMwNHCpHilaILdth5Y5JFB6KmOXxnFUIVa5TZZkg/5k0N7Py4qHNAtiSBK6KvM+RhyXnCNaUwAZBh7jdb1AN5zVmVXXJC23CGCMyjTM89s0YUWup1nl+5GmywvpRqDjziNTpgrjcDf1+V5H1bVNcJWF+NaGfdacDga7LwkpxP1Wq/7s82h67qStothl13GNDSBByRBD6G9KQ/R+owimlh2yMA4b7tiaRIgXgy0K9E0IORZQogoiZtiuUYLzqiqhiKiImE0AFQEAFS+xZrEi3kbxxt9AoWc/Hhm89L3t26tfHd2RgtwDQdkRB7FRtAu51MUs2+VxJQm4ADRdx5635JN6erU8PXnAiWBowzvd8XfvX01Ad42l2XT+Lk1lDs5Xm7t3J1bvM1akZq9NQWi3Orvcbbfa6Vm80AXLMCaf3o3t1i+uz//wbvmU5JtA7B3a8ytfjsvDP7lvx+av+G+yOFEEoolEkTTmBfe4qCqjKikVjOXFrj3cy7D/23+8UDRqD45NUZUx1i1tF7w3D+6lVSZ6IgVemESNTOLbPPTy7kfF9tvFpuBa7nT64l3rGJ9q7+Zf//I//P7JwxEGyvkvQlVis9ytKjfHs6/+8mcgqcdK+7itnZ9t1ufv3QYNR4dFmfG2+i8Pf3rxYXMRrb88vSe1++E7d/Pu/SZOju4eYcyyPCvzsz279/b5BZ/4rSIPd5vb65oNS0vVsvwKBUaGrhkAVJZyQ6uLUiWymTTYRBiR1tGRd7vorpaHxyeSZD97tapA1HG01oG4vOqt1qxq0jR8D1iRhAQxutule4N2p4y+TV1dMoAyKdMiTJffXAVeeP01u+lcamWZb3dbhthkb+DSXXhLMrkjUoyN48skkoIIt9uz1Xr6/vdFBv/i46+ePnqapKk7m3vb5mAipFkdux5tmjhgtVQK5S2EQPJBwXb5jv7j9a+qmv3VX/3bj+8eF1kRxVjUMkbbecrCJEFAqSrKYAKJWjSw3LpiRrZ7u865bxwMyrLKr6fdJ6bRHkTvNT+u2pKkdj6XRGG99gLbGJdSXzwOvFRUDFMg3lVs2O1dWmR5rvWGNfdErvhhjtJ8unrx5h+irpM5LevgYN+I1K34lnMBAqnV6QEoFki1FT9rsmydAV6iVqdmECAQE40BNFCVJqnJmN3bqbcLPpQIHVdJRt9PsxIzaBj5NFHU7vj+EPiwLhtJEHsH/ZWyul3ebN7c4IDs/6C7e+ev81ram4ykhNUAWGMORpVj4zWizSosJbNNCwa0mtdZvtM7vusxVt89PeYOx2oL8gCjfj1bk2OTEPH2tpr9Zjr8cixLepb7VZV/9OQHSRxrYTK9vt4UBeB0u1ssB7VyT0a4TAhrMEpKFlCpYw3e/WH9q//4P2FcHe6Pul0dAvby5TPT1o8/PXx7/fz65lcPn/wpkWQvjHHqtsYHFoh3UvNPt/PIfYPXoKlAeOaT+cv3bBcxWWiNW2Fu92y86iTu7xe+O82Vw16PedHM3e0WxP2Lf/5ALPpllja1YuTSDsdCl9SAyQBDJOS5VxYl59Z4ZHLGt1wKlsvSNs0y0ATFMFUsA34rFkip4I7nJK6yjqHmyTYJo9WHN2WViYrGSoCI6miFKItKR5SUYn5LeyObjO1swQSPDlpMkW02GUreMiUJgzXALEuTnZzlLBEEwdiqx6PhFkhVBVtdkqTwB9//CQCo3lyhCjQmQ7koDwVBYLAvF5meo0YtpIYxq0mXMFfaPq1B7fM8LQd01+SsiPPYTWlRIx4vZmXCLxjUx6LQ+vihZOX2rFXYvRlamHNqtYEujjuDpqk8Q8YYu8X7+N7d+yCLeqLER4eGtbdaQWXUr2Dlx+GevWfk12KTnr3z6yYV7k1qQZWkpihqpSVBgDEqIACsDiADBGMB+Ixbai2UYg0hkESSBBGQzbIoNctiaTKvGjdYVRRIysgxB2np89UNi0MEDQlyUWC9/UOwWkdxKAhBswVANcvV2kPXQts5bWstk7elmrHVm2/OO3ee7JufdVvmmzga2R9nMNhu/tbstKWRQ2QRiXC3c/0Fq6uNDqRtupgEDuPqcWd/+PhT2u65b+ZllVmmGZbgfTSX5kgUO8vp+q+fffg3X/xXx9PpFV6ZeplMI6bW6XKVqwK/TruW5Z2/cIi6WhRZFj24N9GVhwf7vSLPF9sbZKq7azVMLlbL9fHxgcZBZzJ8uXHDq+u7n38y3W2+PvugStKDo46Sqzsvv+cMrOxzQzV/9eGDYYKGiU53kqfxLqtZWfZtMStznELDHLQm0LJY4DUTTPHe55ezN+9uLkTBPpicgjQCaXb0dNha5J63Xa0pj281zUyX6cm49WDvqWMej1snsMG0am4DoSnZ9izQQW51xmtetAqpOD/DQlk7iqm0QFkWSTTs9qHSRYJyEczagoKFpr7NN413MD6Yl2tmluaYW1q9XK4krYUi3HGU18/fuJwOJj2g86wq+p2n3c5Y7Vbt/YPpP/3GD2fWvf1GuH7z3XcxGz1+fOfGXV2uc2DvS9NFJYtm1W1IfawNJc4+/eizp3/+l8k2ef/mTGt1+xolQZhvvVlR4YaDuM7noaZkxaJRKIvX/j9e/iKvyv/dVz/69OOPF5sLwIM8t7VaD+M5o0CAapizqs05Nks3ccwqaLyI7MaLmmo2z7SCwUzXJjIsz7e/+uUvBjC6EIOnChLggacAJWW5KkALX3/Y3P/oTgXXgNViXTJawDJxrPbrt5GqrkQ/nrRtkh6qT/XF8rdHhyedzw/PLr+2VbOyBmlQTVfzpJayJmooKsHGEIwali0kUgFBnRpKm1GQ+Q1Beun5psDjdJ2ku6wkrCJyf0/O2zTBlaCwvCi8JN6td5vF87OvP/rZD83O3sP+R6uf/z879z/5cvBR2IJFYxQglmoUl/Xb373f/5z8K3viY5asRlQmmGHQYK4YTa3VLoNAqmjhZyWHDpIMETmaKK5INQrq7uRu35EkIPvrrW1XR4fdumjc7ZTPfH5y76Rbmod78cvXW12/efPd4wf7UUN3MDehuQ64bg7l8Z7lrf78Z//doFf3B0MskNVifnHxwTLKTm5jdzbp9cddo5BJkE4T+TJOd8zqPBlPZMV89uE95TzM8hsUkZdvvtsz9M7Rg7LKaM1t+/ijTn/4k867r9t1E1X1VFNG6kQ96e/hTbQhdNRYTI+CAOqp4sGmYGtF4eW2oRXCRFYlSVXNReLXPkESC5euz2t5qAk7KiKVNynstp2iEuqmSCqCSLp4c7MoDc76hiEQdad31ELNkykhmFUtrOq8v9wAFxf94UGH5MS/Cq6W53IR6rZqGiFAgu8SGStqqSS4kWpeAXpRNjqWJF0JM9Jqt0bDI+7N013eNFWV1UjmgkhSl0RVnZMg5ACXy6qu81xr6LrwBZDWoAryKsJNlputm3fnu42HUdu292v816/X+c8Gyt4f/8CPttu4vBWSu6OPOpYtVZylTBKEjlJjdYgwJ3ChKRJu6vdv3ohMVjCRaL836oMuOdWszeKCc6qJhqpJB/ez5A3T9lTsdJG/NGWJZwjVqWHFDatBCTjnCHJRRDVOciJxhAipBVkkRCWG6u3YanVLC0qjUMCmZh0YJ6bJpLhsGCtVA+5ZZKY5CUqX3o0kZVarq4mIDlrdkfrd6xupwcrWbZq0TBQVA0Oz+0+6lT6IZtMcZVki6By3+4Ph6N+hTpH/4+tcFI0xPLUeCSKv6xYQ8SPyWBAFjGXLbq+D8OL6Giik1/RaD0/X//R8t63nlFaG2lTkm/9wVq3+w5/94KvVr1ID1LuX8bTw9Dxd5NVlBfr96LTsTsxa1iSDjE7M+9cFO7v+2vXc3UZQ1B2EoNU68oI4L3JiBeH01hb2//zf/9d3x6P/x//9v3T6qYg/Es2efSBeXawkDpgqlXk21nSCCvGPxlVVHAoHHwmD5/M/PH/2T667CcNgKBIVc1tWTau7YXzSPxyPxn3r6Wz9fHu9a0yT+5kXzgakzMNl4J0bst4+HDx4+P357MP52XvH3OjFINi4802tDoZM5D3diPyQVhIilOtS9+mJvPOnbsZhkReF5W2jyKthVzOFj+8/lcTu5GCPYGF7883ZN+/GXZaxptyxOrpkmzVsZbONdDXlVcmqMhOOhC4wA+85bYayGPT2f9QImGjiLj+X/dgIVGpq3/389U/+958/fHj/6ucXy+Bqva73RNKdHGzOl3+7WBcJbUPjMyTNf7cOZl9PPvnX7Vbzfnnje7WGzKZieV7SIvPjqoECD4Kz93+zXK3/+3/+59//q3+z2/y+SO31xm93BFbxcLdyrHaOYM0TNUdJHWtddP7S9avlKbRD70UeeVGUnNz/9MH+p/7r9d+8/weIefugp6u20zvxcyTiDlGli/nFiHdEohWwtiPIeIPlmQjbsp+qiHdbNpn6F1fnv1r9ygDq/+V/+D/v7/HMia+un59dXmraHUneFBWp08JSxf7kzrPpCyEoPEpBlTaq2nHMhlOOqoAHXXlk2Vb3uPfm1c+nt+vhWJUUparQzToyEgNyUeRB7LrP3+z2W9Uff/nDr98+n31z1RoVyunjTz77t8Eu+vkvr9skIxBnZfQivG2oGPm8+a1ffvpVKQkceyZRcVXZEGt1g1Atc0i7fUYVRGSGcEprjgkB3IECzrZ6Yd8/vCsBTq8zeprkBHeM4YfpLZGcVpK6nK+vrgXHHlTF8GDcCCNfjapK/+7tfDjZ64uQxAVviGmI46GFq37oxUqpjumgun3vV+HJUFWHw/fhi/R5sQMrYhLZkGjHBkG5L0qt/+r7r84X4tvtw08+IaeTvdGgJ7SFRjT8Mnq3eKOCjqGbD//ZMRSb7Xun3TM6UpWlYb2shTyQa56s4XK1VLVeVzc7xqmq2ypRJa5ACRSuvJgvoygVmNAbDQ8OTreuS7MF2KMNA9dnZDfd1UW+nX/AsnR9dgPn2+PxYLJ3yHgTRYqmELnYvaVNtlipi6k2ZjtNsZ3jMn7nVA8RZoHY+AnTkxkUDws3AYw3BtgV2racCgiEEGKO+FmotVK7qxO3+slXHzuinngRdRoMgBTIDMnl1kuIWEHOkQBoVZY0yUzAG1pZasvPSAoKqYpw1h+6XvD/ff0yDOiTPa9FL69JNjwRre89kh1UoTLzE2izZDsviKt2P5bB1r39cPHLdZ4nezJxHmpt2r5++0qUzB/f++MV3+bUhxi8fu2LYp4lIeDNbrOsqvQHD0eKqrTbd7AoqIBDCCDPOGtK6hA1lksIOaqo1mgGcCuIKkFUENGBrgHKyqSOUkA5VHWrbsT+GCQVQoYe6yorCCSYdETKARJkrJEGM7UQBm1savsXV+l6eTGSVIngwg1YLVGXUYU6+5JYGbGYv73yWFSJipxhceGx8aBsbvCcL4TSapaGIGmNrTQxUAC5cvmgi5Sq4DzERWAx1tbHl4krXJLvzs9pWV4G3qFjt6whOlDcs3AuJ6ba3v7hNRG6Ogk6bWOxnD+xMCyATmWCTkCLQwJcaZOFAGKoSOTp471SKFSMWrZ55/QntLxKEpwp8sGdiULYyw+vNtGZYh/qulnTs7Bs2UZGd1GqrBBSBp19BFmhIxXJkiSutsEnn/4IRsEvot+7flAq9I4EgIGIQGRRrJqrOkBcy/u6djTqSo1cC5WViGF6tQCXLOHCaV927p793dfT2wUZjLwM+sE1A1DGHMXu/5+l/2iyLUusNLEtjtbnXH2v3+va/fnTL3RGRGogkUChCkCjUexCVxnZxSKbHJBlxik54U/ggDTOaE0a2dZkN1A6gQKQAFJGZKinn/tzfbU8Wou9Oaj6E8vWt77BGsaeUdsRGeR7qWEUq2GltlW+pu9ZQmftUI2VtcPicj1ejCvK/fWbVyKC9w5+0G3q73IPJyu/pHEQrBbOxlA8luXjKK0qLkryxw+/dXC4Nd/Mnk5uJPmk3hCvh1dByKUh4XZ6TNb0o7RhcZeX1xc2GHw+3f6OdvTDH/Tnm2/Yca8/6HR76OLVgzv3fvPrLy+uLr76t5/X5GJvu87SF/NxhqOGWeEpWQOCeN+ltp1RgEL79aufvHn97Mff/c79R79dBG8uXrxSlS1ASVqeVslgsrxlBIRQRtZRkfaIKqTOxptfyRGbYfv68qrIaYWlCkDPEEdf//Ls6pRBsFI7b+1XXHG28SLGVdoHx6Vk5hUyNaOicOaHkMCiKJI0oxQEdqRFUODoe3t7x6b1xen16dOfSCpPV9V56lzBrBZnQQSowxrEJIUADOoGoV7SRBJgGPlRABlOYBGvwa42KMrScX2QadsnPyxs+vPRGX9dWAetwLb5Kq+i0PecZtN4V+0MBjVFze/ff6IaxrOF9+uf/7vd1uNyvnr94vlWt7eztfPd73/6H/7+3251DuelISxjTWDLInO1kMcyrXLMSIhQQEtaMQYHRUYEDMAIeVnGiiyifM6IHsoByB/v3U+rOTATXq/eerakaKraSNIqH91IjaYgCwxDb+cz//ylU3vdqnXoEGoUvCsczrJFtc72j/vLq2B0zbDcRlE22/oA1XLXF0VB9YLhNNzc3tj7Dw4Fg3UWZBml9ckkTAHmJRMz//SH37UfV5omM/WDI0YTrydvHf5NTjqyLNyO51wsD8wB3KaQ0DWxy0Vw+no5uv3VnRNOtO69PVuEcc6LlcVxlJR5mLrRoq01g00MJnBEhmxlWap+eXXBcwhAygM2DkNAuU5HajQGpKqEDV2yAYZVc/+o8eRh+Pr5erXCUKhlDdHa+16TPnv5UwI2aK3syPLq/MzlKkM+UmqZtb3jt0xUECFJp8YdTTULuywXz0h7JyXjPE0x6u2emA+N/en46WDvk3v3Pk5QbhNUFDWR22QRiYtglqUCBYDHKi+oVCplLYjSYBaXGYlndB7YDSoQGc085/bL23Vc5gBs+BQ0jK1u87d//NsVIL4WMhuhsqLH1geXWVyGiOVRff+x0qrxr17+5qtfi0Sp4yOh16qlkGPzyPdDxivK0q3KrV2xyNDeXqdu6d7mressg4W3guKnrsdiEBKgRaEgaA1aOXlcAoekjCzLrJJlVKk0RiooEkXECmwi2Lk9j1OuLGRlhxY8wq9DViwABgVOK0JUmd8Vt+AxYGbH8l0ik4pOGoap85JzvYRkBsoK+knYrMOS7Pa2LVVb26vl1cZbzcdAJlBwcaOriqDWksusAvw0vSbQ4nmUZE4uC3UkJAJHqdbTQ0R0jvNgkVJKWvXW4nrFucksmrK4aNbb+9uHmqEJPB8l6WoRfXn+XNe1DRbMnGgYrhNY3zluidu5UdVNq1qV/uWU6et2cJO4G0OxYCkGcFiU8YLqt6/P77dEU9WNniDIRjCMhpvrkOMFTZM4v9UUV3bf4LOZ7Fe4RHHJN6QKu/3azm1uM6IwGo203paL0E6//l3jUxK4blGchSFaeHWj3f2oiRgwHL9kSTu9Xqf1FnWC2YQztMCo31ElU1MsbyW/vf3F083qqL2XLTcNtQYQ5kKvc3hQioY2aXBdJSJ5SxKzQO/tRePpVbBJra2adAhZpKjiAdO+O3/6NfCLB/v3Nvbl//jX/59ard7b6s2nY0orUWNYe1psibzJJHlR26s1Ywsw6Gc3E9tZpHka5+XWthT43nj4GiMOlOVOy8ii0mwwvKIxDe9nN9M798x8Pb03+J5ue4vFgmcZ6sf1g97vfPppOdlk6RnUBs5mcnE1x1t7LO+woiynCqniCqmwHoPTuEgymlXf/+73dw8+ePnyz5WxRkgVRFdQkpaEYjRaVpNtrLklWjuvTvo1lKRfPhtKBY2CrEzPdN345utXzW77+fPfSEmsFoGuaaKmr5brbut+V5Sf5ss0qlhOzLzkenLj39k5cSTdqi2Cq9QuNGWO6ygj8Px8OOgOdncff/sfP/xoM4vjV1c/v7D6RjbA23A3jMWeulM12XrURZTzV2spYmB/oGC0mNu4LF+9fHVwcmJ7o72dXdcpZEUORqCvCs2D7/Oz1dObvJ3nfCbSg2x6PZoMx6Jy+FH7IJ4twwUepzeD/b175l7GNu92aoEqucsZAuXxMZPnr5ez4e3NqPfoPok1WhUGD8MMSga3WtqKIJWQKhwXFgDyHmIRFQBPeb6iGKpxFgqizDP8ur2MUAExR5PbCrCSjwN547nVYOskFHJZFuIsKZL03UeP4yP9iy++1n2eEnzHPLQUicr3VjfT1XBSiWyXQUUcC6VIlDCL4yrJqpx4V85QK6noAM5L5pXAqdOz01QQGJYdE/rpd77zV2/+jmfZx/ABs/H9SeGBKmdl5G9e2HGmo70ivHW5pTdW9/kdTWCeXtrj2y8OBFUUt/OMLUsq8aKp76OWVuQcdW2BRa43K6d+kiREgQQu0pUfI9fNHClBcc8I0xhRKZ6tZhfXRRSVLKNr6vc+/ABFwXJyc/nilJMaat3Yrlki36tIdnWz/YvRVTQZf6/7sG7htqQL1Y33gmntsZyTrGUtLYhlNFRShzILHsr1Ih9n7QS+FYW2XspxWN5cwz/5g2/VxDJOcgRZxBTTNRBRxTXMAUu9NWOLIQiYNaRcUKiobSjYrYBIiQTrYlImbu5PkvP4/ORRY/exftw++sZ+kWQRlJIz/0K5NSzTXJzNK5S6OeR4xXYXnnP24MGTTq+tXRgxIMVy/dVomSXFbkdw6wVictMyWVKZOg78JE2CxfzaUOv7B79VbKcY2Vq9lrgRIytekhR8RUwjnw5ZlRMNAXPSJiWGLmAWsJrORa5HoJd5OYUUgDwvKcAcRogd8EqCKsSoDF/m69CL0+ySm8Sf29j8GWLIt462RVFwbuzr51dABAY1cpFEJYokjZuuN9AmoCQQkYqxGs15mLcFRDKIltxGXvkMqCi2Gve88Hluyt08zHEbCRyNOVneTtNoNmZOuqK/LqNNgHwQzPwA+7vbO4etI0ZkJuvAuXSkA4kXypP2iUudgbHDakjh5MAO5Q4LViQXSJrlFV/aqdAk8e5OL7wKOJMHqRjTcLs28KgtdkxKN8PxosZZq5X/5elG1lSDvVOrMcFGcWZANj0Lg+XtUurL24JUq0tarbkW2GgYA0uWfVVhcJE7CwfvDHa++4f/xeyboUtmBywhXfr06dfZTRrGEWYnpIRJHEicWm9UPGs6i/WL8/PdwQAKhiAevv/eO41KiNTCC6emojUenAThxL58Ucr64oVdljByCMeTGjalZmm1yXx2FXqMFffNu2Wv9/GDR++MZvNSUg7u7u4en8yKLLwe/eDjpj2+lVWF+9YdXjCKMt9s3Iqz8lCxU4IVyDBS7LmYgY4TMYwKEa5ZFhtGPJIZq4jNhvnIOVD0KZ34QZxV+ZvRRLHdunRHFTkvZ1J3yAUoYsBqM8uTjOTcyp2wcc6iEBLJ43VJMY2sba8n9nStCKppyX44/sWvXuqW0elvpaTMu+06X6ztDOC5yqymazyjpy6Kanh2M7TDwGn2pf/4839dD51P3/99Rnq1czAALMdsGW7IjZ6+vWftxQyb+SGfVo4XNdqd/e7uNAekg5PJfAXo1lYHlXi2PmVF+cufvEoFZbhMx9X4+fJ/gmD44O5hpUqkD8VdJZiMXS+2rEZvaxC/LjleAISFBdvt3G0eWXPHlj8y2u3Wn/93fxZCtCkgHA13d7ZHt+NOrb/m2W2r+YM7/0hQy3nlrrNN5+jd3Y7/63//bx/cf4Dsanwx63101CqPUV5gOj/c2XNWawRAzZBUEJqmVNH88SNtUnLf3fvoZTQMQj9JE5FjIj/iWFbkEYZQY3HBkgQCCACIaMUVPCNCkFaVxIqJa89FgJuckkfhlWeLFRLN9hYGe7X6zcULBijd/Uc3w7e8YOw+6Y+G63907/c4AeVxIUtmOgr1dqFsw9wTkyQeB7OEjcONJyZsRxEMTV1MR7bt7XWs5v6P4px40dd9fF/Qg6rMjJphIRqfvW6nOTsmAb5gTvOb8c9OtROmLWiChDRDpxVt8Q0KcNsQV+vretGplgAh1P/wXVIlge+zYaTWG7pcLM6vCeW7tVoSe4IH/WAOEJX4GkuZAod77T7LD4LYj8dTIEssVikJqzjmBabN4ub945CBdpkvpgvS6uzsHpQAhrysluLbt28VyUjjYjYBy6e3jYd6GUWkzCNXIIXFCaBTlwFr0SGw6horyunw6obB89mL/GLdvw/mE2WF9vrdvZ3GowoEhCaEYAILXhMURqogh/KEZaot0CklWkYh4dkgnUEOpXglSEZXkVxndjVdncZvW93tBw+36nW51VY3k8WerGLMdaNGwOQMhzr37oRpooCU0zU03XhB8uLZN+1Wd3t7BwLSPjkSL6+KpoZYQGlO6TJL1zFLVi+RolmxnJEFuwnLpv68Yd1vNj6QW4fzsZfmleqr0IK8oEUM05FZ0ZRDnxNElCaFwIAC4VKVMzcuAAA4rlmDoMUyHK8KSvEmTEhFCIUMLESOptBo66q6N+N/lTvVeL66Xqw5FwXXdhYWOmOmeRYWGVxHjKGFlIuyCpflWKhKltchRqIUSDJP9EpOnBtvfbEo7cv7H/4Q1rcsmG8IZIpKWGYMZhfrU8y0MUYwRKfeuUjLQ6WtiN6jo7utbit47V3BSavdPsXeXsZWIVPxQtswhZTneJC5jsFaGYkWtm01TJmXVnEO5SLDHgGZxEMNpwshK7LMXTzHLcCuVVWWbXa9Wmwo3m4WtAb2woltly7PCpRXEaRzQGuKUoXFbD7jjvlUqG5nVYIrkbhyT/FhKNJu6YWnby9N/bg1QN7pKMyL7DSynRUrYsM0CCG1uzWEm9KCskgEVCPgWm91rZNHQRbloJNl6Sm7lMJGq9mgzmp685vF9Xqe5o1mx/XcncF+S6L+5DaHfjHNqMQSGkOKhUSTrmsXVz+NOb7avS9r2pm9oBIf+27RI27hyXeVZCNtJklVem7gcIKosHqVg8XtG6O2c9hvSUngc8LVVUAJbDZarXpDqpHhy9ehogki8pDVaIXbouHrXJJW3iZqq0a7vc0Sh7MkXPiaYFiyGLjx2yvWUBlNRSwbrdcuK5UgjsaTs43QDt3F23x+gpWqytPEA4hUKJsKFRP6bEQusySM1y4rSZHMmS+Y1Ko39F+9+IUs1Z980Pof/4dfPn05UkRwlw+1trWJ7XrrXT/yJrYPdTVNY6nIzUYtDnNN1mlJZ/MZBUiqtyAtJ7MNz3GWZc21tsQpqGEzefnh0Z5MhfPk5mc3o/lmc/9H3964n7352WeEsUqVGY2mHHPK2orIJQiwWZ54/qlKPp598dXWx5+ESZEw5OVsZoXuXBI4rt4y9Pki152FgrGqaqnKtGVDV3eEOCMUb+/eN/T21fkla+yGLscup1DXcpNnYYYcp9XflTUV93WOxcPFFIECL1bsYdXf6ga+o/AGYpmiqGhFEaIcZlAJJYFmkCCR6LrpuiksuJwpG5rAxnSaF4zA8Yaee6PtQV+3mhwbyaH6dvb6p3/1ZqvfuXvnkGXobHz92b/XZrPr33tw5LhulgeXizkEeNu9L8kNQeZTeaUVWFkwcs4Heem4uaETjuVa9eZ6g4La+O/+9ld8KgTfMVBab3e5usKeLYZs4udj0DZNTd5jwrdOBSq9NujKtUV2zmOOEdJoNuYFNKuaEYpXaxSFr2v1WiNN57ORO76dev6jgyNMSlMRqKJFm1VZJJSrSE1otttwQzErLshyOLtpkgK0axqPoCQ8fvAkzzNJVjClm/mI12vL+TyxxwiziiRzDEzigpXYNEgnkzEaVB1BoR3SsJrShsmbcRzkWz2eqWDWyuhsYvW1kA9tO6xtPTJNBYhEU7b4ex9vETi+nFly97sfvw+ACgGxbwlnVCnJeBFWOZ1vliAtZZFlMccJOLdq6QIjpRCTOPajMi9pUoSxH2czDtPBbucmu/AvBQy2unnNS9L56qldBoP6QeBsGvsHOHE1o86JrcTqeDeToAItDB3XMUzt9etrDMjhoMGmDmIYgOuGkBtcBo+QwIHA06Rd/jxKZ1N+OT+f7iY7O+8wIqSJTfrITwKIiWppgiJzPMcKTJ5khFQaoSHDQAwUJh0mOQ2IxFfYCaW7+x2OD9hNi1OzuLII5CkZTcZoA5laihneDhYduBNeBW+tCxaS+p5lnztJlVcQpFkuFnnJoMJJaK8heTrqWiEDPM9uAWYtQBqCvMhgBLI8P3961XyHnzhvVKW5Xim97t6T3g6gT2omief2ePX22NM0iT3e+/QN89dlsAnnEQH+NsZUgvfubZM87nXqBWIYLlfkAkuyZZg0dG+STOyyJRH5WqelzGIXji5nqlhCZoVkk46pZtXYOOZcDgHiLO2+uD1fzFEXp06aDvKVz1eRYXS0hq60YQPQq+L97SB2w4Vb5VVaRQUSTGQGBTIrERf8LLhpSg2McBTPWjvWHfqwSpajN8/CsJJxVXSyWq2psZqfcrEkm1Wd5SQRRqaha3xtert2+NnW9u7cX2Z0xKwElRXG49vYjT757YdcJiWHIkTdFy+f/t4//47qyn93/leeOz862lZZjgtUxBLj2IpmXDK/xNY9ypEgXuXlBUCREwc2EOUCu25Cuw1OkWRRXifpQO0gfJeXtOu3rzRDrURxYd+2mrtbvd7wl1+5NGsJUi0rg7wCuThd2lVVNPcbItBLAQTjVZVNttrUXjqRhNkOkYP01tngYjk5nWZV8sPf+oFX1xWU43oLqQjI9KT5pH1mM1UwvX61WMxOw+yQhp3CXpKMr9qu583Z5aPHTxLX10ErARGjqNJoSWP9anP29dd/IdcAhCDPkNuyPELaR+LFdHO9nj5qG7LC+wtfyMFk5QfxWubaZZh2xLomSJyqUciWJR5eT7mK4ei9bpcXBSMXnck6OOZOpmn62i7ekzr7Rz/++U//H4NBv9uCq7UzGl1v7+z7m1RS1PXFmqLWbLagFKaX9tyPtabZajfo1fl6Mbs2ps36lqx6TpWyAWEzPYoSgrxQ17UifX41r4PCdaqLxVJE5AdcLVAtlmEbosZyOOEFUGUZwiY2otjHGGqKpB7UpuNFFEEesaiiOmO4qVtWJZMjifgUk6zkVE0oURY4scjJsmSQqiIAFKJqypqimDLSrq6Hx8dP8vIqT6r19QqWqN+3ppPZ3/0//1ozou//wZ9utR9ml+/TrSS++rpa0cPj/eWCfXF1PYr+Zueg11WFJlbM7iCJstEQBEEsixXPChybp/ORX04++O0/jt3VeEydL1//Itz8n/6P/we1mHObWsnm/Pag0xgwzQ/2OoxZr7hlfq0JIiVEc0Nea7OMBZ3Ih2UUBiEpuxwIgxAB1Kq3dUXr6OXS2eQlqFxbykqDZpiUxAvzmlA6achycltlBI6XhSjLLpNE45jFyl/b16qqFlkmxlFxe7bMKeAoK0lIaizdjSrvAlhdpmekXXXKtnD0Ya1c3LprWmQ9Wq2Xy2Xbb/RUhRZeIdazcM0VWpqg9MYVV6bWu9veVcvWcmm76+D+/s7ek/sA4OnCL2TD0howCaI8LUmG9YjTOZahKEJlBZI5hCUCKivLTCPolEy4Hi+HtyMMy1632a1rWqkVGC7xhqvDOEskVZHRMVcFi/myWr8NF5FDoHZCKBQBxrPp2ExzTd856ndLFsSRc/b2jJJ8p2coDKUZNRQ9yZI5M8NQlsWuDqlYqwd+ZOeSTxHkmwgFQa4m4bTeqrW3TNtbB7OcgooXxFpdAxDyZW6Pl24RMAAjXuTrdRjNTJZXEIYEKhTYEdljocKzW43G6yFurapO84TLdpavbtykPP1iDctKDa6fzzxJRz9+/IDhBdeQG2EimjJgzaUFl+uMAlvXe9BShITkaSnaHDL4Wq3PclzbMjudHww6e2pSxnE5D9y3X/5VXdsP82srVKNoTQ3ZdV+omsQJzHqzWKfL7snecjksJNUUa8JAEBpNKVb8+SLOb+2qhniuLloje0GKBQGtmUdOuq27Xd2zn89f2RhL1han+aXdIXEWZz5Z08ziMAJ87Ec2IzQBQ0qnbqn72wNSFJffPFvEQ4HEoJFHXlDLCs48cpOVUNGsUlZZQnnB0NqwoqnIsSlXVmVGKPWqqtFU2vX9HQYIsNls+aHaMhrEwrGbXb89dz0Pq3RaLLOmIEBFa4BOoQBeKoOZ50d8waYamS+n/pRbxPl7H8l+svnl3zx7+EH/zr3jKGxlSZDm4Pr00s/fyIu7smFmtS6+eOUsZ+wBEGWEIJ/xJLhaM7BrNttyl9uM3orNrrrJ3754w3GKKPOw07ux11Yy2xbNuiRbUT5DtC/qsDegeQGQ0HnwALbqo8++WZwtyzr1Nt6gs/1wt376N6+zTUb40fWtQCgcFuidg7u1nd1lunl5frZggGRJqjPXGmoSBq9Hz9zUtlSDv7+zLxjT82+OBu3Wh09u//6nRq3kW53H+qPE0PP+5Gr0dgPs6NZjkOQvvsiN/uP3fiuMQ+uxujLlvfxD43hQQXy30YYctr1VMHM2tmPpVhfBtP/wrqTFpGIxKrx5yUQ9Q4eAgQKXJexwsXCcpBwIxQqzLhcdy86zQIUZVRplG+3hk+29k5qJpGL99vWrcUl1zoTJMAhWiiwjcufo+FB7+KCexHfBvTB0q61e8rN/w1bLMNaLsmQYaTZ2GZyYW4OigmVcMe1GvUJb1fajx/fsVevpb/5fV7an5PlyGY5Gk3a7iRiaF7mqqlUEYjsx75uLi7FlaFWFmnrDstrj4RzyoZIauRoVOAskThYZDkMCEK0EhsGEVmVVIMBwAEAocGHMNZtWp2kabc8Zbl7O1s5sY5OHTw46tJpA8Pn0lF2D6v/333e6M0W0y2koy5woSRsBtZ60oSsIXiP2hKdvv0nWL7fFN837j+u7dXqdUQQYhrVqzb8bXs+er6353xmmuU7kq6t1VYD/y//1//bpuw+UIqi3Wg8ffH82mzP+0mHUtWjK/FTAkGkYeZ7VNYFZbYKoyEopu/Jm6dq+nFcunvGI8phJkogbCilbKwhoM1Vu1Zx4lJz5nh/Kqd00OjKSPLtUG9ZkYzvOZrFa1lu7JQcj4stKfXRx+s67h+vFqi9rIr9/Pr0K3Oluc79h8d5VqBEGggqgzGps5UuGxNPzxDs4/vT4SCxSXzQ0dznvtdvOfJHZrxPuOOQ4FS1m/uab+XyrhE+/dExt8P77+01gOiUgVOBE2ceMTxO7gLQgIpY7Qs2xN5BhWJ7hu2x8Q7J5dlOVaZwIeXLpn2dF1JYbz1Yv9o/Mumqcno3e799frc7cUSQrCqol1ACy2rYYGR1szTfz2flNUaLdnd3Kq+ZV0G+rBZOQKi9K9oMP33OcDSCFP3tBKo5HDIM4nohIbK/Wq7Mof7+5PePc9lgMKEYCB1GPqBkMoaFoIuVlVZ07i5JQrBtpmqVZMh4NC5IDAEXxyNzR9zT14kjKSloCwvM4SwMNV3zGQkppSYSkWE43ABMkcNt37oPEV6Lm0+GvVIDv77ZSWpTNLqGT5WLN1HklY6fJJi2JWasrR++tpivVUHhQEQV3aiz1GP3utyGtkjQkq8VkMg3ckK5W64htCLnnrxfuuNN9xLCPOG66XnsIZ7eTqajwck+BtQLSQuf1lYdceyaOLyRLbDAYsTBczZ0VUXWzTOMEJGpDe6RV//6rv75v7cr8cQ4dMePYJb/xNoVcyqVKSyhQNcz0klI3CACiDAOau2Zq09kiXi/e9iStmAdfLCb31vxllt3+q5f3f6d3YNaXocuv7EzgpDgOozjFWNH0LN2sprtVLgIdwVA2aD44+ERSxFE5S5bJer2s8qooagkn7H3UiIOAUTmZgW5Arux1Sst8s7IqoleLMM/khlRRxJmZu/JYXvwn//T33WA6vL1J4vhmtZELenf/jifXuu3tiiPEqVpmmkgay9HAcwjIG3WjKBNe1u2NXzppX2mNk0gazupWr7HXywpudjrc4nchQlGwetC/A0ixHC3nk9Xgo4/Ss4t6o7HauK8vv/r049/e+i//4Dd/8ZMGi7la58SokQJ+Nf5GUZj7qok5LMpya3tXEg0C8LHWWNM9PB+FtSCc+GfzleT6flbF6+XJ/Xs905SP9P/5YVO5K883JMrDLLMkhRvBq3Je7dYO3937vZW/Kdl8No6Ng3ZTk5h7qsziK1hydAVX5erVq8ROOK5LcFHyLFRrZlEUObmz/ykvCrpAf/n5da20WSFQUcxIUhylCU0R3mVaaK+94+dpY7fNRIzUFaM4S29LnwDLaiqydvYff8HL6P7RwfFWf73KyjAhSmRqne9//8lsvk7bW4N+99WbU4rg7c3Q4llr/5F98wWppnKuMa2jxSqcLtZqu2LZmiB5oqo+lo1oLL59Uxpao935pHSTv7v4+7vtlizzN9dDVsCmaTKPD2kwYm2JWTCJmyW4YCpys168twdEQak4SPQKl0xZZUxFKaQCgAkueAJFY3uxDioQsZDJFFEAsFRl7Kd7UvsXQeT+bFKviyd3787jBW6gg+7Ogah6tjM789MoWpMXwwzs1HSAvMnaKyL5huAtVlfb9cajzn135+bXT/3507OXL1P90DTVopD8MJLu6gcDIykLhLHQPvqw2+/19thZ8t7jFoOqbqeZzaP1N28vXj5lBGUe+67OFoiqTcmqqmi9AVHgvXz7NqNpoWAVsvLuXWWzDoZvSwZnCFJAfCcx5Nhh+Dwt3HLpvpqGYShLgrqlWnytQuJwvnZVKaF0RYCm7R8eD9ZRFIlSaTvrzYqSR5iV5+s1S8RhPJU97Uef3N3MF9PwGgDU4GqRmGGBqJqRFW1hhWaO3eO3kpx3RrG9Lpqt+Gj7PifmhgQxzm03u69pv/e9fyxyjf3uT1Pme7v9IwCACwq+WU/WOVluEk6oFUlYctAHa+quy7SuihKDKIAAwYoSSkCalmotylKhqDLcKzk+LQmOijD28pezm8IO84xhODKxX7aFZsaW9pTnO7usULfHb0c/f1Z9B378259oflBUCVPCjRdxMPedW54XqrwoJI1vMCjmQzfKopLfK0SB7/rhcDT0yk1L6YESAoYAC5gIpXUrLYqc0ojlxN0ebzMyzMIsqCgwGzVBEjleqAiahwkV0vR27ETx7pNH+/1mMltcgZKVc0xIHsVABQbPXn0zTQvBbKqE0K2PdnIVH+/X15PF+dd/H5WpyOPOVuFW6Xhu84rR2PuofdhtihIzmRlF5jdl13cYx05oFp0/P3tzRjQDVSRn+Mrb1AThaFtBktyS+Ju/R0AkogyXl678yfuzr3/9zTdXgx1df6inr4CvRbehBM1a+Obm7XITyuEfvvt7nmtraq+VO6qKONtac0vkrlhReagd721bcQBLKi4WGRKh5wRAhF5lkJQUCBhLd71iYav2h3/83mg8jKO4akskAs2te6IW7/Q/hi9/appKO6z+5mr8+ulp76N3RR/xAidkMGLjvGGIBVlGkcmgVi1bYm9+Og8hzDF+dXurs4avSSLUhG1ZsjdJFJN8DSSDQt7dzArc0pSSEtqRhLE9j9JQNZpca00xzasyqYTZKv7v//zf/bf/qx8AXYvb+tWFe3XjHA9aSDi6v2uESfHLn/76xehpvS188K33TZPfwep8Fi6vFlajzVJEGDnK1zjLWpW03LhryoOKb7BQEnkZeIN+o1w+LH0/HN3cenZSpLLjl42273vioItug6+efsXnLk9YgREPto90ST//YkVL5NkBNDv6Hb3dVBKZW7iRe7N447mm1QryBUhhqJi3p581m/eO93qfv6IIMPP56urs9sHdO8k365IUUR5VXd6tJvYsYADnv/7G3blyliNBkQhonXovjrTDGUAKC8Q+5DSx1rPatXfnLxZjZxhHvnt21qy3Hj947+j+TlsYnF/ezif5VloGMtGsVsGFHMf4XoQQkkS4KvJ0uYwF4eTRg7wslsuFf376O//L/1bQxNO//o3n2Tu7O5K/qZLcLEDMlCmNm8quv74iZZBlwdkbmxH4LI8h5o6PDxngN7KsDo5AbBNVXgQ2QtzDe0+aWx3bebbV0m9j33b9ZKliLFFK827FOipQNdNsdB4+wQL483/7Z+OzL/6QUEGXC0C6H+00iw+GV18f7n7csBS1poTrChOWVFRRlaIqWMctkJVIShYwSZXkbMCwLM8qCLEFAAIAvCTQ2Ou0TUHSWxJAXCzwUnwTa1zdUfXIz4+Pesf1e0wPzhYwTNycZiUsvDbRzVa6Tmdu6mYL+eK21+22n+y1U+OXn//CVoJCZJSIY7ZFzPLvfPL7Jx+lF1W+2mxqbbNes24/v+zJO5UXrlcOz6A3L95krMBsKyySTA6yOWfnC3R5euvYcVlB3ag5dYGp8sQZrW4mcsYYVl2OAwzgYPuQHTRdz+NDdB37UuQiCIoC5GWxfLUIlMx89CQTa4ok6p22Bc2EuF8v17EXaGHgCbkgyPO5bUBjdrOp7XKPHv6+zLOO7Z+9+nmj1lSM7sXPfq4qWuPovdF89t4n35oOZ1mRJRlN8mrQ3ren2enX2Xzyty9fLn7w/ZO9Q4MW8nI+Xw7/MvYyBh/+s//dI54V0wqIfqLJQgjLxGMlzqMgE8IsKKIE0IaoJVl+44TAExmGJxUNw6jSKy/wJovrzWZlduSjwWAdzDdcsHt052Z1swicXX2/0WnbeZLTrZbKfb36Epy6jeZ2s7ZFBgxcMdllNsymRaaZVogZoOjC9eiNprTb9Qy5QRWZIyZM48xgeTCNYlq9RWEz80Kakf0+xAgCyHEsBjRwXMvUk2pWhSiL+SKtVnkumzIvyiIoCKVhnGclDEYF4yQ8J0v7XVJBQBFstDo3t5jleY73/UBIGL3ZE3cl8HY+P7/OVP7mlNRUOl0kz15/U4qSni0dzMktvWWqd97ZY1ijAuJytR6nqZyni8X4+esXOcgaxybnCoskYXmgQCZn2UHLVPsNfYA3s+zlq6eoudfdEiKaYF7nWluL+Wp4O5VqKtQF14/bluHxkmjTVVQIx3eRNFu8/eX/+//7Zxxk/tEjDqD8KgLe9QXTlosMcAwESSUukbHd+Ki3v0wchoO1WiP208TggiwOA8OB/uNPHhy9c3DlTZ+Nv650UyjMqMj98ZgA8uBBt/2wwwmw3tD/STA4fcGBMZp7K46r410tjnJjtVhyuGKRrJp+tvnyy8/nt/OGKvO11ot//avakyd1y1REkcwTTeb5BkhJfLacsEnAAQwY6McEE4YY1V5/8PWvPnNDZ6tbhxH65tY/eaJ++79+uLFXYaIWwSWSjaOjozvHv7OPrZuZ84vffD27HGVZ7ruAgLRK8yJHpBB0LWdxN04YnWttdwcsfhoREhBy0m0B2Xp5vWTSsLnTw4SJNu71zRm7nEq6MlBNQQCLyWKI8v72gKyWnTbSG8LqGXPn8Tv3ekdzNw9y0hls/an2z8PNUDzh4uzlyo2X10pJgHVP5dYS70UX2TC9TSHLyxIWNy46eJD6ZNwWt/DWyQONK3kqFaJkcZKAcDybLphA7Sj1KXidzKCaGuM4U9mgnjhlaReYMS2FyRVyS5cgrHeKLU3ZUXc3KJ7RpMGzgkZGw9fPrr703ExhJQUxZS4y/no9fBu0mQQZ2ztHN8vgZj0xuPyN70JY9nZ6FY3kQ1HzVxDkQ/dWGxzhzPnyV5998PA+12zusOJ5wYSJ29zq08X08vxqLTi//KVXa3UFQRWFRU9qpaI0C0OdlKKh04I4scAwJPBHPC9QQgSeSeONF1/xPM9QHkUMf1T7bz7+X2t1cfX1K54HH/YGoqap9x8JqjC+uvyLv3yzWt5enq2T4PX21pMCkHrNwgjHiV9UVRKHKYtNlkEMppRWJQrDjOEwQ/OKVDngAQCyblYsqqC05CTJXaXII8tc7HR7H31r2yw2HmOi2k36lfOCLwpKhBKVbmSBTqvdkbfnKpGFRpsR8tsgyxaJF7dMSdHvYtEYTi5EY+tbvcFZaIMqlnWZDReT2SqOA90h+9tbDp7nfIqvnNNruzMQW1qLGT5f1y1stNSzvzo3RFtm9dBfK5qJUraLcdbtiseHr16eQsQe3/mwSFfz6WTBwvRqJLI6jKUCYpmv162GqmwMoxZGiRvE0GUt3rTd+bOnZyhcaPfvDQKviVle1jZ50ZCOdcNEsdLr09JqhPNbS+1fblb+hdvKeCnnlhBqDCZVJvBSxgmOE1AIxR4rQIUQcP/eAz+bqpLQuos++/zm159f//Ef/ZfQzO3ZWRd2fvinP9ozHlUAxBhEbqRDHqzHPSnzUwTdZQQBKTNK6CZalrhmcelCLSOUxCgqadTE2a+vZ6PpK77DMjy+oX7GFL3OAVKtfU4QsbnRoNHpKT7jBZv7tfqP7v34m7UjlTBLM3lHFjdsMyM+307oqFcxDozDiNy9e+CJhV1il1NKAgcUs5gNokSEGPYQnZE5O1f5jiIFWGA4n8PFpkoSkROrxNeEyhSQVMNpXBJqBjHd5GkeFqkgZEkqK5o2ULisLHNQJdk6Drl8zje3Qb0GIJPl8XqzvBpe3xCDEt9nBK5X5yRe6ovhmyHDgpxQXVc9hfAqjLMkDDACNIpfF5ucOFkYRF9E0ZGq60YostvF8lKyzDvHrcWc7baPHQ9JenE6fY4nPFOKlZRBSFrtxsx2McK7tdrrl8/O0uqjbss8MrMyjqCgqAfOZrxaec2mxgvW/v4P1B3u2d/+5O+uzw/4pnKgW9u7LEtNo1aVV+H88iLwsuvzHIFiZtswoapOFHl9FTTrql5PFekhd7D93Jmf3vyF2BcYLotCH7HM9sG259s34Y3IFPMgFAtPYE1md+v97t1f/NK9uXnrh2GYEEr4e/ea4YV/bX+tasJmubh/V9k+/LYNWXZkDzchnK7D3X1nPTcs5c6dtqDyNaUHUJqmhFLMXixZth6VIMs7Vtsajy/VcfL4nXfMe7Wl5tTzDMBqlWTfnE8gNzrcu1s31TFgpf5ua1VBItZo7CczzYT9/mA1v3UZKUDNTQ6qArjT+N0P44pW6zkfifGbcKET3kCadaclrbzXZ5uGlXU1vXa0Vdne7t6OU5LZZl5PTq9uX+eo7Db6YEIYhpGs+svl2humTrrZ5viAq9Ik8W9n09vL1v2BuFVfRe7VEDA4Ddfjlb+ebcDePnvn5ITeoqqEA45nZgHfl6bjyGpyVeqZdaOQysl0mcSimGPXCTrKPiSRNAD9Ke3K0kaKdTk94OpzkrU4fDXJBN7AN+M8w/VeLV+OJV0REcTrJc+JYezBuJCVNtepyskqsu12vy0e8NM1DzjGJ4v6jrln1ZqgCEGSLjdcLb1/9wCCcjR8O3zxc4P6myA1NAlBQAnht7cte7pYLu6/cxKEqzgN6zrUMVPxAiVrMeANQ/3yZ68CD+7u34ecCmmh9lik11LXSfI0jN20sJWjYyN3a2k4mo34VE4QnZnjcC57HLF42Xjy6OD51dXtNcOzBIM4SzvdjrcugyhOmYTnBQTZzdohtGg2LZr5XJpzsghWdqIIEsSsRnSjI2AxBYD9T5fKQGMlRpCIJJl/983Pa212phY/+Ph9zwv9iW/LxVNvvnGix48eODcLiSLS2+ZmE9zseog4568WandLEKawYP15TdVzut1szwTWKAre8zdjuH3v4HianXrh/O3bM13d0hVjR2abNYNe2EnpqgpotXC/2/A8mbm8XUc31Gbty4Q0N6uTO8bJw+P5zDZw/VdffF3S/O7De92t3SRPgcFus/e2Do7quPnLX7zEKihJx4LtB+17RbGZ05+zgMuJIjEFm0kEAa/Mq/XIy7xPakrKTBvcCQyzxXp98GhPlKV15TtIw9nGau2GbJEMNw+f3DMYBlfomFcrTdosgudvXu8fHpy/Ob2djGRVbHVa907MZq3ZtGRJvPNJj7kWbn/1q8///F/9rcCrjVrnD/7Z7x/t/y4AAAGACsqweRDP66pQLHyODYsskzASimKe5TkgZlO23cBihSTMVFqmZbhez5PbpN1tgg63lBye5wRZzGfR8ury8Oik4jETOd7wZjafc6R4zoqK2TW9nORCp918TzEns+u5O8wXyd27csYZV28ngNmUNJsL6E7NKropqMgyVm7Ho+e3sx+e3Kltbx00EpnTzl3captVdiWTbliWgECUAzkgHM/FQVKxRCxZnywg1eqMGMiqqMhErDLEkApXkcBxYr5wCDsCkpJtsnlY7HpVrkY5zZIagRkE2BQMtt5o+2EeZjHYN+fnU7PbxE0m9nwpkbAmLfLw8vJzEPOYp+l01msP3qsNFsuFKR92OrK9sao040rgLtaum0CWsyqjtLMbt9ge6KZVW6CyR6pS3kQVAxhT0rW9vFff2jKBNULjHLb5Jdfq39E8mi1ChdbO5nFoxe9993tvv/zqC+AduAGo7N2H754//Q+rqxkelJGaghHjltBo1tSS5AQuEqKpnCA2BK3Z3965PfvFlJ3MF2NFVQIv0fU6yAyZiAKL3SoXUtdIBFFZhDKePH3xwf6T/gfvjdO5lJKCcyHG7huHFxirodbqpqoI+3c6k81ybsctpbOuoA3L7c1KZTHBkxwym5eBt/20KtXlItnbPRA5lTPN8fUrVlz98P7dz8MFxOU3r7/Z+ni/VrBXaTNY3brDt1LziCt5d2GyKE1SP88roU4MwjmT8P77/bIkVxeLCjElo3JsaZZlltQqEVxe5gKr8N26XqUorrIgJSqX5mHixiw/3+/fhxuf1y27qH519nrtuu8e79Z3TiIytJPN2p46K0fV6m6eBm+CfleTOCtyh13aYFu5tXW0CWay1AjKvBwmaV2ggKd1WCbM9iFvWJ2L6wkd5cf1Tqd3dHt1fjE87TaUMphzElvmpN95cDFdwsRrmE/mN18aCkJqEvoENviNY3canYwpp+B6tpgvN81+p1bMEmqAJIyT2YKn8dxbbMJYU7Wtd3v1hGv11Iv1iIRlb7d1G7hzFPQyrO4Ul7Nf5waPs4Pl4poSUmXLpTdGCP7oRz+qspskyf/wo49HoWOvvm6ate6BXga5B9dbd/Eim80DtWFYT9+cmZE6uK+6r8+kSt37vT9Z3d6yvPTw3s7Sh3FVQigub5yGhXZrA5f3a5Jxt35nOfMlkU99WaPcmpnfOTksy2S5nFcovpis+GYzedBuluX20dHTm9v3t+uIKU6O31mvCSwUxMPYjwRRSDMwm29E0cCSlKcUWk0VUcNqYY5HlCf/Of0JACgFKANKjeT/8IP/arq+urj9tSQq2NTeXF+06EGbyLs7atl26hJrtzrPnj+7d++3UoXbl9sv3n5jCbUEcCSMd2t6ttNiUuQk2XwTWFsVVff9+Wt7vH7QbRwph9+4V9363aYgxjNHDeHlxTSNM3a1zlS5Ih2SV9fhJfNHf/BfJbNw8vJXEg1khUG7AyNMeUlQT452s51X/jlkTQLxe2oTMvSbsy/zLL975/7huy2WSMVZuX3vzsk7dye/eZsVtY1tJ1GVV8VwPNaOD+7Lj5QCEXHVbNQ9HiTXjru+4LlOlCUv316uXWfr6DAHgQBcrmTjLMpAOQ/y28ubME5YcCBo0cnRfcDgvfe+KytPKaQ7d3YCL5KEeLkqGfwL1No1pfpHH/ygXt9q1ZoPHr1zcvguACwBZUxyjoFVVgQgE4pM3KrANUyK1PdjioBCYYBAtHQqVC09qcqpIJZOyOdZrKleVnCSwcVMWpW8JJtlLS1u0mmRBKQMF9SPhpymS7Wd8jaWaFoAxKtNejsZpW7sL2XOqOn5chF/Mf2moJW8pUxXYcdsXvNJ5HoMau+a9aaqQ9b/11+/Uc9vux8178ucvSzrRT5AqEzymmpkSUmiYSRwMCagKPxRxjC4VDKR08oSwlylM1QYQpmWdF0W2CWVpio55lQoywSCMsClWMRx6KjSA/iIgOzLxYySTGLjYknm1WJjL5LIa8jRe/rjWXqbJ8AUZCw2y3xcsaCrIO9AgKwc+HGelXYVimoQxYkoso6rh9GsUa+JuqnwLFR8SxDjnOxLR69Gr9YNTfRFn7qvs3R3UGc5bhGmr5yh2bQ2Yil7l3pVB2mNwVoSke5xD80uGYg+fPLxs9MXo9LuNOull65hDvplQTI/LHlZUqikAJDKUmkDToCYaQ12zYKmn09+Ha2vCynhsyREAMYEyVRKwMJfzdczjgPwns7YRRzrnU7fUdJT36sg3H74SRxH4nQmlGloVExEsmya8XlqpcPJbUwsmRVXiG7v9OIl549GvqWba/Ly6o3r2DvFTtVZUZj7UbKMU0xnfJzczMenWeRJAZ2kZVWc/ZtVoPCc1mwkksjJKWt9uDWwWQZrKoFulIdh4oAyZppsdEs5oxZBjUdeVoR5VrEM5IQM9rQsLhRjexnOeb7RRLK8w4Hl7fl8ThbXLMOFie8VK9318gpvbe/A9HzKRnGGAAEAAElEQVR080bFLdn0jV3tJgjXnI3No83GLrX83Ik7oAIYRv5Mlegq3NiOG9+SogCaoatuAASmaFrvvNuczEevXr2uS/3GvTsb+9yq3eczK5ldQ/MkZ2XOKFZzZ8/sOSQY/9xP6CpNODrYi9ZfMgWFCLEqnYub1XqlawYj8BhExUosC596KbvOFyUXA8I2azHarC6WiiadJ0lDocAwl2dhVHr3D7ZXQjGL8+Ubh+3y79TeLVIS+SBzVhTrZje1GGM1mcSXIC2qRFfULBC7e0JFp9N5Tiiv42napzUjJ13IdnkBq1BdXE71Xr3Vfogm47O1nTuiqlqdLc0bxmEW7dboVms/DLz4Nqr2uVEwuzi70VSpXsOdFu5wranrYiEqmyliIHODs/WioezXW3Rze8Oxq+kmZdoJWuvzWb7f71ZlZTu2KMqQAoYTBM7I3SpNA7MpSppQEMqgAMMSATYBiGYg5xEDQLQmhOG23zv4480fbr5/h3ApihjJqFEbRVGWZFFeuGsOl0gyDJHBNLtYvmxFYVTutjvcthXmKVz40zg6MlqwtJntfsJGFQfN/e3lzc1fSfmDun5xfVWRchY9L5/HK7XBLZek0QMZrcRqOn+7xHpne5tZLXy0gt3ut3vtmzjwiuk65SlCuBi9NO8o9wbtsgDj89lnz78a1KlhcJEfvX69KhL5eO8u0vqlFm5WY1Hz77V+dHv7mWI7YWKcJcueoiOeg10tnN9skmWtZWUoEUHzzt27Oa0NdusnfJRnBYKyWKzCihZ5CiDDcmL95EROKrO/l5Hq57dfJBtkILbV2M6q1J/HHCsFroTaWDYOj417h/vHplk/3LqDAAsA85/gqgJMRcuKALXWcjZDlCS+w+EGQloh3OZJkSdpamEUIV5llcNa8zy6RRikcZLaJE0jnmPDJEhhjCtxHUZpmaONk+dviG69+9HJfNK203Rd2VpXnS2iyqb3dqk9D0hwQ0mYJunuwb0kjVevXVlXrJ1HJxxTlamh6CV/729ev01nOS9IW+2PSTtznHNnHa4iafXMCz6NELsNGEJWIBEz1moEaQiLABoQ5TCmEJV6UVRpEgRpwbN1gABEgBE4TFEczomNEbzE/R7FkiQJRZkhhMafXdxMPitJKG1JgnUwXk80swbWBEKQl9ny7dJvhBgryzwZX49rXL9mNrxgmu1oaqG+/uIbY2egWVZVlH5EwlwkDKfgFIkibPSuR0Po+5rcwbIZemEMIqNXE0WlAgHjwQLj0A2g2M7hElZBwmGGRSUPyqJielSg8vx64s7XGgKFovRV/SP+t+00K9PwpfPMDMtan0q+UnCJxAhgC9r5xl5CBDlL3WL66nUSe3m8dNZJFdAg9pPSEvAmC8PlDEGnXlpbB0ca4iMcjclwV1cSYGXxrXM+VXY609Hs9vqa3eCmwqzdpW3fShI+OfjtKZfnbKVJiOWleBoXoJQ4Th4MJIljuNYPu23Mswtmen3lpuvx9eXXhrlHV6F7E7Qy5kJftNotTYOj0QSsSV1ts6XasExsNdROD2QpYRkjLGhGMoHnoMx5dgYrTwQSgKroAVastepJHI/iaEcE9tSe3F69+/3fKi6vZr4TdFo9cHQbBdPb8f7WtlbilAaHj/f9zTJL1o6TQUNZrIJsnSAhKTapljXH+WobsSTOhCyHkIGMjtzNnPp2iXFUPHhn63YYOiTk63vZzRi396e3L9J8RinoNHVJqljEjjfzPG+qkir05abemm7m+RwSlnLtZoPq5u/yycv07M033tJ5YLAnH+7mQUoAyYSMY8QsLsusytaF2a6IXGZ5JkuCNNh6c/qM5hvF1IS6ULUB+dVivtXHHM1UNkjT8cvT7eOmqqyLO6ah1f2140wDyzAPFMsWU/dZhRs028Sm2keInWGAs4qsCMFEV/VYraO0lS5svjhwVuG9A+PTb38n1dNqzpRyA4b4YpPaq5XBMhjJ84k7vbT3HrWAp3EsqNW1+dLhmO4mDfzEa3YMSecJMEbDr7vdre1W44svhu1ut95rAgCWm/jpmxsVspwllKSQ/CxHXH1gYYPBGDGYyfOyw7RyzLieUxYZwZWO9ziRVoTMJ1QVKkHlEUYUVBwAAACsI8dxwqUt8KHoJtyR/nrxHHE6owmbci27nOP66XKWW01ZYVtlQvt6lC2dKC7S5PTVc4bPVGHfVLhYZ1VFVl3/6uLazMrSqkEQB2dXn11Uo8u3oNXkE9pr9XTRVBmhAK36gdnf3m7M8DI539g+A+kBaBX9I+X1i2CVppLI0vUiRiETIoK2dKMsCqqU7JPdLoUBZqkuMggxy6sy9vDhnVoWV7/87L+LQndnu88wHNeqi5zIneHR+DzJK/lIBUDnDKnByOa7hr2wXD8f51M+sl+S2ZPag6K4gSXUOMHQZeNg290A+9dP//76Fn3x4tHj9350//f5/f2BabFFpcpimqVx6G71ZVk+kESFw1xcRggoXukjxtABAwDwChAkIExZLADqQkpwpih5MoaLjKCKFZi8zJw4JGkZhXOItoQ+YxYMgYIbnG+ysTe84uqa4uEiRVwT0dVmx2rUHz5CSLi5uUmCwJ7d5A2Ljcs8DVRZaJaGDJJn+bDOsw3pWNNZCiDe3XvsunK3rW7vrDYe68ellw/a7aM9VgvqjrPc3tGrqkqSRTaJvrCniZOvXJHjhVxLsISQAwuMCcO4AdUwTCqESkhRDjFiCkbhSFwmFlYUVVzJmNqMQnkGu1mSi6AkVXp7Q7CVaSIW+CL0vcnsRl/p+9/bt2p6lIYIVpLIsagWLRezgrRaW2ZKYOiTSnDW66BA8iJNUt8uQ3nhZVt9kHlE1hIvhjn/drLiGYkPFDnXin7Nu7yAwMiyLPJRmbNtsKwqyovCcjnnrDqt3Hbvnh4OZ0ySxjkjibM0SoZex9rZ6jdZG+RVsjk/z+S2BS1BYIxmSx18t5hfOKshVhRe4UY5Ki88DiUF4jVlK84SLi9KlqRJ4YcJQA1CHZYtCOEVzajndQ4LRGNzUT2dvEWhBYkRpp2kSsTmHX7QHN1OGIbZZrZzM4+icHR7m2eAa+EsATXY4OltWLoezrEvroKZJEu6IvIQzd589RTVNUYXFOlkcHK/tvOv/vJfo2iymC9JWezvf4wblZTMm3I91JO2VLtz59uRRoq85M3awnUCCIw0cpxlTNKcDagX5SZFEUr9kOlauNdN1/ZmeB14nojRuENqgiCLnESq7v7esSJoLHr71gbLK43BSbTIQ58QGQ1dy+z0duXba2A1dEFhF+vXgc+SZrWl17nR5ma6JKLOV6yWcCWz0oNZc9DeuKtictX9cE/Smef/0yvU7KacIF/eIk7ClH3SenyenNEcdDFq9z79/HwEDLbbZTY8OwoLg6RwZ3B7s2g1tzHDdPeYL3+lkBKdebR7W6qW6Pgul3CwQZ6/mO3tHtZ2Ch3UpxsPZzmBFRnOiV1yHKcf7kXlLC5aoA+a7c7ZLKvVdyeTWSUuL2dLpUXkCZjXVs2iR0robnypKqCdF0U+n44pYWFlOKKumVreEFtFK8sTj/HzlexR0upqjF0Py7NFupZ3lcwt+a3d2PXCxYVp1rq9oyZPvBhprCDvmiauArJKl5VdFJIIBDntyRw6OlBVNilCzcBWo6XozbKs7NDmHfnZi+vdOz2ZwcSP+w+t6bQomAKQAc+JOJV7cj/epJAAjmEzISkrqSgNUaSCwCiqyGYlkVCnYeZ+HoQFxgWENI6hoak8C+siX280xgw/nsxqReCmKBY342SjxSbuqkmz0uCOxPMpgKPplZ2teZ3fvWd6EUrDVSvudu7qm1EcuWNxW8nfXuMwcwBT3M63RLZWb8wX853evmHpiKKG1a35XO3YdDbD2Sz88ukE9ISTex/FTMSsRK9mqCWhm5ljTxfYlEGaebLf6HS7LZDerDM9g5CEnsGJCRwzJagWpJT4uBCm5x4XTC51FDIGfTG5EkuampKkdxm5TmkY2BE78m/CYvM/fPb3acawAsMz+weHThyIGsQ2Pc8+UxFSWS5CyHcWf/Oz5/6NHGJxa/vwT7/9v/j0f/ZdgasDgAAASe5zAGFOBlUMygjwJgAYgFJl8qJiZNwCABCQrQFPC8ByQCgxTEEOaCl3EbU5FmXxzcqL5YoUVUkhmySR7wWXl392ca7Xamb9yf2CQ+lyqmMsm7pqCB0OBXHwJZqLpTg6G3Xk/jLwNY/kcdTmGjNQWDzaQvLr9TfDeclC0DctAEteZs8Dv4ba6vHdeRRNfvFrb+Pv3DtxJ8to4csSXxRRkabV9j4YjtJa852m+fLpS0aXfuu3fsxxYlXGhBCMUQ4hwthDjBJVYZU1KE0Ax1QwAoAjPMqy2RgcHkiZW2XuFUKlkvvTJOzfXArWiSCuvWk7kIPAm9UsS5K5CCfAC2KeZ3mG45DvxQzLcBwbxfHm9Mxecu27e/c7rVcj2YQbFkTT2ytMlHeevDcNgqdvv/60/i2kN2+SpDfYq4L8KpokKL1XcdPtAxGJ4XDJMgpiRUxK3w0gwL3uHs/BxfU60TZJKgCW5aDr+ZEgcGEUrcNvAKqnvGzWlf69u1mWablRZPl1eGp4tKzK9Tq7nU+KPP30O+11AfmB0WM4WRqQojq7fav0t6SySvMyzXKESwbv0QoJXJhIJVkkgb9C3pLJMM/KJ537mlz7zfkZr4RXz18IutVr9mLXaxWVi7jak98zFPXVzS98L4Mtn1FU2QvlMgFGW+JxDPVykqgCxkpDslXVUkkEPDPUmu3v/eGfhi+WnvvLd/a+1TlQaF4Uq0QWtOg24xsM6pmUVEG4ZBHEcuX4cR4Fs2oMnQTRUuKy4EahJbm/v+XHaTIaJUnOtBr7zQaAdMPhxKlkvXM9WxbRpqNK6yxYrTyEqjqPZZEZe5twPp760mHkWM3dVle/HXo8l+cu5RXRPnWT3roBuHADeFwVBYywrZSAcIYaODANucYWTpkcoo8Pd2rN5tuL8WQ6n0zGUKdVc6NKur123iwmdgnSMOcsJo/Z6Or0+HBnZbsVhB1ZofOo0MW9vZ1/+S//5c3z1dXtL5aLELK8YqqzkR1Vqiq0Rc7iWNWn1A9moqJGgUMZ5sH9e6Is5yx6vkqkYgZU7RejmcnUar22aeQbF8zykIzhPAr9a1I7olvSHvRIyidNi+z0+8PhxeXFENXBgMG60dFAzYv9RKwYtp2UTOmFeUIElvKs6d7E+8bxqnqTjGcFzzeOT8oxZbmY1zQr4pgyKaRc4jh3vVzhq5ihiirP5meiKMEOs6rELEnl2w0CGcazzYIdtLuNZj/DqAxjtmYJnCDzbVUcxagChIviqGRqiUoqRBHkFEmraBGlSwB6HC9SiJ3AmaUiRpwp0W2B4xAgCCEZEkrdOGxpKlAloLbMwyPw8me0hdseeJsgjNHr8Er8ze2c2zx8IEbVMkmSJYEHmpGs6EaQBTHe3nmAWT3nFCJAjiAOKbv7uw+xjHK8cDe5K2LKKAJq7DSxwJQkg1V1nocFNErZ2hjuNJ5mtwkjQUPXmWd/+VnjYRcYumforF2nYLVa+p5bJG/H/zG6zXLQ29INSRD8cQkrXZNbrcaeJOba4XXsT69egyXUD7hjpXTKdYyAsw6YsMAq1ThFOdbztf29g+OIZajvXVcUIrRJEybJtvuHX/m/UYkM3M0myWvmE1VrWrp3FnKfHj/+B3/8J/tbDwGA6wKSAkiwgIQlGGJCAOIBhwAIAZAAoABAjCoAAAA5AlETYCAxNgCJAOgC5Hme08zFlVJBpTWwwJL460I0GDVnAk0Sw29Wf1/Oxt87eF/NgiR2Ea0oquyby7BhsAY3Kuad5p2e3o0Wkag3DD4kYWyHwSEop5t1piovV8MsSu+paonx0M0NkdYkPqfEq8bOKqNEYhp7llKZLMIdJbFjhVFEQXTd7Pz8LAyjGh8b5s47777bNncV1ShKmBY1AvOCrTTdiGMUr701KjmhCjk1iVNe5GAEEymQMjVmfSE62u9UlwHCmCVewmXRuoibfKqxbZptfvXlW0pB7Cqtuw3T1GlFZ2kSB4GsKgwDJQYRjoeQ0VX+9uJN/haVXuCMAYsvU45mOdDDaHE17g+65clH9tL2SSmG0ZxMBgLfuHPinLobtvS+ueCarTJvffhRv6jobDrWdCPR9asvvyxITRbyvivTVFJP6nGG4o2tA4wZtqaZl2vS8L0vb06tWq0/6Kc4KbycqxkpBd4rb+UnV5exLINHSarVKi6n83XJ8W9TwkNGvroZddqdJ4+f/OrzL/EGw8x+PbzNM9rdEt/93ids6eY47MIuqjXLZXY6n2TrhVQ/WNzM2HXIxtSeu4amHB7sN0pqWjxmRCSUbAyzyNZUUhTNlFlMXyb1/f08cyoomGL9oLuLTHi7Gi3mk9X0/ODj73tydO/4Tk833De2JHhVliVdUd45+O4P/jTEyWJ4G23cMvSTLGla6m2y6DBlxqfVvPBY3VTixWLOGse5vZZ1/Wj7XuA6Pq0kVcgms4Ze83ltHQQSJzOo2nhOxJUkcCUZxdmkLqdFXvzFT9zfsNNW5+mn3/mOKGi8sCd2YBTY9uaKod2DnTtEqhdBJTAchlBMp1wO4thXNWVlr1avFmazQcwmdbC3dt978O79e3cLOBYharNdXWYjHaMVONm5U9dr51Fg1mnoTHJZIXloVFQxpNP58swLBVZhFf6jnY836StncTl/uTw42KeC1qx31dyIAtcJ7JRhERYIZhItgxD8cjKCCFOGx7WWB8DV9asP2u3L12+/Wrx69+hAbTw8dV7IXHP49WfnXw4Frs9CyrDjOwpz794hYspOb0u5c8fSGhsv/Nnz/zCfw5osy43GwYOmG8DZDIgCk+sqz8h36p3dtHSj8cIN7cWsxXXBGp4t3tTyWlkrRA5EkwVP0oODrbfjm06vcWGvSynCcZNsrlhQh7xxvDPgxdZ8/qpMBIyZeO2a+3uhH/hulCVrBoHXny93D4wFDnoJIQuWKBiLjJ9FDAQcZ0maJmo4CUiakiz3GaxvoighykADCWJrGCYYShxT/GcnrLZ6h816D+NOGl7c03Sooa5V+dNpFGBJJgwR641aoyyrtIxducM25gnoalY4nLrFZBU4Ogevb54maSav/bxqNhttwi+4Ta0JeClnBVm7HH+lqQeE8yNA5Q5zr3/y+L3t8/PZeHh7fh0x3cf9kqnmDE2rwq3QbC4sF7M8ByisZFnptRrv1R85cMr28TsP9+xXl6dnbzRLX0zg89u3vNhVdvUC0OEScrqSioydrXiBMDD00qBWNdV++9X5Dcoyud0PN6MKlZevLwxdYSy9q7cXZwvFgoGfLOdf3+32M2nvH3/nvY//5J/K2AIAZSWoYsCqQAEsoCyFFEAKAAUQA4AAIACIAAgI/qflnwFABwADUHCAJRUocgpZgLSSS6FLWS8sSVV5pc5VMYuwoZuKoPQIMWpG796uHzjF7YrJA1oEhGSFV/oZF7rh9onAFFW/3292TNEtAOK6/aOyKA2ztn4xM3HZU5CsiKPRiME0YreqRSZwTFZXZhuvvtc2DCs+e32Ry2rgdWrNb96+Mlt3uvtbfYLTKPpm+eWvp5/zU6H/D343UOq+k5MKEIySkAkLyjGa3Cw9fz3fhIaaW7q1GDm4wiwWkiQtCGfPNpasDITWlXtW53GZUi9gIi5SLLGSmdK5mU4o086yhPhamDpZZ29wO6X+5rQgml/GksBx9oob7HePHjNYNA3Ovn0dx/63f/A9GJLZ+IvJ6DZ0bo8P36dIAgiFgXztjBfD67EHuqqGYh3SqL/dSEPfcdy5nwKAavVGuoF37z3eHWylaXV+fjObnpdLV+mQw9b2ZD1FmFtcbajCkRKblrmxzzVDkSWl3++FYRySEPZxWZx0P6FV5WaZy8hsUeo8Jhs7rFlKDqCU5MmmyCp3a2uQNarh32+KZBTFFaQYR1GauLuNlg/s/M2qypG9iWr1Dl2hltJAPDaoYgxkDuOr5HRv7+TixXL/ruavPA4rebmczFZRsEhtCGUDXKRuarcNQ9cZxhtGSTIpY1naC9LLzcpbXlyIbMUM2Cw1Omoz4uxq43ZYrhDyi9vg/PT1YKsjyyIq8Hx9W4ZlqaZpzGgtJnZ5aQ330L5/5QG+NmCb/KqaJsucM52FLSqs78CiyuuikVThijCMbMDzrz/59s7IW83cSKmq7a3en/wjuxiWNxV69c2LuvHw8NPkza8mURI8evju/YNddxPF43kF9SJbJn7kcdPCz3ku6m110wZrezhn2MoNFvmsZpo8CyM3z3PVSTdZmpzcv+ddPLf0gSBIz07PYuBFHCnKvKUdMyXxRdUdurtmg2fsPBFSmLBHHPe2Ida8x53OZu2oFbQk6/XsbVHmVcVsYX7NBzzLL0K8HH2W8q2TB/dZzyem2eCFD771sZCJoR9t72w3OX5ycft+cz/Pyc8noFyD3h2ps9W4uh6/ZVH+4rzeNO9/674XrWfz2au3twxbJ9sZkQrPO/vJN1cPTCtMBUtwgZ+5IEzbnR998OFfZXg9/yZYIkcb7Tb5XmFGsVNG+cRJH/ZUkAkrZ9VgVLxOj0255FpeGHp51e30aBUJOnd5fu15szgIzZbZuGMqJcfXu54TrKYZYitBwlXBM0tA6wcnJ09UeQvRTMVcibOKAMJQ34dlRTHm+QbnVzFrswzmZzErK7FPeUQgQjQjFUQcA0BLbx3vPHl69RtLa1HoXr621bq+de/R+AwvZpf5+Vw9FHhZVrNWRW4R08qvR0/PRhBiQIGVgoov9TTmBWFvf2829zANTQCCzFP1XiF4tIgzn9OPM9slUzTa7d2x/Zc8w/K9pFHT8bLG1LetIst0qovbPM8y8899y7TuNj4+eH9LELVOp39x/Wp1OWtZTUkbBHucRDmcCW+e/h0lldDWt1sH89G5WznQzl3XS+I8ZXLhkMuLCjkYY6suc0anEUDUPT5UXO/6AxPFo5QNpIgXBblu7olscX01itDWj77/o48+/TFTsV7sZQWLVBXpgAEgqgCoAMcATClCFQAcAP8JAioASgCK/6x+AQKAuiGMGZhEfIEoEYs4LViGIXlGOVDhWqMZRjGlC7AS7R2hef/+jxVVkFtC6qzCZLrJNgFrb+3U9w4GFcAvX6HZfO4AWKw2QZwkcSwIB3Gcf372OgjnlmXsqEa8cSqg7x3uRVHik5xYtaoqG5Lw6zCQ0qxcLEvf57PQF0LGhtiqV1z1anqbxfnDeuNh/S7G5cgK3jnZMamwBACrMIdFlQKQC3kapHnFoR4nxGGSm3rplUEDWUkQg6Koy3UbFBpIxSazk7JRTthWq6CbNL4ShFRYHe60tl9Nvtrf3a5IuhHWGPKz5Xl/0M+ynWpo5wXcGCh3glGRZLgwoqUyeCSrW7s797tl03a+Cm42zS2+GBdvr/6D0Dns9vZqCo8NjmpHSyJsJsFk9uW+2SmKPI6jV9dXhqAYvLbiK1GHGjtwXVqriT/4wfefv3j9i6/PmZW/cO2yyu/f/SDhotXKOXl4DP2lxKdVFs/WtqrwSbSBUHt4UNce7AicMJ/Pg2Rhe9N6w+REHjIhSFfW1v35phRUzPNiRTDPMnd/2GHQAxClL3/5ZRQm7ZbKIyjFiJG8WrfDM4DhcFbaOypfsvXKH1Ka48CSK5aZpnI9Hg1tFrF7j+79+Z99tbazjw76jcOGZB0uy/V7+gM2XVxfnb8eOrSuyLwQ4hiUPZ7ji0xWBcNfs5gN8xh4qY90TbXM+Wp6evOG0UUwMFfOKHOj29spINXwOjzYFxfzmF8HE5RLqbAKQ09S5IZuGE1PDrSyTvpMDrKcZs4UMdSWa3KPwzltXefF27PXhwd79+/fi8JIjFMe8JmVWlcgx2qk1tZXFUn97b1trtP56mqZDTNN0sTUttUVtqi4sSTJpiTJM4KgJUuVwKt8XfHDDCOaxWmSpBzPbW/dSSM/j8D11TrPZqudnRjFHSr5SsQJYHx9qfLS1F0apuyQ2L9OYvjcXQMnr2uFKYiCWRuUBA2vl9mCq8NaVma1pmE7i2x6SyQ+9cOE4p13On4YRlmOIGVnRWene3l6Ud9udIQWt7ErSlxVG2Dxd//FH1387XLvwTZjVpe3dd3q775b6w9ajr/RZAYK6Eltz5W1qgJf/ObLQa2urJhU8lw+MnOO47jZKvvJT/7yX/yLf/4PfvDxX1AoF4Wp6cAblmXebqnqrrrnr4JXdgWJgKS4rc9dtxyXGv8qxRYmVcPIMdraOAvAvNRNurff4LhgwB1FfmgZ1kFXcUMHgl5NFUnFpUne3N9iuzvRcA1AESBS1IwaIVValSELVVxgWgmkWzBun6t8UgBQEqUEOYcghqz4n5srAFxdURrJvMzmc8yQXn9rYa9W8Wm9vqWpg0j5Kjh95dSLgG8kSf3q+npg9i/X47phqgQAL6CcKhDcNw1VVtguG4Qhy4vbgzqF5HQ8kxpWvUsdMk6XC/HYWG1eYSbN4lCvafUgKAuN6Zqqv6qwP86QqmCl2RTutP+os681zK31S9/h3YrQwlcUTp9+dbPIPFU1K6k6e5NvDcA7u51bJ8C5IW/L15NzVBSdTlto9xLEqjiTXJekcYqR0+/d3F5niytR2GMYvDqdiT4jb+9dzu31NDw++v3HT7b7W0c/+PYfYqAABlYYyRIoAfACEOaA4QBmQAKIxEIJcBRUJSCAiiGMYAw0FiH2PxFAnkYkK2iVinVUTngCUwBigliGJ2JckVTI3YwtsQI4ojGIJfzW7oDnSTJfhKE9nN5iXAkMrmhxe3Vzde7NdVKVUAvcjtCvKMpJ29KcgPqCQHnRfPjg/v629OonX11PbznMbtYOK4np+WVkNrznL3CtU8apWquLvTuchd3Z9TKK42COxLQuN86nV1fpJSijdrPR6D843NnleI6CFGJQpGXOEskUmJBN0zVBmOOZnCRFmaXZeky9cJPq6mBPHjhkGW20jMlzFWZppriZVXEVi4dZ1gVzGbNpGjEMyNMMTG9b9TYHUDg/B6o8ImOOF9ZLP3eSw71usy6OrkYAtWs1DbHcufOGIfRgd0+Q+ExMXdWdv5x+k6x3dw5GU2I0Ks9NVV39pPVQqAmb6+uNkOzIMsLIk4hShDTnFuVLWMHXr/yDo8Ggf/SHjdr1cElo8fL1T9+8+qXefZjG4pdvbvgma1l9WAEY2LAGDdze7fPOSlAFMpxkmsrVB/uTX90uS1+X5TCcrs8Wu2ybQ0mvIH4Qb+KihpsWNPKI2epXO598DJlN6CxK5COWzTwUVjm2/UZDv87yBLfvbgufDeeH1o50h43PMp5P7/ItpwgDlHOs8MOj7woCX3uyu3k6jvyCLElQmwDknxzf8SLXU3Inkne01tt5WGzoncP7kFIvTUxTVwlfSgnLQl7gbkYX8XK4szMYr1cv/vqvY5B3OtKuflLEqShkpUtjP5pP3WUGlj64fwSzJJdEBRds5kZJgm00ZxnIsT1Zwi9H02eTq//tH36b9w7nzGS5XJdlKYmyk8RBGMZj7/prf/egn8BkOfSM+tGW1SfrAnqsaSkghyV2OZYdnt1WyRxj4Fb591R10Oz/5unXdSJWDAtipPNangaSmLACCgL38uyC19eCIHBce2f/yC1G+caPo6iKM9vhpwSJGBMgvxqloiwTlm7m4cuvL0S23OlJv7Xda733uPeYXVwuzj4/e+6d3olUliZFVW5pssBySn+30+pc0irDfD6L4tV629rdsrSSS9+cPmsIwt17B2GUhFn2+OG7W9jDSKjtGVtib/BA9BMv8icpyVgQQmi6pMSr+fbdd+gn74uiHNpucX3dRJUfJyLHa2pfZ/mfvnozYKG3CoXK3TBZh0Pdbh91UAkk1Tr0LI9sxrLEgpBuEHcRh3Eu1nlre8tq1OtffP31/p0BL0COEWVFfH49EqKqVa9Nhs8QUUWmotNA5M1O//DB3Xd+73c/MkT9tKkJGiSbEghxyeQJZDjCAEAIKCHCHs8KG4Hj+FKoChWnCGQgaAGTAkAAwAAAwBzu3r3ZOd8IOKV+mWjvvfvt4XzxLDhFKa2nFhH30gQ1De/Djx7f/HoMGfpgy8pybvzqlSxZRcIun73sNJoFyRAD+9u909M3nMDdfXj/7sftitLrN8vMj9J6IiNJEsuiACWliVehNI03G2Ye+cnthbi3w0JGhxFbN0k7Tgru11+c0gq2NoAVCTLVUijHOZuF0Z7UZ3n2D/7Bowr2U8MMnv9CN7Rqk9cM/mTnW0qvn0pKk5I8SshgcnmR2M6GzN0gWpFhLNy9rnJKi4yWhaUqGDBvL0ZJ8ld/+k/+zz/+ox9ioOYgogCxUKQABCX1PShJwI8AEgEr4wgWcpFyLM0Ar0NsAiuRsrKCHAgLwMRBFRWkKEqQ47gqZIAhBgSxpCTIr0CIigghKEocB1SCiiSJ/aKyYRWF6SJcDF84q/f7jbwk+SSP6yFjpveMA8rVVElTJTnJCt0qPDd8dnrblnO2DnN6PZxLYl/xnGATOo17TS00pku7pZv7P/ydlOfHaTa+ni39ubVCXE0BLM/FAYfA+OKcyZmaJL6ZO6Mv5n90+KO62YgBRimiCAJcqhAUXg6wgJh+WUWkorqicRw+2OoxbvKSUgn5s2zIs4wsC4jlWFKyWRjPI5UHFWXyvCSEs51pliW/+uXPS5q02gWpcgJooYjpmyIPY+NIP+kehWYhVXGreRLvDDfF+LA1WJ9tbj13n2Ui1mVzRCgRLFE5rPEEDlr94/tyRantOOPhLc3M8asLWZbI2F+1m0YQzrwhnyGmJEjHx722rNRcPyDJ6yQEXpIcD+ruVmOxXPK5f3hPe72wQanC+UzVFF0175rqdTAEZQtTZ73cIll2O74mDWnQOUpS//k3T0WB32kP9nf23fOLsJLYVG2XhKk4Vgw0q3Pz7LQt8bfFQhQ5Z77OJSnwcoGFmGFvNuuSkTJ6tQy2ESwUmLiv5qE/sYxPQt+9Jcl7vTuu4zNC84vZy8elvq6WwOHcjV8mC5bDVRbdrIY6p8lq7WzuxJESuwkpwXIx29nucZxesbSwc5/1K+g8O33tSCGDO+H4c8s0vvvoAYe52IvGK2f2Zn5gNSVLPtg5TuI8qFk7O3vxTTabLL4+f5qXZaO9FQqkK4kjOJUTsc6RCoDnz7+KRnYcBMskybKpxUAEoCyKGisxFBQxFVNu95CTJT6Am8ubt816tyPDNEqCSkmj5OBwj8BuvWnOLt/eluUu5IpCj1JqmcqmYHptMfOK1E3CxbrSiCRm7a3Bnfv38jTdVCmTq2azBjK5QNWDWgdVoChRlCwVMatASBn5wW+9s1vg2HcbTOyFbjwvD2tSU6zzD9k3n99+/jbo7+AHNbXMCyoTBOGbUZhTqNUxURmrPVgFK73WKPOs1uiy2MM8md3eFPWd6c+ednsdeV/fLJclyM9nCzX1i8qVRK4wFSFgoqoSEZ4FgSZxBWKwLCmPHjSS2HWd5SZLbHtqT99RD5rvNIsy99zMdabuYi4pUv5ZnFeVwCofffDJM6805kH90b3W3n71esjlntLZcxLP/vq2TJPL888ugs2hIVUlEUXZ3NGjyxIJXK+2A6sIJkG71Tn86AeHu49FwL0NAfBdVBIIi8LmipTAioEsBgxmCMuJkA4hAYASACGRKiByLAZaCSoIMAbgP4lMtHNSFt/8q7/6v8e5Ixtif/ntisFGz/TeLFy32jgTAuhPf7b+6d8PWZ693+ucbzY9WUw5OFy+nV3Z4yvw6TbRdN6L3DAKCCw5Ho+HN7ZsA0SZaM3kfQlL0QyGCJeFxgsO5bEEH5r3BOblT37CMviDujUipQwDa7dNJJZjKqthG4rBC3sljrYP1j5QNC7W9rdc23eo3GzUJWU7c8r79z/eududz18ZgqiIynXm+bMLV2R3eZ5D+Mm7h6fXozXjQ18RD9Tjww/fb7Zver9eTVYACQf1dp6L//V3/zf/8E++rzJqBQAHpISESZVFLA+SDIhVRWVCQZFUFQ44mUEsZYDCAJQCkAIQ55AHNKFS4PlFUVYVpRRVuQ1ACgChFVY4zd+UWbIuCxlQxLAsYpg0jgSOrSjwYghjaIfZ680Kc9DoaDRhS2mTpRzLE80Y5GVlu97LuZOOL1mO5fq7wWy89+4hELln7rKn5ZpERKCwDJ/eprycSaLAN5uiwE9vSpHne912VKROGpgFQ6tit7HX720v657EiYDCsmoOs8vf/+4/ZIFAYiBwCQSUdasKirywgTTt6mwC4enrnCFE4rGuFXxLfzcSqVjjOEVJiAwIQHG6CaqKcgyf5kWRlyAx58Zivpiroqhm5mV51mruZZkfpTYIeYLp4PCA1ztcllIQcwnNp8OWJrLL9OLZqYlbu50t4i4miwnXYQSZqzwSJXVVM1bV5tlffHXnbr/TbJxoh08nX67tNd3qxFZeLmaxquh1WKyKSEyKIjsNQqbk9k1DUvdiGGlkXmH8qHH0+SadXpxbYm9HEWWMhpXAM3WeZ51L1wuT3T4LEVFzd/dkf9Vmvvhm+OmPH/iu3W10+v0u5vDScQCVCQ2rNEWorqo2mgJfBwi4LsJkUblqTAJgoZpZ0UHvQciFajza//Bbz189H4/9qiobdc1eX9ZrhnpHc4ZTxY4YNSAbzYVckhTnTnxkHKDjElEap7LvOoFCfUCQ5xlmF2Xk7t13zDu1819crhfDb77+iyc/+mfb7+wYU/vyYlNFDKiKtq6bluQmifVOTRLFMi1wI/tWb69M3smCjFzNfEnpq/h4757nx6tk9p33vyM2ZGFLmo2WX56/uk29DJCIw8nlme/6xRXY6mr9/kBYLRfztSJraZIsFpHWVfcGW0btQ8YwYU+djm88FjTbW+46MCpeV7fKYCoyqdkxVLmVZ1mZIiCA66mPLS1Pi6KCbmrP34CCX+WMn3ipqdcYjkGQLp1wdP2Wb7RhmQ6dQHSwKdcFrUsrUlUEQ3D3nqpzpKDYrrRuQcv943pw7iwWAkWOBLc5rv3unf/9URMFqyhb2cwSyojneD63Lmxv6YdAqhOei5br3J6LqEgh96DduI25GzrkjjoaL+UVVg2NUUGVpFyeBakryjgmsbFTX8/WFYR72wfOcJiuZikXqo27GDPYnxdSZlpQUwaQmp5bu724NCRXlnlAy/z8IiySiYcnsc9zvKrS+PJKkkS+rb5+83Z3Z1C3hK9eXaL5WuZ4L4OWqgpNc8qHZV7+7Pq6i3Z2d+oLuqlJ9cgv0thpM/LdO3sHuw98wG0AwArgdZPDeZ67DKfIVsUgmERsUUKYeSCUoMXRiFJCuZxNwCpnZZ1jOYApoABAjwAdAQCY2sN2+O9SAqpXL98U5pbOgLb1/jy+9dezje1NFzlgQV76UQIEBgCMr19d7fVMo147ZDpz5EBQyqJcVHkUhrzMAlLGpa+zhqIKFORBxGJe2OS5dz07vHOXY82XL28Fs6ZQkbGU2nI+j4P4yNKdPBSFMg0nETawgOehw4YrAgNe6qVePF9fO9Ygl5XkPCZC9YNv75ydLZWI5KMbETBzey4f7rJMavXqLGBUhFxn4dAbFjO15uGBFFZptAlfnInrpiTMilocZ3aJ/unv//Fv/Re/qzJ6XlaYwQBAjPg4CAHNKctUZeQpjFyVDPVQSgylzgEBAFAAEGcEFAWtqBsLihZDxFBQBZEIIa2ydZ+X/CqJCk9SFFKsi0KuSAURoJRmVeRuljksFulFGq5Io8gd6sQ+ZKvzIJ/BUd3JrVpjNjcbbf42il+evu12GhSQUGXu1CTuiGM5HtTqy9ktxbhVM2iZujbd22qWpbmm033Y+OxXv2ZYHmiwzu6+u3t/E4dAKZaX59fRjKJyPRnLrKga3HqWtcyd/f5JCaokgqTISKkZis8xpbtMMIQVTkRJ0nUtCVaxF5YMG9q5IBMY2UhDeZpsQlstEkqrPI5lXo7dNaUIYyahvsdAgd+pWmV1efrs+U2WJhjDTl/CCitUQrmwy6osiM7zjLNxnLV90KmPgyBjAUHlnW2x/vBBXEVhHHBcY/zNzZ3de9Nr99/82dO//dun/80Pn3z6D3/nXufxZDIJYk8vzQ30J1kKKg5w66LKAVP4edyw6iPoovhlHAcaIxfjmxu36B21uQW5OM+gTI6bdD6bd7tK3arlJdjeNpLExywAcuHMluPA2T3Q8liDKIFQ9YJgvKrscCyLVCLlhoN8MgNQ9nh/OX9Vz1mvBKjB16na2r2rSvxFwl1NRrHvFoi0Qgfkxbvbe0uX7PR2stAhVeGsX3rhRh2oI2UlYqHG9Hbg7vNn8+v1m4cnD4tGm1uDZGNzIWgTvuJIXkiAEJtzhLVYf6AfoeNgBXrfauZCZK+XceR3rXatoQucwAkzXRGedE8myXQTuLmbIVFvKoUfV1K/I6fQofjFF8NtXhJM0c+cG/dsoPdVi/ud73zrL9+84Zbl6vqL1co/2u89fri9Wk07T7porlTT3v0H7zCY2Xz5JZsnpv5kcO+e74PXbu6EmNcV1eR6zSrYTH33WSXROA2buLBdTmBla3/b9/y3F0tBALIuiNJer69fn39ezz1cB9oh1+wo0ZmvMcjgmpHJY8IX2uqgahvNw1ZDmM0AX68EmrW1Wkgzb14l7Dq3bwrMrzeXwEIpDpzS7qeDCVcozy7zNN7d3YcbIdqwbMIystocDCrLYoKJa6/KJG8xAHc7Trb285XPbWUg46SqXoPebCojHrLtPMnA8MJsGbVayyReDXbLMG5bZprUQZS9mi972ztpwVT2ulVnDGhsNstOoyyTosrCH3/8yTpZnv7s7ytTxQyYsww/2LM4nDsrTREYnV34N2qh5QjjUr2cnX3r0Xd2KBld3lSyulNrJFkyGo2hLa4xXr2+SsSq3dih7e7wdkUL8ujhhx+991G3uQ8AdtfzTKvpOYVMIYqcV1CG9zRRQRSZEoYMcGIxicsyd1lVL3LA8THz/2fiv5p+zQ7rwG/v/eQc/jm9OZ3cGd3IAAEGkBDJoURrNHLNSLKnyuMqu3zhsq/8LVx2+cLjsUoXE6SSRYqiQABEBvp0n+6T33Pe/P5zeHJO+9m+0JTL60OsqnWxfrQA4jIGGLGSCiAAoMihzYEGAsP9ne2978S5zT+wt1XeRoK+LTVX+g++9/HKW3caTYpDb68v3OCSVEKzVwvgJA7pbJoc3X9wsMelSVLjKW81GdY3GxqggLqjiS3l1W/fJnLC8HXMmbJkOKfL9juyU6l6Q/3xb87Wt8/oP/3en//dj/5mcTtHVFTElbQnXn4+Tlq+Im3XzX4eEM/ieBa17h7HC4UO/CLKyK6odHZXK1t0L12eh4QmaWy0zBarnALCcS4A1eliKaccRVuhz0bMjGQKVWxWtkeuKoFV7/ab6cT//uGD33//a6a2A3yX0EwBGZpCURJHQVgTkOcSJTDlMvDyAFFx+2BbAywAoACgBIBAksUFhyANCU4yQoqqSmshkSAJN5t6q6/6MChzWAecgHymSoIEcTQuswKULTW6fj7h2CkPwOnkbbKIZqvpnXdPlJZx6cxsTRd7u2Dtq5KRr2OV5cU4ddod2cAVS/cGxzfppBEnAHCOG8WxydLNlpLHila8ii824wfme2keHQwbjm1fLT7FnBzEUbOh9iRuGiUkcPc6TJZni9eFayf/2//6fyeCdpwXLExFQw69dVWEJXZ1AgFAWVHWLOkO4OyKrNx4ty1BscawUdCwTiu25DUUxUjLFm+LNIkJKHEbo3UNC/fc/uL2pw3jzma5ljQzT0Jd57oi2z86pGnWi8syN+si7zVUf/7W2dhZkuOoVjmGUSLLHvtET6m4wHngeGWxEJaZ+/xlHETbHfit/Za7uf77/+m/U3f2pab57NUp2d6m00LFuaf5KGYoFAQQYACCanNodgtYGBTDhNTcSpZxMXRgUzVxWiGGTKZ2fyQPhqPFK+vWuny4dd9DqeNaJ3d3YVx50e1e+8H5zaqq51Hscw5ieYpSTJpNqsDVONGL5rdvA06uVFRWGmkLQswSyJaXb59FYQrlSlYMVIEwdW8/O01RYvMr1/aeXk1NpAC6qh3PpwEjF6H9fE0FYlUVJtX+uO070gWKFEDtPuo8utOno+y3r/4dgHWTNraP+zs9LjfFLC3vf/f4d7/eAJTfPPut71jSLhsUdtP3DR1Z1xTjwTP+FUfxbEZruuQtQ1yyfpgGMOOgJtY7rQbLdaohI68vr3g2rdZnNhT0vePjoxNi4uXsd/ffee/R8YBB8bCn4FtAbyr/5Rvhg2+CCEVBEq2vmG18+wrEGrHWrgw5mchCtYrCdYvKS3l5dbN8d29flkmZ1+v1qtEalBUlNUT/beI4VwHHv7+zvSt+jzIq2/llcmOl15thy3x2dbPV7fZbe1VVMLyhAG9rt3VxXfjxk5QRxMxzbeg5rsixHGd2thoFKQRStmn/3M72GjwVLMvB6PL2lTO2V850u7eD05rh6Jm7mdPycEd8YI6eVlV+81bZHgkK02TpXiLM4aaGAOfmdFahaSUgWsAuWVVq2+QEoQg2FMhyjlbkPqqEub3UFLlBMdJqEVEwKa/ZoPngwV5HFWbzgqFTNGa/fPO56CyD3C8WHqdqcVHWkDu1rUtrdaAOJbygJAykIKN5VC2jN9hJq29++GHI47dfPo+qCNewsXUE5e69O7vH+5e/+uUvQY3XL63AHX/1K//sO9//A4MRSwCWWU2zQCmDGsOiSBMM48itMbFXs7rGFMsennRERjOb0uXUlUW6In4NKUghhmEYiiIAZQAAAAjO64ibFoDvtnffPf7FT//fanO32TPvdBoTf1VVwSpc//L6F8fKXbomsFcjRfVDt5wxskTXdWZ06Tfk3JQFwGQ6GvE1QBTRzY4gCp1HH0/nC37AGZK8WN0wmxp0yOj+x9cespx5rzf6w+9pr14f0zLP/uXXvz+5+jL2nUIjwCq1gaSpqtIUHXfhZeXuzr1VWlw++RkxtzKOy0jR0CRZFrPlm9nlze31taLIh+/cX3v1r8P5bD05uHdHEdzgEnb2xaoEzZHaLEjORKNe6yEz4mnZ+TKSMqW7097fvW8+/AQAACjCSXQIWN/dAABEHq3tAEIc+qTXYBdBuDcyupQCAIjB2gIil4E6q2KJxZsIwfzGjwxeLSqA64jpiZfTRbNpCKUoJHQA3BJjLkc4LwGq6Dh24hTHDgTzfJ7Hmb148eTKKa0SMFvDeeUxNNNsdfKLXBbF37y9mj79lD86mBVxdPOMtbukLfaaKgUesHKV5EmRGLY1U+ShYMJdtfXr4teWlXuy14U4CiNAisN9Jga9yXhxd+947c6OBjvL+ePgGeq8v8v6YUttvP/o6wAwFEl5mi6ygCGYZkpCQ8STnI3LXCY1pFFna1desTfBwuFMtqAqUFYpZOW8ykHkrv00WpMqx2khKEFJoQS8vijmmNTH7xq9kBBhRKp8IG2oEvoYYlIzCGMUQrb2l8Hl1TkE4O7eLityTFEgUSJRfB5eVfNs/70990sn9tLtre2G2dAls68+gEMabK6b7Ybvh43ukcSI2OJuFmh7mwIrYiOnJcPEIywH9rWuB2hrvjb9NJ6zktltNxsjRQp8f7fVdxhvsYpabbXKOKnXHXKu0OmRZCJhtoZoSRaixJEa94cDL5AEqWp3JctaldQq97GXZX74iimRIItIZOX2KD57U1CILurUmzS2+g2qpRNNkgQLVXg2LSiU+jVA5EjVGd/DEnd1fi6oLCdxFUEEQy7nQxCnjAgp4Ly9piiG7exfX6UCh7b5jAlAp99pvtfs9HqnvrM8f+26V1+sFtUynHZzOqurLDe85u3kNsV5GlTlpq4lQ09VVuwYRjZ2PS6T0goyzb4zq//4Gx//9vFSlhEr6Ivl2XxxPtqHMc+Mb1fXG2b07j7o1jvbW0a/HwU+LqcCx+YMkxjme3/yw2ajeX16e3D0e+MqtZygUgKQ8/ca+5dl2WlHnz99xXI1prHQynVME47yVyAtXYaVZ7Np2uy2dN08xD/+D1XvQV2T4nxzySzLjOYoSRAUl1fpYVFCJgSkjWDVabWHqDEdz+cvrgN6CdaaL9k0wxJezMMQVtXkC1/R/UZX8hKkID+gMo/BJ+wu1e/TTMQ05Z+8+LlpNDpxl1K1s5vrDOv3jqnvvf/Oj/3V+PJi95BRdYnp9eppyR9Jmyvuycuzg5329x99DRd4MZ9zKjS5gMoVlW8GZb4Mss00BJlnrQuEUEhTnczHtezcTpYtbbWcvv10sSWbw/7x5snkfD0zNVl7cAIkU2L4vCB8Y7AtSGV5e/PqhkhgOOxkaXnU2wZdeWnf/volN+oM5J4Oc4IwWhY1Vaa//O2nES+GYXJuVzqwPnj/43/2T/6YA+zzS6DIgMAaosrPAs8NVQVpcmfU304SV1TUNI+YlR3fuMhwJW2boSiagYhvgAIDlENUU4imAa5JjSANIMwhyPMiXoD9k+3Z5ITbNlyQ/f1P/ifJLTr99iSYOZfW6/Q0R/5g2FFkmaMko99exmNZADRq80Gqk3weUxB4/VbPjfW/+3KKqfXdiJ8u56PtRrfb7Rw1rl/dXvtl4ZzpWgMqhvvCou90D75p0q//+l+LkiDIosyLdu7kUaVmMUnKDUEsK6rNer+j9KLiPz73KXLTPX5YYV7khUOOScyd/odNVJMZgFXURnK6Q9F07yQPIZf1GypawdR9ne4edmuG0uQEYaa4qULTnlytcJ8XR03DbAFyA+AIyCYAIAEg59qZU2ZMTACdpqHMR4ED69rrN1sAuICgLMM0CVaUYOJMLEo/QKJQiIStyqUkcRtrsbhpp5mVFxmOKyuMeChgAN2NQ5OSRMj1ibd2Ymrj+eFsdX07G7+9dQqotwcQwM30dsaxpUZR6IiDgKJXXKztxAlGHK827xBN9j2tyM4NUaM5rlsoiSiv8YaCy/Mv0nkD4yCMIuDNgpqXC++q12RW59n05u15nuc3k2nlffTBqN994G9mWQKLov6Lf/YdU+8QgH0/aAi9MJ4KZQ4oNs4KDEuK5phaLMu6zLOqAqzU3CggKGBDkgmomJjJ0sTO7Mpa4yKs8sxXDZ5hgouzjOoIhYBQ//MvHvuCddAbsAmcBQghAqnXeUXyollTjMooaptLbpN0XaSwjhJPrMTA3XTkHq900+CNJHbb3Vrgff7oUG32tSA+e3MuRfLM9aqyjKNU5oNea+uL15vGQKnTHGG0NTzgyiqWp3fUPboYuMkbFnMU0q1ssd3Km1yrZKqraDEw+CIMjAZMktxyLhCQwzhOskQQaIAUD3FHx3tffPH0+eun23viZL5K4mBjN7oHJpWR0/WNokgkBqRByZj5zS8/930sSeBP/6xPU5U50pQQ0SR33ZvFLGN3djrdfppFbUkgGVhsbrfePZydXo2nY0kX1UcqSRUCJJyzCSm5lBOZdPdrD4Q1o2hSVaY4S7kWIxl6FIWzV1PL8W4nNxNSPGq1mIGBW+FslptZRHH8ZDbNs4rhld3db3hk/GI5dxZIllOG3u6i9kwLa9cVN7UuaFObNprNJLqcWVsgB+vF5vDRRzUYNxodReE8e+2Fnu/6rQadZNleYzejhARO80TNYOkmYEFPabxlNj85P/s7I2UXk+fynWE0s+KCFgXQ7w3SZF0kma60k3XAbWMqIxmudVavVouUURCr3rk76LTbFKqXdtZqUqyQKTpy/KyKvArxnr/mhUbLILLqJktgRTMhu9LlbbYfUY2tLC81UfV82bEdjk/qGC9unrFSn6IRw/KrONwv6ao9bACTx5HWaCB26AtJTteKqs1vzyP/9Q//8IdH2ztXhJy9/HTnYFCSFhHg+mpSlIOtbf293mHg+mVWSYw5MlRBLKN4bl17qyqXoqCIWY4XykRGAuYta+5ulCAIknjTR/Es+fI8mOjBR1GUpEGRFHpLVdhqFUXV0u71RqnY9J69QnujkroSWJBl2WLpq4GeNRWY154zlxBp9fTVMo2DpLReKVDFHDhsdne+9f2G2Q2z1lc+/BMI6EsCJBFAABQxXM09WNeKIO2MJEKaGOc0wTxnIZoHu5wYUn5S0gJhaabNUoSuGQgITEtAAVABwCJI8QBihsakxJWTZgUPakOXdva2/v7xX8ONX0FK6QhaRxAEpldrqjyYeKvNbNPHYsRNDZbRCRerXrCORaqKgaPSe4WuZ1jd1rUbh76+Xs5Xr+ZL9osXzz/+2geNVp9JModhHYT0IKCE2F+R8hTTw07jdjyJA1+WlYIVqJIXuKMakoU1UzWZYblJ8OlIfPf3fvC1C8shoIBVkCxvvvTwHUHce+eDYT7Ib35VJFVEs1BR2BmgWZpl6MlqPYx5Wt0VGcNLovNXUb48F2jquNmjIet74XUfHKvqaI0A7c+1tKLbYUk23ma5meb+ZiTsIGq2soMgCN9r92pQIw+BGuRVTjgd4mxNMKhQTWo/jCiuCHOP0KLnub2uRMpGcBXZiGapPPdTL84JBmWdIUiKNCxyqwLJfLm+ebJYQC8vgX6kNjvMbDatcXhzWXzQ3HTaw5/95jeZJzOQA3PD9+Okmalh1hAkUdxPwBQuq4auqF2Rse8CH66Cp4ur0zQCkIDXk9d/+NH3NokVnWcVwN1ulyozOg4dOzs7df/go+HTHNq/Hj/cf/D97/+vEZABAAzHIipGgpAxqKqzqKQpxIAUs1WCJEghNYnrpOQZThvfjBOZ1lMKl25RxHVseU5Ao5Rg7Aa++/npb1envKnu3j/q9Lki4Gp9yLYQZdd1Go4EwWUFuqoQV6dR6btuiAOuT0/Pi0Ri8hJWEiRYX2fuFurnC8aNgySMFUWr8nrxxeNcbaRZkmcJIOTy/LzR7MagGirHr9r+Zu0ejjqDginmSdwoe/oA0cPXTpzFxlAaCWql64WsHCd1XRQJEBFEWSQSwxyZiLuxnS2lllqNkPIS205NGU9eXURQlCW6gKMBT1ot+wrwErTG9jKy5DBSIbBNilomV/6apZV2u0vT8zKlUjDpK21nvVZknWYolmMRwVkZp5rB+nidzYEuKtfjq8l1KbCSrBLM3KRM6ROWRFRFMPIqpEigothZGotDzMXxGo/jliwAJNSltdkkQIJMKfz28lIog51Gf3+vef2zNyxFFVlWllW3MWzmfcAWL5/+GqFN787dBztRkbJ5AkhLS3DdyPnrq5t2q3d5lera4u5d41vsvT5Ff/YsWMMkkgEArTwoeKVratBK4TTMOTYvqozuDvrdrTfu2cs3L3bf5QSD1ttdL3493DGS3G1JwSxPETSXixW13Ohmo0TJ2g63uzxOUlRpWlMpiypapTR0NRWX0/QtLggqixqvN2FfrRDFIo6XWU3Rd93cvVq4o60j13Wvr98eqBwrrnBaiX6F5Ay7kcp3trv3uQ5IyvD8arF3Ym4cdPGmzDf4c+TqEVJVtNE71LFK3HCS4Gox6e11DZ6LVtd14ia3b1U3Nn1D95qVCVyX9VOi6FyD17pmSxPU0PPKPL188gTkccAiFqxhrhRFISM9iBNSwVbBSGUzwJuzMPGDYvnjn31zb/dbe8ag1eFQnSTMsI/avbZX8HGYKJIyrWFvxcPaGBLWRrqq1WajxXLy1VkkUt5dzRQo/m241KlVnOW8zNMQ8XJHzPiGrkPJ8FIIIpGl5OugpGOO1IAURVK50WbdaLc1RQC1xDGbosaygNCaQmVGE8g2h9hPgC8oNUwBoKuSY1kamBDUENQIQABABkCBgVdjBlQc9Ci8URyweP7FAdecZ8XBI23rwSDzEoWTVoWryP02L0Sra26fc5wlxwgj0aAJZVBg/6Ndc9P0nUaf6txGi5KvDu6141wueowXu9bbl3/9/1luj7aO3vlgoBui61QCaJhiUcVFJ6fl3Z0PW53ZdFkDqhblq6tFWMT3Htzb0Q+cbJXmFqpwWD0PKhhON9DU+ZLGRVEmyUVRwcefUfVIC0RAkFhr+YLOwpnRlO1lNY+Wi2k10lou23fDUtcaYuxULKNvPQyffm7qjR/+g7+YfWlPtnB/a98e+/xeO83TptESOXY5B5WXUnRSZvH9Xts4vhPmllYLWU3HGakyPxFhkVc0AKRRqVlRpnFLk4LQi8AtrJQsvrzhWAILusaIFGGRdHQ9J2XtYdZOIicPZvbjF+u1m0GajzOly1TriU3R+ieDTqKEmqLcXL1983lRFk7T6PcGLe2oX9ZFlIbrdShqrKQLlZgEQcHeIAqwDVn/yh//IYKIj+Jnv3iaZx4VW4u3UeG4o0dDaaB12AZg+70XbwAGlHLQw2PZ7P+z//L/2G70/5MOTvNyWeWQgUlO0oKYHIQEWUkdlxAGgOAYUmyJS9Xs94umZZ0vQSok0LfnRQSpGlIC3Nh2GIVPrGVel+/qdy+SALHRQbsRFfPVymdpIIVrS25wSIY86yAQ0VGq5BzHD/jdafdVnM2yVZ605IfDkypULlaV1OpqFbnNq68PTJ6Qcze4Wi51no8cPy/ztVtn9cLDp9IwOjA6lJOuVpnAJttd3SDa2e2ldg+2C1DUCAC0ikqW5TlJn06vPY4XB+a8ILNnr+C7H0O69sdj6aMPBJbBDPHSspxPNZ3RdDoMchni89uzNS53dL5pyi/W11yxiQCEANBOXNek3x/ufeWk2ez8u7/+H96+vry73QGkzsSEiqlue9RpqJnRS8sa5kzSThricZ+G9rPHvir2Cdj71reLAh5IenHAe+s4Z5tcFiUeLItrx3axF3Is0zKkXkeGBSnKKsQJLRRA29oVyYtsA1f0eL785JOvdT5m3rx+UZWFITc8x/eY5MEffPvR9SoMGbbLmfrB4Lgfsos3r16dTieP9hp1km9uzg5E6rOLN3rQODzgYj/bbg4e3nv49qdvA3H27f33fdqSG2KRpoLIIrb246qlyEnsPX/19P7dgwm+OVGOG73WxlrRHLuxXd1ssWozfDMrcmbFiof9HjQb8e3f1aCGFIoDK55HZWzKis8LTGypWhupRDFUrWzVLFCF3E+pqxxUqYx4a2EoxyarBI6bMhEbWo33P2CrhKIhxbJSS7U3IYqrLJtES7O5x3f3PtywCdfc07QNLgYMpypGt9Nv0gr4yV/9m1qnWg21AZBclQIXGVxbRKFcCnmRfO+Pv3Pz5ib60lWP9u5KRxqXpGkROX5UOenZStSMBk0vkGxYK9TlyyScFRwjZJ4DjCbHJBTo4u/0H2zee3/1xRdP1huXER7d/WrF8nyy5jk+r+KpN08nk9bd967nXhMw7S3z3//1E+n4e2gSnr7A+wekvd9Xew4pkCgIy6Vll2Ut86UahsTFeZot5kGivHzyWhXeN3RjZ7QFAE1HtCwC15lKhTiLVoqQm5yodxAChADEsQPAAiCDJPUgBd03gRemW1smBXkAACCcAAACuAYlAQUAXE1gFKUQMnWUkyLM3axKUtgqWDx7997J9uD7vDr8xU//+3sPHvznf/qPf/T4aYDgffkAvtfsI8Q+/L5TnPO4wriKKUKzTE3w2voSUYLZZGxXqSG99pbCgdgWlF/FHLkIQj/66//xv+cM3tBUUkC6DIkk6A2NXj895TheUZsMI9A1c3dv/3r5enq7IpNCHFAjicdZkZKIp9CWzsUpZacBhWvT0POkOH/7lqUsiGWjoYS43haY/eODdQRkbS3GB9azL3A+W80xofWWqRnvfCDbTP942w2DptEjk9uAzZ++uG60Dx/s3RsD0GHF2gJsriaxuiyuHpCGb+I7g04qoMmvp+iAkjv3q/NNjlsm8gmuLZjnVU7BKqx8EfLu+tS9HteiyeNMjUNMQMVkgCaNmHbpWZZn4U1w5oSOG6xWT5abszitcwwIBFGuB7bMJs6G0RuNPUXiLi3/4TYQGg8oSnAcRrSUGzytqtoctQXA0AUh2EJ57Kdx3W8uykKkKLyBFZHvfveT6Mynt/qteRg3BaSP8nIJ1lwtOnJngMvQvX37ys++89HHX/2D74AVBh2CABR5Mciwm2FcESQzSWXiqqxIBEBFCNFVA1BUBURVog0m26aPN+vFPLlmEQ/2cBigYlGFWXrrbEpEGu3e6BvDOHXCYnmdhUFwQ7NE4BiU5UmaQcQmWQqyzCoq3hHAMXusb68fBpAjiKbdi8UvPG5UsXnW1JVGlOXztf0iy08O9yVFxgrLZWWtCEmY3vthhxO3bq+sulpJ9RZkgEJVrE5t6CArLHHIBPQkdzNFkQxFJTHMyTrybFmQVmkZJnVHY/YeHDYZ+s3pi3anzdCsF1qsIIgst3XMJ2GyPgtZXegardtVbvb40HPGN+cbq0BUWCcp16R1QSro3Hcd5XB7NU/uHH7YFAVcXUgtlnJsu3bSK2LnQDaDyuxUmCgdNl2sb+myrylgswKYgDqIY2aTI00tlst1MLugeEbpqrraayJuOXu98SeTeXn6AszH7v5+r3VvtM5LnAQUxT+8/17ERE8e/+3/9f/2//gX/+S/MJsrgommaLYbrZ8+vvPg7te/8TCp6tskvzy7GF/dSoMCArIrjPiSmYSzLK45GbFglfirxe2DnV0mcdLlv/8RYpk8hFF1/eZ2aswNzEQhhHLOS4IRbDy5N8SJkwkax5FKju2LBUHgF7/8nedVz23w5x98fHS4BYD7TuMeYwhz+zJdUZGeq/quIIuxl8mMnFK5ymuNNlm9fgJbw5IOi5ih+2gZXhBY5wRVUxtWVYNcMDWel/Kdo162GtRF5OeRYWpJFieTqvJxXLqc0M8wnyyjNSqYPK9TgWGY7a/dBTXHb5jJl5/P7I23mTV6d2vWNgZyvV7Wa4GBXI7z/mFfVdTOnY/WkaAdsstzKxIXN6lsz9e/2fzmQDF2H+wtZnOKDU1U16Sup1UUR00Du4nS1lVIMQW1pGr+PE8lCux8/RtHGbtKNpsk854uJdpj+JIXWSJkWG/SDMdxeqizibzcPWjx1uTw5GCYvdWUoczJbf1ulePPX89okGy3zRoT0UsCjWF4iCNWYplisJXDenqTvHN/mzW42I8ZpMqSYPlTa7E+Ptkxt1QAMAAAAvU/vXrtN4vFchZsvJ+++nmr9cH/0mgqDSnPQA3/k1VAIUABwAMAAuDWlMGQqkXnOU3sYOKNrxolEQ+2eY0Lz5OCeENjmKaDgpBP3vm6s76Yz85pmtKPDjRJffrLZb9XUjSVhQEiAK15wO7H68obyqGb0boGqCQGBKTJg84Rv8+vJ7OkKNSulkf5sSamFXo5Xlxdj+mMQMcKcVz3xPacZsu6bOiPbHeDWZvyo4JiorDgGELowq0DHkQU5LI8qcoi8hO9quz1hSQqmt59c/Gzp2XJvNivqvL7395/oG+TryDeUJwSlEIrZCN3PeNa6HY839xYI+7OynIzGm7G53/9V//PD/7RP5RU03WN2MlxVbAUGQnVEt+OEnW9ml7/9tfPrmfH09mDP2rotDSNslVNIQzqdEPVLu4YUWwbSaKWnpKtAu85pyg2NWeWTBSHFa4Qm05mFs2j1xevKQpZ68Wbt9dpBngBJD5gBdBTGFmSEjtgDO6rX9m5/tkb3hFsq5jMrXUaaIby7nt/Kjb1qs4KL03daGMFal/s0XIGNkpeVmWR5iXMM1WTJajLA2Y6H3vEY0qB48Sapvl9LrB4OcOUefL2579T5fZ/9Zf/BwDEQMkJgBoABQCA5hi2QfRcwgSToqpSzLNU6rNUTTGwLKvASZCtJFGqQCBLa9P0fd9NQ74ged6ix1eVrBxn3lPfmV1cX0yTW6a1NQAlVnowd9MkqitOSUiSbPK8LCtdxH7nwCSyXBX5oTnktoyN4BXcLCf8ehGztI1mZt2HA7AVR25hDpY3j02JhRRV1KXaVIBAaMEDTJ3iRDScFsOunaoCngTEnCn5jIAw447qepE4izehEzQevJMGUZFVO6IS+AkToYqIOYxTFdQyXZR5lIbQi0BNiFsxSV2yCMeVgIV+j8N1VbCspmnh7MXDB7uzs1maL/Ki7Cp9Li/C6NLaUGYPZWHd4Y+fvfjNamqJWcUPjW2jUamG69esEoq5qjcapL66+83foyHKshzAqCglHuSVAw729/MH7cVymt2cfba4vtO5d+/b96L18OrTnymqbo4oluVBQ43jiTXFNQA9AUzmyLaJrlSfffnlO43maEvcbFaCyHUGnfX08vz6LWN2acQXdF5y+WJlaV4tVTQWxWad8IZy4zihu/ibWQHB7e/tNSRRxLjY3X5w1IKcyPP9TpQFRbtdk6wmMKQoxLLLokBiRdEJpOFvzv+eFHRpwTCoziJQZgBi5fULS5QhrifRxDlPErnZYmjJsfK2aTbLBPJ8iIPF8xe3/u2Nk32tolVTqlhQBiuGkQ475tJzJ2rBsc1wvpE5Pk43S0ts7e+vb85se+GZXoNu3fv6Q1Lv/OKvn7o11TA5QdH0ihGr+DSYaTUnEzElZVXV27sP24cQw/sMTq2MqjJi0s1UY7tmoyg7BMLrm59c/Xf/EkOtcbzbHAyKbEH72eHdnXAmh/EZJwlU7DIcY8+ngsCnSTpfLo7Fu9CdUy2TxagUG0my3mGEiKZImSZVZup6Qon7f8TWHtlcfg7pXC4QJYRlupEEmk0j98o+uTvCdQEFdsB9J4kzf5W+Of0RQzfhQC0i6hfn1wYM0k3KMuDBo4PXp6erWSFxL3qdO7uDP2z0aY0jCcULfFXk5WI6pQnVG7QBAAWgWYABkAAA7sXNbbhs1sqb8y8/++2vf/+PBhDUsAIlBjxDGPD/wx9AARKUUHUeWThtMXBqzSHMkL+2NlNQSus4OR1Pv7a7txL7S3t9frtSFF6Spe5HXzn72d8//vLpX/zFwZ37d8/OznZ3D1V1TMOavStK15Sk7rUH+oy9lA3TpkKX5y7c1dYQ0xyV29Vo672TLj637LSK9a72fkL9Zjqnk0zN+BpIzDQHUbZmWFhGW/0+DBJqtQyLjJNVpBMYQJlBcZhuvIQiKZMGG5GCFC/duf9xlYtu6uZF5AdB5S1Zlnn5LKD1m7os9cw0Hx3UMjcZ+25B2VEBK8o83hHvSI5APv/bv+317n05OzV//jcP/+SHxJ+0+UM+Kx3b9qM4rJOnt4/b7WbgB5W1/BLWvcUVz5sdRZlagBdjJJOsouoyscY3g6EYhEvfm0EXQEmY3CzSi7iuyCY9rw2Z35Tr1UUpwPvf+fZHX3u39T/+m+Wc5rZ558ILaVBW+c7haNFW7gzuzufUj95ccjQ1j8lIpf7w7neoPjsc8W8vPEcuw2jT2O/nThUGZRGDXvMkInaDZSUGll1dyavAnudJYtseREiTeJ5lkxowlOxnUZB51XX0m/PZ/+n//L86/vhhCQBhuTIHEQccAGDGsoSppTwLw8CraAJhmbo1W2buIcdQgAUoTmmAsceWGogqQGFMUpSucc2445dJxTx2zld23OxSYwq4uW1kJuG5495JCleem5pcGobjPC8gAILoO7dgf1iRDX9Jn1UxUvycS8BQM3J24Nvnk/Hpl0tMagBq0DTBwU2miSKBOMozL4m1A7H0S8CAe3ePfvLLSzUp6yYR2klcFXm6UWosKPK6nHKJyLVYVdOZa0USrc21XwGO5BGRWQFXDdHMSp8sPdXQEaK++t63p7e3dZRQ0KMhaImcE+BxGPobYqoSRct91nhdfm7drhoHqqqM8lWuGgbcQXFB7x9zy6Xr2fXhu1+ZvvjJkXF3sHsiSRIjcuv5+s5WY4VBFsURLJAdg+GagnSnbazP7EJGWKmieW5IZWxtIq9Wep2HubxO7HADjjqNBz/4E4WXsr/7cQHrDNfbTUOFSkSxEHFDrX7nk3ejl1aUXs9m086jllGbgYYjXlyPX0JIMKqLIj26d4AieHqWI67g8CJLDYRzuWU8OLnz3rcf/Pt/9a+5GnVaPVVSdFWuALq5eAtW/OBgt2xQuLpNGDZ2aFGQoiTmTtp687ilD3CVIaZUGijLtVfCi2QePbh3+GZ8MWg0mBa4ml4pXUHngNpo0pRCM5V94+Z0sU3W1qvnWRaTpBxUQNjmaoJbDG/XzEDpyAK9WjuREwq9cVZUWq8VL6Ir9+bjO48W05ut0YBrMv5tNF+tu53j937v/SdPvswJ19zpz678T5986axvhBgM5kKLh6wGEtYscZYaXOKPBZpVZH5tX1GA7r/3J+EktCfur6bTFr/lgGSrfL3C7O6OPDpprj5duIwdx5F4+fLk0b3U96qWofXVq2cXHMfHabbgoHXzy/3mftMwqDy/wQUfJXFKatBvK5RskKpmpBI0+4Pzsy8ClPXbIgY5UXBZl4oh5kkZTh0S0BerW8v2BEZumq2qhOe/fauqkCMkg/X+wb5QZLqSfufruyw1PH0R5ynXabV2GppMwQKGWVqH9mbjWg92t3lRACCDgAOAB6ACgN5kMSgLWbBpTHiGfcc8FLp8nQC+AgX/P7d/BjwKyCVBNUAUxCokeZ5uVlMxmOmqiEsl8N0wCjRNt92k39+P7OlWk7YEbnlluQtU9AZcuX764tXB/j5/fHf67DPrqmhsMT3Qi7JpzSwlvsHLbVbt2P6YZWiGYuqqNjumEzqxGZXKyZ5uNJLo4uJsFQNcAlp/p51BWMR5aHMaTeuMGOJV7JYmryIEkpp1rAS2OSu4KuIC0rCunTRTOIHv9xkZHWA/c63Sxe5D84G8m8iaUpOSpNk4vPbOHFYU8Pll1T7JpLqmsdgQjEgJk2Kxmn0++WztLrYeHFez1b//7KeyQhp63/XCPMtd1wbQ49msYhhrHTZNbtTV/+bV54oz+cp/9l8JSlvmE8u3ZE1IUg8iti6csoJR4DW6ptETr3/2/ObpGy8M2ubDeTTjsNwo85/fTgEA6vaPvvXtf3xw1G01vNV6NXq4u/DCWQJrj93f666y/HcvXm4m+W5fGG2198S+zFFe6S422do563D9XK+OVfbtlV2PgST6TbkZpxXTl9qopjwnLcuZ85zLeC4tVHWrQlSGa9dTVLlNEwsT5ezs8oMPPvmzP/tLAJgcgIQGiAYRAACAsiSgxrBApSgwICJA4QR1VwrSuKJo2rN5QeBBXQGIa7qoSFmDMsVJkHtKu/2by1j76KBv8+I4J6imObo1OKxq5iw9U5/MRIEvRDnKOI45YRn/7cvbd9qSkueBE5q1xdRsu9leR4Dl6JqRzDRq3bu7NRxeXPh55oT+DFclAaDd6VxfncdZohkNxsOiKad52ul17grhBIbJdPPOVx5azvK55fASyP2IlkCrLZocy5RFwbu1nSDIiRRhVL6nGZwoZWUUf3k+bKjN5paha4Zh4gryDNhYDvasdVZmaqlxbZEqV8vAj1/de/cP3xuN5rOxeiQ2Rwa1R9WkznOGvXK03SNR4tABJZv0g/t/cLzVUjLpJj9Di3xlva2RgxB93D1cWhea1nw5ea0piuu4cR5Toyrj2FAOYpIIUluqQQDkcLXEokLbzGUKjkSdDOq0KmKRzVmXeEgaVMnGAXCHpbnESph9mbEH6SwvcVs9kPL5bRBtrNQLmeooN2KKr9xb09zu96g4yt+uV52GLDbbZU0Yamx2v/JP//f/zfh3q5qhlNzSFD4KvdsstN3ZnFt2tu9SHCBxZDY6Hl9Xbs2ierfdSETVudw0mGGZ4HbT/NP20eRu9d5geHt5VdA+VkKFapoNweeUEkhnb29GeztZQ0K+TdPbzeYiihhVKcsyqydXWBa4eiRJhcFBb+XitUfZ6wRopZxPo4Bl+RxjxEm4Jucrp7OE8yB5c+rU1OmHd78p0dtlDmaff3Z77lbeAjAtvo0Gg5LFFVuAxMdRkvSEIav2Ss9FfNXsnezvPpqt1p998VhRzcHugw6tDPoIQzpeZElBkiShtqh9bf/pq2gOMbdeKBDWDHr6+OXU8TCuF7Prymg3mK1VtaL9oGXqNC5qOy9MQRooVYYhyYMgTgX+QX9rli8Xiwsx56l+nWxWmtzz6uB8fk2AxkaBJjea2sDUR4aplpjsjdbWhpvOP6UQfHjnHaOhZsVEZrAg7bd06ud//2aPha2mRmW5AAoIasu1CQv27j4CQMYAMYAFIAdAqkOM1ms+jzNABIT/8Phke3sbBLDUCkZiPaqUAZ2BPAOiCKgE03W5TuJUFkzbuolDt/aoZTwBnTKigztbf6Boi/HaK4rc7GyBzZIqoSaCGtH7NLcW9wJBe3y73B4NH37wvelwtVzMH59ecCW3f/cIlNnFq5fGTqSImiiIw8ZWEtzOIas9Mhr0lrt08trCqmKYndBLuz2OPq38llKGfsAKsCLcMs0YipU5Rib4sibz65scwIpi81rh27EmKY6LM4MySYuwWk7Qle9pkoEQ3YQdS3GdvODWdhUlDOAVWUccO84rLg7oZrcnlISh0J5QTWpE1aDi+Tr1g1XvRJk/fvu3f71mmZOG3OM5t6oIAaDZ3OZbmp5Ep8+fqob4vcP+bDb+4q/+28Pf/3OuhI47B5ssUOXL03GJwHKxWlMW5dfFp/kvXr+ZhqFlrfd8Zoonn/DbzffvfDtc52WBVnWJL9CoqOtI8EtdCT5bXCuGImzt8Do3++K0yOtPvnZfU9SW0cqCBLixnm2omlaNJM6XAKBpnYFFiQyWJttEA4xHZcubgKtD3ynK3BQomkW5QWMO8ELie26kRLiCq/lGlhq724f/9J/8X/rd/RkALQA4AGgAfAAIACVbVhlGKUVTPGK7ZbmqSncZU3FRSCFi2TXACKCaAFgJJVIH6Sbyk8CJ7PadrYVn5QHonHyd7TeKsuJFLkk5nmd17VHmXOZZ7TkLP68b/f4Hd+/s9ZUkjx9pConIWrBMxgQk5V2mSDKaSTdxWvOAk8RH39yn0HHqx4VjY4Wqwm4QfsnuU8ArYljxUL25KXXGYttt9MrX28b8ZoFRIfs8iOqqWdxTlDarXoeeZ/uSi2SBYqnA5KSBKM03Fkar+fXUwWmDx95mfPnli09/+WkRJn2ai5IQNHVUCVCDQUEfPtrplqEd9BIqgaQGpL799Ob6NxeSLOt3NMVQzUFjYz/DG+3o/a+9frXMEvb15bWOxYRl+Dj2/GJ6+2nHaEmANhXFKcY98YNg+vLVl8/pY1rOzAjRDEslMQR5STMxz2jVYAQhK9SiwHMLYhvnxKFoWeTohHWRq3FNAOtOt8vFEsQgU5PZyrHy2/yNhUQOg8r1ZoODhyPKABiCIEqJmosFKmjayVsY50VpJdFOs9MZapP0zfJl5Eb5ifz+pqjs9UZlK/FYhgsqcjJqNzxfB2JD1RDj+kA/3AecTDwvnPihFd16gsQyw77wQO8cKjwoq8uBH9srMc75pv7cC2pScoZs7u0lYWhlMRvxM2GxnC+DIDQNQdUUjuJgygh0zhUyjek8AkzENPX7Qb5hpEZhW6xswgxO0qLfHVWo9C5nI8WQZXV260XjUJLhqNe6wZJjXQOGOzDF8eX1b6fjr7+/pw7uGxTNct0sL3FUUbBVuMWLF8+bnePcC0XxUGMo0GPD3Cow32iZI75I03qdFJXn02yRFwWmIAMKRpIBC2YcXRiatUq++uHDYJVLfdbylIXtXc0293bNgi5TkhJQ5qJECGpTmJTElVCjWdghoiXoB4hicV2h8gYYidloGGlWSKzaH56kQXn+Ouj1MQ9Gu0PsWQrH09enF1WvedjRpxno6KjV6X18Hz56dJe3UpwvFHZw60/TLOp235cb+m2eNDhJBlQOaBaQKI7cDQQFCpMYZ5Oj/YN2WwNowobbHgOATAMAa8ATAAIAKlziGjeaBiqz8bOXTmi90+Dmbjn91TXU0Kb3/GYK6N2D+SbViIsD0hCTot+13CgCKL+69cHaME4uz5+F7+53jCbfO2qLDZ6qVab001g9NFSdVZKmUAuNLeXLp/Fm9Xr19Gb4FeHk8MSoBtbMsgr2Tpugnkn7689xwJk0LSIhwxAxbFsucq7nXIez5ayuZVE2ciSEopDE2TSayjy/r+5nYnNxO5fztDsYIMAszqh2G9Uuk+TO2I/oMDnUze2dvfbJyYHAp4bqFjGU+PlsQi9db7Nx1kEROFrbcKzZ3u5WlLhxbHfe6VGVSVOdIi8xBgli8+mEbWn84f7F059orAgR9oL57Hc/bYxGLanlo/ry8pTlWJFm59NZKjRD9xUfCojgk2H7GUmZHq0s2Kwfh9kbo6OWuNywHMkrXtkTW44q7LrT+bGE4KDLlU74Okuz4ljXj/eOwziyfTd+87IoIrBVNRNFRZjdZ8NJsn495XmG39OP+BMW1vwuEMKVgAiNCCGc/8Yrk4jSKWJEiCloXm2X3SRa29Y0C+Hvf+8f/OAHn8Q14OO8UjgGAAJAXVZ1jfiyBBADESeEImQOUFVjAkIHilQpZ1VUVhjUTTUNC8EOCzEPlGRtr9aUW3s2e8K4tXdgSHF6yQptiqo2sxeAkQ7373UenmjGKLid+a5XQ6KyTciWxfo2LQrHcmqdQjwM4qhkZI5jK1wz7bZQ1VYeT95ikfNVadDc3YOBl9G1B4wmVittrUtmnTIts0EBpTscXZ1PaVLitOqPhs3W6Pz0mWLAUOivV6soCt5p7kGFg3XJMVBLalImVOLGSaEorEAgLchKUiSGLiBR6I+yIEGJTBhQUth34Xh6sVz50+k4Saab88etHt1AqCxz3wdpGoZhqKly54+NKIpppXI3NxTHCzyfFyNXwFTh793fJwJNUcfTySusiLXAc/AgDYsnz8YOxe5S1I3rGbRS0wXKiyajOlwryVIUiC0JYIFPyiL3iFt7qqZkaRL6pUSrji3QlIEIgCqJooj24aE6an7jHQouZhevUoa9/8kfCDU9dgONElvbXSWt41Ty5zYpHV3TfSplg6oQ4cLb/uz6OQ47qnQQsICvdigmFZR4YJo5e+uCcjqzkUzLvJaHGRvn4ujA9ZJKNxfOMtqovCkZleg44zmNz05nglKv11NzIFd08XSZFVUVR7nKNorJdb/XYxpmHoxLX291+xBOEcsCXHtBossyKWlE8f5Tr2xyZcpZkQMVlg7yRAZYktLprH81RrXIqb3Z+DnVBVvt4SRZzK+fN3t3CK4oAX3nO3dtdJ9sLm8uzkmFf/qzc986H/Rhq92sBKogHF2i/TsHWzs767wiFSyodS7qarPLWIrLzRfVhgswlGlKoCZWLCHm5L3j519+OYF4v//1t6dv7cAfDj952KZyqerf7Qc1UfSUClmPt+d1nnMgngV9401Lezcu00wQUVFdXL9KcK2M8kT311kNkLy2balkERL8hAAOUmH15W9eNJqsSDE0UQHlbHe3dn74DwInGPD9oJ6pZkNJ02ycXU4eN81e2zSzZMLQYt0i0/MFxWgHB3uriJAcWhxVApCCXChhFDlVucnZmk9rgaJVxWBZx+ZVgSkJ4QQAAwAKACoAJACicE2qks2U0/Gr5eS2ydHXXFh4KYGosdOYvLXdnHm4DXQJUm2Tpl3LySCIRxUIJGlba9D0njZEn/0snP2bf/foq9/mWh22glQcxDgpq/mg4IcQnC4WRGy0Dtr/4hvf/8n0jZ4JHx4djovSW9ed5gMBpnt9ORMSelsyIK0zACI6MjJGFJoyAdNg9tPTtxgDRd3JSxantVDGnGkKKUtkmNP6zcJFrA518/Rqrin60YM9nFpkXoMKDxr9rTar7hzADbiyXMQyznWa6ck6CyGqBZvu9UWGleW6kWVOndF8mCUUd7a2v3E5vUA5BLbr+P3utrT9DWWguTi7vPqs8tJ5EpiSurvXd63FzJor3Q6uCwzhyvHjOEnCyPCiIAmNNivekVvy3v2OzvO8+UDrUFWeB/F2FqRRsqAeW+O7bWMrOMwVZj65MR6YrU5vHURQYPiO3iCt6erWt13/4try8J0Ro9E6VdKRn/gLSoS6D+CguVXbTKEsbmfu9ggTFGGGJiHMvfyLaQEAOM4qpK+9DTcYgTRPAE6NndZR69E//Ed/CQBVRikt8wIAEAAL1FQSwUplGQgQ5TqVLPgpU4uCiCtEgJimSZwmNiewMZPlCU4pi6dsREpQv+VzLyk2qdPoNW9uF4734vrsQlSyZqMhCMNmu+nY0zpimWaqiJK53cZd5tfnz7LK5VaM7EbLeUpllLDLy3pbKFFYhBzPFtbGF7V9RUk0BpaHQChCr1hMLnzXFdhEpbVxLPBifXnl6QZ4fPv8/jFWFOVsvFA1FZfQMLV+4yuWbYuUhqxcQfpihTW2rHG2I0k4xvPZpDKqws0FRZZkw1kF3KDVNhplTjAoenujJM5qXOV1bjkWveHLqpQkRxSY7b19gQePHqnLzUwL3MFwUOFqc5l59gaXRJC568VcN/rmoDGZTN1FGfhjalfOKKbR07rSSVbleYIoxJyeLt+4SbvTAJ1HcnlD17RGc5f1zZPbZxrbYMz9iqWlWAagurWvvaVdeNHBXp/mhYQJAalQTolRzWZ2mkxFZkcdIVXiVVkI57Tc6wsakxabWUWpLArrur4mXjnuKGpBb8QizeMUMD63s4VJ+Mr5Ynw17e10A8p9s7ZJqTYNMMti3gZ8c0SBarMIK7GlruhKktvbXWotZEKSJZn98vlquVA37+gPmpsJvHPE7TJmlDjq/X7oRbRpMkm8c+/BygsqCMpWsSFFFtpY4u/e22mg3Z4flvNJGvlemGzSJHDTCrclXekwbUFO7EVQpQU9Yozc8GOLoRSnRhooNI/q7W9xmOi6YeG0yQFr/euzQruj7S0yG3GMfn/nu+LvS3z97OznRQ4e7o8u5yWJQfPo+EDUaNPQmd74fBbylbZz2KLAhqtcztazSug0IyZMoshI0LauUWW1tteNdrMosxpMt3nG6vUdd8Js3VUKcZLbIvA4DhUDzqSOAPHotFAwpYiaxFMNGhJ/U7C10aWk0Fiul25CQkxxpAPWi5IGSyKyC/5gyHd3+2CnQKTiOMrIozmsVotfI6G1DEK0i5+/etmIhl2Dwx6r8tKjBz+wx5YipqbBRl8s3dXm8PgBxFQRwxoLBSE1hAwAke/21NYGZsWal1Q5ZxiaUX3Xh5rpRFxW1iqF4hRwPKgk0AYg4Jk6q/x6/uzpj4tg3Pjg/sV//HtWoMwHbbP3jtHzo+Xtmy+fLqKpBvW9Tx72NCnPyw0vyJbLser7H2ytN+uP3xnVYDgpEzK93NkaymEqVLnNUwJrnJ5ePH0yZSGvPWb+6Ac/qK7cXrO3N9yvn/sX1+fc3kz2JYcS6LSiy0RomHKRVkUp5NUyuZmfI3Q6m8+Wcbv3TqlT2Sba6m9BJM+d6faefnL8nav5ev12xfPsu+/fb/EnIfDoPLRunBtvMagCGeh888AKfSTBbtVFDVQL6TTFPVmhSJk240avU0ZTxFJDtvUqXHqaLsKj129+uXZeGwbQNWUxD7+KkJ7bVbTOGELc9csoEAuhfedoBfn45oyT2es0jLOU5WmMq7woiqwok8w0W0kHLaLAqRK2bVKCS83E7IQV/TLBjiny8tAs2eLFcrZL7dFZtMqKbYoLw4uAgmJbaeQNQaLJLGgobPX+w7NPv4zd0lzTth9u1imeRP3OB16YLItA39ErVjnYEmSORAlxQxcXeO3AwAEUCywEDJ7f1mS9Q8ogDLxMV7p/+Rd/tj06BlUJBFZDMAZFmRcIE4ZgQCyQlQBAlS8pikpRnlCclZeoJmVJp5jpqpINMkJAiSvC8WKev1pbblazbXa4tYc4wfVdz7d72w8c2/eDZNDbqdP0oHsvQ2Ua0n4dsFIOoniTrbyxA+x6pLXuf+349no1ezJpdgKq19W66txf4qxWBVZXBnYRMUpCO/6sKCBNGp3tVpOHCLfKkkad1q4rtjrxaXE6t7qKcrh3OJ8vKpYwNffofvMXTws5bRYV1elWm80GMjUgZDPbyJBNvSyjciQCys+rEqVBESkxyMqYV5vd9speZXnJNbaGTcnXDBnnP/67f4sr8Od/9N2jo95qPJFtjlwvf/cq/s//Kbc92l1Ob9gFRdOYwiTL1xtkRswKDJnmnlxOs2g8DQIEEau35Mz3SkLJsmgOT1oprR+bcqsFQ4VJbLNhGrtbK8nKk3qDoVTXGxxBLk7TGNQgzss3V+Pt3VH0yk2ask5VujB4/94DnhchkBk23ViLt09OvWSZsqSFUZalnCwxmJA0rRlZqighZ9jICWNXEkWupjFFlsurxRRAnaNllnWp4XH/2nPWEZErJco9FqPnr8Zx5O/tPTREKQziNIGQzhsF4twoiasoKvL0Med9+N5JHwDaDaJNGctqWQsZzypamw3nFolDda8PGC1K3aoAGcoCa65jOWPx7nBrnFf5ddaig/ktU4IkIqCOk62TY8KrL1e3CtaE0d58apt5JtZlCeprGNw9OE4Sv9aHv/+192swzLtlksaz+SyvZYOBize3YlX1Hux97eNPbMd64djGSVukWIamRF56ZS1lN5AFuNXsEy9km6Mjs8l69mlcHpeQUJU1W+pao668SKSWaLFjcAhJrsfLdOU6Thiw9+4WkS6biVAgyHTzqmA2zi2ClL/RQYXdy9xVzludTKZaoCjmk5yAOksHKZPzdr2xvbos9VbjqwdH7x3cQZnhL8fj9bkzn4sNmu0K3HSCewKiy8T1XZrHO5033my/+cgYdpu41ztpn/+HMdJ7EjB8+wtNklumUVdVGsUpx8i1zFGgDip3tWhvi4BgUt2S8k6BqzAAr58/28u6PGtVjaY3A4QGAhXzAZ7k/nx5e/d4/8lP/8Pb6flOXcs8dxrGqR1/vDOkOTGv/H7VCBlnPA3KFgMvl0GrzGOuJW1tdXsSYywupq9+8bdVXesPHmhNmWZpqvBx4NCSWJ4FNZu9fH2Ogao2t+ps/WwcvHr22UcPD6lbeNgT9egg5TMvcqfRpRve0D3xTnRxunIcBmMKUAii4WB4zYVHh8PmsekWle+ms9vrrZ3tndH+x90dtq5/efsrXRsVRXV7e3333nvJmfv4xy9j/1nixw8Pv7K3o+bJ4khpYL6X1RklMRIgRu0zcg870Xo2dnESRG7DNFK2NloNWhQe/PDB73F2GNgf7rQ2bnhPaqkn93mdYWuW03KHYh/1zM72jjw69PIKSiQIXFHkHF0VdEEEhA95TUQnu7tplr58/mLyy9/S2+v+7ldH/ZOAOndfvWke9IXtvWvf8aJQRIquGhhv+Kbw4Xf3Wi0zr3AZBjRkcZ6uVrbvT0hc7G8dHG116wqriiFxksKnL3z3i9vPawLfbz/sbqlZlqPyNg/rgsnKCnMWIjzVGwAGgQFLy4IKOClPC0XULy33+ycHhyfvAMAH4ZpIbIlFAjKECK4AhKDdVMP5mGIYHwGAAKmIUFd8GMEa0LgmcQwMzQncviG5Ah/XznhtXY3PvIsZF6NseKYwsiZKFKIZpjIUpcxyaz3DJfHTudFqGXo/dch8VrrL14I6eufkSKf5elMSQLV3GV6X7SpK/UWWrSlU7g2a/d3O27MbgaLkkx0qlYQyr1WNLuKqgCWd+2XNjDAAqJCkPI+iKNUbLCFMq91UBDxevtUGDw+6u278arNKvAAKzYKOCopFGz8giEOQzi5yngIJU6Wxhxh+ubQ7Bw/oQna9MPBkTqyiZfpkY3f2e+ZXWx/Hh4p0VzSo3zx+oQvVhdQe7ewcATDY+bbtWhv/uVnXpqqtMCupcZ2ECq+GLJunMY1zRxC4iM7yIApYzBCqSm074SRx8GELB/X65hLCEoAgOl22t0xehbmMB4nsUMjM6Lyqei3dLu2W3o1jum3uvvfOtqnrHAJxnFruxnvuOIKTlakfuBSoRVGQaLookhqXXA6dKDFljgWZWyShTmUZdLySZstMzfjUskMn0xSM+5c3SzSbLkJbbmhGpxFOI64U+g3R1Oqjo2++f/f4wvEp04xsl6b4RJb0o/1/sj06j5PN8zXbYjcpThzLjzdMBxaQV5FYx7DV6kwzlL+xeknqVDW1jO5/7UNJUu3Zau5ZdIhuk8QmIk2z5xj7rDie2OH1eavb2Gf7LY2FuKxpXhZFVYm8MOrxfI9mNteTpcE0EfN6/GokcCXrhRcu7hqN7kCkDT+yozQK2Tm5JqOHPUoSDxqNNE2jNCvS+ZPbcV6ZUotb+ok9SdQ8pZhEUfKdd9+3f/FlEq8AT/U0ka+zzACVFwsJQzfavMBv4hmtj/q9zg3yeVGsWHp5sxBUEDwreEUiEhVEWRTGcpTk/SbiOF5Wihsvj7EXRq7rAEgUo6fJ1PF+fzjY3hrtnW3Wv/nRK14o0pBkMal5olKMKBuz8qVS4zguAoUwhSfwCgHO375+i9/8+N2j99ukX9SzHMun52tcJ77YWs1eMfr22g2kOwpXgyyrmDzO05iT6O6gVeVhUZ6VdRZ6Vl6DlXChGS2pTFSGr00ji6IkWxVlonPUs7/6u/Hi9F6/zQaB77z4qNtKJbVGI5qhzq+SnVaPRvQ9Qzs82W886qxWq83Sw2In5+l8dbFZzHBbECqBRYzvRwOln9Y+35XjecALQoarh/dOlO0DHQqe3XpwMnw0/McsQ8fWaZkpFJA0VpHvCIeGEJxt07/64pcsy+6wHanT5jWRspBDiNEIKWa7yLHIgq19nsEwPx8XTPX34QWIaBPJVV/LsyUA5evTz1ePP2f4oM0YH33rO1pTV4MAixTiaptauUGEx+gG3woekmmK06RHDx7yEktRsh9RFa4DSbmMPJWR3vu971LLyWhnCOa1uSXdbjwYTVRRD8dRVGT8boNqjC7GZ2mBE0UyJdHO4pyhwoxkdK60jNjFy6l//uJzbTRimHZyu75Ft2VB9gSuqT+KgmnGsLCmKw8Ciy5EEFgb5p7W7upZkTy3XeDBKg9MIY5DIDQ1HC2C9Uph1NHOlheECi8rvEKqF383zWWFPJ48698+M0xoPjoZcNCt0iTJacDsibLW6mzdv0Nt1myTI5DUGN1MwLHx3h/+yX9jKrsAAM5oQwC8qlJphaNgBIKyyis/ZjGCLVnDNqRBRQiEiBkMKC8FABJacEmFeKqqcq1Hpq/s1fkbuoAMCxKvXoynBHdYyBma4ljrJPGyLKMB1IWdJF7LtGzXwWJl8RBolOGHxbRwuK19QWn47jWb57ZA9VkDNxur8VuOxVrvhAKqSnsNoxXbzunVGGlouznw3UiR6qYpPXsWIhoio2NE+WVelHm1CpKqzBWZCazyQJO6AA3fa6z9++vN765unicXQBSBVoEWB1KFEnmBRkleEtsqKRFEdHLce9ePq/Vq6VYVL/uIJ7qZ+z4NbCpflnEUCXpuZWBrf1tiyigOLnlm9Oig0R08Oz3LusSrFCiEHMVV9VZR5KEXglJiNVYz+iLNYaMWc7bES12CaUGIK1AIcgzIYJbaEd9EoqRkZRwGQW/Im3hkZ1hPar1BKtwq2pUop0e9415HTeNiOZ+cPr+VZGEymaopDAXIFayZJO3+QOqquT0nqrhYuaJUlW4g4wrRXIZ0q3ZBAKWRvnBWjCSwuhQF67dz0NxVu6xcpyy60yrsKWfjdZy2Sac3wu44Kkt+f0ceV0lw/rKC6qA3sAMn2mygKE4luYZk/7uH8SJyb2akYGRRy20f7KUARrjWx/l4yO0vdQkLArUO7NITBAmX0zxbNKmW2efOny/3T+5UKnr2N5Eu6/e++n7qhCtrGbq5eXefjyKepiIMKAoyhhKnWcHU9QTXGUNMWDP02STUdo3U0KlAqiVxdvvq8Gi3LanjReysXNCoPx4MoKzeZlUUuh1iXARuf7jL8ZiExVDmFhFYT+cqL/R4NOrTs1ih6LyAIaAQV1OsSbMJVXdyiCp+aVdFt6EaVYL9kKvmFx21IcpsAPw3Z2/Mdw97vS2eiyo/kBpaljqvXoasFZpSExH6m3/8D+5vjzbWenpraYhdzG7Hv7y+8SOn8Ib9IwGwJXMDIKgwWq7sjFK7RAl0Wg+LIsuzrAKE5gQZynJaJhP2GmK0OPs8S66O7/yA3syCYTZS+NlmOVjINejxcWJqmu0sneeP1eZhNw/t5YoWUK9v3N7+6sWTi/3jD8yGIB9v12RJ1xTMAkVA88nt7Oz6znFztZjwuiRk7JqCpVgwdTmbpIqmAZGj3zq73z4W2wc5XpSTSkZtRsKEjtMs7Nw9bPGcv1kVbA2TwoO2GQrcULKFa1gA1BbxqoIFb1ce3WmmiceizJ5drOYp4qTNfNHmRZrmd7/5deORQQ9H9wUUaWmriqqYZHlOhcxya2tb1ncnc4s2GFQTmWXa4nDtjVW9BE2S0J0lmM2nV66XVgWloPwf/S/+wn+2zuNoU84RqBhElpnNQ7YgJEkS0RBVQ8A+XpYurpfhOOlxUBRlMDyUvTRTlNLxOLleJEn62087d/rpq6Iucror+p5dUcTcGsTQihK37giiqFEgP7vOluvpaPdYMxvL2Uzh6udXp3PAEDv/zndO5t9Pp5tVpz3odfvnbx9TsSvAWhTJTq99dHerdGNrua7NInCdCpSf325CJtU0vboup7eB7oOhWumDXhGS+WwTEI6ocjvJTFNg9r/xXfxThmVYljv8ZN+78RwvCDPCNPos6y+YMHdoRFHsy2uHAdDPgAK0bvPO1sP/+p//Z732RwBAAIANAAdAiRGmSAnBpvCIV0oszUkCYMoCkyrTKFBUpMb2CiIFQkQIEWK2D4lrAU6L7fGTOAzoIDgaCpAq65hCm+Tmdj7YEgXAD9U7tDbLczUIqjv9+5vcd1++sUpsak2DHHQYQlD0/FfP7x9t7x3sF1l18fzFmisZmVW3NDeQVUUwBXPw8V0/FtJXn0osy3sZUSZpskhCSZP7iiwWmJCg/tV4Nr24YFAtsB8c7O+3W7XmQ/Ou6b1+GS+47o7x7W89GLX4xy8/gxScretxCBo4e8dgjUY3DpMLyxcJYIAECCWaPFllrvMauBQr7aShA7iOEPNe6IY11TSq2otXvtSVq6oGflWo8s4vxovz6SSkNUYXUwCWs0zVHJoRRJpABueJiZMowNAP00oMOkpRB34l9/0iC90Zg6th/4iWiRV5mbPGTOpnVnKK9/tKR+xQMidX0rUdRbdWz6A96/z0xQbanjrcsp1FtASMzKuI22pJMsvelKKNQ3vuBcG4zzWSZMlyIKX0FLEoqf10xhKG0BSv4msAQFk2UvbmOhMIouku0LuLt180BjLJ6yCPZuNxxl6sVnBve9fQsvVmnt7eKqpYE5ymKwkgpHU5qsrdIsVZsPGi0nXL8EjQJFG6nJy+x20hUSeQCm8q9gDlRVYESyrzRI4XEDVdOZWLcyEESOXbmrHTcsPw2Yvfto3d4ehYlDlCUWhkWLZDSM0piigKgccIsCyCFKqMJHDsDkuZ3Q6CnqzkeEEgjOlcoGGz13V8SRKE7VFetFovbp/+ZBr+4Ct3j5rbz3929eTN26oA6tdEpbtvb2CEigdHrdtnj6uS+B7fNNiVOyuRwTaUCrswpRBGSkOWQlzhApEmDWKek0AFVeIjreXn6WK87g/aiqEoKdaqqHT8JEzqKKuVRIesMdx5785Hui5lefK7V7fBzRtFEbsczy/jLCWjA6qT6gKHiVbmsbKaThWakw2u1dUTqNHr/PLyytyVuv1hq6n7ce40zJdvXqpR3JZb1nJ1584j/i4Mf3R1/cz/0c+fsyK7+713cKXUkVOqdBrbP/3pZ/fe+z2NJ2kFg8T59PG/xGUuyprrFC9eLKjfcmdpWtWFKPK///1vff43/7Z38MjlVddb7g92AC49z1pcWHiP7fR6XVNP8xxyHFnD5+tX/svTjSg86gzRppJZ+aJIRcviBDpKEgke1vVGkQQZhNaXY1IWoRQtfv72EoJP7j4QAkDcxbk9dt1S01TseoivtcYgXqdVuVK+vGi9d0DfU7djQhbwTato22GJywLQCg06WZwrnitjeY43FKgDU3XX54Ao/dHIQsEWYHBLs1JbaRkMkl3X1huGy7uynQRCheVmmTNpXVmLZbPZlPoNGKGwW8GYjIwdMEjzxIoXETt1fYMVKI1ummVZ5aIIeO716SI+e9tpt9CcQAp3+239jqpRwyJmz1xPwIRT2vff3Xl0p1nRrfV8ygsSU9QNQ9xBetSTf/381PYDesIwZTY82FK3lLOXv+rdVSBOorgOUwvEkGLoKCirkgR5StFCXztaBJ7MxarGvL/dhknJEjotsdrmYbtVExIThS3TvMju/PEfVfkieLtaX9twxPiAJBeBZAybza9G1WRnZ2u6XG6K0pTN3nDUGXS+8437j07+VGDbAEA/CAAlcwyCLGA4BAEoatLi2vI2D0AJChfkhOMFBgsAsojx80yOQx0SVGYBpYLIDfd3OtPpLHNsM9St1Qr3M57hlYDjdJFagbf1BQtAu8ELvBhHNgIsnSC51jpd9YhIlYDHM8SwpSANqRF3cZFfXT754IPD7ft7cTxLaCuLOdddf/nrX3ZrRZFlooI49ncNRe61EYUaqnpzfX07vtLN+9Nbd+ncEi/Y2T7oH2hDTd83tMq7YUTgvr5Zr5Y5Rx4Y71dZ3N/rfU3/mGLoLMm0yLxOr1eW31Tu5K1I959AuvPw0YeW53Ubvd271IsLwHHY0GVA0gD5Wi32B11X5RWRp4tYoCvA8BQAYr93ZDa+XCwAYHhF+nyzuXfcrvmgKA2SB0mGGEbg9BjHAGiIzhFXK1RlbeySJQnPNvi+TOaL2e1MJmKpCjlFJFAqNOPbYcid1YrgvvQJAVfZmzbP1QXlcXwSe8Om5kXu8cG+StOtfd6feY8/PzMRxL1unMZVVRJQpWkSRj7DiVQaF3TuFOV4mfKSmmNK6zYOjmPhYL8EoK4vo7B2f/nkh3/65wd/9Kc+ge1yK0lCDafR9VmZR3HmCTyV4lVCEpnv4Lj0XKrVHpSlBxC7rXVqmY+WbqnCNC6qvJJVSU55dV4mpAoRpctK4FqwrTNhVmm5lPJeemGYhiap3ZwPfevWdi4++5Sy1HZX46jCCwPEagguHJ9u8i2xjgTPFRSVCx0SshwGUlsY7o0AT202ll9oHO3y/c78bLr97u7MnpgNPZvWaR4hltI0/ag8vHz19j/kT795/7sfGZ3uVq0wvDtbtJv9Ss+sq6mBWwqnXd0kH0lT31NbvLLxNpGsUlUWV1lAJUJBWnQX4BrnDidKctSY1Jv586ixL5lEZOhsUJFv/uN/VBEzyyuImDSvbq1lR2Wz3EMVSJLoJ6evqiq7o4r5Ykwj1k8bVR4jEgrrJokdB3m1IgGc8yJF6VWFKACY+eoal0jV1OGw2wPgfLnMSzgSuNMqfeUvbzezk/6oMqPXz370xF2VG2jZjtE0X71usTdeW0szmuJFauwsX82fDfr9GRdsYnvY1wSefvLkWYR97d135p8/fvr2Dc9zssT9m3952tW5VfL8/AvU1JM0D1fhgtk37n7w6OefP177r2n+pNnh1Qf3cysUwuVpLhAGpnkjTZG+Q1FbW/EiScq4lgS2nnI8Wi0CVAe+5496HarJgogWCd2oUkpiL8Y3bM0S5FNsgxe5GlYFSJRtJZuXvxw/49dndMbnOlEunLzPsj2ajfOg19glwJ1NxpBARGGdottFgQS4TBVFFMu8+fryxyNNaRpmhkDwer2YrJan6+/98A9LpxGhcF/TP5v8jmZFiaP7bZ3tdVw3nfuJKFAE1pMzS9lpt5u9gQYJw/h+mELKTzISuY3t3Xjj6CXe/eMhx9nLi6ky0FKKwCxDorAqYtddLJxgdNxuiEI5g/33hLevZriQHi9u2Pn6vX/yX0qX8evzc6qmGJ6NgznAqL/d3975Lktl19OL+fQaY87eWGWY8TSNAwo2yhTTcQqvxu6g0/torwUq1xCb4dIOtIrrnbAsS6JQQnmSl87VxZxGfUXZunP8/M2pP5nHbwHu7D66+17oxvfVEcXQg/379/fvDnd2D/cPdcmggAIACkAKMp4XVZoGEIASgAiABAAWQQ7xVV3jqgYJxZVVwlAl70MJYCA1TBYIZWYnCKU9Q52GWlYkZ2dvrm+uKdt5PM94BxzKmblLQ1Q/Onzgxy8W52BznmlittPVOJWLAzhUACL1L65farFhlVUti+R8+c57O9Uw9SLd8oOSVGVamI1+JcVD4VChlfHlDMXJsGDiIg8BUmGoFToQSKPd9FzWebG5dK8V3Xz33e9LrSaXb6pNaXlnabKECBdG5NMuXJBq/8B/PdYf7Sqyx3JcxDDto+4v//Ure7m6qdk/+5N3Wq13BVxswKJtQAsCqsKGbtBcxtBrTdWHPFOUeVVVPEtXpCwigoQsqUGYFz2GCzabJ09+x2yCuycfLUs/zwQYZCv/DVvTFaQMOcgz1uO4Jqe3Gy2BdSjYZGomZwTfipz5jKN5gQ4ZXe/yWgboxeY6yKhqbntuAs1kGZ0zVabmMY/U2M97/cHd3ZGsNyAB9ZCyzjeXz0Oo6bhMFpI8YOcyxYSRQwTRjTc8x0HAU2qcez4m0CJ4wNBGp2V7rjRQtUZLheC8qbdEejbZfPrrT//8z/9hGeAIMoin2oBm2trkxpovZyzDGALCEQ5AXuG61Wo1G9rr0wu+O7TsEMahUuN0zpGLvDka8EXVZCWpw4oQ8lbE8oyfg0et/pV9jmlM6bR35io7Wp3Lr8NJXeAPH3xUYVD14f43/kWZEUShLI8n/8OvteUU9WqhcxgDmHAKT/GQSQVEGZx+FrzFAWzJbQLCguYyG8fRcj7lk9SjHV/q7u3rwnLppmV2cnLk2TbrRJuzl/Z0cdgfGIOtFElMxW6l8VIvsFAABxc0xdE0jMKyrFvtbpW4WUoxrNAoGEoRvIIpLA/ViiGNtE6LJvJrys0Wq5NvfKU7eJAururILQrqyePniJVU9e7V1WqMyuaQz6JqZ9vwrieVzicMFPpQURpevglXIcCk1+qXCSWZCRL06WKlyjTHR3oDbC7WsuKx1EEH0DoqllFMAG3WFY2AdLT/4cF+Q9UJmRZF+ur1zYnZy7XW3e0DRFM0mtY5sLwha07zPJxVCQw3xTQuinyReqqg7XzjTrOwY47wdPoSe4LJ3h9oGzvkGzgQ6DTFNSiCS/+tMqYNtsg5U+Sh1mc46vRq3F4bapl/Mf5UkWWotsPAf4r9OH4jbX0LEtDotjfQ0JI4DkJZ42g7zGSPoWlZUbvyYPWVXEm5iuBVlM5m0/cfPDJHXHwZ0g2jylKurop4jQ0jvrwoaplme+Jf/+jXl+dP2n/ckgXRnVhNUfbHl1kcGw1N4LFa1n6SBHMkGA3DlG8XT9/+ZLq+y5+8Kx7tHVIi/f969q+M7eFsVrx+eWPZn334/jtZ1QZJ/Ojbh69OfWbpz70Lnd1hJBGqgmVNr375u2sAaY7WWk25ecjRSFSU0N8QKg/T8UvnWq24flMqBbl79w5Ns8v5ynN9iokPD9oZtWXFcRjE08tr7eRYltSr2UWruS0KMgqD1WrM9ditvtbo94Ibsgnnv/r5Ocivo9TGJJZNtSpwrWKVMkCiWc7GunG7O4dpUUicTkHedZJPv7xUEU9j0GUY+iTlNAXliStrJGd7wzsb90rVNWFvu++HkyktH/X/6Pt/9Ad/8A90vcEzEgAc+P9LBgoEEASABQLPg/8EfwYAYABwBJAI0hRkqAAZIwuIFXXAUhoABADLW6V5VnMAiiqxa8KpazuTEr4WgvnjT6+mN52y4iXgeiBXgKTq88Dl+Ea6NvURaehyu93ixbqq1PpmyqsmTTjff17F1MOd/YyibOC9fmsxDKqqjGtCnQIzQailoCYt4gWGLNxttLIsjQLftm1Ud6s4m8xWVZrouu5HwZxmRFbYGm0lKcHzORMjjSKIwoTUDAXxEnEJW7B5GQZ1S2TX65IqQ4p21pWqpSd3R09TDhez//iTnywXyQcfHGzxMmL4fF7xDf3kzmEUJwZHYhy0msOc0GXgLqz5TEemuR/alt4UZCyF0/UUJMHLaz8C/OX5cMDE2VhvHLWUtOZlamMhiNar5dHRHXnYX5+eZSkIKeH+cH+1cTCr7zb7NVGX6+eX159Ob4z+tsnjJtoUP7lwWNr54bsPWJoTITQ7w5oOYjUXujCpAvfapQQtuYh/Nx2HcfS979155zt7v/jF1ZlVbzX5lFJFSZxk/pZQx7GbZmmeA01vFXhDUQwjQ5gzRYbizVlJzI7SG7x/3Hx1vlpuGApJtR3HsaCJQZkafVM/Nq+uXkMK3bjO+ZdB587cSM3zy8uvffKtne1eFOYMXNA58j1RoKmdrkp7UUjym6tbegX3vjsSdHq6uERoi2qCSiJhnEnczhz77KV3sKtn66zd2O8YzasaxEWF84wQxAuiIut/8r/5L+J4M55e3U4uzUab3trya0nhytItl9aq2xv4Ydjp9VSuWKZptK6O3r9DQH1AbW3Wlp8Fk0nlcCzIs3pSvrf74bp6Slahqag0oOK5BbnNMpWytILEiwsCqmo8dS62u83OgAn9S2fZoFBYVaEUKqnS4OTVMpF5pcXKW/v3r55c9t7fu2M0fvHjz9ZrK17NibtGb+BqufnJxdV+9/6dB4agiw3J82wfAz6KvRj7sqBmBvDP+Id9w3FwxWAv9GnEBG5YuoXcrkyS0XUlgE7g41pTIitvNXxINIDf0pzQaXfDIMNVLRFhGp2mMQy8cLvzsH20ZyA6lXKxFnKBef7iGSa0xL65Oq3zPA/DULYNRqBYmY4Pt66LYPn481nkVLAyoHkxP282G/BOT12pSSOrJx5fp7t77SouKSzUNV+D87cX6dFgKPU71tprC2yFMZpReV7f+eCTuCxwXNjTZIyp5Xz+Xa0Z43odFqPW1toeb64/PzLNTrORJsXSun0ThR4pTX078OYML9u2B9mUlAQWqavKJhtyEZ/XwXFP3Tr5kIYs29veF0Sh0W1X44u+iBjGs9YbmqUFiiqDYLFRatk42bpTG7XrW7MFOxx1tN4BLovx1fS9znv/+Ad/GdMawsXWnnBw51vN+8fT+Xm7Lbp5btlOT9GG/UOuVvIye3V2czQ4vHPv2J+NbWueJnkwPQXpaEfnvnjxsp2WaiXs9Pehydn+JrPWXSfR2wLNK+Q8xhQFFVbZ1SZPnoS0YN5/8Pm//6tGo/3h/jtthimLksVpEI/TRf3522V/60DgempOXGJRXgl5OtQMqkhKhs+sJIjWAa9RwDC7u5QuY+gfD0/6vGDP18tlPIlihQNLgxfOXz1khDjPOEQkVZcH+rJqzGjOv56ch+Xh0cN/+Gf//J27n0iCEgCAAGABSAtbYIW0SvKSRRSbsUACAAFAACAEVASDOgE0T8kMIQBgQEGalWFeMwIBAPwnLxDweseb3xS+jyUx61BowadJklMxCIKLPBm2BSSZD1I7CTJ1MBhDdrZgRjucrxqFlwklby1u7YDgOL3apN/w/Hd/+MOP+4dXFy8cWwkp2qXI0cFJs62/fv3SrSqFYaIwVaHcEXFUU/MYmECXRi0F1M1T2gJVCQxVoTOCPD9K03zQUYXGDsszipBMp7gl4pDHQV2vry8bpkpDKIkSrrC9sfdE2ZXKYGHIFEyU9UUU+ZLQvdvZ3/3O6u1mdzc+PuAgQiFW6aQqOD5N1svFlG8xkt7LbJfIdJQW77/3LmBrtuRAw0yLMkJxUpVpjI6+PWL5XdWQNAMEy1gu8lig0uwqKZI+2xHazbfnG2WWI6oe9HfqLLu8GjNl47hrdjil5NCd/ndvx+NPn7w5jZd6V1M5+WD/OA5Lxwn7rYc1uYljv6hckONACYu8ZDqCZtA0GHw4Iq9PvUarqRsyfzh3fefZyuHXki4AmXTSpLLoDR2rG8vutAcMFc9u5xwl7u7vzq1FLfAkBMOjftM0hLtHAs9E3jTwPI2OEdyqsqRWEE36knRH4PlDtRT5l7vN3brGwcRezqcNHUI47LPduRX22qW7DLPUehNEX9U/etfs/uz22vu3b0ffNESeLwvqZnEjb0nFc7qBcUtvbyaLMIYMZGg/vXhzGSaJbJpeoo3L23anwzurdqOljSh8m3U7tYjCzXJV4jDwHI1IZV3pLO/TydVmQ2ijBmJROiRWy2LsKIM5JkZVR3Qqbaxuw7RANr653RHuZPgS1fjszZnACUm7odVemigsw6gSlQ0azfHs9vmr9LhiJFGk9ADO1V6nCVqQrlLXyzdLpDebW43KyyVJNhrGf/iPz+NoxYqqVRY01LhsRUh9X+lvPdhr9Gn/9gYKfMXTFPT8XOD6RpuUmcumVcZ0+Ogq19SmJrWtVcizKs79WlByzFwvx185GuIwyii+2x9QBIrQpig44lBJ6hIARJhIyIOQpAGzM+wXUDb4KAWNAtPj1a0X+JIklzVVQ+b4YIvU5NXMfn3+TLY07f7dnZP9nOS363G9O6qqKoSgMejUg96ZlzQ7Gc9ouCkvosubn19tjRSDdszGcbNjrK9ib32Vra8kUSv9ZlvHezvbIui9dzCI0hhAWB135ot5wbUq2Ui/mBqyZhDt1avFyrK0AsuscP7mMjZ1rT+UStxo3xGkrUYDMFG8Wc7U2o/SYBUHVTsRU+EicDmKxQamf/34s+b9u3ePmyyiFiXekeTpizEiBFb45dNnEEGakRKL7jT520X2xYsnAMFmu6kIiu+XC+8KXwHXcZvtr2g6ihRlWwBFHfp52KG01WS9Cla+M2/6vYKP3s5mAuGn1av1m/SdD/aVsutF4dhzk/Mr9oOTxs5RZ7CbIuC652LNdXoHGzHxccQ5e5Y3T8KS2W7XqKL9aDC6N367HLWbDUH/9rsfOOvVYjZxr9x58ZKx7d17H3rQkGWNI+zVamwmMWoM3fj1vqAilpvXi3pZ5BUKF5dJqUtfN5CC2qumsGSAUo3UY+lb/dGQrcv8N+dTkviIzdwsKW42e9vN6RiJnOI43jxKt7YP/+Ff/NkHj75JAz4GIC9rxEQsAALLASAKtCjQ//MOqAHAdQkRDQChQMUjkoKEIWIW5xUv4RBREDAsSCHwHRCnYNgHCgR8f/u8TLIkwRjXOkfHNab0y2dPMK705lFOws2t7QESFish1mmGmmIo5YbOQydJ3aLJC0iWOC56GkT++ovTJF1QFKDYxfOrIM6AIke2w1mOg1lOPjroMmKOoIWhADlW5DOIIqegah+1u/3NKlo7ebvVZHlHkKRlPXqws55OsFvWGtXuYAQh5bGBN3PLukMziijomhIEYRzRboAmwHJDn+aGgnJsVOXLyQRjrNc99oinM0hYpsxzFfq+kEMv3d7rvfvONsOzfFWkVJHm+rYuZ+XaXTprL9VZURUZuhb3O407vBLnzSLPHq/PQM6VVP3ry/BkRwYx7goNmg8P5IPPA4uQkgDuZnPewKzI0CNdh1G2sN+WZQGR2j04+eM//gRL4OIq8XxLlvfahpQC6JGk9tSaoQAOFcwhSCGa8DxvWXZW0JzQ3f3EUGThl1eXXMn2eztpEpRpnViUV9apFWlt+s7Bva17YosBkGr59nXg+5v5khVjEpGaEsIbJ1rHDRaSMhDAptVRgwCCZJFqmK/NzWppB1Mq7vN7gwfb77UqLQ9Tl2PDRZJEC54RrNCXm/s//ukTQ0Z8JX84eKfZbTGdbz6MLdsNZzduZ990XFcgyhYnDJttn15GeCRQqFOWXGdfZqQ+aERD6sa6/eoHbe6LG+JfjLaVoCpaBP3eyUdzgHinXFsMky9vJta9HWlr9/B2fN1sDxYZBImfZSFD0QDb/XbbtpcaALrRtJw13e5Ekd9mWbY3YDg0/cUcEYCrCvEQrSwfjUSFFviBrhU5ovBgcJln/usZq/f3vjn0o2q92RByvXoZkQLcVzStYzBS63pz8fnfPT7O/5DQhbjXj2MbULhQ6iooZU3RW+J6gxbukgyUBgfTZKkNiB37cX5D2P2GwIEeUwNDP3jw3/7f/1UPo47R5tiG2WvNvNTyrbsPDniul8FNto4ZkG71GY7Sa1xNxraklV20zbWqHWVHkhIGNSSRqsrGerWQ5HJpz6UGKkW2KsjWyZ1wMnlur/dN+cP99mn/696b189evbiLiNE1CIUcz2u1WkGaMPtcTdiuqja6OI0Shpd39E9egGeJD6OsdMPx/uHhaAtDlpvkpVw1o+jtiqgsRYwW/ebi9fO3L7Is/vMf/LneMk5ImU3WB6NWkoDXv3tal7Zre7WgziaLvzs7//Nvf0dudTWzqaO+qZGY9zUkKoKY5SGVxTW5qSkm4UKskzs7HUgB+uDr35V4sN5YwPdoAkmPtZ5sYt/f2h4eHx70u108OpyHRT6t/GzT7X2wvc9d+7m1LOTCYfTDtqJUMaapQtSahrcuzHZZhSXBfBRLTV3ZYsBSEmSSJ+lewxyYqior4WSVv7GCutx+uFM/57+cfRH7PbQpJ4vfCh/eXYchjOy4iOMwzjJwlcTXr16qpvrdalB1G1opwa6mqe0sCHheCl+9ePHFl2UcNlomzwqJxGTO25bWgNbGSwI2h5IjuMbEVJVZcQPXMJdxq9PNC2rn679X+PaWqkRRCvtSG1Wh51jOOk2i8wu61x8l81cAVE6MVy+nCSpEmgEoOTl5BxXxtz7+5J//wT8S9g4AYDIfRBowGMQCGYAEAFSDClcZJoRh0rISaQDrvAIigLCmIEUBSAOhqDHA2GBgqQCeBSwAaxfkZUjA/5eE/2iyLE0Q9LzvO1rrq4Vf1+7hoTJSVlZWVlV3dbVEz2AAzAAzC5KgkcYFdzQjtwRhNBr/ADdcYEEQZoQZwAY4M627ZOqMjMxQrt2vVkdrfQ4X8zteex9+MksGQ4IAyC5OXKJ5nsE6Rgro52R2s7qyTDvlnDTdoofdA4RFcNoVG3TK2bWD9+kWqtJk5d3PL7buNvN5CHwUAWjUbLWqotS39tMdbhoHlo2nusk+UJLQH0+marOmUiYOUKjJ8bZi6IgR8CJEMysxIEZJ6vxuarIcnpXZJkEu2XP3ZuegL0K4jfHSdjWs8XJ2oyKA2DkUsiBCEQ9iYVFrTRLFu2ejHZ0OsQqdh1VeZaKkJnkqVZmfJ3UmFrELMGSgqV6KcpWPMjRSRhu3zrJca291PZguZt3OcL/RQiH0bROpVlev15CiRJFGcfq02VlatkhJqox2qDbFgRSYVMy4ljGSKMhTumkRScHSLTrWdWcdeSGKIoqi2c42/MEjdrX1Iry5TVmWZkjWj3IIAlrr1u1IEEW4pL9YTg5zYvR0T9HaNxNj0GNjpHSW2/MoKFxvoi+75A5JjPqPhRgh9kh8/vqyRbDt9nC5sO4tJwrnge8xHMWyaBIWEBSoEOYYlQcbQhXPnii3k0mFuABBALonUlJMhNU6Fnq7UQSCOLLd2cwdc1SC5EJRZNBIrpfnQVL2e4XWKmXxSJBhjoM3d1eBf1/hBM6CYguydgmJbdkgvTjOOHAwOppPZjdRGk9fST6tBemYl/pag69zzzZ299pfff0NQ8dJOstceotSVY2gjpgJPDBkAiMWJfpU0/7x1//QHh3iODIpU74ipJ0eQUDXcSM/RqQ0sJ2WLKoAeT2esJrqjy1CbZAE4Vg2SVKNRgOhaCuQ5ZHA13gUXVtB3Nndc6/OX21uCXchnhfPv/umxjOWo1g2bw4okhugOLIMFzhGio8aIe4jSpqWsKhbGBUWEcaPhCK3ckysQ45G1sHWv+Kl5WaayTxBQUogLQRX69Kp6TS3Vfloo6fLBODryQc9tEZGzF4ulpDE9jyvdEMUl2siqaaLVfjanZU1pycUC2V1WTaHh0d5VQp1jejT/Gb8dS5kPci++P7VzuPBQFFDKDjLm8wNoR9ebHKFb/DtduPJo3i1Gv/w29trpH30Hk6SCIS6oeMQkTm+TZJB6pA54ZVGBqTDp4f5eLO4c2ezyXL1YnA4KmsFaP0wviPb6mmr8wZ5izIwQYOj/nAaTl+vXlEJxhAMRWZliXS6BQTEE+0nTYk6oNjJ/bjXHQzf/ygsS5RTV9dvQhujUIbVCk0VEAJQDM6yAysy34RlleYRUd8432GAQ7eBv7mxnDY6aLdKyB3+7Jnz6h5FoW3bLMeC5Zrr7ah/0MTnZF6ka2dNx5a8u9uq9nCUYAiUKlvNvdayLnZE9DrzaFRXFdkLfYFlOkr/5uYuk1ujYXN8rSdAk7EaV6np3QWdkRjcT4MF4fnZRcoL3CKY+SsviSFFmQjWx4gyzVjPne+eHHzQeS8EabK+EyUiyqgIy2ihIGz4zXQuEhjHt0ieT6Jw+OiMrcVk4fJ8B4JbnicMpOwfH5v+dtBXYyPbFpMm0/QjPC3REudWZalxtDUbcyxtj00yInbe69tv3xqb6JjGNyjJ3znGKmQY/OkfPnv96sL6xvnZp4//4pM/onkWeA5gZVDHCuBxAABAAOAAABCUEEFhVaKARzEalCEKKhBUoAKAA3WN1EiW5yWGQRoCkgAIAPkCuJnJ1FUK9KxIL95mfOOQbRAsQwaLyvf8Uij1tXV9dRGEGc3i99OQQurOUHvlR+vf/+3u0alubMu8jhXYaB1OjOV2u/5R/zQELY4pbcusy5zhCKXmuN1n1vm5fEwrgnrr2bKixJmZxwyJsARRkbmslx6SSxSVSDtDPb/HfBzk1QOt79BY6GQrLMiTHAA2iXKRAzhD1l4Fd8gD7kMFgLiITd9BUQxhqbDUkToJk8rCcDWRUdXz9JXgB83dgczBds0TkMBCkiK4JI0pGiOZMDIKAK35JtoaMYLC6TohWU6VW87E36A+j1KKInJSSwh8tyzZLIKMINJUyWJJnjxsdLZZ2qTyy89vzzp9lMEpivT8WqMa7QZpr+Z16ORplmVpliRVlYvy41wr40kekr6kajsDtK7rLKySLZPlKE5CP0aWOBXi2XfTGDsraAAwAi1LhChB5hVmBtdpxfJCWYm26ccM4ENM0fDjvV6TaWw2W1BnoZuXVT8v7qyxxfCRxHQIjApcH6c0luvStGGYdRikhw/bLNdXm8ICOZzMLeUsGzYHy8D0Az2OIw3F+brHdkunIhhW+6ev3iAQuO5XzdFHbB8PcJFAY4uJXpwnIgqoGjJk7QRFjpNKVq6jtEjj+Dxo9Y9+8sfdl599ntVhJbBEHK7nUULKLXO7ezQIPLfIiu1qzVeynZEkKwYkVkwsCoaKon70wXur+xtZaeAICnC0y5FlQ5J4tvLdxWpd0ELpBaq/+uKlc9oVdwSpttAA2HvMbvPsbLNeu16YFanGa5CVUzKeOyZWemKNu+t1c+fkPWT5b19emN5vAAo+PWsIxwyBUVlYG7qzuh8HOSE+2ldZNkX8CKYAYnybS6EQSpYHYGWhtSoJfWHAPJhvNpHikmDtAbcyBYbcl4PU74b7sBvHIkqUokiLbVUWnlQkGUJjc+cfHHfzGhZR7rkpW0lJvRi/8F7fxgwJ9nCAZ/XccVH/lvdSDPfIkRtsqTBKxIbIoM3jdnOy8FR8l21S90tXQDBCJNm6MoPLZO0RhPhepztr/dRdXPV3BrrxwnEiwbQ0rUU1Gk6RuWtTE/ZILxYR6ub3l6cn+8/e2XHMW8vmaIZ+c2dBw8EpKp0m4XvtuMBQuCy9+mDvqMwarGWiDBFAiOOSeb8kWa3dPhCV9PDo0H97Hje0XZyOk4IVpfHbSxTHEBxFalhV9eXVlecZAlZSPcxLNj7I0zKdrRwj0bHx+EZKBAyN1aqrV/5mtj3el4XHXrEtOo8/5RjOXRqzyQ2ofQJNTo/2hkH5d8Z6u9qkNNkTGov5vNk4VDXp/MsvgjbGIWUdQYrhbIDCIG+Lp9/H0wT0K0CzalyX+e3FqlxfcxQexvb4N19NFwtZ4GmZEAQBoYfNlhqG2Nu3JorlRQaKIk5WiRlvl3BJK2LM9AJKIFkcu74x15tWysh1ytEcQ9AQ4CUColvDLEMUuI514fvx6cM/PPhxK5ZyekvLspey4RH2+O7uusUf/Oruko34xq54e7EtiFhjmfXm9m5d/AG+2v1oJ08zEh3sA2CT6CdILcAWvmD32ycffvrLP/nLv6RoHjgOqCCoKkrgN2WFoXUOMqkkcAAAhKDGQQXd0MXxAgc5XmSgrgCFAhSBAMXDrMyBRJIAgHAW8yQ9Di5Q2xDlIRZskjThmk3fuw9Ctd0n7hoFXiI+iO8mE910cFKQZDIrbhdfehmcMykhtc6E3iG/c5YUc7ZAiDhuwhoJMQ83GEPHM7wSRaEvbO+2NYY/+vTZdTK/0e9VOMDwjGEgw8JBr/HyxbzdO10Z5/3+HsnTs03YptLd/Z2rye31+WUaZWRAycIxo7COn0JMSWIEQXkml1DesjeFlU1DohhRJCKxkW8Vkm2jWCGlFagSjveoqi5q27Bskj6hGjfXC9jHdMM0g/l+f//pfsvKoWUbjo449x7BJxgnKI1WvqxWjslCqtM9mk7uwshVOfrVt+dtiWV7bYTW1voynXolQrKCVMIi2yay2D1lZ3WZszlSekZblt6sty267RtXtVfQJNdstO5vJ99c3B+04yeD/+iqMfEruTtsu/bmzevv3z88lkfUypvhIuvmoQXL3eEDK9lcfTPbl/i1eelGrkQK9sJARZZvaZPFckRrHbYu8jLut+59g4e37nK5nOnrpS8zaujV73/8ztXb1zdv7zXKoxsCJpKi6PEsC0Axnc0bQrdGEDsE8/vrH4xvvUA9PpUnl+Z9ek2Tcq/T6NOEN/dml6vUZc5G8v/5v/4/4hj7139/lVCwUuo6re6X0WLhjQ76y5WbYfTYTZhL/NGn75CoVt+9zdhG4LrrpUl07F8+fufee7PeriSs/e9+/f1sk31UDT/56QcfnZ1cf/dt5sXUjtZWKEDxTE3qJBgOhm1BJQv0zRffpqJCyfj69gahKXog2Wsfyf2DTtM0XYXFmW6blzCJl3CK4n3GzNcoS9NqY0+iItQ1b7Y1DOezr+MFiakojFck0w4it8aIV+Y1QQCsAp98ojUbEoXT3c7Oi1fnOC4091pRlRdbe5YHJULEMQUiXRLfKwt3u3xtMjxjoZU5FwfSZLvkWeYhpXzGaXCLmmEISJiWgBrjmMLd3C4Odrl//pP/ZLXUt9WGliScb52pzbxMylq09LRuSH5qPP/1jcAIA6niCLZ1uCvTTjQNXdt3a7txQKJAa3ZpOuMIkhgbBkREgkpEStLv9T4t176Ps2l8G7c7VLCeEYJhZp4m0vs/3v/+6sp+dX/4s58VRd/3A6SaECQ2nzjUIEGzEu+rrN+INzGhAJrYPTlqJKVSAhuhqZSiMs+eGBZFqlZW0VV1W2w2xsb3QQtnKpbVQFZW8XquI8gWEhoncu0//oR8e9ncE2i68erikiYIBi8KcB+Gyd98dRuY6xgtkjwRVaH7cTvMEo5nDLNokQcYhqCJEEMTx9AEKSHocZyiIormNc+DIivzNKKg0pEv/MvajXR8uUvsPnv07MXLO5aRMUrQ2n0Aw+ev76vKlHKpprFtCnB9SiComwNJ7Hf7D1NvHYqnDC8Fbto5bCnvsQpPGZul99ZDRP7s2cdOFKEtPnljmkVBcY3Dwb7nS3nBaACgAylNZ3aZJ0UhU6xrTTmH8+ygqcgyxxVzz3Azc2tDBOVFSVEV3TB811mtZgsMb1rTsF0t7l53+sNbM7QnPofBwAOm9+Kw1celkgJxzZOhvcl4UZLEp0KtKsrq5Souoq7YptokcKmhtB8naFpu/7M/+lf7P/opRfMAVEAAAMEAgLpr1SSdlyQaJXYeIQha1RWGQRyHAFBFXpR5iFM5gBBACAAKgArYQAEYADWIAYySvIo1pK4xbDN7DWGFE0SCBnSG0fF9Ve5VdlE2C/jyfjw7t52NH5jjGSApcPBebyhzWydNnbX9ukgrin+I4lkAUMhSVS0WBFpvVmD0pNomBoCAbtGvJtaHdakdP9QvDZwgWDaPY4Pn2e1WFxU5iUKcwDebZSMAWoERvnu99cX9/WOpk2ymxtJ4/fpqdzhCEHS9GU+mTlFwx2d93RDLRgriFkd52yhiqw1QizRKSBzTnYykiCHGlh5y696UUXbANZWiKKOUAMxHQ/H2CiBuZOTL2WR6H93LmpiByrFUO4EAAwBlei1heR6kmvdIHSViUBDZafNsU2/8qzHf6/aaO0gSp3gRpCGoISgDDKHk5iClkcCzKayw66h/0HIdM7USZkDVFkQgxvEP98mxqIwKDE1ijZB9L7SDshTaTY5nx4aFSrwkK1lG4kUa81Q1MwkVmfmBPo8D5xWKY53DE7zZCcJEI7oOjmF2lCPIQ5L665t1KqHW9Dtc7Dx5ehpYsDfAjK23u79/ttN8fX+dFVXkuiVQzbVOEF6jKXc7HILiN8b1OoqztElW+TffznEE4nXZbhrj2+R6E3BhYHvomaiWhDi7e9scnO38aLRZG8p+R/ZZkxrX7ZAIa4zYTZOkO5IVMQM1Vdr149PH8xxBSEcNk7cvLq2VtXu4w0qh2Fb/gP2z7y5fKHzDmBsCze8P91EKq1i0dAHstFm+ke4oJB4XYbK+utx9+o6f5GPDZph2Y/8QQ1DPnIV+kCFEPbu3WkoBDYx1CiqdXhZlIbiW57US4K83t3MMrymK8pN8b9SycrrMi7r08rzmWJnsDEaTm8EDTsUgyeCapIbTOJeqKmMgzk5RJPcSqcNkWIkBsUNjKxxZbxaOPd8slwf77VogzTXjzWpNSmemyTPk0WgPC8Lf/uOvBK7qP/xxHazFVAFMXXgZRzGnR4dsprk+XjoOKmlcVmyygOPDqOCxdKfbrGsGOXvasrYmQbLOzbamWYqsBaHfO3lWBJEdJ3HOhMkmiiGjqsrWIElyejPpizzHMvalIzfbHbSwW5VhbcvCOf/WAc/lfERkebZZrUiGjatCrhtBPO08bCmIdJ86bT1usSM0t7emX7hBOYMo5TV6O5eW1ygr2JPrBeAVVeDYxXwKDftg1KirarbUmUTyDUuS2Ygo4u3GvXOBaL7f6ow+afT4D7KYy5+/VEVus7gEkcnI2h+cfTQp15oqrxfTX3/xO/vf2xRHn/7JaUmXGY5isMTCSxNBCQ/ARC55XpjmlaglBdZbbwJ/vK5MV9w5PFSOXlbfbG3jaNTXpJ2hWeBEA+mi0BRRZFWS3P7eI9M2alj1CNYRJQFPZ9sqcjayWLtG1j1t8sxukTtsGtrm261hhFFUlBXJsayqTHQd6BuGy+3tiskaooBhEd5XNQVBBzXIiFFB0WCl564SexZCecQmwzVqcXkNU4PAye9evhJk4cE7P53OJoQkoQRG0tRmZf//8s/+Yv8vSKZ9N94qish1HQ3HCVTeOFEWpEpHc6xylxO44fuQRjqdlgwCDmUX9ws7sYI0M77z//7ih16v/3/61//l+z/7N53DI1D/h6zrAgQBgA1BmmQZIipRGsuSBG0IAKgqUNYAB0VRhUWWtHguzGJQZTD3qhTluhCAFgAYAHqsW6AmAjeEIC7rGiFgEidZmeEJhiQ0gsB6kyTRoic1Zt767st/7/rmSUOALZpmGIKiKoBKHBB3Wp6V1vF69TwL+ZIjcCKM6xrs4nQpg8quWYLE11jF1VqTyQuTR70DoW8R6ywtKRL3HZ9luHpmJwi8fWH1elrvAyz08zQhQRNZrtcMDjKREeTjh4MEdUDlud1Od7ZsZfF2i6gHT7qvru5JQYrKCCd5UKeuGZAUViNllhYUyWBIp4IJaVJcjSaCODNNhqH9OJwZNYPmsrhnrS42OMOLcpa3kTyk1DZyuaAC7cGT9t1zAyWjJLuapuIuMSxCdOdo1Pb6d8JNUVeT1xeto1HpBGJXw3FhtTWuL2/KND5rikkQVhTKq5EdJ/crk202jw6Gs3tvsyElWdqR/nDjGHbtSj0Or7CKqqoYdI5P+Qp1FmsejUEDJzMgIjGsMYCgfF8MCkjts235A0VuaBSlJxlSLkGJy1FE8oTvB+v5bPX5d/z7HVo6cbwoyHwcVTmecEsn9GOzrlrtYY3WZZ2tLVsSuDqifeBuIYHi7vrcpTneo7IOiisIMYeQ5+AOkp4vEyIiCI84pFj+4BFDNN++fvXrf5hoZy0gtxpljpQFQxEsoNzUZHalXsoJUqPGQBpMLcOyTFprdJTdRhSnlutWdRlGd37guajMC+TJ/o/MSXz/+brusMtFBGEy3B/5Is+m0EnNLDNwko6sAImTB2FINpvNfidKtLN3tO00++z+RRJsMFZlGEwS2U6v64NBUUGCX9BYK4rBvbve1/bowyiKXLmWeZnHKK5aJmGhb/mmHXj+m6uezkry0Wj0MEguEKo27+25XaJqGcQsyiDk1stIZGOvls5SkgdKa6DIip8UlkH3erss0wzcC5oMEUSjSEdwCvfty3Xq7z95r9/vKdxJi6nvjXAObyjdSJlIEygS1whdyHig9yKFxZdehaR1GsR+BMokrMoNW+xVCWf519O7sSixDUV758d/rHvbtWUGYQhgVVZ0lIaK8nC1WGxX9tFxRMuc7VgIVleFz8EmksIiLCCKkRT17vCQFiRXlr+IZ+Z6q2lKi6STah6GoRozjntd06FnU5kXEAzTQQhPUcmqQHAKw/H2qIvoW0hIHLVejn+HDJ+BOm91uFarWc0LFIF8S2ZHxHqRF2zY6bQ3OBKE089vzimM+OMHUkP5RJLozDVoZ4n1G7ixiVJnRPIcIB7/wR83S/pXd9+RDFleFbRKQqbAgpkVWtPGu4/ohlCGdZIkrnOjBzVFUGmVt0ZKPuwbz1/eMNSgsbPIpi9mi5++87jod57/z/+j9Ph0Z7dH605V5F98ORZUFhJIRddFiXEMp/FxXuUcDQLcV9Lg4dnZ8x++1b1VlsbVuNA3NsmTKt2YLU2MIG1JFFuyHxkwh1XZINso8KGOZkWR0QSBjCECC5pG0IIWAUjlOPF1voF4KwBQ8NEnHzAnD9CCgJxIgJRSBgS1/pl0vUTQ8+v53idH6hJmRULmraCoK2aG4TiGarGf0niZUmiZVUQMxC4XZWpUQ+2jYRfb81+7r+Plhz/e/a//L//3o8MPAUBBnCZISIH/wPhTAIBlaWZhqjaAGycL3ej1B6VTQghhCNIcEiRSI5gTZXmeUHjKlNQGKdZzi2qHHWynAmJGJaWYO5ZLbGkUQMhioEaruAZGgiAlovVSLCtT6LlOkrp+OPcjUMUenHhOCcAAmDWQ8DZJsKMRQZBHQV15G92/sB7/4kc3vz2389oWSDxN+4MW02ZXK1vpsLNsgwhVrqa5oZKEqUrawl9kYZn4aZpaoIhs3Tj//GWz1Tx6t7WJA5gvOFrrDk6ef/VtE2CDg53FtcPSuGW7OCnffXe3/2cf+/cztbWvyCqCrqocJyBFYtTrta3ucHv0SRKlb+Yvtl7UwDtiBitBVEWUBKAo0fOre/v6HscRCqsN0m+3T8OCwOJEYYenH44UirqI75PK3C/VFCme//B3Jl78Fzv/2s9sFKKD4f6ab/jmZnF/dSRKc8toNYZ1XkThEm/3aQoNYkdixc+ef8fQAs+rvCh1OjxDijfpOMrcZRkh44ClOwS9YTIqcb0ibt4LRAfQGcGmrt7lGtzRBy/efusm3sBp86LwtDMsWc7O8vF4bmy2umn89OkzFEF9z+13H/zDP/yVuQarN2bn3X0NoWiMKOtAZga7rQGT8q9u72osmWxnrCQPei0CQUAYdah2hiQ3U19iZZJk4jRzKTyrSp6MNYwOLTZZzD89eQBl7rOrl+qYaPN9us/7yfPC3IwXM4WHoNGquULBmDaJAsfB+Xi1/oYiuVa7JQhqbK+cycJieprQffTo6cXVmyvfFypCcKg0gCEgJFVaZvmhgzPa0An0IsUpgUSRmmUFtdVjaamlNrff/ubvvnmFrOaP3n8fbO69S3Jt33288y73KeNFrucMJVXZuPdeiHKMtH/yI7xKULS6txzz61c7Itc+3OO0/Xw7fnm/4ECLlnoH1ShvWu7O6d1Xdy+t+7vQOVJYgm9AwxY1LUXoJNUVtV+5zyMIOp0hK0mT2ewbczYavYejTIdBCUWIwqSI0gNuxAxVbB40u61VYd/+9juud/ju02cAgvHz9f3N/IOd/t7BXmAnWRSCOsw8Z02SxAJaeEmwmCzweaiqDY6Koh8QXZUZmBVnux9vJa+C+dmTvRjmP1wGO4+koOYlMsLFAgslt9YZGo5OHogi029JLlZSHKX135c8x7Ecd+U2HrXETFqRqTlbVSl2gDeXcTi5e0Uf76kVx1ZWlGFlmba7jSHo3gTXqijAKNgRMWHwCFkvzmfxzhExrwotD7iDZ/fzCEeBLPGONfvV2MKRTSvrIPTu7Oo1pR2UZf2DbXW5vKW1qxypivmtt6jyL/YPEHPiEhweeve2EeYxDqU+AZC1Zx30OxE4XsTB9Gb6YesnPCVgb199t//ouC+w0+0mil2OIyGWlSVa1KST6CVeVikMm1KRJdvrKx9P+zxnu8EAwW739ykKMfVZOL2WKZ4VEIphLNzkCiYvLHfjZXIhYwIaFgSIo+A+DI6KJPLdoC45bcis0ZpVUFgNkzhhJRn1QYFhVF0swyXtBDuDfYJtdZC4rHugChzUct2UZ7iRCP34pirxqrIIwB4cHURhJIgy1+jF1hgowDayNF7tfdjcZ35xMV2nJRWNVx6sMgpDV0umS5ZxvjPaX89tI8YaAiVyvB8GCYlFK9utYw8ExJSWGvJ6seWVzv/iL//To8N3a1ClXkIIBAUUAIANDAEkEJDzRajVOwAAnGZSz10t5826RxCwwvw0YyuKBzUX+pt2W+JxEsCwB5pbbxlYwaKx6cG+H8Tkf7CS2xB4MAA1Sog0LPM839R5x0AQHNiuR3Ls9PoHVSEGSE4QRM7WpMTwkpKUrQ2Awd12FTUyLcA3HMcdbMHfT79bbfRwAUNZ5cXmQd7m1ulVUWdrK4sX97tcY3qbkgIRxaQ9rWjA5vNSEWQBa/3ipx0/ju8v3e36S/7yJ6LoIgQsKlvBNu1GEgX4bHEZeM587tFBzO1+hOdWvEJHg9FFsKJsHEX4ksxKrFPVUU/ZHQm7eVBdhleZhx2Lh7ykbBZIExMKLUdMS+C5x48fF3mm//B8Z69XmWW4uNTteOfksdRA89fGvCibpCHwDEWUchS339mNTN/Xr0V6P9jWP2y2nCAxwtHxMw5HmlFkkRTGtRo1YCfTq7KqlsZbwA7zMqYOtAP+QS010GCDiBi8YXGBUHGkRCDdKPDuIbqCOztMHI4jx1T7LXu6dLvtzeIlJncEvpCLAStLSIVsdBtZJXFihggylNX3j08RCPTgzjUTRZj3mirxBCZZHYdVu9WLvIJHren8/n71Oc/KfOsZKbPjTRpG1sZGy8zHMOu6XnA06Tl0kVpVuSFpSlWqNEsKJF8kS8cIXR28hPM96dlqA79O5liNtzuNlraDl2bFg2zmBUao7qipsYR4hUK8yIL9IZZMguldJDCaKIs8IWyXjp763b0dsTtwb1xUlQiSMY2oqjmBx4I8mtGkwPcLyw44ohsHuaZpTSEvfMMKk/BW6gsP7tlVUWe5vwlm0YVHd+UwXgav7bk1K1julf1KEFsJX8CQnM6+b+0cHxwf7xX1/NKFcFNn7PjlN2sdxTG6HFRbNzQNm6LY3p705C+ePd18+Otvv/l2arwr7eRhapcmI7TGaUYwkXx0lrivTeO2W9EftDrX2zx6+xb2djSeNM01JMSjwSOUx8HCSGrUirZSQWgi34Z+bhOvx59Nzu2TA6Wose1ygdYAZrUTJmkYN5QDQa63eRIH0MAWWZmlXtptdfmcZXM9jcJdTeG1fdPxJo5pVJkHHTyncRGrIApK37NtOyb3epokY999PcGicL+xk7NOXsXjeixCYTjac2beBtg4ikLqODaydX4F0T5IInroSyyxzkpRcDOyMCe+nekc5C3P+f1vf7uvKB/9UV1OXPlwN/C9Mk+9jZnBlSZynERAKJ5fvOoBvCEezyFuIjPbNix93jp43N0Zks4iqb0mZAlhiJeZ42wwjIpRdza9iSOHY4WypLbbNU0xXpBPV7VDqQTRlaWKYzhFUrHTboNt0H5V4MgEx3jG1h3ORXEaxZggtMLYy+KUQDc4xYmPJanu2pv7765vcKmBIpWGIiVNQBqJ+aJFdOLYB0YJlVJiRJs1qiIPV5UspR0Bji/mvPgdz8CQQD2/zkVeUDAHRcM0gSmMNJzlaPfinG4QDF1ldo3TdOatM4RJ4nVRAAwSzd0P6JJOLedmezMUBrale44l7DM1iiy3y9isWIEtBSAeNLJSuNB1hoFZkdMc7rhprcl0EIQgBznlp1Dk8ZbWzop672B3NZ+6SbnXVC3HhiXkxW6JBQRCkscnH3/0Zz/9w78EgNTrjBXY/3BpAQDyEjhohWZ+Etdig8gMQKVkM20sthOcInmeB4DMm8kOTlgBVIcNHqBOYM5mK7Uf04KMVXW2zdEWEiEkCaHgkzkyy+pCKtGULNKsjlgWpKmJrDl/QNSeZcVFuRoOIH3WQnPGT5MsBd42tnMbYBhJQl5Qb8d3ZZh1W31BODm3g/1hu4jiJ8MzJzfDIFosbEWlzvrtWuAqJMG5Qu73r67v0NDfwvJ4MHDWBofiqx/MHFTvP22+vugj9tSrCpY1OMDHdb2vyIxGoDmLdPt4no/j7P7VLCfyNMyPHuzc/GqclfRoNCKywXQ9ttGE6SNWZS1/mNdl2WzxLJ1bq01TU7P0LrgUQIVRsMAhFFn5FY5ovk8QaEdrNbhY6me5HpVJEvs+AkC2dQiGDYuMh7wo8JFlYcLgZGd3VBbfLnQ3TUmWwkWT7/GLm1slC3mWtLbFwRENiHZZMbtnrAiZAsDJ7U1ZYj2Z0nYaAxyxcvaHN2/K1FeN2QzCZ+88o3KUEGmNZ6795WOy/X1dxe51r6/FaVI7iUn7JdZi8YqiNEXiGVD7sVu6981UodXSWK15hpX21K+/OTe//H6cRGqDOTk84AI7rQrfTL+e/0/tzif/6//Nv3j+0lYa8Xa91OcVgq9sAzs88JuNERqRM31OB37ClaOTzt//9hVFYE+Ggkyd8rT4i9H72zx7a/m1JChilI+xQZZawpQj5T119z4kfdvjObeoqywF3G53x0epEHVCmAc5xw+LJggdZ28wHE/PXUgJJQJBVxHVqowo36XSmOLxLMWoKgeDItN1n7X8EEOwwglpqs2r7/b0N99HTtoeDUGd13xUpOYscWOibGl8jROlWicuHQtpCpRl6FgTt6H2jn68d9D4wLrXfzu+5fsax1HzxQKiiNzuJml+N75r4GpL6Pz8/fdeLC7zPKf4PlXbG8Nc37wCGNkaNli602FVxLd51hly7QJH7tMyZBSUI1BYEHEWpSikKEgDDaFdXecrjxOK3DdQgHz8kxNrtfLNl6QgxV6AVgAHGIkRShYaccxqNGiK+AJbY5llWWqjUDgYVxGvNibJjWcQnDDECx4t3JDIGJZxEVjTNBJbDM+tNzeR2MLKdB28fq/R3CLTahnQVG74ZinXD6WdEuKmyYhKM4uKv3n9P96NvcfPyPc/fgbyzDQDReTTghsSiVPwSRnhSF5UdQ2BxLKq1pxfbhMvUCgS7XJRbZUz7407VwfaT3b3yCdnhuHerW48L242TgY/+gBbWXES50lGsELplgSZa2JYJw0vdjqt3tnp0fa7NwRO994fLL90LTO6uLNO/vD91e0ySlFFk3l193acX04WWKd3YG/vHHtGUYgimqEZ41HtO7Ojh9yCgBja6VXAgF2YepzHSn0yGXOiCCfnb5EaOnZGuLFI0QnNBlZcA8CQbBoneRqJOJfnOoJ42BZhMYkkCd6K6w6GoYLJl9OtPY5SqayqhtYZNLkqfLWeF9BRmG6fFFZ54RE0lDkiS7iKCTMYcRhhJZDjEAV/opyIyh5zk3XbnbKs0yQpEzB6vCvy4Lu7uzQu4iiMyS3a6REdvjYr+sGhO1u4RbbbG9gbwwzs496j+7q8/tVvUZpy9bUVhDUDjcow306arX733R/5UdoRWs9a/SAvxv6cotoJCkgStADwAIj9jJTQjWlIqVwBkOcgCPyqzhEW+ebLL588ewdXSGvuJLLI+nxs3H92d1uWJU3TSVGwbMhyfJHkawCLEjO9TOgi7rpSKQqmBZbVBVmlVYFjSFZFfnRFo/QPqxd2ZtI8O5/p8XVlGsDFgNjpQDILJbreYGCQgA6WvEwKoPMs++HeaBbbYuDS++z92Nrf78bInEaQ8cTRAIMLgB/03dtrlvAKvL+9nR8UcVXgGFKPxxMPJgfKB2XOgI0Nt3Mgcof/0S7FMDfXd+sUo3mlLWtf/8P3NIlebxeSPLi6efGA2Xn37PHr55vN1y9Gg96jZ8/iKLqYGGxRddQez9LVfBaDWilptMbIWsT4EiPy3KNd//XR0R/veKJhrAWNHfykTRGEZdBsy8/uI45FV9OVXRogLimG0vVtfzggRGa7ffHffx49a40OHr/jg3Ji/rrBiCpPrWCT4URJRewEThOPZ/O1fz1z4xwElK63tG6e55NIxfzwHoPH3d577z75/uaGZzlsE02+uc3jycPuEK2TqAy90OrQqV+lhBewAyKalWhYyzIfGS7NsKkbrmuo5LEfiChvcSQT3eoYs4O2KAAGGFr/7F2l31WjwAptipVzXOGD39y/2Pz7zmeKuYCCK35wusulYcXu4nBC4sVmk5TQ21f2Y83UzTWG9//8j2UY80jGoBYyvhqjPLNfyxDWhOunVhX7IcKjzSBKbfNO4Nrt+mS3FySxb3pbXX9788UI23fKFtcng3XsMj5bSzawnrz/GDyXqSB0srJWGJ6tzE2VBx7JsLCeamogRnV+W4lHnONVIs9s9TjP0rcXd1SDiisbxKUwZBI3NbfZfOVGod96wK+pmZ/myRIJw5aFYj0OTcMAp7rb7WXsV7p03GzUuKbYnoHRMjEY1u42jADDyhAjczLS6RQLkd6+uFovBhXSHjQqTJQxGS6Xhr6osHQ9LEYkRkUMJdFuEvczPrA9sY4aDRHnOAFBEitiYi6tp74bttvSdjLmisS27Qf7R3JNOqkLYF3VRdkqQjOXZIHtk/YsNjeWaa/cbPPg7GeTZEJQJCPsZuZVmiSwBAQaVFIQ14zYlB6qj859L7682B3tJFS2PzxezzejR3v2Yk3iJM8xdQWyLBMJ7nDnY5KiA0ix/a6o0abp/NX3/9Ny6TWa9IMdFSexuvLS3CYTcbX0gO/xCpQp6ZvLV093NJpm0xpBQFhVNRY6CcttHYetSxxtqMQm9PzfXF93Ol2eEVdlRREkALQiq2FYQBCArMSRpC8ogXmfVSRITYTl1t+tDH3GiwLDiOl9mlfgDoK2UBUI8ZW+3jkaMB2lsEUWBhiOYimcN0gK5A7LF4Fn12QCYF+3tu5GJBHUNS87zEldopkVr8iF6/pFVlvk0kcRkuBDw8xAEKBZXZZy1SBRxBJYkEAkNDM3brUUjAggU62v1zzkTZyhHM4NIhRBWJYnlwn5aJ+m8bwq13maJyGhCQRDkT5Cs8nlxeUZqzoxXaTm7vAdjFYwEcUBYm/wOHFKxkNQ3HB913Fgb/C9cf2JZeONQS2qmFpm8+qrtxeflE2mKV+nL5WYPm1pl6sCQ1GO5yQE931PK0nqvXdAJvGSg3cwu5rDbf7gZ7/kZI3a0nh49+H+z8hOP8ryChI4hhFlASJkTSA1BCSg0BrVlzei0ClLEEVhmoZ5nuZEjLfBzdX5/v4hEtWHA+4WiXlfPDh573TYqQFIQWqVZbCFSXanJ3bg+0U+lrWDrMoZTIizqKhzD9ZCVVZlvoocUpJzoVr/5mLrxEhie1bFUczhkWRRQpDk19e3JEk4XmZZwzBIYmdrG5aq+R3qmeF7RyeHV3ebMjBjnRA1rbYqpDank1tk+IQlqRylsUSpG5zsjQkND/SZXaI0BDnBZ2X+XuPgrfH3oECbipomyez17CZNGYa9396uGZcctnr9UdA/22/yz5+bl4F/pLXUkxzoJFZAtKzbqmKvkcC6DkKvUVY1QSFUVadomhRBPOPTdmPnMEgnScRt9c1APX27mdu6+7v/7nciz1IqI7aaVJJRNTV8OOAsHgR13s7Ly3o8mw53dv/h6v716yDJPLTRGu0IZG9kB45VE8dH7VJObs0p32rbwSs9j9dvIzcBYg8HsurJIonURJTA0F1Hel26Cam6Vxb14/Zf/uTkaroszT2SKJLY2SMQP1liNSlruLstubhGqFxC+Wi7jQIiDg3XdrLMz0mUFZTIjlmRH6c5T5uIsXf64ClOkDhavh1PlTiwowutUHKa8JwyKcBsa/W5PSzBX77YksQKrxFR5mLYT/xrzPPnEOEi3/GcL7/4cjTst2ufkDo2khTlCpd3x9/dtZtqszVcrs5JoqRwqsKIqsz1717s/Iv3l5uN7wfBmyDPymJdP3e/Y3i2zbyj0K02TaRE5NiuLPKPd/tfTa53WMZHwGqTYraVYwRE5hiGNNQ2BIXjWtuNF+eqblpBAgcstim3lEXQImdb7uuvJ67vI7ig4LLSkwumTrOwAoBhJJblirSwI9/aWBTveQTjrOHWPK8gb9M5QrY8Fib6mGc4lE/RquKEVJLlVy+27xx3qqrj39/o7ipZZIPBQW8wODx58MXkZqerzLdfbV0X0IWmUIPmyTZBl3wAkb4OthpMVVL2fb+iMC7njMUiRcAC15/u7uFocPXrqwe/OArfRpAiSlhFKYsQTslJd+H9dGL7qiLjBQr6f//2Sv9hA1D0nXYXoVCyJkkaF3H83kmRqOD8kIZMKZEIy6AIutsd2EnebrTnbxaywnXk1toxCLTmumycxUWR3xqRJqgVQpE80X+w+2+6/8ubeXh4QK6tWVxWJCZwTFkWjUM2mEDKWNtm6ZVRgUbknz98/Hev37z83G4eHNqOxUSoxihhOF6jLtVqmatllgaBd9dstizdOjw6JXHgTu40rRc6AUw2Qs5LDSHJayVJ+eYoWyUu5uUaKpKCRkqul0oVIkEfMCf368VQ7Eocd7t4CwWus9cgaAw76HSS7dV3082TllwXZXmVt56EZt5j0DZ+r2M2Xh8lalCMLetgKLS6jTfnRnKLKTmlkpI84mCZwirTQ+il2cbaZI1ALRkoIAAky9m22kYayVIERzcEw9ggtUkyXJS1eo2u8u6jvKqqPHU829j6UTJ90Hxn4S/KqMKZ3QdSzKBMnsYPD97lZHx2PU8gS6C+6y62i4mwNQ7/4B2Sav1//9v/9/br554HsrL6uPlnqUC1Wh21Nu68/gTYrVJE0zqIzu/Jg83NW4+iGwIBAyFEnPXSx3hxt2XqSXig7Y3QjUMTEk7Yd2WU30CUfjga7EZMGoZ9KGV16VUZxJg8BVUJUEGJtkvHcZpP0diNAm9a5Jgf3UUBScUrL4gkbvdHz3YBQOPZdHR4ggMAAFjlYRFXiUfG6aquST6JlD38t7+aj/aGnKxtSgApiikBqPMyzZ2aQDNxG5tvL7///vp7y/Y1qdA6TwmSYRi6L4h+VHzw0Qf//RffdCzMdi7jqH7w4Ek3z0pMIzvortrw0rFVZp2RMjPXGo4ggOt1tEvzu7m+2uufVAWMJcrb6EyD7ey23Nu3oRcm1JYi2L/6+m8ItH7Sb9AVmibZ9W+vrbIuVOLKsFgf+83nrxstPO300USfAN6pHMwhbgGaz1OtqeXj29vP4w+efPCj3dEX59PQcO7nHk3GtMPwHLLKPIbjvZnpr6w3ztT3jd+8fvXL958AgN0VmTVLsDrZx0yNXT46ObZTCzHJ2XpdlYXzyrNC0OvwFVEe0BX78Vmvs/dyfFUDhaZqmuJBlDP8xikQgqxult8AJDOsMMEBP9TwCnO92HjzQ7S2Rx8+OW4qceZbhr6ZXREFOvtSoQC3PF9Q0EXCiuVxpM6bJBNVuUThKY4WWYmlKIqivmMXWR6HJY41Ol2NwiCF5O4dLuw+pJy7GGkffNi3VxXFkBQOOfZIzHR1W9MkEtqiyKE7avtYpi3T9emchHWWojGMK9fsiFx/Rw18cr7eKiTZ6HfDNCogjCiOFVjZiQO6xjeTophHiQIAKouK2pTpQ7Ld3fvmf/51EAavXr3+/p+u64Y6mZiHBHJ8cExR3EpfiyVq2RNFwbgOt9Yj2zFxFe+usCQLMyOgBLqh8BfbGzOwxVG/Kmue525vbtEARXpmtoYJw4wFHweDRXDbE/prz15n6V77jKoxFDETMaMhiUCSh6rpm/rVV0RKjEZPRXa33a4kDE+lxPaWr7y8gQ8IFkVYdj2+8vO0LauGsXIctteT/fDqfhvt7+0fPn1Xn31TelVzv728NkMvKibOdDU7PBanTnDhJGdlsAIrHtMOOoc+oltmalkZwFe71DB2g9l8SwEKpunT9hOJZYN776s3L2JQ59a2224mRe5ONuxoULOKFRLovkBLiItIZBRb331uGGCAYRTJWintrNcSS3X7IwV1QiJFMRaF+S7BGTsjDmMTpH39zT+QhEzQCVHA3Mz99aoqc1AXVV04muNUdfvjv1jnVaDHDQhapw9Y5HpdONfbXOMDN49GKBcVmYmx/Y7cPHqP4fnUTWuvjsOgSdw7E+/0nRN9oWMSmRdFHKpsOymqgiWEDFYIBFnQ1BS+iXTpnIiSVKhdWqMQ6cCZzIylSRGM1OkaV+7fvXqxNaxuT5UkFIM50RsNjo/3NR1h+NiJm62aVNuYJJDG5mZ+K3gMhqJEZMS+HxHrTqNsu2KwvMogQOlVFc6sniha46ChNffxRhGns7Ep0bYEhEUZTy7n89S6mZ0zBFlWcGd3R1I4GBG8jKWBxwpMEhbtnZ5jOkJnJ99sr19flHUlyg0ret2OPrRYWZSDvf2Rri/zZNbuCGWW8QmP0RxN8yuV8VepSGFigzB0owq8/Qb3ZhPKgIuaB9fWFLuYHL/TlbVWVtUIEbRPT8MsMw0vT/IBDlV5Jy51SENttwmgYBiBdKR8OOyzFDud5k2Jeul82ReaoVTxJWJ7tmdfS8SBXzgiWk3W7idnT+u62tzc01SoAyziegXG1yqCpRSKAwavxqEXC5LguWEQcXRRISGI6tz4ehXHvzh92K0NEhzcLe9wlmgBYACwCJPATIqSyrIpgoCypEI/aDfkMIbnl8bBoWCsNxRBBQDGWSwUqg9nvuWcX331j1/8w3g2aR8pRSoCFMuqLMgDPc4Ylcdp9tMfPysR9N/9Py7a7faezKkaPrE2q/Vk9/DA9KwOlHEjERsSS9Ortz/IaKeLj8au68Qk1elQppdGqUjTb1697B0N6VDNylKSH3+V3kzMawEdZeUtThKf/Ok/txy3gqiTp3mc1p0FA7Fms7HyEGtreIkvhGJkxTRBggIMGl2epfPyXiXefdJ9ehOBu8nzNIOSRPKsEOlUvM3fOiFFUARZUBk8ZA8Cqui+/xM+8r784psPmw8oNA3M2cxN9OWqDOL2TruhdYr5VG7hvQ/7pUMr7zwe5FIeAGxXVmiVKipCoo1gRgFewdJvbz8juEJi+doGTkOroFgyGU4Q28U9RwGEACt83m40RDH+8aOf+BbEMH5+62IA7O4MONLI61AFApsDNEmtCRGmie8GvuXBFBTbrHt2EFe+jGVojWznAVfEK4Dm4yuPY9Ng/k//Nj375fvQBb7pPxryCY2cSmdF7sWJPLgj7cRfnN+wTeGRfLQwJg4Ld0g+IPLr8ELxhVIjqQEVLELdMXCtkUUZyN2kLj999uOGRuI1+os//iUGCaFzWnqzut8jKYTn6Xn7UqcN0/WuPIAGpmUBTqpox//k0TvU7HxsnnODnVhwnXXO8CKKBYQGFFneZjbwQYNklt51FKQ8hV7f6vbmShJ4goDtfs/S44jaT2IvXSAGlhEmHemb8dQQT09pIIaBq7S6DEVHdTxbbH1nhTlluIw67+wdjtq+W2dxaBpLVROpgrIKR+RIy3FUSdLO3qlg7eobUVExLCDV/BA9no7HHXmI2laLb2V0kZW5b1iWD6oims43P/34J48PHhmr9XKxLe8hM+Du11WHEk60ChPy+/FdGMYgA6EX8gTZa7RZiluB9OHxkyxlNYYN60duOdcZcsh1xeMTjKTi3M8KNHUzv7z3Jh6eYie/2Pmg/RQlKbbgsCYXmu6rH152DneQvI7CqMpRtEJzmACUmy4WkO65RHzYUkccvi1b09kdCmrPDxRFiJBYHYxqDqQmQQnMeL649C+rIpZ5otkGNc2Sce0kJSwz1KO8KnR1RGYwQYt3NHwWoU+HP317/sPVi9vBYHA3vxYlOi9y2mRKUBVzW2xpJECAAPZg63Kun2j9HKXoMD4Qtc3MCc1ElBAG1CCjvpl+lmfg0dlfaid8sHxVAHIRZsHiNYpCel/EWV5GZMQBMHbR/qCb2HkUY/PZyjfiMgWbpSHyAl5iFMTaAPfNtF3QpLJjzc/pGisSH4EUxDbuJiulnMCZ935+3Gv0/2//1f91pRt/dPYHfEfIqrT0DYJG6jiuK6zK8tH+TqYOfvvr37umWUUBhkFErCX6sDfsEyU6NXKhnxgkjvf5vT0RMQ3T9bESoVBmiDH8vpjpOF4jge7pxRpkjCCmfuZ02zu0xdU40mx1/+Rf/kvT8YPA3tW0xQJifp7i0dsyxFGCliSVFzdmgpaJKIoaQeOINr1bV3ntq42f/vSfeUkY+K8909ljPVmRek06CaTcLbqsdKwJ1EqHEHpGUQIcWALerDgSqQoxFDAF1Prdbbul+p5bZgUo0yoPaiRNI3hAkd1mo0gTYM1efPvNaPcQdIEHQGLqfoaXRZq6LscN/MpBapBkmSLvrlY/HBx92mh3J/f3QtmhonqOTpdX4zfnNxP/OwQr333/ge7E55fjPmKiJLor8blWgM16lt4LktoktPf+9GflNs9rZDq7AVVFiihBIFiFYjiyWa9wB39wfHjUOVjHZYkFeZybs5naUxdllVGk2miByX280uN8QdPYbPMNSYOfHw14SSqr9ujj91mOX+kvTI9JKwh6u/n1rExLnGaizdfL+3tDjyOqebh/9ujhKag8JZNz110uxwyBCexBozEiWlJu5LIiYzjeasurre7a0zevv/Hc+t2nnz54v234ruuphYBHEZCfdPuNtjnbQ6qKQ1lQ1K5G+YHLKXIkYkmp0QqLu56RohSOrI11VVrxatJ/75Sqo2I3b7R6g2XbDza5n1IcTeekr3suSRwcadKnH8fuW0VNKYwn4qwuC/2HH1IntimtKsl9ucWrXBlvEj/lBNpeuHbtBB7gdg8krlnKTFviHN1AXUzdFXpY/5vxc1VtyNSOvbl+dPTpu4+qL1697f3oSU0Sy28XWVXcjn0pTp12LgksRRRPDw6c2OWVtuM641ffiz3FN4wf9PvmUCEB5VReviFaFB8MeyBOlQKSDCbJ/NWrlysaq/N8dT8jsCvbCiAgRFHjeYGhQVLEAAH0sTCUOxj+ciAe/fy0y+8x4x+mvxsvBJKW0GooMjooMeCYjnm1fs2wKEZiUimGLSwmjOga1wuvVfNNrfvF7VVh+n/xZOS7xSxCM9o13E27pyI0VzA1C6mzYzWQVIWls6BQuGGU+dkiIhxMUATbL3vvvvPj4U7iJY6Zc0KJQZDEme8GEOFQIqhRMbTKCsDOoFmXEKlRWPn+PBY7O51e5/ztq6OmLO0fXX39OUOJ2v6hqjRgfvqbz/92fu9ia4Inu/s7R35QuQjJbJewxUzv5yxSQQS9nF9IhSJKUpTbQRjNl+eN4SP+SH1w3DVdO4PAqioKw1FVQ8v8erlZbRweillYFZomHPc6ooil2XezJSnUas1LBNJqc128kSMIrUk1RC0dUByZ2XkGwtXba6nXY8Jk8fpc3dttqW3m4CQO/V/5r4W8QiyA7il+EBqbsuz6VJcqc4QRxWQxdj1dUBgT9bB52JQ7OQcUVMmiKi+c2EUo2cwSiaRonpWAtaIU5fH+0dTaMNm6YIrPfz9L06Id5YfHHxKAdMxNB+/7i7VnuyWS8icnYkl8cPYEQtT2p26QW5bf1A4++fFRa9D8tk5jjd6NwdVrV+t0OzxHIGLgzReWnzqJCr1ThskHKOZbll9HKps5TKTAFoTF9uJa2D2K/IzFZd8u04AhT6XLFzNkEhUk7SfVO0TbZfMoTG682yjPTs5OleMGUQepaWauTuyc8tpODfHH7++GlylBgG6nD0uEVaQo9GWpdbu8vRsrvccfHArS+bd/68sMmiwEFrKKUsmQxtgIr2bnN08evUNw5f3ituBKueorFHbSe+/GeIO0KiynnaJaXS8dEiUJ3kOzywu/KO5A3uRZdmVbWrNTgsC9nZdRWFV1DcFbqiTDdWGvixRUqWm82eAK+Zef/sxG34Aq3dH6928tjpncf3239/gPDRNP07s8imzLV6SmogE8buM+GrX85I6PdxHP1VG5k8MSt/S6iIjEIx0HE7nLbz87fvKpAELP3HjWeiqL7wAAE4AALrGiiPADr1RafnAba8xgMU27ncGvfvMrL/RYmqphFuFTb+H8D1/8t45nsiIRhmEtoF999fb+DSAYAOp7FNf4s1zZlURZQmlQuPV2O+eLmOEp31mMuu2QLC8uXmtpo6e0LP1OUkSCQO/vv2Mr/nioui7qSGJReMGW4WtSqWN9nkRe0CXpIpV0MyboYCCc0RRQZQ/UDJNtzOslSiM4QW1cOzk/19Bi68+x6EfN0T51dPAQoKcMm6cFmK/unQut1TSsW5am4zQsqzvX06Vur1ZLUSQcbzqvvUTlfvHeRxWC0UR7f6TcL+4pok2yOlm0BIHZlKjvhDiG1YbdbPfsyMmqLBc4BIhKR9yiprucMmwbCn6aJntQoRKM10oCZBEsne+3nT8Tnh7wX53bBJklOtpjGVsZGO6rNIhIEsqyVGZpvSJ295TtemtMF+pgxObF+4c/4Tj6zeplEpiiohhG4Hh5d+8IyMj7zz7mcsbZWGtrKVMiz9DjaJzF0XHvZPfowDKdx/Sj0w/3hqNR0eErFGWxCrRk7YS/O19LhGiZkWOspvOLVk/qNRr9PVLXsevxtoyx7uMmP+OXiV9kCSbSUozNEVhtlnGFzlaeQDDvkawi7d8ttk9GO71337+8vMOwjGUfqw366OBZFF2eX7+2XOu9T/6TyWxWV0DoDy0U/fbLm1GL6Cl7NUf1hlFF4Olqg5Fkf3cQJwSRkH6w5ngp92Ldc0mV+ZPhvyxti5CqP3/8NC1jjCTvTbPRO8QFmleHjVaTIA0cAV8/n/dUpJtUn82mJ6p8OVliRc4J3NGTjsoOilZCERRNs/PxFAcFbrf3lYYOvYi7b4tHNEN6OO3niTszTcsRKTBoMDhRq5p2P1kcPDsoop4JogEuao0up/RTEMchF3nR3s7xkXoap+vLu1VjuL9cuSilYG0ZshootgCJzdd3lltvIvNHu7/AkOx6ekfkiMwjl7epac++ffFGbaGfvPPTgos8pPbCSPd9pN2W02ECUodw19ttT+BxzKLaWhpEJoJA07PxhMwjjMVZgV8mFg95gOKzlf6nf/IvJWZ4/vo5JeLIfJ2zhHZ6TL37TFWVwYvB7f1VCWoJJS68AG/zcVjwlacRFVpRd8waMK2VOWOadRjnwmjAUUiGoyWJtBEkStyFhVCay/u5KmErP1O3sy7ZGWFU1hreXN9SKEYz3H/84//49fXb/f2zc8PqNYgq64iVcru6fJzgNQQ4RA3H3OhblMhklHA9c2Y4QSoiMNNyarZcC03x8eP+9NxEqFuWQBuq3BwQm9tXm7GpNkT4J/9yj67tElUL/+5EeoABajmvkLrS1FKVd+ezc2ofC9xsSxZ7DK8ojyWhkYnVZJzTNPvwsfLZF1+t12M5S2gMbUpaFnsJH2++3KwJ+qNud5znnx79EUWRF+evCISqK6tGKj9GdA7wqoJQbFiNaRrNmlJZzZpql4IioVPtbntrR5cXzzmeeXjy5O3Va5AXFM0gyxXdpKQBWjrItR2c9J4gEJ8Znm7coCioQc33WrG+ufeC453RtKg5KypBVzpTGY92GAfCIk3CokqzNFp8Pk2jKY7TKIQfvt+BDmM5+uN3B/pbUz1+l6j4xFkTmlxnAB1veUpuqYej0QeeFOLkiBStf/rs1/2f/Jj2GLSY0TggfS9fryzDCvxM1Y67QznKxM+3X2QF+n/43/9XN9tieT3d3eE3m+vlYn58fByGUlY7HCuGMfHi5Td57X7wnnLz5urim29vF6/TMn/WakeVGOGAaYh/9+JCf/ltu9nRjYXaZJiTvX2JtFlbFjvdkry4uKuKSStvGGWNl2UJuZwsUpDv7O1sozkd5KPjzv1LCxRFV0RKus58fmI6XFNCInwVIo9GzendPVKUKtXAJDQ4t4bNFkRmNSgphur0eytd37AExWsVzlxvjczP1aODh2fH82+/wLs9PIiLrWUs0h2KTUO/Bp7nz2zHHnT2Tn/+6eu/+9X+3qMssWqkZfLxerqJsby/u/Ptd9+J/IiumZpHUx4yJYXnelFkH//yl1+/eiv5IYERvm2XcqHSUhTF+K5STFxsiBkmaembBk1l5paiyGIyRpNSFEW8oWA0SjeSs3fPnn//fLw18HKQFDBgwjbRWyOrwCpYjOYJCMbgX73/0W//8bcYgh4df6Q1pITAJm+/zqOQEkBEepEd+oK2e/SMICVFaBZh9Ltf/YMU0rpniGooiLIPkZJh1UabpJgh1Tz+6QdfffXNq8vnuKINjnYIK2CxUe7PZ7+bkihB4RhJxgDxShDxIpXCfBL4KY6gAp8pfATj3M4sL8wIgsDyJ+rQSWGOS4vbBU3XBxi5mo73MLTf6dHyfrjMAjFHIHn18o4ZoA/PjhkK4px4fXM9X2ySVAz8JcN2Bvt0HufdfjcDSwJFx/fzpqIajiu3G+1B/7m+YXl2NV8Ftu99677/6OMSLFrSoNkRPGMTbO/ZzkFJZpCq8jq6vU1yJDRu7lWOZKkGSdKMtJMUGCfWDF/kCc+1eGvm2CGsiCJfli1elakIxq5DegkMdX+jPTgJUTTNuSKIVZatSTI3g7rMiCgWRXGlBwAgSFIn0d2g363LMpNafBR6bmCZ3Gzx1aA77PUHcZTs4KM1GseVrRGVGZjyTmfYkn73+d9e3WxM3aEIfne4b+p2Vaj/7J8/IVnyb//6PIqiRw94gNQ0TSVV7CR2JEgszSB2TVBIzTaW7pTMici6wQ/3W2W1rkDLjQSVQ4I6hkXd74HpIo7IXk+4nC8f9B9s72/X88sBTiN5oDxpxklOEp0nH+8VCWW8uAEkMi4RC8fjuOCEFmnRkE6uix+y7BbUtSQqCEB4r9158MjdOmmZkFnF5VmR+8NO117ouyiTC83pbz4reHrIUfQBVeMlQ9O1j3lpix+1ooRYjK2A1EsAtKTR73ZD/aY3OkvCcHv/NcEhXhQw3CiviKtoavto/3EPRzZNuu05Lk0zEGOvz83jo2ESbGiGvr36VXYX3WWRF7qYa0Mzbwhc/ET+E4o0FhPguR4CkYlx+3i33dBUr0aywtsbNgSlBWLqyhn7YzkofIGP73NGe7SrdfHYiAldn49vFUGuHeQHxw0DFw8jcTCcL+dHPzshFgwKChwlaQItc7rtp3pcADXG+I6XBWQFSXq/qGVQiwiKTabF/uPDJC8P39sN7lehtZaEopzWduTLHVYu2xWZoTlxPb2uXX8ZeHWJdHdaBInpZeSVaUFRdVm3SlAeHhIoAQuIoAQdsD5hYzhF4ZQRRr1nvc2XxHr5fVHUIaNWAvQt79uvnm8Dak//VjjZJ2k0M25AbWtIK410NxAs7puW3mm8j78Jk+nqXvAPV0m8h2q+P0/nM7SqQV1kWbReXZHshzY+X65d2507qVMHWJna1TTHmVpkKfdymsogLdcYViNYAyC5D4ytFb19/Rt/cy1yCptaNd94efECQsr21MWL1+3u46cf9M9fNUzrB/+H1w9++TGOICSVbouA3Kk6VZ8iqOw6TRDo2Baa4GJbZmgmDDPfzpCbsCwKWBZoxlIdtEY9tfLrPNk9/Jn1/Ft9DRR6H1Z+ZuTr+2lobAr/FsOAINF0TuVoDga4BDU7CPTQDgmKltUCpBfn3yWBzlsRYxakV/AM6pVlRmVM7GVFhGDADzzKDfYPD1CYpnkWkbGub9gGE7LW15tvF/qGPToEKUlGWZcgrrOA9kKm3VqmcYmVrsSLPJ+kllchNZkQRJQ5mJ6tJPs48r0KoDJEQ5qhKCzkMFgCBIV4EsV5EAOQxOnxwQNv+f2tNWvvNOqMNdc6v8v56QI1cLJJS4ochinPSU2tMfzo9O73N6ZtbY26023KSuElNtqkfvLux36srNYrFhMQCJRYVp9y2cu1Igs1qNok2Ws/cCtbYkmZTrzx1+707UArcZJ0tyt3s2n0kaHAdgddz75I4qBGUIpCu91OVkTj8ZgTGGWXn1YBLBgUw2KWJHCSx7HAd3zP90sCIWNBFgZVAShMaKhFRP31X/9tt9XtDw9yq/nW04cDoftoNy3wNHZlEeWOT1tl2/f8brsHUYhiCa8RaU4brvnBp39qR9Y8SECaeUEqZanCN5ybtwKZS73+7TrW9SXJht9bsz1I0xUetzUnnOZRiiCFN48BEtSe+0CTewc7qF5FPg7KXNQEFhdppWRA0/GuIqeQm0kWtSfh+MHuwXYx0To7ske4NNgZNZMogikU0XYaUz5M8VWcoN6eRjgM9IJCfahIYeW7MSg/Yig3C6Nkk+c0z3Ia1QLy7i+fHfSySf7cfH5XXId2sL/b2RrrpEoTLwwZ7OHZR4w9/iL6tixrN/T3jn6aBt7VPM/SJclmu0/PenyVpEmcJxQAaBKsri8QVZFyXEIa1nJJ7hBEgWMiZ2TB2zjlGd+LiTBEeEHNgm3DDwuG8UGR48Tw8Gh947jOpsUye4c7v/t3f+s1fbnV8iP/5fM17YWlljgmmpW1TDG1x1KZfhFlUbBZGd+2uiqJS1FK99sSkpOb12ZdTg2S7rFcidZ4jZVZoGapFYWFsxIUnsLQ9XatNRUqwh0QZ3klMGg2RqeZh3AteyvgoLDwOvJWFcyo9UW42t4v7nujFsOwebbhGw81Ym/3NBVFdjNFNSygm3G88TdlS3wgzgoHz72e1uXwh8hJ/Wjmbo0tdnV1y9Ig4ggCo3fCgdqkl/M1UoNJnLc2X+W5QpUM3+6zDaVMoZnAoqbIVlJb7n22ji4