From 900060c9670699d6e7281ced27e90fabb1e1cab3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Felix=20Geisend=C3=B6rfer?= Date: Fri, 5 Feb 2021 10:44:51 +0100 Subject: [PATCH] more block profile --- block.md | 134 ++++++++++--- sim/block_sampling.ipynb | 328 ++++++++++++++++++++++++++++++++ sim/block_sampling_biased.png | Bin 0 -> 46580 bytes sim/block_sampling_debiased.png | Bin 0 -> 43794 bytes sim/block_sampling_hist.png | Bin 0 -> 32322 bytes 5 files changed, 433 insertions(+), 29 deletions(-) create mode 100644 sim/block_sampling.ipynb create mode 100644 sim/block_sampling_biased.png create mode 100644 sim/block_sampling_debiased.png create mode 100644 sim/block_sampling_hist.png diff --git a/block.md b/block.md index c8dceb3..92ca944 100644 --- a/block.md +++ b/block.md @@ -7,12 +7,12 @@ This document was last updated for `go1.15.7` but probably still applies to olde The block profile in Go lets you analyze how much time your program spends waiting on the blocking operations listed below: - [select](https://github.com/golang/go/blob/go1.15.7/src/runtime/select.go#L511) -- [chan send](https://github.com/golang/go/blob/go1.15.7/src/runtime/chan.go#L279) (except sending to a nil chan which blocks forever but is [not tracked](https://github.com/golang/go/blob/go1.15.7/src/runtime/chan.go#L163)) -- [chan receive](https://github.com/golang/go/blob/go1.15.7/src/runtime/chan.go#L586) (except receiving from a nil chan which blocks forever but is [not tracked](https://github.com/golang/go/blob/go1.15.7/src/runtime/chan.go#L466)) +- [chan send](https://github.com/golang/go/blob/go1.15.7/src/runtime/chan.go#L279) +- [chan receive](https://github.com/golang/go/blob/go1.15.7/src/runtime/chan.go#L586) - [semacquire](https://github.com/golang/go/blob/go1.15.7/src/runtime/sema.go#L150) ( [`Mutex.Lock`](https://golang.org/pkg/sync/#Mutex.Lock), [`RWMutex.RLock`](https://golang.org/pkg/sync/#RWMutex.RLock) , [`RWMutex.Lock`](https://golang.org/pkg/sync/#RWMutex.Lock), [`WaitGroup.Wait`](https://golang.org/pkg/sync/#WaitGroup.Wait)) - [notifyListWait](https://github.com/golang/go/blob/go1.15.7/src/runtime/sema.go#L515) ( [`Cond.Wait`](https://golang.org/pkg/sync/#Cond.Wait)) -Time is only tracked when Go has to suspend the goroutine's execution by parking it into a [waiting](https://github.com/golang/go/blob/go1.15.7/src/runtime/runtime2.go#L51-L59) state. So for example a `Mutex.Lock()` operation will not show up in your profile if the lock can be immediately aquired. +Time is only tracked when Go has to suspend the goroutine's execution by parking it into a [waiting](https://github.com/golang/go/blob/go1.15.7/src/runtime/runtime2.go#L51-L59) state. So for example a `Mutex.Lock()` operation will not show up in your profile if the lock can be aquired immediately or via a short amount of [spinning](https://en.wikipedia.org/wiki/Spinlock). The operations above are a subset of the [waiting states](https://github.com/golang/go/blob/go1.15.7/src/runtime/runtime2.go#L996-L1024) used by the Go runtime, i.e. the operations below **will not** show up in a block profile: @@ -21,16 +21,29 @@ The operations above are a subset of the [waiting states](https://github.com/gol - Syscalls - Internal Locks (e.g. for [stopTheWorld](https://github.com/golang/go/blob/go1.15.7/src/runtime/proc.go#L900)) - Blocking in [cgo](https://golang.org/cmd/cgo/) calls +- Events that block forever (e.g. sending/receiving on nil channels) + +However, some of the waiting states above above can be analyzed using [Goroutine Profiling](./goroutine.md). ## Usage -The block profiler is disabled by default. To record all blocking events regardless of their duration, simply call: +The block profiler is disabled by default. You can enable it by passing a `rate > 0` as shown below. ``` -runtime.SetBlockProfileRate(1) +runtime.SetBlockProfileRate(rate) ``` -You should read the [Accuracy](#accuracy) and [Overhead](#overhead) sections below to figure out if passing `1` as the sampling rate is a good idea for you or not. +The `rate` impacts the [Accuracy](#accuracy) and [Overhead](#overhead) of the profiler. In the [docs](https://golang.org/pkg/runtime/#SetBlockProfileRate) the rate is described like this: + +> SetBlockProfileRate controls the fraction of goroutine blocking events that are reported in the blocking profile. The profiler aims to sample an average of one blocking event per rate nanoseconds spent blocked. +> +> To include every blocking event in the profile, pass rate = 1. To turn off profiling entirely, pass rate <= 0. + +Personally I struggle to parse the second sentence, and prefer to describe the `rate` (aka `blockprofilerate`) like this instead: + +- `rate <= 0` disables the profiler entirely (the default) +- `rate == 1` tracks every blocking event, regardless of the event `duration`. +- `rate => 2` sets the sampling rate in `nanoseconds`. Every event with a `duration >= rate` will be tracked. For events with a `duration < rate`, the profiler will [randomly](https://github.com/golang/go/blob/go1.15.7/src/runtime/mprof.go#L408) sample `duration / rate` events. E.g. if you have an event with a duration of `100ns` and your rate is `1000ns`, there is a `10%` chance it will be tracked by the block profiler. Block durations are aggregated over the lifetime of the program (while the profiling is enabled). To get a [pprof formated](./pprof.md) snapshot of the current stack traces that lead to blocking events and their cumulative time duration, you can call: @@ -44,7 +57,7 @@ Last but not least you can use the [`runtime.BlockProfile`](https://golang.org/p ## Overhead -**tl;dr:** A blockprofile rate `>= 10000` (10µs) should have negligable impact on production apps, including those suffering from extreme contention. +**tl;dr:** A `blockprofilerate` >= `10000` (10µs) should have negligable impact on production apps, including those suffering from extreme contention. ### Implementation Details @@ -83,52 +96,115 @@ The costs of updating the hash map is probably similar to collecting the stack t ### Benchmarks -Anyway, what does all of this mean in terms of overhead for your application? It means that block profiling is **low overhead**. Unless your application spends literally all of its time parking/unparking goroutines due to contention, you probably won't be able to see a measurable impact even when sampling every block event. +Anyway, what does all of this mean in terms of overhead for your application? It generally means that block profiling is **low overhead**. Unless your application spends literally all of its time parking and unparking goroutines due to contention, you probably won't be able to see a measurable impact even when sampling every block event. -That being said, the benchmark results below (see [Methodology](./bench/)) should give you an idea of the **theoretical worst case** overhead block profiling could have. The graph `chan(cap=0)` shows that setting `blockprofilerate` from `1` to `1000` on a workload that consists entirely in sending tiny messages across unbuffered channels decreases throughput significantly. Using a buffered channel as in graph `chan(cap=128)` greatly reduces the problem to the point that it probably won't matter for real applications that don't spend all of their time on channel communication overheads. +That being said, the benchmark results below (see [Methodology](./bench/)) should give you an idea of the **theoretical worst case** overhead block profiling could have. The graph `chan(cap=0)` shows that setting `blockprofilerate` from `1` to `1000` on a [workload](./bench/workload_chan.go) that consists