Return-Path: X-Original-To: archive-asf-public-internal@cust-asf2.ponee.io Delivered-To: archive-asf-public-internal@cust-asf2.ponee.io Received: from cust-asf.ponee.io (cust-asf.ponee.io [163.172.22.183]) by cust-asf2.ponee.io (Postfix) with ESMTP id 88098200C05 for ; Mon, 23 Jan 2017 19:42:32 +0100 (CET) Received: by cust-asf.ponee.io (Postfix) id 868E3160B49; Mon, 23 Jan 2017 18:42:32 +0000 (UTC) Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by cust-asf.ponee.io (Postfix) with SMTP id BCA82160B3C for ; Mon, 23 Jan 2017 19:42:30 +0100 (CET) Received: (qmail 97994 invoked by uid 500); 23 Jan 2017 18:42:29 -0000 Mailing-List: contact user-help@flink.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@flink.apache.org Delivered-To: mailing list user@flink.apache.org Received: (qmail 97985 invoked by uid 99); 23 Jan 2017 18:42:29 -0000 Received: from mail-relay.apache.org (HELO mail-relay.apache.org) (140.211.11.15) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 23 Jan 2017 18:42:29 +0000 Received: from mail-io0-f174.google.com (mail-io0-f174.google.com [209.85.223.174]) by mail-relay.apache.org (ASF Mail Server at mail-relay.apache.org) with ESMTPSA id 701371A06AA for ; Mon, 23 Jan 2017 18:42:29 +0000 (UTC) Received: by mail-io0-f174.google.com with SMTP id j18so116966490ioe.2 for ; Mon, 23 Jan 2017 10:42:29 -0800 (PST) X-Gm-Message-State: AIkVDXKyrAPyrE0thP6sxawCFn6U/aklBhEC3860Q3d02LIgk83bMJP5qKzsXkehgMTDy/DgQYFInsGla5Sw/A== X-Received: by 10.107.170.7 with SMTP id t7mr25621697ioe.32.1485196948888; Mon, 23 Jan 2017 10:42:28 -0800 (PST) MIME-Version: 1.0 Received: by 10.107.19.101 with HTTP; Mon, 23 Jan 2017 10:42:28 -0800 (PST) In-Reply-To: <145B3E4A-93CA-4B9C-A96B-28E47051D0E5@tetrationanalytics.com> References: <8D214890-46F4-479F-B5F2-22A8AA8494A6@tetrationanalytics.com> <1485164840364-11204.post@n4.nabble.com> <8CC92C17-B0D3-403E-B125-916A5178BE38@tetrationanalytics.com> <83A053B2-9E07-4DB5-A0CF-08CCC5DAA2C3@tetrationanalytics.com> <44DF34DE-CC30-4B9C-937D-8C92E8C23780@tetrationanalytics.com> <145B3E4A-93CA-4B9C-A96B-28E47051D0E5@tetrationanalytics.com> From: Stephan Ewen Date: Mon, 23 Jan 2017 19:42:28 +0100 X-Gmail-Original-Message-ID: Message-ID: Subject: Re: weird client failure/timeout To: user@flink.apache.org Content-Type: multipart/related; boundary=001a11425ba88f8d180546c75cb0 archived-at: Mon, 23 Jan 2017 18:42:32 -0000 --001a11425ba88f8d180546c75cb0 Content-Type: multipart/alternative; boundary=001a11425ba88f8d150546c75caf --001a11425ba88f8d150546c75caf Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hi! I think what you are seeing is the effect of too mans tasks going to the same task slot. Have a look here: https://ci.apache.org/projects/flink/flink-docs-release-1.2/concepts/runtim= e.html#task-slots-and-resources By default, Flink shares task slots across all distinct pipelines of the same program, for easier "getting started" scheduling behavior. For proper deployments (or setups where you just have very , I would make sure that the program sets different "sharing groups" (via "..slotSharingGroup()") on the different streams. Also, rather than defining 100s of different sources, I would consider defining one source and making it parallel. It works better with Flink's default scheduling parameters. Hope that helps. Stephan On Mon, Jan 23, 2017 at 5:40 PM, Abhishek R. Singh < abhishsi@tetrationanalytics.com> wrote: > Actually, I take it back. It is the last union that is causing issues (of > job being un-submittable). If I don=E2=80=99t conbineAtEnd, I can go high= er (at > least deploy the job), all the way up to 63. > > After that it starts failing in too many files open in Rocks DB (which I > can understand and is at least better than silently not accepting my job)= . > > Caused by: java.lang.RuntimeException: Error while opening RocksDB > instance. > at org.apache.flink.contrib.streaming.state.RocksDBStateBackend. > initializeForJob(RocksDBStateBackend.java:306) > at org.apache.flink.streaming.runtime.tasks.StreamTask. > createStateBackend(StreamTask.java:821) > at org.apache.flink.streaming.api.operators. > AbstractStreamOperator.setup(AbstractStreamOperator.java:118) > ... 4 more > Caused by: org.rocksdb.RocksDBException: IO error: > /var/folders/l1/ncffkbq11_lg6tjk_3cvc_n00000gn/T/flink- > io-45a78866-a9da-40ca-be51-a894c4fac9be/3815eb68c3777ba4f504e8529db6e1 > 45/StreamSource_39_0/dummy_state/7ff48c49-b6ce-4de8-ba7e- > 8a240b181ae2/db/MANIFEST-000001: Too many open files > at org.rocksdb.RocksDB.open(Native Method) > at org.rocksdb.RocksDB.open(RocksDB.java:239) > at org.apache.flink.contrib.streaming.state.RocksDBStateBackend. > initializeForJob(RocksDBStateBackend.java:304) > ... 6 more > > > On Jan 23, 2017, at 8:20 AM, Abhishek R. Singh < > abhishsi@tetrationanalytics.com> wrote: > > Is there a limit on how many DataStreams can be defined in a streaming > program? > > Looks like flink has problems handling too many data streams? I simplifie= d > my topology further. For eg, this works (parallelism of 4) > > > > However, when I try to go beyond 51 (found empirically by parametrizing > nParts), it barfs again. Submission fails, it wants me to increase > akka.client.timeout > > Here is the reduced code for repro (union at the end itself is not an > issue). It is the parallelism of the first for loop: > > int nParts =3D cfg.getInt("dummyPartitions", 4); > > boolean combineAtEnd =3D cfg.getBoolean("dummyCombineAtEnd", true); > > // create lots of streams > List> streams =3D new ArrayList<>(nPar= ts); > for (int i =3D 0; i < nParts; i++) { > streams.add(env > .readFile( > new TextInputFormat(new Path("/tmp/input")), > "/tmp/input", > FileProcessingMode.PROCESS_CONTINUOUSLY, > 1000, > FilePathFilter.createDefaultFilter()) > .setParallelism(1).name("src")); > } > > if (combineAtEnd =3D=3D true) { > DataStream combined =3D streams.get(0); > for (int i =3D 1; i < nParts; i++) { > combined =3D combined.union(streams.get(i)); > } > combined.print().setParallelism(1); > } else { // die parallel > for (int i =3D 1; i < nParts; i++) { > streams.get(i).print(); > } > } > > > > On Jan 23, 2017, at 6:14 AM, Abhishek R. Singh < > abhishsi@tetrationanalytics.com> wrote: > > I even make it 10 minutes: > > akka.client.timeout: 600s > > But doesn=E2=80=99t feel like it is taking effect. It still comes out at = about the > same time with the same error. > > -Abhishek- > > On Jan 23, 2017, at 6:04 AM, Abhishek R. Singh < > abhishsi@tetrationanalytics.com> wrote: > > yes, I had increased it to 5 minutes. It just sits there and bails out > again. > > On Jan 23, 2017, at 1:47 AM, Jonas wrote: > > The exception says that > > Did you already try that? > > > > -- > View this message in context: http://apache-flink-user- > mailing-list-archive.2336050.n4.nabble.com/weird-client- > failure-timeout-tp11201p11204.html > Sent from the Apache Flink User Mailing List archive. mailing list archiv= e > at Nabble.com . > > > > > > --001a11425ba88f8d150546c75caf Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hi!

I think what you are seeing is the = effect of too mans tasks going to the same task slot. Have a look here:=C2= =A0https://ci.apache.org/projec= ts/flink/flink-docs-release-1.2/concepts/runtime.html#task-slots-and-resour= ces

By default, Flink shares task slots across= all distinct pipelines of the same program, for easier "getting start= ed" scheduling behavior.
For proper deployments (or setups w= here you just have very , I would make sure that the program sets different= "sharing groups" (via "..slotSharingGroup()") on the d= ifferent streams.

Also, rather than defining 100s = of different sources, I would consider defining one source and making it pa= rallel. It works better with Flink's default scheduling parameters.

Hope that helps.

Stephan




On Mon, Jan 23, 2017 at 5:40 PM, Abhish= ek R. Singh <abhishsi@tetrationanalytics.com> = wrote:
Actually, I take it back. It is the last union that is causing issues (of= job being un-submittable). If I don=E2=80=99t conbineAtEnd, I can go highe= r (at least deploy the job), all the way up to 63.=C2=A0

After that it starts failing in too many files open in Rocks DB (which I c= an understand and is at least better than silently not accepting my job).

Caused by: java.lang.RuntimeException: Error while opening = RocksDB instance.
=C2=A0 =C2=A0 =C2=A0 =C2=A0 at org.apache.flink.contrib.streaming.state.RocksDBStateBackend.initializeForJob(Rock= sDBStateBackend.java:306)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 at org.apache.flink.st= reaming.runtime.tasks.StreamTask.createStateBackend(StreamTask.java:821)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 at org.apache.flink.streaming.= api.operators.AbstractStreamOperator.setup(AbstractStreamOperator= .java:118)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 ... 4 more
Caused by: org.rocksdb= .RocksDBException: IO error: /var/folders/l1/ncffkbq11_lg6tjk_3cvc_n00= 000gn/T/flink-io-45a78866-a9da-40ca-be51-a894c4fac9be/3815eb= 68c3777ba4f504e8529db6e145/StreamSource_39_0/dummy_state/7ff48c49= -b6ce-4de8-ba7e-8a240b181ae2/db/MANIFEST-000001: Too many open fi= les
=C2=A0 =C2=A0 =C2=A0 =C2=A0 at org.rocksdb.RocksDB.open(Native Meth= od)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 at org.rocksdb.RocksDB.open(RocksDB.jav= a:239)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 at org.apache.flink.contrib.streamin= g.state.RocksDBStateBackend.initializeForJob(RocksDBStateBac= kend.java:304)
=C2=A0 =C2=A0 =C2=A0 =C2=A0 ... 6 more


On Jan 23, 2017, at 8:20 AM, Abhishek R. Singh <abhishsi@tetrationa= nalytics.com> wrote:

Is there a limit on how many DataStreams can be defined= in a streaming program?

Looks like flink has problems = handling too many data streams? I simplified my topology further. For eg, t= his works (parallelism of 4)

<Paste= dGraphic-2.png>

H= owever, when I try to go beyond 51 (found empirically by parametrizing nPar= ts), it barfs again. Submission fails, it wants me to increase=C2=A0akka.client.timeout
<= div>
Here is the reduced code for repro (union at the end its= elf is not an issue). It is the parallelism of the first for loop:
int nParts =
=3D cfg.getInt("dummyPa=
rtitions", 4);
boolean combineAtEnd=
 =3D cfg.getBoolean("dummyCombineAtEnd", true);

// create lots of streams
List<SingleOutputStreamOperato= r<String>> streams =3D new ArrayList<>(nParts);
for (int i =3D 0; i < nPart= s; i++) {
streams.add(env
.readFile(
new TextInputFormat(new Path("/tmp/input")),
"/tmp/input", FileProcessingMode.PROCESS_CONTINUOUSLY,
1000,
FilePathFilter.createDefaultFilter())
.setPara= llelism(1).name("src"));
}

if (combineAtEnd =3D=3D true) {
DataStream&l= t;String> combined =3D streams.get(0
);
for (int i =3D 1; i < nParts; i++) {
combined =3D combined.un= ion(streams.get(i));
}
combined.print().setParallelism(= 1);
} else { // die parallel
for (int i =3D 1; i < nParts; i++) {
streams.get(i).print= ();
}
}


On Jan 23, 2017, at 6:14 AM, Abhishek R. Singh <abhishsi@tetration= analytics.com> wrote:

I even m= ake it 10 minutes:

akka.client.timeout<= span style=3D"font-variant-ligatures:no-common-ligatures;color:#d53bd3">: 600s

=
But doesn=E2=80=99t feel like it is taking effect. It sti= ll comes out at about the same time with the same error.