dkkX6JMZ3NFipRVbv7u5SEKsBgqu7XItHDcKlXC/zarGZwFWGMFDkGRdwNMc3EzeJnJqhWS6M9Olyi5VVu3USXKwKQLdOmhVdrBez11/9yjSNXWlIPEPZKY5ZaCoX0/EUlFjsUEaaPHj0MAN5o6lt0zWINjgeihmBdlq4V7S4pgSay5XOilzExsCDRVlEfqLmjcXr28RZt6QmwynS7lnprfWyunxtJSWohsTHnNRoitsYd1aok3vOBu1pbwfpzl14s71dffNyTu93N+4CqREn1VGYywPVfPnaNf1v7ma7glI3mqzMn406v3lxa62mDU1txjDw78uxx9cWPhj2inRtQ7hb1MjWl/Qkc394+fqH19+c7ClorVsrk2FuQ0c/ePTe497Iu5ok0Xx6a7FMpRu4F2aOPSFwzqddrsgwgtjvdb/7q+9iLyUaFBYXbLsRemmuVmreIkchzDKJpaoi2oItZ7NZzkQREkfB0QFMgyRkebyCa2ui23rtVWIBSoC2mzIvcJ7v3K9nRQD41DYkOaRYlmYwd5Pf5ZHE9QiCzp3UNSWlX9nxKvOSNG4XESsygsLP7sz1ep2mKUvRqtpMNqAksM9uvxg9HA0GraJOWixTeBuAIOOC95cXvNTtnJ0AxJdUHqlxcrVuCwrNAIDmeFagZE7g2no+VxS1SKzV3LY8m6fIU3XHjJdhlE6Xy3oA2ED97W8nf/hHD975ybv6P11GJjo4YCZMVEzKJmg+eu8pViK57eMkcXr2oCyK29//7ub+VhLOzh6/4+OzuXHnMgrDUQvdePP6a45paIqiqVo+LLlY7LQ4lBAczK2IwCPOs3HsIxTGKe6r2netZlAt5UnjYIhwjfXdFKXPmJ5ml/RR+zSJIte1amNJ0njFMjiJVZW4x6FbJ+MrjGXkRKqTKK5MJ2rLIMkFmizRGoaJQ2Cnez1YVovo6NdfXx0gxH/+858rHMYQIU4DHankgvZNPcboVqslKqhZpm7qwgRDS7YAY1DB2XRtOpXUaKdy4/L2khMZZW+/YtS6JUmev+BptoI10lBZy3GTqw3Yf0CngOIwyIqERgSE3EXrbQMqwKxLGpEZ/M14SuRFJBowIrqNfHlbfvjBzlgfIwxep90CrMPcT5e3RbXmo4JmGhrPVzGD41CRoziOCLkj8LVlbzSaXFNWsnXuNiFNVWyjckoeEbh5es/UuAgV83zVavZEiRs+Gn4A3717eff1v/v/ZONrDBQiQ0BzPTUnD3/ySfDM15Ldvf4jRStxrEronjv3MVw5Oh2SrBxz/mbu1ZGjtGSI8fYm4WAeNaSKQLJmjKagZNDKCGmKKEpUECnErdbO69JpoBW9zdfs4ADDo3vTdO/NLiscP3h3FxNLLRYecJzIsSRjL51lEMiNbm7SDIWWeZZlsSygtxkMQ0doUSlCd3oNnh1gRrHUfbEMixTGSUwJIoiDpAZUHcQhhqAYLbMRRuHA3KyXPh6Ul4Ai8Va7vbc7LBJ0gkN9rn/0o5M//WePtpZTFrk7M0uvQ2WzSualWIFAqUBhVQWl5d9/95Woww/f72CWk2KauiMbnm5vrgVyEGM1xcG0vAqmHklj0+UMwArr90QUwjAMX7264no50T7kRc533TgD/3jv/RKH+0+OPQLx3dh3wxSoElcnNudxIZFXSZ2GQeamSBOnihAVukdGxLjxhHn4oJHnWebfAn/Y3gsCPCWi1EVbDCkkSkIHQRUlaSoJog9LU58StKJpYpmBCuWrYT3aGWyjlZEa+pVvlfaZzPXVBlFgk3hs265qNmhH+Ga9bbVaqjzEaLyu6xhu3Mz2c1cZyoFXOL6O5YhregUIqpLW1zp7SBcph8UQiecCw8NGv3DHluWfsU/LTVnL7Nn7nzz6Uf1mdb54rf83/8//5uDPz/ZOH1F4Bfb6MHQ+tyaNcM2e8H/9N1/niP5HH/4kKl3Pj6emlcVGO84XusXV+Jdvv31BcJ+6dHPQJg5wgqi++/e/ERj/7PSEJvFawPxZ3PMxpzCX2P30C29xt56uXooqnsd+AsKX1ysaBRKu6UjZaSuL1IoDavjhGeMjIL1M0vh4p8xKsFjodX+jXZaEQFVmTo4GoKygAAu6VSzHLC2s5+NIkNEwyhOCpQmI1Vm0Qssqz6teV5A47Ppcn71+wdJMl+PwJI/q7vHgkCAxvAYFiDP/zrIcgsRJQIkpg9O0tnt0F3s5jpatpswTgW1VcYSxCNodpUFWR57S5LKyFkKxFpJET0mKevPmLUXRBLFb1ndzGGZF9vDktKRrGvZarbyi8QpWKIZSJE5QGKoIAILpF99jPYQmiTiHDE0LjQzmWZbWKIp2B1qnSquJl6U1oNjdRluWhGKT3cfjHYa2atg26Z2fdZ9/N//yy1prno329p4v3ghbNQ7LFsvSBDWfJyoPd6RdTMAwWiqrWmlsWUlEsOGW0pduudffGez2N6uX69ULgW+DsjTNNU42lbZ6b0/9KKynY8dxWQwt225T0zAW2y7uszQa9liAgEwPQ0urSBRtaLcXY6QAWKXcETO0qDC8slyvsjNMrvPmsMqmizsS45kKI8PKL+KyLCHNszRNESiOYDDCcu1IqL14CfK26f388R/84vEvxRMZhaRlIStY92kMbrCCVUPZSbzIC2d4kcCGIAMNN7AkuHcCm+9KJJZ2W93V7O3u8BRpauvcPkgzatCMdGMyXewoAlkRYRSyPLM2wge/OJLqHFRZUSWRZdChHUazUk+tdCHQUktqKa3WftGr99kSYrwgtOTm5fd/NX1bzIO7PIu7Hfl+mXYHB2UZEgQ2GnVW+hLkoEvlGzukmwAiwDZmeRRB1HdCElK0r29i363VDqvxWRK5TlwgBSaKeV2QNA9r5zf/9NdNlj3c2SFbvV/+l/86ss1kMg7TMC2Ti9BdXV20BLTdxBA883EOtTcirEyyVgckXWRJGHrraxJHUJqIoOf6UwJ3nwyeeARvGHbs+3eLzU5fltRat+5o7sD1LELAe41HMHctJ6hLoet6OYqLLjIcNY+O9r1J+Vz/Lrxz9ntHNrQYlxEbSuy4ruO67oZhZQQDVhKQNcnjHMbRbLuiZqOqBpvtzWZjkwROSk+kPTnSUwaDUUy2BAINyaLALd+yiWS7vcwD78Gexpcq7iN+nFUrY6HHRU0Io/1et6/Pjc6xDdFqOasEERU6VOS21b3RcK+T59nMi1QHW61Wgb+iWK3ZflcQzuNxWNc506YL29NdPaLIgdwooEif5nVVkXPqjWdhn/zRnwwG/SDwSyewznUrdJWm/PXmLUTBrgiYB4Iv+74eotJIaitBxWCJK4ouGmtLr0yTvBQrAsPrmmIF4ELEde90Y/1zoTErApmmuIzEiArihtaW9MLFM3RN3dMUQ4o4A4QLxxOQDtsXeVmEOOq6/vXNbZrFcaFO774cyQ2l4feBpjbEcGnPdRMFqCxKgR9tMKrdOX30eIiQiA8BB9JFYUGuRmtyY8VpyCqKUBCrwHF3hweOhSyD1QhyLyugUUpRxa7pf/ioz5KnLXaHaTH1ArlBJhgocAT9+M/+lfNJMputC3sa5/crWx8G5jQ1Tdt5e/fX/+KP/3dnbXgVolKD+eqz33vjKaXim9fjx31Bbgu7Z3+iXr5ZLs2/ufq3zIo5Ds/+8PEZysUoSDxvtY3DRVyY02mQ3E/ns7UFN0RVAs9NsBYDKRa81zyUuE4WV5a5JWyfPhFAiKbbTU5FeuRjod/vdAxPJbDtDgfCpHBcudMkC2a9XCwKNSv06kAhpxNisVztHZz402WGcxgHEj+uKeywK7lbqQbrYk3gFfVeT327MTmGaGRMTMWdNqdRCoIu4iB0potIVZI4PJaYHaKZZKnuWgwKMODPfniDDgZhQJAkxlaFZ2xDh5d5TY9TyUdjKJV9LYgtRQh+5eunLCxI3La+3yTR/sFx48Ehw1Uo6dVIdfdiNfejltYpq4pdLwAmcmUFlwuLsR8pO3LVTrrX165B2JDE0STNfTelKVOqRBTBVVLKkthJiNd//22UbDuDTvv9D8yvbt+u7kd5//TZcDMb00yyrrFO6/HkxZu7u6nfbH/6yX+eJVmrUblJbuten2FIPMRZtrK98/CKdzSswWonD51w9eVX93WW7x5gho2gPMmWcyMMwkRQO2dlMWYVv3c4EBtA8pmQcH0rte70BXpPMXivOwQJZ6OAtqzWjx4wnvvqu+/nRtam93w29AwUCIgQe7R/190dUixH87HjiRIAJs2QeFWFEbrZBEEAaSLFsSithSImKoTkcjvbrMfW086HQVkEDkQh8BCfF4skXd1+kZAkCLCSRkiBcmocp3qi+zprNeTM9+Llpre/a1xeLqIxzPDCC2fsJF3akeNnaYYnJSiMIpNRPD572LOj/JV5T4OszsLI8TtlyZHkerNicVqiEc8N0mT+1dsL6y3HyeqnP/s5pjVOH9CS1uN85HY8tT0HT8W4lgmakQt6s7p0394jFSL01RY7XNzWBOtJBRXlJaCocOtDOna3JnvA90mx3qxTmcRYDCZokPpMUSPNurNziG6NersO9dXy/I3ruQLNFlVRI1VeZipZF9tw41GSyKKIhfoeSVG+Y3KSDHQkRq0s24667cy1DHfTrSkcRXOtC4rtbPUSwwUCk/dFeqhRGbASBreclzUASAH1dC74QpkiEfnqt1O1jTf29x+ORvzvf/+yLDEIvdIyEGZYQSHh2J320BUhUrpcBVyYxWl0ssNuDNQ0vML1FKZNc/RysxTqnsSVBMkIshiuNlESSRBBOWo6DenYTsKwwipGoDoKGcnNEGQ4IyNRmhGczLa6smQYDsuKJQW++P6rXMrSuoiSaLMmxE7FySrlVGThioLiVRU11FIMf/+DD37y00+Ojg8uz1fOxPQ9RJKV00fyLMSNi/NvwvzJPnPUbqqilOct7qsvsUrVlnm5sXwIi+vF4umgV0XhaF9+v99VH56hMM3AquQjHCsQDItLL+UpLCXiNA+yCBFIipDqEjhJ0oRtZRfpPRpO7IVhOYDASTYRRUGQGZpmZ7MFSaSFgMgojghcGMecQslhXVS+2thdXKym8wXbZz3fAUiF52meR01KI2CcINBc68ks2q42tumM9o85GT9qn7B9VrfiMrWSLHZbch5kMRIlSZEmOYS843gsowG+XgA/glFaxXfXN+btRtg9zErH99beIhKLbO9YscabC2BzPMlL7LR2idrAefEP/vQP54vP1tu40EwqdxWkrESaZZH5xe3+iaKirbSIo8l97PsRVowOUL7N4RYS3L60HLPZVTiOh0h9eNS/MO53A0fHq6toI7lBksQ3YwOj+tNQX2+TLYJr7frxJydMHVUwdkMYROMsSWSN35QyVPAmKpZk3aqBX6232bbCPEkJIULEaSaImDu2n38BkAKgi3HsFzEHZt9PJF7g1QYncEYcsjRe5NmAYxw33lZMR+IsK4ooF3FQbuepvLLyKL0F12Vc02YOGKcozKrM5Ya2d3ZsThc0rFEUT1K/KEpNovrD0dXymljreb8COXKTVbsZjPmILypcayOUxNEM1pCdV+HlZnz64PjDgw++ffErD8LTJ3/03s/fmwcXq/W02aBns+s48J8evWNlNUIwchBwWW7bdhAGuR5vKOd2OafomBhyFE3moAQIAfLST4mlm4osg5IUVpA5i23sjWXVG2tx8KB85+cf+t8SF5ukI0oQdsd3C47nmGGPfe+jw8fvdhiR5RV7G45Tt4VUkMW2/gQSqKGHCNIf9FWswWh0d11nly8XFMkjeLG4WWU1w3IbcHhw8OTsy9++zaTWfu+DzMNVDuoXL642L9WOUHt1V9ml+5iXhCUuUwy/3LhIXSRuhOMo/+g0OPcyFknjmuzSFMFh2+TypTPojU6OBy9efjeevrH5Q44PGuqIFRgC4P3RaJ3HP6GwPI5j5K5Dd3KNmP3+e8/zv/w9Ns8jdihyZYUHwFn4zSYdJnFZt4FAlWiVWRaBY6osoJrgW2aYhGkYsgSmKJLKN7bQMHwz35g5WeAkqXTbbbXreAUrAsOxp77l2lZFkSFIhaoQBN4KHZqp9z59/8X/8HsakxRWsUyj4ETPMZbmOoit5r/+N/bb+Va7ae3ui3y20YuTxwdvl7aUwc8Wn+PbBShLGhBMW+a2CVw5PpJ4mGNVjtRSyhriKJbjZJvakyXpdjwxYKk2G5Zt6oZxONyt/Wz66qt2s10pvq1XeZqhdTWfz7WmRh7j498tkxZa+R6BkXhBZc0OrBGkVfek9rpYBwhsoSON0SPHITOrAUFUBQhZSQG3DTwkr3qdfjulSFWmGet+5mW67dMgy0EWA6YE7lynEBztEAzG8vsDkU9/8/e3WbkiqZ7KdcGeYNIQmn5cokTskixTMoR2cKj/8BlVlYu5GeXk3sl7aVRskxBlWiIWMW5JVCAXIKVb6/G11OhOdZN2Pa3ZQ1Kiq1KEKviljXOqTCaRW2U5ClWew6TUI/xaGR21EIhO6niv20HQOuQl6Mtnj6PFahPMQdGXVUQz187V3U2zT3E8LX/w3jKI1r/77z7//f3BDs5U7MUyLrWmJFAdrRutlgFivHlDoIj/s48/vTq/xuIwOb8/D2Kv3Wr0+0TKBH5lgBju/nhI0slstbZtrOwqte3eWDoIE2ehWzgll/TWTbCQG+7u0iWLpMS9O5Y9fMHHgogwOCyL3EUwmcedcFnjA0mV1uE8qhMvLQZukWVRzOxIiuqViGGY//RX/y+IorviJ6omMyyD8Cxmg6r0X17M6XXcFBXb9ECJtLVOt/1jciSvo/V2smZ4GtRSTBK2M65de/edHSXNLy6vMDQLQz9Lt7WV1Awga1JGBVHID3nMi1fvvfvs+gqmLJCGDX1y9zbwy6rEAaxwKMfY7fRWYJDS35SVN4SsTBJhFjQRQKqyQOBeaQpZjovc7OVF5+jwsCwn/qWAZBdfXP/s/Q8wCvlf/W9/SpKncZoFoQ+xjJXZq+WaK4pt8pLzEISiao6eG0sXK2d1wnUKZH8nK7YL3yT9OomSMi9ACyeXNYUj24mJK3XNaP0K4blOuLy2J8bOSGRr5iqym2129C5jrywCIQOIEjBDojJjCZYXCWwHJzGVJJsYsvAq33EYEqvzcjqbt1Dwzul7y3yx+v75YrliqU4Lb3e7yjhaVkiRkjAKIwkX4vtJCaDSbKUpCfpMsbh9+/bln/2zf/0nT+F0Ni/bounccTmb5VZVkSFW1CSxZegSQTolxknED28cmWm4mC9IraO9odxibm5/63pOgDA8YqkpUnSGSZLWoKq9dDzzURAxw+GDXkcShcn6mqjCsqwUlMtobL3algVoNjtoTaCk7JleXUEMQ9C0VluNvePTx49Yz81neNghW1lQ4GIosEUU4p5pY6IIECi0+5YBa7eECEoCMi1YBNZQzRoqAYrSNAuWqZh+z1nOvvlsjBYpyyqDYevy9T/WAN2Yc//zv/3wD/6oUpHp9TbYlYpVQeDLPZqkecUKszpMEJrhYzG3yrqtTm4Lji3dHADdxnmZzXgd2TR5LIkpPMYBAwHNKLbz4q9fPYdvb6uU6QlDhbw111wWOaUNPchtJ5JMbLG8LDxNUcxsu/mn9Wy2wTEMUluBzpm4jIhkG9AQw8uyucbuaP+2g8pZs1UiDGbUy/ldp9Xw1/dZ6J80923HJmLj6kKPYKV1VI/1KIpHpC6KUYYxt3VzlckCSy5MhwQVz9UAReu8S7pRhYKV7x41Rijy8v522/voGUY0HqOstrd8efkyyRw/3oJhNH55RatoQ1bRgl3ZYRxeBykKiEhpyLHjR24cTx0nc+VWjyWOgzTlOaQWad2bREWk7J5gFHe3mkWiXFVJkiZqQwvMTV7kWp1IDTSK09AxQFDv7ozEgTB9NSNaOCEfosiU2pZhiuDHJMdJPMeGFarRKsd6XHoSqbABCdfx4GqBKVji5GyCNuVuiLg4SxRrOP/qIpK4thfYVbWJ7IwEagEMBuQhYHCKPNCoHGu1+tFG75e1vbYFLM9yaaDSYeVVFQBZjcstkeNEIOUZ9IqwBvm7nxysbpe6np2fL68u/5amHvAsdoju1FW0Ds6vbzaPH6i4pmBV9aDVmi+DmC5TC5F5DDBUlKUVBv04LqKUxOgaQKaQpUEr44oMVl+8uWc65LMPn11OL2FIcxodZGFegsaj082LyWQxi6OsrYw+ff/P2/v028CvnXBTpFvfPhv2GxJVYbRSUvbcHgx2ebn/yrBA2nn32Z4kSI6x9dwYa7RUBAdX52/xMNr50WF0bxBMw419tXV6N38+yWNhJFpvjc1mU2QVR4ut0VAiabrGhiVZyqJuOEUUkkQEyjpJowSNUggSJRK0YZ1lBdwCUoF4TvHNcrIgUTyMY51x536S6KbQ3y0ruM80j4+fMfs9N7GzYDzoHSIgGnT7qjC6uvhVq6xlWuFYGRdwrfuYRLmrzboEriQLASgCypdwwwVT3QAAUw1JREFUPEtrZq/N0508WfM0W1ftOMo4siJKDs8Ciq5lXpIUFEHSpjO0NutWnqcEiaLKAjUpllIbWmhHRZqCumIojaDFsCbtlVWH00FXjZbUq+0blk2KvX1ra0BVDnQvyaHGSzRNARyKLNnrT9/99L3txsoXWLKZbc1tnHgYWQURLBJubLm7MpYxBauRcc24LCHVO3/5Ln1+8WYocefBeYFj2oPePsejhkMg2rhtswmLQKyczOLUvOH5dsQ9PHv4d9980S+qnU93TrnTOCxxjGh4LA5wbLlNwmyBoFqzXUZ8zbh3Ez0kYqfINYoqGaoqqJ0mdvLsWaOhFTHy/J++1reGn+V1MuaZKgpyjs6UJkUiI702vZkrq7KgNveGg29+95bucZ29h7/9/HfdwQ/aqHnuTWCS7u3/FN1sQkegqiyL6rba05MiF1lLkB1P5vkOkGXbcabBPcb0gmWUgQLAUN0haj8rk5TwPEtFeeVQd/SOSlG4fPbOe3sNbbVeUndIUV2bSGEUGQ8BJyugBBhOhk48mc9lToYAcp4HyipO42Oh4HO2oDyQGtqQAyht6gYvchTWCNAJmybP55NBDhGHtwmP0Ihj7mi9/TpJooJhY1zhG42ONtjOzmGa/ebvrzaL9cPTs7Ig9MymHgwZvNZfb7bW8pu722Ca3Jx/3tgOP3z/qRodpmrUPDxq5cq59UZutoLVbFoRWk2jmI5hHYJIBaFKYtfl6iqZhoEiyoRb5VUGYoppvffB8uvnvxpbkgJojqN3ube/myy0fCRhDfL/XxBcNWuSJohhfpOZMz/Gw6ewq7u6GqZ7ZpZjV2Otdr3WhSAcYTmscPjKV/4vvrXDCodRlq3Rzg7tUHNVddHhcz6mZOZMP4+6XPq/+Ie791qAxfBUiMejUehHcQhoBdHYxPUDHwMg25KKDPlklfcaMGrg5LnrHZNNBQtytHK2vkpTPErWWChyReWGHk1222FF4YTU/v16C3sVnPnunTMcjCoYUAjilEZepygnYEwse6LrehBGKwL14uVy2IU+/M//pftuoz19+OZ8khRVxMtZkYZpFOWul+jGVbgW1hJWMwxFSWLN3VMflqDqZYm//sMXDEsiJHbv5HOOKM58U8RUXCifm/Ocwni5qfBUTpBmCuAqpFC3SDHP9aPlFG6pNM1mrgnKDakzFQatZpvFZOV4Lufyx6jwq+tZBcqiAD8asVgKZbFPcgwgVjcXCEtsVUVau3oaB1WASFLtoV6zasqaZL67gx1C6PUlIe2mxSYxmIp4qO599fb2IgE0D6AKgKHUxfhaEJKwIAGxm6d0lcdxxGpMU1bDjVUVReA7MA4nQEfGRyQvSkaAYjBacoiIz28hkqJXi5Ai3iKqGnmoCikHwtH+oURoOC0TlhnajokItIyLJEHAJQrqKklSAAGKpquKBHXdaHcIuKfbSVMiKZw4OKavzMt67VAckZURHm2CkjDz/HBbDgYnMuHq394lkdEYH+Z17b2aVWNuvvqS8Rh4v1HYXrw0RfnUc9eplHIkvb6YdcciLzy9PL+4+vu/d5MQPeVoiGM+bDf+72//D2tXCjRLAFQQBYTw3tp2EseRWeQFJKkdiBkiIjyGCVqQUzbhAwEn6FCWSggqUiwtg4ADO2PmRZvQ0C3YbtawEVNdjc2yHNQBxovLNFEZhpXGAyoJ6tqPk10YfKQo4sEgR/O22LbPtpa+ZUUOQRDPDRqs0qgkTNwnEQiBksQn1rAJpIjAGZzEKgKWvcAOlhiAGz0tC/xgOk+D5KNxI8OJMK/SKMNQ2LXu3nx/9/iRjMJohgX22wVVZqGPAAQxDF+UNVAYfmCTOYnKsGxD1u16A2GQYWjwLna0MIjbWuv49LR3MlhFsB/GAVP7cRbWKF7BJtDbvARV3Nn51fcvXvl+7AYUqIuiwCkKydN5GKgoTGAMq/VPYQKrSkAzmL/Z3iSFydFe6CHEsIqNOM0NKL1bTDnacG2v3iHseF/AcNBsUpMZuyf0m4/Qly9urDL/f75vHjRxlWAoFipr521Q0xWvtRoFjBL4zfxttK1xEmFDvmhBO8eQAT4aKzzLcGMZTWlMEn/y3/53gROuvt1kWeo4c6cwCA63cRxazgMqQPYQUNQHbcUJbErkRUXrK/35YvnuzeUJPDgaf3Tz5jvL0tMiUwaUaJJBnb3SdQgn2pgMrWohZQZ7p+urq7QrmAA9gmFNUVGG8N1dMg99NNcrnRAoCcNQPEmyHKfHAttINuTXlwsMxL1OLy/DFpzbIDfSQpO0MIACR+dYqaFsAufKe+l5GLY/3n/v4XunvT39cuWzDsMySAHCJOyynZW9GDRIgdFgxPzgvmabycGgkZbltMhWm69rlMH3OlfXr5eJSzn6QNhs3gUdAm0wwfjJB41mM0mLJENhMadDID4SVvMVRFQRFXAM6VqLt2/qZ9178TYvq9KCzM1kBuV4VlEVNsw1Bc7S0t+GvlWxaokVKlFVjwf1whFkNUtjAsWqu6L0KlDCoAIIjL7f+khrNcfHT/A4OmqNMIj57P7nTx/dhr5BYLtqXmZCcbB3SOKLLM/enV+9MMDTzlp+9JDzCZhBEj6iqkZjYTk2BrNVjIcdQSqYFIMjAwoxUMxWizwJshZRh/kWzqzZH3QrRGny8eO/9uDzBYB5QWHLNLMihsm6vRO2CnMr5kiuEjTfDx/f+7DCW1GNbOB353//TcKHIicKgZBXdVpkMIoKktwf+nTNFFXlu5sKwSuEuluGLGQfHWLyX903X9hJmG+330bYMCHDO7eEs1zAsxIlGBSOqmUVSLRvhrI05KRUAlhdcMPDMd1GkrT2GaZAB48GsRddX10jCJI2OkfKe9utff/ekz35ob+dN3uNye1NnmZcgBYFK3Ci2FYkQpxDlcznmCgXUUxZFHyPXdoIa/liew8vuaCI3fnZ8cMP6Di/eHsZoIBCQK952Ogd4WyMBRHIi4qmO6rG1EwY+nKSWW+ypfE1DFeHB3u1JE6mE16g+SAiajSskW0FCagAFXtxvDg+/qvxEHp4TyNh/Lvv/1ACoLKsM6D8KvHsVVrFTujCkosgUlLlPEUgMNJSGBiHyxyQLFMTAuQFaVbGQmqX0EvDfHh4JIHGLljXUNHq0+s05IRI1Mn15i1D9lr3D6SjODCtXz//+bjTe/Lp8cqxJhaFkrjvhbATCFyPQf2T/UFZ83Sb/q/+5m/U4+7N7buf/+zvDWMHygJ9577qQr3x3miMj6CyVnsETTN3y8m7s3W9Xg/UR9NyFzUqPMS0XgtBwcR2a33DrVOn2jVViucGOEnZlgWqomCoNikzkRVlLFRXBagVGiS1Q1Y4Azhx0Cmma88214aJ0aVpp+2yDpLItM0mkiZREtK6cMRnaQznFcvwC33OyhogONfHSZiS+aHAy1CxKxTTrrIb4yJBY4kTnaWuqFoTIyfnG6SCT9A2XNQmyFCU9GiHjkB//7FUTSBQCBCLOWnE4m5U7Pf24qRoq0Wa0Z7rd7X3GmppQ2WUhGSJHPGD1qGXm6+wFLUZ5LTVxjXp3Sb0ZotcUWNOAiDn0iJlORjZv/DWeRpE324ct+r3OJWq4hDz8KQMMaHBASTfGx8vrSjD7dxCdlmNBDSvNKrcVzvty9u3LBMyCBeYfgAz+spBMTwjiwBjYsOaFzkI/NVqff7qu83hM6iC6yT/wg7gtUOiiCppP/74c4THjcgMb1OyRXmLmec7qapZqxJBMyqh7vU6EEOXceivzr7fzN9ebVVxT0ZIGZMZVeW1nPYZeXEDyhLBQDZu5IQUoKEdme8c4+Lb+V5rzNiEzlhd7Tipmav51X2h07t3YrhnNUBrL7MJ6uRwxKJ85SKIRtZwoTGdZqWqe0qjBtt0gFPFLsg3i7vxPgGrsJzCPWx0uDd+Pt14rhcttncwKf2xUqQpjiKFkWeow8mcnmwtEGMMnwZ5FfOxG++mUXxmdhrd4cl7qlDImIwAuloHsW/f64g4TdRRgJYg8S+7DGuFNomnrAjBhskA0nLdgqZEWUgrHCYRUWNGYDybTt3AmYIa7SFcmtuBHKmhW+0cGEPpgonJHIp80YZ1sI+jnZ7cfPTXlmH5XsCwfF9RovUEpdklTkIhg4hxHpuUhZZQXVS1Ioh7XdaLfDeIcnsawgEfRZxTU6q8a+TQdjsYPfsT1fGS+srb7d7Sp+1Ts7AWLlQ5l9vtbMDibB6LaosdMyIhZK10u9aLoixTQJOgO26hvBPaMo4SdV3AERnFIE/jInEZEgtLF4MzCKyJLChxFILrvMrCNE0STy8RwJA1WWRlsdwsUAITWAlUdUniktbEEawoquV6hVgYd3jAplC8LQlFpVgcR4rjpx9PzAVA1LhMyCbzoP4sq6IKYFEG1HZPtxwahzZVoVJOv6NiFJYWYLtNGo2aecQKa1qfr3bRVFSUph9nCpfKbBqFOA1XWEnCetCPRTgnpDFRAjJhmWFnLKrR3ND+9CcsxVdWHeTB08FTvMIrqoAxZPpm9pcf/F3dSO7uCo4VPv5o5KeWNV9JTQQCzHKjn7t3OQTBEEjjoAmX7c4ItqHYqFX58eSuHBzU/WZ/fn47/9W7JI0mKFA4dXDQGp7sBzaZuDaKyIgKICd0HccubWi7ISroeNRgWXyznk8ubw4P9lRBslw3iQnqXpstQIdt3iRL0zaAOvr4k/EmZhoMSUJY/Y6Ale46vtAnS6YFQSAUNAmncjtHMKIiamy+naIILEoKK3ajcFcgHA+wusRgiIoKRxI5jWMcwy1D+FvzdxUgiqM9GkvKuGWzaAPjsjT2N2uKFw7Z5levvri9PiM1YdjqhPsHv//t75EuftB7VoPKQ3PW0GNs56SOpDUmd9MXL3YYBe+NO1qKo8GVn96LHdKRZDGNYyOOG0A0Sef8/MvvdFNOv/YzIamz4+Mfwggdb01W5AUoZ/LEdTbr9dU3X36nNjVeazbkVp37qJSTnGgaeVZEcZbDstQFGJTzoRdEJFZKCuI4fQwT91sbK7m6WfUHffHBKU5g787eNNXR7Pp6Z25wFBv3HyxuJgAoonCCCgWNgzSrFv5yYThMsbHrjJaRPK3SEtp7cn+fbEBZArKqLxy0RmDn18mlJStoH5fwOm7KdTpo+e7im5c/k2SthuCTk/uZGyAI32kf2zjdsq1Bv+n7dq5vS1IqCiKZe5fRuZTbMYW1WA7aUz0A/KDwODHnCdJ0TTINcYXNalVW9XXchBl6tNhHO7ZV1AiPY5lQQwjvYDhdA04SRza+CcMMrcTddv756f1L19tur2O4onkEp2gCG6dx/b6kqp/+NW1bb3UHiqzF82+Z1kltW1i0cXxmubr94P5HiIRK0wmCYg218fLrF1eG08aZdvdhgmXLcFPFWX9vbGG4SwZPWloQBWEYMHCFrFcbGO53taW/xrHNtgSZO0HRhCAoDR/wAxaGAEGhNK3IYtjU62v4xnHD/mmfrOldFTN2k+NlJDdcT9/aLN6itOYQTaIMTSiTmEyvbFrT8tpBpbPLd2qL5zwknNvY3lBk4NBJIaE+Vo/S4haPY5nEEY4NMLTBNRVMoz8/TiaxBuDl+Z3caoo9gPj5+dm7dbw2WYzxxDSumlpbQyT97Vlg+jWrjjUZoF7gWfF2vc7uWJVeOxSYTURBFkUZU9t2EVAEiJZr2pGDlV82A6rZWM2d8tYgD2h35/ZgGyMEnCYwGC3v7ji55VaYs/42gxpMYxCROwSLOC5Zzqcoy5E0/lp/Ced4R6ZppPpkr0uf3Lv4+a+uz8573XEjrV3WoStOIykYxSfXF2pL407aEF7fOjc7a1Mv5yRNwwyDDIdZVotBWigDje0RfbVEgJfxMJ7AqHjy9H5mpNH6IYHFhT/HIsOcmd/dfsfSzMM/f5Ql6XxuGg4YSQA0WcNMmmqSZSrnBQKPIyKNQ4YsgbKOeSKs1j5KaohKbM+WCAARGRcxWJFJDdPdXoOMa9+HdGvZ4CQ8x2EWKYqsqmvHuYxDnQFNWlRjN7LcqCQyyIa38Yteh+lozSACd69mQYaZW4uFADXor8PK8cRWPwsrGMJYOoV2qw0B4SQDRcDudzs5nVEYJH3aK38dUpYNCAlhyLKOk7WOYPAqMnAKaC0pnTrUodhEomoO324dGQ+22wRHusWirjQ2tMyygFQ02/pG7UhTkJaCbIQptHWv9bzhew8eNDS6ySFkXmduFKEkQdeMLKMUAnu6RYXJtKqPqHa7QxclWibvxnADsrwv7uZNFDlpaa2m1hr14KO9uaGHrtPttsPAJ7Cuk14GjiMHSZmnJcnSDEVhdObHa2O3mc0Zjr1bb9fJ2ScB0f5o6OfGerPBCUxpqmUBzEs3JqDHfToqO36xxjWe5WqM8BmBgFAIxkjb8wsACi799P1Pd4GnFXziFWYKDRDWdFNnvujtw30Sw5AqNIJ1ETeknIrUJA++eHE+ahIcL9G+D3EKjmJWXbeyumKzvf2B63lfff31s2cfPbj3IPC4Il1+s/qG2OllwYmMg2IAP6I3xvJyPvF8ThRFjsJpjkXvdx9e3lwqYrPx+Nnbb/6BKBWa5FSx8WTYP43Db9ZLNCDMAKchCoURjGdohioyKwqsTz9+JIofTb67+/Wrs27D2SyytIjjdkhR6BCp3QrSC0QNs63pKQIu7b0PbbJYX0EYkudxnDuCCH38rBOdx/qVHSKEk8LrVy/jKvYv0qwJ4phb3SzTVDrgDIVv05oo450o3Mk9YIexF09HhwemVW7XG92NTee6ykPEz/ePUNf0WTb7eLwXkRlFMXDAvj1/wyHkYND6V6f/ZUbUqZ4zwHMExa0wN7Ita6dQxM1yvtlu68jHCIIn5ToNvLJMaxgbjlDRD2NgRRYGuAoBXA6K4SBfLuDa8etSkJm4muao0sJbi2Dz/dc3ckeQD4UejtzOVhYEowwRmuvCDGsyePzggUsG7wIzL1O5283KotVpFFVRxHGabV44BakbBck2ex212agxtMzLq9d3qsr883/2sadvUH2hIk+/WVw9eTo8PTlJMxUqYiukl/rrJKqrHi40VJ9mg6nDkPB6slq9vrLDzcdPDmsZnr1aD9pDpYMi41NnPodShCPa1+VssfqDFHFvX13sdaGTBwOZVNoP+02/r0dGBUsk0l7tDDd4/fjTPzN3gRtibFFmO8cst3iCIhCoGAQjeQxHPZqCENBuNdttNaG3VMFLkoDhOVaSVm5vIztyTI4lrSTtkbLz/S4xqkJkBy77LvCctxsmqVp1jruUjaRRlTGkKpPtsKEVtyWLyuOH6qOD++k8xWAyy/yL6boGtNjE9pyHNrTJs3iv34eqmkBAlkdIkhyq3Y2cpVkmDPbfLdbjB+9jSs9amJRGYT7GkIrSkFVNzfPcISNCG5uTbaPX/vEf/Umj29Hd5z///WuHATILG26QEXVP2HMi+/L8LUcxE2drffP1YrZgabbOfHG8N10uE26PLZGr81uK5R7c/6AScDzRNUkW68pJ4te6tbreQNX2sNPvHh2xOfVqsdjMd42eAlhYgiOMadiet9vucIsEUhWtN2mSsBaVd8bG5CYqNZQirRwoTdD/VAQIkyqhW8oeBktFO6z8ONc5VggNFwWIgNQ4RmdRRSEwRdNB5OUoVCpNmgi9MNZnOi/KBCUzDURN1NVkQUb9ooH6vp2lpiLfOyClLIfzAi3TEq8jDDataHk7ESx/dzg4dd8fYdNUx6wAg0kCzbKyZgWLzhNgEihO4EwU1oAonRq4vpkiq0bKMhyN+UPQTohKRAIIobAUphSxaj39MEzfLW/v0jjFG1QT1FCZ4zhL1DmKIkiNx3F4vtigntUrhCIvdTmpcExf3kUYHBUEhCxYXGqNWhhRv7i97nW6JKlUUSSrWgH5RZ5lfuBu14jjbiE4THIzCnnxHkVZP3nyQH362HXtn6zuS4JMHhwURUVIwq++uaHIfLinmE6FgJyJXbaWhh88YhuqoxuW7XjLJKEwefys3dtenL8z3KDmWKdMF2jI4si7q50T+aPe+60+5wVpptUDQvVQpK2olLfmyBqK0svJVnjIZqC6L+AfjT6eGbsqKl4sn/urYF0pNMpVFK825Hyzo47aHEvo9k6HkJKlRgnpOnGtO/IpiV1H1JCEkVhqQCvIxTIatTK/wi6QToLwtEizSfW1ad0TG/fH42zN7sJt/biJwEAKxSSPo41/V29gumSGeFJm8W0QQzA6ONiLoGLydqUoUYvoljC4jBJSp1sfPIHqxfffmIQXaiCScFxlObcsW3GOQvy7fJpGsW3mFMM0AcgzReQwgoImblz6adGhiLSmYbqWOw1aQkuGZ+SyjdckmmWrqHIgKF3NfZk7wJHKLwIVp63Lm+vpdW9/fzA6kODHHCk8Gv/guN8kBAxJUByQeeFUaTDg1CMRRm3MMF0/kVBBRWnR1q9pokD3wdZc1kVJJzAg0yqO3SC8vb2hKa5EitWdvnBfjh8/zb3tjKBAHqWK1u127m7vlvPpT57+YLW6UzXJNRyEDjAkPTk5qfg6CE3dNkUeU9RhUeNoCicUU+obPHZwlUGpeY084ACVxEENk3gmDrXu4f1hliUm466wXITHPbJRVoU7UsAKBHCZzzb0HsWIJzvrtqzR2WICwVPPNt1bT9l/3O3uB5sdijRwinr60bP1YpWOZ48ePSWRUozTFSsyDIZD9XyLoXs5tkfNfv8NAvdaHYFvVAUrwgEW7zI8oYcJdHd5hYUmn+S781XjqSrazvXuBdQYZxgpDQZlJy+LVIlagU8hKn78Cbp58/bVK5OX68ckqrTbWndveo5Z7PqLL38niMhnP/7hlfnWLLY80oyknN4qJIHRNB8Q6bAvneDifLPWYYijmSbFPL73sNgRvuevduVgb28f+C5FGuZ77uRdYLjsSKYJcp6t8UtdaEsfKep2tTKMbzOPojiqgCsBpdE99tXduyq830aryNtcfDH48Q+O0XuI/fIs8AJZU9Ki0p0aydLcTUBpe3itr5eyLPUGPdMOxCbvJSEkcrBVu1d3f/jd9eHR4cHePlzCSLA2/ev5nAxjbzDqhcBFoKQFpxf2Zv7iD8PDv33c+3wxfx3EVck1ijx0qnS3O8cLDlC2jLZ366SMagpFqzQ2Fnc9BJJFhSewtZNAKP4nf/RPMAx6bb2kffTj496q0gpm7xlZxQn+i5/9LgBQo9E/m15WHa5z2migtes6liR/cqJ+++7q4uuLxCwC26c5bP8QmqLGoTr62cuzzjdfvf+3f42L/4BRqIGvrXyiNo8CouSMPG2XbIyAgxHlAKiw/MVrHnvoJtZmMut/fJJN1zdVHaWFTKKKfFKWZhVmUezAeByYFY5CBAYxNBLXsR+9YxmayoJZhvur9LRB8giWCZQoww2SmV3vrqdr144URUU4BUzr5jE9JMj1esnSgodALHNtUM5YoHiIydBMbjZ9LANZtIv0aOm99wBXWDEpiyzCAznmMNylO45lJUDJqwmNUwLiRH60Tpda0Sh9N0NxfNBPbnKoxdQI7rRqLE/kkEeAOXjwhFTFBaynLnuReZS/W+d5hAb41uBowcnKHsSjGJzasRUscAThNFZ0PeC6UZ0K4K7ZYK5DPfjiZ0lX7J8OUITytisvIl3dlqEcq7GiRNE4GSgyBsDOeBlhpZAmXJYHJsryDYIiYyHmtYNHPWV2PhEBkTQtQtU8CsGZQmuoyoCXC9xkmG7BkCjuGtbnnd4NY8fuZkiJmAbdnu9sx/y9aP7Lf/pJu+XZu8iL0JKDE1Qyo5wml7O45QSbpqSayUWMAsrCAnf72/N14FsXN7M/Nxu3IOP+kD77FAmJYe7GNYEJrWG1LpE5jwE2R7KQK2Csuvnm+w3NBt5ks55xb20Ignm57bKEGe+gZnLcUVOB85yUb/T3hweo5wW9TvcmT6E8M0xdUdXU1OsKoCgB1/vPuvqyDQYP7qmcOKtL3E0CFuXr6p7Sqeoq1GN8iGoPnk4n1wxLbBf2Hk+iTerupVHTwWYZ1IvbZ+/9QOJFPiYFEv+/twtV5vAiJA34Hq94jnt+7uw/ao8anMTf/8l//adjgeN50cVwwiOZHtVUGhSCZ6Vum5ebbUTQhB1s5Y6ch77uG0qzA1GSH3g4KXzw5MS3jXpyXYH6bLp4/1SsYXi9K6hKcMz4yaPDSq/+YL4bpbSbQ1ldeqFrJ7GXhc7F1cP7j0mFO2QIvMhah4e3d7f+cr2NKrGt3E7Oo9z55P09pTe4vPx9vKHiPMc1RWmpZngX+d5+HWJET18bBMI/6O7N6+uDXvvr719WHtQu+2W7hyNklNYczWV4aBg60qdgpCcJ/Op2G8EBQABJYizDpc08Du/ioB1zXLHTQQUaarNOo+GDQ5ENnMmtkMMKwPQ6ODo5uf8nn50vb/KqqGFANJ2IwgFGxeGWyUqSwBlGxF18b4QR/c+i65XnW1VZYBznumD04WlQ52nqRjFjWY7KCvi+n0MYwqHCewo9LavA//XXviaYGsmxfPd2ajZajX/7b/+u0++4ob7JTGyvNRYlUmI4EkNTkKYpAqP/6e0blCUN6/v9/tj1rZ/+u6DKSghC+vtP11trsZ6Njw55QSx7x2FxzkdkFKdwWUlAjIwlTUgiQ6+mIYEAQGD/cDY5OOl8pmqrqR4EOEQgoQQL7vr6Chr2Rww70qlNoO84VuYAlmZmWaAhw622RuKF6/VGkiRFlm/f3Lx48/bkk4+l9p6qPjId/fzq3a0x+aNPH5wefX4d3VpJKEuiBsHbNHwPRiqtdQZdnJmG+ptf/Mm/+tcUyfqhb5p8Ek3po1GE01mR8YXk7EIG50icxiAiSeL9vXGJoH7kuUmB4OJ/9i/+kgPkxeXZnXVzoo1heUAj5swIkjRpQkyz0yRw2o9Tx7WKhoY4qyWZFRzflIQgS6HVovOgfUKLFEHxXWUZBvq7zfE+/fsvf3MWBP+02zurwZnrc0yTaxCFW+1Kh88gFaVnZWg/f14IyonSGI8esaPm5W8v0iAiqEHBXBQxVMeY/Xqn/XETjYgjoanjCeP7NEso7dFkuqTodFulYYRBsEQlkchwZYEgKN4VW3b9SlF5XGBqCIgH0KvfT5T7e3uSdvbiZ5z9kSsVZauVpNsKYapeN91cOT5GYtXOtDyCgmIfEfEAZrI8QX2c7eyR5Hqe+AQuwgx+588uNmc4iRdhJih8l2CJqsQQoOcbWzezuKDNeI3SD+s2M2SvbrcyGmdMGhk2ClWyyy6tTYVmLJtUao75fgOio0IPjCBmRf98TpA4AAUCVTAbREkNC/lqOhdCgSLwYX/UzRE7iqCCiGEmd8AKilAerBYO0tGaDU1NSJe6qSsoCdwoCAAMUTSd14FVzR51fzxP4mJt5VhFivLDzwZIS0KndxPPqV0zb3CNRr9A/OuLHU/0hGajSKjtYqEoHB8XgemGx3s08BUoc10UjTDTfudynDju8NXhYrtVMDUo4DjMlvFNFO9onIpjByVrAFpRENw/PJSj4O8+fLLR5999/3yAoiLBkVhzB3koLFWuP41iqqiUA4UNWNELAjZ1PN33FxhCSZwABVFZ1zscDWMf4JA67jmZBBfBYHi0qOhJxaALoxClIgaVkSV0t2PHcZMgIgJLI1uR26f3Dz45fpyU1WK51V9ONA1VZa3MVjTV9u2Zu3a05kmraU9+M2PbapNlTdwO3/lv1+EPjk4LpajcZDqdVt1aUlQUAYqS89A2dCcFhsErHvWzp+qDf/Lkxz/92f87aIoKaKSg3Lh+RpCCC5V30dX8LIjnRer2uh2tSVlRfP32rWqL+x82by/C6fx3w/HnKHBwMrq+usnioJEUGIw0eC5Nkzs95RGJ6pEq6OCxkNf+e62PfShW0H35vjSZXlf6RrfPB53O8XgcmdvKCfwoNd6cv1svQpo4HBxzJN1oNOGO3Ov0rxYTeIdyw16rrFIKLtGSx5TgbXAB3TwU3iNlLgXQ7dVsMp9k9Z2+jXO0yqBKZUIjzRC9ZGUlbcqs7xdUF67r61evPMNFFSRMQgLl87SkKCaPyjBKIz+kCJZh2CQKp+d3UlN1Fj4NccRhL9RdyLXzGrInie9vuaa6pXDcTO8RtY1AaVlyclsoYpDnpnnRkIV67aEYWlRlCWpJFPygQovEjy6juOYEkZM4Cq/9ko+i0NULAhX2HyEoUjwq5E0OMT6UJMGo1fzs80aS+//wj2+wfUo7GVdQjhZi7lueB6YX1wSCbDwrD/2CxK6/v+P+HO4XlSwiRIi/uLXuPbh3V8TL86uKqcGq+mzQFwSNRmh/NWsUKc9wRTnvNI6ZY4ohNgDUT/7ZPx39/U+/PD+/fDE9GjxY7Exa4KWigFkWgaq6TiHCUeK8YLRt7CEYEgRZCldcpmZJDGCvquBvvvn2/b98akSWeMgt3r5BM/bk5Oi7t25PBIvt5Le/MO4dfgwx5KEAUKTCCGQJ8Zt63YQ7n4xGdVnM7uZ306kkqxUMYJyarKYHvHaESiDFcj84v36nRFnG0GIJq43Oja5nojAgyGLQb/dHQ633n777d6vIOG5IBRk///01iqCMLHhJ9J2+g/JYVsQaCUgSaYnstMRpPAJIzNNlBlxB5LmWUKLNFMMXiYuV8LA3EKTsvUd/XpZzz3Xbjdabq/MTid8kdSEU+0i3LKEYjrCN/eiffPpnx0+RGliTVzeza0XUqHbf9jyh+95n95mLm5uNvll9NUkEMS/rU7JTUxFMRmmSRlG4jSPduSZldIRAoNnCU/G4DTMQlHpzJCpcfQfTTFqHNZFWSjT99lvwdP/kcJ+msKDMiYLM46qgpjSLoiS9CHVQcCQTJ37Y6x5zwMfo4/0Ohq5DX88hrV1vX4kBgrRGiDVHkTYGU276qtKbUgfi3CoBVh5k7XabENjZzL7ZLRwfCLPxbPa2++TeM5m5Nu0egXbGh0i0clwTaUKiNAjreVWQha/hSnlK8bss8TBA0cAKrNvFTGVxrcLkIyW5SKereZylpVBj7TFSodvMhTk0h0RnuRFlEkfx0gRkm0b4D65u33DLBSUw4n0R7TeZpHFY83mVMgx2G9gH8hEGp3ZamalNtJpHPTorsuuZObl7C0GY1m6iHgzVEe7ZvL/1RQhRWorA4jScu9bp8enjx3tuuVi6WXBuRlIlPFTLgjWqLA2ruoIYam9vMMSLMk6pMkvKvARR+dnxZ9QRVcO5Yq1Gwy46RP0wWF+d2TGGqjGJtfI0dZJFtU1DlY/htEhS/mgfgSCwmUGUrBzsTebfczRJkGTOx7KsrVZAxu/NZpXnxBhcopvdm/k2e++v/no6v3Msm0rSHMAETH7/7fln+xlU1fMvv1oU5WS7ZthTjisj3+r2MJLo3tx8QbQ7GOHiW+zp0yckia1W2+A8OI/S7dq5liONGpnATCYZhq0//cEneyf3eAl//t3/6WeFgjWa7Tbo4FLjkW5sUBL64fBkVWHT3KYCJ7CsnZ+QdWNfLVsnR5TnJL53OZ9v1pM2z5EwjIP64877L7wVVpV1QVQYYlvT9yVpttZJjGBYznnrwhTdOxxMJpPA36HdkTvzeY6f/mEdifF+9IAd02/OdrW+/aP/4kd45cTn0+1kRhFkXWZ9RSxp+XhwaqYxRTmEAGM8a73+bq99nEGc5e8oL0QJqEYrgeKTWQydIHiJZSArdHe71g9PP+wfLPKayVoQQTClWwEFJWIQmCUsqCfD5stXP1ULuTu8tyP0IKHgEKrRGkaJvA6zOAdFyclMlmYYxaIgFbAKJIVTl9JiFnCqrLaPpaEN0tiLKCUlKQhFUAMCUJ61m61ksdxGwqBLYqoMQ7BhGK5rmbZJxzTNUOoIQWM3z0OaoXV9Y5omf3jkJFYRBiiMWG7xMneUlIX2CFmg++OeZVaet7N965u35ziaU+zRbHmd3c1hmDB3lsywLE62FBmKAxSO7p3et+E57LsJqEf7x2mY5V+e3VxdHBzfA5+cpnl6d3kpomg63/3o+H0DOaNgEi6qui7q6q6OpW6nC0Fg+/Lr28ub660x9kNSHvE0E3geDKBeR3CdwDCMgSiShYlhdUeR1qaZIZCw19tOZk2C4Ab3mPUEw+D58+Vmt2l2Wj97N70Pk5/++aE4k+SuDAvq7Hyy3ZxRFLmMWd3Y/PhPf/hs9GyzWxrwDGNQDaG2KRon17zEJ0Wawdmhd28sjXwovL2N8Awa3f/YX/lQ7JmAblLDjrz1oQJnaZi1IJJ+FT9PnITmYGMzhfJe2YwJQoBC6c1btyjinlgjrEcyDYwl7SQsajerk5bUGlFyVSbFfQ5UEuTQZVileF56ZRICUBM/ev/9yTR//v/8lH1wTN49T1O5dnyHcZCGjJAqnBZYo9mpKX23VdQYZbDGXjtuw9ZcP7+8IsnOJx89aH302Xy6fP79t4289L3g/MULFKWUtnZ0v9zl65od5ljWZ1tCH2MdtKyS2E3xIAmh3DZ3gkw3ZLUSKsOM2y0tUsp7wjH2Y3pjOy9ns8FgxCIcybUtfy7I77FUCsqgUStuXDQoKi2wWjdvEuxu8uYRyo7e/5hAsSIvaisMakehlSZJlfl9vEQwC9nZtqAw0dqpAMwMlVE69gkaxlqQnBMpT0uETtRxttmxCmLMHBE3K9S7CzhjRlMJXGVJWtYQ7tMc0ifrwEoglicosd+y8rt330/un3ZQgszzwuIriuyRqGgbwLZ1i2KENEmzHJZFcdjw9WAGm7zEHSPSWaXevT1/8x++eE+74jqdyXrz9Omnrb6GU73moCeJ4nQ9B6kXwHRYL+rIQ+sZKXAS38ChcBWG/HLYltCZDA4k2Umd3as37LDFdUfLyZLXFhhJIDlFdQYQViy+fZWSFMYLWZxhERQvtjZNOZabisUBusdLwmH74bC3HzvWL//xf9fNJScQ2W0SyYkgqiwxcGah7r8T7z1sIPh6t5oZqyh2SYbsKRQKByZsmpuN9zuPIsmTv3y/KO0wdCAYIykXhkxRPJAY/F5zgHIi8/bthPjmi86908V6Baclg2GirDmbZZjgzs4PkwqmKZ7ncTz2vBinhe2ufvSYZ8UOhoY41p3aryorpkk8DIMoTGWUGjz50+PWIOFx1q+TiEfRZhh5v/zF/3l983VqeU+fPVVUuZv3qhy5u7lbbXcCYAxrsgqslUl12gACUOtkf4BzooCk7jT0I4rEZQ52VqVpbC5Db+S2825NxaoqFD4vwZYhoj0j9XS9+PD98buvljeGGUWJRKsI0j9t1DhHEQoTAfRw2EQQNKzd1Wqttlp7e3uDTmd7dXZ3eUFjqCoJh419JwtTpelWoRPuPNtRJOElmCGaMssL3HM1AYlz3PAsmqgwCE6ygtwFUZY0KJqXtF3WoiiKJLAMRgIWdx2X4GXCKVEcqd15t3tE2Ho9L2vVKysyvcvIIRHpEcqgSA0zGMS6MEtqR8Kj2/VlB2PywfBAESMn4gShoBQkSAPDX+++DRXh6Om9s4t3aTrvYAdz6/X7j5+wtX1bZ494GIQZHOFBtZNksQIFzTEAZio4Rhjyyp5FlUsWWp54zWauNZQs2ZTKKIvTaquDnLAsLydvH/Yf/ftfv8rC7ODoSKPoDC32+73cD9Zri5Bx2ig8x3raFTsNJdWjoHY+/OhIVJVHXmP11uIlPvMjHFD7vfblxbkXZuP9j6G2UESJd3VH4Yip6ywkFAlMDgiwBJPlxDZfSJJs6LvlchU3WgzPmwh65wUnB621g2Zp6HlFUeaouXMxwB6OqjevK0zkZG5nTbeXMCjVjPelshWgGwjU/+HVLYyCPSE6vdfvdORMv6ziyprUSY6KDB36mSCrRwQtqy3n8qY7AN176uW59e78jNrfSxA8vPXxEZpk0Xx2KyhCBVfFNK8rJIsqnyCTzKPojhfG71Z3woikGLLW+Bpx/ShzZpcus3FMx3PRUwEl+dMoKkxz12x3bXe7jOda2aRYHy1LJIn2eUl5ohF5gWSbsqDalXSZ1HgwgZsdDrDtPcm9cmY7b3r96j15/ya6IouqNdgzLEPTRnJv33Qyy96hyKYqqKUnbMPz47LlBR7Nd4oq/3axsA1PYND/9X/8OclQ0mNJQnC5qjDKP+of3m+p7WfduGheGn8v8XxUnw6lTrhw7M2EU/rpzCv5DOGIxA9IFHIEO8hjhqLGNWAaPI0jv/yH/2TQdbjwaJKoKjf2HFKkQV2SJMoyErrLcR4LA3Pr2nCYgKw2dd2gipOUxpvNja/D/pqOkoxarIsmhqB5ZEa5CCoSWiYgBBAFuJjKaSha4XltaQPpo7/61Av82/UUjKUwTs7vbiEU5HkdhSkMo7I0IlC0zB3glsXUJ6kMWChQoV2a8ASJYn0v2t7exUOFS0KWRJtlCb2+mdRuqjY0jRZFBRF5tijLLHa1JhWiyM4y2t32E4ZRKPL87m4GV8cB2EaO7UNYZJ+tZxlHmrbhI0ZMYrOJkdS2IJX9UZNA0MAzIETZp9gS3b29vNo4Zh/0mwcN6b3HBgZ8z9M6fJKnPgQhqtYHIErqqtmpa6iqIIYmRUb0Y5ggUUmiRns9qKrgGhboxt//6h9ZnqFlkYPSq6s3C6h6XCqZWGR5MRgPRWEvIJDz1TysqkGnRcFaXiYRXLl+QpDoHnsw1+b8vX2OEXNA+CFMYIfdYoOHRMk6fabfkTBUleR2o2m5d/hOkQUxpdIMw2mccExz6axL14XQloMYvue3WkKaJiiFL8KdqjMky5y90yUVAkmFYSiBEQIHk/vHYr9dB/CXq106M/kDEiSR5VhffT15e/YbNUtMfYXZNc9w7+C3w95hEhcIhDQ7jRhQdjztt1GS9Lr9LkFilmnoXoJXsdRucGUxX0waTbXO0uTamd7eKmET7sQgCL11IpOQKmtlwsdkdvHl1sxz0zKHg7Hca1+8spEKOazktN8ms6pEyXYFl5RQQYmKFCNRCYLg8uV5u6GVXkDiOElgq+nCtrdUe4AAgiaPVA7JqgzGxW2Uozxm21UJc1gXxaxttnP3aZzE45BhCg+xzArANeuZL4M4a+pIjEQVkCosqfkDOIUgrgMqK0iPKGaXQcbORVActmGVG8z8kA4DmmwWhV+mme/sam/5/ObccWc2SfAY3ha10KsYICvt/hc///r4czEL6tS9ZREyZoM2Ibdq9e67t3xewXsgXKwZDAEYW2RQXRONBsuyjYm3xYwUgbdRGZL1pCBJQKZRELIMJIpCEBSK+qDK26a2/qTV2JWVyAYf/9kHaILHZbid6JiiCRwlwpnWf0zsk65hh2vf5SWUzludZlkWoHbvnx5Z16/gDJAl5lfhhz/5i08J/tsXdgCntZt0B4O67MTrd7Nina1DXqQ9Z53kCcW0sELb+dOoysO67hHwSy/96PjJKjMdALEspW83kEp0QIX12Z3vc6VPKbLhhkizwfX7eex2tJ6kyCddafI8iFj4M9eJNDmI/PuNJtNua6q2t6D8ChnvaReX3mIZNLV0MU2derJY3r3gyB8/HpMw7iGGw56QuOXwDBGTXsRECfto8MwmPaiuVBJL/DRPfEegCE1VUUuIT1NoHRQhFKBVgmNZhODwbHkDKphv9FJJgrY7stlrk72yjGGstN/suGOQZ5XSKGx/xzD7oldvc3uaFkRa4Gl7qjvDwR5aZuv13LzaoVHe1LqmNb1xawvT1apB083vb7+2yrxDdGbrtw1FiabR/rDVabWKzMmLjBeEEkb/4eL3m+VWGbyPq2JVFCUC8qLiSlDkZSMln/7Fw37/w4ub3z5/88tim2a5zZJk7rq+bjUrxJ35MIHgFWPMF1WcZ3gyfzM7v5p4HP3Hf3GCQKKhfx3FWRthXZKM/DDNUie0uJKtoRWSwMWWodsUx3Ucx8tgQEC5KnFYv4fYgbV63eg3U3cLgpgEDFmSFR3CdT8P4awS1aJe54YiqwRAZrMl2j34bP/BN1sjyHI3cHmB25pVkoI+S7gQWpQFBNU4xvKsXJcoSfMSnxBxgVO445eEBudJpCZS4CcMTmujB9ZsmucwTWG76yXP8lXBcnSXwAGDMpEXVySgW7Jr7LgiS2u43Bavog1I6sbx/gFKVkVI99tCaSy8mTPVoirMqzmMH2t43wjvECKTKSRMuCRbdjQ4yVLbXrBox7OSeOP05X6cxrOz87iEFZm4d3KkF56IRjFX+1a2xXiOUVsQzjulA7tBGHqIryr7DU2wdy7K0zsj0K/d3+rfrdfbT08HAEpFWRseH1RZ3Hm/kWfKqLnPdMbOJPfWy6agsgTWRBkCD5brnUvpElZtbgOYAA/+6odhmCqKaFgoo99yZua1+lEdn9KisZ1XhYVKQmWbBgIhm+Vy2BmDsHr+zYt7w5MH9/+UM51MSiAit26cMi5uAl/WeEBUFMpkoGYZJUreclEEjo6ZOBHwhuWsZ7Pt+qIuWO7x5w/RGCmLzGit2RIDpX1fHvB95MQfWuvdZrX2rMAx4vsPfnw4VG/mV88vFkKrmWUOjkOT2C8upwWB77e0wI+I7ZWLi67vwnWljeX7xIM4SxM6B265mK85jkM2lF2bcMwrrRE7og7Q/OR+K2fU53evq6qKtrgEk1qNUjhTBuEVhIHcrMicUzgMI5bLFYJgJD6amr8hSQLB0G6nwwFMBwgGg9GhUMZGmvhxnHIU5yZxGcdEi16vV43Yw4USQHKWekJebC1qYyw1KYVg5t6AlUbdtMoRrJdlWBplKEALKg+toPCjox/90XsMdXl3O3OsOHAyLJQZnhd7WBzFkHR61K3t4sooP9l/EBkD130d+MFqFzdUIa/yRov/H/77f0mQ6B+mF9266SgwWEKKKodOVGY5Q1EYViKJz7eOIA71q6LD9EnCuLl8Z/nh/ZPj0ArfnG0TMcrKMiKw43/NzE1/u3rD8ns8O8igKo7ywM8WM7+pdqkt/+bqhpIBS2pyjhR+ATmQ6NVaV1kJ4ovp/I84Tmw0tsluM9kpLYXlOVLk+rKSxikIIBddBVlcVgjAoVTPFq8nQ7XFwXKORTRJoIhRpShxMLy7umJBOYOiSmT39+67UHT07AMbQ8H5brWYnRyM0B6drc01x8oJjmDAXN4J2p5bIRQMjU72VtZ3vhTde3LSJ7X5JUEJ5LO/+Tv/4l0oHx8/kt/OnN3ZtKwLqCSguvfx/U+ui5fumSEcDnrZsVvdLObrn2exoLJqYxxcviYPGmeXHies1yswfNQnGbyw0oyJYI8IAJLT9L5AGWlOVxLU8OqdJ9c4yrhuEtQ5YVTFZhtpCh5FdkhckumQ0PENtLF9HdMrjCA1qRMnRlpwOMYwXHO5ctbb7wukETvFwnr78vuJJp62pXZGZDiMHbbGfQDnez/q88LPL3QR4rRR426+iJ2kOgQSdSjLULNqALSMoss0SZjmEYWicK3kk0rg1fZQM+xwMB75F+5mMo9JnmyRo7bgvXO/Wv5fN7NJkCYZw7CKmm2t2NsRADO9nI1rI3XzAi2CHctji8licHzvrZUdErTIPw7CcLO+ZUShwJkIJfG7Am4YMETZ5lRrIFzBBL6XxeFZ8YpxyQqv8QxbZ2W32SIY2o3dUmccjyiaEKggkGOURWM5MACUQRbH0JfX8x5LD6RxhBCKi+kNM8w3qs9svv22PmiSOIZ6hW35cqcHYXBVVyZsYgitCjIMk2dffklzQwVHtlksZhmpdCUO3W3jXEhkvo3EaJ3HLMdxPNTWVJYUeU4ynSz0s3ZHBW0oqSvIT5KCQUHN81VeoTAWWtOIYhnXi89vzP3jj/0gKbVaGbxvm/PJ9hf9h+8NUBRpomGKrjeXcezTiEgTxPpuOmXKMaPKoz1NGCRRmWRIXIaLaJ2fLZl7J53ucQmgAA1fmDrYeDgnm2aMtXFNIm3Tygp/vUzqOn3+m8sojiVhEIK8ddrLpA6K5XVqYWNKcSht2K2rHk1Kd57lYelC1yl/xkHVV5Pw0X1Rk8QHjcHdbLGEdKXZHUr9rX6eXE8bB6fR4UlQoaW5OtB413n+i198geMQWkehxns4fPBqOhFRvqzqttrCUwyCIAwnCJxwXFvRVC8DgXMWpT0h9gQc3jF2r/9YUeTzwHUMk/QSyr/KylzRmjgTfrUK0QBp0aMoXHEqq0gyPF9jR214u+R5IbJdUtU4SnQ979Xb3371QuNbEi3zVrmzwyBJ6+WlfnB0TObQxfltpyNHtBbGeRRFEE5wu1zi+SIJ0YYiNvGF54uCskoXmt/IMT+2Swu4081d66FCoGmb0lgWjVn4zrt99/zLHvUA1NBwMOJG3UVm7LcQFvCvb77tEzLGCTBFhll6dXuLUNgWL82N3e+MzOVtKaQ+5kIxPw88TiJyqYoLh8iywC9aDOTBDpK34IzhBwzOh1qr5YXGTtdd2JvcpShlqh1g3JUaL7qhmyYpVMPzn34JMxQnNzRFMdOEyLAszlG0tKzaszdYEHWpVmnd7WqW4YL7p/f0enO7KMK3F5MMstbOeHzy7OQBZWz8rYkrAa0RNWazudBtdwksKXUnJqK7cFbrSbRBUNwOKVaOUrzouhvzYDD64u0kNv20AnJT2+fkXOl9fbPwnKVDETQtqX4iDNunTQSLlADnXBgtCpcA2Xa+GR/t9fYf2h4QOOvZsx/cCGc5Q1uGUbR5kqour64Go+GDpsZQTGlDXuZlVjJNZhS1z7IMRpCuv02DnZ8U4fWlKPBQWtr68gmCKfg4hUIFx7WRSA0628lVGylsU0/lqg483VhQjFoNynh1VeYCClnLiB4wGQVV2zBtF9mj/QcVDUdp8s3m9a1ndu383bcmxIKDDw5e3a0D268yXFVUFMbuZpOTo32230Pq2HIuNLb92Z//+fSLfxQFLs3DDsOm+wcESdbVbGPuOsP3DnrHhmFANVp54K25HUtjv0BdGgHrnBOhMEj1NNnmLva6BDTJH6LL7VaK8P6TJ1HERgmZwIBczgiCdA2XbqEHtFQjUegkBZykcLQI7NvNOrXsBh6sZhAK8IfDT/Y/Py4qkKbpCEU1nj+/ufrty9c0TrS6uAM5PVIePhi6a3e5WfoOSlBQC9KwMItBLCjC5Tc38p7aU3sSr8LkiMXJAC0iJ2xqrc6g9/Rhw1mvA9vyPTvV0xIrs7IpABgOEjeIUycUSBaqqwrzNKQVe0EM4EdPfjxdvGVF9d/8iz8TevchCJpur+aT8vCh5myMAcv4fJ1QozreEmiMW6LcJPptzrbTzF54SspLLB1gAwXPEgEA0nSdtbMpWzXIYQSlYYIPTQvHIlFtV0XkrXRJU4o4iusIkqncL2uSOXp0lGRkubtGIIFHQCjxZM1bdzOvLNu9Dk/wJJYn0VKSHx7/4Im1DmSSJPyiKGA7sH09SxNiXxah2otHD8OggrGQpmI34CMv1DdrSTmUFLLVrkkKu1zpJJ7hqOmFmc+RYbir1xVNVbgsFi6G4ZbtcAhCMDXj7XYtRjSN63e//F7dg7vyMV2oGQnnMhnoZ0ybOvj86H3lCQGRV8/Pdvi6jlm2h3aTpg4lgW3Nf/GfwOkRdXjcbHafIfG6rRhvgizdpA7N8bTM8ObcypOEFxiSpOXBmJBpppQYjquWpRtkAcHXWC6fgpShciKbmN5267bbA6kr4G5IIPCeQAk4enFzYV9laLsYdxsYXS82b7Ik1hQtnWX42hAO5LIWSNTDWTRPPYrA0Vf//nd6FJHSS8zHEUEiDw4aZD/z0KurW6UsKRbGM4SXuCcV/3WR6fZVlWOlSnMZnWWJL3s9tk0xPegy2MzOdGvHSdzBh0cmu7zRp+IRBTAPxQXnauEZawpGEbBcLQqWpNK4igQRDpkClLrj3DHg4IhwqgigrkYwPodWuesYOUVXOIXxAv/F97+x4vTPPv2csswthuFZtNytaYQocQbK9bamskGIkj2IpCaFqzQ8iMa9NOtLbaR0gVf+8f6ji/T59PbLC8P6W+lH3ZZ4gksIDp+HUxprr5iMgzy20Qe5F8Y+jtEtngElVIUeDOsjRN3h5AoJ6KSMbaaAM9QKXOdWk3klg1OYC+KwymIthgOygvKySEi/RFM76aj0TE8xeL9qnCEkxqcg83O6MWRhiKZI14krGlcabZQko7UOx/DotGvdUFEQ1/i8KYi9domhHE/x1sT88MkjoIakY5QZyH0nyb2Hw2MlN5bpwstiEiOdtGRRjPETFAElB4VRWcAzpjdWYp5PMi/Ay2aqL3W8Rn5ydC9BC2p/oHT22bjA6ir1riYXlqxwf/ynfzs4+aAjiPMg2kzm0nh3T0qXE0iDYhqppcT3yjzP87lF9hOQlRWMkhWClRCHk/HoyUOF5K/dl6xE26aTU7RTFYtVfvQxQhJ0TUIfHp8mfgiqEhV7zvr7ugRzP/nHX/+eE1hm0ME1ceab5e6Kl7o4g/FtnszHzvb57Hp+b5z09vdsU13udFmUOIZCKLwtCtu3Zw4VtvqdW2c1D/yP9jpRv/f1l9+Ued5uI+F2YpirJIHl3iiqRAFlaTZyvKW5cz+4d3oX39wsbgc53/+ka77YyrLs6gGCTL/cmQ2uxQrj8f6p6UYvzLMsVSg/KiE4FGDgFeUmHfQQElYbfKmkx3740g08NMnkkj4g2AsQhedG1jBCg4qnrihow+HHR0JbawGGxcuNb0UhIXOelTuGz4oiA4HaZVv8er89kvffPzddAKFuWji+ZlthWNYABohUE7gynW7m8I7mOXhIeGcESWPumeEBZ9yDCRSH6ppnSei17x3peI1CyXr3OkYqmDpkaQpBieLuzcbzbjHPLdIExhC6Zi7iuYlnsNMF21uJYQEMDYiRGUbbbG74Rm5an7QUgXhUutajT5+8O3P/40//45s3tyQFCHzIsgJLCy5ftKJwHehMQEMg/s1XN3GenwxxvssO5dOqmhVUUtdYcHm2SAtaVHAYGhMP55Uro3QGsWmqFxXkBGZqbmvHEnlm1GNImgK0UqoIi+KYqOU2ef/ZcFkG+nx6oMjZ2u0jyLrMnbszt/AHDx/EiRLP72DUiCFjXpNWYJIISTiUzHMpVscezPEARpyQ0pEMA6CkVIJNOZrEq8qT64Chj3EC00jdtbC4qKs6w3YeL4lcr/TrOCsIo5jwkPLpp+O3365d20GSpDXk8oSfeFFi6isPEyWSYaEH3UdnIgsj9DHLp0WcLW8qDa6TKiAhiYOrMm8jbeXPjmdffePZlvG7f6T++GnkGy2K4FrVvDkKtkFw66uM8Hgk7taBbd+dfPyBIinPlyakI1Qbi6s7BsdbXSwr8Roq7CIlMNyyr2B0NB50ydWmAIeuYSDSVs6zj5uqbhsplBJxhMMVhCvQQAu3FkkoItMSMPp8dqe4EDTKWnnc0xjUgBFckDebbQ7yxWRa3qX7x/eqOucFvg2QLVhLKGchhlSxoz2RP2tiKujIPMyRYRIVoDB5h9axEN2q4/bw3gAGEJRn7Q7iZzbZ8oFUl1vn5fwPxBrvD2Wab6TFzV5jUJawuQ3Qp406Q410s/+4dxdc/Px/+vcCBBp/9YiHKivMoqxCGBYnsO9ePF9sNzIv1qCW9voKCu6gmocyHCFrb75+ve33BjjDXns3VQ1zPN/ea+dsOX3zAlHSR7393A68fPn4Jz8kfvoFoSr3Pzgq6iVU1f/xxYWhux/+2V/Gl/qVl2dJ3iCJIo0dLxQUCa5qUBY0VA8PDxuFN5CZy9vvLpKUcfEg2CVBMGjyEIIxHIBhMsfhuKCCtJ5MrxmQvDC390Vh/5/8RXdY10z5xfzLhb4+6rUhUbV8g2RpHuNwUDq542wTEkNkRqlBma2cXlOA1G6S3B1RI4k7bbURUSYDP+Sc2EjMD3p7y8Xq5duzduDBSGZ7O+4Yp3E6CKo0jxKczKoCjeOSSigqyRBSkkTOTDWBionItUCO0dOLycnJSVfWsAaNEOFuvcJs7P7pn3/wHtlg4X7CXL2YzqHzyjHikFlBaaOpPTzlnhtSloZwGNEUjgi8UYOEETBXuZvNj5h9rcEODj/cZTe/ufrm9pvVT/70uOYBQ/iquueorp153irpdYaBKOIJAnKPOqAr4T4C1z3P/fXtlgSBaui9e0wJSgx0na3pq0gUmYOW3IDobcc7W/k4uR0PG6/f3m0Wi7Yi3iX5g0eP+BYfp+mlEwg8r4qwgAu9njz8L/65Y+1YTti4t3wbaqRxJQyg3c6L7N7TU8w0L67Pv9tu9zGU3xt7u1thj240NVChX8wM2MBQhk3G1Y8/+yiP0Nt8znHyYjmP2UIWeiqNBDhBQU67c3R3tgS16S+/29lbjK3igKLtRkN8eIN84bpxYUCQUIj9zod796EMbEGdJDESAxtNE7piKXeWL4oifaL2XJdTGiUm9G+vdr958b/hOK2OTji22W7Xi9lGEJXDB3tNiep0mi9nlzXgipIKQ+v+/Yeu6zlAj9/5Bt4eQWDiTAROoiGBNKtPjj+pfATj4fH94c5YT5dzfWMKQcKhBY0iPpEFZdhSOsgdiboA1wRUe19j6ThcvdCvuLSdEeW9D56pMruNPaGUiq5w9i7PCnB4+Be287+32q3lysRxPQwHVzev3nvy3hDpCmIotw9d61dXnllZMNKGk/juxjaBU9+TyB1KuH7QyQMDodAWEZ1DIKlJOK+ZLnfUgMPt7eX3SRiAkkUPG4ZZCM7cxNm6VqrFhOeqPKKZFKFhAr+94wv0+NkpKykTZ/7dm28sd/qj4+73u4tvX7wcDodplgkq+YC5V3Wg2vO5ficC4fnF/PRwrOS0Ye18LozdJU8ysnrECywUz6A6tyzDuLtLyRZPUGlSRlFIUEXBEfrVyi7e7fceHnc6aeAa+ot79x6Ebh26UUNWYWg2X1pOYHreEYmLK2XBFsg3v/zd6oFQhnHtF5om+V4tipg+C6+M6yMW1w6faI8lc2usJ1X7eu7g7mq5o/CTpXf3SDv97trbeZen+/tio3oZV66l20EAGYEfFYQVwniZR4EqSGnBfmdtxDAoi6rIMwJf6TfXs8mFxh8G3juBxV3DHH9+jJhZkMAXb26/2+yePDOffviDWxvk0bmAqQ1NwXfienPjbZezZdaRK1QOQoZlPvrP3kur9mBvHIThZLGAPCSy4mVWiL2Gj/g4QYIqJYiq1cIDuPYpYnm+QLnnMQqtlouhMCYVNVuVnSb3dmPSiIkBJFl4C+qmUcmGN4ETqH9IoEhNEAjfaxdY1dW628qyXpjw+xrJOiGiuOmG5iC8wCGkOte3e0q31R415LbPmBAE9br9PMl+9cvfaAfaUW80SawqtFEk5HIS53jL3HHjcePyRq+Q4XjsphGCYEtl3sSb+iyE4DlO7bFc+uHfPXs/k3CMK6siiM/tnW17fnC2yTotiC4zG8Laye1rM4nj7HrCt/vl2vh+udb6b9I6qTAYx+nHlLyjI98qBV6AIaDrdqumYZWCdNwlfEKUSajIFjdQhU8S6P2KpJj8/7v5hfXOSoIcKZQxMNICI6juFlrmDRgq606jjcWrMrLdEkEitKL0uiRRGCUxosyn80n+9o0L0NpAYWN290ju0Vqz8f27xI/cYHdrzg9SlX+gUFh33OdNK2eHg8X89yBHBJGvohLDkThOFVUzFvpysTzcO8ZqmKVZnGSiM6/ei5JYKhL3WJMVXkR3JoDcppyhNUW1xlUB5wnBUFGSRdF6TjLEFk1tw6K4e01BMd3J+LilsseLm5sXNy+/uf2O6OLQFliGfTd/Pup++PYiRCETx3MYKHWJKIpCQEi/v29vwzzWD7u9PMkZAjr+8fLrC13Hje++fdHst1iR1Cp0oecUA1/d/WaMiixar8JoNVkefrj/wUe0bfr2auVa0Hb1fYc5WocbnABRCr87e+O33SIq8aww07TJq3pmZmGCyGKdG0QXrQpZKOKcRIUTKt+sth6l1XwhZvOlIRRcleFFjrI0f3x4sr93QOfSz2a/nPxuCkk1KeMgLCWRUglq6ax6e/twVS23b+s0nfleBaE//PC95HpXTjzxj6Qf/fBPCCLRdSuNKQQlCijV/RWKIaQv1e1aYygeobdRFjmJ2haaNGsE+fmFyXnxTZw9lJT2B58UEOQEwSzJMzwoC5tlseXMuNje6NNZ4/1HCo5DCOZHieV4NN56+IkgAs6f2PAK2KmhHWhIB0mzbHL5DYmKnv56Z2xDkrr3+KDImbOX/wGBagQAYMH/uJ2WCKLe/xCTqC5U8DjuL3itmRcxCHx078F7gETQfLOkoDZOuDl0r30i9OOc/puBjKxWS3Or4gJ8dmaF/hcDimSEhnraOOnu43GMFPC7d/FIThsqdetnb4sQhqAhDmMHPfvNLs5jSULNECIYOvac3eUvE8OnFOrgWbtRSxyDfLc46+Jio3EPTeki15fbJAyn7/XHLba13s0ZhjVms/n8mhs82h+e8M5svb54dvCJGVlB4H3U7s9MM/Be53kTw/Dbd38QSPxWP6eITJIVVjRJXEyrMI3s5Xo6f7GKQdk+nTAcTaQ4gbnV2s8gPdlYX/zegiiktdeQFWGkikWZ/vT//feiKIWBk6ZXy0sg9yXHNGgCw3wyRqKdnbEb8rj76eTRyt/OBJyuxIBiuxRHpVGRo0if4VlVcEqDoiipcR9PLlf2HOXHu82LYZcp7zwH2SlCUFd0TmdJkQy6TI4R9t2b/uEDGCYd3YmdAHLvPL1NsnWxjEkU0UEksixFnMoAvqnKVruQ+IPtV7//rb429SXHUtJjmcKwqijwkERBDQI7zSABKIHvVp2Wopnub2Y+AmhSQX/8b/4bFMNs/7U1D9b6ORQmKJUbgbO0lwKjHgwPg4ssQlKsRbAlQCTaWywXVZRXYSkhh4h2Pb/yis5AaQfO1oQRiaxJ7jizrWZfYRyHpMSwLkSEv51GSbz+k8+fbW8W6xS+yDfaycNnf3P/m8kVfzwUGeLdZnX0qIfwaIbkIs8wwiECZC+oXn/1a3MLSIYxz626TEg2SrnR2a/Ps9x58v4gSWFRYWEs3m7P6jRyZ9amO5K6Izv0leY9DlS5vyPRtlWZ0U2CJ5y2l6UFzfN24MUIgRyf7hdBaF1eFygQ1KKqwPAARCupywv61k4JVtG0cBbEcXSVZY/eH97dzqIwe/zwo63+Oxv1XT1IUFjRGtSQroIQlEDmMvHTpyPDl/cJURKm25vN13OWITWp9eTJKL50Y4DgEhixTV5o7TZvkdz1dyCxt9R4RB4y8Rau42h5dwGyKo19gpNYuaSauJ2izQdjiCgFmnn84D2k1y5f/0oOERdkiYGhKFhvFkePHgAMV4ddQWKKrHj1h9cI5J60uoDc11ql6acTY8OReGP4KMznUqd/5RsYC3qK6AZVo6xvdgGLYyrXKvPCdywMlLG3ygIB31cVArYt81j4hGMehWFCotwcj2CAzjfLX/zqZyEMGh1x2O+4QtjvjhCYpDlZt77/7GRvFvt2smHIvrldFTk07GNl6SIotHDerRyHJqR99f6nyiJB9u58x/a89cbkRvvHmkQRwuuzn6Ycjuk4XFZVTBg7B4IrTulnFe5srF//5vzZB2JmuQyCxXFsGUZXHBBRaWw8s45KBkaQWgBE6cc1waaJONDwIHWMspDLlqgO8lo3Sc82jW6vHfgJ2xf+1R//nW47iqZ195T/+ff/x+1vLid3S/wQPmQ+0+REyztYSo7hkMvKja5LbVnLoAjOp9ObktC8Bh4kTvXdb7VxM4fl+e3bvcEBDKPhUqtiVjnos3i2TeY1jOcwsl58n60qpNtAwXFT+Qa/hbd28EGvt/fg3surc1riGFmhqiRrI54ZkIR0uzxfrNcYAZ5gCC5yaGqaJgtQyoxmj3t7aZamRUWiJKjyFK/tuylO8E7hMRWc6cVkFx1KHAkQWtAK+vDd2fPR0YA+kb5/d90Z7HVG+6mTXb2e7pa/fbT3CYCIdkvp32eX+qW+neCDzhFP7Qi2QZJe7n53cxvZ0Vxg1E4vJw22Kv7Z3/64G1TL6VsUq3jfefjBD2PbyKv81WzehIn+8FPf/e12Ex12eHRAlSBGqcKKVxGJZSDY2maWBhCL0V0ahvEm8hiH9CyH+hyHQhoCeyQbG8sVl8VkWEIsuYOzIIqwOkOQwre8GHgVhnEUs/KWmCH8+PTBz9+8TdwUqpHVXfzkaTvLy0V61+i1/6L3cZIZi8VXJUT29pEc2KszpyauMZ/pNNQKIYO8MqOYpfmZH/IQp+1JssEZxuJhV1O6UlFu8gJgNKRwWOA5itAt6pV+d1tCCeIQCagyQY9jLvQWC2LZglGaVwThqIbvoqj2wbYwIVFT7x/+EUuTN1e/4LOUJNWY8pfT6OEerdVPKlCP9vmtcSex3QEFxThi2j7LkHBZRAmAXcvbGFgHFsTD5XaDZC5R9wV4+f18phCNSCzxLEhRKI3K2Y3ZG42HR09yssryuEQqrXsg/LPD8//ll/ZsYS96WViQlbSptuSyQJtGVm0x3EMhkDjZ/w9KdYIelpaGzgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=512x422 at 0x12C879758C8>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_step(image)\n",
+    "train_step(image)\n",
+    "train_step(image)\n",
+    "tensor_to_image(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "mNzE-mTbBVgY"
+   },
+   "source": [
+    "Since it's working, perform a longer optimization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "rQW1tXYoLbUS"
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "start = time.time()\n",
+    "\n",
+    "epochs = 10\n",
+    "steps_per_epoch = 100\n",
+    "\n",
+    "step = 0\n",
+    "for n in range(epochs):\n",
+    "  for m in range(steps_per_epoch):\n",
+    "    step += 1\n",
+    "    train_step(image)\n",
+    "    print(\".\", end='')\n",
+    "  display.clear_output(wait=True)\n",
+    "  display.display(tensor_to_image(image))\n",
+    "  print(\"Train step: {}\".format(step))\n",
+    "  \n",
+    "end = time.time()\n",
+    "print(\"Total time: {:.1f}\".format(end-start))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "GWVB3anJMY2v"
+   },
+   "source": [
+    "## Total variation loss\n",
+    "\n",
+    "One downside to this basic implementation is that it produces a lot of high frequency artifacts. Decrease these using an explicit regularization term on the high frequency components of the image. In style transfer, this is often called the *total variation loss*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "7szUUybCQMB3"
+   },
+   "outputs": [],
+   "source": [
+    "def high_pass_x_y(image):\n",
+    "  x_var = image[:,:,1:,:] - image[:,:,:-1,:]\n",
+    "  y_var = image[:,1:,:,:] - image[:,:-1,:,:]\n",
+    "\n",
+    "  return x_var, y_var"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Atc2oL29PXu_"
+   },
+   "outputs": [],
+   "source": [
+    "x_deltas, y_deltas = high_pass_x_y(content_image)\n",
+    "\n",
+    "plt.figure(figsize=(14,10))\n",
+    "plt.subplot(2,2,1)\n",
+    "imshow(clip_0_1(2*y_deltas+0.5), \"Horizontal Deltas: Original\")\n",
+    "\n",
+    "plt.subplot(2,2,2)\n",
+    "imshow(clip_0_1(2*x_deltas+0.5), \"Vertical Deltas: Original\")\n",
+    "\n",
+    "x_deltas, y_deltas = high_pass_x_y(image)\n",
+    "\n",
+    "plt.subplot(2,2,3)\n",
+    "imshow(clip_0_1(2*y_deltas+0.5), \"Horizontal Deltas: Styled\")\n",
+    "\n",
+    "plt.subplot(2,2,4)\n",
+    "imshow(clip_0_1(2*x_deltas+0.5), \"Vertical Deltas: Styled\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "lqHElVgBkgkz"
+   },
+   "source": [
+    "This shows how the high frequency components have increased.\n",
+    "\n",
+    "Also, this high frequency component is basically an edge-detector. You can get similar output from the Sobel edge detector, for example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "HyvqCiywiUfL"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(14,10))\n",
+    "\n",
+    "sobel = tf.image.sobel_edges(content_image)\n",
+    "plt.subplot(1,2,1)\n",
+    "imshow(clip_0_1(sobel[...,0]/4+0.5), \"Horizontal Sobel-edges\")\n",
+    "plt.subplot(1,2,2)\n",
+    "imshow(clip_0_1(sobel[...,1]/4+0.5), \"Vertical Sobel-edges\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "vv5bKlSDnPP7"
+   },
+   "source": [
+    "The regularization loss associated with this is the sum of the squares of the values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "mP-92lXMIYPn"
+   },
+   "outputs": [],
+   "source": [
+    "def total_variation_loss(image):\n",
+    "  x_deltas, y_deltas = high_pass_x_y(image)\n",
+    "  return tf.reduce_sum(tf.abs(x_deltas)) + tf.reduce_sum(tf.abs(y_deltas))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "s4OYBUX2KQ25"
+   },
+   "outputs": [],
+   "source": [
+    "total_variation_loss(image).numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "pu2hJ8zOKMc1"
+   },
+   "source": [
+    "That demonstrated what it does. But there's no need to implement it yourself, TensorFlow includes a standard implementation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "YQjWW04NKLfJ"
+   },
+   "outputs": [],
+   "source": [
+    "tf.image.total_variation(image).numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "nTessd-DCdcC"
+   },
+   "source": [
+    "## Re-run the optimization\n",
+    "\n",
+    "Choose a weight for the `total_variation_loss`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tGeRLD4GoAd4"
+   },
+   "outputs": [],
+   "source": [
+    "total_variation_weight=30"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "kG1-T4kJsoAv"
+   },
+   "source": [
+    "Now include it in the `train_step` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "BzmfcyyYUyWq"
+   },
+   "outputs": [],
+   "source": [
+    "@tf.function()\n",
+    "def train_step(image):\n",
+    "  with tf.GradientTape() as tape:\n",
+    "    outputs = extractor(image)\n",
+    "    loss = style_content_loss(outputs)\n",
+    "    loss += total_variation_weight*tf.image.total_variation(image)\n",
+    "\n",
+    "  grad = tape.gradient(loss, image)\n",
+    "  opt.apply_gradients([(grad, image)])\n",
+    "  image.assign(clip_0_1(image))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "lcLWBQChsutQ"
+   },
+   "source": [
+    "Reinitialize the optimization variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "a-dPRr8BqexB"
+   },
+   "outputs": [],
+   "source": [
+    "image = tf.Variable(content_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "BEflRstmtGBu"
+   },
+   "source": [
+    "And run the optimization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "q3Cc3bLtoOWy"
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "start = time.time()\n",
+    "\n",
+    "epochs = 10\n",
+    "steps_per_epoch = 100\n",
+    "\n",
+    "step = 0\n",
+    "for n in range(epochs):\n",
+    "  for m in range(steps_per_epoch):\n",
+    "    step += 1\n",
+    "    train_step(image)\n",
+    "    print(\".\", end='')\n",
+    "  display.clear_output(wait=True)\n",
+    "  display.display(tensor_to_image(image))\n",
+    "  print(\"Train step: {}\".format(step))\n",
+    "\n",
+    "end = time.time()\n",
+    "print(\"Total time: {:.1f}\".format(end-start))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "KKox7K46tKxy"
+   },
+   "source": [
+    "Finally, save the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "SSH6OpyyQn7w"
+   },
+   "outputs": [],
+   "source": [
+    "file_name = 'stylized-image.png'\n",
+    "tensor_to_image(image).save(file_name)\n",
+    "\n",
+    "try:\n",
+    "  from google.colab import files\n",
+    "except ImportError:\n",
+    "   pass\n",
+    "else:\n",
+    "  files.download(file_name)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "style_transfer.ipynb",
+   "private_outputs": true,
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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+++ b/24. GANS_Generative_Networks/MNIST DCGAN.ipynb	
@@ -0,0 +1,918 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "_jQ1tEQCxwRx"
+   },
+   "source": [
+    "##### Copyright 2019 The TensorFlow Authors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "cellView": "form",
+    "colab": {},
+    "colab_type": "code",
+    "id": "V_sgB_5dx1f1"
+   },
+   "outputs": [],
+   "source": [
+    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#\n",
+    "# https://www.apache.org/licenses/LICENSE-2.0\n",
+    "#\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "rF2x3qooyBTI"
+   },
+   "source": [
+    "# Deep Convolutional Generative Adversarial Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ITZuApL56Mny"
+   },
+   "source": [
+    "This tutorial demonstrates how to generate images of handwritten digits using a [Deep Convolutional Generative Adversarial Network](https://arxiv.org/pdf/1511.06434.pdf) (DCGAN). The code is written using the [Keras Sequential API](https://www.tensorflow.org/guide/keras) with a `tf.GradientTape` training loop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "2MbKJY38Puy9"
+   },
+   "source": [
+    "## What are GANs?\n",
+    "[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A *generator* (\"the artist\") learns to create images that look real, while a *discriminator* (\"the art critic\") learns to tell real images apart from fakes.\n",
+    "\n",
+    "![A diagram of a generator and discriminator](./images/gan1.png)\n",
+    "\n",
+    "During training, the *generator* progressively becomes better at creating images that look real, while the *discriminator* becomes better at telling them apart. The process reaches equilibrium when the *discriminator* can no longer distinguish real images from fakes.\n",
+    "\n",
+    "![A second diagram of a generator and discriminator](./images/gan2.png)\n",
+    "\n",
+    "This notebook demonstrates this process on the MNIST dataset. The following animation shows a series of images produced by the *generator* as it was trained for 50 epochs. The images begin as random noise, and increasingly resemble hand written digits over time.\n",
+    "\n",
+    "![sample output](https://tensorflow.org/images/gan/dcgan.gif)\n",
+    "\n",
+    "To learn more about GANs, we recommend MIT's [Intro to Deep Learning](http://introtodeeplearning.com/) course."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "e1_Y75QXJS6h"
+   },
+   "source": [
+    "### Import TensorFlow and other libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "WZKbyU2-AiY-"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "wx-zNbLqB4K8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.1.0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "YzTlj4YdCip_"
+   },
+   "outputs": [],
+   "source": [
+    "# To generate GIFs\n",
+    "!pip install -q imageio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "YfIk2es3hJEd"
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import imageio\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import PIL\n",
+    "from tensorflow.keras import layers\n",
+    "import time\n",
+    "\n",
+    "from IPython import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "iYn4MdZnKCey"
+   },
+   "source": [
+    "### Load and prepare the dataset\n",
+    "\n",
+    "You will use the MNIST dataset to train the generator and the discriminator. The generator will generate handwritten digits resembling the MNIST data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "a4fYMGxGhrna"
+   },
+   "outputs": [],
+   "source": [
+    "(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "NFC2ghIdiZYE"
+   },
+   "outputs": [],
+   "source": [
+    "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n",
+    "train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "S4PIDhoDLbsZ"
+   },
+   "outputs": [],
+   "source": [
+    "BUFFER_SIZE = 60000\n",
+    "BATCH_SIZE = 256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-yKCCQOoJ7cn"
+   },
+   "outputs": [],
+   "source": [
+    "# Batch and shuffle the data\n",
+    "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "THY-sZMiQ4UV"
+   },
+   "source": [
+    "## Create the models\n",
+    "\n",
+    "Both the generator and discriminator are defined using the [Keras Sequential API](https://www.tensorflow.org/guide/keras#sequential_model)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "-tEyxE-GMC48"
+   },
+   "source": [
+    "### The Generator\n",
+    "\n",
+    "The generator uses `tf.keras.layers.Conv2DTranspose` (upsampling) layers to produce an image from a seed (random noise). Start with a `Dense` layer that takes this seed as input, then upsample several times until you reach the desired image size of 28x28x1. Notice the `tf.keras.layers.LeakyReLU` activation for each layer, except the output layer which uses tanh."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "6bpTcDqoLWjY"
+   },
+   "outputs": [],
+   "source": [
+    "def make_generator_model():\n",
+    "    model = tf.keras.Sequential()\n",
+    "    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))\n",
+    "    model.add(layers.BatchNormalization())\n",
+    "    model.add(layers.LeakyReLU())\n",
+    "\n",
+    "    model.add(layers.Reshape((7, 7, 256)))\n",
+    "    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size\n",
+    "\n",
+    "    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n",
+    "    assert model.output_shape == (None, 7, 7, 128)\n",
+    "    model.add(layers.BatchNormalization())\n",
+    "    model.add(layers.LeakyReLU())\n",
+    "\n",
+    "    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n",
+    "    assert model.output_shape == (None, 14, 14, 64)\n",
+    "    model.add(layers.BatchNormalization())\n",
+    "    model.add(layers.LeakyReLU())\n",
+    "\n",
+    "    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\n",
+    "    assert model.output_shape == (None, 28, 28, 1)\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "GyWgG09LCSJl"
+   },
+   "source": [
+    "Use the (as yet untrained) generator to create an image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "gl7jcC7TdPTG"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x29e15c11dc8>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "generator = make_generator_model()\n",
+    "\n",
+    "noise = tf.random.normal([1, 100])\n",
+    "generated_image = generator(noise, training=False)\n",
+    "\n",
+    "plt.imshow(generated_image[0, :, :, 0], cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "D0IKnaCtg6WE"
+   },
+   "source": [
+    "### The Discriminator\n",
+    "\n",
+    "The discriminator is a CNN-based image classifier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "dw2tPLmk2pEP"
+   },
+   "outputs": [],
+   "source": [
+    "def make_discriminator_model():\n",
+    "    model = tf.keras.Sequential()\n",
+    "    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n",
+    "                                     input_shape=[28, 28, 1]))\n",
+    "    model.add(layers.LeakyReLU())\n",
+    "    model.add(layers.Dropout(0.3))\n",
+    "\n",
+    "    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n",
+    "    model.add(layers.LeakyReLU())\n",
+    "    model.add(layers.Dropout(0.3))\n",
+    "\n",
+    "    model.add(layers.Flatten())\n",
+    "    model.add(layers.Dense(1))\n",
+    "\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "QhPneagzCaQv"
+   },
+   "source": [
+    "Use the (as yet untrained) discriminator to classify the generated images as real or fake. The model will be trained to output positive values for real images, and negative values for fake images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "gDkA05NE6QMs"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tf.Tensor([[0.00099409]], shape=(1, 1), dtype=float32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "discriminator = make_discriminator_model()\n",
