From 4fafcefe5b9c623cb282acc1371c092189f93f99 Mon Sep 17 00:00:00 2001 From: Caleb Johnson Date: Wed, 26 Oct 2022 13:30:24 -0500 Subject: [PATCH] Remove quantum_serverless from circuit cutting source code * Remove quantum_serverless from cutting source code * Fix linter errors * Modify language in CC tutorial 3 * Fix linter error * Small edits and fix typos (#33) * Change to functional workflow in all circuit cutting tutorials * Fix bad notebook output in CC tutorial 2 * Capture output from MIP model in CC tutorials * update 3 cutting tutorials * add manual cutting fig * Fix conflicts between divergent branches * Create an img directory in cutting tutorials dir * Clean up output that is rendering poorly on Github * Fix linter errors * Fix broken links in CC tutorials * Add circuit cutting tutorial README * Update README.md Co-authored-by: Jen Glick <41485571+jenglick@users.noreply.github.com> Co-authored-by: Jen --- .../wire_cutting/wire_cutter.py | 13 +- .../wire_cutting/wire_cutting_evaluation.py | 31 +- .../cholesky_decomposition.py | 4 +- .../entanglement_forging_knitter.py | 4 +- docs/circuit_cutting/tutorials/README.md | 5 + .../tutorials/img/how-to-manual-cut.png | Bin 0 -> 120601 bytes ...=> tutorial_1_automatic_cut_finding.ipynb} | 365 +++++------ ...ial_2_circuit_cutting_manual_cutting.ipynb | 472 --------------- .../tutorials/tutorial_2_manual_cutting.ipynb | 533 ++++++++++++++++ ...l_3_cutting_with_quantum_serverless.ipynb} | 571 ++++++++---------- 10 files changed, 954 insertions(+), 1044 deletions(-) create mode 100644 docs/circuit_cutting/tutorials/README.md create mode 100644 docs/circuit_cutting/tutorials/img/how-to-manual-cut.png rename docs/circuit_cutting/tutorials/{tutorial_3_circuit_cutting_with_quantum_serverless.ipynb => tutorial_1_automatic_cut_finding.ipynb} (54%) delete mode 100644 docs/circuit_cutting/tutorials/tutorial_2_circuit_cutting_manual_cutting.ipynb create mode 100644 docs/circuit_cutting/tutorials/tutorial_2_manual_cutting.ipynb rename docs/circuit_cutting/tutorials/{tutorial_1_circuit_cutting_automatic_cut_finding.ipynb => tutorial_3_cutting_with_quantum_serverless.ipynb} (50%) diff --git a/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutter.py b/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutter.py index 9f53ef3d0..8863634f6 100644 --- a/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutter.py +++ b/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutter.py @@ -20,7 +20,6 @@ Options, QiskitRuntimeService, ) -from quantum_serverless import run_qiskit_remote, get from .wire_cutting import find_wire_cuts, cut_circuit_wire from .wire_cutting_evaluation import run_subcircuit_instances @@ -148,7 +147,7 @@ def decompose( "A circuit must be passed to WireCutter before decompose() is called." ) - cuts_futures = cut_circuit_wires( + cuts = cut_circuit_wires( self.circuit, method, subcircuit_vertices=subcircuit_vertices, @@ -158,7 +157,6 @@ def decompose( max_subcircuit_cuts=max_subcircuit_cuts, max_subcircuit_size=max_subcircuit_size, ) - cuts = get(cuts_futures) return cuts @@ -173,13 +171,12 @@ def evaluate(self, cuts: Dict[str, Any]) -> Dict[int, Dict[int, NDArray]]: - (Dict): the dictionary containing the results from running each of the subcircuits """ - subcircuit_probability_futures = evaluate_subcircuits( + subcircuit_instance_probabilities = evaluate_subcircuits( cuts, self._service, self._backend_names, self._options, ) - subcircuit_instance_probabilities = get(subcircuit_probability_futures) return subcircuit_instance_probabilities @@ -202,13 +199,12 @@ def reconstruct( - (NDArray): the reconstructed probability vector """ - reconstructed_probability_futures = reconstruct_full_distribution( + reconstructed_probabilities = reconstruct_full_distribution( circuit=self.circuit, subcircuit_instance_probabilities=subcircuit_instance_probabilities, cuts=cuts, num_threads=num_threads, ) - reconstructed_probabilities = get(reconstructed_probability_futures) return reconstructed_probabilities @@ -238,7 +234,6 @@ def verify( return metrics, real_probabilities -@run_qiskit_remote() def cut_circuit_wires( circuit: QuantumCircuit, method: str, @@ -298,7 +293,6 @@ def cut_circuit_wires( return cuts -@run_qiskit_remote() def evaluate_subcircuits( cuts: Dict[str, Any], service_args: Optional[Dict[str, Any]] = None, @@ -331,7 +325,6 @@ def evaluate_subcircuits( return subcircuit_instance_probabilities -@run_qiskit_remote() def reconstruct_full_distribution( circuit: QuantumCircuit, subcircuit_instance_probabilities: Dict[int, Dict[int, NDArray]], diff --git a/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutting_evaluation.py b/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutting_evaluation.py index 93844cc7f..6c2a4234f 100644 --- a/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutting_evaluation.py +++ b/circuit_knitting_toolbox/circuit_cutting/wire_cutting/wire_cutting_evaluation.py @@ -12,6 +12,7 @@ """Contains functions for executing subcircuits.""" import itertools, copy from typing import Dict, Tuple, Sequence, Optional, List, Any, Union +from multiprocessing import Pool import numpy as np from nptyping import NDArray @@ -21,7 +22,6 @@ from qiskit.circuit.library.standard_gates import HGate, SGate, SdgGate, XGate from qiskit.primitives import Sampler as TestSampler from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Session, Options -from quantum_serverless import run_qiskit_remote, get from circuit_knitting_toolbox.utils.conversion import dict_to_array @@ -63,19 +63,21 @@ def run_subcircuit_instances( backend_names_repeated = [None] * len(subcircuits) subcircuit_instance_probs: Dict[int, Dict[int, NDArray]] = {} - subcircuit_instance_probs_futures = [ - _run_subcircuit_batch( - subcircuit_instances[subcircuit_idx], - subcircuit, - service_args=service_args, - backend_name=backend_names_repeated[subcircuit_idx], - options=options, - ) - for subcircuit_idx, subcircuit in enumerate(subcircuits) - ] - - for i, partition_batch_futures in enumerate(subcircuit_instance_probs_futures): - subcircuit_instance_probs[i] = get(partition_batch_futures) + with Pool() as pool: + args = [ + [ + subcircuit_instances[subcircuit_idx], + subcircuit, + service_args, + backend_names_repeated[subcircuit_idx], + options, + ] + for subcircuit_idx, subcircuit in enumerate(subcircuits) + ] + subcircuit_instance_probs_list = pool.starmap(_run_subcircuit_batch, args) + + for i, partition_batch in enumerate(subcircuit_instance_probs_list): + subcircuit_instance_probs[i] = partition_batch return subcircuit_instance_probs @@ -280,7 +282,6 @@ def measure_state(full_state: int, meas: Tuple[Any, ...]) -> Tuple[int, int]: return sigma, effective_state -@run_qiskit_remote() def _run_subcircuit_batch( subcircuit_instance: Dict[Tuple[Tuple[str, ...], Tuple[Any, ...]], int], subcircuit: QuantumCircuit, diff --git a/circuit_knitting_toolbox/entanglement_forging/cholesky_decomposition.py b/circuit_knitting_toolbox/entanglement_forging/cholesky_decomposition.py index d94e71daa..995f3caf4 100644 --- a/circuit_knitting_toolbox/entanglement_forging/cholesky_decomposition.py +++ b/circuit_knitting_toolbox/entanglement_forging/cholesky_decomposition.py @@ -19,9 +19,7 @@ from qiskit.opflow import ListOp, PauliSumOp from qiskit.quantum_info import Pauli from qiskit_nature.converters.second_quantization import QubitConverter -from qiskit_nature.drivers.second_quantization import ( - ElectronicStructureDriver, -) +from qiskit_nature.drivers.second_quantization import ElectronicStructureDriver from qiskit_nature.mappers.second_quantization import JordanWignerMapper from qiskit_nature.problems.second_quantization import ElectronicStructureProblem from qiskit_nature.properties.second_quantization.electronic.bases import ( diff --git a/circuit_knitting_toolbox/entanglement_forging/entanglement_forging_knitter.py b/circuit_knitting_toolbox/entanglement_forging/entanglement_forging_knitter.py index c4dc484bc..1bf7b2f1a 100644 --- a/circuit_knitting_toolbox/entanglement_forging/entanglement_forging_knitter.py +++ b/circuit_knitting_toolbox/entanglement_forging/entanglement_forging_knitter.py @@ -19,9 +19,7 @@ from qiskit import QuantumCircuit from qiskit.quantum_info import Pauli from qiskit.primitives import Estimator as TestEstimator -from qiskit_ibm_runtime import ( - QiskitRuntimeService, -) +from qiskit_ibm_runtime import QiskitRuntimeService from qiskit_ibm_runtime.estimator import EstimatorResultDecoder from quantum_serverless import get, run_qiskit_remote diff --git a/docs/circuit_cutting/tutorials/README.md b/docs/circuit_cutting/tutorials/README.md new file mode 100644 index 000000000..8d62e440d --- /dev/null +++ b/docs/circuit_cutting/tutorials/README.md @@ -0,0 +1,5 @@ +# Tutorials + +- [Tutorial 1](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/docs/circuit_cutting/tutorials/tutorial_1_automatic_cut_finding.ipynb): Perform circuit cutting with automated cut finding, using a mixed-integer programming model. +- [Tutorial 2](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/docs/circuit_cutting/tutorials/tutorial_2_manual_cutting.ipynb): Perform circuit cutting by manually specifying wire cut locations. +- [Tutorial 3](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/docs/circuit_cutting/tutorials/tutorial_3_cutting_with_quantum_serverless.ipynb): Use Quantum Serverless to allocate steps of the circuit cutting workflow to various compute resources (e.g., the cloud). diff --git a/docs/circuit_cutting/tutorials/img/how-to-manual-cut.png b/docs/circuit_cutting/tutorials/img/how-to-manual-cut.png new file mode 100644 index 0000000000000000000000000000000000000000..662b9dfcf109212be92f0716fb4a2b168a2e699a GIT binary patch literal 120601 zcmeFZby$^Ow=N8bA|hSFBBYh>E+wVAL+S2rMM>%I4(aX?knZm8?pWk`_?>ru+r{4B zKi@z5T<3f7g0;XW)*N%pImUgDdp!O!QX+`2a9=?|K_Pw;6_kU5dY%de^^Ec*4Dgp5 z4r>tb0&OcN@)4?N0B;-k<-5MhCj&`IC`#b{OQ`42Xi(1{4*~u`LE}O_|K~jvlo&MV z|9LM5{qA4K0N3<2hJyXqF>1i;UpU8_sJw5y_|2J>b|9W2y zEiYK#UTGreCszl4g-0ra`1JqW%*q)W4Ud-Z2Q1`=c|02!z>9wm;NLUwmzVzAg&tSX zzZda;ZWjOEh5oPEMv-GeQazwElhK`p&P`kQraiRMopsAu*o{j0mEzygRMj}lQ@MZx zf76RgQ@zK)9-%2T(`@{0wY+Y){VS(j$e<`IsxWsm8?}(QkUc~mGIGGd=kl~1-#LUO zei7M?Ue@Wb;;Jgn%f7s$J{g#0SUFNkA61O5N{M;z;AU>Dzq@0f{I(U1n&&*kTW20x zoJw7GuvehL%^P#m`V~arTzU&lAZv}nJU_+HZ|coz)G7vjRQP?B`R`C|6$*qtxDTU2 zdAPWvc6VEMa7lqwM9_EY_DTMaFAKC0lE%k z#-Kpudo zlh(a?{3WODa?P&qN7yG>LiDT9r4oezmm=a&=i@W&?p?I>`kEdu90~{{dhy>qSMkoc z_o-OIfH5{ioG&Wk#h~-z z>tDeExaMarXo^Pf)yqXsLN*Hx;Hy}xu?f79@B;x`Dw%uQL3Xk*OTJruA$cXvmbc$Z zO={zlstu)cdD@?2P)>RK2F7tGQCK)pPY>RrXBL5Y@1HdJl}iDgT~uq@oDm}5eP*P9 zCfC~p$Di(%+BfjNQf5@tJ)@59@62yN+KWWh2!W@B34D~zL)NPi?m7BD5NLOQf8MG|&0N)ADZlWI*W7z5)DAMt&b_9nC<-id3^*x8yD@hDxiNK8L4`A2OxaV*~0oq6h@MnTLA!(jm#cyx^fAgtL0dZ@PPPwc8u{+SU4X zruAt|df9N|?;$2s1`aw$+zOAwMsKFV_;SJZaJRU*D5Pz2^0J#EC6UwK1iw|zVmuJM z5`-C52G2Ng%CXYOvQk8pUfGt?IOU<>c2Ym2@M6(kBU2mYG6;h=ISQFwPdZ~*0{^a6 z;M8Z{u3fjkZMKW|!M;B+?(jSYId6{RLeckiS_3*qc-N51U2ux@XQ{6#UnaxaQt2R! z%#R2ob?MiGK-aKzc=&L8ItV(ux!9+wu6NkaqkB(9rCwn)EHBM-z8b=ZyOV3E+wEXY zr2S@IA{{}qIv+E|9HTu8DXNPGL)&abpDpjF2of=gI`!iEP|F*~=t%lZceCan`Nuun6olaTLh>WQBFTkN)7i+f{!@=SYCGn1Jj=XdU(IRmYx*Al^1u&$`C65=`|AGr?j$k}Vpfs+dOP zRQx=k_WX(5?JIvam{YJ&HMB@k<5*u>m~iQKbeEQ-j)ZiAz*6*UF}k+ou<7O?&VN#o zPp^!WA$!)-(1OLeOX&yOg4Jq=yEu)R`O+F&xbS^%I2AbH*J-@t8v`SUVf6Xevj(gu zCnqq^p7kq)`fl_mINt16Clt5ILOAqqDVVC8O?&m%G9Lcn{TNtSxVTi5l-EaX$bjSQ zZ|2-g+QgM?4QKfBW`u=?q7V=?8IO_+=+|4oerH8A8Tt4?QoTQ;+#jUuiIpj*2_D>w zy4Y`&pJmKPY;wtJ@FpR1pL_`N6c_sr`+bpjBTKWpc}Bgxd-f9H6`Mx4O-A-@WQ0h| z5k$28N(nPy4<~ZE(Z3#^E*?Xh#9^E5R>psyaXs(2=zewd1uTboS0kOmOA~?X^M;t1 zSkM1U^>xQmi}$&7jRIdLowH*l`MQK8+^#z91Bd(2@2}dt=EEdV(RNBQ)X7)E>e+?I zJ`v8AxAkqx4^^4(6UL^URNN}FI`HB`axNrhK7e@B%Z^DTJq+ew&=#k2bu{U)=P1uI z`0P`HPH5Bo(J6S(OS2-P+||tP3dBIH3z{eM;U&@0Rp5DteoHv&LNg(rxG&5P`J}UY z{^(SnIiCL=vCYVqG~kiIv{N`t1`W3-y^XGy2XnRM4cY<^dI4Xck-#)#SL)FoKc1gr zZ8Y1d+K4xUFZv0_3+dlTqqO!6Ih` znKlnJ@%Y)JdKF^z2>Sb+w#5Pus+wUbFo8`->;R;r9BQK7kMlXsJJDH zePk<}3NPFn|77ykb>w*-2U(B`Zz)MG%hRHak}}YijjS*t@@EWp?M_gnj-Zi-!8hK4 zqUdNxp#;*!A^lev2G`uY_uW)32~~pg-T5S;)6&>c(%3Q z>LUIW9f6C;{+3l+2efe?;57m$&w{ERM@PwIwJbGCM*P~^nzEKN86%?{SYAQkJJEgX>BzJ9Cl^64 z9=hQ4&f1PnO-lAU!VxV~*llH`!^Uj7yrj?vHANG6m-nsR55j-gMi0z*y4gjz$R((1$Pw_!i;+(^fJSB;hIP>bj9d3KiN-oV~@hOP*IOov)k9s7 zv>`#@OaCZc?OOW}CLQ-#pog2WZ>AS`(@UJg^l}iM4fVASo*hWw?E`&gD6yQbqYw4Y zNL=SQnYXhWJMU}KclBzyvM={fMA|98Lz*C02q4ka4Agk6U7WA&KE26HcJjygdk;e4 z=YMtN_bLV&V(K5GX-xxwCSp=P!juGgO|bS4LBbU;e(#r|OGYRToRwnyy5p*edg#zc z=B=V6f9LI=*`O>)b}-X0mvnqX(zC~*n%rcMa*lvGZ&@JVPL+4f zR@CY&UkBO+C*MDDZNd>x+f<0XTZ&%Xt9OY#|ZfAED?|7Ryvx&*B z@b0kIDio3D6(m=fhq;uZugG-{3u%Pl!9xzf=&w_A@wk5;c2W-@=={logqHy&XJh6ff9~!6fxc~;b3?rWr|b}65XCCg zYX%+fgzJlq&ydkxYvFi6^fD>CnL&cJ=$ihkFDczo+r zuqfE&2K{M7_MFIE!e@F=YJp-SyAKm3Fml3_CQy+GZSygHC{T?$x{)RWzG$pGP z1V$!%S@Oac%^>W}f0?P;$2HIZwZ=DQxDgP}IKMQw?l&S1qQ!yt7Jf2Ezp-Gr({&Jp z;HpaLB&)|-zuJWtO9uOt}=~%`Is-FT{*$j z6N4;=->pgZQY&`XBwz3=HV+nep;6fvxdwo6d1LggA*eX^Ti^SK(w?LN~@N_Lfx zkg*aamw2fGKG=41V1Kch4@d%uh|(%5Dl#+w%+A*LY~h!yhuZSm5AIwQt#UWGo%2Lv ztml|%Pc*dAr+CTK2Y=wTDYMELni%rckgEtwQK?=O{3eimJPSQcE>;ovYgw-yGo^?7 zx(kjZAOOJzp{(OFiMOn?@EXN)9xSA=8eN#M9nQ1s;EqW!(qMk773{C2RkeHRW?EKE zP$+zL^CE#;ulZx4lB?0E@^Ik|+;>5c%cq44_tk8CT%ii)$P!56p(fpniJUzkAmE_+ z;T~Wgw8>dAxBvK~laP>L)2NZk6Ci#}w7)(&akQ-2FI@c^l&}N$R-T}2%_rynr$ou2 z^7{CV;Qe}amI~@kSyH-rg+rzK(Hglj$nw!CI{&$>O^8kXMlQf&z zkCQHTHN7UxVG5y9yJ?*reXvq7YtIrApJ>iHl%n-k(~z4YPEYcvR+OlIA!-#F)wAm` z=Pn+Ej%T{DIPALODQd;OmzPTN_r_Qf@i>fZ$%HDq?Gb=80rEp$4i4~)X|ZOLn{x`U z`<~!ZML$+d03Y(Csol0}-E$Fi;+=87-lVyl`I_Oz?0!{F(}RK}+#vGoPpy6g)%S;; zzQ{S|50ig5w32y|0=qhU4-2=Ock5kOcexnLf}82hYuj9MAU*^?zaorOb=Q_O-`&}E zPKw9Hv?1G^m3K&pG>}yhx}Oa_uYtZ-RnW>ps!5k|o z`-1!Be4%=Ec2-t@9Fqv`;Fny3!=T80aYQN$j17aCdamjFFecWmwJuc<^NNimGkh@4 zwCzp~)dN%Ns-QD^XRv_jx4VQrj=HqechU=r%QnkX4!@0WJgI2=SSnQ2G|c_g+89_N zH?2ujt0Gw626^4P>+_RnMBU#^hIZO#i10{p^IH>zv)I~hVM$5m8vSbyE-rO$Gwo|c z?#rJRty^h&!u2WdwH`|AMZlCfS!6#9W(wFd$9tgrlIjc6GilW+8hqggPqGMk<^%_? z@+e$2;~O)vR&+eF1WqPG$~o4_CjY(MjHsw!v7 zA|o?En?;O~v5QBv<|7Ic%-T80BFeW7#FqUG2q2FAaS{N z_WqJ7VKXJm`IY!X72|@M>-|w$OMj~b1^e7w9)6yx!;N(LT z+yY_QxqaL zt=+RVk!r*1u{$pCp!%3Ifo!%H)#x&vPdCDr=ykh@AQo=Emn|7PurTy0D5R5_|4?J& zMByMh_xjv6nY^oT6KK-E$WE|ely8<*iH*xR=ffZ%Fz|pfsCyY~q{FTzM_Gl>vKM7( zXGXauiRU_+M>}rav;uxcRMD=4oND4}aLFSu+p|iPMs%@D`;NYA4@Ckm(l{_p(_&}N zri7b3;;r=Dt&l_Vw&QUZ0>H``L?btqYA5>No2fB>Uhk8>Fh@`@{Jo~QKSlJRIL5$w8Q%yUbwQ;S(rsnbj> z0)I*JLpiMOd!=AJx-&13)9#utq+vGadZ~hAS*ZEIpuIM3eT*JAHe|?l7UcUvsE05D zzqYpaa%X(^Vv`!*?c#l@b6R(_nbOtoJLK?vN`5-iJ~=GVDpT1N$mSog%043{^vp+gGkdw#)0Z1 zMssWSvzg9Wis~;UTv)iP657pWP$AN5E4fE9S2Sm6Xb6==c&o)>Z)$mUb+ky6%X_iK z8xG;stF@U|zDW2dM@L4BO^x;S^~dUM9eGkmq<8S(7EIb zEv2i5(k?1x3N07A2f3Ye5_0M<4@jEz*AutL9qe`ucyO(LC#}G-em=Y|VzICoFP^Qs zdl+7NrVFV-0Ez~Q2dGh$MBUixc6(E@q}liak_ zAXI#NHfxo;+A{B>3d@tuoivV>Kb9V@cem|ll=^003QbQbU3OXGa5R%$!{LjK2&BgUPv?i1M55fh7R84?4% zWOx0hJ5iuYnL_G}-O(*kx?2P9P#?A^T*mZyMz@4@ zuIY)emt@8_hy3^bCF=3tzj9F7kCz$gc_abT{*A+)trLVD~C__fvo`JqI0+aM@dVl=GM1>zaI z#aTRj(7OW4Z@q-zR*XC1j8E_aYa#Kfe)texBjXQ1kjVt2T1pW+zXM-sNtgzjEOMM4 z$a`kJ0uYRxVRQ{-G60Jr69$})Dao1^k3JO7)y44lUJum!D6nO2NGQMZ5>HJA+mb5x zFX>W2AM(qpndNs#RIDn-blccs+i_slv>KMX8F2thOqgEGvz0i3#59<32Npd@>ngXiKP=Wc}o&PdxT*svLlW zDU+O9ArmbC2Y&)^a3piWpKlOE4*=-J05wvs-3mMi!g#<4$$;6A88Jgcc)A->Vgi7E z#Gk_KiAi81NdZ9rsr(c5laKo{R{;R@Vid_GkhTYm91a-y|HOeTrDc9D@LQi(T(R=O z+*EZD`}jSUw{N89`Ls?OH-N~Mi}$05vm@+tY@+26sHe#%_N!N4x^uS&PR^FP$3t3w z=Y8r07kkC|S^mCVVIx_GwkG!x(9?37zy2@N0s9k}s5ho1 z`}|+AwNNJJ7l{YMH&34O9lu5S_f4(d$d(vq{jS9eOC&L_s z?qaXbEv;|egw#2H0A%@mQJ)2X(S0hhC1I$7qb)W zUU@peRmazDy@m0FYkK(-4Nn7h;$`9>DgvvY?xqQMb*|uT9CZ~QDhb!l2p*=RF||r+ zuLTBHbjo{MJOST;O4A^dv7zifi?c1FDsw8n#FXM*Yi5&7(;#F>U-jAnirNC_G5qt; zYtP;n7t+Vf(=)58k$cRM@oLfOHD%l~S*31`Zk`NeHGAGEIuiQ!X~%-M4(I9h*v?pH zE73!^bv8(mo^0K@kMxww#tds%avz4RCu(iX;qQ{nVa?9=Rz6xo`jwSGzJviS-c(7s zo=RESc)q;#X`YPjR%+E2I=#^|hd1 zW%KjYG*-~708d(%+6=DW#oOSWs7CE@AgL0kND$Rq`+)la= z=Icp_i7o2)tI7=r4`wQNC)6!jaJ(B4n56O59;WZw4zIe{N=loGZv!`#`s?j~XDSP% zt4-Hm?(9EoMwp6=1?0sna-K|IZAp#ynyZ|QYOlBzZC}hW*ZmOUnUf@p%Hli3`a~lr zIF$A38n4s58XoKxuuM{&U(lI0XrbWD*k0z!_A0=>Iu777GHz4{UE%wVAY=So3=sQ?1}@f_f@B&0`O0`52{z}y5Dpco7_o6g8O=V zLCi)&08HNKiyH-yqBk5etHQSodbZQ9V0|(nun#;f4Q(hHPbZ(nJL@&!OC{wV%e=Bq zz6%;22a`idfnMe5*C%v?%~Qto*e8EF+9s-Lniq-}NXRc6=ROv4AeokfPJUGK?Ne$K z3mTxSiTDHIh1pTSJqgUm3QC)!D-%aBi>csXGUlPg@TEyh zBWU*`kZkt*GC6#l;Gjmob) z4+Ctfo`k-g_e*xBRt1MF03DCz=gIrT;^Wm#bNX!b zxO9Qhp8eQ{kk!3uu_)>qM}dhLHjyq@wx8voYhk@lvcFV3WquxL{8FqxSi|&RL>`}N zJ3t4N1>bj-Nq?0_Ya*H5AH?yPZsNq#_VqKGl|NyhiXPFQ-oVOGnti=F(Xx$!B(5MT<>_bp}#G19HhY1{U)L9W!(nW`*xsdQX}C3esIhM%J%9tkYh)LIQS#-rI^zI-X8W?&e$5?rWfu+(bj69n}H zv6YpTv6@YN){xWZvaxdDSBPR9tvA1($D69TUGPpkHTHB2%c}M?iur{J=Y1#W=G0#FMZ7438vD|_@kWQl7J zkf6W^A6d=EYyg`WZtFczr<^R(bRAbz#u0lBI&8dLIG@zEoMNdR{w1G7=*fH;vZhY! z&_&JQoQL>o-JJ-Z%b})!4~o)Wevib&BR5S>($w()IBT0oS>4(pPN|$zoOegoBfs}x zwT^=NC*N?;pBmbgKA~gt_B|Xzx ztC;>n>;RiyZba{bxyUMAow5GtL80}C!l_*|FBT zM48UIg@uI;&nP`td@i+UonN|>3Xr3weq`E20Ckmc5N@T_a%*hto7$}oy5`$5;U>w| z6BxXXWA2kK5*!?ylMSTR)}7h3w6qM7kRmOMcb~M#7@1zXvMvsN_hBXAnVW<9Sx?=C z1=>qo3f2Cg%c%;cvFp5C(U1Z=4Q{>y>)yU^ilm@?hqWHT;a=-MRc;o9`fv>4u*Y%F@!YLO=TL9ueb_a>gfJ)M)Ke9s}NcXMph5@&Q-e`QVx z-Sk_R!Ck{2N!@za>4VE+t9(%&nUv!Rn(PtU$#va>;VI9a|@ zVR*GGyFonhO5Nh=2PuA|pJ`kVp+>4`sM@EO$^we678125zpC#C)7#&hF$QE}otq2O ze!#_-t3|zQ1kF|{Qbrw0OjmqT@O0e6RE{0A?!x51w~RHyRr|$IJ6igyMxTHDBivqf z@W}bDCYTiwnax^Ldbn{$lN#wwNJ71DgN1Gc<-DBTE=Yv)b@GPS!Zr*9HYv&HUq94x z*zd{fsJ2|Bl_#(Xlf9dM6zoL+Z-Ph& zE()L|^AQN1JF>dOIO&v4sT%%h@Q$&si0X?6wfBqyZxN8U)H=2u2~Q#OVN}nPd)!s7 z;5h?{p>?dgQdU?>jb-Lk0(ta43Ltm=SaUF5vN6e1Xtao=o|F*y{blK0u7xq0^Fy~^ zaJuEUQ1J@Zv7BrB-R*g+4C8mtuOT2G*(>&Wd_2Wa{cmko7eU{@e+R@N;?Z4^zPDXj zoOxr0lO8Y@xYmlwy9psL(HhZzO0j|U0*LB~s5;u=<5qKR2z{Rq=5-kga57_-#t`I& zfe>ToheUP{X8u8d=9LM=JpfMYufpZ%yOO34O{;>oDr8!blVzhjH1c(59JY1ehm zK2cPtFltvF`L~D+z?Zo~zmI}OKRvUUF4Nc3%ZxyhifY4MRIFgKUz9K?QmGX&6Jz~K ze2Oc79oV(h>r35dv6Y|D5F(JW8oGb<6wxe&dzDkL$C4YOhlZ$0fZn~Rs@gr1YZWSSott2i*zk({YXfh=IRNB>=9s6K8rOytDs)EhLLDZndj_e4Txb zOLO;Zc$0micyl3$vs4xbVo}LFiWb_XOUqxpOHnL6AhE?e@qL1(pkRbys2NSZw~)iU z93z$KR5v#<@v(uxihvT%q}e;eHPMgFqtmxAuD{tZQ_PZJfw)RJiV<}9!e_STL%c28 z*9qCy-sI+ff&(eEcWsdBX$gd>Q>w6Sf~5Qk>2K&kBIN97FW85j-UJ}PR|KLy{ZOfD zfcRVX-?9S;7x@3ApFm#!uA@i_RPzF#D_~RW7mLZb5+H{@E2iex* zu>Z|?;PKHhmhH2DbYu$~n+x6_Wrm1)GSZI<0*?inaHbt&ql2^PWqVO8jLkqEzIfBZ z`_37yPv0``0|usACooD6yzt>R)<1k}3vOTz`g;`T*h{EU*Psy?7)q3(ytl$1A=aca z7SI#dQ{FiRiLL-=TzaIGXkj7T@^Li-U2cG+X88nN6iSLZRQ;VpR=~4ufMSe{-hOd? z{R(wnlvEgqWdNV&?>jIA35k@^fxDS7bvS8$cv~z}Fhz6EPAw)zg?Q;!6w2p1^5=3^ z=#rg*+@D#5jL-q{)O>)oQIYKQe;JD<%0sbHjE;RGNjM%8Z4Af{N^fdv^u3Jm^^3?m zAe8gy>SRA(+hU|}^l1}gDlg1jL-pJGYiZ+WL7izWT-Js{WOWqs0j4$Y)-vA7!@(0uqeFKg zn6|vI9^NCNA(tx86Lp-Aj((G(rVuKxCTFJ6$_n8gO93!a@k>|BzJ%8o`ydRA$k5QE z7C6GbBreCZtzpFV1Z%z78%Nm9L}{kiZ`n>Wsk=p|O#l?%QTYI$a&)+~!&e{fX}?Cl zl^^hXrr93BPwlds3XShhfBu&}yLdM1n!T&tq|K@=Z&UI5g8gZb_!J}`ry=_r2OB4e z&2mB0ah^rLClZd(jh>8b{r=_<836$S5z$^q+w~`ScN*u~0K3S;Wwu4pa;{vfd3S%^ zFyz2!6b%|&6}fn!E?3&YY12Q@8Dy1|?uyVSS>H7<~Y;*TY>mL4JIHO)8Y;K*oy&0p!-`PUUKwUEKv z<#{4)%P}CC_x1JV1BB^59NU1bcn+X;@^8yiQ$G)<3wb`=Iez-|>GEK{xcS}%85!<} zjD}Sy{oywc1)=BIh;6#hpx*=ZGU6K~Lw4d8S&dlJn(+11O8x8!!O9QJ%#g5_UE%kK zX=nu9aO1!%@t7W8d2SvmEZxXYh|VrCw3=%vKuW9L?9n(kVZ;oi?tDx+iu%8DU>h#R+XB1G{qp^e1o-Fi!fZFMp8u`>P6OR zV>X>$UKJHVl4AUdJH`2aia|s#BRKIDcB^v8ayDuU3pdSJm$Z}OeNu#JAnrn4xJ&ld z6a&>_PPjXRV25dURKrSRLP2Z=qxp2HZfAeWRE_njmlxEjJv(H8ocfe{5<|mfuVY=l zy}b>fV58COpko~MDzmU^z+|Zh(7UXV_qlc-&pbNn4rC)mV3h5_J?RPB&fn z+YcAQ8JP@P`n{(+yhIKfPNBudxm#j!OqFSsk17NIg5w6BvXm@zYl0gbzn8`_K z;VVMtwXpRBz7j>#fw=+9QQj?jr=9Xa?t0yJ5Yn4X#D{6TX*tLIu-os?14KHv+4Aqf zOIvwlwiABOV&tuU8E@(o`xmOUmEC=fSQNzt(*f0K+0kY{$-Fn6o#iVu+eCbPe5_T` zrQPGU7R`4K;%XU45UcL9E%c_CRTqd>?|W@6=3#nQZ*PR9tnp+~QiyccIQDug9>7n? z13XG!U>Da1Tox0(>|h`lH*QT2@vUx-kp?$QVLRWb+XVyq^@9z#&vQ~TpF@v}c&hGh z-Dn6ycwQ`(ur@X}=xH}K@do=(&NmW&B1cyrpIkjU$6&k()h4A}V0G(GuNd)&8_zhu z2xB8wJv%-+VbSQWL#O@}pkY!oQR?if&iNwng%}yc?wv{t15gD3JNBcY{SIhjNd~m+ zeUe?i@@uP3r?kddG9NgUA*)4bOxxTR2NM z1)ceDu-kRD757iaK|dt2dFmC`cEX*q`3luLVw*K2QyhNT0h;6kuejB7p0&eG%k!z7 zS+-3`i6Yj^8daH2YehY|)nO6B`37fOK!8vTsDh(u)OQw<^0mF%=z{@bv+GdpM6nD2 zyR?*fi}h5mgUY`0^T4L}&r|3b*RvY`85zih%yZv~|E@*D8&PWFTdzNC0x=m#0CnIs zA2;a$>apgJ?JB(V^fD8Xm)2dAJ-9Kmy0f!7gll_o%ksdfushy;ZI4 zypGAnR-KtidUp`VWcbb7yOEZrIMtI^h?dL5@-npRk9B$Uo68k+6t%2BSGjFf#ao=J z_v{5+oQxzWgygtzezxaMAA&-1wmvT^Z@pw=yWE-Fk8<=dBd2{r!50xP{e(x-t>2%m zrXVNBUEry8@K1YbSVs6eDssq=!u&chV5_^#y7umA%%mlw0le z^#EKY>cYVNu93T4qW|BC$IB`8-_y;qQBhIdHyp2D0m7%YT-l5@T)d{Hrqa^VYjuv- zBGde-(9c~c{QBRoY^j?1yj`TGIwf&?62l9ytS(?X0o{-+8L;K00>q#4dp+#0FR-mf z{bpYTOhyMHfXO>f<`{{zNfQirFey)#BaOYk$CN+N#=i_8hFAl?onaF#!elqozL35g zQ+(T{JjWpjt4uATKFn`W#GXP`f%$x0y(y*)gxWHW^b}#pU;~K2emI>LrV?Jz*Y8He zzouknN0XBPhx!0ShDJ=CCjq(QprDyx=CCYZ_zJvKA$fbaTH{z_!4u5lD%2q~O2d4D zE-({tU-+GiLwNM#6YOUXWJOa4KXoqKU+c{gj844??WywAVzx419{d| zQtW^8xP=9vwz5Yfar5%sM=wkP42B@szha!%x94L`FFMzF({smyPj@x3KNmv{Zz^#i z3A666TP|K0H>SY?l`VI){V5Ld^cmEc?7tIEK)GrUzBdoNPb>@?f}+3;OGr4f+!I{= z@{X1iWs7PJnGj+U0WmUndiu;S3M+^|1KH`nT1G#SLWFx@@VQC=j5Wzg{{8oDAJbWx zU?AaJ?;{I?Bza!G1rQM$US5(53?%0Ulz=%NOTbJqg8!uiZ1PwFcG!W(gFu7;EL#VZ z>M{cwXz&n`1W0u99!tRgg99Pxfp5>?5rit%#ZaPI^!M%VW0)%Pu8u zr<%*o_8qZ%%0HTF;|43+>VvTtyi|1Zc(?6Mx_-Mf-!f?SS3(4I2I2ovS&wSYaSr{z zsH{Z>1J+)rm4;L)`L%{~GHWUk4`gfPS%yG&55Gi>mhnAa)7QiW!XSx=wK4a)=foG8 z0lCV|p@Rs_(O*a{$xUxBk}6iC_SWN_b2(qiVg7Au4GsQ%cqBk-*=))+tiU<$m1sgQ z#r!G8cSVACfH}IC_Z9*fy<`{kVqwPv^lneRp1h0; zBxb56dhsFU$>G76fQq2gg(|DM9D5}B)~Mm9aK`YJ)AI_`;hVY^u9M=*o|WC zAeZjHAn8b07)A^Rf!HjoduzRH(CVOG(v@3(dA;03BhMYONN`I{u`S0+e!q@qowaJa zgG1AlmCWTRla|C5`E3Iw(zNkz*ROd%n1r}zXRfu}PRKTJH|cuE>$1g<-n;0plk;Vm z8p2gmH2{0`B&pwsOb``$_HGT75qd5zZDK80U9@(;03&&BRa|Ycg*k$Y5SgqkrBFo% z{EWSY1ViK0SlKgC;|;zPx>n(k&F)0A-^Nb|RTJDg>xHPB4>3zVS#_~!09=!&j2xH! zEtydtIyaBIq?isj(w@kIrO$T8(5=X^ppmAY+4d#H22b+Y5<9gpCxq}07FWZIiyhbP zm4*`(T(D(rtmA#(5A>7*w>;^aW1_F9NJzp4R6xmkEiZkX?+#5UU$peS(4Kg{3w^VS z+fjZji`>=BscIA;Wp^aF7Xn)E%vKYD_Uy))ph8Zhizj=B8O5XOJ2Eno$Mr%`Q1EPT znu(2#jf;zmlXJ%5>ttG10|Jh3wcBXb1BNuA=bdr;^IA{KE7G)1!RDnSPG-G~JidF5 z;(~$-7hU%m?Qv4}Y?oRdu?jU!xi80bbS`zS`g+-{uYNi$T_d04C$ZZa-5nHL;$G*@ z@i@3FW;c7*c^o~v!12D?LnbQ6Bn|L+-%A>xfw0b+^oNoNpKA~&# zqU+fuPy%iEwugPF@=N>cc}!BE_bslYDT{`K^@&;8ToU)*@wKDj7lfIzvU$_Z4hI5E z3@lT2{Y6Vckw2G4a}|wg-w`3U+kbkzQ8D8NVwsKQdZTG9>$bCb-LGGRSoeS?QE@Rb zlXr`IA!`$r13b(ydAl}U~}AcwZx6Sa@@%dx`~d8j&8VEAItnsSks6@ky*l&t(*5XnGF=KcOVywzof|Jl@lraTsa)$&ZSTS002dvTV6i(( zzXvbKx36>4)za6napw!b z6DnuTm_J_%;~mOJk67_?mD^6RFu*TVfj-iDkGZ+I>>!WhOCIs2`~AxI%Dk9pD7=7P zc~O(GQSwx)Fu~rEaCWk&aTKyS2w*C6>b+Nil>Q#T-pyAl2yyeFqbQh|=RlbR3lr0R ze@2m~e8naV5bAADerf#{Renx_j2M8IXxA2xpEA$Ax9AX%m8ycrt+`nBz^S!}Ma{9; zJGz;j7UEui-=BC(t?DVWxx0og%x4MSiVho8v*EOVlB5B(s5&n81)z8UAU#A}<}Ui{ z*f;ZaXh7|j0eDUT(3RPE(nAHb4_hoYAziZ_HBZtVU07xk3Vd03zS@#&rJiAZdWtmC zOejcr-`|$p;5m@`th2fYgl=<6cUyNF2ropT2w!eI| z#9p1go;8Eqeu}6b+hJ?3J34|ufb8Rw?hv5Y?bcicB&*$lbWO2gVMNLVEJdpBTr4d1 z%dJ1g0cC0&DjUL~!OBPgn$O1wkTIOc>CxFZA?5o4Hq{)HG$ksyqubLIMVjI-i{h~4 z&i%Q|spbKm*V~;;e0yajh3~4H`ng{u6yV|D-rSW(m2tx}eDVqB5I5B1yK1p0EOIki zC<#7(_U!JX1S>IuwhNf7kiPV@iR3xrIv6@hXqCZdUh9~+#LcFsH=XYMy>{k`D)_u$ zTUDoc*i})>^HO&(J;KL3l z`Hvr=Whj+JB#`a1<-DnXTrVvxEkNg}Ppj)e?dG&$N)n4{bXsOMBA7C|laTE)I>0n# zi*UX!&Z4DnhPN%dAK7e9L7QsRZRXasx4J)(LfM(!W^F5f5P+G-+h1Dxc|BX3;DgD; zo79`Y&sL@S&3fUeEI=$%63-)i*G%QhYzAK+@(K|&e(7p^(BwQ_24Tk*yhkwPz_YUz`licoI?OyKcz(g~@&1ixRi=WNRo}x^`KqfjNV&v{>z}sviP)u* z64cXiJqwFfUU6&{9QJ8CV?AVmEJ$7@3_0W+7&>oorM#=d>B%?4z{XB^Pz*nBYi$*^ zQn1#={yQhZBY*TMZSDEyRzfa^K9mPQOSC@84aN(&uf#9A?WJozJ3GT;vq&U^4+dm7 zQYpOiLu%)UY+TJQtmNb~w@^QQ*H*ReU;eaCGq3i2jXzSBlNF*z`a<;-cYFiYoG?kj zW3=bU;=wOuhjj6cPHYq9@*L3gp`V^9mPS?|f0W!YUPXv5GVli5@6SurX;D%`zX(jr z=3zy4C9opXYG)9dz>EUtXj#(dVuNT!X4!n8xjedoPromAc;1JV&ARCqv1H&tc7VCk z#`3c4Zq!_pyKBO^2t=ow7eD^_aZ3e>9yTItcz&=#CG@tL9)RkLo8%mCrL(jp^~b;tb1i_dfRAF8J42Wx&`%tlzm zV~iSy|JWx0%adA`lvoH=<_^^a;1j6{C)}*ub$~2uRP7NDq_Gc+3%3mSn*z-5CW0F6 zfEE06i?ck$hx_L6=G$FfvT3)+Kbm(kOv$mZ!9CajT*6s~M5|7GM<22`7bepxWr_v) zi>CU0j!XONcV*MsWT!c{kUgp;)m{mJ;?VSl(DSyGJInLo9{9Qh9sz(Z%}wNph+x&F zS$n_L2aD}@ zCtT*H%MC%1>#GzGK#^ED7|-}!>8MYLr>M@Le@Bq36IkIr1Mg1B{#4h+N#~dO@tH6; zuDt`2-9i1FoXo=j<8XE$Fgv)Tn>7EqCTG9qWED8?9cXXykYwF|81C%#)JUmMfK}GI zuTWZaQ4SrHP;9nXt^Zl**v1UW3^O-)jq;?+{SYK)=d|BhJJ13vro>Qo8!E1;gC5Ji zVK66AY(61-j-&S}HVCv=PT+a=wXpP++rde0!^svqW1{q(Qc6=fTU7ZQ&NmYxpF_hy zWl;r*OkBthHVk~Hc})+KEY?@H2$mI1#sd&l6Ihg7@}-}SR&S6`05AMmv@Aq%x;e;J z)LrHMd~Nr$wuYT;e)ha{vUsow-opugeifbygKlfR2k%!KQgih))#_b@dZg}tNr?lJ z1-179;*w{!8|k9mDAQnaq;6s$0R0g|Hi<1Vus1f4>PRYfjIJ)lz{J+*;yDE__P}|f ze(0ckQ!(0@!qOmndYi71584!j$jzXD^2I>%QqB2c^$a=VFhZ-Nr##fm@(t|3zOseL){dP9ojC>&4dk11VsaZ$8V5V;t~5MYsZqLG{>@Tw8xUeBl!vP=GusRE*r1l zkdG$8f&I4I1j_?3btRSa4S%^&3#lt@92{7`Gk5(cG1;o)OyS?rJ1WA;b?ECG|D&2< zW_%>(e8QGA5whPpSMX6cO+It+rKkA7pFvYxEU;2-9v2;GMckjaNCd=eK(Fe#30H^Y zst1q^@4K>$>&PJ-7gN!w$z(`I)>uaRnd{_XCPZf?@nE25tG6_i||?9MVS^8j zuC}z3Y#^rKvpu~}Nlzf{5@Eta|LUf+elNRHtZ;lo1PG9ODx&J5E*&d^u%4;;|LGE2 zznC31nk(A#2sG0!sxMs~!Ubsn_tv~61PjHZoCbBRThBDgj@hnvz!Z-NM0sdl) zb(J-+S|s5F`)t9r-}Qty_*aH@q~whYoCLwe_x|f#aRCclBA|}W_q=`Ok8@aDzPj7k ztN=3-hNQrE4ac^yl-G@hFqsKK>W><^3c3!Wi-AFY@W1 zyLkmXAG`MxQ%SYxesSa8>b)~m)u9RNgJ8b?oN?F6qfn8hb+mdy3=^~AR*1g_#Q_u| zOcFCwur*LwHOI5ht4gq7|JHcTaM)FEXGmu~&xQq^7Xs&dF(DXLINb8?_kCr0@(8j2 zO(TWm{=1+5$t3>&;K2mI#`1s16BhnGkf(~N|LJF5{Ch_JPn(g)r;wohCyDR%3Kofw zH!&iD>_<%J(D)e6Oyd;pXO6!wV798r>buRhWSiVhPyl z1LpT>WE06qJsyD_cV{_(P{0jDP5f`weOv><6YhD)Cm(t_he04C-?KK?wf!?~ zr|BzUE%Hzwg$ez?G2-+r5T(Ycj;Ck4DV1xr*sd4JV_)vb$cV~EMg$Fme&qKLhMJnx zeYck8=9=UVu<2k*gEzKI`k}y(GhQ-8FRL{%t$KZao*^;M@1CY(OeX3{bfvz>xKC$4=_X?Hb41o<17P9`c5s_d_zfo zZ?50>-tUmVKg1f|Zd4O}iEx|bmp$@1sguQGcTI`Sk)OJb(eB3F8GEbYs74Lv-6+~=S?4vQkOlS+-A zW-~-?i!ky~-cZwUvR2<9BY}zp1YNE!_5PJ+^HK9`!nNOy6uG4Y#Ia5a-Arhx@b3*^ z1UP$kNJS?L)2gI91hdSX?!%oXSvXZPh5Tg;xQa~-xGi~q?t2$>@qpCKoHD+G-CAe} zxRa;LyRCEbKVb`e{9vu!d!5FzSt0s*l+pLHhx4FL*L#z9@p7HfH$T3g;}j6iq;FEy zrT9R<`^zcdrqCwYJ2_V0yd<6{L>}g+|Ap^Htu8GRr5EInqVtj;wBZs z@na!LUOKM?qA-a9CPdweBa()(Fs!Zm++LfcG*uhjRcoh}rE%*rJrH?ZBh}B78*--< zvl@5}A!;@7Cq((*cWwCp-XSAsed3?;o{V?$+d;{V=UrbG^tt|4P&)}J^A_Frc>*gdD`P$Sm@&xM&JWT^zEvdIN;AlRYm^OAVF_ZVVO0g zo8nPUYFkuv-T3E#Ah9N5v(DMHdUJb4N|7`T);UGq`{oQKwi_6|A1l9LcZ0{*RWr!X zS}VhLApm=W6s|~vA0VatbjUmKSfhZE>HS!{Pl8K@(7sXGe>^B~V%JeG-GNj|yZ}PGDnxeTsPLJThV1Nq`?PMBC@i4D zVf1#LQE;7F;dg3AUgFccJi@wo^h3BF31R;R4sw%%Pl85NVY!K~#KU?TtU$=1z_?j9J zg%8EoIkh#I_dXb2!YI^7D=)613?&}9I9XMx-9s$#n-9mQgN?Vp9dmy>5psbw?y`x# z8o;Glxg#?`So#M0^gw@ZL?yeg>l}R>$zEsG6Q`v~*6X)VLRZ7{j?2C<^n`TrXy1n6 zxPR{sx|Eds0VBWti$L`5Bxej8T=-~{WhUIzHcmvp5z4VC&2hw;CGhWmFnWMFH<^+3 z=;-?60EyW2ojEu?&sT_eK*W4{?hpP!KwtfR_(k%C>{Kr2m7HtNlDxXKFaS(D-&y$P z%l9dNN7=6A=?A|vh#2Itb3WQ)bgTaelgo3(A|t>8oy~&QLC#3=dWih(68Y0t3COi| zhf>zb*G)89n6NkAA!|Vmfv+d9EChWIBTg={dbNFTPgio4Lnc!Z90`n7SBP77M$g1o zHax0|OSU3a1H+FAfWQByH%7o^=&IqlYliP0lTp@Nw}*A@fOgmF!o0Yiir`tR&tBVi zcgMt|uMFsRj(k;rGPjiLhz`KuIu2N#{nDS{qua7Xej*2w(dz#GTpwp+tikWZ*e$9->i4X|a%6;h59ree)eWWfmB zWRK;p@t*rlKQ2Ao&qnwt5swmSa~#@wQgIwf~`hzhN6WlL3A>lKyCIU|!E zHtU?kta%@=04U#WaFe~%eeTh~)EDMCuD7lZrCA|sXR{5X_`t#e2P>KW3UhyFf0B#g zJuGRkjh(S6I&$C*c7O8d%%~vGqVZWaiGlBAX4%Qk^t*0Ek>Y75`>lY-^l=M9;b zQ!G|rI)j*IjhylIrd7NZ7(K`D7U!f-ps9erE#E)&JuNiYN|WRWnj*cN%7wrXP)Y|o zaO@7wo8qK-$do^JMF%YDzYv@`S0=^93z$WcdV*;g*^5AC*f?G;~Ahx!K;=ntDfWG9(o4z*LC0-xp@ zhk--+*puwAqo=q3d|!H5ySHCr7}t>2_EOfp3*T+tCNvQT5okTt0Hdfo7ctFC%~QJ^M0(!svLanf8^Mxm)C(yIg6NO5UM?{_3#MLo7((M^_Jtc*3u6tH zy=sKksjZ|Cy2MKS8fI;jC=nOhM!(kXZfp$ZFz^*;tRs6hBZi9d=C3hV?5B^MhJrhBzLbLJilAopnikk_1N_(d%tdynn zw!7MuQa@cYP=$b3e4b@MfAb|gt-x*eVi?B)xM7`1f~kjSvTfTT{k)#m;c5_+!y(`; z`SFn*wDB)F0UQQkX6~qMi++B)7u^?BaDe&|@y_y~J$8rBtf)4S@N2fCdyfUi@Op;D zh?`Wc?nQd389iMf_K<)o5E4nAnD7btLYci;KbHMBCq9Ee(R;$II6Ep0%8*r}X|%BJ?rc0Tt5C$jp1GO@^C-}B!w4fX({n5 zQux#aStMzlDe`e7VsGU z+L@ylWYvlZwn%5H>r2TBWH5I_#a^amHjpS$!o|_nN*Z;w$OFjeMq<-ZD1{Y}N{1Nn zzSY86Thi866zT4BA7N=vIzXS3bVScg*;v=05GnJvv|M&IHZpmMjE(Y4Bx zZm>19uvBcIK)??OA@TDsO3$(0L?%Nj3a2)vbNwp2|AW~3-0BzSmS>|q9&$Afp2+?8 zDUWMi=H{*8I!zVE1Y=(KX(t4w^IVSWXmfVo)JoO)<}S$k?F_e&|EpnEYBX}i8*B3y z+T+MB`km9LLEbMjmNNU3f)~sc62qBp#k4o-f9bE9GkR(ztDJG=?={7yN{ZHg%SBtP zrK_N#@|QC?A?8M(xJ8%=L|8>O6Mc=QwzX32R`#g@ybKSw9?0=)LOad?bt%eM4kGi6 zc2fyQTkQ|T1llJAv#BU7&oW>*IC9+r*KhI(vOE~o)N@kKs%p!yA6K067PRX`Q1K|H z{fGFgl-D_+))nZ-^hsuYS$1QH(J@Z^mhD#AY(4oXz=&K+(I09 zS*p&S)6{#~MvDY)*)(=7+1_YDIwa#fTMc3LtFZ8u^!_d>z3d}y_|(N!`p@59vMkrY z?(D{Z$j(F*IQpje8F3npz`aJ@V$U^)j_Y6PWh^}}ggOt+1%4Q@G(kOH=mXf>@n zoa`(ygp?)7;=*WkuJ)Lo(#0x7mE<1=Djg)36B?TQLC}h!JTjtKcjh`OjS~{`#V;+WF-J)(aFC@OflbyddrSUV@|E!-T zw!#Vt+zdwaq{mB;uv90xpGGWEhml8O!6F3z66Ep{@bgC(EjP1A)!bs9u*CN@S?%C~ z6nuxHi%$K6)d$q9o^XG?l)EabSuOOL+SVn$!X460A1 z95qW+I2_F6nJ1gE)L0-ndvPN^7!o8az*Pz<6%dKlc>! zx9acRrANXipFbytUc1S%oKPyc5*jv6H>ItgKm$@$Fg_{HLaQx24Bx`@QXcUhID#I^s-in0tO?_OjTCUj*l#uO zC%G4gBSgkwDO*dpuq2mnV(Mh9&QjAVXn0fSIb+)H7QcM3VJ~dkNE_^j`E+SRaNLj6 zJ&f=%GLcQ_M0YAC=@aei~-mK~8(+m|zjX3N-n4;*;QmU&* zF5$zP=BjHb(k!x-#IfL(g4fgAoxZ`|S@55-XXy??U z|Kp0#f845D-9@4fjV69I>3_^OrhGB2dpJ_ zoBL%Jow9H^*v(h8r$rWe^F8xGpiEkoBK3i0f*@i%eN2riUHmkrEDd#Hd_GS#`O2gH zSU#_B`d~VVPM6S@dX!ub4!s0Vn>C`N#!zbm28X?emX=#84$QYc;@4+H23pFXf;X(e z!(Dn-{*h*R8s&AcI`MAf2emU0 zMP@Dp{I4N`?Gg52x%2%vLML9>Dog_@Eq{8qs67sy^I^D1v2`17ByvyfR_nrWE-Uz< zC0{;#7Zy{NM)+8zIuFPRW9x$(*kU2^Og1Jf_a_izxpYociwByP{;mw#imfey3h_^3H*xvJK-bwPsNFr-?6BYr2q34q$lWo(I_jSVO-P9M6eSW zz9U3HgV+6n`_!q%zfg>Xk(Upz%YSh)mTOKr8!e``5eih!P>{?;LdufyPR@tZli!S) zRyY+D4=3h}$IfoF61mBm+6Cy?)YE^uM@>p5qF!L{9R3!&oBH)b&~d5EsNYT(>?;Po z)ZSKK7(G6ofB~a-1L}gJReOLRN~6t*3OPWZmdwsLQAl=@vK}AQh)eEC#MiFn z06@U+Jgrz=Qf!?BDJ%EhT?DlTmXYSBWm~9yPjL%j+$0_ZQys}pN7e4P+aDo5sTuU>|*0C@wZCNc7vjO zLJPPBaRn222>5$&yd^FqMY*RCuViofHP@f;ivxKxhXcicNV<`(gIqR8)`g`0UX2Ku z7`46inc%OARRsp|6#~{=1=a7dh|Rn!jrnqI6k|jv360a1UfGGh1V7`Y2)wDLJUf>_ zL?!BU_3ppM_%IVl$f`yqNEF+G?=M-$va2Af8X06;mQNE09Li*#HD%gE2t?VhD~59u z2T0@MPwj^SchBF!-e|GGC>KGlOQ_@rsP45eepp`I7hJTG0T~IFu0(%D(!QJYF&_vf zD5OfVtmJd{<*N)9ugd*<^)ZvZC>=^Z_CMXonSi+R^o;zW>fAj{j05lhm*C)#F`m|T zZnqa=btrr8ZkbLlKZ0dz0zcvR;iog|DSdSsSY9>8GL+}@Qr$HS2m4L)6oK$rui>zVVOCdx;N8^r-mx0UZq9=gkbXWZG*zkYaHy`)7ff?f5X&;S&~N4SLni z)ifvusYCh4?4Q5QzbQ8(0bbH&#i4N*gPixkv+0>QsLU^LyvZ&fJaz#mbYEBC1hLrN-lY+vcZnLTSF-v#jR$ zxln>H0V%vJk8aEk-v4Av``Wg$8&9Q!R|52K zXPeUGxQ?MJRdk|{`bozP_WN$7LRB8g=zq%eOgYzezmQLK&ThHe1Hw21w**%K3|=9Y z`QH0k^-L&&P4VQD```EO{xizQ6fKbW(YT?jY{uO<%1(sIgjOsDuyI62Zwwv%u9iWQ zOMX5Sf)M!Ts9W^5uX%y?~Wa zh=DWjc*w631S2V2cMAz};xkntc}czTbH+5SNVT7Q{Ia7VTUnOvlCI0j+zE zhjNAhL}=s-Fft=;&X4S$>ZRIZ?U;5n{@%&ja-M=OL2){t`(QnBv;eQBMsAi&1(HL0 zI%eX_OoZXzs@%&DDc>ZF?!lYT-$c#_l6PRu))!i`zC9t=m#ZbG{{Cqp?7IsPmrXxe zk;oPwfJRTW3&iIF%7)c|PhWmDrrCdMl>eb7v1{|yZc+(BaEIuD4|XQ=*x8FJ5=Owt z)Zh5%F}%_?g_cI_Hx*lJd?ecn0xA07w&W@y_8TcQ&&8oU-`)A**#rxFasrFF?qp+$N8~pQctmM29|2W{ zh#(#~OZg@%lK20tYTeJit@~{wWuDuXBA^i~x;C1)RA{vBUmvSbu6^4VYa@2s8<#;Z) z#HUtX>hh=pgXo|MwiEQHo)U3+MU+j1)1iH^s}!SCu)yI~ldAlnT7vFj!-zDd6`SI@ z3>uhv`?jC(W^Q%JRIJHe`a3*0h@DeJT1_QMgpa#wT8=|0Dw{BWjz}*gr69pA*Wdc~ z4}b_ImHWAIM*7A%{V3>IG(q3^)t1Ey2_b^*IoZ0v)`)FHprErsBahA%t`E-=!1RkQ zfS$I~`a8Iqo6|ih-L%ioA=LSC0S<#soDpXxeP&<8_C!z|=9dCcy&n+WPkAXQIp*wB zn&}H){Tb#+^IX*Qv=GK>2j65u^~c@nz8b$1Bc6*)Pfj2eQZY?`428U zFYHr_O-FCa_+sgb>g&GNEvzf|;0H#vl_&-&Lj zPuw08k46M!)$yGr;5iuBLO)RN3QXbYSP3G|Ht7`aT#C9tPc@bdl=h=Zmo{*h7N%;g zuNd#EB2LmE$3_5kUxSAz2!C8)C`!IHuingQzr2pquL2AeYt>OFst^%zEWqo7J+4M+ z5h8Es!B?Hy`ot$nbf&PI?8^fBp^lj>3nRv?~Y58g1Z>fdw1}eJapbt<7#))~w4@vFsRs0n{yw%p?)R6`Mc0S-EUw|!Al>KSQj zeAs{lIz94Cq|w1u>92CFpV}IjPP#>fEQP7Oz!={dG=XNzOW5@n9-fQ$ZMN-kYx6^U z&=4)*9+JTmy>6vY%h)eNbOcfJx=~(@l`25bi>Ml*Ip)tnF63|)gIs+gW8gEYhJqa= zRt0EZ_e4NJi}njxy++G-^rCuk6`Ebi7Zx3!1l47kT3vybFRI1K12X zSCqhc19vnE>*qC80Vist8WbTBOyoq^d#TZ1o4?IPP|TM#DXKl^Afhr~!jU&)joF|J z4Ui&I+HA6d`8~fCI=VB6&0vp)uaFriU?7omcD3f0?23WrlfS zo9zg$U!jv&F>+?=5X-Bzw5DYKHGukf_0JwH#*Ah#EA?$f-Pm`cGBH@-BPj@Zuo9$X zaYKLtH@WNim1*= z{mlUl6z2lV$5YB4YN8Ao*53sG3+mt#>P(W>$)3V#C{*%5io7xhbt9P} zFk-btZMfJ~)dGdX77D;yggxxMtunu#lS=*Mm~?QoGbjSv9#ZqR~vFi*hb9b}+n6 zW6mZfU7FSanN?^WdbHmMvw&9GMB@YfaVhKoaG}A@S253m;W8;Olj5lkp9wK91X@YV zkXDI}q=1oPJiJ`eNGG!Uqr=xF@x_(^;#Mv?oaKRs-_d{_{{2j|?H)13%t6Suco?G7 zAX&9IK(m#`B5MfhbNTIxZFmkHlbzn8v~eihT$233-uzd(E~sB~Jb-!?vg%FqSSP2u zmbm6WFbc%dC-&$}l%^dB*zR2%K5p}#bCokwx8;>nc#Zl#Y0UhFioWzwoXI--4)B6Q z#H>6l0>a;lc3vn-Rhb z5~N>4G{kgzf^i&G6QbuG!MnE^TQx^a$6`MVBcj2j?y=#*&HMIxKjX<<{-9=xBu8OF zwoh)>V@`M^pR!t|0eE751&rrX=b zqQ-sv7)L2J1y1Cm)A}-#|88pY+j)Jc$OP8q^l)#Kpj(KM(aV#`d_@?OH2`6+`d;+N zpUy&vYuF>5+vZ$B0UuODf!P4>S`udw6-AonqT$Xq&Bf?j$eVPVoo^_@gUD&WPK~kb zg3!#Z50u{G!@*vGg1(BDF8l?`NQ@a?jXT^)%eB1ssuC4TeO zq7f5n+0HNlGF8K7vKv@gkFm|uC>BPj`k7>&EtnHyhNc1*M*IUo`*;tm1hM-2Ze%9k z4vYcLQQ?4799la+qE_;{X55J|F2~?K<}aoc2!&po&4|u`)yRvJCjoN4{aVX2<=;zc zD_{o1BbXE^a7Wvb62I5*kLp_Ugt_5>*??JLXwUHiHik5lg33=_$I?X3W&#O!6%VfL}>3#N)P$;sM5Vr;v}1u-)ZBo4 z@)wCLbjRWhxU1!ad}t>g#fpWbx_Qx1Yu-aX7~C8TgZKED?m<`|!kNDd;+TBj>c;Qo>Q(@5 zf@{C&#X`4k3`6arik8dvk`_iZOoapHUc;+TfJZq&X%rM6L?6_~z|2`BKMu*0%P zHs&EKn^KU*(n1m^9;+r7f`7-OHH)Q;zk|#KfJ^GwmG*+k3k7_rEyheSK^upwY>iUm zlX1>N_+&gZk5kurX8#tx<{0(0RU4 z@#RGKI0wPyTAG(OL~_h&+{R=3H|p1}#A@;kifGR_i0Vz6kv}i9dm$|sN%b>+*F|K} zxvgo3<42qlAL&o$G;f_~tTk`dCS_SjYR?nst>~XuDsi&Sqe`sx>;znl>z!yU2fU`Y zxIo89sR!8%wHIpKlbmlLsoyR@>t7b$_CQHIrW37r(#O;EM8+%v26tbbGCn zSJKl`^e778rN1ZJk-bn51AkszCsy3?p~HR<8UV4(hv#m-6afU@a&fUTSIC7nsAzS& z8jM<#a!39s?0`2VWOQr<&kZ!Q#a#ECt&~42?C2^)<5}xc$^;pm}1Kw{QCpqI}+QSe(_nGBz9YR*`hdcJgDrim zfDawwdTZ=riY$ZtRxFvirJqFFrG~`*Tk9X>=)aTEZ>*%sMo%a{$8FHB5s$!)RIC~( z8u|lJ?rL&=;YC7&{ny+Qg3C8EZwOi$BM)}4{CKy=7C!R_^Bn$r9wdUEU|I->HOpMRa)E;jN-T?)icmpaFQ{(oFjRd)K(Z*bftfUi)N-geN z$k0wA;qVD3D%Y$};h_$g#0ces{pt-jRS0v8yr|r$%vcDE?KpnX)64xlsLY}Ws~mZJ zPVUTVGuUXbv9ZBz+5=6UU+t%f5&q`}$?&^9Wi8_k%bol%Df}Os@q$Sq1E1C(COcS1 zLhJvr9hyR}`ax(j^z|KEq%;SIvyb`ToCtPsfAM z8YN-jYU~Yyw216dVtDV6Y!D8e38Ci>IR!qi{qbBo%e(M9=zmJH1p(Y9Ldjn?A@mGJ znt-DAZQ%RG`+~1&$;M3A`dz(&=gNrRvownp%Wx1|Q_n69+v8=VX!rgv|LysIIlP{2 zyoLt|Zt!FOSI@7?|9?k?27j*IC zZsLpIpBmnuz_O4!Tz!_~S-hyKyMMaw?+X!yttod(G5=M_JSz+qW5zSz4SYKYL|8N$ zu*IqP3rQ26LN+^BOAsQ0aCcVO;fSJv!bflE{~VZ_53n^l16bQ2NUH39IPkS;*=V0L zgC9~)YW@6+L!cP#XBhtxIkzD7QIBKg_)vAGKB$`Z#loI5iVySz*lmjpSjzi zPSp~}=RP9{NdSSm*o=1H-@71P$&Z%=uNCmTwh{1;Yx~E0!E8-P-c$&m+597bcp>!~ z2`B#ZcvG;%YlQ!&xoW{9Xf=fZFP|%Ag;Ac*FM?pvA0YTk+fjK=^V$2Mtmqd!$ODU~ z5IOm-tvNOhAhg^)_alm2+CpFxxBQ#jm25UErhMU!-n+UMSxS~H$LL28lud3x^^4$n z=T5rYGNj{0(_6Y|&c@R#pjFUjS~W_Tx-a}Mo~yNcY_};Y!iBJmYo^&7#Zc$X@E|OV z(FYGdkl=b#zaitV4ait)oAO5@#xHJZBCt1E6D6^`pwno}o?U8bAHS1i5`m42_-3_;R*6e|FW;jwD?0>#x zIJ%dRoVCuWSf~!!8Qx_3Lb6N+55sirkfTE(cv+@~->@UNZ|%={`%l^&p-pIv2gt3B z2HzokUJ%b!Xy+E1(?esVFmq4u{hE^~rx;O#%K$t9RDOn(W`x=x#Qxu$-gae7g&>5A zgWmEH+EEqfMgzb4-h)A9e~01 zDt!N2V-v&vGW8LHku|=*J%_3;{=DtyxAc$xhiF_PbZTEhZWM0!6J78W3OS}*oHVVr z;sJ&ql321*L@IAw(9$zF5Lo&SfiIk>ba6Gfi~zgB=Pno$*~R}rH;PB8)3-F>O(y6QB6{ zIBqnFb_RhTXCMq@+dqnlY)D=?`Fw&Y2MP1rT#?H3bqGOs|GXjOMsMd{edA-lGc@1k z3f|@mR{08O;iPH{1^|#_QzM!t1x{+a&VN8VQ(rSB!Ou~KehPp9fw!e}@?R%{+oXn| zP~9bzNgFWy4-z_#Y46G%x9+}0afI{EW3F9g!)y9_t6 zsT_s&7(NXw7(p1IP6$7zC6Rs(YxKil4w+0P+-)$y zVT^EWD%BaGG9~kB?<5-!2&$aXPmtnD{_}J_^!`I6>zh}SCasR#uxQC5-3N?gfOrBU1BozOsRVZs$5()(gZFU{+0MlbhCf%71;>u($eI$GBny+1MK$T5Q~BPU#Ol?6k< z!5RPlpmuKAS%`o*V9-9Ae;A66StOK*k8()b8h|39fiBDev>ITH^c(%qtw!KHqcKAij4wNgZ)G$(2;$3BC|ov zgi0a=BSAL~Rz%|29CpzioNy<9m_2(sb&rq=%pJnwlk}>SAL^eV)VU)A^gaG^s^og% zh-k!|Wk_PGcTKBNeQ69Ij9sJ2GH=?4jBZzziga_T0444L1SDpv%D0xPV?u+$q5lY- z91(w;>oytv#Cm))gK5e`w5E&?Ij&A%q-9@0o`9g|P94BgITG z&-~5ysZ2muGvHZDl~;?fTC)k6@5yQcQg8jH~jQlwF~ z?VpwDO5%uf6CU#E6s2DpH|MFWnGHdG{IMqv3RV#GI-E-sIHvdY3(hPuWH!SiKysML zVPm&;p8?GwMj6j`m`dYcNffWVs6U(K@K}{S<$31T)QGHOC&sKznB3sI)0_2hT20~% zRSm;rsODuEQCwO`y8g48PWw=M(vPSRE|ih)A8F~bHzxWN;^GwB0q+DK_(0e$TeQE0 z3$n|0r-YHXlx{6^G>*qT5|fg*4l9$Bg~`PXdQ6mV{SYk;PYuzas<_Rz?8*46K>JX~ z=R%bJ*_buX@07(FR@)QS!catfaX~-#@>dvVxoK-V7;WEO2ZbVd2>W!H8*Lu1;j8pQ zW3-TJz26?woPe_Z0%I)&0$icG<=zIE0&A$Gx{-ZMRM~WSm<*=nBurRc_>!~n<|$%32i4)?;7QA?DJKnsj`FRO_M0l_ z0+z^7Uo;5wheY)fm{Vbgu8=aoAkJW&!bVq}>nt5)lHc9JDKsxB^20{v{xuuv`y}$H zQ4_cUeEv!4H)=LuiB4!6^KqwRnBqFwMdNwd#3+X7JR5=qiu&c?K|@UvzBnlBn;Tve z!_lwFubFOp8MX47HSa0EPaS4hF+;vd!WXg*rzNAgjU8X2sq`u*B|Lr2YqZ31Wyb{W z)&IwYap$@Prd_Flpp_}+lbwj4x#o17bd=Rb-}fe95th9bNQugg*lh!i^$q!z(jKeI zZ)X`UkTCv6xy^6YeE~Q%p+~c>Bq31GVP`;Q>kUV`*9Lwv-WXhbD7=i_k$c7A@X5d9 z4LSv$NnZz~`K@1VOxvDEKAF3bB-xWcQj-@;;G686J4Xt>yc~#jtccG^16rBGbHB?}&V;yr%O`9?IYHkL8>|DModMkGpG$ zla8F3H6~*!Cq3rMAmL1eAdhs&(GfH~YpZ~<-NxEVRcvxg!bL0)$1{p79>SC{2C970)NZ+5g;d%P3?v672Q^aU<;*;<^_5Kn*WFCd`=s{aXo`6} za1_Y~2cH)a|Muyb*v!98;;e5fL&*6ZWGBbWgZl9=RS4IB!Ph<^a~n;ISp_>g6!!FQ zW5bYWl#V@esfh=>BI=hytcq6&%k0{DS=(LX51_joc0E%{!hQ!I;BPvceo_Hk+x3JN zPWyQ1th;0@Di)!1z2ip!CmUgk+m#G1{!DlreGMdyd@e% zcGqm0G{TNJmic7G4mcg%QJFpczcnI!lLKma1epVSXk-Go<1}IfE8N z>ZEeU5tC8WWzEV0uDLWY9vqI5j9)0Aqpu*spHCI4k}}=a-6-`bGJ@yIA7Y5LOeWG6 ztecQ!{_0(8Lsz`kUlC?%z$3oFnkbx_A#whZPg>uR8&YzEw;J&u<;pKo;nUc>7*B%Z z)}GP)APiO&YZ)3}-{FWk{PBV&V{_|r_iR~TdwcxO`iv+z-+10&ZOC!=vv({!c$8HCl%Qam_07WQ3J zn^6T7K7#z#un1io*TQIicgOEMf|rO1kgG2tn+|wmET0CFiX*LQS_R9j3jvZ!`4;En|0_!j%RkzjzwvOwIN2Lg!yC4?e9k?*tA>rcX<=)n;RUM~|UMVs?dUhc5XIyqBs|2P2YV*X^us5Z&MBwB;mph z=w-)*v1v6XTyz2~&n?U)-^6unX_d#B&##(*^xWT*ItO@Zc;}%yl(E5cxET!+?C0}{ zZvvNQY@NKN-wG1N<$o)s^S?@2c?DS_mKA*I3!p)?ik9VIi-|6z?iO2U0QO<9gJE9F z3yT75#&ugbHi<<$F4Nu2^~N34_Y)N(%dr7gOf(@w^nW_XtTikH4>&OrrX7PU%>I!xgTvDJTa$|laWEPhv4d*aqEP(W*6@kuq z>Pi?o6?3FqRTT3eDCiVUq}ZtldCWTC0DBVOsYZ&bmMRLmqZcl~1563!_5*qLfERlY zAX|RaSU7p|fvPHDy{#-3%XgVFb6$rLvveoE(Dk8g_M?+I0gD?W>ercs_GV-wx-V~> z72o!qgSBt%puZ+2jH~EFJsa`36gPcTFE+VUkfr-A_zTHKDC_T57q&B<3cr23%x|Y3 z)K(XF!`I9^PlN(xK(ANL%%Xte%|d>ZHmrq|vmt0(3e1?C()zrnJ8~vLcQqs=v}_fI zj8Ir}8jGm1&Bhn?0TcLH$I5*N*AvNC)yCl6O3{-4FQJw|gcf{F#P?6&*mRW@ua#?UVuo?C3FNDLtB)gV6& z8y@TZUGSZvt_s+g>K%`Rb8Q_L5cbUm4={g;gc!HiM)BH9W%U5S3E|y7OM-3E=>1Gt zP~Db#6{k;|&zvL0E+L{YjVS3+I1cyO7$3=|v37CR4i$m^wA-aZJJ(@~bJDj8FQz;>aBb}g4@gE99z4MYg0G}G9$1iTCN z+>DX9q9633|1NMaf|{1e9cZk)(_Ji}UW=2v0vIO>d-!Z?k>C+`1yj!)OYsT{yP~Bq zTuu^g^;Ik)Sy7;mzI{k&I%HQ=N3YuT+eQf8U&#aO)?nKugbdm3@DAK1gDJO)#og0u zNQ}H@dkvJopK#5a#OH32hSD;oX-ay5JBK6U5>3QZj!S?bsOvFm5`s5rHrPmUM%Z*c z(jPeM--{*0DA-)`C@3kOG@)n@)#H#1e}P;D2-)U#0Vb6)U=xgnEP>5tOJSCnuz(CZ zrZ;SiV7=`qaZYjNQb|)Rux#*Po!KT}oQ%k%bG@lM3;p2|A7D5SU7!?ojoo3LfTU88+|s;z2|OP-4Z}Zo&Y%@u0Ckz*nBTt#|2f@H_huaVIusLz-~m;GBC`_ zH|%4IqoK_y){@BsMa>B^$0vN-FpDqs*~>^8%GG_wm#>Sv5XJOCH*Plyu^Ts-CyMD; z3T!{|mzZ3-I-S38l!u)Hl6Z@TL_s}4Hxxd|s2s8g>3kz4(E&%wjS&h1Aevjr3>U^9 z>~<)mdh(JvcIJt%RcQV%3vV`pFS#T;%dU*Ilr^6PBOW@X>|85 zBWA~>0%b#V>XD&~0|@gpe!kzjiI>Qhk_(YJ()cw|qZlv=et}Oz3aTU*N9>!LI7$;# z7|BZL>8P;y%b+yU+i#1Fp+4<7eNkn--wQpRchma2rV`gjTlk}EEc$4^zbtaisoT*$ z=VLBANIE+ZqPd>y5>zd_AhHiihgWauPwJ3$=Y&zo%4EXPyVP$SeFk{~JXZec97?v*Bv0stHvLl$vB?(F{bl6tSPhCUYVF2(+A)_$jK zK(`d%8^sHAzpTh17DYN135FDhxN9i%*TPLgK9Fl}?$__+8X)#dV!DJefV`G2qBC18 z+)1L`QBW#QNqn?*i28U%b;ZD)vcgc*eqL7bKIZnwPbZXw;Y&Z-ZqxFaX`MYJ2UZkx zeP91X=RsUug{!TpltTZT*}D2kAGyZ7k%GrpcvvKZ=#Nv-BGoIQfJp`%MUXG)cT%E( zChw+aTWk#dA6gwjBMR*Vs+r3lxcT-B#pX`oSD^#{!t9NJdkQz;&*5FaocfS#YzlVu zCcKEu^4Z-oH0lfvyFoTMal=sGiGhyqjA-OPU1rKniuT;uu~o9NT#{{e>Fc`g3>Elh z6S^<|7-#GoOCJ%5-|3?}0Fo();OA^~yips%C~>Nw)Xb^6d+4K{lZzBiA%-2xNyqKBY5!5& z0^o>TnEGw?Y7CBsET(w4ViDNyP;J1F5>iBlf!Sd?Clujz**d2mVIg#u^mJD-7`I=F zje_olaI@8{vE5#16MP#vGgWmuElo*b4f7NsqQgETm=*QyOM%ucXwh|10F`fN=|OZ+ zv*ONF8D~ctBc>$_f3x4D^Kx(@dJg7do>z$2Wv%y{=u0vu(uP*Ta4m;tDjyyN>_H~x zvd2|(+^B1Ag8Tns>@9=h?6x&p++7=Ycc*c82=2i>xVyW%26uu(2oQq11_+kmZXtMp z0J(4Py}x_TK6QWG>QvEGQEBK})@zPA9@6AWRz6gyc@D$J>2uok`Qz);R-J080OK1@ z^(`7+w`>{f8CF=Sz~YfX{`8%onIRKU>)F{j1$fJTVo-sS%_C_Ts*=3*4M}brO}VDi zc!g1l3J$7xtswpW>i6d?L1LL&Wk0(Y_a5!l1>KzZ%DZOc5lQZocw=Hn&g zDgKzE-^rkMr>7pcL)2yyd2aKgKTUmXE@ifC-9+iTKYhcA>Pv}UwJ^^ zz_Is=!yNX;cvK}NI#MF5fqMhd1cS9M>zk#ZO&MEx?V+!)+VPq_nL-E@@$#6#T4{om z1Y`3yr+w9xdflXXVAe&C0a!&{NfuZ6Q{P8)*bl1_mYwX(p7BFxEAO(eitq9Z7s7^e zRnOK>5=^t-NceD9`&Up~qbJXC$w=#3qahXnB4AgMIpLo$xTPMF zkK7AwFa=DL;7+te|^%R`2LUfc;>DZ;W2jd0dv zm05)lPOnx>jPGL)JXY{o^7V$iR#-@SS>i6F+PVa~MRN0pvMQew@`Q|)i7-&trlzSj zU+6M9>7qIIT6L<<`r8cYqb=$4SNpJ!-X$k(^E+dlARlu?%0L!}wrf zSw0eGhpaRa{Q^YZHfWd1KK1*>xMOd5tkN+gHVNRq*T{)tQ#&v3aP+r`D6MIn2$m@* z)K1^Zn{gf5VlNE~9?J@e0(|rD^bN&MC}pE7CSxR;;%2rn>tK87>@A{DQf7o4JQ-Z7 z?0i9!z4>4Lah}^&Hws8sLl;^OP_yJiO_8Sl%e6FNe?F1uyzShRec&ULh)v$1F$iPj zFY-al=zaPs#W3hxT$qh4L>LxOcfxo>WaGuKNI{98z%qhP#q5CN=t}<>yfG-ThAo>K z_*D{7F@_^0`RRrG*9XdWs+f5kE%W|tybSJEMPFBS7`fVn%D_+&6LdXP=g>?Ha@nR^ zA84`|>HvoC80%TvNa{)W<2|;>k2eoMF}qG&s*$cE+97^Y)zEbGh3;FJvFWIHw_noD z_Ax96A^};}H|bf4X_+6C26#DSa~CM7m)qkn_^$NHZLQLTQK9OC{?#?|tPHno&JqGW zm)nd?4!*8PLGc5hwmzc35_0{hI=1L*7@O~ zP%)S8?&zOXVmi%xM8B!n{HRr6B5fxW;MI4iK#PS9ADxP`ILcxugN99pmBG}-yRA)0 zA~KQvImRgCsZeeD>IGF2g|wI61pOEtCp{>kVXw{-D%4f#Dp*+dWz*g%(fbgU=?|2V z8X2tA$QL|!`!CI<`X{*&IwrwwP8fGsub=pIjD_Xa!kXeyS`+Sh*9ZYC zNGiQ0K_(tKo%IUa1}7E-4(Y)8Z<=7)0j=|)KWgl}SSlC?+j-gS;@+$7TIr6}DefO^ zCpru~ETQE(y`yX|?eJf$i@q3|>!o)XQi|sl*~PC*RTONvS6^wQBe(K_YxK&yf9M{7 z6o5hlx&>fTSMvD*PUn9f_1GX+=@^cgKQNh1jh!#^GrP_go-FZiMjNqj-evp)*66cv ze#IkwH}cLo!|}gf|DPX#eARE`a-HW3@OSe5zck1H{{9^-LVmdA{AO!Gn4kO8;d7a! zF`9%k5)ZRL1_wg3bvR)Ac?O~mQ+`u1()^wf{c`V-ZO_{nzz+<)z1mcE_u$b(S1oN~zsUleP>s{n1L`AlXU8kk21GjtRFURtj zq|=uU6c7D~^+|D=N-GfN!(KdS%T4f7x9$H>J7#PH{lqofm$f{`g6tu+raKwM&sK-S zl^51ogQiICc2?6*z%Ahh@QL6PP{&vJ?O`J~C56E6QQcrZ)Zc1u22|p14aO>u*+p1Q z0E?2zZ5fNVtMNWHA^7&IbgkI$t&qQ!xt0-Y|6#uVO@0AmqmHZDBJi|bKVTb{!uR8c zw?84ypy6oNzqn_tJurHwble&2fsQN~=3(QHK#Bi*o|JgCmihJ`3?hGXj&6gw89dkJ z@4ml5O#YDi^&LbLW&bANdwj?nbD*La0NjgGRRF@3$4SlU_JRKyK>Au-{afn-rDKoE z7F3!1Z+`II{cIAw$v1@`iy}1XJSqy<1W=YOpsNcYKt6K>?x3o8{|3B4JtP*VQzrMV z5Tg)@OV5>)vqu48?`2f!x9yK^DFN#dL2n8ns0MQ}fNET^%O<;?tpW;lmw(U|%DBR3 z0JeCc7wDP*JSa|_TbI!S`)7S~K^GnXt_6`1xgE;vLIhDiT|xp1En4r~0m>)~AA(nG zo~Zd;pFG5f>{E$#>k`cHquq!CuoDoEdiL(^nV}C*5w9G>rVk2wS}g7bh#ge>)4l^X z7p8!m<*(bH)@(>hAVu*0tTQ$tuiY!YCcmWGpsNJaee_@er6DYOd!(`a4A3$qNWsp& zLnLD7=9qt;R{F>*z4m1aCrQ4Y0E||}_XzS^%9G;P+v>vifK16pi6vK?ne(U=GpHsE zc?H6GJpzB1>S1bF?ec3jF)W5!j-bc|@YY;-6tg_kEN+00-VbSQa{)dLK;=b!qILp7 zSNd4+XH%8gPXhtkBzQvEq)Y)`Pc{?X&&%Lc9MGe|a#KXE=F;xNVv4xty!CFe6_#|$ zGJig|6&KzPh4s9xw-r4D-mX6o>Z7$ug+{(Hy%d{s5yDo{1Wm#PfmV+IY|W95Djr2q z@!n!x!9o+@af%@|lY6NKPbB?PxB8Gy!C&f?TLksFmI z^~7_sfy^7AUe4jceFcG*_7L(3z)w1hATi)K^Z>1St-PI&!C)@A1;j{gt~@Wq=JI!m zmr|(v0rM@=ZA}V^1)Z9oZXTi!CK!s>?H9;N5V9#?vtMAzFQJaK*Fj#>Tx0shApmq> zL0dQ$Hq>SSWe`8*jDS(1^A4mhgT}h-9}G|u2jd{WT;c|vkw!lVbQMd3slI3ed$i3G z*I$-mO9j>P6|NHEf7x?v$8e6=0vNU{9MqGVk4_+cbF@_7qstViz`j2lDN_j8`6dzk z<`L-p{j;O$5CSQn2PoVt`gQ`A3g(w#1~ConI@`qsi}BLBR53vRey~@>#9>E@L<{n) z-2V$93Zau+*<%D7ZZUj1BY1ztQKL=jH2LeKIN!$RKeBK~T}Wepj5U$k3+r{a(NSz_`i>{(Cce@CbMcZ8wqISf@zBHIc)AbynuxSv(Bagh zgts1Nt2lPBTVRTXhVZNa30U84?5TSuEET^ynDzwVZk%oM^CT~jyhbr&{*n$Lkf>AJ z!gIpHGQwVe!1Jp78i(@r7=Qd0g|-KeHD|0sJ1Y(m4lm$CL&xNWp~aHJmagmEhA4Uu(sHLP;0{Oj6SA;Aa1DR0eOOt(+9)- z77e!YiAkf}T<(`GxI}Dn+3d87cjix(V^%_y^(UcE69DZ6=+<%%C~t&iC~tDWT<$NH zCvK*|QtNEZxr#uFy7^hN8lMks8jSF2zfe3Re$C^M-YRe%W<59K4V*MvhDV#3bbm^# zfGaKc&p&Mie~)&caUVbGyablIfU9x4dYGj+d0OO{B)w$nQY?UWQ)vF<9E_$_} zx-OHFkcay>L=fkmYmO^LA1*-=aNd%31N<$TGctJ0ztZ3ZmF!7-D4DB$&uJJ2B#bGckN+s)=tBwDy%C^;Rf9qJbJ__1!Sy_&iP_qY{$RL}>dzYvS zUmf+ipPhTj?Xcuv`qYUpN+u##3R|VO0)3B3@~X~WAB;5MR_%^qDLtu4lks-!m^N6) zt(?kDultfp&Io(9;R$kkEnILq9(gC?p6<-Q+SE-HO3L%XD8M1g6-kWe_bFlYlb_)&Z-ayKnIKswHNPYZOCh zhlm%>!#C>r4W{b%yJ_8vG}UY;>c8adAK&5T*&o7nm8)InTkWz+C#4Hkb08$WLrbb? zX|W7|nCVEcYC=V+_aq(D2x$$~%v!~+LPKW*PQXRk0IIzGZ7(TB%XPTh5I;i-@%UAp z*;y5Vlw=VhZor2ean`N{ZZK@4p?2v~oGzoIO3mZ-=%fBwo~9W1^tC=mZ2+6WcOoD@Q-&}U-13w|`S7e6wB{r@YxGbMg06IN342-T}{^@8<$^h$? zE%EY|TN}=2b{to>rz-CiNtq~V!7Ywz4vPor%Xy8)a4IUXxa%)zj}Y1#q(E06vfk%O zypIW^XNP9*V?hoMp?1tMCs=c-5&cfZc5kl&L~ z+x~-+#yVUK7bm(%^Nvc~;DTjWcgQ+bz<8z6P0It))L=QH!r7Gyb=Q z+b`R2Xi&wt)lBoI7*iUTGUpa2%d#l*hObLt@AN6;e2c~~+;`}3B#E%Mo|yiW1(rHd-Au{o z_%100{{6GXaVC?$N$@x+`T)?LM!hP1cWE*nCTyeqBlfg;;RN+);;12~Kdw`^anNfL z4r3W~IH+M;I9=pDuH(zDbD{@_Ya=IB>}hA;rEix@j%HBiJl&CRV4ag)iG5x_V7GN- zb=geNi<9RyUGbxA>+GlO7{`0bjJ^`E+HIGzSqZfJ8_{+jGQugLcoa>1pQ{WwJ6ZT+ zm)$U7XHnOsI6hm~TcD{gkJWye_HbR+_V*OiRnn`o+kZM*I~}KoS0TlxA5NQL=4r73 z7-r65Q0GG+M#8zd+56s3kV4>1!Io8pD#mIrK0h^S#2PLG+k-LL_4Ihx*ON)YZi>Oc zZ&U~w3rxl>+qdu#zB=~U%C`lr0{%1w==QV9F2nfkqWWO`Q0NDmPgPOKkB4q{Vdx)^ zGc}}AP+*m%zv4LSl-=%*@KYO_&HuvSDecKyPf zhpZIYrgXThc5R?-&{WD^8U{ekX-wO`ltSB%Ld)OOKY+*FPS-!yi;~3n7fw-$tjZh5 zFwUo9kVO8-NidtNtVs@{GF`=EGb))~;t|_8kOR3Drr{=Mc*c6=dN%DYb>Yc=s10_? z0C2+h(ev3{eAPTf)ueTCp}$-iR;aRg?8_!DBqg@SZb*VXSd4?P#HrlKU? zV=B_~HdA6szE#6w2=(W5l%PMj23jdm4Ks;JPUUS;0-oEJO+$ii*;c!1ID5+77zc4- z6q<&a_{>_LT)PfR#@gnxjFNHok(|4-G^#Ai-1pfu#`-(SsN5Q`C~&mE-Crz z{guYP)So-A`_H1_WZe;+O(vHJ=;8V#abqFsGyb%8TcaaAnUoRY8*?HW6IdIt&?-i4 zqe*!cb>wu3VQl}dBNl+>b zeTrzL=er94(f9ZNRqFHLGajSLAG{wgJH8wZ5x&-l2UV=P!(qfxAwSGW2kqm9!|Emp zOY53_%1n-KoE#$OVv9e!0c)}*2mAS19pvHC6>#5hGFrrv-Y$dDa0fP{T7-D$%23_k z<{4ClUF%QMa!S|{G1BgPmIX6Wz>uWCN~+!=Off0Q6YiPVWhUb-2)TaW6EW)#;6)4U zFzpHF15Qkca<7hNu}h<%5yg7Rk!6QR#JkgFum3Wq20`Sb#0> zQ`kc40?>A@fTb|b+!@1SauqLAzb;(pK;9xl+Yb3yoBuT$>B~+q&eAE(NyO<#7FI~jT~>Jr~~F)Z+PDPu`eJ!PAW{okY-EDi;Yp4)A8c_|X^E0D_L zf_%6`-Ul++3M~xXYzk+RHyjo4;@j6`C_WC{3m=i=y{HkI1xBrEd3>cd?mIrwxku)z ziZb8V$wVC}vFfqmlj2dAxJ_WdBd#ghqs-yw#w(3$RmQ!bfd?JI5^17HN69CitJ-kH z6G_i<{znK00{r zPbaKR1njIv;SYFGMsK1fMulw@gsW5}HtHsuYbl#sGG@WdUM7#0B9(bce$0d||CEJ0 zjQBFf1Xe9Ujr(vZyVgSo1Di!w9qw!XTXc=)*23gNd1@)wn|?PA7D~NkkGSW_r$jI7 zJ^7&7T;0~3vv#_e&kCO-wTcxCwEWkz-Mo#&-NG051e>N7jSZ`u?D%$t5-Y64WG#}7 z|7xK-k}BEi|HalOZ#>|7L8;RT)j`2<0Z*7A+W%}ZH*4gQZ~_;H%sPfc6T&)gLR78# zE@)ZbSG%G*EWTRq_u64G7>IaZY?riYpCT0ts-9yUM%QWj;IddEg;t`>tgb5EZ7TMdu#9yE^+2&io)DPpB3KWG^VltiT{upp}E9B-hdQl zysw1B_NAK>%g0qvnr>2DKKCHa$CWcx)9w2_*XL`L2K~rn$1UA)lE9cP!vkhUvJvY5 z8TH|LEe~r$_Cl!%x!*fkzMQVuFDb7< zjcy3@FY%JKUh-U!H|E}T3im|&XiH)%SwM-!WicMiQODqsfypd#mki62!zPm#o87|< z53H9~JitYhPudOZs(9fhpI*`fiW4&4_gJ-yrL`_TWoxuqNlrzlT_BNhpPhv~^fNOS zn!k96MHO4MoYwH83gO+Yh5pfU2BX8?q=bch4xcN)h{h;FM9f>erw`Z;9{jr|*fJY@ zlA@JF+kUzaCNKN6cDss$J`p=G{H7l<^E z6xPgr4K(CKj1H(`LP{WAL4M?S&%TDY4r#u(NXeuSNV;uD6{cK?*|9;t|4=i58J_

~EN=+ba!CHHo^Z zO^&u;$5OGo`2G8xaW0Tiu$6}R;tcVyY}4FDf@V|Pwzo78|#{ZLrU12Pl$Xdhl|S?SC5Q?#-EVKTig z38&pD;!j0WJ37m@2CDgssir$+UBA~zT4#sjtW@KkvYFngWs!HiRuiwvWUBFz--|ob z??~3}$Y_5!pX9Mgul2XjB0)HWgY_UTlWL1a=_HPj4;4LpAgr404Euq#KcOmmv2VLd z)wv+qPhU&ZgYTQ*cbg^oLr`RST|r4tYSLP&VxxZ&H+1h+JR6QHOh|M86n$D0CzK5~ zEQUFGi<6TyNO1!6>?Y@mm@*^`D1ns=oj|mVZ)16A(OgT+R;X99uvx6D_34UhwkMUN z!vqkLIyWd!&EV`^sg$aeQB(--X#aLjw!m@IIpI~M`qd&T$MyxsRr2{1)9GW9G8hbCfz!totPwqTwLKLDm ziggO5ykX=42B!&zGmVo=g~)n-_CRDp3UP!R5I#UH*&*4yM8KFJ?XWbb!5D0_1-S-+ z2)ZQrdB<`a{9e-8HDB^KG}c_RJ1|;|(cYNyp*vAtw5kqU^A~Zzx5uEFXB?E48Ri zkfh>4(gJNhA@7<7|BILnKf@*RbO>0C#WfwE)dg}Sd2tzLM{@4F_EJUML2MqfoZH*% zd|B-a{3qjSvFwVXX4@xH9yrl-M)h1R~W_!F14{{vatY z_wb#t(sa|P17fFKH{=V~T$&?6cAmkjYj&!o#Oiv|dB!v?lar-B1XQ~AC?}Mxvr7K$ z&VaG#H*H%{!Eqji0iBv!5yQZ!binv!M;#9X{^`@giPdDYo?IU2L%K&IZxSYOWu0U+ zcd*TJ)>cnX7OfbgaCqdO!Q)1~rc~BfG=;9S{GPdRk7?zvHf%xcLhb0@ut2&aH{6nm z0jJqo9UU=(F6zHTpfm|5r9r?%4??60Gn*qz>&;dQ@0Xa2vTh_ri&*tckG3p;rl66H zk|b!&h=3hkXBjz;(TUloA?59@2#t43rqnWT^L23Ds6avPQt)`jt$Gi&7*fc6qLmD^ zmJqyUc7l<{N=^@#|1h2B2hwNP)eRz|LWuqH{i9WFJNp>DX7WVlP#;xk!+V8VDc%$z zkeCVsZNI)RZ|vZRDnfz&;DjfJnEQ2FN32m&KZUPuBp+`DC$%4NwE?Zj5oWxGD~?Q z8xwled>TXLnI7=8&Y;qLd=8Hpk7ASAdXQ&7H~zw$dGg)`tw{{6PG_@@Q3YfOHrBWA zJx-ILrWy~ITvdhUCDv}l%RG!7PhEdP!B9PxWE4CfZUt7x;~Ji{R}?~nCaV ziT1dZW?dYv028plBvz}|!c+0#$cZTM&4F<(jtJI-hZnKf&KP~{A_R^j=^197Q zJd*+}>I5>O&WEf@7T?Wfs`u##%x;}`L96zU(;vhe=?fZAJmvP7(dLfOc9&$3t}b;o zJfa0wPs+j-rMa7XW=j^EMH>YX9;mZ&+%orSIe>UUIV@vc{9U5uf>6$-b4a~F_)Rn|wHzlGW*M^OL=yr#j zIOuM+Uukl!JR()7k8X(aFQasb?fHWn%1A>l4GZzfDa^x)J1a3s2X+@G66T(i#qN_# z(0D*kA(G~J@)8ZWg{30d~L73zNBEl|KRJ9fxB*xPK` zSxnr~x!5_K6Z&lDa^CJ(U@4F)po7F#+s#TjolYS#SqEZoepRp8&hNBGV8?MoG0F&`yOLWoW67{ z@Ik@ek>tp!xjr=LsS_ckh(2 zM#p5?HBfii7BN;(7wopBi|5y6P%V~LPXL@1PP9$7_q1U@lz2wp&6&?^?|D*K4@B247mKK*nDgm zS|ulvrW}}?V2}9+KbtCMFELbx5Gv`LE87s1A>mYF*i(}7%Ycg4JA9%zvZ$#q31(6x z><&5=xpqt|#}x=3%(?CEa@N(crF-7r{a6vt8;QEJE@jJ*5AtbY)G898Bj5QObg8T~ zE!O4eUXT?ybc_&Kc3Oy5f8CXL&K>GJQ~7`J__q z{&W5gId>87db?=!6jQHlYF z*ACL6#&!&OOh|BaFGg_{S0`Sa1tUT~4Tm_&2gblxOLp=XZ265hr1pJ7!o)Gu5|Q|b zbxF{d4;M{Srmci4Ytkj;OpB>r$~-jO;6%JUaj3-$?c#GeHw zEdvLF)~E72iUoa_F1vA6JKc7~H?{xUZ8cY-0!X36UxC^gIT6%F^lHE+rd0qN| zk3flG383}&btd1N{~OzQo%?HEYF$T0X!U&zOV7a{onVT}t?=cL#v>c1DtVnxS{;w} zT{z7jvK*#dcteGA8d$ZnN@_PdqNlX3r+kNeG5-;2*%VIciqn7H7a+ca!sE3-h%x)s zb2Ga~xwtMc+EByY*Sfbp3-x1oqcyk+xV5V1q}4DeEH8Nnz2YhWZyE0+nox zPw4LdpVE{5Nz48j;!MKE)@$gGZslO0#w$44m3hnV(>2v8#MWnSHVaJLoWxo1^7qUC zerx{u4mfYE7pi#d>@^V@yAF(Vd04vfXe>G>%BW2l%Yy94Nk1H0e=PlP9_4??6v(s0 zQHh&vP*P6zkN-8i|G^gi`Pv*0fm8l}{9pZb8m^sAe}L-xB~axboxjn~Ux(D|KcBZI zaKy8rKa4~=qypC>KOi-}5xDPWd}#FuXq{%ybR2#YV*o4ys&D%Ihu}LPIamQxe13xo zsi{LO*-0Vm>KlH}4nST_%$Sb;+ee;0;*}nkPaEDBvL0!FPV?PKoc_<7-0yAyaq@~? z-UFJFeIS2IxkP%9umGqE|6*tGRug-)qFBDiZap~nv@(tS1jILySZgqhQN9s6slW`} zhP5utb_VHJf-bMxfF!I^NDDa=5rf*m%1H{KUTK>X6L#nYQoC+yy-fuRR$Wh+)djn2 zKVWQ;9ag`0pBzA>5V^)pnJ2)~Qr=J{+kVcOeRfzja`_^S0p16gmMx#HAeKc>6=UC> zsGmQM@bNzWOMLV{I1(Spzfzz8(FsHW<8J+h0b(~rNiJC5r)TL!Dtg{$=z~sLr(BIS2M{;X|9;e?gL38J5u1_%fQ&9UD2{EQC@V;H1Wf&@oGYNx zb*<*pz3A2GQ3%0I$R^~WP^&Sse~@e(%K<4xi{rioq?drb#1$yW()IuT@++ttP^I*y zg6Dhz)CQnJ0s?2=nUo&`NY0mbQ+u%E6eJpFAmvF~C@J5Hy#DbmfS>?AmG8h0yo90X zIn2{h_5mJ>SchQ0im69{=wJb)4}DL7vqaQu4pIC;GB1$#30!w@38Rqs3~4k6Bz84t zCcnG?rD*{aZgLvW?)#e_bD|JykW1?XefSSmNR}RAU;W|&IxI_g7?3C8QZ{rEcqII* z`>YY+63}W?izjNm5nbXB9Kr%su=XOn?9+WYwI) z2@E3{MYps+B&!d+Ko@=6;)6O$wUJ|_zzIxi8-ja$cDV=Ayqkayb`AgU_SJsveUL_c zqG+dr25xi5x(#5G0H2+`Xfh^osJO?+08z`muXt}X4{8n;QcKzngQVhWPre9{54PMS z*vq<*01c`G$45!xmep`KIg-Q)otDi4_JrCToDXtHJIG4Sr6>U6@%tdN$tHT|Enp{- z1jE;5HZcj6!N|R3Lr7t)AP4fkVC=ZANaK8H{{p;`{b6VOajs?Z#27H&r<_HuP-HOS zA7{#dl9Mo2UEFuoK&co7o%#Q7o=&*l>p@;%D zUoR&+_5D#^k-{%4=fwX$Llz}+`&8y;bD`4-^T}DOo6l}+MmAr^Cb!^3tXWAz#S6wDqK0CHf=`D1`Sr4b=0M?*VX%B8A$zL`ou9T>c}d2>Nf^ zONiX;%f2~g0LYOuAiSTMyt1MeSloh*`D?_F`54tR(=W=}U-;4LSrHgHSQQ2tyeh2U zl&%q_1h8mdGH_2d`qU^>0LmJThV_ovI3tm)&0T^|B_|mWGDhhZ7{|W=%aAvBl(=K} zNfl0PL;z`K8Th`F)IS@zZHdMqkHBM0Vm}`{`+N3=sa0aWB$jv=7}MRP1sHUt%t^hI zrk_x2CHPYF^l`y*HNqrX(hg*mElo+MhAZ9KKkJjlGigaQ6DKvq>8XE)FEJ){upgos zp6Ue$nIP+yC3n{=a+GWNzaK9ZyNt#7ywB%;EW8iL#BMs{I8^QfrMhp1L5Xu=9tJ1- z%D?U^eF70_VZ=hLUIrPj3ii8Wr`HQ7iDfX|#2m@N#(Yh`AP7qb+x&laIVjln{=XG% ze;)&eX~?-?4MF$Koew|lNX8`Hn}b3J#I-bkC+I3C*BDhLkp3KIvYnwqZbjZ1pRop< z1@%v;l6pr=w*kfarCSvTfRjxe;ZS1$TOTQzLYO>75;b<$(Z_w2lj1TIk+S(=qedc; z0X7imq)8Zq#62n@Uta0Ix^Jm_UUVHz4oB^ch^DiY%|b?R3Iy>Gy*ze|+s4ru+jYGbEzHRH|0IeFd~&jQO9N@cMxU zb1=CE%esg?8~G2QI5ue!YC0-M;XR29}n>Bk?wiDD>0K#79EhA#`b&3a!>b>ce}WQJBv z?J{3b&w*7+s8YL92_Rw% z0XgLCXL#0e|AnuV@n^;$YJCVh(7Hp zdJBu4D%t2l=k7lP+( zEg9DyFcy1L2lm1#c^+{~2*Mj@Kb&AXT0{kBWG`|yKZA0|u(IOcp(hv%dO@sbg!-lQ zMVlP2MQB3@Bq0&y2jH zgH-feP^a3lBVPni(Rdj|NB)7|1Xx3!;CuxMlX%ljYUpfUUkE(+6|wr z7bU%JoE7GjM~1_ZSNQB~^FpuX(unage;B=^_*4jNs)0^^tbU90Ez~#&h#hrfR;08e zGlfmbBveS1xd%c><#+f2S=qYpUJ^P8#@?l^p^K-}2^eN@4`Da)9r)kqT#@Zc1tDHH z{5)Q!=ekMwupO^J0{Zl0)IED;l4ri2i--sYITgP_?7T)4$Lm)Ci zXsSsl7(6JEyqzf_oRpq(b{-2w;9qInjssp(^8pHS5V@gQ(ioJfl=Cet-5EK4_X=ivf}X=`iw((j}53o*D$N3Kq^t#7rRiePMiG+}?-fa^9h$V$n z|KqxHPq-DNZl72mn^@nUtQ1Y9-2B_73T_mll4_r{V%S-SY@^5u&)m(2B4W*ZA`4pg zj3i2Xhdj1*YJ1&SI12m%(JCAo)E^ljVxqN{JlArXB1fEOSc7J|UO+K z8XeXjDvYfpn*TFe`jj%pED^I`BnqTBULkBo^WC;MND6G%;DA#XWA{M{QQ@&B>yjnG zbQ*pIK~{wRnHYInN@a3VQs-QkPu9p4dR&tkIh^ZL!Uy96=CnK`ee5*B+h~KnbcvkF zxCm)NdvQ(luQJVvRH_G`EZb-}EpO;!%u_tZcbz2t1vt_uf#@qsD3NdOw6I56cVngO zEHdHCp)sgiC_==u7SxiidYk1bcf=&^AHLeBr|_U*cAP}uNYP2Y`8h7J@b)ya$B4m% zF)RCn1mj0GPPLF%iCENvN!bJ0T($khxLEV)WM2>@hjV=FKl`)(6j$ih;r#T~%n41; zH+uyNpch+Xb085{Qk7r!aUFBsL(ljz$-#MzWbOTrat~t-eKA43r(?mKpLZ*!TU`^2 z8Ci{sY4WzaJ24dO;R|aFDofym6Lh&jI)lb79L7^#w?K~&g}T{KyMX8|&2q4!^X;WG z#-c*M4`b}P_Y3$HTE)54Joxx`5%}BgMU1-x338LfX1+1F)pI>l2ed%A4WiA0FGbzi zDLYQT!+HDYz=?U<9O8OG4jxNus=_Og)1{Py9#7#j4XZInz|Tzq3rT?WgrwP!6?phN zsDI7!!AvXnC@D{{&o2PJPSoLkW-ZsuHgsxy@W8J|0B~M!Z^fw6?e6nvZfNX(n6?!w z&m741O4~x@&-onF$rpB8nN3@IhpXj9ob@N-B;sw^dz`n)a2{6WhtuK1X17_d;`g&K zCUv_nL&~v*`k`jf{N7Xu7SQw^s~hq9t8s<;jAr2tvGLEG{euq$NbzN>p-9Zu;ja|F zcU~g4zt=r}Uu=F3_~j$sJ)Ot%_};_EX47xW2d85o;TtW!(F)SG=coXe??EdHy^28u zJP*Z@H2bK+kA8D1)t(u55y?iVMb<;!!~?sxE!h}*ZJ_wv-g z_lLw1$0Ea&UhHYaE9DuTeSe}o-V!e1)1#n^92ZnJvgiZ;_DcJy>%o z=aOu%a+U5_pbH7@pff4$VEbxRG*1ZI1cb&_FjR>ai#XZh6dE6_^AEX1ce^f83Fd%3 zSOHF2Ytj;DbdC&#Sn#{qVLUZCx(&NsSMbvb15D{NaVgHP>Jb`7bH>SbBp10yim=x(;*J^FiCss80Omz?CKd-cL?i<{%Hl70cD6*T;5 zw&&U}dQY(RTRi7oDy2iq9jqKtVh5`!=s3&;(m{hiNR7rRqr~s;*79Jv1^ZL2cnhNN zjW`r|9gCuaMdxPkeFkECY2Q#>qs=(_n}<4X4m6+yB}kMaWwwOU$e2l43=^=T4L%r~ zM-qz$-0vSD_%WOw(C($6=TV2`>DT9?D>wF#o7O5h6=UVO)%ZEft~G_!c+SvnzGKpa z5)B;Q!JFmh3&=WG{66cNR;SfM4HX8b1;01spB@KK!mT3t9;AtIZ@cQp2_5qj=Q8K) z4_gs8xmm1icbFxTv8!9oJQ-$Y_H?w7Hrj62`5Tf6Boe->&pIjQ%8`9;{F>*sVK8|* zu8e3DRi?4N$6^JB2JocjKOMuV2qfvy;dwI%c48wlECzYaJ2hXFBnYz=bB9dxTDS}( zpN1-M(ebq0C96W0czML}gs2T#O{(n4aY(D)Au+XPW2IQ7Pm19T67#P=%rHQU`<%a) zo`$<%PL4WQ)rfZn(9|^H#Q-$NU)i^_-&8=8f&>@-)nv&3D~FguqMpC4ZL#G*o_ zLG?iUWhTj79+n)z)B{e@QkEN*RhP-MH&$*ZLM@=Xt{towTwIBDcXx|F$Q=d5<#np3@wr&+Iur z&okoI9C2@rWvL0yGc7%@jA~D!e;@@UUZFzws4Cr-W6zjD9z!vK->0%D;y^{iUFdsX z&o!-;hf*#yJT*ijOWf=C_4ymFw*mjpqjcm(LJRZRsw)t@j0Bua$uh24*X(_7$TXUW zbvzVnM35(2Dd$QY1>^E zE0r3QJ1?|&cRTyK`*VUgBSC-Q422Ad{+8<(Uj@QvEF>u*;+EZCM!wR zYShtAi%EQ0rTo!LVmRTas6X9tK+Mt=>J%b_pJ4qc4`biqRW4^xl=->!!<`Hwxu_@s z1FHg7@C_~|+#tS<9t<`k4m52#Bk!8y&<8Wl(bk*>a&P5_1e-RPDH9d2Z$O%SNl93a z>Kjtz%cJS-NxYa$yS!HG$T_n2vz%XBKlOW~W6(SlSY*UFp$B7qjiZc~5+r~dKw*^r zV2UFHJH3>v9~#Sx7p0ax14Co5@SBC`ZH4|mlA{3jN!7?Ww%}Q0?kx-kj^3)szA>Yg zE30w^v^Nn2-`8;S!6abhd$pi++(RXqiu0%Y>L3pk5L{V<<{df zX~F>`!+{POq3Wv+ff977qO5z4^gBEcI~}G+g~{{!r94`mK%R0Nb-B7(Ou?H-g&z-G z4D>li!X1dK^ybQ;=;A+w&E3`fVl=X7vHEpcwL}d0M9&nk^=O7qZ4%(kr-dFXV=yVtxH99 zXsPUhq)kgU@J&kMmC_d>7XF}0R(=qF4Ga}Ld8m4@bk7VjQU{Xc{Xl-q2y&9V=~UuN z54#*;HVf+)33Jq3UzMKP_OHs#%TILgz`A6P#^vcV+T>YMx+$?{`Uv_;z)p0BE1^B` zW4wSUd3YHu7#*p(^PoVS2rg}$kx2egS$-j`heq$+T7YD1zbX2{o8dGCmgYfgn;{}O zEAtseg1d-0^!+RLZwQWb;0kP)!&0|vF@wlcmhg6y9$CmnuIJrq~dA@#Za z9x}mNu9o2*-NTA8v7}B&j0nOsOn@Q&)cX1s*Tt*!ct9k@OYp69%>B#nEqNM3Wzj!$ z?Wa9D-g#A~+1x~hl~*nq7MFI3hR7{t3??50%_J@*ldA6=}#tzb;+lmdRn+i)cWwsB6Mk+q4pLeBrof$H+N=Zn=G)smgp%Q~0KA(} zH47tGt`+KOt($XK=`yRrI|X=|5>B{OgXLCCWo*{Q4_#U(EY>7>=vKj+G;I5X*;1(% z!!EL#x-aRK#DiW0E+%*j7WLW>ir2s@STc-!2}ARcB5*y;GiP-e);a8QB``ukw%0fo z3H^HO`*&uYCh?Q&llV{H%}9`(zc#IYs@7+Qp(%P?jy@F%iDLd|548*~>%R2)%38)g zUW0-d7jN$+*NjZlJbQPlduz4PCeQp~B^nrNN*J#Us8)BYvrQiaPLSPDCHZM@ewJ; z*&q%&xA&|O?=0_6WGF_@%`3F><|H!h%MQ)sGWc|9dhHBE4zB&W8k9D^iWeaYQ=o{8 z6UpaniP}TXWhlvvZ{oc-%w0`IJ3&yD8bNp=;{%8?OAG_kq@*&j;O;gs|u zXj~rA`?{8XVX+5QGHTEmOV|*dP_`PONT$#Pv!u74=Hw1tmfO50HSlV$KgNBcfvq#? zRl*8$pzQAik#iF-k9SCPD)(>Ug?M(|>pv`7I%3R8p(ZH(ttLCH<5al;{n03uDj&wR zB+c1ECKJRBi_XcQPi-}wI9|}sG|l(DX-2cP7!Eev6ppty3}>dukFYsSr~k6>ufq(I zHUo4|d_FE6^T<{P_>Y+n=R!2xoDXZCM*HydaBHq;Y?Q%=y$?TL+|2H0Z&y*8zWyS2 z7FM4gh7wsw7hz|qXG!4}RhFU+Xt+v5Ns9oR5gX>* z(#*PFV2_Sv3CCWi(Waz5P6Omk$1CWZmBFI7k!U*^D?wn~wa&bx%6j?{ax~NCfff;M zbCC54f6L|A=;m(DUytAqw})5F}NG8mCCVoL_2-$ih5uLxwVCHMW%v zJ{mme6F#y~N(7Nnkaphif67{0m_+juI z8QC`wIjH_}-bytTFCt^529FQ@1F<6FXkX;UCB+fb`~C0~&OoGQH1jbSQ$0rxy4*tg zTd7YnB``2in(!K6YZ<5p-$1Mq{OCS8xmZA{p0RbH?o^-nR8|!a95GQd1&CJoW6^-r z8*Hq9{|gJpk{E!FlKjgVR-xY(VWxValN*O}Q3;HFGmSw6lBm_vllP9Yf>grMRJ0OP z{|{Yn9Tj!A_7B4_#J~_kx5UuhNJ%%+NQn%MG$Wkty%a7GBA7Z@7~w`)Rm`9c6YsoM4xe_q&ZpNfYCiR`T;Eu+>RvU z7{!|WmV__-Q<{Z2Y%knSV>nhv_tr!rJ;X!BMOhFoXx_4g#7sM!Bq{H$vgJBGCezb> zHfrcL@}U(qw1Zy|j~uL)w&2|tbtV6Fq79qlNg&oUQMNwTP|1l>P-oUNPmgZn9J~Yb zSobLzvzzpuRD$zLmQHddV~dg&S%m}!`C8zQe>P1bFiB99H@d|FdFk;#pYeY_EH^fU z-$qzqLwcU@h5U#Ig=d}cAJtekcWHX=?qlVq^w%NT(K6N4+oJSp_~G+xyGPPL)MGF` zPA(ZSRHh<@9ZkGBuFIL1=4rgG)Dha8MaoL*izOC6xLaAE$aQW4bt<;2z2x67gAW8~ zmk-uzKT$q#y+CH|BAil^L`7G!w6JpV;z&!@|M;)q#|%87-cGS=__5RL&&yX=n$8jw z1&}P*8Ui6TpECZrB>dOc1qn#UQQp$iGlJK5{_iLKJGn6FQ<8rk$N%#+OCB30J@p$= zhr?!|K_GXz-^SP;!`IyK>lZkiyn1%1NdjL4cb&3dk2{>#5gH|a@KLH`#7a8w$o3rt z$*|zDe_hbOKO{K)xximJlwPNZAPX-eTW`3u=?`Wln6SLqds!~jcZ30sujgNG7RgBD zijTb7rr}nf6#q8H;KisA5*VsyB*=9Hn9Hxld(eCMWmh+$f4vr%AmrW)Mb3@I35p(^ z=rMY?t3c(or}w|!^aLUCAV7WK4z;jBUk1^VCKbnj4dwmhFeu~wgZTPqYzDG0@w*Fq zAeZ1hNNw+b(B?FI;vF4c-}Lk@@Iv?e*nP~Ws}-T0U09`9St|+24xW3x$ZLxHkGC@* zgAfxLB@iOODhd~eR_W&Zf`r*n5L?aHfW~w6Y zjVLnpBwNZ9Kp&6l;@~=+TtU9(zE+x)c*4nd0J-GIF)yj1JJ61DsF?ah0yXTNqIdnxb{I+N)u{6JFaJ(f() znR6QGCq5noE@Hh4K;b$8820+nAP$X%142L3fXLP6(Xaa}KSycqV4*%Yc9FOR0a}pt z2WYY$QH1B-{!-m$_v?!r`fXR)1OpE*LqYnxTT{<2NH@RPn$E6)iRHgC`XMwJMSw6O zn0kKeL&OMQW4qCZ=S8*8z0@G5j!S4%xq7mtQ@&#$)XKA`lH({A*Hq^6pzHlrs?4qln@(8-7g$bFa=L-}r*bHP%U_79g`BTOY3Rq60&HlP~!>IcX+? zXZO%&iMokwm+Sgb&{O(jvKIvRX~`*uw+CN+|M)7_rv2{gI)Edrvg!T^m4eHGnMam9 zJB`4)=9jEQ|CxU~_fBZg-IiW_MsLNuwc$x7{m@!&rC@{eckYGohz(*u) zj<#m>Prhl^CZga096M@Ml{siT9fd5%8qWWP5rcaGg~peG%O);cu0hYy zXOAV^b|>qYZTX_(p#lxThPhf zIh88w!0Bs`-iS*4^6XEsS^votUxQbCZB`i0ebS#k&x_?5UgP9!HauB&ENeFy9NIbk z_=@Mpg0cSW%0si!RmX6>>qy(F(MlmSV2ELib@;2B-qa%;tD`tIR8&GgKvNGTvakXy zSe`PgAL}nV=fWY5+bXu<-P@@*ey{s(j>PMJ#LyLIPj+=&Lkt7m#VAJ+=*!W@9s8}P zQmY16fEG&ZGE$Ok7i1WxIImQxyRR4}jNu)n`g~LNK-CRq-om+Fk}6Jx2D%#TY|$PN zZXuz$a8hV7FZoW8@BN_H)UmLo zL^K8WKdiCo4Te8`V%%EV26h3O+xHN`x$tp!q;N#HL&c*%zmufTP%~w^tv@Mz!gGzk zVHpJyeqW2Iqp2a+j>7WeeO&M0kqTwBXjw?tgh_vzh%}Q@D~W-jL!F$43l>M{7U_3& zb!GWEEAhtP+<9~$kIIeT2g?>;B%9p3q*PHoCHq@hUcJcB$#lM$b1*F(c08iNTvv7Q z7;Hl=BGJT&)fx@xPj&Tdbu4&Ra8*+C=<_veY_~u0T*Z5^JlV4Ph??lUZA-y{b34np zjnog0C@%(2UHzW>VADJo;LIJ#`x&iC;cbhwgXyw`vBg(^u%(~BaXdmXVjcQpC+g~f zd-k?Bz{?Ko+mflrSd1>6cZxy3j*9ki=OSVWHv^F!PwOs@o-L{Y6BwyXkAqj|-=n}F zfBSOvA1Y)7_pDg-XQlzveadFA=hiNQzaa$w@2s6fuqMn9%7`pB)hKjtAPwkqu&%N2!QjP-=& zJjPZcr4C{&xM`ttxh5QDgaeyM!9DCy1F|14^ zu+76Bjt&5u_>BUZ33l&yLy~IB0!f=$ZBxAUfvzs5=7IZ5El{kTE*HF*IqEOzhayo) zfxk-bJ{$ebfI=#SY-L1#>HRX$8~W=RlgTS#yQA@;fbCakl!_{A&9H8PI4tvv3cAN&t||!w8UmKas>g*r%qft#P7qV! zJVwG5w!vDCjEDqj?r&omp6Lw2$zNi_ir9rUOUJ{PciB_rBDSi99~KCQb_n*L8f8f# z3RoOalqynD2hfwU+Yui*Znl!0w$+CDar0tosoA9kRME=x;)HYsFF9DTb92_@oJ)xl zP6!+=#mI)*JQUUTooz`IDe$dTmm7~{Z&g;XvVSWR(xR1R+9i*>FTFRbF2m;4|IKWd zRx>VdSeVMfcap8_7$bj|1@ZH0Ne4NKn;jZ3unyYC92aDsjHr!7$Xrz%Vm)?A-)eJNw5N8#*o-M7XI83UyTI5Gb<8n48K;TA5jGnI5d zu2dyo0(*or5M}t$4erNdopM#2j#pPnCMy0w*!Gpalq8_4`?edFnXl?dG%^G&qgGdv z3$~R)EpjVRmpuqjM(;F&s;(Ax|+R!!CqFZAT(WHAjcqSLya*r{7KE<|S7rj$$IY z_e(vAHhhcMgwqvX2CZNS9CZo~YxeOMqIEF5p42XOs}iYll5!rVM^bSHPv`IBaRxo2 zMsIY9lp2xGu|J(_x-F|J2G5<5*Bx~5P=vQ1k1??4Dp6u)!i(V4KUVFsQ%<9rP`|XP zvSv6qmN;XxL-t$7F$wrxAjU(zAo59{COoJdR zCsW0@^W{3zk>$p^8%qy29Qx6PrxGzel!CV)JQDI*x>rYeopM+Qw@exQ2*sK-q*?&3 zf3yU>)36N3`=T;ICk{g4JrE0Boy7*`nMrO~UWzwgE-48)+AH!1@(o(0s!O6fhT^bS z)3qV&(iyucc>K*aH$8*Svu8RqVomSdNit?qgaTKHSJ(X@)#a_I2@r5OCWKg3E-t@3dy)t8g_4EKu%A8V8>CDq zZuQw7QviGtQV4@+Rb{3Y5$ZoB4@k#WBh)<%j25D*+zAE?NTZ-s=kJL~1T~*;lh!@t z5p|=K(Hxe>eCbRsMo>^qDUla>3M_OlHPA^7?sRr!&PyCJoZa7B zg&APyxXyd34Io4CldQNQYEDD&g^LT!9ZP1fB(l2#WSNGLjgZp#!TlJW;(y7O8Zm6; z-OhX9>n}_D%k>61@w6v<^V>;L<|}yu(iU+OB7O}P@Og2T{1pN!GLpy2n?W*W#-OVe zrWkJJ5)sAmO3B(Q0>d`!h2*TzxEy9%%j2Fov={M(lFKHe8OO{#kJsI^Vjl;2DUGC6 z)kJBKEh;a$+drgN8RSH+5^@PMHfcNv6idDyfKpD9rPI4(UxEFUUjy=}o-VgOQ2l@! z8^KKh+21H1ic5O3wb?KU?@wxY7g9wTmCqXle@n@HhGpLw3u0PCF#F1Zx>Hy>_c|d* zyi-4F-AT~6;h8!w68mAYSe-(uND7{3RHW$oLERK$>aoDyM7anQbF#J|B4zz{ga;jF zzj7a(4KtG%K~=)`kBMq*$Ec>^o<E1En14`Cz>-j^kfkzd zOcigEQFg=u{p{PWIRWvy6S^!L88uzz(%5n#HM*MCqUP^36R+}n$CcN#{DV8rMLS1wyZY`i!aK^5!&c-l zmY24%31!l!P+aQ6&DDi@k`D5HXO#z|SS=`0h!L%o-c7-_BFe9h> z0(q^>EJvG&w!IYG71zpvTdcfrW@aYI2SXkxxRBy!d}zcZMykq+#KNsowBQ;*oe3At z`DAcBGnoXH+cuh*FcDx0ij7=@g9$7OY>k?G5*qhVxM3&=^EG85s)j%j7ParJ#c$YV ziPV33DklXs50+;Ch_qF8qP3GjSR!y3Ktn1s`jmU_CbnNO=#Kiq&2Sdwayo+6;f~n%t;pUF@7h3 zX0#zv#Jyw{r^43gb6|{b*>~=6zZ7Wr&|=ZC%)BN4G)ohz`ygi-hrW)$rEi)U zea65xg_^>sV&m#SsdG3>sa0X!Y$}d8*CE$LJ%R8(PR`vf9D|E1Dl{^aSC(hbdK$m~ z^12jx)Q09)mu-+wlC&(KpZ1aRLBK>s81)mK(i%(>bUE#yuC&&T!dOuxKs89(P0gbI z=ALx)$Q@o2BiD)!xvM+U{=-}U+*=8-@pxWkYcAh`GA8-Nz0n&T^xFtpxV6QsHOO}R zlBF-3ajYEwgy56%H!Kf>K4D?OHb=W2+Uz6ocoW<<@r(-&_r_V$deEuMbLfqVPMn&yG$_$W z)DWn_6$GKjBQWgGvUUx^$+uS;h2LR%rmEACw)E>3CE!wa>pJpl8E**Gh7()n`F?bD z7#30gZ6`Z9l50EimD!Wbe^`bfCjFhr7BaEl*oibMV=WlatxfV1O^bUWDi}BDJ4!@P;M6WAO=hHjs4WA`G zzI4(fQ!k@?Y;a#$AkK9nkBaH(xGk5Gx#2q$^EE0F`x-Qq@}bIesw#cWw7KJ>Nsj#qqys-1l1yuc*JFMnM9DCOizpvl>iNgt8*UnYC-Dg#w^!GaD4>g~Bp3Z}l^xVZ8P3f#Q%SJ7re8cy(xXeq6d${2ZQ|vZ~ zO#GOwIX~+18%8)YPFlkx*gAG?0gB022&OLS=J-HGZPu{01%+jQR+u3dA!k*iYr0Eb zaD5sDLXwHtuQ<9m;(g_GAhF7}65L4|KCtwBX>I#?FpOHUKK&yFdwItbO-d1OqJ&O@ zCV95_OrhCKnqa}=8H}r32*(YG&>X~NrECC&TDW0oz`^*4O%oMIDxbm%X|oQwRj2-< zQt=0QTDL$*gOF?Jl@E$$?SGaYkoLQ)TE1YdLO7c@lwH3|$*1=9Y4j{lG}_L2 zDD@OsPpM$!v`rqZ6vDefkE4b&!NQrmXt#C)&h?aLVC+N8{3_&!f`5=Q&;?jV6Lz81Lju1}H zrj1_d=l-d^z8IYxbWF3f2QWs^!KKSA#U!0ZE;`B z>E+QB^C`Zs&H51i$e1 z^pPcrulM#uJ*sNcplTz@1Qvm6A8|=jZi@7lo3;z^^FB;Q2}Vz6D+cit)^vbn)e(=S zg3u@|7<0ORMxQmWWAobL=oXH}AA2}x%!+L2k=(#H?;3m%-_gF@Y%ytNqm)OWG&jkKbf$HFV$ zW4_>hv{3V-$tFBCXkD@AG`X9@G9iQ&@iW5L{Kr=2+j`-*gw`3aXxwm!J710S6Vp=* zw@8+J#qdWL(e_3&<3ngvH62ErV%cy1@Qx=Zgf%!&+VJ}0`Z8MjLbT9PZhk` z6ZXP6>USI^5(#mF8TkVQ@UEHfzk3iNZi;@PpVQD;DLt$_Wl-wjx~P4f7x%G2ma6&f z@&P6K#Sf*UZmG=j22+EsxRUI96-Py{KT&_Mn2=DUks?q#j{2(2Hd^(uwR(e$`V1?L zuE#a?eVqx`EWL2qLBk2<%O7X0#Yg)QHS;4)L8f?*;OOr<-uz* z_qcbN&leOqpw$fBkRiUiKD-S^wdPstHtn~2{d=^RUCNWar6UeinxF|ZU_BFJ|5&df zl;5EOxWs?G^t(;v{wE9I>Ra3}$Tyubs=HDD^|$>muVoSrDm2ru>IN`cJNjW;@1@DV zJo7s;kFUKj0~^>I%pgcM1Z*ihOhc!GeZO$9&cmuC$mo;+(Lk;%X;(G$W5&zha&Jh(D(yurPvEOG3&da`Rz7nooiWD zG)}bgifNaz$lL4RHC%3cD>fwC-tE6eZJ3$N++*zHx`)#V-@9`*oWC@l=(Z zAzZwHT6ST;rd9)tx1%xrCV{(ZV>RvJoP`20UhICB5oQFhoWAa-dc05F6u56 zb({hO-fD?b^!JYs*B+j}1VrE$fXi|z%FP}not6cBc?Y@-%MkK7jNnz6)|)vf z8SbR1kOTpSu=WCwkGGu;$Ndn2fHbN(d@lRt=XxY5wN2m#r(Qu+01W)0Z`!HpQ~~k% zlR8EcxJ(I>TXuJNM-YYr$))My{O@k+k8OmQ;lejo0da{MjseJ}mx&(Ez8Qp#(i@MM z{vM$J6hIDn%?AI|y)qr4{`RhAoSPZdUrhfT4U>vD0M=k@JoV0lgN~rRVg>jdEpwqf z3khQTIB2+UX#!_c0J6>$uo^CJvNe`$h`0bh(;M)`=GpJ}3CTTsJG{c~ssEIWe6SEe zA||1K76YYWk&jqUDx41BKaUEsCU>%S*S6n@)LlXKNsfSLNW8J$@=$M8gr_LSqok3mogP-Iv<&WOY?K1f?qv-)PUlQ;cB|SP0UFiP zU+39Yifj`aLkOBbD|Bh1CpjOpq@tnFD-Q`<*ODF4^mC4$eZfWX)w0@@#B2uG_T){?Xp_##=tR!hZigy*lL$&2R%BpTCwjt29xN-asYY^ zC_=uZY7~PswHDN)JKH96m<=j2Ac@s2vQeV!Eh>GWGhwq34sL(b&5Dk!u?gXYT zG25~@O>|nND>WT&>s$5dY;mps2so2ztMj>ufsSK-h^Gw{#4$N}5^n}%HR z0N@9a_Fj(wJp@lwQ#;JmS742?AkjITLvg)D0Q$L1i{?&<$mhEzI{fTzUUf?#w%Qeb zsL9g2Q}1`yT%J&?=y_(5z~sqVM$Zl<;J;Xg0g<3RkIHj@HC}V%su&Eo;Mm&>Y-W zUkXjH#hY-njQ$Udz?_@K?110f!%=YzgYqZ6bYt;e568DV zia5ZdZzJMID~>qwOhMvIG-DEmKpcJKIaVW?;PsBIe8vD!HmyyiYcR)0C$4dfDQK$9 zl{qM@O#T@$J^Es(!K7v{JkG-2qf4S(jL#g1C8|k54a5e5FSBi#Cu3+&;xyy;!dMgn zfs{gk8JifJ?Z-`H0`r`^@(IKN$|Fh^J047%GG+(q?Oh z6+vFJ=ADgnm$Ts=JhN`Z6j58T_y|S8zk^Vcucu|e&kswmBlt>tlX`e%K?vq>z*MX; zp~*g+g_71A_v?5+HEG$rrfu9^2_2Pu>yBeI=5pp`An{F|lP2Ofk1S&!GB0RzfRI@G zMc!vBHf+Vg2xjgtw5eF-E1*5atQh&=;kW&^7h~etKk%BSz_CgB?wfZZ+_EV<*uPJ% zZ+QRM7B;OjT0i~j`w10y07OQua`j2ciV(z61*@43Sh(9Ha(7(1D`r+=f#Rd2I4lSI zRaFDbNImAW`E&&Pq_;ey5dLi}w)_&|ILk2Xx0xbkUzMuXMNHvQ0u|~j)cFv$XEdU8?lGAFf-SZsgyg% z?lL)Lu&XC2%BSISsYphW5Id2ErAGJvf&+VzVEdeM%_Zw&&SqKnv8QPc-RhfNG%s;B z;R6R;Odnyd)5*8Aw<(v9Moeazv`)d3%!6K(YArv8WT<=9Mkp5UPw`YAC1XXHKCK>+ zl@Nhr_gIeJzR(5<5q6aVX>I33lPHMWzJJM<{ zA6p+(*I0k^<#XJqa3F;}UabV=HDKKpv#^5lY{MqsY5B4){W+O1m{EP1f%pm>dArLf zVP!*DGnvFm))2-zdY0CP_Z@U!%kW?oJObU*_@#I|N59b$5lQ@S3_X2BXY_f$?WY;B zQf?)W#=Ze$`WOkLDE(PZ1KovSz*QxCNnvuSR^m>84mYmt^lgAeg!tpJK|&u(M&l7= z^Gdqey(KuY>i%R~7P%2QyFu$B^mLEynbiUo15aM4_h4*(4f4%sAr|)d`cNdoTln22 zWvNj#@)j!K_`McOJrVrVCMc|&`u9AKL}Y1p%cW0h8p4JP5PxjI@fGioL;cR~aP;pi zuJ#4_LF6{i1rm9r`>m;XZ2nd<34>tM!c1Ko9rQm~c`0nx;|@$8j#)zmCh8u+GB2^8?)j#N;m=O0q6@y7DZf~msm~ZNB2iJbF8{c?2edZ?$zXH`6g!j zzbKYJPIMT(kQz^JsnC$x25}6IXnP(Vds;r9%5B%p@PU7C-|{{!tk*bM`ot+adV9C~yi>K7j~fnzSWGWQ&4}^SmbYy8RBus!#5>US zWM!a|!(}eyNj4%`xu#h}c8jA=`Q5L~n=+Fj-ACqzV?$(1PD0#k29$?0LD+fN8O%jY zXJc~Z_^laQfA#}YG^NNq<^qrb6OCyBB?VEcshZ7d;U^p{6xk;a{qOb=EQCU4oX~o4 z+(Eu4xbouKctm7>lGjan(3H1TBj%bsfbSNY~B$*Z36TVrWBp@>-om`wMPy%n4h9_ z2oR{4xXYTYcZ0H+hgYdPZ7!yv%eyRmMJ&#n-wQ@oE=*cwT56%4N3%xkaBY9d`?O(H z>=wyuH=>(D4YyH$FN=DRpMk!2rFG2i4BB7Ed||aZ{Oy*nG0j8#5s%Je_z9L|YDoFZ zN$HeAOa;RTnkWLR;P%67De>*3Zmy6xG4op#w)fOCA(x1+ho=E$3x9+g+FUo*;9o5& zEMnMXG*%nExmFslbS3!RXzLeu5jfY*%6D$Clr)KJ=6b{(UF)$sQ4g;DuxH<0WCP zn3l(0s<6(^c5(h<3#8v^_w;9hcQcZDxpQ{PTzb^TXz$3?eNrntV)`kPsuchW=e?VelL%{jh`b#X{;_=DBpi-I&~%c*r_h> zj8`mTAIxJSd3$*(q)?jeN zYzW@<=kgs}%9@c+h>#==8$Ht;kusLuK$MwBqsG!8n8Y>nd=N+~{l=LBSY)n)Rwj8W z=E>>&p_8|Fja+o>Dlvl8VgeP{CkWU)Is2%;cEs=yKjvc2uOe%H>1(=lQ_|mHR?Xuz zN$*Yw#ZB^M-)Vu#;;=X^oR#VcHv_9mS>5!Oa;Jhly>E()Iu+3sN#kVkG~))W2sZ%{=K=Ev4Vj;UEo0-TsI2yH377!dKwIXX^Fr_KHC2jI8Yi zP0rfX$Rdl}-H|h~%^Tlxq?w-t==eH1aF2ZDkmW|&MuZ!|2sx83cjcjuIR=arJjngq z+h&0lwJEW5e$m6he5TS{(u*qn558ADqjf>c4+hQ}DN)k%_P7SI3gLwW3aBOr4@Y zQrE*Wl|8V2>T6mNc(s+(_&{hcSltGlknS)xSGE=$4)QOgQRxe@FpO}kJ9)_?RJvIS z(|1pi?sgVzlIk8+M3`{vM|70+#+ATgWS?S$VouUCebB4LfD6w`Eit}*DszW$709OZ zj6@5LW<|D<<9*s+*URMgVc_}K`w+8OJb=b-&vmuxX3DZwc*?-d z@YpACF$=dkw}V(6L{Dl_LJ>nC8v=ZG2Iooi6bEK^kev+s zk4ip^oh)yRXU)%0#8SDT67`MK=RsYbUz2r?WAydEG2}g{bWhSN zBX8HJKWdVsXfwsJq2EE;w+I-YYEfLCevDL=eS`f3b*+Yk7_m53$dGUkCw7_taTma^ zgp;FvlA}hjZy^Yn$bM~6Zx=r}914Yg)m<5TO4$SFLa$5Xs44%{j^Y@xieBb9?4L+! z(#V^~@V6}=48EXbF3>GVZRDU*!g*}*l=_7cDoJZz#7r$N?DOdWoEt39cljp ztntGbuIG_>L>PWQOosIYXbzab$Hz=Kh6z-RrBdd2>%nIKu9<9wvCo|msc*RSwJe+N zhCbK+gF@XY=h(05R~V?YDNDa{HjYrI=JCFJE}HO)rLg$?q~`BN{l6IWl`T=&dtH(G zY=8c%Qt9xFR)a%Mt14$+jqO^PJ?A_N4GU3mBSG8dx0rkp+!SUxJOmDe2>4j^+}PmH z81T@gwrUX&SZ*L3krY$N{>Nm`sGNrcPFcPiY(W5HGBY74$b&nX=6sdY{`c06l>j+$^ z#@u6*LddXKw~mrv&1uUq=aKAyOJL6bpr#NVHJYGS7-fgMVa>^l5H-^*aMsXnziB|T zsn+o{aG=NCtKWk#3lSdN-eK)1cDL(4kl?EotpxK6)(0X=jVd-pO<7U{=gn<4Qw)Ws|^r(N}Mc(_y=ZY68A70jFMMsjvvNIBA znqKE9B@^|fxm9G*XjXqhs8=8!k6-k;;k%r{_EbbX#qIStDPz3JY~5o=4FJuzK5)2; z{y%??0#q8GIFqn1jq}`gs@i%7fW6$r5MNPC`WH%#{|o#63sUC9Ho;lx@=E=fT){9p z($K0_!U@xiEa&`K@A+?t_}@PSOiXEb8cM=-WdEPvEx5AL=*{0+V>1|st&{FwnOaCX zQ~V}Y9+K32plt)-iu~ubpKOVSRUJ|Oz8J=x4{wBb$o~h5zWP1kbrb+v{{%OU03&M& z0btvxb4NAjjH?IG6@I71EgS>@alj-;ZW6QnihUh3w|f2}?@G`{G>y@Kl;}x=yv={U z8~Ett;dnpK1B18RThMdP`M7ZCdFl5n{{*Vm@b9JVCZ`RG-TSmLzK6~`%cxxOOBUVbELqcT$v|bEMAqs#maIp61cug-^ z$e!Q-=l5lc2WpfVGwM1D0CAlH;f$lJCU&-iVc?%%9dB&xc?K|qeC7~(?<;4+C=lIG z+Z z9n~&lWn*l51!TUld2nE6<*_4{gMOonN1|}PzTD>A+_PJN>cg-ZC_~NN_x?`sD5p07 zl-f?5YiE25r~qL0L^m0pIdbpQ0xTW7-VGB%BE62~1t%wtG5-rlSKvUqku)&CUx*66 zfAB?K|0mda$o&A}5tM|>9Jr3XS$hxIl~@Pyug9&m;_U;EMV#DK(;he0$(lFhgM8NgU27R~KF4=XJ`JfM1baf*sgMOR(kvtXGB> z$rTrNx3_7mD%}!JGqi$W2b~$@P&`&tk5YPb0VTDOKX=p7yM7X6cR=|xXAtx{< zhjj;B=HT+5fVU}rc(x3jTVIo<-kAfk#zOcJ@DasqCh#>W>S3i1B?=A1{fx*K>uTyd z(LUTf4EI`G%9$Sq+IXn>ldY}D6~p)Id8AqV=9PZ{*Hs)-c?neNCjezHP4oe8l*%BL zqBiiJBWykLBT1xuFe8CB?HjI}g_=JBuC0|RxpvR^WB};6oBK`(ZN%b~5~F$-;)c`l zuZ58PLInGO7MHqt!q^3PYF!1!aXl4np3h!bwrD+3c}ouXBS@Ld+q&`tDV_ZA5!-<9z| zg#Qgs@+&KJ^f7d#WMGKng61l(GlEEvc1kbq2H>H8=dB4!!xg2K+P+wiN62~jluY8JR^g+I5gttedq3^l* zSvnPAn+=Z?YRGM&vK}k#SC%Cxx7(FBf`b5a#VDj(d}A>$e01@#Ixx%D7_yTHK3?8w z)=bw!{sUWFTs#QP17Uooq;wEfro#I6yZPYN+!i=t1b=Ovj+t|sKHz}M9&Hu8bC1&Kd z$;qJ{Cq8XP+ecmuDIfQNM2POUXVK-=os3TQ>AjB;Wbdzg$3g*DqO70EM8MAhWS*Pe z6Xo6{G0so0%1y>BFiB>`Op&jy7F{EDC*d65$X53I)(pesky0wm#ZV;vr2uJ3aL`p3 zN~7&s}IPO8(Z_c4!a`x+i3c zF2}<5r^(AaH>p@Rz5Hrt=?jJn4CQZ>4u6uH*s0a5YA!Ld-A&+KoMFUmyUk$0BC?;@ zdU{W=#tMH)po~7`B4tv!mE~OIVamc^PApZF-R zS6IYOVW{z{RYxPC*udZzQ~>6rc08YNpN=bL=|HTha@35_rx>#x##E9XYzSbg8c+eQ ztZZ^BA)!@Qrd`FqF2(}LbMaA33XGB1ibv?^mqxvq7S55#|V-e4>QC+}Jd0$iZfcEzMs4 zgQddmk5!K`uR3l@Z$K&We60JyvTk8jItS*p7K{ZsFsHtAA@Usfu^X6*$;;Ato6HiP zopg?qsFU-Vn^(OXmDB^0lI>jLH9WT2vmyf{l=<@=Z+xmL--FcO6Kp}My%sSd$aQy` z6M`h6_x-zjee6`w{M;dG>|DHCjI|X151XaKbf0MhWo+xhu`-#fNb(*8-Y#|4P7L{2nG5PaLP0tWm{{rY=`q&h z>+TcYc!W0U7l6x3Kmu|3rFY+N58(@b)8iPDG zgRH|iDS=3Jkv~0Zgc0~28GS|&s~r7mqPNyFv@&j+?RD|%ronj|vaVBfIH)PLZi{No z0jqv%8!k)7r${TVrRhY zkk{#xuS31Pic!ku=*&O_turQ=SvZ_0k3^liBh8U2v|9?(--LT-`QlJdB1iTf#Bc;k zD+F2`^t5t)pHAcm%M|+LIB(a6Xs`e^l8c=;#&MF+szY9AU-vrsX#eBbgAEH~*~1t7 z8CdE=)Fjn}fm>{JyTdjFFQRD!M5p{v#`w`@({63j?YH-h-m@97Lo0?18#j@roDjCO z;z1)Ff#_Z2C212|;#@ckTRYo^K$dg{HusW6&+L-&XYk3}sm{p9`DlraWdU0XexkM{ zXUVZMlNz?6imA3d41tyINM8Dg0<7#U+cK7U$nhwwynkZKA5`QH-7wjdC5#~5(Cl{#{HGL=4NtsoEf20N$VI%p zJr=C)Zybuu|B>dK2(vq92^tEEHe0TIBqrx0d0kxueo7s$04W#yge+|nFp=9AROc?# z_42Q>(Tona22lW1)Napv!kpR=Rrq=HX)KK1!>7zm+OG*X*WFWRh}y$?7JZ)_lC^!H z(sFu1GfW{AMHgMI zO$y;<+Zfb*xC4FnPu1v*9ugxQah5zSv;nD@=r6mb!9~KaRFa9Cl_-REB{=v=GX*&H z;jbrHG{;&R7~Sk}6% zos)CE+**PSdDUsd`{C3N9xT+gq#|(=({R=X$#;SFl77i}`XYBhlhgx;E-*Bic+9nrp^3x9NbvN|&W`_hq@y5kkvQ?| z(bog*gw;gtcxDM-Z^omfBFboWmsy2hP@?*gN4)&KkL=mBmnyNSUvTikq@N4FluLgn ztcolQpiVCibnSukwBA}05)yuQbIgoW_^^H2SRGU zOHGuYI`T|P@RVDC_C7?1+TX;RN1IBwF3+PR)b*`~4LyT~d3m0*@z2bcxR1+ThGyMp zsT=)DXDm_}#s*o3TRt{Qa~w;eTk2qYIru5BTx&wjuzeAi@ttw=G>(t-@yIc4Ya1-` zK-^NmpIQiSl7K%i#3;-bJ*-Iz~n>TTg2v}cmB%S)q|z`rW0~e5s2k4cDsW|4xiF! zrCEqk+scT@R3argA7v~n(!&)c*Www$gB*?E5~H05f}~O4k=d~zNXz WO(=jSIO zI`etsXWiL83hVrQdT*^)>V)6KI6vRE43ovFtFlxup2r;DrPDiIk9lBX*$~!9E7-PDZ7wRw zalVd9BzK4VCmWm`Rg8>F{f5 z-sE9t(5Jla5iK{iM$1f5%Pn_yD5$AmTVctmYDQjA9Y&Kprf5p*0-WR?lkVsY>#FRq zx!8D;GCEy%0~y?c+!>BCV*jPQSL@18tO)A%J%1Xi7kAM7E9gQIMsAy@@-(~@UnNYS zg03PhHC8X##k6ET?p&7ADhk%EVkW@X_=#Vgu#2PGhm2N8#l5+`c(kW1wBG2{)`U47>BJqT;GIjwn-%lXaxzILbp+pKf#+AX&Y**N&_-u`?3CWknMU z%RBBOw551W6?nPCWwSiZ{S%@kokvH#OzT28fJPVqE8IMgs>->*KgA<-;?ynM3`UrS0yQuj{e2qfdMxJyu|327L+3b{E~?*j#ew$rb+6=! zaIFnO(3NwaO2!u9F!|da-Dw-6Ok7=9|io92IA-W z&l)U+7f${R6-U7L?XNrRp&S_!!Wyr(oC7h*q>k@bneg>>T=Z~FT$1jq^w%M2tW*TL zWCHywO+Rg>jFF0kZZ7D7O{qW{x0kCloXS4==X(BQRYBe$L1^n+-&|0C;e?uYSS-WM z6(Z#dW03{}(9xAPm<}~^)HD`127lC0FcvlLoI$_h;YNf%$FBJ%omLb45;)E;6S7oC zeOhYZa?>NVuioaC@#qTl3qA2ZdhVY!a=E|^&qk4KUy<`v!ry?$FHW)}@!3b(7(LDI z3WEyd5FrxahD9R0GXCHASPU;MsdN#9w2g1H8Jn1ru?8~snmAp~U)Nch(r#HJpj=a2 z)@Y^TeBH(SHl10;#3OqObsmRBiAskgh<_!4BNZ@^r3%$~iPcVxOoqfH%J;)iES(Ip zX7ruxnd<*W75|lH2^5mU0x7{idDOoWjE{kg!U|;VTVV(3mqZ#Eie<=}PrKra|NZ~} z^;w}5Vw9;=D*xt@hx$Ls+rK{B0PqDD@?Uv~zux^n${T&L05rXRy*B>0>0f9dKopSm z4_T6bD~CLuNu9@=;{P%zHSO{28;g>vNdW)50Wd&ztNBX+AR3zRG7o&X=pJbK$XbKmfA;C+XA0Mav*Gy3;TXr*32s3n1p{nn%uWLc;~S;mGJQS3wQoolXDaHkxcpLLztGgC*AX1 zMy#Y^!;M_e$4VV@`>zwOsc8xep#=*6=t=(D>q;NO%KwcQav60+}_-v)YImu#e%k){VsVH^|+A4-)yhWL1CUsLzs-QUu&9qrTC1QVREHO%A?s_=Y}HR%iezl`%GsPD0} z&Zmn#phD%>@?1W(5#X{Xi#WT5)&QIepcw7p(Mbl3fi_XRrbz-o75fKK8YsO<=3>E0 zxVMwJo2=Y4i)ZI&4Y!THum7=hhsxG$BByZFGn3#?%04To%+w%+%#bV)(NrvQROJ#CW-&>-NFgFJ2`V_f_HPpqUm8Jr4Khr@47Rw*lt7> zVWaN`$gZHdUsT0ke*{IbV^yk=N>h0xuEHLLng3h_VAo}&S}H&)ZA29<&~l-^>ijCv zqBMcWamRJ{ZeQ0Bq))*ukiDPfEYsTr0xw`{fMVgAqJc(Sn0pV6H6aQGWxx(Kv`lnSIyv01JrfM3W<7{ zKok;e+Xw0a&7cgb`6&%8PMy*3eCSYb7f;$q2)EoAe`k3_kVGHHZNt{A z^xp>wC^1oZxq8^bNEWy!zB2m;%0~5NVW6SN9*3UPIk1YQ}tGz+5imt>w^3_nU#nBSn*eOYT{sue~28X)8Nv6C|jO z%wj6n>;FTr?vG*bq*SPhTJruFm2p57{?Ee#JgOIOp}UJUD}lz# zPe7v(aD&hc-}$oq0ebw$n+N|Yw2jP_$Z739scE{61ZaN$V%_}D2O&R*On>itzd==) zzcIexS_%Z|LCPU9mjAK+|GL2dgGAyn1^!9jy!3CsI~iP4w$FqA?V|qcetW_8h%TIg zRkr_|ttkXq0bW)ho8mv^!2kW*g%U79%5s3@Ya-p>T~8Q~0({9qhX48Y{%n@M&__o2 zMa#I(-#xAu7mxy6k$w8_b;Ex?x`N50=U0i4yYUE-_^*FDz|D$jn)&Q+zn7H~%qfAN znO*(A78Oj3>JGLf4Q z8$UE&wuyxE-<}-!FZt47m5lNPPiqE76ceVpOx)A%ti5MEOuA&ho-Un|n#5@^S1u$b zgsH`SUDXpn4gwMV?e%WiyRJ}8P%_o}{$BB@t)vB@Z-7i$38;NPG7lmP6)M=JmO&Zr z66npT>3u$z2qVDB!e=ubBI0%01I3$qP^-huy4BJHX7$T`i^Xp`mA&7R9?MlXcZ!E# zvjF+*gnT2H;xxHFoeQIlPL_vAE3uz<%xtGEgb7J${R5Zmc9QX6+xd|*19u@SoW~pp z5_+tLaZ*s<16~wJwOdlLHusNaNge{3Kp?o-18ma?op7B@9F+ACuJ(sf01*?szoh)x z6NF834=$C+p0(GyJ&ziVQAO@|*Zi($(?IqO-0JIVd-a4gUv{Cl(fNjCSZrbH7D<(M z(D}k&|1mv4nG+nZ&m-31Np#hh3X^u71&^En?0BM_f~Vzyz$qqF>tK@iCOLvTSY-6t;yL1B;! z)EckPcR3w*#-#xqAc1u%@6DG3(8_H_-v<@=fTieyx6FZR<&8*>I5fm&*NeTn0oHyE zd4;BF07*CiRi^E+yc&xst5{VvVLMy!%oG_?TRVW4_DBNDO!q1_0-M8Xx}2P86M)ed zn>{@Gs{n`dk!#6jGEfUXV)c!dAbMTG{v-vYYesCujwRANFPi0_438EGIQvw~m%laT zZ>a4z`!%x(v)?z`Y-I1VB2?XQ+CEF<;~(=7d-jOT5K=`dXY8a{ZbM-zRzI}<*p%z% zrQp+pNh!exXknm|I|LplL?zRDsi|(KJp@5}7r^G9%^iaMI20So`T!0W;O@Orhv|&R zgI){ja*z+d zUvZ%D%9ac4QwBQ#zi9%!1(5`Syk#H_Uk-wX$0LX3aT665mG-S*;|DASLq?XDPr+Mt zCZs@e(4>R_CyAE?IV7RnDHSserdWM4%oSW2L_nu4w2?-Kf>+yhJBPs?6ul&2 z+IWN3C}g({`oxU0z!qCeG);90l#E#Iz?P?K(GH^x$ooEpRS?`gel4`KgC)qVSzp_g}YIdN4%hbxSKeng}Y#81TP4HlAr$$?_k9LtX1pOY6j&`gw;b;o*jl z|+Y=kS? zdkfKIsQdY%X%^89AO~*mSLtCfar!WU)0#`@xr`aN8E%hj{UnOs%?M*APo6ZS;}eQI zXZ511g}hHbOl+Dst|p`=3#h6r=4Xfo_(zgRB)66TwjCx*faFT!yNXWSRjh(!NyF1` zz>F!Eb&nqisouAcV|xf)!D$!34NhK+z4&H3YbJa(0d^Yj_i$c_52x~zjyVmC=u ztCUw%ERQ_#OJ}|Dx3SY|jL!DzF&EJtOJ2A=UmeJ1aA3DkL#Og;Vt>QsUyo6~#FqGx zCTPBfC&eIff> z-kpHuEI$cd3hvNwTdTm2=*6IkclurQ?Xz4V10(9|m|28yDR_dBka3ad+sFNRwUNRm3F25Sp~o1xT;7 zWf{24aleMs)P8HjuGJ}cYv(UDwHi2>L)lQc!{k4`NBtAYp;L8h%X)NtJgxjw^y zAh_SvB<#ZlRy!o#j2fAkULASJ>cd5+;`+`@De~ANr2_HvGsNYdXzDF+`E1lCg1M0dkkvV9cSiRx( zpi_YkA69h($u=|}JrGy!VZw%aEMpyjSRU)=mfAT+E#Ngqk~!?p#_HWXVvAqX6MRu? zyxu8#1x4Pes{U3S3gJ7ID4HE0I#Loe0JgK7bL9EmFJj7mtk+^G;xx^Inm7Fw^c`$` zbp;{Rm7LX+%_7&Z!`EsrSoxvvyz)$Q{#ikfr9LVfvNX2G<>UWkOXTOtVW+`rzVrU; z93#=Ej{}ByTJOo99DbZJj%U`KWX9H`_pJnm&z;v#^Kj`E!T9iiAUKvw!!Y(!l9xO> zTTA;b&4)vXXfec`?i(kX^p{h`9@+&8qlib74CO5_;&e^bIH6uh z$OOf2M%R8fy#=7SOEKoqZ)%yQe`x{Sif1hi;qu?K>=mtA`LOXux;h(jICFm<`_9ex zGh8OGNKAoF;KdG|zoqSeE6>EZrRr zNjKlxgv+s~RmJraHP*pBoGv(L3{Zc%rST6@hQGC#`}z9-;rG71#w&5m(%-vR_eu=2 z4hM~^9rAi~BFD6>GEfiRz|RTn+TlTfRvxvUC{%S$n-F&nQb$8$6t079rEbFF!kpye z(jr)+%;<*=dF2wIS>-F9@M$y{=dUsaJfB^2=;5L1u@zgFOIoT-HJV;?(qn}Q zqKN`9nrUqZ9{U?zz6$=19`MA!5m02FhUX(PDJFgR7Bux9x~Kx|7`Q9g01Xxrg|#3t z#&ZM`XeG*cU|2Bs3RNB>k{vWJEG(O8S~yXh(4u{4J^(h7&E|st$@rO$X?@k8>}%NX zm>{+U?k1=`wwvJrYmZxdmfv~~oCNFxHZtvpEPg>pYySSIyXv`J4=RcUA@Jv$)3^-G z54RJ_Z*ykV=& zkch0bz#yE2?u;C?$AU0tzLerdQr%MXgB!ilGqIHpyG6pFQQEDo*xwt__8tWns$A^5z=x^SbcKVpb?0cJN=>Q3c<#h zY^A;TYG*hBZVE}$>ng5d!6Q4D+Q27VdwW1hRmjFOlC1J9a8k9((HH)^DSj2*g?FfK)D*({i*N@r7v7uIJ5Tg0AWK=$4vRT3W!G1E8$J?J~E zhpgIPEoLzH1e%t1Yo@ic6hSrQ3ip|5pmVqVsEsAK~Ub?q-9!X9lYr{^n?zXObMn2-pB zfa6u4N5u?u{hJnAEuH(@lYh1~;zP?cyWiPKb4tmEH4b2I*6K=?AgSD;^X)!*uq7^{ zQM{RgM@9nFcJcNFM_Hj7(3Vx$Z@9dayDslSLz^ciRnDHQ;ec3S6G%3`8!8aH1eIjorzTijJYI zcd>)tc04}(piBDn#&tkLiy>NGVjVy?CGnIWg|3=nn){KU*IU(1Ek$&Yh znzn&spN!+|cQe@xSp*z{n3kFnK4P>?`S6xyqA=j~c2H=-CRxV-pyXIe#3E2ZknK@` zIu3mat45qT5wjgz>KFTmtO3=3U+Yho&9W{|N zRgU@9_2;)XCL_6u^|#P#aLpKVV%yO}>BcooRd%J{Zk6%t z-L3JB@N>~YtM)f71@~R2A+0c8qs^{r5KZ2mOYiw)ab!}l@0w#O)KP2{$1-ZMSTP{$ z(gI9<5VEDnbMyPT;?G3)o046wAt6uSk5QaXYbPw3ZtC6c$g!cQF58nmw8bhz`U0U) z_~HUTlvKV4ZC8mSRlpIP0|?n(8M=}Vf>=jNnznC|L!=FLW=0C#O`dfq-EJVsSaBAk zN)hmpwcF5qou^{&=GRTSc8yh_2*~P3r1HJi(Qzy{a9@j*Bp`jNDKB{E(4^TqtC^}2 z<_fq|RRDVK@d4&3g=X2THCl;bMn6#YYt?f7JoOY>7nX#SfT$ti1i+@ix>GmQHGj)@rJh_>g2# z8$-IBI`Hd2#GbH_zrJ7iM}Uz=S8%oza%iLOZS!#teL~^PyYuxmpTie_9j78(5A{0$ z7C(M;yR>eH#OiYej{shw-e?o5fHC#?7vnwP=uL7Ek^JI7xr2-QjmTTLgb{HiI;do* zt2hqt`6GY5$ObcC`O%-p%A2g7Pj%7@0jmoQbKP!vz7p7wB&c--37ca@^B!rcmg3l+0y=cpgOs?l9Fg~o;o z=U>rz6373dC2PLbAvkCp!10M06%zC-kPUfLuzEuF-hr6ZV&vy*@iK<)oV0y^clKrBZLq~LoyBn@oN&$mPT0yL}h5Z&%X^6{0o@sYp z=!`SgnGE|}0b&mmMB)8{Oe!wf{tMBy$0v*Gyc(Llv%LYU!-0YT(}Gf-*rvk8{rhWQ zKrjf$>Zzhhb@pz?RM8CQYIHX0o6xK0o#8!%SnV*Yh>_UewfqfUatex>ielXU6{Du=En6%jMIk{NmFd?%F3G z6P6rropQbi9p0`96YD-(#Gks)H_`>!7$+LZ%bzQUT;ExnsVYQi+#B2TYCqpiqo34# zJ%ZsAY++)9-i_y5J-LZPe-_6#S%7#t)d(oqz|Se~+sFT4eo5K(8^`D|csn$`ncs_A zyBcX*A{Ou(FV@5+ba5x%UV5NMtxnlQeubRj{$)+x|9srw&}hEaR?NCXTk>>cP?4Dr z@F>4`VC-B!Ylv23pucWZmBKyFR z1UVldl_INq*9VZG(D~S~Dy7mP9(_tZ&Eof|Pl2k(NrsSw_z<4h1WBTgdw3T;i`=wM zE%7O$h-n1jU1;Y_2w8NDD$CMxI;Mfo8oYep7*^wpx>g^S-gB(Q#VbqywBqH2W&0Rf zSvW_WvR#E?H`lc+N;N{2$dSrQ0z;afB{k&850l0C_)l4!=V~B(_#dDKFSduG3RR0F z;6;3w9ws#$40|6yFPYm+3HVH5Q)an!tVyB%7Jq%DBDfJG@v&&WH59&(iJ(~FXK&v? zq>2#}lb%q~TNCvhSw8>q{K%&py{Kg9=nE`e2DL`k5Hs}K{^A#2mK9o~Y}D^>6|v?8 z>5w*!bq($@%cVJ}VJp^+$~&)Cmvs6MC{4P;KX< z4dvetGI`v$Xukc3xIp|c#|Nf?^Z<3=6Fy;23a`_WnY#a|z{#xj<2Rwgl2RtRa2abXPgm2&0iy>(Yls zz{^=v6!SaHK_b|CQK=v@tRwN9%i%#oj-u>lYM9S$%a6ny0D;LD_bOu>$;c3mf;Z4$M&E7Bz1?~Cs3;9!k!*N`$h2ga|TKj80lardBUyMlNQV$ z>Xo2e?4vG6!cJf0OJ5=UOin%Z-Q|SIQ1uA1G>ECegP8EHzIejFQ7sWI)dQCfaOl)@fELPZ9{ovqivkHVnzJNwYRhbu z#T8E94WH^Q93Ttr+GcbTx|dTm=lsH4*1UW>$RgxvwLeK0^S%;o>xl*|V*tT9a<>>UN@$!iJiz5C;b?}t{T>s4PHXFz9d}o4k2bm-4 zQmw|Q&Dh~#R>^bcti1|Wp8P(Fho85!576R9;w5Q`K!?z3Qd!!!kJ>2 zh}Tm6&$J$pu9jFbg_xS1Ezw&>^YfVAz+cLUrZqhWhOV>|c2SpRp zSsSOi?!0s}YQeMUdsYvfg7QSlXTjSLMwBkuSi_PSU*pMBX%4ITXRHUjMWQ$D=HILq z03Xwa%QANBN%))IbkRiW)>a!St&CctyPllK3G+QpxQow2V4@%cU)^{Rt;#TDwqBi> ztJqCIwf<|Nngn}cVV~dq0!cmROPs%E{m;qsg+sYAa$QOmWB}S)cmWnEgpo|96C&rN ze~&MVAr#5wx`Bm?xT2lW;f01M7{uVcD&Ms;lHnt&kuHQ*2ZCwl^xO(O>F}x|UvY83Ya0Wc0{yB-i zet4}(KplPs6&)AQhROR*QlV!Sz(@#CVYFVuuwl=eYMqqB`-*+n4%ARJ!szPp^R-8% z$iosj2O?ca&$$qTypKS0siiVK!J@j93{N312Qm=`5`Kq#bq4?4FZyjT;yB2ooWk4f zjE#2dGFmf^{KXYS_zwIUqnY9%Bls1SH2J~$&wZFPbg!hv<^WS>yLzjQizo6mSnWVc zm=&W};rcS_-kxM%Fd*emwabO&bPKzNRkPextp^p#U|@iHA|35$H79wYT+u# zvn|%y=S}ni)DW^@rX|l_ivE>1J94UUiO7ECOrodi8+dp~e6m=upTfwx?C`JJGmv(l zt9XatE?VLbQo7U^$cv&SSah(lK5UA)<}Wod28=fb5@vA%sd33gxA4s~-qGVrQTP+` zyS@UJ`N$qLDYAK?<=ML{>zj6oOdiKku3Z^SMz0DUR9{l)71!Q-DP6GJ`Fx==LgGHC z_16@BelwwHA0-))^ngaBsC`k8qdgE6C9ZNOQ>cie+IOo#@p1z5%jxPdHW_$w)RW7z zwOgw9K1VJrh8=;RsacdK<$LLL53g=Hn8fr9&*yDm{qTpN1KtTVPCSAjK(&s-4MMIJ zRO4+Qc6GY>du!BZx&qBW#~lMFcFO&$9{}ZGPMm|Pp3p}1oRz(lo%AqZ6y7}w1v>6Z z1mb?DDK{N$)uaCf+M%bzdV4+LgF6tZs3pHZzF>E9#Cq*~wA^|``A7N3Qr)8h~sxKa_J?j0+($ z7EQg@&>XM<`TqtGC;Dz|Wh{G52qdoo`#{DDPNi6vr1|4#JdA03g+a|MwGXPfdEfG} z`Fcn#Cg!UOb*U%nt7+f9+KHq1s)N{bb$sdG2k+wM!zaMcEf}Tj97*d1Y=@Ru~5jxn-6A-E}CvO-ly!DE8 z25_VxIG-mU;1*cYFgh5?O@2{E$4FHO4KRf!I-^7hSN8uy$oK788L3460(@n-qFK z#cx7$Faj`oc^6RM$q83ReF7_M*8(wGjh31|UryzN0F!ylEqd?{Z*hgrtSv^3hgT z&-ZTuEeAg4Z4_0MvL7~{T{G(GI9g}+iJg;SivIrDq5ZhdlkSi~362+tSTAk7Jd;WB z^n>z&B}asL8S3daFZ*jSJ_iLxS497g@qC8#*oaeuNC)Zj&&up<$v-AV<6kD__!7SN z?2q+4LIeBF*_6rXxwp^`MY&zWwNu9St5*j>0^14q)YL40-d5ee-d036r&KNt7}47^ z4+F!yU3rc?8WW;~2`w-vr<43Pj}X(dJq6*8EjT+{?9Z60e>Wz<v$6=2^+FVY?8Db2Op=!RIPe!)`9D6v zFoi-(C0le57+DC5vi$cKt%`~B>nH8gb@Gng1<0yRIF56sL8RWNJz z#TG>oJDl8|_Sp4n_X^xz#tv^DAogBWz&>LnodY2%3EHp0Da*mH*ZOS|>$m7uM(TBP*Y|^j_)N?!r6?_Ex%3{y`PboQ_b@>RD?^#_1-@R#c zP^{Y0%yKr6Z|;q_Q~i7!Lb~CqO2&2T@)ZCuCmPvz4jFc``wERMC(08~jKxz97p({G z=ayg}V(Bwy*^ihW`nh*bx$g@%s>rxt45P5Yl;4AmN=XnS_83U5gtnY89PlK6Yq`At zXo}sgaH>{Fd^0{_O3)cn>&&dxq>6|}^VMZ{cK>Roy8DuTwe{5vUF>NzwZG6BZ7=Ep za%nc6(e_+f0xmAINv53~ff&&HlVb-KP&-v_PQ!SRP*pq_N_C~$z~y3BJu3bY+d z+6j3gAQG=Tt@VSa<%QOZy?4Dq*j-g+h0-2ylPHla`vwox$c26=`R&~>RgW`6DhIo%1KKO)W}>N`@p`777t&x*rB@>-cRyAiOj~*Q1$4tbc>!ZJJUsfY3n6+U z@{*sv?<&a6ZU9xrBP@vCWTFq%N|+oU!{*PF>AE-OTz2}G20he1TyoG{ZR&piVy;D5 zRxG+;-*mmBE9f$M=t9%b%DT+^BH~Nx)pP8IUteym1(&`GhqKl_1rx(VL~Q*9Lyrl( zL9be}a)X)%D-$D)A3vY8PiearwBi*xg~6oUR_hQ8>#o9^*1$MEOn zSpQ@`pA0i^MO5YKLFg6?pl~E{ITMu(`+-2o{_zcU?k=|np)&53cIv3qT}|3b-kTU@ z!f#V%Az=^h2W17i9NB?Sk->?o#t7{8(XD*MhIT`jE~5$CzZqaWJS<`Su&=J}C^_?k6)cqR6bG2137V{yQD&vSk#i6ZPmGeW4BY7_y z-%)fT4hi=>fm_XzB{L`O%)rsAAc9$K_JUt}A({luXC^%KXX=@O1fTHs>JIBmB^Ksl zt2;8P9sPrY**D_&yBy1|GHSC+3N>~4f0wAf#DDZ>(SafDK#<%P2d61hQvc(KX)>ba z#|LZ$)4@$;$0CT)uBvin{1uZ6mR+pQV5{mdMe=)y&i;b;#Qpa2amf;XVfIxd^O-uk z`j`_tf;vBqS2>*xNYi)|M;v15q>$FNuA;c-D4BODRrU{?2-{rVkgzHL5{rhI3fKQa zU5!pKk;pSndis#dsJ4X8-WPksu&{>b?wGml5bgUhh}L)5lYh3 zHc|<~e7L!{CyaCO@4O=0%8BuSP*+viD2kJyp}TcV{w zHY<0w&pir)0=06EA7Z2hVq&f>QktLL5KN;oUe0{?XQ4|`tuf!XM2h&vyC706tjd03 zetxEYA@k>AcY8EA0$ego;5xwIhk2;AZE1!55*v)Ccty_5s#IWz&z0y%jf|yb?HT^ko!{eY=QC)K^0x6y?5yb#<16^h+b(+ z#Zuh`Z=&BU)4t)T{<1mRsKX0ZZhz0BAJK$-O*eK<_I}OsZ58b=Wwi(9Y76d#T3D6l zTwfSo`n#=pkOwS$y)Ku=7rd`yS$VCzoboaO5lzO5e@?z8P( zatH;>9HVa9_PP70>uODCPXUu&fN`+(snzNk!l2o_L)^?4atCB?*SxNKt@}^=uEm0G zgiZuyZxlossfcQ$7rJb6-`;sPQmQxkW|4JJZ#Y71#|nHI=4)f~aPdoK46Hq7rJM9f zEAjq>ODmzC7(}-4uQC=|Bg+;tIQEoo&=OY%G>uuu9O)9PLG0@VA}Sj0>yBu(SKFS1 zB^W#o0|x!L@wOIO3DG@N@X+0h;%rh{#+zf7uSzI-I+z0xe^6`2{yAjd7KZQbz#-#L zo?o5`c>5a)6AD{69sB3+Q#lg@5s5FXmKEP97V3Sqp2!~DjbziP8F%x>Cd(q#GR+4VDwG9pz?IAg_y;xjVCfhvtEhe9q_5q>Zh zxxc^hrPceuldTX1k>0f7D|!R#Hw>(g@jKb(V?@8n5@9TMw)LDzskx-jsDtZW-3n>* z`L4R+Tf(BQ3Bzs8SwtA_2q=&92=PH%{WvVf?O~bvNrpjnGLKr_ocUeES{^|ptx#Zs zTk2;)Vu+F~AyKt-NZy-FW}WcXTg`+@9m`heN&DC8jtG|ZY}D-`-d_4y{d@atIsE5@ zF=;yeiV5dA6D}FI?l|bP8hh|AugTW75F!c%_Q$)MzrqUJFK_#Jh}4v-j_IR<;SwK z1w_CxzQ#je%j(5>3jCL^d@ombKsXF%=S~5&YYsBo5uQfoYIWfGy~Yb+Q?2auXyP8b zGj;gOdJgt-qt-hZo746wT0OMd>vv^56U*7?}y}p_axtVuhhbG5skX%%{q0 z??v1<`c9(NL2v#0x1{ws;&6!rg(+49X_u$a>YFse7BQ#T&>HF>CF%^JQm*WqxRS;Y zD{%9Pr{CFi`gXj)hdtZfbp>5bQJAbUb4DZM4H~D-$qOyJJY9{1UN|#uk?CCG+IiS9 za+VQ!fO$yQh+p3NKGt3+?dW(q!v%e8%zfQB(Z$q1p7-&jYPiO!VbnAmzduea!)H0r zRVfmpcsBAzG?2K`H7QbUv|Ij!NAa~*<%09)OnjzX`A-usgL~-jyH3)8$ufcki~0>( zcO@x2!8kETEY0aao#`(7KtM7!q>W$cY#}(C3_}mn_f%P%Rma|V#y(-?eK765A#73% z^{#5=tIE-e)h?wZ_YaQoq~TwEviX8Ni%z4{*@5#0Rb2Gw9K%EvXs(0xM$CNL+?v7m zs@03c_YIzrZ|INgl4fLytOfv#xH~`NPllV;?IlHl~hSTcB zL7_SRc^}o-W-mxBf?X6Ms=mz+HJ)6HK@QZz5FN_8LkUM~W#=KKd+XUmpP2XEbT%J0*) zGsvfLHU$nWW1qJnZuHz z-rFK3NG$y=T~t$`{v0xU(&DC=)RHr#%COr|A5uB8&u+r&Gt8NK+4ccpl#Rb#4=LVf zSTb!EV!7{B8TZ|>3_lg#!$4|C3PB-$H;wPdu82d;pQFZsc|_4#;e5REhzOsqSOuJ$ zAE19V0BmR%sg;2Gps{^{OA)t3gkIh*f`778q_+uCnN4X(VB_5wh2xvbFLJATh@=n0 ziuFjl{aUoQ-i8`1P?ZQ&BtwML&) zbyPFfYyziv+9rGinKkn@HY@@WkLJb8YrFY{T(!2j=(`UvepPh&+Cyrb*>v=mb1%js z11M1s-|=)lxP?m#ZP7^dClsB_m2SRlDuXBd7^=jhj~rpoFcyesG1Hunn04dU1g{#F zK}t9Ng5C*!Xnpjd-7yg5ewJmwC;s=_ts>na=bio*yza#Hi640RbUas*V!IFzQmPnb zqVPAO7et}jQY+ ztWucIla+z51J58Vtt*sX&W8?TV0WTsHI<^Lc zl}N-}_OF6&Si!#aAK!lr;_-)7iX1<=uY`kwhtTzQiUGct2%OtPoeIBOP=!gu)n^nZ z*c#$VMp**f9&JCKXtmzo%&1JLPd{4j%-uOHD{b;{oD8}Uy~cjG;G|k9LY5p{8pe0M zef~N6!Lrb$blzM*JWqA)RD1f03nBcPAUayEA@tYhJN|ZKolzv~;;!qILJ98B4z{pB zi>QxziMe#M)CfKf)+B}#7p+UFAJW$-q(L-bDAA$~>Fx-^J_M?6d7xSWWCabSlQ^xd zfc78YC@8E4y7;+NkXzf@s~KCYH0}@U70sr`l)RzfytnXi{glXKE0VCYu#F^hpA)q6 zlcP}dE;KXwnslOR(_`RiuEn9S;FaAw^``yJaXzEue9U}3>AOgSc|y4?6SF4P&M+p^ zf-v4Y_|osR+9Jol?gns!4>?9brIxuqn=cI5S08GpxR(U@McCYexKDqvmU0BXS%XIp zFfxw=KJ@5(<7khDLO+O?G0EH0-U1_#!`kU)+=h@C#>bP|V|x`@>udT2R&xBPDM7+> z3UT`Zts2J3r_2g=xR;9bm*MP+>?9*5RgMgJ6CxBCo%VV?PDn4PW;yCX!u)Jx51vw` zKq&_#3_%MUL4XlH^Hd-K2e(kG%3MoJ3nq6tmQmfR|LFs3wHc-gwJij}gzQjmxWkHB zde9*>rNlg*lY1{&Hp^qM!%9PxX(_YmPWkP!pnzJ1ksYa*gzETw6|@t5BGN?hR>L{p zH0ry4#^}`-dbGRD=j083C^ zPwb_et&;q zX3ExVV9LSEoRsx4zV_^t)sZTu!jr`I#L!VZpab<_&t*=aO8-aYNPTK`hlAwG&TiO* z8f%bfx(cRhS(H}O#5tlfS=k1we(<{|UB=+q5WN7;&&Y|yq;5yTO>S2ghf7N!y$=dT51^V@fx>$NYQd}Xb=&ue z#5k$VvbzqFRmwNoV?++sW062g$HaJP><+qyzV{F>U!3q(UE7J&d~D-M5p7dKF5VBF z!ma`VJ;VpxE!*2~`u!*$Q%RRFDbLg@j$O`&?>}-uhcXZUSo8l(*xv8~b?T>@J7|%# zayY3*Hp4|WQeS=jDQU^;HBo&?DF9TllxBmvskHzs0-8?qVm33c!`4XC=T8Pd1vI&DHFDKAH0xvHnR_QX#;QP;gxwETH82A>(}e;P@Membn{Ms%qPW1LE}#C{0{4Xcyc_LqH1 zRpfcvkP*Onf+I6O;)P?frWIqBl)?5@G3M%_S-nL2)U^6d8zBF%eSE2ID+HA@7{|Wg zveI*(AKFtJ)ffThfB9~OM!7^W3x0>t$;n5ATaaW)Y=$htTH)Z0yw%Zsr=G-P_$W*k zgn5L~s#s|v=b6fGfosLpLnPql+~Nt%wNXxFqcO+qaql+H54hy2MN~!`Kqm2#``tqu zfyX$TRQ*9uKfFv<39W(Ofm30nWv+9BADYEZTQO)6P9bej|aU)DQ#RawC$%awuNWo36V z(;?44h=9i#8j_-UREJ+Qz{i+?4S90#=2J>+@4?H;K%MpHFmNXY2Xf=49uSd98Y>RC z)1-IO)5{&?$#(yun7l-Ual!P$ z5O`{5fe+Fg_dpAhkmCcF-CED1v>(td?OaLpeA%H?k=voy4f?p*PnR2j?N*5#&1A2{ zXQavr$MSM%Xu1v8Nh_WBx{*C+o@&Q_ro!lFR*Sm|t>A`B1tw^^l7>Oat3YKdaj5}@d)UZUkk z9n!=HYO6nWsSpMtfZ4d&aBWCFi2?B78U9c}?{gSV z^;8~oJ>TI@q#830(JHhOud>sow^%v9Z&Q9}_DnRX&{)NX_Iaz9vCP3YLfIw_@(J2O zDlTR%ZgvL$390CLF(|Z^73B$E@#8b@V0Fb?t3YEJs{@O#6}GS5cY65EBBDiM<-VOZ z8W>b_W7S0Rt#DcuJ}!OJMN^llbbVfgLEK9CZDi)G;RUYW+B?SoaD2*)C*#j_O1dE zxbZ-NIsv={A$mk2UIX~>lp4l{4cZYmF<)wvU}u~#V?wN%uKSIw)#i)+IZjiskyPcI$mE1dR~+Qdt(qx2EEu z8V!0#{2OVzZQ%=bis9b~4K~SfyoF>DF`P2w63sO<{}hc;fAMnk>)NVol|`Aq)sDrD z&(!petps*RKWtduKa963Bc5WD#JR#rBGa2+hi-QPrECMP|&g2O#?r5aS*tmrtgA)19K(e*yg*&JDntxvi29d!> zaoQE%mZ3ZIcB`s8Qon`CruL06&rH^|lX7udO^u9Ql?>lPU5QvalDy;Wq)YNIbk+jl z+x=IzP%BlhXUOa&^*Ku$mG&1)7#n1NoXE8O&Udt^G71urcbfgaXG3J?L*tfb1ocnc zPCNiLiFK}vL}@nKKs)Nbo+znEtWjfF%m-<@PiTeMG%BMag-S0Sm8czM_UAJ=qVOFw zlj1Qv>o88*UK3*N>+Jm^2jcjmw^Q%6yHwu{$?N(96I75g@o znVO0I4_kj3R#n%%al;bQjdXWPcY{cGBVB@YOLvNNcS?763#dpV-AE%{Qt#Ye_y0Yf z<2jyB*B7t7H*2jq*O+6R=kN5-t0GkvRIJj;zdqr8cKcIVtP~x+6-Trzq4D3l8v%M1 z4Cc^Ij^5&OGHB^E)i^vQQvaLG4r4}`dC0`pXn;xWAQaSmbd$~FBQakd*YMetywe_k zYZQ;2g8RTH+2K}$_O!oL_O_a!5FXN$18-G`K1nDDv>-_r5Yof`ZQ55dP~%8cHJll$KV+?;tf;OETE#89DVpn_?4JeIVKG9BV+!F8UB4(1mwe( zU!^Df_dY;}^7a@33jm!|C@t61fq=tI9E?^<^}wEmV=5N41*j|-O?5a;b`@IH00t9k z6&G)wyM0A+l*_PW7MKfu z4%h!TI|hAiC@?)9fyty+hm6j|du*3jj!o}NYGeRlkCVkkO% z*7F>S{jI>~(&}rE-4bOaraTR22iUEsTZ>qPpn=$8ES1%$FX-h!6m|0u8w%X%MnA2B zfYat+Je4#O4&$KEK*V7FEx_mu$C9zC70D3)O<;k1D-ASaXN$p@`oJge|Gt`Z(icuAcpYw?5Op{R1d2a4~=p*blha!Mn#I`Dn4}5a@xd z(sL>$I8@aru4&J{7xLEeuhgItxN=dtX12-#zHKH%4Fd#LV|!oZl0UJ9&zD2UKJR~K zKr$7Zcc$bpWL|=MEfZ>x!VQLvKfYI$VuYv5|Et=4JhJIQ~%H%7D7e=+f6!ux{I*U!euZ)~5EEvpI5IJ;W zB1Zw8SFY0#0ozNLolycnApjWr`(7CIyuK3Eg~kul0RIC1DpGp1*maFgk28=9`+Gb$ zSHWI!e>c~kMI|rbiiZ5pG(NZW3_#*D!AdU&CZi=Hf<9)#2VRTU{}#a@8PsrQ?4WsME>aPMD*5A zq0RpP@A3?f|J`(3g!zFt=c?MJ(dJ9Wy0Ik~0b93smb3Z6i>>TOoe>x1SA02MjFqFe zXSxkUv*l&L{r^QJKd|cE2ToX^4BbpO#Ub;%-3UV^V(C1Uh+3I=;V1!8D|rxNz)BGT zZf)RJpFdh?D9Zrpy8t2(+=F;2$Xx3FXZa4eM3l1ynsn++fw!9Wn$W!_7aZ7q0qA&V zJQp2r0?7RU)FTP$Z8> znQV)pcKO=U26XU@6&gP0|&IiLH69e;~C47AKh^9KRPnul+JWXrKL>1gBA8|FPmG zFt<{o19~>Q<<>fT=vFB$;r?Z7cxe|_+vf* zDQH|H!n&PK0(l$>tl!@s_?Lla-ztwTF9dO0}Z-FyfyyE3HsSiM>p>hKasLNn6X0&zAr6ID6aH_XZ~I z{7Yxzg^hoI2x6a1<-4X&<@PJ>Bz%qmpM?!TaPqqx-V2Zb{BUUWT(0&7scxWY^zmlR z;VSH7Y_VddT8ugHqK@Rb^hI^ii1B~eo5*i}ouaOlJO;5kx0eGuJaB$eq084B_dy#} zWa|B+na^XJlM}ka9TKsapf;k|B1rMRd$GIFWIaoXP6cyk>Sk(z(G3_z802-M-;)HPIin>8Kb&If6$08tAzN5SZD2EI2Z@$V?5@4rW zrcx=1DH~Sp@heIh^m!X=HiA(ynrg(TPxNQ)gt^7wS*_`y$K%;&8x#0TN@aKgURY!5 z&s1o$swBm4{qAwx==W%4e%DBUCZ41C(G!_M;>mY3n_~*>5y}Gl)QuD>*3w1VOir5*5&)@sIa6>IfeaCIc5C0#E_SV+(0xfCPc2?`~nu=B~D> zKw+LI-yfwf6N)?!`wD%ZpdHpdj0^a)FUgNc`~dn~=Rl!t?5z^7W6!5v;2!!}AcjarY%Zh17NcYp10M~}I*$<0esHh@ zVy%!ufE72|w{`iIp60&E#}>OKTy|F-My*N&UzlC2WjQ&>Aq`Z7ua;eP>;F{~6c`g5 z6x>bX+|^qliwIDOuMnXlrC!8rg_C)F{mg|HZzLV7weR5NS69=mmTG_-xT2FP`h1+s z^n7@GW;FFi`#UNr;_9pF;2z^EiUEJ)2-O0~NU(x1MZfsmoo~l)(|L9XX<@~o(a5I^ zPVYiod5658(4+8JrP4ln(??cRw;v?PvrOXWy8Y6{?=dz%qq)p_jTsf9q=HgLCV5?D z(1H6SmB((89BmdFy+4<+QNp(BQP%^Bxz<<@5>8M`AV=`SzDpvK=gh|2K@AL_1N;qGRg2suxUH zu#JyD{HGJ&sdma{)KPAVDb_^gd*QH&&$3KI zh|$_5776ne!*5d^W;l1W90oIjgU*i6?B-8=o8kW5dg^5)U5=xM(}%f+bv114a5NHU znS<6Ukig-V1aEY(K{+bTJ87=s=)wRI4AMi!{Bn7c3SiR%DoXKQaXXoZx$T8_y0Qrr z6IGetoGuSbC>@E#kECd=-<0(Ygkf%QehY~IO^pCm&>lm;xdZAPiG%+4X`jc^yR3-kOl75ab=cm|e_#cC!^a51w*RYzHqvmleYDEa5XI+b6 z-(A74BCuq2ehqGiE(N)gRD*=f!M~DPh7$I6HMEb7`WdX}QbAPo<@JSF@2Q8q>bcC_#UQS77I+!m|@Nno9t`p283|hc> z0(~<-0X~MlGZur^uw#BmC|_rK&c{_xSi3%l(pkqp%WbgIbAhUr64`;5ed+WUElJG9 z3h5j!I||20gA~N)=fKpJPb@H09;#%@*Dat@&}0T`5QOl8mua0e1T4Wd7?eM1#eY@y z%gVKp$5A3gz)63yZDdvlcU6r9G};tW?+`IOEZ*3>3F;~Sn;v(yiv!Cw94HD9m;H(a z)-WH5dWxhqOPwBW*B|fiH@&tBV_(^U!KNxz09_O)rt|^ECwXnGHE9e^6qj~lWQToo_|C9R9417s7lC2LX3C~lySx!sejEh0TtyiN_bp5NasP&{3r9LU`>T= zz(6;FWZ%xL3%P#CQ1DHCFl5Rf%3cOmYGI5VBCNy4J9Osrp}{vz-b{d^x?d*aJYSz~ z$Bo+jDN-sAP|5&fYE#n(c))>Ih_=9PsTqSTnvlC*DjZtu5!8=<>jor$#IT9y?R6I6 zQ>Y0@mXlCEQFIn;Ef5MLupo|ol7eGBND*r#b#;)8{+;~{U`4cys$YFn=mKMF2akR+Y2_yex@e}P^l)nkz-(=3fm078NwLJc_(vDP`)&GKgXK8z%8toYWlT}X5r4s{v0SyKPhw;mBYW}P`mL{vMqAe)nV`v1HOLYh{k+nAl_WGH z<@hl7%959l|Mp;*WCch#jx)bb_TS!tRde0Xe1UJU<+q0U83X4J09b8~k~#S3UXw$k z!S>t5Ew7{H%_xgT|CvMQ4GX-j>G&AC*2Kizk-x;MSDfSbuIsV6p~bun?IRGSE2og8 zxcxgtV<1Chx!#5MZim1Fxc6{^20JkDG(4QGQQ=`G%%dQv`rg>MfL&k04h?vX_53O?oZ1 zg!+uSrY!pEaIOU)3w^vOx5{L99?W@c=f#TNbbj~0oRm(P=IcMfWX33Qg{0vK;N?jM z^fK>;rD{>vy&YZ={w@hPzV&-3LcNgKK5&fd01c13v@8p4N@EI)PH|v4UZ3ae>AW?q zczT5Y{i8+1->;Eps6?IUIF`8XN61fB-2!k}XW)-nzkopiQdnU`HHv>V(|2-mRSmWG z71+%a%byB34lJ+2<;koqb1S`?W$-bkob)ko!IA8aF~$PwY{}KbGpl;dby_jk|A~KX zR9X0H1~kadF>}~G6+?5#(ezWcrbFg5guYOLpgiRfv7`bM6?j;W;=8+euQ zLrt#Bz?M&0_G8{UnOM2Q^bn=CX!