entirely in sending tiny messages across unbuffered channels decreases throughput significantly. Using a buffered channel as in graph `chan(cap=128)` greatly reduces the problem to the point that it probably won't matter for real applications that don't spend all of their time on channel communication overheads. -It's also interesting to note that I was unable to see significant overheads for `mutex` based workloads. I believe this is due to the fact that mutexes employe spin locks before parking a goroutine when there is contention. If somebody has a good idea for a workload that exhibits high non-spinning mutex contention in Go, please let me know! +It's also interesting to note that I was unable to see significant overheads for [`mutex`](.bench/workload_mutex.go) based workloads. I believe this is due to the fact that mutexes employe spin locks before parking a goroutine when there is contention. If somebody has a good idea for a workload that exhibits high non-spinning mutex contention in Go, please let me know! -![block_linux_x86_64](./bench/block_linux_x86_64.png) +Anyway, please remember that the graphs below show workloads that were specifically designed to trigger the worst block profiling overhead you can imagine. Real applications will usually see no significant overhead, especially when using a `blockprofilerate` >= `10000` (10µs). -### Initialization Costs - -The first call to `runtime.SetBlockProfileRate()` takes `100ms` because it tries to [measure](https://github.com/golang/go/blob/go1.15.7/src/runtime/runtime.go#L22-L47) the speed difference between the wall clock and the [TSC](https://en.wikipedia.org/wiki/Time_Stamp_Counter) clock. However, recent changes around async preemption have [broken](https://github.com/golang/go/issues/40653#issuecomment-766340860) this code, so the call is taking only `~10ms` right now. +block_linux_x86_64 ### Memory Usage -Block profiling utilizes a shared hash map that takes up 1.4 MiB even when empty. Unless you explicitly [disable heap profiling](https://twitter.com/felixge/status/1355846360562589696) in your application, this map will get allocated regardless of whether you use the block profiler or not. +Block profiling utilizes a shared hash map that [uses](https://github.com/golang/go/blob/go1.15.7/src/runtime/mprof.go#L207) `1.4 MiB` of memory even when empty. Unless you explicitly [disable heap profiling](https://twitter.com/felixge/status/1355846360562589696) in your application, this map will get allocated regardless of whether you use the block profiler or not. Addtionally each unique stack trace will take up some additional memory. The `BuckHashSys` field of [`runtime.MemStats`](https://golang.org/pkg/runtime/#MemStats) allows you to inspect this usage at runtime. In the future I might try to provide additional information about this along with real world data. +### Initialization Time + +The first call to `runtime.SetBlockProfileRate()` takes `100ms` because it tries to [measure](https://github.com/golang/go/blob/go1.15.7/src/runtime/runtime.go#L22-L47) the speed ratio between the wall clock and the [TSC](https://en.wikipedia.org/wiki/Time_Stamp_Counter) clock. However, recent changes around async preemption have [broken](https://github.com/golang/go/issues/40653#issuecomment-766340860) this code, so the call is taking only `~10ms` right now. + ## Accuracy -🚧 +### Sampling Bias -- `rate` concept [same as memory profiler](https://codereview.appspot.com/6443115#msg38)? -- sampling (should cycles < rate events be multiplied?) -- multi socket cpus / tsc +**tl;dr:** Setting your sampling `rate` too high will bias your results towards infrequent long events over frequent short events. -- max stack depth 32 -- spin locks -- [runtime: problems with rdtsc in VMs moving backward · Issue #8976 · golang/go](https://github.com/golang/go/issues/8976) -- https://github.com/golang/go/issues/16755#issuecomment-332279965 ? +As described in the [Usage](#usage) section, the block profiler will sample as follows: -## Relationship with Time +- Events with `duration >= rate` will be sampled 100% +- Events with `duration < rate` have a `duration / rate` chance of getting sampled. -🚧 +The [implementation](https://github.com/golang/go/blob/go1.15.7/src/runtime/mprof.go#L408) for this looks like that: + +```go +func blocksampled(cycles int64) bool { + rate := int64(atomic.Load64(&blockprofilerate)) + if rate <= 0 || (rate > cycles && int64(fastrand())%rate > cycles) { + return false + } + return true +} +``` + +This means that if you set your profiling `rate` low enough, you'll get very accurate results. However, if your `rate` is higher than the `duration` of some of the events you are sampling, the sampling process will exhibit a bias favoring infrequent events of higher `duration` over frequent events with lower `duration` even so they may contribute to the same amount of overall block duration in your program. + +#### Example 1 + +A simple example: Let's say your `blockprofilerate` is `100ns` and your application produces the following events: + +- `A`: `1` event with a duration of `100ns`. +- `B`: `10` events with a duration of `10ns` each. + +Given this scenario, the `blockprofiler` is guaranteed to catch and accurately report event `A` as `100ns` in the profile. For event `B` the most likely outcome is that the profiler will capture only a single event (10% of 10 events) and report `B` as `10ns` in the profile. So you might find yourself in a situation where you think event `A` is causing 10x more blocking than event `B`, which is not true. + +#### Example 2 & Proposal for Improvement + +For an even better intuition about this, consider the [simulated example](./sim/block_sampling.ipynb) below. Here we have a histogram of all durations collected from 3 types of blocking events. As you can see, they all have different mean durations (`1000ns`, `2000ns`, `3000ns`) and they are occurring at different frequencies, with `count(a) > count(b) > count(c)`. What's more difficult to see, is that the cumulative durations of these events are the same, i.e. `sum(a) = sum(b) = sum(c)`. + + + +So given that your application might produce events like this, how will they show up in your block profile as you try out different `blockprofilerates`? As you can see below, all is well and fine until a `blockprofilerate` of `1000ns`. Each event shows up with the same total duration in the profile (the red and green dots are hidden below the blue ones, but they are there, trust me). However starting at `1000ns` you see that event `a` starts to fade from our profile and at `2000ns` you'd already think that events `b` and `c` are causing twice as much blocking time as event `a`. + + + +So what can we do? Do we always need to live in fear of bias when working with block profiles? No! If the [Overhead](#overhead) for your workload allows it, the simplest solution is to use a low enough `blockprofilerate` that allows you to capture most blocking events. + +But perhaps there is an even better way. I'm thinking we could correct for the current bias by keeping the same logic of sampling `duration / rate` fraction of events when `duration < rate`. However, when this happens we could simply multiply the sampled duration like this: + +``` +duration = duration * (rate/duration) +``` + +Doing so could be done with a trivial patch to the go runtime and the picture below shows the results from simulating it. So from my point of view it should be possible to eliminate this bias from future versions of Go and I'm planning to send a patch for it soon. That being said, I'm not trained in statistics, so