-Abhishek-

O= n Jan 23, 2017, at 6:04 AM, Abhishek R. Singh <abhishsi@tetrationanalytics.com> wrote:

yes, I had increased it to 5 minutes. It just sits t= here and bails out again.

On Jan 23, 2017,= at 1:47 AM, Jonas <jonas@huntun.de> wrote:

The exception says that

Did y= ou already try that?



--
View this message in context: http://= apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/weir= d-client-failure-timeout-tp11201p11204.html
Sent from the = Apache Flink User Mailing List archive. mailing list archive at Nabble.com.

<= /div>


=


--001a11425ba88f8d150546c75caf-- --001a11425ba88f8d180546c75cb0 Content-Type: image/png; x-unix-mode=0666; name="PastedGraphic-3.png" Content-Disposition: inline; filename="PastedGraphic-3.png" Content-Transfer-Encoding: base64 Content-ID: <1152EEA8-BD78-40D0-8740-E606E41FF2A7@cisco.com> X-Attachment-Id: f003052f8166a7f3_0.1.1 iVBORw0KGgoAAAANSUhEUgAAAYgAAAEcCAYAAADdtCNzAAAYK2lDQ1BJQ0MgUHJvZmlsZQAAWIWV eQk4Vd/X/z733MnlmudZZjLPJPM8z0Mq1zzTNUWRkAyVZEghhUSKRlNChpRkylCKFEKpVIZMeQ+q 7+/9vv/n/z7vfp59zueuvfban7X32nvfdS8AHKykkJAAFC0AgUFhZGsDbV5HJ2de3DsAAXpAAZgA ILmHhmhZWpoCpPx5//eyNIRoI+WFxJat/9n+/y10Hp6h7gBAlgh28wh1D0TwPQDQ7O4h5DAAML2I nD8yLGQLLyCYkYwQBACL38LeO5hzC7vtYOltHVtrHQTrAoCnIpHI3gBQb9nnjXD3RuxQhyBt9EEe vkGIaiKC97r7kDwAYG9DdHYHBgZv4XkEi7j9hx3v/2bT7a9NEsn7L97xZbvgdX1DQwJIUf/H6fjf S2BA+J8xdiGVyodsaL3lMzJvZf7BJluYCsENQW7mFgimR/ATX49t/S084hNuaPdbf849VAeZM8AM AAp4kHRNEIzMJYo53N9O6zeWJZG3+yL6KHPfMCPb39iNHGz92z4qIijA3PS3nWQfT6M/+JJnqJ7N Hx0vX30jBCORhroX7WPrsMMT1Rbha2+OYGoE94b625j87jsW7aNj/keHHG69xVkAwQteZH3rHR2Y NTD0j1+wpDtpeyxWBGuG+dga7vSFHT1DHU3/cPDw1NXb4QB7eAbZ/eYGI9Glbf27b1JIgOVvffiS Z4CB9c48w7dCI2z+9O0PQwJsZx7g934kY8sd/vBSSJil7Q43NBqYAh2gC3hBOFLdQDDwA77dczVz yKedFn1AAmTgDTyBxG/Jnx4O2y1ByNMGRIPPCPIEoX/7aW+3eoIIRL7xV7rzlABe260R2z38wQcE B6LZ0XvRamhT5KmJVFm0MlrlTz9emj+jYvWwulhDrD5W9C8Pd4R1AFLJwPf/ITNB3p6Id1tcgv74 8I89zAdMH+Y9ZhAzjnkF7MHktpXfWgd948n/Ys4LzMA4Yk3/t3duiM3ZPzpoIYS1AlobrY7wR7ij mdHsQAItj3iihdZAfFNApP/JMPwvt3/m8t/jbbH+T39+y6nFqBV+s3D7uzI6f7X+bUXnP+bIA3mb /FsTTobvwh3wI/gp3ADXAF64Ca6Fu+CHW/hvJExuR8Kf0ay3ufkjdnz/6EhXSM9Kr/+P0Um/GZC3 1xuEeR4O29oQOsEhUWRfb58wXi3kRPbkNQpyl9zNKystowTA1vm+c3z8sN4+tyHmnn9kJOT8VpYF gKD9jywYOQcqs5GwvvCPTAjZm2wqANyxdg8nR+zI0FsPDCAAGmRnsAFuwA9EEJ9kgSJQA5pADxgD C2ALnMABZNZ9QCDCOhIcBcdBEkgDZ0E2uAgKQTEoAzfBHVADGsAj8Bg8A71gELxGYmMKfALzYAms QRCEg4gQA8QG8UCCkDgkCylDeyE9yBSyhpwgV8gbCoLCoaNQApQGnYMuQlegcug2VAc9gp5CfdAr 6B00C32HVlEwigrFiOJCCaGkUMooLZQJyha1H+WNOoSKRiWizqByUUWoG6hq1CPUM9Qgahz1CbUI A5gSZob5YAlYGdaBLWBn2Asmw7FwKpwDF8GVcD2y1i/gcXgOXkFj0QxoXrQEEp+GaDu0O/oQOhZ9 Cn0RXYauRrehX6DfoefRvzBEDCdGHKOKMcI4YrwxkZgkTA6mFHMf047snSnMEhaLZcYKY5WQvemE 9cMewZ7CFmCrsM3YPuwEdhGHw7HhxHHqOAscCReGS8JdwN3ANeH6cVO4n3hKPA9eFq+Pd8YH4ePx Ofjr+EZ8P34av0ZBSyFIoUphQeFBEUWRTlFCUU/RQzFFsUagIwgT1Am2BD/CcUIuoZLQTnhD+EFJ SbmLUoXSitKXMo4yl/IW5RPKd5QrVPRUYlQ6VC5U4VRnqK5RNVO9ovpBJBKFiJpEZ2IY8QyxnNhK HCP+pGaglqQ2ovagPkadR11N3U/9hYaCRpBGi+YATTRNDs1dmh6aOVoKWiFaHVoSbSxtHm0d7TDt Ih0DnQydBV0g3Sm663RP6WbocfRC9Hr0HvSJ9MX0rfQTDDADP4MOgztDAkMJQzvDFCOWUZjRiNGP MY3xJmM34zwTPZM8kz3TYaY8podM48wwsxCzEXMAczrzHeYh5lUWLhYtFk+WFJZKln6WZVYOVk1W T9ZU1irWQdZVNl42PTZ/tgy2GrZRdjS7GLsVeyT7JfZ29jkORg41DneOVI47HCOcKE4xTmvOI5zF nF2ci1zcXAZcIVwXuFq55riZuTW5/bizuBu5Z3kYePby+PJk8TTxfORl4tXiDeDN5W3jnefj5DPk C+e7wtfNt7ZLeJfdrvhdVbtG+Qn8yvxe/Fn8LfzzAjwCZgJHBSoERgQpBJUFfQTPC3YILgsJCzkI nRSqEZoRZhU2Eo4WrhB+I0IU0RA5JFIkMiCKFVUW9RctEO0VQ4kpiPmI5Yn1iKPEFcV9xQvE+3Zj dqvsDtpdtHtYgkpCSyJCokLinSSzpKlkvGSN5BcpASlnqQypDqlf0grSAdIl0q9l6GWMZeJl6mW+ y4rJusvmyQ7IEeX05Y7J1cp9kxeX95S/JP9SgUHBTOGkQovChqKSIlmxUnFWSUDJVSlfaViZUdlS +ZTyExWMirbKMZUGlRVVRdUw1TuqX9Uk1PzVrqvN7BHe47mnZM+E+i51kvoV9fG9vHtd917eO67B p0HSKNJ4r8mv6aFZqjmtJarlp3VD64u2tDZZ+772so6qToxOsy6sa6CbqtutR69np3dRb0x/l763 foX+vIGCwRGDZkOMoYlhhuGwEZeRu1G50byxknGMcZsJlYmNyUWT96ZipmTTejOUmbFZptkbc0Hz IPMaC2BhZJFpMWopbHnI8oEV1srSKs/qg7WM9VHrDhsGm4M2122WbLVt021f24nYhdu12NPYu9iX 2y876Dqccxh3lHKMcXzmxO7k61TrjHO2dy51Xtynty9735SLgkuSy9B+4f2H9z89wH4g4MDDgzQH SQfvumJcHVyvu66TLEhFpEU3I7d8t3l3Hffz7p88ND2yPGY91T3PeU57qXud85rxVvfO9J710fDJ 8Znz1fG96PvNz9Cv0G/Z38L/mv9mgENAVSA+0DWwLog+yD+oLZg7+HBwX4h4SFLI+CHVQ9mH5skm 5NJQKHR/aG0YI/JVpytcJPxE+LuIvRF5ET8j7SPvHqY7HHS4K0osKiVqOlo/+uoR9BH3Iy1H+Y4e P/ouRivmSiwU6xbbcoz/WOKxqTiDuLLjhOP+x5/HS8efi19IcEioT+RKjEucOGFwoiKJOomcNHxS 7WRhMjrZN7k7RS7lQsqvVI/UzjTptJy09VPupzpPy5zOPb15xutMd7pi+qWz2LNBZ4cyNDLKztGd iz43kWmWWZ3Fm5WatZB9MPtpjnxO4XnC+fDz47mmubUXBC6cvbB+0efiYJ52XlU+Z35K/nKBR0H/ Jc1LlYVchWmFq5d9L7+8YnClukioKKcYWxxR/KHEvqTjqvLV8lL20rTSjWtB18bLrMvaypXKy69z Xk+vQFWEV8zecLnRe1P3Zm2lROWVKuaqtFvgVvitj7ddbw/dMbnTclf5buU9wXv59xnup1ZD1VHV 8zU+NeO1TrV9dcZ1LfVq9fcfSD641sDXkPeQ6WF6I6ExsXGzKbppsTmkee6R96OJloMtr1sdWwfa rNq6203anzzWf9zaodXR9ET9ScNT1ad1ncqdNc8Un1V3KXTdf67w/H63Ynd1j1JPba9Kb33fnr7G fo3+Ry90XzweMBp4Nmg+2DdkN/Ry2GV4/KXHy5lXAa++jUSMrL2Oe4N5kzpKO5ozxjlW9Fb0bdW4 4vjDd7rvut7bvH894T7xaTJ0cn0q8QPxQ840z3T5jOxMw6z+bO/HfR+nPoV8WptL+kz3Of+LyJd7 XzW/ds07zk99I3/b/H7qB9uPawvyCy2LlotjS4FLa8upP9l+lq0or3SsOqxOr0Wu49ZzN0Q36n+Z /HqzGbi5GUIik7a/CsBIRXl5AfD9GgBEJwAYkDyOQL2Tf/0uMLSVdgBgD0lCn1BtcALaBqOJFcax 41kpeAjqlOZU/sSz1HU0c3QS9J4MxYwTzGIsUaxN7DQcDpwlXD949vAm8j3npxOwFjwt9EwEiMqJ eYmf390psSwlIm0lEydbITeogFKUUdqvnKpSrfpuD1Fdea+rRormba03OnhdRT13/bMGtYZjxpCJ gKmBmZ95usU9y5dWP22YbeXsLOwDHU47Vjo9c363b95lef/aQeBKILG5SbhreVh7HvTy9Cb52Pju 8eP1h/zHA5oCLwclBPuEWB5SJvOG4kO/hg2FN0aURWYejo0KiHY6YnRUPUYpVvGYSpzWcZN4hwTP xLATJ5KyTpYk301pTu1KGzr19vT0mc/p388uZiydW8xczFrNQZ9nyt19weCie96x/NyCyktNhc8u D1wZKRovni1ZKIWvMZWJlWtfd6mIvJF1805lX9W323R35O7a3Au9f7a6vKa+9lFda33zgwcN9x9W NZY3FTcXPMpuSW092ubXbvNYsYO1Y+XJ+NOezsfPWrsePW/orurJ7Q3t0+kn9r94kTfgNagwhBka Hi57GfFKcwQ70oHEl8Kb6dGMMbWxibenx9XGP70rfG89AU9UTdpNrkxlfdj9oWnaenpy5sSs1Ozk x7JPQXNyc4ufq764f6X7en/ecv7Dt6PfWb4//pG+ELRIWvJC4mhytX1DcnNze/35oVsoP1gWnkHf xsRhHXHqeAkKYYIw5S4qaaIqtRWNO20sXSF9I8MsEy2zMguJNZntHvsYJyWXHPc+njjeK3xNu17z LwpSCvEIK4gYibqKRYln7r4t0SU5I42W4ZPdI+csH6aQpliiVKf8XOW96sIerDrHXhkNM80ArXTt Wzq9up/18QZchrJGesZ2Ju6mQWaHzWMtEixPWCVZJ9uk2p6yS7VPdIhy9HGyddbdp+Giv9/5QOTB bNdbpBa3Tvd2j/ue+V5HvB18pH2pfOf8ev3rA8oD84LSg+NDyIdcyJqhPKFrYYPhNyOSIt0O60VJ Rwsc4TrKFsMUS3sMe2wp7v3xzvjbCdmJkSf2Jxmf1E02TSGlHk+7eurx6bEzX9IXzy5nLJ77kTmf 9Tl7LufL+Z8XaC+q5AXllxZ0X5oonL08deVt0avivpInVxtLG651ln2+zlex/0b+zVdVjLfMbycj p9fKfclqj5q82v56zAP5hoMPTzSWNjU0Nz663nK2NaYtsj3ucXpHwZPip5c6zzwL77J5LtGN7h7p udOb1ufXb/VCb0Bv0GrIbTj8ZeKrkyMxr73e6Iyyj86N1b09Oe74TuI9/v2HidbJgqlDHzSnqaYH Zopnj330/eQx5/M58EvI15D5kG/k7xE/ohYiF32XDJZplu/+1Pv5bMV55fNq7zrVxsj2+ouDNsgE eonyhLFwOloc3YOJxkphZ3FX8T4UUhQrhE7KQqpIojW1LA01zRLtK7pm+nKGTMYYJm9maxZ1VlE2 JrZ19hmOfs5GrkruYp483hy+rF3p/EkCEYIkIT1hXuGfIl2ihWKh4oa7+SRQErOSw1JPpOtlrsvm ysXJuyqoKGIVe5SylR1V2FReqRaoeeyRVceqj+2t1kjX9NHS1RbSodUFuj/0pvWHDB4Y5hh5Ggsa j5vkmlqY4cxazRMsjCxZLT9aNVpn2vjYqtkR7cbsbzocdTR2YnJ661y2Lxi5/1f2PzwQd1DHFe/a R8p383ff40HlMeJ5zeuQt7L3uk+Tb5yfpj/wbw44HqgThA5qDz4RohXy81AF2Qm5s8vDLMIWwnMj 9kSMRcYd5jr8MMo1mjl65EjF0YQYx1iR2KVjrXGZx73jdRPEEllPUCaBpIWTE8nPU6pST6WRTsmf xp0eOXMrPfWsf4bBOfpzjzP3Zc5lRWdr5WifT76Av5iaN1nAdkm2UOWyyhWFIqlikRK+q2yldNcI ZRTlNEgkqd9wvXmy8mbVi1vrd0TuOt87d7+vhrHWqS6/frgB81C00aDJrfnYo0stja1v2zYf83Xo PPF+eqrz9rOhro1u0Z59vef7xl7IDpwe/DJs87JuhO919qjUW+p3kZNpM1Gfzb8vrVhtrf/O73Bb BasIQCaSZ9qfRuosABk1SJ75AAAWAgCWRABsVQDqZCVAGVQCyP/E3/sDQhJPPJJzMgMeIArkkUzT FDgjmfNhkIJklDdAI+gHH8A6RA+JQppIfhgKnUbywXZoAgWh+FDaKA/USSTL60etwvywGRwNl8HD aDxaFR2ILka/wtBjTJCMrBULYTWxcdgWHAZnjDuLe4nnwwfg6yhwFA4UZRSrBDPCFcIypTllGRWa yo2qlShITCF+obalbkAynQxaQHuIdpLOia6HXp/+IYMyQzWjKmMrkzXTBHM4C5Ylh1WItZbNnG2G PZlDhmOCs5DLjVuc+yfPY95sPo9d8vxY/tcCdwXThQKETUTERYmi82KD4g92X5KIlXSRUpFmlJ6X eS57XS5F3kfBWFFSiUlpU/mzyphqv1rnnnb1tr0dGt2aI1oz2ku6QA+LnHN4Q7wRhTGVCaMpn5m8 ublFkGWWVYP1lC3RTt7eySHG8bJTm/O0C+V+6QP2B4+6lpC63X56CHjaeJ3wbvBZ9dPxvxCwEuQe 3H9In9wQJh9eFSlx+Hb0niO9McHHOOOG4rMSTU8sncxK2Z3afsrzDFP624znmaPZm7m8F1XyTS8d vBxVdLlk5JpE+eUb0pXjt6/cO1BDWVfZsL9JvIWnXf9JURdVj0jf0kDGsMirvjeX3p5/3//BdXbl M/3XG9/BgvSSyvLmSupq7drA+oON4l8hm0rb5we0/ZsDPeAAQkAWaAAz4AICQSzIACWgDvSAKbAB MUNSkDHkBSVARdAj6D0KjRJGmaLIqIuoVtRXmBM2gY/CVfAkmh1tjU5Dt2MgjDrmCOYBZh2rgU3A PsXR4pxwV3Hf8Vr4TPwHCjWKTIo5gj6y5uuUjpT3kEyYTDVAVCFepqakPkw9TeNE002rT9tMt5eu iV6HvpPBhmEUyUxXmdKZxZifsRxiZWatZrNi+8AexUHkKOHU5JzkyuA25qHmGeW9y3dmly+/tgCr wCfBh0Jnhb1EtEUFxejF8bsxEnhJail6aToZvMyK7IzcsHynwiPFR0qdyq9VvqtR75FWt9rrqxGm Sdby0XbUMdBV0ZPXVzYwMDxoFGt8xaTDdN6cw0LP0h+507Jszttm22XZX3ZocvzmrLAvzuX5Ae6D Ya49bvzuXh7Znve9ur0nfdb8mP3lAmwDI4IuBjeHfCSzhOqHRYRfixg5TBtlFp1+5GWMUGzMsYnj 3gm0iZ1JYcnYlJNp6FPJZzjSWzPiMx2zdc6rXVDLUytQKRS9gi56XBJRynHtYblbBdON0cr2Wz13 Fu/L1Byte9ZA06jbTG4pbZvt0H56p0umO793tH9h4NvQ9MuJkZk3C2+hd4QJximBacPZnDmlr6k/ SpcDVrrXEtdbNxZ+rWyvPwrZ/XSAG0iAvcAKeIEYkANugS7wEaKAxCEziAzlQs3QRxQzShcVhipF jcB0sBGcCDfDG2g1dDS6Hr2O0cKkYoaxotjj2FHcXlwRHo8Pxg9QqFAUEFAEP8IgpS7lAyoVqkdE S+IH6ngaPppmWhfaJbqz9BL0zxmCGImMZUzaTG+Yo1i4WbpZz7C5sWtziHEycq5xjXLX8pzjDeQz 3SXNzyqAFVgR/Cb0VfiHyIYYtbjAbk0JV8k4qQLpWpkXsj/k2RWMFOOVWlWoVF3UbqnjkO+qjVq7 tDN1mfUqDZyN6Iz7TC+aB1vaWcvajNg523c5Gjq92Ofl8vNAgitECnEb9FDyzPem8DnuR/AvDjQL BiE15OAw7vDWyPAojyNfYkvioo4Pxa8nok7gk2hPyiWHpgyk2Z2aPZN8VjLjVWZytlrOt9zyiwfy CQXXCpUuPyzSKG6+qlvaWWZZPlBhe6O3Ur+q7rbInfP38PdjqtdrU+qFHvQ+jG9SbJ5tyW+zeIzu ePA09Jl412T3pV7HfsYX/YPpw8YvN0duvLEYnXkbPr7xPn4SnoqfRs0kfER/Ojb35Yv+16j5gm+n v4f/0P2xvHB90Xzx9ZLP0tJyxPLsT5efPSs6KxWrxNWQ1f41hbXctW/rRutF62sbths3f8G/HH/d 2IQ27Tavb61/qJec7Pb1AVFpA4AZ29z8IQQA7hwAGxmbm2tFm5sbxUiy8QaA5oCd/3a27xpaAPLf bqFOscG4f//H8l+E581kkUfmWwAAAZ1pVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1l dGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDUuNC4wIj4KICAg PHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50 YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAg eG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZXhp ZjpQaXhlbFhEaW1lbnNpb24+MzkyPC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4 aWY6UGl4ZWxZRGltZW5zaW9uPjI4NDwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgIDwvcmRm OkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgo3phIYAABAAElEQVR4Ae29 D0ybV5rv/yUmxkaleRM6CWaSwRkyWdxtLuZ2lw2dzATSn4IjTRVno5sxczuKMx2NiO5PCpFWqqt7 R4quZgdntFKofr+rspHaOLOdxoq0U0etJtDdFFepBoqWwdlkA6Fh4ixs7Gab4hQKhsHhPud9bTD2 S0IxNtg8J8F+3/OeP8/5PM85zznvax/nzVAABybABJgAE2ACCQTWJZzzKRNgAkyACTABmQA7CDYE JsAEmAATUCXADkIVC0cyASbABJgAOwi2ASbABJgAE1AlwA5CFQtHMgEmwASYADsItgEmwASYABNQ JcAOQhULRzIBJsAEmAA7CLYBJsAEmAATUCXADkIVC0cyASbABJhAPiNIP4Gx6Uf49Mtw+iviGlYt gWclPQrW5S27fF9MRXB3bHLZy+UCUyOgJV3/Oek82wM7iAxoUDiHk58MZaAmrmK1EvjN3m/jm4Xr