|aO94LeLv!@#a!4;jl{h8xEQ`qT* z+}0S+$#ZTdyp=xzWfK!t5_mG1-Gdot+(FB|{h%BqA3|_%fEZaKbp!ze*6P|F6O8KY zg}=G|&QxRkGeizkxW#q>%k!s~)dpyB0L~YVMO>T9?qa6A2r+aY#5+w=(Tmz`g*gLv zX5_zCN5$2lT3^)u=&qR`)YZoMJZFTIFebGx@bY}o?exy%rE~CZlU>r?e?2yIgjam~*k~z4o~rC~NIY{@VNPmEkexy_-`lnL#s<-%r!Ejuq^< zuQiZ;n}U$%I3~Ca&aWYO%JONvLm*+%iW{jSZnWB$@h5PdYX|!+YJcr`iy#qpuEG6iR8qEb)Nw&bIg>#=GXhjd_YqXX zz_f&7o{5#yk;_fNXfVdCW#}B8@34rKLCa;hE&PHJ8}RNNJWN~4h$8xxVKcb&DtXqk z<&Kswk#UQNqfz3h2ks=zMiLq=jVC53Bv7{jyL3ul4~QDzi=>Oi2v%7ly)aX2DX#ms z$A*1^VdPJN;HX+T>Hq2=vW*{I>W}^DG?UB8_ z&X3lHLjP~Qol~vz2L+ECbp{&mE_Zx%Sl&9*`8Et9BYimTS|qgaxuK25HKk_y(Zq#f z3x+8e;4vU)-ox37IY5qldc5D_fFZ(o%s}$}1_lc#T&$HJPO+8%EgvN5ZRy8J@L_gJ z7=l(&$_s*3Mm%O6F^6jj4@4FU>7&p-&+eA3AHnAU>H)nKWDF%YanySm(6SKMWJtgL zGyJxOnIIKo6h0Hd5IrGzu{WFUJ09T3oy^sceaGZ8^aQNUOBR3`q$=XoNUiPh_?0(N zj}b^Yd?E#k#mea5v0G5af&LuQPZOL?EtqU_7Aj(jpxgDZU)B;**zPzS{+vLON3{&U zwMdi5mzzyQtP{j5WAC_&`sc!Kp`Dw#2`VCEh3`b@BJJ#=X-*@V@x6%t-wS28v#;N; zqjxyjLEz(*fpL?5q zt@n4>1JveF2c;2AH^vFm<*q?#(*mU4-p-tR#nr!9+cuv((lO7e-!0+NlTR=B5@$eOpTJ_5{ z81%jSqxRE36Z2zjqL`rJ(42}eX;q3nmVnL`u#1n+8U{^v@66N-NoX;lXoA)gDht1o z76pNlprP66MFvu}RD)-5_!7|WJo0+%j=7xg`uqLBy@ztyiNez+^Fv@IqNjWAg<+(M zu+3Y}$>fa<&OC$+h^~cY&mMr!4gDf|1>W?@KH25=v2nuVR{gmLuUmONWcVi<%H^LV zShOUU`EN2oVq=sTw{gi0TVbSj6M5o3=0v>qS=V?GynM{1o*}=F>0MzM6op}ND}VgS zh$YsR=PldeZdi(KL=S(_E#&#~H;WWe5bJgXDgg(q8q8wQjVwY8JgLVE(m?G?fFAp> z-W$k9E-BFlMhH&kdG|9E#!h?q_6YKy(a*QXr`Om9WH`whdv9}|L8C|TbA)asRk`(# zRs>Uiqqh&*e!K@xXV!={@t1u@x>TJoF`~JiI|O$4&{Y8v!=DK_Z^vTak-4JN9e~ul zG|*%MO-#?%A%p{pVOryn=MOI!ptNpfC6ndijgXT!lXLj<-bfz7wW7o@Vfqk5Xp=F1J{i*GnG-?usyB~XxpW9NV-p&?SMsJ4t8XC^e8_fXsW{4 zee&f@A&lZJ{n?=zeO|7sI9qOyi`*CFyyML6AHCSEoXnbkprK}vSx))#v8p?(P$ai- zSoNn(Sw+X~#Xq&RB+w5apmlp~h+|Y~dvk>V;Y%TDB^mCW;V>2L+A#e_A6|Pq*ehFFxil6Fysiz~N+_$#$AA$jo&l1|7p8 z$xs>khpvexFKomWLeFxpVWfl}s!2(Nu-Iu^LSN^&H09c=MAAuhX?hOmy4j)MaF%-` zL`em_e0@fUdks!zMO7Ou^hZ)BtN9jEpH;$4oQanPp6iPI`9P0EgN5A*ls>B+=B!=6 zUvK4J(u>ugsAT*Awuhb_VfdddkHD1(2W#=Eti1R`VS0)!o4uF%PrbsL2hk30Zo_UR zO$O|Y9i7&()+e`@6sF3_@)dOVL|w|9nHsMv9Mf6bhunRrSrJ+#df&jpk1HsUMETxB z-@bakP-5H|JBd{HcxxEmB$N)i>0>NMBapcWzNY}vZ;n3*#LO#7ntNl@&zmPb(cZg# zB!9}Bf*Fr^Ia&1do5~Vpi&VrfH~@lTYg^&BzJe~+tqr^Hb%Cc*w3}Q=kI2O+=kY4b z0J2PXiJRS=*39jhES6 z4Gc@x)riBq{E8}_ahV!)h40^D;&hqPTdycpL-#5qhRSZC5U#V@I)!)Q>DEEiD0ath z1vflRAL4%Gf~Yhn+&}ad#o_D zz{i2z#Gq?5pfcCqlbpp_5QFHQqLQgK&t`A5cArI*s{L zIQ9NWceUu@5<^H?m`ZA?7jQ(nKd789T4(B68hLJI9p+8*zQzg6gu!9tGSlCid-@@i z1#9D3alGRv-+WLRu|Gfy_V)v!HL${c@yE3$14r>%K{QoZ2zXfj`D|GsId5EGAE|VB zI@Ixn3rJV2(|pofm)Co-?1R-V;lfv1H<6l5-W0*WC<<98weJ z2BDYBZLO4~tbA~=L{_Y6aP$qIVF^{{G}R~->!gmW_Bp2$LRP6OTnr^UJKxox4s6~} zvln%0U#lMez*svMpT>V;t0N(zf09u!vX;H$bhqdcXsTE_e&38Kq%II1Z_~&$E!uhE zE69NlmQ(nO7~D_)qz0CuE`O;4c-J7dtC|Gy_ZBm0q_S;IPN8BxsKoRdl{%BvQT- z6-f?ZUpexYXPT#D()MaLGok^dqceGUQI)7?y$z;#lrdH$ z*q=Uvvv`<{N}@#v^(vyxBcw8&nyU~m?I%$dLD+KMtFAs1h_vSmQDNvxM*_{PoP*Q5utJR}R-Hl6ZShOM7ioF3JCwV>ew2myf%` z=xacZ#~#*~ZxNYmTu(Z7Na%akA_=~KE6TOBl9|h;##QK08nR~o&#y}>$aZSbVE)gp z`}h~FqJaO) zMN)Zf{DxUmvdNn7-Zpt|8-Y&dq+V^QKB!5N!8`2zvpq=Fxh|b$qd22It}9?nXST{&CHs&8rGvLY43{r8T}& znGj|+>RuLiJXN~)f4=d@FWYd1NrdVy)q4yd%|lk?&mhOIZ-Rnqwk;L z&|AdIyo1;L1j!sJv*Ait6Mhaa<;EQ!kRLEYD_X0kYEVOj2Y`ThSK%c~6We!g3}-9%$t zE1^7Rf&pq@6hzIQ2C+YkXf#!x3TCFvh@FH*#XR|h<(Zdxh+>`nPp2yV2SM*uL z2O}MMVgXnBYM0Y}8AO3sgQ35fg!{Bk9r`mFudQ0MdfCK(*cb_cWQ(fVtkJ12@}A=% zE964Epj(X}vvLdM3hV-6P$9ISsZ6sab94@E$oHB2*rsNrth63-;F0#|NX62uLEDIL(Xn|8ew)Xzce4uAP_+E&FXN z)>g2$JHgRA$$SL&3ejG-zmDiuIM7nMOJtyQxUp5`udGbGgsJ+Rgd7AU zk*I&#?;YYcDaH)Q;oCP>Rw3avB0;C;7s4D3mAQ@XF%~_B<1nHZjTF|bzSrrKKGpWJ zm)GRTHtpS`n%97Z!EFo}c-*?zO+r-b@P#o--}K{w#m31FsIYifB!XMTJ4R~H)wQ&D z)@oEK#qXxBBpM)@kw1e7?THr%os%J8_E88Wl4M*60V|+unc?pb2`vAmd4N zCp(xpVK+b@FJs#IA0gL@P>q&ob#A_{gQ#K_A#K6e${}oci|rwJ`Jhe^81e;&@LFoh zQY34thvgOrGB{>fyGbX#qkbQnoqFJ$xo%N^e+491YH5ika(O(p2@@{Cive@B%HKqG zHGa6D%!_dGt!Dx=&CC6Pv)+I`AT{6RGnu{waSO(?Bg&*Kb!p@(W5C>nh#GjPQnd-? zFAl4FJ#+pRl^qHZI-zwHVP}|#%PQ{!a!h)07oQovOg(E7#s=~=?3!}cQaY!lbMGF! z>DO-72ibz%^~4XUvN1l_4vvlptB@B>hZ|=T9VPXAbhqzMe|9?NwnovIuKjK(iR*^< zs6MqBLj0DOd_ZH_hG{lii zRZnN*(U?XVF8-FEy!aYoCaWwnRh57*r{vd}o5TC8>@(WuD-2EAm8~J5m|neH^xSs zS@aP>=Mej7iY|`6+jUF-4kkepBWziC*yDr+f~YU3Cc542+R3a0WwI#h3A7Lu(E973 zU}18$A{-*DP3EzJ~zcsG#0I>GEaaDWc;GpC1?Hh*F4@#Ia=S4 znFGt~Lh%aFnep-mG@4S->DZ(j1ijL-@+5o#K6#@GXUV(+nwGEuJ0mQ1V0$K(QJBd zH<9O2V~G73;==X4HzVnc#Y(4QgqW*|3$}r@u@PO`@X#i%u5p0PEb`p1aE+`-DR(WZpQ4oR0_O#*N)qAZEMgAoEEZ_htA{5aXo9PJPJY?Sh@ z&FpQv8)6fF+VXwAtnxag1|6~GrtFM=zQ%ZpUpR@DYzl$=KFn2kPD$FP3L}#g;mh-T zqkF2@7-xHt+_h=xDDPR>j z8z^VGS6oP!)|_xpF(M9cpb5m|!UkcU>I!W1xwYv9LGeJ5t6B}KydB>LBl#`t1u)wM zBF|rdo7W-VznpFTDYn|z(Y3UO#V+xUOyy#+)DylJGcze!2Y#0v_jck0G^|eBvydhW zt-XkAtQsLE)B}QqFoa4bPA_R|u65o;5{<(A3KaC(QJ16jIM5(Hh4~t}HFvv3lIwir zbnG(=qM3<$FqU*ul_@q@{kct>Jq1nii2_u0Kl$@^Adz;RnsGEY@Qln1&d~{oUu%r8UjPqTj2?ry7JF+PPhnd3|+%-}b-_ zhlobYKUNpaR=3R9V9)xJW%}Z=bjgzu@ea!5^;Ymq=TkT|R$0)DCXzA(<8%~ax$UWms z>krTm2E6q;!FAbCe>xXrdi(PIyY}MGRb}Q76b>@1P)VQ3Pdu^$pH5+57^%cMI*|SK z#asK;Bhcj>j$pvq6`!` z21x9aZH3g$E|@O##lceHb-GC|AP&F4K@Yz}wBsk*c;7!(@Y?jF4$*HNS#sP9)Uidg z`8@NEzUir%BH3a)1ch)a%!5|@6_0wN%jbS%KjD~lgzwAb^CjCJz(zkzG43U{Y~9O> z;eqD+2@(0>Nb0biwnKbN?u%j%4)wB$;l%d^z3B5XEbX~u`TMV)pMtsR)h381bLxp7 zlcBn+6;YXWpY(0sE$m%hsBt?59{6T-(On%n_ZY=0d?CO)S;0E3%TUGr`+V#j?Z-<0 zqkgBo`rzBscQt;*g`E+0n4YTCk6ct+DJ)VUcvsUBFeP+F3rrHtaS}ix=ol{6ZbSZ2 z(OHI2cmwbar>4Zl@>MO;go4DzaSygtx{J|CH64^RPb}ue1hl5xC@?#TEu8laU>z-c zy{YmG>((QjQ}`%o#bBVcC`0hvTtnFc({E>E@r=Gmb@+}>7fthzIE zuY)!HY_obofBU>@Ld#w!54*nJ_dg~q{q$v2{#chPLsjDYOxZRoZQjwi{HRcj(vpy| z{$oA6O=zD*O5wPk-9x>CuUE<9>8F!%Oj_hZ?9fP=A3Ow>s5cEn^CCUY*9J7Oh+Ufm6VJS*LL|7k%G@8&JMpCHE$mlU8|0E~kYS}r8()l{@68)^{6o+54uK{2QIWX`aqny-tFv`Imx`;En zTQdHhV6JN?HQ0V{LhN|4raQaDgPMplB9F1#KK5^6HJ^gUH8I=4T7HCq#NW9O#Z1$w zH5dE}8hb^@!B6Y;gUS?E$e-{ZsFGRBQba1c(+Vj+E9Ipi=W^gEOfhCqXdvORJ?8uo zw&L+G$Zaq4F5c|{wr#SVOic>))MD2JS<*UV?BwUq+&T9=6XGi z>qYEvM80%q5vfR`v^_b0ouw&xS&$T-6fYr8-i?4!@zRJ$!fp^xb6*kT6am_)8>~=; z6sVRa!H*g3M2Hr=VyC}Tah?=&ebkbfEICL%v&dr9QoImUVe{`@OpU9~SP|RaZ0`P8 zjF0adS8GzY9OTSz-%P;Gqu);qbMs|wl>5+0I}n&Pi}`4%w;J4X~5bQmIkZkpi3P%KVK=pJwG| z8Ec5m1yN+WzL)z8x;@|Z+ZWB`k34>ru*2BbY9P8@B>&DZCh6k;jspg)^9Xoo#I`;Y zf?`GW*3~plI#QR>@>r9BSAEUW(P{?m<}{2~4B;=#B~xhwHTA9;+O3vXtS8#a&Eo^a zj*)h}?<-)SX|^4=H)ICxO9jWcXPHJ9FOA)AediE?Z$GExwwti>K=aI~KR9pe^zt`g zb3Dd+U`q9zqpb11nPK=qD`-Z;m&yvaB`|mvVl_+0HlR+wpV0h7q2j@yIyhI_?(qFh zxw4>o-=N$!BGdiWBhunm#v};Sc<^&TTInE4HrFLpoE|M>ACco;Y1vM=wsr3K!*mo+ z-s6dMZ6|QhI7tWak`*i+*wZ}OEN>*Ey$oD_EB2pN_XZ6b)vK&+hAbGUh!Hdso!llb4)EIq9*AVmv~de(=!s3jywkiAj8@|2+n#=E!i_J}P{4_$b%Do6eU#qdu{?E!NRvN9p z9I>8%F?ffV<9?8|9SK-0%MI`x%2{ON1$Y;OJ|ellzDBf#kD#7uzbn?#i61G%V_vt> zd2PFNq`i*2-HVA3Q|c8|6|h3Dsey_T63Rt{MO7?5J0Yx~cVx*cp)FqquB&+{(5C8A zP+vi;BJv1F!bSi1xPPmxNg4un*2&Nb{)6Mn&A1@ngZ(2=|KI7QD zLWq$>q4sT;Ag{?H(~gp(-RP_bLJ9ASncvA5TZ)(FTI}R1J>}NgYilgu16rea@nb)$ z^C|dlNgdfk3lPRjJE**7i?gW0Zi}3O%)&{a*j4KaDeiPIPGchFm>W`gawK1zn7aHb z#nDS9(D-SwiCFNDIeRA)zh~}$LGi>i69((A$h9+hnX5gf%w;1hm;S$>aQlBh;XlQJ zFmyp*{G}c<@+;VLENIEdd*7;z7NzWnz$FbWCa|V}Yz`JEWYx?IEz*O;NX3kA z#JNr47wbgP)I-Vc%&Oisyn)bD2Q`za#x^LnYPI(JW5sg#M9Vt$JWT;S>E;OYC-&}e z!``>S{{%`Tu!E8`Zr1{;OgPxdk`$SykJW2m{B+FQXCoZ?F+ar8mP;-0T{1d|IK5yu zG4ObPptjxjEHpcD0GbR#5!qEyQ9~)^o&VIjaujj+$Hz%QD!5Ew3t5md{_TEs+<)6Z zG>SYFlCv+Gi5Q#&7`?FsoMC=I!q;H&16Ro2yg~T`DJyKJzu#ull+a@A4P=DOO7{9B zmyETL&IQ7S6epjB=(J|t^?xxT03Ry^x=P$J9nCx!+=leXDQ6qWn+v9ZqCjE5MJMzf z#7pM@b;W#pf>xwh@769Iru>+mua^(x&%bp#zr8!-^WqTx?_y#=E~ewO#kZhM-U8gfpSiUo)#o}pMWBVf1J&W>=Dz&t&_lilmTklQtc02aFZKXv-} zb;jn5sz4-Pfin=D{><`6-6ryNL|DhjSnbKy`xS&X9w`%Msn4{SZ}NdyGZ+q40>28? z12vzgpzn3Kg^;(OGd}z{+U~-?RcARQ!akluc27;i3egt;94j|0^`}pB?vgv8rHKLj zc+v8sN%RzE%u=?AxUyNixd1p^ZPY_b5&2Ar$bdfMdHUc13Q5JjV2DyAd`wzP{cQd2 zoAr0K|9xQGJ)jcB-+PmDg2xFvP#Au;(~ZB&RmxUJHyI6wFNK$^^6c(gR>`%gRqgHk}}$K$e#EnyF5f2kY@xXk5QtTijJWGwbaTU6^mZ$prn zJtesapEI^E0kFL3!d{cAF*Wau9T22W9)AEqnOM)WXc0FTIRc`q;Y_=hdu0%8)GoRh zkM!lt-M5{0MQ#847&Iz!LSlZDRustlj22p+hiO0`S<$E?dT zez_~Uz~+*9w}O6dQxPDUCB%?dz_H*_ zL9W?w^bKv9>_zqwoWZDGnc>sW!|7px;FoI>wg0F)vv&*Ly&g|YS4+--g1X3itahTj zgU}`@#09`)ciU-1Nef8K^_|P>@n^D4@3bNHu7)6Kz2UzVR1bOLnJKU!7TO3-Xd?HH z!_(omgA+xQ%%fFM`(cA5o6sfxkVEP3E~HS}-Z#IQn_P^F_+zr6ZZOo%VV6fK`%WyvA4m5N2kj}pI04}p+(OI z4jfzyk5eqeXyQZYBI|O)?#60^`F+6=?us-pT9nR zGi|B&>yWEBvi}3Ha_hg50K4gb2t9xrD)W>vpZ)i6OG12^lmS!_wtE}~tUH8`h--?w<&AO& z=hfwA*hc%CBE-`2IBcB?$BDMW?g@v<|`Kbk&74&5K709k;Ozu{(n5ki-AgG zF8hP@5AH-C%dvSRD%H{znkMx}?ji#cAXLQv%b}8u!2W@IR zdJGR%khlAs|0b}83~*8yb?b`^M4UXqVySozdWoFEsQJD9vq<7^!om|qxNo3;oz3Ue zZ#{yg6X7OHD-YFCyX0d`wbJ~`^WUFh`lc5XktRdQN5rVBJRKIV5=~!=vFd!OX9V85 ze@9|0-`OHH49TP;^;0Va25}$nHsCp~#=4+FqhK?;uQUgEBxaPGe#jJ%&ZAXh+2NNw z;L<%XeE}Xu^97%|Ltwte|;INH0;W_r~DHe1KRdo0Bv7Yckru95I77DIsPi7 z*c4Sj)NsJc@cMOhsN`A$P20zlRUn&AW>7B%inY6Qi+be!sV_fverCRw{j&xlwny5j z6Tbu)%-b0vptVFq{!47Z3*3T|;~lC_ap3a%cA77SXGhrnhwk3RQMiaJ0vnqBWM$^% z_f=f2t)#@6(2`+}*WRLajmif+3-I6q6^W4RGPCSJ2)(!?L+^h6m<2;)tH%+l*kuqO zQ0Jal*Qa4t-fN57=GSNjchnJxy(HqcPI^Q$+Y9`-51erdxgx@8>uKy} za3_Hfp&rNzGu`92%((os;ys=tT&si`7F9chQ03fe4P-@t<5Er92&U~ZlRuJ~Zh%2_ zzaKnC^ENbW}KgeC)GRUkb5D>J;PxDvbPr$+$Tj~WHW-Bn-!mA?h%jJgORxObiN z*PS+qZ?h3wq~Jo?XNY0f@aNUFQ~{QVUU0S5X$we97c5>I0hcm7z!go&vo>YGkPOnN zE;j?`^PB9!@~L#@m&E7QETQX;V1I}t6D3wPS&QIQ@N@z zuXjLijL#yEi+O=dGhE;2#BAW;!^UrHs?ybIz0}KHPoem(*>+*Go$5o}7J~W3gU-d( zokqUSGhCREsMa_`&bJ*PC1ly96Z9u%zTAdu$s2jU6Gmj82hSG+(Y%^FM{SMlFjnrA zWH}?%6n~Av8-MK{oPJ7+XgAYjqiv%VgMvgGgH>a2@No{I;k$D*=HiZyCV2Sx(Ko^d ziYU!r7r|86EY#C@;liVn`T$q!B;w!oVDvGei%~}d{_hk=kkShH_`uD(m_9p4Pd7bd z&>EV2bD8?Qwwf-sq?+W}*-t1j4@*gfK@A5hg$pAA`)b&P6K0!@&6W|3v5-7SAV*wv zXRmd`iR2}o$}7Iga0^%qcmf`?ei0M$N-TV7M1(h3xQLy121tk8ANQATcDgeCR(eD> zvb$$zXRjW++het#!4hibfuwZ%#D91IQSM715Zv!9%1GaJ@+~qR3;4C1QH=`gL!;@O zyW_bK?aWEzv=Zb$Ku_l}qbiL#Tz4@hIE0~tyhj%%m5sVAT@fyY{djar>MV}>cMXqmKk&W(X=9u3yuF5c zB@jX^%HMswB;L4>Cj@V+$Z{p@_Yf4jicpp?Bu?(DUiz@-bU1UKX~@55Xpykc+^G@z zRv_KXtju=5i4(%sL-#v{nE{#eoAoik-Rlp6-yWWBdW=-o#U}aI17JJ~-KS*f@?Cwd zDvXYs(B+MufgM=DC7>1y?pz-&q!mubqdZ9Oo6E6pq6zA)W5%*N-8G?V)n7b?}vX7#`-#HrDqad&sK@ z1&_aB=-B-}wLO7`N4l|S2|L_HM^mT;hHmsQGWfOZxRmyJazV4V_KrJ|(p* zv)e(jI;4|-c}dv$L}LMSA>|Au$u!2d^%)X{+|S#&f%>f)xJ-#ZE<0%(xZz+Qf#{B0 z$9_cEp^}O$M`T{CDdk1zO{y4RU{A`9r=jpsNCt}3@7}kU@!Au)e`iXjhSin$Lz9_% z9)7a|y}pv6%Eg37q%4E#BXBS2Hd;*zuAapNqX}5e8GKay2t=NK)5VJS%ex|Y^0(Px z(XPQVPdewJtUe~!M?DWcbg&tVCgCKTUqE2yC5WWn#(m$D7e^C}15!}A}k{vd)fu=b?;o%r=&b9mc1mrE)?usaj*m-y%Pk)T_EvY+~a)v zTiK(ka2t44d9oDJ&{TSb{sbo$wmlh63w22<>CP5 zkE$!{;y{fmbTG=ln>A3&O6iEP6jazC{25WPedx;Efne6kwEER#6hb1p4iVc0uP`YmdNSh<%Xen_i}tg zw?)1yhOB2DhKNGG(23meG$^&@?`Vtcu_sV7asH--63<#^-|7N^)v||iwkFjwveQ11 zZ=aZ7F92`{Vr|b+{f`m9@A|+^cXn3o;CGR^c00toNtIS{Aw^Ob@EgYIdJK-hrm9J+ z(i6PPd8?pgwbO+-FM2jVF^SwSjGuoZ@}>S#Ks}WsORlbNj!fI4;2Mt@al8f+-pv}) z5^1}V^0bWmhlm`AYtFF|$*%(k;T2dLu1()|4t^%l46TeVYso6XJy(wNuEpc0-IoN< z)C#bBW0+1wN4zrUr0AiVLRonuD~?W?6o6zvF_<&KzKJ$ZPIVQx@3iPoJv=lYUXAbK zC4G>ChXE<+5Ewv^L1aKcK245)Mx>DvC59SFL70b@mX;0)De3%<&-*>^_r@>ZAMl)?&ULNpoW0lDd#}CLS?9iY zxU}N7+#!X+t(D}Kjj%8}Ja*m@K1cQLQkApJ`OA!1=Y96)hB1T;KYZG(P2n@g1X}Ql zI75#q$OZ3bAwS-CP8Pc{>k^P&yXP&Hb<-@W6cYFer-JN zOR|%oRR9$NgTOhs=x`M^6ew;%6tCIkSRfcl`j^)!#(75#7$M9@I0qqQ{%pXLu#$z1 zy-6OQjQKVdfLO@-WB337^&x?^gd7`0KyB^s_{>qH?C%U{@Oy+6t%mz?pZ7Vw*&)n=B&Xnr%A8(bi~~q*$lj z8O??Zg2Xl$w<_Ug1qwTRJLtzL7F8xEt?-yE=I7LR6|MR1VnH?>f~LJ8P8{seVA;Hb z`&MR7h{q$#2kL3%jl4UZ>TiWdrE-?*K1kj3#bP97@w=^2pTIi`-*WG3>!p;y zkMMDT*$g~f6ep<_U#y%U5iSX*-@U#5iI8E|W%nk6XFWpZlU-nC(GdqKVE7YR<&xy` zwCStRcYO1c-CtaP$y7f5QBp=duK$ssr(i;MY*cHtwy}OU$II#mR5z@IT70|y61_L} zE}xH+{D|Bs@O@$|J0tJ+-6O8KNF)6Uxhtc^Q@qNcQRShqrR6SxQ$jO!wS0h*6T$ov z@%gx}vZ~E=q)fvb0xXJcB?mp#7sGsJzl!HRA}=K>#Yq&(M7PLoQbYho#pAiqq8sl10R;B zO2MJ0)GJ`CBin4^ea^iz3{*Bz3M7b&w6AEu4`!$AOh%JtY##0lGMi5@JzDr(Grjl7 zo!?B}!XO#stZpJ=;T1l+)?ZGgDa^<*(D9UE0?~&+lt27>`yB5)(40 zSu(;7-2dEEDVSl)x(svFX$j9XV-6}e^el@|8|P}4hO$xt2iTOJ8V+3oUb2IYhJ3QW z1C7o}6Uke_;OrMxo84Abm(fU!9Qt+0CVu%J?{O8N-2>eNWX(KlmIBahrZIGuNtpIP zm>HqY95j6_0U7UK<)4$BG&KUPE>Sb4Sl2LX%%4C`|-n;Lzy%R zDS3Zzn11YLOZ80TtAtmPw=iJA^a5i&RW zW9&tjVPrtS(cS>JnoiBt0P8N1#j4G{!J8kaJ?kCcLWnn~5rLHM8iBLDFLl4{FH`%8 z7(C*DDbJ*NTO(|+Ht_{Io{D!@PoZDTC}=t-#D&1N-;!OOPqeNn$zZcMtR@`9qFZ=M zNUuWPpQaSDBgU3rA7(WX8JW={wA7d|)18TSt)=^9m+$~0Tw*F8lr=P;!A9;$ndU{a zn=*%8QW1PGS~Fp@Fjo3j+T9RNW0kQu2LpBW>~(uMn-PXl=Xo|j z&q2iC_#-heDpsCj!<3fhO%Hz)fT(dB+0b>&bLZ6>`Ld<@VhK)J+3b}VQO@8sy_a1DZ^x_g9bO5p4HRBwQK4|i-R|94 z0AHBhk5rguVdve_B6RFtlTk7^lWDJ>Zn*WU$&7d8VVZ&oBf;Hj*^n^qLSxo%HU`%6 zG2_K|s>yt3+GV8`NE)_){b8W5 zwys`wu6lh+*o>fWJMEjx9b+ns*Ud1>h_?)Np3YiBiw|b9_YHD(1dBn3A^|#?_mO3_ z!f1Ln+ODN_1sZA{{h}%xC&?vo9~c8nDg9`LrFB6mVf+I^{kIdw>g?3~K#yDXt0J@P z?Z&S6+Tx0(;#$;bVfxENX*%DX25S-M&PKvB5iaYUj4;Zzy_7;e#8NdKkrv)uI+z5Z zIdT_3ohy>&51_bvF3*0AA`APUqqqiYy%l38m2sn4!H#M_rjzT}RVVBuDfN;Srd1WW zICZ9uxM$nJtnJp5LHLX8=r*g5-A^>wyp<7|YH!ktlh0_HjuZN+)8&$eC7P^svwxh6 z)LDh*6we{Il|7t+GsR!QQ9hHVk^Am+)}`1pJNNf86wIUU#h8b#R05p43{y^+@hA({ z64333lNb&jad&^JCe8w<1V~EQ1K{v;<&yL4CoEABSDx??^45k~WSin@?N8Sdd;%RK zw%}0z^5Ml}fReauKZO*ND8{@r@=e$HD;slrLL(6a7rVt_H+s#R88_$b*JD*9CSpVL z8}u{!snW=JQ61YXt!|@xQviCagPitK{QGg+Ik~XyRrNE~zyY2R2hN`(C!P-OE=_Um zp||Na6bQWU7p*ohQL+qeojwm}aJ8?bI{hRPxm+UAV#KK3yqc9E#YHeM**FGJ?A(_1 zxi)fkG0{Csl& zsLU`nKB{GM#D;FYEKtFP z<@ET~D97j1_0@` zl}MVxg6Q&{#f@~s-@H(lJ^ zdlTt0BHDp>b1Bd@FTRzvIB32Z`?XUqSFE^E8ei(Q2>NS8=m6#mc>#wwKGxJ91~|xE z1L?l&cf>D_Woi0kTrN|(e4F`Fb5Jn4OB_NMIPHk>x0b zmQ&7LZQSi>RXwq!&Cx+Gq#;n`&5BK(4gVR%TNPGv$l=mPm5RHMQ(DA*_a2B>1Nj5` z5gdh-pJmNi>NCD}E7ZnS*akmXKuK-^c+p%1hwdsc9RKFN4NZjKxCA3^er;u(8)jp2e)orFuErbTM z%ZBJB6)SsQ!O^;#q^%u^<+1HaloC>JT>$#A8jUCxWJ-A0*xMl+@K`k;$*G?KUB;~d zm#yVV&x~yWfJ?@P$WwkOM5whjq1Vh`sEcKmO<=zdFSWKV(E)Ta=hnwJ1s=R$L7n^P zZ+o4FZ*ytJfa>Luj9e5(QsyOwye~%@!5cD!NBGcRtw}Gp*Oijq?oG7 zn~`Jhp1#F*Gabp5+e?x`h>8}w(~D0LOH)$#LlEXF5nKkC>O*I&lg&JAxr#k@23V>) zzd*oSUEw(AI%LpoA^ZDKD6_=?8{v4+-sfa(LdUH={%kIg)iya4;oy&nN*Th&={M(( z7{~7s?{Nc-2$Cj`_|$Pg4s$`&Sq1m1p7^B3SJ{)8xJE`jW0|rqJN!xUv$m!3P@dFIJ0&}78wD5p?Sw% z+msJ|>m1k5czmEH_t4}$`DsVLjFp#Tqf7v1xU5r-gl(aymB?|oSAh2^t}=x{qHqFu zLu4){n4i0Yi&6H?DoO~!cD##6p=mR5abUMopb?cX)XVtcaB z%Q!|L;;aH#6r_}ja03g1FIzW?Gd)DphCoQfw;|;ik&%sKwpB^>bq}}KE)=#UB~lzb ztj{0K=ub`*OnkM`GZXGkh(v!M=$fpHG~}uEx6xw7CVV1o&ia&9jXkpT!9^(MBf#|V zGDZg;XYmPw2;T^!5ayll?r}-96U#>Jt%Q*yqQVPtiV};7_Stz_B{nwC{pe@VSwz3o z_kS7exR5J5h{*|RLCxg?EqQ+WF1RzBm`wH;!B%6Vhr+cz1 zN+`Evk2gMs4Dcee`BLv!i0^ryFu)+53E$~yu+x5$I}IJXV$h!1tLT=hf$*>ji#OE4 z@3`@U%cZBzMOddTlGR4MzmON+4NLl&bL9zV5GsMmWw-wq%IfA+YY6wb}+t=dpVY(K&u_ie(uY5nDaw zF1I)9a@Xi#)IQje40yc5(gei@!SkeXdwE(oe6{$DKQ$QsZwMHw z;P%lQK>Z&Fr_3majATPdjmF&;t7YtrTFFzennyGwSc@_tz5odrkf2?EZEX ie|hP@9mW68QCu^V3U`E+jDS3`fJ;RQp;)0{67oNP5Wa!{ literal 0 HcmV?d00001 diff --git a/docs/circuit_cutting/tutorials/tutorial_3_circuit_cutting_with_quantum_serverless.ipynb b/docs/circuit_cutting/tutorials/tutorial_1_automatic_cut_finding.ipynb similarity index 54% rename from docs/circuit_cutting/tutorials/tutorial_3_circuit_cutting_with_quantum_serverless.ipynb rename to docs/circuit_cutting/tutorials/tutorial_1_automatic_cut_finding.ipynb index 7f5819c1e..0b358b98d 100644 --- a/docs/circuit_cutting/tutorials/tutorial_3_circuit_cutting_with_quantum_serverless.ipynb +++ b/docs/circuit_cutting/tutorials/tutorial_1_automatic_cut_finding.ipynb @@ -5,23 +5,17 @@ "id": "c6cd641f", "metadata": {}, "source": [ - "# Circuit Cutting with Quantum Serverless\n", + "# Tutorial 1: Circuit Cutting with Automatic Cut Finding\n", "\n", - "**Circuit cutting** is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n", + "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n", "\n", "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n", "\n", "This wire cutting technique is comprised of the following basic steps:\n", "\n", - "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use an automatic method to find optimal cut(s). See [tutorial 2](tutorial_2_circuit_cutting_manual_cutting.ipynb) to manually cut a circuit.\n", + "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use an automatic method to find optimal cut(s). See [tutorial 2](tutorial_2_manual_cutting.ipynb) to manually cut a circuit.\n", "2. **Evaluate**: Execute those subcircuits on quantum backend(s).\n", - "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution).\n", - "\n", - "--------\n", - "\n", - "**[Quantum serverless](https://github.com/Qiskit-Extensions/quantum-serverless)** is a platform built to enable distributed computing across a variety of classical and quantum backends.\n", - "\n", - "In this demo, we will show how to send each of the three circuit cutting steps to a cloud provider." + "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution)." ] }, { @@ -31,12 +25,12 @@ "source": [ "## Create a quantum circuit with Qiskit\n", "\n", - "In this case, we'll create a hardware-efficient circuit with two (linear) entangling layers." + "In this case, we'll create a hardware-efficient circuit (`EfficientSU2` from the Qiskit circuit library) with two (linear) entangling layers." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "eb859bde", "metadata": {}, "outputs": [ @@ -47,7 +41,7 @@ "

" ] }, - "execution_count": 1, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -71,94 +65,6 @@ "circuit.draw(\"mpl\", fold=-1, scale=0.7)" ] }, - { - "cell_type": "markdown", - "id": "461e57e3", - "metadata": {}, - "source": [ - "## Set up the Qiskit Runtime Service\n", - "\n", - "The Qiskit Runtime Service provides access to IBM Runtime Primitives and quantum backends.\n", - "Alternatively, a local statevector simulator can be used with the Qiskit primitives." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d1fb2ca", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import (\n", - " QiskitRuntimeService,\n", - " Options,\n", - ")\n", - "\n", - "# Use local versions of the primitives by default.\n", - "service = None\n", - "\n", - "# Uncomment the following line to instead use Qiskit Runtime.\n", - "# service = QiskitRuntimeService()" - ] - }, - { - "cell_type": "markdown", - "id": "c6382121", - "metadata": {}, - "source": [ - "## Set up the QuantumServerless object\n", - " * We will use our local CPU cores as our cluster for this demo\n", - " * We will see commented examples of how one might change the quantum serverless context throughout the workflow" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "175b4f9e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from quantum_serverless import QuantumServerless, get\n", - "\n", - "serverless = QuantumServerless()\n", - "serverless.providers()" - ] - }, - { - "cell_type": "markdown", - "id": "0cab5dd8", - "metadata": {}, - "source": [ - "## Set the runtime options and backends\n", - "\n", - "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3cc622d9", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the Sampler and runtime options\n", - "options = Options(execution={\"shots\": 4000})\n", - "\n", - "# Run 2 parallel qasm simulator threads\n", - "backend_names = [\"ibmq_qasm_simulator\"] * 2" - ] - }, { "cell_type": "markdown", "id": "0aa14d2f", @@ -166,79 +72,31 @@ "source": [ "## Decompose the circuit with wire cutting\n", "\n", - "In this example, we will use an automatic method to find cuts matching our criteria. See [tutorial 2](tutorial_2_circuit_cutting_manual_cutting.ipynb) for how to manually cut a circuit.\n", - " * `method='automatic`: Use a mixed integer programming (MIP) model to find optimal cut(s)\n", - " * `max_subcircuit_width (int)`: Only allow subcircuits with 6 qubits or less\n", - " * `max_cuts (int)`: Cut the circuit no more than two times\n", - " * `num_subcircuits (list)`: The number of subcircuits to try, in this case only 2 subcircuits\n", - " \n", - "We will call the `cut_circuit_wires` function within a `QuantumServerless` context, which means it will be run on the specified cluster. Remember, the default cluster for this demo will use the cores on our local CPU. To specify a new cluster, the `QuantumServerless.set_provider` method should be used.\n", - "\n", - "Since the `cut_circuit_wires` function is annotated with a `@run_qiskit_remote` decorator from `quantum-serverless`, it is a remote function and will return a futures object. This means we should use the `get` function from `quantum-serverless` to retrieve the results from the remote function.\n", - "\n", - "Note, the `get` function is a blocking command. No lines of code after it will be executed until the results are retrieved via the futures object." + "In this example, we will use an automatic method to find cuts matching our criteria. See [tutorial 2](tutorial_2_manual_cutting.ipynb) for how to manually cut a circuit.\n", + " * `method='automatic'`: Use a mixed integer programming (MIP) model to find optimal cut(s)\n", + " * `max_subcircuit_width=6`: Only allow subcircuits with 6 qubits or less\n", + " * `max_cuts=2`: Cut the circuit no more than two times\n", + " * `num_subcircuits=[2]`: A list of the number of subcircuits to try, in this case 2 subcircuits" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "8c11457a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Exporting as a LP file to let you check the model that will be solved : inf \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Version identifier: 22.1.0.0 | 2022-03-27 | 54982fbec\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m CPXPARAM_Read_DataCheck 1\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m CPXPARAM_TimeLimit 300\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Warning: Non-integral bounds for integer variables rounded.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Tried aggregator 3 times.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m MIP Presolve eliminated 37 rows and 8 columns.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m MIP Presolve modified 7 coefficients.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Aggregator did 103 substitutions.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Reduced MIP has 366 rows, 127 columns, and 1072 nonzeros.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Reduced MIP has 121 binaries, 6 generals, 0 SOSs, and 0 indicators.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Presolve time = 0.00 sec. (2.10 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Found incumbent of value 2.000000 after 0.00 sec. (3.52 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Probing fixed 18 vars, tightened 0 bounds.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Probing changed sense of 36 constraints.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Probing time = 0.00 sec. (1.05 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Cover probing fixed 4 vars, tightened 14 bounds.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Tried aggregator 2 times.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m MIP Presolve eliminated 347 rows and 108 columns.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m MIP Presolve modified 102 coefficients.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Aggregator did 19 substitutions.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m All rows and columns eliminated.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Presolve time = 0.00 sec. (0.90 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Root node processing (before b&c):\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Real time = 0.01 sec. (5.60 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Parallel b&c, 16 threads:\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Real time = 0.00 sec. (0.00 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Sync time (average) = 0.00 sec.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Wait time (average) = 0.00 sec.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m ------------\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m Total (root+branch&cut) = 0.01 sec. (5.60 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m --------------------\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m subcircuit 0\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=13574)\u001b[0m ρ qubits = 0, O qubits = 2, width = 5, effective = 3, depth = 8, size = 19\n" - ] - } - ], + "outputs": [], "source": [ + "%%capture\n", + "\n", "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import cut_circuit_wires\n", "\n", - "with serverless:\n", - " cuts_future = cut_circuit_wires(\n", - " circuit=circuit,\n", - " method=\"automatic\",\n", - " max_subcircuit_width=5,\n", - " max_cuts=2,\n", - " num_subcircuits=[2],\n", - " )\n", - " cuts = get(cuts_future)" + "cuts = cut_circuit_wires(\n", + " circuit=circuit,\n", + " method=\"automatic\",\n", + " max_subcircuit_width=5,\n", + " max_cuts=2,\n", + " num_subcircuits=[2],\n", + ")" ] }, { @@ -248,15 +106,15 @@ "source": [ "**The results from decompose includes information about the wire cutting process, e.g.,**\n", "\n", - "- `subcircuits`: list of QuantumCircuit objects for the subcircuits\n", - "- `complete_path_map`: a dictionary mapping indices of qubits in original circuit to their indices in the subcircuits\n", + "- `subcircuits`: list of `QuantumCircuit` objects for the subcircuits\n", + "- `complete_path_map`: a dictionary mapping indices of qubits in original circuit to their indices in the subcircuits. Note that some qubit indices may be mapped to more than one subcircuit.\n", "- `num_cuts`: the number of times the circuit was cut\n", - "- `classical_cost`: the final value of the cost function used to find optimal cut(s)" + "- `classical_cost`: the final value of the objective function used to find optimal cut(s). The objective function represents the postprocessing cost to reconstruct the original circuit output and is set to be the number of floating-point multiplications involved in the reconstruction. This quantity is also returned in the case of [manual wire cutting](tutorial_2_manual_cutting.ipynb). See [Section 4.1.4 of CutQC](https://doi.org/10.1145/3445814.3446758)." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "id": "465733e2", "metadata": {}, "outputs": [ @@ -282,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "938f7733", "metadata": {}, "outputs": [ @@ -293,7 +151,7 @@ "
" ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -305,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "id": "1c3a5712", "metadata": {}, "outputs": [ @@ -316,7 +174,7 @@ "
" ] }, - "execution_count": 19, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -331,36 +189,89 @@ "id": "742ec1e1", "metadata": {}, "source": [ - "## Evaluate the subcircuits with Qiskit Runtime\n", + "## Evaluate the subcircuits" + ] + }, + { + "cell_type": "markdown", + "id": "461e57e3", + "metadata": {}, + "source": [ + "**Set up the Qiskit Runtime Service**\n", "\n", + "The Qiskit Runtime Service provides access to Qiskit Runtime Primitives and quantum backends. See the [Qiskit Runtime documentation](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/) for more information.\n", + "Alternatively, if a Qiskit Runtime Service is not passed, then a local statevector simulator will be used with the [Qiskit Primitives](https://qiskit.org/documentation/apidoc/primitives.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5d1fb2ca", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import QiskitRuntimeService\n", "\n", - "Note that two local cores will be used to support each of the parallel backend threads we specified earlier.\n", + "# Use local versions of the primitives by default.\n", + "service = None\n", "\n", - "We will call the `evaluate_subcircuits` function within a `QuantumServerless` context, which means it will be run on the specified cluster. Remember, the default cluster for this demo will use the cores on our local CPU. To specify a new cluster, the `QuantumServerless.set_provider` method should be used.\n", + "# Uncomment the following line to instead use Qiskit Runtime Service.\n", + "# service = QiskitRuntimeService()" + ] + }, + { + "cell_type": "markdown", + "id": "0cab5dd8", + "metadata": {}, + "source": [ + "**Configure the Qiskit Runtime Primitive**\n", "\n", - "Since the `evaluate_subcircuits` function is annotated with a `@run_qiskit_remote` decorator from `quantum-serverless`, it is a remote function and will return a futures object. This means we should use the `get` function from `quantum-serverless` to retrieve the results from the remote function.\n", + "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Qiskit Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits. Backends could be [simulator(s) and/or quantum device(s)](https://quantum-computing.ibm.com/services/resources?tab=systems). In this tutorial, two local cores will be used to support each of the parallel backend threads we'll specify below.\n", "\n", - "Note, the `get` function is a blocking command. No lines of code after it will be executed until the results are retrieved via the futures object." + "If no service was set up, the `backend_names` argument will be ignored, and Qiskit Primitives will be used with statevector simulator." ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, + "id": "3cc622d9", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import Options\n", + "\n", + "# Set the Sampler and runtime options\n", + "options = Options(execution={\"shots\": 4000})\n", + "\n", + "# Run 2 parallel qasm simulator threads\n", + "backend_names = [\"ibmq_qasm_simulator\"] * 2" + ] + }, + { + "cell_type": "markdown", + "id": "4e5d9696", + "metadata": {}, + "source": [ + "**Evaluate the subcircuits on the backend(s)**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "2ae5160c", "metadata": {}, "outputs": [], "source": [ "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import evaluate_subcircuits\n", - "from circuit_knitting_toolbox.circuit_cutting import WireCutter\n", "\n", - "with serverless:\n", - " subcircuit_probabilities_future = evaluate_subcircuits(\n", - " cuts,\n", - " service_args=(None if service is None else service.active_account()),\n", - " backend_names=backend_names,\n", - " options=options,\n", - " )\n", - " subcircuit_instance_probabilities = get(subcircuit_probabilities_future)" + "subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n", + "\n", + "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n", + "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n", + "# service_args=service.active_account(),\n", + "# backend_names=backend_names,\n", + "# options=options,\n", + "# )" ] }, { @@ -370,12 +281,12 @@ "source": [ "**Inspecting the subcircuit results**\n", "\n", - "In this case, the original circuit was cut 2 times (we can also get this info from `cuts['num_cuts']`):" + "In this case, the original circuit was cut 2 times (we can also get this info from the previous step: `cuts['num_cuts']`):" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 13, "id": "fa22661e", "metadata": {}, "outputs": [ @@ -399,12 +310,12 @@ "From these two wire cuts, there are $4^2=16$ variants of the first subcircuit corresponding to the combination of measurement bases: $P_i\\otimes P_j$, for the Paulis $P_i \\in \\{I, X, Y, Z \\}$. And there are $4^2=16$ variants of the second subcircuit corresponding to the combination of initialization states: $|s_i\\rangle\\otimes|s_j\\rangle$, where $|s_i\\rangle \\in \\{ |0\\rangle, |1\\rangle, |+\\rangle |+i\\rangle\\}$. \n", "\n", "\n", - "Note that some subcircuit probabilities can be negative (and not sum to unity). This is because the raw probabilities from subcircuits must be modified to account for the measurement bases of ancillary qubits. See Section 3 of [CutQC](https://doi.org/10.1145/3445814.3446758) for more details." + "Note that some subcircuit probabilities returned by the evaluate step can be negative (and not sum to unity). This is because the raw probabilities from subcircuits must be modified to account for the measurement bases of ancillary qubits. See Section 3 of [CutQC](https://doi.org/10.1145/3445814.3446758) for more details." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 14, "id": "7e57f303", "metadata": {}, "outputs": [ @@ -436,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 15, "id": "3ec4d42c", "metadata": {}, "outputs": [ @@ -467,33 +378,25 @@ "source": [ "## Reconstruct the full circuit output\n", "\n", - "Next, the results of the subcircuit experiments are classical postprocessed to reconstruct an estimate of the original circuit's full probability distribution.\n", - "\n", - "We will call the `reconstruct_full_distribution` function within a `QuantumServerless` context, which means it will be run on the specified cluster. Remember, the default cluster for this demo will use the cores on our local CPU. To specify a new cluster, the `QuantumServerless.set_provider` method should be used.\n", - "\n", - "Since the `reconstruct_full_distribution` function is annotated with a `@run_qiskit_remote` decorator from `quantum-serverless`, it is a remote function and will return a futures object. This means we should use the `get` function from `quantum-serverless` to retrieve the results from the remote function.\n", - "\n", - "Note, the `get` function is a blocking command. No lines of code after it will be executed until the results are retrieved via the futures object." + "Next, the results of the subcircuit experiments are classically postprocessed to reconstruct the original circuit's full probability distribution." ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 16, "id": "5aceecc0", "metadata": {}, "outputs": [], "source": [ "%%capture\n", + "\n", "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import (\n", " reconstruct_full_distribution,\n", ")\n", "\n", - "\n", - "with serverless:\n", - " reconstructed_probabilities_future = reconstruct_full_distribution(\n", - " circuit, subcircuit_instance_probabilities, cuts\n", - " )\n", - " reconstructed_probabilities = get(reconstructed_probabilities_future)" + "reconstructed_probabilities = reconstruct_full_distribution(\n", + " circuit, subcircuit_instance_probabilities, cuts\n", + ")" ] }, { @@ -501,12 +404,12 @@ "id": "3dbae8e0", "metadata": {}, "source": [ - "Here are the reconstructed probabilities for the original 8-qubit circuit:" + "**Here are the reconstructed probabilities for the original 8-qubit circuit:**" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 17, "id": "919958cb", "metadata": {}, "outputs": [ @@ -536,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 18, "id": "5353b0c8", "metadata": {}, "outputs": [], @@ -551,31 +454,33 @@ "id": "03f63d3b", "metadata": {}, "source": [ - "The verify step includes several metrics, including the chi square loss. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting_toolbox/utils/metrics.py)." + "**The verify step includes several metrics**\n", + "\n", + "For example, the chi square loss is computed. Since we're using the Qiskit Sampler with statevector simulator, we expect the reconstructed distributed to exactly match the ground truth. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting_toolbox/utils/metrics.py)." ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 19, "id": "673d3cb3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'nearest': {'chi2': 0.01493107383072922,\n", - " 'Mean Squared Error': 5.382563197894436e-07,\n", - " 'Mean Absolute Percentage Error': 111.92728200840769,\n", - " 'Cross Entropy': 3.725665476499916,\n", - " 'HOP': 0.9955995089285713},\n", - " 'naive': {'chi2': 0.01413976015817252,\n", - " 'Mean Squared Error': 6.057006747639112e-07,\n", - " 'Mean Absolute Percentage Error': 236.46136537000393,\n", - " 'Cross Entropy': 3.6905343536390607,\n", - " 'HOP': 0.9923360798300288}}" + "{'nearest': {'chi2': 0,\n", + " 'Mean Squared Error': 8.13178352181795e-35,\n", + " 'Mean Absolute Percentage Error': 4.4880309854901524e-10,\n", + " 'Cross Entropy': 3.564551116068219,\n", + " 'HOP': 0.9945381353717198},\n", + " 'naive': {'chi2': 0,\n", + " 'Mean Squared Error': 3.7794080473092745e-35,\n", + " 'Mean Absolute Percentage Error': 4.4880563544629694e-10,\n", + " 'Cross Entropy': 3.564551116068219,\n", + " 'HOP': 0.99453813537172}}" ] }, - "execution_count": 40, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -589,12 +494,14 @@ "id": "ec8c120e", "metadata": {}, "source": [ + "**Visualize both distributions**\n", + "\n", "If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 20, "id": "c8cc97e9", "metadata": { "scrolled": false @@ -602,12 +509,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "execution_count": 41, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -647,14 +554,14 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 21, "id": "5e6e41aa", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ibmq-provider0.19.2
qiskit-nature0.4.5
System information
Python version3.10.6
Python compilerClang 13.1.6 (clang-1316.0.21.2.5)
Python buildmain, Aug 11 2022 13:49:25
OSDarwin
CPUs8
Memory (Gb)32.0
Sun Oct 23 12:02:26 2022 CDT
" + "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ibmq-provider0.19.2
qiskit-nature0.4.5
System information
Python version3.10.6
Python compilerClang 13.1.6 (clang-1316.0.21.2.5)
Python buildmain, Aug 11 2022 13:49:25
OSDarwin
CPUs8
Memory (Gb)32.0
Wed Oct 26 11:27:39 2022 CDT
" ], "text/plain": [ "" @@ -704,7 +611,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/docs/circuit_cutting/tutorials/tutorial_2_circuit_cutting_manual_cutting.ipynb b/docs/circuit_cutting/tutorials/tutorial_2_circuit_cutting_manual_cutting.ipynb deleted file mode 100644 index 46df22956..000000000 --- a/docs/circuit_cutting/tutorials/tutorial_2_circuit_cutting_manual_cutting.ipynb +++ /dev/null @@ -1,472 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c6cd641f", - "metadata": {}, - "source": [ - "# Circuit Cutting with Manual Wire Cutting\n", - "\n", - "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n", - "\n", - "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n", - "\n", - "This wire cutting technique is comprised of the following basic steps:\n", - "\n", - "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use a manual method to specify the cut(s). See [tutorial 1](tutorial_1_circuit_cutting_automatic_cut_finding.ipynb) to automatically cut a circuit.\n", - "2. **Evaluate**: Execute those subcircuits on quantum backend(s).\n", - "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution)." - ] - }, - { - "cell_type": "markdown", - "id": "3c17f515", - "metadata": {}, - "source": [ - "## Create a quantum circuit with Qiskit\n", - "\n", - "In this tutorial, we'll use the example circuit shown in [CutQC](https://dl.acm.org/doi/10.1145/3445814.3446758)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "eb859bde", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from qiskit import QuantumCircuit\n", - "\n", - "num_qubits = 5\n", - "\n", - "circuit = QuantumCircuit(num_qubits)\n", - "for i in range(num_qubits):\n", - " circuit.h(i)\n", - "circuit.cx(0, 1)\n", - "for i in range(2, num_qubits):\n", - " circuit.t(i)\n", - "circuit.cx(0, 2)\n", - "circuit.rx(np.pi / 2, 4)\n", - "circuit.rx(np.pi / 2, 0)\n", - "circuit.rx(np.pi / 2, 1)\n", - "circuit.cx(2, 4)\n", - "circuit.t(0)\n", - "circuit.t(1)\n", - "circuit.cx(2, 3)\n", - "circuit.ry(np.pi / 2, 4)\n", - "for i in range(num_qubits):\n", - " circuit.h(i)\n", - "\n", - "circuit.draw(\"mpl\", fold=-1, scale=0.75)" - ] - }, - { - "cell_type": "markdown", - "id": "461e57e3", - "metadata": {}, - "source": [ - "## Set up the Qiskit Runtime Service\n", - "\n", - "The Qiskit Runtime Service provides access to IBM Runtime Primitives and quantum backends.\n", - "Alternatively, a local statevector simulator can be used with the Qiskit primitives." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d1fb2ca", - "metadata": {}, - "outputs": [], - "source": [ - "from qiskit_ibm_runtime import (\n", - " QiskitRuntimeService,\n", - " Options,\n", - ")\n", - "\n", - "# Use local versions of the primitives by default.\n", - "service = None\n", - "\n", - "# Uncomment the following line to instead use Qiskit Runtime.\n", - "# service = QiskitRuntimeService()" - ] - }, - { - "cell_type": "markdown", - "id": "5fb383d2", - "metadata": {}, - "source": [ - "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d409553d", - "metadata": {}, - "outputs": [], - "source": [ - "# Set the Sampler and runtime options\n", - "options = Options(execution={\"shots\": 4000})\n", - "\n", - "# Run 2 parallel qasm simulator threads\n", - "backend_names = [\"ibmq_qasm_simulator\"] * 2" - ] - }, - { - "cell_type": "markdown", - "id": "61d2944a", - "metadata": {}, - "source": [ - "## Set up the Wire Cutter from the Circuit Knitting Toolbox\n", - "\n", - "Instantiate a `WireCutter` with the circuit and runtime information." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "57c8dccb", - "metadata": {}, - "outputs": [], - "source": [ - "from circuit_knitting_toolbox.circuit_cutting import WireCutter\n", - "\n", - "cutter = WireCutter(\n", - " circuit, service=service, backend_names=backend_names, options=options\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "30e45e68", - "metadata": {}, - "source": [ - "Note: if only a circuit is passed to `WireCutter`, a local Qiskit Sampler with the statevector simulator will be used instead:
\n", - "```cutter = WireCutter(circuit)```" - ] - }, - { - "cell_type": "markdown", - "id": "0aa14d2f", - "metadata": {}, - "source": [ - "## Decompose the circuit with wire cutting\n", - "\n", - "In this example, we will use a manual method to specify the wire cuts. See [tutorial 1](tutorial_1_circuit_cutting_automatic_cut_finding.ipynb) for how to automatically cut a circuit.\n", - " * `method='manual`: Manually specify the wire cuts\n", - " * `subcircuit_vertices`: A list of vertices to be used in subcircuits" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8c11457a", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-10-22 12:32:03,763\tINFO worker.py:1518 -- Started a local Ray instance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m --------------------\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m subcircuit 0\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ρ qubits = 0, O qubits = 1, width = 3, effective = 2, depth = 6, size = 12\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ┌───┐ ┌─────────┐┌───┐┌───┐\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m q_0: ┤ H ├──■───────────────■──┤ Rx(π/2) ├┤ T ├┤ H ├\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ├───┤┌─┴─┐┌─────────┐ │ └──┬───┬──┘├───┤└───┘\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m q_1: ┤ H ├┤ X ├┤ Rx(π/2) ├──┼─────┤ T ├───┤ H ├─────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ├───┤├───┤└─────────┘┌─┴─┐ └───┘ └───┘ \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m q_2: ┤ H ├┤ T ├───────────┤ X ├─────────────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m └───┘└───┘ └───┘ \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m subcircuit 1\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ρ qubits = 1, O qubits = 0, width = 3, effective = 3, depth = 6, size = 11\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ┌───┐\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m q_0: ───────────────────────■───────■─────┤ H ├\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ┌───┐┌───┐ │ ┌─┴─┐ ├───┤\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m q_1: ┤ H ├┤ T ├─────────────┼─────┤ X ├───┤ H ├\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m ├───┤├───┤┌─────────┐┌─┴─┐┌──┴───┴──┐├───┤\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m q_2: ┤ H ├┤ T ├┤ Rx(π/2) ├┤ X ├┤ Ry(π/2) ├┤ H ├\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m └───┘└───┘└─────────┘└───┘└─────────┘└───┘\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m Estimated cost = 1.280e+02\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8239)\u001b[0m --------------------\n" - ] - } - ], - "source": [ - "cuts = cutter.decompose(method=\"manual\", subcircuit_vertices=[[0, 1], [2, 3]])" - ] - }, - { - "cell_type": "markdown", - "id": "742ec1e1", - "metadata": {}, - "source": [ - "## Evaluate the subcircuits with Qiskit Runtime\n", - "\n", - "\n", - "Note that two local cores will be used to support each of the parallel backend threads we specified earlier. See [tutorial 1](tutorial_1_circuit_cutting_automatic_cut_finding.ipynb) for more info about the subcircuit results." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2ae5160c", - "metadata": {}, - "outputs": [], - "source": [ - "subcircuit_instance_probabilities = cutter.evaluate(cuts)" - ] - }, - { - "cell_type": "markdown", - "id": "17e8511c", - "metadata": {}, - "source": [ - "## Reconstruct the full circuit output\n", - "\n", - "Next, the results of the subcircuit experiments are classical postprocessed to reconstruct an estimate of the original circuit's full probability distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5aceecc0", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture\n", - "\n", - "reconstructed_probabilities = cutter.reconstruct(\n", - " subcircuit_instance_probabilities, cuts\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "8f6d26ee", - "metadata": {}, - "source": [ - "Here are the reconstructed probabilities for the original 5-qubit circuit:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "fe5d901c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Size of reconstructed probability distribution: 32\n" - ] - } - ], - "source": [ - "print(\n", - " \"Size of reconstructed probability distribution: \", len(reconstructed_probabilities)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "40277ef0", - "metadata": {}, - "source": [ - "## Verify the results\n", - "\n", - "If the original circuit is small enough, we can use a statevector simulator to check the results of cutting against the original circuit's exact probability distribution (ground truth)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5353b0c8", - "metadata": {}, - "outputs": [], - "source": [ - "metrics, exact_probabilities = cutter.verify(reconstructed_probabilities)" - ] - }, - { - "cell_type": "markdown", - "id": "b220335e", - "metadata": {}, - "source": [ - "The verify step includes several metrics, including the chi square loss. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting_toolbox/utils/metrics.py)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "8d54b767", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'nearest': {'chi2': 0.0016480859293148851,\n", - " 'Mean Squared Error': 2.2735561224425387e-06,\n", - " 'Mean Absolute Percentage Error': 9.116161020394516,\n", - " 'Cross Entropy': 2.6013770444786495,\n", - " 'HOP': 0.9068189188838005},\n", - " 'naive': {'chi2': 0.0016480859293148851,\n", - " 'Mean Squared Error': 2.2735561224425387e-06,\n", - " 'Mean Absolute Percentage Error': 9.116161020394516,\n", - " 'Cross Entropy': 2.6013770444786495,\n", - " 'HOP': 0.9068189188838005}}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metrics" - ] - }, - { - "cell_type": "markdown", - "id": "ec8c120e", - "metadata": {}, - "source": [ - "If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c8cc97e9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from qiskit.visualization import plot_histogram\n", - "from qiskit.result import ProbDistribution\n", - "\n", - "# Create a dict for the reconstructed distribution\n", - "reconstructed_distribution = {\n", - " i: prob for i, prob in enumerate(reconstructed_probabilities)\n", - "}\n", - "\n", - "# Represent states as bitstrings (instead of ints)\n", - "reconstructed_dict_bitstring = ProbDistribution(\n", - " data=reconstructed_distribution\n", - ").binary_probabilities(num_bits=num_qubits)\n", - "\n", - "\n", - "# Create the ground truth distribution dict\n", - "exact_distribution = {i: prob for i, prob in enumerate(exact_probabilities)}\n", - "\n", - "# Represent states as bitstrings (instead of ints)\n", - "exact_dict_bitstring = ProbDistribution(data=exact_distribution).binary_probabilities(\n", - " num_bits=num_qubits\n", - ")\n", - "\n", - "# plot a histogram of the distributions\n", - "plot_histogram(\n", - " [exact_dict_bitstring, reconstructed_dict_bitstring],\n", - " number_to_keep=8,\n", - " figsize=(16, 6),\n", - " sort=\"asc\",\n", - " legend=[\"Exact\", \"Reconstructed\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6a3261e8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ibmq-provider0.19.2
qiskit-nature0.4.5
System information
Python version3.9.13
Python compilerClang 12.0.0
Python buildmain, Oct 13 2022 16:12:30
OSDarwin
CPUs4
Memory (Gb)32.0
Sat Oct 22 12:32:36 2022 MDT
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import qiskit.tools.jupyter\n", - "\n", - "%qiskit_version_table" - ] - }, - { - "cell_type": "markdown", - "id": "d55d9f98", - "metadata": {}, - "source": [ - "This code is a Qiskit project.\n", - "© Copyright IBM 2022.\n", - "\n", - "This code is licensed under the Apache License, Version 2.0. You may\n", - "obtain a copy of this license in the LICENSE.txt file in the root directory\n", - "of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n", - "\n", - "Any modifications or derivative works of this code must retain this\n", - "copyright notice, and modified files need to carry a notice indicating\n", - "that they have been altered from the originals." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/circuit_cutting/tutorials/tutorial_2_manual_cutting.ipynb b/docs/circuit_cutting/tutorials/tutorial_2_manual_cutting.ipynb new file mode 100644 index 000000000..82beebab0 --- /dev/null +++ b/docs/circuit_cutting/tutorials/tutorial_2_manual_cutting.ipynb @@ -0,0 +1,533 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c6cd641f", + "metadata": {}, + "source": [ + "# Tutorial 2: Circuit Cutting with Manual Wire Cutting\n", + "\n", + "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n", + "\n", + "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n", + "\n", + "This wire cutting technique is comprised of the following basic steps:\n", + "\n", + "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use a manual method to specify the cut(s). See [tutorial 1](tutorial_1_automatic_cut_finding.ipynb) to automatically cut a circuit.\n", + "2. **Evaluate**: Execute those subcircuits on quantum backend(s).\n", + "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution)." + ] + }, + { + "cell_type": "markdown", + "id": "3c17f515", + "metadata": {}, + "source": [ + "## Create a quantum circuit with Qiskit\n", + "\n", + "In this tutorial, we'll use the example circuit shown in [CutQC](https://dl.acm.org/doi/10.1145/3445814.3446758)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "eb859bde", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from qiskit import QuantumCircuit\n", + "\n", + "num_qubits = 5\n", + "\n", + "circuit = QuantumCircuit(num_qubits)\n", + "for i in range(num_qubits):\n", + " circuit.h(i)\n", + "circuit.cx(0, 1)\n", + "for i in range(2, num_qubits):\n", + " circuit.t(i)\n", + "circuit.cx(0, 2)\n", + "circuit.rx(np.pi / 2, 4)\n", + "circuit.rx(np.pi / 2, 0)\n", + "circuit.rx(np.pi / 2, 1)\n", + "circuit.cx(2, 4)\n", + "circuit.t(0)\n", + "circuit.t(1)\n", + "circuit.cx(2, 3)\n", + "circuit.ry(np.pi / 2, 4)\n", + "for i in range(num_qubits):\n", + " circuit.h(i)\n", + "\n", + "circuit.draw(\"mpl\", fold=-1, scale=0.75)" + ] + }, + { + "cell_type": "markdown", + "id": "461e57e3", + "metadata": {}, + "source": [ + "## Set up the Qiskit Runtime Service\n", + "\n", + "The Qiskit Runtime Service provides access to IBM Runtime Primitives and quantum backends.\n", + "Alternatively, a local statevector simulator can be used with the Qiskit primitives." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5d1fb2ca", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import (\n", + " QiskitRuntimeService,\n", + " Options,\n", + ")\n", + "\n", + "# Use local versions of the primitives by default.\n", + "service = None\n", + "\n", + "# Uncomment the following line to instead use Qiskit Runtime.\n", + "# service = QiskitRuntimeService()" + ] + }, + { + "cell_type": "markdown", + "id": "5fb383d2", + "metadata": {}, + "source": [ + "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits.\n", + "\n", + "If no service was set up, the `backend_names` argument will be ignored, and Qiskit primitives will be used with statevector simulator." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d409553d", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the Sampler and runtime options\n", + "options = Options(execution={\"shots\": 4000})\n", + "\n", + "# Run 2 parallel qasm simulator threads\n", + "backend_names = [\"ibmq_qasm_simulator\"] * 2" + ] + }, + { + "attachments": { + "how-to-manual-cut.png": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAAMqCAIAAAD7OXEbAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEbAAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAABsAAAAAQAAAGwAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAABaCgAwAEAAAAAQAAAyoAAAAA4m4NyQAAAAlwSFlzAAAQnAAAEJwBJs06NAAAQABJREFUeAHs3Qd8FEX7wPG59B4SSKckIaGX0HsVpAuKFXx5VbD+wRfs7RVfe+++CK+oKApWFFSQpgiKdJAiLQECIRBKQkhv95/jwuWSXC6Xy93lbvd3n3xgd3Z2Zp7vbF7fe9id1Wi1WsEHAQQQQAABBBBAAAEEEEAAAQQQcGUBN1cePGNHAAEEEEAAAQQQQAABBBBAAAEEdAIkOLgOEEAAAQQQQAABBBBAAAEEEEDA5QVIcLj8FBIAAggggAACCCCAAAIIIIAAAgiQ4OAaQAABBBBAAAEEEEAAAQQQQAABlxcgweHyU0gACCCAAAIIIIAAAggggAACCCBAgoNrAAEEEEAAAQQQQAABBBBAAAEEXF6ABIfLTyEBIIAAAggggAACCCCAAAIIIIAACQ6uAQQQQAABBBBAAAEEEEAAAQQQcHkBEhwuP4UEgAACCCCAAAIIIIAAAggggAACJDi4BhBAAAEEEEAAAQQQQAABBBBAwOUFSHC4/BQSAAIIIIAAAggggAACCCCAAAIIkODgGkAAAQQQQAABBBBAAAEEEEAAAZcXIMHh8lNIAAgggAACCCCAAAIIIIAAAgggQIKDawABBBBAAAEEEEAAAQQQQAABBFxegASHy08hASCAAAIIIIAAAggggAACCCCAAAkOrgEEEEAAAQQQQAABBBBAAAEEEHB5ARIcLj+FBIAAAggggAACCCCAAAIIIIAAAiQ4uAYQQAABBBBAAAEEEEAAAQQQQMDlBUhwuPwUEgACCCCAAAIIIIAAAggggAACCJDg4BpAAAEEEEAAAQQQQAABBBBAAAGXFyDB4fJTSAAIIIAAAggggAACCCCAAAIIIECCg2sAAQQQQAABBBBAAAEEEEAAAQRcXoAEh8tPIQEggAACCCCAAAIIIIAAAggggAAJDq4BBBBAAAEEEEAAAQQQQAABBBBweQESHC4/hQSAAAIIIIAAAggggAACCCCAAAIkOLgGEEAAAQQQQAABBBBAAAEEEEDA5QVIcLj8FBIAAggggAACCCCAAAIIIIAAAgiQ4OAaQAABBBBAAAEEEEAAAQQQQAABlxcgweHyU0gACCCAAAIIIIAAAggggAACCCBAgoNrAAEEEEAAAQQQQAABBBBAAAEEXF6ABIfLTyEBIIAAAggggAACCCCAAAIIIIAACQ6uAQQQQAABBBBAAAEEEEAAAQQQcHkBEhwuP4UEgAACCCCAAAIIIIAAAggggAACJDi4BhBAAAEEEEAAAQQQQAABBBBAwOUFSHC4/BQSAAIIIIAAAggggAACCCCAAAIIkODgGkAAAQQQQAABBBBAAAEEEEAAAZcXIMHh8lNIAAgggAACCCCAAAIIIIAAAgggQIKDawABBBBAAAEEEEAAAQQQQAABBFxegASHy08hASCAAAIIIIAAAggggAACCCCAAAkOrgEEEEAAAQQQQAABBBBAAAEEEHB5ARIcLj+FBIAAAggggAACCCCAAAIIIIAAAiQ4uAYQQAABBBBAAAEEEEAAAQQQQMDlBUhwuPwUEgACCCCAAAIIIIAAAggggAACCJDg4BpAAAEEEEAAAQQQQAABBBBAAAGXFyDB4fJTSAAIIIAAAggggAACCCCAAAIIIECCg2sAAQQQQAABBBBAAAEEEEAAAQRcXoAEh8tPIQEggAACCCCAAAIIIIAAAggggAAJDq4BBBBAAAEEEEAAAQQQQAABBBBweQESHC4/hQSAAAIIIIAAAggggAACCCCAAAIkOLgGEEAAAQQQQAABBBBAAAEEEEDA5QVIcLj8FBIAAggggAACCCCAAAIIIIAAAgiQ4OAaQAABBBBAAAEEEEAAAQQQQAABlxcgweHyU0gACCCAAAIIIIAAAggggAACCCBAgoNrAAEEEEAAAQQQQAABBBBAAAEEXF6ABIfLTyEBIIAAAggggAACCCCAAAIIIIAACQ6uAQQQQAABBBBAAAEEEEAAAQQQcHkBEhwuP4UEgAACCCCAAAIIIIAAAggggAACJDi4BhBAAAEEEEAAAQQQQAABBBBAwOUFSHC4/BQSAAIIIIAAAggggAACCCCAAAIIkODgGkAAAQQQQAABBBBAAAEEEEAAAZcXIMHh8lNIAAgggAACCCCAAAIIIIAAAgggQIKDawABBBBAAAEEEEAAAQQQQAABBFxegASHy08hASCAAAIIIIAAAggggAACCCCAAAkOrgEEEEAAAQQQQAABBBBAAAEEEHB5ARIcLj+FBIAAAggggAACCCCAAAIIIIAAAiQ4uAYQQAABBBBAAAEEEEAAAQQQQMDlBUhwuPwUEgACCCCAAAIIIIAAAggggAACCJDg4BpAAAEEEEAAAQQQQAABBBBAAAGXFyDB4fJTSAAIIIAAAggggAACCCCAAAIIIECCg2sAAQQQQAABBBBAAAEEEEAAAQRcXoAEh8tPIQEggAACCCCAAAIIIIAAAggggAAJDq4BBBBAAAEEEEAAAQQQQAABBBBweQEPl4+gfgGknhN5RfVrwuzZfl6ieWOzNThosUBWmigttrh23St6+YvAMEtPKykUF9ItrWxdvcBw4eVn6amns0VmrqWVrajnphGtIq04j1MQQAABBBBAAAEEEEAAAQcJqD3B8f12kZxhR+uW4WLGcDu2r6qm9/0s8s7bMeLwVqLTVZa2n5cpdnxtaWXr6nWeIMISLD31j4Ni3QFLK1tRz8tDvHyDFedxCgIIIIAAAggggAACCCDgIAEeUXEQNN3YQEBrgzZowkoB8K2E4zQEEEAAAQQQQAABBBBwkAAJDgdB042zC2icfYCMDwEEEEAAAQQQQAABBBBAwIwACQ4zOBxCAAEEEEAAAQQQQAABBBBAAAHXEFD7GhyuMUuMsjaBlJN/JafvkrXcNG4hAREd4vp7efrUdhLHEUAAAQQQQAABBBBAAAEElCNAgkM5c6nmSDbsWfLVuldlXqO0tPhI+m6Nm9vLd6yOjWyvZhNiRwABBBBAAAEEEEAAAQRUJcAjKqqabiUHGxkS+8K05S/fufqTx1L8vAM/W/2skqMlNgQQQAABBBBAAAEEEEAAgcoCJDgqe7Dn+gK+Xv5tmvc6c+G464dCBAgggAACCCCAAAIIIIAAApYKkOCwVIp6riKQefH0X8nrEqK7uMqAGScCCCCAAAIIIIAAAggggED9BViDo/6GtOAUAmlnD93xWqfSspL080d6tB5566jn9MNKO3s4NDDS1ztA7p7PPqUV2sZBUU4xYgaBAAIIIIAAAggggAACCCBgOwEnvYPj999/HzduXFxcXK9evb755puFCxdOnjzZdlHXraVTyVvenuKZcz7NcFpO5klZcip5s6HE8Runs8XmFPHHIZGSIcrKHN9/pR5Ly8ShU+L3Q2LrEXEup9Ihh+00Doq+e/ybd4x9tV/7CYdObCssytN3fSht+xtf3yG3C4vzH5s/Kr/QceOTfY142NPkz5FTexwmY+iorLREXrcpO34wlMiND6bH7F47z7iEbQQQQAABBBBAAAEEEEDAFQWc8Q6OxYsX33zzzaNGjXr88cdTUlKmTJkSGxvr7e3dcL5abVmJVqutGIDcKSsRxiUVx+y+lZUnFm0UB05VdNQ4QNzUWyREVJQ4cmvPCfHlZpGdX9Fn52bi+l7C37Ez5uPl3yVhqBxEzzaj7n2nz/9+fOjhmz6Ru4M7X79u15cbdi/ZfWT98G5TmoYlVgzUzltySAsePqTvZP5Pj57LPvnQjQv0u02CY+zcuenmq1+3Zbpru6EzZKYHSykCCCCAAAIIIIAAAgggUAcBp0twHDlyZNq0affee+/rr7+uj8PX1/fJJ5+cOnVqHcJSbtWL+eKNFZWyCTLW8znivTXiriGitcOfvdh+VHzyu9BUBt91XKRfEPeNFD6elQ84ZE+j0Uwd/cJD84bddMVjzcPbyD7/dc2cme/1lw+q3Dn2VYcMobwTOZLI0Fj9jp9PYE6Bn2HXkcOgLwQQQAABBBBAAAEEEEBADQJO94jKCy+84O/v//zzzxv0+/btK7e7du2qLykrK1u0aNFTTz31448/GuqoZ2PJdnEhXxjdTKILXberFZ9tFMWlDpXILRRfbNJlN6qMRw4iI1ss3+XQwRh3lpQwpH1sv4WrntYXlpWVFpcUNgoId3NzugveeNhsI4AAAggggAACCCCAAAIIWC3gXHdwyOSFXHHj+uuv9/HxMYRUWqr71m5IcMgnVk6cOHHdddc99thjW7dunT17tqFm9Q15bn6+0bMT1WqUlvoKIb/0VrkFoVo9IT6aGWuitJYibWlpWU6OuQHU0kDlw/nFYsdRf5OjlSkG+ZDI9uT89tGOS3JsPuJRWFIxU5UHKzYe1g5tletuu5RCWZnfpcmq0o9ut1+HCQnRScYHZl0778DxLfKKkkmNN7+9a8bV767cumD97m8HdLzGuJrRtrakpDQnp8CoxNxmXr4MTI7Hjp/8gvycHEtns6jYSwh5w0ztV/KyNyZojKpptRZ1IRdnzcnJNR+t/LX18HCu/0kxP2COIoAAAggggAACCCCAgJIEnOvbSHp6+vnz5zt06GBMvG3bNnd3906dOsnCffv2ff/992lpaUFBQSNGjOjYseOsWbPktnF94+3169cPGTLEuKTK9rVPrItuPUBT+7dCcd2TG/xDyp8Aycs69cVTvas0VX1XrtGxYcOGWaMGVz9kXUlUYr/rZ28wc+7MR1/581tzGR8z51pxaMitczoOuV3j5m7y3KJSTWyrrlmnyxehMFmnToUfPrS/WVhrk6e0jO4sf4wPyYdT9M+nrN620N8nuHe7sa2b93zw/aGdWw4O8gs1rqnflrmw77777pmJ11U/ZLIkMabbf2duNXnIVoU33nDjxn1LLWxt4OQ3kkb+y5Ir+Yqp85p3GGZodtET3QzbZjby8vICAwPNVJCH5s2bd/vtt5uvw1EEEEAAAQQQQAABBBBAwE4CtvvndVsMUH6Jks0UFhYaGisqKvrwww9bt27t56f713KZsOjRo4c+o5GQkBAeHr5p0yZDZbtuBIQ2DWrSQv8jt+3aV82NV38WpFJd+c/slfbtvVPbMqsNvnqlvIMjJX3X/41/S0qEBITfPualXcm/2lvFydv3Cwo3XMlyo6b8lJNHwfAQQAABBBBAAAEEEEAAgSoCznUHR/PmzeWSokuXLp05c6Z8skD+o/rdd999+PBh+VIV/bhPnz4tkxqGGCIiImSJYbf6xuDBg/VPuFQ/pC95d7Um5UxNB+tbLv85fdDAgV8+bdEjAJZ0VlAsnvhGW6at8YaTeW/8u8NX/7akKZvU2XhYfLWlxhyZj6f2TNpBGz6isvEjTX5m3QYur6I7xr5iOKdX2zGG7Sob8i6hiRMnPvWxpZOVfVps/axKGzbeXfLdkrCWlrb53XbNbwcsrWxFPZlhNP+rJNtkiRMrYDkFAQQQQAABBBBAAAEEbCXgXAkO+S7YGTNmvPzyy7179+7cubO8X0P/ZIphAQ75LdT4W5Z+hQXzFua/dFlyS7/59s0e1a114OZWYz7C7LkmDvp5i26xYssRE4dkH8F+ol2M+XBNnFifoq6xYtlOUVhs+r6RvgkaTw+bxS7Hacu2TIQt33lSh8my3ayaGIq+yE1Th9m0M47El6Oxdyc1UnAAAQQQQAABBBBAAAEEEKhVoMZ/fq/1TDtVePHFF+fPn9+mTRsvL6+33nrrnnvukR116dJF312zZs2OHz9u6Do1NbVFixaGXTttePkEynU63D29De3LbVni6VPLkgSG+jbcmNBNhPpXbU9+75TfPW/uKzxMr4ZRtb6t9n29xKQ+usaqf/GNbiRG6FZN4VMuENMkMS6y0uIyDUCj0cjr1ieg0hIkkQm9/RuVLy7TAEOiSwQQQAABBBBAAAEEEEDARgIabW3LKNioIyubef311x944IHMzMzg4GDZxMmTJ+XSG3v37o2Li/vtt9/kMwVywVGZCrGydSHeWSWSM6w+u/YTW4aLGcNrr1anGhcLxJebxO4TFSdFBosbe4vYJhUljtw6kK57Wex5ozds9IgTE3sIH/lOD5t+/pgv8uryiEpRSaG8CSK/MOdi3vnI0LhabofQiPBE0ekqS0ecfUpsXmhpZevqdZ4gwhIsPXXJVrHOno+oeLmLl2+0dDDUQwABBBBAAAEEEEAAAQQcL+Bcj6hUj3/79u3x8fH67IY8Gh0d/fTTTw+69Fm9evWcOXPqk92o3p1LlAT6iKmDRGaueOUnkVckbhkgkpo35MBbR4knxovfD4lvtujuLpk5QgTJd+86wWfeDw+2iGj3V/K60KCog8e3PDf1J7+GuOnGCSQYAgIIIIAAAggggAACCCCgfAFnT3CcPXt2+PBKt0DIGzquueaa5OTkV155JTIyUvlTVEOEIf7C89IDKfL2jQb/yAdk9MPw82qw7MaSDW+fOHNQT5HUcsiAThPPXUibPuHtcX3ukoVzls7af3xz18QrGtyKASCAAAIIIIAAAggggAACCNhDwNkTHCtWrKgetrynQ36ql1OiZgEPN89Av9AhSTe9vHjKlCufOpy2IyGmfOmW/KLc/ambJw9z3Ptl1DwRxI4AAggggAACCCCAAAIINIiA0y0y2iAKdKoAgbPZJ7u3utLfJ1gu5xns3+T3Pd/1bT9exiVX4njh88m3jXo+yK/S4poKCJkQEEAAAQQQQAABBBBAAAEEDAIkOAwUbLi2QPLJnS2jk5JPyhs3uspIjpzaHRfVsbik6IXPJk3oO71zy0GuHR6jRwABBBBAAAEEEEAAAQQQMCtAgsMsDwddR8DNzd3XOyAzJ+PvYxtPZx6LCImVY1+5dUFqxv6lG//71IJr9hzZ4DrRMFIEEEAAAQQQQAABBBBAAIG6CTj7Ghx1i4baKhZ4+pbvZPQje9w6NOmmvcf+uKLrZLk7pvft8kfFKoSOAAIIIIAAAggggAACCKhFgASHWmZaPXF6efp0SRiqnniJFAEEEEAAAQQQQAABBBBAQArwiAqXAQIIIIAAAggggAACCCCAAAIIuLwACQ6Xn0ICsI2A1jbN0AoCCCCAAAIIIIAAAggggECDCJDgaBB2OrVKQGPVWZxkEwHwbcJIIwgggAACCCCAAAIIIGA3AbWvwTGxuygotpuuED6edmxcbU13GCPK7DlZnr51EPULFd1vrEN9K6r6N67DSQPbiM7N61C/rlXdyIXWlYz6CCCAAAIIIIAAAggg4FgBtSc4okMc601v9RAIiqjHybY+1cNLNGpq60br0V7jACF/+CCAAAIIIIAAAggggAACqhXgn2VVO/UEjgACCCCAAAIIIIAAAggggIByBEhwKGcuiQQBBBBAAAEEEEAAAQQQQAAB1QqQ4FDt1BM4AggggAACCCCAAAIIIIAAAsoRIMGhnLkkEgQQQAABBBBAAAEEEEAAAQRUK0CCQ7VTT+AIIIAAAggggAACCCCAAAIIKEeABIdy5pJIEEAAAQQQQAABBBBAAAEEEFCtAAkO1U49gSOAAAIIIIAAAggggAACCCCgHAESHMqZSyJBAAEEEEAAAQQQQAABBBBAQLUCJDhUO/UEjgACCCCAAAIIIIAAAggggIByBEhwKGcuiQQBBBBAAAEEEEAAAQQQQAAB1QqQ4FDt1BM4AggggAACCCCAAAIIIIAAAsoRIMGhnLkkEgQQQAABBBBAAAEEEEAAAQRUK0CCQ7VTT+AIIIAAAggggAACCCCAAAIIKEfAQzmhWBXJqj0iI9uqMy07KTxIDO9gWVVq1SZw+DdRmFNbpXocD4oUzbpaen7+BZHyu6WVravXrJsIirD01G1HxP50SytbUc/DXdzQy4rzOAUBBBBAAAEEEEAAAQQQcJCA2hMc8jthcobQaOzCrdWKluEkOGxmm3FI5GUKYZ/JElpRWlKHBEdxvkjfdyk0u40nvJUQFic4Us+JLUfseCV7keCw2YVMQwgggAACCCCAAAIIIGAXAbUnOPSoMhPBx2UE7DRZVucp7DQeq+aDK9kqNk5CAAEEEEAAAQQQQAABJQiwBocSZpEYEEAAAQQQQAABBBBAAAEEEFC5AAkOl7wA5D/UZ+aKkjLd4M/liNJLGy4ZCYNGAAEEEEAAAQQQQAABBBBAwBYCPKJiC0UHtrEvTbfUwoF0kVdU3uv/fhXubiIuTHRtIbrHCS9VTunqbQtXb/9Uimg0biEBEYM6X9er7RgHTgtdIYAAAggggAACCCCAAAIINLAAd3A08ARY3r1cRfLV5WLer2LHsYrshv50eQfH4dPiy83i6e/F5hTLm1ROzZPnko+d3jes6z8Gd77B08P7iQ/Hrtr6iXLCIxIEEEAAAQQQQAABBBBAAIHaBFT5z/21oTjh8T+TxVeba38UJadAfL5R916Y63oI+V5PVX0CfBoN63azDHlEj1tOZx5dtf3T4d2nqEqAYBFAAAEEEEAAAQQQQAABNQtwB4cLzP6avWLxn7VnNwyRbEoWH6wTZSpemMPXO1Cr5vgNlwIbCCCAAAIIIIAAAggggIBqBLiDw9mneu8J8cNO04McmyT8vMSKv0R2QdUK+9PFd9vFNd2rlit4v6ikIOXkX6VlJfuObdy4d+nMa+cqOFhCQwABBBBAAAEEEEAAAQQQqCLgvAmOc+fO7d+/PyIiIiEhobS0tKSkxNvbu8roHbNbkHM+dc/q+K7jPLx89T2WFOWnbF/WvMMwn4BQu45BPnKycKPQ1tCHXFK0kZ9Yt99EgkOe8dsB0SZKtIup4WTFFZ/KPPrIByOKigtyC7LG950+sset+hB/3flFVOOWrZvpkj3rdn0VHtK8bfNejoleXrbr93xrsq8erUb4+wabPGS/Qq1We2jTV9Gt+weERBt6Sd72feOmHRpFtDSUsIEAAggggAACCCCAAAIIuKKAMyY4ioqK7r///vfff18mNaTpxIkTIyMjFy1aJFMeDUKcdfrw8ndvuO2t44GNm+oHIFMesuSGp/6MTLDvV+XV+0T+5belWBH7sp2ibbR8sYgVp7reKc3D2vzvgd1y3Bv3LfvPgon9Ol7dJWGo3E1s2u2ZT697a/ofpzOPLVr7wtvT/3BYbMWlRUvWv6XvTi6DWlxS2CKinX63VUy3BkhwlJXK63bcfUuNExxr5t/Re+J/SHA47KqgIwQQQAABBBBAAAEEELCTgDMmOKZNm7Z06dKFCxeOHz8+OTn52muvXb58eZ8+fexE4LTNFpeKPw/Xa3TpWboFRxMi6tWIy53cp9240b2mzVk6a+6snRqNJqZJwoget/7vx4cPp23/18Q5Xp4+DovIx8vvrem/67t74+s7MrKOvzBtucN6pyMEEEAAAQQQQAABBBBAQFUCTrfI6E8//fTpp59+9tlnN9xwg4+PT/v27R999NG8vLyuXbuqamJksAdPiYLi+ga9K7W+Lbji+ZOuePxExoH1u7/RD35CvxlbD/wcH9XZYQ+nuCIaY0YAAQQQQAABBBBAAAEEXFrA6e7gePXVV7t37z5mzBgDa2xsrNw2JDjefffdrVu3pqWlzZkzRy7PYahm742U7UsNK24U5mbauzvZ/pEzNujkyFkbNOJyTTQJjhnZc+qnK/8zoONEeRPHjkNrgv2b7Dm6oaAoT95V4XLh2HbAaft/Ky7MNbRZWlxtiVrDMTYQQAABBBBAAAEEEEAAAdcRcK4Eh7xTY/369fKWDWPAM2d0X/QNCQ6Z3ejWrdsXX3yRnZ1tXM3kdnp6unzaxeQhfeFJ33HCLcqSZSr+Xv+xu0f5KqelJYVm2qw4pNXKAcydu6yipC5bJ7yHCc/ypR893ETP+Kone12avaQW4mJ+pUPy1o+zOeUlJ88Vzp37caXD9tnJdY8SvledPXtm7lzTy2rWv9tmOTd4iUYm27l52L8nD3vC+NCMq98tKyuV2Y28govvL7vv2ak/btj97UcrHr/7qjeMqxm2tdqylJSjG+euMpSY3/AubdJUTDRfp55HV/y8Im/NMQsbOe3VR3h2tORKPrLzx1OH/zQ0K1fMNWyb2SguKZ4790MzFeShQYMGtWnTxnwdjiKAAAIIIIAAAggggAACdhJwrgSHTAfIhUWjoyte8SDDXrNmTUBAQGJiop7g448/lhuPPfaYftf8nwcOHLjrrrvM1Ln2ibbRraMsWYVzzL++NSwymnM+bf695QuOmmlcvv3k4MFDLzxnbgBmTh//4E+xncsTHN6e4vpepuuO6lS1/NPfKxIcpVpP8wJVT7Z2P6bt4Gsfvyo19fhL/7Yy3lp7/vChwc3CTCc43NyqPmwlUxvu7rrLe+4PD0zof294o2byQZUH3h+y9+gf7WP7Vu+rrKxs+/btz3xq6eATY7r9d6Z9Exxz/jtn4z5z6TnjKAZOfiNpZEdLruT+N74kXwlkOHfePRGGbTMbcunfWi+kefPmkeAwY8ghBBBAAAEEEEAAAQQQsKuAcyU4vLy8ZLQnT540xCwXGZUZDXnLhvy+aii0fEO+Zfamm24yU79RWJiZo/U/FB4eZn4AZroIjmhiOFpSKrYdNeyVb3RsKuRNHHvTqi7Vcb7i+QMhtMVWD6Bqf2b3PUPbyuMhoSH26y4oMMjsEEwclGmLoV1u6txysDwmkyBP3PxFdp7pd/HIC6xZs2aWD76xX5yJ/mxaNGDggNjO/hY2GdCmtYU1ravm4e5eK44jHxmzLgrOQgABBBBAAAEEEEAAAQULOFeCQ37DbNGixfz58ydNmiT/KXjTpk1Tp07Nz883PJ9S15lo27bt559/buasd1bp3jNip4/8zty2bbsZ95obgJmuv90qfjtQfrywRMj7Mqp8nrpal+BYul2crvlhnYgQ77fMClRp0+rdQ6fFe6tFXGzce/dYGW+tXf8xX+TVcfETmdTQZzf0jYcGRcofkx25ubn36tXr9ucsHXz2KbF5ocmWbFb4wP0PhCVY2tqSrWLd5avF0nPqUs/b28f8r1JdGqMuAggggAACCCCAAAIIIGB7gao39tu+hzq2+L///e/ChQsyMeHr63v11VfPmDFDNmB1gqOOnTtX9eaNbTAemzRig3HQBAIIIIAAAggggAACCCCAAAL2FHCuOzhkpMOHD09NTd2yZUtQUJB8ncqyZboVOrt06WJPhFraDgqLG3rbXB//EEM9b/8QWSLLDSX22GgTJdw0okyu5FGPT/uYepzMqbYTuKLrzQVFxs8O2a5pi1vSaNzkddukeaVVWwZMejU8tpvFbVARAQQQQAABBBBAAAEEEHBSAadLcEin0NDQESNG6MF27Njh4+PTrl07g9/BgwcLCgrk2gqHDh3y8PBo3769u7u74ag9NvyCwjoOvcO4ZU9vvyolxkdttR3gIzo1EztTrW9PttCh9rVQrW/fyc88eGKbp7v37iO/ZeWc6dV2dOtmPRpwwJ3iBzZg7/quNW5u1a/btv3/0eADYwAIIIAAAggggAACCCCAQP0FnO4RlSohyRdbdOzYUSYyDOVPPvnktGnTZF7jtddekxs5OZdfiGqooaCNER11N3FY/bmyg/C0b/LH6qE54sRvfnv9/MX0JsFNe7cb+9539548m+yIXukDAQQQQAABBBBAAAEEEECgIQQqEgcN0XvtfQ4bNkyuPGpcb/Hixca7yt6OaiRkjmP5X6ajPHtRFBaLkjLTRxPCRf/yV+uarqCw0le/nPr3sT9DAyNPnD046YrHr+z+Txlgt1bD9WEmxHQ5l30yukn5a3cVFjvhIIAAAggggAACCCCAAAIIOHuCY9asWSqfJHkXxslMseu4CYZ3V5so1BeF+otbBsgXo9ZYQXkH2sf2TWo5ZEiXmx6Zd+W4Pnf9sXdp18Ty7IZ8ViX9/JH2sf2UFzURIYAAAggggAACCCCAAAII6AXU9A3YNedcoxH/6Cd61GU90+hGYvowIRfgUNXn0IntiU27Hs/Y3zyirQx8098/yCdT5EbKyb8+XP7YE5MXy1fGqgqEYBFAAAEEEEAAAQQQQAABVQnwlc8FptvDXUzuK67pLrwsuOGmZ7z41wgRGuACcdl2iCfOHGga1jr55M7YyA5yDdrsvPPB/k2Onf771S9vmzb6xZyCrIKiPNv2SGsIIIAAAggggAACCCCAAALOI2DBN2bnGay6RzKwtUhqLtbtF1uOiOz8qhZyMVH5ypXBbUWz0KqHVLLfr8PV7m7uESEtft315b5jG9u36CsDz7x4qmP8gFXbPpHbw7tNkStxqESDMBFAAAEEEEAAAQQQQAABtQmQ4HClGQ/yFeO6iLFJIj1LnMwSF/KFViv8vEREsGjRWMgbPdT8Gd/v/2T4HeL6yx/5whT9IqNJCUPkj5pZiB0BBBBAAAEEEEAAAQQQUIkACQ7Xm2i5Kkd0iO6HT00CvC2lJhnKEUAAAQQQQAABBBBAAAGlCrAGh1JnlrgQQAABBBBAAAEEEEAAAQQQUJEACQ4VTTahmhPQmjvIMQQQQAABBBBAAAEEEEAAAScXUPsjKoE+ItjXjnMkV83gYysB70BRWmyrxky041WXyXJzF3I8wn5pEY1w8zQxyJqK/LxFIz/dmix2+vh42alhmkUAAQQQQAABBBBAAAEEbCOg0drvK5FtRkgrriFw6LR4b7VoGiIeGO0aA2aUCCCAAAIIIIAAAggggAACShLgERUlzSaxIIAAAggggAACCCCAAAIIIKBSARIcKp14wkYAAQQQQAABBBBAAAEEEEBASQIkOJQ0m8SCAAIIIIAAAggggAACCCCAgEoFSHCodOIJGwEEEEAAAQQQQAABBBBAAAElCZDgUNJsEgsCCCCAAAIIIIAAAggggAACKhUgwaHSiSdsBBBAAAEEEEAAAQQQQAABBJQkQIJDSbNJLAgggAACCCCAAAIIIIAAAgioVIAEh0onnrARQAABBBBAAAEEEEAAAQQQUJIACQ4lzSaxIIAAAggggAACCCCAAAIIIKBSARIcKp14wkYAAQQQQAABBBBAAAEEEEBASQIkOJQ0m8SCAAIIIIAAAggggAACCCCAgEoFSHCodOIJGwEEEEAAAQQQQAABBBBAAAElCZDgUNJsEgsCCCCAAAIIIIAAAggggAACKhUgwaHSiSdsBBBAAAEEEEAAAQQQQAABBJQk4KGkYKyI5d1V4nCGFedZekpChJg+zNLKBdliwzxLK1tXr+2VIqaTdadyllMLLNkm1u234wh9PMWL19uxfZpGAAEEEEAAAQQQQAABBOopoPY7OLT19KvtdK29O6htABxXi4Cdr7SyMrVAEicCCCCAAAIIIIAAAgi4qIDaExwuOm0MGwEEEEAAAQQQQAABBBBAAAEEjAVIcBhrsO1AgRdeENbd3yLPkufyQQABBBBAAAEEEEAAAQQQQMBIQO1rcBhRsGmlwLkckZwhDqTrTs/KE2v2iqahIi5MeJm5uN54Qzz1lFi1SqxZIzSaOnQssxs9e4q9e0VWlnjppTqcSFUEEEAAAQQQQAABBBBAAAFFC5j5DqrouAmu3gIy1bD9mPj1b3H8fEVbOYVi2U7drsxudG0hhrUXTQIrjpZvyezGo4+KoiLxyy/iiivqkOPQZze2btW18847uj/JcegU+CCAAAIIIIAAAggggAACCAgSHFwE1giczxELNohj52o8t6hE/JksthwRIzvq0hwVd2ksWKDLbhQWlp8pcxxDh4q1a41q1NCmzG706CG2bSs/nJ+vy3E0aqRrjQ8CCCCAAAIIIIAAAggggIDqBViDQ/WXQN0B5AMpr60wl90wNFlaJn7cJT5eL2S+o/wzcKDo0+fyzqW/f/1Vdx+HzF+Y+ejv3TBkN/Q127UTjzxi5iQOIYAAAggggAACCCCAAAIIqEeABId65to2kZ7MFHN/EbmX78CwpNFdx8XCPy5nMOLixIcfisGDK52of1alphyHPruhfzLFcFq3bmLLltrv+zDUZwMBBBBAAAEEEEAAAQQQQEDRAiQ4FD29tg4ur1B8sM7odgyj9uPDxMs3iH9daVRktPnXcbFyz+X9mnIc8lmV6jkOWSKfTCG7cRmPvxFAAAEEEEAAAQQQQAABBEwKOOkaHDk5OZ988snu3bsjIiJuu+22s2fP7tq169ZbbzUZg70LM08dWv/Z/cPv+Mg3sLG+r/yL51bNu3XApFdDolrZu/fq7f/nk2tLSoqql9868tn46E7Vy21Y8tNf4nyu6fbcNLqFRT3cTR+VpT/vFkktRETQpQr6HMdttwn5fIrhI7errMehz25UeTLFsns3Cop1t5kE+ph9mYuhaztvyDguFogyrQjyFRKqoT5lZaU/vHF1zwlPRLbsaRjDz3OmtO47KbbzSEMJGwgggAACCCCAAAIIIICAKwo4Y4Jj3759I0eOzMvLGzRo0KZNm+bMmdOtW7eDBw82VIKjMDfzyI5lJUX5hgkuLS6QJT3HP24oceRG91ZXym+qssc/9i09dmrvTUPLV9kM8i/Pv9hpMBfyxMbD1rctv96v3iMm973cQq05DmuzG/KFtT/tKl8iRGYTWkeJsUkiJuRyv479u6RUrP1brD+gS3DIj6+X6JMgRnQQ3p6OHYe+N61WXrcdhtxu3Pex3T9HtTLMivERthFAAAEEEEAAAQQQQAABVxJwugRHbm7uhAkTYmNjly1bFhwcLC0nTZq0aNGi6667zpVc7TnWMb3v0Dd/5sKJrJyMcX3vtmdvFW1vPSLkoqH1+exMFRN7CB/Dd3szOY41a0TPnhXvTNH3asG9G+v2iyXbhOEmCZlV2X9SHDwlbhso2sfUZ+zWnFtcKuasESlnKs7NLxJr94m9J8S9Vwp/74pythBAAAEEEEAAAQQQQAABBOop4HRrcLz99tupqamfffaZPrshw5s8ebL8s2vXrvpQi4qK9uzZI59ekRv1DJ7T6yTw98k6VTdRWX7hP3y6crk+x1FlzVH5rEpEhBXZjaNnddkN+TF+I4vcLisTC9aLixW34FQeg932lm6vlN0w9HM6Wyz+07DHBgIIIIAAAggggAACCCCAgA0EnO4Ojo8++mjs2LHNmjUzBOfv7y+39QmOlStXyhs64uLiSkpK5MIcX3/9da9evQw17bqxat4tHp6++i5Kii89b2DX/pyv8ePnbTCmE5miQ9PK7Zi8j+Ps2UqVLLh3Q9aXN0fIezeMsxv6RmRJUalYf1CM7lypVbvu5BWJ3w/V2MPuEyIjW4TrVySpsZZdDmz+7pk9a+cZmi7MzTJss4EAAggggAACCCCAAAIIuK6AcyU4ZM7i0KFDd955pzGoLJG7Xbp0kX/K1Ia8fSMyMlJuP/3007Lmzp07jStX2ZZHZ8yYUaXQeLfVVe8ERHXWaAzPNBgfrLTdrP0wb/9G+iL5nfD43tWVDpva0Wq1cgADnr7X1EETZUE+4bNGfGPigO2KXnrppR2pP1jRnpunb5epKw0nJjUXA1sb9nQb+gdPwgPFvcMrlcudt1dVlHy6eOlz97xSsX95K7Kg4LHg4C4XLlwuqPj7QEDANB8fMXBgRVENW51v+dHDx3TOQKst+2blrhemWzoXNfRQh+KgZr0Sx7xq5oQ77n/x3P4fzVSo06GmfWeEd7zOkis5PK57k+adDI2f2L/OsG1mI78gf8CAK81UkIceeuihcePGma/DUQQQQAABBBBAAAEEEEDATgLOleA4f/68jNNHfps1+ixevLhp06ZhYWGyLDEx0XBE3rsxb17FP0Qbyo03srKyNmzYYFxSZTtycHZAVJUy07tt+t0c2Lj83oOc82l/fFm+tKfp2pdLL1zINj+AyxV1f4cFNxMjjAtsv3348OENm82B1NSlt39ol6kVB4P9RHx4xa5hS66dabLcUOHM2cyaQLYLsV6IaEPVSxvyRo52OTllv/9eudj0XtK0SleOcSWNxi2/SFtT18Y1bbWd0DOy4mI11eixE6d3mr04TZ1UY9nAFhPDO9Z41PhAbOdR8V0r0hB/fjPb+GhN22VlZbXqTZkypabTKUcAAQQQQAABBBBAAAEE7C3gXAmO8HDdl+atW7cawv7000/Xrl171VVXGUoMG++99971119v2DW50alTpx9/NPeP5FsudsgsMXmqDQrlfSEdO3a4zewAKnVT7CNqfqihUk1rd+69997pIVdbcXaZVrM6Sz7qUX6ry+7juicsjD/RjcS4LuLMRfFtxewZHy/fHjl84MzxpmZEq+03c2bw4cNVzmkiREanTpuef15YcJfN7xeKcss8DYOs3JS2Y2LUzZbPReWTrdjLLgn686K582beOTlsxnBzNepybH9e29TCupxQx7re3t7mf5Vkex07WpZiqWPXVEcAAQQQQAABBBBAAAEELBFwrgRHo0aNRo0atXDhwpiYGLnoxi+//LJGvk3j8gIcxvE88sgjGRkZ8uYO48Lq26GhoaNHj65ebihJXiUyMwx7Nt/QhIY2Hj3c3ACMuyzIFhvsnOCQX0FjOln5LXTH9+JcTvl4z+cK+WP8Kb6UJyooFubXIu3ZKa5/qzjjE3Xb8o2w8p0p1bIb+mqN//pr9GuvCXkl1JbjcN8tlv9Vte3L+5oxfaI6NbPsdp3L59TnbxnT4WXi3EUTa4LIZv28xE1ju3vZ7vevcKtIPVCf8dZyroe7h/lfpVrO5zACCCCAAAIIIIAAAgggYGcBp3uLyoIFC+RrYufOnfvggw+6u7u/8847UkC/AIeBYvbs2XK10RUrVvj5+RkK2bC3QLzuIaH6fkw0IjMBPXrI+3YqNd1E3rph9PnlFzF0qC4PYvYzuI0IC7x8k0nlmm2iRKeKhWsrH7PPnszGXNejxqav6S5smN2osRsOIIAAAggggAACCCCAAAKqEXC6BIdca+Orr746c+ZMcnLym2++efToUTkXhnfEyu1nnnlmyZIlq1atkrd7OGaaIuK63/NBTkBojKE7/5BoWRIRX/P3V0NVe25MufKpt/7PosUpbDKKpBb1bUYuQRodUrkR/b0b27ZVKpXvTNm0SVR/d+wVV5jPccgVQP5vmGgaWqkxudOxqbhlQNVCB+y3jtL16135Ng0PN3FdT9G92l0sDhiPm7uHvG5jkyrdUnTrG0c6DJ7mgN7pAgEEEEAAAQQQQAABBBCwq0Dl71527cqqxrdv3y5THnKRUf3Z33zzzZNPPvnPf/5Tvg1ElshbPF544QWrGq7DSRo3N08f3atqDR/5rooqJYZDjtzwcPcU8sdRn7bRQmYoMsyuK2F+LIPaVD6uz25UuXfD8EbYDz8Ut90mfv214hx5H4fMcZh9VqWRn7hvpNibJpbtEKezRUK47tWw5tc9rWjfDludm4uECLH9qG5pEnn/yYiOom+CkEu0NtSn+nXr6d1wo2koBfpFAAEEEEAAAQQQQAABJQo43R0cVZBlgsP49o2EhIS33npLlsiUh/zIpTqq1GfXfgJuGnFVV+ubjwgSvROMTjf5ZIohuyErxsUJmeOoch+HBc+qyGdDOjQVsZceqJEbDZjd0Efr7y0GtBZul37VBrRqyOyGkT6bCCCAAAIIIIAAAggggIDSBJz9Dg654Ki/f8XdE50vfZQ2Ca4Tj8wXDGot1plazPJcrvhpl7iQbzoY+ZjGrQOFuyGfps9uVH8yZcuWSiuJ6nMcVe7jkPd0yPU41q6tVNN0t5QigAACCCCAAAIIIIAAAgioRcDwjdNJA27ZsmVkZKSTDk6VwxrfVbQzdd9MZq5YuUdsSjaBIvMaU/qLyODLhyzMbuirm7yPQ5/jkO3wQQABBBBAAAEEEEAAAQQQQOCSgLMnOJgmZxOQj1pMGygGtrZ0XAE+Yvow0d6QE6lTdkPfCTkOS7GphwACCCCAAAIIIIAAAgioV4AEh3rn3urIZY5DvuX0toGicYC5NjRCdIsVD44Wccbvl50xQ+zbV+k043U3Kh0w2jGZ4/jjD/Hpp0aV2EQAAQQQQAABBBBAAAEEEFCvgLOvwaHemXH6yDs1Ex1ixJ40seOYSM4Q2ZdX35APpEQ1Em2jRM94ERZULYx33xVyUZV33hH5l06wJLuhb6PKehze3kK+QGfKlGodUIAAAggggAACCCCAAAIIIKBGARIcapx1W8Usb+WQaQ75Iz+FxSKvSLeMaIB3+RtDauzl0it+dTmOdu1ElVVFazzn0gFDjmPjRl12Y9Ys89U5igACCCCAAAIIIIAAAgggoB4BEhzqmWv7RurtKeSPpR+Z48jN1eU45Dtd6/TR5zjWr+fejTqxURkBBBBAAAEEEEAAAQQQULwACQ7FT7GzBiifVbHuI3Mc8ocPAggggAACCCCAAAIIIIAAAkYCLDJqhMEmAgjUJFDHW21qaoZyBBBAAAEEEEAAAQQQQMBOAmq/g6N7nIg3fseHrZnNv2ekSm8e3iK2V5UyG+8Ghtu4QZpzEoHWUcLLnr/NHu5OEijDQAABBBBAAAEEEEAAAQRMC9jzK5HpHp2rtE+CE41HJjgSBjjReBiKCwm0ixHyhw8CCCCAAAIIIIAAAgggoFoBHlFR7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBD+WEYlUkB0+JiwVWnWnZSYE+olWkZVWFKC0WZw5bWtm6ekFRwq+RdadyllMLHD8vMrLtOEJ3N5HU3I7t0zQCCCCAAAIIIIAAAgggUE8BtSc4ft4tkjPqaWju9JbhdUhwFOeLPT+aa63+x9peSYKj/orO2MLWFLHugB0H5uVBgsOOvDSNAAIIIIAAAggggAAC9RfgEZX6G9ICAioQ0KogRkJEAAEEEEAAAQQQQAABVxYgweHKs8fYEUAAAQQQQAABBBBAAAEEEEDgkgAJDi4EBBBAAAEEEEAAAQQQQAABBBBweQG1r8Hh8hNIAE4skFsodqWKA+kiPUuUlukG+voKEd1IJEaKpBYi2NeJh87QEEAAAQQQQAABBBBAAAFXEyDB4WozxnhdQSCvUKzYLf44LEpKKw33fK6QP3vSxPfbRfc4MSaJNEclH3YQQAABBBBAAAEEEEAAAasFSHBYTceJCJgWSD0n5q8TF/JNH9WXlmnF5hSx+7i4ZYBoHWWuJscQQAABBBBAAAEEEEAAAQQsEWANDkuUqIOApQK7T4i3V9aS3TC0lV8s3l8rNh42FLCBAAIIIIAAAggggAACCCBgpQAJDivhOA2B6gJpmeKTDaLk0nIbVY52iBEdmwr3ar9w8u2rX27SrdPBBwEEEEAAAQQQQAABBBBAoD4C1b5v1acxhZ5bVlZamJet1cqvouUfuS1LZPnlAof+nVdwMbcgu/pPaQONx6HBO3FnRSW6J1OKa7go5KMoUwcJH08TAcgLa8EGcdHsIy0mTrOqSHfdlpYYn1pUkFNaUmxcwjYCCCCAAAIIIIAAAggg4IoCTprg+OGHH/r27RsUFJSQkPDBBx/MnTt39OjRDeWbcWTb+3cE55xPMwwgN/OkLMlI2WooceTGtf8Jn/Dv4Oo/u5J/deQw6KuKwNq/dQuIWvfJKxI//WXdqXU4S6Y25HV7dNdy43M+vq/l3nXzjUvYRgABBBBAAAEEEEAAAQRcUcAZFxmdN2/eXXfdNWXKlAcffDAlJeX+++8PCwsLDQ11RV97jPnr2Rn6ZheufnrbwVVv3LNev+vt5WeP7mjTEgH5tpTf9ltSscY6cs3RMZ1FgE+NFTiAAAIIIIAAAggggAACCCBgRsDpEhz79++fMWPG7Esf/bhLS0sffvjhYcOGmQlDVYf8fAL18Xq4e7m5uRt2VYXgbMHKRTTkXRj1+ZSWiV2pol+r+rTBuQgggAACCCCAAAIIIICAegWcLsHx/PPPh4eHP/bYY4Y56dGjh9zu2rWr/LOgoGDRokV///13SUlJ9+7dr7/+eg8PB4VQXHCxMO+CflRFBRcNw2MDASlw6LQNGA5nOCLBUVyYa7iS5aC1ZabWRLVBNDSBAAIIIIAAAggggAACCDhUwEHZAQtjkmmLpUuXyodTPD0rFmOUSQ15epcuXeSf586dW79+vdzWaDQvv/zyihUrPvnkEzONFxYWnjp1ykyFgoIIIbyF0Jipoz/06cPtaq1TrYK2oKDw2DFLv/sW57kL0bRaI7YskIAlx3Js2aKztpWTI59pCjyfef7YMUdko46cChfCV4/hphHBNTwt1MhPeFX+nZNrixreupJ6pujYMSvfp5J9MUTGa8mVvOK9m6yYtDJt2bFjx82f2Lhx44CAAPN1OIoAAggggAACCCCAAAII2Emg8pctO3VicbPp6ekXLlxo06aN8RlbtmyRt2l07NhRFsbExHz44Yf6o3IV0n79+plPcGzcuHHIkCHGrVXZvvaJddGtB2hqz2+IKS/v9w+N0Z8uFxn95MHWVZqqvivfu7Jp06aHrhpc/ZDJkrDgZp8/kWrykK0KH3nkkeWbP7BVa87czhXTPugweOqzzzy7Y8UbDhjnDf/ZHNlSd6uR/IQFikfH6Ter/vng6Kolc9aIA5dTcKlp52Kvia1aw7L9gZPfSBr5L0uu5FHTv4hNqhjHx7PiLekhPz8/NraWscnVc26//XZLWqMOAggggAACCCCAAAIIIGBzAedKcMgbLmSEubkV76LIy8uTb1Fp166dj0+l1Rflwhw//fRTr169zIvIO0EaNWpkpo67h7xpwqKPh7e/l0/5v04XeftbdI4QHh7u5gdg3E5QYJDxrj22/fx8LR+PPQbgsDa9vbxkX76+Po6J1929IkkmE1vV1+Pw9RQy+5BfJCreNnzJorTSfpnVo/X2kTciWfTx8PI1XMm6EyxJilxquNaxeXtbOgaLBkolBBBAAAEEEEAAAQQQQKAuAs6V4GjRooW8xf3rr7+eOXOmzE3Ih1Pk4yrHjx+/4oorjIOKi4uThVFRUWvWrDEur74tb/HIzMysXm4oeWeVSM4w7Nl4Qz5H069f/8VPmRuAcZcF2WLDPOMC22+/9dbbMZ3etn27ztfioj/FpmTx3HPPD277vANG98E6sedEeT8ZF8VjX1Xt89UbhUymPbtU5OqSeKY/rVvGmL9cTZ92qXTJVrHugJnj9T3k7+dv9djq2zfnI4AAAggggAACCCCAAAIWCLhZUMdxVWRS49FHH928eXP79u2vueaali1b6t8Oq1+AwzCOI0eOyO9a8lWyo0ePLiqq37srDI2ygUA9BKLN3Sdkabs2acTSzqiHAAIIIIAAAggggAACCChLwLkSHNJWvj/lu+++GzNmTIcOHeSGfE+KLNS/QsVYPjAwUNaU93HI18oal9tj29s/JL7bBHljv6FxuS1LvAPkMpYN+WkR0S6ppbkVRhpycCrru3WUDQK2SSPmxqHRyOvWLzjSuE5s51HBYXHGJWwjgAACCCCAAAIIIIAAAq4o4FyPqOgFx1/66LdfeeUVNze3pKQk/e6ZM2eCg4O9Li2voH8+RS47am/3kMjEcbOWGPfiExBapcT4qMO2r+g6Wf44rDs6MiMQHyYaB4hz9XhBjY+n6GDfV+gINzf36tftlXd+bCYuDiGAAAIIIIAAAggggAACriLgjAkOY7vt27cnJiYa3j25dombleIAAEAASURBVO3aGTNmyEdX5PIcJ0+elOuPyjdTGtdnG4EGEZArdV7RTny52frOB7UR3s7+62h9dJyJAAIIIIAAAggggAACCNhbwNm/UZWVlcn7OQwKN9xww6hRo+QaHL6+vrGxsfpbOQxH2UCgAQV6J4jNKeLoWWuGIN8sK/MjfBBAAAEEEEAAAQQQQAABBKwWcPYExxdffFEltqCgoM6dO1cpZBeBBhdw04jbBorXlosL+SbG8twyIV8km2fqFSry4ZRpg4SXs/8umgiKIgQQQAABBBBAAAEEEEDAeQScbpFR56FhJAjUVSDIV9w5RARXLEdb0UBmrjifK7QVBeVbvpeyGxHB1Q5QgAACCCCAAAIIIIAAAgggUBcBEhx10aIuArUJRIeI+0eJuLDa6l06Hhks7hslEiIsqkwlBBBAAAEEEEAAAQQQQAABMwLcFm8Gh0MIWCMg7+O4d7huPY41e0XGRdMtNPITQ9qK/q2EOzlG00KUIoAAAggggAACCCCAAAJ1EyDBUTcvaiNgiYB8qUqvlrqfY2fFgVMiPUtcLBBarQjwFvKujcRIER8u5JodfBBAAAEEEEAAAQQQQAABBGwlQILDVpK0g4AJgRZNhPzhgwACCCCAAAIIIIAAAgggYG8B7o+3tzDtI4AAAggggAACCCCAAAIIIICA3QVIcNidmA4QQAABBBBAAAEEEEAAAQQQQMDeAiQ47C1M+wgoQoAVQxQxjQSBAAIIIIAAAggggICCBdS+BsfkPqKo1I7z610XYK8A0edWOw5GNu3lb9/2ab2hBIa1F30S7dg5S6LaEZemEUAAAQQQQAABBBBAwBYCdfn+bYv+nK2N0AAnGpGbm/Bv7ETjYSguJBDoK+QPHwQQQAABBBBAAAEEEEBAtQI8oqLaqSdwBBBAAAEEEEAAAQQQQAABBJQjQIJDOXNJJAgggAACCCCAAAIIIIAAAgioVoAEh2qnnsARQAABBBBAAAEEEEAAAQQQUI4ACQ7lzCWRIIAAAggggAACCCCAAAIIIKBaARIcqp16AkcAAQQQQAABBBBAAAEEEEBAOQIkOJQzl0SCAAIIIIAAAggggAACCCCAgGoFSHCoduoJHAEEEEAAAQQQQAABBBBAAAHlCJDgUM5cEgkCCCCAAAIIIIAAAggggAACqhUgwaHaqSdwBBBAAAEEEEAAAQQQQAABBJQjQIJDOXNJJAgggAACCCCAAAIIIIAAAgioVoAEh2qnnsARQAABBBBAAAEEEEAAAQQQUI4ACQ7lzCWRIIAAAggggAACCCCAAAIIIKBaARIcqp16AkcAAQQQQAABBBBAAAEEEEBAOQIeygnFqkiW7RDpWVadadlJUY3EuC6WVRWiKE/sW2FpZevqNesqGsdaeurp/SJ9n6WVravXabxwc7fu1AY+Ky9THPzFvmOI6y2Coy3t4o9DYs8JSytbUc/TXdw60IrzOAUBBBBAAAEEEEAAAQQQcJCA2hMcR8+K5AyhsY+2VojCkjo0XVYizqZcqm+PAcnRCBGWUIfx5GVdGo89BiNHcWk85X/WYVDOUrWk0O6TFdOpDsGeviD2nbTjlezlmnmoOghSFQEEEEAAAQQQQAABBFxcQO0JDv306b9rO9FUOtWAnGowTjRJl4fiTD7ONJbLPvyNAAIIIIAAAggggAACCDhEgDU4HMJMJwgggAACCCCAAAIIIIAAAgggYE8BEhz21KXthhAo04qMbHEhT9f3mYsiM7chBkGfCCCAAAIIIIAAAggggAACjhXgERXHeiult7MX0p78aLyMRqPR+HgFtGvR+7rBDwb5hTZsfCkZ4vdDuqUo8ovKByJ35U+Qr+jUTPRvJSKDHTTA2R9ffSbruOzMw8OreXjba/r/Kz66LitqOGiYdIMAAggggAACCCCAAAIIKEeABIdy5tKRkRSXFB5K2zZ9wrtRjePPZJ34fM2zu1LWvT39D0eOwbivczniq81if7pxWcV2dr7YcFD30zNeTOgm/LwqDtlp68ip3Z3iBw3sdG1e4cUfN87913v9PnroQBPL34lip2HRLAIIIIAAAggggAACCCCgXAESHMqdW/tH1iGuf8vozrIff9/g5xbecPbCyQb5Dn/otPjoN5F3+a4NM3FvThFHzohpg0VEkJlaNjikEZpmYa17thkl2+qWOPya2Y23Hvx5ZI9bbdA0TSCAAAIIIIAAAggggAACCJgSYA0OUyqU1VHATaO7kNzdGuBVogfSxZw1FmU39DHJVTneXKFbpMNhn3IcTQPgOCxGOkIAAQQQQAABBBBAAAEEGlyAOzgafApceAAHjm+5mHf+zIUTn6x8qk/78SGBEQ4ORmYrPl4v5Kqi1T/9EkWrSLEpRexLq3owv1h88KuYNVL42vNZlZPnknce/kU+orJs45zGQVG92o2tOg72EUAAAQQQQAABBBBAAAEEbCfgvAmOlJSUPXv2RERE9OzZMzc3Nzs7Ozo62naB16GlvAsZh7d807b/FE8ff/1pxQW5f2/4JKHHRL/g8Do0ZKOqP/45r6ystHpjfdpf1SQ4pnq5PUq0Wl1SYfEvL3q6e2VkpjYKjHjg+g/1HZ04c2j97q9vGvqo3D12et+mv3+6fvAD9hmD+OwPIbMVJj/NQkXn5iLljMmDIuOi+H67uLG36aP1L9UK7R97v9979PesnIy8guwX71ipX4H1l52Lg/wad2s1XHZx8myy3J087PH6d2dhC9qyst1r58Z2HhUUFms4Zd9vH0fE92jctL2hhA0EEEAAAQQQQAABBBBAwBUFnPERFZnOmDRpUsuWLW+66aY+ffoMGzbsvvvuS0pKaijf7LNHf/n4noLcTMMACvOyZEn2mSOGEkdubD24cvP+5fLnm/VvzF/+qH5b/pmde85hw5AvT5F9zZ7yzfwH9y1+8qT80v72N3fre28alvh36qaN+5aVlpa89uXUpIQhdhrVnjRx9Kz1bW9KFmfs9qCKXIPj2oH3ffDAni+fPDW2z10vfD45r+CiHGtSwtC5y+7PzjsvM0SvfTXNfjgmXbTaMnndnj2+2/johsUPpx1Yb1zCNgIIIIAAAggggAACCCDgigLOeAfH5MmT//zzz1WrVsnURnp6+pgxYxYsWDBkiL2+J7vctM2e8rV+zB8uf3zLgRXP3La0YUPw9wmade3cu9/sds3Rme1i+8jBzLzm/YfnDf8reV2XxGGtmnaz0/A2HKhXw/IWFPkGWflSFbt+3Nzcpo1+8fc9S77+7bUpVz4VEhA+edi/310yvU3zXq2bdW8f29euvdM4AggggAACCCCAAAIIIKAeAae7g+Prr7/+/vvvv/jiC5ndkNMQFRX14IMPFhUVde3aVT2z4nKRJsR06ddhwoKVs/UjDw2KHJx0w5odn9087N92iqWgWBw8Xd+2/zpe3xYsOd/L0+emoY99u/7NnPwsWX9Q5+vkE0Y/b/nolpHPWnI6dRBAAAEEEEAAAQQQQAABBCwRcLo7ON58881+/foNGjTIMPqYGN26El26dDGUyI1Fixa98sor7777bt++Dvo38L/XL/D2b6QfQ2HeBePBqHBbo3Hz8w7Svx9EH/7Nw568f86gA8e3yhsTcvMv/Lrzi9jIDlv2L+/bYbw9fFLPiUvLgNSr7fO54mKBCPSpVyMmT/b1DvBwr1jCdESPW+XzRD/8OffGIQ/L+r3bjQs5Hunl4W3yXHsXHtn548VzqYZeSgrzDNtsIIAAAggggAACCCCAAAKuK+BcCQ65+sbGjRuffPJJY9BTp07JXeM7OGTJiy++mJGRIVceNa5ZfTs1NXXhwoXVyw0lmRE3C+9m4tKKEoZCkxtpB37z8PTVHyopzjdZp2qhVisH8Pzzn1Utr2HfSwQmec2o4aBtin/66cczP+yysK0ot77NPAabrBwZGvv9s5WyPAkxSYaSOUtnXT/4wW6trnxo3rCO8QMD/UJMNiILX3r5Ja0wsWBqTfUN5fkBnUXjMYbdXvEisHxyysuiL/UpX6TiWfn1rOlZYq/Re1Vef/djz6KThnYs3/DTRHbwvK2m+u/P2mF8yNPD66OH9huXWLL91VdfZWkPWVJT1rkYMkwE9rDkSj5zbGdelu53Sv8pLSm6vGnu76Lioueff9VcDSFGjx7dgGvlmB8bRxFAAAEEEEAAAQQQQEDxAs6V4JArbpSVlck3pxi7//zzz8HBwfHx8YbCe+6559lnn501a5ahpKYN+SqWxx8395aKa5/oH926mW7BzNo+w6bND2zcVF8r53za/HvLt82cJ1d5OHLk6KvPmRuA8elhwc0+f8K+CY4lS75bvvkD407NbMsHK24bNdhMBZOH5GtTsnLPDO8+RR69YcjD/1068+EbF5isKQtnPzm7uLSwpqNmypNGzhx0c0WCY0Br0TTURPX2MUL+GH+2pFRKcMz/+LPje1cbV7BwOzGm239n1pjgsLAR89Vkbm7jPksXWBk42S9pZA9LruSe4x+P7zrO0PW8eyr9uhnKq2wUFxeb/1WS9cPCwkhwVHFjFwEEEEAAAQQQQAABBBwm4FwJDl9f3b/CHzt2zBD/7t275dc8+RyK/rUdsvzLL7/09PQcN26cJQmOpk2bTp8+3dBa9Q03O796NiYm2vwAjIfk417+CIxxoW23hwwd0rKnpY9kJAZZ8xrV5hFtH51UftPK8G7/iI/qJN8YYpi+KuHcfffdZaKkSqElu5rwnsbVth8TR84YF4jECBHZSCRniJMVb7/RVThW+VUz48eNEkPaVDrTsp1gr2aWVTRRq1fbMZa8P2XMmNHdhjY3cb6pIk2LzqaKbVbm4eFR65Xcvn17m/VHQwgggAACCCCAAAIIIIBAHQWcK8Ehl9to1arVBx98MHbsWLnoxq+//iqzGHKFUcMCHGfPnpUPsKxbt87CMBMSEt555x0zld9ZpfsObKeP/FbfMiFxxt3mBmDcdUG22DDPuMD225NumhTTaZKF7R75UyRvsLBuRbWo0LiKHSFaRpv74v3GG2+4WXUN7k8X76+t6Gftvopt/daNvXQJDrmM6Dqzj4Y8++/7gv2qnmvJfvYpsXmhJRVN1JHP7ASKGh/bMZxwxx13hiUY9mrZWLJVrKvfa2XMd+Dt5f2y2V8l86dzFAEEEEAAAQQQQAABBBCwt4CbvTuoa/sff/yxPKV///7+/v733XffE088IXcNC3DIXbn+6L59+3755Zf8/Pxdu3YdPXpUVuCjNoFmocKSxzHMswT7CuuyG+ab5SgCCCCAAAIIIIAAAggggIDjBaz613N7DrNPnz7Hjx/fs2dPUFCQvJtDrrMoezMkOORbY7ds2fLaa6/JwqysrG+//bZ58+axsbH2HJFoFJk4+t6vfQMbG3rxCWgsS2S5oaRBNoZ2mZSUMLRBum7wTv29RVyYSKn8WEpdR9WhaV3PcO36Gjd3ed1GxHU3DmPY7fMbx7QzLmEbAQQQQAABBBBAAAEEEHBFAadLcEhEHx+f7t3Lv4Nt377dz8+vdevWetzZs2cblOXjJ//5z39GjhxpKLHTho9/SGLPicaNe3j5VCkxPuqw7djI9vLHYd3V2pF8O+zuI+vPZaenpO+KaZI4ts9ddn0Tar/E+iY4+jo2Q/XLzsXBfk027f/Rx8t/XJ+7mwRXXv60Vt96V5DPTFW/buO7jK13wzSAAAIIIIAAAggggAACCDS8gNM9olKFRCY4OnXq5O5e+VWflyrJZ1XkWxuq1GfXkQLylTeGj+x30/6fcgsu+Hj53TjkkaLi/M/XPGfXwXRpIaLqsSprUnMRU/s6GNZHYJCRG7KVrJwzOw//cjE/86q+/9c1cfhzC2+0vmnORAABBBBAAAEEEEAAAQQQqCbgjHdwGA/yxhtvjK7hRSfz5883rsm2gwXk62BfXHRzq6bdT55Ljmoc//Idq7YcWDFjwrt+PoFyJG1b9Fm36wu7DsnNTUzuI95aKYpLTfRz5qJIyRDZ+SYOySK5+sbESg9qmK5mdWlmTsadr3eWt7EUFRdcyD3z8h2r/zryW+92Y/u0072fNbxR8/yiXKsb50QEEEAAAQQQQAABBBBAAIHqAs6e4Lj11lurD5oSZxDoFD+wQ2z/Z25b+tLif94y4unikqLikkJ9diO3IHvhqqdnXjvX3uNsGiom9RELNpjoZ80+IX9MfjzcxbRBIlD3SmJ7fUICwmMjO7wwdfm6XV/mF+VEN2m5YOWT919XnpKbs3TW9YMesFfftIsAAggggAACCCCAAAIIqFLA2R9RUeWkuEbQySd3xl96BWxGZmpESItdyb8mtRwih55fmCOfv5g25sWYJgkOiEQ+qHJLf+Fp4hkm050H+Yjpw0SzihVjTVerZ6l8LEVbVubu7nEobXtCTNfCYnknicbL00c2O++Hh5qFtx7a1dKX9dZzJJyOAAIIIIAAAggggAACCKhEgASHSiba9mHKr+6JMV1LSou1Wt0aExv3Le3dbpy8j+OJD8f1ajvGTeN+8myy7Xs11WJSC/GvKy1aUKNNlLhvlIhtYqoVm5alnT0UE6ZbwjQl/a/m4W22HVzVrdVwubvg59nnL6bLO18Op+2waYc0hgACCCCAAAIIIIAAAgioXcDZH1FR+/w4cfxNm7RKbNpNIzTyWYy0s4fPZ6c3CY6Wt290TbwiJz9z8/6fopskyEczHBOBfFbl/lHir1Tx+yGRnCHKtJW6lfd3tI0WA1qJxMhK5fbb0Wjcrux+i2xfrrux49Aa+eaUqaNekLvyzSke7p4SR97QkRDTxX4DoGUEEEAAAQQQQAABBBBAQG0CJDjUNuM2i7dHm/IX9N57zXvyPo6po1+UTft6B0we9oTN+qhLQ24aIW/lkD8FxeLEeZGZq1t81NtDNA4UTUOEXHfDkZ+muts3dHdwXN3/XvmnvJsjyF/3VMyY3nc4chj0hQACCCCAAAIIIIAAAgioR4AEh3rm2o6RyrsSLn2lt2MXljft4ykSIiyv7oiaLSLaOaIb+kAAAQQQQAABBBBAAAEEVCzAGhwqnnxCRwABBBBAAAEEEEAAAQQQQEApAiQ4lDKTxIEAAggggAACCCCAAAIIIICAigXU/ohKWKBuyQb7fcKD6tC2xl0Ehgtt5QUy63B+rVU1wtO31koVFbz8RUC4EHYbj0Yjl9qs6M61ttw97T5ZHt51IGnkZ9F7ZOrQYuWqcjUTPggggAACCCCAAAIIIICAMwtotHb8Pu3MgTM2BBBAAAEEEEAAAQQQQAABBBBQjgCPqChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHgASHcuaSSBBAAAEEEEAAAQQQQAABBBBQrQAJDtVOPYEjgAACCCCAAAIIIIAAAgggoBwBEhzKmUsiQQABBBBAAAEEEEAAAQQQQEC1AiQ4VDv1BI4AAggggAACCCCAAAIIIICAcgRIcChnLokEAQQQQAABBBBAAAEEEEAAAdUKkOBQ7dQTOAIIIIAAAggggAACCCCAAALKESDBoZy5JBIEEEAAAQQQQAABBBBAAAEEVCtAgkO1U0/gCCCAAAIIIIAAAggggAACCChHwEM5oRAJAggggAACCCCAgAoEDh8+nJOTIwNt0aJFSEhITRGfO3fu+PHj8qi3t3fbtm1rqkY5AggggIBiBFzjDo6srKy0tDStVutU7vn5+SdOnKj/kEpKSlJTU3Nzc6s39dVXX/Xp06djx46LFy+uftTmJUePHh11+SO39e2bHIMc8/z58//5z3/qq3///fc2H8zFixf/8Y9/xMfHX3vttXLb5u1Xb9DxPVYfg3pKzFz2ikEoLS09duxYdna2YiJyqkDkdxv5v5zFxcVONSoGU13ACf8LXlRUJL/0ZmRkVB9tA5bIIV3+L/Aow3+C7TceB3dn20BWrlw5YMCAESNGjB07Vv7/QzONy/9zNXHiRFlz8ODBn3/+uZmaHKoioIb/TCvpvyMu/Rtd5dqTuya/fVSvRgkC/8/enQfeV00NA3/NFBkiQ4pQhsg8hqhkLDIPydBgnhJRCaFZlFmZUoYoFEIpJDJlnmcPKUOZXrPneT8s1rPfc+7d99z7ne7399vfP+53nz2svfc6e6+9xn1GYmCuPTio5w8++OBPfOIT2COjv+xlL3uDG9zg3ve+9+Mf//iLXvTfqhlahnPOOUfpeuutRxEwcpKLm0nP8opXvOI973nPt7/9bQKMfm9605vuu+++N7vZzabqyMlx3HHHHXXUUT/4wQ/A0ZYVYosttthrr7023XTTAHXooYcSkKRf+tKXPvShD50K/gyVE5na/vGPfwwII8fw2te+9kUvelF2sRRs4oc+9CEcjC7OPPPMU0455cEPfnB2t0SJkT0mTpZtgS3R7OYE7JBlb6grgvYvfvGLj370o//7v/87cXX88cfPYPED4dWvfvVJJ530rW996y9/+QtoV77ylS3gpz/96Ze//OUT+FInvvOd7/zyl7/Uy+abb36FK1xhqbtL+CjkzW9+86BpkXmRi1xko402gkkCyX3ve9+sWSYIIXe+852JnZH5jGc847GPfWxZIdOEmZe85CWf//zngzbKR4F33XXXZSAROYaWGIKB+TzBP/CBD9ie1k9M4TKXucwd73hHe/NWt7rVkElFnaOPPvqII46I9EEHHYQtKdvaevTyYZKxql/1qleVpZU0WSv4GXXyCK7UH140kqIuXXfDB1avOY6Iffe7333c4x4XtPo5z3nOjW984wqcjTfe2Dt68pOfrM6ee+6Jv7r1rW9dqd+KBh7T497OMiDwkEMOeetb3xodTbXFcmyr/RxZpTs68d9JjFxLI6WPTsP22DAwDgPzq+D45Cc/iWEtuWSHMabE34c//OHXvOY117jGNczqM5/5zEMe8hAJJ9b73//+cfNcrHzWQsfke9/73gTIPEsCv9/97odnute97pX59YSD+eEPf/jHP/7xshp+3d+pp576whe+kKClaN11140K66yzTllzOdMjx0CxGmO40pWuZGxLIT6VUy7TSzf3spdML/MCW7rZzQPkgcveUJcf7bSohOTzzjuvRBQ+r3wckqbsw3l/6lOfKiv/6le/Qh8++tGPvvvd795ggw3KoqVL0xGgluBT02y11VZL11Ef8s9//vNO5vnnn28wWFKk4/DDD+8jAXJKh7gTTjhhpILj5JNP3mOPPTpOMV/+8pef8pSnfOxjH6N6vtjFLtbpuj2uCAbm8wR//vOfTztfIoSc4My1/CzObbfdtiyqpC+44IJU6x9wwAH3uMc9yoVH+rLgo3mYZyqglqdo+SnqosxrHBGD81AA3eEOd3jiE584sa8HPehB3jJXUyzlC17wAkquiU3W2grDj+lxb2epUffNb36TejFP59/+9rfT9rgGnCOrdEePe1Mj19JI6WMchJbfMNDBwJyGqDB7ctMotRvluKn6MmZhmf2TTzzxxNRu2HsCKBgnjQ2TxAQ03OTCcaOj3cgJ/vnPf0a+45GXxF3velcemI7zrLDMif4YSC9M0zGMY4455gtf+ALuYdFHdbe73W333Xe/7W1v+9SnPnW77bZbdPh9gCN7XOYF1h/VmpQzcNmb8jKjna2VhBwCtjjtheD8uc99bke7kdD4fO299975uNSJZcbhwOkQM3bZZZd+ZQ4vZSZtSF9L8sMf/vAJT3hCR7uRrehEaJHysSVWEAPzeYLbgK9//esDLRe/+MVvcYtbrL/++vFIqMNhz4Yxjiqp8Qfha1/7GvFpNlBL12o+qcHE+Y4c9le+8pUPfvCD2l7ucpd75Stfmf68dWiswVe72tXUQVtOO+20euW1uXRuj+l4KQ5rbjip3ZjhTa0Z58jIrTEDNuakycjp9KWPORltG8aqwMCcenBgEcK5GhJxtHQHpA5mOpEghBBW0Otf//rUH0x2boIIRON6qQxYUbibJuq//vWvA6UJp0RuzGJAsogegQY0Hvmmco125rG68HV07cU4IQfpjyabbbYZI4CwBfz6TjvtJJMWGZfjWorsopI444wzopQHhFY3utGNDJLqhCMlzw6/UXqVq1zF9KXTRcKMWIPlcHrnee6khwSD4fsdyk7KEUE9pmNSJhJw/OLtwjqtoe6+9KUvqXb1q1/9Nre5zbWvfe2s1k90xmAA3/jGN7IaONQ68BBSx6UvfWkqiSwVIe8s8YiPHOdEagpgGhsfHH+JeRwne1qY1NC+S17ykpzYwyINoNnhUE1W/QwicJeYKAOZ7htjsuYVnyNJvF3zmte87nWvK7+Edstb3lIAVKdHa2niAkv4LTEEA0OW/QL39WzrnA95xENd4hKX4D/F53nIdPp1uJenH5klethhh13vetdDZ1784hdbmepbh5SzOPL6Smb1Vd9Gs+wRLnsnXYqyU5vL3rHaFdkCW265Ze4d+sfPfvazuU+/+tWvakUbmztiCPzsSIICFyFFgW0x5MLUYhOVdUam+buJftdc/BftcKiQjI06Y4cddsgmKjChx2MSEwSWT03WkfBqgg2Clv3220/UHjZXQ+gNbQjVEkVV2aSlVwQDi3WCVxbqDCc4tgHDwMFKKOixxx7rmEBtrM9Pf/rTsMQjw54SzjADxuz0+9///paltnl8j4Qzw1bi15n3cTj0neABGTEZx96UXQ+hqFHfmYhEi/7AIVD3dxQHlXdRdlemxx3uvHHZRaImhi2cX77//e8HfQjepkLE0sgk3m3DDTcsewTB24Qug0fxsAFYrKggPPARj3iEaF+PQoyDtSjbtnRgYMgxXXk7icbK+pztmA7IPK3y9My+pkpMdY4k6xjL8txzzxVUjmMU2hadxjRdfuFMdFhbdTmYbFthO2egY8u8o8liwUvgjjhM5eySLiGkKGrkV6hEyXKXDDwj8Th2pSN9ZNcVVn/iMKICbp8ExFjOF8/4iR7iasNcnb20xKrHAG3oHP6R+fkwx9/ZZ5+dI8QcEJjjUeI/Vf73P+Y7Sm0Auob/LfhXyvUWLEtRwY7N0re85S0CXvLRLV80HVGt/EXXsg6WOovoFyJ/xx13zMx6glohmuCuypr2m42XOY95zGOimoiVyCxzygmScIwZaxX145f6cyQoGpmsZuK8MKJaiROkud+jHCPJtpl42tOeFumrXvWq2MR+p/vvv39mZkLNhz3sYQlEggYK/xEV+oMpcwhL7G+aUABFfWovR0sJjcjnNIpS3EwUkYsixxGblUmAMkv4pl9fYAGk/U6FgSHLvo72ifu63CDj1nlnzFQJNH2xGGgw6exyYWDQO5Xrj5ylo62liC3Iyhbb7W53O3fuEMhlliutv5KZfylkcwwS2CYsXUKjTBQTV1aQxnJlnRxGWYcSJyBMhJ8dRYJfN/6sBGWbs6GVlKpsIj8rM5hnEf7GRUVRJJov8yVohSKftMPJJdKkl7IO4TPBInRl0VlnnUWval5lZkuvIAYWfoIbfH2hlpto4AkeCKGFJGAncnhH5roaee5nzTLhdrBsFQmqExU+97nPdfJZLLLhxK1UTiqOYDcF0G8GTO6cMfKJZDB7lKhQ1LK7t7/97RSgOXhemZSJCaf+LrJaJuqHuyjj7IjCN1rRZkbmzjvvLKdCxJhzoibzUvYo8bKXvaxDqXAUSfdUQM+jodvcSIll25ZODAw5pitvB5yJ63OGYzqGZ4eyGXiJJGpnULxNeqsc/MTEtOdIOVQBbtEjWSCm2eeHnV/klBjGtGznQDq2zDuaIiBm7ZcCMTGcc3/e854XmXUqUVKbku2prKUS+dlvndVXrT4MFSDQfcM5qUiQ++RnLy2xBmBgTkNUSlcLjqNu3MA3YOs5CNTdDVLh5AMcbqbMx0i86U1vEr/dyfS4zz77lA5vbAty+tXoaDOTISXT6ZsQ9ofMryRyglhzJkf+lrYWwYBgM1CJyLmgnKATBUciuL3slPcmsGWONEeVMFZHvok7JwhgnWpTPVLxhIXZlsC7RFua6TQFlNbaKFWT/Jlm28h0MGAch7iPCgQosY3RQSUxf+WwWaJ222238s2WpS29/BhY+LIfvq+Hr3Obbvvtt4cNq7TjMjAtihghowlBPQ2tcmwQdkWiThmoHzU7K9m+FsHRCSrmnkDa5wkfTez0fhQM6sQ0PTFKbgj86CV+7SyRYnn3Z2TavLSiuLGy5sQ0EhF4VtPeLOtnfAqOkOk4iqi2049PTk5funOnI7OSykL5SpgtvYIYyJ1uDLOd4FMt1IEneCCEpY63I59Bp55lzJUj8kP7NjPSHL52n/NrHIQZthKdoFOSsgBMSlhfAAk/zeFkcNxg+vkEs7gROYr4w3JziPRU70KTBR7u/bGVOSyuya7QGmcRgn/ggQd2KBWOQmZSG/fQB6NSAkkILREYyM07M3c6fH0OP6ZjbAxU4SzMZhZ7Ydq3NvM54gQniZTdEb8jVKrMdP3wSPGhrDMuPRUdGwck8xdrRzte0w0q2Xu0AlmIvuJYn4pKdNieHPPExERWf8gwXIBA29Lpi9zHEa+T2R5XNQbmVMFBZZCeh2QGl0JhXskh7H5vfvObKQIgnfyApUiPKYoPj8Fe8G1OHzbS++mnn55R35R/edrlmwOKahY5YJmMTBZFxv+sEInyAsJSz5LpskKnbefRnWSRYy6uAEQoHdX8Qt030VdJdNrGI16K84Ip52nEaZCPHAy4vz3q4DNSxZBAcAA8TVzRRGGZmYhypusJpAFtyjoGbAzbbLNNXpORahccUohbRsgEnU0iQcnKVB5pAzYAX3GLR684EpXfCy+8UKkQXJTXNPmqRGUGHNZdH1ng6C4HFctApAq0kUWVBTayfsuciIEhy76C9qn29fB1jk963eted+SRRzIATpxCvUJuXuuwXjNLOyuZ92wUUaG+853v5I4bPvOsqeFcrVQEjTHz3mR8JvOktE/HwUSpAnqScDzaszYpk4WdkvkV+DGA+KWpibeGaBiMzcWfM4ps4bLmkHSihQaHjBFNaEJxLZGm4BBYl9rSJCZKU3kknXAQauSi/AtpMKC135XCwAJP8GkX6sATvMQGNYePLzzrWc+KkE8m/c7lo2XlShoHoq0KlHHUo6yd0mJV7JdOqxm2Et0QPw5wRFDa6XGFxFRkUNsKRS1HqBqLyLOf/ez0vY9dOe27AHOBh3uMahwRSy4LlYCWnIIguEjjo9iH0QTxKZzq3/jGN2IdsxprbaT5AmRmS5QYGHJMj3s74Ey1Pocf0yCTXUPpZueO+xpXOZGR6ZnPkTipGSB5PTNUUG3QqkcXTBd8FVM1b6vixkf2Xs8cSMeWeUebbH4/IXcZdU+w94Qm8SbTUomS7amspQ669FJn9YcMw8aPeGHA+Z5gIfxiqPiFNQVHB+Gr/XFO7+BwxL785S93i35eJhqIpmPzR+nL6uJs45mGoQ9jJnbfY1RLDwUCPw5GJr2vJhGf4tguDzyl9KahFOCiGXde0DuIbo0PtQRMv8EJxWMZnppp8A2Y1J1NxiU4d5H/k1hENXueSMOL1W9pmhgJRMwYvY8iNopUGMuhaIAHZ4x8pf3vw3OedxWfMYtgx5wFuU9dw8i+ykyu4ExJrCKRqTsjkWb9jshYHB67E3qRwkmabUs4XmI8ghZf0dt6661JXz6dg1yWNUem8S7E0dCCkevSpIMjZPLSRMAtCcrBM+R1jOyissBG1m+ZEzEwZNlX0D7Vvp52ncfHmCZOoVLBsheAGhVcEFOpWRaVK5lTUhoWOHZRSajp5os4d5NcyCdCuHGDs64KnHUxW452adsZ6bCDaF0pbeX4s7P0IjEQ/r8a/fsHKSYhgJ/+EcLo5CguOcWySSVN3shSGuSwC1HCQp18rx55gTqjDeqB+eCvHk1Kj61Er8+mdGgXxU3KMNlXSywzBhZ4gju2hmyEnNTAEzzrdxJOK8GSs92+4SR16HCeAjMdElmw80Kc7GvarYQT8Kc5AUP8C7k9QE1FBjWpUNQcm4QvvwTB4S0bdpFgHqZ9F0At8HCPUY0jYqTcqIDli0T8Bg2JNAUTGkJQF0qACSyraRUXmjQFR4mWMj3kmB73dsCZan0OP6YxtLRv4DtEkv8shz0wvZBzhNOidWUj64uEEj3iQsM0IoJMqDiXYeIDq2roPQeOKqoNpGPLv6Od/uHpRtoKGSffMvsKpc+0rAU0JgNv7iPZlT7q9FJn9YcMg7d7Qhan5vU96UlPoo9muREikEUtsQZgYCgXvvxTpaMlKttUmAZrOsT1GAYNBVlaFPq4UTnVoggPnZ4FWTnv68qcZB3SiUBR34MjTZdKQ5wICJlGeZGeBFtJ4IrQSuw7QYUaOI0SmrBnPvOZz+QcWGmuKE/3DJCRGSTVMLjEk4vklHgLgOqkRsZ847sD/Q8WROXhv4QoFhU6XSp5rwzVS2e2fnwKsGnrTh8cXCYea2CPPEfSxydBaevejYCwFB92GTi2Vm0cBha47Kfa10u0zsdNTb7174C0f6UdnJWaZVG5knOCKuCfwiE2lSY2FytxbHy/hB80xGmNnSICRShW3MFZwi/Tw+GXrbAvsTdRCfQkXXzrfZUQMl2ipXSXiwp0kXFpdEL2qVFq3yC8zFYj4WRmS8wVBhblBDejcRuhnOzAE7xsYnicfRhabR+yMW0gwT6WX1ltSJoxme9DXOWrPh5AJGxo7TvNp9pKKUHZFy6cSlC5iweyN9mwnki1IMoZCo6w0GZ3mo97F8mNRBd5Is92uNfHGaNSJ650zco4EI4bHqnGQhtLtYH38C7Kmql46rNGCWotT8znMU0edqulV8MGGZr92V7TzOcIy0H6tug69fu5yHkNi4EK74By1wwf5wx0rAJ8EXc07zMmQ36XjmZyGfY+lblhvyznO45KlEMt2Z4yv55OwqLaSFZ/yDAsHvxM6EP5wPrzSGHHM51bX30ArXR1YWB+FRzwyIGCyja0tjRz/BpSABZ3UFFwpPOzMyzk/PKtZGmZGWkLHQsSCouI9Cvr8DXIR+PJsAu3FkU+D1LNs87EhGM4TmJCEY8vOy08zIkQBplhb3U4ZJt+hbRw9ovKnFTHdGL+yzoD07TadEnhxMHGwhcm8DwyPgXMfAvJcAzsKKqVyElQimaDNlXXrfICMTDzss8XPe2+XsR1Xpm7vW+1h4Wh7zk1ruG4lTzSxxUGyBL4ePxBagHGQe7nJwIVVeCXDZEm8lvHS6KsMFU60eKNhFDE6y09UyhQuNqVAClE6En5msncZJNNsggc/JZHQQEU1oBw5cjSlpgTDCz8BDeRcQt15BzrJ3jZBDvhkTXSN9pYGqT98g91WW9ZbUjaxufkzPcwKvO9KoWohDDzVqLiZLJOq3Xu4mnJYI6knkj7R1TL7jyOexcdBUc2WYrjOO1MnSu3hNxypC/vRCCCkoqRFzQkR5Kt8st0dWystaXzdkzn/RcOhXBppJGMt8OpUA4ZNVyQ6q9s5nOkPKl1kQ7dpVtiMt5ZWh/MuNLhdGwchDJ/4TvaHCl3gmaeeeaZrisKO7GNH87mueX1O45KlEPqILMsqqTLXnJHl/XLCuOGYcxWiw8jsMVGW8oODvWsKVQzScZLsC29SjEwvwoO7Dtp2UdGYnMKuubXwKEjHCvKy+f6qCddh5mRQjT9tLNa5zCWny4YPCkynerPbFhGrAhgydD3dKYtK2SrcQnUmcbXh+tUYPjl3oa/5+wa9U1wNhIwrruR+eml0p/syPr1zIxS4X6Sp8jI+BRwvNB4RxGMF5CxgDM4iWVAPiCgxVsIg3aeNznytP/ki86illgGDCxk2U+7r3M6i7vOE2w/weE2FBw0sGW0mu2M/+a47jqMkQdzgDLBhIlwpT0nMy11AVnuFQrtBiMGsDh+X2Mdsp6HwM++IuEut9BuYLm4LrPukhZCIOzUnPhozOnVBVFRH2ORIsdICPrqKziYjwI54ajlnoKm4BiJvRXMXOAJniMftxHSrqBmLv76Ca6mUZGU3MllPXvkHEFHluvZQppBwQEOj2t/FjMjR4SryOz8zbCVbJOwSbpRQuRpeGvOTAY74xn4OAPRmOpwdyKHAjpfYn1g6fmFqGqSJiVA3CIvtI2HL9kGHQ7C4kPaGBL+HQE27+hJOPXu1s7SOTym48jzOvq+UTw7Dj30UJ4d0yo4FnKOiGgLDra0DqZZtBPvNpDtzC0wkY4tZFnOsKN1h5MPBQdFQHo6UCmGgDYEZkmxZxv/RFZ/yDB07ZPefOIImO5qpAYNGktxhoA0Bcdsr2Y+W110PodFOnV1P65dYLyzyraXgwtJQSX0Agafx1s5kYy6RIBsRVTPH4cLd/X7LT+AEq2S7T7hhBMSTumvEZlu7kjXOFGmpHH5TgIHbVTIazt9JM8+cf0HR48EWCb4a6hAJ0plEzRRRKgLUKMOdWkqCMpWi5J22MfBz/6T5o7+ZGfoK6JUNKTchYGAMDI+RVFOkD44NKnQSDFMiJoYntMZW4KSn45znH1o9N0cGQqUNPuw0UXzcJbpgOo/jlxg/WotZwgGhi/7kWifal8vxTqfuK9TnSdyBL8VZhwrzSd+KCa4aLnIqjQydJCWYr98bYNw+dUEDydBKhPRltHmPmrrO0ECzUojUsAcicAh8DtDynB6pJh7BapVAulU7j9i7KACZ+MWYcxQuvXa5lE5byoVcUa9m38iCKICoZGqSJpuRYRzZLozFXcS7CDgbl2N/PY7JxhY4AlerrFxG6Gc6cATHP/AHckV1FiLNN9lMDmA6XnBRciicjNX8PRlX+PSnDgU8XOOEP1+tWm3kkFa5AFNHI2P3MeCn4oM5jBGEoQsrSSmfRdA5Yk87nDP41jlOJGJr2koysGMHDNeJewW1liyXtHkxS9+MckQccDY0FWlOJSHvl6ySUcEzU5bYuAxPfLtwN5U63P5j+nFOkdSFnBCBTEpL4MLJOQ6zxVYZzsH0rFYouPwP3EBz7CjwaTADUWkrzSkoJRW3tlg5lAHziUJi4YjWf2BwyAYuu8PS0P+strz01f8/cso2hxeS6xSDMypBwcTZcjewr1yX5WGPv4OgfG4V1wa60wnYgPgMNg6XIOHJ0B3XIGJsRbHQa9PiUDj4HvvnbAODkvEBudlitb8n4nHnZdqExIn8Bny0SnmU2ctV46oJgI/bjl1gnJ/in3iJE7P0oTGoc7ll6HT5ZbijzGhvPKKFFRGjWbDRUnQN5OUoAVdTlnLxwsA52CPnaL4kLb/qTmnunlR24xSCTv2uPgU8OGKulTCaxXE6IWK+6WMYIHxKjFGigb+rb/++lRF4egO22IgIR836Rcv6/oiSpbU7OJ7DJKuKiWrei8jF1i9SSsdiYGplv1ItE+1ryvrvD88Zj0id8gPuSlUI2PY1xKu73V21ve1araM29TD5QHBYXHlCF36KFmoPLPKzV4OBj9krcY3U9mpHL38P1EYZBDJwjMJhS1dnFwZYCP7DFNKa8cddxy23nYGCg2JfH7a8g2ebnci/HI80tmdMZgRb/n8OizNLJi+x9RpUj7+M8L1gAPKHGnDcKejBHkj41OgrtRTUBzrUR1TIIKGKx8ZEuWUiXiyk9OGINSlsUtR+5sHDCzwBB+yEcppDjzBHQchaeAuyDl2k6BudClA0RLGhdkeXU0VxxOWwNk0xMNR+D0fjYoBcNqtZM1jDDiYhHcSY+M73vEOG2cqMphYGklRs7SSmPZdADXxcM/jWGXKX9EQqFxqP3Mw44iYDy0FhyAYNhGOREcUgwg+HAWqmwFx6QrnbYYjgEvK8g6v7K4lYGD4MT3u7Uy1Pocf0ynQ5mty9MQtvE5Jpww+UNFE9pvHwaKcI6ZJa0/KwMHi2I0hR+hUCsNervOBbOdAOhYYWM4drUfsBFNKqDbIDnIsgLwIYwYqEbOI33FrqawjPYTVn8jhoDOuFMXy4a/MCL+Uhl5koSMbdgbQHlcXBubUg4PxM300IBQF8ZeYFX+e2gdfZgoXKWIJYhc8sfOMsiPqI6A0GhR1IVTwV6RnTVCRoIawY1O7IdOHi8KFtVPTaYqWRSZdCS481AHGwHoQnyZFYVMLKOqkA8Ej1a9re0udZSnwkH+YIPqtFisHnYIBTEBeJkwvG58qIMuFpkNfWIe8NW1410HWs34atDMnE9jB+NyJHOM55phj4r1AS5jCsuaQxL777hvaZecNakURHq+Av3HcchJe7gGKmpazTN/oPbKjkQtsZM2WWcfAVMt+JNqn2teVdd4fJ/JC8jnnX38RXBp1uI9FJsF+4r7WxOq1edNySGNSajdISv2Iuc5gLP4MDtc1QStomvUc/BMZLE3EPhxrYTPzhtgPFMVryAkoUt6v7CBHG4PITITfGQ/9cuQwetiwND4kw4gCo3rwtaZO/YmPXOrwhUG0TS19erEaZVs7NxhWmRlB4HPU+cFv+dQcCCwkizrME6EE0tIrhYEFnuCGPdVCHXiCU0BYvYETblCOidRuyOT0l+Gr6SvqQMmghonIpHSLhT2y5mxbyY0ecd0MmD6RiBBNRQZzJCMpapbWE1O9C6AmHu5ks8SGXUxFy30jTvByJOOIWHIpSX+oLRiWo+1nP/tZsfT0sKGw5haKbkRRSjIJoeyupWFg+DE97u1MtT6HH9PU4p2/DAkXaKYoBP4hx/SinCMoSR5GeAbKR13HEtp7771J7NLTsp0D6Vj0spw7OnoMXjrSfnmsG3A+TkslsqHEuLVU1on0RFZ/4jA4w4Z1GW/mrVEqpe8YY3O/x5azejEwpwoO2g2WPVoAX0AsBVE0hWtGfhUV3tE17LLfTmQ7BwThCanjVBPFYRKhyGDS77wwxtL0N9MdJj7cNDrVPFIB2BLoWjLfRBruAD58TaEb9dH3CFEzpPj6bAeOzczARcYmgZSmIXLLox71KCPMgBE1o23SkX6iAzweUw2Z9bOao9294pnPLEx+yPs+kA8mo3zUqj+GzFGacAJ+RqnEY0ffEZn5i7LQCmVfDFyYHnazEMzKXmI6ZV85wYDmFfguF4tc5nMeYZPnYBxwiIUc7BMma3wZtB+tshTMhDNugeUsWmIgBqB34LIHcBzah+/r+jofOOasZu1N3NdR2cgpB+kCkkTIt7XpTDl3xLqqrGSO08R+0n7SPU3QQ84aoRxB09Cr4J9Atn0sbD6WdK+h+MilKyyZB1ZJYdSfCD9mkb8oYaoe0DqUzSxAjmiR7Cvrj0uwZdnXJDQySfqRpkcuXHXuPrBaUizh28W9LiCz1AnMSVdVWLLrX/va15bYHjeGlr9sGFj4CT7VQh14glskRFx8RW6fQIjTwUmRWkKZDm6ilwTx2I6eAW82S6fVkK3UP4NoN+g4AhSbBG8s6eFkMMcwkqL2u1O/pE7RfKp3EU3qh7s6aAiKGpWREVar3O8lVRlJxHh7hWMdAsL3FhB0gBELhSy5OxNhueG8Fr4zvEoj4Misk1uLAbTfxADkDD+mR74doIavz4Uc0/2FquuBx/TwcyR7yUTiCvtKK1oePY456zADMIewnQlNYiAdiybLvKN12mHvO/bLiVSiRGC5x2M6I9dSEqhsO5HVnzgMs2ArioDf6NovsETLzoyytCVWKQb+/cWQeR49OyHdPHbB0RWn2lSjFcErSJuut1R2gMCVwxIPUBhoDBljDjZaIjibib1wgAScZmSkr4cx01P0jRJ9sFys2YtUNsI+V9SvP1vOYx/72DB3CNihRMAquVkHLVj0q7YEOQfPQcXAljJktMywrGS0Vx0t1ZC2nTq+p8DUTKNvap0ij6x2uBzvy1vrU9h+/ZazdBhY4LIft6+Xep0P39dQZ1XzS+KRwadj2rWNFlnJDMhUkP221rn9qxQ/NxvRqMPvvHfeH6goJiC9SzoVlv+Rax78IBojye/yj6f1OA4DCzzBxy3UhZzg/KEcOv6cFBGY2R883yImWbuvX7SQnMXdSuPI4EJGWGk77l1UmkBy5XBnTVVK41lKiRVoWSQWlZuGR5Em+Ld0cpHDHQ9lwHrhAZKXQzCFB4apVphPtE1oLTEOA2v8Mb0o5wgsWecoSZrrSnzW2c6F0LGyl9nSM+zoiR0tBcx+p3VWX/2Jw+DhxfXGAsCe5e1L/Y5azurFwCpQcCwRcjtkJY0JS9TdioPtCH6LPh5OXxG0TBEbYTvi87nqLXpHDWDDQAUDS73OK123ooaBhoFlw8DadoIvG2JXRUcuHqawoMgwWnZXbrz1YXP7jTosOu4SWnS7Tr33VtrBQDumEyGNjiUqWqJhYHEx8L8BVIvZT5wMAABAAElEQVQLt0Fb2zBASy3COWctmFmkTz62RMNAw0DDQMNAw0DDQMPAwjHA9UZcsKgimg6Oou4Ur1yrQYYUh0upwaHD3RxNu7Fw/DcIDQMNAw0Dc46BpuCY8xe0Kofn4xF8N+LK1VU5gTbohoGGgYaBhoGGgYaBecWAiIDyYvjKMF0Y5GbxSoVW1DDQMNAw0DCwhmFg7Q1R+fOf/xyfMhXBTiDPy2zWsBec04lgM48uHcxLTLN04QkBbz6FBY7rTvIu+oWDbRAaBqbCwFKv86kG0yo3DDQMLBEG1rYTfInQ2MA2DCw/BtoxnThvdCxR0RINA4uLgbVXwbG4eGzQGgYaBhoGGgYaBhoGGgYaBhoGGgYaBhoGGgYaBlYQA3P6mdgVxEjrumGgYaBhoGGgYaBhoGGgYaBhoGGgYaBhoGGgYWDVYaApOFbdK2sDbhhoGGgYaBhoGGgYaBhoGGgYaBhoGGgYaBhoGOhioCk4uhhpzw0DDQMNAw0DDQMNAw0DDQMNAw0DDQMNAw0DDQOrDgNNwbHqXlkbcMNAw0DDQMNAw0DDQMNAw0DDQMNAw0DDQMNAw0AXA03B0cVIe24YaBhoGGgYaBhoGGgYaBhoGGgYaBhoGGgYaBhYdRhoCo7V8cr++Mc/PuQhD3njG98Yw+08DpzDb3/729122+2d73znwPqVasccc8yDH/zgP/zhD5U6S1f0f//v/9V7YqPf0bve9a5ddtnFfPtFLWctxIAvsb3kJS/58Ic/vObNfeLUPve5z73iFa/461//uubNvc2oYaBhoGGgYaBhoGGgYaBhoGGgg4FVrOA4+uijr/r//9385jd/5jOfeeGFF3YmuQY8EmM+9rGPffe73425dB4HTvAnP/nJSSed9IEPfGBg/Uq1b37zmx//+MdXSmr6y1/+ovfERn+cJ5988vvf/37zjaL/+q//+shHPqJVv2bLmTcMUOTdu/f3oAc9aCHjpBA88sgj7aCBQCwVZOR//ud/BtZfwWoTp/ayl73sxS9+8de//vUY5NJNbekgryB6W9cNAw0DDQMNAw0DDQMNAw0DqwsDq1jBEeLHdttt98R//bHYX+Yylzn22GMf97jHra53sGyjvfGNb0zBcdhhhy1bjyvV0aGHHkrBcZOb3CQG8NGPfvSRj3xkc+hYqdcxVb9f/OIXv/zlL1/k//+75CUvORWQBVZ++9vffoMb3OCXv/zlAuHMQ/OnPe1pz3/+8zfffPMYzNJNbekgzwMa2xgaBhoGGgYaBhoGGgYaBhoGVgUGVrGCI/D7qEc9Cvvu74ADDvjUpz616aabsu3/6Ec/WhXYX+ZBkhlve9vbbrDBBrP1+8AHPvBqV7vac57znNmaL2crnj23vvWts0chLZluifnHAGmcfqr8O+6445Zz2P/4xz+Ws7sl7cuWpwFODdG0U6MrvO997/uqV71q4iCnhTwRYKvQMLBADNRjFX/9618LdXzHO96xwF4qzd/85jdXYjmHB43W4VQGMLFoGZAwcQytwtqMgYkRx4uOnE6Id51KLGLvjsjjjz+ezIIm/P73v58Ieel2/cSup6qwbAicalStcsPAqldwdF7hNttsIydjE0QxMN2Tl250oxvttNNOZVDDa17zmnvd616f+MQntt5664033phTfMRuyL/b3e62ySabPOABD/j+979fwv/BD36ANvGDuOENb/iwhz0svb73228/MkAZr3HBBRcAnpdEVIZRwv/v//5v/uRGYjy3uc1tXvjCF84mmZvItttue+1rX/uWt7zl7rvvLkAjekFSjcoE4zEwwGBuUte97nX1+NrXvrYcDy/9PffcU+APbPCU+cIXvrD//vuDUNbJ9MA5Zn2JT3/60/AGuFfwlre8hWvJDjvsAAmKvEEdlayn+AI5n/3sZ0sIprDVVltd5zrXuf/9728iWRRTM18c5D3ucQ9CmqKHP/zhIJx33nlZrSVWIwasQ0vl0Y9+9F577UWb2ZnC2WefbeM85jGPcfFE7tCs8/nPf54ydNddd7XU//a3v2V+JrA+hxxyiNUo59WvfrWVYy/r7pRTTsk6X/nKV+T/9Kc/zRz32riVJh95oLjywxiMpLNis04nQcx4+ctfzg3tRS960de+9rUPfvCDHsNJTZHuzCubCBCT06FO0PKCF7zA1F75ylf+7ne/y8rcl1T+05/+1J/aOJ8mBESsn8EceOCBhrHOOuusu+66AXAk8iuQBdMhg09+8pOf9KQnSXjMgbVEwwAMdEJNHa8PfehDFyWOEvBOrGIH4Q6IpTaHfPvb39ZFyRuUYxgeNFqHU8KcNr0MSOgMaaUiRleq387016TH61//+oxJGODOpP7+97/f/va3V+RI6hT1H8U22iMlc96vs7g5nRDvOpUou17gEiJuPOUpT8HVX+ISl7jYxS5WQpbuA1+6Xd/peqrH/jiHI3CqjlrlhoGFYgAPvUr/Xv/613NGOPXUU8vxP+IRj5D5i1/8QiY5gZBP+n3Ws54lsF8+WRrJiPo8EeRsttlmFB877rijNLUCgYQqBC9OqpeDRqPUUf8b3/gGUDKJ+ttvv33UJ70ofcMb3uDxQx/6UNT0S/Mq54wzzpCuDyObSDz72c/W6p73vCev8pve9KbSTK9RgZDj0ZhHPpZA3CnIz0J0BhGFcmejjTai34kLBTpAAgM8HShr4MdppIsTTzwxoFE03O9+9wOK+oNeAyqUImRRGm0BjMfhc8yhfuYzn9lwww3BvOMd7/iEJzzBi6CO8RgI/9a3viX90pe+NOufcMIJctwTKScmcqtb3YqmCYrCLcXrM4yon8MjwtF9wIC2NEfS559/fsJsiTnEACcsW2zcwOi8vMprXvOat7vd7ST8UWRkZdK4nNAP/qvw37syFgz11rWudS0LPorsjmyYiV/96leARwXrU/rHP/6xJXrXu94169ieKmS/zDLGTLMQFd70pjdd/epXVwEl8WtbHXHEEdl2ZEIXt7jFLVS+xjWuIXG9612PVs7j8L1wn/vcJxSa0TXk5DrPvTByav3xPPe5z43BQxdoCEhuq3HIHwfZGO5yl7uAduc739lWlfCYRKPfdctZCzEQ57hTmCxE+Ugt6CywVPbZZ5+FY4M6O87okaB++MMf6ogWb2TpomTm7hsJzSFLcZlbdWSdyKzDqTScWLQMSOiMAYWE9iGz7jRc4ONK9bvAYc9zcwefV+mUpO4vx8lCIN+fHV3mj0zH6WyFjyxdisxOj3UqUQ5ggUsINuDEcVnCzHQf+NLt+ux0hkR/nMMROEN3rUnDwMwYWPUeHL/5zW+oM/wxhrAQ0imQK65ylatQ/LCFwovbJZlksebvfve7qU5FspQ6IRTnrW99K6meaEStKw0CEyg1AZGGjfScc86J+i7q89EQYjYZ200Wp59+OsWz5kpJFxe96EXf9773JeT3vve966+//p3udCc5Q4YRDa90pSs99alPDeOtfkkX73nPe8YZf7KvToLBFttE58LszKpswPvuu+8VrnCFTrV8JK297W1vgx81ZQqkjyIqBiE/RE3Kmuc973ls1CJckLZsWCaGzzFbsSozoRvnmWeeyVTud1rrrpd71llncZ7nCcLzRfPDDz884UdivfXWMy/OOB4tD2kHTKdOe5w3DNinNmD5l0Fn1Ii8k77zne946Spc7nKXowULrx8moIMOOmjLLbfkYUF9xo2CpoNxiZogJggIfSiBRykqYSPTWnbmbtsyUDz+8Y+Xbw9KA0I+VxOpkWnV2WIS+U0WRfRo6shU32Zxf4dS/iOICccoNKffkcr5h0BhEO19Y+YiYcfx0cjSIQm7FX3jn2LMVC18zRC0TsORU+vUQUVtSarDr371q6zoaBEqd/DBB0e1ccgfB1lDE0E0GOg4WKGW8GCynU7bY8MANbrlQceBSjt36NOPOuooe3yBmKFeLGMVFwht0ZsvMGh00cfTB4hg0rdiRRbxQqLZ/FL7Y5s2Z6X6nXacq6s+3hKPitvMYfMWdChjvTJnzhPDqcQCl1CdmV8g8GVDcn+cwxG4bINsHTUMwMCcKji+9KUvceXKP/qCcW+LtwVvBX/4GLZHoebhFMdrmnQhAkLwRbSlbmCNFOlARElohKVIRwKcEID5j0W0C4lFBST7tNNOc9Kz5UZ9TgF6JI2ce+65mhCrcP8holNn4sxoPQAZOIyAyeWe2SrSNCasuIzDoEXOwN+LX/ziaqZAZZA8fittxeNEKZ7SWcWYHI8/+9nPJGTGI9T5y9LIjN/6HEe+SqoNGg3mdFgKIIHDEuzENA3UFa94RdWwiTxQrnzlK9M6TWzVKqwgBgSgUmbl38ggEcOjqrAwyj9NYtjEHgKz64Q9Uj3wMrD87EGPdHA0HbyNLn/5y3vkfESfZUnEjpBDW8fiJMHZQTCUBEWJ34l/lBeIRgSJkPypUwVG0SaIXtGWls1vKDgoCjExrNA3u9nNZFKjuPNY2whgYSzNuUsQ5NSxx9E3q5e5xq8cxMTalhj+Bw8coNQ3awpNESWhrxwOIWoGQmAe/ZFjFnZ9alsqyO93BA+0pQbmT+mlLnUp4XJ0uLMNrA+/5aypGKAvo55zelJb5xzrIZB2pWOOlxAHKK6LfI6iYcYqxiNtnRPWmci13ia1kRN+JOq9RB32AEs6GIPIEVG/xx57JCiUQQXq18xhKRkZB9oJGlV/3EQC1Dg42VEkkEEfjRL46YRFSaRLnmciEihn7373uyOSlLPmBQLPVnFqgNchj4uNzeFVIkbHxf9m20xQWFOS8gUzQi6ZXnHMrhLWWuk3wbbEbBigzbdaHHwZMklHiWstdwTIVs7A+GsHB5deOygjTIfsyhg8bpNLLzbeQY/5jxN24rw6VGLkMh64hMYtY83NiGXUYHwSTroMI60Dr+z6IZjhtK47n18o8cCIy6OZXCOzAiQwQxbgZ4fAMqKMjPjuIHDcDtXXAglITMF+xz4R0NA3dICtFBMVRUlRTZDEx+meaJDmsajTftceDPxTGJ7DP1uOLJQDw/EIl8jHMoFYoLByWPKZWAkb3Lw9kiX8kknEX2R9q9+W5htJDIjMFH44fZCW81FpyEK4AWlky2/qAqKtR9Zg1EePiCmJnWkX1SCu2IHCXlQbOIwAaPPzN2FexniR/Zhz5ZesSVSr/5Lc7G3RLhCCk0MFoC7ElZENc8rq8A1Ovk2Qjvp4nbBmE3IwECPlrvocR75KFNO8OtByJCPH2c8s6+OGyZxkJzInIapfueXMAwbYeUodGaZfMGp/YFxeOUyV+amm9NIp7wgP9ohz1OGtWhxvsQ6JLtmQBiTTEpe+9KXzMZZ33xaRFcoEjYMFRmvpdKfoxOvvvffeuDr7Hf3BRTlo/WlitfvFT2RzG5BsH/kkH6JCFmHj7nCHO/z85z+32UHTRRaFOJGPExNlfdofO8sVrVioac1oNDL6MlNjk7DluZaklrOC/P4IcRXei+ZwlaUU0HYoa3A42WV+SzQMlBggu4onTTU9USeUbnzxrEluBTYdi4IDSyvUQL7FSYNJVHDjnQr2pjWmMiNEKlIpNeTbj85uMpirYcpO671kTcc9mAaAdZapFQclvdvaHMrksKyoUBIfZhjWEccx+wpHFQbPYA8MTE3hqAG8MpGoMA5OlOYvyxBRROSma6dElboPCG1MbVEdCdiYnXfe2eBdcuSYdveQtsSJINQVyIgb3zE4dxk54uztQNEnP/nJ0nuUKYLu9bKXvSw2DDX2IoIzwV14vzxkaVRl0kprawowlpPKxGMf+1hsCaRhdTBLDFqgEaf9QmaarNRHauRgC8f1mzBbYiEYwG16I9wnacAdOhRq3o6zMmyNAZn1kWciLTydlF1gRVkk/bursYXWGBaaup8RUduBu1JNh6wt6V1T7TliyAIendclSzBymiWVGLeMLdSRS7cEWFnGRuVc9qs+OM7BSEdz6Qrwcbt+IGZQHhOEfGGA0R1Bw5vCZRlSHUhghs6IJKWyTTpynCUCdTFuhypaIAGJ8T/96U+nUENGvGi8Cq6SRjvuTQuKGlZhzJXVaLEJRvZb8lcBp/2u8RiYUwUH/gNhSuxjCDLdSZDe40jDTHMFZ8V9xjOeoU44U+Dvy5AENFfRVAs9yFBAs8PL3uMxiug1+F9Q/UpgKShQsBfTDsO+NX5j3mKLLfxONc4cGL0M9u51r3uds1/oDS6BHRXPlxUqiZLmUirRDWnLaQWvieFQGrjtQKijeuSrjCZYmQ6ohTzGdyJCIbUQOK3t0mGA5yqHi4BvOZViedkpRwY30ZQ5mRYMRX+H27DOiROUlVlEfSk9UmOSdTIx1eZylLIMO0o1x3zbERhxbtuEJdy8jebgD8gRxlKq2PDxphn5FCXlXaShtQlLTohGObwFJgIahCAjU4EivFG1ULMyShMyiWo4zlB2gFNBfr8XoqZMYwjtcFRgY/eHwerXbzlrEgZIL4x+5Yy4OIWXU5lZSdMY2l+kVudshkDGliGE23eOe1w7CAR4xMSixcp71IqvULkroxfWCEWup9EqXP84AqBIUeq33ktWc7I7a8APBYc9ooiylZoj6ABhAHUqmRZxoG4HU43QRcBzqoaCI2FGYuJEhsAhoTn0dRcBm9Qu1By8TtwZ7ALXiUiIKFS/KhsVnsRoqQngrQ4Zbu1rAW6hlaCsYfsptRugIUeMEFQ87nhm50/eLON/w0OW0EUvrJoQuQ6KIJAsbUZm56Xj+tiWYF58X6dm+Tiu37JOSycGrB8xj/lIeeeyeb+Z00lYKvhwWglvgWLdYWfveztltYi/Dg9l68R5Kv6a0i3YtqwppBQcEnKymgN3JQiOfhSA6iSMDY5at++JHKd/SfgTE5VlPHLplgAryzhWIF2/DYL+4K7LhvX1OW7XD8QMVselgbYbTSVOXr8IFBUAXYP0ECAoMHGMvgnfRUvS37/lXCo7lH9NhTRVMF/CF+tKu0G1AZM4Kww/GkXSwZjJjJqYLkIQXQyyzPHEeSF0d6S2tITc0mseBsbSrJWdqnOxczROHI/zmz6YrxQzAv4ed64JH0tMw8S2EysEXQgJJysjE9JRhHzj3lABhAM3sNtuu4VpYvgw6CBpNxzwSECcDfEJg+xueAKV57WlPuMwEm+r022HQ8pwIGoiDZQ1EsywGA5yTti9O0AmzrH/Kvm9AwJRJSiiVD6G/BmK2MgsS0fmIGHO4JKtTGgtMScYiFtpZh4Mrssed0MEhV0oR8o9EiZTzHFYfmbuZWRD/kFYaiZNyyzu8bEjKDS5kLjBNyU31AC3TRLI0xSXr0Iw7tQH/jrwYy+4N6STn49BSdxMljn9RGd3gGYHpZ9av34lB2eJRzEviomgJLxA1a8jvw8wyAL9pqChfmnLWbMx4Bo/t/CWc2Rby21S5o9Lh6YSHGcrJYLDKN24ymhTZn/nMg+O0G6AZmM6TDvWCPk4YL8Y9NBuSNM1pIIjAi3H9VJKayAzRMfpH2A5TAktcfpTcIQJERxF+WcXRLoTB5oVJKgCJ05kCJxQCmSAAAGSMgiLz4ZJEK0jwTCQOOxTklBWUAoObncUHHXIIQBzugnS5zcS5RxHpumwoM7rCyKpjt61pYsRe9ghmNRYKhCk442gcvil0jVvZBctcyoMeNGOtrIJXyda/jKnk+ZwgVf0awF4j47LTlwA+182caKxNVKiYfBKRyfcO80IaTyZ9uG7EnAnTjlstkbcb7okZ+/1xPIs4/oYOqUjd/1UmKF+ouCgOQq1EdWS7cMBaiAQipukrp2x9R8rO3RRCAgNmk4pl4MDZCVliKXgsPxSwWEF0m6o5m1y60P9ULCB5Kg/o5azejEwpwqOGRCK/rLbEOw5L9A3U5Hihyx6StNY6zgPug9bfaTxpN4jHouCAE+TztVOZRpH4nTw8ZoDiw/DzRM29BIAhw8jLvHCOSUvFS739YH1S1lOyGAi/xUxMtveFByEk2kVHNTwCAeM5bUg/b4iZ/gcE4I34u2QA7FTvlUhn6q1lPGMXCaeLJtEMEI+StAHQ3WgywRVYBYIabCs1tJrDAYo7Lxxp1ecbebFNzJn5+27JNiWT+7coU7fvyi3WuLYeEISh9gMw2XMr7hW1MYAkjVn3WWfYTXK0zTuHiYe5Dg7CeIEtQjZJrl5XeQlAioHix+G4mgbAS8lHPJV7gX3aNDycBIOLWFZbWKa2wVxiEtn6V0crerI70PmrI42eh1Y26DA9jhGGf+K9ezXbzlrEgacjAxr5YwYG8rHiWmmObSdkT9Wu8N3ZLRpKP7CZpsw+9oNRSHqlHGRIcxEqzhtx/XS0RXSsKAqjmyEiCsHszMOAT8AlFMJb9DR5GZHjqcyDjQHLBGxe/WJDIETE8ECJSMRvlSRX0eCYaCipoAcUW14FGniN8SbOuRpY2OBjb+J8b//qfjP/yE2l1Jx026U+FmUNPa1wy52/Cz6vTjCqA4jTGAkx8hrg+hrs/Dtwor3468ZBmIl0BImFzfVrjQqhxQ/C1wB9tUxKsex2B9tJWd5lnFlAP2ikbt+KsygvThqrAgFB+Q7lO1upBWfoLuJRA9x649qXE5lhy4KAemTC46x6DBLUg6p5HziBoPm2Z3IWasSa46Cw2vjN8H1kcgh4aJBdJarG1930r5dTbnrM9RhjZzhHYMmKpVUQ3fIuMSGjI9hQE7qoyPHAPLKJBsX/kUvA4dBKgOWWx26E965okJAIC9xnSAhIPpUACboUowIXs3HcjoYL9PkHGGoRshhD6+ZOuCyZj0tioR+l2sr/hIPgVviiEEyQU2kQ2IRyEeBwq1j4BzLHhlhuN6xvJkO+PAWHF7UAZ9MiPISt2iOEE2BCWVzaUoNkpgDie3LNOV0Yqqzfhgfjj76aK6A4+pk5ZZYcQxQe5V3VRiPBc865IaI2COEZ3uENZJuXmnsEddh+CCr6wkxTxZqhGVarprYCFNNqr9g8P3W5HHHHee8FLwKGnM0yDaIK82SA8AaWoqcJ61Y9gQh6Lg6QxXWXhmAvYDzIHoJDDFHBpY4xaOJfulNsIZKwccl9EOXcSq6MGsKHVtJQ5trZI/9qZXVbHkmcUZRmxHOy11fRz6q1YfsulMaUqNCkFFgBhxcbKp+yn5beg3DgB0h6n4hk7LOnVyO1HoIZJSmRFTpsR4XWe+lAzYUHFwMDI/ajvbTfO27uB5I5dAOdFrFY+odOqXDJ1KHg9bBBtQlfHuTvBrXHteRoAkxlTlUDIgpEBfNyOyCn6lD1sVssbExpI5OKh6jKCciETmlAFOWtvSiYABP5W9aUC735YPs1HNW9ttOjL8moLq7yonp1MClh9tvvG5smzWcMMdFmn/ve9/TuztK6eP8jdtoCWdkYnmW8ciuh2TmpKbCjFZeKMkIUXWycykNE+xUQIYMT53KDl0UAhK8XKnWRA1wYtFvf5CJsX5Ry1njMbCKFRxxyJWcDaUGflpcH8stgQFjTdhALsNdk5gheMwNW+VLLU/KMp11Ej79Bfc5/iCEKKX64rJe2pRQYSKKAFFBodlcYsgwVEPByfD0F5QFHrEUQBH+TYGQr5SahlMfqV4mmad8LLtjxMZZ0ubQ11K+YFNcERTkIObSmea4R368RDj+IPwswEc+6BEglhEbD0QxQXKDAXig4Bg4x3KcnHi5H1PHMPAaJ47KHElWWQdwL4sE649I6YCUE6ONiWiCUsunqqfB9U1Kw4jmnZmi5hDixclvCo7E8HwmvGKm0dBY5QhtLgoOXHt9j3DCFFlK8eHPkqB989Ktrs56SLCdxZ/5/QUDCF2JNc8BPqrZgxzOKRNLx3sduR0ZwbF5sftqEg9cula3gMVKthdQmNgLNDjlXkDT7AXaAX/2AuXFyL2g3wXuBXJa7Hob3+A7u76O/D7SOGv0KbBYwsRzSzQMjMQAuRojbv8qDR/JcdGmYaHldDASTpmZcZFp5Citu/VeSjjSjrzw3WCX5mBF6RlB9VwObRyRIBkF02lYeQyvhCETqQBRZCK8vSiI+5GhSutIiOZMIwgdLTNqgOrmFTx1yNqSKmeIjY0gXz4jIOQf24Z0FGWmBC7OLyyFvqYsCmJeD2st67f04mLA6YzVHOkfPST+2vHKBCUeiqLEp9YjwG2qXSmSgqEeDxC3YtM8hnfwtNNchmU87ZD69afCjOakEgoOdwbz4bJT4ragaYH0h9HPqezQRSEgaAL6hj5YMNE7mYiTWp9W9MfWctY6DDjj1/g/1lSn9WJNE+NFD4qzmRbgwGG48MLVidMC79Q3PBpx276TP/yRMgjfVo7ErDF2lDgVIAPnmBDYdfGyjMZynI50HI6lLDULANEvWvnM7CTc0ejy6rJVp0J7XPMwUN8jNHGWRGXNLANCfJ/FwjaS4X3Nw14YsuvryB8532nJwkggLXONxAClHrJPG5izI6NSbcikW49MEgs/qTgm5Nja/JU4GkSpS3lo/XKvCVph43Wtg1LyOTg06dLqS1OIRyu/dIhyuGFGTr2XbBUJCnqDFJtGqxg53CQ5kfHEtImycjmAyKT1Ez4WaQMzAHXiceBE+nAiJ35jmhSLmUnbyybhkyJyJiLB9QdULTz8s3km6pD5szDPZGXRCqbmu5iZkwkRPYpK1oKsQjwWvBB1+JERk7xxJDFbRcL9gtoaZOabKbMWemt5KOIglkWqyaFrjpx+v6ZJlZPrKhu2xEAM8HfgGjCyMnYR8mMvWIGdt8anT45zQdtyF2D5iOKKBHwF2OG7kg2S/i7pgFBNcHyCpD+8skel5SatL+P+EiqBT1zGTI+GhECVrTLdB14OLKqV1GM4ZqIto6nmeHhO6NlpHUh/ABrWx1nZoYtCQCiw4DBppvFQqsphfJXuvFk5TFBKRRNLt7+1DQOr2INjuC4qdIrD69drlvGf9Zqd0oHDmE3r3OmLX5ZPQHcyp3qkImUNjs8xRENWZQwHnqACZ+AcEwJ7ta8q5GMnYRYTARphRNl12rbHNRgD9T0iVmLFlwQDb2Vhj3w187AXhuz6OvJHTm3iLh7ZqmWuPRgQ6uVqW+wX+4EE1bwbtTLSsx4C6b4JQV6cCnk5OZ74ybuPidNyB3uYe7oMHSkiButIaFtZp95LWVNalEqE0WG1o4h7PE8r6byRp9Nk4uPAidThkA+5jLkLzJ0+NCnEPKx/RqxMRAIJgd+Wj7BEIB5cUdnwIfVlqzrk4bGx/Vg2mK/H/+aUvWXOa2L0LBVEnjgqqte74CKHftbDWvv9chZwH4GV5lsS2UVLLDoG6vHXXlz2iOVz1xUrmlfDddcrG74rie5Cs7k3ujyYmsOVpfyLmTpcSxkOC9nLzCHe/SWUMCWGL+OyVabrwLNaJoZjJprYvxHoXV74Oi0QoOrjrOzQRSEgFGq8e5BuBJw7bUQB8w2Jz1QlclqiYeCfGFjbNDptvgMxgF+h+HQycaxABFnMeN7KwU8MhDBttb4Hx7QQWv2GgTUDAyu1F5Z/168Z76vNYmYM8Et3rOSfE8ddM2SSDkDeFgTaqEY9ESJu1uEvwLE8StlR6TiiSEipzPRHYDHmZxHVxDjoRTo9ODSp95LdSbgAS1vxKVwII5+wLcdlfmlDlt8ZgBwqBpcURBMhlpo4XuPR78CJdOBk80gI8cPuU2oAzh7Dr4RfetapI4H7txtVXbjjRfg1HUCIjuEjWYEsNp53DJO++tBCJ8Jun52WCToXgXuq6SjzGWZ1J9MfIBWLK8UNISpqEmxwJvl+3cJA2RFFkHzQQQdJhy+Pjvr9Mu+rILQhh9ESU2HAlhSTMrJJeHBQAkapi6V8gDxeDVcLboCUDh5Fr/R3AR2HInsn2g7cleAIUI0VyLxnCfnz+KhHPao/Qq4ltoZqispNWl/G/SXUgVxfxna6eY3z4OgDLwcWHZXUQ85AzETbIFkUlx3P7gqQ8ODI/RVwJo6zskMXhYBQwnqnqEcsJ/QtbkA3vPDgKClqeHAIfYrBt9+1CgMXMdum6WkY6GPAwuBRxoGTKyn/T0HFzgOHWUa+9ZssMAcNooAnYsWNCQuE1po3DKxeDKzUXlj+Xb9631Eb+fJjgNWOT0F8V6jTu6Wr1G1TnS+edKp5FAohKqHi4VjppQ9t0XOGT6TetVObXkNoOnt1v+ZIJBARBYaQEtPGK1pEDBEdEJMpuTTgVCAbPMmWu4dbk/qdTszx/QtBChy+Jl4N6A0Skn2LreOqYwCAcELxfjtF/d5pbYx25gsp+wBbTh0DQsW9FHJpvdrI0oG7kpex6Gx6jXEXbI0E3slctmXc6Xe2x4GYqQNfFCBlF+N2qDqLQkCQJmMWlBQeJWXXLd0wEBhoCo62EhoGGgYaBhoGGgYaBhoG1moMuPjAhcfsn0J+EhFu1mDu9nWq8kr1LG2JhoGGgYaBhoGGgTnEQFNwzOFLaUNqGGgYaBhoGGgYaBhoGFg+DHDwdoEIj3TxHe7v8E0KEQT+eEMI53GjwfINpfXUMNAw0DDQMNAwsAAMNAXHApDXmjYMNAw0DDQMNAw0DDQMrBEYcEOHL837ApSvrrj90c3ibu50K4pPxq4R82uTaBhoGGgYaBhYKzDQFBxrxWtuk2wYaBhoGGgYaBhoGGgYaBhoGGgYaBhoGGgYWLMxcNE1e3ptdg0DDQMNAw0DDQMNAw0DDQMNAw0DDQMNAw0DDQNrAwaagmPR3rKbgXly+ub8okFcDEBuCDMqN5MvBrBlgvGPf/zj+OOP9yEoI/fJPb32c5ZpKK2bRcXAqaee6pt8rr9eVKgN2PJhwLcwb3nLWy4FlfvgBz9obUykVG9605tcEHDWWWct35xbTw0DDQMNAw0DDQMNAw0DDQOrBwNroILDp9F83NT9WEv9Fj72sY8JVc1e9Pjxj3/8u9/9bubMQ+KrX/2qUfnq1TIPpoOcqXr35WpfqqMw8kWx+NZXP2cqgGXlhQyshNPSM2Dg9NNPf8UrXuEbfjO0bU0GYsBX7pdIhYS0PvKRj9xiiy0e/ehHDxzM8Gof+MAHrI2JlErXt771rWk/fd9xOPBWs2GgYaBhoGGgYaBhoGGgYWAtwcAaqODwqTNcOC5/qV/hk5/8ZObEpe5llcJfCHJoZMz6da973XHHHbfOOutI93NmRstCBjZzp61hw8CyYWDLLbf0Wcel6C48LF760pde9KIrdnBc5CIXOfTQQ6k+999//6WYY4PZMNAw0DDQMNAw0DDQMNAwsKoxsGJ86rRYe+ADH3i1q13tOc95zsSGLP8T6yy8AlfqJTKTLnxsKw5hgcj561//2plCP6dTYeDjAgc2sJdWrWFgBTHw97//fSl6/9a3vvXe9773aU972op/T2G99dZ7xjOeIaTlK1/5ylLMtMFsGGgYaBhoGGgYWFMxwN/cN6HH+UtiIZQ2T9s19e2vPfOaXwXHhRdeuOeee9785jffZJNNtttuuy984QtMdve61728G2Lqy172snvf+94bb7zxbW5zmxe+8IWh1OC1cY973IN9T52HP/zhKp933nnxLkWOcOvYfPPNb3SjG+20005lIImd/PSnP/0mN7nJta99bTDf8IY35Ou/z33uc+Mb3/jHP/5x5kTi1a9+9e1vf3udYrL1cthhh5UVXvnKV97pTnfy6fiHPOQhPrpWFs02DPM98sgjt95662td61p3vetdpccRJn19+tOfvu997wtp6r/lLW8pe5fW0OC32WYboO5yl7vAlestOnXi8TWveY2pffGLX+QNft3rXheeXedR1vzBD36gCH5ueMMbPuxhD/v6178epXXkRJ3KG9TpSSedpNqDHvQg6Z/+9KednN/97ndKK5hUKjDnoQ99qIEJ13/mM5/5ta99TeaQganW/pYaAw5Olzg87nGPe+5znyv4qNPdOeecw1PgMY95zIte9CLpTunZZ59tvysVzpBLLup8+ctffslLXqJIBVdFlA15dVnqP//5zwMyH4TYmCTkAw88cJdddrFnO0FtlWH87Gc/O+CAA3T0rGc96+1vf/vf/va3sq9MI0eukqERMNOXv/zlI33KBH0Y2Oc+97lsJSGKCklJ9mLcSGJSf/rTn77zne+8/vWvN/3TTjttr732Qjm/9KUvAYs6BdhAON+lJz3pSTCfkM8880zVUIzsHcGU8773vS9zMvH+97+f4waaFjn6VdMYPv/5z3MY2XXXXdGHDiqg9M1vfrNwM/0aYUf1jPBCC+R70bFDs69IjJu4Urfz+IblySef3GnSHhsGlh8Dlv0DHvAA51TcGHXMMcdYn3/4wx9iJJ3HgcOzWQBZistuBg6gVWsYWEQMLNZ6fte73uXIGHmYGq0zxa55xzvesYgjD1DTXgPn4CuJwKKPZyEAsdAEpfvf//7jbLQOboPfdttt8RIL6ai1bRhYYQyQeOfwjwB8v/vdj8sGEZpegzZhgw02wM7GUJ/97Gd7vOc970l4uOlNbyr9xCc+URGqZ9PSYsihqpA+//zz5eOeKS9oHAgkGHSl5P9vf/vbAY1wLoc4rfQOd7iDNO2JIgzKVa96VY9u9Iia+Ssyhe5DEbWIXkLjgLbKoZHxh6EXqe7xjne8I21oNJxtGNqaHVDbb789B5aYHY1MDqZMfOYzn9lwww2j3yc84QmbbbaZGwE95hie+tSneoQKoezXv/71pXfeeecSQqb1pVS4O/xAWqDixBNPjAouHwFEBbonA5OgbCJVKh2JnAQbifobpDQBEIbhljDpt8xBneuYNIyNNtrIKyZYUnNIe02/+MUvhgysM872uLgYiEXl7PR2YiV70a961auyl6OPPtqu33TTTSnyFEl7a1lKHo6VRt0m4e9DH/pQlKp29atfXQ4Npl/L9YgjjsiG0S+1I53X9a53PRVovtx4eo1rXMMKj7VtqWT9yjCoO20rEGztG9zgBhI77LADwSbbRgItomGMjugHJVCqCy64oFMNkaFqhJAyn+bRBCOnMpKYFI0G4P5wVBQNgQQzuuY1rxlUAg0EUIU73/nOpi/hEbECnzrDFGT+8pe/9IjqcpQDITZyOSTpu93tbmaamUHuCHXGD4f/HMEGG1AiZwUwAwMoA/KrFHWl0IkK0OgVyPQKJLwULJfHpFSViQcEFHurrbbK7lpi1WHgqKOO8sZtw87I99lnH/lU2J38uX10HYwB+6PsM8jYmLHF+o8DZxH7C6iB9Vu1kRg444wz6MFHFrXM5cTAYq1nR4yNxjIxcvCxExktRpYuJNP1Yfqlx2Q6pazpgPrJT37y4Q9/mOUg8ztEIPOXOTFy/WPpcV/nnntuZTDYBhwL3rtSpxU1DMw5Bv5p0p/DP/IzapKyN/KBZc/NdtBBB7HExrApVu1DTDmlY+Tst99+2oZqI3LI57jw733ve/HIaKwC1YlHJAmHjVeOIqEQhxxySLD7ckhNHvvkTBGRBhA+JtHQb1BwuhhCi8ew6qiT0sJsw2DdBYRLdnRkmoisHE4K2XUmyCeKUhMECSFGhtjAIqqUIBczMvfHP/7xcphhE0ImgkAfe+yxkfPJT35SzUQUKu+RBThKaRy8ICqneOwjJ8FGov4GGfYB/9WvfpWtOjkVTGrCgYVMlfwxszZdTOBk4sCyx5ZYCgzEoqKXZDqwcy08wi1RnG+F7nhVUM/tuOOOtAMeOSaQyb1KXjzxSN+h9De/+Y1HTW51q1tRBNho6AMgdCIcjhThaCndLMhkbaNf8r+NoOsXvOAFFpi+Ym8Stvk0yYn69WHQZqrJnUFHNAJIBG1diuUy48+2pQfBXnhEVXStVWhC/13jP/9233135IsCLjKMQc2DDz7YY30kMSk3bhgDahCbur/C99hjD6hwhSeAtjx1EvjUItHdKaec4pG60yOnLelQ70Zp+WsTmWnmBLmjTvKaZOIsQ42SOKcvBo1rDI2ksbFje3zsYx8bEKiAPSLjQWyRFyRaTmCyPvGAgM576Tmellh1GODU442PU3DEuloVk0IH6P5QKm5NBhwb0waJwXceB85osQTCgd2tqdXwPyWTtqZOc/7ntVjrmVI+Oer+rJdOwREneMmUlr07gpGyUuiYbdeXMBcl3V//mC5D5Xk6Ef4JJ5ygZnAOEyu3Cg0Dc4iBOQ1RYbfn2UJZEP4tjPD+MlQEd87IE0W8ppn+CEsZjdJxiSHPEDMIveyoUcSQCxo/cO9Dc38kqAh8cHcdvvzKV75y1MT3e4x7Ljtgxz2y66677rpKeVDzbpCIYc88jPAzJ6VEj5e85CVpMaSNP3Lyl6RH40BO4PsQmcgT+ScrkMqkaQdiRpe61KWocuVUPL2ZbaO5d3GFK1wh5oKNozVgdDXZKEVGWXGFEdEKR079d6o32AFVxyQBj8s9/x3Di4ZkV/qXxEkHWntcfgzgOC9zmcvYdyz8j3jEI8j/sTJ5llrDwkxcsmBU/DiUksmj9J3vfCdBgj/X5S9/eaWUHQ5gdhUbTUNAxIzc7GY3U0Tq5rxjdxOqy9lRJfgoj67VlM/TyqKVIJnE8qAD9VgfRniexw5y4SUSQRkR3/op+6J8oW0h88tEVXjVSnz/+98v60Sa3hb5yj347ne/W75Mv/WRRHNeLcbAxWkkmYIWrAw/C3/q2/KQ4BINqIvmiCdVLyIDsTBvR3PyiqLyl4bCxKmEykxpJM5rkuCjQcaTIJf69R7dEOwd8T673OUuZ2wsb96LV4lHNF/XeSCzuMAgtsgU/ZSG8Tdk4rRCZtd8aP+Ds/Z/xTBASeeGGvaYS1/60is2iNZxDwPOC9xCL7tlrGIMUNbHqb3Mc3DWVHp0PlZKV6po5Prn2erIJgVMHBUmxJHNKjOxZqvQMDCfGLj4fA6L7GFgnL64GEh885vfZKRNDti+FTYiHIPZEyct+Fwd8szIudDpyhe6H8J81CEGk9IpXLHg9AVve9vbiOhkeLEMpAXqj5GghmSWog73b034XPideRjRkExImooBsG8nwMiJX1ohSEgsRSbxL+u4NUM6dRbS5Aqyx0i5q9OcOMoizcNcfh9OgKVZB4pHTLSt/E71Bjtw6pgkPqlPy1O2ItOWjy29shgoRXGCNwVBKM5Cv+DiGIstRhhay1if8d7FVeXgKSYizVtHImT4yKHVIsxHftZP8UNDOpRyVQgoUy3YlPow7n73u7v+wz0+vEu4n9hudBnZRZmwYaktbBkblkiviHtCWSHSIFAd0jjwbrAvRIEZTKjn6iOJ5hwr+jAzhyuErilw995778ykJBUsQyq7ylWuItOVJZ/61Kd4dlB8+E3kZ30Jaia/NDVlpnSiVDqIduBQvybLpSuplgpeENc8k+J5gW7DZEkty1UxZOJmAWYMTKL9rcEYsJEp4HggOuBsOtbUUKs5x3Hq3LWsPRuHep3LITzYR1yHKNGQDhuE25eLYMqlqI59IV/YFC1boM7VMz7d5UYYcZ2RE46TdIh1mHSv4tW5FEWrqX5tk8MPP5yG0ZYxNacznqccqomA73An1yGVNIkBHyq4XKEbNouIPzp9VpDcUPVS5nTTZMuFQJwPSSY0sCAPQV1OECeAejBsQCbl8r777gtalBob6kdnCnv8s9Zff330Ldi5qDC8o3EowhMCSK/EKTVgsvpwueWgx7PPlVuOEuQIbrmVUaZjonLkLVHHwLgdp5UXR0hm4uI2iH+28CA2rHpKx72s7G7ces4KEpX1GevKjqA3VxN3jTIQB1go7WVx2SUc6cpEOjXHbRnHN9nBYlOfC7Mjj/49bDBylCJB9JvS/JqdSu7NgZYAjv6gISPX//CBjUPpDOsfe0xuKqWJCqpNgbc4QoENyxnFvNpvw8CqwMD/Sr9zNVzHIZrC9sii6PDGfzvyUYoYpNhyBA6JIQn4zUN95BSCA1aNO0NWYLeUjoauysMeMS2K6/M5UiyOfnl5ZOWZEyWbMvMwKGKIHPTWOQwTwd6FsTozJaILcl2ZWaaB8ljKJDBAaBkoJOR0on4pHwIbjwNBTfUGyylIRxfjXmiUjhTSOnDa4zxgwHs0DHy2X7yCNYbRLwdG/g91VdTpy9gqC1rxW37dwyInMEd+CS3THaIRCyZWeH0YhBCiFO5ZEAr/EbeB0pDSCyTkSBBIyCq4hyte8YpErNw7nWoesUQktLe+9a2a0IYIveF+EtXqI+mD6ueAIBPqQikZFVy64Q+fGo92Lh0NfgvVNdo+EDnyoSigjawgs0Rp/42oEC9IUbz04FBHQhsy8RhM+dJHgmqZqx0DpFMOVpYfbhtPL6QFP+DyHQp3ajLStQmKdSIGsDfGZKkzeELd9ra3JXKEmE09KvaqRIXlSixXLRUcFCKgcckOBQfZSWkKzxWYikrIU6WJ/Qw5wmw5QJE9COeO6XTYVAQayY38RnrnBuU3NpozlKB12ctelujOaREh4qiY14rXSw2YdsMVNtQlZ511Fu0nEqGJvirT7MyL2cl74diFFcF1IIbeC4yFgd2bgkykjK5WjmFTS2FjKJUCzvCOxqEIonRBtZQDo7eVE8QH/ccpOU2gC4mr8EXZvCUCA5Udp4J4Yfc92SP0Yl6ro5ChMU/AcS8rIFfWc4n8yvqMdWXjR33OmFayl+u0JYe7SLuEU59IWVN63JZxdls/cYLT4zivy9NcWqZtaDXiOoykZD5FYo5c/1MNbBxKZ1j/EVQeruUx/QqqVYBVCg5vOclgB2ntsWFgrjFAbTmff85sggEXA27eu+22W1hvDJXa2LmFtmLQY+S+aBAnWTx27uCgs1TqCJ84TewO5oaqUncTK2MIgKUKzZqEGTm4pcyJyz7iGouZhyGaA1ie2Al2XAKroSYzaVmBSlsm5k+msUlTJ2cFPIocXuuZk4mobFKZQwzDh3mMmYr6ySIJVBgoYpJ0HzllzYlvsHPjhrZlTh2TvHIMA19b9pjp+sCyWkssEQZiUXmDCZ95zftidpMTtzYQXLO0TPgajpqY+DIz0nFnbRmay6qpMrNkVOgvZp4geaGMOqLY1I/A1Powyt75IyBEGhKfynxpNES+2UU+hswjdqdTLR5tSaWuJHje855HBogbSRTVR9KflCadFY71BBnYkf1GZryCuEED8RxXkza5HH+d3HEP0W+JYWBJOzIZu0hrEq6RLvuKq5SDUtUnHq0IgVzkSggtvbowEHdwOEMlyj9HmOWRd3AwkFJx2tExu/IKrViEjmzfVsgbfIffWuUqKB3FZXtc0PEbHnlmRUc4EI+0Hh6Hw1S5szE7jwE8fmPjk6yCmbH42cNpOpCLmNq4K73q12nVS9kAxt07NtU0HbLwM+4erpj1uDu8hndUQRFKYgA+iZUo5c8ihxQdOR1imNVaoo6Byo7TsHKBWuVl1ddzOZ7K+lSt3E0Ugl63/ZJ7n4uTnLxktD6RstP6llGzZEHLhpHuCB0y6+t/+MAqKJ1h/XM+hZ9kseqoNgs6FPXZgPtTbjkNA/OPgX+7gs+bDoYOngcHmdn2JnvgfsL/2Tixzn7pj1ONGr7r46bALOn2DWacDJPDPQg7f8973qMJtgY1jMgLmlc+1dy2w99MKQczRglvcRzw4fkzD4MZSi9Er+wLQlhy+sHnFMlm6iJ3puCojGHilpINb3e720nHjQaRGenIz2oTE6ymXEhE/cS7UB8dpEcnm6UDbQXItG+wA6qOSeRYAAImOO5K0NbJ6rUaXgdOe1wpDJRrkjeEYYQ7Upj+fFo1B4Z7pikIixzXCfk2cpbyBWV/sPZij5QfN430tAs7INeH4WYN9quoyQ+Cmk+apiNy8ldUCGMOYSly2J2yqJ8gxhDSaFfpGlhr0x20PpI+nH4OamZLltQPTeDXra+ojFaQr2xnOWyhPvWK5PbhyDEYW35kUT/TzRrERa/P7otSr4ngYTzy+W7wyyNA5pU9lMtUVwlnyMQ1HxcclHBaYqUw4EPF9mb+UROMG4mLcoQ2lH+OsKxcv3EpqpFweFGl85FNJD+dIFhcx91aFXdIxarWqSODSs7pHweoBQZOXGI1HGaOfEiCa71qbr0JZoavAQ8ItCsDS40wnP87V3rFwU1MisAupCYicIM81kvxOf5G3js2fJq285B7uFiAAw/lHV5yhnc0EUVD8NzqDMfAxB1XuUBt4ssat57L4VXWZ1lNmtOQX+GiuffZG7LOxIlkTYn6lilrTpUeuf6nGthElE41HvY/bibhQanhRFSr7MozZpKpemmVGwbmBANzGqJCs4gKuBqDR4A95vgXps64gdsQ5MlNPe7kZ9ghuNJBwCapI7wVeIt59JVB2zg81txISldCymVCpN3AyhOA4/4/lgQxgTpS5OoNzD0SgC0DgYcqjt8wMBx9SckY/HFGcAePwzs48vpLnW0YJChdiDMkDHCgYNci15URK2WnWCWfqODKS4aBNwaNuN0g6mAEaXAhSqZLCkwW9ST/uMqxBDIkbS4uK4EfPBY8MKDheKiKgjP7F27GImfiG5w4gAomtWUShAdI4L5INhbN5JPAhqSoPrCJ/bYKi4IBL4iGEeOOm2c54VgebsZeGX9XxhDqAEvdW3M5OZE4ginIHkJ/RXS7cwcpCH9sBME75Qug4Rve8AYOyRy2Y2EjDsKzZxhwfRgWuUA2NMeYrXn9ki5S/ZrdUdlwlrYOsVwMiRQHGAUOUxip/n23+Aye26KLNc9APOn6SLKvMtFf4UTHXXfdFcaoilA/ooXxB72iVkAoaH4RCpISL3caGdSPS2rc5FpCFtZHfPK+QtNUFo1McxuxAWHJR3CRBe8aLRWTb4Tqw4yZij9SagFQN5dBNBMnzsWX9rnjkDxyGC1zRTDg4C41lbTSNunIkYguST1gVHDeWS2RDuvFuCu0KC9U64QpRRNEJk0gEc3UN4RQkDklAbf7CEvWvDXpLCO6I1AUHDSYcQnucJgj5zguM8Lx8i4h1YynrBzRKJFTXunVvwYLJbSFbQqV66U24Lh7x4ZPs9+FfolznXu4UlODxOUdXmoO72giigI57XexMBCvZtyOo3x3HOMbR16BN/FljVvP5eAr67OsJh2GSYdI5ud6kzNxItlKor+eyw1V1pwqneMp1/9UA5uI0qnG48Q3kmwyBNVeWTBg2aolGgZWCwbmVMFBI4vpoX0IYw59ByGBYM8dkb6WSoLAQMqFZa4cDIPYdJKGY5sBHyNFhUEWspODA8bcK8Xoh1WH5CMcNL6kgIpxjiBTYbN4anDdpAoJuUhz/BOmnADTf50y6VOOOuooFl0qbQJDEI6SgkerzJl5GFguVlZ9GSEGiL3atWSptC7HJlKOyZTDvyGhrZxIqUI0jzoYPkwnJMBYeDSIwsVNBptYwsl0Dj5y8pG2iFuNwB94VoQ1NKQwInnsIycBSnhH9TdYkuBo2MmpYFJ9OhfKKaGDdD0eWadJxcFD1wcWfbXfpcNAvEc6AoswHHncBmdhhyiiNJa6pZVL3ZYPAcZhzA5M/CaK+MPNCyW1/IKHyIUtRN/4Ry7sXL0qlOmcbwyvPgyqVd6qKEwYWjlcWGm+6JFAIiHIgkHYTQE0GlSuFJTmiMiQ3PoKDk3oQSg47MSytD6S6Kgzkf4KF1nWp36CdzSHPQKJawJ9hMWjWRgnUkM3gSwE/Py15YmpNKS0TpnZ6V1+5gg0CBIRdzQGiUCRoq0EHzSUSp2gVMhCUqqJEzcMANGBHElLzBUGyLrUBDmkcRp5FfjylDcHyymPNke/HLYKyyOhlVdoZWYmqB2tn7JHbUfeWmWzoD9ENW1p/Vy8xQUSe0DBx0eSgjJPtOEwcxhDEjG73DL1JqmvUc14/JbaEEBQyABYL9Vw3L1jw6cZHcXFWznseIyizMxEZ/wD39FUKMq+WmJmDATCKzvOATHuCrypXla5HjqjHbc+O9WiO7u4kx+PEydStpq4ZcrKs6VzvlMNbCqUThwYdS2JBnucSKujmkKEdjiUvBOBtwoNA3OHAYLEHP5RQLBXcKbIsdF6MpNS1mYOH8uyQuZXmLBbbAAAQABJREFUErShvE9HVrCNM8o3K1AWRIBu5ixKYtphRKfkJRZg9G7iGNhp+cyrP64mm60KdEbjKgzP5wzi1UQI8fBWUXOGN9jpooJJQzKwvM6g07A9rjgG+CKVl3GU46kvdevWRhBcVjaJ9CIubAArw7C6GEszlrU/ksgxSEONeyXG1RmSXxnJkOZRp7JZBgLhI0NWzKj7ga0qJGIipRo5cUoZw3Ct3cABtGrziQG6Le+RRrIzPKYL+XEHR/3GJQe0msLdSwjDb63Sio6PKsRJ4deHjeVo7l5AYXQgn3LKKQF5KpjlNQGadx4DYPzGXTNsJGVmpPtTK6/0Cphc1bIhOmPAcZ1WvTSbSBBg6GqZ5ePeseHTjOFV7uHqzzrv8NLv8I4qKOIlZMoq5Iyoj+W0OzgSITMk6juufoFa5WXV1/O4cXbWp2rluiImeN1CtrM5Rww5LChy6hPJJpGYuGVmu4PDrLOjXP9TDayC0hnWPxIHP7CUo8pEH9WKiEvqM2lktZZoGFhFGPhfb6W5Ur1wkLPfymv2earjhiMaP4a64YYb2ntTDZtxZtxHTGmsWW860JiO+7bZTp0ZHqcdRnTBpZ/SJzWvlX5ZRPnWRuzuyGqs3ypELM/ICsMzudcKcknl9PCGas7wBjvwK5g0JAPL6ww6DdvjimOATZVHwMhh1Je6dWsjMFf22y7iwga8Mgyry10SiEZ/DGWOQRrqQAtt2bCTroykU7PyWNkslVZlEb99ISeEKLqSMr+erpCIiZSqP3FuyZzseOgQyer9ttI1AAP1G5dGTnD4rVWauxQAu8b90K/oNjmCN12C62NqXAz4Z0UXU8EcOaqRmfG5lriQIipw47KwWVlH1s/M+nVa9dLKvWPDp7nAe7iGd1RBUfBm3G0SLaIaMz0uIciuvO5nXLW1Nr++4+oXqFVe1nB8VtZnB4g4Vjlx31YUeblZpz6RrBaJ+pbpVF7g41QDq6B0hvXPYc3g43YhiYmojvD/DMBn0aGPztsMF4iH1rxhYKkxMKchKpgMZg37CiPLYRVTi3K56kaU+FJjpMFvGGgYaBhoGOhjgKZGPJFLfJiLaQ/7FZYhh+OMgEQXAI3UcC3DAFoXy4yB+o1L/cFMdWsVGYkdhcu9i2BCGy5qVaSYaCnX6KQGcyqYtHJGZaeIoeN22nksB+yuGbqV8EWiCeWaJAJOpIwBhGt6WblM16/TqpdW7h2bapreS+UernK0/fTwjioooh4llzKGiU12iwqtq9DXsi8kwl95URoNiFA7qiuX+HSubikbruXpyo6rX6BWeVn19VwivLI+y2rSfCI4a7hBz1smLHAVdDaVdSoTKatJ17dMp3L/sX/xX79OmTN8YBWUzrD++aYJCTzppJMiVnQiqgXYMkN66TF4X7JzDxcLh7Dccjot3TAwpxiYT28THuC8DW0kByGbofsFba3SIXM+h91G1TDQMNAw0DDQMNAwMBEDEfHkPp1OTbdl8c2kycp8zAD5X6Y/kkxGJ4XfO2kha0aCndnN2aJO1OdAhH9gI+nUyUfXbajm9pnMUb+To2g4TOGflBQgcMfQsPOYvURCDCwZRmV/lIbu/+KmqsgNNXLKqUWISnzKWgVz51FFKfOvpv/8JHMZj1kvdeEI01Hghx6Hf36Gvg6fpjG4FIliKAbAIy8/CqsofP5jLh79EUfdTxxpv8M7GociQIQVB6qNAfD47q/LX7IX1x7xXFMaX5MViyTtOvmJ0YUJYe1MjNtxsOFC6Lvf/e7x0mmLBBq7N8pjfMF93MuauJ5LPFfWp3vf9JXrilbLdT8xGCuZQC4dISoBsDKRskfp+paxE0EWXdJpFY/i6WwoFezHyOmMU2Zn/Q8f2DiUgjnt+tfE9VU2Pq1fjLOCat+TMp3yG7G+E2+OcV9hNG+/DQPzjIGLGNycql7asBoGGgYaBhoGGgYaBhoG/s//YaFlqh0XZNrHEFdqeg0WyyFxnf3mI3OWAqaO3DVDUBQka4Ij+x2XKZgFWkjsIwNO66UicWgZ+pG5+ppqmj56xSJFjzBDpOrwjsahCAdrABQ0YgaHYO+8887jINPcN8atqDK/suPczgDbxN2yfqbHvaysMCRRWZ+d5py79WgBdPLzsTKRrBOJ+pbpVF744/CBjUPptOvfTvHZR7rUE088MTfsSFS7rZ+jkzuYwwfNZN0mRkXIDSQbLhwDDULDwNJhoCk4lg63DXLDQMNAw0DDQMNAw0DDQMNAw0DDQMPAymPAzRqUF66y8gG1caM54IADOMq5lPQud7nLuDotv2FgzjEwp5eMzjnW2vAaBhoGGgYaBhoGGgYaBhoGGgYaBhoGVgsG3J1Mc+HjrxdccMHIMXNjcTNR026MRE7LXEUYaB4cq+hltaE2DDQMNAw0DDQMNAw0DDQMNAw0DDQMNAw0DDQMjMZA8+AYjZeW2zDQMNAw0DDQMNAw0DDQMNAw0DDQMNAw0DDQMLCKMNAUHKvoZbWhNgw0DDQMNAw0DDQMNAw0DDQMNAw0DDQMNAw0DIzGwKpRcLht+8EPfvAb3/jG0fNYmtx3vetdu+yyiy+KLQ34xYRqkLvttts73/nOxQTaYDUMrAYMuGD8JS95ycc+9rEYrK+7+xbaX//61zkfu08PPO5xj9t6662Nf9xQ3Xluah/+8IfHVVhd+TO/Gte8b7fddu5Fc5H76ppyG23DQMNAw0DDQMNAw0DDQMPAcmJgfhUcPvD2kY985C9/+UugQ8IXm7/73e8uJ3ZOPvlkH9b2EbLl7HRkX4S3b3zjG2VRJ8cgTzrppA984ANlnZZuGFilGPDxs4c+9KH3vve9KRknTsHX6Y888sizzz47ar7sZS/zqXZfOJvYcOkqUDi6qasO3x3mp5xyysEHHzzyE4/RFhBTS91NHeD8l878atZbb71DDjnk9NNP33fffSvTdExceOGF7dvnFRS1ooaBhoGGgYaBhoGGgYaBNRsD86vg+OhHP/rIRz5yZb0nDj30UAqOm9zkJiu+CJ785Ce/6U1vKofRybnxjW9MwXHYYYeVdVq6YWCVYoC24owzzvj85z8/g9PW0572tOc///mbb775Cs59yy23NIbKAL797W+//vWvf+ITn3jrW9+6Um0NK1rIq7nZzW6mOTL4ta99bRxa3v72t9/gBjf45S9/Oa5Cy28YaBhoGGgYaBhoGGgYaBhYszEwvwoOMSkrjvqrXvWq8yB+cGXvWIP7ORe5yEVue9vbbrDBBrMh7YEPfODVrna15zznObM1b60aBhYXAyeeeOLFLnaxe93rXuecc86PfvSjqYDbCBQHl7zkJadqtbiVJ0ZSUJ5e8YpXfMpTnrK4/c45tAW+msc//vFoMleOcdP8xz/+Ma6o5TcMNAw0DDQMNAw0DDQMNAysDRiYRwUHr4173OMeBAAv4OEPfzgh57zzzsuX8cpXvvJOd7rTda5znYc85CEdyUcAC6cPltsb3ehGO+2008h4FhzwDjvscNBBByXA973vfbr4whe+kDnPeMYznv70p3t8zWteo+j3v/+9tF9pOWzLz372s8GP+kM6jZpkHqz5Xe5yl2tf+9r3v//9gQpXatElIL/jHe+Ian55pMv57Gc/K/3qV7/69re/PXXPBz/4QZl8NPo5quXwAkiM/Itf/OKjHvWo6173ure5zW1e+9rXRlH8cuTec889b37zm2+yySaC201///33B7+s09INAyuCgb/97W+iw+585zvvuuuuBvCe97ynP4xTTz3VNnzCE57wlre8paNN4PyFevzpT3+KVujJ8ccfz/jvwouXv/zlHacwO4snxZOe9CTbQafldRjuv+A/wlVKqYTHHEZ0wVPgzW9+s7tv9tprr6985StRetppp3m0v770pS8Zhm2brTJhbKrd9773XXfddSOTyhIhMsjHPOYx9ng/voYzC5cQCLGR4SdBSdi8mjz60Y/Wrzi+LNKLARiqOYqFQROiqDKvbBuJOuqizplnnqmXEqtGCOEJSrChCt///vfllK9mhuFd5jKXQTkFqpSvKTqiAkZdLQaPKKQeL7jgAmgx8RyJdyT/pz/9aea4tOiYY47Jxy9/+ctuPPEKRA8F+c2ilmgYaBhoGGgYWCIMOKTqt+ytohvxBqJo4pQHwolqTtj99tsvcbjmoWsqbLTKDQP/xAAZe97+8Mq4WHoK/giC8KXPP//8X//61x4J5P7IG1tssYXHO97xjmSbGD+/ZYoDio9nPetZdB9Kie78wPuzI8bf8pa3zHyCgco42sjB/V/zmtck7Xjk0aBI19IxgHve857XuMY1ZNJTyBzeqcqULxre7W53Y7alcZB+6UtfKv9b3/pWpj36O+GEE+S4WVCaS/Z97nMfjyJloEJAfj9HtRieAf+z/X9Gzv3kYQ97GGwwe4LAKh6lpKn73e9+XDaoP+g1KFCUku6itP02DKwsBigvLEjCJ3WkZU+h2RmPeytU8Ccyy653qEsfeOCBUa3ctoiJWzyV3uIWt6Dpk7jpTW9K9I2aZO/b3e52MoU/bLrpphKIg3sclKI59rgcepYb3vCGEh6DFCiNLuzH6//rTymyQNGgiBri6le/uhybDiWhKo2+yl+3C6lANZCZWBM5Rqg7G1NDah2lsa+N6lrXupbtrI4/lCQbUozKUT8mIu2C1SiNti7mDFrqV359XglWoo66rEkvo1N6nMihPvCIDrvkNXLoO+QEKS5fzWzDo3cATeRgDiAS7mGBBEX+NtxwQ+kf//jHDoi73vWuWZP+SGnix+ry0umMogK6Gi+Oflw1r++II47Iti3RMNAw0DDQMAADtMD/pLP/+XvEIx4xA1oY9rC4+O1oG8dBcrB9gME/U1L3i1ZLjqhbposc7cQpZ80hCbIJ4cjNZccee6z6C0FX59X0e59Yod+k5TQMLD8G5lHBEVgIjh87Ho9BC4jibHdy2DAf8IAHILD43ahAjCcDfO9734vHT3ziE0oJ8PFY/nLfUHTuuefKxIXjxT2SK6LOpz/9aY/vfe97PfbZcbIHzWgKSMM7/eQnPwksaYdyAWR6GbobviQmUldwqMw7Q9vQucQg+zkdWhkjD0qnSfT+oAc9KJq7rxTAnXfeOR5RK9w8aS0e22/DwMpigF/GRhtt5MMZhvG85z3PWqVJzCGxVNiGSIE9bjchCGRUdUYqOHy2Y7PNNsNYaG6z0z6oSUsY0CLkIaRlW5Loq9Q9Dkr32GMPm8KtvdKYsFe96lWKuEhEw9hfaJT9C+zrXvc6pVSlUdrfnpGfv5wp1OcAEjm6pqahiAFNDoc1nmvcr6RjXxO5v/Od73j84Q9/GGqX5JN0qjL/BaVEeioSoIju2VZHe++9N4cR2hyZ9XmpkH911GU1I4Qod39GDpcWPfrjSxI56Ax9dKT7FFXNqYZnVWgyTvXQOTVANra4dtRSoQ7Tlr44BvPVr37VIw8Oj2ggnQhtSKAderfffnttE8/RpP02DDQMNAys5RjAdTtD2R6o9SWCW54WJxTKyG+Hw3dAjIPjWExuf1ydOc9nYyjZ+A7TvpDB44IgM/kToBaCrs6r6Q9sYoV+k5bTMLD8GJjHEJWKaw2LXDh1X/ziFxdYoSae3i/+ngzD5Rt/H81ZfclIYj3gtAMQEDnxzQWO30gDyZ+W4Wc/+5l8Cg6/7gjstIpHKgnXVQie9zhVp2y2mjz1qU91WYaE+wXe/e53M36aSEBeil/eIgGWNHiFK1whcCUnZiozSiHKX5YuxUgazIYBGBC5IHwg/8jzfbTYVsIKtt1228td7nJKd9xxR79llIo0YdX3m+1xuwlBEFPWhxM5t7rVrYislAIeL3GJS2glEeESQiSAuvvd707VKNOWFI1CLGcDobOg5uA34U/RpS51qd133/1KV7oS1yqP+Sfmxf4FlpZhnXXWoYPIonoCV+eKkCtf+cpRDR7oUPQS1IBcfdxxx3EqSSBoHV8Dj5QXSJBE9nXUUUfhNsRuyNx4440NGAJpb7OtuTO4bbPNNgT4gfOKthXUJXAJaKE4+NSnPhWZNCn843wXJiieN4WielNlkzI97fCsCsDLoMUSWicdausg9dQZv/jFL7baais0n4ZazbPOOsuvOn45wkCO4JRAO42Sl+vsiAAWeqVctBI52U537bFhoGGgYWCNxwBfQm7IDiNnnwTGe4YpC9CYqpVjcR5uxJtqzGVlR6GjucxZxLTDqwNtIeia+GomVugMpj02DKwIBpZQul6K+RBCEqzb8qXjO7IYUGmMrIiMrMCOSoYhS7D3ZqYExv3Sl760ymQn4dyXv/zlCUj8MrDmPD6w4zzSU/YoG0rj5jNnqk7juhASSDY3hkwvUSK1Jxe96EXZsZlwoyOebBL8A1mwJb75zW8yYLJeLtEwGtiGgcCAOxFKPZrthkPqIIdgjA/APIUYScikO2Aj2meffUI5aK1qEqqHaKtCB0j5qJXwK4tfzAXXBkU8JvwaCeBCTMrKQVXsVjVFW3AByFIqCYIxt4urXOUqkZlbWBGROPSGWb+SQJfKiYNDF2k/Ug3QRLCMIU3lt2OzIzBj8yaHYY9zyOJtQXo3L5Eg6sQ0YwA81HIkA+eV9cehLitEgtaAhwvfCjoazmIiQXBX3qOP9RobtKvQaZKPMwwPtumDEkIlQU/tyEDSXeqEvFsnXiiVlqtAKLUtME5//kCIC5vKReVFmE7k04mIYcyOeBjd4Q53yMeWaBhoGGgYWMsxgN/m0czxzekjvNRpSB8hmNRR3sGME0FNNkX53A/Rc1dc+Y1qbtkb2Zzbo3NcUVg+OFf66DiX7fXXX9+5ydOTla7TkUdn4uGHHy6C0tknTNU5i+kNRsIlWa7fEijqNHF8c39Iqs634kUvepFjwqS4XfBuDtNIwHcoiOx2/Tn+AUxuoWF+6PeeOdTi7odyahsJr0Demlw5snTclFUY0hfgGCSVBaEL4eGi6JL1El35ajAP6rB/cO4eOceRr6YUoEZWIEYByI81X4GoYQYVmI85OkA5VzpDLQlIMza2Iu/ONQLMS7xKS8ku0dISDQMLxMAqU3CUsw0iFTnB76633nrctLIOpla6v3Owra7A+MxnPqOUwZZ1kd8HNpe7h7g1gfSlliSh9RNTdRqV+4Ppg12inBJdjgRzZKPmzILEYPSV/j/2zgJequL9w0uInViAYAE2diEGYgEmttjdiljYiZ1YKGCCrYiK/DDAwvibCIqi2IAIKmWC/h949XU8Z/fcc/fu3rt773c/usyZM/mcc3dm3nnfdxK2wYvUJBVb1wjgdMY3MXjlsgomTFmDBTMf50MuxkjbwGEhTbzNcjxBrgDTLOY0jOVoXbGWDv8KGPXJFQoavBBGccIkGDdunEci9+TDPozHhIFK/WkjP6VHKG547Qz59BfVFSYKTBDRuRgwYACTp7AKC0cqYh7G9AgJJnMsmCDPjWfxmEr1KwGdF2gBlCBoA7+oTFLpF7M3OshMDk0Z054wLYlIrvhlmuYxPeK58AsWzx6PQUgEQ1PKYxLGzx0KGjjaYOaKIh4NZi5ouX766ScCofwaoRLvp8UjKAl9kbqeYLxGxYiACIhAHSTAcMbk2XTrkBQwTNuMmu/ImMUojN4lP84I+vmZZULO+GXEkPITyJqdjQ3KN61PJgPYpbLTwM84kv0hQ4YwiUW2jp5yhDwuqCgTx1toPvKDf8EFF1ApK2oEBwhZaAmjFZsWaPNxiRDcNjxQS2GMQC6PdIZbiMUZdOzkAcQTjBo0GBt5moS/MKqmAewgRqoOLxlKWJsgZAEF6pZ02e8mdDllXRRu4iFK5mPhEJc9GmpkywGJD9Icwln7CK5cj8YanPXZmQMyOMCQZDSbhRVbL0g0bJ6Gfj3PzvZ3wYi+JE8f8gzNbHphgGzewa0KfYtAoQiUsYAjRGBCYsQWLAzC+FxhhBo9e/bk5wZ5J+rlJOOPDZkif5lYrJgNS668Hl+pSm2fEJlxqHluRdmvf6h0zSTeaylSAD18xOEUzjY1e5XsSdrOcJGqU7EiAIEEUwXjw3qSiQUbMkxBnBhSBvZn2HkwAYftEvCX6/stnjIeYIhFuoFjCFxwcZfRHQ+UlsyGW1QM4rnsTxvJCBs18btVj2FLhL9xNlLsZ4ECmVVwKAwfpl+IeOg+m1pZBRxh7WyCIRZBJQ0FNJMW4YiEk2LCNGG4Uv1KQBeWSZjDX5mxMVlB+sOkEw0UU3JBpoDVNDM/FDoiWbJepmkeEhymawgpspYQj2SSyj4Swho23PjNJwE/d+z+oeqCbw6XvCDnZU7mQjSSIZ0hgY0FTOD4xAtXjAiIgAiIgBNAYMGan+GMoZZJJvoR/PDawO1p2Ilkso3iA/6nGeZsV9LMBtNkpxwmCYw1ffv2tZJRYOTnOi7dQDaB+AAlEYYAVubsHGBNw+88puJsLbRs2RLdBxuCkV9zOgE24wzBaIWThoHDzgJjuGFI9WMT2VFg7EY/0cTcdBAhCyMLcwzvYDzAKoNkZEF/kJO8wgQJXU5ZF4XTL5RPmeSEB0SGtRDGVhcvYwx/bPbk6mPWRxOWkzUBUgykKsiPTMCBOIMsvABgtC0E9jnYgGEaQBuQbrDa4tkxY6EZyDuY2jFVIDKsSGERqDqBv+WmVS+oZktgQ4/fDlbsrrnNrxIK8LYbHG8b01Z+pPjV49v+rvizR+SMkhWi2TQLJ8qsVKWs2cjCrqY3hj1bDphkw9Mm68i5/ZbpmftlwQMsI9HgQFiLzJsfI/T0JN0oOGQVmAcBVp785fJy4oPDP7ZXM2jQILO8YDeGkkN3GMgNc9WF/I61t69jzbzFEjPcIuxgZPUfDRa0KKMyKrM+Z7Ed/p4wWqPaSvNyVVSpeJuWmXYDGemyLcIJswND92kbEpwKy5wwYQK/YPyamXSD9GEH49kr1a8EdJGSmaIh40DAwY8YP6RMJflNQ7OXaSiTHocfyRW/TNM8aiEjMp149qwxzFOZCqM6xMvDG0UavnnQ/NQTdlk27eeSCa4XYmHOpvEYBURABESg1hNguc72vn/MDDxlr/lFRbpBYjbwQzd5hc1uxte+OcF4ijAlXoX5gcrq+Y7xndHcNxjMD5cZcbME4MPwZ7qiiEKQfZjROtP19M7+4u3JGpOLWMHrCh0IJvQxayOTI5m0sMDx+QxzKgzeIYZGDBmZ3qC+YUMtDt2JQQ5iMxbmZsx2iLHd1uRadFcEKkugdDU4zAS9T58+iAxx/ldhxzDRZ8WOCBPncPxF8ePFIQvmXDCelzUSQkfM+VDWMgMzlJCRQbIRioYFNcazZI1JXyn6bKxh2G5lQYKolY1NZMZsaFMpP9ZMo/l1QMSL7T16Jeh7h9XxC8sHJ3k4rMYzKL/m8ZgwfYVh7GX49UQHHj09FicsSBB+8xMPBMIVZlcCESgSASSSKDTZbMOrYDDGspetHnZL+ItmswLLXkw5eHuRR6CggZdNTxwJ8OfM4MoUh5RombJlhM8dnM4w0LK3wHYNDiP4m2KTh20NfhDQiSCGQjgWBMEKLUEKye8JokkEHyaYiFQRv6zwzxM9WCZY2M3ajge/BqzA2QLClSn6KYgd0efiMOx4yZEYfr6oi18SRAPM1ZhYgIg0FIVOls0CI1nS9ysZXaRY5AhsNxHppsXIETjrhJj0Ag4SV9g85FwoU3AsS6QBdhkfNdCCYcKN01Z+dbEKtvYAjV8/NE3cJoVjs9nNY1uJn19E3jwCJscg5QiYrBUpUgREQARqJQEGptBVFkoBoUlFcpdDaxRzaFUp+UjK7KzVmSQzfDOjZlBGqs4POPOESNv4MScml+c7tgfYJmFLgA0/2zthck56Rgem4gwQyOiZGzBMoCJheqOV8rsXaUyuy1xdLnhdPtgl9zFXO5PjWctceeWVGPsguWBXA48bVMcOB7nYf0Iv1bR3zeYX3VgvjUUHWczvu0cqIAIFIRD9RShIoQUphB8sLN6ZIpvVif14hb8FVovHsBRhhsofGNrduAviBxoVOLziZ20MuUxNw9Wi+BuzU0V8T4+M8Uq9Ois2faUsNhg2qAIhBR6AUH5DvM0JCLYIQfbBmofFCY1HX8uM/bwuRheWKyy9QGELmHhMvKm00Euw1volazlWHWxco5LNVB6VQlaPiGBYBlhKfYtA9RNAVMHbyJsZjsTWDH4NCLC45Zs/GQSRyCgZR/lLR9KBvCNXa9l7QYjAXw3ST8SRSCuwwmVmwxlGZGHbB31RRl/clTEk44ocZVTW6txCpMLvCQYR9nvCVInfk9DTGGn8D8pq98v4n6clCL9x98NeENanRDLG4xCHXmMii4yDfQ9aiwIat7L+XRNvdaHowTyPdThdQ4f2/fffRyMXSz37JbTqvFV2maZfljIZnaXxb5Ni8GiMHvGmLkHt9M6TxbtTqeah2oa1c9euXb3ASCAyanCXJlkDmARbYuQd9lMfSl5oGL/J5n+0R48eyMcZIHjfzJ45UosuRUAERKC2Erj33nsZg+zDGtU0MvLobBV3yxKyY+/ArBXpM/tzTJiZHttWQaSRCZ7v8HCJ1h4DLp4jGPcjrrWYFfBhmECPkhGWgE28Q797WNbYB2d/TCQiA1mkJSkvwy4Xu65cfUzZ1Egyk1+wVQNP1F2ZBqDEgXsU84BOYltwMdcizFPz7HBDomSd9UgFRKAwBJBZ1rIPSxEsrqu5U+krZRuZDWR+UiMt5BeWQpBixG9FUlb9kqUawnUWb14U0mKsVPhJ8hgFRKDECfACI/iPN/LMM89k5oG4xG/xN8UfHeOux4QB/vR4/83cI4y3cPo/7XjehBhGelQkWJBTuyfDyyYTL79MH0CfNvxzTpMxZb+S0aWpKL80WZuHb3Z21XCTlF+ZaXIh9mVOxq90msRKIwIiIAJ1kwCycmTr3ncGXIZdBl+PQYeaGHYgPCYMsKPAXR+2KsweH9atNFxZIfKmKI5OD8snjIyeePw+ROK5ZP+AW6zG7RZKmlyiNRlJiWYHwm60vNka4dbkyZNJhs/RSLI0l4wp5EXD0RMnd7lSdeHBhMLZ7/HCQ1zxijwZgUgfiYk8mjCxheMJmCeglIqvNPZamNiQDOkGTULFBv0XNpIto7WKfSwvk4kZydjX8RgFRKBQBEpXgyNv+Q2/udXvEC59pShRI1xAZhnpILJbCkGPI34rkrLql5jD8KNm/o2tNFTc+Ym3IwOqXr5KEIFqIMC4GB4yGtbItkC4S8DfFH90ufZY+NNjbA7PQguLSv+nHeaqMEzzGPtRoUKjxBNjHJffCR04E4GGl5MmkLJfyejSVJRfmnjzsAxnYsR+Wt47imlagiofmoNm6pImvdKIgAiIgAhUJwG8ipoTJSrF35M5+2B3MNKGBM93LL9RtMT8xLKYPYiFWavjq9v9cWD2zvTYCq/Q7x5uO1DMxAA80pI8LiusK48yPUtCHz1NpQLMEzC0R6kWS17Tiwcaara45eJhmX4HBZpPK/PEYeVbWL6uKkVbiVMSKF0fHCk7oGR5EEBbG6k2tosokmGuwk85nq75xefEpjxKUxYRKBEC7GNgZcA34yUr1RJpVa5moLSJURh2JTQ4bpWTK1fdjGfKiEAWXLncKtVNLOq1CIiACFQzATw9s2pFdYJdMWyucXJhNqSVakbcX1L67Lh7QD0E71oYQqILiUkm1pqhZwcrKsHzHQrLZMffljnnwuW/O+diSowYHR8cuPMzl1iomeBy1cpM9rvXvXt3DBvJGD9/DREAn9CVXoX9Ta6rwuwJCXh8CX2s8NFkTYAUg5PXqBQ1DasaSQfGv4Td8B/nKZzkgo8wfAiwDGHHAl9XbC8lmJ0m9EK3RKACAoVSBVE5ZUQArXh8EKAVhr9GRog999yT3+VQbayM+qKmioATQAuUNxnXNogMPFIBERABERABERCBghBAwQGFQf8wgaRYs4NgTe5VmIkK7qU8JgxgUcIGG4XYoemcbEU4ITuGISRAy5hCEGrgqA5PHMSgr41PUBxsh4V7GCkMYg6S8WEhjdsOK4HqkFlYCeiBYkfDh0tMV8iLGgJtQ2hCLgwuWLHjJMLLZPKMQujcIpdmmxBNTL/FUWvE57JhweUHmokkwKc4WSrsMmkS6vJKCdApig1NVEJc8UdDloQ+Rh5NWJGFsybAXwlt4HGgxmLJcMlBDCo2ocknjQEyD51bfHh5clkHx+tVjAhUikA9UlcgAtFtERABERABERABERABERABESgBAixeMC3hONIKzz3EbRNuqjD/RIcibDgaKLjxQq6R1XyVhTqnFuQyGkUHgdIi5vA4+UaGwrlmaJKGFVUxnLWuKpZp2ZP7WJAqshaCSiadQkFG1qBZ+SiyIAQk4CgIRhUiAiIgAiIgAiIgAiIgAiJQ5wiwaOdcRfxhcQxNeB5KnQOhDotAaRCQgKM0noNaIQIiIAIiIAIiIAIiIAIiUG4EcGaH3QeHy+DOo9zarvaKQC0kIAFHLXyo6pIIiIAIiIAIiIAIiIAIiIAIiIAI1DUCtfCY2Lr2CNVfERABERABERABERABERABERABERABCTj0DoiACIiACIiACIiACIiACIiACIiACJQ9AQk4yv4RqgMiUEcIPPvssxy0hiuvrP39/fffb7nlFg54z3q39CM5iO7SSy/FDXvpN7XEWzh48GDeEw7DLvF2qnkiIAIiIAIiIAIiIAIFJyABR8GRqkAREIGiEOCs+169eiEIyFr66NGjL7zwwuuuu87vTp06NZc0xNPkFyhGyTT1xhtvHD58eH5NUi4n8PTTT/Oe6AR0B6KACIiACIiACIiACNQdAhJw1J1nrZ6KQG0msMYaa5x77rndunXzTm622Wbnn3++XxYwULySC9hIFSUCIiACIiACIiACIiACdY2ABBx17YmrvyJQOwk0atTo+OOP33DDDb17s2bN8nCFgauuumqXXXa5+eabK0xJgkqVnKZApak2ApV60NXWKlUkAiIgAiIgAiIgAiJQEAINC1KKChEBERCBNASmTJly7733fvDBByussMJuu+321VdfffLJJyeddFK9evW41a9fv80333yTTTaxoj766KOnnnqqS5cuK6+8sheOiQrJ3njjjSWWWKJjx45bbLGF3frll19uuumm9dZbr0OHDs899xwOO3788cf33nuPBS3KHZ06dfISPIAtA2YvP/30U8uWLclLUQsuuKDdxQgFdxivvvoq1a222mqHHHKIHW6fq2SSDRgw4J133sEyYv31199vv/3mm28+KwpnEE8++SQVTZs2jZbQZr69DfHA22+/TfpvvvlmnXXWOfDAAxdZZBFPk1ALaV5//XXa/MUXXwBh6623Dmt5//33Ifnpp5+CnQZstNFGXubzzz9Ps/fff/++fft+9tlna6655u67706ykSNHwocs66677hFHHDHvvPN6FtLj54LEK620UufOnanOb0UC9GXYsGGjRo1aZplleARbbrmlJfCHBVU6++23326wwQaHHXbYPPPMEynBLpM7Hnmp4iUkPOjkkuNFKUYEREAE6giBCRMmIM1v3rx5FfvLeDp+/HjGi3AcqWKZyi4CEHj44YeHDBly7bXX2gwtwoS5wTHHHMMccp999onc0mXtJ8B0XB8REAERqAYCX375JYvhpZdeumnTpgQQK+ywww5cMoWi9jFjxhC+5pprvCWPPvooMSzaLebMM8/kcq+99lpxxRVZvRPmg86F3WUk45I0XGKZ0qRJEy5ZVy+33HInn3yyl+mBHj16kGD11Vdn4U1i5nCsw+0uszEEBNylkchWCKy99to//PBDrpK/++67rbbaimSISJCGEOCS9lhp5513HjGUw91ll12W9jz++OPeDA9Y+3fccUeEC4hIrP3Ieijc0iTXcv3111NLixYtEF4Q4MOobxnvvPNOK43OEg+TG264wes1qsiVaDlPhAT0GvEQzwh1GBITw+TA0/fp04detGrVqn379twiTPl+Nww88MADJKC/9IIAH3ONQRrrLOSXX355arG7BxxwQJjdw8kdT36pKCThQSeX7A1QQAREQATqDgGE8gzENl7w48xP9Omnnz5jxgwn0Lp1a+IRmnsMAYTFRB555JFhJKMAw5n9wjMMbbvttu+++64nYCfAbsW/mzVr5smqHmArhYkELax6USqhpAgwbeDlYT8ma6s+//xz7l522WVZ7yqydhPI1O7uqXciIAKlQ+C4445jsOGEi++//55WvfLKK6xviamUgAN1D/xxzp49G9UDluKsn9llorRQwMHl9OnTKfnUU0/N2n1WxdxlgU05JGDKxaUvsDmKhQkcqgfc4nAWxCXcxQOoFRUv+ZRTTkEQgJoACZhCIXMh/RlnnMElXUNggbjkjz/+4HLixIlM6cIZnpXJt7UfEcNbb73FJZ0666yzKIf1uaVJqAUtGAQNaMSgjWJ5UYhA0kGlTOxAhDDCKsUV60477URrCVixJuCgjzQVsBdccAGVMrl88803SQAoNGKIsfSoh3CLipABcZd6kdogVUHfxEoLvw8++GA6S5lWDiIeUBhw6ywTaErgLrMQkxB5q8JyEjpOsuSXKvlBJ5cctkFhERABEagjBI4++mh+85F6o/945ZVXIpXgkt9zBB9GAAE3MUgu7OfdIk3AgbqfU7Khs02bNhdddBF6lwzHbCQwgrhkpH///ow49mHMokyy/BNxgZdT9QBSeAr33YKqF6gSSoQAcyqbq2RtjwQcWbHUkUgJOOrIg1Y3RaCGCbB+ZmbDmtbEGdYa5kxMOywmpQbH119/7T1hl4nsd9xxBzGVEnCgoUBGjpX1opirMcHyy5kzZ3oYGQGJEaxYTETA8dtvv7ExhQ2Lp0essOqqqyKqIAZbDEQPWIX43VwBa/+JJ57oCZg7oquCqIWY5FouvvhiWojhj+dlYW8bbkxPuXXPPff4LSaXxJj8hUgTcGAkYglMIoAiiafnYBrSP/HEE8RcfvnlhD/88EO/27t3b2Juu+02j/EAHGi2X55wwgmktMdnnQ01a1Ax5W5ctyW54xW+VAkPOrlkb7YCIiACIlB3CLBc5Kd4jz32CIUXDExEolNpHEzAQQzji5OJCDjMMpGxz8TuloxRnpER60tGRs9oAfQEKdAk4JFbuS6xpkRY72NZrmTEY4JK4RJwJCCqlbck4KiVjzVlp+SDo/ZbIamHIlAKBFBJYMW7/fbbN2jQwNuzwAILeDhlIMyCCsYVV1zBmjxlXk+GnIXwa6+9hn0mAUQY6CBsuummngCfIPiGYDWOqgJTLuJZS/vdMIBSAwnIjsKFx+PxFJMWFFWWWmoptr9QjkWAgirEWmuthfrDQgst5CkjgbB3888/P5oXyCNw3sE2RUItjOKUs8oqq3hpaFVYeOzYsQQA5bdoBobQFu+R7jGEjHj9oGq/xf4bYSQ+fOOSg++77767fv2/HVTTMGLwx8F35NOwYUNEIajDTJo0CXoff/wxCQympfRKucTTB99Wi92172S8CCmSX6qEB51cMg8ubIbCIiACIlAXCCCtppucuR6OAoi2d955Z8YOJ4DhIaMkgoO9997bhxu/S8AMEtEBCZ0jMEihx4HZ5iOPPILjpzB9mjB+tS699FKUNxGaIGRhFEM3hO0E8qJdguklTrIQ9LOpgHMoKsKpFiM4LUSwQho0Chma0SUhAZeW3TxnoQ2K8ghlWjMQwbNtQEXsZ2APixMHXETZrfA7V6WkISP1oi9JGDsd5hKIdTjozUY6Im+99VbmGEiImMMwFVlyySVpMwqbjJtWBYWDd+DAgebuipkDmwQwt7v4F7v99ttffvll5EoMc0h52rZta7esZDZ+zPkUnrzCCYClGTduHI8YJ1mMyDTsnHPOcaddlh3O2L2iZtu4ceNDDz0UpR7LGPlmqUl6JF/MDdiPoQvoRfocr8KiEh5BpCL6wl4LtdAeZlOQNNcwVsWDDz648MILk4UpAa/E0KFDmTUBxCZ4YVHpawxzKVyOBCTgKMenpjaLQPkRYLyh0TYIFar15oCTmURlC8TBBKIWRkE8emA3YdYoPhaizoBUginO4osvjl80n1JkrYX5E/G0gRmDJ8CfBR8mKMQwAGO08swzz9x3331MJpg34I6U6ZQnTggYLgpPrsUIZPXQyUSQ8nHI6rUgVkCMYvEeGQZ8dmKRJsswCDSDAKKBMD1SGEyNwhgLM0FkfkZ2Zo10hN2zeBqPiVTq8ckdr/ClSnjQySV7AxQQAREQgbpDAEk0a0gTDXuvEXaE0g2LZ1XMgha5AAYgntIDyBQox5fNHo9VI2GTOHhkmgBraVba+NJm7MbgkSEVHRN+4a1ARAN33XUXS1+EEcOHD0c9E8E6Iy8DFmIONhXQGWHsQ7hvIxrmkCgqEkYNhB0OdP1GjBiB4ypUJmkMogSkG3jFZnqAr3F2LxhkUTmMtDNXpSRD8o74gE0OpPYMkSy8KWfXXXfFP7cxoVIS0CO60K5dO0QzDJfMPUzAZG1ADLTxxhsjl0EUwsyBnQwW+dxilY70hK5h/UoVlMwlJdgmh5V87LHHwooHt+eee0aajft2+o6OJ6INgOABnb5Tlx1CZ9mPOuoodG2IASaPGE0ZJCyRcriECeIb8NISxDRXX301mjtsgVjK5KKSH0FYF3a7hx9+OLsOKBbxWHlMNBjhy2KLLWZVQNvSo0vLg6NTTOHYfeFkvbCc9DWGuRQuVwIpNT2UTAREQASqQoC1KDqiDPBhIYzBRJqJCu4YCLvLCZJldTI6efJkL4G5AllMS9asHszJKAmYjnArlw8OErBvg/AC9QSmREwOTMZhJWNFTF4Kt0vGTi7x9WCXkZIZbrnLfoLdTfhmNobJMRMFLypMbO2PaNsyMcL+BT7JtXTv3p02uJPUsFicw3ErNFJlD4QY9qMsmZmoULvnYpLElMgvIUP6+++/n5jTTjuNMKIBv5srwBSKnrJx5MY+PXv2JC/CEbJEHhYxL730EneRAUUKTO54hS8VpeV60MklR5qhSxEQARGoCwRY0rM4TO4pMmsUOkjDOMLvto2eERMVykESES+HEYEsqG9EblVoooI+IBnRR7CMrGwZYhBn2CU6Jrj3sjAbCfgFZ+hErGAx5uo7NFFh0Ecuz2hoCWwAOuigg7ikIwgdfBDEDxdzDHMcZon9O6FSG+OYXZh7cnY7GP1pPxINy24jr9vJMh5ts802JGARTgI0Hwl369bNEtMRBDHEmCEqkg7GVhtMSYBAgVtogoQl00Fcb4LCIsNvJCakR/vDIt2YKMzuYzFyBBI7jbAcxlZu8eBslIebeW9BxJCmqIRHENZC2GxjfRpDwCYk3AonMByuR3t4M405d2FCjDsZTV9jpAG6LEcCf6sZl6t4Ru0WAREoEwLs4aPBiIyf4+KsyQy9jKzefKYUhJkqeQzbFB72AGO2hx977DHC7EJ4TMoAS3S2AvAqyvmpbA6wBYGTS8+LvQk7ALYvRCQTKb8VD7CrwEyODRbGeLuLPIJhHo1KLhGO4F2CbRnC7KXsu+++zMnCLkQKtOmLRSLxYa7Dua1oNyTXYvogtMFLQwuXPSj2rNj/IRIPGn7Lwn4Wr8enCdgOz0MPPeSJmSQhD4rrg5jnVzam3OgmGaMXGAkkd7zClyrhQSeXHGmGLkVABESg1hNgFEMQjx1Hyp7iEBS1iLPPPtu30C0jhfBxa4uwNItkoyKMTBO2YdQtSbFQ4OMGquwN0AwrB70MDmhjgmEWlPHCaRszjV122QVNELuLR1VKQ1uBhRzZ+TANMA1BVCOR7GNCEi+nwkrZokAPlIxoW7C6phDUJcJyEFvYJeMRkgLCtIFv9lf4xtyDbz48EWYsBOwuUw4mGK44iR4Nt7CCmZP0nw/iHkxi6cg/EX//y6wAXQ+0MxidLQqNEkZ21El8bkY8Qi67C3AUJZzz36XM/Qf7Wf6lXzbKM2tihkNMOBXhMmtRyY9gbvH/ftk7g4TLomitsfo3xdyQsUV2ZsyJQ/7laSpVo+dSoHwJyESlfJ+dWi4CZUYAR2XsSDDkYxiCBiweJUOzDvRIWY0jg+cuih7sUaBfGu8hShlss5AYjUo2ECiHfY94MiYlfJAXcPoJI7StzD0ZQx36mZTAsM2ozOSDIZxZwmabbUYYiQmDPa1ldERfA5VRrIhRSWA4R7EzXjKauuhPkp2lPvM8piYvvvii1chWA55QUZ3l8DzmT2yGMOVCg8NbEgkg0WCHiqI49AQFFu7i3NTSJNTC7grmx1jMIq8hr+mI0heaCkn2iPr27ctuD0qkNADDHCZG1BKpOs0lszFKYzcMaQVbZCgzo5zMzMyMccISeC7UjnNT7lIdMw9EISQABaZAWSe+YfYwnNBxkiW/VMkPOrnksA0Ki4AIiECtJ8DAiiA+Iq1I6DVHdGEFgGECHh9CLxWseDEgRfchntcizQtG/G5CjHmvwKGV+YNgRGYYYjphWRiDGNqYP6AKQfsZColHWpG1QPNahQWHLcgtDWM9i3+0PGgb0gQsSVn5M3lATIA3dIbveFEVVhqOdGxUYPPCsI5+gRuNhgkYoKnC5BTWQmY7TEisXrOptHhi2EKgKAiwu4Aoh5hIZ70Ky+7fNulyoYPFc8lcBWcfts9EpDcMEQlmOxHpieWKF8Wsg0cf8cmVtagKH4E3mABKGdjvoI7KnJCZFdZSTGzishtrpL8SYS8IV6rGsHaFy5SABBxl+uDUbBEoPwIoRuIkDG1PnGMx5nHJ6hcpg/cE+1JMKJEO8GHmxNoe9Qf3zmDjGdqGbGugL0ouTkJlXmUzALvriRFbsJDmLpICtlkiAg7GfhQ00ALlQzmoVjK5QesBYQTif7ZrEH9g54lEA+NSfFZRBfshNBUBR7xkHImx7Ge1bPstrOexWbXZHuM9upRIBEx7ltkDGqoIaLzLHrD2GyLKYcrCRAEFSwQWliahFkQJuCJDZQM5Ah8ybrfddog8bGKBwgUFMi/EzJiimGMh9Ins0Tk3EoThSPNo5MMPP4zZLU+Q6RR7d8ikgBafS6GowowE2ZDtqqGpaxNTQDF3RHk4a0VZq07oOIXEXypUUmmktTz5QSeX7H1XQAREQATqCAEGKdOVSNlfBByMcYjXTcXAc+F51FfjHknAIl37ILyVHMajB/II6kL1AG1QvDAwLrNlYrkYlfA0iVQFs1O+sw4lXj4jPmGSMVh4JEofhC0jvlGRpzABQN2S2QiHhVEvWh6e2AKVqpQsNuxibxIpxy7Du4haGG0ZRj0lTcUyyPRVsaxBGoKoCEUMPi4E8cQJAes7YqwwjV3arTDewrnKp5EkYBrgWaDHbCRXOSTzoip8BF4mATrO7IWngAALvV1mYjwOH+U9pZXJDM1jwkClagwzKlyuBJik6iMCIiAC1UaA3RVUDe0Q09AHhzWALRF0DfBAxuCd0CTMN0JnHAkps95CAIHr9dAil1kXExpk/56eBrBHZP5BPDI5QMuZGmZNw/aLm/tmTRBGohmbUHVCLYhpyJgVHbrHYCdBWFHeYR4fFTFjqLAEFH1DzhWmT0iQ0PHwpQpLSPOgSZ9QcliawiIgAiJQuwkgf2ctzRAcdpOBA2sU9ics0n1w2CXidbLwY8s3aowWaSfLsiINyyGMugfJELtH4iv0wUF6TFzxnMW2ASalVIQKpxWCqiZlonHJ/MFiQpdPxER8cDB5ID2uQy1xwjc7DRiDoNPh/fLEyZVmdaqFiAT5EWVSiDmPCKcxgwYNolVsGHCXXRnCDtwrtYA9I0Q8dsnwR2L37RW6pYhk5NIahnlOeIt9F0rAKJjIeHZ2AlDYDNNb2FKiFuq3mBVQDvqqYQJq9AReVPpH4HktgB1N165dqYVJIDFha+0NxPjIs6BjQkrzwZF3jV6aAuVFIGqdVa5yGrVbBESgTAigVsAJI6jCZm0vAn72dvBzyT5A1gQWyQSL/ZyEBMm30E1lkmFnlFhKdEmYJYS+JGgAQpDkjaBILbTcNTwjt9gscnPfyK34JQ1LqDqhFvRNyJgVHWodYCdBvLo8Ynh8VJRrqyQssFmzZswwwpi8wwkdz/VSpXnQtCeh5Lxbq4wiIAIiUHYEWMnTZsQZoaIBsgCU78xqMt4jVsuYcvTp0ye8hWYHoznrTxaWHo9CBJp9KHdgYuCRKQOMzmhwsBRnRc0iFi1CP3LVNDrRE3QFgazKI14RkweG49BzFqM/UgPMZknDDgFLYrN3QI2iY8eOTA/ix76kqZTDXFkTWr2UgFEnCiamrWmRaJh6q5ATETYdDXOehZ6C36XLeA1D5EEMZjgMvtjO2N3kznoJFkCrkekTI6O1n0gUMXALirYIrj0iiZMvzZMXei6ezMJpPHwlPwIv0AJo2qK+YWHUP1FQJRx/IkhhiA89jiECs1x8V6pGz6VA+RKQiUr5Pju1XAREIE8C2HAyscBuBXsN/FExlWEgZNLA5lKeJSpbSRLQgy7Jx6JGiYAIlCgB9CPwyIBFAAt7fB+w2udAVrT/2HsPvWxEWs8hphFnWKyiOWoUK0XzuoXNKYWwYmfBj/QkqxQ+UmbkEoVBfCrhGgNFA8wikGW45yycZVAgLp8QpiOtZrmOdgPZzeUTkSbZRwTDToMdHYrtJLIS+kgjkW6gpsFBKuZrnOU0Uhgq4pZ5zkIJEQvQSHuSKzX7UBbhSCXAiAoMEiJKsNq9KPxK4OCDRTtSG6QD+K4yTxzIjLAkxTwWhQXW7WgrYELrFitIdnAThg2vuQnjKPrQTZgXnitA36mU54V/ULhh64GMA5mOtTlXrng8/erVqxeQ8WXGUGsevpCSoGERTxyPSXgEkcTYNfN06DJtpqmQBEXEjQhZeEXpBQ+OTjG7o1W0LSwqfY1hLoXLlUB5KZyotSIgArWJAEMRR5yaxmZ19gtFVsw4UaRkGsGQiZNOmhFqWlZnY1RX8QjoQRePrUoWARGolQQwzERggWtJRAN82JDHw5RZlVp/Ud/bbbfdIn23Q8TxABrGI9HgkC8rB0MPRlu2FsIEHja7g4TJAAIO3Hwgv0CywAcVDCv23nvvpRCULziV1mIQW2AaicMsLu30dAQEbGZwSRu8RuYAdMSysB7GgaXfwu8G6VlFcxeVCvRQstpjJlRqliDILwCC/IJyWPnjk9urMNsKXJLTF2sDDbbTxywNGhYwsTag88IshZ0Yu4UeBwIXREhkxGaHI135cIkSCgmsZBReLHHWb7RFEKZYvWQkuyeLZ0dwwEP0BGGAblIpVK0oGhl2ocKiEh5BWAtCDdydWH9RkuU1wNeYJUC3iKq9s1ibIviwxvDgkBkRNhMVS5+yxrB2hcuUQD3aXa6yGbVbBERABERABERABERABESg0AQQE6AogZlhFQtmQc7xYXgGDR1SVrZM9D5wI4rogSWr5WU1iwoDehwsdy0GH1js3nuCNFWwz0+WrLalOMNCylChbWnWSjkqBZtQlERYXaO+QRrW56HFa48ePVDKwGkFNiPopPCd1eoW51nINUAXNwhF8YTzSiLFpumyp+EQFvYAkBm5aY/fqlQAzRowou2Snw1swiMIm8FyFWMcjtpFDSeMj4fRxkUeh+gnfstiUtaYK7viy4KATFTK4jGpkSIgAiIgAiIgAiIgAiJQTQSwKylITdiB8qliUWkcKuUhi2F5n6thLKQrXEuTt8JKzalWrlqIR0iR6665u8p6F7kMGihZb6WMRDEkZcrkZObhKzlNwt2ERxDmQgqTILMIU5oGRxgTCaesMZJLl+VFQAKO8npeaq0IiIAIiIAIiIAIiIAI1CECcqhUhx62uioCVSYgAUeVEaoAERABERABERABERABERCB4hA49dRT2cAfOnQo5h741EAlBKcbuALBK2pxKqxSqViU4F60Xbt2uUrZaKONcDiC7kOuBIoXARGoCgH54KgKPeUVAREQAREQAREQAREQAREQAU+4VTwAAEAASURBVBEQAREoCQL1S6IVaoQIiIAIiIAIiIAIiIAIiIAIiIAIiIAIVIGABBxVgKesIiAC5U+A4984Eu9///tftXXl2WefveSSS3A8Xm015lfR77//zoHz//d//5df9srmoqJevXpRaWUzFiQ9x/XxUHApX5DSVIgIiIAIiIAIiIAIiECNEJCAo0awq1IREIHsBPbee+/OnTuPHDkyvM1xaEReeeWVYWShwggabrzxxuHDhxeqwHg5U6dODcUZnGnHSh7BSjxlzcZE2jl69OgLL7zwuuuuq55WUREiBiqtnuoitTz99NM8FJ2bHsGiSxEQAREQAREQAREoLwIScJTX81JrRaCWE3j33XffeuutM888M1xqsq9O5KefflqmnccL2vnnn1/6jY+0c4011jj33HO7detWPS0/6aSToESl1VOdahEBERABERABERABEah9BHSKSu17puqRCJQ9gbfffvv+++/fb7/9yr4nczswa9assuhIpJ2NGjU6/vjjq63lG8/9VFt1qkgEREAEREAEREAERKD2EWhwwQUX1L5eqUciIAJlSgAzgbXWWmueeeZ57rnnunbtOt9889ERTlPDfmHVVVfdaaeduJwyZcrNN9/coEGD5ZZbzrr50Ucf3XXXXcsuu+wSSyzxyy+/XH/99RiAjBs3DqsWHF4Qs9pqq82cOXPgwIGU/+KLL5KsadOmlpe7lLbuuuty7Nztt99+3333ffXVV9TFMW+WgG9Ku+eee/r27Yshw3fffUdpDRv+LR1+/vnnH3300fXWW+/zzz8nQDzN8Iz0onfv3q+//joKKWTEBqRVq1ZkQVHlkEMOefzxx6n61VdfXWbux3MReOedd6juzjvv/OCDDxZeeOEmTZqEdwl//fXXtBYZRLNmzfwWhjaPPPIIjbHm5SrEEVEtjaGRNCzeTk/G4XxWBQzh3K9fv2HDhv32229UTQPsVgIiFHAGDRp06623PvTQQ6jhLLrooksvvbS32QNOkqfvVf/www80rH///uPHj1977bV56J4+DNAYHpw9IA4R5PF5w7zYyAPiLQLgHXfcgT1U48aNURH68MMPu3fvXr/+34qNuejlKjBsj8IiIAIiUCsJfPbZZ4yw/J5Pnz6dcTO5jwwZ+++/f5qUyeWU5t3k3jGqnnDCCYxla665Ju1nCoG3r44dO/rYlHenkuvNu9jSzMhIffDBBzMJNIyl2Ui1qhQJMO3WRwREQARKhADr/5133hlJBGvgM844w1rF4pnLI444wi7HjBnD5TXXXONtRrJADI5CiWE4JNy2bVtEGBtssAFhPsgROJS+ZcuWyCa4RF7w5ptvWnZLv+OOO66wwgrrr78+t0iwySabII+wBAS22morIrfYYgvLziW57C7WNNyiqXzzYf1v8faNzYUViCgBcczJJ59MvGXp0qXLKnM/5KKpKK14xj59+iAlAUX79u25SxhJh9+1wIwZM5Zffvm99torjKdhG220kcUkFGJdPvTQQ7EHoXy+s7bTkpm5EGUiUgEL6ddZZx3aRqBTp05IFriVjOi8884j8corrwxA+gIHJDthsy1sWAysVU359HHDDTckOx+eYDwXMd9///3WW29NghYtWvAQCfD0aW1YbOQBffnll4iBSAl5ArwYO+ywA5fosFiuBHrJTzxrCxUpAiIgArWDAEt05Br77LMPMuV4j9geYCBmyLZbkUEknr56YiKtKlSlyb1DdM6Y4sNWOMBVtgGR9ifXW9nCSzw9OxNgvOyyy7K2M0ImaxpF1k0C8sFRilIntUkE6jgBZBzIGu6++270F/JD8dNPP7300ksczIGuAaMjzjLZNqE05hyXX345uwGcDxKWjNAE3Qe28dm3P/zww9H+YJPKElxxxRVoiCBiQPUDzQtkAWz1RzyevvLKKyRjb3/PPfcMi0VF7pNPPiGGWQ5L7tBhJ9sRo0aNokkXX3wx62pUSywja2+q2HTTTWkM7kgpuXXr1sSgmBCWvOCCC26//fYvv/wyy3uLp1V8dt99dy7TFPLUU0/tsssuAwYMGDJkSEI7vVJ2n5hqoL4BBIDgnoMWPvbYYyRIQARqdq7oLIQB+N577yHmQAzhxSYEvvjiCxRwEEW98cYbq6++OvNmOhhPz7OAJGIUvvFRevXVV6MnArEwZeQBkeWbb7458cQT33//fURLiKXokadPQy9SoOdVQAREQARqKwF0B/jB3G677bAhRcUy3k0GQQY7lBfit2owpkZaxaiH6iLjUdU7XiPtr3qzq6EEkakGyGVahQQcZfrg1GwRKD8CWCggVvAPZ6Pk6kO9evVMYG/qA7mSJcQzA0NlgAQsjNFrIHDggQdi8ILtBhqzCyywQMRlKcoCiFRIhorBOeecg/gArRAuObWUmRx3+XCJ3cqRRx6JhYvdJcY+SEPQomRCQ8n/xFXw71FHHUVjMMcgI7lMDkKeBx54ADJIExZZZBEu0ZVgHsmGGPKISInogCA+ePLJJy0eAQ0BIvlOUwhYqKVDhw5u6RMpP7xEzxa1C0QqHGdDPKYiuOdAYME+XjIi+kLj4WZWM2iyYG+CDkhYeK4wD5HucxeBCDIvAk7Js1A+BfLUjj32WGx5IMn0mocOrsmTJ3uy8AEBDRWhJZdckreLb9LgXRVlGU+chl5YoGdUQAREQARqMYEKj/FGAlKC3a+RVjGNwa8U+ytVB1Ij7a96s6uhBJGpBshlWoUEHGX64NRsESg/AmxloEnhn+SpEg4XEEmgI/Dggw/m0VVz3mEZcepBYP7557dLZAr4aIiMi6FggpQsd9EBmTZtGkoErIfZ7T/rnw+aAiiDcMtVJyjWvVRYFWm+vYWUxoLc22OSF7RX/qnwLFx4UCCWz5FiaeRiiy1mohb8XKBM0aZNG6wtSJamkEq1GaUGtBwxqQnbAEYukxHRzW233ZZNP+RHp59++r333otxTVhIQtgRkcaMvZ2S56J29F/QmmY26ZEmjQplWGFnJ0yYgFgEYU3o0SN8AQpOzxumgAiIgAiUOAEUGA866CDk9Zhk7rvvvn50N5sTnONO4xlr+I3lMuwIWhsY+l111VVE4iCcBBMnTvQEN9100+abb87vMCXwo+3xBMaOHYtUGktJxkG2H7gM74ZhfupRvkM0j8gbUT5+QBiVSICdAtUhmPbEuKMiBu2/XK3CnRZbIHEvhAz3CNNR8/SinnjiCYpiCPMYThbD2tQvCWTtHZ5HyEgjw5RhOE3Hc7Xfyslar91KU7ilBMU222wDUsZo9m9QNfVGJheC/ik7HLwkmHnivgoNSs+Y6xUiAUDAgh4o7xiPAKPa2267zTMSwO717LPPxjSV+QauyiZNmhTe9XCcDFqulXp2vDy8w+zxYAnLS8Wry9P38hUodwI6RaXcn6DaLwJlQwCvGT///LM1l+VouKTM2ocePXqgnoCWAdOmrAlSRobrWMuCI8lwPRwvB10AIpmgMIhagAHbkzGi80Gm4DFVDIQtpEbaFpkCIstgDI7UgmQEr6uIDBjXmZSwbkcrxNKkLyRSZq5LUHAL2VA8QYWImNDgA+WZZ57BZpsJBA8UuximRPGiEmJCRGEy5FBcolMTRtql3QrjLYxwioA94vhdYgpOL2stihQBERCBUiOAsR4eqRBDo2eH5h1mkiNGjEA9kAUnQzaDDg1mD4CPhb39DFtoPi600EKo+yGbJq87bMa6kGS4RuK3F9ED4gy+7Scd6QnVkRLjSuQU2CRSHVaTGGZ6yR7AbxRFsfnBOha7RcQT1HXKKafwjQCCVbqnxDs1MQwBuVrFXfpIXZ7FArSKQYr+ot9nMaj7kRgpgOl4svzmLgt7z5ird4jRyUhrPWUYSNnxXO2nqFz1citl4aRkDwmb3KWWWmqPPfZAlAB5mGCAyd5JciHY//LIUMxE0ZIR8+GHH+bZYTBCUQmvEDXylMHCXIXNGF4q3gTsSVHt3G233bjLB6EG5fD+sDWCjCzXUW5xMsxPKvXskFIhFOONxYfXa6+9xg4cMhr2lqwZ+i53AhJwlPsTVPtFoGwIsIFTqbZyrAmCfHYGIgJ+mzaFBgiVKjZrYtsI8lu46mCig+GDzeEYaC+66CK/W9QAGymYfnDAh5moJNfFnAABx+DBgxFwMN77FKFShSRXYXfx30kgqwsM6uJWAiJmvehu8DE7F+ajOPKorIDDmhH/XnHFFYlklhbeYu7Fpd0K4y3cvHlzAjzi+C2LKTi9XBUpXgREQARKisAll1zCyh/FwHbt2tEwVrlscbMEZZTBmhK3TSjuoTEX6jhY+xmwyEVKxmt+4c00A0EDd5F3sPplIEAFA9EArqPwdcXillu4oGLwHTp0qJmUcouVds+ePfGLZMX6N8eNsaRHN+Taa69lsGMpS2MYK3Gl5GnigaytIhn6oTSGxXk8C16iqIINAxyEI6TAmRdpWHKjvEkA1QOUTw2O5U3oXbxwj0nZ8aztLyBV+sVWDQeQ2eNg6ERXFOkG7UxuIRsVPAUenCmNUg7aH0idEFclvELe/ZNOOsl8uPBYUcbBENhmLyjdUBTuzHkBmAGSHp0d9sY8oweykkn/7HiOSDcQbdB3JHfIrZB3oJqERI9Ir0WB8iUgE5XyfXZquQjUfgLMZrBNiCh5cvIFPcd7qPc/QanV0yQHULZ0GQeOHpjVoaCLjIPtCJa7KJK4cQSzoqOPPpr9nOQC875r8wz8lXgJzPk4QSarPgK+SJHC0Bhmn+yPEbZclSrEK0oIsMHCobCM/c7hxx9/ZF7C/DIZERNEJovmIZVNPxSeKSpBuJDQhqy3cKKB2jOIcCxvCRCjMA+mVaFZSpgX3Q1kH+zYcPSsxTNXDtVrC04vrF1hERABEShNAvx4cmo4S1ZfwGM5wu8hW+7+a5lHyykN6QYZbcOfADaPfKPRyTiOnMKkG8SwC4IAml19H46JtA9raQKIM1hXE2B0RpMC+xHz7vR3otT/MKyzaeHdDPNZpFmGIjpH3IPvcJxk2yjGwEFi3DZ5lly98wTxQKU6Hs9uMbnqrVThRs+3LnjWppySXAganXDAIZdJN2gPUjDGUKQbKV8hdkSsF8xhkKfY+0AMcwy+sVQy6QZhcytmiSv8Tv/szKkZBlOmSozCCJMTynenZhXWpQQlTkAaHCX+gNQ8EajTBFDWYKcoYqLCVAnfXewVcEjqrrvuytEeWEBUERMSDbZ0GLCR65tXC3YYrEx8jqLDyS2kDCzXESWwqrc1cJpKUZvkgwDlxhtvZCyvMCNqn9iFslmEJifarR9//DEHuLBcz2oRAx/2PUwAhGGwt6dShViuCtuJCgZM6AJSJyYf+EZhzmeUEhCxIcP2C7sxbO8wc0X3FaXT4447zpta9QDnuaDUin4y7wPTNWa9TM7YX6JHuQpnlgwuDH/IguE3/lNDE6Q86OWqSPEiIAIiUC4E7GfQF5/WbC75GWdj37YW8ugLwgjPZZ6b2DAnhrGbb0QJtra0NCyeWSRz9LjL6y3ezDZNl9BiQidNFlOQb06Xp2RaxdjKenvRRRdlsMAEA9EPbiNY2GOgat6prbpcvUtoTKU6nqucXPVWqnCMfXDkweDOJIrJCXIK5lTMK5ILMf3ZiNksGxg0NeUr5GIp6sIcyR1/WICh2XvtKT0mIZD+2cXbidAKYUfc2VlCdbpVygQk4Cjlp6O2iUCdI8CYHQ7b9B9LYMT5GGKE8Zy3ypoWQQMfDgFhmY2aQJggV9iBMqxa2AIs2lFJwHqCzXwGOc49teNCSIOfCyQOrOG5yyWDOlq4hx12mBdFIKwujCfMzsAxxxxzxx13IDo544wzmENYjZEsfsld5lJoS2Klwi4WMy2kOcgIIm4mvBbztYYpDZsnHpmmEK/RclXYTjZ2UM1FbdUOu+V8E04S2XLLLcmegIhJA9qnyGtQW6U7TJGxo2aa6E31QBxLpIWkjMcQiccyWOGTlfZwyXyUl4EH6iXHM9rjRveVjMyfuEShGuyWJQ96YV0Ki4AIiEA5EuDEK5ptK1Vvv13aLY/MO2D6F5bdysTWIDxqxPYz4j/1ljgen3dLcmVkKMTzJTsoJGCCgVIJCiaM++Y9BGWWUBwTKSTsXeRWeFmpjocZc4XDeitVOOM41kO9e/emvxhosD3AeM1QmFyI3bUhO9Iku1WpVyjeeB5BpNiUl+mfHUI0ygxlZLxabIpY+1NWp2QlTYAZpz4iIAIiUHYE0GhgSwclAlbdhWo8LtDwj4URStYCqQ4l1ay3ihGJZiyNYbitSuEFKSRsANjZ28E+OYz0cAIi/JBxOomnLEYANVfaRgtTFo4+Dqq5IMqVvuD0clWkeBEQARGocQIY+iFrwGYkbAkiaSKxAyUS7w+EkdSHCcIwsmwSoH9hkVagHfduMbi0IAEOp7lEEYAwsumwhFzh0047jcTsE8QT0DZukcBvsVAnBltFi4m0ypPlCtxwww2YUjKa8M0x5CSjy5hjYFxJsTjMtozJvYvcBQJ5iSRvpTpO+kj7IyWTIG+q1hH7xgoJvxg0Eivd5BbyfEnGxkCY3cLWtoRXKORgWdggQVnVwuweUTKGS3bJN6oWxFx22WUeEwYiZLiV8tlZM1Ap9dKYblERqqYeo0BZE5APjpIWP6lxIiACuQgg9WdThYPEEiwRcuXNFY93BhRoc+0RUV3eOrq5akyIxxKHxuS9lWElF6SQsJFgx3o5ojzsCRIQsU3nhtaevrABtJdpW7gdlFw+uhsoG5txeNaUBaeXtRZFioAIiEApEEBPkC19rDP8EHQ2unH6yDqfn9aCt7Bx48YMCqGLK4TO6PdhMxivyw4xwUTUb2Gbid0oBol4AyUSDQu/hWmnh7MGsJEhPcv4rHdx5cDSDk1Avs3lJLYb7H+g6YDaAu6usuZKH1mpjqcv1lJWqnC8itIpywhG87rKvlFyIQgCGG2RqrAHYHmRa+B6lleliq+QHTqDaxXvNf5oPZwmkPLZ4ceU0swThxVrYYtPU5HSlDgBmaiU+ANS80RABERABERABERABESg6AQ4uYzTUnBphP9FNg+wVkDGwf55SlcIHLpJE/v06YNEO9cBn2EfqA4NEdbGnBuKdAOH2Sybt9pqqzCNhXGNhMQBVxEIHRD94xbknnvuwX4E80zaxroUuQwnsOA4A0VC/EqEJcRbhdkp7r3Ijq5HmNLCLLPZ7cDPFB6aTJqPrSUVQQObWboWz1JhjEnSMVZFiIDr9PQdp+R4+5OrS184dpoAx+6GJ86DBgvCLHPCklzIqaeeiisrHgrGwnhAhxVCJdttImPerxDaHLxsHCRPUdjzopUZP08n7HucTMpnh/ORXr16UThVIL1ClQMvtkjx7GyXsAqFy5VAWeufqPEiIAIiIAIiIAIiIAIiIAIFITBw4EAW9uzS80Ghw8xJrGSWskQmmKhg3YBjJtIgFyALnq0Is+L1hpkxBY4ePAYRAwILkvFhTWsiDL8bBrChYEVtKVmL4hec9lgC7B+RVtgtziBHeEEYt6B2N9IqIvH5TQIcaoblh+EDDjiABJy04pGcpRKJSe5d5C7mkNZCFE+szPQdj7Q/UjKl5U0VoQYOv3jKdA0FVSRE+FX1Lie38O6778Zsh4x8EAAh4/CMCa+Q2Yb4gyMLQg0emedFPoWExYpFnIRiBeFcJioRMlZImmdHSrROUBfiRbW6eL65bG+9bQqUEYF6tLVcZTNqtwiIgAiIgAiIgAiIgAiIQEEJcIwX/oywOkxv9FeV+tlIZ9M+jREoZhHffPMNti2mL+CVspyhzXit4oDwyC1P4wFKwOaFxXZ4+pjfrc5A+o7n0aqUhYMOhyMcDZNVOSWhEDJyF1edWa1Wq/IKTZo0iQed66z3PFDkyoKJE13giDdTBsmVTPFlR0ACjrJ7ZGqwCIiACIiACIiACIiACIhAPgQ4Bw2TCryQJvhgyqdc5REBESgNAnIyWhrPQa0QAREQAREQAREQAREQAREoJgFUA9q3b4/pjaQbxcSsskWgJglIg6Mm6atuERABERABERABERABERABERABERCBghCQBkdBMKoQERABERABERABERABERABERABERCBmiQgAUdN0lfdIlDHCeAP7NJLL8UOto5zyNp9DqjnGLPff/896938IotRprWEdnLCHOXn1zDlEgEREAEREAEREAEREIGqE5CAo+oMVYIIiEBaAlOnTsVntacmzGltw4cP9xgFnMB11113ySWXjB492mOqHihGmdYq2nnhhRdSvjcy8qw9XgEREAEREAEREAEREAERKBKBBhdccEGRilaxIiACIhAhsMEGG3A823bbbWfxnMHOoffrrrvuNttsE0mpy+WWW65169Y77LBDgwYNCkWjGGVa2xo3bswpa3vssUezZs0sJvKsC9UFlSMCIiACIiACIiACIiACuQg0zHVD8SIgAiJQcAKzZs0qeJm1tcCN534K27tilGktbNSo0fHHHx+2Vs86pKGwCIiACIiACIiACIhANRCQBkc1QFYVIiACmeeee653796vv/76X3/99d1332G/0KpVK9fgWGyxxbjbv3//8ePHr7322qHOAn467rnnnr59+z799NNkXG211Ro2nCOZvf/++7FtYcUewsVpxbfffkuaMJLw22+/PWDAgNtvv/21115jKb7CCitYAhpw/fXXU8WPP/7IXc6N++qrr1ZdddV5553XEjz//POPPvooBVLdbbfdNmLECLIvv/zydte+33nnHZp35513fvDBBwsvvHCTJk38Lt0cNGgQJQ8cOPCzzz6jnPnmmy8seb311vv888+pgk4tu+yynpGAVU2CeeaZx9v5ww8/5AIV5p05c+Zdd93Vr1+/YcOG/fbbb2hV0OxcZS6zzDLUxQPacMMNrRAeEz26++67v/76a/Qyll56aeKnTJmCug2PBjUQS/bRRx9RC81eYoklvIUrrbRS1mdtWfQtAiIgAiIgAmVEgGGuZ8+eHTt2tGG0OlteU1U//PDD11577VZbbeUzlrDXTAYOPvjg2bNnr7nmmmG8wiJQQgRYbOgjAiIgAsUmcP7557PyZ6nMcpoV8sknn0yNDJPEdOrUCZEBq2vCfA444ABvDBINhlgit9hiC6QDBLgkFwkuu+wyLt977z1P/O677xJz1VVXeYwFHnjgAeKpdJNNNiHABzmI3bIG7Ljjjog81l9/fWshyajXEpx55pmk32uvvVZcccU11lhjbu6lWed7FX369GGFj7Cmffv23CWMpMPuIt3YeuutiURIsfLKKxNAdoOEIiz5jDPOsDKZx3iZYQLrbIWgwrxIJayn66yzDg2jfAgj5iCNdScs89BDD7V+8W2FIPEhS4sWLTbaaCNr25AhQ7g1ZswYLq+55hqvC7kMMfiIJcZaSPmEsz5rz6WACIiACIiACJQsATYG8CrlzQvHTY8sUqAGqw57xDSMwX3kyJFhpIfZleEuczCPUUAESo2AnIyWkLBJTRGBWkwAdz+ffPIJHWTgZAUeeqP84osvnn322TfffPONN95YffXVWTB/+OGHhuKKK65ATQCRwYsvvoj8gpUzt6688kru4u6Bb9bYltLDFu+RBFif4+OD2lHf4JgPlCxYpf/555+ehqX7I4888tZbb6GLcfjhh48bN45Fvt8lgOCD6Q6DPXocCEoY1ydOnEj8l19+SZM23XRT8r7wwguvvPIKXjOIQYuEu9SIw5EHH3wQ/RF6ccwxx0yYMAElkbBkstBHFCj23HPPMD5rOAFUmJ6DaZh/oL4BMeo999xzad5jjz0WpvHwU089tcsuu6DeAiUix44de/nll2+22WZ0lsfx/vvvI+k477zzKmVvkvCsvV4FREAEREAERKAECWBuyayjRhpWg1WH/WWjiLnBWmutFUYqLAJlREACjjJ6WGqqCNROAvgcRdGAvqFGsfPOOxMwUQgnjyJQQPuAD5GYjRx55JFYQ5hQo2XLlihEPP744yaqQFuSMGogbn7isO644w4mK/PPPz8xLNcpjdNbsIXxBMSgvsEl+hfnnHPOggsuGMpNiD/11FPJXr9+fTQyunbtSsMY+4lHN+SPP/5AoLDIIotwSS+4i8GL3cXLJiIGVE64hZnJYYcdRgBDFb79gyQFVU/0PBdYYAGPzBXIBSpMj6kIHLbffvvOnTsTj0UJEybEQ/vss0+YzMM0j/Z36NDBDE8Qx8DzoosuWnTRRUkDEFAguzGzIM+lgAiIgAiIgAjUPgKMgOFZb9XZwRqsOtJNNG3dZDVyS5ciUBYEJOAoi8ekRopAbSYQGnlyogpdxYUE3ygsILZACeKsfz6oEmAEO23atO+//54EKGtMmjQJJQjCL7/8MpFZ9SBYnH/66ad4lGBTAuWLjz/+mPSUzLd9QuECggyMTX766Sdq+ed+Jkxg0hZ0N7hLsXxT8j8NPAvvFcS4FKNevXpPPvnkLbfcQr0k41ZEFQKPFUSm/OQCFWanYSgKrrLKKmEkXkXCyzAcaQCqH9wNsyMSQuITZlFYBERABESgVhJgjY1+JfJxs1Lk8G8bjuns9OnTGf5unfvZcsstGTu6dOmCEN85cIcEo0aNQk+THQgsJS+++OJwyKNwDoZnnwCjVMZZwoxWnh2D02OPPRatAbYK0CvE45XfspJRjcShFcL6wYMH+y0PoHp50EEHsVuANeu+++4bnrBu2WkqCTAXxfoSj1qeMQwwWKOSSZepgr5cffXVfpdhPVf25H55CQToL1WwowABdheYk/hUpOBV+/NiWnL66afvv//+YUssjGsz9FvNRJcNJLRrLd6IUYJdYuJ69tlnI/JgbnDIIYcw74oUhe4nDx1DV5RwqYjLSAJdikA1E5CAo5qBqzoREIEkAqF7UXxYkJQhlomLf5i7MDFiPsGtXXfdFa0KM77AxgQtCWZF8dKZY5EFM1pkDUhD8K8RTxPGYMPCpQ/t4S3Cpqxhd2khIgwEMd48dn6oi7kLKZmNoRiCzQvTOOQvGMhEiqrKZQgqLMcaBoowMn24itnTV6SUIiACIiACpUagR48eWCmyDkd4ga4iq27WxtZI1BUxt8T4ESdWyBHwwvDqq68yCrsoARfdJMCvE+NIu3bt2Jy46aabwtO1TjjhBBQGcVy93377sSFB+JRTTrHCWRLvvffeaAtuvvnmeI/CmJRL240ggZWM+AOLS5brKCpGuGGMue2222JoiUdt5COUg3iCQiyZZT/qqKPoAqt0LrEkRdUxUgiXbGbQLwKMsOx2uLtxYo477rhc2RP6FakC72PIjJgzIOXB0BUBCrgsTcGrtufFtGf33Xdnf8UsZ8P2YLvK/ARpBXtFyCaghyNVdndIY8QowdIj1MDdGHMnHivpw2dKAl4A1EsRSCEXa9OmDRbH6JCaHm5YncIiUK0E+BXTRwREQASqgQCTHqYOmHt4XaFnSot86aWXSIOjCi4ZRwkzofH08QCbOWzIcAYKTkCx9YgnYN6DsiVzHfZk7C7u0CmWGQaX1gA8fYYZkVDgdIN9JyLNu9jkyZM9ARs7ZMcPCDGnnXYaYcQcfjcMHHHEEdwlvUUyV+CSSZJdVui3LEyQDCqslDkTtWRFQbLkMknQvXt3srMFF5ZpYeYr3GL667ewXiEm7mSUBPFn7bkUEAEREAERKE0CSDcuueQSaxvKBdiBMhqaj2obhpDam6tsthk4+IwhgCW6pbfxBZmIXTKCox1AAhbAxCBuINytWze7S5ksvInh9DFiUB5kmLZxmUsW5NzCQZUltpIReeAcilZZZPiNxIT0bCRYJEMY4z7L9TC7zSuIYZ+DxOh7hiV4OD54We25sif3y4slgJMv6mXSYrMRrFmPPvpoYnDCZckKW7U9L+xMORLFvZuH7eFZUzvuzyySAEbBFg6nCnjjIhnmw14Iz4UYdzLKc2FTB4VWy2uzOLRd7FLfIlAjBKTBUa3iJFUmAiKQnsBSSy2F5iT7D64ii9CBCQFKlV4IOw/MCdDRIE1W+xT8evLbyraDm5mwNeHZLWATLAuzjGc2xvZUqCLBpMqzmMIIW0zEmJHqQw895HeZYCHXsD0Q9q/Y/+H8F7sbr9dzFTDArI4tLPavHBrSH/bicMORphYOfCEZzD0x+3VsT7Fj1rRpUyLx8e63pIbqKBQQAREQgVpAAHE/xgjWERQkd9hhBwQK5lTbItkAWHzxxQmjvcjKdskll2S4CTuO2MIuGcHN9xMHuhODrJ9vV9nA2tQ8gttdxnpGdlN+JJn5kHKLCWL4IHnBgIVW2aV/MzxxNjlGMQz0Fok+AqMz6iShsy1UPOwuRiicTG92pl5IhYFc2ZP7FRZrzrmAZrMRpgeY0pAgHHDD9B6uStUIJuBsj8wLtIC51nKf7hCz5xVJZs8XwxMvhBmFp0FrlVkByrNsNVkkOjjNmzfnsTL18mQKiEA1E2hYzfWpOhEQgTpLALsJPkgTMNlghmHSgWQauPxEhZK5DlIDNCCYSbBQDzMy/WKuwNYK8xU2i+KlYRFKpffccw+TLSZPDNXIIEjGsawca2IDPBKNAw88kFqw0TX3oieddFJYFFonbEfgigJDGDZhKNPqYibHbhWeQRBesNOFPi3eTKnILGgQgjDBOvHEE5kNIIXB6BfPnWiUMMvhYNqw/MKG0Sim/RBmU4sZCX5DOSYm0qNcNSIkuuGGG3B9yvkpAGEfjJ00DlWBIaw23nhjNnM4NBe1ZDbcOC43VzlznnQln3WuohQvAiIgAiJQPQQYvIYOHcrvPPoXjLkMBNQbrlRt0LTGsA2AMw4GTfb28f9tkWECDDGINDmFOXhiMEUyYinNCtXiiWE3gqIYTNkhMM8UYb0k8Cosu39jIkrYpQAWzyUqCTjOMNE8kd4wRCScdxaRnnhpuQK5slfYLy8w3k7GViYw7rTLU0YCVak6FzSqQPaBDRETBoZyplV4BjGz30jtBgrBlsd7e4ix7mM3ZMIaS8OEB6kTJi3oj3guBUSgOglIwFGdtFWXCNRpAuxXIFPgTBPkCGwTMaDaVkyoK2GAPGannXZCgoCYw7Z9kFBgOmvHkVhKhA7otSJ3YAOBHaE4XzQaGMIRLtiuFOq1mFSgBkKxbGugfEsWdlRwKUoVTKqYbaB+adtHXhobR1ijmGdT/JPRBZui0X6UP7GqxfMZUzGcgCIC4Axam1KQZcaMGdi1ItHA6pijSchFUbTWBRzeU6/LA3E48cTxGLKzCYPtNFotdhYvBskILJiGcqvCMpFKDBw4EJUNJEF8oIFtLSIPm9BQILa4yJj4cOoKQpNrr73W2hApOf6svV8KiIAIiIAIlCYBhjNk4riawpkC31mHmLDlNuxibxJGeji8y4qXYYIR2e9i5sDwZOqQGDggDWHkQhGDjwtBPHFCAFsP7tpBaZ7MLu2WR3qgUuV7Lg+E2ZP75VkIkJLv0Fk4eBlzczUyzOvh/Kr27GEA+PjL6N27N/IsVFOZmWCiwpQmTEPYmseYHom3S7vLq8LT9ATsPBGu8OXx9AqIQMEJSMBRcKQqUAREICcBpAwmaLAUqF1EXH6i3BiJQUuCD9qkzAN8KyasgK0YLrPap1gyNiX44GGLEmwMdo1Q9p1Iw6wLn+G41SANQ358VEZsgS4DFhlILho3bhzWjoIu+iPYg7DLgR+QcBKA9gS6KmyCsT/jxaJVYdmxX+UTFhUJ44CNj0VSbwRLHFSYHYUUXJoDjblUuIWSXKaVgNCHiQ6iGUxsUDoFmpfMJaINdtiY0+BCn1sIquxu/FFGnrUXooAIiIAIiEAJEmCYQ7qBoQdDgC2kGaSQj4dNjWhVoNKI+DsUW4QJuEteO4McIxRsGXCxyWARFmhhRP9ISRCvo3tIDOao2FrGk2WNYeQlPuLG2040s1tZcxUqMrlfYS00BgI0DMUNi0e5EjWWvBuZvuqwGWEYWRInuRCD+gxbMsg7mOcwXQnTYG/CJeqoduY94fAR0wZi2Pjx6QqX+ohAjROIWrLVeIPUABEQARGIE0B3I6t0g5RIK7gb2q3EsxPDbCncYYin4fAUjlONSzc8JaN+RLrht1AkIW8o3fBbSAGSi/WUhQ0wPWXmEUo3KlU+Kic0O5RuWHaKhTZnxcVvVap8JRYBERABESgpAqaliJ6jqwm4/Yi3k8NcfX3L+hzDTHQ9TIPP0oQHxyKwINJ0NNgnIIyOgBfFah9BPF6iiMEihgEUFxt2N16v54oH2ABgdEZwYO0nAboSeO5E7GLL73iWAsYk9yusiHNzuTRPHBZvYYsPU6YMp686a4G4R0V9w26xsYG2JmGeaSQx5rfEPPHEEx5vciu7ZFLEzgfTMHf7xaYOJr1+SA0SHOQmftcLUUAEikpAGhxFxavCRUAEikgA/5doXqBQgBeMIlajokVABERABESgthPANyeSa1xWsRmAIBsZAWd/0unQZRULYKQSuG/AzwKWntyNHBqKTweO8WLBzGElLOBxWWWeODAjRaURU00cf7Jmxp83I7irfqy77rroCGD2aC6rGNkr5bIKhUEqxTcWBqd0ATsLZByon4TeIlI+PbLzSe8sLLlfYaVA44RdYKJcicML+ODuBBFM165dLVnxqg6b4WG0VjnxBOxwAxdPk8cRcWVCYiyFITlgwACax04SjacLXggB4HOSDmfW2EG8OIulWFf34Gg2xB9HHnkkZrNhLoVFoKgEJOAoKl4VLgIiUEQCLVq0wPEVthK4IM2vGraMmKu56/V4IShemmOO+C3FiIAIiIAIiECtIYBcI6XLKlx15HJZhalCxGWV8UHLw1xW4cQKHRBzWYUXJzubgyxITEKXVeTCniWlyypW13jCOuuss/AYRUaEI5Qcur0kMqKeGbm0RvLNrCCNszDPntwvL5YAejGcuYa3LxyBITwiBt9YCH3cfVgxqvZ2hi2x8L333su578gj0M5AEtS2bdtjjz3WXISYSo7l5RYPjlkWHsf5oEuLnzIkGl4gPsvivtJw12UJrED79iwKiECxCdRzTbNi16TyRUAEREAEREAEREAEREAESplA6LLK24nLqtVWW42VLfv5CCPiLqtYLaOUwUlh2IxkdVllRWV1WWW3Ii6rvOr0ATQ6OQgG9RO3skmft4opE/oVKZmjVdGDwLcFpqCRW/ldpq86Xj7LQAyC8CaGo9D43TAGMyKcc7GlFEaGYTqFlkfEmhh3KpRfWcexYbEKi0AeBKTBkQc0ZREBERABERABERABERCBWkigQgef5rIqoecRR5VhSnNZFcZ4mOUxvp/8Mo8Aep155CpIloR+RcrneDJERZHIqlymrzpeC5KgBJlFmB4FHz5hTCSMXCkSwyUKIAkvQzy9YkSgIATkZLQgGFWICIiACIiACIiACIiACIiACIiACIhATRKQBkdN0lfdIiACIiACIiACIiACIlDiBOSyqsQfkJonAiLgBOSDw1EoIAIiIAIiIAIiIAIiIAIiIAIiIAIiUK4EZKJSrk9O7RYBERABERABERABERABERABERABEXACEnA4CgVEQAREQAREQAREQAREQAREQAREQATKlYAEHOX65NRuERABERABERABERABERABERABERABJyABh6NQQAREQAREQAREQAREQAREQAREQAREoFwJSMBRrk9O7RYBERABERABERABERABERABERABEXACJSrgGD9+/KhRo3755RdvqAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIikItAyQk4Pv/886233rpZs2ZrrbXWsssu+9RTTzVu3Pjss8/O1YFqiP/2x8zAdzK3D8v0eynzwoeZmb9VQ505q/jl98zwMZk7X8r0HpZ5/O3M11NyptQNERABERABERABERABERABERABEag7BBqWVFenTJmy5ZZbItd4/fXXV1555Yceeqhr167Tpk1r06ZNjbTzr78yT7ybGf7RnMrrzf1/5NeZ/32QOahdZvVmNdCiTybOEW388sfcxmQyH43PvDgm0651psv6mfolJ6qqAT6qUgREQAREQAREQAREQAREQAREoM4SqPcXi/iS+Rx00EHPP//86NGjF110UWtUu3btXn311U8++aRVq1bV38whIzNDPohWi6Sjfr3MSdtnWjSO3irq9fgfM9cOycz+MxN/YFuvltl5vaJWrsJFQAREQAREQAREQAREQAREQAREoKQJlNC+/4QJE/r379+9e3eXbkBupZVWWnjhhVu2bGkU77rrro033nijjTYiUGyuU3/OPDsqSyXIF/78KzPw7Sy3ihr15LvZpRtUOmxMZsqMolauwkVABERABERABERABERABERABESgpAmUkInKs88+O3v27E6dOoXAJk+evO6669arN8dA5JlnnjnnnHMGDx7MZefOnZdZZpmOHTuGiSNhUk6cODESmf5ySv3Ws+dplzU9Mo5xk/66vd/9DTO/Zk1Q8MjZmYYfNdo/Uy+7QAotnD6PvrHU7NEFr1cFGoENNtigpuyk9AhEQAREQAREQAREQAREQAREQATSECghAcdXX31Fi3Ev6u3+9ddfR4wYcfDBB1vMHXfcccIJJ9g6k0Dv3r2TBRyXX375yy+/7KVVNrDpHpdstGt2AcecourVO+fia77/4p3KFptf+sWarHLQVQfmyvvXX38Ofva1l/p3y5VA8VUk0LNnTwk4qshQ2UVABERABERABERABERABESgqARKSMCxyCKL0FUOiG3durX1+Zprrpk6dep66/3tXmLkyJFHHnmk3VpnnXVuvfXWZDQdOnQIxSXJieN3F1ixRTwyjNm2fds/f/m7qWF8McL1Gi2cUGy9evVbrdS06T77JKTRraoQWH311auSXXlFQAREQAREQAREQAREQAREQASKTaCEBBxYAdDb8847r1+/fg0aNEB+wbY5MS7gmD59+kILLWRECHC6SjKd888/PzlB8l2cel45OHsSDGYWXzBzfb9e2W8XJ/bypzLfTc3iYdRqO/uEvVZcaq/i1KxSRUAEREAEREAEREAEREAEREAERKDUCWT36VAjrW7bti2GJw8++CCqHMgvhg4dinHK/PPPv9pqq1l7iJw5c6aFCbiwo0itbbp4Zq3lspeND45Oa2e/VbxYaqTerJ/Wy2ZWXCrrHUWKgAiIgAiIgAiIgAiIgAiIgAiIQJ0gUEICDnjfeOONH3/88ZAhQzgXFhehX3zxBY4P0OawR4GZACfIWnjUqFFrrrlmsR/Rvptmmi/xn0rmODvNZDqsntlgxf/EV8NFm+aZjm3m1GNt8BqbLJY5cDO/UkAEREAEREAEREAEREAEREAEREAE6iKBen9xAkepfpo0abLrrru6r43HHnvs1FNPHTZsWP369du3b3/FFVfsvvvuxW77rNmZlz/JvPpJZvLcc1hXaZLZatXMak2LXW3O8sdOzLzwUWbM+DnaHJjJbNYqs8UqmUYlZGmUs+W6IQIiIAIiIAIiIAIiIAIiIAIiIALFI1C6K+MJEyZwyKs74ABBly5dOGllhx12IIwxSzVIN6ioYYNM+9Uyqyw7xx9H44Uyx2xdvGeRquRWy2b474qnMxN+mqO4IcuUVNSUSAREQAREQAREQAREQAREQAREoLYTKF0Bx6xZsy699NLtt98+fAQnz/2EMQqLgAiIgAiIgAiIgAiIgAiIgAiIgAiIQOkKOJo3b37WWWfpCYmACIiACIiACIiACIiACIiACIiACIhAhQRKy8lohc1VAhEQAREQAREQAREQAREQAREQAREQARGIE5CAI85EMSIgAiIgAiIgAiIgAiIgAiIgAiIgAmVGQAKOMntgaq4IiIAIiIAIiIAIiIAIiIAIiIAIiECcgAQccSaKEQEREAEREAEREAEREAEREAEREAERKDMCEnCU2QNTc0VABERABERABERABERABERABERABOIEJOCIM1GMCIiACIiACIiACIiACIiACIiACIhAmRGQgKPMHpiaKwIiIAIiIAIiIAIiIAIiIAIiIAIiECcgAUeciWJEQAREQAREQAREQAREQAREQAREQATKjIAEHGX2wNRcERABERABERABERABERABERABERCBOAEJOOJMFCMCIiACIiACIiACIiACIiACIiACIlBmBCTgKLMHpuaKgAiIgAiIgAiIgAiIgAiIgAiIgAjECUjAEWeiGBEQAREQAREQAREQAREQAREQAREQgTIjIAFHmT0wNVcEREAEREAEREAEREAEREAEREAERCBOQAKOOBPFiIAIiIAIiIAIiIAIiIAIiIAIiIAIlBkBCTjK7IGpuSIgAiIgAiIgAiIgAiIgAiIgAiIgAnECEnDEmShGBERABERABERABERABERABERABESgzAhIwFFmD0zNFQEREAEREAEREAEREAEREAEREAERiBOQgCPORDEiIAIiIAIiIAIiIAIiIAIiIAIiIAJlRkACjjJ7YGquCIiACIiACIiACIiACIiACIiACIhAnIAEHHEmihEBERABERABERABERABERABERABESgzAhJwlNkDU3NFQAREQAREQAREQAREQAREQAREQATiBCTgiDNRjAiIgAiIgAiIgAiIgAiIgAiIgAiIQJkRkICjzB6YmisCIiACIiACIiACIiACIiACIiACIhAnIAFHnIliREAEREAEREAEREAEREAEREAEREAEyoyABBxl9sDUXBEQAREQAREQAREQAREQAREQAREQgTgBCTjiTBQjAiIgAiIgAiIgAiIgAiIgAiIgAiJQZgQk4CizB6bmioAIiIAIiIAIiIAIiIAIiIAIiIAIxAk0jEcpxgn8+kdm9DeZTydlvp4yJ+6nnzO3vZBptnhm1aaZlZfO1K/nCRUQAREQAREQAREQAREQAREQAREQARGoSQIScGSnP/O3zNBRmdfGZn6f/W+C2X9mxkyY89/zH2aWWDCz3ZqZjVaWmONfPgqJQMEJ/Prrr2PGjFliiSVatGhR8MKtwHHjxk2bNq1169YLLLBAkapQsTVI4Isvvvjpp59WX331Ro0aFaMZM2fOHDt27KKLLrriiisWo/xaX2axH5D+wGv3K1Ts90d/4LX7/VHvREAEaiUBmahkeayfTcpc9mTmxTH/kW5E0v0wM/PAG5mbn8vM+DVyR5ciIAKFITB79uyjjjpq++23f+ONNyIl/vbbb99++20kMr/L0aNH77DDDocccsisWbPyK0G5SpYAb84WW2zRvXv3P/74I9LIiRMn/vzzz5HI5EteyK+++iqSq379+j169Nh8881ffvnl5Oy6GyeQ6wHpDzzOSjFxArneH1LqDzyOSzEiIAIiUEcISMARfdBvjpsrtvgtGp/1GlHItUMyk6ZlvalIERCBKhG4+OKLhwwZcuaZZ+65555e0PPPP488YuWVV15vvfXYlj/iiCPYn/e7uQJ//vknq9C15n522223MNlOO+10/vnnDx8+/JxzzgnjFS53AojADjzwwMaNGw8YMGDBBRe07vzyyy8nn3xymzZt1l57bd6iDh06PProoxX2dNSoUXvsscfyyy+/4YYboqnRtm3bJ5980nLNP//89957b9OmTQ8++GA2kyssSgmcQNYHpD9w56NAMoGs74/+wJOh6a4IiIAI1AUCEnD85ymPnZh54PXMn3/9J5KLRg0yKy6Vab5ENJ5rVDluH575+fcstxQlAiKQN4HPPvvs9ttvRyLBctQLGTx48EEHHfTuu+9isbLllluyLT9o0CAEFj/88IOniQdQzTjuuOP69es3derUSZMmTZky16dOkO6YY47ZYIMN7rrrrg8//DCIVrC8CVx22WUIv3r27LnMMstYT1C+2G+//e6///7p06e3a9dulVVWQXJx7LHHIqFI6Or777+/4447oqCBrGTjjTdu2LAhL+fhhx/ev39/y7XkkkteeeWVM2bMuOSSSxLK0a0IgfgD0h94BJEuEwjE3x/9gSfg0i0REAERqDsEJOD491lP+yVz58tZpBukaLxw5qTtMge1+zdxGJo8PdN/RBihsAiIQFUJsC7FIuDCCy+sV+9vd76mhYFQg8gRI0Y89NBD77zzzrrrrotUom/fvrnqw4sHW+uPPfYY2++9e/fOlYwy//rrr0svvTRXAsWXFwHeikceeWTTTTft2LGjt/zhhx/mzVlppZXQbEdxA7WdW265hRfsvPPOw9Lek0UC3bp1Y1sYydpbb72FQG3kyJH77LMPaS644AJeUUu81VZbtW/fHrUOpG+R7LrMSiD+gPQHnhWUIrMSiL8/JNMfeFZWihQBERCBukag5AQc7LVed91166+//gorrNClS5evv/6aaeWdd95ZDQ/m6ffyV8QY/W3mw8I4BKiGjqoKEagmAr///jurQXTO8eLpVbLJhg4FW+geY4Eff/zRdSu4+7///W+NNdbYbLPNPBnLUcyq+XE4+uijLXLhhRe+9tprCT/wwAOIJzylB1h/7rvvvs8+++yqq67K4hMZh9+KBNDg4PPCCy94GyIJdFkjBL777jseCouZsHaeEZ+IzxQkWUS6nAL5Ba8EPlzCjLwnXCI7W3rppS1+9913RwLCO4nkIkzpYcpEZaNJkybkmmeeeYhHj+PEE08kwFuNKoentNcSqYrH1PpA3n/gkIk/IP2B1/oXJt5B/YHHmShGBERABESgigRKS8DBBg5CDTbTOnXqxOYYW7VMPe+55564f7gqdjuefeovmf/7PB5diRiOVtFHBETACdx2220tW7bcdtttMQrAFqBz5862TP2///s/fGdge/L55//+ybGIRQaB1Yk51EAmwl/9zjvv7KURQE7BNy4zwkiKYkP+m2+++eijj8J4Czdo0AApCd46Bg4c6HYK8WQWQ8n8BFktudIovtoIfPnll/z+4ywDERXKEWuuuaYbgOARlueOcxZvDJIsfGQQ2adPH4vEewt+N7beemtPg6gClZ9FFlkEt6MeScDeqKFDh4aRHkacwa0XX3wRMYdH4rxjscUW4xKpnEdi87L44otTr8fU7kBV/sAhE39A+gOv3S9MpHf6A48A0aUIiIAIiEChCJSWgIPNWKaSbOPgXBCtctYkuFKnq6xPCtXhXOW892V245Rc6ePxOBz9MaeOczy5YkSgNhPAJQGeO1kTshY95ZRT+BNGvZ8/auQXSDHYV0fn//jjj0egAIXJkyfblvhNN91k68ZXXnmF+HB1yiXqG3zHz4u1GHYCuRv/8GPyxBNPsPKM34rEWHU6CyOCpUYuEUZst912yCPweXHqqafuvffe6Gv06tXL5BfYlSCnwHEGcjFr3g033IDgjMS8VMTwMnz66aeo/8w777zefnt/lltuOcReHkkg+f2xlJwCG2bBK4dJ4sKjYXnbOUsFWVtdcDVaxT/whAekP/DwTautYf2B19Ynq36JgAiIQCkQKCEBB0rCuIxi5YMKuqFhGorLemaNbPYSwwSXaSUKHSxXCs5ubPbFUeXqKUghlatSqUWg9AjgyJMl6HzzzceW7OWXX37GGWc8/fTTu+66K+YD/AnT3rPPPpvNdkQeN998M5dIQL7//ns8feLIwHozfvx4ApGlDsUSyY66pfFvXDwStrse6QE8LDRq1MgvEwLNmzfn7oQJExLS6Fb1ELjxxhuRIHTt2pVf+9NOO41LLBqQVrz66qs0ACHFVVddRQAHtCRDDnL11VcjGrv11ltNeJH1/eEdI8sSS0SdRSe/P1n7y7EsxDM8uamLJbM3tta/QvoDz/pWKDI9Af2Bp2ellCIgAiIgApUl8K/ObWVzFjw9e3EchYA2clgyaupomNsuHHt3zGIXWmghDO932WWXMFnWMBNfLISz3opHfjuFWe/fNBrUz8w/x9T6388Cc9dH+Dpc6N/twL/vzggOlB03fuYKC1WHFsfsWXNa++OPPyz416x/W6lQ0Qjw1i2wwAJFK762FYyBCQoarE7R5Pe+XX/99fhEMIyyVqJnAABAAElEQVT8RaPfzhY9+8Dsq+NugzM7kXp4YjZ4OX3TtDk80o5KiWy/c9e8kCYfpOKFJASoEUWPXJogCRl1q+AE7BBWRBvuYpaBAP+dLt5CXsZrhqNZZBxYJ2GiggJgs2bNrCX2EDm6NWxYod4fKuXAnfr168fPFbYaa/0rVJA/cB5NkR5Q+NDDsP7AQxo1G9YfeM3yV+0iIAIiULsJlJCAw+zz2dcNibPHi/W1xaDcgRc3ZpYJJyaEeTHgT69tfnTvH+ddcI5NNZ+1lsscvLkF//PdeKHMJXv8J4aLUwb8a9vS9+4H9+1zWDRFEa736/n+Ui3adO6848RPXytC8SoySgD/gj169IjG6joHAVPRb9WqVXif1UV4iVcODi5BuQPBJb4SOBHWPDhaGuQjocsDizR5h1m1hEWZe9GIEUGYIH2YSqk6fXqlLAYBLBNRweCtwLVnWL5LNywSjT8OQ3nmmWe4xBc1Tl48sT3EyCtUkPcHC0oUjqgIL1GbbLKJ12gBq7HWv0IF+QOHWDEeUOSJRC71Bx4BUiOX+gOvEeyqVAREQATqDoESEnDYKgVDFYyr7QE89dRTTKQ4BtIufSsv5eNhr5iN95SJM5k5vgDsM/vPzG9//HMx9190Nxo1zHBKw+8xhYnw5IaGDetXpsb/VFGpiwb159gWLTD//NVTXaXaVisTp7RxqJV9z6NTtoONiUpyXpyPIjmaOnUqx5dwalKYGE0KXNAhy2Cf3OPNHCCuqWGOHiv0IerlJAQwoilIOQlV6FaFBPDJwnBQ4fvDrx9eXRCTUaCd2+olm8sVP1HF4qv+/jAkIWRHr/Dcc89F4O7VecBqTOPwxbOUY6Agf+B0vOAPqEKY+gOvEFE1JNAfeDVArn1V2BohYSHAhAGtbWZr4bSh4Bz4/UdhsMLhqeD1qkAREIFKESghAQc7ujSdY/xQOSYwevToI444gkDeHkYr5c3+sicz3037G90H32TOeOjvsP3TZLHMGZ0zU2ZkLhn0n/jIxfFHHfzMrQdHIotxecXTmQk/ZZ57/vkVlypG8SpTBKpEwDbezeVBQkGXXnop0g0ScEQFHhY4sNMTUwLaW5j6L7vssh5pogdzr+CRBL799lu+I94QwgQpwxi1IWCNaA2kzKtkBSTAo2SGOmPGDFQhIoo/YS0cIo7dk8WgVYGhk7sUtYdoL4ZnsTcEBxlMlMNZcsr3B5+mZjKDpeQBBxzgxYYBK6rWv0LWwSr+gcMt8oD0Bx6+S7U4rD/wcnm4iAw47OyDDz5AcICRILP0iNZVtXWEBrRt25bqRowYkWvDiTk/Ts1xSs0PdTEaxqHgnOTFSQg4BBw2bFhE37wYNapMERCBvAn8uzuadxGFysiJgNjhM0/FMp/wNttss+mmmzLNXWeddQpVRUI5iDCq/mm6eNXLUAkiUPYE7GgJsx3wzjAnePPNN/0S/6O44cBpAv4aWW1y5Ge42rESxo4d6+kJcPwK3/grDSPHjRs3ZsyYpZZaquqzDasuPBcjrEjhaiOAsRJuRNFj90NSrOpRo0aZRIxLXieUKbjEVKRDhw644UAbyFtIdibikfeHZRUvCRpAr7/+uqckMHjwYL7dwW14y8Oc0sK8GQFKv379ckk3SEyNvMwR57heSK0JVP0PPOsD0h94rXlDkjuiP/BkPiVyl3Op8KOMWAFttRNOOIFTxvj9jIy/1dZU9CZw18WHQHKlDA3JCfK+izktsxocBTIEuKZ53qXFMyI0wQY/Hq8YERCBPAiUkICDueNLL73E5hi/oQg4Ro4ciQ5Y69atscTOo2OVzbLqf2y9K5t7TnrMWFotk09G5RGBWkZg2223xRMwXnVCiQZOTHbaaScbv5mmcC4s4ksOW2F1ykp12rRpTKFMBxUalMC3rTwdDqJPjsDgHI1HHnnEIlkDm2+UPffc052P3n///UzIUADxjCkDVh2i1ZTplax4BPbYY467o/vuu89nq8gOcCzKJPvXX3/l1hVXXMEjRirBy3PNNdfwviEvs9OFucuowbycCXpEy8AsWTjAePr06dZ4fGoMGjSILcEuXbpYDOe8du/eHfUi9/ZCeqQnOHl5+OGHGZssWfwbaQu7i9hb1XoTlar/gWd9QPoDj79UtTVGf+Al/mTxAosvf34M+WPnqHV+bxlkGaYPPfRQTsCp/sbzi8ECgdG/elYEWTvI+ILcnEUKH0S0WdNUJZIzIvFKVpUSlFcERMAJlJCJCm1CJmr+26x9HP7nR8Z6i4sUaNMi89hbmd9n51/86k0zC1XgcyD/wpVTBMqIAAs8lohsrbNo3HHHHZFUvv3225988slKK63EGpV1IyfCspHOH7u5aWRQx76AuQvHfB577LH0lFUih3cyx2Jq5Tqx7Pvh+6Bbt26IQvr374+ePEtcXHWwYX7kkUcaHxRZTz/9dL5ZwdpZnlSNeojdtbXxxx9/bAIUIlmv2m8OrWKhy0+Q6cFaen3XFAFeAx4fGhzImxgFcPrA64EB0VlnncXrhE1Tr1698DnKNxoTvAkXXXSRvRgILMzdLE+WCTHP9LDD/nX8jIADK0jOKm7Xrh3vHt5bEEmwJYiYzI/sQUCGYIWOd+rUCQ9QeG1AdMIlycKDfowMIng76phLPHSQhlx2qxZ/V/0PHDjxB6Q/8Fr8zkS6pj/wCJCSumTnAKkuniaQZey9997WNhwe4TYL/QWW9wg7qt8Qj/lDzVJiXlHUBoC9qOWrcBGoUwRKS8ARoscAmy27ww8/3CNfe+213XbbDatsPljm8wvL7NbvVjHAQbDtWmde+Cj/YrZZI/+8yikCtYwAIgwkFMg4Hn/8cbrGrgsyBfxBsoxEa8M2utH5t17jZwETAI494lwMlj0owKPcwXSKidTdd98dLlCZYLEKYjudEsiL7GPjjTdm/emTLWLQ8kBDxA9VYVWMOlgEr8e4BRzLaXarjj/+eMqPJNZl9RNAI+O5557DHxNau5if0ABECXvttRf7hwwNxx13HMo+vDPmVoO7vBhPPPEE0g1euf9n7zzApCi2NlxDzjnHBclIUBAkSVBEwcA1/ASzYhYxIaJy9ap4FdPFnAMmUARzAokigiggSJacc5Ac+n97a7antyfs9OzMbu/MqYdnqa46lb6q3u3++oTnnnuOEsi1J554gr8RVFmOPDgVHEgOJ9/iPv/8c8QwbmLTUQMhr5PlZVYfIY6iLg95kCwtD559mQ/nnD9MGT0l8//ZvMGBJuQGyQ2ezIfGtja5wW1geC7L9wMsRtHgsNgNPUXYf35b8neZ33X8dqWQQO/85EEdXhjeGdf+qGRefvnldidH+NLi7zi0Mr8ksUMnfrzDp7h9/dgP8reY8AI8P8AvMwHrlzzj8vuWDxiWPNapCPPXvHbt2j169LDK7RlsavgjwvcVZDp16sTvZ/vc7JLk+dDC3x0+gdSsWRMbeX4d6d//LA22necKNAqhfpDkT4b11GHvZNy4cURvxFtH8+bNUTA844wztMcQmtMJDHvjxo21PHQGTzIMBHpU8TlHP9Xo/nn+4UEIZp/PM3D6AB7O84h9dMkLAoJAAAEeE72ZcDLK7xd+2VnT4zcLCnJWguawquKSOXjE+M94Y9AHIf498bU5wra9Iaq0/OjANOMylyw6YT6Mu3JrFmJSLQjkOgKwBrgo48u225nwHqu9mvGZPbgtriJ5YOKdM7gKgwUeiXjuCa4KV4K6hx4LK4NwMlKeKwjwe57zoz2Dup0AuhWcBB6LgxvycIlHD9R/gqsYkedvnq2DqyKUvPjii4zF2Ysgk5RVMd/goBFhg+QGT8rTErwoucGDMcn1Et7e+W2Gn6zgmeBzlCpMBXUVr+tQz3x65KMjVhtUkdCzsxpiJ8jfVgqRbNasGRkUMXjbtwTsGWwAzfbpzpKqVatGBkKE3//WWHRiyf/000/8okYGokFnME3lEtZAy8CGYAmrBdAJRdOTPOQLPIvViZVBGF1OBEisCJqbDCQsbxzI8G0mvSbwg8cPq63O8JADK6ElWKPOMAFdy6wogW2xWtEzJWj8URLcPywJ5egS6n6w9rUaSkYQEASiQYDQp5ICCGzYaQweHYLFuPNDY9hY44FPQ1RBNDz7nXH0WKCTHMgJwZEDIMsQuY4ATwM8ZxBdJeQTScjp4XOBpyi+nITkPkI24bmEDzU8RvDtJaSAFOZRBLBI4vGRx1++4EW/BBRAOAyE9Ym+CeYzPNxjTYM9S/StRDKGDZIbXI6NhUAM54e2coNbAIbLnHPOOfwOhMsIFsBuBaWDk046SVfBOCCJMiav63yTwIsEf7IhO1AAQYC/rSh98BsY5kLLQ5ogwN/okL8q+cNNbyjiIcxf8Ndffx09CJhoayyL4OCRgACLCLObHAME+CXcqFEjSiyCQ9Ml6KGgB4EAM0R9AwFUPnWH9p9o9lHF3wu9aj6roKRGCdQDYiyN2MawJNAuZEjB32zeeecd5FFEhfOlCRroKLNQQs9cRiY4dP9YTSKv+9fPPHzohR5q06YNId7ss5W8ICAIZImAh5yMBrRKci9HGJQBnVWRIBX1E4bae0j9E8o+rlZ5NaCLKpA/9yYtIwsCSYoATxvowfLAEb1XszfeeINXTR41LKuELLFBGE1XnmP4DJWlsAjkIQRwXE1sV6yQUPpFuziamaPTQRRATGP4dheNPDKo/+CbgydvlIbQ0I6ylYiBQAwbJDe4nBwLgRjOj9zgFnoRMqBEbchAIZhslChRgl96UAC6B0wnCJ6KzREGeoQXQfcNbQhtV4jf7hUrVlx11VXapyzysMBQHjAOeEcKnsDatWv5Fcr7PFX8Bb/++utxocUbfrAkv6XhEeBZ+F3NMUCAbu3WK5Rosw7oDFgDLpkhfprIwJvw05GIOM7SENbmM9jSQp0Q5Y1fOLAPLA2XTwhg3kKGZLkGs/rBgxiF/KQVhfXq1SPqCjaS+Fa3ZMJldP/aUbruX5vKEnIBfmfWrFmWyW24HqRcEBAEHAh41weHY6I5dtmgirr7HPXWNLV5T9ZjtqmrLm2jCgq7kTVUIiEIxIIAOp+k6FviLZIUvTySPEWRXDUR4byCAA5ZHLF4Is8cO21X8vTGczMaIpG7ldpwCLjdILnBwyGZmuVuz4/c4NGck2gcUVky/ALUFIPuGa8TxAdADYFLTXOgsoGZiTUuShloW+DnwiqxMlThh+Kaa67BTwdECd8qgnkELQxvQsbxTYKBrK7QgEAGXRJ0vki6HPaE2cKMoD8Cp2AJo46Bkw40U0hWIYvCvzVEDP1YvrqsWkcGm0f8hkBqoOJhVcGVaLrEKpGMICAI5BgCQnCEgLpiKTW4p/pluZq8WO3cH0KAIiLCnt1M4sKGBkdKBQFBQBAQBAQBQUAQEATyHAK8lqPyBi8Af+SYPFwA5hJoGaDH4ajSl1rXAO1xLnnn5ye+w0m61vqJkx0rb2WeeuopfGTAF2vKGLoBnQsMSIPdgmICQyucZVhtHRlYDBRJNm7cGDLuO6tD+8Nqsm7dOkxvcDhtlegMjk7JoEOaJcHBcEhaobgc/cilICAI5DwCQnCExjx/PtWpoRlXZc0OtWKL2rpXHTisfD5VupiqVkY1qqrKhf7dHro3KRUEBAFBQBAQBAQBQUAQEAQ8jgB6Ljj4R6mhYcOGjqkSHwQuIErFBE12YP4ZTBBAkTh65hINCOJbocSBDgg/iS2C40/G6tChg0NYm8/gp8NRbl3qobEc1NFerHKdsetZUKKJCXx5OMR03FaiYjvKgy/1cBHmo5to3ie4uZQIAoJA3BEQgiMSpDAaaRXMf5IEAUFAEBAEBAFBQBAQBASB5EYA75hjxox57bXXgv1HUMjau3btGg0CWksCMxPcZ1rysAaERLRbiFhVZBDGyIWEcw0ipxJC9ZNPPgkmOAiJgvD333/fr18/q7mdoYBAgXQgQi0ePXSoVy2G65BgwgJFFQrRN0Hpwy68cuVKWtl1PayxHBl0PVjRokWL0BmxsyeoimAmgzChXvlJRNtevXrptujCODqRS0FAEIgjAuJkNI5gSleCgCCQEASwFFuwQU1eqsbPVaN+VW/NUK9NM3+Sp4RyasNZkyVkQtJpnkJAzo/Ht0s2yOMb5PHpyfmJ7wYRCgTXnkR4HTZsGM4sdOdoH+A1E1fK0AFEGIlmxO7du+Oq4+WXX965c6fVya233go/om067J1g+YI1Cp5BrULts9magFVOBgYEl5xEioVQ0OVMz+G49LzzztuxYwd+Q62Gc+fOJfYK3kOtEisDlYMzUaZqKVkQzGXmzJnaFYglFiFD6BlqoWMsGUgihhs9ejQl6KHwc+LEiXAoWoDJ60yEn/gKIZY2sFuziiAsVYKAIGBHQDQ47GhIXhAQBDyEAKGLlmxWy7eqfU7VUXOSR4+b/w4cUVv2qaVbzJKSRVT9SqpRFVXK/FgiKdURkPPj8RMgG+TxDfL49OT8JGiD8HkxYsSIK6+8koAjaEmgf4HHTQKaoKGAJsKTTz4ZzgGHYz681d94440vvvgifAQhWom3gnON+fPn9+jRg9ixDmFcfhAVldd+mIVOnToR1fWLL75A5rbbbnNIcknPffv2/fDDD7t160aIFrQnsGfRwV8s4SFDhqADgoEMPAXuQrG4IfYK9jV2dRJLePDgwfAjjz76KJFucdsxZ84c5ok6CSXBHkCsVvYMMVxwUI23EVDCOci8efNYLGRQx44dEUMphjwhUZhJ7969WWywN2vUQAguq9dLWFnMcL777rtHHnmE5uih4IHVPpzkBQFBIDICQnBExkdqBQFBIBcQ2HNQzVmjVmxVpqeyqBM8yB9r1dy1ql4l1bq2Kl006pYimFwIyPnx+H7KBnl8gzw+PTk/id6g1q1bT5o0iZf26dOna60E6Im2bdtCfDRq1MgaHYMOSyXBKiSjI56SQQeE9/ann34a5QguidiKmgZv75aA1ap+/fq88999991ERcUBB+XQFg8//LAVJtYxFjOBbYGPIJIrHETnzp2HDx9uVy0hqMqECRPgC+A1SIyIOgaWL+3atbMGtTJMDPWKgQMH4nzkrbfeopz5oE4CH2HJ2K1XrEIrA+fyww8/MJzmZVBdgZV48MEHiQWDDGwF3TI9VE6AQoehveGGG6zmZNBtofbTTz8lz8L5qQPcMm5IlyUISBIEBIFwCPhE8SkcNF4uf/IbtWm3GnS2quP0+uzlWcvcBIGsETh+Qv2+Vs1bp0644jaCOs7nUy1rqla1FA6DJaUOAnJ+PL7XskEe3yCPT0/OTw5vEPwFYUTwmoErinBBW7OcEi8adAINUadOnSyFUbJA2wI+Al+n9gC0IRvi0YOIs7jkiBDBZN++ffjXqFu3bjjHH/aeMVRhqiiY6BAq9qoo83v27Fm7di1waRMbeyuWhl8PfIXgTrVo0ai+wDBz+tFMh70ryQsCgkBkBITgiIyPR2uF4PDoxsi0sofA7oNqwiK1I0xs5hj6Ll9cdW+iykT1IBFD99LEWwjI+fHWfgTNRjYoCBIpcIGAnB8XYImoICAICAIpjIB83EzhzZelCwJeQmD9LvXZH/FkN1gcXAl90rOkpEdAzo/Ht1g2yOMb5PHpyfnx+AbJ9AQBQUAQ8A4CQnB4Zy9kJoJA6iKwcrv6dqHpNDTuiT7pmf4lJTECcn48vrmyQR7fII9PT86PxzdIpicICAKCgKcQEILDU9shkxEEUhEBPs1NXJxdpxsRgMOdB/0ziqSkREDOj8e3VTbI4xvk8enJ+fH4Bsn0BAFBQBDwGgJCcHhtR2Q+gkBqIYBZ9Q+LEshuaDThOBiFsSQlGQJyfjy+obJBHt8gj09Pzo/HN0imJwgIAoKABxEQgsODmyJTEgRSBQFc4uNVNBGWKcEIMgpjMaKkpEFAzo/Ht1I2yOMb5PHpyfnx+AbJ9AQBQUAQ8CYCQnB4c19kVoJASiBARNg4xkzJEjLGYkRJSYOAnB+Pb6VskMc3yOPTk/Pj8Q2S6QkCgoAg4E0EhODw5r7IrASB5Edgz0E1b11OL5MRGVdSEiAg58fjmygb5PEN8vj05Px4fINkeoKAICAIeBYBITg8uzUyMUEgyRGYsybhrjeCEcQZB+NKSgIE5Px4fBNlgzy+QR6fnpwfj2+QTE8QEAQEAc8iUMCzM5OJCQKCQBIjsPeQWrHV9foMw9i5T23YoQ4eUVXLqqrlVMECPre9MO5paapUEbftRN5DCMR2fnbsNWC47Kl8KZXP5+4IyfmxAxguH9sGyQ0eDs9UK4/t/MgNnhfPyebdxvw1qm19VaaYu1/FOb/YnxYa0xf7hy1fUg08J1ETHjfb+HONf6C0iurqLokaKOcxlBEFgZxBQAiOnMFZRhEEBIFMCCzZrDK/aWaqDXmxYLUx7heT2rASb6Yt6hgXt3dHczAuo7dJs7qRTN5DIIbzM3+l8fE050r/3U8VK+wsjHwt5ycyPro2hg2SGzwaYFNEJobzIzd4XjwbU/4yej9jTrx0UbXoWaN4YU+/yY/9VX38ix/melUgOPx5mNmlG9W8NervLeqkyqplbdWwmvJlRZ3v3m/M/tvfA/+VLKLaNfAv/8s56vM5/qrmtSA4AmKSEwQEgWgQEIIjGpRERhAQBOKMwPIt7jqcssD4/ndnE8NQ81aqHfvU1WcaxYu4eDBavlUIDieYeeva7fnZtsf4LOPBNPsrlfOTJYZA5CrJDe4KrqQXdnt+5AbPE0filreMfw6ZM31lgNJcxrQl/onjcgU9jvYN8sQ6UDZRVcv4p3rwiHHTm+qLDD5Cl17YWr06wChaKOxjyYkTxrWvqkl/BdYLkTHtYf8lCqqFC6jDxwK1khMEBAFXCIgPDldwibAgIAjEAYGd+9W+wy762bo7BLthtV+3TU3+07qKKrPvkGIOkvIoAm7Pz9FjxgeT1ZH4PSzK+Yl8cswNSn+NiSxm1coNbkEhGRBwe37kBs8rx+bbeerL381/Vmz4Nif55160kGpaI6+sQ91zvvrqXj950e95J7vBMuA7KI+QnvkmE7vhkPxvP9+YQY4yuRQEBAEXCAjB4QIsERUEBIG4ILBht7tuZmRYvdIMw9dbeqnBF6t2jQKd/LZcHT7qzuTF7RwCg0kutxFwu3ef/6q2ZBy5OpVVoXhoLrqdQ25jlqPjuwVHbvAc3R7PD+b2/MgNnltbeuy4gf5ClKPv2GfsPeCUPaelb9Iw9daNasEIVTrIB8c/hyJ1Hnn0Q0eNvQcjNXdOJabrOX8bUxb5W57RSP23n+KnTpRTG7LXaYuN/37ur6leNqSIFAoCgkC2EIjHg162JiCNBQFBIOUQ2PaPiyUfOWb8sSIg37udqlXR/HJy4elq6XpjZ3pXh4+q+StVm4YBsSxz293MIcveRCAnEXB1fuYsN37POD8liqr+ndWzGU+W2ZmznJ8I6LkCR27wCEimZpWr8yM3eHwPSa8nje37zC7v760ubO1XUnj9J+OtyWbhWSer4X3NwrcnG6OmqcUb1JHjqn4Vo3Nj9dAlqkS6oejNbxp/rDaFB3TFPZb69FdVrawqWVSNnhGIm9b9MZUvn/HpHWrzbjXwXVN4xFfq10cN7bdi9VbjgTFqzkq1ZY+qUc7o2lT17xDwT4FwhNEPHDZGfqc+/Fmt