my naive ideas might be hilariously misguided. + + + + + +### Time Stamp Counter + +`amd64` and other platforms use [TSC](https://en.wikipedia.org/wiki/Time_Stamp_Counter) for implementing the `cputicks()` function. This technique has been historically challenged by problems with frequency scaling and other kinds of CPU power transitions. Modern CPUs should provide invariant TSCs, but at [least some Go users](https://github.com/golang/go/issues/16755#issuecomment-332279965) are reporting issues. I can't tell whether those are due to broken hardware or issues regarding multi-socket systems, but hope to do more research on this in the future. + +Please also note the bug description in the [Initialization Time](#initialization-time) section which may impact the accuracy of converting cputicks to wall clock time. + +### Stack Depth + +The max stack depth for block profiles is [32](https://github.com/golang/go/blob/go1.15.7/src/runtime/mprof.go#L31). Block events occurring at deeper stack depths will still be included in the profile, but the resulting data might be more difficult to work with. + +### Spin Locks + +As described earlier, contended Go mutexes will first try to [spin](https://en.wikipedia.org/wiki/Spinlock) for a bit before yielding to the scheduler. If the spinning is successful, not block event will be tracked. This presents another subtle bias in the block profiler towards events of longer duration. + +🚧 Todo: Research this a bit more + +## Relationship with Wall Clock Time + +🚧 To be written ... ## Relationship with Mutex Profiling -🚧 +🚧 To be written ... ## pprof Output -🚧 +🚧 To be written ... ## pprof Labels -🚧 +🚧 To be written ... ## History @@ -136,6 +212,6 @@ Block profiling was [implemented](https://codereview.appspot.com/6443115) by [Dm ## Disclaimers -I work at [Datadog](https://www.datadoghq.com/) on [Continuous Profiling](https://www.datadoghq.com/product/code-profiling/) for Go (you should check it out) and they generously allowed me to do all this research and publish it. +I work at [Datadog](https://www.datadoghq.com/) on [Continuous Profiling](https://www.datadoghq.com/product/code-profiling/) for Go. You should check it out : ). The information on this page is believed to be correct, but no warranty is provided. Feedback is welcome! \ No newline at end of file diff --git a/sim/block_sampling.ipynb b/sim/block_sampling.ipynb new file mode 100644 index 0000000..406733e --- /dev/null +++ b/sim/block_sampling.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "classified-alcohol", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from plotnine import *\n", + "import plotnine\n", + "import random\n", + "plotnine.options.figure_size = (12, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "union-circle", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " event duration\n", + "0 a 954.462517\n", + "1 a 982.854305\n", + "2 a 1037.667967\n", + "3 a 1002.143116\n", + "4 a 1020.217687\n", + "... ... ...\n", + "18328 c 3040.239624\n", + "18329 c 2941.655774\n", + "18330 c 3053.683405\n", + "18331 c 2980.200817\n", + "18332 c 3050.789074\n", + "\n", + "[18333 rows x 2 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "points=10000\n", + "mu=1000\n", + "sigma=50\n", + "events = pd.concat([\n", + " pd.DataFrame({'event': 'a', 'duration': np.random.normal(mu, sigma, points)}),\n", + " pd.DataFrame({'event': 'b', 'duration': np.random.normal(mu*2, sigma, int(points/2))}),\n", + " pd.DataFrame({'event': 'c', 'duration': np.random.normal(mu*3, sigma, int(points/3))}),\n", + "], ignore_index=True)\n", + "events" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "right-discovery", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + "ggplot(events)\n", + "+ aes(x='duration',color='event')\n", + "+ labs(x='event duration (ns)',title='Block events from 3 sources with different means, but the same sum(duration)')\n", + "+ geom_histogram(binwidth=10)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "brief-bailey", + "metadata": {}, + "outputs": [], + "source": [ + "# sample implements the sampling used by the go block profiler. Returning 0 is the\n", + "# same as false in the go implementation, otherwise the actual number of cycles to\n", + "# track is returned. This allows to adjust them for bias if the debias paramter is\n", + "# set to True.\n", + "#\n", + "# \tif rate <= 0 || (rate > cycles && int64(fastrand())%rate > cycles) {\n", + "# \t\treturn false\n", + "# \t}\n", + "# \treturn true\n", + "def sample(cycles, rate, debias):\n", + " if cycles >= rate:\n", + " return cycles\n", + " elif cycles > 0 and random.getrandbits(64)%rate <= cycles:\n", + " if debias:\n", + " return cycles*(rate/cycles)\n", + " else:\n", + " return cycles\n", + " return 0\n", + "\n", + "def simulate_rates(rates, debias):\n", + " frames = []\n", + " for rate in rates:\n", + " rf = (events\n", + " .groupby('event')['duration']\n", + " .agg(lambda durations: sum(durations.apply(lambda d: sample(d, rate, debias)))).reset_index()\n", + " )\n", + " rf['rate'] = rate\n", + " frames.append(rf)\n", + " return pd.concat(frames)\n", + "\n", + "rates = range(0, 5000, 100)\n", + "profiles = simulate_rates(rates, False)\n", + "profiles_debiased = simulate_rates(rates, True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "relative-minnesota", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(\n", + "ggplot(profiles)\n", + "+ aes(x='rate', y='duration',ymin=0,color='event')\n", + "+ labs(y='block profile duration (ns)', x='blockprofilerate (ns)',title='Simulations for different blockprofile rates')\n", + "+ geom_point()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aerial-trunk", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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u+927dwtJksTIkSOLHWP48OFCpVLZHSMqKkoAED/88IPdtu+9954AIP7880+7WEpqo7dTtI8kSWLHjh1260aOHCkAiO+++67Yud1YnxMnThQAxOTJk+3237hxowAg2rVrZ1u2c+dOAUA0adKkxGvnxnvAja/R4sWLhV6vF23atBHp6el2+xTFFBERIdLS0mzL8/PzRePGjYVKpRInT560LW/VqpUAIF599dVix3fkmhPi1veQkq71ktqWI+3qVoruHc2bNxcmk6nY+pLqOScnR1SqVElUr169xLJuvA8VmTZtmgAgli5darfcZDKJevXqiZiYGNuyxx9/XHh7e5cYz51YLBbh6+srOnfubFs2adIk4evrK7p16ybi4+Ntyz/99FMBQGzfvt22rKR6vt15FbWf2rVri/z8fNvy7OxsERgYWOx9oyRF977IyEi79pmbmyvq1q1b7LpctmyZACCmTp1arKzHHntM+Pj4iMzMTNsyAAKA+Omnn+y2nTp1arGyi94LbrxuZVkWOTk5xY61Zs0aAUB8+OGHtmWjR48WAMSlS5fueN43U/re4sh9oKT1Fy9eFIGBgXZtRAjlr33Rsk6dOtkdKzk5WWi1WtGnTx/bsrS0NGE0GkVsbKzda5Kfny8aNWqk+J5bUFBQ4vUwe/ZsAUD8+OOPtmWO3peLzqdBgwZ2bTg9PV1ERkYKb29vkZGRYVvuivtT0fvAJ598YrftwoULbe2XiB5sfKyJ6F8oNjYW+/btw8iRI2E0GvHzzz/j1VdfRfPmzVGxYkWsXr1acVljxoyx+7tVq1YAgK5du9o9UqDX6/HQQw/h+PHjTjmHGxmNRgCFXdQzMzORkpKC0NBQVKlSBTt27Ch1ubIsY9myZahSpQr69u1rt65///6Ii4vD0qVLiz0qNHjwYPj7+9v+VqlUaNeuHS5evGjrCu3r6wugsIv8zWP83I3Vq1cjOzsbI0aMsOv9oNFo8MYbbwAofJznZq+++qriaTaL9n/ttdfs9omJibH9+uxsGRkZ+O2339C5c2f4+PggJSXF9s/LywtNmjTBn3/+WWy/4cOHF/s1e8GCBRBC4Nlnn7UrJyUlBV27doUsy1i7dq3dPuHh4cV6fHXo0AEAnNamO3TogMaNG9stK+oNUtJrdqOff/4ZPj4+ePnll+2Wt2rVCm3atMH69euRlpYGALZHBt577z3btXMjlar4W/eUKVPQu3dv9OzZE6tWrbK135u98MILdoO96vV6jBkzxnYt3ezVV1+1+7u011xplbZd3cqYMWNKHDPixnrOycnBtWvXkJubi7Zt2+Lw4cPIyspSVP78+fMRHR2NFi1a2MWakZGBrl27IjEx0dYe/fz8kJubi99//x2yLCs+B6BwnKjWrVtj8+bNMJvNAIB169ahdevWePjhh7Fz505bzOvWrYOvry8aNmzo0DFKMnz4cLtBY41GI5o2baroGiu69w0fPtyufRoMBowdO7bY9vPnz4fBYMBTTz1V7D7QvXt3ZGZmYtu2bXb7VK5cGT179rRb9uKLL8LPz++O16gkSbbxh2RZRnp6OlJSUlC3bl34+vravVcVXUM//fSTrf6VUvre4sh94Mb1WVlZuHbtGjQaDR566KG7eo8FgNGjR9sdq0KFCqhSpYrda7569Wrk5OTghRdesJuNTa/XY/To0YqPpdPpbNenxWKxvQZFj+SVdC6O3pfHjBlj14Z9fX3x4osvIisryzbYvVKO3p9+/vln+Pn54YUXXrArp3fv3qhUqZJDxyaisonJGaJ/qaioKHz66ac4ffo0Ll++jGXLlqFv3744c+YMHn/8cbuxNG4nNjbW7u+ipMTNy4vWXbt27e6Dv8nmzZvRvn17GI1G+Pr6Ijg4GMHBwTh48GCx58gdcfXqVWRlZaFmzZrF1kmShBo1aiAtLc32pbdISeceGBgIALbzj4qKwltvvYU1a9YgPDwcderUwYsvvujwh7ebnT59GgBsjyLcqGjZqVOniq2rXLmy4mMU7V/SYxE1atRQXI4jjh8/DlmWsWDBAtvre+O/tWvX4vLly8X2K+m8jhw5AgCoU6dOsXI6duwIAMXKUvKa3q2S6jMkJASBgYF3vB5Pnz6NSpUqlTgIaa1atSCEQGJiIoDryaT69esriuuXX37BuHHj8PTTT2P+/Pm3HWi3pHMoWnbzOQQHB9slMYHSX3OlVdp2dSu3uo7OnDmD/v37IzAwEF5eXggKCkJwcDBmzZoFAIrP58iRIzhz5kyJsRaN6VIUb9EU9U888QSCgoLw2GOP4ZNPPlF8Pu3bt0d2djZ27NiBnJwcbN++He3bt0f79u1hsViwadMmWK1WbNy4Ea1atbrtY3dK3eo6U3KNOXpfOnLkCPLy8lC+fPlidVk0W+HNdVVS2TqdDnFxcYreM5ctW4b4+HgYDAb4+/vbjpeRkWH3XjV8+HA0bNgQI0aMgL+/Pzp06IDJkyfbruHbUfre4sh9YP/+/ejevTt8fHzg4+Nja78rVqy4q/dYQNlrXvS+VrVq1WLb3u6x5JLMnj0b9erVs3sNih75KelcHL0vO3IPvBNH70+nTp1CxYoVS7xH3+4xRiJ6cHDMGaL7QEhICLp164Zu3bohMjIS77//PhYtWmTraXE7t/pArvSD+q16a1gsFkX7//3332jXrh1iY2MxefJkxMbGwtPTE5IkYdSoUcjJyVFUjjPd7txv/MX/7bffxqBBg7By5Ups2bIFP//8M2bMmIFu3brhl19+UdyTxRn+7TPKFP3y36tXLwwZMkTxfiWdV1FZy5cvv+XUrjd/YVD6mpZFjRs3RlJSEn799VckJCTcdrwLR/wb2lxp29WtlHRO2dnZaNmyJTIyMjBq1CjUrl0bPj4+UKlU+Pbbb7Fw4ULFPVtkWUaVKlXwxRdf3HKbosRWXFwcDh06hI0bN2LdunXYsmULxo4di4kTJ2LFihVo2bLlbY/Vvn17AMDatWuRlZUFk8mE9u3bo1KlSoiMjMTatWsRGBiIzMzMWw4u7yhnJHiUkmUZvr6+WLJkyS23cWay+ddff8Xjjz+Ohg0bYurUqYiMjITBYABQ2LPhxjYQEBCAHTt2YOvWrVi7di22bNmCSZMmYdKkSZg/f75tjKJbceZ7y7lz59C8eXN4eXnhtddeQ9WqVWE0GqFSqfDee+9h/fr1pauQf9zqNXfFffXTTz/FSy+9hPbt22PGjBkIDw+HXq+HxWJBp06dHO5h5mrOvj8RETE5Q3SfKRrY7/z58/fkeAEBASX+WlX0S9mdLFiwABaLBStXriz2hfratWvFehI48qE0ODgY3t7eOHToULF1QggcOnQI/v7+xX79d0RUVBSGDRuGYcOGwWKxYODAgViwYAE2bdpU4ixWd1L0C2DRIIA3Kpq96m4HBiza//Dhw2jUqJHdupLqyhkqVqwIlUqFvLw825fG0qpcuTJWrVqFsLAwxb1HlLqbhFpJM+hcuXIF165dQ9OmTW+7b9Ev9wUFBcUSTgcPHoQkSbaZpCpXroyVK1diz549tscQb6d8+fKYP38+2rdvj0ceeQS//PKLrYdRSefQrVu3Es+rYsWKdzzWvbjmbuTMdnUr69evtw2gOnjwYLt1s2fPLrb97dpQ5cqVcfbsWbRu3VrRQO5arRYdOnSwPYK3f/9+NGzYEG+++eYdB1KtWrUqypcvb0vOlC9f3tZzoV27drbkDABFdefqZPON96VHH33Ubl1J7aly5co4evQo6tWrZzuPOynpGjWZTLYeC7czd+5ceHh4YNOmTXZJvJycnBJ7TqlUKjRv3hzNmzcHUDgwbP369fHqq6/eMTkD3Pm9Rel9YOnSpcjKysKyZcvsZuQCrj/e42pF7+1Hjx4t9toW9YRUYu7cuYiOjsaff/5p9yjV7cpw9L58+PBh1KlTp8QylNwDb+To/anofcBkMhXrPXOnGdqI6MHAx5qI/oU2btx4y6lFf/nlFwD3rgtslSpVcPToUbtkkCzL+PjjjxXtX/Sr282/ss2cObPE7vteXl5IS0tT9KucSqVC9+7dcfTo0WK/ri5YsACnTp1Cjx49SvWlIyMjo9hYAhqNxvahrrSPynTo0AFeXl744osv7D7wW61WTJ48GQDwxBNPlKrsIkWzEL333nt29ZiYmHjLKVDvVmBgIDp37ow//vjjltOCKn1co3///gAKx8wpaTyHjIwMFBQUlCrOovFtStPVf82aNdi5c6fdsqLXrKjOb6VHjx7IyMgoNm31li1bsH79erRt29aW0CgaF2jChAnIy8srVlZJ10a5cuWwadMmVK1aFV27dsVvv/1WYhwzZsxAenq67e+CggJ8/PHHUKlUxZI2JXHlNVcSZ7arW7nVPWr//v0ljsNzuzY0YMAApKWl2drF7WK9evVqsfXVqlWD0WhUfH9p164ddu7cid9//92ud0z79u1x6NAhLFy4EGFhYYreL+7m2lCiY8eOMBqN+OKLL5CRkWFbnp+fjylTphTbfsCAAQCAV155pcQ2X9Lrfvz48WLtcvr06UhPT7/jNapWqyFJUrHeGf/973+LLSvptatQoQJCQ0Pv+NopfW9Reh+4VftduXJlsfuVq3Ts2BGenp6YMWOG3fhMBQUFdjMv3knRudxY30IIvPPOO7fcx9H78scff2z3/pGRkYHp06fDy8vLliRVytH7U48ePZCeno4ZM2bYbbNo0SKcOHHCoWMTUdnEnjNE/0LTpk3Dhg0b0KVLFzRo0AD+/v5ISUnBH3/8gU2bNqFmzZrFfuF1lZEjR2LhwoVo27Ythg0bBiEEfvzxR8Vfvnr06IGpU6eiU6dOGDp0KDw9PZGQkIA///wTcXFxxR6PatKkCZYvX47hw4cjPj4earUabdu2RUhISInl/+9//8PatWvRp08fbNiwAbVq1bJN61uhQoVbfkm6kw0bNmDIkCF4/PHHUaVKFfj5+eHw4cOYOXMmypcvX+pf8X19fTFt2jQMGTIEDRs2xODBg2E0GrFkyRL89ddfGDJkiO2X2NJq164devbsiSVLlqB9+/bo1q0bUlNT8eWXX6J69er4+++/76r8W5k5cyaaN2+ODh06oG/fvmjUqBFUKhWSkpKwYsUKNGzYEHPmzLljOQ0bNsS7776LN954AzVr1kSfPn0QERGBK1eu4MCBA/j1119x+PBhuwGtlQoMDETFihWxaNEixMXFITQ0FEajEY899tgd961Xrx7at2+PF154wfbYyC+//ILmzZvbvkjeyrhx4/Dzzz9j3Lhx2LdvH+Lj421Tafv6+uKzzz6zbduoUSNMmDAB//vf/1C7dm307dsXFSpUwLlz5/Drr7/iu+++Q926dUs8t/Xr16NTp0544okn8P333xf7BT80NBSNGjXC4MGDbVPd/v333xg/frziX41ddc3dirPa1a00a9YMYWFhGDNmDE6fPo3o6GgcOXIEs2fPRq1atYpdL0XTB7/66qt4+umn4eHhgZo1a6JmzZoYNWoU1q1bh7fffhubN29Gx44dERAQgLNnz2Lr1q04ffq0rdfhww8/DG9vb7Rs2RKRkZHIzc3FokWLkJ6eruiRVaDwWp83bx5OnDiBiRMn2i2XJAlHjhxRPAj47c7LGXx9ffHBBx9g+PDhaNSoEQYNGgSdTofvv/++xEdnnnjiCQwZMgSzZ8/Gvn370L17d5QrVw4XLlzA33//jRUrVhRLctSqVQsDBw7E5s2bUa1aNezatQtz5sxB5cqVSxx0+EZPPvkklixZglatWmHgwIEQQuDPP//E4cOHi01dP3ToUCQnJ6Njx46Ijo6GxWLB8uXLcejQIQwfPvy2x1H63qL0PtCpUycYjUb0798fL774IoKCgrB7924sWLAAtWrVwoEDB5S8PHfFz88P7733HkaNGoXGjRvjmWeesd1finrAKPnM8OSTT+LVV1/Fww8/jJ49eyI3Nxe//PILTCbTLfcpzX05Pj4effv2hclkwnfffYfk5GTMnDkTPj4+Dp+7I/ensWPHYuHChXj55Zexf/9+NGrUCIcPH8a33357z14rIvqXu6dzQxGRItu3bxfjxo0TjRs3FqGhoUKj0Qhvb2/RoEED8c4779hNVSnE7afSvtntphK+1T4LFiwQ1apVE1qtVpQvX1689tpr4ujRo4qn0v7tt99Ew4YNhaenp/D39xePPfaYOHToUIlTVebk5IjBgweLkJAQoVKp7KYkvtVUr2fPnhXPPvusCAsLExqNRoSHh4shQ4aICxcu2G1X0vSpRW4u+/Tp02LYsGGievXqwsfHRxgMBlGxYkUxYsQIcfbs2WL7l+RWUwgLIcSKFStEq1athJeXl9Dr9aJ27dri888/LzZd9q1ekzspKCgQb7zxhqhQoYLQ6XSiSpUqYvr06SVO8+ysqbSFECI1NVWMHz9eVK1aVej1euHt7S2qVq0qhgwZYjeV762OeaNVq1aJzp07i8DAQKHVakV4eLho06aN+Pjjj0VeXp5tO0emexdCiB07doj4+Hjh6ekpANxy6vaSyvnxxx9F3bp1hV6vF6GhoWLEiBHFrsdbnVtqaqp46aWXRFRUlNBqtSIoKEj07t1bHDt2rMTjLlmyRLRs2VJ4e3sLDw8PERsbK4YMGSJSUlJs25T0GmVlZYk2bdoItVpta+tFMa1Zs0a88847Ijo6Wmi1WlGpUiUxbdq0Yse+3ZT2Qii/5oS4+6m0hVDerm7ldtNECyHEgQMHROfOnYW/v7/w9PQUTZo0Eb/++ust9/vggw9ETEyM0Gg0xdqYxWIRM2bMEA899JDw8vISHh4eIjo6WvTo0UMsXrzYtt3s2bPFww8/LMLCwoROpxPBwcGiZcuWdtvcyfnz521T8J4/f95uXa1atQQA8e233xbb71b1fKvzut316ug96vvvvxe1atUSOp1OhIWFidGjR4tDhw7d8n3phx9+EK1btxa+vr5Cp9OJChUqiE6dOokvv/zSbruia2H9+vUiPj5eGAwG4e/vL/r3719syutbvRd88803ombNmsLDw0MEBweLvn37irNnzxZrwz///LPo1q2bqFChgtDr9cLf3180btxYzJw5027a6ZI4+t6i5D6QkJAgWrZsKXx8fIS3t7do27atSEhIUPzZ4HZTaZd0zdzq/jBnzhxRo0YNodPpRHh4uBg7dqzYsWOHACA++OCD29aLEIXTg3/wwQeiUqVKQq/Xi/DwcPGf//xHpKamFrvXOXpfLjqfQ4cOidGjR9uuu1q1aokFCxYoOkdn3J8uXLgg+vXrJ/z9/YXBYBDx8fFi/fr1pX6vJ6KyRRKijI+USERE9ICbM2cOBg0ahA0bNpRqrCQiotL46aef0KtXLyxatEjRWDxKnTlzBjExMXjrrbfw9ttv33H7t99+G5MmTUJiYmKpel0SEd0LHHOGiIiIiIhKLT8/v9i4NwUFBfjoo4+g1WrRpk0bN0VGRHT/4JgzRERERERUagkJCXjxxRfRs2dPREdH4+LFi1i4cCGOHj2KN99885bjxhER0XVMzhARERERUanFxsaiZs2amDdvHq5evQqNRoMaNWrg22+/xaBBg9wdHhHRfYFjzhARERERERERuRHHnCEiIiIiIiIiciMmZ4iIiIiIiIiI3IjJGSIiIiIiIiIiN2JyhoiIiIiIiIjIjZicISIiIiIiIiJyIyZniIiIiIiIiIjciMkZIiIiIiIiIiI30rg7ALKXkpLi7hDuSJIkGAwG5OXlQQjh7nDKHJ1OB5PJ5O4wyhS2Wddhe3U+tlfXYpt1PrZZ12F7dT62V9dim3W+e9Vmg4KCXFY2KcOeM+QwlUoFT09PqFRsPq6g1+vdHUKZwzbrOmyvzsf26lpss87HNus6bK/Ox/bqWmyzzsc2++DgK0xERERERERE5EZMzhARERERERERuRGTM0REREREREREbsTkDBERERERERGRGzE5Q0RERERERETkRkzOEBERERERERG5EZMzRERERERERERuxOQMEREREREREZEbMTlDRERERERERORGTM4QEREREREREbkRkzNERERERERERG7E5AwRERERERERkRtp3B0AERERERER3XsmYcI2006kyekIUQXjIV1DqCW1u8MieiAxOUNERERERPSAKRAF+Djrc1yWr9qW7TbvxQvGIVBJfMCC6F7jVUdERERERPSA2ViQgMvyVVhv+N9xyyn8bd7r7tCIHkjsOUNERERE97Vj5hP4KW8ZMkUWwlWh6OvZCyHqYHeHReRSSYkWJGwqQH6eQOWqWjRrpYNKJSneP0W+BiusdstUUCFFTnF2qESkAJMzRET3CeuFc8hdvADWq1eg8vODocdT0Fas7O6wSk3OzEDB1i0Q2VlQR1SArnE8JBU7dBKRY85YkjA9ZzZkyACA09YkTM3+Aq97j4O3ysvN0ZXOiZ2XkbA6A2aLGlWqSmjRK8ahL91U9p08bsasz3MgAEAAx45YcOG8BU/1MyouI1AVADXUdgkaAYFAVaDzAyaiO+KnYCKi+4Ccnoas6Z/AeuE8YCqAfOUycr76ApbzZ90dWqnI6WnI+vh/KNiwBqYdW5G39Efkzv8WQgh3h0ZE95m/CnZA4Pq9Q4aMPJGPA+ZDboyq9A5uOIev5mlx6FIQjqcE4o8EX/w49bi7w6J/meW/5EEIoKjpyzKwa7sZV69Yb7vfjVrrmyNIFQg11FBBBTXUiFFHoYG2bqliMpkEZJnv40SlxZ4zRET3AdP+vYDVCgjZfvn/7YCmfAX3BHUX8v5cAZGXV/hpEgCEgPngPlhOHoe2UhX3BkdUAiEETLu2w3L0EKDVQd+kGTQxce4Oq0wQZjMsZ04DZjPUFSKh8vZxaH8TTHbJGQBQQYIJZmeGec8s/80MAQlAYU8ZGWr835lQdEzOQECkr3uDcyORlwfzkYMQJhM00bFQlwtzTxxWKwr+2gzruWSovLyhb9EaKv+Aex5HZoZc4vKsTIHgEGVleEgeeMV7FBIKtiFNzkCoOhjxuoccnq3pwjkr5n6dg2spMjSaDDzSxQOt2ukhSfe+t9cu026syl+LAlGAypqKeNKzOwySwaEyzlrO46e8X3BNTkWoOgRPGXogVK2wUonuApMzRET3A0sJXzKEAMz355cP+dqV64mZImo15PQ09wREdAd5fyyDafOGf9qtBPPuXTAOHgZttRruDs3tZCGXutebnJmJ7C+nQU65CkgSoNEU1qsDj2xW01TBHvM+yDckaCywopIm1qFY8kU+/sj7E0nWs/BT+aKTRweEqcs5VIYz5Fj0KErM3CjzUpZDyRnrlcvIX7Uccloq1OHl4dG5K1RGxx7zEkLgoOUIzlnPw1vyRiNdPeglvUNlOIOcloqsLz6GyM4ubCdWGZ59B0BXr6FD5ViSk5D9289Iz8yAKiQUHo/3gjowSPH+QpZxdtYCLD1aF5flGjBIeWiX8CuavPqYQ+UAQHaWjKREK1RqICZOAw8PxxIZYZrLyEYg5Bu+zqlgRbCvGY58xdNLerTzaO3QsW+Uky3jy8+ykZ9XeP1ZLMAfv+bD6KVCoya6UpdbGrtMuzEvd6EtWfu3eS+uZqdgtNeLimefumy9iqnZX8ACCwQEsizZ+Dj7c0zwHgs/1YObHKV7g481ERHdBzQVqxT2nLl5eZXqbojm7qnLhQPqm36Zs1ihDnLPAJ6W5DPI37AGBX9thpyV6ZYY6N9LzsqEaeO6GxKKAhACeb/97Na43C3TmoXPzn2AESlj0P/4YCw69w2sQvkjFQCQ++P3kFOvFSabZRkwmZAz5yuIggLFZTyka4g2upa2v9VQY4BnH5RXhysuwywsmJY1A5tNW3HaegZ7zPvxYdanuGS97ND5OEOYIRWqmwZpVcOC4BjlPYqs11KQ8clHOLQ3HzsTQ3F8x2VkfzbFoXoFgB/zfsFXOd/hz/x1+CnvF7yf9Qly5VyHynCG3CULIbKyC98HLRZAyMhdNB9yTrbiMqyXLiJ7xiewJCXCmnoN5uNHkf3ZFMjZWYrLyD58Et8ebo0LcnmYoUOm8MUvud2wf9H/OXQ+SYkWvD8pE3O/zsF3s3Lw4X8zkXLVsWvnEfUyeCEbKliggRkSZDymXQpDzkWHyrlbp09aUJAvcGN+Vgjg752OtTVnWJO/3q4XnRVWJFqTcN56QXEZ2007IUO2lSNDhkmY8Ldpj9PjJbqZ23vOLF++HOvXr8eZM2fQtGlTjBs3zrYuKSkJn3/+Oc6cOYPQ0FAMHToUderUsa3/66+/MHfuXKSmpqJq1aoYOXIkQkKudzn7/vvvsWrVKlgsFjRr1gzDhg2DVqsFAGRnZ2P69OnYvXs3DAYDHn/8cXTr1u2eHJuIHjzW1GswbdkIOTsLmsgo6OJbQro5OXEbmsgoePZ6Grk/LQRkKwAJ+ke6QFerzh33/TfyePhRmI8cgsjM+OdXUCt0TZu75TGRgu1/Ie/nRYBaAwiB/D+Xw2vEGKiDQx0qRwgBkZUJWKyQ/PzcOrhxnsjDNWsqfFQ+8FF5uy0OZxFmE0RuLiRvH7fUq8i6/uXNpAHUcuG/BzmRJwsZX57/GOeNWZBVEmQIbNEdgHR2LnpGDlZcjjUpsXjiOT8f8rUUqMPLKypDkiQ87vkY2ni0RKachSBVIDxVjj3GcMh8BBfkS7ZBhQUErLBiTf4G9Df2dqis7CwZyWesUGsKe0TodI71iOg1OADTp+cgRxhRWLMq9Gp+DsbguorLyNm8CQty+uKsHAUVZFgtajS4vBOP79sNfeOmiso4ZUlEQv52qHbVAy6Wg2TMRWqT3VihWY2ent0VxyILGWvyN2Cv5QA0UKOFPh6NdQ0U7w8A1vPn/nnvu3GhFXLKVcW9gQp2/FWYABTXB2kR+Xkw79sDfbOWt9/5HyeOmpGLQMi48f1bwq6z4ainqARAlgW++yoHBQXXQ8nOEvj+u1y89Iry+7WvhwnP6z/HSbkyCoQeFVRJCFZdhaTrpLgMoPDHibxlSyCnpUIVEgrPnr0dfv8roaOXw4QQ+GuzCXv/NkGlAh6K16NBY8d63hTAdIvlyhNF+XI+ZNlq34XBKiNf3PtkEz143J6cCQgIQK9evbB3715k3fDhx2Kx4L///S86duyI9957D9u3b8d7772HmTNnws/PD2fPnsWnn36K1157DdWrV8f8+fPx4YcfYsqUKQCA1atXY9OmTZgyZQo8PT0xefJkLFiwAAMHDgQAzJo1C2azGd999x2uXLmCiRMnIiIiAg0aNHD5se9nwmJG9sFDyLGooC8fBCnY8V+5hRAwnT6NzPOp8An3hy4urlTPpFquXEbGibPw8NbDs1pVSKVIfsk52cg8fAJCBnyrxzn8nDsACIsFuUeOIi8jD35xEdCEle4ZaNPp08g4dw2quHCI8PBS1skVZJxIhoeXHp7Vq0DSOt6d1K5OqsVB5VOKOrFakXf4KHIzcktdJ0IImE+eQvr5a/AJ9YG+atVS1YkQApmZFhgMjv0idbO8XBlWIeBldOw5bACwplxF1icfwGKSkSu08N63F+YTx2Ac9LxD56Rr1ATqGrWRcT4N3uV8ofUu3SwkQghkZ1uh06mg15f+i25urhUQplJ9KFMZveAz5jXk7vw/ZKfnwi82DLoatUoVhywLZGVZ4eWlhlrtWDBydhbyli6GkAVyhBZaYYZe5CP3p4XwfuElxeWIvDxkz52NtKQzMKvUCPT2hveQFxzu5g4AFrNARrql1IMq7jLtxvdZi2HN9QAMeejo2RqPGTo5fP0IIZCbI6DRStDrS//JOz9fwGoR8DRKpYohf92fSFu/Cql6D5SzWuDbf0ipxiWSZYGcbAGDwfF6VQUEIsNfgx+6qXA+wAhJMqP+0QJ0P1KKe5vZjJwTZ2ApsMC7UgTUXqVLnlksAnm5AkYvqdSz+JwpuIDE1BQEeXmihles4m7/AHAt5zzO+mTjxhuArJawU3sYPR0JwsOA5JwQ7LY0hAVaxKlOoK76b0ieno6UAgDwU/mW+rGDbJENFVS25AxQ+It5llDeMwMAEk9Z8PWMbJjNhV+8/QNU+M8oL/gHKK/boKrhGPt6Gg6vPg1ZViGypi/CGtZ1KI5tJ0JxTo6EDLUtkfC3pTFqnD6JWo2VlXHJfAXaH3pCOh4HCAlQycCu+kgetQZw4OX5Me8XbDPtgPWfuj2Tm4x8kY+W+maKy5C8fSBnZyHDBzBrJfinCWjkwuVKifz84o/TShKESfmXbtnLHxJuvodIsGiVz5CUmSGQnWVfhiwXjtsihFB8n9S3bgd5yUJU1B2AWQcY89VQR8RAVU55jzHr5UvImj4NZ8yRyJTjEJiZigqffQzvcW8o/vxXlIC8sfeMJAH1Gzn2GXTFb/nYtC4fslx4/omncpCXK9C8tfLH6KpqKmN7wU5YpaLx7Aof2wpXKb9Xx5w2I6G8wI33NotKRnSSDFRVXAxRqbg9ORMfHw8AOH36tF1y5sCBAygoKEDPnj2hUqnQokUL/P777/jrr7/w6KOPYuPGjahfvz7q1SvMU/ft2xf9+/dHcnIyIiMjsXbtWnTr1g3lyhU+K9y7d29MnToVAwcORH5+Pv766y988skn8PT0RHR0NDp27Ig1a9agQYMGLj32/cycnY3ps7fj7KkGkCBBMmagb/sE1OvQXHEZQgj8OncFEvY2BiyBgMaMJrVX4IlBnR360L5/w1Z8vzocclZFCAiUj9mMEUMbQ+ut/ENu6qkzmPZDKnIvFz6T7hF8BiOeMiC0aiXFZVhzczBj1lacOdXwnzrJRO+2W9Dg4RaKyxBCYPn8Fdi0pxFgDgDUZjxU6w/0HNzZoV+ID27cjnl/htrqJCz6L4wa2gBaB5IraYlJmLYgBTmXCnsv6IOTMKKnHuVqKH/2X87Px4yZm5B4qhEkoYLkmYUnW29C486tFJchhMCK+X9gw55GgDkIUFvQsMZyPPXcow7VyfGzV/H1rBzI6X4QSEd43WSMHFgTWo3yBEtWrgmffJOEzGOFiUh9hVSMfL4cQv2UfzLNW/UHFvi0wOlzLf6pk2x0PfcDmpw64dC4Cmv3nsWqeXrAZATU+WjS4wp6tnJsTIWTF9Mxe2YmrNcK20W5xpcxqm9Fx+okKxdTZx5HVlI0gCzoQi5h+JBghIcpn3pTCIFv1pzD0dU1AKGCZMzDk89dQeNKjv1at2H/OfwxVwsU6AG1FY26p+OpNsp738jXUpCs98cCuR+sGYWvcWD5PRhyZTUc+cp85aeFmJ7RCLlZAwAAat0FDJw3B1VHvay4zQoh8Ouqa0hYqQLkVGiMZgx8zoiqlZS3tXPWC5i/dzu0P46GNt8DQmXFuk5rUK7t32isVz42w7UUK76ZmYMrlwo/4NZvpEWvvp7QaJXfp00mgUXzcrF/b+G4SOHlVRg8zAt+/sqvYdOeXZh9KAtJ2W9CylRBGHLR9rcf8cigIKgDlLe340fMmP9tLvLyBFSqTHR53AMt23go3l/odZjTMwYZizvB40phL9n9tQ7C2OM8eikuBShISceCD47jcF7hdR+iuoBBg7UIruvYdbxw9Rn8vdwHkFWQDAV4YpCEJtUdG6zy+927sOf7SEimSAgI+LXfivFdH4JWpeyHDmtOFlDCdyZZciz5lVyzF+avCf/n664Kx+TqSA2uiSf8/B0q525VUJeHRVigOl4R0sVQwCsHqH0E0R6RisuwWAS+m1XYI6JIerqMhfNy8MJLjiXhjOX80WhAA3h7e9t9PlbqMsJhveljvhpWXIHyL+55p0IgHfOFJP65Zq0qiHwJBevqAwOUlZEr5yHBtM1umYDAivzVDiVn1I89hi8PbcalxKaQCnRQ1z2F5/xS4OfAfUAbVwnmv3faJ2gsFod6bMY1LgdpRRogX//yLsGKWi2VX38Gz5Lvo3oPxxLYmsYP4bfQv7HTLwkAEJCjw3/8noS3A5+T8rdvxZL8XjhurQoVrLBaNGhk3YFue/+GR8s2isrw8lZh2Egj5nyVg/Q0AbUa6NhZ79B4M2aTwIY1+bgxISKEhJW/5TiUnOmW2RgXL2/D6ajCcvQmgUEnq8GzmfL30RrHBVokW7G5mcbWtemRDTJiPa1MzpDL/WvHnElOTkZ0dDRUN9xgYmNjkZRUeANKSkpCTEyMbZ2npyfKlStnW5+cnIzY2Fi7fTMyMpCWlobz589DCIGoqCjb+piYGCQnJ7v82PezuYu242xiPUhFswfkemPBn7G4dk75c5w7N+zAlr+bApZ/PvxZtNi2pym2rt2quIz0S1cwb2UkrNmFXy4lSDifVBffLNyh/GQAfPpjCnJSrn/oyrsWjs+W5Do0qOH8RdtwJrH+DXXihYWrK+Jq8jnFZfy9eSc27moKmP95E7NqsX1fPLasVl4nmVeuYc7K8rBmF/5iKEHCxeTamP3DLsVlAMCniy8j++r16yL/Whg++7nAoTr5fvEWJJ5uaPswJ+cZ8eO6KriUmKS4jD0J/4f1u+IB8z9vyFYNdu1vjo0rExSXkZ1vwuzp+bBmXG8nF/ZH4KtFjj0zPG3+aWScvP4lIf+8Lz6dedGhOlmW6YvT51vcUCee+DX9GVy6ck1xGYfPpmDlt54Qpn+uHasG25b4Y+NB5VNp55ssmPVFNixp13/hu/h/QZj16wnFZQDAtK8PIfNchO3vgpRgfP7VVVhLGBPnVn5OOIUjqwOBojrJ0ePHLyVcSFP+K/XR89ew/GsDREHRtaPGzp8DsG5/suIyzEYfzLcMhCXr+kwbKZdqYZ5fO8VlAMDMy3HIuXA9sWu5ForvMrrAnJ6uuIz1269hywoVIBfWiTlHg69n5OBaqvJBn//v0H5oF/QC8guvHUlWQ/PHw9i2+YziMqxWga++yEHKletfYPbtNuP3ZXmKywCAX37MxaED12O/dFHG7BnZDvUI+nn/OSSdbAfpnzpBngHrzz6NEwcPKi4j5aoV38zKQV5e4fnIMvD70nwc2Fty9/eSXDZfQ8YPXSClXP8iqDpcFbtWOzZY7NLPjuNY3vXPBylyEL7+Voa1QHksq3Yl4/9+97G1EzlPhyVfqZB8RfkjVnsunsWeuTGAqfDakSAhfV11LNimvF6D/WIRdE1AZRWARQXIEtQWgZpnHevxsmJf5D8zExWej4AK2y5WuuVMNLcihEDiKQv27jbh4gXHe0pGaSJRccVQaL/vBc3G5tD8/ggMXw5FC1nZF1QASEuVkZt7U48IK3A26e56bpaGX8VykCT748oqNfxilScRjBlhUKntz0eS1fBKVz5DYJ74575hlSBdDYCU6geIwsGXHTEvLRVX1veG6nQ0VOfLQ97eDF8n1USBrLzXi7ZBY+jib/jxTJJg6P4kNNHKk6O+/kDIwG0Q+uvXrGiyF9VaK79P6/US4mtl2o0rJEFGx6Y5issAgN/zV+Fvv+ufN9ONFky3zIVJKI9l15lQnLBWgYAKVmgBSPg/S2McS3bsscCIChq88V9fvPuRL774OgLtHjY4lGjKzbGgpG64BSbJofcM67JlGLjAjBEzC/D8dwUY92kBIn7ZDuu1FMVlqL190XGzwMtfFODZ+SaM+6wAzXfIpepdT+Qot/ecuZW8vDwYjfZdBI1GI65cuQIAyM/PL3F9Xl5eieuL/jsvLw/5+fnwvKm77I37uvLY/v72vwRdvHgRFy9eH7hLr9cjPFz5rxr30onz5SHJ139dl4QKosAD/3fsBDpHKXuj3n7GVNgt1npDXlCSseOMjJYKx9/YfSwJIi/u+i85KPywcPp8JNQKyyjIz0P2+ThbUqWwDA0KLkUhJzMTvgHKpkQ8ej6seJ2Yddh19Dgei4m6zZ7XbT+dX0KdCOxMEmij8Hz2nTgDkRN70/moceZClOI6MZtMyDpf8aZ61cB0pQIyU9MREKLs8Ywj58qVUCda7Dp2Gt0rKvsAtON0LqCy2teJSsausyq0V3g+Bw6fhMgOt3url2Q1zhwNUFwnsiwj/VCw/fnIapjOBSIttwDBPsq6MR/Jqnj9yyX+qROLBrsLPBCtMJath64CqmBI8o1nJLBrXyba1VFWxqELlyAyvIrVSdIeT6h7Ke85k34mBpL1+tuHJKthvloOV1JSERGu7Mvqvt0W+7YGFYRVhR3HL6FnvLJHVrYeulK8TiSBXXuz0LGesvM5lmuFyPK3rxOrBheuVFHcTgAg80K1YnUip5TDpbxcxCgsJ2FXCiRxvf4kSBCyhISjZ9GjhbIefWmHPAEIu3sBIJB9uDzUjyiL48olC66l2H8xtlqB/XvM6NlbeZ3s32u2G0pEloHLF2VkZUoICFT4+qRG3tROJAgh4e9UFWoorNcTx0yQYQVuGCNCCIG9+0yo20DZF5DMVBVU1+x/oZesGoiDlRxqJ4dSK9j1ZpChxjU5CKln0lGuurJu97v25ADiei+MwjoB/jpyCTFhynqbHDyeDUgG+3YiJJw5qIW6pbLzUXsa8UxmH8xcrIXlQiQgCfhW3Itefao7VCeFj3cU/1KWmyPBP0BZOVarwNzZWTi43wy1urC9du5qQIdOyhNFJ4+bcX57CCSBwmQTAJHqi81rrOjaQ1kvKy/vkr+Mehgkh+rkRpJUun1DWiVD3u4D5OshyWoItQWWoGsIqytDrVbWQzE8XAdY7ZMoKjUQWV6vOKZAVQCMqeVh/q47VGmF7VOOPIvwgdscOq/Tf5Yv9rlPHKqM41dTUTc84jZ72rtW7wksPdIBGekCIeVU6FXNF54OxHHCfBqJsZsgxm+BlO4LYcyDyrMAq8wpeMajr6IyhMWCNmemwahpiMPWmlBBRn3NLtQ5fhFq9STFsewx74P1hgSPDBnpIgOXxWVEa5R9Br2sqgD5putPDSsuq5V/fryR0QtQq1UO7+ujzoQRWciBEUWJWglWBEtXoFEHKO+BmnIVOVYjTl2qgwLhAZPqDOLUJyFlpEMdoqzdG1q0RsG2BARk5SAg3Qqo1VD5B8LwUFOHxgoEgPPnLDiXbIGnUYVq1bUO9T69UVF9lvY+QvePf21yxmAwICfHPoOck5MDg6Hwg5SHhwdyc+1Hi8/NzbVbf+P+RdsaDAbk5eXZEikl7evKY99s1qxZmDTp+o14woQJmDx5cvEK+ReQ1CVnrj18vYolnW5FrSuhyUmAWqdWXIaHr3fhs883UwnFZeSbjYCUXWI5/qGh8