+    "decision = discriminator(generated_image)\n",
+    "print (decision)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "0FMYgY_mPfTi"
+   },
+   "source": [
+    "## Define the loss and optimizers\n",
+    "\n",
+    "Define loss functions and optimizers for both models.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "psQfmXxYKU3X"
+   },
+   "outputs": [],
+   "source": [
+    "# This method returns a helper function to compute cross entropy loss\n",
+    "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "PKY_iPSPNWoj"
+   },
+   "source": [
+    "### Discriminator loss\n",
+    "\n",
+    "This method quantifies how well the discriminator is able to distinguish real images from fakes. It compares the discriminator's predictions on real images to an array of 1s, and the discriminator's predictions on fake (generated) images to an array of 0s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "wkMNfBWlT-PV"
+   },
+   "outputs": [],
+   "source": [
+    "def discriminator_loss(real_output, fake_output):\n",
+    "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
+    "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
+    "    total_loss = real_loss + fake_loss\n",
+    "    return total_loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Jd-3GCUEiKtv"
+   },
+   "source": [
+    "### Generator loss\n",
+    "The generator's loss quantifies how well it was able to trick the discriminator. Intuitively, if the generator is performing well, the discriminator will classify the fake images as real (or 1). Here, we will compare the discriminators decisions on the generated images to an array of 1s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "90BIcCKcDMxz"
+   },
+   "outputs": [],
+   "source": [
+    "def generator_loss(fake_output):\n",
+    "    return cross_entropy(tf.ones_like(fake_output), fake_output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "MgIc7i0th_Iu"
+   },
+   "source": [
+    "The discriminator and the generator optimizers are different since we will train two networks separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iWCn_PVdEJZ7"
+   },
+   "outputs": [],
+   "source": [
+    "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
+    "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "mWtinsGDPJlV"
+   },
+   "source": [
+    "### Save checkpoints\n",
+    "This notebook also demonstrates how to save and restore models, which can be helpful in case a long running training task is interrupted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "CA1w-7s2POEy"
+   },
+   "outputs": [],
+   "source": [
+    "checkpoint_dir = './training_checkpoints'\n",
+    "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
+    "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
+    "                                 discriminator_optimizer=discriminator_optimizer,\n",
+    "                                 generator=generator,\n",
+    "                                 discriminator=discriminator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Rw1fkAczTQYh"
+   },
+   "source": [
+    "## Define the training loop\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "NS2GWywBbAWo"
+   },
+   "outputs": [],
+   "source": [
+    "EPOCHS = 50\n",
+    "noise_dim = 100\n",
+    "num_examples_to_generate = 16\n",
+    "\n",
+    "# We will reuse this seed overtime (so it's easier)\n",
+    "# to visualize progress in the animated GIF)\n",
+    "seed = tf.random.normal([num_examples_to_generate, noise_dim])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "jylSonrqSWfi"
+   },
+   "source": [
+    "The training loop begins with generator receiving a random seed as input. That seed is used to produce an image. The discriminator is then used to classify real images (drawn from the training set) and fakes images (produced by the generator). The loss is calculated for each of these models, and the gradients are used to update the generator and discriminator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "3t5ibNo05jCB"
+   },
+   "outputs": [],
+   "source": [
+    "# Notice the use of `tf.function`\n",
+    "# This annotation causes the function to be \"compiled\".\n",
+    "@tf.function\n",
+    "def train_step(images):\n",
+    "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
+    "\n",
+    "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
+    "      generated_images = generator(noise, training=True)\n",
+    "\n",
+    "      real_output = discriminator(images, training=True)\n",
+    "      fake_output = discriminator(generated_images, training=True)\n",
+    "\n",
+    "      gen_loss = generator_loss(fake_output)\n",
+    "      disc_loss = discriminator_loss(real_output, fake_output)\n",
+    "\n",
+    "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
+    "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
+    "\n",
+    "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
+    "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "2M7LmLtGEMQJ"
+   },
+   "outputs": [],
+   "source": [
+    "def train(dataset, epochs):\n",
+    "  for epoch in range(epochs):\n",
+    "    start = time.time()\n",
+    "\n",
+    "    for image_batch in dataset:\n",
+    "      train_step(image_batch)\n",
+    "\n",
+    "    # Produce images for the GIF as we go\n",
+    "    display.clear_output(wait=True)\n",
+    "    generate_and_save_images(generator,\n",
+    "                             epoch + 1,\n",
+    "                             seed)\n",
+    "\n",
+    "    # Save the model every 15 epochs\n",
+    "    if (epoch + 1) % 15 == 0:\n",
+    "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
+    "\n",
+    "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
+    "\n",
+    "  # Generate after the final epoch\n",
+    "  display.clear_output(wait=True)\n",
+    "  generate_and_save_images(generator,\n",
+    "                           epochs,\n",
+    "                           seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "2aFF7Hk3XdeW"
+   },
+   "source": [
+    "**Generate and save images**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "RmdVsmvhPxyy"
+   },
+   "outputs": [],
+   "source": [
+    "def generate_and_save_images(model, epoch, test_input):\n",
+    "  # Notice `training` is set to False.\n",
+    "  # This is so all layers run in inference mode (batchnorm).\n",
+    "  predictions = model(test_input, training=False)\n",
+    "\n",
+    "  fig = plt.figure(figsize=(4,4))\n",
+    "\n",
+    "  for i in range(predictions.shape[0]):\n",
+    "      plt.subplot(4, 4, i+1)\n",
+    "      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n",
+    "      plt.axis('off')\n",
+    "\n",
+    "  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
+    "  plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "dZrd4CdjR-Fp"
+   },
+   "source": [
+    "## Train the model\n",
+    "Call the `train()` method defined above to train the generator and discriminator simultaneously. Note, training GANs can be tricky. It's important that the generator and discriminator do not overpower each other (e.g., that they train at a similar rate).\n",
+    "\n",
+    "At the beginning of the training, the generated images look like random noise. As training progresses, the generated digits will look increasingly real. After about 50 epochs, they resemble MNIST digits. This may take about one minute / epoch with the default settings on Colab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Ly3UN0SLLY2l"
+   },
+   "outputs": [],
+   "source": [
+    "train(train_dataset, EPOCHS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "rfM4YcPVPkNO"
+   },
+   "source": [
+    "Restore the latest checkpoint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "XhXsd0srPo8c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f89c41bfba8>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "P4M_vIbUi7c0"
+   },
+   "source": [
+    "## Create a GIF\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "WfO5wCdclHGL"
+   },
+   "outputs": [],
+   "source": [
+    "# Display a single image using the epoch number\n",
+    "def display_image(epoch_no):\n",
+    "  return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "5x3q9_Oe5q0A"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=288x288 at 0x7F8AECFBC7F0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display_image(EPOCHS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "NywiH3nL8guF"
+   },
+   "source": [
+    "Use `imageio` to create an animated gif using the images saved during training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "IGKQgENQ8lEI"
+   },
+   "outputs": [],
+   "source": [
+    "anim_file = 'dcgan.gif'\n",
+    "\n",
+    "with imageio.get_writer(anim_file, mode='I') as writer:\n",
+    "  filenames = glob.glob('image*.png')\n",
+    "  filenames = sorted(filenames)\n",
+    "  last = -1\n",
+    "  for i,filename in enumerate(filenames):\n",
+    "    frame = 2*(i**0.5)\n",
+    "    if round(frame) > round(last):\n",
+    "      last = frame\n",
+    "    else:\n",
+    "      continue\n",
+    "    image = imageio.imread(filename)\n",
+    "    writer.append_data(image)\n",
+    "  image = imageio.imread(filename)\n",
+    "  writer.append_data(image)\n",
+    "\n",
+    "import IPython\n",
+    "if IPython.version_info > (6,2,0,''):\n",
+    "  display.Image(filename=anim_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "cGhC3-fMWSwl"
+   },
+   "source": [
+    "If you're working in Colab you can download the animation with the code below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "uV0yiKpzNP1b"
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "  from google.colab import files\n",
+    "except ImportError:\n",
+    "   pass\n",
+    "else:\n",
+    "  files.download(anim_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "k6qC-SbjK0yW"
+   },
+   "source": [
+    "## Next steps\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xjjkT9KAK6H7"
+   },
+   "source": [
+    "This tutorial has shown the complete code necessary to write and train a GAN. As a next step, you might like to experiment with a different dataset, for example the Large-scale Celeb Faces Attributes (CelebA) dataset [available on Kaggle](https://www.kaggle.com/jessicali9530/celeba-dataset). To learn more about GANs we recommend the [NIPS 2016 Tutorial: Generative Adversarial Networks](https://arxiv.org/abs/1701.00160).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "dcgan.ipynb",
+   "private_outputs": true,
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/1. Generative Adverserial Neural Networks Chapter Overview.srt b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/1. Generative Adverserial Neural Networks Chapter Overview.srt
new file mode 100644
index 0000000000000000000000000000000000000000..0f162b2ffb9b871541c9b5ead30b62e68302651a
--- /dev/null
+++ b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/1. Generative Adverserial Neural Networks Chapter Overview.srt	
@@ -0,0 +1,55 @@
+1
+00:00:00,360 --> 00:00:05,600
+Hi and welcome to Chapter 24 way introduce generative adversarial networks.
+
+2
+00:00:05,640 --> 00:00:07,310
+Also known as guns.
+
+3
+00:00:07,310 --> 00:00:11,040
+So in this section we start with the introduction to guns.
+
+4
+00:00:11,130 --> 00:00:13,370
+I lead you up to how we got here to guns.
+
+5
+00:00:13,380 --> 00:00:19,430
+Give you some examples and give you a very high level intuitive explanation of guns and then explain
+
+6
+00:00:19,440 --> 00:00:21,660
+how guns are trained.
+
+7
+00:00:21,660 --> 00:00:24,540
+Next we go into the mathematics behind guns.
+
+8
+00:00:24,540 --> 00:00:30,590
+Now I try to make the math as simple as possible and give you a general algorithm on how guns operate
+
+9
+00:00:32,300 --> 00:00:35,740
+and next we actually go and implement a gun in Paris.
+
+10
+00:00:35,770 --> 00:00:39,230
+So we start generating digits from the amnestied asset.
+
+11
+00:00:39,310 --> 00:00:42,480
+And finally we do face aging gun.
+
+12
+00:00:42,520 --> 00:00:46,090
+This is going to make you look much older up to 60 years plus.
+
+13
+00:00:46,120 --> 00:00:47,140
+So stay tuned.
+
+14
+00:00:47,140 --> 00:00:49,250
+It's going to be fantastic.
diff --git a/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/3. Mathematics of GANs.srt b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/3. Mathematics of GANs.srt
new file mode 100644
index 0000000000000000000000000000000000000000..a199b6d9cf52d929f0e3fdab389078073c07c9e1
--- /dev/null
+++ b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/3. Mathematics of GANs.srt	
@@ -0,0 +1,207 @@
+1
+00:00:00,380 --> 00:00:06,240
+Hi and welcome to Chapter 24 Point two we take a look at the motto Be hand-guns.
+
+2
+00:00:06,700 --> 00:00:11,620
+So I remember I mentioned guns are basically two antagonistic networks that are sort of opposing each
+
+3
+00:00:11,620 --> 00:00:12,300
+other.
+
+4
+00:00:12,490 --> 00:00:19,300
+And it's also named generative adversarial network so adversarial basically adversarial games are so
+
+5
+00:00:19,300 --> 00:00:25,480
+few in AI where two or more agents play an opposing game with each other in a zero sum game that is
+
+6
+00:00:25,600 --> 00:00:28,410
+there is zero sum means your loss is my gain.
+
+7
+00:00:28,690 --> 00:00:32,760
+So an example of a zero sum game is Tic-Tac two if someone wins someone has to lose.
+
+8
+00:00:32,830 --> 00:00:35,190
+They can't be any ties.
+
+9
+00:00:35,260 --> 00:00:37,790
+I mean they can be ties but it's effectively no winner.
+
+10
+00:00:38,350 --> 00:00:45,130
+So in Gans we have two adversarial networks that generates the discriminator where each network is being
+
+11
+00:00:45,130 --> 00:00:47,860
+trained to win over the heart of each other.
+
+12
+00:00:48,130 --> 00:00:49,450
+That sort of training process works.
+
+13
+00:00:49,450 --> 00:00:50,640
+Remember right.
+
+14
+00:00:50,680 --> 00:00:56,190
+So the generator tries to keep making better fix to be the discriminator while they discriminate to
+
+15
+00:00:56,270 --> 00:00:58,450
+Lind's to get better at spotting fix.
+
+16
+00:00:58,450 --> 00:01:00,680
+So these guys just fighting it out here.
+
+17
+00:01:01,480 --> 00:01:04,540
+So now this is the core mathematics behind guns.
+
+18
+00:01:04,540 --> 00:01:06,200
+Now it's not that simple.
+
+19
+00:01:06,250 --> 00:01:12,010
+And if you're not familiar with basically a lot of these complex mats it's going to seem totally you're
+
+20
+00:01:12,010 --> 00:01:13,610
+going to lose you totally.
+
+21
+00:01:13,630 --> 00:01:17,120
+However I'll try my best to explain it to you in simple point form here.
+
+22
+00:01:17,470 --> 00:01:22,270
+So let x be a true dataset and Z the normally distributed noise.
+
+23
+00:01:22,290 --> 00:01:22,880
+OK.
+
+24
+00:01:23,230 --> 00:01:26,390
+And we let z be data from o late in space.
+
+25
+00:01:26,410 --> 00:01:34,050
+Z OK so Z is from all that in space and Z is B is normally distributed noise G India differentiated
+
+26
+00:01:34,050 --> 00:01:38,800
+all functions of the generative network and the discriminative network respectively.
+
+27
+00:01:39,100 --> 00:01:43,800
+So VFX represents a probability that came from the real data set.
+
+28
+00:01:44,100 --> 00:01:44,840
+OK.
+
+29
+00:01:45,490 --> 00:01:53,530
+We train d to maximize the probability log D and try and minimize the probability log 1 minus D D of
+
+30
+00:01:53,530 --> 00:01:54,560
+g g of Z.
+
+31
+00:01:54,720 --> 00:01:56,990
+OK this is what we are actually training.
+
+32
+00:01:57,040 --> 00:02:02,120
+Mathematically what defects and sorry what we are trying to do with guns.
+
+33
+00:02:02,140 --> 00:02:08,140
+So basically in short they play a min max scheme as explained above with each other trying to turn global
+
+34
+00:02:08,140 --> 00:02:11,120
+optimality.
+
+35
+00:02:11,130 --> 00:02:13,290
+So this is how the function is written here.
+
+36
+00:02:13,290 --> 00:02:17,190
+This is going to seem a bit complex but this is effectively what guns are doing here.
+
+37
+00:02:17,190 --> 00:02:23,170
+So the above functions of Cives the last function for a generative adversarial network.
+
+38
+00:02:23,250 --> 00:02:31,370
+Now it is important to note that this tomb here Laaga one minus D D being g of Z here saturates.
+
+39
+00:02:31,380 --> 00:02:34,740
+So we don't minimize it so so we don't minimize it.
+
+40
+00:02:34,740 --> 00:02:37,590
+Rather we maximize this system here.
+
+41
+00:02:38,010 --> 00:02:42,480
+So in order to prove that the sample generated by that generates and that work is exactly the same as
+
+42
+00:02:42,570 --> 00:02:49,530
+X which is Expwy no real data set we need to go deeper into the mathematics and use a cop callback to
+
+43
+00:02:49,560 --> 00:02:55,380
+kill their visions Terrel and the Jensens Jenson's challenge the evidence.
+
+44
+00:02:55,530 --> 00:02:59,670
+So I'm not going to get into the mathematics being to kill the visions and Jeanson channel divisions
+
+45
+00:02:59,970 --> 00:03:03,510
+but effectively if you want to know what we're trying to do here is I've been trying to figure out the
+
+46
+00:03:03,510 --> 00:03:08,730
+probability that x is a synthetic data came from a real data set.
+
+47
+00:03:08,760 --> 00:03:15,750
+So kill evidence sort of as a measure of basically how much this new distribution matches or Brydges
+
+48
+00:03:15,780 --> 00:03:18,200
+from the original distribution.
+
+49
+00:03:19,020 --> 00:03:22,340
+OK so that's it for the mathematics behind guns.
+
+50
+00:03:22,380 --> 00:03:28,950
+I know the Shapter was a bit confusing and all with how they actually achieve their purpose.
+
+51
+00:03:29,220 --> 00:03:35,220
+So next we're going to move on to building a simple gun in Chris and this kind is going to be used to
+
+52
+00:03:35,220 --> 00:03:36,960
+generate fake Henricksen agents.
diff --git a/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/4. Implementing GANs in Keras.srt b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/4. Implementing GANs in Keras.srt
new file mode 100644
index 0000000000000000000000000000000000000000..122a2e71f7ab6ca44613394357802f89484975a9
--- /dev/null
+++ b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/4. Implementing GANs in Keras.srt	
@@ -0,0 +1,675 @@
+1
+00:00:00,810 --> 00:00:05,100
+So in a section we're going to use again to generate some fake handwritten digits.
+
+2
+00:00:05,130 --> 00:00:06,340
+So let's get started.
+
+3
+00:00:07,410 --> 00:00:14,190
+So you're very familiar with amnesty to said by now I assume and this is some real data from the amnestied
+
+4
+00:00:14,200 --> 00:00:14,520
+assets.
+
+5
+00:00:14,530 --> 00:00:20,260
+We can see there's some variation in it here in this picture it is not that much variation however.
+
+6
+00:00:20,680 --> 00:00:22,120
+It's 60000 digits.
+
+7
+00:00:22,140 --> 00:00:23,590
+So it's a lot of variation.
+
+8
+00:00:23,790 --> 00:00:27,260
+So we're going to use this now to actually build that again.
+
+9
+00:00:27,690 --> 00:00:32,280
+That's a discriminator and it generates a network that creates fake handwritten digits.
+
+10
+00:00:32,280 --> 00:00:34,520
+So let's take a look at how we do this.
+
+11
+00:00:34,540 --> 00:00:39,740
+Firstly before we dive into the code does one explain a to you but how generators work.
+
+12
+00:00:39,810 --> 00:00:40,330
+OK.
+
+13
+00:00:40,710 --> 00:00:47,720
+So this is the printed output from Chris of generator network noticed is only 800000 parameters.
+
+14
+00:00:47,730 --> 00:00:51,350
+That's quite small which is going to be relatively quick to train.
+
+15
+00:00:51,390 --> 00:00:52,310
+We hope.
+
+16
+00:00:52,380 --> 00:00:52,930
+Get.
+
+17
+00:00:53,190 --> 00:00:58,700
+But what I want you to note is that remember previously in CNN's We had a soft Max Liard end and it
+
+18
+00:00:58,700 --> 00:01:01,670
+was operating a class probability right.
+
+19
+00:01:01,680 --> 00:01:03,720
+However our generator doesn't do that.
+
+20
+00:01:03,720 --> 00:01:08,400
+Our generator mix images so taken note of the output here.
+
+21
+00:01:08,730 --> 00:01:12,400
+It is basically that the dimensions of eminence digits.
+
+22
+00:01:12,420 --> 00:01:13,460
+So that's pretty cool.
+
+23
+00:01:13,470 --> 00:01:14,440
+It's right here.
+
+24
+00:01:14,910 --> 00:01:21,330
+And also instead of downsampling which we have done in previous CNN's we're actually upsampling a bit
+
+25
+00:01:21,510 --> 00:01:26,980
+and then we finally sample up sample and then we sort of do a kind of a Godalming sample right here.
+
+26
+00:01:27,280 --> 00:01:32,370
+It's only a sample is just the connective nodes will be from this to this and that's how we get our
+
+27
+00:01:32,370 --> 00:01:34,520
+image.
+
+28
+00:01:34,800 --> 00:01:36,440
+And what about the discriminator now.
+
+29
+00:01:36,710 --> 00:01:42,870
+Now does discriminator is tiny in terms of number parameters which is great for us and note its final
+
+30
+00:01:42,870 --> 00:01:43,270
+shape.
+
+31
+00:01:43,290 --> 00:01:46,290
+It's a binary but it's basically just one.
+
+32
+00:01:46,290 --> 00:01:50,660
+So yes but he basically puts this digits is real or fake.
+
+33
+00:01:50,710 --> 00:01:52,100
+And that's what he learns.
+
+34
+00:01:52,500 --> 00:01:58,300
+So before we dive into the code I just want to discuss with you how we actually construct the gun.
+
+35
+00:01:58,320 --> 00:02:00,450
+Basically this is a stacked again in Chris.
+
+36
+00:02:00,450 --> 00:02:01,040
+All right.
+
+37
+00:02:01,290 --> 00:02:03,230
+So I'm going to quickly go over this code.
+
+38
+00:02:03,270 --> 00:02:05,330
+Maybe you should go at it go it over on your own.
+
+39
+00:02:05,350 --> 00:02:06,350
+So it makes sense to you.
+
+40
+00:02:06,420 --> 00:02:06,980
+OK.
+
+41
+00:02:07,320 --> 00:02:13,990
+So remember no again the discriminator weights are made or frozen effectively.
+
+42
+00:02:14,010 --> 00:02:18,150
+So we basically make them non-tradable by putting a trainable flag is false.
+
+43
+00:02:18,480 --> 00:02:26,750
+Then we basically give our input basically to the dimensions of the lead in space that we defined previously.
+
+44
+00:02:26,760 --> 00:02:28,600
+So this is all again in Perth here.
+
+45
+00:02:29,040 --> 00:02:31,540
+This is basically where the random noise comes from.
+
+46
+00:02:31,740 --> 00:02:37,900
+And what we do is we put that into generator input here and then we call that X..
+
+47
+00:02:37,980 --> 00:02:41,910
+And what we do here now this is a part that's a bit tricky to understand.
+
+48
+00:02:41,910 --> 00:02:43,360
+So we have a generator.
+
+49
+00:02:43,500 --> 00:02:45,690
+Again input which is this here.
+
+50
+00:02:46,080 --> 00:02:49,400
+And this is basically effectively a model in a way here.
+
+51
+00:02:49,410 --> 00:02:56,750
+So now what we do we give or discriminate a data model and that basically becomes an output.
+
+52
+00:02:57,030 --> 00:03:01,760
+And then what we do here with this modeling is that we start to get input and output.
+
+53
+00:03:02,040 --> 00:03:06,720
+And this is how we basically combine or gun or discriminate or generators together.
+
+54
+00:03:07,080 --> 00:03:13,110
+And then we just compile this model choose an optimizer and this model basically has a binary cross
+
+55
+00:03:13,260 --> 00:03:18,420
+entropy because it's a binary model that's going to basically say yes or no to data being real or fake
+
+56
+00:03:18,870 --> 00:03:20,250
+that it generated.
+
+57
+00:03:20,820 --> 00:03:23,690
+So let's see what kind of outputs we actually got.
+
+58
+00:03:23,700 --> 00:03:29,490
+So this is after one epoch Wanny book doesn't it looks a bit like digits you can kind of see that this
+
+59
+00:03:29,490 --> 00:03:30,180
+is this.
+
+60
+00:03:30,220 --> 00:03:36,090
+And this is part of it for it is not very good it looks like someone it looks like fake faded handwriting.
+
+61
+00:03:36,090 --> 00:03:40,090
+To be fair I've messed up this is clearly me and not a number.
+
+62
+00:03:40,170 --> 00:03:42,720
+So let's see what happens after e-books.
+
+63
+00:03:43,530 --> 00:03:44,870
+OK things are getting a little better.
+
+64
+00:03:44,910 --> 00:03:46,560
+I'm seeing some trees here.
+
+65
+00:03:46,770 --> 00:03:51,970
+It looks like a 9 8 4 basically maybe a TD of these things can pass for digits.
+
+66
+00:03:51,980 --> 00:03:53,160
+If so much.
+
+67
+00:03:53,160 --> 00:03:55,200
+So let's see if the tree box.
+
+68
+00:03:55,620 --> 00:03:57,480
+Definitely I'm seeing some more improvement.
+
+69
+00:03:57,630 --> 00:04:00,060
+Some zeros and fours some sevens.
+
+70
+00:04:00,120 --> 00:04:06,310
+So you can see how a gun is progressively leaning and generating data that looks more like a real amnesty
+
+71
+00:04:06,400 --> 00:04:07,090
+set.
+
+72
+00:04:07,440 --> 00:04:09,810
+So after 40 bucks it's actually definitely getting better.
+
+73
+00:04:09,810 --> 00:04:11,610
+Now you can see it tree.
+
+74
+00:04:11,640 --> 00:04:14,480
+It looks like maybe a child is writing learning to right.
+
+75
+00:04:14,490 --> 00:04:17,550
+Or maybe me my handwriting sucks.
+
+76
+00:04:17,550 --> 00:04:23,160
+And then after five e-books it actually looks quite good maybe but maybe 80 percent of them I would
+
+77
+00:04:23,160 --> 00:04:28,320
+say look are 75 percent of them look like religious and that's only half to five bucks we can leave
+
+78
+00:04:28,320 --> 00:04:29,810
+this training for longer.
+
+79
+00:04:30,090 --> 00:04:31,340
+That's at the end of the chapter.