l128f/n8K/zyWmDZy+UCUyOwqUCD3+7bkVohqyA332JaBUpgEZgAE2ACq5EAryAyrJX/YdqMHU8X ZLhWrm4lCPR8Po63Bx9krOr/WWnAMzru0hkDrlLRxT9+gc7//ErlSnZGsTVlWG/COVRtKsxwrVzd ShD4PDyd0WrFc4503MbKaCOyvLIr977M8hbMF59vMc3nwWdMgAkwASYQJcAOgk2BCTABJsAEVAmw g1DFwpFMgAkwASbADoJtgAkwASbABFQJsINQxcKRTIAJMAEmwA6CbYAJMAEmwARUCbCDUMXCkUyA CTABJsAOgm2ACTABJsAEVAmwg1DFwpFMgAkwASbADoJtgAkwASbABFQJsINQxcKRTIAJMAEmwA6C bYAJMAEmwARUCbCDUMXCkUyACTABJsAOYhXYQL/XBbvFAovVAY8vuKIS+dpaYbWYSZYmuLu+pizh EIKh5fnlvC53C5xOp/zncDSh1RuEt8WBtmAYQeIlzp8YQv1wOewwmy1wtHqwiBxPLJITLEBAlbUf LU2t8C+QxdvagsWoUaSLN0Vvq5PyLY+dLSDaoqIXK/+iClulidhBrLBiQl1OmOrcsDlpQGwswaEq A9z+lTF+X6sFVQc8sDvdJEsFGmoMcCymB0cZ9rs2wuDyLwPRMPzOk/DCiIqKChrgd8Mo6VBirkWJ TodQfwuOP9GRhuDcaEJLiQ1ujxMlnkMwWNzLIBsXkUxgIdYSzLUV0CVnoBjS8fGT8D1xoFfSdc2m E+evUT7VQjMaGVyU/BkVadkrYwex7Ei/XoFBv5cymGE00kBoacKdzg6YaTAEQvA4LcjLy6M/uzJj 8rthsbupa1EIemGVj/1wWqxwOCit2YWgzw2znCcPTW6fSEkDqgeWaJwy8w6jjWbWnnmOqB+njrfj XJ8HVrOQpRF3Os6hQnRkdxPs7n65rKDXKR+HSW67WciWB0uTGyG/B6bjlOSkieqltDSjdFqU62Y7 yUWXgm1OKteqyGJugsvlUNpndqI/wSeKU5vVBqvVCpvNBotZQrDLDb9YoehKUEmOQmYk2i3aZlXq oMhooBUHHdUayckYzWjy3EGHwyxfi+dhb/XKPNscVrijQnijx36S12J3yPK20ojkjtVldsAXEkWR jmJxSfXLVa3oS5KOSBrRTjutyPLyLDTI+uCI1+GC0pK9tLSiyy83WiXVQqxD6HJ1ESVhoxbY7Yqu LA6yF1FKJSAJPZItW/LMxD+kYpdKOlndsZopnziX9UMrXats2xa4hVKCbbQCtsNuVWzPEe0D/TRJ UPpSHhwess8EmZpaXHBG8zjbxHVgLo/ofyImDG+rXS7HbKcVNsXIXVVcytHADmKFFVthc+Hc0TaY NioG3eoHzZbJfN12HHJbEZiZQd+7EuoMLQiGw2g/L4ZOMlUagC/Jx2F0tV/CaTQi4K1BY1UDmnpH MDPSC3+DMGw/7KZDsPeNYCLQgdY6A7whHXTkkOYZN6W7hIPYXUE9LxqMtdTRao0Ih/tpQFTqDfW7 5cGx31OH8+YLCIzcgd0YQr9kRe+ZelSe6USLrQIeuwluaycmZkZwCsdgcNJAEfLiWnstXBT3rvl1 HPNZMDMTQPO119CW4CF0RuCYSWEiOraLBg+/+zwxiElHY4Fg1G/HyMwEOnZTHQ7v3EVyba7Oc/Ae MikDg62VwFKhNDAIHtbOAGYm+iAdr0ML3b8Itl1SBi3hEE8rx+FQF/FuQ2NvAHX9jWhos1FdM+ht 6keV0/uE+uNEWaHDJB2RDkO+Szh/WofeADlbexV8Tb2kgxHY+xvgTFgthsnexB8Nx6gw69C4fSNN QprQ5vMntGgh1jSgXiIHTPV629tRQROFmZFOtJ92wk/Fkp9HP01orIY62Hq9sFVIyXaZUFP8aZjs qf1SCVpJJ53N7Wjw+qmuEC61+2BvncBIZzNON3QJjaLNE0JnYAITfRdw+lATTUgUmcyNLjnu9ZPH YBZ5Oprx2gGSOeiG6VA/+iZmEOiooP7nRJBsv+54CL0jM+hy7Kb+kvuBf1FuhXUc8odgafFhxkVm 7PPiVBV1FvTRLP0STtCshvoQSqyNqKfYu5NN8qxLiExD/KzkYZqKdZ6yooSM+hLegJtm22JV4pnx CU+DOjoL04rBRe/GynoEaZS1NTroLC5INCun3MEQQP1UDuJevytohlWcxaZwoleHqXRbL5q9VTBs BOpPnEMriRMW+eQXPzzUe1pcu2UpracuANt9mDxH4p+zUJskVNSSMwnLrSOZaC4+1xy5burTaKZB 3EFtoepEg+UZm3JRvE6iiwZ1MdLYLS6CVI96KSinlYui5yEhiWbJNHiEQ350uU+hbjuRvWOjVr4K 126l7qYL9djedRck2mzQkTxynWJwbHbRikqi1cMlnHG5SXJqu90DKpWc4GPqny1t5Q7UdOQPUpN6 T8Fc4sep9ko4PWZZQFvbDFnYXAj3u6A3HZMj3uidQCNNFnwzdlrFtcJQtZ0UeQETPptihQuxnnEq BcoKqYe1VjAPkS2LdwoUf7qhgQ7q4RCzIgq1iXYpx8a/KJZPmpGVVHmO7J4OQ6T/yrCoiK5UNqG2 hI5D0XooxW6LhFN2a9SMdYp+qV4L2YGO+gpZD4yUR4rmuetto7JohWWz0LsO9ZVG/OGql+AROyEq TYiEzVBtOR14BbHC6vW1mGCobaVbMDoY6V67pZ7GvBAN0rZ6vN7ilQ0w1NWGdlhQtoGM/pqPuhjZ fig46yxodBR9jd7MNMgfV5bDNOsXS2+vJIwf8kPaNk8rbJZauSP4u7ygSflc0FXAQQnrxO0iERui zlF3DF06mtWRc7gWnTWGgu1yHl9bG4ynaKVCK4Da14+hUV62k3hiBCJ5LDTItkSX6l0eGgROVKBA 5FTpUSpRcjJJkmTHJOqX2yfXHHspICdDlVjowT7J0tpop2cVxrl0YR9MJgPErSGdZIR5dy1l9CNM jOppvaWsWILwNLSTaFuU2awATzX7r8XqIMTyMouGEFslTrq88oV+WtnlOboeX/9cESt2lKyjWwpX GaYR1sprcMmrhjDcdDsw/nmTrsJOup2R/xpp9RCm21Euup1mqDmOE2+8izveqHMQrVuIdYJi5cVI PA0/cIZWZ7000tZQHxD0k+yS4sSc5DixF8WF/W1oIP3M2kNCHZSEjFBocS4IZ1dDK4kWshNPSyNd mLsaL9NcLLCFnnfRTU60eihPq4Nuje7Gs9+zAa/RyouukKekdXGcHCIuBwOvIFZYqbXOPjRbyUnk iRv4FA6+gYC9gh7GttKMdTv0eSKyEhf6vCgxhnGmsoHSvo7KepoxGe3i4lygQd59uRkmg5wJ9Wc6 UCtRWfFxzR3wSDQg1NQhRDNDcetACTrYPH3wkywb88SsjuZ1lN9jpUE32ITKhjpQtaivr6TBklYi ZiOqqjZCTln5KnqtFagINdF9oTpYKvpo0O6A20C3d+QEJ2hZXgt6qCKXm/giJIjvnOK6alxJNF30 YoXNjebdVMdpkaMeHQGPOFCCVIs+0W566B8lizc6AjDTaOPqOAODSa/IfuICRixGhHXNaKDbb0SW ZouV82bTokC5LnLm5HMpiLp200rrMfWLZCscknX0Z+gnFSisSd/uy2ghJ6o06QwCtQRYNZC92Gn1 QLdjRmba5FXUvGQLsg7NDeTzMkRPqDod6UM8o3r1mAGn2iwwH0i2S6urAwfpNpRe1jN1EbJLG90K 9XepFUpxNG+YDXSsq7BSvzkOk1Ae6VZ4kFOeP84miR0Is5KDnMeGy6/upr72mhzV3HEHxooSvPtq C7YrRiDHz+ZRcubcax7NEmZyrlWrrEG9X4zj5CdDslRn/mobqjYVJktIU5kwdafYnZzZBGKKkxCp EjWbXDkQ944Ty4qWn5BS9XQBWdTqVYuLL/NJ1+PTLvVY3CcXq4yFwkLXk2UTQ+fC5Yjy1cpSixNp /4l+wP5vrwXEIX6z99v4ZuF6+Xg5Xz6gOn75hDqS2zlfgiddn5/68WcLsXh8rkVeTVHQJWVXy6QW F23C390I4v2hh9hUoMFv9+1YZMNWbzJeQawW3dAApzo0qQx8KlEJrUh0DuLyAuUn5JRPF5BFrV61 uPgin3Q9Pu1Sjx/nHESZC11Plk1VA/PEUitLLW5ephU+SW7nfIGedH1+6sefpZVFioIuKbtaJrW4 x2PJ2qv8DCJrVceCMwEmwATSS4AdRHr5culMgAkwgawlwLeYslZ1LPhaI/Do0SP89Kc/xcjICPbu 3YtDhw4B6zeuNQzc3gwSYAeRQdhcFRNIhYD4wuD69esxNDSEX//61/B6vSg/+N/pU8X/JZViOS8T WJAAO4gF0fAFJrB8BH7+859j/djIogrcunUrhoeHVdPeunVLjl+3bp2c5t7Vqyj8b+wgVGFxZMoE 2EGkjDDXCpjGaGgMEfqmMlAESVL5SO6CTZ7E6Cig10whoi1CQYJ1jY+OQ6sFJiJaFBUmXFywTJUL 05MkXUFS+SopV03UkSNH8Izm0aLk0Wg0iEQiSWnFLaYzZ87g008/la+J840bNuBPSSkzEDE9jtDY FH2hneykSIJUKH8NcnEVU95xsgFtZELFThQbKtJGMJqqnZBs0wUFSMHSFteeHE7F7HJYuUtq2vgA Ll78GFoaeDD2EBPSc/jJ4RcW18ko74ULfvzF5vu4U34Eh3cVxYkwiisXLmLr3nLcDWylbRd2xF1b /OFnt33wftiNwj0NeOnZ+PIXX8ZKpNy1a1fK34MQX1l6+umnIRxDWVmZ/Bxiy4sH8ffDNFBnOIwP enHxowA2bNCTmYxBeu4HOPxC6aKkGB/4AG/37cQeTSf6Eu0kakOH9xfhqn+pdjKO276r+LB7GHte fgXPfp05zqJasHYSsYNYO7pedEs1mjI0/LAe+dM3cfatHtz9vAif/LYbUxqg2LQfuyau4oPBMejL qrD5/r9imCa7WsmEI/slaKGBmAHTf3zm+x0u99AXxfRbceDIC3L8I5oZT1L6od+/hysDD2imrEfN 4QO4/54bAxOUT6uHpI/gwcMpmA4cxtSVi5je8zLqdyi9fPT+Z3hILSmi8tdaEA5iAznuo0ePyg+o