PVSOJZHSXUiJOi9jYpjUelL7kKucqRO/G7xjL2kvE7MFgsgpaPYo9QA/MoFw9/XT1E50i3eN3ReDtSJR+GvN8WWa5XXif5W718O1Iksy4B0tcR1Pt6eDp5PcYaQYMVlaPWa4q+NJKDSqhSXYdBeusUaq+IyAACqi0AJPwR7eymvE6mkdx8hQW/QKS5DV1KdAFBpJMVleKVkALAUj0+CY3UildyD0C8sXHE5FmvJv5qaclLg769skIgYTTkcKuHaKWdRfp/Oys4FkFFseT6K/8BwOxbpEoDibd/H1wf+/sq6u6s9dBDFkk2AMbSc4ljSdRcgC/VN6SqBq9IV+PtHKyojNs8TQPFeajq11qE6UUlnS77HhoQ4cO2U/F6s8VB+f/QzepcQh4CHXvn1J8sCv6+rDlw2FdatkJBzuj627PTF0wOV10mFyBycPJlvN9arRiMhrmIgDJ7KvpCt+iMNRw4V9hYoSgiu/D0P9RoEoFIVZQm4rMwMaDQSzOYbZnqxAlevKL/H+vsD0c0SkbjN25bUF5KMh7pbHLufoHBQ0oO5h1FQYEIlrziEaB0b4++s6ShML+6HelN8YRKh3BVIrXfgnGd31PZT9mxGg0ZAi6Y52LLV/E8vDwn+XsBTT8fAy0vpjxwCPgsGIS39esJXdT4cgcv7wv8VB+okt+QEmV7yVVy3Z5ML8MXUs5BlASEkZCcKfPpRJt77OBI+vsq+EpkyTdBkqWHWWiCCUwEAMoB0VYbyz1vXUpBqNuEhzVbU99gKSQY0MiCnqODn56e4x4kmQwOU0MHM18cP/h7KYgmpaoX6eCqsN860pNKgXNVw+Ps7Nn7TvpwDOF2QCO88b8T7NnFoUG7h7Y1ehtfxQ14/mKGDAOCJXDzp/wcCApUPFZAcUBUzL7WF5Z/3nr+srdBaXoOnY2KhVXoN+vvD990Pkfb7LzBfuQxdeAT8H3scaqPycYUAYM3KdPwwr/C+YrUKlK+gw4S3IhTf10riU4pxIOn+8q9NzkRGRuLnn3+GLMu2x4sSExPRsmXhaOpRUVE4ffq0bfu8vDxcunTJ9qhSZGQkEhMTUb164TSzp0+fhq9v4Q28KElSNEZMUdlF/+3KY9/s+eefR9euXW1/6/X6f+3jT5XrZ+LQynK2Xy6EygrJmIO6NWMUx9y0aRgSd0q2D9sCApBVeKhpsOIyatWIwVLfSxBZXtc//KhkVKyX6lDdeVU9hezj13sACLUFHjFnIZsDFZdTrUEO9l+U7evEMxf16sQpLiM+Phyntt9UJ0JCkyZBisuoXiUSkt95iEyfG+rEiph6KQ7ViXe1U8g6GmtXJ/qoc1DJyuukRsM87D5/Q51IVkiGPDSop7xOmjWNxPGtKFYnjZsGKH9tYsIB/2sQGX52dRIVexxpacrGeRGyDJ/o/cg8U9uuTnRhSdBkqJCm8ANUzSYydp68sU5kSB4FqBfjr/h8Gtfwwf5fAQEZUtETqUJCw3oeisuIMmdCChCQ0/3t6qR82F6kpVVUVAYA+FU6hvRj1ezqRBt2Hl7GOopjqd1YYNtR+3YCvQkN4pTXSYNQM/ZIorA+b/hFtW6Y8ntBlJ8npJBEyCn27aTCQ9ccunbKNbyKSzvK2V87Edfgq1Z+PpHhR3H01PUecEKSAa0ZVX2UPxZbt1UUVm3OgAUyrv/6KKFlq3DFZaTKpyCHFkC6GmRXJ5bau5CWpmz2HACw1t0LsaP+9TLUFojwS0gHoElTNk5Ek2a++PPw9b+FJEPSWNC0Rnnlr090IqCOhZBvbCcS1DWPIC1NWY8knV6gXARw6YIV+Od8JJWMZk2NDrWTBtXz8NchvW2AVhUsKGfMgi5A+b2tTiMJG3df/1tIMqCxomEVP8VltKgcgk2aaxCyyq5O2tX1VFxGylUrks/YP45ltQJ/bclE527KP1727OuBz6YUIDdXhiQBQpbw9EAj8gsykK/wiZUjh7KKTfik1gCHDqQhKETZ4zM6vQlWi4wbfxhQSTK8fayK6yRPzsOxh2dAZWgC9eGqEBorrA/9H7ZVvYhH0yYqOxkA6dZ0TMn8HGlyOlRQQUBgqPdA1NbVVFyGxWQBfLNg6brKtkwDDQryCpAmlJ2PnJuDlsf+h3K6CFySy8GIbNTM34ec/UNgrqYslrRUK9IuA3YjKVjVOH7A4tC1ExpjxcVDVruerNBaEOnjq7icP1dkwSrk64/TykBungWbNlxF81bKekd5m71gFvYJfTXUCBUhiuMQFityvFRY2F2NM9FqQAjUOiyjxyYD0h14FLaptjF+s6ywDWKthgrBqiB45xqRlqcslkZNJGxYo0Z+voAsA2o14OevRrWayts9ACzN+Q1r8zdCDTUAgV+u/Y7xvqPhpVI+cUFI34bQSdORlRsJqASC1WcQ6P2kQ3Esz+8EM4TdbFobrR3QIkOFAI1j3680j3a3fVHONJkAkwOPwl60YMHcwh85ihK+F84VYMHc83iit+OTOajVavj4+CAzM9OhR8gd5WgSmZzP7WPOWK1WmEwmyLIMWZZhMplgsVhQq1Yt6HQ6LF26FGazGQkJCUhKSkKzZoWDh7Vu3Rq7d+/G3r17YTKZ8MMPPyA6OtqWYGnXrh1+/fVXXLp0CVlZWVi0aBHatSscQ8DDwwPNmjXD/PnzkZubi6SkJKxevRodOnQAAJce+2ZhYWGoX7++7V9oaCisVuu/8t8zjzRCVNujEDoTBARUwSkYMFyCr4en4jIaxMWgzTPnAe9/xpXwykGL/kloUrmS4jK8tXoMflEDVegVCAgIrQkRLY/i2S6NHTqf0c9Wh7FqIoTKCqGywqNSEl4aEudQGf07NEJM+2MQuoLCOgm6hn4vCgR4GhWXUTc6Gm0HXQB8/hnwz5iDZk+fQXy1yorLMGp0eG64HqpylyEkGUJrQnjzYxjStZFD5/Py4Jrwqn4aQv1PnVRMwujnYx0qo2+7RojreL1OpOBU9H3RjCCjl+IyakZGoMPgy4DPP2MoGHPRpO8ZtKxeRXEZBh8/DO52BarQC4V1ojEjpMYODO0Zr7gMWQg8V7AJXjH7bXWiiziFZ00/QTjwGvdqXA2VuyVB6K/XSZ+RVgQ7UEa1kFA8MiwN8C28doRnLh4acAGtKsUoLsPDwxN9td9BHXreVidBlf7CM7mHHHqNX+pfC96VjxR+2ZZk6CITMfKZANs9Xcm/JxpWRdXHkyD0+YV1EpSOp0aYEGr0VlxGzKULaBs9z9ZOhGcu6lT+Cc2unFFchlZSYdhwH2gir1xvJy2S8J9HazhUJyOerIKQRpdsdeIZdxWjh0VACKG4jKd8DPButdxWJyIwFfVqfYdqYcrvj34hejz/HwP8PAp7hxrUBejTw4xKDQMVlyGlp8M08AfIUWf/qRMLrE13QtN0s0N1gjZrYG2021YnckwSTP0WQ84vUFxGxzpReKh3CuBRWCfqgCz0GwmE+ih/3wnOS4Vl4DwIv/TCdmLIg/WJZQg3nFJchhAynn/BBxUr6iBJgEYDNG/pgUceNThUJ/HPBUB6aJ+tTqyxZ9F6nAlCyIrL6BxdHpXqLof45zWGXzo6NFiJaB9/xWUYzx7FM/pvYPC+AqisUHtkoUPgj6iRtN2x17gEQii/D1itVuj8c6AbtQD53X5HfueVkEd8B6+a5x0qw9MI3PzUg5ABg6fyWOJC0hGtOgUVLCgc+cYCvchDy3KHFZeRZkmHUMmwtt4K0wvfwjx0LuQ6h5ApZzp0PvOyFyFdzoAMGRZYYIUVX2fNQ7YlW3EZDbX2EztLkKCCCjXUVRWXUXD4IJCfi8qqI2ip2YAGml3QSybkbd2iuAyVquSxg1SS8nuj1WrF833KwyvIDEgyoLJC0ljx9GAPeHhIistIyr2Em4eLkyUrkrIvKS4jUhWBtrpWkCBBAw3UUCNA5Y/O+o7Kz0elwg9DQpFc4Z9GK0k4XFWFP56JcKhO2upaoqO+LbTQQoKEKHUkhns9D0lWXide3gKjx3ujQWMtYiuq0aSZDiPHekGrVX5PSixIwpr8DRAQsPzTYtPlDCzL+UNxGRaLBd9U3ItrUQWQqx+HXPUEzle0YkHEHofq5Gqa+qZpzgtb/9UrZofKsVqtuHTRhKOH83H1isnhfc8lm6C+acwnq1VC4rFch8sq+ufIZ6y7OQa5lyQcGdXSBX744QcsWrTIblnbtm3x0ksv4cyZM/jiiy9w5swZhISE4Pnnn0edOnVs2yUkJGDu3LlIS0tDlSpVMGrUKISEFA50JoTAggULsHLlSlitVsTHx+M///kPtP9Mt5ydnY0vvvgCu3fvhsFgQI8ePdCtWzdb2a489u2kpCgfsMpdLJIFOi8DkGMtfOyjFKzCiixLNrw1XlDf6jmYOxBCINOaBQ+VB/Qqx6eMLpJjLRwE2EvjWHfFG5lkE3Kt+fDVeJdqumfgep2E+4chNzv3zjuU4Hqd6KFXKR/d/mbOqBOzMCPHkndXdSILGZmWrLtqJ9b0NGRePIXAwHJAcHnIDsZiOXUC2V9NR55egiwBxlwLPPsNgq5OfYdjKaoTH42XQ1PW3uhu6kQIgZyvv4T55DFk62TozAJ6qwTPpwdBV9fx88nKToXBoIdKMtgNoO4Is7Agx5JbqjopSNiIvOXLIFstyDKo4JUvQw0VdE2awbPHUw6VVXjtZEOv0sJDpXwWn5vJsgxZCGhK8Vy4KChAxsxPcMzjIrK8NAi7ZEZszYdhePjRUsVitQqHpxYHAEtGOj46/w4uhkqQJVXh43OyQLvdBnTr8K7ichaseRk76kuQVRIgJKiEjPCLAmNDXoEmxLGBdIHSn0/O5nWY6bsCyRGAVUhQQ8AzD3gpoRJC+wxzuDxZFpAklOq+9mnWlzhlTYQs5H+mJhbwgB7/830bOknZLEl5vy1FwV+bkK+xItOghn+WFVqhgvGZIdAqnI6+4K9NyPt9GWCxHzhUU70mvAYrqxNZFpj6XhauXJFtjySp1UDjeB2eeEr5WC+zc+bgoPmI3dgZRskT//WZqLhOrl6xYurkdFisgIAaKljg7wu8/Gag4mngC3ZuQ/bPP+H/ChrgslwOXlI2Gmm2wb9mDLwGPa+oDJMw4ZWMN2G54VFJCRLCVKGY4DNWURkA8GrGW8gRxR9zfMXrJURqlI+vsse0Hz/mLUW2yEGQKhADPfsiSqN89inT/+1A7k8LAat9TxFN1erweu4FRWUIixlfjdmNU9Y4yP/0Q1DBinif3ej+vw6KYwEKeyAknrLAVCBQIUoDXz/H3jM+S9iEpMW17HpaCgg8NOIYelVp4lBZR8zHkGw9B2/JCw10daGXlH/mypKz8Vrm28WWa6HBVN/3HL63CCEKfyAs5eeKu7XLtBsLcn+0a/cAEK2OxFjvkYrKyJZzMD7zrWLLVVBhmu/7is/ty0+zcPqktVgS7rW3vREYpOx9WQiB5b/kYdP66z1lOnf1QNuOyj8bHNuQiNk/++LGfhASZFTyvYChk5X3gCuiVhc+tpqWlubSJEpQkLLxJcl13P5YU9++fdG3b8kDaEVHR2PKlCm33Ld58+Zo3rzkaZwlSUK/fv3Qr1+/Etd7eXlh/Pjxtyzblce+3+lVevjrfZGWW/rHr9SSGn5ax55lvZkkSfDV3P2zl0a1YzNLlESn0kF3Fwki4HqdlDYJAfy76kQraeGnICF5OypJddftRO3nj6DAh2xvanDwTU0TVwneY16Dx4G9gCxDU60GNBHKP9zeyN11IkkSjAOfQ96K36E+fgSS3gP6Nu2hq1W3VOV5ewWUeprXIlpJAz9t6dqstlZd5K1aDpVVhm/uP4liCdA1fMjhsgqvHcemui2JSqUqdZdUSa+H74tj0eDYYRisFhS0DISqgrJBHUtSmkQGAGh8/TBkRzP8kJOA09GAxgo03SXjkWr9HSqn8x5fWKU0/F1XDVklEHtGxpO/WaF+za9UcZX2fLS+/nhmoRk76ku4HCLBOxuI/z8Bn3qOjeFRRKUqXRwAcNZ6rvARBAnAP1NO56MA1+RrCFMrS1hZU64CVis8rIBHwT/3M60acpry2d9UwaHFvnBDrYa6nPIJCVQqCUNe9MKc2Tm22YjqNdShaw/HZnk5YTlll5gBgByRi6tyCsqrlQ2S7Hv5AAZrfsNG0Q6ZwhflpItoY14HTebLQLCy2YkktRpqWPGQZlux5UrpJB36XG2M7wP/gtpamH9TywJ9rW0BB25zXpKxxOSMl8qx9+Z6utqop6sNIUSpkonq2IrFh39TqaCtUVtxGXJGBh7XLMIq8RiOy1ULB79V70JLyyYAjiVntFoJlauW/j20Qv0sJJ7dCfVfhYkYIcmQH/sTEQ7M1lSkmrYKqmmVDV7vapIkFRuX617yV/kWu4ZVUCFQpWxyDQC3/Oyrgsqhc+ve0xOff1z4mKMQhf/adNArTswAhbMTbt5g/wjTyt/zUSFKjUpVlLW/KN80VNCm4qw1CpA1gCRDgozWPjsAOJ6coQeH25MzRET/duqQUKjbPezuMJxC0urg2e0Jd4fhFCpfP3i/MBo5i+dDvnIFKh8fGHo8BU1ktLtDKzVJo4G+dj343oNfyG4nsEMPDNsTCfO6o5B0OugaN3U4Ken9RF90/eoLPLbGUvirrixg6NYTkkfpeyaVhrZmHejLR6HZ38mFCQm1GpKHAfrWjk2X7gyekifyRfGBVIyS8l6K6tBysBw7bJ9otlihClI+RbKmUhVoGz4E8987C58HEgKqoGB4tHXsy7KvnwqjxnnDYlYhMMgf2dnpDrdZPfTIRfFp2j2gvCeC+cA+BEtX8KRu4fWFKg0sx49CrTA5o6lcDZJOD5GfX/hMFABIEnSNmiqOQ5hMqPLNZrwQaMXJGBXUVqD6MRkBxlXAGOW9E7sZOuOrnDnXTwUqNNLWR4ADX3ZvVNreq+qAQBgHPY+ced8ABYVj9+hatoGuSTPFZai8vKFTWdBVt9Q+Jm+/UsV0N1p7NMe2zh8jv+n/QWQaoQrIRKCPBxroetzTOLwkI2LV0UiynrUlNdRQo762bqlfK3eKU8eijrYmDpgPwQoZaqihgxaPeXRSXIZB8kAtTQ0cthy1q5OmukYO1UlYeTXGTPDGru0mFOQLxFbUoFZdx348PX3SUpiUvKH3jVoNJJ6yKk7OiIhgXBv2Gaz7mwPnKgDGHJiab0NWip9DsdCDh8kZIiK6b6nDy8Nn9K17QVLpSJIEXf1G0NVvVOoyNHGV4P3SqzDt2QVYLNBUrQ5t5WpOjFIZSa2G17CRKNi8HtYL56APDoUqvgVUPnfXK680uho6Y27uD4WDnKPwS3dTXWP4qJT32tK37QDzgb2Q01IBSICQoa1dD5oqyutWkiR49noa5hq1IF+8AMnbB7r6jSDpStcDVO8hQVvKKWIf9miHxXlLbXWihhrVNFUQoHJktjSp8N/NzzI48KVO5e0NrxdGIef7OZCvXILkaYShaw9oq9VQXIackQZYLAi7DIRdvmGK49ySZ/27ldramnjROASbChJgUVtRVaqEtvpWDpXhLNoq1eA76T3IaWmQjF5QeTrWe0fS6+HxaDfk//Gr3XLPHr2cGaYi/io/jPcejZW6tcgol4lQEYtO+g4OPZLkDJIkYahxEL7NmY/j1pOQIKGetjae8ry3SSJnkSQJgz37I8