+
+80
+00:04:31,500 --> 00:04:36,570
+But we can leave this training for much longer and we can actually see it evolving to get even better
+
+81
+00:04:36,630 --> 00:04:39,360
+and better at making fake digits.
+
+82
+00:04:39,360 --> 00:04:40,770
+So that's our test again.
+
+83
+00:04:40,800 --> 00:04:44,690
+Let's go into it and I buy the book and actually play with it.
+
+84
+00:04:45,200 --> 00:04:45,500
+OK.
+
+85
+00:04:45,540 --> 00:04:46,840
+So this is all file here.
+
+86
+00:04:46,890 --> 00:04:49,340
+Amandus DC gun.
+
+87
+00:04:49,380 --> 00:04:52,060
+So let's open it up and we have it here.
+
+88
+00:04:52,140 --> 00:04:56,420
+So this is I could use this could have sort of taken from here but I've made some changes.
+
+89
+00:04:56,430 --> 00:04:58,300
+Not much.
+
+90
+00:04:58,420 --> 00:05:00,470
+All important we need.
+
+91
+00:05:01,080 --> 00:05:07,730
+And it is all leads and dimensions says 10 looks better but use 100 because 100 is what was in our that
+
+92
+00:05:07,730 --> 00:05:10,110
+we have actually begun original people.
+
+93
+00:05:10,550 --> 00:05:13,560
+So we do some administrative processing here.
+
+94
+00:05:13,760 --> 00:05:14,450
+OK.
+
+95
+00:05:14,680 --> 00:05:17,820
+The final optimizer and now we create our generator.
+
+96
+00:05:17,820 --> 00:05:22,700
+So remember before we used to call this a model now we give it a specific name because there's two models
+
+97
+00:05:22,780 --> 00:05:24,020
+in it again.
+
+98
+00:05:24,050 --> 00:05:25,520
+So the generators defined here.
+
+99
+00:05:25,640 --> 00:05:33,760
+These are parameters for a generator and displaces a 128 by 28 Fincham up which is basically our ominous
+
+100
+00:05:34,010 --> 00:05:39,920
+data set image and a printed summary and compile the model for the generator here.
+
+101
+00:05:40,210 --> 00:05:41,160
+So we have the generator.
+
+102
+00:05:41,320 --> 00:05:45,850
+And similarly we do this exact same thing for the discriminator.
+
+103
+00:05:46,240 --> 00:05:53,300
+This should probably be in line too long and exactly as we said one thing to note is that for remember
+
+104
+00:05:53,300 --> 00:06:01,790
+for binary updated operating CNN's We need to use sigmoid activation not so of Max.
+
+105
+00:06:01,850 --> 00:06:05,080
+So this is a part of that could I discussed in those slides.
+
+106
+00:06:05,390 --> 00:06:09,890
+This is the part where we actually create and combine those models to create or gun model.
+
+107
+00:06:10,280 --> 00:06:11,610
+So now we're moving on from there.
+
+108
+00:06:11,630 --> 00:06:16,480
+We define areas for losses and this is how we plot our losses in real time.
+
+109
+00:06:16,500 --> 00:06:18,700
+Is there some help or functions to see if our models.
+
+110
+00:06:18,700 --> 00:06:22,190
+Well so this is a printed outputs of those models.
+
+111
+00:06:22,190 --> 00:06:27,260
+When you run this code on your own and I encourage you to do so and I also encourage you to mess around
+
+112
+00:06:27,260 --> 00:06:31,400
+with some of these parameters not so much these parameters because you don't want to mess around with
+
+113
+00:06:31,400 --> 00:06:34,670
+this too much because you don't know exactly what you're doing just yet with guns.
+
+114
+00:06:34,820 --> 00:06:40,700
+If you're feeling adventurous feel free to probably change kernel sizes all add in some new convolutional
+
+115
+00:06:40,910 --> 00:06:44,360
+as well maybe just a linear it's if you want.
+
+116
+00:06:44,900 --> 00:06:47,660
+So anyway let's go back to this.
+
+117
+00:06:47,660 --> 00:06:50,320
+So now this is how we train our guns here.
+
+118
+00:06:50,410 --> 00:06:50,980
+OK.
+
+119
+00:06:51,380 --> 00:06:53,850
+This part of the code is definitely a bit tricky.
+
+120
+00:06:54,050 --> 00:06:59,810
+I have it set to a box but you can probably put it as whatever you want but at five and you probably
+
+121
+00:06:59,810 --> 00:07:03,400
+get this book with five because I'm going to save it now.
+
+122
+00:07:03,440 --> 00:07:05,510
+So even checkpoint.
+
+123
+00:07:05,510 --> 00:07:10,540
+So this is this is this loop here is how we actually train to guns.
+
+124
+00:07:10,640 --> 00:07:16,640
+So we're going a step through this loop and hopefully I don't lose you because guns are definitely different.
+
+125
+00:07:16,670 --> 00:07:19,390
+Training CNN's said sometimes a bit confusing.
+
+126
+00:07:19,430 --> 00:07:21,840
+So that's fiercly see what we do.
+
+127
+00:07:21,890 --> 00:07:23,790
+So right now I'm running this loop here.
+
+128
+00:07:24,170 --> 00:07:29,540
+So by the way these nice little visuals here that basically update us on the progress and you can see
+
+129
+00:07:29,540 --> 00:07:31,250
+guns are quite slow to train.
+
+130
+00:07:31,260 --> 00:07:37,750
+They started this like maybe two or three minutes ago because in a way those visuals are generated by
+
+131
+00:07:38,030 --> 00:07:40,880
+UDMA which is a nice function we can use inside of loops.
+
+132
+00:07:41,000 --> 00:07:45,620
+That basically tells you the progress of this faster and gives you a nice little display.
+
+133
+00:07:45,680 --> 00:07:47,450
+So let's go back to the loop.
+
+134
+00:07:47,520 --> 00:07:52,090
+So remember we have to generate some fake images here and mix it with some real images.
+
+135
+00:07:52,100 --> 00:07:57,080
+So this line here is a lead in dimension and about size to get some noise.
+
+136
+00:07:57,110 --> 00:08:00,070
+And this is noise is used to generate fake images here.
+
+137
+00:08:00,410 --> 00:08:04,790
+So we give it to the generator to predict and he gives us fake images.
+
+138
+00:08:04,910 --> 00:08:10,670
+Image Bache comes from a real image data that takes some random real images and then we mixed them together
+
+139
+00:08:10,700 --> 00:08:11,560
+in x.
+
+140
+00:08:11,750 --> 00:08:12,350
+So that's good.
+
+141
+00:08:12,350 --> 00:08:18,900
+So for now these little labels here we generate all generated data and real data.
+
+142
+00:08:19,310 --> 00:08:20,880
+That's how we compile it here.
+
+143
+00:08:21,260 --> 00:08:23,970
+And then this is a part where we actually trained the discriminator.
+
+144
+00:08:24,200 --> 00:08:30,450
+So we we unfreezes it temporarily and then we give them this basically batch of data to look at.
+
+145
+00:08:30,500 --> 00:08:37,040
+And then with the labels that we got here and then we have him basically he trains and dispatch and
+
+146
+00:08:37,040 --> 00:08:37,940
+we get his loss back.
+
+147
+00:08:37,940 --> 00:08:41,630
+Remember we created loss functions up here.
+
+148
+00:08:41,930 --> 00:08:45,510
+Actually it was that there might have been.
+
+149
+00:08:45,800 --> 00:08:47,050
+That's a different loss of function.
+
+150
+00:08:47,170 --> 00:08:49,450
+But either way we create his last function here.
+
+151
+00:08:49,640 --> 00:08:51,560
+And I'm guessing we're going to stick it to get the pendant here.
+
+152
+00:08:51,590 --> 00:08:52,440
+Yep.
+
+153
+00:08:52,460 --> 00:08:58,860
+So a loss or individual losses are done here and then this is how we trained the discriminator.
+
+154
+00:08:58,860 --> 00:09:00,900
+Next we trained a generator.
+
+155
+00:09:01,220 --> 00:09:09,140
+So just like we did before we take some we get some random noise here random images again get the labels
+
+156
+00:09:09,140 --> 00:09:15,810
+here freeze discriminator let's remember this process and then when this is how we train to generate
+
+157
+00:09:16,100 --> 00:09:23,100
+with images noisy images and all labels and what we do is we get lost.
+
+158
+00:09:23,090 --> 00:09:29,200
+Here we append this loss here and we will print this out and plot at the end of an epoch.
+
+159
+00:09:29,300 --> 00:09:34,910
+What we do here in this loop here is that it's IPAC so one or divisible by five.
+
+160
+00:09:35,120 --> 00:09:37,030
+We just plot the images here.
+
+161
+00:09:37,370 --> 00:09:38,540
+All right.
+
+162
+00:09:38,930 --> 00:09:41,110
+So this is going to take quite some time.
+
+163
+00:09:41,130 --> 00:09:44,060
+We also see if our models and plot these things here as well.
+
+164
+00:09:44,070 --> 00:09:45,920
+I mentioned before.
+
+165
+00:09:46,100 --> 00:09:47,520
+So I'm going to leave just to run.
+
+166
+00:09:47,570 --> 00:09:53,000
+And in the end we will actually come back to you and decide and we will see our results.
+
+167
+00:09:54,300 --> 00:09:57,950
+Actually we don't need to because to be fair the results are here.
+
+168
+00:09:58,310 --> 00:10:00,670
+It was exactly after five ebox.
+
+169
+00:10:00,770 --> 00:10:04,930
+So you we wait what five 10 minutes for Spock to finish.
diff --git a/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/5. Face Aging GAN.srt b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/5. Face Aging GAN.srt
new file mode 100644
index 0000000000000000000000000000000000000000..213e33f8bc4358dbebd7064e6a8c7044798ceb53
--- /dev/null
+++ b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/5. Face Aging GAN.srt	
@@ -0,0 +1,331 @@
+1
+00:00:00,930 --> 00:00:05,760
+And welcome to chapter twenty four point three where we're going to build a feast in Ghana.
+
+2
+00:00:05,930 --> 00:00:08,090
+Basically this is called The Edge Seaghan.
+
+3
+00:00:08,140 --> 00:00:12,620
+See sense for conditional again which is faces up to 60 years plus.
+
+4
+00:00:12,620 --> 00:00:14,420
+So let's take a look of this project.
+
+5
+00:00:14,430 --> 00:00:20,580
+Now this is an example of the face aging effect that it can do can take this guy who looks maybe like
+
+6
+00:00:20,760 --> 00:00:27,910
+20 and age him up to 20 years at age differently says it's him at 50 to 60 years old.
+
+7
+00:00:27,990 --> 00:00:29,640
+So pretty cool.
+
+8
+00:00:29,640 --> 00:00:34,770
+We're going to do this for a sample test face and I'm going to show you how to implement this project.
+
+9
+00:00:35,070 --> 00:00:39,170
+Now remember I told you guns are notoriously hard to train.
+
+10
+00:00:39,480 --> 00:00:43,770
+So what we're going to do in this project we're going to actually use their sourcecode because an open
+
+11
+00:00:43,770 --> 00:00:49,560
+source project and it's a fairly recent 20 18 people that was published in computer vision and pattern
+
+12
+00:00:49,560 --> 00:00:51,360
+recognition conference.
+
+13
+00:00:51,360 --> 00:00:56,790
+So it's actually one of the top computer vision conferences in the world and these are the guys who
+
+14
+00:00:56,790 --> 00:00:57,510
+made it.
+
+15
+00:00:57,510 --> 00:01:01,820
+Zong we Wang Zugang and this guy these guys here.
+
+16
+00:01:02,460 --> 00:01:05,910
+And if you want to learn more about their sourcecode you can visit dig it up.
+
+17
+00:01:05,920 --> 00:01:10,520
+This is a could we'll be using it as project of already cloned at two or three.
+
+18
+00:01:10,590 --> 00:01:14,770
+Essentially this is an illustration of can and how it works.
+
+19
+00:01:15,060 --> 00:01:18,350
+And now we're about to implement this project on our own.
+
+20
+00:01:18,360 --> 00:01:25,290
+So let's go to a virtual machine and I'll show you how to execute this code on you own test images.
+
+21
+00:01:25,320 --> 00:01:33,930
+OK so what I've done I've actually cloned these guys repository here by doing this here and I've already
+
+22
+00:01:34,320 --> 00:01:38,950
+put it few here in a 24 24 generative guns.
+
+23
+00:01:38,970 --> 00:01:45,140
+So what we do we go to I picked a book or two men'll here and it's navigated under a tree so letters
+
+24
+00:01:45,230 --> 00:01:52,500
+lists and CD deep in atlast again and CD 24.
+
+25
+00:01:53,310 --> 00:01:55,010
+We pressed tab to autocomplete.
+
+26
+00:01:55,050 --> 00:01:57,800
+By the way kiss you wondering how I typed all of us.
+
+27
+00:01:57,800 --> 00:01:59,500
+So it was clear this is clear.
+
+28
+00:01:59,520 --> 00:02:03,740
+And now we see the face aging directory.
+
+29
+00:02:03,750 --> 00:02:05,120
+So this is a directory here.
+
+30
+00:02:05,130 --> 00:02:12,520
+We've navigated it in our terminal display the contents file we want to run is testing images not pi.
+
+31
+00:02:12,570 --> 00:02:17,880
+So before we do that let's let's just go into this territory and I'll show you what images we're actually
+
+32
+00:02:17,880 --> 00:02:18,900
+going to test it on.
+
+33
+00:02:19,200 --> 00:02:24,700
+So there's a director made a few guys called your test images here and I have a picture of Justin Bieber.
+
+34
+00:02:24,930 --> 00:02:29,220
+He's a guy we're going to age first thing to do is that is a real image here.
+
+35
+00:02:29,240 --> 00:02:31,600
+I want to show you how we actually tested properly.
+
+36
+00:02:31,680 --> 00:02:36,400
+So what we do we have to extract his fees from the image so let's do that here.
+
+37
+00:02:36,450 --> 00:02:43,480
+Let's try to get it in a nice quick shape as it is roughly square.
+
+38
+00:02:44,000 --> 00:02:49,350
+And what we do we just go to image here and crop to selection.
+
+39
+00:02:49,400 --> 00:02:53,060
+So we opened with this program called Pinto and then we cropped it here.
+
+40
+00:02:53,090 --> 00:02:54,390
+So let's see that.
+
+41
+00:02:54,830 --> 00:02:59,300
+And then secondly what we have to do is resize it to 400 by 400.
+
+42
+00:02:59,510 --> 00:03:05,410
+So let's resize our pixels let's forget about the aspect ratio 400 by 400 is going to squash his head
+
+43
+00:03:05,410 --> 00:03:07,150
+slightly but that's OK.
+
+44
+00:03:07,760 --> 00:03:08,780
+And we see it again.
+
+45
+00:03:08,810 --> 00:03:09,940
+So let's close this.
+
+46
+00:03:10,190 --> 00:03:12,810
+So we have all saved image of Justin Bieber here.
+
+47
+00:03:13,200 --> 00:03:16,110
+Let's just copy his image name here.
+
+48
+00:03:16,510 --> 00:03:17,160
+Actually we don't need it.
+
+49
+00:03:17,160 --> 00:03:23,000
+Sorry we just run what this program does it looks in images an artist folder and an ages all of them
+
+50
+00:03:23,540 --> 00:03:28,610
+and outputs them to our test for that I believe or training for the.
+
+51
+00:03:28,650 --> 00:03:30,500
+Actually no it's a different story.
+
+52
+00:03:30,780 --> 00:03:32,650
+I'll show you guys shortly.
+
+53
+00:03:32,700 --> 00:03:34,980
+So let's just run this code now.
+
+54
+00:03:34,980 --> 00:03:39,650
+Forget about what I've been doing here and it's clear to us again.
+
+55
+00:03:39,740 --> 00:03:42,220
+So what we do do fall we want to run.
+
+56
+00:03:42,640 --> 00:03:46,390
+Let's just go back to it here is called test images of Pi.
+
+57
+00:03:46,440 --> 00:03:48,440
+This is a fellow when to copy them here.
+
+58
+00:03:49,400 --> 00:03:53,380
+And we go on test images by.
+
+59
+00:03:54,320 --> 00:04:02,200
+Good point does happen because we're not working in virtual machines let's source it the environment.
+
+60
+00:04:02,290 --> 00:04:11,320
+So let's sort of activate C.V so environments and know we can run this.
+
+61
+00:04:11,490 --> 00:04:13,470
+There we go running.
+
+62
+00:04:13,520 --> 00:04:16,190
+It doesn't take that long to execute on an image.
+
+63
+00:04:16,250 --> 00:04:18,640
+Maybe about 10 to 20 seconds.
+
+64
+00:04:18,650 --> 00:04:21,350
+So it's put the images one by one here.
+
+65
+00:04:21,560 --> 00:04:25,420
+So let us wait for this to finish and I'll come back and she or her results.
+
+66
+00:04:25,430 --> 00:04:25,760
+OK.
+
+67
+00:04:25,790 --> 00:04:27,180
+There we go it's actually done.
+
+68
+00:04:27,230 --> 00:04:28,820
+So let's find our images now.
+
+69
+00:04:29,120 --> 00:04:31,710
+So we go to h here.
+
+70
+00:04:32,270 --> 00:04:33,700
+And these are images that are produced.
+
+71
+00:04:33,710 --> 00:04:39,830
+So let's sort by date is November 15 and go in your own.
+
+72
+00:04:40,240 --> 00:04:41,130
+So here we go.
+
+73
+00:04:41,130 --> 00:04:43,300
+So these are the images we just created.
+
+74
+00:04:43,300 --> 00:04:46,000
+So let's take a look him here.
+
+75
+00:04:46,040 --> 00:04:51,100
+Ten years older 20 years older two years older 40 years older.
+
+76
+00:04:51,110 --> 00:04:56,280
+And that's it does it look drastically different just has some things on his face.
+
+77
+00:04:56,660 --> 00:04:58,740
+But definitely the aging effect we can see.
+
+78
+00:04:58,760 --> 00:05:04,920
+So let's take a look at the original him here and progressively older.
+
+79
+00:05:05,360 --> 00:05:06,560
+There we go.
+
+80
+00:05:07,310 --> 00:05:07,670
+OK.
+
+81
+00:05:07,820 --> 00:05:09,560
+So that's it for this project.
+
+82
+00:05:09,980 --> 00:05:14,510
+Hope you had fun and try your images try a friend's images.
+
+83
+00:05:14,800 --> 00:05:16,620
+It'll be a lot of fun to do.
diff --git a/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/5.1 Download Data.html b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/5.1 Download Data.html
new file mode 100644
index 0000000000000000000000000000000000000000..6a4d7b5d78948eb96ee326a46a5e8397e8506368
--- /dev/null
+++ b/24. Generative Adversarial Networks (GANs) Simulate Aging Faces/5.1 Download Data.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1SjB1dekhpYTLsp6f-ze9esVv_zgxbVAQ/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/25. Face Recognition with VGGFace/1. Basic Face Recognition using LittleVGG CNN.html b/25. Face Recognition with VGGFace/1. Basic Face Recognition using LittleVGG CNN.html
new file mode 100644
index 0000000000000000000000000000000000000000..0fc7b84dd5fe0f8d99c58c958b3e87ac7e99e406
--- /dev/null
+++ b/25. Face Recognition with VGGFace/1. Basic Face Recognition using LittleVGG CNN.html	
@@ -0,0 +1 @@
+<p><strong>Intro and Lesson:</strong></p><p>In this section, I've included the code to build a dataset of faces (you'll manually have to sort them through though) and the code required to train a very simple VGG based model to recognize faces.</p><p>It's purpose is to demonstrate the shortcomings of using a regular CNN in face recognition and we'll later include more advanced Face Recognition Algorithms. </p><p>I've included the code to create your own face dataset from a video (Face Extraction from Video - Build Dataset.ipynb)</p><p>and the code required to train and test your model.</p><p>Using a simple VGG based CNN for facial recognition is not a good idea, later on I'll be introducing more advanced methods to do this more effectively.</p><p><strong>The iPython Notebooks to load first are:</strong></p><ul><li><p>25.0 Face Extraction from Video - Build Dataset.ipynb</p></li><li><p>25.1 Face Recognition - Friends Characters - Train and Test</p></li></ul><p><br></p><p><br></p>
\ No newline at end of file
diff --git a/25. Face Recognition with VGGFace/1.1 All Facial Recognition Code.html b/25. Face Recognition with VGGFace/1.1 All Facial Recognition Code.html
new file mode 100644
index 0000000000000000000000000000000000000000..5d7b0241a6047d1d25ff1bd7ddb1d9dc60e29578
--- /dev/null
+++ b/25. Face Recognition with VGGFace/1.1 All Facial Recognition Code.html	
@@ -0,0 +1 @@
+<script type="text/javascript">window.location = "https://drive.google.com/file/d/1dRA2Rlf3gCrs6mPOQrJW5o_8yt3Pf0TZ/view?usp=sharing";</script>
\ No newline at end of file
diff --git a/25. Face Recognition with VGGFace/2. Face Matching with VGGFace.html b/25. Face Recognition with VGGFace/2. Face Matching with VGGFace.html
new file mode 100644
index 0000000000000000000000000000000000000000..36be18226ecaa21b96772469af2b7ac75564670a
--- /dev/null
+++ b/25. Face Recognition with VGGFace/2. Face Matching with VGGFace.html	
@@ -0,0 +1 @@
+<p><strong>Intro:</strong></p><p>In this section, you will be running code that loads a pre-trained model called VGGFaces</p><p>http://www.robots.ox.ac.uk/~vgg/software/vgg_face/</p><p>The VGG-Face CNN descriptors are computed using our CNN implementation based on the <strong>VGG-Very-Deep-16 CNN </strong>architecture as described in [1] and are evaluated on the<strong> Labeled Faces in the Wild </strong>and the<strong> YouTube Faces</strong> dataset.</p><p><strong> The iPython Notebook in this lesson is:</strong></p><ul><li><p>25.2 Face Recognition - Matching Faces.ipynb</p></li></ul><p><strong>Lesson:</strong></p><p>The code in this notebook loads VGGFace and defines a similarity function. This similarity function compares two faces using the VGGFace output descriptors. We then define a threshold metric that determines if the two faces under comparison are the same person.</p><p><strong>References</strong>: </p><p>Labeled Faces in the Wild - http://vis-www.cs.umass.edu/lfw/lfw.pdf</p><p>YouTube Faces - https://www.cs.tau.ac.il/~wolf/ytfaces/WolfHassnerMaoz_CVPR11.pdf</p>
\ No newline at end of file
diff --git a/25. Face Recognition/25.0 Face Extraction from Video - Build Dataset.ipynb b/25. Face Recognition/25.0 Face Extraction from Video - Build Dataset.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3c211e88ca6ebc792f42cf7d1c28509c2f4c066c
--- /dev/null
+++ b/25. Face Recognition/25.0 Face Extraction from Video - Build Dataset.ipynb	
@@ -0,0 +1,93 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extracting the faces from a video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import os\n",
+    "import cv2\n",
+    "import dlib\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define Image Path Here\n",
+    "image_path = \"./images/\"\n",
+    "\n",
+    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "               font_scale=0.8, thickness=1):\n",
+    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
+    "    x, y = point\n",
+    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
+    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
+    "    \n",
+    "detector = dlib.get_frontal_face_detector()\n",
+    "\n",
+    "# Initialize Webcam\n",
+    "cap = cv2.VideoCapture('testfriends.mp4')\n",
+    "img_size = 64\n",
+    "margin = 0.2\n",
+    "frame_count = 0\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    frame_count += 1\n",
+    "    print(frame_count)   \n",
+    " \n",
+    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "    img_h, img_w, _ = np.shape(input_img)\n",
+    "    detected = detector(frame, 1)\n",
+    "    faces = []\n",
+    "    \n",
+    "    if len(detected) > 0:\n",
+    "        for i, d in enumerate(detected):\n",
+    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
+    "            xw1 = max(int(x1 - margin * w), 0)\n",
+    "            yw1 = max(int(y1 - margin * h), 0)\n",
+    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
+    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
+    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
+    "            file_name = \"./faces/\"+str(frame_count)+\"_\"+str(i)+\".jpg\"\n",
+    "            cv2.imwrite(file_name, face)\n",
+    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
+    "\n",
+    "    cv2.imshow(\"Face Detector\", frame)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/25. Face Recognition/25.1 Face Recognition - Friends Characters - Train and Test.ipynb b/25. Face Recognition/25.1 Face Recognition - Friends Characters - Train and Test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ccb0a2615fc2e31ab9e820bc942e240a2cfe4c13
--- /dev/null
+++ b/25. Face Recognition/25.1 Face Recognition - Friends Characters - Train and Test.ipynb	
@@ -0,0 +1,521 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Deep Learning Face Recogntion\n",
+    "## Building a Friends TV Show Character Identifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The learning objective of this lesson (25.1) is the create a 'dumb' face classifer using our LittleVGG model. We are simply training it with 100s of pictures of each Friends Character, and testing our model using a Test Video. \n",
+    "\n",
+    "## You will see how this is an in-effective way to do Face Recognition, why?\n",
+    "Because a traditional CNN will be looking at small subsections of a face to identify it. However, the angle and deformation of a face can easily throw off our model, making it mis-classify and match it to faces that aren't correct - this will happen a lot especially when our training data looks different to our test data. Imagine if the training data, one face was titled downward mostly. Our CNN will more likely identify any future face on our test data that looks downward, to be that face."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's train our model\n",
+    "I've created a dataset with the faces of 4 Friends characters taken from a handful of different scenes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 2663 images belonging to 4 classes.\n",
+      "Found 955 images belonging to 4 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "import tensorflow as tf \n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "import os\n",
+    "\n",
+    "num_classes = 4\n",
+    "img_rows, img_cols = 48, 48\n",
+    "batch_size = 16\n",
+    "\n",
+    "train_data_dir = './faces/train'\n",
+    "validation_data_dir = './faces/validation'\n",
+    "\n",
+    "# Let's use some data augmentaiton \n",
+    "train_datagen = ImageDataGenerator(\n",
+    "      rescale=1./255,\n",
+    "      rotation_range=30,\n",
+    "      shear_range=0.3,\n",
+    "      zoom_range=0.3,\n",
+    "      width_shift_range=0.4,\n",
+    "      height_shift_range=0.4,\n",
+    "      horizontal_flip=True,\n",
+    "      fill_mode='nearest')\n",
+    " \n",
+    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
+    " \n",
+    "train_generator = train_datagen.flow_from_directory(\n",
+    "        train_data_dir,\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True)\n",
+    " \n",
+    "validation_generator = validation_datagen.flow_from_directory(\n",
+    "        validation_data_dir,\n",
+    "        target_size=(img_rows, img_cols),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Our Keras imports\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import BatchNormalization\n",
+    "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+    "from tensorflow.keras.layers import ELU\n",
+    "from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a simple VGG based model for Face Recognition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "conv2d (Conv2D)              (None, 48, 48, 32)        896       \n",
+      "_________________________________________________________________\n",
+      "activation (Activation)      (None, 48, 48, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization (BatchNo (None, 48, 48, 32)        128       \n",
+      "_________________________________________________________________\n",
+      "conv2d_1 (Conv2D)            (None, 48, 48, 32)        9248      \n",
+      "_________________________________________________________________\n",
+      "activation_1 (Activation)    (None, 48, 48, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_1 (Batch (None, 48, 48, 32)        128       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d (MaxPooling2D) (None, 24, 24, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout (Dropout)            (None, 24, 24, 32)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     \n",
+      "_________________________________________________________________\n",
+      "activation_2 (Activation)    (None, 24, 24, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_2 (Batch (None, 24, 24, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "conv2d_3 (Conv2D)            (None, 24, 24, 64)        36928     \n",
+      "_________________________________________________________________\n",
+      "activation_3 (Activation)    (None, 24, 24, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_3 (Batch (None, 24, 24, 64)        256       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "dropout_1 (Dropout)          (None, 12, 12, 64)        0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_4 (Conv2D)            (None, 12, 12, 128)       73856     \n",
+      "_________________________________________________________________\n",
+      "activation_4 (Activation)    (None, 12, 12, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_4 (Batch (None, 12, 12, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "conv2d_5 (Conv2D)            (None, 12, 12, 128)       147584    \n",
+      "_________________________________________________________________\n",
+      "activation_5 (Activation)    (None, 12, 12, 128)       0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_5 (Batch (None, 12, 12, 128)       512       \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_2 (Dropout)          (None, 6, 6, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "conv2d_6 (Conv2D)            (None, 6, 6, 256)         295168    \n",
+      "_________________________________________________________________\n",
+      "activation_6 (Activation)    (None, 6, 6, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_6 (Batch (None, 6, 6, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "conv2d_7 (Conv2D)            (None, 6, 6, 256)         590080    \n",
+      "_________________________________________________________________\n",
+      "activation_7 (Activation)    (None, 6, 6, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_7 (Batch (None, 6, 6, 256)         1024      \n",
+      "_________________________________________________________________\n",
+      "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "dropout_3 (Dropout)          (None, 3, 3, 256)         0         \n",
+      "_________________________________________________________________\n",
+      "flatten (Flatten)            (None, 2304)              0         \n",
+      "_________________________________________________________________\n",
+      "dense (Dense)                (None, 64)                147520    \n",
+      "_________________________________________________________________\n",
+      "activation_8 (Activation)    (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_8 (Batch (None, 64)                256       \n",
+      "_________________________________________________________________\n",
+      "dropout_4 (Dropout)          (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 64)                4160      \n",
+      "_________________________________________________________________\n",
+      "activation_9 (Activation)    (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_9 (Batch (None, 64)                256       \n",
+      "_________________________________________________________________\n",
+      "dropout_5 (Dropout)          (None, 64)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 4)                 260       \n",
+      "_________________________________________________________________\n",
+      "activation_10 (Activation)   (None, 4)                 0         \n",
+      "=================================================================\n",
+      "Total params: 1,328,548\n",
+      "Trainable params: 1,326,372\n",
+      "Non-trainable params: 2,176\n",
+      "_________________________________________________________________\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "\n",
+    "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
+    "                 input_shape = (img_rows, img_cols, 3)))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
+    "                 input_shape = (img_rows, img_cols, 3)))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
+    "# layer set\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "# Block #5: first set of FC => RELU layers\n",
+    "model.add(Flatten())\n",
+    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Block #6: second set of FC => RELU layers\n",
+    "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation('elu'))\n",
+    "model.add(BatchNormalization())\n",
+    "model.add(Dropout(0.5))\n",
+    "\n",
+    "# Block #7: softmax classifier\n",
+    "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
+    "model.add(Activation(\"softmax\"))\n",
+    "\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training our Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 166 steps, validate for 59 steps\n",
+      "165/166 [============================>.] - ETA: 0s - loss: 0.8249 - accuracy: 0.6743\n",
+      "Epoch 00001: val_loss improved from inf to 2.41761, saving model to face_recognition_friends_vgg.h5\n",
+      "166/166 [==============================] - 73s 437ms/step - loss: 0.8229 - accuracy: 0.6747 - val_loss: 2.4176 - val_accuracy: 0.3623\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.optimizers import RMSprop, SGD, Adam\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
+    "\n",
+    "                     \n",
+    "checkpoint = ModelCheckpoint(\"face_recognition_friends_vgg.h5\",\n",
+    "                             monitor=\"val_loss\",\n",
+    "                             mode=\"min\",\n",
+    "                             save_best_only = True,\n",
+    "                             verbose=1)\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor = 'val_loss', \n",
+    "                          min_delta = 0, \n",
+    "                          patience = 3,\n",
+    "                          verbose = 1,\n",
+    "                          restore_best_weights = True)\n",
+    "\n",
+    "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
+    "\n",
+    "# we put our call backs into a callback list\n",
+    "callbacks = [earlystop, checkpoint, reduce_lr]\n",
+    "\n",
+    "# We use a very small learning rate \n",
+    "model.compile(loss = 'categorical_crossentropy',\n",
+    "              optimizer = Adam(lr=0.01),\n",
+    "              metrics = ['accuracy'])\n",
+    "\n",
+    "nb_train_samples = 2663\n",
+    "nb_validation_samples = 955\n",
+    "epochs = 1\n",
+    "\n",
+    "history = model.fit_generator(\n",
+    "    train_generator,\n",
+    "    steps_per_epoch = nb_train_samples // batch_size,\n",
+    "    epochs = epochs,\n",
+    "    callbacks = callbacks,\n",
+    "    validation_data = validation_generator,\n",
+    "    validation_steps = nb_validation_samples // batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Getting our Class Labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class_labels = validation_generator.class_indices\n",
+    "class_labels = {v: k for k, v in class_labels.items()}\n",
+    "classes = list(class_labels.values())\n",
+    "class_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load our model\n",
+    "from tensorflow.keras.models import load_model\n",
+    "\n",
+    "classifier = load_model('face_recognition_friends_vgg.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing our model on some real video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'dlib' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-10-d316c51cf110>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mimg_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m64\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[0mdetector\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_frontal_face_detector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[0mcap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVideoCapture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'testfriends.mp4'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'dlib' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import os\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "face_classes = {0: 'Chandler', 1: 'Joey', 2: 'Pheobe', 3: 'Rachel'}\n",
+    "\n",
+    "def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "               font_scale=0.8, thickness=1):\n",
+    "    size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n",
+    "    x, y = point\n",
+    "    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n",
+    "    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\n",
+    "    \n",
+    "margin = 0.2\n",
+    "# load model and weights\n",
+    "img_size = 64\n",
+    "\n",
+    "detector = dlib.get_frontal_face_detector()\n",
+    "\n",
+    "cap = cv2.VideoCapture('testfriends.mp4')\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    frame = cv2.resize(frame, None, fx=0.5, fy=0.5, interpolation = cv2.INTER_LINEAR)\n",
+    "    preprocessed_faces = []           \n",
+    " \n",
+    "    input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "    img_h, img_w, _ = np.shape(input_img)\n",
+    "    detected = detector(frame, 1)\n",
+    "    faces = np.empty((len(detected), img_size, img_size, 3))\n",
+    "    \n",
+    "    preprocessed_faces_emo = []\n",
+    "    if len(detected) > 0:\n",
+    "        for i, d in enumerate(detected):\n",
+    "            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()\n",
+    "            xw1 = max(int(x1 - margin * w), 0)\n",
+    "            yw1 = max(int(y1 - margin * h), 0)\n",
+    "            xw2 = min(int(x2 + margin * w), img_w - 1)\n",
+    "            yw2 = min(int(y2 + margin * h), img_h - 1)\n",
+    "            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)\n",
+    "            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)\n",
+    "            #faces[i, :, :, :] = cv2.resize(frame[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))\n",
+    "            face =  frame[yw1:yw2 + 1, xw1:xw2 + 1, :]\n",
+    "            face = cv2.resize(face, (48, 48), interpolation = cv2.INTER_AREA)\n",
+    "            face = face.astype(\"float\") / 255.0\n",
+    "            face = img_to_array(face)\n",
+    "            face = np.expand_dims(face, axis=0)\n",
+    "            preprocessed_faces.append(face)\n",
+    "\n",
+    "        # make a prediction for Emotion \n",
+    "        face_labels = []\n",
+    "        for i, d in enumerate(detected):\n",
+    "            preds = classifier.predict(preprocessed_faces[i])[0]\n",
+    "            face_labels.append(face_classes[preds.argmax()])\n",
+    "        \n",
+    "        # draw results\n",
+    "        for i, d in enumerate(detected):\n",
+    "            label = \"{}\".format(face_labels[i])\n",
+    "            draw_label(frame, (d.left(), d.top()), label)\n",
+    "\n",
+    "    cv2.imshow(\"Friend Character Identifier\", frame)\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/25. Face Recognition/25.2 Face Recogition - Matching Faces.ipynb b/25. Face Recognition/25.2 Face Recogition - Matching Faces.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cf38d5b8ab4c7619a620eee4d2f614e526515d40
--- /dev/null
+++ b/25. Face Recognition/25.2 Face Recogition - Matching Faces.ipynb	
@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Face Similarity Matching, the basis our our Face Classifier\n",
+    "\n",
+    "In this section, we will load a pre-trained model of VGGFace (trainined on thousands of faces) and use it, together with a similarity metric, to define whether two faces are of the same person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import our libaries\n",
+    "from tensorflow.keras.models import Model, Sequential\n",
+    "from tensorflow.keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "from tensorflow.keras.preprocessing.image import load_img, save_img, img_to_array\n",
+    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define our VGGFace Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model created\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))\n",
+    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(Convolution2D(4096, (7, 7), activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Convolution2D(4096, (1, 1), activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Convolution2D(2622, (1, 1)))\n",
+    "model.add(Flatten())\n",
+    "model.add(Activation('softmax'))\n",
+    "print(\"Model created\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load out VGG Face Weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#you can download the pretrained weights from the following link \n",
+    "#https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing\n",
+    "#or you can find the detailed documentation https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/\n",
+    "\n",
+    "from tensorflow.keras.models import model_from_json\n",
+    "\n",
+    "model.load_weights('vgg_face_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define our very important findCosineSimilarity function.\n",
+    "We also load our findEuclideanDistance() function and will show the distance metric used to create a similarity score between the faces under comparison "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_image(image_path):\n",
+    "    img = load_img(image_path, target_size=(224, 224))\n",
+    "    img = img_to_array(img)\n",
+    "    img = np.expand_dims(img, axis=0)\n",
+    "    img = preprocess_input(img)\n",
+    "    return img\n",
+    "\n",
+    "def findCosineSimilarity(source_representation, test_representation):\n",
+    "    a = np.matmul(np.transpose(source_representation), test_representation)\n",
+    "    b = np.sum(np.multiply(source_representation, source_representation))\n",
+    "    c = np.sum(np.multiply(test_representation, test_representation))\n",
+    "    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\n",
+    "\n",
+    "\n",
+    "vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define our verifyFace function where we load to images of faces and compare them.\n",
+    "\n",
+    "We set **epsilon** to be the threshold of whether our two faces are the same person. Setting a lower value makes it more strict with our face matching."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epsilon = 0.40\n",
+    "\n",
+    "def verifyFace(img1, img2):\n",
+    "    img1_representation = vgg_face_descriptor.predict(preprocess_image('./training_faces/%s' % (img1)))[0,:]\n",
+    "    img2_representation = vgg_face_descriptor.predict(preprocess_image('./training_faces/%s' % (img2)))[0,:]\n",
+    "    \n",
+    "    cosine_similarity = findCosineSimilarity(img1_representation, img2_representation)\n",
+    "    \n",
+    "    f = plt.figure()\n",
+    "    f.add_subplot(1,2, 1)\n",
+    "    plt.imshow(image.load_img('./training_faces/%s' % (img1)))\n",
+    "    plt.xticks([]); plt.yticks([])\n",
+    "    f.add_subplot(1,2, 2)\n",
+    "    plt.imshow(image.load_img('./training_faces/%s' % (img2)))\n",
+    "    plt.xticks([]); plt.yticks([])\n",
+    "    plt.show(block=True)\n",
+    "    \n",
+    "    print(\"Cosine similarity: \",cosine_similarity)\n",
+    "    \n",
+    "    if(cosine_similarity < epsilon):\n",
+    "        print(\"They are same person\")\n",
+    "    else:\n",
+    "        print(\"They are not same person!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's a run a few tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cosine similarity:  0.2544904947280884\n",
+      "They are same person\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's compare two faces images of Angelina Jolie\n",
+    "verifyFace(\"angelina.jpg\", \"angelina2.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cosine similarity:  0.4739535450935364\n",
+      "They are not same person!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's now compare it to the Friends character Monica, it should return that these are two different people\n",
+    "verifyFace(\"angelina.jpg\", \"Monica.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cosine similarity:  0.7327100932598114\n",
+      "They are not same person!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's try it with Rachel\n",
+    "verifyFace(\"angelina.jpg\", \"Rachel.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cosine similarity:  0.6882018446922302\n",
+      "They are not same person!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And now Pheobe\n",
+    "verifyFace(\"angelina.jpg\", \"Pheobe.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cosine similarity:  0.38444697856903076\n",
+      "They are same person\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now let's try it with another image of Angelina Jolie\n",
+    "verifyFace(\"angelina.jpg\", \"angelina3.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### There we see that our Face Matcher is working quite well as it's able to distinguish Angelina Jolie from other actresses"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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",
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diff --git a/25. Face Recognition/25.3 Face Recogition - One Shot Learning.ipynb b/25. Face Recognition/25.3 Face Recogition - One Shot Learning.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e5b71ea7cf46e0cf69e3448c7dfbca16bdbd13d4
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@@ -0,0 +1,406 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Extract faces from pictures of people \n",
+    "### Instrutions:\n",
+    "- Place photos of people (one face visible) in the folder called \"./people\"\n",
+    "- Replace my photo titled \"Rajeev.jpg\" with a piture of your face for testing on a webcam\n",
+    "- Faces are extracted using the haarcascade_frontalface_default detector model\n",
+    "- Extracted faces are placed in the folder called \"./group_of_faces\"\n",
+    "#### We are extracting the faces needed for our one-shot learning model, it will load 5 extracted faces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collected image names\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The code below extracts faces from images and places them in the folder\n",
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import cv2\n",
+    "\n",
+    "# Loading out HAARCascade Face Detector \n",
+    "face_detector = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
+    "\n",
+    "# Directory of image of persons we'll be extracting faces frommy\n",
+    "mypath = \"./people/\"\n",
+    "image_file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
+    "print(\"Collected image names\")\n",
+    "\n",
+    "for image_name in image_file_names:\n",
+    "    person_image = cv2.imread(mypath+image_name)\n",
+    "    face_info = face_detector.detectMultiScale(person_image, 1.3, 5)\n",
+    "    for (x,y,w,h) in face_info:\n",
+    "        face = person_image[y:y+h, x:x+w]\n",
+    "        roi = cv2.resize(face, (128, 128), interpolation = cv2.INTER_CUBIC)\n",
+    "    path = \"./group_of_faces/\" + \"face_\" + image_name \n",
+    "    cv2.imwrite(path, roi)\n",
+    "    cv2.imshow(\"face\", roi)\n",
+    "    \n",
+    "    cv2.waitKey(0)\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Load our VGGFaceModel \n",
+    "- This block of code defines the VGGFace model (which we use later) and loads the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Loaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "#author Sefik Ilkin Serengil\n",
+    "#you can find the documentation of this code from the following link: https://sefiks.com/2018/08/06/deep-face-recognition-with-keras/\n",
+    "\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "\n",
+    "from tensorflow.keras.models import Model, Sequential\n",
+    "from tensorflow.keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation\n",
+    "from PIL import Image\n",
+    "from tensorflow.keras.preprocessing.image import load_img, save_img, img_to_array\n",
+    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
+    "from tensorflow.keras.preprocessing import image\n",
+    "import matplotlib.pyplot as plt\n",
+    "from os import listdir\n",
+    "\n",
+    "def preprocess_image(image_path):\n",
+    "    \"\"\"Loads image from path and resizes it\"\"\"\n",
+    "    img = load_img(image_path, target_size=(224, 224))\n",
+    "    img = img_to_array(img)\n",
+    "    img = np.expand_dims(img, axis=0)\n",
+    "    img = preprocess_input(img)\n",
+    "    return img\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))\n",
+    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(64, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(128, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(256, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(ZeroPadding2D((1,1)))\n",
+    "model.add(Convolution2D(512, (3, 3), activation='relu'))\n",
+    "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
+    "\n",
+    "model.add(Convolution2D(4096, (7, 7), activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Convolution2D(4096, (1, 1), activation='relu'))\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Convolution2D(2622, (1, 1)))\n",
+    "model.add(Flatten())\n",
+    "model.add(Activation('softmax'))\n",
+    "\n",
+    "#you can download pretrained weights from https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing\n",
+    "from tensorflow.keras.models import model_from_json\n",
+    "model.load_weights('vgg_face_weights.h5')\n",
+    "\n",
+    "vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)\n",
+    "\n",
+    "model = vgg_face_descriptor\n",
+    "\n",
+    " \n",
+    "print(\"Model Loaded\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Test model using your Webcam\n",
+    "This code looks up the faces you extracted in the \"group_of_faces\" folder and uses the similarity (Cosine Similarity) to detect which faces is most similar to the one being extracted with your webcam."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Face representations retrieved successfully\n"
+     ]
+    }
+   ],
+   "source": [
+    "#points to your extracted faces\n",
+    "people_pictures = \"./group_of_faces/\"\n",
+    "\n",
+    "all_people_faces = dict()\n",
+    "\n",
+    "for file in listdir(people_pictures):\n",
+    "    person_face, extension = file.split(\".\")\n",
+    "    all_people_faces[person_face] = model.predict(preprocess_image('./group_of_faces/%s.jpg' % (person_face)))[0,:]\n",
+    "\n",
+    "print(\"Face representations retrieved successfully\")\n",
+    "\n",
+    "def findCosineSimilarity(source_representation, test_representation):\n",
+    "    a = np.matmul(np.transpose(source_representation), test_representation)\n",
+    "    b = np.sum(np.multiply(source_representation, source_representation))\n",
+    "    c = np.sum(np.multiply(test_representation, test_representation))\n",
+    "    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\n",
+    "\n",
+    "#Open Webcam\n",
+    "cap = cv2.VideoCapture(0) \n",
+    "\n",
+    "while(True):\n",
+    "    ret, img = cap.read()\n",
+    "    faces = face_detector.detectMultiScale(img, 1.3, 5)\n",
+    "\n",
+    "    for (x,y,w,h) in faces:\n",
+    "        if w > 100: #Adjust accordingly if your webcam resoluation is higher\n",
+    "            cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n",
+    "            detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face\n",
+    "            detected_face = cv2.resize(detected_face, (224, 224)) #resize to 224x224\n",
+    "\n",
+    "            img_pixels = image.img_to_array(detected_face)\n",
+    "            img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
+    "            img_pixels /= 255\n",
+    "\n",
+    "            captured_representation = model.predict(img_pixels)[0,:]\n",
+    "\n",
+    "            found = 0\n",
+    "            for i in all_people_faces:\n",
+    "                person_name = i\n",
+    "                representation = all_people_faces[i]\n",
+    "\n",
+    "                similarity = findCosineSimilarity(representation, captured_representation)\n",
+    "                if(similarity < 0.30):\n",
+    "                    cv2.putText(img, person_name[5:], (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n",
+    "                    found = 1\n",
+    "                    break\n",
+    "\n",
+    "            #connect face and text\n",
+    "            cv2.line(img,(int((x+x+w)/2),y+15),(x+w,y-20),(255, 0, 0),1)\n",
+    "            cv2.line(img,(x+w,y-20),(x+w+10,y-20),(255, 0, 0),1)\n",
+    "\n",
+    "            if(found == 0): #if found image is not in our people database\n",
+    "                cv2.putText(img, 'unknown', (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n",
+    "\n",
+    "    cv2.imshow('img',img)\n",
+    "\n",
+    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "        break\n",
+    "        \n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test on a video\n",
+    "### Since we're using the Friends TV Series characters, let's extract the faces from the images I placed in the \"./friends\" folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collected image names\n"
+     ]
+    }
+   ],
+   "source": [
+    "from os import listdir\n",
+    "from os.path import isfile, join\n",
+    "import cv2\n",
+    "\n",
+    "# Loading out HAARCascade Face Detector \n",
+    "face_detector = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
+    "\n",
+    "# Directory of image of persons we'll be extracting faces frommy\n",
+    "mypath = \"./friends/\"\n",
+    "image_file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n",
+    "print(\"Collected image names\")\n",
+    "\n",
+    "for image_name in image_file_names:\n",
+    "    person_image = cv2.imread(mypath+image_name)\n",
+    "    face_info = face_detector.detectMultiScale(person_image, 1.3, 5)\n",
+    "    for (x,y,w,h) in face_info:\n",
+    "        face = person_image[y:y+h, x:x+w]\n",
+    "        roi = cv2.resize(face, (128, 128), interpolation = cv2.INTER_CUBIC)\n",
+    "    path = \"./friends_faces/\" + \"face_\" + image_name \n",
+    "    cv2.imwrite(path, roi)\n",
+    "    cv2.imshow(\"face\", roi)\n",
+    "    \n",
+    "    cv2.waitKey(0)\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Again, we load our faces from the \"friends_faces\" directory and we run our face classifier model our test video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Face representations retrieved successfully\n"
+     ]
+    }
+   ],
+   "source": [
+    "#points to your extracted faces\n",
+    "people_pictures = \"./friends_faces/\"\n",
+    "\n",
+    "all_people_faces = dict()\n",
+    "\n",
+    "for file in listdir(people_pictures):\n",
+    "    person_face, extension = file.split(\".\")\n",
+    "    all_people_faces[person_face] = model.predict(preprocess_image('./friends_faces/%s.jpg' % (person_face)))[0,:]\n",
+    "\n",
+    "print(\"Face representations retrieved successfully\")\n",
+    "\n",
+    "def findCosineSimilarity(source_representation, test_representation):\n",
+    "    a = np.matmul(np.transpose(source_representation), test_representation)\n",
+    "    b = np.sum(np.multiply(source_representation, source_representation))\n",
+    "    c = np.sum(np.multiply(test_representation, test_representation))\n",
+    "    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\n",
+    "\n",
+    "cap = cv2.VideoCapture('testfriends.mp4')\n",
+    "\n",
+    "while(True):\n",
+    "        ret, img = cap.read()\n",
+    "        img = cv2.resize(img, (320, 180)) # Re-size video to as smaller size to improve face detection speed\n",
+    "        faces = face_detector.detectMultiScale(img, 1.3, 5)\n",
+    "\n",
+    "        for (x,y,w,h) in faces:\n",
+    "            if w > 13: \n",
+    "                cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n",
+    "\n",
+    "                detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face\n",
+    "                detected_face = cv2.resize(detected_face, (224, 224)) #resize to 224x224\n",
+    "\n",
+    "                img_pixels = image.img_to_array(detected_face)\n",
+    "                img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
+    "                img_pixels /= 255\n",
+    "\n",
+    "                captured_representation = model.predict(img_pixels)[0,:]\n",
+    "\n",
+    "                found = 0\n",
+    "                for i in all_people_faces:\n",
+    "                    person_name = i\n",
+    "                    representation = all_people_faces[i]\n",
+    "\n",
+    "                    similarity = findCosineSimilarity(representation, captured_representation)\n",
+    "                    if(similarity < 0.30):\n",
+    "                        cv2.putText(img, person_name[5:], (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n",
+    "                        found = 1\n",
+    "                        break\n",
+    "\n",
+    "                #connect face and text\n",
+    "                cv2.line(img,(int((x+x+w)/2),y+15),(x+w,y-20),(255, 0, 0),1)\n",
+    "                cv2.line(img,(x+w,y-20),(x+w+10,y-20),(255, 0, 0),1)\n",
+    "\n",
+    "                if(found == 0): #if found image is not in our people database\n",
+    "                    cv2.putText(img, 'unknown', (int(x+w+15), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n",
+    "\n",
+    "        cv2.imshow('img',img)\n",
+    "        if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
+    "            break\n",
+    "\n",
+    "#kill open cv things\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+  "kernelspec": {
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+   "language": "python",
+   "name": "python3"
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+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+}