v/Wtb0F8UQ7DypfxMssjAq1hL3740g5M3noP5z8ewH3pOt77eJj0rMXOA0dQ2vePqnbykkmxD1U7 +cE22YYi0yp28lIVejxeTJBxafUS9JEQHk5IOPCTAxh4++05O5mewv3PxMYdIGvkkAoB/phrKvRy Ma8Y2SN38dabb5Jz+JicwPMoy9diy04Tnt8pIXDjOh6OT0Bfvg8/rv8Oikp3ovr5ckzd78PgRByQ R8O4QjM4Y00NtlF5H3TeUy5GxjE2OoUHgQAiRdtg2rkNhZoIxsk51Bx9BXukCWh2HcbLNRIGrwfw 3J592LWF7ktFw44XalFGx8k3YGIpcvddPHf41a9+hRMnTkA4hxUN5ASmAh/izTfP4jytJMpqzNBq ilD+XDXKi6dwo2cQkwvYiT9+wZNoJz3R5zRqdoIp+mfAy680oHgshF1HXkFNcQjXByPz7SRfwgsv Pi87h7VoJ8tpF7yCWE6auVCWfO/bgINHX0IxtSefLMTvfYc2EDSijBwENNNYR11PMpRi0t+NG7TB 3Z7ycujhj7Y+2iUfic4s1hNaSNu2Qrv5KYwOzAEqNlWjmk79nd0Y0mymFUH8rJJy0UoiMjWD4m2l iGjizTSyJp3DHLlVchSZgmZzDY7S3l1kJfRvEt53+hAy1qC8SA/tpJhoPM5Oou1ItJNiHQY//WK2 kfPs5NEusqfoPzGRof96vQZT5HCKdybYicoznNlC+WDRBHgFsWhUayghdT7xDFk4BxH02kJEHgyi xzdEnf4+QvKz1gg0evGg8QGuf3IdY+QOhoLUU0XHpaDJ/zY5Dj0GujvRMxCCtGXuRrBIMeq/js7u m7SDpxalpU+LHKK/y51evImgpQeVH5x/m25T0JPvuEDb3MRSx8XyYWYJCIeukV2DYibkDAo1eDB4 HdeHyBruD5NFiJBsJ/fIgBQrUbGTZ+hhd8yGKPc8OynRz9qH4iZE+WIyMaViJ1FHIpJwWDIB3qxv yegWn3FRm/UtvrgVSTktPq0iPhEyPY1p8hyzc/rZTxRR/DTFz15QxJzNpyK1uBbRZNenkVSasWDU atmsb0EB03BhknRaQHYyPTmN/PiPsa0RO+HN+tJgVFzk6ieQT51eDvHOQUTk0wCvXEhyDsrlaD45 zfwXUWaCP5mfgM+yjoBwDiLMcw5KBNuJTCa7XvgWU3bpi6VlAkyACWSMADuIjKHmipgAE2AC2UWA HUR26YulZQJMgAlkjAA7iIyh5oqYABNgAtlFgB1EdumLpWUCTIAJZIwAO4iMoc7yiqZH4b99G/dC YhO/ZQqT4xgdn16mwriYVUEgDXYyOT4KNpOV0S47iJXhnl21hm7iH966gKvXruGDi+fh+X1024wl tuJ2+zv43a1xTAa6aTsO/xJL4WyrjkCa7CTQfQXd/vFV19y1IBB/DH0taDnFNl6/Qru7PncQP3xh C23vfAuu81fw6ZZSdFwZpP13qHCxUd/hGgz87iJ6ArRPk6EaR17U4+L5D+kb1nr8ZfVm/IH2ZRKb uJVXGzFwl7YTv3sZt78vyRv33aNN/d6n69BswJ7Dh1HYc5G+PU1bOdB3cTdX/wCmz2iAKPgeflRr TLElnD2dBFKzEx2+szUfnwZI79pi1H2/GB9F7WQjqV1s4MJ2kk7tqZfNKwh1Lhw7S2AcQw+AZ0pp HyYRCrZhM/3ew8gYzeiKq2F/5WcoH+/DP3d60T1cgOo9z9Puop3o/I8wJvAU9r38Y2zXz23i1ucv xh6DFpv37Mf2PNq478sArSIeYN9PfoaG6nx87B2QN3krrjmCo/sMCNBeT6Xm76F6p9gZisPqJZCq nfwIpVIpnqs2o3gigH/7atesnWyaHMVo+C7byQoonx3ECkDPrioL8WyZFv6bflns6Xs9tL23hC06 carsqFNEm61OT4lnE2J/Hj1Kt5Zhs9g2RyOhtHASvh7axI2uSWITt9g+O9F3zDyi2SH9gBCtZfXi 14TkTdbE/jqibLGfDlBYbMC2YiqQwyomkKKdaIfRc8NPPyD0FIqeEraj2FbsHY/EJo1sJ5k2AHYQ mSaehfUZX9wPw/2PcPbsWbz1/gBM+w/gG3S7CA864XKdRfeYAd/d+33sfCqE7s5ODNKKQ9qgmBZt 1Za0iRvIowQ+eg9D9Hxas+4bqN0JXDr7Jt766AHNIMuTCN26fB7nL8dtBZuUgiNWA4HU7ERL275P we/rAe31hwfD91AQtZPPaXNITX4Z28kKKJk368sA9FzYrE9gmhyn20qFhfKeOuM3PfSrYOX42WFl u+cYxthmbbHz2HssPmkTt1gC2sxtmvZ1yqWHYmtxsz6hzqXbybT8C6YFtMnfJG0KWZC486MofJXb CW/WJ5TEYU0SKCDnEAtaYzX2S+InP+cP6bHN2mLpYu+x+KRN3GIJcsw5xJq1Ft+Xbif5YsNgOag6 B3GF7UQBlKHX+b07Q5VyNdlPIL+wFMY5f5H9DeIWpIUA20lasGasUH4GkTHUXBETYAJMILsIsIPI Ln2xtEyACTCBjBFgB5Ex1FwRE2ACTCC7CLCDyC59sbRMgAkwgYwRYAeRMdRcERNgAkwguwiwg8gu fbG0TIAJMIGMEeCPuWYMtVLR/7l5H0+tV7YRyHDVXF2GCXwxmdmtzH/ho28fa3jOl2E1z6tu6Kup eefZfsIOIsMavD26jL+nkGHZubrVTaDvYXh1C8jSZR0BdhAZUFlRvgaVm3izuQygXrVVaNflpUW2 Tdp8tq20kE2t0A05cpeA92JKzQ44NxNgAkwgZwnwDcucVS03jAkwASaQGgF2EKnx49xMgAkwgZwl wA4iZ1XLDWMCTIAJpEaAHURq/Dg3E2ACTCBnCbCDyFnVcsOYABNgAqkRYAeRGj/OzQSYABPIWQLs IHJWtdwwJsAEmEBqBNhBpMaPczMBJsAEcpYAO4icVS03jAkwASaQGgF2EKnx49xMgAkwgZwlwA4i Z1XLDWMCTIAJpEaAHURq/Dg3E2ACTCBnCbCDyFnVcsOYABNgAqkRYAeRGj/OzQSYABPIWQLsIHJW tdwwJsAEmEBqBNhBpMaPczMBJsAEcpYAO4icVS03jAkwASaQGgF2EKnx49xMgAkwgZwlwA4iZ1XL DWMCTIAJpEaAHURq/Dg3E2ACTCBnCbCDyFnVcsOYABNgAqkRYAeRGj/OzQSYABPIWQLsIHJWtdww JsAEmEBqBNhBpMaPczMBJsAEcpYAO4icVS03jAkwASaQGgF2EKnx49xMgAkwgZwlwA4iZ1XLDWMC TIAJpEaAHURq/Dg3E2ACTCBnCbCDyFnVcsOYABNgAqkRYAeRGj/OzQSYABPIWQLsIHJWtdwwJsAE mEBqBPJTy865F0Pg9peT+P/6PltMUk6TowT+V2UpvqFb/u72L5+P4x8GP89RatnbrA3rNfjf//Wb 2duAqOTLb7FZj2T5GzA6HcG1LyaWv2AuMWsITD2aSYusX0xNs22lhWxqhW4q0KRWwCrJzQ4iw4oo LyrAU+v5zl6Gsa9IdSOTEfz7V1MZq7tigw4FmryM1ccVJRMYIn1/QXrPlcAOIsOa/H+f3YyqTYUZ rpWrWwkC/3TvS/zttUDGqv65uRTfLFyfsfq4omQCf3cjiPeHHiZfyNIYnspmqeJYbCbABJhAugmw g0g3YS6fCTABJpClBNhBZKniWGwmwASYQLoJsININ2EunwkwASaQpQTYQWSp4lhsJsAEmEC6CbCD SDdhLp8JMAEmkKUE2EFkqeJYbCbABJhAugmwg0g3YS6fCTABJpClBNhBZKniWGwmwASYQLoJsINI N2EunwkwASaQpQTYQWSp4lhsJsAEmEC6CbCDSDdhLp8JMAEmkKUE2EFkqeJYbCbABJhAugmwg0g3 YS6fCTABJpClBNhBrALF9XtdsFsssFgd8PiCKyqRr60VVouZZGmCu+tryhIOIRgKL4v8Xe4WOJ1O +c/haEKrNwhviwNtwTCCxEucPzGE+uFy2GE2W+Bo9WAROZ5YJCdYgIAqaz9amlrhXyCLt7UFi1Gj SBdvit5WJ+VbHjtbQLRFRS9W/kUVtkoTsYNYYcWEupww1blhc9KA2FiCQ1UGuP0rY/y+VguqDnhg d7pJlgo01BjgWEwPjjLsd22EweVfBqJh+J0n4YURFRUVNMDvhlHSocRcixKdDqH+Fhx/oiMNwbnR hJYSG9weJ0o8h2CwuJdBNi4imcBCrCWYayugS85AMaTj4yfhe+JAr6Trmk0nzl+jfKqFZjQyuCj5 MyrSslfGDmLZkX69AoN+L2Uww2ikgdDShDudHTDTYAiE4HFakJeXR392Zcbkd8Nid1PXohD0wiof ++G0WOFwUFqzC0GfG2Y5Tx6a3D6RkgZUDyzROGXmHUYbzaw98xxRP04db8e5Pg+sZiFLI+50nEOF 6MjuJtjd/XJZQa9TPg6T3HazkC0PliY3Qn4PTMcpyUkT1UtpaUbptCjXzXaSiy4F25xUrlWRxdwE l8uhtM/sRH+CTxSnNqsNVqsVNpsNFrOEYJcbfrFC0ZWgkhyFzEi0W7TNqtRBkdFAKw46qjWSkzGa 0eS5gw6HWb4Wz8Pe6pV5tjmscEeF8EaP/SSvxe6Q5W2lEckdq8vsgC8kiiIdxeKS6perWtGXJB2R NKKddlqR5eVZaJD1wRGvwwWlJXtpaUWXX260SqqFWIfQ5eoiSsJGLbDbFV1ZHGQvopRKQBJ6JFu2 5JmJf0jFLpV0srpjNVM+cS7rh1a6Vtm2LXALpQTbaAVsh92q2J4j2gf6aZKg9KU8ODxknwkyNbW4 4IzmcbaJ68BcHtH/REwY3la7XI7ZTitsipG7qriUo4EdxAortsLmwrmjbTBtVAy61Q+aLZP5uu04 5LYiMDODvncl1BlaEAyH0X5eDJ1kqjQAX5KPw+hqv4TTaETAW4PGqgY09Y5gZqQX/gZh2H7YTYdg 7xvBRKADrXUGeEM66MghzTNuSncJB7G7gnpeNBhrqaPVGhEO99OAqNQb6nfLg2O/pw7nzRcQGLkD uzGEfsmK3jP1qDzTiRZbBTx2E9zWTkzMjOAUjsHgpIEi5MW19lq4KO5d8+s45rNgZiaA5muvoS3B