32l2W6KI0bWJGtpbNanhX4tZGr/0Zjos9FeiiBp7F+y577quRt3blbbEobZ1hoqKNeaWPcZ1r/mhuKStqllePfetVSkZQUAQiA8CQnDEB0fpRRAQBKJHYHfQZ5wIbXf/E1BnzedTdasEZOtVU7OX+S95EnKVXM3BVc8inGgEXO3dgtX+6eDxrd8ZqqT5kTD0VzVX03Y1B1c9J4GwK3DkBk+CHY/vElydH7nB4wt+pVJqxlKzy89mQXD4+x4zU+FEkwRnQbrhDeOTmWZGp2WbFP+mLlbfDzXKlfAt3eQXfnWi6XSThHeJKmXUAZuD8OWbzXIMVfYc8Aub1+kJrQfcjmqCgAJ4ivenq3Gz1dSHjHpVTJIi8ui3vBVwz4kw/Xz1h/rxTyxKjDb1MnEc380zbn4zfchQPz64TXVslEk+lJT6a52/GPsa2A0u+Nm0hjErnVW3au1tCY+yO91CtkFVNfJq9fRX9krJCwKCQHwQEIIjPjhKL4KAIBA9Av+4ccDBA5CVihdR+SE5MlKpYhk5pYJ1XwN1oXIOJyCVK1cOJSVluYnAyJEj+/btGzwDV+fnim7qq9lq1lJ19inqpKqBwxPcrasSx/lx1TbphV2BIzd40p+HcAsMd4O7Oj9yg4eDN7ZySI3xv5lNf1poGn4WLujbttf4Y5VZAkd8fis1ZVGA3WgBc1FWTVygjp8wOY7nv1MPX2pK6qTZDZ1vUVv9udbU19Dp5JqqQD5VuKD/0v7f0NEBduPUOurvzQr/o/sPq6teVjMeyWL0q7sYVvAR/JWWLqZ+XmL6A2pVVzGiI+GVKQKVtiM6Hc+NGStqUiPQPXlNcFi1gTqlujf3fXufMfAd9e7N2tNqHAh3e/+SFwQEARAQgkOOgSAgCOQ0Aq7cPe6xeQN1eE+wX9rFolmPqzlE06HI5BgCrvauQH7fv9qpU+oatSvFc4Ku5hDPgfNCX67Asd+59juahdov7WLRYOBqDtF0KDI5hoCrvZMbPL77cnZzhb9PwrHDKUxbzNu4qf5AwDJS23ooYviGfOR/IYfdmPqwSRk//rkx4ktT4J0pmQgOSm4921TfqF9VnVrH172Zcfbjphjp6yEEIjHbLl6f6fV+0Xrjt4zIqS9fh2WKb/Nu4/QHzWncdJbZkCF0Cjl6p8b+Wv577XqsP3wbdhqvTlBDLiQcuJPdxnCmXf2AvCNXvoSjIMQl5nUEcdOphC3cuJWnFhmt2WFvf9pJvl8fsxdIXhAQBOKMgBAccQY0od3Bka/doTbsUvsOmuPMWWUy03UrmrZ/kgSBPIQAJzn6xJNWNOmAG60QOnTMYcsWl3Fro5mTyCQGAcfeRTNIWmXn0200rSLIxDCHCL0lWZUrcOQGT7Ldz/5yXJ0fPZzc4NmHXfcAEQATQaAT0vfzTWZhQkaQMm2xsiLduoRaDEzu/dCkJ1ZlxIRG1QJPombL9NSzpd9hR0ZB1v9bSh9oi/Q8xZSHUlkx0oDG0o0jj55WQaHieSJ9Ci3uVSdVNlCmOKsZihIhhoZl+G5oiPLoi1BCyZ/P+Sxhb04tMpIEAUEg5xEQgiPnMY9lRPToJi9Sv63KZMQ4Y7niH7/161dRXRurxtVi6VnaCAI5jwB/9Y9FzXFglhIu6c9KutYtzVcgf7hepdzrCLg6PwlajJyfCMC62iC5wSMgmZpVrs5PgiBK5Rv8glZmJFcScUAMw5i2xI8x5aQN6c47ySzaYP5zpE0ZJhuUn1zLUZn1pdV5ofx+FQ/aWOwGeUsg5OgHj6o+7dTHv5gDQXPg6YN/RGwd84saPcjQPlDNuvSEs8/5azIugv7v2DCE0odDKl8+X5XSBh8dSVv2BiqtfJXS+FKNM7ceGEZygoAgEB4BITjCY+OZmmlL1VdzA34WHfOCql622fwHwXFZO9HmcMAjl15EAM3zY9HpZTB7zGitdOiolTUzh48FLksXD+SjyRUUgiMamDwp4+r8JGgFcn4iAOtqg+QGj4Bkala5Oj8JgiiVb/AeLVThAuafV/QpJixQOlRZ67qqejnzXb1Sab/risbVlaY87FtQo7z9ynW+cml/E9RDiPNapKCTHchy9FcG+Lo2NbBkweUHVjY6/bzUdBTS+7RM8/l1uenXI1x675aAj9VwMpRXK2dqVZMWrg1IWXlqJQkCgkCuICAER67AHu2gx46rT2ar2Sujkl+8UT37vRrQ2YzIJUkQ8DICJQtn0kWKPFU7c4EdytFjRsF0X+W0spvl2x2ORu5Q1zIHSXkUAVfnJ0FrlPMTAVhXGyQ3eAQkU7PK1flJEESpfIOXLOrrdrLx3TwT2sfG+QG2IqqcVNn0J0qCgxja209A4HkU45EB3fRlwErF3zjq/+pkeEpC/wL/oFiX4Oi008OqSXV189k4AfFlOfq81cYvy9Snd/K1z7d2u9FnpBnLlkShg+CIelKRBFH00E5DiB2zepuRVtHHT/I6UUtas914faIZL6ZPe9Upisgs/sbynyAgCGQDASE4sgFe4puOmWWapUSfdu5XL/2k7j5HlYvCPVL03YqkIBBfBMoUU1syXHNl2XOZ4miKKu1iA5uUJetVszSz0bHjxlKbfmx1lx+OmIOkPIqAq/OToDXK+YkArKsNkhs8ApKpWeXq/CQIohS/waEzNMGBHoROF2SEjL2krb8KRqPVUKNDA7Vrv/p2numKomZ5o0cLp86FtUENq5lug7UH2Tvfw5Wp8fJ1TmFinUBhaE8c171K0BaDCcCn8G/6UrX0WSPy6ASj7f20qWCCcc25LfHuGTBpqZtBnVjzaXOS+t+V1pUzQ9iXaNKAbur5782186/rI8RqMRau83vlwNLq+jPNPoaPVzqqLu5aFz9rEAmOULgPjfV3v2abP8Oqez1pckOwOU9d7kTGLyT/CQKCQHQICMERHU65ITVlcVh24/buqmoZ9cok0+eoI6GS9+ZUdec5KpUVLB2YyKXXEKhQQi2N2qcn9rdtGhhTFvgXMX6mqcRRqKCas1z9k+5tl4oSRdXJ0T2OWFAwB0l5FAFX54c14vdui80y3O7CcNkGVaig+UzJ+0y18i6eKeX8RDg8rjZIbvAISKZmlavzA0Ryg8f9nJzb0nyGREdDp5a1Ve0K/l+PF7f1vTPFwOiDxDu5JiO02LjZCvOWcKl0MdN96TdzzXoi0eKbgjgpjsRvg0f/z7jsRTNuCy5LP/g5UH/fBaYzjovbEkgl7OgdGpqu90mrt6lXJgTa8sB8UZvApc5VLeu7uouz0O01ZjuDzzee+MJsB9EzPcNfCZeDz0ef2gRt7XazlrR1rxmeBn9hSM5IB1CX6588vetCdLclCQKCQDYR8CLBsXjx4tGjR+/YsePUU0+96qqrxo4dW6tWrXbt2mVzqXmrOUaPX6frB4acdpGCZhyvcK6LiLz90yJ1TrOQTeNfCB//zyHTSXXIkObxHy9ij6g1EmIGZEoWjSgnlbmKQPUy7oZv10hNW+h3jY4qxye2hx7dUbuGmfyQRdO72zlE06fI5AwCbvdu0Vr1zW+hpzZ6mr+8VT11acfQMiFL3c4hZCfJWugWHLnBk/UkxLYut+dHbvDYcI7QCjKiSxMDBxw6WfYp+nLc3aZWwtuT/WwChbUrqFvOVjekKyz42yjTkYcjDe9jOsX/fI5JQOgnRsuZq/VNrucpvq8GG3eMUjpgCo9zLdOIF2vZv6gIo/t8vgZVjaEfK9QoLHYGzuXR/8N1iAv+2jHtyJf3XeirXs546svAF8ea5dW9F6grOvlHROsEfx8klFMcjk5D9hyMW0gxKRQEBIEICAT9+okgmyNVL7zwwp133tmsWbO6deved999U6ZM+fDDD5944olUIzhgKKIPMxG8M2h/dGmk4EESmnCtBAuDtaEOylWnourZQtWvnNAxw3aO+8kfF6qZK0yCnFSqqDqjoRlcBi1BSV5DoFxxVbJI4Nkoy+mVLu7r19kYMx2zlBCyzdPUGSeHKI9QxOjMQVIeRcDt+Yn7MuX8RIbU7QbJDR4Zz1SrdXt+4o6P3OBA+umdYRmBQgV8/7lUPXyJsWa7+X0LdgO3HdYuTBoWyFuFOpNWyfcwDS8NFHdt6tv9duBS5zo28s15HEsTY912VbNCIJyKro08+un1fZP/bap5rtxqPkWnVeTzW9j5OAeO+hqTk027jOF9/T3DZVzRCW+sxvqdqkY5jMQzjYhrknenmqYr2iUHg3RvHmLV9sHfnmyg5CJJEBAEYkbAWwTHxIkT77jjjqeeeuquu+5iSZs3b27UqNGJEydQ5Yh5hXmxIW9xc1Zla+K87c9fq9qelK1OIjf+a4N6e5o6cUKZ6t3pCUr+pYnq0jaqQ/2Mopz6H9W+53/MFKZr70E/+XJjV77t59Q8ZJyoEahfSf2RYdwbTaNmab4yxY0vZ6mNO/0GrrQqW0Lx4f3MForvNtF0YskwuqQ8jYCr84MldpYpGhmrEzk/FhThMq42iE7kBg+HZGqWuzo/0dy80chYUMsNbkERIcOfXeiDxKUyxXxlwgeajTw6nshx+ZG4hGeQA0fU8L6ZRoDXCOn/DrYCdoOvbgPPySQf4WLa4kzWLhEkpUoQEARCIhDFc1/IdgkoJNo2uhs9evTQ7AYjVKlSpVu3buPHjz/llFP0gFAeq1atqlq1alpaWgKm4JUuV27LFP8ytmkRVCVxBAdmIO9Nz8RuMEnNdIydrWqXNznsnEyjf83EblhDL9+ivv1TXeA/PlaxZHIfgUZV1Ny1AXYsmgnVrOi79TzYDWPLLpPjKF8q6zD1IbuFC2F0SXkaAVfnp21DX9t0b/ZxWbKcn2hgdLVBukO5waMBNkVkXJ0fucFT5FTk+jLrV1XtMj7gRRkQd/EG45FxqnRR9caNql4V/npElRpUCwxUTx5XosJMhASBTAh4iOD47bffFi5cOHz4cPsEixUrBpdRrpz5uty/f/+pU6c2adLkr7/+wmJlzJgxBQp4aP72aWczv2FnNjswm6Mpl7j08zJ1JJSxgB5x8mJ1RYfEDe7seds+tWC9s9C6nrbEdEfi6uuN1VYyiUOgVBFVr5JavtX1CHggr+YyYIpjDMZldEl5CIFffvllxowZgwcPtuYc8/mxeog5I+cnGuhi3iC5waOBN8lk5AZPsg1N1uXc1ct3Vy93i2tc3bf6BXdNkL6f+Lu9XbeSBoKAIGAh4CGCYObMmUzr9NNPtyZHZtmyZZZ9CpodH3zwQb58+fbv39+8efP333//mmuusQs78jfeeOPcuen+mh0Vnr9M63RHtVP7W9McdLbTkUSFkmZl39P9Abcsye8XqEUb/Fdbdh9q0+YMqyq+maYXvVyqxqk+Xwj/Fuhx/LJgywt3nh/fESP0VrHxefXP/nc4AYwwz73khn0bw7tsDdfSVn7rrbfi79ZWINk4INC6tvp7m9+BSxy6i64LPJYxrqS8ggBvPgMHDly/3qQwO3To0L59e2vmcn4sKLyZkQ3y5r54alZyg3tqO2QygoAgIAgkBwIeIjj27t0LpoUKFbKQhd2YM2fOhRdeqEtat/aH4S5evHj9+vV37dplSYbMEI0FrZCQVR4vLNZ4p914sGa50F4kqpR2rqNE4UCJz1cgccuve55ROhS7oYc38hVK3NCBFWbkWpRpm6EzmFGU+f+/V61b82e2TsLGjRszdylXcUAApc2WNd154sj+qIzIuJK8jwBvPvhj4idTrVGjBuobdnaDQjk/Ht9E2SCPb1DuTk9u8NzFX0YXBAQBQSCJEfAQwVGzZk2AnjVrFm44yBw+fHjAgAE45rAccFjbMH/+fNQ9Xn31VaskZOb555/fvXt3yCqPFy7am7ZsX2COr0xSDrs9dDdQ4sDbxeY9ATFyW0yOyJ+KFDwxefLkjKs4/z9nZ831B9HVcMxLj2JULZMvcUMHr2TrobK/7AguDpS89MxDxQsMCVy7zxHTx30jaZE1AqfWUmt2qB37s5aMi0T54qpVeKdlcRlCOsk+Arz5ECkcI0Td1T333GM3TrH3L+fHjoYH87JBHtyUXJ+S3OC5vgUyAUFAEBAEkhsBDxEc5557bqlSpa699tohQ4bkz5//9ddfh90AfctERe/E6tWr0emA3cjSz2jLli3z6OaVWKWWmZ8t/envID8FR46ZVet3qdXbM4SC/q9evlCXLl2CiuNTUHmjei0seeLr1qJ0p4aJGjp4AQSdWfC5GavMPC6ZEwQM7k57nZXJ7imziFzlJgIF8qnuTdRnfwRC1iduNgXzm2NJ2ODEIZz9ntetW4fWRjTUhh5Lzk/2MU9oD7JBCYU3z3UuN3ie2zKZsCAgCAgCeRGBED4UcmsZlStX/v777xs2bDhy5Mgvvvji0UcfxeKagCnEUrGmxF/HM888c9iwYf369bMKky9zUjxiWMalk3DYNq6mWpgKNyFSrfKqfWSLkRCNslVEFFhi04ZkN/LlU//XJludS+NEI1CmqOrRROEaI6GJ/hmFsSR5FgGoDewQNbvRp0+fLVu2hFPcsC9Bzo8dDQ/mZYM8uCm5MiW5wXMFdhlUEBAEBIEURMCntSS8ufK2bdtWrFjx66+/1tPbsGEDKgm4Gr355pu9OeE4zuq5703V/XDp3p6qWln1vx8iaXAM7qmqlw3XQRzKjx5XY39Ts/7O1BXExxXtCd6ZqTBnLv5YrT6ZrQ4dDYxG1PErO6h6lQMlkvMsAiu3q4mLE+VwFHbjrMaqbgXPrj7VJ8abz9NPP61RwNEG1oXaYjF6XOT8RI9VrkjKBuUK7B4ZVG5wj2yETEMQEAQEgRRBwLsEx7Fjx0qWLMkXvEceeURvRseOHVetWoUli74855xzLrnkkmTdp3lr1Ls/h11clgRH/crq1rPCNo9jxabd6os/1JJNCk+oF5+m0nL1HfLAETX7b/X5H6Y6wOXtVbOaCqsESXkFAUyuflgUf1sVzgC6GzUSSfblFYQ9OE98bQwaNEhPLDZqw1qUnB8LCm9mZIO8uS8JnZXc4AmFVzoXBAQBQUAQCImAh3xwOOZHDJRDhw7ZPYwOHTp0z56AU8169eo5miTTZYtaqm5FtXJbLGvi9f7CU2NpGEObqmVUgyomwYFlSu6yG0y+WCHVKs0kODBaOTUthtVIk9xEAA7i4lPVhEXx9DmKV1H8bohlSm7ua5ixcTRoxX+F2ggOkhKmXdhiOT9hofFGhWyQN/Yhh2YhN3gOAS3DCAKCgCAgCAQh4F0NjqNHj+7bt6906dI4HA2adkoU7D6gnv1O7T0UYrHNa6rihdWCdeqfwyFqLzlNdWwQojxBRZMWqS/nqg71TUcYuZ72HVTDxqlCBdSIPrk+F5lALAgcO2EGjp23LrvmKtB8RIQlZop4FY1lGxLZxv7mo+O/9u3bN14DyvmJF5IJ6uf4CfW73OAJAtcb3Sb0Bpfz441NllkIAoKAIOBpBLyrwVGwYMFy5cp5GrwET65MMXVdZ0WMWLtfCT3mn+vCjt25UY6yG2HnIRWCQEwIEHahTZpqWFnNWaNWbA3hOzbLXnFXWq+Sal1blRaXolmClbMCvPlgjc9Pho07taGXIucnZ7fU9WgQjnKDu0YtjzTIgRtczk8eOQsyTUFAEBAEchMB72pw5CYqXhp72171xlS1dW/Wc+KT9UWtc4HdEA2OrPdGJGJCAPWlJZvV8q1qXyg9puAuSxZR9SupRlVUqSLBlVKSmwi4DQ8Zl7nK+YkLjInrRDbIie2UKWroUDVzprM8Qdc1a6rp01VaWva7lxs8+xhKD4KAICAICALxQkAIjnghmcB+Dh9TExaqaUvVkWNhR8GrKH43auSGyosQHGF3RSrihMDO/WrDbrX9H4Xd1r7D5o2AojKf8rBFKllYoetUoYSqXkaVKx6n8aSb+CGQK28+junL+XEA4rVL2SBzR2A3rrxS7d6tLrtMvfJKwveodWu1ZIlCT3batOxwHHKDJ3ynZABBQBAQBAQBlwh410TF5UKSWbxwAXVeS9WtsZq3Vi3eqDbsUnsOqhMnVNFCqlIpdVIldUrt3KE2khl0WZuXEIC5EPLCSxsS7Vzs4SH79OlD/NdoW8ZVTs5PXOGMf2eyQWr1anXVVWpduvXpqFHK51Mvvxx/oK0eTztNrVih9u83/3XurKZOjY3jkBvcQlQygoAgIAgIAt5BQAgO7+xFFjMpVli1r2/+kyQICAKCgMcRiGN4SI+vVKYnCMQBAdiNtWv9/Rw4oDTH8dJLceg5uIs2bUx2w4pJx7hdupj6I25sVeQGD8ZVSgQBQUAQEAQ8goAQHB7ZCJmGICAICALJgIC8+STDLsoachKBrl1NOxF7QrHivfdMPY4XX7QXxyGv2Q0MYexpzRrFHCZPjobjkBvcjpzkBQFBQBAQBDyIgBAcHtwUmZIgIAgIAnkPAXt4yPbt2w8ePJifeW8ZMmNBICcRqFtXrVoVYkCL43jhhRC1sRW1batWrlS7doVojY1Mt25q0qQIHIfc4CFwkyJBQBAQBAQB7yEgBIf39kRmJAgIAoJAnkLA/uaToPiveQoPmawgEB0CVauqzZvDiv7zj1+PIy7OazS7sWNH2OHgWdq1U5s2BQvIDR6MiZQIAoKAICAIeBYBITg8uzUyMUFAEBAEvI4Abz44GuQnExVqw+u7JfPzGgJEhMU2BO2JcGnfPj/HMXJkOJGoygcNMvVEtm+PJMxM0ODInOQGz4yHXAkCgoAgIAjkAQSE4MgDmyRTFAQEAUHAawh4ITyk1zCR+QgC7hDAryeeL/DxiReMcGnvXj/H8b//hRPJonz0aMW/bdsiiRFLJTO7ITd4JLikLjwChw8fzpcvX8GCBcOLJLbGMAwG8OHCJkw6ceLEkSNHChUqxDzDiMSh+OjRo8ePHy9SpEgc+pIuBAFBwCUCCby3Xc5ExAUBQUAQEATyAAK8+dx+++2tW7ceM2YM0yX+65YtW/C4kQemLlMUBLyGABwHEUxq1Yo0LyKe4HP0zjsjyYSr++QTs+HWreHqzXJGZw4ZSW7wDCRS/X+IgL///vvzzz//5JNP/vrrr2PHjkWDyCmnnNKqVatoJBMhA3NxWnoiE67/77//vnbt2s8880w4gWyWA9o111yTlpbGKIsWLcpmb9JcEBAEYkBANDhiAE2aCAKCgCCQoghgkPL000/rxUNtPB8X7wApiqUsWxBIRwCOY+pUdcYZat26sIgQ90THVXn22bAywRWffqoGDFDYuURINWuaPWckucEzkEj1/3/77bebbrpp/fr1FhClS5d+7rnnevXqZZXs2rXriy++aJmerEIyUVIh9ibxyqM3sTndrw0ZdDQidJu4Sb755pvffffd5ZdfzuilSpWKMIfYqiZPnrxmzZqrr746tubSShBIBQSE4EiFXZY1CgKCgCCQXQQkPGR2EZT2gkA4BOA4iBTbqZOyvU86ZYl+ojmOKL88f/aZuuoqdfCgsx/7dY0aatQo00ZGYcUyehCuOtIT8Y/gLmvCfUhKSQS++uqrG2+8EQuL7t27n3HGGTAFc+bMGTt27LXXXvvAAw+gwadR2bRp05AhQ+655x4oDo/gVLx48WnTpuXPn59Mbk3p559/LlCggPUlIO7TGDVq1LfffisER9yBlQ6TCQEhOJJpN2UtgoAgIAjEHwHHm4/Ef40/xNKjIADHMX266thRbdgQFoydO/0cR4YWVVjJcePUFVdkwW5Ur67efx92Q27wsDCmZAVONB566CHYDUgu1PQ0BrxO9+/fv1+/fry3X3rppVUJAKQUkh5EqC6hl3M1RbCOicu8vAl7XJYmnQgC8UJACI54ISn9CAKCgCCQbAjYw0PyUVeojWTbYFmPpxCA4/j5Z9Whg9q4Mey8iPOq9TieeiqszPjxWbMb1aqpDz74pVChga1aaTMEucHD4pliFR9++OGGDRsuvPBCi93QAHBCbrvtNgiOkSNHPvroo4899tjs2bOpmjJlyj///IO/CfQ7LKh27tz58ccfT506tVixYmeeeSb2Gnavn1u3bn3vvffmz58PF9CiRYvLLrsMjxVW2xEjRpC/9957cfwxadIkNIl69+5t1erMr7/++tFHH61evbpChQp4/WCqlSpV0lXMEO8hNLeaLFmyBOE///yTSfbo0cMqt2e++eYbTD+WLVuGTKdOnSBx7BO2S5L/4YcfJk6cuHTpUubWrl07qB/tr5T1MmFsZLB/gSRCEjMfTQY5ehg3btz06dPx1tG8efMuXbpoNRlkaE4nffv2bdy4sW4CnfH4448z0IABA6hCuUaHLdP9QzzVqVMHN1j33XcfDk3ZmsiGOY5pyKUgkLQI4G1YkiCQHQR++ssY9IHxyazs9BG3tnsPmJMZPDpuHUpHgkBqIjBjxgyeKXlkJJ166qk8qqYmDrJqQSCnEVi50qhWjTgQkf5VrGgMHhx6YuPHG8WLR2pLz9WqzXjxRbnBQwOY8qW8k/Nrf8KECcFIrFq1iqpu3bodOHCAjD1dcMEFWp438wYNGvzrX/+qUqUKscO1zP3332/1hnePpk2bUo5ks2bNyKBzwYu9JUA5aejQobotllNWlc58+umnuqpJkybVqlUjD0uyYMECXaubW01++ukn2BNkIBp0BusbLmENtAxsCEY3WgDn2bVq1SIPIwP5YnViZRC+6667ECCxzMqVK5O56KKL9u7di8x//vOf9JrAD0gcq63O4BwEVkJLsHCdYQK6lllRAttitaJnSnr27ElJcP+wJJR/8MEHuh8oJ6uhZASBVEYgX9IyN7IwQUAQEAQEAfcI8HWIZ1MSGR5P+SL0+++/80HJfU/SQhAQBNwjUKeOqcdRv36klsR8RY9jyBCnzJdfKlwb7t/vLLdd/1K16r94/3zkEbnBbahINoAAahFc1KtXL1CUkePlv3Dhwni4LFq0KPFBnk13eQshQv59zJ0y0u7du0866SS0IVCdQJ8CVYi33357Y7peEqoNEBbod7z00ku0QqsChZH9+/dTCGmS0YHasWMHfjqvv/76N95449Zbb7XKdYa2ZHRslxUrVqBLwotcSIUL2AQUD+kZugR2hjR+/Hj0IOwdfvbZZ1hpoYgxb948yJeFCxd27tz5xx9/ZM52MZ3HqSpsAjzIrFmz0OBggRdffDFON3C/isDdd9/NokAJHxxkSFAwjk5ojgeN008//Y8//kCDg28JsDxMgGg1DsngS92/tsHR/bdt2xYxdFgqVqwIfQPnEtxKSgSBFERACI4U3HRZsiAgCAgCIRDQ4SE1tUE1ruOE2ggBkxQJAolGAI7jhx94xYw0DpFfR49W6W96frGvvlKXXRaB3ViXP//tlSv/6/jxX9JDV8oNHgneFK6Dv2D1IcN/YIhRokSJffv2ET+lfPny5JHkJ3m7PFYSkA4lS5bE0+cVV1zB6zeKD4sXL0aYd3soiauuuuqSSy7RGJ911lkYv2Bk8SX0nC0Ri4ROUAypH0T2rV27FsuXNm3aIA7VAg/Cnyq0Qmyt/Vl4CiywYFvuuOMOqBlKGctuvUKJNut49dVXUYLgkmnfmR6S+fXXX/f3Yvvvf//7HyAgrG1qypQpA7VRvXp1iBjsdFgvUCAA20KGBNNha21mX3nlFQr5SSsuIZJY6QsvvHD++ec7JIMvdf+4UKVK91+wYEHy0CjwMnAuRLoJbiUlgkAKIuC88VIQAlmyICAICAIpjgDUBl+Q+NSmcZD4ryl+HmT5uY+A5jjat1dbtoSdzNq1Ck8cPp+65Rb19deqf3/1zz8hhaE2Rhcp8jRxJQyMV5Tc4CFRkkKNgH5njoxGZBk4As0m6E5wMIG2wvbt27nUNAeWHViUWEPghwJFBhQirBLe3lu1amVdOjLIo390zTXX4LwD9gRNw2AeQTeBTCEDa2/vgdGtS+xQkMGaZlt60uWwJywBZgTVEns0FvRBUEvBHQbJ6oGVMlXYGfrJMpoMDjVQkIHUwLLG6gGuhGRdSkYQEASyj4AQHNnHUHoQBAQBQSAPI/DUU08JtZGH90+mnqwIEAzi448V1mEoa4RLfGyHl5w0Sf34Yzh246nixU1qIz0JtREOSCm3EOBlGzeZvO+XK1fOKtQZ3vAxP4F90Lobjtpwl1qtACsSBLT9y3/Tk0OeoLOOknCX/M3CRwY+QUnIQDegc4Gnz2ArFbylIhDBcAMWA+0SzGdQJAkeDhzQ/rDK+RJAcBmMQawSncHRKRnsX7IkOLRDX/Q+HD3IpSAgCMQXASE44oun9CYICAKCQJ5BwBEekqCA9g9TeWYZMlFBIFkR6NrVz3HgdCNc7c8qUAAAQABJREFUWrVKQXOcOBFcj9bGoFKldHn7Jk2eHzVKbvBglKTEgQBhRIhRgqpCw4YNHVX4jOANPzvqBprswFlmMBcAb+IYLtwlGhC4vUCJA8UQfuJTA8efzKoDEYgyJ204Y/fukble6fngl/SJJ55wVHFp17PgUhMThw4dckjquK1ly5Z1lAdf6uEizEc30WRQcHMpEQQEgSgREIIjSqBETBAQBASB5EHAQW1I/Nfk2VpZSZIh0K2byXH066cicBxB7EYmakOpwcOGEeEzyYCR5SQIAaK6jhkz5rXXXgv2CkEhg3aFd4s1aYUILErwsmn1AUGA81G7MYhVFS5DD1i+kHCuQeRUQqjiczSY4MDfJz18//33/biDMpKdoYBVgXQgbC0ePXSoVy2Fk5FgwgKVFgpRQkHpwy68cuVKWtl1PTKGcv6PrgfLxD8oOiN29gRVEcxkkC5SpAg/8b3aq1cv3RitGWcvci0ICAJZISBORrNCSOoFAUFAEEgiBPjehcEwLutZE+7WcClPIpNES5SlCALJhcCZZ5ocR7oafJYL+6VQoVbly2vFjfZHj443jPEffSTsRpa4iYCFAH49cdhJPJFhw4bhokKXo1OAL8yPPvqIl/ybb77ZEnab6d69O/47Xn75ZQKpWD0TJwXSRJtvZNkhNjJYo+AZ1JLEZQZ5a6pWORkYEFxyEilWx3ChhIU4vJmed955BG3Bb6jVcO7cuQRH14FRrEKdgfTBmSjzt5QspkyZMnPmTO0KxCEc8vKcc86hHDrGqoVOYji+OlCitWMmTpwIh6IFmLwlGS6DrxAiy7BB1qzCSUq5IJAiCIgGR4pstCxTEBAEUh0BqA1Ml/kJEHhlQ2tDgr+m+pmQ9ecVBDTHgT+OHTvCTRlqA3cbv6RHVahx/Pjg/fv74nrjo48UbSUJAlEjgCeLESNGXHnllYQRQfcBVQv8aBKmBL0D9AuefPJJywGH9j1BYBQCr3Ts2DGaPyi8wN94440vvvgi1MOll15KvBX8aMyfP79Hjx5Vq1aNZo44B/nrr7947YdZ6NSp0969ewndSsPbQukoMRyzIhJtt27diNuC9gT2LDpMjDXWkCFD0AHBagaeAvYf2xxir2CJY9cxsYT5uwk/8uijj44aNQq3HXPmzGHyqJNQEuwBxGplzxDDBcRwQgKeOAchNi0IQBsBIGKoz5AnJAoz6d27N4tF2N6cPLAvX75cr/fxxx/HDIeQuo888ghV6KHggdUhL5eCQAoiIARHCm66LFkQEARSCwEHtcEDH09pqQWBrFYQyOsI4ASRb7yhOA4HtdH30CHYDcJImvKhXCfmdSRk/olGoHXr1pMmTeJVfPr06VrXACaibdu2EB+NGjWyRocCQP3hhx9+4F0d7kATHNhuWNoHliQZHdyUDIohvKLj2Ro9CC4JzopGBi/qlkC4HnRvRI3lnf/uu+8mKqrm6xn64YcftsLEOpozZ3gZ+AgiucJBdO7cefjw4XYlFIKqTJgwAb4AXoPENFDHwPKlXbt2ekT7T2aLesXAgQNxU/LWW29RxXxQJ4GPsMTs1itWoZWBcwExhtO8DPossBIPPvggXx2Qga2gW6aHygn46DC0N9xwg9WcDAov1H766afkWTg/dYBbxo3ejwmtJAkCSYyAT9SZknh3c2ZpkxapL+eqDvXVpWZI8lxO+w6qYeNUoQJqRJ9cnokMLwh4AQG8vqO1gQasnsw999wj1IYX9kXmIAjEiMCECerCC9XBg7r5uvz50doYk263T8k9+/eb1AapaFHFZ+3u3bWY/BQEYkMAqoLgIDjIwMFEuFCssfXM2wc9wzjUISJyTAklC7Qt4CPwimqPShuyM9x8EIYWlxwRIpjs27cP/xp169aFMQnZib0QQxXmj9aJVmOxV0WZ37Nnz9q1awFWm9jYW7E0/HrgKwR3qkW5l6NIzJx+NNMRhbiICAJJjoBocCT5BsvyBAFBIDURgNrAplfiv6bm7suqkxkBXngOHoTawJNoIP7roUPP790bWDUBHQitIkkQyB4CKAVE4zszhkGgNqASYmhoNYHaIPqJdRk5AwOSpe1GyZIlmzVrFrkfqxY7neiFrVb2DM5Nw/XA0lAMsQtnmdfOO7IUEwFBIEUQEIIjRTY6JZZ5/ITats9c6QlDbd+nypVQ+XwpsXBZpCDgQACtDTu1gdaGhId0QCSXgkDeQ2DiRNNEZedOtDbs1AZaGzWPH8+0nI0b1YgRZsmAAZnK5UIQEAQEAUFAEEh2BITgSPYdToH1HTuufl9t/lu5VR1LdztNyWNfqsIFVIMq6rS66uQawnTk1XOAiuaSJUvwuaWDvSViGSiC4qUMX1/BaqKJGC7RfTrivz7//PMpTm2guIvX/SZNmmBDngjw9+/fj783vsXFrGidiFnloT4TvUHJc4OnsxujDxwYVKmS3l+CpKC14aQ2rL1fvlw99ZTy+dR111llyZdJ9PmRGzz5zoysSBAQBJIeAQkTm/RbnOQLnL9WPfqF+vhXtWyzn92wFnz4mFqwXr09TT35tVqxxSqWTJ5BADNU3K3jXB1fYo5J88pqRX1zVLm9xB87YduuueYazIzdtvWUPNRGcPzXFGc3ODn46scd3dGjR+2bxdHC+PnAgQP2wizzGF3jft8RjBAV7qFDh+LMH298WfYgAg4Ewm2Q3OAOoNRPP43u1w83hoH4r7t3j9+1Kyy7odsvW6aeflq9/bazt2S5Dnd+5AZPlh2WdQgCgoAgEAsCQnDEgpq08QICxAj//A/1znS1x+9tLeyktuxVL01UU5eEFZAKbyJA3DVi1N13330Ek9Mz5N0SlYSTTz65YcOG+DnHghd34sRRi37+77zzDlavpDvvvFO3Iqz9Qw89RMA53JhH34+nJPEkr6mN9evXt2/ffnx6IuOpSeb8ZDZs2ECkQ7zKf/TRR5bTOMLvESwQp3SnnXYaOhegRIDALOeGl1YksUXH3T3KRL169bLoDDzAvf/++9WqVbv66qv5mJxlVyJgIRC8QXKDW+DYM7+8+GKr/v0H5cu3Pn9+tDbGp1Mb7Y8cscuEzS9Zop55Jik5juDzAwhyg4c9CVIhCAgCgkDKICBRVFJmqxO20NyKojLqZ/WHSx9qZ5+serZIGBDScVwR+Pvvv/kqjmUB8dt0eHmcrt90002ff/4538xPPfXUrVu38hGeMXlNhZ4gDlyW49OE11Q8pSPZs2dPyA6rCa+sv//+O4HxGNEq9H7GEf8VXxs6UJ/3Z54DMyQOH4H03n333XPPPVcPN3/+/AsvvPDgwYNVqlSB42DHtdrOs88+e9lll4Wb0n//+9///e9/1MKVwG7QCZEFcAI3duxYi0XiBPbp0wey7M033wzXj5Q7EHBskNzgDny4NG/wBx74ZdEi8jWOH8fXBiFgg8VUvnwKyj9CatpU3XWXuvbaCCJ5rspxfpi/3OB5bhNlwoKAICAIJAIB0eBIBKrSZ8IR+HFhaHYDvxuXt1d924aegNlqdegqKfUaAo8//jhqxv/5z380u8H0vvvuO9gNYrwRhf6bb76ZPXs2YeRxnEGoNjtVEWEhjzzyCOwGb6HBMgzE+9Xw4cODq7xZwpvPv9ITmRo1ahD/ldd1YTeszVq0aBEERLt27Sx2gyrUdmA3rrrqqjlz5nz55Zd//vmnRuzhhx/msFlt7RnMWOiHQ/jKK69gzYRKET23bNkSec16aOEuXbp07doVZZC5c+fam0s+HALBGyQ3uB2rwA2+aBHUBvFff9+xIzS7UbKkuv12VaGCvbkz/9dfCpLORuk6BfLadfD5YQVyg+e1bZT5CgKCgCCQEAS8SHDwmvHbb7/xHLlp0yYWvXPnTiyfE7J66TRvIrBup/pufuipFyygWtdRp9QOXUvpmFlqjzu7+7BdSUWWCKBwzjvkTz/9hBdPS5g3xh07dmg1CquQzK5duyjXJdT+8MMPTZs27dChgyWD0w0Cs+HvgHIKeec8/fTT8dBB/o8//rDEwmWw1uZ7PgoavN8Gy7ROT2hwWHMIlvFICfFfb7/9dsgNXoGYkqY20N3wyPTiO40tW7awKbzM2Ltlj0gOnyn4o6UQj4Ba8rPPPuNPCT5crIbUFihQoGrVqnBnBQsWpByNDJAkw/lEY8iStGdg0NAhQg3koosu0lxb2bJlB6RHpkAZ3i6JehGXsCH2wuTOx3yDA0vwBskNrk+L8wZPpzbQ3Qh9lkqUUB99pJ57ziQvqlYNLaNLFywwOY6VKyPJ5Hid3OA5DrkMKAgIAoJA8iPgOYKDz1+8gWDn3Lt3b9zj8VjJx8knn3wy+bdCVhg1Al/PVUbUwg5BPI+ixyEpBxB49dVX69Wr17179/79++MyAxsQ/ZoKfck9jhcMNC+safAS26hRo86dO+NckEI4EbxCXnDBBZYAGd4qFyxYQG/2QngJLiFH7IXBeb6348uD8ieeeALjgmABStDswPSAt9mQtV4o5M2H+K8sGZcQzAebCF4PkpXawJ0nyhfNmzfv168fyhE4Xnnsscf0LuARliOkN1SXsL941qDQshCBIsfvRrdu3ayNg8748ccfp06dCs1hFeJWA50gLiMcIYL44IbWakJGE3YVMn8z79ixI9wH49olkzifnRscWII3SG5w5w3u823ZujUstQGImt047zzzmPHztddUlSpmPlz680918cXK9os3nGAOlMsNngMgyxCCgCAgCKQmAt4iOLCoP+uss7Co5888isR84bnlllvI4E0wNbdHVh2MwLZ9aunm4GIXJb+tVNAckhKKwIgRI/DcyZsk76J33XUXLjMwCsALI/wFLAbf1bmvsaCGUGAa27dv1x/SX3zxRf22+fPPP1NufzvVs+VzuiPYJ74PqOKXhhYI9/Ptt9+GXuEduG3bMPZLGcNZziPDdZVb5ZraeJqYCOnUBnjibzW3JpPocdG2OPvss1HMYb9QUYHKQV/jhRde0PzFyy+/XKpUKVx7wovpmYwcORLiDGEOFSXwPitWrED9p3Dhwo6pEs/VXoLRvubUsjxCVisMVd544w0uOU5WIRlOO15j8POaCq5Gs3mDh9sgucH9N3jnznPy539+yxb7AXPmixdXH34ILxsoJ//AAyojiGyg3J6bN4+Dm+sch9zg9j1J8fzm3cYP843dB2L+bpVz+P200Hj4U/+/F75P4ITHzQ4M9O6UBA6Uc9jJSIJAziIQ+JCVs+OGHm3QoEF8ZBs3bpz2F4g3OCyoeYTl7Sh0AylNPQQWrMvumo8cV0s3qeY1s9uPtA+HAL48eQXlLkYbgi/kiA0ZMgRSA9qC90kIjgceeAAegVf0l156aeDAgTAgREK5+eabcWSg+9QhYHHoGG4IS4wP8uTP098ww0gzH7TA+J7/73//O4yIWawjqmrLuAhiOV9F/Fd+N+px8WoJr5H0wV9ZI7wDjj+feeYZbRgCrYBCx4wZM/jOj1ofdA8n6o477pg2bdrKlSt5LYQaw02GVs+J8vwAKQFW+ElElUqRXwuV4pWeOaBDROwGuAyOKxF8HIdBn1iOUFpamqMqmS7lBo/vbjpv8H79ahLjKd1EN+xAsBsffKAy67iZwhB8uF5+910VIbYUBn0dOypI5Kx44bCjZ7tCbvBsQ5gkHUz5y+j9jLmW0kXVomeN4oV9Xl7Y2F/Vx6ZhqJnqVVEDbYp9M5YaizeozbtVjfKqWU3Vqm4WC8GCcuZyNX+N2r5PlS+h6lRSXZuqIgX9rb6coz6fo8dRzWupq7v48/KfICAIRImAhwgOvnrhQRCtV3s0hOrVq6MerB8W+V7HIybfxyjhwRe3cARTiHKdIpY0CKzeHoel0IkQHHHAMUwXGJigoMFNqtkNLYVHRv6i84WWS76rc6fziZ47evPmzbjbaNGiBayH1R8feIm+qbU5rEJHBtc8DIEXABgTkqPWfql9i6JRUrlyZXu5I8+ImBgwtKM8Fy958+FNnl96zAFqA2sUK2xHLs4qB4bWoVtZr2Y3GBHHKxgwwoDr0bFh5Jh98skncByLFy/GRIVIKPy90LV6EwndGnmqNMcKkr8j0UQIho/TbBp9cpCYT7Ctkx7RU0coMgKx1cblBmfoyBuUojc4wV8J6LNxY6St4bco7Ebv3qFlRoxQhmFyHNvD/7GkfziOGTNULjFxcoOH3rtkL73lLeOf9ChArwxQmsuYtsS/5j0Hzbf99g3yBgRliqmqpmmjmZZsMG56U81bo6/8P7s1NV6+TlUpE5rmWLjOuP51BSFiT5VLqyf7G71PM5tULatwmS+6xnZ8JC8IuELAQwQHz0xMHYt9+wL4FGbZp2C0j9EKsf34Xoe1PJnrrrvOLuzIv/feexh5OgrlMu4IHCzdTlXoztP/4u+/jXvnwR3uqXGDKuw3My5bXLWpk0mkUEHzMn8+1ePkTOVcTFkS+GsxffaieV+NdUqEvz7zzDPt3i7DC0qNiYBW0a9fv74dDugD+yVeOQhcgmYHRgfoVrz++uva76OWgR+xO0qwN9T5w4cP8yUfqxN+D8CVBAtYJQRbwbcok7n++uutwnAZBmXocLU5WY4DUXRbLGoDG43UiZDC5qKCwanAIagdc4vd0IVEb8VxLKE3uMRxLE5eLGG9iZGPEMZNqA7RBK4cb7VW23AZIvXgwRRfpPBxeBLFFga1I4wo7fJ6RI8cIfvE4puPyw3OlCJsUIre4FOmqMsvz5rdeP/9sOyG3umnnvJzHBlum0McAM1xoMeRlhaiNpFFcoMnEl1P9/3tPLU73Vvu89f459nGVPE0U9FCqmkNnc0DP+85X93Ww8dEsa9BAwXFDUea9JeC9Rh/t2Fx9JbAsk1GrycUhI4jbdmjBryuapY30P74bz9fj+Z+3RaHmFwKAoJANAh4iODQSsV2PWEcufGUb6kBE5lPL6lx48aYOts9FIZc6ltvveVZc/qQE86jhaf2vKdT/+4YwE9+96EcWMKVT/crm+FHrVxxdW6LEGMWyB+i/Ne/AwTH8r/XfvGUi9ny7i0ERwigwxTpL9h2VayQgrgLJZ7Fnj178JrpUOlHkwJ2Eg8dIbW0+LR7xRVX8MsBvwlYtKHSFbJ/CvmwD4dCxgqcEU5Sl/MGG1nLI3LzuNSyLrQ2+Elv2GKgxZA61IYGEJ8sKPtkeX4IqYNXF2gyWjkg4vxQyG7qDoN/fv311wQ9wZHtsGHD7JFWgiWtEggXEn+hMJyEnkNzBP0jWDa7Uxg9oh7daph8mbjc4MASboNS9AaH3bjiCrVhQ6QDA008apS66KJIMroOZz1aj2PnzrDCjNWpk5o+PYc5DrnBw+5IXqs4dtw4ehxuwnzVzzLt2GfsDYphd05L36RhxqqtqnNjVbqYs59/DhklijgLrYEij37oqHHkmCpVNGxzq5/sZJ79xs9u+HyqfwfoCfX2ZLV1r9nllEXqr/Xq5CBr6CIFVdkSJsFRqIC6rqtqVkt9N1d9lR4I7thxNW425i3ZmZG0FQQEARMBDxEc+usctAWaGnpz+LBGRElLg4NCnI9ig433OF6nI5vTI8wjL9FYdFfyM3EInKh0Bo4iMTE49e67EzeK1fOxUsWt/O4DanKm8JGKMLEdGyj+SExfakn5M3Zlvzq1q93tZrYRPFM6h5FrtCvTP7zzfTsyGMOHD4fdQAbNf5SzLsa9f0aiB3SCMPWvkkFmZdSYHkm5tXGFQHwNHChUrFjRqgrOTJ48GS0PWBJeaElaAKMYMsT4vPfee+vWrauje1KC0wd+4Ti0BoL7TFwJpAY2KTpCCqPgXDNZI6RExhASgS3jLRdVCIfij70hfw6we9Il6GKgWGG5FNWbiLMMu7yVx9pRG7/guQOmzCqPPoN/KIgzpscfI0K3WA31iLl4hKyZJDSjF5jNG5wZhtygFL3BYTeuvFKlG6OF3buSJdVbb5lhUKJMzzzj5zgiBJlixDPOUNOm5STHITd4lBuYK2K9njTwCkG6v7e6sLWfHXj9J+OtyWbhWSer4X3NwrcnG6OmmUYWODWrX8WAnnjoEqXJiJvfNP5YbQoP6Go+kn36q6pWVpUsqkbPUCcMs5zU/TGVL5/x6R0mOzDwXbNkxFfq10f9+g6rtxoPjFFzViqUGmqUM3BOAXfQrkGAqogw+oHDxsjv1Ic/q/XpzF6JIkbXJmpob9WkRqC5OV6c0kmVVVpFtXqburuXevAic4jT6wU0LxauC0Fw1Krg++4+46qXzVl1bWo2ubiNUTkjoPma8IZlcZqydCMIpAQCHiI4tG05r50onKO5ig8qVDDYBLuHUT4cYcmyZMkSXnusZ9lwG+VQHg4nJuXZRGDSIvXlXNNBwKVt2mezq2iavzVVLVjvF9zxj/pibqZGJYqYBMfxE87yTEJKdW7XvPetTzsK5TJeCOiAFNgOwCBYfRIFg6AYFueI/1HudJwmoK2AKw1CfmImYPlQ0D0sX77cQXDwTvt///d/GKnhdAOTAb6oW/2HzKALTTmaIBisOQSI2UQhLIlFcDAcMtFH03B0mJ1L1gUOQm1oDFGYQnWFDcLDtN3wBE4K76o6DArHiY2DIIMHR1OPvwso6WhtDjqhOX9E9IY69oV4K0iiHsLxw2upozbkJVpCeLG1a2qgXqSPVkneOW2JEVFIztI5rq1Fnsxm/wYPt0Gpe4PffjsfcCKdBqjekSPVpZdGkgmue/ZZP8eRHn47uN4sYVx8GOGtOS0ttEC8S+UGjzei8eyvUik1I/370GezIDj8PY+ZqZZuNPNwFqQb3jA+mWlmdFq2SfFv6mL1/VCjXAkfTty18KsT1d/pLq1wk1mljDpwJKOBUsvTY+Gh/bHngF/Yqpvzt0kQaFcdFMJTvD/d1GuY+pBRr4pJB0Qe/Za3Au45EaYflCN+/FN9da/Rpp7Z3ErfzTNuftO6cmY+uE11bJRJ3imRfn3jWb4bzjTdhTbxO4BSpUw/Y/7EE2nIVLWs78eAz7FM/jsaZ/QTsqEUCgKCQJQIeMhJJxYo2FTzxRXHhBjM8+SKWTVKyHZLflTZic/H4ywawnaXhFGuVsSSAIHaFeKwiLR4dBKHeSRpF3jS4cUP1Qn8X1hLHDp0KK5z8OlICToUxIXlKz3BVvBvwpsq9mi4nMAwQctrXzzffvut1ZzM0qVLec+E3SA854cffhiO3fj4448xOkABhCZdu3Yl40j4+6CqY8eOlH+Ar76MpIcjUnVGQQ79D7XBbzbNbuBrAxo3NRU37HDrCKzsDkSGLoc7wLEokYMPHTpECWFx2D7C7nB4iLTCeYOw0NGFqeVsQLmi6OfQMsDRLOwGFAluWcKxG/g9gWdHvUjHMIZAIWAKh1OPS+cYVhD9hwzsmz2cDX+z0MFhK5PeRCX7N3jIDUrdG7xLF7VgAScqbCLEz3PPqf/7v7ACESpoeNVVKnN0ZKc4gVeYw+rVzvKEXcsNnjBos9uxRWr8tFAdPmr+Rd621/hjldmtz6fOb4XlRYDdaFFL9Whhej0jwXE8bzpECiTNbujrFrVNjsNKGG60rK0KF7QKApmhowPsxql1zOgqpP2HFSoPpMijr95mWMFH8Fd6bktVMp1iwOgj2FQEAxa0gMP94/tZlAlSu30DX5nifjYEExWdgKtlWqQ+Fq03zh5uNL3b6PG4X6xGOXVTTj+ARJqh1AkCeReBAp6aOl9xeVrFsShPjVAbBFmA9QhphA/rYSmce2oJMplEI3ByDfX1vGwNUiCfapTJd2G2epPGwQjwgscrIm+GF110EZQEX8t///33ZcuWYQ/COyrvjbwx7ty5E7MC7dwRbX/sC7A+I8ynVrziLbFChQp42n/00UctT4QwF9q6hBehnj17OsZ97LHHsCQiqApqI/zct28fBiwEbdFxW+zCfCXmslSpUva3U2b15ZdfUpiTYUqgNrCS0HOD2oDXsE/JPudUy3MM2D40OOCbWrVqBenD8cCA6P777+c4YdP0wgsvYNXITx4usZggUM6dd94JDYHrUK3iAX9BBFn21PJFDTEBCQKSuN4I5scbNGigaQuOmaa9OGMYSLIvlBDhC/YE30+c25kzZ3LA6Adixb4v/Emi5+CTaZdJjnz2b3BwCN6gFL3Bu3UztSciJNiNvn3NfzEnLLm0P4696b4BQvazZg18sJo8OWf0OOQGD7kJXig8u7np7/PgEZNTmLZYdW9uqj/oTw9t65lhQYZ85P8OAbsx9WHzrf7xz40RX5pzf2eKejizjtGtZ5tRTutXVafW8XVvZpyd8Sb/9RBVJt3jxuL1/t702nnn/+1vnVVEIenfwYcXz9MfNKeh3/wZQqeQo3dq7K/lv9euxyOGb8NO49UJasiFqlhQAFoMZ9rVD8g7coRujSG9MsH44Gd/uys7YV8TSQcE7ZLZGYulDWFZvrpXlS8ZqUkMU5ImgkBqIuAtgoM9IDCkFRsShXZ0162Nee6553DPgQtAggLyYhBNYD+rrWSSBoEqpVW9SmrF1tgX1KoOwcZjby4to0EACgOGAo5j/PjxyPPBlq++WBBwd6O1oT9042NCd4WfBQwHLrjgAnS4eO1BAR5aE/+RvPxjRWK9oFpROXHAETyHXel25rAh+ByFB9FvucFiVonFm+gSXqf5dE9oDHswF0s47hl8bQwaNEh3C6WCRZ5QG3aQ0ciYOHEijjzR6eMXPlUQEFgnXXvttfjmwPM0yj6cGcspNQ5rv/jiC9gNjhx/KZCHXHviiSdgQKjSjjwsrhyi5M8//7QPR17ra5CxvMzqI4RP6x9//JFuMbnShxkZ/hLhBOoMnBdkJCgP5sM5v9StEUFGD3nr/2ze4Cw2eINS8QbHXVG6S6Cwu1+hgsk4YJySzUQPmuPYl+5fIWRvaHC0a6c2bQpZGd9CucHji2cce4MIgIn48nezy+/nm8zChIxfllq5Y0W6dQm1GJjc+6FJT+AiVCccZ+JJ1H+hVM+WfocdVkmWGUvpA/WHnqeY4lAqK0YaBfL7X/sjj45ybj6f39NHi3vVSZWNJjXUWc0ISRti5NNO8n03NER5bEX8SXp0nMLnqE7onjzWx11Pm3ar80eor+810ioJx+EOOpEWBIIR8Fk64cF1uVuCATYBIFFox1BFz+S1117jE9mOHTuqVavWr18/u0vC3J1qio+ufXB0qK8ubZNDSKzapkb+GHosLB4fuxi9SjXkk9ACBFi5/zxVLiZuPnSPUhoRAfwIQj00atTIQShEbGRW8r0djQwsFH799VeL9MyyFQLPPvss9gu4n+S3RDTyyPDOjC4JY2FTgxJHlK1iE4PagJ+14r+itZGTOiOxzTkXW2EYgiNPyDKHN5ZopgSVBqeGRo8rj8KMCKmBkgIMu30UrKj4q4SOBkaUwYcE7Q+0SNBF0qFn7Q2TOx/zDQ4ssW1QUt3gcApocKxaFfqQlC+vTjpJzZoVujaG0oEDFUaC/4RRvq9TR61cGUOv2WkiN3h20EtQ27G/GsQrJeFBc87j6qRBamf6kfnraVW9nC/tNgOzjnDp5/+o299V2qTl3gvwVBp4V5+9IqDBsfpFvwbHhD+NS/2uotWut9RrE9V9H5t9Fy6gtrweaGsNl+XoL/2gPv7FEvdnOjZUowf5faBadVv2GPPXWFfODE2ClT7wn6o7h7zQYWJ1MyK24M5j/G/+Tjo1Uu/f5l+gs1/b9fETBkocG3aa8VbuH+2vgEV67xZz4VP+8jsrRQVmWrqmjK2pZAUBQSALBDynwWHNFx+ifLw955xzrBLs6knWpWRSFoE6FdVZTdXEv0IAcPy4Wrje/LAQLl3SWtiNcNgkpBy/oZbrUFcD8DGcMJx8sScSJ7rrUSpWEIIBVpTAFtjCRDkcvAa/WHDWgP1C8ItrlJ1EI4beCjYUFrWB7YMjuGk0naSaDAYpJ598cmyrRvfnm2++wZAEIxe8dUTZCccAFQ+H+QltORvhZsLOoi1id1gb5VhJIBbzDc7aY9igZLvB09LUpEmmbQhMhyMR6rhu3XiyG/T/wgt+PY7gCMrcINin5HiSGzzHIc96QNxqwC8QeA59igkL/OxG67omu0HjSqVNvxUk3GFe0MrZW43yzhJX15VL+8V5ioM1KFLQHNGeshz9lQG+rk0NLFn+XGta2ej081I1cYHqfZq9J/Xrcr9fj0ylGRfv3RLwsZpRFvr/7XuNfi8oy7Lmuq7qyf7KUjkJ2ebIMQO/rZd3NCPjli5GhBf100IDpyek2StCtpBCQUAQcIdAumsgd01ySBo94SuvvNLSQM6hUWWYPIJAzxbq5FC+pg8eVW9OVe9l2EA6VtO5IRG8HGVy6V0EcGfAeyOho7HgiHKWOCEmQAMBViKEF3V0hTCBmfjU/69//ctRFa9LXoDpnAS7wfSwZcDYQdiNeMEbrh9YciLC4sgJ36Lae0s4Sauc8CgYpGAagwGFVRg5g7cXPHqg9KF9vkQWllo7AjFsUBLe4HAcMAu1a9uRwVhX1aunbE6aM9Vm5+LFF02fo474U0RRyQ12IzvroG0M50du8GgwL1nU1y2DVX5snL+F5XwUtQ6d4CCG9vbpf+0aqIqlzEvtWSOaUULK1KnkLyag7M9LzDyOTts8YFz9sjFrhcFllqPPW238skx9eqfa8IrvzxEmC6MThYlIyzYZZw33sxvYPr9ynXrmCp+D3Viz3XhgtHHb28b0JeYS1u0wzvmvGvgOvkv8Mzp23Fi03p8P6Xg1ETOXPgWB5EbAuyYqyY17Mq0u501UNHrEgh37m5oZNdt9bnN19smmG3BJgkCOIQC1gU2KxH/NMcBlIEEgJxGI2w2OBgcsA9FMSEQ8qV9f/Zah756I9dxyiyJy9oH0D/G18BU5VcGzSBIEMhAY/YtxU+YQqvNHqNoVzOenz2YZ173ml4Nu6NBA7dqvvp2neCQbM4igKr5uj/qjrjhMVPYcMOrfoYhdQvrXaaYr05ev8zlMVOik7YP+4LLETyFoC4oY/COVL6mWPqu+mKMijE6glgufMhVM0iqaIVQKFTD9nu49aDZ/op+6qXum579Nu4wf5ptVIVOXpnSSSR6xYBOV/4w1nvs20EHdDIJGF3VtavIdVlxbQvAuflZd9IwZUlenmuVxv6pmLlNb9/pL+rVXKKFwISYqfkTkP0EgJgS8a6IS03KkUQohQGSyPm0VPqW+nBtQRAy5/nLF1SWnBaKUh5SRQkEgvggQqwVfG0JtxBdV6U0Q8AgCcb7B09JMlgG3tTt3mrobCWU3QPDllxX+TZ98UpUrZzIdjC5JELAhADtQMH/A2peQrprdQOTitr53phgYfZCwYbHcgnI5brYZNTZcwhwD96XfzDXrcVeBN1DipDgSug+P/p9x2YumKRUuS62IJIjdd4Fp93FxWziLsKN3aKj2mWHE1ept6pUJgb4JUHJRm8ClzlUt67u6i7Mwm9crM/yt6n60/sja7f5eYTEIT4MBy3kj1PZ0b7/rdqDQERgTgubfFwcuJScICAIxI+BdE5WYlyQNUwqBtiepf1+o/tVK1SqvHGQ7DEi9yqp/O3X/+cJupNShyP3FQm0Q6VazG/jaIMopzkRzf1oyA0FAEIgHAgm5wWEZpk1TuN6YMycec8yqj2HDFMG2R41SXbpkJSr1KYcAZESXJoFVW/Ypumjc3WrQuapkkYBA7Qrme/urAwIl5HDk4UjD+6g7zjXVK8za9GB2+H3XCT5Fp56n+L4arOpV8V/Cg6Dj8PTl6voz/Y94EUa/8gzfN0PUKWkmO2MlOJcvBuM6xPGEaNUnMMNTKOmStv4hUHgpUcTXqLpvykOqTztTwcRK5Akr++P9CtrFKpSMICAIxIyAmKjEDJ009COQWyYqwRtw6Kip5odXap/P/NOLNqD970ewvJQIAnFHgDcfotvqbiX+a9zhlQ4FgdxFQG7w3MVfRvcOAgRhXLPdfOKC3cBtR9wntvuAsW67qlkhdDiSyKMfPWagTHHshEmmFC8ct7lZJipENiFUyvC+0fbc8SFj4Tp11Rlq5NWBJnhRRbkDAx8UN2qUU/nhcjLS25MNlFymp3shkSgqGajI/4KACwRs/KGLViIqCHgRATw8occhKfkQ2Llfbdittv9j2tbuO2zqzR47jraq+ZWmZGGeflSFEqp6GYUtUi4mfG0MGjRIT0CojVzciOCh88T5CZ526pTkiQ2SG9yzBzJPnB/PohfzxHw+n9bFiLmHyA3xV1qmVliRyKMXLOBrWC1s2+xX4BbkwBEIjqh6gq2A3ShVVA0MhIU0GxIjpkHV0D1MW+xnN0JXS6kgIAhkhYAQHFkhJPWCgCCQSwjsPaSWbFbLt/qtah2zgObgHw8ZW/appVvMStR26ldSjaqoUjbVWUerRFziaNAe/xVrFAiORAwkfbpCIK+cH1eLSibhvLJBcoN789TllfPjTfRkVjEgUL+qalff3y7KgLiLNxiPjFM4TH3jRuxuAjoakUdvUC0wkGWtE7mJ1AoCgoAdATFRsaMh+VgQ8I6JSiyzlzaeRADvYnPWqBVblRlUzWXiCaJeJdW6tvlIkehkf/Mh/ivUhgR/TTTm0fSfV85PNGtJSpm8skFyg3vz+OWV8+NN9GRWgoAgIAgkPQKiwZH0WywLFATyEgJEift9rZq3Tp2IgdtIXyjtUPr4e5tqWVO1qoVda0KWz5sP1vj8pHehNhICcUyd5pXzE9PikqFRXtkgucG9edryyvnxJnoyK0FAEBAEUgQBIThSZKNlmYJAHkBg90E1YZHasT8OU4Uf+WOtWrNDdW+iysRVlSPO4SHjsFbpwo9Anjg/qbxbeWKD5Ab37BHNE+fHs+jJxAQBQUAQSB0ExEQldfY6USsVE5VEIZti/a7fpX5YZLrViG/CEWmPJqpG2Tj0Km8+cQAxYV14//wkbOl5o2Pvb5Dc4F4+Sd4/P15GT+YmCAgCgkBKIZAY7e2UglAWKwgIAtlGYOV29e3C+LMbzAvGhJ7pP5sJg5TWrVuPGTOGfvr06bNlyxY8bmSzT2keLwS8f37itdI82o/3N0hucC8fLe+fHy+jJ3MTBAQBQSDVEBCCI9V2XNYrCHgOAT7NTVwcu9ONLNeDuQr9M0psiTefypUrP/300zQnPMqcOXOef/752LqSVolAwOPnJxFLzlt9enyD5Ab3+HHy+PnxOHoyPUFAEBAEUhABIThScNNlyYKAhxDArBrLlJhdika5EvpnFMZylUaPHu2gNsaPH1+zZk1XnYhwQhHw8vlJ6MLzSude3iC5wb1/irx8fryPnsxQEBAEBIHUREAIjtTcd1m1IOAJBHCJj1fRuPvdCLk2RmEsRowmEUOhVatWgwYNQhitDXgNoTaiwS2HZTx7fnIYB88O59kNkhvcs2fGPjHPnh/7JCUvCAgCgoAg4DUEJIqK13ZE5iMIpBACRISNS8yUKCFjLEZskxZJnDefgQMHrl+/HiGJ/xoJKQ/UefD8eAAVD03BgxskN7iHzkdWU/Hg+clqylIvCAgCgoAgkPsICMGR+3sgMxAEUhOBPQfVvHU5vXRGbFhZlQ4VOJY3H6zx+cmchNrI6Y1xP57Xzo/7FSR5C69tkNzgeevAee385C30ZLaCgCAgCKQyAkJwpPLuy9oFgdxEYM6ahLveCF4ezjgY98xGzprbb79dR0ih4p577pEIKU6AvHftqfPjPXhyf0ae2iC5wXP/QLicgafOj8u5i7ggIAgIAoJAbiIgPjhyE30ZWxBIWQT2HlIrtubO6hmX0e1p3bp1Ev/VDoj38546P96HK+dn6KkNkhs85w9ANkf01PnJ5lqkuSAgCAgCgkAOIyAaHDkMuAwnCAgCJgJLNivDPRKHjhhbdqvte9XRY6pCaVW5tCpZzOe2G8ZldLsnDgKjjBw5sm/fvm67EvncQiCG88PhWbtN7Tmgjh1XZYqrciVU5bKuDw/rDT4/uQWCl8eNYYNYjtzgXt7TnJxbDOdHbvCc3CAZSxAQBAQBLyMgBIeXd0fmJggkLQLLt7hb2pGjxpSF6ue/1JFjmRq2rGv0bK1KuaQ5lm/NRHDQo7AbmWD1/IWr88Obzze/qbkrTWrDnqqWNc5qqZrWdk1zBJ8fe7eSBwEgcpXkBncFV9ILuzo/coMn/XmQBQoCgoAg4AoBMVFxBZcICwKCQBwQ2Llf7Tvsop9jx433JqlJ853sBl3MW6le/sb88OuiO6X2HVLMQVIeRcDV+dl7wHj+K/Xbcie7wdo37VLvT+YIuTs8NJTzE/nkmBuU2Qossrzc4JHxSbVaV+dHbvBUOx6yXkFAEBAEskRACI4sIRIBQUAQiDMCG3a76/DHuervTWGb7N6vvp0TtjZchds5hOtHynMeAVd79+UstXNfpDmOnQH34ZrjcDWHSMMnY51bcOQGT8ZTEPuaXJ0fucFjB1paCgKCgCCQpAiIiUqSbqwsSxDwMALb/nExuQOHjZlLAvKt6qkOjdWBw+rzX01nHDot2xAQiDK33c0couxTxHIGAVfnp9dpatNOtWOfyudTPU9TzWqbmSkL1IzF/slit7J+u0qr7G7ucn4i4OUKHLnBIyCZmlWuzo/c4Kl5SGTVgoAgIAhEQEA0OCKAI1WCgCCQEAR2H3DRbf58qvPJqmwJs0ndKurSjr5q5X31qvkuaBvoBCWOw0fdfYR3NYfASJLzAAKu9q5sCd9N56q0SuryrqpjE1/p4j4c0559qklzWGm/G3sK3crVHKyBUiTjChy5wVPkVES/TFfnR27w6IEVSUFAEBAEUgQB0eBIkY2WZQoCHkLgHzcOOAoX9OEJ8swWxt+bVbHCgVUULhjI845UIH/gMpqcKycg0XQoMjmGgKvzw6xgNG7qmWl2G3eqEzZCrFKZTLXRXMj5iYCSK3DkBo+AZGpWuTo/QCQ3eGqeE1m1IJAdBAzD2Llzp72H0qVLFygQeC/ev3//oUOBrx+FChUqWbKkXT5xecZldKv/ggULlipVyrqMPpPlGqPvKgbJXbt2nThxwmrIEliIdUkmSwG7sNt8YCPdthR5QUAQEARiQ8ARCSWaTnw+X72qmQQXrA5cViqt8tu/yAdqwuZCzqFyZZeGCmG7l4q4IRAcwTfk3kUz3o9zDQIMEyx23baAeJOaqmJpmzpHoCZSLuY5ROo0WepiAEdu8GTZfNfrkBvcNWQ53mDBggVHjhwpWrRokyZNcnxw/4CTJ09+5ZVXHnrooaZNm2ZzDtu3b7/22muPHj366aefliiRrh2azR7dN3/zzTffeeed66+//uqrr3bfOm+08PgaYRAc53nixInNmjWzwB0xYsSrr75qXfbo0WPUqFHWpc6sXr2a+yLuj45jx469++67rbHq1q07c+ZM6zL6TJZrpKtNmzbt3r07LS2NhUTfsyW5d+/edevWlSlTpmrVqvnyZbIL6dKly+bNmy1J7t+LLrrIuiSTpYBd2G1eCA63iIm8ICAIZBeB4wFKN8aulm80fsnwoUAXbRq67if7c3A9pDSIEwIx793UBcreFlbjlJPUhafHMi17P7G0T+o22QdHbvCkPiBZLC7m8yM3eBbIxlrNy9WwYcNo/cgjj9x4442xdpOtdhs2bJg6dSqffLPVS3rjNWvWzJo1i+z69esbNWqU/Q5j6IF36RUrVkyfPj2Y4JgxYwbvgW+//XavXr1i6DlnmvTu3XvPnj2wThGGi7DGCK1yuGrQoEHnnnuuHrRx48aO0cuVK/fRRx/pwgoVKthrn3/+ed7YtQ5I9erVhw8fbvVjF4uQp23//v3nzp2LzOeff96uXTtL+JxzzrGIPAZavNj2vGsJRZ0Jt0YGhTHUHATfGNhTVlG+fPkoO964cePgwYPZZS1fpUqVBx988NJLL7WaAx3EKJfctv369UOdxKrSmSwFHPKuLoXgcAWXCAcQWL1d/blOrd6mNqZHxJi9Uq3fqWqVVyfXUPWrZLJvD7SRnCCQjgAWJceywXEsXW98MDlgYlC9vGrbwDWyIU1atmzZ4rojaZDjCGTz/Fjz9flUoVj/BoY8P1bPKZ7J5gbJDS7nJzt/ICz05Aa3oMhm5oYbbqAHOI5///vfZHKL48jmKqzmp5xyCq+mxYsXzy12g5nwJglt1LVrV2tWjgwKJo4Sr13qd9cIs8pyjRHa5lhVzZo1OQ/hhsOkImQtpAOra9myJXoWBw8efPfdd9EJ+uCDD84888xwXTnKIez69OmzfPny+vXr89Px8g+ZYvEpVsbRQ/SX/9/emUDbUZR5vMIaQtgEIiMQJAYERhYDQgQUx2EzA+QwMGwqDiqjIII6nkFR5ui44XEdBRdQFDish2UEZQcZNmPCPmEgrBIYRwkIQxIWCbz5XQrrNXfp2/fmvZt+3b8+4Z2+1dXV9f2+uo9X//7qq7Y2XnnllXyRafywww7bYIMNfvGLX1x00UULFy4888wzi7RM4MbMmTPnz5+/995777TTTvfee++FF1541FFHEcex8847xxaSRvP444+3bbNrhbZ3FSzs94+7gs1brYoE7vtDuOT28OhrVq6FF18KjzzZ+HfDfWHd1cJ7tgrT3lhF47VpJAgwq1zSUHX7OWbfN/QfvxlWN1iccthuYbke16fw4BV7zNnRT1+9Z3QI9D1+NlinsWXswude7RZpOGbNa2yhcuTfDfU6hBw/Ob7t20G06Rc8B2xNLvU9fvyCj94IqZLGQSB9U6j86HHr1PKbXjnaXmWS2ba8VIVFOpljY6ls6bUzL7zwwg9+8APUMSIg4rKO/ffff8cdd/z+979fUOBA0TjggAN4o3bSSSehdHz1q1/ttQ9LX//rX/86X4Rzzz33LW95C60dccQRW2+9NeEYrFhBpOjaPoYTYbTuuut+7GMfi5U32mijL37xi5dcckkSOLo2MqoVFDhGFW/VGidZzMV3hOu6hUotWBhOvyn812Ph4On9vyCtGjvtyRBYbeXwbO8CBwr3ZbeG6+cONzR53XDou8PE8T0nUKAJ+uAxRgn0N34w9ogZjaHyzLNDJHC5ZPar1j/2ZLjrd2GbKb3BcPzk8OrPQX7Bc5DW6lJ/4wdEfsFHdZwsvcbx05/+lLfcMW0BL2+ZIKUFGrz+JWUD80ZyAUQrzjjjjLPPPpvCN7zhDcmuJUuWHHfccVdddRUT7OnTp59wwgkExqernHALNxLST9oC4iOOPvrolJ2RJSF8TJWnTp3Ke/j0kVfQn/rUp9LHU045hZUsv/zlL++8805eUPPQeIlJKVH9c+bMWbRo0Tve8Y6DDjpo1113TXftt99+06ZN+9znPhdL+J2GjXSDSP5Y8oUvfGH27L/8vycEmtphhx3ipUsvvfTEE0/kFma/lHzpS186+eST4yVekjP/jOf5Pz/wgQ+svPLK6UYqMxtnLs30+9hjj433EhvyrW996/LLL3/44YeBsO+++zJHZYUCV7tCeO9738tyA2yfN28et8yYMSP1h6lyzMGZY2OqnI8xp4exhQcffJAEGYTA0BkcvdVWW0F48uTJqf1RPbn22mt5LqMlJa1AEQAFiTNYtZEdrp26wSok1qecdtppu+22G0mIOlUb1fLzzjtv7ty5Ud3gQcsvvzzqzDnnnMOwLyJwENuCo7M9jAlNusb1ZG8Z1XMFjlHFW6nGWRb7k/8M9/y+qFG3PxJQOo782zBhpaK3WK8mBNacEP7Y4yuKF5cMnXtDmPvIMKFtp4Z9387mKY3/K/dx0AePMUqg1/Hz8tDQvY+FLTZ8daisPmHcTluEOx8eIttoPB59omeBw/GTM3h6dRBN+QXP4Vm3S72OH7/gAxshS6NxIF7wgpe3+sy3Se15/fXXM+UmAUGc4DHjvfXWW0nrkGxhrkhJ03yJRf4sLdl+++0JsL/sssvIfspccfz48fGub37zm9/4xjfiI5ir80YdsYDlA3H2zgYZa621Vqx58803N60BSVeZPDPzv+KKKz772c/yiht9JKWiJNKeSSBTU2QLJrdMdJEJiO3fdtttY7OYkya9lKBWYMLGG28cr/ITCSD2gcQHTC+z9mJFvMSMHVWFrI2pt5ynFvJPkHuwFzkm6UTXXXfdrFmzEGLSjYgUZDMhvyPSDN1DSUEPIpSACl0hxJ7QMeIO2CAj9ZDpcdp/JMfG2IeuGHN6SAssjiAAB4nhne98JzIZPfnVr36FPETsQPQUQgPkk73phIQaBSMs0i1tT8ipSfmb3/ya3G/xI1ldiggc6HrAZ2FI2/YHU0iujV122SX7LL4yfCyibmTviuc4JcpqWb2vtdogSxQ4Bkl7bD/r/Dnt1Y1Jq4f112poGeTgaDooOf3G8E/vYgVB0xU/1prAOhPDvF6SXSx8bui0axpLCeLBCv+9tw/TN+tT2oiN0AePMUqgp/Gz+Pmhc64P9/8+vGfboV22fHXM8NfPoueHrX/ppeHzgmeOnxxQPTmIdvyC58Cs4aWexo9f8BEfIRdffDGqQU6zTBSZ5Peaj4N4fnQNJqJpuk50RjaxYs4T06Xtttvuu9/9Lh9JfMArdOI+2Anl/e9/PyVIJCgaBERQQhQDJUT+I0AgJcQ5G3P+lF+g9bnEMsSrSCQIJd/5zncIryAiI7tBBhEfCxYsoFqcxdHy7rvvjnyQBI7Uz04naWsMet4UlPHuVw5upM8EYnz84x8n6WOndjqV02EEDqb63B7rcM7+pigy6SPqxqc//ekUVEL4BooAT0Q26goh6iA0hVLAVqaJZ7Y/OTbGavkY6XBOD2mBBLGQ/+QnP/mZz3wmNoisQDxCShGK7dndSVPfyKYxIgIHu/DQJgIBg4E+oPIwZmJuTkrS4/JPlq260do3pMA77rgDhYiVJq1XO5XwewAdECB8C1C4kCzTSOt0y8DKFTgGhnpsP2jOQ+E3D7Q34a/XDzOnhRvvC+e3CBzccO//hivnhj23an+vpfUksH7RtxGv4jnj2mF1g6It39jIwXHzPa9R6KesF9ZbqwfJo9c+1NNT5bS6uO+eeGboJ1eEpxc37Lj8tvDUoqEtJhP1E25/qJGMIx0s3e/1KN6HXluuQP1e4fgFr4DTR9CE4uPHL/gIYk9NoW4wSU4fc06+9rWvscBh0qRJOXXSJWLaec/PhpRJ4Pjwhz+crhY82WeffWJNAiWYkyMT0NsocBBPwZT7ox/9aFQ3qMbkk6Np68qCD2JHjCOPPLKpMuoMIQPpHTXhEkRAYFdTtWX48W1vexs7eiBRRYGD+BfkEmb1aZ0O8Sbw+cQnPpE6CTEEjmuuuQaBIxXGk7YQmur08TEfY9ceRuDPPvssKkZ0LmJBdnkRTnmp3VuLFOnTR5+zt8S4Gx7Ng1jExCU0HcY2J9mQnOwtJT8nHCZ+iaJqWby3eIEvIJvRsnZsjTXWSMOseAujV7N0Agcru1jVwyK9J598kpVsLNjje8hvtOzCudHDYcttCZBA9OLGNkZ9HlfdHd4+NazhioA++VXwttetGlYbHxZmXqHnG/n8a7OJ3/FQ4F/Tsc8OCBxNZR0/8nT64DFGCRQfP+NXHN4XlpBVUoryr+lYZaWw6fpNZV0+On7yARV3UGzHL3g+z7pdLT5+/IKPxthgOQOBEp1aZurIZrH8iY7EQLaLguoGrcWoBEInyM7IzhG8K+ZlLyedHtS2nGCEVM6EfMKECaxQiCWk9uAkLc3gvD9pI7aW3e0ylpDMglUzJGKMH+PPUqkbdClu9kmcBZkUeBVPKATJShChUp9ZgEOdQw89NJWgCnH+0EMtf1SF0Aoh3dX3SVeMXXuI3xlCP/7xj9Fl0JsYQpSgxSRhq8gikb77z41xYQ5TejKbsNiEGB9ChOKKpyTeLU37A76X1SXve9/7WJZFLEbOtj5te4W0xEorUBBWw1ozss/wjeCb3rbygAvLJXCgNRLuBWU0DhbREWa211578S0F/YC5+LgsgVse7mEumr0xnpO8g61V9tqm9Yol9SWwyaRw2/xlZj5P94BIz7wAABXaSURBVBjTBAqOn4mrjPvHXYd+fvXwzilNVrP9ziHvCmTlaCrP/+j4yefD1YIO6tpOfxV0UH/cynNXwfHjF3w0XEYkP0fblnlnTmgA6gYvw1E3yLLZtlrbQlJvkI2CP+yZEf3617/mRT0BIGzlkPKMtr0rpxD9gqj4lKQjTtTji/ScuwpeYhlOU00exNrGkWq/qfER/Eh+CgQOFnqwZyehHGhATKxS+7xyRyRqkqVIz4lSkOqkk1YI6VLfJ10xdu0hFmEd6TCJHSBtxI033si7cFJgsAYqdrjTEhXEr7Qwp+/+cyNbh/CTbwHJTRnAsSk+pkuxZEz8ZE0NgiZfSdSNbFxPT53na0giXsQdVlmdddZZChxt6JF/iIxBJOlhGRiXyS675ZZbctLpV22bJiwaBQK3/W5pG6UFBY6lhVit+zdbL9w+P7xmkUlnA1cqsKUriTkKHsxlebrHmCZQfPysv/a4o/ceum5uuPWB8Hxm7x4GzNYbh3dvHdZZvTd1w/FTZOQUdxCt+QUvgrRWdYqPH7/gAxsYUd3gtTnqBnHWPakbsZMsoOCI58ypiIr/9re/HQWOGG3BFpVNIRJN1mWTRxJ0wMvn9Lo+bqJBAsum7I9NLfT9MebOpP2cFrACE3IqDOAS+2Iwh0LaICMs61MIbcjmPUVjYj3Cstq5A/O7YizSQ5ZCYB0HDbI+goQppF8h1WhcZ0GK1qYMshF728I+PEIPuevuu+/OxjvwkdCYnhJY9PHokb2F6CeCdIgqYFedPiIJCObK6n2MOn4zLPPxnxCVKIKD31N85Y455piobsQu8puOxCdNyiJ5iVD4XLSSvDiqJ8RfPFw0aU7HjvxpcXhqcVjLRQEdCdXuwurjw9RJ4f68PxWGmRy1d29T0OE7253xXJ7uMaYJ9DR+Vpswjqy0M7YbIhnH4udCGBdWW6WxaG454jd6Pxw/RZj15CC/4EWQ1qpOT+PHL/gAxsZSqhsEsfOum6yZ6e955uHkZUTmiNOkmHORPBp77rlnNCfu6ZBVNChn7pqEFebwlKQtTuKyGgpTBd6YEg9OUtINN9xwRBChzrApyfz589OOpMxZsCjOtHlEjNhHBIkhEm1N6NqTuOdL12o5FViTQtpLRCgmVtn1KdzCEiHCZ9iABuEjtsBmq8hMUCJ5R06bTZeWppP5GLv2kMwXmECS0Ti7Zled6Po0tWaDnqbejuxHdm9ZZ511SLB62GGH8XQaZ6kB2+4w8JpCY4gqimOD5K8zZ84c2W7E1ohkIacJC0PYxCQrN3R9Frsmo26QJZSEO3vssUen+p1MIIiG/XdIghO/udyOwETltO1xpwYHVl4igYN9rdHh9t9//6zxlBDEkTYf4tLVV1/NIh/CkLoKHCgjZPTItuZ5HwSe+fNKS14eDl1j/7ZVG9mph481VmmcU8heKtnjhRfDExn8N932wPoTM2n9slVLf84rgmwm7dL3d2x0cLuNwoMLGulCB3kwpeW5HhUg0Ov4WX65cWuvFvi3NIfjpzi9Xh1UvOWcmjooB87YutTr+PELPnr+XUp1g47xfpt5FHOwD37wg+TXI9IBLQMBgr/546yMeRHv9s8991yWMLCD6Q033MC0rdUiFl+QVAJRg51N2XeWtAspHp4Xoogjp59+Or0lVJ75289+9jOElTTrYy53wQUXxDaZ+bMPyymnnMJHlmzQCDtBXHTRRXxk/sxPduVgEssJf/6ljSHYfISpCpIBwfzMbJEJ0FOygf0sBqFjBx544CGHHMKckzwRtJA9mMLwwpwSlBp+MitmbswJ2RyS9MPCB0rI+pm25OASFSgseLBKBYEDcYdsEcDM3nX44YefeuqpbODC5ik0e9ddd0GJF+9RsCgCIba2zTbbYH4EGEuInqAdzrvamI8xv4e0T/8JI0JpQi/jjThzPbrBSEgJaGN/Ru8nc9KPfOQjX/nKVxgJKBeMWLpEeEjTtjh0AIULF3PSlLyTl/RxMyAuzZkzh5/IJVjECflEUhZbPnY9EBoATk/SOO96S6xADl1yyiAnMeDjmI/liHfZaI5OJjC/ZujyjeMbTbZdvrDs3Mwoak3NW7A/I16tRAIHYxTzsuEb/JKaPXs2aTiS2QAlVy35jdLISJdaTz70oQ9BvLXckp4IvH7K9gf922/TLXtuGaY31g81H2/dKPAve9z/h3DSNcMF//wvx98365zhz2PqjM3GWJ82pro8BjqLNLbNhoPOxMEToyQ3BgDZxVwCjp9cPMv+og5a9j4Yyz1w/JTHe7wtX5qVKRiC+sCM/bjjjiOsAGGCkilTprA4hReW0UzUBMoJiDj7lYP5M1fRROLSFerEGThpO1iMEKcA3PKjH/0o+878hz/8IY9AJeEpzPdQUlj5ntawMJ37/Oc/Hx8Xf8aPvDFF4CAuI3s1zTJ4LZ8EDqwghwgbdjBFpwUezft5NmpJbdJ/hBVeZdMUU26e3rSNC4lLWDaS6tPPeI5dyA3xnIyV3IXtiCyxhKf0JHDAFpUE8QKppSkNKgksUI7oJ6sSiI5B3CGe4stf/nKkVARC7BI74LA56/HHH59CbMixEgWOrjbmY8zvIU9H5CJxAboMkHk69VHH2NOXzKOxbwP4GV+xM2KjckHwCxoHslrTo5HwKGEIZee2lBDp0LRKiO9XvBeVpCeBIz6CWJKmR3f9SLZX6hC/w5GtjMKSFTg6mXDwwQezUIggjpSFhNSZ0GgS1LItD/h8XBqaA35w6+PYd5rsL48++mgKd0HQgjK/v5DKYn0yxyDcIi/xywWVtLWRbAn33nLLLdkSz/sgsPp6W+x4+IXpxhlbh60np0+NkwkrNXbEePbPzYlIH3kinJX51tx+/jF/vKchZI7Fg/+fpRDEsdj/0vZ5ycvhotvDk6/s4jmATq69avj7t4bi2ToG0CUfsTQEHD9LQ28A9+qgAUCu8CMcP5V0Li9+mVczjW+1joALMmswJycfZM4iCNaA8L6TVAht6/AunSgJZuwTJ05sfcSIlNABAsxjLobWBsk3yXIJ0jEQk9J6tSQl9J8JF5yjKrFMepWPsWsPYwXUHIZTH/1nCDEnZ+4ZM3e0toBoQlAPOlHrpVTC3j0oWW0HM3WOPfZYhCq0j6SRpRuLnzA15m39rFmzWm8hxoelXptvvnmM/mit0NXG1luaSvJNQENgqBP4w8w9Znhtuj1+xNGsxkAPIuClvwpt78ovLFEERxQvkTOQEuk03oqBLinDKA4mput73/te27i1VjuTMtp6yZLiBJ55LvzrsL4RLr2z8S97/M3mYea0QBrR8xthVh2Pn5/87xs3Eg97SGCYwArLhd22CBfcFtiKeLSPFZdvPEt1Y7Q5D7J9x88gaffxLB3UBzRvSQQcPwlFlU5yEjES/L/pppt2NZbQCY5O1dBHijTS6fYi5TlP53Zyi3AUaaenOiQrIVlG21vY16PXzWhYYjPIkIe23c7H2LWHXSu0fWhTIQEITDxjIVEhTSOHqTv7g8SrO++8c4pWSI1ktyVOhemEFDMs38jJcJFqNp2w8OSEE06IhaQCpZGmCvEjK7846er6fBvbtpwK801AYURJ5Ej1syesIYrLrGK0SPZSPO9aofWW4iUlEjhYukZCWhY1kbOE33HITqynIntKXI0GHfQOYsPa6rXFDbZmrwRWXyVMHB8WPd/rfa+pP25c+Ks1X1PiBwlEAmuuEvbYIlw6d3STcbAyn6fwLI+KEXD8lNyhOqjkDip59xw/JXeQ3RsYAZbtE27Q9nHEtned5ba9sc6FhK5klxeBoikGga082fo0Idpkk03SecETpA2CRLJ5JAveSHBQNiUluVTa3kg5uVSyNZuqdbWxqX7rx75NoCniVpjLpzbTIqxU0rVCqtnHSYmWqNB74spQN1gDxoCYMWMGUUPz5s2L0UFEUqVUycSwoXcg3REYM3oRaH3QrOotZ94c5jSSIrU/YgTHjfflRXBMWTccvXv72y2VAAQeeiJcfc9oaRyoG7tuHqY08oV5VJOA46fkftVBJXdQybvn+Cm5g+zeYAiQnLLtg0g4wkKJtpcslEA9CZRL4GjyATIHqVlYv9RUTv6eIjk4mu7yY98EHvhjOPHqjncXETgOnh52eFPHFrwgAQg89lS44r9Hfq0KK1OI3digvfwt+OoQcPyU3Jc6qOQOKnn3HD8ld5Ddk4AEJFAeAsuVpytNPXn66adJNUQO5KZyPhLtQ/hGa7klo0Rg6uvDmzquduz+zLUnhm3f2L2aNWpOAA1iv2mBPKAjeNAabapujCDS0jbl+Cmta2LHdFDJHVTy7jl+Su4guycBCUigPATKK3CQc5VlRW33m2FFkNujDHgM/cP2gTfhbY+hEIb4r8MxLoQDtg8rdLi3w00W15QAy633fWuYNjmwqGQpD1qgHfZMoU2PmhBw/JTc0Tqo5A4qefccPyV3kN2TgAQkUBICpV6iUhJGdiMSuOvRcOr1PcNggxXWsHhIoCcC//dcuOWR8MDjobN01rE9tJGpk8J2G4U1lDY6Qqr4BcdPyR2sg0ruoJJ3z/FTcgfZPQlIQALLloACx7LlP8ae/tsHw3mzw0svF+32e7YKe2xZtLL1JNBE4Jnnw71/CPc/HhYW28RntfFhk0lhs/XC6uObWvJjHQk4fkrudR1UcgeVvHuOn5I7yO5JQAISWFYEFDiWFfmx+tyHF4TTbwpPLe7S/1VXDgfuELbasEs1L0ugCIE/LQ7/83RYsCg8/WxY9EL485KGyrb8cmGlFcLElcOaE8K6E8P6a4bXjWj+jiIds86YIOD4KbmbdFDJHVTy7jl+Su4guycBCUhgwAQUOAYMvAqPY3p50/2BfWGfHN7eeNgu3qK/fWp412ZhgltWDVPxTAISkIAEJCABCUhAAhKQgARGl4ACx+jyrXbrj/0p/O6JhszxwothxRUa7883WidMfl1Yrry5a6vtEK2TgAQkIAEJSEACEpCABCRQXwIKHPX1vZZLQAISkIAEJCABCUhAAhKQgAQqQ8BX7ZVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4EFDjq63stl4AEJCABCUhAAhKQgAQkIAEJVIaAAkdlXKkhEpCABCQgAQlIQAISkIAEJCCB+hJQ4Kiv77VcAhKQgAQkIAEJSEACEpCABCRQGQIKHJVxpYZIQAISkIAEJCABCUhAAhKQgATqS0CBo76+13IJSEACEpCABCQgAQlIQAISkEBlCChwVMaVGiIBCUhAAhKQgAQkIAEJSEACEqgvAQWO+vpeyyUgAQlIQAISkIAEJCABCUhAApUhoMBRGVdqiAQkIAEJSEACEpCABCQgAQlIoL4E/h8QM8OEp9IJdwAAAABJRU5ErkJggg==" + } + }, + "cell_type": "markdown", + "id": "0aa14d2f", + "metadata": {}, + "source": [ + "## Decompose the circuit with wire cutting\n", + "\n", + "In this example, we will use a manual method to specify the wire cuts. See [tutorial 1](tutorial_1_automatic_cut_finding.ipynb) for how to automatically cut a circuit. The figure below shows the steps for producing the `subcircuit_vertices` argument for the `cut_circuit_wires` function.\n", + "\n", + " * `method='manual'`: Manually specify the wire cuts\n", + " * `subcircuit_vertices`: A list of lists containing the two-qubit gate indices appearing on either side of the cut(s)\n", + "![how-to-manual-cut.png](attachment:how-to-manual-cut.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8c11457a", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import cut_circuit_wires\n", + "\n", + "cuts = cut_circuit_wires(\n", + " circuit=circuit, method=\"manual\", subcircuit_vertices=[[0, 1], [2, 3]]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7f826b8c", + "metadata": {}, + "source": [ + "**The two subcircuits produced**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5816c27f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# visualize the first subcircuit\n", + "cuts[\"subcircuits\"][0].draw(\"mpl\", fold=-1, scale=0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5f605d57", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# visualize the second subcircuit\n", + "cuts[\"subcircuits\"][1].draw(\"mpl\", fold=-1, scale=0.6)" + ] + }, + { + "cell_type": "markdown", + "id": "742ec1e1", + "metadata": {}, + "source": [ + "## Evaluate the subcircuits" + ] + }, + { + "cell_type": "markdown", + "id": "f47fc118", + "metadata": {}, + "source": [ + "**Set up the Qiskit Runtime Service**\n", + "\n", + "The Qiskit Runtime Service provides access to Qiskit Runtime Primitives and quantum backends. See the [Qiskit Runtime documentation](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/) for more information.\n", + "Alternatively, if a Qiskit Runtime Service is not passed, then a local statevector simulator will be used with the [Qiskit Primitives](https://qiskit.org/documentation/apidoc/primitives.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7fd84c6e", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import (\n", + " QiskitRuntimeService,\n", + " Options,\n", + ")\n", + "\n", + "# Use local versions of the primitives by default.\n", + "service = None\n", + "\n", + "# Uncomment the following line to instead use Qiskit Runtime Service.\n", + "# service = QiskitRuntimeService()" + ] + }, + { + "cell_type": "markdown", + "id": "b2f2d1aa", + "metadata": {}, + "source": [ + "**Configure the Qiskit Runtime Primitive**\n", + "\n", + "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Qiskit Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits. Backends could be [simulator(s) and/or quantum device(s)](https://quantum-computing.ibm.com/services/resources?tab=systems). In this tutorial, two local cores will be used to support each of the parallel backend threads we'll specify below.\n", + "\n", + "If no service was set up, the `backend_names` argument will be ignored, and Qiskit Primitives will be used with statevector simulator." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "aafc01ef", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the Sampler and runtime options\n", + "options = Options(execution={\"shots\": 4000})\n", + "\n", + "# Run 2 parallel qasm simulator threads\n", + "backend_names = [\"ibmq_qasm_simulator\"] * 2" + ] + }, + { + "cell_type": "markdown", + "id": "f1f09e56", + "metadata": {}, + "source": [ + "**Evaluate the subcircuits on the backend(s)**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2ae5160c", + "metadata": {}, + "outputs": [], + "source": [ + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import evaluate_subcircuits\n", + "\n", + "subcircuit_instance_probabilities = evaluate_subcircuits(cuts)\n", + "\n", + "# Uncomment the following lines to instead use Qiskit Runtime Service as configured above.\n", + "# subcircuit_instance_probabilities = evaluate_subcircuits(cuts,\n", + "# service_args=service.active_account(),\n", + "# backend_names=backend_names,\n", + "# options=options,\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "id": "3a5ee37e", + "metadata": {}, + "source": [ + " **See [tutorial 1](tutorial_1_automatic_cut_finding.ipynb) for more info about the subcircuit results.**" + ] + }, + { + "cell_type": "markdown", + "id": "17e8511c", + "metadata": {}, + "source": [ + "## Reconstruct the full circuit output\n", + "\n", + "Next, the results of the subcircuit experiments are classically postprocessed to reconstruct the original circuit's full probability distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "5aceecc0", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import (\n", + " reconstruct_full_distribution,\n", + ")\n", + "\n", + "reconstructed_probabilities = reconstruct_full_distribution(\n", + " circuit, subcircuit_instance_probabilities, cuts\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "40277ef0", + "metadata": {}, + "source": [ + "## Verify the results\n", + "\n", + "If the original circuit is small enough, we can use a statevector simulator to check the results of cutting against the original circuit's exact probability distribution (ground truth)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5353b0c8", + "metadata": {}, + "outputs": [], + "source": [ + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import verify\n", + "\n", + "metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)" + ] + }, + { + "cell_type": "markdown", + "id": "b220335e", + "metadata": {}, + "source": [ + "**The verify step includes several metrics**\n", + "\n", + "For example, the chi square loss is computed. Since we're using the Qiskit Sampler with statevector simulator, we expect the reconstructed distributed to exactly match the ground truth. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting_toolbox/utils/metrics.py)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8d54b767", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'nearest': {'chi2': 0,\n", + " 'Mean Squared Error': 1.554441697306333e-32,\n", + " 'Mean Absolute Percentage Error': 4.748059826691265e-14,\n", + " 'Cross Entropy': 2.599681088367844,\n", + " 'HOP': 0.9004283905932716},\n", + " 'naive': {'chi2': 0,\n", + " 'Mean Squared Error': 6.5760386594463524e-34,\n", + " 'Mean Absolute Percentage Error': 5.260720927719812e-14,\n", + " 'Cross Entropy': 2.5996810883678423,\n", + " 'HOP': 0.9004283905932736}}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics" + ] + }, + { + "cell_type": "markdown", + "id": "ec8c120e", + "metadata": {}, + "source": [ + "**Visualize both distributions**\n", + "\n", + "If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c8cc97e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.visualization import plot_histogram\n", + "from qiskit.result import ProbDistribution\n", + "\n", + "# Create a dict for the reconstructed distribution\n", + "reconstructed_distribution = {\n", + " i: prob for i, prob in enumerate(reconstructed_probabilities)\n", + "}\n", + "\n", + "# Represent states as bitstrings (instead of ints)\n", + "reconstructed_dict_bitstring = ProbDistribution(\n", + " data=reconstructed_distribution\n", + ").binary_probabilities(num_bits=num_qubits)\n", + "\n", + "\n", + "# Create the ground truth distribution dict\n", + "exact_distribution = {i: prob for i, prob in enumerate(exact_probabilities)}\n", + "\n", + "# Represent states as bitstrings (instead of ints)\n", + "exact_dict_bitstring = ProbDistribution(data=exact_distribution).binary_probabilities(\n", + " num_bits=num_qubits\n", + ")\n", + "\n", + "# plot a histogram of the distributions\n", + "plot_histogram(\n", + " [exact_dict_bitstring, reconstructed_dict_bitstring],\n", + " number_to_keep=8,\n", + " figsize=(16, 6),\n", + " sort=\"asc\",\n", + " legend=[\"Exact\", \"Reconstructed\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6a3261e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ibmq-provider0.19.2