G0DcmWcwjw8EdTqTH8VX4OlTPQ2BeLcn/GPvMBSFChia4hHjc85nA8gUFqPNLFsR58N/LwkKCS7CelFAD0DjST0/7ZSNMCosMWu0K2RnijQakjowcBkzNERETkEuqwcBjCut15QxeTtFp4/NP77W4fxbsbDXX1oJN02FLwF0zCgtra6mijb+lQGSqDJ7xHj4dp13bIWRlQlwuHtm4Dh39xlyQJupp1gJp17ryxCzXXN4Uaaqwv2AwzzKihqYbuhkcdOh9dg8aFvYCKSBKgVkNb3bHHB9Rh5eEz7vVSPwak8vIpMUmk8nb8kcmix2bc2V6LSBqt4h5IJfFo3R6qwCCYDx+EpFZD16AxNDFxToxQuQBVAAZ49bkn43fcjpfKiJHew2AWFqgg3dUj7f8GKkmFFurGkDMrwds7FLmy40Oa6iU9njGWPNTFvdQ4Xoctmwogy4WXskoF6PUS6jVSnrw2CTNUksr+cS8JKJDMt96JCEzOEBEREd0ztbU1UFurvDdGSSS9Hvrm7ulJ4QpN9Y3RVN+41PtrK1eF59MDkffLTxC5OVAFBMLz6UFQ+ZVuWtjSPloiGQzQP9IFBauW2y03ODhAeVmkq1W31OOblWVaqWx8FTOfPI6cObOB/DxkAdA1bwVD1ycglXKyAHcKClZjxBhv/LokF6nXZISWU6P7kwb4+Cg/l2hNZLGxctRQoYbm3vcepftL2bgjEBEREdEDS1evIXT1GkLIslu/EBraPQx1cAjMRw5BUmuga/QQNFExbouHyNXkjHTkfDMTMF8fRNe0dQvUgUHQt2jjxshKL7y8Gv8ZVfpJAvxVfhhifAbf5MyDCYW9Zepqa6OjR1tnhUhlFJMzRERERFQm/Bt+qdfVrgdd7XruDoPonrCcOQ3INz0eJsswHdh73yZnnKGGthom+76FK9arMEqeCFIHujskug8wOUNEREREREQOk9Sa4oNxo3CsogedQfJAlKaCu8Og+4j7f14gIiIiIiKi+46mYiVIXt6FI+cWkSTomrZwX1BE9ykmZ4iIiIiIiMhhkocBXi+OhrpCJKDRQuXrB89eT0NXy70zwRHdj/hYExEREREREZWKOjAI3iPGAsC/Yvp3ovsVe84QEREREREREbkRkzNERERERERERG7E5AwRERERERERkRsxOUNERERERERE5EZMzhARERERERERuRGTM0REREREREREbsTkDBERERERERGRGzE5Q0RERERERETkRkzOEBERERERERG5EZMzRERERERERERuxOQMEREREREREZEbMTlDRERERERERORGTM4QEREREREREbkRkzNERERERERERG7E5AwRERERERERkRsxOUNERERERERE5EZMzhARERERERERuRGTM0REREREREREbsTkDBERERERERGRGzE5Q0RERERERETkRkzOEBERERERERG5EZMzRERERERERERuxOQMEREREREREZEbMTlDRERERERERORGTM4QEREREREREbkRkzNERERERERERG7E5AwRERERERERkRtJQgjh7iDouszMTOj1eneHcVuSJEGn08FkMoHNx/k0Gg0sFou7wyhT2GZdh+3V+dheXYtt1vnYZl2H7dX52F5di23W+e5Vm/23fwd9EGjcHQDZM5lMMJlM7g7jttRqNXQ6HXJycmC1Wt0dTpnj7e2NrKwsd4dRprDNug7bq/OxvboW26zzsc26Dtur87G9uhbbrPPdqzbL5Iz78bEmIiIiIiIiIiI3YnKGiIiIiIiIiMiNmJwhIiIiIiIiInIjJmeIiIiIiIiIiNyIyRkiIiIiIiIiIjdicoaIiIiIiIiIyI2YnCEiIiIiIiIiciMmZ4iIiIiIiIiI3IjJGSIiIiIiIiIiN2JyhoiIiIiIiIjIjZicISIiIiIiIiJyIyZniIiIiIiIiIjciMkZIiIiIiIiIiI3YnKGiIiIiIiIiMiNmJwhIiIiIiIiInIjJmeIiIiIiIiIiNyIyRkiIiIiIiIiIjdicoaIiIiIiIiIyI2YnCEiIiIiIiIiciMmZ4iIiIiIiIiI3IjJGSIiIiIiIiIiN9KUdscjR47g4sWLyMvLQ2BgICpXroyAgABnxkZEREREREREVOYpTs7Isozly5dj7ty5WL9+PTIzMyGEsK2XJAnVqlXDk08+iYEDByIqKsolARMRERERERERlSWKHmtauHAhqlSpgqeffhparRaTJk3C+vXrceDAARw/fhw7d+7E4sWL0aVLFyxduhSVKlXCkCFDcOHCBVfHT0RERERERER0X1PUc2bSpEl4/fXX8dRTT8FgMJS4TcOGDdGzZ0+8//77OHToEKZOnYp58+Zh/PjxTg2YiIiIiIiIiKgsUZScOXLkCCRJUlxojRo18M0339g99kRERERERERERMUpeqzJkcSMM/YjIiIiIiIiInpQODyV9qpVq7Bw4ULb32fPnkWHDh0QERGBgQMHIicnx6kBEhERERERERGVZQ4nZ958802cP3/e9vfw4cNx5MgR9O7dG6tWrcKbb77p1ACJiIiIiIiIiMoyh5MzJ06cQJ06dQAAmZmZWLVqFaZNm4YpU6bg/fffx9KlS50eJBERERERERFRWeVwcsZisUClKtxt8+bNEELgkUceAQDExsbi0qVLzo2QiIiIiIiIiKgMczg5U7VqVSxYsAA5OTn46quvEB8fDy8vLwDAxYsXERgY6PQgiYiIiIiIiIjKKkVTad9o4sSJePLJJzF37lyo1WosX77ctm7VqlWoX7++UwMkIiIiIiIiIirLHE7OdO3aFUeOHMGePXtQu3ZtVKpUybauadOmqF27tlMDJCIiIiIiIiIqyxxOzgCFY8vExsYWWz506NC7DoiIiIiIiIiI6EFSquRMWloaVq5ciXPnziE/P99unSRJmDhxolOCIyIiIiIiIiIq6xxOzqxevRo9e/ZEdnY2DAYDdDqd3XpnJ2d69epl97fJZELDhg3xxhtvAACee+45pKen22aQCg4OxvTp023bHzx4EDNnzsSlS5cQGRmJESNGICYmxrZ++fLlWLJkCXJzc1GvXj2MGDHCNsCx2WzG7NmzsWXLFqhUKnTs2BEDBgyAJEkAgKtXr+Lzzz/HkSNH4OvriwEDBqBly5aKj01ERERERERE5HByZsyYMWjUqBG+/fZbREVFuSImOz/++KPtv61WK5599lk0a9bMbpvXXnsNDRo0KLZvZmYmJk+ejCFDhqBFixb4448/8O6772LmzJnQarXYs2cPFi5ciHfeeQdhYWH4/PPPMWPGDLzyyisAgMWLF+PUqVP48ssvYTab8eabbyI4OBidO3cGAEyZMgXR0dF4/fXXcfz4cbz77ruIiopCVFTUHY9NRERERERERASUYirt06dPY/z48fckMXOz3bt3Iz8/H/Hx8Yq237ZtG8LCwtC2bVtotVp069YNQgjs3bsXALB+/Xq0a9cOcXFx8PT0RP/+/bFt2zbk5OQAANauXYvevXvDz88PwcHB6N69O9auXQsAuHDhAo4fP47+/ftDr9ejVq1aaNy4MdavX6/o2EREREREREREQCmSM/Xr18fZs2ddEcsdrVu3Di1atIBer7dbPm3aNPTr1w8TJkzA4cOHbcuTk5PtHiOSJAnR0dFITk4GACQlJdmtDw8Ph0ajwblz55CdnY3U1FS79bGxsXb7BgcH2x6BAoCYmBgkJSUpOjYREREREREREVCKx5q+/PJL9OvXD+XLl0e7du2g0ZRqTGGHZWZmYufOnXjvvffslr/88suIi4sDUJi8mTRpEj7//HOEhIQgLy/PLnkCAEajEXl5eQCA/Pz8W64v2sZoNNqtM5lMsFqtt90XwB2PXeTixYu4ePGi7W+9Xo/w8HBlleImarXa7v/JuSRJYt06Gdus67C9Oh/bq2uxzTof26zrsL06H9ura7HNOh/b7IPD4cxK06ZNYTab0blzZ6hUKhgMBrv1kiQhIyPDaQEW2bhxI8LCwlClShW75dWrV7f9d+fOnbFlyxb8/fff6NSpEwwGA3Jzc+22z8nJscXs4eFhe4SpSG5uLgwGg22bor+L9tXpdFCr1bfdF8Adj11k1qxZmDRpku3vCRMmYPLkycoqxc18fHzcHUKZdfNA2+QcbLOuwfbqGmyvrsM26xpss67B9uoabK+uwzbrGmyzZV+pBgQumq3oXlq3bh3at29/x+1UKhWEEACAyMhI/Pnnn7Z1QgicOXMGnTp1AgBERUUhMTERrVu3BlA4jozZbEZERASMRiMCAgJw+vRpBAYGAgASExMRGRlp2/fq1avIzs629ZA5ffq0bSyeOx27yPPPP4+uXbva/tbr9UhLS3Oobu41tVoNHx8fZGZmwmq1ujucMsdoNBZL/NHdYZt1HbZX52N7dS22Wedjm3UdtlfnY3t1LbZZ57tXbdbf399lZZMyDidn3n77bReEcXunTp1CcnKyLYlS5OrVq7hy5QoqV64MoHCA3xMnTmD48OEACnv5zJkzBxs2bEDz5s2xYsUKAEDdunUBAG3btsWUKVPQsmVLhIeHY8GCBWjatKntUaZ27dph8eLFqFy5MkwmE5YtW4YuXboAKByfpmLFivj+++8xaNAgnDhxAjt37sSHH36o6NhFwsLCEBYWZvs7JSXlvnmjsFqt902s9xMhBOvVRdhmnY/t1XXYXl2DbdZ12Gadj+3VddheXYNt1nXYZss+SRR1M/kXmzVrFlJSUvD666/bLU9OTsbHH3+MixcvQqPRoEKFCujXrx9q1apl2+bAgQOYNWsWLl26hMjISAwfPhyxsbG29cuXL8eSJUuQm5uLunXrYuTIkbaeMGazGV999RUSEhKgUqnQsWNHDBgwwNZz6OrVq/jss89w5MgR+Pn5oX///mjVqpXiY5ckJSXlruvL1dRqNfz9/ZGWlsYbhAt4e3sjKyvL3WGUKWyzrsP26nxsr67FNut8bLOuw/bqfGyvrsU263z3qs0GBQW5rGxSRlFy5qmnnsJrr71WrNfHreTn5+Prr7+Gp6cnBg8efLcxPlCYnCG+qTkf26zrsL06H9ura7HNOh/brOuwvTof26trsc06H5MzDw5FjzVFRkaiWbNmqFy5Mnr27IlmzZqhdu3aCAgIAACYTCYkJibi77//xsqVK/Hbb7+hcuXKmDlzpkuDJyIiIiIiIiK636mUbPTRRx/h5MmT6Nq1K7755hu0bdsWwcHB0Gq18PT0hMFgQPXq1TFw4EBkZmZiwYIF2LVrFxo0aODq+ImIiIiIiIiI7muKBwQOCwvDpEmTMGnSJJw6dQq7du3CxYsXkZ+fj4CAAFSpUgWNGzeGp6enK+MlIiIiIiIiIipTHJ6tCQDi4uIQFxfn7FiIiIiIiIiIiB44ih5rIiIiIiIiIiIi12ByhoiIiIiIiIjIjZicISIiIiIiIiJyIyZniIiIiIiIiIjciMkZIiIiIiIiIiI3KtVsTUWuXLmC/Pz8YssjIyPvplgiIiIiIiIiogeGw8mZa9euYcSIEVi6dCnMZrPdOiEEJEmC1Wp1WoBERERERERERGWZw8mZ5557Dps2bcJrr72G6tWrQ6fTuSIuIiIiIiIiIqIHgsPJmQ0bNuCzzz7DgAEDXBEPEREREREREdEDxeEBgf38/BAUFOSKWIiIiIiIiIiIHjgOJ2deeeUVfP7557BYLK6Ih4iIiIiIiIjogeLwY01HjhzB4cOHERcXh1atWsHPz89uvSRJ+PTTT50VHxERERERERFRmeZwcmb58uVQqQo73GzZsqXYeiZniIiIiIiIiIiUczg5k5iY6Io4iIiIiIiIiIgeSA6POUNERERERERERM7jcM8ZADh//jymTZuGhIQEpKamIiAgAC1atMCoUaNQvnx5Z8dIRERERERERFRmOdxz5uDBg6hVqxZmzpyJsLAwtG3bFmFhYZg5cyZq166NQ4cOuSJOIiIiIiIiIqIyyeGeM2PHjkVcXBxWr14Nf39/2/K0tDR07NgRY8eOxcqVK50aJBERERERERFRWeVwz5mEhAS88cYbdokZAPD398frr7+OhIQEpwVHRERERERERFTWOZyc0Wg0KCgoKHFdQUEB1Gr1XQdFRERERERERPSgcDg50759e7z++us4fvy43fITJ05g4sSJ6NChg9OCIyIiIiIiIiIq6xxOzkydOhUWiwXVq1dH3bp18fDDD6NevXqoVq0aLBYLpk6d6oo4iYiIiIiIiIjKJIeTM5GRkThw4ACmTp2KypUrQ5ZlVK5cGZ988gn279+PChUquCJOIiIiIiIiIqIyyeHZmgDAy8sLI0eOxMiRI50dDxERERERERHRA8XhnjNEREREREREROQ8inrO+Pj4YMOGDWjQoAG8vb0hSdItt5UkCRkZGU4LkIiIiIiIiIioLFOUnBkzZgzCwsJs/3275AwRERERERERESmnKDnz1ltv2f777bffdlUsRERERERERERu8fbbb6Njx46Ij4+/58d2eMyZtm3b4ujRoyWuO378ONq2bXvXQRERERERERER3UuTJk3C1q1b3XJsh5MzGzduRGZmZonrMjMzsXnz5rsOioiIiIiIiIjoQVGq2ZpuNebM1q1bERISclcBEREREREREVHZt23bNrRt2xZGoxG+vr7o27cvrly5AgCIiYnB8OHDi+0zduxYREREQJZlAEBBQQEmTJiAqKgo6PV6VKtWDT/88IPdPgMHDkTNmjWxceNG1KtXD0ajEY0bN8bff/9t26YozzFu3DhIkgRJkrBx40YXnXlxipIz7733Hnx8fODj4wNJktCmTRvb30X/9Ho9Ro8ejSeeeMLVMRMRERERERHRfWzbtm1o3bo1fH19sXjxYnz11VfYtWsXunXrBgDo3bs3lixZAqvVattHCIHFixfjqaeegkpVmM7o1asXZs2ahTFjxmD58uV45JFH0K9fP6xcudLueJcuXcLIkSMxbtw4/Pjjj8jPz8fjjz8Os9lsiwcARowYgW3btmHbtm2oX7/+vagKAAoHBI6Pj8eYMWMghMA777yDPn36ICIiwm4bnU6HatWq4bHHHnNJoERERERERERUNowfPx4NGzbE0qVLbb1WatWqhZo1a2LFihXo06cP3n//faxfvx4dOnQAAGzZsgXnzp1Dnz59AAAbNmzAb7/9hj///BMdO3YEAHTo0AEXL17EW2+9hU6dOtmOl5qaik2bNqFGjRoAAKPRiDZt2mDHjh1o3rw5mjRpAgCIjIy0/fe9pCg506pVK7Rq1QpAYVefIUOGIDw83KWBPah0Oh30er27w7itogvHaDRCCOHmaMoejUYDb29vd4dRprDNug7bq/OxvboW26zzsc26Dtur87G9uhbbrPOV1Tabm5uLv/76C1OmTLHrGVO5cmVUqFABu3btQufOnVG9enUsWrTIlpxZtGgRKlWqhIYNGwIAVq9ejYCAALRt2xYWi8VWTocOHTBs2DBYrVao1WoAQHh4uC0xAwDVq1cHAJw7d87l56uEouTMjW6cVpucz2QywWQyuTuM21Kr1dDpdMjJybG7kMg5vL29kZWV5e4wyhS2Wddhe3U+tlfXYpt1PrZZ12F7dT62V9dim3W+e9Vm73UHgbS0NFitVowePRqjR48utv7s2bMAgD59+uDjjz/Gl19+CZVKhSVLluA///mPbbuUlBSkpqZCq9WWeJyLFy/anvrx8/OzW6fT6QAA+fn5zjilu+ZwcgYATp48iTlz5uD48eMlnshvv/1214ERERERERERUdnj5+cHSZIwYcIEdO/evdj6oKAgAIXjzkycOBGrVq2CXq/H1atXbY80AUBAQACCg4OxYsWKEo9zP01Y5HByZteuXWjVqhWioqJw/Phx1K5dGxkZGThz5gwiIiJQsWJFV8RJRERERERERGWA0WhE06ZNceTIEbz77ru33K5ixYpo1KgRFi5cCL1ej7p166Jq1aq29e3bt8eHH34InU6H2rVr33VcWq3WbT1pHE7OvPLKK+jVqxe++eYbaLVafPPNN6hfvz62bt2KPn364NVXX3VFnERERERERERURnz00Udo27YtnnrqKfTu3Rv+/v44d+4c1qxZg0GDBqF169YACh9tmjhxIjQaDV5//XW7Mjp06IDHHnsMjzzyCF555RXUrl0bOTk5OHToEE6ePImvv/7aoZiqVauGX3/9FS1atIDRaESVKlXu2ThKiqbSvtG+ffvQp08f27RVRVml+Ph4vP322xg/frxzIyQiIiIiIiKiMiU+Ph4JCQnIzs7GoEGD0LlzZ7zzzjvw9PS0eyLnqaeeQl5eHjIzM9G7d+9i5SxZsgTDhg3DjBkz0KlTJzz77LNYvXq1bVIjR0yfPh2yLKNTp05o1KgR/v7777s6R0c43HNGkiTodDpIkoSQkBAkJSUhPj4eABAREYHjx487PUgiIiIiIiIiKlsaNmyIP/7447bbhIeH33YwZJ1OhzfffBNvvvnmLbeZM2dOsWV+fn7FZsBq3rz5PU3I3MjhnjPVq1fHqVOnAABNmzbFxx9/jIMHD+LYsWN4//33ERcX5/QgiYiIiIiIiIjKKod7zgwdOhRnzpwBAPzvf/9Dx44dUadOHQCFg/osWbLEqQESEREREREREZVlDidn+vfvb/vvatWq4ciRI9i2bRvy8vLQpEmT+2qqKiIiIiIiIiIid3MoOZOfn49evXph7NixaNmyJQDAy8sLHTp0cElwRERERERERFR2ZGVluaTcezWrkqs4NOaMh4cHNm3adNvBeIiIiIiIiIiISDmHBwTu2LEjVq9e7YpYiIiIiIiIiIgeOA6POTNo0CA8//zzyMrKQufOnREaGgpJkuy2qV+/vtMCJCIiIiIiIiIqyxxOznTp0gUAMGPGDMyYMcMuMSOEgCRJfOyJiIiIiIiIiEghh5MzGzZscEUcREREREREREQPJIeTM61atXJFHEREREREREREDySHBwQmIiIiIiIiIiLncTg5o1KpoFarb/uPiIiIiIiIiKg0hNmM3M0bkLX0R+Ru3gBhNpe6rI8++ggVK1aEt7c3qlWrhp9//tmJkTqPw481TZ06tdjsTGlpaVi9ejUuXLiAl156yVmxEREREREREdEDRJjNSJ32ISznz9qW5e3choBR4yBptQ6XFxMTg02bNiEsLAxLly5F//798dBDDyEiIsKZYd81h5Mzt0q+vP322xgwYABSU1PvNiYiIiIiIiIiegDlbUsoTMzcMAu05Vwy8rYlwLNlG4fL69mzp91/T548GTt27PjXJWecOuZMv379MGvWLGcWSUREREREREQPCGvK1ZKXX0spVXnz5s1D3bp14efnBz8/Pxw4cAApKaUry5Uc7jlzO8eOHYMsy84skoiIiIiIiIgeEOqg4JKXBwY5XFZSUhKee+45rF27Fs2aNYNarUa9evUghLjbMJ2uVGPO3MxkMuHIkSP46aef0LdvX6cERkREREREREQPFkPT5sjbsdVuzBlNRCQMTZs7XFZOTg4AIDi4MOEzb948HDx40DmBOpnDyZmxY8cWW6bX6xEREYFRo0Zh4sSJTgmMiIiIiIiIiB4sklaLgJdeQd62BFivpUAdGARD0+alGgy4evXqGDduHJo1awaVSoUBAwYgPj7eBVHfPYeTM3xsiYiIiIiIiIhcRdJqSzX4b0kmT56MyZMnO6UsV3LqgMBEREREREREROQYRT1n5s2b51ChAwYMKFUwREREREREREQPGkXJmYEDB9r9LUkSANiNcFy0DGByhoiIiIiIiIhIKUWPNaWlpdn+7dq1C1FRUXjjjTewb98+XLp0Cfv27cPrr7+OqKgo7Nixw9UxExERERERERGVGYp6zvj6+tr+e/z48Rg6dCjGjx9vWxYSEoJatWrBYDDg1Vdfxbp165wfKRERERERERFRGeTwgMBbt25FgwYNSlzXoEEDbN++/a6DIiIiIiIiIiJ6UDg8lXZISAgWL16MDh06FFu3aNEiBAcHOyUwIiIiIiIiIipbvL293R3Cv5LDyZkJEybg+eefx6lTp9C9e3eEhITgypUr+OWXX7B582bMmjXLFXESEREREREREZVJDidnhgwZgrCwMEyePBnjxo2DxWKBRqNB/fr18euvv+Kxxx5zRZxERERERERERGWSw8kZAOjSpQu6dOkCWZZx9epVBAcHQ6VyePgaIiIiIiIiInqAZGVluaTc+/1xqVIlZ4qoVCqEhoY6KxYiIiIiIiIiogcOu7sQEREREREREbnRXfWcuRemTZuGzZs3Q6O5Hur06dNts0JdvXoVn3/+OY4cOQJfX18MGDAALVu2tG178OBBzJw5E5cuXUJkZCRGjBiBmJgY2/rly5djyZIlyM3NRb169TBixAh4eXkBAMxmM2bPno0tW7ZApVKhY8eOGDBgACRJcsqxiYiIiIiIiIjui54z3bp1w48//mj7d+N03VOmTEFYWBi+//57jBo1CtOnT0dSUhIAIDMzE5MnT0aPHj2wcOFCtGzZEu+++y7MZjMAYM+ePVi4cCEmTpyIOXPmQKVSYcaMGbayFy9ejFOnTuHLL7/EtGnTsH37dqxcudIpxyYiIiIiIiIi14mOjsaqVavcHYYi90Vy5lYuXLiA48ePo3///tDr9ahVqxYaN26M9evXAwC2bduGsLAwtG3bFlqtFt26dYMQAnv37gUArF+/Hu3atUNcXBw8PT3Rv39/bNu2DTk5OQCAtWvXonfv3vDz80NwcDC6d++OtWvXOuXYRERERERERETAXSZnzp49i61bt9qSGa7y559/om/fvhg5ciTWrFljW56UlITg4GDbY0gAEBMTY+u9kpycbPcYkSRJiI6ORnJysm3/G9eHh4dDo9Hg3LlzyM7ORmpqqt362NhYu33v5thEREREREREREApx5z56quvMGnSJFy6dAkAsGvXLtSvXx+PP/44WrdujVGjRjktwMceewyDBw+G0WjEoUOH8MEHH8BoNCI+Ph75+fl2yREAMBqNyMvLAwDk5eXddv3t9i/axmg02q0zmUywWq13fewiFy9exMWLF21/6/V6hIeHK6scN1Gr1Xb/T84lSRLr1snYZl2H7dX52F5di23W+dhmXYft1fnYXl2Lbdb52Gbv3p49ezBu3DgkJyejffv2+Oabb+Dn5+fusIpxODkzbdo0vPrqq3j55ZfRrl07dOzY0baudevW+Omnn5yanImLi7P9d+3atfHoo4/ir7/+Qnx8PDw8PIr12snNzYXBYAAAGAwG5Obm2q3Pycmxrb/d/kXb3FheTk4OdDod1Gr1XR+7yKxZszBp0iTb3xMmTMDkyZMV1Iz7+fj4uDuEMkun07k7hDKJbdY12F5dg+3VddhmXYNt1jXYXl2D7dV12GZd40Frs2ZhRkLeNly1piBYHYTmhqbQStpSlTVnzhysXLkSwcHB6NOnD0aOHIl58+Y5OeK753By5vPPP8fEiRPxxhtvwGq12q2rUqUKjh075rTgSiJJEoQQAICoqChcvXoV2dnZtl4qp0+fRlRUFAAgMjISf/75p21fIQTOnDmDTp062fZPTExE69atARSOI2M2mxEREQGj0YiAgACcPn0agYGBAIDExERERkY65dhFnn/+eXTt2tX2t16vR1pamnMqy0XUajV8fHyQmZlZrA3Q3TMajS5/VPBBwzbrOmyvzsf26lpss87HNus6bK/Ox/bqWmyzznev2qy/v7/LynaUWZjxYeo0nLWcty3blrcT4wJGlSpBM3z4cMTGxgIAJk+ejMaNG9smBPo3cTg5c/78ecTHx5e4TqvVIjs7+66DulFCQgLq168PDw8PHD16FH/88QeGDh0KoHCMmIoVK+L777/HoEGDcOLECezcuRMffvghAKBp06aYM2cONmzYgObNm2PFihUAgLp16wIA2rZtiylTpqBly5YIDw/HggUL0LRpU9ujTO3atcPixYtRuXJlmEwmLFu2DF26dHHKsYuEhYUhLCzM9ndKSsp980ZhtVrvm1jvJ0II1quLsM06H9ur67C9ugbbrOuwzTof26vrsL26Btus6zxIbTYhbxvOWs7Diuvnm2w5h4S8bWjj2dLh8oo6WACFnSxMJhOuXr2K0NBQp8TrLA4nZ6KiorBz5060bdu22LodO3agcuXKTgmsyPLlyzF9+nTIsoygoCD069cPLVtef0HGjRuHzz77DE8//TT8/Pzwwgsv2Hqv+Pj4YMKECZg1axamT5+OyMhIvPHGG9BqC7Nt9erVQ58+ffDf//4Xubm5qFu3LkaOHGkru3fv3sjMzMSwYcOgUqnQsWNHu54vd3NsIiIiIiIiIrJ31ZpS4vIU67VSlXfjpDzJycnQarUIDg4uVVmuJImiZ4QUmjJlCt5++218+umn6NGjBwIDA7Ft2zakpKSgX79+mDx5Ml544QVXxVvmpaSU3BD/TdRqNfz9/ZGWlvbAZG/vJW9vb2RlZbk7jDKFbdZ12F6dj+3VtdhmnY9t1nXYXp2P7dW12Gad71612aCgIJeVfbM7tZENuZuxOGupXc8ZNdR4yrvHbXvOeHt7F1sWHR0Ng8GAFStWICgoCE8//TR8fX0xf/780p+Aizj8kNXYsWMxePBgDB061JZtatasGbp164b+/fszMUNEREREREREpdLc0BQVNOWhvuF/kZoINDc0LVV5AwYMQNeuXREREQG1Wo1PP/3UyRE7R6mm0v7ss8/w0ksvYc2aNbh27RoCAgLQrl07VKpUydnxEREREREREdEDQitp8UrAS0jI24YU6zUEqQNLPVvTmTNnAACvvfaak6N0vlIlZwAgNjYWzz//vDNjISIiIiIiIqIHnFbSlmrw3/uZouTM7t27HSq0fv36pQqGiIiIiIiIiOhBoyg507BhQ0iSdMfthBCQJImDaxERERERERERKaQoObNhwwZXx0FERERERERE9EBSlJxp1aqVq+MgIiIiIiIiInogOTyVNhEREREREREROY+injM+Pj7YsGEDGjRoAG9v7zuOP5OZmemU4IiIiIiIiIiIyjpFyZkxY8YgLCzM9t9KBgcmIiIiIiIiIrqRt7e3u0P4V1KUnPH394dGU7jp4MGDERYWBq1W69LAiIiIiIiIiIgeBIqSM6NHj0bTpk0REhKCmJgYbNu2DY0bN3Z1bERERERERERUhmRlZbmk3Pu9R46iAYEDAwNx6tQpAIAQgo81ERERERERERE5iaKeM48++igGDBiA8ePHQ5IkdO/eHXq9vsRtJUmyJXKIiIiIiIiIiOj2FCVnvvrqKzRv3hxHjhzB1KlT0bJlS5QrV87VsRERERERERERlXmKkjNarRbPPvssAODnn3/G+PHjUadOHZcGRkRERERERET0IFCUnLlRYmKiK+IgIiIiIiIiInogOZycAYDz589j2rRpSEhIQGpqKgICAtCiRQuMGjUK5cuXd3aMRERERERERERllqLZmm508OBB1KpVCzNnzkRYWBjatm2LsLAwzJw5E7Vr18ahQ4dcEScRERERERERUZnkcM+ZsWPHIi4uDqtXr4a/v79teVpaGjp27IixY8di5cqVTg2SiIiIiIiIiB4MZrPAtoQ8pFy1IihYjabNDdBqpVKVdeHCBbz88svYsGEDTCYTWrVqhWXLljk3YCdwODmTkJCABQsW2CVmAMDf3x+vv/46+vfv77TgiIiIiIiIiOjBYTYLTPswFefPWmzLdm7Lw6hxAQ4naKxWKx577DE89NBDOHHiBAwGA7Zu3erskJ3C4eSMRqNBQUFBiesKCgqgVqvvOigiIiIiIiIievBsS8jD+bMWWK3Xl51LtmBbQh5atvF0qKxdu3YhMTER27Ztg06nAwC0atXKmeE6jcNjzrRv3x6vv/46jh8/brf8xIkTmDhxIjp06OC04IiIiIiIiIjowZFy1Vri8mspJS+/neTkZERGRtoSM/9mDidnpk6dCovFgurVq6Nu3bp4+OGHUa9ePVSrVg0WiwVTp051RZxEREREREREVMYFBZf8NE5gkONP6URGRiI5ORlms/luw3I5h5MzkZGROHDgAKZOnYrKlStDlmVUrlwZn3zyCfbv348KFSq4Ik4iIiIiIiIiKuOaNjegfAUN1GrY/kVEatC0ucHhsho1aoSoqCi8/PLLyMrKgtlsxqZNm1wQ9d1zaMyZ/Px89OrVC2PHjsXIkSMxcuRIV8VFRERERERERA8YrVbCS68EYFtCHq6lWBEYVPrZmtRqNX7//Xe89NJLiI2NhdVqRZs2bf6V4844lJzx8PDApk2bMHr0aFfFQ0REREREREQPMK1Wcnjw31uJiIjAkiVLnFKWKzn8WFPHjh2xevVqV8RCRERERERERPTAcXgq7UGDBuH5559HVlYWOnfujNDQUEiSffei+vXrOy1AIiIiIiIiIqKyzOHkTJcuXQAAM2bMwIwZM+wSM0IISJIEq9XxKa6IiIiIiIiIiB5EDidnNmzY4Io4iIiIiIiIiIgeSA4nZ/6NoxoTEREREREREd2vHE7OFDl+/Dh27tyJixcvIiwsDI0aNUKVKlWcGRsRERERERERUZnncHImOzsbQ4cOxY8//ghZluHh4YH8/HyoVCo8+eSTmD17Nry8vFwRKxERERERERHdx7y9vd0dwr+Sw1NpjxgxAsuXL8fs2bORkZGB3NxcZGRk4KuvvsIff/yBESNGuCJOIiIiIiIiIqIyyeHkzM8//4wPPvgAgwYNsmW8vL29MXjwYLz//vtYunSp04MkIiIiIiIiIiqrHE7OeHh4ICYmpsR1sbGx0Gq1dx0UEREREREREdGDQhJCCEd2ePXVV3H06FEsW7YMkiTZlgsh0K1bN1SpUgUfffSR0wN9UGRmZkKv17s7jNuSJAk6nQ4mkwkONh9SQKPRwGKxuDuMMoVt1nXYXp2P7dW12Gadj23WddhenY/t1bXYZp3vXrXZf/t30AeBwwMCBwQEYPfu3ahUqRIee+wxhISE4MqVK/j9999RUFCA5s2bY+rUqQAKG9Lo0aOdHnRZZjKZYDKZ3B3GbanVauh0OuTk5MBqtbo7nDLH29sbWVlZ7g6jTGGbdR22V+dje3UttlnnY5t1HbZX52N7dS22Wee7V22WyRn3c7jnjEql/EkoSZJ403NQSkqKu0O4I7VaDX9/f6SlpfH1dQG+qTkf26zrsL06H9ura7HNOh/brOuwvTof26trsc06371qs0H/396dx1VV4P8ff1+RXdkUETUWl8rK0pyxUFN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