Q+iMwDGTwkR0bBcNHn73eWIQk47GAsGo346RmQl07KY6HN65i+TaXJ3n4D1kUgYGWyuBpUJpYBA8 rJ0BzEz0QTpehxa6fxFsu6QMWsIhnlaOw6Eu4t2Gxt4A6vob0dBmo7pm0NvUjyqn9wn1x4myQodJ OiIdhnyXcP60Dr0Bcrb2KviaekkHI7D3N8CZsFoMk72JPxqOUWHWoXH7RpqENKHN509o0UKsaUC9 RA6Y6vW2t6OCJgozI51oP+2En4olP49+mtBYDXWw9Xphq5CS7TKhpvjTMNlT+6UStJJOOpvb0eD1 U10hXGr3wd46gZHOZpxu6BIaRZsnhM7ABCb6LuD0oSaakCgymRtdctzrJ4/BLPJ0NOO1AyRz0A3T oX70Tcwg0FFB/c+JINl+3fEQekdm0OXYTf0l9wP/5OgK6zjkD8HS4sOMi8zY58WpKuos6KNZ+iWc oFkN9SGUWBtRT7F3J5vkWZcQmYb4WcnDNBXrPGVFCRn1JbwBN822xarEM+MTngZ1dBamFYOL3o2V 9QjSKGtrdNBZXJBoVk65gyGA+qkcxL1+V9AMqziLTeFErw5T6bZeNHurYNgI1J84h1YSJyzyyS9+ eKj3tLh2y1JaT10AtvsweY7EP2ehNkmoqCVnEpZbRzLRXHyuOXLd1KfRTIO4g9pC1YkGyzM25aJ4 nUQXDepipLFbXASpHvVSUE4rF0XPQ0ISzZJp8AiH/Ohyn0LddiJ7x0atfBWu3UrdTRfqsb3rLki0 2aAjeeQ6xeDY7KIVlUSrh0s443KT5NR2uwdUKjnBx9Q/W9rKHajpyB+kJvWegrnEj1PtlXB6zLKA trYZsrC5EO53QW86Jke80TuBRpos+GbstIprhaFqOynyAiZ8NsUKF2I941QKlBVSD2utYB4iWxbv FCj+dEMDHdTDIWZFFGoT7VKOjX9RLJ80Iyup8hzZPR2GSP+VYVERXalsQm0JHYei9VCK3RYJp+zW qBnrFP1SvRayAx31FbIeGCmPFM1z19tGZdEKy2ahdx3qK434w1UvwSN2QlSaEAmbodpyOvAKYoXV 62sxwVDbSrdgdDDSvXZLPY15IRqkbfV4vcUrG2Coqw3tsKCMfpQe13zUxcj2Q8FZZ0Gjo+hr9Gam Qf64shymWb9YenslYfyQH9K2eVphs9TKHcHf5QVNyueCrgIOSlgnbheJ2BB1jrpj6NLRrI6cw7Xo rDEUbJfz+NraYDxFKxVaAdS+fgyN8rKdxBMjEMljoUG2JbpU7/LQIHCiAgUip0qPUomSk0mSJDsm Ub/cPrnm2EsBORmqxEIP9kmW1kY7PaswzqUL+2AyGSBuDekkI8y7aymjH2FiVE/rLWXFEoSnoZ1E 26LMZgV4qtl/LVYHIZaXWTSE2Cpx0uWVL/TTyi7P0fX4+ueKWLGjZB3dUrjKMI2wVl6DS141hOGm 24Hxz5t0FXbS7Yz810irhzDdjnLR7TRDzXGceONd3PFGnYNo3UKsExQrL0biafiBM7Q666WRtob6 gKCfZJcUJ+Ykx4m9KC7sb0MD6WfWHhLqoCRkhEKLc0E4uxpaSbSQnXhaGunC3NV4meZigS30vItu cqLVQ3laHXRrdDee/Z4NeI1WXnSFPCWti+PkEHE5GHgFscJKrXX2odlKTiJP3MCncPANBOwV9DC2 lWas26HPE5GVuNDnRYkxjDOVDZT2dVTW04zJaBcX5wIN8u7LzTAZ5EyoP9OBWonKio9r7oBHogGh pg4hmhmKWwdK0MHm6YOfZNmYJ2Z1NK+j/B4rDbrBJlQ21IGqRX19JQ2WtBIxG1FVtRFyyspX0Wut QEWoie4L1cFS0UeDdgfcBrq9Iyc4QcvyWtBDFbncxBchQXznFNdV40qi6aIXK2xuNO+mOk6LHPXo CHjEgRKkWvSJdtND/yhZvNERgJlGG1fHGRhMekX2ExcwYjEirGtGA91+I7I0W6ycN5sWBcp1kTMn n0tB1LWbVlqPqV8kW+GQrKM/Qz+pQGFN+nZfRgs5UaVJZxCoJcCqgezFTqsHuh0zMtMmr6LmJVuQ dWhuIJ+XIXpC1elIH+IZ1avHDDjVZoH5QLJdWl0dOEi3ofSynqmLkF3a6Faov0utUIqjecNsoGNd hZX6zXGYhPJIt8KDnPL8cTZJ7ECYlRzkPDZcfnU39bXX5KjmjjswVpTg3VdbsF0xAjl+No+SM+de 82iWMJNzrVplDer9YhwnPxmSpTrzV9tQtakwWUKayoSpO8Xu5MwmEFOchEiVqNnkyoG4d5xYVrT8 hJSqpwvIolavWlx8mU+6Hp92qcfiPrlYZSwUFrqeLJsYOhcuR5SvVpZanEj7T/e+xN9eC4hD/Gbv t/HNwvXy8XK+fEB1/PIJdSS3c74ET7o+P/XjzxZi8fhci7yaoqBLyq6WSS0u2oS/uxHE+0MPsalA g9/u27HIhq3eZLyCWC26oQFOdWhSGfhUohJakegcxOUFyk/IKZ8uIItavWpx8UU+6Xp82qUeP845 iDIXup4sm6oG5omlVpZa3LxMK3yS3M75Aj3p+vzUjz9LK4sUBV1SdrVManGPx5K1V/kZRNaqjgVn AkyACaSXAK8g0suXS2cCy0ZA3A3+m7/5G2zduhV//dd/jbKysmUrmwtiAmoE2EGoUeE4JrAKCQgH MTo6it/85je4evUq9uzZg5L/x7oKJWWRcoUAO4hc0SS3Y1UTuHHjBoKaR4uSUaPRIBKJJKUVDmJs bEyO//d//3e8/fbbKB4aAfa/nJSWI5jAchBgB7EcFLkMJvAEAm63G+vHaDBfRBC3kIaHh1VT3rt3 bzZ+3bp1ePjwIVQ+Ezebhg+YQCoE2EGkQi8n805jNDSGCH1TGSiCJH2d4WeSboEAes0UItoiFCRY 1/joOLRaYCKiRVFhwsWvw3J6kqQrSCr/6xSR6bS/+MUvUv6Y66NHj9DY2Ig//OEPsvjCkWz/7nfR m+nGiPqmxxEam6IvtJOdFEmQCuWvQS5OEso7TjagjUyo2IliQ0XaCEZTtROSbbqgAClY2uLak8Op mF0OK3dJTRsfwMWLH0O7YQMw9hAT0nP4yeEXFtfJKO+FC378xeb7uFN+BId3FcWJMIorFy5i695y 3A1spW0XdsRdW/zhZ7d98H7YjcI9DXjp2fjyF19GtqYUe1L96U9/wre+9S3s3bsXhw8fxs11T6M3 +j2ITLZrfNCLix8FsGGDnsxkDNJzP8DhF0oXJcL4wAd4u28n9mg60ZdoJ1EbOry/CFf9S7WTcdz2 XcWH3cPY8/IrePbrzHEW1YK1k4gdxNrR9aJbqtGUoeGH9cifvomzb/Xg7udF+OS33ZjSAMWm/dg1 cRUfDI5BX1aFzff/FcN0u1wrmXBkvwQtNBD30Ok/PvP9Dpd76Iti+q04cOQFOf4R3VufpPRDv38P VwYe0L12PWoOH8D999wYmKB8Wj0kfQQPHk7BdOAwpq5cxPSel1G/Q+nlo/c/w0NqSRGVv9aCcBDn zokdgObCTfqi3MqECLSGvfjhSzswees9nP94APel63jv42HSsxY7DxxBad8/qtrJSybFPlTt5Afb ZBuKTKvYyUtV6PF4MUHGpdVL0EdCeDgh4cBPDmCAnsfM2sn0FO5/JjbuAFkjh1QI8PcgUqGXi3nF yB65i7fefJOcw8fkBJ5HWb4WW3aa8PxOCYEb1/FwfAL68n34cf13UFS6E9XPl2Pqfh8GJ+KAPBrG FZrBGWtqsI3K+6Azeu88Mo6x0Sk8CAQQKdoG085tKNREME7OoeboK9gjTUCz6zBerpEweD2A5/bs w64tdF8qGna8UAvx4c7kR7ixFPyeEQLkBKYCH+LNN8/iPK0kymrM0GqKUP5cNcqLp3CjZxCTC9iJ n+5MzYZEO+mJPqdRsxNM0T8DXn6lAcVjIew68gpqikO4PhiZbyf5El548XnZObCdzJJe0gGvIJaE LYczyZ+eMeDg0ZdQTM3MJwvxe9+hDQSNKCMHAc001lHXkwylmPR34wZtcLenvBx6ZQszyhHtko9E ZxbrCS2kbVuh3fwURgfmuBWbqlFNp/7ObgxpNtOKIH5WSbloJRGZmkHxtlJENPFmGonVMFcYH2We QGQKms01OEp7d5GV0L9JeN/pQ8hYg/IiPbSTYqLxODuJipxoJ8U6DH76xWx75tnJo11kT9F/YiJD //V6DabI4RTvTLATlU+BzRbKB4smwCuIRaNaQwmp84lnyMI5iKDXFiLyYBA9viHq9PcRkj+tGYFG Lx40PsD1T65jjNzBUJB6qui4FDT53ybHocdAdyd6BkKQtszdCBYpRv3X0dl9k3bw1KK09GmRQ/R3 udOLNxG09KDyg/Nv020KevIdF2ibm1jquFg+zCwB4dA1smtQzIScQaEGDwav4/oQWcP9YbIIEZLt 5B4ZkGIlKnbyDD3sjtkQ5Z5nJyX6WftQ3IQoX0wmplTsJOpIRBIOSybAm/UtGd3iMy5qs77FF7ci KafFp1XEJ0KmpzFNnmN2Tj/7iSKKn6b42QuKmLP5VKQW1yKa7Po0kkozFoxaLZv1LShgGi5Mkk4L yE6mJ6eRH/8xtjViJ7xZXxqMiotc/QTyqdPLId45iIh8GuCVC0nOQbkczSenmf8iykzwJ/MT8FnW ERDOQYR5zkGJYDuRyWTXC99iyi59sbRMgAkwgYwRYAeRMdRcERNgAkwguwiwg8gufbG0TIAJMIGM EWAHkTHUXBETYAJMILsIsIPILn2tnLTTo/Dfvo17IbFH0zKFyXGMjk8vU2FczKogkAY7mRwfBZvJ ymiXHcTKcM+uWkM38Q9vXcDVa9fwwcXz8Px+bkfRpTTkdvs7+N2tcUwGuunb1v6lFMF5ViOBNNlJ oPsKuv3jq7HFOS8Tf8ow51WcegOvX6HN+547iB++sIV277wF1/kr+HRLKTquDNL2ClS+2IfpcA0G fncRPQHahsNQjSMv6nHx/If0BTo9/rJ6M/5A226IPXrKq40YuEu7xd69jNvfl+R9me7Rnk3v03Vo NmAPbUBX2HORvhxH39Slr1ptrv4BTJ/RAFHwPfyo1ph6Y7iEtBFIzU50+M7WfHwaIL1ri1H3/WJ8 