qiskit-nature0.4.5
System information
Python version3.10.6
Python compilerClang 13.1.6 (clang-1316.0.21.2.5)
Python buildmain, Aug 11 2022 13:49:25
OSDarwin
CPUs8
Memory (Gb)32.0
Wed Oct 26 11:28:28 2022 CDT
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import qiskit.tools.jupyter\n", + "\n", + "%qiskit_version_table" + ] + }, + { + "cell_type": "markdown", + "id": "d55d9f98", + "metadata": {}, + "source": [ + "This code is a Qiskit project.\n", + "© Copyright IBM 2022.\n", + "\n", + "This code is licensed under the Apache License, Version 2.0. You may\n", + "obtain a copy of this license in the LICENSE.txt file in the root directory\n", + "of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.\n", + "\n", + "Any modifications or derivative works of this code must retain this\n", + "copyright notice, and modified files need to carry a notice indicating\n", + "that they have been altered from the originals." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/circuit_cutting/tutorials/tutorial_1_circuit_cutting_automatic_cut_finding.ipynb b/docs/circuit_cutting/tutorials/tutorial_3_cutting_with_quantum_serverless.ipynb similarity index 50% rename from docs/circuit_cutting/tutorials/tutorial_1_circuit_cutting_automatic_cut_finding.ipynb rename to docs/circuit_cutting/tutorials/tutorial_3_cutting_with_quantum_serverless.ipynb index af7f2afc0..f8aed1cc8 100644 --- a/docs/circuit_cutting/tutorials/tutorial_1_circuit_cutting_automatic_cut_finding.ipynb +++ b/docs/circuit_cutting/tutorials/tutorial_3_cutting_with_quantum_serverless.ipynb @@ -5,17 +5,21 @@ "id": "c6cd641f", "metadata": {}, "source": [ - "# Circuit Cutting with Automatic Cut Finding\n", + "# Tutorial 3: Circuit Cutting with Quantum Serverless\n", "\n", - "Circuit cutting is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n", + "**Circuit cutting** is a technique to decompose a quantum circuit into smaller circuits, whose results can be knitted together to reconstruct the original circuit output. \n", "\n", "The circuit knitting toolbox implements a wire cutting method presented in [CutQC](https://doi.org/10.1145/3445814.3446758) (Tang et al.). This method allows a circuit wire to be cut such that the generated subcircuits are amended by measurements in the Pauli bases and by state preparation of four Pauli eigenstates (see Fig. 4 of [CutQC](https://doi.org/10.1145/3445814.3446758)).\n", "\n", "This wire cutting technique is comprised of the following basic steps:\n", "\n", - "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use an automatic method to find optimal cut(s). See [tutorial 2](tutorial_2_circuit_cutting_manual_cutting.ipynb) to manually cut a circuit.\n", + "1. **Decompose**: Cut a circuit into multiple subcircuits. Here, we'll use an automatic method to find optimal cut(s). See [tutorial 2](tutorial_2_manual_cutting.ipynb) to manually cut a circuit.\n", "2. **Evaluate**: Execute those subcircuits on quantum backend(s).\n", - "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution)." + "3. **Reconstruct**: Knit the subcircuit results together to reconstruct the original circuit output (in this case, the full probability distribution).\n", + "\n", + "**[Quantum serverless](https://github.com/Qiskit-Extensions/quantum-serverless)** is a platform built to enable distributed computing across a variety of classical and quantum backends.\n", + "\n", + "In this demo, we will show how to use quantum serverless to run portions of this workflow on a remote cluster." ] }, { @@ -25,7 +29,7 @@ "source": [ "## Create a quantum circuit with Qiskit\n", "\n", - "In this case, we'll create a hardware-efficient circuit with two (linear) entangling layers." + "In this case, we'll create a hardware-efficient circuit (`EfficientSU2` from the Qiskit circuit library) with two (linear) entangling layers." ] }, { @@ -67,261 +71,181 @@ }, { "cell_type": "markdown", - "id": "461e57e3", + "id": "c6382121", "metadata": {}, "source": [ - "## Set up the Qiskit Runtime Service\n", + "## Set up Quantum Serverless\n", "\n", - "The Qiskit Runtime Service provides access to IBM Runtime Primitives and quantum backends.\n", - "Alternatively, a local statevector simulator can be used with the Qiskit primitives." + "We can use Quantum Serverless to allocate the steps of wire cutting to various compute resources. For this tutorial, we will use our local CPU cores as our cluster. See the [Quantum Serverless](https://github.com/Qiskit-Extensions/quantum-serverless) documentation (and below) for more informatin about how to use other clusters." ] }, { "cell_type": "code", "execution_count": 2, - "id": "5d1fb2ca", + "id": "175b4f9e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from qiskit_ibm_runtime import (\n", - " QiskitRuntimeService,\n", - " Options,\n", - ")\n", + "from quantum_serverless import QuantumServerless, get\n", "\n", - "# Use local versions of the primitives by default.\n", - "service = None\n", - "\n", - "# Uncomment the following line to instead use Qiskit Runtime.\n", - "# service = QiskitRuntimeService()" + "serverless = QuantumServerless()\n", + "serverless.providers()" ] }, { "cell_type": "markdown", - "id": "0cab5dd8", - "metadata": {}, - "source": [ - "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3cc622d9", + "id": "0aa14d2f", "metadata": {}, - "outputs": [], "source": [ - "# Set the Sampler and runtime options\n", - "options = Options(execution={\"shots\": 4000})\n", + "## Decompose the circuit with wire cutting \n", "\n", - "# Run 2 parallel qasm simulator threads\n", - "backend_names = [\"ibmq_qasm_simulator\"] * 2" - ] - }, - { - "cell_type": "markdown", - "id": "61d2944a", - "metadata": {}, - "source": [ - "## Set up the Wire Cutter from the Circuit Knitting Toolbox\n", + "**Use `quantum-serverless` to send the `cut_circuit_wires` method to a remote cluster**\n", "\n", - "Instantiate a `WireCutter` with the circuit and runtime information." + "Here we create a wrapper function for the `cut_circuit_wires` function and annotate it with the `@run_qiskit_remote()` decorator from `quantum-serverless`. This allows us to call this function from a serverless context and have it sent for remote execution on the specified cluster." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "57c8dccb", + "execution_count": 3, + "id": "8c11457a", "metadata": {}, "outputs": [], "source": [ - "from circuit_knitting_toolbox.circuit_cutting import WireCutter\n", - "\n", - "cutter = WireCutter(\n", - " circuit, service=service, backend_names=backend_names, options=options\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9c18caea", - "metadata": {}, - "source": [ - "Note: if only a circuit is passed to `WireCutter`, a local Qiskit Sampler with the statevector simulator will be used instead:
\n", - "```cutter = WireCutter(circuit)```" + "from typing import Optional, Sequence, Any, Dict\n", + "from nptyping import NDArray\n", + "from qiskit import QuantumCircuit\n", + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import cut_circuit_wires\n", + "from quantum_serverless import run_qiskit_remote, get\n", + "\n", + "# Create a wrapper function to be sent to remote cluster\n", + "@run_qiskit_remote()\n", + "def cut_circuit_wires_remote(\n", + " circuit: QuantumCircuit,\n", + " method: str,\n", + " subcircuit_vertices: Optional[Sequence[Sequence[int]]] = None,\n", + " max_subcircuit_width: Optional[int] = None,\n", + " max_subcircuit_cuts: Optional[int] = None,\n", + " max_subcircuit_size: Optional[int] = None,\n", + " max_cuts: Optional[int] = None,\n", + " num_subcircuits: Optional[Sequence[int]] = None,\n", + ") -> Dict[str, Any]:\n", + " return cut_circuit_wires(\n", + " circuit=circuit,\n", + " method=method,\n", + " subcircuit_vertices=subcircuit_vertices,\n", + " max_subcircuit_width=max_subcircuit_width,\n", + " max_subcircuit_cuts=max_subcircuit_cuts,\n", + " max_subcircuit_size=max_subcircuit_size,\n", + " max_cuts=max_cuts,\n", + " num_subcircuits=num_subcircuits,\n", + " )" ] }, { "cell_type": "markdown", - "id": "0aa14d2f", + "id": "467a0e87", "metadata": {}, "source": [ - "## Decompose the circuit with wire cutting\n", + "**Decompose the circuit in a serverless context**\n", + "\n", + "In this example, we will use an automatic method to find cuts matching our criteria. See [tutorial 1](tutorial_1_automatic_cut_finding.ipynb) for how to configure the inputs for the automatic method. See [tutorial 2](tutorial_2_manual_cutting.ipynb) for how to manually cut a circuit.\n", + " \n", + "We will call the `cut_circuit_wires_remote` function within a `QuantumServerless` context, which means it will be run on the specified cluster. Remember, the default cluster for this demo will use the cores on our local CPU. To specify a new cluster, the `QuantumServerless.set_provider` method should be used.\n", "\n", - "In this example, we will use an automatic method to find cuts matching our criteria. See [tutorial 2](tutorial_2_circuit_cutting_manual_cutting.ipynb) for how to manually cut a circuit.\n", - " * `method='automatic`: Use a mixed integer programming (MIP) model to find optimal cut(s)\n", - " * `max_subcircuit_width (int)`: Only allow subcircuits with 6 qubits or less\n", - " * `max_cuts (int)`: Cut the circuit no more than two times\n", - " * `num_subcircuits (list)`: The number of subcircuits to try, in this case only 2 subcircuits" + "When the remote function is called, it will return a \"future\" object, and Python will continue interpreting the next line of code. The `get` function from `quantum-serverless` is a blocking command which should be used to retrieve the results of the remote function via the \"future\" object. The program will not continue past the `get` call until the results of the remote function are returned." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "8c11457a", + "execution_count": 4, + "id": "ecad9a4a", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-10-22 12:34:10,133\tINFO worker.py:1518 -- Started a local Ray instance.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Exporting as a LP file to let you check the model that will be solved : inf \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Version identifier: 22.1.0.0 | 2022-03-27 | 54982fbec\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m CPXPARAM_Read_DataCheck 1\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m CPXPARAM_TimeLimit 300\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Warning: Non-integral bounds for integer variables rounded.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Tried aggregator 3 times.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m MIP Presolve eliminated 37 rows and 8 columns.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m MIP Presolve modified 7 coefficients.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Aggregator did 103 substitutions.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Reduced MIP has 366 rows, 127 columns, and 1072 nonzeros.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Reduced MIP has 121 binaries, 6 generals, 0 SOSs, and 0 indicators.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Presolve time = 0.00 sec. (2.10 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Found incumbent of value 2.000000 after 0.01 sec. (3.52 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Probing fixed 18 vars, tightened 0 bounds.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Probing changed sense of 36 constraints.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Probing time = 0.00 sec. (1.05 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Cover probing fixed 4 vars, tightened 14 bounds.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Tried aggregator 2 times.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m MIP Presolve eliminated 347 rows and 108 columns.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m MIP Presolve modified 102 coefficients.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Aggregator did 19 substitutions.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m All rows and columns eliminated.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Presolve time = 0.00 sec. (0.90 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Root node processing (before b&c):\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Real time = 0.01 sec. (5.60 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Parallel b&c, 8 threads:\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Real time = 0.00 sec. (0.00 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Sync time (average) = 0.00 sec.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Wait time (average) = 0.00 sec.\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ------------\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Total (root+branch&cut) = 0.01 sec. (5.60 ticks)\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m --------------------\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m subcircuit 0\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ρ qubits = 0, O qubits = 2, width = 5, effective = 3, depth = 8, size = 19\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ┌─────────┐ ┌─────────┐ ┌───────┐ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_0: ─┤ Ry(0.0) ├───■──┤ Ry(π/2) ├──────────────────■────────┤ Ry(π) ├───»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ├─────────┴┐┌─┴─┐└─────────┘┌───────────┐ ┌─┴─┐ └───────┘ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_1: ─┤ Ry(π/16) ├┤ X ├─────■─────┤ Ry(9π/16) ├───┤ X ├──────────■───────»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ├─────────┬┘└───┘ ┌─┴─┐ └───────────┘┌──┴───┴───┐ ┌─┴─┐ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_2: ─┤ Ry(π/8) ├─────────┤ X ├─────────■──────┤ Ry(5π/8) ├────┤ X ├─────»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ┌┴─────────┴┐ └───┘ ┌─┴─┐ └──────────┘┌───┴───┴────┐»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_3: ┤ Ry(3π/16) ├────────────────────┤ X ├─────────■──────┤ Ry(11π/16) ├»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m └┬─────────┬┘ └───┘ ┌─┴─┐ └────────────┘»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_4: ─┤ Ry(π/4) ├─────────────────────────────────┤ X ├──────────────────»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m └─────────┘ └───┘ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_0: ────────────────────────────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « ┌──────────────────────┐ \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_1: ┤ Ry(3.33794219443916) ├────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « └──────────────────────┘┌──────────┐\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_2: ───────────■────────────┤ Ry(9π/8) ├\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « ┌─┴─┐ └──────────┘\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_3: ─────────┤ X ├──────────────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « └───┘ \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_4: ────────────────────────────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m subcircuit 1\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ρ qubits = 2, O qubits = 0, width = 5, effective = 5, depth = 8, size = 19\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ┌──────────────────────┐»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_0: ────────────────────────────────────■───────┤ Ry(3.73064127613788) ├»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ┌──────────┐ ┌─┴─┐ └──────────────────────┘»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_1: ───────────────■──┤ Ry(3π/4) ├────┤ X ├────────────────■────────────»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ┌───────────┐┌─┴─┐└──────────┘┌───┴───┴────┐ ┌─┴─┐ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_2: ┤ Ry(5π/16) ├┤ X ├─────■──────┤ Ry(13π/16) ├─────────┤ X ├──────────»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m └┬──────────┤└───┘ ┌─┴─┐ └────────────┘ ┌──┴───┴───┐ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_3: ─┤ Ry(3π/8) ├────────┤ X ├──────────■─────────────┤ Ry(7π/8) ├──────»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m ┌┴──────────┤ └───┘ ┌─┴─┐ ┌┴──────────┴┐ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m q_4: ┤ Ry(7π/16) ├─────────────────────┤ X ├──────────┤ Ry(15π/16) ├─────»\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m └───────────┘ └───┘ └────────────┘ »\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_0: ───────────────────────────────────────────────────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « ┌──────────┐ \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_1: ┤ Ry(5π/4) ├───────────────────────────────────────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « └──────────┘┌─────────────────────┐ \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_2: ─────■──────┤ Ry(4.1233403578366) ├────────────────────────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « ┌─┴─┐ └─────────────────────┘ ┌───────────┐ \n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_3: ───┤ X ├───────────────■────────────────┤ Ry(11π/8) ├──────\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « └───┘ ┌─┴─┐ ┌────┴───────────┴─────┐\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m «q_4: ─────────────────────┤ X ├─────────┤ Ry(4.51603943953533) ├\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m « └───┘ └──────────────────────┘\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Estimated cost = 4.096e+03\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m Model objective value = 2.00e+00\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m MIP runtime: 0.009525060653686523\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m NOT OPTIMAL, MIP gap = 0.0\n", - "\u001b[2m\u001b[36m(cut_circuit_wires pid=8281)\u001b[0m --------------------\n" + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Exporting as a LP file to let you check the model that will be solved : inf \n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Version identifier: 22.1.0.0 | 2022-03-27 | 54982fbec\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m CPXPARAM_Read_DataCheck 1\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m CPXPARAM_TimeLimit 300\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Warning: Non-integral bounds for integer variables rounded.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Tried aggregator 3 times.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m MIP Presolve eliminated 37 rows and 8 columns.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m MIP Presolve modified 7 coefficients.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Aggregator did 103 substitutions.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Reduced MIP has 366 rows, 127 columns, and 1072 nonzeros.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Reduced MIP has 121 binaries, 6 generals, 0 SOSs, and 0 indicators.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Presolve time = 0.00 sec. (2.10 ticks)\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Found incumbent of value 2.000000 after 0.01 sec. (3.52 ticks)\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Probing fixed 18 vars, tightened 0 bounds.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Probing changed sense of 36 constraints.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Probing time = 0.00 sec. (1.05 ticks)\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Cover probing fixed 4 vars, tightened 14 bounds.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Tried aggregator 2 times.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m MIP Presolve eliminated 347 rows and 108 columns.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m MIP Presolve modified 102 coefficients.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Aggregator did 19 substitutions.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m All rows and columns eliminated.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Presolve time = 0.00 sec. (0.90 ticks)\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m \n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Root node processing (before b&c):\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Real time = 0.01 sec. (5.60 ticks)\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Parallel b&c, 16 threads:\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Real time = 0.00 sec. (0.00 ticks)\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Sync time (average) = 0.00 sec.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Wait time (average) = 0.00 sec.\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m ------------\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m Total (root+branch&cut) = 0.01 sec. (5.60 ticks)\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m --------------------\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m subcircuit 0\n", + "\u001b[2m\u001b[36m(cut_circuit_wires_remote pid=46539)\u001b[0m ρ qubits = 0, O qubits = 2, width = 5, effective = 3, depth = 8, size = 19\n" ] - } - ], - "source": [ - "cuts = cutter.decompose(\n", - " method=\"automatic\",\n", - " max_subcircuit_width=5,\n", - " max_cuts=2,\n", - " num_subcircuits=[2],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "fb52c53e", - "metadata": {}, - "source": [ - "**The results from decompose includes information about the wire cutting process, e.g.,**\n", - "\n", - "- `subcircuits`: list of QuantumCircuit objects for the subcircuits\n", - "- `complete_path_map`: a dictionary mapping indices of qubits in original circuit to their indices in the subcircuits\n", - "- `num_cuts`: the number of times the circuit was cut\n", - "- `classical_cost`: the final value of the cost function used to find optimal cut(s)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "465733e2", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "dict_keys(['max_subcircuit_width', 'subcircuits', 'complete_path_map', 'num_cuts', 'counter', 'classical_cost'])\n" + "/Users/caleb/projects/repos_public_acct/circuit-knitting-toolbox/ckt/lib/python3.10/site-packages/ray/_private/worker.py:976: UserWarning: len(ctx) is deprecated. Use len(ctx.address_info) instead.\n", + " warnings.warn(\"len(ctx) is deprecated. Use len(ctx.address_info) instead.\")\n" ] } ], "source": [ - "print(cuts.keys())" + "with serverless:\n", + " cuts_future = cut_circuit_wires_remote(\n", + " circuit=circuit,\n", + " method=\"automatic\",\n", + " max_subcircuit_width=5,\n", + " max_cuts=2,\n", + " num_subcircuits=[2],\n", + " )\n", + " cuts = get(cuts_future)" ] }, { "cell_type": "markdown", - "id": "2bd643fc", + "id": "27e3d3eb", "metadata": {}, "source": [ - "**The two subcircuits produced:**" + "**The two subcircuits produced**" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "938f7733", + "execution_count": 5, + "id": "13dc6132", "metadata": {}, "outputs": [ { @@ -331,7 +255,7 @@ "
" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -343,8 +267,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "1c3a5712", + "execution_count": 6, + "id": "c5eebeaa", "metadata": {}, "outputs": [ { @@ -354,7 +278,7 @@ "
" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -366,172 +290,189 @@ }, { "cell_type": "markdown", - "id": "742ec1e1", + "id": "4200f91f", "metadata": {}, "source": [ - "## Evaluate the subcircuits with Qiskit Runtime\n", + "## Evaluate the subcircuits\n", "\n", + "**Use `quantum-serverless` to send the `evaluate_subcircuits` method to a remote cluster**\n", "\n", - "Note that two local cores will be used to support each of the parallel backend threads we specified earlier." + "Here we create a wrapper function for the `evaluate_subcircuits` function and annotate it with the `@run_qiskit_remote()` decorator from `quantum-serverless`. This allows us to call this function from a serverless context and have it sent for remote execution on the specified cluster." ] }, { "cell_type": "code", - "execution_count": 9, - "id": "2ae5160c", + "execution_count": 7, + "id": "3234f5f8", "metadata": {}, "outputs": [], "source": [ - "subcircuit_instance_probabilities = cutter.evaluate(cuts)" + "from qiskit_ibm_runtime import Options\n", + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import evaluate_subcircuits\n", + "\n", + "# Create a wrapper function to be sent to remote cluster\n", + "@run_qiskit_remote()\n", + "def evaluate_subcircuits_remote(\n", + " cuts: Dict[str, Any],\n", + " service_args: Optional[Dict[str, Any]] = None,\n", + " backend_names: Optional[Sequence[str]] = None,\n", + " options: Optional[Options] = None,\n", + ") -> Dict[int, Dict[int, NDArray]]:\n", + " return evaluate_subcircuits(\n", + " cuts, service_args=service_args, backend_names=backend_names, options=options\n", + " )" ] }, { "cell_type": "markdown", - "id": "66997ed4", + "id": "461e57e3", "metadata": {}, "source": [ - "**Inspecting the subcircuit results**\n", + "**Set up the Qiskit Runtime Service**\n", "\n", - "In this case, the original circuit was cut 2 times (we can also get this info from `cuts['num_cuts']`):" + "The Qiskit Runtime Service provides access to IBM Runtime Primitives and quantum backends. See the [Qiskit Runtime documentation](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/) for more information.\n", + "Alternatively, a local statevector simulator can be used with the [Qiskit Primitives](https://qiskit.org/documentation/apidoc/primitives.html)." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "fa22661e", + "execution_count": 8, + "id": "5d1fb2ca", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of subcircuits: 2\n" - ] - } - ], + "outputs": [], "source": [ - "print(\"Number of subcircuits: \", len(subcircuit_instance_probabilities))" + "from qiskit_ibm_runtime import (\n", + " QiskitRuntimeService,\n", + " Options,\n", + ")\n", + "\n", + "# Use local versions of the primitives by default.\n", + "service = None\n", + "\n", + "# Uncomment the following line to instead use Qiskit Runtime.\n", + "# service = QiskitRuntimeService()" ] }, { "cell_type": "markdown", - "id": "2ee40d04", + "id": "0cab5dd8", "metadata": {}, "source": [ - "From these two wire cuts, there are $4^2=16$ variants of the first subcircuit corresponding to the combination of measurement bases: $P_i\\otimes P_j$, for the Paulis $P_i \\in \\{I, X, Y, Z \\}$. And there are $4^2=16$ variants of the second subcircuit corresponding to the combination of initialization states: $|s_i\\rangle\\otimes|s_j\\rangle$, where $|s_i\\rangle \\in \\{ |0\\rangle, |1\\rangle, |+\\rangle |+i\\rangle\\}$. \n", + "**Configure the Qiskit Runtime Primitive**\n", "\n", + "The wire cutter tool uses a `Sampler` primitive to evaluate the probabilities of each subcircuit. Here, we configure the options for the Runtime Sampler and specify the backend(s) to be used to evaluate the subcircuits. Backends could be [simulator(s) and/or quantum device(s)](https://quantum-computing.ibm.com/services/resources?tab=systems). In this tutorial, two local cores will be used to support each of the parallel backend threads we'll specify below.\n", "\n", - "Note that some subcircuit probabilities can be negative (and not sum to unity). This is because the raw probabilities from subcircuits must be modified to account for the measurement bases of ancillary qubits. See Section 3 of [CutQC](https://doi.org/10.1145/3445814.3446758) for more details." + "If no service was set up, the `backend_names` argument will be ignored, and Qiskit Primitives will be used with statevector simulator." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "7e57f303", + "execution_count": 9, + "id": "3cc622d9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of variants of 1st subcircuit: 16\n", - "Number of variants of 2nd subcircuit: 16\n" - ] - } - ], + "outputs": [], "source": [ - "print(\n", - " \"Number of variants of 1st subcircuit: \", len(subcircuit_instance_probabilities[0])\n", - ")\n", - "print(\n", - " \"Number of variants of 2nd subcircuit: \", len(subcircuit_instance_probabilities[1])\n", - ")" + "# Set the Sampler and runtime options\n", + "options = Options(execution={\"shots\": 4000})\n", + "\n", + "# Run 2 parallel qasm simulator threads\n", + "backend_names = [\"ibmq_qasm_simulator\"] * 2" ] }, { "cell_type": "markdown", - "id": "dfdf82a5", + "id": "742ec1e1", "metadata": {}, "source": [ - "The first subcircuit has two ancillary qubits (induced by the two wire cuts) that do not appear in the original circuit. This means that the first subcircuit has $5-2=3$ qubits from the original circuit and a probability distribution of size $2^3=8$. The second subcircuit has 5 qubits, all from the original circuit, and so its probability distribution is size $2^5=32$." + "**Evaluate the subcircuits on the quantum backend(s)**\n", + "\n", + "We will call the `evaluate_subcircuits_remote` function within a `QuantumServerless` context, which means it will be run on the specified cluster. Remember, our local CPU is the default cluster for this demo. To specify a new cluster, the `QuantumServerless.set_provider` method should be used.\n", + "\n", + "When the remote function is called, it will return a \"future\" object, and Python will continue interpreting the next line of code. The `get` function from `quantum-serverless` is a blocking command which should be used to retrieve the results of the remote function via the \"future\" object. The program will not continue past the `get` call until the results of the remote function are returned." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "3ec4d42c", + "execution_count": 10, + "id": "2ae5160c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Size of 1st subcircuit probability distribution: 8\n", - "Size of 2nd subcircuit probability distribution: 32\n" - ] - } - ], + "outputs": [], "source": [ - "print(\n", - " \"Size of 1st subcircuit probability distribution: \",\n", - " len(subcircuit_instance_probabilities[0][0]),\n", - ")\n", - "print(\n", - " \"Size of 2nd subcircuit probability distribution: \",\n", - " len(subcircuit_instance_probabilities[1][0]),\n", - ")" + "with serverless:\n", + " service_args = None if service is None else service.active_account()\n", + " subcircuit_probabilities_future = evaluate_subcircuits_remote(\n", + " cuts,\n", + " service_args=service_args,\n", + " backend_names=backend_names,\n", + " options=options,\n", + " )\n", + " subcircuit_instance_probabilities = get(subcircuit_probabilities_future)" ] }, { "cell_type": "markdown", - "id": "17e8511c", + "id": "b7890513", "metadata": {}, "source": [ "## Reconstruct the full circuit output\n", "\n", - "Next, the results of the subcircuit experiments are classical postprocessed to reconstruct an estimate of the original circuit's full probability distribution." + "**Use `quantum-serverless` to send the `reconstruct_full_distribution` method to a remote cluster**\n", + "\n", + "Next, the results of the subcircuit experiments are classically postprocessed to reconstruct the original circuit's full probability distribution.\n", + "\n", + "Here, we create a wrapper function for the `reconstruct_full_distribution` function and annotate it with the `@run_qiskit_remote()` decorator from `quantum-serverless`. This allows us to call this function from a serverless context and have it sent for remote execution on the specified cluster." ] }, { "cell_type": "code", - "execution_count": 13, - "id": "5aceecc0", + "execution_count": 11, + "id": "9a74644d", "metadata": {}, "outputs": [], "source": [ - "%%capture\n", + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import (\n", + " reconstruct_full_distribution,\n", + ")\n", "\n", - "reconstructed_probabilities = cutter.reconstruct(\n", - " subcircuit_instance_probabilities, cuts\n", - ")" + "\n", + "@run_qiskit_remote()\n", + "def reconstruct_full_distribution_remote(\n", + " circuit: QuantumCircuit,\n", + " subcircuit_instance_probabilities: Dict[int, Dict[int, NDArray]],\n", + " cuts: Dict[str, Any],\n", + " num_threads: int = 1,\n", + ") -> NDArray:\n", + " return reconstruct_full_distribution(\n", + " circuit, subcircuit_instance_probabilities, cuts\n", + " )" ] }, { "cell_type": "markdown", - "id": "3dbae8e0", + "id": "17e8511c", "metadata": {}, "source": [ - "Here are the reconstructed probabilities for the original 8-qubit circuit:" + "**Reconstruct the output**\n", + "\n", + "We will call the `reconstruct_full_distribution_remote` function within a `QuantumServerless` context, which means it will be run on the specified cluster. Remember, our local CPU is the default cluster for this demo. To specify a new cluster, the `QuantumServerless.set_provider` method should be used.\n", + "\n", + "When the remote function is called, it will return a \"future\" object, and Python will continue interpreting the next line of code. The `get` function from `quantum-serverless` is a blocking command which should be used to retrieve the results of the remote function via the \"future\" object. The program will not continue past the `get` call until the results of the remote function are returned." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "919958cb", + "execution_count": 12, + "id": "5aceecc0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Size of reconstructed probability distribution: 256\n" - ] - } - ], + "outputs": [], "source": [ - "print(\n", - " \"Size of reconstructed probability distribution: \", len(reconstructed_probabilities)\n", - ")" + "%%capture\n", + "\n", + "with serverless:\n", + " reconstructed_probabilities_future = reconstruct_full_distribution_remote(\n", + " circuit, subcircuit_instance_probabilities, cuts\n", + " )\n", + " reconstructed_probabilities = get(reconstructed_probabilities_future)" ] }, { @@ -546,12 +487,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "5353b0c8", "metadata": {}, "outputs": [], "source": [ - "metrics, exact_probabilities = cutter.verify(reconstructed_probabilities)" + "from circuit_knitting_toolbox.circuit_cutting.wire_cutting import verify\n", + "\n", + "metrics, exact_probabilities = verify(circuit, reconstructed_probabilities)" ] }, { @@ -559,31 +502,33 @@ "id": "03f63d3b", "metadata": {}, "source": [ - "The verify step includes several metrics, including the chi square loss. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting_toolbox/utils/metrics.py)." + "**The verify step includes several metrics**\n", + "\n", + "For example, the chi square loss is computed. Since we're using the Qiskit Sampler with statevector simulator, we expect the reconstructed distributed to exactly match the ground truth. More info about each metric can be found in the [utils metrics file](https://github.com/Qiskit-Extensions/circuit-knitting-toolbox/blob/main/circuit_knitting_toolbox/utils/metrics.py)." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "673d3cb3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'nearest': {'chi2': 0.010877629252533529,\n", - " 'Mean Squared Error': 2.2715289367877793e-07,\n", - " 'Mean Absolute Percentage Error': 33894.26768956667,\n", - " 'Cross Entropy': 3.6320286857414312,\n", - " 'HOP': 0.9947113653872088},\n", - " 'naive': {'chi2': 0.010555995982443323,\n", - " 'Mean Squared Error': 2.2611297333591976e-07,\n", - " 'Mean Absolute Percentage Error': 35040.90391229953,\n", - " 'Cross Entropy': 3.6246776301991215,\n", - " 'HOP': 0.9934968235036893}}" + "{'nearest': {'chi2': 0,\n", + " 'Mean Squared Error': 8.13178352181795e-35,\n", + " 'Mean Absolute Percentage Error': 4.4880309854901524e-10,\n", + " 'Cross Entropy': 3.564551116068219,\n", + " 'HOP': 0.9945381353717198},\n", + " 'naive': {'chi2': 0,\n", + " 'Mean Squared Error': 3.7794080473092745e-35,\n", + " 'Mean Absolute Percentage Error': 4.4880563544629694e-10,\n", + " 'Cross Entropy': 3.564551116068219,\n", + " 'HOP': 0.99453813537172}}" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -597,12 +542,14 @@ "id": "ec8c120e", "metadata": {}, "source": [ + "**Visualize both distributions**\n", + "\n", "If we calculated the ground truth above, we can visualize a comparison to the reconstructed probabilities" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "c8cc97e9", "metadata": { "scrolled": false @@ -610,12 +557,12 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -655,14 +602,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "5e6e41aa", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ibmq-provider0.19.2
qiskit-nature0.4.5
System information
Python version3.9.13
Python compilerClang 12.0.0
Python buildmain, Oct 13 2022 16:12:30
OSDarwin
CPUs4
Memory (Gb)32.0
Sat Oct 22 12:35:13 2022 MDT
" + "

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ibmq-provider0.19.2
qiskit-nature0.4.5
System information
Python version3.10.6
Python compilerClang 13.1.6 (clang-1316.0.21.2.5)
Python buildmain, Aug 11 2022 13:49:25
OSDarwin
CPUs8
Memory (Gb)32.0
Wed Oct 26 11:26:52 2022 CDT
" ], "text/plain": [ "" @@ -712,7 +659,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.6" } }, "nbformat": 4,