FLWTjaR28f18tpO0qW7BgnkFsSAavqAQGMfQA+CZUtpmQ4SCbdhM23mPjNGMrrga9ld+hvLxPvxz pxfdwwWo3vM8MNyJzv8IYwJPYd/LP8Z2/dwePX3+YuwxaLF5z35sz6N9mb4M0CriAfb95GdoqM7H x94BeQ+f4pojOLrPgABt5VFq/h6qd4qNPzisXgKp2smPUCqV4rlqM4onAvi3r3bN2smmyVGMhu+y nayA8tlBrAD07KqyEM+WaeG/6ZfFnr7XQ7u3StiiE6fKhglFtJfe9JR4NiG2X9CjdGsZNotdETQS Sgsn4euhPXromiT26IltoxB9x8wjmh3S70PQWlYvfixC3kNHbJ8gyhbbJQCFxQZsK6YCOaxiAina iXYYPTf89PsQT6HoKWE7im3F3vFI7MHFdpJpA2AHkWniWVif8cX9MNz/CGfPnsVb7w/AtP8AvkG3 i/CgEy7XWXSPGfDdvd/HzqdC6O7sxCCtOKQNimnRTjxJe/SAPErgo/cwRM+nNeu+gdqdwKWzb+Kt jx7QDLI8idCty+dx/nLcTn9JKThiNRBIzU60tKvvFPy+HtBWTngwfA8FUTv5nPb+0uSXsZ2sgJJ5 L6YMQM+FvZgEpslxuq1UWChvmTB+00M/+lKOnx1WdvOMYYztxRM7j73H4pP26IkloL16pmnbjlx6 KLYW92IS6ly6nUzLP1BXQHs4TdKeXwWJG3uJwle5nfBeTEJJHNYkgQJyDrGgNVZjvyR+0W3+kB7b iyeWLvYei0/aoyeWIMecQ6xZa/F96XaSL/aDlIOqcxBX2E4UQBl6nd+7M1QpV5P9BPILS2Gc8xfZ 3yBuQVoIsJ2kBWvGCuVnEBlDzRUxASbABLKLADuI7NIXS8sEmAATyBgBdhAZQ80VMQEmwASyiwA7 iOzSF0vLBJgAE8gYAXYQGUPNFTEBJsAEsosAO4js0hdLywSYABPIGAH+mGvGUCsVDX45iTz6xyH3 Cdwdm8poI/tCE/jPMG+fnlHoCZV9MSm2FcydwA4iw7r8//vuZ7hGrm6tEPjFtcBaaSq3M0ME+BZT hkBzNUyACTCBbCPAezFlQGNj04/w6ZfhDNTEVaxWAs9KehSsW/5bi19MRXB3bBl/5W+1AswyubSk 6z8nnWd7YAeR7Rpk+ZkAE2ACaSLAt5jSBJaLZQJMgAlkOwF2ENmuQZafCTABJpAmAuwg0gSWi2UC TIAJZDsBdhDZrkGWnwkwASaQJgLsINIElotlAkyACWQ7AXYQ2a5Blp8JMAEmkCYC7CDSBJaLZQJM gAlkOwF2ENmuQZafCTABJpAmAuwg0gSWi2UCTIAJZDsBdhDZrkGWnwkwASaQJgLsINIElotlAkyA CWQ7AXYQ2a5Blp8JMAEmkCYC7CDSBJaLZQJMgAlkOwF2ENmuQZafCTABJpAmAuwg0gSWi2UCTIAJ ZDsBdhDZrkGWnwkwASaQJgLsINIElotlAkyACWQ7AXYQ2a5Blp8JMAEmkCYC7CDSBJaLZQJMgAlk OwF2ENmuQZafCTABJpAmAuwg0gSWi2UCTIAJZDsBdhDZrkGWnwkwASaQJgLsINIElotlAkyACWQ7 AXYQ2a5Blp8JMAEmkCYC7CDSBJaLZQJMgAlkOwF2ENmuQZafCTABJpAmAuwg0gSWi2UCTIAJZDsB dhDZrkGWnwkwASaQJgLsINIElotlAkyACWQ7AXYQ2a5Blp8JMAEmkCYC+Wkql4uNIzA2/QiffhmO i+HDtUbgWUmPgnV5y97sL6YiuDs2uezlcoGpEdCSrv+cdJ7tgR1EBjQonMPJT4YyUBNXsVoJ/Gbv t/HNwvXLLt6/fP4VfnktsOzlcoGpEdhUoMFv9+1IrZBVkJtvMa0CJbAITIAJMIHVSIBXEBnWyv8w bcaOpwsyXCtXtxIEej4fx9uDDzJW9f+sNOAZHXfpjAFXqejiH79A539+pXIlO6PYmjKsN+EcqjYV ZrhWrm4lCHwens5oteI5RzpuY2W0EVle2ZV7X2Z5C+aLz7eY5vPgMybABJgAE4gSYAfBpsAEmAAT YAKqBNhBqGLhSCbABJgAE2AHwTbABJgAE2ACqgTYQahi4UgmwASYABNgB8E2wASYABNgAqoE2EGo YuFIJsAEmAATYAfBNsAEmAATYAKqBNhBqGLhSCbABJgAE2AHwTbABJgAE2ACqgTYQahi4UgmwASY ABNgB8E2wASYABNgAqoE2EGoYuFIJsAEmAATYAexCmyg3+uC3WKBxeqAxxdcUYl8ba2wWswkSxPc XV9TlnAIwdDy/HJel7sFTqdT/nM4mtDqDcLb4kBbMIwg8RLnTwyhfrgcdpjNFjhaPVhEjicWyQkW IKDK2o+Wplb4F8jibW3BYtQo0sWborfVSfmWx84WEG1R0YuVf1GFrdJE7CBWWDGhLidMdW7YnDQg NpbgUJUBbv/KGL+v1YKqAx7YnW6SpQINNQY4FtODowz7XRthcPmXgWgYfudJeGFERUUFDfC7YZR0 KDHXokSnQ6i/Bcef6EhDcG40oaXEBrfHiRLPIRgs7mWQjYtIJrAQawnm2grokjNQDOn4+En4njjQ K+m6ZtOJ89con2qhGY0MLkr+jIq07JWxg1h2pF+vwKDfSxnMMBppILQ04U5nB8w0GAIheJwW5OXl 0Z9dmTH53bDY3dS1KAS9sMrHfjgtVjgclNbsQtDnhlnOk4cmt0+kpAHVA0s0Tpl5h9FGM2vPPEfU j1PH23GuzwOrWcjSiDsd51AhOrK7CXZ3v1xW0OuUj8Mkt90sZMuDpcmNkN8D03FKctJE9VJamlE6 Lcp1s53kokvBNieVa1VkMTfB5XIo7TM70Z/gE8WpzWqD1WqFzWaDxSwh2OWGX6xQdCWoJEchMxLt Fm2zKnVQZDTQioOOao3kZIxmNHnuoMNhlq/F87C3emWebQ4r3FEhvNFjP8lrsTtkeVtpRHLH6jI7 4AuJokhHsbik+uWqVvQlSUckjWinnVZkeXkWGmR9cMTrcEFpyV5aWtHllxutkmoh1iF0ubqIkrBR C+x2RVcWB9mLKKUSkIQeyZYteWbiH1KxSyWdrO5YzZRPnMv6oZWuVbZtC9xCKcE2WgHbYbcqtueI 9oF+miQofSkPDg/ZZ4JMTS0uOKN5nG3iOjCXR/Q/EROGt9Uul2O20wqbYuSuKi7laGAHscKKrbC5 cO5oG0wbFYNu9YNmy2S+bjsOua0IzMyg710JdYYWBMNhtJ8XQyeZKg3Al+TjMLraL+E0GhHw1qCx qgFNvSOYGemFv0EYth920yHY+0YwEehAa50B3pAOOnJI84yb0l3CQeyuoJ4XDcZa6mi1RoTD/TQg KvWG+t3y4NjvqcN58wUERu7AbgyhX7Ki90w9Ks90osVWAY/dBLe1ExMzIziFYzA4aaAIeXGtvRYu invX/DqO+SyYmQmg+dpraEvwEDojcMykMBEd20WDh999nhjEpKOxQDDqt2NkZgIdu6kOh3fuIrk2 V+c5eA+ZlIHB1kpgqVAaGAQPa2cAMxN9kI7XoYXuXwTbLimDlnCIp5XjcKiLeLehsTeAuv5GNLTZ qK4Z9Db1o8rpfUL9caKs0GGSjkiHId8lnD+tQ2+AnK29Cr6mXtLBCOz9DXAmrBbDZG/ij4ZjVJh1 aNy+kSYhTWjz+RNatBBrGlAvkQOmer3t7aigicLMSCfaTzvhp2LJz6OfJjRWQx1svV7YKqRku0yo Kf40TPbUfqkEraSTzuZ2NHj9VFcIl9p9sLdOYKSzGacbuoRG0eYJoTMwgYm+Czh9qIkmJIpM5kaX HPf6yWMwizwdzXjtAMkcdMN0qB99EzMIdFRQ/3MiSLZfdzyE3pEZdDl2U3/J/cC/KLfCOg75Q7C0 +DDjIjP2eXGqijoL+miWfgknaFZDfQgl1kbUU+zdySZ51iVEpiF+VvIwTcU6T1lRQkZ9CW/ATbNt sSrxzPiEp0EdnYVpxeCid2NlPYI0ytoaHXQWFySalVPuYAigfioHca/fFTTDKs5iUzjRq8NUuq0X zd4qGDYC9SfOoZXECYt88osfHuo9La7dspTWUxeA7T5MniPxz1moTRIqasmZhOXWkUw0F59rjlw3 9Wk00yDuoLZQdaLB8oxNuSheJ9FFg7oYaewWF0GqR70UlNPKRdHzkJBEs2QaPMIhP7rcp1C3ncje sVErX4Vrt1J304V6bO+6CxJtNuhIHrlOMTg2u2hFJdHq4RLOuNwkObXd7gGVSk7wMfXPlrZyB2o6 8gepSb2nYC7x41R7JZwesyygrW2GLGwuhPtd0JuOyRFv9E6gkSYLvhk7reJaYajaToq8gAmfTbHC hVjPOJUCZYXUw1ormIfIlsU7BYo/3dBAB/VwiFkRhdpEu5Rj418UyyfNyEqqPEd2T4ch0n9lWFRE VyqbUFtCx6FoPZRit0XCKbs1asY6Rb9Ur4XsQEd9hawHRsojRfPc9bZRWbTCslnoXYf6SiP+cNVL 8IidEJUmRMJmqLacDryCWGH1+lpMMNS20i0YHYx0r91ST2NeiAZpWz1eb/HKBhjqakM7LCjbQEZ/ zUddjGw/FJx1FjQ6ir5Gb2Ya5I8ry2Ga9Yult1cSxg/5IW2bpxU2S63cEfxdXtCkfC7oKuCghHXi dpGIDVHnqDuGLh3N6sg5XIvOGkPBdjmPr60NxlO0UqEVQO3rx9AoL9tJPDECkTwWGmRbokv1Lg8N AicqUCByqvQolSg5mSRJsmMS9cvtk2uOvRSQk6FKLPRgn2RpbbTTswrjXLqwDyaTAeLWkE4ywry7 ljL6ESZG9bTeUlYsQXga2km0LcpsVoCnmv3XYnUQYnmZRUOIrRInXV75Qj+t7PIcXY+vf66IFTtK 1tEthasM0whr5TW45FVDGG66HRj/vElXYSfdzsh/jbR6CNPtKBfdTjPUHMeJN97FHW/UOYjWLcQ6 QbHyYiSehh84Q6uzXhppa6gPCPpJdklxYk5ynNiL4sL+NjSQfmbtIaEOSkJGKLQ4F4Szq6GVRAvZ iaelkS7MXY2XaS4W2ELPu+gmJ1o9lKfVQbdGd+PZ79mA12jlRVfIU9K6OE4OEZeDgVcQK6zUWmcf mq3kJPLEDXwKB99AwF5BD2Nbaca6Hfo8EVmJC31elBjDOFPZQGlfR2U9zZiMdnFxLtAg777cDJNB zoT6Mx2olais+LjmDngkGhBq6hCimaG4daAEHWyePvhJlo15YlZH8zrK77HSoBtsQmVDHaha1NdX 0mBJKxGzEVVVGyGnrHwVvdYKVISa6L5QHSwVfTRod8BtoNs7coITtCyvBT1UkctNfBESxHdOcV01 riSaLnqxwuZG826q47TIUY+OgEccKEGqRZ9oNz30j5LFGx0BmGm0cXWcgcGkV2Q/cQEjFiPCumY0 0O03Ikuzxcp5s2lRoFwXOXPyuRREXbtppfWY+kWyFQ7JOvoz9JMKFNakb/dltJATVZp0BoFaAqwa yF7stHqg2zEjM23yKmpesgVZh+YG8nkZoidUnY70IZ5RvXrMgFNtFpgPJNul1dWBg3QbSi/rmboI 2aWNboX6u9QKpTiaN8wGOtZVWKnfHIdJKI90KzzIKc8fZ5PEDoRZyUHOY8PlV3dTX3tNjmruuANj RQnefbUF2xUjkONn8yg5c+41j2YJMznXqlXWoN4vxnHykyFZqjN/tQ1VmwqTJaSpTJi6U+xOzmwC McVJiFSJmk2uHIh7x4llRctPSKl6uoAsavWqxcWX+aTr8WmXeizuk4tVxkJhoevJsomhc+FyRPlq ZanFibT/RD9g/7fXAuIQv9n7bXyzcL18vJwvH1Adv3xCHcntnC/Bk67PT/34s4VYPD7XIq+mKOiS sqtlUouLNuHvbgTx/tBDbCrQ4Lf7diyyYas3Ga8gVotuaIBTHZpUBj6VqIRWJDoHcXmB8hNyyqcL yKJWr1pcfJFPuh6fdqnHj3MOosyFrifLpqqBeWKplaUWNy/TCp8kt3O+QE+6Pj/148/SyiJFQZeU XS2TWtzjsWTtVX4GkbWqY8GZABNgAuklwA4ivXy5dCbABJhA1hLgW0xZqzoWfK0RePToEX76059i ZGQEe/fuxaFDh4D1G9caBm5vBgmwiCO3lgAAC65JREFUg8ggbK6KCaRCQHxhcP369RgaGsKvf/1r eL1elB/87/Sp4v+SSrGclwksSIAdxIJo+AITWD4CP//5z7F+bGRRBW7duhXDw8OqaW/duiXHr1u3 Tk5z7+pVFP43dhCqsDgyZQLsIFJGmGsFTGM0NIYIfVMZKIIkqXwkd8EmT2J0FNBrphDRFqEgwbrG R8eh1QITES2KChMuLlimyoXpSZKuIKl8lZSrJurIkSN4RvNoUfJoNBpEIpGktOIW05kzZ/Dpp5/K 18T5xg0b8KeklBmImB5HaGyKvtBOdlIkQSqUvwa5uIop7zjZgDYyoWInig0VaSMYTdVOSLbpggKk YGmLa08Op2J2OazcJTVtfAAXL34MLQ08GHuICek5/OTwC4vrZJT3wgU//mLzfdwpP4LDu4riRBjF lQsXsXVvOe4GttK2Czviri3+8LPbPng/7Ebhnga89Gx8+YsvYyVS7tq1K+XvQYivLD399NMQjqGs rEx+DrHlxYP4+2EaqDMcxge9uPhRABs26MlMxiA99wMcfqF0UVKMD3yAt/t2Yo+mE32JdhK1ocP7 i3DVv1Q7Gcdt31V82D2MPS+/gme/zhxnUS1YO4nYQawdXS+6pRpNGRp+WI/86Zs4+1YP7n5ehE9+ 240pDVBs2o9dE1fxweAY9GVV2Hz/XzFMk12tZMKR/RK00EDMgOk/PvP9Dpd76Iti+q04cOQFOf4R zYwnKf3Q79/DlYEHNFPWo+bwAdx/z42BCcqn1UPSR/Dg4RRMBw5j6spFTO95GfU7lF4+ev8zPKSW FFH5ay0IB7GBHPfRo0flB9Tf+ta3IL4oh2Hly3iZ5RGB1rAXP3xpByZvvYfzHw/gvnQd7308THrW YueBIyjt+0dVO3nJpNiHqp38YJtsQ5FpFTt5qQo9Hi8myLi0egn6SAgPJyQc+MkBDLz99pydTE/h /mdi4w6QNXJIhQB/zDUVermYV4zskbt46803yTl8TE7geZTla7FlpwnP75QQuHEdD8cnoC/fhx/X fwdFpTtR/Xw5pu73YXAiDsijYVyhGZyxpgbbqLwPOu8pFyPjGBudwoNAAJGibTDt3IZCTQTj5Bxq jr6CPdIENLsO4+UaCYPXA3huzz7s2kL3paJhxwu1KKPj5BswsRS5+y6eO/zqV7/CiRMnIJzDigZy AlOBD/Hmm2dxnlYSZTVmaDVFKH+uGuXFU7jRM4jJBezEH7/gSbSTnuhzGjU7wRT9M+DlVxpQPBbC riOvoKY4hOuDkfl2ki/hhRefl53DWrST5bQLXkEsJ81cKEu+923AwaMvoZjak08W4ve+QxsIGlFG DgKaaayjricZSjHp78YN2uBuT3k59PBHWx/tko9EZxbrCS2kbVuh3fwURgfmABWbqlFNp/7Obgxp NtOKIH5WSbloJRGZmkHxtlJENPFmGlmTzmGO3Co5ikxBs7kGR2nvLrIS+jcJ7zt9CBlrUF6kh3ZS TDQeZyfRdiTaSbEOg59+MdvIeXbyaBfZU/SfmMjQf71egylyOMU7E+xE5RnObKF8sGgCvIJYNKo1 lJA6n3iGLJyDCHptISIPBtHjG6JOfx8h+VlrBBq9eND4ANc/uY4xcgdDQeqpouNS0OR/mxyHHgPd negZCEHaMncjWKQY9V9HZ/dN2sFTi9LSp0UO0d/lTi/eRNDSg8oPzr9NtynoyXdcoG1uYqnjYvkw swSEQ9fIrkExE3IGhRo8GLyO60NkDfeHySJESLaTe2RAipWo2Mkz9LA7ZkOUe56dlOhn7UNxE6J8 MZmYUrGTqCMRSTgsmQBv1rdkdIvPuKjN+hZf3IqknBafVhGfCJmexjR5jtk5/ewniih+muJnLyhi zuZTkVpci2iy69NIKs1YMGq1bNa3oIBpuDBJOi0gO5menEZ+/MfY1oid8GZ9aTAqLnL1E8inTi+H eOcgIvJpgFcuJDkH5XI0n5xm/osoM8GfzE/AZ1lHQDgHEeY5ByWC7UQmk10vfIspu/TF0jIBJsAE MkaAHUTGUHNFTIAJMIHsIsAOIrv0xdIyASbABDJGgB1ExlBzRUyACTCB7CLADiK79MXSMgEmwAQy RoAdRMZQZ3lF06Pw376NeyGxid8yhclxjI5PL1NhXMyqIJAGO5kcHwWbycpolx3EynDPrlpDN/EP b13A1WvX8MHF8/D8PrptxhJbcbv9Hfzu1jgmA920HYd/iaVwtlVHIE12Eui+gm7/+Kpr7loQiD+G vha0nGIbr1+h3V2fO4gfvrCFtne+Bdf5K/h0Syk6rgzS/jtUuNio73ANBn53ET0B2qfJUI0jL+px 8fyH9A1rPf6yejP+QPsyiU3cyquNGLhL24nfvYzb35fkjfvu0aZ+79N1aDZgz+HDKOy5SN+epq0c 6Lu4m6t/ANNnNEAUfA8/qjWm2BLOnk4CqdmJDt/Zmo9PA6R3bTHqvl+Mj6J2spHULjZwYTtJp/bU y+YVhDoXjp0lMI6hB8AzpbQPkwgF27CZfu9hZIxmdMXVsL/yM5SP9+GfO73oHi5A9Z7naXfRTnT+ RxgTeAr7Xv4xtuvnNnHr8xdjj0GLzXv2Y3sebdz3ZYBWEQ+w7yc/Q0N1Pj72DsibvBXXHMHRfQYE aK+nUvP3UL1T7AzFYfUSSNVOfoRSqRTPVZtRPBHAv321a9ZONk2OYjR8l+1kBZTPDmIFoGdXlYV4 tkwL/02/LPb0vR7a3lvCFp04VXbUKaLNVqenxLMJsT+PHqVby7BZbJujkVBaOAlfD23iRtcksYlb bJ+d6DtmHtHskH5AiNayevFrQvIma2J/HVG22E8HKCw2YFsxFchhFRNI0U60w+i54acfEHoKRU8J 21FsK/aOR2KTRraTTBsAO4hME8/C+owv7ofh/kc4e/Ys3np/AKb9B/ANul2EB51wuc6ie8yA7+79 PnY+FUJ3ZycGacUhbVBMi7ZqS9rEDeRRAh+9hyF6Pq1Z9w3U7gQunX0Tb330gGaQ5UmEbl0+j/OX 47aCTUrBEauBQGp2oqVt36fg9/WA9vrDg+F7KIjayee0OaQmv4ztZAWUzJv1ZQB6LmzWJzBNjtNt pcJCeU+d8Zse+lWwcvzssLLdcwxjbLO22HnsPRaftIlbLAFt5jZN+zrl0kOxtbhZn1Dn0u1kWv4F 0wLa5G+SNoUsSNz5URS+yu2EN+sTSuKwJgkUkHOIBa2xGvsl8ZOf84f02GZtsXSx91h80iZusQQ5 5hxizVqL70u3k3yxYbAcVJ2DuMJ2ogDK0Ov83p2hSrma7CeQX1gK45y/yP4GcQvSQoDtJC1YM1Yo P4PIGGquiAkwASaQXQTYQWSXvlhaJsAEmEDGCLCDyBhqrogJMAEmkF0E2EFkl75YWibABJhAxgiw g8gYaq6ICTABJpBdBNhBZJe+WFomwASYQMYI8MdcM4Zaqej/3LyPp9Yr2whkuGquLsMEvpjM7Fbm v/DRt481POfLsJrnVTf01dS882w/YQeRYQ3eHl3G31PIsOxc3eom0PcwvLoFZOmyjgA7iAyorChf g8pNvNlcBlCv2iq06/LSItsmbT7bVlrIplbohhy5S8B7MaVmB5ybCTABJpCzBPiGZc6qlhvGBJgA E0iNADuI1PhxbibABJhAzhJgB5GzquWGMQEmwARSI8AOIjV+nJsJMAEmkLME2EHkrGq5YUyACTCB 1Aiwg0iNH+dmAkyACeQsAXYQOatabhgTYAJMIDUC7CBS48e5mQATYAI5S4AdRM6qlhvGBJgAE0iN ADuI1PhxbibABJhAzhJgB5GzquWGMQEmwARSI8AOIjV+nJsJMAEmkLME2EHkrGq5YUyACTCB1Aiw g0iNH+dmAkyACeQsAXYQOatabhgTYAJMIDUC7CBS48e5mQATYAI5S4AdRM6qlhvGBJgAE0iNADuI 1PhxbibABJhAzhJgB5GzquWGMQEmwARSI8AOIjV+nJsJMAEmkLME2EHkrGq5YUyACTCB1Aj8X8cj Y+lVyuTGAAAAAElFTkSuQmCC --001a11425ba88f8d180546c75cb0--